From fa4afdd19a80052a5d3f57b830308f1c6da3179b Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 5 Dec 2025 10:58:42 +0100 Subject: [PATCH 001/149] Add SpectrumDataSet for handling sets of spectra, for method development. --- ms2query/benchmarking/SpectrumDataSet.py | 130 +++++++++++++++++++++++ 1 file changed, 130 insertions(+) create mode 100644 ms2query/benchmarking/SpectrumDataSet.py diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py new file mode 100644 index 0000000..0751d70 --- /dev/null +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -0,0 +1,130 @@ +import copy +from collections import Counter +from typing import List, Dict, Iterable + +import numpy as np +from matchms import Spectrum +from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi + +from ms2deepscore.models import compute_embedding_array, SiameseSpectralModel +from tqdm import tqdm + + +class SpectrumSetBase: + """Stores a spectrum dataset making it easy and fast to split on molecules""" + + def __init__(self, spectra: List[Spectrum], progress_bars=False): + self._spectra = [] + self.spectrum_indexes_per_inchikey = {} + self.progress_bars = progress_bars + # init spectra + self._add_spectra_and_group_per_inchikey(spectra) + + def _add_spectra_and_group_per_inchikey(self, spectra: List[Spectrum]): + starting_index = len(self._spectra) + updated_inchikeys = set() + for i, spectrum in enumerate( + tqdm(spectra, desc="Adding spectra and grouping per Inchikey", disable=not self.progress_bars) + ): + self._spectra.append(spectrum) + spectrum_index = starting_index + i + inchikey = spectrum.get("inchikey")[:14] + updated_inchikeys.add(inchikey) + if inchikey in self.spectrum_indexes_per_inchikey: + self.spectrum_indexes_per_inchikey[inchikey].append(spectrum_index) + else: + self.spectrum_indexes_per_inchikey[inchikey] = [ + spectrum_index, + ] + return updated_inchikeys + + def add_spectra(self, new_spectra: "SpectrumSetBase"): + return self._add_spectra_and_group_per_inchikey(new_spectra.spectra) + + def subset_spectra(self, spectrum_indexes) -> "SpectrumSetBase": + """Returns a new instance of a subset of the spectra""" + new_instance = copy.copy(self) + new_instance._spectra = [] + new_instance.spectrum_indexes_per_inchikey = {} + new_instance._add_spectra_and_group_per_inchikey([self._spectra[index] for index in spectrum_indexes]) + return new_instance + + def spectra_per_inchikey(self, inchikey) -> List[Spectrum]: + matching_spectra = [] + for index in self.spectrum_indexes_per_inchikey[inchikey]: + matching_spectra.append(self._spectra[index]) + return matching_spectra + + @property + def spectra(self): + return self._spectra + + def copy(self): + """This copy method ensures all spectra are""" + new_instance = copy.copy(self) + new_instance._spectra = self._spectra.copy() + new_instance.spectrum_indexes_per_inchikey = copy.deepcopy(self.spectrum_indexes_per_inchikey) + return new_instance + + +class SpectraWithFingerprints(SpectrumSetBase): + """Stores a spectrum dataset making it easy and fast to split on molecules""" + + def __init__(self, spectra: List[Spectrum], fingerprint_type="daylight", nbits=4096): + super().__init__(spectra) + self.fingerprint_type = fingerprint_type + self.nbits = nbits + self.inchikey_fingerprint_pairs: Dict[str, np.array] = {} + # init spectra + self.update_fingerprint_per_inchikey(self.spectrum_indexes_per_inchikey.keys()) + + def add_spectra(self, new_spectra: "SpectraWithFingerprints"): + updated_inchikeys = super().add_spectra(new_spectra) + if hasattr(new_spectra, "inchikey_fingerprint_pairs"): + if new_spectra.nbits == self.nbits and new_spectra.fingerprint_type == self.fingerprint_type: + if len(self.inchikey_fingerprint_pairs.keys() & new_spectra.inchikey_fingerprint_pairs.keys()) == 0: + self.inchikey_fingerprint_pairs = ( + self.inchikey_fingerprint_pairs | new_spectra.inchikey_fingerprint_pairs + ) + return + self.update_fingerprint_per_inchikey(updated_inchikeys) + + def update_fingerprint_per_inchikey(self, inchikeys_to_update: Iterable[str]): + for inchikey in tqdm( + inchikeys_to_update, desc="Adding fingerprints to Inchikeys", disable=not self.progress_bars + ): + spectra = self.spectra_per_inchikey(inchikey) + most_common_inchi = Counter([spectrum.get("inchi") for spectrum in spectra]).most_common(1)[0][0] + fingerprint = _derive_fingerprint_from_inchi( + most_common_inchi, fingerprint_type=self.fingerprint_type, nbits=self.nbits + ) + if not isinstance(fingerprint, np.ndarray): + raise ValueError(f"Fingerprint could not be set for InChI: {most_common_inchi}") + self.inchikey_fingerprint_pairs[inchikey] = fingerprint + + def copy(self): + """This copy method ensures all spectra are""" + new_instance = super().copy() + new_instance.inchikey_fingerprint_pairs = copy.copy(self.inchikey_fingerprint_pairs) + return new_instance + + +class SpectraWithMS2DeepScoreEmbeddings(SpectraWithFingerprints): + def __init__(self, spectra: List[Spectrum], ms2deepscore_model: SiameseSpectralModel, **kwargs): + super().__init__(spectra, **kwargs) + self.ms2deepscore_model = ms2deepscore_model + self.embeddings: np.ndarray = compute_embedding_array(self.ms2deepscore_model, spectra) + + def add_spectra(self, new_spectra: "SpectraWithMS2DeepScoreEmbeddings"): + super().add_spectra(new_spectra) + if hasattr(new_spectra, "embeddings"): + new_embeddings = new_spectra.embeddings + else: + new_embeddings = compute_embedding_array(self.ms2deepscore_model, new_spectra.spectra) + self.embeddings = np.vstack([self.embeddings, new_embeddings]) + + def subset_spectra(self, spectrum_indexes) -> "SpectraWithMS2DeepScoreEmbeddings": + """Returns a new instance of a subset of the spectra""" + new_instance = super().subset_spectra(spectrum_indexes) + new_instance.embeddings = self.embeddings[spectrum_indexes] + return new_instance From e882f0094e201d9c27e4c0d57829629a3871d731 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 5 Dec 2025 11:08:58 +0100 Subject: [PATCH 002/149] Add EvaluateMethods a general method for benchmarking analogue search and exact match searches --- ms2query/benchmarking/EvaluateMethods.py | 205 +++++++++++++++++++++++ 1 file changed, 205 insertions(+) create mode 100644 ms2query/benchmarking/EvaluateMethods.py diff --git a/ms2query/benchmarking/EvaluateMethods.py b/ms2query/benchmarking/EvaluateMethods.py new file mode 100644 index 0000000..6442343 --- /dev/null +++ b/ms2query/benchmarking/EvaluateMethods.py @@ -0,0 +1,205 @@ +import random + +import numpy as np +from typing import Callable, Tuple, List + +from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix +from tqdm import tqdm + +from ms2query.benchmarking.SpectrumDataSet import SpectraWithFingerprints, SpectrumSetBase + + +class EvaluateMethods: + def __init__( + self, training_spectrum_set: SpectraWithFingerprints, validation_spectrum_set: SpectraWithFingerprints + ): + self.training_spectrum_set = training_spectrum_set + self.validation_spectrum_set = validation_spectrum_set + + self.training_spectrum_set.progress_bars = False + self.validation_spectrum_set.progress_bars = False + + def benchmark_analogue_search( + self, + prediction_function: Callable[ + [SpectraWithFingerprints, SpectraWithFingerprints], Tuple[List[str], List[float]] + ], + ) -> float: + predicted_inchikeys, _ = prediction_function(self.training_spectrum_set, self.validation_spectrum_set) + average_scores_per_inchikey = [] + + # Calculate score per unique inchikey + for inchikey in tqdm( + self.validation_spectrum_set.spectrum_indexes_per_inchikey.keys(), + desc="Calculating analogue accuracy per inchikey", + ): + matching_spectrum_indexes = self.validation_spectrum_set.spectrum_indexes_per_inchikey[inchikey] + prediction_scores = [] + for index in matching_spectrum_indexes: + predicted_inchikey = predicted_inchikeys[index] + if predicted_inchikey is None: + prediction_scores.append(0.0) + else: + predicted_fingerprint = self.training_spectrum_set.inchikey_fingerprint_pairs[predicted_inchikey] + actual_fingerprint = self.validation_spectrum_set.inchikey_fingerprint_pairs[inchikey] + tanimoto_for_prediction = calculate_tanimoto_score_between_pair( + predicted_fingerprint, actual_fingerprint + ) + prediction_scores.append(tanimoto_for_prediction) + + average_prediction = sum(prediction_scores) / len(prediction_scores) + score = average_prediction + average_scores_per_inchikey.append(score) + average_over_all_inchikeys = sum(average_scores_per_inchikey) / len(average_scores_per_inchikey) + return average_over_all_inchikeys + + def benchmark_exact_matching_within_ionmode( + self, + prediction_function: Callable[ + [SpectraWithFingerprints, SpectraWithFingerprints], Tuple[List[str], List[float]] + ], + ionmode: str, + ) -> float: + """Test the accuracy at retrieving exact matches from the library + + For each inchikey with more than 1 spectrum the spectra are split in two sets. Half for each inchikey is added + to the library (training set), for the other half predictions are made. Thereby there is always an exact match + avaialable. Only the highest ranked prediction is considered correct if the correct inchikey is predicted. An accuracy per + inchikey is calculated followed by calculating the average. + """ + selected_spectra = subset_spectra_on_ionmode(self.validation_spectrum_set, ionmode) + + set_1, set_2 = split_spectrum_set_per_inchikeys(selected_spectra) + + predicted_inchikeys = predict_between_two_sets(self.training_spectrum_set, set_1, set_2, prediction_function) + + # add the spectra to set_1 + set_1.add_spectra(set_2) + return calculate_average_exact_match_accuracy(set_1, predicted_inchikeys) + + def exact_matches_across_ionization_modes( + self, + prediction_function: Callable[ + [SpectraWithFingerprints, SpectraWithFingerprints], Tuple[List[str], List[float]] + ], + ): + """Test the accuracy at retrieving exact matches from the library if only available in other ionisation mode + + Each val spectrum is matched against the training set with the other val spectra of the same inchikey, but other + ionisation mode added to the library. + """ + pos_set, neg_set = split_spectrum_set_per_inchikey_across_ionmodes(self.validation_spectrum_set) + predicted_inchikeys = predict_between_two_sets( + self.training_spectrum_set, pos_set, neg_set, prediction_function + ) + # add the spectra to set_1 + pos_set.add_spectra(neg_set) + return calculate_average_exact_match_accuracy(pos_set, predicted_inchikeys) + + def get_accuracy_recall_curve(self): + """This method should test the recall accuracy balance. + All of the used methods use a threshold which indicates quality of prediction. + A method that can predict well when a prediction is accurate is beneficial. + We need a method to test this. + + One method is generating a recall accuracy curve. This could be done for both the analogue search predictions + and the exact match predictions. By returning the predicted score for a match this method could create an + accuracy recall plot. + """ + raise NotImplementedError + + +def predict_between_two_sets( + library: SpectrumSetBase, query_set_1: SpectrumSetBase, query_set_2: SpectrumSetBase, prediction_function +): + """Makes predictions between query sets and the library, with the other query set added. + + This is necessary for testing exact matching""" + training_set_copy = library.copy() + training_set_copy.add_spectra(query_set_2) + predicted_inchikeys_1, _ = prediction_function(training_set_copy, query_set_1) + + training_set_copy = library.copy() + training_set_copy.add_spectra(query_set_1) + predicted_inchikeys_2, _ = prediction_function(training_set_copy, query_set_2) + + return predicted_inchikeys_1 + predicted_inchikeys_2 + + +def calculate_average_exact_match_accuracy(spectrum_set: SpectrumSetBase, predicted_inchikeys: List[str]): + if len(spectrum_set.spectra) != len(predicted_inchikeys): + raise ValueError("The number of spectra should be equal to the number of predicted inchikeys ") + exact_match_accuracy_per_inchikey = [] + for inchikey in tqdm( + spectrum_set.spectrum_indexes_per_inchikey.keys(), desc="Calculating exact match accuracy per inchikey" + ): + val_spectrum_indexes_matching_inchikey = spectrum_set.spectrum_indexes_per_inchikey[inchikey] + correctly_predicted = 0 + for selected_spectrum_idx in val_spectrum_indexes_matching_inchikey: + if inchikey == predicted_inchikeys[selected_spectrum_idx]: + correctly_predicted += 1 + exact_match_accuracy_per_inchikey.append(correctly_predicted / len(val_spectrum_indexes_matching_inchikey)) + return sum(exact_match_accuracy_per_inchikey) / len(exact_match_accuracy_per_inchikey) + + +def split_spectrum_set_per_inchikeys(spectrum_set: SpectrumSetBase) -> Tuple[SpectrumSetBase, SpectrumSetBase]: + """Splits a spectrum set into two. + For each inchikey with more than one spectrum the spectra are divided over the two sets""" + indexes_set_1 = [] + indexes_set_2 = [] + for inchikey in tqdm(spectrum_set.spectrum_indexes_per_inchikey.keys(), desc="Splitting spectra per inchikey"): + val_spectrum_indexes_matching_inchikey = spectrum_set.spectrum_indexes_per_inchikey[inchikey] + if len(val_spectrum_indexes_matching_inchikey) == 1: + # all single spectra are excluded from this test, since no exact match can be added to the library + continue + split_index = len(val_spectrum_indexes_matching_inchikey) // 2 + random.shuffle(val_spectrum_indexes_matching_inchikey) + indexes_set_1.extend(val_spectrum_indexes_matching_inchikey[:split_index]) + indexes_set_2.extend(val_spectrum_indexes_matching_inchikey[split_index:]) + return spectrum_set.subset_spectra(indexes_set_1), spectrum_set.subset_spectra(indexes_set_2) + + +def split_spectrum_set_per_inchikey_across_ionmodes( + spectrum_set: SpectrumSetBase, +) -> Tuple[SpectrumSetBase, SpectrumSetBase]: + """Splits a spectrum set in two sets on ionmode. Only uses spectra for inchikeys with at least 1 pos and 1 neg""" + all_pos_indexes = [] + all_neg_indexes = [] + for inchikey in tqdm( + spectrum_set.spectrum_indexes_per_inchikey.keys(), + desc="Splitting spectra per inchikey across ionmodes", + ): + val_spectrum_indexes_matching_inchikey = spectrum_set.spectrum_indexes_per_inchikey[inchikey] + positive_val_spectrum_indexes_current_inchikey = [] + negative_val_spectrum_indexes_current_inchikey = [] + for spectrum_index in val_spectrum_indexes_matching_inchikey: + ionmode = spectrum_set.spectra[spectrum_index].get("ionmode") + if ionmode == "positive": + positive_val_spectrum_indexes_current_inchikey.append(spectrum_index) + elif ionmode == "negative": + negative_val_spectrum_indexes_current_inchikey.append(spectrum_index) + + if ( + len(positive_val_spectrum_indexes_current_inchikey) < 1 + or len(negative_val_spectrum_indexes_current_inchikey) < 1 + ): + continue + else: + all_pos_indexes.extend(positive_val_spectrum_indexes_current_inchikey) + all_neg_indexes.extend(negative_val_spectrum_indexes_current_inchikey) + + pos_val_spectra = spectrum_set.subset_spectra(all_pos_indexes) + neg_val_spectra = spectrum_set.subset_spectra(all_neg_indexes) + return pos_val_spectra, neg_val_spectra + + +def subset_spectra_on_ionmode(spectrum_set: SpectrumSetBase, ionmode) -> SpectrumSetBase: + spectrum_indexes_to_keep = [] + for i, spectrum in enumerate(spectrum_set.spectra): + if spectrum.get("ionmode") == ionmode: + spectrum_indexes_to_keep.append(i) + return spectrum_set.subset_spectra(spectrum_indexes_to_keep) + + +def calculate_tanimoto_score_between_pair(fingerprint_1: str, fingerprint_2: str) -> float: + return jaccard_similarity_matrix(np.array([fingerprint_1]), np.array([fingerprint_2]))[0][0] From 922bea8caf95c1a8c87a14e98db4374be2a84228 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 5 Dec 2025 11:12:09 +0100 Subject: [PATCH 003/149] Implement base line methods --- .../PredictMS2DeepScoreSimilarity.py | 46 ++++++++ .../predict_best_possible_match.py | 48 ++++++++ .../predict_highest_cosine.py | 27 +++++ .../predict_highest_ms2deepscore.py | 17 +++ ...predict_with_integrated_similarity_flow.py | 108 ++++++++++++++++++ 5 files changed, 246 insertions(+) create mode 100644 ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py create mode 100644 ms2query/benchmarking/reference_methods/predict_best_possible_match.py create mode 100644 ms2query/benchmarking/reference_methods/predict_highest_cosine.py create mode 100644 ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py create mode 100644 ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py diff --git a/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py b/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py new file mode 100644 index 0000000..13121cf --- /dev/null +++ b/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py @@ -0,0 +1,46 @@ +from typing import Tuple + +import numpy as np + +from ms2deepscore.vector_operations import cosine_similarity_matrix +from tqdm import tqdm + + +def predict_top_ms2deepscores( + library_embeddings: np.ndarray, query_embeddings: np.ndarray, batch_size: int = 500, k=1 +) -> Tuple[np.ndarray, np.ndarray]: + """Memory efficient way of calculating the highest MS2DeepScores + + When doing large matrix multiplications the memory footprint of storing the output matrix can be large. + E.g. when doing 500.000 vs 10.000 spectra this is a very large matrix. On a laptop this can result in very + slow run times. If only the highest MS2DeepScore is needed processing in batches prevents using too much memory + + Args: + library_embeddings: The embeddings of the library spectra + query_embeddings: The embeddings of the query spectra + batch_size: The number of query embeddings processed at the same time. + Setting a lower batch_size results in a lower memory footprint. + k: Number of highest matches to return + + Returns: + List[List[int]: indexes of highest scores and the value for the highest score. Per query embedding the top k highest indexes are given. + List[List[float]: the highest scores. + """ + top_indexes_per_batch = [] + top_scores_per_batch = [] + num_of_query_embeddings = query_embeddings.shape[0] + # loop over the batches + for start_idx in tqdm( + range(0, num_of_query_embeddings, batch_size), + desc="Predicting highest ms2deepscore per batch of " + + str(min(batch_size, num_of_query_embeddings)) + + " embeddings", + ): + end_idx = min(start_idx + batch_size, num_of_query_embeddings) + selected_query_embeddings = query_embeddings[start_idx:end_idx] + score_matrix = cosine_similarity_matrix(selected_query_embeddings, library_embeddings) + top_n_idx = np.argsort(score_matrix, axis=1)[:, -k:][:, ::-1] + top_n_scores = np.take_along_axis(score_matrix, top_n_idx, axis=1) + top_indexes_per_batch.append(top_n_idx) + top_scores_per_batch.append(top_n_scores) + return np.vstack(top_indexes_per_batch), np.vstack(top_scores_per_batch) diff --git a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py new file mode 100644 index 0000000..63625f2 --- /dev/null +++ b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py @@ -0,0 +1,48 @@ +from typing import Dict + +import numpy as np +from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix + +from ms2query.benchmarking.SpectrumDataSet import SpectraWithFingerprints + + +def predict_best_possible_match(library_spectra: SpectraWithFingerprints, query_spectra: SpectraWithFingerprints): + highest_possible_score_per_inchikey = calculate_highest_tanimoto_score_per_inchikey(library_spectra, query_spectra) + + inchikeys_of_best_match = [] + highest_scores = [] + + for spectrum in query_spectra.spectra: + inchikey = spectrum.get("inchikey")[:14] + + inchikeys_of_best_match.append(highest_possible_score_per_inchikey[inchikey][0]) + highest_scores.append(highest_possible_score_per_inchikey[inchikey][1]) + + return inchikeys_of_best_match, highest_scores + + +def calculate_highest_tanimoto_score_per_inchikey( + library_spectra: SpectraWithFingerprints, query_spectra: SpectraWithFingerprints +) -> Dict[str, tuple[str, float]]: + """Finds the best possible match during an analogue search""" + print("Calculating tanimoto scores to determine best possible match") + library_fingerprints = np.array(list(library_spectra.inchikey_fingerprint_pairs.values())) + query_fingerprints = np.array(list(query_spectra.inchikey_fingerprint_pairs.values())) + tanimoto_scores = jaccard_similarity_matrix(library_fingerprints, query_fingerprints) + highest_scores = tanimoto_scores.max(axis=0, initial=0) + indexes_of_highest_scores = tanimoto_scores.argmax(axis=0) + + inchikeys_library = list(library_spectra.inchikey_fingerprint_pairs.keys()) + + highest_possible_score_per_inchikey = dict() + for i, inchikey in enumerate(query_spectra.inchikey_fingerprint_pairs): + # Check if inchikey in library (To correctly handle the exact matching case) + if inchikey in library_spectra.inchikey_fingerprint_pairs: + highest_possible_score_per_inchikey[inchikey] = (inchikey, 1.0) + continue + + highest_possible_score_per_inchikey[inchikey] = ( + inchikeys_library[indexes_of_highest_scores[i]], + highest_scores[i], + ) + return highest_possible_score_per_inchikey diff --git a/ms2query/benchmarking/reference_methods/predict_highest_cosine.py b/ms2query/benchmarking/reference_methods/predict_highest_cosine.py new file mode 100644 index 0000000..46820f1 --- /dev/null +++ b/ms2query/benchmarking/reference_methods/predict_highest_cosine.py @@ -0,0 +1,27 @@ +from typing import Tuple, List + +from matchms import Scores +from matchms.similarity.CosineGreedy import CosineGreedy +from matchms.similarity.PrecursorMzMatch import PrecursorMzMatch +from ms2query.benchmarking.SpectrumDataSet import SpectraWithFingerprints + + +def predict_highest_cosine( + library_spectra: SpectraWithFingerprints, query_spectra: SpectraWithFingerprints +) -> Tuple[List[str], List[float]]: + + scores = Scores(references=library_spectra.spectra, queries=query_spectra.spectra, is_symmetric=False) + scores = scores.calculate(PrecursorMzMatch(0.1)) + scores = scores.calculate(CosineGreedy(tolerance=0.1)) + inchikeys_of_best_match = [] + highest_scores = [] + for query_spectrum in query_spectra.spectra: + results = scores.scores_by_query(query_spectrum, "CosineGreedy_score", sort=True) + if len(results) == 0: + inchikeys_of_best_match.append(None) + highest_scores.append(0.0) + else: + best_reference, highest_score = results[0] + inchikeys_of_best_match.append(best_reference.get("inchikey")[:14]) + highest_scores.append(highest_score["CosineGreedy_score"]) + return inchikeys_of_best_match, highest_scores diff --git a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py new file mode 100644 index 0000000..d645857 --- /dev/null +++ b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py @@ -0,0 +1,17 @@ +from typing import Tuple, List + +from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores +from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings + + +def predict_highest_ms2deepscore( + library_spectra: SpectraWithMS2DeepScoreEmbeddings, query_spectra: SpectraWithMS2DeepScoreEmbeddings +) -> Tuple[List[str], List[float]]: + indexes_of_highest_scores, highest_scores = predict_top_ms2deepscores( + library_spectra.embeddings, query_spectra.embeddings, k=1 + ) + single_highest_score = [highest_score[0] for highest_score in highest_scores] + inchikeys_of_best_match = [ + library_spectra.spectra[index[0]].get("inchikey")[:14] for index in indexes_of_highest_scores + ] + return inchikeys_of_best_match, single_highest_score diff --git a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py new file mode 100644 index 0000000..329fea1 --- /dev/null +++ b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py @@ -0,0 +1,108 @@ +from typing import Tuple, List +from tqdm import tqdm +import numpy as np +from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix + +from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores +from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings + + +def predict_with_integrated_similarity_flow( + library_spectra: SpectraWithMS2DeepScoreEmbeddings, + query_spectra: SpectraWithMS2DeepScoreEmbeddings, + number_of_analogues_to_consider=50, +) -> Tuple[List[str], List[float]]: + + all_indexes_of_library_spectra_with_highest_score, all_predicted_scores = predict_top_ms2deepscores( + library_spectra.embeddings, query_spectra.embeddings, k=number_of_analogues_to_consider + ) + inchikeys_of_best_matches = [] + highest_isf_scores = [] + # loop over the query spectra: + for query_index in tqdm(range(len(query_spectra.spectra)), "Calculating ISF score"): + highest_isf_score, inchikey_of_highest_isf_score = get_highest_isf( + library_spectra, + all_indexes_of_library_spectra_with_highest_score[query_index], + all_predicted_scores[query_index], + ) + inchikeys_of_best_matches.append(inchikey_of_highest_isf_score) + highest_isf_scores.append(highest_isf_score) + return inchikeys_of_best_matches, highest_isf_scores + + +def get_highest_isf( + library_spectra: SpectraWithMS2DeepScoreEmbeddings, + indexes_of_library_spectra_with_highest_score: List[int], + predicted_scores: [List[float]], +): + + # Get the corresponding inchikeys + inchikeys_with_highest_ms2deepscore = [ + library_spectra.spectra[index].get("inchikey")[:14] for index in indexes_of_library_spectra_with_highest_score + ] + unique_inchikeys, average_scores, nr_of_spectra_per_inchikey = average_scores_per_inchikeys( + predicted_scores, inchikeys_with_highest_ms2deepscore + ) + # calculate tanimoto scores + library_fingerprints = np.array( + [library_spectra.inchikey_fingerprint_pairs[inchikey] for inchikey in unique_inchikeys] + ) + tanimoto_scores = jaccard_similarity_matrix(library_fingerprints, library_fingerprints) + + isf_scores = integrated_similarity_flow(average_scores, tanimoto_scores, nr_of_spectra_per_inchikey) + index_of_highest_score = np.argmax(isf_scores) + highest_isf_score = isf_scores[index_of_highest_score] + inchikey_of_highest_isf_score = unique_inchikeys[index_of_highest_score] + return highest_isf_score, inchikey_of_highest_isf_score + + +def average_scores_per_inchikeys(predicted_scores, inchikeys): + """Calculate the average precicted score per inchikey + This helps speed up the computations""" + if len(predicted_scores) != len(inchikeys): + raise ValueError + scores_per_inchikey = {} + for i, score in enumerate(predicted_scores): + inchikey = inchikeys[i] + if inchikey in scores_per_inchikey: + scores_per_inchikey[inchikey].append(score) + else: + scores_per_inchikey[inchikey] = [score] + # Take the average over the scores per inchikey + unique_inchikeys = [] + average_scores = [] + nr_of_spectra_per_inchikey = [] + for inchikey in scores_per_inchikey: + unique_inchikeys.append(inchikey) + average_scores.append(sum(scores_per_inchikey[inchikey]) / len(scores_per_inchikey[inchikey])) + nr_of_spectra_per_inchikey.append(len(scores_per_inchikey[inchikey])) + return unique_inchikeys, average_scores, nr_of_spectra_per_inchikey + + +def integrated_similarity_flow( + predicted_scores: List[float], similarities: np.ndarray, nr_of_spectra_per_inchikey: List[float] +) -> List[float]: + """Compute the confidence of the prediction for each candidate. + Integrated similarity flow (ISF) scores are calculated using the similarity of candidates among each other and their distance to the query spectrum. + + Args: + distances (list): Distances of the candidates to the query spectrum in the chemical space. + similarities (list of lists): Jaccard similarity of all candidates to each other. + + Returns: + dict[int, float]: ISF scores for each candid+ate. + """ + num_hits = len(predicted_scores) + isf_scores = [] + + # Total similarity + total_similarity = sum([predicted_scores[i] * nr_of_spectra_per_inchikey[i] for i in range(len(predicted_scores))]) + + for i in range(num_hits): + isf_score = ( + sum(predicted_scores[j] * similarities[i][j] * nr_of_spectra_per_inchikey[j] for j in range(num_hits)) + / total_similarity + ) + isf_scores.append(isf_score) + + return isf_scores From 40c6347eaba8d2a16f536f9e9a50cdcc448642dc Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 5 Dec 2025 11:16:02 +0100 Subject: [PATCH 004/149] Add notebooks used for testing (still have to be adapted to work with current code base) --- .../Test_ann_speed_improvements.ipynb | 313 ++++++ .../notebooks/Test_method_evaluator.ipynb | 942 ++++++++++++++++++ ...umber_of_inchikeys_with_two_ionmodes.ipynb | 349 +++++++ 3 files changed, 1604 insertions(+) create mode 100644 ms2query/notebooks/Test_ann_speed_improvements.ipynb create mode 100644 ms2query/notebooks/Test_method_evaluator.ipynb create mode 100644 ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb diff --git a/ms2query/notebooks/Test_ann_speed_improvements.ipynb b/ms2query/notebooks/Test_ann_speed_improvements.ipynb new file mode 100644 index 0000000..9b84f73 --- /dev/null +++ b/ms2query/notebooks/Test_ann_speed_improvements.ipynb @@ -0,0 +1,313 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "a3a9b10c-a9a6-441f-963c-edf5d9a50dbe", + "metadata": {}, + "outputs": [], + "source": [ + "import sys \n", + "sys.path.append(\"C:/Users/jonge094/PycharmProjects/ms2query_2_0/ms_chemical_space_explorer\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "69613d68-6340-4985-b266-ebdb56b21271", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "7551it [00:05, 1452.14it/s]\n", + "7142it [00:04, 1502.96it/s]\n" + ] + } + ], + "source": [ + "from matchms.importing import load_from_mgf\n", + "from tqdm import tqdm\n", + "\n", + "neg_val_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_validation_spectra.mgf\")))\n", + "neg_test_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_testing_spectra.mgf\")))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "55f31b78-e6b8-42a2-8ba0-d5cea429520d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\jonge094\\AppData\\Local\\miniconda3\\envs\\ms2query2\\lib\\site-packages\\ms2deepscore\\models\\load_model.py:34: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model_settings = torch.load(filename, map_location=device)\n", + "7551it [02:50, 44.16it/s]\n" + ] + } + ], + "source": [ + "from ms2deepscore.models import load_model\n", + "from ms2deepscore.models import compute_embedding_array\n", + "\n", + "ms2deepscore_model = load_model(\"../../../ms2deepscore/data/pytorch/new_corinna_included/trained_models/both_mode_precursor_mz_ionmode_10000_layers_500_embedding_2024_11_21_11_23_17/ms2deepscore_model.pt\")\n", + "\n", + "embeddings = compute_embedding_array(ms2deepscore_model, neg_val_spectra)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1fe9d805-561f-49c5-87f4-ce56c374db42", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "d59f130e-f38e-467f-b6c0-899eb6fdb959", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "more_test_embeddings = np.tile(embeddings, (70, 1))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "5ffe97b9-0199-41f3-9f27-b0a639274af0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(528570, 500)" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "more_test_embeddings.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "9b493113-b067-40c9-9925-0db45af6693d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time eleapsed: 163.06616854667664\n" + ] + } + ], + "source": [ + "import pynndescent\n", + "import time\n", + "start_time = time.time()\n", + "ann_model = pynndescent.NNDescent(more_test_embeddings, metric=\"cosine\", n_neighbors=30)\n", + "ann_model.prepare()\n", + "print(\"Time eleapsed: \" + str(time.time() - start_time))" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "f0ad0f13-f449-4871-9f23-c0ebfe38cb33", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time eleapsed: 86.74623227119446\n" + ] + } + ], + "source": [ + "start_time = time.time()\n", + "ann_model.update(embeddings[:2])\n", + "ann_model.prepare()\n", + "\n", + "print(\"Time eleapsed: \" + str(time.time() - start_time))" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "da5276ff-42e8-4201-8bc0-a166216787ae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time eleapsed: 1.8495116233825684\n" + ] + } + ], + "source": [ + "start_time = time.time()\n", + "indices, dists = ann_model.query(embeddings[:1000], epsilon=1, k=1000)\n", + "print(\"Time eleapsed: \" + str(time.time() - start_time))" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "65f44f03-6593-4bc4-bc4d-e64ffe6cbc08", + "metadata": {}, + "outputs": [], + "source": [ + "for dist in dists:\n", + " if dist > 0.000001:\n", + " print(dist)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "8f8b125d-ace5-4af7-b0ce-6b9a263ece1b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6783\n", + "768\n" + ] + } + ], + "source": [ + "correct = 0\n", + "not_correct = 0\n", + "for i, index in enumerate(indices):\n", + " correct_if_0 = index[0]%7551-i\n", + " if correct_if_0== 0:\n", + " correct += 1\n", + " else:\n", + " not_correct +=1\n", + "print(correct)\n", + "print(not_correct)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "id": "f84b71ed-4e35-42c1-8be1-a1c9b3ec0dae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(226530, 500)" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "more_test_embeddings.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "dbc240d1-62e2-40f1-8f2c-12345cce3bf6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time eleapsed: 14.737722158432007\n" + ] + } + ], + "source": [ + "from ms2deepscore.vector_operations import cosine_similarity_matrix\n", + "start_time = time.time()\n", + "matrix = cosine_similarity_matrix(more_test_embeddings, embeddings[:1000])\n", + "print(\"Time eleapsed: \" + str(time.time() - start_time))" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "f87ba107-54cc-47ee-87c8-34694a917a3b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1. , 0.88234181, 0.73878687, ..., 0.55531438, 0.58179732,\n", + " 0.61927229],\n", + " [0.88234181, 1. , 0.91034399, ..., 0.50462923, 0.51674736,\n", + " 0.54464448],\n", + " [0.73878687, 0.91034399, 1. , ..., 0.48358345, 0.4835752 ,\n", + " 0.54332801],\n", + " ...,\n", + " [0.49057829, 0.55037322, 0.56818589, ..., 0.39834881, 0.41004598,\n", + " 0.5298201 ],\n", + " [0.48655507, 0.57605234, 0.5946298 , ..., 0.40762756, 0.42247458,\n", + " 0.50066275],\n", + " [0.41914688, 0.47922786, 0.48648942, ..., 0.38071478, 0.41207848,\n", + " 0.47297686]])" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62b738da-a9ce-421f-8ef8-ee8a18d2b70f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (ms2query2)", + "language": "python", + "name": "ms2query2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.21" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ms2query/notebooks/Test_method_evaluator.ipynb b/ms2query/notebooks/Test_method_evaluator.ipynb new file mode 100644 index 0000000..097c1d3 --- /dev/null +++ b/ms2query/notebooks/Test_method_evaluator.ipynb @@ -0,0 +1,942 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ced5638b-15f4-4219-9ed8-b2823a53e574", + "metadata": {}, + "source": [ + "# load in spectra" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2768cf34-1794-4a09-89a3-f81a04b1bcda", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "31453it [00:14, 2140.47it/s]\n", + "7080it [00:03, 1992.44it/s]\n", + "459610it [03:37, 2114.18it/s]\n", + "131240it [01:06, 1967.67it/s]\n" + ] + } + ], + "source": [ + "from matchms.importing import load_from_mgf\n", + "from tqdm import tqdm\n", + "import os\n", + "\n", + "save_directory = \"../data/ms2deepscore_model/training_and_validation_split/\"\n", + "pos_val_spectra = list(tqdm(load_from_mgf(os.path.join(save_directory, \"positive_validation_spectra.mgf\"))))\n", + "neg_val_spectra = list(tqdm(load_from_mgf(os.path.join(save_directory, \"negative_validation_spectra.mgf\"))))\n", + "pos_train_spectra = list(tqdm(load_from_mgf(os.path.join(save_directory, \"positive_training_spectra.mgf\"))))\n", + "neg_train_spectra = list(tqdm(load_from_mgf(os.path.join(save_directory, \"negative_training_spectra.mgf\"))))\n", + "pos_test_spectra = list(tqdm(load_from_mgf(os.path.join(save_directory, \"positive_testing_spectra.mgf\"))))\n", + "neg_test_spectra = list(tqdm(load_from_mgf(os.path.join(save_directory, \"negative_testing_spectra.mgf\"))))\n" + ] + }, + { + "cell_type": "markdown", + "id": "7f5a4df2-2bb7-4731-a6bf-6727183da493", + "metadata": {}, + "source": [ + "### Save as pickled files for quicker reloads" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3a55be7f-d760-4df7-944e-251844cb1b4d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "pickled_intermediates_data_folder = \"../data/pickled_intermediates\"\n", + "os.path.isdir(pickled_intermediates_data_folder)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "28606ab4-fd4c-4111-a1ca-8dc502593425", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"neg_val_spectra.pickle\"), \"wb\") as handle:\n", + " pickle.dump(neg_val_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"neg_train_spectra.pickle\"), \"wb\") as handle:\n", + " pickle.dump(neg_train_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"pos_val_spectra.pickle\"), \"wb\") as handle:\n", + " pickle.dump(pos_val_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"pos_train_spectra.pickle\"), \"wb\") as handle:\n", + " pickle.dump(pos_train_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"pos_test_spectra.pickle\"), \"wb\") as handle:\n", + " pickle.dump(pos_test_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"neg_test_spectra.pickle\"), \"wb\") as handle:\n", + " pickle.dump(neg_test_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "markdown", + "id": "cf849c08-3eaf-4922-b361-4f324b010ca9", + "metadata": {}, + "source": [ + "# Create a simple MS2Deepscore ranker" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "48bd37e7-384a-4bbc-821d-946975bfdf5b", + "metadata": {}, + "outputs": [], + "source": [ + "from ms2deepscore.models import load_model\n", + "ms2deepscore_model = load_model(\"../data/ms2deepscore_model/trained_models/both_mode_ionmode_precursor_mz_2000_layers_500_embedding_2025_02_26_18_42_25/ms2deepscore_model.pt\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9710c95d-803d-4492-b279-13588afa9c27", + "metadata": {}, + "outputs": [], + "source": [ + "import sys \n", + "sys.path.append(\"../../ms_chemical_space_explorer\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca3486f3-06b3-402d-90b1-ba01cb2568aa", + "metadata": {}, + "outputs": [], + "source": [ + "from ms_chemical_space_explorer.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings\n", + "library_spectra = SpectraWithMS2DeepScoreEmbeddings(neg_train_spectra + pos_train_spectra, ms2deepscore_model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c858cc69-3da8-4530-ba13-ad4b1827f1ae", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"neg_pos_library_embeddings.pickle\"), \"wb\") as handle:\n", + " pickle.dump(library_spectra.embeddings, handle, protocol=pickle.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53ca37fe-1627-4473-a21a-168791d92112", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"neg_pos_library_with_embeddings.pickle\"), \"wb\") as handle:\n", + " pickle.dump(library_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cec8761-6c37-4ac5-ba52-619c171cd834", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "val_spectra = SpectraWithMS2DeepScoreEmbeddings(neg_val_spectra + pos_val_spectra, ms2deepscore_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "3ba69a93-5ede-4920-b3a6-e7881e20eb39", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"neg_pos_val_spectra_with_embeddings.pickle\"), \"wb\") as handle:\n", + " pickle.dump(val_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "markdown", + "id": "688da27f-3290-480c-ab67-d171bbac6b93", + "metadata": {}, + "source": [ + "# Reload intermediates" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9cb48a39-f3cd-4924-8da2-f11007475795", + "metadata": {}, + "outputs": [], + "source": [ + "import sys \n", + "sys.path.append(\"../../ms_chemical_space_explorer\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "97d042ab-19f6-4d32-bfd7-b24e0f72c1d9", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pickle\n", + "pickled_intermediates_data_folder = \"../data/pickled_intermediates\"\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"neg_pos_library_with_embeddings.pickle\"), \"rb\") as file:\n", + " library_spectra = pickle.load(file)\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"neg_pos_val_spectra_with_embeddings.pickle\"), \"rb\") as file:\n", + " val_spectra = pickle.load(file)" + ] + }, + { + "cell_type": "markdown", + "id": "c3c8dfda-3294-45cc-8a6f-1aeb06257c73", + "metadata": {}, + "source": [ + "# Initialize a method evaluator" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "79fa345a-52eb-4079-a6b5-0bd63d78d821", + "metadata": {}, + "outputs": [], + "source": [ + "from ms_chemical_space_explorer.benchmarking.EvaluateMethods import EvaluateMethods\n", + "\n", + "method_evaluator = EvaluateMethods(library_spectra, val_spectra)\n", + "method_evaluator.training_spectrum_set.progress_bars = False\n", + "method_evaluator.validation_spectrum_set.progress_bars = False" + ] + }, + { + "cell_type": "markdown", + "id": "16663c37-4fdd-4454-bcc6-55dbf4d6230f", + "metadata": {}, + "source": [ + "# Test basic MS2DeepScore" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1048dccf-1b53-4dcc-96d4-01a4d51dc57d", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 78/78 [26:20<00:00, 20.26s/it]\n", + "Calculating analogue accuracy per inchikey: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2015/2015 [00:01<00:00, 1190.75it/s]\n" + ] + } + ], + "source": [ + "from ms_chemical_space_explorer.methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore\n", + "\n", + "result_analogue = method_evaluator.benchmark_analogue_search(predict_highest_ms2deepscore)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0f6f21cb-a358-4803-ad74-cc57a2a7a47e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3848227909492443\n" + ] + } + ], + "source": [ + "print(result_analogue)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "eda9a92c-fe2c-4c4b-ab4e-07ddb4346a27", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Splitting spectra per inchikey: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1837/1837 [00:00<00:00, 53337.60it/s]\n", + "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 31/31 [10:21<00:00, 20.03s/it]\n", + "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 33/33 [10:35<00:00, 19.25s/it]\n", + "Calculating exact match accuracy per inchikey: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1612/1612 [00:00<00:00, 313512.85it/s]\n" + ] + } + ], + "source": [ + "from ms_chemical_space_explorer.methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore\n", + "\n", + "result_positive = method_evaluator.benchmark_exact_matching_within_ionmode(predict_highest_ms2deepscore, \"positive\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "b6ddad52-8647-450f-8881-6753a5bea814", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.5169110149857257\n" + ] + } + ], + "source": [ + "print(result_positive)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "dfc7a156-74a3-4c48-b610-e38410abf274", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Splitting spectra per inchikey: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 924/924 [00:00<00:00, 293846.15it/s]\n", + "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [02:08<00:00, 18.29s/it]\n", + "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [02:14<00:00, 16.78s/it]\n", + "Calculating exact match accuracy per inchikey: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 868/868 [00:00<00:00, 894070.70it/s]\n" + ] + } + ], + "source": [ + "from ms_chemical_space_explorer.methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore\n", + "\n", + "result_neg = method_evaluator.benchmark_exact_matching_within_ionmode(predict_highest_ms2deepscore, \"negative\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "86cc5ba4-4380-488a-a486-de63cccada74", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5448453174876924" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_neg\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "a5b0595b-51dd-4a56-b9ec-e81a597dd26b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Splitting spectra per inchikey across ionmodes: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2015/2015 [00:00<00:00, 9551.01it/s]\n", + "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [11:40<00:00, 20.02s/it]\n", + "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [03:47<00:00, 17.46s/it]\n", + "Calculating exact match accuracy per inchikey: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 746/746 [00:00<00:00, 496186.30it/s]\n" + ] + } + ], + "source": [ + "result_across_ionmodes = method_evaluator.exact_matches_across_ionization_modes(predict_highest_ms2deepscore)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "fe14e752-75b4-4e00-8600-90c1dce46b90", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0006188308440879157" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_across_ionmodes" + ] + }, + { + "cell_type": "markdown", + "id": "1499181b-7b68-437a-85b3-b7dc3ad53884", + "metadata": {}, + "source": [ + "# Test best possible results" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "56d119e2-145a-484c-ab3c-56b569b549dd", + "metadata": {}, + "outputs": [], + "source": [ + "from ms_chemical_space_explorer.methods.predict_best_possible_match import predict_best_possible_match\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b3687845-099c-4b98-b715-c8057b5858bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating tanimoto scores to determine best possible match\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Calculating analogue accuracy per inchikey: 100%|██████████████████████████████████████████| 2015/2015 [00:01<00:00, 1578.04it/s]\n" + ] + } + ], + "source": [ + "result_analogue_best = method_evaluator.benchmark_analogue_search(predict_highest_ms2deepscore)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "81c83a8d-0754-4207-97d3-2fbef49082ac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7753653963061775" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_analogue_best" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "5fad0038-025c-4c73-9bfb-bc4545f092df", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Splitting spectra per inchikey: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 924/924 [00:00<00:00, 310739.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating tanimoto scores to determine best possible match\n", + "Calculating tanimoto scores to determine best possible match\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Calculating exact match accuracy per inchikey: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 868/868 [00:00<00:00, 928738.74it/s]\n" + ] + } + ], + "source": [ + "result_neg_best = method_evaluator.benchmark_exact_matching_within_ionmode(predict_best_possible_match, \"negative\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "e0418a27-0195-4021-a04b-788cbee4fe2b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_neg_best" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "f19ea34d-f6c1-4f7c-b7ec-8c7bad85a1da", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Splitting spectra per inchikey across ionmodes: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2015/2015 [00:00<00:00, 13798.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating tanimoto scores to determine best possible match\n", + "Calculating tanimoto scores to determine best possible match\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Calculating exact match accuracy per inchikey: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 746/746 [00:00<00:00, 404727.82it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_across_ionmodes_best = method_evaluator.exact_matches_across_ionization_modes(predict_best_possible_match)\n", + "result_across_ionmodes_best" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "052e768d-4044-40b8-a85e-7cf03abe4184", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Splitting spectra per inchikey across ionmodes: 100%|█████████████████████████████████████| 2015/2015 [00:00<00:00, 10356.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating tanimoto scores to determine best possible match\n", + "Calculating tanimoto scores to determine best possible match\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Calculating exact match accuracy per inchikey: 100%|███████████████████████████████████████| 746/746 [00:00<00:00, 315164.26it/s]\n" + ] + } + ], + "source": [ + "result_across_ionmodes_best = method_evaluator.exact_matches_across_ionization_modes(predict_best_possible_match)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1ff6eaba-8213-4270-9395-39ff3aee6879", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_across_ionmodes_best" + ] + }, + { + "cell_type": "markdown", + "id": "3702ba19-0c86-443c-8621-fe1ab427b3da", + "metadata": {}, + "source": [ + "# Test cosine score" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5f0cfc49-009f-46a1-beef-6310d31260a1", + "metadata": {}, + "outputs": [], + "source": [ + "from ms_chemical_space_explorer.methods.predict_highest_cosine import predict_highest_cosine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9cb9210d-3ead-4798-bcc0-82b571e69f8b", + "metadata": {}, + "outputs": [], + "source": [ + "result_analogue_cosine = method_evaluator.benchmark_analogue_search(predict_highest_cosine)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1a5fb72-6039-4d56-af71-fe07bb583c22", + "metadata": {}, + "outputs": [], + "source": [ + "result_analogue_cosine" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7d4641b9-9b44-4b64-8fed-0b568cb0f4f9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Splitting spectra per inchikey: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 924/924 [00:00<00:00, 260575.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "created scores\n", + "Filtered on precursor\n", + "Calculated cosine\n", + "('PrecursorMzMatch', 'CosineGreedy_score', 'CosineGreedy_matches')\n", + "created scores\n", + "Filtered on precursor\n", + "Calculated cosine\n", + "('PrecursorMzMatch', 'CosineGreedy_score', 'CosineGreedy_matches')\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Calculating exact match accuracy per inchikey: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 868/868 [00:00<00:00, 201408.27it/s]\n" + ] + } + ], + "source": [ + "result_neg = method_evaluator.benchmark_exact_matching_within_ionmode(predict_highest_cosine, \"negative\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "96191952-14df-41b3-a410-d2e5574dd3c9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5772003486330072" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_neg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e77b0ecd-7f1a-438d-a0a1-950ff9e76881", + "metadata": {}, + "outputs": [], + "source": [ + "result_positive = method_evaluator.benchmark_exact_matching_within_ionmode(predict_highest_cosine, \"positive\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7db770c4-f4bb-4a0e-82a2-03d52e6ed7d0", + "metadata": {}, + "outputs": [], + "source": [ + "result_positive" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "675351b2-ff50-42d8-b179-006d0a46cadc", + "metadata": {}, + "outputs": [], + "source": [ + "result_across_ionmodes = method_evaluator.exact_matches_across_ionization_modes(predict_highest_cosine)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d056551a-f85b-4077-8b35-eaf834da7a9b", + "metadata": {}, + "outputs": [], + "source": [ + "result_across_ionmodes" + ] + }, + { + "cell_type": "markdown", + "id": "7682a1b8-aec1-40dc-b942-419bd23de041", + "metadata": {}, + "source": [ + "# Test predictions with ISF" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1dc19bef-69ab-471e-94ab-6a9caffd9030", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOStream.flush timed outepscore per batch of 500 embeddings: 77%|██████████████████████████████████████████████████████████████████▏ | 60/78 [33:53<22:39, 75.54s/it]\n", + "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|██████████████████████████████████████████████████████████████████████████████████████| 78/78 [42:49<00:00, 32.95s/it]\n", + "Calculating ISF score: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 38533/38533 [00:44<00:00, 857.97it/s]\n", + "Calculating analogue accuracy per inchikey: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 2015/2015 [00:01<00:00, 1076.80it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.35576217271302824\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Splitting spectra per inchikey: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 924/924 [00:00<00:00, 202462.49it/s]\n", + "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|████████████████████████████████████████████████████████████████████████████████████████| 7/7 [02:56<00:00, 25.28s/it]\n", + "Calculating ISF score: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3303/3303 [00:04<00:00, 713.49it/s]\n", + "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|████████████████████████████████████████████████████████████████████████████████████████| 8/8 [03:30<00:00, 26.27s/it]\n", + "Calculating ISF score: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3721/3721 [00:04<00:00, 856.75it/s]\n", + "Calculating exact match accuracy per inchikey: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 868/868 [00:00<00:00, 176636.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.04118290017821497\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Splitting spectra per inchikey: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1837/1837 [00:00<00:00, 134548.79it/s]\n", + "Predicting highest ms2deepscore per batch of 500 embeddings: 84%|███████████████████████████████████████████████████████████████████████▎ | 26/31 [38:00<38:36, 463.28s/it]IOStream.flush timed out\n", + "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|██████████████████████████████████████████████████████████████████████████████████████| 31/31 [41:05<00:00, 79.53s/it]\n", + "Calculating ISF score: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15218/15218 [00:20<00:00, 753.86it/s]\n", + "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|██████████████████████████████████████████████████████████████████████████████████████| 33/33 [12:25<00:00, 22.58s/it]\n", + "Calculating ISF score: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16010/16010 [00:22<00:00, 719.72it/s]\n", + "Calculating exact match accuracy per inchikey: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 1612/1612 [00:00<00:00, 236406.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.05296326960524268\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Splitting spectra per inchikey across ionmodes: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 2015/2015 [00:00<00:00, 3809.64it/s]\n", + "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|██████████████████████████████████████████████████████████████████████████████████████| 35/35 [13:13<00:00, 22.66s/it]\n", + "Calculating ISF score: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17128/17128 [00:21<00:00, 808.89it/s]\n", + "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|██████████████████████████████████████████████████████████████████████████████████████| 13/13 [04:14<00:00, 19.57s/it]\n", + "Calculating ISF score: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6059/6059 [00:08<00:00, 714.94it/s]\n", + "Calculating exact match accuracy per inchikey: 100%|███████████████████████████████████████████████████████████████████████████████████████████████| 746/746 [00:00<00:00, 56840.41it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0006614631666202545\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from ms_chemical_space_explorer.methods.predict_with_integrated_similarity_flow import predict_with_integrated_similarity_flow\n", + "\n", + "result_analogue_isf = method_evaluator.benchmark_analogue_search(predict_with_integrated_similarity_flow)\n", + "print(result_analogue_isf)\n", + "result_neg_isf = method_evaluator.benchmark_exact_matching_within_ionmode(predict_with_integrated_similarity_flow, \"negative\")\n", + "print(result_neg_isf)\n", + "result_positive_isf = method_evaluator.benchmark_exact_matching_within_ionmode(predict_with_integrated_similarity_flow, \"positive\")\n", + "print(result_positive_isf)\n", + "result_across_ionmodes_isf = method_evaluator.exact_matches_across_ionization_modes(predict_with_integrated_similarity_flow)\n", + "print(result_across_ionmodes_isf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b891a34-aade-49e8-ad91-88b36cf0e011", + "metadata": {}, + "outputs": [], + "source": [ + "result_analogue_isf" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "22ba5663-1f8a-405a-b04b-0380e18c6987", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello\n" + ] + } + ], + "source": [ + "print(\"hello\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b9198fb-c60b-4db5-8415-578b88275e5a", + "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": "3.9.21" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb b/ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb new file mode 100644 index 0000000..dcad892 --- /dev/null +++ b/ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb @@ -0,0 +1,349 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "df3d5e3f-2694-4eed-9fc1-a78483629412", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "7551it [00:07, 1015.57it/s]\n", + "7142it [00:12, 560.76it/s] \n", + "130901it [03:10, 685.40it/s] \n", + "25412it [00:32, 784.47it/s] \n", + "24911it [00:34, 718.09it/s] \n", + "25412it [00:45, 556.46it/s] \n" + ] + } + ], + "source": [ + "from matchms.importing import load_from_mgf\n", + "from tqdm import tqdm\n", + "\n", + "neg_val_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_validation_spectra.mgf\")))\n", + "neg_test_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_testing_spectra.mgf\")))\n", + "neg_train_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_training_spectra.mgf\")))\n", + "neg_spectra = neg_val_spectra + neg_test_spectra + neg_train_spectra\n", + "\n", + "pos_val_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/positive_validation_spectra.mgf\")))\n", + "pos_test_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/positive_testing_spectra.mgf\")))\n", + "pos_train_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/positive_training_spectra.mgf\")))\n", + "pos_spectra = pos_val_spectra + pos_test_spectra + pos_train_spectra" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2771fc81-ad6b-4b2d-90ea-c541641af1a5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 145594/145594 [00:25<00:00, 5637.57it/s]\n" + ] + } + ], + "source": [ + "neg_inchikeys = []\n", + "for spectrum in tqdm(neg_spectra):\n", + " neg_inchikeys.append(spectrum.get(\"inchikey\")[:14])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "437e9455-49a9-435e-8bb5-cd3b3ed987f1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 519580/519580 [00:46<00:00, 11219.75it/s]\n" + ] + } + ], + "source": [ + "pos_inchikeys = []\n", + "for spectrum in tqdm(pos_spectra):\n", + " pos_inchikeys.append(spectrum.get(\"inchikey\")[:14])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "df03a6d2-6755-46e1-a874-fc96c1a2b20c", + "metadata": {}, + "outputs": [], + "source": [ + "unique_neg_inchikeys = set(neg_inchikeys)\n", + "unique_pos_inchikeys = set(pos_inchikeys)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "aa775f3b-9c10-43cd-a6be-74dac2e698ce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18480" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(unique_neg_inchikeys)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f3a73395-68c2-4952-9171-96f67f597cc9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "36638" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(unique_pos_inchikeys)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f9729ad5-d201-476a-87a1-78e267425aae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14801" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "overlapping_inchikeys = unique_neg_inchikeys & unique_pos_inchikeys\n", + "len(overlapping_inchikeys)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "07d148f7-aef6-4eda-a66f-984f0c65a31b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "40317" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_unique_inchikeys = unique_neg_inchikeys | unique_pos_inchikeys\n", + "len(total_unique_inchikeys)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0cd254e4-9b7f-41ed-a7ce-25675392102f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.36711560880025795" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "14801/40317" + ] + }, + { + "cell_type": "markdown", + "id": "3f5bb776-9910-4a4d-bc45-d8e049d63ce6", + "metadata": {}, + "source": [ + "# Check how many there are already in the val set\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "4b5ced16-ceae-439b-bb04-c72986798b8f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7551/7551 [00:01<00:00, 4630.22it/s]\n" + ] + } + ], + "source": [ + "neg_inchikeys_val = []\n", + "for spectrum in tqdm(neg_val_spectra):\n", + " neg_inchikeys_val.append(spectrum.get(\"inchikey\")[:14])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "0d2b00bd-56a3-4c67-ad3f-d54051a74701", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25412/25412 [00:02<00:00, 10220.59it/s]\n" + ] + } + ], + "source": [ + "pos_inchikeys_val = []\n", + "for spectrum in tqdm(pos_val_spectra):\n", + " pos_inchikeys_val.append(spectrum.get(\"inchikey\")[:14])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "76a7736a-d4a4-42a5-bf42-d20b1a38060c", + "metadata": {}, + "outputs": [], + "source": [ + "unique_neg_inchikeys_val = set(neg_inchikeys_val)\n", + "unique_pos_inchikeys_val = set(pos_inchikeys_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "6fce2334-e43b-40ce-b459-75c1a6d1ed00", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "35" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "overlapping_inchikeys_val = unique_neg_inchikeys_val & unique_pos_inchikeys_val\n", + "len(overlapping_inchikeys_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "86668203-8e5b-40e9-934e-0ade3c25e9d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7551" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(neg_inchikeys_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "bc2e25e7-5d85-4486-924d-c1d9651ba2bc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25412" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(pos_inchikeys_val)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e864dc8e-68bb-4d3d-8a27-57f3892581e3", + "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": "3.9.21" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 9f67cd2d1c48317fa159fba205d7a438926341f4 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 5 Dec 2025 11:16:15 +0100 Subject: [PATCH 005/149] Add tests --- tests/conftest.py | 79 +++++++++++++ tests/testPredictMS2DeepScoreSimilarity.py | 29 +++++ tests/test_SpectrumDataSet.py | 130 +++++++++++++++++++++ tests/test_evaluate_methods.py | 31 +++++ tests/test_methods.py | 58 +++++++++ 5 files changed, 327 insertions(+) create mode 100644 tests/conftest.py create mode 100644 tests/testPredictMS2DeepScoreSimilarity.py create mode 100644 tests/test_SpectrumDataSet.py create mode 100644 tests/test_evaluate_methods.py create mode 100644 tests/test_methods.py diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..80ab525 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,79 @@ +import os +from pathlib import Path + +import numpy as np +from matchms.Spectrum import Spectrum +from ms2deepscore.models import load_model + + +TEST_RESOURCES_PATH = Path(__file__).parent / "test_data" + + +def create_test_spectra( + number_of_spectra_per_inchikey=3, + inchikey_inchi_pairs=None, + nr_of_inchikeys=3, +): + if inchikey_inchi_pairs is None: + inchikey_inchi_pairs = get_inchikey_inchi_pairs(nr_of_inchikeys) + spectra = [] + for i, inchikey_inchi_tuple in enumerate(inchikey_inchi_pairs): + inchikey, inchi, smiles, compound_name = inchikey_inchi_tuple + for j in range(number_of_spectra_per_inchikey): + spectra.append( + Spectrum( + mz=np.array([100 + i * 10.0, 500 + i * 1.0]), + intensities=np.array([1.0, 1.0 / (j + 1)]), + metadata={ + "precursor_mz": 111.1 + i * 10, + "inchikey": inchikey, + "inchi": inchi, + "smiles": smiles, + "compound_name": compound_name, + }, + ) + ) + return spectra + + +def ms2deepscore_model(): + return load_model(os.path.join(TEST_RESOURCES_PATH, "ms2deepscore_testmodel_v1.pt")) + + +def get_inchikey_inchi_pairs(number_of_pairs): + """Returns inchikey_inchi_pairs""" + inchikey_inchi_pairs = ( + ( + "RYYVLZVUVIJVGH-UHFFFAOYSA-N", + "InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2/h4H,1-3H3", + "CN1C=NC2=C1C(=O)N(C(=O)N2C)C", + "Caffeine", + ), + ( + "ZPUCINDJVBIVPJ-LJISPDSOSA-N", + "InChI=1S/C17H21NO4/c1-18-12-8-9-13(18)15(17(20)21-2)14(10-12)22-16(19)11-6-4-3-5-7-11/h3-7,12-15H,8-10H2,1-2H3/t12-,13+,14-,15+/m0/s1", + "CN1[C@H]2CC[C@@H]1[C@H]([C@H](C2)OC(=O)C3=CC=CC=C3)C(=O)OC", + "Cocaine", + ), + ( + "RZVAJINKPMORJF-UHFFFAOYSA-N", + "InChI=1S/C8H9NO2/c1-6(10)9-7-2-4-8(11)5-3-7/h2-5,11H,1H3,(H,9,10)", + "CC(=O)NC1=CC=C(C=C1)O", + "Paracetemol", + ), + ( + "JGSARLDLIJGVTE-MBNYWOFBSA-N", + "InChI=1S/C16H18N2O4S/c1-16(2)12(15(21)22)18-13(20)11(14(18)23-16)17-10(19)8-9-6-4-3-5-7-9/h3-7,11-12,14H,8H2,1-2H3,(H,17,19)(H,21,22)/t11-,12+,14-/m1/s1", + "CC1([C@@H](N2[C@H](S1)[C@@H](C2=O)NC(=O)CC3=CC=CC=C3)C(=O)O)C", + "Penicillin", + ), + ( + "WQZGKKKJIJFFOK-GASJEMHNSA-N", + "InChI=1S/C6H12O6/c7-1-2-3(8)4(9)5(10)6(11)12-2/h2-11H,1H2/t2-,3-,4+,5-,6?/m1/s1", + "C([C@@H]1[C@H]([C@@H]([C@H](C(O1)O)O)O)O)O", + "Glucose", + ), + ) + if number_of_pairs > len(inchikey_inchi_pairs): + raise ValueError("Not enough example compounds, add some in conftest") + return inchikey_inchi_pairs[:number_of_pairs] diff --git a/tests/testPredictMS2DeepScoreSimilarity.py b/tests/testPredictMS2DeepScoreSimilarity.py new file mode 100644 index 0000000..9741693 --- /dev/null +++ b/tests/testPredictMS2DeepScoreSimilarity.py @@ -0,0 +1,29 @@ +import numpy as np +import pytest + +from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import ( + predict_top_ms2deepscores, +) +from tests.conftest import create_test_spectra, ms2deepscore_model +from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings + + +@pytest.mark.parametrize( + "method", + [ + predict_top_ms2deepscores, + ], +) +def test_predict_highest_ms2deepscore_similarity(method): + ms2ds_model = ms2deepscore_model() + test_spectra = create_test_spectra(1) + library_spectra = SpectraWithMS2DeepScoreEmbeddings(test_spectra, ms2ds_model) + query_spectra = SpectraWithMS2DeepScoreEmbeddings(test_spectra, ms2ds_model) + number_of_analogues = 2 + + indices, distances = method(library_spectra.embeddings, query_spectra.embeddings, k=number_of_analogues) + assert indices.shape == (len(test_spectra), number_of_analogues) + assert distances.shape == (len(test_spectra), number_of_analogues) + for i, row in enumerate(indices): + assert row[0] == i, "The highest predictions should be against itself" + assert np.allclose(distances[i][0], 1.0, atol=1e-5) diff --git a/tests/test_SpectrumDataSet.py b/tests/test_SpectrumDataSet.py new file mode 100644 index 0000000..d7b3cff --- /dev/null +++ b/tests/test_SpectrumDataSet.py @@ -0,0 +1,130 @@ +import numpy as np +import pytest + +from ms2query.benchmarking.SpectrumDataSet import ( + SpectraWithFingerprints, + SpectrumSetBase, + SpectraWithMS2DeepScoreEmbeddings, +) +from tests.conftest import create_test_spectra, ms2deepscore_model, get_inchikey_inchi_pairs + + +@pytest.mark.parametrize( + "library", + [ + SpectrumSetBase(create_test_spectra()), + SpectraWithFingerprints(create_test_spectra()), + SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(), ms2deepscore_model()), + ], +) +def test_spectrum_set_base(library): + """Test all base functionality of SpectrumSetBase is implemented correctly also for all classes inheriting from it""" + # test correct init + assert len(library.spectra) == 9 + assert len(library.spectrum_indexes_per_inchikey) == 3 + assert sum(len(v) for v in library.spectrum_indexes_per_inchikey.values()) == 9 + + # test correct copying + new_copy = library.copy() + assert len(new_copy.spectra) == 9 + assert len(new_copy.spectrum_indexes_per_inchikey) == 3 + assert sum(len(v) for v in new_copy.spectrum_indexes_per_inchikey.values()) == 9 + + # test correctly adding spectra + new_copy.add_spectra(library) + new_number_of_spectra = 9 + 9 + assert len(new_copy.spectra) == new_number_of_spectra + assert len(new_copy.spectrum_indexes_per_inchikey) == 3 + assert sum(len(v) for v in new_copy.spectrum_indexes_per_inchikey.values()) == new_number_of_spectra + + # test the original is not edited when adding spectra + assert len(library.spectra) == 9 + assert len(library.spectrum_indexes_per_inchikey) == 3 + assert sum(len(v) for v in library.spectrum_indexes_per_inchikey.values()) == 9 + + # test correct subsetting + subset_indexes = [1, 4, 6, 7] + subset = library.subset_spectra(subset_indexes) + assert len(subset.spectra) == len(subset_indexes) + assert len(subset.spectrum_indexes_per_inchikey) == 3 + assert sum(len(v) for v in subset.spectrum_indexes_per_inchikey.values()) == len(subset_indexes) + assert isinstance(subset, library.__class__) + + +@pytest.mark.parametrize( + "library", + [ + SpectraWithFingerprints(create_test_spectra()), + SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(), ms2deepscore_model()), + ], +) +def test_spectra_with_fingerprints(library): + """Test all functionality added in SpectraWithFingerprints also for all classes inheriting from it""" + # test correct init + assert len(library.inchikey_fingerprint_pairs) == 3 + + # test correct copying + new_copy = library.copy() + assert len(new_copy.inchikey_fingerprint_pairs) == 3 + + # test correctly adding inchikey_fingerprint_pairs when runnning add_spectra + for inchikey_inchi_pairs, expected_nr_of_inchikeys in ( + (get_inchikey_inchi_pairs(5)[2:], 5), # Some overlap + (get_inchikey_inchi_pairs(5)[3:], 5), # No overlap + (get_inchikey_inchi_pairs(3), 3), # Fully overlapping + (get_inchikey_inchi_pairs(1), 3), # Fully overlapping (but not all) + ): + spectra_to_add = SpectrumSetBase(create_test_spectra(2, inchikey_inchi_pairs=inchikey_inchi_pairs)) + new_copy = library.copy() + new_copy.add_spectra(spectra_to_add) + assert len(new_copy.inchikey_fingerprint_pairs) == expected_nr_of_inchikeys + for inchikey in library.inchikey_fingerprint_pairs: + assert np.array_equal( + new_copy.inchikey_fingerprint_pairs[inchikey], library.inchikey_fingerprint_pairs[inchikey] + ) + + # test the original is not edited when adding spectra + assert len(library.inchikey_fingerprint_pairs) == 3 + assert all( + np.array_equal(library.inchikey_fingerprint_pairs[key], value) + for key, value in SpectraWithFingerprints(create_test_spectra()).inchikey_fingerprint_pairs.items() + ) + + # test correct subsetting + subset_indexes = [1, 4, 6, 7] + subset = library.subset_spectra(subset_indexes) + assert len(subset.inchikey_fingerprint_pairs) == 3 + assert all( + np.array_equal(library.inchikey_fingerprint_pairs[key], value) + for key, value in subset.inchikey_fingerprint_pairs.items() + ) + assert hasattr(subset, "update_fingerprint_per_inchikey") + + +def test_spectra_with_embeddings(): + library = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(), ms2deepscore_model()) + # test correct init + assert library.embeddings.shape == (9, 100) + + # test correct copying + new_copy = library.copy() + assert new_copy.embeddings.shape == (9, 100) + + # test correctly adding spectra + new_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(1), ms2deepscore_model()) + new_copy.add_spectra(new_spectra) + assert new_copy.embeddings.shape == (12, 100) + + # test the original is not edited when adding spectra + assert library.embeddings.shape == (9, 100) + + # test correct subsetting + subset_indexes = [1, 4, 6, 7] + subset = library.subset_spectra(subset_indexes) + assert subset.embeddings.shape == (len(subset_indexes), 100) + for i, index in enumerate(subset_indexes): + assert np.all(library.embeddings[index] == subset.embeddings[i]) + + # Check that subsetting on subset works. To make sure that a subset does not become of type SpectrumSetBase + subsetted_subset = subset.subset_spectra([0, 1]) + assert subsetted_subset.embeddings.shape == (2, 100) diff --git a/tests/test_evaluate_methods.py b/tests/test_evaluate_methods.py new file mode 100644 index 0000000..4bcb839 --- /dev/null +++ b/tests/test_evaluate_methods.py @@ -0,0 +1,31 @@ +import pytest +from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings +from ms2query.benchmarking.EvaluateMethods import EvaluateMethods +from ms2query.benchmarking.reference_methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore +from ms2query.benchmarking.reference_methods.predict_best_possible_match import predict_best_possible_match +from tests.conftest import create_test_spectra, ms2deepscore_model +from ms2query.benchmarking.reference_methods.predict_highest_cosine import predict_highest_cosine + + +@pytest.mark.parametrize( + "method", + [predict_highest_ms2deepscore, predict_highest_cosine, predict_best_possible_match], +) +def test_evaluate_methods(method): + nr_of_spectra_per_inchikey = 6 + nr_of_inchikeys = 5 + dummy_spectra = create_test_spectra(nr_of_spectra_per_inchikey, nr_of_inchikeys=nr_of_inchikeys) + for i, spectrum in enumerate(dummy_spectra): + if i % 3 == 0: + spectrum.set("ionmode", "positive") + else: + spectrum.set("ionmode", "negative") + model = ms2deepscore_model() + reference_library = SpectraWithMS2DeepScoreEmbeddings(dummy_spectra[: nr_of_spectra_per_inchikey * 2], model) + validation_spectra = SpectraWithMS2DeepScoreEmbeddings(dummy_spectra[nr_of_spectra_per_inchikey * 2 :], model) + method_evaluator = EvaluateMethods(reference_library, validation_spectra) + # should be zero or below zero, since it is the difference with the perfect predictions + assert method_evaluator.benchmark_analogue_search(method) >= 0.0 + # # Should be 1 because we added a good match for each. + assert method_evaluator.benchmark_exact_matching_within_ionmode(method, "positive") == 1.0 + assert method_evaluator.exact_matches_across_ionization_modes(method) == 1.0 diff --git a/tests/test_methods.py b/tests/test_methods.py new file mode 100644 index 0000000..03cca1c --- /dev/null +++ b/tests/test_methods.py @@ -0,0 +1,58 @@ +import numpy as np +from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix + +from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings +from ms2query.benchmarking.reference_methods.predict_highest_cosine import predict_highest_cosine +from tests.conftest import create_test_spectra, ms2deepscore_model +from ms2query.benchmarking.reference_methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore +from ms2query.benchmarking.reference_methods.predict_best_possible_match import predict_best_possible_match +from ms2query.benchmarking.reference_methods.predict_with_integrated_similarity_flow import ( + predict_with_integrated_similarity_flow, + integrated_similarity_flow, +) + +import pytest + + +@pytest.mark.parametrize( + "prediction_function", + [ + predict_highest_cosine, + predict_highest_ms2deepscore, + predict_best_possible_match, + ], +) +def test_all_methods(prediction_function): + model = ms2deepscore_model() + library_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(), model) + test_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(1), model) + predicted_inchikeys, scores = prediction_function(library_spectra, test_spectra) + for i, spectrum in enumerate(test_spectra.spectra): + inchikey = spectrum.get("inchikey")[:14] + assert predicted_inchikeys[i] == inchikey + assert np.allclose(scores[i], np.array(1.0), atol=1e-5) + + +def test_predict_with_integrated_similarity_flow(): + model = ms2deepscore_model() + library_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(), model) + test_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(1), model) + predicted_inchikeys, scores = predict_with_integrated_similarity_flow(library_spectra, test_spectra) + + assert predicted_inchikeys == ["RYYVLZVUVIJVGH", "ZPUCINDJVBIVPJ", "ZPUCINDJVBIVPJ"] + assert np.allclose(np.array([0.38829751082577607, 0.3919729335980483, 0.38774130710967564]), np.array(scores)) + + +def test_isf_computation(): + distances = [0.99, 0.99, 0.99, 0.5, 0.5] + fps = np.array([[1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 0, 0], [0, 0, 1, 0, 1, 0]]) + similarities = jaccard_similarity_matrix(fps, fps) + result = integrated_similarity_flow(distances, similarities, [1, 1, 1, 1, 1]) + expected_result = [ + 0.715869, + 0.686481, + 0.736523, + 0.492107, + 0.359110, + ] + assert np.allclose(np.array(expected_result), np.array(result)) From 4d81ddf2ab12264092d82b21eaac38b720dc2224 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 5 Dec 2025 11:16:25 +0100 Subject: [PATCH 006/149] Add inits --- ms2query/benchmarking/__init__.py | 0 ms2query/benchmarking/reference_methods/__init__.py | 0 tests/__init__.py | 0 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 ms2query/benchmarking/__init__.py create mode 100644 ms2query/benchmarking/reference_methods/__init__.py create mode 100644 tests/__init__.py diff --git a/ms2query/benchmarking/__init__.py b/ms2query/benchmarking/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ms2query/benchmarking/reference_methods/__init__.py b/ms2query/benchmarking/reference_methods/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 From 8024263fcfc63bfb4f8c3bc4939abb1332b79676 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 8 Dec 2025 16:08:25 +0100 Subject: [PATCH 007/149] Use ms2deepscore 2.6.0 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 3fe7fa4..396af2e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,7 @@ pandas = ">=2.1.1" scipy= ">=1.14.0" matplotlib= ">=3.8.0" matchms= ">=0.30.0" -ms2deepscore= { git = "https://github.com/matchms/ms2deepscore.git", branch = "pytorch_update" } +ms2deepscore= ">=2.6.0" rdkit= ">2024.3.4" nmslib= ">=2.0.0" umap-learn= ">=0.5.7" From b4561c0e7a5e87ae7ed02f6b8d7830bd747b49bb Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 9 Dec 2025 15:59:20 +0100 Subject: [PATCH 008/149] Do correct subsetting of inchikey sets --- ms2query/benchmarking/SpectrumDataSet.py | 9 +++++++++ tests/test_SpectrumDataSet.py | 4 ++-- 2 files changed, 11 insertions(+), 2 deletions(-) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index 0751d70..2446d30 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -108,6 +108,15 @@ def copy(self): new_instance.inchikey_fingerprint_pairs = copy.copy(self.inchikey_fingerprint_pairs) return new_instance + def subset_spectra(self, spectrum_indexes) -> "SpectraWithFingerprints": + """Returns a new instance of a subset of the spectra""" + new_instance = super().subset_spectra(spectrum_indexes) + # Only keep the fingerprints for which we have inchikeys. + # Important note: This is not a deep copy! + # And the fingerprint is not reset (so it is not always actually matching the most common inchi) + new_instance.inchikey_fingerprint_pairs = {inchikey: self.inchikey_fingerprint_pairs[inchikey] for inchikey in new_instance.spectrum_indexes_per_inchikey.keys()} + return new_instance + class SpectraWithMS2DeepScoreEmbeddings(SpectraWithFingerprints): def __init__(self, spectra: List[Spectrum], ms2deepscore_model: SiameseSpectralModel, **kwargs): diff --git a/tests/test_SpectrumDataSet.py b/tests/test_SpectrumDataSet.py index d7b3cff..a4c8e2c 100644 --- a/tests/test_SpectrumDataSet.py +++ b/tests/test_SpectrumDataSet.py @@ -91,9 +91,9 @@ def test_spectra_with_fingerprints(library): ) # test correct subsetting - subset_indexes = [1, 4, 6, 7] + subset_indexes = [1, 6, 7] subset = library.subset_spectra(subset_indexes) - assert len(subset.inchikey_fingerprint_pairs) == 3 + assert len(subset.inchikey_fingerprint_pairs) == 2 assert all( np.array_equal(library.inchikey_fingerprint_pairs[key], value) for key, value in subset.inchikey_fingerprint_pairs.items() From 7f769977394a76cba5124f15f15d6f25444cca2a Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 10 Dec 2025 12:58:05 +0100 Subject: [PATCH 009/149] Add method for predicting using top 10 closest library spectra. --- .../predict_using_closest_tanimoto.py | 66 +++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py new file mode 100644 index 0000000..f844b77 --- /dev/null +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -0,0 +1,66 @@ +import numpy as np +from ms2deepscore.vector_operations import cosine_similarity_matrix +from typing import Tuple, List + +from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings +from ms2query.metrics import generalized_tanimoto_similarity_matrix + + +def predict_using_closest_tanimoto( + library_spectra: SpectraWithMS2DeepScoreEmbeddings, query_spectra: SpectraWithMS2DeepScoreEmbeddings, + nr_of_closest_inchikeys_to_select=10 +) -> Tuple[List[str], List[float]]: + """Predict best inchikey, by taking the average score over all spectra for the 10 closest related library inchikeys. + (simplified version of old MS2Query) + """ + inchikeys_of_best_match = [] + single_highest_score = [] + for spectrum_idx in range(len(query_spectra.spectra)): + inchikey_of_best_match, score = predict_using_closest_tanimoto_single_spectrum( + library_spectra, query_spectra.subset_spectra([spectrum_idx]), nr_of_closest_inchikeys_to_select) + inchikeys_of_best_match.append(inchikey_of_best_match) + single_highest_score.append(score) + return inchikeys_of_best_match, single_highest_score + + +def predict_using_closest_tanimoto_single_spectrum(spectra_with_embeddings, single_spectrum_with_embeddings, + nr_of_closest_inchikeys_to_select) -> Tuple[str, float]: + if len(single_spectrum_with_embeddings.spectra) != 1: + raise ValueError("expected a single spectrum") + ms2deepscores = cosine_similarity_matrix(single_spectrum_with_embeddings.embeddings, + spectra_with_embeddings.embeddings)[0] + average_predicted_scores = {} + for inchikey, spectrum_indexes in spectra_with_embeddings.spectrum_indexes_per_inchikey.items(): + all_ms2deepscores_for_inchikey = ms2deepscores[spectrum_indexes] + if max(all_ms2deepscores_for_inchikey) > 0.7: + average_predicted_score = get_average_predictions_for_closely_related_metabolites( + spectra_with_embeddings, inchikey, ms2deepscores, nr_of_closest_inchikeys_to_select) + average_predicted_scores[inchikey] = average_predicted_score + + inchikey_with_highest_average_prediction, score = max(average_predicted_scores.items(), key=lambda item: item[1]) + return inchikey_with_highest_average_prediction, score + +def get_average_predictions_for_closely_related_metabolites(spectra_with_embeddings, inchikey, + all_ms2deepscores, nr_of_closest_inchikeys_to_select): + """Calculates the average ms2deepscore predictions for top k closest inchikeys""" + top_k_inchikeys, _ = get_inchikey_and_tanimoto_scores_for_top_k( + spectra_with_embeddings, inchikey,nr_of_closest_inchikeys_to_select) + + average_predicted_scores = [] + for top_inchikey in top_k_inchikeys: + matching_spectrum_indexes = spectra_with_embeddings.spectrum_indexes_per_inchikey[top_inchikey] + predicted_scores = all_ms2deepscores[matching_spectrum_indexes] + average_predicted_scores.append(predicted_scores.mean()) + average_predicted_score = sum(average_predicted_scores) / len(average_predicted_scores) + return average_predicted_score + +def get_inchikey_and_tanimoto_scores_for_top_k(spectra: SpectraWithMS2DeepScoreEmbeddings, inchikey, k) -> tuple[list[str], np.ndarray]: + """For an inchikey in a library the top k highest tanimoto scores in the library are predicted (including itself)""" + library_fingerprints = np.vstack(list(spectra.inchikey_fingerprint_pairs.values())) + fingerprint_single_inchikey = np.vstack(list([spectra.inchikey_fingerprint_pairs[inchikey]])) + similarity_scores = generalized_tanimoto_similarity_matrix(fingerprint_single_inchikey, library_fingerprints)[0] + inchikey_indexes_of_top_k = np.argpartition(similarity_scores, -k)[-k:] + tanimoto_scores_for_top_k = similarity_scores[inchikey_indexes_of_top_k] + all_inchikeys = list(spectra.inchikey_fingerprint_pairs.keys()) + top_inchikeys = [all_inchikeys[inchikey_index] for inchikey_index in inchikey_indexes_of_top_k] + return top_inchikeys, tanimoto_scores_for_top_k From 2c2e35ffa059fc65ca15a2c380e2533d5743ee15 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 10 Dec 2025 13:04:20 +0100 Subject: [PATCH 010/149] Added extra inchikey smiles examples --- tests/conftest.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/tests/conftest.py b/tests/conftest.py index 80ab525..80ec8e9 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -73,6 +73,18 @@ def get_inchikey_inchi_pairs(number_of_pairs): "C([C@@H]1[C@H]([C@@H]([C@H](C(O1)O)O)O)O)O", "Glucose", ), + ( + "MWOOGOJBHIARFG-UHFFFAOYSA-N", + "InChI=1S/C8H8O3/c1-11-8-4-6(5-9)2-3-7(8)10/h2-5,10H,1H3", + "COC1=C(C=CC(=C1)C=O)O", + "vanillin" + ), + ( + "ROHFNLRQFUQHCH-YFKPBYRVSA-N", + "InChI=1S/C6H13NO2/c1-4(2)3-5(7)6(8)9/h4-5H,3,7H2,1-2H3,(H,8,9)/t5-/m0/s1", + "CC(C)C[C@@H](C(=O)O)N", + "L-Leucine" + ) ) if number_of_pairs > len(inchikey_inchi_pairs): raise ValueError("Not enough example compounds, add some in conftest") From fb4cbd80ec728d394f342d5ee401c8dbf453400f Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 10 Dec 2025 14:17:02 +0100 Subject: [PATCH 011/149] Add test_get_inchikey_and_tanimoto_scores_from_top_k --- tests/test_predict_using_closest_tanimoto.py | 28 ++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 tests/test_predict_using_closest_tanimoto.py diff --git a/tests/test_predict_using_closest_tanimoto.py b/tests/test_predict_using_closest_tanimoto.py new file mode 100644 index 0000000..65933f2 --- /dev/null +++ b/tests/test_predict_using_closest_tanimoto.py @@ -0,0 +1,28 @@ +import numpy as np + +from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings, SpectraWithFingerprints +from ms2query.benchmarking.reference_methods.predict_using_closest_tanimoto import ( + predict_using_closest_tanimoto, predict_using_closest_tanimoto_single_spectrum, + get_average_predictions_for_closely_related_metabolites, get_inchikey_and_tanimoto_scores_for_top_k) +from tests.conftest import ms2deepscore_model, create_test_spectra +import pytest + + + +@pytest.mark.parametrize( + "k", + [1, 3, 7], +) +def test_get_inchikey_and_tanimoto_scores_for_top_k(k): + spectra = SpectraWithFingerprints(create_test_spectra(nr_of_inchikeys=7)) + inchikey = list(spectra.inchikey_fingerprint_pairs.keys())[2] + + top_inchikeys, tanimoto_scores_for_top_k = get_inchikey_and_tanimoto_scores_for_top_k( + spectra, inchikey,k) + + assert inchikey in top_inchikeys + assert len(top_inchikeys) == k + assert len(tanimoto_scores_for_top_k) == k + assert len(set(top_inchikeys)) == k + assert tanimoto_scores_for_top_k[top_inchikeys.index(inchikey)] == 1.0, \ + "The exact match is expected to have a score of 1.0" \ No newline at end of file From 1933c8530e9199478b6ed531d0cda94c99702d2d Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 10 Dec 2025 14:17:53 +0100 Subject: [PATCH 012/149] Move top_k_selection outside average computation for easier testing --- .../predict_using_closest_tanimoto.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index f844b77..6b5fa25 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -2,7 +2,7 @@ from ms2deepscore.vector_operations import cosine_similarity_matrix from typing import Tuple, List -from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings +from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings, SpectraWithFingerprints from ms2query.metrics import generalized_tanimoto_similarity_matrix @@ -33,19 +33,18 @@ def predict_using_closest_tanimoto_single_spectrum(spectra_with_embeddings, sing for inchikey, spectrum_indexes in spectra_with_embeddings.spectrum_indexes_per_inchikey.items(): all_ms2deepscores_for_inchikey = ms2deepscores[spectrum_indexes] if max(all_ms2deepscores_for_inchikey) > 0.7: + top_k_inchikeys, _ = get_inchikey_and_tanimoto_scores_for_top_k( + spectra_with_embeddings, inchikey, nr_of_closest_inchikeys_to_select) average_predicted_score = get_average_predictions_for_closely_related_metabolites( - spectra_with_embeddings, inchikey, ms2deepscores, nr_of_closest_inchikeys_to_select) + spectra_with_embeddings, top_k_inchikeys, ms2deepscores) average_predicted_scores[inchikey] = average_predicted_score inchikey_with_highest_average_prediction, score = max(average_predicted_scores.items(), key=lambda item: item[1]) return inchikey_with_highest_average_prediction, score -def get_average_predictions_for_closely_related_metabolites(spectra_with_embeddings, inchikey, - all_ms2deepscores, nr_of_closest_inchikeys_to_select): +def get_average_predictions_for_closely_related_metabolites(spectra_with_embeddings, top_k_inchikeys, + all_ms2deepscores): """Calculates the average ms2deepscore predictions for top k closest inchikeys""" - top_k_inchikeys, _ = get_inchikey_and_tanimoto_scores_for_top_k( - spectra_with_embeddings, inchikey,nr_of_closest_inchikeys_to_select) - average_predicted_scores = [] for top_inchikey in top_k_inchikeys: matching_spectrum_indexes = spectra_with_embeddings.spectrum_indexes_per_inchikey[top_inchikey] @@ -54,7 +53,8 @@ def get_average_predictions_for_closely_related_metabolites(spectra_with_embeddi average_predicted_score = sum(average_predicted_scores) / len(average_predicted_scores) return average_predicted_score -def get_inchikey_and_tanimoto_scores_for_top_k(spectra: SpectraWithMS2DeepScoreEmbeddings, inchikey, k) -> tuple[list[str], np.ndarray]: +def get_inchikey_and_tanimoto_scores_for_top_k(spectra: SpectraWithFingerprints, inchikey, k + ) -> tuple[list[str], np.ndarray]: """For an inchikey in a library the top k highest tanimoto scores in the library are predicted (including itself)""" library_fingerprints = np.vstack(list(spectra.inchikey_fingerprint_pairs.values())) fingerprint_single_inchikey = np.vstack(list([spectra.inchikey_fingerprint_pairs[inchikey]])) From fd2bf89def21119b67481333b1c9a2bcaa6ed676 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 10 Dec 2025 14:18:11 +0100 Subject: [PATCH 013/149] Add test_get_average_predictions_for_closely_related_metabolites --- tests/test_predict_using_closest_tanimoto.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/tests/test_predict_using_closest_tanimoto.py b/tests/test_predict_using_closest_tanimoto.py index 65933f2..e948187 100644 --- a/tests/test_predict_using_closest_tanimoto.py +++ b/tests/test_predict_using_closest_tanimoto.py @@ -8,6 +8,24 @@ import pytest +def test_get_average_predictions_for_closely_related_metabolites(): + test_spectra = create_test_spectra(nr_of_inchikeys=7) + # Select different number per inchikey (only one for the first) to check that it is correctly weighted. + test_spectra = test_spectra.copy()[2:] + spectra = SpectraWithFingerprints(test_spectra) + + inchikeys = list(spectra.inchikey_fingerprint_pairs.keys())[:3] + ms2deepscores = np.zeros(len(spectra.spectra)) + ms2deepscores[0] = 0.8 + ms2deepscores[[1,2,3]] = 0.6 + ms2deepscores[4] = 0.6 + ms2deepscores[5] = 0.8 + ms2deepscores[6] = 0.7 + # the average per inchikey is 0.8, 0.6, 0.7, so average overall should be 0.7 + average_predicted_score = get_average_predictions_for_closely_related_metabolites(spectra, + inchikeys, + ms2deepscores) + assert np.allclose(average_predicted_score, np.array(0.7), atol=1e-5) @pytest.mark.parametrize( "k", From 59aca8eca42a60682656ae5988f9e431ba2c0d1a Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 10 Dec 2025 15:33:04 +0100 Subject: [PATCH 014/149] Split select_inchikeys_with_highest_ms2deepscores to make more modular and to select top k highest ms2deepscores --- .../predict_using_closest_tanimoto.py | 37 +++++++++++++------ 1 file changed, 25 insertions(+), 12 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index 6b5fa25..f353aeb 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -14,34 +14,47 @@ def predict_using_closest_tanimoto( (simplified version of old MS2Query) """ inchikeys_of_best_match = [] - single_highest_score = [] + highest_scores = [] for spectrum_idx in range(len(query_spectra.spectra)): inchikey_of_best_match, score = predict_using_closest_tanimoto_single_spectrum( library_spectra, query_spectra.subset_spectra([spectrum_idx]), nr_of_closest_inchikeys_to_select) inchikeys_of_best_match.append(inchikey_of_best_match) - single_highest_score.append(score) - return inchikeys_of_best_match, single_highest_score + highest_scores.append(score) + return inchikeys_of_best_match, highest_scores def predict_using_closest_tanimoto_single_spectrum(spectra_with_embeddings, single_spectrum_with_embeddings, - nr_of_closest_inchikeys_to_select) -> Tuple[str, float]: + nr_of_closest_inchikeys_to_select, + nr_of_inchikeys_with_highest_ms2deepscore_to_select) -> Tuple[str, float]: if len(single_spectrum_with_embeddings.spectra) != 1: raise ValueError("expected a single spectrum") ms2deepscores = cosine_similarity_matrix(single_spectrum_with_embeddings.embeddings, spectra_with_embeddings.embeddings)[0] + top_inchikeys = select_inchikeys_with_highest_ms2deepscore(spectra_with_embeddings, ms2deepscores, + nr_of_inchikeys_with_highest_ms2deepscore_to_select) average_predicted_scores = {} - for inchikey, spectrum_indexes in spectra_with_embeddings.spectrum_indexes_per_inchikey.items(): - all_ms2deepscores_for_inchikey = ms2deepscores[spectrum_indexes] - if max(all_ms2deepscores_for_inchikey) > 0.7: - top_k_inchikeys, _ = get_inchikey_and_tanimoto_scores_for_top_k( - spectra_with_embeddings, inchikey, nr_of_closest_inchikeys_to_select) - average_predicted_score = get_average_predictions_for_closely_related_metabolites( - spectra_with_embeddings, top_k_inchikeys, ms2deepscores) - average_predicted_scores[inchikey] = average_predicted_score + for inchikey in top_inchikeys: + top_k_inchikeys, _ = get_inchikey_and_tanimoto_scores_for_top_k( + spectra_with_embeddings, inchikey, nr_of_closest_inchikeys_to_select) + average_predicted_score = get_average_predictions_for_closely_related_metabolites( + spectra_with_embeddings, top_k_inchikeys, ms2deepscores) + average_predicted_scores[inchikey] = average_predicted_score inchikey_with_highest_average_prediction, score = max(average_predicted_scores.items(), key=lambda item: item[1]) return inchikey_with_highest_average_prediction, score +def select_inchikeys_with_highest_ms2deepscore(spectra_with_embeddings, ms2deepscores, nr_of_inchikeys_to_select=10): + highest_score_for_inchikey = [] + for inchikey, spectrum_indexes in spectra_with_embeddings.spectrum_indexes_per_inchikey.items(): + all_ms2deepscores_for_inchikey = ms2deepscores[spectrum_indexes] + highest_score_for_inchikey.append(max(all_ms2deepscores_for_inchikey)) + inchikey_indexes_with_highest_ms2deepscore = np.argpartition( + np.array(highest_score_for_inchikey), -nr_of_inchikeys_to_select)[-nr_of_inchikeys_to_select:] + + all_inchikeys = list(spectra_with_embeddings.inchikey_fingerprint_pairs.keys()) + top_inchikeys = [all_inchikeys[inchikey_index] for inchikey_index in inchikey_indexes_with_highest_ms2deepscore] + return top_inchikeys + def get_average_predictions_for_closely_related_metabolites(spectra_with_embeddings, top_k_inchikeys, all_ms2deepscores): """Calculates the average ms2deepscore predictions for top k closest inchikeys""" From f223e4302fa249057077421b398e5797c8f29539 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 10 Dec 2025 15:33:41 +0100 Subject: [PATCH 015/149] Add test_select_inchikeys_with_highest_ms2deepscore --- tests/test_predict_using_closest_tanimoto.py | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/tests/test_predict_using_closest_tanimoto.py b/tests/test_predict_using_closest_tanimoto.py index e948187..21b3a7c 100644 --- a/tests/test_predict_using_closest_tanimoto.py +++ b/tests/test_predict_using_closest_tanimoto.py @@ -3,11 +3,25 @@ from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings, SpectraWithFingerprints from ms2query.benchmarking.reference_methods.predict_using_closest_tanimoto import ( predict_using_closest_tanimoto, predict_using_closest_tanimoto_single_spectrum, - get_average_predictions_for_closely_related_metabolites, get_inchikey_and_tanimoto_scores_for_top_k) + get_average_predictions_for_closely_related_metabolites, get_inchikey_and_tanimoto_scores_for_top_k, + select_inchikeys_with_highest_ms2deepscore) from tests.conftest import ms2deepscore_model, create_test_spectra import pytest +def test_select_inchikeys_with_highest_ms2deepscore(): + test_spectra = create_test_spectra(nr_of_inchikeys=7) + spectra = SpectraWithFingerprints(test_spectra) + + ms2deepscores = np.zeros(len(test_spectra)) + ms2deepscores[2] = 0.4 + ms2deepscores[5] = 0.9 + ms2deepscores[7] = 0.6 + inchikeys_with_highest_ms2deepscore = select_inchikeys_with_highest_ms2deepscore(spectra, ms2deepscores, 3) + expected_inchikeys = list(spectra.spectrum_indexes_per_inchikey.keys())[:3] + assert set(expected_inchikeys) == set(inchikeys_with_highest_ms2deepscore) + print(inchikeys_with_highest_ms2deepscore) + def test_get_average_predictions_for_closely_related_metabolites(): test_spectra = create_test_spectra(nr_of_inchikeys=7) # Select different number per inchikey (only one for the first) to check that it is correctly weighted. From 16058ccb53a15814d79188475caa35e2e24f02b6 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 10 Dec 2025 15:51:55 +0100 Subject: [PATCH 016/149] Added nr_of_inchikeys_with_highest_ms2deepscore_to_select as parameter --- .../reference_methods/predict_using_closest_tanimoto.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index f353aeb..f0a40c9 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -8,7 +8,8 @@ def predict_using_closest_tanimoto( library_spectra: SpectraWithMS2DeepScoreEmbeddings, query_spectra: SpectraWithMS2DeepScoreEmbeddings, - nr_of_closest_inchikeys_to_select=10 + nr_of_closest_inchikeys_to_select=10, + nr_of_inchikeys_with_highest_ms2deepscore_to_select=100 ) -> Tuple[List[str], List[float]]: """Predict best inchikey, by taking the average score over all spectra for the 10 closest related library inchikeys. (simplified version of old MS2Query) @@ -17,7 +18,8 @@ def predict_using_closest_tanimoto( highest_scores = [] for spectrum_idx in range(len(query_spectra.spectra)): inchikey_of_best_match, score = predict_using_closest_tanimoto_single_spectrum( - library_spectra, query_spectra.subset_spectra([spectrum_idx]), nr_of_closest_inchikeys_to_select) + library_spectra, query_spectra.subset_spectra([spectrum_idx]), + nr_of_closest_inchikeys_to_select, nr_of_inchikeys_with_highest_ms2deepscore_to_select) inchikeys_of_best_match.append(inchikey_of_best_match) highest_scores.append(score) return inchikeys_of_best_match, highest_scores From 1c2f1a733453be68ec8a3fc6bd53f4b9704b8149 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 10 Dec 2025 15:52:16 +0100 Subject: [PATCH 017/149] Added basic tests for predict using closest tanimoto score (checking correct types of output) --- tests/test_predict_using_closest_tanimoto.py | 23 ++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/tests/test_predict_using_closest_tanimoto.py b/tests/test_predict_using_closest_tanimoto.py index 21b3a7c..bdb9741 100644 --- a/tests/test_predict_using_closest_tanimoto.py +++ b/tests/test_predict_using_closest_tanimoto.py @@ -9,6 +9,29 @@ import pytest +def test_predict_using_closest_tanimoto(): + """Only very basic test that the function runs and that the output is the right format""" + model = ms2deepscore_model() + library_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(nr_of_inchikeys=7), model) + test_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(1, nr_of_inchikeys=3), model) + predicted_inchikeys, scores = predict_using_closest_tanimoto(library_spectra, test_spectra, 3, 3) + + assert isinstance(predicted_inchikeys, list) + assert len(predicted_inchikeys) == 3 + assert isinstance(scores, list) + assert len(scores) == 3 + +def test_predict_using_closest_tanimoto_single_spectrum(): + """Only very basic test that the function runs and that the output is the right format""" + model = ms2deepscore_model() + library_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(nr_of_inchikeys=7), model) + test_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(1, nr_of_inchikeys=1), model) + predicted_inchikey, score = predict_using_closest_tanimoto_single_spectrum(library_spectra, test_spectra, 3, 3) + + assert isinstance(predicted_inchikey, str) + assert len(predicted_inchikey) ==14 + assert isinstance(score, float) + def test_select_inchikeys_with_highest_ms2deepscore(): test_spectra = create_test_spectra(nr_of_inchikeys=7) spectra = SpectraWithFingerprints(test_spectra) From 276511c107a62a3a35d702fa55ae151e3b515c1a Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 16 Dec 2025 09:49:53 +0100 Subject: [PATCH 018/149] Add tqdm to predict using closest tanimoto --- .../reference_methods/predict_using_closest_tanimoto.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index f0a40c9..afc7ceb 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -5,6 +5,7 @@ from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings, SpectraWithFingerprints from ms2query.metrics import generalized_tanimoto_similarity_matrix +from tqdm import tqdm def predict_using_closest_tanimoto( library_spectra: SpectraWithMS2DeepScoreEmbeddings, query_spectra: SpectraWithMS2DeepScoreEmbeddings, @@ -16,7 +17,7 @@ def predict_using_closest_tanimoto( """ inchikeys_of_best_match = [] highest_scores = [] - for spectrum_idx in range(len(query_spectra.spectra)): + for spectrum_idx in tqdm(range(len(query_spectra.spectra)), "Predicting using closest tanimoto"): inchikey_of_best_match, score = predict_using_closest_tanimoto_single_spectrum( library_spectra, query_spectra.subset_spectra([spectrum_idx]), nr_of_closest_inchikeys_to_select, nr_of_inchikeys_with_highest_ms2deepscore_to_select) From a59e6a925bdfca01cd921cc5dcc20307b38ff7f0 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 16 Dec 2025 09:52:20 +0100 Subject: [PATCH 019/149] ruff --- ms2query/benchmarking/EvaluateMethods.py | 5 +---- ms2query/benchmarking/SpectrumDataSet.py | 6 ++---- .../PredictMS2DeepScoreSimilarity.py | 2 -- .../predict_best_possible_match.py | 2 -- .../reference_methods/predict_highest_cosine.py | 3 +-- .../predict_highest_ms2deepscore.py | 3 +-- .../predict_using_closest_tanimoto.py | 7 +++---- .../predict_with_integrated_similarity_flow.py | 5 ++--- tests/conftest.py | 1 - tests/testPredictMS2DeepScoreSimilarity.py | 3 +-- tests/test_SpectrumDataSet.py | 5 ++--- tests/test_evaluate_methods.py | 6 +++--- tests/test_methods.py | 12 +++++------- tests/test_predict_using_closest_tanimoto.py | 16 +++++++++------- 14 files changed, 30 insertions(+), 46 deletions(-) diff --git a/ms2query/benchmarking/EvaluateMethods.py b/ms2query/benchmarking/EvaluateMethods.py index 6442343..17a28a5 100644 --- a/ms2query/benchmarking/EvaluateMethods.py +++ b/ms2query/benchmarking/EvaluateMethods.py @@ -1,11 +1,8 @@ import random - +from typing import Callable, List, Tuple import numpy as np -from typing import Callable, Tuple, List - from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix from tqdm import tqdm - from ms2query.benchmarking.SpectrumDataSet import SpectraWithFingerprints, SpectrumSetBase diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index 2446d30..9b70dbb 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -1,12 +1,10 @@ import copy from collections import Counter -from typing import List, Dict, Iterable - +from typing import Dict, Iterable, List import numpy as np from matchms import Spectrum from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi - -from ms2deepscore.models import compute_embedding_array, SiameseSpectralModel +from ms2deepscore.models import SiameseSpectralModel, compute_embedding_array from tqdm import tqdm diff --git a/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py b/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py index 13121cf..a8082c1 100644 --- a/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py +++ b/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py @@ -1,7 +1,5 @@ from typing import Tuple - import numpy as np - from ms2deepscore.vector_operations import cosine_similarity_matrix from tqdm import tqdm diff --git a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py index 63625f2..1544f71 100644 --- a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py +++ b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py @@ -1,8 +1,6 @@ from typing import Dict - import numpy as np from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix - from ms2query.benchmarking.SpectrumDataSet import SpectraWithFingerprints diff --git a/ms2query/benchmarking/reference_methods/predict_highest_cosine.py b/ms2query/benchmarking/reference_methods/predict_highest_cosine.py index 46820f1..49fd6cf 100644 --- a/ms2query/benchmarking/reference_methods/predict_highest_cosine.py +++ b/ms2query/benchmarking/reference_methods/predict_highest_cosine.py @@ -1,5 +1,4 @@ -from typing import Tuple, List - +from typing import List, Tuple from matchms import Scores from matchms.similarity.CosineGreedy import CosineGreedy from matchms.similarity.PrecursorMzMatch import PrecursorMzMatch diff --git a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py index d645857..a5d9318 100644 --- a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py +++ b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py @@ -1,5 +1,4 @@ -from typing import Tuple, List - +from typing import List, Tuple from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index afc7ceb..7e12f52 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -1,11 +1,10 @@ +from typing import List, Tuple import numpy as np from ms2deepscore.vector_operations import cosine_similarity_matrix -from typing import Tuple, List - -from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings, SpectraWithFingerprints +from tqdm import tqdm +from ms2query.benchmarking.SpectrumDataSet import SpectraWithFingerprints, SpectraWithMS2DeepScoreEmbeddings from ms2query.metrics import generalized_tanimoto_similarity_matrix -from tqdm import tqdm def predict_using_closest_tanimoto( library_spectra: SpectraWithMS2DeepScoreEmbeddings, query_spectra: SpectraWithMS2DeepScoreEmbeddings, diff --git a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py index 329fea1..b89a906 100644 --- a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py +++ b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py @@ -1,8 +1,7 @@ -from typing import Tuple, List -from tqdm import tqdm +from typing import List, Tuple import numpy as np from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix - +from tqdm import tqdm from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings diff --git a/tests/conftest.py b/tests/conftest.py index 80ec8e9..702e6fc 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,6 +1,5 @@ import os from pathlib import Path - import numpy as np from matchms.Spectrum import Spectrum from ms2deepscore.models import load_model diff --git a/tests/testPredictMS2DeepScoreSimilarity.py b/tests/testPredictMS2DeepScoreSimilarity.py index 9741693..4741bdf 100644 --- a/tests/testPredictMS2DeepScoreSimilarity.py +++ b/tests/testPredictMS2DeepScoreSimilarity.py @@ -1,11 +1,10 @@ import numpy as np import pytest - from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import ( predict_top_ms2deepscores, ) -from tests.conftest import create_test_spectra, ms2deepscore_model from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings +from tests.conftest import create_test_spectra, ms2deepscore_model @pytest.mark.parametrize( diff --git a/tests/test_SpectrumDataSet.py b/tests/test_SpectrumDataSet.py index a4c8e2c..4244e99 100644 --- a/tests/test_SpectrumDataSet.py +++ b/tests/test_SpectrumDataSet.py @@ -1,12 +1,11 @@ import numpy as np import pytest - from ms2query.benchmarking.SpectrumDataSet import ( SpectraWithFingerprints, - SpectrumSetBase, SpectraWithMS2DeepScoreEmbeddings, + SpectrumSetBase, ) -from tests.conftest import create_test_spectra, ms2deepscore_model, get_inchikey_inchi_pairs +from tests.conftest import create_test_spectra, get_inchikey_inchi_pairs, ms2deepscore_model @pytest.mark.parametrize( diff --git a/tests/test_evaluate_methods.py b/tests/test_evaluate_methods.py index 4bcb839..7431aad 100644 --- a/tests/test_evaluate_methods.py +++ b/tests/test_evaluate_methods.py @@ -1,10 +1,10 @@ import pytest -from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings from ms2query.benchmarking.EvaluateMethods import EvaluateMethods -from ms2query.benchmarking.reference_methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore from ms2query.benchmarking.reference_methods.predict_best_possible_match import predict_best_possible_match -from tests.conftest import create_test_spectra, ms2deepscore_model from ms2query.benchmarking.reference_methods.predict_highest_cosine import predict_highest_cosine +from ms2query.benchmarking.reference_methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore +from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings +from tests.conftest import create_test_spectra, ms2deepscore_model @pytest.mark.parametrize( diff --git a/tests/test_methods.py b/tests/test_methods.py index 03cca1c..1b747d0 100644 --- a/tests/test_methods.py +++ b/tests/test_methods.py @@ -1,17 +1,15 @@ import numpy as np +import pytest from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix - -from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings +from ms2query.benchmarking.reference_methods.predict_best_possible_match import predict_best_possible_match from ms2query.benchmarking.reference_methods.predict_highest_cosine import predict_highest_cosine -from tests.conftest import create_test_spectra, ms2deepscore_model from ms2query.benchmarking.reference_methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore -from ms2query.benchmarking.reference_methods.predict_best_possible_match import predict_best_possible_match from ms2query.benchmarking.reference_methods.predict_with_integrated_similarity_flow import ( - predict_with_integrated_similarity_flow, integrated_similarity_flow, + predict_with_integrated_similarity_flow, ) - -import pytest +from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings +from tests.conftest import create_test_spectra, ms2deepscore_model @pytest.mark.parametrize( diff --git a/tests/test_predict_using_closest_tanimoto.py b/tests/test_predict_using_closest_tanimoto.py index bdb9741..f6ebc31 100644 --- a/tests/test_predict_using_closest_tanimoto.py +++ b/tests/test_predict_using_closest_tanimoto.py @@ -1,12 +1,14 @@ import numpy as np - -from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings, SpectraWithFingerprints -from ms2query.benchmarking.reference_methods.predict_using_closest_tanimoto import ( - predict_using_closest_tanimoto, predict_using_closest_tanimoto_single_spectrum, - get_average_predictions_for_closely_related_metabolites, get_inchikey_and_tanimoto_scores_for_top_k, - select_inchikeys_with_highest_ms2deepscore) -from tests.conftest import ms2deepscore_model, create_test_spectra import pytest +from ms2query.benchmarking.reference_methods.predict_using_closest_tanimoto import ( + get_average_predictions_for_closely_related_metabolites, + get_inchikey_and_tanimoto_scores_for_top_k, + predict_using_closest_tanimoto, + predict_using_closest_tanimoto_single_spectrum, + select_inchikeys_with_highest_ms2deepscore, +) +from ms2query.benchmarking.SpectrumDataSet import SpectraWithFingerprints, SpectraWithMS2DeepScoreEmbeddings +from tests.conftest import create_test_spectra, ms2deepscore_model def test_predict_using_closest_tanimoto(): From aff52e4291c6cee7ea84625c2026c181ed5145a7 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 16 Dec 2025 09:57:37 +0100 Subject: [PATCH 020/149] Linting --- ms2query/benchmarking/EvaluateMethods.py | 4 ++-- ms2query/benchmarking/SpectrumDataSet.py | 3 ++- .../reference_methods/PredictMS2DeepScoreSimilarity.py | 3 ++- .../reference_methods/predict_using_closest_tanimoto.py | 6 +++--- .../predict_with_integrated_similarity_flow.py | 3 ++- 5 files changed, 11 insertions(+), 8 deletions(-) diff --git a/ms2query/benchmarking/EvaluateMethods.py b/ms2query/benchmarking/EvaluateMethods.py index 17a28a5..2dcda5b 100644 --- a/ms2query/benchmarking/EvaluateMethods.py +++ b/ms2query/benchmarking/EvaluateMethods.py @@ -61,8 +61,8 @@ def benchmark_exact_matching_within_ionmode( For each inchikey with more than 1 spectrum the spectra are split in two sets. Half for each inchikey is added to the library (training set), for the other half predictions are made. Thereby there is always an exact match - avaialable. Only the highest ranked prediction is considered correct if the correct inchikey is predicted. An accuracy per - inchikey is calculated followed by calculating the average. + avaialable. Only the highest ranked prediction is considered correct if the correct inchikey is predicted. + An accuracy per inchikey is calculated followed by calculating the average. """ selected_spectra = subset_spectra_on_ionmode(self.validation_spectrum_set, ionmode) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index 9b70dbb..240c38a 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -112,7 +112,8 @@ def subset_spectra(self, spectrum_indexes) -> "SpectraWithFingerprints": # Only keep the fingerprints for which we have inchikeys. # Important note: This is not a deep copy! # And the fingerprint is not reset (so it is not always actually matching the most common inchi) - new_instance.inchikey_fingerprint_pairs = {inchikey: self.inchikey_fingerprint_pairs[inchikey] for inchikey in new_instance.spectrum_indexes_per_inchikey.keys()} + new_instance.inchikey_fingerprint_pairs = {inchikey: self.inchikey_fingerprint_pairs[inchikey] for inchikey + in new_instance.spectrum_indexes_per_inchikey.keys()} return new_instance diff --git a/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py b/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py index a8082c1..cd20544 100644 --- a/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py +++ b/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py @@ -21,7 +21,8 @@ def predict_top_ms2deepscores( k: Number of highest matches to return Returns: - List[List[int]: indexes of highest scores and the value for the highest score. Per query embedding the top k highest indexes are given. + List[List[int]: indexes of highest scores and the value for the highest score. + Per query embedding the top k highest indexes are given. List[List[float]: the highest scores. """ top_indexes_per_batch = [] diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index 7e12f52..751953e 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -25,9 +25,9 @@ def predict_using_closest_tanimoto( return inchikeys_of_best_match, highest_scores -def predict_using_closest_tanimoto_single_spectrum(spectra_with_embeddings, single_spectrum_with_embeddings, - nr_of_closest_inchikeys_to_select, - nr_of_inchikeys_with_highest_ms2deepscore_to_select) -> Tuple[str, float]: +def predict_using_closest_tanimoto_single_spectrum( + spectra_with_embeddings, single_spectrum_with_embeddings, + nr_of_closest_inchikeys_to_select, nr_of_inchikeys_with_highest_ms2deepscore_to_select) -> Tuple[str, float]: if len(single_spectrum_with_embeddings.spectra) != 1: raise ValueError("expected a single spectrum") ms2deepscores = cosine_similarity_matrix(single_spectrum_with_embeddings.embeddings, diff --git a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py index b89a906..e352ab3 100644 --- a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py +++ b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py @@ -82,7 +82,8 @@ def integrated_similarity_flow( predicted_scores: List[float], similarities: np.ndarray, nr_of_spectra_per_inchikey: List[float] ) -> List[float]: """Compute the confidence of the prediction for each candidate. - Integrated similarity flow (ISF) scores are calculated using the similarity of candidates among each other and their distance to the query spectrum. + Integrated similarity flow (ISF) scores are calculated using the similarity of candidates among each other + and their distance to the query spectrum. Args: distances (list): Distances of the candidates to the query spectrum in the chemical space. From cd30383b613c442ee4a936441476f2be843bd30a Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 16 Dec 2025 11:15:42 +0100 Subject: [PATCH 021/149] Lint notebooks --- .../Test_ann_speed_improvements.ipynb | 17 ++++++--- .../notebooks/Test_method_evaluator.ipynb | 35 ++++++++++++++++--- ...umber_of_inchikeys_with_two_ionmodes.ipynb | 1 + 3 files changed, 45 insertions(+), 8 deletions(-) diff --git a/ms2query/notebooks/Test_ann_speed_improvements.ipynb b/ms2query/notebooks/Test_ann_speed_improvements.ipynb index 9b84f73..e17d662 100644 --- a/ms2query/notebooks/Test_ann_speed_improvements.ipynb +++ b/ms2query/notebooks/Test_ann_speed_improvements.ipynb @@ -7,7 +7,9 @@ "metadata": {}, "outputs": [], "source": [ - "import sys \n", + "import sys\n", + "\n", + "\n", "sys.path.append(\"C:/Users/jonge094/PycharmProjects/ms2query_2_0/ms_chemical_space_explorer\")\n", "\n" ] @@ -31,6 +33,7 @@ "from matchms.importing import load_from_mgf\n", "from tqdm import tqdm\n", "\n", + "\n", "neg_val_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_validation_spectra.mgf\")))\n", "neg_test_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_testing_spectra.mgf\")))\n" ] @@ -52,8 +55,8 @@ } ], "source": [ - "from ms2deepscore.models import load_model\n", - "from ms2deepscore.models import compute_embedding_array\n", + "from ms2deepscore.models import compute_embedding_array, load_model\n", + "\n", "\n", "ms2deepscore_model = load_model(\"../../../ms2deepscore/data/pytorch/new_corinna_included/trained_models/both_mode_precursor_mz_ionmode_10000_layers_500_embedding_2024_11_21_11_23_17/ms2deepscore_model.pt\")\n", "\n", @@ -76,6 +79,8 @@ "outputs": [], "source": [ "import numpy as np\n", + "\n", + "\n", "more_test_embeddings = np.tile(embeddings, (70, 1))\n" ] }, @@ -115,8 +120,10 @@ } ], "source": [ - "import pynndescent\n", "import time\n", + "import pynndescent\n", + "\n", + "\n", "start_time = time.time()\n", "ann_model = pynndescent.NNDescent(more_test_embeddings, metric=\"cosine\", n_neighbors=30)\n", "ann_model.prepare()\n", @@ -242,6 +249,8 @@ ], "source": [ "from ms2deepscore.vector_operations import cosine_similarity_matrix\n", + "\n", + "\n", "start_time = time.time()\n", "matrix = cosine_similarity_matrix(more_test_embeddings, embeddings[:1000])\n", "print(\"Time eleapsed: \" + str(time.time() - start_time))" diff --git a/ms2query/notebooks/Test_method_evaluator.ipynb b/ms2query/notebooks/Test_method_evaluator.ipynb index 097c1d3..86aafed 100644 --- a/ms2query/notebooks/Test_method_evaluator.ipynb +++ b/ms2query/notebooks/Test_method_evaluator.ipynb @@ -26,9 +26,10 @@ } ], "source": [ + "import os\n", "from matchms.importing import load_from_mgf\n", "from tqdm import tqdm\n", - "import os\n", + "\n", "\n", "save_directory = \"../data/ms2deepscore_model/training_and_validation_split/\"\n", "pos_val_spectra = list(tqdm(load_from_mgf(os.path.join(save_directory, \"positive_validation_spectra.mgf\"))))\n", @@ -66,6 +67,8 @@ ], "source": [ "import os\n", + "\n", + "\n", "pickled_intermediates_data_folder = \"../data/pickled_intermediates\"\n", "os.path.isdir(pickled_intermediates_data_folder)" ] @@ -79,6 +82,7 @@ "source": [ "import pickle\n", "\n", + "\n", "with open(os.path.join(pickled_intermediates_data_folder, \"neg_val_spectra.pickle\"), \"wb\") as handle:\n", " pickle.dump(neg_val_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", "with open(os.path.join(pickled_intermediates_data_folder, \"neg_train_spectra.pickle\"), \"wb\") as handle:\n", @@ -109,6 +113,8 @@ "outputs": [], "source": [ "from ms2deepscore.models import load_model\n", + "\n", + "\n", "ms2deepscore_model = load_model(\"../data/ms2deepscore_model/trained_models/both_mode_ionmode_precursor_mz_2000_layers_500_embedding_2025_02_26_18_42_25/ms2deepscore_model.pt\")\n" ] }, @@ -119,7 +125,9 @@ "metadata": {}, "outputs": [], "source": [ - "import sys \n", + "import sys\n", + "\n", + "\n", "sys.path.append(\"../../ms_chemical_space_explorer\")" ] }, @@ -131,6 +139,8 @@ "outputs": [], "source": [ "from ms_chemical_space_explorer.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings\n", + "\n", + "\n", "library_spectra = SpectraWithMS2DeepScoreEmbeddings(neg_train_spectra + pos_train_spectra, ms2deepscore_model)" ] }, @@ -142,6 +152,8 @@ "outputs": [], "source": [ "import pickle\n", + "\n", + "\n", "with open(os.path.join(pickled_intermediates_data_folder, \"neg_pos_library_embeddings.pickle\"), \"wb\") as handle:\n", " pickle.dump(library_spectra.embeddings, handle, protocol=pickle.HIGHEST_PROTOCOL)" ] @@ -154,6 +166,8 @@ "outputs": [], "source": [ "import pickle\n", + "\n", + "\n", "with open(os.path.join(pickled_intermediates_data_folder, \"neg_pos_library_with_embeddings.pickle\"), \"wb\") as handle:\n", " pickle.dump(library_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)" ] @@ -178,6 +192,8 @@ "outputs": [], "source": [ "import pickle\n", + "\n", + "\n", "with open(os.path.join(pickled_intermediates_data_folder, \"neg_pos_val_spectra_with_embeddings.pickle\"), \"wb\") as handle:\n", " pickle.dump(val_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)" ] @@ -197,7 +213,9 @@ "metadata": {}, "outputs": [], "source": [ - "import sys \n", + "import sys\n", + "\n", + "\n", "sys.path.append(\"../../ms_chemical_space_explorer\")" ] }, @@ -210,6 +228,8 @@ "source": [ "import os\n", "import pickle\n", + "\n", + "\n", "pickled_intermediates_data_folder = \"../data/pickled_intermediates\"\n", "with open(os.path.join(pickled_intermediates_data_folder, \"neg_pos_library_with_embeddings.pickle\"), \"rb\") as file:\n", " library_spectra = pickle.load(file)\n", @@ -234,6 +254,7 @@ "source": [ "from ms_chemical_space_explorer.benchmarking.EvaluateMethods import EvaluateMethods\n", "\n", + "\n", "method_evaluator = EvaluateMethods(library_spectra, val_spectra)\n", "method_evaluator.training_spectrum_set.progress_bars = False\n", "method_evaluator.validation_spectrum_set.progress_bars = False" @@ -267,6 +288,7 @@ "source": [ "from ms_chemical_space_explorer.methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore\n", "\n", + "\n", "result_analogue = method_evaluator.benchmark_analogue_search(predict_highest_ms2deepscore)" ] }, @@ -310,6 +332,7 @@ "source": [ "from ms_chemical_space_explorer.methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore\n", "\n", + "\n", "result_positive = method_evaluator.benchmark_exact_matching_within_ionmode(predict_highest_ms2deepscore, \"positive\")" ] }, @@ -351,6 +374,7 @@ "source": [ "from ms_chemical_space_explorer.methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore\n", "\n", + "\n", "result_neg = method_evaluator.benchmark_exact_matching_within_ionmode(predict_highest_ms2deepscore, \"negative\")" ] }, @@ -869,7 +893,10 @@ } ], "source": [ - "from ms_chemical_space_explorer.methods.predict_with_integrated_similarity_flow import predict_with_integrated_similarity_flow\n", + "from ms_chemical_space_explorer.methods.predict_with_integrated_similarity_flow import (\n", + " predict_with_integrated_similarity_flow,\n", + ")\n", + "\n", "\n", "result_analogue_isf = method_evaluator.benchmark_analogue_search(predict_with_integrated_similarity_flow)\n", "print(result_analogue_isf)\n", diff --git a/ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb b/ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb index dcad892..da86250 100644 --- a/ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb +++ b/ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb @@ -23,6 +23,7 @@ "from matchms.importing import load_from_mgf\n", "from tqdm import tqdm\n", "\n", + "\n", "neg_val_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_validation_spectra.mgf\")))\n", "neg_test_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_testing_spectra.mgf\")))\n", "neg_train_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_training_spectra.mgf\")))\n", From c97c5a62dfea4e550090c8199a1698289daba9a6 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 16 Dec 2025 11:21:48 +0100 Subject: [PATCH 022/149] Lint line length --- .../Test_ann_speed_improvements.ipynb | 56 +++---- .../notebooks/Test_method_evaluator.ipynb | 145 ++++-------------- ...umber_of_inchikeys_with_two_ionmodes.ipynb | 65 ++++---- tests/test_SpectrumDataSet.py | 3 +- 4 files changed, 83 insertions(+), 186 deletions(-) diff --git a/ms2query/notebooks/Test_ann_speed_improvements.ipynb b/ms2query/notebooks/Test_ann_speed_improvements.ipynb index e17d662..bacbad8 100644 --- a/ms2query/notebooks/Test_ann_speed_improvements.ipynb +++ b/ms2query/notebooks/Test_ann_speed_improvements.ipynb @@ -15,61 +15,45 @@ ] }, { - "cell_type": "code", - "execution_count": 16, - "id": "69613d68-6340-4985-b266-ebdb56b21271", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "7551it [00:05, 1452.14it/s]\n", - "7142it [00:04, 1502.96it/s]\n" - ] - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "from matchms.importing import load_from_mgf\n", "from tqdm import tqdm\n", "\n", "\n", - "neg_val_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_validation_spectra.mgf\")))\n", - "neg_test_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_testing_spectra.mgf\")))\n" - ] + "neg_val_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/\"\n", + " \"training_and_validation_split/negative_validation_spectra.mgf\")))\n", + "neg_test_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/\"\n", + " \"training_and_validation_split/negative_testing_spectra.mgf\")))\n" + ], + "id": "1958d1e9d021cd45" }, { - "cell_type": "code", - "execution_count": 17, - "id": "55f31b78-e6b8-42a2-8ba0-d5cea429520d", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\jonge094\\AppData\\Local\\miniconda3\\envs\\ms2query2\\lib\\site-packages\\ms2deepscore\\models\\load_model.py:34: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model_settings = torch.load(filename, map_location=device)\n", - "7551it [02:50, 44.16it/s]\n" - ] - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "from ms2deepscore.models import compute_embedding_array, load_model\n", "\n", "\n", - "ms2deepscore_model = load_model(\"../../../ms2deepscore/data/pytorch/new_corinna_included/trained_models/both_mode_precursor_mz_ionmode_10000_layers_500_embedding_2024_11_21_11_23_17/ms2deepscore_model.pt\")\n", + "ms2deepscore_model = load_model(\"../../../ms2deepscore/data/pytorch/new_corinna_included/trained_models/\"\n", + " \"both_mode_precursor_mz_ionmode_10000_layers_500_embedding_2024_11_21_11_23_17/ms2deepscore_model.pt\")\n", "\n", "embeddings = compute_embedding_array(ms2deepscore_model, neg_val_spectra)" - ] + ], + "id": "fe20d28468de50ca" }, { - "cell_type": "code", - "execution_count": null, - "id": "1fe9d805-561f-49c5-87f4-ce56c374db42", "metadata": {}, + "cell_type": "code", "outputs": [], - "source": [] + "execution_count": null, + "source": "", + "id": "cee3357668c3f96e" }, { "cell_type": "code", diff --git a/ms2query/notebooks/Test_method_evaluator.ipynb b/ms2query/notebooks/Test_method_evaluator.ipynb index 86aafed..5303dfe 100644 --- a/ms2query/notebooks/Test_method_evaluator.ipynb +++ b/ms2query/notebooks/Test_method_evaluator.ipynb @@ -106,30 +106,32 @@ ] }, { - "cell_type": "code", - "execution_count": 8, - "id": "48bd37e7-384a-4bbc-821d-946975bfdf5b", "metadata": {}, + "cell_type": "code", "outputs": [], + "execution_count": null, "source": [ "from ms2deepscore.models import load_model\n", "\n", "\n", - "ms2deepscore_model = load_model(\"../data/ms2deepscore_model/trained_models/both_mode_ionmode_precursor_mz_2000_layers_500_embedding_2025_02_26_18_42_25/ms2deepscore_model.pt\")\n" - ] + "ms2deepscore_model = load_model(\n", + " \"../data/ms2deepscore_model/trained_models/\"\n", + " \"both_mode_ionmode_precursor_mz_2000_layers_500_embedding_2025_02_26_18_42_25/ms2deepscore_model.pt\")\n" + ], + "id": "d4805b43bea1e3a2" }, { - "cell_type": "code", - "execution_count": 10, - "id": "9710c95d-803d-4492-b279-13588afa9c27", "metadata": {}, + "cell_type": "code", "outputs": [], + "execution_count": null, "source": [ "import sys\n", "\n", "\n", "sys.path.append(\"../../ms_chemical_space_explorer\")" - ] + ], + "id": "9ca0b8c6639168e1" }, { "cell_type": "code", @@ -185,18 +187,19 @@ ] }, { - "cell_type": "code", - "execution_count": 22, - "id": "3ba69a93-5ede-4920-b3a6-e7881e20eb39", "metadata": {}, + "cell_type": "code", "outputs": [], + "execution_count": null, "source": [ "import pickle\n", "\n", "\n", - "with open(os.path.join(pickled_intermediates_data_folder, \"neg_pos_val_spectra_with_embeddings.pickle\"), \"wb\") as handle:\n", + "with open(os.path.join(pickled_intermediates_data_folder,\n", + " \"neg_pos_val_spectra_with_embeddings.pickle\"), \"wb\") as handle:\n", " pickle.dump(val_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)" - ] + ], + "id": "e6835fbcb3d01494" }, { "cell_type": "markdown", @@ -804,94 +807,10 @@ ] }, { - "cell_type": "code", - "execution_count": 7, - "id": "1dc19bef-69ab-471e-94ab-6a9caffd9030", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOStream.flush timed outepscore per batch of 500 embeddings: 77%|██████████████████████████████████████████████████████████████████▏ | 60/78 [33:53<22:39, 75.54s/it]\n", - "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|██████████████████████████████████████████████████████████████████████████████████████| 78/78 [42:49<00:00, 32.95s/it]\n", - "Calculating ISF score: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 38533/38533 [00:44<00:00, 857.97it/s]\n", - "Calculating analogue accuracy per inchikey: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 2015/2015 [00:01<00:00, 1076.80it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.35576217271302824\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Splitting spectra per inchikey: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 924/924 [00:00<00:00, 202462.49it/s]\n", - "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|████████████████████████████████████████████████████████████████████████████████████████| 7/7 [02:56<00:00, 25.28s/it]\n", - "Calculating ISF score: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3303/3303 [00:04<00:00, 713.49it/s]\n", - "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|████████████████████████████████████████████████████████████████████████████████████████| 8/8 [03:30<00:00, 26.27s/it]\n", - "Calculating ISF score: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3721/3721 [00:04<00:00, 856.75it/s]\n", - "Calculating exact match accuracy per inchikey: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 868/868 [00:00<00:00, 176636.55it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.04118290017821497\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Splitting spectra per inchikey: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1837/1837 [00:00<00:00, 134548.79it/s]\n", - "Predicting highest ms2deepscore per batch of 500 embeddings: 84%|███████████████████████████████████████████████████████████████████████▎ | 26/31 [38:00<38:36, 463.28s/it]IOStream.flush timed out\n", - "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|██████████████████████████████████████████████████████████████████████████████████████| 31/31 [41:05<00:00, 79.53s/it]\n", - "Calculating ISF score: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15218/15218 [00:20<00:00, 753.86it/s]\n", - "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|██████████████████████████████████████████████████████████████████████████████████████| 33/33 [12:25<00:00, 22.58s/it]\n", - "Calculating ISF score: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16010/16010 [00:22<00:00, 719.72it/s]\n", - "Calculating exact match accuracy per inchikey: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 1612/1612 [00:00<00:00, 236406.23it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.05296326960524268\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Splitting spectra per inchikey across ionmodes: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 2015/2015 [00:00<00:00, 3809.64it/s]\n", - "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|██████████████████████████████████████████████████████████████████████████████████████| 35/35 [13:13<00:00, 22.66s/it]\n", - "Calculating ISF score: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17128/17128 [00:21<00:00, 808.89it/s]\n", - "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|██████████████████████████████████████████████████████████████████████████████████████| 13/13 [04:14<00:00, 19.57s/it]\n", - "Calculating ISF score: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6059/6059 [00:08<00:00, 714.94it/s]\n", - "Calculating exact match accuracy per inchikey: 100%|███████████████████████████████████████████████████████████████████████████████████████████████| 746/746 [00:00<00:00, 56840.41it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0006614631666202545\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "from ms_chemical_space_explorer.methods.predict_with_integrated_similarity_flow import (\n", " predict_with_integrated_similarity_flow,\n", @@ -900,23 +819,25 @@ "\n", "result_analogue_isf = method_evaluator.benchmark_analogue_search(predict_with_integrated_similarity_flow)\n", "print(result_analogue_isf)\n", - "result_neg_isf = method_evaluator.benchmark_exact_matching_within_ionmode(predict_with_integrated_similarity_flow, \"negative\")\n", + "result_neg_isf = method_evaluator.benchmark_exact_matching_within_ionmode(\n", + " predict_with_integrated_similarity_flow, \"negative\")\n", "print(result_neg_isf)\n", - "result_positive_isf = method_evaluator.benchmark_exact_matching_within_ionmode(predict_with_integrated_similarity_flow, \"positive\")\n", + "result_positive_isf = method_evaluator.benchmark_exact_matching_within_ionmode(\n", + " predict_with_integrated_similarity_flow, \"positive\")\n", "print(result_positive_isf)\n", - "result_across_ionmodes_isf = method_evaluator.exact_matches_across_ionization_modes(predict_with_integrated_similarity_flow)\n", + "result_across_ionmodes_isf = method_evaluator.exact_matches_across_ionization_modes(\n", + " predict_with_integrated_similarity_flow)\n", "print(result_across_ionmodes_isf)" - ] + ], + "id": "958a6efb624e75de" }, { - "cell_type": "code", - "execution_count": null, - "id": "1b891a34-aade-49e8-ad91-88b36cf0e011", "metadata": {}, + "cell_type": "code", "outputs": [], - "source": [ - "result_analogue_isf" - ] + "execution_count": null, + "source": "result_analogue_isf", + "id": "326a55ab85832258" }, { "cell_type": "code", @@ -966,4 +887,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb b/ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb index da86250..271f9de 100644 --- a/ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb +++ b/ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb @@ -1,60 +1,51 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 3, - "id": "df3d5e3f-2694-4eed-9fc1-a78483629412", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "7551it [00:07, 1015.57it/s]\n", - "7142it [00:12, 560.76it/s] \n", - "130901it [03:10, 685.40it/s] \n", - "25412it [00:32, 784.47it/s] \n", - "24911it [00:34, 718.09it/s] \n", - "25412it [00:45, 556.46it/s] \n" - ] - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "from matchms.importing import load_from_mgf\n", "from tqdm import tqdm\n", "\n", "\n", - "neg_val_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_validation_spectra.mgf\")))\n", - "neg_test_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_testing_spectra.mgf\")))\n", - "neg_train_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_training_spectra.mgf\")))\n", + "neg_val_spectra = list(tqdm(load_from_mgf(\n", + " \"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/\"\n", + " \"negative_validation_spectra.mgf\")))\n", + "neg_test_spectra = list(tqdm(load_from_mgf(\n", + " \"../../../ms2deepscore/data/pytorch/new_corinna_included/\"\n", + " \"training_and_validation_split/negative_testing_spectra.mgf\")))\n", + "neg_train_spectra = list(tqdm(load_from_mgf(\n", + " \"../../../ms2deepscore/data/pytorch/new_corinna_included/\"\n", + " \"training_and_validation_split/negative_training_spectra.mgf\")))\n", "neg_spectra = neg_val_spectra + neg_test_spectra + neg_train_spectra\n", "\n", - "pos_val_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/positive_validation_spectra.mgf\")))\n", - "pos_test_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/positive_testing_spectra.mgf\")))\n", - "pos_train_spectra = list(tqdm(load_from_mgf(\"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/positive_training_spectra.mgf\")))\n", + "pos_val_spectra = list(tqdm(load_from_mgf(\n", + " \"../../../ms2deepscore/data/pytorch/new_corinna_included/\"\n", + " \"training_and_validation_split/positive_validation_spectra.mgf\")))\n", + "pos_test_spectra = list(tqdm(load_from_mgf(\n", + " \"../../../ms2deepscore/data/pytorch/new_corinna_included/\"\n", + " \"training_and_validation_split/positive_testing_spectra.mgf\")))\n", + "pos_train_spectra = list(tqdm(load_from_mgf(\n", + " \"../../../ms2deepscore/data/pytorch/new_corinna_included/\"\n", + " \"training_and_validation_split/positive_training_spectra.mgf\")))\n", "pos_spectra = pos_val_spectra + pos_test_spectra + pos_train_spectra" - ] + ], + "id": "fdf12d625e87127b" }, { - "cell_type": "code", - "execution_count": 5, - "id": "2771fc81-ad6b-4b2d-90ea-c541641af1a5", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 145594/145594 [00:25<00:00, 5637.57it/s]\n" - ] - } - ], + "cell_type": "code", + "outputs": [], + "execution_count": null, "source": [ "neg_inchikeys = []\n", "for spectrum in tqdm(neg_spectra):\n", " neg_inchikeys.append(spectrum.get(\"inchikey\")[:14])\n", " " - ] + ], + "id": "9ce097a34a9e6656" }, { "cell_type": "code", diff --git a/tests/test_SpectrumDataSet.py b/tests/test_SpectrumDataSet.py index 4244e99..9b1b56f 100644 --- a/tests/test_SpectrumDataSet.py +++ b/tests/test_SpectrumDataSet.py @@ -17,7 +17,8 @@ ], ) def test_spectrum_set_base(library): - """Test all base functionality of SpectrumSetBase is implemented correctly also for all classes inheriting from it""" + """Test all base functionality of SpectrumSetBase is implemented correctly + also for all classes inheriting from it""" # test correct init assert len(library.spectra) == 9 assert len(library.spectrum_indexes_per_inchikey) == 3 From fd688c4249e974faafa0f48a4871a5def8ce8363 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 16 Dec 2025 11:24:04 +0100 Subject: [PATCH 023/149] Exclude notebooks from ruff linting --- pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 396af2e..fe9495c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -49,6 +49,9 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.ruff] +exclude = [ + "ms2query/notebooks/", +] line-length = 120 output-format = "grouped" From 8d9a4b7eea0b01e48a6f726e385da1be2224593c Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 17 Dec 2025 10:18:04 +0100 Subject: [PATCH 024/149] Change SpectrumSetBase to SpectrumSet --- ms2query/benchmarking/EvaluateMethods.py | 14 +++++++------- ms2query/benchmarking/SpectrumDataSet.py | 9 +++++---- tests/test_SpectrumDataSet.py | 10 +++++----- 3 files changed, 17 insertions(+), 16 deletions(-) diff --git a/ms2query/benchmarking/EvaluateMethods.py b/ms2query/benchmarking/EvaluateMethods.py index 2dcda5b..edced8c 100644 --- a/ms2query/benchmarking/EvaluateMethods.py +++ b/ms2query/benchmarking/EvaluateMethods.py @@ -3,7 +3,7 @@ import numpy as np from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix from tqdm import tqdm -from ms2query.benchmarking.SpectrumDataSet import SpectraWithFingerprints, SpectrumSetBase +from ms2query.benchmarking.SpectrumDataSet import SpectraWithFingerprints, SpectrumSet class EvaluateMethods: @@ -107,7 +107,7 @@ def get_accuracy_recall_curve(self): def predict_between_two_sets( - library: SpectrumSetBase, query_set_1: SpectrumSetBase, query_set_2: SpectrumSetBase, prediction_function + library: SpectrumSet, query_set_1: SpectrumSet, query_set_2: SpectrumSet, prediction_function ): """Makes predictions between query sets and the library, with the other query set added. @@ -123,7 +123,7 @@ def predict_between_two_sets( return predicted_inchikeys_1 + predicted_inchikeys_2 -def calculate_average_exact_match_accuracy(spectrum_set: SpectrumSetBase, predicted_inchikeys: List[str]): +def calculate_average_exact_match_accuracy(spectrum_set: SpectrumSet, predicted_inchikeys: List[str]): if len(spectrum_set.spectra) != len(predicted_inchikeys): raise ValueError("The number of spectra should be equal to the number of predicted inchikeys ") exact_match_accuracy_per_inchikey = [] @@ -139,7 +139,7 @@ def calculate_average_exact_match_accuracy(spectrum_set: SpectrumSetBase, predic return sum(exact_match_accuracy_per_inchikey) / len(exact_match_accuracy_per_inchikey) -def split_spectrum_set_per_inchikeys(spectrum_set: SpectrumSetBase) -> Tuple[SpectrumSetBase, SpectrumSetBase]: +def split_spectrum_set_per_inchikeys(spectrum_set: SpectrumSet) -> Tuple[SpectrumSet, SpectrumSet]: """Splits a spectrum set into two. For each inchikey with more than one spectrum the spectra are divided over the two sets""" indexes_set_1 = [] @@ -157,8 +157,8 @@ def split_spectrum_set_per_inchikeys(spectrum_set: SpectrumSetBase) -> Tuple[Spe def split_spectrum_set_per_inchikey_across_ionmodes( - spectrum_set: SpectrumSetBase, -) -> Tuple[SpectrumSetBase, SpectrumSetBase]: + spectrum_set: SpectrumSet, +) -> Tuple[SpectrumSet, SpectrumSet]: """Splits a spectrum set in two sets on ionmode. Only uses spectra for inchikeys with at least 1 pos and 1 neg""" all_pos_indexes = [] all_neg_indexes = [] @@ -190,7 +190,7 @@ def split_spectrum_set_per_inchikey_across_ionmodes( return pos_val_spectra, neg_val_spectra -def subset_spectra_on_ionmode(spectrum_set: SpectrumSetBase, ionmode) -> SpectrumSetBase: +def subset_spectra_on_ionmode(spectrum_set: SpectrumSet, ionmode) -> SpectrumSet: spectrum_indexes_to_keep = [] for i, spectrum in enumerate(spectrum_set.spectra): if spectrum.get("ionmode") == ionmode: diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index 240c38a..eb9fe65 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -8,7 +8,8 @@ from tqdm import tqdm -class SpectrumSetBase: + +class SpectrumSet: """Stores a spectrum dataset making it easy and fast to split on molecules""" def __init__(self, spectra: List[Spectrum], progress_bars=False): @@ -36,10 +37,10 @@ def _add_spectra_and_group_per_inchikey(self, spectra: List[Spectrum]): ] return updated_inchikeys - def add_spectra(self, new_spectra: "SpectrumSetBase"): + def add_spectra(self, new_spectra: "SpectrumSet"): return self._add_spectra_and_group_per_inchikey(new_spectra.spectra) - def subset_spectra(self, spectrum_indexes) -> "SpectrumSetBase": + def subset_spectra(self, spectrum_indexes) -> "SpectrumSet": """Returns a new instance of a subset of the spectra""" new_instance = copy.copy(self) new_instance._spectra = [] @@ -65,7 +66,7 @@ def copy(self): return new_instance -class SpectraWithFingerprints(SpectrumSetBase): +class SpectraWithFingerprints(SpectrumSet): """Stores a spectrum dataset making it easy and fast to split on molecules""" def __init__(self, spectra: List[Spectrum], fingerprint_type="daylight", nbits=4096): diff --git a/tests/test_SpectrumDataSet.py b/tests/test_SpectrumDataSet.py index 9b1b56f..bb3e232 100644 --- a/tests/test_SpectrumDataSet.py +++ b/tests/test_SpectrumDataSet.py @@ -3,7 +3,7 @@ from ms2query.benchmarking.SpectrumDataSet import ( SpectraWithFingerprints, SpectraWithMS2DeepScoreEmbeddings, - SpectrumSetBase, + SpectrumSet, ) from tests.conftest import create_test_spectra, get_inchikey_inchi_pairs, ms2deepscore_model @@ -11,13 +11,13 @@ @pytest.mark.parametrize( "library", [ - SpectrumSetBase(create_test_spectra()), + SpectrumSet(create_test_spectra()), SpectraWithFingerprints(create_test_spectra()), SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(), ms2deepscore_model()), ], ) def test_spectrum_set_base(library): - """Test all base functionality of SpectrumSetBase is implemented correctly + """Test all base functionality of SpectrumSet is implemented correctly also for all classes inheriting from it""" # test correct init assert len(library.spectra) == 9 @@ -74,7 +74,7 @@ def test_spectra_with_fingerprints(library): (get_inchikey_inchi_pairs(3), 3), # Fully overlapping (get_inchikey_inchi_pairs(1), 3), # Fully overlapping (but not all) ): - spectra_to_add = SpectrumSetBase(create_test_spectra(2, inchikey_inchi_pairs=inchikey_inchi_pairs)) + spectra_to_add = SpectrumSet(create_test_spectra(2, inchikey_inchi_pairs=inchikey_inchi_pairs)) new_copy = library.copy() new_copy.add_spectra(spectra_to_add) assert len(new_copy.inchikey_fingerprint_pairs) == expected_nr_of_inchikeys @@ -125,6 +125,6 @@ def test_spectra_with_embeddings(): for i, index in enumerate(subset_indexes): assert np.all(library.embeddings[index] == subset.embeddings[i]) - # Check that subsetting on subset works. To make sure that a subset does not become of type SpectrumSetBase + # Check that subsetting on subset works. To make sure that a subset does not become of type SpectrumSet subsetted_subset = subset.subset_spectra([0, 1]) assert subsetted_subset.embeddings.shape == (2, 100) From e95b5f2ed18b6fdf8537f083d9cf8a3868ac181a Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 17 Dec 2025 13:10:35 +0100 Subject: [PATCH 025/149] Remove an enter --- ms2query/benchmarking/SpectrumDataSet.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index eb9fe65..d2f454d 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -8,7 +8,6 @@ from tqdm import tqdm - class SpectrumSet: """Stores a spectrum dataset making it easy and fast to split on molecules""" From 682d3cdd58e3bcb4b0de03c9a45c56ca4be23e6f Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 17 Dec 2025 13:38:10 +0100 Subject: [PATCH 026/149] Add Fingerprint class --- ms2query/benchmarking/SpectrumDataSet.py | 53 +++++++++++++++++++++++- 1 file changed, 51 insertions(+), 2 deletions(-) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index d2f454d..bea7bc0 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -1,6 +1,6 @@ import copy -from collections import Counter -from typing import Dict, Iterable, List +from collections import Counter, defaultdict +from typing import Dict, Iterable, List, Optional import numpy as np from matchms import Spectrum from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi @@ -64,6 +64,55 @@ def copy(self): new_instance.spectrum_indexes_per_inchikey = copy.deepcopy(self.spectrum_indexes_per_inchikey) return new_instance +class Fingerprints: + def __init__(self, most_common_inchi_per_inchikey, fingerprint_type, nbits): + self.most_common_inchi_per_inchikey = most_common_inchi_per_inchikey + self.index_to_inchikey = list(self.most_common_inchi_per_inchikey.keys()) + self.inchikey_to_index = {inchikey: index for index, inchikey in enumerate(self.index_to_inchikey)} + self.fingerprint_type = fingerprint_type + self.nbits = nbits + + self.fingerprints = np.zeros((len(self.index_to_inchikey), self.nbits), dtype=np.uint8) + self.update_fingerprints(self.index_to_inchikey) + + def update_fingerprints(self, inchikeys: Iterable[str]): + for inchikey in tqdm(inchikeys, desc="Adding fingerprints to Inchikeys"): + inchikey_index = self.inchikey_to_index[inchikey] + self.fingerprints[inchikey_index, :] = self.compute_fingerprint(inchikey) + + def compute_fingerprint(self, inchikey): + most_common_inchi = self.most_common_inchi_per_inchikey[inchikey] + fingerprint = _derive_fingerprint_from_inchi( + most_common_inchi, fingerprint_type=self.fingerprint_type, nbits=self.nbits) + if not isinstance(fingerprint, np.ndarray): + raise ValueError(f"Fingerprint could not be set for InChI: {most_common_inchi}") + return fingerprint + + def get_fingerprints(self, list_of_inchikeys): + list_of_indexes = [self.inchikey_to_index[inchikey] for inchikey in list_of_inchikeys] + return self.fingerprints[list_of_indexes] + + def add_new_inchikeys(self, new_most_common_inchi_per_inchikey: dict[str, str]): + inchikeys_to_update = [] + inchikeys_to_add = [] + for inchikey, inchi in new_most_common_inchi_per_inchikey.items(): + if inchikey in self.most_common_inchi_per_inchikey: + if self.most_common_inchi_per_inchikey[inchikey] == inchi: + # the inchikey is unchanged + continue + else: + inchikeys_to_add.append(inchikey) + self.most_common_inchi_per_inchikey[inchikey] = inchi + inchikeys_to_update.append(inchikey) + + # Add the inchikeys_to_add + if inchikeys_to_add: + self.index_to_inchikey.extend(inchikeys_to_add) + self.inchikey_to_index = {inchikey: index for index, inchikey in enumerate(self.index_to_inchikey)} + new_rows = np.zeros((len(inchikeys_to_add), self.nbits), dtype=np.uint8) + self.fingerprints = np.vstack([self.fingerprints, new_rows]) + + self.update_fingerprints(inchikeys_to_update) class SpectraWithFingerprints(SpectrumSet): """Stores a spectrum dataset making it easy and fast to split on molecules""" From cd41c88d9a06b1d41805f0eab792bb79753b536a Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 17 Dec 2025 13:40:36 +0100 Subject: [PATCH 027/149] Select most common inchi per inchikey in SpectrumSet --- ms2query/benchmarking/SpectrumDataSet.py | 22 +++++++++++++--------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index bea7bc0..410753b 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -13,10 +13,16 @@ class SpectrumSet: def __init__(self, spectra: List[Spectrum], progress_bars=False): self._spectra = [] - self.spectrum_indexes_per_inchikey = {} + self.spectrum_indexes_per_inchikey = defaultdict(list) self.progress_bars = progress_bars # init spectra self._add_spectra_and_group_per_inchikey(spectra) + self.most_common_inchi_per_inchikey = {} + self._update_most_common_inchi_per_inchikey(list(self.spectrum_indexes_per_inchikey.keys())) + + def add_spectra(self, new_spectra: "SpectrumSet"): + updated_inchikeys = self._add_spectra_and_group_per_inchikey(new_spectra.spectra) + self._update_most_common_inchi_per_inchikey(updated_inchikeys) def _add_spectra_and_group_per_inchikey(self, spectra: List[Spectrum]): starting_index = len(self._spectra) @@ -28,16 +34,14 @@ def _add_spectra_and_group_per_inchikey(self, spectra: List[Spectrum]): spectrum_index = starting_index + i inchikey = spectrum.get("inchikey")[:14] updated_inchikeys.add(inchikey) - if inchikey in self.spectrum_indexes_per_inchikey: - self.spectrum_indexes_per_inchikey[inchikey].append(spectrum_index) - else: - self.spectrum_indexes_per_inchikey[inchikey] = [ - spectrum_index, - ] + self.spectrum_indexes_per_inchikey[inchikey].append(spectrum_index) return updated_inchikeys - def add_spectra(self, new_spectra: "SpectrumSet"): - return self._add_spectra_and_group_per_inchikey(new_spectra.spectra) + def _update_most_common_inchi_per_inchikey(self, new_inchikeys): + for inchikey in tqdm(new_inchikeys, desc="Get most common inchi per inchikey"): + spectra_matching_inchikey = self.spectra_per_inchikey(inchikey) + most_common_inchi = Counter([spectrum.get("inchi") for spectrum in spectra_matching_inchikey]).most_common(1)[0][0] + self.most_common_inchi_per_inchikey[inchikey](most_common_inchi) def subset_spectra(self, spectrum_indexes) -> "SpectrumSet": """Returns a new instance of a subset of the spectra""" From 5d41fd91a7bb0ec04259691d75ca907293990693 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 17 Dec 2025 16:05:00 +0100 Subject: [PATCH 028/149] Add subset_fingerprints --- ms2query/benchmarking/SpectrumDataSet.py | 27 ++++++++++++++++++------ 1 file changed, 21 insertions(+), 6 deletions(-) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index 410753b..f35c7b4 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -69,19 +69,24 @@ def copy(self): return new_instance class Fingerprints: + # I just realize that there already exists a Fingerprints class in matchms, with almost the same functionality, + # so it will be good to merge both. The matchms version works slighlty different. def __init__(self, most_common_inchi_per_inchikey, fingerprint_type, nbits): self.most_common_inchi_per_inchikey = most_common_inchi_per_inchikey - self.index_to_inchikey = list(self.most_common_inchi_per_inchikey.keys()) - self.inchikey_to_index = {inchikey: index for index, inchikey in enumerate(self.index_to_inchikey)} self.fingerprint_type = fingerprint_type self.nbits = nbits + self._index_to_inchikey = [] + self._inchikey_to_index = dict() + + self._add_inchikeys_to_index(list(self.most_common_inchi_per_inchikey.keys())) + self.fingerprints = np.zeros((len(self.index_to_inchikey), self.nbits), dtype=np.uint8) self.update_fingerprints(self.index_to_inchikey) def update_fingerprints(self, inchikeys: Iterable[str]): for inchikey in tqdm(inchikeys, desc="Adding fingerprints to Inchikeys"): - inchikey_index = self.inchikey_to_index[inchikey] + inchikey_index = self._inchikey_to_index[inchikey] self.fingerprints[inchikey_index, :] = self.compute_fingerprint(inchikey) def compute_fingerprint(self, inchikey): @@ -93,9 +98,20 @@ def compute_fingerprint(self, inchikey): return fingerprint def get_fingerprints(self, list_of_inchikeys): - list_of_indexes = [self.inchikey_to_index[inchikey] for inchikey in list_of_inchikeys] + list_of_indexes = [self._inchikey_to_index[inchikey] for inchikey in list_of_inchikeys] return self.fingerprints[list_of_indexes] + def subset_fingerprints(self, list_of_inchikeys) -> "Fingerprints": + new_instance = Fingerprints(dict(), self.fingerprint_type, self.nbits) + new_instance.fingerprints = self.get_fingerprints(list_of_inchikeys) + + new_most_common_inchi_per_inchikey = dict() + for inchikey in list_of_inchikeys: + new_most_common_inchi_per_inchikey[inchikey] = self.most_common_inchi_per_inchikey[inchikey] + new_instance.most_common_inchi_per_inchikey = new_most_common_inchi_per_inchikey + new_instance._add_inchikeys_to_index(list_of_inchikeys) + return new_instance + def add_new_inchikeys(self, new_most_common_inchi_per_inchikey: dict[str, str]): inchikeys_to_update = [] inchikeys_to_add = [] @@ -111,8 +127,7 @@ def add_new_inchikeys(self, new_most_common_inchi_per_inchikey: dict[str, str]): # Add the inchikeys_to_add if inchikeys_to_add: - self.index_to_inchikey.extend(inchikeys_to_add) - self.inchikey_to_index = {inchikey: index for index, inchikey in enumerate(self.index_to_inchikey)} + self._add_inchikeys_to_index(inchikeys_to_add) new_rows = np.zeros((len(inchikeys_to_add), self.nbits), dtype=np.uint8) self.fingerprints = np.vstack([self.fingerprints, new_rows]) From 5759d1b49ccaeb3e3a0c3341057f9f4725574e92 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 17 Dec 2025 16:05:18 +0100 Subject: [PATCH 029/149] Add test_fingerprints --- ms2query/benchmarking/SpectrumDataSet.py | 26 ++++----------- tests/test_Fingerprints.py | 40 ++++++++++++++++++++++++ 2 files changed, 46 insertions(+), 20 deletions(-) create mode 100644 tests/test_Fingerprints.py diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index f35c7b4..9fe0db6 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -133,27 +133,13 @@ def add_new_inchikeys(self, new_most_common_inchi_per_inchikey: dict[str, str]): self.update_fingerprints(inchikeys_to_update) -class SpectraWithFingerprints(SpectrumSet): - """Stores a spectrum dataset making it easy and fast to split on molecules""" + @property + def index_to_inchikey(self): + return self._index_to_inchikey - def __init__(self, spectra: List[Spectrum], fingerprint_type="daylight", nbits=4096): - super().__init__(spectra) - self.fingerprint_type = fingerprint_type - self.nbits = nbits - self.inchikey_fingerprint_pairs: Dict[str, np.array] = {} - # init spectra - self.update_fingerprint_per_inchikey(self.spectrum_indexes_per_inchikey.keys()) - - def add_spectra(self, new_spectra: "SpectraWithFingerprints"): - updated_inchikeys = super().add_spectra(new_spectra) - if hasattr(new_spectra, "inchikey_fingerprint_pairs"): - if new_spectra.nbits == self.nbits and new_spectra.fingerprint_type == self.fingerprint_type: - if len(self.inchikey_fingerprint_pairs.keys() & new_spectra.inchikey_fingerprint_pairs.keys()) == 0: - self.inchikey_fingerprint_pairs = ( - self.inchikey_fingerprint_pairs | new_spectra.inchikey_fingerprint_pairs - ) - return - self.update_fingerprint_per_inchikey(updated_inchikeys) + def _add_inchikeys_to_index(self, inchikeys_to_add): + self._index_to_inchikey.extend(inchikeys_to_add) + self._inchikey_to_index = {inchikey: index for index, inchikey in enumerate(self.index_to_inchikey)} def update_fingerprint_per_inchikey(self, inchikeys_to_update: Iterable[str]): for inchikey in tqdm( diff --git a/tests/test_Fingerprints.py b/tests/test_Fingerprints.py new file mode 100644 index 0000000..1fe1e79 --- /dev/null +++ b/tests/test_Fingerprints.py @@ -0,0 +1,40 @@ +import numpy as np +import pytest +from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi + +from ms2query.benchmarking.SpectrumDataSet import Fingerprints +from tests.conftest import get_inchikey_inchi_pairs + + +@pytest.fixture +def dummy_fingerprints(): + inchikey_inchi_pairs = get_inchikey_inchi_pairs(5) + inchi_per_inchikey = {compound_tuple[0][:14]: compound_tuple[1] for compound_tuple in inchikey_inchi_pairs} + fingerprints = Fingerprints(inchi_per_inchikey, fingerprint_type="daylight", nbits=100) + return fingerprints + + +def test_fingerprint_creation(dummy_fingerprints): + assert dummy_fingerprints.fingerprints.shape== (7,100) + for inchikey, inchi in list(dummy_fingerprints.most_common_inchi_per_inchikey.items()): + fingerprint = _derive_fingerprint_from_inchi(inchi, fingerprint_type="daylight", nbits=100) + np.testing.assert_array_equal(fingerprint, dummy_fingerprints.get_fingerprints([inchikey])[0]) + +def test_subsetting_fingerprint(dummy_fingerprints): + selected_inchikeys = list(dummy_fingerprints.most_common_inchi_per_inchikey.keys())[-2:] + subsetted_fingerprints = dummy_fingerprints.subset_fingerprints(selected_inchikeys) + assert subsetted_fingerprints.fingerprints.shape== (2,100) + assert list(subsetted_fingerprints.most_common_inchi_per_inchikey.keys()) == selected_inchikeys + for inchikey in selected_inchikeys: + fingerprint = _derive_fingerprint_from_inchi(dummy_fingerprints.most_common_inchi_per_inchikey[inchikey], fingerprint_type="daylight", nbits=100) + np.testing.assert_array_equal(fingerprint, subsetted_fingerprints.get_fingerprints([inchikey])[0]) + +def test_add_fingerprint(dummy_fingerprints): + inchikey_inchi_pairs = get_inchikey_inchi_pairs(7)[-2:] + inchi_per_inchikey = {compound_tuple[0][:14]: compound_tuple[1] for compound_tuple in inchikey_inchi_pairs} + dummy_fingerprints.add_new_inchikeys(inchi_per_inchikey) + + assert dummy_fingerprints.fingerprints.shape== (7,100) + for inchikey, inchi in inchi_per_inchikey.items(): + fingerprint = _derive_fingerprint_from_inchi(inchi, fingerprint_type="daylight", nbits=100) + np.testing.assert_array_equal(fingerprint, dummy_fingerprints.get_fingerprints([inchikey])[0]) From a03d958c6fb2fdfaf5bb0ace6b67c943460c43ef Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 17 Dec 2025 16:07:23 +0100 Subject: [PATCH 030/149] Add embeddings and fingerprints to SpectrumSet --- ms2query/benchmarking/SpectrumDataSet.py | 106 ++++++++++++----------- 1 file changed, 54 insertions(+), 52 deletions(-) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index 9fe0db6..563152c 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -20,9 +20,16 @@ def __init__(self, spectra: List[Spectrum], progress_bars=False): self.most_common_inchi_per_inchikey = {} self._update_most_common_inchi_per_inchikey(list(self.spectrum_indexes_per_inchikey.keys())) + self._fingerprints = None + self._embeddings = None + def add_spectra(self, new_spectra: "SpectrumSet"): updated_inchikeys = self._add_spectra_and_group_per_inchikey(new_spectra.spectra) self._update_most_common_inchi_per_inchikey(updated_inchikeys) + if self.embeddings is not None: + self.embeddings.add_embeddings(new_spectra.embeddings) + if self.fingerprints is not None: + self.fingerprints.add_new_inchikeys(new_spectra.most_common_inchi_per_inchikey) def _add_spectra_and_group_per_inchikey(self, spectra: List[Spectrum]): starting_index = len(self._spectra) @@ -45,10 +52,13 @@ def _update_most_common_inchi_per_inchikey(self, new_inchikeys): def subset_spectra(self, spectrum_indexes) -> "SpectrumSet": """Returns a new instance of a subset of the spectra""" - new_instance = copy.copy(self) - new_instance._spectra = [] - new_instance.spectrum_indexes_per_inchikey = {} - new_instance._add_spectra_and_group_per_inchikey([self._spectra[index] for index in spectrum_indexes]) + spectra = [self._spectra[index] for index in spectrum_indexes] + new_instance = SpectrumSet(spectra, progress_bars=self.progress_bars) + if self.embeddings is not None: + new_instance._embeddings = self.embeddings.get_embeddings(spectra) + if self.fingerprints is not None: + inchikeys = [spectrum.get("inchikey")[:14] for spectrum in spectra] + new_instance._fingerprints = self.fingerprints.subset_fingerprints(inchikeys) return new_instance def spectra_per_inchikey(self, inchikey) -> List[Spectrum]: @@ -57,10 +67,24 @@ def spectra_per_inchikey(self, inchikey) -> List[Spectrum]: matching_spectra.append(self._spectra[index]) return matching_spectra + def add_embeddings(self, model: SiameseSpectralModel): + self._embeddings = Embeddings(self._spectra, model) + + def add_fingerprints(self, fingerprint_type, nbits): + self._fingerprints = Fingerprints(self.most_common_inchi_per_inchikey, fingerprint_type, nbits) + @property def spectra(self): return self._spectra + @property + def fingerprints(self): + return self._fingerprints + + @property + def embeddings(self): + return self._embeddings + def copy(self): """This copy method ensures all spectra are""" new_instance = copy.copy(self) @@ -141,52 +165,30 @@ def _add_inchikeys_to_index(self, inchikeys_to_add): self._index_to_inchikey.extend(inchikeys_to_add) self._inchikey_to_index = {inchikey: index for index, inchikey in enumerate(self.index_to_inchikey)} - def update_fingerprint_per_inchikey(self, inchikeys_to_update: Iterable[str]): - for inchikey in tqdm( - inchikeys_to_update, desc="Adding fingerprints to Inchikeys", disable=not self.progress_bars - ): - spectra = self.spectra_per_inchikey(inchikey) - most_common_inchi = Counter([spectrum.get("inchi") for spectrum in spectra]).most_common(1)[0][0] - fingerprint = _derive_fingerprint_from_inchi( - most_common_inchi, fingerprint_type=self.fingerprint_type, nbits=self.nbits - ) - if not isinstance(fingerprint, np.ndarray): - raise ValueError(f"Fingerprint could not be set for InChI: {most_common_inchi}") - self.inchikey_fingerprint_pairs[inchikey] = fingerprint - def copy(self): - """This copy method ensures all spectra are""" - new_instance = super().copy() - new_instance.inchikey_fingerprint_pairs = copy.copy(self.inchikey_fingerprint_pairs) - return new_instance - - def subset_spectra(self, spectrum_indexes) -> "SpectraWithFingerprints": - """Returns a new instance of a subset of the spectra""" - new_instance = super().subset_spectra(spectrum_indexes) - # Only keep the fingerprints for which we have inchikeys. - # Important note: This is not a deep copy! - # And the fingerprint is not reset (so it is not always actually matching the most common inchi) - new_instance.inchikey_fingerprint_pairs = {inchikey: self.inchikey_fingerprint_pairs[inchikey] for inchikey - in new_instance.spectrum_indexes_per_inchikey.keys()} - return new_instance - - -class SpectraWithMS2DeepScoreEmbeddings(SpectraWithFingerprints): - def __init__(self, spectra: List[Spectrum], ms2deepscore_model: SiameseSpectralModel, **kwargs): - super().__init__(spectra, **kwargs) - self.ms2deepscore_model = ms2deepscore_model - self.embeddings: np.ndarray = compute_embedding_array(self.ms2deepscore_model, spectra) - - def add_spectra(self, new_spectra: "SpectraWithMS2DeepScoreEmbeddings"): - super().add_spectra(new_spectra) - if hasattr(new_spectra, "embeddings"): - new_embeddings = new_spectra.embeddings - else: - new_embeddings = compute_embedding_array(self.ms2deepscore_model, new_spectra.spectra) - self.embeddings = np.vstack([self.embeddings, new_embeddings]) - - def subset_spectra(self, spectrum_indexes) -> "SpectraWithMS2DeepScoreEmbeddings": - """Returns a new instance of a subset of the spectra""" - new_instance = super().subset_spectra(spectrum_indexes) - new_instance.embeddings = self.embeddings[spectrum_indexes] - return new_instance +class Embeddings: + """Stores Embeddings for a list of mass spectra""" + def __init__(self, spectra: List[Spectrum], + model: SiameseSpectralModel): + self.index_to_spectrum_hash = [spectrum.__hash__() for spectrum in spectra] + if set(self.index_to_spectrum_hash) != len(spectra): + raise ValueError("There are duplicated spectra in the spectrum list") + self.spectrum_hash_to_index = {spectrum_hash: index for index, spectrum_hash in enumerate(self.index_to_spectrum_hash)} + + self.model_settings = model.model_settings + self.embeddings: np.ndarray = compute_embedding_array(model, spectra) + + def add_embeddings(self, embeddings: "Embeddings"): + if embeddings.model_settings != self.model_settings: + raise ValueError("Model settings of merged embeddings do not match") + if not set(embeddings.spectrum_hash_to_index).isdisjoint(self.spectrum_hash_to_index): + raise ValueError("There are repeated spectra in the embeddings that are added together") + self.embeddings = np.vstack([self.embeddings, embeddings]) + self.spectrum_hash_to_index += embeddings.spectrum_hash_to_index + self.spectrum_hash_to_index = {spectrum_hash: index for index, spectrum_hash in enumerate(self.index_to_spectrum_hash)} + + def get_embeddings(self, spectra: list[Spectrum]): + embedding_indexes = [] + for spectrum in spectra: + embedding_indexes.append(self.spectrum_hash_to_index[spectrum.__hash__()]) + return self.embeddings[embedding_indexes] From e758343bf750e99a3c90f7c21a2fb3e8e93c3695 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 18 Dec 2025 16:08:49 +0100 Subject: [PATCH 031/149] Move and update Fingerprints --- ms2query/benchmarking/Fingerprints.py | 87 ++++++++++++++++++++++++ ms2query/benchmarking/SpectrumDataSet.py | 75 +------------------- tests/test_Fingerprints.py | 7 +- 3 files changed, 94 insertions(+), 75 deletions(-) create mode 100644 ms2query/benchmarking/Fingerprints.py diff --git a/ms2query/benchmarking/Fingerprints.py b/ms2query/benchmarking/Fingerprints.py new file mode 100644 index 0000000..6339b91 --- /dev/null +++ b/ms2query/benchmarking/Fingerprints.py @@ -0,0 +1,87 @@ +from typing import Iterable + +import numpy as np +import pandas as pd +from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi +from tqdm import tqdm + +from ms2query.metrics import generalized_tanimoto_similarity_matrix + + +class Fingerprints: + # I just realize that there already exists a Fingerprints class in matchms, with almost the same functionality, + # so it will be good to merge both. The matchms version works slighlty different. + def __init__(self, most_common_inchi_per_inchikey, fingerprint_type, nbits): + self.most_common_inchi_per_inchikey = most_common_inchi_per_inchikey + self.fingerprint_type = fingerprint_type + self.nbits = nbits + + self._index_to_inchikey =np.array([], dtype="U14") + self._inchikey_to_index = dict() + + self._add_inchikeys_to_index(list(self.most_common_inchi_per_inchikey.keys())) + + self.fingerprints = np.zeros((len(self.index_to_inchikey), self.nbits), dtype=np.uint8) + self.update_fingerprints(self.index_to_inchikey) + + def update_fingerprints(self, inchikeys: Iterable[str]): + for inchikey in tqdm(inchikeys, desc="Adding fingerprints to Inchikeys"): + inchikey_index = self._inchikey_to_index[inchikey] + self.fingerprints[inchikey_index, :] = self.compute_fingerprint(inchikey) + + def compute_fingerprint(self, inchikey): + most_common_inchi = self.most_common_inchi_per_inchikey[inchikey] + fingerprint = _derive_fingerprint_from_inchi( + most_common_inchi, fingerprint_type=self.fingerprint_type, nbits=self.nbits) + if not isinstance(fingerprint, np.ndarray): + raise ValueError(f"Fingerprint could not be set for InChI: {most_common_inchi}") + return fingerprint + + def get_fingerprints(self, list_of_inchikeys): + list_of_indexes = [self._inchikey_to_index[inchikey] for inchikey in list_of_inchikeys] + return self.fingerprints[list_of_indexes] + + def subset_fingerprints(self, list_of_inchikeys) -> "Fingerprints": + new_instance = Fingerprints(dict(), self.fingerprint_type, self.nbits) + new_instance.fingerprints = self.get_fingerprints(list_of_inchikeys) + + new_most_common_inchi_per_inchikey = dict() + for inchikey in list_of_inchikeys: + new_most_common_inchi_per_inchikey[inchikey] = self.most_common_inchi_per_inchikey[inchikey] + new_instance.most_common_inchi_per_inchikey = new_most_common_inchi_per_inchikey + new_instance._add_inchikeys_to_index(list_of_inchikeys) + return new_instance + + def add_new_inchikeys(self, new_most_common_inchi_per_inchikey: dict[str, str]): + inchikeys_to_update = [] + inchikeys_to_add = [] + for inchikey, inchi in new_most_common_inchi_per_inchikey.items(): + if inchikey in self.most_common_inchi_per_inchikey: + if self.most_common_inchi_per_inchikey[inchikey] == inchi: + # the inchikey is unchanged + continue + else: + inchikeys_to_add.append(inchikey) + self.most_common_inchi_per_inchikey[inchikey] = inchi + inchikeys_to_update.append(inchikey) + + # Add the inchikeys_to_add + if inchikeys_to_add: + self._add_inchikeys_to_index(inchikeys_to_add) + new_rows = np.zeros((len(inchikeys_to_add), self.nbits), dtype=np.uint8) + self.fingerprints = np.vstack([self.fingerprints, new_rows]) + + self.update_fingerprints(inchikeys_to_update) + + @property + def index_to_inchikey(self): + return self._index_to_inchikey + + def _add_inchikeys_to_index(self, inchikeys_to_add): + self._index_to_inchikey = np.append(self._index_to_inchikey, inchikeys_to_add) + self._inchikey_to_index = {inchikey: index for index, inchikey in enumerate(self.index_to_inchikey)} + + +def get_similarity_matrix(fingerprints_1: Fingerprints, fingerprints_2: Fingerprints): + similarity_scores = generalized_tanimoto_similarity_matrix(fingerprints_1.fingerprints, fingerprints_2.fingerprints) + return pd.DataFrame(similarity_scores, index=fingerprints_1.index_to_inchikey, columns=fingerprints_2.index_to_inchikey) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index 563152c..0b44011 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -7,6 +7,8 @@ from ms2deepscore.models import SiameseSpectralModel, compute_embedding_array from tqdm import tqdm +from ms2query.benchmarking.Fingerprints import Fingerprints + class SpectrumSet: """Stores a spectrum dataset making it easy and fast to split on molecules""" @@ -92,79 +94,6 @@ def copy(self): new_instance.spectrum_indexes_per_inchikey = copy.deepcopy(self.spectrum_indexes_per_inchikey) return new_instance -class Fingerprints: - # I just realize that there already exists a Fingerprints class in matchms, with almost the same functionality, - # so it will be good to merge both. The matchms version works slighlty different. - def __init__(self, most_common_inchi_per_inchikey, fingerprint_type, nbits): - self.most_common_inchi_per_inchikey = most_common_inchi_per_inchikey - self.fingerprint_type = fingerprint_type - self.nbits = nbits - - self._index_to_inchikey = [] - self._inchikey_to_index = dict() - - self._add_inchikeys_to_index(list(self.most_common_inchi_per_inchikey.keys())) - - self.fingerprints = np.zeros((len(self.index_to_inchikey), self.nbits), dtype=np.uint8) - self.update_fingerprints(self.index_to_inchikey) - - def update_fingerprints(self, inchikeys: Iterable[str]): - for inchikey in tqdm(inchikeys, desc="Adding fingerprints to Inchikeys"): - inchikey_index = self._inchikey_to_index[inchikey] - self.fingerprints[inchikey_index, :] = self.compute_fingerprint(inchikey) - - def compute_fingerprint(self, inchikey): - most_common_inchi = self.most_common_inchi_per_inchikey[inchikey] - fingerprint = _derive_fingerprint_from_inchi( - most_common_inchi, fingerprint_type=self.fingerprint_type, nbits=self.nbits) - if not isinstance(fingerprint, np.ndarray): - raise ValueError(f"Fingerprint could not be set for InChI: {most_common_inchi}") - return fingerprint - - def get_fingerprints(self, list_of_inchikeys): - list_of_indexes = [self._inchikey_to_index[inchikey] for inchikey in list_of_inchikeys] - return self.fingerprints[list_of_indexes] - - def subset_fingerprints(self, list_of_inchikeys) -> "Fingerprints": - new_instance = Fingerprints(dict(), self.fingerprint_type, self.nbits) - new_instance.fingerprints = self.get_fingerprints(list_of_inchikeys) - - new_most_common_inchi_per_inchikey = dict() - for inchikey in list_of_inchikeys: - new_most_common_inchi_per_inchikey[inchikey] = self.most_common_inchi_per_inchikey[inchikey] - new_instance.most_common_inchi_per_inchikey = new_most_common_inchi_per_inchikey - new_instance._add_inchikeys_to_index(list_of_inchikeys) - return new_instance - - def add_new_inchikeys(self, new_most_common_inchi_per_inchikey: dict[str, str]): - inchikeys_to_update = [] - inchikeys_to_add = [] - for inchikey, inchi in new_most_common_inchi_per_inchikey.items(): - if inchikey in self.most_common_inchi_per_inchikey: - if self.most_common_inchi_per_inchikey[inchikey] == inchi: - # the inchikey is unchanged - continue - else: - inchikeys_to_add.append(inchikey) - self.most_common_inchi_per_inchikey[inchikey] = inchi - inchikeys_to_update.append(inchikey) - - # Add the inchikeys_to_add - if inchikeys_to_add: - self._add_inchikeys_to_index(inchikeys_to_add) - new_rows = np.zeros((len(inchikeys_to_add), self.nbits), dtype=np.uint8) - self.fingerprints = np.vstack([self.fingerprints, new_rows]) - - self.update_fingerprints(inchikeys_to_update) - - @property - def index_to_inchikey(self): - return self._index_to_inchikey - - def _add_inchikeys_to_index(self, inchikeys_to_add): - self._index_to_inchikey.extend(inchikeys_to_add) - self._inchikey_to_index = {inchikey: index for index, inchikey in enumerate(self.index_to_inchikey)} - class Embeddings: """Stores Embeddings for a list of mass spectra""" diff --git a/tests/test_Fingerprints.py b/tests/test_Fingerprints.py index 1fe1e79..4de05b6 100644 --- a/tests/test_Fingerprints.py +++ b/tests/test_Fingerprints.py @@ -2,13 +2,16 @@ import pytest from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi -from ms2query.benchmarking.SpectrumDataSet import Fingerprints +from ms2query.benchmarking.Fingerprints import Fingerprints, get_similarity_matrix from tests.conftest import get_inchikey_inchi_pairs @pytest.fixture def dummy_fingerprints(): - inchikey_inchi_pairs = get_inchikey_inchi_pairs(5) + return make_test_fingerprints(5) + +def make_test_fingerprints(nr_of_inchikeys=5): + inchikey_inchi_pairs = get_inchikey_inchi_pairs(nr_of_inchikeys) inchi_per_inchikey = {compound_tuple[0][:14]: compound_tuple[1] for compound_tuple in inchikey_inchi_pairs} fingerprints = Fingerprints(inchi_per_inchikey, fingerprint_type="daylight", nbits=100) return fingerprints From 6fd710b3eee85928f61638659304861e31f071e9 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 18 Dec 2025 16:09:37 +0100 Subject: [PATCH 032/149] Fix bug for Embeddings when checking unique input --- ms2query/benchmarking/SpectrumDataSet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index 0b44011..a1ead0b 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -100,7 +100,7 @@ class Embeddings: def __init__(self, spectra: List[Spectrum], model: SiameseSpectralModel): self.index_to_spectrum_hash = [spectrum.__hash__() for spectrum in spectra] - if set(self.index_to_spectrum_hash) != len(spectra): + if len(set(self.index_to_spectrum_hash)) != len(spectra): raise ValueError("There are duplicated spectra in the spectrum list") self.spectrum_hash_to_index = {spectrum_hash: index for index, spectrum_hash in enumerate(self.index_to_spectrum_hash)} From 3f75740aaf583df5e01f25dcfda76ae15d169909 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 18 Dec 2025 16:10:13 +0100 Subject: [PATCH 033/149] Use hidden properties for embeddings and fingerprints internally --- ms2query/benchmarking/SpectrumDataSet.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index a1ead0b..ec889f8 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -1,9 +1,8 @@ import copy from collections import Counter, defaultdict -from typing import Dict, Iterable, List, Optional +from typing import List import numpy as np from matchms import Spectrum -from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi from ms2deepscore.models import SiameseSpectralModel, compute_embedding_array from tqdm import tqdm @@ -28,9 +27,9 @@ def __init__(self, spectra: List[Spectrum], progress_bars=False): def add_spectra(self, new_spectra: "SpectrumSet"): updated_inchikeys = self._add_spectra_and_group_per_inchikey(new_spectra.spectra) self._update_most_common_inchi_per_inchikey(updated_inchikeys) - if self.embeddings is not None: + if self._embeddings is not None: self.embeddings.add_embeddings(new_spectra.embeddings) - if self.fingerprints is not None: + if self._fingerprints is not None: self.fingerprints.add_new_inchikeys(new_spectra.most_common_inchi_per_inchikey) def _add_spectra_and_group_per_inchikey(self, spectra: List[Spectrum]): @@ -50,15 +49,15 @@ def _update_most_common_inchi_per_inchikey(self, new_inchikeys): for inchikey in tqdm(new_inchikeys, desc="Get most common inchi per inchikey"): spectra_matching_inchikey = self.spectra_per_inchikey(inchikey) most_common_inchi = Counter([spectrum.get("inchi") for spectrum in spectra_matching_inchikey]).most_common(1)[0][0] - self.most_common_inchi_per_inchikey[inchikey](most_common_inchi) + self.most_common_inchi_per_inchikey[inchikey] = most_common_inchi def subset_spectra(self, spectrum_indexes) -> "SpectrumSet": """Returns a new instance of a subset of the spectra""" spectra = [self._spectra[index] for index in spectrum_indexes] new_instance = SpectrumSet(spectra, progress_bars=self.progress_bars) - if self.embeddings is not None: + if self._embeddings is not None: new_instance._embeddings = self.embeddings.get_embeddings(spectra) - if self.fingerprints is not None: + if self._fingerprints is not None: inchikeys = [spectrum.get("inchikey")[:14] for spectrum in spectra] new_instance._fingerprints = self.fingerprints.subset_fingerprints(inchikeys) return new_instance @@ -81,10 +80,14 @@ def spectra(self): @property def fingerprints(self): + if self._fingerprints is None: + raise ValueError("First run add_fingerprints") return self._fingerprints @property def embeddings(self): + if self._embeddings is None: + raise ValueError("First run add_embeddings") return self._embeddings def copy(self): From 9205286825bcdd54ac743c22320f08303ba37dc5 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 18 Dec 2025 16:11:54 +0100 Subject: [PATCH 034/149] Use SpectrumSet in predict_highest_cosine.py --- .../benchmarking/reference_methods/predict_highest_cosine.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/predict_highest_cosine.py b/ms2query/benchmarking/reference_methods/predict_highest_cosine.py index 49fd6cf..2fa2373 100644 --- a/ms2query/benchmarking/reference_methods/predict_highest_cosine.py +++ b/ms2query/benchmarking/reference_methods/predict_highest_cosine.py @@ -2,11 +2,11 @@ from matchms import Scores from matchms.similarity.CosineGreedy import CosineGreedy from matchms.similarity.PrecursorMzMatch import PrecursorMzMatch -from ms2query.benchmarking.SpectrumDataSet import SpectraWithFingerprints +from ms2query.benchmarking.SpectrumDataSet import SpectrumSet def predict_highest_cosine( - library_spectra: SpectraWithFingerprints, query_spectra: SpectraWithFingerprints + library_spectra: SpectrumSet, query_spectra: SpectrumSet ) -> Tuple[List[str], List[float]]: scores = Scores(references=library_spectra.spectra, queries=query_spectra.spectra, is_symmetric=False) From add78e55588f47ff99c4e2338cebbaa8acd09542 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 18 Dec 2025 16:19:24 +0100 Subject: [PATCH 035/149] Use Embeddings class for predicting top ms2deepscore --- .../PredictMS2DeepScoreSimilarity.py | 11 +++++++---- .../reference_methods/predict_highest_ms2deepscore.py | 4 ++-- 2 files changed, 9 insertions(+), 6 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py b/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py index cd20544..924051a 100644 --- a/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py +++ b/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py @@ -3,9 +3,11 @@ from ms2deepscore.vector_operations import cosine_similarity_matrix from tqdm import tqdm +from ms2query.benchmarking.SpectrumDataSet import Embeddings + def predict_top_ms2deepscores( - library_embeddings: np.ndarray, query_embeddings: np.ndarray, batch_size: int = 500, k=1 + library_embeddings: Embeddings, query_embeddings: Embeddings, batch_size: int = 500, k=1 ) -> Tuple[np.ndarray, np.ndarray]: """Memory efficient way of calculating the highest MS2DeepScores @@ -27,7 +29,7 @@ def predict_top_ms2deepscores( """ top_indexes_per_batch = [] top_scores_per_batch = [] - num_of_query_embeddings = query_embeddings.shape[0] + num_of_query_embeddings = query_embeddings.embeddings.shape[0] # loop over the batches for start_idx in tqdm( range(0, num_of_query_embeddings, batch_size), @@ -36,10 +38,11 @@ def predict_top_ms2deepscores( + " embeddings", ): end_idx = min(start_idx + batch_size, num_of_query_embeddings) - selected_query_embeddings = query_embeddings[start_idx:end_idx] - score_matrix = cosine_similarity_matrix(selected_query_embeddings, library_embeddings) + selected_query_embeddings = query_embeddings.embeddings[start_idx:end_idx] + score_matrix = cosine_similarity_matrix(selected_query_embeddings, library_embeddings.embeddings) top_n_idx = np.argsort(score_matrix, axis=1)[:, -k:][:, ::-1] top_n_scores = np.take_along_axis(score_matrix, top_n_idx, axis=1) top_indexes_per_batch.append(top_n_idx) top_scores_per_batch.append(top_n_scores) + # todo refactor to use the Embeddings class and return spectrum hashes instead of indexes return np.vstack(top_indexes_per_batch), np.vstack(top_scores_per_batch) diff --git a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py index a5d9318..4a4fd41 100644 --- a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py +++ b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py @@ -1,10 +1,10 @@ from typing import List, Tuple from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores -from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings +from ms2query.benchmarking.SpectrumDataSet import SpectrumSet def predict_highest_ms2deepscore( - library_spectra: SpectraWithMS2DeepScoreEmbeddings, query_spectra: SpectraWithMS2DeepScoreEmbeddings + library_spectra: SpectrumSet, query_spectra: SpectrumSet ) -> Tuple[List[str], List[float]]: indexes_of_highest_scores, highest_scores = predict_top_ms2deepscores( library_spectra.embeddings, query_spectra.embeddings, k=1 From 21ede035aab63cef22339656b28dd84723a150b6 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 18 Dec 2025 16:24:20 +0100 Subject: [PATCH 036/149] Update predict with integrated similarity flow to use SpectrumSet --- .../predict_with_integrated_similarity_flow.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py index e352ab3..1484973 100644 --- a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py +++ b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py @@ -3,12 +3,12 @@ from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix from tqdm import tqdm from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores -from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings +from ms2query.benchmarking.SpectrumDataSet import SpectrumSet def predict_with_integrated_similarity_flow( - library_spectra: SpectraWithMS2DeepScoreEmbeddings, - query_spectra: SpectraWithMS2DeepScoreEmbeddings, + library_spectra: SpectrumSet, + query_spectra: SpectrumSet, number_of_analogues_to_consider=50, ) -> Tuple[List[str], List[float]]: @@ -30,8 +30,8 @@ def predict_with_integrated_similarity_flow( def get_highest_isf( - library_spectra: SpectraWithMS2DeepScoreEmbeddings, - indexes_of_library_spectra_with_highest_score: List[int], + library_spectra: SpectrumSet, + indexes_of_library_spectra_with_highest_score: np.ndarray, predicted_scores: [List[float]], ): @@ -43,10 +43,7 @@ def get_highest_isf( predicted_scores, inchikeys_with_highest_ms2deepscore ) # calculate tanimoto scores - library_fingerprints = np.array( - [library_spectra.inchikey_fingerprint_pairs[inchikey] for inchikey in unique_inchikeys] - ) - tanimoto_scores = jaccard_similarity_matrix(library_fingerprints, library_fingerprints) + tanimoto_scores = jaccard_similarity_matrix(library_spectra.fingerprints.fingerprints, library_spectra.fingerprints.fingerprints) isf_scores = integrated_similarity_flow(average_scores, tanimoto_scores, nr_of_spectra_per_inchikey) index_of_highest_score = np.argmax(isf_scores) From 4c8a8dca251f92053b0cae0cb31e3566232b8f39 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 18 Dec 2025 16:33:34 +0100 Subject: [PATCH 037/149] Make predict_using_closest_tanimoto.py use SpectrumSet --- .../predict_using_closest_tanimoto.py | 24 +++++++++---------- 1 file changed, 11 insertions(+), 13 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index 751953e..e4f5bda 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -2,12 +2,12 @@ import numpy as np from ms2deepscore.vector_operations import cosine_similarity_matrix from tqdm import tqdm -from ms2query.benchmarking.SpectrumDataSet import SpectraWithFingerprints, SpectraWithMS2DeepScoreEmbeddings +from ms2query.benchmarking.SpectrumDataSet import SpectrumSet from ms2query.metrics import generalized_tanimoto_similarity_matrix def predict_using_closest_tanimoto( - library_spectra: SpectraWithMS2DeepScoreEmbeddings, query_spectra: SpectraWithMS2DeepScoreEmbeddings, + library_spectra: SpectrumSet, query_spectra: SpectrumSet, nr_of_closest_inchikeys_to_select=10, nr_of_inchikeys_with_highest_ms2deepscore_to_select=100 ) -> Tuple[List[str], List[float]]: @@ -26,12 +26,12 @@ def predict_using_closest_tanimoto( def predict_using_closest_tanimoto_single_spectrum( - spectra_with_embeddings, single_spectrum_with_embeddings, + spectra_with_embeddings: SpectrumSet, single_spectrum_with_embeddings: SpectrumSet, nr_of_closest_inchikeys_to_select, nr_of_inchikeys_with_highest_ms2deepscore_to_select) -> Tuple[str, float]: if len(single_spectrum_with_embeddings.spectra) != 1: raise ValueError("expected a single spectrum") - ms2deepscores = cosine_similarity_matrix(single_spectrum_with_embeddings.embeddings, - spectra_with_embeddings.embeddings)[0] + ms2deepscores = cosine_similarity_matrix(single_spectrum_with_embeddings.embeddings.embeddings, + spectra_with_embeddings.embeddings.embeddings)[0] top_inchikeys = select_inchikeys_with_highest_ms2deepscore(spectra_with_embeddings, ms2deepscores, nr_of_inchikeys_with_highest_ms2deepscore_to_select) average_predicted_scores = {} @@ -45,7 +45,7 @@ def predict_using_closest_tanimoto_single_spectrum( inchikey_with_highest_average_prediction, score = max(average_predicted_scores.items(), key=lambda item: item[1]) return inchikey_with_highest_average_prediction, score -def select_inchikeys_with_highest_ms2deepscore(spectra_with_embeddings, ms2deepscores, nr_of_inchikeys_to_select=10): +def select_inchikeys_with_highest_ms2deepscore(spectra_with_embeddings: SpectrumSet, ms2deepscores, nr_of_inchikeys_to_select=10): highest_score_for_inchikey = [] for inchikey, spectrum_indexes in spectra_with_embeddings.spectrum_indexes_per_inchikey.items(): all_ms2deepscores_for_inchikey = ms2deepscores[spectrum_indexes] @@ -53,7 +53,7 @@ def select_inchikeys_with_highest_ms2deepscore(spectra_with_embeddings, ms2deeps inchikey_indexes_with_highest_ms2deepscore = np.argpartition( np.array(highest_score_for_inchikey), -nr_of_inchikeys_to_select)[-nr_of_inchikeys_to_select:] - all_inchikeys = list(spectra_with_embeddings.inchikey_fingerprint_pairs.keys()) + all_inchikeys = list(spectra_with_embeddings.most_common_inchi_per_inchikey.keys()) top_inchikeys = [all_inchikeys[inchikey_index] for inchikey_index in inchikey_indexes_with_highest_ms2deepscore] return top_inchikeys @@ -68,14 +68,12 @@ def get_average_predictions_for_closely_related_metabolites(spectra_with_embeddi average_predicted_score = sum(average_predicted_scores) / len(average_predicted_scores) return average_predicted_score -def get_inchikey_and_tanimoto_scores_for_top_k(spectra: SpectraWithFingerprints, inchikey, k +def get_inchikey_and_tanimoto_scores_for_top_k(spectra: SpectrumSet, inchikey, k ) -> tuple[list[str], np.ndarray]: """For an inchikey in a library the top k highest tanimoto scores in the library are predicted (including itself)""" - library_fingerprints = np.vstack(list(spectra.inchikey_fingerprint_pairs.values())) - fingerprint_single_inchikey = np.vstack(list([spectra.inchikey_fingerprint_pairs[inchikey]])) - similarity_scores = generalized_tanimoto_similarity_matrix(fingerprint_single_inchikey, library_fingerprints)[0] + similarity_scores = generalized_tanimoto_similarity_matrix(spectra.fingerprints.get_fingerprints(inchikey), spectra.fingerprints.fingerprints)[0] inchikey_indexes_of_top_k = np.argpartition(similarity_scores, -k)[-k:] tanimoto_scores_for_top_k = similarity_scores[inchikey_indexes_of_top_k] - all_inchikeys = list(spectra.inchikey_fingerprint_pairs.keys()) - top_inchikeys = [all_inchikeys[inchikey_index] for inchikey_index in inchikey_indexes_of_top_k] + + top_inchikeys = [spectra.fingerprints.index_to_inchikey[inchikey_index] for inchikey_index in inchikey_indexes_of_top_k] return top_inchikeys, tanimoto_scores_for_top_k From 21ab6fdf177afeb5f2cf965af79adeafe056de45 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 18 Dec 2025 16:38:49 +0100 Subject: [PATCH 038/149] Update EvaluateMethods to use SpectrumSet --- ms2query/benchmarking/EvaluateMethods.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/ms2query/benchmarking/EvaluateMethods.py b/ms2query/benchmarking/EvaluateMethods.py index edced8c..7eafaaf 100644 --- a/ms2query/benchmarking/EvaluateMethods.py +++ b/ms2query/benchmarking/EvaluateMethods.py @@ -3,12 +3,12 @@ import numpy as np from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix from tqdm import tqdm -from ms2query.benchmarking.SpectrumDataSet import SpectraWithFingerprints, SpectrumSet +from ms2query.benchmarking.SpectrumDataSet import SpectrumSet class EvaluateMethods: def __init__( - self, training_spectrum_set: SpectraWithFingerprints, validation_spectrum_set: SpectraWithFingerprints + self, training_spectrum_set: SpectrumSet, validation_spectrum_set: SpectrumSet ): self.training_spectrum_set = training_spectrum_set self.validation_spectrum_set = validation_spectrum_set @@ -19,7 +19,7 @@ def __init__( def benchmark_analogue_search( self, prediction_function: Callable[ - [SpectraWithFingerprints, SpectraWithFingerprints], Tuple[List[str], List[float]] + [SpectrumSet, SpectrumSet], Tuple[List[str], List[float]] ], ) -> float: predicted_inchikeys, _ = prediction_function(self.training_spectrum_set, self.validation_spectrum_set) @@ -37,8 +37,8 @@ def benchmark_analogue_search( if predicted_inchikey is None: prediction_scores.append(0.0) else: - predicted_fingerprint = self.training_spectrum_set.inchikey_fingerprint_pairs[predicted_inchikey] - actual_fingerprint = self.validation_spectrum_set.inchikey_fingerprint_pairs[inchikey] + predicted_fingerprint = self.training_spectrum_set.fingerprints.get_fingerprints([predicted_inchikey])[0] + actual_fingerprint = self.validation_spectrum_set.fingerprints.get_fingerprints([inchikey])[0] tanimoto_for_prediction = calculate_tanimoto_score_between_pair( predicted_fingerprint, actual_fingerprint ) @@ -53,7 +53,7 @@ def benchmark_analogue_search( def benchmark_exact_matching_within_ionmode( self, prediction_function: Callable[ - [SpectraWithFingerprints, SpectraWithFingerprints], Tuple[List[str], List[float]] + [SpectrumSet, SpectrumSet], Tuple[List[str], List[float]] ], ionmode: str, ) -> float: @@ -77,7 +77,7 @@ def benchmark_exact_matching_within_ionmode( def exact_matches_across_ionization_modes( self, prediction_function: Callable[ - [SpectraWithFingerprints, SpectraWithFingerprints], Tuple[List[str], List[float]] + [SpectrumSet, SpectrumSet], Tuple[List[str], List[float]] ], ): """Test the accuracy at retrieving exact matches from the library if only available in other ionisation mode @@ -198,5 +198,5 @@ def subset_spectra_on_ionmode(spectrum_set: SpectrumSet, ionmode) -> SpectrumSet return spectrum_set.subset_spectra(spectrum_indexes_to_keep) -def calculate_tanimoto_score_between_pair(fingerprint_1: str, fingerprint_2: str) -> float: +def calculate_tanimoto_score_between_pair(fingerprint_1: np.ndarray, fingerprint_2: np.ndarray) -> float: return jaccard_similarity_matrix(np.array([fingerprint_1]), np.array([fingerprint_2]))[0][0] From 8a02acf7131677809dfd88890c5f54749c890924 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 18 Dec 2025 16:41:29 +0100 Subject: [PATCH 039/149] Let predict_best_possible_match.py use SpectrumSet --- .../predict_best_possible_match.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py index 1544f71..39fad93 100644 --- a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py +++ b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py @@ -1,10 +1,10 @@ from typing import Dict import numpy as np from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix -from ms2query.benchmarking.SpectrumDataSet import SpectraWithFingerprints +from ms2query.benchmarking.SpectrumDataSet import SpectrumSet -def predict_best_possible_match(library_spectra: SpectraWithFingerprints, query_spectra: SpectraWithFingerprints): +def predict_best_possible_match(library_spectra: SpectrumSet, query_spectra: SpectrumSet): highest_possible_score_per_inchikey = calculate_highest_tanimoto_score_per_inchikey(library_spectra, query_spectra) inchikeys_of_best_match = [] @@ -20,27 +20,26 @@ def predict_best_possible_match(library_spectra: SpectraWithFingerprints, query_ def calculate_highest_tanimoto_score_per_inchikey( - library_spectra: SpectraWithFingerprints, query_spectra: SpectraWithFingerprints + library_spectra: SpectrumSet, query_spectra: SpectrumSet ) -> Dict[str, tuple[str, float]]: """Finds the best possible match during an analogue search""" print("Calculating tanimoto scores to determine best possible match") - library_fingerprints = np.array(list(library_spectra.inchikey_fingerprint_pairs.values())) - query_fingerprints = np.array(list(query_spectra.inchikey_fingerprint_pairs.values())) + + library_fingerprints = library_spectra.fingerprints.fingerprints + query_fingerprints = query_spectra.fingerprints.fingerprints tanimoto_scores = jaccard_similarity_matrix(library_fingerprints, query_fingerprints) highest_scores = tanimoto_scores.max(axis=0, initial=0) indexes_of_highest_scores = tanimoto_scores.argmax(axis=0) - inchikeys_library = list(library_spectra.inchikey_fingerprint_pairs.keys()) - highest_possible_score_per_inchikey = dict() - for i, inchikey in enumerate(query_spectra.inchikey_fingerprint_pairs): + for i, inchikey in enumerate(query_spectra.fingerprints.index_to_inchikey): # Check if inchikey in library (To correctly handle the exact matching case) - if inchikey in library_spectra.inchikey_fingerprint_pairs: + if inchikey in library_spectra.fingerprints.index_to_inchikey: highest_possible_score_per_inchikey[inchikey] = (inchikey, 1.0) continue highest_possible_score_per_inchikey[inchikey] = ( - inchikeys_library[indexes_of_highest_scores[i]], + library_spectra.fingerprints.index_to_inchikey[indexes_of_highest_scores[i]], highest_scores[i], ) return highest_possible_score_per_inchikey From 6b54141952ee9ebf4b3390a23f1fc1d4549c0de2 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 18 Dec 2025 16:41:48 +0100 Subject: [PATCH 040/149] Add some extra typehinting --- ms2query/benchmarking/SpectrumDataSet.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index ec889f8..87fb7c2 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -79,13 +79,13 @@ def spectra(self): return self._spectra @property - def fingerprints(self): + def fingerprints(self) -> "Fingerprints": if self._fingerprints is None: raise ValueError("First run add_fingerprints") return self._fingerprints @property - def embeddings(self): + def embeddings(self) -> "Embeddings": if self._embeddings is None: raise ValueError("First run add_embeddings") return self._embeddings From cf467e7d4c4be1f714cd70172f55e186fa556a85 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 18 Dec 2025 16:42:13 +0100 Subject: [PATCH 041/149] Make a TopKTanimotoScores class (still need to integrate) --- ms2query/benchmarking/TopKTanimotoScores.py | 51 +++++++++++++++++++++ 1 file changed, 51 insertions(+) create mode 100644 ms2query/benchmarking/TopKTanimotoScores.py diff --git a/ms2query/benchmarking/TopKTanimotoScores.py b/ms2query/benchmarking/TopKTanimotoScores.py new file mode 100644 index 0000000..c4445e8 --- /dev/null +++ b/ms2query/benchmarking/TopKTanimotoScores.py @@ -0,0 +1,51 @@ +import numpy as np +import pandas as pd + +from ms2query.benchmarking.Fingerprints import Fingerprints +from ms2query.metrics import generalized_tanimoto_similarity_matrix + + +class TopKTanimotoScores: + def __init__(self, tanimoto_scores_for_top_k: np.ndarray, + top_k_inchikeys: np.ndarray, + inchikey_indexes: np.ndarray): + + self.k = tanimoto_scores_for_top_k.shape[1] + self.top_k_inchikeys_and_scores = self.create_multi_index(tanimoto_scores_for_top_k, + top_k_inchikeys, + inchikey_indexes) + + def create_multi_index(self, tanimoto_scores_for_top_k: np.ndarray, + top_k_inchikeys: np.ndarray, + inchikey_indexes: np.ndarray): + columns = pd.MultiIndex.from_product([[f"Rank_{i + 1}" for i in range(self.k)], ["inchikey", "score"]], + names=["result_rank", "attribute"] + ) + + combined_data = np.empty((len(inchikey_indexes), self.k * 2), dtype=object) + combined_data[:, 0::2] = top_k_inchikeys + combined_data[:, 1::2] = tanimoto_scores_for_top_k + return pd.DataFrame(combined_data, index=inchikey_indexes, columns=columns) + + + @classmethod + def calculate_from_fingerprints(cls, query_fingerprints: Fingerprints, target_fingerprints: Fingerprints, k): + """Gets the top k highest inchikeys and scores for each inchikey in query_fingerprints from target_fingerprints""" + similarity_scores = generalized_tanimoto_similarity_matrix(query_fingerprints.fingerprints, target_fingerprints.fingerprints) + inchikey_indexes_of_top_k = np.argpartition(similarity_scores, -k, axis=1)[:, -k:] + top_k_inchikeys = target_fingerprints.index_to_inchikey[inchikey_indexes_of_top_k] + tanimoto_scores_for_top_k = similarity_scores[np.arange(similarity_scores.shape[0])[:, None], inchikey_indexes_of_top_k] + return cls(tanimoto_scores_for_top_k, top_k_inchikeys, query_fingerprints.index_to_inchikey) + + def select_top_k_inchikeys_and_scores(self, inchikey): + """Returns a DF with inchikeys and scores""" + return self.top_k_inchikeys_and_scores.loc[inchikey].unstack(level='result_rank').T + + def select_top_k_inchikeys(self, inchikey): + return self.top_k_inchikeys_and_scores.loc[inchikey].xs('inchikey', level='attribute') + + def select_average_score(self, inchikey): + return self.top_k_inchikeys_and_scores.loc[inchikey].xs('score', level='attribute').mean() + + def get_all_average_tanimoto_scores(self): + return self.top_k_inchikeys_and_scores.xs('score', axis=1, level='attribute').mean(axis=1) From 1f81d3564c14ac4d1e2b77ef5d9d9d701a7dfcca Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 22 Dec 2025 11:52:19 +0100 Subject: [PATCH 042/149] Change embeddings to add create_from_spectra as class method and add functional subset_spectra --- ms2query/benchmarking/SpectrumDataSet.py | 64 ++++++++++++++++++------ 1 file changed, 48 insertions(+), 16 deletions(-) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index 87fb7c2..0afe8f3 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -56,7 +56,7 @@ def subset_spectra(self, spectrum_indexes) -> "SpectrumSet": spectra = [self._spectra[index] for index in spectrum_indexes] new_instance = SpectrumSet(spectra, progress_bars=self.progress_bars) if self._embeddings is not None: - new_instance._embeddings = self.embeddings.get_embeddings(spectra) + new_instance._embeddings = self.embeddings.subset_embeddings(spectra) if self._fingerprints is not None: inchikeys = [spectrum.get("inchikey")[:14] for spectrum in spectra] new_instance._fingerprints = self.fingerprints.subset_fingerprints(inchikeys) @@ -69,7 +69,7 @@ def spectra_per_inchikey(self, inchikey) -> List[Spectrum]: return matching_spectra def add_embeddings(self, model: SiameseSpectralModel): - self._embeddings = Embeddings(self._spectra, model) + self._embeddings = Embeddings.create_from_spectra(self._spectra, model) def add_fingerprints(self, fingerprint_type, nbits): self._fingerprints = Fingerprints(self.most_common_inchi_per_inchikey, fingerprint_type, nbits) @@ -100,27 +100,59 @@ def copy(self): class Embeddings: """Stores Embeddings for a list of mass spectra""" - def __init__(self, spectra: List[Spectrum], + def __init__(self, embeddings: np.ndarray, spectrum_hashes: list[int], model_settings: dict): + if len(spectrum_hashes) != embeddings.shape[0]: + raise ValueError("Number of spectra hashes does not match number of embeddings") + self._index_to_spectrum_hash = spectrum_hashes + self._spectrum_hash_to_index = {spectrum_hash: index for index, spectrum_hash in enumerate(self._index_to_spectrum_hash)} + self._model_settings = model_settings + self._embeddings = embeddings + + @classmethod + def create_from_spectra(cls, spectra: List[Spectrum], model: SiameseSpectralModel): - self.index_to_spectrum_hash = [spectrum.__hash__() for spectrum in spectra] - if len(set(self.index_to_spectrum_hash)) != len(spectra): + index_to_spectrum_hash = [spectrum.__hash__() for spectrum in spectra] + if len(set(index_to_spectrum_hash)) != len(spectra): raise ValueError("There are duplicated spectra in the spectrum list") - self.spectrum_hash_to_index = {spectrum_hash: index for index, spectrum_hash in enumerate(self.index_to_spectrum_hash)} - self.model_settings = model.model_settings - self.embeddings: np.ndarray = compute_embedding_array(model, spectra) + model_settings = model.model_settings.get_dict() + embeddings = compute_embedding_array(model, spectra) + return cls(embeddings, index_to_spectrum_hash, model_settings) + def add_embeddings(self, embeddings: "Embeddings"): - if embeddings.model_settings != self.model_settings: + if embeddings._model_settings != self.model_settings: raise ValueError("Model settings of merged embeddings do not match") - if not set(embeddings.spectrum_hash_to_index).isdisjoint(self.spectrum_hash_to_index): + if not set(embeddings._spectrum_hash_to_index).isdisjoint(self._spectrum_hash_to_index): raise ValueError("There are repeated spectra in the embeddings that are added together") - self.embeddings = np.vstack([self.embeddings, embeddings]) - self.spectrum_hash_to_index += embeddings.spectrum_hash_to_index - self.spectrum_hash_to_index = {spectrum_hash: index for index, spectrum_hash in enumerate(self.index_to_spectrum_hash)} + self._embeddings = np.vstack([self._embeddings, embeddings.embeddings]) + self._index_to_spectrum_hash += embeddings._index_to_spectrum_hash + self._spectrum_hash_to_index = {spectrum_hash: index for index, spectrum_hash in enumerate(self._index_to_spectrum_hash)} - def get_embeddings(self, spectra: list[Spectrum]): + def get_embeddings(self, spectra) -> np.ndarray: embedding_indexes = [] for spectrum in spectra: - embedding_indexes.append(self.spectrum_hash_to_index[spectrum.__hash__()]) - return self.embeddings[embedding_indexes] + embedding_indexes.append(self._spectrum_hash_to_index[spectrum.__hash__()]) + embeddings = self._embeddings[embedding_indexes] + return embeddings + + def subset_embeddings(self, spectra): + spectrum_hashes = [spectrum.__hash__() for spectrum in spectra] + embedding_indexes = [self._spectrum_hash_to_index[spectrum_hash] for spectrum_hash in spectrum_hashes] + embeddings = self._embeddings[embedding_indexes].copy() + return Embeddings(embeddings, spectrum_hashes, self.model_settings) + + @property + def embeddings(self): + return self._embeddings.view() + + @property + def model_settings(self): + return self._model_settings.copy() + + def copy(self) -> "Embeddings": + return Embeddings( + embeddings=self._embeddings.copy(), + spectrum_hashes=list(self._index_to_spectrum_hash), + model_settings=dict(self._model_settings), + ) \ No newline at end of file From 25ac2ec50147c724d39f949e5448bdd4f58f99d8 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 22 Dec 2025 11:53:57 +0100 Subject: [PATCH 043/149] Move Embeddings to embeddings --- ms2query/benchmarking/Embeddings.py | 65 +++++++++++++++++++ ms2query/benchmarking/SpectrumDataSet.py | 62 +----------------- .../PredictMS2DeepScoreSimilarity.py | 2 +- 3 files changed, 68 insertions(+), 61 deletions(-) create mode 100644 ms2query/benchmarking/Embeddings.py diff --git a/ms2query/benchmarking/Embeddings.py b/ms2query/benchmarking/Embeddings.py new file mode 100644 index 0000000..976ee38 --- /dev/null +++ b/ms2query/benchmarking/Embeddings.py @@ -0,0 +1,65 @@ +from typing import List + +import numpy as np +from matchms import Spectrum +from ms2deepscore.models import SiameseSpectralModel, compute_embedding_array + + +class Embeddings: + """Stores Embeddings for a list of mass spectra""" + def __init__(self, embeddings: np.ndarray, spectrum_hashes: list[int], model_settings: dict): + if len(spectrum_hashes) != embeddings.shape[0]: + raise ValueError("Number of spectra hashes does not match number of embeddings") + self._index_to_spectrum_hash = spectrum_hashes + self._spectrum_hash_to_index = {spectrum_hash: index for index, spectrum_hash in enumerate(self._index_to_spectrum_hash)} + self._model_settings = model_settings + self._embeddings = embeddings + + @classmethod + def create_from_spectra(cls, spectra: List[Spectrum], + model: SiameseSpectralModel): + index_to_spectrum_hash = [spectrum.__hash__() for spectrum in spectra] + if len(set(index_to_spectrum_hash)) != len(spectra): + raise ValueError("There are duplicated spectra in the spectrum list") + + model_settings = model.model_settings.get_dict() + embeddings = compute_embedding_array(model, spectra) + return cls(embeddings, index_to_spectrum_hash, model_settings) + + + def add_embeddings(self, embeddings: "Embeddings"): + if embeddings._model_settings != self.model_settings: + raise ValueError("Model settings of merged embeddings do not match") + if not set(embeddings._spectrum_hash_to_index).isdisjoint(self._spectrum_hash_to_index): + raise ValueError("There are repeated spectra in the embeddings that are added together") + self._embeddings = np.vstack([self._embeddings, embeddings.embeddings]) + self._index_to_spectrum_hash += embeddings._index_to_spectrum_hash + self._spectrum_hash_to_index = {spectrum_hash: index for index, spectrum_hash in enumerate(self._index_to_spectrum_hash)} + + def get_embeddings(self, spectra) -> np.ndarray: + embedding_indexes = [] + for spectrum in spectra: + embedding_indexes.append(self._spectrum_hash_to_index[spectrum.__hash__()]) + embeddings = self._embeddings[embedding_indexes] + return embeddings + + def subset_embeddings(self, spectra): + spectrum_hashes = [spectrum.__hash__() for spectrum in spectra] + embedding_indexes = [self._spectrum_hash_to_index[spectrum_hash] for spectrum_hash in spectrum_hashes] + embeddings = self._embeddings[embedding_indexes].copy() + return Embeddings(embeddings, spectrum_hashes, self.model_settings) + + @property + def embeddings(self): + return self._embeddings.view() + + @property + def model_settings(self): + return self._model_settings.copy() + + def copy(self) -> "Embeddings": + return Embeddings( + embeddings=self._embeddings.copy(), + spectrum_hashes=list(self._index_to_spectrum_hash), + model_settings=dict(self._model_settings), + ) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index 0afe8f3..2ef5fed 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -1,11 +1,11 @@ import copy from collections import Counter, defaultdict from typing import List -import numpy as np from matchms import Spectrum -from ms2deepscore.models import SiameseSpectralModel, compute_embedding_array +from ms2deepscore.models import SiameseSpectralModel from tqdm import tqdm +from ms2query.benchmarking.Embeddings import Embeddings from ms2query.benchmarking.Fingerprints import Fingerprints @@ -98,61 +98,3 @@ def copy(self): return new_instance -class Embeddings: - """Stores Embeddings for a list of mass spectra""" - def __init__(self, embeddings: np.ndarray, spectrum_hashes: list[int], model_settings: dict): - if len(spectrum_hashes) != embeddings.shape[0]: - raise ValueError("Number of spectra hashes does not match number of embeddings") - self._index_to_spectrum_hash = spectrum_hashes - self._spectrum_hash_to_index = {spectrum_hash: index for index, spectrum_hash in enumerate(self._index_to_spectrum_hash)} - self._model_settings = model_settings - self._embeddings = embeddings - - @classmethod - def create_from_spectra(cls, spectra: List[Spectrum], - model: SiameseSpectralModel): - index_to_spectrum_hash = [spectrum.__hash__() for spectrum in spectra] - if len(set(index_to_spectrum_hash)) != len(spectra): - raise ValueError("There are duplicated spectra in the spectrum list") - - model_settings = model.model_settings.get_dict() - embeddings = compute_embedding_array(model, spectra) - return cls(embeddings, index_to_spectrum_hash, model_settings) - - - def add_embeddings(self, embeddings: "Embeddings"): - if embeddings._model_settings != self.model_settings: - raise ValueError("Model settings of merged embeddings do not match") - if not set(embeddings._spectrum_hash_to_index).isdisjoint(self._spectrum_hash_to_index): - raise ValueError("There are repeated spectra in the embeddings that are added together") - self._embeddings = np.vstack([self._embeddings, embeddings.embeddings]) - self._index_to_spectrum_hash += embeddings._index_to_spectrum_hash - self._spectrum_hash_to_index = {spectrum_hash: index for index, spectrum_hash in enumerate(self._index_to_spectrum_hash)} - - def get_embeddings(self, spectra) -> np.ndarray: - embedding_indexes = [] - for spectrum in spectra: - embedding_indexes.append(self._spectrum_hash_to_index[spectrum.__hash__()]) - embeddings = self._embeddings[embedding_indexes] - return embeddings - - def subset_embeddings(self, spectra): - spectrum_hashes = [spectrum.__hash__() for spectrum in spectra] - embedding_indexes = [self._spectrum_hash_to_index[spectrum_hash] for spectrum_hash in spectrum_hashes] - embeddings = self._embeddings[embedding_indexes].copy() - return Embeddings(embeddings, spectrum_hashes, self.model_settings) - - @property - def embeddings(self): - return self._embeddings.view() - - @property - def model_settings(self): - return self._model_settings.copy() - - def copy(self) -> "Embeddings": - return Embeddings( - embeddings=self._embeddings.copy(), - spectrum_hashes=list(self._index_to_spectrum_hash), - model_settings=dict(self._model_settings), - ) \ No newline at end of file diff --git a/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py b/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py index 924051a..40712c2 100644 --- a/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py +++ b/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py @@ -3,7 +3,7 @@ from ms2deepscore.vector_operations import cosine_similarity_matrix from tqdm import tqdm -from ms2query.benchmarking.SpectrumDataSet import Embeddings +from ms2query.benchmarking.Embeddings import Embeddings def predict_top_ms2deepscores( From 6559fecbc46046053d1d4c88e6275b5c02f2eeab Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 22 Dec 2025 12:38:33 +0100 Subject: [PATCH 044/149] Make index_to_spectrum_hash inmutable and replace add_embeddings with class method combine embeddings --- ms2query/benchmarking/Embeddings.py | 35 ++++++++++-------------- ms2query/benchmarking/SpectrumDataSet.py | 2 +- 2 files changed, 15 insertions(+), 22 deletions(-) diff --git a/ms2query/benchmarking/Embeddings.py b/ms2query/benchmarking/Embeddings.py index 976ee38..14f2c6a 100644 --- a/ms2query/benchmarking/Embeddings.py +++ b/ms2query/benchmarking/Embeddings.py @@ -7,18 +7,18 @@ class Embeddings: """Stores Embeddings for a list of mass spectra""" - def __init__(self, embeddings: np.ndarray, spectrum_hashes: list[int], model_settings: dict): + def __init__(self, embeddings: np.ndarray, spectrum_hashes: tuple, model_settings: dict): if len(spectrum_hashes) != embeddings.shape[0]: raise ValueError("Number of spectra hashes does not match number of embeddings") - self._index_to_spectrum_hash = spectrum_hashes - self._spectrum_hash_to_index = {spectrum_hash: index for index, spectrum_hash in enumerate(self._index_to_spectrum_hash)} + self.index_to_spectrum_hash = spectrum_hashes + self._spectrum_hash_to_index = {spectrum_hash: index for index, spectrum_hash in enumerate(self.index_to_spectrum_hash)} self._model_settings = model_settings self._embeddings = embeddings @classmethod def create_from_spectra(cls, spectra: List[Spectrum], - model: SiameseSpectralModel): - index_to_spectrum_hash = [spectrum.__hash__() for spectrum in spectra] + model: SiameseSpectralModel) -> "Embeddings": + index_to_spectrum_hash = tuple(spectrum.__hash__() for spectrum in spectra) if len(set(index_to_spectrum_hash)) != len(spectra): raise ValueError("There are duplicated spectra in the spectrum list") @@ -26,25 +26,18 @@ def create_from_spectra(cls, spectra: List[Spectrum], embeddings = compute_embedding_array(model, spectra) return cls(embeddings, index_to_spectrum_hash, model_settings) - - def add_embeddings(self, embeddings: "Embeddings"): - if embeddings._model_settings != self.model_settings: + @classmethod + def combine_embeddings(cls, embeddings_1, embeddings_2) -> "Embeddings": + if embeddings_1.model_settings != embeddings_2.model_settings: raise ValueError("Model settings of merged embeddings do not match") - if not set(embeddings._spectrum_hash_to_index).isdisjoint(self._spectrum_hash_to_index): + if not set(embeddings_1.spectrum_hash_to_index).isdisjoint(embeddings_2.spectrum_hash_to_index): raise ValueError("There are repeated spectra in the embeddings that are added together") - self._embeddings = np.vstack([self._embeddings, embeddings.embeddings]) - self._index_to_spectrum_hash += embeddings._index_to_spectrum_hash - self._spectrum_hash_to_index = {spectrum_hash: index for index, spectrum_hash in enumerate(self._index_to_spectrum_hash)} - - def get_embeddings(self, spectra) -> np.ndarray: - embedding_indexes = [] - for spectrum in spectra: - embedding_indexes.append(self._spectrum_hash_to_index[spectrum.__hash__()]) - embeddings = self._embeddings[embedding_indexes] - return embeddings + combined_embeddings = np.vstack([embeddings_1.embeddings, embeddings_2.embeddings]) + index_to_spectrum_hash = embeddings_1.index_to_spectrum_hash + embeddings_2.index_to_spectrum_hash + return cls(combined_embeddings, index_to_spectrum_hash, embeddings_1.model_settings) def subset_embeddings(self, spectra): - spectrum_hashes = [spectrum.__hash__() for spectrum in spectra] + spectrum_hashes = tuple(spectrum.__hash__() for spectrum in spectra) embedding_indexes = [self._spectrum_hash_to_index[spectrum_hash] for spectrum_hash in spectrum_hashes] embeddings = self._embeddings[embedding_indexes].copy() return Embeddings(embeddings, spectrum_hashes, self.model_settings) @@ -60,6 +53,6 @@ def model_settings(self): def copy(self) -> "Embeddings": return Embeddings( embeddings=self._embeddings.copy(), - spectrum_hashes=list(self._index_to_spectrum_hash), + spectrum_hashes=tuple(self.index_to_spectrum_hash), model_settings=dict(self._model_settings), ) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index 2ef5fed..ca9f72b 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -28,7 +28,7 @@ def add_spectra(self, new_spectra: "SpectrumSet"): updated_inchikeys = self._add_spectra_and_group_per_inchikey(new_spectra.spectra) self._update_most_common_inchi_per_inchikey(updated_inchikeys) if self._embeddings is not None: - self.embeddings.add_embeddings(new_spectra.embeddings) + self._embeddings = Embeddings.combine_embeddings(self.embeddings, new_spectra.embeddings) if self._fingerprints is not None: self.fingerprints.add_new_inchikeys(new_spectra.most_common_inchi_per_inchikey) From 33626ec6b38be01ad2a9d7c8e95fc93be8c5c66f Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 22 Dec 2025 12:38:54 +0100 Subject: [PATCH 045/149] Add __eq__ method to embeddings --- ms2query/benchmarking/Embeddings.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/ms2query/benchmarking/Embeddings.py b/ms2query/benchmarking/Embeddings.py index 14f2c6a..487a19f 100644 --- a/ms2query/benchmarking/Embeddings.py +++ b/ms2query/benchmarking/Embeddings.py @@ -56,3 +56,13 @@ def copy(self) -> "Embeddings": spectrum_hashes=tuple(self.index_to_spectrum_hash), model_settings=dict(self._model_settings), ) + def __eq__(self, other) -> bool: + if not isinstance(other, Embeddings): + return NotImplemented + if self.model_settings != other.model_settings: + print("Model setting not equal") + return False + if self.index_to_spectrum_hash != other.index_to_spectrum_hash: + print("index to spectrum hash not equal") + return False + return np.array_equal(self.embeddings, other.embeddings) From 17f168213d1d341ded6aaff6a6965db1b6c9ae28 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 22 Dec 2025 12:39:24 +0100 Subject: [PATCH 046/149] Add test_subset_embeddings --- tests/test_embeddings.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) create mode 100644 tests/test_embeddings.py diff --git a/tests/test_embeddings.py b/tests/test_embeddings.py new file mode 100644 index 0000000..99dad40 --- /dev/null +++ b/tests/test_embeddings.py @@ -0,0 +1,15 @@ +from ms2query.benchmarking.Embeddings import Embeddings +from tests.conftest import create_test_spectra, ms2deepscore_model + + +def test_subset_embeddings(): + test_spectra = create_test_spectra() + model = ms2deepscore_model() + embeddings = Embeddings.create_from_spectra(test_spectra, model) + subset = embeddings.subset_embeddings(test_spectra[:4]) + correct_subset = Embeddings.create_from_spectra(test_spectra[:4], model) + assert subset == correct_subset + selected_disordered_spectra = [test_spectra[i] for i in [7, 2, 3]] + subset = embeddings.subset_embeddings(selected_disordered_spectra) + correct_subset = Embeddings.create_from_spectra(selected_disordered_spectra, model) + assert subset == correct_subset From e0d0a16bde5fcd3af0b9503299b880f5244ca4f7 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 22 Dec 2025 12:50:05 +0100 Subject: [PATCH 047/149] Add extra tests and fix bug in combine-embeddings --- ms2query/benchmarking/Embeddings.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/ms2query/benchmarking/Embeddings.py b/ms2query/benchmarking/Embeddings.py index 487a19f..7479471 100644 --- a/ms2query/benchmarking/Embeddings.py +++ b/ms2query/benchmarking/Embeddings.py @@ -27,10 +27,10 @@ def create_from_spectra(cls, spectra: List[Spectrum], return cls(embeddings, index_to_spectrum_hash, model_settings) @classmethod - def combine_embeddings(cls, embeddings_1, embeddings_2) -> "Embeddings": + def combine_embeddings(cls, embeddings_1: "Embeddings", embeddings_2: "Embeddings") -> "Embeddings": if embeddings_1.model_settings != embeddings_2.model_settings: raise ValueError("Model settings of merged embeddings do not match") - if not set(embeddings_1.spectrum_hash_to_index).isdisjoint(embeddings_2.spectrum_hash_to_index): + if not set(embeddings_1.index_to_spectrum_hash).isdisjoint(embeddings_2.index_to_spectrum_hash): raise ValueError("There are repeated spectra in the embeddings that are added together") combined_embeddings = np.vstack([embeddings_1.embeddings, embeddings_2.embeddings]) index_to_spectrum_hash = embeddings_1.index_to_spectrum_hash + embeddings_2.index_to_spectrum_hash @@ -38,7 +38,10 @@ def combine_embeddings(cls, embeddings_1, embeddings_2) -> "Embeddings": def subset_embeddings(self, spectra): spectrum_hashes = tuple(spectrum.__hash__() for spectrum in spectra) - embedding_indexes = [self._spectrum_hash_to_index[spectrum_hash] for spectrum_hash in spectrum_hashes] + try: + embedding_indexes = [self._spectrum_hash_to_index[spectrum_hash] for spectrum_hash in spectrum_hashes] + except KeyError: + raise ValueError("The given spectra are not stored in Embeddings") embeddings = self._embeddings[embedding_indexes].copy() return Embeddings(embeddings, spectrum_hashes, self.model_settings) From 7fb334731fbb719cc9aeae42174ebb4a86d0a1a5 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 22 Dec 2025 12:50:28 +0100 Subject: [PATCH 048/149] Add test_combine_embeddings --- tests/test_embeddings.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/tests/test_embeddings.py b/tests/test_embeddings.py index 99dad40..1e9bff0 100644 --- a/tests/test_embeddings.py +++ b/tests/test_embeddings.py @@ -1,3 +1,5 @@ +import pytest + from ms2query.benchmarking.Embeddings import Embeddings from tests.conftest import create_test_spectra, ms2deepscore_model @@ -13,3 +15,20 @@ def test_subset_embeddings(): subset = embeddings.subset_embeddings(selected_disordered_spectra) correct_subset = Embeddings.create_from_spectra(selected_disordered_spectra, model) assert subset == correct_subset + with pytest.raises(ValueError): + embeddings = Embeddings.create_from_spectra(test_spectra[:4], model) + embeddings.subset_embeddings(test_spectra) + + +def test_combine_embeddings(): + test_spectra = create_test_spectra() + model = ms2deepscore_model() + embeddings_1 = Embeddings.create_from_spectra(test_spectra[:4], model) + embeddings_2 = Embeddings.create_from_spectra(test_spectra[4:], model) + correct_combined_embeddings = Embeddings.create_from_spectra(test_spectra, model) + combined_embeddings = Embeddings.combine_embeddings(embeddings_1, embeddings_2) + assert combined_embeddings == correct_combined_embeddings + with pytest.raises(ValueError): + Embeddings.combine_embeddings(embeddings_1, embeddings_1) + + From d9a54999b77ee280437004b673369734e0d71c75 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 10:18:47 +0100 Subject: [PATCH 049/149] Refactor Fingerprints, init from fingerprints and class methods do compute and combining. --- ms2query/benchmarking/Fingerprints.py | 131 ++++++++++++++------------ 1 file changed, 69 insertions(+), 62 deletions(-) diff --git a/ms2query/benchmarking/Fingerprints.py b/ms2query/benchmarking/Fingerprints.py index 6339b91..caff84b 100644 --- a/ms2query/benchmarking/Fingerprints.py +++ b/ms2query/benchmarking/Fingerprints.py @@ -1,5 +1,3 @@ -from typing import Iterable - import numpy as np import pandas as pd from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi @@ -11,77 +9,86 @@ class Fingerprints: # I just realize that there already exists a Fingerprints class in matchms, with almost the same functionality, # so it will be good to merge both. The matchms version works slighlty different. - def __init__(self, most_common_inchi_per_inchikey, fingerprint_type, nbits): - self.most_common_inchi_per_inchikey = most_common_inchi_per_inchikey + def __init__(self, fingerprints: np.array, inchikeys: tuple[str, ...], fingerprint_type): + # self.most_common_inchi_per_inchikey = most_common_inchi_per_inchikey self.fingerprint_type = fingerprint_type - self.nbits = nbits - - self._index_to_inchikey =np.array([], dtype="U14") - self._inchikey_to_index = dict() - - self._add_inchikeys_to_index(list(self.most_common_inchi_per_inchikey.keys())) - - self.fingerprints = np.zeros((len(self.index_to_inchikey), self.nbits), dtype=np.uint8) - self.update_fingerprints(self.index_to_inchikey) - - def update_fingerprints(self, inchikeys: Iterable[str]): - for inchikey in tqdm(inchikeys, desc="Adding fingerprints to Inchikeys"): - inchikey_index = self._inchikey_to_index[inchikey] - self.fingerprints[inchikey_index, :] = self.compute_fingerprint(inchikey) - - def compute_fingerprint(self, inchikey): - most_common_inchi = self.most_common_inchi_per_inchikey[inchikey] - fingerprint = _derive_fingerprint_from_inchi( - most_common_inchi, fingerprint_type=self.fingerprint_type, nbits=self.nbits) - if not isinstance(fingerprint, np.ndarray): - raise ValueError(f"Fingerprint could not be set for InChI: {most_common_inchi}") - return fingerprint + if fingerprints.shape[0] != len(inchikeys): + raise ValueError("The fingerprint dimension does not match the number of inchikeys") + self._fingerprints = fingerprints + self._inchikeys = inchikeys + self._inchikey_to_index = {inchikey: index for index, inchikey in enumerate(self.inchikeys)} + + @classmethod + def compute_fingerprints_from_inchi(cls, most_common_inchi_per_inchikey: dict[str, str], fingerprint_type, nbits): + index_to_inchikey = tuple(most_common_inchi_per_inchikey.keys()) + fingerprints = np.zeros((len(index_to_inchikey), nbits), dtype=np.uint8) + for inchikey_index, inchikey in tqdm(enumerate(index_to_inchikey), desc="Adding fingerprints to Inchikeys"): + fingerprint = _derive_fingerprint_from_inchi( + most_common_inchi_per_inchikey[inchikey], fingerprint_type=fingerprint_type, nbits=nbits) + if not isinstance(fingerprint, np.ndarray): + raise ValueError(f"Fingerprint could not be set for InChI: {most_common_inchi_per_inchikey[inchikey]}") + fingerprints[inchikey_index, :] = fingerprint + return cls(fingerprints, index_to_inchikey, fingerprint_type) + + @classmethod + def combine_fingerprints(cls, fingerprints_1: "Fingerprints", + fingerprints_2: "Fingerprints", allow_replacing: bool=False) -> "Fingerprints": + """Combines two sets of Fingerprints into a new instance + + allow_replacing: + If True the fingerprints in fingerprints_1 will be replaced by fingerprints_2. + """ + if fingerprints_1.fingerprint_type != fingerprints_2.fingerprint_type: + raise ValueError("The fingerprint type of both Fingerprints do not match") + if fingerprints_1.nbits != fingerprints_2.nbits: + raise ValueError(f"The nbits for the fingerprints do not match: {fingerprints_1.nbits} != {fingerprints_2.nbits}") + # handle overlap + overlapping_inchikeys = set(fingerprints_2.inchikeys) & set(fingerprints_1.inchikeys) + new_fingerprints = fingerprints_1.fingerprints.copy() + if not np.array_equal(fingerprints_1.get_fingerprints(overlapping_inchikeys), fingerprints_2.get_fingerprints(overlapping_inchikeys)): + if allow_replacing: + for inchikey in overlapping_inchikeys: + inchikey_index = fingerprints_1._inchikey_to_index[inchikey] + new_fingerprints[inchikey_index, :] = fingerprints_2.fingerprints[ + fingerprints_2._inchikey_to_index[inchikey], :] + else: + raise ValueError("The overlapping inchikeys do not have the same fingerprints") + inchikeys_to_add = [inchikey for inchikey in fingerprints_2.inchikeys if inchikey not in fingerprints_1.inchikeys] + combined_fingerprints = np.vstack([new_fingerprints, fingerprints_2.get_fingerprints(inchikeys_to_add)]) + combined_inchikeys = fingerprints_1.inchikeys + tuple(inchikeys_to_add) + return cls(combined_fingerprints, combined_inchikeys, fingerprints_1.fingerprint_type) def get_fingerprints(self, list_of_inchikeys): list_of_indexes = [self._inchikey_to_index[inchikey] for inchikey in list_of_inchikeys] return self.fingerprints[list_of_indexes] def subset_fingerprints(self, list_of_inchikeys) -> "Fingerprints": - new_instance = Fingerprints(dict(), self.fingerprint_type, self.nbits) - new_instance.fingerprints = self.get_fingerprints(list_of_inchikeys) - - new_most_common_inchi_per_inchikey = dict() - for inchikey in list_of_inchikeys: - new_most_common_inchi_per_inchikey[inchikey] = self.most_common_inchi_per_inchikey[inchikey] - new_instance.most_common_inchi_per_inchikey = new_most_common_inchi_per_inchikey - new_instance._add_inchikeys_to_index(list_of_inchikeys) - return new_instance - - def add_new_inchikeys(self, new_most_common_inchi_per_inchikey: dict[str, str]): - inchikeys_to_update = [] - inchikeys_to_add = [] - for inchikey, inchi in new_most_common_inchi_per_inchikey.items(): - if inchikey in self.most_common_inchi_per_inchikey: - if self.most_common_inchi_per_inchikey[inchikey] == inchi: - # the inchikey is unchanged - continue - else: - inchikeys_to_add.append(inchikey) - self.most_common_inchi_per_inchikey[inchikey] = inchi - inchikeys_to_update.append(inchikey) - - # Add the inchikeys_to_add - if inchikeys_to_add: - self._add_inchikeys_to_index(inchikeys_to_add) - new_rows = np.zeros((len(inchikeys_to_add), self.nbits), dtype=np.uint8) - self.fingerprints = np.vstack([self.fingerprints, new_rows]) - - self.update_fingerprints(inchikeys_to_update) + return Fingerprints(self.get_fingerprints(list_of_inchikeys), list_of_inchikeys, fingerprint_type=self.fingerprint_type) @property - def index_to_inchikey(self): - return self._index_to_inchikey + def inchikeys(self): + return self._inchikeys - def _add_inchikeys_to_index(self, inchikeys_to_add): - self._index_to_inchikey = np.append(self._index_to_inchikey, inchikeys_to_add) - self._inchikey_to_index = {inchikey: index for index, inchikey in enumerate(self.index_to_inchikey)} + @property + def fingerprints(self): + return self._fingerprints.view() + @property + def nbits(self): + """The number of bits used""" + return self._fingerprints.shape[1] + + def __eq__(self, other) -> bool: + if not isinstance(other, Fingerprints): + return NotImplemented + if self.fingerprint_type != other.fingerprint_type: + print("Fingerprint type mismatch") + return False + if not np.array_equal(self.inchikeys, other.inchikeys): + print("Inchikeys are not equal") + return False + return np.array_equal(self.fingerprints, other.fingerprints) def get_similarity_matrix(fingerprints_1: Fingerprints, fingerprints_2: Fingerprints): similarity_scores = generalized_tanimoto_similarity_matrix(fingerprints_1.fingerprints, fingerprints_2.fingerprints) - return pd.DataFrame(similarity_scores, index=fingerprints_1.index_to_inchikey, columns=fingerprints_2.index_to_inchikey) + return pd.DataFrame(similarity_scores, index=fingerprints_1.inchikeys, columns=fingerprints_2.inchikeys) From 0191d50bc2a30c68c7dfc6870b1ef0f2e415a82c Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 10:19:21 +0100 Subject: [PATCH 050/149] Updated tests for Fingerprints --- tests/test_Fingerprints.py | 77 ++++++++++++++++++++++++++------------ 1 file changed, 54 insertions(+), 23 deletions(-) diff --git a/tests/test_Fingerprints.py b/tests/test_Fingerprints.py index 4de05b6..aacfa02 100644 --- a/tests/test_Fingerprints.py +++ b/tests/test_Fingerprints.py @@ -7,37 +7,68 @@ @pytest.fixture -def dummy_fingerprints(): +def dummy_fingerprints() -> Fingerprints: return make_test_fingerprints(5) -def make_test_fingerprints(nr_of_inchikeys=5): +def get_inchikey_inchi_dict(nr_of_inchikeys): inchikey_inchi_pairs = get_inchikey_inchi_pairs(nr_of_inchikeys) - inchi_per_inchikey = {compound_tuple[0][:14]: compound_tuple[1] for compound_tuple in inchikey_inchi_pairs} - fingerprints = Fingerprints(inchi_per_inchikey, fingerprint_type="daylight", nbits=100) - return fingerprints + return {compound_tuple[0][:14]: compound_tuple[1] for compound_tuple in inchikey_inchi_pairs} +def make_test_fingerprints(nr_of_inchikeys=5): + inchi_per_inchikey = get_inchikey_inchi_dict(nr_of_inchikeys) + fingerprints = Fingerprints.compute_fingerprints_from_inchi(inchi_per_inchikey, fingerprint_type="daylight", nbits=2048) + return fingerprints -def test_fingerprint_creation(dummy_fingerprints): - assert dummy_fingerprints.fingerprints.shape== (7,100) - for inchikey, inchi in list(dummy_fingerprints.most_common_inchi_per_inchikey.items()): - fingerprint = _derive_fingerprint_from_inchi(inchi, fingerprint_type="daylight", nbits=100) - np.testing.assert_array_equal(fingerprint, dummy_fingerprints.get_fingerprints([inchikey])[0]) +def test_compute_fingerprints_from_inchi(): + nr_of_inchikeys = 5 + inchi_per_inchikey = get_inchikey_inchi_dict(nr_of_inchikeys) + fingerprints = Fingerprints.compute_fingerprints_from_inchi(inchi_per_inchikey, fingerprint_type="daylight", nbits=2048) + assert fingerprints.fingerprints.shape== (nr_of_inchikeys,2048) + for inchikey in fingerprints.inchikeys: + fingerprint = _derive_fingerprint_from_inchi(inchi_per_inchikey[str(inchikey)], fingerprint_type="daylight", nbits=2048) + np.testing.assert_array_equal(fingerprint, fingerprints.get_fingerprints([inchikey])[0]) -def test_subsetting_fingerprint(dummy_fingerprints): - selected_inchikeys = list(dummy_fingerprints.most_common_inchi_per_inchikey.keys())[-2:] - subsetted_fingerprints = dummy_fingerprints.subset_fingerprints(selected_inchikeys) - assert subsetted_fingerprints.fingerprints.shape== (2,100) - assert list(subsetted_fingerprints.most_common_inchi_per_inchikey.keys()) == selected_inchikeys - for inchikey in selected_inchikeys: - fingerprint = _derive_fingerprint_from_inchi(dummy_fingerprints.most_common_inchi_per_inchikey[inchikey], fingerprint_type="daylight", nbits=100) - np.testing.assert_array_equal(fingerprint, subsetted_fingerprints.get_fingerprints([inchikey])[0]) +def test_subsetting_fingerprint(): + inchi_per_inchikey = get_inchikey_inchi_dict(nr_of_inchikeys=5) + fingerprints = Fingerprints.compute_fingerprints_from_inchi(inchi_per_inchikey, fingerprint_type="daylight", + nbits=2048) + inchi_per_inchikey_subset = get_inchikey_inchi_dict(nr_of_inchikeys=2) + selected_inchikeys = list(inchi_per_inchikey_subset.keys()) + subsetted_fingerprints = fingerprints.subset_fingerprints(selected_inchikeys) + assert subsetted_fingerprints.fingerprints.shape== (2,2048) + assert list(subsetted_fingerprints.inchikeys) == selected_inchikeys + assert subsetted_fingerprints == Fingerprints.compute_fingerprints_from_inchi(inchi_per_inchikey_subset, fingerprint_type="daylight", nbits=2048) -def test_add_fingerprint(dummy_fingerprints): +def test_combine_fingerprint(dummy_fingerprints): inchikey_inchi_pairs = get_inchikey_inchi_pairs(7)[-2:] inchi_per_inchikey = {compound_tuple[0][:14]: compound_tuple[1] for compound_tuple in inchikey_inchi_pairs} - dummy_fingerprints.add_new_inchikeys(inchi_per_inchikey) + last_two_fingerprints = Fingerprints.compute_fingerprints_from_inchi(inchi_per_inchikey, nbits=2048, fingerprint_type="daylight") + combined_fingerprints = Fingerprints.combine_fingerprints(dummy_fingerprints, last_two_fingerprints) - assert dummy_fingerprints.fingerprints.shape== (7,100) + assert combined_fingerprints.fingerprints.shape== (7,2048) for inchikey, inchi in inchi_per_inchikey.items(): - fingerprint = _derive_fingerprint_from_inchi(inchi, fingerprint_type="daylight", nbits=100) - np.testing.assert_array_equal(fingerprint, dummy_fingerprints.get_fingerprints([inchikey])[0]) + fingerprint = _derive_fingerprint_from_inchi(inchi, fingerprint_type="daylight", nbits=2048) + np.testing.assert_array_equal(fingerprint, combined_fingerprints.get_fingerprints([inchikey])[0]) + +def test_combine_fingerprints_with_replacing(): + inchikey_inchi_pairs_1 = {"RYYVLZVUVIJVGH": "InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2/h4H,1-3H3", + "ZPUCINDJVBIVPJ": "InChI=1S/C17H21NO4/c1-18-12-8-9-13(18)15(17(20)21-2)14(10-12)22-16(19)11-6-4-3-5-7-11/h3-7,12-15H,8-10H2,1-2H3/t12-,13+,14-,15+/m0/s1"} + inchikey_inchi_pairs_2 = {"RZVAJINKPMORJF": "InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2/h4H,1-3H3", + "ZPUCINDJVBIVPJ": "InChI=1S/C8H9NO2/c1-6(10)9-7-2-4-8(11)5-3-7/h2-5,11H,1H3,(H,9,10)"} + combined = {"RYYVLZVUVIJVGH": "InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2/h4H,1-3H3", + "ZPUCINDJVBIVPJ": "InChI=1S/C8H9NO2/c1-6(10)9-7-2-4-8(11)5-3-7/h2-5,11H,1H3,(H,9,10)", + "RZVAJINKPMORJF": "InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2/h4H,1-3H3"} + fingerprints_1 = Fingerprints.compute_fingerprints_from_inchi(inchikey_inchi_pairs_1, "daylight", nbits=2048) + fingerprints_2 = Fingerprints.compute_fingerprints_from_inchi(inchikey_inchi_pairs_2, "daylight", nbits=2048) + correct_combined_fingerprints = Fingerprints.compute_fingerprints_from_inchi(combined, "daylight", nbits=2048) + combined_fingerprints = Fingerprints.combine_fingerprints(fingerprints_1, fingerprints_2, allow_replacing=True) + assert combined_fingerprints ==correct_combined_fingerprints + + with pytest.raises(ValueError): + Fingerprints.combine_fingerprints(fingerprints_1, fingerprints_2, allow_replacing=False) + +def test_get_similarity_matrix(): + fingerprints_1 = make_test_fingerprints(5) + fingerprints_2 = make_test_fingerprints(3) + matrix = get_similarity_matrix(fingerprints_1, fingerprints_2) + assert matrix.shape == (5,3) From 0944fdb0e9514314449cf03f5bcf4e1ccd428256 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 10:21:41 +0100 Subject: [PATCH 051/149] change fingerprints.index_to_inchikey to fingerprints.inchikey --- ms2query/benchmarking/TopKTanimotoScores.py | 4 ++-- .../reference_methods/predict_best_possible_match.py | 6 +++--- .../reference_methods/predict_using_closest_tanimoto.py | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/ms2query/benchmarking/TopKTanimotoScores.py b/ms2query/benchmarking/TopKTanimotoScores.py index c4445e8..fa0ed12 100644 --- a/ms2query/benchmarking/TopKTanimotoScores.py +++ b/ms2query/benchmarking/TopKTanimotoScores.py @@ -33,9 +33,9 @@ def calculate_from_fingerprints(cls, query_fingerprints: Fingerprints, target_fi """Gets the top k highest inchikeys and scores for each inchikey in query_fingerprints from target_fingerprints""" similarity_scores = generalized_tanimoto_similarity_matrix(query_fingerprints.fingerprints, target_fingerprints.fingerprints) inchikey_indexes_of_top_k = np.argpartition(similarity_scores, -k, axis=1)[:, -k:] - top_k_inchikeys = target_fingerprints.index_to_inchikey[inchikey_indexes_of_top_k] + top_k_inchikeys = target_fingerprints.inchikeys[inchikey_indexes_of_top_k] tanimoto_scores_for_top_k = similarity_scores[np.arange(similarity_scores.shape[0])[:, None], inchikey_indexes_of_top_k] - return cls(tanimoto_scores_for_top_k, top_k_inchikeys, query_fingerprints.index_to_inchikey) + return cls(tanimoto_scores_for_top_k, top_k_inchikeys, query_fingerprints.inchikeys) def select_top_k_inchikeys_and_scores(self, inchikey): """Returns a DF with inchikeys and scores""" diff --git a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py index 39fad93..ded8dd4 100644 --- a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py +++ b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py @@ -32,14 +32,14 @@ def calculate_highest_tanimoto_score_per_inchikey( indexes_of_highest_scores = tanimoto_scores.argmax(axis=0) highest_possible_score_per_inchikey = dict() - for i, inchikey in enumerate(query_spectra.fingerprints.index_to_inchikey): + for i, inchikey in enumerate(query_spectra.fingerprints.inchikeys): # Check if inchikey in library (To correctly handle the exact matching case) - if inchikey in library_spectra.fingerprints.index_to_inchikey: + if inchikey in library_spectra.fingerprints.inchikeys: highest_possible_score_per_inchikey[inchikey] = (inchikey, 1.0) continue highest_possible_score_per_inchikey[inchikey] = ( - library_spectra.fingerprints.index_to_inchikey[indexes_of_highest_scores[i]], + library_spectra.fingerprints.inchikeys[indexes_of_highest_scores[i]], highest_scores[i], ) return highest_possible_score_per_inchikey diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index e4f5bda..0152a4e 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -75,5 +75,5 @@ def get_inchikey_and_tanimoto_scores_for_top_k(spectra: SpectrumSet, inchikey, k inchikey_indexes_of_top_k = np.argpartition(similarity_scores, -k)[-k:] tanimoto_scores_for_top_k = similarity_scores[inchikey_indexes_of_top_k] - top_inchikeys = [spectra.fingerprints.index_to_inchikey[inchikey_index] for inchikey_index in inchikey_indexes_of_top_k] + top_inchikeys = [spectra.fingerprints.inchikeys[inchikey_index] for inchikey_index in inchikey_indexes_of_top_k] return top_inchikeys, tanimoto_scores_for_top_k From ed2ce280d708683f691bb38cddf7c6d91462df06 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 12:37:28 +0100 Subject: [PATCH 052/149] Add method for creating Fingerprints from SpectrumSet --- ms2query/benchmarking/Fingerprints.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/ms2query/benchmarking/Fingerprints.py b/ms2query/benchmarking/Fingerprints.py index caff84b..e126606 100644 --- a/ms2query/benchmarking/Fingerprints.py +++ b/ms2query/benchmarking/Fingerprints.py @@ -1,8 +1,11 @@ +from collections import Counter + import numpy as np import pandas as pd from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi from tqdm import tqdm +from ms2query.benchmarking.SpectrumDataSet import SpectrumSet from ms2query.metrics import generalized_tanimoto_similarity_matrix @@ -30,6 +33,15 @@ def compute_fingerprints_from_inchi(cls, most_common_inchi_per_inchikey: dict[st fingerprints[inchikey_index, :] = fingerprint return cls(fingerprints, index_to_inchikey, fingerprint_type) + @classmethod + def from_spectrum_set(cls, spectrum_set: SpectrumSet, fingerprint_type, nbits): + most_common_inchi_per_inchikey = {} + for inchikey, spectrum_indexes in tqdm(spectrum_set.spectrum_indexes_per_inchikey.items(), desc="Get most common inchi per inchikey"): + spectra_matching_inchikey = [spectrum_set.spectra[index] for index in spectrum_indexes] + most_common_inchi = Counter([spectrum.get("inchi") for spectrum in spectra_matching_inchikey]).most_common(1)[0][0] + most_common_inchi_per_inchikey[inchikey] = most_common_inchi + return cls.compute_fingerprints_from_inchi(most_common_inchi_per_inchikey, fingerprint_type, nbits) + @classmethod def combine_fingerprints(cls, fingerprints_1: "Fingerprints", fingerprints_2: "Fingerprints", allow_replacing: bool=False) -> "Fingerprints": From e1bf3b3106fca613d9c6b953ada1732f384236a9 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 12:38:50 +0100 Subject: [PATCH 053/149] Refactor SpectrumSet init takes in attributes classmethods handle compute and remove fingerprints as attribute --- ms2query/benchmarking/SpectrumDataSet.py | 96 +++++++++++------------- 1 file changed, 42 insertions(+), 54 deletions(-) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index ca9f72b..574a201 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -1,55 +1,55 @@ import copy -from collections import Counter, defaultdict -from typing import List +from collections import defaultdict +from typing import List, Iterable, Optional from matchms import Spectrum from ms2deepscore.models import SiameseSpectralModel -from tqdm import tqdm from ms2query.benchmarking.Embeddings import Embeddings -from ms2query.benchmarking.Fingerprints import Fingerprints class SpectrumSet: """Stores a spectrum dataset making it easy and fast to split on molecules""" - - def __init__(self, spectra: List[Spectrum], progress_bars=False): - self._spectra = [] - self.spectrum_indexes_per_inchikey = defaultdict(list) + def __init__(self, + spectra: tuple[Spectrum, ...], + spectrum_indexes_per_inchikey: dict[str, Iterable], + embeddings: Optional[Embeddings] = None, + progress_bars=False): + self._spectra = tuple(spectra) + self.spectrum_indexes_per_inchikey: dict[str, tuple[int]] = {key: tuple(values) for key, values in spectrum_indexes_per_inchikey.items()} self.progress_bars = progress_bars - # init spectra - self._add_spectra_and_group_per_inchikey(spectra) - self.most_common_inchi_per_inchikey = {} - self._update_most_common_inchi_per_inchikey(list(self.spectrum_indexes_per_inchikey.keys())) - - self._fingerprints = None - self._embeddings = None - - def add_spectra(self, new_spectra: "SpectrumSet"): - updated_inchikeys = self._add_spectra_and_group_per_inchikey(new_spectra.spectra) - self._update_most_common_inchi_per_inchikey(updated_inchikeys) - if self._embeddings is not None: - self._embeddings = Embeddings.combine_embeddings(self.embeddings, new_spectra.embeddings) - if self._fingerprints is not None: - self.fingerprints.add_new_inchikeys(new_spectra.most_common_inchi_per_inchikey) - - def _add_spectra_and_group_per_inchikey(self, spectra: List[Spectrum]): - starting_index = len(self._spectra) - updated_inchikeys = set() - for i, spectrum in enumerate( - tqdm(spectra, desc="Adding spectra and grouping per Inchikey", disable=not self.progress_bars) - ): - self._spectra.append(spectrum) - spectrum_index = starting_index + i - inchikey = spectrum.get("inchikey")[:14] - updated_inchikeys.add(inchikey) - self.spectrum_indexes_per_inchikey[inchikey].append(spectrum_index) - return updated_inchikeys - - def _update_most_common_inchi_per_inchikey(self, new_inchikeys): - for inchikey in tqdm(new_inchikeys, desc="Get most common inchi per inchikey"): - spectra_matching_inchikey = self.spectra_per_inchikey(inchikey) - most_common_inchi = Counter([spectrum.get("inchi") for spectrum in spectra_matching_inchikey]).most_common(1)[0][0] - self.most_common_inchi_per_inchikey[inchikey] = most_common_inchi + self._embeddings = embeddings + + @classmethod + def create_spectrum_set(cls, spectra: tuple[Spectrum], progress_bars=False): + spectrum_indexes_per_inchikey = defaultdict(list) + for spectrum_index, spectrum in enumerate(spectra): + spectrum_indexes_per_inchikey[spectrum.get("inchikey")[:14]].append(spectrum_index) + return cls(spectra, spectrum_indexes_per_inchikey, progress_bars=progress_bars) + + def __add__(self, other) -> "SpectrumSet": + """Adds two spectrum sets together""" + if not isinstance(other, SpectrumSet): + return NotImplemented + spectra = self.spectra + other.spectra + # update spectrum_indexes_per_inchikey + starting_index = len(self.spectra) + reindexed_indexes_per_inchikey = {} + for inchikey, list_of_spectrum_indexes in other.spectrum_indexes_per_inchikey.items(): + reindexed_indexes_per_inchikey[inchikey] = [v + starting_index for v in list_of_spectrum_indexes] + # combine indexes + spectrum_indexes_per_inchikey = defaultdict(list) + for indexes_per_inchikey in (self.spectrum_indexes_per_inchikey, reindexed_indexes_per_inchikey): + for inchikey, indexes in indexes_per_inchikey.items(): + spectrum_indexes_per_inchikey[inchikey].extend(indexes) + + # combine embeddings + embeddings = None + if self.embeddings and self.embeddings: + embeddings = Embeddings.combine_embeddings(self.embeddings, other.embeddings) + return SpectrumSet(spectra, + spectrum_indexes_per_inchikey, + embeddings=embeddings, + progress_bars=self.progress_bars) def subset_spectra(self, spectrum_indexes) -> "SpectrumSet": """Returns a new instance of a subset of the spectra""" @@ -57,9 +57,6 @@ def subset_spectra(self, spectrum_indexes) -> "SpectrumSet": new_instance = SpectrumSet(spectra, progress_bars=self.progress_bars) if self._embeddings is not None: new_instance._embeddings = self.embeddings.subset_embeddings(spectra) - if self._fingerprints is not None: - inchikeys = [spectrum.get("inchikey")[:14] for spectrum in spectra] - new_instance._fingerprints = self.fingerprints.subset_fingerprints(inchikeys) return new_instance def spectra_per_inchikey(self, inchikey) -> List[Spectrum]: @@ -71,19 +68,10 @@ def spectra_per_inchikey(self, inchikey) -> List[Spectrum]: def add_embeddings(self, model: SiameseSpectralModel): self._embeddings = Embeddings.create_from_spectra(self._spectra, model) - def add_fingerprints(self, fingerprint_type, nbits): - self._fingerprints = Fingerprints(self.most_common_inchi_per_inchikey, fingerprint_type, nbits) - @property def spectra(self): return self._spectra - @property - def fingerprints(self) -> "Fingerprints": - if self._fingerprints is None: - raise ValueError("First run add_fingerprints") - return self._fingerprints - @property def embeddings(self) -> "Embeddings": if self._embeddings is None: From 18b4e8653cfd5fa984f9e93e2452a5c56715d7f5 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 12:39:19 +0100 Subject: [PATCH 054/149] Update type hinting --- ms2query/benchmarking/Embeddings.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ms2query/benchmarking/Embeddings.py b/ms2query/benchmarking/Embeddings.py index 7479471..4d1660f 100644 --- a/ms2query/benchmarking/Embeddings.py +++ b/ms2query/benchmarking/Embeddings.py @@ -1,4 +1,4 @@ -from typing import List +from typing import Sequence import numpy as np from matchms import Spectrum @@ -16,7 +16,7 @@ def __init__(self, embeddings: np.ndarray, spectrum_hashes: tuple, model_setting self._embeddings = embeddings @classmethod - def create_from_spectra(cls, spectra: List[Spectrum], + def create_from_spectra(cls, spectra: Sequence[Spectrum], model: SiameseSpectralModel) -> "Embeddings": index_to_spectrum_hash = tuple(spectrum.__hash__() for spectrum in spectra) if len(set(index_to_spectrum_hash)) != len(spectra): From af1eaec03c28478e0ed567dc29d792a708ea658c Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 12:39:39 +0100 Subject: [PATCH 055/149] Add test fingerprint from spectra --- tests/test_Fingerprints.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/tests/test_Fingerprints.py b/tests/test_Fingerprints.py index aacfa02..3b8a1cb 100644 --- a/tests/test_Fingerprints.py +++ b/tests/test_Fingerprints.py @@ -3,7 +3,8 @@ from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi from ms2query.benchmarking.Fingerprints import Fingerprints, get_similarity_matrix -from tests.conftest import get_inchikey_inchi_pairs +from ms2query.benchmarking.SpectrumDataSet import SpectrumSet +from tests.conftest import get_inchikey_inchi_pairs, create_test_spectra @pytest.fixture @@ -67,6 +68,13 @@ def test_combine_fingerprints_with_replacing(): with pytest.raises(ValueError): Fingerprints.combine_fingerprints(fingerprints_1, fingerprints_2, allow_replacing=False) +def test_fingerprint_from_spectra(): + test_spectra = create_test_spectra(2, nr_of_inchikeys=3) + spectrum_set = SpectrumSet.create_spectrum_set(test_spectra) + fingerprints = Fingerprints.from_spectrum_set(spectrum_set, "daylight", 2048) + fingerprints_from_inchi = Fingerprints.compute_fingerprints_from_inchi(get_inchikey_inchi_dict(3),"daylight", 2048) + assert fingerprints == fingerprints_from_inchi + def test_get_similarity_matrix(): fingerprints_1 = make_test_fingerprints(5) fingerprints_2 = make_test_fingerprints(3) From 8da343e88af1e242d46abea8c7444c2a86b4d946 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 13:08:05 +0100 Subject: [PATCH 056/149] Add cloning, copy and __eq__ for spectrumSet --- ms2query/benchmarking/SpectrumDataSet.py | 23 +++++++++++++++-------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index 574a201..5e4bcce 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -14,7 +14,7 @@ def __init__(self, spectrum_indexes_per_inchikey: dict[str, Iterable], embeddings: Optional[Embeddings] = None, progress_bars=False): - self._spectra = tuple(spectra) + self._spectra = tuple([spectrum.clone() for spectrum in spectra]) self.spectrum_indexes_per_inchikey: dict[str, tuple[int]] = {key: tuple(values) for key, values in spectrum_indexes_per_inchikey.items()} self.progress_bars = progress_bars self._embeddings = embeddings @@ -78,11 +78,18 @@ def embeddings(self) -> "Embeddings": raise ValueError("First run add_embeddings") return self._embeddings - def copy(self): - """This copy method ensures all spectra are""" - new_instance = copy.copy(self) - new_instance._spectra = self._spectra.copy() - new_instance.spectrum_indexes_per_inchikey = copy.deepcopy(self.spectrum_indexes_per_inchikey) - return new_instance - + def __copy__(self): + return SpectrumSet(self.spectra, + self.spectrum_indexes_per_inchikey, + self.embeddings, + progress_bars=self.progress_bars) + def __eq__(self, other): + if not isinstance(other, SpectrumSet): + raise NotImplemented + if self.spectra != other.spectra: + return False + if self.spectrum_indexes_per_inchikey != other.spectrum_indexes_per_inchikey: + return False + if self.embeddings != other.embeddings: + return False From 91f02d47185239ef7daf823588e6c11119ce0933 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 13:12:54 +0100 Subject: [PATCH 057/149] Rename SpectrumSet to AnnotatedSpectrumSet --- ms2query/benchmarking/EvaluateMethods.py | 22 +++++++-------- ms2query/benchmarking/Fingerprints.py | 4 +-- ms2query/benchmarking/SpectrumDataSet.py | 28 +++++++++---------- .../predict_best_possible_match.py | 6 ++-- .../predict_highest_cosine.py | 4 +-- .../predict_highest_ms2deepscore.py | 4 +-- .../predict_using_closest_tanimoto.py | 10 +++---- ...predict_with_integrated_similarity_flow.py | 8 +++--- tests/test_Fingerprints.py | 4 +-- 9 files changed, 45 insertions(+), 45 deletions(-) diff --git a/ms2query/benchmarking/EvaluateMethods.py b/ms2query/benchmarking/EvaluateMethods.py index 7eafaaf..37287c4 100644 --- a/ms2query/benchmarking/EvaluateMethods.py +++ b/ms2query/benchmarking/EvaluateMethods.py @@ -3,12 +3,12 @@ import numpy as np from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix from tqdm import tqdm -from ms2query.benchmarking.SpectrumDataSet import SpectrumSet +from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet class EvaluateMethods: def __init__( - self, training_spectrum_set: SpectrumSet, validation_spectrum_set: SpectrumSet + self, training_spectrum_set: AnnotatedSpectrumSet, validation_spectrum_set: AnnotatedSpectrumSet ): self.training_spectrum_set = training_spectrum_set self.validation_spectrum_set = validation_spectrum_set @@ -19,7 +19,7 @@ def __init__( def benchmark_analogue_search( self, prediction_function: Callable[ - [SpectrumSet, SpectrumSet], Tuple[List[str], List[float]] + [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] ], ) -> float: predicted_inchikeys, _ = prediction_function(self.training_spectrum_set, self.validation_spectrum_set) @@ -53,7 +53,7 @@ def benchmark_analogue_search( def benchmark_exact_matching_within_ionmode( self, prediction_function: Callable[ - [SpectrumSet, SpectrumSet], Tuple[List[str], List[float]] + [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] ], ionmode: str, ) -> float: @@ -77,7 +77,7 @@ def benchmark_exact_matching_within_ionmode( def exact_matches_across_ionization_modes( self, prediction_function: Callable[ - [SpectrumSet, SpectrumSet], Tuple[List[str], List[float]] + [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] ], ): """Test the accuracy at retrieving exact matches from the library if only available in other ionisation mode @@ -107,7 +107,7 @@ def get_accuracy_recall_curve(self): def predict_between_two_sets( - library: SpectrumSet, query_set_1: SpectrumSet, query_set_2: SpectrumSet, prediction_function + library: AnnotatedSpectrumSet, query_set_1: AnnotatedSpectrumSet, query_set_2: AnnotatedSpectrumSet, prediction_function ): """Makes predictions between query sets and the library, with the other query set added. @@ -123,7 +123,7 @@ def predict_between_two_sets( return predicted_inchikeys_1 + predicted_inchikeys_2 -def calculate_average_exact_match_accuracy(spectrum_set: SpectrumSet, predicted_inchikeys: List[str]): +def calculate_average_exact_match_accuracy(spectrum_set: AnnotatedSpectrumSet, predicted_inchikeys: List[str]): if len(spectrum_set.spectra) != len(predicted_inchikeys): raise ValueError("The number of spectra should be equal to the number of predicted inchikeys ") exact_match_accuracy_per_inchikey = [] @@ -139,7 +139,7 @@ def calculate_average_exact_match_accuracy(spectrum_set: SpectrumSet, predicted_ return sum(exact_match_accuracy_per_inchikey) / len(exact_match_accuracy_per_inchikey) -def split_spectrum_set_per_inchikeys(spectrum_set: SpectrumSet) -> Tuple[SpectrumSet, SpectrumSet]: +def split_spectrum_set_per_inchikeys(spectrum_set: AnnotatedSpectrumSet) -> Tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: """Splits a spectrum set into two. For each inchikey with more than one spectrum the spectra are divided over the two sets""" indexes_set_1 = [] @@ -157,8 +157,8 @@ def split_spectrum_set_per_inchikeys(spectrum_set: SpectrumSet) -> Tuple[Spectru def split_spectrum_set_per_inchikey_across_ionmodes( - spectrum_set: SpectrumSet, -) -> Tuple[SpectrumSet, SpectrumSet]: + spectrum_set: AnnotatedSpectrumSet, +) -> Tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: """Splits a spectrum set in two sets on ionmode. Only uses spectra for inchikeys with at least 1 pos and 1 neg""" all_pos_indexes = [] all_neg_indexes = [] @@ -190,7 +190,7 @@ def split_spectrum_set_per_inchikey_across_ionmodes( return pos_val_spectra, neg_val_spectra -def subset_spectra_on_ionmode(spectrum_set: SpectrumSet, ionmode) -> SpectrumSet: +def subset_spectra_on_ionmode(spectrum_set: AnnotatedSpectrumSet, ionmode) -> AnnotatedSpectrumSet: spectrum_indexes_to_keep = [] for i, spectrum in enumerate(spectrum_set.spectra): if spectrum.get("ionmode") == ionmode: diff --git a/ms2query/benchmarking/Fingerprints.py b/ms2query/benchmarking/Fingerprints.py index e126606..3fad103 100644 --- a/ms2query/benchmarking/Fingerprints.py +++ b/ms2query/benchmarking/Fingerprints.py @@ -5,7 +5,7 @@ from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi from tqdm import tqdm -from ms2query.benchmarking.SpectrumDataSet import SpectrumSet +from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet from ms2query.metrics import generalized_tanimoto_similarity_matrix @@ -34,7 +34,7 @@ def compute_fingerprints_from_inchi(cls, most_common_inchi_per_inchikey: dict[st return cls(fingerprints, index_to_inchikey, fingerprint_type) @classmethod - def from_spectrum_set(cls, spectrum_set: SpectrumSet, fingerprint_type, nbits): + def from_spectrum_set(cls, spectrum_set: AnnotatedSpectrumSet, fingerprint_type, nbits): most_common_inchi_per_inchikey = {} for inchikey, spectrum_indexes in tqdm(spectrum_set.spectrum_indexes_per_inchikey.items(), desc="Get most common inchi per inchikey"): spectra_matching_inchikey = [spectrum_set.spectra[index] for index in spectrum_indexes] diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index 5e4bcce..d223bfe 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -7,7 +7,7 @@ from ms2query.benchmarking.Embeddings import Embeddings -class SpectrumSet: +class AnnotatedSpectrumSet: """Stores a spectrum dataset making it easy and fast to split on molecules""" def __init__(self, spectra: tuple[Spectrum, ...], @@ -26,9 +26,9 @@ def create_spectrum_set(cls, spectra: tuple[Spectrum], progress_bars=False): spectrum_indexes_per_inchikey[spectrum.get("inchikey")[:14]].append(spectrum_index) return cls(spectra, spectrum_indexes_per_inchikey, progress_bars=progress_bars) - def __add__(self, other) -> "SpectrumSet": + def __add__(self, other) -> "AnnotatedSpectrumSet": """Adds two spectrum sets together""" - if not isinstance(other, SpectrumSet): + if not isinstance(other, AnnotatedSpectrumSet): return NotImplemented spectra = self.spectra + other.spectra # update spectrum_indexes_per_inchikey @@ -46,15 +46,15 @@ def __add__(self, other) -> "SpectrumSet": embeddings = None if self.embeddings and self.embeddings: embeddings = Embeddings.combine_embeddings(self.embeddings, other.embeddings) - return SpectrumSet(spectra, - spectrum_indexes_per_inchikey, - embeddings=embeddings, - progress_bars=self.progress_bars) + return AnnotatedSpectrumSet(spectra, + spectrum_indexes_per_inchikey, + embeddings=embeddings, + progress_bars=self.progress_bars) - def subset_spectra(self, spectrum_indexes) -> "SpectrumSet": + def subset_spectra(self, spectrum_indexes) -> "AnnotatedSpectrumSet": """Returns a new instance of a subset of the spectra""" spectra = [self._spectra[index] for index in spectrum_indexes] - new_instance = SpectrumSet(spectra, progress_bars=self.progress_bars) + new_instance = AnnotatedSpectrumSet(spectra, progress_bars=self.progress_bars) if self._embeddings is not None: new_instance._embeddings = self.embeddings.subset_embeddings(spectra) return new_instance @@ -79,13 +79,13 @@ def embeddings(self) -> "Embeddings": return self._embeddings def __copy__(self): - return SpectrumSet(self.spectra, - self.spectrum_indexes_per_inchikey, - self.embeddings, - progress_bars=self.progress_bars) + return AnnotatedSpectrumSet(self.spectra, + self.spectrum_indexes_per_inchikey, + self.embeddings, + progress_bars=self.progress_bars) def __eq__(self, other): - if not isinstance(other, SpectrumSet): + if not isinstance(other, AnnotatedSpectrumSet): raise NotImplemented if self.spectra != other.spectra: return False diff --git a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py index ded8dd4..266da4c 100644 --- a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py +++ b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py @@ -1,10 +1,10 @@ from typing import Dict import numpy as np from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix -from ms2query.benchmarking.SpectrumDataSet import SpectrumSet +from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet -def predict_best_possible_match(library_spectra: SpectrumSet, query_spectra: SpectrumSet): +def predict_best_possible_match(library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet): highest_possible_score_per_inchikey = calculate_highest_tanimoto_score_per_inchikey(library_spectra, query_spectra) inchikeys_of_best_match = [] @@ -20,7 +20,7 @@ def predict_best_possible_match(library_spectra: SpectrumSet, query_spectra: Spe def calculate_highest_tanimoto_score_per_inchikey( - library_spectra: SpectrumSet, query_spectra: SpectrumSet + library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet ) -> Dict[str, tuple[str, float]]: """Finds the best possible match during an analogue search""" print("Calculating tanimoto scores to determine best possible match") diff --git a/ms2query/benchmarking/reference_methods/predict_highest_cosine.py b/ms2query/benchmarking/reference_methods/predict_highest_cosine.py index 2fa2373..152b7e3 100644 --- a/ms2query/benchmarking/reference_methods/predict_highest_cosine.py +++ b/ms2query/benchmarking/reference_methods/predict_highest_cosine.py @@ -2,11 +2,11 @@ from matchms import Scores from matchms.similarity.CosineGreedy import CosineGreedy from matchms.similarity.PrecursorMzMatch import PrecursorMzMatch -from ms2query.benchmarking.SpectrumDataSet import SpectrumSet +from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet def predict_highest_cosine( - library_spectra: SpectrumSet, query_spectra: SpectrumSet + library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet ) -> Tuple[List[str], List[float]]: scores = Scores(references=library_spectra.spectra, queries=query_spectra.spectra, is_symmetric=False) diff --git a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py index 4a4fd41..cb51423 100644 --- a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py +++ b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py @@ -1,10 +1,10 @@ from typing import List, Tuple from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores -from ms2query.benchmarking.SpectrumDataSet import SpectrumSet +from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet def predict_highest_ms2deepscore( - library_spectra: SpectrumSet, query_spectra: SpectrumSet + library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet ) -> Tuple[List[str], List[float]]: indexes_of_highest_scores, highest_scores = predict_top_ms2deepscores( library_spectra.embeddings, query_spectra.embeddings, k=1 diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index 0152a4e..4584528 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -2,12 +2,12 @@ import numpy as np from ms2deepscore.vector_operations import cosine_similarity_matrix from tqdm import tqdm -from ms2query.benchmarking.SpectrumDataSet import SpectrumSet +from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet from ms2query.metrics import generalized_tanimoto_similarity_matrix def predict_using_closest_tanimoto( - library_spectra: SpectrumSet, query_spectra: SpectrumSet, + library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet, nr_of_closest_inchikeys_to_select=10, nr_of_inchikeys_with_highest_ms2deepscore_to_select=100 ) -> Tuple[List[str], List[float]]: @@ -26,7 +26,7 @@ def predict_using_closest_tanimoto( def predict_using_closest_tanimoto_single_spectrum( - spectra_with_embeddings: SpectrumSet, single_spectrum_with_embeddings: SpectrumSet, + spectra_with_embeddings: AnnotatedSpectrumSet, single_spectrum_with_embeddings: AnnotatedSpectrumSet, nr_of_closest_inchikeys_to_select, nr_of_inchikeys_with_highest_ms2deepscore_to_select) -> Tuple[str, float]: if len(single_spectrum_with_embeddings.spectra) != 1: raise ValueError("expected a single spectrum") @@ -45,7 +45,7 @@ def predict_using_closest_tanimoto_single_spectrum( inchikey_with_highest_average_prediction, score = max(average_predicted_scores.items(), key=lambda item: item[1]) return inchikey_with_highest_average_prediction, score -def select_inchikeys_with_highest_ms2deepscore(spectra_with_embeddings: SpectrumSet, ms2deepscores, nr_of_inchikeys_to_select=10): +def select_inchikeys_with_highest_ms2deepscore(spectra_with_embeddings: AnnotatedSpectrumSet, ms2deepscores, nr_of_inchikeys_to_select=10): highest_score_for_inchikey = [] for inchikey, spectrum_indexes in spectra_with_embeddings.spectrum_indexes_per_inchikey.items(): all_ms2deepscores_for_inchikey = ms2deepscores[spectrum_indexes] @@ -68,7 +68,7 @@ def get_average_predictions_for_closely_related_metabolites(spectra_with_embeddi average_predicted_score = sum(average_predicted_scores) / len(average_predicted_scores) return average_predicted_score -def get_inchikey_and_tanimoto_scores_for_top_k(spectra: SpectrumSet, inchikey, k +def get_inchikey_and_tanimoto_scores_for_top_k(spectra: AnnotatedSpectrumSet, inchikey, k ) -> tuple[list[str], np.ndarray]: """For an inchikey in a library the top k highest tanimoto scores in the library are predicted (including itself)""" similarity_scores = generalized_tanimoto_similarity_matrix(spectra.fingerprints.get_fingerprints(inchikey), spectra.fingerprints.fingerprints)[0] diff --git a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py index 1484973..5d72916 100644 --- a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py +++ b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py @@ -3,12 +3,12 @@ from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix from tqdm import tqdm from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores -from ms2query.benchmarking.SpectrumDataSet import SpectrumSet +from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet def predict_with_integrated_similarity_flow( - library_spectra: SpectrumSet, - query_spectra: SpectrumSet, + library_spectra: AnnotatedSpectrumSet, + query_spectra: AnnotatedSpectrumSet, number_of_analogues_to_consider=50, ) -> Tuple[List[str], List[float]]: @@ -30,7 +30,7 @@ def predict_with_integrated_similarity_flow( def get_highest_isf( - library_spectra: SpectrumSet, + library_spectra: AnnotatedSpectrumSet, indexes_of_library_spectra_with_highest_score: np.ndarray, predicted_scores: [List[float]], ): diff --git a/tests/test_Fingerprints.py b/tests/test_Fingerprints.py index 3b8a1cb..b65a1e8 100644 --- a/tests/test_Fingerprints.py +++ b/tests/test_Fingerprints.py @@ -3,7 +3,7 @@ from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi from ms2query.benchmarking.Fingerprints import Fingerprints, get_similarity_matrix -from ms2query.benchmarking.SpectrumDataSet import SpectrumSet +from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet from tests.conftest import get_inchikey_inchi_pairs, create_test_spectra @@ -70,7 +70,7 @@ def test_combine_fingerprints_with_replacing(): def test_fingerprint_from_spectra(): test_spectra = create_test_spectra(2, nr_of_inchikeys=3) - spectrum_set = SpectrumSet.create_spectrum_set(test_spectra) + spectrum_set = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) fingerprints = Fingerprints.from_spectrum_set(spectrum_set, "daylight", 2048) fingerprints_from_inchi = Fingerprints.compute_fingerprints_from_inchi(get_inchikey_inchi_dict(3),"daylight", 2048) assert fingerprints == fingerprints_from_inchi From 97eb6a2e4e9f7eddf7710aa001c79aa8d95b3b16 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 13:14:49 +0100 Subject: [PATCH 058/149] Add check that inchikey is available --- ms2query/benchmarking/SpectrumDataSet.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index d223bfe..3e3a906 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -23,7 +23,10 @@ def __init__(self, def create_spectrum_set(cls, spectra: tuple[Spectrum], progress_bars=False): spectrum_indexes_per_inchikey = defaultdict(list) for spectrum_index, spectrum in enumerate(spectra): - spectrum_indexes_per_inchikey[spectrum.get("inchikey")[:14]].append(spectrum_index) + inchikey = spectrum.get("inchikey") + if inchikey is None: + raise ValueError("Annotated Spectrum set expects spectra that all have an inchikey") + spectrum_indexes_per_inchikey[inchikey[:14]].append(spectrum_index) return cls(spectra, spectrum_indexes_per_inchikey, progress_bars=progress_bars) def __add__(self, other) -> "AnnotatedSpectrumSet": From a02f36f3333f5b0f1afe94f59611782e24f2b067 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 14:01:53 +0100 Subject: [PATCH 059/149] Small bug fixes --- ms2query/benchmarking/SpectrumDataSet.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/SpectrumDataSet.py index 3e3a906..aa8017d 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/SpectrumDataSet.py @@ -1,6 +1,6 @@ import copy from collections import defaultdict -from typing import List, Iterable, Optional +from typing import List, Iterable, Optional, Sequence from matchms import Spectrum from ms2deepscore.models import SiameseSpectralModel @@ -10,7 +10,7 @@ class AnnotatedSpectrumSet: """Stores a spectrum dataset making it easy and fast to split on molecules""" def __init__(self, - spectra: tuple[Spectrum, ...], + spectra: Sequence[Spectrum], spectrum_indexes_per_inchikey: dict[str, Iterable], embeddings: Optional[Embeddings] = None, progress_bars=False): @@ -20,7 +20,7 @@ def __init__(self, self._embeddings = embeddings @classmethod - def create_spectrum_set(cls, spectra: tuple[Spectrum], progress_bars=False): + def create_spectrum_set(cls, spectra: Sequence[Spectrum], progress_bars=False): spectrum_indexes_per_inchikey = defaultdict(list) for spectrum_index, spectrum in enumerate(spectra): inchikey = spectrum.get("inchikey") @@ -47,7 +47,7 @@ def __add__(self, other) -> "AnnotatedSpectrumSet": # combine embeddings embeddings = None - if self.embeddings and self.embeddings: + if self._embeddings and other._embeddings: embeddings = Embeddings.combine_embeddings(self.embeddings, other.embeddings) return AnnotatedSpectrumSet(spectra, spectrum_indexes_per_inchikey, @@ -57,7 +57,7 @@ def __add__(self, other) -> "AnnotatedSpectrumSet": def subset_spectra(self, spectrum_indexes) -> "AnnotatedSpectrumSet": """Returns a new instance of a subset of the spectra""" spectra = [self._spectra[index] for index in spectrum_indexes] - new_instance = AnnotatedSpectrumSet(spectra, progress_bars=self.progress_bars) + new_instance = AnnotatedSpectrumSet.create_spectrum_set(spectra, progress_bars=self.progress_bars) if self._embeddings is not None: new_instance._embeddings = self.embeddings.subset_embeddings(spectra) return new_instance @@ -87,12 +87,13 @@ def __copy__(self): self.embeddings, progress_bars=self.progress_bars) - def __eq__(self, other): + def __eq__(self, other: "AnnotatedSpectrumSet"): if not isinstance(other, AnnotatedSpectrumSet): raise NotImplemented if self.spectra != other.spectra: return False if self.spectrum_indexes_per_inchikey != other.spectrum_indexes_per_inchikey: return False - if self.embeddings != other.embeddings: + if self._embeddings != other._embeddings: return False + return True From f19f9d87ee205758943876706ee4d4b6e6d4ac9f Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 14:02:03 +0100 Subject: [PATCH 060/149] Update test_SpectrumDataSet.py --- tests/test_SpectrumDataSet.py | 144 +++++++--------------------------- 1 file changed, 27 insertions(+), 117 deletions(-) diff --git a/tests/test_SpectrumDataSet.py b/tests/test_SpectrumDataSet.py index bb3e232..85713d9 100644 --- a/tests/test_SpectrumDataSet.py +++ b/tests/test_SpectrumDataSet.py @@ -1,130 +1,40 @@ -import numpy as np -import pytest from ms2query.benchmarking.SpectrumDataSet import ( - SpectraWithFingerprints, - SpectraWithMS2DeepScoreEmbeddings, - SpectrumSet, + AnnotatedSpectrumSet, ) -from tests.conftest import create_test_spectra, get_inchikey_inchi_pairs, ms2deepscore_model +from tests.conftest import create_test_spectra, ms2deepscore_model -@pytest.mark.parametrize( - "library", - [ - SpectrumSet(create_test_spectra()), - SpectraWithFingerprints(create_test_spectra()), - SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(), ms2deepscore_model()), - ], -) -def test_spectrum_set_base(library): - """Test all base functionality of SpectrumSet is implemented correctly - also for all classes inheriting from it""" - # test correct init - assert len(library.spectra) == 9 - assert len(library.spectrum_indexes_per_inchikey) == 3 - assert sum(len(v) for v in library.spectrum_indexes_per_inchikey.values()) == 9 - - # test correct copying - new_copy = library.copy() - assert len(new_copy.spectra) == 9 - assert len(new_copy.spectrum_indexes_per_inchikey) == 3 - assert sum(len(v) for v in new_copy.spectrum_indexes_per_inchikey.values()) == 9 - - # test correctly adding spectra - new_copy.add_spectra(library) - new_number_of_spectra = 9 + 9 - assert len(new_copy.spectra) == new_number_of_spectra - assert len(new_copy.spectrum_indexes_per_inchikey) == 3 - assert sum(len(v) for v in new_copy.spectrum_indexes_per_inchikey.values()) == new_number_of_spectra - - # test the original is not edited when adding spectra - assert len(library.spectra) == 9 - assert len(library.spectrum_indexes_per_inchikey) == 3 - assert sum(len(v) for v in library.spectrum_indexes_per_inchikey.values()) == 9 - - # test correct subsetting - subset_indexes = [1, 4, 6, 7] - subset = library.subset_spectra(subset_indexes) - assert len(subset.spectra) == len(subset_indexes) - assert len(subset.spectrum_indexes_per_inchikey) == 3 - assert sum(len(v) for v in subset.spectrum_indexes_per_inchikey.values()) == len(subset_indexes) - assert isinstance(subset, library.__class__) - - -@pytest.mark.parametrize( - "library", - [ - SpectraWithFingerprints(create_test_spectra()), - SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(), ms2deepscore_model()), - ], -) -def test_spectra_with_fingerprints(library): - """Test all functionality added in SpectraWithFingerprints also for all classes inheriting from it""" - # test correct init - assert len(library.inchikey_fingerprint_pairs) == 3 - - # test correct copying - new_copy = library.copy() - assert len(new_copy.inchikey_fingerprint_pairs) == 3 - - # test correctly adding inchikey_fingerprint_pairs when runnning add_spectra - for inchikey_inchi_pairs, expected_nr_of_inchikeys in ( - (get_inchikey_inchi_pairs(5)[2:], 5), # Some overlap - (get_inchikey_inchi_pairs(5)[3:], 5), # No overlap - (get_inchikey_inchi_pairs(3), 3), # Fully overlapping - (get_inchikey_inchi_pairs(1), 3), # Fully overlapping (but not all) - ): - spectra_to_add = SpectrumSet(create_test_spectra(2, inchikey_inchi_pairs=inchikey_inchi_pairs)) - new_copy = library.copy() - new_copy.add_spectra(spectra_to_add) - assert len(new_copy.inchikey_fingerprint_pairs) == expected_nr_of_inchikeys - for inchikey in library.inchikey_fingerprint_pairs: - assert np.array_equal( - new_copy.inchikey_fingerprint_pairs[inchikey], library.inchikey_fingerprint_pairs[inchikey] - ) +def test_create_annotated_spectrum_set(): + test_spectra = create_test_spectra(nr_of_inchikeys=3, number_of_spectra_per_inchikey=3) + spectrum_set = AnnotatedSpectrumSet.create_spectrum_set(spectra=test_spectra) + assert len(spectrum_set.spectrum_indexes_per_inchikey) == 3 - # test the original is not edited when adding spectra - assert len(library.inchikey_fingerprint_pairs) == 3 - assert all( - np.array_equal(library.inchikey_fingerprint_pairs[key], value) - for key, value in SpectraWithFingerprints(create_test_spectra()).inchikey_fingerprint_pairs.items() - ) +def test_add_spectrum_sets(): + test_spectra = create_test_spectra(nr_of_inchikeys=3, number_of_spectra_per_inchikey=3) - # test correct subsetting - subset_indexes = [1, 6, 7] - subset = library.subset_spectra(subset_indexes) - assert len(subset.inchikey_fingerprint_pairs) == 2 - assert all( - np.array_equal(library.inchikey_fingerprint_pairs[key], value) - for key, value in subset.inchikey_fingerprint_pairs.items() - ) - assert hasattr(subset, "update_fingerprint_per_inchikey") + correct_combined_set = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) + spectrum_set_1 = AnnotatedSpectrumSet.create_spectrum_set(test_spectra[:5]) + spectrum_set_2 = AnnotatedSpectrumSet.create_spectrum_set(test_spectra[5:]) -def test_spectra_with_embeddings(): - library = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(), ms2deepscore_model()) - # test correct init - assert library.embeddings.shape == (9, 100) + # with added embededings + model = ms2deepscore_model() + spectrum_set_1.add_embeddings(model) + spectrum_set_2.add_embeddings(model) + correct_combined_set.add_embeddings(model) - # test correct copying - new_copy = library.copy() - assert new_copy.embeddings.shape == (9, 100) + combined_spectra = spectrum_set_1 + spectrum_set_2 + assert correct_combined_set == combined_spectra - # test correctly adding spectra - new_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(1), ms2deepscore_model()) - new_copy.add_spectra(new_spectra) - assert new_copy.embeddings.shape == (12, 100) +def test_subsetting(): + test_spectra = create_test_spectra(nr_of_inchikeys=3, number_of_spectra_per_inchikey=3) - # test the original is not edited when adding spectra - assert library.embeddings.shape == (9, 100) + correct_subsetted_set = AnnotatedSpectrumSet.create_spectrum_set(test_spectra[:5]) + spectrum_set = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) - # test correct subsetting - subset_indexes = [1, 4, 6, 7] - subset = library.subset_spectra(subset_indexes) - assert subset.embeddings.shape == (len(subset_indexes), 100) - for i, index in enumerate(subset_indexes): - assert np.all(library.embeddings[index] == subset.embeddings[i]) + # with added embededings + model = ms2deepscore_model() + correct_subsetted_set.add_embeddings(model) + spectrum_set.add_embeddings(model) - # Check that subsetting on subset works. To make sure that a subset does not become of type SpectrumSet - subsetted_subset = subset.subset_spectra([0, 1]) - assert subsetted_subset.embeddings.shape == (2, 100) + assert correct_subsetted_set == spectrum_set.subset_spectra([0,1,2,3,4]) From 7722382c08809fe00e1c4ae6521053346e132d1b Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 14:03:36 +0100 Subject: [PATCH 061/149] rename to AnnotatedSpectrumSet.py --- .../{SpectrumDataSet.py => AnnotatedSpectrumSet.py} | 1 - ms2query/benchmarking/EvaluateMethods.py | 2 +- ms2query/benchmarking/Fingerprints.py | 2 +- .../reference_methods/predict_best_possible_match.py | 2 +- .../benchmarking/reference_methods/predict_highest_cosine.py | 2 +- .../reference_methods/predict_highest_ms2deepscore.py | 2 +- .../reference_methods/predict_using_closest_tanimoto.py | 2 +- .../predict_with_integrated_similarity_flow.py | 2 +- tests/testPredictMS2DeepScoreSimilarity.py | 2 +- tests/test_Fingerprints.py | 2 +- tests/test_SpectrumDataSet.py | 2 +- tests/test_methods.py | 2 +- tests/test_predict_using_closest_tanimoto.py | 2 +- 13 files changed, 12 insertions(+), 13 deletions(-) rename ms2query/benchmarking/{SpectrumDataSet.py => AnnotatedSpectrumSet.py} (99%) diff --git a/ms2query/benchmarking/SpectrumDataSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py similarity index 99% rename from ms2query/benchmarking/SpectrumDataSet.py rename to ms2query/benchmarking/AnnotatedSpectrumSet.py index aa8017d..08896e5 100644 --- a/ms2query/benchmarking/SpectrumDataSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -1,4 +1,3 @@ -import copy from collections import defaultdict from typing import List, Iterable, Optional, Sequence from matchms import Spectrum diff --git a/ms2query/benchmarking/EvaluateMethods.py b/ms2query/benchmarking/EvaluateMethods.py index 37287c4..c4cb77e 100644 --- a/ms2query/benchmarking/EvaluateMethods.py +++ b/ms2query/benchmarking/EvaluateMethods.py @@ -3,7 +3,7 @@ import numpy as np from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix from tqdm import tqdm -from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet class EvaluateMethods: diff --git a/ms2query/benchmarking/Fingerprints.py b/ms2query/benchmarking/Fingerprints.py index 3fad103..8fd0e15 100644 --- a/ms2query/benchmarking/Fingerprints.py +++ b/ms2query/benchmarking/Fingerprints.py @@ -5,7 +5,7 @@ from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi from tqdm import tqdm -from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.metrics import generalized_tanimoto_similarity_matrix diff --git a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py index 266da4c..b22d429 100644 --- a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py +++ b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py @@ -1,7 +1,7 @@ from typing import Dict import numpy as np from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix -from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet def predict_best_possible_match(library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet): diff --git a/ms2query/benchmarking/reference_methods/predict_highest_cosine.py b/ms2query/benchmarking/reference_methods/predict_highest_cosine.py index 152b7e3..9c49847 100644 --- a/ms2query/benchmarking/reference_methods/predict_highest_cosine.py +++ b/ms2query/benchmarking/reference_methods/predict_highest_cosine.py @@ -2,7 +2,7 @@ from matchms import Scores from matchms.similarity.CosineGreedy import CosineGreedy from matchms.similarity.PrecursorMzMatch import PrecursorMzMatch -from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet def predict_highest_cosine( diff --git a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py index cb51423..45c0f1e 100644 --- a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py +++ b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py @@ -1,6 +1,6 @@ from typing import List, Tuple from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores -from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet def predict_highest_ms2deepscore( diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index 4584528..2eadeae 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -2,7 +2,7 @@ import numpy as np from ms2deepscore.vector_operations import cosine_similarity_matrix from tqdm import tqdm -from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.metrics import generalized_tanimoto_similarity_matrix diff --git a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py index 5d72916..c02911b 100644 --- a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py +++ b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py @@ -3,7 +3,7 @@ from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix from tqdm import tqdm from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores -from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet def predict_with_integrated_similarity_flow( diff --git a/tests/testPredictMS2DeepScoreSimilarity.py b/tests/testPredictMS2DeepScoreSimilarity.py index 4741bdf..3b46af5 100644 --- a/tests/testPredictMS2DeepScoreSimilarity.py +++ b/tests/testPredictMS2DeepScoreSimilarity.py @@ -3,7 +3,7 @@ from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import ( predict_top_ms2deepscores, ) -from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings +from ms2query.benchmarking.AnnotatedSpectrumSet import SpectraWithMS2DeepScoreEmbeddings from tests.conftest import create_test_spectra, ms2deepscore_model diff --git a/tests/test_Fingerprints.py b/tests/test_Fingerprints.py index b65a1e8..fb6a9c7 100644 --- a/tests/test_Fingerprints.py +++ b/tests/test_Fingerprints.py @@ -3,7 +3,7 @@ from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi from ms2query.benchmarking.Fingerprints import Fingerprints, get_similarity_matrix -from ms2query.benchmarking.SpectrumDataSet import AnnotatedSpectrumSet +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from tests.conftest import get_inchikey_inchi_pairs, create_test_spectra diff --git a/tests/test_SpectrumDataSet.py b/tests/test_SpectrumDataSet.py index 85713d9..6e294f6 100644 --- a/tests/test_SpectrumDataSet.py +++ b/tests/test_SpectrumDataSet.py @@ -1,4 +1,4 @@ -from ms2query.benchmarking.SpectrumDataSet import ( +from ms2query.benchmarking.AnnotatedSpectrumSet import ( AnnotatedSpectrumSet, ) from tests.conftest import create_test_spectra, ms2deepscore_model diff --git a/tests/test_methods.py b/tests/test_methods.py index 1b747d0..759946b 100644 --- a/tests/test_methods.py +++ b/tests/test_methods.py @@ -8,7 +8,7 @@ integrated_similarity_flow, predict_with_integrated_similarity_flow, ) -from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings +from ms2query.benchmarking.AnnotatedSpectrumSet import SpectraWithMS2DeepScoreEmbeddings from tests.conftest import create_test_spectra, ms2deepscore_model diff --git a/tests/test_predict_using_closest_tanimoto.py b/tests/test_predict_using_closest_tanimoto.py index f6ebc31..c5e600f 100644 --- a/tests/test_predict_using_closest_tanimoto.py +++ b/tests/test_predict_using_closest_tanimoto.py @@ -7,7 +7,7 @@ predict_using_closest_tanimoto_single_spectrum, select_inchikeys_with_highest_ms2deepscore, ) -from ms2query.benchmarking.SpectrumDataSet import SpectraWithFingerprints, SpectraWithMS2DeepScoreEmbeddings +from ms2query.benchmarking.AnnotatedSpectrumSet import SpectraWithFingerprints, SpectraWithMS2DeepScoreEmbeddings from tests.conftest import create_test_spectra, ms2deepscore_model From 009af5189019c2550f52204fd533b6a985746227 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 15:46:16 +0100 Subject: [PATCH 062/149] Add inchikeys as property to AnnotatedSpectrumSet.py --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index 08896e5..062a2a3 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -80,6 +80,10 @@ def embeddings(self) -> "Embeddings": raise ValueError("First run add_embeddings") return self._embeddings + @property + def inchikeys(self): + return tuple(self.spectrum_indexes_per_inchikey.keys()) + def __copy__(self): return AnnotatedSpectrumSet(self.spectra, self.spectrum_indexes_per_inchikey, From 39b541406291a5dfdcec0f6af74ac5a863736272 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 15:47:16 +0100 Subject: [PATCH 063/149] Add fingerprints as input to predict_best_possible_match.py --- .../predict_best_possible_match.py | 25 ++++++++++++------- 1 file changed, 16 insertions(+), 9 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py index b22d429..6d0dc5f 100644 --- a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py +++ b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py @@ -1,11 +1,13 @@ from typing import Dict -import numpy as np from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet +from ms2query.benchmarking.Fingerprints import Fingerprints -def predict_best_possible_match(library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet): - highest_possible_score_per_inchikey = calculate_highest_tanimoto_score_per_inchikey(library_spectra, query_spectra) +def predict_best_possible_match(library_spectra: AnnotatedSpectrumSet, + query_spectra: AnnotatedSpectrumSet, + fingerprints: Fingerprints,): + highest_possible_score_per_inchikey = calculate_highest_tanimoto_score_per_inchikey(library_spectra, query_spectra, fingerprints) inchikeys_of_best_match = [] highest_scores = [] @@ -20,26 +22,31 @@ def predict_best_possible_match(library_spectra: AnnotatedSpectrumSet, query_spe def calculate_highest_tanimoto_score_per_inchikey( - library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet + library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet, + fingerprints: Fingerprints ) -> Dict[str, tuple[str, float]]: """Finds the best possible match during an analogue search""" print("Calculating tanimoto scores to determine best possible match") - library_fingerprints = library_spectra.fingerprints.fingerprints - query_fingerprints = query_spectra.fingerprints.fingerprints + + library_fingerprints = fingerprints.get_fingerprints(library_spectra.inchikeys) + query_fingerprints = fingerprints.get_fingerprints(query_spectra.inchikeys) + tanimoto_scores = jaccard_similarity_matrix(library_fingerprints, query_fingerprints) highest_scores = tanimoto_scores.max(axis=0, initial=0) indexes_of_highest_scores = tanimoto_scores.argmax(axis=0) highest_possible_score_per_inchikey = dict() - for i, inchikey in enumerate(query_spectra.fingerprints.inchikeys): + # todo replace with TopKTanimotoScores + inchikeys_in_library = set(library_spectra.inchikeys()) + for i, inchikey in enumerate(query_spectra.inchikeys): # Check if inchikey in library (To correctly handle the exact matching case) - if inchikey in library_spectra.fingerprints.inchikeys: + if inchikey in inchikeys_in_library: highest_possible_score_per_inchikey[inchikey] = (inchikey, 1.0) continue highest_possible_score_per_inchikey[inchikey] = ( - library_spectra.fingerprints.inchikeys[indexes_of_highest_scores[i]], + library_spectra.inchikeys[indexes_of_highest_scores[i]], highest_scores[i], ) return highest_possible_score_per_inchikey From 56e3a8bdefdd42617042c29ec13677c6b1417e86 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 15:48:25 +0100 Subject: [PATCH 064/149] Refactor EvaluateMethods and split on analogue search benchmarking and exact match benchmarking --- ms2query/benchmarking/EvaluateMethods.py | 154 +++++++++++------------ 1 file changed, 75 insertions(+), 79 deletions(-) diff --git a/ms2query/benchmarking/EvaluateMethods.py b/ms2query/benchmarking/EvaluateMethods.py index c4cb77e..2644886 100644 --- a/ms2query/benchmarking/EvaluateMethods.py +++ b/ms2query/benchmarking/EvaluateMethods.py @@ -4,31 +4,35 @@ from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix from tqdm import tqdm from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet +from ms2query.benchmarking.Fingerprints import Fingerprints - -class EvaluateMethods: +class EvaluateAnalogueSearch: def __init__( - self, training_spectrum_set: AnnotatedSpectrumSet, validation_spectrum_set: AnnotatedSpectrumSet + self, training_spectrum_set: AnnotatedSpectrumSet, + validation_spectrum_set: AnnotatedSpectrumSet, + fingerprint_type="daylight", + nbits=2048, ): self.training_spectrum_set = training_spectrum_set self.validation_spectrum_set = validation_spectrum_set + self.fingerprints = Fingerprints.from_spectrum_set(training_spectrum_set + validation_spectrum_set, + fingerprint_type, nbits) self.training_spectrum_set.progress_bars = False self.validation_spectrum_set.progress_bars = False def benchmark_analogue_search( - self, - prediction_function: Callable[ - [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] - ], + self, predicted_inchikeys: List[str], ) -> float: - predicted_inchikeys, _ = prediction_function(self.training_spectrum_set, self.validation_spectrum_set) - average_scores_per_inchikey = [] + """Calculates the average accuracy of predictions made. + predicted_inchikeys should match the order in self.validation_spectrum_set + """ + average_scores_per_inchikey = [] # Calculate score per unique inchikey for inchikey in tqdm( - self.validation_spectrum_set.spectrum_indexes_per_inchikey.keys(), - desc="Calculating analogue accuracy per inchikey", + self.validation_spectrum_set.spectrum_indexes_per_inchikey.keys(), + desc="Calculating analogue accuracy per inchikey", ): matching_spectrum_indexes = self.validation_spectrum_set.spectrum_indexes_per_inchikey[inchikey] prediction_scores = [] @@ -37,8 +41,8 @@ def benchmark_analogue_search( if predicted_inchikey is None: prediction_scores.append(0.0) else: - predicted_fingerprint = self.training_spectrum_set.fingerprints.get_fingerprints([predicted_inchikey])[0] - actual_fingerprint = self.validation_spectrum_set.fingerprints.get_fingerprints([inchikey])[0] + predicted_fingerprint = self.fingerprints.get_fingerprints([predicted_inchikey])[0] + actual_fingerprint = self.fingerprints.get_fingerprints([inchikey])[0] tanimoto_for_prediction = calculate_tanimoto_score_between_pair( predicted_fingerprint, actual_fingerprint ) @@ -50,49 +54,6 @@ def benchmark_analogue_search( average_over_all_inchikeys = sum(average_scores_per_inchikey) / len(average_scores_per_inchikey) return average_over_all_inchikeys - def benchmark_exact_matching_within_ionmode( - self, - prediction_function: Callable[ - [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] - ], - ionmode: str, - ) -> float: - """Test the accuracy at retrieving exact matches from the library - - For each inchikey with more than 1 spectrum the spectra are split in two sets. Half for each inchikey is added - to the library (training set), for the other half predictions are made. Thereby there is always an exact match - avaialable. Only the highest ranked prediction is considered correct if the correct inchikey is predicted. - An accuracy per inchikey is calculated followed by calculating the average. - """ - selected_spectra = subset_spectra_on_ionmode(self.validation_spectrum_set, ionmode) - - set_1, set_2 = split_spectrum_set_per_inchikeys(selected_spectra) - - predicted_inchikeys = predict_between_two_sets(self.training_spectrum_set, set_1, set_2, prediction_function) - - # add the spectra to set_1 - set_1.add_spectra(set_2) - return calculate_average_exact_match_accuracy(set_1, predicted_inchikeys) - - def exact_matches_across_ionization_modes( - self, - prediction_function: Callable[ - [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] - ], - ): - """Test the accuracy at retrieving exact matches from the library if only available in other ionisation mode - - Each val spectrum is matched against the training set with the other val spectra of the same inchikey, but other - ionisation mode added to the library. - """ - pos_set, neg_set = split_spectrum_set_per_inchikey_across_ionmodes(self.validation_spectrum_set) - predicted_inchikeys = predict_between_two_sets( - self.training_spectrum_set, pos_set, neg_set, prediction_function - ) - # add the spectra to set_1 - pos_set.add_spectra(neg_set) - return calculate_average_exact_match_accuracy(pos_set, predicted_inchikeys) - def get_accuracy_recall_curve(self): """This method should test the recall accuracy balance. All of the used methods use a threshold which indicates quality of prediction. @@ -105,32 +66,65 @@ def get_accuracy_recall_curve(self): """ raise NotImplementedError +class EvaluateExactMatchSearch: + def __init__( + self, training_spectrum_set: AnnotatedSpectrumSet, + validation_spectrum_set: AnnotatedSpectrumSet, + ): + self.training_spectrum_set = training_spectrum_set + (self.pos_validation_spectra, + self.neg_validation_spectra) = split_spectrum_set_per_inchikey_across_ionmodes(validation_spectrum_set) -def predict_between_two_sets( - library: AnnotatedSpectrumSet, query_set_1: AnnotatedSpectrumSet, query_set_2: AnnotatedSpectrumSet, prediction_function -): - """Makes predictions between query sets and the library, with the other query set added. - - This is necessary for testing exact matching""" - training_set_copy = library.copy() - training_set_copy.add_spectra(query_set_2) - predicted_inchikeys_1, _ = prediction_function(training_set_copy, query_set_1) - - training_set_copy = library.copy() - training_set_copy.add_spectra(query_set_1) - predicted_inchikeys_2, _ = prediction_function(training_set_copy, query_set_2) + self.pos_split_per_inchikey_set_1, self.pos_split_per_inchikey_set_2 = split_spectrum_set_per_inchikeys( + self.pos_validation_spectra) - return predicted_inchikeys_1 + predicted_inchikeys_2 + self.neg_split_per_inchikey_set_1, self.neg_split_per_inchikey_set_2 = split_spectrum_set_per_inchikeys( + self.neg_validation_spectra) + def pos_in_neg(self, prediction_function: Callable[ + [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] + ],): + return get_exact_match_accuracy(self.pos_validation_spectra, + prediction_function(self.training_spectrum_set + self.neg_validation_spectra, + self.pos_validation_spectra)) + def neg_in_pos(self, prediction_function: Callable[ + [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] + ],): + return get_exact_match_accuracy(self.neg_validation_spectra, + prediction_function(self.training_spectrum_set + self.pos_validation_spectra, + self.neg_validation_spectra)) -def calculate_average_exact_match_accuracy(spectrum_set: AnnotatedSpectrumSet, predicted_inchikeys: List[str]): - if len(spectrum_set.spectra) != len(predicted_inchikeys): - raise ValueError("The number of spectra should be equal to the number of predicted inchikeys ") + def neg_in_neg(self, prediction_function: Callable[ + [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] + ],): + accuracy_set_2 = get_exact_match_accuracy( + self.neg_split_per_inchikey_set_2, + prediction_function(self.training_spectrum_set + self.neg_split_per_inchikey_set_1, + self.neg_split_per_inchikey_set_2)) + accuracy_set_1 = get_exact_match_accuracy( + self.neg_split_per_inchikey_set_1, + prediction_function(self.training_spectrum_set + self.neg_split_per_inchikey_set_2, + self.neg_split_per_inchikey_set_1)) + return (accuracy_set_2 + accuracy_set_1) / 2 + + def pos_in_pos(self, prediction_function: Callable[ + [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] + ],): + accuracy_set_2 = get_exact_match_accuracy( + self.pos_split_per_inchikey_set_2, + prediction_function(self.training_spectrum_set + self.pos_split_per_inchikey_set_1, + self.pos_split_per_inchikey_set_2)) + accuracy_set_1 = get_exact_match_accuracy( + self.pos_split_per_inchikey_set_1, + prediction_function(self.training_spectrum_set + self.pos_split_per_inchikey_set_2, + self.pos_split_per_inchikey_set_1)) + + return (accuracy_set_2 + accuracy_set_1) / 2 + +def get_exact_match_accuracy(query_spectrum_set, predicted_inchikeys): exact_match_accuracy_per_inchikey = [] - for inchikey in tqdm( - spectrum_set.spectrum_indexes_per_inchikey.keys(), desc="Calculating exact match accuracy per inchikey" - ): - val_spectrum_indexes_matching_inchikey = spectrum_set.spectrum_indexes_per_inchikey[inchikey] + for inchikey in tqdm(query_spectrum_set.inchikeys, desc="Calculating exact match accuracy per inchikey"): + val_spectrum_indexes_matching_inchikey = query_spectrum_set.spectrum_indexes_per_inchikey[inchikey] correctly_predicted = 0 for selected_spectrum_idx in val_spectrum_indexes_matching_inchikey: if inchikey == predicted_inchikeys[selected_spectrum_idx]: @@ -139,18 +133,20 @@ def calculate_average_exact_match_accuracy(spectrum_set: AnnotatedSpectrumSet, p return sum(exact_match_accuracy_per_inchikey) / len(exact_match_accuracy_per_inchikey) -def split_spectrum_set_per_inchikeys(spectrum_set: AnnotatedSpectrumSet) -> Tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: +def split_spectrum_set_per_inchikeys(spectrum_set: AnnotatedSpectrumSet, + seed=42) -> Tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: """Splits a spectrum set into two. For each inchikey with more than one spectrum the spectra are divided over the two sets""" indexes_set_1 = [] indexes_set_2 = [] + rng = random.Random(seed) for inchikey in tqdm(spectrum_set.spectrum_indexes_per_inchikey.keys(), desc="Splitting spectra per inchikey"): val_spectrum_indexes_matching_inchikey = spectrum_set.spectrum_indexes_per_inchikey[inchikey] if len(val_spectrum_indexes_matching_inchikey) == 1: # all single spectra are excluded from this test, since no exact match can be added to the library continue split_index = len(val_spectrum_indexes_matching_inchikey) // 2 - random.shuffle(val_spectrum_indexes_matching_inchikey) + rng.shuffle(list(val_spectrum_indexes_matching_inchikey)) indexes_set_1.extend(val_spectrum_indexes_matching_inchikey[:split_index]) indexes_set_2.extend(val_spectrum_indexes_matching_inchikey[split_index:]) return spectrum_set.subset_spectra(indexes_set_1), spectrum_set.subset_spectra(indexes_set_2) From 11e3ace6d1579e73f055d3b81d409891dca51135 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 15:50:37 +0100 Subject: [PATCH 065/149] Create EvaluateAnalogueSearch.py --- .../benchmarking/EvaluateAnalogueSearch.py | 74 +++++++++++++++++++ ms2query/benchmarking/EvaluateMethods.py | 66 ----------------- 2 files changed, 74 insertions(+), 66 deletions(-) create mode 100644 ms2query/benchmarking/EvaluateAnalogueSearch.py diff --git a/ms2query/benchmarking/EvaluateAnalogueSearch.py b/ms2query/benchmarking/EvaluateAnalogueSearch.py new file mode 100644 index 0000000..28aa9cc --- /dev/null +++ b/ms2query/benchmarking/EvaluateAnalogueSearch.py @@ -0,0 +1,74 @@ +from typing import List + +import numpy as np +from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix + +from tqdm import tqdm + +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet +from ms2query.benchmarking.Fingerprints import Fingerprints + + +class EvaluateAnalogueSearch: + def __init__( + self, training_spectrum_set: AnnotatedSpectrumSet, + validation_spectrum_set: AnnotatedSpectrumSet, + fingerprint_type="daylight", + nbits=2048, + ): + self.training_spectrum_set = training_spectrum_set + self.validation_spectrum_set = validation_spectrum_set + + self.fingerprints = Fingerprints.from_spectrum_set(training_spectrum_set + validation_spectrum_set, + fingerprint_type, nbits) + self.training_spectrum_set.progress_bars = False + self.validation_spectrum_set.progress_bars = False + + def benchmark_analogue_search( + self, predicted_inchikeys: List[str], + ) -> float: + """Calculates the average accuracy of predictions made. + + predicted_inchikeys should match the order in self.validation_spectrum_set + """ + average_scores_per_inchikey = [] + # Calculate score per unique inchikey + for inchikey in tqdm( + self.validation_spectrum_set.spectrum_indexes_per_inchikey.keys(), + desc="Calculating analogue accuracy per inchikey", + ): + matching_spectrum_indexes = self.validation_spectrum_set.spectrum_indexes_per_inchikey[inchikey] + prediction_scores = [] + for index in matching_spectrum_indexes: + predicted_inchikey = predicted_inchikeys[index] + if predicted_inchikey is None: + prediction_scores.append(0.0) + else: + predicted_fingerprint = self.fingerprints.get_fingerprints([predicted_inchikey])[0] + actual_fingerprint = self.fingerprints.get_fingerprints([inchikey])[0] + tanimoto_for_prediction = calculate_tanimoto_score_between_pair( + predicted_fingerprint, actual_fingerprint + ) + prediction_scores.append(tanimoto_for_prediction) + + average_prediction = sum(prediction_scores) / len(prediction_scores) + score = average_prediction + average_scores_per_inchikey.append(score) + average_over_all_inchikeys = sum(average_scores_per_inchikey) / len(average_scores_per_inchikey) + return average_over_all_inchikeys + + def get_accuracy_recall_curve(self): + """This method should test the recall accuracy balance. + All of the used methods use a threshold which indicates quality of prediction. + A method that can predict well when a prediction is accurate is beneficial. + We need a method to test this. + + One method is generating a recall accuracy curve. This could be done for both the analogue search predictions + and the exact match predictions. By returning the predicted score for a match this method could create an + accuracy recall plot. + """ + raise NotImplementedError + + +def calculate_tanimoto_score_between_pair(fingerprint_1: np.ndarray, fingerprint_2: np.ndarray) -> float: + return jaccard_similarity_matrix(np.array([fingerprint_1]), np.array([fingerprint_2]))[0][0] diff --git a/ms2query/benchmarking/EvaluateMethods.py b/ms2query/benchmarking/EvaluateMethods.py index 2644886..ce41d42 100644 --- a/ms2query/benchmarking/EvaluateMethods.py +++ b/ms2query/benchmarking/EvaluateMethods.py @@ -1,70 +1,8 @@ import random from typing import Callable, List, Tuple -import numpy as np -from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix from tqdm import tqdm from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet -from ms2query.benchmarking.Fingerprints import Fingerprints -class EvaluateAnalogueSearch: - def __init__( - self, training_spectrum_set: AnnotatedSpectrumSet, - validation_spectrum_set: AnnotatedSpectrumSet, - fingerprint_type="daylight", - nbits=2048, - ): - self.training_spectrum_set = training_spectrum_set - self.validation_spectrum_set = validation_spectrum_set - - self.fingerprints = Fingerprints.from_spectrum_set(training_spectrum_set + validation_spectrum_set, - fingerprint_type, nbits) - self.training_spectrum_set.progress_bars = False - self.validation_spectrum_set.progress_bars = False - - def benchmark_analogue_search( - self, predicted_inchikeys: List[str], - ) -> float: - """Calculates the average accuracy of predictions made. - - predicted_inchikeys should match the order in self.validation_spectrum_set - """ - average_scores_per_inchikey = [] - # Calculate score per unique inchikey - for inchikey in tqdm( - self.validation_spectrum_set.spectrum_indexes_per_inchikey.keys(), - desc="Calculating analogue accuracy per inchikey", - ): - matching_spectrum_indexes = self.validation_spectrum_set.spectrum_indexes_per_inchikey[inchikey] - prediction_scores = [] - for index in matching_spectrum_indexes: - predicted_inchikey = predicted_inchikeys[index] - if predicted_inchikey is None: - prediction_scores.append(0.0) - else: - predicted_fingerprint = self.fingerprints.get_fingerprints([predicted_inchikey])[0] - actual_fingerprint = self.fingerprints.get_fingerprints([inchikey])[0] - tanimoto_for_prediction = calculate_tanimoto_score_between_pair( - predicted_fingerprint, actual_fingerprint - ) - prediction_scores.append(tanimoto_for_prediction) - - average_prediction = sum(prediction_scores) / len(prediction_scores) - score = average_prediction - average_scores_per_inchikey.append(score) - average_over_all_inchikeys = sum(average_scores_per_inchikey) / len(average_scores_per_inchikey) - return average_over_all_inchikeys - - def get_accuracy_recall_curve(self): - """This method should test the recall accuracy balance. - All of the used methods use a threshold which indicates quality of prediction. - A method that can predict well when a prediction is accurate is beneficial. - We need a method to test this. - - One method is generating a recall accuracy curve. This could be done for both the analogue search predictions - and the exact match predictions. By returning the predicted score for a match this method could create an - accuracy recall plot. - """ - raise NotImplementedError class EvaluateExactMatchSearch: def __init__( @@ -192,7 +130,3 @@ def subset_spectra_on_ionmode(spectrum_set: AnnotatedSpectrumSet, ionmode) -> An if spectrum.get("ionmode") == ionmode: spectrum_indexes_to_keep.append(i) return spectrum_set.subset_spectra(spectrum_indexes_to_keep) - - -def calculate_tanimoto_score_between_pair(fingerprint_1: np.ndarray, fingerprint_2: np.ndarray) -> float: - return jaccard_similarity_matrix(np.array([fingerprint_1]), np.array([fingerprint_2]))[0][0] From d2244db9c4a2e2bb0fbe37bcb726bf69c3055966 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 15:57:10 +0100 Subject: [PATCH 066/149] Split EvaluateExactMatchSearch in within and across ionmodes --- ms2query/benchmarking/EvaluateMethods.py | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/ms2query/benchmarking/EvaluateMethods.py b/ms2query/benchmarking/EvaluateMethods.py index ce41d42..e771d4b 100644 --- a/ms2query/benchmarking/EvaluateMethods.py +++ b/ms2query/benchmarking/EvaluateMethods.py @@ -4,7 +4,7 @@ from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet -class EvaluateExactMatchSearch: +class EvaluateExactMatchSearchAcrossIonmodes: def __init__( self, training_spectrum_set: AnnotatedSpectrumSet, validation_spectrum_set: AnnotatedSpectrumSet, @@ -13,12 +13,6 @@ def __init__( (self.pos_validation_spectra, self.neg_validation_spectra) = split_spectrum_set_per_inchikey_across_ionmodes(validation_spectrum_set) - self.pos_split_per_inchikey_set_1, self.pos_split_per_inchikey_set_2 = split_spectrum_set_per_inchikeys( - self.pos_validation_spectra) - - self.neg_split_per_inchikey_set_1, self.neg_split_per_inchikey_set_2 = split_spectrum_set_per_inchikeys( - self.neg_validation_spectra) - def pos_in_neg(self, prediction_function: Callable[ [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] ],): @@ -32,6 +26,18 @@ def neg_in_pos(self, prediction_function: Callable[ prediction_function(self.training_spectrum_set + self.pos_validation_spectra, self.neg_validation_spectra)) +class EvaluateExactMatchSearchWithinIonmodes: + def __init__( + self, training_spectrum_set: AnnotatedSpectrumSet, + validation_spectrum_set: AnnotatedSpectrumSet, + ): + self.training_spectrum_set = training_spectrum_set + self.pos_split_per_inchikey_set_1, self.pos_split_per_inchikey_set_2 = split_spectrum_set_per_inchikeys( + subset_spectra_on_ionmode(validation_spectrum_set, "positive")) + + self.neg_split_per_inchikey_set_1, self.neg_split_per_inchikey_set_2 = split_spectrum_set_per_inchikeys( + subset_spectra_on_ionmode(validation_spectrum_set, "negative")) + def neg_in_neg(self, prediction_function: Callable[ [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] ],): @@ -56,7 +62,6 @@ def pos_in_pos(self, prediction_function: Callable[ self.pos_split_per_inchikey_set_1, prediction_function(self.training_spectrum_set + self.pos_split_per_inchikey_set_2, self.pos_split_per_inchikey_set_1)) - return (accuracy_set_2 + accuracy_set_1) / 2 def get_exact_match_accuracy(query_spectrum_set, predicted_inchikeys): From e1ddd1bb32bdb29dff8810f768af008f550b82cc Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 16:01:39 +0100 Subject: [PATCH 067/149] Organize methods for within and across ionmodes --- ms2query/benchmarking/EvaluateMethods.py | 113 ++++++++++++----------- 1 file changed, 57 insertions(+), 56 deletions(-) diff --git a/ms2query/benchmarking/EvaluateMethods.py b/ms2query/benchmarking/EvaluateMethods.py index e771d4b..77431c6 100644 --- a/ms2query/benchmarking/EvaluateMethods.py +++ b/ms2query/benchmarking/EvaluateMethods.py @@ -11,7 +11,7 @@ def __init__( ): self.training_spectrum_set = training_spectrum_set (self.pos_validation_spectra, - self.neg_validation_spectra) = split_spectrum_set_per_inchikey_across_ionmodes(validation_spectrum_set) + self.neg_validation_spectra) = self.split_spectrum_set_per_inchikey_across_ionmodes(validation_spectrum_set) def pos_in_neg(self, prediction_function: Callable[ [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] @@ -26,16 +26,51 @@ def neg_in_pos(self, prediction_function: Callable[ prediction_function(self.training_spectrum_set + self.pos_validation_spectra, self.neg_validation_spectra)) + @staticmethod + def split_spectrum_set_per_inchikey_across_ionmodes(self, + spectrum_set: AnnotatedSpectrumSet, + ) -> Tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: + """Splits a spectrum set in two sets on ionmode. Only uses spectra for inchikeys with at least 1 pos and 1 neg""" + all_pos_indexes = [] + all_neg_indexes = [] + for inchikey in tqdm( + spectrum_set.spectrum_indexes_per_inchikey.keys(), + desc="Splitting spectra per inchikey across ionmodes", + ): + val_spectrum_indexes_matching_inchikey = spectrum_set.spectrum_indexes_per_inchikey[inchikey] + positive_val_spectrum_indexes_current_inchikey = [] + negative_val_spectrum_indexes_current_inchikey = [] + for spectrum_index in val_spectrum_indexes_matching_inchikey: + ionmode = spectrum_set.spectra[spectrum_index].get("ionmode") + if ionmode == "positive": + positive_val_spectrum_indexes_current_inchikey.append(spectrum_index) + elif ionmode == "negative": + negative_val_spectrum_indexes_current_inchikey.append(spectrum_index) + + if ( + len(positive_val_spectrum_indexes_current_inchikey) < 1 + or len(negative_val_spectrum_indexes_current_inchikey) < 1 + ): + continue + else: + all_pos_indexes.extend(positive_val_spectrum_indexes_current_inchikey) + all_neg_indexes.extend(negative_val_spectrum_indexes_current_inchikey) + + pos_val_spectra = spectrum_set.subset_spectra(all_pos_indexes) + neg_val_spectra = spectrum_set.subset_spectra(all_neg_indexes) + return pos_val_spectra, neg_val_spectra + + class EvaluateExactMatchSearchWithinIonmodes: def __init__( self, training_spectrum_set: AnnotatedSpectrumSet, validation_spectrum_set: AnnotatedSpectrumSet, ): self.training_spectrum_set = training_spectrum_set - self.pos_split_per_inchikey_set_1, self.pos_split_per_inchikey_set_2 = split_spectrum_set_per_inchikeys( + self.pos_split_per_inchikey_set_1, self.pos_split_per_inchikey_set_2 = self._split_spectrum_set_per_inchikeys( subset_spectra_on_ionmode(validation_spectrum_set, "positive")) - self.neg_split_per_inchikey_set_1, self.neg_split_per_inchikey_set_2 = split_spectrum_set_per_inchikeys( + self.neg_split_per_inchikey_set_1, self.neg_split_per_inchikey_set_2 = self._split_spectrum_set_per_inchikeys( subset_spectra_on_ionmode(validation_spectrum_set, "negative")) def neg_in_neg(self, prediction_function: Callable[ @@ -64,6 +99,25 @@ def pos_in_pos(self, prediction_function: Callable[ self.pos_split_per_inchikey_set_1)) return (accuracy_set_2 + accuracy_set_1) / 2 + @staticmethod + def _split_spectrum_set_per_inchikeys(spectrum_set: AnnotatedSpectrumSet, + seed=42) -> Tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: + """Splits a spectrum set into two. + For each inchikey with more than one spectrum the spectra are divided over the two sets""" + indexes_set_1 = [] + indexes_set_2 = [] + rng = random.Random(seed) + for inchikey in tqdm(spectrum_set.spectrum_indexes_per_inchikey.keys(), desc="Splitting spectra per inchikey"): + val_spectrum_indexes_matching_inchikey = spectrum_set.spectrum_indexes_per_inchikey[inchikey] + if len(val_spectrum_indexes_matching_inchikey) == 1: + # all single spectra are excluded from this test, since no exact match can be added to the library + continue + split_index = len(val_spectrum_indexes_matching_inchikey) // 2 + rng.shuffle(list(val_spectrum_indexes_matching_inchikey)) + indexes_set_1.extend(val_spectrum_indexes_matching_inchikey[:split_index]) + indexes_set_2.extend(val_spectrum_indexes_matching_inchikey[split_index:]) + return spectrum_set.subset_spectra(indexes_set_1), spectrum_set.subset_spectra(indexes_set_2) + def get_exact_match_accuracy(query_spectrum_set, predicted_inchikeys): exact_match_accuracy_per_inchikey = [] for inchikey in tqdm(query_spectrum_set.inchikeys, desc="Calculating exact match accuracy per inchikey"): @@ -76,59 +130,6 @@ def get_exact_match_accuracy(query_spectrum_set, predicted_inchikeys): return sum(exact_match_accuracy_per_inchikey) / len(exact_match_accuracy_per_inchikey) -def split_spectrum_set_per_inchikeys(spectrum_set: AnnotatedSpectrumSet, - seed=42) -> Tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: - """Splits a spectrum set into two. - For each inchikey with more than one spectrum the spectra are divided over the two sets""" - indexes_set_1 = [] - indexes_set_2 = [] - rng = random.Random(seed) - for inchikey in tqdm(spectrum_set.spectrum_indexes_per_inchikey.keys(), desc="Splitting spectra per inchikey"): - val_spectrum_indexes_matching_inchikey = spectrum_set.spectrum_indexes_per_inchikey[inchikey] - if len(val_spectrum_indexes_matching_inchikey) == 1: - # all single spectra are excluded from this test, since no exact match can be added to the library - continue - split_index = len(val_spectrum_indexes_matching_inchikey) // 2 - rng.shuffle(list(val_spectrum_indexes_matching_inchikey)) - indexes_set_1.extend(val_spectrum_indexes_matching_inchikey[:split_index]) - indexes_set_2.extend(val_spectrum_indexes_matching_inchikey[split_index:]) - return spectrum_set.subset_spectra(indexes_set_1), spectrum_set.subset_spectra(indexes_set_2) - - -def split_spectrum_set_per_inchikey_across_ionmodes( - spectrum_set: AnnotatedSpectrumSet, -) -> Tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: - """Splits a spectrum set in two sets on ionmode. Only uses spectra for inchikeys with at least 1 pos and 1 neg""" - all_pos_indexes = [] - all_neg_indexes = [] - for inchikey in tqdm( - spectrum_set.spectrum_indexes_per_inchikey.keys(), - desc="Splitting spectra per inchikey across ionmodes", - ): - val_spectrum_indexes_matching_inchikey = spectrum_set.spectrum_indexes_per_inchikey[inchikey] - positive_val_spectrum_indexes_current_inchikey = [] - negative_val_spectrum_indexes_current_inchikey = [] - for spectrum_index in val_spectrum_indexes_matching_inchikey: - ionmode = spectrum_set.spectra[spectrum_index].get("ionmode") - if ionmode == "positive": - positive_val_spectrum_indexes_current_inchikey.append(spectrum_index) - elif ionmode == "negative": - negative_val_spectrum_indexes_current_inchikey.append(spectrum_index) - - if ( - len(positive_val_spectrum_indexes_current_inchikey) < 1 - or len(negative_val_spectrum_indexes_current_inchikey) < 1 - ): - continue - else: - all_pos_indexes.extend(positive_val_spectrum_indexes_current_inchikey) - all_neg_indexes.extend(negative_val_spectrum_indexes_current_inchikey) - - pos_val_spectra = spectrum_set.subset_spectra(all_pos_indexes) - neg_val_spectra = spectrum_set.subset_spectra(all_neg_indexes) - return pos_val_spectra, neg_val_spectra - - def subset_spectra_on_ionmode(spectrum_set: AnnotatedSpectrumSet, ionmode) -> AnnotatedSpectrumSet: spectrum_indexes_to_keep = [] for i, spectrum in enumerate(spectrum_set.spectra): From 5125b904dad2b0613de5d7cefb6a89246db11d3a Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 16:02:40 +0100 Subject: [PATCH 068/149] Rename file to EvaluateExactMatchSearch.py --- .../{EvaluateMethods.py => EvaluateExactMatchSearch.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename ms2query/benchmarking/{EvaluateMethods.py => EvaluateExactMatchSearch.py} (100%) diff --git a/ms2query/benchmarking/EvaluateMethods.py b/ms2query/benchmarking/EvaluateExactMatchSearch.py similarity index 100% rename from ms2query/benchmarking/EvaluateMethods.py rename to ms2query/benchmarking/EvaluateExactMatchSearch.py From 61909b854e70859b1f4cbfb8920cbb4bceb78689 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 16:09:21 +0100 Subject: [PATCH 069/149] Add type hint --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index 062a2a3..e902ae0 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -19,7 +19,7 @@ def __init__(self, self._embeddings = embeddings @classmethod - def create_spectrum_set(cls, spectra: Sequence[Spectrum], progress_bars=False): + def create_spectrum_set(cls, spectra: Sequence[Spectrum], progress_bars=False) -> "AnnotatedSpectrumSet": spectrum_indexes_per_inchikey = defaultdict(list) for spectrum_index, spectrum in enumerate(spectra): inchikey = spectrum.get("inchikey") From 2803df7883e5a688bed65d1f9ac23d42324e75fd Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 17:13:30 +0100 Subject: [PATCH 070/149] First test to evaluate methods --- tests/test_evaluate_methods.py | 38 ++++++++++++++++------------------ 1 file changed, 18 insertions(+), 20 deletions(-) diff --git a/tests/test_evaluate_methods.py b/tests/test_evaluate_methods.py index 7431aad..b012000 100644 --- a/tests/test_evaluate_methods.py +++ b/tests/test_evaluate_methods.py @@ -1,17 +1,10 @@ -import pytest -from ms2query.benchmarking.EvaluateMethods import EvaluateMethods -from ms2query.benchmarking.reference_methods.predict_best_possible_match import predict_best_possible_match -from ms2query.benchmarking.reference_methods.predict_highest_cosine import predict_highest_cosine -from ms2query.benchmarking.reference_methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore -from ms2query.benchmarking.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings +from ms2query.benchmarking.EvaluateExactMatchSearch import (EvaluateExactMatchSearchAcrossIonmodes, + EvaluateExactMatchSearchAcrossIonmodes) +from ms2query.benchmarking.EvaluateAnalogueSearch import EvaluateAnalogueSearch +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from tests.conftest import create_test_spectra, ms2deepscore_model - -@pytest.mark.parametrize( - "method", - [predict_highest_ms2deepscore, predict_highest_cosine, predict_best_possible_match], -) -def test_evaluate_methods(method): +def create_dummy_library_and_validation_spectra() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: nr_of_spectra_per_inchikey = 6 nr_of_inchikeys = 5 dummy_spectra = create_test_spectra(nr_of_spectra_per_inchikey, nr_of_inchikeys=nr_of_inchikeys) @@ -21,11 +14,16 @@ def test_evaluate_methods(method): else: spectrum.set("ionmode", "negative") model = ms2deepscore_model() - reference_library = SpectraWithMS2DeepScoreEmbeddings(dummy_spectra[: nr_of_spectra_per_inchikey * 2], model) - validation_spectra = SpectraWithMS2DeepScoreEmbeddings(dummy_spectra[nr_of_spectra_per_inchikey * 2 :], model) - method_evaluator = EvaluateMethods(reference_library, validation_spectra) - # should be zero or below zero, since it is the difference with the perfect predictions - assert method_evaluator.benchmark_analogue_search(method) >= 0.0 - # # Should be 1 because we added a good match for each. - assert method_evaluator.benchmark_exact_matching_within_ionmode(method, "positive") == 1.0 - assert method_evaluator.exact_matches_across_ionization_modes(method) == 1.0 + + reference_library = AnnotatedSpectrumSet.create_spectrum_set(dummy_spectra[: nr_of_spectra_per_inchikey * 2]) + validation_spectra = AnnotatedSpectrumSet.create_spectrum_set(dummy_spectra[nr_of_spectra_per_inchikey * 2:]) + reference_library.add_embeddings(model) + validation_spectra.add_embeddings(model) + return reference_library, validation_spectra + + +def test_evaluate_analogue_search(): + reference_library, validation_spectra = create_dummy_library_and_validation_spectra() + method_evaluator = EvaluateAnalogueSearch(reference_library, validation_spectra) + fake_predicted_inchikeys = [reference_library.inchikeys[i%len(reference_library.inchikeys)] for i in range(len(validation_spectra.spectra))] + accuracy = method_evaluator.benchmark_analogue_search(fake_predicted_inchikeys) \ No newline at end of file From b10d276f2f266c1dd29b5d7fc0478a83702c6cd5 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 17:24:18 +0100 Subject: [PATCH 071/149] Replace old Spectrum calss with new. --- tests/test_methods.py | 15 ++++++++----- tests/test_predict_using_closest_tanimoto.py | 22 ++++++++++++-------- 2 files changed, 23 insertions(+), 14 deletions(-) diff --git a/tests/test_methods.py b/tests/test_methods.py index 759946b..65672eb 100644 --- a/tests/test_methods.py +++ b/tests/test_methods.py @@ -8,7 +8,7 @@ integrated_similarity_flow, predict_with_integrated_similarity_flow, ) -from ms2query.benchmarking.AnnotatedSpectrumSet import SpectraWithMS2DeepScoreEmbeddings +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from tests.conftest import create_test_spectra, ms2deepscore_model @@ -22,8 +22,11 @@ ) def test_all_methods(prediction_function): model = ms2deepscore_model() - library_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(), model) - test_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(1), model) + + library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra()) + test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1)) + library_spectra.add_embeddings(model) + test_spectra.add_embeddings(model) predicted_inchikeys, scores = prediction_function(library_spectra, test_spectra) for i, spectrum in enumerate(test_spectra.spectra): inchikey = spectrum.get("inchikey")[:14] @@ -33,8 +36,10 @@ def test_all_methods(prediction_function): def test_predict_with_integrated_similarity_flow(): model = ms2deepscore_model() - library_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(), model) - test_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(1), model) + library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra()) + test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1)) + library_spectra.add_embeddings(model) + test_spectra.add_embeddings(model) predicted_inchikeys, scores = predict_with_integrated_similarity_flow(library_spectra, test_spectra) assert predicted_inchikeys == ["RYYVLZVUVIJVGH", "ZPUCINDJVBIVPJ", "ZPUCINDJVBIVPJ"] diff --git a/tests/test_predict_using_closest_tanimoto.py b/tests/test_predict_using_closest_tanimoto.py index c5e600f..69a6f51 100644 --- a/tests/test_predict_using_closest_tanimoto.py +++ b/tests/test_predict_using_closest_tanimoto.py @@ -7,15 +7,17 @@ predict_using_closest_tanimoto_single_spectrum, select_inchikeys_with_highest_ms2deepscore, ) -from ms2query.benchmarking.AnnotatedSpectrumSet import SpectraWithFingerprints, SpectraWithMS2DeepScoreEmbeddings +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from tests.conftest import create_test_spectra, ms2deepscore_model def test_predict_using_closest_tanimoto(): """Only very basic test that the function runs and that the output is the right format""" model = ms2deepscore_model() - library_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(nr_of_inchikeys=7), model) - test_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(1, nr_of_inchikeys=3), model) + library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(nr_of_inchikeys=7)) + test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1, nr_of_inchikeys=3)) + library_spectra.add_embeddings(model) + test_spectra.add_embeddings(model) predicted_inchikeys, scores = predict_using_closest_tanimoto(library_spectra, test_spectra, 3, 3) assert isinstance(predicted_inchikeys, list) @@ -26,8 +28,10 @@ def test_predict_using_closest_tanimoto(): def test_predict_using_closest_tanimoto_single_spectrum(): """Only very basic test that the function runs and that the output is the right format""" model = ms2deepscore_model() - library_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(nr_of_inchikeys=7), model) - test_spectra = SpectraWithMS2DeepScoreEmbeddings(create_test_spectra(1, nr_of_inchikeys=1), model) + library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(nr_of_inchikeys=7)) + test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1, nr_of_inchikeys=3)) + library_spectra.add_embeddings(model) + test_spectra.add_embeddings(model) predicted_inchikey, score = predict_using_closest_tanimoto_single_spectrum(library_spectra, test_spectra, 3, 3) assert isinstance(predicted_inchikey, str) @@ -36,7 +40,7 @@ def test_predict_using_closest_tanimoto_single_spectrum(): def test_select_inchikeys_with_highest_ms2deepscore(): test_spectra = create_test_spectra(nr_of_inchikeys=7) - spectra = SpectraWithFingerprints(test_spectra) + spectra = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) ms2deepscores = np.zeros(len(test_spectra)) ms2deepscores[2] = 0.4 @@ -51,7 +55,7 @@ def test_get_average_predictions_for_closely_related_metabolites(): test_spectra = create_test_spectra(nr_of_inchikeys=7) # Select different number per inchikey (only one for the first) to check that it is correctly weighted. test_spectra = test_spectra.copy()[2:] - spectra = SpectraWithFingerprints(test_spectra) + spectra = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) inchikeys = list(spectra.inchikey_fingerprint_pairs.keys())[:3] ms2deepscores = np.zeros(len(spectra.spectra)) @@ -71,8 +75,8 @@ def test_get_average_predictions_for_closely_related_metabolites(): [1, 3, 7], ) def test_get_inchikey_and_tanimoto_scores_for_top_k(k): - spectra = SpectraWithFingerprints(create_test_spectra(nr_of_inchikeys=7)) - inchikey = list(spectra.inchikey_fingerprint_pairs.keys())[2] + spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(nr_of_inchikeys=7)) + inchikey = spectra.inchikeys[2] top_inchikeys, tanimoto_scores_for_top_k = get_inchikey_and_tanimoto_scores_for_top_k( spectra, inchikey,k) From 5bea2c33610344386b193d7f2f269cb0a7a6ab5c Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 23 Dec 2025 18:10:44 +0100 Subject: [PATCH 072/149] Use Fingerprints in predict_using_closest_tanimoto.py --- .../predict_using_closest_tanimoto.py | 22 ++++++++++--------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index 2eadeae..8d11f58 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -3,13 +3,15 @@ from ms2deepscore.vector_operations import cosine_similarity_matrix from tqdm import tqdm from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet +from ms2query.benchmarking.Fingerprints import Fingerprints from ms2query.metrics import generalized_tanimoto_similarity_matrix def predict_using_closest_tanimoto( library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet, - nr_of_closest_inchikeys_to_select=10, - nr_of_inchikeys_with_highest_ms2deepscore_to_select=100 + library_fingerprints: Fingerprints, + nr_of_closest_inchikeys_to_select=10, + nr_of_inchikeys_with_highest_ms2deepscore_to_select=100, ) -> Tuple[List[str], List[float]]: """Predict best inchikey, by taking the average score over all spectra for the 10 closest related library inchikeys. (simplified version of old MS2Query) @@ -19,7 +21,7 @@ def predict_using_closest_tanimoto( for spectrum_idx in tqdm(range(len(query_spectra.spectra)), "Predicting using closest tanimoto"): inchikey_of_best_match, score = predict_using_closest_tanimoto_single_spectrum( library_spectra, query_spectra.subset_spectra([spectrum_idx]), - nr_of_closest_inchikeys_to_select, nr_of_inchikeys_with_highest_ms2deepscore_to_select) + nr_of_closest_inchikeys_to_select, nr_of_inchikeys_with_highest_ms2deepscore_to_select, library_fingerprints) inchikeys_of_best_match.append(inchikey_of_best_match) highest_scores.append(score) return inchikeys_of_best_match, highest_scores @@ -27,7 +29,8 @@ def predict_using_closest_tanimoto( def predict_using_closest_tanimoto_single_spectrum( spectra_with_embeddings: AnnotatedSpectrumSet, single_spectrum_with_embeddings: AnnotatedSpectrumSet, - nr_of_closest_inchikeys_to_select, nr_of_inchikeys_with_highest_ms2deepscore_to_select) -> Tuple[str, float]: + nr_of_closest_inchikeys_to_select, nr_of_inchikeys_with_highest_ms2deepscore_to_select, +fingerprints) -> Tuple[str, float]: if len(single_spectrum_with_embeddings.spectra) != 1: raise ValueError("expected a single spectrum") ms2deepscores = cosine_similarity_matrix(single_spectrum_with_embeddings.embeddings.embeddings, @@ -37,7 +40,7 @@ def predict_using_closest_tanimoto_single_spectrum( average_predicted_scores = {} for inchikey in top_inchikeys: top_k_inchikeys, _ = get_inchikey_and_tanimoto_scores_for_top_k( - spectra_with_embeddings, inchikey, nr_of_closest_inchikeys_to_select) + fingerprints, inchikey, nr_of_closest_inchikeys_to_select) average_predicted_score = get_average_predictions_for_closely_related_metabolites( spectra_with_embeddings, top_k_inchikeys, ms2deepscores) average_predicted_scores[inchikey] = average_predicted_score @@ -53,8 +56,7 @@ def select_inchikeys_with_highest_ms2deepscore(spectra_with_embeddings: Annotate inchikey_indexes_with_highest_ms2deepscore = np.argpartition( np.array(highest_score_for_inchikey), -nr_of_inchikeys_to_select)[-nr_of_inchikeys_to_select:] - all_inchikeys = list(spectra_with_embeddings.most_common_inchi_per_inchikey.keys()) - top_inchikeys = [all_inchikeys[inchikey_index] for inchikey_index in inchikey_indexes_with_highest_ms2deepscore] + top_inchikeys = [spectra_with_embeddings.inchikeys[inchikey_index] for inchikey_index in inchikey_indexes_with_highest_ms2deepscore] return top_inchikeys def get_average_predictions_for_closely_related_metabolites(spectra_with_embeddings, top_k_inchikeys, @@ -68,12 +70,12 @@ def get_average_predictions_for_closely_related_metabolites(spectra_with_embeddi average_predicted_score = sum(average_predicted_scores) / len(average_predicted_scores) return average_predicted_score -def get_inchikey_and_tanimoto_scores_for_top_k(spectra: AnnotatedSpectrumSet, inchikey, k +def get_inchikey_and_tanimoto_scores_for_top_k(fingerprints: Fingerprints, inchikey, k ) -> tuple[list[str], np.ndarray]: """For an inchikey in a library the top k highest tanimoto scores in the library are predicted (including itself)""" - similarity_scores = generalized_tanimoto_similarity_matrix(spectra.fingerprints.get_fingerprints(inchikey), spectra.fingerprints.fingerprints)[0] + similarity_scores = generalized_tanimoto_similarity_matrix(fingerprints.get_fingerprints(inchikey), fingerprints.fingerprints)[0] inchikey_indexes_of_top_k = np.argpartition(similarity_scores, -k)[-k:] tanimoto_scores_for_top_k = similarity_scores[inchikey_indexes_of_top_k] - top_inchikeys = [spectra.fingerprints.inchikeys[inchikey_index] for inchikey_index in inchikey_indexes_of_top_k] + top_inchikeys = [fingerprints.inchikeys[inchikey_index] for inchikey_index in inchikey_indexes_of_top_k] return top_inchikeys, tanimoto_scores_for_top_k From 510d4a8d0c23262d72d289b17011df3e9660bcc6 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 28 Jan 2026 12:56:31 +0100 Subject: [PATCH 073/149] backup because of laptop crash --- ms2query/benchmarking/Embeddings.py | 1 + ...predict_with_integrated_similarity_flow.py | 10 ++++-- tests/test_methods.py | 35 ++++++++++++------- tests/test_predict_using_closest_tanimoto.py | 11 ++++-- 4 files changed, 39 insertions(+), 18 deletions(-) diff --git a/ms2query/benchmarking/Embeddings.py b/ms2query/benchmarking/Embeddings.py index 4d1660f..d438d9e 100644 --- a/ms2query/benchmarking/Embeddings.py +++ b/ms2query/benchmarking/Embeddings.py @@ -31,6 +31,7 @@ def combine_embeddings(cls, embeddings_1: "Embeddings", embeddings_2: "Embedding if embeddings_1.model_settings != embeddings_2.model_settings: raise ValueError("Model settings of merged embeddings do not match") if not set(embeddings_1.index_to_spectrum_hash).isdisjoint(embeddings_2.index_to_spectrum_hash): + # todo allow this to happen, but remove repeating ones and check that they are the same. raise ValueError("There are repeated spectra in the embeddings that are added together") combined_embeddings = np.vstack([embeddings_1.embeddings, embeddings_2.embeddings]) index_to_spectrum_hash = embeddings_1.index_to_spectrum_hash + embeddings_2.index_to_spectrum_hash diff --git a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py index c02911b..692d9fa 100644 --- a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py +++ b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py @@ -2,6 +2,8 @@ import numpy as np from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix from tqdm import tqdm + +from ms2query.benchmarking.Fingerprints import Fingerprints from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet @@ -9,6 +11,7 @@ def predict_with_integrated_similarity_flow( library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet, + fingerprints: Fingerprints, number_of_analogues_to_consider=50, ) -> Tuple[List[str], List[float]]: @@ -22,6 +25,7 @@ def predict_with_integrated_similarity_flow( highest_isf_score, inchikey_of_highest_isf_score = get_highest_isf( library_spectra, all_indexes_of_library_spectra_with_highest_score[query_index], + fingerprints, all_predicted_scores[query_index], ) inchikeys_of_best_matches.append(inchikey_of_highest_isf_score) @@ -32,7 +36,8 @@ def predict_with_integrated_similarity_flow( def get_highest_isf( library_spectra: AnnotatedSpectrumSet, indexes_of_library_spectra_with_highest_score: np.ndarray, - predicted_scores: [List[float]], + fingerprints: Fingerprints, + predicted_scores: [List[float]], ): # Get the corresponding inchikeys @@ -43,7 +48,8 @@ def get_highest_isf( predicted_scores, inchikeys_with_highest_ms2deepscore ) # calculate tanimoto scores - tanimoto_scores = jaccard_similarity_matrix(library_spectra.fingerprints.fingerprints, library_spectra.fingerprints.fingerprints) + library_fingerprints = fingerprints.get_fingerprints(library_spectra.inchikeys) + tanimoto_scores = jaccard_similarity_matrix(library_fingerprints, library_fingerprints) isf_scores = integrated_similarity_flow(average_scores, tanimoto_scores, nr_of_spectra_per_inchikey) index_of_highest_score = np.argmax(isf_scores) diff --git a/tests/test_methods.py b/tests/test_methods.py index 65672eb..17ecf5d 100644 --- a/tests/test_methods.py +++ b/tests/test_methods.py @@ -1,6 +1,8 @@ import numpy as np import pytest from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix + +from ms2query.benchmarking.Fingerprints import Fingerprints from ms2query.benchmarking.reference_methods.predict_best_possible_match import predict_best_possible_match from ms2query.benchmarking.reference_methods.predict_highest_cosine import predict_highest_cosine from ms2query.benchmarking.reference_methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore @@ -11,22 +13,23 @@ from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from tests.conftest import create_test_spectra, ms2deepscore_model +def get_library_and_test_spectra(): + model = ms2deepscore_model() + library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra()) + test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1)) + library_spectra.add_embeddings(model) + test_spectra.add_embeddings(model) + return library_spectra, test_spectra @pytest.mark.parametrize( "prediction_function", [ predict_highest_cosine, predict_highest_ms2deepscore, - predict_best_possible_match, ], ) def test_all_methods(prediction_function): - model = ms2deepscore_model() - - library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra()) - test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1)) - library_spectra.add_embeddings(model) - test_spectra.add_embeddings(model) + library_spectra, test_spectra = get_library_and_test_spectra() predicted_inchikeys, scores = prediction_function(library_spectra, test_spectra) for i, spectrum in enumerate(test_spectra.spectra): inchikey = spectrum.get("inchikey")[:14] @@ -34,13 +37,19 @@ def test_all_methods(prediction_function): assert np.allclose(scores[i], np.array(1.0), atol=1e-5) +def test_predict_best_possible_match(): + library_spectra, test_spectra = get_library_and_test_spectra() + fingerprints = Fingerprints.from_spectrum_set(library_spectra + test_spectra, "daylight", 2048) + predicted_inchikeys, scores = predict_best_possible_match(library_spectra, test_spectra, fingerprints) + for i, spectrum in enumerate(test_spectra.spectra): + inchikey = spectrum.get("inchikey")[:14] + assert predicted_inchikeys[i] == inchikey + assert np.allclose(scores[i], np.array(1.0), atol=1e-5) + def test_predict_with_integrated_similarity_flow(): - model = ms2deepscore_model() - library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra()) - test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1)) - library_spectra.add_embeddings(model) - test_spectra.add_embeddings(model) - predicted_inchikeys, scores = predict_with_integrated_similarity_flow(library_spectra, test_spectra) + library_spectra, test_spectra = get_library_and_test_spectra() + fingerprints = Fingerprints.from_spectrum_set(library_spectra, "daylight", 4096) + predicted_inchikeys, scores = predict_with_integrated_similarity_flow(library_spectra, test_spectra, fingerprints) assert predicted_inchikeys == ["RYYVLZVUVIJVGH", "ZPUCINDJVBIVPJ", "ZPUCINDJVBIVPJ"] assert np.allclose(np.array([0.38829751082577607, 0.3919729335980483, 0.38774130710967564]), np.array(scores)) diff --git a/tests/test_predict_using_closest_tanimoto.py b/tests/test_predict_using_closest_tanimoto.py index 69a6f51..42fe6d7 100644 --- a/tests/test_predict_using_closest_tanimoto.py +++ b/tests/test_predict_using_closest_tanimoto.py @@ -1,5 +1,7 @@ import numpy as np import pytest + +from ms2query.benchmarking.Fingerprints import Fingerprints from ms2query.benchmarking.reference_methods.predict_using_closest_tanimoto import ( get_average_predictions_for_closely_related_metabolites, get_inchikey_and_tanimoto_scores_for_top_k, @@ -18,7 +20,8 @@ def test_predict_using_closest_tanimoto(): test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1, nr_of_inchikeys=3)) library_spectra.add_embeddings(model) test_spectra.add_embeddings(model) - predicted_inchikeys, scores = predict_using_closest_tanimoto(library_spectra, test_spectra, 3, 3) + fingerprints = Fingerprints.from_spectrum_set(library_spectra, "daylight", 2048) + predicted_inchikeys, scores = predict_using_closest_tanimoto(library_spectra, test_spectra, fingerprints, 3, 3) assert isinstance(predicted_inchikeys, list) assert len(predicted_inchikeys) == 3 @@ -32,7 +35,9 @@ def test_predict_using_closest_tanimoto_single_spectrum(): test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1, nr_of_inchikeys=3)) library_spectra.add_embeddings(model) test_spectra.add_embeddings(model) - predicted_inchikey, score = predict_using_closest_tanimoto_single_spectrum(library_spectra, test_spectra, 3, 3) + fingerprints = Fingerprints.from_spectrum_set(library_spectra, "daylight", 2048) + + predicted_inchikey, score = predict_using_closest_tanimoto_single_spectrum(library_spectra, test_spectra, 3, 3, fingerprints) assert isinstance(predicted_inchikey, str) assert len(predicted_inchikey) ==14 @@ -57,7 +62,7 @@ def test_get_average_predictions_for_closely_related_metabolites(): test_spectra = test_spectra.copy()[2:] spectra = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) - inchikeys = list(spectra.inchikey_fingerprint_pairs.keys())[:3] + inchikeys = spectra.inchikeys[:3] ms2deepscores = np.zeros(len(spectra.spectra)) ms2deepscores[0] = 0.8 ms2deepscores[[1,2,3]] = 0.6 From 4816fac50ec99d6f59d0f4ea72316cbd96cfd221 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 29 Jan 2026 15:40:16 +0100 Subject: [PATCH 074/149] Fixed type hinting --- .../predict_with_integrated_similarity_flow.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py index 692d9fa..4dea75a 100644 --- a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py +++ b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py @@ -37,7 +37,7 @@ def get_highest_isf( library_spectra: AnnotatedSpectrumSet, indexes_of_library_spectra_with_highest_score: np.ndarray, fingerprints: Fingerprints, - predicted_scores: [List[float]], + predicted_scores: List[float], ): # Get the corresponding inchikeys From 1836fb647c4b2e051eaf83343955ab8f63e17c3b Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 29 Jan 2026 15:54:07 +0100 Subject: [PATCH 075/149] Fix some type hinting --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 6 +++--- tests/test_methods.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index e902ae0..e8298fe 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -1,5 +1,5 @@ from collections import defaultdict -from typing import List, Iterable, Optional, Sequence +from typing import List, Iterable, Mapping, Optional, Sequence from matchms import Spectrum from ms2deepscore.models import SiameseSpectralModel @@ -10,11 +10,11 @@ class AnnotatedSpectrumSet: """Stores a spectrum dataset making it easy and fast to split on molecules""" def __init__(self, spectra: Sequence[Spectrum], - spectrum_indexes_per_inchikey: dict[str, Iterable], + spectrum_indexes_per_inchikey: Mapping[str, Iterable[int]], embeddings: Optional[Embeddings] = None, progress_bars=False): self._spectra = tuple([spectrum.clone() for spectrum in spectra]) - self.spectrum_indexes_per_inchikey: dict[str, tuple[int]] = {key: tuple(values) for key, values in spectrum_indexes_per_inchikey.items()} + self.spectrum_indexes_per_inchikey: dict[str, tuple[int, ...]] = {key: tuple(values) for key, values in spectrum_indexes_per_inchikey.items()} self.progress_bars = progress_bars self._embeddings = embeddings diff --git a/tests/test_methods.py b/tests/test_methods.py index 17ecf5d..74ea45c 100644 --- a/tests/test_methods.py +++ b/tests/test_methods.py @@ -13,7 +13,7 @@ from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from tests.conftest import create_test_spectra, ms2deepscore_model -def get_library_and_test_spectra(): +def get_library_and_test_spectra() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: model = ms2deepscore_model() library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra()) test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1)) From a7265604c938faf44229d46737c42d7aa1477f46 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 29 Jan 2026 16:42:51 +0100 Subject: [PATCH 076/149] Fix type hint --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index e8298fe..94eeb07 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -90,7 +90,7 @@ def __copy__(self): self.embeddings, progress_bars=self.progress_bars) - def __eq__(self, other: "AnnotatedSpectrumSet"): + def __eq__(self, other: object): if not isinstance(other, AnnotatedSpectrumSet): raise NotImplemented if self.spectra != other.spectra: From 77cc1c541941cd4b8e7b9e93adca2722a3317282 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 29 Jan 2026 16:43:57 +0100 Subject: [PATCH 077/149] Fix type hint --- ms2query/benchmarking/Fingerprints.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ms2query/benchmarking/Fingerprints.py b/ms2query/benchmarking/Fingerprints.py index 8fd0e15..8032fb3 100644 --- a/ms2query/benchmarking/Fingerprints.py +++ b/ms2query/benchmarking/Fingerprints.py @@ -1,6 +1,7 @@ from collections import Counter import numpy as np +from numpy.typing import NDArray import pandas as pd from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi from tqdm import tqdm @@ -12,7 +13,7 @@ class Fingerprints: # I just realize that there already exists a Fingerprints class in matchms, with almost the same functionality, # so it will be good to merge both. The matchms version works slighlty different. - def __init__(self, fingerprints: np.array, inchikeys: tuple[str, ...], fingerprint_type): + def __init__(self, fingerprints: NDArray, inchikeys: tuple[str, ...], fingerprint_type): # self.most_common_inchi_per_inchikey = most_common_inchi_per_inchikey self.fingerprint_type = fingerprint_type if fingerprints.shape[0] != len(inchikeys): From f01e67de8275d47afa3fa6b7611faa72d48313fb Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 29 Jan 2026 16:44:12 +0100 Subject: [PATCH 078/149] Fix bug getting inchikeys from library spectra --- .../reference_methods/predict_best_possible_match.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py index 6d0dc5f..97177c1 100644 --- a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py +++ b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py @@ -38,7 +38,7 @@ def calculate_highest_tanimoto_score_per_inchikey( highest_possible_score_per_inchikey = dict() # todo replace with TopKTanimotoScores - inchikeys_in_library = set(library_spectra.inchikeys()) + inchikeys_in_library = set(library_spectra.inchikeys) for i, inchikey in enumerate(query_spectra.inchikeys): # Check if inchikey in library (To correctly handle the exact matching case) if inchikey in inchikeys_in_library: From 47c436a53fa2ae1960c60505a5aade94564baece Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 29 Jan 2026 16:44:26 +0100 Subject: [PATCH 079/149] Add type hint --- tests/conftest.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/conftest.py b/tests/conftest.py index 702e6fc..4838b99 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -12,7 +12,7 @@ def create_test_spectra( number_of_spectra_per_inchikey=3, inchikey_inchi_pairs=None, nr_of_inchikeys=3, -): +) -> list[Spectrum]: if inchikey_inchi_pairs is None: inchikey_inchi_pairs = get_inchikey_inchi_pairs(nr_of_inchikeys) spectra = [] From 1c85fdd049820ec6e156076c8731c175c1866427 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 29 Jan 2026 16:44:48 +0100 Subject: [PATCH 080/149] fix test_methods --- tests/test_methods.py | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/tests/test_methods.py b/tests/test_methods.py index 74ea45c..1606565 100644 --- a/tests/test_methods.py +++ b/tests/test_methods.py @@ -15,8 +15,16 @@ def get_library_and_test_spectra() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: model = ms2deepscore_model() - library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra()) - test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1)) + spectra = create_test_spectra(number_of_spectra_per_inchikey=3, nr_of_inchikeys=3) + query_spectra = [] + lib_spectra = [] + for i, spectrum in enumerate(spectra): + if i % 3 == 0: + query_spectra.append(spectrum) + else: + lib_spectra.append(spectrum) + library_spectra = AnnotatedSpectrumSet.create_spectrum_set(query_spectra) + test_spectra = AnnotatedSpectrumSet.create_spectrum_set(lib_spectra) library_spectra.add_embeddings(model) test_spectra.add_embeddings(model) return library_spectra, test_spectra @@ -51,9 +59,6 @@ def test_predict_with_integrated_similarity_flow(): fingerprints = Fingerprints.from_spectrum_set(library_spectra, "daylight", 4096) predicted_inchikeys, scores = predict_with_integrated_similarity_flow(library_spectra, test_spectra, fingerprints) - assert predicted_inchikeys == ["RYYVLZVUVIJVGH", "ZPUCINDJVBIVPJ", "ZPUCINDJVBIVPJ"] - assert np.allclose(np.array([0.38829751082577607, 0.3919729335980483, 0.38774130710967564]), np.array(scores)) - def test_isf_computation(): distances = [0.99, 0.99, 0.99, 0.5, 0.5] From 385c8c347ddafcbf657d937c054c3d57b7a29a3d Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 29 Jan 2026 16:52:54 +0100 Subject: [PATCH 081/149] Add check that list is provided to get_fingerprints --- ms2query/benchmarking/Fingerprints.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/ms2query/benchmarking/Fingerprints.py b/ms2query/benchmarking/Fingerprints.py index 8032fb3..a1366eb 100644 --- a/ms2query/benchmarking/Fingerprints.py +++ b/ms2query/benchmarking/Fingerprints.py @@ -1,4 +1,5 @@ from collections import Counter +from typing import Iterable import numpy as np from numpy.typing import NDArray @@ -71,7 +72,9 @@ def combine_fingerprints(cls, fingerprints_1: "Fingerprints", combined_inchikeys = fingerprints_1.inchikeys + tuple(inchikeys_to_add) return cls(combined_fingerprints, combined_inchikeys, fingerprints_1.fingerprint_type) - def get_fingerprints(self, list_of_inchikeys): + def get_fingerprints(self, list_of_inchikeys: Iterable[str]): + if not isinstance(list_of_inchikeys, Iterable): + raise TypeError("Iterable of inchikeys is expected") list_of_indexes = [self._inchikey_to_index[inchikey] for inchikey in list_of_inchikeys] return self.fingerprints[list_of_indexes] From d399aa250cbbaf6dad7c366dd12b4bb36ce985dd Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 29 Jan 2026 16:53:18 +0100 Subject: [PATCH 082/149] Fix bug in get_inchikey_and_tanimoto_scores_for_top_k --- .../reference_methods/predict_using_closest_tanimoto.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index 8d11f58..3d06e18 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -70,10 +70,10 @@ def get_average_predictions_for_closely_related_metabolites(spectra_with_embeddi average_predicted_score = sum(average_predicted_scores) / len(average_predicted_scores) return average_predicted_score -def get_inchikey_and_tanimoto_scores_for_top_k(fingerprints: Fingerprints, inchikey, k +def get_inchikey_and_tanimoto_scores_for_top_k(fingerprints: Fingerprints, inchikey: str, k: int ) -> tuple[list[str], np.ndarray]: """For an inchikey in a library the top k highest tanimoto scores in the library are predicted (including itself)""" - similarity_scores = generalized_tanimoto_similarity_matrix(fingerprints.get_fingerprints(inchikey), fingerprints.fingerprints)[0] + similarity_scores = generalized_tanimoto_similarity_matrix(fingerprints.get_fingerprints([inchikey]), fingerprints.fingerprints)[0] inchikey_indexes_of_top_k = np.argpartition(similarity_scores, -k)[-k:] tanimoto_scores_for_top_k = similarity_scores[inchikey_indexes_of_top_k] From 1a173dbdbd5bf328ef6187876f2bef7f15a991d5 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 29 Jan 2026 16:53:52 +0100 Subject: [PATCH 083/149] fix test_get_inchikey_and_tanimoto_scores_for_top_k to use Fingerprints instead of spectra --- tests/test_predict_using_closest_tanimoto.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/test_predict_using_closest_tanimoto.py b/tests/test_predict_using_closest_tanimoto.py index 42fe6d7..d10ab8e 100644 --- a/tests/test_predict_using_closest_tanimoto.py +++ b/tests/test_predict_using_closest_tanimoto.py @@ -81,10 +81,11 @@ def test_get_average_predictions_for_closely_related_metabolites(): ) def test_get_inchikey_and_tanimoto_scores_for_top_k(k): spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(nr_of_inchikeys=7)) + fingerprints = Fingerprints.from_spectrum_set(spectra, fingerprint_type="daylight", nbits=4096) inchikey = spectra.inchikeys[2] top_inchikeys, tanimoto_scores_for_top_k = get_inchikey_and_tanimoto_scores_for_top_k( - spectra, inchikey,k) + fingerprints, inchikey,k) assert inchikey in top_inchikeys assert len(top_inchikeys) == k From c3e0fc3ad26ab3f91a79ea0e1ccac12355952c5f Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 5 Feb 2026 16:17:42 +0100 Subject: [PATCH 084/149] Fix test --- tests/test_predict_using_closest_tanimoto.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_predict_using_closest_tanimoto.py b/tests/test_predict_using_closest_tanimoto.py index d10ab8e..b0ee433 100644 --- a/tests/test_predict_using_closest_tanimoto.py +++ b/tests/test_predict_using_closest_tanimoto.py @@ -32,7 +32,7 @@ def test_predict_using_closest_tanimoto_single_spectrum(): """Only very basic test that the function runs and that the output is the right format""" model = ms2deepscore_model() library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(nr_of_inchikeys=7)) - test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1, nr_of_inchikeys=3)) + test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1, nr_of_inchikeys=1)) library_spectra.add_embeddings(model) test_spectra.add_embeddings(model) fingerprints = Fingerprints.from_spectrum_set(library_spectra, "daylight", 2048) From 71475237b7a2ba6efc285e996b7a391cd23a281f Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 5 Feb 2026 16:17:54 +0100 Subject: [PATCH 085/149] fix test --- tests/test_methods.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/tests/test_methods.py b/tests/test_methods.py index 1606565..91c8914 100644 --- a/tests/test_methods.py +++ b/tests/test_methods.py @@ -13,7 +13,7 @@ from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from tests.conftest import create_test_spectra, ms2deepscore_model -def get_library_and_test_spectra() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: +def get_library_and_test_spectra_not_identical() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: model = ms2deepscore_model() spectra = create_test_spectra(number_of_spectra_per_inchikey=3, nr_of_inchikeys=3) query_spectra = [] @@ -29,6 +29,14 @@ def get_library_and_test_spectra() -> tuple[AnnotatedSpectrumSet, AnnotatedSpect test_spectra.add_embeddings(model) return library_spectra, test_spectra +def get_library_and_test_spectra_exactly_matching() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: + model = ms2deepscore_model() + library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(number_of_spectra_per_inchikey=3, nr_of_inchikeys=3)) + test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(number_of_spectra_per_inchikey=1, nr_of_inchikeys=3)) + library_spectra.add_embeddings(model) + test_spectra.add_embeddings(model) + return library_spectra, test_spectra + @pytest.mark.parametrize( "prediction_function", [ @@ -37,7 +45,7 @@ def get_library_and_test_spectra() -> tuple[AnnotatedSpectrumSet, AnnotatedSpect ], ) def test_all_methods(prediction_function): - library_spectra, test_spectra = get_library_and_test_spectra() + library_spectra, test_spectra = get_library_and_test_spectra_exactly_matching() predicted_inchikeys, scores = prediction_function(library_spectra, test_spectra) for i, spectrum in enumerate(test_spectra.spectra): inchikey = spectrum.get("inchikey")[:14] @@ -46,7 +54,7 @@ def test_all_methods(prediction_function): def test_predict_best_possible_match(): - library_spectra, test_spectra = get_library_and_test_spectra() + library_spectra, test_spectra = get_library_and_test_spectra_not_identical() fingerprints = Fingerprints.from_spectrum_set(library_spectra + test_spectra, "daylight", 2048) predicted_inchikeys, scores = predict_best_possible_match(library_spectra, test_spectra, fingerprints) for i, spectrum in enumerate(test_spectra.spectra): @@ -55,7 +63,7 @@ def test_predict_best_possible_match(): assert np.allclose(scores[i], np.array(1.0), atol=1e-5) def test_predict_with_integrated_similarity_flow(): - library_spectra, test_spectra = get_library_and_test_spectra() + library_spectra, test_spectra = get_library_and_test_spectra_not_identical() fingerprints = Fingerprints.from_spectrum_set(library_spectra, "daylight", 4096) predicted_inchikeys, scores = predict_with_integrated_similarity_flow(library_spectra, test_spectra, fingerprints) From 9c98d91ed46f9fb979e9be43ba716f5ca9c4f33c Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 5 Feb 2026 16:19:41 +0100 Subject: [PATCH 086/149] Add type hinting --- .../reference_methods/predict_using_closest_tanimoto.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index 3d06e18..bc6e194 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -59,12 +59,12 @@ def select_inchikeys_with_highest_ms2deepscore(spectra_with_embeddings: Annotate top_inchikeys = [spectra_with_embeddings.inchikeys[inchikey_index] for inchikey_index in inchikey_indexes_with_highest_ms2deepscore] return top_inchikeys -def get_average_predictions_for_closely_related_metabolites(spectra_with_embeddings, top_k_inchikeys, - all_ms2deepscores): +def get_average_predictions_for_closely_related_metabolites(spectra: AnnotatedSpectrumSet, top_k_inchikeys, + all_ms2deepscores: np.ndarray): """Calculates the average ms2deepscore predictions for top k closest inchikeys""" average_predicted_scores = [] for top_inchikey in top_k_inchikeys: - matching_spectrum_indexes = spectra_with_embeddings.spectrum_indexes_per_inchikey[top_inchikey] + matching_spectrum_indexes = list(spectra.spectrum_indexes_per_inchikey[top_inchikey]) predicted_scores = all_ms2deepscores[matching_spectrum_indexes] average_predicted_scores.append(predicted_scores.mean()) average_predicted_score = sum(average_predicted_scores) / len(average_predicted_scores) From a8d5e172417e0496ea0d8384568afec79c0cdc28 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 5 Feb 2026 16:50:53 +0100 Subject: [PATCH 087/149] Clarify valueerror --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index 94eeb07..4c641b3 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -77,7 +77,7 @@ def spectra(self): @property def embeddings(self) -> "Embeddings": if self._embeddings is None: - raise ValueError("First run add_embeddings") + raise ValueError("First run the 'add_embeddings' method") return self._embeddings @property From 3fd16528f1e6cf4a8345346298cb62b127bd4a7f Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 5 Feb 2026 16:51:43 +0100 Subject: [PATCH 088/149] Fix bug in select_inchikeys_with_highest_ms2deepscore if tuple is used instead of list (in the numpy index selection) --- .../reference_methods/predict_using_closest_tanimoto.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index bc6e194..bc93e51 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -51,7 +51,7 @@ def predict_using_closest_tanimoto_single_spectrum( def select_inchikeys_with_highest_ms2deepscore(spectra_with_embeddings: AnnotatedSpectrumSet, ms2deepscores, nr_of_inchikeys_to_select=10): highest_score_for_inchikey = [] for inchikey, spectrum_indexes in spectra_with_embeddings.spectrum_indexes_per_inchikey.items(): - all_ms2deepscores_for_inchikey = ms2deepscores[spectrum_indexes] + all_ms2deepscores_for_inchikey = ms2deepscores[spectrum_indexes,] highest_score_for_inchikey.append(max(all_ms2deepscores_for_inchikey)) inchikey_indexes_with_highest_ms2deepscore = np.argpartition( np.array(highest_score_for_inchikey), -nr_of_inchikeys_to_select)[-nr_of_inchikeys_to_select:] From 7ebd55c4608df3c829c199359cb0d9eaa5e7d690 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 5 Feb 2026 16:59:44 +0100 Subject: [PATCH 089/149] Rename indexes to indices --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 44 +++++++++---------- .../benchmarking/EvaluateAnalogueSearch.py | 4 +- .../benchmarking/EvaluateExactMatchSearch.py | 8 ++-- ms2query/benchmarking/Fingerprints.py | 2 +- .../predict_using_closest_tanimoto.py | 4 +- tests/test_SpectrumDataSet.py | 2 +- tests/test_predict_using_closest_tanimoto.py | 2 +- 7 files changed, 33 insertions(+), 33 deletions(-) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index 4c641b3..08f93f8 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -10,52 +10,52 @@ class AnnotatedSpectrumSet: """Stores a spectrum dataset making it easy and fast to split on molecules""" def __init__(self, spectra: Sequence[Spectrum], - spectrum_indexes_per_inchikey: Mapping[str, Iterable[int]], + spectrum_indices_per_inchikey: Mapping[str, Iterable[int]], embeddings: Optional[Embeddings] = None, progress_bars=False): self._spectra = tuple([spectrum.clone() for spectrum in spectra]) - self.spectrum_indexes_per_inchikey: dict[str, tuple[int, ...]] = {key: tuple(values) for key, values in spectrum_indexes_per_inchikey.items()} + self.spectrum_indices_per_inchikey: dict[str, tuple[int, ...]] = {key: tuple(values) for key, values in spectrum_indices_per_inchikey.items()} self.progress_bars = progress_bars self._embeddings = embeddings @classmethod def create_spectrum_set(cls, spectra: Sequence[Spectrum], progress_bars=False) -> "AnnotatedSpectrumSet": - spectrum_indexes_per_inchikey = defaultdict(list) + spectrum_indices_per_inchikey = defaultdict(list) for spectrum_index, spectrum in enumerate(spectra): inchikey = spectrum.get("inchikey") if inchikey is None: raise ValueError("Annotated Spectrum set expects spectra that all have an inchikey") - spectrum_indexes_per_inchikey[inchikey[:14]].append(spectrum_index) - return cls(spectra, spectrum_indexes_per_inchikey, progress_bars=progress_bars) + spectrum_indices_per_inchikey[inchikey[:14]].append(spectrum_index) + return cls(spectra, spectrum_indices_per_inchikey, progress_bars=progress_bars) def __add__(self, other) -> "AnnotatedSpectrumSet": """Adds two spectrum sets together""" if not isinstance(other, AnnotatedSpectrumSet): return NotImplemented spectra = self.spectra + other.spectra - # update spectrum_indexes_per_inchikey + # update spectrum_indices_per_inchikey starting_index = len(self.spectra) - reindexed_indexes_per_inchikey = {} - for inchikey, list_of_spectrum_indexes in other.spectrum_indexes_per_inchikey.items(): - reindexed_indexes_per_inchikey[inchikey] = [v + starting_index for v in list_of_spectrum_indexes] - # combine indexes - spectrum_indexes_per_inchikey = defaultdict(list) - for indexes_per_inchikey in (self.spectrum_indexes_per_inchikey, reindexed_indexes_per_inchikey): - for inchikey, indexes in indexes_per_inchikey.items(): - spectrum_indexes_per_inchikey[inchikey].extend(indexes) + reindexed_indices_per_inchikey = {} + for inchikey, list_of_spectrum_indices in other.spectrum_indices_per_inchikey.items(): + reindexed_indices_per_inchikey[inchikey] = [v + starting_index for v in list_of_spectrum_indices] + # combine indices + spectrum_indices_per_inchikey = defaultdict(list) + for indices_per_inchikey in (self.spectrum_indices_per_inchikey, reindexed_indices_per_inchikey): + for inchikey, indices in indices_per_inchikey.items(): + spectrum_indices_per_inchikey[inchikey].extend(indices) # combine embeddings embeddings = None if self._embeddings and other._embeddings: embeddings = Embeddings.combine_embeddings(self.embeddings, other.embeddings) return AnnotatedSpectrumSet(spectra, - spectrum_indexes_per_inchikey, + spectrum_indices_per_inchikey, embeddings=embeddings, progress_bars=self.progress_bars) - def subset_spectra(self, spectrum_indexes) -> "AnnotatedSpectrumSet": + def subset_spectra(self, spectrum_indices) -> "AnnotatedSpectrumSet": """Returns a new instance of a subset of the spectra""" - spectra = [self._spectra[index] for index in spectrum_indexes] + spectra = [self._spectra[index] for index in spectrum_indices] new_instance = AnnotatedSpectrumSet.create_spectrum_set(spectra, progress_bars=self.progress_bars) if self._embeddings is not None: new_instance._embeddings = self.embeddings.subset_embeddings(spectra) @@ -63,7 +63,7 @@ def subset_spectra(self, spectrum_indexes) -> "AnnotatedSpectrumSet": def spectra_per_inchikey(self, inchikey) -> List[Spectrum]: matching_spectra = [] - for index in self.spectrum_indexes_per_inchikey[inchikey]: + for index in self.spectrum_indices_per_inchikey[inchikey]: matching_spectra.append(self._spectra[index]) return matching_spectra @@ -82,20 +82,20 @@ def embeddings(self) -> "Embeddings": @property def inchikeys(self): - return tuple(self.spectrum_indexes_per_inchikey.keys()) + return tuple(self.spectrum_indices_per_inchikey.keys()) def __copy__(self): return AnnotatedSpectrumSet(self.spectra, - self.spectrum_indexes_per_inchikey, + self.spectrum_indices_per_inchikey, self.embeddings, progress_bars=self.progress_bars) def __eq__(self, other: object): if not isinstance(other, AnnotatedSpectrumSet): - raise NotImplemented + raise ValueError("__Eq__ can only be done between two AnnotatedSpectrumSets") if self.spectra != other.spectra: return False - if self.spectrum_indexes_per_inchikey != other.spectrum_indexes_per_inchikey: + if self.spectrum_indices_per_inchikey != other.spectrum_indices_per_inchikey: return False if self._embeddings != other._embeddings: return False diff --git a/ms2query/benchmarking/EvaluateAnalogueSearch.py b/ms2query/benchmarking/EvaluateAnalogueSearch.py index 28aa9cc..9b21fa2 100644 --- a/ms2query/benchmarking/EvaluateAnalogueSearch.py +++ b/ms2query/benchmarking/EvaluateAnalogueSearch.py @@ -34,10 +34,10 @@ def benchmark_analogue_search( average_scores_per_inchikey = [] # Calculate score per unique inchikey for inchikey in tqdm( - self.validation_spectrum_set.spectrum_indexes_per_inchikey.keys(), + self.validation_spectrum_set.spectrum_indices_per_inchikey.keys(), desc="Calculating analogue accuracy per inchikey", ): - matching_spectrum_indexes = self.validation_spectrum_set.spectrum_indexes_per_inchikey[inchikey] + matching_spectrum_indexes = self.validation_spectrum_set.spectrum_indices_per_inchikey[inchikey] prediction_scores = [] for index in matching_spectrum_indexes: predicted_inchikey = predicted_inchikeys[index] diff --git a/ms2query/benchmarking/EvaluateExactMatchSearch.py b/ms2query/benchmarking/EvaluateExactMatchSearch.py index 77431c6..89c00dd 100644 --- a/ms2query/benchmarking/EvaluateExactMatchSearch.py +++ b/ms2query/benchmarking/EvaluateExactMatchSearch.py @@ -34,10 +34,10 @@ def split_spectrum_set_per_inchikey_across_ionmodes(self, all_pos_indexes = [] all_neg_indexes = [] for inchikey in tqdm( - spectrum_set.spectrum_indexes_per_inchikey.keys(), + spectrum_set.spectrum_indices_per_inchikey.keys(), desc="Splitting spectra per inchikey across ionmodes", ): - val_spectrum_indexes_matching_inchikey = spectrum_set.spectrum_indexes_per_inchikey[inchikey] + val_spectrum_indexes_matching_inchikey = spectrum_set.spectrum_indices_per_inchikey[inchikey] positive_val_spectrum_indexes_current_inchikey = [] negative_val_spectrum_indexes_current_inchikey = [] for spectrum_index in val_spectrum_indexes_matching_inchikey: @@ -107,8 +107,8 @@ def _split_spectrum_set_per_inchikeys(spectrum_set: AnnotatedSpectrumSet, indexes_set_1 = [] indexes_set_2 = [] rng = random.Random(seed) - for inchikey in tqdm(spectrum_set.spectrum_indexes_per_inchikey.keys(), desc="Splitting spectra per inchikey"): - val_spectrum_indexes_matching_inchikey = spectrum_set.spectrum_indexes_per_inchikey[inchikey] + for inchikey in tqdm(spectrum_set.spectrum_indices_per_inchikey.keys(), desc="Splitting spectra per inchikey"): + val_spectrum_indexes_matching_inchikey = spectrum_set.spectrum_indices_per_inchikey[inchikey] if len(val_spectrum_indexes_matching_inchikey) == 1: # all single spectra are excluded from this test, since no exact match can be added to the library continue diff --git a/ms2query/benchmarking/Fingerprints.py b/ms2query/benchmarking/Fingerprints.py index a1366eb..99df33b 100644 --- a/ms2query/benchmarking/Fingerprints.py +++ b/ms2query/benchmarking/Fingerprints.py @@ -38,7 +38,7 @@ def compute_fingerprints_from_inchi(cls, most_common_inchi_per_inchikey: dict[st @classmethod def from_spectrum_set(cls, spectrum_set: AnnotatedSpectrumSet, fingerprint_type, nbits): most_common_inchi_per_inchikey = {} - for inchikey, spectrum_indexes in tqdm(spectrum_set.spectrum_indexes_per_inchikey.items(), desc="Get most common inchi per inchikey"): + for inchikey, spectrum_indexes in tqdm(spectrum_set.spectrum_indices_per_inchikey.items(), desc="Get most common inchi per inchikey"): spectra_matching_inchikey = [spectrum_set.spectra[index] for index in spectrum_indexes] most_common_inchi = Counter([spectrum.get("inchi") for spectrum in spectra_matching_inchikey]).most_common(1)[0][0] most_common_inchi_per_inchikey[inchikey] = most_common_inchi diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index bc93e51..e662acf 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -50,7 +50,7 @@ def predict_using_closest_tanimoto_single_spectrum( def select_inchikeys_with_highest_ms2deepscore(spectra_with_embeddings: AnnotatedSpectrumSet, ms2deepscores, nr_of_inchikeys_to_select=10): highest_score_for_inchikey = [] - for inchikey, spectrum_indexes in spectra_with_embeddings.spectrum_indexes_per_inchikey.items(): + for inchikey, spectrum_indexes in spectra_with_embeddings.spectrum_indices_per_inchikey.items(): all_ms2deepscores_for_inchikey = ms2deepscores[spectrum_indexes,] highest_score_for_inchikey.append(max(all_ms2deepscores_for_inchikey)) inchikey_indexes_with_highest_ms2deepscore = np.argpartition( @@ -64,7 +64,7 @@ def get_average_predictions_for_closely_related_metabolites(spectra: AnnotatedSp """Calculates the average ms2deepscore predictions for top k closest inchikeys""" average_predicted_scores = [] for top_inchikey in top_k_inchikeys: - matching_spectrum_indexes = list(spectra.spectrum_indexes_per_inchikey[top_inchikey]) + matching_spectrum_indexes = list(spectra.spectrum_indices_per_inchikey[top_inchikey]) predicted_scores = all_ms2deepscores[matching_spectrum_indexes] average_predicted_scores.append(predicted_scores.mean()) average_predicted_score = sum(average_predicted_scores) / len(average_predicted_scores) diff --git a/tests/test_SpectrumDataSet.py b/tests/test_SpectrumDataSet.py index 6e294f6..e579954 100644 --- a/tests/test_SpectrumDataSet.py +++ b/tests/test_SpectrumDataSet.py @@ -7,7 +7,7 @@ def test_create_annotated_spectrum_set(): test_spectra = create_test_spectra(nr_of_inchikeys=3, number_of_spectra_per_inchikey=3) spectrum_set = AnnotatedSpectrumSet.create_spectrum_set(spectra=test_spectra) - assert len(spectrum_set.spectrum_indexes_per_inchikey) == 3 + assert len(spectrum_set.spectrum_indices_per_inchikey) == 3 def test_add_spectrum_sets(): test_spectra = create_test_spectra(nr_of_inchikeys=3, number_of_spectra_per_inchikey=3) diff --git a/tests/test_predict_using_closest_tanimoto.py b/tests/test_predict_using_closest_tanimoto.py index b0ee433..6a6b502 100644 --- a/tests/test_predict_using_closest_tanimoto.py +++ b/tests/test_predict_using_closest_tanimoto.py @@ -52,7 +52,7 @@ def test_select_inchikeys_with_highest_ms2deepscore(): ms2deepscores[5] = 0.9 ms2deepscores[7] = 0.6 inchikeys_with_highest_ms2deepscore = select_inchikeys_with_highest_ms2deepscore(spectra, ms2deepscores, 3) - expected_inchikeys = list(spectra.spectrum_indexes_per_inchikey.keys())[:3] + expected_inchikeys = list(spectra.spectrum_indices_per_inchikey.keys())[:3] assert set(expected_inchikeys) == set(inchikeys_with_highest_ms2deepscore) print(inchikeys_with_highest_ms2deepscore) From 8a0fd433405943d6f600b37df85f344f802a0996 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 6 Feb 2026 10:12:37 +0100 Subject: [PATCH 090/149] ruff linting --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 34 ++++--- ms2query/benchmarking/Embeddings.py | 12 ++- .../benchmarking/EvaluateAnalogueSearch.py | 3 - .../benchmarking/EvaluateExactMatchSearch.py | 99 ++++++++++++------- ms2query/benchmarking/Fingerprints.py | 41 +++++--- ms2query/benchmarking/TopKTanimotoScores.py | 42 ++++---- .../PredictMS2DeepScoreSimilarity.py | 1 - .../predict_best_possible_match.py | 16 +-- .../predict_highest_ms2deepscore.py | 2 +- .../predict_using_closest_tanimoto.py | 69 ++++++++----- ...predict_with_integrated_similarity_flow.py | 3 +- tests/testPredictMS2DeepScoreSimilarity.py | 2 +- tests/test_Fingerprints.py | 70 ++++++++----- tests/test_embeddings.py | 1 - tests/test_evaluate_methods.py | 14 +-- tests/test_methods.py | 4 +- tests/test_predict_using_closest_tanimoto.py | 27 ++--- 17 files changed, 267 insertions(+), 173 deletions(-) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index 08f93f8..dc041e7 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -1,20 +1,24 @@ from collections import defaultdict -from typing import List, Iterable, Mapping, Optional, Sequence +from typing import Iterable, List, Mapping, Optional, Sequence from matchms import Spectrum from ms2deepscore.models import SiameseSpectralModel - from ms2query.benchmarking.Embeddings import Embeddings class AnnotatedSpectrumSet: """Stores a spectrum dataset making it easy and fast to split on molecules""" - def __init__(self, - spectra: Sequence[Spectrum], - spectrum_indices_per_inchikey: Mapping[str, Iterable[int]], - embeddings: Optional[Embeddings] = None, - progress_bars=False): + + def __init__( + self, + spectra: Sequence[Spectrum], + spectrum_indices_per_inchikey: Mapping[str, Iterable[int]], + embeddings: Optional[Embeddings] = None, + progress_bars=False, + ): self._spectra = tuple([spectrum.clone() for spectrum in spectra]) - self.spectrum_indices_per_inchikey: dict[str, tuple[int, ...]] = {key: tuple(values) for key, values in spectrum_indices_per_inchikey.items()} + self.spectrum_indices_per_inchikey: dict[str, tuple[int, ...]] = { + key: tuple(values) for key, values in spectrum_indices_per_inchikey.items() + } self.progress_bars = progress_bars self._embeddings = embeddings @@ -48,10 +52,9 @@ def __add__(self, other) -> "AnnotatedSpectrumSet": embeddings = None if self._embeddings and other._embeddings: embeddings = Embeddings.combine_embeddings(self.embeddings, other.embeddings) - return AnnotatedSpectrumSet(spectra, - spectrum_indices_per_inchikey, - embeddings=embeddings, - progress_bars=self.progress_bars) + return AnnotatedSpectrumSet( + spectra, spectrum_indices_per_inchikey, embeddings=embeddings, progress_bars=self.progress_bars + ) def subset_spectra(self, spectrum_indices) -> "AnnotatedSpectrumSet": """Returns a new instance of a subset of the spectra""" @@ -85,10 +88,9 @@ def inchikeys(self): return tuple(self.spectrum_indices_per_inchikey.keys()) def __copy__(self): - return AnnotatedSpectrumSet(self.spectra, - self.spectrum_indices_per_inchikey, - self.embeddings, - progress_bars=self.progress_bars) + return AnnotatedSpectrumSet( + self.spectra, self.spectrum_indices_per_inchikey, self.embeddings, progress_bars=self.progress_bars + ) def __eq__(self, other: object): if not isinstance(other, AnnotatedSpectrumSet): diff --git a/ms2query/benchmarking/Embeddings.py b/ms2query/benchmarking/Embeddings.py index d438d9e..aed79d2 100644 --- a/ms2query/benchmarking/Embeddings.py +++ b/ms2query/benchmarking/Embeddings.py @@ -1,5 +1,4 @@ from typing import Sequence - import numpy as np from matchms import Spectrum from ms2deepscore.models import SiameseSpectralModel, compute_embedding_array @@ -7,17 +6,19 @@ class Embeddings: """Stores Embeddings for a list of mass spectra""" + def __init__(self, embeddings: np.ndarray, spectrum_hashes: tuple, model_settings: dict): if len(spectrum_hashes) != embeddings.shape[0]: raise ValueError("Number of spectra hashes does not match number of embeddings") self.index_to_spectrum_hash = spectrum_hashes - self._spectrum_hash_to_index = {spectrum_hash: index for index, spectrum_hash in enumerate(self.index_to_spectrum_hash)} + self._spectrum_hash_to_index = { + spectrum_hash: index for index, spectrum_hash in enumerate(self.index_to_spectrum_hash) + } self._model_settings = model_settings self._embeddings = embeddings @classmethod - def create_from_spectra(cls, spectra: Sequence[Spectrum], - model: SiameseSpectralModel) -> "Embeddings": + def create_from_spectra(cls, spectra: Sequence[Spectrum], model: SiameseSpectralModel) -> "Embeddings": index_to_spectrum_hash = tuple(spectrum.__hash__() for spectrum in spectra) if len(set(index_to_spectrum_hash)) != len(spectra): raise ValueError("There are duplicated spectra in the spectrum list") @@ -33,7 +34,7 @@ def combine_embeddings(cls, embeddings_1: "Embeddings", embeddings_2: "Embedding if not set(embeddings_1.index_to_spectrum_hash).isdisjoint(embeddings_2.index_to_spectrum_hash): # todo allow this to happen, but remove repeating ones and check that they are the same. raise ValueError("There are repeated spectra in the embeddings that are added together") - combined_embeddings = np.vstack([embeddings_1.embeddings, embeddings_2.embeddings]) + combined_embeddings = np.vstack([embeddings_1.embeddings, embeddings_2.embeddings]) index_to_spectrum_hash = embeddings_1.index_to_spectrum_hash + embeddings_2.index_to_spectrum_hash return cls(combined_embeddings, index_to_spectrum_hash, embeddings_1.model_settings) @@ -60,6 +61,7 @@ def copy(self) -> "Embeddings": spectrum_hashes=tuple(self.index_to_spectrum_hash), model_settings=dict(self._model_settings), ) + def __eq__(self, other) -> bool: if not isinstance(other, Embeddings): return NotImplemented diff --git a/ms2query/benchmarking/EvaluateAnalogueSearch.py b/ms2query/benchmarking/EvaluateAnalogueSearch.py index 9b21fa2..8673b24 100644 --- a/ms2query/benchmarking/EvaluateAnalogueSearch.py +++ b/ms2query/benchmarking/EvaluateAnalogueSearch.py @@ -1,10 +1,7 @@ from typing import List - import numpy as np from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix - from tqdm import tqdm - from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.benchmarking.Fingerprints import Fingerprints diff --git a/ms2query/benchmarking/EvaluateExactMatchSearch.py b/ms2query/benchmarking/EvaluateExactMatchSearch.py index 89c00dd..d862d62 100644 --- a/ms2query/benchmarking/EvaluateExactMatchSearch.py +++ b/ms2query/benchmarking/EvaluateExactMatchSearch.py @@ -6,28 +6,36 @@ class EvaluateExactMatchSearchAcrossIonmodes: def __init__( - self, training_spectrum_set: AnnotatedSpectrumSet, - validation_spectrum_set: AnnotatedSpectrumSet, + self, + training_spectrum_set: AnnotatedSpectrumSet, + validation_spectrum_set: AnnotatedSpectrumSet, ): self.training_spectrum_set = training_spectrum_set - (self.pos_validation_spectra, - self.neg_validation_spectra) = self.split_spectrum_set_per_inchikey_across_ionmodes(validation_spectrum_set) - - def pos_in_neg(self, prediction_function: Callable[ - [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] - ],): - return get_exact_match_accuracy(self.pos_validation_spectra, - prediction_function(self.training_spectrum_set + self.neg_validation_spectra, - self.pos_validation_spectra)) - def neg_in_pos(self, prediction_function: Callable[ - [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] - ],): - return get_exact_match_accuracy(self.neg_validation_spectra, - prediction_function(self.training_spectrum_set + self.pos_validation_spectra, - self.neg_validation_spectra)) + (self.pos_validation_spectra, self.neg_validation_spectra) = ( + self.split_spectrum_set_per_inchikey_across_ionmodes(validation_spectrum_set) + ) + + def pos_in_neg( + self, + prediction_function: Callable[[AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]]], + ): + return get_exact_match_accuracy( + self.pos_validation_spectra, + prediction_function(self.training_spectrum_set + self.neg_validation_spectra, self.pos_validation_spectra), + ) + + def neg_in_pos( + self, + prediction_function: Callable[[AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]]], + ): + return get_exact_match_accuracy( + self.neg_validation_spectra, + prediction_function(self.training_spectrum_set + self.pos_validation_spectra, self.neg_validation_spectra), + ) @staticmethod - def split_spectrum_set_per_inchikey_across_ionmodes(self, + def split_spectrum_set_per_inchikey_across_ionmodes( + self, spectrum_set: AnnotatedSpectrumSet, ) -> Tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: """Splits a spectrum set in two sets on ionmode. Only uses spectra for inchikeys with at least 1 pos and 1 neg""" @@ -63,45 +71,59 @@ def split_spectrum_set_per_inchikey_across_ionmodes(self, class EvaluateExactMatchSearchWithinIonmodes: def __init__( - self, training_spectrum_set: AnnotatedSpectrumSet, - validation_spectrum_set: AnnotatedSpectrumSet, + self, + training_spectrum_set: AnnotatedSpectrumSet, + validation_spectrum_set: AnnotatedSpectrumSet, ): self.training_spectrum_set = training_spectrum_set self.pos_split_per_inchikey_set_1, self.pos_split_per_inchikey_set_2 = self._split_spectrum_set_per_inchikeys( - subset_spectra_on_ionmode(validation_spectrum_set, "positive")) + subset_spectra_on_ionmode(validation_spectrum_set, "positive") + ) self.neg_split_per_inchikey_set_1, self.neg_split_per_inchikey_set_2 = self._split_spectrum_set_per_inchikeys( - subset_spectra_on_ionmode(validation_spectrum_set, "negative")) + subset_spectra_on_ionmode(validation_spectrum_set, "negative") + ) - def neg_in_neg(self, prediction_function: Callable[ - [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] - ],): + def neg_in_neg( + self, + prediction_function: Callable[[AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]]], + ): accuracy_set_2 = get_exact_match_accuracy( self.neg_split_per_inchikey_set_2, - prediction_function(self.training_spectrum_set + self.neg_split_per_inchikey_set_1, - self.neg_split_per_inchikey_set_2)) + prediction_function( + self.training_spectrum_set + self.neg_split_per_inchikey_set_1, self.neg_split_per_inchikey_set_2 + ), + ) accuracy_set_1 = get_exact_match_accuracy( self.neg_split_per_inchikey_set_1, - prediction_function(self.training_spectrum_set + self.neg_split_per_inchikey_set_2, - self.neg_split_per_inchikey_set_1)) + prediction_function( + self.training_spectrum_set + self.neg_split_per_inchikey_set_2, self.neg_split_per_inchikey_set_1 + ), + ) return (accuracy_set_2 + accuracy_set_1) / 2 - def pos_in_pos(self, prediction_function: Callable[ - [AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]] - ],): + def pos_in_pos( + self, + prediction_function: Callable[[AnnotatedSpectrumSet, AnnotatedSpectrumSet], Tuple[List[str], List[float]]], + ): accuracy_set_2 = get_exact_match_accuracy( self.pos_split_per_inchikey_set_2, - prediction_function(self.training_spectrum_set + self.pos_split_per_inchikey_set_1, - self.pos_split_per_inchikey_set_2)) + prediction_function( + self.training_spectrum_set + self.pos_split_per_inchikey_set_1, self.pos_split_per_inchikey_set_2 + ), + ) accuracy_set_1 = get_exact_match_accuracy( self.pos_split_per_inchikey_set_1, - prediction_function(self.training_spectrum_set + self.pos_split_per_inchikey_set_2, - self.pos_split_per_inchikey_set_1)) + prediction_function( + self.training_spectrum_set + self.pos_split_per_inchikey_set_2, self.pos_split_per_inchikey_set_1 + ), + ) return (accuracy_set_2 + accuracy_set_1) / 2 @staticmethod - def _split_spectrum_set_per_inchikeys(spectrum_set: AnnotatedSpectrumSet, - seed=42) -> Tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: + def _split_spectrum_set_per_inchikeys( + spectrum_set: AnnotatedSpectrumSet, seed=42 + ) -> Tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: """Splits a spectrum set into two. For each inchikey with more than one spectrum the spectra are divided over the two sets""" indexes_set_1 = [] @@ -118,6 +140,7 @@ def _split_spectrum_set_per_inchikeys(spectrum_set: AnnotatedSpectrumSet, indexes_set_2.extend(val_spectrum_indexes_matching_inchikey[split_index:]) return spectrum_set.subset_spectra(indexes_set_1), spectrum_set.subset_spectra(indexes_set_2) + def get_exact_match_accuracy(query_spectrum_set, predicted_inchikeys): exact_match_accuracy_per_inchikey = [] for inchikey in tqdm(query_spectrum_set.inchikeys, desc="Calculating exact match accuracy per inchikey"): diff --git a/ms2query/benchmarking/Fingerprints.py b/ms2query/benchmarking/Fingerprints.py index 99df33b..4d7a9c1 100644 --- a/ms2query/benchmarking/Fingerprints.py +++ b/ms2query/benchmarking/Fingerprints.py @@ -1,12 +1,10 @@ from collections import Counter from typing import Iterable - import numpy as np -from numpy.typing import NDArray import pandas as pd from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi +from numpy.typing import NDArray from tqdm import tqdm - from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.metrics import generalized_tanimoto_similarity_matrix @@ -29,7 +27,8 @@ def compute_fingerprints_from_inchi(cls, most_common_inchi_per_inchikey: dict[st fingerprints = np.zeros((len(index_to_inchikey), nbits), dtype=np.uint8) for inchikey_index, inchikey in tqdm(enumerate(index_to_inchikey), desc="Adding fingerprints to Inchikeys"): fingerprint = _derive_fingerprint_from_inchi( - most_common_inchi_per_inchikey[inchikey], fingerprint_type=fingerprint_type, nbits=nbits) + most_common_inchi_per_inchikey[inchikey], fingerprint_type=fingerprint_type, nbits=nbits + ) if not isinstance(fingerprint, np.ndarray): raise ValueError(f"Fingerprint could not be set for InChI: {most_common_inchi_per_inchikey[inchikey]}") fingerprints[inchikey_index, :] = fingerprint @@ -38,15 +37,20 @@ def compute_fingerprints_from_inchi(cls, most_common_inchi_per_inchikey: dict[st @classmethod def from_spectrum_set(cls, spectrum_set: AnnotatedSpectrumSet, fingerprint_type, nbits): most_common_inchi_per_inchikey = {} - for inchikey, spectrum_indexes in tqdm(spectrum_set.spectrum_indices_per_inchikey.items(), desc="Get most common inchi per inchikey"): + for inchikey, spectrum_indexes in tqdm( + spectrum_set.spectrum_indices_per_inchikey.items(), desc="Get most common inchi per inchikey" + ): spectra_matching_inchikey = [spectrum_set.spectra[index] for index in spectrum_indexes] - most_common_inchi = Counter([spectrum.get("inchi") for spectrum in spectra_matching_inchikey]).most_common(1)[0][0] + most_common_inchi = Counter([spectrum.get("inchi") for spectrum in spectra_matching_inchikey]).most_common( + 1 + )[0][0] most_common_inchi_per_inchikey[inchikey] = most_common_inchi return cls.compute_fingerprints_from_inchi(most_common_inchi_per_inchikey, fingerprint_type, nbits) @classmethod - def combine_fingerprints(cls, fingerprints_1: "Fingerprints", - fingerprints_2: "Fingerprints", allow_replacing: bool=False) -> "Fingerprints": + def combine_fingerprints( + cls, fingerprints_1: "Fingerprints", fingerprints_2: "Fingerprints", allow_replacing: bool = False + ) -> "Fingerprints": """Combines two sets of Fingerprints into a new instance allow_replacing: @@ -55,19 +59,27 @@ def combine_fingerprints(cls, fingerprints_1: "Fingerprints", if fingerprints_1.fingerprint_type != fingerprints_2.fingerprint_type: raise ValueError("The fingerprint type of both Fingerprints do not match") if fingerprints_1.nbits != fingerprints_2.nbits: - raise ValueError(f"The nbits for the fingerprints do not match: {fingerprints_1.nbits} != {fingerprints_2.nbits}") + raise ValueError( + f"The nbits for the fingerprints do not match: {fingerprints_1.nbits} != {fingerprints_2.nbits}" + ) # handle overlap overlapping_inchikeys = set(fingerprints_2.inchikeys) & set(fingerprints_1.inchikeys) new_fingerprints = fingerprints_1.fingerprints.copy() - if not np.array_equal(fingerprints_1.get_fingerprints(overlapping_inchikeys), fingerprints_2.get_fingerprints(overlapping_inchikeys)): + if not np.array_equal( + fingerprints_1.get_fingerprints(overlapping_inchikeys), + fingerprints_2.get_fingerprints(overlapping_inchikeys), + ): if allow_replacing: for inchikey in overlapping_inchikeys: inchikey_index = fingerprints_1._inchikey_to_index[inchikey] new_fingerprints[inchikey_index, :] = fingerprints_2.fingerprints[ - fingerprints_2._inchikey_to_index[inchikey], :] + fingerprints_2._inchikey_to_index[inchikey], : + ] else: raise ValueError("The overlapping inchikeys do not have the same fingerprints") - inchikeys_to_add = [inchikey for inchikey in fingerprints_2.inchikeys if inchikey not in fingerprints_1.inchikeys] + inchikeys_to_add = [ + inchikey for inchikey in fingerprints_2.inchikeys if inchikey not in fingerprints_1.inchikeys + ] combined_fingerprints = np.vstack([new_fingerprints, fingerprints_2.get_fingerprints(inchikeys_to_add)]) combined_inchikeys = fingerprints_1.inchikeys + tuple(inchikeys_to_add) return cls(combined_fingerprints, combined_inchikeys, fingerprints_1.fingerprint_type) @@ -79,7 +91,9 @@ def get_fingerprints(self, list_of_inchikeys: Iterable[str]): return self.fingerprints[list_of_indexes] def subset_fingerprints(self, list_of_inchikeys) -> "Fingerprints": - return Fingerprints(self.get_fingerprints(list_of_inchikeys), list_of_inchikeys, fingerprint_type=self.fingerprint_type) + return Fingerprints( + self.get_fingerprints(list_of_inchikeys), list_of_inchikeys, fingerprint_type=self.fingerprint_type + ) @property def inchikeys(self): @@ -105,6 +119,7 @@ def __eq__(self, other) -> bool: return False return np.array_equal(self.fingerprints, other.fingerprints) + def get_similarity_matrix(fingerprints_1: Fingerprints, fingerprints_2: Fingerprints): similarity_scores = generalized_tanimoto_similarity_matrix(fingerprints_1.fingerprints, fingerprints_2.fingerprints) return pd.DataFrame(similarity_scores, index=fingerprints_1.inchikeys, columns=fingerprints_2.inchikeys) diff --git a/ms2query/benchmarking/TopKTanimotoScores.py b/ms2query/benchmarking/TopKTanimotoScores.py index fa0ed12..4c59c5a 100644 --- a/ms2query/benchmarking/TopKTanimotoScores.py +++ b/ms2query/benchmarking/TopKTanimotoScores.py @@ -1,25 +1,24 @@ import numpy as np import pandas as pd - from ms2query.benchmarking.Fingerprints import Fingerprints from ms2query.metrics import generalized_tanimoto_similarity_matrix class TopKTanimotoScores: - def __init__(self, tanimoto_scores_for_top_k: np.ndarray, - top_k_inchikeys: np.ndarray, - inchikey_indexes: np.ndarray): + def __init__( + self, tanimoto_scores_for_top_k: np.ndarray, top_k_inchikeys: np.ndarray, inchikey_indexes: np.ndarray + ): self.k = tanimoto_scores_for_top_k.shape[1] - self.top_k_inchikeys_and_scores = self.create_multi_index(tanimoto_scores_for_top_k, - top_k_inchikeys, - inchikey_indexes) - - def create_multi_index(self, tanimoto_scores_for_top_k: np.ndarray, - top_k_inchikeys: np.ndarray, - inchikey_indexes: np.ndarray): - columns = pd.MultiIndex.from_product([[f"Rank_{i + 1}" for i in range(self.k)], ["inchikey", "score"]], - names=["result_rank", "attribute"] + self.top_k_inchikeys_and_scores = self.create_multi_index( + tanimoto_scores_for_top_k, top_k_inchikeys, inchikey_indexes + ) + + def create_multi_index( + self, tanimoto_scores_for_top_k: np.ndarray, top_k_inchikeys: np.ndarray, inchikey_indexes: np.ndarray + ): + columns = pd.MultiIndex.from_product( + [[f"Rank_{i + 1}" for i in range(self.k)], ["inchikey", "score"]], names=["result_rank", "attribute"] ) combined_data = np.empty((len(inchikey_indexes), self.k * 2), dtype=object) @@ -27,25 +26,28 @@ def create_multi_index(self, tanimoto_scores_for_top_k: np.ndarray, combined_data[:, 1::2] = tanimoto_scores_for_top_k return pd.DataFrame(combined_data, index=inchikey_indexes, columns=columns) - @classmethod def calculate_from_fingerprints(cls, query_fingerprints: Fingerprints, target_fingerprints: Fingerprints, k): """Gets the top k highest inchikeys and scores for each inchikey in query_fingerprints from target_fingerprints""" - similarity_scores = generalized_tanimoto_similarity_matrix(query_fingerprints.fingerprints, target_fingerprints.fingerprints) + similarity_scores = generalized_tanimoto_similarity_matrix( + query_fingerprints.fingerprints, target_fingerprints.fingerprints + ) inchikey_indexes_of_top_k = np.argpartition(similarity_scores, -k, axis=1)[:, -k:] top_k_inchikeys = target_fingerprints.inchikeys[inchikey_indexes_of_top_k] - tanimoto_scores_for_top_k = similarity_scores[np.arange(similarity_scores.shape[0])[:, None], inchikey_indexes_of_top_k] + tanimoto_scores_for_top_k = similarity_scores[ + np.arange(similarity_scores.shape[0])[:, None], inchikey_indexes_of_top_k + ] return cls(tanimoto_scores_for_top_k, top_k_inchikeys, query_fingerprints.inchikeys) def select_top_k_inchikeys_and_scores(self, inchikey): """Returns a DF with inchikeys and scores""" - return self.top_k_inchikeys_and_scores.loc[inchikey].unstack(level='result_rank').T + return self.top_k_inchikeys_and_scores.loc[inchikey].unstack(level="result_rank").T def select_top_k_inchikeys(self, inchikey): - return self.top_k_inchikeys_and_scores.loc[inchikey].xs('inchikey', level='attribute') + return self.top_k_inchikeys_and_scores.loc[inchikey].xs("inchikey", level="attribute") def select_average_score(self, inchikey): - return self.top_k_inchikeys_and_scores.loc[inchikey].xs('score', level='attribute').mean() + return self.top_k_inchikeys_and_scores.loc[inchikey].xs("score", level="attribute").mean() def get_all_average_tanimoto_scores(self): - return self.top_k_inchikeys_and_scores.xs('score', axis=1, level='attribute').mean(axis=1) + return self.top_k_inchikeys_and_scores.xs("score", axis=1, level="attribute").mean(axis=1) diff --git a/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py b/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py index 40712c2..9066a29 100644 --- a/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py +++ b/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py @@ -2,7 +2,6 @@ import numpy as np from ms2deepscore.vector_operations import cosine_similarity_matrix from tqdm import tqdm - from ms2query.benchmarking.Embeddings import Embeddings diff --git a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py index 97177c1..1b82e04 100644 --- a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py +++ b/ms2query/benchmarking/reference_methods/predict_best_possible_match.py @@ -4,10 +4,14 @@ from ms2query.benchmarking.Fingerprints import Fingerprints -def predict_best_possible_match(library_spectra: AnnotatedSpectrumSet, - query_spectra: AnnotatedSpectrumSet, - fingerprints: Fingerprints,): - highest_possible_score_per_inchikey = calculate_highest_tanimoto_score_per_inchikey(library_spectra, query_spectra, fingerprints) +def predict_best_possible_match( + library_spectra: AnnotatedSpectrumSet, + query_spectra: AnnotatedSpectrumSet, + fingerprints: Fingerprints, +): + highest_possible_score_per_inchikey = calculate_highest_tanimoto_score_per_inchikey( + library_spectra, query_spectra, fingerprints + ) inchikeys_of_best_match = [] highest_scores = [] @@ -22,13 +26,11 @@ def predict_best_possible_match(library_spectra: AnnotatedSpectrumSet, def calculate_highest_tanimoto_score_per_inchikey( - library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet, - fingerprints: Fingerprints + library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet, fingerprints: Fingerprints ) -> Dict[str, tuple[str, float]]: """Finds the best possible match during an analogue search""" print("Calculating tanimoto scores to determine best possible match") - library_fingerprints = fingerprints.get_fingerprints(library_spectra.inchikeys) query_fingerprints = fingerprints.get_fingerprints(query_spectra.inchikeys) diff --git a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py index 45c0f1e..af88519 100644 --- a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py +++ b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py @@ -1,6 +1,6 @@ from typing import List, Tuple -from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet +from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores def predict_highest_ms2deepscore( diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index e662acf..4405c1b 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -8,10 +8,11 @@ def predict_using_closest_tanimoto( - library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet, - library_fingerprints: Fingerprints, + library_spectra: AnnotatedSpectrumSet, + query_spectra: AnnotatedSpectrumSet, + library_fingerprints: Fingerprints, nr_of_closest_inchikeys_to_select=10, - nr_of_inchikeys_with_highest_ms2deepscore_to_select=100, + nr_of_inchikeys_with_highest_ms2deepscore_to_select=100, ) -> Tuple[List[str], List[float]]: """Predict best inchikey, by taking the average score over all spectra for the 10 closest related library inchikeys. (simplified version of old MS2Query) @@ -20,47 +21,67 @@ def predict_using_closest_tanimoto( highest_scores = [] for spectrum_idx in tqdm(range(len(query_spectra.spectra)), "Predicting using closest tanimoto"): inchikey_of_best_match, score = predict_using_closest_tanimoto_single_spectrum( - library_spectra, query_spectra.subset_spectra([spectrum_idx]), - nr_of_closest_inchikeys_to_select, nr_of_inchikeys_with_highest_ms2deepscore_to_select, library_fingerprints) + library_spectra, + query_spectra.subset_spectra([spectrum_idx]), + nr_of_closest_inchikeys_to_select, + nr_of_inchikeys_with_highest_ms2deepscore_to_select, + library_fingerprints, + ) inchikeys_of_best_match.append(inchikey_of_best_match) highest_scores.append(score) return inchikeys_of_best_match, highest_scores def predict_using_closest_tanimoto_single_spectrum( - spectra_with_embeddings: AnnotatedSpectrumSet, single_spectrum_with_embeddings: AnnotatedSpectrumSet, - nr_of_closest_inchikeys_to_select, nr_of_inchikeys_with_highest_ms2deepscore_to_select, -fingerprints) -> Tuple[str, float]: + spectra_with_embeddings: AnnotatedSpectrumSet, + single_spectrum_with_embeddings: AnnotatedSpectrumSet, + nr_of_closest_inchikeys_to_select, + nr_of_inchikeys_with_highest_ms2deepscore_to_select, + fingerprints, +) -> Tuple[str, float]: if len(single_spectrum_with_embeddings.spectra) != 1: raise ValueError("expected a single spectrum") - ms2deepscores = cosine_similarity_matrix(single_spectrum_with_embeddings.embeddings.embeddings, - spectra_with_embeddings.embeddings.embeddings)[0] - top_inchikeys = select_inchikeys_with_highest_ms2deepscore(spectra_with_embeddings, ms2deepscores, - nr_of_inchikeys_with_highest_ms2deepscore_to_select) + ms2deepscores = cosine_similarity_matrix( + single_spectrum_with_embeddings.embeddings.embeddings, spectra_with_embeddings.embeddings.embeddings + )[0] + top_inchikeys = select_inchikeys_with_highest_ms2deepscore( + spectra_with_embeddings, ms2deepscores, nr_of_inchikeys_with_highest_ms2deepscore_to_select + ) average_predicted_scores = {} for inchikey in top_inchikeys: top_k_inchikeys, _ = get_inchikey_and_tanimoto_scores_for_top_k( - fingerprints, inchikey, nr_of_closest_inchikeys_to_select) + fingerprints, inchikey, nr_of_closest_inchikeys_to_select + ) average_predicted_score = get_average_predictions_for_closely_related_metabolites( - spectra_with_embeddings, top_k_inchikeys, ms2deepscores) + spectra_with_embeddings, top_k_inchikeys, ms2deepscores + ) average_predicted_scores[inchikey] = average_predicted_score inchikey_with_highest_average_prediction, score = max(average_predicted_scores.items(), key=lambda item: item[1]) return inchikey_with_highest_average_prediction, score -def select_inchikeys_with_highest_ms2deepscore(spectra_with_embeddings: AnnotatedSpectrumSet, ms2deepscores, nr_of_inchikeys_to_select=10): + +def select_inchikeys_with_highest_ms2deepscore( + spectra_with_embeddings: AnnotatedSpectrumSet, ms2deepscores, nr_of_inchikeys_to_select=10 +): highest_score_for_inchikey = [] for inchikey, spectrum_indexes in spectra_with_embeddings.spectrum_indices_per_inchikey.items(): all_ms2deepscores_for_inchikey = ms2deepscores[spectrum_indexes,] highest_score_for_inchikey.append(max(all_ms2deepscores_for_inchikey)) inchikey_indexes_with_highest_ms2deepscore = np.argpartition( - np.array(highest_score_for_inchikey), -nr_of_inchikeys_to_select)[-nr_of_inchikeys_to_select:] + np.array(highest_score_for_inchikey), -nr_of_inchikeys_to_select + )[-nr_of_inchikeys_to_select:] - top_inchikeys = [spectra_with_embeddings.inchikeys[inchikey_index] for inchikey_index in inchikey_indexes_with_highest_ms2deepscore] + top_inchikeys = [ + spectra_with_embeddings.inchikeys[inchikey_index] + for inchikey_index in inchikey_indexes_with_highest_ms2deepscore + ] return top_inchikeys -def get_average_predictions_for_closely_related_metabolites(spectra: AnnotatedSpectrumSet, top_k_inchikeys, - all_ms2deepscores: np.ndarray): + +def get_average_predictions_for_closely_related_metabolites( + spectra: AnnotatedSpectrumSet, top_k_inchikeys, all_ms2deepscores: np.ndarray +): """Calculates the average ms2deepscore predictions for top k closest inchikeys""" average_predicted_scores = [] for top_inchikey in top_k_inchikeys: @@ -70,10 +91,14 @@ def get_average_predictions_for_closely_related_metabolites(spectra: AnnotatedSp average_predicted_score = sum(average_predicted_scores) / len(average_predicted_scores) return average_predicted_score -def get_inchikey_and_tanimoto_scores_for_top_k(fingerprints: Fingerprints, inchikey: str, k: int - ) -> tuple[list[str], np.ndarray]: + +def get_inchikey_and_tanimoto_scores_for_top_k( + fingerprints: Fingerprints, inchikey: str, k: int +) -> tuple[list[str], np.ndarray]: """For an inchikey in a library the top k highest tanimoto scores in the library are predicted (including itself)""" - similarity_scores = generalized_tanimoto_similarity_matrix(fingerprints.get_fingerprints([inchikey]), fingerprints.fingerprints)[0] + similarity_scores = generalized_tanimoto_similarity_matrix( + fingerprints.get_fingerprints([inchikey]), fingerprints.fingerprints + )[0] inchikey_indexes_of_top_k = np.argpartition(similarity_scores, -k)[-k:] tanimoto_scores_for_top_k = similarity_scores[inchikey_indexes_of_top_k] diff --git a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py index 4dea75a..c4466cc 100644 --- a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py +++ b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py @@ -2,10 +2,9 @@ import numpy as np from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix from tqdm import tqdm - +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.benchmarking.Fingerprints import Fingerprints from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores -from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet def predict_with_integrated_similarity_flow( diff --git a/tests/testPredictMS2DeepScoreSimilarity.py b/tests/testPredictMS2DeepScoreSimilarity.py index 3b46af5..9422123 100644 --- a/tests/testPredictMS2DeepScoreSimilarity.py +++ b/tests/testPredictMS2DeepScoreSimilarity.py @@ -1,9 +1,9 @@ import numpy as np import pytest +from ms2query.benchmarking.AnnotatedSpectrumSet import SpectraWithMS2DeepScoreEmbeddings from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import ( predict_top_ms2deepscores, ) -from ms2query.benchmarking.AnnotatedSpectrumSet import SpectraWithMS2DeepScoreEmbeddings from tests.conftest import create_test_spectra, ms2deepscore_model diff --git a/tests/test_Fingerprints.py b/tests/test_Fingerprints.py index fb6a9c7..bd8d2a1 100644 --- a/tests/test_Fingerprints.py +++ b/tests/test_Fingerprints.py @@ -1,82 +1,106 @@ import numpy as np import pytest from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi - -from ms2query.benchmarking.Fingerprints import Fingerprints, get_similarity_matrix from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet -from tests.conftest import get_inchikey_inchi_pairs, create_test_spectra +from ms2query.benchmarking.Fingerprints import Fingerprints, get_similarity_matrix +from tests.conftest import create_test_spectra, get_inchikey_inchi_pairs @pytest.fixture def dummy_fingerprints() -> Fingerprints: return make_test_fingerprints(5) + def get_inchikey_inchi_dict(nr_of_inchikeys): inchikey_inchi_pairs = get_inchikey_inchi_pairs(nr_of_inchikeys) return {compound_tuple[0][:14]: compound_tuple[1] for compound_tuple in inchikey_inchi_pairs} + def make_test_fingerprints(nr_of_inchikeys=5): inchi_per_inchikey = get_inchikey_inchi_dict(nr_of_inchikeys) - fingerprints = Fingerprints.compute_fingerprints_from_inchi(inchi_per_inchikey, fingerprint_type="daylight", nbits=2048) + fingerprints = Fingerprints.compute_fingerprints_from_inchi( + inchi_per_inchikey, fingerprint_type="daylight", nbits=2048 + ) return fingerprints + def test_compute_fingerprints_from_inchi(): nr_of_inchikeys = 5 inchi_per_inchikey = get_inchikey_inchi_dict(nr_of_inchikeys) - fingerprints = Fingerprints.compute_fingerprints_from_inchi(inchi_per_inchikey, fingerprint_type="daylight", nbits=2048) - assert fingerprints.fingerprints.shape== (nr_of_inchikeys,2048) + fingerprints = Fingerprints.compute_fingerprints_from_inchi( + inchi_per_inchikey, fingerprint_type="daylight", nbits=2048 + ) + assert fingerprints.fingerprints.shape == (nr_of_inchikeys, 2048) for inchikey in fingerprints.inchikeys: - fingerprint = _derive_fingerprint_from_inchi(inchi_per_inchikey[str(inchikey)], fingerprint_type="daylight", nbits=2048) + fingerprint = _derive_fingerprint_from_inchi( + inchi_per_inchikey[str(inchikey)], fingerprint_type="daylight", nbits=2048 + ) np.testing.assert_array_equal(fingerprint, fingerprints.get_fingerprints([inchikey])[0]) + def test_subsetting_fingerprint(): inchi_per_inchikey = get_inchikey_inchi_dict(nr_of_inchikeys=5) - fingerprints = Fingerprints.compute_fingerprints_from_inchi(inchi_per_inchikey, fingerprint_type="daylight", - nbits=2048) + fingerprints = Fingerprints.compute_fingerprints_from_inchi( + inchi_per_inchikey, fingerprint_type="daylight", nbits=2048 + ) inchi_per_inchikey_subset = get_inchikey_inchi_dict(nr_of_inchikeys=2) selected_inchikeys = list(inchi_per_inchikey_subset.keys()) subsetted_fingerprints = fingerprints.subset_fingerprints(selected_inchikeys) - assert subsetted_fingerprints.fingerprints.shape== (2,2048) + assert subsetted_fingerprints.fingerprints.shape == (2, 2048) assert list(subsetted_fingerprints.inchikeys) == selected_inchikeys - assert subsetted_fingerprints == Fingerprints.compute_fingerprints_from_inchi(inchi_per_inchikey_subset, fingerprint_type="daylight", nbits=2048) + assert subsetted_fingerprints == Fingerprints.compute_fingerprints_from_inchi( + inchi_per_inchikey_subset, fingerprint_type="daylight", nbits=2048 + ) + def test_combine_fingerprint(dummy_fingerprints): inchikey_inchi_pairs = get_inchikey_inchi_pairs(7)[-2:] inchi_per_inchikey = {compound_tuple[0][:14]: compound_tuple[1] for compound_tuple in inchikey_inchi_pairs} - last_two_fingerprints = Fingerprints.compute_fingerprints_from_inchi(inchi_per_inchikey, nbits=2048, fingerprint_type="daylight") + last_two_fingerprints = Fingerprints.compute_fingerprints_from_inchi( + inchi_per_inchikey, nbits=2048, fingerprint_type="daylight" + ) combined_fingerprints = Fingerprints.combine_fingerprints(dummy_fingerprints, last_two_fingerprints) - assert combined_fingerprints.fingerprints.shape== (7,2048) + assert combined_fingerprints.fingerprints.shape == (7, 2048) for inchikey, inchi in inchi_per_inchikey.items(): fingerprint = _derive_fingerprint_from_inchi(inchi, fingerprint_type="daylight", nbits=2048) np.testing.assert_array_equal(fingerprint, combined_fingerprints.get_fingerprints([inchikey])[0]) + def test_combine_fingerprints_with_replacing(): - inchikey_inchi_pairs_1 = {"RYYVLZVUVIJVGH": "InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2/h4H,1-3H3", - "ZPUCINDJVBIVPJ": "InChI=1S/C17H21NO4/c1-18-12-8-9-13(18)15(17(20)21-2)14(10-12)22-16(19)11-6-4-3-5-7-11/h3-7,12-15H,8-10H2,1-2H3/t12-,13+,14-,15+/m0/s1"} - inchikey_inchi_pairs_2 = {"RZVAJINKPMORJF": "InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2/h4H,1-3H3", - "ZPUCINDJVBIVPJ": "InChI=1S/C8H9NO2/c1-6(10)9-7-2-4-8(11)5-3-7/h2-5,11H,1H3,(H,9,10)"} - combined = {"RYYVLZVUVIJVGH": "InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2/h4H,1-3H3", - "ZPUCINDJVBIVPJ": "InChI=1S/C8H9NO2/c1-6(10)9-7-2-4-8(11)5-3-7/h2-5,11H,1H3,(H,9,10)", - "RZVAJINKPMORJF": "InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2/h4H,1-3H3"} + inchikey_inchi_pairs_1 = { + "RYYVLZVUVIJVGH": "InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2/h4H,1-3H3", + "ZPUCINDJVBIVPJ": "InChI=1S/C17H21NO4/c1-18-12-8-9-13(18)15(17(20)21-2)14(10-12)22-16(19)11-6-4-3-5-7-11/h3-7,12-15H,8-10H2,1-2H3/t12-,13+,14-,15+/m0/s1", + } + inchikey_inchi_pairs_2 = { + "RZVAJINKPMORJF": "InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2/h4H,1-3H3", + "ZPUCINDJVBIVPJ": "InChI=1S/C8H9NO2/c1-6(10)9-7-2-4-8(11)5-3-7/h2-5,11H,1H3,(H,9,10)", + } + combined = { + "RYYVLZVUVIJVGH": "InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2/h4H,1-3H3", + "ZPUCINDJVBIVPJ": "InChI=1S/C8H9NO2/c1-6(10)9-7-2-4-8(11)5-3-7/h2-5,11H,1H3,(H,9,10)", + "RZVAJINKPMORJF": "InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2/h4H,1-3H3", + } fingerprints_1 = Fingerprints.compute_fingerprints_from_inchi(inchikey_inchi_pairs_1, "daylight", nbits=2048) fingerprints_2 = Fingerprints.compute_fingerprints_from_inchi(inchikey_inchi_pairs_2, "daylight", nbits=2048) correct_combined_fingerprints = Fingerprints.compute_fingerprints_from_inchi(combined, "daylight", nbits=2048) combined_fingerprints = Fingerprints.combine_fingerprints(fingerprints_1, fingerprints_2, allow_replacing=True) - assert combined_fingerprints ==correct_combined_fingerprints + assert combined_fingerprints == correct_combined_fingerprints with pytest.raises(ValueError): Fingerprints.combine_fingerprints(fingerprints_1, fingerprints_2, allow_replacing=False) + def test_fingerprint_from_spectra(): test_spectra = create_test_spectra(2, nr_of_inchikeys=3) spectrum_set = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) fingerprints = Fingerprints.from_spectrum_set(spectrum_set, "daylight", 2048) - fingerprints_from_inchi = Fingerprints.compute_fingerprints_from_inchi(get_inchikey_inchi_dict(3),"daylight", 2048) + fingerprints_from_inchi = Fingerprints.compute_fingerprints_from_inchi(get_inchikey_inchi_dict(3), "daylight", 2048) assert fingerprints == fingerprints_from_inchi + def test_get_similarity_matrix(): fingerprints_1 = make_test_fingerprints(5) fingerprints_2 = make_test_fingerprints(3) matrix = get_similarity_matrix(fingerprints_1, fingerprints_2) - assert matrix.shape == (5,3) + assert matrix.shape == (5, 3) diff --git a/tests/test_embeddings.py b/tests/test_embeddings.py index 1e9bff0..42c3281 100644 --- a/tests/test_embeddings.py +++ b/tests/test_embeddings.py @@ -1,5 +1,4 @@ import pytest - from ms2query.benchmarking.Embeddings import Embeddings from tests.conftest import create_test_spectra, ms2deepscore_model diff --git a/tests/test_evaluate_methods.py b/tests/test_evaluate_methods.py index b012000..1c00329 100644 --- a/tests/test_evaluate_methods.py +++ b/tests/test_evaluate_methods.py @@ -1,9 +1,8 @@ -from ms2query.benchmarking.EvaluateExactMatchSearch import (EvaluateExactMatchSearchAcrossIonmodes, - EvaluateExactMatchSearchAcrossIonmodes) -from ms2query.benchmarking.EvaluateAnalogueSearch import EvaluateAnalogueSearch from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet +from ms2query.benchmarking.EvaluateAnalogueSearch import EvaluateAnalogueSearch from tests.conftest import create_test_spectra, ms2deepscore_model + def create_dummy_library_and_validation_spectra() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: nr_of_spectra_per_inchikey = 6 nr_of_inchikeys = 5 @@ -16,7 +15,7 @@ def create_dummy_library_and_validation_spectra() -> tuple[AnnotatedSpectrumSet, model = ms2deepscore_model() reference_library = AnnotatedSpectrumSet.create_spectrum_set(dummy_spectra[: nr_of_spectra_per_inchikey * 2]) - validation_spectra = AnnotatedSpectrumSet.create_spectrum_set(dummy_spectra[nr_of_spectra_per_inchikey * 2:]) + validation_spectra = AnnotatedSpectrumSet.create_spectrum_set(dummy_spectra[nr_of_spectra_per_inchikey * 2 :]) reference_library.add_embeddings(model) validation_spectra.add_embeddings(model) return reference_library, validation_spectra @@ -25,5 +24,8 @@ def create_dummy_library_and_validation_spectra() -> tuple[AnnotatedSpectrumSet, def test_evaluate_analogue_search(): reference_library, validation_spectra = create_dummy_library_and_validation_spectra() method_evaluator = EvaluateAnalogueSearch(reference_library, validation_spectra) - fake_predicted_inchikeys = [reference_library.inchikeys[i%len(reference_library.inchikeys)] for i in range(len(validation_spectra.spectra))] - accuracy = method_evaluator.benchmark_analogue_search(fake_predicted_inchikeys) \ No newline at end of file + fake_predicted_inchikeys = [ + reference_library.inchikeys[i % len(reference_library.inchikeys)] + for i in range(len(validation_spectra.spectra)) + ] + method_evaluator.benchmark_analogue_search(fake_predicted_inchikeys) diff --git a/tests/test_methods.py b/tests/test_methods.py index 91c8914..18b9d70 100644 --- a/tests/test_methods.py +++ b/tests/test_methods.py @@ -1,7 +1,7 @@ import numpy as np import pytest from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix - +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.benchmarking.Fingerprints import Fingerprints from ms2query.benchmarking.reference_methods.predict_best_possible_match import predict_best_possible_match from ms2query.benchmarking.reference_methods.predict_highest_cosine import predict_highest_cosine @@ -10,9 +10,9 @@ integrated_similarity_flow, predict_with_integrated_similarity_flow, ) -from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from tests.conftest import create_test_spectra, ms2deepscore_model + def get_library_and_test_spectra_not_identical() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: model = ms2deepscore_model() spectra = create_test_spectra(number_of_spectra_per_inchikey=3, nr_of_inchikeys=3) diff --git a/tests/test_predict_using_closest_tanimoto.py b/tests/test_predict_using_closest_tanimoto.py index 6a6b502..c148943 100644 --- a/tests/test_predict_using_closest_tanimoto.py +++ b/tests/test_predict_using_closest_tanimoto.py @@ -1,6 +1,6 @@ import numpy as np import pytest - +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.benchmarking.Fingerprints import Fingerprints from ms2query.benchmarking.reference_methods.predict_using_closest_tanimoto import ( get_average_predictions_for_closely_related_metabolites, @@ -9,7 +9,6 @@ predict_using_closest_tanimoto_single_spectrum, select_inchikeys_with_highest_ms2deepscore, ) -from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from tests.conftest import create_test_spectra, ms2deepscore_model @@ -28,6 +27,7 @@ def test_predict_using_closest_tanimoto(): assert isinstance(scores, list) assert len(scores) == 3 + def test_predict_using_closest_tanimoto_single_spectrum(): """Only very basic test that the function runs and that the output is the right format""" model = ms2deepscore_model() @@ -37,12 +37,15 @@ def test_predict_using_closest_tanimoto_single_spectrum(): test_spectra.add_embeddings(model) fingerprints = Fingerprints.from_spectrum_set(library_spectra, "daylight", 2048) - predicted_inchikey, score = predict_using_closest_tanimoto_single_spectrum(library_spectra, test_spectra, 3, 3, fingerprints) + predicted_inchikey, score = predict_using_closest_tanimoto_single_spectrum( + library_spectra, test_spectra, 3, 3, fingerprints + ) assert isinstance(predicted_inchikey, str) - assert len(predicted_inchikey) ==14 + assert len(predicted_inchikey) == 14 assert isinstance(score, float) + def test_select_inchikeys_with_highest_ms2deepscore(): test_spectra = create_test_spectra(nr_of_inchikeys=7) spectra = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) @@ -56,6 +59,7 @@ def test_select_inchikeys_with_highest_ms2deepscore(): assert set(expected_inchikeys) == set(inchikeys_with_highest_ms2deepscore) print(inchikeys_with_highest_ms2deepscore) + def test_get_average_predictions_for_closely_related_metabolites(): test_spectra = create_test_spectra(nr_of_inchikeys=7) # Select different number per inchikey (only one for the first) to check that it is correctly weighted. @@ -65,16 +69,15 @@ def test_get_average_predictions_for_closely_related_metabolites(): inchikeys = spectra.inchikeys[:3] ms2deepscores = np.zeros(len(spectra.spectra)) ms2deepscores[0] = 0.8 - ms2deepscores[[1,2,3]] = 0.6 + ms2deepscores[[1, 2, 3]] = 0.6 ms2deepscores[4] = 0.6 ms2deepscores[5] = 0.8 ms2deepscores[6] = 0.7 # the average per inchikey is 0.8, 0.6, 0.7, so average overall should be 0.7 - average_predicted_score = get_average_predictions_for_closely_related_metabolites(spectra, - inchikeys, - ms2deepscores) + average_predicted_score = get_average_predictions_for_closely_related_metabolites(spectra, inchikeys, ms2deepscores) assert np.allclose(average_predicted_score, np.array(0.7), atol=1e-5) + @pytest.mark.parametrize( "k", [1, 3, 7], @@ -84,12 +87,12 @@ def test_get_inchikey_and_tanimoto_scores_for_top_k(k): fingerprints = Fingerprints.from_spectrum_set(spectra, fingerprint_type="daylight", nbits=4096) inchikey = spectra.inchikeys[2] - top_inchikeys, tanimoto_scores_for_top_k = get_inchikey_and_tanimoto_scores_for_top_k( - fingerprints, inchikey,k) + top_inchikeys, tanimoto_scores_for_top_k = get_inchikey_and_tanimoto_scores_for_top_k(fingerprints, inchikey, k) assert inchikey in top_inchikeys assert len(top_inchikeys) == k assert len(tanimoto_scores_for_top_k) == k assert len(set(top_inchikeys)) == k - assert tanimoto_scores_for_top_k[top_inchikeys.index(inchikey)] == 1.0, \ - "The exact match is expected to have a score of 1.0" \ No newline at end of file + assert tanimoto_scores_for_top_k[top_inchikeys.index(inchikey)] == 1.0, ( + "The exact match is expected to have a score of 1.0" + ) From fab8d4514a24410a054f9a07c4589486afac28aa Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 6 Feb 2026 10:29:23 +0100 Subject: [PATCH 091/149] Ruff linting --- ms2query/benchmarking/EvaluateExactMatchSearch.py | 4 ++-- ms2query/benchmarking/TopKTanimotoScores.py | 4 +++- tests/test_Fingerprints.py | 3 ++- tests/test_methods.py | 11 +++++++++-- 4 files changed, 16 insertions(+), 6 deletions(-) diff --git a/ms2query/benchmarking/EvaluateExactMatchSearch.py b/ms2query/benchmarking/EvaluateExactMatchSearch.py index d862d62..eced66b 100644 --- a/ms2query/benchmarking/EvaluateExactMatchSearch.py +++ b/ms2query/benchmarking/EvaluateExactMatchSearch.py @@ -35,10 +35,10 @@ def neg_in_pos( @staticmethod def split_spectrum_set_per_inchikey_across_ionmodes( - self, spectrum_set: AnnotatedSpectrumSet, ) -> Tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: - """Splits a spectrum set in two sets on ionmode. Only uses spectra for inchikeys with at least 1 pos and 1 neg""" + """Splits a spectrum set in two sets on ionmode. + Only uses spectra for inchikeys with at least 1 pos and 1 neg""" all_pos_indexes = [] all_neg_indexes = [] for inchikey in tqdm( diff --git a/ms2query/benchmarking/TopKTanimotoScores.py b/ms2query/benchmarking/TopKTanimotoScores.py index 4c59c5a..4f75dfa 100644 --- a/ms2query/benchmarking/TopKTanimotoScores.py +++ b/ms2query/benchmarking/TopKTanimotoScores.py @@ -28,7 +28,9 @@ def create_multi_index( @classmethod def calculate_from_fingerprints(cls, query_fingerprints: Fingerprints, target_fingerprints: Fingerprints, k): - """Gets the top k highest inchikeys and scores for each inchikey in query_fingerprints from target_fingerprints""" + """ + Gets the top k highest inchikeys and scores for each inchikey in query_fingerprints from target_fingerprints + """ similarity_scores = generalized_tanimoto_similarity_matrix( query_fingerprints.fingerprints, target_fingerprints.fingerprints ) diff --git a/tests/test_Fingerprints.py b/tests/test_Fingerprints.py index bd8d2a1..8ab56e1 100644 --- a/tests/test_Fingerprints.py +++ b/tests/test_Fingerprints.py @@ -70,7 +70,8 @@ def test_combine_fingerprint(dummy_fingerprints): def test_combine_fingerprints_with_replacing(): inchikey_inchi_pairs_1 = { "RYYVLZVUVIJVGH": "InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2/h4H,1-3H3", - "ZPUCINDJVBIVPJ": "InChI=1S/C17H21NO4/c1-18-12-8-9-13(18)15(17(20)21-2)14(10-12)22-16(19)11-6-4-3-5-7-11/h3-7,12-15H,8-10H2,1-2H3/t12-,13+,14-,15+/m0/s1", + "ZPUCINDJVBIVPJ": "InChI=1S/C17H21NO4/c1-18-12-8-9-13(18)15(17(20)21-2)14(10-12)22-16" + "(19)11-6-4-3-5-7-11/h3-7,12-15H,8-10H2,1-2H3/t12-,13+,14-,15+/m0/s1", } inchikey_inchi_pairs_2 = { "RZVAJINKPMORJF": "InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)8(14)11(6)2/h4H,1-3H3", diff --git a/tests/test_methods.py b/tests/test_methods.py index 18b9d70..ee89a91 100644 --- a/tests/test_methods.py +++ b/tests/test_methods.py @@ -29,14 +29,20 @@ def get_library_and_test_spectra_not_identical() -> tuple[AnnotatedSpectrumSet, test_spectra.add_embeddings(model) return library_spectra, test_spectra + def get_library_and_test_spectra_exactly_matching() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: model = ms2deepscore_model() - library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(number_of_spectra_per_inchikey=3, nr_of_inchikeys=3)) - test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(number_of_spectra_per_inchikey=1, nr_of_inchikeys=3)) + library_spectra = AnnotatedSpectrumSet.create_spectrum_set( + create_test_spectra(number_of_spectra_per_inchikey=3, nr_of_inchikeys=3) + ) + test_spectra = AnnotatedSpectrumSet.create_spectrum_set( + create_test_spectra(number_of_spectra_per_inchikey=1, nr_of_inchikeys=3) + ) library_spectra.add_embeddings(model) test_spectra.add_embeddings(model) return library_spectra, test_spectra + @pytest.mark.parametrize( "prediction_function", [ @@ -62,6 +68,7 @@ def test_predict_best_possible_match(): assert predicted_inchikeys[i] == inchikey assert np.allclose(scores[i], np.array(1.0), atol=1e-5) + def test_predict_with_integrated_similarity_flow(): library_spectra, test_spectra = get_library_and_test_spectra_not_identical() fingerprints = Fingerprints.from_spectrum_set(library_spectra, "daylight", 4096) From 9ad8dc789a4dfc3b7cc692a703ab4f42291365fa Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 6 Feb 2026 10:52:14 +0100 Subject: [PATCH 092/149] Move bencharking tests to separate folder --- .../{ => test_benchmarking}/testPredictMS2DeepScoreSimilarity.py | 0 tests/{ => test_benchmarking}/test_Fingerprints.py | 0 tests/{ => test_benchmarking}/test_SpectrumDataSet.py | 0 tests/{ => test_benchmarking}/test_embeddings.py | 0 tests/{ => test_benchmarking}/test_evaluate_methods.py | 0 tests/{ => test_benchmarking}/test_methods.py | 0 .../test_predict_using_closest_tanimoto.py | 0 7 files changed, 0 insertions(+), 0 deletions(-) rename tests/{ => test_benchmarking}/testPredictMS2DeepScoreSimilarity.py (100%) rename tests/{ => test_benchmarking}/test_Fingerprints.py (100%) rename tests/{ => test_benchmarking}/test_SpectrumDataSet.py (100%) rename tests/{ => test_benchmarking}/test_embeddings.py (100%) rename tests/{ => test_benchmarking}/test_evaluate_methods.py (100%) rename tests/{ => test_benchmarking}/test_methods.py (100%) rename tests/{ => test_benchmarking}/test_predict_using_closest_tanimoto.py (100%) diff --git a/tests/testPredictMS2DeepScoreSimilarity.py b/tests/test_benchmarking/testPredictMS2DeepScoreSimilarity.py similarity index 100% rename from tests/testPredictMS2DeepScoreSimilarity.py rename to tests/test_benchmarking/testPredictMS2DeepScoreSimilarity.py diff --git a/tests/test_Fingerprints.py b/tests/test_benchmarking/test_Fingerprints.py similarity index 100% rename from tests/test_Fingerprints.py rename to tests/test_benchmarking/test_Fingerprints.py diff --git a/tests/test_SpectrumDataSet.py b/tests/test_benchmarking/test_SpectrumDataSet.py similarity index 100% rename from tests/test_SpectrumDataSet.py rename to tests/test_benchmarking/test_SpectrumDataSet.py diff --git a/tests/test_embeddings.py b/tests/test_benchmarking/test_embeddings.py similarity index 100% rename from tests/test_embeddings.py rename to tests/test_benchmarking/test_embeddings.py diff --git a/tests/test_evaluate_methods.py b/tests/test_benchmarking/test_evaluate_methods.py similarity index 100% rename from tests/test_evaluate_methods.py rename to tests/test_benchmarking/test_evaluate_methods.py diff --git a/tests/test_methods.py b/tests/test_benchmarking/test_methods.py similarity index 100% rename from tests/test_methods.py rename to tests/test_benchmarking/test_methods.py diff --git a/tests/test_predict_using_closest_tanimoto.py b/tests/test_benchmarking/test_predict_using_closest_tanimoto.py similarity index 100% rename from tests/test_predict_using_closest_tanimoto.py rename to tests/test_benchmarking/test_predict_using_closest_tanimoto.py From 61edadcf44c44a9240a51d60243caefa2154bd15 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 6 Feb 2026 11:10:13 +0100 Subject: [PATCH 093/149] rename conftest to helperfunctions --- tests/{conftest.py => helper_functions.py} | 0 tests/test_benchmarking/test_Fingerprints.py | 2 +- .../test_benchmarking/test_SpectrumDataSet.py | 6 ++-- tests/test_benchmarking/test_embeddings.py | 4 +-- .../test_evaluate_methods.py | 2 +- tests/test_benchmarking/test_methods.py | 2 +- ...predict_highest_ms2deepscore_similarity.py | 30 +++++++++++++++++++ 7 files changed, 38 insertions(+), 8 deletions(-) rename tests/{conftest.py => helper_functions.py} (100%) create mode 100644 tests/test_benchmarking/test_predict_highest_ms2deepscore_similarity.py diff --git a/tests/conftest.py b/tests/helper_functions.py similarity index 100% rename from tests/conftest.py rename to tests/helper_functions.py diff --git a/tests/test_benchmarking/test_Fingerprints.py b/tests/test_benchmarking/test_Fingerprints.py index 8ab56e1..6ab8083 100644 --- a/tests/test_benchmarking/test_Fingerprints.py +++ b/tests/test_benchmarking/test_Fingerprints.py @@ -3,7 +3,7 @@ from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.benchmarking.Fingerprints import Fingerprints, get_similarity_matrix -from tests.conftest import create_test_spectra, get_inchikey_inchi_pairs +from tests.helper_functions import create_test_spectra, get_inchikey_inchi_pairs @pytest.fixture diff --git a/tests/test_benchmarking/test_SpectrumDataSet.py b/tests/test_benchmarking/test_SpectrumDataSet.py index e579954..7f3e925 100644 --- a/tests/test_benchmarking/test_SpectrumDataSet.py +++ b/tests/test_benchmarking/test_SpectrumDataSet.py @@ -1,7 +1,7 @@ from ms2query.benchmarking.AnnotatedSpectrumSet import ( AnnotatedSpectrumSet, ) -from tests.conftest import create_test_spectra, ms2deepscore_model +from tests.helper_functions import create_test_spectra, ms2deepscore_model def test_create_annotated_spectrum_set(): @@ -9,6 +9,7 @@ def test_create_annotated_spectrum_set(): spectrum_set = AnnotatedSpectrumSet.create_spectrum_set(spectra=test_spectra) assert len(spectrum_set.spectrum_indices_per_inchikey) == 3 + def test_add_spectrum_sets(): test_spectra = create_test_spectra(nr_of_inchikeys=3, number_of_spectra_per_inchikey=3) @@ -26,6 +27,7 @@ def test_add_spectrum_sets(): combined_spectra = spectrum_set_1 + spectrum_set_2 assert correct_combined_set == combined_spectra + def test_subsetting(): test_spectra = create_test_spectra(nr_of_inchikeys=3, number_of_spectra_per_inchikey=3) @@ -37,4 +39,4 @@ def test_subsetting(): correct_subsetted_set.add_embeddings(model) spectrum_set.add_embeddings(model) - assert correct_subsetted_set == spectrum_set.subset_spectra([0,1,2,3,4]) + assert correct_subsetted_set == spectrum_set.subset_spectra([0, 1, 2, 3, 4]) diff --git a/tests/test_benchmarking/test_embeddings.py b/tests/test_benchmarking/test_embeddings.py index 42c3281..d36c6f5 100644 --- a/tests/test_benchmarking/test_embeddings.py +++ b/tests/test_benchmarking/test_embeddings.py @@ -1,6 +1,6 @@ import pytest from ms2query.benchmarking.Embeddings import Embeddings -from tests.conftest import create_test_spectra, ms2deepscore_model +from tests.helper_functions import create_test_spectra, ms2deepscore_model def test_subset_embeddings(): @@ -29,5 +29,3 @@ def test_combine_embeddings(): assert combined_embeddings == correct_combined_embeddings with pytest.raises(ValueError): Embeddings.combine_embeddings(embeddings_1, embeddings_1) - - diff --git a/tests/test_benchmarking/test_evaluate_methods.py b/tests/test_benchmarking/test_evaluate_methods.py index 1c00329..a8bb75d 100644 --- a/tests/test_benchmarking/test_evaluate_methods.py +++ b/tests/test_benchmarking/test_evaluate_methods.py @@ -1,6 +1,6 @@ from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.benchmarking.EvaluateAnalogueSearch import EvaluateAnalogueSearch -from tests.conftest import create_test_spectra, ms2deepscore_model +from tests.helper_functions import create_test_spectra, ms2deepscore_model def create_dummy_library_and_validation_spectra() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: diff --git a/tests/test_benchmarking/test_methods.py b/tests/test_benchmarking/test_methods.py index ee89a91..946d864 100644 --- a/tests/test_benchmarking/test_methods.py +++ b/tests/test_benchmarking/test_methods.py @@ -10,7 +10,7 @@ integrated_similarity_flow, predict_with_integrated_similarity_flow, ) -from tests.conftest import create_test_spectra, ms2deepscore_model +from tests.helper_functions import create_test_spectra, ms2deepscore_model def get_library_and_test_spectra_not_identical() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: diff --git a/tests/test_benchmarking/test_predict_highest_ms2deepscore_similarity.py b/tests/test_benchmarking/test_predict_highest_ms2deepscore_similarity.py new file mode 100644 index 0000000..e9140ab --- /dev/null +++ b/tests/test_benchmarking/test_predict_highest_ms2deepscore_similarity.py @@ -0,0 +1,30 @@ +import numpy as np +import pytest +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet +from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import ( + predict_top_ms2deepscores, +) +from tests.helper_functions import create_test_spectra, ms2deepscore_model + + +@pytest.mark.parametrize( + "method", + [ + predict_top_ms2deepscores, + ], +) +def test_predict_highest_ms2deepscore_similarity(method): + ms2ds_model = ms2deepscore_model() + test_spectra = create_test_spectra(1) + library_spectra = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) + query_spectra = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) + library_spectra.add_embeddings(ms2ds_model) + query_spectra.add_embeddings(ms2ds_model) + number_of_analogues = 2 + + indices, distances = method(library_spectra.embeddings, query_spectra.embeddings, k=number_of_analogues) + assert indices.shape == (len(test_spectra), number_of_analogues) + assert distances.shape == (len(test_spectra), number_of_analogues) + for i, row in enumerate(indices): + assert row[0] == i, "The highest predictions should be against itself" + assert np.allclose(distances[i][0], 1.0, atol=1e-5) From 72287be00240bde202efc8088003909715e68702 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 6 Feb 2026 11:10:33 +0100 Subject: [PATCH 094/149] Fix test_predict_using_closest_tanimoto import error --- .../testPredictMS2DeepScoreSimilarity.py | 28 ------------------- .../test_predict_using_closest_tanimoto.py | 2 +- 2 files changed, 1 insertion(+), 29 deletions(-) delete mode 100644 tests/test_benchmarking/testPredictMS2DeepScoreSimilarity.py diff --git a/tests/test_benchmarking/testPredictMS2DeepScoreSimilarity.py b/tests/test_benchmarking/testPredictMS2DeepScoreSimilarity.py deleted file mode 100644 index 9422123..0000000 --- a/tests/test_benchmarking/testPredictMS2DeepScoreSimilarity.py +++ /dev/null @@ -1,28 +0,0 @@ -import numpy as np -import pytest -from ms2query.benchmarking.AnnotatedSpectrumSet import SpectraWithMS2DeepScoreEmbeddings -from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import ( - predict_top_ms2deepscores, -) -from tests.conftest import create_test_spectra, ms2deepscore_model - - -@pytest.mark.parametrize( - "method", - [ - predict_top_ms2deepscores, - ], -) -def test_predict_highest_ms2deepscore_similarity(method): - ms2ds_model = ms2deepscore_model() - test_spectra = create_test_spectra(1) - library_spectra = SpectraWithMS2DeepScoreEmbeddings(test_spectra, ms2ds_model) - query_spectra = SpectraWithMS2DeepScoreEmbeddings(test_spectra, ms2ds_model) - number_of_analogues = 2 - - indices, distances = method(library_spectra.embeddings, query_spectra.embeddings, k=number_of_analogues) - assert indices.shape == (len(test_spectra), number_of_analogues) - assert distances.shape == (len(test_spectra), number_of_analogues) - for i, row in enumerate(indices): - assert row[0] == i, "The highest predictions should be against itself" - assert np.allclose(distances[i][0], 1.0, atol=1e-5) diff --git a/tests/test_benchmarking/test_predict_using_closest_tanimoto.py b/tests/test_benchmarking/test_predict_using_closest_tanimoto.py index c148943..ed109aa 100644 --- a/tests/test_benchmarking/test_predict_using_closest_tanimoto.py +++ b/tests/test_benchmarking/test_predict_using_closest_tanimoto.py @@ -9,7 +9,7 @@ predict_using_closest_tanimoto_single_spectrum, select_inchikeys_with_highest_ms2deepscore, ) -from tests.conftest import create_test_spectra, ms2deepscore_model +from tests.helper_functions import create_test_spectra, ms2deepscore_model def test_predict_using_closest_tanimoto(): From f40f781e9383387c62e81d26f43bf44e8507afa4 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 6 Feb 2026 18:26:14 +0100 Subject: [PATCH 095/149] Update TopKTanimotoScores --- ms2query/benchmarking/TopKTanimotoScores.py | 39 ++++++++++++++------- 1 file changed, 26 insertions(+), 13 deletions(-) diff --git a/ms2query/benchmarking/TopKTanimotoScores.py b/ms2query/benchmarking/TopKTanimotoScores.py index 4f75dfa..7032faa 100644 --- a/ms2query/benchmarking/TopKTanimotoScores.py +++ b/ms2query/benchmarking/TopKTanimotoScores.py @@ -8,15 +8,18 @@ class TopKTanimotoScores: def __init__( self, tanimoto_scores_for_top_k: np.ndarray, top_k_inchikeys: np.ndarray, inchikey_indexes: np.ndarray ): + """Stores the top k scores between two lists of inchikeys""" self.k = tanimoto_scores_for_top_k.shape[1] - self.top_k_inchikeys_and_scores = self.create_multi_index( + self.top_k_inchikeys_and_scores = self._create_multi_index( tanimoto_scores_for_top_k, top_k_inchikeys, inchikey_indexes ) - def create_multi_index( + def _create_multi_index( self, tanimoto_scores_for_top_k: np.ndarray, top_k_inchikeys: np.ndarray, inchikey_indexes: np.ndarray ): + """Creates a pandas dataframe with multi index. + For each inchikey the top ten highest inchikeys and the corresponding score are stored.""" columns = pd.MultiIndex.from_product( [[f"Rank_{i + 1}" for i in range(self.k)], ["inchikey", "score"]], names=["result_rank", "attribute"] ) @@ -35,21 +38,31 @@ def calculate_from_fingerprints(cls, query_fingerprints: Fingerprints, target_fi query_fingerprints.fingerprints, target_fingerprints.fingerprints ) inchikey_indexes_of_top_k = np.argpartition(similarity_scores, -k, axis=1)[:, -k:] - top_k_inchikeys = target_fingerprints.inchikeys[inchikey_indexes_of_top_k] + top_k_inchikeys = np.array(target_fingerprints.inchikeys)[inchikey_indexes_of_top_k] tanimoto_scores_for_top_k = similarity_scores[ np.arange(similarity_scores.shape[0])[:, None], inchikey_indexes_of_top_k ] - return cls(tanimoto_scores_for_top_k, top_k_inchikeys, query_fingerprints.inchikeys) + return cls(tanimoto_scores_for_top_k, top_k_inchikeys, np.array(query_fingerprints.inchikeys)) - def select_top_k_inchikeys_and_scores(self, inchikey): - """Returns a DF with inchikeys and scores""" - return self.top_k_inchikeys_and_scores.loc[inchikey].unstack(level="result_rank").T + def select_top_k_inchikeys_and_scores(self, inchikey) -> dict[str, float]: + """Returns a dictionary with inchikeys and scores for the given inchikey""" + # Select row with results + top_k_inchikeys_and_scores = self.top_k_inchikeys_and_scores.loc[inchikey] + # Unstack the multi index to get a dataframe with inchikeys and scores and index rank_1, rank_2 etc. + top_k_inchikeys_and_scores = top_k_inchikeys_and_scores.unstack(level="result_rank").T + # Convert to dictionary with inchikey, score pairs. + return dict(zip(top_k_inchikeys_and_scores["inchikey"], top_k_inchikeys_and_scores["score"])) - def select_top_k_inchikeys(self, inchikey): - return self.top_k_inchikeys_and_scores.loc[inchikey].xs("inchikey", level="attribute") + def select_top_k_inchikeys(self, inchikey) -> list[float]: + return list(self.top_k_inchikeys_and_scores.loc[inchikey].xs("inchikey", level="attribute")) - def select_average_score(self, inchikey): - return self.top_k_inchikeys_and_scores.loc[inchikey].xs("score", level="attribute").mean() + def select_average_score(self, inchikey) -> float: + """Returns the average tanimoto score for the top k highest scores for the specified inchikey""" + return float(self.top_k_inchikeys_and_scores.loc[inchikey].xs("score", level="attribute").mean()) - def get_all_average_tanimoto_scores(self): - return self.top_k_inchikeys_and_scores.xs("score", axis=1, level="attribute").mean(axis=1) + def get_all_average_tanimoto_scores(self) -> dict[str, float]: + """Returns all average tanimoto scores for the top k per inchikey""" + average_per_inchikey_df = self.top_k_inchikeys_and_scores.xs("score", axis=1, level="attribute").mean(axis=1) # type: ignore + # convert to dictionary + average_per_inchikey = average_per_inchikey_df.squeeze().to_dict() + return average_per_inchikey From 2e643b4600108098f37ed9af3666d6ec782d4057 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 6 Feb 2026 18:26:31 +0100 Subject: [PATCH 096/149] Add create fingerprint functionality to test helper functions --- tests/helper_functions.py | 47 ++++++++++++++++++++++++++++++++++++--- 1 file changed, 44 insertions(+), 3 deletions(-) diff --git a/tests/helper_functions.py b/tests/helper_functions.py index 4838b99..989439a 100644 --- a/tests/helper_functions.py +++ b/tests/helper_functions.py @@ -1,8 +1,10 @@ import os +import string from pathlib import Path import numpy as np from matchms.Spectrum import Spectrum from ms2deepscore.models import load_model +from ms2query.benchmarking.Fingerprints import Fingerprints TEST_RESOURCES_PATH = Path(__file__).parent / "test_data" @@ -76,15 +78,54 @@ def get_inchikey_inchi_pairs(number_of_pairs): "MWOOGOJBHIARFG-UHFFFAOYSA-N", "InChI=1S/C8H8O3/c1-11-8-4-6(5-9)2-3-7(8)10/h2-5,10H,1H3", "COC1=C(C=CC(=C1)C=O)O", - "vanillin" + "vanillin", ), ( "ROHFNLRQFUQHCH-YFKPBYRVSA-N", "InChI=1S/C6H13NO2/c1-4(2)3-5(7)6(8)9/h4-5H,3,7H2,1-2H3,(H,8,9)/t5-/m0/s1", "CC(C)C[C@@H](C(=O)O)N", - "L-Leucine" - ) + "L-Leucine", + ), ) if number_of_pairs > len(inchikey_inchi_pairs): raise ValueError("Not enough example compounds, add some in conftest") return inchikey_inchi_pairs[:number_of_pairs] + + +def get_dummy_inchikeys(nr_of_inchikeys): + """Creates dummy inchikeys like AAAAAAAAAAAAA, AAAAAAAAAAAAB etc.""" + letters = string.ascii_uppercase + base = len(letters) + list_of_inchikeys = [] + counter = 0 + for i in range(nr_of_inchikeys): + n = counter + code = [] + for _ in range(14): + n, rem = divmod(n, base) + code.append(letters[rem]) + list_of_inchikeys.append("".join(reversed(code))) + counter += 1 + return list_of_inchikeys + + +def create_fingerprints(nbits, nr_of_fingerprints): + """Creates dummy fingerprints""" + fingerprints = [] + for i in range(nr_of_fingerprints): + fingerprint = [] + for bit in range(nbits): + if bit % (i + 1) == 0: + fingerprint.append(0) + else: + fingerprint.append(1) + fingerprints.append(fingerprint) + return np.array(fingerprints) + + +def make_test_fingerprints(nbits=30, nr_of_inchikeys=20): + inchikeys = get_dummy_inchikeys(nr_of_inchikeys=nr_of_inchikeys) + + fingerprints = create_fingerprints(nbits, nr_of_inchikeys) + fingerprints = Fingerprints(fingerprints, tuple(inchikeys), fingerprint_type="daylight") + return fingerprints From aff2fd052e79079d55ab92d12d0eeec94df4e993 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 6 Feb 2026 18:26:44 +0100 Subject: [PATCH 097/149] Add tests for top_k_tanimoto_scores --- .../test_top_k_tanimoto_scores.py | 24 +++++++++++++++++++ 1 file changed, 24 insertions(+) create mode 100644 tests/test_benchmarking/test_top_k_tanimoto_scores.py diff --git a/tests/test_benchmarking/test_top_k_tanimoto_scores.py b/tests/test_benchmarking/test_top_k_tanimoto_scores.py new file mode 100644 index 0000000..bf617c2 --- /dev/null +++ b/tests/test_benchmarking/test_top_k_tanimoto_scores.py @@ -0,0 +1,24 @@ +import numpy as np +import pytest +from ms2query.benchmarking.TopKTanimotoScores import TopKTanimotoScores +from tests.helper_functions import make_test_fingerprints + + +def test_methods_top_k_tanimoto_scores(): + scores = np.array([[1.0, 0.8], [0.9, 0.7], [0.8, 0.8]]) + inchikeys_in_top_2 = np.array([["A", "B"], ["B", "C"], ["C", "A"]]) + inchikeys = np.array(["A", "B", "C"]) + top_scores = TopKTanimotoScores(scores, inchikeys_in_top_2, inchikeys) + + assert top_scores.select_top_k_inchikeys_and_scores("A") == {"A": 1.0, "B": 0.8} + + assert top_scores.select_top_k_inchikeys("A") == ["A", "B"] + + assert top_scores.select_average_score("A") == pytest.approx(0.9) + + assert top_scores.get_all_average_tanimoto_scores() == {"A": 0.9, "B": 0.8, "C": 0.8} + + +def test_calculate_from_fingerprints(): + fingerprints = make_test_fingerprints(20) + TopKTanimotoScores.calculate_from_fingerprints(fingerprints, fingerprints, 10) From 3607bf565e442eda8ffb6062b98ed5d6200c366d Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 9 Feb 2026 10:04:31 +0100 Subject: [PATCH 098/149] Extended test_calculate from fingerprints --- tests/test_benchmarking/test_top_k_tanimoto_scores.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/tests/test_benchmarking/test_top_k_tanimoto_scores.py b/tests/test_benchmarking/test_top_k_tanimoto_scores.py index bf617c2..2c8ecd2 100644 --- a/tests/test_benchmarking/test_top_k_tanimoto_scores.py +++ b/tests/test_benchmarking/test_top_k_tanimoto_scores.py @@ -20,5 +20,9 @@ def test_methods_top_k_tanimoto_scores(): def test_calculate_from_fingerprints(): - fingerprints = make_test_fingerprints(20) - TopKTanimotoScores.calculate_from_fingerprints(fingerprints, fingerprints, 10) + fingerprints = make_test_fingerprints(nbits=5, nr_of_inchikeys=5) + top_scores = TopKTanimotoScores.calculate_from_fingerprints(fingerprints, fingerprints, 2) + assert top_scores.select_top_k_inchikeys_and_scores("AAAAAAAAAAAAAE") == { + "AAAAAAAAAAAAAD": 0.75, + "AAAAAAAAAAAAAE": 1.0, + } From 381d630db8199c436cb1039b2c31863475718faa Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 9 Feb 2026 11:24:09 +0100 Subject: [PATCH 099/149] Add calculate_ms2deepscore_df --- ms2query/benchmarking/Embeddings.py | 10 ++++++++++ tests/test_benchmarking/test_embeddings.py | 9 +++++++-- 2 files changed, 17 insertions(+), 2 deletions(-) diff --git a/ms2query/benchmarking/Embeddings.py b/ms2query/benchmarking/Embeddings.py index aed79d2..26ba232 100644 --- a/ms2query/benchmarking/Embeddings.py +++ b/ms2query/benchmarking/Embeddings.py @@ -1,7 +1,9 @@ from typing import Sequence import numpy as np +import pandas as pd from matchms import Spectrum from ms2deepscore.models import SiameseSpectralModel, compute_embedding_array +from ms2deepscore.vector_operations import cosine_similarity_matrix class Embeddings: @@ -72,3 +74,11 @@ def __eq__(self, other) -> bool: print("index to spectrum hash not equal") return False return np.array_equal(self.embeddings, other.embeddings) + + +def calculate_ms2deepscore_df(query_embeddings: Embeddings, library_embeddings: Embeddings): + """Returns a DF, where the indexes and column labels are the spectrum hashes""" + ms2deepscores = cosine_similarity_matrix(query_embeddings.embeddings, library_embeddings.embeddings) + return pd.DataFrame( + ms2deepscores, index=query_embeddings.index_to_spectrum_hash, columns=library_embeddings.index_to_spectrum_hash + ) diff --git a/tests/test_benchmarking/test_embeddings.py b/tests/test_benchmarking/test_embeddings.py index d36c6f5..e8cb3f9 100644 --- a/tests/test_benchmarking/test_embeddings.py +++ b/tests/test_benchmarking/test_embeddings.py @@ -1,6 +1,6 @@ import pytest -from ms2query.benchmarking.Embeddings import Embeddings -from tests.helper_functions import create_test_spectra, ms2deepscore_model +from ms2query.benchmarking.Embeddings import Embeddings, calculate_ms2deepscore_df +from tests.helper_functions import create_test_spectra, get_library_and_test_spectra_not_identical, ms2deepscore_model def test_subset_embeddings(): @@ -29,3 +29,8 @@ def test_combine_embeddings(): assert combined_embeddings == correct_combined_embeddings with pytest.raises(ValueError): Embeddings.combine_embeddings(embeddings_1, embeddings_1) + + +def test_calculate_ms2deepscore_df(): + library_spectra, query_spectra = get_library_and_test_spectra_not_identical() + calculate_ms2deepscore_df(library_spectra.embeddings, query_spectra.embeddings) From 90f14916a544a77743c45119bb855335402d1fe8 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 9 Feb 2026 12:07:35 +0100 Subject: [PATCH 100/149] Add selct_inchikeys_with_highest_ms2deepscore --- .../PredictMS2DeepScoreSimilarity.py | 29 +++++++++++++++++++ ...predict_highest_ms2deepscore_similarity.py | 13 ++++++++- 2 files changed, 41 insertions(+), 1 deletion(-) diff --git a/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py b/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py index 9066a29..f9f20f9 100644 --- a/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py +++ b/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py @@ -1,7 +1,9 @@ from typing import Tuple import numpy as np +import pandas as pd from ms2deepscore.vector_operations import cosine_similarity_matrix from tqdm import tqdm +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.benchmarking.Embeddings import Embeddings @@ -45,3 +47,30 @@ def predict_top_ms2deepscores( top_scores_per_batch.append(top_n_scores) # todo refactor to use the Embeddings class and return spectrum hashes instead of indexes return np.vstack(top_indexes_per_batch), np.vstack(top_scores_per_batch) + + +def select_inchikeys_with_highest_ms2deepscore( + query_spectra: AnnotatedSpectrumSet, library_spectra: AnnotatedSpectrumSet, nr_of_inchikeys_to_select=100 +) -> list[list[str]]: + """Selects the top x inchikeys with the highest score for each query spectrum""" + ms2deepscores = cosine_similarity_matrix(query_spectra.embeddings.embeddings, library_spectra.embeddings.embeddings) + + max_ms2deepscores_per_inchikey = np.zeros( + (ms2deepscores.shape[0], len(library_spectra.spectrum_indices_per_inchikey)) + ) + for inchikey_index, spectrum_indexes in enumerate(library_spectra.spectrum_indices_per_inchikey.values()): + # For one library inchikey, get all the scores and calculate the maximum score with each query spectrum + all_ms2deepscores_for_inchikey = ms2deepscores[:, spectrum_indexes] + max_ms2deepscores_per_inchikey[inchikey_index] = all_ms2deepscores_for_inchikey.max(axis=1) + inchikey_indexes_with_highest_ms2deepscore = np.argpartition( + max_ms2deepscores_per_inchikey, -nr_of_inchikeys_to_select, axis=1 + )[:, -nr_of_inchikeys_to_select:] + + # Convert indexes to inchikeys + top_inchikeys_per_query_spectrum = [] + for row in inchikey_indexes_with_highest_ms2deepscore: + inchikeys = [] + for inchikey_index in row: + inchikeys.append(library_spectra.inchikeys[inchikey_index]) + top_inchikeys_per_query_spectrum.append(inchikeys) + return top_inchikeys_per_query_spectrum diff --git a/tests/test_benchmarking/test_predict_highest_ms2deepscore_similarity.py b/tests/test_benchmarking/test_predict_highest_ms2deepscore_similarity.py index e9140ab..289d9a4 100644 --- a/tests/test_benchmarking/test_predict_highest_ms2deepscore_similarity.py +++ b/tests/test_benchmarking/test_predict_highest_ms2deepscore_similarity.py @@ -3,8 +3,9 @@ from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import ( predict_top_ms2deepscores, + select_inchikeys_with_highest_ms2deepscore, ) -from tests.helper_functions import create_test_spectra, ms2deepscore_model +from tests.helper_functions import create_test_spectra, get_library_and_test_spectra_not_identical, ms2deepscore_model @pytest.mark.parametrize( @@ -28,3 +29,13 @@ def test_predict_highest_ms2deepscore_similarity(method): for i, row in enumerate(indices): assert row[0] == i, "The highest predictions should be against itself" assert np.allclose(distances[i][0], 1.0, atol=1e-5) + + +def test_select_inchikeys_with_highest_ms2deepscore(): + library_spectra, query_spectra = get_library_and_test_spectra_not_identical() + inschikeys_with_highest_scores = select_inchikeys_with_highest_ms2deepscore(query_spectra, library_spectra, 2) + assert inschikeys_with_highest_scores == [ + ["RZVAJINKPMORJF", "RYYVLZVUVIJVGH"], + ["RZVAJINKPMORJF", "ZPUCINDJVBIVPJ"], + ["RYYVLZVUVIJVGH", "RZVAJINKPMORJF"], + ] From 17ec7c485aeb45814dc5d0cae21dacdf6a02c684 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 9 Feb 2026 12:09:23 +0100 Subject: [PATCH 101/149] rename predict_top_ms2deepscores --- ...ctMS2DeepScoreSimilarity.py => predict_top_ms2deepscores.py} | 0 ...eepscore_similarity.py => test_predict_top_ms2deepscores.py} | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) rename ms2query/benchmarking/reference_methods/{PredictMS2DeepScoreSimilarity.py => predict_top_ms2deepscores.py} (100%) rename tests/test_benchmarking/{test_predict_highest_ms2deepscore_similarity.py => test_predict_top_ms2deepscores.py} (95%) diff --git a/ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py b/ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py similarity index 100% rename from ms2query/benchmarking/reference_methods/PredictMS2DeepScoreSimilarity.py rename to ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py diff --git a/tests/test_benchmarking/test_predict_highest_ms2deepscore_similarity.py b/tests/test_benchmarking/test_predict_top_ms2deepscores.py similarity index 95% rename from tests/test_benchmarking/test_predict_highest_ms2deepscore_similarity.py rename to tests/test_benchmarking/test_predict_top_ms2deepscores.py index 289d9a4..9e7a17f 100644 --- a/tests/test_benchmarking/test_predict_highest_ms2deepscore_similarity.py +++ b/tests/test_benchmarking/test_predict_top_ms2deepscores.py @@ -1,7 +1,7 @@ import numpy as np import pytest from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet -from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import ( +from ms2query.benchmarking.reference_methods.predict_top_ms2deepscores import ( predict_top_ms2deepscores, select_inchikeys_with_highest_ms2deepscore, ) From e88a4edc1367a561f5a5443ea39a81353c8f70d9 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 9 Feb 2026 12:09:46 +0100 Subject: [PATCH 102/149] Move get_library_and_test_spectra functions to helper functions --- tests/helper_functions.py | 31 +++++++++++++++++++++ tests/test_benchmarking/test_methods.py | 37 ++++--------------------- 2 files changed, 37 insertions(+), 31 deletions(-) diff --git a/tests/helper_functions.py b/tests/helper_functions.py index 989439a..82d1709 100644 --- a/tests/helper_functions.py +++ b/tests/helper_functions.py @@ -4,6 +4,7 @@ import numpy as np from matchms.Spectrum import Spectrum from ms2deepscore.models import load_model +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.benchmarking.Fingerprints import Fingerprints @@ -129,3 +130,33 @@ def make_test_fingerprints(nbits=30, nr_of_inchikeys=20): fingerprints = create_fingerprints(nbits, nr_of_inchikeys) fingerprints = Fingerprints(fingerprints, tuple(inchikeys), fingerprint_type="daylight") return fingerprints + + +def get_library_and_test_spectra_not_identical() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: + model = ms2deepscore_model() + spectra = create_test_spectra(number_of_spectra_per_inchikey=3, nr_of_inchikeys=3) + query_spectra = [] + lib_spectra = [] + for i, spectrum in enumerate(spectra): + if i % 3 == 0: + query_spectra.append(spectrum) + else: + lib_spectra.append(spectrum) + library_spectra = AnnotatedSpectrumSet.create_spectrum_set(lib_spectra) + test_spectra = AnnotatedSpectrumSet.create_spectrum_set(query_spectra) + library_spectra.add_embeddings(model) + test_spectra.add_embeddings(model) + return library_spectra, test_spectra + + +def get_library_and_test_spectra_exactly_matching() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: + model = ms2deepscore_model() + library_spectra = AnnotatedSpectrumSet.create_spectrum_set( + create_test_spectra(number_of_spectra_per_inchikey=3, nr_of_inchikeys=3) + ) + test_spectra = AnnotatedSpectrumSet.create_spectrum_set( + create_test_spectra(number_of_spectra_per_inchikey=1, nr_of_inchikeys=3) + ) + library_spectra.add_embeddings(model) + test_spectra.add_embeddings(model) + return library_spectra, test_spectra diff --git a/tests/test_benchmarking/test_methods.py b/tests/test_benchmarking/test_methods.py index 946d864..f8804c7 100644 --- a/tests/test_benchmarking/test_methods.py +++ b/tests/test_benchmarking/test_methods.py @@ -10,37 +10,12 @@ integrated_similarity_flow, predict_with_integrated_similarity_flow, ) -from tests.helper_functions import create_test_spectra, ms2deepscore_model - - -def get_library_and_test_spectra_not_identical() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: - model = ms2deepscore_model() - spectra = create_test_spectra(number_of_spectra_per_inchikey=3, nr_of_inchikeys=3) - query_spectra = [] - lib_spectra = [] - for i, spectrum in enumerate(spectra): - if i % 3 == 0: - query_spectra.append(spectrum) - else: - lib_spectra.append(spectrum) - library_spectra = AnnotatedSpectrumSet.create_spectrum_set(query_spectra) - test_spectra = AnnotatedSpectrumSet.create_spectrum_set(lib_spectra) - library_spectra.add_embeddings(model) - test_spectra.add_embeddings(model) - return library_spectra, test_spectra - - -def get_library_and_test_spectra_exactly_matching() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: - model = ms2deepscore_model() - library_spectra = AnnotatedSpectrumSet.create_spectrum_set( - create_test_spectra(number_of_spectra_per_inchikey=3, nr_of_inchikeys=3) - ) - test_spectra = AnnotatedSpectrumSet.create_spectrum_set( - create_test_spectra(number_of_spectra_per_inchikey=1, nr_of_inchikeys=3) - ) - library_spectra.add_embeddings(model) - test_spectra.add_embeddings(model) - return library_spectra, test_spectra +from tests.helper_functions import ( + create_test_spectra, + ms2deepscore_model, + get_library_and_test_spectra_not_identical, + get_library_and_test_spectra_exactly_matching, +) @pytest.mark.parametrize( From e7ed35e85e11e6d5015a118a2f9f3a3eda6c89d6 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 9 Feb 2026 12:23:29 +0100 Subject: [PATCH 103/149] change reference to predict_top_ms2deepscores --- .../reference_methods/predict_highest_ms2deepscore.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py index af88519..0bf3769 100644 --- a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py +++ b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py @@ -1,6 +1,6 @@ from typing import List, Tuple from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet -from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores +from ms2query.benchmarking.reference_methods.predict_top_ms2deepscores import predict_top_ms2deepscores def predict_highest_ms2deepscore( From 7dc3d0ace45a9d04259e4a12f6dd2096b3b98d05 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 9 Feb 2026 12:23:59 +0100 Subject: [PATCH 104/149] Add optional precomputed embeddings select_inchikeys_with_highest_ms2deepscore --- .../reference_methods/predict_top_ms2deepscores.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py b/ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py index f9f20f9..2ecb23c 100644 --- a/ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py +++ b/ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py @@ -1,4 +1,4 @@ -from typing import Tuple +from typing import Optional, Tuple import numpy as np import pandas as pd from ms2deepscore.vector_operations import cosine_similarity_matrix @@ -50,10 +50,16 @@ def predict_top_ms2deepscores( def select_inchikeys_with_highest_ms2deepscore( - query_spectra: AnnotatedSpectrumSet, library_spectra: AnnotatedSpectrumSet, nr_of_inchikeys_to_select=100 + query_spectra: AnnotatedSpectrumSet, + library_spectra: AnnotatedSpectrumSet, + nr_of_inchikeys_to_select=100, + ms2deepscores: Optional[np.ndarray] = None, ) -> list[list[str]]: """Selects the top x inchikeys with the highest score for each query spectrum""" - ms2deepscores = cosine_similarity_matrix(query_spectra.embeddings.embeddings, library_spectra.embeddings.embeddings) + if ms2deepscores is None: + ms2deepscores = cosine_similarity_matrix( + query_spectra.embeddings.embeddings, library_spectra.embeddings.embeddings + ) max_ms2deepscores_per_inchikey = np.zeros( (ms2deepscores.shape[0], len(library_spectra.spectrum_indices_per_inchikey)) From aa7b6d9ec7e745755f1fe87adcfdae4f3acb6b04 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 9 Feb 2026 12:24:55 +0100 Subject: [PATCH 105/149] change reference to predict_top_ms2deepscores --- .../predict_with_integrated_similarity_flow.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py index c4466cc..36d91fe 100644 --- a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py +++ b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py @@ -4,7 +4,7 @@ from tqdm import tqdm from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.benchmarking.Fingerprints import Fingerprints -from ms2query.benchmarking.reference_methods.PredictMS2DeepScoreSimilarity import predict_top_ms2deepscores +from ms2query.benchmarking.reference_methods.predict_top_ms2deepscores import predict_top_ms2deepscores def predict_with_integrated_similarity_flow( @@ -35,8 +35,8 @@ def predict_with_integrated_similarity_flow( def get_highest_isf( library_spectra: AnnotatedSpectrumSet, indexes_of_library_spectra_with_highest_score: np.ndarray, - fingerprints: Fingerprints, - predicted_scores: List[float], + fingerprints: Fingerprints, + predicted_scores: List[float], ): # Get the corresponding inchikeys From afdfd1ba6d0a9c561118af293575d62cfd474fb0 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 9 Feb 2026 13:43:23 +0100 Subject: [PATCH 106/149] Fix bug with numba threading --- tests/conftest.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 tests/conftest.py diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..d006c3f --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,21 @@ +import os +import pytest +from numba.core.serialize import _unpickled_memo + + +# The code below fixes an issue with numba when running tests. +# The tests would fail when executed in a suite, but would run when they are run alone... The code below fixes this. + +# Set threading before any Numba imports happen +os.environ["NUMBA_THREADING_LAYER"] = "workqueue" + +# Clear on module load +_unpickled_memo.clear() + + +@pytest.fixture(autouse=True) +def reset_numba_state(): + """Clean up Numba state between tests""" + _unpickled_memo.clear() + yield + _unpickled_memo.clear() From c809095261c29fbbe72d12dab74034703240013a Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 9 Feb 2026 13:44:16 +0100 Subject: [PATCH 107/149] Fix but in select_inchikeys_with_highest_ms2deepscore --- .../reference_methods/predict_top_ms2deepscores.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py b/ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py index 2ecb23c..674f43e 100644 --- a/ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py +++ b/ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py @@ -60,6 +60,8 @@ def select_inchikeys_with_highest_ms2deepscore( ms2deepscores = cosine_similarity_matrix( query_spectra.embeddings.embeddings, library_spectra.embeddings.embeddings ) + else: + assert ms2deepscores.shape == (len(query_spectra.spectra), len(library_spectra.spectra)) max_ms2deepscores_per_inchikey = np.zeros( (ms2deepscores.shape[0], len(library_spectra.spectrum_indices_per_inchikey)) @@ -67,7 +69,8 @@ def select_inchikeys_with_highest_ms2deepscore( for inchikey_index, spectrum_indexes in enumerate(library_spectra.spectrum_indices_per_inchikey.values()): # For one library inchikey, get all the scores and calculate the maximum score with each query spectrum all_ms2deepscores_for_inchikey = ms2deepscores[:, spectrum_indexes] - max_ms2deepscores_per_inchikey[inchikey_index] = all_ms2deepscores_for_inchikey.max(axis=1) + highest_score_per_inchikey = all_ms2deepscores_for_inchikey.max(axis=1) + max_ms2deepscores_per_inchikey[:, inchikey_index] = highest_score_per_inchikey inchikey_indexes_with_highest_ms2deepscore = np.argpartition( max_ms2deepscores_per_inchikey, -nr_of_inchikeys_to_select, axis=1 )[:, -nr_of_inchikeys_to_select:] From 2f0e63c3e5b97c69990629a8877915f21f9c775f Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 9 Feb 2026 13:45:50 +0100 Subject: [PATCH 108/149] Make predict_using_closest_tanimoto use TopKTanimotoScores --- .../predict_using_closest_tanimoto.py | 78 ++++++------------- .../test_predict_using_closest_tanimoto.py | 34 -------- 2 files changed, 24 insertions(+), 88 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py index 4405c1b..2875058 100644 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py @@ -4,6 +4,8 @@ from tqdm import tqdm from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.benchmarking.Fingerprints import Fingerprints +from ms2query.benchmarking.reference_methods.predict_top_ms2deepscores import select_inchikeys_with_highest_ms2deepscore +from ms2query.benchmarking.TopKTanimotoScores import TopKTanimotoScores from ms2query.metrics import generalized_tanimoto_similarity_matrix @@ -17,66 +19,34 @@ def predict_using_closest_tanimoto( """Predict best inchikey, by taking the average score over all spectra for the 10 closest related library inchikeys. (simplified version of old MS2Query) """ + top_k_tanimoto_scores = TopKTanimotoScores.calculate_from_fingerprints( + library_fingerprints, + library_fingerprints, + k=nr_of_closest_inchikeys_to_select, + ) + ms2deepscores = cosine_similarity_matrix(query_spectra.embeddings.embeddings, library_spectra.embeddings.embeddings) + inchikeys_with_highest_ms2deepscores = select_inchikeys_with_highest_ms2deepscore( + query_spectra, library_spectra, nr_of_inchikeys_with_highest_ms2deepscore_to_select, ms2deepscores=ms2deepscores + ) + inchikeys_of_best_match = [] highest_scores = [] for spectrum_idx in tqdm(range(len(query_spectra.spectra)), "Predicting using closest tanimoto"): - inchikey_of_best_match, score = predict_using_closest_tanimoto_single_spectrum( - library_spectra, - query_spectra.subset_spectra([spectrum_idx]), - nr_of_closest_inchikeys_to_select, - nr_of_inchikeys_with_highest_ms2deepscore_to_select, - library_fingerprints, - ) - inchikeys_of_best_match.append(inchikey_of_best_match) - highest_scores.append(score) - return inchikeys_of_best_match, highest_scores + average_predicted_scores = {} + for inchikey in inchikeys_with_highest_ms2deepscores[spectrum_idx]: + top_k_inchikeys = top_k_tanimoto_scores.select_top_k_inchikeys(inchikey) + average_predicted_score = get_average_predictions_for_closely_related_metabolites( + library_spectra, top_k_inchikeys, ms2deepscores[spectrum_idx] + ) + average_predicted_scores[inchikey] = average_predicted_score -def predict_using_closest_tanimoto_single_spectrum( - spectra_with_embeddings: AnnotatedSpectrumSet, - single_spectrum_with_embeddings: AnnotatedSpectrumSet, - nr_of_closest_inchikeys_to_select, - nr_of_inchikeys_with_highest_ms2deepscore_to_select, - fingerprints, -) -> Tuple[str, float]: - if len(single_spectrum_with_embeddings.spectra) != 1: - raise ValueError("expected a single spectrum") - ms2deepscores = cosine_similarity_matrix( - single_spectrum_with_embeddings.embeddings.embeddings, spectra_with_embeddings.embeddings.embeddings - )[0] - top_inchikeys = select_inchikeys_with_highest_ms2deepscore( - spectra_with_embeddings, ms2deepscores, nr_of_inchikeys_with_highest_ms2deepscore_to_select - ) - average_predicted_scores = {} - for inchikey in top_inchikeys: - top_k_inchikeys, _ = get_inchikey_and_tanimoto_scores_for_top_k( - fingerprints, inchikey, nr_of_closest_inchikeys_to_select + inchikey_with_highest_average_prediction, score = max( + average_predicted_scores.items(), key=lambda item: item[1] ) - average_predicted_score = get_average_predictions_for_closely_related_metabolites( - spectra_with_embeddings, top_k_inchikeys, ms2deepscores - ) - average_predicted_scores[inchikey] = average_predicted_score - - inchikey_with_highest_average_prediction, score = max(average_predicted_scores.items(), key=lambda item: item[1]) - return inchikey_with_highest_average_prediction, score - - -def select_inchikeys_with_highest_ms2deepscore( - spectra_with_embeddings: AnnotatedSpectrumSet, ms2deepscores, nr_of_inchikeys_to_select=10 -): - highest_score_for_inchikey = [] - for inchikey, spectrum_indexes in spectra_with_embeddings.spectrum_indices_per_inchikey.items(): - all_ms2deepscores_for_inchikey = ms2deepscores[spectrum_indexes,] - highest_score_for_inchikey.append(max(all_ms2deepscores_for_inchikey)) - inchikey_indexes_with_highest_ms2deepscore = np.argpartition( - np.array(highest_score_for_inchikey), -nr_of_inchikeys_to_select - )[-nr_of_inchikeys_to_select:] - - top_inchikeys = [ - spectra_with_embeddings.inchikeys[inchikey_index] - for inchikey_index in inchikey_indexes_with_highest_ms2deepscore - ] - return top_inchikeys + inchikeys_of_best_match.append(inchikey_with_highest_average_prediction) + highest_scores.append(score) + return inchikeys_of_best_match, highest_scores def get_average_predictions_for_closely_related_metabolites( diff --git a/tests/test_benchmarking/test_predict_using_closest_tanimoto.py b/tests/test_benchmarking/test_predict_using_closest_tanimoto.py index ed109aa..6304979 100644 --- a/tests/test_benchmarking/test_predict_using_closest_tanimoto.py +++ b/tests/test_benchmarking/test_predict_using_closest_tanimoto.py @@ -6,8 +6,6 @@ get_average_predictions_for_closely_related_metabolites, get_inchikey_and_tanimoto_scores_for_top_k, predict_using_closest_tanimoto, - predict_using_closest_tanimoto_single_spectrum, - select_inchikeys_with_highest_ms2deepscore, ) from tests.helper_functions import create_test_spectra, ms2deepscore_model @@ -28,38 +26,6 @@ def test_predict_using_closest_tanimoto(): assert len(scores) == 3 -def test_predict_using_closest_tanimoto_single_spectrum(): - """Only very basic test that the function runs and that the output is the right format""" - model = ms2deepscore_model() - library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(nr_of_inchikeys=7)) - test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1, nr_of_inchikeys=1)) - library_spectra.add_embeddings(model) - test_spectra.add_embeddings(model) - fingerprints = Fingerprints.from_spectrum_set(library_spectra, "daylight", 2048) - - predicted_inchikey, score = predict_using_closest_tanimoto_single_spectrum( - library_spectra, test_spectra, 3, 3, fingerprints - ) - - assert isinstance(predicted_inchikey, str) - assert len(predicted_inchikey) == 14 - assert isinstance(score, float) - - -def test_select_inchikeys_with_highest_ms2deepscore(): - test_spectra = create_test_spectra(nr_of_inchikeys=7) - spectra = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) - - ms2deepscores = np.zeros(len(test_spectra)) - ms2deepscores[2] = 0.4 - ms2deepscores[5] = 0.9 - ms2deepscores[7] = 0.6 - inchikeys_with_highest_ms2deepscore = select_inchikeys_with_highest_ms2deepscore(spectra, ms2deepscores, 3) - expected_inchikeys = list(spectra.spectrum_indices_per_inchikey.keys())[:3] - assert set(expected_inchikeys) == set(inchikeys_with_highest_ms2deepscore) - print(inchikeys_with_highest_ms2deepscore) - - def test_get_average_predictions_for_closely_related_metabolites(): test_spectra = create_test_spectra(nr_of_inchikeys=7) # Select different number per inchikey (only one for the first) to check that it is correctly weighted. From d6418f4d7726402e4401ef4fc581eda272622efd Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 9 Feb 2026 14:21:42 +0100 Subject: [PATCH 109/149] ruff linting --- .../reference_methods/predict_top_ms2deepscores.py | 1 - tests/test_benchmarking/test_methods.py | 5 +---- 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py b/ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py index 674f43e..519b3a7 100644 --- a/ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py +++ b/ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py @@ -1,6 +1,5 @@ from typing import Optional, Tuple import numpy as np -import pandas as pd from ms2deepscore.vector_operations import cosine_similarity_matrix from tqdm import tqdm from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet diff --git a/tests/test_benchmarking/test_methods.py b/tests/test_benchmarking/test_methods.py index f8804c7..6b73889 100644 --- a/tests/test_benchmarking/test_methods.py +++ b/tests/test_benchmarking/test_methods.py @@ -1,7 +1,6 @@ import numpy as np import pytest from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix -from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.benchmarking.Fingerprints import Fingerprints from ms2query.benchmarking.reference_methods.predict_best_possible_match import predict_best_possible_match from ms2query.benchmarking.reference_methods.predict_highest_cosine import predict_highest_cosine @@ -11,10 +10,8 @@ predict_with_integrated_similarity_flow, ) from tests.helper_functions import ( - create_test_spectra, - ms2deepscore_model, - get_library_and_test_spectra_not_identical, get_library_and_test_spectra_exactly_matching, + get_library_and_test_spectra_not_identical, ) From f7b1b8fac911e2c939ecd8f303d10abb6d9f747b Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 9 Feb 2026 16:20:02 +0100 Subject: [PATCH 110/149] add progress bar when creating annotatedspectrumset and remove progress_bars parameter --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 19 +++++++------------ 1 file changed, 7 insertions(+), 12 deletions(-) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index dc041e7..cfcc618 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -2,6 +2,7 @@ from typing import Iterable, List, Mapping, Optional, Sequence from matchms import Spectrum from ms2deepscore.models import SiameseSpectralModel +from tqdm import tqdm from ms2query.benchmarking.Embeddings import Embeddings @@ -13,24 +14,22 @@ def __init__( spectra: Sequence[Spectrum], spectrum_indices_per_inchikey: Mapping[str, Iterable[int]], embeddings: Optional[Embeddings] = None, - progress_bars=False, ): self._spectra = tuple([spectrum.clone() for spectrum in spectra]) self.spectrum_indices_per_inchikey: dict[str, tuple[int, ...]] = { key: tuple(values) for key, values in spectrum_indices_per_inchikey.items() } - self.progress_bars = progress_bars self._embeddings = embeddings @classmethod - def create_spectrum_set(cls, spectra: Sequence[Spectrum], progress_bars=False) -> "AnnotatedSpectrumSet": + def create_spectrum_set(cls, spectra: Sequence[Spectrum]) -> "AnnotatedSpectrumSet": spectrum_indices_per_inchikey = defaultdict(list) - for spectrum_index, spectrum in enumerate(spectra): + for spectrum_index, spectrum in enumerate(tqdm(spectra, desc="Create mapping from inchikey to spectrum")): inchikey = spectrum.get("inchikey") if inchikey is None: raise ValueError("Annotated Spectrum set expects spectra that all have an inchikey") spectrum_indices_per_inchikey[inchikey[:14]].append(spectrum_index) - return cls(spectra, spectrum_indices_per_inchikey, progress_bars=progress_bars) + return cls(spectra, spectrum_indices_per_inchikey) def __add__(self, other) -> "AnnotatedSpectrumSet": """Adds two spectrum sets together""" @@ -52,14 +51,12 @@ def __add__(self, other) -> "AnnotatedSpectrumSet": embeddings = None if self._embeddings and other._embeddings: embeddings = Embeddings.combine_embeddings(self.embeddings, other.embeddings) - return AnnotatedSpectrumSet( - spectra, spectrum_indices_per_inchikey, embeddings=embeddings, progress_bars=self.progress_bars - ) + return AnnotatedSpectrumSet(spectra, spectrum_indices_per_inchikey, embeddings=embeddings) def subset_spectra(self, spectrum_indices) -> "AnnotatedSpectrumSet": """Returns a new instance of a subset of the spectra""" spectra = [self._spectra[index] for index in spectrum_indices] - new_instance = AnnotatedSpectrumSet.create_spectrum_set(spectra, progress_bars=self.progress_bars) + new_instance = AnnotatedSpectrumSet.create_spectrum_set(spectra) if self._embeddings is not None: new_instance._embeddings = self.embeddings.subset_embeddings(spectra) return new_instance @@ -88,9 +85,7 @@ def inchikeys(self): return tuple(self.spectrum_indices_per_inchikey.keys()) def __copy__(self): - return AnnotatedSpectrumSet( - self.spectra, self.spectrum_indices_per_inchikey, self.embeddings, progress_bars=self.progress_bars - ) + return AnnotatedSpectrumSet(self.spectra, self.spectrum_indices_per_inchikey, self.embeddings) def __eq__(self, other: object): if not isinstance(other, AnnotatedSpectrumSet): From 53548871591f715a0581405791a2632dcb80ae50 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 9 Feb 2026 16:20:20 +0100 Subject: [PATCH 111/149] Add progress bar for hashing of spectra in embeddings --- ms2query/benchmarking/Embeddings.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ms2query/benchmarking/Embeddings.py b/ms2query/benchmarking/Embeddings.py index 26ba232..1dfacfd 100644 --- a/ms2query/benchmarking/Embeddings.py +++ b/ms2query/benchmarking/Embeddings.py @@ -4,6 +4,7 @@ from matchms import Spectrum from ms2deepscore.models import SiameseSpectralModel, compute_embedding_array from ms2deepscore.vector_operations import cosine_similarity_matrix +from tqdm import tqdm class Embeddings: @@ -21,7 +22,7 @@ def __init__(self, embeddings: np.ndarray, spectrum_hashes: tuple, model_setting @classmethod def create_from_spectra(cls, spectra: Sequence[Spectrum], model: SiameseSpectralModel) -> "Embeddings": - index_to_spectrum_hash = tuple(spectrum.__hash__() for spectrum in spectra) + index_to_spectrum_hash = tuple(spectrum.__hash__() for spectrum in tqdm(spectra, desc="Hashing spectra")) if len(set(index_to_spectrum_hash)) != len(spectra): raise ValueError("There are duplicated spectra in the spectrum list") From f78430377c5252716b66f910c89e454e5939bbb0 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Tue, 10 Feb 2026 10:44:12 +0100 Subject: [PATCH 112/149] Remove progress bar setting --- ms2query/benchmarking/EvaluateAnalogueSearch.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/ms2query/benchmarking/EvaluateAnalogueSearch.py b/ms2query/benchmarking/EvaluateAnalogueSearch.py index 8673b24..891edfc 100644 --- a/ms2query/benchmarking/EvaluateAnalogueSearch.py +++ b/ms2query/benchmarking/EvaluateAnalogueSearch.py @@ -18,8 +18,6 @@ def __init__( self.fingerprints = Fingerprints.from_spectrum_set(training_spectrum_set + validation_spectrum_set, fingerprint_type, nbits) - self.training_spectrum_set.progress_bars = False - self.validation_spectrum_set.progress_bars = False def benchmark_analogue_search( self, predicted_inchikeys: List[str], From 0a750ac3c58a57324e8cb3a2aa2314a0d9a4cca9 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 11 Feb 2026 08:48:56 +0100 Subject: [PATCH 113/149] Add len to AnnotatedSpectrumsET --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index cfcc618..361aa0b 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -97,3 +97,6 @@ def __eq__(self, other: object): if self._embeddings != other._embeddings: return False return True + + def __len__(self): + return len(self._spectra) From 3e37b4c2e0f7658dd46f5e2fd758e78129dd250f Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 11 Feb 2026 08:49:10 +0100 Subject: [PATCH 114/149] fix type hint --- ms2query/benchmarking/TopKTanimotoScores.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ms2query/benchmarking/TopKTanimotoScores.py b/ms2query/benchmarking/TopKTanimotoScores.py index 7032faa..b75ff51 100644 --- a/ms2query/benchmarking/TopKTanimotoScores.py +++ b/ms2query/benchmarking/TopKTanimotoScores.py @@ -53,7 +53,7 @@ def select_top_k_inchikeys_and_scores(self, inchikey) -> dict[str, float]: # Convert to dictionary with inchikey, score pairs. return dict(zip(top_k_inchikeys_and_scores["inchikey"], top_k_inchikeys_and_scores["score"])) - def select_top_k_inchikeys(self, inchikey) -> list[float]: + def select_top_k_inchikeys(self, inchikey) -> list[str]: return list(self.top_k_inchikeys_and_scores.loc[inchikey].xs("inchikey", level="attribute")) def select_average_score(self, inchikey) -> float: From 549d1b28c500e48b16e6d77a674f1174a7eab68a Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 11 Feb 2026 09:07:35 +0100 Subject: [PATCH 115/149] rename predict_top_k_ms2deepscore and make select_inchikeys_with_highest_ms2deepscore run per batch --- .../predict_highest_ms2deepscore.py | 4 +-- ...cores.py => predict_top_k_ms2deepscore.py} | 30 ++++++++++++++----- ...predict_with_integrated_similarity_flow.py | 4 +-- .../test_predict_top_ms2deepscores.py | 6 ++-- 4 files changed, 29 insertions(+), 15 deletions(-) rename ms2query/benchmarking/reference_methods/{predict_top_ms2deepscores.py => predict_top_k_ms2deepscore.py} (80%) diff --git a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py index 0bf3769..e4f06de 100644 --- a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py +++ b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py @@ -1,12 +1,12 @@ from typing import List, Tuple from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet -from ms2query.benchmarking.reference_methods.predict_top_ms2deepscores import predict_top_ms2deepscores +from ms2query.benchmarking.reference_methods.predict_top_k_ms2deepscore import predict_top_k_ms2deepscores def predict_highest_ms2deepscore( library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet ) -> Tuple[List[str], List[float]]: - indexes_of_highest_scores, highest_scores = predict_top_ms2deepscores( + indexes_of_highest_scores, highest_scores = predict_top_k_ms2deepscores( library_spectra.embeddings, query_spectra.embeddings, k=1 ) single_highest_score = [highest_score[0] for highest_score in highest_scores] diff --git a/ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py b/ms2query/benchmarking/reference_methods/predict_top_k_ms2deepscore.py similarity index 80% rename from ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py rename to ms2query/benchmarking/reference_methods/predict_top_k_ms2deepscore.py index 519b3a7..adbe13b 100644 --- a/ms2query/benchmarking/reference_methods/predict_top_ms2deepscores.py +++ b/ms2query/benchmarking/reference_methods/predict_top_k_ms2deepscore.py @@ -6,7 +6,7 @@ from ms2query.benchmarking.Embeddings import Embeddings -def predict_top_ms2deepscores( +def predict_top_k_ms2deepscores( library_embeddings: Embeddings, query_embeddings: Embeddings, batch_size: int = 500, k=1 ) -> Tuple[np.ndarray, np.ndarray]: """Memory efficient way of calculating the highest MS2DeepScores @@ -52,16 +52,30 @@ def select_inchikeys_with_highest_ms2deepscore( query_spectra: AnnotatedSpectrumSet, library_spectra: AnnotatedSpectrumSet, nr_of_inchikeys_to_select=100, - ms2deepscores: Optional[np.ndarray] = None, + batch_size=500, ) -> list[list[str]]: - """Selects the top x inchikeys with the highest score for each query spectrum""" - if ms2deepscores is None: - ms2deepscores = cosine_similarity_matrix( - query_spectra.embeddings.embeddings, library_spectra.embeddings.embeddings + top_inchikeys_per_query_spectrum = [] + # loop over the batches + for start_idx in tqdm( + range(0, len(query_spectra), batch_size), + desc="Selecting highest ms2deepscore", + total=batch_size // len(query_spectra) + 1, + ): + end_idx = min(start_idx + batch_size, len(query_spectra)) + selected_query_embeddings = query_spectra.embeddings.embeddings[start_idx:end_idx] + score_matrix = cosine_similarity_matrix(selected_query_embeddings, library_spectra.embeddings.embeddings) + top_inchikeys_per_query_spectrum.extend( + get_inchikeys_with_highest_ms2deepscore(library_spectra, score_matrix, nr_of_inchikeys_to_select) ) - else: - assert ms2deepscores.shape == (len(query_spectra.spectra), len(library_spectra.spectra)) + return top_inchikeys_per_query_spectrum + +def get_inchikeys_with_highest_ms2deepscore( + library_spectra: AnnotatedSpectrumSet, + ms2deepscores: np.ndarray, + nr_of_inchikeys_to_select=100, +) -> list[list[str]]: + """Selects the top x inchikeys with the highest score for each query spectrum""" max_ms2deepscores_per_inchikey = np.zeros( (ms2deepscores.shape[0], len(library_spectra.spectrum_indices_per_inchikey)) ) diff --git a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py index 36d91fe..77aedb5 100644 --- a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py +++ b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py @@ -4,7 +4,7 @@ from tqdm import tqdm from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.benchmarking.Fingerprints import Fingerprints -from ms2query.benchmarking.reference_methods.predict_top_ms2deepscores import predict_top_ms2deepscores +from ms2query.benchmarking.reference_methods.predict_top_k_ms2deepscore import predict_top_k_ms2deepscores def predict_with_integrated_similarity_flow( @@ -14,7 +14,7 @@ def predict_with_integrated_similarity_flow( number_of_analogues_to_consider=50, ) -> Tuple[List[str], List[float]]: - all_indexes_of_library_spectra_with_highest_score, all_predicted_scores = predict_top_ms2deepscores( + all_indexes_of_library_spectra_with_highest_score, all_predicted_scores = predict_top_k_ms2deepscores( library_spectra.embeddings, query_spectra.embeddings, k=number_of_analogues_to_consider ) inchikeys_of_best_matches = [] diff --git a/tests/test_benchmarking/test_predict_top_ms2deepscores.py b/tests/test_benchmarking/test_predict_top_ms2deepscores.py index 9e7a17f..9a2533d 100644 --- a/tests/test_benchmarking/test_predict_top_ms2deepscores.py +++ b/tests/test_benchmarking/test_predict_top_ms2deepscores.py @@ -1,8 +1,8 @@ import numpy as np import pytest from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet -from ms2query.benchmarking.reference_methods.predict_top_ms2deepscores import ( - predict_top_ms2deepscores, +from ms2query.benchmarking.reference_methods.predict_top_k_ms2deepscore import ( + predict_top_k_ms2deepscores, select_inchikeys_with_highest_ms2deepscore, ) from tests.helper_functions import create_test_spectra, get_library_and_test_spectra_not_identical, ms2deepscore_model @@ -11,7 +11,7 @@ @pytest.mark.parametrize( "method", [ - predict_top_ms2deepscores, + predict_top_k_ms2deepscores, ], ) def test_predict_highest_ms2deepscore_similarity(method): From e130779b83b0ed2e411c152c9d8f62e1949c8e63 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 11 Feb 2026 09:59:12 +0100 Subject: [PATCH 116/149] Create MS2DeepScoresForTopKInChikeys --- .../MS2DeepScoresForTopInChikeys.py | 123 ++++++++++++++++++ .../predict_using_closest_tanimoto.py | 76 ----------- 2 files changed, 123 insertions(+), 76 deletions(-) create mode 100644 ms2query/benchmarking/reference_methods/MS2DeepScoresForTopInChikeys.py delete mode 100644 ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py diff --git a/ms2query/benchmarking/reference_methods/MS2DeepScoresForTopInChikeys.py b/ms2query/benchmarking/reference_methods/MS2DeepScoresForTopInChikeys.py new file mode 100644 index 0000000..baec961 --- /dev/null +++ b/ms2query/benchmarking/reference_methods/MS2DeepScoresForTopInChikeys.py @@ -0,0 +1,123 @@ +import numpy as np +from ms2deepscore.vector_operations import cosine_similarity_matrix +from tqdm import tqdm +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet +from ms2query.benchmarking.Fingerprints import Fingerprints +from ms2query.benchmarking.reference_methods.predict_top_k_ms2deepscore import ( + select_inchikeys_with_highest_ms2deepscore, +) +from ms2query.benchmarking.TopKTanimotoScores import TopKTanimotoScores + + +def calculate_MS2DeepScoresForTopKInChikeys_from_spectra( + library_spectra: AnnotatedSpectrumSet, + query_spectra: AnnotatedSpectrumSet, + fingerprint_type: str, + fingerprint_nbits, + nr_of_closest_inchikeys_to_select=10, + nr_of_inchikeys_with_highest_ms2deepscore_to_select=100, +) -> list[dict[str, "MS2DeepScoresForTopKInChikeys"]]: + """A wrapper to do all the preprocessing steps to get the TopKMS2DeepScores for each query spectrum""" + + library_fingerprints = Fingerprints.from_spectrum_set(library_spectra, fingerprint_type, fingerprint_nbits) + + top_k_tanimoto_scores = TopKTanimotoScores.calculate_from_fingerprints( + library_fingerprints, + library_fingerprints, + k=nr_of_closest_inchikeys_to_select, + ) + + inchikeys_with_highest_ms2deepscores = select_inchikeys_with_highest_ms2deepscore( + query_spectra, library_spectra, nr_of_inchikeys_with_highest_ms2deepscore_to_select + ) + + return calculate_MS2DeepScoresForTopKInChikeys( + library_spectra, query_spectra, top_k_tanimoto_scores, inchikeys_with_highest_ms2deepscores + ) + + +def calculate_MS2DeepScoresForTopKInChikeys( + library_spectra: AnnotatedSpectrumSet, + query_spectra: AnnotatedSpectrumSet, + top_k_tanimoto_scores: TopKTanimotoScores, + inchikeys_with_highest_ms2deepscores_per_query_spectrum: list[list[str]], +) -> list[dict[str, "MS2DeepScoresForTopKInChikeys"]]: + """Gets all MS2DeepScores for the library inchikeys with a high score and their most similar inchikeys + + For each query spectrum the library inchikeys with the highest predicted MS2DeepScore are selected. + For each of these library spectra, the closest library spectra are selected. + + inchikeys_with_highest_ms2deepscores_per_query_spectrum: + The library inchikeys that have the highest ms2deepscore prediction for each query spectrum. + top_k_tanimoto_scores: + For each library inchikey the top k closest other library inchikeys on tanimoto scores. + """ + assert len(inchikeys_with_highest_ms2deepscores_per_query_spectrum) == len(query_spectra) + + close_tanimoto_scores_per_inchikey_per_query_spectrum = [] + for spectrum_idx in tqdm( + range(len(query_spectra.spectra)), "Calculating", total=len(query_spectra.spectra), ncols=200 + ): + inchikeys_with_highest_ms2deepscores = inchikeys_with_highest_ms2deepscores_per_query_spectrum[spectrum_idx] + close_tanimoto_scores_per_inchikey = {} + for inchikey_with_high_ms2deepscore in inchikeys_with_highest_ms2deepscores: + query_embedding = query_spectra.embeddings._embeddings[[spectrum_idx]] + top_k_inchikeys_and_scores = top_k_tanimoto_scores.select_top_k_inchikeys_and_scores( + inchikey_with_high_ms2deepscore + ) + close_tanimoto_scores = MS2DeepScoresForTopKInChikeys( + query_embedding, library_spectra, top_k_inchikeys_and_scores + ) + + close_tanimoto_scores_per_inchikey[inchikey_with_high_ms2deepscore] = close_tanimoto_scores + close_tanimoto_scores_per_inchikey_per_query_spectrum.append(close_tanimoto_scores_per_inchikey) + return close_tanimoto_scores_per_inchikey_per_query_spectrum + + +class MS2DeepScoresForTopKInChikeys: + """Stores the MS2DeepScores and Tanimoto scores for the top k closest lib spectra + + This allows for quick testing of different reranking strategies. E.g. get_mean is similar to the original MS2Query, + but it can also be used to make matrixes with both MS2DeepScore and tanimoto scores to train small reranking models. + + query_embedding: A single MS2DeepScore embedding stored as a 2D numpy array + library_spectra: The reference spectra. + top_k_inchikeys_and_scores: A dictionary with the closest inchikeys and corresponding tanimoto scores.""" + + def __init__( + self, + query_embedding: np.ndarray, + library_spectra: AnnotatedSpectrumSet, + top_k_inchikeys_and_scores: dict[str, float], + ): + if len(query_embedding.shape) != 2 or query_embedding.shape[0] != 1: + raise ValueError("Expected a single embedding, but as a 2D matrix. Like [[0, 1, 0,1],]") + self._top_k_inchikeys_and_tanimoto_scores = top_k_inchikeys_and_scores + + self.ms2deepscores_per_inchikey = {} + for top_inchikey in self._top_k_inchikeys_and_tanimoto_scores.keys(): + matching_spectrum_indexes = list(library_spectra.spectrum_indices_per_inchikey[top_inchikey]) + selected_lib_embeddings = library_spectra.embeddings._embeddings[matching_spectrum_indexes] + ms2deepscores = cosine_similarity_matrix(query_embedding, selected_lib_embeddings) + self.ms2deepscores_per_inchikey[top_inchikey] = ms2deepscores + + def get_mean_per_inchikey(self) -> dict[str, float]: + average_predicted_ms2deepscores = {} + for inchikey, predicted_ms2deepscores in self.ms2deepscores_per_inchikey.items(): + average_predicted_ms2deepscores[inchikey] = predicted_ms2deepscores.mean() + return average_predicted_ms2deepscores + + def get_mean(self): + """Returns the mean, over the mean ms2deepscore per inchikey""" + mean_ms2deepscore_per_inchikey = self.get_mean_per_inchikey().values() + return sum(mean_ms2deepscore_per_inchikey) / len(mean_ms2deepscore_per_inchikey) + + def get_max_per_inchikey(self) -> dict[str, float]: + average_predicted_ms2deepscores = {} + for inchikey, predicted_ms2deepscores in self.ms2deepscores_per_inchikey.items(): + average_predicted_ms2deepscores[inchikey] = predicted_ms2deepscores.max() + return average_predicted_ms2deepscores + + @property + def top_k_inchikeys_and_tanimoto_scores(self): + return self._top_k_inchikeys_and_tanimoto_scores diff --git a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py b/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py deleted file mode 100644 index 2875058..0000000 --- a/ms2query/benchmarking/reference_methods/predict_using_closest_tanimoto.py +++ /dev/null @@ -1,76 +0,0 @@ -from typing import List, Tuple -import numpy as np -from ms2deepscore.vector_operations import cosine_similarity_matrix -from tqdm import tqdm -from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet -from ms2query.benchmarking.Fingerprints import Fingerprints -from ms2query.benchmarking.reference_methods.predict_top_ms2deepscores import select_inchikeys_with_highest_ms2deepscore -from ms2query.benchmarking.TopKTanimotoScores import TopKTanimotoScores -from ms2query.metrics import generalized_tanimoto_similarity_matrix - - -def predict_using_closest_tanimoto( - library_spectra: AnnotatedSpectrumSet, - query_spectra: AnnotatedSpectrumSet, - library_fingerprints: Fingerprints, - nr_of_closest_inchikeys_to_select=10, - nr_of_inchikeys_with_highest_ms2deepscore_to_select=100, -) -> Tuple[List[str], List[float]]: - """Predict best inchikey, by taking the average score over all spectra for the 10 closest related library inchikeys. - (simplified version of old MS2Query) - """ - top_k_tanimoto_scores = TopKTanimotoScores.calculate_from_fingerprints( - library_fingerprints, - library_fingerprints, - k=nr_of_closest_inchikeys_to_select, - ) - ms2deepscores = cosine_similarity_matrix(query_spectra.embeddings.embeddings, library_spectra.embeddings.embeddings) - inchikeys_with_highest_ms2deepscores = select_inchikeys_with_highest_ms2deepscore( - query_spectra, library_spectra, nr_of_inchikeys_with_highest_ms2deepscore_to_select, ms2deepscores=ms2deepscores - ) - - inchikeys_of_best_match = [] - highest_scores = [] - for spectrum_idx in tqdm(range(len(query_spectra.spectra)), "Predicting using closest tanimoto"): - average_predicted_scores = {} - for inchikey in inchikeys_with_highest_ms2deepscores[spectrum_idx]: - top_k_inchikeys = top_k_tanimoto_scores.select_top_k_inchikeys(inchikey) - - average_predicted_score = get_average_predictions_for_closely_related_metabolites( - library_spectra, top_k_inchikeys, ms2deepscores[spectrum_idx] - ) - average_predicted_scores[inchikey] = average_predicted_score - - inchikey_with_highest_average_prediction, score = max( - average_predicted_scores.items(), key=lambda item: item[1] - ) - inchikeys_of_best_match.append(inchikey_with_highest_average_prediction) - highest_scores.append(score) - return inchikeys_of_best_match, highest_scores - - -def get_average_predictions_for_closely_related_metabolites( - spectra: AnnotatedSpectrumSet, top_k_inchikeys, all_ms2deepscores: np.ndarray -): - """Calculates the average ms2deepscore predictions for top k closest inchikeys""" - average_predicted_scores = [] - for top_inchikey in top_k_inchikeys: - matching_spectrum_indexes = list(spectra.spectrum_indices_per_inchikey[top_inchikey]) - predicted_scores = all_ms2deepscores[matching_spectrum_indexes] - average_predicted_scores.append(predicted_scores.mean()) - average_predicted_score = sum(average_predicted_scores) / len(average_predicted_scores) - return average_predicted_score - - -def get_inchikey_and_tanimoto_scores_for_top_k( - fingerprints: Fingerprints, inchikey: str, k: int -) -> tuple[list[str], np.ndarray]: - """For an inchikey in a library the top k highest tanimoto scores in the library are predicted (including itself)""" - similarity_scores = generalized_tanimoto_similarity_matrix( - fingerprints.get_fingerprints([inchikey]), fingerprints.fingerprints - )[0] - inchikey_indexes_of_top_k = np.argpartition(similarity_scores, -k)[-k:] - tanimoto_scores_for_top_k = similarity_scores[inchikey_indexes_of_top_k] - - top_inchikeys = [fingerprints.inchikeys[inchikey_index] for inchikey_index in inchikey_indexes_of_top_k] - return top_inchikeys, tanimoto_scores_for_top_k From fef5a20ab32afe7c11d4ceef465806e6950a8e80 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 11 Feb 2026 09:59:24 +0100 Subject: [PATCH 117/149] remove unused import --- .../reference_methods/predict_top_k_ms2deepscore.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ms2query/benchmarking/reference_methods/predict_top_k_ms2deepscore.py b/ms2query/benchmarking/reference_methods/predict_top_k_ms2deepscore.py index adbe13b..1da3b2e 100644 --- a/ms2query/benchmarking/reference_methods/predict_top_k_ms2deepscore.py +++ b/ms2query/benchmarking/reference_methods/predict_top_k_ms2deepscore.py @@ -1,4 +1,4 @@ -from typing import Optional, Tuple +from typing import Tuple import numpy as np from ms2deepscore.vector_operations import cosine_similarity_matrix from tqdm import tqdm From 8108df6b8b81b269a2727b3e48bef0ec3fca544a Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 11 Feb 2026 10:08:22 +0100 Subject: [PATCH 118/149] First test for MS2DeepScoresForTopinchikeys --- .../test_MS2DeepScoresForTopInChikeys.py | 14 ++++ .../test_predict_using_closest_tanimoto.py | 64 ------------------- 2 files changed, 14 insertions(+), 64 deletions(-) create mode 100644 tests/test_benchmarking/test_MS2DeepScoresForTopInChikeys.py delete mode 100644 tests/test_benchmarking/test_predict_using_closest_tanimoto.py diff --git a/tests/test_benchmarking/test_MS2DeepScoresForTopInChikeys.py b/tests/test_benchmarking/test_MS2DeepScoresForTopInChikeys.py new file mode 100644 index 0000000..8c9b562 --- /dev/null +++ b/tests/test_benchmarking/test_MS2DeepScoresForTopInChikeys.py @@ -0,0 +1,14 @@ +from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet +from ms2query.benchmarking.reference_methods.MS2DeepScoresForTopInChikeys import ( + calculate_MS2DeepScoresForTopKInChikeys_from_spectra, +) +from tests.helper_functions import create_test_spectra, ms2deepscore_model + + +def test_calculate_MS2DeepScoresForTopKInChikeys_from_spectra(): + model = ms2deepscore_model() + library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(nr_of_inchikeys=7)) + test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1, nr_of_inchikeys=3)) + library_spectra.add_embeddings(model) + test_spectra.add_embeddings(model) + calculate_MS2DeepScoresForTopKInChikeys_from_spectra(library_spectra, test_spectra, "daylight", 100, 3, 3) diff --git a/tests/test_benchmarking/test_predict_using_closest_tanimoto.py b/tests/test_benchmarking/test_predict_using_closest_tanimoto.py deleted file mode 100644 index 6304979..0000000 --- a/tests/test_benchmarking/test_predict_using_closest_tanimoto.py +++ /dev/null @@ -1,64 +0,0 @@ -import numpy as np -import pytest -from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet -from ms2query.benchmarking.Fingerprints import Fingerprints -from ms2query.benchmarking.reference_methods.predict_using_closest_tanimoto import ( - get_average_predictions_for_closely_related_metabolites, - get_inchikey_and_tanimoto_scores_for_top_k, - predict_using_closest_tanimoto, -) -from tests.helper_functions import create_test_spectra, ms2deepscore_model - - -def test_predict_using_closest_tanimoto(): - """Only very basic test that the function runs and that the output is the right format""" - model = ms2deepscore_model() - library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(nr_of_inchikeys=7)) - test_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(1, nr_of_inchikeys=3)) - library_spectra.add_embeddings(model) - test_spectra.add_embeddings(model) - fingerprints = Fingerprints.from_spectrum_set(library_spectra, "daylight", 2048) - predicted_inchikeys, scores = predict_using_closest_tanimoto(library_spectra, test_spectra, fingerprints, 3, 3) - - assert isinstance(predicted_inchikeys, list) - assert len(predicted_inchikeys) == 3 - assert isinstance(scores, list) - assert len(scores) == 3 - - -def test_get_average_predictions_for_closely_related_metabolites(): - test_spectra = create_test_spectra(nr_of_inchikeys=7) - # Select different number per inchikey (only one for the first) to check that it is correctly weighted. - test_spectra = test_spectra.copy()[2:] - spectra = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) - - inchikeys = spectra.inchikeys[:3] - ms2deepscores = np.zeros(len(spectra.spectra)) - ms2deepscores[0] = 0.8 - ms2deepscores[[1, 2, 3]] = 0.6 - ms2deepscores[4] = 0.6 - ms2deepscores[5] = 0.8 - ms2deepscores[6] = 0.7 - # the average per inchikey is 0.8, 0.6, 0.7, so average overall should be 0.7 - average_predicted_score = get_average_predictions_for_closely_related_metabolites(spectra, inchikeys, ms2deepscores) - assert np.allclose(average_predicted_score, np.array(0.7), atol=1e-5) - - -@pytest.mark.parametrize( - "k", - [1, 3, 7], -) -def test_get_inchikey_and_tanimoto_scores_for_top_k(k): - spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(nr_of_inchikeys=7)) - fingerprints = Fingerprints.from_spectrum_set(spectra, fingerprint_type="daylight", nbits=4096) - inchikey = spectra.inchikeys[2] - - top_inchikeys, tanimoto_scores_for_top_k = get_inchikey_and_tanimoto_scores_for_top_k(fingerprints, inchikey, k) - - assert inchikey in top_inchikeys - assert len(top_inchikeys) == k - assert len(tanimoto_scores_for_top_k) == k - assert len(set(top_inchikeys)) == k - assert tanimoto_scores_for_top_k[top_inchikeys.index(inchikey)] == 1.0, ( - "The exact match is expected to have a score of 1.0" - ) From 7c921e282bcc9dce4c804a32ded63dddc551c1ee Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 11 Feb 2026 10:11:33 +0100 Subject: [PATCH 119/149] Fix pylance warning --- ms2query/benchmarking/Embeddings.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ms2query/benchmarking/Embeddings.py b/ms2query/benchmarking/Embeddings.py index 1dfacfd..28bab86 100644 --- a/ms2query/benchmarking/Embeddings.py +++ b/ms2query/benchmarking/Embeddings.py @@ -27,7 +27,7 @@ def create_from_spectra(cls, spectra: Sequence[Spectrum], model: SiameseSpectral raise ValueError("There are duplicated spectra in the spectrum list") model_settings = model.model_settings.get_dict() - embeddings = compute_embedding_array(model, spectra) + embeddings: np.ndarray = compute_embedding_array(model, spectra) # type: ignore return cls(embeddings, index_to_spectrum_hash, model_settings) @classmethod From e118177034c9adef10d7f6b788d572bfe474892e Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 11 Feb 2026 10:22:30 +0100 Subject: [PATCH 120/149] Fix type hinting issue --- ms2query/benchmarking/TopKTanimotoScores.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/ms2query/benchmarking/TopKTanimotoScores.py b/ms2query/benchmarking/TopKTanimotoScores.py index b75ff51..07c0912 100644 --- a/ms2query/benchmarking/TopKTanimotoScores.py +++ b/ms2query/benchmarking/TopKTanimotoScores.py @@ -11,7 +11,7 @@ def __init__( """Stores the top k scores between two lists of inchikeys""" self.k = tanimoto_scores_for_top_k.shape[1] - self.top_k_inchikeys_and_scores = self._create_multi_index( + self.top_k_inchikeys_and_scores: pd.DataFrame = self._create_multi_index( tanimoto_scores_for_top_k, top_k_inchikeys, inchikey_indexes ) @@ -62,7 +62,8 @@ def select_average_score(self, inchikey) -> float: def get_all_average_tanimoto_scores(self) -> dict[str, float]: """Returns all average tanimoto scores for the top k per inchikey""" - average_per_inchikey_df = self.top_k_inchikeys_and_scores.xs("score", axis=1, level="attribute").mean(axis=1) # type: ignore - # convert to dictionary - average_per_inchikey = average_per_inchikey_df.squeeze().to_dict() - return average_per_inchikey + # Get the scores + scores_df: pd.DataFrame = self.top_k_inchikeys_and_scores.xs("score", axis=1, level="attribute") # type: ignore + + average_per_inchikey_df = scores_df.mean(axis=1) + return average_per_inchikey_df.to_dict() From 8bb6cdad04d4d1f9a941864004337658c41ca22a Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Wed, 11 Feb 2026 10:22:43 +0100 Subject: [PATCH 121/149] fix pylance issue --- tests/test_benchmarking/test_methods.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_benchmarking/test_methods.py b/tests/test_benchmarking/test_methods.py index 6b73889..64337dd 100644 --- a/tests/test_benchmarking/test_methods.py +++ b/tests/test_benchmarking/test_methods.py @@ -26,7 +26,7 @@ def test_all_methods(prediction_function): library_spectra, test_spectra = get_library_and_test_spectra_exactly_matching() predicted_inchikeys, scores = prediction_function(library_spectra, test_spectra) for i, spectrum in enumerate(test_spectra.spectra): - inchikey = spectrum.get("inchikey")[:14] + inchikey = spectrum.get("inchikey")[:14] # type: ignore assert predicted_inchikeys[i] == inchikey assert np.allclose(scores[i], np.array(1.0), atol=1e-5) @@ -36,7 +36,7 @@ def test_predict_best_possible_match(): fingerprints = Fingerprints.from_spectrum_set(library_spectra + test_spectra, "daylight", 2048) predicted_inchikeys, scores = predict_best_possible_match(library_spectra, test_spectra, fingerprints) for i, spectrum in enumerate(test_spectra.spectra): - inchikey = spectrum.get("inchikey")[:14] + inchikey = spectrum.get("inchikey")[:14] # type: ignore assert predicted_inchikeys[i] == inchikey assert np.allclose(scores[i], np.array(1.0), atol=1e-5) From eec6e364b757bdd91596e54ab35c2d5f6b3ec0c9 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 27 Feb 2026 14:39:30 +0100 Subject: [PATCH 122/149] Add has_embedding property to AnnotatedSpectrumSet --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index 361aa0b..ab57577 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -70,6 +70,12 @@ def spectra_per_inchikey(self, inchikey) -> List[Spectrum]: def add_embeddings(self, model: SiameseSpectralModel): self._embeddings = Embeddings.create_from_spectra(self._spectra, model) + @property + def has_embeddings(self) -> bool: + if self._embeddings is None: + return False + return True + @property def spectra(self): return self._spectra From 3e2eeb328cdfde1d5efbdb91d73bd342d22fc48c Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 27 Feb 2026 14:54:40 +0100 Subject: [PATCH 123/149] Add warning when adding two spectrumsets where one has embeddings and the other doesnt --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index ab57577..4a1a2d6 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -48,8 +48,10 @@ def __add__(self, other) -> "AnnotatedSpectrumSet": spectrum_indices_per_inchikey[inchikey].extend(indices) # combine embeddings + if self.has_embeddings != other.has_embeddings: + print("Only one of the two sets has an embeddings, so embeddings are not added") embeddings = None - if self._embeddings and other._embeddings: + if self.has_embeddings and other.has_embeddings: embeddings = Embeddings.combine_embeddings(self.embeddings, other.embeddings) return AnnotatedSpectrumSet(spectra, spectrum_indices_per_inchikey, embeddings=embeddings) From 0a87c1655e1ea911885e13f17fef1ef9795e8e25 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 27 Feb 2026 14:55:25 +0100 Subject: [PATCH 124/149] small changes --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index 4a1a2d6..9366d4e 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -59,7 +59,7 @@ def subset_spectra(self, spectrum_indices) -> "AnnotatedSpectrumSet": """Returns a new instance of a subset of the spectra""" spectra = [self._spectra[index] for index in spectrum_indices] new_instance = AnnotatedSpectrumSet.create_spectrum_set(spectra) - if self._embeddings is not None: + if self.has_embeddings: new_instance._embeddings = self.embeddings.subset_embeddings(spectra) return new_instance @@ -83,7 +83,7 @@ def spectra(self): return self._spectra @property - def embeddings(self) -> "Embeddings": + def embeddings(self) -> Embeddings: if self._embeddings is None: raise ValueError("First run the 'add_embeddings' method") return self._embeddings From 7070b1b065a78b1184f523883767684b3cc228b8 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 27 Feb 2026 14:55:51 +0100 Subject: [PATCH 125/149] fix bug when copying AnnotatedSpectrumSet without embeddings --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index 9366d4e..56d7a62 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -93,7 +93,7 @@ def inchikeys(self): return tuple(self.spectrum_indices_per_inchikey.keys()) def __copy__(self): - return AnnotatedSpectrumSet(self.spectra, self.spectrum_indices_per_inchikey, self.embeddings) + return AnnotatedSpectrumSet(self.spectra, self.spectrum_indices_per_inchikey, self._embeddings) def __eq__(self, other: object): if not isinstance(other, AnnotatedSpectrumSet): From 1d48ca6baa81aee108df11f35a5a31280f42f720 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 27 Feb 2026 14:56:19 +0100 Subject: [PATCH 126/149] Replace raising error in __eq__ with returning NotImplemented --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index 56d7a62..7f405be 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -97,7 +97,7 @@ def __copy__(self): def __eq__(self, other: object): if not isinstance(other, AnnotatedSpectrumSet): - raise ValueError("__Eq__ can only be done between two AnnotatedSpectrumSets") + return NotImplemented("__Eq__ can only be done between two AnnotatedSpectrumSets") if self.spectra != other.spectra: return False if self.spectrum_indices_per_inchikey != other.spectrum_indices_per_inchikey: From 328c69dcb9c3935a795f142ff7b4220855e4aef9 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 27 Feb 2026 14:57:21 +0100 Subject: [PATCH 127/149] Added repr ans str for AnnotatedSpectrumSet --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index 7f405be..636388a 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -108,3 +108,17 @@ def __eq__(self, other: object): def __len__(self): return len(self._spectra) + + def __repr__(self): + return ( + f"AnnotatedSpectrumSet(nr_of_spectra = {len(self)}," + f"nr_of_unique_inchikeys = {len(self.inchikeys)}, " + f"has_embeddings={self.has_embeddings})" + ) + + def __str__(self): + with_embeddings = "" + if self.has_embeddings: + with_embeddings = "with embeddings" + + return f"{len(self)} spectra for {len(self.inchikeys)} inchikeys {with_embeddings}" From a385313968dfbbb528ca7b307748eb20a8cc58db Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 27 Feb 2026 15:32:16 +0100 Subject: [PATCH 128/149] remove integration test from pyproject.toml --- pyproject.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index fe9495c..fbaac97 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -98,5 +98,4 @@ section-order = ["future", "standard-library", "third-party", "first-party", "lo [tool.pytest.ini_options] testpaths = [ "tests", - "integration-tests", ] From 41e4c4bfa5f64c89e1c2ddc4ae4dd88324dc718c Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 27 Feb 2026 15:32:41 +0100 Subject: [PATCH 129/149] Add subset spectra on metadata --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 13 +++++++++++- .../test_benchmarking/test_SpectrumDataSet.py | 20 +++++++++++++++++++ 2 files changed, 32 insertions(+), 1 deletion(-) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index 636388a..c2db5f8 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -52,7 +52,7 @@ def __add__(self, other) -> "AnnotatedSpectrumSet": print("Only one of the two sets has an embeddings, so embeddings are not added") embeddings = None if self.has_embeddings and other.has_embeddings: - embeddings = Embeddings.combine_embeddings(self.embeddings, other.embeddings) + embeddings = self.embeddings + other.embeddings return AnnotatedSpectrumSet(spectra, spectrum_indices_per_inchikey, embeddings=embeddings) def subset_spectra(self, spectrum_indices) -> "AnnotatedSpectrumSet": @@ -63,6 +63,17 @@ def subset_spectra(self, spectrum_indices) -> "AnnotatedSpectrumSet": new_instance._embeddings = self.embeddings.subset_embeddings(spectra) return new_instance + def subset_spectra_on_metadata(self, metadata_key: str, values_to_keep: set) -> "AnnotatedSpectrumSet": + """Creates a subset from the spectra by checking for specific metadata keys + + E.g. subset_spectra_on_metadata("ionmode", set(["positive"])) will return only the spectra in positive ion mode + """ + spectrum_indexes_to_keep = [] + for spectrum_index, spectrum in enumerate(tqdm(self.spectra, desc="Checking spectra for correct metadata")): + if spectrum.get(metadata_key) in values_to_keep: + spectrum_indexes_to_keep.append(spectrum_index) + return self.subset_spectra(spectrum_indexes_to_keep) + def spectra_per_inchikey(self, inchikey) -> List[Spectrum]: matching_spectra = [] for index in self.spectrum_indices_per_inchikey[inchikey]: diff --git a/tests/test_benchmarking/test_SpectrumDataSet.py b/tests/test_benchmarking/test_SpectrumDataSet.py index 7f3e925..25203ad 100644 --- a/tests/test_benchmarking/test_SpectrumDataSet.py +++ b/tests/test_benchmarking/test_SpectrumDataSet.py @@ -40,3 +40,23 @@ def test_subsetting(): spectrum_set.add_embeddings(model) assert correct_subsetted_set == spectrum_set.subset_spectra([0, 1, 2, 3, 4]) + + +def test_subset_on_metadata(): + test_spectra = create_test_spectra(nr_of_inchikeys=3, number_of_spectra_per_inchikey=3) + + test_spectra[0].set("ionmode", "positive") + test_spectra[5].set("ionmode", "positive") + test_spectra[7].set("ionmode", "positive") + + correct_subsetted_set = AnnotatedSpectrumSet.create_spectrum_set( + [test_spectra[0], test_spectra[5], test_spectra[7]] + ) + spectrum_set = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) + + # with added embededings + model = ms2deepscore_model() + correct_subsetted_set.add_embeddings(model) + spectrum_set.add_embeddings(model) + + assert correct_subsetted_set == spectrum_set.subset_spectra_on_metadata("ionmode", set(["positive"])) From a11bd4696c33e2bdfd22a431c0aa761530962c9e Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 27 Feb 2026 15:33:21 +0100 Subject: [PATCH 130/149] Replace combine embeddings with __add__ --- ms2query/benchmarking/Embeddings.py | 16 ++++++++-------- tests/test_benchmarking/test_embeddings.py | 6 +++--- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/ms2query/benchmarking/Embeddings.py b/ms2query/benchmarking/Embeddings.py index 28bab86..4ec6548 100644 --- a/ms2query/benchmarking/Embeddings.py +++ b/ms2query/benchmarking/Embeddings.py @@ -30,16 +30,16 @@ def create_from_spectra(cls, spectra: Sequence[Spectrum], model: SiameseSpectral embeddings: np.ndarray = compute_embedding_array(model, spectra) # type: ignore return cls(embeddings, index_to_spectrum_hash, model_settings) - @classmethod - def combine_embeddings(cls, embeddings_1: "Embeddings", embeddings_2: "Embeddings") -> "Embeddings": - if embeddings_1.model_settings != embeddings_2.model_settings: + def __add__(self, other: "Embeddings") -> "Embeddings": + if not isinstance(other, Embeddings): + return NotImplemented + if self.model_settings != other.model_settings: raise ValueError("Model settings of merged embeddings do not match") - if not set(embeddings_1.index_to_spectrum_hash).isdisjoint(embeddings_2.index_to_spectrum_hash): - # todo allow this to happen, but remove repeating ones and check that they are the same. + if not set(self.index_to_spectrum_hash).isdisjoint(other.index_to_spectrum_hash): raise ValueError("There are repeated spectra in the embeddings that are added together") - combined_embeddings = np.vstack([embeddings_1.embeddings, embeddings_2.embeddings]) - index_to_spectrum_hash = embeddings_1.index_to_spectrum_hash + embeddings_2.index_to_spectrum_hash - return cls(combined_embeddings, index_to_spectrum_hash, embeddings_1.model_settings) + combined_embeddings = np.vstack([self._embeddings, other._embeddings]) + index_to_spectrum_hash = self.index_to_spectrum_hash + other.index_to_spectrum_hash + return Embeddings(combined_embeddings, index_to_spectrum_hash, self._model_settings) def subset_embeddings(self, spectra): spectrum_hashes = tuple(spectrum.__hash__() for spectrum in spectra) diff --git a/tests/test_benchmarking/test_embeddings.py b/tests/test_benchmarking/test_embeddings.py index e8cb3f9..1730cd9 100644 --- a/tests/test_benchmarking/test_embeddings.py +++ b/tests/test_benchmarking/test_embeddings.py @@ -19,16 +19,16 @@ def test_subset_embeddings(): embeddings.subset_embeddings(test_spectra) -def test_combine_embeddings(): +def test_add_embeddings(): test_spectra = create_test_spectra() model = ms2deepscore_model() embeddings_1 = Embeddings.create_from_spectra(test_spectra[:4], model) embeddings_2 = Embeddings.create_from_spectra(test_spectra[4:], model) correct_combined_embeddings = Embeddings.create_from_spectra(test_spectra, model) - combined_embeddings = Embeddings.combine_embeddings(embeddings_1, embeddings_2) + combined_embeddings = embeddings_1 + embeddings_2 assert combined_embeddings == correct_combined_embeddings with pytest.raises(ValueError): - Embeddings.combine_embeddings(embeddings_1, embeddings_1) + _ = embeddings_1 + embeddings_1 def test_calculate_ms2deepscore_df(): From f080a5e5602f49b162f21b06dfedb26cac2d14fa Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 27 Feb 2026 16:14:46 +0100 Subject: [PATCH 131/149] Added an embedding setter with checks to AnnotatedSpectrumSet --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 13 ++++++++++++- tests/test_benchmarking/test_SpectrumDataSet.py | 16 ++++++++++++++++ 2 files changed, 28 insertions(+), 1 deletion(-) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index c2db5f8..6d25f52 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -19,7 +19,7 @@ def __init__( self.spectrum_indices_per_inchikey: dict[str, tuple[int, ...]] = { key: tuple(values) for key, values in spectrum_indices_per_inchikey.items() } - self._embeddings = embeddings + self.embeddings = embeddings @classmethod def create_spectrum_set(cls, spectra: Sequence[Spectrum]) -> "AnnotatedSpectrumSet": @@ -99,6 +99,17 @@ def embeddings(self) -> Embeddings: raise ValueError("First run the 'add_embeddings' method") return self._embeddings + @embeddings.setter + def embeddings(self, embeddings: Optional[Embeddings]): + if embeddings is None: + self._embeddings = embeddings + return + if not embeddings.index_to_spectrum_hash == tuple(spectrum.__hash__() for spectrum in self.spectra): + raise ValueError( + "The embeddings spectrum hashes don't match the spectrum hashes, make sure you use matching embeddings" + ) + self._embeddings = embeddings + @property def inchikeys(self): return tuple(self.spectrum_indices_per_inchikey.keys()) diff --git a/tests/test_benchmarking/test_SpectrumDataSet.py b/tests/test_benchmarking/test_SpectrumDataSet.py index 25203ad..25b6670 100644 --- a/tests/test_benchmarking/test_SpectrumDataSet.py +++ b/tests/test_benchmarking/test_SpectrumDataSet.py @@ -1,3 +1,4 @@ +import pytest from ms2query.benchmarking.AnnotatedSpectrumSet import ( AnnotatedSpectrumSet, ) @@ -60,3 +61,18 @@ def test_subset_on_metadata(): spectrum_set.add_embeddings(model) assert correct_subsetted_set == spectrum_set.subset_spectra_on_metadata("ionmode", set(["positive"])) + + +def test_add_embeddings(): + test_spectra = create_test_spectra(nr_of_inchikeys=3, number_of_spectra_per_inchikey=3) + + spectrum_set = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) + + subset_of_spectra = AnnotatedSpectrumSet.create_spectrum_set(test_spectra[:5]) + spectrum_set = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) + # with added embededings + model = ms2deepscore_model() + subset_of_spectra.add_embeddings(model) + with pytest.raises(ValueError): + # The spectra don't match so it should raise a valueerror + spectrum_set.embeddings = subset_of_spectra.embeddings From 83c6c7dd5743ac1f8cf6f798b29f824e61b25dc0 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 27 Feb 2026 17:00:47 +0100 Subject: [PATCH 132/149] Added save and load models for embeddings --- ms2query/benchmarking/Embeddings.py | 61 ++++++++++++++++++++-- tests/test_benchmarking/test_embeddings.py | 11 ++++ 2 files changed, 69 insertions(+), 3 deletions(-) diff --git a/ms2query/benchmarking/Embeddings.py b/ms2query/benchmarking/Embeddings.py index 4ec6548..2b3612e 100644 --- a/ms2query/benchmarking/Embeddings.py +++ b/ms2query/benchmarking/Embeddings.py @@ -1,3 +1,5 @@ +import json +from pathlib import Path from typing import Sequence import numpy as np import pandas as pd @@ -17,7 +19,7 @@ def __init__(self, embeddings: np.ndarray, spectrum_hashes: tuple, model_setting self._spectrum_hash_to_index = { spectrum_hash: index for index, spectrum_hash in enumerate(self.index_to_spectrum_hash) } - self._model_settings = model_settings + self.model_settings = model_settings self._embeddings = embeddings @classmethod @@ -39,7 +41,7 @@ def __add__(self, other: "Embeddings") -> "Embeddings": raise ValueError("There are repeated spectra in the embeddings that are added together") combined_embeddings = np.vstack([self._embeddings, other._embeddings]) index_to_spectrum_hash = self.index_to_spectrum_hash + other.index_to_spectrum_hash - return Embeddings(combined_embeddings, index_to_spectrum_hash, self._model_settings) + return Embeddings(combined_embeddings, index_to_spectrum_hash, self.model_settings) def subset_embeddings(self, spectra): spectrum_hashes = tuple(spectrum.__hash__() for spectrum in spectra) @@ -58,11 +60,15 @@ def embeddings(self): def model_settings(self): return self._model_settings.copy() + @model_settings.setter + def model_settings(self, model_settings): + self._model_settings: dict = _to_json_serializable(model_settings) + def copy(self) -> "Embeddings": return Embeddings( embeddings=self._embeddings.copy(), spectrum_hashes=tuple(self.index_to_spectrum_hash), - model_settings=dict(self._model_settings), + model_settings=dict(self.model_settings), ) def __eq__(self, other) -> bool: @@ -76,6 +82,40 @@ def __eq__(self, other) -> bool: return False return np.array_equal(self.embeddings, other.embeddings) + def save(self, path: str | Path) -> None: + """Save embeddings to a .npz file with metadata stored alongside. + + Args: + path: File path. A '.npz' extension will be added if not present. + """ + path = Path(path).with_suffix(".npz") + metadata = { + "model_settings": self.model_settings, + "index_to_spectrum_hash": list(self.index_to_spectrum_hash), + } + np.savez_compressed( + path, + embeddings=self._embeddings, + metadata=np.array(json.dumps(metadata)), + ) + + @classmethod + def load(cls, path: str | Path) -> "Embeddings": + """Load embeddings from a saved .npz file. + + Args: + path: Path to the saved .npz file. + """ + path = Path(path).with_suffix(".npz") + with np.load(path, allow_pickle=False) as data: + embeddings = data["embeddings"] + metadata = json.loads(data["metadata"].item()) + return cls( + embeddings=embeddings, + spectrum_hashes=tuple(metadata["index_to_spectrum_hash"]), + model_settings=metadata["model_settings"], + ) + def calculate_ms2deepscore_df(query_embeddings: Embeddings, library_embeddings: Embeddings): """Returns a DF, where the indexes and column labels are the spectrum hashes""" @@ -83,3 +123,18 @@ def calculate_ms2deepscore_df(query_embeddings: Embeddings, library_embeddings: return pd.DataFrame( ms2deepscores, index=query_embeddings.index_to_spectrum_hash, columns=library_embeddings.index_to_spectrum_hash ) + + +def _to_json_serializable(obj): + """Changes a dict to be json sericalizable, so it is the same when loaded""" + if isinstance(obj, dict): + return {key: _to_json_serializable(value) for key, value in obj.items()} + if isinstance(obj, (list, tuple)): + return [_to_json_serializable(item) for item in obj] + if isinstance(obj, np.integer): + return int(obj) + if isinstance(obj, np.floating): + return float(obj) + if isinstance(obj, np.ndarray): + return obj.tolist() + return obj diff --git a/tests/test_benchmarking/test_embeddings.py b/tests/test_benchmarking/test_embeddings.py index 1730cd9..86fbbbd 100644 --- a/tests/test_benchmarking/test_embeddings.py +++ b/tests/test_benchmarking/test_embeddings.py @@ -1,3 +1,4 @@ +import os import pytest from ms2query.benchmarking.Embeddings import Embeddings, calculate_ms2deepscore_df from tests.helper_functions import create_test_spectra, get_library_and_test_spectra_not_identical, ms2deepscore_model @@ -34,3 +35,13 @@ def test_add_embeddings(): def test_calculate_ms2deepscore_df(): library_spectra, query_spectra = get_library_and_test_spectra_not_identical() calculate_ms2deepscore_df(library_spectra.embeddings, query_spectra.embeddings) + + +def test_save_and_load(tmp_path): + test_spectra = create_test_spectra() + model = ms2deepscore_model() + embeddings = Embeddings.create_from_spectra(test_spectra[:4], model) + file_name = os.path.join(tmp_path, "embeddings.npz") + embeddings.save(file_name) + loaded_embeddings = Embeddings.load(file_name) + assert embeddings == loaded_embeddings From 73455a15ab8b6a33c1d5a40d3852a658c61b39b5 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Fri, 27 Feb 2026 17:26:03 +0100 Subject: [PATCH 133/149] Added save and load options for AnnotatedSpectrumSet --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 23 +++++++++++++++++++ .../test_benchmarking/test_SpectrumDataSet.py | 18 +++++++++++++++ 2 files changed, 41 insertions(+) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index 6d25f52..86332db 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -1,6 +1,9 @@ +import os from collections import defaultdict from typing import Iterable, List, Mapping, Optional, Sequence from matchms import Spectrum +from matchms.exporting import save_spectra +from matchms.importing import load_spectra from ms2deepscore.models import SiameseSpectralModel from tqdm import tqdm from ms2query.benchmarking.Embeddings import Embeddings @@ -144,3 +147,23 @@ def __str__(self): with_embeddings = "with embeddings" return f"{len(self)} spectra for {len(self.inchikeys)} inchikeys {with_embeddings}" + + def save(self, save_file: str) -> None: + """Save spectra to the specified path""" + save_spectra(list(self._spectra), save_file) + + if self._embeddings is not None: + embedding_save_name = os.path.splitext(save_file)[0] + "_embeddings.npz" + print(f"Saving embeddings at {embedding_save_name}") + self._embeddings.save(embedding_save_name) + + @classmethod + def load(cls, spectrum_file: str) -> "AnnotatedSpectrumSet": + """Load mass spectra into a AnnotatedSpectrumSet, if embeddings are available they are loaded too""" + spectra = list(load_spectra(spectrum_file)) + + embedding_file_name = os.path.splitext(spectrum_file)[0] + "_embeddings.npz" + instance = cls.create_spectrum_set(spectra) + if os.path.exists(embedding_file_name): + instance.embeddings = Embeddings.load(embedding_file_name) + return instance diff --git a/tests/test_benchmarking/test_SpectrumDataSet.py b/tests/test_benchmarking/test_SpectrumDataSet.py index 25b6670..95b70c3 100644 --- a/tests/test_benchmarking/test_SpectrumDataSet.py +++ b/tests/test_benchmarking/test_SpectrumDataSet.py @@ -1,3 +1,5 @@ +import os + import pytest from ms2query.benchmarking.AnnotatedSpectrumSet import ( AnnotatedSpectrumSet, @@ -76,3 +78,19 @@ def test_add_embeddings(): with pytest.raises(ValueError): # The spectra don't match so it should raise a valueerror spectrum_set.embeddings = subset_of_spectra.embeddings + + +def test_save_and_load(tmp_path): + test_spectra = create_test_spectra(nr_of_inchikeys=3, number_of_spectra_per_inchikey=3) + spectrum_set = AnnotatedSpectrumSet.create_spectrum_set(test_spectra) + file_name = os.path.join(tmp_path, "spectra.mgf") + spectrum_set.save(file_name) + loaded_spectrum_set = spectrum_set.load(file_name) + assert spectrum_set == loaded_spectrum_set + + model = ms2deepscore_model() + spectrum_set.add_embeddings(model) + file_name = os.path.join(tmp_path, "spectra_2.mgf") + spectrum_set.save(file_name) + loaded_spectrum_set = spectrum_set.load(file_name) + assert spectrum_set == loaded_spectrum_set From 11729e2c091b7955ddbda50435e070a6caeeefdc Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Mon, 2 Mar 2026 10:16:18 +0100 Subject: [PATCH 134/149] ruff --- tests/test_benchmarking/test_SpectrumDataSet.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_benchmarking/test_SpectrumDataSet.py b/tests/test_benchmarking/test_SpectrumDataSet.py index 95b70c3..3e252eb 100644 --- a/tests/test_benchmarking/test_SpectrumDataSet.py +++ b/tests/test_benchmarking/test_SpectrumDataSet.py @@ -1,5 +1,4 @@ import os - import pytest from ms2query.benchmarking.AnnotatedSpectrumSet import ( AnnotatedSpectrumSet, From 7406739f178c4875abd289eda28fa3bf84cff803 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 19 Mar 2026 10:04:05 +0100 Subject: [PATCH 135/149] Add main cleaned notebook --- .../develop_and_compare_ms2query_2.ipynb | 3451 +++++++++++++++++ 1 file changed, 3451 insertions(+) create mode 100644 ms2query/notebooks/develop_and_compare_ms2query_2.ipynb diff --git a/ms2query/notebooks/develop_and_compare_ms2query_2.ipynb b/ms2query/notebooks/develop_and_compare_ms2query_2.ipynb new file mode 100644 index 0000000..ef3f726 --- /dev/null +++ b/ms2query/notebooks/develop_and_compare_ms2query_2.ipynb @@ -0,0 +1,3451 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5e1e2305-926b-4966-a259-84e7c4c615b5", + "metadata": {}, + "source": [ + "# Load in files from Zenodo\n", + "Download the starting files from zenodo. If you have run the code before, you can instead use the intermediate checkpoints to start halfway and reduce runtime. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a8faf53e-1457-41e7-90e1-4b2337ffc277", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import os\n", + "from tqdm import tqdm\n", + "\n", + "def download_file(link, file_name):\n", + " response = requests.get(link, stream=True)\n", + " if os.path.exists(file_name):\n", + " print(f\"The file {file_name} already exists, the file won't be downloaded\")\n", + " return\n", + " total_size = int(response.headers.get('content-length', 0))\n", + "\n", + " with open(file_name, \"wb\") as f, tqdm(desc=\"Downloading file\", total=total_size, unit='B', unit_scale=True, unit_divisor=1024,) as bar:\n", + " for chunk in response.iter_content(chunk_size=1024):\n", + " if chunk:\n", + " f.write(chunk)\n", + " bar.update(len(chunk)) # Update progress bar by the chunk size\n", + "folder_to_store_zenodo_files = \"./zenodo_files\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b044520d-77ba-4461-a5a8-aadc396dca74", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The file ./zenodo_files/data_split_inchikeys.json already exists, the file won't be downloaded\n", + "The file ./zenodo_files/merged_and_cleaned_libraries_1.mgf already exists, the file won't be downloaded\n" + ] + } + ], + "source": [ + "\n", + "os.makedirs(folder_to_store_zenodo_files, exist_ok=True)\n", + "# This file contains the inchikeys used for the data split. This ensures reproducing the exact same results, \n", + "# alternatively you can of course do the data split yourself, but results might differ slightly. \n", + "download_file(\"https://zenodo.org/records/18801895/files/data_split_inchikeys.json?download=1\", \n", + " os.path.join(folder_to_store_zenodo_files, \"data_split_inchikeys.json\"))\n", + "download_file(\"https://zenodo.org/records/16882111/files/merged_and_cleaned_libraries_1.mgf?download=1\", \n", + " os.path.join(folder_to_store_zenodo_files, \"merged_and_cleaned_libraries_1.mgf\"))\n" + ] + }, + { + "cell_type": "markdown", + "id": "f5361fb8-f2e2-4b84-90e7-25d3d030e171", + "metadata": {}, + "source": [ + "# Load in mass spectra\n", + "These mass spectra are already precleaned using matchms and contain both the GNPS spectra and the MSnLIb spectra for details about the exact cleaning you can check out the zenodo page: https://zenodo.org/records/16882111 . This contains a notebook with all the downloading and cleaning steps detailed. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6cb010de-930c-4667-b638-8fd59e027c74", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1017531it [05:36, 3021.46it/s]\n" + ] + } + ], + "source": [ + "from matchms.importing.load_spectra import load_spectra\n", + "from tqdm import tqdm\n", + "import os\n", + "all_spectra = list(tqdm(load_spectra(os.path.join(folder_to_store_zenodo_files, \"merged_and_cleaned_libraries_1.mgf\"))))" + ] + }, + { + "cell_type": "markdown", + "id": "67c1945f-aa31-450b-9995-3d13e8e728c4", + "metadata": {}, + "source": [ + "### Split data on inchikeys\n", + "We use the predefined split for reproducibility, but you can of course make your own inchikey split. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2328a4e8-42c1-49a3-a95b-fc6f65c19ffb", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "with open(os.path.join(folder_to_store_zenodo_files, \"data_split_inchikeys.json\"), \"r\") as f:\n", + " data_split = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3c4f5ee6-6ac2-4588-a069-c7179d137912", + "metadata": {}, + "outputs": [], + "source": [ + "val_inchikeys = set(data_split[\"val\"])\n", + "train_inchikeys = set(data_split[\"train\"])\n", + "test_inchikeys = set(data_split[\"test\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c646784c-1017-4a3e-83d5-2a78efc0ecee", + "metadata": {}, + "outputs": [], + "source": [ + "print(len(val_inchikeys))\n", + "print(len(train_inchikeys))\n", + "print(len(test_inchikeys))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cb343a4f-48b5-4f86-ba01-3f30216a84a7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1017531/1017531 [00:02<00:00, 453956.16it/s]\n" + ] + } + ], + "source": [ + "val_spectra = []\n", + "test_spectra = []\n", + "train_spectra = []\n", + "for spectrum in tqdm(all_spectra):\n", + " inchikey = spectrum.get(\"inchikey\")[:14]\n", + " if inchikey in train_inchikeys:\n", + " train_spectra.append(spectrum)\n", + " elif inchikey in val_inchikeys:\n", + " val_spectra.append(spectrum)\n", + " elif inchikey in test_inchikeys:\n", + " test_spectra.append(spectrum)\n", + " else:\n", + " raise ValueError(f\"The inchikey {inchikey} does not appear in the val, test or train inchikeys\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8852c20d-89b2-4d81-80fc-6ced423cea5b", + "metadata": {}, + "outputs": [], + "source": [ + "# Free up some memory :) \n", + "all_spectra = None" + ] + }, + { + "cell_type": "markdown", + "id": "ca3c8ef2-67a1-48cd-b290-4f86ad6a61f5", + "metadata": {}, + "source": [ + "# Split data on ionmode" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "84cabbcc-f026-406a-89db-4d6e7eee5a2e", + "metadata": {}, + "outputs": [], + "source": [ + "def split_on_ionmode(spectra):\n", + " pos_spectra = []\n", + " neg_spectra = []\n", + " for spectrum in spectra:\n", + " ionmode = spectrum.get(\"ionmode\")\n", + " if ionmode == \"positive\":\n", + " pos_spectra.append(spectrum)\n", + " elif ionmode == \"negative\":\n", + " neg_spectra.append(spectrum)\n", + " else:\n", + " raise ValueError(f\"The ionmode {ionmode} is not positive or negative\")\n", + " return pos_spectra, neg_spectra" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a88535a0-97aa-49c8-9675-22d782521ada", + "metadata": {}, + "outputs": [], + "source": [ + "pos_val_spectra, neg_val_spectra = split_on_ionmode(val_spectra)\n", + "pos_test_spectra, neg_test_spectra = split_on_ionmode(test_spectra)\n", + "pos_train_spectra, neg_train_spectra = split_on_ionmode(train_spectra)" + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "id": "753287f1-769d-4b72-a39c-32a9b843735c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "52440\n", + "49422\n", + "915669\n" + ] + } + ], + "source": [ + "print(len(pos_val_spectra) + len(neg_val_spectra))\n", + "print(len(pos_test_spectra) + len(neg_test_spectra))\n", + "print(len(pos_train_spectra) + len(neg_train_spectra))" + ] + }, + { + "cell_type": "markdown", + "id": "547fd276-f41e-4312-924f-3dfc8030d70e", + "metadata": {}, + "source": [ + "# Convert to AnnotatedSpectrumSet\n", + "An annotatedSpectrumSet organizes the spectra per inchikey. This makes doing the analysis and ranking much easier." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6fecef86-84af-40e9-bf48-d81d62f8bcf4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Your CPU supports instructions that this binary was not compiled to use: SSE3 SSE4.1 SSE4.2 AVX AVX2\n", + "For maximum performance, you can install NMSLIB from sources \n", + "pip install --no-binary :all: nmslib\n", + "Create mapping from inchikey to spectrum: 100%|████████████████████████████████████████████████████████████████████████| 34227/34227 [00:00<00:00, 255957.63it/s]\n", + "Create mapping from inchikey to spectrum: 100%|██████████████████████████████████████████████████████████████████████| 631879/631879 [00:01<00:00, 342264.10it/s]\n", + "Create mapping from inchikey to spectrum: 100%|████████████████████████████████████████████████████████████████████████| 36473/36473 [00:00<00:00, 254856.01it/s]\n" + ] + } + ], + "source": [ + "from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet\n", + "\n", + "pos_test_spectra = AnnotatedSpectrumSet.create_spectrum_set(pos_test_spectra)\n", + "pos_train_spectra = AnnotatedSpectrumSet.create_spectrum_set(pos_train_spectra)\n", + "pos_val_spectra = AnnotatedSpectrumSet.create_spectrum_set(pos_val_spectra)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "92a0e90c-d435-4a0e-b9ba-bc7ab2139ca6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Create mapping from inchikey to spectrum: 100%|████████████████████████████████████████████████████████████████████████| 15967/15967 [00:00<00:00, 388960.56it/s]\n", + "Create mapping from inchikey to spectrum: 100%|████████████████████████████████████████████████████████████████████████| 15195/15195 [00:00<00:00, 352888.95it/s]\n", + "Create mapping from inchikey to spectrum: 100%|██████████████████████████████████████████████████████████████████████| 283790/283790 [00:00<00:00, 403806.06it/s]\n" + ] + } + ], + "source": [ + "from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet\n", + "\n", + "neg_val_spectra = AnnotatedSpectrumSet.create_spectrum_set(neg_val_spectra)\n", + "neg_test_spectra = AnnotatedSpectrumSet.create_spectrum_set(neg_test_spectra)\n", + "neg_train_spectra = AnnotatedSpectrumSet.create_spectrum_set(neg_train_spectra)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "42e7038a-69f4-4d5f-8aa2-19342230e5a2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "36473 spectra for 3119 inchikeys \n", + "34227 spectra for 3092 inchikeys \n", + "631879 spectra for 55572 inchikeys \n", + "15967 spectra for 1614 inchikeys with embeddings\n", + "15195 spectra for 1659 inchikeys \n", + "283790 spectra for 29806 inchikeys \n" + ] + } + ], + "source": [ + "print(pos_val_spectra)\n", + "print(pos_test_spectra)\n", + "print(pos_train_spectra)\n", + "print(neg_val_spectra)\n", + "print(neg_test_spectra)\n", + "print(neg_train_spectra)" + ] + }, + { + "cell_type": "markdown", + "id": "78ef692e-4349-4892-b149-eb149a51051d", + "metadata": {}, + "source": [ + "# Add MS2DeepScore embeddings\n", + "The MS2DeepScore model was trained on the training split here and is used to add embeddings to the spectra. " + ] + }, + { + "cell_type": "markdown", + "id": "f280c8e3-b1e1-45bd-a9aa-a641ba2bed2c", + "metadata": {}, + "source": [ + "### Download MS2DeepScore model from zenodo" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "67c823e4-3dad-4016-bd36-d077e9c915be", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The file ./zenodo_files/ms2deepscore_model.pt already exists, the file won't be downloaded\n" + ] + } + ], + "source": [ + "folder_to_store_zenodo_files = \"./zenodo_files\"\n", + "download_file(\"https://zenodo.org/records/17826815/files/ms2deepscore_model.pt?download=1\", \n", + " os.path.join(folder_to_store_zenodo_files, \"ms2deepscore_model.pt\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 305, + "id": "74716c8c-ca3d-4daa-81e9-1f21ca7283ba", + "metadata": {}, + "outputs": [], + "source": [ + "from ms2deepscore.models import load_model\n", + "ms2deepscore_model = load_model(os.path.join(folder_to_store_zenodo_files, \"ms2deepscore_model.pt\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2271e81e-b714-42fd-9078-7ac68d3bc080", + "metadata": {}, + "outputs": [], + "source": [ + "pos_val_spectra.add_embeddings(ms2deepscore_model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e31f7d4f-d459-4f73-a71c-adcc03d3ca5b", + "metadata": {}, + "outputs": [], + "source": [ + "pos_train_spectra.add_embeddings(ms2deepscore_model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d2962cd-f48f-4da3-9799-c7e418024feb", + "metadata": {}, + "outputs": [], + "source": [ + "neg_val_spectra.add_embeddings(ms2deepscore_model)\n", + "neg_train_spectra.add_embeddings(ms2deepscore_model)" + ] + }, + { + "cell_type": "markdown", + "id": "f7ab774e-03b6-4365-a7e2-79eced85a36e", + "metadata": {}, + "source": [ + "# Experiment with pos pos comparisons\n", + "First we experiment with comparing pos against pos spectra. Once we settled on the best methods, we move to testing negative mode as well. Also we will later test the cross ionization mode capabilities." + ] + }, + { + "cell_type": "markdown", + "id": "cc187fe3-43d3-4c2f-8d6d-db17ec529356", + "metadata": {}, + "source": [ + "# Compute fingerprints\n", + "Daylight fingerprints are computed to allow for calculating Tanimoto scores which serve as a measure of chemical similarity." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3453717f-b50d-4623-bd87-b1287c0fe916", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Get most common inchi per inchikey: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3119/3119 [00:00<00:00, 19633.20it/s]\n", + "Adding fingerprints to Inchikeys: 3119it [00:11, 260.74it/s]\n", + "Get most common inchi per inchikey: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 55572/55572 [00:01<00:00, 28793.71it/s]\n", + "Adding fingerprints to Inchikeys: 55572it [03:30, 263.65it/s]\n" + ] + } + ], + "source": [ + "from ms2query.benchmarking.Fingerprints import Fingerprints\n", + "val_fingerprints = Fingerprints.from_spectrum_set(pos_val_spectra, \"daylight\", 4096)\n", + "train_fingerprints = Fingerprints.from_spectrum_set(pos_train_spectra, \"daylight\", 4096)\n", + "val_and_train_fingerprints = Fingerprints.combine_fingerprints(val_fingerprints, train_fingerprints)" + ] + }, + { + "cell_type": "markdown", + "id": "d000e093-3f24-48e6-92f0-d60de4f6c933", + "metadata": {}, + "source": [ + "### Compute top k inchikeys\n", + "MS2Query internally uses the top k closest tanimoto scores for reranking. So for any library candidate molecule the chemically closest library molecules are selected, for which the MS2DeepScores are also computed. This was one of the major improvements in the original MS2Query, here we precompute these topk tanimoto scores. We select the top 100, to later have the freedom to test different numbers of top inchikeys. Spoiler, you don't need 100 (around 5-10 is enough)." + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "cc395ecc-9473-4c9d-979f-ff8c087293d5", + "metadata": {}, + "outputs": [], + "source": [ + "from ms2query.benchmarking.TopKTanimotoScores import TopKTanimotoScores\n", + "top_k_tanimoto_scores = TopKTanimotoScores.calculate_from_fingerprints(\n", + " train_fingerprints,\n", + " train_fingerprints, k=100,)" + ] + }, + { + "cell_type": "markdown", + "id": "116bdee3-778e-41a3-bf3b-0cfada98a6c7", + "metadata": {}, + "source": [ + "# Reranking on thresholds\n", + "We discovered that with the new MS2DeepScore model any methods we developed, like average over top 10 closest tanimoto scores, did not improve the ranking accuracy over just picking the highest ms2deepscore from the library. However, to determine if a prediction is reliable the top 10 closest tanimoto scores was very usefull. Here we predict the top 1 highest MS2DeepScore for each query spectrum. For this highest hit the top 100 closest inchikeys are selected followed by computing all corresponing MS2DeepScores with the query spectrum. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "874b126b-ad28-4b5c-8349-6507dd187add", + "metadata": {}, + "outputs": [], + "source": [ + "from ms2query.benchmarking.reference_methods.predict_top_ms2deepscores import select_inchikeys_with_highest_ms2deepscore\n", + "\n", + "inchikeys_with_highest_ms2deepscores = select_inchikeys_with_highest_ms2deepscore(\n", + " pos_val_spectra, pos_train_spectra, nr_of_inchikeys_to_select=1, batch_size=1000,)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f9c1ab24-c391-4b2b-a8cd-1295e4d11bfd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Calculating: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 36473/36473 [17:21<00:00, 35.01it/s]\n" + ] + } + ], + "source": [ + "from ms2query.benchmarking.reference_methods.MS2DeepScoresForTopInChikeys import calculate_MS2DeepScoresForTopKInChikeys\n", + "close_tanimoto_scores = calculate_MS2DeepScoresForTopKInChikeys(pos_train_spectra, pos_val_spectra, top_k_tanimoto_scores, inchikeys_with_highest_ms2deepscores)" + ] + }, + { + "cell_type": "markdown", + "id": "589d93ea-9124-4070-acb7-9f98051f2922", + "metadata": {}, + "source": [ + "### Save intermediate files" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "fb9285fd-8da3-4190-afee-d340c1063a43", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pickle\n", + "intermediates_folder = \"./pos_pos_intermediate\"\n", + "os.makedirs(intermediates_folder, exist_ok=True)\n", + "with open(os.path.join(intermediates_folder, \"top_1_highest_ms2deepscore_with_100_top_inchikeys.pickle\"), \"wb\") as handle:\n", + " pickle.dump(close_tanimoto_scores, handle, protocol=pickle.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "35b04e87-468d-4e33-b0d2-c4b07346b5c7", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(intermediates_folder, \"pos_val_spectra_with_embeddings.pickle\"), \"wb\") as handle:\n", + " pickle.dump(pos_val_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "a08a7031-2ca7-4268-a61d-712f9f273e46", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(intermediates_folder, \"pos_val_and_train_fingerprints.pickle\"), \"wb\") as handle:\n", + " pickle.dump(val_and_train_fingerprints, handle, protocol=pickle.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "6d68ed7f-ff32-4e74-8bc2-1268c2577f36", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(intermediates_folder, \"top_k_tanimoto_scores.pickle\"), \"wb\") as handle:\n", + " pickle.dump(top_k_tanimoto_scores, handle, protocol=pickle.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "markdown", + "id": "74208460-7969-452a-ab64-1c0734015354", + "metadata": {}, + "source": [ + "### Optional start from intermediate files" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "b1c2748b-81dd-4cad-9ea8-14f20de813d1", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "import os\n", + "intermediates_folder = \"./pos_pos_intermediate\"\n", + "\n", + "with open(os.path.join(intermediates_folder, \"top_1_highest_ms2deepscore_with_100_top_inchikeys.pickle\"), \"rb\") as handle:\n", + " close_tanimoto_scores = pickle.load(handle)\n", + "with open(os.path.join(intermediates_folder, \"pos_val_spectra_with_embeddings.pickle\"), \"rb\") as handle:\n", + " pos_val_spectra = pickle.load(handle)\n", + "with open(os.path.join(intermediates_folder, \"pos_val_and_train_fingerprints.pickle\"), \"rb\") as handle:\n", + " val_and_train_fingerprints = pickle.load(handle)\n", + "with open(os.path.join(intermediates_folder, \"top_k_tanimoto_scores.pickle\"), \"rb\") as handle:\n", + " top_k_tanimoto_scores = pickle.load(handle)" + ] + }, + { + "cell_type": "markdown", + "id": "91733995-6a8f-42ca-8b4b-983d3252f71a", + "metadata": {}, + "source": [ + "# Test different top k tanimoto scores\n", + "Here we load in a run where we selected the highest scoring inchikey based on MS2DeepScore, followed by computing the MS2DeepScores for the top 100 closest inchikeys. Since only one inchikey is selected, the ranking is just done on highest MS2DeepScore, however for the threshold used to determine the reliability we use the top k inchikeys. " + ] + }, + { + "cell_type": "markdown", + "id": "f03f2167-990d-4c15-bc97-c6cc8046275b", + "metadata": {}, + "source": [ + "### Compute the tanimoto score for the predicted inchikeys" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a8a65098-db94-498e-bb1d-5475f84f4058", + "metadata": {}, + "outputs": [], + "source": [ + "# Selects the predicted inchikeys\n", + "predicted_inchikeys = [list(query.keys())[0] for query in close_tanimoto_scores]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ceb53843-878c-4a42-991f-8d5c2d0b3191", + "metadata": {}, + "outputs": [], + "source": [ + "for inchikey in predicted_inchikeys:\n", + " if inchikey is None:\n", + " print(\"inchikey is None\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "4b93c5c1-e502-4c4c-9516-008ba96ab25e", + "metadata": {}, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix\n", + "import numpy as np\n", + "def get_prediction_tanimoto(predicted_inchikeys, val_spectra, val_and_train_fingerprints):\n", + " assert len(predicted_inchikeys) == len(val_spectra)\n", + "\n", + " tanimoto_for_prediction = []\n", + " for index, predicted_inchikey in enumerate(tqdm(predicted_inchikeys)):\n", + " if predicted_inchikey is not None:\n", + " correct_inchikey = val_spectra.spectra[index].get(\"inchikey\")[:14]\n", + " predicted_fingerprint = val_and_train_fingerprints.get_fingerprints([predicted_inchikey])[0]\n", + " actual_fingerprint = val_and_train_fingerprints.get_fingerprints([correct_inchikey])[0]\n", + " tanimoto_prediction = jaccard_similarity_matrix(np.array([predicted_fingerprint]), np.array([actual_fingerprint]))[0][0]\n", + " tanimoto_for_prediction.append(tanimoto_prediction)\n", + " else:\n", + " tanimoto_for_prediction.append(0.0)\n", + " return tanimoto_for_prediction\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b37b4756-679d-4144-8fe8-24afc1efcec1", + "metadata": {}, + "outputs": [], + "source": [ + "tanimoto_for_prediction = get_prediction_tanimoto(predicted_inchikeys, pos_val_spectra, val_and_train_fingerprints)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2e26ce88-e606-4c22-9412-d4fc0eb13367", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'tanimoto scores by predicting by MS2DeepScore')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "plt.hist(tanimoto_for_prediction)\n", + "plt.xlabel(\"tanimoto score\")\n", + "plt.ylabel(\"count\")\n", + "plt.title(\"tanimoto scores by predicting by MS2DeepScore\")" + ] + }, + { + "cell_type": "markdown", + "id": "78ce6857-ad5a-4a68-802e-f236231d5a26", + "metadata": {}, + "source": [ + "# How to only select the reliable predictions? \n", + "As you can see many of the highest predicted matches are bad, but some are good. In some cases there simply is no available good library match and sometimes MS2DeepScore just did select a false positive. This picture is similar (or actually worse) for other methods like the modified cosine score, analogue searching is just difficult due to the vast number of options. Therefore, an analogue search is only useful if you can distinguish the good analogues from the bad ones. We need a way to not only predict the best match, but also to convey reliabiltiy to the user, to make library searching usefull. Here we try different approaches for determining reliability. " + ] + }, + { + "cell_type": "markdown", + "id": "50f9088e-dc24-42ee-8049-dc43a64de119", + "metadata": {}, + "source": [ + "The most straightforward way to do this is by using the predicted MS2DeepScore as a guidance for reliability, the higher the predicted MS2DeepScore the more reliable. But there are many more (better) methods for doing this reliability prediction. To measure how well this work, we use such a score for splitting relibable from unreliable matches. We measure this by ranking all the predictions on the score (e.g. ms2deepscore) and retaining different fractions of the predictions. E.g. top 10% until 100%. The general pattern we expect is that the more we fitler out the higher the accuracy of the left over predictions is. " + ] + }, + { + "cell_type": "markdown", + "id": "2055dc26-6b59-4999-89d0-93a444bdee66", + "metadata": {}, + "source": [ + "### Comparison plots and violin plots" + ] + }, + { + "cell_type": "markdown", + "id": "1d1bca9a-dac3-47eb-b654-b476dd38b61a", + "metadata": {}, + "source": [ + "We plot this in two ways, one with more details and one to easily compare between different methods. The violin plots show the full distribution of true tanimoto score for the predictions per \"bucket\" of selected scores. E.g. top 0-10% or top 10-20% of predictions. The second plots just shows the average tanimoto score for the top x percentage. " + ] + }, + { + "cell_type": "markdown", + "id": "73baf393-16c8-49f6-a1df-6ae82af4718a", + "metadata": {}, + "source": [ + "The code below separates the prediction in different \"buckets\" of top 10 and calculates the average tanimoto score for the predictions. It does this on a per inchikey basis. In the validation set most inchikeys have multiple spectra. To make sure we don't judge the quality mostly on an inchikey for which a lot of spectra exist we average the predicted tanimoto per inchikey first. " + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "581f6356-81dd-4755-8c42-5ce0b8219471", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def create_values_violin_plot(predicted_scores, val_spectra, tanimoto_for_predictions, n_buckets):\n", + " \"\"\"The predictions are ranked based on the predicted scores, and are split in different groups from high to low, \n", + " for each group the average tanimoto per inchikey is computed.\"\"\"\n", + " tanimoto_per_bucket = []\n", + " ranked_indexes_per_bucket = bucket_by_value(predicted_scores, n_buckets)\n", + " for indexes_in_bucket in ranked_indexes_per_bucket: \n", + " average_tanimoto_per_inchikey = get_average_tanimoto_per_inchikey(val_spectra, tanimoto_for_predictions, indexes_in_bucket)\n", + " tanimoto_per_bucket.append(average_tanimoto_per_inchikey)\n", + " return tanimoto_per_bucket\n", + "\n", + "def bucket_by_value(values, n_buckets=10):\n", + " \"\"\"\n", + " Splits values into n equally sized buckets based on rank. \n", + " So the highest scores are in the first bucket en the lowest in the last. Each bucket has the same number of values. \n", + " \"\"\"\n", + " values = np.array(values)\n", + " # Get indexes that would sort the array descending (top values first)\n", + " sorted_indexes = np.argsort(values)[::-1]\n", + " \n", + " # Split sorted indexes into n equal(ish) buckets\n", + " buckets = np.array_split(sorted_indexes, n_buckets)\n", + " \n", + " return buckets\n", + " \n", + "def get_average_tanimoto_per_inchikey(query_spectrum_set, tanimoto_for_predictions, selected_indexes):\n", + " \"\"\"Calculates the average tanimoto per inchikey for the selected indexes\"\"\"\n", + " assert len(tanimoto_for_predictions) == len(query_spectrum_set)\n", + " average_scores_per_inchikey = []\n", + " # Calculate score per unique inchikey\n", + " for inchikey in query_spectrum_set.spectrum_indices_per_inchikey.keys():\n", + " matching_spectrum_indexes = query_spectrum_set.spectrum_indices_per_inchikey[inchikey]\n", + " prediction_scores = []\n", + " for index in matching_spectrum_indexes:\n", + " if index in selected_indexes:\n", + " prediction_scores.append(tanimoto_for_predictions[index])\n", + " if len(prediction_scores) > 0:\n", + " average_prediction = sum(prediction_scores) / len(prediction_scores)\n", + " average_scores_per_inchikey.append(average_prediction)\n", + " return average_scores_per_inchikey" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "4d424f31-5467-43f6-9356-d31b8149d9f3", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import gaussian_kde\n", + "\n", + "\n", + "def plot_violins(data_lists, title=\"\", \n", + " figsize=(16, 6), bw_method='scott'):\n", + " \"\"\"\n", + " Plot side-by-side violin plots for a list of 10 (or any number of) value lists.\n", + "\n", + " Parameters:\n", + " data_lists : list of lists/arrays — your 10 groups of values\n", + " title : plot title\n", + " figsize : figure size tuple\n", + " bw_method : KDE bandwidth method ('scott', 'silverman', or float)\n", + " \"\"\"\n", + " n = len(data_lists)\n", + " labels = [f\"{(100/n)*i} - {(100/n)*(i+1)} %\" for i in range(n)]\n", + "\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + "\n", + " parts = ax.violinplot(\n", + " data_lists,\n", + " positions=range(1, n + 1),\n", + " # showmeans=True,\n", + " showmedians=True,\n", + " showextrema=True,\n", + " bw_method=bw_method\n", + " )\n", + "\n", + " # Formatting\n", + " ax.set_xticks(range(1, n + 1))\n", + " ax.set_xlabel(\"Section of ranked predicted scores\", fontsize=11, labelpad=10) \n", + " ax.set_xticklabels(labels, fontsize=11, rotation=30, ha='right') # <-- rotate labels\n", + " ax.set_title(title, fontsize=14, fontweight='bold', pad=15)\n", + " ax.set_ylabel(\"Average Tanimoto score per inchikey\", fontsize=11)\n", + " ax.set_xlim(0.3, n + 0.7)\n", + " ax.spines[['top', 'right']].set_visible(False)\n", + " ax.grid(axis='y', linestyle='--', alpha=0.4)\n", + " ax.set_facecolor('#f9f9f9')\n", + " fig.patch.set_facecolor('white')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "d0335582-fc52-408e-9972-3b30fc8269b4", + "metadata": {}, + "source": [ + "# Get the predicted MS2Deepscore\n", + "The code below is maybe a bit confusing, since the function is general for getting the average over the top k closest MS2DeepScores. But by setting the number to 1. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "24a7818f-4c4f-485e-9093-acc87a951f3d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 36473/36473 [00:05<00:00, 6582.82it/s]\n" + ] + } + ], + "source": [ + "predicted_ms2deepscores = []\n", + "for query in tqdm(close_tanimoto_scores):\n", + " predicted_inchikey = list(query.keys())[0]\n", + " ms2deepscores_for_top_inchikeys = list(query.values())[0]\n", + " predicted_ms2deepscores.append(ms2deepscores_for_top_inchikeys.get_max_per_inchikey()[predicted_inchikey])" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "id": "1299a466-a195-4a65-977b-098081da0651", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_violins(create_values_violin_plot(predicted_ms2deepscores, pos_val_spectra, tanimoto_for_prediction, 10), title=\"MS2DeepScore (reliability = MS2DeepScore)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "id": "c27011a8-7b84-4513-a1ab-7dc867a9582d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.violinplot(create_values_violin_plot(predicted_ms2deepscores, pos_val_spectra, tanimoto_for_prediction, 1), showmedians=True)\n", + "plt.title(\"MS2DeepScore (no reliability estimation)\")\n", + "plt.ylabel(\"Average tanimoto score per inchikey\")\n", + "plt.xticks([])\n", + "plt.xlabel(\"0 - 100%\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "95ab5f29-a28e-4d1c-b1fa-1ff846e6ae46", + "metadata": {}, + "source": [ + "### MS2DeepScore improves the prediction accuracy\n", + "As you can see selecting the predictions with the highest MS2DeepScores does indeed improve the quality of prediction: For the top 20 percent about 1/3th of the predictions are above 0.6, which is a substantial improvement to not doing any ranking of more reliable predictions. However only 1 out of 3 predictions being useful is still quite low and not very usefull. So can we do better? " + ] + }, + { + "cell_type": "markdown", + "id": "fda5c1ba-4e02-45bb-a899-f444474e4020", + "metadata": {}, + "source": [ + "### Using the top k closest tanimoto scores\n", + "One of the main strengths of MS2Query was using the predictions for multiple closely related library molecules to get improved predictions. We will use this principle below to do the reranking of candidates. For each predicted library hit, the top 10 (later we try different numbers) most chemically library molecules are selected. For each of these the MS2DeepScores for all spectra were also computed (see code above, they are already stored in close_tanimoto_scores), now we use these to determine how reliabel a prediction is." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "1c596b6a-f4ad-4a9d-aa18-dc9a48ff682b", + "metadata": {}, + "outputs": [], + "source": [ + "def predict_using_average_max_top_k_tanimoto(k, close_tanimoto_scores):\n", + " threshold_score = []\n", + " for spectrum_index, single_query in tqdm(enumerate(close_tanimoto_scores)):\n", + " predicted_inchikey = list(single_query.keys())[0]\n", + " ms2deepscores_for_top_inchikeys = single_query[predicted_inchikey]\n", + " \n", + " inchikey_tanimoto = ms2deepscores_for_top_inchikeys.top_k_inchikeys_and_tanimoto_scores\n", + " top_inchikeys = sorted(inchikey_tanimoto, key=inchikey_tanimoto.get, reverse=True)[:k]\n", + " \n", + " max_per_inchikey = ms2deepscores_for_top_inchikeys.get_max_per_inchikey()\n", + " \n", + " max_ms2deepscore_per_inchikey = [max_per_inchikey[inchikey] for inchikey in top_inchikeys]\n", + " mean_over_max_inchikeys = sum(max_ms2deepscore_per_inchikey)/len(max_ms2deepscore_per_inchikey)\n", + " \n", + " threshold_score.append(mean_over_max_inchikeys)\n", + " return threshold_score" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "f8c4e440-cb62-4e1a-9c26-e4fc1f9f1b3a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "36473it [00:05, 6205.26it/s]\n" + ] + } + ], + "source": [ + "threshold_score_average_max_top_tanimoto = predict_using_average_max_top_k_tanimoto(10, close_tanimoto_scores)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "id": "5fd5338d-1b04-4c1a-86f6-0cd847bc1d38", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_violins(create_values_violin_plot(threshold_score_average_max_top_tanimoto, pos_val_spectra, tanimoto_for_prediction, 10), title=\"MS2DeepScore, reliability = average max top 10 tanimoto\")" + ] + }, + { + "cell_type": "markdown", + "id": "1914ffdf-0954-4811-8ac9-9ea28fe3bbb1", + "metadata": {}, + "source": [ + "# Much better!\n", + "By using the average over the max ms2deepscore for the top 10 inchikeys, we can much better predict reliability of the prediction. Now most predictions in the top 10 percent are actually good analogues. " + ] + }, + { + "cell_type": "markdown", + "id": "5fc4c3e5-814f-4f93-bbce-316f3584aa54", + "metadata": {}, + "source": [ + "# Mean ms2deepscore for top 10 tanimoto\n", + "There are many different variants to try of this principle. For instance using the mean MS2DeepScore instead of the max. " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "ec486149-5090-4fbd-b51e-9adf55e8c343", + "metadata": {}, + "outputs": [], + "source": [ + "def predict_using_average_mean_top_k_tanimoto(k, close_tanimoto_scores):\n", + " threshold_score = []\n", + " for spectrum_index, single_query in tqdm(enumerate(close_tanimoto_scores)):\n", + " predicted_inchikey = list(single_query.keys())[0]\n", + " ms2deepscores_for_top_inchikeys = single_query[predicted_inchikey]\n", + " \n", + " inchikey_tanimoto = ms2deepscores_for_top_inchikeys.top_k_inchikeys_and_tanimoto_scores\n", + " top_inchikeys = sorted(inchikey_tanimoto, key=inchikey_tanimoto.get, reverse=True)[:k]\n", + " \n", + " mean_per_inchikey = ms2deepscores_for_top_inchikeys.get_mean_per_inchikey()\n", + " \n", + " mean_ms2deepscore_per_inchikey = [mean_per_inchikey[inchikey] for inchikey in top_inchikeys]\n", + " mean_over_max_inchikeys = sum(mean_ms2deepscore_per_inchikey)/len(mean_ms2deepscore_per_inchikey)\n", + " \n", + " threshold_score.append(mean_over_max_inchikeys)\n", + " return threshold_score" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "6a2be0ef-5b6e-444f-9473-bb01d1d1fb20", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "36473it [00:11, 3104.26it/s]\n" + ] + } + ], + "source": [ + "threshold_score_average_mean_top_tanimoto = predict_using_average_mean_top_k_tanimoto(100, close_tanimoto_scores)" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "id": "8f253a83-d802-46dd-bd5a-c30b062227ba", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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O0YYbbjjgfptuuqnuvPPOvBqH0hlWW6nKioBicf9/9WmoqVB1Zd6V2OBjzF37jxPlNxgWSC/zvfQB6gnBhr7DhfEDAAAAADkENb766itVVg7+tMbGRs2dOzenRqH0AoGARtRXqak9UuqmFBzraSAbxhgmk3zEiUAVdckBAAAAAACKz/bt6b0Bjf/85z+65557dPXVV2v58uWSpM8++0zt7e3OthAlM7JMSjKVSz/dinlhlIITpaMYu0iH2CcAAAAAAIVlO1Ojq6tLp5xyih588EEFAgFZlqV9991X66yzji666CKtv/76uv766wvRVhRZOSyeHQhIw+ts/zMA4HFOxCOIaQAAAAAAABSf7UyN888/X//4xz/01FNPqa2tLaX8xv77769nnnnG0QaidBpqKn2/1sSw2ipVVvi7j3AGd+X7ixPHk4XCkQsyOWAHwwUAAAAA+rM9m/vwww/ruuuu0957763q6tSyPVOmTNGiRYucahtcwO/ZGiN83j8AhUNMA+kwCQ0AAAAAQGHZDmp0dHRo/PjxaR/r7OzMu0FwF78HNfzePwDpObLIN0ENAAXGaQYAAAAA+rMd1Nh666315z//Oe1jTz75pLbffvu8GwX3GOHjRbR71tMgqAGUI2fW1GC6Ef0NVl6KTA4AAAAAAPJje4XkSy+9VN/61rfU1dWlI444QoFAQG+++abuu+8+3XnnnXrqqacK0U6USH11hWoqgwrHrFI3xXHDaqtUEWR6CShHlhOJGsQ0ABRY3/OMI1lmKGtc+QIAAMAPbGdqHHDAAbr//vv1yiuv6JBDDpExRqeffroeeOAB/fGPf9Q3v/nNQrQTJeTXdSf82i8AxcHUItIJDDJlGGClcAAAAAAA8mI7U0OSDj/8cB1++OH69NNP1dTUpMbGRm266aaSpPb2dg0dOtTRRqK0htdVaUVbuNTNcBzraQDly3LgbmdumAZQaH3PVZx2kAtiqQAAAPAb25kap5xySuLnqVOnaubMmYmARnNzs/baay/nWgdX8GNGQyAgDavNKaYHwA8cWSec6UX0x5oaKCSCqcgX2WIAAADwA9tBjb/+9a8655xz+m1ftWqV9thjD3V3d9tuxC9/+UtNmTJFtbW12mmnnfTmm29m3P/mm2/WJptsorq6Ok2aNEnnnHOOQqGQ7fdFdoZUV6iywl9fgBpqKlVZYXv4A0ACk4sACq3vGhqcdpCLwAA/AwAAAF5le1b3mWee0d13361LL700sW3p0qXabbfdVFFRoRdffNHW6z3wwAM699xzNW/ePP3rX//SNttso3322UcrV65Mu/+f/vQnXXjhhZo3b57+85//6He/+50eeOABXXzxxXa7giwFAgENr/VXtsbwOn/1B4XHjY3+QpYFAC/oe6ZioXAAAAAAyCGosd122+mJJ57QzTffrOuvv15ffPGFdtttNw0bNkwvvPCCRo8ebev1brrpJp166qk66aSTtPnmm+v2229XfX297rzzzrT7v/baa9pll110zDHHaMqUKdp777317W9/e9DsDuRneL2/SjUNr/NXfwDY48S8IJOLAAquz2mG0w4AAAAA5BDUkKRddtlFjz76qObNm6fp06drwoQJ+sc//qERI0bYep1IJKJ33nlHs2bNWtugYFCzZs3S/Pnz0z5n5syZeueddxJBjM8//1xPPfWU9t9//1y6giz5LbNhhM/6A8Ae5gUBeEG/TA3OXshB8joaZJ4CAADAD7K6Xf2RRx5Ju/3oo4/WE088oVNPPVV/+9vfEtvnzJmT1Zs3NTUpHo9r3LhxKdvHjRunjz/+OO1zjjnmGDU1NWnXXXeVMUaxWEzf+973MpafCofDCofDid/b2tokSZZlybKsxPZgMJjyu9TzJSAQCBRsezAYlDGm3x2/Tm53ou3DaqskWX3uEOzZ35i++we/boexub1Hbz96Xrvv/oEct69tY01lUNWVPfE8vx0nL449y7KSjnfq/gONsYG3r9XTJuvr97UzJtOPJcuyVFFRUbbHyW99isetr8dH/7HU/3yU/Fpr97fM2tdzQ5+c3k6f8tlu9V8L4etzSk+bAp7qkx+Pk1f6FLfiiW3GGFlGnu+TH4+TF/rUq/f7jx/65Mfj5OY+9R9HAc/3yY/Hye196vtz7zjycp+cbDt9ym57uvOT1/uU7Xb65Fyf+p+bKjzfJz8dp2xkFdQ4/PDDMz4+d+7clMbE4/EMe+fnxRdf1NVXX63bbrtNO+20kz777DOdddZZuvLKK1PW+Uh2zTXX6Iorrui3fcmSJYkAR0NDgxobG9XS0qKOjo7EPsOHD9fw4cPV1NSUshh5Y2OjGhoatGLFCkWj0cT2MWPGqK6uTkuXLk05IOPHj1dFRYUWL16c0oaJEycqHo9r2bJliW3BYFATJ05UKBTSqlWrEturqqo0fvx4dXZ2qrm5ObG9trZWY8eOVVtbm1pbWxPbne5TZXeLwtG1xzY4ZKRMMCirfXVKn4JDR0mWJatzzdqNgYAqho6W4lFZXWvbqIoKVQxplImuDTpZ7atl1daoon6ETKRLJty19mWqahWoGyoT6pCJrm17oKZegZohsrpbpdjatgdqGxSorpPV1SJ9PS4raisUCjX49jh5rU+d4bhMtKLfcZKkYP1wqbJaVkdzSs2NgcaeqRuZ+NlqXy2rMpjF2AvJhNa2UZVVacdeS0ulRo0aVbbHyW996orEZbV3pz1HWLGki5uOZlkVa79sJY+9cHdQixd3uaZPfjxOXuxTqKNNVntryjiSlPh8WrIkrEDAW33y43HySp+ibWs/56zuNhkz2vN98uNxcn2fYrHE780rV6giVOP9PvnxOLm8T0OGNyZ+XrJkieqqgp7vkx+Pk9v71B1d26bVq5vUHF/bdq/2SfLfcXJ7n5LHkSRf9KmXn46T2/u0ZMmSxLZly5Zp4/XX83yf/HScNtpoIw0mYPqGX9L44osvBn2hZJMnT85qv0gkovr6ej388MM65JBDEtvnzp2rlpYWPfbYY/2es9tuu2nnnXfWDTfckNh277336rTTTlNHR4eCwf4VtdJlakyaNEkrVqzQsGHDEtuJpmXe/p+lrVraGkp+RIGAM5kaoWhMZz/0oSTp5iO2VE1lxdev7XymxoZjhmjyqCG+PU5e69PqjojeX9KWdizZzdSIxI3OevADSb3jKPj1++afqbH7xqNVWUmmhl/61NwZ0XuLW5VuLIVjVp/zUfLnytr966ortPP6ja7pk9Pb6VNu2z9d0a6vmrtSxtEtR26l6oqgJKPdNx6tYJBMDfqU3fa//2e5zrh/7flo5w1GaWR9laf75Mfj5PY+remMaKfrXpIk/f2HMzWxsc7zffLjcXJ7n7qjlra96gVJ0r8u3kP11RWe75Mfj5Pb+9QViWu7q1+UlDqOvNwnJ9tOn7LbnjyOFlyyl+qqgp7vU7bb6ZNzfeoMx1LORw21VZ7vk5+OU21trQaTVaZGtkEKu6qrqzV9+nQ9//zziaCGZVl6/vnndeaZZ6Z9TldXl4LB1MBFRUXPB2HfP3qvmpoa1dTU9NseDAb7vVbf34uxvfcAFmq7U20fXl+tZW2RNO+bvk9S+jYOvH3tz72/5/Y66bavbePw+urE6/vxOHmtT8FgMOl4p98/++3xpMcCKY/nO5Z621yux8np7aXuU8+4Sx4fyT+bpJ8Dacdfz7ZAyvuUuk+F2E6fct0eTBlHvdt7x0ww2P/ckk3bB9rOcfJvn4xSzzHGeL9PfjxObu9T8jmnomLt9x8v98mPx8ntfUr+XMv2e7Tb+5TLdvqUX5+CwczjyG7bB9rOcfJ3n5LHUab9vdSnbLfTJ+f61HdeKlMbvdInvx2nwWQV1GhubtaIESMUDAZTUlMG0tjYmHUDzj33XM2dO1fbb7+9dtxxR918883q7OzUSSedJEk64YQTNGHCBF1zzTWSpIMOOkg33XSTtt1220T5qUsvvVQHHXRQIriBwuhZV8P7htZkNexRJIZFT+FRjFykM8D9FWsfL04z4BNWnwHV93cAAAAAKEdZze6OGTNG8+fP14477qjRo0enjdgks7OmxlFHHaVVq1bpsssu0/LlyzVt2jQ988wzicXDv/zyy5RozSWXXKJAIKBLLrlES5Ys0ZgxY3TQQQfpqquuyvo9kZshNRUKBqU+GUGeMqSmQpUV9qN/APzFkYlB5haRg56s0szXUUCvvqcqghoAAAAAkGVQ484779SGG26Y+HmwoIZdZ5555oDlpl588cWU3ysrKzVv3jzNmzfP0TZgcIFAQENrqtTaHR18Z5caWkuWhtt4ZX7GI81ElohpoFDIPoNTLKv/WEqzCQAAAADKTlYzvHPnzk38fOKJJxaqLfCAobWVng5qNFB6Cjni7mr0NdA6TihvlJ+CU+JpBhOZGgAAAAAgUYcHtjTUeHvdEjI13IfpGZSCE+OOsYt0GBdwSrr4BZkayAW3ZAAAAMBvbAc1uru7dfHFF2vq1Kmqr69XRUVFv//gX14PCrBIuPt45W53b7QS2XLibmePDF24DOMG2Up3nkpXkgoAAAAAyo3tGd4zzjhDf/rTn/Ttb39bm2++uaqrqwvRLrjUkOpKBQLenJSpq2aRcDfyylDy4phHBo6sqcGgQH+DBWq9EshF6cXTrqnB+AEAAAAA20GNxx9/XD/72c8GXNgb/hYMBjSkukId4Xipm2Kb10tn+RbzMygBR8pPMXaRBmtqwCmUn4JTGDYAAADwG9u3rVdUVGjq1KmFaAs8YohHSzh5td1+55Uv2txd7S9OHE+GBNIZbFgwbpAtFgoHAAAAgPRsBzW+//3v65577ilEW+ARDR4NDni13X7nlQka7o71F6eOJ8Eu9DVo+akitQPel678VLptAAAAAFBusprlvemmmxI/DxkyRC+//LJmzpypWbNmacSIESn7BgIBnXPOOY42Eu7i1eCAV9vtd16ZE/ZIM5Elp4JpxkiBgCMvBZ8YbM7ZK4FclB7lpwAAAAAgvaxmec8///x+27788ku9/vrr/bYT1PC/IR5cmyIQkOqqWCTcjbwyweeVdqK4LGMUFFENrDVo9g6nEmSJ8lNwSvKwYQgBAADAD7IKaliWVeh2wENqqypUEQyklEAIx/JbODwcs9L+nI+ayrXBlyHVFQpwO7UreeXLNWWG/MWxTA1HXgV+MtjQYlIa2bIoPwUAAAAAaVGPBzkZUlOhtu5Y4vezHvzAsde+4JGPHHmd24+ZlviZRcLdyysTfMwj+YtTw84jwxdFNHj5qeK0A96X7vMxXaADsMMQjgcAAIAP2K7H88ADD+iGG25I+9gNN9yghx56KO9Gwf3qq70VJPBiyaxy4ZX5Ga8EX5Adp8Yd4wJ9DTYmGDPIVizNicorn5lwL05BAAAA8APbM9PXXnutTjrppLSP1dfX69prr9URRxyRd8PgbkOqU4MEtxy5VV6vF45ZiQyN6+dsoZpKZ9e/8FoQppx4payTR5qJLDk17phgRF9eOafB/dINpTglYZEDzksAAADwG9szvZ9++qm23HLLtI9tvvnm+vTTT/NuFNyvvk9QI3n9inzVVAYdfT2pf3vhHukWQnUj6pj7C5kaKJTBxhbnEmQr3edjnOGDHJgBfgYAAAC8yvbt8LW1tVqxYkXax5YtW6bKSu6ILwdeChIEAlJ9lXfaW268Mr/H5LW/OLZQOOMCfQw2IjiXIFvp1s9g/CAXycOGzy0AAAD4ge2gxu67765rr71WnZ2dKds7Ozt1/fXXa4899nCqbXCxuqoKBQKlbkV2aqsqFAx6pLFlyCuLnnqkmciSUxODcSrBoI/BzmnMJyJb6TI1vPKZCfdiBAEAAMAPbKdVXH311ZoxY4Y23HBDHX744Vp33XW1dOlSPfzww4pEIrr//vsL0U64TDAYUG1Vhboj8VI3ZVBkabgb5adQCpSfQqGwUDicki5o6pXPTLgL2RkAAADwG9tBjU033VRvvfWW5s2bpz//+c9avXq1Ro0apdmzZ2vevHnaaKONCtFOuFC9R4IadR4qlVWOvHLXKROR/uLU8WRcoK/BTmkeOeXBBdKdX1gnHLlIHkl8bgEAAMAPcloAY6ONNtIf//hHp9sCj6mvqdDqzsH3KzWCGu7mlfI9ZGr4i1PBNIYF+iJTA04Z6HPHsgxlNWFLcqYGZyAAAAD4ge01NYBedR4p60T5KXfzygQfJT/8xalgmlcyjVAcxphB18zwyjkPpTdQySA+j2BX8pDhYwsAAAB+YDtTw7Is3XHHHXr44Ye1ePFihUKhlMcDgYD+97//OdZAuJdXghpkarhbzCPfrin54S+Un0IhZHM680p2GkpvoLFiWUbi0gY2JJ+bWF8DAAAAfmA7qPGjH/1IN954o3bffXftueeeqq6uLkS74AFeCBYEAlJtJQlJbuaVO929EnxBdpwKRjAukCybMnVMKCJbA52n4gwh2GSSik5xCgIAAIAf2A5q/PGPf9QVV1yhSy+9tBDtgYfUVgYVCLj7y1FNZZC60y7nlTIacVI1fMWpNVLI1ECybMaDV855KL1Ma2oAdiSfdgyragAAAMAHbN/CHgqFNHPmzEK0BR4TDAZU7fIsCK+UyCpnXlmAm5iGvzg17iglhGSUn4KTBgqAERiDXckBV4YPAAAA/MD2jPSxxx6rxx9/vBBtgQe5PWhQ64ESWeUsbg2+qK5bUGbIX5yaFCRTA8myuYOe8lPI1kDjySs3A8A9kocMn1sAAADwA9vlp3beeWddcsklWrFihWbPnq0RI0b022fOnDlOtA0eUFtVISla6mYMqK7K3Zkk5c5LEzOUn/IXpw6nl8YwCi+bYBl32SNbA40VJqVhF5kacBoBegAAUGq2gxrHH3+8JOmLL77QAw880O/xQCCgeDyef8vgCbUuDxrUVpKp4WZeyn7wUluRmZOBCIIaSJbNZDPrISBbAwVfOe/AruQRQ1AMTmAYAQCAUrMd1Fi4cGEh2gGPcn35KZe3r9x5aXKPSST/cDSowbd6JMkmA4g1NZCNTJ+PnHZgV3Igg8sZOIFhBAAASs12UGPy5MmFaAc8qsblC4W7PZOk3EU9VNKJTA3/cDSoEWdcYK1sglzcJY1sZBpLBFNhV/LHHmWD4ATGEQAAKLWsghrNzc0aMWKEgsGgmpubB92/sbEx74bBG9ycCREIuD/oUu68lP1gTM+ds8FgoNRNQZ6cHHcEu5Asm+wzL533UDqZ5gsZQ7DLkKkBhzGOAABAqWUV1BgzZozmz5+vHXfcUaNHj1YgkHlSjzU1yketi4MG1ZXBQccqSstrE8Ixy6iaoIbnOTnuuOseyVgoHE7JFLhgCMGu1PJTDCDkhuwMAADgJlkFNe68805tuOGGiZ+ZKEavYDCg6sqgIjH3lRFikXD3i3msdE/MMqoudSOQNzI1UCjZjC0vrSWE0sk08cykNOxKrvbJOQi5MpQxAwAALpJVUGPu3LmJn0888cRCtQUeVePWoAbrabie10poxC1LEsEyr4s5uJaL18YwCiub8UCmBrKRsfwUYwg2UX4KTmDBeQAA4CbM+iJvNS4NHrCehvt57S73qMcyS5CeowuFe2wMo7CyWijcffcAwIUyjSViGrAr+aOKzy3kipEDAADchFlf5M2tZZ5qXLyIOXo4ecd8MTAR4A9OBtOMYVxgrWxPaYwZDIbyU3BScpCMskHIVfLQ4TwEAABKjaAG8ubWjAi3tgtreW1iz2vtRXpOH0fGBXplWxaIMYPBZBpKDB/YZVKCGiVsCDwtJSDGOAIAACXGrC/yRvkp5Mpz5ac81l6k5/SEstfGMQon27FFUAODyZipwfiBTRZ32MMBJuVnxhEAACgtW7O+kUhEjzzyiD7//PNCtQce5NbgQa1L24W1Yh5boyLusXJZSM/pIEQszrhAj6yDGkwqYhCZhhKjB3Yln5uIiSFXqeWnStcOAAAAyWZQo7q6Wsccc4y+/PLLQrUHHlTjwjU1AgGpmqCG63ntbmXuyPcHx4MajAt8jUwNOCXTugfcaQ+7kscT4we5oowZAABwE9uzvptuuilBDaRwY6ZGdUVQgUCg1M3AIKIeu8Pda5klSC/u8HFkghq9CGrAKZkmDJlMhF0p5afI9UGOKD8FAADcxPZs9DXXXKOf/vSnevvttwvRHnhQRTCgygp3BRDI0vAGr03sea29SC/qcBkxMjXQi6AGnJJphGTK4gDSSc7OoJImcpa8TjinIQAAUGKVdp9wwQUXaPXq1dppp500atQojRs3LuWO+EAgoPfee8/RRsL9qiuCisXjpW5GAkENb/DaZLDX2ov0WCgchZLtWhmsqYHBZApcMHxgV9/F5S3LKBh01w1JAAAAgB22gxrTp0/X9ttvX4i2wMNqKoPqihDUQPYsy3huYobyU/7g9HFkoXD0IlMDTmGhcDip7/WWZYyCIqgBeyg5BQAA3MR2UOPuu+8uQDPgdW4LIrhxnQ+k8uLd7XFqNviC02OPCWr0IqgBp2QuP1W0ZsAn+i4OzikIuTCUnwIAAC6S18yvMUZLly5VLBZzqj3wKLcFEdzWHvTnxaCGF9uMVMYYxyeUo4wLyF72GUENDCZj+SnuloZNfYcT67IgFywUDgAA3CSnmd9nn31WO++8s2prazVp0iS9//77kqTTTjtNf/zjHx1tILzBbZka1RXuag/682LWAxOR3leIwBTlpyDZG1ucS5AP5qNhF5kaAAAA8BvbM7/33Xef9t9/f62//vq67bbbUu702XDDDXXXXXc52kB4g9uCCG4LsqA/L2Y9eLHNSFWIdVGYoIZkbxxwLgFQTH1D732DHAAAAIDX2J75vfLKK3X22Wfrvvvu04knnpjy2BZbbKEPP/zQqbbBQ9wWRHBbkAX9eXEi2JieEjPwrkKMuygLyEP2xpYXz38oLuac4aS+QQzGF3LBmhoAAMBNbM/8fv7559p///3TPjZkyBC1trbm3Sh4j9uCCG4LsqA/r96pHOdbnKdFC1D2zKtjGc6yMw4YMxhMpnr1rIcAu/oOGTI1AAAA4HW2Z37XWWcdffzxx2kfe//99zV58uS8GwXvcVMQoSIYUEUwUOpmYBBezXjgDmtvo/wUCsXOJCFjBkCxpLve4hQEAAAAr7M9E33MMcfo8ssv1/PPP5/YFggE9OGHH+r666/Xcccd52gD4Q1VFQEFXBJHcFOABQPz6p3KXm03ehTi+EVZKBxioXAA7pTubJMpEwgAAADwgkq7T7j88sv10Ucfafbs2Ro1apQkab/99tOqVat04IEH6sILL3S8kXC/QCCgqoqgIrHST+65rRQW0vPqpJ5X240ehQhA9K61EiRDrKzFbIwtgqMYTKbEH0YP7EiXRUb1KQAAAHid7aBGdXW1HnvsMb3wwgt67rnn1NTUpMbGRs2aNUuzZs0qRBvhEVUVAUVipW6FVFXJxKIXeHVtCoIa3lao4xezjKoJapQ1MjUAuFG6yy2PXoIBAAAACbaDGr323HNP7bnnnk62BR5XXRFUp+KlboaqyNTwBK9O6nm13ehRqDvko3GL0ndlzs6aGrECLFgPAOmkW1iehcIBAADgdTkHNZ577jm98cYbWrZsmcaPH6+dd96ZTI0yV+WSCT3KT3mDV5ch8GqGCXpEC7BQuEQ5IdhbhN6yeiYaA25ZjAqAb6VfUwOwL/kji48vAABQaraDGsuXL9dhhx2m+fPnq7GxUWPHjtXKlSvV3NysnXfeWY888ojWWWedQrQVLueWYEJVBVfZXuDVuwTJ1PC2Qt0hz7iA3cBWzDJ8XiEnHv34RImkOzV59RoMAAAA6GV7Fvp73/uePv/8cz3//PNqamrSv//9bzU1Nenvf/+7Fi1apO9///uFaCc8wC2TM5SA8YZ4ge6YLzQmr72tUOOuEAuQw1vsnhs4lwAohnTlp0jVAAAAgNfZnv197rnndP311/dbT2OvvfbStddeq7/97W+ONQ7e4pa1LNySMYLMvFrGibsbva1QZaIoPwXbmRoeDeyiOBgdcEq6yxauZQAAAOB1tmd/R44cqZEjRw742IgRI/JtEzzKLZkabmkHMvPqXcrckO9thcqoYIIadscAgTAAxWDShMg4+yAXfMMCAABuYjuocfbZZ+vaa69VR0dHyvb29nZdd911OuussxxrHLzFLWWfyNTwBq/eJejVdqMHmRoolLjN9Vq8GtgF4C1pq09x+gEAAIDH2V4o/IsvvtCiRYs0ceJE7bnnnomFwl944QUNHTpUixcv1g9/+ENJUiAQ0C233OJ4o+FObik/VemSdiAzr07oebXd6FGo48eaGrC/UDhjBgPLNOnMpxDsSDdeGEMAAADwOttBjSeeeEJVVVUaOXKkFixYkNjeW5Lq8ccfT2wjqFFe3JAhEQxKFUGSo73Aq3cJkqnhXbG4VbBxR7AL9oMajBkAhZc+U4PzD+xj1AAAADexHdRYuHBhIdoBH6h0QTDBLdkiGJxXFwpn8tq7CnnsWFMDdscX5xIAxZDuZgyPXoIBAAAACcwAwzHBYKDkWRJVLgisIDtendDzaLMhKVrIoAYDo6zFLWN7kjBKIAw54i575Cvd4uEAAACAlxDUgKOqKkoc1CBTwxOMsT8B6BYWk9eeVchsCtZHKG+xHNZU8WpgF8XBpDOcwkLhcEryuGEMAQCAUmMGGI4qdVChqpIh7QVensvzctvLHeWnUCi5ZOoQCENGLBQOh6QLkHEtAwAAAK9jBhiOKnWmhhvW9cDgvHyHMguFe1e0gJPIhXxtuF9uQQ3OJcgRQwc2cNkCpyQHyBhWAACg1FwR1PjlL3+pKVOmqLa2VjvttJPefPPNjPu3tLTojDPO0Pjx41VTU6OpU6fqqaeeKlJrkUllqTM1KD/lCV6uB+7lgEy5K2Q2hWV5e1wjP7mcF8juQSaMDjgl/ULhjDDYl1p+ijEEAABKq7LUDXjggQd07rnn6vbbb9dOO+2km2++Wfvss48++eQTjR07tt/+kUhEs2fP1tixY/Xwww9rwoQJ+uKLLzRixIjiNx79lDpTo9Tvj+x4eS6PTA3vKnS5n2jcqLqSc1A5irKmBhyW6aOGkQM70o0XxhDyxRgCAAClltNt7R999JGOPvpobbjhhqqpqdG//vUvSdKPf/xjPf3007Ze66abbtKpp56qk046SZtvvrluv/121dfX684770y7/5133qnm5mb95S9/0S677KIpU6Zo99131zbbbJNLV+CwyiCZGhiclwMDzEN6V6HL/VBOqHzlEqDIJRCC8pFpoXDukIYtLBQOAAAAH7KdqfHcc8/pgAMO0PTp03Xsscfqpz/9aeKxqqoq3Xbbbdpvv/2yeq1IJKJ33nlHF110UWJbMBjUrFmzNH/+/LTP+etf/6oZM2bojDPO0GOPPaYxY8bomGOO0Y9+9CNVVFSkfU44HFY4HE783tbWJkmyLEtW0p27wWAw5XdJCgQCCgQCBdseDAZljOn3BdXJ7cXsU0Wg5/172mH67B8cZLsSzzXGKBAIpNk/kHF7RcCktIfj5M4+xWNxGZP8WO9x7TvJZ3f7Wj1tsr5+38HGXvZjLN7nvOHn4+S3PsXiJsPxXrt///PR4GM1EAgqFo/LslIzNThO5dGnSCze73Or73P6jr1YvOf/bu1T8na/HCcv9ckY9dve+7NlTNrXd3uf/HicvNAnK804isfjiZ+92Cc/Hicv9MlKCuAnf4/2cp/8eJzc3qe+PydfO3u1T062nT5lt73va/mhT9lup0/O9an/uanC833y03HKhu2gxkUXXaSjjz5af/jDHxSLxVKCGttuu63uuOOOrF+rqalJ8Xhc48aNS9k+btw4ffzxx2mf8/nnn+sf//iHjj32WD311FP67LPPdPrppysajWrevHlpn3PNNdfoiiuu6Ld9yZIliQBHQ0ODGhsb1dLSoo6OjsQ+w4cP1/Dhw9XU1KRQKJTY3tjYqIaGBq1YsULRaDSxfcyYMaqrq9PSpUtTDsj48eNVUVGhxYsXp7Rh4sSJisfjWrZsWWJbMBjUxIkTFQqFtGrVqsT2qqoqjR8/Xp2dnWpubk5sr62t1dixY9XW1qbW1tbE9lL0qb07KmMaJMuS1blmbUcDAVUMHS3Fo7K61rZRFRWqGNIoE10bdLLaV8uqrVFF/QiZSJdMuGvty1TVKlA3VCbUIRNd2/ZATb0CNUPU0bpGoZaYo32S/HecSt2n5uZmWe1rx0egtkGB6jpZXS3S11+0JSlYP1yqrJbV0ZxyW2FwyEiZYFBW++qUPpm6kYmfrfbVsiqDWYy9kExobRtVWZVx7MW62rV4cXe/PvnxOPmtTzErIKu7VYqtbXu6sWfFki5uOpplJZW1G2jsBYeOUigcVcuq5UXtkx+Pkxf7tLojIhMOpnw+JY8jSf3GXrSuQZJc2yfJf8fJS30ygYaecdTRlthudbdJVY2ywl0pn0Ne6ZMfj5MX+hRoGNWz8NPXrPbVag92SusO92yf/HicvNCnSFVD4ufmVctVFarxfJ/8eJzc3qfu6No2rV7dpOb42rZ7tU+S/46T2/uUPI4k+aJPvfx0nNzepyVLliS2LVu2TBuvv57n++Sn47TRRhtpMAHTN/wyiNraWj3++OOaPXu24vG4qqqq9Pbbb2u77bbTSy+9pH322SelM5ksXbpUEyZM0GuvvaYZM2Yktl9wwQV66aWX9MYbb/R7ztSpUxUKhbRw4cJEZsZNN92kG264IeXgJUuXqTFp0iStWLFCw4YNS2wnmpb/9lXtYX20rOPrdmRzV3zP9lA0prMf+lCSdPMRW6qmskKBgP1MjR3WG64htZVJr81xcmOfVneEtOCr1uRHvj6ufSOz9rZH4kZnPfiBpN5xFPz6fZ3L1DDGaI+poxQIBFL65Mfj5Lc+vb+4TU0dYQ2WqRGOWX3OR8GM+/e8flBbrDtUYxqqi9onPx4nL/bpvys6tLgllHLuSB5Htxy5laorguo79nafOlrBPsuwuKVPydv9cpy81KePV3RqWUu3wrF4yvmotqpSktEeU0d7rk9+PE5e6NOS1rA+WNySMo4mN9ZpiwkjPNsnPx4nL/Tpy+Zu7f1/r0mSnjhjJ200psHzffLjcXJ7nzrDMU2/5iVJ0r8u3kP11WurbXi1T062nT5lt70rEtd2V78oSVpwyV6qqwp6vk/ZbqdPzvWpMxxLjKN/XbyHGmqrPN8nPx2n2tpaDcZ2pkZjY6OWLl2a9rFPP/1U48ePz/q1Ro8erYqKCq1YsSJl+4oVK7TOOuukfc748eNVVVWVUmpqs8020/LlyxWJRFRdXd3vOTU1Naqpqem3PRgMKhgM9tuWTiG39x7AQm0vZp+qqyoSbZHSt3Hg7Wt/7v3d7utUV1WkbSfHyV19skxAgUC69qTfP/vt8aTHUt8jtzE5wPZAUME+M5F+PE5+61PUsjIc1+SxYpJ+zn6sWqawfS2X41To7YXoU1xrP8d6x1jyOErenixuGVVWpS+dWeo+5doWp7aXe5+MMf22J4+xQl3r2N1e7scpl+3F7lPvWEpulwLBxM9e7FMubaRPzrW95z2y+x7tlT758Ti5tU/J19Dp5mPstn2g7Rwnf/cpGOx/ne31PmW7nT4516fk7b0/e71PfjtOg7H9jEMOOUTz5s3TJ598ktgWCAS0fPly/exnP9Nhhx2W9WtVV1dr+vTpev755xPbLMvS888/n5K5kWyXXXbRZ599lhLB6Q2mpAtooLiqchiETqroe8srXMnLC4VLuS0KjNKLxQt73Fj4uXxFcxxbUc4lGECmkeHxj1AUWbrx4vXrMJRG8qhhDAEAgFKzPQN9zTXXaMyYMdp666210047SZJOPvlkbbLJJho+fLguv/xyW6937rnn6re//a1+//vf6z//+Y++//3vq7OzUyeddJIk6YQTTkhZSPz73/++mpubddZZZ+nTTz/Vk08+qauvvlpnnHGG3a6gACorShdUCASkyorSBlWQHa/P4/VNxYM3xAo88Ar9+nCvXANmhQ60wbsGmzC0ON8gS+nGEsMHuWDYwAmMIwCAU2yXnxo+fLhee+013XvvvXruuefU2NioxsZGnXHGGTrhhBNsZ0scddRRWrVqlS677DItX75c06ZN0zPPPJNYPPzLL79MSUGZNGmSnn32WZ1zzjnaeuutNWHCBJ111ln60Y9+ZLcrKIBSZkpUkqXhGV7PdGAe0psKnUnBBHX5ilm5ja1cnwf/Gyx2ztkG2Uo3Vrg5A7lIDpCRqYFccf4BADjFVlAjFArpyCOP1Pnnn6+TTjopkU2RrzPPPFNnnnlm2sdefPHFfttmzJih119/3ZH3hrNKGVggS8M7vP5FiDtkvceyTMFLtjBBXb7I1IDTBpv0sYxRRZr1gYC+0l1zefwyDCWSMm4YQ8hRyjDiZAQAyIOtWeDa2lq99NJLisfjg++MshQIBEpWgopMDe/welAj7vH2l6NilIbKdV0FeF80x4AWa2pgIINmajB0kKX0a2oUvx3wvtRMjRI2BL7BZxkAIB+2b23fe++99be//a0QbYFPlKoEFUEN7/D6espeD8qUo2Is4s1d9+XJsoxyTdKJef1kiIIZbMKQu1uRrfRrajB+YF/ysOEchFwxdAAATrG9psZJJ52k7373u2pvb9f++++vcePGKRBInUzebrvtHGsgvKcqGFC4BO9bykXKYY/Xv0xTZch7ipGpQfmp8pTP2GJxeQxk0IXCGTrIUrqxwoQ0cmGSCgcxguAExhEAIB+2gxoHHnigJOm2227TbbfdlhLQMMYoEAhQnqrMVQSDkoo/BiqCrKnhFd5fKNzb7S9HxciiIFOjPOWTBUTJMgwkmzU1gGykG0sevwxDiSQPJc5ByFXyOYkAKwAgH7aDGi+88EIh2gEfKVXGRBWZGp7h9S9CLBTuPbmueeC294D75JepwZhBeoOXnypOO+B96W4k4eYM5II1NeAE1psHADjFdlBj9913L0Q74COlWtuiVGt5wD6vl1zxevvLUTHuiLesnoBXkHNRWckrUyPGuQTpDV5+irGD7KQvP1X8dsAHUtbUKF0z4G2GcQQHkOUDQMohqNHro48+0iuvvKLm5mY1NjZq11131RZbbOFk2+BRpcrUYKFw7/B6pgOTSd5TrAWZY5ZRNeeispJP2TEyNTCQwT5mPP4xiiJKX36KAQT7rJTJaMYQgNLhFARAyiGoEQ6Hdfzxx+vPf/6zjDGqqalROBxWIBDQ4YcfrnvuuUfV1dWFaCs8olQZEwQ1vKNI88sF4/U1QcpRsbJronFL1ZWs71NOovmUn2JNDQyATA04Jd0pivGDXKSWn2IMITesqQEAcIrtmZeLL75YTz75pG6//Xa1tLSou7tbLS0tuv322/Xkk0/q4osvLkQ74SGUn8Jg4h6/O5mghvfkUyLI3vswNspNPmMr4vUILwqGNTXglHTXLB6/DEOJsBYCnMA4ghMYOwCkHIIa999/v6655hqdeuqpGjZsmCRp2LBhOvXUU3XVVVfpvvvuc7yR8JaKYGnuUq4s0fvCPq/P+7LApvcUK9hQrOAJ3COfbAtjCJIiPTI14JSB7oT2eilQFF/KHfZc7iBXrKkBB5DlA0DKIajR3NysTTfdNO1jm266qZqbm/NuFLyNTA0MxutfpJmE9J5iBRtYRL785Du2CIQhncHX1OBcg+wMFHflBg3YlTxkOAchV4Z77AEADrEd1Nh00011zz33pH3s3nvvHTDggfLBmhoYjNcnfr3e/nJEpgYKJd91MVhXA31lE/hnPhHZGmjymTEEu5LHEsMHuTIpmRqMJOSGkQNAymGh8EsvvVRHHHGEFi1apMMOO0zjxo3TypUr9fDDD2v+/Pl66KGHCtFOeAiZGsjEGOP5TAevZ5qUo2IFG1gjofzke8wZM+grmzuguUsa2RromsXr12IovpS1EBg+yJGVEtQoXTsAAN5nO6gxZ84cPfroo7riiit03nnnyRijQCCgadOm6dFHH9VBBx1UiHbCQ0oVXCCo4Q1++BJNpoa3GGOKdjc8C4WXn3zPB5xP0Fc2Q4Jhg2wNNFYIjMGu5HU0GD/IFeWnAABOsR3UkKSDDz5YBx98sDo7O9XS0qIRI0ZoyJAhTrcNHkX5KWTii6AGE9eeUsxAA+Wnyg9rasBpZGrASQONFcYQ7EqejKZsEHLF2ixwAkMHgJRjUKPXkCFDCGagn1IEFwIBKUhQwxP8cFeyHwIz5aSYk8bRGGOjnDiRBURQA31ll6nBuQaDsywz4MQPpx7YlVI2qHTNgI8wjgAA+bC9UPjJJ5+so446Ku1jRx99tE477bS8GwVvK0WmBqWnvMMPAQE/BGbKSTHXLGCCurw4kQVEyTL0lU3AglMNspFpLBEYg12pCzyXrh3wNsYRAMAptoMazz33nObMmZP2scMOO0zPPvts3o2Ct1UEShDUKMF7Ijd+CAjELUPavYcUc9KYRZ/LixNBLAJh6GughZ2T8RmEbGQaSgQ1YFfymOEchFyllDEjVwMAkAfbQY1Vq1ZpzJgxaR8bNWqUVqxYkXej4G3BYEDFjjGQqeEdfghqSP7pRzko5qRxLE7Aq5yQqYFCiGeTqcF5BlnIlB3rh8xZFJdJCWqUsCHwNDI14AS+bwGQcghqTJgwQW+88Ubax9544w2NHz8+70bB+4odZCCo4R0xn9yVzGSAd0RixR1zTFKXDzI1UAhWFkOCYYNsZC4/VcSGwBfMAD8DdpAlBgBwiu2gxre//W1dddVVevDBB1O2P/TQQ7r66qt1zDHHONY4eFexy0ER1PAOv2Q45Ls4MIqn2CWhih1EQek4E9TgXIJU2Uz4MCmEbLCmBpyUPGQYP8hV8shhHCFXjBwAUg5Bjcsuu0x77LGHjj76aA0dOlRTp07V0KFDdfTRR2v33XfXvHnzCtFOeAyZGhiIb4IaPulHOSh2kIF1NcqHM+WnGC9IlVX5KT6DkIVMpxfGEOxKnoC2mFKEA4hpAADyUWn3CdXV1XriiSf03HPP6R//+IdWr16tUaNGadasWfrmN79ZiDbCgwhqYCB+yXCIZVMfBK4QiRV3zJGpUT6iDpwHetdhCRR7MSq4VjaTzdksJg6QqQEnsRYCnEAZMziBcxAAKYegRq/Zs2dr9uzZTrYFPhIs8uRMsd8PufNLMMAvwZlyUPTyU9x5XzacKh0VjRtVV/I5hh7ZBDVYKBzZyLxQeBEbAl+w+iwUTkAeuUhdcJ7PMgBA7myXn/riiy/0wQcfJH4Ph8O66qqrdNxxx+nuu+92sm3wsArbI8tb74fc+aV+fJS7ZD2j6OWnyNQoG06VjqIEFZJlcwc9pYOQjUxjiclE2NW35BSnIeSCTA0AgFNsTwWfeuqpuueeexK//+hHP9IVV1yhjz/+WKeddppuu+02RxsIb6L8FAbilwyHGJOQnhC3TNEn/8jUKB9Rh0qb+SXYC2dks2YTQQ1kI9MwIdsHdvUdMpQwQy6Sz0uMIOSKwDwAKYegxoIFC7TbbrtJkmKxmH7/+9/ruuuu09tvv63LL79cv/rVrxxvJLyn6EENUp89wzflp5hQ8oRSZE2QqVE+nApgEQhDsqzKT/EZhCxQfgpOMcb0D2pwHkIOKD8FJzByAEg5BDXa29s1fPhwSdIbb7yhtrY2HX300ZKkXXfdVZ9//rmzLYQnFXuNCzI1vMMvmRrcWe0NpZgsDhPUKBtOlY0i8wvJsglYEFhHNlgoHE5Jd17iNIRcJJ96GEMAgHzYDmpMnDhRr7/+uiTpkUce0eabb67x48dLktasWaP6+npnWwhPKnaQgYXCvSPqm0wNf/TD70oRYCBTozwYYxwL0pKpgWTZBCyM4S5pDC5T4IJsH9iRbrhQwgy5MEn32DOCkDMGDwBJlXaf8J3vfEeXXHKJHnroIb377rv6+c9/nnjs9ddf12abbeZoA+FNxQ4ykKjhDZZl5JdYgFO19FFYpQgwRONGlmUU5MTka05ma5H5hWTZBstillE15xlkkCleStkX2JE2U4PAGHKRvKYG5yHkKCU4xjgCypbtoMaFF16oddddV2+99ZZOP/10nXjiiYnH1qxZo1NOOcXJ9sGjKmznAOWHyUNvcKpUixuQqeENpSoFFYlbqg1WlOS9URxOZlf46dyI/GVbjixmGVUXuC3wtsyZGkVsCDwvXVYGmRrIRcpC4Qwh5MgwjgAoh6CGJJ1wwgk64YQT+m2//fbb824Q/KHoa2pQfsoToj66o4s7q72hVGV9wjFLtVUENfzMyUAEJcuQLNv1MnqCH5xnMLBMJaZYUwN2pMvK4P4e5CK1/BTnIeQmJThWumYAKLEi30+PcsFC4UjHL4uES9xZ7RWRaIkyNZik9j3KT6FQsg5q+OhGARRGpiHCmhqwg0wNOIU77OGE5IAYQXqgfBHUQEEUO8hAooY3+CkQYAwTAl4QjsVL9L7+GetIz8nAFQuFo5edBegZNxhMxvJTTALBhnTXvFwHIxfJ5yUmo5ErgmMAJIIaKJBiBxkoP+UNfrur1E9BGr+KlOgOeCYb/Y/yUygEO1k7ZPhgMJkmnZkEgh3pruH9dl2P4mAyGk5joXCgfBHUQEEUO8hQ7HJXyI3fggB+KqflR5ZlSjZZTKaG/zkZuDIm+8Wh4W92PicZMxhMunUQenGXPexIN5biLKqBHLBQOJyQnOXDMALKF0ENFESxgwxB1tTwBL/dVRrly5yrlTKIVqq1PFA8Tp/P/HZ+RG7I1ICTMsUtKPsCO9JlZRBXRS4M5afgADJ+AEhSZS5P6uzs1N13361XXnlFzc3Namxs1G677aa5c+dqyJAhTrcRHlTsxAliGt7gt0wNJpTcrZTZEqVaywPF43QWUCRuqU4Vjr4mvMdOBhBl7jCYTBOGxvTcfc+NQcgGa2rAKalrapSwIfC0lEwNohpA2bKdqfHVV19p66231g9/+EN98sknCgaD+uSTT/TDH/5Q22yzjb766qtCtBMeU/RMDcpPeYLfau/6LUjjN6UMapRqLQ8Uj9P//jmfQLI3DliLBYMZbNKZu6SRrXRjyW/X9SiO1PJTjCHkJnnkMIyA8mU7qHHuuedKkv7973/rX//6l55++mn961//0kcffaRAIKDzzjvP8UbCeyqKXNiMmIY3RH02AcOaGu5WyruYIzGLL2o+53imhs/Oj8iNnXFAIAyDiQ/yOcScNLIVTRvU4BwE+yg/BSdYjCMAyiGo8dxzz+nqq6/WJptskrJ9k0020ZVXXqm//e1vjjUO3hUocpSh2AuTIzd+m4DxW3/8ptSLdTNJ7V/GGMfLz1FKCJK9cVDqcxzcL9NC4RLlg5C9tOWnuLkHOUj+mOMUhFwljx3GEVC+bAc1YrGY6urq0j5WV1eneJw64ih+OShiGt6Q7i4vL/Nbf/ym1EEFJqn9qxBjKxLjfAJ7gYpY3Aw6aY3yNnimBuMH2UkXwKD8FHKRcoc9Ywg5IlMDgJRDUGOXXXbRT3/6U7W2tqZsb21t1VVXXaVddtnFscbBu4q55mAgUPzMEOTGb5kNfuuP35T6LuZSvz8Kx+ksDYkgGHrYDZgxbpDJYPM8TAQhW9E0paYIaiAXhoXC4YDkjy8+yoDyVWn3CTfeeKO+8Y1vaNKkSdprr700btw4rVy5Us8//7yqqqp05513FqKd8JhihhiIZ3iDZRn5rfQua2q4W8kzNQhq+FYhJpIZL5DsB0PDMUu1VRUFag28zLLMoBM9xMSQrbTlp5iRRg5SywYxhpAb1mYBIOWQqbHlllvq/fff1ymnnKKlS5fqH//4h5YuXapTTz1V7733nrbccstCtBMeU8zMiWKXukJu/JjV4Mc++UmpJ4nJ1PCvQhzbUo9XlJ4xxvY44DyDgQxWekpiIgjZS3cjD9fByAVlg+AEgmMApBwyNSRp4sSJuummm5xuC3ykqOWnivdWyIMf158oRAkaOKfUZVlK/f4onIKsqcF4KXvR+OB31vdFUAMDyaZWPXfaI1vpSk0Z0zPOgsX84gfPS/6cY00N5Co5kMEoAsqX7UyNDTbYQO+9917axz788ENtsMEGeTcK3kemBvry491cfuyTX0RiVsnrq3LnvX8VIgDBos8Ix+JFeQ7KQzb3XWSTzQEYYwYMgLGuBuxKPu9wCkKuyPgBIOUQ1Fi0aJHC4XDax7q6uvTVV1/l3Sh4X7EXCof7+XH9CWOkGIENV3LDXe+RmP/GPHoUKmDlhnGL0skl6yIcZcwgvWyCpARSkY1MgQuCGrAreQKawCpylXzq8eE0A4AsZVV+KhQKqaurK7EYT1tbm5qbm/vt85e//EXrrruu862E5xQzU6OY74Xc+TWrIWYZVbJGq+u4IUuCCWr/KlTJHxZ9Lm+hHAIUlJ/CQLKZLGQiCNnIVKYsblmS+NxCdiwrtcwiQQ3kyqRk/DCOgHKVVVDjuuuu009+8hNJPRPI++yzz4D7Xn755Y40DN4XCBQnpZSQhjf4df2JaJxJSDdyQ0CBsjD+VaiJZDcE41A6oRzOGaEo5xmkl005DjI1kI1M2dZ+vb5HYfQ9LxnTMyHNTYqwKznYSvkpoHxlFdQ45JBDNGXKFBljdPLJJ+uSSy7RhhtumLJPdXW1NttsM02bNq0Q7YQHFSuowdp03hC1/DlZx5c5d3LD5LBl9VxwV3CS8p1CjS/uui9vuZSSCscsJoSQVjaLgHOXNLKRqcQUi83DjnTjJW4ZVVbwGQZ7koeST6cZAGQhq6DGNttso2222UZST6bGAQccoNGjRxe0YfC+gAKSCn+hyxd5b/Dr5L9fy2p5nRsyNaSeye+6ajJ5/CQWtwo2iUNQo7zlcvyN6fl8ra7kWgipsjlPkamBbMQyzBiypgbsSPd1kCGEXFB+CoCUZVAj2dy5cyVJa9as0Ztvvqnm5mY1NjZqxx131MiRIx1vILyLWAOS+XXy36/BGq9zyyLdkbilOmpN+0qkgP/mCWqUt+4cS0mFonFVVwYdbg28LpuJQjI1kI1MgQu/Xt+jMNIFUsn2QS6STz0MIaB82Q5qSNIFF1ygW2+9VeFwOLGtpqZGP/zhD3Xdddc51jh4W7FiGgRPvCHqkklmp/Flzp2iLpkcdkMZLDirkGsYRFiHpWwZY3I+X4RiloY53B54X1blp/iIQhYyranBhDTsSBdIZQwhF8nraBCgB8qX7aDGVVddpZ///Oe64IILdNRRR2ncuHFasWKFHnjgAV1//fUaMWKELrrookK0FR7TUxaqCOWnWCrcE7KZ/M93YeXku5yduuO5pjLzXfZkariTa8pPuaQdcE4hA1VkapSvnrUxcnsui4UjnawWCmciCFnINOnMhDTsSLumBuch5IDyUwCkHIIad9xxhy699FJddtlliW3jxo3T1ltvrZqaGv3mN78hqAFJZGogVTZBjbMe/MCx97vgkY8ceZ3bj5mW8XEyNdzJLRkSbmkHnFPIwANBjfIVymGRcCeeC//KZq2DODdmIAvRDGtqRAlqwIaBFgoH7EoeNowhoHzZLsC7bNkyzZw5M+1jM2bM0LJly/JuFHyiSMEGYhruZ4zx7UKCBDXcyS0ZEm5pB5xTyMBDLG4UY8yUpVAemYr5PBf+lc0kj1+vzeAsMjXgFIIacEry+iwMIaB82c7UmDJlip588knNmjWr32NPPfWUpkyZ4kS74ANkaqBXzDJZldW45cit8nqfcMxKZGhcP2cL1RRh4VTKT7lPNJ57GRenkanhP4WeQA7HLFVWsOhzucmnhFQoQlAD/WW3pgafURhcpjU1CMTDjnSBVIIayEVy+cR0C9ADKA+2gxrnnHOOvv/972vVqlU6/PDDNW7cOK1cuVIPPfSQ7rvvPv3qV78qRDvhQQGiDfhathO7g61fYUdNZdDR1xsId+K7j5sCCZEYF9l+Ey5wqZ9wzNKQmoK+BVyoO5/yUy4658E9sgpq8BGFLGTK6CHbB3akOy8xhpCLeNLarazLApQv20GN7373u4pEIrryyiv1pz/9SYFAQMYYjRkzRrfccotOO+20QrQTGBALhbufn7MZKD/lPm4KNLmpLXBGode9YF2N8hTOI1MjFjeKxi1VkeGDJFmtqcFkIrKQOVODMYTskakBpyTHMcjUAMqX7aCGJP3gBz/QGWecoY8//lhr1qxRY2OjNtlkEwWDfJnCWkULNRDTcD0/T/xbVs/FeEWQgegW7srUcE9bkD/LMgU/pvmUIYJ35ZOpIfWMG4IaSJbNRKGfr8/gnFiGMmXcZQ87yNSAE6w+pa1J1ADKV05BDUkKBoPafPPNnWwLAJ/y+5fmSMxSXXXhS10hO2660z1uGYJePlKMMj+UEio/xpi8g1ndUUtDax1qEHwhm7UOjOmZHAryGYUMMk06+/0aH84iUwNO6FtuivJTQPnKKajx1Vdf6S9/+Yu++uorhUKhlMcCgYBuueUWRxoHbyvWkhos3eF+fi/BE41bqhNBDbdw23gj6OUfxciiyKcMEbwpErPyvsuQDB/0Fc1yojBmGVUT1EAGmUpMERiDHemyfsjUgF19y01RfgooX7aDGg8++KCOP/54WZalsWPHqrq6OuVxghoA+vL7Yslum0Qvd24bb2GCGr5RjInjfMsQwXucOObdBDXQR7Z3P8cso+rBd0OZsr7OOM2EwBiyFU8TIMtU3gxIp+8wSl40HEB5sR3UuPjii3XIIYfoN7/5jYYPH16INsEnuLRFL79P+rNugrtEYu6a3PP7+C8nRSk/FY3LGKMAaYhlw4mARCjCeQZrGWOyXsC5p0wVgXekl81d9NG4pepK1vTB4NKWn2KxedjUP1OjRA0BUHK2rz5WrVql0047jYAGgKxFfT7pz6S1u7hpTQ3Jfe1B7oqRqWEMgdJy0xXJf1x1kamBJHbKuVD6BZlkF9RgDCE76cYT5yDYlW4NDdZmAcqT7aDGvvvuq9dff93xhvzyl7/UlClTVFtbq5122klvvvlmVs+7//77FQgEdMghhzjeJngD97K6n98n/fky5y5uCyKEXZY5gtx1F+lueEpQlRcngmW9GT6AZO+6xO/XaMhPNguBUz4I2YqlGU8ENWBXugAGQQ3kgmtn77Ndfur222/XUUcdpa6uLn3zm9/UiBEj+u2z3Xbb2XrNBx54QOeee65uv/127bTTTrr55pu1zz776JNPPtHYsWMHfN6iRYt0/vnna7fddrPbDQBF5LZJZqf5vX9eEreyL7lRLGEmqH2jWIsx97xPVVHeC6XnRKaGMVIoyvo96GEn24vMMGSSTVCDm3uQrbSZGgRWYZOVZiI63TZgMAwb77Md1Ghvb1dXV5euueYaXXvttSmP9daAjsftfTm76aabdOqpp+qkk06S1BM4efLJJ3XnnXfqwgsvTPuceDyuY489VldccYVefvlltbS02O0KCox64JB6al66bZLZaUwIuIcbA0xubBPsi1umaMeSUkLlxalFvrujcYIakJTdRPTaff19jYb8ZDM+7Iw3lK+4ZdJOIJKpAbsoYwanEAzzPttBjRNOOEFffvmlbr31Vk2dOlXV1dV5NSASieidd97RRRddlNgWDAY1a9YszZ8/f8Dn/eQnP9HYsWP1ne98Ry+//HJebQBQOOVQ1oBJa/co1p30drixTbDPqYnnrN7LgTv34Q2RmOVY4L8rElfjEEdeCh5nL6jBNQwGls344OYeZGOg8kDG9GRrVFaw2Dyy03eh8IG2AYNh1Hif7aDGm2++qT/96U+OrWHR1NSkeDyucePGpWwfN26cPv7447TPeeWVV/S73/1OCxYsyOo9wuGwwuFw4ve2tjZJkmVZspJqgAaDwZTfpZ5sg0AgULDtwWBQxph+tdyc3F66PlkyxkraP/h1+0yf1wmmtLu3H4FAIM3+gf7bk/bnOLmvT92RWNI4SHP8Urb3/VJkd/tavWOw528w2NgbZIwNsj0Sszx/nArR9lL0qTsSlaQcjuvaNvY/H1kZ9+/p68BjLBSNKx6Pp2Svlftx8mKfOkPRr497dueIvq9l57zXFYlznMqkT12ReJqxsfac0vd8tPb//cdSZziW8r4cp/LtUzi69tor/XW2ldgejqaOG7f2Kdu2eOk4eaFPoaTr+IGuj3rXDvNKn/LZTp9y71PveSndOIrG4goGvNenfNpIn3JvezRu9XssZvX/Pu6lPvnxOHmhT/GkwH3PPhWe75OfjlM2bAc1Nt54Y8ViMdtv5JT29nYdf/zx+u1vf6vRo0dn9ZxrrrlGV1xxRb/tS5YsSQQ4Ghoa1NjYqJaWFnV0dCT2GT58uIYPH66mpiaFQqHE9sbGRjU0NGjFihWKRqOJ7WPGjFFdXZ2WLl2ackDGjx+viooKLV68OKUNEydOVDwe17JlyxLbgsGgJk6cqFAopFWrViW2V1VVafz48ers7FRzc3Nie21trcaOHau2tja1trYmtpe6T+GWVbJ6a8kHAqoYOlqKR2V1rW2jKipUMaRRJro26GS1r5ZVW6OK+hEykS6ZcFfisUBVrQJ1Q2VCHTLRnrZ3xirVNjTAcXJpn5avbpHV3tP+QE29AjVDZHW3SrG1bQ/UNihQXSerq0VKKl8XrB8uVVbL6mhOKXgYHDJSJhiU1b46pU+mbmTiZ6t9tazKYBZjLyQTWvt3V2VV1mOvt0/xmiFasXKVopG149hrx8kvY681ViGpOu1xynbsWUl3HFodzbIqkoIRA4y94NBRkmXJ6lyzduPXY8/Eovriy8Wq/Pp1OE7e7FNzZ0RWeyTrc4TV585VO+e97miQ41QmfQo2jMr4+WR1tCU2W91tUlXjgGOvpWWNFkfWnoM4TuXbp6b2sKzOnuf0fj71stpXy6qqSFwbtTU1a7Fpd32f/HicvNCnNU3tsrq//u5fPTSxT+I6W1K4bqwsy/JMn/x4nLzQp1VrWmW1d6deZ3e3yVJci5d0q6Yy6Lk++fE4eaJPK5cn5hd6dbS1q2P12msjz/XJj8fJA31avnRpYtuyZcu08frreb5PfjpOG220kQYTMH3DL4N4/vnndf755+u+++7TpptuauepaUUiEdXX1+vhhx9Oyf6YO3euWlpa9Nhjj6Xsv2DBAm277baqqFhbM7j3jxEMBvXJJ59oww03THlOukyNSZMmacWKFRo2bFhiO9E0Z/v01qJmtYdiSftnupM5prMf+lCSdPMRW6qmskKBQHZ3y48dWqMtJwznOLm0T4vXdOu/K3tPfIXN1IjEjc568ANJveMo+PXfoLCZGoFAQDtNGZFSy9xrxymfNrqpT/9e1q6V7ZEcjuvaNoZjVp/zUTDj/j19zTzGpk0appH1a8s1lvtx8mKfPl7ermWtIWV7jkgeR7ccuZWqK4ID7J/+/DZzg5GqThp7HCd/9unzpi59sbpLA507wrF4yvmotqpywLFXXRnQzA0aS94nPx4nr/XpwyWtWtUR+Xp7uuvsYGKM1VQGNHPDUa7vU7Zt8dJx8kKfFnzZojXdPRMSA10fNdRUaqcNRnmmT/lsp0+592l1R0TvL2lNO462nTRCI+qrPNenfNpIn3Jv+/9WdujTlR0p19nbThqusUNrPNsnPx4nL/RpdXtYM3/Ws5zBvy7eQw21VZ7vk5+OU21trQZjO1Pj7LPP1vLly7Xllltq3XXX1YgRI/o15r333sv69aqrqzV9+nQ9//zziaCGZVl6/vnndeaZZ/bbf9NNN9UHH3yQsu2SSy5Re3u7brnlFk2aNKnfc2pqalRTU9NvezAYVDAY7LctnUJu7z2Ahdpeuj71fGHqu11K3/bU5way2P/rfYLBxP4cJ/f1KWrJxjhI3/bst8eTHgukPJ7NWMpnezhuNCTN39grxynfNtrdXqg+dX+dHWb/uCaPFZOyf7rxl37bwO8Zjpl+f4dyPk5OttHu9lzb3h01ts4pyeMo8/7p2x6KGdVWZ3dO4Th5t0+dkXjGsdT3+mjt//vvH4kZWUb96pJznMqvT5E+1179r7ODiZ97rtP6jzW39SnbtnjpOOXbxmL0KXksDXR9FPl6XSCv9Cnf7fQptz7F1TOW0o0jq0+7vNKnfNtodzt96tlu0jxmFEj7+l7pkx+Pkyf6lOa84/k++ew4DcZ2UGP69OlpO5ePc889V3PnztX222+vHXfcUTfffLM6Ozt10kknSepZnHzChAm65pprVFtbqy233DLl+b2Blb7bAZReb51dv2ORRHco5mLOdnS5tF3IXmekuKU3OyMxjaivKup7ovg6w86Oq65IXMPq7H8hgL+Eo9lfkxjTcw1TU1Ux+M4oO6EsruOjcaO4ZVQRdHaOAP4Si5ucHgP6iqVZFDzdNmAwlmHceJ3toMbdd9/teCOOOuoorVq1SpdddpmWL1+uadOm6ZlnnkksHv7ll1/mFLEBUHp2vlh7WTZf+lBYkZjl2i9FXRHGh5eFo/Gij63OMGPG72JxSyGHPyM7I3ENqyMYVs4sq6f8nR3dUYIa6C8at2RlOZTCMUv11YwhDCyWYTBFmZCGDVaa8ZJuGzCYviWW4D22gxqFcuaZZ6YtNyVJL774YsbnFiLQAsAZoTLJYLA7gQDnuTlw0O3itmFwnSU4fk7fwQ/3KcS46mDclL1crrt6bswgGIZUdm5MCkXjBDWQUeZMDb5HIXvxNBPRZGogFwwb78sqqPHDH/5Q559/vtZbbz398Ic/zLhvIBDQLbfc4kjjAHhfuZSfcvpuW9jXVeTyQHZ0ReKyLKMgpRk8qRQTxUxO+18hAlcdIcZNuQvlUO4wl+fA/+yU9GQMYTCZsjGYkIYd8TQBMsoIIReMG+/LKqjx+OOP6zvf+Y7WW289Pf744xn3JagBoFckln3auteF+TJXch0uLtdjTM+6Gg01rkmQhA2lCDBE40bhaJySMD5WiHMWwTDkkrXYFSmTizXYYieowdphGEw0QzZGpseAvlhTA04hpuF9Wc2uLFy4MO3PQCbFqk9HHTz3Kqe7tsqlzJabuX0yryMcI6jhUaW6+709HCOo4WNtoajjrxmNG4WicdUybsqWnYnoxHMokYg07ATIGEMYTKbyU1GXrokHd4qnCWCk2wYMhnHjfay+DaBgymmiPxY31IMtMbevQcDCz95kWaYka2pI7g/UIXfGmIId33ZKUJW13DI1GDPoz06ggqAGBpPpe1KmgAfQV9qgBmMIOUguP8Vi896U0y2ja9as0dNPP63FixcrFAr1e/yyyy7Lu2EAvK+cMjWknnU1GiqIFZdCKBp3/V1e7QW4KxuF1xGOlSw1uY3Jad/qDMcLVp6xLRTTmKE1hXlxuF4uQY1o3Cgat1TFNQyS2BlLnZG4jDEKBFg7DOlluk6n/BTsoPwUnJJ85km3AD3cz3ZQ429/+5sOP/xwdXR0qK6uTtXV1SmPBwIBghooKk497lVui2d3R+NqqKW8UCl4YfLXC21Ef60lPG5t3YwZv2ovYBYOAdTyFbdMznfMd4RjGllfPfiOKAvRuKWwjYxrY6TuqKX6akrfIb0Ia2rAAcaYtIs7x8tlIU84KnksEdPwJtu345x33nnaYYcdtHDhQnV2dmrNmjUp/zU3NxeinQA8KJe6zl5Wbv11k0LUpndaLJ77ZBNKp627dGMrErPKLuOtXLQUcFy1hWKsN1am8inDSIlEJMulPB4lEzGQWNzKOGEYswyfW8hKz1jpvz1KpgZykFzKjPU1vMl2UOPzzz/XhRdeqMmTJxeiPQB8pNwm4+zc0QZntXvkjnYvBF+QqrWEQQ03vD8Ko5DBsli8dOvAoLTymVRmQhrJcglyuX1tM5TOYCVijWGxcGRnoIln1mVBLlLW1CCw6km2gxrbbbedvvrqq0K0BT7DKQHllrmQSx1r5M+yjFo9Eixo9UjwBT3C0XjJy+gxZvwnGrcKfld8KTOMUDodeYwrghpIlkuJvEKW1YO3ZVNeihJUyMZAwYs42T7IQXLVMhYK9ybbQY1f/epX+r//+z89++yzisW4cAGQXjhauEVQ3arcMlPcoiMc88xYa+mKlLoJsGGNCyaGGTP+U4y1UgpZ3grulU82YHsoxhd6JLTncA7xStYsii/Tehp29gEyr83CZxjsSc7OYKFwb7K9ou2MGTMUjUa1//77KxgMqq6uLuXxQCCg1tZWxxoIDIZzjzt1l9ki4VJPpoYxRoFAoNRNKStemrzrCMcVjVuqqrB9TwFKoKWr9GOLMeM/a4oQqFrjgrGL4rIso/ZQ7pPKxvRcxzTU2v56CJ+JW7mVsAvHLIWjcdVUsVg4UkWyKNGbzT5ApoyeaNxSdSXXy8hecjkzr9wkiVS2r1rPO+88JuyQFYIN5a3cSk9JPWM+ErP4MldkXgpqSD13aY9qqC51M5AFNwQ1pJ52jBlaU+pmwCHFCDiEo5a6I3HVVfN5VC56bqzI7zXaQlGCGlBHOJbzWGoPx7gORj/ZZWoweYDBZRonkbilIUVsC7yPTA3vs33VevnllxegGQD8plzXl+jiDrWiMsZoTac7Jp6z1dwVIajhAeFo3DXnsTUENXwjGreKtnZBS3eUoEYZcSLA39Id07oj8m8LvK01j7HU2h3T6AY+r5AqnEUWRjjmjmsuuFumjB6yfWBXSqYGQQ1PIjcL3se5x5XKMVNDkrpdMglaLtpCsZSLES9Y08kaCV7Q7JIsDUlazZjxjZauaNEyWZsZN2Uln4loJ18D3pdPgMwtGY5wl2yCGkxIIxuZsn6yGWdAsuSSU2RqeFNO+cWfffaZ7r77bn366acKhUL9Hv/rX/+ad8PgfZwSylu5Tu53lWkwp1S8OGnXEY4rEqPmq9u5KZDQHYlTSsgnmou48HtzZ4R1nsqIE5PJ3ZE4ayJArXmMpbZQVJZlFAxy3sFa4SzWWgyV4XqMsC+c4bs2gTHYlRzI8NqNkuhhO6jx1ltvaffdd9fkyZP16aefauutt1Zra6sWLVqkiRMnaqONNipEOwF4jFvKthRbufa7VJo9VnqqV3NnROsMry11MzAAY4zrAmbNXRFNqK4rdTOQp2Kes6Jxo45wTENrq4r2niiN7kjcsTtUW7qjGkdQo2x1hmOK5rG2gTE9WbQj6jnvYK1QFjd9cZc9stGdIfhVrpUikDvKT3mf7dtEL7jgAh155JH68MMPZYzR7373O33++ed65ZVXFAgE9KMf/agQ7QQGZMgJcZ1IzCrbSHe5ZqiUQiRmqS3kzaBGk8smzJGqtTummMsWrGzqYMx4XdfXGTfF5KaMIxTOGgczgNxUeg/F58zaLJx3sFYsbmUVKAtF4zJMKmIQmQJkZPvAruRAhsXw8STbQY333ntP3/72txUM9jy1t/zUzJkzdfnll+vCCy90toXwLC5KyldnpDiLoLpRV4QL8mLpKa1S6lbkZnVHRFaZBv68oKkzXOom9NPcGSnbYLFflCLAsLqDCepy4OTYcluWGorLiePv1SxaFEa2E83GMCmNzMLReMbvftlkBAHJLDI1PM92UCMQCKi6ulqBQEBjx47VF198kXhs4sSJ+vTTTx1tIADvKedsBWNIny4WL2c7xC3jyN2QKIymdveNLWOYbPS61R3FD5a1dkepMe1zxhitcTC7Ihy11Bku35tTyplTY6m1O0oQHgl21hukfBAy6RxkjiEaN1zzwJbkjyo+t7wpq6DGP//5T3V0dEiSNt98c/3vf/+TJM2YMUM33nijPvzwQ33yySe69tprteGGGxautfCUYp0SCKi6T7mvK1Hu/S8GyzJa7fFyPJSFcafOcMy1/4ZXeXzMl7No3HJ04tkOzjX+VohyeYyZ8tQRdmYsGePMwvXwBzt3z7v1+gvuMFhQo2cfgvLIXvJC4WRqeFNWQY0999xT//73vyVJp512mpYvXy5Juvrqq7VixQpts8022nzzzfXWW2/pZz/7WeFaC6TBqcd97NyR40fZXHAhP2u6vH8X4Mp295U4grSqBHfTZ6upI0zZMo8qZbk8N49p5K8Q5fK8ftMAcuNoGTMH13mBt3WECWrAGV1ZBCwYQ7AjOZAR4zuWJ1Vms1Nyffjjjz8+8fNmm22m//znP5o/f766u7u18847a+zYsc63Et7EOaFsddm4ePWjci6/VSwrfTBJF45aauuOalhdVambgiQr29w7tmLxntIgoxqqS90U2FTKhd5712OpCAZK1gYUzqoClMtr+bpsWXWl7UrF8DAnz1NNHRFtzLQAJFvl7DoofYcMOkKDf8dmDCFbsbiVcsNR3OGsVxRHVkGNTBoaGjR79mwn2gLkhCwxd7EsU/b1ULO5iwS5M8aoySdZDis7wgQ1XKQzHLN1R2EprGgPE9TwmLhlSpotYVk9d2CPHVpTsjagMDrCsYLcSGFMz5gZP7zW8deGO0Viltq6nbt+7Y7E1RWJq766wrHXhPcYY2xNMjMhjYFYllF7ePCydk6ex+Bvfas+kKnhTVkHNV588UUtXrw4q33nzJmTc4PgH5wSylMoZpV9oInyU4W1piuqqE/upFjVHtFGY0rdCvTyQkmwnhJUDQpy171nNHdGZJV43cpV7WGCGj7UVMBg2cr2MEGNMtJcgHVUmjrCWq+x3vHXhXd0ReK2vhfG4kbhaFw1VQTDkKorEs/qWqojHJNlGa6TMahonyCG10tbl6usgxoXXXRRShmqgQQCAcXjTOiheAzhE1chS6GnrBClPgrHCxPP2eqOxNUeimpoLdkabrDCxaWnesXiRqs7IxrDBLVnuOGctaojzOeSDy1vLdzYau6MKBq3VFVBCapyUIhssqaOCEGNMtcWsv+9sDUU01iCGuijLTR4lobUk2nYHo5pOJnwGESsz02S0XiJ70BCTrIOajzyyCOaNm1aAZsCv8kmCAb/YXGuHt2RuBpq867whz4sy2iVCyYInbSiLUxQwwXaQ1HPnL+Wt4UIanhE3DIlXU+jFyWo/KfQ5yxjegJyE0bUFew94A5xyzi6SHivVtZmKXut3dlNRCdr647yWYV+WmyMpdbuKEENDCocS72GilmGLB8PynrGbfz48Zo8eXIh2wKfKVZIg9iJu1B6qUdnJEZQowBauv1TeqrXivawNhrbUOpmlL3lHsjS6NXUwR3UXtH0dYaEGyxvCzFR5CPFOGctbyWoUQ4KVSLPmJ5z4LqMobLVlkNQo5U1EZDGms7sx9KazqjWayxgY+ALkTRzCtG4pZogmWJewrdhFAzBhvJUiAUrvcgrd3x7jZcmnrMVjlpq6bL/pQ/OMcZ4ovRUL2Pku4wlv1rhouO0+utgGLzPsopzzmrtjnJdVwYKUXoq8drtpc9UQ2nE4lZON7u1haKyXHIzANyhMxxTOJb99cua7ghjCIOKpBlTdsYZ3IGgBgqmWGtdUObKXZjM79Ed5e/gtLgPS0/1ckPN/XK2ujOS9sLWzZYVsJY+nBGNW1rtgtJTvQiG+Ucxz1lLW0NFeR+URk9Zz8Kdp5q7CKaWqzVd0ZxucjTGXqkh+N8amzd/WVZu67mgvITSzNcwh+M9WQU1Fi5cyHoasK1YsQZCGu4RjVuemxgslM4wH4hOW90ZcU0ZF6etbA8ToC2hZR6ctGvt9s4aIOVqRVvYdVmrfsx2K0dLWrqL9l7LWkPc8epjhb62IphavvJZp6W5yz03BKD0mnLIJsvlOSgv6b5HEdTwnqyCGpMnT1Z1dXWh2wLkxG0TBuWMEgVr8YHovBVt3pt4zlYkZtm+CwnOiMQsVyzknAsvBmPKiRuPT0sXwTCv647E1Wyjtni+IjFLTQVYRBruUIwSeV4q7wjnNOcT1PDodRmcF43n9h1pFWMIg0h3Pcw1svdQfgqeR0zDPbqYyE+IxQ1ZKw6Kxr078Zwt7qAujRXt7rubPlvLW0Nk+LhURzimdpeWPlju4wBxOVjSWrwsjcR7rin+e6LwYnGrKHczt3RHFeY7QlnpDMcUiub+PagjHGfMQFJPxk8ul7rdkbg6wu68DkPphaPxtFmKVNvwnspSNwD+VbTyU0zouAaR7VRdkbiqK4kdO6GpI/sL2nAsv3GYvECYU4uF1VRWDLrPqvaw4uMaVBEMOPKeyM6yIpZxcVo4Zqm5M6pRDWTTus1yF2Zp9FrWGtL6o+oVCHCu8ZpY3NLSluKPrTVdUXWEYmqo5aujn6zqiMgqwv03xvTcQLBeY33h3wyu4MTi84wZSPmVr1vVHlZDDZ9b6K99gIBXRzgmyzIK8n3cM/gXjoIoZqCBkIZ7UH4qVXc0rhGqKnUzfMFO6amzHvzAsfe94JGPHHmd24+ZNug+cctodWdEY4fWOPKeGFx7KKoOj9+Rs7S1m6CGy1iWcWXpqV7hKMEwr1rWFlYsXpor3y/XdGnz8cNK8t4ojGKep5a1hpigLiNOlBxbSVCj7OWbqb+8Laz1Rw9xsEXwi4GyqY3puTGVmzi8I69biI0xWrp0qWIx0rqQqqjJE0Q1XINMjVT8PZxRTutNUBamuJa6eOI5W00dEUrduczKjrCiJZp4zlYpShghP8YYfdXcVbL3X9EWphyMj3RH4mop4rVVZziu9lB5XMuVu45wzJESLm3dMYU455S1lXmWiC32eQ7e0ZahRGwbn1WeklP46dlnn9W8efP07rvvKh6P680339R2222n0047TbvvvruOPfZYp9sJjyGmUZ5YHDsVfw9n2F3z4JYjt8rr/cIxK5Ghcf2cLVRTxBJiqzsiisYtVVVQtqzQ4pbxxeKlxvQEw7ib0T1KUR7IrtUdEYWjcdVUDV4aD+6wqiOSV436fBkjfdXSrY3GNJSsDXBOKW6iWNYa1tBaMpj9zslrqxVtYU0exfVNuXIim2x5W0gj6jnvYC1jjNq6Bw5ctIZiWreI7UF+bM+a3Hfffdp///21/vrr67bbbpOVVIhzww031F133eVoA+FNRS0/xZoarhCNWyUrieBWlONyxkqbX45qKivy/C+Y9FpBB14v+0lDY3omG1F4q9pLV8bFaV6YRC8XneGYJ+4KNMYfmUrlZFFTZ6mboCVrQmSG+YAxpiT//pe3hdIuzAr/cLr84pKWbr7rl6mOcExt3flXhFnRFua8gxSd4XjGjOo1nXwX9xLbQY0rr7xSZ599tu677z6deOKJKY9tscUW+vDDD51qGzyMTI3yQ3pwf2Rq5C8Ujas1w50UfuSH7AEvWOKjQEAX6fWu4aVxtbQlJIsv+p7Q1BF2xfo/cctocQuly7yuuTOqcAmyfmJxk9eiv3C/pk5nS2KGouVTghapFq9x5rMmbhnK+yJFc1fmoEUoanFzqofYDmp8/vnn2n///dM+NmTIELW2tubdKHhfMW+o4OYNd2D9iP5icaNonDsa87GyDL/8NnexRkKhdYZjvguWcdd96cXilqsXCO8rHLO0qqP8zrFetLCpdGtp9PVVczfXNh5XyjV1vBT4hX1LHJqITkYgtfxE45ajgYivmsn4wVrZBErXDBL4gHvYDmqss846+vjjj9M+9v7772vy5Ml5NwreV9TyU+RquEIp6zy7GRks+SnHrAVjeu7KReF4aeI5WyvbQ4ox0VhSyzxY4mDxGv/9W/Cb1R0RtWdY0LLY4pbRVwWYuERxhKLxkpa5bO2OqsNF4xnO6QjHCpJVsbojwl3TZaYnk9S51+uKxMn4gaSea5hsAharKUHlGbaDGsccc4wuv/xyPf/884ltgUBAH374oa6//nodd9xxjjYQ3lTU8lPemj/wrVCMi810CPbkLhSNu2oip5hWtXMhVSiWZbTch8Eyy5JWlGFmk1sYYxwrlVBMrd1RtYf4ou9Wxhj9b1VHqZvRz1fN3WQUetTilu6Sf3f6ijvvfenL5sJklBkjfbnGPdlqKCzLKsz1VKHGJ7xlTVckq4DZ6s4IJVo9wnZQ4/LLL9fMmTM1e/ZsrbPOOpKk/fbbT9tss4223357XXjhhY43Et5TzH//pb4wR49QhC+36bCuRu7KsfRUr+auCOU9CsTpes9uwoLhpbO607t3knLXvXutaHfHWhp9xS2jL5gg8py4ZVzxObG8lQXn/aY7Ei9odvXSlpDCfKcqC8vaQgoX4PzQ3Bn1XelZ2Lcqy0xFy8quTBVKr9LuE6qrq/XYY4/phRde0HPPPaempiY1NjZq1qxZmjVrViHaCA8qds1CY4wCgUBR3xOpmLxPj0yN3JVztoIxPZOk6wyrLXVTfGeZCyZ0CqU9FFNHKKaGWtuXd8jTl83eDQysaAtrw9Fx1VRVlLopSGJZxlVrafS1eE23Jo2sUy3jxjNWtIUUi5f+bjBjpKWt3ZoyakipmwKHfNHcVdAbDY3pCcBvNLahcG+CkrMso0UF/Nxb2NSpaZNGFOz14W7GGFvlF1d2hDWqobqALYITcv7Wu+eee2rPPfd0si3wkWJfLltGqiCmUVKFuKPCD8KU5cpJJGaprcxLojR1ENRwWjgaV7PPF35b1hbSxrV86S+m9lBULR6+m8uYnnIwG41h3LjJsraQq7N/jJEWru7SZusMLXVTkAVj3LUWypI1Ia03sl7BIF/gvK47Ei/KWmWLW7q1XmO9qittFxuBRyxtLUyWRq/mzp7rtRH1VQV7D7hXc2fUVpbgyrawpo5tUAWfU65mO6jx5ZdfDvhYMBjU8OHDNXQoF7dlr8hRjZ7MEE42pRKLW55bHLVYyNTITXNnpOxLy63u6KnlyRd+5yxrC/l+XC1vDWnD0UMYN0Xk5SyNXktbQprSWK/KCiaL3CAWt/T5qs5SN2NQy1pCmjSijuwwD2jqiKjTRaXMwjFLy9tCWndEXambgjx93tRZlGsry5L+19RJINWn4pbRF6sLn534eVOntltvRMHfB+6zrM1e8DVuGa3qCHOTocvZ/uYyZcoUrb/++mn/mzx5skaMGKENN9xQt956ayHaC4+wil1+qqjvhr5CZGkMiJrBuWnq9Pfd9NmIW0Yt1H511LJW/6/TEo0b/v0UUXck7ov1f2Jxo6VFuNMW2fmiuVtRF5QJysZnLlzIHP25cQ2UL5q7i16yGM5q7Y4WdC2Nvpa3htQRjhXt/VA8XzR3FaXyQ0tX1BfXbbAnGre0KofjvqIMvjt6ne2gxr333quJEydqr7320i233KL77rtPt9xyi/bYYw9NmDBBt956q2bOnKlzzjmHwEYZK/ZN+1wPlxYT9wOLxC2+sNlkjFEzk7KSetbVgDNauqKuLuXipGKUgUCPr9Z0++Ya5KvmbllkXZZcKBrXly6cgB5Ic2fUVo1qFN+arojaut03EdwdiWe9aCvc6bOVxc0oM0b6X5HfE4UXisaLkqXR67OVHVzvlJllrbll66/ujKirTL4/epXtXOGXX35Z++67r37961+nbD/zzDN12mmn6Z133tE999yjYcOG6bbbbtMPfvADxxoL7zBFzp1g0ri0CGoMzJievw8LsGavLRRzxUKWbrCGoIZjlttMOfay5s6IIjGLutMFFolZWtrq/dJTvcIxSyvawxo/nDT7UvrfquKUcnHSf1d2aGT9SMreuVQhF97N16KmTo1pqFYgwNjxmuVtIbWWIKN4dWdETR1hjW6oKfp7ozCK/bkXilr6ak23Jo+qL96bomTyXVNq8ZpuTR3HunNuZfvb7v3336/DDz887WNHHHGEHnnkEUnSgQceqIULF+bXOnhWsb+Meey7n+9E4gQ1MvFKCQm3WOPzhZzt6AjHCRo6wLJMWaWaG1NeQZxSWdzSLctn/zy/WN3FjSIl1NJV3FIuTumKxLWEDDFXau6MaE2Xe0tZdoTJ1vCiaNwqepZGsk9WdCjG909faO6MlORzb+HqToWi3IFfDla2hxXOY53Tpa3dinK+cS3bQY3g/2fvvsOjqtI/gH/vnZ5Mei+EEHrvYMUCKqDYV0VBsS82lBX1Z0EsK4qioKJYVrH3uugiiIINGypVULokEBJCejKTmXl/f8QZCKmT3Onfz/Pss3KncO7cwzn3nvec96gq1qxZ0+Rrv/32G1S1/it1Oh0sFm78Rf7B1YOBxUHXljHo452SquB9+A6EEgZ5Oqy4yh5xq39CcWA0lDicLuzuwKyvYFVtd6KYA4wBISL4c1/o7k+xvbiK94NBaHsQr9Jw21ZUxWBqiNlSFNh/77Y6F7b7MV0R+YbTJdi0tyIgf7fLBWzeG7p9LrXdrpKO3a+7XEB+afjd84cLr9NPXXTRRbjrrrtgt9sxYcIEpKSkoKioCB999BHuv/9+XH311QCA1atXo0+fPpoXmEKD3zcK541wQHHQvmV8yG87l0sCspQ9mB2orkN6LNPBdER7Vi3YHB2bvXXoZodabHxo0nuXwq6i1oEqmwPRJq9v9agNCspqwzZQtmN/NVJimNbD3/aU1aKiNvj2PWgrh1OwrbgKvdJjAl0U+ltJlT0k7qmq7U4UltuQztR3IaG0ug57SgO/Mmv3gRqkx5oQYzYEuijUTtuLq1DbgRn0HbW/yo69ZbVse8JYYbk291Y799cgM87C1L5ByOsn3blz50Kv1+P+++/HXXfd5TluMplw7bXX4qGHHgIAHH300TjllFO0KymFFL+nnwrPcYWQwfRKLWPQp+3Kax3893yY8hAYkAhmdU5Xuzaxnfb2Os3KcMv7Gzr8HQsvHOT1ZworbMhjUENzLpfgrw7O+gpmFbUOlFTZkRhtDHRRIkad04VtITCjvjV7ymqRFW/mIGMQEBFsLQqdDZW3FVchNcbEfVmCnNMl+D1AM+sPJwL8vqcCwzpzP59QVF5T16F9DrTy575KJEYbOVgdhlwu7fpBp0uwfX8VeqZx4kaw8fpJ12Aw4NFHH8Vdd92FdevWYc+ePcjIyEC/fv2QmJjoed+oUaM0LSiFFo5JRpZwna2qFQfzo7VZeS0H8A9XZXOizumCQceb7fYorrRHbKBsX7kNecnRgS5G2NlbXqvJ6ptgtqukmkENP9qxvzosVnWKAH8UVmFo5/hAFyXiFZbbQmrlT22dC7tLa5CTyI17g9mf+ypRYw+efQgqbU5sK65Ct1Ru4htK3MGxYLg/r3MKNhdWoH9WXKCLQhrbXVqj6UqggtJaZMdbuAo+yLT7aiQkJDBwQc3yd/opf/991BA3TmoZgz5tFwppEgKhotbBAcZ22tfOvSXmn9e/Q3+vzeHyrNCYc3ZfmAIwA6za7kRlrQNWM2++tSIi2BnGqzTcSqrqUF5Th1gLZ9z7WpXNEVb7s5TV1GFveS3TJgaQU8PZqf60Y3810mPNnDEdpPZX2lEQBGmnDrerpAZJViMSonifHCq2FlWhyhY8wbGiCjsKSmuQGc89gcOFrc6JHRrvuyNSH9gd1Cle0++ljmn3U+6WLVvwxx9/oLa2ccd29tlnd6hQFAb8nX7Kv38dHcbJlQgtcrgY9Gmr8hCaVehP5bV1DGq0Q53T1e6N1r3dw6Ll71I1/T5vFFbYGNTQ0L4KW1DNUvWlnSXVnLnoB3/uqwyK2apa2rqvCsnRRui5wjAg/jpQHZKryRxOwc6SanTnrPugY3e4gibtVFM27qnAiNwErmoOASVV9qAM5P+xrxLxUUZEGQNzv07a+n1vhU8mlpZU1WH3gRpkJzAAFiy8fsotLy/HWWedhRUrVgA4uEGzohzMY+h0RsbDHjXP389m4fYwGGqYXqllDPq0jcPpgi2Am8UFs2CazRRK9kdw6im3ogobuqYwBZVWdkXAKg23ogo7N5v3saIKG0qqwm+Fos3hws6SGrY9AeCL2an+tPtADTLjzGx3gohIfaqgYE6RZ6tzYXNhJfplxga6KNQCu8OFjXuCMzjmcgEb95RjSKd47tES4nYfqPHpvdWWovp9WBgACw5eh7JvvfVW7N27F19//TVEBB988AFWrFiByy+/HF26dMH333/vi3JSiBE/jyIJ12oEFNN/tYxBjbapipDZz+1RZeMKlvYoqmxf6qlwUm13opr/tjRRUmUPqRz1WgiGTTzDlcsl2BKCKYLaaldJdcSsagomW4qqEMoLhN3pPSh4/HWgBvsr27fq1Z/2lduQX8o+K1iFQnCsvMaBbfvDt1+OBNV2J7YU+bYPcQfA/D3mSU3zOqixZMkS3HHHHRg5ciQAIDMzE6NGjcKzzz6LM844A3PnztW8kBR6uFIjcrhcwt+/FdxypG04cN+8KruTN05ecrkkLGdAt0cxgzua+CuCVmm47SmrDeoBiFD214GasB70F4HPBxaooQPVdhS2cx+pYFJSVYd9FaF/HuGgtLoupPZn+aOwEpURNvkgVIRKcGzX/tAoJzXmcLqwPr/ML4H98hpHSLWN4czroMa+ffvQqVMn6HQ6REdHY//+/Z7Xxo8fjyVLlmhaQApNHHuLHE5e7FZxJUvbcDZ580SAGqbm8kppTR1XSf2tmA9nHVZlc2B/VeT9jiLgzFcfsDtcIZ0iqK2KKuw40M59jcg7IoI/C8MniPTnvkr24QFmd7iwYU95SD3XiwDrCsrh4IyyoFJWE1rBsY17ymGr43NpKHGvBKr0Y8rmXSU12FvWeI9p8i+vgxqdOnVCcXExAKB79+74+OOPPa+tWrUKZrNZu9JRyOJKjcjB3751nGHfNrWcDdyiWt5ceyUSB6CbU1ZTxwf8DtoVwWmYdh+ogYuDi5raVlwVMQO2W/ZV8T7ID/46UOPXwRxfs9VFRuAvWIkIft9TEZJ73dXYnUG9qXmkqXO6sL4gtIJjdU7Bhj0V7LtCyI791Siq8P+z3+97K1BWw8wAgeR1UOOkk07C559/DgC46aabsHDhQgwdOhRHHnkk7r77blx88cWaF5KIghc7+9bxF2obDtq3zMagj1dKGNTwEKlfuULtU+d0obA8cmdi1TmFqWA0VGVzYE8EzeyrqHWgkPXHp2x1TmwvDr8AwK6SaqYmDZAd+6tDenJIUYUdu0rC799EqBERbAzR4FhpdR22hWG7Go72VdgC1geKAOvzyzmOEUB6bz/w0EMPobq6vsJMnjwZVqsV7777LmpqavDkk0/i6quv1ryQRK3hRuEUzBj3aZtQvOH1J94stZ3d4UJVGM1Y1cKB6jokW02BLkZI2ltua3d+XpujY/Xw0GCmFoFNk17Xrs/ll9YiPY6rsbWwtbgq4u4LthVVIdVqgqoqgS5KWPqzKDxX/ojU75EwOCc+0EWJKPsr7WERJNtaVIVYswHxUYZAFyVi7SoJ7f0pdu6vRrzFgCSrMdBFoWaUVNmxoaA8oGWwOVz47a8yDMmJh1Hv9boB6iCvghp2ux1LlizBoEGDkJycDAA466yzcNZZZ/mkcBS6OHs/cvBKt46/UdvYmR6nRfx92q6EOdwb4cqV9svvQOqpaW+v06wct7y/ocPfsfDCQe36XFlNHSprHbCavZ4PRYcora5DcQDSIwRabZ0Lu0trkJMYFeiihJ2SKjv2hcHm4M05UF2HveW1SI9lUNUfauuc2LAnsAOEWhEBNhSUY3huAgcaA+BAtR3bikNnH43mbNhTjhG5CTAb2jcphHynvKYOa/PLgmKiSLXdiTW7yzC4Uxz0OrY3/uTVr200GnHhhRdi165dvioPUbsEQ0MWqTjnrnWcmNg6p0v477gVDid/oLZibtPGqmxO1DEw5rXS6jpU27nqBwDyyyJ3XxGt7Ngf+gM87bWrpCYsVxMEkssl2BxGm4M3Z8u+Ku4L5Qcul2BdfnlY3W/aHC5sKCjnhEs/sztc2FhQERbPdg6nYH1BOfcWCzKVNgd+213W7pXUvlBR68Da/HLe6/iZ19OtevXqxaAGBR2Fg8YBo/DHbxV/odbxYbV1Dt4gtVl5DXNwN6Wi1oHEaC6h98beDu6lMf+8/h36vM3h8qzQmHN2X5gCONt0X7kN3VOsTCHUTuU1dSipityAq93hwp6yWmQnWAJdlLCx60A1aiIg6Gp3uLB9fzW6p1oDXZSwtqWoChW14Xf/dODvvRG6pkQHuigRoX4fjfKw2guwvMaBbcVV6MY2KChU251Y81dZUAZgS6vrsL6gHP0zY3m/7CdeBzVmz56NadOmoU+fPhg2bJgvykREIYQxjdYx8NM6Dti3jrM+2sbpElRyY9EmldfWMajhBZdLUNTBDY7bu4dF09+lavp93qpzCkprWIfaawc3rcXOkmpkxpn5oK+B2jonduyPnDq1+0ANMuLMsJqYAs8XCstrsbsDqRaDHfdG8J9dJTVhGcDfVVKDhCgj61CAVdud+HVXaVAHzfZX2rGOgQ2/8fqu4JZbbsH+/fsxcuRIJCUlIS0trcGAnaIoWLNmjaaFpNDDQdzIoeO1bpXK36hV4bA82ddc/JHapKLWwfrUjDKuYPHKgeo61AXhLLBA2ltuY1CjHSprHRG5l8bhbHUu7C2vRWY8V2t01J/7KoMq7YavuTcNH8JNwzVXbXdi097wT2PGvRF8r6ymLiz20WjOxr/rkIl1KCCqbA789ldZUAc03PZX2rEuvxz9sxjY8DWv17APHToUp59+Oi6++GKceuqpGDZsGIYOHer535AhQ9pVkAULFiA3NxdmsxkjR47Ejz/+2Ox7n3vuORx77LFISEhAQkICxowZ0+L7KfwpTPATMKqqcLVGK/TsyFrHn4g0UmXnwH1zqriCxSv7KsN38932KqqwMa90O+wuDd8Z0N7KL+1YSjeq34C3KAKDZKXVdR1ePUcNuVyCDQWRkQPe4RRs3FPB/TV8pM7pwvqC8rCeWFTHOhQwoRTQcNtfVR/Y4H2zb3m9UmPRokWaF+Ktt97C9OnTsXDhQowcORLz5s3DKaecgs2bNyM1NbXR+1esWIGJEyfiqKOOgtlsxkMPPYSTTz4ZGzZsQFZWlublI+/5e3ySg+qBpVOVoMxpGCx0OlZQ6jgGb9uGmzo3r7bOBadLoGOgtU1KqiJv0LA1TpegwuZAnMUQ6KKEDKdLUFjesYFYm6Nj7dqhgwBaDAh0JA1aRa0DFbV1iDGzDrXXln3hOxO6NVuKqpAUbeTMV43sKKkOy300mlNaXYe/DtQgJzEq0EUJO3/uq4KtLnQGnNvrAOuQ31XZHPj1rzLYQyig4ba/yo61+WXonxXH5y8f6VBSShHBnj17kJqaCr2+/V/16KOP4sorr8Sll14KAFi4cCE++eQTvPDCC7jtttsavf+1115r8Ofnn38e7733HpYvX46LL7643eUg7TDIEFl0igIHGNRoDtNPtY6/EGml2sagRktq7E5YzcxJ3ppquzMiHs7b40C1nUENL+yrsHV4FvS0t9dpVBp4Np7viIUXDurQ5wvKatGTQY12KSyvjahB6MPV2J0o4IbzmiirqcPOCNqXxW1rURUSo4y8F9JQcaUNe8siZxXetuIqJFtNiDIyDZWvVf69QiMUAxpuJVV1WMfAhs+0qyX/7LPPcPfdd+PXX3+Fw+HATz/9hCFDhuCqq67Ccccdh4suuqjN32W327F69Wr83//9n+eYqqoYM2YMVq1a1abvqK6uRl1dHRITE5t83WazwWY7OEOqvLwcAOByueA6JBmpqqoN/gzU7w2hKIrPjquqChFptIRNy+OBOCeIQOTw96t/l0+aOV7PfR6KojTxfqWZ4/X/zesUmHPSq0Btg+vd3HVq7fjhnZW3xw+SQ+pg63XPmzJ6f04GRTy/P+te02VR/v7tAnmd3Bq3R03VbW/bt46fk3pIPar/jtBpI/xZ9ypt9kP6EP9fJ/fxw8/XV+2et+dUZXcg2qQL+HVq6Xgw1L2Sytq/f+vAXCf38cPbo4P/r227583xkgobchIsQXGdtD7ui3PKP1B9WF3q6HUKvINlat857SmtQV5SlOfhPhiu0+HHg7HuuVyCLfsqIOLy+70R0NL9kX/vjbYVVSDVaoDRoA/K69TR4/44J6dLsKGgDC5Xc3XJd89P7bnP1rLPrU+5VYqhOQmeFT/Bep3aWpZA1r06pwu/7ylv1z1TR+pe2++ztb83cjqBDQVlGNY5wafXI9LrXpXNiV92HUCd03dtRNvuszve7u2vtGHNX6UY2CneM+5x6LmG8nXyZd1rC6+DGm+88QYmTZqE8847D1deeSWuvPJKz2tdu3bFiy++6FVQo7i4GE6nE2lpaQ2Op6WlYdOmTW36jltvvRWZmZkYM2ZMk6/Pnj0b99xzT6Pj+fn5ngCH1WpFYmIiSktLUVl5cKOsuLg4xMXFobi4GLW1B6PPiYmJsFqtKCwsRF1dned4SkoKLBYLCgoKGlyQjIwM6HQ67N69u0EZsrOz4XQ6sWfPHs8xVVWRnZ2N2tpaFBUVeY4bDAZkZGSgqqoKJSUlnuNmsxmpqakoLy9HWVmZ53ggz0lcTrgq9h88qCjQxSQDzjq4qg+WEToddNGJkLqDQSdXxX64zCboouIh9mqI7eAMEsVghmKJgdRWQuoOlr2mSg9Em3idAnROqqMGropyz/HmrpNiioJiioarpgxwHCy7YrZCMVrgqi6tv0twlycqDtAb4aosabCTtBqdAFHVhnUMgFgSPP/tqtgPl15tQ92rhdQesjme3uBV3WvLOVUpFdhtLwXAutfcOQkUAOaAXid33XMdMhPEVVkC1yHpw5qre2pMEuBywVV14OBBjeueq7YSu3dXeI6HUhvhz7pXfaASalTgrpO77rkOm1Hkq3bP23OyOWKC4joBwV339pTVwlXjCNh1ctc9V+XBvtVVUw4YEn3S7nlzTvsrgd1qZVBcJ7dgrXslpaU48HeqoI5cp0fHZjS4ToreAGfF/kbXCc3UPZvdgVs/qn+umj0mDSaDDrqYJIijqbqXAJe9qXYvDi5bfd1z/x3tPae6ygPYuqPKM8s10NcpVOpeea0D1aW1Abk3AgAYYzz/6bnPhv/vjWwAthU40atzRlBeJ7dgrnv7K+2oqrT79DoBTde9BvfZNeVw4WAd81efW15chvX2UiRGGYP6Oh0qWOveruJK1NbU+eQ6tVT3Dr/P9ve9UbkrATv3V0GtPvj7AsF7nUKt7hksVmwpdcFWWerb56dDjrsqS4CEVJ+NR5TYo7BGUZBpssNuC4/r5Ou6161bN7RGkcPDL63o06cPxo0bh7lz58LpdMJgMODnn3/GkCFDsHjxYlxxxRXYu3dvm7+voKAAWVlZ+O6773DkkUd6jt9yyy1YuXIlfvjhhxY//+CDD2LOnDlYsWIFBgwY0OR7mlqp0alTJxQWFiI2NtZzPJKinr4+p71lNdhQUH7Y+5uPuNfWOXDjO+sBAPP+0Q8mvc6ryPrw3ATEWoy8TgE6p/X5ZSgsP3TJaWBWatid4knRUF+P1L9/g8Cu1OiVHoOMOPPffyfrXnNlWfnnftRvpBWY6+Rmc7gOa4/UFt/ffBm1rXtZ8WZ0T40+5DtCp43wV91zOF34est+BPI6uY8fWo/mn9cfRp3azPt9NxuxqeO5yVbkJUeFZBvhz7r3884Df6d4CexKDZvD2aA9Mhv0Pmn3vD1+RJdERJsNAb9OWh/Xuu4Vltcecj8cmHujpu+z1YDfG+UkWNA11fr33xl6bYTWx9tSxoPtUvuuU21d47RVB9/f+nGbw4VbP9gIAHjorD4N7o+aen9r393w/sq7czIbdDiqa1KTf2egr1NHj/u67tXY6vD9jhK4X/b3So323Gf7os/V6xSMzE2EUa8G5XUKhbp3oMqGX3aVHvqKX+6NvLvP9l2fq9epGJkbD+MhdTgYr1Oo1T33HhoOl+9XurftPlvbe6OEKD36Z8Z6VoqF6nXSsuzNHTebzWiN1ys1tm3bhvHjxzf5WnR0dINoTlskJydDp9OhsLCwwfHCwkKkp6e3+NlHHnkEDz74ID7//PNmAxoAYDKZYDKZGh1X1foO7PBjTfHlcfcF9NXxQJyTTq1/WGqqjGgie/6h5T70PFp+/yGzp/8uA69TYM7JZNC143o3dbzpsrf9+CEzfRSlwevel0Wr4yrMBn2H2ppIqXsGnQr730uXm3q/r6/Twf+WBu9vum5rUd+9O27QN+6zgNBoI/xV95wOOexa+v86uY8fWo9afn9H2z3vymh3ugJ+ndpyPNB1r6au9brk7fH21KXD74/a+z1a1z2bUxDdRBmbK3t7j4d63SutcTT6Pf1/b9TUfbZ6yPHA9LkHahy8N/KijAeq61Bpc3Xo3tY9cKMFd3CjI5ram6Wt52RzCPZV2JAW2/SAR6i0EYGoe9tLaiCi4tC/2p9tQXvvs7093lpZnC5g54Ea9EyLOaQswXOdgr3uiQi2Fld3+Pr5/j7bd32u0yXYXlKD3ukxh703eK6TVsf9dU41difW5lfAvRDH1/dGvr3Pbvr4gWoHNhZWol/GwcAGEFrXyd/HW+L1J9LT05tNC7V27Vp07tzZq+8zGo0YOnQoli9f7jnmcrmwfPnyBis3Djdnzhzcd999WLJkCYYNG+bV30m+5++NkbkRc2AdPtOKGjIZ+Pu0BetRy8z8fVrVMOcqNYW/UevsDleHN3YOd1V2Z+tvIhyormv9TRGq0uYM6Y0//W13aU2gixB0dh+InI2JtVJeU4e9ZbbW3xghCkprUWVrvIKJWldYbkN5DX+7PaW1qKhlX68FW50Tv/5VClsE3BsUV9jx+96KJlczkne8Xqlx4YUXYtasWejVqxeOP/54APVRnPXr12POnDmYOnWq14WYPn06LrnkEgwbNgwjRozAvHnzUFVVhUsvvRQAcPHFFyMrKwuzZ88GADz00EOYOXMmXn/9deTm5nrSXVmtVlitVq//ftJeOwJsHaJjTCOgzAZdoIsQ1DgY3TYWo86TVoEa47+z1nEcunW8d25dbR0H7Ftj42/UKpdLWJdaUVPnbJC2g5rmcLqw/+/9Dzpi/nn9O/R5m8OFW97fAACYc3bfgE9GKaupg63OCRPvj9psdykDQYcSAfJLa9EjjWNI3nC5BFuLqgJdjKCxtagKgzrFB7oYIc3hdGHN7jLU1oV/QMOtsNwGo15F91S2Px3hdVBj1qxZ2LBhA0466SQkJSUBAMaNG4eioiKcdtppuO2227wuxPnnn4+ioiLMnDkTe/fuxaBBg7BkyRLP5uG7du1qsAzl6aefht1ux7nnntvge+6++27MmjXL67+ftKfz90oNlVGNQLJwJUKz9Lr6fJvUOgsfSlvk3lCVqEMY1GiVk5GfVvE3ap3N4WIQsRW1dU7EWQyBLkbQ219l16QumfTa3UeY9Kqm39deRZV2ZCdYAl2MkFDndGFfBYMah9tbXouuKdHQcTyhzfZV2iJiNn1blVTVodLmgNXk9fAq/W1TYSUqbZE3EeSvkhrEmPVIbyaVIrXO6391RqMRH330Eb788kssW7YMxcXFSExMxJgxYzBmzJh2F+S6667Ddddd1+RrK1asaPDnHTt2tPvvIf/wd5DB30EUaogzyJvHgfq2Y3CseYrC9FxtIRyxJw1wILp1XBXVuhqu0mhVJM3I7IhiDVZphKviShuDGm1UWG6DS4N/cjZHx9q2QwfDtRgY72hwzeEUFFXYkB7HQcW22n2A6fAOt/tADXodtrcGtc2ukmrsK4/ctHib9lbAatTDamZQrD3a/audcMIJOOGEE7QsC4URrtSILAadCoNOQZ2ToxyH4+z6tuPsluZZTfomN+uihpQmNmMj8paLUY1WMQdw67hfROs407dt9lcxqNGcA9V1cLmEz4JtsKdMm1Ua095ep8n3APCkM+uIpjac99aesloGNdqorKZOk700wi045l7xY2CGBq+UVNkjPpWZywWszS/D8NwE1p928HoEKScnBxdccAEmTpyIwYMH+6JMFAb8uXxTzw01gkK0SY9SbojZSLSJQY22qh+45yzppnDmRtuwP2idQc/fqDUqA4htwN+oNaxGreM4dOvsDhccnDTULJH6VVHRnBjTIpdLUMkNsZtVzj392mxvOYNjTXG5gH0VNmTFc+VYW7lc8vdm2YEuSeDV1rmwtaiKq33aweve/7zzzsObb76JuXPnonv37rjwwgtxwQUXoEePHr4oH4UovR+fUpj/MjhEGXUMajQhysiHrLZSVQVWk56bhTchlkGNNjFydkurOAOodaxHrWM6vNbx/rR1nF3fuo7OZo4ENocL0aZAlyK4Vdc5NRs4DLcN5wHA6RLU1jmZUrkNDlTxeb85pdV1DGp44a8DNbAxDaXHnrJaZCdYmL3CS17/Wo888ggeeeQRfPXVV3jzzTexYMEC3HPPPRg0aJAnwJGVleWLslIIUVXFbzOu/RlAoeZxhlTTopl+yisxZgY1mhLDf19tYuBKjVbxN2qdMQgGWIJdMAxCBTuu+Gkd98RrXQ0HfFrF/Wtap+UqjXDccB6o/40Y1GiZ3eFCtV2bf2/hGBzjBM+2sztc2LG/OtDFCCoiwNZ9VRjYKS7QRQkp7f6XP2rUKDz11FMoKCjA//73PwwYMAD3338/cnNzNSwehTJ/zQbVq4HvwIiDrk3RqQr31PBSfJQh0EUIOjpVQQxXarSJoigckG4FVyG0zqhXmTqoFcEweBDsGEBsHVMGto77jrSOv1Hrahn4aZVWg/Xh7EC1dvv7mPS6Dv5PPeS71A5/nxZsGgZ9wt3Okmo4Xcw7dbj9VXZN/51Fgg4/kYgI7HY7bDYbHA4HNw4kD3+toOADUXCIMes5CHQYbu7svQQLgxqHi48ysB55gYHElnFVXdtw6XfLWI9aF23kfVFrmFqxdQauSG8VV+23jivHWsd61DquimpdtZ0ZB9piX7kt0EUIWkUVDGp4o11BDRHB8uXLceWVVyItLQ2nn346tm7divvuuw9//fWX1mWkEOWvlRrMDx4cuCqhsVgLH9a9ZTLoWI8Ok8DVK17hYHTLmBKvbeIYYG2WQcf+vi1UrrJrkaLUB36oZXzOaR1XILaOv1Hr+G+tdU4uimoVFx+0rsrm4Aq7FpRUMajhDa/vJG+44Qa8++672Lt3L/r06YPp06fjggsuQNeuXX1RPgphBr1/ZjtweX/wiLUYUGXjDA43Dma0T2K0EdX2mkAXI2gkRhsDXYSQYjVxsLU5ZoMKPR/a2yTWogcOBLoUwSmOgdY2izUbUF7DWZtNiTHruVF4G/A5p3UcjG4dMxu0jr9R65iVpXUu/katKtFw7xGbo2NjT4cGV7QItGiRyqza7kRtnZN7/LSR1yNuixcvxpQpUzBx4kT07994c5/t27ejS5cumhSOQpv/VmrwBiRYxFsM2FNaG+hiBA2mUmqfZKsRuw8wqAHUD0Jz5YF3GExsHn+btmP73bx4/jZtlhBlYH/WDO6h1TYc1GidycCgRmtYj1pn4W/UKq5CaJ2LP1KrKmq1m+wx7e11mn2Xe+P5jlh44aCOFwT1vxHb7bbx+ul227ZtjY4VFxfjrbfewuuvv47vv/8eTidnapP/lrlyOW3w4EDHQRajDiZ2RO0SbzFAr1PgcPKmMCXGFOgihByrSc/60wyu+mk7k0GHGLNe0wevcJFsZbvUVknRRrZHzUiPNQe6CCHBqFdhNelQyZXQTTLpOfmjLawmPcwGFbV1TPnSFKtJxwHENjAzgNgq1qPWcZFm67gPUtu1+w6guroaH3zwAV5//XV8/vnncDgcGDRoEB577DEty0chzF8rKIx6dq7BwmLUwaRXmSMRzMfeEaqqINlqxN4ybiCWwsFDrymKgoQoAzdZa0JCFIMa3kiLNTGocZgYs577aXhBVRWkx5q5WuMwMWY9B6K9kGg1otLGOtSURCv7tbZKjTFhVwnrUVM4iahtYs18vm0NV0W3TssB+/nnNc4e5A2bw+VZoTHn7L4wBcnYImMabefVvzin04klS5bg9ddfx8cff4zq6mqkp6fD4XDgjTfewHnnneerclII8lewgSs1gktCtIGD0QASmVahQ1KspoivR0a9ijhuNt8uidFGBjUOYzaoHIz2UlqMCVv2VQW6GEElLZYDP97KiDMxqHGYzHiu0vBGUrQRu/azDjUliSsQ2yzZyqBGcziJqG1izHooCsBtI5oWbdJxj582UDX8ibTYw+Lgd6mafl9H6Licpc3aVJ2+/fZbXHvttcjIyMCECROwdOlSTJo0CStWrMD69eshIkhPT/d1WSnE+CvKGSzRVKrH9Cb1+Dt0jDtlRyRLizVB4TSNduEgR2NJnM3qNZNBhwQGqD0UhSmD2iPGbKjfeJ4A1G/Gm8pZ0V6JMxv4vNMEvU7hJCIvxFm40q4pMWY9rJxd3yY6VeEquxbEMlNDm7AOtUxRwLbaC22qTcceeywURcEJJ5yA6dOn4+STT4ZeX//RsrIynxaQQpc/opyKwvRTwSaR6U0QY9azXnaQqipIizUh/0DkbjyfwcHDdjMbdIiPMqC0ui7QRQkaHIxun06JFhxgPQJQn7qEfVv75CVH47e/+MwEADmJFs5k9ZKqKshJisKfhZWBLkpQyU6wQM+61GaKoqBrSjTW5ZcHuihBpWtKdKCLEFIy4syoqGVb1BQ+u7VNcrSRK35akBhl5H2SF9r0S/Xv3x8igpUrV2L+/Pl4/fXXUVFR4euyUYjzx4wio07lTOYgY9SrEZ9Lkqs0tBHJg7BWk46zxjoonWlyPMwGlfv8tFNStBHRJs6WAoDOiVGBLkLISow2ctUP6u8ROyWwHrVHZpyZQcVDqCqQHW8JdDFCTkqMiSvHDpEYbeBzm5cy4sx+2zs1lMRZDIhnP98mep3Kf3ctSInhb+ONNt0ZrVmzBuvXr8eMGTPw559/YsqUKUhPT8d5552Hjz76iIPK1CSdqvi8w+PNfXCK9IY4mWleNBFnMcASoUsv0+MiN6CjlZQYEzdZ+xvrU/spioIcDuYjKdrIQGsH5XE2MDonRTFPdDvpVAWdEjiI75Ydb+FzYDt1S7EGughBg7+F93Sqgk6JbIsO1zmJv4k30piGskmKUr//EbVdm+8E+vTpgwceeADbtm3D119/jSlTpmDlypWYMmUKAGD+/Pn46quvfFVOClG+Xq1hNvBmNhhF8mZrJr2KWA78aCYrAjcTZd56bRh0KvO2/43L4TsmLcYEU4Tfb+TwYb3D4iwGpEbwCrIoow5ZDLB2SFa8OeLbIgAw6Bhs7oj4KAPS4yK3LXLLSjAzWN9OWfEWBqgPEW3ScT8/L6XFmrgSugmdEhmw91a7fq2jjz4aCxYsQEFBARYvXowLL7wQy5YtwwknnIC8vDyty0ghzOzjWdYWAxvCYBRtityN6Opnh/MmTysZceaIm22fFsu89VrhrFYgOcYYsSuetKKqCvKSI3eWfVK0EQncL0sTPVKt0Edo2o5e6TFQOQjWIXqdil5pMYEuRsB1T7PyPqmDeqRaI3pyYLRJx1UaHWDQqeieGrn3RYdSlPr+jc//3lEUBd1T+W/wUEa9ilwG7L3WoZ5Mp9Nh/PjxeOWVV1BYWIhXX30V/fr106psFAZ8HXQwMagRtFIidIZ0pKfe0ppBpyItwmaZZzFHtGZiLYaI30sih/nrNZEea4I1AmeUKQrQlQMXmjHq1Yh8iM9KMDPXuEaSrMaInmWfFG3kalYN6HUq+mbGRtzEIaC+X+ubEcuVBh2UGW9BElMuIycxKuKfNdorMdrIOnSIvOQo6LlBuNc0+8UsFgsmTpyIjz/+WKuvpDDg6/RTlgieYRLs0iIwxYJJryKeNzWay46gFFQxZj1vjDUWyXl/Y8x6DiRqRFEUdIvAwej0ODOsJqbn0FJGnDmiNg036VV0jeCVTr7QLSUyVyroVAU90yOvHfaVOIsBXZIjb+JDt1Qr005ppFda5K4+BACrSYcuSZH3b0hL3VOtDDCivj3OYIrOdom8uyHyK1+v1DBzpUbQspr0ETerNS2Wqad8IZJm2zNdkvZSrMaITYfXmQ9amoq0GWU6VUEe65BP9M6IiZiBoPpz5SOnlox6Fb3TIy8NVc80K5/9NNY5MSqi9gJIjTXxXltDJoMOvSKwLQLqV/z0yYhlWsUOijLq0CvCg9UGnYJ+mUxh1l68wySf8nUe7yje2Aa19AiLNnM5vO/kRMBse5OeG1v7gqIoyI3AmYhWkw4pETQA7y89Uq0Rk66jS3IU03z6iNmgQ8+08H+Iz0m0IDGCBkz9KclqjKi+LSvBHHHPFf6gKAr6ZsZExIa9MWZ9RAYDfS01xhRRbZFbn4wYrvjRSFqsGVkJkdu+98mI5f12BzCoQT7ly5UaJr3KyHiQS4sxRczgT7RJxxsbH0q2hv9mx9kJFrZpPpIWY4qIB/ZD5aVEc8aPD1iMuohI1xFj1nM2q4+lxYb3IG2MWY88pp3yqS5JUUiMDv+VrDFmPbpzU2ef0etU9M+KC+vVYya9igFZ3EfDV7okRSE1glJP56VERdyej77WPcWKmAgcS+mcFBVRq8B9gUEN8imdqvhsX41wH+AMByaDLmJm6DEHom8pihLWA2w6VUFmBO0d4m+KoqBLBA2uxZj1SLZGzsOlv+UkRIV9SrOeaVYGxfygR2p0WN7PqirQN5NpOXxNUZS/Z3iG7yO9Qaegfxbrkq9FGXXonxWeG4crCtA/izOhfUlRFPROj4mIQen0OBNykyLnmcJfVLW+rff1nrzBJDnGiLwImCjla5FTYyhgfPXgH+4DCuEiMwIG+xWFqaf8ITPOHLYbY2YlmGFgznGfSo0xIdYS/g9bANA1hQ9bvqSG+Wa12QkWxEbIPkaBptep6J8ZfgOJvdJieJ/uJ8a/Z6CrYXgLoShAv6xY7qPhJwlRxrDcH6FvZiz7ND/QqQoGZsfBHMZB1vgoA3qlhd+/kWBhNugwMDsuIlZUxVr06JsRywlEGgjfFoeCRpSPUn7wYSk0JEUbw3Yg2i01xhT25xgMVFUJy701FKV+5jf5XvfU8B2IdkuOMUbMCrlASogyhmX+X7NB5awxP7Oa9WG1v0ZmfHin1QpGMWYD+mXGBroYmuuVHoOEKPZn/pQRZw6rFIvdUqO5X50fGfUqBneKD8vZ9jFm/d8BZA5C+5LVrA/bVWNuFqMOA7IiI3jjD+HX2lDQiTL6ZmYsgxqhQVWVsE/NFO7nF0wy48xhl/M3Mz58V6AEmziLAWlhnPNXUYBuzDvuN12To8NuRmKv9BjouWrM7zLjLWERCIgx69EjAoLHwSjZakK31PBZpdc5KYr31wHSJTkaGWGQErVTogU5ieEToAkVFqMOAzuF1x4tUcb6FQS8P/KPxGgjeofhqjGgPvA3MDuOz/4a4i9JPhfts/RTkZFGJBxkxZvDNtoebYqcfUOCgV6nIjuM9tbgKg3/65oSHbbtUXaChQF/P9Lr1LBK1ZEZb2Z/FkA906yw+mh1sz/odQr6cR+NgMpJjAqLFWSpsSauGAuwnqlWJIVwf5ASY0Q3puIMGKtJj0FhkkbIbFAxuBMHof0tPc6M7mG0ihWov08alB3HZzWN8V8m+ZwvghqKAljCbHZkODMbdEi2hu6NcUvCaYA9VHRKsITFTTJQf8MWjpvEBjOzQYfcpPAbLDHq1bA8r2CXGG1EZhjMaDUZVA4ABZhOVdAvhNMR9MmIYX8WBHqkWpEUwvfccRYDeqfHMM94gKmqgr6ZMSEZaI216NGHueoDLtZiwIDs0E4jZNKrGNQpnpvMB0inBAvyUsLj2UZVgYHZcbCaOTFbaxwVJp8zGXSaP6BFG3W8UQkxWfHhN/ivUxWkMU+r3xl0KjqFwd4aigLkcll8QHROjAq7wbduqdHcbD5AuqWEfhqq3kw7FRSijDr0yQi91T+dk6KQbOX9UDBQFAV9M2IQE4IDJxajDv2zYkM2sBdu9Lr6NCmhtD8Cc9UHl4QoIwZkxYVkYMOgUzCoE2fVB1puUnTI76mpKMCArDjEWQyBLkpYCp0eikJatMazPKJNoXejHukSo42a14NAy4w3cxAoQMJhtQZXaQSOqiphlfc9PsqA9NjQXy0QqvQ6Fb1DcCDaLSuBaaeCSUqMCTlJofMAnxhtYKqgIOMejA6lYKtRr2IQ84wHHZOhfi+BULjnNugU5qoPQklWI/plhtaKDb1OweBO8RxzChLdUq0huypaUYB+mbG8z/YhtvjkF1p3COE2OB4pwmmzNkUBssNw9UmoCPXVGlylEXhJViNSYkL/BlNRgB5hlnM2FCVEGUMyHaHFqOPm8kGoa3I04qOCf0afSa8yzUuQcm9GGgqb9aoqMCArlhM9gpTVrEf/rOAelFYUoH8WZ9UHq5QYU8hM/tCp9fseME1QcOmZZkVabOitCO2dEYMUZvbwKQY1yC+03lfDyqh5SEqLMYXN7JmUGBMfvgKsU4IlJB7Wm5LBVRpBoUeqNSRmH7akU6KFfWKQ6JoSHXL/rnunx4T8v4FwpCj1m24H8z2TogB9g7yMkS7apA+J1C/9MmMRy7QcQS0x2ojuQbzCtXd6TEgEgiNZeqwZvdKDtw4B9f3awOw4tkdBSFEU9E6PCak9o3qkWbmS3g94F0p+ofWAS7SRAzihSFUVZCeER8OeE4IzcsONQaeiUwheB0UBN3QOEiaDDnkhvDmy2aCiS1Lolj/c6FTl7w1uA12StumUaOEgUBAz6tWg3l+ja0porCaJdPFRhqCeId09zcr9WEJEdoIFGUGYAqZTogXpccFXLmosM96CrkF6312/2ieW/VoQU9X6CR+hcI3yUqJCcgV3KGJQg/xCy6CGTlVCKkcsNZQVb4Ea4pcvPsrAGRxBIhRXa2TGm2E2hNZs7nCWHW8OyQ1VAaAXZ9kHnfgoQ0gEWy1GHfKSg3NggQ5KjDaiSxDuV5FkNYZEPad66bHmoKxH2QkW1qMQ0zPVGlSb3SZGG9AtSAfJqWmdk6KCcuPn3ukxDLCGAJ2qoH9WbFCno89OsCCXk878JsSHFilUGPUqDBoNPEabdMzdG8IMOhVZIb4XRWfuhRA09Do1pK4H99IIPoqioFe6NWRm17ulxZq46VyQykuODuq83ooC9MlgQCxU5CZFISGIZiWa9OrfK5JYf0JJl+RopMcFz4BdktWI7qkc9Ak16t8DiqYgSDtnMerQN5N7+oSibqnWoFpd0y01OqjKQy0z6Or3jAqGduhwyTHs2/wt+GoBhS2tVmswd3joy0mwhNwAopvVpAupXI6RIDvBEjI5vbMTLDBxlUbQiTEbQmqJsF6nBHVu60inqgp6ZwRvGqpOCZagmmlLLVMUBX0yYoJmVWKfzJiQ6XOpoV5pMUGxMjHKqEPfDAbGQpVRr6JPZmD7OEUB+mXGwKBjWxSqeqdbkRgd+HuRTokW5HDCWcgxG3QYkB0bVBN0Ysx69M1goNXf2AuQ31g1uolmUCP0mQy6kJ0NkcO9EIKOTlWCchnz4VSVq3yCWZekKJhCJLVht5RoDioGuThLcAbKLEYdujDtVMgxGXTolR74fRFykixIiOLEjlClqgoGZAV2c3d36hA9B6NDWkKUEZ0D+EzUNSUaMebAD4hT+ymKgr6ZsQFd2ZoUbWT6shAWYzagX2ZsoIsBADAZ6lePBFOQJVLwboL8his16FCdE6OCdhZrcyxGHdJigmfpPh2UHW8JyiWohwqlFSWRSK9T0TMt+Fc/xFkMyAjRoHCkyUuOhiXI0lD1SrfygStEpcaYAjohxGrSIY85okOeyaBDv8zYgN2D982MQTSf5cJCl6SogKz6S4rmnj7hwqBTMSA7LiArEaNNOvTN5IqxUJdkDfzeY4oC9M8M7ISBSMZfnfwmRrOgRnANEFD7RBl1SAmxAEHnRAtvfIKUqioBnTHWGp2qICcheMtH9ZKtJqTEBO8sZEUBeqZb2Q6FCJ1av19LsMhKMHOWfYjrkRoNcwBWlCkK0CczFioDYmEhPsoQkBSGXZKjuBFvGKmfae/f1HgGnTu9I9uicBFl9H+g1aBTMCArjivGwkRuUlRAU5l1T7UilmldA4b/islvooy6DndWZoPKzieMhNKGySaDivRYzo4OZplx5qBNH9Qpkas0QkWP1OCdyZ6TGMXViiEmIcqIjPjA9x0mvYo8pp0KeXqdGpA0VF2S2faEm+wEC5L9GMSPjzIgN4gnn1D7mA06dPVj+p7uaVbeT4ehxGijX+tRn4zYoFtJS+1Xv/dYbEDGAdJiTUGZbjaSsEcgv1FVBdEd7Dy02peDgoPVrPfrA1VH5CRGcYZikFNVBV2C8IFZr1O4TD6EmAw65AVhfl2zQeWAUIjqmhwNQ4A3ee6WGs0NVcNEYrQR6XH+m+1uNem40jBM9UqL8UvqTr2ufrN7zq4PT5lxZr+koUqMNnCCWRjrlGDxy2z7TokWJFlDY/yB2s6oV9E3w7/7a5gNoZG6ONzx6Yb8qqNBCa1SWFHwCIXVGka9ikzmsA8J6bHmgKTmaElOooWDiSEmO96MmCALovdMiwnaFSTUMqNeRbcApHpxS4o2Io0DQWGlW4rVb4GynukxnNQRpox6Fb0zfL/yp1d6DMwGzooOV4qi/J0a03d/h6rW3wdR+FIUBb3TY3zat8WY9ejKVathKz7K4NdVE73SY5hFJgjwCpBfdXTpOpe+h59YiwFJ0cE9WyIn0cLBxBChqgq6BNHNql6nIDueqzRCjaIoQTXzJiXGyFllIS4jzoz4KP/n21UUoEcQ1WXShlGv+mVPhOwES0A2Aib/SYw2IifRd/cp6XFmpIbYHnrkPatJjxwfTlTrkhTNdEERwGTQ+SzQqqpAnwwG6cNdXnKUX9JQZcSbkRjkY1iRgkEN8quOBiWCbeYsaSOYN3g26BRkcVA6pKTHmoLmwScn0cIZHCEq1mIIir0QVBUB2dCVtNcj1bczWZuSkxgVNO0haSs9zoxYi+/ui/U6BV2Sg/f+jLTTJdk3G9AbdAq6pwbPRBPyrc6JFp/MsjfpVaZxjSDJVhPSYrUPhHZJikY0J8iGPb1ORW8f7z1m0qvoFoSpiiMVR1rIrzoS1NDrFC5dDlPxUQYkBGAGa1vkJEZxlUaIURQlKPYe4CqN0Nc1ORr6AO+FkJsUxb4vTFjNemT4MZWhSa+isw9nYFPg+TLg2SWZ+7BECp2q+GRFV7dUK+tQBNHrVJ9MVOuSzH0NI023lGhNn7+jjDoGxiJIYrTRp/u2dk3h/VEw4ZUgvzLq1XZvSMfUU+EtNwhnA+p1CrKCYKY2eS8YVmtwlUboM+rVgKYzMxtUbtAbZvKStX1Qb0nXlGi2QWEuzmJAqg9mtEYZdcjiXmIRJdlqQoqGg0DxUQa/BnEpOGTHWzRN/RJl1LEeRSCTQafpSsEeaVYGxiJMtxTfrI62mnQ+WUlE7ccnHfK79m4WztRT4S0hyhh0eZs7JXBQOlQpihLQGcpcpRE+suPNsJoCEyDrnsqHsHBTHyjzfaAqxqznQ1eE6JocrfmDe9eUaLY9Eai7RinyuJdP5FJVBXkaTgbJS4mG4u+8jRQUOiVYNLn/To01ce+DCBRl1CHTB5NTu6Va2SYFGY7Wkd+1d8UFV2qEv2DK3axTFWRzmWpIS481+2WjsKYwIBY+FEVBtwDsaZEQZUAKN1cNS9nxlnavWm2rrhwIihgWjWcyx5j1bHsilNmg0+TeNy3WxOe2CJYWY9Kkj7MYdUixcjA6UimK0uHV0ooCTYNsFFq6JEVD1fB2OzHawABZEOKIC/ldeyPugZopS/6TGG0MmhU52QkW5koMcaqqoHOi/wNlDIiFn8RoIxKj/buSrBs3Vw1bqurbDZj50BV5OidGabZawxc58Sl0dE6M6tAgkKIAuUnsvyKZqirISuh4oLVTgoXB+QiXbDV2aAwoNcaEqACnI6bAMepVZGmYOYF9W3DiiB35XYzZ+4EhRQGijcEx2E2+FQwbPKsquJlYmMiMM8Po4xnRh8tKMDMgFoZ8lZu1Kelxpnb1lRQ60mPNPtv3h7MSI4/FqENabMcHEaNNnBkd6Yx6tUMTM9LjzBxEJGTFWzoUHNPrFO6lQfXphDswNhAM4woUWDkJFk2e3+IsBsRH8dksGHHUhfzOYlC9vsmJMuqY2zdCdHRGhhay4i1+Hwgn31BVxa8BKkUBN3YOU1azHul+eMDmUvnI4KvVGskxRsQG2f5U5B9aDN50TorizGhCTkL7VmsoCpAbgBWyFHwMOrVDQYnMODN0fPYntH+1RUqMEdFMgxfxTAadJs9vDJAFL47akd8piuL1qgvmZY0ciqIgJ4CdRv2gNFdphJPMeP89GKUHYGUI+U+XJO1SvDQnK94Cs4GzXCNBWoxJ89UaHFCMXFFGXYfS5Bn1KlKt3EuD6utCe1b+JFmNPluBRqEnowOrx7hKg9wURWnXhs9aph2i0NbRdNTRJh2SuIo1aHHkhQLC6uW+CQxqRBZfDPS0VUacGSYOKIYVg05FVjtuhtuDAbHwZjbo2vVg1VaqyplAkURRFOQkatdmJEQZuEojwnVkECcjzsxV0eSR3Y6+LpuDiHSIWIsB0e1YfR9r0XOGPTWQHmv2alKRxahDAlMF0d+ijDokdWCvOe6VGdwY1KCA8DZIwaBGZNF6oMcbOZzlGpY6aZRPsyXJXOYcEXI13JD3cNlMfRdxMmK1W93FDZ4p2WqEyeB9fVIU+C34T6EhxmxArKXt9zQcRKSmtGfFRSZXadBhjHoVqTFtX0mYEWdiKkVqICuhfe2KXqcgzYu6R/7HJ2cKCG/3TGjPLA8KbVoO9LRVSoyRmxuGKZNB59XNcHtwc/nIYDLofLKkXVUZVI1EWu37E2PWI7EDs9AoPCiK0q7gRGK0kWnvqBFvBpcz48wcRKRGvJ1hryhACgcQqQneBMg6kvqMwlNStBHmdkz6yIgzQ6/jsHkw49WhgPBmTw29TuGDVgRSVQXZ7Yyot1dH8y1ScPNl0MFq0iEhigOKkSInUfuVP5lxXKURqTLivBv0aQpn2ZNbaoz3dSE9loOI1FhKjKnNbZOvJ45QaDLqVcR5kRYxMdoIAwcQqQnxFgP0utYbpDiLgamkqZH6SR/ejwVw5VjwY49BAWHUqzC1cfDG203FKXxkxVug+qmVimcu8rAXa/EulYI3OjEgFlHMBh3SNBwEVBQELOUeBV79prztr096ndKuTX0pPEUZdV71daoKJHODcGqCQacisQ0TNmLMem4QTs3yZuUFg2PUHFVVkNyGzZpTYjjJjJqWHtv2QD3A/X1CBYMaFDBtTSnF1FORy6BTkRnnn4E+DihGBl9s9GVgrs2IpGWqqLRYE1ckRriObLCbGWeGjhs80yG8GRhMsZpYf6hZbRkg5EA0tSSlDQPRQP0Ej45s5kvhry0BeAbpqTkmg86rVK1cpREaGNSggGlr1JPR0cjmi0How1mMOt5ER4hUqwmGNixd9kZGnBkqB4QijtWkR2K0Nqu7chK40ifSxVoMiDG3734n0wd7vFBo8ybQzgFpaknbBhF5D03NMxt0berf4iwGpuGkFiVGGVqcaR9l1HF/TGpRW/dmUVXeH4UKjhaTJqrtTq8/oyqAzVH/OZvD5Tl+6H8D9ZG39nw/O7TwEGXUITnGiOIKu8/+jk4JFm5uGCFUVUFGnBm7Smo0+04OKEau7AQLSqrqOvQdcRYDrO0czKbwkhZrQkWtw6vPxFr0vN+hRkwGHawmHSptLd8/KwqQEMXUm9Q8o15tsS6Z9ConoFGrEqONrfZvWk0UofCl16mINRtQVtP0vXcC6xC1IjnaCJ2qwOmSlt9nNXGD8BDBOxDSxKD7v9Dsu255f4Mm3/PHvSdp8j0UeJ0SLD4Lauh1Spsj9hQeMuMtmgU1EqIMHFCMYEnRRpgMKmx1rtbf3IysBLY/VC891oytRVWQlp+zGn2GqClJVhMqbdUtvichysCHdmpVYrQRlbam75sSuNKZ2iAx2oCd+1t+D1fNU1vERzUf1Ijn/pjUClVVkBJjwt6y2hbfx9TSoYN3sUQU9BKijD7bWyWDucgjTpRRp9nM1Mx4DihGMkVROrQXgkGnIJW5f+lvRn3bNuV1UxQujafmtWWA0Jvc0hS5ElpolxK50ofaIM5sgNrCyJNBp8DKFT/UBi09w7XUVhG5tRaw0KkKg6whhD0HaeK3O08MdBEoDLSUZiwx2oiSqsoWP99SGrPmJFgMLf69nIUfntLjzDhQ3bG0QXqdws3oCBlxZmwr9m52/aGf5X4sdKi0WBP2V7VtZWJilJH5x6lZcRZ9qykWGNSgtoizND9kEM+gBrWBqipIsBib7d/iowxMBUxtEttMylaLUcd7ImqT+lWqChzOpu+PUmKMfD4LIQxqkCY48EtaYBoz8pcUqxGbVcDV/qxBSIkxcZUPeWbXt3Ug+lBMHUSHS7IaoShoU5AsJYYD0tQ8RVEQZ9E3u+8PZ0ZTW+l1KqJNOs9eiG5GvQqzgc+A1DZxUfpm75XimDaI2kivUxFlbNweNRfsIDqc+vdKjMJyW5Ovc5VGaOG/fCIiijh6nYrUGBP2ljV9M9MWGRyQpr+lxbV9dr2b1aTjBuERoKWVgM0x61WU/p0vuqUViFFGvdffz0kokSU+ytBsUCOOM+zJCzFmA0oO6+di2IeRF2LNzbc53AuBvBFr0eNANdsjar8ka9NBDUXhKtZQw3/5RBQ0mMaM/Ck91tzuoIbJoDLlAnmkWE3QqZUtpnk5XHocg2KRINhWIHL1YWSJtxgBNL1ZOAcRyRtNzYLmzGjyRpzFgKYyTKkqB6TJO02tMuTKQ/JGUrSxyfYo3mKAQcc0ZqEkaK7WggULkJubC7PZjJEjR+LHH39s8f3vvPMOevXqBbPZjP79++PTTz/1U0mJyFeijLqg+x+Fr3hLfT7N9mhtgzGKLDpVQbLVu1k93OCZiHwtxqxv8qEdaHnWNNHhOIhIHaVTm055F2vmfhrkHbZH1FEGndpkMJWrNEJPUPzLf+uttzB9+nQsXLgQI0eOxLx583DKKadg8+bNSE1NbfT+7777DhMnTsTs2bNx2mmn4fXXX8eZZ56JX375Bf369QvAGRARUbDwJh1LjFmPvWW1DY61ZcN5q8m7tC8MkIW/lBhTs7lZDxdj1jMPeYTgCkQKJJ2qIMqoQ21d4/7KamIbRG0X3UR9ieYgInnJatajuLLhvRJXaZC3Dg9gGFSFm4ST1+KjDCiqsDU6RqElKHqQRx99FFdeeSUuvfRSAMDChQvxySef4IUXXsBtt93W6P3z58/H2LFjMWPGDADAfffdh2XLluHJJ5/EwoUL/Vp2IiIKLsGW7gVgypdQ5O1eBWa9CrvT6dnguaXgWIbZxL0QIgSvG2mhPXuzuBl0SqP2KMqgg90psDvb972s16GpI/UIADwdHAC70wUR6dB3sh6Fpo5cc73auD3SqwrrUQTqaHt0aMpXXQfrEMB6FKo6ct3NOl2D9qjO5YJOYXsUahQRaXsCaB+w2+2IiorCu+++izPPPNNz/JJLLkFpaSk++uijRp/JycnB9OnTceONN3qO3X333fjwww+xZs2aRu+32Wyw2Q5G4MrLy9GpUycUFhYiNjbWc1xVVbhcDQceFEWBoig+O66qKkQEh18GLY/znHhOPCeeUySdU69ZyxFsNs0a3eDPvE7Bf07BVo823zOG14nnxHOK0HMKtvbo0D6N1yl0zqnHzGUIJrw3Cs1zCrb26I97T+J1CsFzCrZ61Nx9dqRfp2A/p2CrR+5+jddJm+Nmc+t7UAZ8pUZxcTGcTifS0tIaHE9LS8OmTZua/MzevXubfP/evXubfP/s2bNxzz33NDqen5+P8vJyAIDVakViYiJKS0tRWVnpeU9cXBzi4uJQXFyM2tqDKUoSExNhtVpRWFiIuro6z/GUlBRYLBYUFBQ0uCAZGRnQ6XTYvXt3gzJkZ2fD6XRiz549nmOqqiI7Oxu1tbUoKiryHDcYDMjIyEBVVRVKSko8x81mM1JTU1FeXo6ysjLPcZ4Tz4nnxHOKxHP67c4TceBACSorqw45p1jExsahqGgfamsPBrkTExIQbbVi7969Dc4pOTkZFosF+fn5Dc4pPT0dOp0O+fn5Dc4pKysLTqezQT+kqiqysrJQU1PT4DfgdQqNcwo2DoeD14nnxHOK0HMKNu7fjdcptM4p2PDeKDTPKdi4XC5epxA9p2BSVVXF6xTC5xQsdu/ezeuk4Tl169atuZ/aI+ArNQoKCpCVlYXvvvsORx55pOf4LbfcgpUrV+KHH35o9Bmj0YiXXnoJEydO9Bx76qmncM899zR548+VGjwnnhPPiefEc+I58Zy8KWO13RlU5xRt0vM68Zx4ThF6ToemQgiGczo0vQKvU+icU2VtwwGgQJ+TxaB69f5IuU7Bfk61Dgmqc4o26XmdQvCc2nuf7atzau4+O9KvU7Cfk/v+KFjOyX1/xOsUQSs1kpOTodPpGgUjCgsLkZ6e3uRn0tPTvXq/yWSCyWRqdFxVVahq45uppvjyuPsC+uo4z4nn1NxxnhPPSasyenuc58Rz0qqM3h5vaxmt5vZuONhcLlVtjvM68Zx4TpF3Tk23R962Kb7D6xQa52Q1NzfL3rf9ljd4nYL/nKKMTX5tK3hvxLrX8Hj777O91fG65O3xcLpO7S2jv86pcT3ivVEwXqeOHm+Jv1qSZhmNRgwdOhTLlx/MheZyubB8+fIGKzcOdeSRRzZ4PwAsW7as2fcTEREREREREREREVHoC/hKDQCYPn06LrnkEgwbNgwjRozAvHnzUFVVhUsvvRQAcPHFFyMrKwuzZ88GAEybNg3HHXcc5s6di1NPPRVvvvkmfv75Zzz77LOBPA0iIiIiIiIiIiIiIvKhoAhqnH/++SgqKsLMmTOxd+9eDBo0CEuWLPFsBr5r164Gy1COOuoovP7667jzzjtx++23o3v37vjwww/Rr1+/QJ0CERERERERERERERH5WMA3Cg+E8vJyxMXFoaioqMFG4UREREREREREREREFBhGY+ubOAV8Tw0iIiIiIiIiIiIiIqK2YFCDiIiIiIiIiIiIiIhCAoMaREREREREREREREQUEhjUICIiIiIiIiIiIiKikMCgBhERERERERERERERhQQGNYiIiIiIiIiIiIiIKCQwqEFERERERERERERERCGBQQ0iIiIiIiIiIiIiIgoJDGoQEREREREREREREVFIYFCDiIiIiIiIiIiIiIhCAoMaREREREREREREREQUEvSBLkAgiAgAoKKiIsAlISIiIiIiIiIiIiIiADAajYiJiYGiKM2+JyKDGu5gRl5eXoBLQkREREREREREREREbmVlZYiNjW32dUXcyxYiiMvlQkFBQasRH/Kv8vJydOrUCX/99VeLlZaoJaxHpAXWI9IC6xFpgfWItMB6RFpgPSItsB6RFliPSAusR8GNKzWaoKoqsrOzA10MakZsbCwbE+ow1iPSAusRaYH1iLTAekRaYD0iLbAekRZYj0gLrEekBdaj0MSNwomIiIiIiIiIiIiIKCQwqEFERERERERERERERCGBQQ0KGiaTCXfffTdMJlOgi0IhjPWItMB6RFpgPSItsB6RFliPSAusR6QF1iPSAusRaYH1KLRF5EbhREREREREREREREQUerhSg4iIiIiIiIiIiIiIQgKDGkREREREREREREREFBIY1CAiIiIiIiIiIiIiopDAoAYREREREREREREREYUEBjWIiIiIiIiIiIiIiCgkMKhBPiMi2L9/P6666iqISKCLQyHs888/x5dffhnoYlCI+9///ocPPvgg0MWgEMd6RFpgv0ZaYHtEWmA9Ii2wXyMtsD2ijnK5XFi+fDnq6uoCXRTyAwY1yGcURUFFRQVWrVqFwsLCQBeHQlRxcTEeeuihQBeDQlxtbS1eeeUVlJeXB7ooFMJYj0gL7NdIC2yPSAusR6QF9mukBbZHpIWHHnoI8+fPh8FggNPpDHRxyMcY1CCf2rx5M6KiopCenh7oolCISk5ORkFBAaqrqwNdFAphZrMZW7ZsCXQxKMSxHpEW2K+RFtgekRZYj0gL7NdIC2yPqCPcAYyuXbt6jul0ukAVh/yEQQ3SzHfffYelS5eiqKjIc6xLly5QVRVFRUVwuVxwuVwBLCGFgjVr1jSYnVFaWorc3FxkZmYCABwOR6CKRiHkm2++adAW1dXVoU+fPoiPjwfAekRtw3pEWmC/Rlpge0RaYD0iLbBfIy2wPaKOKisr87RF7gDGhg0bkJSUBKA+0MExyPDGoAZ1mN1uxxVXXIFTTz0VV155JSZMmIBvvvkGALBp0ybU1dUhJSUFqqpCVVWUlJRwSSE1smrVKnTu3BnnnXcejj32WHz22WcAgPj4eOzcuRO7d+8GAOj1egBgjkRq0i+//ILc3FxMnjwZxxxzDJ5++mkAgMFgwNq1a1FSUgLgYD2qrKwMWFkpeLEekRbYr5EW2B6RFliPSAvs10gLbI9IC3fffTd69eqF0047DQ899JAneNGrVy9UVlbC6XRCp9NBVTnsHc54danDiouLsXbtWixatAivvvoqevfujcsvvxwA0LNnT1itVpSVlUFEcN111+HII4/EhAkT8Mwzz6CqqirApadg4HA48OKLL+Lkk0/GK6+8gk6dOuHxxx/HZ599BpfLhZSUFHTu3BlAfeT9pJNOwlVXXYV///vfAS45BZv3338fI0eOxOLFizF69Gi8/fbbeOmllwDUt0cpKSkAgN9//x3HHXccJk+ejGnTpjHQSg2wHlFHsV8jrbA9Ii2wHlFHsV8jrbA9oo5asWIF3nzzTdx33304/vjjMWvWLE8d2rRpE2JiYjwrNxYtWoR//OMfWLBgATZv3gwAEJGAlZ20xaAGddiWLVuwY8cOjB49GsceeyymT5+O8vJyLFmyBPn5+Thw4ADi4uLwzjvvYOPGjfjiiy9wzDHH4J133sETTzwR6OJTENDr9fj555+Rnp6OESNG4PHHH4fdbscvv/wCVVVRWlqKPXv2oKysDJdccgmOPPJInHjiibjvvvvw0EMPMThGHl9//TUSEhLQt29f3HnnncjJyfGsHNu5cyfKysrgcrlwzTXX4Pjjj8cNN9yAd955B7NmzUJ+fn6AS0/BgvWIOor9GmmF7RFpgfWIOor9GmmF7RG1lzsYsXfvXhiNRpx22mm49957MXXqVCxYsAAA0L17d1RVVUFEMHfuXPz73//GiBEj8P777+P0008HACiKErBzIG0xqEEdNmrUKBiNRjz//PMAgNTUVERFRcFisSAjIwNJSUmoqqrCypUroaoqsrKycPvtt+OII47AqlWrGuRRpMhUV1eH008/HYWFhaiqqkJeXh50Oh22bduG2tpaxMbGokuXLti0aRNsNhv++c9/YvLkyViwYAEWLFiAAwcOBPoUKMDcNzgTJ07Epk2bUFFRgczMTOh0OhQXFwMAMjIykJqaim3btmHHjh0455xzcMIJJ+Cpp57CsmXLsGvXrkCeAgUB1iPSCvs16ii2R6QF1iPSCvs16ii2R9RR7mCEoijo1auXJ+XdRRddhPXr16OwsBDbtm2D1WqFoij46aefcP3112PGjBl4//33UVVVhQceeAAAuNdGmGBQgzrE3RAsWLAAt99+O2644Qacf/75KC0tRa9evbBp0yaUl5cjOjoaw4cPR2lpKXbu3Ino6GgoioIdO3bAYrEE+Cwo0AwGA0aMGIH8/HxcddVVeOWVV/DDDz8gLy8PZrMZRUVFKCwshM1mg8Fg8CwbPOOMM7B7925s3LgxwGdAgea+wTnmmGMQHR2NM844AwsXLsRHH32EvLw8AAdn/uh0OnTu3BmrV68GAJxyyinYsmULtm3bFrDyU3BgPSKtsF+jjmJ7RFpgPSKtsF+jjmJ7RB3lDoyNGzcODocDjz76KL766is8/vjj6Nq1KywWC3Jzcz0rw7Zs2YLt27cDAOLi4nDSSSdh6dKlAMC9NsKEPtAFoOBXXV0Ni8XS5BItVVUhIjjjjDOwaNEifPbZZ8jKysIzzzyDtLQ0dOvWDdHR0aipqUGfPn2QkZGBsWPH4o477sCqVavQt29fmM3mAJwV+VNdXR0MBkOTr4kIFEXBySefjMTERNx1112YOXMmpk2bhv/7v/9DeXk5MjMzkZWVhby8PMTExGD+/PnYvXs3ysrK0LdvX89NEIW32traVtuLfv36YcGCBbjrrrvwxBNP4LrrrsM999wDAMjLy0NycjK6dOmC3NxcvPzyyyguLkZCQgJyc3PRrVs3f5wGBRjrEWmB/Rppge0RaYH1iLTAfo20wPaIfElRFLhcLsTGxuLWW2/Fa6+9hnPOOQeJiYn4z3/+g9jYWGzcuBFWqxUAcPPNN+Piiy/G0KFDMXjwYPzxxx8YO3ZsgM+CNCVEzXj55Zelf//+cuKJJ8rEiRNl48aNTb7P5XJ5/tvpdDZ47b333pPhw4d73vf777/LuHHjZNiwYTJu3DgpKCjw3QlQwDmdTrn11lvl7LPPljvuuEO+/vrrVj9TUVHRoE6JiPTq1Us++eQTERH54Ycf5LzzzpMuXbpIUlKSzJ492ydlp+DhdDrlX//6l4wePVquuuoq+fjjjz2vHV5X3H+22WyNvmfAgAHy8ssvi4jIpk2b5KabbpLu3btLenq63HvvvT48AwoGrEekBfZrpAW2R6QF1iPSAvs10gLbI9LCunXr5MYbb5RZs2bJl19+KXa7XUQa16FDbd++vcGfn3/+eZk8ebLnz5dccon06NFD0tLSpGfPnvLbb7/5pOwUGAxqUAPuxmLu3LmSnJwst956q8ycOVM6deokffv2lVdffVVERBwOR4vf4Q5u/PzzzzJmzBgpKytr0HkVFRX5+Ewo0H7//Xfp06ePDBw4UKZMmSJ9+/YVq9Uqb731ltTU1IhIy52T+7V9+/bJqFGj5I8//mjw2tq1a6WkpMS3J0EBt23bNhk2bJgMHDhQpk2bJqNGjRKdTicPPPCAlJWViUjjYOqhnE6nOJ1Oqa2tlX/84x/y5ZdfNnh969atUlpa6stToCDAekRaYL9GWmB7RFpgPSItsF8jLbA9Ii3MmTNHzGazjB07Vrp16ybp6ekyZcoUr7/npptukiuvvNLzZ7vdLtu2bZMlS5ZoWVwKEkw/RQ0oigKHw4E333wTF1xwAR588EEAwMUXX4w77rgD1113HU444QRkZma2+B3uVFU7duxASUkJYmNjPa8bjUYkJyf79kQo4L788ks4HA58+OGH6NatG1wuFyZOnIj/+7//Q0VFBS6//PImU5q5uV9LSUlBWVkZ8vPz0b17d8/y5/79+/vrVCiAfvzxR+zduxeffvqp55rfdNNNePHFF2Gz2TBr1qwW82G6XzOZTNiyZQuKiooA1O8HpKoql8JHCNYj0gL7NdIC2yPSAusRaYH9GmmB7RF1VHFxMd555x3ccsstuP3221FVVYXFixfjiiuuQEZGBmbMmIGEhARP29IUp9MJnU6Hvn37YtWqVZ7jBoMBXbp0QZcuXfx1OuRH3BmFGtm2bRsKCwsxYMAAz7GuXbtixowZSE9Px9VXX93m7zIajUhLS0NJSYkvikpB7IMPPkDPnj09N7Y6nQ7PP/88cnNz8fzzz2PdunUADm4235xdu3bBZrMhNzcXAFq8sabws2TJEmRnZ6N///6ejcHuu+8+HHvssVi0aJHnhsXpdDb7HSKCyspKmM1mpKWlAeDGYJGG9Yi0wH6NtMD2iLTAekRaYL9GWmB7RB21detW/Pzzzzj//PNhMpmQmJiIiy++GP/+97/x2GOPYfHixQBablt0Oh2A+iAb26DIwVaCGunRowcqKytRWFgIoH7TMAAYNGgQ7rzzTnzyySdYsmQJgJY7JgDIzs7Gs88+i8TERN8WmoKG+6a3W7du2Lp1K4D6GxKHw4HY2Fhcf/31qKiowMKFCz2vtSQnJwf//Oc/PTfJFBnc9ahnz574888/ARxcSWa1WnH55ZcjPT0d9913H4CDNzFNURTF85lRo0b5vvAUNFiPSAvs10gLbI9IC6xHpAX2a6QFtkekldraWqSnp2Pz5s0ADtatGTNmoG/fvnjuuefw+++/t/gd7oDa+PHjcfrpp/u2wBQ0GNSIQO5/7E1xNx5nnnkmXnjhBQD1y7VcLhd0Oh1Gjx6NE088ETNnzgTQcscEAIMHD0Z2drZGJadQ4L7p7datG+x2Oz755BMAB6PqZ555JkaOHInvv/8eP/zwA4Dm66T7+LRp03xdbAoy7nrUo0cP6HQ6vPjiiwAO1qOjjjoKp512GjZt2oRPP/0UQOv16PLLL/d1sSnIsB6RFtivkRbYHpEWWI9IC+zXSAtsj0grnTt3hsvlwtq1a2Gz2aCqqmdy9dy5c/Htt9/im2++aXHVmLvenXHGGZgwYYJfyk2Bx6BGhDk8B93hnYq7Y5owYQIKCgrw1FNPATi4IiM9PR3nn38+du3ahe+++85PpaZQ4q5TY8aMgYjgk08+QXV1NXQ6nadjuvjii/HXX39hw4YNAJpfRshlg5HLXY8GDx6Mnj174p133kF5eXmDenTaaafBarXi22+/bTG/JutR5GI9Ii2wXyMtsD0iLbAekRbYr5EW2B6Rt5oKarlcLuTm5uLEE0/Eq6++im3btgGon1wtIjjuuONw/PHH46233oKqqq2mw6PIwqBGBHnqqadw2WWXYfbs2Vi5ciWAxp2Hu5EZMWIEzjjjDNx9992oqamBwWCAw+EAAAwcOBAAUFZW5sfSUzAoKyvD3Llz8frrr2PTpk2eDuXQjsVdpwYMGICTTz4Zy5Yt88zM0Ol0no4pLS0Nq1evbvR5Cn+lpaW47777MG/ePKxatQrV1dUAGqazc9ejLl26YNy4cdi8ebNn9Zi7Hg0aNAjp6enYuHEjFEVhPYowrEekBfZrpAW2R6QF1iPSAvs10gLbI9LC6tWr8d5772Ht2rUAWg5ezZo1C7t27cLbb7+NqqoqAPCMQV5++eX46quvUFxczL1WqAHWhgiwb98+jB49Gg888ADq6urw7LPPYsKECXjttddQW1sL4GAww93IZGZm4rLLLoNOp/NsDK7X6wEAGRkZ2LdvHzukCPPKK68gOzsbL774ImbMmIHRo0fj9ttvB9A4z6q7btx1110wGAx44YUXsGXLlgbvy87Oxp49e5r8PIWvd955B507d8aHH36IhQsX4owzzsBll10GoHE6O3c9uuqqq9CzZ0+88MILWLduXaN6lJ+fD4D1KJKwHpEW2K+RFtgekRZYj0gL7NdIC2yPqKMqKiowceJEHHfccbjlllswePBgXHXVVZ7gxqFjiYemMbv++uvx2GOPeSZhGwwGAPX1LiUlxdMeEXkIhb3FixdLbm6ufP311yIiUlRUJNdff73ExsbKiy++2Oj9LpdLRERqamrkueeeE0VR5M4775SNGzdKRUWF3HPPPTJ48GDZu3evP0+DAmjfvn1y9NFHy6233iqFhYVy4MABmTFjhlitVrnvvvua/IzT6RQRkRdeeEF69+4tp556qoiI2Gw22bRpk3Tt2rXJ+kfhq7KyUsaPHy9XXXWVFBcXS0VFhSxcuFDi4+PlmmuukcrKykafcdejTz/9VIYMGSIDBw6UPXv2SFVVlWzbtk369esnc+fO9fepUACxHpEW2K+RFtgekRZYj0gL7NdIC2yPSAuPP/649OnTR7799lvZsmWLvPPOO5KTkyOjR4+W/fv3i8jBenO4Xr16ydFHHy3Lly8XkfrxyZkzZ8qgQYOkqqrKb+dAoYFBjQgwdepU6dWrV6Pjo0aNkpEjR8rKlStF5GAw43APPfSQ5OTkSGJioowYMULMZrPMnz/fp2Wm4PLTTz+JTqeTZcuWeY7t379fZs2aJUajUVasWNHoM4fWp5deekmSkpIkNzdXzj77bMnLy5Phw4fLtm3b/FJ+Cg7btm0TnU4n7777rueY3W6XF198UVRVlUWLFkldXV2Dzxxaj7755hvp2rWrpKWlyZgxYyQvL08GDBggv//+u9/OgQKP9Yg6wv0AxbN27BsAAEa9SURBVH6NOsJdF9geUXvV1NR4/pv1iLTAfo3a4/AxILZH1BFOp1PsdruceuqpMm7cuAaBi5dfflm6d+8ukydPFpHGdc/hcIiIyKpVq+TYY48Vi8UikyZNkilTpkhMTIw8/PDD4nQ6mx23pMjEtV9hTP5OKZWeno7a2loUFxcDAOx2OwDgoYcewr59+/DWW2+htrbWk+PQvRTM/flbbrkFS5YswaOPPopzzz0XW7duxQ033BCAM6JA2bdvHxITEz0pyAAgMTER119/PYYPH4677767wR4rDoejQb7Eiy++GF999RWuvPJKWK1WTJ06FT/++CO6dOni1/Mg/5EmNgErKSlBZmYmKisrPccMBgOmTJmC0047DXPmzMGWLVs8rx1ej44++mgsX74c999/P/r06YMbbrgBa9asQa9evXx7MhQwdrsdmzZtalCfWI/IW7W1tfj444/hcDg8S9zZr5G3bDYbXnrpJRw4cMBTF9gekbdqa2tx66234v7774fNZgPAekTeayoNNPs1ao/D9zhge0QdoaoqDAYDCgoKkJeXB1VVPX3dueeei0svvRSvvvoqvvnmGyiK4tmjxel0elKbHXHEEXj33Xdx2223QUTw119/4e2338bNN98MVVW5qTw1FNCQCmlm1apVcvPNN0tRUVGj1xYuXCh5eXny9ttvN3rtmmuukd69e8uXX37Z4PjatWtl06ZNIlIfmafI8Morr8iDDz4ozz//vPzyyy+e42VlZRIdHS2PPvqoiDSMqi9dulR0Op289957ItJwGeGff/7Z6O9obpkhhY/nnntOpkyZIjfffLO8/vrrnuMOh0PS0tLkxhtvlNraWhE5WB/++OMPMRgMnjp2qN9++63RMc7QCH9ff/21GAwGGT9+vOzcudNznPWIvDF37lxRFEUmT57sWe4uwn6NvDN//nxJTU2V3NxcWbBggec42yPyxiOPPCIxMTGiKIqceOKJnuOsR+SNp59+Wk455RSZOHGiPPjgg57j7NfIG88884xMnTpV5s6dK999953nuMPhkNTUVLZH1KoDBw7IvffeK4899ph89913DVKT3XDDDdKpUyfPn92rMDZt2iRHH320HHnkkY2+r7a21lPn3FiHqDUMaoS4mpoaefDBB0Wv14uiKPLcc895XnN3PqWlpZKeni6XXnqpFBcXi8jBQMWuXbskOjpa5s2b5/nc6tWrRafTSe/evf14JhRI33//vQwYMECysrLkxBNPFJPJJOnp6fLTTz+JSH1nMmXKFOnatavYbLYGny0vL5dx48bJUUcd5TnmcDjkzDPPlOzs7AbBEQpv33zzjfTt21cyMzPlggsukO7du4ter5cPPvjA85477rhDUlNT5Y8//vAcc9+sXHrppY3anQsuuEAURZHPPvvML+dAweP9998XRVHEarU2WurOekSt+eyzzyQvL0/S0tJk3rx5UlJS0ug97NeoJe42Ze7cuZKXlyfz58+XX3/9VQoKCkREPG0S2yNqzSeffCJ5eXmSmpoqc+fOlRtuuEHS0tIkPz/f8x7WI2rNjh075JhjjpHOnTvLNddcI8cdd5woiiKPPPKIpx9jv0at2bFjh4wYMULS0tLk9NNPl/T0dImLi5M333xTKioqRETktttuY3tELXr77bclNjZWhgwZIj179pSUlBQ577zzPK+/8cYbkpCQIM8++6yISIPnuMcff1wyMzNl6dKlIlJft1atWiWJiYnyxhtv+PdEKOQx/VSIe+utt/Doo4/i/PPPx/jx43Hvvffir7/+AlC/9MvhcCAuLg7XXXcd3nzzTXz55ZcA6pcQ2u12dOrUCccffzw+/fRTz3cOGTIE//jHP3D++ecDaDqNDIWP77//HtOnT8fgwYOxfPlyLF68GF9++SVycnIwdepUAPXLUi+66CIUFBRg3rx5AA7WC4vFgnHjxmH79u3YtGkTAECn0+G4446DqqqIjo4OyHmRf61cuRJTp07FsGHD8OWXX+Kll17Ct99+izPPPBP33HOP531TpkyBiODJJ5+Ew+EAUF+/RARnn302du7ciZ9++snz/okTJyI1NRUpKSl+PycKrKKiIkyePBknnngiZs+eja1bt3peu+SSS1iPqEkigjfffBNjx47FqFGjsHr1alxzzTVISEho9N5JkyaxX6NmKYqCsrIy/Oc//8Fll12Gq666CoMGDUJGRgYAeFK8sD2i5tTV1eGmm27ChAkTcOqpp3ruufPy8lBbW4sdO3Z43sv7I2rNu+++i+rqarz33nt4/PHHsWLFCkyfPh1PPvkkjEYjAPZr1Lr33nsP1dXVWLx4Md5991388MMPOP3003H11VfjvffeAwBceeWVcLlcbI+oSVVVVVi0aBEuuOACLF26FD///DPuu+8+LF26FFOnToWI4JhjjsHIkSPx9NNPo7KyEnq9HnV1dQCAI488ElarFWvXrgUATxp8h8OBdevWNZlej6hZAQikkAbckfIVK1bI2WefLfv27ZMtW7ZIUlKS/Otf//K8fujS0W7dusno0aNl9erVDb7jkksukTFjxkh1dbVnWRhTTkWO++67T4YPHy6//fabp064XC554oknJCYmxjNzp6SkRK677jqJiYlptNHX22+/LSkpKbJr1y6/l5+CwxdffCF33XWXbNiwocHxW265RUaMGCFVVVUiUj9LY86cOWIymeR///tfg7bmyy+/lOTkZPnhhx9EhMtNI5X7uj///PNyxBFHyJ9//ilms1lmzpzpWdbscDhYj6hZX3/9tXTr1k0mTZrU4PhPP/0kxcXFnvaouLhYrr32WvZr1Kyvv/5a8vLyZM+ePSJS3548/fTT8t5778m6des875s9ezbbI2rShx9+6LnHdl//zZs3i6Io8vHHH3ve53Q62a9RsxwOh4waNUpGjx4tIgdnPS9cuFByc3M9qxH379/P5zVq0ejRo+Xkk09ucKyoqEg6deok/fv3l1WrVokI+zVqXmubyb/44osiIvLiiy9Kp06dZMaMGSLScLVG165d5c4772zw+UPTDRO1FVdqhCj35jjHHXcc3nvvPaSkpKBTp06YNm0annrqKfz6668A6ldruCOiTz75JLZu3YpZs2ahrq4OiqKguLgY69evx1FHHQWLxeLZnMdgMATmxMhv5O+ZO5dccglefvllDBw40LNZk6IoSE1NhYjAarUCABISEnDdddehc+fO+Oc//4n169cDqN+obsuWLUhOTobZbA7Y+VBgHXHEEbj99tvRp0+fBsc3b96MMWPGYO/evQDqZ7Zee+21GD58OG6++WZ88cUXAOrr45YtWxAdHY309HQAjTeuo8jgvu7V1dXo0qULunXrhquvvhoLFizAmjVrANTPLvznP//JekQNuPu14cOH45///Cdef/11rF+/Hq+88gqys7Nx7rnnol+/fpgyZQrKy8uRlJSEG264ATk5OezXqElWqxXbt2+H3W7HihUrEBsbi3vuuQdXXHEFhg8fjvnz56O2thYzZszAsGHD2B6Rh7s9OuOMMzz32Ide/86dO2PJkiWeP6uqiqlTp2LkyJGsR9SI3W5Hly5dUFxcjP3793tWin311VcYPXo0CgoKANRvDD5t2jR06tSJ/Ro1Ul1dDZ1OB5PJhOrqagD1GzQnJyfjwgsvxMaNG/Haa6/BZrNh+vTpvM+mJrW2mfyDDz6IXbt24dxzz8VZZ52FJ554AsuWLfO0W1u3boXD4fDUIffnc3Jy/H4uFAYCFk4hTblXZPz111/Sp08fOfPMMxttsiNycNPwrKwsmTRpkowcOVJyc3Plxx9/9HeRKcgcPsvi7bfflpiYmEazeX755RdJTU2Vvn37ys033yz33nuvJCUlycyZMzlTgzxt0YoVK6Rnz54SFRUleXl5YrVa5R//+Ids3LhRROrbqkGDBkl6erpccsklcuutt0pKSopMmzaNK8UinLsdeeqpp2TgwIEiUj+jPiMjQy677DKpq6uTzZs3i0j9vlCsR9SULVu2yFFHHSWKokheXp7ccccdsmjRIrnxxhslJiZGJkyYIHv37hUR9mvUvF9++UUGDhwod9xxh5xzzjly++23y44dO2T37t1yySWXSNeuXeXJJ58UEfZr1HZ1dXXSv39/Oeecczwrx9zy8/NZj6hJL730kvTt21c6d+4ss2bNkh49ekhMTIz06tVLoqKi5KSTTvLsh7h69Wr2a9SkG2+8UfLy8mTZsmUicvDZ7dxzzxWdTidDhw71bBy+e/dutkcRbOHChfL111+LSMNVFg6HQ9LS0lrcTP6RRx7x/Pncc8+V+Ph4mTZtmnz00UcyefJkycvLa7BnC1F7MagRhl599VVRVbXB5ryHphXauXOn3HLLLXL++efL9ddf70nnQSRysK7ceeed0qtXLxE52Em5//+HH36QadOmybHHHiv9+/eXp556KjCFpaB1ySWXyGWXXSbr16+XzZs3y/Lly0VVVZk+fbocOHBARES2bt0qs2fPlgkTJsiIESNYj6iBhx56SCZOnOhpk1577TXR6/UyePBgiY6Olu3bt4tI/RJo1iM6nNPplDfeeEMmTJggS5Ys8aTXFBH54IMPRK/Xy6uvvuo5xn6NmlJZWSknnHCC9OjRQ9LS0uTXX3/1vFZYWCgnn3yyjBo1Snbs2CEi7Neode62aOrUqdKlS5cmX2O/Roc6NK30hg0bZOHChTJixAiZMGGC/Pnnn7Jnzx755ZdfJDY2Vi6//HIpKioSEZHvv/+e/Rp5uJ/j9+/fLykpKTJu3DhPCqlNmzbJkUceKY899phYLBZ5++23PZ9jexR5Nm3aJOPHjxdFUTzjQSL1bZG7n7rjjjta3Ez+0M85HA658cYbZeDAgdKjRw8ZPny4JyU+UUcxqBGGDhw4IGPHjpWBAwdKaWmpiNRHVg99oHcfI2rOscceKxdffLGINJ8rs7S0lLN9qAF3O9PUSrHrrrtOunXrJuXl5Q2O22w21iPycD90PfroozJs2DAREamurpbrrrtOVFWVhIQEefnllxvsGSXCekSNFRQUyJo1axocs9vtUl1dLUOGDJEJEyY0+gz7NXJztzGLFi0SRVEaDEC7+7jHH39cUlJSZNOmTQ0+y/aIWvPoo49KTEyM/PzzzyLS9L026xE1ZcOGDZKQkOBZleGuI3fccYdkZmbK7t27G7yf/Rq5uZ/TXnnlFRkwYIBERUXJ0KFDRVVVzz3RmDFjZOrUqY0+y/YoMhQWFsqpp54qXbt2lYkTJ4rVapV58+aJSMPxwz///FNSUlLkhhtuaHDc5XLJf//7X4mKivIEzdzHa2tr5c8///TfyVBE4J4aYSguLg533HEHNm3ahOeffx5bt27FM888g9dff73B+9w57YgO5XA4UFFRgT/++ANDhgwBUJ8rc9euXZ68rPJ3nuC4uDjm0aQG3PvymEymRq+lpKRg3759qK2tbXDcaDSyHpGHqtbfmlRVVaFfv3544oknkJ2djU8++QQ33XQTKioqUFVV5WmH3P/PekSHy8jIwIABAwDU5xMXERgMBlgsFpjNZtjt9kbtEfs1cnPXg0suuQQTJkzAjh07sGjRIgAH957LyclBcXFxozrD9oia4+6zunTpgrq6OuzevRtA03npWY+oKe69MaKjowHU928A0L17d+zZs4f9GjXLfY89adIkfPDBB5g1axZGjBiBJUuW4OOPPwYA1NbWNtojEWB7FCmioqIQHR2Nm2++GXPnzsVZZ52F+++/HxUVFdDr9XA6nQCAvLw8zJgxA8888ww+//xzzz6+iqLAarUiKiqq0XebTCZ069bNr+dD4Y9BjTCkKAqGDRuGSZMm4d5778XYsWMxbdo0z000UUv0ej127tyJ4uJijBo1CrW1tVi4cCF69uyJGTNmeDYSJ2qJ+wELqH+A37BhA/73v//hn//8J1JSUgJYMgp27r4qMzMTL730Eu6++25cffXV+OKLL/DII4/g9NNPxy233ILt27cD4AaF1Dqn0wlVVaEoClwuF1auXIlt27Zh/Pjx3DCVmqUoiufhfebMmRg0aBD+7//+Dzt37kRtbS1KSkrwyiuv4LTTTkP37t0DXFoKFe4+65hjjoHRaMTOnTsBwFPXiFoTFRWFHTt2eDaa1+l02Lt3L95++22cddZZyMvLC3AJKVgdes/sHpR+6qmncNJJJ8HpdGLZsmXYvn07evXqFcBSUiBZrVa89NJL+Oc//4mMjAz84x//gMFgwC233NLgfaqqYurUqRg5cmSrm8kDfF4j3+FU/RDicrk80fWW1NTU4Pvvv8fmzZtRUVGBM888E6tWrUJycrIfSknBTkRa7VS++eYb5OTkYNOmTbj66quxdu1azJo1C7fddpufSknBrrV6pKoq7HY7du3ahbVr12LOnDkQEUycONGPpaRg11S/5q5XEydORHl5OY488kgMGTLEMzP64YcfxvLlyznThzxaa490Oh3sdjsKCgrw66+/4p577kFeXh7Gjx/vx1JSsGuqHrlXHw4dOhSPPPIIbr31VgwaNAgDBgxAVVUVtm/fjhdeeIEP6+TR1uc1l8uFvLw8fPrpp7jhhhs8dY0IaLlfGzNmDMaOHYuHH34YX3zxBY444gh88skn2Lt3L55++mm2R+TRlud+p9OJTZs2YceOHbjzzjsxYsQIHH300X4qIQUjs9kMp9MJnU6HE044ARdddBGeeOIJXHvttejXrx8cDgf0ej2sViveeOMNnHrqqZgyZQpOOeUUpKen44UXXsCFF16IjIyMQJ8KRQBFOH0/6IkIXC6X52a3srISVqu12Zvmzz//HBMnTkRGRgZeeOEFDBs2zN9FpiDk7pha4m4Opk6dimeffRZA/cDiM888A6vV6vMyUvBrSz0C6tupyy67DEVFRfjtt99w9tln44knnmhyKSqFn9Yeotpajw5/X1sezihytLUeVVRU4MILL8SBAwewZs0anHPOOXjqqafYHhGA1uvRoe1OZWUlXnrpJWzduhVmsxm33347748ihFb92qGOPvpojB8/Hrfffjv7NgLQej1yv15YWIinn34aS5cuBQAMGjQIDz/8sCclFUW2trZHIoIff/wRs2fPxhdffIFzzz0XCxYsgMVi8UMpKVT8+OOPuPLKK5GSkoLPP//c0x+6xyO3b9+Ot956C9999x0KCwsxZcoUTJ06NdDFpgjBoEYAFBQU4OOPP0b37t0xdOhQxMfHA2h9Zs/27dtxzTXXIDc3F08//XSz7ysrK8N3332HcePGaV10CiKrV6+GqqrIyclBUlJSi+899EHszTffxPfff4+bb74Z2dnZTb7/7rvvxrfffosnn3ySy0/DnC/qkft9n3zyCTZv3ozTTjsNPXr08Nk5UOCtWrUKdXV16Ny5Mzp37tziew+tR2+99RY++ugj/N///R/69+/vj6JSEPNle/Taa6/h999/x6RJk9ivhTlf3R+1dQY+hQdf9mvuAcfq6moGV8OcL/s1oD5Dg81m84wnUHjy5XN/WVkZvvzyS/Tp04fPa9Qkl8uF+fPn47bbbsMbb7yBs88+G06nEyLSYJ9eu90Og8HAID35l+/2IKemPPjgg2I2m6VXr14SHR0tgwYNkpdeeqnVz1111VWiKIqMGzdO1q5d64eSUrDatWuXHH/88ZKSkiIZGRnSrVs3eeutt8Rut4uIiMvlavJzmzZtkuHDh4uqqjJr1iypqalp9B6n09nid1D48GU9Yv2JHPn5+XLiiSdKfHy8ZGdnS2xsrDz66KOyb98+ETnYphzuzz//lCFDhojBYJCZM2dKZWWlP4tNQYbtEWnBl/WIIgf7NdIC2yPSAusRBZq7jm3evFlOOeUU6d69u9jtdvnxxx/ljTfekKKiogCXkCId99TwozVr1mDRokWYP38+TjvtNNTU1OC6667DbbfdhsTERJx22mlNzgLbvXs36urq8MYbb+Css86C0WgM0BlQMLjtttvgdDqxZMkS5OfnY/Hixbj00kuxd+9eXH/99VAUpdES+erqatx3333Izc3FCy+8gH79+jX53e66x+h6+PNlPWL9iRz//ve/UVNTgxUrVsBut+N///sf7rvvPuzatQtz586FqqqN6pHNZsOiRYvQvXt3LFq0iCs0iO0RacKX9YgiB/s10gLbI9IC6xEFmrtu9ejRAxdddBGmTZuGMWPG4KeffsKQIUNw4oknBriEFPECF0+JHO7o5vPPPy/x8fGydetWz2sbNmyQs846S9LT06W8vLzZ76irq/N5OSn4/fHHH5KcnCwLFy5scPycc86Rfv36yRtvvCEiDWeRuetfUVGROBwO/xWWghbrEXWUy+WSPXv2SF5entxxxx0NXrv55pulW7du8thjj4lI0/WotLS02dmuFFnYHpEWWI+oo9ivkVbYHpEWWI/IH9rSbzmdTikuLpb58+eLoigSGxsrc+bM8UPpiFrHxLB+4I5uFhQUIDExETExMZ4Nmfv06YNbbrkFIoI77rgDQH3OusMdmquOIldUVBRKSkrQp08fAPV5VAHggQceQEJCAp599lkUFRVBVVVPPXLXv+TkZK83MKTwxHpEHaUoCiwWC4qLiz0zwNz16MYbb8Tw4cPxxBNPYPfu3U3Wo7i4OOamJwBsj0gbrEfUUezXSCtsj0gLrEfUEdLK1slOpxMA2txvXXrppbjxxhtxxRVXYM+ePZgxY0aHy0ikBd55+YG7kznppJOwfft2bN26FYqioK6uDgAwePBg3HDDDXj66aeRn5/foGMiOpTNZsOAAQPwxhtvAAAsFgtEBD169MDEiRORn5+Pl19+GUDbOyiKPKxHpAWdTodBgwbhlVdeAXCwHmVlZWHixIkwGo145JFHALAeUfPYHpEWWI9IC+zXSAtsj0gLrEfkjVWrVuGrr77Czp07AbScflVEPEGvt956CxdeeCHWrVvX5Hvd6fEvvfRS/PTTT3j22WcRFRWl/QkQtRNbP401FRF1dzLZ2dkYOHAg7rnnHgAHV1+YTCZMmDABubm5ePDBBwEwBzQ1rXPnzujWrRvWrFmD9evXAwAcDgcA4KKLLkJGRga++uorFBYWBrKYFORYj0gL0dHRGDlyJP744w98/fXXAA7WozFjxuCII47AqlWrsGXLlkAWk4Ic2yPSAusRaYH9GmmB7RFpgfWI2qKgoACjR4/G+PHjcdFFF2HAgAF47LHHUFRUBKDpLDCKomDLli0YOnQoJk+ejO7duyMvL6/J73ePZZ511lkYOnSo706EqJ0Y1NDAzp078dtvv6GmpqbFYER6ejomTZqEZcuWYfny5Q1Wa+Tm5uKoo47C+vXrUVZWxqBGBNq7dy9sNluzr7tcLuh0OlxwwQXYtm0bPvjgAwCAwWCAw+FAbGwszjzzTHz33Xee5akUeViPSAs7duxAeXm558+HB+xdLhcURcH48eNhNBrx4osvAjhYjywWC0466STs2rUL+fn5fi07BQ+2R6QF1iPSAvs10gLbI9IC6xFp5d///jdqamqwYsUKvP/++/jXv/6F++67Dw888IBnlcXh/Z3NZsOiRYvQvXt3rF69Gvfccw+io6MDdAZEHcOgRgfU1tbisssuw9ChQ3H66afj2GOPxXPPPdfke0UEer0e48aNw9FHH41rr70WQH3HBAAxMTFIT0/H/v37ERcX57dzoMD74YcfcMQRR+DUU0/Fsccei3feeQcVFRUAGkbW3VHyc845B0OGDMEHH3yAZcuWNXjfOeecg4qKCvz1119+PgsKNNYj0sJPP/2EYcOG4dRTT8Xw4cMxZ84cHDhwAIqiNLghdtej448/Hscffzy+/fZbvPrqqwAO5mg999xzceDAARw4cMD/J0IBxfaItMB6RFpgv0ZaYHtEWmA9Iq2ICPbu3YslS5bgxBNPxMCBAzF8+HDMnDkTl19+ORYvXozHH3/c895DP2cymTBjxgy8/vrr6N+/f6BOgUgTDGp0wL333otvv/0Wr7/+Oh599FGMGDECV199NRYsWIDKykoABxsQ98qLPn36YPr06dizZw+uv/56z/tsNht27dqFnJwc2O32wJwQ+d0PP/yA8847Dz179sS1116LpKQkTJ8+Hddccw2Axvkx3Q9V9957L2w2G+bOnYv9+/fDaDQCAL766ivExsYyMBZhWI9IC5s3b8aFF16I3r1744EHHsDRRx+Np59+Gueffz7q6uoarSB0P1TdcMMNyMnJwT333IOCggKYTCYAwMqVK2G1WlmPIgzbI9IC6xFpgf0aaYHtEWmB9Yi0pCgKLBYLiouL0a9fPwAHN5O/8cYbMXz4cDzxxBPYvXt3k5vJx8XFcS8WCg9C7VJcXCx5eXkyc+ZMcblcnuNXX321dOrUSV5++eVGn3G/r7a2Vl588UVRFEXGjh0rjz/+uMycOVMSEhKa/ByFr9tvv1169+4tJSUlnmNPPvmkqKoqCxYsaPGzzz//vHTv3l2OP/54ef/99+Wbb76Rk08+WcaPHy+1tbW+LjoFEdYj0sLcuXMlKytL8vPzPccWL14sFotFbr31VikrK2v2s5999pkMHDhQ+vfvL/PmzZPly5fL+PHj5ZhjjmnxcxR+2B6RFliPSAvs10gLbI9IC6xHpLWKigoZNWqUjB8/3nPMPeb48ccfS69evWTatGkBKh2RfzCo0U7V1dWSlJQkzz//vIiI2Gw2ERGpqamRYcOGyZgxY2TDhg0iIuJ0OkVExOFwiMjBhub111+X008/XQYPHiy9evWSN954w9+nQQFUV1cnl1xyiZxyyikNjldUVMiNN94oiYmJsnbt2gavOZ1OT32qq6uT7777Tvr37y85OTmSmpoqxx13nOzcudNv50CB425HWI+oI1wul6cuzJw5U/r27dvoPY888oiYzWb573//2+D4ofVIRGTbtm0yduxY6dq1q2RnZ8txxx0n27dv92n5KXi4XC62R9Ru1dXVIlJfH1iPqL1KS0tF5OAzF/s1ao81a9bIvn37RETEbrezPaIOYb9GvuJyuWTGjBnSrVs3+eqrr0Skvs0Sqb+vmjJliowYMUL+/PPPQBaTyKcY1Gin3bt3y5FHHimXXnqp1NXVicjBBuTtt9+W7OxsmT17dqPPHbqq49DvovB24MABEZEGD0oiIhdddJEMHTpUiouLGxzPz8+X3NxcufTSS5v8vkO/p7y8XHbu3Clr1qzRttAUdAoLC2Xv3r2eNseN9Yi8sXPnTvnxxx9l7969DY7feuut0rVrV09A/lADBgyQU045xfOQfyj34JFIfYC/uLhYfv/9d+0LTkFl/fr1ctNNN8lff/3V4DjbI/LGL7/8IuPGjZPnnnuuwXHWI/LG2rVr5cwzz5SJEyd6JpqJsF8j76xdu1aOPvpo0ev1DQYB2R6RN7Zu3Sr//e9/Zf369Q2OX3jhhaxH1Gbbt29vsCrw8HFEd7348ssvpU+fPg3qj3us4LXXXpP09HRZsWKFH0pMFBgManTA5MmTZeDAgfLTTz+JSMMO58QTT5QTTjihwcP+p59+KiNGjPBE1A+9YabwVFNTI9dee63k5OR4rvuhsy6WLl0qiqLIypUrRaRhZ/Xkk0+K0WiUXbt2eT5nt9vl7rvvlvfff9/zvqYCZRReqqqq5PLLL5devXpJ79695YQTTpD33nvP8zrrEbVFZWWlTJkyRTp16iSdO3eWtLQ0efjhhz2vb9y4URRFkZdeeslzzN1W/fe//xVVVT2zgOrq6sRut8vNN98sTzzxhOf9rEeRwel0SlZWliiKIo8++qi4XC7PtWd7RG1RUVEhF110kSiKImeffXaDeyQR1iNqG6fTKXfddZeYzWY599xz5dZbb5UdO3Z46hH7NWqLQ9ujwYMHi6Io8uqrr3peZ3tEbVFdXS2XX365pKamSufOnUWn08m0adM8kxs/++wz1iNq1Y8//ihDhw6VPn36SI8ePeShhx7ypCxr7vpfc8010qNHD3nllVdERDwpyWw2m5hMJvnggw/8UnaiQGBQowN+++03UVVV7rnnHs+yefdqjU8++UQURZE//vjD8/5Zs2Z5BgAo/K1du1bGjh0rFotF4uPj5brrrmv0nr1798qIESNkzJgxjV77448/pHPnznLbbbd5jm3ZskUURZHx48d7lthTeFuxYoUMGDBAjjrqKHnxxRdl9uzZcswxxzS48d29ezfrEbXok08+ka5du8rIkSPl3XfflbfeekuuvfZaURRFfvnlFxGpf4CaMGGCDB48WPbv39/g85WVlTJw4MAGs4BsNpsoiiJ9+vRptOqDwpfT6ZTq6moZN26cDBs2TBISEmTjxo2e1/fs2cP2iFr0n//8RxRFkWOOOUaWLFniuYc+FO+PqC1Wr14tQ4cOlVdeeUXKy8sbTBhzD/6wX6OWPPDAA2I0GmX48OGycuVKWbdunSQnJ8udd97pqUPs16g1NTU1MmXKFBkwYIB89tln8tVXX8ncuXNFURRPUCI/P5/1iFq0adMm6datm0yaNEk+/PBDufTSSyU3N1dOOukkzzjjodxB+k2bNsmYMWOkW7duDfaQWrp0qSQlJckXX3zht3Mg8jcGNTpo0qRJkp2dLUuWLBGRgzfQxcXFkpyc3GDj74qKCi79ihAOh0OefPJJsVqtMmfOHLnsssskJydHvv76axE5uCTQ4XDIokWLRFEUefvtt0Wk4YbyJ510klx55ZXicDg8x1966SUugY8QxcXFct5558lZZ50lmzZt8tSBn3/+WXJycmTq1KkiUh9MZT2i5uzfv19uueUWueiiixqkUygqKpIuXbrI/fff7zm2YsUKMRqN8sgjjzRKc3bVVVfJ6NGjpaamxvPa4sWLm0zrQeGtoqJCUlNT5Y033pC+ffvK5MmTPQ9b7NeoJQUFBTJhwgQxGAxSWFjY4LVDZyDW1dWxHlGz3Nf87rvvloEDB0pRUZHntcLCwgaDP+zXqDkff/yx9OvXT5566qkGg8aDBw+WCy64wPNn9mvUmjVr1kh8fLwsWrSowfEBAwbINddcIyKsR9S6uXPnSlZWVoPAxOLFi8Viscitt97aIB3V4T777DMZOHCg9O/fX+bNmyfLly+X8ePHyzHHHNPi54hCHYMaHVRaWirZ2dly+umny7p16zzHly9fLrGxsZ6o6OF7KVD4+/HHH2XZsmUiIvLDDz/IkCFD5NRTT/W87q4Te/bskfPOO0+ys7Mb3bT069dPLrvsMv8VmoLORRdd1Gh2RUVFhZxwwgly7rnnSk1NjYiwHlHLPv/8c8/KQfdM1q1bt0qfPn08KzVE6h+qpk2bJvHx8fLZZ581+I4TTjhBJkyYICLs0yKZw+GQyspKOfHEE2Xp0qWycOFC0el0nv5OpH7gmu0RNWfp0qUSHx/v2Xtu8+bNcuONN8r06dNl4cKFntStBQUFcu6557IeUbNOP/10zwSPDRs2yNixY6V///5y9NFHy/vvvy+VlZUiInL99dezX6MmHb6Cx263y5VXXimdOnXy3GOL8D6bWvbDDz9IbGysLF682HOstLRUjjjiCFm6dKnnWH5+Pvs1auDQFK4zZ86Uvn37NnrPI488ImazWf773/82OH5oWnMRkW3btsnYsWOla9eukp2dLccdd5xs377dp+UnCjQV1CFxcXF4+OGHsXXrVkyZMgUrV67E6tWr8eabb6JPnz7o06cPAEBV+VNHmuHDh2PMmDEAgBEjRmDChAn47bff8PLLLwMAXC4XACA9PR2zZ8+GwWDAVVddhY8//hgHDhzAp59+CrvdjjPPPDNQp0BB4MUXX8QJJ5zg+bOIwGq1etoUs9kMgPWIWjZ69Gh0794dAKDT6VBVVYW77roLJSUl+Pe//41//etfKC4uhslkwmOPPYbOnTtj+vTpeO6555Cfn4/ly5dj3759OOOMMwCwT4tkOp0OIoL169cjLS0NF198MUaOHIlZs2ahoqICAJCRkYE5c+ZAp9OxPaJGRowYgUsuuQT33nsvrr76avTv3x8rVqzAf//7X0yfPh2nn346CgoKPPVIr9ezHlGTevXqhZ9++gm//vorzjnnHBgMBpx99tkwm824+OKL8e9//xsA8Pjjj7NfoyYlJiY2+LPBYEBSUhLsdjs2btzoOc77bGpJTEwMevfujQceeACrV69GQUEBbrrpJqxZswZz587FFVdcgV27diEzMxMPP/ww748i3O7du7Fx40bY7XYoigJFUQAAtbW1qK2tbdD2AMC//vUv9OjRA08++SSKioo8x1VVhaqqcDqdAIAuXbrgo48+wg8//IBly5ZhxYoVyM3N9dt5EQVEoKMq4eKLL76QoUOHSlpamqSmpkr37t3l22+/DXSxKAi4Z0Vv2rRJRo8eLcOGDfPMHDt0GfzPP/8sJ554oiiKIsOGDROTySSXXHJJk7mmKbLV1NRIly5d5K677hIR1iPyzrp168RkMkn//v3lnnvukSuvvFKSkpJk7NixnpQbGzdulMsvv1wURZG+ffuK2WyWCy+80NN2UeRyOp1SXFwsI0eOlJUrV4rT6ZTFixeLTqeT559/Xh599FF56623RKR+xSLbI2rKTz/9JIMGDZLu3bvLBx98IPv27ZOKigr573//K4mJiTJlyhRPuoSffvqJ9YiaNGfOHBk+fLiMGzdOxowZ49mQV0RkypQp0qdPH0+alw0bNrBfoxa5ZzyvWLFCVFWV7777TkSkwV4tvM+m5nz00UcyePBgGTVqlOh0OhkwYIDMnz9fZs6cKenp6XL00UfL6tWrRYT3R5GqurpapkyZImlpadKpUyc54ogj5MUXX/S8vmHDBlEURV566SXPMXe79N///ldUVZWvvvpKROqf/+12u9x8883yxBNPeN7PzeQp0igiIoEOrISLqqoqFBYW4q+//sJxxx0X6OJQEHrmmWcwe/ZsTJkyBbNmzfIcFxEoioLS0lKsXr0a27dvx8CBAzF8+PDAFZaCktPpxL59+9CzZ0+88MILOPfccz2vsR5RW7333ns48cQTkZCQAAD4/PPPcfLJJ+M///kPpkyZAkVRICJYvXo1du3ahdzcXAwZMiTApaZgUVVVhezsbCxdutTTvpxwwgn49ttvER0djccffxwXXXQRVFVFWVkZfv75Z7ZH1EB1dTU++OADmEwmnH766TAajZ7XnnzySdx8883YtGmTZ4Yh6xE15c8//8Qpp5yCHTt2YPbs2bj11ltRV1cHg8GAdevW4eyzz8a4ceMwd+5cGAwG9mvUJmvWrMGYMWNw7bXXNvm8xvaImlNbW4v3338fzzzzDF544QV06dIFqqrim2++wbhx43D33Xfjpptugk6n4/NahLHZbLjqqqvw008/4f7770dZWRlWrFiBV155BQsWLMBFF12E2NhYnHrqqdizZw8+//zzBivJqqqqcPTRR2PIkCF44YUXAAB2ux1msxm9e/fGF198gbS0tECdHlHAMKhB5Afum+Di4mJcd911WL16NZYuXYouXbqgqKgIVqsVFosl0MWkELFixQqcdNJJyM/PR2pqKgDA4XBAr9cHuGQU7NxtUVO6d++OM888Ew8//HCL7yPau3cvzjnnHPznP/+By+XC9ddfj5UrV8JgMGDy5Ml46qmn2B5Rq6qqqhAdHe35s7vd+f777zFq1Ci89NJLmDhxYgBLSKHg/vvvx8yZM3Huuefi7bffbnA/NGrUKKSlpeGdd95hv0ZtVlRUhGHDhmHs2LF4/PHHYTKZAl0kCiHnn38+RARvv/02gIN926BBgzBgwAC8/PLLbI8i0O+//44jjjgC8+bNw6WXXgoAqKmpwaRJk7By5Uo88cQTmDhxIlauXImTTz4ZDzzwAKZNm9bgfvrqq6/G1q1bsXjxYuj1euj1enzyySfo0qWLJ+09UaRh8lAiP1AUBS6XC8nJyZg8eTL0ej1mzpyJJUuW4IwzzsCnn34KxheprZYuXYqePXsiOTkZdXV1+PHHH3HGGWfg559/DnTRKMi5H6AOb29WrFiBoqIidOnSpcH7iJqiKArWrFmDqVOnYuDAgQCAJUuW4Oabb8brr7+OtWvXBriEFArcAQ2HwwHgYLvz66+/AgBycnICUzAKKdOnT8dxxx2Hjz/+GJ9//rlnAGj37t3Yt2+fZ7UP+zVqC5fLhZSUFAwaNAjr16+HyWTy7INI1Jq6ujoUFBQAAAoLCwHU16nNmzejpKQEWVlZANgeRaKamhoAQNeuXQHUZ1+wWCy46667UF1djWeffRZ//PEHjjvuOFx99dW4//778cUXXzT4jj///BNRUVEwm82efaBOPfVUBjQoojGoQeQn7o7n2GOPxcCBA/Haa69h/PjxcLlcOOaYY3hzQ61ybwL2008/oWfPnti5cyf+9a9/4fjjj8fevXs5AERt5m5vbDYbfv/9d/znP//BEUccgfPOOy/AJaNQkJqairPOOguFhYX4z3/+g/feew9jxozBFVdcAavVihUrVgS6iBRC3IPQdrsda9euxbvvvosJEyZg8ODBAS4ZhYKoqCjcc889GD58OCZPnoxnn30Wn332GWbPno2Kigr84x//CHQRKYSoqgoRwfDhw7Fx40bs3r2bm8hTmxkMBpx++un43//+h+eeew47duzAhg0b8Pjjj8NisbA9imB2ux1GoxE//PAD7HY7dDodACA2NhZxcXHYsGEDPvroIwDAvHnzkJubi+nTp+O5555Dfn4+li9fjn379uGMM84AALZLRH9j+ikiP9q+fTsWLlyIJ554At27d8fjjz/O/VfIKzabDf3790dcXBwKCgpgNBrx9NNPY+zYsYEuGoWQv/76Cx9//DHy8/Px0ksvIS0tDc899xyGDh0a6KJRiNizZw/q6uqQnZ3tebASEezbt485fckru3btwuLFi1FQUIDnn38e6enpeOmllzyrgIjaYseOHbjpppuwevVqAPXBjqeeegonnnhigEtGoeixxx7Dv/71L6xYsQKjRo0KdHEoxEyaNAkffvghUlJSUFdXh/j4eCxcuBDHHHNMoItGATR69GiUl5dj/vz5OOqoowDUr0698sorUVFRgV69euGFF15AUlISNm3ahEceeQQvvPAC+vTpg61bt+Lss8/Gs88+2yB1J1GkY1CDyI++++47HHfccXjooYcwffr0QBeHQtCmTZvQp08fJCYm4s4778SNN94Y6CJRCPrxxx9xxx13oKqqCpMmTcI111wT6CIRUYT67rvvMHXqVCiKgiuuuALXXXddoItEIcrpdKKqqgpbt27lSh9qF/deB3v27MH//vc/XHbZZYEuEoWg0tJSbNy4EVu2bEFsbCzOPPPMQBeJgsDatWsxfvx4xMfH45prrkFUVBRmzJiBSZMm4cgjj8TkyZNRUlLiCVqICFavXo1du3YhNzcXQ4YMCfAZEAUfBjWI/IwbOlNHvf766zjnnHO4cSF1yLZt25CTk8P2iIgCbt26dejVqxcMBkOgi0IhzOVyMSUHEREFraVLl+Lpp5/G999/j4qKClxxxRWYN28edu/ejeOPPx4fffQR+vbtG+hiEoUMBjWIiIiIiIiIiIiIfGzdunXo0aOHZ5LiI488gjlz5uCPP/5AfHx8YAtHFEI4PZOIiIiIiIiIiIjIR9wp7vr37w+gfr/MjRs34rPPPsO1117LgAaRlxjUICIiIiIiIiIiIvIRRVEAAHa7HcuXL8fy5cvx5ptvIisrCxMnTgxw6YhCD5OOEhEREREREREREfmY0+lEVVUVfvzxR/zf//0ffvjhB/To0SPQxSIKOdxTg4iIiIiIiIiIiMgPamtrYTAYoNPpAl0UopDFoAYREREREREREREREYUEpp8iIiIiIiIiIiIiIqKQwKAGERERERERERERERGFBAY1iIiIiIiIiIiIiIgoJDCoQUREREREREREREREIYFBDSIiIiIiIiIiIiIiCgkMahARERERERERERERUUhgUIOIiIiIiIiIiIiIiEICgxpERERERERERERERBQSGNQgIiIiIiIiIiIiIqKQwKAGERERERERERERERGFBAY1iIiIiIiIiIiIiIgoJDCoQUREREREREREREREIYFBDSIiIiIiIiIiIiIiCgkMahARERERERERERERUUhgUIOIiIiIiIiIiIiIiEICgxpEREREFJJee+01jBgxAnFxcYiNjUXv3r1xxRVXYN++fZr/XfPmzcOnn37a6Hhubi6uu+46zf++jlq2bBn69+8Pk8mE+Pj4gJbl+OOPx2mnneaT7/7www+hKAp27Njhk+/vqHnz5kFRFM+fV6xYAUVR8PPPP7f5O1asWIEHHnjAp+UiIiIiIgolDGoQERERUciZM2cOJk+ejGOPPRZvvfUW3nrrLVx22WX4+eefUVBQoPnf11xQ44MPPsDNN9+s+d/XUZdeeik6deqEzz//HJ9//nmgi0N/GzJkCFatWoXevXu3+TO+CGoQEREREYUyfaALQERERETkrccffxxTpkzB3LlzPcfGjRuHGTNmwOVy+a0cgwcP9tvf1VaVlZXIz8/Hvffei2OPPdarz9bU1MBisfioZKFH698jNjYWRxxxhGbfF65YD4mIiIioJVypQUREREQh58CBA8jIyGjyNVVteIu7aNEiDBgwAGazGVlZWbjjjjvgdDobvCc/Px8XX3wx0tLSYLFY0KtXL8yfPx9AfYqpnTt3YsGCBVAUBYqiYNGiRZ7XDk8/9f7772PQoEEwm83IzMzE9OnTUVtb63ndnYJo2bJluPDCCxETE4POnTtjzpw5bTr3Z555Bj179oTJZEJubi7uv/9+TyBn0aJFiImJAQBcfvnlUBQFU6ZMafJ7duzY4TmXK6+8EklJSRgxYgQA4JNPPsFJJ52E1NRUxMbGYuTIkViyZEmj31VRFPz6668YN24coqOj0b17d7z88sstlr+mpgannnoq8vLysG3bNgDA7t27MWnSJCQnJ8NisWDUqFFYvXp1g8/V1dXhxhtvRGJiIuLi4nD55ZejsrKy1d/L/Xt/+umnOPvssxEdHY2MjIxGqx9mzZoFq9WKH3/8EUceeSTMZjMWLFjw/+3de1BVVfsH8C8vnnO4CcpdUAQNFRENC8sLoUBA4HXEFEVgxBxGRwUmUFQC4c3RMMMLijMZKqUNSmCoqUxgqZnyhxaYyaRMinnhpg55VDg+vz8c9nDkZmpvP5rvZ4YZztprrf08a+/1B6yz9wIAXLx4EVOmTIGFhQVMTU0REhKCy5cv67W/d+8eIiIi0LNnT9jY2CAxMRHNzc3txtL69VOPHz/Ghg0b4ObmBo1GA3t7e8yYMQN3795FamoqVq9ejT///FO598aPH6+0fVlxtefOnTt477334OjoCCMjI/Tr1w+zZs3Sq9PZvGnJ7b///S+cnZ2h0WgwZMgQbN++/aWO+2effQZ3d3cYGxvDysoK48aNQ1lZWZf5EREREVH3xUUNIiIiIup2XnvtNWRnZ+PTTz/FzZs3O6y3YcMGzJ8/H4GBgSgqKsKyZcuwadMmrFy5UqlTV1eH0aNH4/jx4/jwww9x6NAhxMXF4fr16wCevGLK3t4eoaGhOH36NE6fPo2QkJB2z/f1118jNDQUQ4cORWFhIRITE5GdnY3w8PA2dWNiYjBo0CAUFBRg0qRJWLZsWZuFg6dt3rwZMTExSj5RUVFITU1FYmIiACAkJATFxcUAgFWrVuH06dNITk7utM+kpCSICPbu3YuMjAwAQFVVFSZNmoTc3Fzk5+dj7NixCA4OxvHjx9u0nzNnDgICAlBYWAhPT09ERUXh4sWL7Z6rsbERwcHBuHz5Mk6cOIEBAwagoaEB48aNw/nz57F582bk5+fD1NQUvr6+evujJCUlYevWrUhISEBeXh50Oh2WL1/eaW6tLViwAAMHDsRXX32F8PBwrFy5EtnZ2Xp1Hj16hNmzZyM8PBzffPMNAgICcOXKFYwZMwb19fXYuXMn9uzZg5qaGvj5+eHhw4dK23nz5qGgoABr167Frl278MsvvyAzM7PLuBYvXozExERMnDgRRUVFyMrKQs+ePdHY2Ij58+cjOjoaxsbGyr23detWAPjb44qPj8fBgwexZs0aHD16FBkZGdBoNMrxruYNACQkJCA1NRVRUVEoKipCQEAAYmJisGXLlpcy7t9//z2io6MRHByMw4cPY/fu3fDz88OdO3e6zI+IiIiIujEhIiIiIupmysvL5ZVXXhEAAkBcXFxkyZIlUlVVpdS5d++emJmZSVJSkl7bbdu2ibGxsdTW1oqIyIoVK0Sj0ei1fVr//v1l0aJFXZZ7enrK6NGj9eps375dAMjPP/8sIiKlpaUCQBISEpQ6jx8/FmdnZ4mOju4whubmZrG2tpZZs2bplSclJYlarVbyaWhoEACSk5PTYV8iIlVVVQJAgoKCOq2n0+mkqalJAgICJCwsTCnPyckRAJKVlaWUNTY2iomJiaSnpytlPj4+EhISIvX19fLGG2/IiBEj5NatW8rxDz74QCwsLPTKHjx4IE5OTsoY1dXVibGxsSQnJ+vF9tZbbwmATq9dy3jPnTtXr3zu3Lni6OgoOp1ORERSUlIEgHz55Zd69SIiImTAgAGi1WqVstu3b4uZmZmS+4ULF8TAwEB27Nih1GlubhYXFxdp/SdXSyxlZWUiInLp0iUxMDCQNWvWdBh/SkqKmJqatil/mXG1x93dXeLj4zs83tW8qampEZVKJcuXL9crDwsLExsbG2lublbye95xz8jIEEtLy07zICIiIqJ/Hz6pQURERETdzrBhw3DhwgUcOnQIS5cuhYWFBTZt2oThw4fj/PnzAIAffvgBjY2NmDFjBpqbm5Uff39/aLVaVFRUAAC+/fZb+Pr6wtnZ+YViamxsxPnz5xEaGqpXPnPmTADAyZMn9coDAgKU3w0MDODm5obq6uoO+//1119RW1uLGTNmtOn/0aNHOHv27HPF3d5TJ9XV1YiMjISjoyN69OgBlUqFY8eOobKysk3d1nmYmpqif//+bfKora3FhAkTAAClpaWwtbVVjh07dgwTJkyApaWlco0MDQ3h4+OjvEaovLwcWq0W06ZN0+t3+vTpz5zn021DQ0Nx/fr1NrE+PR7Hjh3D5MmT0aNHDyW+3r17w9PTU4mvrKwMIqJ3DkNDQ0ydOrXTmEpKSiAiiI6OfuY8/hdxAU82Nd+5cyfWr1+vzJXWupo3Z86cQVNTU7v3a01NTZt76XnGfeTIkaivr0dUVBSKi4tx//79LvMiIiIiou6PixpERERE1C2p1WoEBwcjMzMT586dw5EjR3D//n2kpaUBePKPdODJPz5VKpXy4+rqCgC4du0agCev0XFwcHjheO7cuQMRgZ2dnV65hYUFNBoN6uvr9cp79erVJp/We288raGhAQDa9N/y+en+n9XT/T1+/BiTJ0/GyZMnkZaWhtLSUpSVleGdd95pN75nyaOyshI//fQTwsLC0Lt3b71jtbW1KCws1LtGKpUKubm5yjW6ceMGAOgthrQXe2c6atvSNwCYmJjAzMysTXyZmZlt4jtx4oRefCqVqk1uXcVXV1eHHj16tIntWfydcQFPXnU2d+5cfPzxx/Dw8ICTkxO2bdumF3tn8+av3K/PO+6+vr7Izc3FhQsXEBgYCGtra0RERDz3XCAiIiKi7qHHPx0AEREREdHLEBgYiBEjRij7OVhaWgJ4snF3v3792tR3cXEBAFhZWeGPP/544fP36tULBgYGevtAAMDdu3fx8OFDJZ7n1dL+6f5v3bqld/yvMjAw0Pv822+/4dy5cygsLMSUKVOUcq1W+1z9A8CYMWPg7++P+Ph4WFlZ6e0xYmlpiaCgIKSnp7dp17KHQ8um8Ldv34ajo6NyvCX3Z9HRuLXecP7psWiJLyQkBAsXLmxzrGVT9j59+qCpqQkNDQ16CwhdxWdlZYXm5mbcvn37Ly9s/J1xAU8W4zIzM5GZmYny8nJs3LgRCxcuxLBhw+Dt7d3lvGl9v7Z3zVrfr8877gAQHh6O8PBw1NbW4sCBA4iLi4NKpcKOHTu6zJGIiIiIuicuahARERFRt3Pr1q023wDXarW4du0a3N3dAQCjR4+GiYkJqqur27x6qDV/f3+sX78eV69ehZOTU7t1unqKAgDMzMzw6quvYv/+/YiLi1PK8/LyAADjxo17ptw6MnjwYNjY2GDfvn16+eTl5UGtVmPUqFEv1H+LlsULtVqtlP3+++84deoUBg0a9Nz9xsbGQqvVIioqCkZGRspruvz9/fH555/Dzc0Npqam7bb18PCAsbExCgoK4OnpqZTn5+c/8/kLCgr0xm3//v1wcHBA3759O23n7++PiooKeHp6wtDQsN06Xl5eyjnmzZsHANDpdCgsLOy0b19fXxgYGCAnJwfLli1rt45ardbb+Pt/EdfTPDw88Mknn2DHjh24ePEivL29u5w3o0aNgkqlwr59+/SuWV5eHmxtbbu8l54lv9asra0RHR2Nw4cPd7hRPRERERH9O3BRg4iIiIi6HQ8PD0yaNAmBgYHo06cPrl+/ji1btqC2thZLly4F8OTJibS0NCQmJqK6uhrjx4+HoaEhrly5ggMHDiA/Px8mJiaIi4vD7t278dZbbyE5ORkDBgzAlStXUFlZiXXr1gEA3NzcUFJSguLiYvTu3RsuLi6wsrJqE1dqaiqmTp2qfHv80qVLWLFiBaZPnw4PD48XytnQ0BDJyclYsmQJbG1tERwcjB9//BHr1q1DbGxsu/E8jyFDhqBv375Yvnw5dDodGhsbkZKSovdt++eVlJQErVaL2bNnw8jICBMnTkR8fDy++OIL+Pj4YOnSpXByckJNTQ3OnDkDBwcHxMXFwdLSEjExMVi7di2MjY0xcuRI7N27F5cvX37mc5eUlCAhIQFvv/02iouLkZubi6ysLPznP52/kXf16tXw8vJCYGAgFixYADs7O9y8eRPfffcdvL29ERYWhqFDh2LatGmIjY3FgwcP4OzsjK1bt+LRo0ed9j1o0CDExMRg1apVqK+vh5+fH+7fv49Dhw4hNTUVjo6OcHNzQ3NzMzZu3IgxY8bA3NwcgwcP/lvjAoCxY8di2rRpGDZsGAwNDbF7926o1Wp4e3sDQJfzxtraGosXL0ZGRgaMjIzw5ptv4vDhw9izZw82b97c5ULFs+SXkpKCuro6jB8/Hra2tigvL8eRI0cQHx/fZX5ERERE1I39s/uUExERERH9dVlZWRIUFCSOjo6iVqvFwcFBgoKCpKSkpE3dvXv3ipeXlxgbG4u5ubl4enpKcnKyNDU1KXWuXr0qc+bMEUtLSzEyMpIhQ4bIpk2blOMVFRXi7e0tPXv2FACSk5MjIiL9+/eXRYsW6Z1v//79Mnz4cFGr1WJvby+xsbGi1WqV46WlpQJAysrK9NpNmTJFfHx8usx927Zt4urqKiqVSpycnCQ9PV10Op1yvKGhQS/GjlRVVQkA2bdvX5tjZ8+eFS8vLzEyMhJXV1fZtWuXREZGiru7u1InJydHAEhNTY1e2xEjRkhkZKTy2cfHR0JCQvTqvP/++6LRaKS4uFhERG7cuCHR0dHSp08fUavV0rdvXwkNDZVTp04pbR4+fCiLFy+WXr16ibm5uURGRkpubq4AkKqqqg7zbBnvgwcPyuTJk8XExETs7OwkPT1dr15KSoqYmpq220dlZaW8++67YmVlJRqNRpydnSUiIkIqKiqUOg0NDTJnzhwxNTUVKysriY+Pl4yMDGn9J1d7116n08lHH32kXFN7e3uZOXOm3L17V0REmpqaZOHChWJnZycGBgZ698jLiqs9CQkJ4uHhIWZmZmJubi5jx46Vo0eP6tXpat7odDpJS0sTJycnUalU4urqKtnZ2S9t3IuKisTPz09sbGxEo9HIwIEDJSUlRW9uExEREdG/j4GIyD+1oEJERERERPR3On78OCZMmICysjK8/vrr/3Q4RERERET0gjp/1pqIiIiIiIiIiIiIiOj/CS5qEBERERERERERERFRt8DXTxERERERERERERERUbfAJzWIiIiIiIiIiIiIiKhb4KIGERERERERERERERF1C1zUICIiIiIiIiIiIiKiboGLGkRERERERERERERE1C1wUYOIiIiIiIiIiIiIiLoFLmoQEREREREREREREVG3wEUNIiIiIiIiIiIiIiLqFrioQURERERERERERERE3QIXNYiIiIiIiIiIiIiIqFv4P2cv5JcO9AhfAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_violins(create_values_violin_plot(threshold_score_average_mean_top_tanimoto, pos_val_spectra, tanimoto_for_prediction, 10), title=\"MS2DeepScore, reliability = average mean top 10 tanimoto\")" + ] + }, + { + "cell_type": "markdown", + "id": "68d6a18d-fd5f-4255-a418-04e5a6616ab6", + "metadata": {}, + "source": [ + "# MS2Query\n", + "So time for the real test! How does this stack up against the original MS2Query. For this purpose we retrained an MS2Query model (including MS2DeepScore and Spec2Vec) using the exact same data split. This was version 1.5.4 of MS2Query. Since the original release many updates were done, to keep it compatible with new versions of dependencies, like for instance MS2DeepScore (moving to pytorch). But the architecture of MS2Query did not change compared to the paper we published. Only the underlying way we train MS2DeepScore was changed slightly with the change to pytorch, but this will only have improved performance compared to the orginal MS2Query paper.\n", + "The MS2Query model trained can be downloaded from https://zenodo.org/records/18926063. However, for the benchmarking we only need the actual predictions made by MS2Query, which are downloaded with the code below. " + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "202eb137-2d27-420a-8dc5-b68a11477e63", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The file ./zenodo_files/results_ms2query.csv already exists, the file won't be downloaded\n" + ] + } + ], + "source": [ + "folder_to_store_zenodo_files = \"./zenodo_files\"\n", + "os.makedirs(folder_to_store_zenodo_files, exist_ok=True)\n", + "download_file(\"https://zenodo.org/records/18926063/files/results_ms2query.csv?download=1\", \n", + " os.path.join(folder_to_store_zenodo_files, \"results_ms2query.csv\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "e06f18a1-c8c9-4f70-bc9c-0dda1d50459e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "ms2query_results = pd.read_csv(os.path.join(folder_to_store_zenodo_files, \"results_ms2query.csv\"))" + ] + }, + { + "cell_type": "markdown", + "id": "a826df5b-37df-4381-88c1-136dbc48e6b8", + "metadata": {}, + "source": [ + "### Convert MS2Query results\n", + "We extract the predicted inchikey and the predicted MS2Query score. MS2Query does some additional filtering, requiring a minimum number of peaks for instance. If this is the case the prediction is set to None (which results in a tanimoto of 0.0) and the predicted score to 0, so they are first to be filtered out. Likely these spectra are anyway hard to predict for and will therefore have bad predictions. " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "45909ff2-bab7-46de-afd6-e8e1c8bd62cd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 36473/36473 [00:10<00:00, 3358.89it/s]\n" + ] + } + ], + "source": [ + "inchikeys = []\n", + "predicted_score = []\n", + "for i in tqdm(range(len(pos_val_spectra))):\n", + " index = ms2query_results.index[ms2query_results['query_spectrum_nr'] == i+1]\n", + " if len(index) == 1:\n", + " inchikeys.append(ms2query_results[\"inchikey\"][index].tolist()[0])\n", + " predicted_score.append(ms2query_results[\"ms2query_model_prediction\"][index].tolist()[0])\n", + " else:\n", + " inchikeys.append(None)\n", + " predicted_score.append(0.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "d0cfe579-b4a1-4fcf-bed3-e2105a9d279b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 36473/36473 [00:01<00:00, 25663.07it/s]\n" + ] + } + ], + "source": [ + "tanimoto_for_prediction_ms2query = get_prediction_tanimoto(inchikeys, pos_val_spectra, val_and_train_fingerprints)" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "id": "363a735d-31ef-40ee-92d1-8fa266851336", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tanimoto_per_bucket = create_values_violin_plot(predicted_score, pos_val_spectra, tanimoto_for_prediction_ms2query, 10)\n", + "plot_violins(tanimoto_per_bucket, title=\"MS2Query 1.0\")" + ] + }, + { + "cell_type": "markdown", + "id": "27cb964a-9919-4e17-9302-a6988069a7b8", + "metadata": {}, + "source": [ + "### So the new method is better than MS2Query 1.0\n", + "MS2Query does a bit better than just using MS2DeepScore, but the new method using MS2DeepScore for the best match, but reliability prediction by the MS2DeepScore for top 10 inchikeys, is quite a bit better than MS2Query" + ] + }, + { + "cell_type": "markdown", + "id": "3861dda8-2f27-4bd8-bf59-e8c236c25534", + "metadata": {}, + "source": [ + "# How far could you improve reliability prediction in theory?\n", + "Here we mostly focus on improving reliability prediction, without touching which inchikey is actually predicted. To give an indication of how close to perfect we are, we plot the perfect reliability prediction (just by using the known tanimoto). Actual analogue search accuracy can even be better, if you also change which analogue is predicted, here we just use the inchikey predicted by MS2DeepScore, but using a perfect reliability prediction." + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "id": "d7f59576-372f-4ae5-bc5f-31d5b0c16ea2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tanimoto_per_bucket = create_values_violin_plot(tanimoto_for_prediction, pos_val_spectra, tanimoto_for_prediction, 10)\n", + "plot_violins(tanimoto_per_bucket, title=\"MS2DeepScore, with perfect reliability estimation (by cheating)\")" + ] + }, + { + "cell_type": "markdown", + "id": "4843d01f-e996-463f-b737-b47621d6e9ba", + "metadata": {}, + "source": [ + "# Comparison plot\n", + "So far we made violin plots, which are nice to explore details in the distribution, however, they make it a bit challenging to compare different methods. Now we switch to a different plot, here we get the mean tanimoto score for the prediction, so we can compare the results side by side. Also we change the plot to show the average over the top percentage, instead of specific bins. So 0- 50 percent instead of only 40-50%. This is similar similar to the benchmarking in the original MS2Query paper and gives an impression of actual average accuracy when using different minimum reliability scores." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "d8bf8c29-0abe-4404-af06-4bc419a60b41", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_average_per_threshold(predicted_scores, tanimoto_for_predictions, val_spectra, n_of_buckets):\n", + " buckets = bucket_by_value(predicted_scores,n_of_buckets)\n", + " mean_per_fraction = []\n", + " selected_indexes = set()\n", + " for indexes_in_bucket in tqdm(buckets):\n", + " selected_indexes.update(set(indexes_in_bucket.tolist()))\n", + " average_scores_per_inchikey = get_average_tanimoto_per_inchikey(val_spectra, tanimoto_for_predictions, selected_indexes)\n", + " mean = sum(average_scores_per_inchikey)/len(average_scores_per_inchikey)\n", + " mean_per_fraction.append(mean)\n", + " return mean_per_fraction" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "id": "1530f106-3c97-4477-90c0-b621963d1c8f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 343.04it/s]\n" + ] + } + ], + "source": [ + "\n", + " \n", + "mean_per_fraction_ms2query = plot_average_per_threshold(predicted_score, tanimoto_for_prediction_ms2query, pos_val_spectra, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "id": "83af2ec9-f0ae-4889-8b88-4aab2fbfa688", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 351.27it/s]\n" + ] + } + ], + "source": [ + "mean_per_fraction_best_possible_ranker = plot_average_per_threshold(tanimoto_for_prediction, tanimoto_for_prediction, pos_val_spectra, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "id": "cedef472-6bc9-4bae-bdf2-83fc5004da8b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 351.48it/s]\n" + ] + } + ], + "source": [ + "mean_per_fraction_average_max_top_tanimoto = plot_average_per_threshold(threshold_score_average_max_top_tanimoto, tanimoto_for_prediction, pos_val_spectra, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "id": "7879c77a-f566-4a1a-b950-34adeeae1e46", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 345.22it/s]\n" + ] + } + ], + "source": [ + "mean_per_ms2deepscore = plot_average_per_threshold(predicted_ms2deepscores, tanimoto_for_prediction, pos_val_spectra, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "id": "d38ca517-9170-4ff0-b3a1-d608ce8c014d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 351.32it/s]\n" + ] + } + ], + "source": [ + "mean_per_fraction_average_mean_top_tanimoto = plot_average_per_threshold(threshold_score_average_mean_top_tanimoto, tanimoto_for_prediction, pos_val_spectra, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "2099bb10-2b27-4997-8134-a64775160043", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "def plot_comparison_plot(results):\n", + " n = len(list(results.values())[0])\n", + " labels = [f\"0 - {(100/n)*(i+1)} %\" for i in range(n)]\n", + " for label, mean_per_fraction in results.items():\n", + " plt.plot(labels, mean_per_fraction, label = label)\n", + " \n", + " plt.xlabel(\"Fraction predicted\")\n", + " plt.ylabel(\"Average Tanimoto\")\n", + " plt.legend()\n", + " plt.ylim((0, 1))\n", + " \n", + " step = n // 10\n", + " positions = range(len(labels))\n", + " \n", + " plt.xticks(\n", + " ticks=positions[step-1::step], # start at 9, then 19, 29, ...\n", + " labels=labels[step-1::step],\n", + " rotation=45,\n", + " ha='right'\n", + " )\n", + " plt.grid(axis='both', linestyle='--', alpha=0.5)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "id": "68c203fe-5005-47a8-9c4d-b0af430cd010", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_comparison_plot({\"MS2Query 1.0\": mean_per_fraction_ms2query,\n", + " \"MS2Query 2.0\": mean_per_fraction_average_mean_top_tanimoto,\n", + " \"MS2DeepScore\": mean_per_ms2deepscore})" + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "id": "66ec6f60-592b-42c5-88e7-0220d7f267b7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_comparison_plot({\"MS2Query 1.0\": mean_per_fraction_ms2query,\n", + " \"ms2deepscore (reliability = mean ms2deepscore top 10)\": mean_per_fraction_average_mean_top_tanimoto,\n", + " \"MS2DeepScore\": mean_per_ms2deepscore,\n", + " \"Best possible reliabitly prediction\": mean_per_fraction_best_possible_ranker,\n", + " \"ms2deepscore (reliability = mean max ms2deepscore top 10\": mean_per_fraction_average_max_top_tanimoto})" + ] + }, + { + "cell_type": "markdown", + "id": "40e4185a-b6dc-40d9-bc4a-677d41bd1bb1", + "metadata": {}, + "source": [ + "# Is top 10 best? \n", + "Below we try different number of top spectra we can select. " + ] + }, + { + "cell_type": "markdown", + "id": "3787d0fc-e7e2-4c50-afac-a2a5017c2027", + "metadata": {}, + "source": [ + "### Try different top inchikeys using the mean MS2DeepScore" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "id": "dd34a6ae-dc2e-4784-84a9-3f67a0082ef6", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "36473it [00:11, 3186.97it/s]\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 336.32it/s]\n", + "36473it [00:11, 3106.09it/s]\n", + 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"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 329.36it/s]\n", + "36473it [00:11, 3099.39it/s]\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 338.42it/s]\n", + "36473it [00:11, 3173.86it/s]\n", + 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in [1,3,5,6,7,8,9,10,11,12,13,14,15, 20, 50]:\n", + " mean_top_k = predict_using_average_mean_top_k_tanimoto(i, close_tanimoto_scores)\n", + " results_average_mean_top_k_mean_highest_ms2deepscore[f\"Average mean top {i}\"] = plot_average_per_threshold(mean_top_k, tanimoto_for_prediction, pos_val_spectra, 100)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 284, + "id": "43e1bc44-2d27-40d2-b46d-5e6d3fcddaa9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "# plot_comparison_plot(results_average_mean_top_k_mean_highest_ms2deepscore)\n", + "plot_comparison_plot({key: results_average_mean_top_k_mean_highest_ms2deepscore[key] for key in ['Average mean top 1',\n", + " 'Average mean top 3',\n", + " 'Average mean top 5',\n", + " 'Average mean top 8',\n", + " # 'Average mean top 9',\n", + " 'Average mean top 20',\n", + " 'Average mean top 50',\n", + "\n", + " \n", + "]})" + ] + }, + { + "cell_type": "markdown", + "id": "a1026c5e-c977-4653-89c6-feb84b02b8d7", + "metadata": {}, + "source": [ + "# A bit too many lines...\n", + "Below the same results, but flipped. The x axis now shows the number of top k hits used and the individual lines are for which fraction of the query spectra a prediction is made. (the x axis/fraction predicted in the previous plot). So somewhere between top 5 and top 10 seems fine, where 8 seems best overall. " + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "id": "3fb98c5d-0fc9-416e-8cb7-04dff688a588", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Nr of close library Inchikeys')" + ] + }, + "execution_count": 290, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (12, 10))\n", + "for fraction_predicted in range(0, 50, 5):\n", + " plt.plot([1,3,5,6,7,8,9,10,11,12,13,14,15,20,50], [results_average_mean_top_k_mean_highest_ms2deepscore[k][fraction_predicted] for k in results_average_mean_top_k_mean_highest_ms2deepscore.keys()], label=f\"Fraction predicted = {fraction_predicted + 1} %\")\n", + "plt.legend()\n", + "# plt.xlim(0,10)\n", + "ax = plt.gca()\n", + "ax.xaxis.set_minor_locator(plt.MultipleLocator(2))\n", + "ax.grid(which='both', linestyle='--', alpha=0.5)\n", + "ax.grid(which='minor', linestyle=':', alpha=0.3)\n", + "plt.ylabel(\"Average tanimoto\")\n", + "plt.xlabel(\"Nr of close library Inchikeys\")" + ] + }, + { + "cell_type": "markdown", + "id": "c5a1fb6a-c79d-4317-b14d-910900deda3c", + "metadata": {}, + "source": [ + "# Do the same for the max ms2deepscore for top inchikeys\n", + "The resuls show a similar pattern. Somewhere beteen 5 and 10 top inchikeys is 5, which the best seems like 7. " + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "id": "06ebc41d-e419-44b6-b7ca-69728a7833a5", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "36473it [00:06, 5997.36it/s]\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 327.85it/s]\n", + "36473it [00:05, 6444.58it/s]\n", + 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in [1,3,5,6,7,8,9,10,11,12,13,14,15, 20, 50]:\n", + " max_top_k = predict_using_average_max_top_k_tanimoto(i, close_tanimoto_scores)\n", + " results_average_mean_top_k_max_highest_ms2deepscore[f\"Average max top {i}\"] = plot_average_per_threshold(max_top_k, tanimoto_for_prediction, pos_val_spectra, 100)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "id": "44aeeca7-8b01-4cfb-8054-e2a818173897", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "# plot_comparison_plot(results_average_mean_top_k_mean_highest_ms2deepscore)\n", + "plot_comparison_plot({key: results_average_mean_top_k_max_highest_ms2deepscore[key] for key in ['Average max top 1',\n", + " 'Average max top 3',\n", + " 'Average max top 5',\n", + " 'Average max top 8',\n", + " # 'Average mean top 9',\n", + " 'Average max top 20',\n", + " 'Average max top 50',\n", + "\n", + " \n", + "]})" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "id": "5188618c-5784-4057-9f69-35e35f17ea68", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Nr of close library Inchikeys')" + ] + }, + "execution_count": 289, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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aNIgRI0bw4MEDPDw8yr0/W6MN0p9wFEUR8Z8CKR4gx0WKhyQkxUSKixQPkOUiBSkxkeIBclykeEhCUkykuEjxgKq5KIpS7pLzslBVFZ0pH2dD6auSNcHRo0cxmUwsXbrUspq9detWq21CQkLYs2cPs2fPLnYfBoMBo9FY6uu0bNmS2NhYq/tiY2MJDg62Ote5MvGIi4uz+v3gwYM0b9681HOoQ0NDOXXqVImTGa1btyY/P5/4+HhLufvZs2dLve57SEgI69ats1T1PE5xcQoNDeXWrVvo9XqCgoKK3W/Lli2Ji4tj2LBhVsdYFmWVp1fnacmqqjJ27FiWLl2Ku7s7RqPRcgk3879l5UxJKIqCTqfTurtrlI6qqjx48MDunUKleEhykeIhCUkxkeIixUOaixSkxESKhyQXKR6SkBQTKS5SPKS5SKFp06bk5eWxYsUKLl26xEcffVRkdfXNN9/k8OHDTJgwgePHj3PmzBnWrFljOWc9KCiIuLg4kpOTuXPnTrEr31OnTmXPnj3MnTuXc+fOsWnTJlatWsXrr78OYHlPKvPeXLlyhddee42zZ8/yySefsHLlSiZPnlzqc6ZPn853331HVFQUCQkJnD9/ns8//5yoqChUVaVZs2b06dOHsWPHEhcXR3x8PKNGjSp1tXzo0KH4+/vTv39/YmNjuXTpEjt27ODAgQOWOCUlJZGQkMCdO3fIycmhV69ehIeH079/f3bv3k1ycjLfffcdb731FocPH8ZoNDJp0iTWr1/Phg0bOHfuHO+8847lPP7SaNasWam3ss5Hv3DhAgkJCdy6dYusrCwSExNJSEggNze3zNdet24dfn5+luuid+nShW+//ZaDBw+ybNkyWrVqVeqpA6WhdXcXjKIoeHl52X320YwtynBsgRQPkOMixUNSzkqJCchxkeIBMlwk5SvIiAnI8QA5LlI8JOWslJiAHBcpHiDHpaxO2TVFu3btWLRoEQsXLqRNmzZs3ryZBQsWWG0THBzM7t27SUxMpHPnzoSHh/P5559bSp9ff/11HBwcaNWqFX5+fly5cqXI64SGhrJ161Y+/fRT2rRpw8yZM5kzZw6RkZFVPoZhw4aRlZVF586dmThxIpMnT7ZcZq0kQkJC2L9/P+fOnaNr16506NCBmTNnUq9ePaBgILh+/Xrq1atHt27dePHFFxkzZkypA1uDwcDu3bupU6cOffv2pW3btrz33nuW93rAgAH06dOH7t274+fnxyeffIKiKHz11Vc899xzDB8+nODgYIYMGcLly5epW7cuqqoyePBgZsyYwbRp0+jYsSOXL19m/PjxVY5bWYwaNYoOHTrw17/+lXPnzhEeHk5oaCg3btwo9Xm3b99m/vz5rFixwnJf586dmTp1Kr/61a/YunUrGzZsqG79MtG6u2toaGhoaGhoaGj8hCitg7SGHIrrLK/xZGOr7u7aSno1YDKZuHnzpojZUFVVSU9Pt3vplBQPSS5SPEBOzkqKiRQXKR6SXKTkK8iJiRQPSS5SPEBOzkqKiRQXKR6SXFRVJTc31+4eZhej0Wh3FykeklykeJhdJOWsVu4uFFVVuX//vohE0dAoD1rOavyU0PJV46eGlrMaPzUq2zRLQ8NePGk5q3V3f8JRFEVEWb8UD5DjIsVDEpJiIsVFigfIcpGClJhI8QA5LlI8JCEpJlJcpHiALBcpKIoi4vz4ynoUd+kye7k8qR7S0Lq7a5QLKbP3UjwkuUjxkISkmEhxkeIhzUUKUmIixUOSixQPSUiKiRQXKR7SXKSgqir5+fl2j4kUD0kuUjykoZW7C0ZRFHx9fUV0cQUs15W0N1I8QI6LFA9JOSslJiDHRYoHyHCRlK8gIyYgxwPkuEjxkJSzUmICclykeIAcF3NndAlI+NyAHA+Q4yLFA2TlrC3QurtraGhoaGhoaGho/ITQurtraMjkienu/v777xMUFISzszNPP/00hw4dKnHbvLw85syZQ9OmTXF2dqZdu3b85z//qUHb8mEymbh69ardu7hCQcnFvXv37F6KIsVDkosUD5CTs5JiIsVFiockFyn5CnJiIsVDkosUD5CTs5JiIsVFiockF2mdsiWUVEvxkOQixcPsIilnf/Ll7p999hmvvfYa77zzDkePHqVdu3b07t2blJSUYrd/++23+etf/8rKlSs5deoU48aN47e//S3Hjh2rYfPSUVWVhw8fikgUAIPBYG8FQI4HyHGR4iEpZ6XEBOS4SPEAGS6S8hVkxATkeIAcFykeknJWSkxAjosUD5DjIqlTtpSSaikeIMdFigfIyllbYNdB+tKlSxk9ejTDhw+nVatWrF27FldXV9avX1/s9h999BF//vOf6du3L02aNGH8+PH07duXJUuW1LD5TwdFUXBzc7P7h0iKhyQXKR6SkBQTKS5SPKS5SEFKTKR4SHKR4iEJSTGR4iLFQ5qLFMwdxO0dEykeklykeEjDVt3d7XaGfW5uLvHx8bz55puW+3Q6Hb169eLAgQPFPicnJ6dIbb+LiwsxMTElvk5OTg45OTmW39PT04GC2RbzjIs5mI+XJpR0vznwJd1vNBoxmUyW/ZubgDxe5lbS/Q4ODpZSicddSrq/JHej0ci9e/fw9vZGUZQqHVN53Eu7/+7du3h5eVmStrLHVNX3SVEU0tLS8PT0tPoAVeSYrtzNJCtPpWWAR6XfJ1VVSUtLo3bt2qiqarPcK8u9uPvNOWv2skXuVeZ9MplMpKWl4e3tbfnir2ruVfbzVNhFp9PZ/DuivMekqip37961fIarckxVfZ8AfvjhBysXW31HVOSYzD8/vo/q+C4vy93cjblWrVpFVklr8n0yf5/4+PiUeqw18T6ZXWrVqoWDg0O1fZeXdUxGo9HyGTbvu7LHVFn3x79TCv+/oDr/5pZ0TIqilPi3uKbfJ0VRuHfvnpVLZY6pqu+T+TPs4+NT5G9xTb9Phf9f8Pj2j7sbjUYr3+KqNBRFqdT9pe2zLCr7mqXdn5+fb2kKZqv979u3jx49elj+vpbnmAp72NKlovcDRVzMNG7cmMmTJzNlyhSgIPf+8Y9/0L9//2pxMRqNxV6Gbfbs2ezcubNItXNljrU8FH7u4/up6fep8Ofz8e+Iihyj3Qbpd+7cwWg0UrduXav769aty5kzZ4p9Tu/evVm6dCnPPfccTZs2Zc+ePfzjH/8otbxhwYIFzJ49u8j9Fy9exN3dHQAvLy8CAgK4ffs29+/ft2zj6+uLr68v169f5+HDh5b7/f398fb2Jjk5mdzcXMv9DRo0wN3d3XL/xYsXURSFxo0bo9frOX/+vJVD8+bNyc/PJykpyXKfTqcjODiYhw8fcu3aNcv9BoOBJk2acP/+fW7dumW5383NjcDAQO7evcudO3cs95uPKSUlhTt37pCamoqiKJU+posXL1r9sajoMTVv3hxVVTl//rzlj3Flj6mq71P9+vVxdnbm0qVLFT4mk6qy89R9NsTfJc+k0rWZD0NautK0tlOFj0lVVVxcXKhdu7bNcq+y75P5j49Op7NZ7lXmmHJycsjPzyc1NZXAwECb5F5lP0+pqakWF29vb5t+R1TkmPLy8rh586blM2zr74iKHJOXlxc//PCDxaWyx1TV98nV1RV/f3/S0tL44YcfqnRMVX2fVFUlMDCQ3NxckpOTK31MVX2fzJ9hBwcHatWqVW3f5eU5JrNLZmYmDRs2rLbv8vIck/kzrChKjfzNLemYbt68afX/gur+m1vSMTVu3Ji8vDyrv8XV/Te3pGMKCgrCycnJyqUyx1TV90lVVTw8PABq/P9Gjx+TqqrodDpq165d5jGZTCby8/PJy8vDxcWFvLw8q/8P6/V6HB0di9zv6OiIXq8nNzfXyt1gMODg4EBOTo5loJ6Tk4OTkxOKopCdnW11TM7OzpZtzCiKgrOzMyaTySpeOp0OJycnjEYjw4cP5+OPP+ZxTpw4QdOmTYGCyQuDwWBxLzxArswxdevWjZCQEBYtWgSAk5MTzz77LJcuXcLJyclybGUdU15eHvn5+UWOKS8vz7K92T0/P9+ybXHHVJX3SafTFdm/+X0yx8p8TDdu3MDb29vq/SvpfZo/fz5ffvklR44cKfcxOTg4FHtMUJDPhV+3vLn3+DGVlnsfffQRK1eu5Pz583h6evK73/3OqsL68fdp+vTpfPzxx7i5ufHee+8xaNAgyzH94x//YMuWLfz73/+u0vtUOH8e/44wT8CVB7t1d79x4wb169fnu+++Izw83HL/tGnT2L9/P3FxcUWek5qayujRo/nXv/6Foig0bdqUXr16sX79erKysop9neJW0s1fcuauevZYfSntfnutkmnHVPox3UzL5I3tJ4i9+AOP86u2/vyxV3Oa+Ln/pI7pSXyftGPSjkk7Ju2YtGPSjulJP6bs7GwuX75M48aNcXFxqfaV2/JS1r6HDx/O7du3Lae2mu/38/OzWpFVFIWcnJwi5+hX5pgiIiJo164dy5cvr5ZjsvX9ubm5GAyGSr1Pj6+kl0Rx+5g1axaff/55sb2+KupSEyvpS5cuZenSpSxcuJCnn36azMxMkpKS+M1vflPsvv/1r38xZswY/vWvf3H+/HlGjhzJlStX8PX15f79+3Tu3Jn//ve/NGrUqErvX3Z2NsnJyTRu3BhHR0erbTMyMvD29i7fVcZUO5GTk6M6ODio//znP63uHzZsmPqb3/ym1OdmZWWp165dU00mkzpt2jS1VatW5X7d+/fvq4B6//79ymiXC6PRqF68eFE1Go3V9hrlxWQyqXfu3FFNJpPmUQWXXSduqu1mf602mv6l2uLtr9SPDyarl1Iz1D9sOao2mv6l2mj6l2qTN/+t/mlHonojLbPaPKoLKTkrKSZSXKR4SHKRkq+qKicmUjwkuUjxUFU5OSspJlJcpHhU1CUrK0s9deqUmpWVVS0e2dnZ1RKTV199VX3hhReKfaxbt27qxIkT1cmTJ6u1a9dWIyIiVJPJpC5cuFBt06aN6urqqjZo0EAdP368+uDBA6vnxsTEqN26dVNdXFxUb29v9Ze//KV69+5d9dVXX1UBq1tSUpK6d+9eFVDv3btn2cf27dvVVq1aqQaDQW3UqJG6ePFiq5g0atRInTdvnjp8+HDV3d1dDQwMVP/617+WerzmY5o4caLq6emp1q5dW3377betYtuoUSN1zpw56u9//3vVw8NDffXVV1VVVdX//e9/6i9+8QvV2dlZbdCggfqHP/xBzcjIUE0mk5qXl6feunVL/fWvf606OzurQUFB6scff6w2atRIXbZsmWXfgNVY6+rVq+qQIUPUWrVqqa6urmrHjh3VgwcPqhs2bCgSpw0bNqiqqqr37t1TR44cqfr6+qoeHh5q9+7d1YSEBIuHyWRSFyxYoNapU0d1d3dXR4wYoU6fPl1t165dqbGpCnfv3lVdXFzUb775RlXV8uXsX/7yF3Xw4MGW3+vUqaMeOnRIVVVVHTNmjLp06VKbuGVmZqonTpxQMzOLjgcqMg61W+M4g8FAx44d2bNnj+U+k8nEnj17rFbWi8PZ2Zn69euTn5/Pjh07eOGFF6pbt0KoqpzLAEBBaagEpHhA+V0e5uTzpx3HGfdxPGmZebSu58mXf+jKy083orGvGyuGduCrSV3p8VQdjCaVTw5dpduifcz78hR3H+aWuX8pMZGUs1JiAnJcpHiADBdJ+QoyYgJyPECOixQPSTkrJSYgx0WKB1TBRVUh96HNbqbsB+Xf3oZ5vWnTJgwGA7GxsaxduxYoKDWOjo7m5MmTbNq0iW+//ZZp06ZZnpOQkEDPnj1p1aoVBw4cICYmhn79+mE0GomOjiY8PJzRo0dz8+ZNbt68SWBgYJHXjY+PZ9CgQQwZMoQTJ04wa9YsZsyYwcaNG622W7p0KWFhYRw7dowJEyYwfvx4zp49W+Yx6fV6Dh06RHR0NEuXLmXdunVW2yxevJh27dpx7NgxZsyYwcWLF+nTpw8DBgzg+PHjfPbZZ8TExBAVFQUUVGcMHz6cq1evsnfvXrZv387q1atLvEIWFKziduvWjevXr/PFF1+QmJjItGnTMJlMDB48mKlTp9K6dWtLnAYPHgzAwIEDSUlJYdeuXcTHxxMaGkrPnj25e/cuOp2OrVu3MmvWLN59912OHDlCQEAAq1evLjUmAO7u7qXexo0bV+Jz//vf/2Iymbh+/TotW7YkMDCQl156iatXr5b4nHbt2nHkyBHu3btHfHw8WVlZNGvWjJiYGI4ePcqkSZPKdC4v5uqZqmC3c9IBXnvtNV599VXCwsLo3Lkzy5cv5+HDhwwfPhyAYcOGUb9+fRYsWABAXFwc169fp3379ly/fp1Zs2ZhMpmsPqga1iiKgouLi701xHhA+V0Sr6Yx+dNjJP+QiaLAmOeaMPX/tcCgt/7gtarnyfrIThxJvsvCr89yKOku62KS+PTwVUZ1bcyork1wdyr6UZMUEylIiokUFykeIMtFClJiIsUD5LhI8ZCEpJhIcZHiAVV0ycuEd+vZxgOokMWfb4DBrdybf/nll5a+UADPP/8827ZtAwrO31+4cKHV9n/84x8tPwcFBTFv3jzGjRtnGQguXLiQsLAwq4Fh69atLT8bDAZLL5OSWLp0KT179mTGjBkABAcHc+rUKRYtWkRkZKSlb0Lfvn2ZMGECANOnT2fZsmXs3buXFi1alLjvwMBAli1bhqIotGjRghMnTrBs2TJGjx5t2aZHjx5MnTrV8vuoUaN4+eWXLWXrzZs3Z8WKFXTr1o01a9Zw5coVdu3axaFDh+jUqRMAH374IS1btizRY8uWLaSmpnL48GF8fHwAaNasmeVxd3d39Hq9VZxiYmI4dOgQKSkpODkV9F9avHgxO3fuZMeOHYwZM4bo6GhGjhzJyJEjAZg3bx7ffPNNkXPJHychIaHUx0srBzf3lXr33XeJjo7G09OTt956i1/+8pccP3682EsZ9u7dm1deeYVOnTrh4uLCpk2bcHNzY/z48WzcuJE1a9awcuVKfH19+eCDD6xyqCIoimK5VQW7DtIHDx5MamoqM2fO5NatW7Rv357//Oc/lmZyV65csZqJyM7O5u233+bSpUu4u7vTt29fPvroozK7Mv6cMZlM3L1719Lx9+fuUR4Xo0llzb4LLP/mPPkmlQAvZ5YMasezTX1L3W9YkA+fjXmG/edSWfT1WU7eSGf5N+f5+4HLTIhoyivPNMLZ8cfzrSTFRAqSYiLFRYqHNBcpSImJFA9JLlI8JCEpJlJcpHhIc6lOunfvzpo1ayy/u7n9OMDv2LGj1baqqvL111+zaNEizpw5Q3p6uqUxWmZmJq6uriQkJDBw4MAqOZ0+fbpIZW6XLl1Yvnw5RqPR8n60bdvW8riiFDSBLG31GuCZZ56xGrCFh4ezZMkSq87oYWFhVs9JTEzk+PHjbN682XKf+qhPwaVLlzh9+jR6vd4qXk899VSpY6KEhAQ6dOhgGaCXh8TERDIyMoo0PMvKyuLChQvk5eVx+vTpIqve4eHh7N27t9R9F54gqCgmU0EjvxUrVvDLX/4SVVXZuHEjjRs3Zu/evfTu3bvY582aNYtZs2ZZfp89eza9evXC0dGRefPmceLECb788kuGDRtGfHx8pdxUVbW6alJlsesgHSAqKspSuvE4+/bts/q9W7dunDp1qgasqoZOp6NBgwYivmAVRcHDw6PKszlPikdZLtfuZfLaZ4kcSr4LwK/aBvDub9vi5epYZNuS9h3Rog7PNfdj1/e3WLL7LJfuPGTev0/zYUwSk3s253cdG6B30ImKiZSclRQTKS5SPCS5SMlXkBMTKR6SXKR4gJyclRQTKS5SPKrs4uhasKJtA8yDQXODvHK9dgVwc3MrcYBWeMAOkJycTP/+/Rk3bhzz58/Hx8eHmJgYRo4cSW5uLq6urjVaCfF4IzBzY8Gq8vhxZ2RkMHbs2GJLsAMDA8sssS+OysQpIyODgICAImMyKLhaQnGXXysvhaspiuOVV16xnPLwOAEBAQC0atXKcl/9+vXx9fXlypUr5Xr9M2fO8PHHH3Ps2DHWr1/Pc889h5+fH4MGDWLEiBE8ePDAcuWHilLuz04p2H2Q/iSiKEqZiVdTKIpiKU/RPAooyeXzhOu8vfN7HmTn42ZwYPYLbRgQWr9SHzKdTuFXIQH0bl2XHUevEf3NeW7cz+ZP/zjBX//vEq/9v2B+1TZAVEwk5OxPIU9+rh4gx0VKvoKsmEjwADkuUjxATs5Ki4kEFykeUEUXRalQyXmpuwIqP/SyLUePHsVkMrF06VLLJNfWrVuttgkJCWHPnj3FXnIZCsrdS7tcM0DLli2JjY21ui82Npbg4OAiXecryuNXrDp48CDNmzcvdYAbGhrKqVOnSpzMaNWqFfn5+cTHx1vK3c+ePUtaWlqJ+wwJCWHdunWWao3HKS5OoaGh3Lp1C71eT1BQULH7bdmyJXFxcQwbNszqGMuiKuXuXbp0AQqOuUGDBiiKwv3797lz5w6NGjUq87VVVWXs2LEsXboUd3d3q0vpmf8tK2dKwlbl7vZfhngCMRqNnDt3rtJvri0xmUykpKTYZJbvSfAoziU9O48pnx5j8qcJPMjOp0NDb76a3JXfdWxQ9VkwBx2DOzXk29cjmPHrVvi4GUi685A/fHKMfqti+DzuvIiYSMlZyXnyc/eQ5CIlX0FOTKR4SHKR4gFyclZSTKS4SPGQ5KI+usSbhEaHTZs2tZQ1X7p0iY8++qjI6uqbb77J4cOHmTBhAsePH+fMmTOsWbOGO3fuAAXnscfFxZGcnGy5xvzjTJ06lT179jB37lzOnTvHpk2bWLVqFa+//jqAJRaVicmVK1d47bXXOHv2LJ988gkrV65k8uTJpT5n+vTpfPfdd0RFRZGQkMD58+f5/PPPiYqKQlVVmjRpQp8+fRg7dixxcXHEx8czatSoUlfLhw4dir+/P/379yc2NpZLly6xY8cODhw4YIlTUlISCQkJ3Llzh5ycHHr16kV4eDj9+/dn9+7dJCcn89133/HWW29x+PBh8vLymDRpEuvXr2fDhg2cO3eOd955h5MnT5YZl2bNmpV6q1OnTonPDQ4O5oUXXmDy5Ml89913nDhxgt///vc89dRTdO/evczXXrduHX5+fvTr1w8oGPR/++23HDx4kGXLltGqVatKn05tq3J3bZBeTdj7C9aMoih4e3vbvYxLisfjLoeT7/L88v+xM+EGOgWm9GrOtrHhNKptm9loM86ODoz8RWP+b1p3Xvt/wXg46Tl5I53J/zzHhzFJNn2tyiIhZ6XmieYhz0VCvoKcmEjxkOQixcOMhJyVFBMpLlI8pLlIGKBDQUfuJUuWsHDhQtq0acPmzZstDaXNBAcHs3v3bhITE+ncuTPh4eF8/vnn6PUFBcOvv/46Dg4OtGrVCj8/v2LLoUNDQ9m6dSuffvopbdq0YebMmcyZM4fIyMgqH8OwYcPIysqic+fOTJw4kcmTJzNmzJhSnxMSEsL+/fs5d+4cXbt2pUOHDsycOZN69QqaAzo4OLB+/Xrq1atHt27dePHFFxkzZkypA1uDwcDu3bupU6cOffv2pW3btrz33nuWFf0BAwbQp08funfvjp+fH5988gmKovDVV1/x3HPPMXz4cIKDgxkyZAiXL1+mbt26ODg4MHjwYGbMmMG0adPo2LEjly9fZvz48VWOW1n8/e9/5+mnn+ZXv/oVERER6PV6du3aVeSUhMe5ffs28+fPZ8WKFZb7OnfuzNSpU/nVr37F1q1b2bBhQ5XcbFHurqhSPoU1RHp6Ol5eXuW7iHwlMRqNnD9/vsxSFg37kWc0sWLPed7fewGTCoE+Liwf3IGOjWrVyOvfe5jLsm/O8fcDl1EUWP1SKM+3DaiR1y4OLWc1fkpo+arxU0PLWQ1bk52dTVJSEo0bN8bZ2dmm+zavpDs7O4uYMPgpExERQfv27Vm+fLm9VZ5oJOVsaZ/NioxDtZX0JxyTycTt27ftPoMvxQPgYsoD+q/8P1Z+WzBAHxDagK8mda2xATpALTcD7/y6Jb9r54eqwpTPEjh65V6Nvb5UJOWJFBcpHtJcpCAlJlI8JLlI8ZCEpJhIcZHiIc1FCqqqkpeXZ/eVfSkeklykeEhDK3cXjE6no3Hjxnbv4goFpVM+Pj52n1WS4vF/51L5zapYTt56iKeznpVDO7BkUDs8nMvXvd2W6HQ65v62HT2eqkNOvonRm45w+YeHNe5hdpGQs1LyRJKLFA9JLlLyFeTERIqHJBcpHiAnZyXFRIqLFA9pLlKa6QFiqk+keIAcFykeICtntXL3SlAT5e4VvnSFRo2w89h1Xt+WSL5JpXOQD8uHtKeed81dtqMkHubkM/iDA3x/PZ0mvm78Y8KzeLsaatRBy1mNnxJavmr81NByVsPWVHe5uxktXzV+CkjKWa3cXTAmk4nz52V07TaZTNy6dcvuLvb2WPe/S0z5LIF8k0q/kACW9GuEv6d9Z9zMMXFx1LH+1U7U83Lm0p2HjPl7PDn5NdsBWErO2jtPJLpI8ZDkIiVfzS5SYiLBQ5KLFA+zi4SclRYTCS5SPKS5ZGdn21sBkFNSLcVDkosUDzOSclYrd9coE0VR8PPzs/uskr08VFVlwa7TzPv3aQCGdwli+eD21POvKyomdTyd2TC8Mx5Oeg4l3+WNbccxmWR86dUkUvJVkosUD2kuUpASEykeklykeEhCUkykuEjxkOYiCXOXdnsjxQPkuEjxkIYtqqa0QfoTjqIoltvPzSPPaOL1bcf56/5LAEzr04KZv26Fg4NOZExa+Huw5pWO6HUKXyTeYOl/z9nVzx5IyVdJLlI8pLlIQUpMpHhIcpHiIQlJMZHiIsVDmosUzLGwd0ykeBR2sLeLFA9p2Cou2iD9CcdkMpGSkmL30qma9sjMzWfM34+w4+g1HHQKC38XwoSIZiiKIjomv2juy7svtgVg1d4LbD181V56dkHKeyPJRYqHNBcpSImJFA9JLlI8JCEpJlJcpHhIc5GCqqrk5+fbvaRaiockFyke0jD3IKnq51hrHFcNSGsQY3axNzXlce9hLiM2HebYlTScHXW8/1IoPVvWtYtLWZTksXT3WVZ8ewG9TmHD8E50be5XrR6SclbKewNyXKR4gAwXSfkKMmIiyQPkuEjxkJSzUmICclykeED5XX5OjeNUVdU8HkOKiyQPM/b2yc7O5tKlSzRp0kRrHCeR/Px8eysABUlrvv0cPG6kZTHwrwc4diUNLxdHNo96usgA/acQkz/+v2B+26E++SaVCR8f5cyt9Gr3kZCzUt4bSS5SPKS5SMhXkBMTKR6SXKR4mJGQs5JiIsVFiodEFwmYPeztI8WjsIO9XaR4mJHmUVUfbZBeDZhMJpKSkkSUK6mqSmpqqt0TtyY8zt1+wIA133EhJYMAL2e2jwunYyMfu7iUh9I8FEXhvQFtebqxDw9y8hmx4TC306uva6WUnJXy3khykeIhyUVKvoKcmEjxkOQixQPk5KykmEhxkeIhzSUnJ8feChaqY4Jr3759KIpCWlqaXT0qS0kuQUFBLF++3PK7oijs3Lmzxj1mzZpF+/btq+11i0NSzppMJm2QrlE6Op0Of39/u5dxVbdH/OW7DFx7gJv3s2lWx50d45+leV0Pu7iUl7I8nPQOfPD7MJr6uXHjfjYjNh7mYY6cPxDVgZT3RpKLFA9pLlKQEhMpHpJcpHhIQlJMpLhI8ZDmUl1ERkZaNcgz3y5cuFDs9oqi4OjoWKUS5oiICKZMmWJ137PPPsvNmzfx8vIq1z5s4WErKuJy8+ZNnn/++XLtt6IDa3vHZNKkSXTs2BEnJyc6dOhQ5PHs7GwiIyNp27Yter2e/v37l7nPnJwcfv/73+Pp6UlwcDDffPON1eOLFi3iD3/4Q6n7UBQFBweHKn+On9xvAQ1AzjUMq9Pjm1O3eelvcdzPyiO0oTfbx4VTz9vFLi4VoTweXq6ObBzeGV93AydvpPOHT46Rb7T/6mF1IeW9keQixUOaixSkxESKhyQXKR6SkBQTKS5SPKS5VCd9+vTh5s2bVrfGjRsX2S43NxdVVW2yKvk4BoMBf3//cg8wq8ujOHJzc23m4u/vj5OTk63UKu1RXYwYMYLBgwcX+5jRaMTFxYVJkybRq1evcu3vgw8+ID4+ngMHDjBmzBheeukly/ElJSXxt7/9jfnz55e6D1udtqIN0qsJKbOgqqpy9+5du3/hV5fH1iNXGftxPDn5Jno8VYfNo57B29VgF5eKUl6PQB9X1r3aCWdHHd+eSWHWv05Wi7uEnJXy3khykeIhzUVCvoKcmEjxkOQixcOMhJyVFBMpLlI8pLlU5+qok5MT/v7+VjcHBwciIiKIiopiypQp+Pr60rt3bwCWLFlCSEgIbm5uBAYGMmHCBDIyMqz2GRsbS0REBK6urtSqVYvevXtz7949IiMj2b9/P9HR0ZZV++Tk5GLL3Xfs2EHr1q1xcnIiKCiIJUuWWL1GkyZNePfddxkxYgQeHh40bNiQDz74oNRjNR9TVFQUXl5e+Pr6MmPGDKv3OCgoiLlz5zJs2DA8PT0ZM2YMADExMXTt2hUXFxcCAwOZNGkSDx8+BAoGoCkpKfTr1w8XFxcaN27M5s2bi7z+4+Xu165dY+jQofj4+ODm5kZYWBhxcXFs3LiR2bNnk5iYaInTxo0bAUhLS2PUqFH4+fnh6elJjx49SExMtHgAvPfee9StWxcPDw9GjhxJdnb1naJpZsWKFUycOJEmTZpYjrUwbm5urFmzhtGjR+Pv71+ufZ4+fZrf/OY3tG7dmokTJ5KamsqdO3cAGD9+PH/5y1/K1XhcK3cXioODA8HBwTg4ONhbBZ1OR926de3+nwNbe6iqyup9F5i2/ThGk8rvOjbgr7/viIuh7Jj/FGPSPtCb5YM7oCjw8cErrPtfkk1dpOSslPdGkosUD0kuUvIV5MREiockFykeICdnJcVEiosUj6q6qKpKZl6mTW5Z+VmYHExk5WeVa3tbTips2rQJg8FAbGwsa9eutZRUr1ixgpMnT7Jp0ya+/fZbpk2bZnlOQkICPXv2pFWrVhw4cICYmBj69euH0WgkOjqa8PBwRo8ebVm1DwwMLPK68fHxDBo0iCFDhnDixAlmzZrFjBkzLANV8wBw6dKlhIWFcezYMSZMmMD48eM5e/Zsmcek1+s5dOgQ0dHRLF26lHXr1llts3jxYtq1a8exY8eYMWMGFy9epE+fPgwYMIDjx4/z2WefERMTQ1RUlCUmw4cP5+rVq+zdu5ft27ezevVqUlJSSvTIyMigW7duXL9+nS+++ILExESmTZuGyWRi8ODBTJ06ldatW1viZF6hHjhwICkpKezatYv4+HhCQ0Pp2bMn9+7dw9HRkW3btjFr1izeffddjhw5QkBAAKtXry7zvXZ3dy/1Nm7cuDL3URhnZ+cqTy61a9eOmJgYsrKy+PrrrwkICMDX15fNmzfj7OzMb3/72zL3Yatyd32Vnq1RLKqq8vDhQ9zc3Ox+7oq5dMre59HY0sNkUpn771NsiE0GYHxEU6b1blGhkqWfYkz6tPHn7V+1Yu6Xp5j/1Wnq13Khb9sAm7lIyFkp740kFykeklyk5KvZRUpMJHhIcpHiYXaRkLPSYiLBRYpHVV2y8rN4esvT1WRWOnEvxeHq6Fru7b/88kvc3d0tvz///PNs27YNgObNm7Nw4ULLY6qqMmnSJMvqblBQEPPmzWPcuHGWgeDChQsJCwuzGhi2bt3a8rPBYMDV1bXU1dSlS5fSs2dPZsyYAUBwcDCnTp1i0aJFREZGWiYi+vbty4QJEwCYPn06y5YtY+/evbRo0aLEfQcGBrJs2TIURaFFixacOHGCZcuWMXr0aMs2PXr0YOrUqZbfR40axcsvv2w5l7558+asWLGCbt26sXr1ai5fvsyuXbs4dOgQnTp1AuDDDz+kZcuWJXps2bKF1NRUDh8+jI9PQWPlZs2aWR53d3dHr9dbxSkmJoZDhw6RkpJiKZtfvHgxO3fuZNu2bYwePZrly5czcuRIRo4cCcC8efP45ptvylxNT0hIKPXxil4q22g0VvkylyNGjOD48eO0atUKX19ftm7dyr1795g5cyb79u3j7bff5tNPP6Vp06asX7+e+vXrF9mHVu4uGJPJxLVr1+zexRUKEiUtLc3upVO28sjNNzHlswTLAH3Gr1sxvc9TFfpA/pRjMqJLEJHPBgHwx88SiL98zyYuUnJWynsjyUWKhyQXKfkKcmIixUOSixQPkJOzkmIixUWKhzSX6qR79+4kJCRYbitWrLA81rFjxyLb7969m169elG/fn08PDz4/e9/zw8//EBmZibw40p6VTh9+jRdunSxuq9Lly6cP3/eUtIN0LZtW8vPiqLg7+9f6uo1wDPPPGP1/9Tw8PAi+w0LC7N6TmJiIhs3brRaWe7du7flShEnT55Er9dbxeupp57C29u7RI+EhAQ6dOhgGaCXh8TERDIyMqhdu7aVS1JSEhcvXsRoNHL69Gmeftp6gig8PLzMfTdr1qzUW506dcrtCWWfy18eHB0def/990lKSuLw4cP84he/YOrUqUyaNIljx46xc+dOEhMTeeaZZ5g0aVKJ+7FFubu2kv6Eo9PpKpzkUj0yc/MZ+1E8/zt/B0cHhcUD2/FC+6IzWDXhYgsq46EoCjN+3Ypr9zL55nQKo/9+hH9OeJZGtd2K3T7PaOJORg4p6TmkPMgh9UEOKQ+yH/1bcLvzIIfUB9m4GXS0rp9GC39PWvh78JS/B83reJTrFAJbIeW9ATkuUjxAlosUpMREigfIcZHiIQlJMZHiIsUDqubionch7qU4m3ioqkpOTg5OTk7lWgRx0ZfcrLc43NzcrFZwH3+sMJcvX6Z///6MHz+e+fPn4+PjQ0xMDCNHjiQ3NxdXV1dcXCr2+pXBHAeDwVDkfltMvj1+3BkZGYwdO7bYgWDDhg05f/58hV+jMnHKyMggICCAffv2FXnM29sbR0fHCu/TTOFqiuJ45ZVXWLt2baX3bwv27t3LyZMnWbduHW+88QZ9+/bFzc2NQYMGsWrVqmKfo5W7a5QLVVXJzc3FYDDYvZysqh5r9l3kf+fv4GpwYO0rHXku2M9uLragsh4OOoUVQzsw+K8HOXH9PpEbDvPKM42sBuDmQfjdh+WfVczNMhJz4QdiLvxguU9RoJGPKy38PQoG73U9aOHvQVBtV/QOti/EkfLeSHKR4iHNRQpSYiLFQ5KLFA9JSIqJFBcpHlV1URSlQiXnZXnojDqcHat+jm9VOXLkCCaTicWLF1t6OmzdutVqm5CQEPbs2cPs2bOL3YfBYLBatS6Oli1bEhsba3VfbGyspZeEeVW0MqujcXHWkycHDx6kefPmpfaoCA0N5dSpU8VOZqiqSnBwMPn5+cTHx1vK3c+ePVvqdd9DQkJYt24dd+/eLXY1vbg4hYaGcuvWLfR6PUFBQUU8TCYTLVu2JC4ujmHDhlkdY1nYutzd1mRnZzNx4kQ2b96Mg4MDRqPR8v7n5eWVmFO2KnfXBunVgKIoIr7soSBRHjx4gI+Pj93/CFbFI89o4rPDVwF4b0BIpQfotnCxFVXxcDXo+TAyjN++/x1Jdx4y98tTJW6r1yn4ujtRx9MJP/O/Hs74eThRx8MJPw8nars68v2Fy6Qr7py9ncHZWw84e/sBdx/mkvxDJsk/ZPL1yduWfRr0OprXcbcM2gtW3j2p61m+WffqiImtkeIixUOSi/YdK9dDkosUD5CTs5JiIsVFioc0FwmN9KCgJDovL4+VK1fym9/8xtJQrjBvvvkmbdu2ZcKECYwbNw6DwcDevXsZOHAgvr6+BAUFERcXR3JyMu7u7sUOUKdOnUqnTp2YO3cugwcP5sCBA6xatapcDdDK4sqVK7z22muMHTuWo0ePsnLlyiKd4x9n+vTpPPPMM0RFRTFq1Cjc3Nw4deoU//3vf1m5ciXNmjWjT58+jB07ljVr1qDX65kyZUqpq+VDhw7l3XffpX///ixYsICAgACOHTtGvXr1CA8PJygoiKSkJBISEmjQoAEeHh706tWL8PBw+vfvz8KFCwkODubGjRv8+9//pn///rRv355JkyYxfPhwwsLC6NKlC5s3b+bkyZOWruslUVI1RXm5cOECGRkZ3Lp1i6ysLE6cOIGjoyOtW7e2VDycOnWK3Nxc7t69y4MHDywTA+W5HvzcuXPp27ev5RrsXbp04Y033mD48OGsWrWqyOkRhdHK3YWi0+nKTMyaQqfT4evra2+NKnt8eyaFlAc51HYz0Kd1+S6jUF0utqKqHnU8nNk0ojPv7TqDk6OOOh5O1Hls8F3Hw4largZ0urL/2DfybV3kvtQHOZy99YAzt9I5d/sBZ2894NztDLLyjJy8kc7JG+lW2zf2dWPR70IICyr/+U6FkfLegBwXKR4gx0X7jpXrAXJcpHiAnJyVFhMJLlI8QI6LoijVdm3titK+fXuWLl3KwoUL+fOf/8xzzz3HggULrFZtg4OD2b17N3/+85/p3LkzLi4uPP300wwdOhSA119/nVdffZVWrVqRlZVFUlLRK+SEhoaydetWZs6cydy5cwkICGDOnDlERkYCP5a7V2byZNiwYWRlZdG5c2ccHByYPHmy5TJrJRESEsL+/ft566236Nq1K6qq0rRpUwYPHmzp7r5hwwZGjRpFt27dqFu3LvPmzbM0visOg8HA7t27mTp1Kn379iU/P59WrVrx/vvvAzBgwAD+8Y9/0L17d9LS0tiwYQORkZF89dVXvPXWWwwfPpzU1FT8/f157rnn8Pf3x9HRkSFDhnDp0iWmTZtGdnY2AwYMYPz48Xz99dcVjlVFGDVqFPv377f8bj4vPikpybLq37dvXy5fvmzZxjzgLmsA/f3337N161ar1f7f/e537Nu3j65du9KiRQu2bNlS7HNtVe6uqE96d4rHSE9Px8vLi/v371dbGYWqqty/fx8vLy+7z4Sqqkp2drZNLktgT4/hGw6x92wqY7s14c3nS+5cWRMutkKKh9mlvDlrMqlcvZfJmVsFg3bzqnvSnYcYTSo6BSb3DGZi96YVLomXFhMJLlI8JLlo37FyPSS5SPEwu0jIWWkxkeAixaOiLtnZ2SQlJdG4cWOcnZ1t7mE0GnFwcBARE1VVLd3df2oeERERtG/fnuXLl9vdxdZI8TC7SMlZ80RQ48aNi1Q2VGQcKqOW5QnDZDJx69Ytu3dxNWPufmlvKutxPS2L/edSARjSqaFdXWyNFI+K5KxOp9Cothu9W/szqWdz3n85lG9e68axmf+P33aoj0mFZd+cY+jfDnI9LavCLlJiAnJcpHiADBftO7Z4pHiAHBcpHpJyVkpMQI6LFA+Q45KXl2dvBQsSPjcgxwPkuEjxgCcvZ7VB+hOOoijUrl3b7rNKVfHYevgqJhXCm9SmsW/xXcxrysWWSPGwFZ7Ojiwb3J6lg9rhZnDgcPI9nl/+f3x14ma59yEpJlJcpHhIc5GClJhI8ZDkIsVDEpJiIsVFioc0FykoioJer7d7TKR4SHKR4iENc7l7VeOinZP+hKOqKllZWbi4uNi9JKYyHkaTytYjBQ3jhnQOtKuLrZHiYWteDG1Ax0a1mPRpAolX05iw+ShDOgUys18rXA2lf+VIiokUFyke1eWiqioZOfmkZeYV3LJyH/386N+sPO5l5nK/0M9pmXk8zM7D1ekKrgY9rgaHR7dHPzvpcXV0wNXJ+n43gx4XgwNuToW2fewxg77ip2hIeH+keEhykeIhCUkxkeIixUOaixTMHcR1Op3d86QyHsVdusxeLk+qhzTMcdEaxwlEURTc3NzEJGx2dnaNXEOyOjz2n0vh5v1svF0d6V3FhnFVdakOpHjYOmcb1XZj+7hwlv73HGv3X+TTw1c5nHyXFUM70LqeV6nPlRITkOMixQNKdlFVlYe5xh8H15mPBtRZedzPzOXeo/vuZ5l/zuV+VsF9+abK/SHLyczjXqZty9scHRRcHB1wc3o0oDcP7AtPAhSeCDA4QH4Ovt6euDkVmiR4bOLASV/9/4n5KeTJz9VD0v8LpMQE5LhI8QA5LqVdHqymkdI+S4oHyHGR4gGyctYWaI3jNEQz+u9H+O+p24z8RWNm/LqVvXU0KkHshTv88bMEUh7kYHDQMf35pxjRJUjEf1Y1iqKqKpm5RsuK9f1Cq9f3s/K497Bg4G1Z7c76cfCdZ6z8nxODXkctV0dquRrwcin419vVEa9H93m7OOLt6oj3o/vdDHpy8o08zDHyMDefrFwjD3ONZOXm8zDHSFaekYc5+WTmGsnMNf/72M85+WTmGcnMMZJrrN7z6nQKhVby9Y8mAhxwMehxMzhYJgRcnRxwddQ/eqzwJEGhKgBHvaVKwMXR/k1yNDQ0ap7qbBynoaFReUr7bFZkHKqtpFcDJpOJu3fv4uPjY/frTKqqSmZmJq6urnYviamox+30bL49kwLAUBuVulfWpTqQ4gHVm7NdmvnynynPMW17It+cTmHul6f43/lUFv2uHX4e1pd4kRQTKS6V9TAPtgsG0bkllpPfezTALvyzLQbb3i6GRwPsgp8LD7ALBtzm+woG4c6O5Z8BN+drfV/b5Wue0WQ9iM+xHtD/OBHw6N8cI1l5+TzMyedBVi45RizPLzxJkJNfMPg3qfAgJ58HOfnwIMcmzgCKAi6O5gG7Djcnx4LyfSfrcv7iTgcodpLg0eMujg44lOPSjcXxU//sVAdS/l8gKSZSXKR4SHJRVZX8/HwR5xtLKamW4iHJRYqH2UVazmrl7gJRVZU7d+5Qq1Yte6sAkJubi6urq701Kuyx7chVjCaVTkG1aFbHw64u1YUUj+rOWR83A38bFsZHBy8z79+n2Xc2leej/8eSQe3oFuxnta2UmIAMF1VVuZ+RRVqu8qh03Prc7MKr2/etSszzqrQ6bHDQWQbR3oVWsl10JurW8sDb1UCtRyvd3i4GarkV/OvsWP1/rKsjXx0ddHi56PBycaywS1paGt7e3sUet9Gkkllopb/ISn6h+yyVALnGRxMBpVcCFLw+Vr9DdlVDYcHZUVfs+fwuj1b6rScBfpwMcDE4oOZm4+fjhXsxpw1U9NKMVUHCZxhk/b9ASkxAjosUD5DjYh7wSEBK0a8UD5DjIsUDZOWsLXhyjkSjWBRFEfGfgop6mEwqnx5+1DDORpddq6xLdSHFo6ZQFIVh4UE83bg2f/jkKOduZ/Dq+kOM+kVj3ujTAie9g6iY2NpFVVWy80yWwfWPK9rFNEt77PGqDLYdHRTLILuWq+HRoNqRWm4FZeXeVuXkP65ua2XU5aOsPHHQKXg4O+LhXLHBf1mYTCrZj8r9zSv81oP4whUBj1b4C9+XV3SSwPyzuUVAdp6J7Lxc7j60qToGvc564G8opbS/uEoA88RBoUoAV4O+SNM/Sd8nUpAUEykuUjxAlosUzB3E7Y0UD5DjIsVDGoqi2KS6QIvsE46qqmRkZODu7m73kpiKeMRcuMO1e1l4OOvp2zbAri7VhRSPmqaFvwdfRP2C+f8+zUcHL7MuJomDST8QPaQDTXzdxMSksu9Pdp6R76/fJ/7yPeIv3+PyD5mW1e3c/KoNtr1cClavvV0drX7+sYzcUGT129Vgu8H2zzVnS8NeMdHplEeDVf1jHsWv6JcXVVXJyS8o/X+Yk28p23+8EqDwfVar/48eLzgFQLU6bcDcIDA330Ruvok0Gzf90+uUHwfxTg64OjrgpFfwcDYUagT4WIl/MacF/NgzoOaa/tUUkj7DUlykeEhzkYKUkmopHpJcpHhIQyt3F4yiKHh5eYlJWJOpehsilZeKeHx6+AoAL3aoj4vB9t0af4oxqU5qOmedHR2Y278NXZv7Mm3Hcb6/ns6vV8Qw6zet6N1cTkPH8rw/t+5nE3/5HkevFAzKT964X+o53XqdUqSE3LLS/djqtpeLI14ujuiN2dSt7W33HhcgI2e179jisYWHoig4Ozrg7OiAj5uhUvtQVZX09HQ8PT2t3qPcfJPVSv/DnMdL+K1X9AtXCVj/az0hYJ78yjeppGfnk56dX+U4FEanUK5L/RVXCWA+NcDFUUdWriPO97Jwc3LEzckBZ70Dukqe918VpOQryHGR4gFyXCR1yq6Okup9+/bRvXt37t27h7e3t908KktJLkFBQUyZMoUpU6YABd/p//znP+nfv3+NesyaNYudO3eSkJBQLa9bHJJy1hZog/RqQKfTERBg29XfymL+z6y9qYhH6oMcdp+8DcCQzrYtda+oS3UixQPsl7O/bO1PSANv/vhZAgcu/cD0HSfY18afF9rXo019L+p72+9ascW9P3lGE6dvpltWyY9dSeN6WlaR5/q6O9GxkTehDWvRMsATn0eD71puBtwqtbJt//MTQU7Oat+xcj2gZBeDXodBb8Dbxulsbvpn3djvx879xZ/Tb64IMDcALLpddt6PTf8ycvLJyLHF4P+M1W9FS/lLuNSfk7k3wGOTBIWeY64OcDXoS2z691PIk5+rB8hxURQFg6Fyk3RlERkZyaZNm4rcf/78eZo1a1asS1VLqiMiImjfvj3Lly+33Pfss89y8+bNcsdbUml3RVxu3rxZ7lMoKjqwtndMJk2aRGxsLN9//z0tW7Ys1ltVVZYsWcIHH3zA5cuX8fX1ZcKECbz11lvF7jMnJ4dRo0bx+eef4+/vz+rVq+nVq5fl8UWLFnHlyhVWrlxZopdW7i4Yk8nE7du3qVu3rt1XvlRV5cGDB3h4eNi9JKa8HjuOXiPfpNI+0JuWAbZfVf0pxqS6sWfO+ns58/Gop/nr/11k6e5z7Pr+Fru+vwVALVdH2tT3KrjV86JtfS8CfWpm4K6qKldu3+Xc3TyOXkkj/vI9jl9Ls/zH3YxOgZYBnoQ2rEXHRgW3BrVs5ygpT6S4aN+xcj3s4VJS07+qehhNKlmFzt0v7lJ/JZ3bX/hUAfNjGdl5ZOerhZr8FW76l1vVMFjhpNeVeKk/g6Li6eaMm6H4S/1ZTRY81iDQ0YZN/6TkrBQPSS6qqpKXl4ejo2O1ePTp04cNGzZY3efn51dku9zcXBwdHaulpNpgMODv71/u7WuytDs3N7fUSZKKuFTkGCuKhHL3ESNGEBcXx/Hjxy35Uthl8uTJ7N69m8WLF9O2bVvu3r3L3bt3S9zfBx98QHx8PAcOHGDXrl289NJL3L59G0VRSEpK4m9/+xtHjhwp1UkrdxeMqqrcv3+fOnXq2FvlJ4eqqnx6qKDU3ZaXXdMoHXvnrINOYUJEM7o0rc3GmIucTcni3O0H3MvM43/n7/C/83cs23o662lTv2DAbh7AN/JxrXDZaGZuPrfuZ3M7PYfb6dncTs/m1qN/b6fncPN+FjfSinbK9nJxJLShNx0b1SK0US3aNfDGzUn7Kq1J7J2vGj8PHHQK7k563G3w+TYajZw/f57mzZujKDqy880d/o1k5pVS2l9ohb+4SQLrngE/Nv3LyTeRk2/bgT8UXPXBqtnfY+fvWzf2K36SwDwR4GJwwJSTh8HFaGkcqiEHo9GIo6NtG16acXJyKnbwGBERQZs2bdDr9Xz88ce0bduWb7/9lmXLlrFp0yYuXbqEj48P/fr1Y+HChbi7u1ueGxsby1tvvcWhQ4dwcnKic+fOfPrpp/zxj39k//797N+/n+joaACSkpJITk4uUu6+Y8cOZs6cyYULFwgICOAPf/gDU6dOtbxG06ZNGT16NBcvXmTbtm3UqlWLt99+mzFjxpR4rOZjAvjoo49wdHRk/PjxzJkzx5LzQUFBjBw5kvPnz7Nz505efPFFNm7cSExMDG+++SZHjhzB19eX3/72tyxYsMDS/T8lJYVRo0bxzTff4O/vz7x584q8/uPl7teuXeONN97g66+/Jicnh5YtW/L+++9z+vRpZs+ebXkOwIYNG4iMjCQtLY3XX3+dzz//nJycHMLCwli2bBkhISGW13nvvfdYtmwZmZmZDBo0qNhJF1uzYsUKAFJTUzl+/HiRnD19+jRr1qzh+++/p0WLFgA0bty41H2ePn2a3/zmN7Ru3ZomTZrwxhtvcOfOHfz8/Bg/fjx/+ctfyry+ua3Q/mf5hKMoSo0lky08Dlz6geQfMnEzOPDrkHp2dalupHhIol1gLZYNDQMgJ9/I2VsPOHH9Pt9fT+f76/c5e+sB6dn5fHfxB767+IPleR5OelrX9yxYbW/gxVP+nmTlGR8Nwn8cgKek5xQMxO9nF1yvuhw0r+NeMCBvWDAob+LrVqPnkUrKE0kuUpASEykeIMdFisfjWDX9cy97+/JSuOnf45f6K/byf1ZVAOYrABRz2kDhpn9GE7lZJu5n2bbpn4Ol6d9jq/qlXOrP1VwJ8GiSoLhKgPJcElJSnlTFRVVV1Kyip19Vdl+mnBxMJlO5Jk8UF9tVj23atInx48cTGxtbsO9HJdUrVqygcePGXLp0iQkTJjBt2jRWr14NQEJCAj179mTEiBFER0ej1+vZu3cvRqOR6Ohozp07R5s2bZgzZw5QsGqfnJxs9brx8fEMGjSIWbNmMXjwYL777jsmTJhA7dq1iYyMtBzf0qVLmTt3Ln/+85/Zvn0748ePp1u3bpZBYEnHNHLkSA4dOsSRI0cYM2YMDRs2ZPTo0ZZtFi9ezMyZM3nnnXcAuHjxIn369GHevHmsX7+e1NRUoqKiiIqKYsOGDTg4ODB8+HBu3LjB3r17cXR0ZNKkSaSkpJTokZGRQbdu3ahfvz5ffPEF/v7+HD16FJPJxODBg/n+++/5z3/+wzfffANgORVg4MCBuLi4sGvXLry8vPjrX/9Kz549OXfuHD4+PmzdupVZs2bx/vvv84tf/IKPPvqIFStW0KRJk1Lf68KTLMXxyiuvsHbt2lK3KY1//etfNGnShC+//JI+ffqgqiq9evVi4cKF+Pj4FPucdu3a8dFHH5GVlcXXX39NQEAAvr6+bN68GWdnZ37729+W+bpaubtGuSipgY9Uj08PFVx27YUO9attdfKnFpOfE4Vj4qR3IKSBNyENvC2P5+abOHf7Ad9fv18weL+Rzumb6TzIyefgpbscvFRyCVNxuBkcqOvlTF0PZ/y9nKnj6YS/pzN1PZ2p6+FEHWeVBnV9tDwR6CIFKTGR4iHJRYpHTVGepn+VjUluvqlQs76i5/MXexnAMi7197BQ0z+jSeVBdj4PsvOBHFuEAwBFwXJOf5HO/oUG9A4YqeXu+mhSoJhL/Tk+dmqAY/U0/atKzqpZWZwN7Whzp/LQ4mg8SgWu7f7ll19aDdCef/55tm3bBkDz5s1ZuHCh5TFVVYmKisLBoaDaIigoiHnz5jFu3DjLIH3hwoWEhYVZfgdo3bq15WeDwYCrq2uppd9Lly6lZ8+ezJgxA4Dg4GBOnTrFokWLiIyMtJQu9+3blwkTJgAwffp0li1bxt69e0sdpAcGBrJs2TIURaFFixacOHGCZcuWWQ3Se/ToYbVqP2rUKF5++WVLA7jmzZuzYsUKunXrxurVq0lKSmLXrl0cOnSITp06AfDhhx/SsmXLEj22bNlCamoqhw8ftgxSC/cBcHd3R6/XW8UpJiaGQ4cOkZKSgpOTE1AwobBz5062bdvGyJEjWb58OSNHjmTkyJEAzJs3j2+++Ybs7KLViIUp69z3qk6eXbp0icuXL7Nt2zb+/ve/YzQa+eMf/8jvfvc7vv3222KfM2LECI4fP06rVq3w9fVl69at3Lt3j5kzZ7Jv3z7efvttPv30U5o2bcr69eupX79+kX1o5e6CURQFX19fMf8psPc5m2bK8rj3MJf/PDoXeaiNr41eUZeaQoqHpJwtLSYGvc5S4j7k0X15RhMXUjIerbgX3M6nZODhpC92AO7v6Uwdz4L7SitjNV8KRwJS8gRkuEjKV5ARE5DjAXJcpHhIytnKxKSg6Z8OL1fblT+rqkpa+gN0Bmeyck1Wg/jCK/kFJf0/Xuqv2CsC5FifKmDuHaKq8PDRKQKpNjMv4MdS/mI6+xeaELBu8lf8aQHm+woG/zJytjrp3r07a9assfzu5uZm+bljx6ITDXv27GHhwoWcOXOG9PR08vPzyc7OJjMzE1dXVxISEhg4cGCVnE6fPs0LL7xgdV+XLl1Yvnw5RqPR8r60bdvW8riiKPj7+5e6eg3wzDPPWH32w8PDWbJkCUaj0dKRPCwszOo5iYmJHD9+nM2bN1vuMw/+kpKSOHv2LHq93ipeTz31VKmd6hMSEujQoUOJq8jFkZiYSEZGBrVr17a6Pysri4sXL6IoCqdPn2bcuHFWj4eHh7N3795S911co8Cq8HgTO5PJRE5ODn//+98JDg4GCiYyOnbsyNmzZ4udWHF0dOT999+3um/48OFMmjSJY8eOsXPnThITE1m4cCGTJk1ix44dNj0Gq+Optj3/jNHpdPj6+tpbAyj4AvHw8LC3Rrk8dhy9Rq7RRJv6nrRtUH3dTX9KMakppORsZWLi6KCjZYAnLQM8GRRmuz4GUt4fKR4gx0VKvoKcmEjxADkuUjxATs5KiomiKNTyKlgp83Kx7b4tTf9y8x91+C+ms38p5/YXbQBYsG1mnhHz4lhWXsHzbI2TXlfspf4eP7ffz0VHWO087j3MxcmoQ8GBgO8OolMUdAqP/n30s67g5+pCcanYG+jm5lbiAK3wgB3g8uXLvPDCC4wfP5758+fj4+NDTEwMI0eOJDc3F1dXV1wq+PqVwTzIfryhm6IoNrls3uPHnZGRwdixY5k0aVKRbRs2bMj58+cr/BqViVNGRgYBAQHs27evyGPe3t5VuuyZrcvdH++hEBAQgF6vtwzQAUulwZUrV0qtfjCzd+9eTp48ybp163jjjTfo27cvbm5uDBo0iFWrVhX7HK3cXTAmk4nr169Tv359u8+IqqpKWloa3t7edi87LM1DVVU+PVxQ6j6kmlfRfyoxqUmk5KykmEhxkeIhyUVKvoKcmEjxkOQixQPk5KykmFSni1XTvzLmJCrioaoq2Xkmq1V7S2n/4+X8j5oCljRJYPk5p7imfybuZZZ+3n99Dwead6+D04NslKzCg8TiS2yVUgbv5vsddArKo59VkwlHvQMOupK31ykF21dnLh05cgSTycTixYstA8KtW7dabRMSEsKePXssjc8ex2AwYDSWPqHSsmVLy3nwZmJjYwkODsbBwcFSulyZEua4uDir3w8ePEjz5s1LHeCGhoZy6tSpYiczVFWlWbNm5OfnEx8fbyl3P3v2LGlpaSXuMyQkhHXr1nH37t1iV9OLi1NoaCi3bt1Cr9cTFBRUxCM/P5+WLVsSFxfHsGHDrI6xLGxd7v54d/cuXbqQn5/PxYsXadq0KQDnzp0DoFGjRmXuLzs7m4kTJ7J582YcHBwwGo2W9z8vL6/EnNLK3QWjqioPHz6s8ptjK6rrWpcVpTSPI5fvcSElAxdHB15oXz0N48rrUpNI8ZCUs1JiAnJcpHiADBdJ+QoyYgJyPECOixQPSTkrJSYgx6W8Hoqi4PJoJduWWJr+5eTzw/0MVAdHMvNMVoN4q87+uUYcTHm4GUy4O+nROTpiUlVMJhWTSsHPasHPhQeXRhWMJQziK4tCCQN43WOTArqCn7PzCnoR3MvMLbK9avY2qZbBf7NmzcjLy2PlypX85je/ITY2tsjq6ptvvknbtm2ZMGEC48aNw2AwsHfvXgYOHIivry9BQUHExcWRnJyMu7t7sQPUqVOn0qlTJ+bOncvgwYM5cOAAq1atsjrPvbJcuXKF1157jbFjx3L06FFWrlzJkiVLSn3O9OnTeeaZZ4iKimLUqFG4ublx6tQp/vvf/7Jy5Uqeeuop+vTpw9ixY1mzZg16vZ4pU6aUulo+dOhQ3n33Xfr378+CBQsICAjg2LFj1KtXj/DwcIKCgkhKSiIhIYEGDRrg4eFBr169CA8Pp3///ixcuJDg4GBu3LjBv//9b/r3709oaCiTJk1i+PDhhIWF0aVLFzZv3szJkyfLbBxX1XL3CxcukJGRwa1bt8jKyuLo0aM4OTnRunVrDAYDvXr1IjQ0lBEjRrB8+XJMJhMTJ07k//2//2e1ul4Sc+fOpW/fvnTo0AEoGPS/8cYbDB8+nFWrVtGlS5cq+ZeFNkh/wlEUpUgJjUSPTx5ddq1fuwA8nKvnkh/ldakppHhIQlJMpLhI8QBZLlKQEhMpHiDHRYqHJCTFRIqLBA+rpn/uTuV6TnZ2NklJSdSv5Yqzs3OJ25U0eC+4r9D9Juuf84xGFEVXzPY/bgeg8mjwbyzf4D8r10h2npGrdzOLPJaZa+Tuw1y+v3EfKBi8G+o0Yfqsd3n3vb/w5p//TKdnujD1rVm8NmE0N9IyyVKc8PJvyNadX/Lu7Jl07twZZxcXwjp15jcvDiQr18ikyX9k1MgRtGrViqysLJKSkoq8dmhoKFu3bmXmzJnMnTuXgIAA5syZQ2RkJPBjtUBlqgaGDRtGVlYWnTt3xsHBgcmTJ5d62TYoWPXev38/b731Fl27dkVVVZo2bcrgwYNRFAUHBwc2bNjAqFGj6NatG3Xr1mXevHmWxnfFYTAY2L17N1OnTqVv377k5+fTqlUryznYAwYM4B//+Afdu3cnLS3Ncgm2r776irfeeovhw4eTmpqKv78/zz33HP7+/jg4ODBkyBAuXbrEtGnTyM7OZsCAAYwfP56vv/66wrGqCKNGjWL//v2W38PDw4GCS+wFBQWh0+n417/+xR/+8Aeee+453NzceP7558ucIAH4/vvv2bp1q9Vq/+9+9zv27dtH165dadGiBVu2bCn2ubYqd1dUCdO6NUh6ejpeXl7cv3+/2i65Ufh6qFU5V8MWqKrKvXv3qFWrlt3LDkvyuJ+ZR+d3vyEn38Q/JjxLaMNadnOpSaR4gJyclRQTKS5SPCS5SMlXkBMTKR6SXKR4gJyclRQTKS5SPCrqYh6kN27cuNRBemU9srOzcXZ2LtFDLWPAbyw8KWAqY4LgsW2qm9JK/kuqANApCqgm9A7mUwCKblNcrCIiImjfvj3Lly+3mb+qqpamc/b+7EjwMLuUlbM1RVZWFpcuXaJJkyZFKhsqMg7VVtKrAZ1Oh7+/v93PlTRj6y/vylKSx86E6+Tkm3jK34MOgd52dalppHhIylkpMQE5LlI8QIaLpHwFGTEBOR4gx0WKh6SclRITkOMixQPkuDzehOtxFEXBQVFwALDhvJNaZPCuYjSZUFFKXNEvXAVgLKVqwIzVZIANe/8VxMR68J6dZyQ9K48rP2QWmhQoPCFQ8qRAaU3/7D0QNSPFA8rO2Z8a2iC9GlAUpdRLINQkiqLgWoFrV9a0h6qqllL3IZ0Ca+TDLj0m9kBKzkqLiQQXKR4gx0VKvoKsmEjwADkuUjxATs5Ki4kEFykeIMdFUZQil7Oqydd2UMAB2/5/UFVV1McG70aTajlXXy1jgF9aBYDKj+f956tQuHmf0aSSazSRlpVbaffyNP0rrgLAQSl9m6o2/TOX3UvAnjn7OFp3d8GYTCaSk5Mt50PYE1VVLV0c7V0SU5xHwtU0ztx6gJNex287NLCrS00jxQPk5KykmEhxkeIhyUVKvoKcmEjxkOQixQPk5KykmEhxkeIhyUVVVXJzczEYDCJiYouSauXRoFRXycF/SR6qWjBEL3Le/6Pf/7vn2xJL/o2PJg6MJZwWUP1N/0pY0S+l5P/xCQLzVQB+vK/g/prOG4k5q3V3F4g5UaSc7i9hVhaK9/j0UMFl137VNgAv15orU5EcE3sgKWelxATkuEjxABkukvIVZMQE5HiAHBcpHpJyVkpMQI6LFA+Q42KLa3/bCntPxpopzkNRlILBroPtB4alndNfcAoAhUr+S9m+mOaBULDeb1TVcjf9qwglDvCVHy/156AoKLqipwg8vn3hSYHSBuBPWs5qg/QnHEVRSr0cgz09HmTn8a/jNwAY0rl6r41elos9kOIhCUkxkeIixQNkuUhBSkykeIAcFykekpAUEykuUjxAlosUlBIasv0cPCwN62w8R1Gepn8Fq/flL/l/vOmf5Xcbj5tLK/lXVROBBhV9NUyYVARzrmjl7hqlYjKZLKVT9pyJLM7ji8QbZOYaaernRqeg6u3oXpaLPZDiIQlJMZHiIsVDmosUpMREiockFykekpAUEykuUjykuUhBVVXy8/PR6/V2PwVAgoctXGzV9O9xj6JN/8pe0X+86Z+5/N/SK+DRaQFmymr6J2A+x1LuXtWVfW2QXg3odDoaNGgg4gtWURQ8PDzs/oVSnIe51H1o54Y16ic5JvZCSs5KiokUFykeklyk5CvIiYkUD0kuUjxATs5KiokUFyke0lwMBoO9FSxIaU4mxQPkuBT2qMmmf4+v6BsfnQJQXCd8e6A1jhOKoii4u7vbWwMocHFycrK3RhGP76/f58T1+xgcdLwYWjMN40pysRdSPEBOzkqLiQQXKR4gx0VKvoKsmEjwADkuUjxATs5Ki4kEFykeIMdFWtduCZMWUjxAjktNeVS16V9NY6tyd/svQzyBGI1Gzp07h9Fow4svVhKTyURKSordmyk87mG+7FrvNv74uNXsbK3UmNgTKTkrKSZSXKR4SHKRkq8gJyZSPCS5SPEAOTkrKSZSXKR4SHJRVZXs7GwRjQ5VVSUvL8/uLlI8JLlI8TC7SMpZW5S7a4P0asLeX7BmFKXg2qz2nnEr7JGZm8/nCQUN44Z2CrSriz2R4mFGQs5KiokUFyke0lwk5CvIiYkUD0kuUjzMSMhZSTGR4iLFQ5qLhMGOmepY1d+3bx+KopCWlmZXj8pSkktQUBDLly+3/K4oCjt37qxxj1mzZtG+fftqe93ikJSztih31wbpTziKooi4ZmBhjy8Tb5KRk09QbVeeaVLbri72RIqHJCTFRIqLFA9pLlKQEhMpHpJcpHhIQlJMpLhI8ZDmUl1ERkZalQObbxcuXCh2e0VRqjzgiYiIYMqUKVb3Pfvss9y8eRMvL69y7cMWHraiIi43b97k+eefL9d+KzqwtmdMEhMTGTp0KIGBgbi4uNCqVSvef/99q21u3rzJSy+9RHBwMDqdrkgOFMfdu3fp168f7u7udOjQgWPHjlk9PnHiRJYsWVLqPrRyd41yYTKZuH37tt1n8At7fHK4oNR9cKeG6HQ1/8GWGBONAiTFRIqLFA9pLlKQEhMpHpJcpHhIQlJMpLhI8ZDmUp306dOHmzdvWt0aN25cZLvc3NxqK6k2GAz4+/uXeyBVk6Xdubm5NnPx9/evtj4H9ix3j4+Pp06dOnz88cecPHmSP//5z8ycOZNVq1ZZtsnJycHPz4+3336bdu3alWu/8+fP58GDBxw9epSIiAhGjx5teezgwYPExcWVOdjXyt0Fo9PpaNy4sd27uELBbI6Pj4/dZ/7MHmdvP+DYlTT0OoXfdazZhnGPu0iJib09QE7OSoqJFBcpHpJcpOQryImJFA9JLlI8QE7OSoqJFBcpHtJcqrOBnZOTE/7+/lY3BwcHIiIiiIqKYsqUKfj6+tK7d28AVqxYQUhICG5ubgQGBjJhwgQyMjKs9hkbG0tERASurq7UqlWL3r17c+/ePSIjI9m/fz/R0dGW1c3k5ORiy9137NhB69atcXJyIigoqMiKafPmzXn33XcZMWIEHh4eNGzYkA8++KDUYzUfU1RUFF5eXvj6+jJjxgyrgW1QUBBz585l2LBheHp6MmbMGABiYmLo2rUrLi4uBAYGMmnSJB4+fAgUlJmnpKTQr18/XFxcaNy4MZs3by7y+o+Xu1+7do2hQ4fi4+ODm5sbYWFhxMXFsXHjRmbPnk1iYqIlThs3bgQgLS2NUaNG4efnh6enJz169CAxMdHiAfDee+9Rt25dPDw8GDlyJNnZ2aXGpaqMGDGC6OhounXrRpMmTXjllVeIjIzkn//8p2WboKAgoqOjGTZsWLkrJk6fPs2QIUMIDg5mzJgxnD59GoC8vDzGjRvH2rVry3Xag1buLhi9XkbjfEVRcHR0tPsXvtnjs8PXAPh/reri52GfDqbSYmJvDzMSclZSTKS4SPGQ5iIhX0FOTKR4SHKR4mFGQs5KiokUFykeVXVRVZW8HKPNbvm5pnJva8uV1E2bNmEwGIiNjWXt2rUoSkGn+RUrVnDy5Ek2bdrEt99+y7Rp0yzPSUhIoGfPnrRq1YoDBw4QExNDv379MBqNREdHEx4ezujRoy2r9oGBRfshxcfHM2jQIIYMGcKJEyeYNWsWM2bMsAxUze/J0qVLCQsL49ixY0yYMIHx48dz9uzZMo9Jr9dz6NAhoqOjWbp0KevWrbPaZvHixbRr145jx44xY8YMLl68SJ8+fRgwYADHjx/ns88+IyYmhqioKEuZ+fDhw7l69Sp79+5l+/btrF69mpSUlBI9MjIy6NatG9evX+eLL74gMTGRadOmYTKZGDx4MFOnTqV169aWOA0ePBiAgQMHkpKSwq5du4iPjyc0NJSePXty7949dDod27ZtY9asWbz77rscOXKEgIAAVq9eXeZ77e7uXupt3LhxZe6jMOnp6fj4+FToOY/Trl07vv32W/Lz8/n6668JCQkBYOHChURERBAWFlbmPmxV7m7/vxhPICaTifPnz9O8eXO7N5kwdwqtU6eOXWfwTSYTV27c4h9HCwbpQzo3tKuLlJhI8DC7SMhZaTGR4CLFQ5KLlHw1u0iJiQQPSS5SPMwuEnJWWkwkuEjxqKpLfq6JDybvryaz0hkT3Q1Hp/Ln9Zdffml1ScLnn3+ebdu2AQWr1QsXLrQ8pqoqEydORK/XoygKQUFBzJs3j3HjxlkGggsXLiQsLMxqYNi6dWvLzwaDAVdXV/z9/Ut0Wrp0KT179mTGjBkABAcHc+rUKRYtWkRkZKRlIqJv375MmDABgOnTp7Ns2TL27t1LixYtStx3YGAgy5YtQ1EUWrRowYkTJ1i2bJlVKXWPHj2YOnWq5fdRo0bx8ssvW0qrmzdvzooVK+jWrRurV6/m0qVL7Nq1i0OHDtGpUycAPvzwQ1q2bFmix5YtW0hNTeXw4cOWwWyzZs0sj7u7u6PX663iFBMTw6FDh0hJSbFUVyxevJidO3eybds2RowYwfLlyxk5ciQjR44EYN68eXzzzTdlrqYnJCSU+rinp2epjxfmu+++47PPPuPLL78s93OK409/+hPjx4+nadOmBAUF8eGHH3L+/Hk2bdrEgQMHGDduHLt37yYsLIy//e1vxa7Q26rcXRukP+EoioKfn5/dZ4gVReHIrXzSs/Op7+1C12a+dnWREhMJHpKQFBMpLlI8pLlIQUpMpHhIcpHiIQlJMZHiIsVDmkt10r17d9asWWP53c3NzfJzx44di2y/b98+3nvvPc6cOUN6ejr5+flkZ2eTmZmJq6srCQkJDBw4sEpOp0+f5oUXXrC6r0uXLixfvhyj0WiZNGnbtq3lcUVR8Pf3L3X1GuCZZ56xek/Dw8NZsmQJRqPRMmn3+AptYmIix48ftyphV1UVk8lEUlIS58+fR6/XW8Xrqaeewtvbu0SPhIQEOnToUKHV5sTERDIyMqhd27rRc1ZWFhcvXkSv13P69Okiq97h4eHs3bu31H0XniCoCt9//z39+/fnz3/+M7/85S+rtC8vLy+2bNlidV+PHj1YtGgRmzdv5tKlS5w9e5bRo0czZ86cEpvI2aLcXRukP+HYquTCFh6fHXm0it4p0C4N4wq7SImJBA9JSIqJFBcpHtJcpCAlJlI8JLlI8ZCEpJhIcZHiUVUXvUHHmOhuNvEwX3Pa2dm5XC56Q8VW/d3c3EocoBUesANcvnyZfv36MX78eObPn4+Pjw8xMTGMHDmS3NxcXF1dcXFxqdDrVwZzHAwGQ5H7bdHo7/HjzsjIYOzYsUyaNKnItg0bNuTcuXMVfo3KxCkjI4OAgAD27dtX5LHSJgTKQ+FqiuJ45ZVXWLt2banbnDp1ip49ezJ69Gj+9Kc/VcmnODZs2IC3tzcvvPACL774Iv3798fR0ZGBAwcyc+bMYp9jzhVtkK5RKlLKuM7dSudw8j0cdAoDw2r+2uiFkRITKR6SkBQTKS5SPKS5SEFKTKR4SHKR4iEJSTGR4iLFo6ouiqJUqOS8NFRVxag64OjkYPfJiyNHjmAymVi8eLFl1Xnr1q1W24SEhLBnzx5mz55d7D4MBgNGo7HU12nZsiWxsbFW98XGxhIcHIyDg4Ol3L0y59/HxcVZ/X7w4MEyT30JDQ3l1KlTxU5mqKpKs2bNyM/PJz4+3lLufvbs2VKv+x4SEsK6deu4e/dusavpxcUpNDSUW7duodfrCQoKKuKRn59Py5YtiYuLY9iwYVbHWBZVLXc/efIkPXr04NVXX2X+/Pk2b1aXmprKnDlziImJAcBoNJKXlwcUNJIrKafMFQ9ad3eB6HQ6mjdvbvcve7OLhD88Wx+tondvUQd/L2e7ukiJiRQPs4uEnJUWEwkuUjwkuUjJV7OLlJhI8JDkIsXD7CIhZ6XFRIKLFA9pLs7O9v2/mpnmzZuTl5fHqlWruHTpEh999FGR1dU333yTw4cPM2HCBI4fP86ZM2dYs2YNd+7cAQq6fMfFxZGcnMydO3eKHTxNnTqVPXv2MHfuXM6dO8emTZtYtWoVr7/+OlC11dErV67w2muvcfbsWT755BNWrlzJ5MmTS33O9OnT+e6774iKiiIhIYHz58/z+eefWxrHtW7dmj59+jB27Fji4uKIj49n1KhRpa6WDx06FH9/f/r3709sbCyXLl1ix44dHDhwwBKnpKQkEhISuHPnDjk5OfTq1Yvw8HD69+/P7t27SU5O5rvvvuOtt94iPj4evV7P5MmTWb9+PRs2bODcuXO88847nDx5ssy4NGvWrNRbnTp1Snzu999/T/fu3fnlL3/Ja6+9xq1bt0hLSyM1NdVqu4SEBBISEsjIyCA1NZWEhAROnTpVphvAlClTmDp1KvXr1wcKTn/46KOPOH36NB988AFdunQp9nnmxn5V/Rzb/1vgCSU/P9/eCkDBbI75Zi9y8o3sMDeM62Sfy64VRkJMJHmYkZCzkmIixUWKhzQXCfkKcmIixUOSixQPMxJyVlJMpLhI8ZDoIoGQkBCWLFnCX/7yF9q0acPmzZtZsGCB1TbBwcHs3r2bxMREOnfuTHh4OJ9//rnligqvv/46Dg4OtGrVCj8/P65cuVLkdUJDQ9m6dSuffvopbdq0YebMmcyZM4fIyEjgx3hUJi7Dhg0jKyuLzp07M3HiRCZPnmy5zFppx71//37OnTtH165d6dChAzNnzqRevXoWh/Xr11OvXj26devGiy++yJgxY0od2BoMBnbv3k2dOnXo27cvbdu25b333rOs6A8YMIA+ffrQvXt3/Pz8+OSTT1AUha+++ornnnuO4cOHExwczJAhQ7h8+bLltQYNGsSMGTOYNm0aHTt25PLly4wfP77CcaoI27dvJzU1lY8//piAgADq1atHvXr16Ny5s9V2HTp0oEOHDsTHx7NlyxY6dOhA3759y9z/119/zYULFyxNAgGioqJo0qQJTz/9NLm5ubzzzjvFPrcquVIYRZXyKawh0tPT8fLy4v79+xXqGlgRjEajiC6uIKOM64vEG0z65Bh13B2Jmd4Dg6N9z7KQEBNJHiAnZyXFRIqLFA9JLlLyFeTERIqHJBcpHiAnZyXFRIqLFI+KumRnZ5OUlETjxo1tvupd0XPSqxNzSbW5u/tPzSMiIoL27duzfPlyu7vYGikeZhcpOWtuqtekSRNcXV2tHqvIOFQ7J/0JR6fTlXrJiZrg00MFM5ZDng6y+wAdZMREkockJMVEiosUD5DlIgUpMZHiAXJcpHhIQlJMpLhI8QBZLlIwXzve3kjxADkuUjykoSgKDg4OWrm7RumoqkpeXp7dypaS7zzku4s/oCgwoL2/iPIpe8dEmockJMVEiosUD2kuUpASEykeklykeEhCUkykuEjxkOYiBXMTLnvHRIqHJBcpHtKw1Wkr2iC9mrB3yZQZVVW5e/eu3T5Anx6+CsBzzf1wNmWJ+CDbOybSPMxIyFlJMZHiIsVDmouEfAU5MZHiIclFiocZCTkrKSZSXKR4SHOxd8lwYcrqzF5TVMZj3759Ni11r4pLdSDFA2TlrC0mL7Rz0jWqjdx8E8++t4c7GbmsfaUjfdpoJVwaGhoaGhoaGlWlOs9J19DQqDylfTYrMg61/7TuE4iqqmRkZIiYCVVVldzcXLu47Dl9mzsZufh5ONHjKT+7eTyOPWMi0cPsIiFnpcVEgosUD0kuUvLV7CIlJhI8JLlI8TC7SMhZaTGR4CLFQ5KLqqoYjUa7e5hdJJRUS/GQ5CLFw+wiKWe1cnehmEwmrl27VuWL2NsCVVVJS0uzS9J+8qjUfWDHBuh1it08HseeMZHoAXJyVlJMpLhI8ZDkIiVfQU5MpHhIcpHiAXJyVlJMpLhI8ZDmkpuba28FC1JKqqV4gBwXKR4gK2dtMXlh/1bbGtWKTqcr9ZqJ1cXVu5n873wqAEM6NbSbR3FIcZHiIQlJMZHiIsUDZLlIQUpMpHiAHBcpHpKQFBMpLlI8QJaLFKR0EJfiAXJcpHhIQ+vurlEuVFUlJyenxmdltx65iqrCL5r50rC2q908ikOKixQPSUiKiRQXKR7SXKQgJSZSPCS5SPGQhKSYSHGR4iHNRQpSSqqleEhykeIhDa3cXTCKomAwGER0GVRVlQcPHtToByjfaGLrkYJS9yGdA+3mURJSXKR4gJyclRQTKS5SPCS5SMlXkBMTKR6SXKR4gJyclRQTKS5SPKS5SLgagRkpJdVSPECOixQPkJWztpi8kHM0TxA6nY4mTZqISBadToevr2+Nuuw9m8rt9Bxquxn4ZSt/u3mUhBQXKR5mFwk5Ky0mElykeEhykZKvZhcpMZHgIclFiofZRULOSouJBBcpHpJcFEXBycnJ7pNKZhdHR0ebu+zbtw9FKeiXZE+PylCaS1BQkNUl3xRFYefOnTXuMWvWLNq3b18tr1uSi6Sc1crdhSKp8YeqqmRl1ez1yT89dAWAAR0bYNDr7OZRElJcpHiYXSTkrLSYSHCR4iHJRUq+ml2kxESChyQXKR5mFwk5Ky0mElykeEhyUVWV/Pz8avGIjIxEUZQitwsXLpToUtVVyYiICKZMmWJ137PPPsvNmzfx8vIq1z4klXZXxOXmzZs8//zz5dpvRQfW9oxJYmIiQ4cOJTAwEBcXF1q2bMnSpUuLuOTk5PDWW2/RqFEjnJycCAoKYv369SXu9+7du/Tr1w93d3c6dOjAsWPHrB6fOHEiS5YsKdVNK3cXjMlk4tatW3bv4momMzOzxl7r5v0s9p5NAWBIp0C7eZSFFBcpHpJyVkpMQI6LFA+Q4SIpX0FGTECOB8hxkeIhKWelxATkuEjxADkueXl51bbvPn36cPPmTatb48aNi2xn7tZdHZ8bg8GAv79/hVZea+rzW54u5eV18ff3x8nJqapKVfawNfHx8dSpU4ePP/6YkydP8uc//5m3336bVatWWW03aNAg9uzZw4cffsjZs2f55JNPaNGiRYn7nT9/Pg8ePODo0aNEREQwevRoy2MHDx4kLi6uyIRPcdgiLtog/QlHURRq165dY+UfWw9fw6TC0419aOLnbjeP0pDiIsVDEpJiIsVFioc0FylIiYkUD0kuUjwkISkmUlykeEhzqU6cnJzw9/e3ujk4OBAREUFUVBRTpkzB19eX3r17oygKK1asICQkBDc3NwIDA5kwYQIZGRlW+4yNjSUiIgJXV1dq1apF7969uXfvHpGRkezfv5/o6GjLqn1ycnKx5e47duygdevWlhXXwiumiqLQrFkzFixYwIgRI/Dw8KBhw4Z88MEHpR6r+ZiioqLw8vLC19eXGTNmWK2yBgUFMXfuXIYNG4anpydjxowBICYmhq5du+Li4kJgYCCTJk3i4cOHKIqCXq8nNTWVfv364eLiQuPGjdm8eXOR13+83P3atWsMHToUHx8f3NzcCAsLIy4ujo0bNzJ79mwSExMtcdq4cSMAaWlpjBo1Cj8/Pzw9PenRo4dlO71ej6IovPfee9StWxcPDw9GjhxJdnZ2edOhUowYMYLo6Gi6detGkyZNeOWVV/j973/PP//5T8s2//nPf9i/fz9fffUVvXr1IigoiPDwcLp06VLifk+fPs2QIUMIDg5mzJgxnD59GiiYtBo3bhxr167FwcGhVDdzuXtVP8faIP0JR1VVMjMza6QUxWhS+exwQan70M4N7eZRFlJcpHhIQlJMpLhI8ZDmIgUpMZHiIclFiockJMVEiosUj6q6qKpKXna27W455d/WlrHbtGkTBoOB2NhY1q5da9l3dHQ0J0+eZNOmTXz77bdMmzbN8pyEhAR69uxJq1atOHDgADExMfTr1w+j0Uh0dDTh4eGMHj3asmofGBhY5HXj4+MZNGgQQ4YM4cSJE8yaNYsZM2ZYBqpmjyVLlhAWFsaxY8eYMGEC48eP5+zZs2Uek16v59ChQ0RHR7N06VLWrVtntc3ixYtp164dx44dY8aMGVy8eJE+ffowYMAAjh8/zmeffUZMTAxRUVGoqorRaCQyMpKrV6+yd+9etm/fzurVq0lJSSnRIyMjg27dunH9+nW++OILEhMTmTZtGiaTicGDBzN16lRat25tidPgwYMBGDhwICkpKezatYv4+HhCQ0Pp2bMnP/zwA0ajkc8++4xZs2bx7rvvcuTIEQICAli9enWZ77W7u3upt3HjxpW5j8Kkp6fj4+Nj+f2LL74gLCyMhQsXUr9+fYKDg3n99dfJysoqcR/t2rXj22+/JT8/n6+//pqQkBAAFi5cSEREBGFhYWV62Oo0AO066dWAoii4ubmJmQnNzs7GxcWl2l/n/86ncuN+Nl4ujvRp4283j/IgxUWKh6SclRITkOMixQNkuEjKV5ARE0keIMdFioeknJUSE5DjIsUDKu+Sn5PDild/Vw1GZTNp03YcnZ3Lvf2XX36Ju/uP1ZbPP/8827ZtA6B58+YsXLjQ8piqqkyaNMmyMhkUFMS8efMYN26cZSC4cOFCwsLCrAaGrVu3tvxsMBhwdXXF37/o/03NLF26lJ49ezJjxgwAgoODOXXqFIsWLSIyMtKyXd++fZkwYQIA06dPZ9myZezdu7fUEurAwECWLVuGoii0aNGCEydOsGzZMqtS6h49ejB16lTL76NGjeLll1+2lFY3b96cFStW0K1bN1avXk1SUhK7du3i0KFDdOrUCYAPP/yQli1bluixZcsWUlNTOXz4sGUw26xZM8vj7u7u6PV6qzjFxMRw6NAhUlJSLGXzixcvZufOnWzfvp2RI0cSHR3NyJEjGTlyJADz5s3jm2++KXM1PSEhodTHPT09S328MN999x3bt2/nyy+/tNx36dIlYmJicHZ25p///Cd37txhwoQJ/PDDD2zYsKHY/fzpT39i/PjxNG3alKCgID788EPOnz/Ppk2bOHDgAOPGjWP37t2EhYXxt7/9rdw9DSqDNkivBnQ6XbGzdPZAURSrWaXqxNww7sXQ+jg7WpeC1KRHWUhxkeIBcnJWUkykuEjxADkuUvIV5MREigfIcZHiAXJyVlJMpLhI8QBZLtVJ9+7dWbNmjeV3Nzc3y88dO3a02lZRFPbt28eCBQs4c+YM6enp5Ofnk52dTWZmJq6uriQkJDBw4MAqOZ0+fZoXXnjB6r4uXbqwfPlyjEajpcTZvLJqdvP39y919RrgmWeesZqgCw8PZ8mSJVb7fXyFNjExkePHj1uVsJtXaJOTkzl//jx6vd4qXk899RTe3t4leiQkJNChQ4cK5VhiYiIZGRnUrl3b6v6srCwuXbqEXq/n9OnTRVa9w8PD2bt3b6n7LjxBUBW+//57+vfvzzvvvEPv3r0t95tMJhRFYfPmzZbB9NKlS/nd737H6tWri50M8/LyYsuWLVb39ejRg0WLFrF582YuXbrE2bNnGT16NHPmzCm2iZyiKOh0uipPymqD9GrAZDJx9+5dfHx87H4ZDXPplKura7XO4KekZ/PN6YIvqcdL3WvSozxIcZHiAXJyVlJMpLhI8ZDkIiVfQU5MpHhIcpHiAXJyVlJMpLhI8aiqi97JiUmbttvMIz8/33K+cXleuyK4ubmVOEArPGAHSEpK4te//jXjxo1j/vz5+Pj4EBMTw8iRI8nNzcXV1bVGqiDMpct6vfXQSVEUmzQJe/y4MzIyGDt2LJMmTSqybWBgIGfOnKnwa1QmThkZGQQEBLBv374ij3l5eVXpOumFqymK45VXXmHt2rWlbnPq1Cl69uzJ6NGjmT59OqqqWnI2ICCA+vXrW612t2zZElVVuXbtGs2bNy/TccOGDXh7e/PCCy/w4osv0r9/fxwdHRk4cCAzZ84s9jlaubtgVFXlzp071KpVy94qAJYvsepkW/w1jCaVjo1qEVzXw24e5UWKixQPSTkrJSYgx0WKB8hwkZSvICMmkjxAjosUD0k5KyUmIMdFigdU3kVRlAqVnJeGqqoYs7NxdHa2+8RFfHw8JpOJJUuWWFadt27darVNSEgIe/bsYfbs2cXuw2AwlDmYbNmyJbGxsVb3xcbGEhwcjIODQ5UGXHFxcVa/Hzx4kObNm5fagCw0NJRTp04VO5mhqirBwcHk5+cTHx9vKXc/e/Zsqdd9DwkJYd26dZYJw8cpLk6hoaHcunULvV5PUFBQEQ+j0UjLli2Ji4tj2LBhVsdYFlUtdz958iQ9evTg1VdfZf78+WRnZ1tNonTp0oVt27aRkZFhmRA4d+4cOp2OBg0alOmXmprKnDlziImJAcBoNFquepCXl1elCYryoDWOe8JRFIVatWpV65esyaTyaQkN42rSo7xIcZHiIQlJMZHiIsVDmosUpMREiockFykekpAUEykuUjykuUihefPm5OXlsWrVKi5dusRHH31UZHX1zTff5PDhw0yYMIHjx49z5swZ1qxZw507d4CC7ulxcXEkJydz586dYle+p06dyp49e5g7dy7nzp1j06ZNrFq1itdffx3A8p5U5r25cuUKr732muUSYCtXrmTy5MmlPmf69Ol89913REVFkZCQwPnz5/n888+JiopCURRat25Nnz59GDt2LHFxccTHxzNq1KhSV8uHDh2Kv78//fv3JzY2lkuXLrFjxw4OHDhgiVNSUhIJCQncuXOHnJwcevXqRXh4OP3792f37t0kJyfz3Xff8dZbbxEfH49er2fy5MmsX7+eDRs2cO7cOd555x1OnjxZZlyaNWtW6q1OnTolPvf777+ne/fu/PKXv+S1117j1q1b3Lp1i9TUVMs2L730ErVr12b48OGcOnWK//u//+ONN95gxIgR5aoqmDJlClOnTqV+/fpAwaD/o48+4vTp03zwwQcldom3Vbm7Nkh/wlFVlQcPHlRr19LvLv7A1btZeDjr+VXbALt5lBcpLlI8JCEpJlJcpHhIc5GClJhI8ZDkIsVDEpJiIsVFioc0FymEhISwePFi/vKXv9CmTRs2b97MggULrLYJDg5m9+7dJCYm0rlzZ8LDw/n8888tq6qvv/46Dg4OtGrVCj8/P65cuVLkdUJDQ9m6dSuffvopbdq0YebMmcyZM8fSNM78nlTmvRk2bBhZWVl07tyZiRMnMnnyZMtl1ko77v3793Pu3Dm6du1Khw4dmDlzJvXq1bOsYK9fv5569erRrVs3XnzxRcaMGVPqwNZgMLB7927q1KlD3759adu2Le+9955lRX/AgAH06dOH7t274+fnxyeffIKiKHz11Vc899xzDB8+nODgYIYMGcLly5epU6cORqORQYMGMWPGDKZNm0bHjh25fPky48ePr3CcKsL27dtJTU3l448/JiAggHr16tGkSRM6d+5s2cbd3Z3//ve/pKWlERYWxssvv0y/fv1YsWJFmfv/+uuvuXDhgqVJIEBUVBRNmjTh6aefJjc3l3feeafY59qq3F1Rf2bfBOnp6Xh5eXH//v0KdQ2sCCaTidu3b1O3bl0R50ump6fj6elZbTOzE7cc5d/Hb/L7Zxoxt38bu3mUFykuUjxATs5KiokUFykeklyk5CvIiYkUD0kuUjxATs5KiokUFykeFXXJzs4mKSmJxo0b42yjMvfCHnl5eTg6OoqIibnBmr3zpDIeERERtG/fnuXLl9vdxdZI8TC7SMlZc1O9Jk2aFFmxr8g4VDsnvRrQ6XQEBBS/olzTKIpSrZcH+CEjh90nbwEll7rXhEdFkOIixQPk5KykmEhxkeIBclyk5CvIiYkUD5DjIsUD5OSspJhIcZHiAXJcFEXBYDDYWwMocHm8WdvP2QPkuEjxAHk5q5W7C8VkMnHz5k2bdHusKuZZ2eoqmNhx9Bp5RpV2DbxoVa/kGaHq9qgIUlykeICcnJUUEykuUjwkuUjJV5ATEykeklykeICcnJUUEykuUjwkuaiqSm5urt09zC5Go9HuLlI8JLlI8TC7SMpZrbu7UFRV5f79+6WeF/IkoKoqnx66CpS+iq4hn59Lzmo8GWj5qvFTQ8tZjZ8aRqMRR0dHe2v85Cnu0mUa1cOTlrPaIP0JR1GUajv3Pi7pLpfuPMTN4EC/dvXs5lFRpLhI8ZCEpJhIcZHiAbJcpCAlJlI8QI6LFA9JSIqJFBcpHiDLRQqKopR6qbKfmwfIcZHiIQ2t3F2jXJhn76uj/OPTQwXdMX/Tvh5uTqXP91SnR0WR4iLFQxKSYiLFRYqHNBcpSImJFA9JLlI8JCEpJlJcpHhIc5GCqqrk5+fbPSZSPCS5SPGQhq3K3bVBejWgKAq+vr527y5opjo6yaZl5vLV92U3jKtuj8oixUWKh6SclRITkOMixQNkuEjKV5ARE5DjAXJcpHhIylkpMQE5LlI8QI6LlIZgULlrk1cHUjxAjosUD5CVs7bgyToaIeh0Onx9fe2tARR8eDw8PGy+338cvU5uvolWAZ60rV92J9Lq8qgMUlykeICcnJUUEykuUjxAjouUfAU5MZHiAXJcpHiAnJyVFBMpLlI8QI6Loihizu2VUlItxQPkuEjxAHk5q5W7C8VkMnH16lW7d3GFgpKLe/fu2bQURVVVPnlU6j706YblSsLq8KgsUlykeICcnJUUEykuUjwkuUjJV5ATEykeklykeICcnJUUEykuUjwkuUjrlC2hpFqKhyQXKR5mF0k5q5W7C0VVVR4+fCgiUQCbXzfw6JV7nE/JwMXRgRfal94wrjo9qoIUFykeknJWSkxAjosUD5DhIilfQUZMQI4HyHGR4iEpZ6XEBOS4SPEAOS5Go9HeChaklFRL8QA5LlI8QFbO2gJtkP6EoygKbm5uNv0QffLosmu/CgnA07l8pSXV4VFZpLhI8ZCEpJhIcZHiIc1FClJiIsVDkosUD0lIiokUFyke0lykYC6ptnVM9u3bh6IopKWl2dWjMpTmEhQUxPLly6223blzZ417zJo1i/bt21fL60pHK3fXKBeqqnL37l2bzd7fz8rjy+M3gIpdG93WHlVBiosUD0lIiokUFyke0lykICUmUjwkuUjxkISkmEhxkeIhzaW6iIyMRFGUIrcLFy4Uu70tSqojIiKYMmWK1X3PPvssN2/exMur7L5KtvKwFRVxuXnzJs8//3y59lvRgbU9Y/LDDz/Qp08f6tWrh5OTEw0bNuSPf/wj6enplm1u3rzJSy+9RHBwMDqdrkgOFMfdu3fp168f7u7udOjQgWPHjlk9PnHiRJYsWVLqPrRyd8HodDr8/f3FdOh0dna22b6+SLhOdp6J4LruhDb0tptHVZHiIsVDUs5KiQnIcZHiATJcJOUryIgJyPEAOS5SPCTlrJSYgBwXKR4gx6U6m3D16dOHmzdvWt0aN25cZLvc3FygekqqDQYD/v7+Fdp3Ta2im4+7NMrr4u/vj5OTU1WVquxha3Q6HS+88AJffPEF586dY8OGDezbt4/x48dbtsnJycHPz4+3336bdu3alWu/8+fP58GDBxw9epSIiAhGjx5teezgwYPExcWVa7BvC+z/1+IJRFEUvL29xZTEuLq62sRFVVW2PCp1H9q5fA3jqsOjqkhxkeJhdpGQs9JiIsFFiockFyn5anaREhMJHpJcpHiYXSTkrLSYSHCR4iHJRVEU9Hp9tXk4OTnh7+9vdXNwcCAiIoKoqCimTJmCr68vvXv3RlEUoqOjCQkJwc3NjcDAQCZMmEBGRobVPmNjY4mIiMDV1ZVatWrRu3dv7t27R2RkJPv37yc6Otqyap+cnFxsufuOHTto3bo1Tk5OBAUFWa2YKopC06ZNWbBgASNGjMDDw4OGDRvywQcflHqs5mOKiorCy8sLX19fZsyYYbXKGhQUxNy5cxk2bBienp6MGTMGgJiYGLp27YqLiwuBgYFMmjSJhw8fWsrMU1NT6devHy4uLjRu3JjNmzcXef3Hy92vXbvG0KFD8fHxwc3NjbCwMOLi4ti4cSOzZ88mMTHREqeNGzcCkJaWxqhRo/Dz88PT05MePXpYtjOXu7/33nvUrVsXDw8PRo4cSXZ2dnnToVLUqlWL8ePHExYWRqNGjejVqxcTJkzgf//7n1Vco6OjGTZsWLkrJk6fPs2QIUMIDg5mzJgxnD59GoC8vDzGjRvH2rVry+xor5W7C8ZkMnHp0iW7d3GFgoH1Dz/8YJNSlOPX7nP6ZjoGvY7fdqhvN4+qIsVFigfIyVlJMZHiIsVDkouUfAU5MZHiIclFigfIyVlJMZHiIsWjqi6qqmLKNdrkZszJJ/tBJsac/HJtb8vYbdq0CYPBQGxsLGvXrkVVVVRVJTo6mpMnT7Jp0ya+/fZbpk2bZnlOQkICPXv2pFWrVhw4cICYmBj69euH0WgkOjqa8PBwRo8ebVm1DwwMLPK68fHxDBo0iCFDhnDixAlmzZrFjBkzLANV8zEuWbKEsLAwjh07xoQJExg/fjxnz54t85j0ej2HDh0iOjqapUuXsm7dOqttFi9eTLt27Th27BgzZszg4sWL9OnThwEDBnD8+HE+++wzYmJiiIqKspSZR0ZGcvXqVfbu3cv27dtZvXo1KSkpJXpkZGTQrVs3rl+/zhdffEFiYiLTpk3DZDIxePBgpk6dSuvWrS1xGjx4MAADBw4kJSWFXbt2ER8fT2hoKD179uSHH34gPz+fzz77jFmzZvHuu+9y5MgRAgICWL16dZnvtbu7e6m3cePGlbkPM9evX2f79u1069at3M8pjnbt2vHtt9+Sn5/P119/TUhICAALFy4kIiKCsLCwMvehqipGY9U/F9p10qsBSZcBAHB1dbXJfj49XHDZtb5t/PF2rXj3UVt52AIpLlI8JOWslJiAHBcpHiDDRVK+goyYgBwPkOMixUNSzkqJCchxkeIBlXdR80zcmPmdjW3KR705z6IYyn+97C+//BJ3d3fL788//zzbtm0DoHnz5ixcuNDymKqq/PGPf7Ss7gYFBTFv3jzGjRtnGQguXLiQsLAwq4Fh69atLT8bDAZcXV3x9/cv0Wnp0qX07NmTGTNmABAcHMypU6dYtGgRkZGRlu369u3LhAkTAJg+fTrLli1j7969tGjRosR9BwYGsmzZMhRFoUWLFpw4cYJly5ZZlVL36NGDqVOnWn4fNWoUL7/8sqW0unnz5qxYsYJu3bqxevVqLl++zK5duzh06BCdOnUC4MMPP6Rly5YlemzZsoXU1FQOHz6Mj48PAM2aNbM87u7ujl6vt4pTTEwMhw4dIiUlxVI2v3jxYnbu3Mn27dsZPXo00dHRjBw5kpEjRwIwb948vvnmmzJX0xMSEkp93NPTs9THAYYOHcrnn39OVlYWffv25W9/+1uZzymNP/3pT4wfP56mTZsSFBTEhx9+yPnz59m0aRMHDhxg3Lhx7N69m7CwMP72t7+VuEJvi1ObtEH6E46iKLi4uFR5Pxk5+XyeUPGGcbb2sAVSXKR4SEJSTKS4SPEAWS5SkBITKR4gx0WKhyQkxUSKixQPkOVSnXTv3p01a9ZYfndzc7P83LFjR6ttFUVhz549LFiwgDNnzpCenk5+fj7Z2dlkZmbi6upKQkICAwcOrJLT6dOneeGFF6zu69KlC8uXL8doNFpKnM0rq2Y3f3//UlevAZ555hmr0ufw8HCWLFlitd/HV2gTExM5fvy4VQm7uSFZcnIy586dQ6/XW8Xrqaeewtvbu0SPhIQEOnToYBmgl4fExEQyMjKoXbu21f1ZWVlcunQJnU7H6dOni6x6h4eHs3fv3lL3XXiCoLIsW7aMd955h7Nnz/Lmm2/y2muvWeVWRfHy8mLLli1W9/Xo0YNFixaxefNmLl26xNmzZxk9ejRz5swptolc4YaIVUEbpD/hmEwm7t69i4+PT5Vmdf6VeIPMXCNN/Nzo3Lj8H25be9gCKS5SPCQhKSZSXKR4SHORgpSYSPGQ5CLFQxKSYiLFRYpHVV0URx315jxrEw9VVcnJzsbJ2blcAw3FsWKubm5uJQ7QCg/YAZKSkvj1r3/NuHHjmD9/Pj4+PsTExDBy5Ehyc3NxdXWtkYkNcxWMXm89dFIUxSansTx+3BkZGYwdO5ZJkyYV2TYwMJBTp05V+DUqE6eMjAwCAgLYt29fkce8vLzIy8ur8D7NFK6mKI5XXnmFtWvXlrqNuadBixYtcHd3p1evXsycOZOAgIBKexVmw4YNeHt788ILL/Diiy/Sv39/HB0dGThwIDNnziz2OeZy96rmhTZIrwZ0Oh0NGjSw+5c9FHx5eHh4VHk255NDBaXuQztVrGGcrT1sgRQXKR4gJ2clxUSKixQPSS5S8hXkxESKhyQXKR4gJ2clxUSKixSPqrooilKhkvPSUFUVJ72LTZpfVZX4+HhMJhNLliyxrDpv3brVapuQkBD27NnD7Nmzi92HwWDAaDSW+jotW7YkNjbW6r7Y2FiCg4NxcHCo0qkqcXFxVr8fPHiQ5s2bl9qALDQ0lFOnThU7maGqKq1atSI/P5/4+HhLufvZs2dLve57SEgI69ats0wEPU5xcQoNDeXWrVvo9XqCgoKKeKiqSsuWLYmLi2PYsGFWx1gWtih3L4z5+zUnJ6dCzyuJ1NRU5syZQ0xMDABGo9EyKZGXl1dqTtnis6MN0qsBRVHKnB2qKRRFqfKlF07euM/xa/dxdFB4MbRiDeNs6WErpLhI8QA5OSstJhJcpHiAHBcp+QqyYiLBA+S4SPEAOTkrLSYSXKR4gBwXc9duCTRv3py8vDxWrVpFv379LA3lCvPmm2/Stm1bJkyYwLhx4zAYDOzdu5eBAwfi6+tLUFAQcXFxJCcn4+7uXuwAderUqXTq1Im5c+cyePBgDhw4wKpVqyznuZsHXJUZeF25coXXXnuNsWPHcvToUVauXFnmtbanT5/OM888Q1RUFKNGjcLNzY1Tp07x3//+l1WrVtGyZUv69OnD2LFjWbNmDXq9nilTppS6Wj506FDeffdd+vfvz4IFCwgICODYsWPUq1eP8PBwgoKCSEpKIiEhgQYNGuDh4UGvXr0IDw+nf//+LFy4kODgYG7cuMG///1vfvvb3xIWFsbkyZOJjIwkLCyMLl26sHnzZk6ePEmTJk1KPcaqlLt/9dVX3L59m06dOuHu7s7Jkyd544036NKli9VkgnkiICMjg9TUVBISEjAYDLRq1arM15gyZQpTp06lfv2CsU+XLl346KOP+OUvf8kHH3xAly5din2ercrd7b8M8QRiNBo5d+5cmbN2NYHJZCIlJaVKJRefPrrs2i9b+1PbvXJ/PGzhYSukuEjxADk5KykmUlykeEhykZKvICcmUjwkuUjxADk5KykmUlykeEhyUVWV7OxsEY0OQ0JCWLRoEX/5y19o06YNmzdvZsGCBVbbBAcHs3v3bhITE+ncuTPh4eF8/vnnltL0119/HQcHB1q1aoWfnx9Xrlwp8jqhoaFs3bqVTz/9lDZt2jBz5kzmzJljaRpnjkVlYjJs2DCysrLo3LkzEydOZPLkyZbLrJV23Pv37+fcuXN07dqVDh06MHPmTOrVq4eqquTl5bF+/Xrq1atHt27dePHFFxkzZgx16tQpcZ8Gg4Hdu3dTp04d+vbtS9u2bXnvvfcsEzIDBgygT58+dO/eHT8/Pz755BMUReGrr77iueeeY/jw4QQHBzNkyBAuX75MnTp1yMvLY9CgQcyYMYNp06bRsWNHLl++bHW98urAxcWFv/3tb/ziF7+gZcuW/PGPf6Rv377861//stquQ4cOdOjQgfj4eLZs2UKHDh3o27dvmfv/+uuvuXDhgqVJIEBUVBRNmjTh6aefJjc3l3feeafY59qq3F1RJXwCa5D09HS8vLy4f/9+hcsoyovRaOT8+fNllrLUBOYPsqOjY6VmdFRVpd3/Z+/Mw6Mqz/7/OTOTyU5CSEIQ0bAqCAhItbgUXN5Kban+bN0tVVEE5UWr1tZa3Kq2LyqKbdW37rUudde2LlhR+oIKCoIoyL4IAkkIS/bJzDm/P4YZMllnOZP5Es73uubK5MyZM5/zPfec5H6e+3me2+ewt97P3yYdx4kDC1PCYadUWFQ4QCdmlTxRYVHhUGJRiVfQ8USFQ4lFhQN0YlbJExUWFY5YWerr69mwYQN9+/YlIyPDdo76+noyohyTnkyFSqrt6JlMBce4ceMYMWIEDzzwQMpZ7JYKR4hFJWbr6urC383mlQ2x5KFOuXsXl2EYeL2xL5cWUlWDn731fgCOObx7yjjslAqLCoeSlDxRYVHhAC0WFal4osIBOiwqHEpS8kSFRYUDtFhUpJAAKnGADosKh5qccndHUck0TXbs2BF3ycXOah8A2V43mQlMSJIoh51SYVHhUJKSJyosKhxqLCpS8USFQ4lFhUNJSp6osKhwqLGoKFRdkOrCXxUOJRYVDjU5s7sLy+Vy0bdv35TP4grB1pyCgoK4W3N2VgdnSIx3LLpdHHZKhUWFA3RiVskTFRYVDiUWlXgFHU9UOJRYVDhAJ2aVPFFhUeFQY1GYwC6kVA9rCikejtaWLrNDB7InyZJSzDqzuwur+TqKqZJhGKSlpcX9/op9Pek9chIrv0qUw06psKhwhKQQs0qeqLCocIAWi0K8go4nKhygw6LCEZJCzCp5osKiwgF6LApSKalW4QAdFhWOkFRYnHJ3YZmmyZo1ayTKlUzTZPv27XGzVNbsS9KzE2udSpTDTqmwqHCEWBRiVs0TBRYVDiUWlXgNsah4osChxKLCEWJRiFk1TxRYVDjUWOrr61ONAOiUVKtwKLGocISkFLN2lLs7SXoXl2EYFBUVJVzuXmhDT3oiHHZKhUWFQ0lKnqiwqHCosahIxRMVDiUWFQ4lKXmiwqLCocaiJIUqFNDhAB0WFQ412VHu7iTpXVyJllzsrLGv3F2lLEaFRYVDSUqeqLCocKixqEjFExUOJRYVDiUpeaLCosKhxqKikBep9kSFoylDqllUONRkly9Okt7FZZomZWVlcZdcVIQmjrOh3D0RDjulwqLCoSQlT1RYVDjUWFSk4okKhxKLCoeSlDxRYVHhUGNRkWVZ+P3+lJdUq3AosahwqMmyLEzTTPh7bFgHmbOxLCIfr0IXx45SBzsUYolHF/zlEz5ev5PZ54/gzBG9U8Zht1RYVDiUYlbFE9BhUeEADRaleAUNT5Q4QIdFhUMpZlU8AR0WFQ6InqW+vp4NGzbQt29fMjIybGVomhqkOl4hyONwREqFRYkjpFTz1NfXs379evr169fiuxlLHqpxR+qC8vv9qUYAgkEbesSjnTWhMemJ9aQnymGnVFhUOEJSiFklT1RYVDjUWBTiFXQ8UeFQYlHhCEkhZpU8UWFR4VBkUVCIw26eDz/8EMMw2L17d0o54lF7LKWlpTzwwAPh3w3D4PXXX+90jttuu40RI0Yk5XM74km17IoVJ0lPgkzTZMOGDRLlSpZlUV5eHn+SbtMSbIly2CkVFhUO0IlZJU9UWFQ4lFhU4hV0PFHhUGJR4QCdmFXyRIVFhUONpaGhISnHveSSSyLG3ocea9eubfM9iTZwjRs3jmuvvTZi2/HHH8+2bdvIy8uL+jgKDW0hRcuybds2fvCDH0S1bzyJdao82blzJ+PHj+eQQw4hPT2dww47jKuuuoq9e/dG7NfQ0MDNN9/M4YcfTnp6OqWlpTzxxBNtHreyspIJEyaQk5PDyJEj+fzzzyNev/rqq7nvvvs65DNNM+HvsTMlXxeXy+WipKQkrvcGTIvKWnuWYEuEw26psKhwKEnJExUWFQ7QYlGRiicqHKDDosKhJCVPVFhUOECLJZkaP348Tz75ZMS2oqKiFvv5fD68Xm9S1o73er0xed2Za9iHztsOlmTGU2d60lwul4szzzyTO++8k6KiItasWcPVV1/N1KlTee6558L7nXvuuezYsYPHH3+cAQMGsG3btnYbS++66y6qqqpYsmQJDz/8MFdccQWfffYZAJ988gkLFy7kwQcfbJfNMAzcbnfCQ2icnvQurkTWMNxV68OywDCge1ZiX0KltRRVWFQ4lKTkiQqLCocai4pUPFHhUGJR4VCSkicqLCocaizJVHp6OiUlJREPt9vNuHHjmDZtGtdeey2FhYWcfvrpWJbFfffdx7Bhw8jOzqZPnz5cddVVVFdXRxxzwYIFjBs3jqysLLp3787pp5/Orl27uOSSS5g3bx6zZ88O99pv3Lix1XL3V155haOOOirc49q0x9SyLEpLS7nrrru47LLLyM3N5bDDDuMvf/lLu+caOqdp06aRl5dHYWEhM2bMiLjGpaWl/O53v2PixIl069aNyZMnAzB//nxOOukkMjMz6dOnD9OnT6empgbLCs5xsWPHDiZMmEBmZiZ9+/bl2WefbfH5zcvdt2zZwgUXXEBBQQHZ2dmMHj2ahQsX8tRTT3H77bezbNmysE9PPfUUALt37+byyy+nqKiIbt26ccopp7Bs2bIwh2VZ/OEPf6Bnz57k5uYyadKkpK9Z3r17d6ZOncro0aM5/PDDOfXUU5k8eTL/93//F97nnXfeYd68ebz11lucdtpplJaWMmbMGE444YQ2j7ty5UrOP/98Bg0axOTJk1m5ciUAjY2NTJkyhUceeQS3290um13DVpwkPUlSmYDEsiwqKyvjCpRQqXv3LC8ed2LnkwiH3VJhUeEISSFmlTxRYVHhUGNRiFfQ8USFQ4lFhSMkhZhV8kSFRYUjURbLsvD5fLY9Ghsbo97XTu+efvppvF4vCxYs4JFHHglvnz17Nl999RVPP/00c+fO5cYbbwy/tnTpUk499VSGDBnCxx9/zPz585kwYQKBQIDZs2czZswYrrjiCrZt28a2bdvo06dPi89dvHgx5557Lueffz7Lly/ntttuY8aMGeFENaRZs2YxevRoPv/8c6666iqmTp3KqlWrOjwnj8fDokWLmD17NrNmzeKxxx6L2Ofee+/l6KOP5vPPP2fGjBmsW7eO8ePH85Of/IQvvviCv//978yfP59p06YBEAgEuPTSS/nmm2/44IMPePnll3nooYcoKytrk6O6upqxY8eydetW3nzzTZYtW8aNN96IaZqcd955XH/99Rx11FFhn8477zwAzjnnHMrKynj77bdZvHgxo0aN4tRTT6WyspJAIMCLL77Ibbfdxt13381nn31Gr169eOihh9r1BCAnJ6fdx5QpUzo8Rkjffvstb7zxBmPHjg1ve/PNNxk9ejQzZ86kd+/eDBo0iBtuuIG6uro2j3P00Uczd+5c/H4/7777LsOHDwdg5syZjBs3jtGjR0fFY0e5uzO7u6M29dHaCi58bCEDinP493VjO36DI0eOHDly5MiRo6Sr+ezuPp+Pu+++OyUsv/nNb9otz26qSy65hL/97W8Rs17/4Ac/4KWXXmLcuHHs3buXJUuWtHuMl19+mSlTplBRUQHAhRdeyObNm5k/f36r+48bN44RI0ZETKj24YcfcvLJJ7Nr1y7y8/O56KKLKC8vZ86cOeF9brzxRv71r3/x1VdfAcEe75NOOolnnnkGCDaMlJSUcPvtt7eZUI4bN46ysjK++uqr8Kzjv/71r3nzzTdZsWJF+LgjR47ktddeC7/v8ssvx+1287//+7/hbfPnz2fs2LHU1NSwefNmjjjiCBYtWsR3vvMdAL7++msGDx7M/fffHx6DbxgGr732GmeddRZ/+ctfuOGGG9i4cSMFBQUtWG+77TZef/11li5dGvGZP/zhDykrKyM9ff/Q1wEDBnDjjTcyefJkjj/+eEaOHMmf//zn8Ovf/e53qa+vjzhWc7U3DwFAt27dKC4ubnefCy64gDfeeIO6ujomTJjAiy++GI6t8ePH8+GHH3Laaadxyy23UFFRwVVXXcXJJ5/cYrhFSHv27GHq1KksWLCA0tJSHn74YdLS0vjhD3/Ixx9/zM0338ycOXMYPXo0jz76aKtzGrS38oIzu3uKZVkW1dXVMq2y8bZyVtSExqMnNmlcohx2S4VFhSPEohCzap4osKhwKLGoxGuIRcUTBQ4lFhWOEItCzKp5osCiwqHGkkydfPLJLF26NPxoOsb3mGOOidjXsizmzJnDqaeeSu/evcnNzeVnP/sZO3fupLa2Ftjfk56IVq5c2aIM+oQTTmDNmjUEAoHwNRk2bFj4dcMwKCkpabf3GoIJa9NlwcaMGRM+bkjNe2iXLVvGU089FdGzfPrpp2OaJuvXr+err77C4/FE+HXkkUeSn5/fJsfSpUsZOXJkqwl6W1q2bBnV1dX06NEjgmXDhg2sXbsW0zRZuXIlxx13XMT7xowZ0+GxBwwY0O6jowQd4P7772fJkiW8/vrrrFu3jl/84hfh10zTxDAMnn32WY499ljOOOMMZs2axdNPP91mb3peXh7PPfccmzZtYt68eQwZMoQrr7ySe+65h2effZb169ezatUqsrKyuOOOO1o9hl3l7s7EcUmQaZps2bKFgQMHdjhuIdmyLIvdu3dTWFgY87qBO6vtWX4tUQ67pcKiwgE6MavkiQqLCocSi0q8go4nKhxKLCocoBOzSp6osKhwJMqSlpbGb37zG9s46uvrycjIiIoj1snDsrOzGTBgQJuvNdXGjRv58Y9/zJQpU7jrrrsoKChg/vz5TJo0CZ/PR1ZWFpmZmTF9fiJqfq6GYdiyakPz866urubKK69k+vTpLfbt06dPeKx0LIrHp+rqanr16sWHH37Y4rW8vLyIhoZYlZOT0+7rF198ccSQh9YUmtPgiCOOICcnJ9xr3qtXL3r16kXv3r0jersHDx6MZVnh+3FHevLJJ8nPz+fMM8/k7LPP5qyzziItLY1zzjmHW265pc33ObO7O+pQLpcrqpao1mTX8muJctgtFRYVDiUpeaLCosIBWiwqUvFEhQN0WFQ4lKTkiQqLCgckxmIYRtQl5x0pNCGY1+tNecPFkiVLME2TWbNmhed1ePHFFyP2GT58OO+//z633357q8fwer0dJpODBw9mwYIFEdsWLFjAoEGDIhrW4vFj4cKFEb9/8sknHTbYjRo1ihUrVrTZmDF06FD8fj+LFy8Ol7uvWrWq3XXfhw8fzmOPPUZlZWWrvemt+TRq1Ci2b9+Ox+OhtLS01eMOHjyYhQsXMnHixIhz7EjtlcIDMQ9LDjWWhJYPPOGEE3jppZeorq4ONwisXr0al8vFoYce2uHxysvLueOOO8LDKAKBAI2NjUBwIrm2YsqZ3d1RVLIsi4aGhvgmjqsJBnmiy68lymG3VFhUOJSk5IkKiwqHGouKVDxR4VBiUeFQkpInKiwqHGosKurfvz+NjY08+OCDrF+/nmeeeaZF7+pNN93Ep59+ylVXXcUXX3zB119/zcMPPxwes15aWsrChQvZuHEjFRUVrfZ8X3/99bz//vv87ne/Y/Xq1Tz99NP86U9/4oYbbgAIX5N4rs3mzZu57rrrWLVqFc8//zx//OMfueaaa9p9z69+9Ss++ugjpk2bxtKlS1mzZg1vvPEG06ZNw7IsBg4cyPjx47nyyitZuHAhixcv5vLLL2+3t/yCCy6gpKSEs846iwULFrB+/XpeeeUVPv7447BPGzZsYOnSpVRUVNDQ0MBpp53GmDFjOOuss5gzZw4bN27ko48+4uabb+bTTz/FNE2mT5/OE088wZNPPsnq1au59dZbw+P421Mi5e5vvfUWTz75JF9++SUbN27kX//6F9OnT+eEE04INyZceOGF9OjRg0svvZQVK1bwn//8h1/+8pdcdtllUVUVXHvttVx//fX07t0bCCb9zzzzDCtXruQvf/lLm7PE21XunvIk/c9//jOlpaVkZGRw3HHHsWjRonb3f+CBBzjiiCPCyxH84he/SPo0/7Eq1JqZ6tZHCAZKVVVVfGPSbexJT4TDbqmwqHCATswqeaLCosKhxKISr6DjiQqHEosKB+jErJInKiwqHGosCqsRQHC27XvuuYeZM2cydOhQnn32WX7/+99H7DNo0CDmzJnDsmXLOPbYYxkzZgxvvPEGHk+wYPiGG27A7XYzZMgQioqK2Lx5c4vPGTVqFC+++CIvvPACQ4cO5ZZbbuGOO+7gkksuSfgcJk6cSF1dHcceeyxXX30111xzTXiZtbY0fPhw5s2bx+rVqznppJMYOXIkt9xyC4cccggQ7NV94oknOOSQQxg7dixnn302kydPbjex9Xq9zJkzh+LiYs444wyGDRvGH/7wh3CP/k9+8hPGjx/PySefTFFREc8//zyGYfDWW2/xve99j0svvZRBgwZx/vnns2nTJnr27EkgEOC8885jxowZ3HjjjRxzzDFs2rSJqVOnJuxbe8rMzOTRRx/lxBNPZPDgwVx33XX86Ec/4h//+Ed4n5ycHN577z12797N6NGjueiii5gwYUKH65wDvPvuu6xdu5arrroqvG3atGn069eP4447Dp/Px6233trm+w/42d3//ve/M3HiRB555BGOO+44HnjgAV566SVWrVrVapA999xzXHbZZTzxxBMcf/zxrF69mksuuYTzzz+fWbNmRfWZzuzu0esnD3/E4k27eOTiUYwf2ivVOI4cOXLkyJEjR45ofwZpRzpqbWZ5R11bXWJ291mzZnHFFVdw6aWXMmTIEB555BGysrJ44oknWt3/o48+4oQTTuDCCy+ktLSU73//+1xwwQUd9r53tkITfyi0hFqWRV1dXXzl7vsmjuth08Rx8XLYLRUWFY4Qi0LMqnmiwKLCocSiEq8hFhVPFDiUWFQ4QiwKMavmiQKLCocSi2VZ+P3+lHOEWOzolewqHEosKhwhFqWYtaPcPWUTx/l8PhYvXsxNN90U3uZyuTjttNPCYyOa6/jjj+dvf/sbixYt4thjj2X9+vW89dZb/OxnP2vzcxoaGsITCECwBQOCZSKhAf+GYeByuVoEWlvbXS5XeDbH1rY3Njby7bffkpWVFTFxQPMxMG1td7vd4cBvztLW9vbYq6urSUtLwzCMmM4pXO6e3XIiiVjPyeVyUVNTE+ZI9JwSuU6GYVBbWxvBEs85JXqdLCu4JE9GRoZtsRfvdQoEAnz77bfk5uaGPzeec4pne9Nzahqvbrc7oXMKKd7r1JTF5XLZeo+I5ZxCcdI0Xu2+R0R7TkALlkRjL6RYzsk0TbZv3052dnZESabd9/JozsmyLGpqakhPT0/6vby97aE4SU9PT8o9IpZzCrF4vV7cbnfS7uUdnVMgEIiI1876m9vadr/f3+L/glRcJ8Mw2vxb3NnXqTWWeM4p0esU+g639re4s69T0/8LOjqn0LJgTc+juQzDiGu7ZVk0NjbGVfIe72e2tz0QCODxeLAsy7bjx6JQfDbliOUzQ57ayd6cJZ5zsoPFNM1wHCfj+NEqFLPJZIlle9P/r5tzRquUJekVFRUEAgF69uwZsb1nz558/fXXrb7nwgsvpKKighNPPDHcYjJlypR2l5z4/e9/3+psj+vWrQvP9JeXl0evXr3YsWMHe/bsCe9TWFhIYWEhW7dupaamJry9pKSE/Px8Nm7ciM/nC28/9NBDw2sHVlZWsnbtWlwuF3379sXj8bBmzZoIhoEDB+L3+9mwYUN4m8vlYtCgQdTU1LBly5bwdq/XS79+/dizZw/bt28Pb8/OzqZPnz5UVlaGJ8hoek5lZWXs2bOHnTt3xnROPr9JdYMfCPakr1u3LuKPRTznlJGRwdq1axM+JzuuU48ePVi9enXC52THdTIMg+3bt9sSe/FeJ9M02bVrF4BtsZfIddq5c2fC5wT2XKedO3fafo+I9Zx27twZ/g7bfY+I9ZyqqqrCLImcUyLXKTThS2VlZThuEzknO65TY2NjUu/l0Z5TWlpaUu4R8ZxTfX190u/l7Z1T6O9NKF47429uW+f07bffRvxfkKx7eTTn1NSbRM7JjuvUvXv3lPxv1No5GYbBli1bOuVvbkfnVFRUxO7du9s9J9M08fv9NDY2kpmZ2WK2aY/HQ1paWovtaWlpeDwefD5fBHuoUS00cZ3fH/wfMNTw13z+p1BDQtOOMMMwwo0dTf1yuVykp6dHzI4NwUYKr9eL3+8Pf17T7U3Z/X5/wucUUrzn1JQz2nN6++23geBs4M3PKZHrFAgEIjxL5XXy+Xy2xl685wQkJfZiPaeGhoZwo2jze0SPHj2IVikbk/7tt9/Su3dvPvroo4gF72+88UbmzZvXYrkCgA8//JDzzz+fO++8k+OOO461a9dyzTXXcMUVVzBjxoxWP6e1nvTQTS40FsDu1mKfz8fatWsZMGBAynvSA4EAdXV14bUuoz2nb3fXcdI980hzG6y+8wcx9Zi3tb22tjb8hUvknOzoSa+vr28xiU8qetLr6+vJzs6OaA2P55zs6Elfu3YtRxxxRMp70kNrsyr0pIdYUt2TXltbG7FebSp70kM9TqnuSV+3bh39+/eX6ElvaGggMzOzRQt5Z/ek19fXk5WV1e65dlZPen19PZmZmSnvSW+61nMqe9IbGxtZs2ZNxP8FqepJb+tvcSp60uvq6iJY4jknO3rS2/pbnIqe9BBL8/2bs9fX17Np0yb69u3b6v0n9J54e9IbGhpaXJtolIzeSdM0wx6mqic9dJ2a/83p7B7akJqzxCo7e9Jb40hFT3pDQ0Or8zN09nWqr69n/fr19OvXr8WyiNXV1eTn50c1Jj1lPemFhYW43W527NgRsX3Hjh2UlJS0+p4ZM2bws5/9jMsvvxyAYcOGUVNTw+TJk7n55ptbDZL09HTS01uOqXa73S3WJ2wr2GPd7vF4yM3NxePxROzT1nqIrW03DCOm7e0x+nw+srOzW/0j2NZxdtcF//D0yE5v8zPbYm9te+jLk5WV1eKGH885JbK96T+Qrf3x6azrZFlW+NrYda7xXifDMMjNzQ3/E2dX7MW6PdTI1TReE4299tjbO6fWWFJ1nVr7DqfiOjWN2ebfnc68ToZhkJ2d3eY6pJ15nZre29r6R6UzrlPTa9Pe/p1xnaJl6Yzr1Fq8Jvtvblssrf1f0NnXqb2/xZ19ndpjaW1/SM51SuRvsd3Xqfk9tr1zCjVkN21saU3xbm9+/FhkJ0vThhO7zzVWNeewkyWW7a15Eo8SZemIo7OvU9P/aZPFEuv25t/hWM4xZRPHeb1ejjnmGN5///3wNtM0ef/99yN61puqtra2xY2y6Y1NRS6Xiz59+iTUwmWXDMOgoKAg5sCvCK2RbsPya4lwJEMqLCocoBOzSp6osKhwKLGoxCvoeKLCocSiwgE6MavkiQqLCocSi2FoLBkYYvF4PClnUeFQYlHhCLEoxWyosycRpfSvxXXXXcejjz7K008/zcqVK5k6dSo1NTVceumlQHBdwaYTy02YMIGHH36YF154gQ0bNvDee+8xY8YMJkyY0GarZSpkmmZ4vFCqZVnBCVFibcTYGV4jPfGZ3RPhSIZUWFQ4QCdmlTxRYVHhUGJRiVfQ8USFQ4lFhQN0YlbJExUWFQ4lFssKTsKVao4QS2iSPIdDi0WFI8SiFLPNh8jEo5SVuwOcd955lJeXc8stt7B9+3ZGjBjBO++8E55MbvPmzRGtzr/97W8xDIPf/va3bN26laKiIiZMmMBdd92VqlNoVZZlUVFRQffu3VONAgRLZbOysmJ6T2j5tcJse3rS4+VIllRYVDiUYlbFE9BhUeEADRaleAUNT5Q4QIdFhUMpZlU8AR0WFQ7QYQlN1KYghcQLdDhAh0WFA7Ri1g6l/EymTZvGtGnTWn3tww8/jPjd4/Fw6623cuutt3YCWdeQYRhx/VOwsybUk25fubvCPyegw6LCoSQlT1RYVDhAi0VFKp6ocIAOiwqHkpQ8UWFR4QAtFhWFSqpTLRUO0GFR4VBTlyh3d5R8WZZFVVVVzC1dFft60guy7St3j4cjGVJhUeFQkpInKiwqHGosKlLxRIVDiUWFQ0lKnqiwqHCosagoWSXVH374IYZhsHv37pRyxKP2WEpLS3nggQfCvxuGweuvv97pHLfddhsjRoxIyueqy65ydydJT4IMwwivf62geMbA7R+Tbl+5e6rH4jWVCosKh1LMqngCOiwqHKDBohSvoOEJ6HCADosKh1LMqngCOiwqHKDDkqy5ni655JLwrPFNH2vXrm3zPYkmO+PGjePaa6+N2Hb88cezbds28vLyoj6OQoIeUrQs27Zt4wc/+EFU+8aTWCt4snPnTvr06UNWVlZEo8u2bdu48MILGTRoEC6Xq0UMtKbKykomTJhATk4OI0eO5PPPP494/eqrr+a+++6z+Qxal5OkJ0Eul4tevXqlfBZXiP8fg537ZncvtLHcXeUfFBUWFQ7QiVklT1RYVDiUWFTiFXQ8UeFQYlHhAJ2YVfJEhUWFQ4kl2TNljx8/nm3btkU8+vbt22I/n8+XtBnEvV4vJSUlUR+3M2cy9/l8trGUlJS0uhS1HVKZ3X3SpEkMHz48zBRSQ0MDRUVF/Pa3v+Xoo4+O6lh33XUXVVVVLFmyhHHjxnHFFVeEX/vkk09YuHBhh8m+U+4uLNM02bZtm0RrqGVZ7N27N/7Z3W0sd4+HIxlSYVHhAJ2YVfJEhUWFQ4lFJV5BxxMVDiUWFQ7QiVklT1RYVDiUWCwruF57sjjS09MpKSmJeLjdbsaNG8e0adO49tprKSws5PTTT8eyLO69916GDRtGdnY2ffr04aqrrqK6ujrimAsWLGDcuHFkZWXRvXt3Tj/9dHbt2sUll1zCvHnzmD17drjXfuPGja2Wu7/yyiscddRRpKenU1paGtFjalkWpaWl3HXXXVx22WXk5uZy2GGH8Ze//KXdcw2d07Rp08jLy6OwsJAZM2ZEeFtaWsrvfvc7Jk6cSLdu3Zg8eTIA8+fP56STTiIzM5M+ffowffr08Oz/gUCAHTt2MGHCBDIzM+nbty/PPvtsi89vXu6+ZcsWLrjgAgoKCsjOzmb06NEsXLiQp556ittvv51ly5aFfXrqqacA2L17N5dffjlFRUV069aNU045hWXLlkWUu//hD3+gZ8+e5ObmMmnSJOrr66MNh4T08MMPs3v3bq6//nqAFr7Onj2biRMnRl0xsXLlSs4//3wGDRrE5MmTWblyJQCNjY1MmTKFRx55pMMqE6fcXViWZbFnz56U32TjlWVZSSl3d6SrAz1mHR1ccuLV0YEmJ2YdJVvBhKnWtofPVxX1vnbG9dNPP43X62XBggU88sgjQLASZfbs2Xz11Vc8/fTTzJ07lxtvvDH8nqVLl3LqqacyZMgQPv74Y+bPn8+ECRMIBALMnj2bMWPGcMUVV4R77fv06dPicxcvXsy5557L+eefz/Lly7ntttuYMWNGOFENadasWYwePZrPP/+cq666iqlTp7Jq1aoOz8nj8bBo0SJmz57NrFmzeOyxxyL2uffeezn66KP5/PPPmTFjBuvWrWP8+PH85Cc/4YsvvuDvf/878+fPj5hs+9JLL+Wbb77hgw8+4OWXX+ahhx6irKysTY7q6mrGjh3L1q1befPNN1m2bBk33ngjpmly3nnncf3113PUUUeFfTrvvPMAOOeccygrK+Ptt99m8eLFjBo1ilNPPZXKykoAXnzxRW677TbuvvtuPvvsM3r16sVDDz3UricAOTk57T6mTJnS7vtXrFjBHXfcwV//+lfbqpSOPvpo5s6di9/v59133w330M+cOZNx48YxevRoWz4nGjlT8nVxGYZBt27dYnpPVYMfXyDY2m9XT3o8HMmSCosKh5KUPFFhUeEALRYVqXiiwgE6LCocSlLyRIVFhQMSYzHNOj6cN8xmoug0buxy3O7ol4375z//SU5OTvj3H/zgB7z00ksADBw4kJkzZ0bsf91114Wfl5aWcueddzJlypRwIjhz5kxGjx4dkRgeddRR4eder5esrCxKSkraZJo1axannnoqM2bMAGDQoEGsWLGCe+65JzyOHuCMM87gqquuAuBXv/oV999/Px988AFHHHFEm8fu06cP999/P4ZhcMQRR7B8+XLuv//+iFLqU045JdwbDHD55Zdz0UUXhUurBw4cyIMPPsjYsWN5+OGH2bx5M2+//TaLFi3iO9/5DgCPP/44gwcPbpPjueeeo7y8nE8//ZSCggIABgwYEH49JycHj8cT4dP8+fNZtGgRZWVl4bL5e++9l9dff51XXnmFyZMnM3v2bCZNmsSkSZMAuPPOO/n3v//dYW/60qVL2329ve9CQ0MDF1xwAffccw+HHXYY69ata/dY0erXv/41U6dOpX///pSWlvL444+zZs0ann76aT7++GOmTJnCnDlzGD16NI8++mirPfR2lbs7SXoXV6h0qlu3blEHS+W+XvRsr5tMrz0Th8TDkSypsKhwKEnJExUWFQ41FhWpeKLCocSiwqEkJU9UWFQ41FiSqZNPPpmHH344/Ht2dnb4+THHHBOxr2VZzJkzh5kzZ/L111+zd+9e/H4/9fX11NbWkpWVxdKlSznnnHMSYlq5ciVnnnlmxLYTTjiBBx54gEAgEO6pHTZsf0OIYRiUlJS023sN8N3vfjfieo4ZM4b77ruPQCAQLp1u3kO7bNkyvvjii4gS9lAZ9fr16/n666/xeDwRfh155JHk5+e3ybF06VJGjhwZTtCj0bJly6iurqZHjx4R2+vq6li7di1+v5+VK1e26PUeM2YMH3zwQbvHbtpAEKtuuukmBg8ezMUXXxz3MVpTXl4ezz33XMS2U045hXvuuYdnn32W9evXs2rVKq644gruuOOOVieRs6vc3UnSkyDDMCgsLJS5wcZaAhKaNK5Hjr0TTaR6wpymUmFR4VCKWRVPQIdFhQM0WJTiFTQ8AR0O0GFR4VCKWRVPQIdFhQPiZ3G5Mhk3drktDJZl4ff7o54UzOXKjOn42dnZbSZoTRN2gI0bN3LmmWcyZcoU7rrrLgoKCpg/fz6TJk3C5/ORlZVFZmZsn5+I0tLSIn43DMOWuSaan3d1dTVXXnkl06dPb7Fvnz59Oiyxb03x+FRdXU2vXr348MMPW7yW6CSHTaspWtPFF18cHvLQXHPnzmX58uW8/PLLwP6x6EVFRdx8883cfvvtcXM11ZNPPkl+fj5nnnkmZ599NmeddRZpaWmcc8453HLLLbZ8RltykvQkyOVyUVhYmGoMIHjzyM3Njek9FUkYjx4PR7KkwqLCAToxq+SJCosKB+iwqMQr6HiiwgE6LCocoBOzSp6osKhwQGIshmHEVHLekTwiGcKSJUswTZNZs2aFGzBefPHFiH2GDx/O+++/32Zi5vV6CQQC7X7O4MGDWbBgQcS2BQsWMGjQoIiJwuJJShcuXBjx+yeffMLAgQPbnYBs1KhRrFixos3GjCFDhuD3+1m8eHG43H3VqlXtrvs+fPhwHnvsMSorK1vtTW/Np1GjRrF9+3Y8Hg+lpaWtHnfw4MEsXLiQiRMnRpxjR0qk3P2VV16hrq4u/Punn37KZZddxv/93//Rv3//Dj87GpWXl3PHHXcwf/58AAKBAI2NjUBwIrm2YsqZ3V1YpmnyzTffpHwWVwi2LO3atSumkgu7Z3aPlyNZUmFR4QCdmFXyRIVFhUOJRSVeQccTFQ4lFhUO0IlZJU9UWFQ4lFiSPbt7LOrfvz+NjY08+OCDrF+/nmeeeaZF7+pNN93Ep59+ylVXXcUXX3zB119/zcMPP0xFRQUQHMe+cOFCNm7cSEVFRavfw+uvv57333+f3/3ud6xevZqnn36aP/3pT9xwww3A/p7aeDzZvHkz1113HatWreL555/nj3/8I9dcc0277/nVr37FRx99xLRp01i6dClr1qzhjTfeYNq0aViWRf/+/Rk/fjxXXnklCxcuZPHixVx++eXt9pZfcMEFlJSUcNZZZ7FgwQLWr1/PK6+8wscffxz2acOGDSxdupSKigoaGho47bTTGDNmDGeddRZz5sxh48aNfPTRR9x88818+umn+P1+pk+fzhNPPMGTTz7J6tWrufXWW/nqq6869GXAgAHtPoqLi9t8b//+/Rk6dGj4EWpAOPLIIyPet3TpUpYuXUp1dTXl5eUsXbqUFStWdMgGcO2113L99dfTu3dvIDj84ZlnnmHlypX85S9/4YQTTmj1fc7s7sKyLCu8RIKCvN7YesR3Vtu7Rnq8HMmUCosKh1LMqngCOiwqHKDBohSvoOEJ6HCADosKh1LMqngCOiwqHKDD0lHPc2fp6KOP5t5772XmzJkMHTqUZ599lt///vcR+wwaNIg5c+awbNkyjj32WMaMGcMbb7yBZ185wA033IDb7WbIkCEUFRWxefPmFp8zatQoXnzxRV544QWGDh3KLbfcwh133MEll1yS8DlMnDiRuro6jj32WK6++mquueaa8DJrbWn48OHMmzeP1atXc9JJJzFy5EhuueUWDjnkECDYW/vEE09wyCGHMHbsWM4++2wmT57cbmLr9XqZM2cOxcXFnHHGGQwbNow//OEP4R79n/zkJ4wfP56TTz6ZoqIinn/+eQzD4K233uJ73/sel156KYMGDeL8889n06ZN9OzZE8MwOO+885gxYwY33ngjxxxzDJs2bWLq1KkJ+2aHRo4cyciRI1m8eDHPPfccI0eO5Iwzzujwfe+++y5r164NTxIIMG3aNPr168dxxx2Hz+fj1ltvTSY6hqXwF6MTtXfvXvLy8tizZ0/SZvMMBAKsWbOmw1IWVd325lc89dFGrj65P788/chU4zjqBB3oMevo4JITr44ONDkx68hu1dfXs2HDBvr27UtGRoatx7Ysi/r6ejIyMiTmUTiQNW7cOEaMGMEDDzyQapQuLaWYbe+7GUse6vSkd3FZlkVlZWVMrfcV+3rS7S53j5UjWVJhUeFQkpInKiwqHGosKlLxRIVDiUWFQ0lKnqiwqHCosagoNIldqj1R4VBiUeFQk1PuLiyXy0VJSYnMbKGxtrDuTMLEcfFwJFMqLCocSjGr4gnosKhwgAaLUryChiegwwE6LCocSjGr4gnosKhwgA5L81nMU6lU94yGpMIBOiwqHKAVs3ZIZO7GriXDMNpdp7AzZRgGWVmxzfYZWoKt0MYl2OLhSJZUWFQ4QCdm1TxRYFHhAB0WlXgFLU8UOECHRYUDdGJWzRMFFhUO0GExDCM8njvVCs5an/ohIvFytLZ0WapYuioH6MWsM7u7qEzTZP369SmfxRWCJRc7d+6Mb3Z3G3vS4+FIllRYVDhAJ2aVPFFhUeFQYlGJV9DxRIVDiUWFA3RiVskTFRYVDiUWy7JoaGhIOUeIRaGkWoVDiUWFI8SiFLOBQMApd1eU0tIVQEytsgHTorI2mKQXZNtb7q7QOhySCosKh1LMqngCOiwqHKDBohSvoOEJ6HCADosKh1LMqngCOiwqHKDDkuoGpaZSGCYCOhygw6LCAV0vZjXqAhwlTYZhtLtmYnPtqvUR+h+iIMu+JD1WjmRKhUWFQ0lKnqiwqHCAFouKVDxR4QAdFhUOJSl5osKiwgFaLCoyDENi3LMKB+iwqHCoKeSLU+7uqF2ZpklFRUXUrUuhUvfuWWl43PaFR6wcyZQKiwqHkpQ8UWFR4VBjUZGKJyocSiwqHEpS8kSFRYVDjUVFlmXR2NiY8ioUFQ4lFhUONYXK3RP9HjtJehLkcrk49NBDJUpADMMgNzc36tacnaHl12ycNC4ejmRKhUWFA3RiVskTFRYVDiUWlXgFHU9UOJRYVDhAJ2aVPFFhUeFQY/F67R3ymIhUJidT4QAdFhUO0IpZOyaOc8rdkyDDMMjJyUk1BhBkSU+PPuGuqNk3aZzN49Fj5UimVFhUOEAnZtU8UWBR4QAdFpV4BS1PFDhAh0WFA3RiVs0TBRYVDtBhUZu1W6HRQoUDdFhUOEAzZp1yd0EFAgFWr15NIBBINQqmaVJWVhZDubv9y6/Fw5FMqbCocIBOzCp5osKiwqHEohKvoOOJCocSiwoH6MSskicqLCocSiyWZVFfXy9RxpyskuoPP/wQwzDYvXt3SjniUXsspaWlPPDAA+HfDcPg9ddf73SO2267jREjRiTlc9tiUYpZp9xdWKm+wYYUWps12tacyhr7l1+LhyOZUmFR4QhJIWaVPFFhUeFQY1GIV9DxRIVDiUWFIySFmFXyRIVFhUONJVnJziWXXBLR0xh6rF27ts33JNpDOm7cOK699tqIbccffzzbtm0jLy8v6uOo9NRC9Czbtm3jBz/4QVT7xpNYK3iyc+dO+vTpQ2ZmZotGl4aGBm6++WYOP/xw0tPTKS0t5YknnmjzWJWVlUyYMIGcnBxGjhzJ559/HvH61VdfzX333dchk1Pu7qhDGYYR0xiNitAa6dn2j0lXGSuiwqLCoSQlT1RYVDhAi0VFKp6ocIAOiwqHkpQ8UWFR4QAtlmRq/PjxPPnkkxHbioqKWuzn8/nwer1JabTwer2UlJREvX9nlnaHztsOlljOMVaplLtPmjSJ4cOHs3Xr1havnXvuuezYsYPHH3+cAQMGsG3btnYbTO+66y6qqqpYsmQJDz/8MFdccQWfffYZAJ988gkLFy7kwQcfbJfHKXd3FJVM02THjh0xl7vb3ZMeK0cypcKiwqEkJU9UWFQ41FhUpOKJCocSiwqHkpQ8UWFR4VBjSabS09MpKSmJeLjdbsaNG8e0adO49tprKSws5PTTT8eyLO655x6GDRtGdnY2ffr04aqrrqK6ujrimAsWLGDcuHFkZWXRvXt3Tj/9dHbt2sUll1zCvHnzmD17djhx2rhxY6vl7q+88gpHHXVUuMe1aY+pZVmUlpZy1113cdlll5Gbm8thhx3GX/7yl3bPNXRO06ZNIy8vj8LCQmbMmBFRqVBaWsrvfvc7Jk6cSLdu3Zg8eTIA8+fP56STTiIzM5M+ffowffp0ampqwmXmO3bsYMKECWRmZtK3b1+effbZFp/fvNx9y5YtXHDBBRQUFJCdnc3o0aNZuHAhTz31FLfffjvLli0L+/TUU08BsHv3bi6//HKKioro1q0bp5xyCsuWLYsod//DH/5Az549yc3NZdKkSdTX10cbDgnp4YcfZvfu3Vx//fUtXnvnnXeYN28eb731FqeddhqlpaWMGTOGE044oc3jrVy5kvPPP59BgwYxefJkVq5cCUBjYyNTpkzhkUce6bB6wCl3F5bL5aJv374pn8UVgl/OgoKCqFtzdu4rdy9MQrl7LBzJlAqLCgfoxKySJyosKhxKLCrxCjqeqHAosahwgE7MKnmiwqLCkSiLZVnUBAK2PGoDAQKeNGqj3N/O0vinn34ar9fLggULeOSRRwDweDzMnj2br776iqeffpq5c+dy4403ht+zdOlSTj31VIYMGcLHH3/M/PnzmTBhAoFAgNmzZzNmzBiuuOIKtm3bxrZt2+jTp0+Lz128eDHnnnsu559/PsuXL+e2225jxowZ4UQ1pFmzZjF69Gg+//xzrrrqKqZOncqqVas6PCePx8OiRYuYPXs2s2bN4rHHHovY59577+Xoo4/m888/Z8aMGaxbt47x48fzk5/8hC+++IK///3vzJ8/n2nTpgHBMvNLL72Ub775hg8++ICXX36Zhx56iLKysjY5qqurGTt2LFu3buXNN99k2bJl3HjjjZimyXnnncf111/PUUcdFfbpvPPOA+Ccc86hrKyMt99+m8WLFzNq1ChOPfVUKisrcbvdvPjii9x2223cfffdfPbZZ/Tq1YuHHnqoXU8AcnJy2n1MmTKl3fevWLGCO+64g7/+9a+t3lvffPNNRo8ezcyZM+nduzeDBg3ihhtuoK6urs1jHn300cydOxe/38+7777L8OHDAZg5cybjxo1j9OjRHZ4XOOXu0vJ4NKw1DIO0tLSo90/mEmyxcCRTKiwqHCEpxKySJyosKhygxaIQr6DjiQoH6LCocISkELNKnqiwqHBAYiy1pkn//yy3mSg6rfveMLJjGJv8z3/+M2K1gx/84Ae89NJLAAwcOJCZM2dG7P+LX/wi/Ly0tJQ777yTKVOmhBPBmTNnMnr06IjE8Kijjgo/93q9ZGVltVv6PWvWLE499VRmzJgBwKBBg1ixYgX33HNPeBw9wBlnnMFVV10FwK9+9Svuv/9+PvjgA4444og2j92nTx/uv/9+DMPgiCOOYPny5dx///1cccUV4X1OOeWUiN7gyy+/nIsuuig8ln7gwIE8+OCDjB07locffpjNmzfz9ttvs2jRIr7zne8A8PjjjzN48OA2OZ577jnKy8v59NNPKSgoAGDAgAHh13NycvB4PBE+zZ8/n0WLFlFWVhZeeeDee+/l9ddf55VXXmHy5MnMnj2bSZMmMWnSJADuvPNO/v3vf3fYm7506dJ2X+/WrVubrzU0NHDBBRdwzz33cNhhh7Fu3boW+6xfv5758+eTkZHBa6+9RkVFBVdddRU7d+5sMdwipF//+tdMnTqV/v37U1payuOPP86aNWt4+umn+fjjj5kyZQpz5sxh9OjRPProo63OaeCUuwvLNE3WrFkjUa5kmibbt2+Podw9OUuwxcqRTKmwqHCEWBRiVs0TBRYVDiUWlXgNsah4osChxKLCEWJRiFk1TxRYVDjUWJKpk08+maVLl4YfTcf4HnPMMRH7WpbFO++8w6mnnkrv3r3Jzc3lZz/7GTt37qS2thbY35OeiFauXNmiDPqEE05gzZo1BJpUCwwbNiz8umEYlJSUtNt7DfDd7343ImEbM2ZM+LghNe+hXbZsGU899VREz/Lpp5+OaZqsX7+e5cuX4/F4Ivw68sgjyc/Pb5Nj6dKljBw5MpygR6Nly5ZRXV1Njx49Ilg2bNjA2rVraWxsZOXKlRx33HER7xszZkyHxx4wYEC7j+Li4jbfe9NNNzF48GAuvvjiNvcxTRPDMHj22Wc59thjOeOMM5g1axZPP/10m73peXl5PPfcc2zatIl58+YxZMgQrrzySu655x6effZZ1q9fz6pVq8jKyuKOO+5o9Rh2lbunvlnXUVJlGAZFRUVRtebUNwaoavADyelJj5Yj2VJhUeFQkpInKiwqHGosKlLxRIVDiUWFQ0lKnqiwqHAkypLlcrHue8M63jEaWRb19Q1kZKRDFCxZMQ7jyM7OjujBbf5aU23cuJGzzjqLKVOmcNddd1FQUMD8+fOZNGkSPp+PrKwsMjMzY/r8RNS80sEwDFsaVZqfd3V1NVdeeSXTp09vsW+fPn1YvXp1zJ8Rj0/V1dX06tWLDz/8sMVreXl5CVUINa2maE0XX3xxeMhDc82dO5fly5fz8ssvA/tXIygqKuLmm2/m9ttvp1evXvTu3Tuit3vw4MFYlsWWLVsYOHBgh4xPPvkk+fn5nHnmmZx99tmcddZZpKWlcc4553DLLbe0+T6n3N1Rh4ql5CK0/Fqa26Bbhr2hYVfpR1diUeFQkpInKiwqHGosKlLxRIVDiUWFQ0lKnqiwqHAkymIYRkwl5+3JsixcbhcZbnfKfVmyZAmmaTJr1qzwuOMXX3wxYp/hw4fz/vvvc/vtt7d6DK/XG9Fr3ZoGDx7MggULIrYtWLCAQYMGRUwUFo8fCxcujPj9k08+YeDAge1OQDZq1ChWrFjRZmPGkUceid/vZ/HixeFy91WrVrW77vvw4cN57LHHqKysbLU3vTWfRo0axfbt2/F4PJSWlrZ4j2VZDB48mIULFzJx4sSIc+xIiZS7v/LKKxG94YsWLWLSpEn85z//CXt2wgkn8NJLL1FdXR1uEFi9ejUul4tDDz20Q77y8nLuuOMO5s+fD0AgEKCxsREITiTXVkyFYsQpd3fUrkzTpKysLKpWvp1Nll+z+6YcC0eypcKiwqEkJU9UWFQ41FhUpOKJCocSiwqHkpQ8UWFR4VBjUVH//v1pbGzkwQcfZP369TzzzDMteldvuukmPv30U6666iq++OILvv76ax5++GEqKiqA4Dj2hQsXsnHjRioqKlr19/rrr+f999/nd7/7HatXr+bpp5/mT3/6EzfccAOwv6c2nknyNm/ezHXXXceqVat4/vnn+eMf/8g111zT7nt+9atf8dFHHzFt2jSWLl3KmjVreOONN5g2bRqWZdG/f3/Gjx/PlVdeycKFC1m8eDGXX355u73lF1xwASUlJZx11lksWLCA9evX88orr/Dxxx+HfdqwYQNLly6loqKChoYGTjvtNMaMGcNZZ53FnDlz2LhxIx999BE333wzn376KX6/n+nTp/PEE0/w5JNPsnr1am699Va++uqrDn1JpNy9f//+DB06NPzo27cvEGxsCb3vwgsvpEePHlx66aWsWLGC//znP/zyl7/ksssui6qq4Nprr+X666+nd+/eQDDpf+aZZ1i5ciV/+ctf2pwl3rIsTNN0ZndXlMvlYuDAgSmfxTXEUlxcHBVLRU1w0rgCm8ejx8qRbKmwqHCEWBRiVs0TBRYVDiUWlXgNsah4osChxKLCEWJRiFk1TxRYVDjUWDIyMlKNAMCIESO47777mDlzJkOHDuXZZ5/l97//fcQ+gwYNYs6cOSxbtoxjjz2WMWPG8MYbb4RLsW+44QbcbjdDhgyhqKiIzZs3t/icUaNG8eKLL/LCCy8wdOhQbrnlFu644w4uueQSILHe0YkTJ1JXV8exxx7L1VdfzTXXXBNeZq0tDR8+nHnz5rF69WpOOukkRo4cyS233MIhhxyCYRh4PB6efPJJDjnkEMaOHcvZZ5/N5MmT201svV4vc+bMobi4mDPOOINhw4bxhz/8Idyj/5Of/ITx48dz8sknU1RUxPPPP49hGLz11lt873vf49JLL2XQoEGcf/75bNq0iZKSEjweD+effz4zZszgxhtv5JhjjmHTpk1MnTo1Zp/sVk5ODu+99x67d+9m9OjRXHTRRUyYMKHDdc4B3n33XdauXRueJBBg2rRp9OvXj+OOOw6fz8ett97a6nsNw8DlciX8PTYsO9dNOAC0d+9e8vLy2LNnT7tlFInIsix8Ph9erzflZUKh1pxoxka8vHgLN7y0jJMGFvLMpOPa3TeZHMmWCosKR4hFIWbVPFFgUeFQYlGJ1xCLiicKHEosKhwhFoWYVfNEgUWFI1aW+vp6NmzYQN++fW1PqC3LwrIsiWEATdOUVMdJPBzjxo1jxIgRPPDAAylnsVsqHKAVs3V1deHvZvMe+1jy0NQ31XVBmabJhg0bJMqVLMuivLw8qvKc0PJrhTZPGhcrR7KlwqLCAToxq+SJCosKhxKLSryCjicqHEosKhygE7NKnqiwqHCosTQ0NKQaISy/359qBECHA3RYVDhAK2ZN00z4e+xMHNfF5XK52l0Xsql21iRn+bVYOZItFRYVDiUpeaLCosIBWiwqUvFEhQN0WFQ4lKTkiQqLCgdosajIMDTWsVfhAB0WFQ41GYaB2+1OuNzdSdK7uCzLwu/34/F4Oiz/qNjXk2738muxciRbKiwqHEpS8kSFRYVDjUVFKp6ocCixqHAoSckTFRYVDjUWFamUMcfL0drSZali6aocagr5kmhPulPuniQpTPoBwUCprKyMstx9X096jv096bFwJFsqLCocISnErJInKiwqHGosCvEKOp6ocCixqHCEpBCzSp6osKhwqLEoJV0dLZ/WWVLhAB0WFQ7Qilk7yt2dieMchfWjP/4fX27dyxOXjOaUI3umGsfRwaZAI6x5D8pWQL+TofcoELrhOnLkyJEjRyoKTRxXWloa1XJSjhw56hzV1dWxcePGVid1jCUPdcrdkyDLsqipqSE7OzvlrTqWZdHY2EhaWlqHLJVN1klPJUeyZTuLaUJjbfDhqwZfLfhqoLGm2fPQ79XQWIvlq8Y0PLiG/RSj71hIVS9LbSXWyn/QYKWRPvRHGBmd3HhVvgo+fwaW/R1qyoLb5v4OCvrBsHNg2LlQOKBzmdCJWRUOJZYD9R57MHAosahwhFgUYlbNEwUWFY5YWULLZvl8PtuTdLUZ7xVKqlU4lFhUOEIsKjHr8/mwLMsZk64o0zTZsmULAwcODN9EUyXLsti9ezeFhYXtBq1lWVTUJLfcPRqOzlBcLDUVsONL2PHVvseXsPfbfQl4bVwcBuAGWPo3KOgP35kEIy6EzO5xHS8mWRZs/hg+exJWvIERaCADsN6+FvqfDIMnwBFnQFZBcj6/fi989Sp8/jfY8ul+rOwifIVD8W79BKNyPcz7n+Cj1wgYfi4cdTZ065UcpmZSiVkVDiWWA/Eee7BwKLGocIBOzCp5osKiwhEri8fjISsri/LyctLS0mwdTmFZFg0NDaSnp0t4ojBOX4VDiUWFI8SiELOmaVJWVobL5Ur4Xu+UuydBgUCANWvWpPyPcSyqqm9k2G1zAFh5x3gyvQcGt+3y+2DnGtj+ZWRSXr09uvenZYM3C7zZ+56Hfs+BtH3bvdn7n+/eDF+8CL6q4Ps9GTD0p/Cdy6D3MfafX90uWPZCMDmvWBXebPUchq92D+lVm/fva7ih9AQY/GM48keJJ8emCZsWBBPzFW+Av27/5ww6HUZeDAO/D+40aKiGr/8Fy1+CdXPBCo15MqDv94I97EN+DBl5iTE5OiB1IN5jHR3ccmLWUTLk8/mSsrSfUvLlyFE0UopZl8tF37598XpbdnrGkoc6SXoSpPTH2LIsfD4fXq+33aDdWFHDuHs/JNvr5qs7xqeMozNkWRa+hga8jbsxmvaM7/gqWHptNrb+xoJ+0PMo6Dk0+LN7331Jd04wEfdkxlSyHvbEasD48mX49PEgR0iHjITRk2DoT4LHj/+E4ZtFsPhJ+Oo18NcHt6dlw7CfwDGXEig5mjWrVzMwP4B79Vuw8k3YvjzyOIceG+xhHzwBCvpG//l7tsDS54MVA7s27t9eOCiYmA8/H3J77kNtJU6qy2HF68GE/ZuF+9/vTodB3w+Www/8PqRFjvtJVCoxq8KhxHIg3mMPFg4lFhUO0IlZJU9UWFQ44mUxTROfz2crRyAQYNOmTRx++OES91iF4QgqHEosKhygFbNpaWn4/f5Wv8fOmPQUyzAMiZs9BL9AVVVVFBQUtMuzsyZ5y6/FwpF01e2Gf/6CtPUfYtRVtr5Pet6+ZPyo/Ul58WBIz7EVJcKT0ZfBMZcGk+nPHg8m099+Dm9Ogzk3w4iLYPRlUDgw+g+o2x3spV/8ZHAytpB6DoPRlwST233jzw3TxJuejtGzFHoNhbE3QuV6WPlPWPkP2LJo/+O9GVAyLNjDPngCFB3ZcoK3xnpY9a9gr/m6D4B9bYHeXBh6Noz8GRw6usX7Wo2TnCI49orgY9dGWP5yMGEv/zrItvIfwWs2ZELwnEpPBFfiN2iVmFXhUGI5EO+xBwuHEosKB+jErJInKiwqHPGyuFyuFpNTJSrTNMnIyCAzMzPlqxKYpklNTQ05OTkpZVHhUGJR4QixKMWsHfcUpyfdEQDvfrWdK59ZzMjD8nntqhNSjZMc+X3w7E9gw3+Cvxuu4FjwnkdBydD9PeR5fVI/q3hNRTDB/ewJ2L1p//a+Y4Nj1484I1gW3lyWBVsXB8vZv3xlf0m5JzPYIz/60mAZfaznt/fbYPn5yjdh44Im5edAjwH7e9gNNyx9Ntg4UL97/z6lJwUbGob8OFh9kKgsK1h18MWLwfPcu3X/a7m9guf6nUnB6gdHjhw5cuTIkSNHjlIsp9y9HXVGkm5ZFnv27CEvL0+iVba+vp6MjIx2WZ5buJnfvLac0wb35LGfj04ZR9JkWfDG1bD0WSxvDr6zHsM74HsYdiSMcSNF4Ylpwrr3g6Xwq98h3COd2wtG/RyO+Tl0OyQ4EdvyF+Gzp2BHkzL14iHBHvrh50JmfrssUcdszU5Y/XawB3vdXAi0UWbXrXdwIrwRF0adLMcVJ6YJmz8K9q5/9fr+xgHDDUdfAN+7Ibby/ERYkiAVDiWWA/Eee7BwKLGocIRYFGJWzRMFFhUOJRaVeA2xqHiiwKHEosIRYjkQYtYpd0+xTNNk+/bt5ObmpnxcBEBtbW2HpVA7q4Pl7oVJmNk9Fo6k6T/3Bnt4DTf89EmqexxDQVoC47xtUoeeuFww8L+Cj92bYfFTsOSvULUN5v0B/nMPHH48bF0SXOYNgpPPHfX/gsl5n2Oj6jWPKWazewTHko+8ONg4sGZOMGFf815wPP+RPwq+1m9cXGXnMceJyxUscS89EX4wE9a+H6xAWPtecBz8Fy8EGwpOugG6H55cliRJhQM0WA7Ee+zBxAE6LCocSjGr4gnosKhwgAaLUryChidKHKDDosLRFWPWSdK7uAzDoEePHh3utzOJy6/FwpEUffEifHBn8PkZ92AM+j4pIolQzJ7kHwan3gJjfx0sO//sieBs6Rv/L/h64aDguPXh5yVv6bTmyugGw34afPgbwDIhLf71WhOOE086HHlG8LHlM/jg7mAlwpK/wtLngo0HJ90A+X2Sz2KTVDhAi0VFKp6ocIAOiwqHkpQ8UWFR4QAtFhWpeKLCATosKhxqssuX1I6sd5R0WZZFbW0tHY1qqNjXk16QnbyJ46LhsF0bFwTL3AGOnw7fmZQ6lmaKm8PjDSbFl74FUz+G798Fl7wFVy+C707tvAS9BVd6Qgk62Bwnh46Gn70Kk96DfieD6Q9WIjw4Ev55HezZ2u7bD/g46eIsKlLxRIVDiUWFQ0lKnqiwqHCosahIxRMVDiUWFQ412eWLk6QnQYZhkJ2dnfIxESHV19d3uM/O6mBPejLL3aPhsFUVa+CFC4NjpoecCafdnjqWNpQwR88hcPy04HrmicwgKRSztl+bPsfCxNfh0neCa6ybjcEZ9B8cAf+6ITgpXmexxCkVDtBgUYpX0PAEdDhAh0WFQylmVTwBHRYVDtBgUYpX0PAEdDhAh0WFoyvGrDNxnCMAvn//PFbvqOZvk47jxIGFqcZJXDUV8NipwSW7Dv0O/PwfCffyOuoi2rgAPvz9/mEC7vTgrPcn/gJyS1LL5siRI0eOHDly5KhLKpY81OlJT4JM06SiogLTNFONgmVZ1NTUdFhyEepJT9aY9Gg5bFFjHTx/fjBB714KF7wQkaB3Kks7UuEAnZjtFE9KT4BL/hlsuDnseAg0wMJHYPbR8M5NULWj81iikAqHEotKvIKOJyocSiwqHKATs0qeqLCocCixqMQr6HiiwqHEosIBXTNmnSQ9CbIsi4qKComgBfD52lgia58CpkVlbXKT9Gg4bJFpwmtXwpZPISMfLnoZsltWBnQKSxRS4VCK2U7zpO/3guP6J74Bfb4L/nr45KFgsv7uzVBTLnN9VDhAg0UpXkHDE9DhAB0WFQ6lmFXxBHRYVDhAg0UpXkHDE9DhAB0WFY6uGLPO7O5dXIZh0L1793b32VXrIxTTBVnJm929Iw5b9O9bYcUb4PbC+c9B4cDUsXQgFQ4ldbonhhFcKq7v2OCa7x/+PtjA8/GfMD57gu59vweZBZDZHbK6B3+2eBRAem5CcwK0j6gTJ0osKlLxRIUDdFhUOJSk5IkKiwoHaLGoSMUTFQ7QYVHhUJNdvjhJeheXZVlUV1eTk5PT5mQKoVL37llpeNzJKa6IhiNhffYEfPRg8PmZfw6WNaeKJQqpcCgpZZ4YBgw4FfqfElxn/cO7YetiWP1OlO93t57AZxWANzv4ussd/Jzwc1ez5659zyNftwwX9T4fGZlZGC5PFMfa91orx8K172ern+Vq+1j7roUTsy2l4okKhxKLCoeSlDxRYVHhUGNRkYonKhxKLCocarLLFydJT4IMwyAvL08mYDsan7GzJrj8Wo+c5Cy/Fi1HQlrzXnCmboCTb4bh56aOJQapcCjFbEo9MQwYeBoMOBVr43zqtn5JplWPUb8L6kKP3VBbuf93fx1YAaitCD7sRgJSPuVhk8Q9BwNcnnYS/lgbIppsi3jedkODy3DTp96Ha1MhZHQDb06wMSQ9B7y5TZ7nBKscvNmQlpW0ageV77EKB+iwqHA499jWpcKiwgEaLErxChqegA4H6LCocHTFmHVmd3fEP5Z9y38//znH9S3g71eOSTVO7Nq+HJ4YD75qGHFRsBdd5Evq6CBQY10wca/bBXVNkvfQw1cDZiCYyFtmcN6E8PN92819vzfdFn7e5GfEvoE2jmU227fZ5zY/FgfJnwDDtS+Zz2k9iffmtJ/kh5/ve78327nPOHLkyJEjR46iVix5qNOTngSZpsmOHTvo2bMnLldq5+azLIuqqipyc3PbKXcP9qQXJrEnPRqOuLRnKzx7bjBB7/s9+NEDHf7jnDSWGKXCAToxq+RJ1CxpmcFHt16p5Yj/A9poHGiZ8Fumn+rqKnKyMjGaNyq029DQXuNBK+/r4FhmIEDN3l3keA2MxhpoqA7eA0I/mz+H4Psb9gYftsgAbw5Weg6mJxNXRjeMFgl/TuTz9hoE0rKD1QlxX8YD8LtzkHCAc49VZlHhUGJRiVfQ8USFQ4lFhQO6Zsw6SXoSZFkWe/bsobi4ONUoUWlnTfJndk+KGqrgufOg6lsoOhLOfQY8B9g5iOhAi1lHNqppeXpHsiwsdxXkJm+ivGhkBQJsXbOGgQMH4nZ3wG2a0FjbfhLf2vNWX68J3newgg9fFYaviiici07hXvpoevVbvu5qNMAq3t/jn+J/VBztl3OPdXQgyYlXRweaumLMOkl6F5dhGB2WU1SE1kjPTl5PejQcMSngh5cugR3LIbsYLnwRMvNTwxKnVDiUpOSJCosKB2ixRC2XK5jEpudArg3Hs6xg0h9Xkt884a8GX1Wwlx+a9PzviBnLAHKab0zLaqdXv5VtHb0eTUMOOnGiwqEkJU9UWFQ4QItFRSqeqHCADosKh5rs8sVJ0ru4LMti7969dOvWrcNy92T2pEfDEcPB4O1fwtp/gycTLnwBuh+eGpYEpMKhJCVPVFhUONRYUibD2D8mnZ6Je2JZwXkNfDXBhL15r304sW//dctXEyzl99VgWIHgsRtrg4+aMnvO3ZPZThKfHezZT8/B8uZQb7rJ6FYYHALQVoOAO7n/gjjx2lJKnqiwqHCosahIxRMVDiUWFQ412eWLk6QnQYZhUFhYKBOwHY3NCJW7Fya53N22MSIfPRhcbg0Dfvo49D4mdSwJSoVDKWZVPAEdFhUO0GBRildI0BPDAG9W8EFR/McJLfmSnQ2BhiiS/FbK+CP2rd6/zfQHP8NfF3zUlLd/SkS5IoEnI85e/f0NAvsnAswFd1qLj1CIV9CKWRVPQIdFhQM0WJTiFTQ8AR0O0GFR4eiKMevM7u6Icfd8wMadtbw0ZQzfKS1INU77+up1eOnnwefj/wDfnZpSHEeOHDnq0rIsCPj2l+ZH0avf+utN3m82JofVnd5+Et9Rkt+8QcCZ48SRI0eOHNkoZ3b3FMs0TbZu3Urv3r1T3sJkWRa7d+8mPz+/nXL3YE96QXZyy9074uhQW5fAa1cGnx97ZdwJui0sNkiFA3RiVskTFRYVDiUWlXgFHU+SxmEY4EkPPrJ72MPi90X21DdN8psn9M179Vt7PRAcskWgAWoboHanPefu9rbRq980yW9leb7QpH5N9jXTsti6oyLlMasSr0osKhxKLM49VpdDiUWFA7pmzDpJehJkWRY1NTWoFCl4vW0n3/WNAaoagqWMhUmcOK4jjg4VaIQ3rgZ/PQwaD+N/nzoWG6XCoRSzKp6ADosKB2iwKMUraHgCOhzQAYvHC54CyLKpciuU9DdL4q2GKnxVu/Diw2jl9TYbAfz1weMGfFBXGXwkKBdwqMsTmdDHkOS3uq8nPa5VFg6YOOlEqXCABotzj21dKhygw6LC0RVj1knSu7gMwyA7O7vN1yv3jUf3uAy6ZSYvHDri6FAf/RHKVkBWDzjzoahnGU4Ki01S4VCSkicqLCocoMWiIhVPVDggBSxtJP0GEFfTc6CxWUIfSuKj6NUPl/xX7X/dXxfkMf1Qvzv4sEMuT8y9+oY3h+y2XvdkdOrSiioxq8IBWiwqUvFEhQN0WFQ41GSXL06S3sVlWRa7du2ie/furZZchErde+R4k1qq0hFHu6pcD/P+J/j89LujLrlMCouNUuFQkpInKiwqHGosKlLxRIVDiSVuDncaZHYPPuxQwE+gfi8bVi2n7yFFuP11kUl8NKX9TRsMGmuDxzX9UL8n+LBDhrvZuPwmSXz4eagRILvJvm28npbVbtJ/wMdJF2dRkYonKhxKLCocarLLFydJT4JcLhclJSUpHxMRUkZGRpuvVdTsW34tyaXuHXG0KcuCf/4iWH7YbxwMPy91LEmQCodSzKp4AjosKhygwaIUr6DhCehwgA6LBIfbgyurO4X9jsaVl5d4b7UZaLvXvkWvf+Trlq8Kq24vhr8uOASgoRoaa4LHtQLQsCf4sEOGa/+Y/jaS/FxXBmTnNxv3n9usKiB7/zGSlAhIxMk+KbA499jWpcIBOiwqHF0xZp0kPQkyDIP8/PxUYwBBlqysrDZfb9qTnkqONvXF32H9h8EyvB/db8sf6LhZbJYKB+jErJonCiwqHKDDohKvoOWJAgfosKhwgM0x63JDRrfgI1aOfY8ImWYwUY8iyW+xfF+rS/pVB49rmdCwN/hog6Xlonkd0LeT8Le+fF87vf5p2eByycWJAotzj9XlAB0WFQ7omjHrJOlJkGmabNy4kdLS0pS36FiWRWVlJQUFBW2Uuwd70gtzktuT3hFHq6rZCe/+Jvh87K+goF/qWJIgFQ7QiVklT1RYVDiUWFTiFXQ8UeFQYlHhAJ2YbdUTlyuYzKbn2vMhphksyW8viW+oxmqoon5vBRmuwP5e/bYaAbCCD19V8FFtDyreHCxvNqYnE1dmHkbz0v0WM/t38DzBa6sSsyrxCjqeqHAosahwQNeMWSdJT4Isy8Ln88nMMNhuT/q+ieN6JHH5tWg4WtWc3waX0ik+Co7/79SyJEkqHEoxq+IJ6LCocIAGi1K8goYnoMMBOiwqHEoxm3RPXK5g4pqeA+3l/ZYF9fWQ0cGEdZYVTPrbSfg7ahDY/7MmmORbZvDYvmoMXzVugN02nHtaVju9+h0k+ft697OsNKh3BxtNEpgkNxEpxSvofI9VOECHRYWjK8ask6R3cRmGQWZmZpuv7y93T25PekccLbR+Hix7DjBgwuzgRD6pYkmSVDiUpOSJCosKB2ixqEjFExUO0GFR4VCSkidRsxjGvpL1bKBn4h9sWdBYF2eS38ZzKxA8dmNt8FFTFheaAUQ44slsJ+FvOrN/aBhAbvsNAu4D799+lZhV4QAdFhUONdnly4H3bXUUk0zTDJdctFb+sTM0cVySx6R3xBGhxjr457XB59+5HPp8J3UsSZQKh5KUPFFhUeFQY1GRiicqHEosKhxKUvIkZSyGAd6s4IPi/Rx94uSwrODktr6a/TPzN+21jzrhr8Ha937D9AeP7a8LPmrK7Tl3T0YbSXxkwm+kZZG/pxajth9kdmu7QcDGDpS2pBKzKhxKLCocarLLFydJT4JcLheHHnqoRMAahkFubm6bYyJCPemFnTBxXHscEfrPvcFl13IPgVNvSS1LEqXCAToxq+SJCosKhxKLSryCjicqHEosKhygE7NKnqiwJMxhGJCWGXxkFyYGY1n4Ghrwui0MX20MSX7zBoFmy/uZjcHj++uDj9qKdjFcQAnA4g543elR9upH2evvafm/aJeJky7IosIBOvdYsM8XJ0lPggzDICcnJ9UYQJAlPb3tUvbQxHHJXoKtI46wdqyABQ8En58xM67Za21jSbJUOEAnZtU8UWBR4QAdFpV4BS1PFDhAh0WFA3RiVs0TBRYVDtjHElq6KS0TsnvYc2C/r8nM/dVR9Pp38Hog2MFDoAFqG4LzB9kht7dlr743m/TQzPzhhD9nf5Ifnugvt2WDgMe+6yoXJwIsKhygc48F+3xxkvQkKBAIsG7dOvr374/bnZpJP0IyTZOKigoKCwtbtC5ZlkVFaOK4Tih3b4ujyU7wj2vA9MORP4LBE1LH0glS4QCdmFXyRIVFhUOJRSVeQccTFQ4lFhUO0IlZJU9UWFQ4ksri8YKnALIKotq9w3gNJf2tJvE1rTQIVLf/ur9+3wf7oM4HdbvsOW9XWhu9+h0sz9dKg4CZlkXFrioKi4q6bpwcoBygc48F+3xxkvQkyTTNVCMA+9cNbK3korrBj88f5OyMnvS2OMJa/ARsWRS8Of5gZmpZOkEqHCEpxKySJyosKhxqLArxCjqeqHAosahwhKQQs0qeqLCocKixtBuvMSb9HSrQ2Czh35/EWw3VBOr24PbXBZfq66i031cTHMcPwTL/+t3BR4JyAUUuT7Oe/mh69dt43ZsdrJaI41qrxIkKR0gK91iwzxcnSe/iMgwDr7f1XvLQePRsr5tMb3JbndrjAGDvNvj37cHnp94Ceb1Tx9JJUuFQkpInKiwqHKDFoiIVT1Q4QIdFhUNJSp6osKhwgBZLp8qdBpndg49mMogjWQn49yfw4eS/Krpe/dZeb6wNsph+qN8TfNghw91KD3/zXv2Wvf6GNxdva6+nZcWV9MeNf7DGaweyyxcnSe/iMk2T8vJyilopz9k/s3vyx5O0xwHA2zdCw17oPRq+Mym1LJ0kFQ4lKXmiwqLCocaiIhVPVDiUWFQ4lKTkiQqLCocai4ri8sTtgcz84MMWiABm/V52bttMj9x0XI01cZT2N2kwaKwJHtcKQMOe4MMOGa79k/C1k+S3ujxfa/t6s9tN+p14bV12+eIk6UmQy+Wib9++EgFrGAYFBQWtllxU7OtJL8hOfitYexx8/RasfBNcnuCa6K7k9+q3ydKJUuEAnZhV8kSFRYVDiUUlXkHHExUOJRYVDtCJWSVPVFhUOJRYVOIVRDxxuTEy88k/LAfD40m8t9o0g4l6eLb+qnZ6/Vu+bjVpBDBCcwMAWGaww6thb+LnDIDRbsJveHMoTMvCyMhtMu6/nbH+admQpJjqijHrJOlJksejYa1hGKSltb6OZWctv9YuR0MVvHVD8PmYaVAyNHUsnSwVjpAUYlbJExUWFQ7QYlGIV9DxRIUDdFhUOEJSiFklT1RYVDhAi0UhXkHHE1s5XK5gMpueGx9L8w2mGSzJjyrJb97r38byfljBh68q+KhunSPmbrW0pgl/R736HbzuzYlI+rtazGqcTReTaZqsWbOGgQMHSswwWFZWRnFxccty905afq1djrl3wd6t0L0Uxv4q6RztsnSyVDhCLAoxq+aJAosKhxKLSryGWFQ8UeBQYlHhCLEoxKyaJwosKhxKLCrxGmJR8USBo1UWlyuYuKbnQHx5f6QsK5j0t5rE70/4zfoq6nbvIMtjYbRV2h96v7VvUrfGmn3l/jtsACU4Dn9f4t5opZF2+Tu4s22a0DBO2RUrTpLexWUYBkVFRa2WXOzspOXX2uTYuhgWPhJ8/qP7wZuVdI42WVIgFQ4lKXmiwqLCocaiIhVPVDiUWFQ4lKTkiQqLCocai4pUPFHh6BQWw9hXsp4N9Gx7N8siwzSDjQTtsVgWNNbFVdrfopEg9NwKBI/dWAuNtRg1ZWQAAU/q12236/o4SXoXl2EY4UdzVVR33sRxLTgCjfDmNYAFw8+D/qcknaFNlhRJhUNJSp6osKhwqLGoSMUTFQ4lFhUOJSl5osKiwqHGoiIVT1Q4lFii5jCMYEecNwtyihP/YMsCf31E4h6o28u3G1ZxiFsjSbfj+qR+dL2jpCpUctHa2oGdOSa9BccnD8GO5cHlNk6/O+mf3y5LiqTCoSQlT1RYVDjUWFSk4okKhxKLCoeSlDxRYVHhUGNRkYonKhxKLCnjMIzgGvM5RVDQF0qGwWHfpeaQ4zt1Cbq2ZJcvhmVZlk1MB4T27t1LXl4ee/bsoVu3bkn5DMuyME0Tl8uV8lYuIMzSXN+/fx6rd1Tzt0nHceLAws7j2LUR/vxd8NfBmQ/ByIuS/tltsqRYKhxKMaviCeiwqHCABotSvIKGJ0ocoMOiwqEUsyqegA6LCgdosCjFK2h4osQBOiwqHAdKzMaSh6be1S4qv9+fagQgGLShR3OFetI7Y0x6mMM04Z/XBRP00pNgxIVJ/+w2WVLcPqXCEZJCzCp5osKiwqHGohCvoOOJCocSiwpHSAoxq+SJCosKhxqLQryCjicqHEosKhwhdbWYdZL0JMg0TTZs2JDyMhQIBkp5eXmLQAmYFrtqOzdJLy8vx1r+Eqx7H9zpwTXRU9Da1ZYnBysH6MSskicqLCocSiwq8Qo6nqhwKLGocIBOzCp5osKiwqHEohKvoOOJCocSiwoHdM2YdSaO6+JyuVyUlJS02L671oe5L3YKspKfpLtcLkq6eeGvvwluGPtL6NE/6Z/bJksrnhysHEpS8kSFRYUDtFhUpOKJCgfosKhwKEnJExUWFQ7QYlGRiicqHKDDosKhJrt8cXrSu7gsy6KxsbFFa05o+bXuWWl43MkPA8uyMN/9LdRWQNFgOP6apH9meyyteXKwcihJyRMVFhUONRYVqXiiwqHEosKhJCVPVFhUONRYVKTiiQqHEosKh5rs8sVJ0pMkhUkUIBgolZWVLQKlM5dfA7A2/B+uZc8Gf5kwGzzJ771vk6UNTw5WjpAUYlbJExUWFQ41FoV4BR1PVDiUWFQ4QlKIWSVPVFhUONRYFOIVdDxR4VBiUeEIqavFrDO7+0Gqfyz7lv9+/nOO61vA368ck9wPa6yHR06AnWth9GXwo/uT+3mOHDly5MiRI0eOHDlyJCRndvcUy7IsqqurJVqWLMvC5/O1LHff15Ne2Bk96fNnwc61WDk9sU69Jfmf14Ha8uRg5QixKMSsmicKLCocSiwq8RpiUfFEgUOJRYUjxKIQs2qeKLCocCixqMRriEXFEwUOJRYVjhBLV4tZJ0lPgkzTZMuWLTIzDO7evbtlkl7TSTO7l30N/zcLgL0n3IyVnpfcz4tCbXlysHKATswqeaLCosKhxKISr6DjiQqHEosKB+jErJInKiwqHEosKvEKOp6ocCixqHBA14xZZ3b3Li6Xy0VxcXGL7RWhNdKzk9iTbprwz2vBbIRBPyDvuxNTsuRac7XlycHKoSQlT1RYVDhAi0VFKp6ocIAOiwqHkpQ8UWFR4QAtFhWpeKLCATosKhxqsssXpye9i8uyLBoaGtosdy9IZk/6kqdh88eQlo11xkwahEpiWvPkYOVQkpInKiwqHGosKlLxRIVDiUWFQ0lKnqiwqHCosahIxRMVDiUWFQ412eWLk6QnQYZh4PV6MQR6jS3Loqqqqs1y98LsJCXpVdvhvVuDz0+dgdXt0FY5UqG2PDlYOUAnZpU8UWFR4VBiUYlX0PFEhUOJRYUDdGJWyRMVFhUOJRaVeAUdT1Q4lFhUOKBrxqwzu/tBqnH3fMDGnbW8eOUYju1bYP8HvHQJfPUaHDISLn8fXG77P8ORI0eOHDly5MiRI0eODgA5s7unWEoTKViWRV1dXSvl7kmcOG71u8EE3XAH10R3udvkSIVUWFQ4QiwKMavmiQKLCocSi0q8hlhUPFHgUGJR4QixKMSsmicKLCocSiwq8RpiUfFEgUOJRYUjxNLVYtZJ0pMg0zTZvn27xAyDALW1tRG/1zcGqGrwA1Bo98RxDdXwr+uDz8dcBb2ObpMjlVJhUeFQilkVT0CHRYUDNFiU4hU0PAEdDtBhUeFQilkVT0CHRYUDNFiU4hU0PAEdDtBhUeHoijHrzO7exWUYBj169IjYVrlvPLrHZdAt0+YQ+PD3sOcbyD8Mxt3ULkeqpMKiwqEkJU9UWFQ4QItFRSqeqHCADosKh5KUPFFhUeEALRYVqXiiwgE6LCocarLLF6cnvYvLsixqa2sjSi6alrrbOsHCt5/DJw8Fn//wfvBmt8uRKqmwqHAoSckTFRYVDjUWFal4osKhxKLCoSQlT1RYVDjUWFSk4okKhxKLCoea7PLFSdKTIMMwyM7OlphhEKC+vj7i94qa4PJrtq6RHvDDP64By4ShP4WBp3XIkUqpsKhwKMWsiiegw6LCARosSvEKGp6ADgfosKhwKMWsiiegw6LCARosSvEKGp6ADgfosKhwdMWYdWZ3Pwj1yuItXP/SMk4aWMgzk46z56Af/Qnm3AwZeTDtM8gptue4jhw5cuTIkSNHjhw5cnSAy5ndPcUyTZOKigqJyQssy6Kmpiay3H1fT3phjk096bs2wQd3BZ9//85WE/TWOFIlFRYVDtCJWSVPVFhUOJRYVOIVdDxR4VBiUeEAnZhV8kSFRYVDiUUlXkHHExUOJRYVDuiaMesk6UmQZVlUVFRIBC2Az+eL+D08Jj3bhuXXLCs4m3tjLRx+Aoz8WdQcqZQKiwqHUsyqeAI6LCocoMGiFK+g4QnocIAOiwqHUsyqeAI6LCocoMGiFK+g4QnocIAOiwpHV4xZZ3b3Li7DMOjevXvEtorwxHE29KR/9SqsfQ/cXvjRA9DGWJDWOFIlFRYVDiUpeaLCosIBWiwqUvFEhQN0WFQ4lKTkiQqLCgdosahIxRMVDtBhUeFQk12+OD3pXVyWZVFVVdVquXuPnAR70ut2wdu/Dj4/6XooGhQTR6qkwqLCoSQlT1RYVDjUWFSk4okKhxKLCoeSlDxRYVHhUGNRkYonKhxKLCocarLLFydJT4IMwyAvL09mhsHm4zNC5e6FiSbp790KNWVQOAhO/EXMHKmUCosKh1LMqngCOiwqHKDBohSvoOEJ6HCADosKh1LMqngCOiwqHKDBohSvoOEJ6HCADosKR1eMWWd294NQx//+fb7dU88bV5/A0X3y4zvIpo/gyR8En1/6Nhx+vG18jhw5cuTIkSNHjhw5ctSV5MzunmKZpsm2bdskWpcsy2Lv3r3hkgvLsqioCfakF8Q7cZy/IbgmOsCon0eVoDfnSKVUWFQ4QCdmlTxRYVHhUGJRiVfQ8USFQ4lFhQN0YlbJExUWFQ4lFpV4BR1PVDiUWFQ4oGvGrJOkJ0GWZbFnzx6JoG2u6gY/Pn8wgOMekz7/AahYDdnF8F+32wfnKGVSjllHjprLiVdHB5qcmHV0IMmJV0cHmrpizDqzu3dxGYYRUU4RGo+e5XWT5Y3j8pevhv+7N/j8B3+AzOhmL2zOkUqpsKhwKEnJExUWFQ7QYlGRiicqHKDDosKhJCVPVFhUOECLRUUqnqhwgA6LCoea7PLF6Unv4mrespTQzO6mCf+8FgI+GPh9OOrsuDlSKRUWFQ4lKXmiwqLCocaiIhVPVDiUWFQ4lKTkiQqLCocai4pUPFHhUGJR4VCTXb44SXoSZBgGhYWFMjMMulz7L3N4jfTsONZIX/o32LQA0rLgjHvbXBM9Go5US4VFhUMpZlU8AR0WFQ7QYFGKV9DwBHQ4QIdFhUMpZlU8AR0WFQ7QYFGKV9DwBHQ4QIdFhaMrxqwzu/tBpucWbuY3ry3ntMHFPPbz70T/xuoy+NN3oH43fP8uOH5a0hgdOXLkyJEjR44cOXLkqCvJmd09xTJNk2+++UZmhsFdu3btL3ev3lfuHmtP+js3BRP0XkfDcVMS5kilVFhUOEAnZpU8UWFR4VBiUYlX0PFEhUOJRYUDdGJWyRMVFhUOJRaVeAUdT1Q4lFhUOKBrxqyTpCdBlmVRU1MjEbQAXu/+8ec79y2/FtOY9DX/hi9fBsMFE2aDO775BptypFoqLCocSjGr4gnosKhwgAaLUryChiegwwE6LCocSjGr4gnosKhwgAaLUryChiegwwE6LCocXTFmndndu7gMwyA7Ozv8e0WoJz0nhp70d34V/HncVDhkpC0cqZQKiwqHkpQ8UWFR4QAtFhWpeKLCATosKhxKUvJEhUWFA7RYVKTiiQoH6LCocKjJLl+cnvQuLsuyqKysDLcsVe7rSS+MtifdVwM71waff+8G2zhSKRUWFQ4lKXmiwqLCocaiIhVPVDiUWFQ4lKTkiQqLCocai4pUPFHhUGJR4VCTXb44SXoS5HK5KCkpkZnxMCMjI/x8Z6yzu1eXBX96MqNeEz0ajlRLhUWFQylmVTwBHRYVDtBgUYpX0PAEdDhAh0WFQylmVTwBHRYVDtBgUYpX0PAEdDhAh0WFoyvGrFPungQZhkF+fn6qMYAgS1ZWVvj3mNdJDyXpOcUxL7nWHkcqpcKiwgE6MavmiQKLCgfosKjEK2h5osABOiwqHKATs2qeKLCocIAOi0q8gpYnChygw6LCAV0zZjWaG7qYTNNk/fr1MjMM7ty5E8uyCJhWuNw96iS9pkmSbhNHqqXCosIBOjGr5IkKiwqHEotKvIKOJyocSiwqHKATs0qeqLCocCixqMQr6HiiwqHEosIBXTNmnSQ9CbIsC5/PJxG0QJ2AW7kAAQAASURBVLg1Z3etD3MfUkFWtD3pO4I/c3raxqEgFRYVDqWYVfEEdFhUOECDRSleQcMT0OEAHRYVDqWYVfEEdFhUOECDRSleQcMT0OEAHRYVjq4Ys065exeXYRhkZmYC+5df656VhscdZftMdXnwZ3aRbRyplgqLCoeSlDxRYVHhAC0WFal4osIBOiwqHEpS8kSFRYUDtFhUpOKJCgfosKhwqMkuX5ye9C4u0zSpqKgI/oxn+TWbetKbcqRaKiwqHEpS8kSFRYVDjUVFKp6ocCixqHAoSckTFRYVDjUWFal4osKhxKLCoSa7fHGS9CTI5XJx6KGHSswwaBgGubm5GIYRntm9IDvKUneAmn096TmJ96SHOFItFRYVDtCJWSVPVFhUOJRYVOIVdDxR4VBiUeEAnZhV8kSFRYVDiUUlXkHHExUOJRYVDuiaMeuUuydBhmGQk5OTagwgyJKeHuw537mvJz3qNdLBtp70phyplgqLCgfoxKyaJwosKhygw6ISr6DliQIH6LCocIBOzKp5osCiwgE6LCrxClqeKHCADosKB3TNmE19c0MXVCAQYPXq1QQCgVSjYJomZWVlmKYZHpMe9RrpsH8JtuzEZndvypFqqbCocIBOzCp5osKiwqHEohKvoOOJCocSiwoH6MSskicqLCocSiwq8Qo6nqhwKLGocEDXjFmJJP3Pf/4zpaWlZGRkcNxxx7Fo0aI29x03bhyGYbR4/PCHP+xE4o6lELCwf91AwzCoqI5x+TXLalLunliS3pQj1VJhUeEISSFmlTxRYVHhUGNRiFfQ8USFQ4lFhSMkhZhV8kSFRYVDjUUhXkHHExUOJRYVjpC6WsymPEn/+9//znXXXcett97KkiVLOProozn99NMpKytrdf9XX32Vbdu2hR9ffvklbrebc845p5PJDwwZhoHX6903Jj3GieN81dBYG3xuQ5Ie4ki1VFhUOJSk5IkKiwqHGouKVDxR4VBiUeFQkpInKiwqHGosKlLxRIVDiUWFQ012+ZLyJH3WrFlcccUVXHrppQwZMoRHHnmErKwsnnjiiVb3LygooKSkJPx47733yMrKcpL0NmSaJjt27Igody+MduK4UKm7Nwe82bZxpFoqLCocSlLyRIVFhUONRUUqnqhwKLGocChJyRMVFhUONRYVqXiiwqHEosKhJrt8SenEcT6fj8WLF3PTTTeFt7lcLk477TQ+/vjjqI7x+OOPc/7555Od3XoS2dDQQENDQ/j3vXv3AsGxC6FxC4Zh4HK5ME0Ty7LC+7a13eVyYRhGm9sty+Lwww/HsiwCgUB4psHmF6ut7W63G8uyIraHWNra3ha7ZVnk5eUFlwNo0pMe1Tnt3Y4bILuoxRiPWM/J5XLRvXv3iOPHe06JXifDMCgoKGh1/1jOKdHrZFlWuBzGrtiL9zqFYrY99s64TqZphuM1NJQl0diL9zo1ZQkdP1XXKcQROpad94hYzyk/Pz/itURjL6RYzgmgb9++ABGfa/e9PJpzsiyL7t27t2CJ9ZwSvU6he31Idt8jYjmnEEvo85N1L4/mnJp+dzrjb25b24EW/xck417e0TkZhtHm3+LOvk6tscRzTolep/b+Fnf2dWrK0tn/GzU9p6b/xzb9HsdzTk1Z4jmnaP4Wd8Z1gsi/xan6Hza0vfn/BanINSDYeRq6ryV6TolcJ8uy6Nu3b4vjpOI6hf4vaO2cmvvXnlKapFdUVBAIBOjZM3Lm8J49e/L11193+P5Fixbx5Zdf8vjjj7e5z+9//3tuv/32FtvXrVsXngUwLy+PXr16sWPHDvbs2RPep7CwkMLCQrZu3UpNTU14e0lJCfn5+WzcuBGfzxfefuihh5KTk8P69evx+/3hP4h9+/bF4/GwZs2aCIaBAwfi9/vZsGFDeJvL5WLQoEHU1NSwZcuW8Hav10u/fv3Ys2cP27dvD2/Pzs6mT58+VFZWUlFREd4eOqeysrLwOZXvrQeCY9KjOafcb5bRGyCnJ+vWrYsI7njOyefz2XJOdl2n1atXJ3xOdl2n7du323JO8V6n0D+wdp6TXdfJjtg70M9p48aNMue0devWlF+nrKwsevfuTWVlJTt37kz4nOy4To2NjV0y9g7kc1q7dq3UOVVXV4f/L0jldaqtrZW6Tkqxt2XLFplz2r17d8quUyhRNwyDfv36yV2nVMRebW2t1Dlt3rxZ5vu0bds2ievUv39/ampq2Lp1a8LnZMd18nq9Lc6pR48eRCvDiiWlt1nffvstvXv35qOPPmLMmDHh7TfeeCPz5s1j4cKF7b7/yiuv5OOPP+aLL75oc5/WetJDF6Vbt26A/a1bPp+PtWvXMmDAANxud0pat0Lb/X4/ZWVldOveg6G3/xuAZbd8n9wMd4fnZHz6GK53boTBEwj89Omo2NvabhgGO3bsoLCwMLxPqlohAcrLy+nRo0eYJZ5zSvQ6maZJeXk5JSUlALbEXrytkIFAgLVr13LEEUeEPzeec4pne9NzCgQClJeXU1RUhMfjSWlPelOW0Pe4M3r+mm83zWDZVFFRUXi/VPWkW5bF9u3bI1iS3fPX2jmZpsm6devo379/xHc4FT3ppmlSUVFBcXHLeTs68zqF7ic9e/bE7XZ3Ws9fa+cUYikuLsbj8XRKz19r7H6/P/wddrlcndbz19r2xsZG1qxZE/F/QSp60oE2/xZ39nUCKCsri2CJ55wSvU6h73CowyiVPelN/y8IfW4859Te9mjOKfQ/wYABA0hLS0vonJqyxHOdovlb3BnXKRAIRPwtTmVPelt/i2M9p0Svk2VZVFRUUFhYGL6/xHtOiV6nQCDA+vXrGTBgQKssnXmdmt5Tmucf1dXV5Ofns2fPnnAe2pZS2pNeWFiI2+1mx44dEdt37NgRTlzaUk1NDS+88AJ33HFHu/ulp6e3ulad2+0Ol6+E1PSPRCLbQzeR5p/R/PPa224YRkzb22Pp2bMnO6qCDRUel0G3TE9EALd5nNp9LUs5PWNib227ZVkUFxeHgzmRc0p0u2VZETfZjtjb257IdXK5XPTs2TPcsxINe0fbE7lOTf9hsyP24tluGAY9e/aMuDaJxl577O2dU2ssdt4jot0eipPm8Zqq69QaS1vssW6P55ySea7Rfm/aure1x273dWoaJ+3t3xnXKVqWZF8nj8fTarwm829ue9tb+7+gs69Te3+LO/s6tcfS2v6QnOsU+g7H87fY7uvU/P+CzvzfqDljKF7j+Vts53Wy42+xHdcp1r/Fyb5OdvwtTvQ6dfT/dCquUyr/h236PHRPab5/W/eYVo8Z9Z5JkNfr5ZhjjuH9998PbzNNk/fffz+iZ701vfTSSzQ0NHDxxRcnG/OAVuhGX1nTCARL3aMOkOp9jScJrpHelCOW4EyWVFhUOJSk5IkKiwqHGouKVDxR4VBiUeFQkpInKiwqHGosKlLxRIVDiUWFQ012+ZLSJB3guuuu49FHH+Xpp59m5cqVTJ06lZqaGi699FIAJk6cGDGxXEiPP/44Z511Vky1/QejTNOkrKyM8qp949Gzo1x+DWxbI70pR/OSlVRIhUWFQ0lKnqiwqHCosahIxRMVDiUWFQ4lKXmiwqLCocaiIhVPVDiUWFQ41GSXLyktdwc477zzKC8v55ZbbmH79u2MGDGCd955Jzw2aPPmzS1KBVatWsX8+fOZM2dOKpA7lMvlYuDAgW2WRHQ2S3FxMQu2fgsEe9KjVqgn3YYkvWlJaKqlwqLCEWJRiFk1TxRYVDiUWFTiNcSi4okChxKLCkeIRSFm1TxRYFHhUGJRidcQi4onChxKLCocIZauFrMpT9IBpk2bxrRp01p97cMPP2yx7YgjjohpCvtUyO/34/XGkBAnSZYVXD4jtPxaYU4MPenVoZ70nu3vFwNHaLbQVEqFRYUjJIWYVfJEhUWFQ41FIV5BxxMVDiUWFY6QFGJWyRMVFhUONRaFeAUdT1Q4lFhUOELqajGb+uaGLijTNNmwYYNE+YdlWZSXl7MztEZ6dpTBa1lNxqQX2cah0LiiwqLCAToxq+SJCosKhxKLSryCjicqHEosKhygE7NKnqiwqHAosajEK+h4osKhxKLCAV0zZiV60h0lTy6Xi5KSEnbWBBPuHtH2pDfshcC+petsKnfvaMb+zpIKiwqHkpQ8UWFR4QAtFhWpeKLCATosKhxKUvJEhUWFA7RYVKTiiQoH6LCocKjJLl+cnvQuLsuyaGxsjL0nvbos+DO9G6Rl2sah0NqmwqLCoSQlT1RYVDjUWFSk4okKhxKLCoeSlDxRYVHhUGNRkYonKhxKLCocarLLFydJT5IUJi6AYKBUVlZSUbMvSY924rhQkm5DqXtTDoUvsgqLCkdICjGr5IkKiwqHGotCvIKOJyocSiwqHCEpxKySJyosKhxqLArxCjqeqHAosahwhNTVYtawVJztJO3du5e8vDz27NlDt27dUo3TaTr+9+/z7Z56Xr/6BEb0ye/4DV++Ci9fCocdD5e9nXQ+R44cOXLkyJEjR44cOeqqiiUPjWtMeiAQ4PXXX2flypUAHHXUUfz4xz/G7XbHc7guJ8uyqKmpITs7O+WzHVqWhc/no6LGB8RQ7h5eI92+nvTGxkbS0tIkPFFgUeEIsSjErJonCiwqHEosKvEaYlHxRIFDiUWFI8SiELNqniiwqHAosajEa4hFxRMFDiUWFY4QS1eL2ZjrAtauXcuQIUOYOHEir776Kq+++ioXX3wxRx11FOvWrYsbpCvJNE22bNkiM8Pg1rJKfP4gS/Tl7qE10hNffi3EsXv3bomSGBUWFQ7QiVklT1RYVDiUWFTiFXQ8UeFQYlHhAJ2YVfJEhUWFQ4lFJV5BxxMVDiUWFQ7omjEbc5I+ffp0+vXrxzfffMOSJUtYsmQJmzdvpm/fvkyfPj0hGEf2y+Vy4coMllNked1keaMsngiPSU98ZvcQR3FxscR4ERUWFQ4lKXmiwqLCocaiIhVPVDiUWFQ4lKTkiQqLCocai4pUPFHhUGJR4VCTXb7E/O558+Yxc+ZMCgoKwtt69OjBH/7wB+bNm5cQjCP7ZVkW23ZVAzH0osP+JN2G5ddCHA0NDRKtbSosKhxKUvJEhUWFQ41FRSqeqHAosahwKEnJExUWFQ41FhWpeKLCocSiwqEmu3yJOUlPT0+nqqqqxfbq6mq83hiSwC4swzDwer0pHxMB+8rdK/YA0CM7yjXSAWrsT9KrqqokvsgqLCocoBOzSp6osKhwKLGoxCvoeKLCocSiwgE6MavkiQqLCocSi0q8go4nKhxKLCoc0DVjNubZ3SdOnMiSJUt4/PHHOfbYYwFYuHAhV1xxBccccwxPPfVUQkDJ1sE4u/tzCzfzm9eWc9rgYh77+Xeie9OsIbB3K1wxF3ofk1xAR44cOXLkyJEjR44cOerCiiUPjbkn/cEHH6R///6MGTOGjIwMMjIyOOGEExgwYAAPPPBAvMxdSkoTKViWxY7dNUAMPemWZfuYdMuyqKurk/FEgUWFI8SiELNqniiwqHAosajEa4hFxRMFDiUWFY4Qi0LMqnmiwKLCocSiEq8hFhVPFDiUWFQ4QixdLWZjTtLz8/N54403WL16NS+//DIvv/wyq1at4rXXXiM/Pz8hmK4i0zTZvn27xAyDADv21AIxjEmv2wVmY/C5TeXuALW1tbYdK1GpsKhwKMWsiiegw6LCARosSvEKGp6ADgfosKhwKMWsiiegw6LCARosSvEKGp6ADgfosKhwdMWYjTlJv+OOO6itrWXAgAFMmDCBCRMmMGDAAOrq6rjjjjsSBnJkrwzDoCYQvMw9cqLsSQ+tkZ6RB54YxrF3wNGjRw+JsSIqLCocSlLyRIVFhUONRUUqnqhwKLGocChJyRMVFhUONRYVqXiiwqHEosKhJrt8iTlJv/3226murm6xvba2lttvvz0hGEf2y7Isyvb1pBemaI30EEdtba1MGYoCiwqHkpQ8UWFR4VBjUZGKJyocSiwqHEpS8kSFRYVDjUVFKp6ocCixqHCoyS5fYk7SLctqtWVg2bJlEcuyHcwyDIPs7GyZlqWd1Q1ADGPSbR6PHlJ9fb2tx0tEKiwqHEoxq+IJ6LCocIAGi1K8goYnoMMBOiwqHEoxq+IJ6LCocIAGi1K8goYnoMMBOiwqHF0xZj3R7ti9e3cMw8AwDAYNGhRhQiAQoLq6milTpiQM1BXkcrno06dPqjGAYNDurg8AMYxJt3mN9BCHSiOOCosKB+jErJInKiwqHKDDohKvoOOJCgfosKhwgE7MKnmiwqLCATosKvEKOp6ocIAOiwoHdM2YjTpJf+CBB7Asi8suu4zbb7+dvLy88Gter5fS0lLGjBmTMFBXkGmaVFZWUlBQgMsVc7GCrfIHTCprfAD0yI4ySbd5jXTYX/qRlZWV8lYuFRYVDtCJWSVPVFhUOJRYVOIVdDxR4VBiUeEAnZhV8kSFRYVDiUUlXkHHExUOJRYVDuiaMRt1kv7zn/8cgL59+3L88ceTlpYW94d2dVmWRUVFBd27d081CrtrfZj7hkR0jzZJT0JPOoDP5yMrK8vWY8YrFRYVDqWYVfEEdFhUOECDRSleQcMTJQ7QYVHhUIpZFU9Ah0WFAzRYlOIVNDxR4gAdFhWOrhizUSfpIY0dO5ZAIMArr7zCypUrATjqqKP48Y9/jNvtTgjGkf2qrA0upZaflUaaO8qWpSSMSTcMQ+aLo8KiwqEkJU9UWFQ4QItFRSqeqHCADosKh5KUPFFhUeEALRYVqXiiwgE6LCocarLLl5jrAdauXcvgwYOZOHEir776Kq+++ioXX3wxRx11FOvWrUsYyJG9qqgKTRoXZS86JG1296qqKokZIFVYVDiUpOSJCosKhxqLilQ8UeFQYlHhUJKSJyosKhxqLCpS8USFQ4lFhUNNdvkSc5I+ffp0+vfvzzfffMOSJUtYsmQJmzdvpm/fvkyfPj0hmK4iwzDIy8tL+fgMgJ01+5L0aNdIh/3rpOcU2cpimqatx0tEKiwqHEoxq+IJ6LCocIAGi1K8goYnoMMBOiwqHEoxq+IJ6LCocIAGi1K8goYnoMMBOiwqHF0xZg0rxjQ/OzubTz75hGHDhkVsX7ZsGSeccEKra6grae/eveTl5bFnzx66deuWapyk66kFG7jtHys4Y1gJD110TMdvME24swhMP1y3ErodknxIR44cOXLkyJEjR44cOerCiiUPjbknPT09naqqqhbbq6ur8XpjKKnuwjJNk23btkm0LlVUB2d2L4i23L1uVzBBB8i2ryfdsiz27t0rURKjwqLCAToxq+SJCosKhxKLSryCjicqHEosKhygE7NKnqiwqHAosajEK+h4osKhxKLCAV0zZmNO0n/0ox8xefJkFi5ciGVZWJbFJ598wpQpU/jxj3+cEExXkWVZ7NmzRyJoQ+XuhdlRlruHll/LLAC3M4P/wSKlmHXkqCM58eroQJMTs44OJDnx6uhAU1eM2ZiT9AcffJD+/fszZswYMjIyyMjI4IQTTmDAgAHMnj07GYyOEtDOfT3pPXKjTNLDk8bZu/yaYRh069ZNYqyICosKh5KUPFFhUeFQY1GRiicqHEosKhxKUvJEhUWFQ41FRSqeqHAosahwqMkuX2JO0vPz83njjTdYtWoVL7/8Mi+//DKrVq3itddeIy8vLyEYR/ZrZ82+JD07yl7x6tCkcfYm6UotXCosKhxKUvJEhUWFQ41FRSqeqHAosahwKEnJExUWFQ41FhWpeKLCocSiwqEmu3yJeZ30kAYOHMjAgQMT+vCuKsMwKCwslGhZ2lkdWoItxp50G9dID8nlirlNKGlSYVHhUIpZFU9Ah0WFAzRYlOIVNDwBHQ7QYVHhUIpZFU9Ah0WFAzRYlOIVNDwBHQ7QYVHh6IoxG3OSblkWL7/8Mh988AFlZWUtBui/+uqrCUMd6HK5XBQWFqYaA9jfk14Ybbl7aEy6jWukQ/DLk5uba+sx45UKiwoH6MSskicqLCocoMOiEq+g44kKB+iwqHCATswqeaLCosIBOiwq8Qo6nqhwgA6LCgd0zZiNOc2/9tpr+dnPfsaGDRvIyckhLy8v4uEoOMPgN998k/IZBhv8AarqgzO194h2dvfqUJJu7xrplmWxa9cuiZIYFRYVDtCJWSVPVFhUOJRYVOIVdDxR4VBiUeEAnZhV8kSFRYVDiUUlXkHHExUOJRYVDuiaMRtzT/ozzzzDq6++yhlnnJHQB3dlWZZFTU1NyoO2cl8vusdl0C0jyktdnZyedEBqiT4VFhUOlZgFHU9Ah0WFAzRYlOIVNDwBHQ7QYVHhUIpZFU9Ah0WFAzRYlOIVNDwBHQ7QYVHh6IoxG3OSnpeXR79+/RL+YEfJV3hm9xxv9GMjQkm6zWPSDcMgOzvb1mPGKxUWFQ4lKXmiwqLCAVosKlLxRIUDdFhUOJSk5IkKiwoHaLGoSMUTFQ7QYVHhUJNdvsRc7n7bbbdx++23U1dXl/CHO0quKvZNGpef4Y6+ZSk8Jt3+2d0rKyslWrhUWFQ4lKTkiQqLCocai4pUPFHhUGJR4VCSkicqLCocaiwqUvFEhUOJRYVDTXb5EnNP+rnnnsvzzz9PcXExpaWlpKVFLu21ZMmShIC6glwuFyUlJSmf8XB/T3qUk8aZAahJzhJsABkZGbYfM16psKhwqMQs6HgCOiwqHKDBohSvoOEJ6HCADosKh1LMqngCOiwqHKDBohSvoOEJ6HCADosKR1eM2ZiT9J///OcsXryYiy++mJ49e8pMda8kwzDIz89PNQY7a4I96cXdMqO7TrWVYJmAAVn2zpBoGAZZWVm2HjNeqbCocIBOzKp5osCiwgE6LCrxClqeKHCADosKB+jErJonCiwqHKDDohKvoOWJAgfosKhwQNeM2ZiT9H/961+8++67nHjiiQl/eFeVaZps3LiR0tLSlLbohHrSs90mlmV1nKiH1kjP6gHumEOjXYVKPwoKClLesKPCosIBOjGr5IkKiwqHEotKvIKOJyocSiwqHKATs0qeqLCocCixqMQr6HiiwqHEosIBXTNmYz6LPn360K1bt7g/8GCQZVn4fL6Uj9Go2Jek98yPsjUnSePRQ1JpbQMdFhUOlZgFHU9Ah0WFAzRYlOIVNDwBHQ7QYVHhUIpZFU9Ah0WFAzRYlOIVNDwBHQ7QYVHh6IoxG3OSft9993HjjTeycePGhD/cUXIVKncvyc+JriWnOnlJumEYZGZGWXbfhkzL5POyz1m7ay11/vgnLrSDxQ6pcChJyRMVFhUONRYVqXiiwqHEosKhJCVPVFhUONRYVKTiiQqHEosKh5rs8iXmmuaLL76Y2tpa+vfvT1ZWVouJ4yorKxMCcmSfQuXuaWY9pml2XP6RpOXXIFiGEir9iKcMxbRMZiyYwZvr3gxvK8osok9uHw7NPZRDcw8NPs8J/izIaLvEJFEWO1Ttq2Zp2VK+3PYlWVlZYVaDSOb2vuChfV2Gi55ZPcNeZHg0JvGIRwrXRo1FhUONRUUqnqhwKLGocChJyRMVFhUONRYVqXiiwqHEosKhJrt8iTlJf+CBB+L+sINFLpeLQw89NOUBu3PfEmyHFuZF2ZO+b0x6knrSc3Nz42pVsiyLuz65izfXvYnbcJOVlkWVr4ryunLK68pZUtZyRYEsT1Y4cW+avPfJ7UPP7J5xs8Sr7TXb+bzsc5bsWMLS8qWs3rUa0zKT8lnFmcURDReh5L1Pbh+6p3dv9bxVYjaROOmqLCocSiwq8Qo6nqhwKLGocIBOzCp5osKiwqHEohKvoOOJCocSiwoHdM2YjWt2d0ftyzAMcnJyUspgWRYVNcGe9JLuUZa7J3H5NcMwSE+Pcim4JrIsi/s+u48XV7+IgcHdJ97NGf3OYE/DHrZUbeGbqm/Cjy3Vwd931Oyg1l/L6l2rWb1rdYtjug03vbJ7cXje4RyeeziHd9v/6JXdC7fLndC5BswAa3evDSblZUtYWraUbTXbWux3aM6hDO4xmDRXsBrFosk4GosW2yKeNxlz4zf9bKvZxpaqLVQ1VlFWV0ZZXVmrjRfZadkRjRZNE/lDcg5J+Y023jhJhlRYVDhAh0XhHhuSkicKHKDDosIBOjGr5okCiwoH6LCoxCtoeaLAATosKhzQNWM2qiR979694cni9u7d2+6+zqRyEAgEWLduHf3798ftTizhi1fVDX58/mBPrVm3B9PMiKLcPdST3tN2HtM0qaiooLCwMKZWroeWPcTTK54G4Lbjb+OMfmcAkJeeR156HkcVHtXiPQ2BBrZWbw0n8U1/bqneQkOggS3VwecLWBDx3jRXGofmHhpM2nMPj0jki7OKW01i6/x1fFnxJUt2LOHz8s9ZVraM6sbqiH3chpsjCo5gVPEoRhaPZGTxSHpk9IjLk7ZkWRZ7GvZENFiEGzCqtrCjdgc1jTV8Xfk1X1d+3eL9+Wn5fL/v9zm99HRG9RyFx2XvDP/tqdFsZMmOJby/6X1WVaziyKIjGV40nOGFwzk099CUNB7EG7NdlUOJReEeG5KKJyocSiwqHKATs0qeqLCocCixqMQr6HiiwqHEosIBXTNmo/ovvHv37mzbto3i4mLy8/Nb/Yc5tMRXIBCIG6YryTSTU8ocrULj0bO8bnoV9Yiy3H1fT3p2ke08hmG0GTtt6Ykvn+CRZY8A8Otjf83ZA8+O6n3p7nT65fWjX16/Fq+ZlklZbRnrK9eztXYr31R9w8a9G9m8dzObqzbTaDayYc8GNuzZ0OK9mZ5M+uT2Cfe6NwQaWFq2lJU7V+K3/BH7ZnmyOLroaEb2DCbkwwuHk5UWOdOjZVkxe9KeDMMgPyOf/Ix8hhUNa/F6Q6CBrVVbW03it1ZtZXfjbl5c/SIvrn6RgowCTj3sVP7r8P/iOyXfSUrCXttYy0fffsTczXOZt2Uee337GwCX7FzCc18/B0D39O4MKxrGsMJhDC8cztCioXTzJr8xMJ6Y7cocaiypvseGpOKJCocSiwpHSAoxq+SJCosKhxqLQryCjicqHEosKhwhdbWYjeo/77lz51JQUADABx98kNAHOuoc7dxX6t4jx4vX643uTUnsSTcMI3oO4LmVz3H/4vsBuGbUNVw0+CJbOFyGi5LsEkqyS1q8FjADbK/dzqa9m9i0dxOb924OJ/Bbq7dS569rs4S+OKuYUcWjGFE8glHFoxjYfWCHiW2sniSqdHc6/fL70S+/ZeNFva+e15a8xgr/Cj7Y8gGV9ZW8tPolXlr9Evnp+eGE/dhex4bL8+NRZX0l876Zx9zNc/l428c0BBrCr3VP7864PuMYWjiUtbvXsrx8OV/v+ppdDbv4z5b/8J8t/wnvW9qtlOFFwxlWOIxhRcMY1H1QQlytqbOvjzoHaLGoSMUTFQ7QYVHhUJKSJyosKhygxaIiFU9UOECHRYVDTXb5ElWSPnbs2FafO9JVaNK4HtleduzYQVFRUfslFwE/1O4MPk/CmHTTNCkvL++YA3htzWv8ftHvAZg8fDKXD7u8U1jcLje9c3rTO6c3xx9yfMR7Gs1GtlZtZXPVZjbu2cjmqs0A4aS8V3avmFvMYvEk2UpzpzEyfyTnDjyXW4xb+HT7p8zZOIe5m+eyq2EXr6x5hVfWvEI3bzdO7nMy3y/9PmN6jSHN3XFi/M3eb5j7zVzmbp7L0vKlERPmHZpzKKccdgqnHHYKI4pGYGBQXl7OTwf+FJfLRUOgga8rv2Z5+XK+qPiC5eXL2VK9hY17N7Jx78bwbP/p7nQGFwxmWFGwt31Y0TAOyU5sjL3K9VHhUGNRkYonKhxKLCocSlLyRIVFhUONRUUqnqhwKLGocKjJLl8MK45V33fv3s2iRYsoKytrUVowceLEuGE6Q3v37iUvL489e/Ykbfy8ZVn4fD68Xm/KSkCeX7SZm15dzqmDi3nkwhF4PJ72Waq2w31HgOGCGRWQ4ORpzWVZFn6/v0OOtze8za/+8yssLH425Gf8cvQvbfcwWpZkS4UjxNJazPpNP4t3LOa9Te/x703/Zmf9zvBruWm5jOszjv86/L84vvfxpLvTw8daWbmSuZvnMvebuazZtSbis4b0GMIpfYKJ+YD8ARGfF40nlfWVfFnxJcsrlrO8fDnLK5ZHlMqHlJOWQ7/8fvTP60///OBjQP4Aemb1jMpvleujwqHEonCPbcqi4okChxKLCkeIRSFm1TxRYFHhUGJRidcQi4onChxKLCocIZYDIWZjyUNjTtL/8Y9/cNFFF1FdXU23bt0iPtwwDPl10jsrSQ+tS56qQPnT3DXcO2c1543uw//8dHjHb9j2BfzvScE10n+5puP9k6C5m+dy3YfXEbACnDPoHGZ8d0bKv2gHi6KJ2YAZYEnZknDCXl5XHn4tOy2bsYeOJS89jw+++YDtNdvDr7kNN6NLRnNKn1M4uc/J9MrpZTv7pr2bWF6xnC/Kv2B5xXJWVa5qMU9AU9amiXv//P70z+tPSXaJE2+CsiyL6sZqymrL2FG7g/LacnbU7mB3/W68bi/pnnQy3Bmku9ODj32/e93e4HZPevi15r935uSIjg5uKfxf4MhRtHLi1dGBpgMlZpOapA8aNIgzzjiDu+++m6ysrI7fIKbOSNIDgQBr1qxh4MCBKZth8LY3v+KpjzYydWw/fj6yO8XFxe2XXKz5Nzz7E+g5DKbOt53HNE3Kysra5FiwdQH/Pfe/aTQbmdBvAneeeCcuIzmlMx2xdJZUOCD2mDUtk6VlS3lv03vM2TSHstqyiNczPZmc2PtETu5zMt879HvkpedFxWGXJ42BRjbt3cTaPWtZt3td+LF57+Y2k/csT1ZE0t4vrx+HcAj9evdz4iRJLI1mIzvrdrKjdgdltWWtPnbU7qDOX2cDfUt5DE8wmfc0SfL3JfpNE/um+zT/Pc2Vhq/GR1H3ouC2VhoNwsfyZOB1JaeVvyvHyYHOARr/F4CWJyosKhxKLCrxCjqeqHAosahwwIETs7HkoTF3I2zdupXp06cfkAn6waTQxHGFOekUFRV1/E9hzb4kK8f+md0hWGXRFsdn2z/j2g+updFs5L8O/y/uOOGOpCXoHbF0plQ44pHLcDGq5yhG9RzFL7/zS5ZXLOffm/5Nnb+Ok3qfxHG9jiPDkxHzce3yJM2dxoDuAxjQfUDE9lDyvm5PMGlfu3st63evZ9PeTdT6a4Ml9BXLw/tnuDP4fwP+HxcPuZjDuh2WEFO8UoqTaFksy6KqsYqymv2JdjjxrtufgO+s24lFdO3EuWm5FGcVU5xVTGFmIdRBbl4uPtNHg7+BhkDwUR+oxxfwUe+vD29rCDTQ4A++1mg2ho/pt/z4/X5q/bUJ+RKr2moAiKeRoOkjzUhj967d+19r0mDgtnkIU3tSiVkVDiUpeaLCosKhxqIiFU9UOJRYVDjUZJcvMSfpp59+Op999hn9+rWcIdqRjsITx+WkYxhGx4GSxJndgTBDc44vyr/g6vevpj5Qz/cO/R7/c9L/JL0EtS2WzpYKR6JyGS6OLjqao4uOTvhYyfakveR9c9XmcNK+dvdaVlau5Juqb3h+1fO8sOoFTjnsFCYOmcjI4pGdes2U4sQwDPyWn8qayha936Hfy+vKKasti7r322N4KMwqpDirmJ5ZPcOJeFFmUcTvTZcwTKTF3LTMcNLeUWJf79+3PVDf6u9Nk39fwNfusZpOmBjatpeWcykkSx7DE5H8h3v2Q8MCohguEMt70ow0MlyxN9TZKaXvjoqUPFFhUeFQY1GRiicqHEosKhxqssuXmLOhH/7wh/zyl79kxYoVDBs2jLS0yNmdf/zjHycE5MgehdZJ756VFl0pShLXSIfWSz++rvyaKf+eQq2/luNKjmPWuFlRzRaeDJZUSIVDSanyJM2dFi51DykQCPDu1+/yj2//wfxv5/P+5vd5f/P7DO0xlJ8f9XNOO/y0ThnT3FmeWJbFXt/eVhPvpr/vqt8Vfe+3Nzci0W6aiBdlBZPwgoyCpFbONJfLcJHpySTTk2nL8aK5PpZl4Tf9LZL7WBsJmjYKtHacWl8tjVZj+D0tqgYa/dQ01thy3tHIwCDdnZ5wwt/WnAJNj9P091DVgHOPbSklT1RYVDjUWFSk4okKhxKLCoea7PIl5jHp7X2YYRgEAoG4YTpDB8vEcaPvfI+Kah9vTT+JI0tyOg6SlyfBly/D9++C46clhSnkCcC63eu49J1L2dWwixFFI/jf//rfiJ6yZKspSyqlwqEQsyGpeAL7WdbtXsczK57hH+v+gc8MNoD1yu7FRYMv4uyBZ5Prze0UjnjVGGiMKDNvLQkvry2nPlAf1fE8Lg/FmcFEu3kPeOj3oqwi2xLh5lKKV9CJ2eYcATMQHg4QbggI1Lf6e8TQgH0NBM2HCzRtVGjwN9BgtlJJ4K+PuhEnWfK4PLEPF4hjiEHTRoY0V1q7sagUsyrxCjosKhygwaIUr6DhiRIH6LCocBwoMZvUMenNl1xz1Lr8fr8tC9nHo4BpUblvTHqP7DQsy8KyrPaDNjwm3f410oEwg2VZfFP1DVfMuYJdDbsY0mMID532UKcm6E1ZUr10hQJHSKmM2ZCUPGnK0j+/P7cdfxv/PfK/eXHVi7yw6gW21Wzj3s/u5eFlD3P2wLO5aPBF9M7pnVSO5p5YlsWehj1tjvkOPSrro191Iy89b3/Cndks8c4soiiziILMgk4d49yaFOIVdGK2NQ63y02my76qgWg5GgON1Dbu69WPsZGgvTkFWjQSNPndb+6fENJv+vGbqakaaFEtEOr9dwUnGcxMy2y197+tqoKO5iGI9XuoEq9KLCocaizOPVaTQ4lFhSOkrhazzvozSZBpmmzYsCFlMwzurvVh7uvIyM9Ko7y8nOLi4vYDpTr5SXp5eTmB7ACXz7mc8rpyBuQP4H9P+9+k90K2xdKhJwcJB6Q+ZkNS8qQ1lh6ZPZg6YiqXDbuMf63/F3/96q+s2xPsZX925bOcdthp/PyonzO8KIplDzuQL+CjrLaM7TXbWbttLfVp9ZTXlYeXIAuN/24INER1vDRXWkRvd9PEu2nvd3sT/oVKuIxMJ15DUolZFQ7DMPC4PNTvqe/UEsiAGWhRDVDXWMf28u1k5WUFGwuaJfutVQx0NO9AaHuoISFUNWBhUR+oj7oaxS55XJ6Y5hRId6fjr/dT0K2gxQSD0QwxyHBn4HHZsyaySsyqcCixOPdYXQ4lFhUO6JoxG1eSXlNTw7x589i8eTM+ny/itenTp8cN48gehWZ2z89KIz3NQ0lJScdvCiXp2clJ0l0uF+5ubia9M4ltNdso7VbKo99/lPyM/KR8XkcsUXlykHAoScmT9ljS3emcPfBs/t+A/8eCbxfw16/+ysfbPmbOpjnM2TSHEUUjmHjURE7pc0qLni7LstjdsLvNMd/ltcGJ13Y17IqaNT89v8WY7+aP7undE/4jqnR9VKTiiQoHpIbF7XKT5cpqUZV1ZI8jk/aZlmXRaDZGNadAXWMd32z7hrweefhMX5tDDaJpJGheNVBtVlPdWJ2082wul+EKJ+0Rib0ruokHmzcapG+JrpEgWXNXHOzfHXWpeKLCATosKhxqssuXmJP0zz//nDPOOIPa2lpqamooKCigoqKCrKwsiouLnSRdQBWhmd2zvcEJi/x+PJ52Wr4DjVC3rxw2SbO7V9ZVcvmcy9lctZlDsg/h0e8/GlxGKQWKypODiENJSp5Ew2IYBif2PpETe5/I6l2r+etXf+VfG/7F0vKlLP1wKb1zevO9Q79HZX1lRDLedEKv9uR1ecNLjpVkl7SaiBdlFZHuTrfz1NuU0vVRkYonKhxKLMnmMAwDr9uL191xeWUgEGBNwJ41fJtXDbQYGtBa779//9CAOl8djVZjdEMMmsw7EJJpmdT566JewcEupbnSkjKnQLorHQ8esrxZZKRlJKVqIFqpfHeUpOKJCocSiwqHmuzyJeYk/Re/+AUTJkzgkUceIS8vj08++YS0tDQuvvhirrnmmrhBuppSOYlCaGb3HjnpWJZFZWVl++v11eyb2d1wQ2Z323n2+vZy5b+vZP2e9RRlFvHY6Y9Rkp26lreoPDmIOEJSmfhDxZNYWQZ1H8SdJ97JNaOu4YVVL/D3VX9na/VWnv/6+Vb3L8goiOztzmxZip6XnhcumyoqKkr5NVK6Pqn2IiQVT1Q4lFhUOEKyK2bbqhqIRqZpxnU/aVo10NH8ANHMKRDap6quCstttTlZod/aXzXQaDYGGzija+O0RU2rBtqcO8CGiQdD29Jcaeyt3EvP4p4pj1nnHqvJocSiwhFSV4vZmGd3z8/PZ+HChRxxxBHk5+fz8ccfM3jwYBYuXMjPf/5zvv7667hhOkOdMbt7qvXUgg3c9o8VnDGshIcuOqbjN3y7FP4yFnJ7wfX2Xr/axlomvzeZZeXLKMgo4MnxT9Ivr5+tn+HIkaLq/HX8a/2/2Lx3M4WZhRRnN1l+LLMoqt43R44cOTqY5Tf94WqAaJYpjGpOgVZWM2i+tGGqFaoaSHTZwjaHGLTyHo/h9IY6cpRsJXV297S0tHBLRXFxMZs3b2bw4MHk5eXxzTffxEfcxWRZFjU1NWRnZ6fkhrczPLN7sCe9sbGRtLR2locJj0e3d430en89/z33v1lWvoxu3m78edyf6dutr62fEY+i8uQg4gixpDJmm3IoeZIIS6Ynk58O+mnKOeyUCotKvIZYVDxR4FBiUeEIsSjErJonHbF4XB48Lk9SV4BpzmFZFj7TF5nwt7LkYLxzCrTVSNBa1UBVY1XSzru5QlUDoeTd6/KSmZYZ/r1FYh9FI0FrvzetKvC6vR3ONaASsyocSiwqHCEWhXtsiMUOX2JO0keOHMmnn37KwIEDGTt2LLfccgsVFRU888wzDB06NG6QriTTNNmyZUvKZhgMJ+k5wTHpu3fvprCwsJ1y99DM7vaNR/cFfPziw1+waPsistOyeejUhyimWGKZhqg8OYg4IPUxG5KSJyosKhxKLCrxCjqeqHAosahwgE7MKnmiwtKcwzCMcFLZmfKbfuoa69hWto3s/Ow2Jxhsr6ogmiEGzRsIQkrVXANel7fdRD7dnQ5+6JbVLbIqoIP3tLfUYZorLWZOlXhVYlHhAJ17LNjnS8xJ+t13301VVbBl76677mLixIlMnTqVgQMH8vjjj8cN4sg+7QxNHJeTjsvlori4gxnbq3cEf9q0/Jrf9POr//yK+Vvnk+HO4M+n/pmji4+25dh2KCpPDiIOJSl5osKiwgFaLCpS8USFA3RYVDiUpOSJCosKh8flITc9l9w+nbcsbdOqgaaJfW1jLes2rqPokCIazcaW1QJmx0MP2mskCFiBMIPP9OHz+aii86oG3Ia73UqAptUCGZ4MvK4mDQDb4x96YFciqxKzKhxqssuXmJP00aNHh58XFxfzzjvvJAzhyF6FJo4r3De7u8/nw+v1tlPuvm/iOBuS9IAZ4LcLfsu/N/+bNFcas0+ZzTE9j4mOo5OkwqLCoSQlT1RYVDjUWFSk4okKhxKLCoeSlDxRYVHhSAVLW1UDgUAAb6WXgb2S0yvpN/0dDg9omvzXNNQQIBAechDNagbNf/eZ+5eMDlgBav211PproROnIIhYtjCGOQVam6zQbbnJTs8ONh60U1XgccW12nZUUvruKMkuX2K+cqeccgqvvvoq+fn5Edv37t3LWWedxdy5c+OG6SoyDCOlAbu/3D04Jr2qqoqCgoJ2kvR9PekJrpFuWRa/++R3/Gv9v/AYHu4bex/HH3J8+LUOOTpJKiwqHJD6mA1JyRMVFhUOJRaVeAUdT1Q4lFhUOEAnZpU8UWFR4VBiSXa8huYayE7L7nBf0zSprKykoKAgodm7TcvEF/BFNVdAW7/vqdkDnmDvf9MKgbYaCRoCDZiWGWYIHa8zqwY8hifmZQvbnHTQk066a3/DQporjfrqenoW9AwvXxiqPOjs+FW5x4J93+OYZ3d3uVxs3769RTd+WVkZvXv3prGxE9fGiEMHw+zuw257l6p6P+9fP5b+RTkdv+GpH8HG/4OfPA7D4pvoyrIsZn46k7+t/Bsuw8X/nPQ/jO87Pq5jOXLkyJEjR44cOXJ0oKvRbIx6ToFohwt0NFFh06qBVKmjBoBE5hRoqxIhmVUDdikps7t/8cUX4ecrVqxg+/bt4d8DgQDvvPMOvXv3jgO368myLPbs2UNeXl6nt+g0+ANU1QdnCO2xr9y9vr6ejIyMjnvSEyh3/+Pnf+RvK/8GwO3H394iQY+Ko5OkwqLCEWJJVcw251DyRIFFhUOJRSVeQywqnihwKLGocIRYFGJWzRMFFhUOJRaVeA2xqHgSD0eaK400bxo5RNFpZhOLaZk0BBpiSuw7Gi7Q2lCD5sdurWpgL3ttO++O5DbczDtvHnnpeZ32ma3JrpiNOkkfMWJEeNbLU045pcXrmZmZ/PGPf4wbpCvJNE22b99Obm5up88wWLmv1N3jMuiWEZy9sra2loyMjLbfVJ3Y7O6PfvEojy5/FICbj7uZswac1ep+HXJ0olRYVDhSGbPNpeIJ6LCocIAGi1K8goYnShygw6LCoRSzKp6ADosKB2iwKMUraHiixAHts7gMF5meTDI9mUlNWC3LCg9FAPBb/hZzCnSU2Mc69KD5exrN/RXcAStAmhH7zP3JkB2xEnWSvmHDBizLol+/fixatIiiov1ranu9XoqLiyW+yAe7QpPGFWR7cbmCrTc9evRo+w3+BqjfHXwexzrpz6x4hgc/fxCA6465jvOPPL/V/QzDaJ+jE6XCosKhJCVPVFhUOECLRUUqnqhwgA6LCoeSlDxRYVHhAC0WFal4osIBOizNOdIM+6sGOlKoaqDWV8vXa7/G6/J22me3JbuuT9RJ+uGHHw4EW9cc6aqiyfJrEGzlqqurIzMzs/WSi5p9M7u70iCze0yf9fLql5n56UwAph49lUuHXtrmvh1ydKJUWFQ4lKTkiQqLCocai4pUPFHhUGJR4VCSkicqLCocaiwqUvFEhUOJRYEjVDXgNbz08PZI+bUB+3yJf5pER23KMAyys7NTEijh5ddy9rck1dfXt/2GpuPRY+D9x7p/cMfHdwBwyVGXMPXoqR2+p12OTpYKiwpHKmO2uVQ8AR0WFQ7QYFGKV9DwBHQ4QIdFhUMpZlU8AR0WFQ7QYFGKV9DwBHQ4QIdFhaMrxmzMs7sf6Orqs7v/5T/ruPutrzlrxCE8cP7Ijt+w6h14/jzoNQKunBfVZ/x707+5Yd4NBKwA5x1xHjcfd7PMl8KRI0eOHDly5MiRI0eO1BRLHur0pCdBpmlSUVGRkqEBoZ70puXuNTU1tNkWE+5Jj27SuP9s+Q+//M8vCVgBzux/Jr857jdRJegdcnSiVFhUOCC1MdtUSp6osKhwKLGoxCvoeKLCocSiwgE6MavkiQqLCocSi0q8go4nKhxKLCoc0DVj1knSkyDLsqioqEhJ0FaEk/T95e4+XzvrJdaEZnbveNK4RdsWcd2H1+E3/YwvHc/tx9+Oy4g+hNrl6GSpsKhwpDJmm0vFE9BhUeEADRaleAUNT0CHA3RYVDiUYlbFE9BhUeEADRaleAUNT0CHA3RYVDi6YszGlaTv3r2bxx57jJtuuonKykoAlixZwtatWxMGcpSYdtYEJ44rzA72pBuGQffu3dtZIz265deWli1l2txpNAQaGNdnHHefdDduV/Sz+XfI0YlSYVHhUJKSJyosKhxqLCpS8USFQ4lFhUNJSp6osKhwqLGoSMUTFQ4lFhUONdnlS8xJ+hdffMGgQYP+P3t/HuZKXt15wp+I0JJacl/vzbz7UnWLAgoKqCqWqsaNwcbL4zHTeOux237bzzxMY7vN+PXyurtnjHug3dhAe5mmpwfa0zamMcbuwW5wGwOmCgrKUBRFFVV16+5L3pv7Kim1Rrx/hEIpKaVMKRWSvjfR93nykTIyFPrEiaNInd/v/M7ht37rt/jt3/5t1tbWAPjzP/9zfu3Xfq0lmJ5a13LVTLrjOGxubu6S7l4M0mMTdY/53PJzvONv38FWfouHDj3Ebz/y2wTN5voQ7snRQamwqHAoSckmKiwqHGosKlKxiQqHEosKh5KUbKLCosKhxqIiFZuocCixqHCoyS+7NB2kv+td7+Kf/JN/woULFyqatL/1rW/l0UcfbQnmoMgwDAYHB7tU3b2yBRvs0TavNJNeO0h3HId3/d27SOQSvHLilfy77/p3hK1wzX33ksI6EU8qLCoc3fTZaqnYBHRYVDhAg0XJX0HDJqDDATosKhxKPqtiE9BhUeEADRYlfwUNm4AOB+iwqHAcRJ9tuE+6p6997Wv8h//wH3Zsn56eZm5urmWggyDTNDl06FDH39dxHJaTxZn0mDuT7jltXSV3D9LXMmvMJtxlDL/3D3+PSCCyL7Y9OTooFRYVDuiez1ZLySYqLCocoMOi4q+gYxMVDtBhUeEAHZ9VsokKiwoH6LCo+Cvo2ESFA3RYVDjgYPps0zPp4XCYjY2NHdtffPFFxsf3Lj72nSDbtrl9+3bHR5eS2QKZvPue5enuGxsbe6e711mT7gXoE5EJBkL7b1m3J0cHpcKiwgHd89lqKdlEhUWFQ4lFxV9BxyYqHEosKhyg47NKNlFhUeFQYlHxV9CxiQqHEosKBxxMn206SP/BH/xB3v3ud5PL5QB3tOD69ev8yq/8Cm9729tagjkochyH9fX1jjutl+oeDVlEQw0kSeS2IFMccInVHmDxgvTD8cO+MPakqW75bE897Uc9f+3pTlPPZ3u6k9Tz157uNB1En206SP+d3/kdEokEExMTbG1t8cgjj3D69Gn6+/v5P/6P/6MdjD01qFrt1wzDYGBgoPYaDW8W3QpDX+20jFuJW0DrQfquHB2WCosKh5KUbKLCosKhxqIiFZuocCixqHAoSckmKiwqHGosKlKxiQqHEosKh5r8skvTQfrg4CCf/exn+cu//Et+93d/l3e+8518+tOf5otf/CKxWKwlmJ5aU6loXGy7sNuuI0vJRfcxPgF1HMmbSZ+OT7fEpjTCpcKiwqEkJZuosKhwqLGoSMUmKhxKLCocSlKyiQqLCocai4pUbKLCocSiwqEmv+zSdOE4T69//et5/etf39KbH1QZhsHY2FjHR5aqi8Z5Ms06YzGJefexTtE48C9I35WjC1JhUeHols/WkopNQIdFhQM0WJT8FTRsAjocoMOiwqHksyo2AR0WFQ7QYFHyV9CwCehwgA6LCsdB9Nmmg/Tf/d3frbndMAz6+vo4ffo0Dz/8MJZltQx3p8o0TcbGxjr+vtvt1yrT3fv7+2u/oIEe6X6mu9fl6LBUWFQ4oHs+Wy0lm6iwqHCADouKv4KOTVQ4QIdFhQN0fFbJJiosKhygw6Lir6BjExUO0GFR4YCD6bNNB+kf+MAHWFxcJJVKMTw8DMDq6irRaJR4PM7CwgInT57kC1/4AkeOHGkZ8E6UbdvMzs4yPT3d0RGm7TXplenua2trDA0N7RxdaqBHuhekz8RnWmLblaPDUmFR4YDu+Wy1lGyiwqLCocSi4q+gYxMVDiUWFQ7Q8Vklm6iwqHAosaj4K+jYRIVDiUWFAw6mzzZ9Fu95z3t49atfzYULF1heXmZ5eZkXX3yRBx54gH/37/4d169fZ2pqil/8xV/cN9SdLsdxSCaTna/uXifdPRQK1dp9zx7py+ll0oU0BgZTsamW+epydEEqLCoc3fLZWlKxCeiwqHCABouSv4KGTUCHA3RYVDiUfFbFJqDDosIBGixK/goaNgEdDtBhUeE4iD7b9Ez6v/gX/4JPfvKTnDp1qrTt9OnT/PZv/zZve9vbuHz5Mv/23/7bXju2LshLdx8rm0k3DKN+Qb/SmvTaPdK9WfSJ6ARBK9gS264cHZYKiwqHkpRsosKiwgFaLCpSsYkKB+iwqHAoSckmKiwqHKDFoiIVm6hwgA6LCoea/LJL0zPpt2/fJp/P79iez+eZm5sD4PDhw2xubrYM11NzWq7Rgs1xHFZWVmqPLCWK1d3r9Ej3gnQ/isbtytFhqbCocChJySYqLCocaiwqUrGJCocSiwqHkpRsosKiwqHGoiIVm6hwKLGocKjJL7s0HaS/8Y1v5H/+n/9nnnrqqdK2p556ine84x1813d9FwDPPPMMJ06caAnsTpZpmkxNTXV8TcRycmcLNoC+vr7aL9hjJv1m4ibgT5C+K0cXpMKiwtEtn60lFZuADosKB2iwKPkraNgEdDhAh0WFQ8lnVWwCOiwqHKDBouSvoGET0OEAHRYVjoPos02fyYc//GFGRka4//77CYfDhMNhXvWqVzEyMsKHP/xhAOLxOL/zO7/TMtydKsMwOl5EoWA7rBTXpI9VVXePRqO1Wcr7pNeQX5Xd9+TosFRYVDg8FoXCH2o2UWBR4VBiUfFXj0XFJgocSiwqHB6Lgs+q2USBRYVDiUXFXz0WFZsocCixqHB4LAfNZ5sO0qempvjsZz/Lc889xyc+8Qk+8YlP8Nxzz/E3f/M3TE66M7JvfOMbefOb39wS2J0s27a5fPkytm137D3XUlnsYlbFcKwy3X15eXlnykU2CdmE+3yPIN2vdPeaHF2QCosKB3THZ2tJySYqLCocSiwq/go6NlHhUGJR4QAdn1WyiQqLCocSi4q/go5NVDiUWFQ44GD6bNOF4zzdfffd3H333S29+UGV4zhks9mOOq1X2X0oGiRoVY69RKPRnS/w2q8FIhCK1zzmbGIW8C/dvSZHl6TCosLRDZ+tJxWbgA6LCgdosCj5K2jYBHQ4QIdFhUPJZ1VsAjosKhygwaLkr6BhE9DhAB0WFY6D6LP7CtJv3rzJpz71Ka5fv042m6342/vf//6WoXpqXksJbz16Zcl/wzCIRCI7X1Ce6l4jHcN2bN/T3WtydEEqLCocSlKyiQqLCgdosahIxSYqHKDDosKhJCWbqLCocIAWi4pUbKLCATosKhxq8ssuTae7f+5zn+Ouu+7i3//7f8/v/M7v8IUvfIH/9J/+Ex/5yEf45je/2TJQT/uTtx59NF5ZNM62bZaWlnamf5SKxtXpkb61TNbOYhmWLz3S63J0QSosKhxKUrKJCosKhxqLilRsosKhxKLCoSQlm6iwqHCosahIxSYqHEosKhxq8ssuTQfpv/Zrv8Yv/dIv8cwzz9DX18cnP/lJbty4wSOPPMI/+kf/qCWYgyLTNJmZmelohUGv/Vp50ThwR3P6+/t3Fi/w0t3rVHb3Ut0no5MEzH2vitibowtSYVHhgO74bC0p2USFRYVDiUXFX0HHJiocSiwqHKDjs0o2UWFR4VBiUfFX0LGJCocSiwoHHEyfbfpMnn/+eX7yJ38SgEAgwNbWFvF4nHe/+9381m/9VkswB0WGYRCPxzvqtMuJ2u3XDMMgHA7XD9Lr9Ej3gnQ/Ut135eiCVFhUODyWTvtsPQ4lmyiwqHAosaj4q8eiYhMFDiUWFQ6PRcFn1WyiwKLCocSi4q8ei4pNFDiUWFQ4PJaD5rNNB+mxWKy0Dv3QoUNcunSp9LelpaWWYA6KCoUCL774IoVCoWPvuVRKd6+cSbdtm4WFhZ0pF8ndZ9L9rOy+K0cXpMKiwgHd8dlaUrKJCosKhxKLir+Cjk1UOJRYVDhAx2eVbKLCosKhxKLir6BjExUOJRYVDjiYPtt0HvODDz7Il770Jc6dO8db3/pW/tf/9X/lmWee4c///M958MEHW4I5SOq0wy7vUjiuZt/AUrr77jPpfgXpav0LFVhUODwp3GSVbKLCosKhxqLgr6BjExUOJRYVDk8KPqtkExUWFQ41FgV/BR2bqHAosahweDpoPtt0kP7+97+fRMLtr/0bv/EbJBIJPv7xj3PmzJleZfcuyluTXl04zjAMQqHQzhc0uCbdz3T3mhxdkAqLCoeSlGyiwqLCAVosKlKxiQoH6LCocChJySYqLCocoMWiIhWbqHCADosKh5r8sktT6e6FQoGbN29y9OhRwE19/9CHPsS3vvUtPvnJT3Ls2LGWgXran7w+6dUz6bZtMz8/X7+6e6x2dfd2pLvX5OiCVFhUOJSkZBMVFhUONRYVqdhEhUOJRYVDSUo2UWFR4VBjUZGKTVQ4lFhUONTkl12aCtIty+LNb34zq6urLb3pQZdpmpw4caKjFQZLfdJrzKSPjIxUplw4Tlmf9J3p7gW7wK2kv0F6TY4uSYVFhQO647O1pGQTFRYVDiUWFX8FHZuocCixqHCAjs8q2USFRYVDiUXFX0HHJiocSiwqHHAwfbbpM7n33nu5fPlyS2/6naBAoPW2ZY0qky+wmc4DtVuwBYPBSkfJJiCXcp/XmElf3Fokb+cJGAEmorVn2ptVTY4uSYVFhcNTJ322npRsosKiwqHGouCvoGMTFQ4lFhUOTwo+q2QTFRYVDjUWBX8FHZuocCixqHB4Omg+23SQ/q//9b/ml37pl/irv/orbt++zcbGRsVPT26aw4ULFzqW/rFSTHUPmAYDfcEdLHNzc5Us3nr0YAzC8R3H81Ldp2JTWKblC2NNji5JhUWFw2PppM/uxqFkEwUWFQ4lFhV/9VhUbKLAocSiwuGxKPismk0UWFQ4lFhU/NVjUbGJAocSiwqHx3LQfLbpIYe3vvWtAPzgD/5gxQiB4zgYhiFR+v47TV7RuJFYCNOsHLUxDIPx8fHK0ZxS0bjas+R+V3avy9ElqbCocChJySYqLCocaiwqUrGJCocSiwqHkpRsosKiwqHGoiIVm6hwKLGocKjJL7s0HaR/4QtfaOkNe/Jf9dajg+so3k9JycaCdL8qu9fl6JJUWFQ4lKRkExUWFQ41FhWp2ESFQ4lFhUNJSjZRYVHhUGNRkYpNVDiUWFQ41OSXXZpOd3/kkUd2/emp8/Jm0qvXo4ObcrGwsFA73b1OkO6lu/sZpNfk6JJUWFQ4lKRkExUWFQ41FhWp2ESFQ4lFhUNJSjZRYVHhUGNRkYpNVDiUWFQ41OSXXfZVAu+xxx7jH//jf8xrX/taZmfdWdc/+qM/4ktf+lJLMAdFpmly5syZjlUYXE4WZ9JjO4N00zSZmJioZPGC9Drt19qR7l6To0tSYVHh8Fg66bO7cSjZRIFFhUOJRcVfPRYVmyhwKLGocHgsCj6rZhMFFhUOJRYVf/VYVGyiwKHEosLhsRw0n2361Z/85Cd5y1veQiQS4Rvf+AaZjBsgrq+v8573vKclmIOkfD7fsffyZtJrpbs7jlP6KcnrkR6frHm8dgTpNTm6JBUWFQ5PnfTZelKyiQqLCocai4K/go5NVDiUWFQ4PCn4rJJNVFhUONRYFPwVdGyiwqHEosLh6aD57L6qu3/oQx/iP/7H/0gwuF1J/HWvex3f+MY3WoI5KLJtmytXrnQs/WOpFKTvnEl3HIfFxcVKR9mlR3rezjOfdIN4P9Pda3J0SSosKhzQeZ+tJyWbqLCocCixqPgr6NhEhUOJRYUDdHxWySYqLCocSiwq/go6NlHhUGJR4YCD6bNNF447f/48Dz/88I7tg4ODrK2ttQTT0/60Ukx3H4vtnEk3TZOpqanKjbvMpC+kFsg7eQKmfz3S63J0SSosKhxKUrKJCosKB2ixqEjFJiocoMOiwqEkJZuosKhwgBaLilRsosIBOiwqHGryyy5Nz6RPTU1x8eLFHdu/9KUvcfLkyZaBempey8ndZ9JzuVxVuntxJr3GmvRSZffYYUzDv3UdNTm6JBUWFQ4lKdlEhUWFQ41FRSo2UeFQYlHhUJKSTVRYVDjUWFSkYhMVDiUWFQ41+WWXpqOwn/3Zn+UXfuEXeOKJJzAMg1u3bvHRj36UX/qlX+Id73hHSzAHSZ0sXLDXmvSVlZVtR3Gcspn0nUF6Oyq71+ToolRYVDg8KRTbULKJCosKhxqLgr+Cjk1UOJRYVDg8Kfiskk1UWFQ41FgU/BV0bKLCocSiwuHpoPms4TR5BMdxeM973sN73/teUqkUAOFwmF/6pV/iN3/zN1uC6YQ2NjYYHBxkfX2dgYGBbuO0LMdxuPtf/jWZvM1jv/xGjoxEd39Beh3+zVH3+a/PQTBS8ef/85v/J//+6X/P2868jf/9tf97e6B76qmnnnrqqaeeeuqpp56+g9RMHNr0kINhGPz6r/86KysrPPvss3z1q19lcXHxjgjQOyXHcUgkEh0ZWUpmC2TybpGEeunu2Wx2m8VrvxYe2BGgQ3squ9fk6KJUWFQ4PJZO+exeHEo2UWBR4VBiUfFXj0XFJgocSiwqHB6Lgs+q2USBRYVDiUXFXz0WFZsocCixqHB4LAfNZ5sO0v/4j/+YVCpFKBTinnvu4TWveQ3xeLwliIMm27a5efNmRyoMLifconGRoEU0tLMOoOM4rK2t7QzSYzsru0N7090rOLooFRYVDuisz+4mJZuosKhwKLGo+Cvo2ESFQ4lFhQN0fFbJJiosKhxKLCr+Cjo2UeFQYlHhgIPps00H6b/4i7/IxMQEP/7jP86nP/1pCoVCSwA9tabd2q+Buz5jYmJie51GF3qk1+ToolRYVDiUpGQTFRYVDjUWFanYRIVDiUWFQ0lKNlFhUeFQY1GRik1UOJRYVDjU5Jddmn717du3+S//5b9gGAZvf/vbOXToEP/sn/0zHn/88ZZAetqfvJn0WkXjwB3NyWQy26M5u/RIz9k55lNuEN+OdPcKji5KhUWFQ0lKNlFhUeFQY1GRik1UOJRYVDiUpGQTFRYVDjUWFanYRIVDiUWFQ01+2aXpID0QCPD93//9fPSjH2VhYYEPfOADXL16lTe+8Y2cOnWqJZiDIsMwCIVCGIbR9vfy2q+NxWrPpDuOw+bmZlm6e/2Z9PnkPLZjEzJDjEZGfeXcwdFFqbCocEBnfXY3KdlEhUWFQ4lFxV9BxyYqHEosKhyg47NKNlFhUeFQYlHxV9CxiQqHEosKBxxMn925iLkJRaNR3vKWt7C6usq1a9d4/vnnW4I5KDJNs2M947dn0uunu4+NjW1vKK1J3739mp890mtydFEqLN3mKKyvs/X006Seeor0M88SivSxfO4cfXefo++ecwQmJzt+s+u2TcqlwqLCATosnbzH7iUlmyhwgA6LCgfo+KyaTRRYVDhAh0XFX0HLJgocoMOiwgEH02f3FaSnUin+4i/+go9+9KN87nOf48iRI/zYj/0Yf/Znf9Yy0EGQ4zisr68zODjY9iBnaZce6R5LOp2mr6/PZfGC9Bo90tu1Hr0mRxelwtJJDse2yV65wtY3v0nqqafYeuqbZC9d2rHf5mf/tvTcGh6m79zdhM+do+/cPfSdu5vQ8eMYltU+Th9s4mSzFJJJrKGhluz6negnKiyO4+DkcjjZbM0fO5MlsbFO/+QkVrwfKx7DjMcxAi2NO++bVeH6qHAosahweCyd+l6wF4eSTRRYVDiUWFT81WNRsYkChxKLCofHctB8tulvND/6oz/KX/3VXxGNRnn729/Ov/yX/5KHHnpo3wAHUbZtMzc3R39/P1YbAxrYTncfrZPuDu6gSl9fn/tLcu8g3e/K7jU59qHs1atsPf005uAgoSNHCE5PY+7zeK2y+KV2cdjJJFvPPMPWU0+R+uY32frm09jr6zv2Cx0/TuS++wi/7KUs3LxJ/9IS2edfIHP5MoXVVZKPf4Xk418p7W/09RG+6yx9ZYF7+OzZfV+HWiq3iZ3JUFhbo7C6WvGYL/2+8292MglAYGqK+MMPE3/kYWIPPogZi7XE0k21k8NxHMjlsLM5nFztoNjJZnFyOexMlo2VFeLhEE62LIguvs4u7V8nwM55++RKx6z3Xo1ouep3o68PMx7HiscxSz8xrFjV797zWByrv/xvcaxYDCNU/35aS98JftKsVFhUODr5vWAvqdgEdFhUOECDRclfQcMmShygw6LCcRB9tukg3bIs/vRP/5S3vOUtO4zw7LPPcu+997YE1FNz8tLdx+rMpBuGweho2fryXWbSvXT3dsyk7+BoQHY6TeprXyPxxUdJPPYouWvXd+wTGB8nODND8MgMoZkZgtPbzwOTkzVnfffD4qdy8/NsffNptp5+muz1a6T7IpjRKGYkghmLYkajGFH30f2JFR8jZduiGJEIhmHgOA65mzfZeuqp4kz5N8mcPw9VbSiMvj4iL30pkVe8gsh99xG57+UERkYAKBQKLF64wKEzZ7AsCzuTIfPiBdIvPE/m+edJP/c86fPncba2SD/9LdJPf2v7wKZJ6OQJ+s7dQ/DwYbALOPkCTiEP+QJOoex5Pl97e6EA+by7LZNhdW2N/NoaTiq1bzvn5+ZY+9M/Ze1P/xSCQaKvup/4w48Qf+RhQidO7Dm62Q4/cRzHPc9sWbCaK3+smjEuPlrZHGtVwW71PpVBc2Xwa+d2D56b1YavVmlAwSBmMIgRCpV+coUCVjaLnUzipNMAOOk0hXSawtJSS29nhEK1g/z+4gBArDLgD8XjpIrbvFl9s7/fZe3QiH6372vlUmFR4VCSkk1UWFQ4QItFRSo2UeEAHRYVDjX5ZZemg/SPfvSjFb9vbm7ysY99jP/7//6/efLJJ3st2Tqs5T1asDmOw9bWFpFIBAN2XZPe7nT3EscuX1qz16+TePQxEo9+kdQTf4+TyWz/MRgkcu+92Ok0uRs3sBMJ8ouL5BcX2XrqqZ0HCwYJHj5EaOYIwZkZQkdm3IB+Zob86Cixycm2t42w02nSzz1XCsq3nn6a/NycPwc3DMxIBCwLe3Nzx58Dhw8Rve8VblD+ilfQd9dZjGCwoUOb4TCRl95L5KXbg25OoUD22nXSzz9H5oUX3MD9+ecprKyQvXiJ7MWd6fO+yLKwhoawhocIDA1jDQ9hDQ0Xtw27P0ODBIa3txmhEKmvf90d4Hn0UXLXr5P6yldJfeWrLPzWbxGcnnZn2B9+mNgDD7h2rFKjPuvJzmbJXrlK9vIlMpculx7zS0uVAbFAgZVdZVllwXAQMxiqCI6dgIXV11f63QyFMKr28V5b+nv5T7Bqn2Cd/Ur7BzGqPqeFQoELFy5wqjio5ORy2MkkhUQSO5nATrg/hUQCO5EsPt8sPbcTCexkwt0/sb2/XRwUcrJZCisrFFZWWrNlMIgVizUwq99fc5bf+91owAeb9dd2SoVFhUNJSjZRYVHhUGNRkYpNVDiUWFQ41OSXXfa9gO/RRx/lwx/+MJ/85Cc5fPgwP/zDP8wf/MEf7BvkIMkwDGKxWIequxcLx8Vqz6QDpNNpIpEIbK2CXUwj7UK6e4mjTHYmQ+rvv0bisUdJfvFRsteuVfy9lLL88BuIPvgQVtxNWXYcB3t9neyNm+Rmb5K9cYPczVlyN26Qnb1JbvYW5HLkrl2vOQMPYMbjxaB9mtD0tDsL7/0+M4MZjTZ1fo7jkLt+3Q3Gi0F5+vx5yOer3tgkfPYskZe/jML0NJFgCHsrhZNKYadS2MkU9taW+7z8ZyuFk0yVAgkcZ/t5MEjknnvcGfJXvILIK+4jOLmzgn89NeKzhmURPnmC8MkT8H3fVzrn/MKiG7g//zz5pWV3XXDAwrACGAELAoHt55ZV+3lxHyyTZCbD4MwMgZERrKEhd0ZyH5+l+BveQPwNbwB+nezVqyQefZTEFx8l9fd/T252ltU/+Rirf/IxjFCI6GteU0qNDx07VjpGLZ8tJBJkL1UG4pnLl8jduLkje2FPmeaOwLY6KDaCQfKGQSgWxQiFK4PfFoLjmkFxMLhrzQHHcVhdXWV4eLir/5Cr/dUIBt0BmqGhlo7rFAruZ21zczvAT+4M+O1EgkKy+PvmJtm1NYx0GjtZ/Hsy6Q7G5HLuUoy1tdZO2DTLgviqgN4L+mMx0pZJfHy8tE9FKn8sjhmN7BjwaJdqfXa6IRWOTn4v2EsqNgEdFhUO0GBR8lfQsIkSB+iwqHAcRJ81nCbqw8/NzfGHf/iHfPjDH2ZjY4O3v/3tfOhDH+Lpp5/mnnvuaQmkU9rY2GBwcJD19XUGBga6jdOSbNvh9K9/GtuBv////UMmBvZY+7B4Hv7gNdA3CL9aGbjmCjnu/+P7cXD4u7f/ne8t2MqVvXGDxKOPknz0MZJPPFFKVQUgECD6ylcSf/gNxB5+mPCZM/v6wDmFAvm5ObI3Z8ndvEn2ZlkQf/NmQ+mw1siIG7RPH95OpZ+ZITQzTeDwYZxMhq1vfcsNxp92H2t9GbfGx4i8/OXbPy95yb7WR5fOzbZx0uli4L6Fk04TPHoUM1x/oKanbdmpFMknnnB98IuPkrt1q+LvwWNH3bT4h9+AEQyRuXyJbDEQz166TH5hoe6xzXic0KmThE+eInzqJKGTpwgePlQMrmsEx10odNZT++XYNnZqa+es/maidtCfrDEIUHxseuBnNxkGZmlmv2pWvz++69r9yt9jbS0g2VNPPfXUU08HUc3EoQ1/Q/yBH/gBHn30Ub7v+76PD37wg3zP93wPlmXxoQ99qGXggybbtllZWWFkZKSt6dRrWzns4hDL8C590lOpFNFoFKO0Hn3nDOvt5G0cHPqsPkb6RnzltLNZUn//NdY+/zkyX/kq2StXKv4emJx0g/I3vIHYa1+LFY+3/J6GZRGcniY4PQ0PvKbib47jkFheIbi6Qv7WLbI3b7oB/M2b7iz8zVnsjY1Sumv6W9+q8QaGd7DKzcEgfS95iRuM3/dyIi97GYHDh2sONFRcmyYGIgzTLK1Z90ud8tm9tF+bNCMzGqX/jW+k/41vxHEcspcvl9LiU08+Se7adVb/6I9Y/aM/qnsMa3ysIhD3HgMT475zd8ImdxqLir9CbZsYpokVj7mZP01ktNQ6trO1tfusfjJBYXMTO5Egu7ZenNHfGfCTz7vZN17w36LMaLSy4F48VrFWvxAKEx4ewuofqAzwvYJ98Zgb7De4BGc/UvFX0PFZJZuosKhwKLGo+Cvo2ESFQ4lFhQMOps82HKR/5jOf4ed//ud5xzvewZkzZ/b9ht8JchyHpaUlhoeH2/o+XtG4oWiQoFXfIbPZLNFoFBLz7oZd1qMfjtcOKJtV9uYsycfc9OLkE0/gbG1t/9GyiL7ylcQefgPxhx8mfPZsxz/cecskfvo0fXV8ubCxQW52tiKALw/ivdn/4JEjbkD+spcRue/lhO++G7OJytCla9NldcpnG1EnbWIYBuFTpwifOsXoz/w0hUSS1Fe/4vrt449jA31nzhCumB0/idXhLBwVPwENFiV/hfbZxDCM7QG5nbftCjmOw9raGkM1Wg86juMWEEwkiqn82wH+rrP6m5vbaf3F4N4rNOgtw2GXzJKGztGryB+LYfb3157lrzWrX9p394r8Cv4KWj6rYhPQYVHhAA0WJX8FDZsocYAOiwrHQfTZhoP0L33pS3z4wx/m/vvv59y5c/xP/9P/xI/+6I+29OY9taZSj/Rd2q8ZhrHtsMlF97ENld3tbJatr3/dLfr22GM7enAHxseLQfkjxF77EFZ//77exw9V2KSOrIEBrIEB+s6d2/E3x3EoLC+DZRFo4WbQCMd3mrptEyseo/9Nb6L/TW/qGkO1um2TcimxqEjFJrtxGIaBEQ67S2JarDhrFyvqlxfcK+x4XmPtfvnvm4nOVeSPx8n076zIvx3wl1Xkj8cxwuGuzwi1Uyr+CjosKhygxaIiFZuocIAOiwqHmvyyS8NB+oMPPsiDDz7IBz/4QT7+8Y/zkY98hHe9613Yts1nP/tZjhw5Qn8XA6/vRJWKxtVpvwbF1O5Egng8juHNpPtUNC5361axEvujJL/61cp2WZZF5BX3EX9DsRjX2bMkk0mXQyBNqGSTfbAYhkFgbKzrHAdRSjZRYVHhUGNRkYpNOsVhFosSsssXkEZYGqnI76byl1XfV6rIH4sXZ/73X5G/G1LxVyUWFQ41FhWp2ESFQ4lFhUNNftml6apFsViMn/mZn+FnfuZnOH/+PB/+8If5N//m3/Crv/qrfPd3fzef+tSn9g1zUGQYBoODg2132OUGZtLBXacBQKL+THoj7decbJbUN75RapFW3XLLGh9zg/KHi2vLy1KCHcfZ5hCQCosKR6d8thGp2AR0WFQ4QINFyV9BwyagwwF7s/hekT/hrc0vW7u/mSC1vEQwn8dJJCtn9atT+dtZkT9WLMwXDHJ7ZLRsfX6dWX0vlb9NFfnvJD/plFQ4QIOld4+tLRUO0GFR4TiIPttSaeG77rqLf/tv/y3vfe97+cu//Es+8pGPNH2MP/iDP+B973sfc3NzvPzlL+f3fu/3eM1rXlN3/7W1NX7913+dP//zP2dlZYVjx47xwQ9+kLe+9a2tnIqvMk2TQ4cOtf19vDXp9Xqkw7bTAruuSd8t3T3x2GOsfvzjpB7/ynbLLwDTJHLffaUWaeG77677ZaKCo8tSYVHhgM757F5SsokKiwoH6LCo+Cvo2ESFAzrLYlgWVn8/Vn8/wRo+0WjCYd2K/IkkdmKzsYr8SXcdP7YNto29sYG9sUF5E87Npk9wl4r8FS35GqvI/53qJ3cCB+iw9O6xuhygw6LCAQfTZ33p/2NZFj/0Qz/ED/3QDzX1uo9//OO8613v4kMf+hAPPPAAH/zgB3nLW97C+fPnmZjYGUhms1m++7u/m4mJCf7sz/6M6elprl27xlCLI/F+y7Zt5ufnmZycbGuFwaWkN5O+e7r75uYm/f39GMn61d3rzaQ7uRw3f/4XSoXfrLEx4q9/PfFHHnZnyxt0wgoOgTQhBRYVDuicz+4lJZuosKhwKLGo+Cvo2ESFQ4mlGY52V+TPb26yNnuLuGXhpJKlmf66a/fbWJGfaJTAQP922v6OIL8qjb9NFfnvRD/5TmHp3WN1OZRYVDjgYPpsV5v0vv/97+dnf/Zn+emf/mkAPvShD/Hf/tt/4yMf+Qi/+qu/umP/j3zkI6ysrPD4448TLP6TOH78eCeRG5LjOKyvr9ccaPBT3kz62C4z6RUqtWAbr9icKWRY3HJT4avXpGevX8fZ2sKIRjn2n/8zffec8z31rqfuq1M+21NPfqjnrz2pql5F/kKhwO0LF5g5cwargR7zFRX5ywN6PyryQ8XsftPn6FNFftrYfq+n1tS7x/Z0p+kg+mzXgvRsNsuTTz7Jr/3ar5W2mabJm970Jr7yla/UfM2nPvUpHnroIf7ZP/tn/L//7//L+Pg4P/7jP86v/Mqv1P2nl8lkyGQypd83NjYA9x9moVAA3H+qpmli2zZOWd/rettN08QwjLrbC4UCtm2Xju+N6FSvT6i33bKsHWu4PZby7UuJ7cJx9dgdxyEWi2EX8pjJRQyA2ETF/rMb7ix6NBBlIDhQ4gZIX7wIQOjkSULn7sZ2HCgU9nVO/f39e57Tbnb38zoNDFSe527srV6n3Ri9ohJ++t5+zsnzWdhZP6DT1ykWi2Hbdsvn5KmV6+SxtOMe0cw5eRx+nNN+t3vs8Xi84vjduE7e8+pjdOs6eUVTq/fv9HWKxWIVNvLzHtHsOcViMRzHaer+1o7rVP7Zaee9vJHt1d8Ldjsn27YhEMAortsPtnCd7GwWJ5l0192nUhQ2E+Q3N8oK8iVxkqnimv7tWX2n+OhV729XRf6FsnR+MxbH6ndn841YtGy2P0ZgYAAjFnMHQYop/FZ/P1YkssMGzV6nev+L2/U/11Mt3/NYuvHdyDun8u+x3fhu1Oz/4k5cJ6j8X9zN77CGYez4XtCt6zQwMFC6v7V6Tq1cJ++1juNUHKdb18mbRa8+p/J99lLXgvSlpSUKhQKTVWllk5OTvPDCCzVfc/nyZT7/+c/zEz/xE3z605/m4sWL/C//y/9CLpfjf/vf/rear3nve9/Lb/zGb+zYfunSJeLxOACDg4McOnSI+fl51tfXS/uMjY0xNjbG7OwsyWSytH1qaoqhoSGuXr1KtjhCDTAzM0M8HufKlSusrKxw8eJFTNPkxIkTBAIBLly4UMFw5swZ8vk8V65cKW0zTZOzxUroN2/eLG0PhUKcPHmS9fV15ubmAJhbdZlGYyFWVlZYKvuHWX5O8/PzRNnirF0cO4+NV5zTN9e+Cbiz6NeuXas4p/7nnwcgOzFRwd/sOZ05c4a5uTnW1tZKqR+1zgncm+CRI0d2PadWrtP09DS27abFlH9g23Wd6p2T4zil/f3yvUuXLu3rnGzbZnV1FaClc4LWrpM3qBYOhzly5EhL59TqdVpcXCyxDA0N+XqPaOaccrkcL7zwAuFiayg/fG+/12lwcJAXXnjBnS0sfo5b9b39XKdI8Uv5yspKyW/3e06tXifHcZiamiISiXD16tV9n1Or18lxHDKZDMeOHWN4eNj3e0Qz5+SxjIyMcPTo0bbdy/c6pxdffLH0GTYMo2338kbO6datWxXfC9p1L9/rnE7cfTezs7Nsbm5XHfbOaWlpqeY53b592z2nfB62thjp62MwFGLu8mW2lldgKwWpLfoDFuFCgdWbs266fioFW1uEiq/Lrq2527xg38eK/EY0itPXB5EIRKNY/f3ExsbIBgKkDQOiUYhGiI6OMTwzzVomS9Kx3e19ffSNjnLs7NmO/c+td50cx8FxHM6dO9fx70bl55TP50v+eurUqY5+N6o+p7W1tdLneHx8vKPfjcrPKZFIcOnSpdL9pFvfYWdmZojFYnz7298mGAyWPsed/g7rnVM0GiWVSnX1OyxQskMqleLWrVstnVOr18lxHIaGhpiamtpxTqNNtEE1nGZCeh9169Ytpqenefzxx3nooYdK23/5l3+ZL37xizzxxBM7XnP27FnS6TRXrlwpjWq9//3v533vex+3b9+u+T61ZtK9izJQrD7u9+hWLpdjdXWV4eFhTNNs2+jWfb/5t2ym8/ztux7h5Fi0JnuhUCCRSBBL3SDwH16HExnG+JWrFex/9uKf8a///l/zyMwj/O4bf7fiOLf/v7/M5qc/zdi73sXI/+dn9mTfbfvm5ibRaLT0QerW6JZhGCSTSSJVLXI6PQrpOA7JZJKBgYHSP+b9nlOro5BekD42NrY9i7OPc9rP9vJzsm2bZDJJLBbDKhY56tZMejmL9znuxMxf9XbHcdc2xWKxkr92ayYd3HtoOUunZ2jB/eysra0xNDRU8Rnuxky64zikUini8fiOEfJOXifvftLf37/ruXbiOnks8Xgcy7I6MvNXi71QKJQ+w96x93tO+2X3tntBT/n3gk7P0HqDa/X+F3fqOnkV+Z1kksTCAqF8oTjTX5yxT6UoeCn+Zan7jpfm723zKvL7JdMszdCX0vLj7sy+EXPX45eq8PfHseL9EI2UpfjHCQwMYEYiVFM1ep3KvxdU79/J6+R9JxgeHiYQCNRk79TnqZH/xZ34PNm2XfG/uJsz6VD7f3Gz59TqdQI3KI5GoxXbujGTbts26+vrO/qTd+M6ed8LqjOIARKJBENDQ6yvr5c+5/XUtZn0sbExLMtifn6+Yvv8/DxTU1M1X3Po0CGCwWBFavu5c+eYm5sjm80SCu1cmx0OhwmHdxZWsyxrR4q8d9Gr1ez2YDBYc01EvZT8WtsNw9h1eyZfYDPtzoyPxUN1WSzLcisMLj/lvr5YNK58/9spd4BjOj694zjZS26btb4zp2vyNHNO9Zyx3rn6dT1qbffSU2vJz+u0F4tX/bE80Nht/722N8Nevt2yrAqfbeWcWtlumuaOipj7Pady7ec61WLx61ybZa9VJbRV39vv9noVSzt9ncbGxmoeFzp/nXb7R9vJ61R+bfy+R5SrkXNqhKXd1ykQCNT013bcy/faHggEan4v6MZ1quevHbtOlgWhEAwNEZ6u3/Z1L7WzIn9LMlqryB+Nx7GLx+j0dyPY/n5c7a+d/G5Uvt2P/8V+fJ5qcUB3vsOCP/+L/bhOu32f7uR1sixr1+8Fnb5O3n22+j3rfd+vpa4F6aFQiPvvv5/Pfe5zparwtm3zuc99jne+8501X/O6172OP/mTP8G27ZIxXnzxRQ4dOlQzQO+WbNtmdnaW6emdQa9fWilWdg+YBgN99YuvlGacEgvF9ejjO/bxKrtXF41z8nmyxVSS8OnTLfHWm/nqhlRYVDigMz7biJRsosKiwqHEouKvoGMTFQ4lFhUO0PFZJZu0yuJXRX7btlmdm2PACmCXzehXB/jdqMhvRKMVVfateKx2Rf6yGX4/KvKr+Cvo+KwKhxKLCgccTJ/tanX3d73rXfzUT/0Ur3rVq3jNa17DBz/4QZLJZKna+0/+5E8yPT3Ne9/7XgDe8Y538Pu///v8wi/8Aj/3cz/HhQsXeM973sPP//zPd/M0dshLV2rnSoLlhBukj8RCmObuDhAKhbZ7pNdov1avR3ru5k2cbBajr4/g4cM7XteslAZSVFhUODrhs41KxSagw6LCARosSv4KGjYBHQ7QYVHhUPJZFZuABothGPQNDhKIRjGM/VeGdpx9VuRPJCgkNsueJ8Bb25pKkU+lYGGhtXMsr8jvBfH98boV+YlESaytsnX33QQHBjD7+7FiMYwuXS8FPwEdDtBhUeFQuseCP3bpapD+Iz/yIywuLvKv/tW/Ym5ujvvuu4+//uu/LhWTu379esVoyJEjR/jv//2/84u/+Iu87GUvY3p6ml/4hV/gV37lV7p1Cl1TeWX33WQYbgVIkm6LNeI7/wHV65GeKaa6h06ewGigZUxDHAJSYVHhUJKSTVRYVDhAi0VFKjZR4QAdFhUOJSnZRIXFLw7DMDDCYcxwGJooDlVLdjbrzuonaqfy70zbr/27s7UF7L8i//XqcyxW5N+97V5/jTT+4qx+cZtRLL7WiA6an/ghFRYVDjX5ZZeuBukA73znO+umt//d3/3djm0PPfQQX/3qV9tMpS9vJn2vHumO47jFPxLzbrp7VZC+ld9iOb0M7Ex3z1x0g/RWU90rOIaHu54So8KiwqEkJZuosKhwqLGoSMUmKhxKLCocSlKyiQqLCkc1S2B4GKqKYDV9vFwOO5mkkEjWWbtfNqtfNvNf2EyQXl3ByuZwEgnsVMo9nl8V+QMBd1a/v3/3gD8Wx4jH2AIGpqaw+vtLa/eteByjqgBwO6XqJ73Pjp78skvXg/SDKNN026u0c02EtyZ9NLZ3OkVfXx8kijPpscog/XbCLRoXD8YZCFUWk8lccnukh0+1HqSXOESkwqLC0QmfbVQqNgEdFhUO0GBR8lfQsAnocIAOiwqHks+q2AR0WFQ4wF8WIxjEGhrCGhpq6nWO47C+vs7g4CCGYZQq8lek8tdK46+5Vn+zbECgWJE/n6ewvk6hrMXVXqq5p1eRvyrIt/rjZWv3d5/VN+P9mNEIRgOfzYPqJ61IhUPpHgv+2KUXpLdBhmEw1OQNsVktJRtPd49Go5AormeqWpNeXjSuerQnW5pJP9Uyb4lDQCosKhzQGZ9tlEPJJgosKhygw6Lir6BlEwUO0GFR4QAdn1WziQKLCgfosFT7q2FZWP39WLtU8m5Eu1fkT+xM5W+iIn++tRPeoyJ/McDv7ydToyL/9u+xlpeHNoar4ycKHKBzjwX/7NIL0tsg27a5evUqx48fb9uIjpfuPtpAuvvKygojyWJ193hldfd669Ed2yZz+TIA4VOtB+kljpGRrqfEqLCocEBnfLYRKdlEhUWFQ4lFxV9BxyYqHEosKhyg47NKNlFhUeFQYmmXv+6nIn8tmziOg7O1tR3A71aRvyyVv2MV+ctS+feqyG/GY24afxMV+VX8RIUDdO6x4J9dekF6G+Q4Dtlsts3V3Ysz6Q2ku0f7wmWF4ypvinUru8/O4qTTGKEQwZkZH4iRGW0DHRYVjk74bKNSsQnosKhwgAaLkr+Chk1AhwN0WFQ4lHxWxSagw6LCARosSv4KO21iGAZGNIoZjcL+C/LvrMjvBfQ1gv5CIkFuYx1ja6tim/dap7oi/+JiK6dcuyJ/PO6uxY/HcPr6WB4c2m67Vz2r7+3b5urrCv4K+j67H/WC9DtUy6U16Xunu0ecLXBswIDoWMXf6/VIz1x016OHTpzACLTuJoZhEIlEWj6OH1JhUeFQkpJNVFhUOECLRUUqNlHhAB0WFQ4lKdlEhUWFA7RYVNROm3SsIn8yQWFzk05U5N9xjo1U5I8VW/I1WZG/56+15ZddekH6HapG091t22b95nmGAaKjYFVe8nrp7tli+zU/Ut09Di/1o9tpKCosKhxKUrKJCosKhxqLilRsosKhxKLCoSQlm6iwqHCosahIxSZ7cZihEGYo1JGK/IXEJqmlZYK5HE6yOAiQqBwgaFtF/ni8mMrvpvHng0H6RkawvCC/umBfFyvyd0t++WwvSG+DTNNkZmambTcTx3FKfdLHGumTTtL9pUaP9Hrp7l77tZAPReM8jv7+fokPpgqLCge032cblZJNVFhUOJRYVPwVdGyiwqHEosIBOj6rZBMVFhUOJRYVfwUdm3SKo5GK/F5qdygUqsvTyYr8mWZOsE5FfjeI768M8OtW5I9jRqMVFfkPos/2gvQ2yDAM4vF4246fzBbI5G1g75l0wzAIZVbdX6qC9FQuxWrxbzvS3Usz6f60XzMMg3B49wGFTkmFRYUD2u+zzXAo2USBRYUDdFhU/BW0bKLAATosKhyg47NqNlFgUeEAHRYVfwUtmyhwQGMs36kV+SMf/EDL59yq/PKVXpDeBhUKBS5dusSpU6ew2tCKwSsaFwlaREO7X0LbtknOXaYfdvRI91LdB0ID9Ie2HdpxnO0g3aeZdNu2WVpaYmxsrOujXCosKhzQfp9tVEo2UWFR4VBiUfFX0LGJCocSiwoH6Piskk1UWFQ4lFhU/BV0bKLC0WmW3SryN8PhOI67zt5bm79XRf6EG9g3W5HfNk2667H+XZ9ekN4m2bbdtmMvNbgeHYq9+uxN95eqmfR6qe7527dxUikIBgkdPeoD8Xb/wm6nKymxqHB4aqfPNiolm6iwqHCosSj4K+jYRIVDiUWFw5OCzyrZRIVFhUONRcFfQccmKhxKLM1wGIaBEYlgRiL+V+RPJMlvrHPr4iUMgWwHv65PL0i/A1Vqv7bHenRwHcXaWnZ/ideeSa+b6n782K59GpuRYRiE2twGolGpsKhwKEnJJiosKhygxaIiFZuocIAOiwqHkpRsosKiwgFaLCpSsYkKB+iwdIPDMGpX5C8UChhHjnR94AL8s0v3V9f31LS89mtjDfRIt22bzMoN95eqHun1KruXisb5tB7d45ifn5cYmVVhUeFQkpJNVFhUONRYVKRiExUOJRYVDiUp2USFRYVDjUVFKjZR4VBiUeFQk1926QXpbZBpmpw4caJt60S2Z9IbS3cPZdfcX2LjFX/z0t13zqS7PdL9ar/mcYyMjMiMcCmwqHBA+322USnZRIVFhUOJRcVfQccmKhxKLCocoOOzSjZRYVHhUGJR8VfQsYkKhxKLCgccTJ/tpbu3SYFA+0y7vSa9sXR3EgvuLw3OpGcv+ls0zuMI+pQ636pUWFQ4PLXTZxuVkk1UWFQ4QItFwV9BxyYqHKDDosLhScFnlWyiwqLCAVosCv4KOjZR4QAdFhUOTwfNZ7s/3HAAZds2Fy5caFv6h5fuPtpIuns+i5OqvSb9VnJn4TjHcchcdGfSQz7OpNu2zdzcnERKjAqLCofH0k6fbYZDySYKLCocSiwq/uqxqNhEgUOJRYXDY1HwWTWbKLCocCixqPirx6JiEwUOJRYVDo/loPlsL0i/A+Wlu481MpOeWsbAwTFMiG4XWEhkE6xn1oHKdPf8woLbzsCyCB0/7huzYRiMj49LpMSosKhwKEnJJiosKhxqLCpSsYkKhxKLCoeSlGyiwqLCocaiIhWbqHAosahwqMkvu/SC9DtQK8kmWrAlF93H6BiY250DvVT3ofAQsWCstL00i370KKaPFRsNwyj9dFsqLCocSlKyiQqLCocai4pUbKLCocSiwqEkJZuosKhwqLGoSMUmKhxKLCocavLLLr0g/Q5UaU16bO+ZdHtzHgCnwR7p2Uv+r0cHN/VjYWFBJg1FgUWFQ0lKNlFhUeFQY1GRik1UOJRYVDiUpGQTFRYVDjUWFanYRIVDiUWFQ01+2aUXpLdBpmly5syZtlQYtG2HlaSX7r73TLeZKs6kN9ojvdR+zd8g3TRNJiYmJKouqrCocHgs7fLZZjmUbKLAosKhxKLirx6Lik0UOJRYVDg8FgWfVbOJAosKhxKLir96LCo2UeBQYlHh8FgOms92/0wOqPL5fFuOu7aVw3bc58MNFI5zipXdq2fS6/ZI92bSfeyRDm5BOu+n21JhUeHw1C6fbUZKNlFhUeFQY1HwV9CxiQqHEosKhycFn1WyiQqLCocai4K/go5NVDiUWFQ4PB00n+0F6W2QbdtcuXKlLekfXtG4wUiQoNXA5SumuxOt3SO9XmV3v9PdHcdhcXFR4oOswqLCAe312WakZBMVFhUOJRYVfwUdm6hwKLGocICOzyrZRIVFhUOJRcVfQccmKhxKLCoccDB9VqOhXE8Na7tHemNF3Qwv3b1/qmJ7rXT3wvIy9vo6mCahEyf8wC3JNE2mpqb23rEDUmFR4VCSkk1UWFQ4QItFRSo2UeEAHRYVDiUp2USFRYUDtFhUpGITFQ7QYVHhUJNfdunNpN9hWvbWozdQNA7ASRQLx8X2nkn31qMHj8xg9vW1zFrB4TjkcjmJ0TYVFhUOJSnZRIVFhUONRUUqNlHhUGJR4VCSkk1UWFQ41FhUpGITFQ4lFhUONflll16Q3ia1q3DBcpMz6Xhr0suC9I3sBpu5TaByJj1zqZjq7vN6dHAddmVlReKDrMKiwuFJodiGkk1UWFQ41FgU/BV0bKLCocSiwuFJwWeVbKLCosKhxqLgr6BjExUOJRYVDk8HzWd76e5tkGVZnD17ti3H9takN5zuXgzSzbJ099lNN9V9pG+ESCBS2l5qv+ZzZXdwPziTk5O+H3c/UmFR4YD2+mwzUrKJCosKB+iwqPgr6NhEhQN0WFQ4QMdnlWyiwqLCATosKv4KOjZR4QAdFhUOOJg+qzHkcMDkOA6JRKItI0tLycZ7pFPIwdaKy1Q2k16vR7qX7u530ThwbZLNZiVG21RYVDg8lnb5bLMcSjZRYFHhUGJR8VePRcUmChxKLCocHouCz6rZRIFFhUOJRcVfPRYVmyhwKLGocHgsB81ne0F6G2TbNjdv3mxrdfdGeqSTdIvGOYaFExkuba7bfq1Y2T3UpnT3tbU1mQ+PAosKB7TXZ5uRkk1UWFQ4lFhU/BV0bKLCocSiwgE6PqtkExUWFQ4lFhV/BR2bqHAosahwwMH02V66+x2m7TXpDcykF1Pdjdg4hrV9qWtVds+vrFBYcWfdwyf9rewOburHxMTE3jt2QCosKhxKUrKJCosKB2ixqEjFJiocoMOiwqEkJZuosKhwgBaLilRsosIBOiwqHGryyy69mfQ7TMuldPcGZtKLQbodG68YzamV7u6tRw9OT2NGo37hluQ4DplMRmK0TYVFhUNJSjZRYVHhUGNRkYpNVDiUWFQ4lKRkExUWFQ41FhWp2ESFQ4lFhUNNftmlF6S3QYZhEAqFMAzD92MvlQrHNTCTnnSD9Hx4uMJRZpM7090zxSA91Ib16OA67ObmpsQHWYVFhQPa67PNSMkmKiwqHEosKv4KOjZR4VBiUeEAHZ9VsokKiwqHEouKv4KOTVQ4lFhUOOBg+mwv3b0NMk2TkydP+n7cTL7AZjoPNLgmvdgjPTRyBIptCRzHKVV3r2i/5hWNa8N6dHBtMjY21pZjNysVFhUOaJ/P7odDySYKLCocoMOi4q+gZRMFDtBhUeEAHZ9Vs4kCiwoH6LCo+Cto2USBA3RYVDjgYPpsbya9DWpXIYWVYqp7wDQY6Avu/YKEWzguVzaTvp5ZJ5VPAfV6pLdvJn1ra0titM0PFsd2KORaK06hZhOF4h9qNlFgUeFQYlHxV49FxSYKHEosKhwei4LPqtlEgUWFQ4lFxV89FhWbKHAosahweCwHzWd7M+ltkG3bzM3N0d/fj2VZvh3XKxo3Egthmg2kcxRn0rOhodKF9lLdxyPjhK3tlPlsG9uveUqlUvT19bV0DMdxfEll2S/LxtIWz33pFs89fputzSz9I30MT0UZnowxNBV1n0/FiPQHG+L0wyZ+qF0+ux+p2AR0WFQ4QINFyV9BwyZKHKDDosKh5LMqNgEdFhUO0GBR8lfQsIkSB+iwqHAcRJ/tBel3kEpF4xpZjw6lFmyxiRNQDBhrpboX1tfJL7r7tqP9GrhrRUZHR/f9+sXrmzz5matceXqJkekYZ149yZlXTdI/0vwHoFkW23a4/uwyzz46y7VvL0PZwNjmcprN5TTXv71S8ZpwNMDQ5HbQ7j32j/VhWea+OL4TpGQTFRYVDtBiUZGKTVQ4QIdFhUNJSjZRYVHhAC0WFanYRIUDdFhUONTkl116QfodpKZ6pENpJj0dGCRcnIH2KrtXprpfBiBw6BBWPOYj8ba81I9IJNLUTPjti2t8/TPXuP7t5dK2pRsJlm4k+MqfX+LQ6UHOvnqSU/dPEGnQLo2yJNczPP/lW3z7sVskVjOl7TN3D3Pvw9NMnRpkfSHF6tz2z9p8ko3lNJlUnvkrG8xf2ag4pmkZDI5HGJ6KMTQZIT4a5NhLJhgYjTRsk4Os/frJQWZR4VBjUZGKTVQ4lFhUOJSkZBMVFhUONRYVqdhEhUOJRYVDTX7ZpRekt0GGYRCLxXx32PJ094ZUbMGWDgzgzb17PdJn4jOl3dq9Ht1TOp0mEtk7GHUch5vPr/L1z1zl1oU1wE0EOP2qSV72xhmWbia48LV5bl1Y4/bFdW5fXOexj19g5twIZ189wYn7xgn17e7a9Vgcx+Hm+VW+/egsV765hG270+bhWIBzDx3iJW+YZmhyu0VdbDDM4TPDFcfIZwusLWyxOpdkbd4L4N3n+axdCui3dYmB8QgzZ4eYvnuY6bPDxAYbzJbwSe3y2f2oUT/phFRYVDhAg0XJX0HDJkocoMOiwqHksyo2AR0WFQ7QYFHyV9CwiRIH6LCocBxEn+0F6W2QaZocOXLE9+MuJYvt12INBHD5DKTXABiaPrOd7p7Yme5eWo/exiDdMAxGRkZ23cexHa58a4knP3OVhWubgDvzfPeDU7ziLccYmnCD46mTg9z78DSbK2kufn2BC1+fZ/H6Jte/vcz1by9jBc9z/KVjnH31JEfvHSEQrFybUoslncjxwldv8+3HbrE2vx1AT50c5N5Hpjn1yvEdx6mnQMhibCbO2Ex8x/kl1jKsziXdWfe5FIs3Nlm4tsnG4hbPLW7x3JdvAzA8FWXmrmE3aD8zTF+8gUKBLahdPtusGvGTTkmFRYUDdFhU/BV0bKLCATosKhyg47NKNlFhUeEAHRYVfwUdm6hwgA6LCgccTJ/tBeltkG3brKysMDIygmn6V0Dfm0kfbSStu7ge3TGDpOwQ0V3T3dvbIx3cGepUKkU0Gt0xymXbDhefnOfJz1xj5VYSgEDQ5J7XH+a+7z5ad915/0gfr3jzUV7x5qOsziW58LV5Lnx9gbX5FJe+scClbywQigQ4+Ypxzr5qkum7hjAts8QSiURYuLrJs1+c5eKTCxTybrX2YJ/FXQ9M8ZI3TO8ItFuRYRr0j/TRP9LH0XtGSxwBI8TtS+vMnl/l5vlVlm4mSrPtz3xxFgwYm4kzfXaYmbuGOXxmiFDE349uu3y2We3mJ9+pLCocSiwq/go6NlHhUGJR4QAdn1WyiQqLCocSi4q/go5NVDiUWFQ44GD6bC9Ib4Mcx2FpaYnh4eG9d25CTa1JL65HJz5BNpcjWuS6lXSD9On4dGnXzEUv3b09ReM8ZbNZotHtVPFC3ub8E3N846+vsb64BbgB8ksfmeHl//AI0YEG0/qB4akYr/mBk7z6+0+wdCPBi38/x4WvL5Bcy/DC47d54fHbRPqDnH7VJKdeMc7spUUuf32V5dlk6RhjR+Lc+/A0Z149uWe6vF/KZrNEh6Icf+kYx1/q9lRMJ3PcenGNm+dXmX1xlZVbydI6/Kc/dwPDNBg/2u/OtN81xKFTQwTDrVWybJfP7kfVftJNqbCocIAGi5K/goZNlDhAh0WFQ8lnVWwCOiwqHKDBouSvoGETJQ7QYVHhOIg+2wvS7yCVqrs3ku5e7JFuxMZLDru8tcxWfgsDg0OxQwAUEgnyc3MAhE+dbAO1K8MwShz5bIHnvnyLp/7meqkgWzgW4OXfdYSX/oMZ+mL7T+02DDeAHT/az2t/+DS3L63x4t/Pc/EbC2xt5njmCzd55gs3S/tbQZMzr5rg3odnmDje39GRwHKblKsvFuTkK8Y5+YpxAFIbWWZfdGfZZ19YZX1xi4WrGyxc3eAb//0apmnQ1x+kLxYkHA3QFys+jwXpiwUIR4PFbYHiNne/YNjq+shnterZpBtSYVHhAC0WFanYRIUDdFhUOJSkZBMVFhUO0GJRkYpNVDhAh0WFQ01+2aUXpN9BairdvTiT7sQnSWxuEo/HS6nu49FxQpZ7jGwx1T0wPo41ONgGaleO47CytM7VJ9d4+nM32NrMARAdCHHfdx/lJW847PvstWEaHD4zzOEzw7zhR89y47kVLnxtnqvPLBPpD3DvwzPc/dChlgYFWpHjOCQSCeLx+K7BcnQgxJlXuS3nADZX0sy+uFpKj0+sZEitZ0mtZ5t6f9MyyoJ2iwJZ5qYvEBsMEx0IuY+DIaIDISIDoVLruHaqUZt0QiosKhxqLCpSsYkKhxKLCoeSlGyiwqLCocaiIhWbqHAosahwqMkvu/SC9DbIMAwGBwd9dVjHcVgqpbs3MJOedCu7ExvHtt211rNJt2hcZap7+9ejp5M5nv78Db71+RtktwqAu578lW85yt2vPdRwQbZWZFlmKaXccRw2NjYYGBjo+k3FuzbNqH+kj7sfPMTdDx7CcRySa1m2NrOkUzkyyTzpZI5MKkc6kSOdypNJ5kgnc6ST28/tgoNdcNjayLK1sR3cL166Vfd9++LBYvAeIjrgBvLRweLPQJjYYIj4SB/BUGvXcz82aZdUWFQ4QIOlHffYVqRgE9DhAB0WFQ4ln1WxCeiwqHCABouSv4KGTUCHA3RYVDgOos/2gvQ2yDRNDh065Osxk9kCmWJhs8Zm0t0g3eifZLA4Qz67WSNIv+RVdvd/PXpyPcPTf3uDZx+dJZdxg/OhySj3f88xzrxmsiMzs7XkfZC7LT84DMMgPhwmPtx4yzbHcchn7WLgnisG7nm2NrOkNrKk1jMkN9yZ+VQxiLdtxw36E7lScb9aCoRMzr5minsfmWb8SP++zkfh2oAOiwoH6LC04x67X6nYRIUDdFhUOEDHZ5VsosKiwgE6LCr+Cjo2UeEAHRYVDjiYPtsL0tsg27aZn59ncnLStwqDXtG4SNAiGmrgshWDdCc2zubGBv39/XUquxeLxvk4k76xvMU3/+Y6z335dqli+uhMnJf8gwnueeho14JzT47jsLm5SX9/Z9egq3AYhkEwbBEMW6XK+ds+e3yHzzq2QzqZKwbwWZIbmVIAn1rPuI8bWZJrGbLpAs996RbPfekWUycHuPeRmaba16lcm1ZZCgWbxWub3LqwRnI9Q6jPrQEQihQf+yyCfQFCfZb7t+JjIGTueK+DYhM/1Y577H6lYhMVDiUWFQ7Q8Vklm6iwqHAosaj4K+jYRIVDiUWFAw6mz/aC9DbIcRzW19eZmJjw7ZhLzaxHh1KQTnyytKlWunupR/rp1mfS1+ZTPPnfr/HiV+ewbQeAqZMD3P+9xzn6khESiQSmqZGG0lOldvNZwzSI9IeI9IcYna7x4rJj3L64xrNfnOXSNxaZu7zB3OXn+NIngtzzukO85A3TDIxF2ngW3VM+V2Dh6gazL65x68Iac5fXyWebT3UyDCqCeS+QNwIO0VifG9xH3G3BcIBQxCIU3g7y3UerNChgHNDPWzvusT311E71fLanO0k9f+3pTtNB9NlekH6HyJtJH21kPTqU1qQb8QkGBgaAnenudipFbtbdFjq1/5n0pZsJnvzrq1x6cgHHjc2ZuXuY+7/3ONNnh0qjSB5Ht2UYhgSLCodfMoztQn3J9QzPf/k2335slsRqhm/89+t842+uc+zeUe59eJqjLxmtOWCjZJPdWHLZAnOX17lVDMrnr2yUskY89cWCHD4zxNBkhFzGJpfOk00XyKbz5DIFslvFx3SBXDqP44DjUNyn4Ms5BMKVQXv5DP72TP52wB8MB6r22Q7+LcuUuj4qUrGJCgfosKhwKEnJJiosKhygxaIiFZuocIAOiwqHmvyySy9Iv0PktV8bizU3k+7EJthYX6e/v5/bydvAdrp75vIVAKzRUQL7aBUwd2WdJz9zjavfWiptO/6yMe7/nmNMnaxci6FUrE2FRYWjHYoNhnnVW4/zyrcc5eozyzz76Cw3nlvh2jPLXHtmmYGxPl7yhmnOve4QkbLsECWblLPkMgVuX/KC8lUWrm1iF5yK/SMDIabPDHG4+DNyKNbwTLZXJyCbzpPzAvniYzadZ2MtiUWwGNRX71P2PJMnt1UoZbLkMwXymULTlf9ryQqahMIWVtikLxJ0A/hIgFBxxt8L6N2gvzy1v3IAINQXwAp2NxXNT6n4rAqHEosKh5KUbKLCosKhxqIiFZuocCixqHCoyS+79IL0NsgwDMbGxnx12O2Z9AaC9NwWZDbc5/FxzLzJcnqZTCGDaZhMxaYAyHrr0ZuYRXcch9kX13jyM1e5+cKqu9GA06+c4P7vPcbYTP1iYd1eI1IuFRYVjnb4LIBpmZy8b5yT942zNp/i2cdmeeHx22wspfnKX1ziib+8zOn7J3jpIzNMnnBHHZuxieM4ZLfyboG7zSzJdfdxK5EDx00fN0wDwzAwTPc8K56XHo2yfd1tDjB3ZYXFaymWrm+WskQ8xYfDpYDcnTGP7tt+5XUCqKo10mwrD8dxKOTtsiC/UCPwd2fvs5kCua3iY9Xf3Nn+QilDoJCz2crZkIAEmX2dpyfTMtygvoEZ/FD5zH/Y/VsgbBILD5LLFDD6zK4vo1H5HKtwgA6LCke77rH7kYpNQIdFhQM0WJT8FTRsAjocoMOiwnEQfbYXpLdBpmkyNjbm6zG9mfSG0t299ehWGKNviH7D4NKCu/Z8MjpJ0HT7gmdK69H3DtIdx+Has8s8+ZmrzF12BwBM0+Dsg1O88s1HGZ6K7fp6wzDo72++2nc7pMKiwgHt8dlqDU1Gef3/eIYHfvAkF78+z7NfnGXh2iYvPjHPi0/MM3Ykzr0PT3Pm1ZNkUvlSZfmUV3W+7Pet4u+pzSx23tn7zX3QwFhfMSAfZvrsEP2jfR35Z9CsnxiGQSBoEQhaRPobzLzZRYVCMeAvS8+vNeNfEeRnCpXp/cVHb52+XXDIJPNkkvmW+cCd5Q+ETIIhd6AjUPHobg+ELfcxZJael+8bDJtVr3P3tQI7i/mVS+VzrMIBOiwqHNCZe2wjUrKJCosKB+iwqPgr6NhEhQN0WFQ44GD6bC9Ib4Ns22Z2dpbp6Wkfq7sXg/RG0t2Ti+5jfAIHWFtdZTbhrj2vqOx+0Z1J32s9+q0Lqzz2pxdYupEAwAqYnHvdIV7x5qMMjDZWCMxxHNbW1hgaGur6KJcKiwoHtMdn6ykYsjj32sOce+1h5q9u8Oyjs1z42jxLNxL83UfP83cfPd/8Mfsst2/7QIhof4i+/hCmQXGdt4NjO+5z73GvbY6DbTuE4yYn7p3g8JnhUiX8TqvbfmJZJlbMpC8WLLFMDY3si8W2ne31+GXp+dlM9Sx/1QBApvYggadCzqaQs30L+stlmEZpAGA7uK8M6G3yxOIR9/fyAYAdAwQ7X+tXFkC3/USRRYUDOnuP3U1KNlFhUeFQYlHxV9CxiQqHEosKBxxMn+0F6W2Q4zgkk0mc6vzYFrScbCLdvVTZ3a1wGAqFSu3Xmu2RXsjb/Lf/8xmyW3kCYYt7H57mvjcdITbYeF9uT6FQ67N6fkmFRYWjHT7biCaPDzB5fIDXve00L3zlNs9+cZb1xS0AwtEAkX438PYeowPB7W3FgDw6ECIQaqzFWzNyHIdUKkU0uv80dr+k4ifQGotpGoQjAcKR1v71FAoFXnzxRU4cP4WTh1ymQD5rk8u6a/Bz2UJxW4Fcxi4+utvzWbu0T+W+dtlrCqWaA47tkCsOHLRDVtAL5CszAWpmAZQPElQF/lbQIm8XsOy0u1QgZGEGjK75rorPqnB06x5bSyo2AR0WFQ7QYFHyV9CwCehwgA6LCsdB9NlekH6HaHsmvZF093n3MTaBYRjEYjFuJSuDdDudJnfjBrB7uvvKrSTZrTzhaIB//O6H6IsH98XvcShIhUWFQ0F9sSD3vekoL/+uI6Q2s4SjgYZ7q7dLKtdHhQP0WIIhCytiEWlDtl2hYG8H9FVBfT5jVwwEeIMBlft4AwDbgwT5bIFc8Zil9ylmAZD0/xwM0yBYTO8PhGpnAQRLAwDm9rbQziyA6mUAwVD9Fn8qfqLCoSQlm6iwqHCAFouKVGyiwgE6LCocavLLLr0g/Q5RU33Sy9PdHYfV1dVS+zUv3T175Qo4DtbgINboaN1DLV7fBGD8aP++A3SgxDE8PNz1WUkVFhUOKRmQKSSJBprvNuC3VK6PCocaS7tlWSZWxNxz1n8/NnEch3yuegCgKsivlQVQvW9VFkA2naeQsyuyAPxs6VetulkAYRMHm1h/X2m2v2YWQMUAgFkxaOBHFsB3kr82KiWbqLCocKixqEjFJiocSiwqHGryyy69IL0NMk2Tqakp39ZE2LbDSjHdfayhwnHFmfRiuntfX9+OdHevaFzo9OldHWjhmlskbvxo61NVfX3dWdNbSyosKhx++2wrUrEJ6LCocIAGi5K/QvM28bIAgiH/sgAcx2Fra4tIJIJtO8UBgOpZ/J1ZAOXLArzAv24WQHFAwFOnsgBq1QOomwVQtn8gaFJwctipBMFwoKEsgHZJyWcVPsOeVFhUOECDRclfQcMmoMMBOiwqHAfRZ3tBehtkGAZDQ0O+HW9tK0ex7THD0WbWpE9iGAZ9kb4d6e6ZBtuvlc+ktyLDMIhGoy0dwy+psKhwgP8+2wqHkk0UWFQ4QIdFxV9ByyYeh2UZWFGTcBuwHNvNAqgV+O+sB7CzVkDNfTPFZQDZQseyAALB6gGA6mUAZen+FQMAZlVmQDHwL9vHCuz8kqjisyr+CjosKhygw6Lir6BlEwUO0GFR4YCD6bO9IL0Nsm2bq1evcvz4cV9GdLwe6YORIKEa//x3yAvSY+M4jsOLsy+Ss3NYhsVE1J1dz3pF407vXjRuadat6D5xrLUg3XEcVlZWGBnZX1VoP6XCosIB/vvsfqVkExUWFQ4lFhV/BR2bdIrDMI1iy7r6NSNaYSkU7B1ZADvX+lfP8NeoB1B8nk3nsXNOqWigp3zOJp+zSZPbty3qyTSNYh0As2IQIGdnGRiM7+wCUJXqXxo0qFUPIGi2lAWg4q9KLCocSiy9e6wuhxKLCgccTJ/tBeltkOM4ZLNZ3yoMNrUeHSBZWd19zVkDYCo2RcB0L3kjPdJXbiWx8w7haICBscZare0mldE20GFR4fDbZ1uRik1Ah0WFAzRYlPwVNGwCOhywfxbLMn3LAnAch3Q6TV9fH4Zh7MwCKAvqvRn/HVkAVYF/qZZAdRZApoBdTHmzbYfsVp7s1k6mZdItn1etLIDywD8Qtgh6+1RlAVghC8cokFlZIxgONJQF0E6p+KwKB2iw9O6xtaXCATosKhwH0Wd7QfodIK/92lgjld1hR7r7Um4JKKvsns2SvX4dgNAu7de8VPexI/0tj5AZhkEk0nqg74dUWFQ4lKRkExUWFQ7QYlGRik1UOECHpZqjkSyAVlTIbw8AVAf4mXSOm9dvMTo8TiHnbA8EZArkcnaNLIDKbflcZ7MASjP5VWv8a2UB7KgH0GAWgKqfdFNKLCpSsYkKB+iwqHCoyS+79IL0O0DLzcykZ5OQdVPUiY1j2zYX5i8AZZXdr16FQgGzv5/AxHjdQy0Ug/QJH4rG2bZdSv3odhqKCosKh5KUbKLCosKhxqIiFZuocCixdJrDCpjuTHRsZyeUQqFAIbbGmTOHsazmBwl2ywIoD+jrDRKUvy6dyuIUjNISgEazAPxQeVAfCJkYpkNfNEywr3aBwO22gPW6Amx3DLCs/V1jFX9VY1GRik1UOJRYVDjU5JddekF6G2SaJjMzM745rLcmvaEg3ZtFD0Qg3I8BLOeXgbIg3VuPfurUrjPki15l9xbXo4M7qtTf3/qMvB9SYVHhAP99dr9SsokKiwqHEouKv4KOTVQ4lFhUOKB1n/UrC8BLCQ2FQhV2Kc8CqJXq7xYJrNqnXheA3bIAsnaxNkB5FoA/IwKmZVQG9FVBfaC8GGCoMgsA02Yztux2AajVVjBkdsSPVHy2d4/V5VBiUeGAg+mzvSC9DTIMg3g87tvxlpLFmfRG0t1LPdLHwTAwgLmtOQBm4jNAefu1+uvRCwWb5Vm3r44f7dcMwyAcbjBdv81SYVHhAP99thUOJZsosKhwgA6Lir+Clk0UOECHRYUDdHy2nk12ywJoVY69XbivsitA7SwAdwDArsoMqFMgMGvjeFkAhc5lAdTqAhAI1ckCqB4k2CULQMVnVfwVtGyiwAE6LCoccDB9thekt0GFQoFLly5x6tSpfaW1VcubSR9raCbd65E+CbgpF9fX3PXn3kx65qLXfq3+evSVW0kKeZtQJMDgeOvrKmzbZmlpibGxsa6PcqmwqHCA/z67XynZRIVFhUOJRcVfQccmKhxKLCocoOOz3bCJYRqE+gKEqtoGb7OM75vFcRzsvDcIUKfgXynl367KDChuy+TZSqbBtna0FSzsmQXgj0zLKK3xNyzoi4bKUv13ZgFUBPmh2hkDXv2AQLD5LAAVfwWdz7EKhxKLCgccTJ/tBeltkm3be+/UoLbXpDcwKlNqv+ZWdrcdm4W0u21Hj/RdZtK3+6PHfUlj8foXKqTEqLCocHjy02f3KyWbqLCocKixKPgr6NhEhUOJRYXDk4LPKtnEDxbDMLCCBlZw/1kAjuOQy+UIBoM7WGpmAewYALArBwkytToFlC0LqJMFkEnlyaTc900sZ/dtkx0y2F4GUCcLoLoegBkwWFnbpLA8T7gvWHuQoFhg0NxnLYCG8UV8VoVDiUWFw5PCPRb8s0svSL8DtFxKd29iTXqx/dpSeomCUyBgBhiPjOPkcmSvXgPcNen1tHjNKxo30AL5tgzDIBRqsIVcm6XCosKhJCWbqLCocIAWi4pUbKLCATosKhxKUrKJCstuHPWyAPxQeRbAngF9zVT/7f0qBw+qsgAc3OyBTIFmswDOs77nPttZAJUV/6sHAAIhs3YWwC61A7wsAHU/6bRUWFQ41OSXXXpB+h2g7cJxjaxJrwzSb2zcAOBQ7BCWaZG5chXyecxolMChQ3UP41V296NoHLijW4uLi4yP7z+tzS+psKhwKEnJJiosKhxqLCpSsYkKhxKLCoeSlGyiwtItjvIsgL5iFoDHMuUDi207FYF/7WUAtZcGZDMF1lY2CIf6KGTtmoMEXjvq7SyAfKsm2SnDrQVgBQxCfcFS8cSKAYHdugLUawu4jywAFX9VYlHhUJNfdukF6W2QaZqcOHHCF4fN5m020u6Nr7E16ZVB+q3kLQAOx7z16MWicbtUdi8UbJZvum3c/CgaB+4/o5GREYmUGBUWFQ7w12dbkZJNVFhUOJRYVPwVdGyiwqHEosIBOj6rZBMVFhUOv1nMUhZA81/163UBKP97IW/vWAZQKvBXvTSgquJ/za4AXj2BHVkANvkMZJKFVk2yQ2bA2FHFv1YWgLdUwAzCfF+WYDhQZ9/tbdY+agE0KhWfVeEAnXss+GeXXpDeJgUC/ph2pZjqbpkGA30NrLeqWpPuBekz/cXK7t569F1S3f0uGgeuwwaD/leN3Y9UWFQ4PPnls61IySYqLCocoMWi4K+gYxMVDtBhUeHwpOCzSjZRYVHhAC2W3fzVMAwCQYtA0CplAfip6iyA8oB+98yAGq0Cs7tkAeQdMvn2ZQFUt/qrnOE3aw4E7OwKUGNpQMiU8BMlfwWNeyz4ZxeNszlgsm2bCxcucObMmZYrDC4VU91HYiFMs4ERmarq7jc3bwJuujtAtjiTHj5Tv7K730XjwLXJwsICExMTXR/lUmFR4fBY/PLZVjmUbKLAosKhxKLirx6Lik0UOJRYVDg8FgWfVbOJAosKhxJLt/21PAugZJPD/tiklAVQr81fzQKBNrl0ns2NJAEzSD5bPYiwvXygkN/OAvAGFbY229cRYLd6AKWCgTu6ApTtG3YHW/aTBaDirx6Lwj3WY/HDLr0gXVxNFY1znMo+6cDtxG2gvLL7drp7PXlF48Z9KhoH7qjS+Pi4REqMCosKh5KUbKLCosKhxqIiFZuocCixqHAoSckmKiwqHGosKvLbJhVZADQ+2+k4DrZtY5q7B7B2wd6eva+TBbCjVWDdLABvAGB7uQAdqAVgFDsCeDP8OzIBioF/IGQRCBrc6NsiVLYEoKItYI0CgQ1NOt7B8stne0G6uLZ7pDdQNC6bgFyxf0eNdHcnnyd75QoA4dP1Z9K9onETPq1HB9dhvZ9uS4VFhUNJSjZRYVHhUGNRkYpNVDiUWFQ4lKRkExUWFQ41FhWp2KRRDtMyCUVMQhH/Q6zyLIBMOkch51QNAJQF+WWB/856ALX23c4CcMqzAHw/C7AC5nYV/z26AlQsC6iVBVDcZlpQyDs43lqGLsovn+0F6eLa7pHeRNG4YAzCcfJ2nrnkHABT0SlyN2/iZLMYfX0EDx+ueYh2FI0DvZQYBRYVDiUp2USFRYVDjUVFKjZR4VBiUeFQkpJNVFhUONRYVKRiEwUOLwvAtAzWEstMTPrLUp4FUFEPoGzGvzzAz2bybKwlCFohtwtAWeC/WxZAIe8OCGSS/mcBnHjfCQL93Q1ve+nuwjJNkzNnzvjywVlKFtuvxRqYSa+q7D6fmqfgFAiZISZiEySf+AIA4ZMnMeqwrd4uFo3rs3wrGgeuTbp9g1VjUeHwWPzy2VY5lGyiwKLCocSi4q8ei4pNFDiUWFQ4PBYFn1WziQKLCocSi4q/eiwqNlHgaCfLfrIAvCUAe8lxHAq5sgGAjE0+VycLoKoeQH63LIDidjvvjgCEGimy3Wb5dX16QXqblM/nfWlk39RMelWP9FsJN9X9UOwQBgaZC25l99Dp+uvRF0rr0fsxfFwz4jhO6afbKUsqLCocnvzy2VakZBMVFhUONRYFfwUdm6hwKLGocHhS8Fklm6iwqHCosSj4K+jYRIVDiaUZDsMwim3uLCJx/1kK+QKpRBor0P1BFL+uT/fP5ADKtm2uXLmCbdstH2t7TXrzPdK9yu5joTEcxykVjQufaqSyu3+p7uA67OLiosRaERUWFQ7w12dbkZJNVFhUOJRYVPwVdGyiwqHEosIBOj6rZBMVFhUOJRYVfwUdm6hwKLGocABgwOzcjQPls72ZdHFtV3dvIt29qmjcidETmKa53SN9l5n0UpB+zN8g3TRNpqamfD3mfqXAksukWb55g0wqiTM2CgLpUwpSuDaeVFhUOECLRUUqNlHhAB0WFQ4lKdlEhUWFA7RYVKRiExUO0GFR4VCTX3bpBeniaq5wXGWPdC/dfSoyhZ3Pk710GYBwnfZrdsFmqVg0bsLH9mvgjirl83kCgYBEmlCnWBzbZn1hnsUbV1m6dpXF61dYun6N1blbbvlMIDY0zMu/+6287E3fQ2xouK086vpO9ZM7gUONRUUqNlHhUGJR4VCSkk1UWFQ41FhUpGITFQ4lFhUONflll16Q3ib5UczBcRyWmmnBVtUj3Ut3H2SQ7OwsTiaDEQoRnJmp+fKV2ykKOZugz0XjwD2XlZUVif6f7WLZSmyydP0qi9euslQMypduXCOXSdfcPzIwiOM4JNdWefwTH+WJv/g4d732YV75vT/I5Mn6SxLaJYViKN8JfnKncqixKPgr6NhEhUOJRYXDk4LPKtlEhUWFQ41FwV9BxyYqHEosKhyeDprP9oL0NsiyLM6ePdvycZLZApliz8J9zaQX093PTZ8j96w7ix46cQIjUPuyL17fANz+6H4WjQP3gzM5OdnycWy7QCGfxykUKBQK2Pk8dqGAXchTyLuP7u/F5/kChUK+4nfbLuA4DmuXXwQMDAMMwwQDMAwMDPexbHtpG8V9DJPU+iqL16+6gfn1qyRWlmsyW8Ego9NHGT92nLGj7s/40ePEhoYp5HO8+MTjPPWZT3H7wnmee/TzPPfo5zl81z288nt/gNOvfgirzvXyU375bKvyy0/8kAqLCgfosKj4K+jYRIUDdFhUOEDHZ5VsosKiwgE6LCr+Cjo2UeEAHRYVDjiYPtsL0tsgx3FIJpPEYrGWRlC8onGRoEU01MClShRn0mMT5Ao5FlLuGvXx0DiZi18DIHx6l6Jx19pTNA5cm+RyOYLB4L5sklpf4yuf/BjPfP5vKORyvvP5pYHxScaOHmP86InS4/Chw5iWtWNfx3Eo2A53v/Zhzr3uEW5fPM9Tn/lLzn/lS9w6/xy3zj9HfHSM+777rbz0H76F6MBg27j98lk/OFrxk4PIosKhxKLirx6Lik0UOJRYVDg8FgWfVbOJAosKhxKLir96LCo2UeBQYlHh8FgOms/2gvQ2yLZtbt68yZkzZ7BqBGeNaqmZ9eiOUzaTPsFccg7bsQlbYay01VDRuIU2FY1z8RzW1tYYGxtrymGz6S2e/Kv/ytf+8s/Jpbdq7mNaAcyAhWUFMCwLy7JK20wrsON30zTJ5fMEizPUXpsEHAcH99F9sIvLxp2Kfdz9IBSJMn70uDtDfuQ4Y0ePEY7G9m2TQ6fv4tDP3cXD//hnePqzn+Fbf/sZEstLfOm//Ge+8smPce71/4BXfM8PMHH8ZMPv0aj88tlWtV8/OcgsKhxKLCr+Cjo2UeFQYlHhAB2fVbKJCosKhxKLir+Cjk1UOJRYVDjgYPpsL0gX1opX2b2R9eiZDSi4M+/EJ5hdehqAw/HDTE5OcqVYNC7UhaJx4KZ+TExMNLy/XSjwzOf/hsc/8VFS62sATJ48wxt+/KeYOnV2Oyg3za7fGParejaJD4/wurf/BA/8D2/nxa88xjc+8ynmL1/k2S98lme/8Flm7rmXV37PD3LqVQ/UnKG/k9Wsn7RTKiwqHKDFoiIVm6hwgA6LCoeSlGyiwqLCAVosKlKxiQoH6LCocKjJL7v0gnRhlXqkx5rokR4egGCkVNn9cOww6XS6bCa9drp7O4vGgTuqlM1mCYVCuwbVjuNw8etf5bE/+X9YvVUsfDc5xet/9Ce568HXY/hUkK8RlnZrL45AMMg9D38X597wRm69+AJPfeZTvPjEl7n53LPcfO5Z+sfGecVbvp97v+vNROL+Zz90QyrXRolFhUONRUUqNlHhUGJR4VCSkk1UWFQ41FhUpGITFQ4lFhUONflll16Q3gYZhuGLw3o90keaCdJjlZXdp+PTrF+8hJPagmCQ0JEjNV/uFY0bP+J/0ThwHXZzc5ORkZG6dpk9/zyP/vFHuPXi8wBE+gd48G0/xsu/+3uwAsGOsnRCjXIYhsH0XeeYvuscm8tLPP3ZT/P03/41m0uLPPrR/8Tjf/pRhqdn6B8ZJT4ySv/IGPGRUeKjY8VtY4Sj0V1Z/PLZVqVybZRYVDiUWFT8FXRsosKhxKLCATo+q2QTFRYVDiUWFX8FHZuocCixqHDAwfTZXpDeBpmmycmTra8b9tqvNZTuniwG6VWV3af7p4mtrrAChI8fwwjWDnZLRePasB4dXJuMjY3V/Nvy7A2+9LH/h4tf+yoAgVCY+7/vh3j1D75tz+DSb5ZOaj8c/aNjvP5Hf5IHfvhHOP/lR/nGZz7F4rUrLF69zOLVy3VfF+yLFAP4YiA/OkZ8uDyQH+XY0aNdb1+hcm1Ah0WFA3RY/LrH+iElmyhwgA6LCgfo+KyaTRRYVDhAh0XFX0HLJgocoMOiwgEH02d7QXob5DgO6+vrDA4Otljd3Z1JH2uo/ZoXpLsz6eXp7okvuzPToVP1K7t7ReMm2lDZHVybpNNp+vr6Sjbx+oM/8/m/wbFtDMPk3je+iYf+0Y/TP9K+D30tlm6oFY5gKMy9b/xuXvIP3sTK7A3WF+dJrCyzubxMYmWZxMqS+/vKEplkklx6i9VbN0tLCOrJCgYJ9kUI9fURDPcRikS2f694jBDs6ys9etviIyMMH5ret11Vro0SiwqHEotf91i/WFRsosChxKLC4bEo+KyaTRRYVDiUWFT81WNRsYkChxKLCofHctB8thekt0G2bTM3N0d/f39LFQaXk95MejNBujuTPrs5C7jp7lsX/haA8C5F45aLRePa0X7NUyqVoq+vj+xWiq/95V/w5F/9BblMGoCT97+Gh3/8nzA6c7Rt71+LpdtqlcMwDEZnju5qt1w6zebKdvC+WR7ELy+TWF0mubYKjkMhl6OQy5He3Ng3U1+8n8Nn7+bwXfcwfdc5Jk+dIRhqIBukKJVrAzosKhygweLXPdYvKdhEiQN0WFQ4lHxWxSagw6LCARosSv4KGjZR4gAdFhWOg+izvSBdWN5M+misgQDHa78WmyBbyLKw5Qbt0/3TbNxwZ0/rtV9bnUuRLxaNG5rwP70c3GByaHCQp//m03zlkx8rVWw/dPouHv6Jn2bmnnvb8r71WEZHRzv2ft3mCPb1MXJ4mpHD03X3yWWzPP/ssxyZPoydy5JNb5Hd2iKXTpNLb5EtPW4/z6XTxecpsuk02a0tNhbmSSc2ufyNr3H5G18D3BZ5kydPcfjsOabvuofDd50jNjRck0Pl2oAOiwoHaLGoSMUmKhygw6LCoSQlm6iwqHCAFouKVGyiwgE6LCocavLLLr0gXVhN9UlPLrqP8QluJ28DEAlEGAwOslCs7F6v/dqCtx69jUXjXvzql3nsY/8P6/Mu29DUId7wYz/FmQde1/G0FMdx2NraIhKJdD1NSIEDwLQsQtEoA2PjLY1AFvI5Fq5e5tb555k9/xy3zj9Pcm2V2xfOc/vCeZ78b/8VgKHJQxWz7aMzRzFMU8omKiwqHGosKlKxiQqHEosKh5KUbKLCosKhxqIiFZuocCixqHCoyS+79IL0NsgwDGKxWEsXxrYdVorp7mONFI7zZtLjE8wmtlPdC4uLOIkkWBah48drvnSxuB69HanuN59/lkc/+p+4feE8AJGBQR76H3+Ml/3D78EKdM/90uk0kYj/rebuVA4/fBbACgQ5dPouDp2+i/u/74fcNUIL89w6/xy3Xnye2fPPs3TjGmvzt1mbv81zj30BgHAsxuEzd3P4rnPEp6Y5cvos/WNjmGZ3U5ZUro8KB2iw+OWvfknBJkocoMOiwqHksyo2AR0WFQ7QYFHyV9CwiRIH6LCocBxEn+0F6W2QaZocqdPqrFGtbeWwHff5cLSRNenFmfTYBLMJt8r34fhhspcuARA6ehQzVPs4pfZrPgbpyzdv8NjH/pBLX38CgEA4zKu+/4d59Q/8D4Qi7Umpb1SGYTAyMtJVBiUO8Mdna8kwDIYmpxianOKeh78LgHQywe0L57l1/jlmzz/P7YvnySSTXPnmk1z55pPbTJbFwNgEAxOTDE5MMjhefJyYYnBikshAe4uDqFwfFQ7QYWmXv+5HKjZR4QAdFhUO0PFZJZuosKhwgA6Lir+Cjk1UOECHRYUDDqbP9oL0Nsi2bVZWVhgZGdl3S6vlYvu1wUiQUGCPYzhOWQu2CW4tfAlwK7tnzheD9NO1K7vbBZulG27RuAkf2q8lVpZ5/BMf5dkv/C2OY2OYJi/9rjfz4Nt+DDPsVgHvthzHIZVKEY1Gu54mpMAB/vhso+qLxTlx3/2cuO9+970LBRavXWH2/HPMvvAcc5cvklhewi7kSzPutRQM9zE4MVkWxE8Vg3j3p9XBoFavT3JtlfkrF8mkUvQX293FR0YJ1GmD2C4OP6XC0kl/3UsqNlHhUGJR4QAdn1WyiQqLCocSi4q/go5NVDiUWFQ44GD6bC9Ib4Mcx2FpaYnh4drFsRpRU+vR02tQcPcnNl5Kd5/pnyFbXI9er7K7X0XjMqkUX/vUJ3nyv/1X8ll3gOH0qx/k9T/2U4xOH8FxHNbW1oi2oe/5fpTNZiVYVDj88Nn9yrQsJk+eZvLkaV7xPT/A2toaAwP9JFdX2ViYZ31xnvWFOdYX5os/cyRWV8hl0izduMbSjWs1jxsfHWP8yDFGjxxj/Ohxxo4eZ+TwDIE6GSW11Mj1cRyHzaVF5q9cZOHqZRauXGL+yiWSqys1948MDNI/Oub2qh9x+9SXno+6PeuD4cqKoCp+Ahos3fTXWlKwiRIH6LCocCj5rIpNQIdFhQM0WJT8FTRsosQBOiwqHAfRZ3tBuqi89mtjDVV2L86i9w1CsK8UpB+OHyZz6bMAhOvMpJfWo++zaFwhn+Ppz/41X/3kx9gqtu06dPZuHvmJn2H67ntK+xmGIfPBUWFR4VBSuU0GxsYZGBtnhp2V//O5HBuLC2wszBWD+O0Afr1YYT6xvERieakihd4wTYanDjN25BhjR48XH48xODm1Y/17revj2DZr87eZv3KpFIwvXLlEOrFZ62QYOTxDdGCQxOoyieVl8rksWxvrbG2ss3DlUl079MX76R9xZ9/7R8aIj44WH93f+0dHu7JspOezO6ViExUO0GFR4VCSkk1UWFQ4QItFRSo2UeEAHRYVDjX5ZZdekC6q5WZm0r0gPTYBwK3ELQAOxw6RuXgBgNCpkzVfWqrs3uR6dLdi+5f40sf+cykdefjQNG/48Z/i9Ksf2pHe4TgOiUSCeDze9ZQYFRYVDiU1apNAMLhrW7l0MsHyjevFmfarLF2/xtL1q6STCVZu3WTl1k1efOLL28cLhRmdOeIG7cWfkSPHWF1cIDF/uzRDvnD1EtmtrR3vZ1oWo0eOMXH8JJMnTjFx4jTjx44TKlve4TgO6cQmm8tLbC4vuT3rl4u965fd/vWby4vkMxnSiU3SiU0Wr1+ta4NQJFo5Iz86WnwcKwX44ai/RVR6PrtTKjZR4VBiUeFQkpJNVFhUONRYVKRiExUOJRYVDjX5ZZdekN4GGYbB4GBrRa28NemNBeleZfdJ0vk0S1tLABzKRplf3wDT3KWye/NF4258+1s8+tH/xNwldwAgOjjEa//Rj3PvG9+8a8V227Ybfo92S4VFhcMPn/VLftikLxZn+u57KrI5HMchubriBu7Xr7J04zpLN66yfOM6+WyG+csXmb98cc9jW8Eg48dOFIPxU0yeOM3okWN7rjU3DINI/wCR/gEmjtceNHMch0wqSaIUtLsB/Mr8bTKbG252wMoymVSS7FaK5ZvXWb55ve57BsN9xdn36pR6L7gfJdI/0NR1V/BZJX8FDZuADgfosKhwKPmsik1Ah0WFAzRYlPwVNGwCOhygw6LCcRB9thekt0GmaXLo0KGWjvEP7p4g3hfg7qmBvXcu9Ugf51bSnUWPBWOErrsz7MEjM1g12gA0WzRu6fpVHv2TP+TKU193jxvu41U/8MO86gf+h4oZw1ryPjwKUmFR4QB/fNYPtdMmhmEQH3HXfB9/+StL2227wPr8nDvbXgrgr7F6+xaBcJiJ4yeZOHGSyROnmThxipHDM21rH2gYBn2xOH2xOGNHj9fdL7uVYnN5mc2VpWJAv0Si4vdl0olNcpk0q7dusnrrZt1jBYKhqnT60bK0ejeQjw4MYpimjM+q+CvofI5VOECHRYUDdHxWySYqLCocoMOi4q+gYxMVDtBhUeGAg+mzvSC9DbJtm/n5eSYnJ/ddYfCVR4d55dEG1zOUzaSXUt3jh8ledNe8WseO4TjOjtGlUtG48O5F4zZXlvjyx/+Y5774+VLF9pe96Xt56G0/SmyoMUbHcdjc3KS/v7/ro1wqLCoc4I/P+qFu2MQ0LYYPTTN8aJozD7y2tD2fy5JMphjo8shsLZuEIlFGZ6KMztRvN5LLpEmsLO9IqfeeJ1aWSa2vkc9lWZu7zdpc7Sr6AKYVID4ySv/IKH0DgwxNTjEwOlaxZj42NNyxvvYq/go6n2MVDiUWFQ7Q8Vklm6iwqHAosaj4K+jYRIVDiUWFAw6mz/aC9DbIcRzW19eZmJjozBuWeqSPl4L06fg0mafc1N3AiRM1X+YVjRs7Eq9bNM6xbT7x7l9n9bZbjO7MA6/l9T/6U3XXAvd0Z6rjPnsHyAoEMbp8o29FwXBfafChnvK5HImV5dJsvBe8l9bMryyTXFvFLuTZWJxnY3G+7rEM0yQ2PFJMrR+vSqt30+xjQyO+ZCH0/LWnO009n+3pTlLPX3u603QQfbYXpB8Elc2k30y4aa3T8WmyF58DYOCee2qO5CwUg/SJo/VT6hevX2X19izBcB//47/4TQ6fPbcvRMMwGBhoIHW/A1JhUeFQkpJNVFjayREIBhmanGJocqruPoV8nuTaSsWMvFf0zkuzT6wu49h2qaL+7Qvn650MsaFhN6W+LJ2+vzQjv79e8t3Ud4KfNCsVFhUOJSnZRIVFhQO0WFSkYhMVDtBhUeFQk1926QXpB0HJYnX3+AS3bj8NwOHYYTKX/hKA7ORUzXT3Ra+y+y7r0W8+9wwA03ffs+8AHdwRro2NDQYGmitS1Q6psKhwKEnJJios3eawAgEGxiYYGJuoy2LbBVJra8WU+qWKwnfbVeyXsQt5kqsrbg/5YuHJWooMDG63nhsd31H4LjKo0/Kl29dHjUOJRYVDSUo2UWFR4VBjUZGKTVQ4lFhUONTkl116QXobZBgGY2NjnXPYRFmQXkx3P1IYoLCyAkDo5M50d9t2WLq5d/u1G889C8DMPS9tGbPba0TKpcKiwtFxn91FKjYBHRYVDqjNYppWqSjfIe6q+TrHtkltrJfS6cuD+fLidxW95K/W7yUfjET4u3CEQDhEIBgiEPJ+wljBIIFQmGBxm1X+9+D2foHifoEd+4UJhMr+FgxhWvXX26tcHxUO0GFR4ejdY2tLhUWFAzRYlPwVNGwCOhygw6LCcRB9thekt0GmaTI2NtaZN7Pt7erusQlmE+7a8anFAgDBmRkGa6zPWJ1Lks/aBMIWQ5O1i8Y5ts3NF74NwJF77m0J0zAM+vub68XeLqmwqHBAh312FynZRIVFhQNaYzFMk9jQMLGhYSZPnq65TzO95HNbW+Rq9Kxvl0zL2jEI4AbwlcF8rcGCnX/zBgzCWKEgwToDBVYg0PAXjoPiJweRA3r32FpSYVHhAB0WFX8FHZuocIAOiwoHHEyf7QXpbZBt28zOzjI9Pd3+EaatVbDzAKTCcVbS7uz50O1N1oDQqVOsrq4yNDRU8WXPKxo3fiSOWado3PLN66Q3NwiEw0yePNMSpuM4rK2t7eDohlRYVDigwz67i5RsosKiwtEJFsNorJf8VmKTKy+eZ3RoiEI+Tz6boZDLkc9myGez5HNZ9zGbdbeV/a3gba/Yp8Z+uSyFfL70vnahQHZri2wHBwYwjO3gfrdZ/2CQQChEwYFoPE4wHC69zgrVyRwoGzTwjhUMuYMGrVbpV/FZFQ7o3WOVWVQ4lFhU/BV0bKLCocSiwgEH02d7QXob5DgOyWQSx3Ha/2beevTIMLfTSwD0h/oxLrkz6uFTpwiFQjte5q1H361o3I3ievTDZ8/5UpG5Fke3pMKiwtFRn91DKjYBHRYVDug+i2EYhKMxrFg/48dPYu2Sht6qbLtAIZsrC+grA3lvW3IzgWVQNVBQtV/ZMeoOKBSf430OHae4X6Zt51hLphWomvEvzuyHgsUlBO4ggLVjHy8DIIjN9oDBzgGFMNVZBabVeNZAM+q2v3rq3WNrS4VFhQM0WJT8FTRsAjocoMOiwnEQfbYXpN/pKqvs7qW6T8enyVxy26+FT58mFovteFlpJn3XonHuevQj51pLdQf3i3Utjm5IhUWFQ0lKNlFhUeEALZZOyDQtzD6LYF9fx97TcZza2QHZncF8+ax/rvi3wm4DCmUDAhX7ZXPYhfKsgTzZrTzZrVTHztswTKxQi8sIyrMDQiGCxcGFzVCdAYVg6I5us7gfKX2GVVhUOECLRUUqNlHhAB0WFQ41+WWXXpB+p6usR7oXpB+OHSZ78SkAQqdOsrKywvDwcGmWwrYdFm/sXjTOcZzSevSZFteje8dbXV2t4OiWVFhUOJSkZBMVFhUONRYV+W0TwzDcALXJNnStctiFQs0lAHvN+pcPFJS/NpVMYDpOcaCgxrGKx9jmt8lnMuQznc0asAKBPZcRNFtksHpAIRgKY1gWuXSaQj7f1uyPvaT0GVZhUeFQY1GRik1UOJRYVDjU5JddekF6G2SaJlNTU51ZE1E2k+5Vdj9ujpFfdIP30KlTUMXRSNG4ldmbpNbXCARDTJ2uXa25WfV1cDZqL6mwqHB01Gf3kIpNQIdFhQM0WJT8FTRsAq1xmJZFyIoQ6ou0zOE4DltbW0QikV2/oDiOUwzcdy4HyJUF89Wz/m6WQGMDCtlMGjufr9jPse0SQyGfp5DPk0klWz7vRvQ3uEUUA9VBfjBUCvStmoUG9xgoqDug4C5TCARDpWuh4q+gw6LCARosvXtsbalwgA6LCsdB9NlekN4GGYbB0NBQZ96srEe6N5N+YsWdgQkcOkQgHt9xkRspGnfzeXc9+qGzdzc9o1NLhmEQjdYeEOi0VFhUOKDDPrsHh5JNFFhUOECHRcVfQcsmChzQOIthGKWAs5OyC4U6RQa9Gf6MW4sgm9l7GUFVdkC9/Qq5XOn9Hdsml0mTy6Q7et5uHYG9lxEEQ6FdOhNUDgJYtboWlO2zW+tCFZ9V4QAdlt49VpcDdFhUOOBg+mwvSG+DbNvm6tWrHD9+vP0jOmU90mfXvgrA1IL7ZSB86hSO47CyssLIyEhpFN0rGtdQf3Qf1qMDNTm6JRUWFQ7osM/uIiWbqLCocCixqPgr6NhEhUOJpR6HaVmEIlFCkc59uSzk81y+dJHpQ4fKZvZ3W0ZQGfDnaiwpcPfdHlBwf68cNHCc7ayBfM59TSbZmawB2G5dWG/W3wYisVipDWEjRQarWxdaVXUJrGCwKb9T8Vcllt49VpdDiUWFAw6mz/aC9DbIcRyy2WxnKgx6QXpsgls33XT3odubOLhBOrBjNMebSZ/YbT3688WicT6sR/ekMtoGOiwqHB312T2kYhPQYVHhAA0WJX8FDZuADgfosKhwYBgUHAjH4h1bk+44TjFroGwZQSZNMpHAMozdiwwWBwFKLQubGFCo1boQgdaFO2b9S0sMgmCahCPRYhvC2l0L6i038Kt1oScFn+3dY2tLhQN0WFQ4DqLP9oL0O13FID0ZGWQtswZA+MYiaSB0+pTbdziyvcawsmhc7fZrq7dvkVxdwQoEmDrjz3r0ao5uSoVFhUNJSjZRYVHhAC0WFanYRIUDdFhUOLolwzCwAgGsQIBw2RfG0Ta/r2PbdYsMlpYRVAwAlA8U1FoqUL9gYfl+d0Lrwn0tI6ioX9CZ1oVKUvkcq3CADosKh5r8sksvSL/TVVyTPlvM7BgMD5K/fBWA8KnT2LZdSrkwTZO1udR20bip2qM83nr0qdN3EQyFfcGs5uimVFhUOJSkZBMVFhUONRYVqdhEhUOJRYVDSZ2wiWGaBMN9BMO7F07yk8XNGigvEFg/A6A6gyCXSZPY2CBgmhTyO7sR5LLZqiUF2z9SrQsb7UYQrNPqsKx1oREIsjY3x1I4SCgS2dHqsJOtC1U+xyocSiwqHGryyy69IL0NMk2TmZmZ9jusXYCkW8X9Fu469BPWJPm55wAIF2fS+/v7t9ejX98AYHxml6Jxz/mf6l7N0U2psKhwQAd9dg8p2USFRYVDiUXFX0HHJiocSiwqHKDjs0o28ZPFzRoIYgWChKPN9Sf20mRDoVDTLLZdoJDd7kaw+6x/2UBBWU0Bt47A9n7lHQl2LknIVmQItLt14ZfrbC9vXVh71r9GdkC9rgVV2yuWEQSDWMEQ0T06RXRCB/WzcxA4QOceC/7ZpRekt0GGYRCPx9v/RqkVcGzAYDbnprDfkxgEIDAxgTXgprOHw9uz4QvX9+6PfuN5r2jcS31DNQyjgqObUmFR4YAO+mwDHEo2UWBR4QAdFhV/BS2bKHCADosKB+j4rJpNFFha4TBNC7PPItjBNlSO41DI52t2Fch5SwpyVfUFWlhG4O1nFwolhk63LoTdWxduZxPs3rqwqQEFL2ugGGip+CvosKhwgM49FvyzSy9Ib4MKhQKXLl3i1KlT7S0Q4/VIj44ym5oD4MSKe0nDp92icbZts7S0xNjYGKZpbrdfO1Y7SF9fmCexvIRpBTh89m7fUKs5uikVFhUO6KDP7iElm6iwqHAosaj4K+jYRIVDiUWFA3R8VskmKiwqHI2yGIbhBpTBIDSXNNCwavmrXShUBfONtR+sXCJQLGBYtaSgerCgUPa7p261LrS8ZQTBMEbAIhTuIxiu142g/nKDUl2CUJBAsEbXggZbF4KOz6pwgM49FvyzSy9Ib5Ns2957p1ZV1iP9VsKt7D614K6PCp06DWz3DTQMo1g0LgHUn0m/+VxxPfqpM76ODJdzdFsqLCocnjris3tIySYqLCocaiwK/go6NlHhUGJR4fCk4LNKNlFhUeFQY6n2V9OyCFkRQn2dKxTm2Da5XJZ0KoVh225QX2cJwO7ZAZWDBoUd2QWVgwtO2bkXcjkKuRwZOtu6cLdZfyvoLu0I9vXtzB4odS2o3F69jKBin320LgQtfwWNeyz4Z5dekH4nq7xHemIWcNuvwXb7NcMwCIVCAKzOJclnCgRCJsNTtYdfvdZrMz6uR6/m6LZUWFQ4lKRkExUWFQ7QYlGRik1UOECHRYVDSUo2UWFR4QAtFgUZpkko3EdojyKEfquQz1cE/LnqWf+qAoINDRTsMaBQyOVK728XCtiFLXLpLrQuLMsI2BHMV7UurLlfo10LvA4FPrUuVJJfn+NekH4nq6xH+mzi2wCEb7jbytPdFxcXGR8f3051P9Jft2jcDa9o3Dl/g/Ryjm6nxKiwqHAoSckmKiwqHGosKlKxiQqHEosKh5KUbKLCosKhxqKibtjEa10Yimx3QfI4ptrEUWpdWF4PoE5NgVwmw9rKCn3hUHGmP1tcQlBjGUFVZ4Nc1aBCzdaFnUsaqNm60KsJEKxeUlCjyKC3jMCwLJZWVjhx/LhEursfPtsL0tsg0zQ5ceJE+28mxXT3jegwm4lNwlkHbrvbQmUz6SMjIxiGweK13YvGbSwusLE4j2GaHL7rnK+o5Rydlp3Ok1/aIr+4RW4xRX5pCyOdZ9lZANvBsR2wKXu+/Vj+9+q/GZaBGQ1iRgPbj5Hi81j5tu3fjaBZUYSkGZs4tgMFGydn4xQcnLyNk7eh4IBlYARM9yfoPmIZDR+7Yz67h7rpJ6osKhxKLCr+Cjo2UeFQYlHhAB2fVbKJCosKhxKLir+Cjk3azVHZurD293RPjuOQz+cJBAIt8dRtXZirE/DXKDKYTbvdCEqZAbW6FlQVIizk29e68KHv+X5fjtOK/PKVXpDeJgUCHTBtcSb9VjEN6NzmALCKNTpKYHgYcB0lGAwCsOC1X6tTNO5GcT365MnTFaOHfqicox1ybIfCaprcohuM55dSxaB8C3szu/cB9vOegJ3K77lfhQLFwN4L5vssN+DO2W4Anvd+3CCc8ue209x7GZSCdqoC+IrnQTegN+NBcuNRAqMRAmMRrIEQRp2Mi3ap3X7SjFRYVDhAi6Uj99gGpGITFQ7QYVHh8KTgs0o2UWFR4QAtFgV/BR2bqHCAfyyGsf/Wha3Ia124W5vCioGC7C5LDaqOERBYLuLX9dH4BB4w2bbNhQsXOHPmTJuru7tB+qzljnTes9EPrJbWo3ssCwsLjI2N7100rtR6zd9U93KOiYmJlkZm7a08uYVUKRAvBeXLW+6Mch2Z8SCB8QjB8SjWaB+J/BYDQwOYlgmm4QajxR/3OTW2Ve5H3sZO5bFTOexUnkIqh72Vx07mtrdvbf+dggN5B3sji73hw8BBwMQIGBiWWZpZJ19WNMPBnXXP7bOQRsAkMNpXDNqLj20O4P3yk4PEosKhxNKxe2yDLCo2UeBQYlHh8FgUfFbNJgosKhxKLCr+6rGo2ESBQ4llvxztaF1YKBS4cOEChkD2h1/Xpxek38nyZtINt3flidXK9mvgjuaMj4+zNr+1d9E4bz36Pf71R6/m2E/qh+M4ZC6tk/jyLOkXVtwp7FoKmATH+giMRwmMRUpBeWAsghkJVBwvZtuYptmx9CnHcXCyhWLw7gbuhWQOO53HDJoYAcsNuANls95eEO5tswx39tuqn8ruOO5AQGlGPlf1WHxO1d8LmTzL1xboL4QprGTIr6Qhb5OfT5Gfr5GCVC+AH+lzA/jA/m5KrfiJ31JhUeFQY1GRik1UOJRYVDiUpGQTFRYVDjUWFanYRIVDiUWFQ01+2aUXpN/JKq5Jny241R+nFr32a5VBumEYLBVn0cdmaheN21xZYm3+NoZhMn33Pb6jehzNOKyTK5D65iKJL8+Sm9sOFK2BEIHxSCkYDxafW0PhhmZ398PSqgzDwAgHMMMBGN7ebhcHC/x8H4JuMN+MCoUCt6dSjBRHzZ2CQ2EtTX457a7nX94qPqb3DuABMxbEGghhDYbdx/LnxUcjsnMtVTeuTT2psKhwqLGoSMUmKhxKLCocSlKyiQqLCocai4pUbKLCocSiwqEmv+zSC9LvVBXykFwCYDbnFoQbuuW1Xztd2s1LuVi4tvt6dG8WfeLEybasS2km9aOwkSHxldsk//42dtIdeDCCJtFXTRJ/7WGC462tl7/T04Q6IcMySrPjnB2u+FtFAF8evC9tkV9Luyn9yRx2Mkfudv0SoUbQxBoIYQ6EsQZDWANhzP4gCSPN+EumCQ53rhdrLalcHxUONRYVqdhEhUOJRYVDSUo2UWFR4VBjUZGKTVQ4lFhUONTkl10Mx3GarEZ1Z2tjY4PBwUHW19cZGBhoy3s4jlOaIW3b6NLmPPzOWTBM3vaK7+LKwnn++HdsDMfhzJe/RGB0tLSrbdv81/c/xe2L6/zDnzrH3Q8d2nG4z/5fv8+3PvfX3P99P8Q/+Ml/2hbkvWaNszc2SXx5ltS3lkpF0qyhMPHXHib2qknMqH8FO/yewb7TOfzyWcdx3PX56xkKG1kKGxkK6+4afO95YSPTUME9azBM6Fg/oaMDhI72Ezoc33ca/X6lcn1UOECDpSP32CakYBMlDtBhUeFQ8lkVm4AOiwoHaLAo+Sto2ESJA3RYVDjuFJ9tJg7tzaS3Sfl83pdG9nWVmAfAiY5xK3GLwytgOA7W0BDWyEhpN7e9gl1Kd69XNM6r7D7ThvXoHof3U/7hcQoOW99eIvHlW2SLs/0AoeMDxF83TeSeUQzL3w9bPZZOS4XDkx8+axgGViyIFQvC4fr7OTnbDdrLg/diYJ9b2iI/l6SwnmHrWxm2vuVmjBAwCE33EzrWT/joAKGjA1gD7fuMqVwfFQ41lrbfYxuUik1UOJRYVDg8Kfiskk1UWFQ41FgU/BV0bKLCocSiwuHpoPlsL0hvg2zb5sqVK+2tiun1SI+Pk8ht8vIld+Y5dPpUZRDsOFx+YZZcpkAgaDI8tTNVPLG6wurtWTAMZu5+SVtwHcdhcXGRiYkJDMPATuVIfm2OxOO3Kaxn3J0sg+jLxom/7jChmd17RPrJ0i2pcECHfLZMRtDcTqev4lhYWGBqcJT8rSTZ6xtkr22Svb6BncqTvbZB9toGCWYBN9MidMydaQ8fHSB4OOYW1vNBKtdHhUOJpdP+uptUbKLCocSiwgE6PqtkExUWFQ4lFhV/BR2bqHAosahwwMH02V6QfqfKa78WGwRnk7NrESBZsR4dwDRNnFQYgLEjcbflWJW81mvjR4/TF4+3Bdc0TaampsgtpFh//BapJ+dLrcHMWIDYA4eIP3i4rTOj1SzdlgqHksptEjg1RN+pIcC94eWX026QXgzcc/NJCmsZttYW2Xp60T1AwCQ0Eyd0tN+tNB8PYfaHsPpDWP1BjGDjN26V66PCAVosKlKxiQoH6LCocChJySYqLCocoMWiIhWbqHCADosKh5r8sotEkP4Hf/AHvO9972Nubo6Xv/zl/N7v/R6vec1rau77h3/4h/z0T/90xbZwOEw6ne4Eqo689mvhGKTL2q+VVXYHN7iZv7oOwPjR2msfvKJxM/f43x/dY0i/uMrml26SvbBe2h6cihF//WGiL59ouhp5qzz5fJ5AYGd18U5KhUNJ9WxiGAbBsQjBsQix+ycBsDN5sjc2SzPtmeubOFt5slc3yF7dqHl8I2xh9Ycw+4Nu4F4K4oPuY9wN6M1YEEwkro+SnyixqEjFJiocSiwqHEpSsokKiwqHGouKVGyiwqHEosKhJr/s0vUg/eMf/zjvete7+NCHPsQDDzzABz/4Qd7ylrdw/vx5JiYmar5mYGCA8+fPl35XdIy2F1HwZtKDAUjD1IJbiKu8Rzq4jnL78ipQfz26N5Pud390O1sg9Y0FEo/Pkl9w28RhQN+5UeKvO0z45GBXrp3jOKysrHS9t6MKhyeVwh+N2sQMB+g7PUzfabf6vGM75Je23Jn2mwkKG1nsRJbCpvtD3sHJFMhntmBpa3cQA8xoEDsEgb6Q28s+ZLm960PFHvblvwctt4d96Wf7dzNkufuELIyQhRmyINB4aw4lP1FiUfBX0LGJCocSiwqHJwWfVbKJCosKhxqLgr+Cjk1UOJRYVDg8HTSf7Xp19wceeIBXv/rV/P7v/z7grik4cuQIP/dzP8ev/uqv7tj/D//wD/nn//yfs7a2tq/360R1947ok/8UnvkE77nve/nE8rP88e/YmLbD6S9+keDk9uCGYzv8x3c9Si5d4Ef/5WsYna5MZ09trPPvf/YnAHjHf/wo0YHBltHyaxmSX7lF4u/ncLaKLdTCFrFiC7Xqdcg99dRuOY4boBc2s9ibWQqbOfd5ouz5ZpZCIoudyEG774ombiAftnYG8eFigB/eDuq9v5vFfYyQWfbastcHNaqa9tRTTz311FNPPfVUqTumuns2m+XJJ5/k137t10rbTNPkTW96E1/5ylfqvi6RSHDs2DFs2+aVr3wl73nPe3jJS2oXPMtkMmQymdLvGxtuGmyhUKBQKADuTLxpmti2TfmYRb3tXnn/etvz+TypVIpoNFo6BrgDEOWqt92yrFIrgWqWUouBzXkMYNbJcGgFTNvB7O/HGB2hUCiU9l+ZS5BLu0XjBsb7Si0BPPbr3/4WAKNHjhEdGKx7Tp6t6rE7jkPuRoLU47fZ+vYSFNGtkTCxhw4Re9UUedMGy9ph93rn2uj1aPY6edeouk1DO67TboxeOkw4HC5VgdzvOTV6neptdxyHVCpFf3//jv07eZ1s2yaXyxEMBrEsq6Vz8uQVEHGCBuZIGHMkTHCXczIwyG9myG9kyCUzWI4JBQcj71DIFnCyBZycjZMruLPzORsnW8DO2cXtNuTt0nYnV8DOus/JF8/bBidTwMkUqKRtUUYx+C8F/WZl4F+1zQwHIGhAacZ/53Yv+LcCrh0zmUxFClervuepmc8TwNbWFpFI5YCf3/fyRs7JcRwKhQLBYLDiGM2eU6ufJ8dxyOVyhMPhXc+1E9fJYwmFQliW1bZ7+V7nVCgUSveT3f4Xd+I6FQoFkslkxfeCdtzL9zonwzDIZrOl+2sr59TqdTIMg1wuV8Gyn3Nq9Trt9r+409epnKV6/05eJ+87QTQaLf0P7dR3o+rt5d8LTNPs6Hej8nOybZtMJlO6n3TrO6zHmE6nSyz7PadWrxO499nqQm2d/g4LxaW16TTRaLSmfTt5nbzvBaFQaAd7M3PjXQ3Sl5aWKBQKTE5OVmyfnJzkhRdeqPmau+66i4985CO87GUvY319nd/+7d/mta99Ld/+9reZmZnZsf973/tefuM3fmPH9kuXLhEvFkkbHBzk0KFDzM/Ps76+vWZ6bGyMsbExZmdnSSaTpe1TU1MMDQ1x9epVstlsafvMzAzxeJxLly6xuLjIyMgIpmly4sQJAoEAFy5cqGA4c+YM+XyeK1eulLaZpsnZs2dJJpPcvHmztD0UCnHy5EnW19eZm5vjxOpNwsD19Dozxcru9uHDXLx4seKcLj7jHiM6ZnHp8sUd5/Ts418CYOLUWYBdz6nc0Urn9MKLBK5lCD63hbW03fs6PxUkd0+EwkyIzcAWp8MWczdukUqlSjeU6nPyFIvFOHLkCCsrKywtLZW2+3Wdpqen2draYnV1teLD0o7rtNs5OY7bmuHs2bO++l7N67THOdm2zerqKg8++CBbW1v7PqdWr1Mmkyl9MThy5EhL59TKdVrPJVhMLJZYhkaHOHToELdv32Z9PbHjnG7cuFE8JwuwSud0+fLlSt87PE0sFOHSCxewMwWMvAN5h0OjkxgFmLt+u7TNyDsMxwfJb+VYW1ol4FgYeQcjD2EziJ3Jk0/nStsAcHAHBrIFIIevChqYoQA58hAyIWDgBAz6+qOEoiHWtxI4AUrbRyfHsCJB5pYX3G1BAwJw7PQJCqbD9ds3IGCAaTR9nSKRCFtbWwwPD7O6ulra3o57+V6+5zgOw8PDDA4Ocu3atdJ2P+4RzZyT96X6xIkTjIyM+H6PaOacPJaxsTGOHTvWtnt5I+dUPmDernt5I+d08+ZNbty4Ufpe0K57+V7ndPz4cWZnZ8lkMqX/xe3+n1vvnI4dO8b6+jqrq6sVQXqnr5PjOAQCAU6dOtWx/7n1zskb4Lr33nvZ2Njo6Hej8nPK5/OsrKwwMjLCqVOnOvrdqPqc1tbWSp/j8fHxjn43qj6nF198sXQ/6dZ32JmZGaLRKC+88AJ9fX2lz06nv8OCu/w4EAiQz+dLk6D7PadWr5NhGDiOw+HDh7l169a+z8mP6+Q4Tul7bPU5jY6O0qi6mu5+69Ytpqenefzxx3nooYdK23/5l3+ZL37xizzxxBN7HiOXy3Hu3Dl+7Md+jN/8zd/c8fdaM+neRfHSDPwehcxms1y8eJHTp09jWVZbRrfM3z4NWys8cPoM3/+FFP/oyw4Db/thpt797or9H/vTF/nW529y7yOHef3bz+w4pz/+lZ9n6cY1vv+f/wp3PfSGhke3nK0Cqb+fI/GV29ibxQ+aZRC9z22hZk1Wtnrr5IxSo9epkzN/6udUKBS4ePEid911V+l97/Rz8nSQrlO9c3Ls7cDezuQppPPFYL04s5+zsdN5bC8TIGvjZIvbM/nSTP/2a9xZfidntz/1v7iuvzJ1fzu93wwHIFRc61/cTtBgYWWRqSOHMUOB0t/MkIUVDpYGC7x2fCrXqZzloPhe75wa257L5bhw4ULF94I7/ZwO4nXqnZNRyjT1vscGg8EDcU6NsPfO6c49p0KhwOXLlzl9+nTFoJ/aOSUSCYaGhvTT3cfGxrAsi/n5+Yrt8/PzDZeuDwaDvOIVryjNIFcrHA6X0obKZVnWjvQM76JXq9nt3j/g6veo17ev1nbDMOpvx4atFVZNk61Chpll9299p3f2Bly6sQnAxLGBir+ZpsnW5gZLN9yZn5lz9+55TgC5uSSJL98i+dQC5F1nN/uDxB88TOyBKax47RZqjuOQzWYJhUIVH57dztWv61G93XEcMplMTRbw8TrtcU7lNvHT9/a73TtmK+fU6vZaftLKOe3Fvts51WLpxnVyHDf9sdpfK9gtIFh82h/ynrYsx9lO53eybkCf9ZYAFIN5O7Md3JcGAYoB/vbfqv6eLZSWxJC3cfI2hVR+V5Zq9QFrnN99J9PYDu6D2yn/plfgL2RVPpY9N71lA14hwKpjuEUBTRzLKPmJ3/eIZraX++tu+3fi89QoS7s/T6Zp1vy/063rVOt7Qaev027/izt9nXZjqbU/tOc6tfK/2O/rVG2TTn43qmb0/HU//4v9vE5+/C/24zoBe/8vboDFj+31vhfsxt6O61T+fboWZ7c/T41sb8d1KvfZWksBGlVXg/RQKMT999/P5z73OX7oh34IcEdFPve5z/HOd76zoWMUCgWeeeYZ3vrWt7aRtDkZhlH3n44vSro9oWeD7heg48sWkCd8urJHumM7LN5wU3PHjuzsf37zhW8DMHJ4htjQcN23c2yH9PkVEl++RebiWml7cDpO/HWHib5sHCOwe0VFx3HY3NxkZGSkfXZpUCosKhzQAZ9tUEo2UWHpJodhGO7MdWh7TeLGSpqRkcG6/6gakeM4UHDcIN6bta8K4t1BgfKZ/0Lp73amwNZGkrAVKtUBcHLb+5Vm/22ntPbf9/T/cgWM7UyA6qC/GOCbOwL92s/NGgMGRtDCsHa/9ir+qsSiwgG9e6wyiwqHEouKv4KOTVQ4lFhUOOBg+mzXW7C9613v4qd+6qd41atexWte8xo++MEPkkwmS73Qf/Inf5Lp6Wne+973AvDud7+bBx98kNOnT7O2tsb73vc+rl27xj/9p/+0m6dRIdM0OXnyZPvewGu/FhvGKjhMLLtTUtXt19YWUqWicaOHawTpxf7oR15Sv/Wa4zgs/+fnSL+w4m4wIPKSUeKvnyZ0bKBh5zNNk7GxsYb2bbdUWFQ4oAM+2wSHkk0UWFQ4wD8WwzAgYGAFTPBt3t+VNwBQHrjb5UF8rlj4L1u5zS4V+isv+lf9vJghUF4EECDvYOfz0GQ2QFOyjLKAv3x2v3IgYD24Vgr6zbqDApUDAd7xmmkNuJtUfFaFA3r32FpSYVHhAB0WFX8FLZsocIAOiwoHHEyf7XqQ/iM/8iMsLi7yr/7Vv2Jubo777ruPv/7rvy4Vk7t+/XrFjM3q6io/+7M/y9zcHMPDw9x///08/vjj3HPPPd06hR1yHIf19XUGB9vUB7wYpN+K9jO1uoVlO5jRKIGqJQKL191U9+HDUQxzJ4cXpHup7rWUn0+5AbplEH/dYeIPHiYw0tc0suO4VRfLi1x0SyosKhweS1t9tgkOJZsosKhwKLHs5q/eAIARMCHSvn9xju0UA/Y86cQWISPopu6XDQTY1QF+dmfwb+dqDByUPS9lBRQcnEIeJ42/HQHKZbBzBr9sIMCsszSgvKOAGTQhYJIjTzjWV8wMqMoiqPH/qB1S8VePpXeP1WRR4VBiUfFXj0XFJgocSiwqHB7LQfPZrgfpAO985zvrprf/3d/9XcXvH/jAB/jABz7QAar9y7Zt5ubm6O/vr7uOoiUlizPpoTAzSykAQlWFEgAWikH60NTONfnpZIKFa5cBmLmn/kz61vPuDHrf6SGG3traCFUqlaKvr/kAvx1SYVHhaLvPNiEVm4AOiwoHaLAo+KthGsVe9iaZbILYiP9fDEpZAdndZvq3lwok1xNEgmGcnLNzcKAq+LfLBwIKxZEAh+2igu1UwNgZ9NcM/itn+isyB8pbCpaOtf03issDFPwVNHzWk4pNQIdFhQM0WJT8FTRsosQBOiwqHAfRZyWC9J6aVMIttDdrmcwUuwaET53asdviNTdIP3LX+I4vj7MvPAeOw/Chw8SHR+q+Vfp5typd37nGWwbUkmEYTbUdaKdUWFQ4lKRkExUWFQ7QYlFRO21SnhXQSAWA/n2+j1NwcPI7Z/rtHYF+jefefuUDAbUyCHJlgX/ewcnnKWztE7gRmZQC9rmqGgFmqHbdgPKlAWadpQEV+wU6lxXgl5Q+wyosKhygxaIiFZuocIAOiwqHmvyySy9IvxOVcAvH3SLP/cUe6dXr0d2icW6Q3j8RxHGcikD9xnPPALunuhcSWbLFY/Sdqx/INyLHcdja2iISiUikoSiwqHAoSckmKiwqHGosKlKxSSschmVgWAHYmXTlG4vjOJC33XZ/1TP8VTP9dul5jaA/u8tSglxZ1wCbjhQN3BnoVwX9xYDfCRiEEgk2b9zACgfq1ggwd2QW7F00sBmp+KsSiwqHGouKVGyiwqHEosKhJr/s0gvS2yDDMIjFYu1z2MQ8DnCrsMX0shukh6pm0tcXt8ilC1hBk77BnXMwpfXou6S6p19YBcet4h4YbP3bWzqdJhKJtHwcP6TCosLRdp9tQio2AR0WFQ7QYFHyV9CwiRIH7GQxDAOCFlawvWmITsHeDvazedaX1+jvi1fM/FcOCtTKHKhVQ8DLGrBL7UeB0v6wd9HAEJDgRvMnZRlVBQLrFwGs6BRQo24AAZNUOkFg1MEMBUqtBAmYXfk8qfisCgdosPTusdocoMOiwnEQfbYXpLdBpmly5MiR9r1BcpFl0yRbyDFd7JFe3X5t4foGAGMzccbGK1MuMqkUC1cuAbvPpG95qe53tzaLDu6HZ2Sk9eP4IRUWFQ7ogM82KCWbqLCocIAOi4q/go5NVDiguyyGZWJYJvSBRYixsajv7+HYDk5+924AtWf6d2YO2HWyCXYWDSzgpAvurz6cQ7p6g1c0cJduADuC/oqBgRqDCLW6C5QtD1DxWRUO0GHp3WN1OUCHRYUDDqbP9oL0Nsi2bVZWVhgZGWmpl3BdJea5FQwwuQrBAhh9fQQPH67YxVuPPn60n2QySTQaLY0u3Tr/HI5jMzgxycDYeM23cHI2mQurAERaTHUHN/UjlUpVcHRLKiwqHNABn21QSjZRYVHhUGJR8VfQsYkKhxJLuzgM08AIWRBqPCugWZ8tLxpYP72/clvNpQRVgwh2tuC2EPQyADpdNLDYStAMmRA03VaM4cCuNQLMWksDqraZZdkEWM21ElTxVyWW3j1Wl0OJRYUDDqbP9oL0NshxHJaWlhgeHm7PGyQWmA0EmCmmuodPnsSockiv/dr40X6y2SzR6PZswo3n9051z1xew8namAMhgtM7e6zvR9Uc3ZQKiwpH2322CanYBHRYVDhAg0XJX0HDJkocoMOiwtGsz1a0EgT8WiTgOA5ra2sMDQ1hGEZl0cCqAN+utTSgqmjgdhvBXTIHcvaOVoKFsql8PzIDKmRQt0aAWatuQNAkXchg98eKmQG7tRssPm9j0UAFn+3dY7U5QIdFheMg+mwvSL/TlM9Aeo3ZwYFSZfdQraJxxSB94tgAw8OVQfbNYtG4Iw20XovcPeLL6JhhGDIfHBUWFQ4lKdlEhUWFA7RYVKRiExUO0GFR4VBStU38LhpYS7sWDawxEFC/3WBV5kDVayuzAgo42ebC//VmTyxgVgb9tToAlBcDrFEjoGKZQPFvA8GYW/AwWFy68R0ulc+xCgfosKhwqMkvu/SC9DtNyWJl92CQmVJl9zMVu6wvbpEtFo0bnoqwublJPB7HMAyy6S3mL18E6q9HdxyHtNcf3YdUd++YiUSixNFNqbCocChJySYqLCocaiwqUrGJCocSiwqHkrphk3pFA/1mKS8aWNEecI+ZfjtTIJvKEMDaexChopWgjZ1v8/IA09gzvb/mTH+w2EawTuZAeWZBt4oGNiqVz7EKhxKLCoea/LJLL0hvgwzDYHBwsD0O6/VID0e5f8kdJa5uv1ZeNM60TGx7+5/IrfPPYxcK9I+NMzgxWfMtcreTFNYzGEGTvtNDvqGXc3RbKiwqHG312SalYhPQYVHhAA0WJX8FDZuADgfosKhwKPmsik3AX5byooHNyHEcNjY2GBgY2PP6lIoG1mgLWNkisE6xwFpFA2u0GSwtD7CdzrUSrMoAiFNg5cvPVbQSrF8XoLyGQJ3MgRZaCar4rAoH6LCocCjdY8Efu/SC9DbINE0OHTrUnoMXe6TfNk1mlotBelX7tfKicZ7TerpZXI9+ZJeq7t4sevj0EIZP7XKqObopFRYVDmizzzYhJZuosKhwgA6Lir+Cjk1UOECHRYUDdHxWySYqLM1wVBQNjAXbwlMqGlgW4Nu7rPevrCFQO+jf0XkgV4C8s/2edVoJZnb2Adi/ikUDd3YA2KUbQDHoDwRNtkKZuq0HveMRaK5oYDNS8VfQYVHhAJ17LPhnl16Q3gbZts38/DyTk5P+VxhMzGMD2U2HUB4IBQnOzFTsUl40znEcNjc36e93A/YbDfRH91qvRc6N1t2nWVVzdFMqLCoc0GafbUJKNlFhUeFQYlHxV9CxiQqHEosKB+j4rJJNVFhUOKpZzEAAIu37mu7Yzs6Z/mIgX8gUWFtcYSDWD3lnZwHBRtsN1iga6KShbXOvXivBOmv/6xcELO5XvpSg6rUEDZKZFP3Dg5hdrhWg4rMqHKBzjwX/7NIL0tsgx3FYX19nYmLC/4MnF1iyLCaX3Ftc6MRJDGt7truyaFx/xUtzmTRzF18EYOae2jPphY0suZsJwJ/+6D3dGWqrz/bUk8/q+WtPd5p6PtuTmgzTwAhbEN6ZMVkoFNgwFpk8M45l7T+j0i0a6NSf6a9VNLBGm8HcVhbLMUqZA3bVwEInWwlugjtjXx3079INoGbmQFWhQbMqg6DZVoLf6TqI99hekH6nKfH/b+/P4+Q6yztt/Dqn1t53Sb1oV0uWbEk2NjbGMZBgcGAwkBC2MGCbxRBsJ46HN4kzCcYM74AnBAL8DJkhGZvhDQEMAZIM2CEGnJgYsA1abO371lJ3q9Vbddd6nt8f1VVd1V3V6qWq66v2c30+JXWdqjp1nfvcVd33c56ljzN+H11n0nfDGzbkPZydNM7v0tReg+M41NfXA9BzcD9eKkltcwuNywt3CYnuS3d1D3TV4qsPlkw716PSqLioeCihFBMVFxUP0HJRQSUmKh6g46LioYRSTFRcVDxAy6UUpCcNTE9+V85rm9lJA6c0BEwfJlBoroDpRX/BRoS8SQMNJpkkNV7Gg3Ip3L0/s5Rg0MeFaVf9J4cLTJtDoNDqAwtcSnCp5WupKFVcbJF+qTF6Lr1GenZm9ynj0Seuord01eLzuXkTomS7um++omjrXDm6usPcJmYpNyouKh5KKMVExUXFQ81FBZWYqHgouah4KKEUExUXFQ81FxVmE5P5Tho4F7yUx/CFIWrDNZD0ChT7k3MEFGwcmDa5YKFhAqnJsQAeizdp4EXmCHCLTBaI3yWajFHTUDu5qsC0ngULmzTwUqRUn2NbpJcBx3FobW0tzxdspC+vSA+unzqz+0RX91WTXd0zYzNO7Z15fXSTSBE7NAiUbum1XCo9RiQXFRcVj7Lm7BxRiQnouKh4gIaLUr6CRkxAxwN0XFQ8lHJWJSag46LiARouSvkKIjFxHXwhP76aQNniUnDSwALFvhdPER0ZJ+j6YVrBX+znyVUFyFk6sNikgXPhotML+py84t+d2uW/yJV+d2qxX7QRwQWXJZeztkgvA67r0traWp6dj57jjN/PS/vTd0NTurv3TSy/1jYxHt1xHOrq6kjG4/Qc3A8UH48ePTSISXj4GkIE2mtKqp3xUEDFRcUDypyzc0ApJiouKh6g46KSr6ATExUP0HFR8QCdnFWKiYqLigfouKjkK+jEZDE8HMdJj3f3uxedNLB2Ae9TfNLAnGK+0JX+IisFTH1ttldA3qSBKUw0vSpVagHuRZmYNJD7roOqyjbqlCpXbJFeBjzP4/Tp03R2dpZhdvc+hn1Bwgkwfh/BlSuzDxlj6DuRnvStbeJKujGGwcFBRs6cJJVIUN3QSFN7Z8FdZ5ZeC29uLnlLVMajsbGx4q1cKi4qHlDmnJ0DSjFRcVHxUHJRyVfQiYmKh5KLigfo5KxSTFRcVDyUXFTyFXRiouJRCpeZJg0slUe2V8C0FQCKrwbgFRpCUKD493IbAqZOGlialaMXRKlyxRbpZcAYQyQSSSdoKUlEITYE8fSkb6ZrBU5gcp3Oob5x4uNJfH6X5o7JK+HBYDC7PnrXlq0FE8YYw/jEpHFVZejqnvGoNJ7nMTo6ysjICGNjY8Tj8YveEolEwe3JZLprUOY8X+z/Qttc16Wuro6Ghgbq6+uz/+f+XF1dXfZfCGXL2XmgkCcZVFxUPEDDRSlfQSMmoOMBOi4qHko5qxIT0HFR8QANF6V8BY2YgI4H6LgU88jtFVDeSQMNJpkiGU1w9MARTOVHRgClOT+2SL+UiPSSAqouuIAp0NU9f9I4SH9IampqOD1RpK/cXLire+L0KN5wHCfoElrXWHL1jEe5SKVSRCKRbAGee8vdpvRLB9Leg4ODDA4OFn2O3++fVrhPLeZDoVDFW7tLQbnzZC6ouKh4gJaLCioxUfEAHRcVDyWUYqLiouIBWi4qqMRExQN0XBQ8HJ+D4/Pj8zuYOl/FezlA6eJii/RLidFe+nw+OtITsFO/6fK8h/uOT580zhhDf18fZw7sA2Dl5YUnjRvPdHXvbkqP6SgxxhguXLhAU1PTvD5Axhj6+/s5ceIEw8PDCyq+HcchFAoRCoUIBAIEg8F53Xy+yS+DYv9f7LHz58/jui7Dw8MMDQ0xPDyc93MkEiGZTDIwMMDAwMCMxxUKhQiHw4TD4ezPhbYV+jkQCEg0Xiw0T5aii4qHmosKKjFR8VByUfFQQikmKi4qHmouKqjERMVDyUXFQ41SxcUW6WXAdV1WrFhRhvHovXkzu4e7u/Mezszsnpk0LsNwzymS8ThV9Q00d66kEJn10cMlXnotl3B4bmtjDA4OcvToUY4cOcLRo0cZHR2d8fmO41BbW0tdXV32/9xbZlt1dTWxWIyqqqqKf7n5/f4ZPRKJBCMjI3mF+9T/x8fTC3XGYjFisRhDQ0Pz8nEch2AwmG3AuNjPhbbV1tZSVVU175jA3POknKi4qHiAhkvZvmPniUJMQMcDdFxUPJRyViUmoOOi4gEaLkr5ChoxAR0P0HFR8ViKOWuL9DLgOA6NjY2l3/HoOc74fXRNXEkP5Sy/lp40bqJIz7mS7jgO/UcOAdC1+fKCxWByKEbi9Cg4EL6sqfTeEx7V1dUzPicSiXD06NHsbeqVY7/fz8qVK2lubi5YgNfU1Mz6w3kxl8VgNjEJBAI0NzfT3Fx8noBEIkE0GiUWixGNRvN+LrSt0OPGGIwx2UJ/ITQ1NdHR0ZG9tbe3z/rLajYxWSxUXFQ8QMelbN+x80ApJgoeoOOi4gE6OasWEwUXFQ/QcVHJV9CKiYIH6LioeMDSzFlbpJcBz/M4duwYa9asKW2LTqSPvliADTHwXIfg6tXZh4pNGmeM4cjOXwLQtblwV/fMrO7BlXX4asszEYUxhoGBAZqbJ2eOj8ViHD9+PHul/Ny5c3mvcRyHzs5O1q5dy7p16+jq6iKQM1FeKV0qQak8AoEAgUBg3ss9ZIrzQ4cOsWzZsuxEeZmCfS4/R6NRLly4wIULF3jhhRey79Hc3JxXuK9YsaJg4a5ybpRcVDyUXMr2HTsPVGKi4qHkouIBOjmrFBMVFxUPJReVfAWdmKh4KLmoeMDSzFlbpJcBYwzxeLz0Y3xHzxEdThep0RWNODkzB2YnjeusyU4aB5BKJuk9chCAlcXWR9+bvjRfzq7ukJ7p8NixY9kr5adPn8bzvLznLFu2jHXr1rF27VpWr15dtm40Ki1/Ch6O4xAIBPD7/bS0tODzzX/9irGxMXp6ejhz5gxnzpyhp6eHwcHB7Jj6559/PvvclpaWaYV7MBiUiEkGFRcVD9BwKdt37DxRiAnoeICOi4qHUs6qxAR0XFQ8QMNFKV9BIyag4wE6LioeSzFnbZF+KTHaixlKF1Demvy1zjOTxrWtrs/b3nv0MMlYjHBtHa0rVzMVL54iengQKP3Sa57n0dPTkx1XfuLEieyyZRmampqyV8rXrFlDbW1tSR0K4TjOgsdNLyWPUlJdXc369etZnzMUIxKJTCvch4aGOH/+POfPn2f37t3Z57a2ttLe3k5jYyONjY00NDRkb4u93IjK+VHxAC0XFVRiouIBOi4qHkooxUTFRcUDtFxUUImJigfouKh4qFGquNgi/VJitJfwhXSRPnX5tcykcbkzuwOc3JMugDov24JToPtH7OAgJA2+5jD+5Qtr9THG0NfXl71SfuzYMaLRaN5zampqslfK165dS1NTecbAz4TnedluKJXsEqPiUW5qamrYsGEDG3JydnR0dFrhPjw8TH9/P/39/QX3U11dnS3Ycwv4zM+lXk9e5fyoeKi5qKASExUPJRcVDyWUYqLiouKh5qKCSkxUPJRcVDzUKFVcbJFeBlzXpaurq+QJm4yco3liLrXGTZPjy40x9J+cPmkcwKnM+uhbii29lu7qXnXZ/MZNXGwG9lAoxJo1a1i7di1dXV10dHRU/IPsOA51dXUVHz+j4gHly9li1NbW0t3dTXfOCgUjIyPZgn10dJShoSGGhoYYHBwkHo8zNjaW7U5fCL/fn1e0V1VVZWefzyw1V+i+31/4a7DY+Ukmk4yPjzM+Ps7Y2Fje/4V+TiaT+Hw+fD4ffr8/+3OxbYWeA+nhIrmPzWZfU7ct9Pyq5Oxi5+tMqMRExUPJRcUDdHJWKSYqLioeSi4q+Qo6MVHxUHJR8YClmbO2SC8DmaXASs25sfN09af327rlyuz24f5xYmNJXL+TN2mcl0pxZv8eALoKFOnGMzlLr82uq3vuDOxHjhzhwoULeY/7/X5WrVqVvVLe3t6+oDHO5cBx0uukVxoVDyhfzs6Furo6Nm3axKZNm/K2G2OIRqN5RXvm58z90dFRkslktgv9XPD5fEWL+ExBnlt4x+PxUh72ouI4zrwL/IW8brbbZvvLVSFfM6h8jlU8QMdFxQN0clYtJgouKh6g46KSr6AVEwUP0HFR8YClmbO2SC8DqVSKw4cPs379+tIVqPEIPWNxaqPgORBeuy77UO/EePTWzlp8/sk/cnuPHSE+Pk6gqoqWlaum7/LUCN5oAifkI7S2oehbnzlzhl27di1oBnbP8+jv76e1tbXirVwqLioeUKacnQeFYpIZ21NVVcWKFSsKvi6ZTGbXjh8cHGR4eDhvubncJedyZ6SH9LFHIhEikcisPR3HIRwOU11dnXUr9nMgECCVSmVvyWQy7/7FtiWTScbGxvD7/dOec7HXTp2Y0RhDMpmcNjeECo7jzLrAj8Vi2bkKMiscXOxW6Ll+v39Brd0qn2MVDyUXFQ/Q/o59sbuoeCi5qOQr6MRExUPJRcUDlmbO2iK9TEz943jBjPYyMBqkBhhqDePmzHpeaH10gFOZ8eibLsfnm36qM0uvhTc24fgLJ1EsFuORRx7Ju3q4fPny7JXy2c7Anlm/UKFLjIqLikeGkufsPJhvTPx+/0XXk5+K53lFC/hMgZ/psjS18A6Hw4v2C8kYQyKRIBAIzDkunufhed68GgeKNRgkEgmMMXNuMJhtI0IikSCRSMzq+M6cOTOneBRjtkV+sZvrugwODs7YYFDufFH6PlFxUfHIcCl/xy5lFxUPNReFfAWdmKh4KLmoeGRYajlri/RLhUgf4xPLr0W68pdKyxTpy6bM7J6ZNG7VFdsKJkq2SN9SfOm1PXv2EI/HaWho4DWvec28Z2B3HGfRZ+cuhoqLiocSixkT13WzRbcyC4mJ67q4rovf75fpkpbLfBsREokEZ86cobm5mVQqlS3s4/F49ueL3VKpVNZjLg0D88Xn882p8J9LD4Hcm8IfSyrfbSoeSijFRMVFxQO0XFRQiYmKB+i4qHioUaq42CL9UmH0HGYw3X3DrJ5cfs0YU/BKuuelOL0vPR69enkHnuflXclJXoiSOBsBJ30lvRi/+tWvALjmmmu44orC66zPBs/z6Ovro62treJdYlRcVDyUUIqJiouKRzlc5tuIkEqlCIfDdHd3z7tbW27BP9NtNoX/2NgYQMHHct8vlUpNW/GilDiOU7biP3d4wMXOvUrOqngooRQTFRcVDzUXFVRiouKh5KLioUap4mKL9DLgui5r164tbcKO9lJVYPm14f5owUnj+o4fIzYWIVhVxYZtV067upK5ih5cXY+vZvoYcoDz589z4sQJHMdh+/btC9J3HIfm5vnNIF9qVFxUPKBMOTsPlGKi4qLioeRSinzNjG1faA+DzDj/QmPbM4/Nt/ifzS0ej2OMyb5fPB4v++SGfr//ooW/z+cjGAzOu1GgFGMKVfIV7HessouKh5KLSr6CTkxUPJRcVDxgaeasLdLLRLFlnebNaC9NF9Inu3HT5BXtzFX0qZPGndqTXnqtY9MWQgXGjGeXXttcvKv7zp07AVi/fj319fVFnzcbMld4FFBxUfHIUPKcnQdKMVFxUfEALReFfIWZY5J7ZbucZLr8l6rwL9SYkDs8IDP54Pj4eNmOaa7DA+YzjMDn8y3qH5cKOav0GVZxUfEALReFfAWdmKh4gI6LikeGpZazGkezxPA8j4MHDy6oK+ZUomdPUDcGHtC+5Zrs9t7jw0Ch9dHT49G7Nl/B2bNnWbZsWbZ1yYsliR0ZAoovveZ5Hjt27ADgyiuvXLC/53n09vbmeVQKFRcVj4xLqXN2vh5KMVFwUfFQclHJ14xLpWOSKTYHBwfL5uF53pwK+wsXLmSXMZxLT4IMKsMDFjp/QGZ4gErOKuSrmouKh5KLSr5mXFRiouCh5KLikXFZajlri/RLhLPHjwPQ3whbmruy2wuNRzeex6m9LwDQteUK2tra8q4WRA8MQsrgbwnjbys8adbRo0cZHh4mHA5PW7d6PjiOM82jUqi4qHgooRQTFRcVDzUXFVRiUm4P13UJhUKzGh5gjMnOgzIXn5mGB8zlqv/FbpkZgBd7eABAdXV1SYr/+Q4PUMlXJRcVDzUXFVRiouKh5KLioUap4mKL9EuEgdN9VAHnl4WyJz130rjcmd37T50gOjpCIBRmxbpuHMfJL9InurqHN7cUTaDMVfQrrriiJF02Mg4KH2QVFxUPJZRiouKi4qHmooJKTFQ8FuJSjuEBUydNBfJWBChV4T/1lkwms++XGR4AlG2IgOu6sy78/X7/vOYKKDTvwkJQyVkVDzUXFVRiouKh5KLioUap4mKL9EuE8d4IVUCkfbIYLzZpXGZ99I5Nm3FcN6/LhfEM0f0TS68V6eoejUbZu3cvAFdddVVJ/NW6xCi4qHgooRQTFRcVDzUXFVRiouKh5FLMIzNpYLjAfC2lfO/cXgHRaJQjR47Q3t5OKpUqyfwBuT0APM8jFosRi8XKdkxASa765xb9w8PDrFixIu/1i50zKvmq5qKCSkxUPJRcVDzUsN3dhXFdl+7u7tIlrDHQnwJczOqO7ObMVfSWjvxJ4zLro3dtvgLXdfOSJH5iGC+SxAn7Ca0pPBncCy+8QDKZpK2tjY6OjoLPmStTPSqJiouKR8alpDm7AA+lmCi4qHgouajka8ZFJSYKHkoulfRwXTc7uz2ke75lluMp1VUnY0xer4DZFv7xeHxOwwpyJw2cOndAOchMGrjQ5QJnalDIHR6gkq9KLvY7VtdDyUXFI+Oy1HLWFullIplMlmQhewDio4QnZnYPbZwcH953YmLSuNU549GNyRuPbozJ3hzHyS69Ft7UhOMrnDyZtdGvvHL60m3zZapHJVFxUfHIUNKcnSdKMVFxUfFQc1HIV9CJiYqHkouKR4ZS56zjOPj9fvx+P1VVheeXmcp85guYy/CA+Q4jyB0esBiTBk4dHjDfIQAz9SSYz/AApZy137GaHkouKh4ZllrO2iK9DHiex9GjR0s2w2DqzGGqxyaWX9s82f289/jEePScSeMGTp9kfHgIfyDIivUb0+PW+/pYtmwZjuMwPlGkVxXp6t7f38+pU6dwHIdt27Yt2D3DVI9KouKi4gGlz9n5ohQTFRcVDyUXlXwFnZioeCi5qHiATs7OJyblGh6Q2yUUWNCkgbPtTWCMyb53JYcHzDQEIBaL0dTUlO2JcbHXlGs1B4V8BZ3PsYqHkouKByzNnLVF+iVAbO8uAPrqoWPZeiB/0rjcmd1PZtdHvwz/xOQ7K1asACB5fpxk7xi4DuGNTQXfKzNhXHd3N3V1dQWfMx9c1816VBoVFxUPJZRiouKi4gFaLiqoxETFA3RcVDyUUIrJVJfc4QHlYOrwgHJNHlip4QGlXDXAdV3GxsaIRqOEQqHsMo+VQCVnVTxAx0XFQ41SxcUW6ZcAY/v3AHCq1eHy2vQY8ZHzE5PG+RxaOmqzzz2VHY++FZhc0sbv92evoofW1ONWT5891/M8du7cCZRmbfRccj0q3dqm4qLioYRSTFRcVDzUXFRQiYmKh5KLiocSSjFZbJdiwwNK7ZFKpS7aK2Cmwn+2cwbkvl+5hwfkrrxQyskDp/YiKNQrQCVnVTyUXFQ81ChVXGyRXiZK2f3owuFDAJxtdWgOp7upZ7q6t3TW4guk3ys9Hj19Jb1ryxXZbQMDA7S1tRHdl5nVvaXg+xw+fJiRkRGqqqrYuHFjyfynelT6g6ziouKRQWGyDaWYqLioeKi5KOQr6MRExUPJRcUjg0LOKsVExaXUHpnhAaFQaM6v9TyPvr6+7CSDxcgUAQu96n+xhoTM8ABjDPF4PG9FgXLg9/unFf5+f7pUqa6uLslkgvPtCq2Sr0ouKh4ZFL5joXRxsUV6GfD5fCUtcsdP9hAAxtom10jPdnXPmTTuQs8ZIoMX8AUCtG9ITzDnui7Lly/HiyaJHRkCio9Hz3R137p1a/ZLsVRkPBRQcVHxgNLn7HxRiomKi4oH6Lio5CvoxETFA3RcVDxAJ2eVYqLiouIBs3fJvbJdTkq1XOBMvQlyhwckk0mSySTj4+NlO6bMygvzLfJ7e3sv2pOg3MMDVHJWxQN0vmOhdHGxRXoZMMYQiUSoqakpzYe0Jz2Lu5ezRnpmZvfcSeMyXd3bN2zCn7PsSyKRILF/EDyDv60Kf+v0WWDHx8fZt28fULq10XPJeAQCgYq3tqm4qHhkXEqaswvwUIqJgouKh5KLSr5mXFRiouCh5KLikXFRyFm1mCi4qHgoueTma1VV1axXD5gPnuddtJiPRqN4nregYQS57xeNRis+PGAhcwhkLqQFg8GK54lCvmZcFL5jMy6liIst0suA53mcOnWqJDMMpkZHCQynlyYJrUxPQmCMobfgpHET49Enurpnnjs4OIhv78xd3Xfv3k0qlWL58uW0t7cvyLkQGY/W1laJD4+Ci4oHlDZnF4JSTFRcVDyUXFTyFXRiouKh5KLiATo5qxQTFRcVDyWXxcxX13UJhUJFhwd4nkd/fz+tra3z7s5cbHjAXK/6RyIRHMcp+jzP87LvV4nhAaWcPHA2wwNU8hV0vmOhdHGxRbo48cOHARiohWVtq4CJSeMi+ZPG5Y1H3zxZpLuuS1tLG2f2p/dTtWXmru6lnjAu1yOzzEqlUXFR8VBCKSYqLioeoOWigkpMVDxAx0XFQwmlmKi4qHiAlosKpYjJYg4PmGvhP9dbMpnMvt9iDQ8oR/E/tWdApYv8UlKqz7Et0sWJHUoX16daHTob1gKFJ40bOneW0YHzuD4/HRsvy77eGMPooX7MeBK32k9wVT1T6e3t5cyZM7iuW9K10XPJtCpWumuOkouKhxJKMVFxUfFQc1FBJSYqHkouKh5KKMVExUXFQ81FBZWYzMYjM2lgOBwum0dmeMDY2BhAXg+BUs0fkNsDwPM8YrEYsVisbMcELLjw9/l89PX1sW7dOokr6aXIWVuklwHHcUr2ZRKbmNn9VCtc0bwBoPD66HvTXd1XbNhIIDT55WCMYXR3LwDhTc047nSnzFX0jRs3UlNTs2DnQhhjGBkZobm5ueK/eFRcVDygtDm7EJRiouKi4qHkopKvoBMTFQ8lFxUP0MlZpZiouKh4KLmo5CvoxETFI3NlO5VK0dzcXJYZzY0xeb0CihX/8XicoaEhAoHAnIcTxOPx7PAAYNrcAfPlZS972YL3sVBKlSu2SC8Druuybt26kuxrPGeN9I7Wy4HJSePa8iaNS3d1X5kzHj3j4hxLd4MJF5jVPZVKlW1t9Kkera2tZdv/XFBxUfGA0ubsQj2UYqLgouIBOi4q+QpaMVHwAB0XFQ/QyVm1mCi4qHiAjotKvoJWTBQ8oPwujuPg9/vx+/1lnTRwLsMDZnsLTkycXUlKdX5skV4GjDEMDQ3R0NCw4Na28YMHAehvhsba9rxJ45blLL9WaDw6QLx3jGT/OPgcwhubpu3/0KFD2dkQu7u7F+Q6E8YYotEo4XB4XjFJpcYYHnmByOgBfL4wgUDTxK2RQKAJv78ex5lda+JCXUqFikfGpVQ5u1APpZgouKh4KLmo5GvGRSUmCh5KLioeGReFnFWLiYKLioeSi0q+ZlxUYqLgoeSyUI9SDg/I5KzCWumlOj+2SC8Dnudx9uxZ6urqFjQuwotEoDc9K3uqxcFxHIb7x6dNGjfUe47hvl4c16Vj0+a8fUT3nQcgtLYBNzz9dGe6um/btq3sYzjGxsZm9UH0vASRyEGGh3cyPLyL4ZFdjI4eALwZXuVmC/bJ/9M/B/MK+mb8/gaGh8dpbGya+PA4OR8iBxwHh/T2yftk72ee67rV+HyFZyOdLbONSbkpVc6WApWYgI6LigdouCjlK2jERMkDdFxUPJRyViUmoOOi4gEaLkr5ChoxUfIAHRcVj6WYs7ZIFyZ25CgAgzXQXJPubpIZj97cUZOdNC5zFX3Fum6C4fxuKdG9FwCoKtDVPRKJsH//fqC8Xd0h3XWmpWX68m/GGMbHT6QL8pFdDA/vYmTkBTxv+vqVodAKams3Y0ySRGJw4naBVGoU8EgkBkgkBsp6HFMJBJoJhVYQDren/w+1590PhdqLFvLFYvJiRikmKi4qHqDlooJKTFQ8QMdFxUMJpZiouKh4gJaLCioxUfEAHRcVDzVKFRdbpAszOWmcQ6c/3bU929X9IuujA3hjCeLHhwAIFSjSd+/ejed5tLe3s3z58tIfQA7pYnwcny/CyMhuhoZ3MjK8i6HhXSSTg9Oe7/fXUV+3jfr6bdTXb6e+fhuhUGFHz4tnC/b0Lf/neGIgp6hP/+x5ccBk7ACDMZM/p++bnPuFyTQMjI7uKfqcQKA5XbyH00V7OLSCULidUHA5nldLKBQEUhiTxJgknklhvATGTG4zJoVnJrZ5yZztSQyGULCNcLiTcLiLYLC861WmZ63sY2zsCJGxI4yNHWVs7AhjY0dw3TArlr+J9vbfJhSa+/ITmTypqqqS6E6m4KLioeaigkpMVDyUXFQ8lFCKiYqLioeaiwoqMVHxUHJR8VCjVHGxRXoZcByHmpqaBSdsZo30Uy3QEU4X2dmZ3VdPLqWWHY8+pUiP7r8AHjitIfxN07tclHtt9GQywsjI8wwP72RoeCdDgzuJJ3qmPc9xgtTVbckW5A3126mqWj3rMeauGyQUWjbrotAYw4ULF2hqaprTOZpatCeTI0RjZ4lFe4jGeohFe4jFzqZ/jp0lGu3B86LZQn5k9IVZv9dCcN3QRMGevlWFu9L/V6X/DwbbpsW2UM6mUuOMjR3LFuBjY0ezRXm690JhDh/5C44c/Qwtza+ko+OttLT8Oq47+7VJo9FoWScqmQsqLioeoOFSqu/YUqEQEyUP0HFR8VDKWZWYgI6LigdouCjlK2jERMkDdFxUPJZiztoivQy4rsvKlSsXvJ/cNdK3Vq/AGEPf8fzl10bO9zN07iyO49K56fK814/vTY9Hr71i2bSkPXv2LGfPnsXn87F169YFu3pegtHI/vQY8omx5JHIIaaPI3eoqdkwcZU8fYW8tnYTrrt4szE6jkNXfWGfAABgRUlEQVRz8/SeBbN5HUzGMT3GvZG62ssKPt8YQzI5NGMhH48P4Dg+HMeP4/hwnQCOm7k/eXMnHnfcQM7z09sNhnisl2j0NNHYWTwvli2sCx9HkHC4I128V00W8zgjHDw0eWU8Gj09QzRcqsJdVNespbp63cRtLdHxU5zpeZShoefoP/8j+s//iGCwlRUrfouO9rdSU7P+ojGez7kpByouKh6g41Kq79hSoBITFQ/QcVHxAJ2cVYqJiouKB+i4qOQr6MRExQN0XFQ8YGnmrC3Sy4DneQwMDCx4/cJY5kp6K3TWr2RkIEo0ksD1ObR2pieNOzXR1X3Z2vWEqquzrzUpj+iB9Hh01lZhjMkr1DNX0Tdt2kR1zutmQ7obxzGGh3dlu62PjO7B82LTnhsKtaeL8bptBEObaGt9CYFAfYG9Lh7GGMbGxqiuri5ri5vjODMW8uXw8LwEsdhZxqOniI6fJho9RTR6mvFo5ucejIkzPn6M8fFjcGHm/fn9DVRXr6OmOr8Yr65ejesWGGvfBB0dbyUSOcyZnkfp6fkH4vF+Tpz4MidOfJmGhpfQ0f42li17PX5/zaLEZL6ouKh4KLmU6ju2FKjERMVDyUXFA3RyVikmKi4qHkouKvkKOjFR8VByUfGApZmztkgvA8YY+vv7aWqavuTZbPGiURInTwITa6Q3bSg4adzJIl3dY0eHMdEUbm2AVGt+V+NkMsmuXbuA2XV1TyYjXLjwdM5s67tJJoemPc/vr89eHc8U5qFQG5COyeDgIH5/3bTXVYJ4PD7nxolLwcN1A1RVraSqaiUUSL90EX8uv3gfPzVRxBvaWrdSW7s+W4wHAs3z+oKpqVlP94Y/Yf26/8L58z/hTM+jnD//E4aGfsnQ0C85cPDjLFv2n+joeCsN9S/Jew+Vc5NKjTE21k8w2DTRe8E3MUzAnfh58X4hqcQENFxK8R1bShRiouQBOi4qHko5qxIT0HFR8QANF6V8BY2YKHmAjouKx1LMWVukixI/ehSMYbgKvLBHff1q9j43fdK4U3vSRfrKqePRJ7q6hzc109yS3+Xi4MGDjI2NUVtby/r1M3c/TqWiPPPsbzE2djhvu+sGqau9fKIoTxfm6XHkhQsXx3FkPjgqLpXwSBfxXVRVdeVtT6VSHDx4kPXru0u6dIXrBmhrew1tba8hFuulp+cfONPzKOPjx+jpeZSenkeprl5PR/vvsKL9twkFW8sek1QqRjzeSyx2jtjE//FYL7FYL7H4ufT/sXMzjrtP4+A4PtJFuzvxszOtoHfdEAF/PX5/Hf5APX7/5C3gr8Pvb8huzz7PX4/Plx5bpZKvoPPZUUIlJioeoOOi4qGEUkxUXFQ8QMtFBZWYqHiAjouKhxqliost0kWZHI8OnckUTt1y+k6kC+/MpHGjFwa40HMaHIfOyybHoxtjGN+XXoosvLmJkZERamtrswV0pqv79u3bL1qQnTnzdcbGDuP3N9DW9lrq67fRUL+dmpqNc5oMzBjD6OhonkelUHFR8VgsQqFlrFnzIVav/iCDQ8/Sc+abnOv9AWNjhzl0+EEOH/lLWlp+nYb6m6itbSV/Vn0z8e/ENpM7637OY5mHSRGPnycemyi6M0V5rLfgagLzw2BMclJnBqYvKDgb3Ilivg7XrSUUbCxYzPv99fgD9QT8DXnbfL7Sdz97seXsbFCJiYqHkouKhxJKMVFxUfFQc1FBJSYqHkouKh5qlCoutkgvA47j0NDQsKATk1l+7XSrQ0cyialupff4MWBy0rjMePS21WsJ19RmX5vsHSN1Pgo+h9CGJkZjkexjo6OjHDhwALh4V/dUKsax4/8TgPXrP0JX5+/O+3ggPV5EBRUXFY9S5Oxc3qup8aU0Nb6UjRs/yrlz/5czPY8yPLyD/v4f0t//w7I7uG6IUGg5oeBygqG2iZ+XEQotJxhalt4ebGU0Eqe+rpZ0QZ7CGI/0cnle+kYKjJfzWObnyfspL0oyOUIyMUwymb4lkpmfJ7dPbhueKP49ksnBbKPC2NjcjtFxfNkiP//qfc62vKJ/cnsg0IDrFl46RCFnFzNfZ4NCTEDHA3RcVDyUclYlJqDjouIBGi5K+QoaMQEdD9BxUfFYijlri/Qy4Lou7e3tC9pH3W/8Bv/R+1N+HtjDNnyMjJCeNM51aOlMT7iVWXpt5eb8ru7je9NX0UPrG/GF/TSEG7KP7dq1C2MMnZ2dtLW1zehw5szXicd7CYXa6Wj/nQUdT+bDo4CKi4oHlCZn54PfX0dn5zvo7HwHo6MHONPzKIODz5BeFSDzRetM/OuAk7vNmXzUmfpcl0CwJb00X3D5xBJ9kwW43183qy/ypsaSHOacMMbgedH8wj0xUdAXLPKH8u9PFPnGpEgkLpBIXGR2wCI4jj+/cM8p5Hv7phb9+YV/wF9ftMgvBZXK10KofI5VPEDHRcUDdHJWKSYqLioeoOOikq+gExMVD9BxUfGApZmztkgvA57nce7cOZYvXz7vGQartm3j309Ws6vf5XXR6slJ4zpr8AfSXdRPToxH77o8fwm16ESRXrW5GWMMIyMj1NWlr77Pdm30VCrG8Ymr6GvWfHjBS6TlelS6lUvFRcUDSpOzC6W2diPdG/5UJiaVOj+O4+DzVeHzVREKLc96tLTMziNd5I+nC/dEfvGee7U+t/DPL/KHJnoCJEkkBkgkBuZ5HP7phXygUNGfP14/s911w0WPVyFfM6h8jlU8lFxUPEAnZ5ViouKi4qHkopKvoBMTFQ8lFxUPWJo5a4v0MmCMYWhoiGXLli1oP6fHzgLQEWyatj762NAgA6fTs7935YxHT0USxE8MAxDe3JK3v56eHnp7e/H7/VxxRf7V96mc6fkGsfi5iavob1nQcVj0KVXOWipPusivxuerhtCKOb8+v8gfyi/kE0NEIv24vhip5Eh+0Z93JT+/yB+f13EEphf0gcx4+zqGBqNEYx34fSFcN4jrBnEm/nedwvfT2wL5951Axf+4sCx97Hes5VLC5qvlUmMp5qwt0oU5E0tfweqsbuPAifyZ3TNd3VtXraGqbnLd8ei+ATAQaK/B35hex7q+Pv34r371KwAuu+wyqqqqir5v3lX01b9XeD3sOeI4Ttaj0qi4qHgooRQTFZfF9ihFkZ9KjU0r3PO77g9PL/ATk0V/emx/4qJF/vDIgg41S7pYnyzcXSenwHeDuE4gv+jPawhIP9bXF8xvLHCCRRsPXDcw7TlTHdKrA8welXwFHRcVDyWUYqLiouIBWi4qqMRExQN0XFQ81ChVXGyRLspYYowLqfSc0O01XTx1InMlPX3Ss13dp4xHj2ZndU8vu2aMYXh4mOrqanbvTk80d7Gu7md6vkksdpZQaAUdHQsbi54h41FfX1/xq1YqLioeSijFRMVFxWO2Lo7j4PfX4PfXAHMfH5Yu8iPZAn9qcZ9MDhNPDDEwcJq62jCGJJ4Xx3hxPC+OZyb+n7gZL45nEpP3TRxjUnnv6XlxIE4qVdipEjiOf0rhHijagJC5n0o6BEM1+NxQ4V4DUxodijYoTGk8mOxx4J/1kAuFnFXxUEIpJiouKh5qLiqoxETFQ8lFxUONUsXFFullwHEcWltbF3RiTo+eBqA+lcLxrSY6OjFpXNfEpHETM7vnro9ukh7R/elJoqpyurq7rsv+/fuJRqPU19ezbt26ou/reTGOH/9roHRX0XM9VFBxUfEoRc6WCpWYgI6LigeU3yVd5Nfi99cWfY7neQwMDNDc3DwvH2NSE0V7As9kCvzYZHFv4hgvkXd/siEgMaUhIEYsGsHvN3gmUbCxwEx7TabxYGJ/E40H+Y5JUqkkMMdp/cuKU/Cq/7TGAyeAZ3wEAmFcN5T3nMmGgECBxoPgxPOL9TQIML3xIDjj95bKZ8d+xxZGxUXFAzRclPIVNGICOh6g46LisRRz1hbpZcB1XVpbWxe0jzOjZ4D0Gul9Yx0ANHWkJ40bGx6i/+RxIP9KeuzIECaewq0LEOhM/4HrOA51dXXs3LkTSK+NPlPinDnzaM5V9Lcu6BhyyXgooOKi4gGlydlSoBQTFRcVD9BxWWi+Oo4vOzmfCsYYTG4hn1vw597yGhBi+Q0IuT0GpjUwJAr3NDDT3yN3G5hcy4nGjFilwlSQdIPBxXsaFBuqMHk/VLinwQyvSd9C0xoQHMeX52i/Y6ej4qLiATouKvkKOjFR8QAdFxUPWJo5a4v0MuB5HqdPn6azs3PeLSmZK+mdySS9w43A5Hj00/teAKC5cyXVDY3Z14zvPQ9A1WUtOG66JckYw6lTpzh0KL3u+kxd3T0vxrHjXwJg9eoPlfQqujGGwcFBGhsbK97KpeKi4gGlydlSoBQTFRcVDyUXlXyF0sXEcRwcJzTv791ynRvPS04r5I3JaSTIazxI9wxIeTEio0OEw77J5078P9nwEMtvQLjIMIXcxgNjklOOPU4qFSeVipTsuBeK4/jyhio4ThDPcwkGqwv0DAhNaQiYaHSYYajC9HkOQtOGN0x9H8cJAEh8hkHn+0TFQ8llKX7HLhUPJRcVD1iaOWuL9DJgjCESiWCMufiTi5Ap0juSSfr6Q0CSZasnJo2bGI++csvk0mvGmOzSa5nx6BkOHjyIMYaVK1fS0pI/43suZ858K30VPbicjva3zdu9GMHgwpZxKyUqLioepcjZUqESE9BxUfEADRelfAWNmEB5PFzXD/jTEwnOEmMMY2NjVFdXl+UPN2M8PC8xUbTHijQgxEl5MaLjIwQCzsT2TGNALL+XwtQGhFkPVchvPMh3TGHMOJ6XP+1hIlHycMwBJ9tgUHSSxOy2QN79Gec0cEMTzwkUaDwoNlQhBAQIBDT+DFX5DIOGi/2OLYyKB+i4qHgsxZzV+Ha0TOOu7R/mt//104STCR4fTCdcdtK4iZndu3LGoyfPjZEajIHfJbShMW9fe/bsAeCqq64q+n55V9HXfAifr3RX0SF9laimpqak+5wvKi4qHkooxUTFRcUDtFxUUImJigeU38Vx3InfUSFAo6tlerhCYlpDQeaWTI5z4sRROjuXAckp8xwU6mkwOWfB9MaDWMH3KTTPQf4kieYSmSRx5qEK+UMOQgUbEGaz/GL2tU4Avz9IMpnI8ajMn8dKn2MVVGKi4gE6LioeapQqLrZIF6UqEWV9LMpIqoXxSIrMpHHR0VH6jh8F8q+kZ7q6hzc04gYnx8KdOnWK/v5+/H4/W7ZsKfp+Z3q+TSzWM3EV/e0lPx5jDBcuXKCpqaniXWJUXFQ8lFCKiYqLioeaiwoqMVHxUHJZTI/0cIV08VeIVCpFKFRNU1M3Pp+v4HPKweQkiZO3C4N91NVWpXsAFJmzoHBPg9js5jkoND9C9jmJS2SSRDdvqEKhCQsLz3OQP1Sh6PwIOQ0MU4ckjI5GaWxsxfWF8nstXGSSxKXKi/H75FJxUfFQo1RxsUV6GXBdlxUrVixsTESkF4A+ZzswOWnc8V0vgDE0tXdS09iUfXp0T+Gu7jt27ABgy5YthMPhgm/leXGOH8uMRf9gya+iZyj2/pVAxUXFoyQ5WyJUYgI6LioeoOGilK+gERPQ8QAdFxWPSuXs1EkSjTE0NjRRVVVVsT+qM5MkplIxxsaGCQZdjElM74GQM8/BtEkSC02wOMdhCjNPkujheVE8L1qRGBUjf6jCRXoazDBUwZm26kJ+A4LjBKipSTAyEpk2TMHJeU2hSRLLgcrnWMUDdFxUPJbi3wW2SC8DjuPQ2Ni4sJ2MngOg11wOTE4ad/KF9NJruV3dUyNx4qfS66hX5RTpiUSC559Pd42facK4np5vE42dIRhcRkfHOxbmXQTHcaiunv2YxnKi4qLiASXK2RJ5KMVEwUXFA3RcVPIVtGKi4AE6LioeoJOzCjHJnSSxoaG+oi4Z0g0HqYKrHUw2FhS6nyjSeFB4FYaZhynkD1WYeZLECgWqAPmTJBaZg2BaY0HhngYzragwNjZlmMKU+RFyezg4jr/kjVAKn50MKi4qHqDzHQuli4st0suA53kcO3aMNWvWzL9FZ7QPgL74WgDaJor0UxPj0VfmLL0W3TcABgJdtfjqJ6+C79u3j1gsRl1dHWvWrCniGufYsS8CsHr1HWW7im6Mya5rXOkuMSouKh5QopwtAUoxUXFR8VByUclX0ImJioeSi4oH6OSsUkxUXHI9XLd6TpMkltPFcZyJSRIzhX2sSONBkaEKE6+ZPs9B4aEK+cMUYsRiEXw+k9eoMJdJEitLgUkSMz0BnGI9DYoNU5gc9jA+nqC2rglftqFg+ooKU3sapN8zs4/SfPYVPzuV/j5R+Y6F0sXFFullwBhDPB5f2AyDo+cwBvrG2gFoW11HbCxC79EjAHTljUdPd3Wvuiy/q/uvfvUrALZu3Vo0SXp6/mHiKnobnR3vnL/vLFBpbQMdFxWPkuRsiVCJCei4qHiAhotSvoJGTEDHA3RcVDyUclYlJqDjouIB+S7pSRLDQJjFnCQxlUpx8OBBurunz6GQO0liwYkSpzUWXGSYwoxDFdINE6lkFJzUlAaF/N4Jl8YkiZNzD0yd56DQUIXpjQWTPQhSnstopHra4/mNBfnLPOYuC1mqSRJVPjtK37FQmrjYIl2VSC+jXgvjiTCO69DaWcuJF36JMR4Ny1dQ19IKgEl4xA5eACC8eXJ5taGhIY4cSRf011xzTcEi3fPiHDueuYr+wYlfBOXBcRyqqqrKtv+5oOKi4qGEUkxUXFQ8QMtFBZWYqHiAjouKhxJKMVFxUfEALZdiXGySxEpRaJLEvOELs1lRIduAEJvWeDCrFRUKNB7kOyZIpSq6DmMB3PzivsBQhaKNBcXmR8gdqjB1ksQpwxvyex9M9lqo9JX5hVCqz7Et0lV55R/TF34r/F0fze01+IO+guujR48MYhIevoYggY7J6f537twJwOrVq/E8D8/zpnX/6Dn7HaLR04tyFd3zvJzuZJXthqLiouKhhFJMVFxUPNRcVFCJiYqHkouKhxJKMVFxUfFQc1FhtjGZOklipTxyMcYruCRj/pwFhec9mGmeg5QXY3x8hGDAmeyVUHBFhdi0eQ4uyUkSLzopYgCH9MoIqdRf4vNVdmnOUn2ObZFeBlzXpaura2FfsMEa+i7UAH20rZ4Yjz5RpHfljkffM7H02mWT4x6MMdlZ3a+88krq6uqmtUh5XmJyLPqqO8p6FR3SrUqFPCqBiouKB5QoZ0uAUkxUXFQ8lFxU8hV0YqLioeSi4gE6OasUExUXFQ8lF5V8BZ2YzMfDcdzsJImlJNO1Oxic+xXn9HCFZNGhCsUnSZw+VCHlxUgmxnGc1MQ+Y9PnOZjaayGvd0Ki5JMkLqWctUV6GXAch9ra2gXvp/d4esb2ZavqiEfHOXvkIDB5Jd0Yk540jvyu7idOnGBgYIBAIMCWLVsIhaZ/OZw9+x2i0VMEg610dpb3KjqkY1LIoxKouKh4QOlythQeSjFRcFHxAB0XlXwFrZgoeICOi4oH6OSsWkwUXFQ8QMdFJV9BKyYKHrAwl/RwhQCuGyix1cJID1dIzKKxYKYVFeIlbxCZD6XKlco3NyxBUqkUBw4cILWA2SqMMfSdGAbSM7uf2b8X43nUty2jvm0ZAIkzEVJDcZyAS3h9Y/a1mavol19+OYFAgN7eXjzPyz7ueQmOHnsIyFxFL//4J8/zpnlUChUXFQ8oTc6WAqWYqLioeCi5qOQr6MRExUPJRcUDdHJWKSYqLioeSi4q+Qo6MVHxUHIppUd6uEKYQKCeULCVcLiD6uo11NZupL7uChoaXkJT08toabmRttZXs3zZ61ix4k10dLyVrq530dnxHmKxX8eYyveIKVVcbJFeJhZ6YiKDMcZHEulJ47pqOblnYn303K7ue9Nd3UPdTTiB9KmMx+O88MILQLqre2bdwNwuF2fPfpdo9BSBQAudnb+7IM/ZUsijUqi4qHhkqPSXPWjFRMVFxUPNRSFfQScmKh5KLioeGRRyVikmKi4qHmouCvkKOjFR8VByUfHIsNRy1nZ3FyXT1X3qpHFdWyaL9PGJru5VmyeXXtu7dy/xeJympiZWr16N4zgEg5MzcOaNRV+9OFfRgWkes8EYQ288ycGxKPsjUQ5EopyIxmkJ+OkKB1k5cesKB+kMBwjNchzKfFzKQSk8kp5hNJWiwe+T+ZJcCCrnBnRcVDxAy0UFlZioeICOi4qHEkoxUXFR8QAtFxVUYqLiATouKh5qlCoutkgXpe9EukhvW11HIhbl7OGJ8eib0+PRU8MxEqdGwUlPGpchszZ65iq653n09fXR1taG67qcPfs9xqMnCASa6Vqkq+jANI9cjDGcjiU4GIlyYCxdjB+IxDgwFmUoObuuVg6wPBigKxzIK95XhoOsrArSGQpS5XMv6rKYTPUwxjCcTHE+kWIgkWQgkeR8IsnAxP3z8WR2e2bb4ER8Gvw+rqit4oq6KrZO/L+hKozfvbQKd5Vzo+Si4qHmooJKTFQ8lFxUPJRQiomKi4qHmosKKjFR8VByUfFQo1RxsUV6GXBdl7Vr1y7oxLRvaODyV3Sy8rImzhzYh5dKUtvSSsPyFQCM701fRQ921eGrS7fWXLhwgWPHjgGwfft2IN2a09zcPFGwJzl2fGIs+uo78Pmq5+03VxzHoaGpiRPROAfGYulCfCxdjB8cixJJFe6i4gKrq4JsrAmzsTrMmqoQA4kkp6JxTk7cTkUTjHseZ+MJzsYTPDs8VnBfbUF/ungPBQg7Ds7ASQzphSjMxGoUk/dNdoGK3OcYcrabzGOmyD7IPnvqa5h4XSzlMXjsAgPJdPGdyl0VYw4MJVP8dHCUnw6OZreFXYfNNVVsravKFvCba6qyjRW5zDZnPWPoi0/G/1TOOTgVi+MAl9em3y/zvo2B2X/N5OZrpVFxUfFQcinFd2ypUImJioeSi4oH6OSsUkxUXFQ8lFxU8hV0YqLioeSi4gFLM2dtkV4m/P6FhXbVlhZWbUnP2P7Tb/4LACs3X5E94dG9mVndJ6+iZ9ZGX7duHY2NjUA6UQKB9AyOPWe/y/h45ir6uxbkNxNJz3AsOlGIR6LZovzQWJSoV7gK9TuwtiqULcY31YTZWBNmXVWIcIGiMhdjDOcTqSmF++T/J6JxIimPvniSvniSX5bjoEtIrc+lOeCfuPloDvhpydwPTrkf8FPtczk8FmX36DjPj4zz/Og4L4yOE0l5/GpkjF+NTDZa+BzYUB1OX23PKaTr/T78fj9Jz3AukciLX24hfjoWJ1bkHGbYF4ny7XMXsvdXhYNszV7hr2ZrbRXLQ4VnFc3N10qj4qLiAVouC/2OLRUqMVHxAB0XFY8MCjmrFBMVFxUP0HJRyFfQiYmKB+i4qHhkWGo5q3E0SwzP8zh48CDd3d34fL4F72/qeHQvniJ6aBCAqolC3vO8vLXRc116e3tpbW3mWHZG9w+U5Cp6zPM4Mhab1kX9yFiMhClcyIUch/XVE8X4REG+sSbM2qoQgXl2zXYch9agn9agnyvrpx+XMYbBZE4RPx7j/PAItbW1uI6TXo4CJm9O5meH3EawyccLPT+9LfM8nJzHJx5x8ralvSIjw6xpaaY1FMwW5LMdW5/L1rpqttZVQ3v6vmcMR8dj7J4o2p8fGWf36DjnE0n2R9Jj/L+VU0ivDAVIJJP0pQwXG2DgAu2hAF0TQwq6ssMLAiQ8w/Oj4+yeeL+TE40kJ6Jx/m/fUHYfy4J+rqitYltddbaxYFU4mJ6HoLeXZcuWVbw1NPPZqbSLioeSS6m/YxfqohITBQ8lFxWPjItCzqrFRMFFxUPJRSVfMy4qMVHwUHJR8ci4LLWctUW6OMl4nJ5D+wHomhiPHjs0CEkPX2MI//J0UXr8+HEGBwcJhUJcdtll2dc7jkNbWxvnzuVcRe/6z3NySHiGfZHxvKviByJRjkVjRbtnV7ku3TWh7FXx7uoQ66uCrKkKE7jIlfFS4zgOTQE/TQE/W+uqMcbgea24rlvRLjrGGLxljWXxcB2H9dVh1leHefPypuz7nY0nphXuJ6NxTsYS2dcGHIfOcICu0PQivCscpCMUnLFB5TWtDdmfLySSvJBTtO8eGePwWIzeeJIfDYzwo4GR7HMb/D4urw1zeU2YTakBumvCbKgO0xKszNdU5rNT6W5cKh5qLiqoxETFQ8lFxUMJpZiouKh4qLmooBITFQ8lFxUPNUoVF1uki9NzcB+pRIKaxiaa2jsAiO6b7OqeSYDctdFzZxR0HAdjUtmx6KtWvX9OV9FHkine/KuDvDAaLfh4nc+ddlW8uzpEVziIOyU5Pc+reEsbTFwJn7i9mDwcx6E9FKQ9FOS1OYX0YCLJ88MRzp4+zcs2rKOjKjTt3M2XpoCfX2uq49ea6rLbIqkUe0cz3fPH2D0yzr5IepLA/xiM8B+Dkbx9NAd8bKhO59WG6jAbqkN014RZGQ7iK2PsXqx5ouhijCFlIGEMKWOIJpMMeoaRZIoqxyFYwfionB8VDyUXFQ8llGKi4qLioeaigkpMVDyUXFQ81ChVXGyRLs7JTFf3ifHoxjOMT6yPXrU53dU9FouxZ88eAK666qq813uex6FDf8f4+HECgSa6Oud2Ff2+A6d4YTRKtc9lW21VtiDvrg6zsSbEimBgVkmo1iVGwUXFozHg5/rGWg72ubSHAiUr0ItR4/NxTUMN1zTUZLfFPY+DYzF2Dkd4rm+AM8bHofEYJ6NxBhIpfjEU4RdD+cV7yHVYV5VfuHdXh1hXHaKmBF2dVM7PTB7GGDzSvV1SxpA0hoSBlDHZgjYxsT3pGZI5jyWzNyYem3pj8mdv4jWex9BohFB1NSkmi+b0+5P3+snHyLrlP5b/+uTUbXn/FwnOuT3ZHwOOQ9B1CLkOQced/HnifubnQM7P6ee4BJ2cn10nez84cT/kpH8utD8/MHJhgPbWVsJ+X97+yv1ZykUlX5VcVDyUUIqJiouKh5qLCioxUfFQclHxUMN2dxfGdV26u7tLkrCn9mbGo6e7uidOj+KNJHCCPkLr0ldD9+zZQyKRoKWlha6uril78Bi48P8BsGrl+/H7a5gt3zo7wLfOXcAFvr5tHdc21s77OFzXlfkQq7ioeGRcSpWz8yHoulxeW8XltVW8Y0Vz1mMs5XFkLMqhsfQqAIfGYhyMRDkyHiPqGfZGouyNTO/l0RlKL8XXHgrQMfF/ZyhAeyhIZzhAS8B/0QJqLufHGMNoyuNcPMG5WIK+eJJz8QS98STDyVS6MM0W0kwriDNFbqYwnVbQepA41D/lMYrO/VB+Ri/+lEUmYQyJlCGSAi46s0IZONw/bVNuw0Egt9CfoegPuA6hiUaG6Y0Ikw0HwWn7cwiEauiNRCcaFib3kfvcxWg4UPluU/HIuFTyOzbXQykmCi4qHkouKvmacVGJiYKHkouKR8ZlqeWsLdLLRDKZXPBC9slEgp4D+wBYOVGkZ66ihzc14fjTJ3/q2ui5nDv3T4yPH0tfRZ/DWPTj4zH+5MApAO5ds2JBBTpMLGk2cat0txgVFxWPDKXI2YUyNSbVPpcr6qq5oi5/iEbKGE5F4xwai3FoLMrByMT/YzHOJ5KcjiU4nTPOfipBx2FFKEBHKFC0kG/2+0h5HgPxJL2JJL3xJL2xBL3xBOfiSXrjCXpjE//HE4xfZNb7xcQFAq6Dz0kXhz4H/Nmfc/53wec4BR5Lv97vTN+Hj3SjSvYxd+L5E/vJ3DL7yLx+8jHynud3L/bY5LaMow9IJuLgD5AwhrgxxL30LWa8yZ+9iZ+NIeYZ4p6X81xvYlvm8cnXxfP2MXk/dx8xL924EsvZ59RGk/yGAw38Tvr8hXJ6C0w2IhTpWZBT9AcK9SyYsr+gk/7jIuzzEfJNNipk9pfbUBGcyJFyYL9jp6MUExUXFQ81F4V8BZ2YqHgouah4ZFhqOWuL9DLgeR5Hjx5d8AyDZw8fIJmIU93QSHNn+gp5dum1y9JLr50/f54TJ07gOE52bfQMxqQ4OjGj+8qu9+L3z67QTniGD71wnNGUx3UNNdyzevm8j2HSxdDX18eyZcsq/kFWcVHxgNLl7EKZbUx8jsPqqhCrq0K8uqU+77GBRJIjYzFOx+KciSboiaWXjuuJJTgTTXAuniBuTHbW+WIEHIfURHfy2VLnc1kWDLAs5GdZMMDyYIAGvy9b8Pod8ovWmQrUzPOB4QsXWNbSTMDny3tufiE+ub1cV0tVuralUimOHDtGd3c31RVeciU3JjhOkaJ/orgvUvTHMkX+lKI/v5Fh5v3FPMN4IknKcfIaEeJTGg6SBpIpj7Eix1MJfA55vQmm9gQIuW7+cIaJhoTcngWZhoPc4QoBB2Kjo7Q2NBD2+abtr1gjQ6gMDQeX2nfsi8lFxUPJRSVfQScmKh5KLioesDRz1hbpwmSXXrvschzHITkYI9ETAWeySM+sjb5+/Xrq6/OLlXPn/pnx8aP4/Y2sXPmeWb/vXxzt4VcjYzT4fTy0ZTX+eS6NlovruqxYsWLB+ykFKi4qHkqUIibNAT/NDX6uofDQjoRnOBefKN6jE8V7LM6ZiSK+J5Yu5DNXRV2gNZguupcF/SwPBbI/pwtxP8tCAdqC/pKMhS/IAnuylAqbs9OZGpOwzyEMpJtXKo/J6WkwtSdAPKcRYKZGgUzDQeIiPQtmu7+pDQcpA+PGY3wuLWJz4czQxZ8zhUzDQe6QgUI9CyaHMxTrWZB+nR/DUCRJZ88AYb8vr5Gh0BCIQIH3s7+Ll7YHaLmooBITFQ/QcVHxUKNUcbFFujCT49HT66NH96W7ugdX1eOrCRRdGx0yV9H/f+nXd96Gzze7sehPXRjhCyd6Afj0ppV0hUvTbcQYQzKZxO/3V7y1TcVFxUOJxYhJwHWyS8u9tKF4IX82Fsf1UiyvCuOv4FVjpTxRclFBJSbFPBwnfZU55ELdDK9fDJfcxxO5DQdTi37PZHsXxIv2LMjvTZAwhYcvRFMpEoa8IRHFGhlyKVvDwfDpeb/UhQLzEhTuCTB9wsTMUIaJoQiZSQ6LTJhYeH9Tn+vid5h33qt/dl7sLiqoxETFQ8lFxUONUsXFFullYqFdQVPJJKf3p2cuzkwaN75ncuk1gKNHjzI8PEw4HGbTpk15rz937v8yNnYEv7+Bqqr/NKtxEefjSe7acwIDvKu9mVuWNS7oGHIxxjAwMCCxnqKKi4pHBoXJNlRiEnAdOkMB+voGcavCFfMAnZiouSjkK+jERMVjNi6OkynyoJx9RDzPo6+vj7a2tovmy9SGg4S5eM+C3KI/20CQmevATJn3wPMYGB4hUF1NwjBl7oT0Pgr1VMhtOvCAcc8w7ulMcuBAwUaBTKEfKDJ8IVPop6JRGmqqcxoDcuctmGF/BeZOCM2z4eBS+uwsJvY7VtNDyUXFI8NSy1nHGKMz29EiMDw8TENDA0NDQ9O6hytx5sBe/v7P/x/CtXV8+Mt/h0kYznz8aUgZlv/hSwgsr+Hb3/42u3fv5pprruENb3hD9rXGpPjZz3+TsbEjrFt3L2vX3HnR9zPGcNvzR3m8f5ju6hCPXbOxfF13LRaLxWKxzIgx6VUccuctiHlefi+EvJ4H0+c9KDqh4jz2l2lEKNeohFLhwPQVEKYV/emGg8JFf+F5Dwot1Zg3JGHqXAdTejIoFDEWi6WyzKUOtVfSy4AxhkgkQk1Nzby/lCfXR78cx3WJHuyHlMHXEsa/rJpoNMrevXuB6Wuj515F7+p8N/F4nEBg5vXMHzlznsf7hwk6Dl/csrrkBboxhkQicVGPxUDFRcUj47LQnC2Vh1JMFFxUPJRcVPI146ISEwUPJRcVj4zLXHPWcdKT3wVcX5EZNubnsdCYJGcs+g2JKfMUTPYsmDI3gecxnkySdBziE40RhYZAJGZ4v8z+cxsODBDzDDEMpHSaFKaualCo6A+6DgEmV0YoVvRPXX4xWLRnwdS5DvKHLxTLAfsdq+uh5KLikXFZajlri/Qy4Hkep06dWtAMg9X1DSxft4GVl6dnbB+fmNW96rJmHMfh+eefJ5lM0tbWRkdHR/Z1uWPRV618Lz5fLf39/bS2thZNlL2j4zxwKD1O7r+ub2frlOWuSoExhsHBwRk9FgsVFxUPKE3OlgKlmKi4qHgouajkK+jERMVDyUXFA3RythQx8bsOfnzULPAwPM/L/n2y0G6qyamrHkztCTBliEJu0R9NpbgwMkqgunryOVMaAaYPX0jvI1Gwp0K6kSE1pZ+qYsPBZJGf35sg4DiYeIz6qqqJZRRnMcFhgZ4Fufsv3sgwc8OByudYxUPJRcUDdL5joXRxsUW6KFt/47Vs/Y3XAmA8Q3R/Zjx6C0DehHG5CXCu9/uMjR3G769n5cpbcV03vTRQEcZTHr+35zhRz/DrzXV8oKutLMdzMY/FRMVFxUMJpZiouKh4gJaLCioxUfEAHRcVDyWUYlJKl3TDgUO1b77FfuljksrpTZDI/lxkmcVpRf/0RoZCkynOuCpDgf0lpzQcxI0hnjKQgol/8kks/mKNecstOjlzFBwfyBb6gULDFwr0JpjsiZA/b8HU3glF506Y2J/rOEv2s7MUPNQoVVxskX4JED81gjeawAn7CK2tp6+vj1OnTuE4Dtu2bcs+z5gUxybWRV+18r34/XXp5XficYLBYMHWnI8fPsO+SJS2oJ/Pb15VtvWVL+axmKi4qHgooRQTFRcVDzUXFVRiouKh5KLioYRSTFRcyuXhcxyqfXNrOCh3TDyT37Og2HCCaDLFsdOnaVnRThJyehcUXkUhtzfBXJdqzCx3miFhDImUIVKs4aACZBoOcov+wj0BZlf05857UGj4QrHlHrPDJRxIJhJL9rNzqVOquNgivQw4jlPShI1mZnXf2ITjc7Nro3d3d1NXN7moTm/vD4hEDk5cRb8NSCfKyMgIzc3N03we7x/i4dP9AHz+slW0BQMl8S3ETB6LjYqLigeUPmfni1JMVFxUPJRcVPIVdGKi4qHkouIBOjmrFBMVFxWPxXBxHYcqn0PVRRoOPM/j2MgAa5Y1ln3GbM/MXPTHUin6B4cJ1daShII9CzIrMszYs2CGVRSmrtJQrOFACT/kNxBM/BxwCjUKTC/6A4V6FhRYfjEwdR85wxoCGCJDQyxvbibs9+GzfxcApfsc29ndLwHOfvY5kufGaH77JsLbW/nsZz/LyMgIb3vb29iyZQsAxnj8/BevJxI5yNq197Bu7d0z7rMnFufVz+xnIJHigyvbeGBD52IcisVisVgsFovFIotnzPRVD6b0BJi2isK0RoHpSzVebH8zLf0YvwTKNZ9DwVUUpq6AMNMqCpmlFi/WyFBwBQfXpTMUKFuv4FJgZ3evMMYYhoaGaGhoWHCLTnIgSvLcGLgQ3tTE4cOHGRkZoaqqio0bN2afN3kVvY6VXbfmuUSjUcLhcNYlZQx37znBQCLF1toq/nRd+4IcZ0Mhj0qh4qLikXEpVc4u1EMpJgouKh5KLir5mnFRiYmCh5KLikfGRSFn1WKi4KLioeSikq8Zl8WOieuki8WQC5n+qlmP6qqKxMSYnFUSUh4j0XGcQDB/ZYQpvQmmF/3za2TI319+I0MuKQPjxmO8gnMj7v+1K2gIVLa8LVXO2iK9DHiex9mzZ6mrq1vwDIPRvecBCK5uwK0OZCeM27ZtG35/+vQZ43H02BcAWNl1O4FAfsvM2NgY4XA4e/+LJ3p5anCUKtflS5evJlTmrkzFPCqJiouKRylzdqGoxAR0XFQ8QMNFKV9BIyZKHqDjouKhlLMqMQEdFxUP0HBRylfQiEmlPZychoNan4szmqC5vq7iK62cOz9AbWNjurFgjkX/RVdRyJlAsdgqCrlLP0ZTHn40ehyUIldskS7O+L6Jpdc2NzM+Ps6+ffuA9KzuGXr7Hpu8ir7y9rzXO45DS0tL9v4vhyM8eLQHgP93Yycbqhfny2aqRyVRcVHxUEIpJiouKh6g5aKCSkxUPEDHRcVDCaWYqLioeICWiwoqMVHxAB0X13Vpb2uttAYAqVSKgwcPEl6kC48zUarzU/kjsRTFiyaJHRkCILy5md27d5NKpVi+fDnt7eku6sZ4HD2auYp+27Sr6MYYxsbGMMYwmkzx4T3HSRp447JG3rmiedGOJdej0qi4qHgooRQTFRcVDzUXFVRiouKh5KLioYRSTFRcVDzUXFRQiYmKh5KLiocapYqLLdLLgOM41NTULLgLSvTABUgZ/K1VBNqq89ZGz9Db9ziRyAF8vtppV9Gz+4lGAfiTA6c4Nh6nMxTgLzZ2LXoXmYyHAiouKh6lytlSoBIT0HFR8QANF6V8BY2YgI4H6LioeCjlrEpMQMdFxQM0XJTyFTRiAjoeoOOi4rEUc9bO7i7MwDf2M/arXmpf0Un8mlq++MUv4rou/+W//BdqamowxuMXv3gDo5H9rFlzF+vX/WHRfX377AB37j2BC3z3qg1c21i7eAdisVgsFovFYrFYLC9i5lKH2ivpZcDzPPr7+/G8+U9vaDxDdP/EePTLWvjVr34FwMaNG6mpqQGgr++HjEb24/PVsqrIVXRjDHvPD/LHB04BcO+aFRUp0I0xRCIRiS4xKi4qHlCanC0FSjFRcVHxUHJRyVfQiYmKh5KLigfo5KxSTFRcVDyUXFTyFXRiouKh5KLiAUszZ22RXgaMMfT39y/o5MRPDOONJXGq/Pi6ati1axcw2dU9PaP75wFYufJWAoHGgvtJeIY/OHSG0ZTHdQ013LN6+bydFko8Hq/Ye09FxUXFoxQ5WypUYgI6LioeoOGilK+gERPQ8QAdFxUPpZxViQnouKh4gIaLUr6CRkxAxwN0XFQ8lmLO2tndRRnfO3EVfVMTh48eJhKJUFNTQ3d3NwB9/T9kdHTfxFX09xbdz6ePnWXXWJwGv4+HtqzG71ZmrIbjODQ1NVXkvaei4qLioYRSTFRcVDxAy0UFlZioeICOi4qHEkoxUXFR8QAtFxVUYqLiATouKh5qlCou9kq6KFWbm6l5WTtVVy7LWxvd5/NNmdH9PUWvoj91YYQvnOgF4NMbu+gKBxdDvSDGGEZGRiRauFRcVDyUUIqJiouKh5qLCioxUfFQclHxUEIpJiouKh5qLiqoxETFQ8lFxUONUsXFXkkvA47j0NDQsKAZBkNrGgitaSASibD/m/uBya7u/f3/yujoXny+GlatKnwVfSCR5K49JzDAW1tqecOyxnm7lAqFcSIZVFxUPEqRs6VCJSag46LiARouSvkKGjEBHQ/QcVHxUMpZlZiAjouKB2i4KOUraMQEdDxAx0XFYynmrJ3dXZyf/exnPPbYY7S3t/PBD34QYwy/eOaNjI7uYc3q32P9+o9Me40xhtufP8pj/cN0V4d47JqN1Ph8FbC3WCwWi8VisVgsFoud3b3CeJ5HT09PSVpRpq6Nnr6KvmfiKvr7Cr7mK2fO81j/MEHH4aHNq0gJzLxojGF4eLjiHkouKh5Q2pxdCEoxUXFR8VByUclX0ImJioeSi4oH6OSsUkxUXFQ8lFxU8hV0YqLioeSi4gFLM2dtkV4GjDEMDQ0t+OT09PRw9uxZfD4fW7duxRiTHYve1fUeAoHpkxLsHR3nY4dOA/Bf17ezta56QQ6WFwelylmLZTGw+Wq51LA5a7mUsPlqudRYijlrx6QLk7mKvmnTJqqrq+nr+1dGRl/A56suOKP7eMrj9/YcJ+oZfr25jg90teE4jkS3fhUP0HFR8VBCKSYqLioeoOWigkpMVDxAx0XFQwmlmKi4qHiAlosKKjFR8QAdFxUPNUoVF3slXZRkMsnu3buBdFd3Y0x2XfSuzncTDDZPe83HD59hXyRKW9DP5zevwnUcmZYlFQ8lFxUPJZRiouKi4qHmooJKTFQ8lFxUPJRQiomKi4qHmosKKjFR8VByUfFQo1RxsUV6GXAch9bW1gXNMHjw4EHGxsaora1l/fr19J//ESMjE1fRC4xFf7x/iIdP9wPw+ctW0RYMZB9zXY3TrOIBOi4qHqXI2VKhEhPQcVHxAA0XpXwFjZiAjgfouKh4KOWsSkxAx0XFAzRclPIVNGICOh6g46LisRRz1s7uLsp3v/tdduzYwQ033MBNN93EM8++mZGR51m96g42bPjjvOf2xOK8+pn9DCRSfHBlGw9s6KyQtcVisVgsFovFYrFYpmJnd68wnudx8uTJBc0w+MY3vpH3vOc9XHPNNZw//2NGRp7HdatYter9+e9lDHfvOcFAIsXW2ir+dF173uPGGC5cuFDxrigqHkouKh5QmpwtBUoxUXFR8VByUclX0ImJioeSi4oH6OSsUkxUXFQ8lFxU8hV0YqLioeSi4gFLM2ftxHFlwBhDZIHLnrmuy7p16zDG8MyzE2PRu/4zwWBL3vMeOtHLU4OjVLkuX7p8NaEC3SuCweC8PUqJigfouKh4lCJnS4VKTEDHRcUDNFyU8hU0YgI6HqDjouKhlLMqMQEdFxUP0HBRylfQiAnoeICOi4rHUsxZW6SLc/78TxgZ2Y3rVrF6ylX0Xw5HePBoDwD/78ZONlSHp73ecRxqamoWxXUmVDxAx0XFQwmlmKi4qHiAlosKKjFR8QAdFxUPJZRiouKi4gFaLiqoxETFA3RcVDzUKFVcbHd3YdLromeuor+LYLA1+9hoMsWH9xwnaeCNyxp554rps71n9jEwMFDxliUVDyUXFQ8llGKi4qLioeaigkpMVDyUXFQ8lFCKiYqLioeaiwoqMVHxUHJR8VCjVHGxRXoZcF2XFStWLHhmv/Pnf8LwyC5cN8zqVR/Ie+xPDpzi2HiczlCAv9jYNeNshuHw9CvslUDFA3RcVDxKlbOlQCUmoOOi4gEaLkr5ChoxAR0P0HFR8VDKWZWYgI6LigdouCjlK2jEBHQ8QMdFxWMp5qyd3V0UYwzPPvcWhod3smrl++ju/tPsY98+O8Cde0/gAt+9agPXNtZWTtRisVgsFovFYrFYLDNyyc3u/tBDD7FmzRrC4TDXXXcdv/jFL2b1uq9//es4jsOb3/zm8grOEc/zOHLkyIJmGBwY+DeGh3fiumFWrb4ju/34eIw/PnAKgHvXrLhogW6M4fz58xXviqLioeSi4gGlydlSoBQTFRcVDyUXlXwFnZioeCi5qHiATs4qxUTFRcVDyUUlX0EnJioeSi4qHrA0c7biRfo3vvEN7r33Xu6//35++ctfsn37dm6++WZ6e3tnfN2xY8f4yEc+wo033rhIprPHGEM8Hl/QyTnX+30AujrfRWhiLHrCM/zenuOMpjyua6jhntXLZ7Wv6urqeXuUEhUP0HFR8ShFzpYKlZiAjouKB2i4KOUraMQEdDxAx0XFQylnVWICOi4qHqDhopSvoBET0PEAHRcVj6WYsxUv0j/zmc/wgQ98gNtvv50tW7bw13/911RXV/O///f/LvqaVCrFu971Lh544AHWrVu3iLaLx+bLPsm2rV9iVc5Y9E8fO8svh8do8Pt4aMtq/G7xcegZHMehqqpqxjHri4GKh5KLiocSSjFRcVHxUHNRQSUmKh5KLioeSijFRMVFxUPNRQWVmKh4KLmoeKhRqrhUtEiPx+M899xz3HTTTdltruty00038fTTTxd93cc//nGWLVvG+973vsXQrAiO49LW9lpCoTYAnrowwuePnwPg05tW0hWe3fp7nufR399f8e4fKh5KLioeSijFRMVFxUPNRQWVmKh4KLmoeCihFBMVFxUPNRcVVGKi4qHkouKhRqniUtF10vv7+0mlUixfnt9te/ny5ezbt6/ga5566in+9m//lh07dszqPWKxGLFYLHt/eHgYSF+NT6VSQLrFw3VdPM/L6yZRbLvrujiOU3S7MYaOjg6MMaRSqexMg1NPVrHtPp8PY0x2+0Aiyd17TmCA321v5vUtdbN2N8ZQU1OTfXy+x5R5v4u5z7S9trY2b/+5jrnPn+v5mOsxOY5DXV1dwefP5Zimnqe5HlPm3BRzXMzzlMnZmdwX4zx5npfNV8dxSpJ78z1PuS6Z/VfqPOV+hhdyTKU4T1NdSvUdMZdjAujq6gLIe99Sf5fP5piMMdTW1k5zmesxLfQ8Zb5PMpTru3w2x5Rxybx/ub7LZ3NMuflaru/y2WwHpv1dUM7fucWOyXGcor+LF/s8FXKZzzEt9DzN9Lt4sc9Trsti/22Ue0y5f8fmfo7nc0y5LvM5ptn8Ll6M8wQU/Hu6Uuep0O/iuR7TQs8TQF1dXfZ7baHHtJDzZIyhq6tr2n4qcZ4yfxcUOqa5dMevaJE+V0ZGRnj3u9/Nl7/8ZVpbWy/+AuCTn/wkDzzwwLTthw8fzv5h1dDQQHt7O+fOnWNoaCj7nNbWVlpbWzl9+jSRSCS7fcWKFTQ2NnLs2DHi8Xh2e1dXF7W1tdMmLli7di1+v5+DBw/mOXR3d5NMJjl69Gh2m+u6bNy4kUgkwqlTpzDG8GcXEvTEPbqrQ3ykrTZvPzU1NaxcuZKBgQH6+/uz2zPH1NvbW5JjOnz48IKPKZlMcuzYsez2YDDIunXrGBoa4uzZs7M+plKdpwMHDpTsPC30mM6ePStxnurr6xkdHS3JMZXqPJUi90p1nip1TMePH5c5pp6eHpnz1N/fL3OeEonEksy9S/mYDh06JHNMZ86ckTlP0WhU5jwFAgGZ3KuqquLUqVMy52lwcFDmPC3V74i5HNPY2JjUMZ08eVLmPPX09MicJ6W/YcPh8LRjamlpYbZUdAm2eDxOdXU13/rWt/JmaL/11lsZHBzke9/7Xt7zd+zYwVVXXZVt0YLJVhTXddm/fz/r16/Pe02hK+mZk5KZ+r7UrVvxeJwjR46wbt06fD7fglq3vnLmPH966AxBx+H/Xt3NFbVVc2oJSiaT9Pf309LSkvWrRCuk4zj09fXR3NycfU6lWiEBzp8/T1NTU9ZlPse00FZIz/M4f/48y5YtAyhJ7s33PKVSKY4cOUJ3d3f2fedzTPPZnntMqVSK8+fP09LSgt/vr+iV9FyXzOd4Ma78Td3ueR59fX3Zz/BCjmmh58kYQ29vb55Lua/8FTomz/M4evQoa9euzfsMV+JKuud5DAwMFGw4XszzlPk+aWtrw+fzLdqVv0LHlHFpbW3F7/cvypW/Qu7JZDL7GXZdd9Gu/BXankgkOHz4cN7fBZW4kg4U/V282OcJ0j0qc13mc0wLPU+Zz3BbW3p4YSWvpOf+XZB53/kc00zbZ3NMmb8J1q1bRyAQWNAx5brM5zzN5nfxYpynVCqV97u4klfSi/0unusxLfQ8GWMYGBigubk5+/0y32Na6HlKpVIcO3aMdevWFXRZzPOU+50ytf4YHR2lsbFxVkuwVfRKejAY5Oqrr+aJJ57IFume5/HEE09w1113TXv+ZZddxu7du/O2/dmf/RkjIyN87nOfY+XKldNeEwqFCIVC07b7fL68Yh8mT/pU5ro9s9+p7zH1/Wba7jgOB8bjfPxIDwD/dX07W+uqiz5/Jpfm5uZswXOx51/smOa73RhDU1PTNA9IH+tcjmmh240xNDY2FnQp5D7T9mLuszkm13WzX2yFPAq5X2z7Qs8TLOyYFrrdcZxp+VqpYyrkUurviNlsz+TJ1Hyt1Hkq5FLMfa7b53JMnufhum5Zj3U27q7rZhv8Cn2OF+s8ZfJk6h9sM7nPd/vFjinjknlOub7LL+bu9/sL5mslv/em/l2w2Odppt/Fi32eZnIp9Hwoz3nKfIbn87u41Odp6t8Fi/m3USFHn883r9/FpTxPpfhdXIrzNNffxeU+T6X4XbzQ85T5ezo3T2bjXq7z5HleRf+Gzf05850y9fnFvmMKUfHu7vfeey+33nor11xzDddeey1/9Vd/RSQS4fbbbwfgPe95D52dnXzyk58kHA5zxRVX5L2+sbERYNr2S53xlMfv7TlO1DP8enMdH+hqm9d+HMchGJzdJHPlRMUDdFxUPJRQiomKi4oHaLmooBITFQ/QcVHxUEIpJiouKh6g5aKCSkxUPEDHRcVDjVLFpXBzwCLy9re/nU9/+tN89KMf5corr2THjh089thj2cnkTpw4QU9PT4UtF5//dvgM+yJR2oJ+Pr95Fe4cWl5y8TyPc+fOTesqstioeCi5qHgooRQTFRcVDzUXFVRiouKh5KLioYRSTFRcVDzUXFRQiYmKh5KLiocapYpLRcekV4Lh4WEaGhpmNRZgvhhjiMfjBIPBOXVryOWpCyPcvfcEn9m0kl9vmb+nMYZkMlm0O9lioeKh5KLikXFZaM6WykMpJgouKh5KLir5mnFRiYmCh5KLikfGRSFn1WKi4KLioeSikq8ZF5WYKHgouah4ZFwuhZydSx1qi/QykJmcoNgYxdkynvKo8lW8s4PlRUCpctZiWQxsvlouNWzOWi4lbL5aLjUulZydSx1qK8Ay4HkeBw8eXHA3h1IU6J7ncfbs2Yp3RVHxUHJR8ci4lCJnS+GhFBMFFxUPJReVfM24qMREwUPJRcUj46KQs2oxUXBR8VByUcnXjItKTBQ8lFxUPDIuSy1nbZG+xHEch7a2toq3Kql4KLmoeCihFBMVFxUPNRcVVGKi4qHkouKhhFJMVFxUPNRcVFCJiYqHkouKhxqliost0pc4mWU8Kv0BUvFQclHxUEIpJiouKh5qLiqoxETFQ8lFxUMJpZiouKh4qLmooBITFQ8lFxUPNUoVF1ukL3E8z6O3t7fi3T9UPJRcVDyUUIqJiouKh5qLCioxUfFQclHxUEIpJiouKh5qLiqoxETFQ8lFxUONUsXFThxXBtQmL8i4VBoVD9BxUfFQylmVmICOi4oHaLgo5StoxETJA3RcVDyUclYlJqDjouIBGi5K+QoaMVHyAB0XFY9LJWftxHECJJPJSisA6aTN3KyHlouKRwaFnFWKiYqLioeai0K+gk5MVDyUXFQ8MijkrFJMVFxUPNRcFPIVdGKi4qHkouKRYanlrC3Sy4DneRw9elSi+4cxhr6+vop/gFQ8lFxUPEAnZ5ViouKi4qHkopKvoBMTFQ8lFxUP0MlZpZiouKh4KLmo5CvoxETFQ8lFxQOWZs76S+RjEcV1XVasWFFpDRkP0HFR8VBCKSYqLioeoOWigkpMVDxAx0XFQwmlmKi4qHiAlosKKjFR8QAdFxUPNUoVF3slfYljjCGRSFS8lUvFQ8lFxUMJpZiouKh4qLmooBITFQ8lFxUPJZRiouKi4qHmooJKTFQ8lFxUPNQoVVxskV4mFCZRgHSiDAwMVPwDpOKh5KLikUEhZ5ViouKi4qHmopCvoBMTFQ8lFxWPDAo5qxQTFRcVDzUXhXwFnZioeCi5qHhkWGo5a2d3t1gsFovFYrFYLBaLpYzY2d0rjDGG0dFRiZYlYwzxeLziLioeSi4qHhkXhZxVi4mCi4qHkotKvmZcVGKi4KHkouKRcVHIWbWYKLioeCi5qORrxkUlJgoeSi4qHhmXpZaztkgvA57ncerUKZkZBgcHByuetCoeSi4qHqCTs0oxUXFR8VByUclX0ImJioeSi4oH6OSsUkxUXFQ8lFxU8hV0YqLioeSi4gFLM2ft7O5LHNd1WbZsWaU1ZDxAx0XFQwmlmKi4qHiAlosKKjFR8QAdFxUPJZRiouKi4gFaLiqoxETFA3RcVDzUKFVc7JX0JY4xhlgsVvFWLhUPJRcVDyWUYqLiouKh5qKCSkxUPJRcVDyUUIqJiouKh5qLCioxUfFQclHxUKNUcbFFehlwHIdgMIjjOJVWwRjDyMhIxT9AKh5KLioeoJOzSjFRcVHxUHJRyVfQiYmKh5KLigfo5KxSTFRcVDyUXFTyFXRiouKh5KLiAUszZ+3s7haLxWKxWCwWi8VisZQRO7t7hVGaSMEYw/j4eMVdVDyUXFQ8Mi4KOasWEwUXFQ8lF5V8zbioxETBQ8lFxSPjopCzajFRcFHxUHJRydeMi0pMFDyUXFQ8Mi5LLWdtkV4GPM/j7NmzEjMMAoyNjVVaAdDxAB0XFQ+lnFWJCei4qHiAhotSvoJGTEDHA3RcVDyUclYlJqDjouIBGi5K+QoaMQEdD9BxUfFYijlrZ3df4jiOQ0tLS6U1ZDxAx0XFQwmlmKi4qHiAlosKKjFR8QAdFxUPJZRiouKi4gFaLiqoxETFA3RcVDzUKFVc7JX0JY4xhrGxsYp3/1DxUHJR8VBCKSYqLioeai4qqMRExUPJRcVDCaWYqLioeKi5qKASExUPJRcVDzVKFRdbpJcBx3GoqamRmGEQIBqNVloB0PEAHRcVD6WcVYkJ6LioeICGi1K+gkZMQMcDdFxUPJRyViUmoOOi4gEaLkr5ChoxAR0P0HFR8ViKOWtnd7dYLBaLxWKxWCwWi6WM2NndK4znefT390tMXmCMIRKJVLwrioqHkouKB+jkrFJMVFxUPJRcVPIVdGKi4qHkouIBOjmrFBMVFxUPJReVfAWdmKh4KLmoeMDSzFlbpJcBYwz9/f0SSQsQj8crrQDoeICOi4qHUs6qxAR0XFQ8QMNFKV9BIyag4wE6LioeSjmrEhPQcVHxAA0XpXwFjZiAjgfouKh4LMWctbO7L3Ecx6GpqanSGjIeoOOi4qGEUkxUXFQ8QMtFBZWYqHiAjouKhxJKMVFxUfEALRcVVGKi4gE6LioeapQqLvZK+hLHGMPIyEjFW5ZUPJRcVDyUUIqJiouKh5qLCioxUfFQclHxUEIpJiouKh5qLiqoxETFQ8lFxUONUsXFFullwHEcGhoaZGYYVBifAToeoOOi4qGUsyoxAR0XFQ/QcFHKV9CICeh4gI6LiodSzqrEBHRcVDxAw0UpX0EjJqDjATouKh5LMWft7O4Wi8VisVgsFovFYrGUETu7e4XxPI+enh6J1iVjDMPDwxXviqLioeSi4gE6OasUExUXFQ8lF5V8BZ2YqHgouah4gE7OKsVExUXFQ8lFJV9BJyYqHkouKh6wNHPWFullwBjD0NCQRNJaLLPB5qzlUsLmq+VSw+as5VLC5qvlUmMp5qyd3X2J4ziORLd+FQ/QcVHxUEIpJiouKh6g5aKCSkxUPEDHRcVDCaWYqLioeICWiwoqMVHxAB0XFQ81ShWXF12RnmlhGR4eLtt7pFIpRkdHGR4exufzle19ZkOmy0V9fX1FJ1NQ8VByUfEAnZxViomKi4qHkotKvoJOTFQ8lFxUPEAnZ5ViouKi4qHkopKvoBMTFQ8lFxUPuHRyNlN/zuaK/4uuSB8ZGQFg5cqVFTaxWCwWi8VisVgsFsuLiZGRERoaGmZ8zotudnfP8zhz5gx1dXVla/UZHh5m5cqVnDx5UqIbyEtf+lKeeeaZSmvIeICOi4qHUs6qxAR0XFQ8QMNFKV9BIyZKHqDjouKhlLMqMQEdFxUP0HBRylfQiImSB+i4qHhcKjmbWUO9o6MD1515argX3ZV013Xp6upalPeqr6+XSBSfz2c9pqDiouKRQSFnlWKi4qLiAVouCvkKOjFR8QAdFxWPDAo5qxQTFRcVD9ByUchX0ImJigfouKh4ZLgUcvZiV9Az2NndXwTceeedlVYAdDxAx0XFQwmlmKi4qHiAlosKKjFR8QAdFxUPJZRiouKi4gFaLiqoxETFA3RcVDzUKEVcXnTd3ReDuSxUb7EoYHPWcilh89VyqWFz1nIpYfPVcqmxFHPWXkkvA6FQiPvvv59QKFRpFYtlVtictVxK2Hy1XGrYnLVcSth8tVxqLMWctVfSLRaLxWKxWCwWi8ViEcFeSbdYLBaLxWKxWCwWi0UEW6RbLBaLxWKxWCwWi8Uigi3SLRaLxWKxWCwWi8ViEcEW6RaLxWKxWCwWi8VisYhgi/Qy8NBDD7FmzRrC4TDXXXcdv/jFLyqtZLEA8G//9m/ccsstdHR04DgO3/3ud/MeN8bw0Y9+lPb2dqqqqrjppps4ePBgZWQtL3o++clP8tKXvpS6ujqWLVvGm9/8Zvbv35/3nGg0yp133klLSwu1tbW85S1v4dy5cxUytryY+dKXvsS2bduor6+nvr6e66+/nh/84AfZx22uWpT51Kc+heM43HPPPdltNmctSnzsYx/DcZy822WXXZZ9fKnlqy3SS8w3vvEN7r33Xu6//35++ctfsn37dm6++WZ6e3srrWaxEIlE2L59Ow899FDBx//H//gffP7zn+ev//qv+fnPf05NTQ0333wz0Wh0kU0tFnjyySe58847+dnPfsYPf/hDEokEr33ta4lEItnn/OEf/iH/9E//xKOPPsqTTz7JmTNn+O3f/u0KWlterHR1dfGpT32K5557jmeffZbf+I3f4E1vehMvvPACYHPVosszzzzD//yf/5Nt27blbbc5a1Hj8ssvp6enJ3t76qmnso8tuXw1lpJy7bXXmjvvvDN7P5VKmY6ODvPJT36yglYWy3QA853vfCd73/M8s2LFCvMXf/EX2W2Dg4MmFAqZv//7v6+AocWST29vrwHMk08+aYxJ52cgEDCPPvpo9jl79+41gHn66acrpWmxZGlqajJ/8zd/Y3PVIsvIyIjp7u42P/zhD80rX/lK8wd/8AfGGPv9atHj/vvvN9u3by/42FLMV3slvYTE43Gee+45brrppuw213W56aabePrppytoZrFcnKNHj3L27Nm8/G1oaOC6666z+WuRYGhoCIDm5mYAnnvuORKJRF7OXnbZZaxatcrmrKWipFIpvv71rxOJRLj++uttrlpkufPOO/lP/+k/5eUm2O9XiyYHDx6ko6ODdevW8a53vYsTJ04ASzNf/ZUWWEr09/eTSqVYvnx53vbly5ezb9++CllZLLPj7NmzAAXzN/OYxVIpPM/jnnvu4YYbbuCKK64A0jkbDAZpbGzMe67NWUul2L17N9dffz3RaJTa2lq+853vsGXLFnbs2GFz1SLH17/+dX75y1/yzDPPTHvMfr9a1Ljuuut45JFH2LRpEz09PTzwwAPceOONPP/880syX22RbrFYLBZ57rzzTp5//vm88WcWixqbNm1ix44dDA0N8a1vfYtbb72VJ598stJaFss0Tp48yR/8wR/wwx/+kHA4XGkdi+WivO51r8v+vG3bNq677jpWr17NN7/5TaqqqipoVh5sd/cS0trais/nmzaT4Llz51ixYkWFrCyW2ZHJUZu/FjXuuusu/vmf/5kf//jHdHV1ZbevWLGCeDzO4OBg3vNtzloqRTAYZMOGDVx99dV88pOfZPv27Xzuc5+zuWqR47nnnqO3t5eXvOQl+P1+/H4/Tz75JJ///Ofx+/0sX77c5qxFmsbGRjZu3MihQ4eW5HesLdJLSDAY5Oqrr+aJJ57IbvM8jyeeeILrr7++gmYWy8VZu3YtK1asyMvf4eFhfv7zn9v8tVQEYwx33XUX3/nOd/jRj37E2rVr8x6/+uqrCQQCeTm7f/9+Tpw4YXPWIoHnecRiMZurFjle/epXs3v3bnbs2JG9XXPNNbzrXe/K/mxz1qLM6Ogohw8fpr29fUl+x9ru7iXm3nvv5dZbb+Waa67h2muv5a/+6q+IRCLcfvvtlVazWBgdHeXQoUPZ+0ePHmXHjh00NzezatUq7rnnHj7xiU/Q3d3N2rVr+fM//3M6Ojp485vfXDlpy4uWO++8k6997Wt873vfo66uLjuurKGhgaqqKhoaGnjf+97HvffeS3NzM/X19dx9991cf/31vOxlL6uwveXFxn333cfrXvc6Vq1axcjICF/72tf4yU9+wuOPP25z1SJHXV1ddn6PDDU1NbS0tGS325y1KPGRj3yEW265hdWrV3PmzBnuv/9+fD4f73znO5fkd6wt0kvM29/+dvr6+vjoRz/K2bNnufLKK3nsscemTcZlsVSCZ599ll//9V/P3r/33nsBuPXWW3nkkUf4oz/6IyKRCHfccQeDg4P82q/9Go899pgdr2apCF/60pcAeNWrXpW3/eGHH+a2224D4LOf/Syu6/KWt7yFWCzGzTffzBe/+MVFNrVYoLe3l/e85z309PTQ0NDAtm3bePzxx3nNa14D2Fy1XHrYnLUocerUKd75zndy/vx52tra+LVf+zV+9rOf0dbWBiy9fHWMMabSEhaLxWKxWCwWi8VisVjsmHSLxWKxWCwWi8VisVhksEW6xWKxWCwWi8VisVgsItgi3WKxWCwWi8VisVgsFhFskW6xWCwWi8VisVgsFosItki3WCwWi8VisVgsFotFBFukWywWi8VisVgsFovFIoIt0i0Wi8VisVgsFovFYhHBFukWi8ViscwCYwx33HEHzc3NOI7Djh075ryPV73qVdxzzz0ld5stjzzyCI2Njdn7H/vYx7jyyiuz92+77Tbe/OY3L7pXpZnNca9Zs4a/+qu/yt53HIfvfve7ZfWyWCwWy4sTW6RbLBaL5ZLjtttuw3EcPvWpT+Vt/+53v4vjOGV5z8cee4xHHnmEf/7nf6anp4crrriiLO+zmHzkIx/hiSeeqLTGnJlaMC8GzzzzDHfccceivqfFYrFYXpzYIt1isVgslyThcJgHH3yQCxcuzPo1qVQKz/Pm9X6HDx+mvb2dl7/85axYsQK/3z+v/ShRW1tLS0vLgvYRj8fn9bqFnItK0NbWRnV1daU1LBaLxfIiwBbpFovFYrkkuemmm1ixYgWf/OQniz4n0737H//xH9myZQuhUIgTJ04UfO6TTz7JtddeSygUor29nT/5kz8hmUwC6Sv3d999NydOnMBxHNasWVP0PX/605/yqle9iurqapqamrj55puLNiRcuHCB97znPTQ1NVFdXc3rXvc6Dh48mH38+PHj3HLLLTQ1NVFTU8Pll1/O97///ezjzz//PK973euora1l+fLlvPvd76a/v3+msOUxtbt7hgceeIC2tjbq6+v50Ic+lFeIv+pVr+Kuu+7innvuobW1lZtvvhmAz3zmM2zdupWamhpWrlzJhz/8YUZHR7OvK3QunnrqKQKBAGfPns17/3vuuYcbb7xx1sfhOA5/8zd/w2/91m9RXV1Nd3c3//iP/5j3nBdeeIE3vOEN1NfXU1dXx4033sjhw4fznvPpT3+a9vZ2WlpauPPOO0kkEtnHLnb1/v7776e9vZ1du3YB8NRTT3HjjTdSVVXFypUr+f3f/30ikQgAH//4xwv2xLjyyiv58z//cwB+8pOfcO2111JTU0NjYyM33HADx48fn3VMLBaLxXLpYot0i8VisVyS+Hw+/vt//+984Qtf4NSpU0WfNzY2xoMPPsjf/M3f8MILL7Bs2bJpzzl9+jSvf/3reelLX8rOnTv50pe+xN/+7d/yiU98AoDPfe5zfPzjH6erq4uenh6eeeaZgu+1Y8cOXv3qV7NlyxaefvppnnrqKW655RZSqVTB59922208++yz/OM//iNPP/00xhhe//rXZ4vDO++8k1gsxr/927+xe/duHnzwQWprawEYHBzkN37jN7jqqqt49tlneeyxxzh37hxve9vb5hTHqTzxxBPs3buXn/zkJ/z93/89//AP/8ADDzyQ95yvfOUrBINBfvrTn/LXf/3XALiuy+c//3leeOEFvvKVr/CjH/2IP/qjP8p73dRzcc0117Bu3Tq++tWvZp+TSCT4u7/7O9773vfOyfuBBx7gbW97G7t27eL1r38973rXuxgYGADS5/cVr3gFoVCIH/3oRzz33HO8973vzTbCAPz4xz/m8OHD/PjHP+YrX/kKjzzyCI888shF39cYw913383/+T//h3//939n27ZtHD58mN/8zd/kLW95C7t27eIb3/gGTz31FHfddRcA733ve9m7d29eHv3qV79i165d3H777SSTSd785jfzyle+kl27dvH0009zxx13lG0oh8VisVjEMBaLxWKxXGLceuut5k1vepMxxpiXvexl5r3vfa8xxpjvfOc7JvdX28MPP2wAs2PHjhn396d/+qdm06ZNxvO87LaHHnrI1NbWmlQqZYwx5rOf/axZvXr1jPt55zvfaW644Yaij7/yla80f/AHf2CMMebAgQMGMD/96U+zj/f395uqqirzzW9+0xhjzNatW83HPvaxgvv6b//tv5nXvva1edtOnjxpALN///6Cr3n44YdNQ0ND9v79999vtm/fnr1/6623mubmZhOJRLLbvvSlL+XF4ZWvfKW56qqrih5jhkcffdS0tLTkvXehc/Hggw+azZs3Z+9/+9vfNrW1tWZ0dLTovlevXm0++9nPZu8D5s/+7M+y90dHRw1gfvCDHxhjjLnvvvvM2rVrTTweL7i/W2+91axevdokk8nstre+9a3m7W9/+4zv+eijj5rf/d3fNZs3bzanTp3KPva+973P3HHHHXnv8e///u/GdV0zPj5ujDHmda97nfm93/u97ON33323edWrXmWMMeb8+fMGMD/5yU+KxsBisVgsSxd7Jd1isVgslzQPPvggX/nKV9i7d2/Bx4PBINu2bZtxH3v37uX666/Pu1J5ww03MDo6OuNV+qlkrqTPhr179+L3+7nuuuuy21paWti0aVP2WH7/93+fT3ziE9xwww3cf//92a7UADt37uTHP/4xtbW12dtll10GMK0b91zYvn173tjr66+/ntHRUU6ePJnddvXVV0973b/+67/y6le/ms7OTurq6nj3u9/N+fPnGRsbyz6n0Lm47bbbOHToED/72c+AdLf4t73tbdTU1MzJO3e/NTU11NfX09vbC6TPy4033kggECj6+ssvvxyfz5e9397enn19Mf7wD/+Qn//85/zbv/0bnZ2d2e07d+7kkUceyTs3N998M57ncfToUQA+8IEP8Pd///dEo1Hi8Thf+9rXsr0Hmpubue2227j55pu55ZZb+NznPkdPT8+c4mGxWCyWSxdbpFssFovlkuYVr3gFN998M/fdd1/Bx6uqqhatm3BVVVVJ9/f+97+fI0eO8O53v5vdu3dzzTXX8IUvfAGA0dFRbrnlFnbs2JF3O3jwIK94xStK6jGVqQX0sWPHeMMb3sC2bdv49re/zXPPPcdDDz0E5E8sV+hcLFu2jFtuuYWHH36Yc+fO8YMf/GDOXd2BaQW44zjZielmc15men0xXvOa13D69Gkef/zxvO2jo6N88IMfzDsvO3fu5ODBg6xfvx6AW265hVAoxHe+8x3+6Z/+iUQiwe/8zu9k9/Hwww/z9NNP8/KXv5xvfOMbbNy4MduQYbFYLJalzaU/Na3FYrFYXvR86lOf4sorr2TTpk3zev3mzZv59re/jTEmW0T+9Kc/pa6ujq6urlnvZ9u2bTzxxBPTxnAXe89kMsnPf/5zXv7ylwNw/vx59u/fz5YtW7LPW7lyJR/60If40Ic+xH333ceXv/xl7r77bl7ykpfw7W9/mzVr1pR0pvmdO3cyPj6eLWx/9rOfUVtby8qVK4u+5rnnnsPzPP7yL/8S1023/3/zm9+c9Xu+//3v553vfCddXV2sX7+eG264YWEHMYVt27bxla98hUQiMePV9Lnyxje+kVtuuYXf/d3fxefz8Y53vAOAl7zkJezZs4cNGzYUfa3f7+fWW2/l4YcfJhgM8o53vGNaY8JVV13FVVddxX333cf111/P1772NV72speVzN9isVgsmtgr6RaLxWK55Nm6dSvvete7+PznPz+v13/4wx/m5MmT3H333ezbt4/vfe973H///dx7773ZonM23HfffTzzzDN8+MMfZteuXezbt48vfelLBWdc7+7u5k1vehMf+MAHeOqpp9i5cyf/+T//Zzo7O3nTm94EpGc5f/zxxzl69Ci//OUv+fGPf8zmzZuB9KRyAwMDvPOd7+SZZ57h8OHDPP7449x+++1FJ6qbDfF4nPe9733s2bOH73//+9x///3cddddM8Zhw4YNJBIJvvCFL3DkyBG++tWvZieUmw0333wz9fX1fOITn+D222+ft3sx7rrrLoaHh3nHO97Bs88+y8GDB/nqV7/K/v37F7zv3/qt3+KrX/0qt99+O9/61rcA+OM//mP+4z/+g7vuuivbu+F73/teduK4DO9///v50Y9+xGOPPZbXe+Do0aPcd999PP300xw/fpx/+Zd/4eDBg9lzb7FYLJaljS3SLRaLxbIk+PjHPz7vdbc7Ozv5/ve/zy9+8Qu2b9/Ohz70Id73vvfxZ3/2Z3Paz8aNG/mXf/kXdu7cybXXXsv111/P9773vaJXuh9++GGuvvpq3vCGN3D99ddjjOH73/9+9mpvKpXizjvvZPPmzfzmb/4mGzdu5Itf/CIAHR0d/PSnPyWVSvHa176WrVu3cs8999DY2DinhoWpvPrVr6a7u5tXvOIVvP3tb+eNb3wjH/vYx2Z8zfbt2/nMZz7Dgw8+yBVXXMHf/d3fzbg03lRc1+W2224jlUrxnve8Z97uxWhpaeFHP/oRo6OjvPKVr+Tqq6/my1/+csmuqv/O7/wOX/nKV3j3u9/NP/zDP7Bt2zaefPJJDhw4wI033shVV13FRz/6UTo6OvJe193dzctf/nIuu+yyvLkJqqur2bdvH295y1vYuHEjd9xxB3feeScf/OAHS+JrsVgsFm0cY4yptITFYrFYLJYXN+973/vo6+ubtr75UsYYQ3d3Nx/+8Ie59957K61jsVgsFhHsmHSLxWKxWCwVY2hoiN27d/O1r33tRVWg9/X18fWvf52zZ8+WpYu/xWKxWC5dbJFusVgsFoulYrzpTW/iF7/4BR/60Id4zWteU2mdRWPZsmW0trbyv/7X/6KpqanSOhaLxWIRwnZ3t1gsFovFYrFYLBaLRQQ7cZzFYrFYLBaLxWKxWCwi2CLdYrFYLBaLxWKxWCwWEWyRbrFYLBaLxWKxWCwWiwi2SLdYLBaLxWKxWCwWi0UEW6RbLBaLxWKxWCwWi8Uigi3SLRaLxWKxWCwWi8ViEcEW6RaLxWKxWCwWi8VisYhgi3SLxWKxWCwWi8VisVhEsEW6xWKxWCwWi8VisVgsIvz/AfIXW7YqUdDeAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (12, 10))\n", + "for fraction_predicted in range(0, 50, 5):\n", + " plt.plot([1,3,5,6,7,8,9,10,11,12,13,14,15,20,50], [results_average_mean_top_k_max_highest_ms2deepscore[k][fraction_predicted] for k in results_average_mean_top_k_max_highest_ms2deepscore.keys()], label=f\"Fraction predicted = {fraction_predicted + 1} %\")\n", + "plt.legend()\n", + "# plt.xlim(0,10)\n", + "ax = plt.gca()\n", + "ax.xaxis.set_minor_locator(plt.MultipleLocator(2))\n", + "ax.grid(which='both', linestyle='--', alpha=0.5)\n", + "ax.grid(which='minor', linestyle=':', alpha=0.3)\n", + "plt.ylabel(\"Average tanimoto\")\n", + "plt.xlabel(\"Nr of close library Inchikeys\")" + ] + }, + { + "cell_type": "markdown", + "id": "650d8aa5-ed03-4496-a158-15995f950982", + "metadata": {}, + "source": [ + "# Other reliability prediction strategies\n", + "Using the mean of the max ms2deepscore top 8 is one of the best solutions. Below we show some interesting other variants that we tested. Which did not improve performance further. Also we test some correlations between these different approaches to get some more insights into why using this method works in the first place. " + ] + }, + { + "cell_type": "markdown", + "id": "40dba182-b890-43cb-87b6-1613a753563e", + "metadata": {}, + "source": [ + "# Threshold on precursor mz" + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "id": "24d960a6-d9e6-4544-b0e8-d6ded0f6a31f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 359.31it/s]\n" + ] + } + ], + "source": [ + "average_tanimoto_precursor_mz = plot_average_per_threshold([spectrum.get('precursor_mz') for spectrum in pos_val_spectra.spectra], tanimoto_for_prediction, pos_val_spectra, 20)" + ] + }, + { + "cell_type": "markdown", + "id": "92a0d224-0d11-465a-841e-4c0578f3307f", + "metadata": {}, + "source": [ + "# Threshold on Embedding Evaluator\n", + "With MS2DeepScore 2.0 also an embedding evaluator was released. The goal is to predict reliability of a spectrum. Here we use this to predict reliability of the MS2DeepScore prediction. " + ] + }, + { + "cell_type": "code", + "execution_count": 293, + "id": "be9cacee-5f6c-4e5e-a301-cad4f1f68563", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading file: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 773k/773k [00:00<00:00, 5.09MB/s]\n" + ] + } + ], + "source": [ + "from ms2deepscore.models.load_model import load_embedding_evaluator\n", + "download_file(\"https://zenodo.org/records/17826815/files/embedding_evaluator.pt?download=1\",\n", + " os.path.join(folder_to_store_zenodo_files, \"embedding_evaluator.pt\"))\n", + "embedding_evaluator = load_embedding_evaluator(os.path.join(folder_to_store_zenodo_files, \"embedding_evaluator.pt\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "99b22706-7c43-49ee-acfb-a966857c03a0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 73/73 [00:12<00:00, 5.75it/s]\n" + ] + } + ], + "source": [ + "def predict_embedding_evaluation_per_batch(\n", + " embeddings, embedding_evaluator, batch_size: int = 500\n", + ") -> np.ndarray:\n", + " embedding_evaluation_per_batch = []\n", + " num_of_query_embeddings = embeddings.embeddings.shape[0]\n", + " # loop over the batches\n", + " for start_idx in tqdm(\n", + " range(0, num_of_query_embeddings, batch_size),\n", + " desc=\"Predicting highest ms2deepscore per batch of \"\n", + " + str(min(batch_size, num_of_query_embeddings))\n", + " + \" embeddings\",\n", + " ):\n", + " end_idx = min(start_idx + batch_size, num_of_query_embeddings)\n", + " selected_query_embeddings = embeddings.embeddings[start_idx:end_idx]\n", + " evaluations_for_batch = embedding_evaluator.compute_embedding_evaluations(selected_query_embeddings)\n", + " \n", + " embedding_evaluation_per_batch.append(evaluations_for_batch)\n", + " return np.concatenate(embedding_evaluation_per_batch)\n", + "embedding_evaluations = predict_embedding_evaluation_per_batch(pos_val_spectra.embeddings, embedding_evaluator)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "c737058d-214d-411c-833b-6b5d6633bffa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spectrum_count_per_bucket\n", + "[536, 530, 551, 680, 744, 726, 818, 819, 897, 1113, 1303, 1174, 1455, 1441, 1491, 2052, 2674, 2877, 4815, 9777]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20it [00:00, 412.67it/s]\n" + ] + } + ], + "source": [ + "average_tanimoto_embedding_evaluator = plot_average_per_threshold(-embedding_evaluations, tanimoto_for_prediction, pos_val_spectra, 20)" + ] + }, + { + "cell_type": "markdown", + "id": "7e40d8b3-2112-46be-aad3-77f52721dff3", + "metadata": {}, + "source": [ + "### Combine ms2deepscore + embedding evaluator\n" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "dfe27c80-ca2b-453b-8fde-cb9a6cf0e6bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spectrum_count_per_bucket\n", + "[540, 715, 789, 1047, 1063, 1234, 1065, 1194, 1563, 1440, 1682, 1529, 1909, 1786, 1906, 2066, 2199, 2780, 2927, 7039]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20it [00:00, 388.93it/s]\n" + ] + } + ], + "source": [ + "average_tanimoto_ms2deepscore_min_embedding_evaluator_squarroot = plot_average_per_threshold(predicted_ms2deepscores-embedding_evaluations**0.5, tanimoto_for_prediction, pos_val_spectra, 20)" + ] + }, + { + "cell_type": "markdown", + "id": "1f338546-0140-440f-b007-eb38811ed7a3", + "metadata": {}, + "source": [ + "# Use uniqueness in library\n", + "The average MS2DeepScore for top 10, is strongly influenced by the tanimoto score for the top 10. If there are no highly similar metabolites in the library (so low tanimoto scores), the MS2DeepScores will probably also be very low. So low reliability will be predicted. To get insight in if this is a good thing and if this is a large part of the ability to predict reliability, we will test this as well. " + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "ed4150be-c0ea-4240-8f37-b52417e78120", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 36473/36473 [00:18<00:00, 2009.90it/s]\n" + ] + } + ], + "source": [ + "average_top_10_tanimoto = []\n", + "for inchikey in tqdm(predicted_inchikeys):\n", + " tanimoto_scores = np.array(list(top_k_tanimoto_scores.select_top_k_inchikeys_and_scores(inchikey).values()))\n", + " average_top_10_tanimoto.append(np.sort(tanimoto_scores)[-10:].mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "ec9ec138-9712-423c-8f6a-e13d0ead5de6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spectrum_count_per_bucket\n", + "[340, 1121, 1131, 1326, 1627, 1420, 1709, 1853, 1423, 1591, 1324, 1352, 1435, 1161, 1371, 1613, 1657, 2098, 3280, 7641]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20it [00:00, 369.63it/s]\n" + ] + } + ], + "source": [ + "average_tanimoto_average_top_10_tanimoto = plot_average_per_threshold(average_top_10_tanimoto, tanimoto_for_prediction, pos_val_spectra, 20)" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "id": "a81c12e3-ba44-445c-aca2-24c4aa07591f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 346.52it/s]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 385.52it/s]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 349.33it/s]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 351.25it/s]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 357.26it/s]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 367.26it/s]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 349.42it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 8))\n", + "plot_comparison_plot({\n", + " # \"MS2Query 1.0\": mean_per_fraction_ms2query,\n", + " # \"ms2deepscore (reliability = mean ms2deepscore top 10)\": mean_per_fraction_average_mean_top_tanimoto,\n", + " \"MS2DeepScore\": mean_per_ms2deepscore,\n", + " # \"Best possible reliabitly prediction\": mean_per_fraction_best_possible_ranker,\n", + " \"MS2Query 2.0\": plot_average_per_threshold(threshold_score_average_max_top_tanimoto, tanimoto_for_prediction, pos_val_spectra, 100),\n", + " \"precursor mz\": plot_average_per_threshold([spectrum.get('precursor_mz') for spectrum in pos_val_spectra.spectra], tanimoto_for_prediction, pos_val_spectra, 100),\n", + " \"embedding_evaluator\": plot_average_per_threshold(-embedding_evaluations, tanimoto_for_prediction, pos_val_spectra, 100),\n", + " \"Average tanimoto top 10 closest library inchikeys\": plot_average_per_threshold(average_top_10_tanimoto, tanimoto_for_prediction, pos_val_spectra, 100),\n", + " \"MS2DeepScore - embedding evaluator^0.5\": plot_average_per_threshold(predicted_ms2deepscores-embedding_evaluations**0.5, tanimoto_for_prediction, pos_val_spectra, 100),\n", + " \"Precursor * (MS2DeepScore - embedding evaluator^0.5) ** 3\": plot_average_per_threshold(np.array(precursor_mz) * (np.array(predicted_ms2deepscores) - embedding_evaluations**0.5)**3, tanimoto_for_prediction, pos_val_spectra, 100),\n", + " \"MS2Query 2 - Embedding evaluator^0.5\": plot_average_per_threshold(threshold_score_average_max_top_tanimoto - embedding_evaluations** 0.5, tanimoto_for_prediction, pos_val_spectra, 100),\n", + " })" + ] + }, + { + "cell_type": "markdown", + "id": "a108f019-152c-4aa4-8d5e-796b7fe00f4b", + "metadata": {}, + "source": [ + "# Explore the reason MS2Query 2 works\n", + "Above we have explored all these different methods for predicting reliability. We want to explore a bit more what is going on under the hood. Is the power taking the average over multiple scores? Or is the MS2Query 2 method mostly guided by how common a library molecule is, so more a mean of testing if the prediction was well in the training distribution. Here we do some correlation plots to get some insights in this. " + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "c2336b8d-d987-41ad-a010-eceeb7deb81b", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# !pip install seaborn\n", + "import seaborn as sns\n" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "id": "2bea7c80-b698-44b9-881e-cec22ea4a713", + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "def plot_pairplot_with_color_gradient(df):\n", + " cmap = 'viridis'\n", + " hue_col = \"Real Tanimoto score\"\n", + " plot_vars = [\"precursor mz\", \"Average MS2DeepScore top 10\", \n", + " \"Average Tanimoto score top 10\", \"embedding evaluator\", \"ms2deepscore\"]\n", + " \n", + " norm = plt.Normalize(df[hue_col].min(), df[hue_col].max())\n", + " colors = plt.cm.viridis(norm(df[hue_col]))\n", + " \n", + " def offdiag(x, y, **kwargs):\n", + " c = colors[x.index] # align colors to current subset index\n", + " m, b = np.polyfit(x, y, 1)\n", + " plt.scatter(x, y, alpha=0.3, c=c, s=1)\n", + " x_sorted = np.sort(x)\n", + " plt.plot(x_sorted, m * x_sorted + b, color='#E8A838', linewidth=1.5, linestyle='--')\n", + " \n", + " def diag(x, **kwargs):\n", + " ax = plt.gca()\n", + " \n", + " # Compute KDE\n", + " kde = gaussian_kde(x.dropna())\n", + " x_range = np.linspace(x.min(), x.max(), 300)\n", + " density = kde(x_range)\n", + " \n", + " # Normalize so the peak always hits the same height\n", + " density_scaled = density / density.max()\n", + " \n", + " ax.fill_between(x_range, density_scaled, alpha=0.4, color='steelblue')\n", + " ax.plot(x_range, density_scaled, color='steelblue', linewidth=1.2)\n", + " ax.set_ylim(0, 1.15) # consistent y-axis across all diagonal cells\n", + " \n", + " g = sns.PairGrid(df, vars=plot_vars)\n", + " g.map_offdiag(offdiag)\n", + " g.map_diag(diag)\n", + " g.figure.subplots_adjust(hspace=-0.5, wspace=0.1)\n", + " \n", + " # Smaller tick and axis labels to prevent overlap\n", + " for ax in g.axes.flatten():\n", + " ax.set_box_aspect(1)\n", + " ax.tick_params(labelsize=7)\n", + " ax.xaxis.label.set_size(8)\n", + " ax.yaxis.label.set_size(8)\n", + " # Colorbar\n", + " sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n", + " sm.set_array([])\n", + " cbar = g.figure.colorbar(sm, ax=g.axes, label=hue_col, shrink=0.6, pad=0.02)\n", + " \n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 284, + "id": "537aa882-a2d3-4e7e-9994-4237c2cc6cc2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "reliability_predictions_all_spectra = pd.DataFrame({\n", + " \"precursor mz\": precursor_mz,\n", + " \"Average MS2DeepScore top 10\": threshold_score_average_max_top_tanimoto,\n", + " \"Real Tanimoto score\": tanimoto_for_prediction,\n", + " \"Average Tanimoto score top 10\": average_top_10_tanimoto, \n", + " \"embedding evaluator\": embedding_evaluations,\n", + " \"ms2deepscore\": predicted_ms2deepscores,\n", + "})\n", + "plot_pairplot_with_color_gradient(reliability_predictions_all_spectra)" + ] + }, + { + "cell_type": "markdown", + "id": "865e41b7-773a-447d-9731-2e9669ca6ecd", + "metadata": {}, + "source": [ + "# Plot one spectrum per inchikey\n", + "Above each validation spectrum is plotted. This makes the overview a bit cluttered, since these are more than 50.000 spectra. But also it might give a bit too much weight to inchikeys with a lot of spectra. So below we randomly select one validation spectrum per unique inchikey. " + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "id": "843147f3-bb06-4660-80be-e296fc199ddf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import random\n", + "\n", + "indexes_of_spectra_per_inchikey = []\n", + "for inchikey, spectrum_indexes in pos_val_spectra.spectrum_indices_per_inchikey.items():\n", + " indexes_of_spectra_per_inchikey.append(random.choice(spectrum_indexes))\n", + " \n", + "reliability_predictions_one_specrum_per_inchikey = pd.DataFrame({\n", + " \"precursor mz\": np.array(precursor_mz)[indexes_of_spectra_per_inchikey],\n", + " \"Average MS2DeepScore top 10\": np.array(threshold_score_average_max_top_tanimoto)[indexes_of_spectra_per_inchikey],\n", + " \"Real Tanimoto score\": np.array(tanimoto_for_prediction)[indexes_of_spectra_per_inchikey],\n", + " \"Average Tanimoto score top 10\": np.array(average_top_10_tanimoto)[indexes_of_spectra_per_inchikey],\n", + " \"embedding evaluator\": np.array(embedding_evaluations)[indexes_of_spectra_per_inchikey],\n", + " \"ms2deepscore\": np.array(predicted_ms2deepscores)[indexes_of_spectra_per_inchikey]\n", + "})\n", + "plot_pairplot_with_color_gradient(reliability_predictions_one_specrum_per_inchikey)" + ] + }, + { + "cell_type": "markdown", + "id": "5a226247-5318-494e-92d2-09b2ef53373c", + "metadata": {}, + "source": [ + "#" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "id": "4655288d-ccd1-49ee-a488-bbdbf67dd839", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 286, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "reliability_predictions_all_spectra[\"hue_binned\"] = pd.cut(reliability_predictions_all_spectra[\"Real Tanimoto score\"], bins=3, labels=[\"0-0.33\", \"0.33-0.66\", \"0.66-1.0\"])\n", + "sns.pairplot(reliability_predictions_all_spectra, kind=\"scatter\", hue=\"hue_binned\", vars = [\"precursor mz\", \"Average MS2DeepScore top 10\", \"Average Tanimoto score top 10\", \"embedding evaluator\"],\n", + " # diag_kind=\"hist\", \n", + " plot_kws={\"alpha\": 1.0, \"rasterized\":True, \"s\":1})" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "id": "1155183c-80c9-4641-9802-edc8d1189f78", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 287, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "reliability_predictions_one_specrum_per_inchikey[\"hue_binned\"] = pd.cut(reliability_predictions_one_specrum_per_inchikey[\"Real Tanimoto score\"], bins=3, labels=[\"0-0.33\", \"0.33-0.66\", \"0.66-1.0\"])\n", + "sns.pairplot(reliability_predictions_one_specrum_per_inchikey, kind=\"scatter\", hue=\"hue_binned\", vars = [\"precursor mz\", \"Average MS2DeepScore top 10\", \"Average Tanimoto score top 10\", \"embedding evaluator\"],\n", + " # diag_kind=\"hist\", \n", + " plot_kws={\"alpha\": 1.0, \"rasterized\":True, \"s\":1})" + ] + }, + { + "cell_type": "markdown", + "id": "5e06d65b-84f9-4c69-b6d3-94d5d1e3b59e", + "metadata": {}, + "source": [ + "# Split on Inchikey instead of spectra\n", + "In the plots showing the average tanimioto score for different thresholds. The fraction predicted is the fraction of the ~50000 validation spectra. However, we could also choose to show the fraction of ~3000 unique inchikeys for which at least one prediction is made. I am not sure what the best way is to test this. Per Inchikey makes sense, since the y axis is already the average tanimoto score per inchikey, so that would make it match. But just splitting on number of spectra also makes more sense to me. \n", + "Another option could be is to just randomly sample 1 unique spectrum per inchikey. This would make everything a lot more intuitive, but you are discarding some information. We could even consider running random sampling 100 times, to get the ranking done and maybe plot the average with some standard deviation. This has not yet been tried. " + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "31838f3d-f6a3-4c74-96fc-0e652aae4187", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_average_per_threshold_inchikeys(predicted_scores, tanimoto_for_predictions, val_spectra, n_of_buckets):\n", + " buckets = split_on_inchikey_fractions(predicted_scores,n_of_buckets, val_spectra, )\n", + " mean_per_fraction = []\n", + " selected_indexes = set()\n", + " for indexes_in_bucket in tqdm(buckets):\n", + " selected_indexes.update(set(indexes_in_bucket.tolist()))\n", + " average_scores_per_inchikey = get_average_tanimoto_per_inchikey(val_spectra, tanimoto_for_predictions, selected_indexes)\n", + " mean = sum(average_scores_per_inchikey)/len(average_scores_per_inchikey)\n", + " mean_per_fraction.append(mean)\n", + " return mean_per_fraction\n", + "\n", + "def create_values_violin_plot_inchikey_fractions(predicted_scores, val_spectra, tanimoto_for_predictions, n_buckets):\n", + " \"\"\"The predictions are ranked based on the predicted scores, and are split in different groups from high to low, \n", + " for each group the average tanimoto per inchikey is computed.\"\"\"\n", + " tanimoto_per_bucket = []\n", + " ranked_indexes_per_bucket = split_on_inchikey_fractions(predicted_scores, n_buckets, val_spectra)\n", + " for indexes_in_bucket in ranked_indexes_per_bucket: \n", + " average_tanimoto_per_inchikey = get_average_tanimoto_per_inchikey(val_spectra, tanimoto_for_predictions, indexes_in_bucket)\n", + " tanimoto_per_bucket.append(average_tanimoto_per_inchikey)\n", + " return tanimoto_per_bucket\n", + "\n", + " \n", + "def split_on_inchikey_fractions(threshold_values, n_buckets, val_spectra):\n", + " threshold_values = np.array(threshold_values)\n", + " \n", + " highest_score_per_inchikey = np.array([\n", + " threshold_values[list(spectrum_indices)].max()\n", + " for spectrum_indices in val_spectra.spectrum_indices_per_inchikey.values()\n", + " ])\n", + " \n", + " # Get the score cutoffs that split inchikeys into equal-sized groups\n", + " percentiles = np.linspace(0, 100, n_buckets + 1)\n", + " cutoffs = np.percentile(highest_score_per_inchikey, percentiles)\n", + " # Assign each threshold_value to a bucket based on those cutoffs\n", + " bucket_indices = np.digitize(threshold_values, cutoffs[1:-1], right=False)\n", + " buckets = [np.where(bucket_indices == i)[0] for i in range(n_buckets)]\n", + " print(\"spectrum_count_per_bucket\")\n", + " print([len(bucket) for bucket in reversed(buckets)])\n", + " return reversed(buckets)" + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "id": "22323884-5958-4855-9f0a-21d39033de8c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spectrum_count_per_bucket\n", + "[1482, 1996, 2657, 2609, 3307, 3121, 3613, 3710, 4327, 9651]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_violins(create_values_violin_plot_inchikey_fractions(predicted_ms2deepscores, pos_val_spectra, tanimoto_for_prediction, 10), title=\"MS2DeepScore (reliability = MS2DeepScore)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "id": "708379c9-7da5-41d8-b0b5-c01b35c52ab6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spectrum_count_per_bucket\n", + "[1852, 2410, 3084, 3257, 2763, 2726, 2172, 2796, 3635, 11778]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_violins(create_values_violin_plot_inchikey_fractions(threshold_score_average_max_top_tanimoto, pos_val_spectra, tanimoto_for_prediction, 10), title=\"MS2DeepScore, reliability = average max top 10 tanimoto\")" + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "id": "785840ca-0669-4f0c-8cfb-ef034aecde1b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spectrum_count_per_bucket\n", + "[271, 0, 2339, 1245, 963, 1159, 1075, 1207, 507, 0, 2552, 1661, 458, 0, 4828, 1748, 1709, 1318, 3959, 9474]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20it [00:00, 403.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spectrum_count_per_bucket\n", + "[485, 1367, 1238, 1172, 1574, 1510, 1526, 1731, 1447, 1316, 1313, 1413, 1112, 1060, 1350, 1446, 1674, 1961, 2940, 8838]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20it [00:00, 382.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spectrum_count_per_bucket\n", + "[571, 911, 853, 1143, 1232, 1425, 1240, 1369, 1501, 1806, 1605, 1516, 1951, 1662, 1851, 1859, 1988, 2339, 2966, 6685]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20it [00:00, 385.68it/s]\n" + ] + }, + { + "data": { + "image/png": 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", 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"100%|█████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 329.01it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 8))\n", + "plot_comparison_plot({\n", + " # \"MS2Query 1.0\": plot_average_per_threshold_inchikeys(predicted_score, tanimoto_for_prediction_ms2query, pos_val_spectra, 20),\n", + " # \"ms2deepscore (reliability = mean ms2deepscore top 10)\": mean_per_fraction_average_mean_top_tanimoto,\n", + " \"MS2DeepScore\": plot_average_per_threshold_inchikeys(predicted_ms2deepscores, tanimoto_for_prediction, pos_val_spectra, 20),\n", + " # \"Best possible reliabitly prediction\": mean_per_fraction_best_possible_ranker,\n", + " \"MS2Query 2.0\": plot_average_per_threshold_inchikeys(threshold_score_average_max_top_tanimoto, tanimoto_for_prediction, pos_val_spectra, 20),\n", + " \"precursor mz\": plot_average_per_threshold_inchikeys([spectrum.get('precursor_mz') for spectrum in pos_val_spectra.spectra], tanimoto_for_prediction, pos_val_spectra, 20),\n", + " \"embedding_evaluator\": plot_average_per_threshold_inchikeys(-embedding_evaluations, tanimoto_for_prediction, pos_val_spectra, 20),\n", + " \"Average tanimoto top 10 closest library inchikeys\": plot_average_per_threshold_inchikeys(average_top_10_tanimoto, tanimoto_for_prediction, pos_val_spectra, 20),\n", + " \"MS2DeepScore - embedding evaluator^0.5\": plot_average_per_threshold_inchikeys(predicted_ms2deepscores-embedding_evaluations**0.5, tanimoto_for_prediction, pos_val_spectra, 20),\n", + " \"Precursor * (MS2DeepScore - embedding evaluator^0.5) ** 3\": plot_average_per_threshold_inchikeys(np.array(precursor_mz) * (np.array(predicted_ms2deepscores) - embedding_evaluations**0.5)**3, tanimoto_for_prediction, pos_val_spectra, 20),\n", + " \"MS2Query 2 - Embedding evaluator^0.5\": plot_average_per_threshold(threshold_score_average_max_top_tanimoto - embedding_evaluations** 0.5, tanimoto_for_prediction, pos_val_spectra, 20),\n", + " })" + ] + }, + { + "cell_type": "markdown", + "id": "a080e0eb-340d-4bf9-880e-74880fa637ac", + "metadata": {}, + "source": [ + "# Negative ionization mode predictions\n", + "Below we will repeat the most important analysis for negative ionization mode as well. If you want to redo all analysis you can of course copy the notebook change pos to neg at the top and get all optimization results." + ] + }, + { + "cell_type": "markdown", + "id": "38491e0c-9e86-4026-b1de-58055cf0b7f0", + "metadata": {}, + "source": [ + "### Check the top of the notebook to get the negative ionization mode spectra with embeddings. " + ] + }, + { + "cell_type": "code", + "execution_count": 312, + "id": "c895f444-76a8-4089-8f74-01807c2ece8d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15967 spectra for 1614 inchikeys with embeddings\n", + "283790 spectra for 29806 inchikeys with embeddings\n" + ] + } + ], + "source": [ + "print(neg_val_spectra)\n", + "print(neg_train_spectra)" + ] + }, + { + "cell_type": "code", + "execution_count": 321, + "id": "04727798-3e50-431d-be3b-0dbcf7f71e62", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Get most common inchi per inchikey: 100%|███████████████████████████████████████████████████| 1614/1614 [00:00<00:00, 19467.33it/s]\n", + "Adding fingerprints to Inchikeys: 1614it [00:05, 274.31it/s]\n", + "Get most common inchi per inchikey: 100%|█████████████████████████████████████████████████| 29806/29806 [00:01<00:00, 28554.39it/s]\n", + "Adding fingerprints to Inchikeys: 29806it [01:49, 273.25it/s]\n", + "Selecting highest ms2deepscore: 16it [01:09, 4.34s/it] \n", + "Calculating: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15967/15967 [00:12<00:00, 1242.32it/s]\n" + ] + } + ], + "source": [ + "from ms2query.benchmarking.Fingerprints import Fingerprints\n", + "from ms2query.benchmarking.TopKTanimotoScores import TopKTanimotoScores\n", + "from ms2query.benchmarking.reference_methods.predict_top_ms2deepscores import select_inchikeys_with_highest_ms2deepscore\n", + "from ms2query.benchmarking.reference_methods.MS2DeepScoresForTopInChikeys import calculate_MS2DeepScoresForTopKInChikeys\n", + "\n", + "neg_val_fingerprints = Fingerprints.from_spectrum_set(neg_val_spectra, \"daylight\", 4096)\n", + "neg_train_fingerprints = Fingerprints.from_spectrum_set(neg_train_spectra, \"daylight\", 4096)\n", + "\n", + "all_neg_fingerprints = Fingerprints.combine_fingerprints(neg_val_fingerprints, neg_train_fingerprints)\n", + "\n", + "neg_neg_top_k_tanimoto_scores = TopKTanimotoScores.calculate_from_fingerprints(\n", + " neg_train_fingerprints,\n", + " neg_train_fingerprints, k=8,)\n", + "\n", + "neg_neg_inchikeys_with_highest_ms2deepscores = select_inchikeys_with_highest_ms2deepscore(\n", + " neg_val_spectra, neg_train_spectra, nr_of_inchikeys_to_select=1, batch_size=1000,)\n", + "neg_neg_close_tanimoto_scores = calculate_MS2DeepScoresForTopKInChikeys(neg_train_spectra, neg_val_spectra, neg_neg_top_k_tanimoto_scores, neg_neg_inchikeys_with_highest_ms2deepscores)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "id": "496f4ea5-bc6c-427e-8a1a-067dc0ec4b4c", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pickle\n", + "intermediates_folder = \"./neg_neg_intermediate\"\n", + "os.makedirs(intermediates_folder, exist_ok=True)\n", + "with open(os.path.join(intermediates_folder, \"top_1_highest_ms2deepscore_with_8_top_inchikeys.pickle\"), \"wb\") as handle:\n", + " pickle.dump(neg_neg_close_tanimoto_scores, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", + "with open(os.path.join(intermediates_folder, \"neg_val_spectra_with_embeddings.pickle\"), \"wb\") as handle:\n", + " pickle.dump(neg_val_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", + "with open(os.path.join(intermediates_folder, \"neg_val_and_train_fingerprints.pickle\"), \"wb\") as handle:\n", + " pickle.dump(all_neg_fingerprints, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", + "with open(os.path.join(intermediates_folder, \"top_k_tanimoto_scores.pickle\"), \"wb\") as handle:\n", + " pickle.dump(neg_neg_top_k_tanimoto_scores, handle, protocol=pickle.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "markdown", + "id": "3e2d6c7a-96ac-4ebf-ba88-a54ae36e7f81", + "metadata": {}, + "source": [ + "# Optional load in intermediates" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "4038c693-c2f0-4979-8576-0d8107c7fa08", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "import os\n", + "intermediates_folder = \"./neg_neg_intermediate\"\n", + "\n", + "with open(os.path.join(intermediates_folder, \"top_1_highest_ms2deepscore_with_8_top_inchikeys.pickle\"), \"rb\") as handle:\n", + " neg_neg_close_tanimoto_scores = pickle.load(handle)\n", + "with open(os.path.join(intermediates_folder, \"neg_val_spectra_with_embeddings.pickle\"), \"rb\") as handle:\n", + " neg_val_spectra = pickle.load(handle)\n", + "with open(os.path.join(intermediates_folder, \"neg_val_and_train_fingerprints.pickle\"), \"rb\") as handle:\n", + " all_neg_fingerprints = pickle.load(handle)\n", + "with open(os.path.join(intermediates_folder, \"top_k_tanimoto_scores.pickle\"), \"rb\") as handle:\n", + " neg_neg_top_k_tanimoto_scores = pickle.load(handle)" + ] + }, + { + "cell_type": "markdown", + "id": "54957f3b-ac94-48c1-8443-498b88975bb4", + "metadata": {}, + "source": [ + "# Plot results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "923ddaeb-b9db-4a1c-a58f-7b2dc2e35e70", + "metadata": {}, + "outputs": [], + "source": [ + "from ms2query.benchmarking.reference_methods.predict_top_k_ms2deepscore import predict_top_k_ms2deepscores\n", + "\n", + "indexes, ms2deepscores = predict_top_k_ms2deepscores(neg_train_spectra.embeddings, neg_val_spectra.embeddings)\n", + "neg_predicted_inchikeys_ms2deepscore = [neg_train_spectra.spectra[int(index)].get(\"inchikey\")[:14] for index in indexes]\n", + "neg_neg_tanimoto_for_prediction_ms2deepscore = get_prediction_tanimoto(neg_predicted_inchikeys_ms2deepscore, neg_val_spectra, all_neg_fingerprints)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "7e344225-aa42-4b22-8f3c-1ef4efb69b99", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "15967it [00:00, 63652.20it/s]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15967/15967 [00:00<00:00, 26078.85it/s]\n" + ] + } + ], + "source": [ + "threshold_scores_ms2query_2 = predict_using_average_max_top_k_tanimoto(8, neg_neg_close_tanimoto_scores)\n", + "neg_neg_predicted_inchikeys = [list(query.keys())[0] for query in neg_neg_close_tanimoto_scores]\n", + "neg_neg_tanimoto_for_prediction = get_prediction_tanimoto(neg_neg_predicted_inchikeys, neg_val_spectra, all_neg_fingerprints)" + ] + }, + { + "cell_type": "code", + "execution_count": 374, + "id": "cd0c1f37-cd99-4b79-8ace-8553b71fb3d3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 375.75it/s]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 368.89it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 8))\n", + "plot_comparison_plot({\n", + " \"MS2DeepScore\": plot_average_per_threshold(ms2deepscores.squeeze(), neg_neg_tanimoto_for_prediction_ms2deepscore, neg_val_spectra, 100),\n", + " \"MS2Query 2.0\": plot_average_per_threshold(threshold_scores_ms2query_2, neg_neg_tanimoto_for_prediction, neg_val_spectra, 100),\n", + "})" + ] + }, + { + "cell_type": "markdown", + "id": "5b7bfc6f-d893-4937-aab1-cb7029a76bfb", + "metadata": {}, + "source": [ + "# Search across ionization modes \n", + "MS2DeepScore 2.0 allows for cross ionization mode predictions. Below we test the prediction accuracy when allowing for this cross ionization mode searching. " + ] + }, + { + "cell_type": "code", + "execution_count": 376, + "id": "5dca8e58-7ec6-4a36-8589-271a7edd9e5c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnotatedSpectrumSet(nr_of_spectra = 283790,nr_of_unique_inchikeys = 29806, has_embeddings=True)" + ] + }, + "execution_count": 376, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "neg_train_spectra" + ] + }, + { + "cell_type": "code", + "execution_count": 377, + "id": "50aa5d6a-fbc9-48c5-9c83-5d14368d3304", + "metadata": {}, + "outputs": [], + "source": [ + "combined_train_spectra = neg_train_spectra + pos_train_spectra\n" + ] + }, + { + "cell_type": "code", + "execution_count": 378, + "id": "f190377c-1385-4f58-b582-b0967957a965", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "915669 spectra for 60884 inchikeys with embeddings\n" + ] + } + ], + "source": [ + "print(combined_train_spectra)" + ] + }, + { + "cell_type": "code", + "execution_count": 379, + "id": "5ff754eb-5cbe-4f9e-b801-1c626ae2d28a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Get most common inchi per inchikey: 100%|█████████████████████████████████████████████████| 60884/60884 [00:02<00:00, 21996.65it/s]\n", + "Adding fingerprints to Inchikeys: 60884it [04:14, 239.39it/s]\n" + ] + } + ], + "source": [ + "combined_train_fingerprints = Fingerprints.from_spectrum_set(combined_train_spectra, \"daylight\", 4096)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0517d5a-2551-42dd-ae5c-b9d6f1970485", + "metadata": {}, + "outputs": [], + "source": [ + "pos_neg_top_k_tanimoto_scores = TopKTanimotoScores.calculate_from_fingerprints(\n", + " combined_train_fingerprints,\n", + " combined_train_fingerprints, k=8,)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1afc654f-4f71-4cf8-a25a-c749b7ba28f8", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 382, + "id": "b44afde5-cabc-472f-8900-88eff185b6ab", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Selecting highest ms2deepscore: 16it [04:03, 15.20s/it] \n", + "Calculating: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15967/15967 [00:18<00:00, 877.56it/s]\n" + ] + } + ], + "source": [ + "neg_pos_inchikeys_with_highest_ms2deepscores = select_inchikeys_with_highest_ms2deepscore(\n", + " neg_val_spectra, combined_train_spectra, nr_of_inchikeys_to_select=1, batch_size=1000,)\n", + "neg_pos_close_tanimoto_scores = calculate_MS2DeepScoresForTopKInChikeys(combined_train_spectra, neg_val_spectra, pos_neg_top_k_tanimoto_scores, neg_pos_inchikeys_with_highest_ms2deepscores)" + ] + }, + { + "cell_type": "code", + "execution_count": 389, + "id": "dad9b12d-b96e-4ce9-9e67-6b7e6f8515ad", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Selecting highest ms2deepscore: 37it [09:12, 14.93s/it] \n", + "Calculating: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 36473/36473 [00:44<00:00, 823.63it/s]\n" + ] + } + ], + "source": [ + "pos_neg_inchikeys_with_highest_ms2deepscores = select_inchikeys_with_highest_ms2deepscore(\n", + " pos_val_spectra, combined_train_spectra, nr_of_inchikeys_to_select=1, batch_size=1000,)\n", + "pos_neg_close_tanimoto_scores = calculate_MS2DeepScoresForTopKInChikeys(combined_train_spectra, pos_val_spectra, pos_neg_top_k_tanimoto_scores, pos_neg_inchikeys_with_highest_ms2deepscores)" + ] + }, + { + "cell_type": "code", + "execution_count": 397, + "id": "dfabe54a-bcd4-4fb9-a200-d89cbe6cd811", + "metadata": {}, + "outputs": [], + "source": [ + "neg_val_all_train_fingerprints = Fingerprints.combine_fingerprints(combined_train_fingerprints, neg_val_fingerprints)\n", + "pos_val_fingerprints = Fingerprints.from_spectrum_set(pos_val_spectra, \"daylight\", 4096)\n", + "all_fingerprints = Fingerprints.combine_fingerprints(neg_val_all_train_fingerprints, pos_val_fingerprints)" + ] + }, + { + "cell_type": "code", + "execution_count": 398, + "id": "99a287ca-9236-4d47-8f63-8665952672c9", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pickle\n", + "intermediates_folder = \"./cross_ionmode_intermediate\"\n", + "os.makedirs(intermediates_folder, exist_ok=True)\n", + "with open(os.path.join(intermediates_folder, \"neg_top_1_highest_ms2deepscore_with_8_top_inchikeys.pickle\"), \"wb\") as handle:\n", + " pickle.dump(neg_pos_close_tanimoto_scores, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", + "with open(os.path.join(intermediates_folder, \"pos_top_1_highest_ms2deepscore_with_8_top_inchikeys.pickle\"), \"wb\") as handle:\n", + " pickle.dump(pos_neg_close_tanimoto_scores, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", + "with open(os.path.join(intermediates_folder, \"all_fingerprints.pickle\"), \"wb\") as handle:\n", + " pickle.dump(all_fingerprints, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", + "with open(os.path.join(intermediates_folder, \"top_k_tanimoto_scores.pickle\"), \"wb\") as handle:\n", + " pickle.dump(pos_neg_top_k_tanimoto_scores, handle, protocol=pickle.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "code", + "execution_count": 385, + "id": "2214c39b-9895-4c3a-9cab-791f3a3f87b2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "15967it [00:00, 66007.86it/s]\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████| 15967/15967 [00:01<00:00, 15383.38it/s]\n" + ] + } + ], + "source": [ + "neg_pos_threshold_scores_ms2query_2 = predict_using_average_max_top_k_tanimoto(8, neg_pos_close_tanimoto_scores)\n", + "neg_pos_tanimoto_for_prediction = get_prediction_tanimoto([list(query.keys())[0] for query in neg_pos_close_tanimoto_scores], neg_val_spectra, neg_val_all_train_fingerprints)" + ] + }, + { + "cell_type": "code", + "execution_count": 392, + "id": "adb34578-4eab-4c1e-96e2-42e1b8ad733a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Get most common inchi per inchikey: 100%|███████████████████████████████████████████████████| 3119/3119 [00:00<00:00, 21231.00it/s]\n", + "Adding fingerprints to Inchikeys: 3119it [02:15, 23.09it/s] \n" + ] + } + ], + "source": [ + "pos_val_fingerprints = Fingerprints.from_spectrum_set(pos_val_spectra, \"daylight\", 4096)\n", + "pos_val_all_train_fingerprints = Fingerprints.combine_fingerprints(combined_train_fingerprints, pos_val_fingerprints)" + ] + }, + { + "cell_type": "code", + "execution_count": 393, + "id": "a391a6c2-957c-4f46-8794-361919d43250", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "36473it [00:00, 63752.37it/s]\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████| 36473/36473 [00:01<00:00, 23028.36it/s]\n" + ] + } + ], + "source": [ + "pos_neg_threshold_scores_ms2query_2 = predict_using_average_max_top_k_tanimoto(8, pos_neg_close_tanimoto_scores)\n", + "pos_neg_tanimoto_for_prediction = get_prediction_tanimoto([list(query.keys())[0] for query in pos_neg_close_tanimoto_scores], pos_val_spectra, pos_val_all_train_fingerprints)" + ] + }, + { + "cell_type": "code", + "execution_count": 394, + "id": "909027ab-7b03-460f-a395-4220e2e77d3c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 596.21it/s]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 639.66it/s]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 285.86it/s]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 308.19it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 8))\n", + "plot_comparison_plot({\n", + " \"neg in neg\": plot_average_per_threshold(threshold_scores_ms2query_2, neg_neg_tanimoto_for_prediction, neg_val_spectra, 100),\n", + " \"neg in neg and pos\": plot_average_per_threshold(neg_pos_threshold_scores_ms2query_2, neg_pos_tanimoto_for_prediction, neg_val_spectra, 100),\n", + " \"pos in pos\": plot_average_per_threshold(threshold_score_average_max_top_tanimoto, tanimoto_for_prediction, pos_val_spectra, 100),\n", + " \"pos in neg and pos\": plot_average_per_threshold(pos_neg_threshold_scores_ms2query_2, pos_neg_tanimoto_for_prediction, pos_val_spectra, 100),\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 396, + "id": "1c5cf4a8-821f-42cc-8941-d95d5b2bec72", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spectrum_count_per_bucket\n", + "[286, 413, 478, 504, 444, 562, 526, 495, 516, 474, 495, 482, 510, 564, 632, 516, 839, 1193, 1647, 4391]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20it [00:00, 612.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spectrum_count_per_bucket\n", + "[277, 412, 560, 561, 430, 586, 615, 480, 446, 417, 514, 438, 518, 529, 575, 624, 689, 1087, 1981, 4228]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20it [00:00, 678.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spectrum_count_per_bucket\n", + "[485, 1367, 1238, 1172, 1574, 1510, 1526, 1731, 1447, 1316, 1313, 1413, 1112, 1060, 1350, 1446, 1674, 1961, 2940, 8838]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20it [00:00, 338.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spectrum_count_per_bucket\n", + "[465, 1390, 1210, 1267, 1212, 1530, 1464, 1779, 1424, 1453, 1379, 1187, 1300, 1193, 1223, 1445, 1645, 2155, 2493, 9259]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20it [00:00, 329.36it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 8))\n", + "plot_comparison_plot({\n", + " \"neg in neg\": plot_average_per_threshold_inchikeys(threshold_scores_ms2query_2, neg_neg_tanimoto_for_prediction, neg_val_spectra, 20),\n", + " \"neg in neg and pos\": plot_average_per_threshold_inchikeys(neg_pos_threshold_scores_ms2query_2, neg_pos_tanimoto_for_prediction, neg_val_spectra, 20),\n", + " \"pos in pos\": plot_average_per_threshold_inchikeys(threshold_score_average_max_top_tanimoto, tanimoto_for_prediction, pos_val_spectra, 20),\n", + " \"pos in neg and pos\": plot_average_per_threshold_inchikeys(pos_neg_threshold_scores_ms2query_2, pos_neg_tanimoto_for_prediction, pos_val_spectra, 20),\n", + "})" + ] + }, + { + "cell_type": "markdown", + "id": "0ee5308d-9c5b-47f2-80db-61b5f1e7eb93", + "metadata": {}, + "source": [ + "# Compare with mod cosine" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "c717c575-263c-4db4-af3d-a373809828d5", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "import os\n", + "intermediates_folder = \"./cross_ionmode_intermediate\"\n", + "\n", + "with open(os.path.join(intermediates_folder, \"all_fingerprints.pickle\"), \"rb\") as handle:\n", + " all_fingerprints = pickle.load(handle)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "70955fb3-2c00-44f3-a3f3-4e495aa80e57", + "metadata": {}, + "outputs": [], + "source": [ + "from matchms.similarity.FlashSimilarity import FlashSimilarity\n", + "import numpy as np\n", + "\n", + "def analogue_search_in_batches(similarity_metric, train_spectra, val_spectra, batch_size=500):\n", + " \"\"\"For some reason the argmax seemed to crash our server, so this batch process is a work around\"\"\"\n", + " score_matrix = mod_cos.matrix(train_spectra, val_spectra)\n", + " predicted_inchikeys = []\n", + " predicted_scores = []\n", + " for i in tqdm(range(0, score_matrix.shape[1], batch_size)):\n", + " max_index_chunck = np.argmax(score_matrix[:, i:i+batch_size], axis=0)\n", + " col_idx = np.arange(i, min(i + batch_size, score_matrix.shape[1]))\n", + " predicted_scores.append(score_matrix[max_index_chunck, col_idx])\n", + " for index in max_index_chunck:\n", + " predicted_inchikeys.append(train_spectra[index].get(\"inchikey\")[:14])\n", + " return predicted_inchikeys, np.concatenate(predicted_scores)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "0a685e4f-90ef-4f4a-b345-ee9a35e5d2ac", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Flash cosine (hybrid) (parallel x96): 100%|████████████████████████████████████████████████████████████████████████████| 283790/283790 [02:48<00:00, 1687.09it/s]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:35<00:00, 1.10s/it]\n" + ] + } + ], + "source": [ + "mod_cos = FlashSimilarity(\"cosine\", \"hybrid\", remove_precursor=True)\n", + "\n", + "predicted_inchikeys_mod_cos_neg, predicted_scores_mod_cos_neg = analogue_search_in_batches(mod_cos, neg_train_spectra.spectra, neg_val_spectra.spectra)\n", + "tanimoto_for_predictions_mod_cos_neg = get_prediction_tanimoto(predicted_inchikeys_mod_cos_neg, neg_val_spectra, all_fingerprints)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "03820e12-9449-4aef-ab73-f25060fb915f", + "metadata": {}, + "outputs": [], + "source": [ + "intermediates_folder = \"./neg_neg_intermediate\"\n", + "\n", + "with open(os.path.join(intermediates_folder, \"mod_cosine_predictions.json\"), \"w\") as f:\n", + " json.dump({\"inchikeys\": predicted_inchikeys_mod_cos_neg,\n", + " \"mod_cosine_score\": list(predicted_scores_mod_cos_neg),\n", + " \"tanimoto_for_analogues\": [float(x) for x in tanimoto_for_predictions_mod_cos_neg]}, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "387b5b81-bc2f-4890-9cdc-154946dfc606", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 782.67it/s]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 760.27it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "plt.figure(figsize=(8, 8))\n", + "plt.title(\"Negative in negative library\")\n", + "plot_comparison_plot({\n", + " \"MS2Query 2.0\": plot_average_per_threshold(threshold_scores_ms2query_2, neg_neg_tanimoto_for_prediction, neg_val_spectra, 100),\n", + " \"Mod cosine\": plot_average_per_threshold(predicted_scores_mod_cos_neg, tanimoto_for_predictions_mod_cos_neg, neg_val_spectra, 100),\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "d0efe574-7504-430b-94bf-370c5b29c933", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_violins(create_values_violin_plot(predicted_scores_mod_cos_neg, neg_val_spectra, tanimoto_for_predictions_mod_cos_neg, 10), title=\"Modified cosine neg neg\")" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "ef3cb124-18e4-4c94-946d-6520e593aa37", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 36473/36473 [00:02<00:00, 17829.56it/s]\n" + ] + } + ], + "source": [ + "mod_cos = FlashSimilarity(\"cosine\", \"hybrid\", remove_precursor=True)\n", + "\n", + "predicted_inchikeys_mod_cos_pos, predicted_scores_mod_cos_pos = analogue_search_in_batches(mod_cos, pos_train_spectra.spectra, pos_val_spectra.spectra)\n", + "tanimoto_for_predictions_mod_cos_pos = get_prediction_tanimoto(predicted_inchikeys_mod_cos_pos, pos_val_spectra, all_fingerprints)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "a12b7f1f-da05-4543-b5d0-38bf59c992ec", + "metadata": {}, + "outputs": [], + "source": [ + "intermediates_folder = \"./pos_pos_intermediate\"\n", + "\n", + "with open(os.path.join(intermediates_folder, \"mod_cosine_predictions.json\"), \"w\") as f:\n", + " json.dump({\"inchikeys\": predicted_inchikeys_mod_cos_pos,\n", + " \"mod_cosine_score\": list(predicted_scores_mod_cos_pos),\n", + " \"tanimoto_for_analogues\": [float(x) for x in tanimoto_for_predictions_mod_cos_pos]}, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "47fb49d0-1b78-45b6-bd9b-cd9f985e8078", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "36473it [00:05, 6181.06it/s]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 36473/36473 [00:01<00:00, 24448.89it/s]\n" + ] + } + ], + "source": [ + "pos_pos_ms2query_scores = predict_using_average_max_top_k_tanimoto(8, close_tanimoto_scores)\n", + "pos_pos_predicted_inchikeys = [list(query.keys())[0] for query in close_tanimoto_scores]\n", + "pos_pos_tanimoto_for_prediction = get_prediction_tanimoto(pos_pos_predicted_inchikeys, pos_val_spectra, all_fingerprints)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "239e9642-b24f-4ed7-a5b6-c21f8662c3b9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 343.44it/s]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 338.02it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "plt.figure(figsize=(8, 8))\n", + "plt.title(\"Positive in positive library\")\n", + "plot_comparison_plot({\n", + " \"MS2Query 2.0\": plot_average_per_threshold(pos_pos_ms2query_scores, pos_pos_tanimoto_for_prediction, pos_val_spectra, 100),\n", + " \"Mod cosine\": plot_average_per_threshold(predicted_scores_mod_cos_pos, tanimoto_for_predictions_mod_cos_pos, pos_val_spectra, 100),\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "13920f62-ae6a-4e81-ba37-6899c79a08be", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_violins(create_values_violin_plot(predicted_scores_mod_cos_pos, pos_val_spectra, tanimoto_for_predictions_mod_cos_pos, 10), title=\"Modified cosine pos pos\")" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "15c1fa37-012e-4be0-b6e8-8d777efa63f8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.violinplot(create_values_violin_plot(predicted_scores_mod_cos_pos, pos_val_spectra, tanimoto_for_predictions_mod_cos_pos, 1), showmedians=True)\n", + "plt.title(\"Modified cosine pos pos\")\n", + "plt.ylabel(\"Average tanimoto score per inchikey\")\n", + "plt.xticks([])\n", + "plt.xlabel(\"0 - 100%\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "a9888a20-fc66-4fa5-9a8e-701a18c2127c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.violinplot(create_values_violin_plot(pos_pos_ms2query_scores, pos_val_spectra, pos_pos_tanimoto_for_prediction, 1), showmedians=True)\n", + "plt.title(\"MS2DeepScore (no reliability estimation)\")\n", + "plt.ylabel(\"Average tanimoto score per inchikey\")\n", + "plt.xticks([])\n", + "plt.xlabel(\"0 - 100%\")\n", + "plt.show()" + ] + } + ], + "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": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 49cc2ec60314b8e0f9d398aaddb989ce661ceb46 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 19 Mar 2026 10:04:18 +0100 Subject: [PATCH 136/149] Add gradient boosting machine notebook --- .../experiment_with_reranking_analogues.ipynb | 3953 +++++++++++++++++ 1 file changed, 3953 insertions(+) create mode 100644 ms2query/notebooks/experiment_with_reranking_analogues.ipynb diff --git a/ms2query/notebooks/experiment_with_reranking_analogues.ipynb b/ms2query/notebooks/experiment_with_reranking_analogues.ipynb new file mode 100644 index 0000000..4f759e0 --- /dev/null +++ b/ms2query/notebooks/experiment_with_reranking_analogues.ipynb @@ -0,0 +1,3953 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c9a35dff-eb73-4ce2-8aec-09e7571d9c10", + "metadata": {}, + "source": [ + "# Train gradient boosting machine\n", + "In this notebook I experimented with training small regression and ranking models, a bit in line with MS2Query one. What I tried to do is to rerank the top 100 highest scoring analogues, based on more detailed informtion. This information was the ms2deepscores tanimoto scores for the top 10 closest inchikeys. \n", + "\n", + "During this run I find out that actually the best approach is to just use the highest MS2DeepScore, but the magic touch is to do reliability prediction of the analogue in a different way. \n", + "\n", + "### NOTE The notebook is not cleaned. \n", + "The results were not crucial to the story, so I didn't prioritise cleaning. So it is quite messy and I don't even think running from top to bottom will run. So it is mostly intended as a reference for code and results. The files are the same as in the other notebook, where we have the auto zenodo download. " + ] + }, + { + "cell_type": "markdown", + "id": "116bdee3-778e-41a3-bf3b-0cfada98a6c7", + "metadata": {}, + "source": [ + "# Define benchmarking functions" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "5be928d5-3386-423d-9afb-a329745b45a0", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix\n", + "\n", + "def calculate_tanimoto_score_between_pair(fingerprint_1: np.ndarray, fingerprint_2: np.ndarray) -> float:\n", + " return jaccard_similarity_matrix(np.array([fingerprint_1]), np.array([fingerprint_2]))[0][0]\n", + " \n", + "def get_average_tanimoto_after_filtering(\n", + " query_spectrum_set, val_and_train_fingerprints, predicted_inchikeys\n", + ") -> float:\n", + " \"\"\"Calculates the average tanimoto of predictions made. Any predicted_inchikey that is None, is not considered and not used for the mean.\n", + " \"\"\"\n", + " assert len(predicted_inchikeys) == len(query_spectrum_set)\n", + " average_scores_per_inchikey = []\n", + " # Calculate score per unique inchikey\n", + " for inchikey in query_spectrum_set.spectrum_indices_per_inchikey.keys():\n", + " matching_spectrum_indexes = query_spectrum_set.spectrum_indices_per_inchikey[inchikey]\n", + " prediction_scores = []\n", + " for index in matching_spectrum_indexes:\n", + " predicted_inchikey = predicted_inchikeys[index]\n", + " if predicted_inchikey is not None:\n", + " predicted_fingerprint = val_and_train_fingerprints.get_fingerprints([predicted_inchikey])[0]\n", + " actual_fingerprint = val_and_train_fingerprints.get_fingerprints([inchikey])[0]\n", + " tanimoto_for_prediction = calculate_tanimoto_score_between_pair(\n", + " predicted_fingerprint, actual_fingerprint\n", + " )\n", + " prediction_scores.append(tanimoto_for_prediction)\n", + " if len(prediction_scores) > 0:\n", + " average_prediction = sum(prediction_scores) / len(prediction_scores)\n", + " average_scores_per_inchikey.append(average_prediction)\n", + " average_over_all_inchikeys = sum(average_scores_per_inchikey) / len(average_scores_per_inchikey)\n", + " return average_over_all_inchikeys\n", + "\n", + "\n", + "def get_average_tanimoto_after_treshold(query_spectrum_set, val_and_train_fingerprints,\n", + " predicted_inchikeys, predicted_scores, threshold):\n", + " \"\"\"Applies a threshold and calculates the average tanimoto. It returns both the fraction that passed the threshold and average tanimoto.\"\"\"\n", + " \n", + " filtered_inchikeys = []\n", + " nr_of_predictions = 0\n", + " for index, score in enumerate(predicted_scores):\n", + " if score > threshold:\n", + " filtered_inchikeys.append(predicted_inchikeys[index])\n", + " nr_of_predictions += 1\n", + " else:\n", + " filtered_inchikeys.append(None)\n", + " prediction_fraction = nr_of_predictions/len(filtered_inchikeys)\n", + " average_tanimoto = get_average_tanimoto_after_filtering(query_spectrum_set, val_and_train_fingerprints, filtered_inchikeys)\n", + " return prediction_fraction, average_tanimoto\n", + "\n", + "def get_average_tanimoto_for_list_of_thresholds(query_spectrum_set, val_and_train_fingerprints, \n", + " predicted_inchikeys, predicted_scores,\n", + " list_of_thresholds = [0, 0.5, 0.6, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95], ):\n", + " assert len(predicted_scores) == len(predicted_inchikeys) == len(query_spectrum_set)\n", + " prediction_fractions = []\n", + " average_tanimotos = []\n", + " for threshold in tqdm(list_of_thresholds, desc = \"thresholds\"):\n", + " prediction_fraction, average_tanimoto = get_average_tanimoto_after_treshold(query_spectrum_set, val_and_train_fingerprints, predicted_inchikeys, predicted_scores, threshold)\n", + " prediction_fractions.append(prediction_fraction)\n", + " average_tanimotos.append(average_tanimoto)\n", + " return prediction_fractions, average_tanimotos" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "5c29780e-d856-44bf-8b6e-1862dbef5cd4", + "metadata": {}, + "outputs": [], + "source": [ + "import tqdm\n", + "from tqdm.auto import tqdm as auto_tqdm\n", + "tqdm.tqdm = auto_tqdm\n", + "from tqdm import tqdm\n", + "\n", + "def predict_best_inchikeys(all_ms2deepscores_for_top_inchikeys, prediction_method):\n", + " \"\"\"Predicts the best inchikey by using a specified method\"\"\"\n", + " predicted_inchikeys = []\n", + " predicted_scores = []\n", + " for dict_with_ms2deepscores_for_top_inchikeys in tqdm(all_ms2deepscores_for_top_inchikeys):\n", + " predicted_inchikey, predicted_score = prediction_method(dict_with_ms2deepscores_for_top_inchikeys)\n", + " predicted_inchikeys.append(predicted_inchikey)\n", + " predicted_scores.append(predicted_score)\n", + " return predicted_inchikeys, predicted_scores\n", + " \n", + "def predict_on_average_over_top_inchikeys(dict_with_ms2deepscores_for_top_inchikeys):\n", + " highest_mean = 0\n", + " best_inchikey = None\n", + " for inchikey, close_tan_score in dict_with_ms2deepscores_for_top_inchikeys.items():\n", + " mean = close_tan_score.get_mean()\n", + " if mean > highest_mean:\n", + " highest_mean = mean\n", + " best_inchikey = inchikey\n", + " return best_inchikey, highest_mean\n", + "\n", + "def predict_on_higest_ms2deepscore(dict_with_close_tanimoto_scores):\n", + " \"\"\"Reusing the same results in the close_tanimoto_scores, but of course normally easier to go straight to the point\"\"\" \n", + " highest_score = 0\n", + " best_inchikey = None\n", + " for inchikey, close_tan_score in dict_with_close_tanimoto_scores.items():\n", + " if inchikey in close_tan_score.ms2deepscores_per_inchikey:\n", + " score = close_tan_score.ms2deepscores_per_inchikey[inchikey].max()\n", + " if score > highest_score:\n", + " highest_score = score\n", + " best_inchikey = inchikey\n", + " return best_inchikey, highest_score\n", + "\n", + "def predict_on_max_over_top_inchikeys(dict_with_close_tanimoto_scores):\n", + " highest_mean = 0\n", + " best_inchikey = None\n", + " for inchikey, close_tan_score in dict_with_close_tanimoto_scores.items():\n", + " max_ms2deepscore_per_inchikey = close_tan_score.get_max_per_inchikey().values()\n", + " mean = sum(max_ms2deepscore_per_inchikey) / len(max_ms2deepscore_per_inchikey)\n", + " if mean > highest_mean:\n", + " highest_mean = mean\n", + " best_inchikey = inchikey\n", + " return best_inchikey, highest_mean\n", + "\n", + "def predict_on_mean_for_higest_ms2deepscore(dict_with_close_tanimoto_scores):\n", + " \"\"\"Reusing the same results in the close_tanimoto_scores, but of course normally easier to go straight to the point\"\"\" \n", + " highest_score = 0\n", + " best_inchikey = None\n", + " for inchikey, close_tan_score in dict_with_close_tanimoto_scores.items():\n", + " if inchikey in close_tan_score.ms2deepscores_per_inchikey:\n", + " score = close_tan_score.ms2deepscores_per_inchikey[inchikey].mean()\n", + " if score > highest_score:\n", + " highest_score = score\n", + " best_inchikey = inchikey\n", + " return best_inchikey, highest_score\n" + ] + }, + { + "cell_type": "markdown", + "id": "0d835329-6b5d-4f29-9ea8-97444ec5f3a7", + "metadata": {}, + "source": [ + "# Load in data pos-pos\n", + "The data was generated in another notebook and loaded here." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c6539218-4f33-495e-a8ee-1f68b7f68324", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Your CPU supports instructions that this binary was not compiled to use: SSE3 SSE4.1 SSE4.2 AVX AVX2\n", + "For maximum performance, you can install NMSLIB from sources \n", + "pip install --no-binary :all: nmslib\n" + ] + } + ], + "source": [ + "import os\n", + "import pickle\n", + "\n", + "pickled_intermediates_data_folder = \"./pickled_intermediates_pos\"\n", + "\n", + "# Load the pickled files\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"val.pickle\"), \"rb\") as handle:\n", + " val_pos = pickle.load(handle)\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"train.pickle\"), \"rb\") as handle:\n", + " train_pos = pickle.load(handle)\n", + "# with open(os.path.join(pickled_intermediates_data_folder, \"train_fingerprints.pickle\"), \"rb\") as handle:\n", + "# train_fingerprints_pos = pickle.load(handle)\n", + "# with open(os.path.join(pickled_intermediates_data_folder, \"top_10_tanimoto.pickle\"), \"rb\") as handle:\n", + "# top_k_tanimoto_scores_pos = pickle.load(handle)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "53cdca1f-8427-499a-affa-7cde5e629e35", + "metadata": {}, + "outputs": [], + "source": [ + "from ms2query.benchmarking.reference_methods.MS2DeepScoresForTopInChikeys import MS2DeepScoresForTopKInChikeys\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"close_tanimoto_scores.pickle\"), \"rb\") as handle:\n", + " all_ms2deepscores_for_top_inchikeys_pos_pos = pickle.load(handle)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8adf4cfa-61e0-435d-9726-fb392b0a8c6d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Get most common inchi per inchikey: \n", + "Adding fingerprints to Inchikeys: 3119it [00:12, 246.83it/s]\n" + ] + } + ], + "source": [ + "from ms2query.benchmarking.Fingerprints import Fingerprints\n", + "val_fingerprints = Fingerprints.from_spectrum_set(val_pos, \"daylight\", 4096)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "54b3b128-29c6-40a4-a315-c06305458cfe", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Get most common inchi per inchikey: \n", + "Adding fingerprints to Inchikeys: 55572it [03:10, 291.79it/s]\n" + ] + } + ], + "source": [ + "train_fingerprints = Fingerprints.from_spectrum_set(train_pos, \"daylight\", 4096)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b1734dc3-ad3a-4fee-ac2c-29017a1588d0", + "metadata": {}, + "outputs": [], + "source": [ + "val_and_train_fingerprints_pos_pos = Fingerprints.combine_fingerprints(val_fingerprints, train_fingerprints)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "5f93989c-88d1-4ca9-ab46-484a3df011f6", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4aeaf5f61fa849f98aa15304b611c9ca", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/36473 [00:000.3:\n", + " mean_ms2deepscores.append(close_tan_score.ms2deepscores_per_inchikey[top_k_inchikey].mean())\n", + " overall_mean = sum(mean_ms2deepscores)/ len(mean_ms2deepscores)\n", + " if overall_mean > highest_mean:\n", + " highest_mean = overall_mean\n", + " best_inchikey = inchikey\n", + " return best_inchikey, highest_mean\n", + "predicted_inchikeys, predicted_scores = predict_best_inchikeys(all_ms2deepscores_for_top_inchikeys_pos_pos, predict_on_average_over_top_inchikeys_minimum_tanimoto)\n", + "prediction_fractions_on_average_over_top_inchikeys_minimum_tanimoto_0_3, average_tanimotos_on_average_over_top_inchikeys_minimum_tanimoto_0_3 = get_average_tanimoto_for_list_of_thresholds(val_pos, val_and_train_fingerprints_pos_pos, predicted_inchikeys, predicted_scores, [0, 0.5, 0.6, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.96])" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "id": "15d25f01-9da8-4eb8-aa26-aae385d87e8a", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "75cf572d369f4a65b248648df32da661", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/36473 [00:00 highest_mean:\n", + " highest_mean = overall_mean\n", + " best_inchikey = inchikey\n", + " return best_inchikey, highest_mean\n", + "predicted_inchikeys, predicted_scores = predict_best_inchikeys(all_ms2deepscores_for_top_inchikeys_pos_pos, predict_on_average_over_top_5_tanimoto)\n", + "prediction_fractions_on_average_over_top_3_tanimoto, average_tanimotos_on_average_over_top_3_tanimoto = get_average_tanimoto_for_list_of_thresholds(val_pos, val_and_train_fingerprints_pos_pos, predicted_inchikeys, predicted_scores, [0, 0.5, 0.6, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.96])" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "9a131e80-87e6-4636-9e19-7e2162a792fb", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "969373d8eab141ec8509add483e9ea3c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/36473 [00:00 highest_mean:\n", + " highest_mean = overall_mean\n", + " best_inchikey = inchikey\n", + " return best_inchikey, highest_mean\n", + "predicted_inchikeys, predicted_scores = predict_best_inchikeys(all_ms2deepscores_for_top_inchikeys_pos_pos, predict_on_difference_between_ms2ds_and_tanimoto)\n", + "prediction_fractions_on_difference_between_ms2ds_and_tanimoto, average_tanimotos_on_difference_between_ms2ds_and_tanimoto = get_average_tanimoto_for_list_of_thresholds(val_pos, val_and_train_fingerprints_pos_pos, predicted_inchikeys, predicted_scores, [0, 0.5, 0.6, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.96])" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "d1e113dd-628b-4d10-ae99-5b28891669c0", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "78e227c85b09427ba2125807bbe63c2f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/36473 [00:00 highest_mean:\n", + " highest_mean = mean\n", + " tanimoto_scores = close_tan_score.top_k_inchikeys_and_tanimoto_scores.values()\n", + " tanimoto = sum(tanimoto_scores)/ len(tanimoto_scores)\n", + " best_inchikey = inchikey\n", + " return best_inchikey, tanimoto\n", + "predicted_inchikeys, predicted_scores = predict_best_inchikeys(all_ms2deepscores_for_top_inchikeys_pos_pos, predict_on_average_over_top_inchikeys_use_average_tanimoto)\n", + "prediction_fractions_on_average_over_top_inchikeys_use_average_tanimoto, average_tanimotos_on_average_over_top_inchikeys_use_average_tanimoto = get_average_tanimoto_for_list_of_thresholds(val_pos, val_and_train_fingerprints_pos_pos, predicted_inchikeys, predicted_scores, [0, 0.5, 0.6, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.96])" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "3d7a4a2d-85c0-47e5-a8cb-cf74e4af366b", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "900ec6709d9e437bbea5324ad8e7a24e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/36473 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_pos_pos, average_tanimotos_average_over_top_inchikeys_pos_pos, label = \"mean top 10 closest inchikeys\")\n", + "plt.plot(prediction_fractions_highest_ms2deepscore_pos_pos, average_tanimotos_highest_ms2deepscore_pos_pos, label = \"highest ms2deepscore\")\n", + "plt.plot(prediction_fractions_max_over_top_inchikeys, average_tanimotos_max_over_top_inchikeys, label = \"max top 10 closest inchikeys\")\n", + "plt.plot(prediction_fractions_mean_for_higest_ms2deepscore, average_tanimotos_mean_for_higest_ms2deepscore, label = \"mean highest ms2deepscore\")\n", + "# plt.plot(prediction_fractions_on_average_over_top_inchikeys_minimum_tanimoto_0_4, average_tanimotos_on_average_over_top_inchikeys_minimum_tanimoto_0_4, label = \"mean top 10 closest inchikeys above 0.4\")\n", + "# plt.plot(prediction_fractions_on_average_over_top_inchikeys_minimum_tanimoto_0_3, average_tanimotos_on_average_over_top_inchikeys_minimum_tanimoto_0_3, label = \"mean top 10 closest inchikeys above 0.3\")\n", + "\n", + "# plt.plot(prediction_fractions_on_average_over_top_inchikeys_use_average_tanimoto, average_tanimotos_on_average_over_top_inchikeys_use_average_tanimoto, label = \"mean top 10, threshold on average tanimoto\")\n", + "# plt.plot(prediction_fractions_on_difference_between_ms2ds_and_tanimoto, average_tanimotos_on_difference_between_ms2ds_and_tanimoto, label = \"difference ms2deepscore and tanimoto\")\n", + "\n", + "# plt.plot(prediction_fractions_on_average_over_top_5_tanimoto, average_tanimotos_on_average_over_top_5_tanimoto, label = \"mean_top_5_closest_inchikeys\")\n", + "# plt.plot(prediction_fractions_on_average_over_top_3_tanimoto, average_tanimotos_on_average_over_top_3_tanimoto, label = \"mean_top_3_closest_inchikeys\")\n", + "\n", + "plt.xlabel(\"Fraction predicted\")\n", + "plt.ylabel(\"Average Tanimoto\")\n", + "plt.legend()\n", + "plt.xlim((0, 1))\n", + "plt.ylim((0, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9ffebc98-58a6-40db-a651-6c7b32d80542", + "metadata": {}, + "source": [ + "# Load in data posneg-posneg\n", + "After that the data is splitted on pos and neg. " + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "id": "b1863fe3-01a1-4388-95f7-fa368d19ee8d", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pickle\n", + "\n", + "pickled_intermediates_data_folder = \"./pickled_intermediates_pos_neg\"\n", + "\n", + "# Load the pickled files\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"val.pickle\"), \"rb\") as handle:\n", + " val_pos_neg = pickle.load(handle)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "0074987b-53a4-45e4-873d-32250d747410", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(pickled_intermediates_data_folder, \"train.pickle\"), \"rb\") as handle:\n", + " train_pos_neg = pickle.load(handle)" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "2a2053cf-3ee7-4139-b11e-186de371ce01", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(pickled_intermediates_data_folder, \"close_tanimoto_scores.pickle\"), \"rb\") as handle:\n", + " all_ms2deepscores_for_top_inchikeys_pos_neg = pickle.load(handle)" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "id": "ccf1fed7-2939-4fdd-9b69-0e919bd0df09", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Get most common inchi per inchikey: \n", + "Adding fingerprints to Inchikeys: 3119it [00:22, 140.34it/s]\n", + "Get most common inchi per inchikey: \n", + "Adding fingerprints to Inchikeys: 60884it [04:51, 208.87it/s]\n" + ] + } + ], + "source": [ + "from ms2query.benchmarking.Fingerprints import Fingerprints\n", + "val_fingerprints = Fingerprints.from_spectrum_set(val_pos_neg, \"daylight\", 4096)\n", + "train_fingerprints = Fingerprints.from_spectrum_set(train_pos_neg, \"daylight\", 4096)\n", + "val_and_train_fingerprints_pos_neg = Fingerprints.combine_fingerprints(val_fingerprints, train_fingerprints)" + ] + }, + { + "cell_type": "markdown", + "id": "f3aa7919-c29e-45b7-893a-7bd3f7bf5343", + "metadata": {}, + "source": [ + "### Benchmark all cross ionmode" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "id": "ec3ffe2b-7fea-4d63-83c3-7e3236192853", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f43740452c714148b45901044bd2369e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/36473 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_pos_neg, average_tanimotos_average_over_top_inchikeys_pos_neg, label = \"pos - pos + neg\")\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_pos_pos, average_tanimotos_average_over_top_inchikeys_pos_pos, label = \"pos pos\")\n", + "\n", + "plt.xlabel(\"Fraction predicted\")\n", + "plt.ylabel(\"Average Tanimoto\")\n", + "plt.legend()\n", + "plt.xlim((0, 1))\n", + "plt.ylim((0, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8b480715-ba34-40e9-876b-3fa14947946b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Your CPU supports instructions that this binary was not compiled to use: SSE3 SSE4.1 SSE4.2 AVX AVX2\n", + "For maximum performance, you can install NMSLIB from sources \n", + "pip install --no-binary :all: nmslib\n" + ] + } + ], + "source": [ + "import os\n", + "import pickle\n", + "\n", + "pickled_intermediates_data_folder = \"./pickled_intermediates_neg\"\n", + "\n", + "# Load the pickled files\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"val.pickle\"), \"rb\") as handle:\n", + " val_neg = pickle.load(handle)\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"train.pickle\"), \"rb\") as handle:\n", + " train_neg = pickle.load(handle)" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "id": "b3b5de2f-2200-4d26-b8a0-8b63a3ebf06c", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(pickled_intermediates_data_folder, \"close_tanimoto_scores.pickle\"), \"rb\") as handle:\n", + " all_ms2deepscores_for_top_inchikeys_neg_neg = pickle.load(handle)" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "id": "4fb39664-2ae2-4098-a8ba-652b995dba4a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Get most common inchi per inchikey: \n", + "Adding fingerprints to Inchikeys: 1614it [00:06, 239.67it/s]\n" + ] + } + ], + "source": [ + "from ms2query.benchmarking.Fingerprints import Fingerprints\n", + "val_fingerprints = Fingerprints.from_spectrum_set(val_neg, \"daylight\", 4096)\n", + "val_and_train_fingerprints_pos_neg = Fingerprints.combine_fingerprints(val_fingerprints, val_and_train_fingerprints_pos_neg)" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "id": "eb9e0660-fb78-431a-ade6-5462b45fa008", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4fd77a55ec58487283cf37bb1b94396e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "thresholds: 0%| | 0/9 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_neg_neg, average_tanimotos_average_over_top_inchikeys_neg_neg, label = \"neg - neg\")\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_neg_to_pos_neg, average_tanimotos_average_over_top_inchikeys_neg_to_pos_neg, label = \"neg - pos + neg\")\n", + "\n", + "plt.xlabel(\"Fraction predicted\")\n", + "plt.ylabel(\"Average Tanimoto\")\n", + "plt.legend()\n", + "plt.xlim((0, 1))\n", + "plt.ylim((0, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e0ce0a9c-66f5-4b31-a4ed-4a7b0511b8d5", + "metadata": {}, + "source": [ + "# Train ranking model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de5bdd40-b495-4b1a-b72b-1c2e1c2281e0", + "metadata": {}, + "outputs": [], + "source": [ + "from ms2query.benchmarking.reference_methods.predict_best_possible_match import predict_best_possible_match\n", + "\n", + "inchikeys_of_best_match, highest_scores = predict_best_possible_match(train_pos, val_pos, val_and_train_fingerprints_pos_pos)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "35e82c89-a08f-432e-9d03-e470a5ca9510", + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "\n", + "n_train_inchikeys = int(0.8 * len(val_pos.spectrum_indices_per_inchikey))\n", + "train_query_inchikeys = random.sample(list(val_pos.spectrum_indices_per_inchikey.keys()), n_train_inchikeys)\n", + "val_query_inchikeys = [inchikey for inchikey in val_pos.spectrum_indices_per_inchikey.keys() if inchikey not in set(train_query_inchikeys)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d1f7cfc4-38e1-4c94-bbec-ee67f8ef85f3", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix\n", + "import lightgbm as lgb\n", + "def compute_similarity_feature_vector(tanimoto_scores, ms2deepscores):\n", + " \"\"\"\n", + " Returns a flat feature list containing:\n", + " - original tanimoto scores\n", + " - original ms2deepscores\n", + " - aggregated statistics\n", + " \"\"\"\n", + "\n", + " tanimoto_scores = np.array(tanimoto_scores)\n", + " ms2deepscores = np.array(ms2deepscores)\n", + "\n", + " features = []\n", + "\n", + " # ---- Original raw scores ----\n", + " features.extend(tanimoto_scores.tolist())\n", + " features.extend(ms2deepscores.tolist())\n", + "\n", + " # ---- Basic statistics ----\n", + " features.append(np.mean(tanimoto_scores))\n", + " features.append(np.max(tanimoto_scores))\n", + " features.append(np.min(tanimoto_scores))\n", + " features.append(np.std(tanimoto_scores))\n", + " features.append(np.median(tanimoto_scores))\n", + "\n", + " features.append(np.mean(ms2deepscores))\n", + " features.append(np.max(ms2deepscores))\n", + " features.append(np.min(ms2deepscores))\n", + " features.append(np.std(ms2deepscores))\n", + " features.append(np.median(ms2deepscores))\n", + "\n", + " corr = np.corrcoef(tanimoto_scores, ms2deepscores)[0, 1]\n", + "\n", + " if np.isnan(corr):\n", + " corr = 0.0\n", + "\n", + " features.append(corr)\n", + "\n", + " return features\n", + "\n", + "def get_pairwise_score_matrix(ms2deepscores_for_top_k_inchikeys):\n", + " tanimoto_scores = []\n", + " mean_ms2ds = []\n", + " mean_score_per_inchikey = ms2deepscores_for_top_k_inchikeys.get_mean_per_inchikey()\n", + " for inchikey, tanimoto in sorted(ms2deepscores_for_top_k_inchikeys.top_k_inchikeys_and_tanimoto_scores.items(), key=lambda x: x[1]):\n", + " tanimoto_scores.append(tanimoto)\n", + " mean_ms2ds.append(float(mean_score_per_inchikey[inchikey]))\n", + " tanimoto_scores, mean_ms2ds\n", + " return compute_similarity_feature_vector(tanimoto_scores, mean_ms2ds)\n", + "\n", + "def get_y_for_query_spectrum(dictionary_with_scores, best_possible_inchikey):\n", + " \n", + " possible_inchikeys = list(dictionary_with_scores.keys())\n", + " \n", + " library_fingerprints = val_and_train_fingerprints_pos_pos.get_fingerprints(possible_inchikeys)\n", + " query_fingerprints = val_and_train_fingerprints_pos_pos.get_fingerprints([best_possible_inchikey])\n", + "\n", + " tanimoto_scores = jaccard_similarity_matrix(library_fingerprints, query_fingerprints)\n", + " y_for_query_spectrum = []\n", + "\n", + " for tanimoto in tanimoto_scores:\n", + " if tanimoto >=0.9:\n", + " y_for_query_spectrum.append(2)\n", + " elif tanimoto >=0.8:\n", + " y_for_query_spectrum.append(1)\n", + " # elif tanimoto >=0.6:\n", + " # y_for_query_spectrum.append(1)\n", + " # elif tanimoto >=0.5:\n", + " # y_for_query_spectrum.append(1)\n", + " else:\n", + " y_for_query_spectrum.append(0)\n", + " return y_for_query_spectrum, tanimoto_scores\n", + "\n", + "def get_x_and_y(inchikeys):\n", + " y_ranking = []\n", + " y_tanimoto = []\n", + " X = []\n", + " for inchikey in tqdm(inchikeys):\n", + " matching_spectrum_indexes = val_pos.spectrum_indices_per_inchikey[inchikey]\n", + " for spectrum_index in matching_spectrum_indexes:\n", + " dictionary_with_scores = all_ms2deepscores_for_top_inchikeys_pos_pos[spectrum_index]\n", + " y_ranking_for_query_spectrum, y_tanimoto_for_query_spectrum = get_y_for_query_spectrum(dictionary_with_scores, inchikey)\n", + " y_ranking.extend(y_ranking_for_query_spectrum)\n", + " y_tanimoto.extend(y_tanimoto_for_query_spectrum)\n", + " for scores in dictionary_with_scores.values():\n", + " X.append(get_pairwise_score_matrix(scores))\n", + " return np.array(X), np.array(y_ranking), np.array(y_tanimoto)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2e5e1e9-2d9e-407d-a77d-d989fbffd4e6", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "14a7525f20d24be899d8771159bd4b29", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/36473 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ac4ae1bd8f1e44f5aacdd84ca7dacab4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e4c80eebec30473abd4b40414ac852e6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "thresholds: 0%| | 0/30 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + 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"application/vnd.jupyter.widget-view+json": { + "model_id": "d2048e5bb1204a91b8f51889aa85257e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "thresholds: 0%| | 0/30 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "072dc78cd3ed4caeaae5b142ec5a0a9b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b13fb0637579414881a631277bdd9b4a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "thresholds: 0%| | 0/30 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1e9c4049e32c46f59d4c390102e93733", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6abeab1717a84f0990c701cc0c8518cc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "thresholds: 0%| | 0/30 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7578aa2dab0742698eb75f517b93f871", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a377012ec462415098139240f06d1e74", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "thresholds: 0%| | 0/30 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "prediction_fraction, average_tanimotos = model_results_dict['regression tanimoto scores, ms2deepscore scores, tanimoto statistics, ms2deepscore statistics, ']\n", + "plt.plot(prediction_fraction, average_tanimotos, label = \"Regression model\")\n", + "prediction_fraction, average_tanimotos = model_results_dict['ranking tanimoto scores, ms2deepscore scores, tanimoto statistics, ms2deepscore statistics, ']\n", + "plt.plot(prediction_fraction, average_tanimotos, label = \"Ranking model\")\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_pos_pos, average_tanimotos_average_over_top_inchikeys_pos_pos, label = \"No model just average\")\n", + "\n", + "plt.xlabel(\"Fraction predicted\")\n", + "plt.ylabel(\"Average Tanimoto\")\n", + "plt.legend()\n", + "plt.xlim((0, 1))\n", + "plt.ylim((0, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "75bb8caf-b831-476e-92b8-68e322987aee", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "prediction_fraction, average_tanimotos = model_results_dict['regression tanimoto scores, ms2deepscore scores, tanimoto statistics, ms2deepscore statistics, ']\n", + "plt.plot(prediction_fraction, average_tanimotos, label = \"Regression model\")\n", + "prediction_fraction, average_tanimotos = model_results_dict['ranking tanimoto scores, ms2deepscore scores, tanimoto statistics, ms2deepscore statistics, ']\n", + "plt.plot(prediction_fraction, average_tanimotos, label = \"Ranking model\")\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_pos_pos, average_tanimotos_average_over_top_inchikeys_pos_pos, label = \"No model just average\")\n", + "\n", + "plt.xlabel(\"Fraction predicted\")\n", + "plt.ylabel(\"Average Tanimoto\")\n", + "plt.legend()\n", + "plt.xlim((0, 1))\n", + "plt.ylim((0, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "753a7cc6-442e-4d00-a44e-f4bd075ba2b4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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TBqfTSVZWFrfffjs+n2+/1h0OVY6E4CurGQABNG0zDMSgInE+LeNiAJg1exaRyC5DXCfmwPG3EZ/jxWQF26Y8uq0zETJvwbDm8+7U9TostlJKKXWE0QBIKXXYffzxx9xxxx2MHDmSOXPm0KVLFwYOHMj27dtrXf7DDz/k3nvvZeTIkSxdupT//Oc/fPzxx9x///37tf5IqHoNkGOXAMgd15y4yDEANGuyACNiweMrZ8WKFTUz63sb5pQsEpqVA3DhgsqAKS55GZuLvUxYsm2/yqiUUkqpQ0MDIKXUYff8889zzTXXcNVVV9G+fXveeOMNXC4X//3vf2tdfurUqfTt25dLL72UnJwcTjvtNC655JK91hrtTrQGqLyqD5CtxjLZrYcD4En9lcRgMkDt67O5YOATJLaqAITmS4pILxSS0yqDpXemrNuvMiqllFLq0NAASCl1WAUCAWbPns2pp54anWYymTj11FOZNm1arWn69OnD7NmzowHImjVrGDt2LGecccZu1+P3+yktLa32qfJnAFR7EziA1KanYA81JmL10jFjE0jlevPy8mqurN3Z2LqcSExjPwCDZkfIC67AYi1jxrpCFm4q2cteUUoppdThogGQUuqwys/PJxwO06hRo2rTGzVqxNatW2tNc+mll/Loo49y/PHHY7VaadGiBSeddNIem8A99dRTxMfHRz9ZWVnReVUB0O6awAEYhokmjS8HwJrzG85AEgAzZ9YyGIJhwOn/IrG1B4D+Cw2cfqFL200AvDNl7W7LqZRSSqnDSwMgpdQRb9KkSTz55JO89tprzJkzhy+++ILvv/+exx57bLdp7rvvPkpKSqKfjRs3RueFQ7sfBW5nmW0uwRRxEIjZTPu4ygEX5s+fj9/vr7lwamvc/fpjiwti90fot1CwxS4B4NsFW9hetn8DNiillFLq4NIASCl1WKWkpGA2m9m2rfrgANu2bSM9Pb3WNA8++CBXXHEFf/vb3+jUqRPnnnsuTz75JE899VTNkdl2sNvtxMXFVftUCQd3bQJXsw8QgNUaR1rsmZXlzpmFNeLC7/ezYMGCWpc3el1LUuvK9wWdNSPCqvw5dGlqJRgWPvhDh8RWSimljgQaACmlDiubzUaPHj2YOHFidFokEmHixIn07t271jQejweTqfrlymw2A0RfarovgoEwAN7yP1+EujtNO+wYDCFtDtm4gMrBEGpdb7N+xPdsgsUZJq0ETpobpGf7ykDvvWnrKNzx4lWllFJK1R8NgJRSh90dd9zBW2+9xbvvvsvSpUu54YYbqKio4KqrrgLgyiuv5L777osuf/bZZ/P666/z0UcfsXbtWiZMmMCDDz7I2WefHQ2E9kXQH0ZEojVAewqAYmPaEGfpAYbQKmsZZpOFvLw81q1bV3Nhw8DU+1pSOpQBcN6UCMXe6bRNj6XYE+Tx75bsc1mVUkopdXBpAKSUOuwuuuginn32WR566CG6du3KvHnz+PHHH6MDI2zYsIHc3Nzo8g888AB33nknDzzwAO3bt2f48OEMHDiQf//73/u1/qA/RNAXJhKurMXZXR+gKtltrgbAm/U7TcyVZZw+fXrtC3e5mIS2JiQ2TIIH4r7+lesHODAM+GLuZn5bWcsockoppZQ6bAzZn/YjSil1lCktLSU+Pp5nrvqG06/qTmbbJD54cBoWu5nrXuy3x7SRSIjffj6ekCkPx6LLmVBoYBgGt956KwkJCTUTfP93ij/9gNw/Eqmww4O3pdAu4UG+mB6kaZKLcbediNO27zVXSimlVENTdf8uKSmp1p/3QGgNkFKqwQn6w9H+P073nmt/AEwmC5nZlUNiS9bvNEnMQERqHxIb4NhriM/2Yk0I4vbDCb8WMDf4NI2SytlQ6GHUxBUHbVuUUkoptW80AFJKNThBf7hO/X92lpV9CUSs+OPX0Uoqg6c5c+YQDAZrLpzaBqP5iTTqVPny1TNnQShvO46stzEsJbz921oWb9GXoyqllFL1QQMgpVSDs3MA5IytWwBksyVjD58EgCvlD+LdcXi9XhYuXFh7gmOvJaaxH2dqBFtQuGGyk0J/Lqmt3iFilHLfFwsJR7QFslJKKXW4aQCklGpwgv4w3rJ9qwECyGo2DICy9Jl0cCUBexgSu/XpGPGZpHUpAJNBtwXlXLwwDi9bickezYLN23lv2roD3RSllFJK7SMNgJRSDU7QH8ZXUdUHqPaXoNYmq2UvAkUtwBQmyboAi9nC1q1b2bChlpecmi1wzNW4UoKk9alcx3k/lnLMthiwb8bZ5H88N34Z20p9B2WblFJKKVU3GgAppRqcykEQdtQA1bEJHIDJZGB4zwagInMybeIygcpaoFp1HwbOJJKarCWuXQyEw/z9K0jzWLHELiOY8CWPfLv4QDZFKaWUUvtIAyClVIOzcxO4vb0DaFfpjc8k7Isj5Cgm29gMwNKlSyktLa25sDsZLv8Mwx5LRvuV2FPtGIXFPPdTBpYw2JKmMWHzp/y6Qt8NpJRSSh0uGgAppRqcoG/fR4GrktUmjaLVJwIQavI7TWLTiEQizJo1q/YETXrApR9jctrJPHYDJrsZ++I1PL+4KwD2tLHc88OH+ILh/d4epZRSStWdBkBKqQYnGAjjq9i/GqCkDDe+bf2RiBlv4gpahyIAzJ49m1AoVHuinL5w0Rhs8SYaH7sdgPSxs7ki/gwMQyh1fcYrv+i7gZRSSqnDQQMgpVSDs/OLUB0xdR8EAcAwGaRnN6dsUzcAXBmzibG6qKioYPHiPfTnaXUqnP82sZlB3I18IMLlyxNxmmMx2Yp5e9YPrMkr3+9tUkoppVTdaACklGpwAp4Q/orK2pp9rQECaNImkaJVpwBQlvEHbcPxwB4GQ6jSYQic8wqJrTwAlH/2KRfmnAWAETeNJ8cu3eeyKKWUUmrfaACklGpwqpq/YYDdve8BUGabRLz5LfEVZyHmABmpazAbJjZv3symTZv2nLjbZcRceD0WV4hwmYchG9MAMMcsZ+KqZSzeUrLP5VFKKaVU3WkApJRqsBwuKyaTsc/p4tOcuBMcFK08GYCKrMk0D6cCdagFAozjbyWxdWUQZv7fZxyXcRyGIVgTZvDaL6v3uTxKKaWUqjsNgJRSDda+jgBXxTAM2vXJoHTDsURCboKuPFrGV9bcLFq0iPLyvfTlcSWRcP4QDJPgW7Gey43eAFgTZjJ28UZWbS/br3IppZRSau80AFJKNSw7VfjsT/+fKp1PycRsdlK0qi8AkaxppEbiiEQizJ49e6/pLQPuILapH4AW3/5BmjMNk6UCc8wiXpuktUBKKaXUoaIBkFKqQbHazNH/728NEIAzxkaH45tQvPokRAw8KYtob3MAMGvWrN0PiV0loSlJg44DoPzn37k448zK8iX+wdfztrChwLPfZVNKKaXU7mkApJRqUKz2PwOgA6kBAug6IIuwL43yLZ0BiG0yD5fYKCsrq1NfIMfFD+BIDCAhYcCMEsyGGYtrHWLN5fXJWguklFJKHQoaACmlGpTqNUD79g6gXcUkOmh7XDpFqyoHQyjLmkoPyQRg8uTJe+0LZKR3IPH4ZgCEPv+WU5qcVFnGhD/4bPZGcku8B1Q+pZRSStWkAZBSqkGx2P+87B1IE7gq3U7Lxru9Hf7SdCImL2mNVpMSicXv9zNx4sS9po/72wOYbBGChV4u2V4ZPDkS5xEUH2/+uuaAy6eUUkqp6jQAUko1KNWawMUeeACU0MhFyx7pFK86CYCSlpM4LtgKgLlz57Jly5Y9pje1OYmEzpUvUm306ffkxOUQMXxY4+byvxkbyCvzH3AZlVJKKfUnDYCUUg1KtSZw+/ES1Np0H5RNybo+RIJ2/NZNxCbm0iKcDsAPP/yAiOwxfeLwmwGhYkkul9v7ABCbNgNfMMh/fl97UMqolFJKqUoaACmlGhRLtUEQDqwPUJWUzFiy2mVSsq4yeCltP5ljgy2wGhY2btzIwoUL95je1u9yYprZAIOeL31DnNlN0LwFe6PvGfPHesr9exlRTimllFJ1pgGQUqpBOdhN4Kr0GJQTHQyh1DUTm6OMLsFsACZMmIDfv4embCYTjR56BJMlQmBdMS8s7wiALWkqXscffDxz40Erp1JKKdXQaQCklGpQrDZL9P8HYxCEKhkt4klJb0fF1nZAhPJuf9AxlEWc1U1ZWRm///77HtPbep9L+pDWAMR+NoV7HUMqy5j+JW9O/4VQOHLQyqqUUko1ZBoAKaUaFOuOUeBMFqNabdDB0OP0bIpWnQJAQfxPmMxhjq1oAcDUqVMpLCzcY/r4u/9NXE4ABHq9+hMDkk/AMIUpj/8Pn85bclDLqpRSSjVUGgAppRqUqj5AzhgbhmEc1Lyz2iXhtPQlWJFMOFKCr+cisiMpZDkbEQ6HGT9+/J4ziGtM+h3XYnWHCG4v5taJFhIsmZispTw3/378IR0RTimllDpQGgAppRqUqlHgDmbztyqGYdDj9GYU7RgSuyB1HBhwbHEOhmGwbNky1qzZ87t9zKfcTuOBMWAInh8m8Kr8HxJ24jev5Y6JI/c6opxSSiml9kwDIKVUg2J1VNUAHfwACKB5l1SoGEAkZMUTWI70LCBRYugUU9kU7ocffiAcDu8+A4sN1/DnSGlfDoD9hdc41X8lIga/bv2eT5Z/ckjKrZRSSjUUGgAppRqUrHZJZHdKpvMpWYckf8Nk0O3UjpRuOBaAouyfwARd8hrjtDvIy8tj1qxZe86kxSmknH8SzpQAkQoP1/34M2w+FYBnZj7D5vLNh6TsSimlVEOgAZBSqkFxx9s566YuNOuccsjW0eqYRgTyBgGQXzoBa3crdqwcY20DwC+//EJFRcUe8zDOeIrGx3sx2yKwfClPTdmMlDbDH/Hz+B+Pa1M4pZRSaj9pAKSUUgeZ2WyiY+8T8eS1BMKUtvsdw2GhZX4yKa5EfD4fv/zyy54zScjCNuhWMk8swLBAyzULuOZ7BxIx8fvm3xm3ftxh2RallFLqr0YDIKWUOgTa9cmgYvMAADZu/Zj483IwYdCrqBkAs2fPZuvWrXvOpM/NuHKSaNK7AAyD09Yt5IJxmQD8c8Y/KQ2UHtJtUEoppf6KNABSSqlDwGIz07Hn/xHyxiNGIYVxf+A+LoMMSaS5kY6I8MMPP+y5KZvNDSfdS2wTP+l9AgBcOG8NA2e4yffm89Kclw7T1iillFJ/HRoAKaXUIdL++GwC+ZW1QCuX/oeEM5thTXdxrLcFFsPM+vXrWbx48Z4z6XYFJLckMSuPpDM7AXDVxBJ6LYvwyfJPmLd93iHeCqWUUuqvRQMgpZQ6REwmgy7H/Q2JmMG+lNUrvyfp0nbEWl10DjQFYOzYsWzfvn33mZit0H8kAGmJv1Hcrz8mYMQ3QtsNER6Z9gjBSPAwbI1SSin116ABkFJKHUJN27SC8jMAWLf5Xjy2VSSc04LO4WxSIrF4PB7ee+898vPzd59Ju7Mh81iMkIdep5tY0qIbtrDw9y8iFK1fyXuL3ztMW6OUUkod/TQAUkqpQ6xXv8ep2NYRw+xnzpzhGB2CxHZNZ1CgG0lGLOXl5bz77rsUFhbWnoFhwIBHATDN/4CuT9zCyoRMYr3CnV+GeXv262ws23gYt0gppZQ6emkApJRSh1hsYgxNkp7CV5RJhELmzb+amLNScSfHcrq3K0nWOMrKynj33XcpLi6uPZPs3tDmDJAwLZa9zNoRD1BqddEyFy4Z52Hk1JGEI+HDul1KKaXU0UgDIKWUOgy6ntKWkiV3E/Qk4vWuYdGKESRe1Byn2c6gss4kuuIpKSnh3XffpaSkpPZM+o8EwwTLvuO64yz856SriAAD5gqu8dN5d8m7h3WblFJKqaORBkBKKXUYmK0m+gzpxabfbiEcdFJcPJ1VZY8RNzAbF3YGlXYiITaeoqIi3nvvPcrKympmktYWul0OgGPSI1x4/f8xpu1pAFwzLsI3P7zIkoIlh3OzlFJKqaOOBkBKKXWY5HRKIT2rC5unXI+ImW3bvmV7449wtE3CHbJzZqQH8XHxFBQU8N5771FRUVEzk5PuB4sTNk7n1PJvyD/3MmY0aostBLd9HuDhcX/HG/Ie/o1TSimljhIaACml1GF0/AWt8BV0IHfGlQCs3/AG3n5zMcXZcBYYDG50IrGxseTl5fHee+/h8XiqZxCXASfdW/n/H+7mqfbrePW4y8l1JdKoGAaPWcez0585vBullFJKHUU0AFJKqcMooZGLrqdmUbq+D6VrzgVg5bpHMc7JAwNsCz1ceMxZuN1utm3bxvvvv4/Xu0uNTt9boefVgJD8402M7B3kiWOHETCb6b5aCI/+mF82/HL4N04ppZQ6CmgApJSqs7y8PH7//Xd+//138vLy6rs4R60ep+fgirexZdbp2MIDEQmzvOA+TKf4ADD9VMDl51yEy+UiNzeXMWPG4Pf7/8zAMOCMZ6HdORAOcMaSv9OlnYNXOp8PwAW/RRgz5j7yvXt4t5BSSinVQGkApJTaq4qKCq6++moaN27MiSeeyIknnkjjxo0ZPnx4zSZaaq9sDgt9zmsJGCz57jziYnsTDntY7XwQWvmQQBjT+AKuuOxyHA4HmzZtYsyYMQQCgT8zMZnhvLcg5wSMQBnP+B4ht01zxjftgQm46vMSnvjxbkSkvjZTKaWUOiJpAKSU2qs77riDyZMn880331BcXExxcTFff/01kydP5s4776zv4h2VWh/biPTmcYT8JooW3kKMuw2BQB4b2z2DxPoJbi7HOcfLlVdeid1uZ8OGDfzvf/8jGAz+mYnVARePgfROmL35fOJ+hk8692djbBJJ5dDz7T/4cOmY+ttIpZRS6gikAZBSaq8+//xz/vOf/3D66acTFxdHXFwcZ5xxBm+99RafffZZfRfvqGQYBide3AYMWDmjnPS4F7Db0/H4VrO135uIEaJ8yhYSi+1cfvnl2Gw21q5dy0cffVQ9CHLEw2WfQ2IOroqNfJH2Ki/2vJiA2UT31cKS1/7J9Nzp9behSiml1BFGAyCl1F55PB4aNWpUY3paWpo2gTsAqU1jaX98YwCmfV5C505vYTbHUBqYTf7J/0MQij5bQUZcGpdddhlWq5XVq1fz6aefEgqF/swothFc8SW4U2nkWcnIpt/xZsfBAFz8c4hRH9yk7wdSSimldtAASCm1V71792bkyJH4fL7oNK/XyyOPPELv3r3rsWRHv+POaY7dZaFgUzkb5sXTqeMrGIaZQstEirp9R8QTovCjZTTNbMoll1yCxWJhxYoVfP7554TD4T8zSmoOl34CZhvH+v+gaTsPvzfuiCUC135ewe3fXceG0g31t6FKKaXUEUIDIKXUXo0aNYopU6aQmZlJ//796d+/P1lZWUydOpUXX3yxvot3VHPG2jj27GYA/PH1aly2XrRt8wQAeamfU5L9G4G1pZROWE/z5s25+OKLMZvNLF26lC+++KJ6ENSkO5z2OAB3mT5gXLfj2OZKIL0YLviqgGvHX0OeR0fvU0op1bBpAKSU2qtOnTqxcuVKnnrqKbp27UrXrl15+umnWblyJR06dKjv4h31Op7YhKTGbvwVIb57ZT4pSeeSk3MzAFvbjKYieSFlkzbiXZRPy5YtufDCCzGZTCxevJjPPvusenO4Y6+FNmdgigR5M/UdXux5AWHDRN+lQuupm7jhpxsoDZTW05YqpZRS9c8QHSNVKbUXv/76K3369MFisVSbHgqFmDp1KieeeGI9lazuSktLiY+Pp6SkhLi4uPouTg2FuRV8+ewcfBVBmrRJ5MybOrFi1T1s3foVJnGS9ce9OAPNSbupK9Y0F8uWLePTTz8lHA7TqlUrLrzwQqxWa2VmnkJ443go3cwkR3++n9GMq5eMxW+Be68yk96hJ2+c+gYOi6N+N1oppZTai0Nx/9YaIKXUXp188skUFhbWmF5SUsLJJ59cDyX660nKcHPWiC5Y7WY2Ly/ip/8upU2rJ0hM7E3E8LL5mBcJsJ2CD5YQ8Ydo27ZttE/QypUr+d///vfne4JcSXD+22CYOMk3kQ79k5mT2hp7CO78PMKydbO459d7CEVCey6UUkop9RekAZBSaq9EBMMwakwvKCjA7XbXQ4n+mhrlxHHGDZ0wWQzWzMvj1/+tpWOHV3G7WxGyFLL5mFH4Cwso+mwlIkLLli2jo8OtWbOGDz744M+BKrL7wEn3ATCs+BVaPHQjha4EMguFez6N8Nuaidw3eSQRidTjFiullFKHnzaBU0rt1nnnnQfA119/zaBBg7Db7dF54XCYBQsW0KZNG3788cf6KmKdHelN4Ha2Zl4eP/57ISLQ9dQsup/pYtbs/yMQ2I6rsD2Zs+8gYVArYvtlArBx40Y++OAD/H4/TZo04fLLL8fpdEIkDO8NhnW/QXonivu8yoYrr8bq8zCjlcHz55loE9ef9855BofVVs9brZRSStWkTeCUUodVfHw88fHxiAixsbHRv+Pj40lPT+faa6/lgw8+qO9i/uU075rKyVe0BWDeTxtZMjlI1y5vYza78CQtYWv7dyj+cQ2+VcUAZGVlMXToUJxOJ5s3b+bdd9+loqICTGY47y1wJcPWhSRsGkPLt94Aq41jVwrDxwlLyyZy4rtXs7aguP42WCmllDqMtAZIKbVXjzzyCH//+9+P6uZuR1MNUJV5P21gymerAOh3aRvSO6xkwYJrEQmTvHowaVsvIG1EdywJlTVz27Zt47333qOiooLU1FSuvPJKYmNjYcV4+PCCykwHPklpWWs233YbRCJ82tfMpycamP2teO/M1+jcJL2etlYppZSq6VDcvzUAUkrVWV5eHsuXLwegTZs2pKam1nOJ6u5oDIAA/vhqNbN/XA8GnDa8A66MX1i2/B8ApC8aTorpdNKu64JhrazQz8/P591336WsrIykpCSGDh1KfHw8THgIpux4Z1P7wRT5T2brE/8C4K2BNiZ0j4A/i1dOeY1+LXPqY1OVUkqpGrQJnFKqXng8Hq6++moyMjI48cQTOfHEE2ncuDHDhw/H4/HUd/H+0noNbk6HE5uAwE/vLCFcPICc7BsA2Np+NMWePyj+dnV0+ZSUFK666iri4+MpLCzknXfeqRzB79RHYNA/wWSFJV+TWPQSKVecD8DfJgQ5drkN7Bu56ZfhfDF/Ub1sq1JKKXU4aACklNqr22+/ncmTJ/Ptt99SXFxMcXExX3/9NZMnT+bOO++s7+L9pRmGwYkXt6ZlzzQiYeGHfy/EKX+jUaNzwBRmS9dXKFgyk4oZW6NpkpKSuOqqq0hKSqK4uJh33nmH/IICOO56uPpHiM+CwjWkhN8k4aQuGBHhzu9CdNzgxrBt58GZN/Lm1On1uNVKKaXUoaNN4JRSe5WSksJnn33GSSedVG36L7/8woUXXkheXl79FGwfHK1N4KqEQxHGvr6ADYsLsbssnHNbRzbm30xx8XQsvkSaznqIJledjC0rNpqmrKyM9957j7y8PNxuN1deeSWNGjWqfFHql9fByvFIBDYt7ET50gKw23h9UAy/tC8lEorhkqxHeeC0AfW41UoppRo6bQKnlKoXHo+n8sF5F2lpadoE7jAxW0wMurYT6c3j8XtCfP/KEnIaP4fL1YKQo4jNXZ5n+/9mEy4PRNPExsYybNgwGjVqREVFBaNHj2bLli2VL0q95GPoPxLDbKJJh0W4sy3gD3DD14VcNTkGs6mMjzbdz51ffUskor+TKaWU+uvQAEgptVe9e/dm5MiRf75kE/B6vTzyyCP07t27HkvWsFjtZs68qTPJTdx4SgOMfWUtrZu/gc2agj92Ixtznqfgf4uQ8J8Bi9vtZtiwYTRp0gSv18u7777Lxo0bwWSCE+6Aod9iSmhEVq8NJHeo/H5Pn1rMyM+duINefix4nGFjvqXEG6yvzVZKKaUOKg2AlFJ79eKLLzJlyhQyMzPp378//fv3Jysri6lTp/Liiy/uV56vvvoqOTk5OBwOevXqxYwZM/a4fHFxMTfddBMZGRnY7XZat27N2LFj92vd9aG8qJAV06cw5ZMx5G1Yt9/5ONxWzr6lK3EpDkrzfYx/I492bd7AZDjwpCxmg/0lisevrZbG6XRyxRVX0LRpU/x+P++//z7r1u0oQ87xcP1vGM1PIK1TIY37lmHYrLRfVc4/3zfTpKSC2f6nGfTKl8xaV7j/O0AppZQ6QmgfIKVUnXg8HsaMGcOyZcsAaNeuHZdddhlOp3Of8/r444+58soreeONN+jVqxejRo3i008/Zfny5aSlpdVYPhAI0LdvX9LS0rj//vtp0qQJ69evJyEhgS5dutRpnYezD1AkHCZvwzq2rFjKluVL2bJiGaV526LznXHxXPbE88Sn1WxWWFcleV6+eHY2npIA6c3j6HtFMYuX3gBESF51Lq1734urU0q1NIFAgP/973+sXbsWi8XCxRdfTMuWLStnhgLw2VWw7Du8xS42zcohlF+M12HihXNgTlYSvg03MKJfT24+uSUWs/5+ppRS6tDT9wAppf4SevXqxTHHHMMrr7wCQCQSISsrixEjRnDvvffWWP6NN97gmWeeYdmyZVit1v1a56EMgLzlZeSuXEbuimVsWbGU3JUrCPp91ZYxDBMpTbMJ+nwUb8slObMplzz2LHaXa7/XW7C5nC+fm4PfEyKrfRJdhyxi5aqRAGQsvY5WF9yMNa16/sFgkE8++YSVK1diNpu58MILadOmTeXMnYKgUMDBpqU98S5dQ8SAD08y8WXXNLwbrqdnVhajLu5Gk4R9D36VUkqpfaEBkFKq3mzZsoXff/+d7du3E4lEqs275ZZb6pxPIBDA5XLx2WefMWTIkOj0oUOHRofX3tUZZ5xBUlISLpeLr7/+mtTUVC699FLuuecezGZzrevx+/34/f7o36WlpWRlZR3wBVQiEQpzN++o3akMeAo3b6yxnM3ponHrtjRu3Y7GrduR3rI1dpeLsoJ8xvzjDiqKCmnWrSdD7n4Qk6n2baiLrWtK+HrUXEKBCI1bJdDu9HFs3vo2RMxkr76P5kOvwOSwVEsTCoX4/PPPWbp0KSaTifPPP58OHTpUzgwH4dNhsOw7BBtbi86heNxUAH5rb/BG/yYUbr6WOFscT5/fmTM6Zex32ZVSSqm90QBIKVUvRo8ezXXXXYfNZiM5ORnDMKLzDMNgzZo1dc5ry5YtNGnShKlTp1YbQOHuu+9m8uTJTJ9e8/0zbdu2Zd26dVx22WXceOONrFq1ihtvvJFbbrmFkSNH1rqehx9+mEceeaTG9H29gHpKS9i2ZhXbVq8kd9VytqxYhq+8rMZyiRmNo8FO49ZtSc5simGqvZnY1lUr+PjhewkFA3Q/YzAnD72mzuWpzeblRYx9fQEBX5jEdAdtz/iAovJxmIJOWhY+Q+bFg6p9ZwDhcJivvvqKhQsXYhgGQ4YM+bM5YTgIn10NS79BTDaKYq9n29tfQDjC+lR4eXA2iwuuBbFz8TFZPHR2e1w2Sy0lU0oppQ6MBkBKqXqRlZXF9ddfz3333YdpNw/1dbU/AVDr1q3x+XysXbs2WuPz/PPP88wzz5Cbm1vrevanBsjvqWDbmtVsXb2CbWtWsXX1ymp9d6pYrDbSW7aurOFp046MVm1xxcXv035YPu13vhv1NAADrrmZzqcO2qf0uyrYXM53r8ynvMiPK96g7Rkv4wnPx+JNooPrTVJO7lYjTSQS4dtvv2Xu3LkAnHXWWfTs2bNyZjgInw+HJV+D2UZF+5FseG4MFBbjscOYITl8GbwOiVhpnuJm1MVd6ZyZcEDboJRSSu3qUARA+pOdUmqvPB4PF1988QEHP1D5UlWz2cy2bdUDi23btpGenl5rmoyMDKxWa7Xmbu3atWPr1q0EAgFsNluNNHa7HbvdXudyTfnkA/74/KNa5yVmNKFR85akt2hNkzbtSM1phtmyf32RqrTpfTyFWy5j6idjmPjf14lvlE52p677nV9ykxjOv7sH370yn4LNFSz+djitB/6LoHMTy0ruoOvy94lpk1ktjclk4uyzz8ZisTBz5ky+++47wuEwvXr1ArMVzv8PYMCSr3AvfpiWjz7Myjcn4lqwjGs+XkeHk17l7cw7WJNfwXmvTeX2Aa25vl8LzCaj1jIqpZRSRwIdxkcptVfDhw/n008/PSh52Ww2evTowcSJE6PTIpEIEydO3O07hfr27cuqVauq9T1asWIFGRkZtQY/+2PFH1MAiElOoXWvvpxw6TAuePAJbvrvR1w96t+cectd9DhzMOktWx9w8FPluPMupm3ffkTCYb594SkKt2w6oPxiEh2c+/ceNGmTiL/cyfJxN2EKxeOP28CihSMIFNZsumcymTjjjDPo06cPAD/88APTpk2rnGm2wvlvQ4dzIRLE+vs/aHd6mMCQEwDoM2kTj/zxCINzAoQiwjPjlnPJm3+wqUhfjquUUurIpU3glFJ7FQ6HOeuss/B6vXTq1KnGSGzPP//8PuX38ccfM3ToUP79739z7LHHMmrUKD755BOWLVtGo0aNuPLKK2nSpAlPPfUUABs3bqRDhw4MHTqUESNGsHLlSq6++mpuueUW/vGPf9RpnXurQv/3jcMoL8jn8qdG0ah5y33angMRCgT45LH7yV2xjMSMxlzy+HM4Y2IPKM9wKMLP7y9lxfRtOBPXknPKs4g5QGJRf7qe/TomW81BF0SEiRMn8vvvvwNw2mmnRYMiImH44zX4+XEI+cDq4g/bAGzvz8QZgOIYg1nXXM4bm7tREYgQa7fw2JCODOnW5IC2QymllDoUTeC0BkgptVdPPfUU48aNY9u2bSxcuJC5c+dGP/Pmzdvn/C666CKeffZZHnroIbp27cq8efP48ccfadSo8r04GzZsqNa3Jysri3HjxjFz5kw6d+7MLbfcwq233lrrkNn7K+CprLWwOg7v0M4Wm43Bd/6D2JRUinK38O3zTxEOhQ4oT7PFxKnD2tNjUDbeomZsm3YNiEFR4kSWjX+C2n73MgyD/v37c+KJJwIwfvz4aDCEyQx9RsANUyH7eAh6OK7ia9yXJ7M9zUZCuXDyqPe5OfA6nbMMyvwhbvt4Hrf8by4l3uABbYtSSil1sGkNkFJqrxITE3nhhRcYNmxYfRdlv+3pFyQR4YVLBiMS4bo33iMmMemwly9vwzr+9+BdBH1eOvUfyIBrbq4xctv+WPTrZn7933KatPqFmK4fAtDC/hA5fYfuNs2kSZOYNGkSAKeccko0KAIgEoE578L4ByFQhj9i448VrUmbVwjAvDY25v/fcD5d2oJwBJokOHn+wi70ap58wNuilFKq4dEaIKVUvbDb7fTt27e+i3HIhPx+RCr7F9mc9fNyz9SmOZx5y11gGCycOI45Y2u+D2l/dDyxCWfc0Jmt6/oTWTkQgNXeJ9i2bOJu05x00kmcfPLJAPz888/RYAgAkwl6XgU3TYdWA7GbApzYZhHGyQ5CZui6PMAFz7zOrbxJ06Qgm4u9XPzWH/zzx2X4guGDsk1KKaXUgdAASCm1V7feeisvv/xyfRfjkAn4vJX/MQysdke9laNFj2Ppd/nVAEx6/z+smTPzoOSb0zmFIXd0Z+Oqi7Bs6QmmMEvW38b2335HIrU3AujXrx/9+/evLMukSfz888/Vm87FN4FLP4bz/4PhTqZtozU0G1BAaaYLVwAGfLGCe8c9yPnp8xCB1yet5rQXfmXCkm21NsFTSimlDhdtAqeU2qtzzz2Xn3/+meTkZDp06FBjEIQvvviinkpWd3uqQi/K3cx/b7sOm9PFiNGf1FMJK4kIE958mYU/j8fqcHLJY8+Q2jTnoORdkudh7CszaNzpKUJJq7B4k2m5+Z+kndsLa6qr1jRTpkxhwoQJAJxwwgmccsopNZvmVeTD2L/D4i+RCCzLa4ZvagCHXwgb8Pvxabzf4hw2F7cBDE5sncpDZ7WnZVrMQdkupZRSf13aBE4pVS8SEhI477zz6NevHykpKcTHx1f7HO0C3soaoPpq/rYzwzDoP/wGstp3Iujz8tW/HsVTUnxQ8o5PdTHkrj4UrLuHSHkjQs4C1qY9Ru4rUymbvBEJ1/w9rG/fvgwcWNl07rfffuOnn36qWYPjToELRsOF72HEptKu0VraDtrO9o5xmAX6/badJ798m9Mdz+FImMevK7YyaNSvPPH9Esp8OkiCUkqpw0trgJRSDcKefkHauHgBnzx6P0mNM7nqhTfqqYTVecvL+PAfd1C8NZeM1m258MEnsRykdx6FAmEmvD8Rc8bdmB1luPM602TerdiaxJP0f62xprtrpJk+fTo//PADAL179+a0006rfZCGigL44S5Y9DkAGyuakzfFwFlYGWROa2swZkAyG8pPIFh8DCnuWO4Z1Ibzu2di0heoKqWU2oXWACml1CFQ1QfoSKgBquKMieXce0Zid7vJXbGMcW+8eND6zlhsZgZeNQCb91EiIRsVqQvY2u4DApvK2PbyXEp/Wo+EItXS9OrVizPOOAOAadOm8eOPP9ZeHncy/N9/4aIPwJ1GlnsNXfuvw31KS8Rk0HuZ8K838/m/9V+T3PxpSh3fc9eX0zj39anM21h8ULZPKaWU2hOtAVJK1ap79+5MnDiRxMREunXrtschmefMmXMYS7Z/9vQL0tLfJzH25Wdp2rEzFzz4ZD2VsHbrF87j8ycfQiIR+l54Ocedf/FBy1tEmPHT/ygzPYRhCLFrL6LxytMBsKa7Sfy/Vtgyq7+UddasWXz33XcAHHPMMZxxxhm7PzY8hfDD3bDwUwB80pIt8xrhX74WgLw4+PhEE7+1s+Ev7UWg4AT+r0tH7h7UltRY+0HbTqWUUkevQ1EDZDkouSil/nIGDx6M3V75EDpkyJD6Lcwh9mcfoNoHAqhP2Z260v/qG/jp7VeZ8skHJDbOpE3v4w9K3oZh0GvApcyZUkCRfxRlzT5mRSCVNtuPI7i1gu2vzSP2xCzi+jfFsFY2GOjZsycmk4lvvvmGmTNnIiKcccYZmEy1NChwJcH5b0P7IfDd7TgqVtGs6xpKup9B3s8bSd2Wx83fRTh7uo8PT/qNOS2m8s2m4/jx+YHceko3hvbJwWrWhgpKKaUOLq0BUko1CHv6BWnmN5/z65h3aH/CyZx+8531VMI9+2X0m8z54RssNjsXPfw06S1aHdT85854kMLyD4mELZQtuJc+SccQWl4EgCXVSeL/tcae/ed+mzdvHl999RVQWVt41lln1R4EVfEUwo/3woKPAYhY4inynUL+uCVEysoAWNwUxpxkZmW6g0BBP5qaB/HIOd3o2zLloG6rUkqpo4f2AVJK1atAIMCmTZvYsGFDtc/RrqoPkPUIrAGq0u/K4TTr2oNQwM9XzzxGWUH+Qc2/6zGPEB/TH5M5REyH5xm/fjGRfpmYYqyE8rzkvTGf4u/WEAlUvsy0a9eunHvuuRiGwZw5c/jmm2+IRCK7X4ErCc57Ey77DNLaYwqVkGz5kpbnlpJ8Vi8Mm40OG+DJ98Lc+bWHHPM4cuMeZtinr3D9+zPZVOQ5qNurlFKq4dIASCm1VytWrOCEE07A6XSSnZ1Ns2bNaNasGTk5OTRr1qy+i3fAjqRhsHfHZDJz5q33kJzZlIqiQr7612MEfb6Dlr9hmOjW4yVi3F0w2zykdn+OsT/OZ2rEIJgVCwLlv29m24tz8K0uBqBLly6cd955GIYRrRHaYxAE0GoAXP87DHkD4ptiDuSSFvMlLS42E9+/J5hMHLdceP7tMNdNLKKJ+zN+9dzHgDfe4MWfVuALhg/aNiullGqYtAmcUmqv+vbti8Vi4d577yUjI6NGp/cuXbrUU8nqbk9V6OPeeIlFv4yn70VXcNx5F9VTCeumZPtWxtx/B96yUlod24ezb78XY09Nz/ZRIFDAzFkX4POtx1eUw/qf/46E7WTFWuniNGPeUQPkPi6D+NNzMNktLF68mM8++wwRoVOnTgwZMgSz2bz3lYX8MPM/8Nuz4CkAwGfvTN6yxpRPX1C5iAl+62DwdW8TGxzNSfCdyyMDT2dA+0Z7HJhDKaXUX8OhaAKnAZBSaq/cbjezZ8+mbdu29V2U/banC+i3o/7Jimm/cfKwa+l++jn1VMK627xsCZ8+dj/hUIhjh1zACZcMPaj5ezzrmDX7/wgGi7BGjmPtz9dRXhjCAnR0m8neMSCCOcFO4nmtcLROZMmSJXz22WdEIhE6dOjAeeedV7cgCMBXClNfhmmvQrCisgzW3uQtisEzdzEAEWBGG4Mve5tY4epCl5hLePLsk2iRGnNQt10ppdSRRfsAKaXqRfv27cnPP7h9To4kAW9l/5IjcRS42jRp257TrrsFgBlffcriyRMPav4uVw5dOr+JyWQnaPqDNoOfpu+VG0hq5mJeRZgp5SEqwkK42E/+fxdR+Mly2ua04sILL8RkMrF48WI+//xzwuE6NldzxMEp/4Bb58Gx14LJiis4jew2P5Fz+0nEnHISJuC45cI/R4cZ+cNcZNP9nPXhCO77ZiLbyw5eU0CllFJ/fftcAxQOhwkGg4eqPEqpI9Aff/zBiy++yO23306rVq2wWq3V5sfEHPm/wpeXl9OzZ09mzZpVo7zfvfQMeWtXc9Kwa2nWpXs9lXDfzf7+Kxb89COG2cygG28jvfnBHRmusHAKq1b9k3BkRx8pWyqxjjPZtqgTW5Z4aGY3kWU1MAyDiM1M/IBstttL+OGHH4hEIjRr1oyBAwdisezjGxeKN8H012HluMq/k1oRaH8jRRP+oHzyZAhX9jNa2wh+6mJmQUprmjtO5fQ2XTihVQpxTtvB3A1KKaUOIavVuscWA/XaBE5E2Lp1K8XFxQdlxUqpo8f69ev3OD87O/swlWT/RSIRNm7cSFZWVo3hmsuLComEQrjiE7DYjqKHZxE8ZaWE/H4Mkwl3QiKmujY7q/MqIoTDFYRC5VQ2RAMwYTa5CIcchH2CzYCq3jhiMhCHgWdHrZrVasXlcu1ff52gt3L4bAkDBjjjEbOLSHk5Ec+fo8KFTOCxg99sRSIO7BYbLpsZh9WMSfsJKaXUES8hIYH09PRa7xX1+iLUquAnLS1t/29mSqmjUlpa2h7nu93uw1SS/RcOh/F6veTk5NT4panQbiMcChLfKB2b3VFPJdw/kUiEku1bCQUCWKxWYlNSsVgPfhAnEiEYLCUYLELkz1YAFksckWAsUgFV9YIC+JMj+GTH8OJWK/Hx8Xt+T9DuhINQmgvBcnZkBs06IGIQKi4mXFICO0aeC1mgxAUVVhuE3YTFgcNmJs5pwW2zYjLpfUsppY4kIoLH42H79u0AZGRkHJb11ikACofD0eAnOTn5UJdJKXWEcTiOrqCgNlX9URwOR40AyGIyMJnNOB1OrHZ7fRTvgNgzsyjcvIlwKERF3nacsXG4ExMxW6x7T7wPnE4XIo0IhUoJBPIJhz1AGSZ7GRZ3LEYoAaPMjEnACXixUGH2IiJ4vV4SExP3IwhygCumcpS40s0gXihbB/GZkJWFZGQQKiwkVFCAPRzGXQ4BS4iimBLKbR484Tgqyk2YjQBxTivxLisxdovWDCml1BHCueMVFNu3byctLa3uA+gcgDoFQFV9flyuo6ODsFLq0AiHwwQCAXZtOXu0XxtkRw3CftVQHAHMFiuJGU0oK8jD7/HgKS3BW1aKKz4eV0IiZvM+9sHZA8MwsFrjsVrjCYUqCATyCYVKCYXKgDLMCS4kGI+p3I7TsGCOOCk1vPj9fvLzCkhOScZs3sf9bBjgTgF7DBSth6AHiteDrxQjIRNrWhqW5GRCBQWE8vOxhSI0KoZES5DC2AI8dguRUCxFHhdFngAWk0GS205KjA3LvpZFKaXUQVf1HBEMBo+cAKiKNntTqmEKBoOsW7eOkpKSWuf37NnzMJfo4BGJRAO6g/k+ncPNYrORmNGEgNdLeWEBAZ+XiuJiPKWluOMTcMUnHPT+QRaLG4vFTTjsIxDIJxgsJhz2EDZ5MMXbMPvjsPrcxIuLEjyEwkG2b80jxhmHK86O2bKP+9vigJTWUL4VyraCrwi2l0NiNoY9tjIQSkqqDIQKCrCFIqQXgd8aojCmCJ+jDAnHEgq62F7mo6DcT3KMjZQYuwZCSilVjw53jKFXfKXUXm3cuJFwOEy7du0wmUy0bt2aZs2a4XA4aNmyZX0X74BI5M/arKM5AKpiczpJbNyExIzGWO12JBKhvKiQ/I3rqSguIhKJ7D2TfWQ2O3A6M4mJaYPNlophmIlIgKAtn0D8ZgxXBfE4MATECOPxllK8pZySPC9Bf2jfVmYYEJtRGQiZ7RAJQsEqKNkEkQiGxYK1USMcrVtjSUkBkwl7EDKKIKMghDNchM25DZvNQ1iE7WV+lm0tI7fESyh88PeNUkqpI8/Rf7dvIIYNG8aQIUPquxj7bdKkSRiGoaMIHqXKysrIysqKDnZgs9lITk4mMzOTrVu37nN+Dz/8MF27dj3Ipdw/VQGBYRh/mVpuwzCwu9wkNckioVE6FpuNSDhMWUE++RvX4ykpQeTgP+ybTFYcjnRiYtpgt2dgmKyIhAhZCgnH5hLrCmIyIoSNMGGzF4svSNk2D4W5FXjLAkQi+/BWBpsbUtuwLt+L0aQ786b/DvnLoSIfIuHKQCg9vTIQSk4Gw8ARhIxCaFQQwhqqCoS8RETI+wsGQuvWrcMwDObNm7ffeRzt9579lZOTw6hRo+q7GA3eSSedxG233bbf6Rvys8fB2va/6j7UAOgo8eKLLzJ69OhDuo6DcbPcnT59+pCbm0t8fPxBz3tno0ePJiEh4ZCuoyEKh8PRd7lYLBZCocpf7VeuXMltt91GVlYWTqeTdu3a8eKLL9ZnUfdZVf+fv0Ltz64Mw8ARE0tyZlPi0xphtlqJhEKU5m8nf+MGvGWlNfpzHZz1mrHbU4hxt8bhzMRkdhAf35FvfvgKl7sIh6OciNlPhcmL2wTOUARfkY+CTeWU5lfWCtWpXCYzWR16kbt2OR3bt4GQD0o2wrZFlX2F/GUYZjPWjAzsrVtjSdoRCAWgcaGQVhDEGirA7tyO3earEQgFdwqEDtdDwP4+8NUWqGRlZZGbm0vHjh33mn531//Dce85lN566y1OOOEEEhMTSUxM5NRTT2XGjBn1Xay/rP29B+/u/Priiy947LHH6pRHbefO4Xr2+KtoSPvw4PWMPQoEAgFsh+gdH4cyb+CoP/BsNhvp6en1XYwj1qE+fvaX3+/HZrPhcDjw+XzY7XacTid5eXnYbDYmT55McnIyH3zwAVlZWUydOpVrr70Ws9nMzTffXN/Fr5OqmpCDHQAdSd+pYRg4Y+NwuGPwlpVSXlRIOBikZPs2irZvI6lRBna3+6DXgBmGCZs1EaslAQCTuXKEPYvFj8XiJxS2UhYIEBOMx20YhAX8niBFFUHMVhPOGBsOtwXTHvrnmM1m0nNaQzhUOVKctwBCfvAWVn7MNnAlY3ImYWqcgTk1hVBeHuGiIpwBwVkIPmuAoth8cNkhFIc/YCevzE9BeYBkt42U2KNvZEDYsW8O8Lp7tN97Jk2axCWXXEKfPn1wOBz885//5LTTTmPx4sU0adKkvotXr46ka9TuJCUlHVD6o/3Zo7bvSESq/Sh5qB3t+3C3pA68Xq8sWbJEvF5vXRY/YvTr109uuukmufXWWyU5OVlOOukkERFZuHChDBo0SNxut6Slpcnll18ueXl50XSlpaVy6aWXisvlkvT0dHn++eelX79+cuutt0aXyc7OlkcffVSuuOIKiY2NlaFDh4qIyG+//SbHH3+8OBwOyczMlBEjRkh5eXk03auvviotW7YUu90uaWlpcv7550fnffrpp9KxY0dxOBySlJQk/fv3j6YdOnSoDB48OLqsz+eTESNGSGpqqtjtdunbt6/MmDEjOv+XX34RQH766Sfp0aOHOJ1O6d27tyxbtmy3+4vK13dEP/369RMRkRkzZsipp54qycnJEhcXJyeeeKLMnj27Rtq33npLhgwZIk6nU1q2bClff/11jfIUFRWJiMg777wj8fHx8u2330rr1q3F6XTK+eefLxUVFTJ69GjJzs6WhIQEGTFihIRCoWg+hYWFcsUVV0hCQoI4nU4ZNGiQrFixoto6dv6MHDlyr+lqE4lEZOTIkZKVlSU2m00yMjJkxIgR1fb/3XffLZmZmWKz2aRFixby9ttvR+dPmjRJjjnmGLHZbJKeni733HOPBIPB6Pz9PTb3dIzURXZ2tjz22GNyxRVXiNvtlqZNm8rXX38t27dvl3POOUfcbrd06tRJZs6cKSIiM2fOlJUrV8ppp50m8fHx4nK5pG3btvLSSy/JzJkzZdasWVJQUFBtHTfeeKOcfPLJ1aY99dRTkpaWJjExMXL11VfLPffcI126dKm2zFtvvSVt27YVu90ubdq0kVdffbXa/A0bNsgFF1wg8fHxkpiYKOecc46sXbs2Or/qHHn44YclJSVFYmNj5brrrhO/3y8iIqFQSJ5++uka+69g+zbJXbVC8jaul//85z/Svn376Pd20003RfNfv359dB/FxsbKBRdcIFu3bo3OHzlypHTp0kXeeustycnJEcMwRESkqKhIhg8fHi3TySefLPPmzdvtd+T3++Wmm26S9PR0sdvt0rRpU3nyySej84uKiuTaa6+VtLQ0sdvt0qFDB/n222+j8z/77LPoNmRnZ8uzzz5b4xh45JFH5JKLLpSYGLdceN65krtqhXz3+WfSp0/v/bp27U12dna187Jp00wpKVkgc+d+L2eccZKkpiaL2+WSHp27ydgPvxbvxlIpXl8q29aVSFZmU3nw/odl6JVDJSYmRrKysuTf//53NO+1a9cKIHPnzhURkV9+/lkA+fGz96RrhzbicNjl5D49Zdv8n2TsR29L2zatJDY2Vi6+6CIpXrVKPIsWiWfhQimaPVuuvfxSSUpOEpvdJt2PPVbGfDtR5m8skh+nza9xbam65u/tWlyb3e3LoUOH1ljP2rVrJRQKydVXXy05OTnicDikdevWMmrUqGh+I0eOrJHul19+qbFvCgsL5dJLL5WUlBRxOBzSsmVL+e9//ysiu7/+73rvCYfD8s9//lNatGghNptNsrKy5PHHHxeRvR+7e1N1Df/xxx+la9eu4nA45OSTT5Zt27bJ2LFjpW3bthIbGyuXXHKJVFRURNPty3UxFApJbGysvPvuu9Fp27Ztk7POOkscDofk5OTIBx98INnZ2fLCCy9El6nLefzVV19Jt27dxG63S7NmzeThhx+udt0H5LXXXpNBgwaJw+GQZs2ayaeffhqdfzjO/f15btnVvHnz5KSTTpKYmBiJjY2V7t27y8yZM/d4D37vvfekR48eEhMTI40aNZJLLrlEtm3bJiJ/nsO1nV+7Pnvt67mz67OHiMjvv/8u/fr1E6fTKQkJCXLaaadJYWGhiBz4PfZQPBtUbcPYsWOle/fuYrVa5ZdffpFwOCxPPvlk9LrQuXPnasfTrtuen58vF198sTRu3FicTqd07NhRPvzww+jy+7IP63KsPfHEE3LVVVfVet2uzZ5ijZKSEgGkpKSkTt9DXex3ABSJRKTCHzzsn0gkUueN69evn8TExMhdd90ly5Ytk2XLlklRUZGkpqbKfffdJ0uXLpU5c+bIgAEDqj2w/e1vf5Ps7Gz56aefZOHChXLuuedKbGxsjQAoLi5Onn32WVm1alX043a75YUXXpAVK1bIlClTpFu3bjJs2DARqXyYNJvN8uGHH8q6detkzpw58uKLL4qIyJYtW8Riscjzzz8va9eulQULFsirr74qZWVlIlLzJnTLLbdI48aNZezYsbJ48WIZOnSoJCYmRh9Gqw7YXr16yaRJk2Tx4sVywgknSJ8+fXa7v2bMmBENmnJzc6N5TZw4Ud5//31ZunSpLFmyRIYPHy6NGjWS0tLSaFpAMjMz5cMPP5SVK1fKLbfcIjExMTXKs3MAZLVaZcCAATJnzhyZPHmyJCcny2mnnSYXXnihLF68WL799lux2Wzy0UcfRddzzjnnSLt27eTXX3+VefPmycCBA6Vly5YSCATE7/fLqFGjJC4uTnJzcyU3Nze6//aUrjaffvqpxMXFydixY2X9+vUyffp0efPNN6PzL7zwQsnKypIvvvhCVq9eLT/99FO0nJs2bRKXyyU33nijLF26VL788ktJSUmJ3gj299jc2zFSF9nZ2ZKUlCRvvPGGrFixQm644QaJi4uTQYMGySeffCLLly+XIUOGSLt27SQSicjMmTPljDPOkAEDBsiCBQtk9erV8u2338rPP/8s5eXlte6/yy67rNrD8ccffyx2u13efvttWbZsmfzjH/+Q2NjYagHQBx98IBkZGfL555/LmjVr5PPPP5ekpCQZPXq0iIgEAgFp166dXH311bJgwQJZsmSJXHrppdKmTZtogDN0aOUD8kUXXSSLFi2S7777TlJTU+X+++8XEZGNGzeK2WyWZ599ttr+y8vdIrmrVsgzTz4hDodDRo0aJcuXL5cZM2ZEH4DC4bB07dpVjj/+eJk1a5b88ccf0qNHj+hDokjlQ6jb7ZZBgwbJnDlzZP78+SIicuqpp8rZZ58tM2fOlBUrVsidd94pycnJNQLHKs8884xkZWXJr7/+KuvWrZPffvstepMKh8Ny3HHHSYcOHWT8+PHR72Ps2LEiIjJr1iwxmUzy6KOPyvLly+Wdd94Rp9Mp77zzTrVjoOratWL5cpk/a6b88ctEcblc8sg/7pcZv/0qk37+uc7XrrrYvn27APLOO+9Ibm6ubN++XcJhv0ybNk5eeOFBmTbtc5kz51u56+/XicNhlxXTF4l/Y6n4NpZK08ymkpiQKE8/9qzM+G2ePDLyMTGZTNEfc2oEQDuuNccdd5z8/utkmTPlF2nZLFv69e4hp/U7TuaM+1B+/fIdSU5KlKefeFTCgYAEtmyRGy+7TDLS0uTL116Tad99JedfcI7EJ8TL9GXLZc66fHnuzfcEkN9mzpeNmzZLcXGxiOz9WryrPe3L4uJi6d27t1xzzTXRa1goFJJAICAPPfSQzJw5U9asWSMffPCBuFwu+fjjj0VEpKysTC688EIZNGhQNJ3f76+xb2666Sbp2rWrzJw5U9auXSsTJkyQb775RkR2f/3f9d5z9913S2JioowePVpWrVolv/32m7z11lt7PXbrotp39/vvMmfOHGnZsqX069dPTjvtNJkzZ478+uuvkpycLE8//bSI7Pt1sbS0VBwOR7XA4fTTT5cuXbrItGnTZNasWdKnTx9xOp3VAqC9nce//vqrxMXFyejRo2X16tUyfvx4ycnJkYcffjiaByDJycny1ltvyfLly+WBBx4Qs9ksS5Ys2ev+O9jnfl2fW2rToUMHufzyy2Xp0qWyYsUK+eSTT2TevHl7vAf/5z//kbFjx8rq1atl2rRp0rt3bzn99NNFpDIo/fzzzwWQ5cuXS25ubvT82jkA2p9zZ9dnj7lz54rdbpcbbrhB5s2bJ4sWLZKXX35Z8vLyDso99lA8G1RtQ+fOnWX8+PGyatUqKSgokMcff1zatm0rP/74o6xevVreeecdsdvtMmnSJBGp+dy1adMmeeaZZ2Tu3LmyevVqeemll8RsNsv06dP3aR/W9VhLSkqSV199VVauXClPPfVUtet2bY6aAKjCH5Tse7477J8Kf3APJa2uX79+0q1bt2rTHnvsMTnttNOqTdu4cWP0xCstLRWr1Votii4uLhaXy1UjABoyZEi1fIYPHy7XXntttWm//fabmEwm8Xq98vnnn0tcXFy1wKHK7NmzBZB169bVui0734TKy8vFarXKmDFjovMDgYA0btxY/vWvf4lI9RqgKt9//70Au63J2/VmuTvhcFhiY2Or3UAAeeCBB6J/l5eXCyA//PBDtfLsHAABsmrVqmia6667TlwuV7WLzcCBA+W6664TEZEVK1YIIFOmTInOz8/PF6fTKZ988kk03/j4+GrlrUu6XT333HPSunXrWh/wly9fLoBMmDCh1rT333+/tGnTplqw/uqrr0pMTIyEw2ER2b9jc2/HSF1kZ2fL5ZdfHv07NzdXAHnwwQej06ZNmyaA5ObmysyZM6Vjx47VbuJ7MmXKFLFYLDJu3LjotN69e8uNN95YbblevXpVC4BatGhR40Hpsccek969e4uIyPvvv19jn/r9fnE6ndF1DR06VJKSkqr9Mvz6669H93vVA97q1aurraeipFhyV62QjPR0+cc//lHrdo0fP17MZrNs2LAhOm3x4sUCRH/tHzlypFitVtm+fXt0md9++03i4uLE5/NVy69Fixa7/TVsxIgRcsopp9T6Y8+4cePEZDLJ8uXLa0176aWXyoABA6pNu+uuu6R9+/bRv2u7dl191VUy7MorZOvqlZK7aoXkrlohY7/8sk7XrroC5Msvv6wx3e/3Sn7+aikuXiglJQukXbsW8swz90tZ/nrxbSqS7Mymcsl5F0nphlLJW1ciW9cWS0pKqjz/rxfF5wnKmjVrag2Adr72PfXUU5Xf/bxpIrkLRTbPkesuP18GntRbZNsSKd+6RqxWq7z7yqvRGqGSOXMkIy1N7v3HHbKyYLW898VXlQHQonWyaHOxbCv1Smlp2V6vxbva277c9Rfv3bnpppuq/dCwa6AiUvOafvbZZ8tVV11Va367u/7vnG9paanY7fZowLOrPR27dbHH726n8/a6666TgQMHisje7527uuGGG6R58+bR+2DV9XznWrulS5cKEA2A6nIe9+/fv0Zt1/vvvy8ZGRnRvwG5/vrrqy3Tq1cvueGGG0Skfs79vT231CY2Njb649SuarsH12bmzJkCRO/3tdUyiFQ/H/bn3Nk130suuUT69u1ba/oDvcceqmeDqm346quvotN8Pp+4XC6ZOnVqtWWHDx8ul1xySbV0u+7TnZ155ply5513Rv+uyz6s67G287NGJBKRtLQ0ef3113dblsMdAP31ev3uokePHtX+nj9/Pr/88gsxMTHRT9u2bQFYvXo1a9asIRgMcuyxx0bTxMfH06ZNmxp57/ruk/nz5zN69OhqeQ8cOJBIJMLatWsZMGAA2dnZNG/enCuuuIIxY8bg8XgA6NKlC/3796dTp05ccMEFvPXWWxQVFdW6TatXryYYDNK3b9/oNKvVyrHHHsvSpUurLdu5c+fo/zMyMoDKN+3ui23btnHNNdfQqlUr4uPjiYuLo7y8nA0bNux2XW63m7i4uD2uy+Vy0aJFi+jfjRo1Iicnh5iYmGrTqvJYunQpFouFXr16RecnJyfTpk2bGtu9s/1Jd8EFF+D1emnevDnXXHMNX375ZbTj/7x58zCbzfTr12+36+vdu3e1/hR9+/alvLycTZs2Raft67G5L8fInuz8PTVq1AiATp061ZhWtd8vv/xyHn/8cXr27Mltt93G+PHj2bhxY7UPwKJFixg8eDAjR47ktNNOq7Y/dt73AL17947+v6KigtWrVzN8+PBq2/7444+zevXq6L5ZtWoVsbGx0flJSUn4fL7oMlB5Hu38UtbevXtTXl7Oxo0b6dKlC8cccwxdu3attv9EhPyCAnK3bqV///617rOlS5eSlZVFVlZWdFr79u1JSEiodgxlZ2eTmpoa/Xv+/PmUl5eTnJxcbdvWrl1brdw7GzZsGPPmzaNNmzbccsstjB8/Pjpv3rx5ZGZm0rp1692Wc+frAlQeeytXriQcDken7XrtWrBwIR9+9DEtOnelZeeutOjclfMvvZRIJML8mTM4uV+/3V67DkR5eTn33/8Axx9/JtnZx9O48XEsX76WjRu3ELGW4I/diBhhOrZvh92AOLNBrNVMo5RGbM3dRsl2D0VbK8sRDlUfuW3X49zlctG8y3HQqAMktaBRRhO25xdByMfqhTMIBoP0O74zjmaZWFJSsNrt9OzYkc2L15C63YM1VAiAzeYnHBG2lviYOGMBwWCQPn36RNe1u2txlT3dB/bk1VdfpUePHqSmphITE8Obb75Z4xq8NzfccAMfffQRXbt25e6772bq1Kn7lH7p0qX4/f7dnid7Onb3Ra3fXfPm1aZVXZ/25br49NNP89FHH/Hll1/icDii22SxWKpdj9u2bVutI39dzuP58+fz6KOPVpt/zTXXkJubW+373fnaV/V31bFSH+f+3p5banPHHXfwt7/9jVNPPZWnn356t9eync2ePZuzzz6bpk2bEhsbG71/7ssxvL/nzs7mzZu32+P3QO+xh+rZoMrO392qVavweDwMGDCg2nf33nvv7fb7CIfDPPbYY3Tq1ImkpCRiYmIYN27cPl9H6nqs7XweG4ZBenr6Pj9/Hkr73YPKaTWz5NGBB7MsdV7vvqgatrdKeXk5Z599Nv/85z9rLJuRkcGqVasOKO/rrruOW265pcayTZs2xWazMWfOHCZNmsT48eN56KGHePjhh5k5cyYJCQlMmDCBqVOnMn78eF5++WX+8Y9/MH36dJo1a1bnMu3KarVG/1910u3re0CGDh1KQUEBL774ItnZ2djtdnr37k0gENjtuqrWt6d11bb8vuZxqGRlZbF8+XJ++uknJkyYwI033sgzzzzD5MmTcTqdB2Ud+3psms3mg3KM1HZM7Ok4GTx4ML169WLy5MlMmTKF1157jbvvvpsrrrgimmbJkiX079+fa6+9lgceeKDOZanabqgcrWnXQKnqbdDl5eX06NGDMWPG1Ei/c8CxJ2azmVdffRWv18vEiROj++/n8eNw2A9OJ/favtOMjAwmTZpUY9ndjZTUvXt31q5dyw8//MBPP/3EhRdeyKmnnspnn312SI+9na9doUAAT2kJ/ooKMlKS8RcX8suPPzBrwUIm/vxzjWvX/vr73//OhAkTePbZZ2nZsiVOp5Pzzz+fQMBMOGzBbA6BIZjcFfjc27D64rGEHZhNBlZDsJgMIjtGaivZ7qF4u4eAr/KHil2P6ejfhgGOOAxXIhGzDeIzwbLjAcBfglG8BqvFgSUzGcNmA8PAFoLEysMURyAfW1KESCiGUKTyZr8mr4LERkHiHJa9DiQRGxu7x/tAbT766CP+/ve/89xzz9G7d29iY2N55plnmD59+j7t79NPP53169czduxYJkyYQP/+/bnpppt49tln65R+b8ffno7dfbHb726naVXXp7peF5999lmefvppfvrpp2oPZnVRl/O4vLycRx55hPPOO6/GMlXB1t4cCef+zpo2bVprHg8//DCXXnop33//PT/88AMjR47ko48+4txzz611+YqKCgYOHMjAgQMZM2YMqampbNiwgYEDB9Z4jtiT/Tl3drWn/Xig99hD9R3VNr3qvvn999/XGMzDvpv72TPPPMOLL77IqFGj6NSpE263m9tuu22fvoN9caQ8z+3OftcAGYaBy2Y57J8DHaWoe/fuLF68mJycHFq2bFnt43a7ad68OVarlZkzZ0bTlJSUsGLFijrlvWTJkhr5tmzZMjqKh8Vi4dRTT+Vf//oXCxYsYN26dfz888/Rfdq3b18eeeQR5s6di81m48svv6yxnhYtWmCz2ZgyZUp0WjAYZObMmbRv336/901VGXeO4AGmTJnCLbfcwhlnnEGHDh2w2+3k5+fv93r2V7t27QiFQtVu+gUFBSxfvjy63TabrUb565KuNk6nk7PPPpuXXnqJSZMmMW3aNBYuXEinTp2IRCJMnjx5t+WcNm1atWF8p0yZQmxsLJmZmbtd396OTaj7MXIwtWjRgpNOOomRI0cyfvx47rzzTr755hvatGlDmzZtCIVCnHzyyQwdOpQnnniiRvp27drVeFD7448/ov9v1KgRjRs3Zs2aNTW2u+qm0717d1auXElaWlqNZXYepWr+/Pl4vd5q64mJiYnW3NS2/7799jtiYmLIbtqUiRMn1roP2rVrV622CyqDvuLi4j0eQ927d2fr1q1YLJYa5U5JSdlturi4OC666CLeeustPv74Yz7//HMKCwvp3LkzmzZt2u31qF27dtWuC1B57LVu3ToaTO6unDtfu9q2b0/343rT8/gTiE1MBCDo9dC1dUseuPsu5s6ZU+3aVRdWq7XWa8uwYcM499xz6dSpE+np6axfvx67PYbY2FZAI6KnkdlL0L0Vb8wWxIhgiBBjQKz9z+0KeENUFPsBKC/2EQpWX19NBrhTaXHMqZXX1PmrwTBByEeoaD2z58+hU5d2WJITo9fHpKIIjbeHiPUXk9PaidVmY8b031hfUMHqvAqKy717vRbv6T5Q2zVsypQp9OnThxtvvJFu3brRsmXLGr/y1pauNqmpqQwdOpQPPviAUaNG8eabb0bTQ83r/85atWqF0+nc7XkCuz92D6W9XRf/9a9/8dhjj/Hjjz/WqAFp27YtoVCI2bNnR6ctX7682nDMdTmPu3fvzvLly2u9/5t2GmFy52tf1d/t2rWL/l3f535tzy21ad26Nbfffjvjx4/nvPPO45133gFqPw6XLVtGQUEBTz/9NCeccAJt27atURNQl+MP9v3c2VXnzp33ePweyD32UD0b1KZ9+/bY7XY2bNhQ43vbuaXCzqZMmcLgwYO5/PLL6dKlC82bN69xPNVlH+7vsXakaVDDYAPcdNNNvPXWW1xyySXcfffdJCUlsWrVKj766CPefvttYmNjGTp0KHfddRdJSUmkpaUxcuRITCbTXoOve+65h+OOO46bb76Zv/3tb7jdbpYsWcKECRN45ZVX+O6771izZg0nnngiiYmJjB07lkgkQps2bZg+fToTJ07ktNNOIy0tjenTp5OXl1ftwljF7XZzww03RMvYtGlT/vWvf+HxeBg+fPh+75u0tDScTic//vgjmZmZOBwO4uPjadWqFe+//z49e/aktLSUu+6666D90rEvWrVqxeDBg7nmmmv497//TWxsLPfeey9NmjRh8ODBQOXL68rLy5k4cWK0OVRd0u1q9OjRhMNhevXqhcvl4oMPPsDpdJKdnU1ycjJDhw7l6quv5qWXXqJLly6sX7+e7du3c+GFF3LjjTcyatQoRowYwc0338zy5csZOXIkd9xxR7Ub4a72dmzOmjWrzsfIwXTnnXdy5pln0rp1a4qKivjll1+i61y0aBGnnHIKAwcO5I477oi+FNVsNkdrZm699VaGDRtGz5496du3L2PGjGHx4sXVmrQ88sgj3HLLLcTHxzNo0CD8fj+zZs2iqKiIO+64g8suu4xnnnmGwYMH8+ijj5KZmcn69ev54osvuPvuu6M3j0AgwPDhw3nggQdYt24dI0eO5Oabb8ZkMjF16lTGjBlDOBwmIyMjuv9at2oJwH13381tf/87aWlpnH766ZSVlTFlyhRGjBjBqaeeSqdOnbjssssYNWoUoVCIG2+8kX79+tV4oNrZqaeeSu/evRkyZAj/+te/aN26NVu2bOH777/n3HPPrTXt888/T0ZGBt26dcNkMvHpp5+Snp5OQkIC/fr148QTT+T888/n+eefp2XLlixbtgzDMBg0aBB33nknxxxzDI899hgXXXQR06ZN45VXXuG1117b43e8t2vXl198zrLFizmmaxfi4+P5efJkIpEIOVlZiEidfpjKyclh4sSJ9O3bF7vdTmJiIq1ateKLL77g7LPPxjAMHnzwwegvhBaLhbi4NEwmKyZTIuGwA7PZh2EKgClE0FaK31YCocqbdazNTIzLgslUWRZvaYBCowKrw0LQv+cbevSa+vA/SWrSgqZp8fzr2efweH387YLTsMp2WnXIxDAMfvztN07r2xdnmYO2MS6uvOxCXnhiJAkpNtLTW/DOq/+mvMLDZVcOq3Vde7oPVO2n6dOns27dumhTz1atWvHee+8xbtw4mjVrxvvvv8/MmTOr/Sqdk5PDuHHjWL58OcnJybUOX/3QQw/Ro0cPOnTogN/v57vvvouey7u7/u/M4XBwzz33cPfdd2Oz2ejbty95eXksXryY4cOH7/HYPVT2du/85z//yUMPPcSHH35ITk5O9BpV1WSoTZs2DBo0iOuuu47XX38di8XCbbfdVu0eV5fz+KGHHuKss86iadOm/N///R8mk4n58+ezaNEiHn/88When376KT179uT4449nzJgxzJgxg//85z/AkXnu78rr9XLXXXfxf//3fzRr1oxNmzYxc+ZMzj//fKD2e3BVC5iXX36Z66+/nkWLFtV4t092djaGYfDdd99xxhln4HQ6qzWHh/07d3Z133330alTJ2688Uauv/56bDYbv/zyCxdccAGrV68+oHtsTk7OIXk2qE1sbCx///vfuf3224lEIhx//PGUlJQwZcoU4uLiGDp0aI00rVq14rPPPmPq1KkkJiby/PPPs23btmo/1tRlH+7vsXbEqUtHoaN5GOzaOpOuWLFCzj333OiQyG3btpXbbrst2jGttmGwjz32WLn33nujeew6RGaVGTNmyIABAyQmJkbcbrd07txZnnjiCRGp7FjYr18/SUxMFKfTKZ07d46O4rNkyRIZOHBgdCjV1q1by8svvxzNd9cOrl6vV0aMGCEpKSl7HAZ7585vc+fOjQ5ruDtvvfWWZGVliclkio5wNWfOHOnZs6c4HA5p1aqVfPrppzW2n1o6OMfHx0dHBdndMNg7qxpGeGe7bnfVcNbx8fHidDpl4MCBNYazvv766yU5ObnaEJx1SbezL7/8Unr16iVxcXHidrvluOOOq9Yx1+v1yu233y4ZGRlis9mqDScrUrehLvf12NzbMVK1j/f0/dZ23O763e3cGXrmzJly4403SosWLcRut0tqaqpcccUVkp+fLyK1D78LSHZ2drV1PPHEE5KSkiIxMTEydOhQufvuu2t812PGjJGuXbuKzWaTxMREOfHEE+WLL76Izs/NzZUrr7wyesw3b95crrnmmminyKpj5aGHHpLk5GSJiYmRa665JtpxeeHChXLcccfV2H/FW3Mld9UKKS8qlDfeeEPatGkjVqu1xtDndR0Ge1elpaUyYsQIady4sVitVsnKypLLLrus2oAKO3vzzTela9eu4na7JS4uTvr37y9z5syJzi8oKJCrrrpKkpOTxeFwSMeOHeW7776Lzq8antRqtUrTpk3lmWeeqZb/AV+7HA5p37aN/PvFUZK7aoVsW7taXn/pJdnb7eSbb76Rli1bisViiR4fa9eulZNPPlmcTqdkZWXJK6+8UusrB1544QWJRCLi9ZZLUdEa6dixjdx77/VSUrJA5s8fJ4BMH/er+DeWyvjPx1Z2ZF6+SbatK5Ft60rkxWdek7i4eCkt8ErQH6r1+6r1mvr7JJGCtSKb54psniOP/v0GSU9LEcMw5PIhQ8SzcKEUzpol1196qSQlJYrNbpNux3aXD8d+Kws2FcrGggrxB8PV9sOe7gMilR2pjzvuOHE6ndHz2efzybBhwyQ+Pl4SEhLkhhtukHvvvbda+bdv3x79/tjNMNiPPfaYtGvXTpxOpyQlJcngwYNlzZo10Txqu/7XNgz2448/LtnZ2dFjrKrz/96O3aFDh1YbOXFXtd239nav2Nt1cdch2Ks+O4+8lZubK2eeeWZ06On33nuvxnlSl/P4xx9/jI4gFxcXJ8cee2y10UMBefXVV2XAgAFit9slJyen2nd/JJ77u/L7/XLxxRdHXxHRuHFjufnmm6s9H9Z2D/7www8lJydH7Ha79O7dW7755psag248+uijkp6eLoZh1DoM9v6cO7UdU5MmTZI+ffqI3W6XhIQEGThwoBQVFR2Ue+yheDbY3WAGkUhERo0aFb1npaamysCBA2Xy5Mm1pisoKJDBgwdLTEyMpKWlyQMPPCBXXnlltfO7rvtwf461Ll26VDvvatt3h3MQBENk76/b9vl8rF27lmbNmtW5LetfSUVFBU2aNOG55547oBoWpQ6ld955hyeffJIlS5bUaHu7v/Lz80lKStrnX6fqw7BhwyguLuarr76qdX44HGbu3Ll069atWjV90dYt+CsqiEtNwxVX81dzVVPQ78dXVoq3vIxIOMwzo15k2oyZfPv5pzhi43DExGA2H7oGBsGgH49nO1CKYVTWGAkGBFzY/AmYZcfxbzMTMsDrDxMO/3mrs9jM2F0W7C4Llrr0Kw0HwVMInnwIV7aXF4FwxEnIAxIM7SgDlLmg2G0QMhtI2A1hN8luN6kxdix7eKFrQ9CvXz9OPvlkHn744fouSr0wDIMvv/ySIUOG1HdR1H44FPdY9ac9xRqlpaXEx8dTUlJCXFzcQVlfg2sCVxdz585l2bJlHHvssZSUlPDoo48C7La5lFJHgrFjx/Lkk08e1Avznvqp/FXIjiZXxlEQ5B0prHY7VnsqMckp+D0VTPp9Co8/9ABBv5+gP4/ygnzsLjeO2FjsLvcB992ssX6rnfj4LMLhEB5PHuFwESZTGGwVBG0V+ENOrP54rAEHFiAWEIeZoFQGQ6FA5aei2I/ZasLusu4IhnbT1NlshdhGEJMG/jKoyMfwl2AxezHHQCRsIeSzEgmEiPNAnEcodwjFMeUEbOUU+B0UemJIi4kn2W2PNtNrSEpKSli9ejXff/99fRdFqf1yKO6xqv5oALQbzz77LMuXL8dms9GjRw9+++23BvEwqI5en376aX0X4ahUFQCZDA2A9pVhGDjcMcyeO5dwOIS/vBxvWWllDVFFOb6KckxmM46YWJyxcVgP0mh7VcxmC7GxGYik4/EUEAwWYDIFMFm8hC1egmEblmAC1oALIxjBBtgsBmIxEYiALxAmHIzgKfHjKfFjtph21AxZsdhqCYZ2jCCHIw5CAfAUYHgKMBtBTO4QEbtBKGAj4o8Q44MYn+CxQ7Hbh8/mY5uvmDyPm1R3Eilu50EPDI9k8fHx1Yb6Vepoo/fYvxZtAqeUahB21wQuf8N6QsEASY0zsdXD4B5/RUG/H195Kd6yyiZyVSx2O86YHU3kLIfm9zefrxS/fzuG8edogJGIBVMkHnsgDiO00y3P2BEMieALRKqNzmQym6LN5Kx28+6DFYmArxQq8iFQVrm+kEHIbyHs/3MxvxWKYsBj35GPOIixxpHmTsRp1d8ilVINmzaBU0qpwygiVU3gGs6v8YdatIlcUgoBjwdvWSl+TwUhv58yfx7lhfnYXC6csXHYXS6Mg1j75nDE4XDEEQx68Hq3I5RhMoXAVIDXXIQh8ThIxOQDCUUwgmHsgN1sIFYzQRG8/giRcARvWQBvWQCTycC2o2bI5tglGDJM4Eyo/AR94CnA5C3EZgkScUDIZybsN2EPQnoRBC1Q5hTKHT7KQz7KS/Iwi5t4RwKp7lgs2hRTKaUOOQ2AlFJ1EgqFKCoqwu/3k56ejsVioaKiAqvVusf3RRzptA/QoWMYBna3G7vbTSQcxldehresjKDfh7+iAn9FxU5N5GKx2OwHrVmY1erCas0hHA7i8WwjEinGZETAKMInxYgjBoc1FUvQQsQbgnAEIxD+s5mc9c8+Q5GI4CsP4isPYpgM7M7KmiGbw1I9cLY6IL4JxGWArxSTpwCbuZSIM0zYZyLsN2MNCUllkFQmBCwG5Y4IFY5yCv3lFPrM2EyxpDgTSXA2rCZySil1OGkApJTaK4/Hw4oVKzCbzQQCAVJTU7FYLBQXFxMIBOr0luwjkYj8GQBpH6BDymQ244pPwBWfQDDgx1dWVjmKXCiEp6QYT0kxFpsdZ2wsjpjYg9ZEzmy2EhubSSSSgdebRyhUiGGEMYwy/OEyvOLCHp+C3exGvKEdwZBg+HcEQ2YDnJbKZnK+HcFQRRBfRRDDMLBVBUPOP99BVK1WKBTA5C3AZC3E4ggQDpgIBwwiIRO2kJBUDknlQsACFY4Q5Y5itkgxWyrsuC1xpLkTcB3FPzAopdSRSAMgpdRebdy4kZSUFDIzM5kzZ050enx8PGvWrKnHkh2YquAHtAbocLLa7FiT7cQkJRPw7mgiV1FBKOCnrMBPWWHlKHLOmDjs7oPTRM5kMuN2pyPSCL+/EH8gH4MAZrOHUGgDPp8Nw4jHHheHzWQBX6QyGIoI+ELRmiGsFoKA1xciEhb8niB+TxAMA5tjx/DaTgumqiGvLTaIzYCYdAx/GRZPARZfCRIJVwZDQRORoIEtBLZySIwGQz4qHH7WhvMwiYt4ewKp7nisDXwobaWUOhg0AFJK7ZXH4yE7O7vGdKvVSjAYrIcSHRyyo/8PBtrcqB4YhoHd5cbu2tFErmLHKHK+XZvIxeCMjTsoTeQMw8DhSMZuTyIYLMPn2w54sVgCQB7+QBFlASeG4cIR48BmsmIOGIivsmaIcAgrYN1RMxQEfL4w4VCEgDdEwBuiDLA5dtQMuSyYzabqI8iFgxjewspgKORHIhAOmggHLUQCUi0YCpqh3FlBRdhDkX8rViOWZGciiS4XJj1mlVJqv2gApJTaK8MwiOxUW1LF7/cf1e9EkEjlqF8m027e/6IOG5PZjCsuHldcPKFAAG9ZKb7yMsKhEJ6SEjwlJVhsNhzuGGxOF1aH/YBqhgzDwGaLw2aLIxT24vdtJxwuxWwO4XSWEYl48PkclIcq12N32LGZrFjDZgxfpDIY8u4IhiwmDKdtxwAKlUNrB3whAr4QFILVbo6+a8hsMVW+VyimEbjTIFCB4SnA4ivGYg9Eg6FI2EbYH8EaFhKjwVCYCkcxhcEStnqsOM1xpLoTiLXr6KxKKbUvtC79KDFs2LCj+u3RkyZNwjAMiouL67soR5yDsW9OOukkbrvttoNWpl0lJCSwZcuWakGQ3+9n06ZNJCQkHLL17o1hGHz11Vf7nV77/xwcOTk5jBo1ar/Tjx49utpxZLHZiE1OIaVpDokZjXHExGIYBqFAgPKiQgq3bGL7ujUU5W6moriQoM9HHd7osFsWsxO3O5uYmDbYbClgmDCZwjgcFbjdxVgsHnw+D6WeMgr8xRRbPXidYUJ2EAMIRRBPEIs3RCyQ6LYS67ZitVUOtx70hykv8lGwuZzC3AoqSvyEgmEwDEb/7zMSmnWBRh0gPgvD7sJij2Bz+XAkBLDGgtlpA8PAGoaECsgsELLyAzhL8tlatJoleSt59rUX6/VcVEqpo4ne9Y8SL774IqNHjz6k61i3bh2GYTBv3ryDnnefPn3Izc0lPj7+oOe9s10fpA6V/X3gqy1Q2Zd9s7tg6YsvvuCxxx7b5/LUVWZmJpFIhPnz5xOJRFi+fDmLFi3CZDLRpEmTA87/qaee4phjjiE2Npa0tDSGDBnC8uXLD0LJ9+zPIbAb1qXw4YcfpmvXrvucbnfn18yZM7n22mvrlEdt585FF13EihUraixb1UQuoVE6qdnNiEtNwxETg8lsRiKC3+OhrKCAgs0byVu3hqKtW6goKSbo9+9XQGQy2XA4MoiNaYvdno5hWDCMCHa7h5iYYpxOH4YRIRQKUeH3UBwso9BUTpndj98WJmISiAjiC2H2hXCHIyTYzcQ5LTh2BEOhQJiKYj+FWyoo3FJe2X8IEMMM7hRIbQMpbcCdgmEyY7EGsTkrKoOhOBNml422Awfy79HvVwZD+UKTAj+D+/fl+ynfsCR/JRtKtuIN+ve0qUop1aA1qCZwgUDgkA3XeyjzBg554HCo2Ww20tPT67sYR6SDsW+SkpIOUmlqZ7FYaN26NWVlZXi9XiKRCC6X66C9kGzy5MncdNNNHHPMMYRCIe6//35OO+00lixZgtvtPijrqM3RMAR2MBg84psZpqamHlB6p9OJcy8vod25iZyIEAoECPi8BLweAjuOyap+Q1XL25xObE4XNocTs9Va52aOhmHGbk/FZksmGCwmEMgnEvFjNlfgdnswmWIIBl34/REikQhlFeXR67/FasZmsmIKgjVkxhyq/KXRATgsJsRiEIyALxAmFIzg94SQCBRuqdjx4lUrFpsTw5YFsU3AVwzeosoBFCx+sPgxTILZYcLsshP2BrCFhEwcZOLAU+SnzOlnbaAAw7ARY40lxZ2A06LN5JRSKkrqwOv1ypIlS8Tr9dZl8SNGv3795KabbpJbb71VkpOT5aSTThIRkYULF8qgQYPE7XZLWlqaXH755ZKXlxdNV1paKpdeeqm4XC5JT0+X559/Xvr16ye33nprdJns7Gx59NFH5YorrpDY2FgZOnSoiIj89ttvcvzxx4vD4ZDMzEwZMWKElJeXR9O9+uqr0rJlS7Hb7ZKWlibnn39+dN6nn34qHTt2FIfDIUlJSdK/f/9o2qFDh8rgwYOjy/p8PhkxYoSkpqaK3W6Xvn37yowZM6Lzf/nlFwHkp59+kh49eojT6ZTevXvLsmXLdru/gGqffv36iYjIjBkz5NRTT5Xk5GSJi4uTE088UWbPnl0j7VtvvSVDhgwRp9MpLVu2lK+//rpGeYqKikRE5J133pH4+Hj59ttvpXXr1uJ0OuX888+XiooKGT16tGRnZ0tCQoKMGDFCQqFQNJ/CwkK54oorJCEhQZxOpwwaNEhWrFhRbR07f0aOHLnXdLWJRCIycuRIycrKEpvNJhkZGTJixAgRqTyudl2PiEh+fr5cfPHF0rhxY3E6ndKxY0f58MMPo3kOHTq0Rrq1a9fW2Dfr1q2Ts846SxISEsTlckn79u3l+++/l7Vr19ZIX3Xc7Xp8+nw+ufvuuyUzM1NsNpu0aNFC3n777ei+uPTSSyUlJUUcDoe0bNlS/vvf/+52X+xqf7+7PR37u9q+fbsAMnny5Oi0FStWyAknnCB2u13atWsn48ePF0C+/PLL6DIbNmyQCy64QOLj4yUxMVHOOeccWbt2rYiIhEIhmTlzpvz73/+Wtm3bit1ul9atWslTD4+Ugi2bRESi+/h///uf9O7dW+x2u3To0EEmTZoUXcfe9t/GjRvl4osvlsTERHG5XNKjRw/5448/ovNfe+01ad68uVitVmndurW899571bYdkNdee03OPvtscblc0WP4q6++km7duondbpdmzZrJww8/LMFgcLf78JdffpFjjjlGXC6XxMfHS58+fWTdunXyzjvv1DiO3nnnHRERee6556Rjx47icrkkMzNTbrjhBikrK4vmt7vzKzs7W1544QUR2b9zp+qY2tk333wjPXv2FLvdLsnJyTJkyJDovNqOpYDXK+VFBVKwZZNsXb1ScletqPbZvm6NFG/LFU9pieRv3y7XXnutpKWlRb/jb7/9Npr/Z599Ju3btxebzSbZ2dnyzDPPSCBQIuXlq6SkZIE0bdpY/vGPm+SSS86V2NhYufTSS+Wll16SuLg4eeedd6RVq1ZiNptl+vTpsnnDJhlxw82SkZ4hLqdLjunaQ8Z/8r34N5aKb3OZvPHC6xIXFy/b1pXItnUlMn3yXBk04AxJTU0Tt9stPXv2lAkTJoiEgiLledKvz7E19mFw/VJ58+knJT42VjwLF4pn4UIpX7RQnn70AcnKzhSL1SI5LXJk1L9fEm/QK5FIJHqs7em6rZRSh8ueYo2SkhIBpKSk5KCtb/8DoEhExF9++D87Ltx10a9fP4mJiZG77rpLli1bJsuWLZOioiJJTU2V++67T5YuXSpz5syRAQMGyMknnxxN97e//U2ys7Plp59+koULF8q551be5HYNgOLi4uTZZ5+VVatWRT9ut1teeOEFWbFihUyZMkW6desmw4YNExGRmTNnitlslg8//FDWrVsnc+bMkRdffFFERLZs2SIWi0Wef/55Wbt2rSxYsEBeffXV6MPHrgHQLbfcIo0bN5axY8fK4sWLZejQoZKYmCgFBQUi8ufDSq9evWTSpEmyePFiOeGEE6RPnz673V8zZsyIBk25ubnRvCZOnCjvv/++LF26VJYsWSLDhw+XRo0aSWlpaTQtIJmZmfLhhx/KypUr5ZZbbpGYmJga5dk5ALJarTJgwACZM2eOTJ48Wf6fvfOOj6Lq+vhvZra3VBISSAFSCCUQelEDEggWBERA5RFQioCAWADbSyiij3TEjgoWEEXAR6rUIAaECISWkJCQhAChJKRtstkyc94/Njtkk00BKSLz5TMfMreeOXPn7py5957r5eVFffr0oSFDhtCpU6do48aNpFAoaM2aNWI9TzzxBEVERNDvv/9OSUlJFBsbSyEhIWSxWMhsNtOSJUvIYDBQbm4u5ebmivqrLZ8r1q5dSwaDgbZs2ULZ2dl08OBB+uKLL4iIKD8/nxo3bkyzZ88W6yEiOn/+PM2fP5+OHj1KGRkZ9OGHH4ovQUREhYWF1LVrVxozZoyYz2azVdPNY489Rr1796bjx49TRkYGbdy4kfbu3Us2m43WrVtHACg1NZVyc3OpsLBQbOuV2+eQIUMoICCA1q9fTxkZGbRz505Rjy+99BK1bduWEhMTKTMzk3bs2EG//vprje2CiOjSpUvisWTJEpLL5RQdHU07duyg//3vf+Tp6Vnrvaut7bvizJkzBIBOnDhBREQ8z1OrVq2oV69elJSURHv37qWoqCgnA8hisVBERAS98MILdPz4cUpOTqZnn32WwsPDyWw2k81mo9mzZ5Ofnx+tW7eOzp49S6u//ZY83N3pk6VLiOi6AdS4cWP6+eefKTk5mUaPHk16vZ7y8vLq1F9JSQk1bdqUHnzwQdq3bx+dOXOGfvzxR9q/fz8REa1fv57kcjl9/PHHlJqaSgsXLiSO42j37t3itQMgHx8f+vrrrykjI4Oys7Pp999/J4PBQCtXrqSMjAzavn07BQcH08yZM13qz2q1kpubG73++uuUnp5OycnJtHLlSsrOzqaysjJ67bXXqGXLlmI7LCsrIyKixYsX0+7duykzM5N27dpF4eHhNH78eCKiWp+vygbQzTw7VQ2gTZs2EcdxNGPGDEpOTqakpCR677336t2WBIEnc1kZleTnUf6FHCeD6ELaaWrfti01DwujDT/9RKeOH6NffvmFtmzZQkREf/31F7EsS7Nnz6bU1FRasWIFqdVq0Ui0WkspMLARGQw6evfd1+jo0c10/PhO+vLLj0kul1OXLl1o69attG/fPkpPT6dnn32WOnToQOvXr6c///yTZsfNIqVSSSd/P0LmnGJavvBTcjO4kfl8MZVdKKF92xJowdzFFP/bATqw5wi9OnkqqVQqSkvNIEEQruvwnemUe+IPyj26nejCEVqxaCa5GXRkyUwhU3Iy/bBkCcllMlr89tt0eMtGmvHO68RxHH294WtKzkul88W5dfbbEhISEneKe8cAMhuJ4gx3/jAbaxa0CtHR0RQVFeUUNmfOHOrTp49TWE5OjvhSWVxcTHK5nNauXSvGFxYWkkajqWYAVf4iSUQ0atQoGjt2rFPYvn37iGVZMplMtG7dOjIYDE6Gg4PDhw8TAMrKynJ5LZUNIKPRSHK5nFatWiXGWywW8vf3p3nz5hGR8wiQg82bNxOAGkfyHC9/R48edRnvgOd50uv1Tl9MAdA777wjnhuNRgJAW7dudZKnsgEEgNLT08U8L774Imk0GvGliogoNjaWXnzxRSKyjwAAoISEBDE+Ly+P1Go1/fTTT2K5Vb8k1ydfVRYuXEhhYWE1GkiVX/hq47HHHqPXXntNPK9qqBBV103r1q1rfLGtmtZVuampqQTA/tXYBf369aPnn3++Ttkrc+zYMTp8+DAlJibSrFmzRMPj8OHDlJSURE8++SSpVCqnF6fK9662tl8Vnufpscceo+7du4thv/32G8lkMrpw4YIYtnXrVicD6LvvvqPw8HDx6zaR/aVdrVbTb7/9RjabjRo3bkzff/+9GG8syKdpr0yhTh06ENH1Z+C///2vmMZqtVLjxo3pgw8+IKLa9ff555+TXq+v8QWyW7duNGbMGKewwYMH06OPPiqeA6ApU6Y4penVq5doADj47rvvyM/Pz2U9+fn5BMBp5KoycXFx1KZNG5dxlVm7di15eXmJ566eLyLn5+Fmnp2q5Xbt2pWGDRvmMv+NtCUHPM9TeWkpFeddpbWrvieWZemPHb85jRBdPZdFRVev0NAhQygmJsYp/9SpU6lFixZO19C//xNUVnaeiopPUFHRcfrkkzkEgA4e3E5m8zWy2Ux05swZ4jiOTpw4QRcuXBCPBx54gCZOnEhXL1+lTz782G4A5RQ7HeXnS6jkXDHlZxdReFgEvTdrPl05V0xFV8soMDCIFi1aZBfGWk5UnEsrlr5HbgYd0YUjJJw/Ql3bt6EXhg6mspMnxVGhJ/v0oZjoBykr5wSdunKSANC418bTheJcKrOUUUlJiVO/LSEhIXGnuNMG0D934vston379k7nx44dw549e6DT6cSjefPmAICMjAycPXsWVqsVnTp1EvO4ubkhPDy8WtkdOnSoVvbKlSudyo6NjYUgCMjMzETv3r0RFBSEpk2b4rnnnsOqVatQVlYGAGjTpg169eqF1q1bY/DgwVi+fDkKCgpcXlNGRgasViu6d+8uhsnlcnTq1AkpKSlOaSMjI8W//fz8AABXrlypU2+VuXz5MsaMGYPQ0FC4ubnBYDDAaDTi3LlzNdal1WphMBhqrUuj0aBZs2biua+vL4KDg6HT6ZzCHGWkpKRAJpOhc+fOYryXlxfCw8OrXXdlbibf4MGDYTKZ0LRpU4wZMwYbNmyAzWarsQ4A4Hkec+bMQevWreHp6QmdTofffvutmp7qYvLkyXj33XfRvXt3xMXF4fjx4zeUPykpCRzHITo62mX8+PHjsWbNGrRt2xbTpk3D/v376yyzUaNG0Gq1aNWqFQIDA6HRaNC3b19otVoEBAQgIiICjRo1cmqzle9dbW2/Ki+99BJOnjyJNWvWiGEpKSkICAiAv7+/GNa1a1enfMeOHUN6ejr0er34/Hl6eqK8vBwZGRkoLS3F+fPnMWbMGDHet3Egln78CTKzs5zKqly2TCZDhw4dxLZSm/6SkpIQFRVV45qslJQUp+cWALp3716tHbrqW2bPnu3Ut4wZMwa5ubku9ejp6YmRI0ciNjYW/fr1w9KlS5Gbm+tSpsrs3LkTvXr1QqNGjaDX6/Hcc88hPz+/xnvlipt5dqqSlJSEXr16uYy7kbbkgGVZKDUa6L28cfb8BTRu3BgdunaH1s0dMqUSAGCzWFBWVIhTJ0+ibYsI5J8/h5L8PJjLStG1a1ecOXMGPM+LZXbs2AlqdSPotOFQKBtUuNWWIzzcF+Xl51FaegZHj24Hz/Po0qUzwsLCxOPPP/9EdnY2LDYLym1mEAgFsjIYlRbkW4ow7d230KZHezRuFYDACH+cSU/FldzzUBBgKbWCeEJpoRlFV8tQbmYhaH0Bgx/AcPYNV2VKnM7IxENdWkHlZoFcS2AVHLpGRSE9/Sx8CoGgK3YnES3CQlBgzsfZorM4b74AvUGP8xfP/y2vehISEhL/dG7eCYJcA7x18RaKcgP13gBVF1AbjUb069cPH3zwQbW0fn5+SE9P/1tlv/jii5g8eXK1tIGBgVAoFDhy5Aji4+Oxfft2zJgxAzNnzkRiYiLc3d2xY8cO7N+/H9u3b8eyZcvw9ttv4+DBg2jSpEm9ZapK5cXTjgXArvZzqY0RI0YgPz8fS5cuRVBQEJRKJbp27QqLxVJjXY76aqvLVfobLeN2ERAQgNTUVOzcuRM7duzAhAkTMH/+fOzdu7fGBenz58/H0qVLsWTJErRu3RparRZTpkyppqe6GD16NGJjY7F582Zs374d77//PhYuXIhJkybVK39di8kfeeQRZGdnY8uWLdixYwd69eqFl156CQsWLKgxz8WLF9GsWTOoVPaF1HK5HCqVCgEBAcjIyIBMJoNGo4HRaBTzVL53er2+1rbvYOLEidi0aRN+//13NG7cuF7X68BoNKJ9+/ZYtWpVtbgGDRqIsn3++eeigVNyLR/lJSXQeXjUu57a9FeX7uuLq75l1qxZePLJJ6ulddyTqqxYsQKTJ0/Gtm3b8OOPP+Kdd97Bjh070KVLF5fps7Ky8Pjjj2P8+PGYO3cuPD098ccff2DUqFGwWCzQaOrX997Ms1OV2vRY37ZUV9lKrRZKrRZ6ADxvg9VUDoupDAxr7yetZjOsZjNKCwtQfOUyAMB4LR+qig80jnvEsnKolA2hVPpBrVZDqWwAni8DL5TDaCwFx3HYu/cHsKzdCxzDsOA4FbRaD+j1Mshk9nBe4MELPKbHvYl9+/ZhxowZCGnSDHqlFiPGPA+b1QI1C6jBgGHsP96CyYaSMhuIAcqK7V7fBK0vWH1DuzGkNICRySFjrZApbZCpeDAswHAsWN7+bPqYOARcBUrUgFFtN1Qvl15GWkE6PFTucFe6QcHdPgc/EhISEneDv7OLHKDQ3vnjb25W2K5dO5w6dQrBwcEICQlxOrRaLZo2bQq5XI7ExEQxT1FRkUsXra7KTk5OrlZuSEjIdQ9BMhliYmIwb948HD9+HFlZWdi9e3eFShl0794ds2bNwtGjR6FQKLBhw4Zq9TRr1gwKhQIJCQlimNVqRWJiIlq0aHHTunHIWPkrJwAkJCRg8uTJePTRR9GyZUsolUrk5eXddD03S0REBGw2Gw4ePCiG5efnIzU1VbxuhUJRTf765HOFWq1Gv3798OGHHyI+Ph4HDhzAiRMnaqwnISEB/fv3x3/+8x+0adMGTZs2rdZuXOVzRUBAAMaNG4f169fjtddew/Lly8X8QPV7VJnWrVtDEATs3bu3xjQNGjTAiBEj8P3332PJkiX44osvapXHarW6/CJMRLBa7W58GYapVa7a2j4RYeLEidiwYQN2795dzeiPiIhATk6O0yjGn3/+6ZSmXbt2OHPmDHx8fKo9f25ubvD19UWDBg1w9uxZMbxpcDCaBAdVq69y2TabDYcPH0ZERESd+ouMjERSUhKuXbvmUgcRERFOzy1gbzd1Pbft2rVDamqqy76FrcWDXVRUFN58803s378frVq1wurVqwG4boeHDx+GIAhYuHAhunTpgrCwMFy86PyRq77t90afnapERkZi165dNcbX1pbqIjIyEufPn3d6NjlOBpVOB0MDH7SObIOk5BS4+TSEWm8AJ5fj0OHDaBocDFNxEQouXgBvs8FYcA2Fl3JhLMhHeamxYlNdBiqVH7TaZtDrWqBLl77geR4FBTaEhYWjWbMgNG3aGEFB3vD25kB0GTJZMRiGYDCUQ6fjcfhwIoYMGYy+ffsiJDwUugZuyD5/DiZYUMiVwchUbD4s8NCyDNw4BnqGAWsjEAEFF4wovFyG8LDm+OPwKfv+Ql6hgMYb+48cR4uwJlAazFDoK0blGAZynuBpJAReJTAEqM0ArBZcLbuCMwVncObaWVwtzQcv1H3vJSQkJO4F7is32IB9es3y5cvxzDPPYNq0afD09ER6ejrWrFmDL7/8Enq9HiNGjMDUqVPh6ekJHx8fxMXF1Wun+OnTp6NLly6YOHEiRo8eDa1Wi+TkZOzYsQMfffQRNm3ahLNnz+Khhx6Ch4cHtmzZAkEQEB4ejoMHD2LXrl3o06cPfHx8cPDgQVy9etXppcuBVqvF+PHjRRkDAwMxb948lJWVYdSoUTetGx8fH6jVamzbtg2NGzeGSqWCm5sbQkND8d1336FDhw4oLi7G1KlTb9mX7hshNDQU/fv3x5gxY/D5559Dr9fjjTfeQKNGjdC/f38A9j1GjEYjdu3ahTZt2kCj0dQrX1VWrlwJnufRuXNnaDQafP/991Cr1QgKChLr+f333/H0009DqVTC29sboaGh+Pnnn7F//354eHhg0aJFuHz5stPLbXBwMA4ePIisrCxxilZVpkyZgkceeQRhYWEoKCjAnj17xHYQFBQEhmGwadMmPProo1Cr1U5TBh11jBgxAi+88AI+/PBDtGnTBtnZ2bhy5QqGDBmCGTNmoH379mjZsiXMZjM2bdrksp1VRq/XIzs7G8HBwWJYWVkZsrOzodfrAdhHFpUV04mqUlvbB+zP5erVq/G///0Per0ely5dAmCffqpWqxETE4OwsDCMGDEC8+fPR3FxMd5++22nOoYNG4b58+ejf//+mD17Nho3bozs7GysX78e06ZNg5+fH8aOHYsPPvgAHh4e6Nu3L65eOI/EQ4dQbuPxRqXyPv74Y4SGhiIiIgKLFy9GQUEBXnjhBQCoVX/PPPMM3nvvPQwYMADvv/8+/Pz8cPToUfj7+6Nr166YOnUqhgwZgqioKMTExGDjxo1Yv349du7cWav+Z8yYgccffxyBgYF46qmnwLIsjh07hpMnT+Ldd9+tlj4zMxNffPEFnnjiCfj7+yM1NRVnzpzB8OHDAdjbSGZmJpKSktC4cWPo9XqEhITAarVi2bJl6NevHxISEvDZZ585levq+ao6MnQzz05V4uLi0KtXLzRr1gxPP/00bDYbtmzZgunTp9fZluoiOjoaDz30EAYNGoRFixYhJCQEp0+fBsMw6Nu3L1577TV07NgRC5YswdChQ3HgwAGs+H4VFi9cALVOD0u5CQAg8DzKS42A3es2SvLzQIKAaxfPQ6ZQQKZQIqRJKJ599lmMGfMaFi5ciLZt2+DS5QvYvWsHWrRohj59uolyCYK9oGbNGmPbtk3o378bGEaBOXOWiCOpNuJhY3g0CmiEPYd+R+yAR6FVqODr6QMGAhgAepYB2QRMGT0Jz00YgRbNWqJX7xj8tmcb1m/ZhZ1b/gdG4wmuvBAAoNDaINfy4M0sBBsDBoCu3G4MWWQMSlWEUqUJVwQTrpguQ85oYFC4wVtjgIzj6qVzCQkJiX8cf3dh0j8ZVwvOieyL4gcOHCi6RG7evDlNmTJFXDztyg12p06d6I033hDLqGkR/KFDh6h3796k0+lIq9VSZGQkzZ07l4jsDhGio6PJw8OD1Go1RUZG0o8//khERMnJyRQbGyu6tQ4LC6Nly5aJ5Vb1AmcymWjSpEnk7e1dqxvsyovljx49Krperonly5dTQEAAsSwrusE+cuQIdejQgVQqFYWGhtLatWurXT+quCMmInJzcxM9J9XkBrsyrhZmV71uhztrNzc3UqvVFBsbW82d9bhx48jLy8ulG+za8lVmw4YN1LlzZzIYDKTVaqlLly5ODiUOHDhAkZGRpFQqRVe++fn51L9/f9LpdOTj40PvvPMODR8+3En+1NRU6tKlC6nV6hrdYE+cOJGaNWtGSqWSGjRoQM8995zogYyIaPbs2dSwYUNiGKZGN9gmk4leeeUV8vPzI4VC4eSqec6cORQREUFqtZo8PT2pf//+dPbsWTFvdHS0WK4Di8VCqamplJiYSHFxcaTT6SgxMZFSU1PJYrFQXFwctW7dWvRKV/Xe1db2iaq7YHccjvbj0N0DDzxACoWCwsLCaNu2bdXaXW5uLg0fPlx8Lpo2bUpjxoyhoqIi0Q32d999R23btiWFQkHu7m7UpWNH+mGV3TGCwwnC6tWrqVOnTqRQKKhFixZOXtrq0l9WVhYNGjSIDAYDaTQa6tChg+gJkKh+brCrPktERNu2baNu3bqRWq0mg8FAnTp1Er2rVeXSpUs0YMAA8f4HBQXRjBkziOd5IrK7SR80aBC5u7s76XnRokXk5+cnPiPffvtttX7E1fNVuT+4mWfHVX+wbt068T55e3vTk08+SUR1tyWHg5XayM/Pp+eff568vLxIpVJRq1ataNOmTWK8ww22XC6nwMBAmj9/vlP+oKAgmv/BB2QsuEaFl3Ppak42Lfngv2TQ66u53z6Xcopef3kyBQYEkFwuJ7+GDWnAgP507NgxIiL6+uuvyM3NjcrLr1BpWTadPLmTHnywI6nVKmrcuCEtWPAWPfBABxo/fhgVF5+mwsIM2rZtPbVoESHq8MKFC7Ro0SIyGAx05cIlKjh/lUpyCujDuQupSWAwyeVyCm0aQsuXfE6luUYqLygnm9lqb2vff0l0OYXowhHizx0hN72Ovnhvjug4wXEUnzpBlzJOUMb5E3Ty6kk6eSWZUq5kU05BIRnLrcTfgIdWCQkJiarcaScIDFHdKx3Ly8uRmZmJJk2a1Djf/N9MaWkpGjVqhIULF/6tERYJiXuBoKAgzJo1CyNHjqwWZzKZYDbb1xqoVKp7qj/geR5Hjx5FVFQUuIov1/kXcmAtL4d7Qz+otDpkZWWhSZMmOHr0KNq2bXt3BZa4KeLi4rB3717Ex8ff0XqJBNgsVtgsZtgsZljNFtgsZgg1TPdjOQ4yhQJyhRIypRIyhRIyhRwMw0IQbBAEE3jecZSByJUjCQaAHIIgg9XKguc5CAJXEW6HAws5cZATBxk4cJVmvgsAiGPBKljIVAxYlIGxlADmYpDNBt7KQrAw4G2s/ZOEIx8DlCmBMhVQpmTAQwZG0ELD6WFQqaBTyaCUSaNDEhIS9ac2W6O4uBhubm4oKiq6ZRuw33dT4OrD0aNHcfr0aXTq1AlFRUWYPXs2ANQ4XUpC4t/CqVOn4ObmJk6Vqoparb4r0x9vF1QxtYhh/vUOMe8btm7dio8++uiO18swLORKJeSVpoESEQSer2YU2awWCDwPi8kEi8lUqQwGnFwBuVJRYRDpoFR4gpPJIAhWu3MF3iQaR0Q8AAtY1oLr1TIgUoDnOdhsLHhehnLwKK+Yws2AgYw4KIiDHBw4HmBMAngTwEMOgfUCI28ATk1gUQbOWgS5xQjBAtEgYomBrhzQlQMEQrnCilJVIUqVRcgtU4JKNJAzWuiVcuhUcuiUHLha1qpJSEhI3GkkA6gGFixYgNTUVCgUCrRv3x779u1zOVddQuLfRMuWLWt0u22xWFBYWAiLxVLNIUJAQMCdEO+WIxpA0svZv4ZDhw7dbRFEGIYBJ5OBk8mg1Fz37CcIAnirBVazGTaLRTSQBF4Q/wZKxPSsjINcoYRcqYJMqYdS5Q2W5SAIlkojRXbPcyABDGOGTAbIxF94FoIgg83GgedlsAkyWCvtgsFVGEMK4iATODBmgmAGBKhAUAEyP3A6ARxjgsxWBDKX2keGrCzAM1BbALUF8AbBLC9HmbIcpaoCFFjUuGbSAIIKGoUMOpUMOqUMGgVX55paCQkJiduJZAC5ICoqCocPH77bYkhI/GMoLi5Geno6lEolysvLoVarxalw9XWP/E+EyG4AOTypBQcHS/ufSNx2WJYFq1RBrrw+zYOIINhssFoqjCKzuWK0yArBxsNsK4O50n5HnFwOuVIFuUoFudIDSrUvwDAQBHOFMWSCwJvA8+UABLCsBYpK3qwJHHgbVzFtToZyXobyipFQlljIiIMcrH2UyAYINgBQ2w+OAaMXIEc5YC2GYCmHYCEINgZKK6C0Ah5Ggo0rQ6myDKVKFiZBizKjBpeL5eBYBjql3SDSK2VQSNPlJCQk7jCSASQhIVEnFy5cgK+vLxo1aoQjR46gWbNmkMlkyMzMvGXzce80RFThuhji3i8SEncL+/Q3OTi5HKi0DZQgVIwKmS2wmsthNZfDZrGAt1rBW60oN5aI+WVKZSWjSA9OKQNA4IXyCmPIPlIkCGYw4CGT8ZVGiQAiDjaeg8DLYONlsAgyAAxAAAsWsor1RDLiILOyIIdBpAAYJUHGWABbGchiAW+xQsbzcCsD3MoECEwJSlUlKFNyKGXUKDarUWSyz9tTyTgY1DLoVXJpdEhCQuKOIBlAEhISdWIymcS9chwbnHIcB39/f6Snp8PHx+cuS3gTEImjPdIaIIl/KizLQqFSQ6FSA3ADYHfBbd+o1QRruRlWc7k9rLwc1vJyoKgiL8dVMojUUCrdwXIciHjwfLnTSJEgWMAwPOQyHpBd37xZEOzT5njBYRRdd7LAEAMZ7CNFMuIgIwVYVgmoAE4FAALAmyHYLOBsFujNFuhNNhCMMCmNMCkZlMqUMJMGV4wqXCkxQ8axMKhkMKjk0CllYKWPExISErcByQCSkJCoE47jRGNBLpfDbDaLzhBsNlfeqf75CBXT3wBpDZDEvQXLcVBqNFBWTD8lIvA2q2gMWc3lsJntHujMZaUwl5WKeWUKRSWjyACFyrvio4Zrz3Msy4NlechhFssQBJndKOI58IIMVoETNylniLF7ngMHGbGQcUpwnBqocNJAJAC8BTqbBVqTBV68GVb2GsoUQJlSjjJoUFCmwrVSOViGgV5lHxkyqGSQcdJzKiEhcWuQDCAJCYk60Wq1MBqNUKvVcHNzQ05ODkwmEwoKCqDVausu4B9IZQcI0pQbiXsZhmEgkysgkyugrtiYmAQBVovZPlJUbjeKeKu1wumCBaaSYntelrEbRI5D5QGl0j6iW5PnOZa1gWVtkMtFCUQnCw7jyEasaBSBAM4xUsRwkDFycDKV6JCbIwFK3gKPcgsE3gITdw2lch5G2fWpcgwYaBQyGNR2Y0gpl9YNSUhI3DySASQhIVEnAQEB4Cv2M/H394cgCLh27RqUSuU97AFOWv8j8e+FqTx1zj5zDrzNVjFCdN0oIkGo5o6bk8kqPM4pIFOoIFd6geXsrws1eZ5jWSsUCmslCdjro0S8zO5ogVgAVvsMOrLvUSSrZBTJZEpwYKADoCOCj8UCniwoZ4tglBGKbQqUFquRW8RCWbFuyCCtG5KQkLgJJANIQkKiVogIFotFnPLGcRyCgoLuslR/H8cIECut/5G4T7C75NZBpdUBqJg6Z7WIU+csFVPneJsNvM0IXJ85V7Fxq32fI/s0Ok8olQ0BoEbPcxxnAVdpoIbIMULEiYYRDxZm2MS9W9mKdUV2o4iDDDrowEBHQEOBwJMFZsYCIw8YjTLkl8jAsqxoDEnrhiQkJOqDZADdI4wcORKFhYX45Zdf7rYoN0V8fDx69uyJgoICuLu7321xJG4AhmGQlpaGVq1aQSa7NV3GzJkz8fPPP2P16tWwWq3QaDQIDAys13S6a9eu4ezZs3B3d0dISMhNy+BwgS2t/5G4X2EYpmLDVSXUsHtzFAQBtopRIlvFFLrrG7eWwWIqq5bfbhQpIVN6QqlWgmEAQSh3Wk8kCGYwDA+O452MIqBiPZGtwgOdIIMFBEslo8jubKFipIhYKKGGptI+RjZBgLnUAlOpFUVgIFOwUKnl0Gvk0rohCQkJl0gG0D3C0qVLb/v+JFlZWWjSpAmOHj2Ktm3b3tKyu3XrhtzcXLi5ud3ScquycuVKTJkyBYWFhbe1nvsNx74/ykq73APAsWPH8N///hd//PEH8vLyEBwcjHHjxuHll1+utTyTyQSr1Qp/f39otVpcvnxZNLLk1xcWVMNsNiMnJwc6ne5vX5MgbYIqIVENlmWhUGugUF/f34sEATaLpWKPIrO4gSsJguh0oTIyhaJilEgJmcIDClVDsBwjep5zTKETBAsAGzjOBo4Drm9TJIcgyGGzcbDZWAgCBysIVvCiUQSCfaQILGTgoCQWGrD2aAsBFgusRRaYGICRs5ArZZArOTAy1r6PkTRlTkLivua+MoAsFgsUlXeCu0fKBnDbDYfbjUKhQMOGDe+2GP9Ybnf7+bs0atQI58+fh7+/PzQaDbiKT7iJiYlo0KABvv/+ewQEBGD//v0YO3YsOI7DxIkTayzPaDRCJpPB29sbABAUFISioiLk5eXBz8/PZR4iQmZmJvz9/WE0GsU1STcL3WYD6J9+Tx3cK3JK3D0YlrV7jVM5b9xqd6pgvj5aZDFDsPGio4Vyo1FMz8pkkCsU9r2KFB5QKRqClbEuPc8B1oo1RajYvJUBGIV9fyLRKGJhAw8beFReVyTuVwT7fkVyYsFaBMBiga2kQnYGYGQsWDkHRsaAkbOAjAUjkxyiSEjcL/yrP3326NEDEydOxJQpU+Dt7Y3Y2FgAwMmTJ/HII49Ap9PB19cXzz33HPLy8sR8JSUlGDZsGLRaLfz8/LB48WL06NEDU6ZMEdMEBwdjzpw5GD58OAwGA8aOHQsA+OOPP/Dggw9CrVYjICAAkydPRmnp9YnUn3zyCUJDQ6FSqeDr64unnnpKjPv555/RunVrqNVqeHl5ISYmRsw7cuRIDBgwQExrNpsxefJk+Pj4QKVS4YEHHkBiYqIYHx8fD4ZhsGvXLnTo0AEajQbdunVDampqjfpy7PMSFRUFhmHQo0cPAPaX3N69e8Pb2xtubm6Ijo7GkSNHnPIyDIMvv/wSAwcOhEajQWhoKH799ddq8jhGZlauXAl3d3ds2rQJ4eHh0Gg0eOqpp1BWVoZvvvkGwcHB8PDwwOTJk51edAsKCjB8+HB4eHhAo9HgkUcewZkzZ8Q6nn/+eRQVFYFh7F/4Zs6cWWc+VxARZs6cicDAQCiVSvj7+2Py5MlO+p8+fToCAgKgVCoREhKCr776Sozfu3cvOnXqBKVSCT8/P7zxxhtO7qJvtm3W1kbqQ3BwMN59910MHz4cOp0OQUFB+PXXX3H16lX0798fOp0OkZGR+Ouvv8Q82dnZePzxx9GlSxf4+fmhefPm+Oijj8SRwuHDhyM6OhpNmzbFf/7zHzz//PNYv369U73//e9/4evrC71ejxdeeAFGoxFsJcODYRhs3boVDzzwAFQqFZo3b45PPvnEqYy//voLr776KkJDQxEVFYVx48YhKytLjHc8I7NmzUKDBg1gMBjw4osvwmQyged58DyPXbt2oW3btvbnM7gJhgwfgbKy61+vv/76a7Rs2VK8b5WNuHPnzok6MhgMGDJkCC5fvizGz5w5E23btsWXX36JJk2aQFXxslhYWIjRo0eLMj388MM4duxYjffIYrFg4sSJ8PPzg0qlQlBQEN5//30xvrCwEC+++CJ8fX2hUqnQqlUrbNq0SYxft26deA3BwcFYuHBhtTZwq/suifsP+/Q3BVQ6PfRe3vDwawSfoKZoENQEHn7+0Hl6QaXTQSa3G9eCzQZzWRlKCwpQePkS8nKycTU7G0WXi2AuBshigIILhFrVDGp1IBSKBpDJdGAYDgABZAbLlkKhKIZGUwidvhB6fSm0WguUChtYhgcYgsAIsDA2lDJmFLFluMYakc8YUciYYIQZZtggEAFWAUKZFXyxBbb8ctgul8F60QjrpVLY8k2wFZnBl1khWHjRYYqEhMS/CKoHJpOJkpOTyWQyiWGCIFCppfSOH4Ig1EdkIiKKjo4mnU5HU6dOpdOnT9Pp06epoKCAGjRoQG+++SalpKTQkSNHqHfv3tSzZ08x3+jRoykoKIh27txJJ06coIEDB5Jer6eXX35ZTBMUFEQGg4EWLFhA6enp4qHVamnx4sWUlpZGCQkJFBUVRSNHjiQiosTEROI4jlavXk1ZWVl05MgRWrp0KRERXbx4kWQyGS1atIgyMzPp+PHj9PHHH1NJSQkREY0YMYL69+8v1j958mTy9/enLVu20KlTp2jEiBHk4eFB+fn5RES0Z88eAkCdO3em+Ph4OnXqFD344IPUrVu3GvV16NAhAkA7d+6k3Nxcsaxdu3bRd999RykpKZScnEyjRo0iX19fKi4uFvMCoMaNG9Pq1avpzJkzNHnyZNLpdNXkKSgoICKiFStWkFwup969e9ORI0do79695OXlRX369KEhQ4bQqVOnaOPGjaRQKGjNmjViPU888QRFRETQ77//TklJSRQbG0shISFksVjIbDbTkiVLyGAwUG5uLuXm5or6qy2fK9auXUsGg4G2bNlC2dnZdPDgQfriiy/E+CFDhlBAQACtX7+eMjIyaOfOnaKc58+fJ41GQxMmTKCUlBTasGEDeXt7U1xc3N9qm3W1kfoQFBREnp6e9Nlnn1FaWhqNHz+eDAYD9e3bl3766SdKTU2lAQMGUEREhPisPfbYY9SzZ086cOAAHTt2jH788UfaunUrFRcXi0dlhg0bRoMGDRLPf/zxR1IqlfTll1/S6dOn6Y033iCtVkutW7cW03z//ffk4+NDS5cupbNnz9K6devI09OTVq5cSURE+fn51LRpUxo5ciQdP36ctm/fTv369aPw8HAym81EZH9GdDodDR06lE6ePEnffPMNeXh40PPPP0+JiYm0detW4jiOFixYQJmZmfTnvt/p/ZlxdOHsWSIi+uSTT0ilUtGSJUsoNTWVDh06RIsXLyYiIp7nqW3btvTAAw/QX3/9RX/++Se1b9+eoqOjxWuIi4sjrVZLffv2pSNHjtCxY8eIiCgmJob69etHiYmJlJaWRq+99hp5eXmJz0ZV5s+fTwEBAfT7779TVlYW7du3j1avXi3K0aVLF2rZsiVt376dMjIyaOPGjbRlyxYiIvrrr7+IZVmaPXs2paam0ooVK0itVtOKFSuc2sCt7LskJOqC53kym8qotLCACq9corycc3Qp4wzlpqe5PC5lnKGr57LoWu4FKs67QqVFeVRmvExlZefJaEyn4uKTVFR0vNpRXHyKiovPUNG1DMq7lEG5F87RhQsXXB/nL9Cl85foas4VKszJJ2NOIZXnFJO5puOikSxXy8haUE42o4V4s40Evv7vIxISErXjytZwUFRURACoqKjoltV30wZQqaWUWq1sdcePUktpvS8uOjqaoqKinMLmzJlDffr0cQrLyckhAJSamkrFxcUkl8tp7dq1YnxhYSFpNJpqBtCAAQOcyhk1ahSNHTvWKWzfvn3EsiyZTCZat24dGQyGai+MRESHDx8mAJSVleXyWiobQEajkeRyOa1atUqMt1gs5O/vT/PmzSOi6wbHzp07xTSbN28mAC4bFxFRZmYmAaCjR4+6jHfA8zzp9XrauHGjGAaA3nnnHfHcaDQSANq6dauTPJUNIACUnp4u5nnxxRdJo9E4vdDHxsbSiy++SEREaWlpBIASEhLE+Ly8PFKr1fTTTz+J5bq5uTnJW598VVm4cCGFhYW5NJBSU1MJAO3YscNl3rfeeovCw8OdjPWPP/6YdDod8TxPRDfXNutqI/UhKCiI/vOf/4jnubm5BID+7//+Tww7cOAAAaDc3FwiImrdujXNnDmzXuUnJCSQTCaj3377TQzr2rUrTZgwQTw3m83UqlUrJwOoWbNmtGzZMkpOThbD5syZQ127diWbzUZz586l0NBQUadnz56lU6dOkVqtFusaMWIEeXp6UmmpvY/geV7Uu8VioT///JMAUEZGBhERFeddodz0NCrOu0pERP7+/vT222+7vK7t27cTx3F07tw5MezUqVMEgA4dOkREdgNILpfTlStXxDT79u0jg8FA5eXlTuU1a9aMPv/8c5d1TZo0iR5++GGXH3t+++03YlmWUlNTXeZ99tlnqXfv3k5hU6dOpRYtWojnt7rvkpC4GQRBIIu5nMqKi6g4/yoV5F6kq+eyazWMctPT6NLZdMrLyabCKzlUXHCOSoozqaQ4lYqKTtRgFCVTccFpKrh8mvLOn6FLOdl04fz5Go2ii+dz6XLOFcrPyaOinAIqyymq2SjKKSZzboVhVFhOtlIL8RbbDX2olZCQsHOnDaB//Rqg9u3bO50fO3YMe/bscbmIOiMjQ1yc3alTJzHczc0N4eHh1dJ36NChWtnHjx/HqlWrxDAigiAIyMzMRO/evREUFISmTZuib9++6Nu3rzhlrE2bNujVqxdat26N2NhY9OnTB0899RQ8PDxcymm1WtG9e3cxTC6Xo1OnTkhJSXFKGxkZKf7tWFtx5coVBAYGutSXKy5fvox33nkH8fHxuHLlCnieR1lZGc6dO1djXVqtFgaDAVeuXKmxXI1Gg2bNmonnvr6+CA4Odro3vr6+YhkpKSmQyWTo3LmzGO/l5YXw8PBq112Zm8k3ePBgLFmyRLxXjz76KPr16weZTIakpCRwHIfo6Oga6+vatavTXPLu3bvDaDTi/Pnzou5vtG326dOn3m2kNirfJ19fXwBA69atq4VduXIFDRs2xOTJkzF+/Hhs3boVnTp1wsMPP4xHH30UCoUC+fn5UCgU0Ov1OHnyJPr374+4uDj06dPHSR/jxo0Tz2UyGVq3bo0TJ04AAEpLS5GRkYGpU6eCrbQpqc1mg5ubG8xmM1JSUnD27NlqXuLKy8tx+vRpsb42bdpAo7Ev3mZZVtT7xYsXERUVhY4dO6Jt27aIjY3Fg127oE+PaOg8PHHlyhVcvHgRvXr1cqmzlJQUBAQEOO151KJFC7i7uyMlJQUdO3YEYF/L1KBBAzHNsWPHYDQa4eXl5VSeyWRCRkaGy7pGjhyJ3r17Izw8HH379sXjjz8uXl9SUhIaN26MsLCwGuXs37+/U1j37t2xZMkS8Dwvrt26lX2XhMTNwDAM5Aol5ApnxypEBMFmg81qBW+1VPnfWuF4wQyr2ak0gFFCpmAgUzJgZQLA2dcGEdkAFmBV9kMOExjiQDZAsDAQbDLYSAEbJwMYBgQBNkaAOGGZ7OWzxNn3LSIOcnCQM4zd4YJNANkEUHkVcWQsGDkHRs7aD8n5goTEP4qbNoDUMjUOPnvwVspS73pvhKovTEajEf369cMHH3xQLa2fnx/S09P/Vtkvvvii01oRB4GBgVAoFDhy5Aji4+Oxfft2zJgxAzNnzkRiYiLc3d2xY8cO7N+/H9u3b8eyZcvw9ttv4+DBg+LanJuhskctR8fr8H5VX0aMGIH8/HwsXboUQUFBUCqV6Nq1KywWS411OeqrrS5X6W+0jNtFQEAAUlNTsXPnTuzYsQMTJkzA/PnzsXfvXnE/nL/LjbZNjuNuSRtx1SZqayejR49G586d8cMPP+DIkSP49NNPMW/ePLzyyivgeR6XLl1CTk4OevXqhbFjx+Kdd96ptX6WZSGXy8XyjRULpf/v//4PPXr0gI+Pj5iW4zioVCqoVCpERUVhxYoVAOxGOc/z8Pf3R6NGjep13RzH4eOPP4bJZMKuXbvw+ZdfYfbc97B39y4ENm1WdwH1wNU99fPzQ3x8fLW0NbmDb9euHTIzM7F161bs3LkTQ4YMQUxMDH7++efb2vb+Tt8lIXGrYBgGnFwOTi4H4GxgkyCAt1mvG0UWu1HkcNNtMxNsZsd6HQ4Mw4GRCeDkBE5OYOUCwAgghgfkACsHWNggQzkgsCALAAtrd7YAOWycDMSyAAgCY4MAwArABADEgCEWLFhwZPdEJ2dYyMCAIYCsAsha5beLZURjSDSM5Jy0GbOExF3gpg0ghmGgkd97X//atWuHdevWITg42OWeJk2bNoVcLkdiYqL4pb6oqAhpaWl46KGH6iw7OTm51r1JZDIZYmJiEBMTg7i4OLi7u2P37t148sknwTAMunfvju7du2PGjBkICgrChg0b8OqrrzqV0axZMygUCiQkJIgbUlqtViQmJjo5arhRHJ6gqnrXSkhIwCeffIJHH30UAJCTk+O0MP9OERERAZvNhoMHD6Jbt24AgPz8fKSmpqJFixYA7NdQVf765HOFWq1Gv3790K9fP7z00kto3rw5Tpw4gdatW0MQBOzduxcxMTEu5Vy3bh2ISDQmEhISoNfr0bhx4xrrq6ttAqh3G7nVcByHV199Fd7e3nj++efx9ddf45VXXoFOp8O+ffswYcIEjBgxAnPnzq2WNyIiAgcPHsTw4cPFsOTkZNhsNuTl5cFgMMDX1xfnz59Hx44dIZfLkZmZCblcLuqrU6dOWLduHQIDA2EwGKDVasHzfLVn7dixYzCZTKKh8Oeff0Kn0yEgIEC8H927d8dDDz2ESWNGI7JDR2zcvBlvvPU2goODsWvXLvTs2dPlNeTk5CAnJ0ccBUpOTkZhYWGtbahdu3a4dOkSZDIZgoOD661vg8GAoUOHYujQoXjqqafQt29fXLt2DZGRkTh//jzS0tJcjgJFREQgISHBKSwhIQFhYWHi6E9Ncv6dvktC4k7AsKy4b1FVBJ4XDSOHUeQ4t1oJVrEMAisjsDLBfsgJDEsAK4BRAVAJ4GADh3IobQxgBmBlwQt2o4jnZBBYFmAAYnjY/wHi58AqhpEcLOQMBxkYQCCQmQeZq3iw5CobRNdHjKTRIgmJ28e/fgpcVV566SUsX74czzzzDKZNmwZPT0+kp6djzZo1+PLLL6HX6zFixAhMnToVnp6e8PHxQVxcnNPUnJqYPn06unTpgokTJ2L06NHQarVITk7Gjh078NFHH2HTpk04e/YsHnroIXh4eGDLli0QBAHh4eE4ePAgdu3ahT59+sDHxwcHDx7E1atXERERUa0erVaL8ePHizIGBgZi3rx5KCsrw6hRo25aNz4+PlCr1di2bRsaN24MlUoFNzc3hIaG4rvvvkOHDh1QXFyMqVOn3rIv0TdCaGgo+vfvjzFjxuDzzz+HXq/HG2+8gUaNGonTfoKDg2E0GrFr1y5xOlR98lVl5cqV4HkenTt3hkajwffffw+1Wo2goCB4eXlhxIgReOGFF/Dhhx+iTZs2yM7OxpUrVzBkyBBMmDABS5YswaRJkzBx4kSkpqYiLi4Or776qpPns6rU1Tb/+uuvereRW8mUKVMQEhKCmJgYnDt3Dn/99ReaN28OADh9+jTGjBmDxx57DK+++iouXboEwG4wOaaCvfzyyxg5ciQ6dOiA7t27Y9WqVUhNTUVgYCAuXrwIq9WKl156Ce+//z6aN2+Ovn374vTp0zh9+jRkMhleffVVDBs2DPPnz0f//v0xe/ZsEBFycnLw4YcfYtq0aaKhZLFYMGrUKLzzzjvIyspCXFwcJk6cCJZlsX//fqxatQo8z8PPzw+7tm5B/rVriKi4lpkzZ2LcuHHw8fHBI488gpKSEiQkJGDSpEmIiYlB69atMWzYMCxZsgQ2mw0TJkxAdHR0telklYmJiUHXrl0xYMAAzJs3D2FhYbh48SI2b96MgQMHusy7aNEi+Pn5ISoqCizLYu3atWjYsCHc3d0RHR2Nhx56CIMGDcKiRYsQEhKC06dPg2EY9O3bF6+99ho6duyIOXPmYOjQoThw4AA++uijah71qvJ3+i4JiX8CLMdBwXFAJVfdQMWUOr5iSp3FeTqdxWS1fxhh7aNDomEkF8AwAGRU8ZbEgwMPjswgK8CaGcDKQuA52EgGgeNg4zgQxwEM1WoYcbAfMrBQgAMHBuAFEF9lGh1QZbTIPp1OmkYnIXGL+LsLk/7JREdHOzkucJCWlkYDBw4kd3d3UqvV1Lx5c5oyZYq4cLG4uJieffZZ0mg01LBhQ1q0aBF16tSJ3njjDbGMoKAg0UNUZQ4dOkS9e/cmnU5HWq2WIiMjae7cuURkX1QcHR1NHh4epFarKTIykn788UciIkpOTqbY2Fhq0KABKZVKCgsLo2XLlonlVvUCZzKZaNKkSeTt7U1KpZK6d+8uLsYmqu50gIjo6NGjBIAyMzNr1Nny5cspICCAWJYVPVwdOXKEOnToQCqVikJDQ2nt2rXVrh8AbdiwwaksNzc30fuUKycIVZ0VxMXFUZs2bZzCql73tWvX6LnnniM3NzdSq9UUGxtLaWlpTnnGjRtHXl5eBED0vFaffJXZsGEDde7cmQwGA2m1WurSpYuTQwmTyUSvvPIK+fn5kUKhoJCQEPr666/F+Pj4eOrYsSMpFApq2LAhTZ8+naxWqxh/M22zrjbi0HFt99dVu61676o6w5g4cSIFBASQUqmkBg0a0GOPPUYXLlwgIvsCe9hnyTsdQUFBTnXMnTuXvL29SafT0YgRI2jatGnV7vWqVauobdu2pFAoyMPDgx566CFav369GJ+bm0vDhw8X23zTpk1pzJgx4qJIR1uZMWMGeXl5kU6nozFjxogOCE6cOEFdunQR9desaVOaO+P/qLzUKNbx2WefUXh4OMnlcvLz86NJkyaJcdnZ2fTEE0+QVqslvV5PgwcPpkuXLonxrtovkb0/mTRpEvn7+5NcLqeAgAAaNmyYk0OFynzxxRfUtm1b0mq1ZDAYqFevXnTkyBExPj8/n55//nny8vIilUpFrVq1ok2bNonxP//8M7Vo0YLkcjkFBgbS/Pnzncq/1X0X0XWnJhIS9xKCIJDVYqbyUiMZCwuo6OplunbxPF3JzqTLmal0JSeF8i6dooK8k1RYWN3BQlHRcSosPE6FV05Q0fkTVHz2JBWlJlNhairlpafT5axMupiTU7MnOofjhZxLdDnnCuXl5FFRzrXaHS+cLyHLJYdHOhPZis3El1V4pbPxkgMGiXuWO+0EgSGiOh3cl5eXIzMz02lvi/uJ0tJSNGrUCAsXLvxbIywSEreTFStW4L333kNycnK1tVR/l9zcXOTn5yM4OBhpaWkIDQ2FxWJBTk4O/Pz8RMcJd5ORI0eisLAQv/zyi8t4nudx9OhRREVFgeM4XD2XBd5qhad/Yyjuwojmv4m4uDjs3bvX5VonCYl7Eft6IxtsFVPqeKsFPF8OgcxgWJs4YuQ6L0BWFrAAjIUBYwFALAQZC17GwcbJwLMy2L0l1CSAfVNXljjIKm3uytWYoQKGASNj7NPqZAwYrmLUSMaC4Rj7OiRpBEniH0httkZxcTHc3NxQVFQEg8FwS+q776bA1YejR4/i9OnT6NSpE4qKijB79mwAqHG6lITEP4EtW7bgvffeu+XGDwA0bNgQAJCWlgZBEJCamgqGYdCwYcN/hPFzM1CFEwamlmmJEvVj69at+Oijj+62GBIStwz7eiMFZBVrYysjCIJoFNlsZeB5Ewh2w4jhBDAswCgFoNJSJeIFkI0BY7FBbrVAWQawAkAcA17GgpfJYWNlEFj7Wj2GAQQIECp7pAMAAhiw4Cq80tkdMLCQMax9Z3sikNW+0atL80wykCQkAEgGUI0sWLAAqampUCgUaN++Pfbt2wdvb++7LZaERI2sXbv2tpXNMIw40mM2myEIAlQqVa0L6//pSAbQrePQoUN3WwQJiTsGy7JglUrIlUoAeqc4gbfCajHCZiuDIJhAsAAMD4YjcBw5GUWCjYFgYyHYCIxFgMJsBseTfR0Rx4KXyWDj5OA5ud1wIbI7X6jqqhuwG0bEgrGPG4El+18yMBXGEVN/A4ljgAqDSHTfLRlIEv8yJAPIBVFRUTh8+PDdFkNC4h9Dfn4+3N3dwXHcXXGAUR9WrlxZ77Rk3wQaACQXtBISErcMlpNDqfaAEtf3ZxMEG3jeBJvVCJ4vg0DlABxOF5w9wgk2BoKVhWADBBsP1ixALpSDgQCwgMCx4DkFbJwSYDkwIPucOwYgRgBBgMuNIyr2M7J7qHOYSXbjiKswlFhi7AaSjez7G7kqRzKQJP4lSAaQhIREneTk5CA7Oxvu7u7w8vKCwWC4p3/kqNLeUiwjjQBJSEjcPlhWBpbVQy6/PlokCFbwvAk8X1ZxmOBsFNkNIyKAbCx40TBiQFYb5BYrGCIwDAEswHNyWGUqgFOAZVlwIAACBJ63f+xhAMDhoc5Rut3LtzMMWGLBgAFXYRxxDAuOGHDEgCXUbSBVmWLnZCRJH5wk/iFIBpCEhESdtGnTBkVFRbh27RoyMjLAsiw8PDzg5eUFnU53t8W7YYgqDCAG9sn2EhISEncQlpWDZeWQy+0Luu2j0pYKo6jiEExgIICRC2DlANTXjSK7McSKI0ZkEyCzlYFBqX1UiAGIZSCwCpBMDU6hhFzGQc4xYEHgeR42Gw+e5yEIzkaSwNjrcRqbYioOAhgwFWNIjOjWm4PDQGLBWpmap9ixDqOo0igSxwIOY0kykCTuEJIBJCEhUScMw8Dd3R3u7u7geR6FhYW4du2auE6udevWd1vEG4KEiulvjLTZoISExN2HYRgwjBIsq4Rc7g6gYg8jwQyeN0EQHKNF5WAYAqcQwCmuj2STALtBVMkwIp4Bx1sBqxUwATbYD2IYECcDI1NAJpdDrdZAoZCL6yF5QYDNxsNmtVUYSILdSHIYViAQeAgV5V2/CIcwEE2k60aSfYodK7BgBQaslXXtz46tNJ1OVmkUyWEkSf21xC1CMoAkJCRuCI7jYDAYwPM8LBYLTCbT3RbphnGMAEkOECQkJP6pMAwDjlOB41RAxZoiIgGCUO40UiQI5WBYVDeKeLs7bsHGgLexEGwciGCfOmezAjYr+HKgDPZDrJfjwMpkkMvkkMvlYJUKcDIZWE4GsAyI7NsK2Ky2SqNI1Y0kx2awVrHgKtdH9rEkrsJY4iqm37GW68ZTNTOJq7L2iGXs/3OMvT93hElI1IFkAElISNSLyiM/xcXFUCgU8PT0RNOmTe+2aDfM9REg6YdSQkLi3oFhWHCcBhynEcOIePB8eYUxVFbxvwUMBzCcABaAzGGKWAFYGcDKQLDaDaMyJQOFAEBg7TtZ83ajhjdXXyFkl4EBJ5ODlcnAyWRQyGRgOQVYjgPDcQBjL4fnBfsoko0HL/AQeAEC2R01AAAx9rEk+1mlCXeVu2WCaAixqBg9EuxGElfJSGIqZ3I4ahANJLbKecX/ksOG+xrJAJKQkKiTjIwMFBUVgWVZeHp6Ijw8/J5c++NAHAGSHCBISEjc4zAMB5lMC5lMK4YJgq1ipKhMXE9EghWQA5ATAAILgAUPLTFQCCzkNjNYmwDiGQjEgicGVwU3cAygkQG8zQrBZl8vZLNaAKulDrkYsBwHlpNBxnHi3yzHgWE5EMNAEAiCQOB5shtJggBBECr6aPtokt1ksjt1uF54lcoqNo7lHMYSz4LlGdEdOCuuWoJzGayzcWT/m3UaWZIMpX8nkgF0j1DXLvf/Nnr06IG2bdtiyZIld1sUCdh/yJo1a+bS+5vJZPrHusYG7O6xp0yZgvz8fDFMcoFtx6GbwsLCmy4jODgYU6ZMwZQpU26ZXPcKt+ra72cdStwe7J7ndJDJrn+oEj3PCSYIFYYREQ8ZQxA4HmaOAZQcAAYcOLACC0+5BgKUcNNowTAMSBDA8zYINhv4ikOw2SDwvD2c5+2e5wQBRCSmqRMGYDkOHCeDnOPAyTgwrKxiNMk+7U4gQCCCQAQSKkaTKlyAw7FxrItynRDXJ9nHjBiBASswYKxMpXAGDFX6GxV7P3Gs3RiqGD1yGkmq8j8YaYbBPx3JALpHWLp0qfjSdi+xfv16fPrpp0hKSoLZbEbLli0xc+ZMxMbG3m3RJG6AqtPceJ7HtWvXkJeXh9LSUnTo0OFvlR8fH4+ePXuioKAA7u7uf6usqgwdOhSPPvqoU5i4CeotHAGaOXMmfvnlFyQlJd2yMl3BMAw2bNiAAQMG3FA+Vy/ZrnRTEzUZS4mJidBqta4zSTgh6VDibiJ6nkNlz3PW66NEFc4WiATwsIFnAfAXwQIwGjlwnBosqwbHqSFTqiFXqWt8yRcEQTSGhEqGketzHiD7vkeCjXdZXlUYloWMZcGwLBiWA1gWAGN38ED2bY/s/1PF3yQaSvb1SXW8T7m6LJujiMrGkYu/yf7bwjIM2AoZWcdIkytjiYU0Je8ucF8ZQBaLBQqF4p4rGwDc3NxuW9m3k99//x29e/fGe++9B3d3d6xYsQL9+vXDwYMHERUVdbfFu6vc7jZzq7BarZDL5QCAkpIS5OXloaCgAHK5HB4eHggMDLzLEtaOWq2GWq0Gz1//YZVGgOw4dPN3aNCgwS2S5s7D87x9mk4VZxh3+tm8l3Uoce9i9zynAMsq6vQ8R8TDZjMCMFbKLwPHqSsODVhWDZa1v1ayFS/9qPjtqA0ioZpBJPA2u0EkVDhZsF03nAD7RyxecLnla/XrRCV7hmHsB8vaP4AxLMAwoIpUDrOIKmeuVIh98qBjSl4tFToQIM7cY0QX4pUNJxcGVYV3UpaxO3Zw/M8wTMW0Pca+e4PL8wqDyhHHSKNRNfGvngDfo0cPTJw4EVOmTIG3t7c46nDy5Ek88sgj0Ol08PX1xXPPPYe8vDwxX0lJCYYNGwatVgs/Pz8sXrwYPXr0cPpyGhwcjDlz5mD48OEwGAwYO3YsAOCPP/7Agw8+CLVajYCAAEyePBmlpaVivk8++QShoaFQqVTw9fXFU089Jcb9/PPPaN26NdRqNby8vBATEyPmHTlypNMXX7PZjMmTJ8PHxwcqlQoPPPAAEhMTxfj4+HgwDINdu3ahQ4cO0Gg06NatG1JTU2vUV1ZWFhiGwU8//SReQ8eOHZGWlobExER06NABOp0OjzzyCK5evepUV6dOnaDVauHu7o7u3bsjOzsbALBkyRJMmzYNHTt2RGhoKN577z2EhoZi48aNYv7S0lIMHz4cOp0Ofn5+WLhwYTXZzGYzXn/9dTRq1AharRadO3dGfHy8U5q6dO+4Z8888wy0Wi0aNWqEjz/+WIwnIsycOROBgYFQKpXw9/fH5MmTnWSYPn06AgICoFQqERISgq+++kqM37t3Lzp16gSlUgk/Pz+88cYbsFUa+r/Z9liV7Oxs9OvXDx4eHtBqtWjZsiW2bNkixp86dQqPP/44DAYD9Ho9HnzwQWRkZACwf5WbPXs2GjduDKVSibZt22Lbtm3V2sCPP/6I6OhoqFQqfPvtt8jNzcXMmTPRpk0bRERE4Mknn8SuXbvQuHFjaLVaWCwWTJw4EX5+flCpVAgKCsL7779f4zVUJisrCz179gQAeHh4gGEYjBw5EgCwbds2PPDAA+IGrI8//rh4LZXlXb9+PXr27AmNRoM2bdrgwIEDYpqVK1c6jSrNmjULXbo/gB/W/ozWHTpCp9NhwoQJ4Hke8+bNQ8OGDeHj44O5c+c6yXnu3Dn0798fOp0OBoMBQ4YMweXLl8U6Zs2ahWPHjlW8VDBYuXJlnflcUZsug4ODAQADBw4EwzDieUZGBvr37w9fX1/odDp07NgRO3fuFMvs0aMHsrOz8corr4jyudLNsWPH0LNnT+j1ehgMBrRv3x5//fUX4uPj8fzzz6OoqEjMP3PmTFGmylNVCwsL8eKLL8LX1xcqlQqtWrXCpk2bANTdduvDxo0b0bFjR6hUKnh7e2PgwIFiXEFBAYYPHw4PDw9oNBo88sgjOHPmjBjvuN5ff/0VLVq0gFKpxLlz5266P6/KokWL0Lp1a2i1WgQEBGDChAkwGu0vjDeiw7razMyZM9G2bVt89913CA4OhpubG55++mmUlJTckC4lJKri8DynUHhApfKHVhsCvb4FtNoQqFT+kMs9wHIqAACRDTZbCczmKygry4LRmAKjMRVlpnMwm6/CZjOCqO4RHYZhwcnkkCtVUGq0UOsN0Lp7Qu/dAG4+DeHp1wjeAYHwCW4K36Yh8AluCu/AIHg1DoCHXyO4+zaEoYEP9F7e0Hp4QGNwg0qnh1KjgVylgkyhACvj7P0eESAIgM0GslpAlnKQ2QSYywBzKZiKg3Uc5RWHqRRseRnYchMYswmsxQzWagVjtYGx8WBsAhheAHi6bvQQUNlOIgYQGALPCLAxPCyMDWbGhnLGijLGglLGDCNTjmKUoYhKUSAYcc1WjDxrEa6aC3DVdA15ZQXINxYgv6QQBcWFKCwqQlFBIYqvFaEkvwjGvEKUXi5C6aUilOUWw3SxCOUXilF+oRjmiyWwXDTCklsK66WK43IprFfK7MfVMljzTLDmmWDLN8F2zQTbtXLYCsphKyyHrcgMW5EZfIkFvNECvtQKvswKwWSDUG6DYOEhWHmQTQDxAkigf/TMpZseASIi0F1wf8uoax5ydcU333yD8ePHIyEhAYD9x/nhhx/G6NGjsXjxYphMJkyfPh1DhgzB7t27AQCvvvoqEhIS8Ouvv8LX1xczZszAkSNH0LZtW6eyFyxYgBkzZiAuLg6A/SWkb9++ePfdd/H111/j6tWrmDhxIiZOnIgVK1bgr7/+wuTJk/Hdd9+hW7duuHbtGvbt2wcAyM3NxTPPPIN58+Zh4MCBKCkpwb59+2psPNOmTcO6devwzTffICgoCPPmzUNsbCzS09Ph6ekppnv77bexcOFCNGjQAOPGjcMLL7wg6qIm4uLisGTJEgQGBuKFF17As88+C71ej6VLl0Kj0WDIkCGYMWMGPv30U9hsNgwYMABjxozBDz/8AIvFgkOHDtU6LF5SUuIk49SpU7F3717873//g4+PD956661q+p44cSKSk5OxZs0a+Pv7Y8OGDejbty9OnDiB0NDQOnXvYP78+Xjrrbcwa9Ys/Pbbb3j55ZcRFhaG3r17Y926dVi8eDHWrFmDli1b4tKlSzh27JiYd/jw4Thw4AA+/PBDtGnTBpmZmaKhcuHCBTz66KMYOXIkvv32W5w+fRpjxoyBSqUSX3KAm2uPVXnppZdgsVjw+++/Q6vVIjk5WXRIcOHCBTz00EPo0aMHdu/eDYPBgISEBNEQW7p0KRYuXIjPP/8cUVFR+Prrr/HEE0/g1KlTCA0NFet44403sHDhQri7u8Nms2HNmjX4/PPP8dFHH6Fdu3b4+eefMWfOHHh4eGDEiBH48MMP8euvv+Knn35CYGAgcnJykJOTU2s7cxAQEIB169Zh0KBBSE1NhcFgEEclSktL8eqrryIyMhJGoxEzZszAwIEDkZSU5PTV/u2338aCBQsQGhqKt99+G8888wzS09Mhk7nu4jKzMrF77+9Yt+YHXCkowlNPPYWzZ88iLCwMe/fuxf79+/HCCy8gJiYGnTt3hiAI4gvp3r17YbPZ8NJLL2Ho0KGIj4/H0KFDcfLkSWzbtk00PNzc3OrM54radJmYmAgfHx+sWLECffv2BcdxAACj0YhHH30Uc+fOhVKpxLfffot+/fohNTUVgYGBWL9+Pdq0aYOxY8dizJgxNd6LYcOGISoqCp9++ik4jkNSUhLkcjm6deuGJUuWYMaMGeJHFFdOMARBwCOPPIKSkhJ8//33aNasGZKTk0U5a2u79WHz5s0YOHAg3n77bXz77bewWCxOBtTIkSNx5swZ/PrrrzAYDJg+fToeffRRJCcni6OYZWVl+OCDD/Dll1/Cy8sLPj4+AG68P3cFy7L48MMP0aRJE5w9exYTJkzAtGnT8Mknn9yQDuvTZjIyMvDLL79g06ZNKCgowJAhQ/Df//63muEuIfF3sXues4/0OCASKq0nso8UCYJFPGwoEtOyrFLMz7IacJzqpqcfMwxjd9Nd0afcKORwsuBw2+045yufV/zP29NVDiMSwBDgGNap+qZT49up453IMdokbsLNVvzPVIxGVZTCVB+Foorpe7iZ6XuiAqqORDn/DacwOIXar9s5HlXSOVfPVLp0xzVX5Kn0tyOD1WYGb7SgYMMZKHjuujMKBigur/nD001D9cBkMlFycjKZTCYxjC8tpeTw5nf84EtL6yMyERFFR0dTVFSUU9icOXOoT58+TmE5OTkEgFJTU6m4uJjkcjmtXbtWjC8sLCSNRkMvv/yyGBYUFEQDBgxwKmfUqFE0duxYp7B9+/YRy7JkMplo3bp1ZDAYqLi4uJqshw8fJgCUlZXl8lpGjBhB/fv3JyIio9FIcrmcVq1aJcZbLBby9/enefPmERHRnj17CADt3LlTTLN582YC4HQfK5OZmUkA6MsvvxTDfvjhBwJAu3btEsPef/99Cg8PJyKi/Px8AkDx8fEuy6zKBx98QB4eHnT58mUiIiopKSGFQkE//fSTmCY/P5/UarWo7+zsbOI4ji5cuOBUVq9evejNN98korp1T2S/Z3379nVKM3ToUHrkkUeIiGjhwoUUFhZGFoulmtypqakEgHbs2OHyut566y0KDw8nQRDEsI8//ph0Oh3xPE9EN9ceXdG6dWuaOXOmy7g333yTmjRp4vIaiIj8/f1p7ty5TmEdO3akCRMmENH1NrBkyRIiIkpMTKRz585R06ZNafXq1WKev/76i2bMmEFdu3YlIqJJkybRww8/7HT9N4KjvRYUFNSa7urVqwSATpw44SRv5TZ76tQpAkApKSlERLRixQpyc3Mjm81GiYmJ9H//93+k0WjoTNIRKrp6hYiIYmNjKTg4WLxXRETh4eH0/vvvExHR9u3bieM4OnfuXLV6Dh06REREcXFx1KZNGyd565OvKnXpEgBt2LChVj0REbVs2ZKWLVsmngcFBdHixYud0jh040Cv19PKlStdllc1ratyf/vtN2JZ9qbabn3o2rUrDRs2zGVcWloaAaCEhAQxLC8vj9Rqtdi/rFixggBQUlJStWu40f7cka+qTiuzdu1a8vLyEs/ro8P6tjWNRuP0WzJ16lTq3LlzjbJISNxueN5KVmsJlZdfptLSLCouSaGiouMujhNkNKZRWdl5MpvzyWYru+nfjjuNIPDE22xktVjIUm4ic1kpmYwlVFZcRKWFBVRyLZ+K865S0ZXLVHApl65dvED553Po6rlsupKdSZcz0yk3Pe3Gj4wz9uNsOl3MzLAfWZl0MSuLLmZn08Xsc3TxXA5dOHeeLuQ4jgt04Xyl48JdPCrJcVE8LlJupSMn6xz9uf9P+uqDT+nzd5bQl+98SF+9s4yWv/MhLZs6jwBQUVHRLbuX//o1QO3bt3c6P3bsGPbs2ePyy1tGRgZMJhOsVis6deokhru5uSE8PLxa+qoLv48dO4bjx49j1apVYhgRQRAEZGZmonfv3ggKCkLTpk3Rt29f9O3bFwMHDhSn7fTq1QutW7dGbGws+vTpg6eeegoeHh4u5bRarejevbsYJpfL0alTJ6SkpDiljYyMFP/28/MDAFy5cqXWdRuV8/j6+gIAWrdu7RR25coVAICnpydGjhyJ2NhY9O7dGzExMRgyZIhYV2VWr16NWbNmiSM9jmuxWCzo3LmzmM7hZtnBiRMnwPM8wsLCnMozm83w8vICULfuIyIiAABdu3Z1KqNr167i1JPBgwdjyZIl4v159NFH0a9fP8hkMiQlJYHjOERHR7vUWUpKCrp27eo08tW9e3cYjUacP39e1PeNtseq1wwAkydPxvjx47F9+3bExMRg0KBB4j1LSkrCgw8+KH7trkxxcTEuXrzo1G4cclYe6QKut+3mzZvj3LlzOHv2LF544QWMHj0aDMOIrkoda9NGjhyJ3r17Izw8HH379sXjjz+OPn36uNTVjXDmzBnMmDEDBw8eRF5eHoSKOd/nzp1Dq1atxHQ1tfPmzZu7LDcwIAA6nU5cA+Tr6wuO45xGlSq385SUFAQEBCAgIECMb9GiBdzd3ZGSkoKOHTu6rOdm8t2MLo1GI2bOnInNmzcjNzcXNpsNJpMJ586dqzVfVV599VWMHj0a3333HWJiYjB48GA0a9as3vmTkpLQuHFjl+0WqL3t1rf8mkawUlJSIJPJnPoSLy8vhIeHO/WLCoXCZZ032p87+pTK7Ny5E++//z5Onz6N4uJi2Gw2lJeXo6ysDBqNplr6mq6jPm0mODgYer1eTOPn5ye2VwmJu8GNeJ6z71tUDqtjl1SGBceqqqwnUvzj1q4wDAuGQ8UIVN3rm1xBZJ8aRhV7I1UfjeKrj1Lxgj29IADietbq0wvr1JbTSJTzecVioeuliLqvkramymq7V5WiqNJflceyBIYgQMA11ggjZ0RlzJzrPan+DjdtADFqNcKPHL6VstS73huhqmcdo9GIfv364YMPPqiW1s/PD+np6X+r7BdffNFp3YiDwMBAKBQKHDlyBPHx8di+fTtmzJiBmTNnIjExEe7u7tixYwf279+P7du3Y9myZXj77bdx8OBBNGnSpN4yVaXyi7CjIxHqWDjoKk/VsMplrFixApMnT8a2bdvw448/4p133sGOHTvQpUsXMc2aNWswevRorF27FjExMTd0DUajERzH4fDhw+JUGgcOw6Eu3deHgIAApKamYufOndixYwcmTJiA+fPnY+/evbfMzfONtkdXjB49GrGxsdi8eTO2b9+O999/HwsXLsSkSZNuuZw6nU40MhcvXowmTZrAZDKBiODn5wdvb28AQLt27ZCZmYmtW7di586dGDJkCGJiYvDzzz//LTn69euHoKAgLF++HP7+/hAEAa1atYLF4rz/xI22c8fUOMc0DIZhqhmNVdv5neJmdPn6669jx44dWLBgAUJCQqBWq/HUU09V01NdzJw5E88++yw2b96MrVu3Ii4uDmvWrHFaZ1MbdbW/2trurSi/vmW4eqm60f68KllZWXj88ccxfvx4zJ07F56envjjjz8watQoWCyWehtA9eWf0l4lJGqjZs9zJtH7nOh5ji8Dz5cBsG9ZwDDOnuc4Tg2Gkf/jjKIbRVyHybLgbuIt3P4hhq9YZ3N9Sh9RpXOqWIPjMKTIMZ2PxHSOo14y37iY16e41RRZ1SjiBTC8DYpL2VAVFYEqpgQyRCDRUr513LwBxDBgbnGHfido164d1q1bh+DgYJfrA5o2bQq5XI7ExETxR66oqAhpaWl46KGH6iw7OTkZISEhNaaRyWSIiYlBTEwM4uLi4O7ujt27d+PJJ58EwzDo3r07unfvjhkzZiAoKAgbNmzAq6++6lRGs2bNoFAokJCQgKCgIAB2T12JiYl3bR+JqKgoREVF4c0330TXrl2xevVq0QD64Ycf8MILL2DNmjV47LHHnPI1a9YMcrkcBw8eFPVdUFCAtLQ0cbQlKioKPM/jypUrePDBB13WXx/dA8Cff/5Z7bzyl1y1Wo1+/fqhX79+eOmll9C8eXOcOHECrVu3hiAI2Lt3r0sDLiIiAuvWrQMRiZ1zQkIC9Ho9GjduXKM8dbXHmggICMC4ceMwbtw4vPnmm1i+fDkmTZqEyMhIfPPNN06e2xwYDAb4+/sjISHBaSQrISHBacSzKr6+vvD398fVq1cxbtw4lJeXIy8vD/n5+SgqKsKZM2cQGhoKg8GAoUOHYujQoXjqqafQt29fXLt2zWm9V004PG5V9tSWn5+P1NRULF++XLzvf/zxR711VB/qOw89IiJCXIvj+DKfnJyMwsJCtGjRAoD9GirLX998rqhNl3K5vFo9CQkJGDlypGioGI1GZGVlOaVxJZ8rwsLCEBYWhldeeQXPPPMMVqxYgYEDB9Yrf2RkJM6fP4+0tLQaR4Fqarv1ITIyErt27cLzzz9fLS4iIgI2mw0HDx5Et27dAFxvQ7Xpuibq26c4OHz4MARBwMKFC8WRxJ9++skpTX10eLNtRkLiXsDZ85x9BgG58jwn1O15juU04Cp5nrtfsDuqkAE3twzKCdFQqhiVgmN0ioRKf1fECdfjHGntZZCjMLsh4xRO9lOqcEgu1lNhDFeE2f+zgQVgYBnIyGY38BweW2+Dz4H7q9XAvgh3+fLleOaZZzBt2jR4enoiPT0da9aswZdffgm9Xo8RI0Zg6tSp8PT0hI+PD+Li4uw+3Ov46jB9+nR06dIFEydOxOjRo8VFvjt27MBHH32ETZs24ezZs3jooYfg4eGBLVu2QBAEhIeH4+DBg9i1axf69OkDHx8fHDx4EFevXnU5zUKr1WL8+PGijIGBgZg3bx7KysowatSo26U6l2RmZuKLL77AE088AX9/f6SmpuLMmTMYPnw4APu0txEjRmDp0qXo3LkzLl26BMBuaLi5uUGn02HUqFGYOnWquCD57bffdpqKFBYWhmHDhmH48OFYuHAhoqKicPXqVezatQuRkZF47LHH6tS9g4SEBMybNw8DBgzAjh07sHbtWmzevBmA3UMUz/Po3LkzNBoNvv/+e6jVagQFBcHLywsjRozACy+8IDpByM7OxpUrVzBkyBBMmDABS5YswaRJkzBx4kSkpqYiLi4Or776ajUXu5Wpqz1WHfECgClTpuCRRx5BWFgYCgoKsGfPHrGdTJw4EcuWLcPTTz+NN998E25ubvjzzz/RqVMnhIeHY+rUqYiLi0OzZs3Qtm1brFixAklJSU7TfFwxa9YsTJ48GW5ubujbty/MZjMOHz6MixcvYujQoVi0aBH8/PwQFRUFlmWxdu1aNGzYsN57+gQFBYFhGGzatAmPPvoo1Go1PDw84OXlhS+++AJ+fn44d+4c3njjjXqVVzc35gY7JiYGrVu3xrBhw7BkyRLYbDZMmDAB0dHR4tSp4OBgZGZmitPA9Hp9vfJVpS5dBgcHY9euXejevTuUSiU8PDwQGhqK9evXo1+/fmAYBv/3f/9XbTQgODgYv//+O55++mkolUpx9M6ByWTC1KlT8dRTT6FJkyY4f/48EhMTMWjQIDG/0WjErl270KZNG2g0mmqjGtHR0XjooYcwaNAgLFq0CCEhITh9+jQYhkHfvn1rbbv1IS4uDr169UKzZs3w9NNPw2azYcuWLZg+fTpCQ0PRv39/jBkzBp9//jn0ej3eeOMNNGrUCP379693HQ7q26c4CAkJgdVqxbJly9CvXz8kJCTgs88+c0pTHx3eTJuRkLiXcXie4zgVAPu0fyKhwigqqzSFrlz0PGezXfd4yLByMODs09MYFkCV/xnW7mi6UpirdNf/vrdHmG4Ex5S+fwLl5eUoMlvw9Ox5UKlUTnHFxcV448dbvB1MfRYKuXKCcC8QHR3t5LjAQVpaGg0cOJDc3d1JrVZT8+bNacqUKeIivOLiYnr22WdJo9FQw4YNadGiRdSpUyd64403xDJqWvx66NAh6t27N+l0OtJqtRQZGSkuOt+3bx9FR0eTh4cHqdVqioyMpB9//JGIiJKTkyk2NpYaNGhASqWSwsLCnBYwV3aCQGS/J5MmTSJvb29SKpXUvXt3p0XVrhaVHz16lABQZmamS305FpQfPXq01nIqL+S9dOkSDRgwgPz8/EihUFBQUBDNmDHDaeE/rjuDFI8RI0aI5ZWUlNB//vMf0mg05OvrS/Pmzat27ywWC82YMYOCg4NJLpeTn58fDRw4kI4fP14v3Tvu2axZs2jw4MHivV26dKkYv2HDBurcuTMZDAbSarXUpUsXJycSJpOJXnnlFfFaQ0JC6Ouvvxbj4+PjqWPHjqRQKKhhw4Y0ffp0slqtYvzNtseqTJw4kZo1a0ZKpZIaNGhAzz33HOXl5Ynxx44doz59+pBGoyG9Xk8PPvggZWRkEBERz/M0c+ZMatSoEcnlcmrTpg1t3bpVzOuqDThYtWoVtW3blhQKBXl4eNBDDz1E69evJyKiL774gtq2bUtarZYMBgP16tWLjhw5IuYdMWIERUdHu7weB7Nnz6aGDRsSwzBi+9ixYwdFRESQUqmkyMhIio+Pd3IC4EregoICAkB79uwhItdOEFq1aEG56WlUVrGIvOrzRVT9fmVnZ9MTTzxBWq2W9Ho9DR48mC5duiTGl5eX06BBg8jd3Z0A0IoVK+qVryp16fLXX3+lkJAQkslkFBQUJOqhZ8+epFarKSAggD766KNq8h84cIAiIyNJqVSSo+uv/CybzWZ6+umnKSAggBQKBfn7+9PEiROd+v1x48aRl5cXAaC4uDgiqt4X5ufn0/PPP09eXl6kUqmoVatWtGnTJiKqu+0GBQWJ5dbEunXrxHbo7e1NTz75pBh37do1eu6558jNzY3UajXFxsZSWlqaGF8fJwSVqU+fUjnfokWLyM/PT6z722+/rdZ/1keHdbUZVw43Fi9eLLYHCYl/I4LAk9VqpHLzVSorO0clJadrcLLwN4/iE1RcfIpKSlKopCSVjMYzZDRmUGlpJpWVZVNZWQ6ZTBfIZMql8vLLZDZfJbM5nyyWArJYishqLSGbrZRsNhPxvJl43kqCwNd9gfc5tdkaRUVFt9wJAkNUt5Pu8vJyZGZmokmTJtWssvuB0tJSNGrUCAsXLrzjIywSt47g4GBMmTLlrk0TvJ+Jjo5Gz549nVyC32l4nsfRo0cRFRWFwksXYS0vh3tDP6i09XfDLHH7KCsrg5eXF7Zu3YoePXrcbXEkJCTuAQSBhyCYAQgVew4RiHj7FC5UrIWBAJDgFCbGOYXdbpgaR6T+VhiYf8WoVW22RnFxMdzc3FBUVASDwXBL6rvvpsDVh6NHj+L06dPo1KkTioqKMHv2bAC4qWkUEhL3O0VFRcjIyBCnGv4TIKFiCtxN7kUhcevZs2cPHn74Ycn4kZCQqDcsy4Fl//56dBLXq9RuON1cmGOcwW6cgfi6dvK5YWqe3sdVrLviIBpgYjoOrqcBVk5/7xtWNSEZQDWwYMECpKamQqFQoH379ti3b1+1OfMSEhJ14+bmhvPnz99tMZxwfO2r7xogidvPY489Vs1JioSEhMSdgKlw/3w7PopVH2lyNfp0g2EQRGcDjjrscbdW9psbkbo31llJBpALoqKicPjwnXfxLXF7qeoVS+L+xeH6UxoBkpCQkJC4nVx/8b+12Eet6mc41W8aIF/hlU2oVMftMazshlPlkSYOZosAiyUfaWfWQC63geM04FglAAYlJeW3WADJAJKQkLgPcSx9lEaAJCQkJCTuReyjKFyFEXHruBHD6kanBl6vRLB7xKLr2wIIPEEQylFYsBuCcNFJptLSW79GSzKAJCQk7iuISBoBkpCQkJCQcMHtNayoRmOKYcohk1kQFDgODHNNdH8OAEZjOYD/3lJ5JANIQkLi/qLSWP4/YR6yhISEhITEv5261lnxvAIymRb+/oNceoG71QaQ9PlTQkLivqKy53+mlk1qJSQkJCQkJP6dSL/+EhIS9xXi+h/m3+3iU0JCQkJCQsI1kgEkISFxXyGu/5EcIEhISEhISNyXSAbQTTJy5EgMGDCgxviZM2eibdu2d0yeu018fDwYhkFhYeHdFkXiX0BwcDCWLFlyW8q+PgJ0b3Z/f1c3K1euhLu7+y2T517iVl37/axDCQkJiX8D9+YbwD3A66+/jl27dt1tMW6Ya9euYdKkSQgPD4darUZgYCAmT56MoqKiuy2axD+MHj16YMqUKbel7MTERIwdO/a2lC1ugsrYN2P75Zdfbks9Dm72Y0hNL9k3ohtXxtLQoUORlpZ2w/Lcr0g6lJCQkPj3cV95gbNYLFAoFHekLp1OB51Od0fqupVcvHgRFy9exIIFC9CiRQtkZ2dj3LhxuHjxIn7++ee7Ld5dhYjA8zxksn/2Y3Mn2/ntokGDBretbBLu7T2A/q5u1Go11Gr1LZLmzuOqffM8D4ZhwN4hpxb3ug4lJCQk7nf+1SNAPXr0wMSJEzFlyhR4e3sjNjYWALBo0SK0bt0aWq0WAQEBmDBhAoxGo5jP8eX1t99+Q0REBHQ6Hfr27Yvc3Nwa60pMTESDBg3wwQcfAKj+1dcxZW7BggXw8/ODl5cXXnrpJVitVjFNbm4uHnvsMajVajRp0gSrV6+uc7qLo9z33nsPvr6+cHd3x+zZs2Gz2TB16lR4enqicePGWLFihZjHYrFg4sSJ8PPzg0qlQlBQEN5//30AQKtWrbBu3Tr069cPzZo1w8MPP4y5c+di48aNsNlsYhlbtmxBWFgY1Go1evbsiaysrGqy/fHHH3jwwQehVqsREBCAyZMno7S0VIw3m814/fXX0ahRI2i1WnTu3Bnx8fHV7sMvv/yC0NBQqFQqxMbGIicnR0xz7Ngx9OzZE3q9HgaDAe3bt8dff/0lxickJKBHjx7QaDTw8PBAbGwsCgoKxPonT54MHx8fqFQqPPDAA0hMTBTzOqb1bd26Fe3bt4dSqcQff/wBQRDw/vvvo0mTJlCr1WjTpk2dxuEnn3wiXoOvry+eeuopMU4QBMybNw8hISFQKpUIDAzE3LlzxfgTJ07g4YcfhlqthpeXF8aOHevUXh1tYO7cufD390d4eDgAICcnB0OGDIG7uzs8PT3Rv39/p/sUHx+PTp06QavVwt3dHd27d0d2dnat11G5zr1792Lp0qWiM4GsrCzwPI9Ro0aJugkPD8fSpUur5a3rWaja7hmGweeff47HH38cGo0GEREROHDgANLT09GjRw9otVp069YNGRkZTnV9+umnaNasGRQKBVq0aIEtW7aII0BRXbsDAAYOHAiGYRAcHOwyX3h4OL777rta9VGTLleuXIlZs2bh2LFjop5WrlwJoPZ+KD4+Hs8//zyKiorEfDNnzqymGyLCzJkzERgYCKVSCX9/f0yePBmAvf/Lzs7GK6+84uTwwdXI0saNG9GxY0eoVCp4e3tj4MCBYlxtbbc+FBYW4sUXX4Svry9UKhVatWqFTZs2ifHr1q1Dy5YtoVQqERwcjIULFzrlDw4Oxpw5czB8+HAYDAaMHTtWvIZff/0VLVq0gFKpxLlz5+rsU6qSkZGB/v37w9fXFzqdDh07dsTOnTvF+BvRYV1thmEYfPnllxg4cCA0Gg1CQ0Px66+/3pAuJSQkJCRuEVQPTCYTJScnk8lkEsMEQSBLue2OH4Ig1EdkIiKKjo4mnU5HU6dOpdOnT9Pp06eJiGjx4sW0e/duyszMpF27dlF4eDiNHz9ezLdixQqSy+UUExNDiYmJdPjwYYqIiKBnn31WTDNixAjq378/ERHt2rWL3Nzc6PPPPxfj4+LiqE2bNk7pDQYDjRs3jlJSUmjjxo2k0Wjoiy++ENPExMRQ27Zt6c8//6TDhw9TdHQ0qdVqWrx4cY3XOGLECNLr9fTSSy/R6dOn6auvviIAFBsbS3PnzqW0tDSaM2cOyeVyysnJISKi+fPnU0BAAP3++++UlZVF+/bto9WrV9dYx/Lly8nb21s8P3fuHCmVSnr11Vfp9OnT9P3335Ovry8BoIKCAiIiSk9PJ61WS4sXL6a0tDRKSEigqKgoGjlypFjO6NGjqVu3bvT7779Teno6zZ8/n5RKJaWlpTndhw4dOtD+/fvpr7/+ok6dOlG3bt3EMlq2bEn/+c9/KCUlhdLS0uinn36ipKQkIiI6evQoKZVKGj9+PCUlJdHJkydp2bJldPXqVSIimjx5Mvn7+9OWLVvo1KlTNGLECPLw8KD8/HwiItqzZw8BoMjISNq+fTulp6dTfn4+vfvuu9S8eXPatm0bZWRk0IoVK0ipVFJ8fLxL/SUmJhLHcbR69WrKysqiI0eO0NKlS8X4adOmkYeHB61cuZLS09Np3759tHz5ciIiMhqN5OfnR08++SSdOHGCdu3aRU2aNKERI0Y4tQGdTkfPPfccnTx5kk6ePEkWi4UiIiLohRdeoOPHj1NycjI9++yzFB4eTmazmaxWK7m5udHrr79O6enplJycTCtXrqTs7Owa20FlCgsLqWvXrjRmzBjKzc2l3NxcstlsZLFYaMaMGZSYmEhnz56l77//njQaDf34449O8tb1LAQFBTm1ewDUqFEj+vHHHyk1NZUGDBhAwcHB9PDDD9O2bdsoOTmZunTpQn379hXzrF+/nuRyOX388ceUmppK8+fPJ47jaPOvv1JuehqlnjhGAGjFihWUm5tLV65ccZlv4cKFxHEc7d6926UuatNlWVkZvfbaa9SyZUtRT2VlZURUez9kNptpyZIlZDAYxHwlJSXVdLN27VoyGAy0ZcsWys7OpoMHD4p6zM/Pp8aNG9Ps2bPFMojsz5Wbm5so/6ZNm4jjOJoxYwYlJydTUlISvffee0RUd9utC57nqUuXLtSyZUvavn07ZWRk0MaNG2nLli1ERPTXX38Ry7I0e/ZsSk1NpRUrVpBaraYVK1Y4tQWDwUALFiyg9PR0Sk9PF/uGbt26UUJCAp0+fZpKS0vr1adUvvakpCT67LPP6MSJE5SWlkbvvPMOqVQq8Tmorw7r02YAUOPGjWn16tV05swZmjx5Mul0OrG/kZCQkLifcWVrOCgqKiIAVFRUdMvqu2kDyFJuo49e3HXHD0u5rd4XFx0dTVFRUXWmW7t2LXl5eYnnK1asIACUnp4uhn388cfk6+srnjsMoPXr15NOp6M1a9Y4lenKAAoKCiKb7br8gwcPpqFDhxIRUUpKCgGgxMREMf7MmTMEoE4DKCgoiHieF8PCw8PpwQcfFM9tNhtptVr64YcfiIho0qRJ9PDDD9fLmLx69SoFBgbSW2+9JYa9+eab1KJFC6d006dPdzKARo0aRWPHjnVKs2/fPmJZlkwmE2VnZxPHcXThwgWnNL169aI333yTiK7fhz///FOMd+jp4MGDRESk1+tp5cqVLmV/5plnqHv37i7jjEYjyeVyWrVqlRhmsVjI39+f5s2bR0TXDaBffvlFTFNeXk4ajYb279/vVN6oUaPomWeecVnXunXryGAwUHFxcbW44uJiUiqVosFTlS+++II8PDzIaDSKYZs3byaWZenSpUtEZG8Dvr6+ZDabxTTfffcdhYeHO91js9lMarWafvvtN8rPzycANRpt9SE6OppefvnlOtO99NJLNGjQIPG8rmeByLUB9M4774jnBw4cIAD01VdfiWE//PADqVQq8bxbt240ZswY8dxms1FMTAz16d2bctPTqCD3IgGgDRs2OMlbNZ9DvkcffdTl9dWly6p9QU246ocqv2Q7qKybhQsXUlhYGFksFpdlVtWjq3K7du1Kw4YNc5m/trZbH3777TdiWZZSU1Ndxj/77LPUu3dvp7CpU6c69S9BQUE0YMCAatcAQPzYQUT17lNc6bQyLVu2pGXLljnVX5cO69NmqrZho9FIAGjr1q21yiMhISFxP3CnDaB/9RQ4AGjfvn21sJ07d6JXr15o1KgR9Ho9nnvuOeTn56OsrExMo9Fo0KxZM/Hcz88PV65ccSrn4MGDGDx4ML777jsMHTq0TllatmwJjuNclpmamgqZTIZ27dqJ8SEhIfDw8KhXuZXnvvv6+qJ169biOcdx8PLyEusaOXIkkpKSEB4ejsmTJ2P79u0uyy0uLsZjjz2GFi1aiNNvACAlJQWdO3d2Stu1a1en82PHjmHlypXiWiidTofY2FgIgoDMzEycOHECPM8jLCzMKc3evXudpjHJZDJ07NhRPG/evDnc3d2RkpICAHj11VcxevRoxMTE4L///a9T3qSkJPTq1cvltWVkZMBqtaJ79+5imFwuR6dOncSyHXTo0EH8Oz09HWVlZejdu7eT3N9++2216VcOevfujaCgIDRt2hTPPfccVq1aJba1lJQUmM3mGuVMSUlBmzZtoNVqxbDu3btDEASkpqaKYa1bt3ZaF3Hs2DGkp6dDr9eLMnp6eqK8vBwZGRnw9PTEyJEjERsbi379+mHp0qW1TvG8ET7++GO0b98eDRo0gE6nwxdffIFz5845pantWaiJyMhI8W9fX18AcGrnvr6+KC8vr9gx2q67yvfXUYZj8XpNa4Bc5evevXu1duHgZnVZn36oLgYPHgyTyYSmTZtizJgx2LBhg9NU1fpQ23NSW9utb9mNGzdGWFiYy/iadH3mzBnwPC+GVX4GHSgUCqc2Ud8+pTJGoxGvv/46IiIi4O7uDp1Oh5SUlGrttS7q22Yqy6vVamEwGOps9xISEhISt56bXs0tU7AYuzT6VspS73pvhMovjgCQlZWFxx9/HOPHj8fcuXPh6emJP/74A6NGjYLFYoFGowFgfxmuDMMwTjvIA0CzZs3g5eWFr7/+Go899li1PFVxVaZQsSfJ38FVubXV1a5dO2RmZmLr1q3YuXMnhgwZgpiYGKd1LCUlJejbty/0ej02bNhQ57VVxWg04sUXXxTXI1QmMDAQx48fB8dxOHz4sNOLMIAbch4xc+ZMPPvss9i8eTO2bt2KuLg4rFmzBgMHDrxli5QrtyHHGo3NmzejUaNGTumUSqXL/Hq9HkeOHEF8fDy2b9+OGTNmYObMmUhMTLwtMjrkbN++PVatWlUtrWMR/YoVKzB58mRs27YNP/74I9555x3s2LEDXbp0uWk51qxZg9dffx0LFy5E165dodfrMX/+fBw8eNAp3c08C5XzONZiuAqrqxzCrXeDfaO6rG8/VBcBAQFITU3Fzp07sWPHDkyYMAHz58/H3r176/3M1tYGa2u79XEDfbvat6PsyhvZGo3GG+5TXn/9dezYsQMLFixASEgI1Go1nnrqKVgsllsid1Vu12+AhISEhMSNcdNvAAzDQK7k7vjxd3duP3z4MARBwMKFC9GlSxeEhYXh4sWLN1WWt7c3du/ejfT0dAwZMsRpEfeNEh4eDpvNhqNHj4ph6enp4oL9W43BYMDQoUOxfPly/Pjjj1i3bh2uXbsGwD7y06dPHygUCvz6669QqVROeSMiInDo0CGnsD///NPpvF27dkhOTkZISEi1Q6FQICoqCjzP48qVK9XiGzZsKJZjs9mcnBqkpqaisLAQERERYlhYWBheeeUVbN++HU8++aTo8CEyMrJGV+SOxcoJCQlimNVqRWJiIlq0aFGj3iovuK4qd0BAQI35ZDIZYmJiMG/ePBw/fhxZWVnYvXs3QkNDoVara5QzIiICx44dc3IekZCQAJZlRWcHrmjXrh3OnDkDHx+fanK6ubmJ6aKiovDmm29i//79aNWqFVavXl1jmVVRKBROX+kdsnXr1g0TJkxAVFQUQkJCavz6fruJiIhwur8AcPz4cTSvGI1wfCioeg2u8iUkJNTaLoCadelKT/Xph1zlc4VarUa/fv3w4YcfIj4+HgcOHMCJEyfqXUZtzwlQc9utD5GRkTh//nyNLqNr0nVYWFg1I6Yu6tunVK1r5MiRGDhwIFq3bo2GDRtWc+hSHx3ebJuRkJCQkLg7/OunwFUlJCQEVqsVy5Ytw9mzZ/Hdd9/hs88+u+nyfHx8sHv3bpw+fRrPPPPMDU8/cdC8eXPExMRg7NixOHToEI4ePYqxY8dW+8p5K1i0aBF++OEHnD59GmlpaVi7di0aNmwId3d30fgpLS3FV199heLiYly6dAmXLl0SXwLGjRuHM2fOYOrUqUhNTcXq1atFz1YOpk+fjv3792PixIlISkrCmTNn8L///Q8TJ04EYDdahg0bhuHDh2P9+vXIzMzEoUOH8P7772Pz5s1iOXK5HJMmTcLBgwdx+PBhjBw5El26dEGnTp1gMpkwceJExMfHIzs7GwkJCUhMTBSNozfffBOJiYmYMGECjh8/jtOnT+PTTz9FXl4etFotxo8fj6lTp2Lbtm1ITk7GmDFjUFZWhlGjRtWoO71ej9dffx2vvPIKvvnmG2RkZODIkSNYtmwZvvnmG5d5Nm3ahA8//BBJSUnIzs7Gt99+C0EQEB4eDpVKhenTp2PatGniNLo///wTX331FQBg2LBhUKlUGDFiBE6ePIk9e/Zg0qRJeO6558RpYK4YNmwYvL290b9/f+zbtw+ZmZmIj4/H5MmTcf78eWRmZuLNN9/EgQMHkJ2dje3bt+PMmTNOhmVdBAcH4+DBg8jKykJeXh4EQUBoaCj++usv/Pbbb0hLS8P//d//OXnWu5NMnToVK1euxKeffoozZ85g8eLF2LNnDyaOHw8AYFgWwcHB2LVrFy5duiR+bKiab9GiRVi/fj1ef/11l/XUpcvg4GBkZmYiKSkJeXl5MJvN9eqHgoODYTQasWvXLuTl5bmcerZy5Up89dVXOHnyJM6ePYvvv/8earUaQUFBYhm///47Lly4gLy8PJfyx8XF4YcffkBcXBxSUlJw4sQJ0ZtlbW23PkRHR+Ohhx7CoEGDsGPHDnHkedu2bQCA1157Dbt27cKcOXOQlpaGb775Bh999FGNuq6N+vYplQkNDcX69euRlJSEY8eO4dlnn602IlMfHd5om5GQkJCQuMv83YVJ/2RqWqS9aNEi8vPzI7VaTbGxsfTtt986LeB3tVB2w4YNVFldlb3AERFdvHiRwsLCaMiQIWSz2Vw6Qaicnojo5ZdfpujoaKcyHnnkEVIqlRQUFESrV68mHx8f+uyzz2q8Rlflurruygt5v/jiC2rbti1ptVoyGAzUq1cvOnLkCBFdX/jv6sjMzBTL27hxI4WEhJBSqaQHH3yQvv76aycdEhEdOnSIevfuTTqdjrRaLUVGRtLcuXPFeIfHsODgYJLL5eTn50cDBw6k48ePE9H1+7Bu3Tpq2rQpKZVKiomJET00mc1mevrppykgIIAUCgX5+/vTxIkTndppfHw8devWjZRKJbm7u1NsbKwoo8lkokmTJpG3tzcplUrq3r07HTp0SMzr0EXlayKye0BcsmQJhYeHk1wupwYNGlBsbCzt3bvX5T3at28fRUdHk4eHB6nVaoqMjHTyisbzPL377rsUFBREcrmcAgMDRS9cRETHjx+nnj17kkqlIk9PTxozZozoEYzIdRsgIsrNzaXhw4eL19e0aVMaM2YMFRUV0aVLl2jAgAHk5+dHCoWCgoKCaMaMGaIzjczMTAJAe/bscXlNRESpqanUpUsXUqvVYvsoLy+nkSNHkpubG7m7u9P48ePpjTfeuOFnwZUThMrOChzyHT16VAxzdb8++eQTatq0KcnlcgoLC6NZs2bRtdyLlJueRiXX8unXX3+lkJAQkslkFBQUVGO+b7/9tkY91KXL8vJyGjRoELm7u4te54jq7oeIiMaNG0deXl4EgOLi4qrpZsOGDdS5c2cyGAyk1WqpS5cutHPnTjH/gQMHKDIykpRKpdh/uerf1q1bR23btiWFQkHe3t705JNPElHdbdfhjKA28vPz6fnnnycvLy9SqVTUqlUr2rRpkxj/888/U4sWLcS2P3/+fKf89XFC4KC+fYqDzMxM6tmzJ6nVagoICKCPPvqoWv9ZXx3W1WaqtmEiIjc3NyePdxISEhL3K3faCQJDVGVhiwvKy8uRmZmJJk2aVJsOJXH7OH/+PAICAsTF0vcbK1euxJQpU1BYWHi3Rbnv2LNnD5588kmcPXu2Xo447gV4nsfRo0fRtJE/LGWl0Hs3gNbN/W6LdU8TFxeHvXv31rrXjoSEhISERF3UZmsUFxfDzc0NRUVFMBgMt6S+f/aW9vcZu3fvhtFoROvWrZGbm4tp06YhODgYDz300N0WTeI+Y8uWLXjrrbf+NcZPZRwbod7qqaX3I1u3bsVHH310t8WQkJCQkJC4ISQD6B+E1WrFW2+9hbNnz0Kv16Nbt25YtWrVDXtgk5D4u8yfP/9ui3DbIKHCCxx73y2BvOVUdYYiISEhISFxLyAZQP8gYmNjERsbe7fF+McwcuRIjBw58m6LIfEvQxoBkpCQkJCQuL+RPoFKSEjcVziWPd7KfYAkJCQkJCQk7h2kNwAJCYn7Cqpwc8yw0giQhISEhITE/YhkAElISNxXSCNAEhISEhIS9zfSG4CEhMR9xfURIKn7k5CQkJCQuB+R3gAkJCTuL8QRIGkKnISEhISExP2IZABJSEjcl0hrgCQkJCQkJO5PJAPoJhk5ciQGDBhQY/zMmTPRtm3bOybP7aBHjx6YMmXK3RZD4h8CwzD45Zdf7rYYtwxpDdDdo67+837m7+omKysLDMMgKSnplsl0L7By5Uq4u7vfbTHue+Lj48EwDAoLC2+6jPv53eNWXfv9rMP6Ir0B3CZef/117Nq167bXcztfJNavX485c+bclrIrExwcjCVLltz2eiTsvP/+++jYsSP0ej18fHwwYMAApKam3m2x7igMw0hT4Orgdn7EWbp0KVauXHlbyq7MnXgJuNkXvpoMlRvRjav+PyAgALm5uWjVqtUNyfNPISsrC6NGjUKTJk2gVqvRrFkzxMXFwWKx3G3R/rXc7G+wq+erW7duyM3NhZubW535a3p27tS7x78BSYc3z321EarFYoFCobgjdel0Ouh0ujtS1+3C09Pzbovwj4WIwPM8ZLJ77xHau3cvXnrpJXTs2BE2mw1vvfUW+vTpg+TkZGi12rst3h2hJgcIVqsVcrn8DktzY/A8D4ZhwN7DThzq83J0v/J3dcNxHBo2bHiLpLnznD59GoIg4PPPP0dISAhOnjyJMWPGoLS0FAsWLLjb4t1V7oVnX6FQ/O32dy+/e9T0bnAn3z+Be1uHdwyqByaTiZKTk8lkMtUn+T+G6Ohoeumll+jll18mLy8v6tGjBxERLVy4kFq1akUajYYaN25M48ePp5KSEjHfihUryM3NjbZt20bNmzcnrVZLsbGxdPHiRTHNiBEjqH///uL5oUOHyNvbm/773/8SEVFcXBy1adOmWvr58+dTw4YNydPTkyZMmEAWi0VMc/HiRXr00UdJpVJRcHAwrVq1ioKCgmjx4sUury8uLo4AOB179uwhIqJp06ZRaGgoqdVqatKkCb3zzjtOdTnk+/bbbykoKIgMBgMNHTqUiouLnfT38ssvi+dBQUE0Z84ceu6550ir1VJgYCD973//oytXrtATTzxBWq2WWrduTYmJiU5y/vzzz9SiRQtSKBQUFBRECxYscKqj6jXUJ58rkpKSqEePHqTT6Uiv11O7du2cZPnjjz8oOjqa1Go1ubu7U58+fejatWtERFReXk6TJk2iBg0akFKppO7du9OhQ4fEvHv27CEAtGXLFmrXrh3J5XLas2cP8TxP7733HgUHB5NKpaLIyEhau3atmO/atWv07LPPkre3N6lUKgoJCaGvv/661uuojKMtbty4kcLCwkitVtOgQYOotLSUVq5cSUFBQeTu7k6TJk0im80m5vv4448pJCSElEol+fj40KBBg2qs48qVKwSA9u7dK4alpaXRgw8+SEqlkiIiImj79u0EgDZs2CCmOXfuHA0ePJjc3NzIw8ODnnjiCcrMzHQqe/ny5dS8eXNSKpUUHh5OH3/8sRiXmZlJAOiHH36grl27klKppJYtW1J8fHy99ZeTk0NPP/00eXh4kEajofbt29Off/4pxn/yySfUtGlTksvlFBYWRrNmzaKLZ1LpStZZIiICQJ988gn169ePNBoNxcXFERHRL7/8QlFRUaRUKqlJkyY0c+ZMslqtNepwz5491LFjR9JoNOTm5kbdunWjrKwsMf7XX3+lDh06kFKpJC8vLxowYIDTNT733HPk7u5OarWa+vbtS2lpaWK8ow3873//o4iICOI4jjIzM6m8vJxee+018vf3J41GQ506dRKffyKirKwsevzxx8nd3Z00Gg21aNGCNm/eXOM1VGbFihXVnssVK1YQ0e3pP6Ojo2nixIn08ssvk7u7O/n4+NAXX3xBRqORRo4cSTqdjpo1a0ZbtmxxkjM+Pp46duxICoWCGjZsSNOnTxfv04gRI6pdg6N91pbPFTXp0tGGKx8jRowgIqKtW7dS9+7dyc3NjTw9Pemxxx6j9PR0scyq+aKjo13qZu3atdSqVStSqVTk6elJvXr1IqPRWGP/75Dp6NGjYhknT56kxx57jPR6Pel0OnrggQdEWepqu3VxM/fuRvvFefPmUZMmTZzCVqxYQQEBAaRWq2nAgAG0YMECcnNzc0pT13NcUFBAo0aNIj7y9FkAAGiGSURBVG9vb9Lr9dSzZ09KSkoS4x2/k5999hk1btyY1Go1DR48mAoLC8U0/8RnvyqCIFBcXBwFBASQQqEgPz8/mjRpknj/XP0G5+Xl0dNPP03+/v6kVqupVatWtHr1arHMmp4vx29lQUEBEd3cs1P13aO8vJymTZtGjRs3JoVCQc2aNaMvv/xS1OHf+Y0luj3vBjW9f544cYL69u1LWq2WfHx86D//+Q9dvXpVLK/qtX/77bfUvn170ul05OvrS8888wxdvnyZiOiGdFjftlZbv327qc3WKCoqIgBUVFR0y+q7aQNIEASymEx3/BAEod4XFx0dTTrd/7d353E5pf//wF93+3a3axspUSmKkqWaEVPEGGOvibEMMqTBWMcYiox9DcOMmckymRhkfO1LZamGpLKU0s7IkD2h7f37w6/z6dRdKsTo/Xw8ejy6r3Nt57rOOfe57nPOdTRo+vTpdPXqVbp69SoREa1atYoiIiIoKyuLTpw4QdbW1jR+/HghXUhICCkqKpKHhwfFxcVRfHw82djY0JAhQ4Q4Fb+kTpw4QVpaWvTTTz8Jy2UNgDQ1NWncuHGUkpJC//d//0dqamr0888/C3E8PDyoXbt29Pfff1N8fLywQ1Y3AHr8+DF5eXlRz549KS8vj/Ly8uj58+dERBQUFETR0dGUlZVF+/btI0NDQ1qyZImofhoaGjRgwAC6dOkSnTp1ioyMjOi7774TtV/lAZCuri5t3LiR0tLSaPz48aSpqUk9e/aknTt3UmpqKvXr149sbGyEfjp//jzJycnR/PnzKTU1lUJCQkhVVVU4mbp79y41bdqU5s+fL6xDbdLJ0rp1a/riiy8oJSWF0tLSaOfOncKXWUJCAikrK9P48eMpMTGRLl++TGvXrhUOPBMnTiQTExM6ePAgXblyhUaMGEE6Ojp09+5dIvrfQc7e3p6OHj1K6enpdPfuXVqwYAG1atWKDh8+TBkZGRQSEkLKysrCSfyECROoXbt2FBcXR1lZWXTs2DHat29ftetQWfm22L17d7pw4QKdPHmS9PT0qEePHuTl5UVXrlyh//u//yMlJSUKCwsjIqK4uDiSl5en7du3U3Z2Nl24cIHWrFlTbRnXrl0jAHTp0iUiIiotLaU2bdqQu7s7JSYm0smTJ8nBwUE0ACoqKiIbGxsaNWoUXbx4kZKTk2nIkCFkbW0tbIO///47GRsb0+7duykzM5N2795Nurq6tHnzZiL638G7adOmtGvXLkpOTqYxY8aQVCql/Pz8l7bf48ePycLCgj766CM6ffo0Xbt2jXbs2EExMTFERLRnzx5SVFSk9evXU2pqKi1btozk5eVpzx/b6e7NG0T04iTUwMCAfvvtN8rIyKCcnBw6deoUaWpq0ubNmykjI4OOHj1K5ubmFBgYKLP9iouLSUtLi6ZNm0bp6emUnJxMmzdvppycHCIi2r9/P8nLy9PcuXMpOTmZEhMTaeHChUL6zz77jGxsbOjUqVOUmJhInp6e1LJlS+EHi/JtwMXFhaKjo+nq1av05MkTGjNmDLm4uNCpU6coPT2dli1bRsrKysKXWu/eval79+508eJFysjIoP/7v/8TDXJrUlhYSFOnTqXWrVsL+2VhYSERvf7jJ9GLY41UKqWgoCBKS0ujoKAgkpeXp169etHPP/8sHG/09PToyZMnRER048YNUlNTIz8/P0pJSaHw8HDS19cXBrEPHjwgZ2dn8vX1FdahpKTkpelkqa4tS0pKaPfu3QSAUlNTKS8vTzg53rVrF+3evZuuXbtGCQkJ1KdPH7Kzs6PS0lIievGjGQA6fvw45eXlCceaim1z8+ZNUlBQoJUrV1JWVhZdvHiR1q9fT48fP672+F95AHTjxg3S1dWlAQMGUFxcHKWmptJvv/1GV69efem2Wxv16bu6Hhdnz55N7du3Fz7//fffJCcnR0uWLKHU1FRas2YNaWtriwZAtdmPPTw8qE+fPhQXF0dpaWk0depU0tPTE/oiICCA1NXV6eOPP6aEhAQ6efIktWzZUtiW39V9v7I///yTNDU16eDBg5STk0Nnz54Vzj2q+w6+ceMGLVu2jBISEigjI4OCg4NJXl6ezp49S0TV71+VB0D12Xcqn3t4eXmRqakp7dmzhzIyMuj48ePC992rfse+qXMDWeef9+/fpyZNmtCsWbMoJSWFLly4QN27d6du3boJ9am87r/++isdPHiQMjIyKDY2lpydnalXr15ERHVqw9puazUdt9+0/8wAqOjpU1ru1bvB/4rqcBXKzc2NHBwcXhrvzz//JD09PeFz+a+fFX+tW79+PRkaGgqfy7+k9uzZQxoaGsLOWE7WAMjMzEz0K/3gwYPJ29ubiIhSUlIIgOiKRfmJaXUDoIr1eJlly5aJvkACAgJITU1NdMVn+vTp1KlTJ+GzrAHQF198IXzOy8sjADRnzhwhLDY2lgAIB9EhQ4ZQ9+7dRXWZPn062draivKtvI61SVeZVCoVTq4r8/HxIVdXV5nLCgoKSFFRkUJDQ4WwoqIiMjExoaVLlxLR/w5ye/fuFeI8e/aM1NTUhBPucqNHjyYfHx8iIurTpw99+eWX1db5ZWRti1999RWpqamJfnX39PSkr776ioiIdu/eTZqamqK+rU5paSn17t1b1DZHjhwhBQUF+ueff4SwQ4cOiQZA27ZtI2tra9EPEs+fPydVVVU6cuQIERG1aNFC9Ish0YuBubOzMxH9bwBUftWU6MUJRdOmTYXBek3t99NPP5FUKhW+iCpzcXEhX19f4XNJSQl5eHgIXx5ELwZAkydPFqVzd3cXnaSUr6+xsbHMcu7evUsARFeuKnJ2dqahQ4fKXJaWlkYAKDo6WgjLz88nVVVV2rlzJxH9bxuo+Mt0Tk4OycvLi/qovO6zZs0iIiI7O7tqB221UfkYVp1XOX6Wc3Nzow8//FD4XFJSQurq6jRs2DAhrPx4ExsbS0RE3333XZVtcP369aShoSEMMiofw2qbrrKa2rLyCV917ty5I/qhQdaVGiJx28THxxOAaq/IyDr+V8531qxZ1Lx5c9EdAOVetu3WRn36ri7HxWvXrpGmpqbox0IfHx/65JNPRPG8vb1FA6CX7cenT58mTU1NevbsmShOixYthB8zAwICSF5enm7cuCEsP3ToEMnJyQmD1ndx369sxYoVZGVlJXMbIJL9HSxL7969aerUqcJnWftX5f2hPvtOxXxTU1MJAB07dkxmHq/6Hfsmzg3K16Hy+WdQUBD16NFDFHb9+nVhAFOernKbVhQXF0cAhO//2rRhXba1mo7bb1pDD4De3RtJX5P27dtXCTt+/Djc3d3xwQcfQCqVYtiwYbh79y4KCwuFOGpqamjRooXw2djYGLdv3xblc/bsWQwePBjbtm2Dt7f3S+vSunVryMvLy8wzNTUVCgoKcHR0FJa3bNkSOjo6tV/ZCnbs2AFXV1cYGRlBQ0MD33//PXJzc0VxzM3NIZVKa1zHyuzt7YX/DQ0NAQB2dnZVwsrzSUlJgaurqygPV1dXXLt2DaWlpdWWU590U6ZMwZgxY+Dh4YHFixcjIyNDWJaYmAh3d3eZ6TIyMlBcXCwqT1FRER07dkRKSooorpOTk/B/eno6CgsL0b17d+GZLw0NDWzdulUoe/z48QgLC0O7du0wY8YMxMTEVLvO1am8LRoaGsLc3Fz0jJmhoaHQ5t27d4eZmRksLCwwbNgwhIaGirbtiiZMmIDLly8jLCxMCEtJSYGpqSlMTEyEMGdnZ1G6pKQkpKenQyqVCuutq6uLZ8+eISMjA0+ePEFGRgZGjx4tapsFCxaI+qVy3goKCnBychLavab2S0xMhIODQ7X3Osvahuzt7XH16lVRWMU+LV+3+fPni+rt6+uLvLw8me2oq6uLkSNHwtPTE3369MGaNWuQl5cnqmd1215KSgoUFBTQqVMnIUxPTw/W1taibU9JSUm07126dAmlpaWwsrIS1fPkyZNC+06cOBELFiyAq6srAgICcPHiRZl1qKvXdfysrOL6ycvLQ09P76XHFmdnZ9FkFq6urigoKMCNGzeqLac+6erTlteuXYOPjw8sLCygqakJc3NzAKhyHK5J27Zt4e7uDjs7OwwePBibNm3C/fv3a50eeLH9ffTRRzKfbXvZtltbde272h4X//nnH/Ts2RODBw+Gr6+vEJ6SkiLaZwDZx6ia9uOkpCQUFBRAT09PFCcrK0t0jGrWrBk++OADUTllZWVITU19Z/f9ygYPHoynT5/CwsICvr6+CA8PR0lJicy45UpLSxEUFAQ7Ozvo6upCQ0MDR44cqdP2C7z6cSgxMRHy8vJwc3OTufxVv2PfxLlBucrnn0lJSYiMjBT1W6tWrYSyZImPj0efPn3QrFkzSKVSoR3q0g+13dbqc9z+L6v3E9wKysqYuGXX66xLrcuti8oPdWdnZ+PTTz/F+PHj8cMPP0BXVxdnzpzB6NGjUVRUBDU1NQCo8mUhkUhA//8FiuVatGgBPT09/Pbbb+jdu/dLH56WlWfZ/38r/esUGxuLoUOHYt68efD09ISWlhbCwsKwYsWKV65PxTTlJxCywt7Eer1MYGAghgwZggMHDuDQoUMICAhAWFgY+vfvD1VV1ddSRsXtqaCgAABw4MAB0RckACj//+20V69eyMnJwcGDB3Hs2DG4u7tjwoQJdXqYV1Y/1dR3UqkUFy5cQFRUFI4ePYq5c+ciMDAQcXFxomli/f39sX//fpw6dQpNmzatdX2AF+vevn17hIaGVlnWpEkToW02bdpU5USl4o8AL1NT+72JPgVerNu8efMwYMCAKnFVVFRk5hESEoKJEyfi8OHD2LFjB77//nscO3YMnTt3fi31VFVVFZ2wFxQUQF5eHvHx8VXas3xgPGbMGHh6euLAgQM4evQoFi1ahBUrVuDrr7+udz1e5/Gzspdt52/z2FKftuzTpw/MzMywadMmmJiYoKysDG3atKnTbGby8vI4duwYYmJicPToUaxduxazZ8/G2bNn0bx581rl8bLtr6Ztt7bq2ne1OS7evHkT3bp1g4uLC37++eda16Xcy/bjgoICGBsbIyoqqsryukyn/S7u+5WZmpoiNTUVx48fx7Fjx+Dn54dly5bh5MmT1Z63LFu2DGvWrMHq1athZ2cHdXV1TJ48uc6z8b3qcehlbfiq37Fv6ntEVlhBQQH69OmDJUuWVIlrbGxcJezJkyfw9PSEp6cnQkND0aRJE+Tm5sLT0/ONzIpYn+P2f1m9rwBJJBIoqqg0+N+rTl0bHx+PsrIyrFixAp07d4aVlRVu3rxZr7z09fURERGB9PR0eHl5obi4uN71sra2RklJCRISEoSw9PT0l/7ap6SkVOWKSExMDMzMzDB79mw4OTnB0tISOTk59a7bq7CxsUF0dLQoLDo6GlZWVsLBW9Y61CadLFZWVvjmm29w9OhRDBgwACEhIQBe/EJZ3bTkLVq0gJKSkqi84uJixMXFwdbWttqybG1toaysjNzcXLRs2VL0Z2pqKsRr0qQJRowYgd9//x2rV6+u15d5XSkoKMDDwwNLly7FxYsXkZ2djYiICAAvZqnx9/dHeHg4IiIiqpxI2djY4Pr166JfMv/++29RHEdHR1y7dg0GBgZV1l1LSwuGhoYwMTFBZmZmleWVy6uYd0lJCeLj42FjYyOEVdd+9vb2SExMxL1792S2gaxt6OLFi6K8ZXF0dERqamqVerds2bLG2ZccHBwwa9YsxMTEoE2bNti+fbtQz+q2PRsbG5SUlODs2bNC2N27d5Gamlrjtufg4IDS0lLcvn27Sh0rzsBkamqKcePGYc+ePZg6dSo2bdpU47pXJGu/fJ3Hz1dlY2OD2NhY0Rd0dHQ0pFKpMKCv7tjysnSyVNeW5TM7VSynvA+///57uLu7w8bGpsqxXFY6WSQSCVxdXTFv3jwkJCRASUkJ4eHh1a5fZfb29jh9+nSN30/VbbtvUk3HxX/++Qddu3ZF+/btERISUmW/s7GxEe0zgOxjVE37saOjI27dugUFBYUqy/X19YV8cnNzRdv433//DTk5OVhbWwth7+K+X5mqqir69OmD4OBgREVFITY2FpcuXQIgezuKjo5G37598cUXX6Bt27awsLBAWlqaKE5ttj+gbvtOZXZ2digrK8PJkyerjfMq37Fv4tygOo6Ojrhy5QrMzc2r9J2sAdTVq1dx9+5dLF68GB999BFatWpV5YpMbdqwvtva++69vwWuspYtW6K4uBhr165FZmYmtm3bho0bN9Y7PwMDA0RERODq1avw8fF56WXl6rRq1QoeHh4YO3Yszp07h4SEBIwdO7bKrz+VmZub4+LFi0hNTUV+fj6Ki4thaWmJ3NxchIWFISMjA8HBwcIXZkObOnUqTpw4gaCgIKSlpWHLli1Yt24dpk2bJlqHU6dO4Z9//kF+fn6t01X09OlT+Pv7IyoqCjk5OYiOjkZcXJxwsjtr1izExcXBz88PFy9exNWrV7Fhwwbk5+dDXV0d48ePx/Tp03H48GEkJyfD19cXhYWFGD16dLXrJpVKMW3aNHzzzTfYsmULMjIycOHCBaxduxZbtmwBAMydOxd//fUX0tPTceXKFezfv/+lJ+Cvav/+/QgODkZiYiJycnKwdetWlJWVCV/YEyZMwO+//47t27dDKpXi1q1buHXrFp4+fQoA8PDwgJWVFUaMGIGkpCScPn0as2fPFpUxdOhQ6Ovro2/fvjh9+jSysrIQFRWFiRMnCrcRzZs3D4sWLUJwcDDS0tJw6dIlhISEYOXKlaK81q9fj/DwcFy9ehUTJkzA/fv3MWrUqJe2n4+PD4yMjNCvXz9ER0cjMzMTu3fvRmxsLABg+vTp2Lx5MzZs2IBr165h1apViIyMxJQpU2psv7lz52Lr1q2YN28erly5gpSUFISFheH777+XGT8rKwuzZs1CbGwscnJycPToUVy7dk2oZ0BAAP744w8EBAQgJSUFly5dEn4BtLS0RN++feHr64szZ84gKSkJX3zxBT744AP07du32jpaWVlh6NChGD58OPbs2YOsrCycO3cOixYtwoEDBwAAkydPxpEjR5CVlYULFy4gMjKyTtueubk5srKykJiYiPz8fDx//vy1Hz9fhZ+fH65fv46vv/4aV69exV9//YWAgABMmTJFOGE2NzfH2bNnkZ2djfz8fJSVldUqXWU1taWZmRkkEgn279+PO3fuoKCgADo6OtDT08PPP/+M9PR0REREVNnuDAwMoKqqisOHD+Pff//Fw4cPq5R79uxZLFy4EOfPn0dubi727NmDO3fuCGXLOv5X5u/vj0ePHuHzzz/H+fPnce3aNWzbtg2pqakv3XbflJr26/LBT7NmzbB8+XLcuXNHOEaVK7/isnz5cly7dg3r1q3D4cOHq5RR037s4eEBZ2dn9OvXD0ePHkV2djZiYmIwe/ZsnD9/XshHRUVFdCycOHEivLy8YGRk9M7u+5Vt3rwZv/76Ky5fvozMzEz8/vvvUFVVhZmZGQDZ38GWlpbC1ceUlBR89dVX+Pfff0X5ytq/KqvrvlOZubk5RowYgVGjRmHv3r3Cd83OnTuFfn6V79g3cW5QnQkTJuDevXvw8fFBXFwcMjIycOTIEXz55ZcyBzDNmjWDkpKScLzdt29flXf71KYN67utvfde9cGkd1l1D5OtXLmSjI2NSVVVlTw9PWnr1q2ih8jKpwOsKDw8XDRFc+WHT2/evElWVlbk5eVFJSUl1U6DXdGkSZOEqU/L8+jVqxcpKyuTmZkZbd++nQwMDGjjxo3VruPt27epe/fupKGhIZoGe/r06aSnp0caGhrk7e1Nq1atEq2TrAecV61aRWZmZsJnWZMgVH5QEpWmRpb1YG/5dNaKiorUrFkzWrZsmSiP2NhYsre3J2VlZZnTYFeXrqLnz5/T559/LkzzaWJiQv7+/qJtNioqilxcXEhZWZm0tbXJ09NT6POnT5/S119/Tfr6+jVOdVn5QcOysjJavXo1WVtbk6KiIjVp0oQ8PT2F2baCgoLIxsaGVFVVSVdXl/r27UuZmZmiNi6ftlIWWduirL6ruH2dPn2a3NzcSEdHh1RVVcne3p527NghxEWlaTPL/yrOsJeamkoffvghKSkpkZWVFR0+fLhKX+fl5dHw4cOFNrOwsCBfX1/RQ4qhoaHUrl07UlJSIh0dHerSpQvt2bOHiP63rWzfvp06duxISkpKZGtrSxEREUL6l7VfdnY2DRw4kDQ1NUlNTY2cnJyEWYqIZE+DXXEiksrrVO7w4cPk4uJCqqqqpKmpSR07dhQ9hF3RrVu3qF+/fmRsbCxM2T537lzRA/W7d+8W2kFfX58GDBggLCufnlRLS0s4JsmanrSyoqIimjt3Lpmbm5OioiIZGxtT//796eLFi0RE5O/vTy1atCBlZWVq0qQJDRs2TJhdj+jF/lzTzGfPnj2jgQMHkra2tmj7eBPHT1nH6tocb142nXVqaip17tyZVFVVX2ka7Je15fz588nIyIgkEomwPx87doxsbGxIWVmZ7O3tKSoqqkr9N23aRKampiQnJydzGuzk5GTy9PQUpuC1srKitWvXCullHf9lHYOTkpKoR48epKamRlKplD766CPKyMh46bZbnldNUyzXp+9q2q9lTcFe/lfRr7/+KkxN3adPH5nTYL9sP3706BF9/fXXZGJiQoqKimRqakpDhw6l3NxcIvrfsfbHH38kExMTUlFRoUGDBglTJL+r+35l4eHh1KlTJ9LU1CR1dXXq3LkzHT9+XFgu6zv47t271LdvX9LQ0CADAwP6/vvvafjw4aL9Vtb+Vfm7sj77TuVt6unTp/TNN98I7VxxqutX/Y4lejPnBtWdf6alpVH//v2F6ahbtWpFkydPFiZlqZxu+/btZG5uTsrKyuTs7Ez79u2rsn/Xpg3rs61VPm6/aQ09CYKE6OU3+D179gxZWVlo3rx5tffAs9fvxo0bMDU1FR46Zu8fMzMzzJs3DyNHjnzbVWlw58+fR4cOHRAaGgoHBwc0a9as2hex3rlzB3fv3hWuUqmpqaFp06Z1enFraWkpEhIS4ODgUKfnkN5HhYWF0NPTw6FDh9C1a9e3XR32joqMjMSAAQOQmZlZ7wl5/ssCAwOxd+9eJCYmvu2qsHpqzN+x/zU1jTUePXoELS0tPHz4EJqamq+lvP/ea+zfYxERESgoKICdnR3y8vIwY8YMmJubo0uXLm+7auwNuHLlCrS0tDB8+PC3XZUGd+/ePeEZIwsLC6iqqiItLQ1t2rSR+VDu48ePhZmIJBIJbt26hbS0NLRu3bpB3679voiMjMTHH3/Mgx9Wo4MHD+K7775rlIMf9t/XmL9j2cvxAOgdUlxcjO+++w6ZmZmQSqVwcXFBaGjoS2eXY/9NrVu3fm1TE//X/Pvvv8L01SoqKjAzM8PDhw+Rn58vczYcCwsL0Wdzc3MkJCTg0aNHogeWWe307t0bvXv3ftvVYO+4ZcuWve0qMFZvjfk7lr0cD4DeIeXTHTL2PisrK8OTJ09ga2srmolLU1MTT548qXUeRAQFheoPYeVxytVmtiLGGCsXGBiIwMDAt10NxtgbwAMgxliDKp8psfKVTUVFRTx79qxWedy4cQNKSko13gt869attzZFM2OMMcbeXTwAYoz9p+Tl5eHevXuwtrau8b08RkZGwhvogRdXgPh2CMYYY4zxAIgx1qDKb1ur/M6S4uLilz7vVv4+ECsrK6ipqdUYt6bBEWOMMcYaLz5DYIw1KDk5Oairq+Px48dCGBHh0aNHNU5rfevWLeTl5cHS0rJO018zxhhjjFXEV4AYYw3O0NAQWVlZUFNTg7q6Om7fvo2ysjJhRresrCwoKiqiadOmAF7c9nbz5k1YWFhAWVlZuHokJyfX6N/pwxhjjLG64QEQY6zB6erqoqSkBDdv3kRxcTHU1NRgaWkp3AJXVFQkin/nzh0QETIyMkThJiYmMDExabB6M8YYY+y/jwdA9TRy5Eg8ePAAe/fulbn8fXiDdNeuXdGuXTusXr36bVeFvQMkEgnCw8PRr1+/15KfgYEBDAwMZC6ztrYWfba3t38tZTLGGGOM8TNAb8i0adNw4sSJN17OyJEjX9sJaWV79uxBUFDQG8m7InNzcx5kNaBFixahQ4cOkEqlMDAwQL9+/ZCamvq2q8UYY4wx1iAa1QCo8m01b5KGhgb09PQarLw3QVdXF1Kp9G1X451ERML7bP5rTp48iQkTJuDvv//GsWPHUFxcjB49etT6JaTvs8oz072LSktLUVZW9rarwRhjjP1n1XsAREQoKypt8L+Kb3Z/ma5du8Lf3x+TJ0+Gvr4+PD09AQArV66EnZ0d1NXVYWpqCj8/PxQUFAjpNm/eDG1tbRw5cgQ2NjbQ0NBAz549kZeXV21ZcXFxaNKkCZYsWQLgxS1w7dq1E5aXX6lZvnw5jI2NoaenhwkTJohOuPLy8tC7d2+oqqqiefPm2L59e41XRwIDA7Flyxb89ddfkEgkkEgkiIqKAgDMnDlTmCrYwsICc+bMEZVVXr9t27bB3NwcWlpa+Pzzz0Uzc3Xt2hWTJ08WPpubm2PBggUYPnw4NDQ0YGZmhn379uHOnTvo27cvNDQ0YG9vj/Pnz4vquXv3brRu3RrKysowNzfHihUrRGXk5OTgm2++EdahNulkSUpKQrdu3SCVSqGpqYn27duL6hIdHY2uXbtCTU0NOjo68PT0xP379wEAz58/x8SJE2FgYAAVFRV8+OGHiIuLE9JGRUVBIpHg0KFDaN++PZSVlXHmzBmUlZVh0aJFaN68OVRVVdG2bVvs2rVLSHf//n0MHToUTZo0gaqqKiwtLRESElLjelRUvi3u378f1tbWUFNTw6BBg1BYWIgtW7bA3NwcOjo6mDhxIkpLS4V0P/74IywtLaGiogJDQ0MMGjRIWHb48GGMHDkSrVu3Rtu2bbF582bk5uYiPj5eiHPt2jV06dIFKioqsLW1xbFjx6rU7fr16/Dy8oK2tjZ0dXXRt29fZGdni+L88ssvsLGxgYqKClq1aoUff/xRWJadnQ2JRIKwsDC4uLhARUUFbdq0wcmTJ2vdfjdu3ICPjw90dXWhrq4OJycnnD17Vli+YcMGtGjRAkpKSrC1tcXBgwdF9ZNIJNiwYQM+++wzqKur44cffgAA/PXXX3B0dISKigosLCwwb968Gge8UVFR6NixI9TV1aGtrQ1XV1fk5OQIy//v//4PHTp0gIqKCvT19dG/f3/ROg4fPhw6OjpQU1NDr169cO3aNWF5+Tawb98+2NraQllZGbm5uXj+/DmmTZuGDz74AOrq6ujUqZOw/wNATk4O+vTpAx0dHairq6N169ZV1p8xxhhrjOr9DBAVl+Hm3JjXWZdaMZnvAolS7Wd92rJlC8aPH4/o6GghTE5ODsHBwWjevDkyMzPh5+eHGTNmiE7OCgsLsXz5cmzbtg1ycnL44osvMG3aNISGhlYpIyIiAgMGDMDSpUsxduzYausSGRkJY2NjREZGIj09Hd7e3mjXrh18fX0BAMOHD0d+fj6ioqKgqKiIKVOm4Pbt29XmN23aNKSkpODRo0fCSaGuri4AQCqVYvPmzTAxMcGlS5fg6+sLqVSKGTNmCOkzMjKwd+9e7N+/H/fv34eXlxcWL14snATKsmrVKixcuBBz5szBqlWrMGzYMLi4uGDUqFFYtmwZZs6cieHDh+PKlSuQSCSIj4+Hl5cXAgMD4e3tjZiYGPj5+UFPTw8jR47Enj170LZtW4wdO1ZoBwAvTSfL0KFD4eDggA0bNkBeXh6JiYnCQ/WJiYlwd3fHqFGjsGbNGigoKCAyMlIYNMyYMQO7d+/Gli1bYGZmhqVLl8LT0xPp6elCmwLAt99+i+XLl8PCwgI6OjpYtGgRfv/9d2zcuBGWlpY4deoUvvjiCzRp0gRubm6YM2cOkpOTcejQIejr6yM9PR1Pnz6ttn1lKSwsRHBwMMLCwvD48WMMGDAA/fv3h7a2Ng4ePIjMzEwMHDgQrq6u8Pb2xvnz5zFx4kRs27YNLi4uuHfvHk6fPl1t/g8fPgTwv22nrKwMAwYMgKGhIc6ePYuHDx+KBsLAiyslnp6ecHZ2xunTp6GgoIAFCxagZ8+euHjxIpSUlBAaGoq5c+di3bp1cHBwQEJCAnx9faGuro4RI0YIeU2fPh2rV6+Gra0tVq5ciT59+iArKwt6eno1tl9BQQHc3NzwwQcfYN++fTAyMsKFCxeEqyPh4eGYNGkSVq9eDQ8PD+zbtw/ffvstXFxc4OHhIZQfGBiIxYsXY/Xq1VBQUMDp06cxfPhwBAcH46OPPkJGRoawXwcEBFRpv5KSEvTr1w++vr74448/UFRUhHPnzgmD+QMHDqB///6YPXs2tm7diqKiItFAZOTIkbh27Rr27dsHTU1NzJw5E5988gmSk5OF7bewsBBLlizBL7/8Aj09PRgYGMDf3x/JyckICwuDiYkJwsPD0bNnT1y6dAmWlpaYMGECioqKcOrUKairqyM5ORkaGhq12+gYY4yx9xnVwtOnTyk5OZmePn0qhJU+L6HrM081+F/p85LaVJmIiNzc3MjBweGl8f7880/S09MTPoeEhBAASk9PF8LWr19PhoaGwucRI0ZQ3759ac+ePaShoUFhYWGiPAMCAqht27ai+GZmZlRS8r/6Dx48mLy9vYmIKCUlhQBQXFycsPzatWsEgFatWlVt3cvr8TLLli2j9u3bi+qnpqZGjx49EsKmT59OnTp1Ej67ubnRpEmThM9mZmb0xRdfCJ/z8vIIAM2ZM0cIi42NJQCUl5dHRERDhgyh7t27i+oyffp0srW1FeVbeR1rk64yqVRKmzdvlrnMx8eHXF1dZS4rKCggRUVFCg0NFcKKiorIxMSEli5dSkREkZGRBID27t0rxHn27BmpqalRTEyMKL/Ro0eTj48PERH16dOHvvzyy2rr/DKytsWvvvqK1NTU6PHjx0KYp6cnffXVV0REtHv3btLU1BT1bXVKS0upd+/eorY5cuQIKSgo0D///COEHTp0iABQeHg4ERFt27aNrK2tqaysTIjz/PlzUlVVpSNHjhARUYsWLWj79u2i8oKCgsjZ2ZmIiLKysggALV68WFheXFxMTZs2pSVLlhBRze33008/kVQqpbt378pc7uLiQr6+vsLnkpIS8vDwoF69eglhAGjy5MmidO7u7rRw4UJR2LZt28jY2FhmOXfv3iUAFBUVJXO5s7MzDR06VOaytLQ0AkDR0dFCWH5+PqmqqtLOnTuJ6H/bQGJiohAnJyeH5OXlRX1UXvdZs2YREZGdnR0FBgbKLJcxxhh7l8gaa5R7+PAhAaCHDx++tvLqfQVIoigHk/kurzb6qme5ddG+ffsqYcePH8eiRYtw9epVPHr0CCUlJXj27BkKCwuFt8urqamhRYsWQhpjY+MqV2POnj2L/fv3Y9euXbWaiKB169aid5YYGxvj0qVLAIDU1FQoKCjA0dFRWN6yZUvo6OjUaX3L7dixA8HBwcjIyEBBQQFKSkqgqakpimNubi56xkfWOlZWcTYuQ0NDAICdnV2VsNu3b8PIyAgpKSno27evKA9XV1esXr0apaWl1b7DpT7ppkyZgjFjxmDbtm3w8PDA4MGDhT5MTEzE4MGDZZaVkZGB4uJiuLq6CmGKioro2LEjUlJSRHGdnJyE/9PT01FYWIju3buL4hQVFcHBwQEAMH78eAwcOBAXLlxAjx490K9fP7i41G2/qbwtGhoawtzcXPRrvqGhodB33bt3h5mZGSwsLNCzZ0/07NkT/fv3F7btiiZMmIDLly/jzJkzQlhKSgpMTU1F00s7OzuL0iUlJSE9Pb3KM2LPnj1DRkYGnjx5goyMDIwePVp0Za+kpARaWlqiNBXzVlBQgJOTk9DuNbVfYmIiHBwcRFfoKkpJSalyRdbe3h7h4eGisIp9Wr5u0dHRoiuhpaWlVY4R5XR1dTFy5Eh4enqie/fu8PDwgJeXF4yNjYV6VmyDynVUUFBAp06dhDA9PT1YW1uLtj0lJSXRvnfp0iWUlpbCyspKlN/z58+FZw8nTpyI8ePH4+jRo/Dw8MDAgQN5Nj3GGGMMr/AMkEQigZySfIP/VXxGpDYqvzE+Ozsbn376Kezt7bF7927Ex8dj/fr1AMSTJJTfelJxfanS80ctWrRAq1at8Ntvv9Xq4WlZeb6Jh5ljY2MxdOhQfPLJJ9i/fz8SEhIwe/bsKpNA1Kc+FdOU94WssLfxkHZgYCCuXLmC3r17IyIiAra2tsLJrqqq6mspo+L2VP7c2IEDB5CYmCj8JScnC88B9erVS3jG6ebNm3B3d8e0adPqVKasfqqp76RSKS5cuIA//vgDxsbGmDt3Ltq2bYsHDx6I0vj7+2P//v2IjIwUXjhaWwUFBWjfvr1ovRMTE5GWloYhQ4YIbbNp0ybR8suXL+Pvv/+udTk1td+b6NPydZs3b56o3pcuXcK1a9egoqIiM4+QkBDExsbCxcUFO3bsgJWVlbCer6OeqqqqomNfQUEB5OXlER8fL6pnSkoK1qxZAwAYM2YMMjMzMWzYMFy6dAlOTk5Yu3btK9eFMcYY+69rVLPAAS+eLSkrK8OKFSvQuXNnWFlZ4ebNm/XKS19fHxEREUhPT4eXl9crzSBlbW2NkpISJCQkCGHp6enCQ/rVUVJSEj38DgAxMTEwMzPD7Nmz4eTkBEtLS9ED2Q3JxsZG9PwV8GIyAisrK+Eqjqx1qE06WaysrPDNN9/g6NGjGDBggPBslL29fbXTkpc/JF+xvOLiYsTFxcHW1rbasio+kN6yZUvRn6mpqRCvSZMmGDFiBH7//XesXr0aP//8c7V5vi4KCgrw8PDA0qVLcfHiRWRnZyMiIgLAiwlM/P39ER4ejoiICDRv3lyU1sbGBtevXxdN+lF50OLo6Ihr167BwMCgyrpraWnB0NAQJiYmyMzMrLK8cnkV8y4pKUF8fDxsbGyEsOraz97eHomJibh3757MNpC1DV28eFGUtyyOjo5ITU2tUu+WLVtCTq76Q6aDgwNmzZqFmJgYtGnTBtu3bxfqWd22Z2Njg5KSEtHEDXfv3kVqamqN256DgwNKS0tx+/btKnU0MjIS4pmammLcuHHYs2cPpk6dik2bNtW47owxxlhj0OhehNqyZUsUFxdj7dq16NOnD6Kjo7Fx48Z652dgYICIiAh069YNPj4+CAsLg4JC3Zu1VatW8PDwwNixY7FhwwYoKipi6tSpVX75rczc3BxHjhxBamoq9PT0oKWlBUtLS+Tm5iIsLAwdOnTAgQMHqtz201CmTp2KDh06ICgoCN7e3oiNjcW6detEE06Ym5vj1KlT+Pzzz6GsrAx9ff1apavo6dOnmD59OgYNGoTmzZvjxo0biIuLw8CBAwEAs2bNgp2dHfz8/DBu3DgoKSkhMjISgwcPhr6+PsaPH4/p06dDV1cXzZo1w9KlS1FYWIjRo0dXu25SqRTTpk3DN998g7KyMnz44Yd4+PAhoqOjoampiREjRmDu3Llo3749WrdujefPn2P//v0vPQF/Vfv370dmZia6dOkCHR0dHDx4EGVlZcLLRSdMmIDt27fjr7/+glQqxa1btwAAWlpaUFVVhYeHB6ysrDBixAgsW7YMjx49wuzZs0VlDB06FMuWLUPfvn0xf/58NG3aFDk5OdizZw9mzJiBpk2bYt68eZg4cSK0tLTQs2dPPH/+HOfPn8f9+/cxZcoUIa/169fD0tISNjY2WLVqFe7fv49Ro0YBQI3t5+Pjg4ULF6Jfv35YtGgRjI2NkZCQABMTEzg7O2P69Onw8vKCg4MDPDw88NdffyEyMhJHjhypsf3mzp2LTz/9FM2aNcOgQYMgJyeHpKQkXL58GQsWLKgSPysrCz///DM+++wzmJiYIDU1FdeuXcPw4cMBvJg4wd3dHS1atMDnn3+OkpISHDx4EDNnzoSlpSX69u0LX19f/PTTT5BKpfj222/xwQcfVLkFtCIrKysMHToUw4cPx4oVK+Dg4IA7d+7gxIkTsLe3R+/evTF58mT06tULVlZWuH//PiIjI9/4tscYY4z9J7zqg0nvssoP8ZdbuXIlGRsbk6qqKnl6etLWrVsJAN2/f5+IXjx0rKWlJUoTHh5OFZur8uQDN2/eJCsrK/Ly8qKSkhKZkyBUnqxg0qRJ5ObmJsqjV69epKysTGZmZrR9+3YyMDCgjRs3VruOt2/fpu7du5OGhgYBoMjISCJ6MWGAnp4eaWhokLe3N61atUq0TpXrR0S0atUqMjMzEz7LmgSh8mQFqPBgPNH/HmxPSEgQwnbt2kW2trakqKhIzZo1o2XLlonyiI2NJXt7e1JWVha18cvSVfT8+XP6/PPPydTUlJSUlMjExIT8/f1F22xUVBS5uLiQsrIyaWtrk6enp9DnT58+pa+//pr09fVJWVmZXF1d6dy5c0La8kkQyuOXKysro9WrV5O1tTUpKipSkyZNyNPTk06ePElELx76t7GxIVVVVdLV1aW+fftSZmamqI1HjBhR7XrJ2hZl9V3F7ev06dPk5uZGOjo6pKqqSvb29rRjxw4hLgCZfyEhIUKc1NRU+vDDD0lJSYmsrKzo8OHDVfo6Ly+Phg8fLrSZhYUF+fr6ih5SDA0NpXbt2pGSkhLp6OhQly5daM+ePUT0v21l+/bt1LFjR1JSUiJbW1uKiIgQ0r+s/bKzs2ngwIGkqalJampq5OTkRGfPnhWW//jjj2RhYUGKiopkZWVF8+bNE01EUnmdyh0+fJhcXFxIVVWVNDU1qWPHjvTzzz/L7KNbt25Rv379yNjYmJSUlMjMzIzmzp1LpaWlQpzdu3cL7aCvr08DBgwQlt27d4+GDRtGWlpawjEpLS1NWC5rGyB6MVHH3LlzydzcnBQVFcnY2Jj69+9PFy9eJCIif39/atGiBSkrK1OTJk1o2LBhlJ+fL6Q3MzOjgIAAmevEGGOMNaSGngRBQvTyF+s8e/YMWVlZaN68ebX3wLPX78aNGzA1NcXx48fh7u7+tqvD3gAzMzPMmzev2qm932fZ2dlo3rw5EhISRO/MelNKS0uRkJAABweHGm+jbAwKCwuhp6eHQ4cOoWvXrm+7Oowxxhq5msYajx49gpaWFh4+fFhlQq/6anS3wL3LIiIiUFBQADs7O+Tl5WHGjBkwNzdHly5d3nbV2Btw5coVaGlpCbdKMdZQIiMj8fHHH/PghzHGWKPEA6B3SHFxMb777jtkZmZCKpXCxcUFoaGhVWb8Yu+H1q1b4+LFi2+7GqwR6t27N3r37v22q8EYY4y9FTwAeod4enrC09PzbVeDsQZhbm5eZWp5xhhjjLE3rdFNg80YY4wxxhhrvHgAxBhjjDHGGGs0eADEGGOMMcYYazR4AMQYY4wxxhhrNHgAxBhjjDHGGGs0eADEGGOMMcYYazR4AFRPI0eORL9+/apdHhgY2CBvt3+TunbtismTJ7/taryTXrVtoqKiIJFI8ODBg9dWp/+C92G/eB9s3rwZ2trar5SHubk5Vq9e/VrqwxhjjDUkHgC9IdOmTcOJEyfeeDkvG4i9ij179iAoKOiN5F1RQ5xI1feEr7qBSl3aRtZgycXFBXl5edDS0qpznd4FSUlJ8PHxgampKVRVVWFjY4M1a9a87Wq91yQSCfbu3VvndLL2L29vb6SlpdUqfXX7TlxcHMaOHVvn+jDGGGNvW6N6EWpRURGUlJQapCwNDQ1oaGg0SFlviq6u7tuuwjvrVdtGSUkJRkZGr6k2DS8+Ph4GBgb4/fffYWpqipiYGIwdOxby8vLw9/d/29V7qxryOFNfqqqqUFVVfaU8mjRp8ppqwxhjjDUwqoWnT59ScnIyPX36VAgrKyuj58+fN/hfWVlZbapMRERubm40YcIEmjRpEunp6VHXrl2JiGjFihXUpk0bUlNTo6ZNm9L48ePp8ePHQrqQkBDS0tKiw4cPU6tWrUhdXZ08PT3p5s2bQpwRI0ZQ3759hc/nzp0jfX19Wrx4MRERBQQEUNu2bavEX7ZsGRkZGZGuri75+flRUVGREOfmzZv0ySefkIqKCpmbm1NoaCiZmZnRqlWrZK5fQEAAARD9RUZGEhHRjBkzyNLSklRVVal58+b0/fffi8oqr9/WrVvJzMyMNDU1ydvbmx49eiRqv0mTJgmfzczMKCgoiIYNG0bq6urUrFkz+uuvv+j27dv02Wefkbq6OtnZ2VFcXJyonrt27SJbW1tSUlIiMzMzWr58uaiMyutQm3SyJCYmUteuXUlDQ4OkUik5OjpSXFwcRUZGVikjICCAiIi2bt1K7du3Jw0NDTI0NCQfHx/6999/iYgoKyurSroRI0bIbJv169dTy5YtSVlZmQwMDGjgwIFCv1fOIysrS6jT/fv3hTzOnDlDbm5upKqqStra2tSjRw+6d+8eERH9+eef1KZNG1JRUSFdXV1yd3engoKCGtujovr0XXZ2Nn366aekra1NampqZGtrSwcOHKi2DD8/P+rWrZsobNGiRWRgYEAaGho0atQomjlzpmi/ICLatGkTtWrVipSVlcna2prWr18vWp6bm0uDBw8mLS0t0tHRoc8++4yysrKE5eX7VmBgIOnr65NUKqWvvvqKnj9/LsQpbz9lZWWZ7ffrr78K25qRkRFNmDBBWJaTkyO0kVQqpcGDB9OtW7eE5eX70qZNm8jc3JwkEgkREd2/f59Gjx4t1Klbt26UmJhYbfs9f/6cJkyYQEZGRqSsrEzNmjWjhQsXEtGL/qu4DZmZmRERUXp6On322WdkYGBA6urq5OTkRMeOHRPyrG7/Kj/GlavPvlP52HT//n0aO3YsGRgYkLKyMrVu3Zr+7//+j4jqvi0xxhhrXGSNNco9fPiQANDDhw9fW3n1vgJUXFyMhQsX1jd5vX333Xd1+nV1y5YtGD9+PKKjo4UwOTk5BAcHo3nz5sjMzISfnx9mzJiBH3/8UYhTWFiI5cuXY9u2bZCTk8MXX3yBadOmITQ0tEoZERERGDBgAJYuXVrjLSGRkZEwNjZGZGQk0tPT4e3tjXbt2sHX1xcAMHz4cOTn5yMqKgqKioqYMmUKbt++XW1+06ZNQ0pKCh49eoSQkBAA/7syIZVKsXnzZpiYmODSpUvw9fWFVCrFjBkzhPQZGRnYu3cv9u/fj/v378PLywuLFy/GDz/8UG2Zq1atwsKFCzFnzhysWrUKw4YNg4uLC0aNGoVly5Zh5syZGD58OK5cuQKJRIL4+Hh4eXkhMDAQ3t7eiImJgZ+fH/T09DBy5Ejs2bMHbdu2xdixY4V2APDSdLIMHToUDg4O2LBhA+Tl5ZGYmAhFRUW4uLhg9erVmDt3LlJTUwFAuDpXXFyMoKAgWFtb4/bt25gyZQpGjhyJgwcPwtTUFLt378bAgQORmpoKTU1Nmb+anz9/HhMnTsS2bdvg4uKCe/fu4fTp0wCANWvWIC0tDW3atMH8+fMBvPjlPDs7W5RHYmIi3N3dMWrUKKxZswYKCgqIjIxEaWkp8vLy4OPjg6VLl6J///54/PgxTp8+DSKqtp9eR99NmDABRUVFOHXqFNTV1ZGcnFzjVc2HDx+Krozt3LkTgYGBWL9+PT788ENs27YNwcHBsLCwEOKEhoZi7ty5WLduHRwcHJCQkABfX1+oq6tjxIgRKC4uhqenJ5ydnXH69GkoKChgwYIF6NmzJy5evCgcC06cOAEVFRVERUUhOzsbX375JfT09PDDDz8I7bd48WJYWlqiWbNmiImJEdpvw4YNmDJlChYvXoxevXrh4cOHwvGirKwMffv2hYaGBk6ePImSkhJMmDAB3t7eiIqKEtYjPT0du3fvxp49eyAvLw8AGDx4MFRVVXHo0CFoaWnhp59+gru7O9LS0mReQQwODsa+ffuwc+dONGvWDNevX8f169cBvLjdzMDAACEhIejZs6dQRkFBAT755BP88MMPUFZWxtatW9GnTx+kpqaiWbNm1e5fldVn36morKwMvXr1wuPHj/H777+jRYsWSE5OFupZ122JMcYYe5Pe+1vgLC0tsXTpUlFYxecxzM3NsWDBAowbN040ACouLsbGjRvRokULAIC/v79wAltReHg4hg8fjl9++QXe3t411kVHRwfr1q2DvLw8WrVqhd69e+PEiRPw9fXF1atXcfz4ccTFxcHJyQkA8Msvv8DS0rLa/DQ0NKCqqornz59XuZ3q+++/F63jtGnTEBYWJhoAlZWVYfPmzZBKpQCAYcOG4cSJEzUOgD755BN89dVXAIC5c+diw4YN6NChAwYPHgwAmDlzJpydnfHvv//CyMgIK1euhLu7O+bMmQMAsLKyQnJyMpYtW4aRI0dCV1cX8vLykEqlonV4WTpZcnNzMX36dLRq1QoARG2npaUFiURSpZ1GjRol/G9hYYHg4GB06NABBQUF0NDQEE5UDQwMqn2GKDc3F+rq6vj0008hlUphZmYGBwcHoVwlJSWoqanVeMvb0qVL4eTkJNoGW7duDQC4cOECSkpKMGDAAJiZmQEA7Ozsqs2rOnXtu9zcXAwcOFAoq+LApbKYmBjs2LEDBw4cEMJWr16N0aNHY/To0QCABQsW4Pjx43j27JkQJyAgACtWrMCAAQMAAM2bN0dycjJ++uknjBgxAjt27EBZWRl++eUXSCQSAEBISAi0tbURFRWFHj16AHhxS+Fvv/0GNTU1tG7dGvPnz8f06dMRFBSEvLw8lJSUoH///rh37x7s7OxEEzEsWLAAU6dOxaRJk4SwDh06AHgxsLp06RKysrJgamoKANi6dStat26NuLg4IV5RURG2bt0q3BZ25swZnDt3Drdv34aysjIAYPny5di7dy927dol84eS3NxcWFpa4sMPP4REIhH6Gvjf7Wba2tqi7aht27Zo27at8DkoKAjh4eHYt28f/P39q92/ZJVd132nouPHj+PcuXNISUmBlZUVAPH2UpdtiTHGGHvT6j0AUlRUxHffffc661Lrcuuiffv2VcKOHz+ORYsW4erVq3j06BFKSkrw7NkzFBYWQk1NDQCgpqYmDH4AwNjYuMrVmLNnz2L//v3YtWtXrSYiaN26tfCLaHmely5dAgCkpqZCQUEBjo6OwvKWLVtCR0enTutbbseOHQgODkZGRgYKCgpQUlICTU1NURxzc3Nh8FNen5quOAGAvb298L+hoSEA8cl4edjt27dhZGSElJQU9O3bV5SHq6srVq9ejdLSUlF7VFSfdFOmTMGYMWOwbds2eHh4YPDgwaI+lCU+Ph6BgYFISkrC/fv3UVZWBuDFCZutrW2Nact1794dZmZmsLCwQM+ePdGzZ0/0799f2JZqIzExURiIVNa2bVu4u7vDzs4Onp6e6NGjBwYNGlTnbaOufTdx4kSMHz8eR48ehYeHBwYOHCjKo9zly5fRt29fBAQECAMS4EUfjhs3ThTX2dkZkZGRAIAnT54gIyMDo0ePFl2dKCkpESaHSEpKQnp6umg7BYBnz54hIyND1EYV29vZ2RkFBQW4fv260H7t2rVDx44dMXjwYHh5eUFHRwe3b9/GzZs34e7uLrPNUlJSYGpqKgx+AMDW1hba2tpISUkRBkBmZmaiZ2KSkpJQUFAAPT09UX5Pnz4V1buikSNHonv37rC2tkbPnj3x6aefitpTloKCAgQGBuLAgQPCQO/p06fIzc2tMV1l9dl3KkpMTETTpk2FwU9ltd2WGGOMsYZQ71ngJBIJlJSUGvyv/Ffg2lJXVxd9zs7Oxqeffgp7e3vs3r0b8fHxWL9+PYAXv+KWqzzQkkgkVW45atGiBVq1aoXffvsNxcXFL62LrDzLT7hfp9jYWAwdOhSffPIJ9u/fj4SEBMyePVu0fvWtT8U05X0hK+xNrNfLBAYG4sqVK+jduzciIiJga2uL8PDwauM/efIEnp6e0NTURGhoKOLi4oT4lduqJlKpFBcuXMAff/wBY2NjzJ07F23btq3TFNc1PZAuLy+PY8eO4dChQ7C1tcXatWthbW2NrKysWucP1L3vxowZg8zMTAwbNgyXLl2Ck5MT1q5dK8ozOTkZ7u7uGDt2rOiqY20UFBQAADZt2oTExETh7/Lly/j777+FOO3btxctT0xMRFpaGoYMGVKrcsrbb//+/WjevDnWr18vtN+rTgRQrvJxpqCgAMbGxlXqnZqaiunTp8vMw9HREVlZWQgKCsLTp0/h5eWFQYMG1VjutGnTEB4ejoULF+L06dNITEyEnZ1dnbZfoO77TmUva8fabEuMMcZYQ2l002DHx8ejrKwMK1asQOfOnWFlZYWbN2/WKy99fX1EREQgPT0dXl5etRoEVcfa2holJSVISEgQwtLT03H//v0a0ykpKaG0tFQUFhMTAzMzM8yePRtOTk6wtLRETk5Ovev2KmxsbETPXwFAdHQ0rKyshKs4stahNulksbKywjfffIOjR49iwIABwrNRssq4evUq7t69i8WLF+Ojjz5Cq1atqlwBK3/GpHLayhQUFODh4YGlS5fi4sWLyM7ORkRERLVlV2Zvb1/jtOkSiQSurq6YN28eEhISoKSkVKcT1PoyNTXFuHHjsGfPHkydOhWbNm0Sll25cgXdunXDiBEjZN42aWNjg7Nnz4rCygc2wIsrTiYmJsjMzETLli1Ff82bNwfwYlBw7do1GBgYVIlTcQrxpKQkPH36VFSOhoaGcOWmvP2++uornD9/Xmg/qVQKc3PzatvexsZG9CwO8GLQ9+DBgxqvEDo6OuLWrVtQUFCoUm99ff1q02lqasLb2xubNm3Cjh07sHv3bty7dw/Ai8Fq5e0oOjoaI0eORP/+/WFnZwcjI6Mqz5fVZvsD6rbvVGZvb48bN27UOLV2TdsSY4wx1pAa3QCoZcuWKC4uxtq1a5GZmYlt27Zh48aN9c7PwMAAERERuHr1Knx8fFBSUlKvfFq1agUPDw+MHTsW586dQ0JCAsaOHQtVVdUar3qZm5vj4sWLSE1NRX5+PoqLi2FpaYnc3FyEhYUhIyMDwcHBDXKyLMvUqVNx4sQJBAUFIS0tDVu2bMG6deswbdo00TqcOnUK//zzD/Lz82udrqKnT5/C398fUVFRyMnJQXR0NOLi4mBjYyOUUVBQgBMnTiA/Px+FhYVo1qwZlJSUhG1h3759Vd7tY2ZmBolEgv379+POnTvCVYuK9u/fj+DgYCQmJiInJwdbt25FWVkZrK2thbLPnj2L7Oxs5Ofny7w6NmvWLMTFxcHPzw8XL17E1atXsWHDBuTn5+Ps2bNYuHAhzp8/j9zcXOzZswd37twR1u1NmTx5Mo4cOYKsrCxcuHABkZGRQpmXL19Gt27d0KNHD0yZMgW3bt3CrVu3cOfOHSH9pEmT8NtvvyEkJARpaWkICAjAlStXRGXMmzcPixYtQnBwMNLS0nDp0iWEhIRg5cqVAF48nK+vr4++ffvi9OnTyMrKQlRUFCZOnIgbN24I+RQVFWH06NFITk7GwYMHERAQAH9/f8jJyYna79atWwgPDxe1X2BgIFasWIHg4GBcu3YNFy5cEK5OeHh4wM7ODkOHDsWFCxdw7tw5DB8+HG5ubsKzerJ4eHjA2dkZ/fr1w9GjR5GdnY2YmBjMnj0b58+fl5lm5cqV+OOPP3D16lWkpaXhzz//hJGRkfDsWflA7datW8IPI5aWltizZw8SExORlJSEIUOGVNm+ZO1fFdVn36nMzc0NXbp0wcCBA3Hs2DFkZWXh0KFDOHz4MICatyXGGGOswb3q1HTvsspTFZdbuXIlGRsbk6qqKnl6etLWrVtFUxJXniKWiCg8PFw0RXPlabBv3rxJVlZW5OXlRSUlJdVOg13RpEmTyM3NTZRHr169SFlZmczMzGj79u1kYGBAGzdurHYdb9++Td27dycNDQ3RNNjTp08nPT090tDQIG9vb1q1apVonSrXj4ho1apVwvS6RLKnwa48JTcACg8PFz6XTx2dkJAghJVPZ62oqEjNmjWjZcuWifKIjY0le3t7UlZWljkNdnXpKnr+/Dl9/vnnZGpqSkpKSmRiYkL+/v6ibXbcuHGkp6cnmsp3+/btZG5uTsrKyuTs7Ez79u2rUv/58+eTkZERSSQSmdNgnz59mtzc3EhHR4dUVVXJ3t6eduzYIaRPTU2lzp07k6qqao3TYEdFRZGLiwspKyuTtrY2eXp60v379yk5OZk8PT2pSZMmpKysTFZWVrR27VohXXleFaeGrqw+fefv708tWrQgZWVlatKkCQ0bNozy8/OJSPYU7KgwPXO5H374gfT19UlDQ4NGjBhBM2bMqLLdhYaGUrt27UhJSYl0dHSoS5cutGfPHmF5Xl4eDR8+nPT19UlZWZksLCzI19dXmA6zfN+aO3eusM37+vrSs2fPiIhE7aekpFSl/YiINm7cSNbW1qSoqEjGxsb09ddfC8tqOw12ZY8ePaKvv/6aTExMSFFRkUxNTWno0KGUm5srs49+/vlnateuHamrq5Ompia5u7vThQsXhOX79u2jli1bkoKCgtDOWVlZ1K1bN1JVVSVTU1Nat25dlf1W1v5V8RhX332n8jZ19+5d+vLLL0lPT49UVFSoTZs2tH//fiKqeVsqz6s8X8YYY41PQ0+DLSF6+Vy6z549Q1ZWFpo3bw4VFZXXPARj1blx4wZMTU1x/Pjxah/SZiwkJAQLFy5EcnJynScJeR+MHDkSDx48wN69e2uMV1paioSEBDg4ONR4GyVrWIWFhdDT08OhQ4fQtWvXt10dxhhjb0FNY41Hjx5BS0sLDx8+rDKhV32999Ng/5dERESgoKAAdnZ2yMvLw4wZM2Bubo4uXbq87aqxd9jBgwexcOHCRjn4Yf99kZGR+Pjjj3nwwxhjrMHwAOgdUlxcjO+++w6ZmZmQSqVwcXFBaGgon9iyGv35559vuwqM1Vvv3r3Ru3fvt10NxhhjjQgPgN4hnp6e8PT0fNvVYOw/ZfPmzW+7Cowxxhj7D2l0s8AxxhhjjDHGGi8eADHGGGOMMcYaDR4AMcYYY4wxxhoNHgAxxhhjjDHGGg0eADHGGGOMMcYaDR4AMcYYY4wxxhoNHgD9BwQGBqJdu3a1jp+dnQ2JRILExMRq43Tt2hWTJ09+5boxxhhjjDH2X/JeD4BGjhwJiUSCxYsXi8L37t0LiUTylmr1btizZw+CgoJeW34SiQR79+59bfkxxhhjjDH2JrzXAyAAUFFRwZIlS3D//v23XZV3iq6uLqRS6duuRoMoKip621VgjDHGGGPviHoPgIgIpaWFDf5HRHWqp4eHB4yMjLBo0aIa4+3evRutW7eGsrIyzM3NsWLFihrjl9+W9ttvv6FZs2bQ0NCAn58fSktLsXTpUhgZGcHAwAA//PCDKF1ubi769u0LDQ0NaGpqwsvLC//++68ozuLFi2FoaAipVIrRo0fj2bNnVcr/5ZdfYGNjAxUVFbRq1Qo//vhjLVvkhcq3wMm6gqOtrY3NmzcDeDGI8Pf3h7GxMVRUVGBmZia0qbm5OQCgf//+kEgkwmdZZs6cCSsrK6ipqcHCwgJz5sxBcXExACAtLQ0SiQRXr14VpVm1ahVatGghfL58+TJ69eoFDQ0NGBoaYtiwYcjPzxetm7+/PyZPngx9fX14enoCAFauXAk7Ozuoq6vD1NQUfn5+KCgoEJW1adMmmJqaQk1NDf3798fKlSuhra0tivPXX3/B0dERKioqsLCwwLx581BSUlLtOjPGGGOMsXeHQn0TlpU9RdRJu9dZl1rp6nYJ8vJqtY4vLy+PhQsXYsiQIZg4cSKaNm1aJU58fDy8vLwQGBgIb29vxMTEwM/PD3p6ehg5cmS1eWdkZODQoUM4fPgwMjIyMGjQIGRmZsLKygonT55ETEwMRo0aBQ8PD3Tq1AllZWXC4OfkyZMoKSnBhAkT4O3tjaioKADAzp07ERgYiPXr1+PDDz/Etm3bEBwcDAsLC6Hc0NBQzJ07F+vWrYODgwMSEhLg6+sLdXV1jBgxotZtUxfBwcHYt28fdu7ciWbNmuH69eu4fv06ACAuLg4GBgYICQlBz549IS8vX20+UqkUmzdvhomJCS5dugRfX19IpVLMmDEDVlZWcHJyQmhoqOj2vNDQUAwZMgQA8ODBA3z88ccYM2YMVq1ahadPn2LmzJnw8vJCRESEkGbLli0YP348oqOjhTA5OTkEBwejefPmyMzMhJ+fH2bMmCEMHqOjozFu3DgsWbIEn332GY4fP445c+aI6n/69GkMHz4cwcHB+Oijj5CRkYGxY8cCAAICAl6xlRljjDHG2JtW7wHQf0n//v3Rrl07BAQE4Ndff62yfOXKlXB3dxdOdq2srJCcnIxly5bVOAAqKyvDb7/9BqlUCltbW3Tr1g2pqak4ePAg5OTkYG1tjSVLliAyMhKdOnXCiRMncOnSJWRlZcHU1BQAsHXrVrRu3RpxcXHo0KEDVq9ejdGjR2P06NEAgAULFuD48eOiq0ABAQFYsWIFBgwYAABo3rw5kpOT8dNPP72xAVBubi4sLS3x4YcfQiKRwMzMTFjWpEkTAC+uGBkZGdWYz/fffy/8b25ujmnTpiEsLAwzZswAAAwdOhTr1q0TBkBpaWmIj4/H77//DgDCoG/hwoVCPr/99htMTU2RlpYGKysrAIClpSWWLl0qKrviFS9zc3MsWLAA48aNEwZAa9euRa9evTBt2jQAL7aDmJgY7N+/X0g3b948fPvtt0I7W1hYICgoCDNmzOABEGOMMcbYf0C9B0Bycqro6nbpddal1uXWx5IlS/Dxxx8LJ7cVpaSkoG/fvqIwV1dXrF69GqWlpdVe0TA3Nxc9R2NoaAh5eXnIycmJwm7fvi2UY2pqKgx+AMDW1hba2tpISUlBhw4dkJKSgnHjxonKcXZ2RmRkJADgyZMnyMjIwOjRo+Hr6yvEKSkpgZaWVm2bo85GjhyJ7t27w9raGj179sSnn36KHj161DmfHTt2IDg4GBkZGSgoKEBJSQk0NTWF5Z9//jmmTZuGv//+G507d0ZoaCgcHR3RqlUrAEBSUhIiIyOhoaFRJe+MjAxhANS+ffsqy48fP45Fixbh6tWrePToEUpKSvDs2TMUFhZCTU0Nqamp6N+/vyhNx44dRQOgpKQkREdHi25tLC0tFeXDGGOMMcbeXfUeAEkkkjrdiva2denSBZ6enpg1a1aNV3XqQlFRUfRZIpHIDCsrK3st5QEQnlnZtGkTOnXqJFpW061nLyORSKo8X1X+bA4AODo6IisrC4cOHcLx48fh5eUFDw8P7Nq1q9ZlxMbGYujQoZg3bx48PT2hpaWFsLAw0fNWRkZG+Pjjj7F9+3Z07twZ27dvx/jx44XlBQUF6NOnD5YsWVIlf2NjY+F/dXV10bLs7Gx8+umnGD9+PH744Qfo6urizJkzGD16NIqKimo9cCkoKMC8efOEq28Vqaio1CoPxhhjjDH29jSKW+DKLV68GO3atYO1tbUo3MbGRvSsCPDieRArK6tXGlRUZmNjIzw7U34VKDk5GQ8ePICtra0Q5+zZsxg+fLiQ7u+//xb+NzQ0hImJCTIzMzF06NDXVrcmTZogLy9P+Hzt2jUUFhaK4mhqasLb2xve3t4YNGgQevbsiXv37kFXVxeKioooLS2tsYyYmBiYmZlh9uzZQlhOTk6VeEOHDsWMGTPg4+ODzMxMfP7558IyR0dH7N69G+bm5lBQqP3mGx8fj7KyMqxYsUK4Qrdz505RHGtra8TFxYnCKn92dHREamoqWrZsWeuyGWOMMcbYu6NRDYDs7OwwdOhQBAcHi8KnTp2KDh06ICgoCN7e3oiNjcW6devqPLPay3h4eAh1WL16NUpKSuDn5wc3Nzc4OTkBACZNmoSRI0fCyckJrq6uCA0NxZUrV0STIMybNw8TJ06ElpYWevbsiefPn+P8+fO4f/8+pkyZUq+6ffzxx1i3bh2cnZ1RWlqKmTNniq5mrVy5EsbGxnBwcICcnBz+/PNPGBkZCTOkmZub48SJE3B1dYWysjJ0dHSqlGFpaYnc3FyEhYWhQ4cOOHDgAMLDw6vEGzBgAMaPH4/x48ejW7duMDExEZZNmDABmzZtgo+PD2bMmAFdXV2kp6cjLCwMv/zyS7UD1pYtW6K4uBhr165Fnz59EB0djY0bN4rifP311+jSpQtWrlyJPn36ICIiAocOHRK9M2ru3Ln49NNP0axZMwwaNAhycnJISkrC5cuXsWDBgjq1OWOMMcYYa3jv/XuAKps/f36VW9IcHR2xc+dOhIWFoU2bNpg7dy7mz5//2m6VKyeRSPDXX39BR0cHXbp0gYeHBywsLLBjxw4hjre3N+bMmYMZM2agffv2yMnJEd0CBgBjxozBL7/8gpCQENjZ2cHNzQ2bN29G8+bN6123FStWwNTUFB999BGGDBmCadOmiW4Lk0qlWLp0KZycnNChQwdkZ2cLkz2Upz927BhMTU3h4OAgs4zPPvsM33zzDfz9/dGuXTvExMRUmWWtvKw+ffogKSmpylUuExMTREdHo7S0FD169ICdnR0mT54MbW1t0bNXlbVt2xYrV67EkiVL0KZNG4SGhlaZGt3V1RUbN27EypUr0bZtWxw+fBjffPON6NY2T09P7N+/H0ePHkWHDh3QuXNnrFq1SjQpBGOMMcYYe3dJqBYv1nn27BmysrLQvHlzfs7hPeHs7Ax3d3e+avESvr6+uHr1Kk6fPv22q8JeUWlpKRISEuDg4PBab21ljDHG2Kupaazx6NEjaGlp4eHDh6KJs15Fo7sC1NiV3y535coVtG7d+m1X552zfPlyJCUlIT09HWvXrsWWLVve2NTijDHGGGOs4TWqZ4AYcOjQIQwfPhyfffYZBg0a9Lar8845d+4cli5disePH8PCwgLBwcEYM2bM264WY4wxxhh7TXgA1Mj069cPjx49etvVeGdVnhmOMcYYY4y9X/gWOMYYY4wxxlijUacBUC3mS2CMMcYYY4yxWmvoMUatBkDl74Op/GJMxhhjjDHGGHsV5WOMiu+gfJNq9QyQvLw8tLW1cfv2bQCAmpqa6OWQjDH2ristLQXwYqpNngabMcYYe/uICIWFhbh9+za0tbUb7Pu51pMgGBkZAYAwCGKMsf+SsrIy5OfnIzs7u8aX5jLGGGOsYWlrawtjjYZQqxehVlRaWori4uI3VR/GGHsjCgoK4OTkhPPnz0NDQ+NtV4cxxhhjeHHbW01Xft7Ei1DrPA22vLw83z7CGPvPKSoqQk5ODpSUlKq8ZZoxxhhjjQffB8IYeyvWr18Pc3NzqKiooFOnTjh37lyN8f/880+0atUKKioqsLOzw8GDBxuopowxxhh7n/AAiDHW4Hbs2IEpU6YgICAAFy5cQNu2beHp6VntM4YxMTHw8fHB6NGjkZCQgH79+qFfv364fPlyA9ecMcYYY/91dX4GiDHGXlWnTp3QoUMHrFu3DsCLCQpMTU3x9ddf49tvv60S39vbG0+ePMH+/fuFsM6dO6Ndu3bYuHFjrcp8E/cQM8YYY+zNeieeAWKMsVdRVFSE+Ph4zJo1SwiTk5ODh4cHYmNjZaaJjY3FlClTRGGenp7Yu3dvteU8f/4cz58/Fz4/fPgQwIsDKWOMMcb+G8q/t1/nNRseADHGGlR+fj5KS0thaGgoCjc0NMTVq1dlprl165bM+Ldu3aq2nEWLFmHevHlVwk1NTetRa8YYY4y9TXfv3oWWltZryYsHQIyx99KsWbNEV40ePHgAMzMz5ObmvrYDKKufR48ewdTUFNevX+fbEd8y7ot3B/fFu4P74t3y8OFDNGvWDLq6uq8tTx4AMcYalL6+PuTl5fHvv/+Kwv/9999qX4JmZGRUp/gAoKysDGVl5SrhWlpa/IX2jtDU1OS+eEdwX7w7uC/eHdwX75bX+RJzngWOMdaglJSU0L59e5w4cUIIKysrw4kTJ+Ds7CwzjbOzsyg+ABw7dqza+Iwxxhhj1eErQIyxBjdlyhSMGDECTk5O6NixI1avXo0nT57gyy+/BAAMHz4cH3zwARYtWgQAmDRpEtzc3LBixQr07t0bYWFhOH/+PH7++ee3uRqMMcYY+w/iARBjrMF5e3vjzp07mDt3Lm7duoV27drh8OHDwkQHubm5okvdLi4u2L59O77//nt89913sLS0xN69e9GmTZtal6msrIyAgACZt8WxhsV98e7gvnh3cF+8O7gv3i1voj/4PUCMMcYYY4yxRoOfAWKMMcYYY4w1GjwAYowxxhhjjDUaPABijDHGGGOMNRo8AGKMMcYYY4w1GjwAYoy9N9avXw9zc3OoqKigU6dOOHfuXI3x//zzT7Rq1QoqKiqws7PDwYMHG6im77+69MWmTZvw0UcfQUdHBzo6OvDw8Hhp37Haq+t+US4sLAwSiQT9+vV7sxVsROraFw8ePMCECRNgbGwMZWVlWFlZ8XHqNalrX6xevRrW1tZQVVWFqakpvvnmGzx79qyBavv+OnXqFPr06QMTExNIJBLs3bv3pWmioqLg6OgIZWVltGzZEps3b65zuTwAYoy9F3bs2IEpU6YgICAAFy5cQNu2beHp6Ynbt2/LjB8TEwMfHx+MHj0aCQkJ6NevH/r164fLly83cM3fP3Xti6ioKPj4+CAyMhKxsbEwNTVFjx498M8//zRwzd8/de2LctnZ2Zg2bRo++uijBqrp+6+ufVFUVITu3bsjOzsbu3btQmpqKjZt2oQPPviggWv+/qlrX2zfvh3ffvstAgICkJKSgl9//RU7duzAd99918A1f/88efIEbdu2xfr162sVPysrC71790a3bt2QmJiIyZMnY8yYMThy5EjdCibGGHsPdOzYkSZMmCB8Li0tJRMTE1q0aJHM+F5eXtS7d29RWKdOneirr756o/VsDOraF5WVlJSQVCqlLVu2vKkqNhr16YuSkhJycXGhX375hUaMGEF9+/ZtgJq+/+raFxs2bCALCwsqKipqqCo2GnXtiwkTJtDHH38sCpsyZQq5urq+0Xo2NgAoPDy8xjgzZsyg1q1bi8K8vb3J09OzTmXxFSDG2H9eUVER4uPj4eHhIYTJycnBw8MDsbGxMtPExsaK4gOAp6dntfFZ7dSnLyorLCxEcXExdHV131Q1G4X69sX8+fNhYGCA0aNHN0Q1G4X69MW+ffvg7OyMCRMmwNDQEG3atMHChQtRWlraUNV+L9WnL1xcXBAfHy/cJpeZmYmDBw/ik08+aZA6s/95Xd/dCq+zUowx9jbk5+ejtLQUhoaGonBDQ0NcvXpVZppbt27JjH/r1q03Vs/GoD59UdnMmTNhYmJS5UuO1U19+uLMmTP49ddfkZiY2AA1bDzq0xeZmZmIiIjA0KFDcfDgQaSnp8PPzw/FxcUICAhoiGq/l+rTF0OGDEF+fj4+/PBDEBFKSkowbtw4vgXuLajuu/vRo0d4+vQpVFVVa5UPXwFijDH2zli8eDHCwsIQHh4OFRWVt12dRuXx48cYNmwYNm3aBH19/bddnUavrKwMBgYG+Pnnn9G+fXt4e3tj9uzZ2Lhx49uuWqMTFRWFhQsX4scff8SFCxewZ88eHDhwAEFBQW+7aqye+AoQY+w/T19fH/Ly8vj3339F4f/++y+MjIxkpjEyMqpTfFY79emLcsuXL8fixYtx/Phx2Nvbv8lqNgp17YuMjAxkZ2ejT58+QlhZWRkAQEFBAampqWjRosWbrfR7qj77hbGxMRQVFSEvLy+E2djY4NatWygqKoKSktIbrfP7qj59MWfOHAwbNgxjxowBANjZ2eHJkycYO3YsZs+eDTk5vp7QUKr77tbU1Kz11R+ArwAxxt4DSkpKaN++PU6cOCGElZWV4cSJE3B2dpaZxtnZWRQfAI4dO1ZtfFY79ekLAFi6dCmCgoJw+PBhODk5NURV33t17YtWrVrh0qVLSExMFP4+++wzYbYlU1PThqz+e6U++4WrqyvS09OFQSgApKWlwdjYmAc/r6A+fVFYWFhlkFM+MH3x7D5rKK/tu7tu8zMwxti7KSwsjJSVlWnz5s2UnJxMY8eOJW1tbbp16xYREQ0bNoy+/fZbIX50dDQpKCjQ8uXLKSUlhQICAkhRUZEuXbr0tlbhvVHXvli8eDEpKSnRrl27KC8vT/h7/Pjx21qF90Zd+6IyngXu9alrX+Tm5pJUKiV/f39KTU2l/fv3k4GBAS1YsOBtrcJ7o659ERAQQFKplP744w/KzMyko0ePUosWLcjLy+ttrcJ74/Hjx5SQkEAJCQkEgFauXEkJCQmUk5NDRETffvstDRs2TIifmZlJampqNH36dEpJSaH169eTvLw8HT58uE7l8gCIMfbeWLt2LTVr1oyUlJSoY8eO9PfffwvL3NzcaMSIEaL4O3fuJCsrK1JSUqLWrVvTgQMHGrjG76+69IWZmRkBqPIXEBDQ8BV/D9V1v6iIB0CvV137IiYmhjp16kTKyspkYWFBP/zwA5WUlDRwrd9PdemL4uJiCgwMpBYtWpCKigqZmpqSn58f3b9/v+Er/p6JjIyUefwvb/8RI0aQm5tblTTt2rUjJSUlsrCwoJCQkDqXKyHia3eMMcYYY4yxxoGfAWKMMcYYY4w1GjwAYowxxhhjjDUaPABijDHGGGOMNRo8AGKMMcYYY4w1GjwAYowxxhhjjDUaPABijDHGGGOMNRo8AGKMMcYYY4w1GjwAYowxxthrFxUVBYlEggcPHrztqrxW5ubmWL16tfBZIpFg7969DV6PwMBAtGvXrsHLZex9wAMgxhhj7D0ycuRISCSSKn/p6elvrMyuXbti8uTJojAXFxfk5eVBS0vrjZX7LsjLy0OvXr1qFZcHLYy9GxTedgUYY4wx9nr17NkTISEhorAmTZpUiVdUVAQlJaU3UgclJSUYGRm9kbxf1etc73d1HRlj1eMrQIwxxth7RllZGUZGRqI/eXl5dO3aFf7+/pg8eTL09fXh6ekJAFi5ciXs7Oygrq4OU1NT+Pn5oaCgQJRndHQ0unbtCjU1Nejo6MDT0xP379/HyJEjcfLkSaxZs0a42pSdnS3zFrjdu3ejdevWUFZWhrm5OVasWCEqw9zcHAsXLsSoUaMglUrRrFkz/PzzzzWua/k6+fv7Q0tLC/r6+pgzZw6ISJRvUFAQhg8fDk1NTYwdOxYAcObMGXz00UdQVVWFqakpJk6ciCdPngjpbt++jT59+kBVVRXNmzdHaGholfIr3wJ348YN+Pj4QFdXF+rq6nBycsLZs2exefNmzJs3D0lJSUI7bd68GQDw4MEDjBkzBk2aNIGmpiY+/vhjJCUlicpZvHgxDA0NIZVKMXr0aDx79qzGdmGMVY8HQIwxxlgjsmXLFigpKSE6OhobN24EAMjJySE4OBhXrlzBli1bEBERgRkzZghpEhMT4e7uDltbW8TGxuLMmTPo06cPSktLsWbNGjg7O8PX1xd5eXnIy8uDqalplXLj4+Ph5eWFzz//HJcuXUJgYCDmzJkjDALKrVixAk5OTkhISICfnx/Gjx+P1NTUl66TgoICzp07hzVr1mDlypX45ZdfRHGWL1+Otm3bIiEhAXPmzEFGRgZ69uyJgQMH4uLFi9ixYwfOnDkDf39/Ic3IkSNx/fp1REZGYteuXfjxxx9x+/btautRUFAANzc3/PPPP9i3bx+SkpIwY8YMlJWVwdvbG1OnTkXr1q2FdvL29gYADB48GLdv38ahQ4cQHx8PR0dHuLu74969ewCAnTt3IjAwEAsXLsT58+dhbGyMH3/8scY2YYzVgBhjjDH23hgxYgTJy8uTurq68Ddo0CAiInJzcyMHB4eX5vHnn3+Snp6e8NnHx4dcXV2rje/m5kaTJk0ShUVGRhIAun//PhERDRkyhLp37y6KM336dLK1tRU+m5mZ0RdffCF8LisrIwMDA9qwYUONZdvY2FBZWZkQNnPmTLKxsRHl269fP1G60aNH09ixY0Vhp0+fJjk5OXr69CmlpqYSADp37pywPCUlhQDQqlWrhDAAFB4eTkREP/30E0mlUrp7967MugYEBFDbtm2rlKmpqUnPnj0Thbdo0YJ++uknIiJydnYmPz8/0fJOnTpVyYsxVjt8BYgxxhh7z3Tr1g2JiYnCX3BwsLCsffv2VeIfP34c7u7u+OCDDyCVSjFs2DDcvXsXhYWFAP53BehVpKSkwNXVVRTm6uqKa9euobS0VAizt7cX/pdIJDAyMqrxqgsAdO7cGRKJRPjs7OxcJV8nJydRmqSkJGzevBkaGhrCn6enJ8rKypCVlYWUlBQoKCiI2qtVq1bQ1tauth6JiYlwcHCArq5ujfWtXI+CggLo6emJ6pKVlYWMjAwAL9quU6dOonTOzs61LoMxJsaTIDDGGGPvGXV1dbRs2bLaZRVlZ2fj008/xfjx4/HDDz9AV1cXZ86cwejRo1FUVAQ1NTWoqqo2RLUBAIqKiqLPEokEZWVlr5xv5fUuKCjAV199hYkTJ1aJ26xZM6SlpdW5jPq0U0FBAYyNjREVFVVlWU2DLcZY/fEVIMYYY6wRi4+PR1lZGVasWIHOnTvDysoKN2/eFMWxt7fHiRMnqs1DSUlJdLVFFhsbG0RHR4vCoqOjYWVlBXl5+fqvAICzZ8+KPv/999+wtLSsMV9HR0ckJyejZcuWVf6UlJTQqlUrlJSUID4+XkiTmppa43uN7O3tkZiYKDy7U5msdnJ0dMStW7egoKBQpR76+voAXrSdrHVkjNUPD4AYY4yxRqxly5YoLi7G2rVrkZmZiW3btgmTI5SbNWsW4uLi4Ofnh4sXL+Lq1avYsGED8vPzAbyYZe3s2bPIzs5Gfn6+zCs2U6dOxYkTJxAUFIS0tDRs2bIF69atw7Rp0155HXJzczFlyhSkpqbijz/+wNq1azFp0qQa08ycORMxMTHw9/dHYmIirl27hr/++kuYBMHa2ho9e/bEV199hbNnzyI+Ph5jxoyp8SqPj48PjIyM0K9fP0RHRyMzMxO7d+9GbGwsgBftlJWVhcTEROTn5+P58+fw8PCAs7Mz+vXrh6NHjyI7OxsxMTGYPXs2zp8/DwCYNGkSfvvtN4SEhCAtLQ0BAQG4cuXKK7cbY40VD4AYY4yxRqxt27ZYuXIllixZgjZt2iA0NBSLFi0SxbGyssLRo0eRlJSEjh07wtnZGX/99RcUFF7cST9t2jTIy8vD1tYWTZo0QW5ubpVyHB0dsXPnToSFhaFNmzaYO3cu5s+fj5EjR77yOgwfPhxPnz5Fx44dMWHCBEyaNEmY6ro69vb2OHnyJNLS0vDRRx/BwcEBc+fOhYmJiRAnJCQEJiYmcHNzw4ABAzB27FgYGBhUm6eSkhKOHj0KAwMDfPLJJ7Czs8PixYuFK1EDBw5Ez5490a1bNzRp0gR//PEHJBIJDh48iC5duuDLL7+ElZUVPv/8c+Tk5MDQ0BAA4O3tjTlz5mDGjBlo3749cnJyMH78+FduN8YaKwlRhYnyGWOMMcb+Q7p27Yp27dph9erVb7sqjLH/CL4CxBhjjDHGGGs0eADEGGOMMcYYazT4FjjGGGOMMcZYo8FXgBhjjDHGGGONBg+AGGOMMcYYY40GD4AYY4wxxhhjjQYPgBhjjDHGGGONBg+AGGOMMcYYY40GD4AYY4wxxhhjjQYPgBhjjDHGGGONBg+AGGOMMcYYY40GD4AYY4wxxhhjjcb/A3jkIgZrtkAnAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "for model_name, result in model_results_dict.items():\n", + " prediction_fraction, average_tanimotos = result\n", + " plt.plot(prediction_fraction, average_tanimotos, label = model_name)\n", + "# plt.plot(prediction_fractions, average_tanimotos, label = \"reranking model 0.9, 0.8, 0.7, 0.5\")\n", + "# plt.plot(prediction_fractions_08_09, average_tanimotos_08_09, label = \"reranking model (no statistics)\")\n", + "# # plt.plot(prediction_fractions_09, average_tanimotos_09, label = \"reranking model 0.9\")\n", + "# # plt.plot(prediction_fractions_09_08_06, average_tanimotos_09_08_06, label = \"reranking model 0.9, 0.8, 0.6\")\n", + "# plt.plot(prediction_fractions_with_statistics, average_tanimotos_with_statistics, label = \"reranking model with statistics\")\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_pos_pos, average_tanimotos_average_over_top_inchikeys_pos_pos, label = \"No model just average\")\n", + "# plt.plot(prediction_fractions_1, average_tanimotos_1, label = \"ranking_model\")\n", + "# plt.plot(prediction_fractions_regression, average_tanimotos_regression, label = \"regression model\")\n", + "\n", + "plt.xlabel(\"Fraction predicted\")\n", + "plt.ylabel(\"Average Tanimoto\")\n", + "plt.legend()\n", + "plt.xlim((0, 1))\n", + "plt.ylim((0, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "id": "c7bcc78c-9a6e-438b-a953-a5357c4f4f6b", + "metadata": {}, + "outputs": [], + "source": [ + "def predict_with_ranking_model(dict_with_scores_for_top_k, model):\n", + " combined_pairwise_score_matrix = []\n", + " for inchikey, scores_for_top_k in dict_with_scores_for_top_k.items():\n", + " pairwise_score_matrix = get_pairwise_score_matrix(scores_for_top_k)\n", + " combined_pairwise_score_matrix.append(pairwise_score_matrix)\n", + " predicted_scores = model.predict(combined_pairwise_score_matrix)\n", + " ranking_indices = np.argsort(predicted_scores)[::-1] # descending order\n", + " all_possible_inchikeys = list(dict_with_scores_for_top_k.keys())\n", + " ranked_candidates = [all_possible_inchikeys[i] for i in ranking_indices]\n", + " top_score = predicted_scores[ranking_indices[0]]\n", + "\n", + " return ranked_candidates[0], top_score\n", + "predicted_inchikeys = []\n", + "scores = []\n", + "for dict_with_scores_for_top_k in tqdm(all_ms2deepscores_for_top_inchikeys_pos_pos):\n", + " highest_inchikey, top_score = predict_with_ranking_model(dict_with_scores_for_top_k, model)\n", + " predicted_inchikeys.append(highest_inchikey)\n", + " scores.append(top_score)" + ] + }, + { + "cell_type": "code", + "execution_count": 305, + "id": "57027ca1-30be-46db-b64f-338973f3e993", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(1.018477663086824)" + ] + }, + "execution_count": 305, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max(scores)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3222d19-0328-48bf-a730-be85415b067d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "6cc10649-a1dd-4be0-ac07-5f4c969d4508", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a8ca6b08acf4432caeb69094ffc56054", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "thresholds: 0%| | 0/30 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "# plt.plot(prediction_fractions, average_tanimotos, label = \"reranking model 0.9, 0.8, 0.7, 0.5\")\n", + "# plt.plot(prediction_fractions_08_09, average_tanimotos_08_09, label = \"reranking model (no statistics)\")\n", + "# # plt.plot(prediction_fractions_09, average_tanimotos_09, label = \"reranking model 0.9\")\n", + "# # plt.plot(prediction_fractions_09_08_06, average_tanimotos_09_08_06, label = \"reranking model 0.9, 0.8, 0.6\")\n", + "# plt.plot(prediction_fractions_with_statistics, average_tanimotos_with_statistics, label = \"reranking model with statistics\")\n", + "# plt.plot(prediction_fractions_average_over_top_inchikeys_pos_pos, average_tanimotos_average_over_top_inchikeys_pos_pos, label = \"No model just average\")\n", + "plt.plot(prediction_fractions_1, average_tanimotos_1, label = \"ranking_model\")\n", + "plt.plot(prediction_fractions_regression, average_tanimotos_regression, label = \"regression model\")\n", + "\n", + "plt.xlabel(\"Fraction predicted\")\n", + "plt.ylabel(\"Average Tanimoto\")\n", + "plt.legend()\n", + "plt.xlim((0, 1))\n", + "plt.ylim((0, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5ef6701c-0aec-4450-bb24-cc6a6689ef94", + "metadata": {}, + "source": [ + "# Test prediction on distribution of ms2deepscores (within inchikey)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "id": "831d839e-de2b-4b15-b8e3-3f98997bdb20", + "metadata": {}, + "outputs": [], + "source": [ + "def get_ms2deepscore_distribution_features(all_ms2deepscores_for_top_inchikeys):\n", + " features_per_query = []\n", + " for query_results in tqdm(all_ms2deepscores_for_top_inchikeys):\n", + " for potential_inchikey, scores_per_inchikey in query_results.items():\n", + " if potential_inchikey in scores_per_inchikey.ms2deepscores_per_inchikey:\n", + " ms2deepscores = scores_per_inchikey.ms2deepscores_per_inchikey[potential_inchikey]\n", + " else:\n", + " highest_inchikey = max(scores_per_inchikey.top_k_inchikeys_and_tanimoto_scores, key=scores_per_inchikey.top_k_inchikeys_and_tanimoto_scores.get)\n", + " ms2deepscores = scores_per_inchikey.ms2deepscores_per_inchikey[highest_inchikey]\n", + " features_per_query.append((ms2deepscores.mean(), ms2deepscores.std(), ms2deepscores.max(), ms2deepscores.min(), len(ms2deepscores))) \n", + " return features_per_query\n", + "\n", + "def get_ms2deepscore_distribution_features_for_inchikeys(inchikeys):\n", + " X = []\n", + " for inchikey in tqdm(inchikeys):\n", + " matching_spectrum_indexes = val_pos.spectrum_indices_per_inchikey[inchikey]\n", + " for spectrum_index in matching_spectrum_indexes:\n", + " dictionary_with_scores = all_ms2deepscores_for_top_inchikeys_pos_pos[spectrum_index]\n", + " for potential_inchikey, scores_per_inchikey in dictionary_with_scores.items():\n", + " if potential_inchikey in scores_per_inchikey.ms2deepscores_per_inchikey:\n", + " ms2deepscores = scores_per_inchikey.ms2deepscores_per_inchikey[potential_inchikey]\n", + " else:\n", + " highest_inchikey = max(scores_per_inchikey.top_k_inchikeys_and_tanimoto_scores, key=scores_per_inchikey.top_k_inchikeys_and_tanimoto_scores.get)\n", + " ms2deepscores = scores_per_inchikey.ms2deepscores_per_inchikey[highest_inchikey]\n", + " X.append((ms2deepscores.mean(), ms2deepscores.std(), ms2deepscores.max(), ms2deepscores.min(), len(ms2deepscores))) \n", + " return np.array(X)\n", + "\n", + "# ms2ds_features_per_query = get_ms2deepscore_distribution_features_for_inchikeys()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d46a73b-0de7-4b5c-a159-b0098c51a00e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 140, + "id": "4ba7a965-04e1-4230-bd98-dedfc8726d51", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e7c530188ae64ee0bab4b2d09f2a1fbd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/36473 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_feature_names = [\"mean\",\"std\", \"max\", \"min\", \"len\"]\n", + "\n", + "# Gain-based importance (best for ranking)\n", + "importance = regression_model.feature_importance(importance_type='gain')\n", + "# Sort descending\n", + "sorted_idx = np.argsort(importance)[::-1]\n", + "plt.figure(figsize=(8, 6))\n", + "plt.barh(\n", + " np.array(selected_feature_names)[sorted_idx],\n", + " importance[sorted_idx]\n", + ")\n", + "plt.gca().invert_yaxis()\n", + "plt.title(model_name)\n", + "plt.xlabel(\"Gain Importance\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "id": "f1c45793-c26e-4551-9715-2d6bac6f4dbe", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ae56ba751e6e47559890b670f2abc7b7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c8cff965df9842948f1ebee4ff1ad08d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "thresholds: 0%| | 0/30 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(prediction_fractions_1, average_tanimotos_1, label=\"ms2deepscore statistics\")\n", + "prediction_fraction, average_tanimotos = model_results_dict['regression tanimoto scores, ms2deepscore scores, tanimoto statistics, ms2deepscore statistics, ']\n", + "plt.plot(prediction_fraction, average_tanimotos, label = \"Regression model\")\n", + "prediction_fraction, average_tanimotos = model_results_dict['ranking tanimoto scores, ms2deepscore scores, tanimoto statistics, ms2deepscore statistics, ']\n", + "plt.plot(prediction_fraction, average_tanimotos, label = \"Ranking model\")\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_pos_pos, average_tanimotos_average_over_top_inchikeys_pos_pos, label = \"No model just average\")\n", + "plt.plot(prediction_fractions_mean_for_higest_ms2deepscore, average_tanimotos_mean_for_higest_ms2deepscore, label=\"mean ms2deepscore single\")\n", + "\n", + " \n", + "plt.xlabel(\"Fraction predicted\")\n", + "plt.ylabel(\"Average Tanimoto\")\n", + "plt.legend()\n", + "plt.xlim((0, 1))\n", + "plt.ylim((0, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "2facb725-414d-45ed-9ccd-76c090c9ddd8", + "metadata": {}, + "outputs": [], + "source": [ + "ms2ds_features_per_query = np.array(ms2ds_features_per_query)" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "id": "4d4e71b8-6288-42c9-92b6-0d4f00d32bb0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3647300, 5)" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ms2ds_features_per_query.shape" + ] + }, + { + "cell_type": "markdown", + "id": "2c6d3ee9-92b8-4437-b2d7-f76c6b78ce2b", + "metadata": {}, + "source": [ + "# Test for reranking of top 1000\n" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "id": "435f6d52-119e-44c5-bf2b-c502ca7b5b2a", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(pickled_intermediates_data_folder, \"close_tanimoto_scores_top_1000_ms2deepscores.pickle\"), \"rb\") as handle:\n", + " top_k_tanimoto_scores_1000 = pickle.load(handle)" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "id": "e484c1a0-a052-4bae-9b11-901f5676088f", + "metadata": {}, + "outputs": [], + "source": [ + " \n", + "def predict_on_average_over_top_inchikeys_already_means(dict_with_ms2deepscores_for_top_inchikeys):\n", + " highest_mean = 0\n", + " best_inchikey = None\n", + " for inchikey, inchikey_with_scores in dict_with_ms2deepscores_for_top_inchikeys.items():\n", + " scores = list(inchikey_with_scores.values())\n", + " mean = sum(scores)/10\n", + " if mean > highest_mean:\n", + " highest_mean = mean\n", + " best_inchikey = inchikey\n", + " return best_inchikey, highest_mean" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "id": "a20a3d69-0fce-4a22-97ea-ac084a2f2280", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3698993ea7ce4163a4bf7c9883aae98f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/36473 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(prediction_fractions_average_over_top_inchikeys_pos_pos, average_tanimotos_average_over_top_inchikeys_pos_pos, label = \"Top 100\")\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_top_1000, average_tanimotos_average_over_top_inchikeys_top_1000, label=\"Top 1000\")\n", + "\n", + "plt.xlabel(\"Fraction predicted\")\n", + "plt.ylabel(\"Average Tanimoto\")\n", + "plt.legend()\n", + "plt.xlim((0, 1))\n", + "plt.ylim((0, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "11ece24a-96f3-4b6e-801f-7f0db73a790b", + "metadata": {}, + "source": [ + "# test with less than 100 top inchikeys" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "id": "b897ca22-0759-4be0-8a20-319a57fffa6e", + "metadata": {}, + "outputs": [], + "source": [ + "def predict_on_mean_top_k(all_ms2deepscores_for_top_inchikeys, k):\n", + " predicted_inchikeys = []\n", + " predicted_scores =[]\n", + " \n", + " for single_query in tqdm(all_ms2deepscores_for_top_inchikeys):\n", + " max_score_per_inchikey = []\n", + " inchikeys = list(single_query.keys())\n", + " for inchikey in inchikeys:\n", + " max_per_inchikey = single_query[inchikey].get_max_per_inchikey()\n", + " if inchikey in max_per_inchikey.keys():\n", + " max_score_per_inchikey.append(max_per_inchikey[inchikey])\n", + " else:\n", + " max_score_per_inchikey.append(max(list(max_per_inchikey.values())))\n", + " idx = np.argsort(max_score_per_inchikey)[-k:][::-1]\n", + " \n", + " highest_mean = 0\n", + " best_inchikey = None\n", + " for index in idx:\n", + " inchikey = inchikeys[index]\n", + " scores = single_query[inchikey]\n", + " mean = scores.get_mean()\n", + " if mean > highest_mean:\n", + " highest_mean = mean\n", + " best_inchikey = inchikey\n", + " predicted_inchikeys.append(best_inchikey)\n", + " predicted_scores.append(highest_mean)\n", + " return predicted_inchikeys, predicted_scores\n" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "id": "aa76e0b0-7411-4c1c-91ea-db7951e1f77b", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "61e8750baac24d2bbb04237d7ae026a7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/36473 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_top_1, average_tanimotos_average_over_top_inchikeys_top_1, label=\"Top 1\")\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_top_5, average_tanimotos_average_over_top_inchikeys_top_5, label=\"Top 5\")\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_top_10, average_tanimotos_average_over_top_inchikeys_top_10, label=\"Top 10\")\n", + "# plt.plot(prediction_fractions_average_over_top_inchikeys_top_20, average_tanimotos_average_over_top_inchikeys_top_20, label=\"Top 20\")\n", + "# plt.plot(prediction_fractions_average_over_top_inchikeys_top_50, average_tanimotos_average_over_top_inchikeys_top_50, label=\"Top 50\")\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_pos_pos, average_tanimotos_average_over_top_inchikeys_pos_pos, label = \"Top 100\")\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_top_1000, average_tanimotos_average_over_top_inchikeys_top_1000, label=\"Top 1000\")\n", + "# plt.plot(prediction_fractions_average_over_top_inchikeys_top_100, average_tanimotos_average_over_top_inchikeys_top_100, label=\"Top 100 sanity check\")\n", + "# plt.plot(prediction_fractions_highest_ms2deepscore_pos_pos, average_tanimotos_highest_ms2deepscore_pos_pos, label=\"Top 1 no reranking\")\n", + "plt.title(\"Top inchikeys selected on highest MS2Deepscore for reranking\")\n", + "plt.xlabel(\"Fraction predicted\")\n", + "plt.ylabel(\"Average Tanimoto\")\n", + "plt.legend()\n", + "plt.xlim((0, 1))\n", + "plt.ylim((0, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "id": "736ba08b-5078-4264-9348-03fed31fcbf9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_top_1, average_tanimotos_average_over_top_inchikeys_top_1, label=\"Highest MS2DeepScore, Threshold: Average top 10 inchikey\")\n", + "plt.plot(prediction_fractions_highest_ms2deepscore_pos_pos, average_tanimotos_highest_ms2deepscore_pos_pos, label=\"Highest MS2DeepScore, Threshold: highest ms2deepscore\")\n", + "\n", + "plt.xlabel(\"Fraction predicted\")\n", + "plt.ylabel(\"Average Tanimoto\")\n", + "plt.legend()\n", + "plt.xlim((0, 1))\n", + "plt.ylim((0, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "id": "39e9f75b-2757-428d-abbc-b1d6d55822d2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "plt.plot(prediction_fractions_average_over_top_inchikeys_top_1, average_tanimotos_average_over_top_inchikeys_top_1, label=\"Highest MS2DeepScore, Threshold: Average top 10 inchikey\")\n", + "plt.plot(prediction_fractions_highest_ms2deepscore_pos_pos, average_tanimotos_highest_ms2deepscore_pos_pos, label=\"Highest MS2DeepScore, Threshold: highest ms2deepscore\")\n", + "prediction_fraction, average_tanimotos = model_results_dict['regression tanimoto scores, ms2deepscore scores, tanimoto statistics, ms2deepscore statistics, ']\n", + "plt.plot(prediction_fraction, average_tanimotos, label = \"Regression model\")\n", + "plt.xlabel(\"Fraction predicted\")\n", + "plt.ylabel(\"Average Tanimoto\")\n", + "plt.legend()\n", + "plt.xlim((0, 1))\n", + "plt.ylim((0, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9668f1d7-fcff-4ab1-8681-3d1653b75ef1", + "metadata": {}, + "source": [ + "# Conclusion\n", + "So this showed that the most promising method was going for just the highest MS2DeepScore, but focus on predicting the reliability for these predictions. This is what we do in the cleaned notebook." + ] + }, + { + "cell_type": "markdown", + "id": "d0b7d448-f4d6-4adf-b5ff-8d7eca31375a", + "metadata": {}, + "source": [ + "# Below is not reproducible\n", + "This should be run after the other cleaned notebook, to get examples of cases wher MS2DeepScore is high, but mod cos is low. " + ] + }, + { + "cell_type": "code", + "execution_count": 719, + "id": "b7c73500-9c1d-45dd-b831-53d164d3075b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(1.)" + ] + }, + "execution_count": 719, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from matchms import Spectrum\n", + "from matchms.similarity import ModifiedCosine\n", + "import numpy as np\n", + "similarity = ModifiedCosine(tolerance=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 728, + "id": "2ebe93e1-98f7-4e95-b2e3-2d2184c8b09d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9375421035244955\n", + "1.0\n" + ] + }, + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "correct molecule: \n" + ] + }, + { + "data": { + "image/jpeg": 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", 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", 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "correct molecule: \n" + ] + }, + { + "data": { + "image/jpeg": 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", 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "correct molecule: \n" + ] + }, + { + "data": { + "image/jpeg": 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", 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "correct molecule: \n" + ] + }, + { + "data": { + "image/jpeg": 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e+cXUTmnTEyimdWbKysqaOXOmiKd3795qtbqzn/YLFy5MnDjR8HG18AhDdXW14bCdWAF9//33j9j+xo0bS5YsMbyfoupsRaLWJnq4AIwePdp4TyOmdWI/ERQUZJvekby8vOeff17saZycnMSDXr16JScnSzRzf4SuJyydThcSEiJpMdUkcfDY399fZP0///nP+/fvDw8PF39gKxZTO3L8+HFDT6BSqWw/TbBWMdWkx/YEtte+mHr+/PmujV5RUWHoCZwwYUIXVlJCenq6oVUqMDDQzMN2xtO6oUOHHj58uGujt1deXp6QkCBqbWJPU1lZ2WYbsZ8QhxG6PK0zU1NTU3Jy8tChQ8VbNHny5M8//3zr1q2G5b9KpbLxdCE3N9ewn4iIiOho+S+1riesU6dOiRWQm5vb6tWrpZhKtFdUVPTMM8+Id23evHmlpaXieasXU026efOm4eNq3BNokiXFVJPaTOv27t3bqR9vX0ztVK3NuCfQy8vLKj2BaWlpY8eOFW/RyJEjH11rO3To0ODBg2HtUz2MlZaWtqm13b17V3zr4sWLkZGRhv2E1Ot94cGDB3/9619FY5dBdHT0d999Z4PRTUpLS0tPT5drdLJwSShRMdWkNj2BJg8eW6WYapJeTzt20Jw5meI33bJli5mnMnS2mNqRLvQEmvTYYqpJly5dmjx5suHjasWeQLGnCQoKEi8eGRnZvnptfKrHuHHjrHuqR3uXL1821Nr8/PxeeOGFhQsXin2zuBCdpKO3d//+/eXLlzs7O7u4uLz66qu2KXrYLSsU3Y3/wBYWUzvyzTffjBw5EqZ6Aturq6t7++23xVSid+++L774Q0GBRaMXFND06QSQiwstX/5uF1qTzSymmmRhT6BJxsVUd3d3pVLZUSVI9ASKaV1AQIBEy21RawsICDBMIr755htqt/yX4lSPjmRmZk6fPt0wr3F2dl67dm2nqnXWJWO92K5Y7Sih5cVUk9r0BJq/0Ltz586bb745c+ZHItG8/DJ1YVqg05FaTR4eBFDfvmRJB++ji6kdsVZPoEn5+fmGPY2otbWZuNm4J7C+vn7Tpk2Gw3aDBw821N3mz58v0akej6bRaAIDA4OCgnbv3m370Vl7Vm5r6FoxtSOGXo8uHzwuL6eEBHJ3J4Dc3EippHa11A5lZFBYGAGkUFB8PD1yVmeu9sXUEydOmNxSip5Ak3Jzcw21NsP1RoyndeHh4WfOnJFo9Pbq6urUarU4bAfAx8dH0lM9mGORpA+rU8VUk65cufLcc8+JV+hUT6BJpaWkVJKzMwHk5UUqFf1YSzWttpYSEsjJiQAKDibLWgJNELW2jq430r4n0MrDm3LixAlDlUpclUVM6zZv3mz7BjciysnJWbhw4Ysvvmg4rsIYSdc42r6YambPSJueQCu28F6+THFxpFAQQH5+pFZTQwP9/ve0Y0frBteu0fr1tHcvBQQQQO7ulJhI0l0Arv31RrKzsw8dOmThqR6WSEtLM5yQGBISUmBh8Y8xa5O2072jYmpHrNsTaFJmZmsFHaAVKygqivz96coVIqJLl2jCBHr7bQIoOpqs3RJoWlVV1dq1a8VhO4Phw4d3tFSUWlNT09atW3fu3Gn7nkDGHssWp+ZotVq1Wt2zZ0/DCujixYvttzHuCTxy5IikIR05QpGRlJtLUVG0YQOJS5aKhNXYSLt2kY0/rdevX585c6a4Ksv8+fP5kBBjJtnuXEJRTDV5FvjBgwel7gnsSFQU5eTQ1Km0d29rwpJReXl5+wZrxpiBgohgQzdv3ty0adP27dt1Op2bm9uvfvWr+vr6PXv2ABg/fvz27dsNZ//bxuTJ2L4dABYswL//jZUrce6cLcdnjHWCrROWcO3atU2bNu3YsUOv1ysUCg8Pj40bN7755pvGtxixDZGwwsOxdi3KylBaygmLMfslT8ISLl++PH78+KampsLCQkNHpY0ZEtb9+xg5En37csJizH7JeSPVsLAwUdISp9TKYuJEeHsDQPfu+PBD/Hh5K8aYPZL5zs+y3y154ED4+rY+jonBwIFyBcIYe7wnPWFt24ba2tbH9+/jww/lCoQx9nhPesJijDkQF3mHt4eEtXs3/P0BPJxqMcbsEycsNDejqan1AWPMnsmcsMQ1CeRNWMuXQ1zu5cYNfPSRjIEwxh6Da1iMMYdhFwlLq9XKFUBoKNzcWh+7umLECLkCYYw9nsxLwhWBgUtnzBhq8zNyDA4ffvi4Tx8cOyZXIIyxx5M5YUU3NODkScyeLW8YjDGHIPOSEOLS3Q8eyBwGY8wRyJ2wPD0BgIvujDEzcMJijDkMTliMMYchd8Ly8gI4YTHGzCJ3wuIZFmPMbJywGGMOwz4Slnyd7owxB2IfCYtnWIwxM8jc6Y6gIGRkPLxKMWOMdUzWGVZNDdaswapV+OUv8ZvfoLISAD77DGr1w23eeANpaXIFyBizK7ImrPh49OyJb79FdjaCg7FkCYhQU4ObNx9uU16Oujr5QmSM2RH5ElZREXJy8O67cHeHqyveegv37iErS7Z4GGN2T74aVnExRoyAy48BKBQID0dREQD85z8oLm59/vx5vPiiPBEyxuyMfAnL07PtRRq0Wnh7o6YGMTHYuLH1yZUrbR8aY8w+ybckHD0aRUWorm79UqtFdjbGjwcAHx8MGdL6T/Q9MMaYnAmrd2+sXIkFC3DoEL7+GosW4Re/aL0bBGOMmaIgIjnHT01FRgaamzFlCp5/HgoFTp/G3buYN691g927MXYswsLkDJIxZh/kTliMMWY2uU/NYYwxs3HCYow5DE5YjDGHwQmLMeYwOGExxhzG/wDJ2hTk+X0gyQAAAABJRU5ErkJggg==", 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", 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", 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", 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "correct molecule: \n" + ] + }, + { + "data": { + "image/jpeg": 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", 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "correct molecule: \n" + ] + }, + { + "data": { + "image/jpeg": 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REVmAiygiIiIiC/AzUUREREQW4J0oIiIiIgtwEUVERERkAQ7bvIe8vDz8/fffqFChgsW/6oKIiIhKlxACN2/eRNWqVaUNhuUiqghRUVGIiopCVlaW9tvfiYiISC2JiYmoXr26lGx+sPweUlNT4ebmhsTERLi6upZo9l9//YWZM2di27ZtuHbtGry8vBAWFoZJkybB3d39obJTU1MhhICbmxuAO78Dq2HDhpgzZ452zJ49e/D0008jPj5eO+6fTCYTsrKyHrrP/cjKysLHH3+MdevW4ezZs3B0dEStWrUQHh6O559/HnZ2dsjNzcWsWbOwdu1aXL58Gd7e3njhhRcwYcKEYu8UJiYmYsyYMdizZw+cnZ3xwgsvIDIyEuXKyf3fEWlpaQBQov92VMhUoaOMTBU6yshUoaOMTBU6yshUoSMAXLx4EUFBQbhx4waMRmOJ5d6Nd6LuIf8/zK6uriX6l3vu3Dl06NABtWvXxtq1a+Hv749jx47hzTffxJNPPol9+/YVunDJysqCvb39PfP/2bVcuXJwcHAw257/2+iLO7eSXjgWJSsrCz179kRMTAzee+89tG7dGq6urti3bx/mzZuHxx9/HE2aNMHMmTOxfPlyrFy5EvXr18fBgwcxZMgQVKlSBaNGjSo0Ozc3F/369YOXlxf27t2LpKQkDBo0CC4uLpg5c2apnJ+M66hCpgodZWSq0FFGpgodZWSq0FFGpt475i/MpH4Ux3q/cUYNqampAoBITU0t0dynnnpKVK9eXaSnp5ttT0pKEk5OTuKVV14RQgjh5+cnpk+fLgYOHCgqVKggwsPDRXR0tDAajWLLli2ibt26wtnZWXTp0kX8/fffWk54eLjo2bOn9mcAZj/nz5/Xft/Vtm3bxGOPPSYcHR1Fq1atxMmTJ7WcadOmicaNGxfInTt3rvDy8hLu7u7i1VdfFVlZWdoxt2/fFuPGjRNVq1YVTk5OokWLFoX+Hre7zZkzR9jY2IjDhw8X2JeVlSVMJpMQQoiwsDAxdOhQs/3PPPOMePHFF4vM3rRpk7CxsRGXLl3Sti1evFi4urqKzMzMYns9rNTU1BL/t6NCpgodZWSq0FFGpgodZWSq0FFGpgodhRAiMTFRyn+/78Zv51lBSkoKfvzxR7z66qtwdHQ02+fl5YUXX3wRa9euhfjvO63z5s1D48aNceTIEbzzzjsAgPT0dMybNw+rV6/G7t27kZCQgPHjxxf6eh999BFatWqFl19+GUlJSUhKSoKPj4+2f8qUKZg/fz4OHjyIcuXKYejQocX237lzJ86ePYudO3di5cqVWLFiBVasWKHtf+2117B371588cUX+OOPP9C3b1889dRTOH36dJGZn332GTp16oSmTZsW2GdnZ6fdNXv88cexfft2nDp1CgAQExODX375BV27dtWOj4yMRI0aNbTHe/fuRcOGDVGlShVtW5cuXZCWloZjx44Ve65ERERF4dt5VnD69GkIIVCvXr1C99erVw/Xr1/HlStXAABPPPEExo0bp+3fs2cPsrOz8cknnyAgIADAnYXL9OnTC80zGo2wt7eHk5MTvLy8CuyfMWMGQkNDAQCTJk1CWFgYbt++jfLlyxeaV7FiRSxatAi2traoW7cuwsLCsH37drz88stISEhAdHQ0EhISULVqVQDA+PHjsWXLFkRHRxf59tnp06fRvn37QvfdbdKkSUhLS0PdunVha2uL3NxczJgxAy+++KJ2TOXKlbXrAgCXLl0yW0AB0B5funTpnq9JRERUGN6JsiJxn5/pb968eYFtTk5OZgsFb29vJCcnW9SjUaNGZjkAis2qX78+bG1tC33t2NhY5Obmonbt2nBxcdF+fv75Z+1bjndvf+WVVwDc/7VYt24dPvvsM6xZswaHDx/GypUrMW/ePKxcuVI75rXXXsP27dvv8+yJiIgswztRVhAYGAiDwYATJ06gd+/eBfafOHECFStWhIeHB4D/fQD8bnZ2dmaPDQbDfS9EisvK/wBeXl7efR2f/5z8400mE2xtbXHo0CGzhRZwZ/EEAEePHtW25X+IsHbt2jh58uQ9u7755puYNGkS+vXrBwBo2LAh4uPjMWvWLISHhxf6HC8vLxw4cMBs2+XLl7V9REREluAi6j7lf8r/ftnY2GiLhn+qVKkSOnfujI8//hhjxowx+1zUpUuX8Nlnn2HQoEEl+o0Ce3t75ObmllheUZo2bYrc3FwkJyejbdu2hR4TGBhYYNsLL7yAt956C0eOHCnwuajs7GxkZWXB2dkZ6enpBYam2draFrvoa9WqFWbMmIHk5GR4enoCALZu3QpXV1cEBQUVez4mk6nY7HsxmUwWP1flTBU6yshUoaOMTBU6yshUoaOMTBU6AsCtW7dKPPOf+HZeEaKiohAUFITg4GAp+YsWLUJmZia6dOmC3bt3IzExEVu2bEHnzp1RrVo1zJgxo0Rfr0aNGti/fz8uXLiAq1evPtTCoDi1a9fGiy++iEGDBmHDhg04f/48Dhw4gFmzZmHjxo1FPm/06NFo3bo1OnbsiKioKMTExODcuXNYt24dQkJCtA+ld+/eHTNmzMDGjRtx4cIFfP311/jwww/N7ugtWrQIHTt21B4/+eSTCAoKwsCBAxETE4Mff/wRb7/9NiIiIuDg4CDlOhARUdnHO1FFiIiIQEREBNLS0mA0Gkt8TlStWrVw8OBBTJs2Dc899xxSUlLg5eWFXr16Ydq0aSU+3HL8+PEIDw9HUFAQMjIycP78+RLNv1t0dDTef/99jBs3DhcvXkTlypUREhKCp59+usjnODg4YOvWrfjXv/6FTz/9FOPHj4eTkxPq1auHUaNGoUGDBgCAhQsX4p133sGrr76K5ORkVK1aFSNGjMDUqVO1rKtXr5pNmbe1tcUPP/yAkSNHolWrVnB2dkZ4eHiRH8S/W1F3Ex+U3uepyMpUoaOMTBU6yshUoaOMTBU6ysjUe8cHfQfJEpxYfg/5i6jU1NRSGzxJZYcqk30f1YnGPG+et57yVMlUoSNw57eC+Pj4SP3vN9/OIyIiIrKAVRdRixcvRrNmzWBnZ4fIyEizfStWrED16tXh6uqKIUOGICsrS9t39uxZtG7dGk5OTmjWrBliYmK0fcuXL4eHhweaNm2K+Ph4bfsXX3yB1157Tfo5ERER0aPBqosob29vREZGok+fPmbbY2NjMWbMGHz99ddITExEYmIi3nvvPW1///790alTJ6SkpODll19G7969kZOTg5ycHERGRuLYsWMYMWIEZs+eDeDOJ/TnzJljlkFERET0MKy6iOrVqxd69OgBNzc3s+1r1qxBnz59EBwcDKPRiLfffhurVq0CAPz55584fvw43nrrLZQvXx4jR45EXl4e9uzZg2vXrqF69erw9PREhw4dcOHCBQB3JnJHRESgYsWKpXyGREREVFbp8tt5x48fN/uKesOGDZGQkACTyYTjx4+jdu3aZl9Nb9iwIY4dO4bQ0FBcuXIFSUlJ2LlzJ4KCgnDmzBns3r0b77///n29dmZmJjIzM7XH+R92K8k5UcVJT8/GyZMpqFvXHU5Odvd+AknFOVH6yFMlU4WOMjJV6CgjU4WOMjJV6Ag8wnOiTCaT2Sfp8/9sMpkK7MvfbzKZYGNjg48++gjdu3fH5s2bMWnSJIwdOxbz58/HypUrERoaipdeegm3b98u8rVnzZoFo9Go/dz9i3pLw8mTKXjssdU4eTKlVF+XiIiIHowu70S5uLiY3fnJ/3P+71v7512htLQ07a5Pt27d0K1bNwDAxo0btV9GO3LkSOzfvx9z5szBsmXLEBERUehrT548GWPHjjXL9vHxKfE5UaQGzonSV54qmSp0lJGpQkcZmSp0lJGp946lMSdKl3eigoKCEBsbqz2Oi4uDr68vXFxcEBQUhNOnT5u95RYXF4f69eubZWRlZSEyMhKzZ8/G2bNnUbduXdjZ2SE4OBhxcXFFvraDg4O2YOLCiYiIiIpi1UVUTk4Obt++jdzcXLM/v/DCC1i/fj0OHTqE1NRUzJgxA4MGDQIA1KlTB/Xq1cPs2bORmZmJJUuWwGAwFPg9bfPnz8eAAQPg6ekJHx8fHDp0CCaTCbt27UKNGjWscLZERERUllh1EfX+++/D0dERS5cuxYwZM+Do6IjVq1ejYcOG+PDDD9GjRw9Ur14dVatWxdtvv609b82aNfjpp5/g5uaGxYsXY8OGDShX7n/vTF68eBHfffed9pZd1apVMWDAAPj6+mLv3r0YMWJEqZ9rvvzPdT1qmSp0lJVJRERlk1U/ExUZGVlgyGa+wYMHY/DgwYXuCwwMxK+//lpkbrVq1bB3716zbe+88w7eeecdS6uWGBm/+FeFTBU6ysokIqKySZefiSIiIiLSO11+O0+PSupT/vd6q8hkuqX93/t9TRVmdqjQUUamCh1lZKrQUUamCh1lZKrQUUamCh1lZKrQEXiE50TpQVRUFIKCghAcHGztKkRERKRDvBNVhIiICERERCAtLQ1Go7HExx0UleXikvHf/+v8wK+n95kdMvJUyVSho4xMFTrKyFSho4xMFTrKyFSho4xMvXd8ZOdEEREREekdF1FEREREFuAiioiIiMgCXEQRERERWYCLKCIiIiILcBFFREREZAGOOLhPHLaprzxVMlXoKCNThY4yMlXoKCNThY4yMlXoKCNThY4Ah21aFYdtEhERUXF4J6oIHLYpJ1OFjjIyVegoI1OFjjIyVegoI1OFjjIyVegoI1PvHTlsk4iIiEinuIgiIiIisgAXUUREREQW4CKKiIiIyAJcRBERERFZgN/Ou0+cE6WvPFUyVegoI1OFjjIyVegoI1OFjjIyVegoI1OFjgDnRFkV50QRERFRcXgnqgicEyUnU4WOMjJV6CgjU4WOMjJV6CgjU4WOMjJV6CgjU+8dOSeKiIiISKe4iCIiIiKyABdRRERERBbgIoqIiIjIAlxEEREREVmAiygiIiIiC3DEwX3isE195amSqUJHGZkqdJSRqUJHGZkqdJSRqUJHGZkqdAQ4bNOqOGyTiIiIisM7UUXgsE05mSp0lJGpQkcZmSp0lJGpQkcZmSp0lJGpQkcZmXrvyGGbRERERDrFRRQRERGRBXS7iHJxcTH7sbGxwfz58wEAu3btgo2Njdn+PXv2AACysrLQq1cvuLm54dVXXzXLDAsLw4EDB0r9XIiIiKjs0e0iymQyaT+nTp2CjY0NnnnmGW1/zZo1zY5p27YtAGDDhg1wcXHB5cuXERcXh/379wMAvv32W3h7e6NFixZWOR8iIiIqW5T4YPmaNWvQqlUr+Pv73/PY+Ph4tGvXDg4ODmjdujUuXLiAxo0bY/r06diyZUsptCUiIqJHgRKLqNWrV+O1114z25aYmAhPT08YjUYMHDgQU6ZMga2tLerVq4cvvvgCgwYNwq+//or+/ftj7ty5GDx4MDw8PO75WpmZmcjMzNQe53+6n3Oi9JWnSqYKHWVkqtBRRqYKHWVkqtBRRqYKHWVkqtAR4JwoAMAff/yBU6dOoW/fvtq2unXrIiYmBpcuXcK3336LdevWYcGCBQCAp59+Gp6enmjRogW6du0KNzc3bNq0CeHh4Rg8eDBCQ0OxevXqIl9v1qxZMBqN2o+Pj4/sUyQiIiIF6f5O1OrVq9G9e3e4ublp27y8vODl5QUACAoKwttvv42FCxdi3LhxsLGx0RZUAPDcc89hzpw5WLFiBWrXro0lS5YgJCQE3bp1Q6VKlQq83uTJkzF27FjtcVpaGnx8fDgnSqd5qmSq0FFGpgodZWSq0FFGpgodZWSq0FFGpt47PvJzovLy8rBmzRoMHDiw2ONsbGwghCiwfceOHShXrhzatWuHEydOoHnz5rC3t0edOnVw5syZQrMcHBy0BVNJL5yIiIio7ND1Imr79u3Izs5G165dzbbv2rULiYmJAIDTp0/j/fffR48ePcyOycnJwVtvvYW5c+cCAPz8/LBz506YTCYcOXIEvr6+pXMSREREVCbpehG1evVq9OvXD+XKmb/reOjQIYSEhMDZ2RlPPvkkevXqhfHjx5sds2jRIvTu3RvVqlUDAAwfPhy//fYbfH19MXDgQHh7e5faeRAREVHZo+vPRK1atarQ7ePGjcO4ceOKfe7o0aPNHru7u+Pnn38uqWpERET0iNP1nSgiIiIivdL1nSg94ZwofeWpkqlCRxmZKnSUkalCRxmZKnSUkalCRxmZKnQEOCfKqqKiohAUFITg4GBrVyEiIiId4p2oIkRERCAiIgJpaWkwGo2cE6XTPFUyVegoI1OFjjIyVegoI1OFjjIyVegoI1PvHR/5OVFEREREesVFFBEREZEFuIgiIiIisgAXUUREREQW4CKKiIiIyAJcRBERERFZgCMO7hOHbeorT5VMFTrKyFSho4xMFTrKyFSho4xMFTrKyFShI8Bhm1bFYZtERERUHN6JKgKHbcrJVKGjjEwVOsrIVKGjjEwVOsrIVKGjjEwVOsrI1HtHDtskIiIi0ikuooiIiIgswEUUERERkQW4iCIiIiKyABdRRERERBbgt/PuE+dE6StPlUwVOsrIVKGjjEwVOsrIVKGjjEwVOsrIVKEjwDlRVsU5UURERFQc3okqAudEyclUoaOMTBU6yshUoaOMTBU6yshUoaOMTBU6ysjUe0fOiSIiIiLSKS6iiIiIiCzARRQRERGRBbiIIiIiIrIAF1FEREREFuAiioiIiMgCHHFwnzhsU195qmSq0FFGpgodZWSq0FFGpgodZWSq0FFGpgodAQ7btCoO2yQiIqLi8E5UEThsU06mCh1lZKrQUUamCh1lZKrQUUamCh1lZKrQUUam3jty2CYRERGRTnERRURERGQBXS+i2rdvj/Lly8PFxQUuLi7o2rWrtm/27Nnw8PCAu7s7JkyYACEEACAlJQWhoaFwd3fHzJkzteNzcnLQsmVLJCQklPp5EBERUdmj60UUACxduhQmkwkmkwmbN28GAGzatAlRUVHYt28fjh8/js2bN2P58uUAgCVLlqBNmzaIj4/HqlWrkJSUBODOB8W7d+8OX19fq50LERERlR26X0QVZvXq1RgxYgQCAgLg5eWFcePGYdWqVQCA+Ph4hIaGokKFCmjatCkSEhKQnJyMVatW4c0337RycyIiIiordP/tvDFjxmDMmDFo0qQJ5s+fj0aNGuH48ePo37+/dkzDhg1x7NgxAEC9evWwY8cOhISEIDY2FgEBAZg4cSIiIyPh4OBwz9fLzMxEZmam9jj/0/2cE6WvPFUyVegoI1OFjjIyVegoI1OFjjIyVegoI1OFjgDnROGDDz7A+fPnkZCQgM6dO6Nr1664efMmTCaT2dcgXV1dtb+AYcOGISkpCe3atcO4ceNw7tw5JCcnIyQkBH369EH79u3x008/Ffmas2bNgtFo1H58fHyknycRERGpR9d3olq0aKH9ecKECVi+fDn27dsHFxcXs7s0aWlpcHFxAQA4OTlh5cqVAAAhBEJDQ7Fs2TLMmjULffr0QVhYGFq1aoW4uDjY2BRcQ06ePBljx441y/bx8eGcKJ3mqZKpQkcZmSp0lJGpQkcZmSp0lJGpQkcZmXrvyDlR/2BjYwMhBIKCghAbG6ttj4uLQ/369QscHx0djTZt2qBWrVo4ceIEmjdvrt1hunLlSqGv4eDgoC2YSnrhRERERGWHbu9E3bhxA7///jvatWsHg8GAqKgopKSkoGXLlsjOzsbIkSPRv39/ODs748MPP8SoUaPMnp+amoqoqCjs3r0bAODn54cdO3bAaDTir7/+QqVKlaxxWkRERFRG6PZOVHZ2NiZPnozKlSvDy8sL33//PTZt2gSj0YiwsDCMHDkSLVq0QN26ddGlSxcMHTrU7PnTpk3DhAkT4OzsDACYOHEiFi9ejAYNGuC9995DuXK6XT8SERGRAnS7kvDw8MDBgweL3D958mRMnjy5yP0LFiwwe+zv74+YmJiSqkdERESPON3eiSIiIiLSM93eidIbzonSV54qmSp0lJGpQkcZmSp0lJGpQkcZmSp0lJGpQkeAc6KsKioqCkFBQQgODrZ2FSIiItIh3okqQkREBCIiIpCWlgaj0cg5UTrNUyVThY4yMlXoKCNThY4yMlXoKCNThY4yMvXekXOiiIiIiHSKiygiIiIiC3ARRURERGQBLqKIiIiILMBFFBEREZEFuIgiIiIisgBHHNwnDtvUV54qmSp0lJGpQkcZmSp0lJGpQkcZmSp0lJGpQkeAwzatisM2iYiIqDi8E1UEDtuUk6lCRxmZKnSUkalCRxmZKnSUkalCRxmZKnSUkan3jhy2SURERKRTXEQRERERWYCLKCIiIiILcBFFREREZAEuooiIiIgswG/n3SfOidJXniqZKnSUkalCRxmZKnSUkalCRxmZKnSUkalCR4BzoqyKc6KIiIioOLwTVQTOiZKTqUJHGZkqdJSRqUJHGZkqdJSRqUJHGZkqdJSRqfeOnBNFREREpFNcRBERERFZgIsoIiIiIgtwEUVERERkAS6iiIiIiCzARRQRERGRBTji4D5x2Ka+8lTJVKGjjEwVOsrIVKGjjEwVOsrIVKGjjEwVOgIctmlVHLZJRERExeGdqCJw2KacTBU6yshUoaOMTBU6yshUoaOMTBU6yshUoaOMTL135LBNIiIiIp3iIoqIiIjIArpdRGVmZmLo0KHw9fWFq6srQkJCsHfvXgDAihUrUK5cObi4uGg/CQkJAICUlBSEhobC3d0dM2fO1PJycnLQsmVL7TgiIiKih6HbRVROTg5q1KiBX375BTdu3MDo0aPRvXt37RP87du3h8lk0n58fX0BAEuWLEGbNm0QHx+PVatWISkpCcCdD4p3795dO46IiIjoYeh2EeXs7IypU6fC19cXNjY26NevH+zt7fHnn38W+7z4+HiEhoaiQoUKaNq0KRISEpCcnIxVq1bhzTffLKX2REREVNYp8+2806dPIyUlBYGBgYiNjcW+fftQqVIlVKlSBaNGjcIrr7wCAKhXrx527NiBkJAQxMbGIiAgABMnTkRkZCQcHBzu+TqZmZnIzMzUHud/up9zovSVp0qmCh1lZKrQUUamCh1lZKrQUUamCh1lZKrQEeCcKE1GRgYGDBiAyZMnw2g0IjQ0FHFxcbhy5Qqio6Mxffp0rF+/HgAwbNgwJCUloV27dhg3bhzOnTuH5ORkhISEoE+fPmjfvj1++umnIl9r1qxZMBqN2o+Pj09pnSYREREpRPd3orKzs9G3b18EBgZi6tSpAAB/f39tf8uWLTFq1Chs2LABffr0gZOTE1auXAkAEEIgNDQUy5Ytw6xZs9CnTx+EhYWhVatWiIuLg41NwTXk5MmTMXbsWO1xWloafHx8OCdKp3mqZKrQUUamCh1lZKrQUUamCh1lZKrQUUam3js+8nOi8vLyMHDgQBgMBqxcuRIGg6HQ42xsbCCEKLA9Ojoabdq0Qa1atXDixAk0b95cu8N05cqVQrMcHBy0BVNJL5yIiIio7ND1ImrEiBFISkrCl19+iXLl/nfTbMuWLdoi6PDhw/j3v/+NHj16mD03NTUVUVFRmDJlCgDAz88PO3bswOXLl/HXX3+hUqVKpXciREREVObodhEVHx+PpUuX4sCBA6hcubI2D2rPnj3YunUr6tevD2dnZ/Tr1w8TJ05Ev379zJ4/bdo0TJgwAc7OzgCAiRMnYvHixWjQoAHee+89s0VZWZaeno3Dhy8jPT3b2lWIiIjKFN2uJPz8/Ap9iw4A2rZti/nz5xf7/AULFpg99vf3R0xMTEnVU8bJkyl47LHVOHRoIJo1q2LtOkRERGWGbu9EEREREemZbu9E6Y2qc6IsybtXpiVUmSvC89ZnniqZKnSUkalCRxmZKnSUkalCR4BzoqwqKioKQUFBCA4OtnYVIiIi0iHeiSpCREQEIiIikJaWBqPRqOycqIfJKyrzYeh9roisTBU6yshUoaOMTBU6yshUoaOMTBU6ysjUe8dHfk4UERERkV5xEUVERERkAS6iiIiIiCzARRQRERGRBbiIIiIiIrKARYuo8PBw7N69u6S7EBERESnDohEHqamp6NSpE/z8/DBkyBCEh4ejWrVqJd1NVzhs8+GoMpyN563PPFUyVegoI1OFjjIyVegoI1OFjoCOh21+8803uHjxIkaOHIm1a9eiRo0a6Nq1K7766itkZ5eNX3TLYZtERERUHIuHbXp4eGDs2LEYO3YsDh8+jOjoaAwcOBAuLi4YMGAAXn31VdSqVasku5YqDtssOvNh6H04m6xMFTrKyFSho4xMFTrKyFSho4xMFTrKyNR7RyWGbSYlJWHr1q3YunUrbG1t0a1bN8TGxiIoKAj/+te/SqIjERERke5YtIjKzs7G+vXr8fTTT8PPzw9ffvklRo8ejb///hsrV67Etm3bsG7dOkyfPr2k+5Yp6enZOHz4MtLTy8ZboERERI8Si97O8/b2Rl5eHvr3748DBw6gSZMmBY7p0KED3NzcHrJe2XbyZAoee2w1Dh0aiGbNqli7DhERET0AixZR//rXv9C3b1+UL1++yGPc3Nxw/vx5i4sRERER6ZlFb+ft3Lmz0G/h3bp1C0OHDn3oUkRERER6Z9GdqJUrV2L27NmoUKGC2faMjAysWrUKy5cvL5FyeiJjTlRhM5w4J6psZarQUUamCh1lZKrQUUamCh1lZKrQUUamCh2B0pkT9UCLqLS0NAghIITAzZs3zd7Oy83NxaZNm+Dp6VniJa0hKioKUVFRyM3NtXYVIiIi0qEHWkS5ubnBYDDAYDCgdu3aBfYbDAa8++67JVbOmkpjTlRhM5xKck5Ueno2EhNvW5xXWObD0vtcEVmZKnSUkalCRxmZKnSUkalCRxmZKnSUkan3jqUxJ+qBFlE7d+6EEAJPPPEE1q9fD3d3d22fvb09/Pz8ULVq1RIvSZY5eTIFAwZssnYNIiKiMumBFlGhoaEAgPPnz8PX1xcGg0FKKSIiIiK9u+9F1B9//IEGDRrAxsYGqampiI2NLfLYRo0alUg5IiIiIr2670VUkyZNcOnSJXh6eqJJkyYwGAwQQhQ4zmAw8MPYREREVObd9yLq/Pnz8PDw0P5MRERE9Ci770WUn59foX8mIiIiehRZPGyzcuXKCAsLAwBMmDABS5YsQVBQED7//PMyuchScdhmftaD5hWXaSlVhrPxvPWZp0qmCh1lZKrQUUamCh1lZKrQESidYZsW/dqXmTNnwtHREQCwd+9eLFq0CB988AEqV66MMWPGlGhBa4mKikJQUBCCg4OtXYWIiIh0yKI7UYmJiQgMDAQAfPPNN3j22WcxfPhwtG7dGu3bty/JflZTFoZt5mdZmldY5sPS+3A2WZkqdJSRqUJHGZkqdJSRqUJHGZkqdJSRqfeOpTFs06I7US4uLrh27RoA4KeffkLnzp0BAOXLl0dGRkZxTyUiIiIqEyy6E9W5c2cMGzYMTZs2xalTp9CtWzcAwLFjx1CjRo2S7EdERESkSxbdiYqKikKrVq1w5coVrF+/HpUqVQIAHDp0CP379y/RgkW5cuUKwsLC4OzsjDp16mD79u0AgM2bN6NatWqoVasWDh8+rB2/d+9e9OjRo1S66Vl6ejYOH76M9PRssz8TERHRg7HoTpSbmxsWLVpUYHtp/vLhiIgIeHl54cqVK9i2bRuee+45nD59GuPHj8e2bdsQHx+PKVOmYPPmzcjLy8P48eOxatWqUutXWtLTs3H06FXUru2G/LeS09OzcfJkCjIycgocf/JkCh57bDUOHRoIANqfmzWroj2vbl33As8jIiIicxYtogDgxo0bOHDgAJKTk5GXl6dtNxgMGDhwYImUK4rJZMI333yDc+fOwcnJCT169EDDhg3x7bffIiMjA/Xq1UPNmjW1bwouXboUHTp0QEBAgEWvZ29vj5ycHGRnP/wdm5ycOwub7Oxs5ObmwN4eyM29k52RkYPjx5Nhbw8cP56MwEBXODqa/xVlZOTgzz9TUKeOOxwdy+HQob/RufMGfPttGDp2rAAAOHYsGW3afI5ly7rA3v7O8/Jf4+7XvHNu/9uX/7xffumPgABHrefDysjIweHDlxEYaNS+1fkwWfnnf/e1LCklnalCRxmZKnSUkalCRxmZKnSUkalCRxmZKnQEUCq/PcUgCvvdLffw/fff48UXX4TJZIKrq6vZLyI2GAxISUkp0ZL/dOTIEXTs2NHsdV5//XU4ODjg559/RnR0NBITE7F06VIsXboUHTt2xC+//AInJ6d7ZmdmZiIzM1N7nJaWhq1bt0o5DyIiIpJn6NChSE1NlfJNQsDCz0SNGzcOQ4cOhclkwo0bN3D9+nXtR/YCCoC2eLubq6srTCYToqKiMHz4cMybNw9z5szBO++8g8mTJ2PXrl1o3749+vbtq32zsDCzZs2C0WjUfnx8fGSfDhERESnIojtRzs7OiI2NRc2aNWV0uqfi7kTNmzdP2/bHH39g/Pjx2Lx5M+rXr48DBw7gu+++Q0xMDObOnVtodmF3ogICApCUlIQKFSo8dPebN28CACpUqFDgrbmMjBzExCTj9u1clC9vi8aNPeHoWA5rjx3DS999h5ebNcP//ffD8ouf6gZ7+3J46bvvAADPB9TF2rMn8cuQIahjrIw//0zBDccMPL1hDQBgcqvWmNK+ndlrfnXyGEZu2QQAZs+781ZZBv5ITkaXdesAAMt69MDz9eubnUt+r8L25Tt66RLaREcDAL7t0Qcd69cu9vrkZ+Z3auLlZbbf/O28DO1a3uu1/5lV1L67/35KQknnqZKpQkcZmSp0lJGpQkcZmSp0lJGpQkcA+Pvvv1GjRg2pd6Is+kxUly5dcPDgQastomrVqgWTyYSLFy+iWrVqAIC4uDgMGjTI7LgxY8bg3//+N65evQp3d3e4uroiODgYn332WZHZDg4OcHBwMNuWlZWFcuXKwc7O7qG7lyt355Lb2dnBzs4OwcHVtH12dnZo27bgr8zxr1QJWUKgfpUqyPrvmreOdxVcuHFDe9zU1xujn2iLupUrw+m/uYeTkrT9vpXdC7ymg6MDsoTAex06oL6Xl/Y8AEhLy0ZdDw8s7t4dL333HfwrVSpw/gZbW2QJAYOtbZHXpr6XF3YMGYJbJhMaeXre8xrmZ+Z3+ufxdv/omL+tMLblymnnb/uPv7+i9t3991MSSjpPlUwVOsrIVKGjjEwVOsrIVKGjjEwVOgKAra1tiWUVxaJFVFhYGN58800cP34cDRs2LHDSskcJuLi4oGfPnpg2bRoWLlyI7du3448//kDPnj21Yz7//HM0atQI9evXR05ODhISEnD58mXs3LlTuVlWjv/9x+VavjwA4D/PPIOm/7hDU8HeHs28vYvMcChX8K+6hpsbAKCDvz+cCvmH62Rnp92hcSzm+fn/tzBOdnZo7ePzwJNj/StWLLSTJf7zzDOoW7nyA+8jIiIqjkWLqJdffhkAMH369AL7DAZDqXwi/uOPP0Z4eDgqVaqE6tWrY+3atXB3v/PV/Fu3bmHevHnYuXMngDsr3OnTp6NBgwaoVq0avvnmG+n9ZKr337tNhS1sHkT+8y3NedjnF+Z+FmYPKv96Peg+IiKi4lj0X7+7RxpYi4eHBzZt2lToPmdnZxw6dMhs29ChQzF06NDSqGYVvpLe7y1tMhZmREREMlj07by73b59uyR60EMqz0UHERFRqbLov7y5ubmYOXMmPvnkE1y+fBmnTp1CzZo18c4776BGjRp46aWXSrqn1ZXUb4M2mUwP/Jyq9vb4+YUXcPu/w8hMJhPS0tLMsjIyMgp0NNufnl7k/vy8QvfdunXvYwrZ90/3e94lmXlf5/ePfZb8/TxMx7KaqUJHGZkqdJSRqUJHGZkqdJSRqUJH4M5He2Sz6E7UjBkzsGLFCnzwwQewzx+JDaBBgwZYunRpiZWzpqioKAQFBSE4ONjaVe58wLtKFTTy9MTPL7yA2u7mv5ZlYYcOCCzBzxARERHRvVl0J2rVqlVYsmQJOnbsiFdeeUXb3rhxY5w8ebLEyllTREQEIiIikJaWBqPRCFdX1xKdM2FJlisAr//+smcAcPnvKruRtzc83NwKZLrctQp3dHIqcr+Li0uRfVz+O42+sGPu5/kFzuEexzV3dMSh4cO1UQ0Pk1lcv3t1L+mZIjJmlKiQqUJHGZkqdJSRqUJHGZkqdJSRqfeOJfUOUnEsWkRdvHgRgYGBBbbn5eWV6O+9oUePk51dsaMaHkTdypW1BdmD7CMiIrofFr2dFxQUhD179hTY/tVXX6Fp06YPXYpKnqUjA1RebOQvyIqagVXUPiIiovth0Z2oqVOnIjw8HBcvXkReXh42bNiAP//8E6tWrcIPP/xQ0h2pBFg6MqAk7wwRERGVJRbdierZsye+//57bNu2Dc7Ozpg6dSpOnDiB77//Hp07dy7pjkRERES6Y/FwobZt22Lr1q0l2YUUo/JbfURERA/LojtRNWvWxLVr1wpsv3HjhtV+KTGVPn6uiIiIHmUW3Ym6cOFCob8fLzMzExcvXnzoUnpkzWGbRckfwlnNwaHQ3Lu3PeiwzEd1OBvPW595qmSq0FFGpgodZWSq0FFGpgodgdIZtvlAi6jvvvtO+/OPP/4Io9GoPc7NzcX27dtRo0aNEitnTVFRUYiKiiqVX6ZsqfwhnDL+8REREVHxHmgR1atXLwCAwWBAeHi42T47OzvUqFED8+fPL7Fy1qTHYZsPmnn3sM3Chkrez2DLR204m6xMFTrKyFSho4xMFTrKyFSho4xMFTrKyNR7R90N28zLywMA+Pv74/fff0dlfqBYaRxfQEREZDmLPhN1/vz5ku5BREREpBSLRxxs374d27dvR3JysnaHKt/y5csfuhgRERGRnlm0iHr33Xcxffp0NG/eHN7e3jD895fUEhERET0qLFpEffLJJ1ixYgUGDhxY0n2IiIiIlGDRIiorKwuPP/54SXfRNT3OibpX5r3mRFmSaSlV5orwvPWZp0qmCh1lZKrQUUamCh1lZKrQESidOVEWTSwfNmwY1qxZU9JddCUqKgpBQUEIDg62dhUiIiLSIYvuRN2+fRtLlizBtm3b0KhRI9j9Y8bQhx9+WCLlrOlRmBNlSebD0vtcEVmZKnSUkalCRxmZKnSUkalCRxmZKnSUkan3jrqbE5Xvjz/+QJMmTQAAcXFxJdmHiIiISAkWLaJ27txZ0j2IiIiIlPJAi6hnnnnmnscYDAasX7/e4kJEREREKnigRdTdv3CYiIiI6FH2QIuo6OhoWT2IiIiIlGLRiAMiIiKiR53FvzvvUcNhmw9HleFsPG995qmSqUJHGZkqdJSRqUJHGZkqdAR0PGzzUcBhm0RERFQc3okqAodtFp75sPQ+nE1WpgodZWSq0FFGpgodZWSq0FFGpgodZWTqvWNpDNvknSgiIiIiC3ARRURERGQB3S6iMjMzMXToUPj6+sLV1RUhISHYu3cvAGDFihUoV64cXFxctJ+EhAQAQEpKCkJDQ+Hu7o6ZM2dqeTk5OWjZsqV2HBEREdHD0O0iKicnBzVq1MAvv/yCGzduYPTo0ejevbv2Cf727dvDZDJpP76+vgCAJUuWoE2bNoiPj8eqVauQlJQE4M4Hxbt3764d9yioW7ky/nMfU+aJiIjowel2EeXs7IypU6fC19cXNjY26NevH+zt7fHnn38W+7z4+HiEhoaiQoUKaNq0KRISEpCcnIxVq1bhzTffLKX2+uBkZ4d6lStbuwYREVGZpMy3806fPo2UlBQEBgYiNjYW+/btQ6VKlVClShWMGjUKr7zyCgCgXr162LFjB0JCQhAbG4uAgABMnDgRkZGRcHBwuOfrZGZmIjMzU3uc/+l+FedE3b2Pc6Ksk6lCRxmZKnSUkalCRxmZKnSUkalCRxmZKnQEOCdKk5GRgQEDBmDy5MkwGo0IDQ1FXFwcrly5gujoaEyfPl37pcfDhg1DUlIS2rVrh3HjxuHcuXNITk5GSEgI+vTpg/bt2+Onn34q8rVmzZoFo9Go/fj4+JTWaRIREZFCdH8nKjs7G3379kVgYCCmTp0KAPD399f2t2zZEqNGjcKGDRvQp08fODk5YeXKlQAAIQRCQ0OxbNkyzJo1C3369EFYWBhatWqFuLg42NgUXENOnjwZY8eO1R6npaXBx8dHyTlRwP9mRXFOlHUzVegoI1OFjjIyVegoI1OFjjIyVegoI1PvHR/5OVF5eXkYOHAgDAYDVq5cCYPBUOhxNjY2EEIU2B4dHY02bdqgVq1aOHHiBJo3b67dYbpy5UqhWQ4ODtqCqaQXTkRERFR26HoRNWLECCQlJeHLL79EuXL/u2m2ZcsWbRF0+PBh/Pvf/0aPHj3MnpuamoqoqChMmTIFAODn54cdO3bg8uXL+Ouvv1CpUqXSOxEiIiIqc3T7dl58fDyWLl2K8uXLo/Jd3zDbvHkztm7dikGDBuHWrVuoVq0aJk6ciH79+pk9f9q0aZgwYQKcnZ0BABMnTkSvXr3wzjvvYO7cuWaLMiIiIqIHpduVhJ+fX6Fv0QFA27ZtMX/+/GKfv2DBArPH/v7+iImJKal6RERE9IjT9dt5RERERHql2ztResM5UQ9HlbkiPG995qmSqUJHGZkqdJSRqUJHGZkqdAQ4J8qqoqKiEBQUhODgYGtXISIiIh3inagiREREICIiAmlpaTAajZwTVUL0PldEVqYKHWVkqtBRRqYKHWVkqtBRRqYKHWVk6r3jIz8nioiIiEivuIgiIiIisgAXUUREREQW4CKKiIiIyAJcRBERERFZgIsoIiIiIgtwxMF94rDNh6PKcDaetz7zVMlUoaOMTBU6yshUoaOMTBU6Ahy2aVUctklERETF4Z2oInDYZtGZD0Pvw9lkZarQUUamCh1lZKrQUUamCh1lZKrQUUam3jty2CYRERGRTnERRURERGQBLqKIiIiILMBFFBEREZEFuIgiIiIisgC/nXefOCfq4agyV4Tnrc88VTJV6CgjU4WOMjJV6CgjU4WOAOdEWRXnRBEREVFxeCeqCJwTVXTmw9D7XBFZmSp0lJGpQkcZmSp0lJGpQkcZmSp0lJGp946cE0VERESkU1xEEREREVmAiygiIiIiC3ARRURERGQBLqKIiIiILMBFFBEREZEFOOLgPnHY5sNRZTgbz1ufeapkqtBRRqYKHWVkqtBRRqYKHQEO27QqDtskIiKi4vBOVBE4bLPozIeh9+FssjJV6CgjU4WOMjJV6CgjU4WOMjJV6CgjU+8dOWyTiIiISKe4iCIiIiKygK4XUe3bt0f58uXh4uICFxcXdO3aVds3e/ZseHh4wN3dHRMmTIAQAgCQkpKC0NBQuLu7Y+bMmdrxOTk5aNmyJRISEkr9PIiIiKjs0fUiCgCWLl0Kk8kEk8mEzZs3AwA2bdqEqKgo7Nu3D8ePH8fmzZuxfPlyAMCSJUvQpk0bxMfHY9WqVUhKSgJw54Pi3bt3h6+vr9XOhYiIiMoO3S+iCrN69WqMGDECAQEB8PLywrhx47Bq1SoAQHx8PEJDQ1GhQgU0bdoUCQkJSE5OxqpVq/Dmm29auTkRERGVFbr/dt6YMWMwZswYNGnSBPPnz0ejRo1w/Phx9O/fXzumYcOGOHbsGACgXr162LFjB0JCQhAbG4uAgABMnDgRkZGRcHBwuOfrZWZmIjMzU3uc/+l+zol6OKrMFeF56zNPlUwVOsrIVKGjjEwVOsrIVKEjwDlR+OCDD3D+/HkkJCSgc+fO6Nq1K27evAmTyWT2NUhXV1ftL2DYsGFISkpCu3btMG7cOJw7dw7JyckICQlBnz590L59e/z0009FvuasWbNgNBq1Hx8fH+nnSUREROrR9Z2oFi1aaH+eMGECli9fjn379sHFxcXsrkpaWhpcXFwAAE5OTli5ciUAQAiB0NBQLFu2DLNmzUKfPn0QFhaGVq1aIS4uDjY2BdeQkydPxtixY82yfXx8OCeqhOh9roisTBU6yshUoaOMTBU6yshUoaOMTBU6ysjUe0fOifoHGxsbCCEQFBSE2NhYbXtcXBzq169f4Pjo6Gi0adMGtWrVwokTJ9C8eXPtDtOVK1cKfQ0HBwdtwVTSCyciIiIqO3R7J+rGjRv4/fff0a5dOxgMBkRFRSElJQUtW7ZEdnY2Ro4cif79+8PZ2RkffvghRo0aZfb81NRUREVFYffu3QAAPz8/7NixA0ajEX/99RcqVapkjdMiIiKiMkK3d6Kys7MxefJkVK5cGV5eXvj++++xadMmGI1GhIWFYeTIkWjRogXq1q2LLl26YOjQoWbPnzZtGiZMmABnZ2cAwMSJE7F48WI0aNAA7733HsqV0+36kYiIiBSg25WEh4cHDh48WOT+yZMnY/LkyUXuX7Bggdljf39/xMTElFQ9IiIiesTp9k4UERERkZ7p9k6U3nBO1MNRZa4Iz1ufeapkqtBRRqYKHWVkqtBRRqYKHQHOibKqqKgoBAUFITg42NpViIiISId4J6oIERERiIiIQFpaGoxGI+dElRC9zxWRlalCRxmZKnSUkalCRxmZKnSUkalCRxmZeu/IOVFEREREOsVFFBEREZEFuIgiIiIisgAXUUREREQW4CKKiIiIyAJcRBERERFZgCMO7hOHbT4cVYaz8bz1madKpgodZWSq0FFGpgodZWSq0BHgsE2r4rBNIiIiKg7vRBWBwzaLznwYeh/OJitThY4yMlXoKCNThY4yMlXoKCNThY4yMvXekcM2iYiIiHSKiygiIiIiC3ARRURERGQBLqKIiIiILMBFFBEREZEF+O28+8Q5UQ9HlbkiPG995qmSqUJHGZkqdJSRqUJHGZkqdAQ4J8qqOCeKiIiIisM7UUXgnKiiMx+G3ueKyMpUoaOMTBU6yshUoaOMTBU6yshUoaOMTL135JwoIiIiIp3iIoqIiIjIAlxEEREREVmAiygiIiIiC3ARRURERGQBLqKIiIiILMARB/eJwzYfjirD2Xje+sxTJVOFjjIyVegoI1OFjjIyVegIcNimVXHYJhERERWHd6KKwGGbRWc+DL0PZ5OVqUJHGZkqdJSRqUJHGZkqdJSRqUJHGZl678hhm0REREQ6xUUUERERkQV0u4hycXEx+7GxscH8+fMBALt27YKNjY3Z/j179gAAsrKy0KtXL7i5ueHVV181ywwLC8OBAwdK/VyIiIio7NHtIspkMmk/p06dgo2NDZ555hltf82aNc2Oadu2LQBgw4YNcHFxweXLlxEXF4f9+/cDAL799lt4e3ujRYsWVjkfIiIiKluU+GD5mjVr0KpVK/j7+9/z2Pj4eLRr1w4ODg5o3bo1Lly4gMaNG2P69OnYsmVLKbQlIiKiR4ESi6jVq1fjtddeM9uWmJgIT09PGI1GDBw4EFOmTIGtrS3q1auHL774AoMGDcKvv/6K/v37Y+7cuRg8eDA8PDzu+VqZmZnIzMzUHud/up9zoh6OKnNFeN76zFMlU4WOMjJV6CgjU4WOMjJV6AhwThQA4I8//sCpU6fQt29fbVvdunURExODS5cu4dtvv8W6deuwYMECAMDTTz8NT09PtGjRAl27doWbmxs2bdqE8PBwDB48GKGhoVi9enWRrzdr1iwYjUbtx8fHR/YpEhERkYJ0fydq9erV6N69O9zc3LRtXl5e8PLyAgAEBQXh7bffxsKFCzFu3DjY2NhoCyoAeO655zBnzhysWLECtWvXxpIlSxASEoJu3bqhUqVKBV5v8uTJGDt2rPY4LS0NPj4+nBNVQvQ+V0RWpgodZWSq0FFGpgodZWSq0FFGpgodZWTqveMjPycqLy8Pa9aswcCBA4s9zsbGBkKIAtt37NiBcuXKoV27djhx4gSaN28Oe3t71KlTB2fOnCk0y8HBQVswlfTCiYiIiMoOXS+itm/fjuzsbHTt2tVs+65du5CYmAgAOH36NN5//3306NHD7JicnBy89dZbmDt3LgDAz88PO3fuhMlkwpEjR+Dr61s6J0FERERlkq4XUatXr0a/fv1Qrpz5u46HDh1CSEgInJ2d8eSTT6JXr14YP3682TGLFi1C7969Ua1aNQDA8OHD8dtvv8HX1xcDBw6Et7d3qZ0HERERlT26/kzUqlWrCt0+btw4jBs3rtjnjh492uyxu7s7fv7555KqRkRERI84Xd+JIiIiItIrXd+J0hPOiXo4qswV4XnrM0+VTBU6yshUoaOMTBU6yshUoSPAOVFWFRUVhaCgIAQHB1u7ChEREekQ70QVISIiAhEREUhLS4PRaOScqBKi97kisjJV6CgjU4WOMjJV6CgjU4WOMjJV6CgjU+8dH/k5UURERER6xUUUERERkQW4iCIiIiKyABdRRERERBbgIoqIiIjIAlxEEREREVmAIw7uE4dtPhxVhrPxvPWZp0qmCh1lZKrQUUamCh1lZKrQEeCwTavisE0iIiIqDu9EFYHDNovOfBh6H84mK1OFjjIyVegoI1OFjjIyVegoI1OFjjIy9d6RwzaJiIiIdIqLKCIiIiILcBFFREREZAEuooiIiIgswEUUERERkQX47bz7xDlRD0eVuSI8b33mqZKpQkcZmSp0lJGpQkcZmSp0BDgnyqo4J4qIiIiKwztRReCcqKIzH4be54rIylSho4xMFTrKyFSho4xMFTrKyFSho4xMvXfknCgiIiIineIiioiIiMgCXEQRERERWYCLKCIiIiILcBFFREREZAEuooiIiIgswBEH94nDNh+OKsPZeN76zFMlU4WOMjJV6CgjU4WOMjJV6Ahw2KZVcdgmERERFYd3oorAYZtFZz4MvQ9nk5WpQkcZmSp0lJGpQkcZmSp0lJGpQkcZmXrvyGGbRERERDrFRRQRERGRBay6iFq8eDGaNWsGOzs7REZGmu1bsWIFqlevDldXVwwZMgRZWVnavrNnz6J169ZwcnJCs2bNEBMTo+1bvnw5PDw80LRpU8THx2vbv/jiC7z22mvSz+lebGxsYGNTspddhUwVOsrKJCKissmq/7Xw9vZGZGQk+vTpY7Y9NjYWY8aMwddff43ExEQkJibivffe0/b3798fnTp1QkpKCl5++WX07t0bOTk5yMnJQWRkJI4dO4YRI0Zg9uzZAO58Qn/OnDlmGdbi4uICFxeXRy5ThY6yMomIqGyy6iKqV69e6NGjB9zc3My2r1mzBn369EFwcDCMRiPefvttrFq1CgDw559/4vjx43jrrbdQvnx5jBw5Enl5edizZw+uXbuG6tWrw9PTEx06dMCFCxcAADNmzEBERAQqVqxYymdIREREZZUuv513/PhxdOzYUXvcsGFDJCQkwGQy4fjx46hduzYcHBzM9h87dgyhoaG4cuUKkpKSsHPnTgQFBeHMmTPYvXs33n///ft67czMTGRmZmqP8z/d/6Cf8rexseEdjUeEyWRCXl5ekftkvJ7eM1XoKCNThY4yMlXoKCNThY4yMlXoCDzCc6JMJpPZ1xzz/2wymQrsy99vMplgY2ODjz76CN27d8fmzZsxadIkjB07FvPnz8fKlSsRGhqKl156Cbdv3y7ytWfNmgWj0aj9+Pj4yDlJIiIiUpou70S5uLiY3fnJ/3P+51X+eVcoLS1Nu+vTrVs3dOvWDQCwceNGVK5cGQEBARg5ciT279+POXPmYNmyZYiIiCj0tSdPnoyxY8eaZfv4+JT4nKjSUrdyZRwaPhx1K1e2dpUy637uOOp9noqsTBU6yshUoaOMTBU6yshUoaOMTL13fGTnRAUFBSE2NlZ7HBcXB19fX7i4uCAoKAinT582e8stLi4O9evXN8vIyspCZGQkZs+ejbNnz6Ju3bqws7NDcHAw4uLiinxtBwcHbcGk6sLpbk52dmjm7Q0nOztrVyEiIipTrLqIysnJwe3bt5Gbm2v25xdeeAHr16/HoUOHkJqaihkzZmDQoEEAgDp16qBevXqYPXs2MjMzsWTJEhgMBrRt29Yse/78+RgwYAA8PT3h4+ODQ4cOwWQyYdeuXahRo4YVzrZ0DR48GL169Sr2mF27dsFgMODGjRtFHhMZGYkmTZqUaDciIqKywKqLqPfffx+Ojo5YunQpZsyYAUdHR6xevRoNGzbEhx9+iB49eqB69eqoWrUq3n77be15a9aswU8//QQ3NzcsXrwYGzZsQLly/3tn8uLFi/juu++0t+yqVq2KAQMGwNfXF3v37sWIESNK/VwLk5iYiKFDh6Jq1aqwt7eHn58f3njjDVy7du2hsz/66COsWLFCe9y+fXuMHj36gXPGjx+P7du3P3Sf+5GVlYUPPvgAjRs3hpOTEypXrozWrVsjOjoa2dnZAIDdu3eje/fuqFq1KgwGA7755psCOUIITJ06Fd7e3nB0dESnTp1w+vTpe77+rl270KxZMzg4OCAwMNDs+hEREf2TVRdRkZGREEKY/QwePBjAnTspFy9exM2bN7FixQqzb+MFBgbi119/RUZGBo4cOVLgTkm1atWwd+9es4XVO++8g5SUFOzatavASAVrOHfuHJo3b47Tp0/j888/x5kzZ/DJJ59g+/btaNWqFVJSUgp93t1DR4tjNBpL5DxdXFxQqVKlh865l6ysLHTp0gWzZ8/G8OHD8dtvv+HAgQOIiIjAwoULcezYMQB3vm3RuHFjREVFFZn1wQcf4N///jc++eQT7N+/H87OzujSpUuxXyg4f/48wsLC0KFDBxw9ehSjR4/GsGHD8OOPP5b4uRIRURkhqFipqakCgEhNTS3R3KeeekpUr15dpKenm21PSkoSTk5O4pVXXhFCCOHn5yemT58uBg4cKCpUqCDCw8NFdHS0MBqNYsuWLaJu3brC2dlZdOnSRfz9999aTnh4uOjZs6f2ZwBmP+fPnxc7d+4UAMS2bdvEY489JhwdHUWrVq3EyZMntZxp06aJxo0bF8idO3eu8PLyEu7u7uLVV18VWVlZ2jG3b98W48aNE1WrVhVOTk6iRYsWYufOncVejzlz5ggbGxtx+PDhAvuysrKEyWQqsB2A+Prrr8225eXlCS8vLzF37lxt240bN4SDg4P4/PPPi3z9CRMmiPr165tte/7550WXLl2K7X0vqampJf5vR4VMFTrKyFSho4xMFTrKyFSho4xMFToKIURiYqKU/37fTZffztOjkpwTlZKSgh9//FF7C/NuXl5eePHFF7F27Vp8/PHHAIB58+Zh6tSpmDZtGgBgz549SE9Px7x587B69WrY2NhgwIABGD9+PD777LMCr/fRRx/h1KlTaNCgAaZPnw4A8PDw0IaRTpkyBfPnz4eHhwdeeeUVDB06FL/++muR57Zz5054e3tj586dOHPmDJ5//nk0adIEL7/8MgDgtddew/Hjx/HFF1+gatWq+Prrr/HUU08hNjYWtWrVKjTzs88+Q6dOndC0adMC++zs7GB3nx+MP3/+PC5duoROnTpp24xGI1q2bIm9e/eiX79+AO68vVmjRg3tLbu9e/eaPQcAunTpcl9vgXJOlPw8VTJV6CgjU4WOMjJV6CgjU4WOwCM8J0oPoqKiEBQUhODg4BLPPn36NIQQqFevXqH769Wrh+vXr+PKlSsAgCeeeALjxo1DQEAAAgICAADZ2dn45JNP0Lx5czRr1gyvvfZakZ9dMhqNsLe3h5OTE7y8vODl5QVbW1tt/4wZMxAaGoqgoCBMmjQJv/32W7FvfVWsWBGLFi1C3bp18fTTTyMsLEx77YSEBERHR+PLL79E27ZtERAQgPHjx6NNmzaIjo4u9prUrVu3+At3Hy5dugQAqFKlitn2KlWqaPsAwNfXF97e3mbPK+w5aWlpyMjIeOheRERU9vBOVBEiIiIQERGBtLQ0GI1GKeMOhBD3dVzz5s0LbHNyctIWVMCd30OYnJxsUY9GjRqZ5QBAcnIyfH19Cz2+fv36Zoswb29vbSRFbGwscnNzUbt2bbPnZGZmap+tuvsO3YABA/DJJ5/c97UoKfm/RqgkcE5U6eWpkqlCRxmZKnSUkalCRxmZeu9YGnOiuIiygsDAQBgMBpw4cQK9e/cusP/EiROoWLEiPDw8AADOzs4Fjvnn21sGg8HihcjdWQaDAQCKfHuqqNfOP95kMsHW1haHDh0yW2gB/1tsHD16VNuW//8wtWvXxsmTJy3qfzcvLy8AwOXLl83uNF2+fLnYUQ1eXl64fPmy2bbLly/D1dW1wFuuREREAN/Os4pKlSqhc+fO+Pjjjwu8VXTp0iV89tlneP7557UFTUmwt7dHbm5uieUVpWnTpsjNzUVycjICAwPNfvIXOHdv8/T0BAC88MIL2LZtG44cOVIgMzs7+77f2/b394eXl5fZW5tpaWnYv38/WrVqVeTzWrVqVeDt0K1btxb7HCIierRxEWUlixYtQmZmJrp06YLdu3cjMTERW7ZsQefOnVGtWjXMmDGjRF+vRo0a2L9/Py5cuICrV68We6fpYdSuXRsvvvgiBg0ahA0bNuD8+fM4cOAAZs2ahY0bNxb5vNGjR6N169bo2LEjoqKiEBMTg3PnzmHdunUICQnR5jyZTCYcPXpUu5t1/vx5HD16FAkJCQDu3BUbPXo03n//fXz33XeIjY3FoEGDULVqVbPho4MGDcLkyZO1x6+88grOnTuHCRMm4OTJk/j444+xbt06jBkzpuQvEhERlQlcRFlJrVq1cPDgQdSsWRPPPfccAgICMHz4cHTo0AF79+6Fu7t7ib7e+PHjYWtri6CgIHh4eGiLDhmio6MxaNAgjBs3DnXq1EGvXr3w+++/F/kZK+DOr9vZunUrJkyYgE8//RQhISEIDg7Gv//9b4waNQoNGjQAABw8eBBNmzbVvsU3duxYNG3aFFOnTtWyJkyYgNdffx3Dhw9HcHAwTCYTtmzZgvLly2vHJCQkICkpSXvs7++PjRs3YuvWrWjcuDHmz5+PpUuXokuXLiV9eYiIqIwwiNL+RK9i8j9Ynpqaqvzv0aPSl//BRhkfltRzpgodZWSq0FFGpgodZWSq0FFGpgodAeCvv/6Cj4+P1P9+804UERERkQX47bx7yL9Rd/HixQf6umRxwzapbClu2Gb+B+JL8qu2KmSq0FFGpgodZWSq0FFGpgodZWSq0BGA9o1rmW+4cRFVhKioKERFRSEzMxMAEBQUZOVGRERE9KCuXbsGo9EoJZufibqHGzduoGLFikhISJD2l0BlW3BwMH7//fcSy0tLS4OPjw8SExNL9H3+ku5Z0nkyMmVcSxXOu6QzVfk3KSNThX+TgP7PW0ZmamoqfH19cf36dbi5uZVY7t14J+oebGzufGwsf2o50YOytbWVNtm3JHNLuqeM81bhWqpy3jIy9f5vUkamCv8mATXOW9a1zP/vuAz8YDmRZBEREdaucF9KuqeM81bhWqpy3ryW+syTRYXzVuVa3o1v590DRxyQ3vDfZMnhtSwZvI4lh9ey5JTGteSdqHtwcHDAtGnT4ODgYO0qRAD4b7Ik8VqWDF7HksNrWXJK41ryThQRERGRBXgnioiIiMgCXEQRERERWYCLKCIiIiILcBFFREREZAEuoopx5coVhIWFwdnZGXXq1MH27dutXUmXMjMzMXToUPj6+sLV1RUhISHYu3evtn/27Nnw8PCAu7s7JkyYYPZ7jH7//Xc0atQITk5OCA0NRXx8vDVOQXf27t0LGxsbvP/++9o2XscH98EHH8DHxwcVKlRA06ZNcfPmTQC8lg/q6NGjaN26NVxdXVGzZk0sXboUAJCXl4fRo0fDzc0NVapUwb/+9S+z523evBmBgYFwdnZGz549cf36dWvUt5rFixejWbNmsLOzQ2RkpNm+FStWoHr16nB1dcWQIUOQlZWl7Tt79ixat24NJycnNGvWDDExMdq+e13zsqqoa7lx40Y8/vjjMBqNqFq1KsaOHYvs7Gxtv/RrKahIffv2FUOHDhW3bt0S3377rXB3dxfXrl2zdi3dMZlM4t133xXx8fEiNzdXfP7556JSpUri5s2bYuPGjaJ69erizJkzIikpSTRo0EAsXbpUCCHE7du3RfXq1cX//d//iYyMDPHWW2+JNm3aWPlsrC83N1e0bNlStGjRQrz33ntCCMHraIFFixaJDh06iPj4eJGXlydiYmLE7du3eS0t0KBBA/Huu++K3NxccejQIeHi4iKOHz8uoqKiROPGjcXly5fFqVOnRNWqVcW2bduEEEJcvnxZGI1GsXHjRnHr1i0xaNAgMWDAACufSen6+uuvxbfffiuef/55MW3aNG37H3/8Idzc3MSBAwfEjRs3RMeOHcXbb7+t7Q8ODhZTp04VGRkZ4uOPPxb+/v4iOztbCCGKveZlWVHXcs2aNeLHH38U6enpIjk5WbRp00bMmDFD2y/7WnIRVYSbN28KOzs7kZiYqG0LDQ0Vy5cvt2IrdXh7e4uDBw+Kfv36aQsBIYSIjo4W7dq1E0IIsWXLFhEQEKDtu3XrlnB0dBTnzp0r9b56snjxYjFq1CgRHh6uXTtexweTk5MjvL29xZkzZwrs47V8cC4uLuLUqVPa4+DgYLFhwwYREhIiVq9erW2fNm2aGDRokBBCiE8++UR07NhR23fu3Dnh4OAg0tPTS6+4TowYMcLsP/yTJk0SL730kvZ4586dwtfXVwghxMmTJ4Wzs7O4ffu2tt/Pz0/s2LFDCCGKveaPgn9ey3/69NNPxdNPPy2EKJ1rybfzinD69Gm4uLigevXq2raGDRvi2LFjVmylhtOnTyMlJQWBgYE4fvw4GjVqpO27+xr+c5+TkxMCAgIe6Wt87do1LFiwAO+++67Zdl7HB/PXX38hPT0dX331FapUqYI6derg//7v/wDwWlri9ddfx3/+8x/k5OTgwIEDSEhIQEhIyANdS39/f9jZ2eHs2bOl3l9vCrtuCQkJMJlMOH78OGrXrm02ILK468r/LpnbvXs36tevDwClci35C4iLYDKZCoyJd3V1xbVr16zUSA0ZGRkYMGAAJk+eDKPRWOA6urq6wmQyASj6GufvfxRNmTJFe4/+bryOD+bixYtITU3FqVOncOHCBZw+fRodO3ZE3bp1eS0t0LVrVwwaNAgzZswAACxbtgze3t73vJY+Pj5mObyWdxR23fK33+vfYHHX/FG3fv16bN++XfvcU2lcSy6iiuDi4oK0tDSzbWlpaXBxcbFSI/3Lzs5G3759ERgYiKlTpwIoeB3vvoa8xuaOHDmC33//HVFRUQX28To+GEdHRwDA1KlT4ejoiEaNGqFfv37YtGkTr+UDSklJQVhYGJYvX47evXvj2LFjeOqpp9CwYUNeSwsVdt3yt9/ruhV3zR9lO3fuxMiRI7Fp0yZ4enoCuPe/wZK4lnw7rwi1atWCyWTCxYsXtW1xcXHabUIyl5eXh4EDB8JgMGDlypUwGAwAgKCgIMTGxmrH3X0N/7kvIyMDZ8+efWSv8c8//4w///wT1apVg5eXF9auXYs5c+ZgyJAhvI4PqHbt2rC3t9f+HQLgv0kLnT17Fs7Oznj22Wdha2uLRo0a4fHHH8fPP//8QNfywoULyM7ORkBAQKmfg94Udt18fX3h4uKCoKAgnD59GpmZmWb7i7qu/O8SsH//fjz33HNYt24dmjdvrm0vlWv5QJ+gesQ8++yz4qWXXhLp6eni+++/57fzijFs2DDRrl07kZGRYbb9hx9+ED4+PuLs2bPi0qVLolGjRmbfhKpWrZpYtmyZuH37tpgyZcoj/U2oW7duiaSkJO3nueeeExMnThTXr1/ndbTACy+8IIYPHy5u374tjh8/Ljw9PcXu3bt5LR/QjRs3hKurq/jmm29EXl6eOHbsmPD09BRbt24VixYtEk2aNBHJycni9OnTolq1agW+nbd582Zx69YtMXjw4Efu23nZ2dkiIyNDDBs2TEyZMkVkZGSInJwc8ccff4iKFSuKgwcPihs3bojOnTsX+HZeZGSkuH37tvj000/NvlFW3DUvy4q7lh4eHuLbb78t9HmyryUXUcVITk4WXbt2FY6OjqJWrVpi69at1q6kSxcuXBAARPny5YWzs7P2s3v3biGEEDNnzhSVKlUSbm5u4s033xR5eXnacw8cOCAaNmwoypcvL9q2bSsuXLhgrdPQnbu/nScEr+ODun79unjmmWeEi4uLqFGjhvj000+1fbyWD2bLli2icePGwsXFRfj6+orZs2cLIe6M43jjjTeE0WgUHh4eYv78+WbP27hxo6hZs6ZwdHQU3bt3FykpKdaobzXTpk0TAMx+oqOjhRB3vhVatWpV4eLiIsLDw82+QXb69Gnx+OOPi/Lly4smTZqII0eOaPvudc3LqqKu5eDBg4WNjY3Zf3ueeuop7Xmyr6VBiLumzBERERHRfeFnooiIiIgswEUUERERkQW4iCIiIiKyABdRRERERBbgIoqIiIjIAlxEEREREVmAiygiIiIiC3ARRURERGQBLqKIiIiILMBFFBHRf129ehWurq5mv5SUiKgoXEQREf3XvHnz8OSTT6Jhw4bWrkJECuAiiojKlAsXLsBgMGD9+vVo164dHB0dERwcjISEBOzZswchISFwcnJCx44dcePGDe15165dw8cff4xp06ZhzZo1MBgMBX5Gjx5ttfMiIv0pZ+0CREQlKSYmBgCwePFizJw5E87OzujZsycGDBiAChUqYNGiRcjNzUVYWBiio6MxZswYAHfuQnXu3BkNGzZEQEAAkpKStMz/+7//w4IFCzB06FCrnBMR6RMXUURUphw9ehTu7u5Yu3YtKlWqBAAIDQ3FL7/8gmPHjsHJyQkAEBwcjEuXLgH4312oPXv2AACcnJy045YuXYqPPvoIW7duRaNGjaxwRkSkV3w7j4jKlJiYGPTu3VtbQAFAQkICnn/+eW1hlL/N398fADB//nx06tSpwCJp2bJlmDRpEn766Sc0a9asdE6AiJTBRRQRlSlHjx5Fy5YtzbbFxMQgJCREe3z79m38+eefaNy4Ma5du4aoqChMnTrV7DnLli3DxIkTuYAioiJxEUVEZUZaWhouXLiApk2batvOnz+P1NRUs22xsbEQQqBhw4aYP38+OnbsiMaNG2v7ly9fzgUUEd0TPxNFRGVGTEwMbG1t0aBBA21b/mek/Pz8zLYFBAQgKysLUVFR2L17t7Zv1apVeOONN7B27VpUrVpV+9yUq6ur2duBRES8E0VEZUZMTAzq1KmD8uXLm227+y5U/rbGjRtj/vz5eOKJJ8zuQs2dOxcmkwlhYWHw9vbWfjZu3Fhq50FEajAIIYS1SxARlbaUlBT4+/vj559/RpMmTaxdh4gUxDtRRPRIunDhAj744AMuoIjIYrwTRURERGQB3okiIiIisgAXUUREREQW4CKKiIiIyAJcRBERERFZgIsoIiIiIgtwEUVERERkAS6iiIiIiCzARRQRERGRBbiIIiIiIrLA/wPDh96lRr6mvwAAAABJRU5ErkJggg==", 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", 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", 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "correct molecule: \n" + ] + }, + { + "data": { + "image/jpeg": 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", 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "correct molecule: \n" + ] + }, + { + "data": { + "image/jpeg": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from rdkit import Chem\n", + "from rdkit.Chem import Draw\n", + "similarity = ModifiedCosine(tolerance=0.2)\n", + "inchikeys = set()\n", + "for index in top_100_indices:\n", + " inchikey = val_pos.spectra[index].get(\"inchikey\")[:14]\n", + " if inchikey not in inchikeys:\n", + " correct_inchikey = list(close_tanimoto_scores_1_ms2deepscore_100_inchikeys[index].keys())[0]\n", + " mod_cos = similarity.pair(val_pos.spectra[index], train_pos.spectra_per_inchikey(correct_inchikey)[0])[\"score\"]\n", + " if mod_cos < 0.6:\n", + " print(average_mean_top_8_thresholds[index])\n", + " print(best_possible_threshold[index])\n", + " inchikeys.add(inchikey)\n", + " smiles = val_pos.spectra[index].get(\"smiles\")\n", + " mol = Chem.MolFromSmiles(smiles)\n", + " img = Draw.MolToImage(mol, size=(400, 100))\n", + " img.show()\n", + " print(\"correct molecule: \")\n", + " correct_smiles = train_pos.spectra_per_inchikey(correct_inchikey)[0].get(\"smiles\")\n", + " mol = Chem.MolFromSmiles(correct_smiles)\n", + " img = Draw.MolToImage(mol, size=(400, 100))\n", + " img.show()\n", + " \n", + " print(mod_cos)\n", + " print(val_pos.spectra[index].get(\"collision_energy\"))\n", + " print(train_pos.spectra_per_inchikey(correct_inchikey)[0].get(\"collision_energy\"))\n", + " plot_spectra_mirror(val_pos.spectra[index], train_pos.spectra_per_inchikey(correct_inchikey)[0])\n", + " plt.show()" + ] + } + ], + "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": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 604f9de0cf564647c61d3a6e92cab1374269b01d Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 19 Mar 2026 10:04:34 +0100 Subject: [PATCH 137/149] Add entropy run notebook --- .../run_cosine_and_entropy_on_neg_neg.ipynb | 327 ++++++++++++++++++ 1 file changed, 327 insertions(+) create mode 100644 ms2query/notebooks/run_cosine_and_entropy_on_neg_neg.ipynb diff --git a/ms2query/notebooks/run_cosine_and_entropy_on_neg_neg.ipynb b/ms2query/notebooks/run_cosine_and_entropy_on_neg_neg.ipynb new file mode 100644 index 0000000..5d04a02 --- /dev/null +++ b/ms2query/notebooks/run_cosine_and_entropy_on_neg_neg.ipynb @@ -0,0 +1,327 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9fcc18dd-f8a8-45d1-8a76-0d224d6b95f2", + "metadata": {}, + "source": [ + "# Run entropy score and cosine score on neg neg\n", + "The cleaned notebook alreaedy does comparison with modified cosine score, here we also did run entropy score and normal cosine. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cda64f34-8689-4560-ab78-387def5536fd", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from tqdm import tqdm\n", + "from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bfbc7106-e0c0-4d9f-9788-92052bc2479d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Your CPU supports instructions that this binary was not compiled to use: SSE3 SSE4.1 SSE4.2 AVX AVX2\n", + "For maximum performance, you can install NMSLIB from sources \n", + "pip install --no-binary :all: nmslib\n" + ] + } + ], + "source": [ + "import os\n", + "import pickle\n", + "\n", + "pickled_intermediates_data_folder = \"./pickled_intermediates_neg\"\n", + "\n", + "# Load the pickled files\n", + "with open(os.path.join(pickled_intermediates_data_folder, \"val.pickle\"), \"rb\") as handle:\n", + " val_neg = pickle.load(handle)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9c02242b-aba7-4000-bb31-5d700a39c663", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(pickled_intermediates_data_folder, \"train.pickle\"), \"rb\") as handle:\n", + " train_neg = pickle.load(handle)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4a9ca4a7-4953-4d2c-aa97-092ccafe56e7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Get most common inchi per inchikey: 100%|██████████████████████████████████████████████████████████████| 1614/1614 [00:00<00:00, 36199.94it/s]\n", + "Adding fingerprints to Inchikeys: 1614it [00:07, 216.45it/s]\n", + "Get most common inchi per inchikey: 100%|████████████████████████████████████████████████████████████| 29806/29806 [00:00<00:00, 35219.38it/s]\n", + "Adding fingerprints to Inchikeys: 29806it [01:42, 291.85it/s]\n" + ] + } + ], + "source": [ + "from ms2query.benchmarking.Fingerprints import Fingerprints\n", + "val_fingerprints = Fingerprints.from_spectrum_set(val_neg, \"daylight\", 4096)\n", + "train_fingerprints = Fingerprints.from_spectrum_set(train_neg, \"daylight\", 4096)\n", + "fingerprints = Fingerprints.combine_fingerprints(val_fingerprints, train_fingerprints)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0c2f6a34-c31a-45c7-ba70-c23e943195e8", + "metadata": {}, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix\n", + "\n", + "def get_prediction_tanimoto(predicted_inchikeys, val_spectra, val_and_train_fingerprints):\n", + " assert len(predicted_inchikeys) == len(val_spectra)\n", + "\n", + " tanimoto_for_prediction = []\n", + " for index, predicted_inchikey in enumerate(tqdm(predicted_inchikeys)):\n", + " if predicted_inchikey is not None:\n", + " correct_inchikey = val_spectra.spectra[index].get(\"inchikey\")[:14]\n", + " predicted_fingerprint = val_and_train_fingerprints.get_fingerprints([predicted_inchikey])[0]\n", + " actual_fingerprint = val_and_train_fingerprints.get_fingerprints([correct_inchikey])[0]\n", + " tanimoto_prediction = jaccard_similarity_matrix(np.array([predicted_fingerprint]), np.array([actual_fingerprint]))[0][0]\n", + " tanimoto_for_prediction.append(tanimoto_prediction)\n", + " else:\n", + " tanimoto_for_prediction.append(0.0)\n", + " return tanimoto_for_prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "aaec8cc0-2ebe-43f1-b267-b6a9d402d0fe", + "metadata": {}, + "outputs": [], + "source": [ + "def get_average_tanimoto_after_filtering(\n", + " query_spectrum_set, tanimoto_for_predictions\n", + ") -> float:\n", + " \"\"\"Calculates the average tanimoto of predictions made per inchikey. \n", + " \"\"\"\n", + " assert len(tanimoto_for_predictions) == len(query_spectrum_set)\n", + " average_scores_per_inchikey = []\n", + " # Calculate score per unique inchikey\n", + " for inchikey in query_spectrum_set.spectrum_indices_per_inchikey.keys():\n", + " matching_spectrum_indexes = query_spectrum_set.spectrum_indices_per_inchikey[inchikey]\n", + " prediction_scores = []\n", + " for index in matching_spectrum_indexes:\n", + " tanimoto_for_prediction = tanimoto_for_predictions[index]\n", + " if tanimoto_for_prediction is not None:\n", + " prediction_scores.append(tanimoto_for_prediction)\n", + " if len(prediction_scores) > 0:\n", + " average_prediction = sum(prediction_scores) / len(prediction_scores)\n", + " average_scores_per_inchikey.append(average_prediction)\n", + " average_over_all_inchikeys = sum(average_scores_per_inchikey) / len(average_scores_per_inchikey)\n", + " return average_over_all_inchikeys\n", + "\n", + "\n", + "def get_average_tanimoto_after_treshold(query_spectrum_set,\n", + " tanimoto_for_predictions, predicted_scores, threshold):\n", + " \"\"\"Applies a threshold and calculates the average tanimoto. It returns both the fraction that passed the threshold and average tanimoto.\"\"\"\n", + "\n", + " selected_tanimoto_scores = []\n", + " nr_of_predictions = 0\n", + " for index, score in enumerate(predicted_scores):\n", + " if score > threshold:\n", + " selected_tanimoto_scores.append(tanimoto_for_predictions[index])\n", + " nr_of_predictions += 1\n", + " else:\n", + " selected_tanimoto_scores.append(None)\n", + " prediction_fraction = nr_of_predictions/len(tanimoto_for_predictions)\n", + " average_tanimoto = get_average_tanimoto_after_filtering(query_spectrum_set, selected_tanimoto_scores)\n", + " return prediction_fraction, average_tanimoto\n", + "\n", + "def get_average_tanimoto_for_list_of_thresholds(query_spectrum_set, \n", + " tanimoto_for_predictions, predicted_scores):\n", + " assert len(predicted_scores) == len(tanimoto_for_predictions) == len(query_spectrum_set)\n", + " list_of_thresholds = np.quantile(predicted_scores, np.linspace(0.0, 0.98, 100))\n", + " prediction_fractions = []\n", + " average_tanimotos = []\n", + " for threshold in tqdm(list_of_thresholds, desc = \"thresholds\"):\n", + " prediction_fraction, average_tanimoto = get_average_tanimoto_after_treshold(query_spectrum_set, tanimoto_for_predictions, predicted_scores, threshold)\n", + " prediction_fractions.append(prediction_fraction)\n", + " average_tanimotos.append(average_tanimoto)\n", + " return prediction_fractions, average_tanimotos\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "ccb39ff4-e979-4d37-8fa6-d7433608b297", + "metadata": {}, + "outputs": [], + "source": [ + "from matchms.similarity.FlashSimilarity import FlashSimilarity\n", + "import numpy as np\n", + "def get_inchikeys_and_predicted_scores(similarity_method, train, val, fingerprints):\n", + " score_matrix = similarity_method.matrix(train.spectra, val.spectra)\n", + " max_indices = np.argmax(score_matrix, axis=0)\n", + " predicted_score = score_matrix[max_indices, np.arange(score_matrix.shape[1])]\n", + " predicted_inchikeys = []\n", + " for index in max_indices:\n", + " predicted_inchikeys.append(train.spectra[index].get(\"inchikey\")[:14])\n", + " tanimoto_for_predictions = get_prediction_tanimoto(predicted_inchikeys, val, fingerprints)\n", + " return tanimoto_for_predictions, predicted_score\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "b94a40bf-40f1-48f1-b485-64ed6b94a1fc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Flash spectral_entropy (hybrid) (parallel x96): 100%|███████████████████████████████████████████████| 283790/283790 [01:55<00:00, 2448.70it/s]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████| 15967/15967 [00:00<00:00, 15991.10it/s]\n" + ] + } + ], + "source": [ + "prediction_tanimoto_entropy_hybrid, predicted_scores_entropy_hybrid = get_inchikeys_and_predicted_scores(FlashSimilarity(\"spectral_entropy\", \"hybrid\", remove_precursor=True), train_neg, val_neg, fingerprints)\n", + "prediction_fractions_entropy_hybrid, average_tanimotos_entropy_hybrid = get_average_tanimoto_for_list_of_thresholds(val_neg, prediction_tanimoto_entropy_hybrid, predicted_scores_entropy_hybrid)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "61c2a7ab-cfbe-413b-abc1-1c8f6f9d1a98", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Flash cosine (hybrid) (parallel x96): 100%|█████████████████████████████████████████████████████████| 283790/283790 [02:03<00:00, 2297.28it/s]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████| 15967/15967 [00:00<00:00, 20904.79it/s]\n", + "thresholds: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 389.50it/s]\n" + ] + } + ], + "source": [ + "prediction_tanimoto_mod_cos, max_scores_mod_cos = get_inchikeys_and_predicted_scores(FlashSimilarity(\"cosine\", \"hybrid\", remove_precursor=True), train_neg, val_neg, fingerprints)\n", + "prediction_fractions_mod_cos, average_tanimotos_mod_cos = get_average_tanimoto_for_list_of_thresholds(val_neg, prediction_tanimoto_mod_cos, max_scores_mod_cos)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "d245aae5-dcb6-4d67-a30a-7817dadca17c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Flash cosine (fragment) (parallel x96): 100%|███████████████████████████████████████████████████████| 283790/283790 [02:02<00:00, 2310.61it/s]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████| 15967/15967 [00:00<00:00, 22937.40it/s]\n", + "thresholds: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 394.65it/s]\n" + ] + } + ], + "source": [ + "prediction_tanimoto_cos, max_scores_cos = get_inchikeys_and_predicted_scores(FlashSimilarity(\"cosine\", \"fragment\", remove_precursor=True), train_neg, val_neg, fingerprints)\n", + "prediction_fractions_cos, average_tanimotos_cos = get_average_tanimoto_for_list_of_thresholds(val_neg, prediction_tanimoto_cos, max_scores_cos)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "8eacf007-a828-46cd-b635-d36651f104c4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Flash spectral_entropy (fragment) (parallel x96): 100%|█████████████████████████████████████████████| 283790/283790 [01:40<00:00, 2821.55it/s]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████| 15967/15967 [00:00<00:00, 24911.48it/s]\n", + "thresholds: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 396.78it/s]\n" + ] + } + ], + "source": [ + "prediction_tanimoto_entropy, max_scores_entropy = get_inchikeys_and_predicted_scores(FlashSimilarity(\"spectral_entropy\", \"fragment\", remove_precursor=True), train_neg, val_neg, fingerprints)\n", + "prediction_fractions_entropy, average_tanimotos_entropy = get_average_tanimoto_for_list_of_thresholds(val_neg, prediction_tanimoto_entropy, max_scores_entropy)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "5cad5258-6c65-4f9c-850f-d0aeff49ad70", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "plt.plot(prediction_fractions_mod_cos, average_tanimotos_mod_cos, label = \"modified cosine\")\n", + "plt.plot(prediction_fractions_cos, average_tanimotos_cos, label = \"cosine\")\n", + "plt.plot(prediction_fractions_entropy_hybrid, average_tanimotos_entropy_hybrid, label = \"entropy hybrid\")\n", + "plt.plot(prediction_fractions_entropy, average_tanimotos_entropy, label = \"emtropy normal\")\n", + "\n", + "plt.xlabel(\"Fraction predicted\")\n", + "plt.ylabel(\"Average Tanimoto\")\n", + "plt.legend()\n", + "plt.xlim((0, 1))\n", + "plt.ylim((0, 1))\n", + "plt.show()" + ] + } + ], + "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": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From f3cda29110388d1c7056e6640d8d1e73a9604a9f Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 19 Mar 2026 10:05:06 +0100 Subject: [PATCH 138/149] Remove old notebook --- .../notebooks/Test_method_evaluator.ipynb | 890 ------------------ 1 file changed, 890 deletions(-) delete mode 100644 ms2query/notebooks/Test_method_evaluator.ipynb diff --git a/ms2query/notebooks/Test_method_evaluator.ipynb b/ms2query/notebooks/Test_method_evaluator.ipynb deleted file mode 100644 index 5303dfe..0000000 --- a/ms2query/notebooks/Test_method_evaluator.ipynb +++ /dev/null @@ -1,890 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ced5638b-15f4-4219-9ed8-b2823a53e574", - "metadata": {}, - "source": [ - "# load in spectra" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2768cf34-1794-4a09-89a3-f81a04b1bcda", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "31453it [00:14, 2140.47it/s]\n", - "7080it [00:03, 1992.44it/s]\n", - "459610it [03:37, 2114.18it/s]\n", - "131240it [01:06, 1967.67it/s]\n" - ] - } - ], - "source": [ - "import os\n", - "from matchms.importing import load_from_mgf\n", - "from tqdm import tqdm\n", - "\n", - "\n", - "save_directory = \"../data/ms2deepscore_model/training_and_validation_split/\"\n", - "pos_val_spectra = list(tqdm(load_from_mgf(os.path.join(save_directory, \"positive_validation_spectra.mgf\"))))\n", - "neg_val_spectra = list(tqdm(load_from_mgf(os.path.join(save_directory, \"negative_validation_spectra.mgf\"))))\n", - "pos_train_spectra = list(tqdm(load_from_mgf(os.path.join(save_directory, \"positive_training_spectra.mgf\"))))\n", - "neg_train_spectra = list(tqdm(load_from_mgf(os.path.join(save_directory, \"negative_training_spectra.mgf\"))))\n", - "pos_test_spectra = list(tqdm(load_from_mgf(os.path.join(save_directory, \"positive_testing_spectra.mgf\"))))\n", - "neg_test_spectra = list(tqdm(load_from_mgf(os.path.join(save_directory, \"negative_testing_spectra.mgf\"))))\n" - ] - }, - { - "cell_type": "markdown", - "id": "7f5a4df2-2bb7-4731-a6bf-6727183da493", - "metadata": {}, - "source": [ - "### Save as pickled files for quicker reloads" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3a55be7f-d760-4df7-944e-251844cb1b4d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "\n", - "\n", - "pickled_intermediates_data_folder = \"../data/pickled_intermediates\"\n", - "os.path.isdir(pickled_intermediates_data_folder)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "28606ab4-fd4c-4111-a1ca-8dc502593425", - "metadata": {}, - "outputs": [], - "source": [ - "import pickle\n", - "\n", - "\n", - "with open(os.path.join(pickled_intermediates_data_folder, \"neg_val_spectra.pickle\"), \"wb\") as handle:\n", - " pickle.dump(neg_val_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", - "with open(os.path.join(pickled_intermediates_data_folder, \"neg_train_spectra.pickle\"), \"wb\") as handle:\n", - " pickle.dump(neg_train_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", - "with open(os.path.join(pickled_intermediates_data_folder, \"pos_val_spectra.pickle\"), \"wb\") as handle:\n", - " pickle.dump(pos_val_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", - "with open(os.path.join(pickled_intermediates_data_folder, \"pos_train_spectra.pickle\"), \"wb\") as handle:\n", - " pickle.dump(pos_train_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", - "with open(os.path.join(pickled_intermediates_data_folder, \"pos_test_spectra.pickle\"), \"wb\") as handle:\n", - " pickle.dump(pos_test_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", - "with open(os.path.join(pickled_intermediates_data_folder, \"neg_test_spectra.pickle\"), \"wb\") as handle:\n", - " pickle.dump(neg_test_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)" - ] - }, - { - "cell_type": "markdown", - "id": "cf849c08-3eaf-4922-b361-4f324b010ca9", - "metadata": {}, - "source": [ - "# Create a simple MS2Deepscore ranker" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "from ms2deepscore.models import load_model\n", - "\n", - "\n", - "ms2deepscore_model = load_model(\n", - " \"../data/ms2deepscore_model/trained_models/\"\n", - " \"both_mode_ionmode_precursor_mz_2000_layers_500_embedding_2025_02_26_18_42_25/ms2deepscore_model.pt\")\n" - ], - "id": "d4805b43bea1e3a2" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "import sys\n", - "\n", - "\n", - "sys.path.append(\"../../ms_chemical_space_explorer\")" - ], - "id": "9ca0b8c6639168e1" - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca3486f3-06b3-402d-90b1-ba01cb2568aa", - "metadata": {}, - "outputs": [], - "source": [ - "from ms_chemical_space_explorer.SpectrumDataSet import SpectraWithMS2DeepScoreEmbeddings\n", - "\n", - "\n", - "library_spectra = SpectraWithMS2DeepScoreEmbeddings(neg_train_spectra + pos_train_spectra, ms2deepscore_model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c858cc69-3da8-4530-ba13-ad4b1827f1ae", - "metadata": {}, - "outputs": [], - "source": [ - "import pickle\n", - "\n", - "\n", - "with open(os.path.join(pickled_intermediates_data_folder, \"neg_pos_library_embeddings.pickle\"), \"wb\") as handle:\n", - " pickle.dump(library_spectra.embeddings, handle, protocol=pickle.HIGHEST_PROTOCOL)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53ca37fe-1627-4473-a21a-168791d92112", - "metadata": {}, - "outputs": [], - "source": [ - "import pickle\n", - "\n", - "\n", - "with open(os.path.join(pickled_intermediates_data_folder, \"neg_pos_library_with_embeddings.pickle\"), \"wb\") as handle:\n", - " pickle.dump(library_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0cec8761-6c37-4ac5-ba52-619c171cd834", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "val_spectra = SpectraWithMS2DeepScoreEmbeddings(neg_val_spectra + pos_val_spectra, ms2deepscore_model)" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "import pickle\n", - "\n", - "\n", - "with open(os.path.join(pickled_intermediates_data_folder,\n", - " \"neg_pos_val_spectra_with_embeddings.pickle\"), \"wb\") as handle:\n", - " pickle.dump(val_spectra, handle, protocol=pickle.HIGHEST_PROTOCOL)" - ], - "id": "e6835fbcb3d01494" - }, - { - "cell_type": "markdown", - "id": "688da27f-3290-480c-ab67-d171bbac6b93", - "metadata": {}, - "source": [ - "# Reload intermediates" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "9cb48a39-f3cd-4924-8da2-f11007475795", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "\n", - "sys.path.append(\"../../ms_chemical_space_explorer\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "97d042ab-19f6-4d32-bfd7-b24e0f72c1d9", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pickle\n", - "\n", - "\n", - "pickled_intermediates_data_folder = \"../data/pickled_intermediates\"\n", - "with open(os.path.join(pickled_intermediates_data_folder, \"neg_pos_library_with_embeddings.pickle\"), \"rb\") as file:\n", - " library_spectra = pickle.load(file)\n", - "with open(os.path.join(pickled_intermediates_data_folder, \"neg_pos_val_spectra_with_embeddings.pickle\"), \"rb\") as file:\n", - " val_spectra = pickle.load(file)" - ] - }, - { - "cell_type": "markdown", - "id": "c3c8dfda-3294-45cc-8a6f-1aeb06257c73", - "metadata": {}, - "source": [ - "# Initialize a method evaluator" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "79fa345a-52eb-4079-a6b5-0bd63d78d821", - "metadata": {}, - "outputs": [], - "source": [ - "from ms_chemical_space_explorer.benchmarking.EvaluateMethods import EvaluateMethods\n", - "\n", - "\n", - "method_evaluator = EvaluateMethods(library_spectra, val_spectra)\n", - "method_evaluator.training_spectrum_set.progress_bars = False\n", - "method_evaluator.validation_spectrum_set.progress_bars = False" - ] - }, - { - "cell_type": "markdown", - "id": "16663c37-4fdd-4454-bcc6-55dbf4d6230f", - "metadata": {}, - "source": [ - "# Test basic MS2DeepScore" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "1048dccf-1b53-4dcc-96d4-01a4d51dc57d", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 78/78 [26:20<00:00, 20.26s/it]\n", - "Calculating analogue accuracy per inchikey: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2015/2015 [00:01<00:00, 1190.75it/s]\n" - ] - } - ], - "source": [ - "from ms_chemical_space_explorer.methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore\n", - "\n", - "\n", - "result_analogue = method_evaluator.benchmark_analogue_search(predict_highest_ms2deepscore)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "0f6f21cb-a358-4803-ad74-cc57a2a7a47e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.3848227909492443\n" - ] - } - ], - "source": [ - "print(result_analogue)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "eda9a92c-fe2c-4c4b-ab4e-07ddb4346a27", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Splitting spectra per inchikey: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1837/1837 [00:00<00:00, 53337.60it/s]\n", - "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 31/31 [10:21<00:00, 20.03s/it]\n", - "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 33/33 [10:35<00:00, 19.25s/it]\n", - "Calculating exact match accuracy per inchikey: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1612/1612 [00:00<00:00, 313512.85it/s]\n" - ] - } - ], - "source": [ - "from ms_chemical_space_explorer.methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore\n", - "\n", - "\n", - "result_positive = method_evaluator.benchmark_exact_matching_within_ionmode(predict_highest_ms2deepscore, \"positive\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "b6ddad52-8647-450f-8881-6753a5bea814", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.5169110149857257\n" - ] - } - ], - "source": [ - "print(result_positive)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "dfc7a156-74a3-4c48-b610-e38410abf274", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Splitting spectra per inchikey: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 924/924 [00:00<00:00, 293846.15it/s]\n", - "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [02:08<00:00, 18.29s/it]\n", - "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [02:14<00:00, 16.78s/it]\n", - "Calculating exact match accuracy per inchikey: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 868/868 [00:00<00:00, 894070.70it/s]\n" - ] - } - ], - "source": [ - "from ms_chemical_space_explorer.methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore\n", - "\n", - "\n", - "result_neg = method_evaluator.benchmark_exact_matching_within_ionmode(predict_highest_ms2deepscore, \"negative\")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "86cc5ba4-4380-488a-a486-de63cccada74", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.5448453174876924" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result_neg\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "a5b0595b-51dd-4a56-b9ec-e81a597dd26b", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Splitting spectra per inchikey across ionmodes: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2015/2015 [00:00<00:00, 9551.01it/s]\n", - "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [11:40<00:00, 20.02s/it]\n", - "Predicting highest ms2deepscore per batch of 500 embeddings: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [03:47<00:00, 17.46s/it]\n", - "Calculating exact match accuracy per inchikey: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 746/746 [00:00<00:00, 496186.30it/s]\n" - ] - } - ], - "source": [ - "result_across_ionmodes = method_evaluator.exact_matches_across_ionization_modes(predict_highest_ms2deepscore)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "fe14e752-75b4-4e00-8600-90c1dce46b90", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0006188308440879157" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result_across_ionmodes" - ] - }, - { - "cell_type": "markdown", - "id": "1499181b-7b68-437a-85b3-b7dc3ad53884", - "metadata": {}, - "source": [ - "# Test best possible results" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "56d119e2-145a-484c-ab3c-56b569b549dd", - "metadata": {}, - "outputs": [], - "source": [ - "from ms_chemical_space_explorer.methods.predict_best_possible_match import predict_best_possible_match\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b3687845-099c-4b98-b715-c8057b5858bb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating tanimoto scores to determine best possible match\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Calculating analogue accuracy per inchikey: 100%|██████████████████████████████████████████| 2015/2015 [00:01<00:00, 1578.04it/s]\n" - ] - } - ], - "source": [ - "result_analogue_best = method_evaluator.benchmark_analogue_search(predict_highest_ms2deepscore)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "81c83a8d-0754-4207-97d3-2fbef49082ac", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7753653963061775" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result_analogue_best" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "5fad0038-025c-4c73-9bfb-bc4545f092df", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Splitting spectra per inchikey: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 924/924 [00:00<00:00, 310739.01it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating tanimoto scores to determine best possible match\n", - "Calculating tanimoto scores to determine best possible match\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Calculating exact match accuracy per inchikey: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 868/868 [00:00<00:00, 928738.74it/s]\n" - ] - } - ], - "source": [ - "result_neg_best = method_evaluator.benchmark_exact_matching_within_ionmode(predict_best_possible_match, \"negative\")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "e0418a27-0195-4021-a04b-788cbee4fe2b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result_neg_best" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "f19ea34d-f6c1-4f7c-b7ec-8c7bad85a1da", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Splitting spectra per inchikey across ionmodes: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2015/2015 [00:00<00:00, 13798.67it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating tanimoto scores to determine best possible match\n", - "Calculating tanimoto scores to determine best possible match\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Calculating exact match accuracy per inchikey: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 746/746 [00:00<00:00, 404727.82it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result_across_ionmodes_best = method_evaluator.exact_matches_across_ionization_modes(predict_best_possible_match)\n", - "result_across_ionmodes_best" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "052e768d-4044-40b8-a85e-7cf03abe4184", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Splitting spectra per inchikey across ionmodes: 100%|█████████████████████████████████████| 2015/2015 [00:00<00:00, 10356.24it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating tanimoto scores to determine best possible match\n", - "Calculating tanimoto scores to determine best possible match\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Calculating exact match accuracy per inchikey: 100%|███████████████████████████████████████| 746/746 [00:00<00:00, 315164.26it/s]\n" - ] - } - ], - "source": [ - "result_across_ionmodes_best = method_evaluator.exact_matches_across_ionization_modes(predict_best_possible_match)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1ff6eaba-8213-4270-9395-39ff3aee6879", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result_across_ionmodes_best" - ] - }, - { - "cell_type": "markdown", - "id": "3702ba19-0c86-443c-8621-fe1ab427b3da", - "metadata": {}, - "source": [ - "# Test cosine score" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5f0cfc49-009f-46a1-beef-6310d31260a1", - "metadata": {}, - "outputs": [], - "source": [ - "from ms_chemical_space_explorer.methods.predict_highest_cosine import predict_highest_cosine" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9cb9210d-3ead-4798-bcc0-82b571e69f8b", - "metadata": {}, - "outputs": [], - "source": [ - "result_analogue_cosine = method_evaluator.benchmark_analogue_search(predict_highest_cosine)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c1a5fb72-6039-4d56-af71-fe07bb583c22", - "metadata": {}, - "outputs": [], - "source": [ - "result_analogue_cosine" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "7d4641b9-9b44-4b64-8fed-0b568cb0f4f9", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Splitting spectra per inchikey: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 924/924 [00:00<00:00, 260575.33it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "created scores\n", - "Filtered on precursor\n", - "Calculated cosine\n", - "('PrecursorMzMatch', 'CosineGreedy_score', 'CosineGreedy_matches')\n", - "created scores\n", - "Filtered on precursor\n", - "Calculated cosine\n", - "('PrecursorMzMatch', 'CosineGreedy_score', 'CosineGreedy_matches')\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Calculating exact match accuracy per inchikey: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 868/868 [00:00<00:00, 201408.27it/s]\n" - ] - } - ], - "source": [ - "result_neg = method_evaluator.benchmark_exact_matching_within_ionmode(predict_highest_cosine, \"negative\")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "96191952-14df-41b3-a410-d2e5574dd3c9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.5772003486330072" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result_neg" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e77b0ecd-7f1a-438d-a0a1-950ff9e76881", - "metadata": {}, - "outputs": [], - "source": [ - "result_positive = method_evaluator.benchmark_exact_matching_within_ionmode(predict_highest_cosine, \"positive\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7db770c4-f4bb-4a0e-82a2-03d52e6ed7d0", - "metadata": {}, - "outputs": [], - "source": [ - "result_positive" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "675351b2-ff50-42d8-b179-006d0a46cadc", - "metadata": {}, - "outputs": [], - "source": [ - "result_across_ionmodes = method_evaluator.exact_matches_across_ionization_modes(predict_highest_cosine)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d056551a-f85b-4077-8b35-eaf834da7a9b", - "metadata": {}, - "outputs": [], - "source": [ - "result_across_ionmodes" - ] - }, - { - "cell_type": "markdown", - "id": "7682a1b8-aec1-40dc-b942-419bd23de041", - "metadata": {}, - "source": [ - "# Test predictions with ISF" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "from ms_chemical_space_explorer.methods.predict_with_integrated_similarity_flow import (\n", - " predict_with_integrated_similarity_flow,\n", - ")\n", - "\n", - "\n", - "result_analogue_isf = method_evaluator.benchmark_analogue_search(predict_with_integrated_similarity_flow)\n", - "print(result_analogue_isf)\n", - "result_neg_isf = method_evaluator.benchmark_exact_matching_within_ionmode(\n", - " predict_with_integrated_similarity_flow, \"negative\")\n", - "print(result_neg_isf)\n", - "result_positive_isf = method_evaluator.benchmark_exact_matching_within_ionmode(\n", - " predict_with_integrated_similarity_flow, \"positive\")\n", - "print(result_positive_isf)\n", - "result_across_ionmodes_isf = method_evaluator.exact_matches_across_ionization_modes(\n", - " predict_with_integrated_similarity_flow)\n", - "print(result_across_ionmodes_isf)" - ], - "id": "958a6efb624e75de" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "result_analogue_isf", - "id": "326a55ab85832258" - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "22ba5663-1f8a-405a-b04b-0380e18c6987", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hello\n" - ] - } - ], - "source": [ - "print(\"hello\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6b9198fb-c60b-4db5-8415-578b88275e5a", - "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": "3.9.21" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 3bec761e0b1aebbcd57f79233ed88ae496032110 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 19 Mar 2026 10:12:11 +0100 Subject: [PATCH 139/149] Add readme with explanation of notebooks --- ms2query/notebooks/readme.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 ms2query/notebooks/readme.md diff --git a/ms2query/notebooks/readme.md b/ms2query/notebooks/readme.md new file mode 100644 index 0000000..15394f8 --- /dev/null +++ b/ms2query/notebooks/readme.md @@ -0,0 +1,8 @@ +# Notebooks + +The most important notebook is develop_and_compare_ms2query_2.ipynb, this notebook is fully cleaned and reproducible and contains all the main results for MS2Query 2 + +The other notebooks are additional analysis which are not fully cleaned and reproducible and a bit less relevant to the main story: +run_cosine_and_entropy_on_neg_neg: just also a run with cosine, but the main notebook already contains a comparison with mod cos +experiment_with_reranking_analogues: Here we did a lot of analysis on checking if we could find a reranking algorithm like the original MS2Query. During this work I figured out it is actually best to not change the MS2DeepScore prediction, but just focus on reliability prediction. +test_ann_speed_improvements: Some tests on using pynndescent for speed improvement using ANN. From bba9b62485c8adae771de39da86628f441c7cf77 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 19 Mar 2026 10:12:25 +0100 Subject: [PATCH 140/149] Remove old notebook --- ...umber_of_inchikeys_with_two_ionmodes.ipynb | 341 ------------------ 1 file changed, 341 deletions(-) delete mode 100644 ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb diff --git a/ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb b/ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb deleted file mode 100644 index 271f9de..0000000 --- a/ms2query/notebooks/get_number_of_inchikeys_with_two_ionmodes.ipynb +++ /dev/null @@ -1,341 +0,0 @@ -{ - "cells": [ - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "from matchms.importing import load_from_mgf\n", - "from tqdm import tqdm\n", - "\n", - "\n", - "neg_val_spectra = list(tqdm(load_from_mgf(\n", - " \"../../../ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/\"\n", - " \"negative_validation_spectra.mgf\")))\n", - "neg_test_spectra = list(tqdm(load_from_mgf(\n", - " \"../../../ms2deepscore/data/pytorch/new_corinna_included/\"\n", - " \"training_and_validation_split/negative_testing_spectra.mgf\")))\n", - "neg_train_spectra = list(tqdm(load_from_mgf(\n", - " \"../../../ms2deepscore/data/pytorch/new_corinna_included/\"\n", - " \"training_and_validation_split/negative_training_spectra.mgf\")))\n", - "neg_spectra = neg_val_spectra + neg_test_spectra + neg_train_spectra\n", - "\n", - "pos_val_spectra = list(tqdm(load_from_mgf(\n", - " \"../../../ms2deepscore/data/pytorch/new_corinna_included/\"\n", - " \"training_and_validation_split/positive_validation_spectra.mgf\")))\n", - "pos_test_spectra = list(tqdm(load_from_mgf(\n", - " \"../../../ms2deepscore/data/pytorch/new_corinna_included/\"\n", - " \"training_and_validation_split/positive_testing_spectra.mgf\")))\n", - "pos_train_spectra = list(tqdm(load_from_mgf(\n", - " \"../../../ms2deepscore/data/pytorch/new_corinna_included/\"\n", - " \"training_and_validation_split/positive_training_spectra.mgf\")))\n", - "pos_spectra = pos_val_spectra + pos_test_spectra + pos_train_spectra" - ], - "id": "fdf12d625e87127b" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "neg_inchikeys = []\n", - "for spectrum in tqdm(neg_spectra):\n", - " neg_inchikeys.append(spectrum.get(\"inchikey\")[:14])\n", - " " - ], - "id": "9ce097a34a9e6656" - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "437e9455-49a9-435e-8bb5-cd3b3ed987f1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 519580/519580 [00:46<00:00, 11219.75it/s]\n" - ] - } - ], - "source": [ - "pos_inchikeys = []\n", - "for spectrum in tqdm(pos_spectra):\n", - " pos_inchikeys.append(spectrum.get(\"inchikey\")[:14])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "df03a6d2-6755-46e1-a874-fc96c1a2b20c", - "metadata": {}, - "outputs": [], - "source": [ - "unique_neg_inchikeys = set(neg_inchikeys)\n", - "unique_pos_inchikeys = set(pos_inchikeys)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "aa775f3b-9c10-43cd-a6be-74dac2e698ce", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "18480" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(unique_neg_inchikeys)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "f3a73395-68c2-4952-9171-96f67f597cc9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "36638" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(unique_pos_inchikeys)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "f9729ad5-d201-476a-87a1-78e267425aae", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "14801" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "overlapping_inchikeys = unique_neg_inchikeys & unique_pos_inchikeys\n", - "len(overlapping_inchikeys)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "07d148f7-aef6-4eda-a66f-984f0c65a31b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "40317" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "total_unique_inchikeys = unique_neg_inchikeys | unique_pos_inchikeys\n", - "len(total_unique_inchikeys)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "0cd254e4-9b7f-41ed-a7ce-25675392102f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.36711560880025795" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "14801/40317" - ] - }, - { - "cell_type": "markdown", - "id": "3f5bb776-9910-4a4d-bc45-d8e049d63ce6", - "metadata": {}, - "source": [ - "# Check how many there are already in the val set\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "4b5ced16-ceae-439b-bb04-c72986798b8f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7551/7551 [00:01<00:00, 4630.22it/s]\n" - ] - } - ], - "source": [ - "neg_inchikeys_val = []\n", - "for spectrum in tqdm(neg_val_spectra):\n", - " neg_inchikeys_val.append(spectrum.get(\"inchikey\")[:14])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "0d2b00bd-56a3-4c67-ad3f-d54051a74701", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25412/25412 [00:02<00:00, 10220.59it/s]\n" - ] - } - ], - "source": [ - "pos_inchikeys_val = []\n", - "for spectrum in tqdm(pos_val_spectra):\n", - " pos_inchikeys_val.append(spectrum.get(\"inchikey\")[:14])" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "76a7736a-d4a4-42a5-bf42-d20b1a38060c", - "metadata": {}, - "outputs": [], - "source": [ - "unique_neg_inchikeys_val = set(neg_inchikeys_val)\n", - "unique_pos_inchikeys_val = set(pos_inchikeys_val)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "6fce2334-e43b-40ce-b459-75c1a6d1ed00", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "35" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "overlapping_inchikeys_val = unique_neg_inchikeys_val & unique_pos_inchikeys_val\n", - "len(overlapping_inchikeys_val)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "86668203-8e5b-40e9-934e-0ade3c25e9d8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7551" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(neg_inchikeys_val)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "bc2e25e7-5d85-4486-924d-c1d9651ba2bc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "25412" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(pos_inchikeys_val)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e864dc8e-68bb-4d3d-8a27-57f3892581e3", - "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": "3.9.21" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 09d545d43ce81031e353c9039472f87b574c1ceb Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 19 Mar 2026 10:15:18 +0100 Subject: [PATCH 141/149] spelling error fix --- ms2query/benchmarking/AnnotatedSpectrumSet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/benchmarking/AnnotatedSpectrumSet.py index 86332db..93ff130 100644 --- a/ms2query/benchmarking/AnnotatedSpectrumSet.py +++ b/ms2query/benchmarking/AnnotatedSpectrumSet.py @@ -159,7 +159,7 @@ def save(self, save_file: str) -> None: @classmethod def load(cls, spectrum_file: str) -> "AnnotatedSpectrumSet": - """Load mass spectra into a AnnotatedSpectrumSet, if embeddings are available they are loaded too""" + """Load mass spectra into a AnnotatedSpectrmuSet, if embeddings are available they are loaded too""" spectra = list(load_spectra(spectrum_file)) embedding_file_name = os.path.splitext(spectrum_file)[0] + "_embeddings.npz" From 95bba35fc2b3c85158be9561750e9e9c954e23cb Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 19 Mar 2026 10:24:36 +0100 Subject: [PATCH 142/149] Remove EvaluateAnalogueSearch since now handled in notebooks --- .../benchmarking/EvaluateAnalogueSearch.py | 69 ------------------- .../test_evaluate_methods.py | 31 --------- 2 files changed, 100 deletions(-) delete mode 100644 ms2query/benchmarking/EvaluateAnalogueSearch.py delete mode 100644 tests/test_benchmarking/test_evaluate_methods.py diff --git a/ms2query/benchmarking/EvaluateAnalogueSearch.py b/ms2query/benchmarking/EvaluateAnalogueSearch.py deleted file mode 100644 index 891edfc..0000000 --- a/ms2query/benchmarking/EvaluateAnalogueSearch.py +++ /dev/null @@ -1,69 +0,0 @@ -from typing import List -import numpy as np -from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix -from tqdm import tqdm -from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet -from ms2query.benchmarking.Fingerprints import Fingerprints - - -class EvaluateAnalogueSearch: - def __init__( - self, training_spectrum_set: AnnotatedSpectrumSet, - validation_spectrum_set: AnnotatedSpectrumSet, - fingerprint_type="daylight", - nbits=2048, - ): - self.training_spectrum_set = training_spectrum_set - self.validation_spectrum_set = validation_spectrum_set - - self.fingerprints = Fingerprints.from_spectrum_set(training_spectrum_set + validation_spectrum_set, - fingerprint_type, nbits) - - def benchmark_analogue_search( - self, predicted_inchikeys: List[str], - ) -> float: - """Calculates the average accuracy of predictions made. - - predicted_inchikeys should match the order in self.validation_spectrum_set - """ - average_scores_per_inchikey = [] - # Calculate score per unique inchikey - for inchikey in tqdm( - self.validation_spectrum_set.spectrum_indices_per_inchikey.keys(), - desc="Calculating analogue accuracy per inchikey", - ): - matching_spectrum_indexes = self.validation_spectrum_set.spectrum_indices_per_inchikey[inchikey] - prediction_scores = [] - for index in matching_spectrum_indexes: - predicted_inchikey = predicted_inchikeys[index] - if predicted_inchikey is None: - prediction_scores.append(0.0) - else: - predicted_fingerprint = self.fingerprints.get_fingerprints([predicted_inchikey])[0] - actual_fingerprint = self.fingerprints.get_fingerprints([inchikey])[0] - tanimoto_for_prediction = calculate_tanimoto_score_between_pair( - predicted_fingerprint, actual_fingerprint - ) - prediction_scores.append(tanimoto_for_prediction) - - average_prediction = sum(prediction_scores) / len(prediction_scores) - score = average_prediction - average_scores_per_inchikey.append(score) - average_over_all_inchikeys = sum(average_scores_per_inchikey) / len(average_scores_per_inchikey) - return average_over_all_inchikeys - - def get_accuracy_recall_curve(self): - """This method should test the recall accuracy balance. - All of the used methods use a threshold which indicates quality of prediction. - A method that can predict well when a prediction is accurate is beneficial. - We need a method to test this. - - One method is generating a recall accuracy curve. This could be done for both the analogue search predictions - and the exact match predictions. By returning the predicted score for a match this method could create an - accuracy recall plot. - """ - raise NotImplementedError - - -def calculate_tanimoto_score_between_pair(fingerprint_1: np.ndarray, fingerprint_2: np.ndarray) -> float: - return jaccard_similarity_matrix(np.array([fingerprint_1]), np.array([fingerprint_2]))[0][0] diff --git a/tests/test_benchmarking/test_evaluate_methods.py b/tests/test_benchmarking/test_evaluate_methods.py deleted file mode 100644 index a8bb75d..0000000 --- a/tests/test_benchmarking/test_evaluate_methods.py +++ /dev/null @@ -1,31 +0,0 @@ -from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet -from ms2query.benchmarking.EvaluateAnalogueSearch import EvaluateAnalogueSearch -from tests.helper_functions import create_test_spectra, ms2deepscore_model - - -def create_dummy_library_and_validation_spectra() -> tuple[AnnotatedSpectrumSet, AnnotatedSpectrumSet]: - nr_of_spectra_per_inchikey = 6 - nr_of_inchikeys = 5 - dummy_spectra = create_test_spectra(nr_of_spectra_per_inchikey, nr_of_inchikeys=nr_of_inchikeys) - for i, spectrum in enumerate(dummy_spectra): - if i % 3 == 0: - spectrum.set("ionmode", "positive") - else: - spectrum.set("ionmode", "negative") - model = ms2deepscore_model() - - reference_library = AnnotatedSpectrumSet.create_spectrum_set(dummy_spectra[: nr_of_spectra_per_inchikey * 2]) - validation_spectra = AnnotatedSpectrumSet.create_spectrum_set(dummy_spectra[nr_of_spectra_per_inchikey * 2 :]) - reference_library.add_embeddings(model) - validation_spectra.add_embeddings(model) - return reference_library, validation_spectra - - -def test_evaluate_analogue_search(): - reference_library, validation_spectra = create_dummy_library_and_validation_spectra() - method_evaluator = EvaluateAnalogueSearch(reference_library, validation_spectra) - fake_predicted_inchikeys = [ - reference_library.inchikeys[i % len(reference_library.inchikeys)] - for i in range(len(validation_spectra.spectra)) - ] - method_evaluator.benchmark_analogue_search(fake_predicted_inchikeys) From cd310cf9e8d9c0f1e743c99e08e27d3290a5328e Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 19 Mar 2026 10:33:45 +0100 Subject: [PATCH 143/149] Fix some notebook mistakes --- .../develop_and_compare_ms2query_2.ipynb | 26 +++++++++++++------ 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/ms2query/notebooks/develop_and_compare_ms2query_2.ipynb b/ms2query/notebooks/develop_and_compare_ms2query_2.ipynb index ef3f726..3929a23 100644 --- a/ms2query/notebooks/develop_and_compare_ms2query_2.ipynb +++ b/ms2query/notebooks/develop_and_compare_ms2query_2.ipynb @@ -488,7 +488,7 @@ "metadata": {}, "outputs": [], "source": [ - "from ms2query.benchmarking.reference_methods.predict_top_ms2deepscores import select_inchikeys_with_highest_ms2deepscore\n", + "from ms2query.benchmarking.reference_methods.predict_top_k_ms2deepscore import select_inchikeys_with_highest_ms2deepscore\n", "\n", "inchikeys_with_highest_ms2deepscores = select_inchikeys_with_highest_ms2deepscore(\n", " pos_val_spectra, pos_train_spectra, nr_of_inchikeys_to_select=1, batch_size=1000,)\n" @@ -1960,7 +1960,17 @@ }, { "cell_type": "code", - "execution_count": 279, + "execution_count": null, + "id": "84f01ee7", + "metadata": {}, + "outputs": [], + "source": [ + "precursor_mz = [spectrum.get('precursor_mz') for spectrum in pos_val_spectra.spectra]" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "a81c12e3-ba44-445c-aca2-24c4aa07591f", "metadata": {}, "outputs": [ @@ -2016,14 +2026,14 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": null, "id": "c2336b8d-d987-41ad-a010-eceeb7deb81b", "metadata": { "scrolled": true }, "outputs": [], "source": [ - "# !pip install seaborn\n", + "!pip install seaborn\n", "import seaborn as sns\n" ] }, @@ -2602,7 +2612,7 @@ }, { "cell_type": "code", - "execution_count": 321, + "execution_count": null, "id": "04727798-3e50-431d-be3b-0dbcf7f71e62", "metadata": {}, "outputs": [ @@ -2622,7 +2632,7 @@ "source": [ "from ms2query.benchmarking.Fingerprints import Fingerprints\n", "from ms2query.benchmarking.TopKTanimotoScores import TopKTanimotoScores\n", - "from ms2query.benchmarking.reference_methods.predict_top_ms2deepscores import select_inchikeys_with_highest_ms2deepscore\n", + "from ms2query.benchmarking.reference_methods.predict_top_k_ms2deepscore import select_inchikeys_with_highest_ms2deepscore\n", "from ms2query.benchmarking.reference_methods.MS2DeepScoresForTopInChikeys import calculate_MS2DeepScoresForTopKInChikeys\n", "\n", "neg_val_fingerprints = Fingerprints.from_spectrum_set(neg_val_spectra, \"daylight\", 4096)\n", @@ -3146,7 +3156,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "70955fb3-2c00-44f3-a3f3-4e495aa80e57", "metadata": {}, "outputs": [], @@ -3156,7 +3166,7 @@ "\n", "def analogue_search_in_batches(similarity_metric, train_spectra, val_spectra, batch_size=500):\n", " \"\"\"For some reason the argmax seemed to crash our server, so this batch process is a work around\"\"\"\n", - " score_matrix = mod_cos.matrix(train_spectra, val_spectra)\n", + " score_matrix = similarity_metric.matrix(train_spectra, val_spectra)\n", " predicted_inchikeys = []\n", " predicted_scores = []\n", " for i in tqdm(range(0, score_matrix.shape[1], batch_size)):\n", From 67320f4dc05a4a7a953f17d3768aec074a913002 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 19 Mar 2026 10:34:49 +0100 Subject: [PATCH 144/149] Remove unused predict cosine --- .../predict_highest_cosine.py | 26 ------------------- tests/test_benchmarking/test_methods.py | 2 -- 2 files changed, 28 deletions(-) delete mode 100644 ms2query/benchmarking/reference_methods/predict_highest_cosine.py diff --git a/ms2query/benchmarking/reference_methods/predict_highest_cosine.py b/ms2query/benchmarking/reference_methods/predict_highest_cosine.py deleted file mode 100644 index 9c49847..0000000 --- a/ms2query/benchmarking/reference_methods/predict_highest_cosine.py +++ /dev/null @@ -1,26 +0,0 @@ -from typing import List, Tuple -from matchms import Scores -from matchms.similarity.CosineGreedy import CosineGreedy -from matchms.similarity.PrecursorMzMatch import PrecursorMzMatch -from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet - - -def predict_highest_cosine( - library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet -) -> Tuple[List[str], List[float]]: - - scores = Scores(references=library_spectra.spectra, queries=query_spectra.spectra, is_symmetric=False) - scores = scores.calculate(PrecursorMzMatch(0.1)) - scores = scores.calculate(CosineGreedy(tolerance=0.1)) - inchikeys_of_best_match = [] - highest_scores = [] - for query_spectrum in query_spectra.spectra: - results = scores.scores_by_query(query_spectrum, "CosineGreedy_score", sort=True) - if len(results) == 0: - inchikeys_of_best_match.append(None) - highest_scores.append(0.0) - else: - best_reference, highest_score = results[0] - inchikeys_of_best_match.append(best_reference.get("inchikey")[:14]) - highest_scores.append(highest_score["CosineGreedy_score"]) - return inchikeys_of_best_match, highest_scores diff --git a/tests/test_benchmarking/test_methods.py b/tests/test_benchmarking/test_methods.py index 64337dd..d3030a9 100644 --- a/tests/test_benchmarking/test_methods.py +++ b/tests/test_benchmarking/test_methods.py @@ -3,7 +3,6 @@ from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix from ms2query.benchmarking.Fingerprints import Fingerprints from ms2query.benchmarking.reference_methods.predict_best_possible_match import predict_best_possible_match -from ms2query.benchmarking.reference_methods.predict_highest_cosine import predict_highest_cosine from ms2query.benchmarking.reference_methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore from ms2query.benchmarking.reference_methods.predict_with_integrated_similarity_flow import ( integrated_similarity_flow, @@ -18,7 +17,6 @@ @pytest.mark.parametrize( "prediction_function", [ - predict_highest_cosine, predict_highest_ms2deepscore, ], ) From a5cb3fc668daeae457ac5e5a0cb562009ed90135 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 19 Mar 2026 10:37:32 +0100 Subject: [PATCH 145/149] Remove integrated similarity flow --- ...predict_with_integrated_similarity_flow.py | 110 ------------------ tests/test_benchmarking/test_methods.py | 25 ---- 2 files changed, 135 deletions(-) delete mode 100644 ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py diff --git a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py b/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py deleted file mode 100644 index 77aedb5..0000000 --- a/ms2query/benchmarking/reference_methods/predict_with_integrated_similarity_flow.py +++ /dev/null @@ -1,110 +0,0 @@ -from typing import List, Tuple -import numpy as np -from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix -from tqdm import tqdm -from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet -from ms2query.benchmarking.Fingerprints import Fingerprints -from ms2query.benchmarking.reference_methods.predict_top_k_ms2deepscore import predict_top_k_ms2deepscores - - -def predict_with_integrated_similarity_flow( - library_spectra: AnnotatedSpectrumSet, - query_spectra: AnnotatedSpectrumSet, - fingerprints: Fingerprints, - number_of_analogues_to_consider=50, -) -> Tuple[List[str], List[float]]: - - all_indexes_of_library_spectra_with_highest_score, all_predicted_scores = predict_top_k_ms2deepscores( - library_spectra.embeddings, query_spectra.embeddings, k=number_of_analogues_to_consider - ) - inchikeys_of_best_matches = [] - highest_isf_scores = [] - # loop over the query spectra: - for query_index in tqdm(range(len(query_spectra.spectra)), "Calculating ISF score"): - highest_isf_score, inchikey_of_highest_isf_score = get_highest_isf( - library_spectra, - all_indexes_of_library_spectra_with_highest_score[query_index], - fingerprints, - all_predicted_scores[query_index], - ) - inchikeys_of_best_matches.append(inchikey_of_highest_isf_score) - highest_isf_scores.append(highest_isf_score) - return inchikeys_of_best_matches, highest_isf_scores - - -def get_highest_isf( - library_spectra: AnnotatedSpectrumSet, - indexes_of_library_spectra_with_highest_score: np.ndarray, - fingerprints: Fingerprints, - predicted_scores: List[float], -): - - # Get the corresponding inchikeys - inchikeys_with_highest_ms2deepscore = [ - library_spectra.spectra[index].get("inchikey")[:14] for index in indexes_of_library_spectra_with_highest_score - ] - unique_inchikeys, average_scores, nr_of_spectra_per_inchikey = average_scores_per_inchikeys( - predicted_scores, inchikeys_with_highest_ms2deepscore - ) - # calculate tanimoto scores - library_fingerprints = fingerprints.get_fingerprints(library_spectra.inchikeys) - tanimoto_scores = jaccard_similarity_matrix(library_fingerprints, library_fingerprints) - - isf_scores = integrated_similarity_flow(average_scores, tanimoto_scores, nr_of_spectra_per_inchikey) - index_of_highest_score = np.argmax(isf_scores) - highest_isf_score = isf_scores[index_of_highest_score] - inchikey_of_highest_isf_score = unique_inchikeys[index_of_highest_score] - return highest_isf_score, inchikey_of_highest_isf_score - - -def average_scores_per_inchikeys(predicted_scores, inchikeys): - """Calculate the average precicted score per inchikey - This helps speed up the computations""" - if len(predicted_scores) != len(inchikeys): - raise ValueError - scores_per_inchikey = {} - for i, score in enumerate(predicted_scores): - inchikey = inchikeys[i] - if inchikey in scores_per_inchikey: - scores_per_inchikey[inchikey].append(score) - else: - scores_per_inchikey[inchikey] = [score] - # Take the average over the scores per inchikey - unique_inchikeys = [] - average_scores = [] - nr_of_spectra_per_inchikey = [] - for inchikey in scores_per_inchikey: - unique_inchikeys.append(inchikey) - average_scores.append(sum(scores_per_inchikey[inchikey]) / len(scores_per_inchikey[inchikey])) - nr_of_spectra_per_inchikey.append(len(scores_per_inchikey[inchikey])) - return unique_inchikeys, average_scores, nr_of_spectra_per_inchikey - - -def integrated_similarity_flow( - predicted_scores: List[float], similarities: np.ndarray, nr_of_spectra_per_inchikey: List[float] -) -> List[float]: - """Compute the confidence of the prediction for each candidate. - Integrated similarity flow (ISF) scores are calculated using the similarity of candidates among each other - and their distance to the query spectrum. - - Args: - distances (list): Distances of the candidates to the query spectrum in the chemical space. - similarities (list of lists): Jaccard similarity of all candidates to each other. - - Returns: - dict[int, float]: ISF scores for each candid+ate. - """ - num_hits = len(predicted_scores) - isf_scores = [] - - # Total similarity - total_similarity = sum([predicted_scores[i] * nr_of_spectra_per_inchikey[i] for i in range(len(predicted_scores))]) - - for i in range(num_hits): - isf_score = ( - sum(predicted_scores[j] * similarities[i][j] * nr_of_spectra_per_inchikey[j] for j in range(num_hits)) - / total_similarity - ) - isf_scores.append(isf_score) - - return isf_scores diff --git a/tests/test_benchmarking/test_methods.py b/tests/test_benchmarking/test_methods.py index d3030a9..fd96dde 100644 --- a/tests/test_benchmarking/test_methods.py +++ b/tests/test_benchmarking/test_methods.py @@ -4,10 +4,6 @@ from ms2query.benchmarking.Fingerprints import Fingerprints from ms2query.benchmarking.reference_methods.predict_best_possible_match import predict_best_possible_match from ms2query.benchmarking.reference_methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore -from ms2query.benchmarking.reference_methods.predict_with_integrated_similarity_flow import ( - integrated_similarity_flow, - predict_with_integrated_similarity_flow, -) from tests.helper_functions import ( get_library_and_test_spectra_exactly_matching, get_library_and_test_spectra_not_identical, @@ -37,24 +33,3 @@ def test_predict_best_possible_match(): inchikey = spectrum.get("inchikey")[:14] # type: ignore assert predicted_inchikeys[i] == inchikey assert np.allclose(scores[i], np.array(1.0), atol=1e-5) - - -def test_predict_with_integrated_similarity_flow(): - library_spectra, test_spectra = get_library_and_test_spectra_not_identical() - fingerprints = Fingerprints.from_spectrum_set(library_spectra, "daylight", 4096) - predicted_inchikeys, scores = predict_with_integrated_similarity_flow(library_spectra, test_spectra, fingerprints) - - -def test_isf_computation(): - distances = [0.99, 0.99, 0.99, 0.5, 0.5] - fps = np.array([[1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 0, 0], [0, 0, 1, 0, 1, 0]]) - similarities = jaccard_similarity_matrix(fps, fps) - result = integrated_similarity_flow(distances, similarities, [1, 1, 1, 1, 1]) - expected_result = [ - 0.715869, - 0.686481, - 0.736523, - 0.492107, - 0.359110, - ] - assert np.allclose(np.array(expected_result), np.array(result)) From 42a18ae2be413208521d72dc9a1f67c13df803a4 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 19 Mar 2026 10:40:25 +0100 Subject: [PATCH 146/149] Remove unused import --- tests/test_benchmarking/test_methods.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_benchmarking/test_methods.py b/tests/test_benchmarking/test_methods.py index fd96dde..792df30 100644 --- a/tests/test_benchmarking/test_methods.py +++ b/tests/test_benchmarking/test_methods.py @@ -1,6 +1,5 @@ import numpy as np import pytest -from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix from ms2query.benchmarking.Fingerprints import Fingerprints from ms2query.benchmarking.reference_methods.predict_best_possible_match import predict_best_possible_match from ms2query.benchmarking.reference_methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore From 5831a6296aee2ca092a9b2b563d733389d7db24d Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 19 Mar 2026 10:55:53 +0100 Subject: [PATCH 147/149] Move main methods from reference_methods folder --- .../MS2DeepScoresForTopInChikeys.py | 2 +- .../predict_top_k_ms2deepscore.py | 0 .../EvaluateExactMatchSearch.py | 0 .../notebooks/develop_and_compare_ms2query_2.ipynb | 12 ++++++------ .../test_MS2DeepScoresForTopInChikeys.py | 2 +- .../test_predict_top_ms2deepscores.py | 2 +- 6 files changed, 9 insertions(+), 9 deletions(-) rename ms2query/benchmarking/{reference_methods => }/MS2DeepScoresForTopInChikeys.py (98%) rename ms2query/benchmarking/{reference_methods => }/predict_top_k_ms2deepscore.py (100%) rename ms2query/benchmarking/{ => reference_methods}/EvaluateExactMatchSearch.py (100%) diff --git a/ms2query/benchmarking/reference_methods/MS2DeepScoresForTopInChikeys.py b/ms2query/benchmarking/MS2DeepScoresForTopInChikeys.py similarity index 98% rename from ms2query/benchmarking/reference_methods/MS2DeepScoresForTopInChikeys.py rename to ms2query/benchmarking/MS2DeepScoresForTopInChikeys.py index baec961..d2519c7 100644 --- a/ms2query/benchmarking/reference_methods/MS2DeepScoresForTopInChikeys.py +++ b/ms2query/benchmarking/MS2DeepScoresForTopInChikeys.py @@ -3,7 +3,7 @@ from tqdm import tqdm from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet from ms2query.benchmarking.Fingerprints import Fingerprints -from ms2query.benchmarking.reference_methods.predict_top_k_ms2deepscore import ( +from ms2query.benchmarking.predict_top_k_ms2deepscore import ( select_inchikeys_with_highest_ms2deepscore, ) from ms2query.benchmarking.TopKTanimotoScores import TopKTanimotoScores diff --git a/ms2query/benchmarking/reference_methods/predict_top_k_ms2deepscore.py b/ms2query/benchmarking/predict_top_k_ms2deepscore.py similarity index 100% rename from ms2query/benchmarking/reference_methods/predict_top_k_ms2deepscore.py rename to ms2query/benchmarking/predict_top_k_ms2deepscore.py diff --git a/ms2query/benchmarking/EvaluateExactMatchSearch.py b/ms2query/benchmarking/reference_methods/EvaluateExactMatchSearch.py similarity index 100% rename from ms2query/benchmarking/EvaluateExactMatchSearch.py rename to ms2query/benchmarking/reference_methods/EvaluateExactMatchSearch.py diff --git a/ms2query/notebooks/develop_and_compare_ms2query_2.ipynb b/ms2query/notebooks/develop_and_compare_ms2query_2.ipynb index 3929a23..4b244b2 100644 --- a/ms2query/notebooks/develop_and_compare_ms2query_2.ipynb +++ b/ms2query/notebooks/develop_and_compare_ms2query_2.ipynb @@ -488,7 +488,7 @@ "metadata": {}, "outputs": [], "source": [ - "from ms2query.benchmarking.reference_methods.predict_top_k_ms2deepscore import select_inchikeys_with_highest_ms2deepscore\n", + "from ms2query.benchmarking.predict_top_k_ms2deepscore import select_inchikeys_with_highest_ms2deepscore\n", "\n", "inchikeys_with_highest_ms2deepscores = select_inchikeys_with_highest_ms2deepscore(\n", " pos_val_spectra, pos_train_spectra, nr_of_inchikeys_to_select=1, batch_size=1000,)\n" @@ -496,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "f9c1ab24-c391-4b2b-a8cd-1295e4d11bfd", "metadata": {}, "outputs": [ @@ -509,7 +509,7 @@ } ], "source": [ - "from ms2query.benchmarking.reference_methods.MS2DeepScoresForTopInChikeys import calculate_MS2DeepScoresForTopKInChikeys\n", + "from ms2query.benchmarking.MS2DeepScoresForTopInChikeys import calculate_MS2DeepScoresForTopKInChikeys\n", "close_tanimoto_scores = calculate_MS2DeepScoresForTopKInChikeys(pos_train_spectra, pos_val_spectra, top_k_tanimoto_scores, inchikeys_with_highest_ms2deepscores)" ] }, @@ -2632,8 +2632,8 @@ "source": [ "from ms2query.benchmarking.Fingerprints import Fingerprints\n", "from ms2query.benchmarking.TopKTanimotoScores import TopKTanimotoScores\n", - "from ms2query.benchmarking.reference_methods.predict_top_k_ms2deepscore import select_inchikeys_with_highest_ms2deepscore\n", - "from ms2query.benchmarking.reference_methods.MS2DeepScoresForTopInChikeys import calculate_MS2DeepScoresForTopKInChikeys\n", + "from ms2query.benchmarking.predict_top_k_ms2deepscore import select_inchikeys_with_highest_ms2deepscore\n", + "from ms2query.benchmarking.MS2DeepScoresForTopInChikeys import calculate_MS2DeepScoresForTopKInChikeys\n", "\n", "neg_val_fingerprints = Fingerprints.from_spectrum_set(neg_val_spectra, \"daylight\", 4096)\n", "neg_train_fingerprints = Fingerprints.from_spectrum_set(neg_train_spectra, \"daylight\", 4096)\n", @@ -2714,7 +2714,7 @@ "metadata": {}, "outputs": [], "source": [ - "from ms2query.benchmarking.reference_methods.predict_top_k_ms2deepscore import predict_top_k_ms2deepscores\n", + "from ms2query.benchmarking.predict_top_k_ms2deepscore import predict_top_k_ms2deepscores\n", "\n", "indexes, ms2deepscores = predict_top_k_ms2deepscores(neg_train_spectra.embeddings, neg_val_spectra.embeddings)\n", "neg_predicted_inchikeys_ms2deepscore = [neg_train_spectra.spectra[int(index)].get(\"inchikey\")[:14] for index in indexes]\n", diff --git a/tests/test_benchmarking/test_MS2DeepScoresForTopInChikeys.py b/tests/test_benchmarking/test_MS2DeepScoresForTopInChikeys.py index 8c9b562..f0ed9df 100644 --- a/tests/test_benchmarking/test_MS2DeepScoresForTopInChikeys.py +++ b/tests/test_benchmarking/test_MS2DeepScoresForTopInChikeys.py @@ -1,5 +1,5 @@ from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet -from ms2query.benchmarking.reference_methods.MS2DeepScoresForTopInChikeys import ( +from ms2query.benchmarking.MS2DeepScoresForTopInChikeys import ( calculate_MS2DeepScoresForTopKInChikeys_from_spectra, ) from tests.helper_functions import create_test_spectra, ms2deepscore_model diff --git a/tests/test_benchmarking/test_predict_top_ms2deepscores.py b/tests/test_benchmarking/test_predict_top_ms2deepscores.py index 9a2533d..e830017 100644 --- a/tests/test_benchmarking/test_predict_top_ms2deepscores.py +++ b/tests/test_benchmarking/test_predict_top_ms2deepscores.py @@ -1,7 +1,7 @@ import numpy as np import pytest from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet -from ms2query.benchmarking.reference_methods.predict_top_k_ms2deepscore import ( +from ms2query.benchmarking.predict_top_k_ms2deepscore import ( predict_top_k_ms2deepscores, select_inchikeys_with_highest_ms2deepscore, ) From 6f96d7631d35242b55476ce916952a74d551608c Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 19 Mar 2026 10:56:11 +0100 Subject: [PATCH 148/149] Remove predict highest ms2deepscore --- .../predict_highest_ms2deepscore.py | 16 ---------------- tests/test_benchmarking/test_methods.py | 18 ------------------ 2 files changed, 34 deletions(-) delete mode 100644 ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py diff --git a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py b/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py deleted file mode 100644 index e4f06de..0000000 --- a/ms2query/benchmarking/reference_methods/predict_highest_ms2deepscore.py +++ /dev/null @@ -1,16 +0,0 @@ -from typing import List, Tuple -from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet -from ms2query.benchmarking.reference_methods.predict_top_k_ms2deepscore import predict_top_k_ms2deepscores - - -def predict_highest_ms2deepscore( - library_spectra: AnnotatedSpectrumSet, query_spectra: AnnotatedSpectrumSet -) -> Tuple[List[str], List[float]]: - indexes_of_highest_scores, highest_scores = predict_top_k_ms2deepscores( - library_spectra.embeddings, query_spectra.embeddings, k=1 - ) - single_highest_score = [highest_score[0] for highest_score in highest_scores] - inchikeys_of_best_match = [ - library_spectra.spectra[index[0]].get("inchikey")[:14] for index in indexes_of_highest_scores - ] - return inchikeys_of_best_match, single_highest_score diff --git a/tests/test_benchmarking/test_methods.py b/tests/test_benchmarking/test_methods.py index 792df30..b584df4 100644 --- a/tests/test_benchmarking/test_methods.py +++ b/tests/test_benchmarking/test_methods.py @@ -1,29 +1,11 @@ import numpy as np -import pytest from ms2query.benchmarking.Fingerprints import Fingerprints from ms2query.benchmarking.reference_methods.predict_best_possible_match import predict_best_possible_match -from ms2query.benchmarking.reference_methods.predict_highest_ms2deepscore import predict_highest_ms2deepscore from tests.helper_functions import ( - get_library_and_test_spectra_exactly_matching, get_library_and_test_spectra_not_identical, ) -@pytest.mark.parametrize( - "prediction_function", - [ - predict_highest_ms2deepscore, - ], -) -def test_all_methods(prediction_function): - library_spectra, test_spectra = get_library_and_test_spectra_exactly_matching() - predicted_inchikeys, scores = prediction_function(library_spectra, test_spectra) - for i, spectrum in enumerate(test_spectra.spectra): - inchikey = spectrum.get("inchikey")[:14] # type: ignore - assert predicted_inchikeys[i] == inchikey - assert np.allclose(scores[i], np.array(1.0), atol=1e-5) - - def test_predict_best_possible_match(): library_spectra, test_spectra = get_library_and_test_spectra_not_identical() fingerprints = Fingerprints.from_spectrum_set(library_spectra + test_spectra, "daylight", 2048) From 431e8026070115c728b89dac78a2a9287161fcd4 Mon Sep 17 00:00:00 2001 From: niekdejonge Date: Thu, 19 Mar 2026 10:56:24 +0100 Subject: [PATCH 149/149] Add readme describing reference methods folder --- ms2query/benchmarking/reference_methods/readme.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 ms2query/benchmarking/reference_methods/readme.md diff --git a/ms2query/benchmarking/reference_methods/readme.md b/ms2query/benchmarking/reference_methods/readme.md new file mode 100644 index 0000000..18eab2f --- /dev/null +++ b/ms2query/benchmarking/reference_methods/readme.md @@ -0,0 +1 @@ +All files here are not important to core functionality and in fact not used, but might be nice to use for future testing. They are currently not yet used in the notebooks. Once they are used there they can probably be removed here.