From 83e084ba494837ce0d9aa50a6c4c6da917ed0279 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Wed, 20 Aug 2025 16:40:41 +0200
Subject: [PATCH 01/48] Add split by ionmode to utils.py

---
 ms2deepscore/utils.py | 24 +++++++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

diff --git a/ms2deepscore/utils.py b/ms2deepscore/utils.py
index ed85d6d0..73d99777 100644
--- a/ms2deepscore/utils.py
+++ b/ms2deepscore/utils.py
@@ -1,6 +1,6 @@
 import os
 import pickle
-from typing import Generator, List
+from typing import Generator, List, Tuple
 import numba
 import numpy as np
 from matchms import Spectrum
@@ -130,3 +130,25 @@ def validate_bin_order(score_bins):
             if low != previous_high:
                 raise ValueError("There is a gap or overlap between bins; The bins should cover everything between 0 and 1.")
         previous_high = high
+
+def split_by_ionmode(spectra:List[Spectrum]) -> Tuple[List[Spectrum], List[Spectrum]]:
+    """Splits spectra into list of positive ionmode and list of negative ionmode spectra.
+
+    Removes spectra without correct ionmode metadata entry.
+    """
+    pos_spectra = []
+    neg_spectra = []
+    spectra_removed = 0
+    for spectrum in tqdm(spectra,
+                         desc="Splitting pos and neg mode spectra"):
+        if spectrum is not None:
+            ionmode = spectrum.get("ionmode")
+            if ionmode == "positive":
+                pos_spectra.append(spectrum)
+            elif ionmode == "negative":
+                neg_spectra.append(spectrum)
+            else:
+                spectra_removed += 1
+    print(f"The spectra, are split in {len(pos_spectra)} positive spectra "
+          f"and {len(neg_spectra)} negative mode spectra. {spectra_removed} were removed")
+    return pos_spectra, neg_spectra
\ No newline at end of file

From efd56c8c3fed8d0a6f9d3b620c9bf24b245fe8de Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Wed, 20 Aug 2025 16:41:37 +0200
Subject: [PATCH 02/48] Make inchikey pair selection clearer

---
 .../inchikey_pair_selection.py                | 23 ++++++++++---------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/ms2deepscore/train_new_model/inchikey_pair_selection.py b/ms2deepscore/train_new_model/inchikey_pair_selection.py
index bc619465..1042606d 100644
--- a/ms2deepscore/train_new_model/inchikey_pair_selection.py
+++ b/ms2deepscore/train_new_model/inchikey_pair_selection.py
@@ -126,7 +126,8 @@ def compute_jaccard_similarity_per_bin(
     selected_scores_per_bin = np.zeros((num_bins, size, max_pairs_per_bin), dtype=np.float32)
 
     for idx_fingerprint_i in prange(size):
-        tanimoto_scores = tanimoto_scores_row(fingerprints, idx_fingerprint_i)
+        fingerprint_i = fingerprints[idx_fingerprint_i, :]
+        tanimoto_scores = tanimoto_scores_row(fingerprint_i, fingerprints)
 
         for bin_number in range(num_bins):
             selection_bin = selection_bins[bin_number]
@@ -238,16 +239,16 @@ def convert_to_selected_pairs_list(pair_frequency_matrixes: np.ndarray,
     selected_pairs_per_bin = []
     for bin_id, bin_pair_frequency_matrix in enumerate(tqdm(pair_frequency_matrixes)):
         selected_pairs = []
-        for inchikey1, pair_frequency_row in enumerate(bin_pair_frequency_matrix):
+        for inchikey1_index, pair_frequency_row in enumerate(bin_pair_frequency_matrix):
             for inchikey2_index, pair_frequency in enumerate(pair_frequency_row):
                 if pair_frequency > 0:
-                    inchikey2 = available_pairs_per_bin_matrix[bin_id][inchikey1][inchikey2_index]
-                    score = scores_matrix[bin_id][inchikey1][inchikey2_index]
+                    inchikey2 = available_pairs_per_bin_matrix[bin_id][inchikey1_index][inchikey2_index]
+                    score = scores_matrix[bin_id][inchikey1_index][inchikey2_index]
                     selected_pairs.extend(
-                        [(inchikeys14_unique[inchikey1], inchikeys14_unique[inchikey2], score)] * pair_frequency)
+                        [(inchikeys14_unique[inchikey1_index], inchikeys14_unique[inchikey2], score)] * pair_frequency)
                     # remove duplicate pairs
                     position_of_first_inchikey_in_matrix = available_pairs_per_bin_matrix[bin_id][
-                                                               inchikey2] == inchikey1
+                                                               inchikey2] == inchikey1_index
                     bin_pair_frequency_matrix[inchikey2][position_of_first_inchikey_in_matrix] = 0
         selected_pairs_per_bin.append(selected_pairs)
     return selected_pairs_per_bin
@@ -392,18 +393,18 @@ def get_nr_of_available_pairs_in_bin(selected_pairs_per_bin_matrix: np.ndarray)
 
 
 @jit(nopython=True)
-def tanimoto_scores_row(fingerprints, idx):
-    size = fingerprints.shape[0]
+def tanimoto_scores_row(single_fingerprint, list_of_fingerprints):
+    size = list_of_fingerprints.shape[0]
     tanimoto_scores = np.zeros(size)
 
-    fingerprint_i = fingerprints[idx, :]
     for idx_fingerprint_j in range(size):
-        fingerprint_j = fingerprints[idx_fingerprint_j, :]
-        tanimoto_score = jaccard_index(fingerprint_i, fingerprint_j)
+        fingerprint_j = list_of_fingerprints[idx_fingerprint_j, :]
+        tanimoto_score = jaccard_index(single_fingerprint, fingerprint_j)
         tanimoto_scores[idx_fingerprint_j] = tanimoto_score
     return tanimoto_scores
 
 
+
 def select_inchi_for_unique_inchikeys(
         list_of_spectra: List['Spectrum']
 ) -> Tuple[List['Spectrum'], List[str]]:

From 31c2fd370a90acb9458408647a0e7107e6fa9615 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Wed, 20 Aug 2025 16:42:04 +0200
Subject: [PATCH 03/48] Create inchikey_pair_selection_cross_ionmode.py

---
 .../inchikey_pair_selection_cross_ionmode.py  | 116 ++++++++++++++++++
 1 file changed, 116 insertions(+)
 create mode 100644 ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py

diff --git a/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py b/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py
new file mode 100644
index 00000000..2c4a14a2
--- /dev/null
+++ b/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py
@@ -0,0 +1,116 @@
+from typing import List, Tuple
+import numpy as np
+from matchms import Spectrum
+from numba import jit, prange
+from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
+from ms2deepscore.train_new_model.inchikey_pair_selection import compute_fingerprints_for_training, \
+    balanced_selection_of_pairs_per_bin, convert_to_selected_pairs_list, tanimoto_scores_row
+
+
+def select_compound_pairs_wrapper_across_ionmode(
+        spectra_1: List[Spectrum],
+        spectra_2: List[Spectrum],
+        settings: SettingsMS2Deepscore,
+) -> List[Tuple[str, str, float]]:
+    """Returns a InchikeyPairGenerator object containing equally balanced pairs over the different bins
+
+    spectra:
+        A list of spectra
+    settings:
+        The settings that should be used for selecting the compound pairs wrapper. The settings should be specified as a
+        SettingsMS2Deepscore object.
+
+    Returns
+    -------
+    InchikeyPairGenerator
+        InchikeyPairGenerator containing balanced pairs. The pairs are stored as [(inchikey1, inchikey2, score)]
+    """
+    if settings.random_seed is not None:
+        np.random.seed(settings.random_seed)
+
+    fingerprints_1, inchikeys14_unique_1 = compute_fingerprints_for_training(
+        spectra_1,
+        settings.fingerprint_type,
+        settings.fingerprint_nbits
+        )
+    fingerprints_2, inchikeys14_unique_2 = compute_fingerprints_for_training(
+        spectra_2,
+        settings.fingerprint_type,
+        settings.fingerprint_nbits
+        )
+
+    if len(inchikeys14_unique_1) < settings.batch_size or len(inchikeys14_unique_2) < settings.batch_size:
+        raise ValueError("The number of unique inchikeys must be larger than the batch size.")
+
+    available_pairs_per_bin_matrix, available_scores_per_bin_matrix = compute_jaccard_similarity_per_bin_across_ionmodes(
+        fingerprints_1, fingerprints_2, settings.max_pairs_per_bin, settings.same_prob_bins)
+
+    pair_frequency_matrixes = balanced_selection_of_pairs_per_bin(
+        available_pairs_per_bin_matrix, settings)
+
+    selected_pairs_per_bin = convert_to_selected_pairs_list(
+        pair_frequency_matrixes, available_pairs_per_bin_matrix,
+        available_scores_per_bin_matrix, inchikeys14_unique_1 + inchikeys14_unique_2)
+    return [pair for pairs in selected_pairs_per_bin for pair in pairs]
+
+
+@jit(nopython=True, parallel=True)
+def compute_jaccard_similarity_per_bin_across_ionmodes(
+        fingerprints_1,
+        fingerprints_2,
+        max_pairs_per_bin,
+        selection_bins=np.array([(x / 10, x / 10 + 0.1) for x in range(10)])
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Randomly selects compound pairs per tanimoto bin, up to max_pairs_per_bin
+
+    returns:
+    2 3d numpy arrays are returned, the first encodes the pairs per bin and the second the corresponding scores.
+    A 3D numpy array with shape [nr_of_bins, nr_of_fingerprints, max_pairs_per_bin].
+    An example structure for bin 1, with 3 fingerprints and max_pairs_per_bin =4 would be:
+    [[1,2,-1,-1],
+    [0,3,-1,-1],
+    [0,2,-1,-1],]
+    The pairs are encoded by the index and the value.
+    So the first row encodes pairs between fingerpint 0 and 1, fingerprint 0 and 2.
+    The -1 encode that no more pairs were found for this fingerprint in this bin.
+    """
+
+    size_1 = fingerprints_1.shape[0]
+    size_2 = fingerprints_2.shape[0]
+
+    num_bins = len(selection_bins)
+
+    selected_pairs_per_bin = -1 * np.ones((num_bins, size_1 + size_2, max_pairs_per_bin), dtype=np.int32)
+    selected_scores_per_bin = np.zeros((num_bins, size_1 + size_2, max_pairs_per_bin), dtype=np.float32)
+
+    for idx_fingerprint_i in prange(size_1):
+        fingerprint_i = fingerprints_1[idx_fingerprint_i, :]
+        tanimoto_scores = tanimoto_scores_row(fingerprint_i, fingerprints_2)
+
+        for bin_number in range(num_bins):
+            selection_bin = selection_bins[bin_number]
+            indices = np.nonzero((tanimoto_scores > selection_bin[0]) & (tanimoto_scores <= selection_bin[1]))[0]
+
+            np.random.shuffle(indices)
+            indices = indices[:max_pairs_per_bin]
+            num_indices = len(indices)
+            selected_scores_per_bin[bin_number, idx_fingerprint_i, :num_indices] = tanimoto_scores[indices]
+            selected_pairs_per_bin[bin_number, idx_fingerprint_i, :num_indices] = indices + size_1
+
+    for idx_fingerprint_2 in prange(size_2):
+        fingerprint_i = fingerprints_2[idx_fingerprint_2, :]
+        idx_fingerprint_corrected = idx_fingerprint_2 + size_1
+        tanimoto_scores = tanimoto_scores_row(fingerprint_i, fingerprints_2)
+
+        for bin_number in range(num_bins):
+            selection_bin = selection_bins[bin_number]
+            indices = np.nonzero((tanimoto_scores > selection_bin[0]) & (tanimoto_scores <= selection_bin[1]))[0]
+
+            np.random.shuffle(indices)
+            indices = indices[:max_pairs_per_bin]
+            num_indices = len(indices)
+
+            selected_pairs_per_bin[bin_number, idx_fingerprint_corrected, :num_indices] = indices
+            selected_scores_per_bin[bin_number, idx_fingerprint_corrected, :num_indices] = tanimoto_scores[indices]
+
+    return selected_pairs_per_bin, selected_scores_per_bin

From 46fb9c4e0689743e5a5f41e9c944b8da2411b7e5 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Wed, 20 Aug 2025 16:42:52 +0200
Subject: [PATCH 04/48] Add first test for
 select_compound_pairs_wrapper_with_resampling_across_ionmodes

---
 tests/test_inchikey_pair_selection.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/tests/test_inchikey_pair_selection.py b/tests/test_inchikey_pair_selection.py
index 87720017..71eb63a0 100644
--- a/tests/test_inchikey_pair_selection.py
+++ b/tests/test_inchikey_pair_selection.py
@@ -299,3 +299,17 @@ def check_balanced_scores_selecting_inchikey_pairs(selected_inchikey_pairs: Inch
     # Check that the number of pairs per bin is equal for all bins
     assert len(set(score_bin_counts.values())) == 1
 
+from ms2deepscore.train_new_model.inchikey_pair_selection_cross_ionmode import select_compound_pairs_wrapper_across_ionmode
+def test_select_compound_pairs_wrapper_with_resampling_across_ionmodes():
+    spectrums_1 = create_test_spectra(num_of_unique_inchikeys=26, num_of_spectra_per_inchikey=1)
+    spectrums_2 = create_test_spectra(num_of_unique_inchikeys=25, num_of_spectra_per_inchikey=2)
+    for spectrum in spectrums_1:
+        spectrum.set("inchikey", "a" + spectrum.get("inchikey"))
+    bins = [(0.8, 0.9), (0.7, 0.8), (0.9, 1.0), (0.6, 0.7), (0.5, 0.6),
+            (0.4, 0.5), (0.3, 0.4), (0.2, 0.3), (0.1, 0.2), (-0.01, 0.1)]
+    max_pair_resampling = 10
+    settings = SettingsMS2Deepscore(same_prob_bins=np.array(bins, dtype="float32"),
+                                    average_inchikey_sampling_count=10,
+                                    batch_size=8,
+                                    max_pair_resampling=max_pair_resampling)
+    selected_inchikey_pairs = select_compound_pairs_wrapper_across_ionmode(spectrums_1, spectrums_2, settings)

From 528b0e5083f68638acd68493af6b7e580455c79f Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Thu, 21 Aug 2025 09:16:08 +0200
Subject: [PATCH 05/48] Move InchikeyPairGenerator to separate file

---
 .../train_new_model/InchikeyPairGenerator.py  | 61 ++++++++++++++++++
 ms2deepscore/train_new_model/__init__.py      |  6 +-
 .../train_new_model/data_generators.py        | 64 +------------------
 tests/test_siamese_spectra_model.py           |  3 +-
 4 files changed, 69 insertions(+), 65 deletions(-)
 create mode 100644 ms2deepscore/train_new_model/InchikeyPairGenerator.py

diff --git a/ms2deepscore/train_new_model/InchikeyPairGenerator.py b/ms2deepscore/train_new_model/InchikeyPairGenerator.py
new file mode 100644
index 00000000..4127e502
--- /dev/null
+++ b/ms2deepscore/train_new_model/InchikeyPairGenerator.py
@@ -0,0 +1,61 @@
+import json
+from collections import Counter
+from typing import List, Tuple
+
+
+class InchikeyPairGenerator:
+    def __init__(self, selected_inchikey_pairs: List[Tuple[str, str, float]]):
+        """
+        Parameters
+        ----------
+        selected_inchikey_pairs:
+            A list with tuples encoding inchikey pairs like: (inchikey1, inchikey2, tanimoto_score)
+        """
+        self.selected_inchikey_pairs = selected_inchikey_pairs
+
+    def generator(self, shuffle: bool, random_nr_generator):
+        """Infinite generator to loop through all inchikeys.
+        After looping through all inchikeys the order is shuffled.
+        """
+        while True:
+            if shuffle:
+                random_nr_generator.shuffle(self.selected_inchikey_pairs)
+
+            for inchikey1, inchikey2, tanimoto_score in self.selected_inchikey_pairs:
+                yield inchikey1, inchikey2, tanimoto_score
+
+    def __len__(self):
+        return len(self.selected_inchikey_pairs)
+
+    def __str__(self):
+        return f"InchikeyPairGenerator with {len(self.selected_inchikey_pairs)} pairs available"
+
+    def get_scores(self):
+        return [score for _, _, score in self.selected_inchikey_pairs]
+
+    def get_inchikey_counts(self) -> Counter:
+        """returns the frequency each inchikey occurs"""
+        inchikeys = Counter()
+        for inchikey_1, inchikey_2, _ in self.selected_inchikey_pairs:
+            inchikeys[inchikey_1] += 1
+            inchikeys[inchikey_2] += 1
+        return inchikeys
+
+    def get_scores_per_inchikey(self):
+        inchikey_scores = {}
+        for inchikey_1, inchikey_2, score in self.selected_inchikey_pairs:
+            if inchikey_1 in inchikey_scores:
+                inchikey_scores[inchikey_1].append(score)
+            else:
+                inchikey_scores[inchikey_1] = []
+            if inchikey_2 in inchikey_scores:
+                inchikey_scores[inchikey_2].append(score)
+            else:
+                inchikey_scores[inchikey_2] = []
+        return inchikey_scores
+
+    def save_as_json(self, file_name):
+        data_for_json = [(item[0], item[1], float(item[2])) for item in self.selected_inchikey_pairs]
+
+        with open(file_name, "w", encoding="utf-8") as f:
+            json.dump(data_for_json, f)
diff --git a/ms2deepscore/train_new_model/__init__.py b/ms2deepscore/train_new_model/__init__.py
index c04e0d39..708a63cc 100644
--- a/ms2deepscore/train_new_model/__init__.py
+++ b/ms2deepscore/train_new_model/__init__.py
@@ -1,9 +1,9 @@
-from .data_generators import SpectrumPairGenerator, InchikeyPairGenerator
+from .data_generators import SpectrumPairGenerator
+from .InchikeyPairGenerator import InchikeyPairGenerator
 from .inchikey_pair_selection import (select_compound_pairs_wrapper)
 
 
 __all__ = [
     "SpectrumPairGenerator",
-    "select_compound_pairs_wrapper",
-    "InchikeyPairGenerator"
+    "select_compound_pairs_wrapper"
 ]
diff --git a/ms2deepscore/train_new_model/data_generators.py b/ms2deepscore/train_new_model/data_generators.py
index 4e638d87..3e422ca6 100644
--- a/ms2deepscore/train_new_model/data_generators.py
+++ b/ms2deepscore/train_new_model/data_generators.py
@@ -1,8 +1,6 @@
 """ Data generators for training/inference with MS2DeepScore model.
 """
-import json
-from collections import Counter
-from typing import List, Tuple
+from typing import List
 import numpy as np
 import pandas as pd
 import torch
@@ -12,8 +10,10 @@
 from ms2deepscore.SettingsMS2Deepscore import (SettingsEmbeddingEvaluator,
                                                SettingsMS2Deepscore)
 from ms2deepscore.tensorize_spectra import tensorize_spectra
+from ms2deepscore.train_new_model import InchikeyPairGenerator
 from ms2deepscore.train_new_model.inchikey_pair_selection import (
     select_compound_pairs_wrapper, compute_fingerprints_for_training)
+from ms2deepscore.utils import split_by_ionmode
 from ms2deepscore.vector_operations import cosine_similarity_matrix
 
 
@@ -317,61 +317,3 @@ def compute_fingerprint_dataframe(self,
 
         fingerprints_df = pd.DataFrame(fingerprints, index=inchikeys14_unique)
         return fingerprints_df
-
-
-class InchikeyPairGenerator:
-    def __init__(self, selected_inchikey_pairs: List[Tuple[str, str, float]]):
-        """
-        Parameters
-        ----------
-        selected_inchikey_pairs:
-            A list with tuples encoding inchikey pairs like: (inchikey1, inchikey2, tanimoto_score)
-        """
-        self.selected_inchikey_pairs = selected_inchikey_pairs
-
-    def generator(self, shuffle: bool, random_nr_generator):
-        """Infinite generator to loop through all inchikeys.
-        After looping through all inchikeys the order is shuffled.
-        """
-        while True:
-            if shuffle:
-                random_nr_generator.shuffle(self.selected_inchikey_pairs)
-
-            for inchikey1, inchikey2, tanimoto_score in self.selected_inchikey_pairs:
-                yield inchikey1, inchikey2, tanimoto_score
-
-    def __len__(self):
-        return len(self.selected_inchikey_pairs)
-
-    def __str__(self):
-        return f"InchikeyPairGenerator with {len(self.selected_inchikey_pairs)} pairs available"
-
-    def get_scores(self):
-        return [score for _, _, score in self.selected_inchikey_pairs]
-
-    def get_inchikey_counts(self) -> Counter:
-        """returns the frequency each inchikey occurs"""
-        inchikeys = Counter()
-        for inchikey_1, inchikey_2, _ in self.selected_inchikey_pairs:
-            inchikeys[inchikey_1] += 1
-            inchikeys[inchikey_2] += 1
-        return inchikeys
-
-    def get_scores_per_inchikey(self):
-        inchikey_scores = {}
-        for inchikey_1, inchikey_2, score in self.selected_inchikey_pairs:
-            if inchikey_1 in inchikey_scores:
-                inchikey_scores[inchikey_1].append(score)
-            else:
-                inchikey_scores[inchikey_1] = []
-            if inchikey_2 in inchikey_scores:
-                inchikey_scores[inchikey_2].append(score)
-            else:
-                inchikey_scores[inchikey_2] = []
-        return inchikey_scores
-
-    def save_as_json(self, file_name):
-        data_for_json = [(item[0], item[1], float(item[2])) for item in self.selected_inchikey_pairs]
-
-        with open(file_name, "w", encoding="utf-8") as f:
-            json.dump(data_for_json, f)
diff --git a/tests/test_siamese_spectra_model.py b/tests/test_siamese_spectra_model.py
index 21e2e2a3..287c685b 100644
--- a/tests/test_siamese_spectra_model.py
+++ b/tests/test_siamese_spectra_model.py
@@ -5,7 +5,8 @@
                                                       train)
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
 from ms2deepscore.tensorize_spectra import tensorize_spectra
-from ms2deepscore.train_new_model.data_generators import SpectrumPairGenerator, InchikeyPairGenerator
+from ms2deepscore.train_new_model.data_generators import SpectrumPairGenerator
+from ms2deepscore.train_new_model import InchikeyPairGenerator
 from ms2deepscore.train_new_model.inchikey_pair_selection import \
     select_compound_pairs_wrapper
 from ms2deepscore.validation_loss_calculation.ValidationLossCalculator import \

From 0aaa092402c1fd9a7721c8c7960a5590d44691ac Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Thu, 21 Aug 2025 09:17:51 +0200
Subject: [PATCH 06/48] Move DataGeneratorEmbeddingEvaluation to separate file

---
 .../models/EmbeddingEvaluatorModel.py         |   3 +-
 .../DataGeneratorEmbeddingEvaluation.py       | 127 ++++++++++++++++++
 .../train_new_model/data_generators.py        | 124 +----------------
 tests/test_data_generators.py                 |   3 +-
 4 files changed, 132 insertions(+), 125 deletions(-)
 create mode 100644 ms2deepscore/train_new_model/DataGeneratorEmbeddingEvaluation.py

diff --git a/ms2deepscore/models/EmbeddingEvaluatorModel.py b/ms2deepscore/models/EmbeddingEvaluatorModel.py
index e8ae2528..059a900b 100644
--- a/ms2deepscore/models/EmbeddingEvaluatorModel.py
+++ b/ms2deepscore/models/EmbeddingEvaluatorModel.py
@@ -7,8 +7,7 @@
 from ms2deepscore.__version__ import __version__
 from ms2deepscore.models.helper_functions import initialize_device
 from ms2deepscore.SettingsMS2Deepscore import SettingsEmbeddingEvaluator
-from ms2deepscore.train_new_model.data_generators import \
-    DataGeneratorEmbeddingEvaluation
+from ms2deepscore.train_new_model.DataGeneratorEmbeddingEvaluation import DataGeneratorEmbeddingEvaluation
 
 
 class EmbeddingEvaluationModel(nn.Module):
diff --git a/ms2deepscore/train_new_model/DataGeneratorEmbeddingEvaluation.py b/ms2deepscore/train_new_model/DataGeneratorEmbeddingEvaluation.py
new file mode 100644
index 00000000..a5697ba9
--- /dev/null
+++ b/ms2deepscore/train_new_model/DataGeneratorEmbeddingEvaluation.py
@@ -0,0 +1,127 @@
+from typing import List
+
+import numpy as np
+import pandas as pd
+import torch
+from matchms import Spectrum
+from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix
+
+from ms2deepscore.SettingsMS2Deepscore import SettingsEmbeddingEvaluator
+from ms2deepscore.tensorize_spectra import tensorize_spectra
+from ms2deepscore.train_new_model.inchikey_pair_selection import compute_fingerprints_for_training
+from ms2deepscore.vector_operations import cosine_similarity_matrix
+
+
+class DataGeneratorEmbeddingEvaluation:
+    """Generates data for training an embedding evaluation model.
+
+    This class provides a data for the training of an embedding evaluation model.
+    It follows a simple strategy: iterate through all spectra and randomly pick another
+    spectrum for comparison. This will not compensate the usually drastic biases
+    in Tanimoto similarity and is hence not meant for training the prediction of those
+    scores.
+    The purpose is rather to show a high number of spectra to a model to learn
+    embedding evaluations.
+
+    Spectra are sampled in groups of size batch_size. Before every epoch the indexes are
+    shuffled at random. For selected spectra the tanimoto scores, ms2deepscore scores and
+    embeddings are returned.
+    """
+
+    def __init__(self, spectrums: List[Spectrum],
+                 ms2ds_model,
+                 settings: SettingsEmbeddingEvaluator,
+                 device="cpu",
+                 ):
+        """
+
+        Parameters
+        ----------
+        spectrums
+            List of matchms Spectrum objects.
+        settings
+            The available settings can be found in SettignsMS2Deepscore
+        """
+        self.current_index = 0
+        self.settings = settings
+        self.spectrums = spectrums
+        self.inchikey14s = [s.get("inchikey")[:14] for s in spectrums]
+        self.ms2ds_model = ms2ds_model
+        self.device = device
+        self.ms2ds_model.to(self.device)
+        self.indexes = np.arange(len(self.spectrums))
+        self.batch_size = self.settings.evaluator_distribution_size
+        self.fingerprint_df = self.compute_fingerprint_dataframe(
+            self.spectrums,
+            fingerprint_type=self.ms2ds_model.model_settings.fingerprint_type,
+            fingerprint_nbits=self.ms2ds_model.model_settings.fingerprint_nbits
+            )
+
+        # Initialize random number generator
+        self.rng = np.random.default_rng(self.settings.random_seed)
+
+        self.on_epoch_end()
+
+    def __len__(self):
+        return int(np.floor(len(self.spectrums) / self.batch_size))
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.current_index < self.__len__():
+            batch = self.__getitem__(self.current_index)
+            self.current_index += 1
+            return batch
+        self.current_index = 0  # make generator executable again
+        self.on_epoch_end()
+        raise StopIteration
+
+    def _compute_embeddings_and_scores(self, batch_index: int):
+        batch_size = self.batch_size
+        indexes = self.indexes[batch_index * batch_size:((batch_index + 1) * batch_size)]
+
+        spec_tensors, meta_tensors = tensorize_spectra([self.spectrums[i] for i in indexes],
+                                                       self.ms2ds_model.model_settings)
+        embeddings = self.ms2ds_model.encoder(spec_tensors.to(self.device), meta_tensors.to(self.device))
+
+        ms2ds_scores = cosine_similarity_matrix(embeddings.cpu().detach().numpy(), embeddings.cpu().detach().numpy())
+
+        # Compute true scores
+        inchikeys = [self.inchikey14s[i] for i in indexes]
+        fingerprints = self.fingerprint_df.loc[inchikeys].to_numpy()
+
+        tanimoto_scores = jaccard_similarity_matrix(fingerprints, fingerprints)
+
+        return torch.tensor(tanimoto_scores), torch.tensor(ms2ds_scores), embeddings.cpu().detach()
+
+    def on_epoch_end(self):
+        """Updates indexes after each epoch."""
+        self.rng.shuffle(self.indexes)
+
+    def __getitem__(self, batch_index: int):
+        """Generate one batch of data.
+        """
+        return self._compute_embeddings_and_scores(batch_index)
+
+    def compute_fingerprint_dataframe(self,
+                                      spectrums: List[Spectrum],
+                                      fingerprint_type,
+                                      fingerprint_nbits,
+                                      ) -> pd.DataFrame:
+        """Returns a dataframe with a fingerprints dataframe
+
+        spectrums:
+            A list of spectra
+        settings:
+            The settings that should be used for selecting the compound pairs wrapper. The settings should be specified as a
+            SettingsMS2Deepscore object.
+        """
+        fingerprints, inchikeys14_unique = compute_fingerprints_for_training(
+            spectrums,
+            fingerprint_type,
+            fingerprint_nbits
+            )
+
+        fingerprints_df = pd.DataFrame(fingerprints, index=inchikeys14_unique)
+        return fingerprints_df
diff --git a/ms2deepscore/train_new_model/data_generators.py b/ms2deepscore/train_new_model/data_generators.py
index 3e422ca6..fb9add77 100644
--- a/ms2deepscore/train_new_model/data_generators.py
+++ b/ms2deepscore/train_new_model/data_generators.py
@@ -2,19 +2,14 @@
 """
 from typing import List
 import numpy as np
-import pandas as pd
 import torch
 from matchms import Spectrum
-from matchms.similarity.vector_similarity_functions import \
-    jaccard_similarity_matrix
-from ms2deepscore.SettingsMS2Deepscore import (SettingsEmbeddingEvaluator,
-                                               SettingsMS2Deepscore)
+from ms2deepscore.SettingsMS2Deepscore import (SettingsMS2Deepscore)
 from ms2deepscore.tensorize_spectra import tensorize_spectra
 from ms2deepscore.train_new_model import InchikeyPairGenerator
 from ms2deepscore.train_new_model.inchikey_pair_selection import (
-    select_compound_pairs_wrapper, compute_fingerprints_for_training)
+    select_compound_pairs_wrapper)
 from ms2deepscore.utils import split_by_ionmode
-from ms2deepscore.vector_operations import cosine_similarity_matrix
 
 
 class SpectrumPairGenerator:
@@ -202,118 +197,3 @@ def create_data_generator(training_spectra,
                                             selected_compound_pairs=inchikey_pair_generator,
                                             settings=settings)
     return train_generator
-
-
-class DataGeneratorEmbeddingEvaluation:
-    """Generates data for training an embedding evaluation model.
-
-    This class provides a data for the training of an embedding evaluation model.
-    It follows a simple strategy: iterate through all spectra and randomly pick another
-    spectrum for comparison. This will not compensate the usually drastic biases
-    in Tanimoto similarity and is hence not meant for training the prediction of those
-    scores.
-    The purpose is rather to show a high number of spectra to a model to learn
-    embedding evaluations.
-
-    Spectra are sampled in groups of size batch_size. Before every epoch the indexes are
-    shuffled at random. For selected spectra the tanimoto scores, ms2deepscore scores and
-    embeddings are returned.
-    """
-
-    def __init__(self, spectrums: List[Spectrum],
-                 ms2ds_model,
-                 settings: SettingsEmbeddingEvaluator,
-                 device="cpu",
-                 ):
-        """
-
-        Parameters
-        ----------
-        spectrums
-            List of matchms Spectrum objects.
-        settings
-            The available settings can be found in SettignsMS2Deepscore
-        """
-        self.current_index = 0
-        self.settings = settings
-        self.spectrums = spectrums
-        self.inchikey14s = [s.get("inchikey")[:14] for s in spectrums]
-        self.ms2ds_model = ms2ds_model
-        self.device = device
-        self.ms2ds_model.to(self.device)
-        self.indexes = np.arange(len(self.spectrums))
-        self.batch_size = self.settings.evaluator_distribution_size
-        self.fingerprint_df = self.compute_fingerprint_dataframe(
-            self.spectrums,
-            fingerprint_type=self.ms2ds_model.model_settings.fingerprint_type,
-            fingerprint_nbits=self.ms2ds_model.model_settings.fingerprint_nbits
-            )
-
-        # Initialize random number generator
-        self.rng = np.random.default_rng(self.settings.random_seed)
-
-        self.on_epoch_end()
-
-    def __len__(self):
-        return int(np.floor(len(self.spectrums) / self.batch_size))
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        if self.current_index < self.__len__():
-            batch = self.__getitem__(self.current_index)
-            self.current_index += 1
-            return batch
-        self.current_index = 0  # make generator executable again
-        self.on_epoch_end()
-        raise StopIteration
-
-    def _compute_embeddings_and_scores(self, batch_index: int):
-        batch_size = self.batch_size
-        indexes = self.indexes[batch_index * batch_size:((batch_index + 1) * batch_size)]
-
-        spec_tensors, meta_tensors = tensorize_spectra([self.spectrums[i] for i in indexes],
-                                                       self.ms2ds_model.model_settings)
-        embeddings = self.ms2ds_model.encoder(spec_tensors.to(self.device), meta_tensors.to(self.device))
-
-        ms2ds_scores = cosine_similarity_matrix(embeddings.cpu().detach().numpy(), embeddings.cpu().detach().numpy())
-
-        # Compute true scores
-        inchikeys = [self.inchikey14s[i] for i in indexes]
-        fingerprints = self.fingerprint_df.loc[inchikeys].to_numpy()
-
-        tanimoto_scores = jaccard_similarity_matrix(fingerprints, fingerprints)
-
-        return torch.tensor(tanimoto_scores), torch.tensor(ms2ds_scores), embeddings.cpu().detach()
-
-    def on_epoch_end(self):
-        """Updates indexes after each epoch."""
-        self.rng.shuffle(self.indexes)
-
-    def __getitem__(self, batch_index: int):
-        """Generate one batch of data.
-        """
-        return self._compute_embeddings_and_scores(batch_index)
-
-    def compute_fingerprint_dataframe(self,
-                                      spectrums: List[Spectrum],
-                                      fingerprint_type,
-                                      fingerprint_nbits,
-                                      ) -> pd.DataFrame:
-        """Returns a dataframe with a fingerprints dataframe
-
-        spectrums:
-            A list of spectra
-        settings:
-            The settings that should be used for selecting the compound pairs wrapper. The settings should be specified as a
-            SettingsMS2Deepscore object.
-        """
-        fingerprints, inchikeys14_unique = compute_fingerprints_for_training(
-            spectrums,
-            fingerprint_type,
-            fingerprint_nbits
-            )
-
-        fingerprints_df = pd.DataFrame(fingerprints, index=inchikeys14_unique)
-        return fingerprints_df
diff --git a/tests/test_data_generators.py b/tests/test_data_generators.py
index 18af2303..a39ce1eb 100644
--- a/tests/test_data_generators.py
+++ b/tests/test_data_generators.py
@@ -6,7 +6,8 @@
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore, SettingsEmbeddingEvaluator
 from ms2deepscore.tensorize_spectra import tensorize_spectra
 from ms2deepscore.train_new_model.data_generators import SpectrumPairGenerator, \
-    DataGeneratorEmbeddingEvaluation, create_data_generator
+    create_data_generator
+from ms2deepscore.train_new_model.DataGeneratorEmbeddingEvaluation import DataGeneratorEmbeddingEvaluation
 from ms2deepscore.train_new_model import InchikeyPairGenerator
 from tests.create_test_spectra import create_test_spectra
 

From f5ff5e2658e54a4f856f7bd392dd08797a2eabd1 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Thu, 21 Aug 2025 09:22:07 +0200
Subject: [PATCH 07/48] Rename SpectrumPairGenerator.py

---
 .../{data_generators.py => SpectrumPairGenerator.py}            | 1 +
 ms2deepscore/train_new_model/__init__.py                        | 2 +-
 ms2deepscore/train_new_model/train_ms2deepscore.py              | 2 +-
 ms2deepscore/wrapper_functions/training_wrapper_functions.py    | 2 +-
 tests/test_data_generators.py                                   | 2 +-
 tests/test_siamese_spectra_model.py                             | 2 +-
 6 files changed, 6 insertions(+), 5 deletions(-)
 rename ms2deepscore/train_new_model/{data_generators.py => SpectrumPairGenerator.py} (99%)

diff --git a/ms2deepscore/train_new_model/data_generators.py b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
similarity index 99%
rename from ms2deepscore/train_new_model/data_generators.py
rename to ms2deepscore/train_new_model/SpectrumPairGenerator.py
index fb9add77..4ed1554e 100644
--- a/ms2deepscore/train_new_model/data_generators.py
+++ b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
@@ -190,6 +190,7 @@ def create_data_generator(training_spectra,
                           json_save_file=None) -> SpectrumPairGenerator:
     selected_compound_pairs_training = select_compound_pairs_wrapper(training_spectra, settings=settings)
     inchikey_pair_generator = InchikeyPairGenerator(selected_compound_pairs_training)
+
     if json_save_file is not None:
         inchikey_pair_generator.save_as_json(json_save_file)
     # Create generators
diff --git a/ms2deepscore/train_new_model/__init__.py b/ms2deepscore/train_new_model/__init__.py
index 708a63cc..14588440 100644
--- a/ms2deepscore/train_new_model/__init__.py
+++ b/ms2deepscore/train_new_model/__init__.py
@@ -1,4 +1,4 @@
-from .data_generators import SpectrumPairGenerator
+from .SpectrumPairGenerator import SpectrumPairGenerator
 from .InchikeyPairGenerator import InchikeyPairGenerator
 from .inchikey_pair_selection import (select_compound_pairs_wrapper)
 
diff --git a/ms2deepscore/train_new_model/train_ms2deepscore.py b/ms2deepscore/train_new_model/train_ms2deepscore.py
index 8d483498..1a46b167 100644
--- a/ms2deepscore/train_new_model/train_ms2deepscore.py
+++ b/ms2deepscore/train_new_model/train_ms2deepscore.py
@@ -10,7 +10,7 @@
 from ms2deepscore.models.SiameseSpectralModel import (SiameseSpectralModel,
                                                       train)
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
-from ms2deepscore.train_new_model.data_generators import create_data_generator
+from ms2deepscore.train_new_model.SpectrumPairGenerator import create_data_generator
 from ms2deepscore.validation_loss_calculation.ValidationLossCalculator import \
     ValidationLossCalculator
 
diff --git a/ms2deepscore/wrapper_functions/training_wrapper_functions.py b/ms2deepscore/wrapper_functions/training_wrapper_functions.py
index 3c878168..9936124b 100644
--- a/ms2deepscore/wrapper_functions/training_wrapper_functions.py
+++ b/ms2deepscore/wrapper_functions/training_wrapper_functions.py
@@ -14,7 +14,7 @@
                                                       train)
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
 from ms2deepscore.validation_loss_calculation.ValidationLossCalculator import ValidationLossCalculator
-from ms2deepscore.train_new_model.data_generators import create_data_generator
+from ms2deepscore.train_new_model.SpectrumPairGenerator import create_data_generator
 from ms2deepscore.train_new_model.train_ms2deepscore import \
     train_ms2ds_model, plot_history, save_history
 from ms2deepscore.train_new_model.validation_and_test_split import \
diff --git a/tests/test_data_generators.py b/tests/test_data_generators.py
index a39ce1eb..37ea3af5 100644
--- a/tests/test_data_generators.py
+++ b/tests/test_data_generators.py
@@ -5,7 +5,7 @@
 from matchms import Spectrum
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore, SettingsEmbeddingEvaluator
 from ms2deepscore.tensorize_spectra import tensorize_spectra
-from ms2deepscore.train_new_model.data_generators import SpectrumPairGenerator, \
+from ms2deepscore.train_new_model.SpectrumPairGenerator import SpectrumPairGenerator, \
     create_data_generator
 from ms2deepscore.train_new_model.DataGeneratorEmbeddingEvaluation import DataGeneratorEmbeddingEvaluation
 from ms2deepscore.train_new_model import InchikeyPairGenerator
diff --git a/tests/test_siamese_spectra_model.py b/tests/test_siamese_spectra_model.py
index 287c685b..a86b69b7 100644
--- a/tests/test_siamese_spectra_model.py
+++ b/tests/test_siamese_spectra_model.py
@@ -5,7 +5,7 @@
                                                       train)
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
 from ms2deepscore.tensorize_spectra import tensorize_spectra
-from ms2deepscore.train_new_model.data_generators import SpectrumPairGenerator
+from ms2deepscore.train_new_model.SpectrumPairGenerator import SpectrumPairGenerator
 from ms2deepscore.train_new_model import InchikeyPairGenerator
 from ms2deepscore.train_new_model.inchikey_pair_selection import \
     select_compound_pairs_wrapper

From 2ae0dc3e4a22e33e363bd9d24a8359e0699309e6 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Thu, 21 Aug 2025 09:25:18 +0200
Subject: [PATCH 08/48] Fix InchikeyPairGenerator import

---
 ms2deepscore/train_new_model/SpectrumPairGenerator.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ms2deepscore/train_new_model/SpectrumPairGenerator.py b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
index 4ed1554e..15dd083b 100644
--- a/ms2deepscore/train_new_model/SpectrumPairGenerator.py
+++ b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
@@ -6,7 +6,7 @@
 from matchms import Spectrum
 from ms2deepscore.SettingsMS2Deepscore import (SettingsMS2Deepscore)
 from ms2deepscore.tensorize_spectra import tensorize_spectra
-from ms2deepscore.train_new_model import InchikeyPairGenerator
+from ms2deepscore.train_new_model.InchikeyPairGenerator import InchikeyPairGenerator
 from ms2deepscore.train_new_model.inchikey_pair_selection import (
     select_compound_pairs_wrapper)
 from ms2deepscore.utils import split_by_ionmode
@@ -27,7 +27,7 @@ class SpectrumPairGenerator:
     """
 
     def __init__(self, spectrums: List[Spectrum],
-                 selected_compound_pairs: "InchikeyPairGenerator",
+                 selected_compound_pairs: InchikeyPairGenerator,
                  settings: SettingsMS2Deepscore):
         """Generates data for training a siamese Pytorch model.
 

From 1ed2816c52a1399d5258bd5330707c400cd84d66 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Thu, 21 Aug 2025 14:01:15 +0200
Subject: [PATCH 09/48] Factor out data augmentation from SpectrumPairGenerator

---
 .../train_new_model/SpectrumPairGenerator.py  | 47 +-----------
 .../train_new_model/data_augmentation.py      | 74 +++++++++++++++++++
 2 files changed, 77 insertions(+), 44 deletions(-)
 create mode 100644 ms2deepscore/train_new_model/data_augmentation.py

diff --git a/ms2deepscore/train_new_model/SpectrumPairGenerator.py b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
index 15dd083b..e1840aa5 100644
--- a/ms2deepscore/train_new_model/SpectrumPairGenerator.py
+++ b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
@@ -7,6 +7,7 @@
 from ms2deepscore.SettingsMS2Deepscore import (SettingsMS2Deepscore)
 from ms2deepscore.tensorize_spectra import tensorize_spectra
 from ms2deepscore.train_new_model.InchikeyPairGenerator import InchikeyPairGenerator
+from ms2deepscore.train_new_model.data_augmentation import data_augmentation
 from ms2deepscore.train_new_model.inchikey_pair_selection import (
     select_compound_pairs_wrapper)
 from ms2deepscore.utils import split_by_ionmode
@@ -113,8 +114,8 @@ def __getitem__(self, batch_index: int):
             # Store batches for later epochs
             self.fixed_set[batch_index] = (spectra_1, spectra_2, meta_1, meta_2, targets)
         else:
-            spectra_1 = self._data_augmentation(spectra_1)
-            spectra_2 = self._data_augmentation(spectra_2)
+            spectra_1 = data_augmentation(spectra_1, self.model_settings, self.rng)
+            spectra_2 = data_augmentation(spectra_2, self.model_settings, self.rng)
         return spectra_1, spectra_2, meta_1, meta_2, targets
 
     def _tensorize_all(self, spectrum_pairs):
@@ -142,48 +143,6 @@ def _get_spectrum_with_inchikey(self, inchikey: str) -> Spectrum:
             raise ValueError("No matching inchikey found (note: expected first 14 characters)")
         return self.spectrums[self.rng.choice(matching_spectrum_id)]
 
-    def _data_augmentation(self, spectra_tensors):
-        for i in range(spectra_tensors.shape[0]):
-            spectra_tensors[i, :] = self._data_augmentation_spectrum(spectra_tensors[i, :])
-        return spectra_tensors
-
-    def _data_augmentation_spectrum(self, spectrum_tensor):
-        """Data augmentation.
-
-        Parameters
-        ----------
-        spectrum_tensor
-            Spectrum in Pytorch tensor form.
-        """
-        # Augmentation 1: peak removal (peaks < augment_removal_max)
-        if self.model_settings.augment_removal_max or self.model_settings.augment_removal_intensity:
-            # TODO: Factor out function with documentation + example?
-
-            indices_select = torch.where((spectrum_tensor > 0)
-                                         & (spectrum_tensor < self.model_settings.augment_removal_intensity))[0]
-            removal_part = self.rng.random(1) * self.model_settings.augment_removal_max
-            indices = self.rng.choice(indices_select, int(np.ceil((1 - removal_part) * len(indices_select))))
-            if len(indices) > 0:
-                spectrum_tensor[indices] = 0
-
-        # Augmentation 2: Change peak intensities
-        if self.model_settings.augment_intensity:
-            # TODO: Factor out function with documentation + example?
-            spectrum_tensor = spectrum_tensor * (
-                        1 - self.model_settings.augment_intensity * 2 * (torch.rand(spectrum_tensor.shape) - 0.5))
-
-        # Augmentation 3: Peak addition
-        if self.model_settings.augment_noise_max and self.model_settings.augment_noise_max > 0:
-            indices_select = torch.where(spectrum_tensor == 0)[0]
-            if len(indices_select) > self.model_settings.augment_noise_max:
-                indices_noise = self.rng.choice(indices_select,
-                                                self.rng.integers(0, self.model_settings.augment_noise_max),
-                                                replace=False,
-                                                )
-            spectrum_tensor[indices_noise] = self.model_settings.augment_noise_intensity * torch.rand(
-                len(indices_noise))
-        return spectrum_tensor
-
 
 def create_data_generator(training_spectra,
                           settings,
diff --git a/ms2deepscore/train_new_model/data_augmentation.py b/ms2deepscore/train_new_model/data_augmentation.py
new file mode 100644
index 00000000..f02fb65c
--- /dev/null
+++ b/ms2deepscore/train_new_model/data_augmentation.py
@@ -0,0 +1,74 @@
+import numpy as np
+import torch
+
+from ms2deepscore import SettingsMS2Deepscore
+
+
+def data_augmentation(spectra_tensors,
+                      model_settings: SettingsMS2Deepscore,
+                      random_number_generator):
+    for i in range(spectra_tensors.shape[0]):
+        spectra_tensors[i, :] = data_augmentation_spectrum(spectra_tensors[i, :],
+                                                           model_settings,
+                                                           random_number_generator)
+    return spectra_tensors
+
+
+def data_augmentation_spectrum(spectrum_tensor,
+                               model_settings: SettingsMS2Deepscore,
+                               random_number_generator):
+    """Data augmentation.
+
+    Parameters
+    ----------
+    spectrum_tensor
+        Spectrum in Pytorch tensor form.
+    """
+    # Augmentation 1: peak removal (peaks < augment_removal_max)
+    peak_removal_for_data_augmentation(spectrum_tensor, model_settings.augment_removal_max,
+                                       model_settings.augment_removal_intensity, random_number_generator)
+
+    # Augmentation 2: Change peak intensities
+    if model_settings.augment_intensity:
+        spectrum_tensor = change_peak_intensity(spectrum_tensor, model_settings)
+
+    peak_addition_for_data_augmentation(spectrum_tensor, model_settings, random_number_generator)
+    return spectrum_tensor
+
+def peak_removal_for_data_augmentation(spectrum_tensor, augment_removal_max,
+                                       augment_removal_intensity, random_number_generator):
+    """Removes small peaks at random for data augmentation.
+
+    Parameters
+    spectrum_tensor:
+        Tensorized spectrum
+    augment_removal_max
+        Maximum fraction of peaks (if intensity < below augment_removal_intensity)
+        to be removed randomly. Default is set to 0.2, which means that between
+        0 and 20% of all peaks with intensities < augment_removal_intensity
+        will be removed.
+    augment_removal_intensity
+        Specifying that only peaks with intensities < max_intensity will be removed.
+    random_number_generator
+        Random number generator used to generate random numbers. Can be generated with np.random.default_rng(42)
+    """
+    if augment_removal_max or augment_removal_intensity:
+        bin_indices_below_removal_intensity = torch.where((spectrum_tensor > 0)
+                                     & (spectrum_tensor < augment_removal_intensity))[0]
+        fraction_of_noise_to_remove = random_number_generator.random(1) * augment_removal_max
+        number_of_peaks_to_remove = int(np.ceil((1 - fraction_of_noise_to_remove) * len(bin_indices_below_removal_intensity)))
+        indices = random_number_generator.choice(bin_indices_below_removal_intensity, number_of_peaks_to_remove)
+        if len(indices) > 0:
+            spectrum_tensor[indices] = 0
+
+def change_peak_intensity(spectrum_tensor, model_settings):
+    return spectrum_tensor * (1 - model_settings.augment_intensity * 2 * (torch.rand(spectrum_tensor.shape) - 0.5))
+
+def peak_addition_for_data_augmentation(spectrum_tensor, model_settings, random_number_generator):
+    if model_settings.augment_noise_max and model_settings.augment_noise_max > 0:
+        indices_select = torch.where(spectrum_tensor == 0)[0]
+        if len(indices_select) > model_settings.augment_noise_max:
+            indices_noise = random_number_generator.choice(
+                indices_select,
+                random_number_generator.integers(0, model_settings.augment_noise_max), replace=False,)
+        spectrum_tensor[indices_noise] = model_settings.augment_noise_intensity * torch.rand(len(indices_noise))
\ No newline at end of file

From 5ea602749c1d046ab6b8cc2827498864557f859b Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Thu, 21 Aug 2025 14:01:54 +0200
Subject: [PATCH 10/48] Fix bug in peak_removal_for_data_augmentation, picking
 with replacing

---
 ms2deepscore/train_new_model/data_augmentation.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ms2deepscore/train_new_model/data_augmentation.py b/ms2deepscore/train_new_model/data_augmentation.py
index f02fb65c..c930c93b 100644
--- a/ms2deepscore/train_new_model/data_augmentation.py
+++ b/ms2deepscore/train_new_model/data_augmentation.py
@@ -57,7 +57,7 @@ def peak_removal_for_data_augmentation(spectrum_tensor, augment_removal_max,
                                      & (spectrum_tensor < augment_removal_intensity))[0]
         fraction_of_noise_to_remove = random_number_generator.random(1) * augment_removal_max
         number_of_peaks_to_remove = int(np.ceil((1 - fraction_of_noise_to_remove) * len(bin_indices_below_removal_intensity)))
-        indices = random_number_generator.choice(bin_indices_below_removal_intensity, number_of_peaks_to_remove)
+        indices = random_number_generator.choice(bin_indices_below_removal_intensity, number_of_peaks_to_remove, replace=False)
         if len(indices) > 0:
             spectrum_tensor[indices] = 0
 

From 7a3c9b264128feb5512d011c7cfdd1955fa011cd Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Thu, 21 Aug 2025 14:02:08 +0200
Subject: [PATCH 11/48] Add test for peak_removal_for_data_augmentation

---
 tests/test_data_augmentation.py | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)
 create mode 100644 tests/test_data_augmentation.py

diff --git a/tests/test_data_augmentation.py b/tests/test_data_augmentation.py
new file mode 100644
index 00000000..1bfae246
--- /dev/null
+++ b/tests/test_data_augmentation.py
@@ -0,0 +1,17 @@
+import numpy as np
+import torch
+from matchms import Spectrum
+
+from ms2deepscore import SettingsMS2Deepscore
+from ms2deepscore.tensorize_spectra import tensorize_spectra
+from ms2deepscore.train_new_model.data_augmentation import (data_augmentation, data_augmentation_spectrum,
+                                                            peak_addition_for_data_augmentation,
+                                                            peak_removal_for_data_augmentation, change_peak_intensity)
+
+def test_peak_removal_for_data_augmentation():
+    spectrum_tensor = torch.tensor([0.0, 0.12, 0.05, 0.78, 0.0, 0.34, 1.0, 0.0, 0.27, 0.65])
+    peak_removal_for_data_augmentation(spectrum_tensor,
+                                       augment_removal_max=0.5 ,
+                                       augment_removal_intensity=0.3,
+                                       random_number_generator= np.random.default_rng(42))
+    assert torch.equal(spectrum_tensor, torch.tensor([0.0, 0.12, 0.0, 0.78, 0.0, 0.34, 1.0, 0.0, 0.0, 0.65]))

From 2b61290088a0ed8c7774246504bb8023c7f1d726 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Thu, 21 Aug 2025 14:36:58 +0200
Subject: [PATCH 12/48] Add docstring to peak_addition_for_data_augmentation

---
 .../train_new_model/data_augmentation.py      | 33 ++++++++++++++-----
 1 file changed, 24 insertions(+), 9 deletions(-)

diff --git a/ms2deepscore/train_new_model/data_augmentation.py b/ms2deepscore/train_new_model/data_augmentation.py
index c930c93b..8ccf5281 100644
--- a/ms2deepscore/train_new_model/data_augmentation.py
+++ b/ms2deepscore/train_new_model/data_augmentation.py
@@ -32,7 +32,8 @@ def data_augmentation_spectrum(spectrum_tensor,
     if model_settings.augment_intensity:
         spectrum_tensor = change_peak_intensity(spectrum_tensor, model_settings)
 
-    peak_addition_for_data_augmentation(spectrum_tensor, model_settings, random_number_generator)
+    peak_addition_for_data_augmentation(spectrum_tensor, model_settings.augment_noise_max,
+                                        model_settings.augment_noise_intensity, random_number_generator)
     return spectrum_tensor
 
 def peak_removal_for_data_augmentation(spectrum_tensor, augment_removal_max,
@@ -64,11 +65,25 @@ def peak_removal_for_data_augmentation(spectrum_tensor, augment_removal_max,
 def change_peak_intensity(spectrum_tensor, model_settings):
     return spectrum_tensor * (1 - model_settings.augment_intensity * 2 * (torch.rand(spectrum_tensor.shape) - 0.5))
 
-def peak_addition_for_data_augmentation(spectrum_tensor, model_settings, random_number_generator):
-    if model_settings.augment_noise_max and model_settings.augment_noise_max > 0:
-        indices_select = torch.where(spectrum_tensor == 0)[0]
-        if len(indices_select) > model_settings.augment_noise_max:
-            indices_noise = random_number_generator.choice(
-                indices_select,
-                random_number_generator.integers(0, model_settings.augment_noise_max), replace=False,)
-        spectrum_tensor[indices_noise] = model_settings.augment_noise_intensity * torch.rand(len(indices_noise))
\ No newline at end of file
+def peak_addition_for_data_augmentation(spectrum_tensor, augment_noise_max,
+                                        augment_noise_intensity, random_number_generator):
+    """Adds noise to a spectrum tensor
+    spectrum_tensor:
+        Tensorized spectrum
+    augment_noise_max
+        Max number of 'new' noise peaks to add to the spectrum, between 0 to `augment_noise_max`
+        of peaks are added.
+    augment_noise_intensity
+        maximum intensity of the 'new' noise peaks to add to the spectrum,
+    random_number_generator
+        Random number generator used to generate random numbers. Can be generated with np.random.default_rng(42)
+        """
+    if augment_noise_max and augment_noise_max > 0:
+        bin_indices_zero = torch.where(spectrum_tensor == 0)[0]
+        number_of_noise_peaks_to_add = random_number_generator.integers(0, augment_noise_max)
+        if len(bin_indices_zero) > number_of_noise_peaks_to_add:
+            selected_bin_indices_to_add_noise = random_number_generator.choice(
+                bin_indices_zero,number_of_noise_peaks_to_add, replace=False,)
+        else:
+            selected_bin_indices_to_add_noise = bin_indices_zero
+        spectrum_tensor[selected_bin_indices_to_add_noise] = augment_noise_intensity * torch.rand(len(selected_bin_indices_to_add_noise))
\ No newline at end of file

From 187df44986a89a082ca4f97fb44df005d5a3a78b Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Thu, 21 Aug 2025 14:37:06 +0200
Subject: [PATCH 13/48] Add test for peak_addition_for_data_augmentation

---
 tests/test_data_augmentation.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/tests/test_data_augmentation.py b/tests/test_data_augmentation.py
index 1bfae246..da029113 100644
--- a/tests/test_data_augmentation.py
+++ b/tests/test_data_augmentation.py
@@ -15,3 +15,15 @@ def test_peak_removal_for_data_augmentation():
                                        augment_removal_intensity=0.3,
                                        random_number_generator= np.random.default_rng(42))
     assert torch.equal(spectrum_tensor, torch.tensor([0.0, 0.12, 0.0, 0.78, 0.0, 0.34, 1.0, 0.0, 0.0, 0.65]))
+
+def test_peak_addition_for_data_augmentation():
+    spectrum_tensor = torch.tensor([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.27, 0.0])
+    peak_addition_for_data_augmentation(spectrum_tensor,
+                                       4,
+                                       0.02,
+                                       random_number_generator= np.random.default_rng(0))
+    assert spectrum_tensor[6] == 1.0
+    assert spectrum_tensor[8] == 0.27
+    assert spectrum_tensor[0] == 0.0
+    assert spectrum_tensor[2] != 0.0 # we know this one is changed because of the random number generator
+

From d71392eb7b876da5daafbded7a083ef709c14fb7 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Thu, 21 Aug 2025 14:47:05 +0200
Subject: [PATCH 14/48] Make change_peak_intensity in place

---
 ms2deepscore/train_new_model/data_augmentation.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/ms2deepscore/train_new_model/data_augmentation.py b/ms2deepscore/train_new_model/data_augmentation.py
index 8ccf5281..7e2edc0c 100644
--- a/ms2deepscore/train_new_model/data_augmentation.py
+++ b/ms2deepscore/train_new_model/data_augmentation.py
@@ -29,8 +29,7 @@ def data_augmentation_spectrum(spectrum_tensor,
                                        model_settings.augment_removal_intensity, random_number_generator)
 
     # Augmentation 2: Change peak intensities
-    if model_settings.augment_intensity:
-        spectrum_tensor = change_peak_intensity(spectrum_tensor, model_settings)
+    change_peak_intensity_for_data_augmentation(spectrum_tensor, model_settings.augment_intensity)
 
     peak_addition_for_data_augmentation(spectrum_tensor, model_settings.augment_noise_max,
                                         model_settings.augment_noise_intensity, random_number_generator)
@@ -62,8 +61,9 @@ def peak_removal_for_data_augmentation(spectrum_tensor, augment_removal_max,
         if len(indices) > 0:
             spectrum_tensor[indices] = 0
 
-def change_peak_intensity(spectrum_tensor, model_settings):
-    return spectrum_tensor * (1 - model_settings.augment_intensity * 2 * (torch.rand(spectrum_tensor.shape) - 0.5))
+def change_peak_intensity_for_data_augmentation(spectrum_tensor, augment_intensity):
+    if augment_intensity:
+        spectrum_tensor.mul_(1 - augment_intensity * 2 * (torch.rand(spectrum_tensor.shape) - 0.5))
 
 def peak_addition_for_data_augmentation(spectrum_tensor, augment_noise_max,
                                         augment_noise_intensity, random_number_generator):

From e01cd1f7145c97265cfdbb69bf64a2b9fd13360f Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Thu, 21 Aug 2025 14:47:22 +0200
Subject: [PATCH 15/48] Add basic check for
 change_peak_intensity_for_data_augmentation

---
 tests/test_data_augmentation.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/tests/test_data_augmentation.py b/tests/test_data_augmentation.py
index da029113..e074e43d 100644
--- a/tests/test_data_augmentation.py
+++ b/tests/test_data_augmentation.py
@@ -6,7 +6,7 @@
 from ms2deepscore.tensorize_spectra import tensorize_spectra
 from ms2deepscore.train_new_model.data_augmentation import (data_augmentation, data_augmentation_spectrum,
                                                             peak_addition_for_data_augmentation,
-                                                            peak_removal_for_data_augmentation, change_peak_intensity)
+                                                            peak_removal_for_data_augmentation, change_peak_intensity_for_data_augmentation)
 
 def test_peak_removal_for_data_augmentation():
     spectrum_tensor = torch.tensor([0.0, 0.12, 0.05, 0.78, 0.0, 0.34, 1.0, 0.0, 0.27, 0.65])
@@ -27,3 +27,9 @@ def test_peak_addition_for_data_augmentation():
     assert spectrum_tensor[0] == 0.0
     assert spectrum_tensor[2] != 0.0 # we know this one is changed because of the random number generator
 
+def test_change_peak_intensity_for_data_augmentation():
+    spectrum_tensor = torch.tensor([0.0, 0.12, 0.05, 0.78, 0.0, 0.34, 1.0, 0.0, 0.27, 0.65])
+    change_peak_intensity_for_data_augmentation(spectrum_tensor,
+                                       0.2)
+    assert spectrum_tensor[0] == 0.0 # Check that zero's are not changed.
+    assert spectrum_tensor[1] != 0.12 # Check that the value is changed.

From 2b176780792e3316260b0af9d8cbb0599c0c10d6 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Thu, 21 Aug 2025 14:48:12 +0200
Subject: [PATCH 16/48] Remove unnecessary imports

---
 ms2deepscore/train_new_model/SpectrumPairGenerator.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/ms2deepscore/train_new_model/SpectrumPairGenerator.py b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
index e1840aa5..a57cddfe 100644
--- a/ms2deepscore/train_new_model/SpectrumPairGenerator.py
+++ b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
@@ -147,12 +147,17 @@ def _get_spectrum_with_inchikey(self, inchikey: str) -> Spectrum:
 def create_data_generator(training_spectra,
                           settings,
                           json_save_file=None) -> SpectrumPairGenerator:
+    # todo actually create, both between and across ionmodes.
+    pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
+
     selected_compound_pairs_training = select_compound_pairs_wrapper(training_spectra, settings=settings)
     inchikey_pair_generator = InchikeyPairGenerator(selected_compound_pairs_training)
 
     if json_save_file is not None:
         inchikey_pair_generator.save_as_json(json_save_file)
+    # todo possibly create a single SpectrumPairGenerator which takes in 3 generators and pos and neg spectra to iteratively select each one.
     # Create generators
+    # todo also make sure that the SpectrumPairGenerator can work across ionmodes.
     train_generator = SpectrumPairGenerator(spectrums=training_spectra,
                                             selected_compound_pairs=inchikey_pair_generator,
                                             settings=settings)

From 41018435abd61ed57ff89eb999e7bf62a6d3894d Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Thu, 21 Aug 2025 14:49:02 +0200
Subject: [PATCH 17/48] Remove unnecessary imports

---
 tests/test_data_augmentation.py | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/tests/test_data_augmentation.py b/tests/test_data_augmentation.py
index e074e43d..c5b5ce89 100644
--- a/tests/test_data_augmentation.py
+++ b/tests/test_data_augmentation.py
@@ -1,12 +1,8 @@
 import numpy as np
 import torch
-from matchms import Spectrum
-
-from ms2deepscore import SettingsMS2Deepscore
-from ms2deepscore.tensorize_spectra import tensorize_spectra
-from ms2deepscore.train_new_model.data_augmentation import (data_augmentation, data_augmentation_spectrum,
-                                                            peak_addition_for_data_augmentation,
-                                                            peak_removal_for_data_augmentation, change_peak_intensity_for_data_augmentation)
+from ms2deepscore.train_new_model.data_augmentation import (peak_addition_for_data_augmentation,
+                                                            peak_removal_for_data_augmentation,
+                                                            change_peak_intensity_for_data_augmentation)
 
 def test_peak_removal_for_data_augmentation():
     spectrum_tensor = torch.tensor([0.0, 0.12, 0.05, 0.78, 0.0, 0.34, 1.0, 0.0, 0.27, 0.65])

From 64cef6b6aaa0d0b55404aa62d53053734648fbc9 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Thu, 21 Aug 2025 14:52:48 +0200
Subject: [PATCH 18/48] Add some typehinting

---
 ms2deepscore/train_new_model/SpectrumPairGenerator.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/ms2deepscore/train_new_model/SpectrumPairGenerator.py b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
index a57cddfe..954e11c6 100644
--- a/ms2deepscore/train_new_model/SpectrumPairGenerator.py
+++ b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
@@ -1,6 +1,6 @@
 """ Data generators for training/inference with MS2DeepScore model.
 """
-from typing import List
+from typing import List, Tuple, Generator
 import numpy as np
 import torch
 from matchms import Spectrum
@@ -84,7 +84,7 @@ def __next__(self):
         self.current_batch_index = 0  # make generator executable again
         raise StopIteration
 
-    def _spectrum_pair_generator(self):
+    def _spectrum_pair_generator(self) -> Generator[Tuple[Spectrum, Spectrum, float]]:
         """Use the provided SelectedCompoundPairs object to pick pairs."""
         for _ in range(self.model_settings.batch_size):
             try:
@@ -118,7 +118,7 @@ def __getitem__(self, batch_index: int):
             spectra_2 = data_augmentation(spectra_2, self.model_settings, self.rng)
         return spectra_1, spectra_2, meta_1, meta_2, targets
 
-    def _tensorize_all(self, spectrum_pairs):
+    def _tensorize_all(self, spectrum_pairs: Generator[Tuple[Spectrum, Spectrum, float]]):
         spectra_1 = []
         spectra_2 = []
         targets = []
@@ -148,7 +148,7 @@ def create_data_generator(training_spectra,
                           settings,
                           json_save_file=None) -> SpectrumPairGenerator:
     # todo actually create, both between and across ionmodes.
-    pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
+    # pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
 
     selected_compound_pairs_training = select_compound_pairs_wrapper(training_spectra, settings=settings)
     inchikey_pair_generator = InchikeyPairGenerator(selected_compound_pairs_training)

From 112674d3553b5f87ec10da9495b0413da29f53f1 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 12:57:21 +0200
Subject: [PATCH 19/48] Include Spectrum selection in InchikeyPairGenerator

---
 .../train_new_model/InchikeyPairGenerator.py  | 23 +++++++++-
 .../train_new_model/SpectrumPairGenerator.py  | 43 +++++--------------
 2 files changed, 31 insertions(+), 35 deletions(-)

diff --git a/ms2deepscore/train_new_model/InchikeyPairGenerator.py b/ms2deepscore/train_new_model/InchikeyPairGenerator.py
index 4127e502..d3083613 100644
--- a/ms2deepscore/train_new_model/InchikeyPairGenerator.py
+++ b/ms2deepscore/train_new_model/InchikeyPairGenerator.py
@@ -2,9 +2,12 @@
 from collections import Counter
 from typing import List, Tuple
 
+import numpy as np
+from matchms import Spectrum
+
 
 class InchikeyPairGenerator:
-    def __init__(self, selected_inchikey_pairs: List[Tuple[str, str, float]]):
+    def __init__(self, selected_inchikey_pairs: List[Tuple[str, str, float]], spectra):
         """
         Parameters
         ----------
@@ -12,6 +15,8 @@ def __init__(self, selected_inchikey_pairs: List[Tuple[str, str, float]]):
             A list with tuples encoding inchikey pairs like: (inchikey1, inchikey2, tanimoto_score)
         """
         self.selected_inchikey_pairs = selected_inchikey_pairs
+        self.spectra = spectra
+        self.spectrum_inchikeys = np.array([s.get("inchikey")[:14] for s in self.spectra])
 
     def generator(self, shuffle: bool, random_nr_generator):
         """Infinite generator to loop through all inchikeys.
@@ -22,7 +27,9 @@ def generator(self, shuffle: bool, random_nr_generator):
                 random_nr_generator.shuffle(self.selected_inchikey_pairs)
 
             for inchikey1, inchikey2, tanimoto_score in self.selected_inchikey_pairs:
-                yield inchikey1, inchikey2, tanimoto_score
+                spectrum1 = self._get_spectrum_with_inchikey(inchikey1, random_nr_generator)
+                spectrum2 = self._get_spectrum_with_inchikey(inchikey2, random_nr_generator)
+                yield spectrum1, spectrum2, tanimoto_score
 
     def __len__(self):
         return len(self.selected_inchikey_pairs)
@@ -59,3 +66,15 @@ def save_as_json(self, file_name):
 
         with open(file_name, "w", encoding="utf-8") as f:
             json.dump(data_for_json, f)
+
+    def _get_spectrum_with_inchikey(self, inchikey: str, random_number_generator) -> Spectrum:
+        """
+        Get a random spectrum matching the `inchikey` argument.
+
+        NB: A compound (identified by an
+        inchikey) can have multiple measured spectrums in a binned spectrum dataset.
+        """
+        matching_spectrum_id = np.where(self.spectrum_inchikeys == inchikey)[0]
+        if len(matching_spectrum_id) <= 0:
+            raise ValueError("No matching inchikey found (note: expected first 14 characters)")
+        return self.spectra[random_number_generator.choice(matching_spectrum_id)]
diff --git a/ms2deepscore/train_new_model/SpectrumPairGenerator.py b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
index 954e11c6..b2234163 100644
--- a/ms2deepscore/train_new_model/SpectrumPairGenerator.py
+++ b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
@@ -1,6 +1,6 @@
 """ Data generators for training/inference with MS2DeepScore model.
 """
-from typing import List, Tuple, Generator
+from typing import List, Tuple
 import numpy as np
 import torch
 from matchms import Spectrum
@@ -27,15 +27,13 @@ class SpectrumPairGenerator:
     In addition inchikeys are selected to occur equally for each pair.
     """
 
-    def __init__(self, spectrums: List[Spectrum],
+    def __init__(self,
                  selected_compound_pairs: InchikeyPairGenerator,
                  settings: SettingsMS2Deepscore):
         """Generates data for training a siamese Pytorch model.
 
         Parameters
         ----------
-        spectrums
-            List of matchms Spectrum objects.
         selected_compound_pairs
             SelectedCompoundPairs object which contains selected compounds pairs and the
             respective similarity scores.
@@ -43,10 +41,6 @@ def __init__(self, spectrums: List[Spectrum],
             The available settings can be found in SettignsMS2Deepscore
         """
         self.current_batch_index = 0
-        self.spectrums = spectrums
-
-        # Collect all inchikeys
-        self.spectrum_inchikeys = np.array([s.get("inchikey")[:14] for s in self.spectrums])
 
         # Set all other settings to input (or otherwise to defaults):
         self.model_settings = settings
@@ -59,14 +53,12 @@ def __init__(self, spectrums: List[Spectrum],
             if self.model_settings.random_seed is None:
                 self.model_settings.random_seed = 0
         self.rng = np.random.default_rng(self.model_settings.random_seed)
-
-        unique_inchikeys = np.unique(self.spectrum_inchikeys)
+        self.inchikey_pair_generator = selected_compound_pairs.generator(self.model_settings.shuffle, self.rng)
+        unique_inchikeys = np.unique(selected_compound_pairs.spectrum_inchikeys)
         if len(unique_inchikeys) < self.model_settings.batch_size:
             raise ValueError("The number of unique inchikeys must be larger than the batch size.")
         self.fixed_set = {}
 
-        self.selected_compound_pairs = selected_compound_pairs
-        self.inchikey_pair_generator = self.selected_compound_pairs.generator(self.model_settings.shuffle, self.rng)
         self.nr_of_batches = int(self.model_settings.num_turns) * int(np.ceil(len(unique_inchikeys) /
                                                                               self.model_settings.batch_size))
 
@@ -84,18 +76,16 @@ def __next__(self):
         self.current_batch_index = 0  # make generator executable again
         raise StopIteration
 
-    def _spectrum_pair_generator(self) -> Generator[Tuple[Spectrum, Spectrum, float]]:
+    def _spectrum_pair_generator(self):
         """Use the provided SelectedCompoundPairs object to pick pairs."""
         for _ in range(self.model_settings.batch_size):
             try:
-                inchikey1, inchikey2, score = next(self.inchikey_pair_generator)
+                spectrum1, spectrum2, score = next(self.inchikey_pair_generator)
+                yield spectrum1, spectrum2, score
             except StopIteration as exc:
                 raise RuntimeError("The inchikey pair generator is not expected to end, "
                                    "but should instead generate infinite pairs") from exc
 
-            spectrum1 = self._get_spectrum_with_inchikey(inchikey1)
-            spectrum2 = self._get_spectrum_with_inchikey(inchikey2)
-            yield spectrum1, spectrum2, score
 
     def __getitem__(self, batch_index: int):
         """Generate one batch of data.
@@ -118,7 +108,7 @@ def __getitem__(self, batch_index: int):
             spectra_2 = data_augmentation(spectra_2, self.model_settings, self.rng)
         return spectra_1, spectra_2, meta_1, meta_2, targets
 
-    def _tensorize_all(self, spectrum_pairs: Generator[Tuple[Spectrum, Spectrum, float]]):
+    def _tensorize_all(self, spectrum_pairs):
         spectra_1 = []
         spectra_2 = []
         targets = []
@@ -131,18 +121,6 @@ def _tensorize_all(self, spectrum_pairs: Generator[Tuple[Spectrum, Spectrum, flo
         binned_spectra_2, metadata_2 = tensorize_spectra(spectra_2, self.model_settings)
         return binned_spectra_1, binned_spectra_2, metadata_1, metadata_2, torch.tensor(targets, dtype=torch.float32)
 
-    def _get_spectrum_with_inchikey(self, inchikey: str) -> Spectrum:
-        """
-        Get a random spectrum matching the `inchikey` argument.
-
-        NB: A compound (identified by an
-        inchikey) can have multiple measured spectrums in a binned spectrum dataset.
-        """
-        matching_spectrum_id = np.where(self.spectrum_inchikeys == inchikey)[0]
-        if len(matching_spectrum_id) <= 0:
-            raise ValueError("No matching inchikey found (note: expected first 14 characters)")
-        return self.spectrums[self.rng.choice(matching_spectrum_id)]
-
 
 def create_data_generator(training_spectra,
                           settings,
@@ -151,14 +129,13 @@ def create_data_generator(training_spectra,
     # pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
 
     selected_compound_pairs_training = select_compound_pairs_wrapper(training_spectra, settings=settings)
-    inchikey_pair_generator = InchikeyPairGenerator(selected_compound_pairs_training)
+    inchikey_pair_generator = InchikeyPairGenerator(selected_compound_pairs_training, training_spectra)
 
     if json_save_file is not None:
         inchikey_pair_generator.save_as_json(json_save_file)
     # todo possibly create a single SpectrumPairGenerator which takes in 3 generators and pos and neg spectra to iteratively select each one.
     # Create generators
     # todo also make sure that the SpectrumPairGenerator can work across ionmodes.
-    train_generator = SpectrumPairGenerator(spectrums=training_spectra,
-                                            selected_compound_pairs=inchikey_pair_generator,
+    train_generator = SpectrumPairGenerator(selected_compound_pairs=inchikey_pair_generator,
                                             settings=settings)
     return train_generator

From d73e746e20ec978bcbc19abb120d8160287c9bee Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 12:57:44 +0200
Subject: [PATCH 20/48] Update tests to handle new InchikeyPairGenerator

---
 tests/test_data_generators.py         | 11 ++++---
 tests/test_inchikey_pair_selection.py | 47 +++++++++++++++------------
 tests/test_siamese_spectra_model.py   |  4 +--
 3 files changed, 35 insertions(+), 27 deletions(-)

diff --git a/tests/test_data_generators.py b/tests/test_data_generators.py
index 37ea3af5..535ab980 100644
--- a/tests/test_data_generators.py
+++ b/tests/test_data_generators.py
@@ -66,7 +66,8 @@ def dummy_data_generator():
     selected_pairs = InchikeyPairGenerator([('CCCCCCCCCCCCCC', 'DDDDDDDDDDDDDD', 0.25),
                                             ('BBBBBBBBBBBBBB', 'DDDDDDDDDDDDDD', 0.6666667),
                                             ('AAAAAAAAAAAAAA', 'CCCCCCCCCCCCCC', 1.0),
-                                            ('AAAAAAAAAAAAAA', 'BBBBBBBBBBBBBB', 0.33333334)])
+                                            ('AAAAAAAAAAAAAA', 'BBBBBBBBBBBBBB', 0.33333334)],
+                                           spectrums)
     batch_size = 2
     settings = SettingsMS2Deepscore(min_mz=10,
                                     max_mz=1000,
@@ -80,7 +81,7 @@ def dummy_data_generator():
                                     augment_removal_intensity=0.0,
                                     augment_intensity=0.0,
                                     augment_noise_max=0)
-    return SpectrumPairGenerator(spectrums, selected_pairs, settings)
+    return SpectrumPairGenerator(selected_pairs, settings)
 
 
 def test_correct_batch_format_data_generator(dummy_data_generator):
@@ -108,6 +109,8 @@ def test_equal_sampling_of_spectra(dummy_data_generator):
     The sampling is random, but for enough repetitions very likely to always happen.
     This test is mostly to make sure we don't accidentally implement something
     where we just resample the same spectrum every time for one inchikey"""
+    spectrums = create_test_spectra(4, 3) # the same spectra used for the dummy_data_generator
+
     tensorized_spectra = []
     epochs = 20
     for _ in range(epochs):
@@ -128,7 +131,7 @@ def test_equal_sampling_of_spectra(dummy_data_generator):
     # but since we sample 640 spectra from 24 options, it is very unlikely (1 in 28 billion)
     # that this will result in not sampling all at least once.
     # Because we have a fixed seed, this should not result in random failing tests.
-    assert len(unique_tensors) == len(dummy_data_generator.spectrums), "Not all spectra are selected at least once"
+    assert len(unique_tensors) == 12, "Not all spectra are selected at least once"
 
     def reverse_tensorize(tensor, list_of_spectra, settings):
         """Finds the spectrum in a list of spectra based on the tensorized vesion"""
@@ -146,7 +149,7 @@ def reverse_tensorize(tensor, list_of_spectra, settings):
     inchikey_counts = Counter()
     for unique_tensor, count in tensor_counts.items():
         spectrum = reverse_tensorize(unique_tensor,
-                                     dummy_data_generator.spectrums,
+                                     spectrums,
                                      dummy_data_generator.model_settings)
 
         inchikey = spectrum.get("inchikey")[:14]
diff --git a/tests/test_inchikey_pair_selection.py b/tests/test_inchikey_pair_selection.py
index 71eb63a0..d6afd22f 100644
--- a/tests/test_inchikey_pair_selection.py
+++ b/tests/test_inchikey_pair_selection.py
@@ -58,12 +58,16 @@ def test_spectra():
 
 
 @pytest.fixture
-def dummy_spectrum_pairs():
+def dummy_inchikey_pair_generator():
     spectrum_pairs = [("Inchikey0", "Inchikey1", 0.8),
                       ("Inchikey0", "Inchikey2", 0.6),
                       ("Inchikey2", "Inchikey1", 0.3),
                       ("Inchikey2", "Inchikey2", 1.0)]
-    return spectrum_pairs
+    return InchikeyPairGenerator(spectrum_pairs, [
+        Spectrum(mz=np.array([90.]), intensities=np.array([0.4]), metadata={"inchikey": "Inchikey0"}),
+        Spectrum(mz=np.array([90.]), intensities=np.array([0.4]), metadata={"inchikey": "Inchikey1"}),
+        Spectrum(mz=np.array([90.]), intensities=np.array([0.4]), metadata={"inchikey": "Inchikey2"}),
+    ])
 
 
 def test_compute_jaccard_similarity_per_bin(simple_fingerprints):
@@ -146,34 +150,35 @@ def test_select_inchi_for_unique_inchikeys_two_inchikeys(test_spectra):
     assert [s.get("inchi")[:15] for s in spectrums_selected] == ['InChI=1/C6H8O6/', 'InChI=1S/C8H10N']
 
 
-def test_SelectedInchikeyPairs_generator_with_shuffle(dummy_spectrum_pairs):
-    selected_inchikey_pairs = InchikeyPairGenerator(dummy_spectrum_pairs)
+def test_SelectedInchikeyPairs_generator_with_shuffle(dummy_inchikey_pair_generator):
     rng = np.random.default_rng(0)
-    gen = selected_inchikey_pairs.generator(True, rng)
+    gen = dummy_inchikey_pair_generator.generator(True, rng)
 
     found_pairs = []
     # do one complete loop
-    for i in range(len(selected_inchikey_pairs)):
-        found_pairs.append(next(gen))
+    for i in range(len(dummy_inchikey_pair_generator)):
+        spectrum_1, spectrum_2, score = next(gen)
+        found_pairs.append((spectrum_1.get("inchikey"), spectrum_2.get("inchikey"), score))
 
-    assert len(found_pairs) == len(dummy_spectrum_pairs)
-    assert sorted(found_pairs) == sorted(dummy_spectrum_pairs)
+    assert len(found_pairs) == len(dummy_inchikey_pair_generator.selected_inchikey_pairs)
+    assert sorted(found_pairs) == sorted(dummy_inchikey_pair_generator.selected_inchikey_pairs)
 
     found_pairs = []
     # do one complete loop
-    for i in range(len(selected_inchikey_pairs)):
-        found_pairs.append(next(gen))
+    for i in range(len(dummy_inchikey_pair_generator)):
+        spectrum_1, spectrum_2, score = next(gen)
+        found_pairs.append((spectrum_1.get("inchikey"), spectrum_2.get("inchikey"), score))
 
-    assert len(found_pairs) == len(dummy_spectrum_pairs)
-    assert sorted(found_pairs) == sorted(dummy_spectrum_pairs)
+    assert len(found_pairs) == len(dummy_inchikey_pair_generator.selected_inchikey_pairs)
+    assert sorted(found_pairs) == sorted(dummy_inchikey_pair_generator.selected_inchikey_pairs)
 
 
-def test_SelectedInchikeyPairs_generator_without_shuffle(dummy_spectrum_pairs):
-    selected_inchikey_pairs = InchikeyPairGenerator(dummy_spectrum_pairs)
-    gen = selected_inchikey_pairs.generator(False, None)
+def test_SelectedInchikeyPairs_generator_without_shuffle(dummy_inchikey_pair_generator):
+    gen = dummy_inchikey_pair_generator.generator(False, np.random.default_rng(0))
 
-    for _, expected_pair in enumerate(dummy_spectrum_pairs):
-        assert expected_pair == next(gen)
+    for _, expected_pair in enumerate(dummy_inchikey_pair_generator.selected_inchikey_pairs):
+        spectrum_1, spectrum_2, score = next(gen)
+        assert expected_pair == (spectrum_1.get("inchikey"), spectrum_2.get("inchikey"), score)
 
 
 def test_select_compound_pairs_wrapper_no_resampling():
@@ -185,7 +190,7 @@ def test_select_compound_pairs_wrapper_no_resampling():
                                     batch_size=8,
                                     max_pair_resampling=max_pair_resampling)
     selected_inchikey_pairs = select_compound_pairs_wrapper(spectrums, settings)
-    inchikey_pair_generator = InchikeyPairGenerator(selected_inchikey_pairs)
+    inchikey_pair_generator = InchikeyPairGenerator(selected_inchikey_pairs, spectrums)
 
     check_balanced_scores_selecting_inchikey_pairs(inchikey_pair_generator, bins)
     check_correct_oversampling(inchikey_pair_generator, max_pair_resampling)
@@ -206,7 +211,7 @@ def test_select_compound_pairs_wrapper_with_resampling():
                                     batch_size=8,
                                     max_pair_resampling=max_pair_resampling)
     selected_inchikey_pairs = select_compound_pairs_wrapper(spectrums, settings)
-    inchikey_pair_generator = InchikeyPairGenerator(selected_inchikey_pairs)
+    inchikey_pair_generator = InchikeyPairGenerator(selected_inchikey_pairs, spectrums)
 
     check_balanced_scores_selecting_inchikey_pairs(inchikey_pair_generator, bins)
     check_correct_oversampling(inchikey_pair_generator, max_pair_resampling)
@@ -229,7 +234,7 @@ def test_select_compound_pairs_wrapper_maximum_inchikey_count():
                                     max_inchikey_sampling=max_inchikey_sampling
                                     )
     selected_inchikey_pairs = select_compound_pairs_wrapper(spectrums, settings)
-    inchikey_pair_generator = InchikeyPairGenerator(selected_inchikey_pairs)
+    inchikey_pair_generator = InchikeyPairGenerator(selected_inchikey_pairs, spectrums)
 
     highest_inchikey_count = max(inchikey_pair_generator.get_inchikey_counts().values())
     assert highest_inchikey_count <= max_inchikey_sampling + 1 # +1 because there is a chance that the last added inchikey is a pair to itself...
diff --git a/tests/test_siamese_spectra_model.py b/tests/test_siamese_spectra_model.py
index a86b69b7..fa1661ff 100644
--- a/tests/test_siamese_spectra_model.py
+++ b/tests/test_siamese_spectra_model.py
@@ -132,9 +132,9 @@ def test_model_training(simple_training_spectra):
                                     num_turns=20,
                                     )
     scp_train = select_compound_pairs_wrapper(simple_training_spectra, settings)
-    inchikey_pair_generator = InchikeyPairGenerator(scp_train)
+    inchikey_pair_generator = InchikeyPairGenerator(scp_train, simple_training_spectra)
     # Create generators
-    train_generator_simple = SpectrumPairGenerator(spectrums=simple_training_spectra, selected_compound_pairs=inchikey_pair_generator,
+    train_generator_simple = SpectrumPairGenerator(selected_compound_pairs=inchikey_pair_generator,
                                                    settings=settings)
     settings.same_prob_bins = np.array([(-0.01, 1.0)])
     validation_loss_calculator = ValidationLossCalculator(

From b3d2a18e2db00559acdaba84fc1a8fc17e7ccd8a Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 13:00:13 +0200
Subject: [PATCH 21/48] Rename SpectrumPairGenerator to
 TrainingBatchGenerator.py

---
 ...irGenerator.py => TrainingBatchGenerator.py} | 17 +++++++----------
 ms2deepscore/train_new_model/__init__.py        |  4 ++--
 .../train_new_model/train_ms2deepscore.py       |  2 +-
 .../training_wrapper_functions.py               |  2 +-
 tests/test_data_generators.py                   |  4 ++--
 tests/test_siamese_spectra_model.py             |  6 +++---
 6 files changed, 16 insertions(+), 19 deletions(-)
 rename ms2deepscore/train_new_model/{SpectrumPairGenerator.py => TrainingBatchGenerator.py} (90%)

diff --git a/ms2deepscore/train_new_model/SpectrumPairGenerator.py b/ms2deepscore/train_new_model/TrainingBatchGenerator.py
similarity index 90%
rename from ms2deepscore/train_new_model/SpectrumPairGenerator.py
rename to ms2deepscore/train_new_model/TrainingBatchGenerator.py
index b2234163..3a4ed47b 100644
--- a/ms2deepscore/train_new_model/SpectrumPairGenerator.py
+++ b/ms2deepscore/train_new_model/TrainingBatchGenerator.py
@@ -1,19 +1,16 @@
 """ Data generators for training/inference with MS2DeepScore model.
 """
-from typing import List, Tuple
 import numpy as np
 import torch
-from matchms import Spectrum
 from ms2deepscore.SettingsMS2Deepscore import (SettingsMS2Deepscore)
 from ms2deepscore.tensorize_spectra import tensorize_spectra
 from ms2deepscore.train_new_model.InchikeyPairGenerator import InchikeyPairGenerator
 from ms2deepscore.train_new_model.data_augmentation import data_augmentation
 from ms2deepscore.train_new_model.inchikey_pair_selection import (
     select_compound_pairs_wrapper)
-from ms2deepscore.utils import split_by_ionmode
 
 
-class SpectrumPairGenerator:
+class TrainingBatchGenerator:
     """Generates data for training a siamese Pytorch model.
 
     This class provides a data generator specifically designed for training a Siamese Pytorch model with a curated set
@@ -21,7 +18,7 @@ class SpectrumPairGenerator:
     inchikey pair.
 
     By using pre-selected compound pairs (in the InchikeyPairGenerator), this allows more control over the training
-    process. The selection of inchikey pairs does not happen in SpectrumPairGenerator (only spectrum selection), but in
+    process. The selection of inchikey pairs does not happen in TrainingBatchGenerator (only spectrum selection), but in
     inchikey_pair_selection.py. In inchikey_pair_selection inchikey pairs are picked to balance selected pairs equally
     over different tanimoto score bins to make sure both pairs of similar and dissimilar compounds are sampled.
     In addition inchikeys are selected to occur equally for each pair.
@@ -124,7 +121,7 @@ def _tensorize_all(self, spectrum_pairs):
 
 def create_data_generator(training_spectra,
                           settings,
-                          json_save_file=None) -> SpectrumPairGenerator:
+                          json_save_file=None) -> TrainingBatchGenerator:
     # todo actually create, both between and across ionmodes.
     # pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
 
@@ -133,9 +130,9 @@ def create_data_generator(training_spectra,
 
     if json_save_file is not None:
         inchikey_pair_generator.save_as_json(json_save_file)
-    # todo possibly create a single SpectrumPairGenerator which takes in 3 generators and pos and neg spectra to iteratively select each one.
+    # todo possibly create a single TrainingBatchGenerator which takes in 3 generators and pos and neg spectra to iteratively select each one.
     # Create generators
-    # todo also make sure that the SpectrumPairGenerator can work across ionmodes.
-    train_generator = SpectrumPairGenerator(selected_compound_pairs=inchikey_pair_generator,
-                                            settings=settings)
+    # todo also make sure that the TrainingBatchGenerator can work across ionmodes.
+    train_generator = TrainingBatchGenerator(selected_compound_pairs=inchikey_pair_generator,
+                                             settings=settings)
     return train_generator
diff --git a/ms2deepscore/train_new_model/__init__.py b/ms2deepscore/train_new_model/__init__.py
index 14588440..858dc12c 100644
--- a/ms2deepscore/train_new_model/__init__.py
+++ b/ms2deepscore/train_new_model/__init__.py
@@ -1,9 +1,9 @@
-from .SpectrumPairGenerator import SpectrumPairGenerator
+from .TrainingBatchGenerator import TrainingBatchGenerator
 from .InchikeyPairGenerator import InchikeyPairGenerator
 from .inchikey_pair_selection import (select_compound_pairs_wrapper)
 
 
 __all__ = [
-    "SpectrumPairGenerator",
+    "TrainingBatchGenerator",
     "select_compound_pairs_wrapper"
 ]
diff --git a/ms2deepscore/train_new_model/train_ms2deepscore.py b/ms2deepscore/train_new_model/train_ms2deepscore.py
index 1a46b167..2f03896f 100644
--- a/ms2deepscore/train_new_model/train_ms2deepscore.py
+++ b/ms2deepscore/train_new_model/train_ms2deepscore.py
@@ -10,7 +10,7 @@
 from ms2deepscore.models.SiameseSpectralModel import (SiameseSpectralModel,
                                                       train)
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
-from ms2deepscore.train_new_model.SpectrumPairGenerator import create_data_generator
+from ms2deepscore.train_new_model.TrainingBatchGenerator import create_data_generator
 from ms2deepscore.validation_loss_calculation.ValidationLossCalculator import \
     ValidationLossCalculator
 
diff --git a/ms2deepscore/wrapper_functions/training_wrapper_functions.py b/ms2deepscore/wrapper_functions/training_wrapper_functions.py
index 9936124b..f6efa957 100644
--- a/ms2deepscore/wrapper_functions/training_wrapper_functions.py
+++ b/ms2deepscore/wrapper_functions/training_wrapper_functions.py
@@ -14,7 +14,7 @@
                                                       train)
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
 from ms2deepscore.validation_loss_calculation.ValidationLossCalculator import ValidationLossCalculator
-from ms2deepscore.train_new_model.SpectrumPairGenerator import create_data_generator
+from ms2deepscore.train_new_model.TrainingBatchGenerator import create_data_generator
 from ms2deepscore.train_new_model.train_ms2deepscore import \
     train_ms2ds_model, plot_history, save_history
 from ms2deepscore.train_new_model.validation_and_test_split import \
diff --git a/tests/test_data_generators.py b/tests/test_data_generators.py
index 535ab980..0a8e88b2 100644
--- a/tests/test_data_generators.py
+++ b/tests/test_data_generators.py
@@ -5,7 +5,7 @@
 from matchms import Spectrum
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore, SettingsEmbeddingEvaluator
 from ms2deepscore.tensorize_spectra import tensorize_spectra
-from ms2deepscore.train_new_model.SpectrumPairGenerator import SpectrumPairGenerator, \
+from ms2deepscore.train_new_model.TrainingBatchGenerator import TrainingBatchGenerator, \
     create_data_generator
 from ms2deepscore.train_new_model.DataGeneratorEmbeddingEvaluation import DataGeneratorEmbeddingEvaluation
 from ms2deepscore.train_new_model import InchikeyPairGenerator
@@ -81,7 +81,7 @@ def dummy_data_generator():
                                     augment_removal_intensity=0.0,
                                     augment_intensity=0.0,
                                     augment_noise_max=0)
-    return SpectrumPairGenerator(selected_pairs, settings)
+    return TrainingBatchGenerator(selected_pairs, settings)
 
 
 def test_correct_batch_format_data_generator(dummy_data_generator):
diff --git a/tests/test_siamese_spectra_model.py b/tests/test_siamese_spectra_model.py
index fa1661ff..fc1f4a83 100644
--- a/tests/test_siamese_spectra_model.py
+++ b/tests/test_siamese_spectra_model.py
@@ -5,7 +5,7 @@
                                                       train)
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
 from ms2deepscore.tensorize_spectra import tensorize_spectra
-from ms2deepscore.train_new_model.SpectrumPairGenerator import SpectrumPairGenerator
+from ms2deepscore.train_new_model.TrainingBatchGenerator import TrainingBatchGenerator
 from ms2deepscore.train_new_model import InchikeyPairGenerator
 from ms2deepscore.train_new_model.inchikey_pair_selection import \
     select_compound_pairs_wrapper
@@ -134,8 +134,8 @@ def test_model_training(simple_training_spectra):
     scp_train = select_compound_pairs_wrapper(simple_training_spectra, settings)
     inchikey_pair_generator = InchikeyPairGenerator(scp_train, simple_training_spectra)
     # Create generators
-    train_generator_simple = SpectrumPairGenerator(selected_compound_pairs=inchikey_pair_generator,
-                                                   settings=settings)
+    train_generator_simple = TrainingBatchGenerator(selected_compound_pairs=inchikey_pair_generator,
+                                                    settings=settings)
     settings.same_prob_bins = np.array([(-0.01, 1.0)])
     validation_loss_calculator = ValidationLossCalculator(
         simple_training_spectra,

From 9c39a44f9b59c5132a574625604f4cf8a88fd505 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 13:04:16 +0200
Subject: [PATCH 22/48] Rename SpectrumPairGenerator to
 TrainingBatchGenerator.py

---
 ...irGenerator.py => SpectrumPairGenerator.py} |  4 ++--
 .../train_new_model/TrainingBatchGenerator.py  | 16 ++++++++--------
 ms2deepscore/train_new_model/__init__.py       |  2 +-
 .../train_new_model/inchikey_pair_selection.py |  6 +++---
 .../inchikey_pair_selection_cross_ionmode.py   |  6 +++---
 tests/test_data_generators.py                  |  4 ++--
 tests/test_inchikey_pair_selection.py          | 18 +++++++++---------
 tests/test_siamese_spectra_model.py            |  4 ++--
 8 files changed, 30 insertions(+), 30 deletions(-)
 rename ms2deepscore/train_new_model/{InchikeyPairGenerator.py => SpectrumPairGenerator.py} (97%)

diff --git a/ms2deepscore/train_new_model/InchikeyPairGenerator.py b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
similarity index 97%
rename from ms2deepscore/train_new_model/InchikeyPairGenerator.py
rename to ms2deepscore/train_new_model/SpectrumPairGenerator.py
index d3083613..3539505a 100644
--- a/ms2deepscore/train_new_model/InchikeyPairGenerator.py
+++ b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
@@ -6,7 +6,7 @@
 from matchms import Spectrum
 
 
-class InchikeyPairGenerator:
+class SpectrumPairGenerator:
     def __init__(self, selected_inchikey_pairs: List[Tuple[str, str, float]], spectra):
         """
         Parameters
@@ -35,7 +35,7 @@ def __len__(self):
         return len(self.selected_inchikey_pairs)
 
     def __str__(self):
-        return f"InchikeyPairGenerator with {len(self.selected_inchikey_pairs)} pairs available"
+        return f"SpectrumPairGenerator with {len(self.selected_inchikey_pairs)} pairs available"
 
     def get_scores(self):
         return [score for _, _, score in self.selected_inchikey_pairs]
diff --git a/ms2deepscore/train_new_model/TrainingBatchGenerator.py b/ms2deepscore/train_new_model/TrainingBatchGenerator.py
index 3a4ed47b..6ee8a347 100644
--- a/ms2deepscore/train_new_model/TrainingBatchGenerator.py
+++ b/ms2deepscore/train_new_model/TrainingBatchGenerator.py
@@ -4,7 +4,7 @@
 import torch
 from ms2deepscore.SettingsMS2Deepscore import (SettingsMS2Deepscore)
 from ms2deepscore.tensorize_spectra import tensorize_spectra
-from ms2deepscore.train_new_model.InchikeyPairGenerator import InchikeyPairGenerator
+from ms2deepscore.train_new_model.SpectrumPairGenerator import SpectrumPairGenerator
 from ms2deepscore.train_new_model.data_augmentation import data_augmentation
 from ms2deepscore.train_new_model.inchikey_pair_selection import (
     select_compound_pairs_wrapper)
@@ -14,18 +14,18 @@ class TrainingBatchGenerator:
     """Generates data for training a siamese Pytorch model.
 
     This class provides a data generator specifically designed for training a Siamese Pytorch model with a curated set
-    of compound pairs. It takes a InchikeyPairGenerator and randomly selects, augments and tensorizes spectra for each
-    inchikey pair.
+    of compound pairs. It takes a SpectrumPairGenerator and augments and tensorizes spectra and combines them into
+    batches.
 
-    By using pre-selected compound pairs (in the InchikeyPairGenerator), this allows more control over the training
-    process. The selection of inchikey pairs does not happen in TrainingBatchGenerator (only spectrum selection), but in
-    inchikey_pair_selection.py. In inchikey_pair_selection inchikey pairs are picked to balance selected pairs equally
+    By using pre-selected compound pairs (in the SpectrumPairGenerator), this allows more control over the training
+    process. The selection of inchikey pairs does not happen in SpectrumPairGenerator, but in
+    inchikey_pair_selection.py. In inchikey_pair_selection.py inchikey pairs are picked to balance selected pairs equally
     over different tanimoto score bins to make sure both pairs of similar and dissimilar compounds are sampled.
     In addition inchikeys are selected to occur equally for each pair.
     """
 
     def __init__(self,
-                 selected_compound_pairs: InchikeyPairGenerator,
+                 selected_compound_pairs: SpectrumPairGenerator,
                  settings: SettingsMS2Deepscore):
         """Generates data for training a siamese Pytorch model.
 
@@ -126,7 +126,7 @@ def create_data_generator(training_spectra,
     # pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
 
     selected_compound_pairs_training = select_compound_pairs_wrapper(training_spectra, settings=settings)
-    inchikey_pair_generator = InchikeyPairGenerator(selected_compound_pairs_training, training_spectra)
+    inchikey_pair_generator = SpectrumPairGenerator(selected_compound_pairs_training, training_spectra)
 
     if json_save_file is not None:
         inchikey_pair_generator.save_as_json(json_save_file)
diff --git a/ms2deepscore/train_new_model/__init__.py b/ms2deepscore/train_new_model/__init__.py
index 858dc12c..f31333e0 100644
--- a/ms2deepscore/train_new_model/__init__.py
+++ b/ms2deepscore/train_new_model/__init__.py
@@ -1,5 +1,5 @@
 from .TrainingBatchGenerator import TrainingBatchGenerator
-from .InchikeyPairGenerator import InchikeyPairGenerator
+from .SpectrumPairGenerator import SpectrumPairGenerator
 from .inchikey_pair_selection import (select_compound_pairs_wrapper)
 
 
diff --git a/ms2deepscore/train_new_model/inchikey_pair_selection.py b/ms2deepscore/train_new_model/inchikey_pair_selection.py
index 1042606d..68d95fc8 100644
--- a/ms2deepscore/train_new_model/inchikey_pair_selection.py
+++ b/ms2deepscore/train_new_model/inchikey_pair_selection.py
@@ -14,7 +14,7 @@ def select_compound_pairs_wrapper(
         spectra: List[Spectrum],
         settings: SettingsMS2Deepscore,
 ) -> List[Tuple[str, str, float]]:
-    """Returns a InchikeyPairGenerator object containing equally balanced pairs over the different bins
+    """Returns a SpectrumPairGenerator object containing equally balanced pairs over the different bins
 
     spectra:
         A list of spectra
@@ -24,8 +24,8 @@ def select_compound_pairs_wrapper(
 
     Returns
     -------
-    InchikeyPairGenerator
-        InchikeyPairGenerator containing balanced pairs. The pairs are stored as [(inchikey1, inchikey2, score)]
+    SpectrumPairGenerator
+        SpectrumPairGenerator containing balanced pairs. The pairs are stored as [(inchikey1, inchikey2, score)]
     """
     if settings.random_seed is not None:
         np.random.seed(settings.random_seed)
diff --git a/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py b/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py
index 2c4a14a2..287c5a9c 100644
--- a/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py
+++ b/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py
@@ -12,7 +12,7 @@ def select_compound_pairs_wrapper_across_ionmode(
         spectra_2: List[Spectrum],
         settings: SettingsMS2Deepscore,
 ) -> List[Tuple[str, str, float]]:
-    """Returns a InchikeyPairGenerator object containing equally balanced pairs over the different bins
+    """Returns a SpectrumPairGenerator object containing equally balanced pairs over the different bins
 
     spectra:
         A list of spectra
@@ -22,8 +22,8 @@ def select_compound_pairs_wrapper_across_ionmode(
 
     Returns
     -------
-    InchikeyPairGenerator
-        InchikeyPairGenerator containing balanced pairs. The pairs are stored as [(inchikey1, inchikey2, score)]
+    SpectrumPairGenerator
+        SpectrumPairGenerator containing balanced pairs. The pairs are stored as [(inchikey1, inchikey2, score)]
     """
     if settings.random_seed is not None:
         np.random.seed(settings.random_seed)
diff --git a/tests/test_data_generators.py b/tests/test_data_generators.py
index 0a8e88b2..beeeb013 100644
--- a/tests/test_data_generators.py
+++ b/tests/test_data_generators.py
@@ -8,7 +8,7 @@
 from ms2deepscore.train_new_model.TrainingBatchGenerator import TrainingBatchGenerator, \
     create_data_generator
 from ms2deepscore.train_new_model.DataGeneratorEmbeddingEvaluation import DataGeneratorEmbeddingEvaluation
-from ms2deepscore.train_new_model import InchikeyPairGenerator
+from ms2deepscore.train_new_model import SpectrumPairGenerator
 from tests.create_test_spectra import create_test_spectra
 
 
@@ -63,7 +63,7 @@ def test_tensorize_spectra():
 @pytest.fixture()
 def dummy_data_generator():
     spectrums = create_test_spectra(4, 3)
-    selected_pairs = InchikeyPairGenerator([('CCCCCCCCCCCCCC', 'DDDDDDDDDDDDDD', 0.25),
+    selected_pairs = SpectrumPairGenerator([('CCCCCCCCCCCCCC', 'DDDDDDDDDDDDDD', 0.25),
                                             ('BBBBBBBBBBBBBB', 'DDDDDDDDDDDDDD', 0.6666667),
                                             ('AAAAAAAAAAAAAA', 'CCCCCCCCCCCCCC', 1.0),
                                             ('AAAAAAAAAAAAAA', 'BBBBBBBBBBBBBB', 0.33333334)],
diff --git a/tests/test_inchikey_pair_selection.py b/tests/test_inchikey_pair_selection.py
index d6afd22f..e0dcac7b 100644
--- a/tests/test_inchikey_pair_selection.py
+++ b/tests/test_inchikey_pair_selection.py
@@ -8,7 +8,7 @@
 from ms2deepscore import SettingsMS2Deepscore
 from ms2deepscore.train_new_model.inchikey_pair_selection import (
     compute_jaccard_similarity_per_bin, select_inchi_for_unique_inchikeys, select_compound_pairs_wrapper, compute_fingerprints_for_training)
-from ms2deepscore.train_new_model import InchikeyPairGenerator
+from ms2deepscore.train_new_model import SpectrumPairGenerator
 from tests.create_test_spectra import create_test_spectra
 
 
@@ -63,7 +63,7 @@ def dummy_inchikey_pair_generator():
                       ("Inchikey0", "Inchikey2", 0.6),
                       ("Inchikey2", "Inchikey1", 0.3),
                       ("Inchikey2", "Inchikey2", 1.0)]
-    return InchikeyPairGenerator(spectrum_pairs, [
+    return SpectrumPairGenerator(spectrum_pairs, [
         Spectrum(mz=np.array([90.]), intensities=np.array([0.4]), metadata={"inchikey": "Inchikey0"}),
         Spectrum(mz=np.array([90.]), intensities=np.array([0.4]), metadata={"inchikey": "Inchikey1"}),
         Spectrum(mz=np.array([90.]), intensities=np.array([0.4]), metadata={"inchikey": "Inchikey2"}),
@@ -190,7 +190,7 @@ def test_select_compound_pairs_wrapper_no_resampling():
                                     batch_size=8,
                                     max_pair_resampling=max_pair_resampling)
     selected_inchikey_pairs = select_compound_pairs_wrapper(spectrums, settings)
-    inchikey_pair_generator = InchikeyPairGenerator(selected_inchikey_pairs, spectrums)
+    inchikey_pair_generator = SpectrumPairGenerator(selected_inchikey_pairs, spectrums)
 
     check_balanced_scores_selecting_inchikey_pairs(inchikey_pair_generator, bins)
     check_correct_oversampling(inchikey_pair_generator, max_pair_resampling)
@@ -211,7 +211,7 @@ def test_select_compound_pairs_wrapper_with_resampling():
                                     batch_size=8,
                                     max_pair_resampling=max_pair_resampling)
     selected_inchikey_pairs = select_compound_pairs_wrapper(spectrums, settings)
-    inchikey_pair_generator = InchikeyPairGenerator(selected_inchikey_pairs, spectrums)
+    inchikey_pair_generator = SpectrumPairGenerator(selected_inchikey_pairs, spectrums)
 
     check_balanced_scores_selecting_inchikey_pairs(inchikey_pair_generator, bins)
     check_correct_oversampling(inchikey_pair_generator, max_pair_resampling)
@@ -234,13 +234,13 @@ def test_select_compound_pairs_wrapper_maximum_inchikey_count():
                                     max_inchikey_sampling=max_inchikey_sampling
                                     )
     selected_inchikey_pairs = select_compound_pairs_wrapper(spectrums, settings)
-    inchikey_pair_generator = InchikeyPairGenerator(selected_inchikey_pairs, spectrums)
+    inchikey_pair_generator = SpectrumPairGenerator(selected_inchikey_pairs, spectrums)
 
     highest_inchikey_count = max(inchikey_pair_generator.get_inchikey_counts().values())
     assert highest_inchikey_count <= max_inchikey_sampling + 1 # +1 because there is a chance that the last added inchikey is a pair to itself...
 
 
-def check_correct_oversampling(selected_inchikey_pairs: InchikeyPairGenerator, max_resampling: int):
+def check_correct_oversampling(selected_inchikey_pairs: SpectrumPairGenerator, max_resampling: int):
     pair_counts = Counter(selected_inchikey_pairs.selected_inchikey_pairs)
     for count in pair_counts.values():
         assert count <= max_resampling, "the resampling was done too frequently"
@@ -265,7 +265,7 @@ def get_available_score_distribution(settings, spectra):
     return score_distribution_per_inchikey
 
 
-def print_balanced_bins_per_inchikey(selected_inchikey_pairs: InchikeyPairGenerator, settings, spectra):
+def print_balanced_bins_per_inchikey(selected_inchikey_pairs: SpectrumPairGenerator, settings, spectra):
     """Prints the available distribution and the balanced distribution
 
     Currently doesn't do any checks, because it is hard to check if the wanted behaviour is achieved,
@@ -288,9 +288,9 @@ def print_balanced_bins_per_inchikey(selected_inchikey_pairs: InchikeyPairGenera
             # assert minimum_available_distribution*settings.max_pair_resampling == min(balanced_distribution)
 
 
-def check_balanced_scores_selecting_inchikey_pairs(selected_inchikey_pairs: InchikeyPairGenerator,
+def check_balanced_scores_selecting_inchikey_pairs(selected_inchikey_pairs: SpectrumPairGenerator,
                                                    score_bins):
-    """Test if InchikeyPairGenerator has an equal inchikey distribution
+    """Test if SpectrumPairGenerator has an equal inchikey distribution
     """
     scores = selected_inchikey_pairs.get_scores()
     # converting to float32 is required, since the scores are float32, otherwise equal numbers are seen as not equal
diff --git a/tests/test_siamese_spectra_model.py b/tests/test_siamese_spectra_model.py
index fc1f4a83..2578b880 100644
--- a/tests/test_siamese_spectra_model.py
+++ b/tests/test_siamese_spectra_model.py
@@ -6,7 +6,7 @@
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
 from ms2deepscore.tensorize_spectra import tensorize_spectra
 from ms2deepscore.train_new_model.TrainingBatchGenerator import TrainingBatchGenerator
-from ms2deepscore.train_new_model import InchikeyPairGenerator
+from ms2deepscore.train_new_model import SpectrumPairGenerator
 from ms2deepscore.train_new_model.inchikey_pair_selection import \
     select_compound_pairs_wrapper
 from ms2deepscore.validation_loss_calculation.ValidationLossCalculator import \
@@ -132,7 +132,7 @@ def test_model_training(simple_training_spectra):
                                     num_turns=20,
                                     )
     scp_train = select_compound_pairs_wrapper(simple_training_spectra, settings)
-    inchikey_pair_generator = InchikeyPairGenerator(scp_train, simple_training_spectra)
+    inchikey_pair_generator = SpectrumPairGenerator(scp_train, simple_training_spectra)
     # Create generators
     train_generator_simple = TrainingBatchGenerator(selected_compound_pairs=inchikey_pair_generator,
                                                     settings=settings)

From 417e890eba41afc486933847c1fa0bd27ec57562 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 13:44:09 +0200
Subject: [PATCH 23/48] Make SpectrumPairGenerator a real generator

---
 .../train_new_model/SpectrumPairGenerator.py  | 33 ++++++++------
 .../train_new_model/TrainingBatchGenerator.py |  7 +--
 tests/test_data_generators.py                 |  2 +-
 tests/test_inchikey_pair_selection.py         | 43 +++++++++----------
 4 files changed, 46 insertions(+), 39 deletions(-)

diff --git a/ms2deepscore/train_new_model/SpectrumPairGenerator.py b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
index 3539505a..4246233f 100644
--- a/ms2deepscore/train_new_model/SpectrumPairGenerator.py
+++ b/ms2deepscore/train_new_model/SpectrumPairGenerator.py
@@ -7,7 +7,8 @@
 
 
 class SpectrumPairGenerator:
-    def __init__(self, selected_inchikey_pairs: List[Tuple[str, str, float]], spectra):
+    def __init__(self, selected_inchikey_pairs: List[Tuple[str, str, float]], spectra,
+                 shuffle: bool = True, random_seed: int = 0):
         """
         Parameters
         ----------
@@ -17,19 +18,27 @@ def __init__(self, selected_inchikey_pairs: List[Tuple[str, str, float]], spectr
         self.selected_inchikey_pairs = selected_inchikey_pairs
         self.spectra = spectra
         self.spectrum_inchikeys = np.array([s.get("inchikey")[:14] for s in self.spectra])
+        self.shuffle = shuffle
+        self.random_nr_generator = np.random.default_rng(random_seed)
+        self._idx = 0
+        if self.shuffle:
+            self.random_nr_generator.shuffle(self.selected_inchikey_pairs)
 
-    def generator(self, shuffle: bool, random_nr_generator):
-        """Infinite generator to loop through all inchikeys.
-        After looping through all inchikeys the order is shuffled.
-        """
-        while True:
-            if shuffle:
-                random_nr_generator.shuffle(self.selected_inchikey_pairs)
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        # reshuffle when we've gone through everything
+        if self._idx >= len(self.selected_inchikey_pairs):
+            self._idx = 0
+            if self.shuffle:
+                self.random_nr_generator.shuffle(self.selected_inchikey_pairs)
 
-            for inchikey1, inchikey2, tanimoto_score in self.selected_inchikey_pairs:
-                spectrum1 = self._get_spectrum_with_inchikey(inchikey1, random_nr_generator)
-                spectrum2 = self._get_spectrum_with_inchikey(inchikey2, random_nr_generator)
-                yield spectrum1, spectrum2, tanimoto_score
+        inchikey1, inchikey2, tanimoto_score = self.selected_inchikey_pairs[self._idx]
+        spectrum1 = self._get_spectrum_with_inchikey(inchikey1, self.random_nr_generator)
+        spectrum2 = self._get_spectrum_with_inchikey(inchikey2, self.random_nr_generator)
+        self._idx += 1
+        return spectrum1, spectrum2, tanimoto_score
 
     def __len__(self):
         return len(self.selected_inchikey_pairs)
diff --git a/ms2deepscore/train_new_model/TrainingBatchGenerator.py b/ms2deepscore/train_new_model/TrainingBatchGenerator.py
index 6ee8a347..93aee9b8 100644
--- a/ms2deepscore/train_new_model/TrainingBatchGenerator.py
+++ b/ms2deepscore/train_new_model/TrainingBatchGenerator.py
@@ -50,7 +50,7 @@ def __init__(self,
             if self.model_settings.random_seed is None:
                 self.model_settings.random_seed = 0
         self.rng = np.random.default_rng(self.model_settings.random_seed)
-        self.inchikey_pair_generator = selected_compound_pairs.generator(self.model_settings.shuffle, self.rng)
+        self.inchikey_pair_generator = selected_compound_pairs
         unique_inchikeys = np.unique(selected_compound_pairs.spectrum_inchikeys)
         if len(unique_inchikeys) < self.model_settings.batch_size:
             raise ValueError("The number of unique inchikeys must be larger than the batch size.")
@@ -120,13 +120,14 @@ def _tensorize_all(self, spectrum_pairs):
 
 
 def create_data_generator(training_spectra,
-                          settings,
+                          settings: SettingsMS2Deepscore,
                           json_save_file=None) -> TrainingBatchGenerator:
     # todo actually create, both between and across ionmodes.
     # pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
 
     selected_compound_pairs_training = select_compound_pairs_wrapper(training_spectra, settings=settings)
-    inchikey_pair_generator = SpectrumPairGenerator(selected_compound_pairs_training, training_spectra)
+    inchikey_pair_generator = SpectrumPairGenerator(selected_compound_pairs_training, training_spectra,
+                                                    settings.shuffle, settings.random_seed)
 
     if json_save_file is not None:
         inchikey_pair_generator.save_as_json(json_save_file)
diff --git a/tests/test_data_generators.py b/tests/test_data_generators.py
index beeeb013..192378c5 100644
--- a/tests/test_data_generators.py
+++ b/tests/test_data_generators.py
@@ -67,7 +67,7 @@ def dummy_data_generator():
                                             ('BBBBBBBBBBBBBB', 'DDDDDDDDDDDDDD', 0.6666667),
                                             ('AAAAAAAAAAAAAA', 'CCCCCCCCCCCCCC', 1.0),
                                             ('AAAAAAAAAAAAAA', 'BBBBBBBBBBBBBB', 0.33333334)],
-                                           spectrums)
+                                           spectrums, True, 0)
     batch_size = 2
     settings = SettingsMS2Deepscore(min_mz=10,
                                     max_mz=1000,
diff --git a/tests/test_inchikey_pair_selection.py b/tests/test_inchikey_pair_selection.py
index e0dcac7b..cb1bd944 100644
--- a/tests/test_inchikey_pair_selection.py
+++ b/tests/test_inchikey_pair_selection.py
@@ -57,19 +57,6 @@ def test_spectra():
     return [spectrum_1, spectrum_2, spectrum_3, spectrum_4]
 
 
-@pytest.fixture
-def dummy_inchikey_pair_generator():
-    spectrum_pairs = [("Inchikey0", "Inchikey1", 0.8),
-                      ("Inchikey0", "Inchikey2", 0.6),
-                      ("Inchikey2", "Inchikey1", 0.3),
-                      ("Inchikey2", "Inchikey2", 1.0)]
-    return SpectrumPairGenerator(spectrum_pairs, [
-        Spectrum(mz=np.array([90.]), intensities=np.array([0.4]), metadata={"inchikey": "Inchikey0"}),
-        Spectrum(mz=np.array([90.]), intensities=np.array([0.4]), metadata={"inchikey": "Inchikey1"}),
-        Spectrum(mz=np.array([90.]), intensities=np.array([0.4]), metadata={"inchikey": "Inchikey2"}),
-    ])
-
-
 def test_compute_jaccard_similarity_per_bin(simple_fingerprints):
     max_pairs_per_bin = 5
     nr_of_bins = 10
@@ -150,14 +137,18 @@ def test_select_inchi_for_unique_inchikeys_two_inchikeys(test_spectra):
     assert [s.get("inchi")[:15] for s in spectrums_selected] == ['InChI=1/C6H8O6/', 'InChI=1S/C8H10N']
 
 
-def test_SelectedInchikeyPairs_generator_with_shuffle(dummy_inchikey_pair_generator):
-    rng = np.random.default_rng(0)
-    gen = dummy_inchikey_pair_generator.generator(True, rng)
-
+def test_SelectedInchikeyPairs_generator_with_shuffle():
+    dummy_inchikey_pair_generator = SpectrumPairGenerator(        [
+        ("Inchikey0", "Inchikey1", 0.8), ("Inchikey0", "Inchikey2", 0.6),
+        ("Inchikey2", "Inchikey1", 0.3), ("Inchikey2", "Inchikey2", 1.0)], [
+        Spectrum(mz=np.array([90.]), intensities=np.array([0.4]), metadata={"inchikey": "Inchikey0"}),
+        Spectrum(mz=np.array([90.]), intensities=np.array([0.4]), metadata={"inchikey": "Inchikey1"}),
+        Spectrum(mz=np.array([90.]), intensities=np.array([0.4]), metadata={"inchikey": "Inchikey2"}),],
+        True, 0)
     found_pairs = []
     # do one complete loop
     for i in range(len(dummy_inchikey_pair_generator)):
-        spectrum_1, spectrum_2, score = next(gen)
+        spectrum_1, spectrum_2, score = next(dummy_inchikey_pair_generator)
         found_pairs.append((spectrum_1.get("inchikey"), spectrum_2.get("inchikey"), score))
 
     assert len(found_pairs) == len(dummy_inchikey_pair_generator.selected_inchikey_pairs)
@@ -166,18 +157,24 @@ def test_SelectedInchikeyPairs_generator_with_shuffle(dummy_inchikey_pair_genera
     found_pairs = []
     # do one complete loop
     for i in range(len(dummy_inchikey_pair_generator)):
-        spectrum_1, spectrum_2, score = next(gen)
+        spectrum_1, spectrum_2, score = next(dummy_inchikey_pair_generator)
         found_pairs.append((spectrum_1.get("inchikey"), spectrum_2.get("inchikey"), score))
 
     assert len(found_pairs) == len(dummy_inchikey_pair_generator.selected_inchikey_pairs)
     assert sorted(found_pairs) == sorted(dummy_inchikey_pair_generator.selected_inchikey_pairs)
 
 
-def test_SelectedInchikeyPairs_generator_without_shuffle(dummy_inchikey_pair_generator):
-    gen = dummy_inchikey_pair_generator.generator(False, np.random.default_rng(0))
+def test_SelectedInchikeyPairs_generator_without_shuffle():
+    dummy_inchikey_pair_generator = SpectrumPairGenerator(        [
+        ("Inchikey0", "Inchikey1", 0.8), ("Inchikey0", "Inchikey2", 0.6),
+        ("Inchikey2", "Inchikey1", 0.3), ("Inchikey2", "Inchikey2", 1.0)], [
+        Spectrum(mz=np.array([90.]), intensities=np.array([0.4]), metadata={"inchikey": "Inchikey0"}),
+        Spectrum(mz=np.array([90.]), intensities=np.array([0.4]), metadata={"inchikey": "Inchikey1"}),
+        Spectrum(mz=np.array([90.]), intensities=np.array([0.4]), metadata={"inchikey": "Inchikey2"}),],
+        True,  0)
 
     for _, expected_pair in enumerate(dummy_inchikey_pair_generator.selected_inchikey_pairs):
-        spectrum_1, spectrum_2, score = next(gen)
+        spectrum_1, spectrum_2, score = next(dummy_inchikey_pair_generator)
         assert expected_pair == (spectrum_1.get("inchikey"), spectrum_2.get("inchikey"), score)
 
 
@@ -190,7 +187,7 @@ def test_select_compound_pairs_wrapper_no_resampling():
                                     batch_size=8,
                                     max_pair_resampling=max_pair_resampling)
     selected_inchikey_pairs = select_compound_pairs_wrapper(spectrums, settings)
-    inchikey_pair_generator = SpectrumPairGenerator(selected_inchikey_pairs, spectrums)
+    inchikey_pair_generator = SpectrumPairGenerator(selected_inchikey_pairs, spectrums, True, 0)
 
     check_balanced_scores_selecting_inchikey_pairs(inchikey_pair_generator, bins)
     check_correct_oversampling(inchikey_pair_generator, max_pair_resampling)

From 1eaae2383e13901a502f8995e9ff0a5856e24e1f Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 13:45:26 +0200
Subject: [PATCH 24/48] Rename self.spectrum_pair_generator in
 TrainingBatchGenerator

---
 .../train_new_model/TrainingBatchGenerator.py       | 13 ++++++-------
 tests/test_siamese_spectra_model.py                 |  3 +--
 2 files changed, 7 insertions(+), 9 deletions(-)

diff --git a/ms2deepscore/train_new_model/TrainingBatchGenerator.py b/ms2deepscore/train_new_model/TrainingBatchGenerator.py
index 93aee9b8..fc8e518a 100644
--- a/ms2deepscore/train_new_model/TrainingBatchGenerator.py
+++ b/ms2deepscore/train_new_model/TrainingBatchGenerator.py
@@ -25,13 +25,13 @@ class TrainingBatchGenerator:
     """
 
     def __init__(self,
-                 selected_compound_pairs: SpectrumPairGenerator,
+                 spectrum_pair_generator: SpectrumPairGenerator,
                  settings: SettingsMS2Deepscore):
         """Generates data for training a siamese Pytorch model.
 
         Parameters
         ----------
-        selected_compound_pairs
+        spectrum_pair_generator
             SelectedCompoundPairs object which contains selected compounds pairs and the
             respective similarity scores.
         settings
@@ -50,8 +50,8 @@ def __init__(self,
             if self.model_settings.random_seed is None:
                 self.model_settings.random_seed = 0
         self.rng = np.random.default_rng(self.model_settings.random_seed)
-        self.inchikey_pair_generator = selected_compound_pairs
-        unique_inchikeys = np.unique(selected_compound_pairs.spectrum_inchikeys)
+        self.spectrum_pair_generator = spectrum_pair_generator
+        unique_inchikeys = np.unique(spectrum_pair_generator.spectrum_inchikeys)
         if len(unique_inchikeys) < self.model_settings.batch_size:
             raise ValueError("The number of unique inchikeys must be larger than the batch size.")
         self.fixed_set = {}
@@ -77,7 +77,7 @@ def _spectrum_pair_generator(self):
         """Use the provided SelectedCompoundPairs object to pick pairs."""
         for _ in range(self.model_settings.batch_size):
             try:
-                spectrum1, spectrum2, score = next(self.inchikey_pair_generator)
+                spectrum1, spectrum2, score = next(self.spectrum_pair_generator)
                 yield spectrum1, spectrum2, score
             except StopIteration as exc:
                 raise RuntimeError("The inchikey pair generator is not expected to end, "
@@ -134,6 +134,5 @@ def create_data_generator(training_spectra,
     # todo possibly create a single TrainingBatchGenerator which takes in 3 generators and pos and neg spectra to iteratively select each one.
     # Create generators
     # todo also make sure that the TrainingBatchGenerator can work across ionmodes.
-    train_generator = TrainingBatchGenerator(selected_compound_pairs=inchikey_pair_generator,
-                                             settings=settings)
+    train_generator = TrainingBatchGenerator(spectrum_pair_generator=inchikey_pair_generator, settings=settings)
     return train_generator
diff --git a/tests/test_siamese_spectra_model.py b/tests/test_siamese_spectra_model.py
index 2578b880..2036b193 100644
--- a/tests/test_siamese_spectra_model.py
+++ b/tests/test_siamese_spectra_model.py
@@ -134,8 +134,7 @@ def test_model_training(simple_training_spectra):
     scp_train = select_compound_pairs_wrapper(simple_training_spectra, settings)
     inchikey_pair_generator = SpectrumPairGenerator(scp_train, simple_training_spectra)
     # Create generators
-    train_generator_simple = TrainingBatchGenerator(selected_compound_pairs=inchikey_pair_generator,
-                                                    settings=settings)
+    train_generator_simple = TrainingBatchGenerator(spectrum_pair_generator=inchikey_pair_generator, settings=settings)
     settings.same_prob_bins = np.array([(-0.01, 1.0)])
     validation_loss_calculator = ValidationLossCalculator(
         simple_training_spectra,

From b514b1154232e980066b45af6a28bd5dfe05bce7 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 13:57:34 +0200
Subject: [PATCH 25/48] Move create data generator to train_ms2deepscore.py

---
 .../train_new_model/TrainingBatchGenerator.py | 21 -----------------
 .../train_new_model/train_ms2deepscore.py     | 23 ++++++++++++++++++-
 .../training_wrapper_functions.py             |  3 +--
 tests/test_data_generators.py                 |  4 ++--
 4 files changed, 25 insertions(+), 26 deletions(-)

diff --git a/ms2deepscore/train_new_model/TrainingBatchGenerator.py b/ms2deepscore/train_new_model/TrainingBatchGenerator.py
index fc8e518a..8df11390 100644
--- a/ms2deepscore/train_new_model/TrainingBatchGenerator.py
+++ b/ms2deepscore/train_new_model/TrainingBatchGenerator.py
@@ -6,8 +6,6 @@
 from ms2deepscore.tensorize_spectra import tensorize_spectra
 from ms2deepscore.train_new_model.SpectrumPairGenerator import SpectrumPairGenerator
 from ms2deepscore.train_new_model.data_augmentation import data_augmentation
-from ms2deepscore.train_new_model.inchikey_pair_selection import (
-    select_compound_pairs_wrapper)
 
 
 class TrainingBatchGenerator:
@@ -117,22 +115,3 @@ def _tensorize_all(self, spectrum_pairs):
         binned_spectra_1, metadata_1 = tensorize_spectra(spectra_1, self.model_settings)
         binned_spectra_2, metadata_2 = tensorize_spectra(spectra_2, self.model_settings)
         return binned_spectra_1, binned_spectra_2, metadata_1, metadata_2, torch.tensor(targets, dtype=torch.float32)
-
-
-def create_data_generator(training_spectra,
-                          settings: SettingsMS2Deepscore,
-                          json_save_file=None) -> TrainingBatchGenerator:
-    # todo actually create, both between and across ionmodes.
-    # pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
-
-    selected_compound_pairs_training = select_compound_pairs_wrapper(training_spectra, settings=settings)
-    inchikey_pair_generator = SpectrumPairGenerator(selected_compound_pairs_training, training_spectra,
-                                                    settings.shuffle, settings.random_seed)
-
-    if json_save_file is not None:
-        inchikey_pair_generator.save_as_json(json_save_file)
-    # todo possibly create a single TrainingBatchGenerator which takes in 3 generators and pos and neg spectra to iteratively select each one.
-    # Create generators
-    # todo also make sure that the TrainingBatchGenerator can work across ionmodes.
-    train_generator = TrainingBatchGenerator(spectrum_pair_generator=inchikey_pair_generator, settings=settings)
-    return train_generator
diff --git a/ms2deepscore/train_new_model/train_ms2deepscore.py b/ms2deepscore/train_new_model/train_ms2deepscore.py
index 2f03896f..fbf43473 100644
--- a/ms2deepscore/train_new_model/train_ms2deepscore.py
+++ b/ms2deepscore/train_new_model/train_ms2deepscore.py
@@ -7,10 +7,12 @@
 
 import numpy as np
 from matplotlib import pyplot as plt
+
+from ms2deepscore import SettingsMS2Deepscore
 from ms2deepscore.models.SiameseSpectralModel import (SiameseSpectralModel,
                                                       train)
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
-from ms2deepscore.train_new_model.TrainingBatchGenerator import create_data_generator
+from ms2deepscore.train_new_model import TrainingBatchGenerator, select_compound_pairs_wrapper, SpectrumPairGenerator
 from ms2deepscore.validation_loss_calculation.ValidationLossCalculator import \
     ValidationLossCalculator
 
@@ -53,6 +55,25 @@ def train_ms2ds_model(
     return model, history
 
 
+def create_data_generator(training_spectra,
+                          settings: SettingsMS2Deepscore,
+                          json_save_file=None) -> TrainingBatchGenerator:
+    # todo actually create, both between and across ionmodes.
+    # pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
+
+    selected_compound_pairs_training = select_compound_pairs_wrapper(training_spectra, settings=settings)
+    inchikey_pair_generator = SpectrumPairGenerator(selected_compound_pairs_training, training_spectra,
+                                                    settings.shuffle, settings.random_seed)
+
+    if json_save_file is not None:
+        inchikey_pair_generator.save_as_json(json_save_file)
+    # todo possibly create a single TrainingBatchGenerator which takes in 3 generators and pos and neg spectra to iteratively select each one.
+    # Create generators
+    # todo also make sure that the TrainingBatchGenerator can work across ionmodes.
+    train_generator = TrainingBatchGenerator(spectrum_pair_generator=inchikey_pair_generator, settings=settings)
+    return train_generator
+
+
 def plot_history(losses, val_losses, file_name: Optional[str] = None):
     plt.plot(losses)
     plt.plot(val_losses)
diff --git a/ms2deepscore/wrapper_functions/training_wrapper_functions.py b/ms2deepscore/wrapper_functions/training_wrapper_functions.py
index f6efa957..4f57b60a 100644
--- a/ms2deepscore/wrapper_functions/training_wrapper_functions.py
+++ b/ms2deepscore/wrapper_functions/training_wrapper_functions.py
@@ -14,9 +14,8 @@
                                                       train)
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
 from ms2deepscore.validation_loss_calculation.ValidationLossCalculator import ValidationLossCalculator
-from ms2deepscore.train_new_model.TrainingBatchGenerator import create_data_generator
 from ms2deepscore.train_new_model.train_ms2deepscore import \
-    train_ms2ds_model, plot_history, save_history
+    train_ms2ds_model, plot_history, save_history, create_data_generator
 from ms2deepscore.train_new_model.validation_and_test_split import \
     split_spectra_in_random_inchikey_sets
 from ms2deepscore.utils import load_spectra_as_list
diff --git a/tests/test_data_generators.py b/tests/test_data_generators.py
index 192378c5..faf540ee 100644
--- a/tests/test_data_generators.py
+++ b/tests/test_data_generators.py
@@ -5,8 +5,8 @@
 from matchms import Spectrum
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore, SettingsEmbeddingEvaluator
 from ms2deepscore.tensorize_spectra import tensorize_spectra
-from ms2deepscore.train_new_model.TrainingBatchGenerator import TrainingBatchGenerator, \
-    create_data_generator
+from ms2deepscore.train_new_model.TrainingBatchGenerator import TrainingBatchGenerator
+from ms2deepscore.train_new_model.train_ms2deepscore import create_data_generator
 from ms2deepscore.train_new_model.DataGeneratorEmbeddingEvaluation import DataGeneratorEmbeddingEvaluation
 from ms2deepscore.train_new_model import SpectrumPairGenerator
 from tests.create_test_spectra import create_test_spectra

From ad9425df83520849b6a206517f04510d80ab927b Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 14:29:55 +0200
Subject: [PATCH 26/48] Directly return a SpectrumPairGenerator instead of list
 of pairs from the inchikey_pair_selection wrapper

---
 .../inchikey_pair_selection.py                |  6 ++--
 .../train_new_model/train_ms2deepscore.py     | 32 ++++++++++++-------
 tests/test_inchikey_pair_selection.py         |  9 ++----
 tests/test_siamese_spectra_model.py           |  3 +-
 4 files changed, 28 insertions(+), 22 deletions(-)

diff --git a/ms2deepscore/train_new_model/inchikey_pair_selection.py b/ms2deepscore/train_new_model/inchikey_pair_selection.py
index 68d95fc8..cc153b86 100644
--- a/ms2deepscore/train_new_model/inchikey_pair_selection.py
+++ b/ms2deepscore/train_new_model/inchikey_pair_selection.py
@@ -8,12 +8,13 @@
 from numba import jit, prange
 from tqdm import tqdm
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
+from ms2deepscore.train_new_model import SpectrumPairGenerator
 
 
 def select_compound_pairs_wrapper(
         spectra: List[Spectrum],
         settings: SettingsMS2Deepscore,
-) -> List[Tuple[str, str, float]]:
+) -> SpectrumPairGenerator:
     """Returns a SpectrumPairGenerator object containing equally balanced pairs over the different bins
 
     spectra:
@@ -53,7 +54,8 @@ def select_compound_pairs_wrapper(
         pair_frequency_matrixes, available_pairs_per_bin_matrix,
         available_scores_per_bin_matrix, inchikeys14_unique)
 
-    return [pair for pairs in selected_pairs_per_bin for pair in pairs]
+    return SpectrumPairGenerator([pair for pairs in selected_pairs_per_bin for pair in pairs],
+                                 spectra, settings.shuffle, settings.random_seed)
 
 
 def compute_fingerprints_for_training(
diff --git a/ms2deepscore/train_new_model/train_ms2deepscore.py b/ms2deepscore/train_new_model/train_ms2deepscore.py
index fbf43473..8107a9e8 100644
--- a/ms2deepscore/train_new_model/train_ms2deepscore.py
+++ b/ms2deepscore/train_new_model/train_ms2deepscore.py
@@ -8,7 +8,6 @@
 import numpy as np
 from matplotlib import pyplot as plt
 
-from ms2deepscore import SettingsMS2Deepscore
 from ms2deepscore.models.SiameseSpectralModel import (SiameseSpectralModel,
                                                       train)
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
@@ -54,23 +53,32 @@ def train_ms2ds_model(
                     checkpoint_filename=output_model_file_name, lambda_l1=0, lambda_l2=0)
     return model, history
 
+# def create_data_generator_across_ionmodes(training_spectra,
+#                           settings: SettingsMS2Deepscore,
+#                           json_save_file=None) -> TrainingBatchGenerator:
+#     # todo actually create, both between and across ionmodes.
+#     pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
+#
+#     pos_spectrum_pair_generator = select_compound_pairs_wrapper(pos_spectra, settings=settings)
+#     neg_spectrum_pair_generator = select_compound_pairs_wrapper(neg_spectra, settings=settings)
+#     pos_neg_spectrum_pair_generator = select_compound_pairs_wrapper_across_ionmode(pos_spectra, neg_spectra, settings)
+#
+#     if json_save_file is not None:
+#         inchikey_pair_generator.save_as_json(json_save_file)
+#     # todo possibly create a single TrainingBatchGenerator which takes in 3 generators and pos and neg spectra to iteratively select each one.
+#     # Create generators
+#     # todo also make sure that the TrainingBatchGenerator can work across ionmodes.
+#     train_generator = TrainingBatchGenerator(spectrum_pair_generator=inchikey_pair_generator, settings=settings)
+#     return train_generator
 
 def create_data_generator(training_spectra,
                           settings: SettingsMS2Deepscore,
                           json_save_file=None) -> TrainingBatchGenerator:
-    # todo actually create, both between and across ionmodes.
-    # pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
-
-    selected_compound_pairs_training = select_compound_pairs_wrapper(training_spectra, settings=settings)
-    inchikey_pair_generator = SpectrumPairGenerator(selected_compound_pairs_training, training_spectra,
-                                                    settings.shuffle, settings.random_seed)
-
+    spectrum_pair_generator = select_compound_pairs_wrapper(training_spectra, settings=settings)
     if json_save_file is not None:
-        inchikey_pair_generator.save_as_json(json_save_file)
-    # todo possibly create a single TrainingBatchGenerator which takes in 3 generators and pos and neg spectra to iteratively select each one.
+        spectrum_pair_generator.save_as_json(json_save_file)
     # Create generators
-    # todo also make sure that the TrainingBatchGenerator can work across ionmodes.
-    train_generator = TrainingBatchGenerator(spectrum_pair_generator=inchikey_pair_generator, settings=settings)
+    train_generator = TrainingBatchGenerator(spectrum_pair_generator=spectrum_pair_generator, settings=settings)
     return train_generator
 
 
diff --git a/tests/test_inchikey_pair_selection.py b/tests/test_inchikey_pair_selection.py
index cb1bd944..e23c2343 100644
--- a/tests/test_inchikey_pair_selection.py
+++ b/tests/test_inchikey_pair_selection.py
@@ -186,8 +186,7 @@ def test_select_compound_pairs_wrapper_no_resampling():
                                     average_inchikey_sampling_count=10,
                                     batch_size=8,
                                     max_pair_resampling=max_pair_resampling)
-    selected_inchikey_pairs = select_compound_pairs_wrapper(spectrums, settings)
-    inchikey_pair_generator = SpectrumPairGenerator(selected_inchikey_pairs, spectrums, True, 0)
+    inchikey_pair_generator = select_compound_pairs_wrapper(spectrums, settings)
 
     check_balanced_scores_selecting_inchikey_pairs(inchikey_pair_generator, bins)
     check_correct_oversampling(inchikey_pair_generator, max_pair_resampling)
@@ -207,8 +206,7 @@ def test_select_compound_pairs_wrapper_with_resampling():
                                     average_inchikey_sampling_count=10,
                                     batch_size=8,
                                     max_pair_resampling=max_pair_resampling)
-    selected_inchikey_pairs = select_compound_pairs_wrapper(spectrums, settings)
-    inchikey_pair_generator = SpectrumPairGenerator(selected_inchikey_pairs, spectrums)
+    inchikey_pair_generator = select_compound_pairs_wrapper(spectrums, settings)
 
     check_balanced_scores_selecting_inchikey_pairs(inchikey_pair_generator, bins)
     check_correct_oversampling(inchikey_pair_generator, max_pair_resampling)
@@ -230,8 +228,7 @@ def test_select_compound_pairs_wrapper_maximum_inchikey_count():
                                     max_pair_resampling=max_pair_resampling,
                                     max_inchikey_sampling=max_inchikey_sampling
                                     )
-    selected_inchikey_pairs = select_compound_pairs_wrapper(spectrums, settings)
-    inchikey_pair_generator = SpectrumPairGenerator(selected_inchikey_pairs, spectrums)
+    inchikey_pair_generator = select_compound_pairs_wrapper(spectrums, settings)
 
     highest_inchikey_count = max(inchikey_pair_generator.get_inchikey_counts().values())
     assert highest_inchikey_count <= max_inchikey_sampling + 1 # +1 because there is a chance that the last added inchikey is a pair to itself...
diff --git a/tests/test_siamese_spectra_model.py b/tests/test_siamese_spectra_model.py
index 2036b193..d69b56f8 100644
--- a/tests/test_siamese_spectra_model.py
+++ b/tests/test_siamese_spectra_model.py
@@ -131,8 +131,7 @@ def test_model_training(simple_training_spectra):
                                     batch_size=2,
                                     num_turns=20,
                                     )
-    scp_train = select_compound_pairs_wrapper(simple_training_spectra, settings)
-    inchikey_pair_generator = SpectrumPairGenerator(scp_train, simple_training_spectra)
+    inchikey_pair_generator = select_compound_pairs_wrapper(simple_training_spectra, settings)
     # Create generators
     train_generator_simple = TrainingBatchGenerator(spectrum_pair_generator=inchikey_pair_generator, settings=settings)
     settings.same_prob_bins = np.array([(-0.01, 1.0)])

From b158ecfc6cc4e2308ae6cce6d9e61977bc7b06cd Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 14:31:48 +0200
Subject: [PATCH 27/48] Remove option for saving the inchikey pairs when
 training model

---
 ms2deepscore/train_new_model/train_ms2deepscore.py           | 1 -
 ms2deepscore/wrapper_functions/training_wrapper_functions.py | 5 +----
 2 files changed, 1 insertion(+), 5 deletions(-)

diff --git a/ms2deepscore/train_new_model/train_ms2deepscore.py b/ms2deepscore/train_new_model/train_ms2deepscore.py
index 8107a9e8..20c48c9a 100644
--- a/ms2deepscore/train_new_model/train_ms2deepscore.py
+++ b/ms2deepscore/train_new_model/train_ms2deepscore.py
@@ -21,7 +21,6 @@ def train_ms2ds_model(
         validation_spectra,
         results_folder,
         settings: SettingsMS2Deepscore,
-        inchikey_pairs_file: str = None,
         ):
     """Full workflow to train a MS2DeepScore model.
     """
diff --git a/ms2deepscore/wrapper_functions/training_wrapper_functions.py b/ms2deepscore/wrapper_functions/training_wrapper_functions.py
index 4f57b60a..01d88865 100644
--- a/ms2deepscore/wrapper_functions/training_wrapper_functions.py
+++ b/ms2deepscore/wrapper_functions/training_wrapper_functions.py
@@ -42,10 +42,7 @@ def train_ms2deepscore_wrapper(settings: SettingsMS2Deepscore,
     validation_spectra = load_spectra_in_ionmode(settings.validation_spectra_file_name, settings.ionisation_mode)
 
     # Train model
-    _, history = train_ms2ds_model(
-        training_spectra, validation_spectra, settings.model_directory_name,
-        settings,
-    )
+    _, history = train_ms2ds_model(training_spectra, validation_spectra, settings.model_directory_name, settings)
 
     ms2ds_history_plot_file_name = os.path.join(settings.model_directory_name, settings.history_plot_file_name)
     plot_history(history["losses"], history["val_losses"], ms2ds_history_plot_file_name)

From 606144191616d694e88c42da60d3550131162178 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 14:39:35 +0200
Subject: [PATCH 28/48] Remove create_data_generator function

---
 .../train_new_model/train_ms2deepscore.py     | 17 +++-----------
 .../training_wrapper_functions.py             |  6 +++--
 tests/test_data_generators.py                 | 22 +++++++++----------
 3 files changed, 17 insertions(+), 28 deletions(-)

diff --git a/ms2deepscore/train_new_model/train_ms2deepscore.py b/ms2deepscore/train_new_model/train_ms2deepscore.py
index 20c48c9a..6d85753c 100644
--- a/ms2deepscore/train_new_model/train_ms2deepscore.py
+++ b/ms2deepscore/train_new_model/train_ms2deepscore.py
@@ -29,11 +29,9 @@ def train_ms2ds_model(
     settings.save_to_file(os.path.join(results_folder, "settings.json"))
 
     # Create a training generator
-    if inchikey_pairs_file is None:
-        train_generator = create_data_generator(training_spectra, settings, None)
-    else:
-        train_generator = create_data_generator(training_spectra, settings,
-                                                os.path.join(results_folder, inchikey_pairs_file))
+    spectrum_pair_generator = select_compound_pairs_wrapper(training_spectra, settings=settings)
+    train_generator = TrainingBatchGenerator(spectrum_pair_generator=spectrum_pair_generator, settings=settings)
+
     # Create a validation loss calculator
     validation_loss_calculator = ValidationLossCalculator(validation_spectra,
                                                           settings=settings)
@@ -70,15 +68,6 @@ def train_ms2ds_model(
 #     train_generator = TrainingBatchGenerator(spectrum_pair_generator=inchikey_pair_generator, settings=settings)
 #     return train_generator
 
-def create_data_generator(training_spectra,
-                          settings: SettingsMS2Deepscore,
-                          json_save_file=None) -> TrainingBatchGenerator:
-    spectrum_pair_generator = select_compound_pairs_wrapper(training_spectra, settings=settings)
-    if json_save_file is not None:
-        spectrum_pair_generator.save_as_json(json_save_file)
-    # Create generators
-    train_generator = TrainingBatchGenerator(spectrum_pair_generator=spectrum_pair_generator, settings=settings)
-    return train_generator
 
 
 def plot_history(losses, val_losses, file_name: Optional[str] = None):
diff --git a/ms2deepscore/wrapper_functions/training_wrapper_functions.py b/ms2deepscore/wrapper_functions/training_wrapper_functions.py
index 01d88865..06151226 100644
--- a/ms2deepscore/wrapper_functions/training_wrapper_functions.py
+++ b/ms2deepscore/wrapper_functions/training_wrapper_functions.py
@@ -13,9 +13,10 @@
 from ms2deepscore.models.SiameseSpectralModel import (SiameseSpectralModel,
                                                       train)
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
+from ms2deepscore.train_new_model import TrainingBatchGenerator, select_compound_pairs_wrapper
 from ms2deepscore.validation_loss_calculation.ValidationLossCalculator import ValidationLossCalculator
 from ms2deepscore.train_new_model.train_ms2deepscore import \
-    train_ms2ds_model, plot_history, save_history, create_data_generator
+    train_ms2ds_model, plot_history, save_history
 from ms2deepscore.train_new_model.validation_and_test_split import \
     split_spectra_in_random_inchikey_sets
 from ms2deepscore.utils import load_spectra_as_list
@@ -127,7 +128,8 @@ def parameter_search(
         os.makedirs(settings.model_directory_name, exist_ok=True)
         settings.save_to_file(os.path.join(settings.model_directory_name, "settings.json"))
         # Create a training generator
-        train_generator = create_data_generator(training_spectra, settings)
+        spectrum_pair_generator = select_compound_pairs_wrapper(training_spectra, settings=settings)
+        train_generator = TrainingBatchGenerator(spectrum_pair_generator=spectrum_pair_generator, settings=settings)
         # Create a validation loss calculator
         validation_loss_calculator = ValidationLossCalculator(validation_spectra,
                                                               settings=settings)
diff --git a/tests/test_data_generators.py b/tests/test_data_generators.py
index faf540ee..51f7e099 100644
--- a/tests/test_data_generators.py
+++ b/tests/test_data_generators.py
@@ -6,9 +6,8 @@
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore, SettingsEmbeddingEvaluator
 from ms2deepscore.tensorize_spectra import tensorize_spectra
 from ms2deepscore.train_new_model.TrainingBatchGenerator import TrainingBatchGenerator
-from ms2deepscore.train_new_model.train_ms2deepscore import create_data_generator
 from ms2deepscore.train_new_model.DataGeneratorEmbeddingEvaluation import DataGeneratorEmbeddingEvaluation
-from ms2deepscore.train_new_model import SpectrumPairGenerator
+from ms2deepscore.train_new_model import SpectrumPairGenerator, select_compound_pairs_wrapper
 from tests.create_test_spectra import create_test_spectra
 
 
@@ -162,21 +161,20 @@ def test_create_data_generator():
     """tests if a the function create_data_generator creates a datagenerator that samples all input spectra
     correct distributions of inchikeys and scores are tested in other tests"""
     test_spectra = create_test_spectra(8, 3)
-    data_generator = create_data_generator(training_spectra=test_spectra,
-                                           settings=SettingsMS2Deepscore(
-                                               min_mz=10,
-                                               max_mz=1000,
-                                               mz_bin_width=0.1,
-                                               intensity_scaling=0.5,
-                                               additional_metadata=[],
-                                               same_prob_bins=np.array([(-0.000001, 0.25), (0.25, 0.5), (0.5, 0.75),
+    settings = SettingsMS2Deepscore(min_mz=10, max_mz=1000,
+                                    mz_bin_width=0.1,
+                                    intensity_scaling=0.5,
+                                    additional_metadata=[],
+                                    same_prob_bins=np.array([(-0.000001, 0.25), (0.25, 0.5), (0.5, 0.75),
                                                                         (0.75, 1)]),
-                                               batch_size=2,
+                                    batch_size=2,
                                                num_turns=4,
                                                augment_removal_max=0.0,
                                                augment_removal_intensity=0.0,
                                                augment_intensity=0.0,
-                                               augment_noise_max=0))
+                                               augment_noise_max=0)
+    spectrum_pair_generator = select_compound_pairs_wrapper(test_spectra, settings=settings)
+    data_generator = TrainingBatchGenerator(spectrum_pair_generator=spectrum_pair_generator, settings=settings)
     tensorized_spectra = []
     epochs = 20
     for _ in range(epochs):

From 7cb6d3120c710e14709d24f1ca2535ed09de1817 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 14:52:57 +0200
Subject: [PATCH 29/48] Remove cross ionmode function from train_ms2ds_model

---
 .../train_new_model/train_ms2deepscore.py     | 21 +------------------
 1 file changed, 1 insertion(+), 20 deletions(-)

diff --git a/ms2deepscore/train_new_model/train_ms2deepscore.py b/ms2deepscore/train_new_model/train_ms2deepscore.py
index 6d85753c..e5ae982f 100644
--- a/ms2deepscore/train_new_model/train_ms2deepscore.py
+++ b/ms2deepscore/train_new_model/train_ms2deepscore.py
@@ -11,7 +11,7 @@
 from ms2deepscore.models.SiameseSpectralModel import (SiameseSpectralModel,
                                                       train)
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
-from ms2deepscore.train_new_model import TrainingBatchGenerator, select_compound_pairs_wrapper, SpectrumPairGenerator
+from ms2deepscore.train_new_model import TrainingBatchGenerator, select_compound_pairs_wrapper
 from ms2deepscore.validation_loss_calculation.ValidationLossCalculator import \
     ValidationLossCalculator
 
@@ -50,25 +50,6 @@ def train_ms2ds_model(
                     checkpoint_filename=output_model_file_name, lambda_l1=0, lambda_l2=0)
     return model, history
 
-# def create_data_generator_across_ionmodes(training_spectra,
-#                           settings: SettingsMS2Deepscore,
-#                           json_save_file=None) -> TrainingBatchGenerator:
-#     # todo actually create, both between and across ionmodes.
-#     pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
-#
-#     pos_spectrum_pair_generator = select_compound_pairs_wrapper(pos_spectra, settings=settings)
-#     neg_spectrum_pair_generator = select_compound_pairs_wrapper(neg_spectra, settings=settings)
-#     pos_neg_spectrum_pair_generator = select_compound_pairs_wrapper_across_ionmode(pos_spectra, neg_spectra, settings)
-#
-#     if json_save_file is not None:
-#         inchikey_pair_generator.save_as_json(json_save_file)
-#     # todo possibly create a single TrainingBatchGenerator which takes in 3 generators and pos and neg spectra to iteratively select each one.
-#     # Create generators
-#     # todo also make sure that the TrainingBatchGenerator can work across ionmodes.
-#     train_generator = TrainingBatchGenerator(spectrum_pair_generator=inchikey_pair_generator, settings=settings)
-#     return train_generator
-
-
 
 def plot_history(losses, val_losses, file_name: Optional[str] = None):
     plt.plot(losses)

From 9df6593951d240893d1439b9bf4bfe379723873d Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 15:02:28 +0200
Subject: [PATCH 30/48] Derive nr_of_unique inchikeys from the nr of pairs

---
 ms2deepscore/train_new_model/TrainingBatchGenerator.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/ms2deepscore/train_new_model/TrainingBatchGenerator.py b/ms2deepscore/train_new_model/TrainingBatchGenerator.py
index 8df11390..34412855 100644
--- a/ms2deepscore/train_new_model/TrainingBatchGenerator.py
+++ b/ms2deepscore/train_new_model/TrainingBatchGenerator.py
@@ -49,12 +49,14 @@ def __init__(self,
                 self.model_settings.random_seed = 0
         self.rng = np.random.default_rng(self.model_settings.random_seed)
         self.spectrum_pair_generator = spectrum_pair_generator
-        unique_inchikeys = np.unique(spectrum_pair_generator.spectrum_inchikeys)
-        if len(unique_inchikeys) < self.model_settings.batch_size:
+        # The number of unique inchikeys derived from the number of spectrum pairs.
+        nr_of_unique_inchikeys = int(len(spectrum_pair_generator) / settings.average_inchikey_sampling_count * 2)
+        # The length of unique inchikeys is len(selected_inchikeys_pairs) / average number of pairs
+        if nr_of_unique_inchikeys < self.model_settings.batch_size:
             raise ValueError("The number of unique inchikeys must be larger than the batch size.")
         self.fixed_set = {}
 
-        self.nr_of_batches = int(self.model_settings.num_turns) * int(np.ceil(len(unique_inchikeys) /
+        self.nr_of_batches = int(self.model_settings.num_turns) * int(np.ceil(nr_of_unique_inchikeys /
                                                                               self.model_settings.batch_size))
 
     def __len__(self):

From 2cfbe1ae74284ea8986a7264317b290506ca7b2a Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 15:11:14 +0200
Subject: [PATCH 31/48] Fix test to calculate unique number of inchikeys
 correctly again

---
 tests/test_data_generators.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tests/test_data_generators.py b/tests/test_data_generators.py
index 51f7e099..f3f002c1 100644
--- a/tests/test_data_generators.py
+++ b/tests/test_data_generators.py
@@ -79,7 +79,8 @@ def dummy_data_generator():
                                     augment_removal_max=0.0,
                                     augment_removal_intensity=0.0,
                                     augment_intensity=0.0,
-                                    augment_noise_max=0)
+                                    augment_noise_max=0,
+                                    average_inchikey_sampling_count=2)
     return TrainingBatchGenerator(selected_pairs, settings)
 
 

From aeea0af91f58f52184f852ff45c92083dfb0384b Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 15:11:29 +0200
Subject: [PATCH 32/48] Add cross ionization mode generators

---
 .../inchikey_pair_selection_cross_ionmode.py  | 139 +++++++++++++++++-
 1 file changed, 135 insertions(+), 4 deletions(-)

diff --git a/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py b/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py
index 287c5a9c..6a2b8264 100644
--- a/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py
+++ b/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py
@@ -1,17 +1,21 @@
+import json
 from typing import List, Tuple
+from collections import Counter
 import numpy as np
 from matchms import Spectrum
 from numba import jit, prange
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
+from ms2deepscore.train_new_model import TrainingBatchGenerator, SpectrumPairGenerator
 from ms2deepscore.train_new_model.inchikey_pair_selection import compute_fingerprints_for_training, \
-    balanced_selection_of_pairs_per_bin, convert_to_selected_pairs_list, tanimoto_scores_row
-
+    balanced_selection_of_pairs_per_bin, convert_to_selected_pairs_list, tanimoto_scores_row, \
+    select_compound_pairs_wrapper
+from ms2deepscore.utils import split_by_ionmode
 
 def select_compound_pairs_wrapper_across_ionmode(
         spectra_1: List[Spectrum],
         spectra_2: List[Spectrum],
         settings: SettingsMS2Deepscore,
-) -> List[Tuple[str, str, float]]:
+) -> "SpectrumPairGeneratorAcrossIonmodes":
     """Returns a SpectrumPairGenerator object containing equally balanced pairs over the different bins
 
     spectra:
@@ -51,7 +55,8 @@ def select_compound_pairs_wrapper_across_ionmode(
     selected_pairs_per_bin = convert_to_selected_pairs_list(
         pair_frequency_matrixes, available_pairs_per_bin_matrix,
         available_scores_per_bin_matrix, inchikeys14_unique_1 + inchikeys14_unique_2)
-    return [pair for pairs in selected_pairs_per_bin for pair in pairs]
+    return SpectrumPairGeneratorAcrossIonmodes([pair for pairs in selected_pairs_per_bin for pair in pairs],
+                                               spectra_1, spectra_2, settings.shuffle, settings.random_seed)
 
 
 @jit(nopython=True, parallel=True)
@@ -114,3 +119,129 @@ def compute_jaccard_similarity_per_bin_across_ionmodes(
             selected_scores_per_bin[bin_number, idx_fingerprint_corrected, :num_indices] = tanimoto_scores[indices]
 
     return selected_pairs_per_bin, selected_scores_per_bin
+
+
+class SpectrumPairGeneratorAcrossIonmodes:
+    def __init__(self, selected_inchikey_pairs: List[Tuple[str, str, float]],
+                 spectra_pos: List[Spectrum], spectra_neg: List[Spectrum],
+                 shuffle: bool = True, random_seed: int = 0):
+        """
+        Parameters
+        ----------
+        selected_inchikey_pairs:
+            A list with tuples encoding inchikey pairs like: (inchikey1, inchikey2, tanimoto_score)
+        """
+        self.selected_inchikey_pairs = selected_inchikey_pairs
+        self.spectra_pos = spectra_pos
+        self.spectra_neg = spectra_neg
+
+        self.pos_inchikeys = np.array([s.get("inchikey")[:14] for s in self.spectra_pos])
+        self.neg_inchikeys= np.array([s.get("inchikey")[:14] for s in self.spectra_neg])
+
+        self.shuffle = shuffle
+        self.random_nr_generator = np.random.default_rng(random_seed)
+        self._idx = 0
+        if self.shuffle:
+            self.random_nr_generator.shuffle(self.selected_inchikey_pairs)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        # reshuffle when we've gone through everything
+        if self._idx >= len(self.selected_inchikey_pairs):
+            self._idx = 0
+            if self.shuffle:
+                self.random_nr_generator.shuffle(self.selected_inchikey_pairs)
+
+        inchikey1, inchikey2, tanimoto_score = self.selected_inchikey_pairs[self._idx]
+        spectrum1 = self._get_pos_spectrum_with_inchikey(inchikey1, self.random_nr_generator)
+        spectrum2 = self._get_neg_spectrum_with_inchikey(inchikey2, self.random_nr_generator)
+        self._idx += 1
+        return spectrum1, spectrum2, tanimoto_score
+
+    def __len__(self):
+        return len(self.selected_inchikey_pairs)
+
+    def __str__(self):
+        return f"SpectrumPairGenerator with {len(self.selected_inchikey_pairs)} pairs available"
+
+    def get_scores(self):
+        return [score for _, _, score in self.selected_inchikey_pairs]
+
+    def get_inchikey_counts(self) -> Counter:
+        """returns the frequency each inchikey occurs"""
+        inchikeys = Counter()
+        for inchikey_1, inchikey_2, _ in self.selected_inchikey_pairs:
+            inchikeys[inchikey_1] += 1
+            inchikeys[inchikey_2] += 1
+        return inchikeys
+
+    def get_scores_per_inchikey(self):
+        inchikey_scores = {}
+        for inchikey_1, inchikey_2, score in self.selected_inchikey_pairs:
+            if inchikey_1 in inchikey_scores:
+                inchikey_scores[inchikey_1].append(score)
+            else:
+                inchikey_scores[inchikey_1] = []
+            if inchikey_2 in inchikey_scores:
+                inchikey_scores[inchikey_2].append(score)
+            else:
+                inchikey_scores[inchikey_2] = []
+        return inchikey_scores
+
+    def save_as_json(self, file_name):
+        data_for_json = [(item[0], item[1], float(item[2])) for item in self.selected_inchikey_pairs]
+
+        with open(file_name, "w", encoding="utf-8") as f:
+            json.dump(data_for_json, f)
+
+    def _get_pos_spectrum_with_inchikey(self, inchikey: str, random_number_generator) -> Spectrum:
+        matching_spectrum_id = np.where(self.pos_inchikeys == inchikey)[0]
+        if len(matching_spectrum_id) <= 0:
+            raise ValueError("No matching inchikey found (note: expected first 14 characters), "
+                             "likely switched pos and neg in entry")
+        return self.spectra_pos[random_number_generator.choice(matching_spectrum_id)]
+
+    def _get_neg_spectrum_with_inchikey(self, inchikey: str, random_number_generator) -> Spectrum:
+        matching_spectrum_id = np.where(self.neg_inchikeys == inchikey)[0]
+        if len(matching_spectrum_id) <= 0:
+            raise ValueError("No matching inchikey found (note: expected first 14 characters), "
+                             "likely switched pos and neg in entry")
+        return self.spectra_neg[random_number_generator.choice(matching_spectrum_id)]
+
+
+def create_data_generator_across_ionmodes(training_spectra,
+                                          settings: SettingsMS2Deepscore) -> TrainingBatchGenerator:
+    pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
+
+    pos_spectrum_pair_generator = select_compound_pairs_wrapper(pos_spectra, settings=settings)
+    neg_spectrum_pair_generator = select_compound_pairs_wrapper(neg_spectra, settings=settings)
+    pos_neg_spectrum_pair_generator = select_compound_pairs_wrapper_across_ionmode(pos_spectra, neg_spectra, settings)
+
+    spectrum_pair_generator = CombinedSpectrumGenerator([pos_spectrum_pair_generator, neg_spectrum_pair_generator, pos_neg_spectrum_pair_generator])
+
+    train_generator = TrainingBatchGenerator(spectrum_pair_generator=spectrum_pair_generator, settings=settings)
+    return train_generator
+
+
+class CombinedSpectrumGenerator:
+    """Combines multiple SpectrumPairGenerators into a single generator
+
+    This is used to combine different iterators for each ionmode pair"""
+    def __init__(self, spectrum_pair_generators: List[SpectrumPairGenerator]):
+        self.generators = spectrum_pair_generators
+        self._idx = 0
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if not self.generators:
+            raise StopIteration
+        current_generator = self.generators[self._idx % len(self.generators)]
+        self._idx += 1
+        return next(current_generator)
+
+    def __len__(self):
+        return sum([len(generator) for generator in self.generators])
\ No newline at end of file

From 555f0d61be06489bb165068624e6b6413ef27ea5 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 18:26:55 +0200
Subject: [PATCH 33/48] Fix the order of pairs in
 convert_to_selected_pairs_list, so pos is always first and neg always second

---
 .../train_new_model/inchikey_pair_selection.py | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/ms2deepscore/train_new_model/inchikey_pair_selection.py b/ms2deepscore/train_new_model/inchikey_pair_selection.py
index cc153b86..9a07786f 100644
--- a/ms2deepscore/train_new_model/inchikey_pair_selection.py
+++ b/ms2deepscore/train_new_model/inchikey_pair_selection.py
@@ -242,16 +242,22 @@ def convert_to_selected_pairs_list(pair_frequency_matrixes: np.ndarray,
     for bin_id, bin_pair_frequency_matrix in enumerate(tqdm(pair_frequency_matrixes)):
         selected_pairs = []
         for inchikey1_index, pair_frequency_row in enumerate(bin_pair_frequency_matrix):
-            for inchikey2_index, pair_frequency in enumerate(pair_frequency_row):
+            for column_index, pair_frequency in enumerate(pair_frequency_row):
                 if pair_frequency > 0:
-                    inchikey2 = available_pairs_per_bin_matrix[bin_id][inchikey1_index][inchikey2_index]
+                    inchikey2_index = available_pairs_per_bin_matrix[bin_id][inchikey1_index][column_index]
                     score = scores_matrix[bin_id][inchikey1_index][inchikey2_index]
-                    selected_pairs.extend(
-                        [(inchikeys14_unique[inchikey1_index], inchikeys14_unique[inchikey2], score)] * pair_frequency)
+                    # This ensures that the order is the same.
+                    # This is important for the cross ionization mode selection.
+                    if inchikey1_index < inchikey2_index:
+                        selected_pairs.extend(
+                            [(inchikeys14_unique[inchikey1_index], inchikeys14_unique[inchikey2_index], score)] * pair_frequency)
+                    else:
+                        selected_pairs.extend(
+                            [(inchikeys14_unique[inchikey2_index], inchikeys14_unique[inchikey1_index], score)] * pair_frequency)
                     # remove duplicate pairs
                     position_of_first_inchikey_in_matrix = available_pairs_per_bin_matrix[bin_id][
-                                                               inchikey2] == inchikey1_index
-                    bin_pair_frequency_matrix[inchikey2][position_of_first_inchikey_in_matrix] = 0
+                                                               inchikey2_index] == inchikey1_index
+                    bin_pair_frequency_matrix[inchikey2_index][position_of_first_inchikey_in_matrix] = 0
         selected_pairs_per_bin.append(selected_pairs)
     return selected_pairs_per_bin
 

From df5015539322ce8d405b4c45a593351837d7adc8 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 18:27:18 +0200
Subject: [PATCH 34/48] Change test training wrapper function to both
 ionization modes

---
 tests/test_training_wrapper_function.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_training_wrapper_function.py b/tests/test_training_wrapper_function.py
index 9fc94d31..d1b870f2 100644
--- a/tests/test_training_wrapper_function.py
+++ b/tests/test_training_wrapper_function.py
@@ -21,7 +21,7 @@ def test_train_wrapper_ms2ds_model(tmp_path):
     settings = SettingsMS2Deepscore(**{
         "spectrum_file_path": spectra_file_name,
         "epochs": 2,  # to speed up tests --> usually many more
-        "ionisation_mode": "negative",
+        "ionisation_mode": "both",
         "base_dims": (200, 200),  # to speed up tests --> usually larger
         "embedding_dim": 100,  # to speed up tests --> usually larger
         "same_prob_bins": np.array([(-0.01, 0.2), (0.2, 1.0)]),

From 7fc9c24f9aa5cfdf145d87dc8a6c44ef71cff2d5 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Fri, 22 Aug 2025 18:27:44 +0200
Subject: [PATCH 35/48] Make train ms2deepscore handle both and single ion mode
 model training

---
 ms2deepscore/train_new_model/train_ms2deepscore.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/ms2deepscore/train_new_model/train_ms2deepscore.py b/ms2deepscore/train_new_model/train_ms2deepscore.py
index e5ae982f..a9cdaa3f 100644
--- a/ms2deepscore/train_new_model/train_ms2deepscore.py
+++ b/ms2deepscore/train_new_model/train_ms2deepscore.py
@@ -12,6 +12,7 @@
                                                       train)
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
 from ms2deepscore.train_new_model import TrainingBatchGenerator, select_compound_pairs_wrapper
+from ms2deepscore.train_new_model.inchikey_pair_selection_cross_ionmode import create_data_generator_across_ionmodes
 from ms2deepscore.validation_loss_calculation.ValidationLossCalculator import \
     ValidationLossCalculator
 
@@ -27,11 +28,11 @@ def train_ms2ds_model(
     # Make folder and save settings
     os.makedirs(results_folder, exist_ok=True)
     settings.save_to_file(os.path.join(results_folder, "settings.json"))
-
-    # Create a training generator
-    spectrum_pair_generator = select_compound_pairs_wrapper(training_spectra, settings=settings)
-    train_generator = TrainingBatchGenerator(spectrum_pair_generator=spectrum_pair_generator, settings=settings)
-
+    if settings.ionisation_mode == "both":
+        train_generator = create_data_generator_across_ionmodes(training_spectra, settings=settings)
+    else:
+        spectrum_pair_generator = select_compound_pairs_wrapper(training_spectra, settings=settings)
+        train_generator = TrainingBatchGenerator(spectrum_pair_generator=spectrum_pair_generator, settings=settings)
     # Create a validation loss calculator
     validation_loss_calculator = ValidationLossCalculator(validation_spectra,
                                                           settings=settings)

From 0cc1cb8de89eaf2b1e1d998b5ea53bf0c1823234 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Mon, 25 Aug 2025 10:22:05 +0200
Subject: [PATCH 36/48] Fix bug in variable naming

---
 ms2deepscore/train_new_model/inchikey_pair_selection.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ms2deepscore/train_new_model/inchikey_pair_selection.py b/ms2deepscore/train_new_model/inchikey_pair_selection.py
index 9a07786f..5de3241d 100644
--- a/ms2deepscore/train_new_model/inchikey_pair_selection.py
+++ b/ms2deepscore/train_new_model/inchikey_pair_selection.py
@@ -245,7 +245,7 @@ def convert_to_selected_pairs_list(pair_frequency_matrixes: np.ndarray,
             for column_index, pair_frequency in enumerate(pair_frequency_row):
                 if pair_frequency > 0:
                     inchikey2_index = available_pairs_per_bin_matrix[bin_id][inchikey1_index][column_index]
-                    score = scores_matrix[bin_id][inchikey1_index][inchikey2_index]
+                    score = scores_matrix[bin_id][inchikey1_index][column_index]
                     # This ensures that the order is the same.
                     # This is important for the cross ionization mode selection.
                     if inchikey1_index < inchikey2_index:

From 301bdba3824fad51ea35f0d361f02232aa7381b8 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Wed, 27 Aug 2025 10:14:38 +0200
Subject: [PATCH 37/48] added basic tests for spectrum pair generation across
 ionmodes.

---
 tests/test_data_generators.py | 54 +++++++++++++++++++++++++++++++++++
 1 file changed, 54 insertions(+)

diff --git a/tests/test_data_generators.py b/tests/test_data_generators.py
index f3f002c1..a05038b2 100644
--- a/tests/test_data_generators.py
+++ b/tests/test_data_generators.py
@@ -8,6 +8,8 @@
 from ms2deepscore.train_new_model.TrainingBatchGenerator import TrainingBatchGenerator
 from ms2deepscore.train_new_model.DataGeneratorEmbeddingEvaluation import DataGeneratorEmbeddingEvaluation
 from ms2deepscore.train_new_model import SpectrumPairGenerator, select_compound_pairs_wrapper
+from ms2deepscore.train_new_model.inchikey_pair_selection_cross_ionmode import create_data_generator_across_ionmodes, \
+    select_compound_pairs_wrapper_across_ionmode
 from tests.create_test_spectra import create_test_spectra
 
 
@@ -235,3 +237,55 @@ def test_epoch_end_functionality(data_generator_embedding_evaluation):
     assert counter == 10
     assert not np.array_equal(data_generator_embedding_evaluation.indexes,
                               initial_indexes), "Indexes not shuffled after epoch end"
+
+def test_create_data_generator_across_ionmodes():
+    """Just a test that is runs, not a test if it is actually well balanced"""
+    test_spectra = create_test_spectra(20, 2)
+    pos_spectra = []
+    for spectrum in test_spectra[:20]:
+        spectrum.set("ionmode", "positive")
+        pos_spectra.append(spectrum)
+    neg_spectra = []
+    for spectrum in test_spectra[20:]:
+        spectrum.set("ionmode", "negative")
+        neg_spectra.append(spectrum)
+
+    settings = SettingsMS2Deepscore(min_mz=10, max_mz=1000,
+                                    mz_bin_width=0.1,
+                                    intensity_scaling=0.5,
+                                    additional_metadata=[],
+                                    same_prob_bins=np.array([(-0.000001, 0.25), (0.25, 0.5), (0.5, 0.75),
+                                                                        (0.75, 1)]),
+                                    batch_size=2,
+                                    num_turns=4,)
+    data_generator = create_data_generator_across_ionmodes(pos_spectra + neg_spectra, settings)
+    for _ in range(len(data_generator)):
+        spectra_1, spectra_2, meta_1, meta_2, targets = data_generator.__next__()
+
+def test_select_compound_pairs_wrapper_across_ionmode():
+    test_spectra = create_test_spectra(20, 2)
+    pos_spectra = []
+    for spectrum in test_spectra[:20]:
+        spectrum.set("ionmode", "positive")
+        pos_spectra.append(spectrum)
+    neg_spectra = []
+    for spectrum in test_spectra[20:]:
+        spectrum.set("ionmode", "negative")
+        neg_spectra.append(spectrum)
+    settings = SettingsMS2Deepscore(min_mz=10, max_mz=1000,
+                                    mz_bin_width=0.1,
+                                    intensity_scaling=0.5,
+                                    additional_metadata=[],
+                                    same_prob_bins=np.array([(-0.000001, 0.25), (0.25, 0.5), (0.5, 0.75),
+                                                             (0.75, 1)]),
+                                    batch_size=2,
+                                    num_turns=4, )
+    spectrum_pair_generator = select_compound_pairs_wrapper_across_ionmode(pos_spectra, neg_spectra, settings)
+
+    for _ in range(len(spectrum_pair_generator)):
+        spectrum_1, spectrum_2, score = spectrum_pair_generator.__next__()
+        assert spectrum_1.get("ionmode") == "positive"
+        assert spectrum_2.get("ionmode") == "negative"
+    # it should be an infinite generator, so it should continue after a loop
+    spectrum_pair_generator.__next__()
+

From 4cd43efb8d2de865f4d9b9b14ff4fbc303c82243 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Wed, 27 Aug 2025 10:18:24 +0200
Subject: [PATCH 38/48] Add SpectrumPairGenerator to init

---
 ms2deepscore/train_new_model/__init__.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ms2deepscore/train_new_model/__init__.py b/ms2deepscore/train_new_model/__init__.py
index f31333e0..0dcaca5f 100644
--- a/ms2deepscore/train_new_model/__init__.py
+++ b/ms2deepscore/train_new_model/__init__.py
@@ -5,5 +5,6 @@
 
 __all__ = [
     "TrainingBatchGenerator",
-    "select_compound_pairs_wrapper"
+    "select_compound_pairs_wrapper",
+    "SpectrumPairGenerator"
 ]

From b17f82b9bf0bdd38056a68144dc307525d8e9725 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Wed, 27 Aug 2025 10:25:49 +0200
Subject: [PATCH 39/48] Remove unused import

---
 tests/test_siamese_spectra_model.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/test_siamese_spectra_model.py b/tests/test_siamese_spectra_model.py
index d69b56f8..28811375 100644
--- a/tests/test_siamese_spectra_model.py
+++ b/tests/test_siamese_spectra_model.py
@@ -6,7 +6,6 @@
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
 from ms2deepscore.tensorize_spectra import tensorize_spectra
 from ms2deepscore.train_new_model.TrainingBatchGenerator import TrainingBatchGenerator
-from ms2deepscore.train_new_model import SpectrumPairGenerator
 from ms2deepscore.train_new_model.inchikey_pair_selection import \
     select_compound_pairs_wrapper
 from ms2deepscore.validation_loss_calculation.ValidationLossCalculator import \

From ded5a1f6a6fcdc4ad8ed1c8584d1fff12cc26222 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Wed, 27 Aug 2025 10:28:00 +0200
Subject: [PATCH 40/48] Remove duplicated test

---
 tests/test_inchikey_pair_selection.py | 15 ---------------
 1 file changed, 15 deletions(-)

diff --git a/tests/test_inchikey_pair_selection.py b/tests/test_inchikey_pair_selection.py
index e23c2343..a6fecd68 100644
--- a/tests/test_inchikey_pair_selection.py
+++ b/tests/test_inchikey_pair_selection.py
@@ -297,18 +297,3 @@ def check_balanced_scores_selecting_inchikey_pairs(selected_inchikey_pairs: Spec
                 score_bin_counts[(min_bound, max_bound)] += 1
     # Check that the number of pairs per bin is equal for all bins
     assert len(set(score_bin_counts.values())) == 1
-
-from ms2deepscore.train_new_model.inchikey_pair_selection_cross_ionmode import select_compound_pairs_wrapper_across_ionmode
-def test_select_compound_pairs_wrapper_with_resampling_across_ionmodes():
-    spectrums_1 = create_test_spectra(num_of_unique_inchikeys=26, num_of_spectra_per_inchikey=1)
-    spectrums_2 = create_test_spectra(num_of_unique_inchikeys=25, num_of_spectra_per_inchikey=2)
-    for spectrum in spectrums_1:
-        spectrum.set("inchikey", "a" + spectrum.get("inchikey"))
-    bins = [(0.8, 0.9), (0.7, 0.8), (0.9, 1.0), (0.6, 0.7), (0.5, 0.6),
-            (0.4, 0.5), (0.3, 0.4), (0.2, 0.3), (0.1, 0.2), (-0.01, 0.1)]
-    max_pair_resampling = 10
-    settings = SettingsMS2Deepscore(same_prob_bins=np.array(bins, dtype="float32"),
-                                    average_inchikey_sampling_count=10,
-                                    batch_size=8,
-                                    max_pair_resampling=max_pair_resampling)
-    selected_inchikey_pairs = select_compound_pairs_wrapper_across_ionmode(spectrums_1, spectrums_2, settings)

From 608f292bc89f95a5cc16af8713935caf1c8eae41 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Wed, 27 Aug 2025 10:28:08 +0200
Subject: [PATCH 41/48] Linting

---
 tests/test_data_generators.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/test_data_generators.py b/tests/test_data_generators.py
index a05038b2..fd2978f6 100644
--- a/tests/test_data_generators.py
+++ b/tests/test_data_generators.py
@@ -288,4 +288,3 @@ def test_select_compound_pairs_wrapper_across_ionmode():
         assert spectrum_2.get("ionmode") == "negative"
     # it should be an infinite generator, so it should continue after a loop
     spectrum_pair_generator.__next__()
-

From 38a85a3fb0a16f106fb566a2188cd1679ad81e66 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Wed, 27 Aug 2025 10:44:21 +0200
Subject: [PATCH 42/48] Move create_data_generator_across_ionmodes to top of
 file

---
 .../inchikey_pair_selection_cross_ionmode.py  | 31 ++++++++++---------
 1 file changed, 16 insertions(+), 15 deletions(-)

diff --git a/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py b/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py
index 6a2b8264..96c17e55 100644
--- a/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py
+++ b/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py
@@ -5,12 +5,27 @@
 from matchms import Spectrum
 from numba import jit, prange
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
-from ms2deepscore.train_new_model import TrainingBatchGenerator, SpectrumPairGenerator
+from ms2deepscore.train_new_model.TrainingBatchGenerator import TrainingBatchGenerator
+from ms2deepscore.train_new_model.SpectrumPairGenerator import SpectrumPairGenerator
 from ms2deepscore.train_new_model.inchikey_pair_selection import compute_fingerprints_for_training, \
     balanced_selection_of_pairs_per_bin, convert_to_selected_pairs_list, tanimoto_scores_row, \
     select_compound_pairs_wrapper
 from ms2deepscore.utils import split_by_ionmode
 
+def create_data_generator_across_ionmodes(training_spectra,
+                                          settings: SettingsMS2Deepscore) -> TrainingBatchGenerator:
+    pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
+
+    pos_spectrum_pair_generator = select_compound_pairs_wrapper(pos_spectra, settings=settings)
+    neg_spectrum_pair_generator = select_compound_pairs_wrapper(neg_spectra, settings=settings)
+    pos_neg_spectrum_pair_generator = select_compound_pairs_wrapper_across_ionmode(pos_spectra, neg_spectra, settings)
+
+    spectrum_pair_generator = CombinedSpectrumGenerator([pos_spectrum_pair_generator, neg_spectrum_pair_generator, pos_neg_spectrum_pair_generator])
+
+    train_generator = TrainingBatchGenerator(spectrum_pair_generator=spectrum_pair_generator, settings=settings)
+    return train_generator
+
+
 def select_compound_pairs_wrapper_across_ionmode(
         spectra_1: List[Spectrum],
         spectra_2: List[Spectrum],
@@ -211,20 +226,6 @@ def _get_neg_spectrum_with_inchikey(self, inchikey: str, random_number_generator
         return self.spectra_neg[random_number_generator.choice(matching_spectrum_id)]
 
 
-def create_data_generator_across_ionmodes(training_spectra,
-                                          settings: SettingsMS2Deepscore) -> TrainingBatchGenerator:
-    pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
-
-    pos_spectrum_pair_generator = select_compound_pairs_wrapper(pos_spectra, settings=settings)
-    neg_spectrum_pair_generator = select_compound_pairs_wrapper(neg_spectra, settings=settings)
-    pos_neg_spectrum_pair_generator = select_compound_pairs_wrapper_across_ionmode(pos_spectra, neg_spectra, settings)
-
-    spectrum_pair_generator = CombinedSpectrumGenerator([pos_spectrum_pair_generator, neg_spectrum_pair_generator, pos_neg_spectrum_pair_generator])
-
-    train_generator = TrainingBatchGenerator(spectrum_pair_generator=spectrum_pair_generator, settings=settings)
-    return train_generator
-
-
 class CombinedSpectrumGenerator:
     """Combines multiple SpectrumPairGenerators into a single generator
 

From 92d0470fbac167d1c1f2d6358240f7f042dc462c Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Wed, 27 Aug 2025 10:55:25 +0200
Subject: [PATCH 43/48] Update CHANGELOG.md

---
 CHANGELOG.md | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 15aa4f9a..5fe22184 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,11 +6,20 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ## [Unreleased]
+### Added
+- The training pair sampling for both ionmodes is now balanced over the different ionmode pairs.
+
 ### Fixed
 - Datasplit of test, train and val, is not done sepparately for ionmodes anymore.
 
 ### Changed
 - Settings include file name of spectra now. This makes tracking of runs more easily and more flexibility for results folder. 
+- Split the different datagenerators to different files, before they were all in data_generators.py
+- Renamed SpectrumPairGenerator -> TrainingBatchGenerator, this better captures what the class does.
+- Moved the data augmentation to a separate file out of the TrainingBatchGenerator.
+- Refactored the data augmentation to make it a bit more modular and testable (also added extra tests)
+- Moved the Spectrum picking from TraininBatchGenerator into InchikeyPairGenerator and renamed InchikeyPairGenerator to SpectrumPairGenerator.
+- Turned the new SpectrumPairGenerator (InchikeyPairGenerator before) into a real generator, before we had a generator method returning a generator.
 
 ## [2.5.2] - 2025-05-26
 ### Changed

From c281a7d6c51be78ab0731ae686ea6fc26fecd006 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Wed, 27 Aug 2025 11:52:28 +0200
Subject: [PATCH 44/48] Update link to zenodo for model to always point to the
 latest version

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 7ead7320..f542e02a 100644
--- a/README.md
+++ b/README.md
@@ -65,7 +65,7 @@ If you are not familiar with `matchms` yet, then we also recommand our [tutorial
 
 ## 1) Compute spectral similarities
 We provide a model which was trained on > 500,000 MS/MS combined spectra from [GNPS](https://gnps.ucsd.edu/), [Mona](https://mona.fiehnlab.ucdavis.edu/), MassBank and MSnLib. 
-This model can be downloaded from [from zenodo here](https://zenodo.org/records/13897744). Only the ms2deepscore_model.pt is needed.
+This model can be downloaded from [from zenodo here](https://zenodo.org/records/10814306).
 The model works for spectra in both positive and negative ionization modes and even predictions across ionization modes can be made by this model. 
 
 To compute the similarities between spectra of your choice you can run the code below.

From e99a31ebffbbf08f4cd2df0e5eb3c1783921c6ab Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Tue, 27 Jan 2026 10:37:25 +0100
Subject: [PATCH 45/48] Change select_compound_pairs_wrapper to
 create_spectrum_pair_generator to better reflect functionality.

---
 ms2deepscore/train_new_model/__init__.py                  | 4 ++--
 ms2deepscore/train_new_model/inchikey_pair_selection.py   | 2 +-
 .../inchikey_pair_selection_cross_ionmode.py              | 6 +++---
 ms2deepscore/train_new_model/train_ms2deepscore.py        | 4 ++--
 .../wrapper_functions/training_wrapper_functions.py       | 4 ++--
 tests/test_data_generators.py                             | 4 ++--
 tests/test_inchikey_pair_selection.py                     | 8 ++++----
 tests/test_siamese_spectra_model.py                       | 4 ++--
 8 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/ms2deepscore/train_new_model/__init__.py b/ms2deepscore/train_new_model/__init__.py
index 0dcaca5f..c9ce05da 100644
--- a/ms2deepscore/train_new_model/__init__.py
+++ b/ms2deepscore/train_new_model/__init__.py
@@ -1,10 +1,10 @@
 from .TrainingBatchGenerator import TrainingBatchGenerator
 from .SpectrumPairGenerator import SpectrumPairGenerator
-from .inchikey_pair_selection import (select_compound_pairs_wrapper)
+from .inchikey_pair_selection import (create_spectrum_pair_generator)
 
 
 __all__ = [
     "TrainingBatchGenerator",
-    "select_compound_pairs_wrapper",
+    "create_spectrum_pair_generator",
     "SpectrumPairGenerator"
 ]
diff --git a/ms2deepscore/train_new_model/inchikey_pair_selection.py b/ms2deepscore/train_new_model/inchikey_pair_selection.py
index 5de3241d..0e2ca8ac 100644
--- a/ms2deepscore/train_new_model/inchikey_pair_selection.py
+++ b/ms2deepscore/train_new_model/inchikey_pair_selection.py
@@ -11,7 +11,7 @@
 from ms2deepscore.train_new_model import SpectrumPairGenerator
 
 
-def select_compound_pairs_wrapper(
+def create_spectrum_pair_generator(
         spectra: List[Spectrum],
         settings: SettingsMS2Deepscore,
 ) -> SpectrumPairGenerator:
diff --git a/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py b/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py
index 96c17e55..d12ada34 100644
--- a/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py
+++ b/ms2deepscore/train_new_model/inchikey_pair_selection_cross_ionmode.py
@@ -9,15 +9,15 @@
 from ms2deepscore.train_new_model.SpectrumPairGenerator import SpectrumPairGenerator
 from ms2deepscore.train_new_model.inchikey_pair_selection import compute_fingerprints_for_training, \
     balanced_selection_of_pairs_per_bin, convert_to_selected_pairs_list, tanimoto_scores_row, \
-    select_compound_pairs_wrapper
+    create_spectrum_pair_generator
 from ms2deepscore.utils import split_by_ionmode
 
 def create_data_generator_across_ionmodes(training_spectra,
                                           settings: SettingsMS2Deepscore) -> TrainingBatchGenerator:
     pos_spectra, neg_spectra = split_by_ionmode(training_spectra)
 
-    pos_spectrum_pair_generator = select_compound_pairs_wrapper(pos_spectra, settings=settings)
-    neg_spectrum_pair_generator = select_compound_pairs_wrapper(neg_spectra, settings=settings)
+    pos_spectrum_pair_generator = create_spectrum_pair_generator(pos_spectra, settings=settings)
+    neg_spectrum_pair_generator = create_spectrum_pair_generator(neg_spectra, settings=settings)
     pos_neg_spectrum_pair_generator = select_compound_pairs_wrapper_across_ionmode(pos_spectra, neg_spectra, settings)
 
     spectrum_pair_generator = CombinedSpectrumGenerator([pos_spectrum_pair_generator, neg_spectrum_pair_generator, pos_neg_spectrum_pair_generator])
diff --git a/ms2deepscore/train_new_model/train_ms2deepscore.py b/ms2deepscore/train_new_model/train_ms2deepscore.py
index a9cdaa3f..60b11e3c 100644
--- a/ms2deepscore/train_new_model/train_ms2deepscore.py
+++ b/ms2deepscore/train_new_model/train_ms2deepscore.py
@@ -11,7 +11,7 @@
 from ms2deepscore.models.SiameseSpectralModel import (SiameseSpectralModel,
                                                       train)
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
-from ms2deepscore.train_new_model import TrainingBatchGenerator, select_compound_pairs_wrapper
+from ms2deepscore.train_new_model import TrainingBatchGenerator, create_spectrum_pair_generator
 from ms2deepscore.train_new_model.inchikey_pair_selection_cross_ionmode import create_data_generator_across_ionmodes
 from ms2deepscore.validation_loss_calculation.ValidationLossCalculator import \
     ValidationLossCalculator
@@ -31,7 +31,7 @@ def train_ms2ds_model(
     if settings.ionisation_mode == "both":
         train_generator = create_data_generator_across_ionmodes(training_spectra, settings=settings)
     else:
-        spectrum_pair_generator = select_compound_pairs_wrapper(training_spectra, settings=settings)
+        spectrum_pair_generator = create_spectrum_pair_generator(training_spectra, settings=settings)
         train_generator = TrainingBatchGenerator(spectrum_pair_generator=spectrum_pair_generator, settings=settings)
     # Create a validation loss calculator
     validation_loss_calculator = ValidationLossCalculator(validation_spectra,
diff --git a/ms2deepscore/wrapper_functions/training_wrapper_functions.py b/ms2deepscore/wrapper_functions/training_wrapper_functions.py
index 06151226..8a00b5b8 100644
--- a/ms2deepscore/wrapper_functions/training_wrapper_functions.py
+++ b/ms2deepscore/wrapper_functions/training_wrapper_functions.py
@@ -13,7 +13,7 @@
 from ms2deepscore.models.SiameseSpectralModel import (SiameseSpectralModel,
                                                       train)
 from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
-from ms2deepscore.train_new_model import TrainingBatchGenerator, select_compound_pairs_wrapper
+from ms2deepscore.train_new_model import TrainingBatchGenerator, create_spectrum_pair_generator
 from ms2deepscore.validation_loss_calculation.ValidationLossCalculator import ValidationLossCalculator
 from ms2deepscore.train_new_model.train_ms2deepscore import \
     train_ms2ds_model, plot_history, save_history
@@ -128,7 +128,7 @@ def parameter_search(
         os.makedirs(settings.model_directory_name, exist_ok=True)
         settings.save_to_file(os.path.join(settings.model_directory_name, "settings.json"))
         # Create a training generator
-        spectrum_pair_generator = select_compound_pairs_wrapper(training_spectra, settings=settings)
+        spectrum_pair_generator = create_spectrum_pair_generator(training_spectra, settings=settings)
         train_generator = TrainingBatchGenerator(spectrum_pair_generator=spectrum_pair_generator, settings=settings)
         # Create a validation loss calculator
         validation_loss_calculator = ValidationLossCalculator(validation_spectra,
diff --git a/tests/test_data_generators.py b/tests/test_data_generators.py
index fd2978f6..a3cabbc4 100644
--- a/tests/test_data_generators.py
+++ b/tests/test_data_generators.py
@@ -7,7 +7,7 @@
 from ms2deepscore.tensorize_spectra import tensorize_spectra
 from ms2deepscore.train_new_model.TrainingBatchGenerator import TrainingBatchGenerator
 from ms2deepscore.train_new_model.DataGeneratorEmbeddingEvaluation import DataGeneratorEmbeddingEvaluation
-from ms2deepscore.train_new_model import SpectrumPairGenerator, select_compound_pairs_wrapper
+from ms2deepscore.train_new_model import SpectrumPairGenerator, create_spectrum_pair_generator
 from ms2deepscore.train_new_model.inchikey_pair_selection_cross_ionmode import create_data_generator_across_ionmodes, \
     select_compound_pairs_wrapper_across_ionmode
 from tests.create_test_spectra import create_test_spectra
@@ -176,7 +176,7 @@ def test_create_data_generator():
                                                augment_removal_intensity=0.0,
                                                augment_intensity=0.0,
                                                augment_noise_max=0)
-    spectrum_pair_generator = select_compound_pairs_wrapper(test_spectra, settings=settings)
+    spectrum_pair_generator = create_spectrum_pair_generator(test_spectra, settings=settings)
     data_generator = TrainingBatchGenerator(spectrum_pair_generator=spectrum_pair_generator, settings=settings)
     tensorized_spectra = []
     epochs = 20
diff --git a/tests/test_inchikey_pair_selection.py b/tests/test_inchikey_pair_selection.py
index a6fecd68..2c451312 100644
--- a/tests/test_inchikey_pair_selection.py
+++ b/tests/test_inchikey_pair_selection.py
@@ -7,7 +7,7 @@
 
 from ms2deepscore import SettingsMS2Deepscore
 from ms2deepscore.train_new_model.inchikey_pair_selection import (
-    compute_jaccard_similarity_per_bin, select_inchi_for_unique_inchikeys, select_compound_pairs_wrapper, compute_fingerprints_for_training)
+    compute_jaccard_similarity_per_bin, select_inchi_for_unique_inchikeys, create_spectrum_pair_generator, compute_fingerprints_for_training)
 from ms2deepscore.train_new_model import SpectrumPairGenerator
 from tests.create_test_spectra import create_test_spectra
 
@@ -186,7 +186,7 @@ def test_select_compound_pairs_wrapper_no_resampling():
                                     average_inchikey_sampling_count=10,
                                     batch_size=8,
                                     max_pair_resampling=max_pair_resampling)
-    inchikey_pair_generator = select_compound_pairs_wrapper(spectrums, settings)
+    inchikey_pair_generator = create_spectrum_pair_generator(spectrums, settings)
 
     check_balanced_scores_selecting_inchikey_pairs(inchikey_pair_generator, bins)
     check_correct_oversampling(inchikey_pair_generator, max_pair_resampling)
@@ -206,7 +206,7 @@ def test_select_compound_pairs_wrapper_with_resampling():
                                     average_inchikey_sampling_count=10,
                                     batch_size=8,
                                     max_pair_resampling=max_pair_resampling)
-    inchikey_pair_generator = select_compound_pairs_wrapper(spectrums, settings)
+    inchikey_pair_generator = create_spectrum_pair_generator(spectrums, settings)
 
     check_balanced_scores_selecting_inchikey_pairs(inchikey_pair_generator, bins)
     check_correct_oversampling(inchikey_pair_generator, max_pair_resampling)
@@ -228,7 +228,7 @@ def test_select_compound_pairs_wrapper_maximum_inchikey_count():
                                     max_pair_resampling=max_pair_resampling,
                                     max_inchikey_sampling=max_inchikey_sampling
                                     )
-    inchikey_pair_generator = select_compound_pairs_wrapper(spectrums, settings)
+    inchikey_pair_generator = create_spectrum_pair_generator(spectrums, settings)
 
     highest_inchikey_count = max(inchikey_pair_generator.get_inchikey_counts().values())
     assert highest_inchikey_count <= max_inchikey_sampling + 1 # +1 because there is a chance that the last added inchikey is a pair to itself...
diff --git a/tests/test_siamese_spectra_model.py b/tests/test_siamese_spectra_model.py
index 28811375..88b225da 100644
--- a/tests/test_siamese_spectra_model.py
+++ b/tests/test_siamese_spectra_model.py
@@ -7,7 +7,7 @@
 from ms2deepscore.tensorize_spectra import tensorize_spectra
 from ms2deepscore.train_new_model.TrainingBatchGenerator import TrainingBatchGenerator
 from ms2deepscore.train_new_model.inchikey_pair_selection import \
-    select_compound_pairs_wrapper
+    create_spectrum_pair_generator
 from ms2deepscore.validation_loss_calculation.ValidationLossCalculator import \
     ValidationLossCalculator
 
@@ -130,7 +130,7 @@ def test_model_training(simple_training_spectra):
                                     batch_size=2,
                                     num_turns=20,
                                     )
-    inchikey_pair_generator = select_compound_pairs_wrapper(simple_training_spectra, settings)
+    inchikey_pair_generator = create_spectrum_pair_generator(simple_training_spectra, settings)
     # Create generators
     train_generator_simple = TrainingBatchGenerator(spectrum_pair_generator=inchikey_pair_generator, settings=settings)
     settings.same_prob_bins = np.array([(-0.01, 1.0)])

From 4417ee8f1bdcc5a91a7937059615d8fca2a96374 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Tue, 27 Jan 2026 11:29:32 +0100
Subject: [PATCH 46/48] Add balanced_sampling_across_ionmodes setting, to have
 the default use the standard sampling algorithm.

---
 ms2deepscore/SettingsMS2Deepscore.py               | 9 +++++++++
 ms2deepscore/train_new_model/train_ms2deepscore.py | 2 +-
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/ms2deepscore/SettingsMS2Deepscore.py b/ms2deepscore/SettingsMS2Deepscore.py
index 2d392262..ab9011ec 100644
--- a/ms2deepscore/SettingsMS2Deepscore.py
+++ b/ms2deepscore/SettingsMS2Deepscore.py
@@ -87,6 +87,12 @@ class SettingsMS2Deepscore:
             The in between layers to be used. Default = (2000, 2000, 2000)
         embedding_dim:
             The dimension of the final embedding. Default = 400
+        ionisation_mode:
+            The ionisation mode that is used for training the model.
+        balanced_sampling_across_ionmodes:
+            If True the model will do separate pair sampling for training for each ionmode.
+            This gives better balance over the ionmodes. Initial results showed a decrease in pos-pos prediction
+            accuracy. Which you can find in the notebook model_benchmarking/Compare balanced cross ion moe sampling.ipynb
         additional_metadata:
             Additional metadata that should be used in training the model. e.g. precursor_mz
         dropout_rate:
@@ -184,6 +190,7 @@ def __init__(self, validate_settings=True, **settings):
         self.embedding_dim = 500
         self.ionisation_mode = "positive"
         self.activation_function = "relu"
+        self.balanced_sampling_across_ionmodes = False
 
         # additional model structure options
         self.train_binning_layer: bool = False
@@ -295,6 +302,8 @@ def validate_settings(self):
         if self.loss_function.lower() not in LOSS_FUNCTIONS:
             raise ValueError(f"Unknown loss function. Must be one of: {LOSS_FUNCTIONS.keys()}")
         validate_bin_order(self.same_prob_bins)
+        if self.balanced_sampling_across_ionmodes and self.ionisation_mode != "both":
+            raise ValueError("Balanced sampling across ionmodes only works if you train on both ionmodes")
 
     def create_model_directory_name(self):
         """Creates a directory name using metadata, it will contain the metadata, the binned spectra and final model"""
diff --git a/ms2deepscore/train_new_model/train_ms2deepscore.py b/ms2deepscore/train_new_model/train_ms2deepscore.py
index 60b11e3c..38f3bfa9 100644
--- a/ms2deepscore/train_new_model/train_ms2deepscore.py
+++ b/ms2deepscore/train_new_model/train_ms2deepscore.py
@@ -28,7 +28,7 @@ def train_ms2ds_model(
     # Make folder and save settings
     os.makedirs(results_folder, exist_ok=True)
     settings.save_to_file(os.path.join(results_folder, "settings.json"))
-    if settings.ionisation_mode == "both":
+    if settings.balanced_sampling_across_ionmodes:
         train_generator = create_data_generator_across_ionmodes(training_spectra, settings=settings)
     else:
         spectrum_pair_generator = create_spectrum_pair_generator(training_spectra, settings=settings)

From a77c84df78f6108e1d515f78496886b2d86675d0 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Tue, 27 Jan 2026 11:41:23 +0100
Subject: [PATCH 47/48] Update pair sampling tutorial to match changes made to
 the sampling algorithm function names

---
 .../tutorials/pair_sampling_tutorial.ipynb    | 189 ++++++++++++------
 1 file changed, 124 insertions(+), 65 deletions(-)

diff --git a/notebooks/tutorials/pair_sampling_tutorial.ipynb b/notebooks/tutorials/pair_sampling_tutorial.ipynb
index 4ce7daef..d5bdcc1c 100644
--- a/notebooks/tutorials/pair_sampling_tutorial.ipynb
+++ b/notebooks/tutorials/pair_sampling_tutorial.ipynb
@@ -20,10 +20,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "7250244e-194b-48ca-85e8-011ba79bb5b5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The file validation_spectra.mgf already exists, the file won't be downloaded\n"
+     ]
+    }
+   ],
    "source": [
     "import requests\n",
     "import os\n",
@@ -48,10 +56,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "67e78338-f404-4112-a184-fb1d9471478e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "25412it [00:23, 1095.42it/s]\n"
+     ]
+    }
+   ],
    "source": [
     "from matchms.importing.load_spectra import load_spectra\n",
     "from tqdm import tqdm\n",
@@ -61,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "c49289c0-b66c-40b8-b639-4d61e3a75c0f",
    "metadata": {},
    "outputs": [],
@@ -71,30 +87,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "2f9872ee-643b-4b82-ab07-3860a5ab334e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1831/1831 [00:05<00:00, 327.18it/s]\n",
+      "Calculating fingerprints: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1831/1831 [00:06<00:00, 269.27it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n"
+     ]
+    }
+   ],
    "source": [
     "tanimoto_scores = calculate_tanimoto_scores_unique_inchikey(spectra, spectra)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "55541454-0880-4315-a017-50104609b649",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.22762467"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "tanimoto_scores.mean().mean()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "257f5cd3-7e03-421b-917e-e2764bf6951b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YHuGycXTJdNsjAEDA6ujjN99NAwAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYJXff4EVuJwE4h+Y4w+1AQg0PDMCAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVwbYHsC0hd4vtEQAAuKx16pmRkpISJSQkKDQ0VMnJydq9e3eH1q1fv14Oh0MzZszozGEBAEAP5HeMbNiwQTk5OSooKNCePXs0duxYpaWl6eTJkxdcd/ToUS1YsEBTpkzp9LAAAKDn8TtGli1bprlz52r27Nm67rrrtHz5cvXu3VurV69ud43b7dY999yjJ598UkOGDPlGAwMAgJ7FrxhpbW1VZWWlUlNTv7qBXr2UmpqqioqKdtf97Gc/08CBA3Xfffd16DgtLS1qamryuQAAgJ7JrxhpaGiQ2+1WVFSUz/aoqCjV1dW1ueadd97RqlWrtHLlyg4fp7CwUBEREd5LfHy8P2MCAIAA0qUf7T1z5oxmzZqllStXKjIyssPr8vLy1NjY6L0cP368C6cEAAA2+fXR3sjISAUFBam+vt5ne319vaKjo8/b/4MPPtDRo0eVnp7u3ebxeL44cHCwampqNHTo0PPWuVwuuVwuf0YDAAAByq9nRpxOp5KSklRWVubd5vF4VFZWppSUlPP2v/baa7Vv3z5VVVV5L7fddpumTZumqqoqXn4BAAD+/9GznJwcZWVlafz48ZowYYKKi4vV3Nys2bNnS5IyMzMVFxenwsJChYaGatSoUT7r+/XrJ0nnbQcAAJcnv2MkIyNDp06dUn5+vurq6jRu3DiVlpZ639RaW1urXr34K/MAAKBjHMYYY3uIi2lqalJERIQaGxsVHh5+SW+bPwePnubokum2RwAASR1//OYpDAAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwqlMxUlJSooSEBIWGhio5OVm7d+9ud9+VK1dqypQp6t+/v/r376/U1NQL7g8AAC4vfsfIhg0blJOTo4KCAu3Zs0djx45VWlqaTp482eb+5eXluuuuu/T222+roqJC8fHx+v73v68PP/zwGw8PAAACn8MYY/xZkJycrBtuuEHPP/+8JMnj8Sg+Pl6PPPKIcnNzL7re7Xarf//+ev7555WZmdmhYzY1NSkiIkKNjY0KDw/3Z9yLSsjdcklvD7Dt6JLptkcAAEkdf/wO9udGW1tbVVlZqby8PO+2Xr16KTU1VRUVFR26jc8++0xnz57VgAED/Dk0gA4KxMAmoIDLm18x0tDQILfbraioKJ/tUVFROnDgQIdu47HHHlNsbKxSU1Pb3aelpUUtLS3e601NTf6MCQAAAki3fppmyZIlWr9+vTZt2qTQ0NB29yssLFRERIT3Eh8f341TAgCA7uRXjERGRiooKEj19fU+2+vr6xUdHX3BtUuXLtWSJUu0bds2jRkz5oL75uXlqbGx0Xs5fvy4P2MCAIAA4leMOJ1OJSUlqayszLvN4/GorKxMKSkp7a579tln9dRTT6m0tFTjx4+/6HFcLpfCw8N9LgAAoGfy6z0jkpSTk6OsrCyNHz9eEyZMUHFxsZqbmzV79mxJUmZmpuLi4lRYWChJ+vnPf678/Hy99NJLSkhIUF1dnSSpT58+6tOnzyW8KwAAIBD5HSMZGRk6deqU8vPzVVdXp3Hjxqm0tNT7ptba2lr16vXVEy4vvviiWltb9aMf/cjndgoKCvTTn/70m00PAAACnt9/Z8QG/s4I0LPx0V6gZ+ro4zffTQMAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwKtj0AACTkbrE9gt+OLpluewSgx+CZEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArOpUjJSUlCghIUGhoaFKTk7W7t27L7j/q6++qmuvvVahoaEaPXq0tm7d2qlhAQBAz+N3jGzYsEE5OTkqKCjQnj17NHbsWKWlpenkyZNt7r9r1y7ddddduu+++7R3717NmDFDM2bM0HvvvfeNhwcAAIHPYYwx/ixITk7WDTfcoOeff16S5PF4FB8fr0ceeUS5ubnn7Z+RkaHm5ma9+eab3m0TJ07UuHHjtHz58g4ds6mpSREREWpsbFR4eLg/415UIH5BFwB0Bl/uh+7W0cdvv54ZaW1tVWVlpVJTU7+6gV69lJqaqoqKijbXVFRU+OwvSWlpae3uDwAALi/B/uzc0NAgt9utqKgon+1RUVE6cOBAm2vq6ura3L+urq7d47S0tKilpcV7vbGxUdIXhXWpeVo+u+S3CQDfRlfPf9X2CPiWeu/JtC653S8fty/2IoxfMdJdCgsL9eSTT563PT4+3sI0AAD0bBHFXXv7Z86cUURERLs/9ytGIiMjFRQUpPr6ep/t9fX1io6ObnNNdHS0X/tLUl5ennJycrzXPR6PTp8+rSuvvFIOh8OfkS+oqalJ8fHxOn78+CV/Lwq+wnnuPpzr7sF57h6c5+7RlefZGKMzZ84oNjb2gvv5FSNOp1NJSUkqKyvTjBkzJH0RCmVlZXr44YfbXJOSkqKysjI9+uij3m3bt29XSkpKu8dxuVxyuVw+2/r16+fPqH4JDw/nF70bcJ67D+e6e3CeuwfnuXt01Xm+0DMiX/L7ZZqcnBxlZWVp/PjxmjBhgoqLi9Xc3KzZs2dLkjIzMxUXF6fCwkJJ0rx58zR16lQVFRVp+vTpWr9+vf7yl79oxYoV/h4aAAD0QH7HSEZGhk6dOqX8/HzV1dVp3LhxKi0t9b5Jtba2Vr16ffUhnUmTJumll17SokWL9Pjjj+uaa67R5s2bNWrUqEt3LwAAQMDq1BtYH3744XZflikvLz9v28yZMzVz5szOHKpLuVwuFRQUnPeSEC4tznP34Vx3D85z9+A8d49vw3n2+4+eAQAAXEp8UR4AALCKGAEAAFYRIwAAwKoeHyMlJSVKSEhQaGiokpOTtXv37gvu/+qrr+raa69VaGioRo8era1bt3bTpIHNn/O8cuVKTZkyRf3791f//v2Vmpp60X8u+Iq/v9NfWr9+vRwOh/dvBOHC/D3Pn3zyibKzsxUTEyOXy6Xhw4fz348O8Pc8FxcXa8SIEQoLC1N8fLzmz5+vzz//vJumDUw7duxQenq6YmNj5XA4tHnz5ouuKS8v13e+8x25XC4NGzZMa9eu7dohTQ+2fv1643Q6zerVq83//u//mrlz55p+/fqZ+vr6NvffuXOnCQoKMs8++6zZv3+/WbRokQkJCTH79u3r5skDi7/n+e677zYlJSVm7969prq62tx7770mIiLC/N///V83Tx54/D3XXzpy5IiJi4szU6ZMMbfffnv3DBvA/D3PLS0tZvz48ebWW28177zzjjly5IgpLy83VVVV3Tx5YPH3PP/+9783LpfL/P73vzdHjhwxb731lomJiTHz58/v5skDy9atW83ChQvNxo0bjSSzadOmC+5/+PBh07t3b5OTk2P2799vfvWrX5mgoCBTWlraZTP26BiZMGGCyc7O9l53u90mNjbWFBYWtrn/nXfeaaZPn+6zLTk52fzHf/xHl84Z6Pw9z//o3Llzpm/fvua3v/1tV43YY3TmXJ87d85MmjTJ/OY3vzFZWVnESAf4e55ffPFFM2TIENPa2tpdI/YI/p7n7Oxs893vftdnW05Ojpk8eXKXztmTdCRGfvKTn5jrr7/eZ1tGRoZJS0vrsrl67Ms0ra2tqqysVGpqqndbr169lJqaqoqKijbXVFRU+OwvSWlpae3uj86d53/02Wef6ezZsxowYEBXjdkjdPZc/+xnP9PAgQN13333dceYAa8z5/n1119XSkqKsrOzFRUVpVGjRmnx4sVyu93dNXbA6cx5njRpkiorK70v5Rw+fFhbt27Vrbfe2i0zXy5sPBZ+K7+191JoaGiQ2+32/mXYL0VFRenAgQNtrqmrq2tz/7q6ui6bM9B15jz/o8cee0yxsbHn/fLDV2fO9TvvvKNVq1apqqqqGybsGTpzng8fPqw//vGPuueee7R161YdOnRIDz30kM6ePauCgoLuGDvgdOY833333WpoaNCNN94oY4zOnTunBx54QI8//nh3jHzZaO+xsKmpSX//+98VFhZ2yY/ZY58ZQWBYsmSJ1q9fr02bNik0NNT2OD3KmTNnNGvWLK1cuVKRkZG2x+nRPB6PBg4cqBUrVigpKUkZGRlauHChli9fbnu0HqW8vFyLFy/WCy+8oD179mjjxo3asmWLnnrqKduj4Rvqsc+MREZGKigoSPX19T7b6+vrFR0d3eaa6Ohov/ZH587zl5YuXaolS5boD3/4g8aMGdOVY/YI/p7rDz74QEePHlV6erp3m8fjkSQFBwerpqZGQ4cO7dqhA1BnfqdjYmIUEhKioKAg77aRI0eqrq5Ora2tcjqdXTpzIOrMeX7iiSc0a9YszZkzR5I0evRoNTc36/7779fChQt9vhcNndfeY2F4eHiXPCsi9eBnRpxOp5KSklRWVubd5vF4VFZWppSUlDbXpKSk+OwvSdu3b293f3TuPEvSs88+q6eeekqlpaUaP358d4wa8Pw919dee6327dunqqoq7+W2227TtGnTVFVVpfj4+O4cP2B05nd68uTJOnTokDf2JOngwYOKiYkhRNrRmfP82WefnRccXwag4ZtNLhkrj4Vd9tbYb4H169cbl8tl1q5da/bv32/uv/9+069fP1NXV2eMMWbWrFkmNzfXu//OnTtNcHCwWbp0qamurjYFBQV8tLcD/D3PS5YsMU6n07z22mvmxIkT3suZM2ds3YWA4e+5/kd8mqZj/D3PtbW1pm/fvubhhx82NTU15s033zQDBw40Tz/9tK27EBD8Pc8FBQWmb9++5uWXXzaHDx8227ZtM0OHDjV33nmnrbsQEM6cOWP27t1r9u7daySZZcuWmb1795pjx44ZY4zJzc01s2bN8u7/5Ud7f/zjH5vq6mpTUlLCR3u/qV/96lfm6quvNk6n00yYMMH8z//8j/dnU6dONVlZWT77v/LKK2b48OHG6XSa66+/3mzZsqWbJw5M/pznQYMGGUnnXQoKCrp/8ADk7+/01xEjHefved61a5dJTk42LpfLDBkyxDzzzDPm3Llz3Tx14PHnPJ89e9b89Kc/NUOHDjWhoaEmPj7ePPTQQ+bjjz/u/sEDyNtvv93mf3O/PLdZWVlm6tSp560ZN26ccTqdZsiQIWbNmjVdOiPf2gsAAKzqse8ZAQAAgYEYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECIDzJCQkqLi42PYYAC4TxAgQoG666SY9+uijXXLb7777ru6///4uue2vczgc2rx5c5cfB8C3W7DtAQB8+1x11VW2R/jWcrvdcjgcfF09cAnxbxMQgO6991796U9/0nPPPSeHwyGHw6GjR4/K7Xbrvvvu0+DBgxUWFqYRI0boueeeO2/tjBkztHTpUsXExOjKK69Udna2zp49693nH1+mcTgc+vWvf61//ud/Vu/evTVy5EhVVFTo0KFDuummm3TFFVdo0qRJ+uCDD3yO9eKLL2ro0KFyOp0aMWKEfve73/kcQ5LuuOMOORwO7/WLrWtLeXm5JkyYoCuuuEL9+vXT5MmTdezYMe/P33jjDd1www0KDQ1VZGSk7rjjDu/PPv74Y2VmZqp///7q3bu3brnlFr3//vven69du1b9+vXT66+/ruuuu04ul0u1tbVqaWnRggULFBcXpyuuuELJyckqLy+/4JwA2tGlX8MHoEt88sknJiUlxcydO9ecOHHCnDhxwpw7d860traa/Px88+6775rDhw+b//qv/zK9e/c2GzZs8K7Nysoy4eHh5oEHHjDV1dXmjTfeML179zYrVqzw7jNo0CDzy1/+0ntdkomLizMbNmwwNTU1ZsaMGSYhIcF897vfNaWlpWb//v1m4sSJ5gc/+IF3zcaNG01ISIgpKSkxNTU1pqioyAQFBZk//vGPxhhjTp48aSSZNWvWmBMnTpiTJ092aN0/Onv2rImIiDALFiwwhw4dMvv37zdr1671fj36m2++aYKCgkx+fr7Zv3+/qaqqMosXL/auv+2228zIkSPNjh07TFVVlUlLSzPDhg0zra2txhhj1qxZY0JCQsykSZPMzp07zYEDB0xzc7OZM2eOmTRpktmxY4c5dOiQ+cUvfmFcLpc5ePDgN/ynC1x+iBEgQE2dOtXMmzfvovtlZ2ebf/mXf/Fez8rKMoMGDfL5evuZM2eajIwM7/W2YmTRokXe6xUVFUaSWbVqlXfbyy+/bEJDQ73XJ02aZObOneszy8yZM82tt97qc7ubNm3y2acj677ub3/7m5FkysvL2/x5SkqKueeee9r82cGDB40ks3PnTu+2hoYGExYWZl555RVjzBcxIslUVVV59zl27JgJCgoyH374oc/t3XzzzSYvL6/NYwFoHy/TAD1MSUmJkpKSdNVVV6lPnz5asWKFamtrffa5/vrrFRQU5L0eExOjkydPXvB2x4wZ4/3fUVFRkqTRo0f7bPv888/V1NQkSaqurtbkyZN9bmPy5Mmqrq6+4HH8XTdgwADde++9SktLU3p6up577jmdOHHC+/OqqirdfPPN7R4rODhYycnJ3m1XXnmlRowY4XM8p9Ppc//37dsnt9ut4cOHq0+fPt7Ln/70p/NeqgJwcbyBFehB1q9frwULFqioqEgpKSnq27evfvGLX+jPf/6zz34hISE+1x0OhzwezwVv++trHA5Hu9sudjtdYc2aNfrP//xPlZaWasOGDVq0aJG2b9+uiRMnKiws7BvfflhYmPf+SdKnn36qoKAgVVZW+kSdJPXp0+cbHw+43PDMCBCgnE6n3G63z7adO3dq0qRJeuihh5SYmKhhw4ZZ+3/qI0eO1M6dO8+b77rrrvNeDwkJOe8+dGRdWxITE5WXl6ddu3Zp1KhReumllyR98YxOWVlZuzOeO3fOJ9b+9re/qaam5oLHS0xMlNvt1smTJzVs2DCfS3R09AXnBHA+nhkBAlRCQoL+/Oc/6+jRo+rTp48GDBiga665RuvWrdNbb72lwYMH63e/+53effddDR48uNvn+/GPf6w777xTiYmJSk1N1RtvvKGNGzfqD3/4g899KCsr0+TJk+VyudS/f/8Orfu6I0eOaMWKFbrtttsUGxurmpoavf/++8rMzJQkFRQU6Oabb9bQoUP1r//6rzp37py2bt2qxx57TNdcc41uv/12zZ07V7/+9a/Vt29f5ebmKi4uTrfffnu792348OG65557lJmZqaKiIiUmJurUqVMqKyvTmDFjNH369Et7MoGezvabVgB0Tk1NjZk4caIJCwszksyRI0fM559/bu69914TERFh+vXrZx588EGTm5trxo4d612XlZVlbr/9dp/bmjdvnpk6dar3eltvYP36G02PHDliJJm9e/d6t7399ttGkvn444+921544QUzZMgQExISYoYPH27WrVvnc9zXX3/dDBs2zAQHB5tBgwZ1eN3X1dXVmRkzZpiYmBjjdDrNoEGDTH5+vnG73d59/vu//9uMGzfOOJ1OExkZaX74wx96f3b69Gkza9YsExERYcLCwkxaWprPJ2LWrFljIiIizjvul59cSkhIMCEhISYmJsbccccd5q9//Wu7swJom8MYY+zmEAAAuJzxnhEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsOr/AfNRrpu1WoaPAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from matplotlib import pyplot as plt\n",
     "plt.hist(tanimoto_scores.to_numpy().ravel())\n",
@@ -113,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 8,
    "id": "df9e4707-9dc8-4d64-a411-53e6ce8faee5",
    "metadata": {},
    "outputs": [
@@ -128,45 +182,46 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Calculating fingerprints: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1831/1831 [00:00<00:00, 11063.43it/s]\n",
-      "Balanced sampling of inchikey pairs (per bin): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 915/915 [00:00<00:00, 13671.38it/s]\n",
-      "Balanced sampling of inchikey pairs (per bin): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 915/915 [00:00<00:00, 13254.73it/s]\n",
-      "Balanced sampling of inchikey pairs (per bin): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 915/915 [00:00<00:00, 15341.70it/s]\n",
-      "Balanced sampling of inchikey pairs (per bin): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 915/915 [00:00<00:00, 12985.29it/s]\n",
-      "Balanced sampling of inchikey pairs (per bin): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 915/915 [00:00<00:00, 14196.94it/s]\n",
-      "Balanced sampling of inchikey pairs (per bin): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 915/915 [00:00<00:00, 14742.81it/s]\n",
-      "Balanced sampling of inchikey pairs (per bin): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 915/915 [00:00<00:00, 14620.09it/s]\n",
-      "Balanced sampling of inchikey pairs (per bin): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 915/915 [00:00<00:00, 14370.67it/s]\n",
-      "Balanced sampling of inchikey pairs (per bin): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 915/915 [00:00<00:00, 14187.13it/s]\n",
-      "Balanced sampling of inchikey pairs (per bin): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 915/915 [00:00<00:00, 16721.88it/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 14.69it/s]\n"
+      "Calculating fingerprints: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1831/1831 [00:00<00:00, 8830.61it/s]\n",
+      "Balanced sampling of inchikey pairs (per bin): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9155/9155 [00:00<00:00, 10997.12it/s]\n",
+      "Balanced sampling of inchikey pairs (per bin): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9155/9155 [00:00<00:00, 14929.39it/s]\n",
+      "Balanced sampling of inchikey pairs (per bin): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9155/9155 [00:00<00:00, 15755.28it/s]\n",
+      "Balanced sampling of inchikey pairs (per bin): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9155/9155 [00:00<00:00, 14699.81it/s]\n",
+      "Balanced sampling of inchikey pairs (per bin): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9155/9155 [00:00<00:00, 16051.44it/s]\n",
+      "Balanced sampling of inchikey pairs (per bin): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9155/9155 [00:00<00:00, 16805.94it/s]\n",
+      "Balanced sampling of inchikey pairs (per bin): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9155/9155 [00:00<00:00, 18401.84it/s]\n",
+      "Balanced sampling of inchikey pairs (per bin): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9155/9155 [00:00<00:00, 17945.20it/s]\n",
+      "Balanced sampling of inchikey pairs (per bin): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9155/9155 [00:00<00:00, 17741.57it/s]\n",
+      "Balanced sampling of inchikey pairs (per bin): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9155/9155 [00:00<00:00, 12120.94it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 10.29it/s]\n"
      ]
     }
    ],
    "source": [
-    "from ms2deepscore.train_new_model.inchikey_pair_selection import select_compound_pairs_wrapper\n",
+    "from ms2deepscore.train_new_model.inchikey_pair_selection import create_spectrum_pair_generator\n",
     "from ms2deepscore import SettingsMS2Deepscore\n",
-    "selected_compound_pairs = select_compound_pairs_wrapper(spectra, SettingsMS2Deepscore(average_inchikey_sampling_count=10, max_inchikey_sampling=13))"
+    "spectrum_pair_generator = create_spectrum_pair_generator(spectra, SettingsMS2Deepscore())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "11a14265-50d0-477c-adc6-a381b6d2fd0c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores = [x[2] for x in selected_compound_pairs]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "6eb910a7-02f8-4cdd-8537-157d24603618",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "plt.hist(scores)\n",
+    "plt.hist(spectrum_pair_generator.get_scores())\n",
     "plt.xlabel(\"tanimoto score\")\n",
     "plt.show()"
    ]
@@ -196,18 +251,6 @@
     "Below we show an example of a badly distributed scores (because we only use the validation spectra, which contain 1800 unique molecules). "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "04549827-e21b-46cb-a7b5-712234bc6b50",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ms2deepscore.train_new_model.data_generators import InchikeyPairGenerator\n",
-    "\n",
-    "inchikey_pair_generator = InchikeyPairGenerator(selected_compound_pairs)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "66368132-1c0a-4da7-8d9a-428eedea9421",
@@ -218,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "a60085fb-3ac7-4a61-b22d-bd53d733ef28",
    "metadata": {},
    "outputs": [],
@@ -277,13 +320,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 12,
    "id": "544bfe70-11b9-468f-9418-5788189a6796",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x800 with 6 Axes>"
       ]
@@ -293,7 +336,7 @@
     }
    ],
    "source": [
-    "plot_diagnostic_plots(inchikey_pair_generator, \"\")"
+    "plot_diagnostic_plots(spectrum_pair_generator, \"\")"
    ]
   },
   {
@@ -339,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 13,
    "id": "42a7cf88-27c7-4f54-a3b2-78cd84fbd357",
    "metadata": {},
    "outputs": [
@@ -354,16 +397,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Calculating fingerprints: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1831/1831 [00:00<00:00, 8846.58it/s]\n",
-      "Balanced sampling of inchikey pairs (per bin): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1525/1525 [00:00<00:00, 7941.05it/s]\n",
-      "Balanced sampling of inchikey pairs (per bin): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1525/1525 [00:00<00:00, 10656.40it/s]\n",
-      "Balanced sampling of inchikey pairs (per bin): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1525/1525 [00:00<00:00, 13589.11it/s]\n",
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 12.70it/s]\n"
+      "Calculating fingerprints: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1831/1831 [00:00<00:00, 11680.24it/s]\n",
+      "Balanced sampling of inchikey pairs (per bin): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1525/1525 [00:00<00:00, 20989.14it/s]\n",
+      "Balanced sampling of inchikey pairs (per bin): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1525/1525 [00:00<00:00, 23527.10it/s]\n",
+      "Balanced sampling of inchikey pairs (per bin): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1525/1525 [00:00<00:00, 18984.89it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 18.57it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x800 with 6 Axes>"
       ]
@@ -373,12 +416,28 @@
     }
    ],
    "source": [
-    "selected_compound_pairs = select_compound_pairs_wrapper(spectra, SettingsMS2Deepscore(\n",
+    "spectrum_pair_generator_new = create_spectrum_pair_generator(spectra, SettingsMS2Deepscore(\n",
     "    average_inchikey_sampling_count=5, max_inchikey_sampling=100, \n",
     "    same_prob_bins = np.array([(0.7, 1.0), (0.4, 0.7), (-0.001, 0.4)])))\n",
-    "inchikey_pair_generator = InchikeyPairGenerator(selected_compound_pairs)\n",
-    "plot_diagnostic_plots(inchikey_pair_generator, \"\")"
+    "plot_diagnostic_plots(spectrum_pair_generator_new, \"\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b57af8d-81d0-4530-890a-a9b50e323e78",
+   "metadata": {},
+   "source": [
+    "# Cross ionmode pair selection\n",
+    "Since version 2.7.0 there is a cross ionmode pair selection. Which is automatically used when you train on both ionmodes. This creates separate inchikey pairs per ionmodes. To make sure both modes are sampled equally. This was not used for the model in the paper. Some initial tests were performed, which can be found in \"model_benchmarking/Compare balanced cross ion mode sampling.ipynb\". The results were not yet very convincing, neg-neg becomes better, but pos-pos became worse. It is probably best to further optimize this first before using for model generation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc461fb7-c8ed-4828-b1b4-77e5843af25d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From cfc1921a1a60e24a64424a6cf33aa44c672e3494 Mon Sep 17 00:00:00 2001
From: niekdejonge <niekdejonge@gmail.com>
Date: Tue, 27 Jan 2026 11:56:17 +0100
Subject: [PATCH 48/48] Add Compare balanced cross ion mode sampling.ipynb

---
 ...are balanced cross ion mode sampling.ipynb | 1703 +++++++++++++++++
 1 file changed, 1703 insertions(+)
 create mode 100644 notebooks/model_benchmarking/Compare balanced cross ion mode sampling.ipynb

diff --git a/notebooks/model_benchmarking/Compare balanced cross ion mode sampling.ipynb b/notebooks/model_benchmarking/Compare balanced cross ion mode sampling.ipynb
new file mode 100644
index 00000000..9013f987
--- /dev/null
+++ b/notebooks/model_benchmarking/Compare balanced cross ion mode sampling.ipynb	
@@ -0,0 +1,1703 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "# Balanced sampling across ionmodes\n",
+    "In the manuscript we had a sampling algorithm that sampled from the training data without considering ionisation mode. In MS2DeepScore 2.7.0 it became possible to do pair sampling per ionmode. Equal pairs are selected in pos-pos, pos-neg or neg-neg. The hope was to have better neg neg prediction quality and better pos-neg prediction quality. Even though there is some improvement for neg neg prediction. The pos-pos prediction accuracy also substantially decreases. Which we don't think outweights the benefits. It might be interesting to further tweak these settings, but I did not have the time to fully explore this.\n",
+    "\n",
+    "The models used here were trained on the spectra here: https://zenodo.org/records/16882111\n",
+    "The model itself has not been uploaded to zenodo. So if you want to reproduce, you will need to retrain the model unfortunately."
+   ],
+   "id": "293195a426211dfe"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "from matchms.importing import load_from_mgf\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "normal_model_file_name = \"/lustre/BIF/nobackup/jonge094/ms2deepscore/data/library_22_07_2025/trained_models/both_mode_ionmode_precursor_mz_10000_layers_500_embedding_2025_08_18_14_48_59/ms2deepscore_model.pt\"\n",
+    "balanced_model_file_name = \"/lustre/BIF/nobackup/jonge094/ms2deepscore/data/library_22_07_2025/trained_models/both_mode_ionmode_precursor_mz_10000_layers_500_embedding_2025_08_24_00_04_17/ms2deepscore_model.pt\"\n",
+    "\n",
+    "test_spectra_file = \"/lustre/BIF/nobackup/jonge094/ms2deepscore/data/library_22_07_2025/trained_models/test_merged_and_cleaned_libraries_1.mgf\""
+   ],
+   "id": "5f4bd4fe1813b454"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ba8bfec2-87f3-491f-860a-b4896cf65b6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "49422it [00:16, 2975.96it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_spectra = list(tqdm(load_from_mgf(test_spectra_file)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "6cb33659-a419-421a-84f8-9b80f8aa876a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'charge': 1, 'description': 'ENAMMOL', 'formula': 'C9H13NO2', 'inchi': 'InChI=1S/C9H13NO2/c1-4-10-6(2)5-8(7(10)3)9(11)12/h5H,4H2,1-3H3,(H,11,12)', 'smiles': 'CCn1c(C)cc(C(=O)O)c1C', 'feature_id': '20240405_pluskal_enammol_5003_B20_id_MSn_positive.mzML msn trees:5', 'adduct': '[M-H2O+H]+', 'feature_ms1_height': '1.028E7', 'spectype': 'SINGLE_BEST_SCAN', 'collision_energy': '20.0', 'fragmentation_method': 'HCD', 'isolation_window': '1.200000047684', 'acquisition': 'Commercial', 'ims_type': 'none', 'ion_source': 'ESI', 'ionmode': 'positive', 'dataset_id': 'MSV000094528', 'usi': '[mzspec:MSV000094528:20240405_pluskal_enammol_5003_B20_id_MSn_positive:390]', 'scans': '390', 'precursor_purity': '1.0', 'quality_chimeric': 'PASSED', 'quality_explained_intensity': '0.77921', 'quality_explained_signals': '0.42857143', 'num_peaks': '28', 'compound_name': '1-ethyl-2,5-dimethyl-1H-pyrrole-3-carboxylic acid', 'parent_mass': '167.09463', 'inchi_aux': 'IVFAZMHRJRGODH-UHFFFAOYSA-N', 'ms_level': '2', 'retention_time': 45.26, 'principal_investigator': 'Tomas Pluskal', 'data_collector': 'Corinna Brungs', 'precursor_mz': 150.09134, 'inchikey': 'IVFAZMHRJRGODH-UHFFFAOYSA-N', 'precursor_formula': 'C9H12NO', 'ms_mass_analyzer': 'Orbitrap'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(test_spectra[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6715584d-222a-4525-b0d5-e978f8080285",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def split_by_ionmode(spectra):\n",
+    "    \"\"\"Splits spectra into list of positive ionmode and list of negative ionmode spectra.\n",
+    "\n",
+    "    Removes spectra without correct ionmode metadata entry.\n",
+    "    \"\"\"\n",
+    "    pos_spectra = []\n",
+    "    neg_spectra = []\n",
+    "    spectra_removed = 0\n",
+    "    for spectrum in tqdm(spectra,\n",
+    "                         desc=\"Splitting pos and neg mode spectra\"):\n",
+    "        if spectrum is not None:\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",
+    "                spectra_removed += 1\n",
+    "    print(f\"The spectra, are split in {len(pos_spectra)} positive spectra \"\n",
+    "          f\"and {len(neg_spectra)} negative mode spectra. {spectra_removed} were removed\")\n",
+    "    return pos_spectra, neg_spectra"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "cad7c4e5-bb6d-4ed7-a47e-92398d85cdb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Splitting pos and neg mode spectra: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 49422/49422 [00:00<00:00, 408628.88it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The spectra, are split in 34227 positive spectra and 15195 negative mode spectra. 0 were removed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pos_test, neg_test = split_by_ionmode(test_spectra)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82c4d220-7872-4890-ab54-4b1b9c068b98",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lustre/BIF/nobackup/jonge094/ms2deepscore/ms2deepscore/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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "34227it [00:37, 900.92it/s]\n",
+      "15195it [00:15, 966.28it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3092/3092 [00:18<00:00, 168.93it/s]\n",
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1659/1659 [00:05<00:00, 323.15it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n",
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "17466it [00:18, 966.30it/s]"
+     ]
+    }
+   ],
+   "source": [
+    "from ms2deepscore.benchmarking.CalculateScoresBetweenAllIonmodes import CalculateScoresBetweenAllIonmodes\n",
+    "scores_normal_model_1 = CalculateScoresBetweenAllIonmodes(normal_model_file_name, pos_test, neg_test, fingerprint_type=\"daylight\", n_bits_fingerprint=4096)\n",
+    "scores_balanced_model_1 = CalculateScoresBetweenAllIonmodes(balanced_model_file_name, pos_test, neg_test, fingerprint_type=\"daylight\", n_bits_fingerprint=4096)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72731826-c613-4ce0-8846-a3b98ac400a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import gaussian_kde\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from ms2deepscore.utils import create_evenly_spaced_bins\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from ms2deepscore.benchmarking.CalculateScoresBetweenAllIonmodes import CalculateScoresBetweenAllIonmodes\n",
+    "from ms2deepscore.utils import create_evenly_spaced_bins\n",
+    "import pandas as pd\n",
+    "def get_predictions_per_bin(predictions_and_tanimoto_scores,\n",
+    "                            average_per_inchikey_pair: pd.DataFrame,\n",
+    "                            tanimoto_bins: np.ndarray):\n",
+    "    \"\"\"Compute average loss per Tanimoto score bin\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    average_per_inchikey_pair\n",
+    "        Precalculated average (prediction or loss) per inchikey pair\n",
+    "    ref_score_bins\n",
+    "        Bins for the reference score to evaluate the performance of scores. in the form [(0.0, 0.1), (0.1, 0.2) ...]\n",
+    "    \"\"\"\n",
+    "    average_predictions = average_per_inchikey_pair.to_numpy()\n",
+    "\n",
+    "    sorted_bins = sorted(tanimoto_bins, key=lambda b: b[0])\n",
+    "\n",
+    "    bins = [bin_pair[0] for bin_pair in sorted_bins]\n",
+    "    bins.append(sorted_bins[-1][1])\n",
+    "\n",
+    "    digitized = np.digitize(predictions_and_tanimoto_scores.tanimoto_df, bins, right=True)\n",
+    "    predictions_per_bin = []\n",
+    "    for i, bin_edges in tqdm(enumerate(sorted_bins), desc=\"Selecting available inchikey pairs per bin\"):\n",
+    "        row_idxs, col_idxs = np.where(digitized == i+ 1)\n",
+    "        predictions_in_this_bin = average_predictions[row_idxs, col_idxs]\n",
+    "        predictions_in_this_bin_not_nan = predictions_in_this_bin[~np.isnan(predictions_in_this_bin)]\n",
+    "        predictions_per_bin.append(predictions_in_this_bin_not_nan)\n",
+    "    return predictions_per_bin\n",
+    "\n",
+    "def plot_comparison_violinplot_three_panels(\n",
+    "    list_a,\n",
+    "    list_b,\n",
+    "    bins\n",
+    "):\n",
+    "    bin_labels = [f\"{a:.1f}–<{b:.1f}\" for (a, b) in bins]\n",
+    "    nr_of_bins = len(bin_labels)\n",
+    "    n_panels = 3\n",
+    "    assert len(list_a) == n_panels and len(list_b) == n_panels, f\"Expected {n_panels} sets of scores in each input\"\n",
+    "\n",
+    "    fig, axes = plt.subplots(\n",
+    "        2, n_panels, figsize=(5 * n_panels, 8),\n",
+    "        sharex='col',\n",
+    "        gridspec_kw={'height_ratios': [1, 4]},\n",
+    "        constrained_layout=True\n",
+    "    )\n",
+    "\n",
+    "    def get_bin_data(scores):\n",
+    "        average_predictions = scores.get_average_prediction_per_inchikey_pair()\n",
+    "        return get_predictions_per_bin(scores, average_predictions, bins)\n",
+    "\n",
+    "    def draw_stat_lines(ax, data, pos, side='left'):\n",
+    "        if len(data) == 0:\n",
+    "            return\n",
+    "        median = np.median(data)\n",
+    "        p1, p99 = np.percentile(data, [1, 99])\n",
+    "\n",
+    "        if side == 'left':\n",
+    "            x_range = [pos - 0.3, pos]\n",
+    "        else:\n",
+    "            x_range = [pos, pos + 0.3]\n",
+    "\n",
+    "        # Median line\n",
+    "        ax.plot(x_range, [median, median], color='black', lw=1.5)\n",
+    "\n",
+    "        # 1st and 99th percentile lines\n",
+    "        # ax.plot(x_range, [p1, p1], color='black', lw=1, linestyle='dotted')\n",
+    "        # ax.plot(x_range, [p99, p99], color='black', lw=1, linestyle='dotted')\n",
+    "\n",
+    "    x = np.arange(nr_of_bins)\n",
+    "\n",
+    "    for i in range(n_panels):\n",
+    "        scores_a = list_a[i]\n",
+    "        scores_b = list_b[i]\n",
+    "\n",
+    "        predictions_per_bin_a = get_bin_data(scores_a)\n",
+    "        predictions_per_bin_b = get_bin_data(scores_b)\n",
+    "\n",
+    "        counts_a = [len(p) for p in predictions_per_bin_a]\n",
+    "        counts_b = [len(p) for p in predictions_per_bin_b]\n",
+    "\n",
+    "        # === TOP BAR PLOTS ===\n",
+    "        bar_width = 0.4\n",
+    "        axes[0, i].bar(x - bar_width/2, counts_a, width=bar_width, label=scores_a.label, alpha=0.6)\n",
+    "        axes[0, i].bar(x + bar_width/2, counts_b, width=bar_width, label=scores_b.label, alpha=0.6)\n",
+    "        axes[0, i].set_yscale('log')\n",
+    "        axes[0, i].set_ylabel('Nr of pairs')\n",
+    "        axes[0, i].set_ylim(100, 2_000_000)\n",
+    "        axes[0, i].tick_params(axis='x', labelbottom=False)\n",
+    "        axes[0, i].legend()\n",
+    "\n",
+    "\n",
+    "\n",
+    "        # === BOTTOM SPLIT VIOLIN PLOTS ===\n",
+    "        ax = axes[1, i]\n",
+    "        for j in range(nr_of_bins):\n",
+    "            data_left = predictions_per_bin_a[j]\n",
+    "            data_right = predictions_per_bin_b[j]\n",
+    "            pos = x[j]\n",
+    "\n",
+    "            if len(data_left) > 1:\n",
+    "                kde_left = gaussian_kde(data_left)\n",
+    "                y = np.linspace(0, 1, 200)\n",
+    "                v = kde_left(y)\n",
+    "                v = 0.3 * v / v.max()\n",
+    "                ax.fill_betweenx(y, pos - v, pos, facecolor='#1f77b4', alpha=0.7)\n",
+    "\n",
+    "            if len(data_right) > 1:\n",
+    "                kde_right = gaussian_kde(data_right)\n",
+    "                y = np.linspace(0, 1, 200)\n",
+    "                v = kde_right(y)\n",
+    "                v = 0.3 * v / v.max()\n",
+    "                ax.fill_betweenx(y, pos, pos + v, facecolor='#ff7f0e', alpha=0.7)\n",
+    "\n",
+    "            draw_stat_lines(ax, data_left, pos, side='left')\n",
+    "            draw_stat_lines(ax, data_right, pos, side='right')\n",
+    "\n",
+    "        ax.set_ylim(-0.05, 1.05)\n",
+    "        ax.set_ylabel(\"Predicted score\")\n",
+    "        ax.set_xlabel(\"True chemical similarity\")\n",
+    "        ax.set_xticks(x)\n",
+    "        ax.set_xticklabels(bin_labels, fontsize=9, rotation='vertical')\n",
+    "        axes[0, 0].set_title(\"Positive vs positive\")\n",
+    "        axes[0, 1].set_title(\"Positive vs negative\")\n",
+    "        axes[0, 2].set_title(\"Negative vs negative\")\n",
+    "\n",
+    "    return fig\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "c811caea-e921-4a7a-96af-8fd7cc61a241",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "d22e6c67-29ee-4d0f-abd6-280174e37dd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores_normal_model_1.neg_vs_neg_scores.label = \"Normal model\"\n",
+    "scores_normal_model_1.pos_vs_pos_scores.label = \"Normal model\"\n",
+    "scores_normal_model_1.pos_vs_neg_scores.label = \"Normal model\"\n",
+    "\n",
+    "scores_balanced_model_1.neg_vs_neg_scores.label=\"Balanced across ionmodes\"\n",
+    "scores_balanced_model_1.pos_vs_pos_scores.label=\"Balanced across ionmodes\"\n",
+    "scores_balanced_model_1.pos_vs_neg_scores.label=\"Balanced across ionmodes\""
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "# We here use a 0.999 1.0 bin as well\n",
+    "This is different from the paper and gave some new unexpected results. The accuracy is a lot lower for identical matches cross-ionmode than almost identical matches. Which is surprising and we don't really know what is going on. However, it doesn't really seem to be an issue with the cross ion mode pair sampling."
+   ],
+   "id": "50b5474615f8a923"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "68c7f80f-a3da-4279-9f66-ffc81b9895f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Selecting available inchikey pairs per bin: 11it [00:00, 22.22it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 21.83it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 40.01it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 41.27it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 80.47it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 81.34it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x800 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bins = [(-1e-08, 0.1),\n",
+    " (0.1, 0.2),\n",
+    " (0.2, 0.3),\n",
+    " (0.3, 0.4),\n",
+    " (0.4, 0.5),\n",
+    " (0.5, 0.6),\n",
+    " (0.6, 0.7),\n",
+    " (0.7, 0.8),\n",
+    " (0.8, 0.9),\n",
+    " (0.9, 0.9999),\n",
+    "        (0.9999, 1.0)]\n",
+    "fig = plot_comparison_violinplot_three_panels([scores_normal_model_1.pos_vs_pos_scores, scores_normal_model_1.pos_vs_neg_scores, scores_normal_model_1.neg_vs_neg_scores],\n",
+    "                                              [scores_balanced_model_1.pos_vs_pos_scores, scores_balanced_model_1.pos_vs_neg_scores, scores_balanced_model_1.neg_vs_neg_scores],\n",
+    "                                              bins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "0e319fb9-0b9b-4935-bc9a-f1469eb8009b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Selecting available inchikey pairs per bin: 13it [00:00, 23.76it/s]\n",
+      "Selecting available inchikey pairs per bin: 13it [00:00, 23.35it/s]\n",
+      "Selecting available inchikey pairs per bin: 13it [00:00, 44.71it/s]\n",
+      "Selecting available inchikey pairs per bin: 13it [00:00, 44.07it/s]\n",
+      "Selecting available inchikey pairs per bin: 13it [00:00, 84.89it/s]\n",
+      "Selecting available inchikey pairs per bin: 13it [00:00, 85.25it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x800 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bins = [(-1e-08, 0.001),\n",
+    "        (0.001, 0.01),\n",
+    "        (0.01, 0.1),\n",
+    " (0.1, 0.2),\n",
+    " (0.2, 0.3),\n",
+    " (0.3, 0.4),\n",
+    " (0.4, 0.5),\n",
+    " (0.5, 0.6),\n",
+    " (0.6, 0.7),\n",
+    " (0.7, 0.8),\n",
+    " (0.8, 0.9),\n",
+    " (0.9, 0.9999),\n",
+    "        (0.9999, 1.0)]\n",
+    "fig = plot_comparison_violinplot_three_panels([scores_normal_model_1.pos_vs_pos_scores, scores_normal_model_1.pos_vs_neg_scores, scores_normal_model_1.neg_vs_neg_scores],\n",
+    "                                              [scores_balanced_model_1.pos_vs_pos_scores, scores_balanced_model_1.pos_vs_neg_scores, scores_balanced_model_1.neg_vs_neg_scores],\n",
+    "                                              bins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "230eadc1-4a74-433f-a843-655e47ab028a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Selecting available inchikey pairs per bin: 12it [00:00, 17.28it/s]\n",
+      "Selecting available inchikey pairs per bin: 12it [00:00, 22.15it/s]\n",
+      "Selecting available inchikey pairs per bin: 12it [00:00, 40.40it/s]\n",
+      "Selecting available inchikey pairs per bin: 12it [00:00, 44.55it/s]\n",
+      "Selecting available inchikey pairs per bin: 12it [00:00, 84.84it/s]\n",
+      "Selecting available inchikey pairs per bin: 12it [00:00, 84.13it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x800 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bins = [(-1e-08, 0.001),\n",
+    "        (0.001, 0.01),\n",
+    "        (0.01, 0.1),\n",
+    " (0.1, 0.2),\n",
+    " (0.2, 0.3),\n",
+    " (0.3, 0.4),\n",
+    " (0.4, 0.5),\n",
+    " (0.5, 0.6),\n",
+    " (0.6, 0.7),\n",
+    " (0.7, 0.8),\n",
+    " (0.8, 0.9),\n",
+    " (0.9, 0.9999),\n",
+    "        (0.9999, 1.0)]\n",
+    "fig = plot_comparison_violinplot_three_panels([scores_normal_model_1.pos_vs_pos_scores, scores_normal_model_1.pos_vs_neg_scores, scores_normal_model_1.neg_vs_neg_scores],\n",
+    "                                              [scores_balanced_model_1.pos_vs_pos_scores, scores_balanced_model_1.pos_vs_neg_scores, scores_balanced_model_1.neg_vs_neg_scores],\n",
+    "                                              bins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "61d20252-813a-4485-8beb-b58ef14d1898",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from ms2deepscore.validation_loss_calculation.PredictionsAndTanimotoScores import PredictionsAndTanimotoScores\n",
+    "from ms2deepscore.utils import create_evenly_spaced_bins\n",
+    "\n",
+    "def plot_loss_per_bin_multiple_benchmarks(list_of_predictions_and_tanimoto_scores: List[PredictionsAndTanimotoScores],\n",
+    "                                          nr_of_bins=10,\n",
+    "                                          loss_type=\"MSE\",\n",
+    "                                          title=\"\"):\n",
+    "    \"\"\"Combines the plot of multiple comparisons into one plot\n",
+    "    \"\"\"\n",
+    "    ref_score_bins = create_evenly_spaced_bins(nr_of_bins)\n",
+    "    fig = plt.figure(figsize=(5,3))\n",
+    "    labels = []\n",
+    "    for predictions_and_tanimoto_scores in list_of_predictions_and_tanimoto_scores:\n",
+    "        bin_content, rmses = predictions_and_tanimoto_scores.get_average_loss_per_bin_per_inchikey_pair(\n",
+    "            loss_type, ref_score_bins)\n",
+    "        plt.plot(np.arange(len(rmses)), rmses, \"o:\")\n",
+    "        labels.append(predictions_and_tanimoto_scores.label)\n",
+    "    plt.title(title)\n",
+    "    plt.legend(labels)\n",
+    "    plt.ylabel(loss_type)\n",
+    "    plt.grid(True)\n",
+    "    plt.xlabel(\"tanimoto score bin\")\n",
+    "    plt.xticks(np.arange(len(ref_score_bins)),\n",
+    "               [f\"{a:.1f} to < {b:.1f}\" for (a, b) in ref_score_bins], fontsize=9, rotation='vertical')\n",
+    "    plt.grid(True)\n",
+    "    plt.tight_layout()\n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "64f87538-e49a-44e7-bf28-799bdcbdb2ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Selecting available inchikey pairs per bin: 10it [00:00, 76.31it/s]\n",
+      "Selecting available inchikey pairs per bin: 10it [00:00, 76.79it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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C23IYjAphc9QMhgYoLCxUmePcuXMnMjMzWehDLVJQUKCStnr1anC5XHTs2FELihiMqsPmqBkMDXDp0iV8+eWXGDx4MGxtbXH9+nX8+uuvaNKkicI/NKPmWbZsGaKiotClSxfw+XycOHECJ06cwKeffvpG27gYDG3Ahr4ZDA0QHx+PqVOn4sqVK8jMzISNjQ169eqFJUuWlLvQiFG9nDp1CmFhYYiOjkZubi7q1q2LUaNG4dtvv2URyBh6AzPUDAaDwWDoMGyOmsFgMBgMHYYZagaDwWAwdBg2SVMGMpkMSUlJMDc3r7LLQAaDwWAwKoKIkJOTA2dnZ3C55feZmaEug6SkJLYilMFgMBjVzrNnzyoMVsMMdRmYm5sDkDdgcfi+N0UsFuPvv/9WuGvURfRBI6AfOplGzaEPOvVBI6AfOt83jUKhEC4uLgp7Ux7MUJdB8XC3hYWFRgy1iYkJLCwsdPqPT9c1Avqhk2nUHPqgUx80AvqhU+c1yqSQPDmHBoW3YJnlCH69jgC37LCsVaEy06vMUDMYDAaDUR7RR4HwWeALkxAAAE83ABbOQM+lgHe/ar88W/XNYDAYDIY6oo8CB0YDwiTldGGyPD36aLVLYIaawWAwGIyykEmB8FkAyvIL9iotfLa8XDXCDDWDwWAwGGXx9KJqT1oJAoSJ8nLVCDPUDAaDwWCURW6qZsu9IcxQMxgMBoNRFmYOmi33hjBDzWAwGAxGWbi2la/uhrotVBzAora8XDXCDDWDwWAwGGXB5cm3YJXJK+Pdc4lG9lOXK6Naa2cwGAwGQ5/x7ge0m6qabuEMDNlZI/uomcMTBoPBYDDKw74hYFoL0rqtcSO/Nnw7BGnMM1llYIaawWAwGIzy8B0OaZMhuByTgPBLd1Eo80YbcFEzZlpHhr7XrVsHNzc3GBkZoVWrVrhy5Yraslu2bEGHDh1gbW0Na2trBAYGKpUXi8WYNWsWfHx8YGpqCmdnZ4wePRpJSeXthWMwGAwGo2zC7yaj/fJ/MXzXfeyM5WHk1mtov/QfhN9NrpHra91Q79+/H9OnT8f8+fNx/fp1NGvWDEFBQUhLSyuzfEREBIYNG4azZ88iMjISLi4u6NGjBxITEwEA+fn5uH79OubNm4fr16/j8OHDiImJQb9+1T+PwGAwGIx3izPX72Pi7utIzi5USk/JLsTE3ddrxFhr3VCvWrUK48ePx9ixY+Ht7Y2NGzfCxMQEW7duLbP8nj17MGnSJPj6+qJhw4b45ZdfIJPJcObMGQCApaUlTp06hSFDhsDLywutW7fG2rVrERUVhYSEhJq8NQaDwWDoMVJxEVoc7YZjgm/ggEylvGKnomHHoiGVleViVHNo1VCLRCJERUUhMDBQkcblchEYGIjIyMhK1ZGfnw+xWAwbGxu1ZbKzs8HhcGBlZfW2khkMBoPxnhAddR4mVIBanJd4ASuVfAKQnF2IK3GZKnmaRKuLydLT0yGVSuHgoOzVxcHBAQ8ePKhUHbNmzYKzs7OSsS9NYWEhZs2ahWHDhqmNLV1UVISioiLFsVAoBCCf7xaLxZXSoY7i89+2nupEHzQC+qGTadQc+qBTHzQC+qFTFzXGGjTA6KL1qMdJhqycfm3yyzyIxWXbF3VU5T71etX3kiVLsG/fPkRERMDIyEglXywWY8iQISAibNiwQW09ixcvRlhYmEr633//DRMTE41oPXXqlEbq0TQyAh4LORCKOYg9dBoeFgRuxXHMtYqutmVpmEbNoQ869UEjoB86dUnjk2wOsmCBKCrfCD+5dxPHn9+oUt35+fmVLqtVQ21nZwcej4fUVGWH5qmpqXB0dCz33BUrVmDJkiU4ffo0mjZtqpJfbKSfPn2Kf/75R21vGgDmzJmD6dOnK46FQqFikVp551UGsViMU6dOoXv37jAwMHirujTNyXupWHz8AVKEJaMJjhaGmNurIYIaV6/v2jdBl9uyGKZRc+iDTn3QCOiHTl3UKJURDq08h1RhUZmBLjkAHC0NMTm4I3hV7OEUj9xWBq0aaoFAAH9/f5w5cwYDBgwAAMXCsMmTJ6s9b9myZVi0aBFOnjyJgIAAlfxiIx0bG4uzZ8/C1ta2XB2GhoYwNDRUSTcwMNDYH4wm69IE4XeTMWXfLZU/vlRhEabsu4UNI5ujZxMnrWirCF1ry7JgGjWHPujUB42AfujUGY1/z4WBMAmrOgzDiL/kRrn072WxWZ7ftzGMDAVVrr4q96j1Vd/Tp0/Hli1bsGPHDty/fx8TJ05EXl4exo4dCwAYPXo05syZoyi/dOlSzJs3D1u3boWbmxtSUlKQkpKC3NxcAHIj/dFHH+HatWvYs2cPpFKpooxIJNLKPeoaUhkh7Fh0eaHQa2QlI4PBYOgkMilwcy9w93e0deJiUhcP2JgqG2NHS6Ma69BofY46ODgYL168QGhoKFJSUuDr64vw8HDFArOEhARwuSXvExs2bIBIJMJHH32kVM/8+fOxYMECJCYm4ujRowAAX19fpTJnz55F586dq/V+9IErcZkqewJLU3olYxuP8kcjGAwG452DwwWG7wce/AWZawccO3wBGXkiTO5cD8LEWPTo0Apt6teq8nD3m6J1Qw0AkydPVjvUHRERoXQcHx9fbl1ubm4gYj1BdWTlibDvauX2k0/ffxP9fJ3xdc+GNfYHyWAwGFqHwwHqBAB1ApBXKEYrdxsUiqUY38ENEacfopW7TY3+Jmp96PudRiYF5+l51M6MBOfpeflwipYZ8ctl/Hmzcu5Uk4WF+PfhC6U/yGvxmUjLUd8bZzAYjHcJcyMDLB/cDBdmd4WJQDt9W53oUb+TRB8FwmeBL0xCAAA83SAPi9ZzaY2ERQOAQrEU/zxIQ8/GjuC+MrZDAurgYNQzJL0sxMt8sdqVjLUsDPHNB43A45UYaSLC5N9uIEVYiAMT2qClu3onMwwGg6GXJN0EHoYD3gOAWg0VyQY8LsRa6myxHnV1EH0UODAaEL7WcxUmy9Ojj1a7BJmMELT6HCbtuY4Lj9MV6aPauOF/Uzpg8SAfAAAPMrTmRqMf9yJac6PBgwwAENavMfr71Uafps6Kc7PyxahlYQgTAQ9N61gq0g9ce4Yv99/ExVLXYTAYDL3k9n4gYjFw/kdcT8hCqlD7I4isR61pZFIgfBagdk01BwifDTToAfAN5XMhGoCIEJ0sRGNnuQHlcjno7GmPv6NTkVsoUZQrHsbu2cQJh7ukwzkyDA7IUOSnwhZJbebDr4yVjDamAhyd3B45hWIYGZQEeDt2Kwn/xabD28kCbT3sAAAiiQyP0nLRyMkcHA3dI4PBYFQ7ru2AzDhQk0GYvv8mnmUVYNuYFujoaa81ScxQa5qnF1V70koQIEwEdvYHnl8B+v4ENB8tz3qZAPwxCTB3Aj7cUnLKg+NAVjzg3hFwbCJPkxQBL2IAQzMUmLmi79rzePwiF+dmdoGLjdyb2ldBXpjXxxt8XhkDJ9FH4Rc5DfTaC0UtZMIhchrgYq12iN7cSHn/39RuDeDtbIHu3iVOUq7EZWLkr5fRtI4ljk5uX057MBgMhg7RqA/QqA8yc4tgbx6F9FwR/F2ttSqJGWpNk5tacRkAKMwGSAbwS7k+zUsH4v8DLOool731G3D/GNB7pcJQv0x+AqtfOwCGFjCe8wxOlkZIflkA2V8zgLSzQOdZsPAfIz+/IAs4MQswtAB6r1Dq9b/e1+WU7vU37A1wKw6N3sLNBi3clOern2flw9iABy8Hc6X0b4/cgbudKQb7u8DSRAecGjAYDEYZ2JoZ4uBnbZGeWwRTQ+2aSmaoNY1ZJV1v9lgIOPoAhqUMmZUrMOgXgPtaD9i1HcAzBOw88SKnCFP2XocoKRq/mzmC8+r8RQN8YG1qAPM/9wA5SYC0lMP3/Ez5vIvAXG6oK9vrf3oRcO9Quft5jaEt62KAX23kFpUMu6cKC7HncgI4HGCgX21F+oucIpgb8ZWG019HKiNcjstEVDoHtnGZNbqHkcFgvAcQyTtEHl2UfpftzFS9VtY0zFBrGte28tXdwmSUPU/Nked7dFXtrZraAk0Hq5whaTEB/NYTAQA2MkLSy0Ikipxwvv95dGggnzepa/sqeEjPxUD7LwGLEkMIIyugx/fyP0Sg8r3+ypZTg5EBT8n4GvK5mNu7EZ5nFcC21B//or+icfJeKsL6N8aQABeVesLvJiPsWPQrJy087Iy9BidLI8zv662zbk4ZDIaekXwLODAKMLZG3JibqG1rAQFfN9ZbM0Otabg8+RasA6Oh1jtszyWVGlJOzi7A4uMP8CQ9F8cmtweHwwGPy8HKIc1Q28oYzlbGqidZ1pF/SmNqC7SdUnJc2V5/ZctVEisTAT7pUE8lPSY1FwViKVxtSiKVPUrLxcGoZ7Aw4mPFyYcqrzwp2YWYuPu6TvskZzAYekR+OmBTD+Tog49330R2vhi/hATAr65256cBtj2revDuBwzZCVi8ZkAsnOXpldxHbSLg41R0Ku4mCnE3sSTSSgs3m7KNdGUp7vWrzFCXwtReXg6Qz6efCgWyn7/5Ncvh+NT2OD61A5qXWrDxd3QKNv37BD+fecR8kjMYjOqnfiAw5TpSOq9CbqEEBWIpGry2xkZbsB51deHdD2jYG5In53Dzv5Pw7RAEfr2OanvSKdmF+OW/JxAWirHso2YAAEtjAywa2ASeDuZoUtuyzPPeiMr0+nuvKtF6bRtw4Sfg0T/AxPOa01F8RQ4H3s7K4USb1bFC+/p2OP9I/d5s5pOcwWBoFA4HTrXscHF2V8Sk5sBMy4vIimE96mpECi4uybxxVNYWl2TekJbT3MJCMX45H4dDUc+R9LJAkT6oeR3NGuliqtLrd/YD3DoArT8rSZPJgPgLJfPeGqZdfTsMDqhTcUGAuTRlMBhvR1660m8Zn8dV+KTQBXTjdeEdpLwFUK3r2eLAtWfgc7kY194dAODpYI6JnT3Q0s0GjhZG5VeuKSrb66/XSf4pbZQfhgP7hsn3do8+qjHHLaWpZV65dqhsOQaDwSiTbb0AaRFy+m+FuZu/ttWowAx1NRB+NxkTd19XuwDq0471sOncE9iYCjC8VV3FyuhZPRuqVlbdcHkg1/ZIvCdEM9f25S9yK22Ms58BfGPAublyukQE8KseRL0sWrrbwMnSCCnZhWp9kjtaGjGf4wwG480RJgEvn4JkUoz8PRUwPI+VQ5qhfi3dmJ8G2NC3xpHKCGHHostdAHX0VhK6eNnj6yCv6uiI1gytJgBf3gPaTStJe/EQWOkF/LNII0PiPC4H8/t6A1Bd9lZ8/GV3T7afmsFgvDkWzsDMx0juvw/RmcDD1FzUqqlRzUrCDLWGuRKX+Wq4u2yKF0B92tEDQ1vWhSG/4m1aOoupLWBSqjd7cw9QkAmk3tPYUHjPJk7YMLI5HC2Vvzh25oawNzfEwWvPIJLINHItBoPxnmJoBudmgbg0pxvWj2wOCyPd8prIhr41TGUXNr2TC6C6hQK1/QFr15K0QiHwvy+Blp8CdVu9UbU9mzihu7cjIh+l4e//LqNHh1awtzDBRxsuIullIVKFhQr/5gwGg/Gm2JoZootXLW3LUIEZag3zXi+A4vJU94hf3wHcPQSk3AYmXVZ1j1pJeFwOWrnbIOM+oZW7DQwMDPDrmBaoZ2+qEy7+GAyGHnJxDRD3H6QtPwOvQVdtq1GLTgx9r1u3Dm5ubjAyMkKrVq1w5coVtWW3bNmCDh06wNraGtbW1ggMDFQpT0QIDQ2Fk5MTjI2NERgYiNjY2Oq+DQAlC6DUDfxyADi9TwugGgQBfqOA9tNLjLRMBtw+CIjfblShpbuNkpGWMccnDAajKtw5CMSexIY//8G0fTeUtsbqElo31Pv378f06dMxf/58XL9+Hc2aNUNQUBDS0tLKLB8REYFhw4bh7NmziIyMhIuLC3r06IHExERFmWXLluHnn3/Gxo0bcfnyZZiamiIoKAiFhdU/3FyZBVDz+3q/Pwug7D2B/msB32ElabF/A4c/ATa0lUfy0gAXH6UjaPU5nf2iMRgMHWTQFqT6z8DW9MYIv5sCU4FuDjJr3VCvWrUK48ePx9ixY+Ht7Y2NGzfCxMQEW7duLbP8nj17MGnSJPj6+qJhw4b45ZdfIJPJcObMGQDy3vTq1asxd+5c9O/fH02bNsXOnTuRlJSEP/74o0buSd0CKEdLI+abGgCkRfJQng17KW8Hy8t4o+pkMsKS8AeITcvFT6drZuSEwWC8A9h7waHvPOyY3AsLBzTR2dC7Wn19EIlEiIqKwpw5cxRpXC4XgYGBiIyMrFQd+fn5EIvFsLGRDyXHxcUhJSUFgYGBijKWlpZo1aoVIiMjMXToUJU6ioqKUFRUpDgWCuV+tcViMcRisUr5ytDNyw6dG3TApccv8E9kFLq28UdrD3vwuJw3rrO6KNZTY7oa9ALqdQckhUDxNTMegb+5A6hRP0j7rQO4r/1pyqSQxp1H7cxISB+bAu7Ke75/Dm6KX8/H4+sgT622b022pUwmg1gsBlVxK5xEIgGfz0dubi74fN3sQQD6oVMfNAL6oVObGt0suXCztEJOTk655SqrkcPhgM/ng8dTv6unKr8RWn1i6enpkEqlcHBQjtLk4OCABw8eVKqOWbNmwdnZWWGYU1JSFHW8Xmdx3ussXrwYYWFhKul///03TEzefjWxvx2QHXsNJ3W8s3fq1CmtXbte2kn4yMRIffYYl8P/VspzenkVPs/3wFiciQAAeLoBBQY2uFNnBJKtWijKBXCBf07F1axwNVR3W/J4PNjZ2cHA4M16AI6Ojnjy5ImGVWkefdCpDxoB/dBZUxo5JANfVgAp1xAyTtXMYGU1ymQy5OTkqDX++fn5lb6mbr5aVZIlS5Zg3759iIiIgJHRm6+injNnDqZPn644FgqFirlvCwuLcs6sGLFYjFOnTqF79+5v/KNa3eiGxl4Qp3wCWy4fvWo1kicV5YC3PQic9IcqpY3EWWgRtxbSD7eBGvZRyT90PREO5obo0MCuuoUrURNtSURITEyERCKBk5MTuFVcSU9EyMvLg6mpKTg67HFHH3Tqg0ZAP3TWpEZOQSY4eWkohBFyjJ1hZWxQqXVDldVIRMjPz8eLFy/g6emp0nEESkZuK4NWDbWdnR14PB5SU1OV0lNTU+Ho6FjuuStWrMCSJUtw+vRpNG3aVJFefF5qaiqcnErmglNTU+Hr61tmXYaGhjA0VN3iY2BgoLEfW03WVV1oXaNLc+Xja3uBMow0AHBAADjgn/oWaNxPaRj82K0kzDlyDxZGfIR/0fHtQoK+IdXZlmKxGIWFhXB2doaZmVmVzy8eMjc2Nq6yka9J9EGnPmgE9ENnjWrkySCBGLmFfLwUceBkYww+r+JrVkWjqakpuFwu0tLS4OTkpDIMXpXfB60+MYFAAH9/f8VCMACKhWFt2rRRe96yZcuwcOFChIeHIyAgQCnP3d0djo6OSnUKhUJcvny53DoZOoiNewUFCBAmAk8vKqX2aOwAf1drjG3nXnMBTmoQqVS+Ul4g0IxPdQbjvcPQDFxrN5hZO8DBwrBSRvpNKJ46fds1K1of+p4+fTpCQkIQEBCAli1bYvXq1cjLy8PYsWMBAKNHj0bt2rWxePFiAMDSpUsRGhqK3377DW5ubop5ZzMzM5iZmYHD4eCLL77A999/jwYNGsDd3R3z5s2Ds7MzBgwYoK3bZLwJokrO4eQqj8gY8nnY92lrGFTTl09X0NUhTAZDH+ByObAyqd6XXU19R7X+SxYcHIwVK1YgNDQUvr6+uHnzJsLDwxVj+gkJCUhOTlaU37BhA0QiET766CM4OTkpPitWrFCU+frrrzFlyhR8+umnaNGiBXJzcxEeHv5W89gMLWCmOq9T2XKljbRMRrj4KF1TqhjvGREREeBwOHj58qW2pQCQ//hXZavpmDFjMHDgwOoTpG8UZANS3dp5UxFVMtTqnJAUI5FIyvUqpo7Jkyfj6dOnKCoqwuXLl9GqVYlP6IiICGzfvl1xHB8fDyJS+SxYsEBRhsPh4LvvvkNKSgoKCwtx+vRpeHp6VlkXQ8u4tpVHtinPz5tFbXk5NUikMny6KwrDf7mM8LvJassxqp8xY8aAw+FgyZIlSul//PEHGx1g1AxSMZD1BJR6F9m5+ZBpIMpfTVAlQ+3k5KRkrH18fPDs2TPFcUZGBpsHZmgOLg/oufTVgRo/bz2XlBtDm8/jwtXWBAI+F4ViFmWrNFIZIfJxBv68mYjIxxmQ1oALViMjIyxduhRZWVkarVckEmm0PsY7ilQMGd8YBWSIhGyx3rgdrpKhft2xQnx8vMokeVWdLzAY5eLdDxiyE7B4zZubhbM8XVwgj9BVDnM+aIhjk9tjgF/tahSqX5yJyUCHZREYtuUSpu27iWFbLqH90n+qfdQhMDAQjo6OijUn6vj999/h4+MDBwcH1KtXDytXrlTKd3Nzw8KFCzF69GhYWFjg008/xfbt22FlZYX//e9/8PLygomJCT766CPk5+djx44dcHNzg7W1NaZOnapYkAcAu3btQkBAAMzNzeHo6Ijhw4dXOHr4OjweD5s2bUKfPn1gYmKCRo0aITIyEo8ePULnzp1hamqKtm3b4vHjx0rnbdiwAR4eHhAIBPDy8sKuXbuU8mNjY9GxY0cYGRnB29u7zP35z549w5AhQ2BlZQUbGxv0798f8fHxVdL/3iAwgdTWE7lmbrA1FVTbIjJNo3GVbAiLoXG8+wFf3IVk5B+45joRkpF/AF/cAVLvAkc+BQ6NK9dnOJ/HhZejueK4UCyFRPr+9q7D76ZgxpEHSBEq+75PyS7ExN3Xq9VY83g8/PDDD1izZg2eP39eZpmoqCgMGTIEwcHBuHDhAkJDQzFv3jylKTBAvkWzWbNmuHHjBubNmwdA7kTi559/xr59+xAeHo6IiAgMHDgQx48fx/Hjx7Fr1y5s2rQJhw4dUtQjFouxcOFC3Lp1C3/88Qfi4+MxZsyYKt9b8YvDzZs30bBhQwwfPhwTJkzAnDlzcO3aNRARJk+erCh/5MgRTJs2DV999RXu3r2LCRMmYOzYsTh79iwA+Q6YQYMGQSAQ4PLly9i4cSNmzZqldE2xWIygoCCYm5vjv//+w4ULF2BmZoaePXuyUQY1GPC4qGVpopVtm28MVQEOh0OpqamKYzMzM3r8+LHiOCUlhbhcblWq1Emys7MJAGVnZ791XSKRiP744w8SiUQaUFY96INGojJ0Jl4n+t6J6NxKIpmsUnU8z8qn3j+fo7Cj92pGYzVQUFBA0dHRVFBQoJSeVySmvCIxyUq1RZFYSnlFYioUS4iISCKVUatFp8l11v/K/LjN+h+1+uE0SaQylXqlpdJEEmmVdYeEhFD//v2JiKh169Y0btw4IiI6cuQIlf4pGj58OHXv3p2kUillZWWRVCqlmTNnkre3t6KMq6srDRgwQKn+bdu2EQB69OiRIm3ChAlkYmJCOTk5irSgoCCaMGGCWp1Xr14lAIpzzp49SwAoKytLpWyxRgA0d+5cRXpkZCQBoF9//VWRtnfvXjIyMlIct23blsaPH69U3+DBg6lXr15ERHTy5Eni8/mUmJioyD9x4gQBoCNHjhAR0a5du8jLy0v5mRcVkbGxMZ08eZKI5O3er18/RVvqKqWfd7UgEVX6d0IdVdWo7rtKVDU7U6UeNYfDQU5ODoRCIbKzs8HhcJCbmwuhUKj4MBg1hrMfMPUG0GE6UMmRnHuJ2bibKMQfNxORnltU8Ql6hHfoSXiHnkRmXklPavO5x/AOPYn5f94DAFyJy1TpSZeGIO9ZX4nLVKS1X3oW3qEn8ehFriLtUFTZveHKsnTpUuzYsQP3799Xybt//z7atWunlNauXTvExsYqDVm/7kMBkO9b9fDwUBw7ODjAzc1NyTGMg4OD0tB2VFQU+vbti7p168Lc3BydOnUCIN9xUhVKO14q3rXi4+OjlFZYWKj4nVR3n8Vtcv/+fbi4uMDZ2VmR//oaoFu3buHRo0cwNzdXbFG1sbFBYWGhyjD7e8/LBEhT7qIoN0vvpmirtI+aiJRWTxMR/Pz8lI7Z0DejRjEvtTVLKgFePgVsPdQW79HYET8M9EFHTzulWNbvC2k5lQv1Wtlyb0rHjh0RFBSEOXPmvNEwMyD3/PQ6r3t74nA4ZabJZPKpj7y8PAQFBSEoKAh79uyBvb09EhISEBQUVOWh49LXKf4dLCut+NqaIDc3F/7+/tizZ49Knr29vcauo/eQDCTKB48keJwtQT1jAp+nP7aqSoa6eO6EwdA5CoXAwTFA8k1g/D+AtZvaosNb1a0pVTVK9HdBAABjg5JV8J929MC49u4KP8a1zCvnS6B0ufOzugAAjPgl9X7kX+et9S5ZsgS+vr7w8vJSSm/UqBEuXLiglHbhwgV4enqWG43oTXjw4AEyMjKwZMkSuLi4AACuXbum0Wuoo/g+Q0JCFGkXLlyAt7e3Iv/Zs2dITk5WuEO+dOmSUh3NmzfH/v37UatWrbeOS/BOw+GiyLYRhMKXMOaa6M0ismKqZKiLh4QYDJ2Dywfy0+XezNIflWuoS3PneTY2/vsYq4KbwZCvWSNQ05iUEfRewOdCUGrNaEt3GzhaGCFVWIiyBv84kMdNb+luU269mvD65uPjgxEjRuDnn39WSv/qq6/QokULfP/99+jVqxfu3LmDtWvXYv369W99zdepW7cuBAIB1qxZg88++wx3797FwoULNX6dspg5cyaGDBkCPz8/BAYG4tixYzh8+DBOnz4NQL5C3tPTEyEhIVi+fDmEQiG+/fZbpTpGjBiB5cuXo3///vjuu+9Qp04dPH36FIcPH8bXX3+NOnXe/oXqXcFIwIeRnZ3eDXsDVVz1LZFIlOI2A/JgF2FhYfj6669x/vx5jYpjMCqNwAQYuhcYFw40CKy4POSrvz/ZeRV/3UnGmjOPqlmgbsDjchDaRx6dTM3OdMzv612pSEKa4LvvvlMZCm7evDkOHDiA/fv3o23btliwYAG+++67Nx4iLw97e3ts374dBw8ehLe3N5YsWaLk5bA6GTBgAH766SesWLECjRs3xqZNm7Bt2zZ07twZAMDlcnHkyBEUFBSgZcuW+OSTT7Bo0SKlOkxMTHDu3DnUrVsXgwYNQqNGjfDxxx+jsLCQ9bDVoI/TsxyqwuvF2LFjIRAIsGnTJgBATk4OGjdujMLCQjg5OSE6Ohp//vknevXqVW2CawKhUAhLS0tkZ2drJMzl8ePH0atXL52NnqUPGoE30CkRAfzyffmee/gCOyOfYlVwM1gYvf2910RbFhYWIi4uDu7u7m/kFlcmk+HI1TgsPxOvtLDMydII8/t6o2cTp3LOrjlkMhmEQiEsLCx0OuKTrmsE9ENntWksyIJYmAaumR14prZvVVVVNZb3Xa2KnanS0PeFCxewdu1axfHOnTshlUoRGxsLS0tLzJo1C8uXL9d7Q814B8iKB/YOA9pOBXyHqS3W0dMeHT3fv0U33bxs0c/fDdeevkRaTiFqmcuHu2uqJ81g1BTSvEwYSPOR9lIIW2Nr8HT0RaU8qqQ4MTERDRo0UByfOXMGH374ISwtLQEAISEhuHfvnmYVMhhvwp2DQFo0EPEDIKn8Nqwz91PxLLOSUbv0HB6XgzYetujvWxttPGyZkWa8kxSZOuMF1w5igZVeGmmgiobayMgIBQUFiuNLly4pBdAwMjJCbm5uWacyGDVL+6+A9tOBcScBfuW2YR249gwf77iG8TuvIV8kqWaBDAajJjAxNoa9owucbK20LeWNqZKh9vX1Vfii/e+//5CamoquXbsq8h8/fqy0OZ/B0BpcLhA4/1X0rcrRvr4d7MwEaOVu887HsmYw3je4ejxiVKU56tDQUHzwwQc4cOAAkpOTMWbMGMX+PkDuu/Z1TzsMhk7w7Apwcw/Q+0e5ES8DZytjhH/R8b10hMJgvHPIZBBnPQPf1BocQ/NKey/URaq8jzoqKgp///03HB0dMXjwYKV8X19ftGzZUqMCGYy3puAlsPtDoEgI2DcCWn+mtmhpI01EeJ5VABcbkxoQyWAwNIko/yUERZkQFQnBd2wCPe5QV81QA3JvOY0aNSoz79NPP31rQQyGxjG2Aj5YBjz4H+A3slKnFEmkmHvkLk7cTcEfn7dD/VpmFZ/EYDB0BhEMkAsLcHgCWOuzlUYV56jPnTtXqU9VWLduHdzc3GBkZIRWrVrhypUrasveu3cPH374Idzc3MDhcLB69WqVMlKpFPPmzYO7uzuMjY3h4eGBhQsX6qU3GoYG8R0GBO8GDCtncDng4GlGPvJFEtx89rJ6tTEYDI1jZmYOK6d6MLPT/zj0VepRd+7cWeHVRZ3h43A4ShFuymP//v2YPn06Nm7ciFatWmH16tUICgpCTEwMatWqpVI+Pz8f9erVw+DBg/Hll1+WWefSpUuxYcMG7NixA40bN8a1a9cwduxYWFpaYurUqZW8U8Y7Sek5qug/gdoBgGXZX2IBn4v1I5sjJiUH7erb1ZBABoOhSbgcDrh6FHxDHVXqUVtbW8PFxQXz5s1DbGwssrKyVD6ZmZkVV/SKVatWYfz48Rg7diy8vb2xceNGmJiYYOvWrWWWb9GiBZYvX46hQ4fC0LDsBT8XL15E//790bt3b7i5ueGjjz5Cjx49yu2pM94zrv4KHBgN7Bsm9w2uBjszQyUjzUZldIMFCxbA19dX2zIqJD4+HhwOBzdv3tS2lGqFw+Hgjz/+0LYMJcR5LyvtP8HNza3M0Vldoko96uTkZBw5cgRbt27FsmXL0KtXL3z88cfo2bNnlf2nikQiREVFYc6cOYo0LpeLwMBAREZGVqmu0rRt2xabN2/Gw4cP4enpiVu3buH8+fNYtWqV2nOKioqUfJgXx4sVi8UQi8VvrKW4jtL/10X0QSOgQZ1uncA3sYXMtT1kxAUqUV9mnghfHLiN4IA66O3jWP0ay0EsFoOIIJPJ3ihkYvELB0klkMVfAnJTADNHoG4bgFt9gUnGjh2LnTt3Ko5tbGwQEBCApUuXKsVyVtH56l5fT9dkuMg3RZ1GoETfmz4nTVKezrclMTER1tbWb12vpjSKJFLwXz4FODLIbBsABhUvBq3sNauqUSaTgYggFotVIr9V5TeiSoZaIBAgODgYwcHBSEhIwPbt2zF58mQUFRUhJCQEYWFh4PMrV2V6ejqkUqkiwHoxDg4OePDgQVVkKTF79mwIhUI0bNgQPB4PUqkUixYtwogRI9Ses3jxYoSFhamk//333zAx0cyK31OnTmmknupEHzQCmtEpqBcGUZEFEH6yctdM5CAygYc7zzIgjr8OQQX2rDrbks/nw9HREbm5uVWOmVyMwaMT4ESEgZubrEiTmTmhoPN8iOt/oCmpSojFYnTr1g3r1q0DAKSlpeH7779Hnz59cPfuXbXn5eTkKB0XFRVBKpUqXqh1gdc1AlA4f8rLy6sxrSKRCAKBev/2Zel8W0xMTFQ6O2/D22osEElgDgEMIUZevhjglN/2MpkMhYWFVXpGldUoEolQUFCAc+fOQSJRdqKUn18FD4j0ljx58oS6dOlCXC6XMjIyKn1eYmIiAaCLFy8qpc+cOZNatmxZ4fmurq70448/qqTv3buX6tSpQ3v37qXbt2/Tzp07ycbGhrZv3662rsLCQsrOzlZ8nj17RgAoPT2dRCLRW33y8vLojz/+oLy8vLeuq7o++qCxWnUWFZEo8U65ZQoKi2j2oZt091mG1ttSKBTSvXv3KC8vj6RSaZU/kjtHSDbfkmTzLYhKfeRpliS9+8cb1VvRZ/To0dSvXz+ltH///ZcAUEpKiiJt5syZ1KBBAzI2NiZXV1f65ptvqLCwUJEfGhpKzZo1UxxfunSJunXrRra2tmRhYUEdO3akq1evKl0HAG3atIn69+9PxsbGVL9+fTpy5IhSmdu3b1OvXr3I3NyczMzMqH379vTw4UNF/qZNm6hhw4ZkaGhIXl5etHbtWpJIJJSVlUUSiYQiIyPJ19eXDA0Nyd/fnw4dOkQAKCoqSm2bbN++nfz9/cnMzIwcHBxo6NChlJycXGldxW26cOFCcnJyIjc3N5JKpXTz5k3q0qULGRkZkY2NDX3yySf07NkzkkgkJJVK6cyZM9SiRQsyMTEhS0tLatu2LT158oSkUildv36dOnfuTGZmZmRubk7Nmzeny5cvq70HAPT7778rjsu6dnZ2tsrfwbJly8jR0ZFsbGxo4sSJVFBQoGhLV1dX+u6772jkyJFkampKdevWpSNHjlBKSgr17duXTE1NycfHR0XXgQMHyNvbmwQCAdV1daXly5cr5ScnJ1Pv3r3JyMiI3NzcaOfOneTq6kqrVq1SlMnIyKBx48aRnZ0dmZubU5cuXej69evy745EQufOnaNOnTpVqn3y8vLo3r17JBQKVb7H6enpBICys7MrtHdV3p4FyN9of//9d2zduhWRkZHo3bs3/vrrL9jY2FR88ivs7OzA4/GQmpqqlJ6amgpHR/VDixUxc+ZMzJ49G0OHDgUgj3n79OlTLF68WClAe2kMDQ3LnPM2MDDQWAQkTdZVXeiDRkDDOiVFwNHPgfv/A8adAJz9yr4mgMUfNtOOxteQSqXgcDjgcrnKEXxEea8ublKycE4iAmRiebxuviEgk4L+ngOAyghzSQA44JycDTTqUzIMXlwv37jEWYxUDPCqdn8cDkehG5D3OH/77TfUr18f9vb2inQLCwts374djo6OuHz5Mr788ktYWlri66+/VtQDQFE+Ly8PY8aMQUBAAIgIK1euRJ8+fRAbGwtzc3PF9RcuXIhly5ZhxYoVWLNmDUaNGoWnT5/CxsYGiYmJ6Ny5Mzp37ox//vkHFhYWuHDhAmQyGbhcLvbs2YMFCxZg7dq18PPzw40bNzB+/HiYmJhg4MCByMvLQ79+/dC9e3fs3r0bcXFxmDZtmkKnukhLUqkUCxcuhJeXF9LS0jB9+nSMGzcOx48fB4AKdXE4HPzzzz+wtLRUjOIUFBTggw8+QJs2bXD16lWkpaXhk08+QV5eHnbv3g2ZTIZBgwZh/Pjx2Lt3L0QiEa5cuQIejwcul4tRo0bBz88PGzZsAI/Hw82bN2FoaFhutKjie8zLyyvz2lOnTsX27dsVzy8iIgLOzs44e/YsHj16hODgYDRr1gzBwcGK57t69Wr88MMPCA0NxY8//oiQkBC0bdsW48aNw4oVKzBr1iyMGTMG9+7dA4fDQVRUFIYOHYoFCxYgODgYFy9exKRJk2BnZ6cIkTpu3DgkJSXh7NmzMDAwwNSpU5GWlqb0dxkcHAxjY2OcOHEClpaW2LRpE7p3746HDx/CysoKn376Kfz9/bFx48YK26f4GZX1e1Cl34cKTXkpLl++TJ999hlZWVmRr68v/fTTT1XqRb9Oy5YtafLkyYpjqVRKtWvXpsWLF1d4rroetY2NDa1fv14p7YcffqAGDRpUWld2dnal33QqQiQS0R9//EEikeit66ou9EEjUTXplEqIdn1IFGZDdHNfpU978iKX1p2NrRmNr1FQUEDR0dFUUFCgnFHcO859UZL27zJ52p+vvmdPzin1otV+npwrqWOpuzwtNbok7dq2KusOCQkhHo9HpqamZGpqSgDIycmJoqKiyiwvlUopKyuLli1bRv7+/iW3OX8+NWvWTO11pFIpmZub07FjxxRpAGju3LmK49zcXAJAJ06cICKiOXPmkLu7u9rn5uHhQb/99ptS2sKFC6lNmzaUlZVFGzZsIFtbW6VnsmHDBgJAN27cUKv1da5evUoAKCcnp1K6QkJCyMHBgYqKihRpmzdvJmtra8rNzVWkHTt2jLhcLiUlJVFGRgYBoIiIiDLrNDc3L3cE8nUA0JEjR9Re+6+//iIul0spKSkKza6uriSRSBRlBg8eTEOGDKGsrCySSqXk6upKI0eOVOQnJycTAJo3b54iLTIykgBQcnIyERENHz6cAgMDiaRSRZmZM2eSt7c3ERHFxMQQALpy5Yoi//79+wRAYUv+++8/srCwoMLCQqV79PDwoE2bNin+trZu3VqptlH7XaWq2Zkq9ahbt26NunXrYurUqfD39wcAnD9/XqVcv379KlXf9OnTERISgoCAALRs2RKrV69GXl4exo4dCwAYPXo0ateujcWLFwOQj/dHR0cr/p2YmIibN2/CzMwM9evXBwD07dsXixYtQt26ddG4cWPcuHEDq1atwrhx46pyq4z3BS4P+OhXIO0+ULd1pU55mS/CwPUX8DJfjFrmRvjIv041i9QguakVl6lKuSrSpUsXbNiwAQCQlZWF9evX44MPPsCVK1fg6uoKQL5t8+eff8bjx4+Rm5sLiURSbrze1NRUzJ07FxEREUhLS4NUKkV+fj4SEhKUypVesGZqagoLCwukpaUBAG7evIkOHTqU2cvJy8vD48eP8fHHH2P8+PGKdIlEoogc+ODBAzRt2lQp5nCbNm0qbI+oqCgsWLAAt27dQlZWlmKBUkJCAry9vcvVVYyPj4/SvPT9+/fRrFkzmJqaKtLatWsHmUyGmJgYdO7cGWPGjEFQUBC6d++OwMBADBkyROEOevr06fjkk0+wa9cuBAYGYvDgwfDw8KjwXipz7eI1SY0bN1ZaXOXk5IQ7d+4o1VX6eRWf5+Pjo5KWlpYGR0dHREffR1DXDpCl3AHHsjY4pnZo164dVq9eDalUivv374PP5ytsFwA0bNgQVlZWiuNbt24hNzcXtrbKcasLCgrw+PFjAMCkSZPw6aefYs+ePVVunzelykPfCQkJWLhwodr8quyjDg4OxosXLxAaGoqUlBT4+voiPDxc8QASEhKUhhOSkpLg51cyNLlixQqsWLECnTp1QkREBABgzZo1mDdvHiZNmoS0tDQ4OztjwoQJCA0NreqtMt4XjCyVjbREJB/WVbOTwcpEgE/au+NszAt09NShPdbfJMn/X3qVa9tpQOtJ8qFvADBzUD2vLEqX++LVDyjfuCTNV/3izPIwNTVVvFQDwC+//AJLS0ts2bIF33//PSIjIzFixAiEhYWhe/fu4PF4+Ouvv8rdtRESEoKMjAz89NNPcHV1haGhIdq0aaOy0O51Y8fhcBSG0djYGOooXhS2ZcsWpWiBxXW8KXl5eQgKCkJQUBD27NkDe3t7JCQkICgoSKG9PF3FlDaKlWXbtm2YOnUqwsPDsX//fsydOxenTp1C69atsWDBAgwfPhx//fUXTpw4gfnz52Pfvn0YOHBgla+jjvKeRVllitu5rLTi86RE4EMCLmQAT/2CuvLIzc2Fk5OTwp6Uptigz549G2PGjMGJEyeqrX1ep0qGujLL0au0kg3A5MmTMXny5DLzXm8sNze3CveympubY/Xq1Tq/L46ho+SmAftGAJ49gI4z1Rb7vEt9fNrRAwK+DkXZEpTxg80XACj1o+XaFmThDAiTX81Jvw5HHnHMtW359VZxflodxXODxeFzL168CFdXV3z77beQyWQQCoV4+vRpuXVcuHAB69evR69evQAAz549Q3p6epV0NG3aFDt27IBYLFYxIg4ODnB2dsaTJ09Udo8Ua2zYsCF2796NwsJCRa/60qVL5V7zwYMHyMjIwJIlS+Di4gIAuHbtWqV1qaNRo0bYvn078vLyFEb8woUL4HK58PLyUpTz8/ODn58f5syZgzZt2uC3335D69byF1ZPT094enriyy+/xLBhw7Bt27ZKGaLKXrs68GnsjchbD1Fo6QGjVx4IL1y4AE9PT/B4PDRs2BASiQRRUVFo0aIFACAmJgYvX75U1NG8eXOkpKSAz+fDzc1N5RrFNtDT0xMNGzascvu8KRr7lSkqKsKqVatQr149TVXJYNQ8j04Dz68AF9cC+eqd93A4HCUjffPZS+QVSXA5LhNR6RxcjsuEVKaDDlK4PFDQEgBlLycDAPRcUm37qYuKipCSkoKUlBTcv38fU6ZMQW5uLvr27QsAaNCgARISErBv3z48fvwYmzZtqtCZRoMGDbBr1y7cv38fly9fxogRIyrVEy3N5MmTIRQKMXToUFy7dg2xsbHYtWsXYmJiAABhYWFYvHgxfv75Zzx8+BB37tzBtm3b8OOPPwIAhg8fDg6Hg/HjxyM6OhrHjx/HihUryr1m3bp1IRAIsGbNGjx58gRHjx5VGa2sSFdZjBgxAkZGRggJCcHdu3dx9uxZTJs2DcHBwXBwcEBcXBzmzJmDyMhIPH36FH///TdiY2PRqFEjFBQUYPLkyYiIiMDTp09x4cIFXL16VW18h8pce8qUKRg1apTKVlxN89VXXyHi7D9YvuonPIx9hB07dmDt2rWYMWMGAMDLyws9e/bEhAkTcPnyZURFReGTTz5R+lsJDAxEmzZtMGDAAPz999+Ij4/HxYsX8e233+LatWsoKCjAzJkz37h93phKzYi/orCwkGbPnk3+/v7Upk0bxQKCX3/9lZycnKhOnTq0ZMmSqlSpk7DFZLpJjek8t5LohepCMXUcuJpA7rP/R15zj5PrrP8pPq1/OE0n7iRpXF55C1Qqg1Qqpdyrv5FsZUPlBWQrGxHd+1PDaksICQkhAIqPubk5tWjRgg4dOqRUbubMmWRra0tmZmY0cOBAWrVqFVlaWiryX19Mdv36dQoICCAjIyNq0KABHTx4UGWxKUoteCrG0tKStm3bpji+desW9ejRg0xMTMjc3Jw6dOhAjx8/VuTv2bOHfH19SSAQkLW1NXXs2JEOHTqkWAAVGRlJzZo1I4FAQL6+vvT7779XuJjst99+Izc3NzI0NKQ2bdrQ0aNHVc4pT1dISAj1799fpd7bt2+XuT1LKpVSSkoKDRgwgJycnEggEJCrqyuFhoaSVCqloqIiGjp0KLm4uJBAICBnZ2eaPHlyuX9rr7ft69ceP368YnGcOs3Tpk2jTp06KS0me32x8OvXiYuLU7SVTCYjIqJDhw6Rt7c3GRgYUN26dWn58uVKdRRvzzI0NKS6desqtmeVvpZQKKQpU6aQs7MzGRgYkIuLC40YMYISEhKooKCABg0aVOn20dRiMs6rBqgUs2bNwqZNmxAYGIiLFy/ixYsXGDt2LC5duoRvvvkGgwcPVvG+oo8IhUJYWloiOzu73EUslUEsFuP48ePo1auXzm590geNgO7qXH3qIVafiVVJL+6vbhjZHD2bOKnkvymFhYWIi4uDu7u70uKlylI8XGthZgrus0vyhWNmDvLh7mr0TFZVFDotLMrdGqRN9EEjoB8631RjkUSKlBcZcOakg29mB46ZapwIbWks77taFTtTpTnqgwcPYufOnejXrx/u3r2Lpk2bQiKR4NatW2+1qILB0FnS7gPnVgD91wEGqkZRKiPsv/aszFPlu5KBsGPR6O7tCJ6uhdrj8gD3DtpWwWC8FVl5YpjIcmDAKQLEBdqWUy1U6dXq+fPniqXtTZo0gaGhIb788ktmpBnvJhIRsGcwcPcQcHZRmUWuxGUiObtQbRUEIDm7EFfiKh+shsFgVJ5a5oYwsHRGoWkdwFSHdmFokCoZaqlUqrRfj8/nw8yscvF9GQy9gy8A+q0B3DsB7b4os0hajnoj/SblGAxG1eByObAyM4aRpX3ZOxTeAao09E1EGDNmjMLdZmFhIT777DOVfXyHDx/WnEIGQ5t4dAHqdVa7p7qWeeXmiGuZGyI5uwBOllVbjcxgMBhVMtSv+8oeOXKkRsUwGDpJaSP9+B/AtBbg2AQA0NLdBk6WRkjJLlS3KxmOlkYQSwjtl57FiFZ18V3/JjUim8F4lxFJpHieVYDanAwYmlrIHRdxdHOh3NtSJUO9bdu26tLBYOg+948BB0IAi9rAp2cBUzvwuBzM7+uNibuvgwMoGeti8z6/rzcuPsmAVEbganA9RxU2bDAY7xyZeWJIi/JhyM0ExC8BhyZQcQ2gZTT1HX03Xz8YjOrAtR1gVVe+UtqwJCpTzyZO2DCyORwtlYfBHS2NFFuzZn/QEIcntcWkziU+gZ9n5WPmwVuIT8+rkoziLZCvu8hkMN4nbEwNYGVmjCIjO8DEVqe2FhZT7KnzbbeTvlGYSwbjvcTEBvjkjPz/r/WMezZxQndvR0Q+SsPf/11Gjw6t0KZ+LaUtWc3rWiudsz7iMQ5GPUdydiF2f6LsQ7o8+Hw+TExM8OLFCxgYGFR5X6xMJoNIJEJhYaHO7qkF9EOnPmgE9EPnm2g0NzIAwR6FAFBY/Qs2K6uRiJCfn4+0tDRYWVm9tX8RZqgZjKpgqhxVB6n3AIfGAAAel4NW7jbIuE9o5W5T4b7p4AAXpGQXYkLHEre7hWIpEl8WwMNe/W4KDocDJycnxMXFVegHuyyICAUFBTA2NtbprZX6oFMfNAL6ofNd1GhlZQVHR8e3vi4z1AzGmyCTAeGzgatbgGH75UE8qkgzFytsHdNCKW3/1WcIO3YPH7d3x7e9vdWeKxAI0KBBgzca/haLxTh37hw6duyoUx7eXkcfdOqDRkA/dFZWY5qwEHuvJOAjx1TUdnQEbOur3ZWhLY2AfLhbU546maFmMN4EDgeQFAAkAzJiAVTdUJfFw9QcyAioa1vxflAul/tGLkR5PB4kEgmMjIx09kcb0A+d+qAR0A+dldV4+HwCNpx/hhFms2EkSQAG7wAaD9ApjZqGGWoG403gcIBeKwGfwYB7R3maTArO0/OonRkJzlMLoF7HKi9wWTTQB8Na1kUDh5Kh70tPMrD70lNM7dYAng7m5ZzNYLz7tKtvh/jkdPDzPYHMl3JfB+84zFAzGG8KX1BipKOPAuGzwBcmIQAAnm6Qx3XuuRTw7lelapvUtlQ6/ul0LCKfZMDKxADfD/DRjHYdRyojRchQ27hMlYV5jPeXFm42aOHWFkBbQFIE8A21LanaYYaawXhboo8CB0YDr7s8ESbL04fsrLKxLs28Pt5Yd/YRJnaur0hLExYiM1+Eho5vF91NFwm/m4ywY9GvfKjzsDP2GpwsjTC/r7dGo5C9LexlQgd4D4w0oAP7qNetWwc3NzcYGRmhVatWuHLlitqy9+7dw4cffgg3NzdwOBysXr26zHKJiYkYOXIkbG1tYWxsDB8fH1y7dq2a7oDxXiOTAuGzoGKkgZK08Nnycm+It7MF1o1ojtpWJe5H1519hJ6r/8NPp1XDa+oz4XeTMXH3dZVAJynZhZi4+zrC7yZrSZky4XeT0X7pPxi59Rp2xvIwcus1tF/6j87oexdJFRbi1/NxyM58ARQKtS2nRtGqod6/fz+mT5+O+fPn4/r162jWrBmCgoKQlpZWZvn8/HzUq1cPS5YsUbvkPSsrC+3atYOBgQFOnDiB6OhorFy5EtbW1mWWZzDeiqcXAWFSOQUIECbKy2kIIkJOkQQcDtDCzVopXZ+RyggLjkaX98qDsGPRkMoIzzLzcevZS6QKSww6ESGvSAKJVFatOvXlZeJdY//VZ1j4v2ic2P4DsNwDOLtY25JqDK0a6lWrVmH8+PEYO3YsvL29sXHjRpiYmGDr1q1llm/RogWWL1+OoUOHKgKDvM7SpUvh4uKCbdu2oWXLlnB3d0ePHj3g4eFRZnkG463ITdVsuUrA4XCwaogvzn7VGW08SvZ1773yDJ/uvIboJN3ubUikMsSm5uC/2BdK6RN2XUOKsHIhQ389H4f+6y5gZ2S8Ir9ALEXj+SdR/9sTyBdJFOmb/n2MzsvPYn3Eo5K6iPDJjqv4fM915BSKFekXH6dj1d8x+OeB8vP663Yywu+mILdQgrBjlXuZYGgWV1sTNHKyQAez54BUBJi//f5kfUFrhlokEiEqKgqBgYElYrhcBAYGIjIy8o3rPXr0KAICAjB48GDUqlULfn5+2LJliyYkMxiqmDlotlwVcLMzVThdkMkI6yMe4e/oVFyOy1B7Tul51ctxmdVuUKKeZmFDxGNcfJSuSEvPFaH7j+cwZttViEv1fiurJC2nEBZGfNS2Moa1SUnY3SJxSV2G/JLV9i9yihCfkY/sghKDLJLKcPp+Gv66o9z7vfQkEz//8whnHyi/REzbdwOf7Y7Cvw9fsPjjWqK/b20cn9oeTh/vByZdAhoP1LakGkNri8nS09MhlUrh4KD8A+bg4IAHDx68cb1PnjzBhg0bMH36dHzzzTe4evUqpk6dCoFAoBL9q5iioiIUFRUpjoVCeY9ELBZDLBaXeU5lKT7/beupTvRBI6CjOp1bgG/uDOQkg1OGqSEAMHeCxLkFIBYD+RlyP+E8gUrZt2XLSD/svJSAj/ycFG30ICUHEimhSW0LnLyXiu+PP0CKsAjFi7QcLQwxt1dDBDWu+ouERCoDnyd/188rkiDsrwd4nlWAnWP8Fekn7iThl/PxGN26Llq4ylezWxtxYW8mQC0LQ6QL82FnJh8dG+TrhDP35dNeXMjQkvsAtfASabDCFVlDyF71K2xN+OjVpR6mdJF7dCu+V1MD4E5oNxSKZZBJJYplAaNa1UH3RvawMxMoykqlMvwwwBtFEhn4kCnSmziZYWQrF/jXtVSkyWSEAFcrFElkeJlXOTeVzzNzIa6rOwv9dPK78xpV0Si1rl98UnVKUkGT7ViVOjikpYmtpKQk1K5dGxcvXkSbNm0U6V9//TX+/fdfXL58udzz3dzc8MUXX+CLL75QShcIBAgICMDFiyVzglOnTsXVq1fV9tQXLFiAsLAwlfTffvsNJiYmVbgrxvuI08uraBG3BoBy8J7iL9ZV9ylItpJ7IGsRtwYW+U9xw3U8Ms28ql3bumguHmZz0dpeiksvigfQVFWO85Shma3yTwERkCcB0guBumZA8YLmiGQOTj7nooU9YZCbvBcrI2DmZR4kxEGonwS2r/yw3Mnk4EYGB95WhAD78n9qZASEXeehleQq5hvshDOnpFeaRDYIE4/GZX4LzG8uhbYWV8dmc7A2uuK98TYCwjwt6nyXEIqA53kcNLQicEE15oWsusnPz8fw4cORnZ0NC4vyX+q01qO2s7MDj8dDaqryXFBqaupb+UZ1cnKCt7ey68VGjRrh999/V3vOnDlzMH36dMWxUCiEi4sLevToUWEDVoRYLMapU6fQvXt3nfUIpA8aAV3W2QvSB/7g/f0NkFNqYZlFbUi7L4Jfwz7wA4D8TPAfzgDE6WjduSdQq1G1qhJJZDibfw9xd1PwuMAYQFnuRjngADiWZAwP7/pwMDdEJ097APJhcp/vTkMsJUR81UGx6jzryjMcib8PnkUt9Orlp6gp2y4BlsYG6ORpB3Mj+fPpVUXNLoLdaH55tUq6IzKxwWA1rrdajWbdR1axVs0hlREOrTyHVGGR2qF6Lgfo5++KPr0aKtKSswvhZFl1L3KaQne/OyWo07jh3yfYFPUIwZ7AEuE3kDXqC1mXUK0YbE22Y/HIbWXQmqEWCATw9/fHmTNnMGDAAADyyCRnzpzB5MmT37jedu3aISYmRint4cOHcHV1VXuOoaFhmYvTDAwMNPZHrcm6qgt90AjoqE6fgUDjfpA8OYeb/52Eb4cg8Ot1BL+0ZzJLB2DqDSDuXxjUblqS/uAvudG2qada71tgYACsHtYcve6l4NNdUWrLEYAXuSJ8+0c0eng7ILCxs/x8AHVtTFAgkiKniBRt3rtpbbR0t4OrrQkMDEp+Qsa0f8sFmzIpAh4sA3FUwwpzOQCBg4AHy4Geo7UW0tAAwIJ+jcuNP7462BedvGop2isuPQ/dVp5Dhwb2+CUkAAY87a3h1cnvzmu8rlFgwIeViQGGmUeBkxAPXmIUeALNTx1VBU20Y1XO16rDk+nTpyMkJAQBAQFo2bIlVq9ejby8PIwdOxYAMHr0aNSuXRuLF8uX4YtEIkRHRyv+nZiYiJs3b8LMzAz168vnLL788ku0bdsWP/zwA4YMGYIrV65g8+bN2Lx5s3ZukvH+wOWBXNsj8Z4QzVzbl21MDM2Ahr1LjvPSgcMTAEkh8MkpwNlP9Zy3pEBcuT3cXo7maOZipZR28ouOivnmYuzNDWFvXg2OJl5tdVPXT+KU3urm3kHz168kxfHHS5yyyHFU45Ql8nEGCACfy1Ey0jIZgcvGxivks04eGNPWDRxxa6CxN2BQsR/8dw2tGurg4GC8ePECoaGhSElJga+vL8LDwxULzBISEpRifiYlJcHPr+SHbMWKFVixYgU6deqEiIgIAPItXEeOHMGcOXPw3Xffwd3dHatXr8aIESNq9N4YjEohygVcWsgXmTk2q5ZL1DKv3JDrgr6NlbZ7AVAx0tXKi5iKywDAqVCgdnOg8zeqYUdriMrEHy9meKu6aF/fDiJpyQtTbpEEPVefQ++mTviimyeMBdoZIdAXjAx4gIEF4N1f21K0gtZdiE6ePFntUHex8S3Gzc2tUk4d+vTpgz59+mhCHoNRvVi7AaOOAAUvgeKXUplU7nrU5yPAe8Bbz8W1dLeBk6URUrILy5xX5UDeG2zpbvNW13ljiID9I+VTAJUh6br8E7igJO3iGuDGHsB/DND6s5L0gizAuHqcHVUl/nhdW+VFqX/dTsLzrAKcupeKWUEN1Zz1fvMyX4TMPBHqlROb/X1B64aawWAAMLYq+fedg8CD/wFx/wH1Or+1oeFxOZjf17vcedX5fb1rzk+1TAqk3AGcfV+J4AACM7kyQwugKAdl76rmAOZOQJc5QE6KfJtbMan3gBf3AVFOSVpRDrDUDTC2Ab64I592AID0R3KHGTb1AIO3WOD1FtHSPvJ3ga2pIQhQDH8TEabtu4muDWuhd1Mnrc5l6wJ7rzzD0vAHGNPWDQuMD8ifV+OBgJHubHurKZihZjB0jUZ9gayncgNd2kjnZbzxUG9V51WrjYKXwPrWQG4a8NUDwKyWPL3T10Dn2XIDfmA0oO6V4gM10ci6zgOafATYuJekZT19dSq3xEgDwIUfgRu7gc5z5NcEAHGBPM22vvzlqKJRjLeMlsbjchDorbx3/b/YdBy9lYQz91PRtVGt995QJ2cXgMsB/GzEwJmf5bHf63VmhprBYOgAAlOg8yzltJQ7wJZuQMA4IOiHkmHyKlCVeVWNkZMCpD8sCQdqbAVY1JYvnku7X2KobV+tGLdxl0cbC5+l7EPdwhnouUS9EbSsLf+UxrEJ8E2SXENpODx5z922JBoZMh4Dx2fIX4xmxZekR20Hsp8DjfoBTq9W6ldTtLTGzhb4qrsnCICFUcmK4J2R8WjpbvNORkorj+/6N8GETh6wQS4g+xZIjwWs1e/eeZdhhprB0AfuHwOkRUBuyhsZ6WKqMq/61jy/BvzaHTCxBaY/AHivfm4+2iofwuar2WLj3Q9o2Ftlq9sbbckSmJa8BBTT72eg70/yHloxHA7g1QswMFYue+cQEP+f3Kg7NZUP2x//CuqjpXHk0dIa9q6yXlszQ0zp1kApLSEjH/OP3gMAnJ/VVSmC2vuA/H6NgY4ztC1FqzBDzWDoA12+Aeq2Vt5rXSgEHp0CGg/SvrcmmRSIOyfXUa+zPM2pmdxI29STByUp7vFWpldUma1ubwOHI+9ZF+PQGBi2V7Wcz+BXRtpXfvz0onzYXi2a3UImJcIHTRxRKJYpGemop5nwcrSAmeG79xOeVyQBX8Z5J+/tTWEtwWDoCx5dlY8vrAb+WwnEnAA+/EUrkhREbQP++gqoHVBiqHkGwORrygvl9A3/EPmnmBqOluZuZ4r1I/yVgqfkiyQYt/0aZDLC4Ult0cDBvJwa9I99155jzT+PMbVbA0yolwnIJIBLq7caSdJ33t87ZzD0HSMr+Wrpmo4ilJMKXFwLPLtaktaoH2BiJx8elpaEmNRrI10WVY2WdmM38PdcIO3NAw0BUJqieJ5VAFtTAWzMBEpblzLzRGVuX63piGlvy6UnmcgTSWFhbAD8uxTY1hOIXKNtWVqF9agZDH2l3VTAdwRgUmr/84O/gCcRQKdZgKld9Vz336XAtV+BpkPlzloA+aKwGQ+15tqzxnBtK1/YJkyG2i1kFs7ycgBw9Rcg6QZg5QrUerVfmt4usISngzlOT++EZGGhwoATEYZvuQQeVx6r3MtR3ssOv5tcaqW/PGKaU02v9K8EpV8mPm7vik87eqBpHUsgxQkwtAQ8P9C2RK3CetQMhj5jalvyoy+VAH/PA65sBq5oKAZ73H/AH5OArPiStGZDgTotVOdg33UjDcjvsefSVwevG9tXxz2XyMsRAe2ny0c8vAeUFHt4Eljf5q2eEZfLUZqzjkvPQ3xGHp68yIOjhXxvePjdZEzcfV0lfnZKdiEm7r6O8LvKsbi1RfjdZLRf+g9Gbr2GnbE8jNoWha8O3sL5R+lAvzXAzEeAvae2ZWoVZqgZjHcFHh/o8yPg0Q1oW8rbX6EQkL1a4azkpOM8FEGb1XFuOXBzD3D7YEmaS0vgk9OAn/aiWGkV737yLVgWr/VILZyVt2ZxOPJ/D94OmNmXlLt3GEiLlm8JK025i9TKp569GSJnd8PGUf6wNDGAVEYIOxatdm06AIQdi9b6MHilXibU7Q54j2BD3wzGu0S9TvJPaY5Oljv/aPIhcHlD2U46PHvKh2kf/AWMPFSyTSlgrHxvc/1uNX0nus3bbCH7YCng1gGo7V+Slv4IWBsgH6UY9ccbjU5YmwoUIUqvxGWqGL/SEOShN6/EZaKNhy0kUhkGb4qEIZ+LX0JaKFZcn4pORURMGtp62KF305IXk52R8eBzuRjg5wwTgbzs86x8JGYVwNHSCK62JYEzMvNEEPC5MDHgKQUhKe9lwhBFMEcBwo5Fo7u3Y815zdNRmKFmMN5lctOAx2fl7jSTb6rmFzvpGLwDuLQeyH4GxByXG3VAPmxb04vV9IU33UJmbA00H6WclhAJgACeQLmeuHOAo0+V3cim5ag30mWVK5LIcCPhJQCAV2r+/EZCFvZcToCAz1UYaiJC6J/yvd09GjsoDPWfN5Ow/GQMggNcsPSjkjCuHZb+gzyRFOdmdlH4PN97JQHLwh8gK19cpq4e3CisNliH3/M64Eqcr0qwmPcNZqgZjHcZs1rA51eBDa3lASpUeOWk4+QcuVMJqQRw71yzGhlyw+3RFSgSlqSJ8oDfggGpGPj8sqrjlnKobMS04nICPhebR/mjSCKDIb9kRrR9fTsI+Fz4lgp/KpUR+jR1QpFEBpNSUb8sjPioZ28KBwvlEKhFEvm0i6FBSb3ZBWK1RhoAvLlPweUQUmADQSVfOt5lmKFmMN51MmLVGOliXjnpsG2g1TjP7z2WtQGUcoOa/RywdgfEecqObu79Ifdf3qC7qie1V1Q1YpoBj4sejR1VyrWtb4e29ZV3D/B5XKwd3lyl7Kg2bhjVxk0l/eH3H0AklUFQynf5YP86sDDi45sjd8vUv0QyDLskgRCBj58r+dLxLsMWkzEY7zo17KSDoSHsvYBJF4HxZ0tW9hMB/ywEDoySu5VVQ3HENADgQYbW3Gj0415Ea240eJD3cGsqYhqXy4HRa/PTtmaGCG5RF06WRipr54tJgj34lk7aC7+qQ7AeNYPxrlNVJx0M3aL0PnmpSO6TPOYE4FVqb/H9/wGxJwG/UfJV+ZAHYTncJR3OkWFwQIaiaCpskdRmPvy0vI9affhVAueV+a7R8Ks6DOtRMxjvOsVOOtT2XTjyiFbFTjoYugvfEOixEJhyTTke943dwPWdQOypkrToP+EXOQ21ShlpAKiFTPhFTpNHAdMyxeFXHS3lw9tGKMI5wRf40WQbNg/10imnLNpEJwz1unXr4ObmBiMjI7Rq1QpXrlxRW/bevXv48MMP4ebmBg6Hg9WrV5db95IlS8DhcPDFF19oVjSDoS9UxUkHQz9pOxloHgL4fCQ/lknlvtdBZTzxV33X8NkV76OvAXo2ccL5WV2xe1wAQmvfRF3uC/Q3v4/uzSq/eO5dR+uGev/+/Zg+fTrmz5+P69evo1mzZggKCkJaWtmb//Pz81GvXj0sWbIEjo6qix9Kc/XqVWzatAlNmzYttxyD8c5TWScdDP3Erb08fKe9l/z46UUg70U5J5SK8gUAmXHA08hXrlFrnuJ5dC+zAki7hYHTfaH2I8LpEFqfo161ahXGjx+PsWPHAgA2btyIv/76C1u3bsXs2bNVyrdo0QItWsj9C5eVX0xubi5GjBiBLVu24Pvvv68e8QyGPqHJOM8M3aaqCwhv7wciFst75f1+LsnfMwQwspSPuJi+2suckwqIcgFzR3m877cl+igQPgt8YRL8AeAp5C+QXD57gXyFVnvUIpEIUVFRCAwMVKRxuVwEBgYiMjLyrer+/PPP0bt3b6W6GYz3nmInHTZtQNUR55mhG1R1AaGBMWDtBljVLckrypUvULtzQNmNZ9Q2YE1zIHyOcl1HJgInvwUKXpakFWbLP2VE9QIgN9IHRgPCJOX0Ykc8OjCPrgtotUednp4OqVQKBwflPyoHBwc8ePDmYeH27duH69ev4+rVqxUXBlBUVISioiLFsVAodzogFoshFqvflF8Zis9/23qqE33QCOiHTqZRc+iDTp3V6NwCfHNnICe5ZE66FPQqypfEuQUgFgMtJ8k/gPwYAKQycPqtByc/HTKukSKdKxaBKzCFzMQesuKyRTkwuPWb/PT2M0rKXlgD3n/LIfX/GDLFOgmAe2Y+YGQN7rVfUdY8OkDy1PDZkHj00JkXSk0+76rUofWhb03z7NkzTJs2DadOnYKRUeU2yi9evBhhYWEq6X///TdMTEw0ouvUqVMVF9Iy+qAR0A+dTKPm0AeduqjRye5DtMhZU+x7TgG9+u9V20FIDj9ZQS1m8s/x46XSmgGNNwC5MkU6T1oIt9rDIJDk4P7pc4qSPs9voh6A2MRMxLwqy5cWoPftdRXq57yaR798cDUyzBtVWL4m0cTzzs/Pr3RZrRpqOzs78Hg8pKYqz6ekpqZWuFBMHVFRUUhLS0Pz5iWec6RSKc6dO4e1a9eiqKgIPJ7y29mcOXMwffp0xbFQKISLiwt69OgBCwuLN9JRjFgsxqlTp9C9e3cYGBi8VV3VhT5oBPRDJ9OoOfRBp25r7AXpA3/w/v4GyCk1tGxRG9Lui+DXsA/8NHq9QQAA99c0iMX58JBJ4VG8naxQCKnpVHASo8BNuFBhra2buIEa99Ko0jdFk8+7eOS2MmjVUAsEAvj7++PMmTMYMGAAAEAmk+HMmTOYPHly+SeroVu3brhz545S2tixY9GwYUPMmjVLxUgDgKGhIQwNDVXSDQwMNPbl02Rd1YU+aAT0QyfTqDn0QafOavQZCDTup7KAkF+TQ8kGlq8d2wJBC+Wxznf0qfB0vmVtQMfaVhPPuyrna33oe/r06QgJCUFAQABatmyJ1atXIy8vT7EKfPTo0ahduzYWL14MQL4ALTo6WvHvxMRE3Lx5E2ZmZqhfvz7Mzc3RpEkTpWuYmprC1tZWJZ3BYDDeed40yld1U+yIR5gMqPNIbuHMHPFABwx1cHAwXrx4gdDQUKSkpMDX1xfh4eGKBWYJCQngcksWpyclJcHPr2TAZsWKFVixYgU6deqEiIiImpbPYDAYjDeh2BHPgdHAa05EmSMeZbRuqAFg8uTJaoe6Xze+bm5uIHVL/dXADDiDwWDoIMWOeMJnKW/RsnCWG2m2jxqAjhhqXaP4RaAqk/3qEIvFyM/Ph1Ao1M05LOiHRkA/dDKNmkMfdOqDRkDHddbpDIy7CEncBdyOPIOmbbqB795O3pPWwG+wJtFkOxbbl8p0PJmhLoOcnBwAgIuLi5aVMBgMxvvGam0LqFFycnJgaWlZbhkOVXUc+T1AJpMhKSkJ5ubm4Lylv9nirV7Pnj17661e1YU+aAT0QyfTqDn0Qac+aAT0Q+f7ppGIkJOTA2dnZ6V1WGXBetRlwOVyUadOHY3WaWFhobN/fMXog0ZAP3QyjZpDH3Tqg0ZAP3S+Txor6kkXo/XoWQwGg8FgMNTDDDWDwWAwGDoMM9TVjKGhIebPn1+m5zNdQR80Avqhk2nUHPqgUx80Avqhk2lUD1tMxmAwGAyGDsN61AwGg8Fg6DDMUDMYDAaDocMwQ81gMBgMhg7DDDWDwWAwGDoMM9Q1ABHh3Llz2pbBqAFSU1OrHDSGoUpBQQHy8/O1LeOdITMzU9sSGG8BM9Q1gEgkQpcuXbQtQ8GDBw+wfft2XL9+XSVvyZIlWlCkym+//YawsDA8ffoUQqEQ48ePR5cuXbB06VJtSysXHx8fpKSkaFuGgte1/Pnnnxg+fDiGDx+Ow4cPa0mVMps3b8bDhw8ByPUGBgbCzMwMFhYW6NmzJ9LS0rSsUM6AAQOwf/9+iEQibUtRS3p6OkaMGIGmTZti7ty5yM3NRZs2bWBnZwdXV1fcunVL2xIBAPv27UPXrl1hZ2cHgUAAJycnDB06FPfu3dO2NCX+++8/jB07Fv7+/vD09IS/vz/GjBmD8+fP16gOtj1LQ9y+fVttnkgkQqtWrSCVSmtQUdkcO3YMw4YNg5eXF+7fv4+hQ4di8+bN4PPl3mQtLCw0EjXsbfjhhx+wZ88ecLlc5OXlYfTo0TAzM4NYLMbPP/+MadOmYfbs2VrV2Lx58zLT79y5g4YNG8LAwKDMF6GapvTz3LVrF6ZMmYIJEyaAy+Vi48aNWL16NUJCQrSqsU6dOrh37x4sLS0RHBwMgUCAsLAwEBG+++47FBYWYv/+/VrVCAAGBgawsrICEWHEiBEYP348mjRpom1ZSgwdOhQFBQUIDg7G3r178fLlS7Rt2xZTpkzB2rVrce3aNZw+fVqrGpcvX461a9fi448/hlQqxbZt2zBu3DgUFhZi27Zt+PPPP9G6dWutagSAX375BTNnzsSIESPg6+sLKysrZGdn4+bNm/jtt9+wfPlyjBs3rmbEEEMjcDgc4nK5xOFwyvxwuVxtSyQioubNm9OxY8eIiCg1NZW6du1K/fr1I7FYTEREZmZm2pRHRET16tWjZ8+eUUJCAnE4HIqKilLkXbhwgRo1aqRFdXKsra0pMDCQduzYQdu3b6ft27fTtm3byNzcnH788Ufavn27tiUSkfLzbN68OZ08eVJxfPr0aWrWrJkWVCljampKMpmMiIicnJwoLy9PkZefn0/29vbakqaEubk5iUQi2r9/PwUFBRGPx6MWLVrQ5s2bSSgUalseERHZ29tTbm4uERFlZWURj8ejgoICIiIqKCggGxsbbcojIqI6depQTEyM4vju3bvk5+dHRES///47tWvXTlvSlHB1daXLly+XmXf58mWqW7dujWlhhlpDODs709WrV8vMKygo0BlDbWlpqXQsEomob9++1KtXLyoqKtIJQ21hYaH4t4mJiVKeTCZTuQdtkJqaSoMHD6bAwEB69OiRIt3Ozo4SExO1qEwZc3Nzxb9tbW1JIpEojqVSqU60pa+vL507d46IiDw9PenZs2eKvMTERLKzs9OWNCVKtyURUXx8PM2fP59cXV3JzMyMxo4dqyVlJdja2lJ+fj4REWVnZxOXy6XCwkIiIiosLCRbW1ttyiMi+UuuVCpVHBcVFSmesUgk0onfICL5C2TxS87r5Ofnk6mpaY1pYXPUGiIgIADXrl0rM4/D4ejMAiNzc3MkJycrjg0MDPD777/DwMAAffr0gUwm06I6OaamphCLxQCAMWPGKOXl5+dXGBKuJqhVqxYOHDiAyZMnIygoCD/88AMkEom2ZakgEomwZs0a/Pzzz+ByuUoLtIqKirSorISwsDCMGDECv/zyC4YNG4a+ffti+/bt2LZtG/r06aPyN6AruLq6YsGCBYiLi8OhQ4eQl5enbUno0KEDRowYgX379mHMmDFo06YNFixYgKSkJHz33XcICAjQtkS0bt0aCxYsgFQqhVgsxvz58xVTSWKxWGdciHbq1AlTpkxBenq6Unp6ejqmTZuGjh071pyYGnsleMdJTk6m1NRUbcuokBEjRtDKlStV0kUiEfXv318nev4fffQR3b17t8y833//nTp06FDDisrn5cuXNGHCBGrSpAmZmprqVI+6U6dO1LlzZ8Xn0qVLirzjx49Tq1attKiuhBMnTlCLFi2Upo9cXV1p0aJFSr0vbaIrPb3ySExMpP79+1PTpk1px44dFBsbS25ubsThcMjT05Pu37+vbYn0+PFjaty4MRkYGJCBgQE1aNCAoqOjiYjo9u3bNHfuXC0rlJOUlEQdO3YkPp9PLi4u5OPjQ3Xr1iU+n0+dOnWipKSkGtPCFpO9Z7x48QK5ublwd3dXyZNIJLh48WLNvilWkYSEBABA3bp1taxElf/++w///PMPvvrqK5iZmWlbToVkZ2dDLBbDzs5O21IU5OfnIysrC2ZmZpWO1VtTPHv2DC4uLtqWUWWICJmZmbC1tdW2FAUymQwxMTGQyWTw8vJSLGbVRZ48eYLbt28jNzcXZmZmaNq0KerVq1ejGpihriGeP3+OOnXqaFsGg8FgMPQM7U/2vSd4e3trW0Kl0JV91OXBNGoOfdA5adIkbUuoFKwtNYM+aASAvXv31ti1mKGuIXRtI7869MGDGtOoOfRBp74M+rG21Az6oBEAFi1aVGPXYkPfDAaDwWDoMLo7g6+HCIVCHDlyBLdv30ZOTg7Mzc3h4+ODgQMH6tzCGAZDF8nJyVF8d8zNzbUtR6/Rh7bUB426ABv61hCXLl1CvXr1sHz5cqSmpkIgECAtLQ0rV65E/fr1cfnyZW1LVLB792506dIFtra2EAgEsLW1RefOnbFnzx5tS1PANGoOXdcpk8nw/fffw93dHVZWVnBxcYGVlRXc3NywaNEindjbXwxry/dDY0VIJJKacx8KsH3UmsLPz4+2bNlSZt4vv/xCvr6+NayobBYuXEh169al5cuX06lTp+jq1at0+vRpWr58Obm6utL333+vbYlMowbRB51TpkwhX19fOnjwIMXGxtKLFy/o0aNHdPDgQWrevDlNmzZN2xKJiLXl+6SxIgoLC2vU5wSbo9YQpqamePnyJQwMDFTyxGIxrKysdMJzkZOTE86dO4cGDRqo5MXGxqJDhw5aj/7ENGoOfdBpZ2eHe/fuwcHBQSUvJSUFjRs3RkZGhhaUKcPaUjPog0YAmD59uto8qVSKtWvX1ligJTb0rSEaNGiAXbt2lZm3e/du1K9fv4YVlU1+fj4cHR3LzHNwcNCJGMBMo+bQB50ymQwcDqfMPF1yv8vaUjPog0YAWLNmDRISEpCVlVXmp0apsb77O8758+fJ2tqafHx8aNSoUTRlyhQaPXo0NW3alGxsbOjChQvalkhERMOGDaMPPviArl+/rnDNKJVK6fr169S7d28aNmyYlhUyjZpEH3R+/vnn5OfnR4cPH6bHjx9TRkYGPXnyhA4fPkz+/v40efJkbUskItaW75NGIiIfHx8KDw8vM6+goIA4HE6NaWGGWoNkZmbSr7/+StOmTaOPP/6Ypk2bRr/++itlZmZqW5oCoVBIo0ePJkNDQ+JyuWRubk48Ho+MjIwoJCREJ8L1MY2aQx90isViCg0NJRcXF6VwsS4uLhQaGqoIwaptWFu+PxqJiObNm0c7d+4sM08sFtOYMWNqTAubo35PKSgoQExMjMJ/rZeXF4yNjbUtSwmmUXPoi86XL18qNFpZWWlbTpmwttQc+qBRF2CGmsFgMBgMHYYtJmMwGAwGQ4dhhprBYDAYDB2GGWoGg8FgMHQYZqg1SH5+Ph48eKCSHh8fj5cvX9a8IAaDwWDoPcxQaxCZTIZWrVohNTVVkSaVStGmTRskJSVpUZkyaWlp+Pbbb1XSf/rpJ9y9e1cLilRhGjWHPuhMTU3FiBEjVNJnzJiB69eva0FR2bC21Az6oBHQoeddYxvB3hOGDx9Oq1atUhyHh4frjJ/v0ri7u1NkZKTiOD09nczNzSk7O1uLqpRhGjWHPuhs0qQJnT59WnH8/PlzsrKyooKCAi2qUoW1pWbQB41EuvG8WY9aw4waNQq7d+9WHO/duxejR4/WoqKyGTlypFLEnwMHDqBHjx6wsLDQoiplmEbNoQ86y/ru9OvXD0ZGRlpUpQprS82gDxoBHXneNfZK8J4glUrJ0dGRHjx4QAUFBWRtbU0pKSnalqVCTEwM1apViyQSCRERdejQgf744w8tq1KGadQc+qDz+fPnZG1tTYWFhURE1Lx5c6Uel67A2lIz6INGIt143sxQVwNffvklffvtt3TgwAHq0aOHtuWopVWrVvTXX39RfHw82dnZ6YzrvtIwjZpDH3R269aN9u/fT9HR0VS7dm1ty1ELa0vNoA8aibT/vPk113d/fxg9ejQGDhyIu3fv6uSwdzGjRo3Crl274Ovri8GDB4PP170/B6ZRc+iDzuLhUF9fXwwbNkzbctTC2lIz6INGQAeed42+FrxHNG7cmCwsLCg/P1/bUtSSnp5OlpaW5OXlpbRYQpdgGjWHPujMyckhKysrcnFxodu3b2tbjlpYW2oGfdBIpP3nzRaTVRMLFixAWFiYTjrrL8bW1hadOnWCVCpF69attS2nTJhGzaEPOs3MzNCrVy9YW1vDx8dH23LUwtpSM+iDRkD7z5sF5XjPef78OXJzc9GwYUNtS1EL06g59EFnTk4OxGIxbGxstC2lXFhbagZ90Aho93kzQ81gMBgMhg7Dhr4ZDAaDwdBhmKFmMBgMBkOHYYaawWAwGAwdRvc2/70DyGQyXLlyBc+fP0edOnXQsmVLcLnsnYjBYDAYVYcZag0TFxeHvn374tmzZ3B2dkZSUhLq1KmDY8eOoV69etqWp0RkZCR27NiheKEICQlBmzZttC1LCaZRc+iDzn379mH79u0KjWPGjMHQoUO1LUsF1paaQR80Atp/3qybp2E+//xz9OzZEy9evMD9+/fx4sUL9O7dG59//rm2pSmxb98+9OjRA0SEDh06gMPhoGfPnti7d6+2pSlgGjWHPuj86aef8NVXX6FTp06YN28eOnfujBkzZmD16tXalqYEa0vNoA8aAR153jXuYuUdx9bWloqKipTSCgsLycbGRkuKyqZx48b077//KqWdO3eOvL29taRIFaZRc+iDzvr166t4p7pz5w55eHhoSVHZsLbUDPqgkUg3njfbR61h6tevj6NHj8Lb21uRdv/+ffTp0wePHz/WojJlrK2tkZ6eDh6Pp0iTSqWws7NDVlaWFpWVwDRqDn3QaWtri5SUFBgYGCjSRCIRHB0dkZmZqUVlyrC21Az6oBHQjefNhr41zMSJExEUFITly5fj4MGDWL58OXr27ImJEydqW5oSjRs3xqZNm5TStmzZovSCoW2YRs2hDzpbtGiB0NBQSCQSAPIfw7CwMLRo0ULLypRhbakZ9EEjoCPPu8b67u8RO3bsoO7du1OjRo2oe/futGPHDm1LUuHq1atkZ2dH3t7e1KtXL/L29iY7Ozu6evWqtqUpYBo1hz7ojI2NpYYNG5KVlRV5e3uTlZUVeXl5UWxsrLalKcHaUjPog0Yi3XjebOhbwxSvCqxsujbJzs7GX3/9pdDWq1cvWFlZaVuWEkyj5tAHnVKpFJcvX1ba2qiLISRZW2oGfdAI6MDzrrFXgvcEc3PzMtOtra1rWEn5LF68uMz0pUuX1rAS9TCNmkMfdE6cOLHM9M8//7yGlZQPa0vNoA8aiXTjebMetYYxNzdHTk6OUlphYSFq166NjIwMLalSxcLCAkKhUCXd1tZWZ3QyjZpDH3Sq02hjY6NTi4tYW2oGfdAI6Mbz1r0xBj3Fz88PHA4HhYWFaN68uVJecnIyOnbsqCVlyty+fRuA3HvanTt3UPo97fHjxzoRP5tp1Bz6oPPo0aMA5MOgx44dU9GoK0PKrC01gz5oBHTrebMetYbYsWMHiAgTJ07Exo0bFelcLhcODg7o2rWrTsy9cLlccDgcAFD6w+NwOHB0dMTChQsxbtw4bckDwDRqEn3Q6e7uDgBISEhA3bp1FenF351vvvkGffr00ZY8JT2sLd8efdBYrEdnnneNDbK/J9y4cUPbEipFy5YttS2hQphGzaEPOgcPHqxtCZWCtaVm0AeNRLrxvFmPmsFgMBgMHYY5PGEwGAwGQ4dhhprBYDAYDB2GGWoGg8FgMHQY7S9DfocQCoU4cuQIbt++jZycHJibm8PHxwcDBw6EpaWltuUxGAquXr0KPz8/ndiJoM8UFBSAiGBiYqJtKXpPZmYmbGxstC1DJ2E9ag1x6dIl1KtXD8uXL0dqaioEAgHS0tKwcuVK1K9fH5cvX9a2xAoRi8Xo2rWrtmUAAE6fPo0FCxbgyJEjKnmTJk3SgiJlJBIJfvjhB4wdOxbXr19HYmIiunfvDnd3d0ycOBGFhYXallguvXv3xosXL7QtAwBUvhs//fQT2rZti7Zt22LVqlVaUqXK5s2b8fDhQwBASkoKAgMDYWZmBgsLC/Ts2RNpaWlaVgj4+vpi6dKlSElJ0bYUtaSnp2PEiBFo2rQp5s6di9zcXLRp0wZ2dnZwdXXFrVu3tC1Rwb59+9C1a1fY2dlBIBDAyckJQ4cOxb1792pWiJZXnb8z+Pn50ZYtW8rM++WXX8jX17eGFVWdwsJC4nK52pZBW7duJWtraxo0aBDVqVOHunXrRkKhUJGvzk1rTfLll19Sly5dqFu3bmRvb0/z5s2jgwcP0p49e8jb25tmzZqlbYlEJHddW9aHy+WSlZWVTri2Lf08V65cSXXr1qX169fThg0byM3NjVasWKFFdSXUrl2bXr58SUREQ4YMoZEjR9Ljx4/p0aNHNHr0aBoyZIiWFRIZGBhQ06ZNycDAgPr160fHjh0jqVSqbVlKBAcHU79+/WjPnj3Up08fat++PX399df07NkzmjVrFnXr1k3bEomIaNmyZVS3bl0KCwuj0NBQcnFxofnz59OsWbOoVq1aFBkZWWNamKHWECYmJiQSicrME4lEZGJiUsOKymbgwIFqP/3799cJQ92oUSO6dOkSEREVFBTQiBEjqGXLlpSdnU1ERGZmZtqUR0REderUoYyMDEpLSyMOh0MPHjxQ5N2+fZs8PDy0qK4EDw8PCgwMpFOnTlFERARFRETQ2bNnydLSkg4ePEgRERHalqj0PJs0aaJ49kTyyEUNGzbUhiwVTE1NSSaTERGRk5MT5eXlKfLy8/PJ3t5eW9IUFL/0XL58mSZMmECWlpbk7OxM33zzDT169EjL6uTY29tTbm4uERFlZWURj8ejgoICIpJ/321sbLQpT0GdOnUoJiZGcXz37l3y8/MjIqLff/+d2rVrV2Na2NC3hmjQoAF27dpVZt7u3btRv379GlZUNsePH0edOnXQrFkzlU+TJk20LQ8AkJiYiFatWgEAjIyMsHv3bjRv3hxdu3ZFVlaWwluQNhEKhbCxsYG9vT1MTU3h5eWlyPPx8dGJYVAAuHv3Lvz8/PDVV1+Bz+ejU6dO6Ny5MwwMDNC2bVt06tRJ2xKVnmdycjJatmypOA4ICEBiYqI2ZKnQoEEDnD9/HoDcp39pf9RZWVlK3qu0TcuWLbFx40YkJSVh0aJFOHfuHDw9PXViaksmk4HLlZseLpcLIlL8DXA4HJ34fgNAXl6e0u92gwYN8OzZMwBA3759a3aIvsZeCd5xzp8/T9bW1uTj40OjRo2iKVOm0OjRo6lp06ZkY2NDFy5c0LZEIiIKCAigP//8s8y8goIC4nA4NaxIFXd3d4qLi1NJnzhxIvn6+pKxsXHNi3oNFxcXys/PJyKiJUuWKOW9fPmS7OzstCFLLdeuXaPmzZvT+PHjKSsri+zt7SkxMVHbsoiISCAQ0Jdffklffvkl2draUnp6uiJPKBTqxPA8EdGff/5JLi4utGXLFpo/fz75+vrStm3baOvWreTn50czZszQtsRyp4UePHhAX3/9dQ2qKZsBAwbQwIEDae/evTRw4EBq164dzZ49mxITE+mbb76hoKAgbUskIqIPPviA5s2bRxKJhEQiEc2ePZt69OhBRER5eXlka2tbY1qYodYgmZmZ9Ouvv9K0adPo448/pmnTptGvv/5KmZmZ2pamYO3atXTkyJEy8yQSCS1YsKBmBZXB+PHjaf78+WXmTZo0SSdeJqZMmaI03F2abdu2Uffu3WtYUcVIJBJavHgxubq6krGxsc4Y6jFjxih9SrvhPXDgAHXs2FF74l7jxIkT1KJFC+JyucThcIjD4ZCrqystWrRIJ+aCdWFaqCISExOpf//+1LRpU9qxYwfFxsaSm5sbcTgc8vT0pPv372tbIhERPX78mBo3bkwGBgZkYGBADRo0oOjoaCKST2/NnTu3xrQwF6IMnUMkEkEikajd8vK6M39dIy8vDxwOR2e37Dx+/Bjnz59HcHAwjIyMtC1HL8nPz0dWVhbMzMzY1ksNQETIzMyEra2ttqUoIZPJEBMTA5lMBi8vL61tZ2SGuoZ4/vw56tSpo20ZDAaDwdAz2GKyGsLb21vbEiqFLuxRrgimUXPog0590Ajoh06mUXPUpE5mqGuIGt8g/4bowwAL06g59EGnPmgE9EMn06g5alInG/pmMBgMBkOHYY5+NYi++frOyclR6DQ3N9e2nDJhGjWHPujUB42AfuhkGjWHtnWyoW8NoS++vmUyGb7//nu4u7vDysoKLi4usLKygpubGxYtWgSZTKZtiUyjBtEHnfqgEdAPnUyj5tApnTW2EewdR198fU+ZMoV8fX3p4MGDFBsbSy9evKBHjx7RwYMHqXnz5jRt2jRtS2QaNYg+6NQHjUT6oZNp1By6pJMZag2hL76+bW1tKSUlpcy85ORknfCzyzRqDn3QqQ8aifRDJ9OoOXRJJxv61hD64utbJpOp9aXL4XB0YsUl06g59EGnPmgE9EMn06g5dEknM9QaYt26dZgxYwaaNm2K0aNHY+rUqQgJCUGzZs0wY8YMbNiwQdsSAQDDhw9Hz549ceTIETx58gSZmZmIi4vDkSNH0Lt3b4wYMULbEplGDaIPOvVBI6AfOplGzaFTOmus7/4eoA++vsVisSK2KofDUfgsdnFxodDQUBKLxdqWyDRqEH3QqQ8aifRDJ9OoOXRJJ9tH/R7z8uVL5ObmwszMDFZWVtqWUyZMo+bQB536oBHQD51Mo+bQtk5mqKsRHx8f3LlzR9syGAwGg6HHMENdjVhYWEAoFGpbBoPBYDD0GLaYrBph70AMBoPBeFuYoa5GNm3apG0JDAaDwdBz2NA3g8FgMBg6DOtRMxgMBoOhwzBDzWAwGAyGDsMMNYPBYDAYOgwz1AwGg8Fg6DDMUDMYeoCbmxtWr16tbRnvHBwOB3/88Yfa/Pj4eHA4HNy8ebPGNDEYr8MMNYOhITp37owvvviiWuq+evUqPv3002qpuzQVGa73DRcXFyQnJ6NJkybalsJ4j+FrWwCDwagYe3t7bUvQWaRSKTgcDrhczfc7eDweHB0dNV4vg1EVWI+awdAAY8aMwb///ouffvoJHA4HHA4H8fHxkEql+Pjjj+Hu7g5jY2N4eXnhp59+Ujl3wIABWLFiBZycnGBra4vPP/8cYrFYUeb1oW8Oh4NNmzahT58+MDExQaNGjRAZGYlHjx6hc+fOMDU1Rdu2bfH48WOla23YsAEeHh4QCATw8vJSiqHu5uYGABg4cCA4HI7iuKLzyiIiIgItW7aEqakprKys0K5dOzx9+lSRf+zYMbRo0QJGRkaws7PDwIEDFXlZWVkYPXo0rK2tYWJigg8++ACxsbGK/O3bt8PKygpHjx6Ft7c3DA0NkZCQgKKiIsyYMQO1a9eGqakpWrVqhYiIiHJ1AkBycjI++OADGBsbo169ejh06JAi7/Wh74iICHA4HJw5cwYBAQEwMTFB27ZtERMTU+F1GIw3psbidDEY7zAvX76kNm3a0Pjx4yk5OZmSk5NJIpGQSCSi0NBQunr1Kj158oR2795NJiYmtH//fsW5ISEhZGFhQZ999hndv3+fjh07RiYmJrR582ZFGVdXV/rxxx8VxwCodu3atH//foqJiaEBAwaQm5sbde3alcLDwyk6Oppat25NPXv2VJxz+PBhMjAwoHXr1lFMTAytXLmSeDwe/fPPP0RElJaWRgBo27ZtlJycTGlpaZU673XEYjFZWlrSjBkz6NGjRxQdHU3bt2+np0+fEhHR//73P+LxeBQaGkrR0dF08+ZN+uGHHxTn9+vXjxo1akTnzp2jmzdvUlBQENWvX59EIhEREW3bto0MDAyobdu2dOHCBXrw4AHl5eXRJ598Qm3btqVz587Ro0ePaPny5WRoaEgPHz5U+9wAkK2tLW3ZsoViYmJo7ty5xOPxKDo6moiI4uLiCADduHGDiIjOnj1LAKhVq1YUERFB9+7dow4dOlDbtm0r/BthMN4UZqgZDA3RqVMnmjZtWoXlPv/8c/rwww8VxyEhIeTq6koSiUSRNnjwYAoODlYcl2Wo586dqziOjIwkAPTrr78q0vbu3UtGRkaK47Zt29L48eOVtAwePJh69eqlVO+RI0eUylTmvNJkZGQQAIqIiCgzv02bNjRixIgy8x4+fEgA6MKFC4q09PR0MjY2pgMHDhCR3FADoJs3byrKPH36lHg8HiUmJirV161bN5ozZ06Z1yKS3+9nn32mlNaqVSuaOHEiEak31KdPn1aU/+uvvwgAFRQUqL0Og/E2sKFvBqOaWbduHfz9/WFvbw8zMzNs3rwZCQkJSmUaN24MHo+nOHZyckJaWlq59TZt2lTxbwcHBwDy0Kql0woLCxUR3O7fv4927dop1dGuXTvcv3+/3OtU9TwbGxuMGTMGQUFB6Nu3L3766SckJycr8m/evIlu3bqpvRafz0erVq0Uaba2tvDy8lK6nkAgULr/O3fuQCqVwtPTE2ZmZorPv//+qzL8/zpt2rRROa6oTUpf28nJCQAqfF4MxpvCFpMxGNXIvn37MGPGDKxcuRJt2rSBubk5li9fjsuXLyuVMzAwUDrmcDiQyWTl1l36HA6Hozatonqqg23btmHq1KkIDw/H/v37MXfuXJw6dQqtW7eGsbHxW9dvbGysuD8AyM3NBY/HQ1RUlNILDwCYmZm99fVeR1famfF+wHrUDIaGEAgEkEqlSmkXLlxA27ZtMWnSJPj5+aF+/foV9vCqi0aNGuHChQsq+ry9vRXHBgYGKvdQmfPKws/PD3PmzMHFixfRpEkT/PbbbwDkvdEzZ86o1SiRSJReZDIyMhATE1Pu9fz8/CCVSpGWlob69esrfSpatX3p0iWV40aNGpV7DoNRk7AeNYOhIdzc3HD58mXEx8fDzMwMNjY2aNCgAXbu3ImTJ0/C3d0du3btwtWrV+Hu7l7j+mbOnIkhQ4bAz88PgYGBOHbsGA4fPozTp08r3cOZM2fQrl07GBoawtraulLnlSYuLg6bN29Gv3794OzsjJiYGMTGxmL06NEAgPnz56Nbt27w8PDA0KFDIZFIcPz4ccyaNQsNGjRA//79MX78eGzatAnm5uaYPXs2ateujf79+6u9N09PT4wYMQKjR4/GypUr4efnhxcvXuDMmTNo2rQpevfurfbcgwcPIiAgAO3bt8eePXtw5coV/Prrr2/YygxGNaDtSXIG410hJiaGWrduTcbGxgSA4uLiqLCwkMaMGUOWlpZkZWVFEydOpNmzZ1OzZs0U54WEhFD//v2V6po2bRp16tRJcVzWYrLSi75eX/REVLLwKSsrS5G2fv16qlevHhkYGJCnpyft3LlT6bpHjx6l+vXrE5/PJ1dX10qfV5qUlBQaMGAAOTk5kUAgIFdXVwoNDSWpVKoo8/vvv5Ovry8JBAKys7OjQYMGKfIyMzNp1KhRZGlpScbGxhQUFKS0cnvbtm1kaWmpct3iFfZubm5kYGBATk5ONHDgQLp9+7ZarQBo3bp11L17dzI0NCQ3NzelFfnqFpOVbtMbN24onjeDUR2weNQMBoPBYOgwbI6awWAwGAwdhhlqBoPBYDB0GGaoGQwGg8HQYZihZjAYDAZDh2GGmsFgMBgMHYYZagaDwWAwdBhmqBkMBoPB0GGYoWYwGAwGQ4dhhprBYDAYDB2GGWoGg8FgMHQYZqgZDAaDwdBhmKFmMBgMBkOH+T//5ihK9YnlxwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure = plot_loss_per_bin_multiple_benchmarks([scores_normal_model_1.neg_vs_neg_scores, scores_balanced_model_1.neg_vs_neg_scores], 10, \"RMSE\", \"negative vs negative\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31aa6288-2882-4448-81fa-0e7f0288ed30",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Selecting available inchikey pairs per bin: 10it [00:00, 20.27it/s]\n",
+      "Selecting available inchikey pairs per bin: 10it [00:00, 20.32it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure = plot_loss_per_bin_multiple_benchmarks([scores_normal_model_1.pos_vs_pos_scores, scores_balanced_model_1.pos_vs_pos_scores], 10, \"RMSE\", \"positive vs positive\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "6abcc1d2-e00a-4ffe-9bfa-42676130437f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Selecting available inchikey pairs per bin: 10it [00:00, 39.85it/s]\n",
+      "Selecting available inchikey pairs per bin: 10it [00:00, 39.69it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure = plot_loss_per_bin_multiple_benchmarks([scores_normal_model_1.pos_vs_neg_scores, scores_balanced_model_1.pos_vs_neg_scores], 10, \"RMSE\", \"positive vs negative\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5425072-e869-43f4-9437-e5f9fdfaa501",
+   "metadata": {},
+   "source": [
+    "# Redo for old test set\n",
+    "There will of course be data leakage, this is just a sanity check, to make sure it is the test set that became more difficult. It seems to be the case that it is something test set specific, but we don't fully understand what is going on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "ba778bfd-a12f-4ab2-8658-0590ad451f95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "7142it [00:02, 2540.73it/s]\n",
+      "24911it [00:09, 2566.56it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "neg_test = list(tqdm(load_from_mgf(\"/lustre/BIF/nobackup/jonge094/ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/negative_testing_spectra.mgf\")))\n",
+    "pos_test = list(tqdm(load_from_mgf(\"/lustre/BIF/nobackup/jonge094/ms2deepscore/data/pytorch/new_corinna_included/training_and_validation_split/positive_testing_spectra.mgf\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "3ddba656-39c9-4753-8d22-6aee7ae5a247",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lustre/BIF/nobackup/jonge094/ms2deepscore/ms2deepscore/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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "24911it [00:25, 973.65it/s]\n",
+      "7142it [00:07, 973.28it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1831/1831 [00:05<00:00, 307.19it/s]\n",
+      "Calculating fingerprints: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 924/924 [00:02<00:00, 309.66it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n",
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "24911it [00:25, 968.49it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1831/1831 [00:05<00:00, 316.83it/s]\n",
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1831/1831 [00:05<00:00, 315.82it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n",
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "7142it [00:07, 971.02it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 924/924 [00:03<00:00, 307.72it/s]\n",
+      "Calculating fingerprints: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 924/924 [00:03<00:00, 305.47it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lustre/BIF/nobackup/jonge094/ms2deepscore/ms2deepscore/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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "24911it [00:25, 972.55it/s]\n",
+      "7142it [00:07, 973.24it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1831/1831 [00:05<00:00, 312.88it/s]\n",
+      "Calculating fingerprints: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 924/924 [00:03<00:00, 307.42it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n",
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "24911it [00:25, 966.07it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1831/1831 [00:05<00:00, 314.51it/s]\n",
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1831/1831 [00:05<00:00, 313.86it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n",
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "7142it [00:07, 962.44it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 924/924 [00:03<00:00, 307.24it/s]\n",
+      "Calculating fingerprints: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 924/924 [00:03<00:00, 307.41it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n"
+     ]
+    }
+   ],
+   "source": [
+    "from ms2deepscore.benchmarking.CalculateScoresBetweenAllIonmodes import CalculateScoresBetweenAllIonmodes\n",
+    "scores_normal_model = CalculateScoresBetweenAllIonmodes(normal_model_file_name, pos_test, neg_test, fingerprint_type=\"daylight\", n_bits_fingerprint=4096)\n",
+    "scores_balanced_model = CalculateScoresBetweenAllIonmodes(balanced_model_file_name, pos_test, neg_test, fingerprint_type=\"daylight\", n_bits_fingerprint=4096)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "6e889a1e-660c-4325-bc42-d87c0c3ef49b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores_normal_model.neg_vs_neg_scores.label = \"Normal model\"\n",
+    "scores_normal_model.pos_vs_pos_scores.label = \"Normal model\"\n",
+    "scores_normal_model.pos_vs_neg_scores.label = \"Normal model\"\n",
+    "\n",
+    "scores_balanced_model.neg_vs_neg_scores.label=\"Balanced across ionmodes\"\n",
+    "scores_balanced_model.pos_vs_pos_scores.label=\"Balanced across ionmodes\"\n",
+    "scores_balanced_model.pos_vs_neg_scores.label=\"Balanced across ionmodes\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "8823d6a0-b348-406d-b1a7-132ce84779f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Selecting available inchikey pairs per bin: 10it [00:00, 60.05it/s]\n",
+      "Selecting available inchikey pairs per bin: 10it [00:00, 59.94it/s]\n",
+      "Selecting available inchikey pairs per bin: 10it [00:00, 94.22it/s]\n",
+      "Selecting available inchikey pairs per bin: 10it [00:00, 120.04it/s]\n",
+      "Selecting available inchikey pairs per bin: 10it [00:00, 257.35it/s]\n",
+      "Selecting available inchikey pairs per bin: 10it [00:00, 261.44it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x800 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_comparison_violinplot_three_panels([scores_normal_model.pos_vs_pos_scores, scores_normal_model.pos_vs_neg_scores, scores_normal_model.neg_vs_neg_scores],\n",
+    "                                              [scores_balanced_model.pos_vs_pos_scores, scores_balanced_model.pos_vs_neg_scores, scores_balanced_model.neg_vs_neg_scores],\n",
+    "                                              10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "947f38c1-94db-478a-a7e0-1e6db2fe0737",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Selecting available inchikey pairs per bin: 11it [00:00, 63.32it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 63.71it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 123.30it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 125.02it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 272.29it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 270.06it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x800 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bins = [(-1e-08, 0.1),\n",
+    " (0.1, 0.2),\n",
+    " (0.2, 0.3),\n",
+    " (0.3, 0.4),\n",
+    " (0.4, 0.5),\n",
+    " (0.5, 0.6),\n",
+    " (0.6, 0.7),\n",
+    " (0.7, 0.8),\n",
+    " (0.8, 0.9),\n",
+    " (0.9, 0.99),\n",
+    "        (0.99, 1.0)]\n",
+    "fig = plot_comparison_violinplot_three_panels([scores_normal_model.pos_vs_pos_scores, scores_normal_model.pos_vs_neg_scores, scores_normal_model.neg_vs_neg_scores],\n",
+    "                                              [scores_balanced_model.pos_vs_pos_scores, scores_balanced_model.pos_vs_neg_scores, scores_balanced_model.neg_vs_neg_scores],\n",
+    "                                              bins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "8506cb4d-4093-473c-9ee4-5f4a6f52a221",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Selecting available inchikey pairs per bin: 10it [00:00, 249.38it/s]\n",
+      "Selecting available inchikey pairs per bin: 10it [00:00, 252.85it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure = plot_loss_per_bin_multiple_benchmarks([scores_normal_model.neg_vs_neg_scores, scores_balanced_model.neg_vs_neg_scores], 10, \"MSE\", \"negative vs negative\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "27254cf3-62dc-4ad4-94f1-584449c3f1de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Selecting available inchikey pairs per bin: 10it [00:00, 59.77it/s]\n",
+      "Selecting available inchikey pairs per bin: 10it [00:00, 59.36it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure = plot_loss_per_bin_multiple_benchmarks([scores_normal_model.pos_vs_pos_scores, scores_balanced_model.pos_vs_pos_scores], 10, \"MSE\", \"positive vs positive\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "329fe156-cf2e-4e44-b7d3-2359bbd166c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Selecting available inchikey pairs per bin: 10it [00:00, 121.71it/s]\n",
+      "Selecting available inchikey pairs per bin: 10it [00:00, 121.31it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure = plot_loss_per_bin_multiple_benchmarks([scores_normal_model.pos_vs_neg_scores, scores_balanced_model.pos_vs_neg_scores], 10, \"MSE\", \"positive vs negative\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed772eda-9e95-47c6-a244-e8c417750dd7",
+   "metadata": {},
+   "source": [
+    "# Run on only GNPS test spectra"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71e0be6a-acde-43fe-9163-74fccfcbe1df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gnps_spectra = []\n",
+    "for spectrum in test_spectra:\n",
+    "    if spectrum.get(\"spectype\") is None:\n",
+    "        gnps_spectra.append(spectrum)\n",
+    "print(len(gnps_spectra))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "bbd020b3-69aa-44f6-b0a1-623aeaf3f270",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Splitting pos and neg mode spectra: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 33709/33709 [00:00<00:00, 643431.89it/s]\n",
+      "/lustre/BIF/nobackup/jonge094/ms2deepscore/ms2deepscore/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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The spectra, are split in 24174 positive spectra and 9535 negative mode spectra. 0 were removed\n",
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "24174it [00:24, 969.83it/s]\n",
+      "9535it [00:09, 973.79it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2218/2218 [00:06<00:00, 316.93it/s]\n",
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1240/1240 [00:03<00:00, 315.59it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n",
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "24174it [00:24, 971.66it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2218/2218 [00:06<00:00, 317.56it/s]\n",
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2218/2218 [00:06<00:00, 319.79it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n",
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "9535it [00:09, 974.63it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1240/1240 [00:03<00:00, 315.72it/s]\n",
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1240/1240 [00:03<00:00, 317.11it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lustre/BIF/nobackup/jonge094/ms2deepscore/ms2deepscore/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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "24174it [00:24, 971.13it/s]\n",
+      "9535it [00:09, 974.63it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2218/2218 [00:07<00:00, 305.83it/s]\n",
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1240/1240 [00:04<00:00, 301.10it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n",
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "24174it [00:24, 969.49it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2218/2218 [00:06<00:00, 320.23it/s]\n",
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2218/2218 [00:07<00:00, 315.87it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n",
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "9535it [00:09, 965.39it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1240/1240 [00:03<00:00, 314.46it/s]\n",
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1240/1240 [00:03<00:00, 314.89it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n"
+     ]
+    }
+   ],
+   "source": [
+    "pos_test, neg_test = split_by_ionmode(gnps_spectra)\n",
+    "from ms2deepscore.benchmarking.CalculateScoresBetweenAllIonmodes import CalculateScoresBetweenAllIonmodes\n",
+    "scores_normal_model_gnps = CalculateScoresBetweenAllIonmodes(normal_model_file_name, pos_test, neg_test, fingerprint_type=\"daylight\", n_bits_fingerprint=4096)\n",
+    "scores_balanced_model_gnps = CalculateScoresBetweenAllIonmodes(balanced_model_file_name, pos_test, neg_test, fingerprint_type=\"daylight\", n_bits_fingerprint=4096)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "1a56d4ce-97ca-4767-baef-9504f83b192f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Selecting available inchikey pairs per bin: 11it [00:00, 42.21it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 42.64it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 76.06it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 78.91it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 152.71it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 149.56it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x800 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_comparison_violinplot_three_panels([scores_normal_model_gnps.pos_vs_pos_scores, scores_normal_model_gnps.pos_vs_neg_scores, scores_normal_model_gnps.neg_vs_neg_scores],\n",
+    "                                              [scores_balanced_model_gnps.pos_vs_pos_scores, scores_balanced_model_gnps.pos_vs_neg_scores, scores_balanced_model_gnps.neg_vs_neg_scores],\n",
+    "                                              bins)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d28c01f-0b60-49d0-a6eb-f7755e74a070",
+   "metadata": {},
+   "source": [
+    "# Run only on spectra which ionize in both modes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "333ae0e2-e0f5-4900-8034-6eaf4bb7c64d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3092\n",
+      "1659\n",
+      "1369\n"
+     ]
+    }
+   ],
+   "source": [
+    "pos_inchikeys = []\n",
+    "neg_inchikeys = []\n",
+    "for spectrum in test_spectra:\n",
+    "    if spectrum.get(\"ionmode\") == \"positive\":\n",
+    "        pos_inchikeys.append(spectrum.get(\"inchikey\")[:14])\n",
+    "    else:\n",
+    "        neg_inchikeys.append(spectrum.get(\"inchikey\")[:14])\n",
+    "    \n",
+    "print(len(set(pos_inchikeys)))\n",
+    "print(len(set(neg_inchikeys)))\n",
+    "print(len(set(neg_inchikeys)&set(pos_inchikeys)))\n",
+    "inchikeys_ionizing_in_both_modes = set(neg_inchikeys)&set(pos_inchikeys)\n",
+    "\n",
+    "both_modes_ionizing = []\n",
+    "for spectrum in test_spectra:\n",
+    "    if spectrum.get(\"inchikey\")[:14] in inchikeys_ionizing_in_both_modes:\n",
+    "        both_modes_ionizing.append(spectrum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "ff385a03-2199-4c4b-86c7-debb4bdeaf93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Splitting pos and neg mode spectra: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 31796/31796 [00:00<00:00, 444444.00it/s]\n",
+      "/lustre/BIF/nobackup/jonge094/ms2deepscore/ms2deepscore/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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The spectra, are split in 18327 positive spectra and 13469 negative mode spectra. 0 were removed\n",
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "18327it [00:19, 964.03it/s]\n",
+      "13469it [00:13, 967.01it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1369/1369 [00:04<00:00, 295.75it/s]\n",
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1369/1369 [00:04<00:00, 317.57it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n",
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "18327it [00:18, 965.99it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1369/1369 [00:04<00:00, 314.73it/s]\n",
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1369/1369 [00:04<00:00, 311.74it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n",
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "13469it [00:13, 963.65it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1369/1369 [00:04<00:00, 314.58it/s]\n",
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1369/1369 [00:04<00:00, 311.56it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lustre/BIF/nobackup/jonge094/ms2deepscore/ms2deepscore/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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "18327it [00:18, 967.43it/s]\n",
+      "13469it [00:13, 969.48it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1369/1369 [00:04<00:00, 309.71it/s]\n",
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1369/1369 [00:04<00:00, 302.92it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n",
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "18327it [01:39, 183.82it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1369/1369 [00:04<00:00, 315.63it/s]\n",
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1369/1369 [00:04<00:00, 316.72it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n",
+      "Calculating embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "13469it [00:13, 971.04it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating similarity between embeddings\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1369/1369 [00:04<00:00, 314.71it/s]\n",
+      "Calculating fingerprints: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1369/1369 [00:04<00:00, 309.37it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating tanimoto scores\n"
+     ]
+    }
+   ],
+   "source": [
+    "pos_test, neg_test = split_by_ionmode(both_modes_ionizing)\n",
+    "from ms2deepscore.benchmarking.CalculateScoresBetweenAllIonmodes import CalculateScoresBetweenAllIonmodes\n",
+    "scores_normal_model_both_modes_ionizing = CalculateScoresBetweenAllIonmodes(normal_model_file_name, pos_test, neg_test, fingerprint_type=\"daylight\", n_bits_fingerprint=4096)\n",
+    "scores_balanced_model_both_modes_ionizing = CalculateScoresBetweenAllIonmodes(balanced_model_file_name, pos_test, neg_test, fingerprint_type=\"daylight\", n_bits_fingerprint=4096)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "2def2c25-1795-4c2b-be7f-c93779df3f34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores_normal_model_both_modes_ionizing.neg_vs_neg_scores.label = \"Normal model\"\n",
+    "scores_normal_model_both_modes_ionizing.pos_vs_pos_scores.label = \"Normal model\"\n",
+    "scores_normal_model_both_modes_ionizing.pos_vs_neg_scores.label = \"Normal model\"\n",
+    "\n",
+    "scores_balanced_model_both_modes_ionizing.neg_vs_neg_scores.label=\"Balanced across ionmodes\"\n",
+    "scores_balanced_model_both_modes_ionizing.pos_vs_pos_scores.label=\"Balanced across ionmodes\"\n",
+    "scores_balanced_model_both_modes_ionizing.pos_vs_neg_scores.label=\"Balanced across ionmodes\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "ed11f62d-25c3-4c6b-a03a-2e0c1f35b255",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Selecting available inchikey pairs per bin: 11it [00:00, 120.74it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 120.28it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 119.77it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 120.53it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 118.35it/s]\n",
+      "Selecting available inchikey pairs per bin: 11it [00:00, 120.38it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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yyy+55J2IKA59+vTBVVddhRdeeAEHDhwIeZ81a9ZAVVVMnDgxIC926dIFRx55pP8ca8OGDaisrMS1114bMAv+yiuvDMhZAJCamuqfTa6qKqqqquDxeDB06NCweTacRJ0zRpO3fJ3xH330Eex2e8xfS15eHv7yl7/4/52RkYGTTjrJn+d9X0f//v1x9NFHB3wdvvGCvq/jww8/BABcf/31Aa9xww03BL1u4zzvdrtRWVmJI444Am3atIn7+zJhwgSkpaUFfF9++eUX/Pbbb/7jL9/XM2LECLRt2zbg6znzzDOhKAq+/PLLuF6fWicW54nisHjxYnzyySf4/PPP8dtvv2Hnzp3+5eW7du1CSkoKjjjiiIDHdOnSBW3atPHPexs5ciQuuugizJ07Fx06dMAFF1yAZcuWBc1b02LQoEE4+uijAxLLqlWr0KFDB38SLC8vR01NDV544QV07Ngx4OPqq68G4F2+H86ll16KdevW+U+cv/jiC5SVlQUkr+uvvx79+vXD2LFj0b17d1xzzTX+xBuNI488Mui2fv36+efJ+d7Xo446Kuh+/fv3958UA8AjjzyCX375BT169MBJJ52EOXPmBBw46CGa92T79u2ora1Fp06dgt57q9Ua8X0nIqLYMYcHSkQOP/zwwwP+7St6+Obc/v777xBC4J577gn6OnzLyn1fx65du4K+PwBC3vbrr7/iwgsvRGFhIQoKCtCxY0d/8SDSDN3mXHLJJUhJSfF/X4QQeP311zF27FgUFBQA8OZ3AJg8eXLQ17N06VI4nc64X5+IqLW7++674fF4mp09v337dgghcOSRRwb9Dd68eXNAPgGC80daWpp/HGpjK1aswHHHHYesrCy0b98eHTt2xHvvvafp73kizhmjyVu9e/fGjBkzsHTpUnTo0AFjxozB4sWLo/7aunfvHjBPH/Dm+sYz8bdv345ff/016Gvo168fgMA8n5KSgt69ewc8X6g839DQgHvvvdc/871Dhw7o2LEjampq4v6+dOjQAWeccQZWr17tv23VqlVIS0vzjyryfT0ffvhh0Ndz5plnBnw9RNHgzHmiOJx00kkYOnRo2Ps0TU6hPv/GG2/g22+/xX/+8x989NFHuOaaa/DYY4/h22+/bXbeaawuvfRSPPDAA6ioqEB+fj7eeecdXH755f7uAF/n1l/+8pegubY+zc3Ja/was2bNwuuvv46bb74Zq1evRmFhIc4++2z/fTp16oSioiJ89NFH+OCDD/DBBx9g2bJlmDRpUsgN74w0ceJEjBgxAm+99RY+/vhjPProo3j44YexZs0a/0w8raJ5T1RVRadOnfDKK6+EfI5Qc4iJiEgb5vDg1zA6h6empoa8XQgR8HXceuut/gslTYU6KQ+npqYGI0eOREFBAe677z707dsXWVlZ+PHHH3HHHXfE3bnerVs3jBgxAqtXr8add96Jb7/9Frt37/bP4m/89Tz66KMBHf6N6fUzQkTU2vTp0wd/+ctf8MILL2DmzJlBn1dVFRaLBR988EHI/BPP39+XX34ZU6ZMwfjx43HbbbehU6dOSE1Nxfz58wM60mOViHPGaPIWADz22GOYMmUK/v3vf+Pjjz/GjTfeiPnz5+Pbb78NmL8fSqQ87/s6Bg4ciIULF4a8b48ePcK+Rig33HADli1bhptvvhnDhg1DYWEhLBYLLrvsMk0r1C677DJcffXVKCoqwuDBg7F69WqcccYZ/v2CfF/PWWedhdtvvz3kc/guOhBFg8V5Ip317NkTqqpi+/btAZuMlJaWoqamBj179gy4/ymnnIJTTjkFDzzwAF599VVceeWVeO211zB16tSQzx+pYNDUpZdeirlz5+LNN99E586dUVdXF7C5TMeOHZGfnw9FUfxXeWPVu3dvnHTSSVi1ahWmT5+ONWvWYPz48UGbs2VkZGDcuHEYN24cVFXF9ddfj+effx733HNPxJNuXxdaY9u2bfN3Nfje161btwbdb8uWLejQoQNyc3P9t3Xt2hXXX389rr/+epSVleGEE07AAw880GxxPtb3PZr3pG/fvvj0008xfPjwgCV5RERkDuZwY3J4JH369AEApKenR/w6evbsid9//z3o9qa3ffHFF6isrMSaNWtw2mmn+W8vLi4Oemw835frr78eW7duxapVq5CTk4Nx48b5P9+3b18AQEFBQdzfFyIiat7dd9+Nl19+OajADHj/Bgsh0Lt377AFUl9O//333zF69Gj/7R6PByUlJQEXt9944w306dMHa9asCcgZTTcNlfWcMVLe8hk4cCAGDhyIu+++G9988w2GDx+O5557Dvfff39cr9tY37598fPPP+OMM84I+z75jsWKi4sDVs+Hyv1vvPEGJk+ejMcee8x/m8PhQE1NTcD9Yv2+jB8/HtOmTfOvNti2bRtmzZoV9PVYrVbmedIFx9oQ6eycc84BgKDduX1XiH27jFdXVwdcSQbg764KtyzeV2BumnCa079/fwwcOBCrVq3CqlWr0LVr14CT1NTUVFx00UV488038csvvwQ9vry8PKrXufTSS/Htt9/ipZdeQkVFRcBSPACorKwM+HdKSor/gCeaMQBvv/22f7kfAHz//ff47rvv/MX0rl27YvDgwVixYkXAe/PLL7/g448/9n9fFEUJWuLWqVMndOvWLWwcOTk5AKJ/34HI78nEiROhKArmzZsX9FiPxxPTaxERkXbM4cbk8Eg6deqEUaNG4fnnnw85Q7jx1zFmzBisX78eRUVF/tuqqqqCOgp9XXyNv08ulwvPPPNM0PPn5ubGtPz9oosuQmpqKv71r3/h9ddfx3nnnRfQADBkyBD07dsXCxYsgNVqDfv1EBFR7Pr27Yu//OUveP7553Hw4MGAz02YMAGpqamYO3duUK4WQvhz2tChQ9G+fXssWbIEHo/Hf59XXnklYBwLEDqnfPfdd1i/fn3A/WQ9Z4yUt+rq6gLeA8BbqE9JSdFtZN/EiROxb98+LFmyJOhzDQ0N/hG0vhV0TfP1U089FfS41NTUoO/xU089BUVRAm6L9firTZs2GDNmDFavXo3XXnsNGRkZGD9+fNDXs379enz00UdBj6+pqQl6P4nCYec8kc4GDRqEyZMn44UXXvAvqf7++++xYsUKjB8/3n9VfsWKFXjmmWdw4YUXom/fvqivr8eSJUtQUFDgLw6EMmTIEADAXXfdhcsuuwzp6ekYN25cQHJt6tJLL8W9996LrKws/PWvf0VKSuB1uYceegiff/45Tj75ZFx77bUYMGAAqqqq8OOPP+LTTz9FVVVVxK974sSJuPXWW3HrrbeiXbt2QVeQp06diqqqKpx++uno3r07du3ahaeeegqDBw8O6E5szhFHHIE//elP+Pvf/w6n04knnngC7du3D1hG9uijj2Ls2LEYNmwY/vrXv6KhoQFPPfUUCgsLMWfOHADeDeG6d++Oiy++GIMGDUJeXh4+/fRT/PDDDwFX3JvKzs7GgAEDsGrVKvTr1w/t2rXDsccei2OPPTbu92TkyJGYNm0a5s+fj6KiIvz5z39Geno6tm/fjtdffx1PPvkkLr744ojvDRER6YM53JgcHo3FixfjT3/6EwYOHIhrr70Wffr0QWlpKdavX4+9e/fi559/BgDcfvvtePnll3HWWWfhhhtuQG5uLpYuXYrDDz8cVVVV/u64U089FW3btsXkyZNx4403wmKxYOXKlUEn8YD3+7Jq1SrMmDEDJ554IvLy8kJ2FPp06tQJo0ePxsKFC1FfXx9USElJScHSpUsxduxYHHPMMbj66qtx2GGHYd++ffj8889RUFCA//znP7q8b0RErdVdd92FlStXYuvWrTjmmGP8t/ft2xf3338/Zs2ahZKSEowfPx75+fkoLi7GW2+9heuuuw633norMjIyMGfOHNxwww04/fTTMXHiRJSUlGD58uXo27dvQLf1eeedhzVr1uDCCy/Eueeei+LiYjz33HMYMGBAwEVYWc8ZI+Wtzz77DNOnT8cll1yCfv36wePxYOXKlf4mAD1cddVVWL16Nf72t7/h888/x/Dhw6EoCrZs2YLVq1fjo48+wtChQzFkyBBcdNFFeOKJJ1BZWYlTTjkF//3vf7Ft2zYACPq+rFy5EoWFhRgwYADWr1+PTz/9FO3btw947cGDByM1NRUPP/wwamtrkZmZidNPPx2dOnVqNt5LL70Uf/nLX/DMM89gzJgxaNOmTcDnb7vtNrzzzjs477zzMGXKFAwZMgQ2mw2bNm3CG2+8gZKSkoAxOERhCSKK2rJlywQA8cMPP4S9n9vtFnPnzhW9e/cW6enpokePHmLWrFnC4XD47/Pjjz+Kyy+/XBx++OEiMzNTdOrUSZx33nliw4YNAc8FQMyePTvgtnnz5onDDjtMpKSkCACiuLhYCCFEz549xeTJk4Pi2b59uwAgAIivv/46ZMylpaXiH//4h+jRo4dIT08XXbp0EWeccYZ44YUXIr8xhwwfPlwAEFOnTg363BtvvCH+/Oc/i06dOomMjAxx+OGHi2nTpokDBw6Efc7i4mIBQDz66KPiscceEz169BCZmZlixIgR4ueffw66/6effiqGDx8usrOzRUFBgRg3bpz47bff/J93Op3itttuE4MGDRL5+fkiNzdXDBo0SDzzzDMBzzN58mTRs2fPgNu++eYbMWTIEJGRkRHwfZk9e7Zo7s9puPfE54UXXhBDhgwR2dnZIj8/XwwcOFDcfvvtYv/+/WHfGyIiih5zeHhG5/CmQr03O3bsEJMmTRJdunQR6enp4rDDDhPnnXeeeOONNwLu99NPP4kRI0aIzMxM0b17dzF//nyxaNEiAUAcPHjQf79169aJU045RWRnZ4tu3bqJ22+/XXz00UcCgPj888/997NareKKK64Qbdq0EQD8+d8X/7Jly4LiX7JkiQAg8vPzRUNDQ8iv/6effhITJkwQ7du3F5mZmaJnz55i4sSJYu3atWHfNyIi+kO4/D158mQBQBxzzDFBn3vzzTfFn/70J5Gbmytyc3PF0UcfLf7xj3+IrVu3Btxv0aJFomfPniIzM1OcdNJJYt26dWLIkCHi7LPP9t9HVVXx4IMP+u93/PHHi3fffTepzhnD5a2dO3eKa665RvTt21dkZWWJdu3aidGjR4tPP/004vOOHDky5Psf6r1xuVzi4YcfFsccc4zIzMwUbdu2FUOGDBFz584VtbW1/vvZbDbxj3/8Q7Rr107k5eWJ8ePHi61btwoA4qGHHvLfr7q6Wlx99dWiQ4cOIi8vT4wZM0Zs2bIl5DHVkiVLRJ8+fURqamrAccDIkSPFyJEjg+Kvq6sT2dnZAoB4+eWXQ37t9fX1YtasWeKII44QGRkZokOHDuLUU08VCxYsEC6XK+J7R+RjESJE+wgRkSRKSkrQu3dvPProo7j11lvNDoeIiIgkdPPNN+P555+H1WptdmM6IiKiSFRVRceOHTFhwoSQI1jIHEVFRTj++OPx8ssv48orrzQ7HCJdceY8EREREREljYaGhoB/V1ZWYuXKlfjTn/7EwjwREUXN4XAEjTv7v//7P1RVVWHUqFHmBEVBeR7w7geUkpISsPcOUUvBmfNERERERJQ0hg0bhlGjRqF///4oLS3Fiy++iLq6Otxzzz1mh0ZEREnk22+/xS233IJLLrkE7du3x48//ogXX3wRxx57LC655BKzw2u1HnnkEWzcuBGjR49GWloaPvjgA3zwwQe47rrr0KNHD7PDI9Idi/NERERERJQ0zjnnHLzxxht44YUXYLFYcMIJJ+DFF19kNx0REcWkV69e6NGjBxYtWoSqqiq0a9cOkyZNwkMPPYSMjAyzw2u1Tj31VHzyySeYN28erFYrDj/8cMyZMwd33XWX2aERGYIz54mIiIiIiIiIiIiIEowz54mIiIiIiIiIiIiIEozFeSIiIiIiIiIiIiKiBGv1M+dVVcX+/fuRn58Pi8VidjhERERxEUKgvr4e3bp1Q0pK67v2znxOREQtQWvP5wBzOhERtQzR5vRWX5zfv38/d3smIqIWY8+ePejevbvZYSQc8zkREbUkrTWfA8zpRETUskTK6a2+OJ+fnw/A+0YVFBSYHA0REVF86urq0KNHD39ea22Yz4mIqCVo7fkcYE4nIqKWIdqc3uqL875lcgUFBUz8RESU9Frr8m/mcyIiaklaaz4HmNOJiKhliZTTW8QQu+LiYowePRoDBgzAwIEDYbPZzA6JiIiIYsR8TkRE1DIwpxMREUWnRXTOT5kyBffffz9GjBiBqqoqZGZmmh0SERERxYj5nIiIqGVgTiciIopO0hfnf/31V6Snp2PEiBEAgHbt2pkcEREREcWK+ZyIiKhlYE4nIiKKnunF+S+//BKPPvooNm7ciAMHDuCtt97C+PHjA+6zePFiPProozh48CAGDRqEp556CieddBIAYPv27cjLy8O4ceOwb98+XHzxxbjzzjtN+ErkNWvNprgfOz99aXwPHPdk3K9J5hBCwOPxQFEUs0MhohBSU1ORlpYm7Qxa5vPEiDenx53PAeb0JMScTiQv2fM5wJyeCKacowPM6UmG+ZxIbnrldNOL8zabDYMGDcI111yDCRMmBH1+1apVmDFjBp577jmcfPLJeOKJJzBmzBhs3boVnTp1gsfjwVdffYWioiJ06tQJZ599Nk488UScddZZJnw1RMnJ5XLhwIEDsNvtZodCRGHk5OSga9euyMjIMDuUIMznRHJgTieSn8z5HGBOJ5IB8zlRctAjp5tenB87dizGjh3b7OcXLlyIa6+9FldffTUA4LnnnsN7772Hl156CTNnzsRhhx2GoUOHokePHgCAc845B0VFRc0mfqfTCafT6f93XV2djl8NUfJRVRXFxcVITU1Ft27dkJGRIXUnD1FrJISAy+VCeXk5iouLceSRRyIlRa493ZnPiczHnE4kt2TI5wBzOpHZmM+J5KdnTje9OB+Oy+XCxo0bMWvWLP9tKSkpOPPMM7F+/XoAwIknnoiysjJUV1ejsLAQX375JaZNm9bsc86fPx9z5841PHaiZOFyuaCqKnr06IGcnByzwyGiZmRnZyM9PR27du2Cy+VCVlaW2SFFjfmcKDGY04nkl8z5HGBOJ0oE5nOi5KBXTpfvMn0jFRUVUBQFnTt3Dri9c+fOOHjwIAAgLS0NDz74IE477TQcd9xxOPLII3Heeec1+5yzZs1CbW2t/2PPnj2Gfg1EyULGrh0iCpSsv6fM50SJlax/K4hai2T+HWVOJ0qcZP5bQdRa6PF7KnXnfLQiLbtrLDMzE5mZmQZHRERERLFiPiciImoZmNOJiIiiI/VluA4dOiA1NRWlpaUBt5eWlqJLly4mRUVERESxYD4nIiJqGZjTiYiI9CV153xGRgaGDBmCtWvXYvz48QC8G2OsXbsW06dPNzc4olZg1ppNCX29+RMGJvT1zPLFF19g9OjRqK6uRps2bcwOBxaLBW+99Zb/72wkU6ZMQU1NDd5++21D46KWg/mcyFzM58ZgPqfWiDmdyFzM6cZgTiczmd45b7VaUVRUhKKiIgBAcXExioqKsHv3bgDAjBkzsGTJEqxYsQKbN2/G3//+d9hsNv/O8ETUek2ZMgUWiwUPPfRQwO1vv/02d7MnSjDmcyKKF/M5kVyY04koXszpRLEzvXN+w4YNGD16tP/fM2bMAABMnjwZy5cvx6WXXory8nLce++9OHjwIAYPHowPP/wwaAMaImqdsrKy8PDDD2PatGlo27atbs/rcrmQkZGh2/MRtXTM50SkBfM5kTyY04lIC+Z0otiY3jk/atQoCCGCPpYvX+6/z/Tp07Fr1y44nU589913OPnkk80LmIikcuaZZ6JLly6YP39+2Pu9+eabOOaYY5CZmYlevXrhscceC/h8r169MG/ePEyaNAkFBQW47rrrsHz5crRp0wbvvvsujjrqKOTk5ODiiy+G3W7HihUr0KtXL7Rt2xY33ngjFEXxP9fKlSsxdOhQ5Ofno0uXLrjiiitQVlYW09dlsVjw/PPP47zzzkNOTg769++P9evX4/fff8eoUaOQm5uLU089FTt27Ah43LPPPou+ffsiIyMDRx11FFauXBnw+e3bt+O0005DVlYWBgwYgE8++STotffs2YOJEyeiTZs2aNeuHS644AKUlJTEFD+1PsznRKQF8znzOcmDOZ2ItGBOZ06n2JhenCci0iI1NRUPPvggnnrqKezduzfkfTZu3IiJEyfisssuw6ZNmzBnzhzcc889AScYALBgwQIMGjQIP/30E+655x4AgN1ux6JFi/Daa6/hww8/xBdffIELL7wQ77//Pt5//32sXLkSzz//PN544w3/87jdbsybNw8///wz3n77bZSUlGDKlCkxf22+A5GioiIcffTRuOKKKzBt2jTMmjULGzZsgBAiYLbnW2+9hZtuugn//Oc/8csvv2DatGm4+uqr8fnnnwPwzgOdMGECMjIy8N133+G5557DHXfcEfCabrcbY8aMQX5+Pr766iusW7cOeXl5OPvss+FyuWL+GoiIiKLBfM58TkRELQNzOnM6xcb0sTZERFpdeOGFGDx4MGbPno0XX3wx6PMLFy7EGWec4U/m/fr1w2+//YZHH300ICGffvrp+Oc//+n/91dffQW32+2/0g0AF198MVauXInS0lLk5eVhwIABGD16ND7//HNceumlAIBrrrnG/xx9+vTBokWLcOKJJ8JqtSIvLy/qr+vqq6/GxIkTAQB33HEHhg0bhnvuuQdjxowBANx0000Bsz0XLFiAKVOm4PrrrwfgXYL87bffYsGCBRg9ejQ+/fRTbNmyBR999BG6desGAHjwwQcxduxY/3OsWrUKqqpi6dKl/pmAy5YtQ5s2bfDFF1/gz3/+c9TxExERxYL53Iv5nIiIkh1zuhdzOkWDnfNE1CI8/PDD/k2pmtq8eTOGDx8ecNvw4cOxffv2gKVuQ4cODXpsTk6OP+kDQOfOndGrV6+ABN65c+eAJXEbN27EuHHjcPjhhyM/Px8jR44EAP8mWtE67rjjAl4DAAYOHBhwm8PhQF1dXdiv0/eebN68GT169PAnfQAYNmxYwP1//vln/P7778jPz0deXh7y8vLQrl07OByOoOV5REREemM+Zz4nIqKWgTmdOZ2iw855ImoRTjvtNIwZMwazZs2Ka3kaAOTm5gbdlp6eHvBvi8US8jZVVQEANpsNY8aMwZgxY/DKK6+gY8eO2L17N8aMGRPzkrPGr+O7Qh7qNt9r68FqtWLIkCF45ZVXgj7XsWNH3V6HiIgoFOZzfTCfExGR2ZjT9cGc3vKxOE9ELcZDDz2EwYMH46ijjgq4vX///li3bl3AbevWrUO/fv2QmpqqawxbtmxBZWUlHnroIfTo0QMAsGHDBl1fozm+r3Py5Mn+29atW4cBAwb4P79nzx4cOHAAXbt2BQB8++23Ac9xwgknYNWqVejUqRMKCgoSEjcREVFjzOfM50RE1DIwpzOnU2Qca0NELcbAgQNx5ZVXYtGiRQG3//Of/8TatWsxb948bNu2DStWrMDTTz+NW2+9VfcYDj/8cGRkZOCpp57Czp078c4772DevHm6v04ot912G5YvX45nn30W27dvx8KFC7FmzRr/13nmmWeiX79+mDx5Mn7++Wd89dVXuOuuuwKe48orr0SHDh1wwQUX4KuvvkJxcTG++OIL3Hjjjc1u5kNERKQn5nPmcyIiahmY05nTKTJ2zhNRs+ZPGBj5TpK57777sGrVqoDbTjjhBKxevRr33nsv5s2bh65du+K+++6Le2ldOB07dsTy5ctx5513YtGiRTjhhBOwYMECnH/++bq/VlPjx4/Hk08+iQULFuCmm25C7969sWzZMowaNQoAkJKSgrfeegt//etfcdJJJ6FXr15YtGgRzj77bP9z5OTk4Msvv8Qdd9yBCRMmoL6+HocddhjOOOMMXqUnIkpSzOexYz4nIiIZMafHjjmdZGcRQgizgzBTXV0dCgsLUVtb22J/qGet2RT3Y+enL43vgeOeDPtpGWNqrRwOB4qLi9G7d29kZWWZHQ4RhRHu97U15LNwWsvXH2/+jDt3AmHzpyn5HGBObwZzOlFyYD4PrzW8BzLmT56jy4P5nCh56JHTOdaGiIiIiIiIiIiIiCjBONZGZzJeASciIqLYsYOMiIioZZBt5RkREZEPO+eJiIiIiIiIiIiIiBKMxXkiIiIiIiIiIiIiogRjcZ6IiIiIiIiIiIiIKMFYnCciIiIiIiIiIiIiSjAW54mIiIiIiIiIiIiIEizm4vyKFSvw3nvv+f99++23o02bNjj11FOxa9cuXYMjIiIi4zCnExERJT/mcyIiouQVc3H+wQcfRHZ2NgBg/fr1WLx4MR555BF06NABt9xyi+4BEhERkTGY04mIiJIf8zkREVHySov1AXv27MERRxwBAHj77bdx0UUX4brrrsPw4cMxatQoveMjIjP956bEvt64JxP7eofMmTMHb7/9NoqKikx5/WiVlJSgd+/e+OmnnzB48GCzwzGMxWLBW2+9hfHjx5sdSlx69eqFm2++GTfffLPZoUTEnE7USjCfS4X5PDkwnxORlJjTpcKcnhxkz+kxd87n5eWhsrISAPDxxx/jrLPOAgBkZWWhoaFB3+iIiMKYMmUKLBaL/6N9+/Y4++yz8b///c/s0EiDAwcOYOzYsWaH0SowpxORDJjPWybm88RhPiciWTCnt0zM6caKuTh/1llnYerUqZg6dSq2bduGc845BwDw66+/olevXnrHR0QU1tlnn40DBw7gwIEDWLt2LdLS0nDeeeeZHVaL4XK5Ev6aXbp0QWZmZsJftzViTiciWTCfG4v5vGVjPicimTCnG4s5veWJuTi/ePFinHrqqSgvL8ebb76J9u3bAwA2btyIyy+/XPcAiYjCyczMRJcuXdClSxcMHjwYM2fOxJ49e1BeXu6/zx133IF+/fohJycHffr0wT333AO3293sc/7www8466yz0KFDBxQWFmLkyJH48ccfA+5jsViwdOlSXHjhhcjJycGRRx6Jd955J+A+v/76K8477zwUFBQgPz8fI0aMwI4dO/yfX7p0Kfr374+srCwcffTReOaZZwIe//333+P4449HVlYWhg4dip9++ini+7Fy5UoMHToU+fn56NKlC6644gqUlZVFHdeUKVMwfvx4PPDAA+jWrRuOOuooAMCmTZtw+umnIzs7G+3bt8d1110Hq9Xqf84vvvgCJ510EnJzc9GmTRsMHz7cvwHZzz//jNGjRyM/Px8FBQUYMmQINmzY0OzXYLFY8Pbbb/v/Hem1fTEvWLAAXbt2Rfv27fGPf/wj4Hvcq1cv3H///Zg0aRLy8vLQs2dPvPPOOygvL8cFF1yAvLw8HHfccUFxvfnmmzjmmGOQmZmJXr164bHHHgv4fFlZGcaNG4fs7Gz07t0br7zyStDXU1NTg6lTp6Jjx44oKCjA6aefjp9//tn/+VjfHz0xpxORLJjPAzGfM5/HgvmciGTCnB6IOZ05PZKYivMejweLFi3CHXfcgX//+984++yz/Z+bO3cu7rrrLt0DJCKKltVqxcsvv4wjjjjCf1ICAPn5+Vi+fDl+++03PPnkk1iyZAkef/zxZp+nvr4ekydPxtdff41vv/0WRx55JM455xzU19cH3G/u3LmYOHEi/ve//+Gcc87BlVdeiaqqKgDAvn37cNpppyEzMxOfffYZNm7ciGuuuQYejwcA8Morr+Dee+/FAw88gM2bN+PBBx/EPffcgxUrVvi/lvPOOw8DBgzAxo0bMWfOHNx6660R3wO324158+bh559/xttvv42SkhJMmTLF//lIcQHA2rVrsXXrVnzyySd49913YbPZMGbMGLRt2xY//PADXn/9dXz66aeYPn06AG9uGD9+PEaOHIn//e9/WL9+Pa677jpYLBYAwJVXXonu3bvjhx9+wMaNGzFz5kykp6dH/FoARHxtn88//xw7duzA559/jhUrVmD58uVYvnx5wH0ef/xxDB8+HD/99BPOPfdcXHXVVZg0aRL+8pe/4Mcff0Tfvn0xadIkCCEAeE9oJ06ciMsuuwybNm3CnDlzcM899wQ875QpU7Bnzx58/vnneOONN/DMM88EHWhdcsklKCsrwwcffICNGzfihBNOwBlnnOH/WdHy/mjBnE5EsmI+Zz5nPo8e8zkRyYw5nTmdOT2ymDaETUtLwyOPPIJJkyYZFQ8RUUzeffdd5OXlAfAmia5du+Ldd99FSsof1x7vvvtu/3/36tULt956K1577TXcfvvtIZ/z9NNPD/j3Cy+8gDZt2uC///1vwHK8KVOm+LuRHnzwQSxatAjff/89zj77bCxevBiFhYV47bXX/H/E+/Xr53/s7Nmz8dhjj2HChAkAgN69e+O3337D888/j8mTJ+PVV1+Fqqp48cUXkZWVhWOOOQZ79+7F3//+97DvxzXXXOP/7z59+mDRokU48cQTYbVakZeXFzEuAMjNzcXSpUuRkZEBAFiyZAkcDgf+7//+D7m5uQCAp59+GuPGjcPDDz+M9PR01NbW4rzzzkPfvn0BAP379/c/3+7du3Hbbbfh6KOPBgAceeSRYb+Gxl599dWwr925c2cAQNu2bfH0008jNTUVRx99NM4991ysXbsW1157rf+5zjnnHEybNg0AcO+99+LZZ5/FiSeeiEsuuQSAt3tj2LBhKC0tRZcuXbBw4UKcccYZuOeee/zv02+//YZHH30UU6ZMwbZt2/DBBx/g+++/x4knnggAePHFFwO+9q+//hrff/89ysrK/MsAFyxYgLfffhtvvPEGrrvuOk3vjxbM6UQkE+bzQMznzOfRYj4nItkwpwdiTmdOjyTmsTZnnHEG/vvf/xoRCxFRzEaPHo2ioiIUFRXh+++/x5gxYzB27Fj/ci0AWLVqFYYPH44uXbogLy8Pd999N3bv3t3sc5aWluLaa6/FkUceicLCQhQUFMBqtQY95rjjjvP/d25uLgoKCvxXZIuKijBixIiQV1dtNht27NiBv/71r8jLy/N/3H///f6la5s3b8Zxxx2HrKws/+OGDRsW8f3YuHEjxo0bh8MPPxz5+fkYOXIkAPhjDxeXz8CBA/1J3xfLoEGD/IkXAIYPHw5VVbF161a0a9cOU6ZMwZgxYzBu3Dg8+eSTOHDggP++M2bMwNSpU3HmmWfioYceClg2GEmk1/Y55phjkJqa6v93165dg66ON/5++Q4YBg4cGHSb73GbN2/G8OHDA55j+PDh2L59OxRFwebNm5GWloYhQ4b4P3/00UejTZs2/n///PPPsFqtaN++fcD3uri42P8+aHl/tGJOJyJZMJ8HYj73Yj6PDvM5EcmEOT0Qc7oXc3rzYi7Ojx07FjNnzsStt96Kf/3rX3jnnXcCPoiIEik3NxdHHHEEjjjiCJx44olYunQpbDYblixZAgBYv349rrzySpxzzjl499138dNPP+Guu+4Ku4nK5MmTUVRUhCeffBLffPMNioqK0L59+6DHNE2eFosFqqoCALKzs5t9ft8stiVLlvgPWoqKivDLL7/g22+/jet9AP5YXlZQUIBXXnkFP/zwA9566y0Af2waEy4un8ZJNlrLli3D+vXrceqpp2LVqlXo16+f/2uZM2cOfv31V5x77rn47LPPMGDAAH9cegn3vQh1H99yvlC3NX2cFlarFV27dg34PhcVFWHr1q247bbbACTm/WkOczoRyYL5/A/M539gPo8O8zkRyYQ5/Q/M6X9gTm9eTGNtAOD6668HACxcuDDocxaLBYqiaI+KiChOFosFKSkpaGhoAAB888036NmzZ8C8zcZX7ENZt24dnnnmGZxzzjkAgD179qCioiKmOI477jisWLECbrc7KCl17twZ3bp1w86dO3HllVeGfHz//v2xcuVKOBwO/5X5SAcFW7ZsQWVlJR566CH06NEDAII2LQkXV3P69++P5cuXw2az+Q8K1q1bh5SUFP9mNABw/PHH4/jjj8esWbMwbNgwvPrqqzjllFMAeJeb9evXD7fccgsuv/xyLFu2DBdeeKFur22E/v37Y926dQG3rVu3Dv369fMvzfN4PNi4caN/ydzWrVtRU1Pjv/8JJ5yAgwcPIi0tDb169Wr2teJ9f7RiTiciWTGfM5/rhfmc+ZyIzMWczpyul5aa02PunFdVtdkPJn0iSjSn04mDBw/i4MGD2Lx5M2644QZYrVaMGzcOgHc22O7du/Haa69hx44dWLRoUcQrnkceeSRWrlyJzZs347vvvsOVV14Z1dXsxqZPn466ujpcdtll2LBhA7Zv346VK1f6l3nNnTsX8+fPx6JFi7Bt2zZs2rQJy5Yt859UXXHFFbBYLLj22mvx22+/4f3338eCBQvCvubhhx+OjIwMPPXUU9i5cyfeeecdzJs3L6a4QrnyyiuRlZWFyZMn45dffsHnn3+OG264AVdddRU6d+6M4uJizJo1C+vXr8euXbvw8ccfY/v27ejfvz8aGhowffp0fPHFF9i1axfWrVuHH374IWDmWziRXttI//znP7F27VrMmzcP27Ztw4oVK/D000/7N/056qijcPbZZ2PatGn47rvvsHHjRkydOjXgZ+XMM8/EsGHDMH78eHz88ccoKSnBN998g7vuugsbNmzQ/P5oxZxORLJgPv8D87m+mM+Zz4kosZjT/8Ccrq+WmtNj7pwnolZk3JNmRxDRhx9+iK5duwLw7vh+9NFH4/XXX8eoUaMAAOeffz5uueUWTJ8+HU6nE+eeey7uuecezJkzp9nnfPHFF3HdddfhhBNOQI8ePfDggw9GtQt7Y+3bt8dnn32G2267DSNHjkRqaioGDx7sn482depU5OTk4NFHH8Vtt92G3NxcDBw4EDfffDMAIC8vD//5z3/wt7/9DccffzwGDBiAhx9+GBdddFGzr9mxY0csX74cd955JxYtWoQTTjgBCxYswPnnnx91XKHk5OTgo48+wk033YQTTzwROTk5uOiii/wHKTk5OdiyZQtWrFiByspKdO3aFf/4xz8wbdo0eDweVFZWYtKkSSgtLUWHDh0wYcIEzJ07N6r3MdJrG+mEE07A6tWrce+992LevHno2rUr7rvvPkyZMsV/n2XLlmHq1KkYOXIkOnfujPvvv9+/OQ3g7RJ5//33cdddd+Hqq69GeXk5unTpgtNOOw2dO3dGamqqpveHiCgqzOfM52A+Zz4nohaBOZ05HczpLS2nW4QQItKdFi1ahOuuuw5ZWVlYtGhR2PveeOONugWXCHV1dSgsLERtbS0KCgo0P9+sNZvifuz89KXxv3CYP9CmxBQhYcgYU2vlcDhQXFyM3r17B2xsQkTyCff7Gm0+a6k5Xe98DsiZq+KNqUUdYwDM6c1gTidKDszn4cmU01tT/pTxuKe1Yj4nSh565PSoOucff/xx/7KFxx9/vNn7WSyWpEv8RERErQlzOhERUfJjPiciImoZoirOFxcXh/xvIiIiSi7M6URERMmP+ZyIiKhliHlDWCIiIiIiIiIiIiIi0iauDWH37t2Ld955B7t374bL5Qr4XCI2ACAiIiJ9MKcTERElP+ZzIiKi5BRzcX7t2rU4//zz0adPH2zZsgXHHnssSkpKIITACSecYESMRJQAUewNTUQm0/v3lDmdqGViTieSG/M5EUWD+ZxIfnr8nsY81mbWrFm49dZbsWnTJmRlZeHNN9/Enj17MHLkSFxyySWaAyKixEpPTwcA2O12kyMhokh8v6e+31utmNOJWhbmdKLkwHxOROEwnxMlDz1yesyd85s3b8a//vUv74PT0tDQ0IC8vDzcd999uOCCC/D3v/897mCIKPFSU1PRpk0blJWVAQBycnJgsVhMjoqIGhNCwG63o6ysDG3atEFqaqouz8ucTtSyMKcTyY35nIiiwXxOJD89c3rMxfnc3Fz/DLuuXbtix44dOOaYYwAAFRUVcQdCRObp0qULAPiTPxHJqU2bNv7fVz0wpxO1PMzpRPJjPieiSJjPiZKDHjk95uL8Kaecgq+//hr9+/fHOeecg3/+85/YtGkT1qxZg1NOOUVTMERkDovFgq5du6JTp05wu91mh0NEIaSnp+vWYefDnE7U8jCnE8mN+ZyIosF8TiQ/vXJ6zMX5hQsXwmq1AgDmzp0Lq9WKVatW4cgjj+Qu8ERJLjU1VfeTBSKSF3M6UcvFnE7UejCfE7VczOdELV/Mxfk+ffr4/zs3NxfPPfecrgERmWXWmk1xPW5++tL4X3Tck/E/lohII+Z0aonizeeAhpzOfE5EJmI+p5aK5+hE1BrEXJz32bBhAzZv3gwAGDBgAIYMGaJbUERERJQ4zOlERETJj/mciIgo+cRcnN+7dy8uv/xyrFu3Dm3atAEA1NTU4NRTT8Vrr72G7t276x0jERERGYA5nYiIKPkxnxMRESWvlFgfMHXqVLjdbmzevBlVVVWoqqrC5s2boaoqpk6dakSMREREZADmdCIiouTHfE5ERJS8Yu6c/+9//4tvvvkGRx11lP+2o446Ck899RRGjBiha3BERERkHOZ0IiKi5Md8TkRElLxi7pzv0aMH3G530O2KoqBbt266BEVERETGY04nIiJKfsznREREySvm4vyjjz6KG264ARs2bPDftmHDBtx0001YsGCBrsERERGRcZjTiYiIkh/zORERUfKKeazNlClTYLfbcfLJJyMtzftwj8eDtLQ0XHPNNbjmmmv8962qqtIvUiIiItIVczoREVHyYz4nIiJKXjEX55944gkDwiAiIqJEY04nIiJKfsznREREySvm4vzkyZONiEMzu92O/v3745JLLuHSPSIioijImNOZz4mIiGIjYz4HmNOJiIiiEfPMeVk98MADOOWUU8wOg4iIiDRgPiciImoZmNOJiIgiaxHF+e3bt2PLli0YO3as2aEQERFRnJjPiYiIWgbmdCIiouiYXpz/8ssvMW7cOHTr1g0WiwVvv/120H0WL16MXr16ISsrCyeffDK+//77gM/feuutmD9/foIiJiIioqaYz4mIiFoG5nQiIqLEiao4/7///Q+qqhoSgM1mw6BBg7B48eKQn1+1ahVmzJiB2bNn48cff8SgQYMwZswYlJWVAQD+/e9/o1+/fujXr19Ur+d0OlFXVxfwQURE1FoYldOZz4mIiBKH5+hEREQtQ1TF+eOPPx4VFRUAgD59+qCyslK3AMaOHYv7778fF154YcjPL1y4ENdeey2uvvpqDBgwAM899xxycnLw0ksvAQC+/fZbvPbaa+jVqxduvfVWLFmyBPfdd1+zrzd//nwUFhb6P3r06KHb10JERCQ7o3I68zkREVHi8BydiIioZYiqON+mTRsUFxcDAEpKSgy7Qt+Uy+XCxo0bceaZZ/pvS0lJwZlnnon169cD8CbyPXv2oKSkBAsWLMC1116Le++9t9nnnDVrFmpra/0fe/bsMfzrICIikoUZOZ35nIiISF88RyciImoZ0qK500UXXYSRI0eia9eusFgsGDp0KFJTU0Ped+fOnboFV1FRAUVR0Llz54DbO3fujC1btsT1nJmZmcjMzNQjPCIioqRjRk5nPiciItIXz9GJiIhahqiK8y+88AImTJiA33//HTfeeCOuvfZa5OfnGx1bzKZMmWJ2CERERFJLhpzOfE5ERBReMuRzgDmdiIgokqiK8wBw9tlnAwA2btyIm266KSGJv0OHDkhNTUVpaWnA7aWlpejSpYvhr09ERNQSJTqnM58TERHpj+foREREyS+qmfONLVu2zJ/09+7di7179+oelE9GRgaGDBmCtWvX+m9TVRVr167FsGHDDHtdIiKi1iBROZ35nIiIyDg8RyciIkpeMRfnVVXFfffdh8LCQvTs2RM9e/ZEmzZtMG/evLg2obFarSgqKkJRUREAoLi4GEVFRdi9ezcAYMaMGViyZAlWrFiBzZs34+9//ztsNhuuvvrqmF+LiIiI/qBnTmc+JyIiMgfP0YmIiJJX1GNtfO666y68+OKLeOihhzB8+HAAwNdff405c+bA4XDggQceiOn5NmzYgNGjR/v/PWPGDADA5MmTsXz5clx66aUoLy/Hvffei4MHD2Lw4MH48MMPgzagIWppZq3ZFPdj56cvjf+Fxz0Z/2OJKKnomdOZz4maF29OZz4nomjwHJ0oMXiOTkRGiLk4v2LFCixduhTnn3++/7bjjjsOhx12GK6//vqYE/+oUaMghAh7n+nTp2P69OmxhkpERERh6JnTmc+JiIjMwXN0IiKi5BXzWJuqqiocffTRQbcfffTRqKqq0iUoIiIiMh5zOhERUfJjPiciIkpeMRfnBw0ahKeffjro9qeffhqDBg3SJSgiIiIyHnM6ERFR8mM+JyIiSl4xj7V55JFHcO655+LTTz/178a+fv167NmzB++//77uARIREZExmNOJiIiSH/M5ERFR8oq5c37kyJHYtm0bLrzwQtTU1KCmpgYTJkzA1q1bMWLECCNiJCIiIgMwpxMRESU/5nMiIqLkFXPnPAB069Yt5k1liIiISD7M6URERMmP+ZyIiCg5xdw5T0RERERERERERERE2rA4T0RERERERERERESUYCzOExERERERERERERElWEzFeSEEdu/eDYfDYVQ8RERElADM6URERMmP+ZyIiCi5xVycP+KII7Bnzx6j4iEiIqIEYE4nIiJKfsznREREyS2m4nxKSgqOPPJIVFZWGhUPERERJQBzOhERUfJjPiciIkpuMc+cf+ihh3Dbbbfhl19+MSIeIiIiShDmdCIiouTHfE5ERJS80mJ9wKRJk2C32zFo0CBkZGQgOzs74PNVVVW6BUdERETGYU4nIiJKfsznREREySvm4vwTTzxhQBhERESUaMzpREREyY/5nIiIKHnFXJyfPHmyEXEQURKYtWZT3I+dn740vgeOezLu1ySi8JjTiVonU/I5wJxOZBDmc6LWi+foRMkv5pnzRERERERERERERESkTdSd8ykpKbBYLGHvY7FY4PF4NAdFRERExmFOJyIiSn7M50RERMkv6uL8W2+91ezn1q9fj0WLFkFVVV2CIiIiIuMwpxMRESU/5nMiIqLkF3Vx/oILLgi6bevWrZg5cyb+85//4Morr8R9992na3BERESJ0NrmLzOnExFRS9Wa5i8znxMRUUvVms7R45o5v3//flx77bUYOHAgPB4PioqKsGLFCvTs2VPv+IiIiMhAzOlERETJj/mciIgoOcVUnK+trcUdd9yBI444Ar/++ivWrl2L//znPzj22GONio+IiIgMwJxORESU/JjPiYiIklvUY20eeeQRPPzww+jSpQv+9a9/hVxCR0RERPJjTiciIkp+zOdERETJL+ri/MyZM5GdnY0jjjgCK1aswIoVK0Leb82aNboFR0QUSbxzyJJtBhmRnpjTiUg2rWmuKJFemM+JSEY8RyeKTdTF+UmTJsFisRgZCxERESUAczoREVHyYz4nIiJKflEX55cvX25gGERERJQozOlERETJj/mciIgo+cW0ISwREREREREREREREWnH4jwRERERERERERERUYKxOE9ERERERERERERElGAszhMRERERERERERERJRiL80RERERERERERERECcbiPBERERERERERERFRgrE4T0RERERERERERESUYCzOExERERERERERERElWJrZARARtSSz1myK+7Hz05fG/8Ljnoz/sURERBTElJzOfE5ERKQrnqOT7Ng5T0RERERERERERESUYCzOExERERERERERERElGIvzREREREREREREREQJxuI8EREREREREREREVGCsThPRERERERERERERJRgaWYHQERExjJld3ruTE9ERKS7eHN63PkcYE4nIiLSGc/RqTF2zhMRERERERERERERJRiL80RERERERERERERECcbiPBERERERERERERFRgrE4T0RERERERERERESUYCzOExERERERERERERElGIvzREREREREREREREQJxuI8EREREREREREREVGCJX1xfs+ePRg1ahQGDBiA4447Dq+//rrZIREREVGMmM+JiIhaBuZ0IiKi6KWZHYBWaWlpeOKJJzB48GAcPHgQQ4YMwTnnnIPc3FyzQyMiIqIoMZ8TERG1DMzpRERE0Uv64nzXrl3RtWtXAECXLl3QoUMHVFVVMfETERElEeZzIiKiloE5nYiIKHqmj7X58ssvMW7cOHTr1g0WiwVvv/120H0WL16MXr16ISsrCyeffDK+//77kM+1ceNGKIqCHj16GBw1ERERNcZ8TkRE1DIwpxMRESWO6cV5m82GQYMGYfHixSE/v2rVKsyYMQOzZ8/Gjz/+iEGDBmHMmDEoKysLuF9VVRUmTZqEF154IRFhExERUSPM50RERC0DczoREVHimD7WZuzYsRg7dmyzn1+4cCGuvfZaXH311QCA5557Du+99x5eeuklzJw5EwDgdDoxfvx4zJw5E6eeemrY13M6nXA6nf5/19bWAgDq6uq0fine57db435sXboz8p2afXDz8ZsSU4T3syXF1KK+b4B8MfH7FuWDJYspwvdtzju/xvW0c9JXxPU4AMDYR5r9lIzft9ifyvtcQgjdnjMWLS2fA3L+7vBvnu/BksXE71uUD2ZMkR+YXD9L8eZzoIXl9BaUzwHm9MZa1N8XQL6YkuxvHr9vvgfy+xbdgyWLiefoUT7YhJwuJAJAvPXWW/5/O51OkZqaGnCbEEJMmjRJnH/++UIIIVRVFZdddpmYPXt2VK8xe/ZsAYAf/OAHP/jBjxb5sWfPHp2ycvwA5nN+8IMf/OAHP7R8yJDPhWBO5wc/+MEPfvBD60eknG5653w4FRUVUBQFnTt3Dri9c+fO2LJlCwBg3bp1WLVqFY477jj/LLyVK1di4MCBIZ9z1qxZmDFjhv/fqqqiqqoK7du3h8ViMeYLiUJdXR169OiBPXv2oKCgwLQ4GmNMyRcPwJiSMR6AMSVjPIBcMQkhUF9fj27dupkaRyitKZ8Dcv1cyBgPwJiSMR6AMUVLtphkiwdgTOHInM+B1pXTZfmZaIwxJV88AGNKxngAxpSM8QByxRRtTpe6OB+NP/3pT1BVNer7Z2ZmIjMzM+C2Nm3a6BxV/AoKCkz/4WmKMUUmWzwAY4qGbPEAjCkassUDyBNTYWGh2SHEraXlc0Cenwsf2eIBGFM0ZIsHYEzRki0m2eIBGFNzkjmfAy0vp8vwM9EUY4pMtngAxhQN2eIBGFM0ZIsHkCemaHK66RvChtOhQwekpqaitLQ04PbS0lJ06dLFpKiIiIgoFsznRERELQNzOhERkb6kLs5nZGRgyJAhWLt2rf82VVWxdu1aDBs2zMTIiIiIKFrM50RERC0DczoREZG+TB9rY7Va8fvvv/v/XVxcjKKiIrRr1w6HH344ZsyYgcmTJ2Po0KE46aST8MQTT8Bms/l3hm8pMjMzMXv27KDlfGZiTJHJFg/AmKIhWzwAY4qGbPEAcsZkFubzP8j2cyFbPABjioZs8QCMKVqyxSRbPABjkh1zupeMPxOMKTLZ4gEYUzRkiwdgTNGQLR5AzpgisRzagd00X3zxBUaPHh10++TJk7F8+XIAwNNPP41HH30UBw8exODBg7Fo0SKcfPLJCY6UiIiImsN8TkRE1DIwpxMRESWO6cV5IiIiIiIiIiIiIqLWRuqZ80RERERERERERERELRGL80RERERERERERERECcbiPBERERERERERERFRgrE4T0RERERERERERESUYCzOExERERERERERERElGIvzREREREREREREREQJxuI8EREREREREREREVGCsThPRERERERERERERJRgLM4TERERERERERERESUYi/NERERERERERERERAmWZnYAZlNVFfv370d+fj4sFovZ4RAREcVFCIH6+np069YNKSmt79o78zkREbUErT2fA8zpRETUMkSb01t9cX7//v3o0aOH2WEQERHpYs+ePejevbvZYSQc8zkREbUkrTWfA8zpRETUskTK6a2+OJ+fnw/A+0YVFBSYHA0REVF86urq0KNHD39ea22Yz4mIqCVo7fkcYE4nIqKWIdqc3uqL875lcgUFBUz8RESU9Frr8m/mcyIiaklaaz4HmNOJiKhliZTTW+cQOyIiIiIiIiIiIiIiE7E4T0RERERERERERESUYCzOExERERERERERERElGIvzREREREREREREREQJxuI8EREREREREREREVGCsThPRERERERERERERJRgLM4TERERERERERERESUYi/NERERERERERERERAnG4jwRERERERERERERUYKxOE9ERERERERERERElGAszhMRERERERERERERJRiL80RERERERERERERECcbiPBERERERERERERFRgrE4T0RERERERERERESUYCzOExERERERERERERElGIvzREREREREREREREQJxuI8EREREREREREREVGCSVOc//LLLzFu3Dh069YNFosFb7/9dsTHfPHFFzjhhBOQmZmJI444AsuXLzc8TiIiIgqPOZ2IiKhlYE4nIiIyljTFeZvNhkGDBmHx4sVR3b+4uBjnnnsuRo8ejaKiItx8882YOnUqPvroI4MjJSIionCY04mIiFoG5nQiIiJjpZkdgM/YsWMxduzYqO//3HPPoXfv3njssccAAP3798fXX3+Nxx9/HGPGjDEqTCIiIoqAOZ2IiKhlYE4nIiIyljSd87Fav349zjzzzIDbxowZg/Xr14d9nNPpRF1dXcAHEZHZhBBmhxBEVVSzQwhitVnNDiGAy6NCcTWYHUbSiyenM58TkaxkzOlClSunu9yK2SGQQZjTjSXj3xdKTjL+LEkZk2T5U7Z4iitsqCzdA5T+BkgQm83pwdaD9XCWbgOs5WaHE7WkLc4fPHgQnTt3Dritc+fOqKurQ0ND84WS+fPno7Cw0P/Ro0cPo0MlIkpKLrfT7BCClOwqNjuEAFU2F1zV+8wOI+nFk9OZz4mIkleN1WZ2CGQQ5nQiakkUVa4LBkKV6+L24s9/x+4f3gXWzgVc9WaHg98O1OGRD7fAte4ZYNuHZocTtaQtzsdr1qxZqK2t9X/s2bPH7JCIiChKqiLXwYhHVQHVbXYYrRLzORFR8pKs1kEmY04nIkpOdpfnj384zS/OV1obNRg2VJkXSIykmTkfqy5duqC0tDTgttLSUhQUFCA7O7vZx2VmZiIzM9Po8IiIkp+Eywo9HrkK4R5FAIon8h0prHhyOvM5EUlLCMBiMTsKqamSdf6RfpjTiYiMI9MZukdR4XQ3GmXjqAMKzYsHACptrj/+YU+e4nzSds4PGzYMa9euDbjtk08+wbBhw0yKiIioZVGF+TPjGlNUIcUcu8bcigoIFue1Yk4nImpdVAkbAEgfzOlE1JLIlq1kmjlvcza50O40f7+QivpGxfkk6pyXpjhvtVpRVFSEoqIiAEBxcTGKioqwe/duAN6lbpMmTfLf/29/+xt27tyJ22+/HVu2bMEzzzyD1atX45ZbbjEjfCKiFke282a3osIiWSHcpaiwKHJ188uAOZ2ISC6y5XT5AqLmMKdTJDJu4kmR8dsWHfneJ3kCqnc2OQ921JoTSCNVtkZjbexVMn4DQ5KmOL9hwwYcf/zxOP744wEAM2bMwPHHH497770XAHDgwAH/AQAA9O7dG++99x4++eQTDBo0CI899hiWLl2KMWPGmBI/EVFLI9uBtkcVgGTd/C6PCrA4H4Q5nYiIwlEl6vyj8JjTiah1k+ucWKZogjrnTS7OCyFQYW3UOa96pOjmj4Y0M+dHjRoVthC0fPnykI/56aefDIyKiFoszoONSLad4F0eFRZVss55jwoozsh3bGWY04mI5CLTyTxwaFQdJQXmdKKWSca/wjKeogvJmsNkGmtT72jSpNZQY0ocPnaXAoe7SQ3DVgFkmTwIPwrSdM4TEZFcZDtg8461kedgBACcHhVQXJHvSERERH6y7WtDREQUimSLyaVS72jSOOeoMSUOnypbiPNye2XiA4mDNJ3zREQkF9mWnLs88s2cd7gVWFSH2WEQERElFUVhtYOIiALJmRnkikqmTv66oM75anMCOaQyVHHeVpH4QOLAznmiFka2OeEUJQm/b0KyJecuRYVFslE7DW4F8LA4T0REf5AwpUt1Mg8cOl6V8Y0iImolZKwbyBmT2REEkmmsTV3Tznmzi/PWEONmbeWJDyQOLM4TEVFIsp3Ie2fOy7X5aoNLAdwNZodBRESUVBSVxXkiIpKfbOfEMnXy1zWEmDlvYjNd6LE27JwnIqIoyXh+qkjWpe6UcKyNzaXA4rKbHQYREUlESHTi7CfZgYa3OC9bwYOIiEwlWa4C5Ovml6lzvrZpcR7C1E1hQxbnOdaGiIiSmWxjbZxuBRZVruK83ekB3FazwyAiIkoqHqFCpu4/IiIyn4xZQbaYZOrkr7GHKIY3VCU+kEOq7SFW2XOsDRERJTNVss55h0dFimRjbaxODywum9lhEBERhSVbcUFRwc55IiIKIFmTOgBAlahTHZAnHiEEakIWw83rVA95scBZD7jl3yOOxXkiapWkW54m3WkzoChyJH4fh1uBRQmRcE1U7/QAzjqzwyAiIonItOTcR7bjHo+isjhPSUG23x3JwqEkxh+lyIQQEJKdEwtJVpLbXIp3RF1TJs14b/ZiAZAU3fMszhNRKyXX4YhsB/4AIGTrnHcr0m0IW9fghsVZzzMlIiJqhDkhEs6cJyIyl4ynL7I1rHlrz5LFpMhxjl4dar47YFrnfINbgbu5Cym2ssQGEwcW54modZLtaES2eCDhWBu3ItVYG7eiosGleHekd3HuPBERecmX0eXL6R5V9eZPIkp6Ep7GSNn4JB0J3yPZQlKFfBeSZSnOh9x8FTCtON9s1zwAWFmcJyKiKHgvyst1NCJL4vexu+TqnA84ADBxV3oiIpKLbBuqy8ityFfwICJqTWTrUpeRd5WXXO+TLKvbq0LNdwdM61KvbWBxnoiIWiCPJInfp8HXOS/JLN/ahkYHJA3V5gVCREQUgWwXDFicJyIyl2Q1ZwCHOtUl4o1HspgUOWbOV1mbKc5by0z54apjcZ6IpCJZQpOXXO+TN+/LdZIqS+L3sTsPXSxQnOYGckiVrdEBgEkb3xARtXZyji6QLyYBuY4xFBbniVoOGf8OyxgTRSTbt01RBSySNawJSc7RK23NnJN7HICzPrHBIFLnfGniAokTi/NERLKQ7GhE8ch1IGJzHToQ8TjMDeSQqsYHJLZK8wIhIiKpqJKs8GpMlaxz3sWZ80RE1IRcmUrOzcsV1SPFSvLK5jrnAVM61escYS5a2Mzp5o8Fi/NELYzkf3PkIdkb5Z35J1dMiiRX5X38nfMeCTvnbeXmBUJERJKRK58D8syo9fEoAhByxUQUkmznDJLFQ8lLxh8l2cbaKKoAVLnOiVUVgAT7sFWELc4nvlM97FgbjxNw1CYumDiwOE9ErZRcid871kaumGQrzlt9nfPuBnMDOaTC2rhzXv45dkRE1HrJtvGf06NIV/AgImpNZMsLgHyrvDwSds57VBVQzC3OuxU1cP+1pqwHExfMIXWOCO+J5M10LM4TtTBypbNDJCs6A/JtjCbjzHmZxtp4FBUOl69zXo6xNgHF+STYZIaISBcS5nTZyLZnCwCoilzHGG6PfGNthARjAoiIEkXGdC5b57y3OC9XrvKoAJQwhfEEqLS6wv/81Ce+cz7szHkAqE/8BYNYsDhPRK2UXIlfxs4FxWP+cjkfm7PRQZEExXkhRGBx3l4FeMw9SCIiIjlIVlsAIF/h2SVhcZ6IqDWRMFV5x8hIxHshWa4L7h7F/JjK6yOMma0/kJhAGolYnJd8pTuL80REElAFpLsqr5i8XK6xgGVqLrt5gRxS5/DA6W5c6BDSJ3wiIkoMIdlKOABQJSuEOyUoLhARtWYy7l8gX+e8CotE58SAb6yNuU1h5dYIzXIJ7lJXVBF+5jwAWDnWhogSSMYTQhnJtjGaKuSbZ6d43NK0/1mdjU7gJZg5X14f4oBE8qVyRER6kO5kXrZ4IF+XOiBfcd7lUaTY0I6IqLWScuW2ZCPYXB75NoT1KMK7wamJyuoivL6zDnDZEhMMvJvBRjwclLyRjsV5IiIJCBXSFefdijxLzgOuhLsTl+ibUxrqgKRuX+IDISIi6cjW+QcAQpEjn/s43OZvaEdE1JoJRb5cpUh2cdst4SovtyJMH/Ma8ly4qbr9xgdySKUtinisiZ+DHwsW54nIcNJ12UHSznnJYnIrQpqutoAZchJ0zpfWhTggSuABCBEReUl4iCFdPgcARbLigtMtX8FDyp8loiQg468Of58jE5CrEA4c2oBVIi5FhcXkETJNuRXF9OJ8WahV5E0lsjhvjeJ7ZKsEFLmOOxpjcZ6ISAKKENIdRbo8ijRdbXWORonUZTUvkEMOhizOs3OeiCjR5MqcXqpknX8AoHrkuWAghPDOnJfkGIOIqDWSrA4OAPBIlKuAQxeSJSvOOz0q4DavOK+oIvKGsEBCN4Utt0Yz5kcADdWGxxIvFueJqHWSbISMqso3z87bOS9HTDX2RgdFCZxf15zS2hAHRLX7pLvAQkREiSfbfHdArk3eHb4N1SUreBARtSYyjmBzSzZz3ulRpMtVLo8KeMxbSV5pdUKJ5spOAhvXohqzA0g9d57FeaKWRsIkKyPZRu0oQp5COAB4FNWbdCU5GAkYa+M0t3NeVUXoznm3Xeqr8URE+pArf8qWzwH55rsLISAkKs43uA+9PxLFBMj2k02UPIRkTU+AnL/PsuUrGVd5eSTLn06PCovJI2SacrhVU8e8hjwPDiWBY23Koo3JVm5sIBqwOE9ErZKQ7GDEI1GXOgA4PIfeH5N3gveplqhzvsLq9H6/Qqndm9hgiIhIOqpkhSpFlWtfG5vz0PGOIscxho9shTMiIiPJ+DfP7ZErfza4FOlyldOjAi67aa9/MNQK8lDqDybk2EMIgX01UV6ssFUYG4wGLM4TtTjyJVkpSXbi7JFsrE2DS66uthp74875OvMCAcIn/9o9iQuEiMgMEp7My0aVbMMxjypgkWSDd6Bx57wcq/OIkomMBVUZ8X2KLKrRJAnmlqxzvsGtSNc53+DymLoHW9Sd86oHsJYaGwy8K+z9tYtIWJwnIpKLEHIlfkWVa2O0P4rz5ncKONxKYMJ11psXDIC91WGK8zUszhNRSyfXybxc0Xgpkm1o51ZUqYrz/s55SVbn+cj4s0REcbJYzI5AerKNtVFVAZdHrovbDS7F1BEyTQkh0OBWzS3OR9s5DyRktM2eqhi+P3YW54laLF6Vj0zGt0hItLwbAFweeea7A4Dd7TtxNr9ToMrW5H3xOEy9kBG+OL87cYEQEZGUx2GqRIVwwLvBu0WiBgC774K7bMV5CX+WiJICf3eiI9n7pErWOe9SVFgkW91udymwuM0bIdOU06PCo6qm7sF2IJbifAJWle+pjuH7w855IiLJSJb43YpcnfM2p+/E2fwLBkHFecDU7vndVWFm3tfuASTrQiEiasmEhP3Oso218XbOy1MItzrlaQAgImqtZNsfxeFWpLuAYXd5pCrO+/OnSZ3zNqcHdQ0x1Cxq9xkXzCG7KmP4/jRUSfcz5sPiPBEZTso/f5IVUF2KKsUIGR+7y3fibP4yvspQxXlHbeIDgfd9KasL831SXAmZrUdEZBrJTmqEZJ1/AKBIdLEdAFweFRaJVufZJC3OC8kKVURERlIUufKnw63CArn+DludHsAdpjErwfyFcZPOhQ/UxlgbqEtEcT6G74/HCbjk+X42xuI8kVaSnaTKSMZlwkKizVcB74mzTCep/s55CWbsVVpDFMMdNQmPAwB2V0VxZb5ml/GBEBGZRracLls8gOqWqzjvlKw47+/8k+AYozEZj1eJKD4y/jbL9idGkaxZze7ySLe63eb0wGLyfmeN1TkO5U9HrSk/UDGNtAGA2r2Gxml1elBeH2ODo73SmGA0YnGeiFol2TbAcXoUwC1PcV6mE+dKqzyd8zvLo7jSXl1ieBxEROQlW7EDAFTJOuedHgUWiRoA/J1/Eo0KICJqbeQrzitSzZxXVQGr89BYG0neq2r7ofNi1WNKB/iBmhiPJRSXoXPeSyrieA9YnCei1kvCM2ch14awDo8qxQgZn3qHPCfOFaE65xuqEx8IgJ3lUcz3qyo2PhAiIpPINkZGxlEkqlueMXXAobE2EhXn632dfy7zjzEaE5IUX4iSjVxZQU4yrsxRFbn+5tmcHlgkOke3ujzeBgChAi45uudr7Y0u/puwknxfTRz1irq9+gdyyM64ivNV+geiAxbniVoYi8VidghB5DsUke8EzOFWpOhS96lrkOfEuTxkcb4m4XEIIfB7WTTF+Z1ytnISEelCrr9vqoR/bz0SbKbeWINbQYpEDQD+ZfkSHfcAcm4uTERkFLcqTyEcAGwuuYrzAYVwE849QwnYi82EInPMM+cBQzeFLY5mVXtT7JwnIpKHUOSaOd/gUqQohPvU+TrnTdoJ3sfpUQIPjHxM6Jw/WOf4o9svHGedtFfkiYhaGim7Ed1yFeedbhUWj0THGP6xNnJtyiZb4wYRkZE8HnkK4QBgdSqwSHTBwH8+DJg2UrWpgL3YGhJ7vulwK6HHzUZSa0znvBACxRVx1CpYnCei1kq2JfCArMV5cwvhjdU2yFGcr2juAMCEpLr1YAzLGat2GBcIEZGJZCuGy7aHDCDfWJsGt4IUSQrhTo/iXS0IAB4nINHxmGw/20ShcIVHcpLxz4uiyFMIB7wXbuXtnDdnpGpTAeNeDZzlHkppXZzj8Qwaa1Npc0XXONeUPbHvW7RYnCdqcWQcayPf0YiqynMyCBzagFWS4rwQ4o/ivMm705c1dxBgwgHS5gMxvBeVvxsXCBGRiWSb8S5jQVXxuKTZPA44tMmeJIXwoNVwkszxBQBVsp9toqQh4VhV2ciXqQCXZDPnaxvcUu0LVx1QnDd/VbRHUVHVeKxNgovzcc2bB7xjbQw4ViuOZ948kPD3LVoszhOR4WQ8cZayOO+Uozhf7/RA9a12cNSZGktpXTPdhw3VCS18CCGw5WAM70XFNuOCISIyk2Q5XcbOeY+qAoo83fN2l28fGfOPM2oamhTnJTn2AQChyPWzTRSSZH+DASlDko6M58Mej1znw3UNbqRIdI5ebW883938USgVVlfg75qtLKGvf6Amzs55t92QzWtL4i3O2yul/KPF4jxRCyPjhrBSkmwZX73D7Z1VLoGqxqNknPWmdv+V1TdzECDUhO5Qv6vSDmssy+Yqf5eiQ5GISG+ync7IVpz3KCo8KgBPnCexBrA6D+UjCY4zqm1NxtWZvEKvMdlWhRARGckt2cz5arsLFjXEXmMmCexSN784H7QZq7XU3NePhQGbwu6uinMvHdUjzZiixlicJyLDybjBlqrIk/iFEKhr8Hi71CW4ihuwCzyEqSfzYWfb2coTFscv+2PcBEhxAzW7jAmGiMhEsnX/yXaM4fQcisctT3He5ivOm7waDmgyJgCQ4oKBD4vzRC2IbLmq0f/Kwi1R57wQAjV2t7zFeQnmlB+sbXJcYatMaDPY/qavH4u6/foFAu/Py65KDRvdSzjahsV5ohZGxsZ5KWfOS9TVbHMp8KjCO2NPgiXnAbvAA6ZeWW52rA2Q0KS6aV+MxXkAKN+ifyBERGaTrODhkWxm7h+bnWroMNOZf+WXBIXwgDEBgBQXDHxUVa4uUiJqOSRLnQAAl0ee/FnX4IGiCqRIUpwXQqC88TmxtdT0b2LwzHeRsNE2HkVFWbjz8kjq9O2cr7G7/2g8iEcCm/yixeI8ERlOqPIdjchUnA9Y4m03f7OZCmuTE2eTNsBxuJXg5e+NJSip2pwe7CiLY6Zd6W/6B0NEZDLZLrgrkhVU7a5D8bg0dHTprN5XnJdgGXdV07yewBF1kbA4T0SticstRyEcACps3sKvLJ3zNpcCh6tRTvA4Tb/Avbc6xEX/ugMJee2yeqe2lZP1+sYZ8r2IRYJHAkWDxXkiMpxsJ/IAICQqztdIthN8RdPOeZMuGES8Op+g4vxvB+riOxgp32x6hwURkd5ku+CuSraHjL9z3i1H57wQAnUSFecrmzYAOOJYmWYQweI8JQW5/gYD4PFuFASEdO+TTGNtKuq9530pkmymXl4fIo568wq6HkUNPfNd54705miaNw/oHufeao0NECzOE5Hx5JtrI9tmbYBcnfO+TgEAUnTOB23CatJMtoPh5s0DgDUxy/j+tzfOwoHLBlSX6BoLEREFkm2sjc3XaeeOY8WVAeqdnj8uMEtwjCFLA0AoqmQ/S0ShyXeuR5F5/wzLU5xXVQGXW54Lkr4RMilqmFXTCVQW6jxU5+7vWByodcCjhPj5SVhxXuM+OrZKwKPf91bT/HvA1O9lc1icJyLDybZZGyBXcT6gi8xu7k7wqiqCO9ZNmskWuThv/BVvIQR+iWfevE/pr/oFQ0QkAdlyumyjSOy+GaguOYrztY1X55l8jOFwK8EzYiVYMeijqvIcGxI1R7LmawCAgFx5AZDzfZKJw6PAIuTJn759xlIUOTZTLw3ZOW9eQbfZzU9r9ybk9YM2o42ZAKwHdYkFAA4Ezd+PUb1+seiFxXkiMpym+WQGEEJAKHLMswOaLJtLUDd4cypsTihNRxaYtOwrYtK1VQAGF2X2VDWgrkHDzwqL80TUwshWhPF45MnnAGD1FZ+d5m/wDjQZnWc3ZyWcT8gxARJ1zkOyC09E1HJIdjoMu0uBRaJ87utUT1Ek7pxPUJd6KMWVzVzwr92TkB+u0khNc9HQaSyQECJyE18kjlqp9gYCWJwnogRQJTsaUVQBSNUp0Ci5mDz/7EBNiERn0gWDiMvnhGJ4F+Av+zXOwi37zfALCEREiSRZSoeiKFIF9Udx3tyN43yq7I0KHbZKwMSVg0EjbQDv+yRJw4Qi2f4FRNRyyLYHW4NLgUXIU5z3nfelKE4pcnrI89Ba84rzO8ubueDvcRq+yl0IoX2sDaBb53y904MGlw75WrLRNizOE5HhZNtgy6MKQJKly0KIwBnvJnfOh9zsxVmX8OX5QojortAbvCTtV63FeY8DqNqpTzBERBKQLae7FVWqi6DSFecb72sDYWr3fFmoznnA9HE7PqokFwmISA/mF3iDSFB09rG7FKQIOc6H6xxu/8gzC4TpG6oLIUKPcak/aMoFbqdHwd7qMO9JzW5DX9+qWzFcn/P2Uj0uFACmroQIhcV5Io0sFm7KE4ls82BdigqLJMX5arsbTnejrgVHDeA2b9Zes4k/wXPZKm0uuDxRdHMYGJdbUbGjTIeLEhxtQ0QtiGyr4dweVarVcP4Z7w45ivMB+9oApq7Qa7Y4b9LG800Jds4TkUFk2xDW6vR4C+ES2N90lKnJxflquxuOUJvlCsWUgm5JhR1q07GzjVWXGPr6pU33o4uXTk2IzR5LxMrElRChsDhPRIaTbfM4l0ee4nzQwQhg6hKrZovzCdpsxifkeJ1QDHyvdpbbvB2ZWrE4T0QtiGw53ds5L0dOB7wdgAC880wlUNG0OK/TzNd4hJzhCwA2c1cN+nBDWApFsuuRcpLxTZIsJrmiAewujzSd88HFeXM3VN9nYpd6KNvL6sPfobrY0NcPWOWvhU7NASH3r4lHXWLrG5GwOE/U0kjYyS/jEniLJAcjIYvhdfsTHwgAj6KGvlgAJLxLIOR4nVAMLM7/XqbTZn4VW6UauUBEpIVsnfNOjzyj6gCg1reJeEO1uYEcEnRSbeIy7mbH1Zk80s+HM+eJyFAS5U+rw+OdOS9BTEHnwwkep9rUvubOhwFzivOlEc5Jq4wtzutWDLdX6vLzFnL/mniwc56IWpuwy7BM4PLIM582ZPI36cT5QK3Du1luKDV7Eh5LVAy8kLGjuY13YuVxGr7ckIgoUVQ9VhTpyKUo0uR0IQRqfGNtPA5Tx9QB3uMd/5gdH5MaAFweFVU2V+hPmjhqpzHVxM1yiahlExIUwRvz748iwcVt2Yrze6vtzX8ywed0qioiN4zZKw1dradbcV716NK4ELQiMF7WUmk2pAdYnCeiBJDtZMfpUWFR5fhDvKcqRPKvTWwh3GdXZZgDkQR3CeyPtnPeWmZYUi2p1PHAsHyrfs9FRGQiVUhWnHergKLTiZpG9U5P4EXuhirzgkEzneoJHlPnU1bvaL5hzsRRO40JiU7SiZonV5HXizFFIhr9rwzqHXIU54UQwcVwl04NUnEKu/lqdXFCVxvsrrKHnn/fVNVOw2LQrTgPALZyzU9RqVfnvFBNa1gIhcV5IjKcdMV5txwz510eNXTnfIK71H2KwxWj7RWAM3EHSgej3oVdGNJxV+9wB3cbalH5u37PRURkEiGEdN1/Do88M+eDNl81eaPTkMX5hipTuhLD5vX6A1KMVlA51oaIWgl/57zJFyVL65xwuptc9HdGmLFuoLBjXgFvbPbEXXjfcjDK96Jyh2Ex6NapDni7/DVQVYFqPc/RWZwnotZElWS5uY/Do0hRnN9b3czO6/UHAU/iuwBLKiKcrNfsSkgc9Q43rI4Yvj8GzIsL2zERDxbniagF8KjC22kkEYdbkaZzvsrWpJvLbm5x/mBzM95N6J5vNhYAcNsBZ13igmmGys55IjKSBBchfep9m5eb/Hdvd6hV5CYW58OOefUxsEu9qa0mF+c9ioraBnmK87UNbn2bRExaTRgKi/NEZDjZlgk73Ip3AxzV3ALDzvLmiuEi4aNtXB419IidxhI0Yy/qefM+BszoL9Nz+R7gHb9j8uxhIiKtFAmL8w1uxfTigk95vVyd8812/1Un5mJ7YwdqIuRACbrXWJwnakEkKoQDvnDkiemPsTYszje2J9y8eZ8q47rUG1NUgW2l0RbnfzfkZ77K7tL3aTXOnK9p0Pnntd78Yw8fFueJWhjJjkMAyNc5b3cdisfkTrvicJ3qCd5sZneVTZouARmK87rO1gMAiITP7Sci0ptHFd6L2xKRqXO+vOkcVGuZOYEcsr+5gniCVsI1FnEvGQNyeaxUxSPngTQRxUGy32UBqf6+1Pk7583Nn7tCjVV1mLeSane4Pdh8DBwh09iuSlt08+YB7+ozHea5N9XsRu7x0lqct+scjwSNAT4szhOR4VSPXJ1IDb7ivMmdAjvKw8xwT3Bx/veyKObPJuhA5GC0m8H6GJBUdU/8gFRX5omI4qEoQqrigqIKb06XpTjfdHSLAXuiREtRRZjO+ZKExiKEiLyXjAEj6mLl/fmW6+ITEbUkcuRPh1v5Y867iSuGhBAoCVUMd9QkPBafXZFWkgPezvkEHAtFPW/ep2K77jHoX5yv0fTwulhG30ZDkj1vABbniSgBFMmK8390zpsXV63dHb47u7o4ccEgwoUCn/oDCdlELubO+dq9uifVWr2XzAHevQSIiJKYW1WlKl7aXB5vqcOj92qn+JTWNYnDxL/7pXVh5uZWlwBK4vbeqbC64PJE+LmRoHNeUeXZXJiIWiBJioD1jQucHvPGblZYXbA7Q/zNddQmPhh4LxaEHLPTlMvmPS82WMzF+Ur9i/M1em6+Cmj+3vr3StCLx2nqxaDGWJwnamHkSPmBZOuct7vM353+9/IIybZ6V8Jm4gsh8HtZFMV5ICGjbSJ21zWluHSf61uv91V5wNQOSiIiPXgUAQh5RtX5Nw83sbjg41ZUVDbdENZZl5CL2qGE3UdG9QB1idsErdkO/sZqErvXTiiKENLsX0BELYuAPCtz6hoXOE3MnyWhRtoA3mKpCRcyyuqdcLiiPMap/N3QWDyKit/LzO+cl604bzXiHL1ejnN0FueJyHCqR47l5j7+wquJy+C3HoxQDFdcCesiK693oi7aTnEDkn5jbkVFRdOZvdHQeQPdqOf7xULjMj4iIrO5FRUWifaR8RcYJOicP1jrCF1LMKl7PuLS/ASNqgOAfdEU5xuqAGeUjQIGUSS7+ESUPCxmB0AxCDjvM/E8fVdz890VN+COooNdZyXh9oNryuBz4l1V9j9GD0WrukT376fuq8ldNk0XXmzRXjyJ6Un1n9UfDxbniVoYkaBu61ioknUhWZ3mF+ej2nk9QTvBbyuN4WTY4JP5snpnfPm6Vt8OQGOK89o2wCEiMptLUaUqXtY1HMrn7hj3KjHAwabz5n1MGtcStnMeSGhxPqrOeUD3C+2xUlSR0HE/RGQgSUbI+AiJNoQNKLh6zMufYYvhJpw3NXuxIBSDi/NbYx1pA3hXxek8GrdO7zEyEIAr/gvxDS4DcjSL80TUWsjXOW9up53N6cHe6mg2m0nM3PmoLhT4VG439MAy5pE2PjoX5xUjvkSTRhsQEenF5ZFr5rx/824Jxtrsq26mwKFzfopGs5vsNWbwkvzG9jb33jRVs9vYQCJwq4Iz54nIQHIU5wM21TTp4rY3T4U5N7JXJS6YQ8LG01TNLkNrCXEV5wGgYpuucUS9uj4WGs6JnZH2r4lHQ+J/1kJhcZ6IDKd63Ambnx6NP8bamFOc315mja6+naAT5+2xzLNz1Oo+372xsuY6DyPRufihGnEBwoTlmUREenJ55Now09/9J8Hf12ZHt5hQnC+3OkNvstdYzW7AbfxFDVUV0V94r95lbDBhCCG4ISwRGUvGznmXOfmzvN6JhnAjShJcMBVCRB4HF/AA1bAVaKoq8Ht5nN3lOhfnDdmHTUNxPuLm8vGQZPQsi/NEGglJkqzMPKowdYRMYw638scf9QSclIay9WBddHes2WX48uoauwtldTFepNA56TfW7FiASOr2SnPA2yzVw+XyRBJjPo/MpaiwSFS8rPZ1zksw1qbZFXEmdIOXVERTYBAJ2eS93OqEW4nyZLq6xNBYwvGownsYoco1ipGI4iMgT2MYcKhnXpKVZwHd0G5zVvZGLIQb2AwWSkybwfoYdE68r6Yh9lh8KvRb5a6qAnYjxshoOGZzRXs8EQtJRs+yOE9EhvOoqmld6k0F7Dhu0oy9zQei7FRX3N6is4G2l8VxVd7A4nxZfZw/Jx4nYK/ULY60FIM2tpKoqEVEFCuHW5GmuAAAVbZDOd2kzj+fBpfS/IVuW3nCLx4UV0SZ2w3M5z5Rj7QBgNrdgEkbDvsvIEi04TERtTRyNAEEzBE3aexmxPnuOp7XRSOmzWB9Ko2ZOx931zzgLTTrNBLI7laM6X3TMIpQVQ0ISMMMfD2xOE9EhvMowtSd4BsLWMZnQud81PPmfQzesC2mefM+Bm6AE3MXf2M6biSXyuI8EUlAtk3enW4VFok6i6tsh3KGySdWEfN6grvnd5ZHWWQweEM7IMy4n1AUN1C337hgwvCvqlTk+fkmopZDCCHNxb+AUSVOc/Lnrkjz3RO8SWdMI218dOxSb2xHPM1zjel04T3ieLx4aWhYUIy4WuCMc76/zlicJyJDCSG83UiSdM5X2RpdJDBhRu220vrYcrjBS863l8aR/Gt2GXLy6vKof2zuF4/afbrFkpmWqttzERG1FA1uRZqLjIoqGnXOm7vhdsTNVxM4rsWjqJE7En0qthk+Eq7ZjXKbU11sTCAR+JfKS/LzTUQti2j0v2YLGGvjjHLcqo6EENgdqRhuLUtMMIfsjjZvNmbQXmwxbUwbik771tnjHa0TiURNHgC8qy8lGG3J4jwlFc6DjUxIkvR9FFVAFTBtvntT1Y2LvyaczG+Jdef1KuNOUu2uGLv4fVSPIYWGCqvGCzg6brqXmcb0SETUVINbgcUjz8V2/3GhyZ3zETsADczlTe2pboh+xruzzvACyL6aGI8zEjAHPxSPcuhnSbaiARHFRcqygQRj4TyKGrgRqwnF+Rq7G9ZIG43aK4EErR6MeTPYxnQeD+dwK9Fvot4cncbtNLgNKs5LMlHhD8I7ItdkrD4QkaEcvmXCGmaL6anSZm5xfmusxXkDN4XdWW6L/8BVpyvyjZXHO2/eR8f5/NkZBnXOp7Ajn4iSl0Oizvmy+kbHFS6bqZWY4kizahPYDR7zcngD5867FRUHa2PM7SZtCvvHWBs5fr6JqGVRhZDiioG16agSR23CY4hqLxLVk7C585U2V/wjXHSeO7+7yq79x6Rqpy4jlBxGFec1HEemWgwaPStBrYrFeaKWRoKk35j/j7oEf/AAoLJxd3aC54tZY503D3iTl0Gbwu7QstmMAZ1tmovztft0+/nPNaw4n27M8xIRJYDNqcCiytHxFLBHieoxZVQd4F2FFrHLrXZvwmaZx5zbK7YaEwiAg7WO2Fe9VhWbcizrH2ujyPHzTUQtjIAUnfP1TTvWPc6Er3DfE+35sLXU2EAO2RNv1zyg+94tmmLxUdy6rCh3egz6edVSnE8xqIQtwX4zLM4TaSRZLVy60T/+ZXMmnTQ3FTA6JcHL+LbHOm/ex6Dl8Jo2mzFgo1rNY23cdu8O9TrIzUzT5XkCWdg5T+aRLDdQcmpweaRZjlxa16SYYNKGXiUVURzfqJ6EbQobe3HeuE1h98eyGayPxwHUH9A/mAj8nfMca0OykzKfyxiTXAQgZ3EeABw1CY0h6r1I6g8aG8ghe2LdG6Wx6hJdV1xFtaogGlXaz9VdRhXn4/g9EELAZrNBdTfA7Yzuw+ZwR18bk+DCvBHVB6JWhYci4Tk9vuK8+Z3zQghU1Df6w5vgZXzbSuMsHNTs0jcQeN+LnZGW4YdTt9/7PU3P0i2mgJFD8ao/AOS00/w0+VkGdLinZQBGLcUjIkoAm0uBRZIN3g82Lc47aoH8LgmPY2dFlMXwyh1A+76GxlJjd6HSGmMurdnt7ZxMy9Q9nn3xFOcB7+q8gm76BhPBH53zHGtDRPqT5ZqKzRXib5y9KqH5M+rcUL/f2EAO0dStrnqA2j1Au966xBLXRe1QdBgRF/X+NTGL/ZfBbrcjLy8vpsesBGB9cQJys6Ioe0tw4Yyd8xKRreOZoiPd902CPyyN+Xf5NnmzNgCobXAHJhlHTUKPlLaVxvkeGDB/tbTOGbgZUMyE7l2AMRcUQqndp/05AOQZ0Tmfqn/hg4haNtmOMewuDywenU4cNQoaJdNQY0ocO8qivNCtQxdbJHGNqxOqIavhAOBAvJvambAprNvDsTaULOTKC4A8hefGZIvJO3Pe/PP0kBux6rTyOBqqKqLf8LQuMauo4r6Q7KNTDhVCYL/WzWB9dFh5b1hxXrZfTkCK3012zhORoWxOecbalDWdaa64vZvIZcZ2FTYeDreCXZVxvgc1e7xJTMeu612VOmyGW7ML6NhP+/McUmXToRtTpw6LwmwDOufTs/V/TiJq2SQ7gal3eGCRYA8Zp0cJHoWW4GX5gPdEOuqCuEEF8MZ+j3dcXdUOoPMAfYOBxs75BPN3znOsDREZwDvWxvycHrpzviJhr19pc0Vf9K3Tp+kqHJdHRVnTlXix0mnTd5tLiX9j2qZqdmuuH3hUg35eLbH3iOfk5MBqtWLZ18X4vqQqqsc8kr8KOaI85tcyC4vzRC2NBFf9GrP7DgBcOhSDNQqaTwt4d4FPQHF+V6U9/g5Il9VbdMhuq188emw2o+O4HZdHDT0DMVYaOyyEELDb7ciAC25ndEUFm8WNgjQBS6SDHxbniSiJeRQVDS5FiuL8gRpHcI3DHt3Jmp5K65ywRXsiXbsXcDcYmgt2lMd5rFX5u76BwNtxF7Bpbyx8m8ImcBSc0+3rnGdxnoj0p6oCMqx6CJmzbIkrzkfdNQ8A1jLvPjdpGYbFU1oX4ngiVjqtctd8kaAxjwOwlQN5neJ+CkWi4rzFYkFubi4KC/ORnhn+HF0IAY/L+17anVFOCrA7kNMmivN5A7E4T8klwQfqyUhIVpy3uRTkAFKMtQl5MGCvANr2NPy141pq3ljtXl2L87rsBK/jWJsau07LyDV2zhs6zy49J/7AiMh4Eh5jCAlO5H2sh07oZRhrE7Ij216Z8Dhiy+3C2xHe+RhDYnErKkri3UumQv/ivLfgEefPr29T2ATOnXcpqnfeK4vzJDmZ8oKfBB3hTcn2PgkBKZrorKGKlQkszodslmuW8HbP6zTPPRRdZrzX7AJUBUhJ1fQ0FXqMeG2sZrem4rxq1O+1hvcpNyPyYz0uB1b+bSRWxvTMa2C1WpGbmxtvaJpx5jxRCyPbfFr/XDunpMV5a1lCXrtYy+argO4z9/bpsRO8TvPdAaDartPJsLVc3s3cMsxL9kSUpCTK6XUNh4rz7gbT49pbHeICswnF+e2xbvRuQIe6z+4qe/xdbvYK74a6OoqpOzIUA/bbCcfFmfNEZCDvzHnzc3pDqLE21tKEvX7QmNlIavcYE8ghQZvLx0Nxey8oa1Rl0zn/1O7V9HDDflxT4u8RN2RfOEm03K8sGUnYsUWRyXZVXoak35h/6ZwEnfMHQiXfBB2MaC/O61cItzo9qG3QoRjusnpP5rMKNT+Vbp3zEICtLO5uO988OwCYsaoIDe7ml8L5lsw9lvUShHDDFmksj5IO2GzIyckxdckckSyEMHf5aDKQaTWcP28I1buPjIkXHPeGusCcwJm5Pr/HuirOwOL8jnjnzftU7QS6Ha9PMNCwGaxPVTHQ81R9gomCy6MiC2BxnkKQ69yKoiPZKbE0G8LaXKE658sSVosqj7U4X2NwcV6vDVhrdgOF3TU9RbVu58OH1GlbUW7UVBukxL+3W16kleoA0jKycNVz/8Uj+auQ7zgY3ROPewI5Oeaucmdxnkgj2RK/DEm/sXpfcd5RZ2oczc4+TcAu8HUON6q1XgmvjzKxREG3gxDAG5cexXk9Lhb41B+Muzjvm2cHAF3aF4YuAB3idjbgtZvOxmtRP/saADebvmSOiCgedY5Gf6ddNtOK80II7A41ms1eBagqkJKYhcE2pwcHamLMpwZuCqu5CaB6l67F+dhGF4SQ4M55p8LOeaKWRLZTdG+h0/yoGkIV5xU30FAN5LQz/PUrbbEW5/UboxpKzJ38zdHYpQ4ANXqtJPfRoZvfEBr2ECjMjlzYt1gsSM/MRm5WOnKjLXkXtDW9UZpjbYi0kqw6Lwy7xBmfet/JvLPO1PfqYG0zs0/rtCfSSHZX6jDf3apfcV7zCXNjOiV9XTr5fXRaDdEuN1OX5yEiipeUnfMA4IxxnIuOauzuP0bmNaZ6vMWFBNkZz+ar9krDNq7VXpwv1ieQQ3QZa5PA40aOtaHmyHVmJd2pJwA5Y5KNkKRzPmRxHtC1Eaw5QghUxjpXvWaXMcEcEnMnf3N0GL8T0AShB43f0xSjatWp8Z9jt8kxaHPgtCxjnjeWEMwOgIj0JUSUO1InSL3DA2TCezDisgKZ+abE0exmL7ZKwO0A0o37gxyu+zpqtgrdlhvqdhAC6Dazv07X4rw+MXXMD3/g4Fsy91TW80hToihCnPhXoM9I05fMERHFI6Cjy2neariQXfM+1lIgt31C4vi9PM4LFFU7de9OtDo92nO7zqMDSrXG46xLWCcn0Kg472FxnmTHcXDJSJFl5nxzIzvrDwCdBxj+2o4wI0NDP6hatzGqTdldnj9G8Gqlw8WNkI0HWjjrNNU5DGsk11B3aZsT/0icZqWkAanml8bZOU+kmflJtjEhSeIHvLEEFF0bakyLZV+zO7ELXZahxffaMdCxI7DCqmNx3lauy9PU63kwolNxvkNe+CvzAUvmstIif7TrjNzcXM7YJqKoybQaLmAWqomj6naFLc4b3/nns6Mszk51A+bOl2jtmge8hRlFnwvlVqcHdj0KHgZ3TDbm9BwqGCk6HiMRER2iqoAMdYNmi+Ma55NHI+6xLdXG5IKYu/jDqT+guQZjC7VZr1b2yrgfmmLUOWtadtwPzclIQ3ZGqo7BAEiPPx49sThPzZOkwCs72TaEFQCgytE9b3cpUBoXFhK43LypfeG61w2ea9ps136sNCTXxir13Alep+K8rsv4bDoV5yN0zscsq42+z0dEupLysEeCJfA+ARt3m9g5vytcIToBy/IBQFVF/GNkDCjO79JjfJ5Qddt8XrcVegbPGm7sj855FuepCSmTA0UiVHnyJ3BoQ1iTY3IrauC5eWMJmE8ef3Fe37FrPlV6nhP75vZrYHcaUMOxV8T90FSj5tpoLIa3z9V5tA2L86EtXrwYvXr1QlZWFk4++WR8//33Ye//xBNP4KijjkJ2djZ69OiBW265BQ6HjvOUiSKR7IBNlp3ggRAFVxOL82FHyxiU8AHv6gHdZrzr9P7putmMTrNzdV3Gp9NFjE4szmvCfE6knUwNAAEXdk1cCRe2cz4BnX8AsL+2Ifal+T5VO3U/dgw76icWOr1/+hXn9R21Ew5nzofHnE7JRp7s6aVKUDNodqQNoNvF2XDibsaqMqg4b9f5772GpjVFFXArBtRwHLVxPzQtJXK5WAgBt7MBDQ4nbA5PdB9uhN4LMEqdCnQeR5xhztjlpswfrNPIqlWrMGPGDDz33HM4+eST8cQTT2DMmDHYunUrOnXqFHT/V199FTNnzsRLL72EU089Fdu2bcOUKVNgsViwcOFCE74Cao20/GExhIA0xfmgTT4bjNkELRKHWwk/yqVqp2GvXe/0NL/xTqx0KoTrOt/dXqnLLHyrXvP+AMBlA9wNmq+CR5o5HzMDZiXKivmckpFk2RyAPJ1/HkVtMqbOnIvtdQ43qsN1uiWoOK9p81WXzTt+Lb+zbvHsqdapOF8rWee8DhvsRcvpUb2jvNk5H4Q5nZKTXFldUQVgMTenh72obC3zdn+nGjDT+5C4z0ENOlev0bs4by0FOh4V10PjvuAfiYbjtbQoOuc9LgdW/m0kVsb0zGtgtVqRm5sbV1wd83Q+R8+QYz84qTrnFy5ciGuvvRZXX301BgwYgOeeew45OTl46aWXQt7/m2++wfDhw3HFFVegV69e+POf/4zLL7884pV8Il1JVpz3zpyXY6xNbdMObZ06mmO1N9JJa/UuwzYAq9Bz81Udxgi4FVXf5K96ALe2ooBbUf/oWNOLDj9rmWmpKNRr05msQik2mkkU5nMivchxjFHT4A483DGpOL+rIkK+sZYCigEzW5vQVJwHdF2x5/QoKNNrhZ5OM/srbTod+9TtS9hxNsfaNI85nUg7GVa3O91hXl+oho+Gi7sZy1rqvbCts6BahVYaGukM6ZoHAKc17oemp0lVLvbrUqhz53xmgb7PFydpKgUulwsbN27ErFmz/LelpKTgzDPPxPr160M+5tRTT8XLL7+M77//HieddBJ27tyJ999/H1dddVWzr+N0OuF0/nHQVVdn3sxMIiOoAtLMnA/qnNep8ztWe6oizHwXivdEOc4r3eHoOt9dw7I0H0Nm2TXUABnxXfkGjJqvVwUUdtf8NJ0LsvQ5cMtpr/05kgTzOZGO5KjNB89lNWklXEllhJNz1eM9iS88zNA4tBfnS4DDT9ElloO1Dv3q1zoVZnTbZE9xe8cE5AV3Z+vNrahAOgAPR680xpxOyUqy/jl4FAGkmlyc90Q456rdC7TpYdjra1opXVUMdDlWv2AA1Ok5VhXQ1Lige6Oa/4k1FOdTI3fOp2Vk4arn/ou/tf8Rg+q+ivykOe2AcU8gJyf+bnXdi/NZLM4HqKiogKIo6Nw5cIln586dsWXLlpCPueKKK1BRUYE//elPEELA4/Hgb3/7G+68885mX2f+/PmYO3eurrFTIkmWZQHIFpN3sxnju8aiURNUnDencz6q5d7lWw0pzuu60YyzXvNT2N0G/Gw4ajUVQgyJSafCUaf8TGw7qP19R3Y77c+RJJjPKVkJoUKyRaUQklxsDxoNp9NIs1iVRFMUr91raHHeo6jhN5mPho7zc/fptek84B1roAPdOucBb/d8Aorzzsad8yb8bMuKOd2XG2Qi17mnl4QxNbfxqUkUCTrnHeE65wFv/jSQTUtDVrX+xfn6eGfgN0dDcd5j1M+rhhUHmVF0zlssFqRnZiM7KxO5rijKy4XtgDjH2fh0baGd83KdgcToiy++wIMPPohnnnkGP/74I9asWYP33nsP8+bNa/Yxs2bNQm1trf9jz57EzTIkSgRVFdK0CgR1HGvYJEWLPdFslFYe+gRDK12Xy2kcHwNEcVAWD40XDex6zeRvTKfNCjvrteFMXkd9nqeFYj4nCk2WolDQhWbFrakbK15hN4P1qdltaAz7axze2cFa1OzSJxh4O+d146zTZaxLtZ7HPgaPWQC8IyH/6CgV3p9vihtzOslApg3VgUMz580eaxOpc77O2OK83aWlc17/ufO67nkGaDon1nxc0Zw4croQAjabDYrLAbezIaqPBoczur0YddiDLT8rHflZOvaZZ7fV77k0kKZzvkOHDkhNTUVpaWnA7aWlpejSpUvIx9xzzz246qqrMHXqVADAwIEDYbPZcN111+Guu+5CSojdhTMzM5GZqfMGApQwQhWwSHZJSZbN2nxUAFDlOKmobrrJissGuOwJ3XRDCBFdV1nZZkM6peLelT4UHWbtRTwoi+tJtRXnddswN+BJ9ZmH3LlAp3yR23qK88zn3r87FnZdJh3pNniHPMX5kGNKbOVAZn7CYoi4GaxPrbHF+d3RXCCIpKEacNTpspT6gJ7FeQCwVWhaeeBwK3DomdfrD+j3XM1wKyKwr8XTAKRlGP66yYA5naIhWyEckKZXzU+O4ry5nfOazvkqd+gXyCH1eo+10XBObFjnvBJ7cd5utyMvLy+mx6wEYH1xAnIjFc116lLv2iYb9XqsbgeA7Db6PI9G0pQ5MzIyMGTIEKxdu9Z/m6qqWLt2LYYNGxbyMXa7PSi5p6amApDzBItaJllOnH0UIaSZOR801gZIePd8aZ0z/OY3Pm67dwasznRN+m7tS9fdHgP+NmrsnjTmgoE+s0o75evVOR/6BLYlYj4n0pEkxxhBY20AbxE3gXZXRlkUr9avKz2UiJvMR0unIki5nhvPA5ovbtfovcGeTqN2wnE13YjPo+NIwiTHnE7JSrYLBh5FguJ8pHPi+oOGbqru0HLOp/OmsEIIONw6n4NqWOVu2N9GSepCfjp1qXdrk63L8wCQZvysNJ3zADBjxgxMnjwZQ4cOxUknnYQnnngCNpsNV199NQBg0qRJOOywwzB//nwAwLhx47Bw4UIcf/zxOPnkk/H777/jnnvuwbhx4/wHAETGkyvxC1WOmfNCCNQ07ZwHvMX5tj0TFkdU8+Z9yn4D2vXW9fV1XS6nwyZlHiNWemg8UDJk1I5Dn+J8x3ydurjyO0e+TwvCfE6kD3lmzofI5wkomjYWdce6tQxwO4B0nWeSHqLbjPfavUDnAZqeQgiB8lAXTrTQuGdLbajGDC2spZHvo1HQRnweHef4twDM6UTaeSQYPRuxIUr1ANaDQGF3Q15f8zmfjpvCNrgV/b8dGs6JDfvJiOOCUE5ODqxWK8rqHZj971+jeszf2v+IHOc3ke+oU3H+sDY6HuPlsDgf5NJLL0V5eTnuvfdeHDx4EIMHD8aHH37o34Bm9+7dAVfh7777blgsFtx9993Yt28fOnbsiHHjxuGBBx4w60ug1kiyDhCPJMX5BrcS+up8Ak6yGotq3rzPwV+Ao8/V9fV1naeuQ+e8asTPq8ZZ+IbsTq9T53xWeioKs9M1FhssQK7xm9nJhPmcoiLZpouSpXMAcnSZCiFQGaoALG0+F96Z7gZs8g7oWZzXPlO7Qe8RMoDmPVt0HecHeJs6DP5bEVSw0mHufkvCnC4XCdJCUpAhfzbmHWtj7gX3iGNtgEObqhtTnNd8zqfjprCG7HmmYUNxw35c43hii8WC3NxcdEjNQHpmdB3q2VmZsLii+Lp1Ks53LdSpcz41HciIbYSPUaQqzgPA9OnTMX369JCf++KLLwL+nZaWhtmzZ2P27NkJiIwoNEOKnRqoQo7ifLPLmhN+Mh/DSXT5Zu/SrxT9unp0HdmiwwZlhvy4auycj+pAMVY6LnvsmJ+prTif26FVzq5lPifSTjQdt2GC2gZ36I3KZO2cB7xj6gwozje4FP02etdhlrquG6/6aLy4Xa93cV5xe+f46jCfvzlB3Zw6NEO0NMzpRNp4VNX0ESNRjXo1cO685uJ8VbE+gQD6j7QB4N9QXKbzPg0XtrPTDVjppFOXetdCnTrncztK0ygkzcx5ko9sV5ulJdnbpKhClyKuVkGbwfrUJ7g4H8tYG3eD7jvBR3UQFC2haJ4DaMiPq8aT2KBZr3rQqXMe0GG0TUH8G+sRUeIImF8IDyLMv9gecqQN4F36niAuj4rSuhhGu+l4At/YwVhiiKRuv+aniGqD3FhpHAtX12DAz6y9Uv/nbCR4rI3Om+wStXAylg1UI0Z5auBWzG+gi6pprG6fIa8thIBb6zlftX653ZDmMABQ4svLhtWHLfGXfNNSU5CRpnPJOKe9Lk9TmJ2OLD0uHuR21P4cOmFxnkgjVZLN2nw8EiR+QI7OeZvTE/uJ68FNusag+SCkKVXbhZcUIxK/xrE2biMOjhS3bhsaaS7OG7Q0lIj0xeJCaCFH2gDeDWET1AW4v6Yhtu+Pjifwjem6+aq9SvPGo7qPkAE0rzzTda8dH42b1EYS1EHJ4jxRTCRMn9IldY+iAiY3AUQ31saY4rxb0eH7UXdAt7FjujbQNRbn6KIUo6rzadrOY3My9By2YtFtrI3FYkGnAh32hsuTZ/Qsi/NEWklw4tyYqkKK4nxlc0VxW3nCTub3VsfR0V36i26vL4QIPQpAizivxv/BgMSv8STWkE1qAcCtz2ibDnlai/PsnCei+Kg6XWTUotnOedXjLdAnQMxz3mv2aC58h1JWr2fRVgA2baOBDOlS17jyzGZEcd5Ro/9zNn76psV5N4vzJC8hZylcOrJNAXCrwvyxNtEU5+v3G1Lf0Od8T3jzuw50HT3bWJzfY8MGq6RqG7GTk6HjaJvstrqOD+6Ur8Nom7zO2p9DJyzOE2kkZOucF6ocY22aK86rHsOXJ/vsjWWkjU/5Vt1O6PWuywPQ3A0uY+e8x5A3CrqdXGsvzh+uSxxEZCzJzuMBAEKC4nylLUyXWoJG28RcnBeKLhuuNlWl9xgZjXP7DSmEaxxVZ8gmexpH7UTSwM55ohZHuuK8RwVMrhtEVZxX3Iacq+t2vlezS5en0Tz/XmepUZ6kCyHgdDhgc3ii+/CkwGazxf37kJupY+d8bgf9ngs6rG4HgLwu2p9DJ9JtCEuUbGRYct6YogrNo0/0EPYEtv5AQpYQxXwyD3gvHlRs02UneEMOCjWuikhLMeCarMYiuO6rC3x0OrnukKdxUx+OtSFKCjJ2IwqTu+wAoCLcKJe6/UDXQYbHcKAmjr/nVTuB9n11jaOyuVUE8dK48sBuxIZ2GovzQYVuPTjr9X/ORoI2hGVxnigmshXCAfn2kXErMmwIG+Xr1+0D8vSdxe3RY6wNANTs1uVpdBmzE0qc42miLc57XA7MuGYCZkT9zGsA3Aar1Yrc3NyY48rVs3Ne5+J8e63n6ACQL09xnp3zlFTkS/uAKsGJc2MeRegw+kS78MX5BHXaxTPWBtBttI0hP69aZ84b8Vdf40ms7MX5tjkZsMQ7BzCvE5Cu027yRGQsCYsLUoy1CZfPddjUNBoHauPI55U7dI+jprnN7uNl11acb3AZ0TmvbTVc0IgYPRhenG861kbbBQpqWeTLDPKR8j2SrIHOW5w3t4Eu6k1Q6w/o/toevfZh02lVnO77wvnEuQFrtMX5RMvL0rNzXt8LPu1ztRbnLVKNtWHnPJFGwqjCYpw8qqrbRpjxEkKEXwZvQMIPFUNcnfMAULZZlxgM2dhF48UgQzrnVY/3Zy41vpRi2K+QThepUlIsaJebHl/HZBuOtCFKFqqMxXmT95ARQqAq3N++BORzt6KiorlNacMxYFPY2gadCyv2Kk0PN2RDO42505DivE57yDQnaBQPO+eJkp5sq9tdHgk656Ods25Ablf0Osaq3avL0xhWnE+J73w42uJ8WkYWFq5cg+tcL0f3xMf/Bej3Z+Tk5MQVl75jbfSdnNA2R2NxPrcDkKZD971OWJynpCLjkjnZEr9HEUCKuZ3zdpcS/oSxzviT+doGNxrinXta+bt37rzGP9aGXADXOtYm1aCr8p4GIDU/roca9nut4wqS9nmZcRbne+oWAyURIeJe1moUIUT8K0DINKrH3OJ8vdMT/gQ2Afm8vN4Z36IG36awOp14qaqAVe8Z7w3aivMuI4oLGi+4R92ZGQuXtm7+SIJG8bBznigmEp6iS1c3cCnmbwgb9bmxAavcdRtr46j1rqbKjO+808ewsTYp6XE9LC3KwoHFYkFmVhZyo70I0LYTEMc4G588iWfOF+bE9177FRymTyA64VgbIo0UyTaEdauq6WNtIhYxE9BpF3fXPOA9Ma3SvhzeYrEgRe8KvcbifEaqQX/2ddp8VVc6HgDHvWyusIduMRCRsWS72A4AwuQN3iPmc3sF4Imjqz0G5eFm3ocjFN1m0wKAzeXRvwDVUKPp4YZtqK7hONKQTfYM/hmzN73ows55otjIVQf3kuwc3elRTB9rE7S/RnOspbq/tq5jTHUYqecx6pgvNc7ivFHn6NltNT08X8+xNjrvOZiXkaatF6qgm26x6IHFeSKNhCLXzHnvhrDmdtqVR1p+bis3fPROXJvHNVa+VZc4or0KHjWNhZp0oxK/R8IuMx1/D9rFW5xvy855omQhWZMdAEDxmHuxvSrciDofg+fOl8VbnAe8m8LqxOY04HjPUavp4apRxfk4C0hCCGNGBRhcLA/aWJed80TJT7LRs06Pauo5uqLG8PfZVqH7zH5dLybr0OhnSOd8akbcK2d1rxn45LTT9HD9Ouctus+cT0mxaIuPnfNELYtsG8K6FdXwDqNIIs6GFSpgKzM0hgN1Gk/kKrbrEofuV8E1roowbKyNlJ3z+h0At42nOJ+SJtUmM0QUgYTVeVX2znnA8OJ8XPPmfXQsztuN2HzVZdNUADFsn4Q4f+4UVRjza6QkuHOexXlqRLrUIF1AgJCsSx0ABOQ6R3d5VM1NVlpEPW8e8J5DaRy71pSunfM6jN1RjOicT8uK+6GGrW7P1lac161zPrtt3KsKwtG0YW2hXMV5zpwn0kAIASHZMniXRwAWc0/mo1qCXn/A0KVEpbUai8U6jLUBgHS9i+EaC87Gdc5LWJzX8eQlrrE2BYcBKam6xUBExpKxuKB6zM3n1Xbzi/NVNg0XpXXcFDZoLrkuhHezU42zc3UX57GGbhv+NWXwsbYtaENYFueJkp1hK4viZHbnfNDG15HYynWdEa7rGBkdivOGjIVLz47rYUIINDTYobgdUf3cOtMcsLk8yMlMDb+fVGaB5n138rN0KqjnG9OwlpvRcjrnWZwn0kBRhXTz7LwbwiZDcV7/jWYaO6i1c76hGrBXaV4KlpkmV+e87hcLfDQU5w3bpFLH3824doMv7K7b6xO1REIIyLRFrWFdyBoIk1fCVdmiOJ6o22toDHFtxu1Ts8fbqahDt1bUs3pjpWFjO8PyZ7zFecnG7ETLxs55SiISpirIOHReSLa63elWTF3dHvVmsD62cgD9dXt93TaEBQCr9hX4usbjk54T18Psdjvy8vKivv9KADMAWF+cgNxwneM6jJHRrXM+V9958/6njXesTUYekFWgbzAacawNkQaqgHTFeaeimL4hbFl9FIVaAzeFdXlUVGvptPOpLtH8FLovUdM61iZFvs55o64XaNshJlD7vHg65+XaZIaIwpNtJRwAKO5k6Jw3dpP3qGJojo6bwhqy0SmgqRCcYlhxPr6ilmGNqgZWI10eNfh7y+I8UUxkvGAgU053KyoUVcBi4oawjlhXf9kqdH19XTvnbeWan8KQzvnM6AvsCaFDt3p2eipS9ZiHr/NmsD5xF+clG2kDsHOekoyQ7Kq8KgQskCfxA4DbowIp5hXnPYqK8vooXt/AzvmIG9JGq7oEOOwETU+Rla7zWBONHReGdc5rmDmvS8I3WE5GGrLSU2M7sJVsqRyRbOTK6HJ2zqsGz9qOJKqRMvX7vZUZAwrFHkVFvUNjMaO6GGjfV3Mshmx0CnjnzsfJqEl18XbOG/Y7ZGAjTFDXPOD9+nVacUHJT7bzT7mi8RIS5k8IeTrn/ecPJm7yHvNYG3ulrq+va6e6s8577pke/4x3jxE5PSM3rofl5OTAarXinrc3oSKK1YLH51biOtfLyMmMUGfQYe8zi8WCgux07Y2PRhXnM+KstUh4ns7iPJEGqhDStQq4PCqQat48uwqrK7oDNAM758u0jrTxqdW+VF/3sTYai/MWiwWpKRb9l55rKCClGtXNb9H3edvlZmB/TQzddAVddX19IjKYZPkcAFS327DCd8TXVgVq7FEUxhW3t8MuT/vy6abqHB7t3xYdVsEBOnf9BTxx/Mcshq2Gk43O+bwxa6jiPODtnmdxnihpyTRz3r9niYkX3GPeN6WhRtfXd+s9RqahCkiPf5WyMZ3z8Y+oy83NRX5eHmqjWLmVmZWF3JQoSrn5XeKKJ+hpstJ0KM7rE0tTOfF2zktYnG8lR3RExpBxrI13J3jzrsofqI2yeGmrNKx7IKqZ99Go26f5KTIl65wHDNoUVkNcaUZ1zlv0fe/bxboprEEHIURkDNk6/4QQUFTVW/w2QZ3DHf17okO+DPm0DTp87dW7tD8HDJpPC2gqzmfo3QDgI9mxrZEXp8IW54koKrLlT0CuTd4drkOxJNPM+YZqXV9f99VnGjv7DdkjJbNQ08N1X3GvUwG6QI9NYQ3qnM+LtHqgORKOn2XnPIUhX5KVjRDybQhr9sz50qi71gVgLQXa9NA9hmiWg0WlTvtSff3H2mg/WUxLtQB613o0/MylGTVqJ0Xf9z6mufOZBZqWWhK1CpKdzMs0nxYAnL452IoTSItj3wuNohpp41N/AMBg3WOod+iwErBmly6rDwxrwtRQrNF9dZ5PnMe2hpXQDeycb/ZnzG037DUpuUiWqiDjObpso38AQJVoQ1h/17qGi7Fa2Vwx5lNHja6v79R73xZ7laaHR9M5L4SAx+WALcUNtyeK90/NAGw25OTkxLVhu+5NfTp2zmuSmgFkabtw0ZzcjP9n79/DZcvK8m74HnPOOtc67b177z7QgBoJ8oL4BSIXEu1EIPDiBTFtFBUEgYtcCEhHVNQQxQPSHgn4IumAQLBjIhE6vmoQExH5QIkYD0DiBwjaNtDHfVh7rTrXnHN8f9Su2nWYhzHHeMYcT+01f9fViJu9qkZX1apnjHvcz/3oOucrcb6i4ppCAhDMxPnRNAZq7sT5Lx0W2HQc329JnCdyJUST2cakdaD9EC3qImuQ7T7HShu8gThvzfnn0bajF3LOW3IHVFRU2CNmlE8LLB1kwwnQKP/5L6lE2syx5Jw3zpsHZuJ37wHjQ6o1Z6hBZwT5Qd4QHTFCCeJ6vkzqZ6xyzlcwhaNLneP0KE4X7oO5MO7QOT8YF9zjjC6TxuqRO+cNnf2RwucjnIxw58tuwZ3Kj3oXAKDX66HTKZ4/T6obNHa1Y3bW2W0Z1uCd6611wHV1Lg68AOjQRzGaUsXaMIJjoWUHs9dIxmDlnJdSzsR5l7E2RTK5LeXOX6AS5wGg96DRjzdrxF+zU/3BcXOsDIU1EBesxOwAgE97/1xInGdY8CsquMFt3xNHzMT5RUatm5p+OCjwvEd26vlxWuRIUQiibax9Wg3eX27Oed+aOG/vEqJyzldsG7wq5wxm5RwAN3F+yTnv6MVKjfBKIw5JLykn1M55Q2e/lcx5Q0h1gz26THXjWJuOPdPaTkNjbd2zVvcVulTO+YoKAyR4DYQdh/FM7HCUTyulxL2qmfPAzDlvgQumA0uW6Z8Hrvv72j9OHmtDsEnybWS8mzj/bIkLPq3V9Ey3wON1zpA+d0VFhX24XRZcjbVxI84Xcs5bumzvU4nzl78A4Ek0j0VNrP/v2K7zOlxa2V8AQGCvdST1AqhyzldUKMMp331OzGhNV2uZnAn0tVbpaxgUjbUBgEkPqLdJnp881mZ0ZPTjKgODg3oT33XHh/Hm1jtQD3v5D/pNvwB0z6Ld1nvNtCNaktijSyfYbRmuy2JHudbadm6gXwgBlThfUWHA7BzPp/AvBr04apm70J9gPC3welg4zI+mUfGBN1kMzhv9OHmszcTcyWVlAKuGuCClxGAwQDQZYjpWOwQPRhNIKdVa54kP86eLOOfbp0mfu6Kiwj6SmXN+tE3O+cGVIe/E2fh9qnp++Qs0j2MDAwGJfI9hiLUB7769mQupQ4crcb6iYmuRUvJ0zgOz7xYH4rxWPR0fkwmr45B4jzU2E+dVnPNCCNQaLXSaNdRDBen09E1G+6AW5YU7oTi/ZxprY1Gc32nWIERBvyxRFj81lTjPCWaOLXbrAT9XG7fM+cHiIO9GnP/SpYIHGQvO+UID7FQwHDbToI61mSjc2ucQ2IiR0chqHgwG6Ha7hX7mTgAPvPU56DQUytc4QltVyFfgoF2HEELte6h1iuQ5KyqubXjV9NjAwWyD0fyy29GF+2ER5zwwu3A/eATpGgZUzvlDxuK8wdDCNqXLjgDPE8UPySoE9gasp8famMcIVlTYgNlxeAazRYWxZBU9uzKMdToAUP45QasTjeDcOWdUxMCnwvjY6MdVnPOFqHeNDQqdBqE4TzjXzzjWxqI473sCO81a+kV74noqcb6i4ppDSmaFf150oykQx4CNwZ8ZfLGoOD+8OBMdCB3Ol4o4/VQYXTb68UZA7Zw3PyxacbaV+Htw7uW/pfg379IeyJOE5wmc6tRwoafwGWvtkzxnRUVFeXBy2QHAcBqhBTiMtSn4vL0H6MV5Kud874GZCG6QMWpv4KG+QNBVuajWQejvH2u+R58tXLMnzh+lDoQdWXvOigojeOngM5iJ81EsIRi9UL3lS0BH3y2FM+cBko7tOeTOecOLg4j6M0vQNc011mavzTdzHpgZ6AqJ85VzviIPPuWDMcwKvwR4ivPALM+OKCNOlS9e0ijgx/eTHuYvF3X65WE4bIZ8IOykP/s9MHCDW8mE1fg9aLfb6PV6uPt8Hz/7gU9n/t3peIhfv+2Zuqsj43S3oSjOH9hfTEXFlsOrogOxgYPZBq7F+cMiBx3AWlQdCXE4E+h3b9R+CKourA1MnPOULjsirIjzgb0IiPRYm8o5XzGD2fGTZb67ZFbRI27O+fG6c75cpJR6znnCtXJzzkfUH4+2eTeEUme4Cu3TQKNYd3oWO41AvXs8ic51ZGtJ4qBdw99dKPADlThfUWGOZJTvPkcw2rH1HIvzX2AgzhcWE/IwdKqTO+chZxulur4b3IpzXsNlJ4RAp9PB6dBHrZF98J4P5PmhU/9fPPL4L9TW863v0h7Ik4Zy7nxzj/R5Kyoq7MNOnJ+3wTsQ50fTCKOirvXeg+TrGFKJ8wBwdJ+ROG8rTt0Ejs75euChT53EZLDnyWIaxelziirnfEWFMtyiZ2euaD66QW+8nDlfvjg/iWKEkcZ7FNJ9D5Jdts8Jx0YdceSxNp0zxg9hHB8zZ//hNI9zBSEEdluBngmysWu1+w2YmeeUET7b2XCVOM8IbrfgUkqLLbx6SOovUUNiKbWytm2x0jJHWExVmIQx7r+scRojdtoVamlSwfBWvhFYiBYaHxsdVP2S447yUHH+zQfytJt1dKYKpaveBQrm2atwRqX4Bw3yYbQV24WUNiMwdOFVPwF+bsSY2UDYRaSLA3H+sk4t5eycB4DjewE8QfvHrTnnDSBz2a1jKM6TY0mcT82bB6qBsBULOJ7RK7KJIgnBKKquN16qqQ7E+f5Ys5YSfg+Si/PA7LVs7Gj9KH2sjbk4v9MkqunE4jwwuzjQEuct5s3POd0tkPXfOWMUcWgTXgpNRUUuvDYjUoJVy9zxSstcuYeK+y4P9TaLxENhtQSFLIyd8xa+Zg3XFPgWBAZPfzPRrlkokJYO8krFv7Fr5bkrKirsIpkNhO0vxHniuqaA1vyW3gPk6yBtgzfcb3B0znfqvp1LAwNx3sq+R1N8ySNzzxhW4nxFhSoxswuDMOblnF+5CCTMcVdFK9IGIDUHkM2QWcZA7yD/zBKI0HTiPO38HwDYa2m6+gk6CvI4U0ScL+GyQJdKnK/YLpgVfikBwcg5v1L4SxbnCw+DnUPtnE8b7KVLNDHKg7XiIDN081uJtTEQ5wPfQ4M6m9/SQf6USqxNFWlTUbGVxFHMap8xmB+mQ+qMkHwu9TVqaf8CENFecJA67QwvDzxbznkDIVwIga6N3HmDmt60cuFO3wkH5HRbVrE2FRXqMKqdABDGMQQTA904jFbncDhwzmsNgwXI9h9SSkvOeX29g/wjS5CrHvgeTUccYWTwnH3dobCWh8ECwHXdArE53XP2FmJIFWtTsV0wKbJzYinhMcqoPV4WpksW579wUXOjQeycz2xR1mU61B6qYkWc15xOL6XEYDBANBlhOs7+fEgpcXh4iAeHI7RVnHnjGOj30W63tVx8nXqA8ZQwusGSOH+6oxBXUznnKxjCMWqHW1RAJOVscKhPlPlpyMChc/7yUOf7WAKD82SDtsIong31o8IwE99aqo3hA3ebAf3ex6Dlu0k+awfWanrmnKJqIGwFU3jG2vBaUxjxGQi78f1s2AGtw2Di1jk/jSRtPZ9jEONL/ntE5BDfa9X0Ox2A2eX6zg0ka1mGtXN+p0isjd3htCZU4nzFVsFtLxLFEh5b53y5hV9rGCwAjA5n7iSiQSE9G+J8ONYW52u+Dee8njg/GAzQLZDD/h8LPfpdAF6FXq+HTqd4pEy3GeBin1KctyOQH3QUNiaWRISKigq7xLGcdUoxEecXhzMHmfOHOrmiANCnE+dHIbGwMrhw5ZZKTwz3beXaCDMxe7dZw30gdnl7+r8D7boFcd5SR1pmrE2VOV9RoQz5cE1DphEf5zwPcV5TryAyB4xCS3qJg87CRPw60Nwneaj9dg33HhrUn72brWSqcxbn2/UAnUagdqlRifMV2wjHW3luLrswlqgzc84v7g1LLPxSSnzhokEROb4POPVlJOs4NrlpTiPSL/xWImQ0nfOc2aEeate0I843Aj+/+Ft67ortgV/1BL/bbfDbZ0TxFec8E/qTEKjBqAbpcklbnDdzpy8zoRbn4xAYXgLap7R+nFOszbwTDgDqcprbDQcA/dEE+1Kqdbf5BrE2CuK8lBKT0RB9ofj7FvpG3XlpHGbNVnAQPVHBFV61it96wG6PEcYSYGKg2zCOORDntQfCEpkDhjby5gEn5oVEumfJ2usO2gVc4IkPQB9pAxiI8wSDclW4bqehKM6Xsx4dKnG+YqtgVvcRxRIiLr/dPI3jUYjT8zNeiYX/cDA1a7/qPUgizo/D2I5zw6DwCyEQ+GLWXkmF5nvbbrfR6/Xwvj//In7/r7Kzd6WU+MfNz+HZw99Si7X56ucCX/VNaLfbWmvbaRI7VS3mvp/q1LM/71WsTUXFVhKDz2EeuOK2qwEIHTjntWJtMHPOE2Eln9ZAnLcWa+MVF+eLdsIBwJ0AHnjrc9TybEch2k1FIX8NlSHv4WSEX3rZ0/FLyo96FwBod+elkdkhEk1nMxQMLioqKqxh0AVkA25n9GkUo87E1He8Pg/NgclKO9aGyIRopZ4DWs7++eX2dDTEVFG/6HtT1PyMmtil6RgEFOebZXHwSJJ1rKOfOV+OGH52p4G7zytoJJVzvqKCBm7OeU4uu9H0yrCZ+fd5iYVfexjsHKKhsNrDbvIwbOkLPA9hRLgp0RTnhRDodDrY7XZRaxzl/v39/X2crSvGDe2fBgwOzGTT6ecQtRYmcdCuZ89YqJzzFRVbSTiPtWFAFMurTjMHzvnLJrE2RIypnfPATJzXxJ5z3kIMTArnXv5ban/xJXcVFsLngocfjXOd/CpO/zLIjW+a9gG/GvJeUZFHxEydDyOJBpMz+hED5/xQVxwneg21nz8PDZOk7uV27x23opN2Xu3SDT09MBXn9x9Os5A1dnWc80EDqOkZ94py3Y7CXDjhW9UITKnE+YpUuLWbA/zE+WkUQzAp/Bt5duPj0p5bO29+Ti/bxa2KvZY5M3GePKfWcFNnZUhtXS+Tf45Wwc+itU/7eEvk5s5XmfMVDFvO+a0I7Kx2cQQ2F+79ZZdbyc55KaV+5vyATpwnj7UBgOGh9o9a86h6xY9j8044APjwZx7Er//pF1L/7nQ8xK/f9kzt5amiI3gACm7+3RuB//tnAEC7Oy+NzFgbAJgMrHbiVWwHzEoV0zM6rzVNo5jNQNgN85gD57z2GZkoIWA0tfReMNmzUQ5gPc1UnN9vaayrfbq0Dp9zuwqGwtaBVrdiWVTifMWWwa/wc4m1OVpvmStRnP+SqXOeSJzXHnaTh+HmjlycD81e77qNIbWGh1ftHLs0Wge0j7dE7loZ38hXVHCC22E+Ah/n/MqFe0g87DOHwSSaCRs6kDrnLbwX4/yusTQ8WwNhNcT5eSccAJw7vYdaI/11D+pNfNcdH8Yb9v4rDgb35D94vQP8839HLoSn0WkE6W5EADg4a9SZl0YYxZt753Wq3PkKcDt98lsPwM9AN4liCCYxdZuxNv3SY4m0O9GIxO+xLee8xudufrn9r+/6FC7lXdBe4c2td6DtZ5y/d+hibU53DcT59mlrJrF64KFV94td9LRPW1lLEme6Cs55zVjDsqjE+YoM+JV+yW4SvGQziGRj2EyJ4vwXmTjntfP08jDcmJCL81NDcd6Gc94wymWbxPn9vEE9lcvuxMNNdAa4ronXYT5mFFV3NFw6zJcszh8ODUwHw4tkooOVWBsDcZ6Tc36ZvJktQgjUGi10mnV0YoXn6uxoieFzweOzDxzj3/6Pz+b+/YNggjeIf4d2IyfWx1I9PxxO8x3RDhyuFQxhVj+lBLvM+ZjZazQJOYnza/uKOJzV9VqrtDXoO+eJMudt1HNA63dzfrndaLVQi9Ri5TrNGkTWXoxSnO8oiMxpWHLNz9lr1Yp9llrlieFKsTaVOF9RQQe3g/w0iiGYiPMb7p+R/gG0CGEU477LhsLB4CLJ0C1reXaGF1U+9ebZUKhp2BDnDYsvqThvOd9uP9c5X4nzFRUqcLswCBmJ8ytt8IYXskXJjfrIIprOBE0C59bYRhv8RN9MYE0HM9z77FIPVNesn3PB4+wpD7VGvuhUr/noCIV/d0uH6Yt9hc+5g2zoigo1eNXPOOZ2RufUCZdw4T3ulSrOa3eiEb2G1pzzBtfmAVUnuRcAbbqhp/XAw16rhss6Ron9R5CtI/Hh2zXcX0T3KVEM32/V4HtiNhMydT3lOfl14Bu4U1GRADfn/CTkI85vZs4fleL0ePB4nP0lqIKMgcEF47VYOcgDxq8jeSu8Yf5ws0Y8fK7WAmqKg2NTMB5+s4zlfLvMi4R6F/CJhZKKimsUbuK8jCWbjFqXznntYbBzBhdJ1jGycZifMhRbPbOaQT5QvW4WIUO+HkvOeSVxflw55ysYwqt0zmC2pplznuFl+xyDLi4dtDPfiboPJrpReRapUZ3Pu+fIc8zPqLjAkziwLM4XzZ0v0TnveQKn86JtSlyPDpU4X7FVSCbtaXMmUQwRjV0vA0BCrE0cltKOe+8hkaOv/5DxQ1jJpwVguuMkj6k1vBAiF+cJbqE7dR+BT/RCWb4VzxxeW7nmKwB2LfAcmQnzvF6nUPJxzq90w5Xs3jWKtQFm0TYEWDnMG3Uh8MmcX6Zd92nj82pm4nynHtDej1s6TKuJ8+UKaBU84da5Hc9jbRgRM3uNJlEEwcY5716c1x6wTvQaas+xycOg2JA55wmHwc65TiU/PYm9m2kXsv7w7YJmAosxs0mcycvrL3k9RanE+YqtgpvLjpNzPnGo1eiy9ee91zTSZg6JOM9rYzjHo3ZxGw4hblGL853rjB9CCIGDvCx3VQhbC5PYzXIFMs+yq6jgguQoLjCKtTkarg2ELTEy4NDUOT+8RLIOK855gz2bvVgbM+e8EAJdSre64QwZ3xPoNAjXY6muXlCKtamc8xX8kMwutoFSS5QSkzCGYFDPo1gmZ3SXFD87R/uym+g1nIaWPrOe/pmWbAbbrgVxXsc57wVWLgqWyY12Xae1b2UdaeQOhWV+Tq/E+YpUuAnhANi0m89ZZM4zeK0Sb+WHh9af9z5Wznlen4859M55M+GkWSf+6u/QiOFk0TZE60kj8D2004SH5r7V567YFtzXhE14rYnb8DjgSua84fcrFUfr7vUS41gumWTOA2TivLbTLwuDWDhrYWkEUWikufME8wJIo20sOecv9BQ6X8fHVp67YrvgdiZmthwA/Gr6OIwhDM1MFPQnKeL2CXPOh7aiiQ1i4epkznm6YbBzzu5qiPO7NxrPsMljv6iRruRz8enKOV9x7cKryAJgJ84vCl3oPtom0TlPdEDOwngY7ByCjNowsvSZFWZOc2Ex/1yHdp24cHdpNiWnt0ScBzKEB+Y38hUVXJBL/8mFWEqyfFVTNmp6idnXWkPIliEyBlhxzjN5f1fwzWtfZkdXUQgO02RD3oVn7TB9XkWcL9ndWlGhAs/6ye+MziFzvp+UNw+U0t2+jHasDFHNjGxd3gSa8S8gjHnduZHmcZY4q+OctxxpA8wGwhaiZOf8qTwtoRLnK1SpbuXzkcx65hYtYgxy55Od83bFeSklHjymEufPGz/ExFbmvDD7qiTNggWMXQzksTZEjgGyWBuCmJ08UsX5yjlfAW5H5hnc1sQx1iaMJBC5P8wDCQJ5ifEah6bO+dEhyTqsdMMxySBegcI5TyWGAySzU8ic/K0D8kF7wGz/eqFXZc5XqMHtjB7Hkl39jJmd0UfTCIJBJxwHcT6OJSJd5zpRrE1syzlvUD8bjGNtzu42i//Q3sPI17FOoVibWptkf1OE052MS41a2+gypwwqcb5iq5DMDlVcnPNSShw7cM5fHk4x1p3+vg6Fc95ay5yZI4081sbQie97As06oUBPJM6fymtFU6VzluZxMkgVHirnfEWFEjOXOi9xIWSSOS+l3BTnSxIJE5+7KETOeW5Rdda64Aic87QxMubOMjLnvKWa2huHajEPJbtbKyq2ldjWwE9NxmEMISPnF+79cYp2UWJXztTk4oRKnLe136u1tX+0RXEWrrWsGLN2GkFxZ//+w8nXsfEURYx0JbvmgRznPHPXPFCJ88zgdUjldmgGAMGsZW5xcJwS5a5rMpxGyZEuQ3PBO4sHjwkvJQguEuyJ82bFm3wgrKGTHwC6ZNE2AuieI3mkM1m33aoIrxSBvJO2oduCwl9RwYHZFoPXPiNiIs4n1vSSDvL9Scp+oghEgubYVjccN3zz2rdDmTnfPm38EIVb39MgWEsS51Vc88Dss8zwPFRxsuH4kYyYOecXZ3SDIeAUpDvnD0tbg1FNZ7AnyqTW0v7RNoU4v3OjlWnxQojiufO7N5GvY5164KlfahB04RXloF1Lfzu24IxeifOM4FZoJXgVWQCQzLJCx0yc80fDlMJJ4EbP4iFKcX46MBrUBkC/ZS8PU+c8tXWeYBPQSRtoWpTudWQta7lDXFRonzK+TFGhmyaEbEHhr7CPtPVdZAC3NXF0zs/Eefdt8IeDhDWU5OA1ds0DZGsdUXXmLUNwuU0Ot4GwJM55ok64tp0ZMkp588Bs1lUVbVPBsX4yu9wOmUTCzVnMLAmJ4lc16aWJ80QdZioYmdeikN1ebQUT5zxFzKuFYbBzzhWJtvECoGu/cxwocPne2LW7kAQC30vfD23BGd3uON+KYnD+4mMC31gbt4U/9TBtOdZG+XCjyujyTOzVhGOeHZAfayOlxHQyRF/VnVCvA/0+2u22dpt9l6oFfpcu3y53iIsKRC7+3KdppGzoqsz5igolYikhuIkLcQzYGixegMSaXpLL7nLSxUBRwhEQTY1rpxXnvMGaVKutlBLD0Rj9pFlASYwjIDar6WSxNs19IDCvxWTOeUsD3i8U2b8OLzlxAFbwQTKrVbPGM15rihjUzmVG0wiow7lzfjBJi7W5DMSxlZka65iZ1+RsVotvVmPI56/NMRCASc7Cu/TDYOecK+Kc754rxZwGzGbE3XeooH01yxfnAeCgU0/eRzuI2SlKJc6zgldR4+ayAwAZMRXnHcfaHCXlzQOzA4WUVtqtgAJtwaqMDo3EeWuT4D2zQ6af8/qHkxHufNk/wbsLPep/RK/XQ6fT0VrTDpVznnAITrPmY6cZJA83VqWEvHkgpfOg1iYRNSq2H46dZ9z2GLEEfEZrklJeGQjr3jl/KWkgq+XL9jmHQ6K6PjoCOmaRJEqZ4EUx6IRT3UqFkxGe/m0vV3/gl9wFAEY1nWwgLJEYTjbg3VKszUNF9q+Di8DBI62so2I7YDcQltl6ACBi5pwfTuOZOO/4jN6fpL0uEhhfLsXNG5rOA4hDbXFeSonBYIDJaIjpOPu9kFIinIxwwT/GDZNp/mV10ABGY7TbvtbFdpfiLLxDPwx2ztmdAs55i5cE6yjPlHHgnAdm0TZ3J/0PlXO+ooKWmJtzfn5ZELmNtUlsgQdmxXTSAxo7Vp73Up9YnDdsHba2VzWc7O2X4IooCpnLjngy/Oluw0yc3ynHOd9OyuyvnHUVFcpIKSEYCQxhfMUb6dhlBwBHSY4fyzF1V5+bSGAZMxXnDeq5UPbOl88uVU0naotnnzlfJJbR8vymCv7wqVQz5NJ/csFYACZESskm1maQNhAWmEXblCHOmxouowlQKyAULzEYDNDtdgv9zK8X+csv/M/aF9sks1qsxtoU2K+UKM4rD4W1pD/lsZe2vi3obq/EeU4wOqRyhVuszXgaz36Lpm4L/2GSy27O4KI9cT7reXUYHxv9uLXfIL+4A2zuFACAcDzIdAtIKfGdb34//m3tDrTrCrf/Bw8H/unr0W7r5+yRueyIh8+c6TZw9/m+/gOUFGvTSYq1cdS+V1GxjcTMBsJO58ICA+d84oV7SQJhaideUQzrObA014eSQH94nKoxL6g38T/+y1vx5OPfz//LjS7wzW8FAKOaTjYQlqiGNms+mnUfo7RIB1Vsxdr0C4jzJV2MVVSow3BmCyPn/DSSV6NO2TrnUVpcnfFMNgb7IhuQGNVsOueLZM5bvCRYZ38LnPOJVLE2FcXgVWQh+dyAz+HmnB+Hc3F+4HQdmQPchheBg0eU/7w6EBzmraDhtNNxCvwnAL133IpO3mZhZw/QbH1fPARZ5jytOH/djlmXQmmxNknOeUebkAp+MDszA+A3UD2KJat9xsKl7bgTDgAuJYnzg0ul5NMmuvZ1ML1sl/LqhQklBs55T1GdF0Kg1WygM1Wos52ucT0HgHrgoVX3MTQVwwkP+AftGu4zWY8XWKmrUkqcPy4Sa3OBfA0VFSbMvhp5bTQiRtGzK9+DjsX51Mx5oLS4OnPnvP6+qN1uo9fr4f2fug+//Yl7M//uPNbmtoOP4bGTT+ab1W7+WuDrXql9sW08SL2xC9T1L9Xz2GkEaNS8mRk0j2554vxBR/F1q5vvbXTYTxtIvwUd7pU4zwmOp3lmSGY3t1wGwmY62C05fqZRbH4IXGdi4Ji2hfCNMmqtUDMvdsYbEmDWitkodgGRx5muYU5tSbE2rXqCc74S5ysYw22LEUtm4vxcCA7dx9okdsPJaOaya5+y+txGsWLLTHpGPx7F0s5nVvOwKKXEaNTPzcydMx8I227kdMMZOPnX2W3VCMR5OhfgqU5DbWhcGu3TVmYmHY3CYhc//YfI11CxXXDLnJcMnfMhJ3F+ykec740zaurwsJQ1RLHhXssg7k8IgU6ng72dLmqN/HpXb7Zxen8H3WOFc+rpG40ut40vtS271YUQOLvTxBcuKphAS3TO76WJ3+u4irVJc/ZX4nxFEbhNgud2Iw/wGggbx0uuLsfO+USX3RxL4nyP6gC/jOFh3koabNDQOhzOnQIA8Gv/8+/w0c+dz/z7HmL8cu3/QTspLmUdAkFceZhLFhby7Yyc87UWUKe9LEijnSjOl/PcFVsAs0MzAHZrmjnn+azp6mW724M8kHHh3j+/PeK8oXN+Yiu/WFOcHwwGeMJXqNe8O6/839xuOM0c3yR2mgEeuGz6ILTOeSNs5c33CrpA+9n7t4prH0alCsCsiYrbOZ1TrM2qOO/W+DXIEudLirUxds6H5h2FzZrC+bYoBLFn+22DS+0SBPHrdhr54rwXlDrsVHmmTEln8nUS1yd8Z+spQiXOs4JXkWVkaFsQMyr8K1moDjPnpZTZg1kt5dRmOgF0MXwdVVvOC1HTc7XNnQIA0O12UWtkbw49xMlRKUkQOLRJxPm9m80fYw0jcb57zorLLolmkLDJdNS+V1GhBq89RhRLgFFU3aKmO3bOSynTh7z3HwKue5TV5++NiToUDTvhjMWENBw5uVIh6ISbY1zX611SZ9mpjmEnnCVx/kKv4O/44EIpkVIVfGFpoGN0YxBGMaSt72wNBss57w6d81JKHrE2kXtxPtHUZErnOuOHOGjX9Tu8SphzdlblXNy5rrTzL1Bgr+HoXLyXJM43dkp9jXSpxHlGcCpqAL98WgCIGcXajEMet/KDSXTV8Zf4F+yI85mbDV0MOxA8G9+5gbmrrR4QH+gIBo/utmoQwnBvT5w3DwCn2nUIIfRaiLvl5M0DgOeJzRxAQpGlYrvh1gIP8NtjzGJt+OwzuDjnj0Zh+vC2/oPWn5/s4t2wE85YTEhD0znVbrfxf+5+AD/zgU8r/f0XnPo/ePLx7+d3wxE6543j6oi74U53DGfIWBPnCwpNcTgzulgaTluxBXCrnzFYuejCWEIwqucrTmjDWmTCOIyzh7FuS+Y8gTifGAdqCoE4v982uEQuyTmfS8m1qeZ76DQC9DP3i8KZOL/TCDa1BALtpAwqcb4iA14bEYBX5vxoWZRzeCt/Mcs1D1hzzo+mFjZhhtn9vs/HOb8MvThv7mzzPYFuIzCLMNh7mPE61gl8D6c79eJt50Cpw3CAWYvmijhfOecrKpQJmTnnFzXNYSccgOxOuJ7d7OswitUGj6kwMbtszxQ0TNA8oAkhsLurlpkLQH0gLGGb9a6pc564pisPjUvDVqxN3r45id4DlTh/guF2Ip45+fmsahLFEIzW018R590Z6LLFS5TonDes6wSz9boNC7IjgSnrtEmHVwnOeSVxvl1+bdpv17I/37WWM6e6EAK7rQCXl7tQt2QuXNWfV5EKN5cdAMQMBrXNWRGnHRb+1Pb3OYMLVp7XSh6soSgS2LDO18ynsNd94q/aFk3msJFbAAD26J3zgEG0TQmbpGUa69E2hA7Iiu2GXws8vzVJZs75MRPnfOaAd8vO+T5lR5xhR2Fsq/vE4HK7Rl3LAdJL3d2sbHulB6Ct6cbO+Y4lcf5Y4/L/+H76hVRsDdy64aQEq1ibSRgzc84viYaGF8Um9Mc5r8nwsJT30fjMTuCcJxfnG7skBjqj+LUSOrav6/JzzgPAfp4ZoG6un5iw0UnILdIwhUqcZwWfIgsAEnza5eawcs6HPFrmLmYd5IHZxYEFJ2BmlI4ups55G3mgBIW/WaMW5/dJHsYon7besXYLrS/Om7c3FqGx3hERmH9WKirswWuPMWuD57PPuOqcZyzO9+yK89pD0ZIwNC1Yc843mInzBAaAOcaZ88QX7saZ80RGhHUu9HXE+fvoF1JRocksFo5PTZ+EMTxG4vyKKO7wjJ4bExeHxsPTVTDPnDffF3VNL4/XIYqU0a5Tfr0UN/apTj3fgE6kCxRhL8/g5zjqdaOTsMF/GCxQifOsYHRGBQAIRkV/ThxF8xH1zhkyaZnLbIGfMzhP/rxWhrVFZjfzNSuxNuYH5w2HtSlErd7K09aT2L3JWrva9jjn10po5ZyvmMOwfnLrhpsNhOUz5H1R0w1nn5hysZ9hQpgPprREf0L4fji+5EhGGDnnySPqAFI3l3mszcNpFnKFeuBhx0SQadOL81LK4gNhAeCoEudPMpLZId1aZ5Em0yhmddm+Ustcxtqo1FRL8bPLGBvqCOp5I/DRoDSr7dxA8jBnVJzpSZQ0hDXwvfxO99aB9XWsk2sGcO6cX9t7VLE2FUXh5lSXjLJg58wyanm454fLsTbTIRC5ERkyXXZz+vQ5tbENoccwtshOy7l5cSF1ztfaJG5+YDahXhviwXHL6InzovTMvQ2hhmB4cMW1AbcWeICbb56hOD+v6eHYqQkg88J9PpjSEqSzZDiK840u4OuLxeQRdQCpOG/knA+aVsTw07rChxdYOUwfj0M9oer4XvK1VGwP3Gp6LMHK1Tf7nYrZGOhWHOsOxfmeylyvgX1xfmoaa0NUz427u5Zx7ZwnGEarSm4uvgNxPjfWxnE3eRVrU2EMN1cbt3xa4Mphnkm0zcbQNEdtc5fyMucBoG8nd56ciKE4T+CcJ51QT5grZ+ScJ3IsJKHlYmifMhJddNgQanzDFv6KawZ+1ZOfuBAxGwi7krfu0D2fG1Vn4bJ9Dqk4TzBAjhzDmJSaL+iNcgQD3udsHEaLsPcwKy5AbeGjdWBlPVqueWAWKeXIhFPBAV71c2aQ4rOmxcwWJhfuK4MqpwNn3Yy5sTZAOc55BgNhAWCvRXhOIpqRUg887Omch0vMeT/dzXndmvulrGOZ3NfMsXN+o2uvEucrisKnxF6B2UEemIvzPIbCDtcPseMjJ+s4dOSct4Lhps5KyznBsLZWjVCcJ3SHGw2EtSjOaznnS3QwzKlVzvmKLYKbOB/GvAbCrgyQc+j6zo2qs1jPx5SzZOLQSMz0bLSLG7rLhBBoUNZzgNQdXg88fTPAPm2kzZxcx18alpyAF3qa8YlxaH0gcwVfeFVPIJKSlXN+PJ/DxqSmrzjW49CZdqAkzpfgnDeu7UR7IiNT2Dq7dOdQrXMnUcSsCqfyhqs7iGzJ7YIg6vLXZWfdrFCvMucrCsOr9HNz8gNXNiNMnHYbg9NGrsR5Bed87wHy57USsxaHRi2RVlrOCYoLqTjfodsMHBg552naCZPo1H00i75mLsT5yjlfkQYzIRxgKM5HEoJJJxzAY4CclDI/qs7iUFhScR4wctt5NvYYBIIvaT0HgCbtoVo7d37vYaTrmKPtnLcQsQMA53Wd8wBw+Yt0C6nYLpicPedEMa+BsIvawaSmH6/HyTiKtlGLtbHf3c5FnDc6d64ggC7dOfQ6rY7t8sT5zEvuWrv0znFAQZx3HGuzMYC4cs5XFIXbsBlORR+YHVojRpnzAwbO+WkUr7bupWHhMB94lr4+DN7fjQGdCUgpMR0P0R9N0B+F+f+EPvr9vpGw1W4QFs3OWbKHOtA9NANWh68KIXAmr4VvHSfi/JJ65AVONkcVPOE2rA0AK5cdMBcX+AgeG23wDuiNQ4RRzmfHosvOeGjcOgZuxcDGZTuB4Es6Q8avkUTnLaOd6WtJnD+jO+DdlnO+r+mcBypx/gTDraSzi7WZ8om1kVLiaEOcd3PhfqzknC9BnDeNrAupnPNEJqbudUBAZ4g6u6vR+Vyqcz7j37XhxhGe75x3203ebawZKbZEnK+UBEawc6pL9wV2men8wMok1ma04Zy/XPoaNpwBaVhwzvtWbG2Yvb+B3mFOZQp8OBnhzpfdgjuVH/UuAECv10OnoxdxQ+ucpxOhdxoBfE/MRLIiNPetF90z3Qa+eKnAZpCwo0CVlQuqyjVfsQKzeg5AMhnUNmcax6wynPsT9wPklDrhLMbaGA+NWyfUF0Kt7DEMM+cBoFMnPDo198nbELXF+V1L4nxeO34aljJ0zx+bOOe/QLeQiu2CmTo/u9zmU9OvZs67v3DvjcNNQ9XYkTg/UqjpJ8g5r91Jtc7OjTSPc4WzOpfIJQ5hzXzd6m5E50bgoeZ76ftGx1Gv3cZ6rI15THEZVOI8J3jV/RlSWsovKc5imAmTw/yAgTh/NFR0mU96s40J4e1qPbAlzps454nbzYnwPYFGzdscIqxDl845L4TAfrtWfEBa175L/XTRFkMHzvlgWTzSvFCquDZhd9kOnrE2XDrhgCuX3fNfY87ivMXhcbmu/aIYmClWOpMykFJiMhqir7Q3bAL92XvbbrchNPa3bUpx3kJ0i5Y4HzStxcicKtoFN8darE3lnK8oDrPyyS5zfjSNUAdYOOcvJ52NHXXDKZnoBhes6y0bUbxFIXr9yMT53ZMlzmd2ujsSnYUQ2G0F6RqCY3G+veycFz55l6ItKnGeEZJRezcAxBKzG3AmUQ3TRZ4dD+f8YLpWcIeXSl+DsnMeAI7vBxp/j+y5674lIdzAdaHSbh7Um/iuOz6MH9//XZzrfzb/Qb/pF4HudWi3zb7U2/UA4ynBZ5d4OvxBu15cnCeM1kmjcKxNie2Fc4Jl8ahyzlcsw+wgDwAxA0fbMtMoZtOhF0bx7PC6EOfduOwOhwrfxRZjbcid8waX7aozZMLJCK972bPxOqW/fdfiv+l2w3XWW6VNIHDyr7O7PgRNhb2HWROGOnVfz5xgwTkvpTSLtTm6d2YQYnIuqigPCT5CODAfqM5nTeNwLs67v3BPFOcdRM9KKdUy58PxzBBgMZ5kZBprM9WfH7OM9oDwdXZvonmcKxSOtfHrpQ487dT9dJd63Z3ovNfKMPi5FueXUwvqHTZm4zyqzPmKVCSzW/nFFxITcX4j1mZ4WPoajscFNkG9+0mfu66Q766FwcZOZYioEAK1RgudZh2dZpD/z8F16HQ6Wg67Zdp1ggN90CCfyK6V/0fo3k/jdGFxnvbSQgW/cs5XpMDtIA8AMaN6DsxqumDgsgNmbfArcHbOTwdAaGcfVDjiLA+D99f3BDxb8XkGdClnyFi4VNZyzhMLHcsIIfTcks098rUcj0OzDsY4JN9LV2wH3LrhuInzC2c2AxPA5aQ66qCmj8NY/cLbcrTNxpy8ohgMd19mr1WjiazbvcH8MZboNoJi8+Fa+6WKvUIIHHRSanvNXVzLTpYZwLE4H/je1bhjR7n8OlRX/4zg1jInAVbD2hZ5aQYZppT0N8T58p3z/XGB9+foPtLnJs1RX8bAaUe/JkHWBkXSCt+5jnwzcNDWOMiXIISfKpJTW2s7GTyzkjlfifMVS3Cr5wCutMPxYRLGgOBx2b7RhTY+drKOI5V8WgAYHVq5JI2oP7ia4ryUEoPBAF44zh1kJ6XEa//9b+BV0Z1o1/30i3QvAP7FOxY1VLcbLvMwWhTiTjgA2G1p7DWIhY51TncauO+woLjT2idfR+EuwSQO77E2PLeCM7zqZxTHgOTjsRwyGgh7KUmcd1DTC3W3D84DB4+wtpaBymDaLOKQpGtICIGDdt0sXgwAduhr1rmdBv5W9XWycHmcx16rjgePEl43h4NXd5sZnwcG5+JW7cr66pU4X6GBZFb4YylZFNk5V53zPMT5wWQ91sZeq3ka/SLF9vhe0udWiZDRQuMzNz/IR5MJpmO1oTX90QRSymxHfK1FJoaTOOctxMlk5til0bafs1fIOV9i7t8yK+4P3/0mpIIRjBxtcySzNU0jCQj3LfBAgijuoAUeAI6GivVveMmKOB+TO+f13t/BYIBut9hh6qcB9N5xKzpph8XOGaDgYybRzTqMFsXCRbdWrA3xcL11CnfCCY+8SxAwzJufc3gP8IivM3+ciq2CW+dZFIPVGX04PxMbGKyouDRIuIRzMBBWaRjsHItxdVLKzTl5OoQjwDevoae6huJ80LRy7ju728DfnlfssGiUL86nmumC8uJ11tnN6tTjIM7XPSBGJc5XaMLMaiclWAkMi4GwRLlnJoRRvNkaOz6ebUp8QldVDoUy5I5pW3GbFGJzEhobO52D/J3IOcgDpDluNOI8/UFeq928hHz3nUaAwBdqAwotDY7LY3UgrNv2vQpexDGf2jmHW+b8OIwAj4dzfkMUd3CQB4o45+0MoCdvrmC0h6TKd890ihXFwnD1zMNyGjvXk69jmcL7jMaulciAh44pxPkvmD9GxdbBLdYmiiQr3WDRTc4gc/5SP0mcL//CvZhz3l6szTiMaSLrwjFJPIhx7vzujVbqw7kiufMOnPP7aeK8Q+d8Zswfg1lsrZoPjOFsaK4OlTjPiJhRkQUw6+BjdJhfiOGhmjPaJhuRNnMsudnSKHQTfnQv6TR4a7E2DDZ2Cwhz3DoUObUd+oO8VqxNCU71eU5tYgufg/UksTIQNnC/CangBLN6DkAyqufA7MAofB7i/IbDzZFzXtlpZ02cp3bO633m2u02er0efv73Po2/eSjfyfb3mkf4/uhdaGcNayWqE1ridxoWarrW5QE3cb5J75oHiJzzl+8xf4yKLYTRRSOAaSxZndEHk3CmKrFwzvMYCLsxyyYLi+I8iWseIEsuONM1dFRbqlfX7RRYV2PHyhqy2Gul1FGHndvZmfPunfPNSpyvMEEwO8zHzAbCcnLOp8bJDC6WKs4vcvhVCEeznFqiA2rN99SdzUXQ2NjND/IA8H3v+UuljoIf3/9dtMPPZ/8lwi9zkssMCwf5wgNhvcBKu3kS+23m4vxK5ry7tsIKfnB0znNz/s0u3HmI80dMMueV58hc4855IQQ6nQ72dnZQO8p/jHpzik6Uc6QhqhNaA1eTqHesHBgD30Or7l8d0JhH68D6Ifp0kRkygDVX4nmKzPneQ7NziEO3YkW5SCk5mdQBzDq4OZ3Re+OIjTh/sZ9wbnCSOV/gtbA4t65QBG4WRDP/tDq2l7GQNw8UdM47EOdTnfMORfCdLDMAA+d83b9yTq/E+QotmFX+GGCVZzcJ49kHdjpwvZT02/CSc+cLxdoAs2gbQiGzVfNxHBF/RjQ+c/ODPADs7nQRJbU0rtFp1iH6OV0EhLE2LaaxNvtFhYbmXmkT6pXX5qC9EFh3zrt3CFQwglk9BwDJqJ4DV2JtBI8ZMkfDdef8MWmnmQpSSnWnnSVxXlJ/bg3Fo06WE74o3MT57jmax0lgt1VTF+dLMJQUj7WxI3xcoHDOQwJHXwJOfwXBY1VsAwzL+cwYxUScj2I5GzjagfO5cNMoTo6TGZXvnN+49M/CYuZ8IQd/FhGNmcJcnLfjnC8mzpefYc5RnM+cccNAnG/M5yPWtsdAx2fMdwX9ocgQySzWZjJ3iXMW5y22pSWx6CZQ5fg+0udv1S3c7xm6LkgH1RLG2pBkzhNl5i4T+F72zfc6JbnmAeBA1dXvSJyvVeJ8RQrcBrwD/Nz8o2kMEbnvhAMSsmFlXLrTbhzG6gNZLYnz5BiL84R7jNY+ycPUfI9mKKxFUbxQTbcwaH6dg3at2D2XhX2GlBIXKJzzAHC5yp0/SbCLnQUwjfk45/uTpfoZuu2GS8ybB4BJHyh5D1Qoc96ic55OnKfpiig8IHwdSxfbnbqvbqRzMGB0PzXWprxZh+tk7oUYiPML53wlzlcURUpeg12AK5sRRk67cXjlomCiOEnbIr20gts/X+o6JkVibQCg9yDp85MIzusYZs6TZuETtkE1TdclPGvxLYWibSxlwSaxp5qH78o5vxJrU7W4VyzBLEIGACSTg/ycURg5P8jPSRzEWnJGbaFcWEtrI9+GGj5g5rCxohDWz8IdZ0nYdM5nudnWsdCRt07ge8XWZGGfcTQKMS1qaEl9sHtpHqdiK4gl2J3RQ0YDYVc6z4jc1bpcHKQ9vwQm5V64F4q1mQ6sRQJtdAbqQjQTTtl4lYali20hBM7uKJ7lHMSk7LZS9kMORfDM7kaHlwZz6sGV9dXokhBsU4nzrOBRZOdICUDycc4v8tUnPbcLQcZteMnO+cWFhSrH95M+v5WhsIYxOaRufk6Z8819wLPzlV1oo1Rizp6yAOIg+w+YOSgXVOJ8xRLchHAArC7bgSuxbAwGvAMph+iS3emFcmGHh1bWQO8QNXu8DmU9b+6TPdSBaVs+YFWcL+Scb9sX54GCr1mdvqbTRNpc4fIX6R6rgj2z70VeZ/RpFLM5o6+ciUO33XAXs7pjSq7phZzzgLX1FV5HGkQxts2aj7buxbtfJ63l6ygPhXUgzrdq/uq5c47nTgRvBClr8mulxkKmsYif3aLu9kqcZ8Ks7vM6zM+c8zwKPzAfHgcWzvnU23CLmXFJTMOCm0Vi5zxJjvo6rJzzdG1rxnE7bfpImzkHnQKFvcRWvl1lcb48N/8y9WBp41ENh6tYgtcxfkZE5RolYjSNIIgGjJkgpcTRMCmjttyD/LDIDJmtibUxdM5TxMfMIYq1AQgycwFrublAUXHe3t5imQPVTjjAyoX7RYVZRMpUzvkThWTonJ8yGgi7EpvC1jmP0nPnCzvWLdX1xM5AHQgNHqeK1INlumetir5nVcV5wthbVYQQyTNvHDvUE/dpDCJtgKX42aCKtakoiFz6Ty5IbuI8o1ib1Fvo/kOlrqNwi26fVpxvBBa+QgyLP2nmPOHNeCMwvDQgFBbWKRRrU6JLXXnwniPn/Eqsjb89t/IV9uHonJeM6rmUcnbhHo6dix7jME6upWWL80VibaYDK5FA9O8Ek1gb4ZFe4hq35QNA16I43yhwWC9LnC9yoWGhpl+gFOd7D5C5SCv4M5shw+uMPgn5iPMrIrRj53xq5jxQalSdlLK4Y93S+i4PeMXaAAXPnctYnpFydldVnHcj9iYOhfUszP8rQOI+jYk4vzinV875iqLMMud5FNk5UQxWbfCjuXN+fOz8MJ96Cz0+KjVDd1xUnB8fA1O6jZNxjnoShpl7XDPnjS8yLLbxFXK0lZjbpuT+8+vOXAO15fd0iwp/RQkwEsLnxIzEpEVMnbwi0Dsk9QBtKTomjUKZ84CVAXLcMucLOcCzaOySuu2MnfN+zaooXqjjwNIsm3WKxefRd+hlinZFkTG52aWCL7EEBDNxfhrFbPYZKzWU8Iypw8V+xhmyxAv30TTl0j8LS0Po6ZzzdJ+3QufOZSzPSDnT5S3OJ3aUO3bOJ+bOexb0IQ0C78q+z/EFRhEqcZ4JEoBg1jInIUlvSU1ZtHzHofPDfGar2qC8obCFB8ICpOuzIs4bFn/SqB1C95a5c97eATp1AnwS9fLE+W4jyNdTHLnmgaV2OaDKnK9YQTKr5wAgGV22rwjRjnPnUw+uo8NS1zEqEmsDWFmfJBegTMV5okMn8dBwY3G+e85qa75yxwFxR0EWiY6/NCxkzl+ico/OIZ7hVMGXmKGBbuac5yHOr9RQx/X8UmasTXni/GWdIaxjOzP1Dqm++wh/B/Sd89eRrSEJJXE+aDjLU08U5x1mzgNAO2k2EBPnvO9VmfMVmkgJNjfgc2IpWbVtruSxWrpdViWz6PbLEefDKEYcaxx8CdfHMdbGWARfhtA5Xzd9rSweoAsdmkvMbRNC5IsMDobyLJ7ar5zzFclIne9my0hG9XxFiJ66Pcxzcc4XypwHrAyg5+acJ4u1adLWz9PG4ry9SBugQMcBcUdBFsVMAPR1/XBI3NXae4D28SrYEsfSecf2MlJKTGKuznnG4nyJNV1LnJ/Qi/NSSr21JD4YpTjP0zl/qlOHyKuJDvPLE+NeHbvUO0mmSMcXBnMWA2GZrEeFSpxnBa9b+dmFASPn/LLTrsTcuHXiWK4Ov1mnJOf8SMc1DwBDuqG1xoJzEobiPFfn/IrLWgdicWGZQpukklv5OnnijKPWQmDt81+J8xXLMHG0LcMp1mbFOT8ZuFsIMga8E9ZKFQrH2lgR53k55+uBhwbFHBniy+1C+elJ7JyjWUgKuXVzjsV9xTrK+wwvsFJPCw9nzKNXxdqcFGJmA2HDWF6JnuWxz1j53XIozk+jGL2snHfuznkLM/V64xARlVmEUJxXnim2Tvs02RqS8D2BU52ctdXcdUpzHAjbTsyc5yGGe/OLFr+KtakoSMxs+Cowd867nbq+zGCyVHBLbjdf5ngUZu/RSnLOF26BnzNgLs4bilp0mfOCdBq77wkzg5pF53y3EVxt/cqjZBE61zlJ+B4VpeZXA2ErkoljXpftAC/n/Mpl+9TtkPfUQzRhrVRhOCn4/lgQ58kbPggErV2KaBtiEbrme9jTdf4B1p3zyh0Hdfps9zSUxfl6x4qbn8w9OqcS508MsZQQjAx0i5ktTKLqVmJtHIrzub/jJWoHh1kO/jQsiPNkkTYAqTifGM+igmVxHlCItnEYY7qb1BUnXDvn+YrzC23D8WtUBBJl7XOf+xx+7/d+D8Ph7AvZxHnzy7/8y3jkIx+JZrOJJz3pSfj4xz+e+fcPDw/xile8AjfccAMajQYe9ahH4f3vf7/28zuFWZ6dlGAVa7PiKivx9nud3OJfkjg/LOqyW/wg3RC5lVgPKgx/D5oULjtgdkD06P79hBCrYm5RLAxImyOEUHcxlCzOJ2bZLePQwRAsX7gEPPL1KKCq6Se5nkt29VxCGg7bpmQlwsXCgbQIR8OUfc6kV+qAdw7O+ZjaHUrweCRDYS1cbhtF23TP0i0kgUbgXW3nzvyL5YnzrZqvtiYLkTaTMMZ4SvydXOJ8KROqM7o5s8x5Ps758WIGG4+avlJDp+464XIFcQtD1NPQEsUt7IUyY36K4tw5L4DmPtka0sidKeOwUzo51satK7ydOBCWh1N94Zxnsh4VjJSnCxcu4GlPexoe9ahH4VnPehbuu+8+AMBLXvISfP/3f3/hx3vPe96DV7/61Xjd616HP//zP8fjH/94POMZz8CDDya7EyaTCZ7+9Kfj7rvvxnvf+1585jOfwdvf/nbcdNNNJv9aTpASEMwO81HMZyCslHLVKV5yFuwyueJ8WbE2DJzzRmJzGsbiPNHtqIVBo8ru9CQsO9yUN0olD3lJnAK/jMPsv5ULl2vAOU9Z009yPQfA7rI9lmDjsgOA/rJL3NIQNFUya3qJ0TaFxXkLRgB657z57wHJUFgL8S1Kg+PS2LHrnBdCqEXblNh5pmwCsCDO97OiKHUpubOmKNUZnY44BquavnDOM7hwn4Tx6nnUQm66KrlDn0eXr7yZZaxFQxS30EVIOgib8IJKa55Mc7eUeBLOznmW4nyS7sIk4/3EifPf933fhyAIcM8996Ddbi/+/LnPfS4+8IEPFH68N77xjXjpS1+KF73oRXjMYx6DO+64A+12G+985zsT//473/lOXLx4Eb/5m7+JpzzlKXjkIx+JW265BY9//OO1/51cISHZZdTGEkA4dr0MALND60pNcBhrw8U5X/ggP4fQOaDkgiqKYfFvUg2EteAoM7rMYCPOV875ladfiPM8NiImUNb0k1zPAX4DYcM4hmBy2Q6sdX5N3A54zxwUWVI9BzQERAuxGuSZ8yTiPIVzfs/8MdY4rS3OC6Btd6gdoCh+1Nv5f4cQpYgiCxcG/aKRUSqMj1iIo2lUZ3Q6Zh1FfMT5hRjO4ML9aH1mSzQtteNsmUv9nOeVcWn6wcW8tSRhwzmvs440CC+omjW/eDRu64Ds+bPInSnj8Ly3GQckSLv8dUg8pzseUjtn4Ylksh4VjN7N//7f/zt+9md/Fg972MNW/vwrv/Ir8Xd/93eFHmsymeDP/uzP8LSnPe3q4jwPT3va0/Cxj30s8Wd+67d+C09+8pPxile8AufOncNjH/tYvOENb0AUpYuW4/EYR0dHK/9wYOZq4ybO88mc39hYl9iatk7mQR6YtZqX0P6o7QQi3Jj4FnJBTWnWiYqUhcO8tnPeC6zHpijn55Y8VCU3psihgwG4ckHl+VtV+NOgquknvZ4DgJDuD83LhJFktaaVoeqOnfOH/QyBrcToisIC4qQHTEeka6CPtTE/zJNkzlvohDvd1azJ7VOl1FE153zJ4rySc55+TdqdprkP7C5iM4/qjE5HFEsIRrE2oykf53yiYW3s5sJdKUqmJP3ggpY4Tx8JpHVJkArt70Bh93wJkTYAcKqdJ86765Su+R5a9aXzJoNBp4mxNkwMa54nAAgrc2xsYaRi9fv9ldv4ORcvXkSjUeyDe/78eURRhHPnzq38+blz53D//fcn/szf/M3f4L3vfS+iKML73/9+/OiP/ih+8Rd/Ea9//etTn+f222/H3t7e4p+bb7650DqtwuAGfJk4lnyc8+O1zZzDdtLcFrE4nDlqLNPTFufp2vqMYlrSuJZjbXSLQwkHaGUBpOTWsHY95/0sOWZnnbrvbVW7XBZUNb2q50DMqAUemInzHpPLdmDtcrmEepmGlJKNc763vs9RoU/rnqePtTEXRWmc8/SdZ2c6mgf0znW0C0l7mrzaCQC1cmPhlN5LC/udEXXe/ByHRqE8qjM6HbPMeT41fcgoc/4oUZx3U9OVomRK+J0No1hzICy9UYFr5jwAdIvW9ia9aS6JU3kX747PnStRfwzOn4nndDaxNtgqYR4wFOe//uu/Hr/6q7+6+P+FEIjjGD/3cz+Hf/JP/onx4vKI4xhnz57F2972NjzhCU/Ac5/7XLz2ta/FHXfckfozP/IjP4LLly8v/vnCF75gfZ0qzCbB87mVB4AwlkDEQ5xn5ZxXuYUu4UCvLc7LmCxGwIo4L8yc7y0qcd5CRq2vGwNUwgFaOdam5I1AI+/9dDiYB7jinHe8UaPCZU2/luo5AMiSck1VmTKLtemNlurXyJ04fzwOEUYZe6+SxPkwijHQqem9h0jXEVOr8wQdoUpu6zwsDIQ9s6P5vV+WOK/iSiy580zJBGAhc36R0U3+wDyc3UlUZ3Q6wliC2jVswmIgLAPn/CEjcT5xLeuUYO672J/oNdBHEyCiNWqSOuepxfnCzvlyxPmDvE5yy53seey2ll43BuJ8ou7CxDk/c81vV2e70Tv6cz/3c3jqU5+K//W//hcmkwle85rX4P/8n/+Dixcv4o/+6I8KPdaZM2fg+z4eeOCBlT9/4IEHcP31yYOTbrjhBtRqNfj+1Rf9q77qq3D//fdjMpmgXt/85Wk0GoUdA2UgOcbaxNJZbtw6G/nqw0sz97eDnC2l4l/C5YG2OA/MBuoSFDmP4W1kzffge2I20NgEC8557buMMpzzLcVyUHKRa+RlEjreAASeB4ntKvxpUNX0k17PAZQ2dEyVMJLwOInzTJzzmZE2ANCnFb9Tn0bHNQ+QO+eN6+Y6LDLnhZWZLafZO+f5ifOunPNhZEucdxvJlUV1RqdDSllKXKkqizMxA3H+clI3uaML94s9Fee8fXH+oZ6BsXHSA1r7JOuQUhI752l/B5QukJexYJpLonUlD3+Sdqnr2JC1y8w5n5hYwCTq1ROANDR9lo3Rah/72Mfis5/9LP7RP/pH+Gf/7J+h3+/j1ltvxV/8xV/gK77iKwo9Vr1exxOe8AR88IMfXPxZHMf44Ac/iCc/+cmJP/OUpzwFn/vc5xAvHYI/+9nP4oYbbkgs+pyRUrKLtZnGMRAOXS8DQIIQXeJQl3WUhquU4LY7Hhl8XohyMq1o8wQP2lJp587DgtNOP9bG/gF6RznWpmxxnnesTc0XLDZHFFDV9JNezwEgZnbZPo1iiGjCRmA4YuKcv9DPOUSXJM5vDNVTpfdA/t8pAHmsDcG+1jhzvt62YuSoB56eq79zmnwtiU+j5JwvVwhVijGwIc7bGtBtYYAjFdUZnQ5uzvlROBfn3Xe3J4q/Ds7nykJ0CefzB48NxXkihtMIY8pIL+J9rVL02jIWTHNJCCGwn+WedxzZsrtcRxm4whuBB7GubzBYF3DFRLpl4ry2ojCdTvHMZz4Td9xxB1772teSLObVr341XvjCF+KJT3wivvZrvxZvetOb0O/38aIXvQgA8IIXvAA33XQTbr/9dgDA93zP9+Atb3kLbrvtNnzv934v/vqv/xpveMMb8KpXvYpkPWUSS8BjlGcHXHFQMXHOJw4/HVyYDdYqkWkUJw+/WaeEm/nEnD9ViDZOVnzzBF/orZq/Gpugg4Ub+o3ipUoJ7jY1d2L5Q1VynfOON0m+5wFy+8V56pp+kus5wDDWZu4cjSbOo6CANTF6fDS7NHDQiXUhz2k3H/BueW364jxxrA315Q2Bs9NcnKd3zc85060X34u1yxHnE4e0rVPy5fZOw81AWPLP9RwmBqZ1qjM6LdwGwl51zrs39SUa1hwMSj4ahmqdXyWczx88MhjUPjoCiNJbcmfkFYVghswybZXurmXq5YjzALDfruPBo5RLFscd2ytmOgYDYYUQaNX91WhGJrE2QuDkiPO1Wg2f/OQnKdeC5z73uXjooYfwYz/2Y7j//vvxNV/zNfjABz6wGEBzzz33wFtyv9x88834vd/7PXzf930fvvqrvxo33XQTbrvtNvzQD/0Q6brKQEoJwUycn0YSmNJPDtchUZzvnwfOfGWp61ByzQOl3MwfmYjPw0OSNdhxzpt/iZIMhbXhnNfNtSlBTFNyzjtoU6szj7XxPVwT4jx1TT/J9RwAZBw5E5yTmEYSNQAIR87F+UkYY7QcVReHMweqhaGdeeTmscbhLKPWsttZuxOO2DlPHmtD4JwvPDRuHYtuu9PdBv7moYLu6VY5phKlPN+SxXk15zztjB0pJQb9PqZjNSF9NBqhPwrRbvj5hoopT3G+OqPTEsdgNRB2NOXjnL+Y5FYnOmMW4XxeF9ycwQW7CwHwQJqoq8KYZiYcUECzUIU44aGjcoG8TIl7xMw5bI7PnSu1nYlDvVXzMFj+2DPpKD9RznkAeP7zn493vOMd+Jmf+Rmq9eCVr3wlXvnKVyb+b3/4h3+48WdPfvKT8T//5/8ke35XSID8RtKUmXPefeEHUg6uJRTYdS6oFroynPO6TjuAzNVgxUhCUPRIYm2a++aPsYZ25rxvX0zr1H0IkfOeOhAaa36ec97tBsD3PCDersKfBnVNP6n1HABiSGbifHxFnHffDZdYu0aXnYjzSgf6wXnr4rxSR14S/QdJP2fkDmOCw7zvCXQaQbJJQwWLzvnTHQ1xu6SOz7bKPqhkkUFJjCGOtRkMBnjm/+fLlP/+nVf+b+8dt6KTd5nAIPM7jeqMTkfIrBNuMaMkclvPpZTJ3WclzF1bR3nwaQndcPcbOefpug5I8+YB8libwkY6C8PC09jPEucdd2yvdLozcahvDIVlIs6LLcycN3rlwjDEO9/5Tvz+7/8+nvCEJ6DTWf2leeMb32i0uJNEHEt2A2GjmI9zPlmct+9OXye3BX6O5YuDDedhUajEeZJHWYPgC72Zl1Ougo2BsNrOefvuNiFmAohxHBAxuc551+L8Ft7Kp1HVdDpmQ95DwOORrXs11sb9hXtiDMjwErB3U+lrUarpvQeA6/6+1XVox9SF45nTjiiGjWOsDTAbWK4vzts70J/pFrw49wKrlwXLKEUGlFw/ldz8JQ+pNYLZrLBlqnpORywlK+f8cHLlc+fYQNcbh8lDMx2I8xdUh7BG01muu6WOqiiWeMgkc55QnD+kjrUh/r4rHGtjYR5JGpnzZBxHyXSZZc4DCRctTAbCCgg2r5EqRp+u//2//zf+wT/4BwBmQ16W0c5WPqHMOon5FH7gilOAScvmxkBYwIlz/rxq8R9ctHozn/h6FIEoc95KjifBYVHJMZZF0LQiiGsPhC2p9TxXnHcgQtfyLjQci/OBLwC5XYU/jaqm0zGbH8enpi8O0Ay64RJd4g4yagHFA30JMXXaznlgZlQgEufJY22InJ27zRrug6Yb0aI4f7pbsDa39kvrplFyqZcca9OqKXToEb9f7XYbH/rU3+Gdf3R37t+VUuKbWn+Fbxx8AFJK9PPMCv0B0O+j3W6zq5FVPacjjHgNhO3PzVmO6/n5tMttBwNhU9eSxOCCNXH+oePxzHCpC+Frd2iyr0iCuFNow22dR4nifGasjWvnfINX5jwANJg65z1v/h/bg9Er96EPfYhqHSeeWMoroXZ8iKScifMMWvOPk9rg++WL88q34XE4K7CtAyvrMBoGC5AJIVY+sgSHxaapON8kmsazhnbmfEkH6G4jQGaCsQNxPsiNtXErjG9jnl0aVU2nY+a049MNN5k750ODdmsiEoXoEqLg1hmHkVrWexkzZExqev8CcOrLSdZBrc3TOecNDsM2Y22KOuctxOWl0VFxJZbcEi+EQLue0wVB7JwXQmBnZwe1Rn6W/XQ8xHd898sLPPpdAP4Ver3ehjPdNVU9p2M2EJbPGf3qQFi3sTaphrVJfxahV0LXb+5akhhcAA4eaWUd9102NDUO6PZCxlrBOsSdl616kbOTKHVeUuYgesfC88rFOxMRvFlbey8dX2DM8YSA3DLnPJmi8MUvfhFf/OIXqR7uxBFLCcHoIA9ccQrIyPnNPJASa9N/qPR1PHhcQNiweHmgPTxuDpU4b8M5TyBEt00HwlpyVHjazvlyilx+x0H5l3S5FxrOxfnty7NToarpZnBrg18456dMxXnCA6kqyjF1Jew1jJzzhOszcvwlQeic18amc75o5ryli/8k2lfmyGTi4GCf6+gnHggLAPW8S/5rnKqemxHGvC7bB/NYG8fd7ZmdZyVH2xQS5y2ez++/bLjHIjQqGO0rkiB2zhfKnK81SzWK7rYyaqNzcX7p+ZmI85uZ8zxqridwsmJt4jjG61//evziL/4ier0eAGBnZwff//3fj9e+9rUrU9srspESrAo/MG/jwyx3vuYuA3I0jZIz7cZHpd/MF8qR6z8EnPl7VtaR2ElQhNHRzPZu+DtK3gIPkLyfxrE2J9Q5n+u0c+Gcz3vNHBdd3xNsNiGmVDWdDm5zZK7G2nAV5xnH1HEX5wkP8yF1TY/pMue1sSjON2s+2o0AA9WowQZN/JAKQgg0az6GWfOJHAyTm+UMp/zueYGVNTXWXX0pBPUm/uuvvwNP7/83tQf+8luAf/gStNvlxS2oUtVzOqJY5mQxlYeUEv1xiF0fM/Ocw+72zDPx6DKwc66UdaQOpk3D4n7jPlNxntCoYKwVrEO8fywUa1PyLJKdLEOA4yiZRuBdnWHHRJxvrM/6Y7IuIQSbtahitNrXvva1i0nwT3nKUwAAH/3oR/HjP/7jGI1G+Omf/mmSRZ4EZrE2fA7ywJVYG2A2OKV9ytk6jrKKy+A8sHtjKesYThRb4Of0H7S2liPjoZ0SmBwbi9ARU+d8y1Sct+Sc5y7OG79uFuDunAdwzcTaVDWdjliCVU0fL5zz7ufIXE4aUsZZnB+ctyqAxLE0myND+NqRd8OFNM75zINyHpZzak936gXEeTt7izQ69SBbnHdwuZ1pnrAkwDTXhYMUhBBoNpvoRIrH43YLYBZnM6eq53SEsYRgMvx3OI2W7gnkTKB3ZKDLFucPS1vHcdpg2jQG9qLq7j8yFLAJjYfGXfbrECcpFHLOlzwfZafJ1zkvhEBnXkc5nIGRFGvDQxCfxdps1xnd6JV797vfjV/5lV/Bc57znMWfffVXfzVuuukmvPzlL68KfwG4DY8DgOm80E0GTtdxNMwoLv3yxPlCkTaAVbed8UBYYOZqMBXnbTjnffNMuZWWLx0sudtyXeBpcIm1cVDgcsV5x875aylzvqrpdEhmNf1qrI3beg4Al7iI88eKwnE0BcbHZENX1zkeh2amTMJhuuTiPFmsDU/nPAActOv4wkXF36uyxflGkH0J5cA5nynIWBIZN4QDKkoWjIpQ1XM6ojhmEz27cf5z2N3+UNZ3y+iotHWcL9LVDljbb0gpzZ3zwKwbbud6o3X0en0cHfeU/v5oNEJ/FKLd8LOHRRM75wuJ8yXmzQNAzffQrPkYTRN+7xkIz+1GAERwfgaes/FessmcB5u1qGL06bp48SIe/ehHb/z5ox/9aFy8WH5+6DYTSwnByGUHAOF82udE7cvdFrnO+ZJ4sGjx71kU5yla1QgO9NPIgvhE4BYoPAF+HUsH6NzhpmmUdADM3Sg5uKH389yqDDLnrxVxvqrpdMxibXg47YDZ8FMALMT5w0GCYDs8BKKw1HbhTGFhnf5D1sR546Ftw0OSdQAWLtypxHmjgbB2nfOnugXqc8PecNoksvPd3VwsZ+7PLHU5NG11BZYsGBWhqud0TCM+M2T64zWtYDoAUH53exRLnM+KkinROX+hX7DOWBryfjwO1buosuifNxLnB4MBdnfVz7F3AngxgN47bkUn6yKceGaR7wnUfE9NS3BwEbrTDPiK8zV/Js4zWAswi9pZgcm6PCEgmXQXqGK0K3v84x+Pt7zlLRt//pa3vAWPf/zjTR76xCGlJMvmpGKRPepYnM/MYrVUYJN48KioOP+AnYUA6Ge1KatC4GpYzCWghGIgrHGsjZ0DdN3XdM6XdOubf6lRfq5lbpKEY2Hc864d53xV0+mIwKumj5k458MoTrlwl6VetgNFZ8jYW1umAUEFQuc8vThP8zuwZyLOlxBro0y9XHG+nTVHxvOdZFVnxudZirVpmxo20ig5B7kIVT2nI2IUa9NbjypxFFV3oT/OHiBeojhf3Dl/cTZ3jZgHKFzzQOl7IWUs7B+Vu5ociL2p0TYMhOdFHWWwFiDJOc/jXCxO2kDYn/u5n8M3fdM34fd///fx5Cc/GQDwsY99DF/4whfw/ve/n2SBJ4UoBqt8WiklpuFcnO87XUtiPu2cEga1zdGKtbGUU9unuJkfE4jzFjY3FE4krrE2NV3nfElDj3OHpjkotkIICCFmF5iJf8HtBkBcQ7E2VU2nQzIbCLtw/ziu54fDaXqEi6FbrAhSymLOeYuHZeNc2EmfZMC7lJJ+5iGRcz4z/zWPWotkDWnstwtcHFiO2Fkn0znv6FCf7Zy3814FvodGzcN4SrxntXzxY0JVz+kIYwkwibXZuMx1VNNzL7fHx+UsBMD5os55Gc0uD4jn6T1Q1MSXhmHsTrvdxqfveRA//f7/X+7flVLiKY2/wa2j35wNG87aj4x7QK8HCIF2u50dgaNII/BxDIU9kIMItm4j5TkZOLEXYjiDtQBAfcM5zyNKxvcEpIPPjglGO7NbbrkFn/nMZ/DWt74Vn/70pwEAt956K17+8pfjxhvLyQG/VoilhJA8buWBmUtgIYaVWGCTSGyBn9OzN3R1ncKxNnE4u53vnCZfC4k4T+C2m4Q8nfPG4rwld9tG8VKlrIGwee4yR7fPngBSmzRci/MADJvQ2FDVdDoiyS3WZh5T51acv5R1iC7RLdYbhxgV6UCzaAQwjrWBBKZ94zi20MYMGSJxvhH4+uJqYFecP1XEOV+ymJvtnHcjzmc6JS3GxLTrAcZTms/jAssXPyZU9ZyOMIrZOOePt0WcJ4xby+NiVrxOGoML5OJ8YRNfGn0zcV4IAb/RQq2R//00HQ/xPS9+Mb5H9cFf/F4AQK/XQ4dgGDZn53zq5TYDt3qz5mMMsBHnN4auM1nXbC4cj7WoYvzpuummm6qhMgTEzA7yk+X8L8exNodZB1eL0THrFGqBn9N/0I44TxFrQ3DpYiVznmAgrHELs6VYm8Z68VKlJHE+d32ONiSeELOYkMT/0XXmvHASDWCLqqbTEEuwqunDec1wXM8vZonzJcbUFa7n3Ae8T8zFeSsD3gl/B/ZaNTw4Lfi+eYH1zrODNl9xvpMVIePITdbI2p9ZvEjpNoLsy0EdSp4hUJSqntMQMpohsxH16kicz8ybB0rOnNc5nz8EnPlK0nVo6QRJDM1nQlg5n1tAeSisg7NeasceA7G3NRfnGawFSHLOu7/AAGbHc8nExa+K0Sv3rne9C91uF9/6rd+68ue/8Ru/gcFggBe+8IVGiztJSAk2t/IAMAmXxXnHbfBZsTbDS7MBJZYn1U+jONvBn0bvQeDsV5GvZzDhEWvDdSCs5wk0634xZ+QyllrPNwamqFKWOJ8ba+PIOe9hNvgmCdeZ89fQQNiqptMRx5Isb5uCURgBPmZtyQ7JHNxWYkxdcXHeYua8sXMeJPs0K855Gc/inQhqx06zVnz2TwmZ4MVibUp2zmd1ETqq5xsOu2UsOue7ph2VSViKQKSgqud0cHLOb4rzbmp6bg0dXLQW7bpO4YGwgJXOezJx3jDWBlCfCRfUm3jnf/pP+Lbhe9Ue+Jm3A3s3od2mqWWZl7XLOHHO882cX3QcMHGob4jzTM7F/kkbCHv77bfjzJkzG39+9uxZvOENbzB56BNHxOhWHlhqgQecH+Yv5YniJbjnL/QmelmsFsSGMIppcjMJ3teVS5wUpJQYjkboj0L1f/r99IxxRTIdY3nUzRyIaWQOQsvC4oF1mXpeJn5JlwTreFmbe8ebJE8INpsjU6qaTkcswSajFgCGEx6xNlzE+cIxdRbFeePMeYBkKGBkY8A7QLa33dXJnbds3ABmHWfKtd1yxM463czMeTdusswYA4sxMV2TuQVpMBbnq3pORxhLCCb1nItzPleIjialrG0wKRhRN8fCfuN8kTk2WQwvGT9ErHiGFkKg2Wyi0wzU/qkLdDodkrx5oIBpzcFZL/VCl8GZbyGGM3HOb7yPDC4wgNkZ/UQ55++55x582Zd92cafP+IRj8A999xj8tAnDm6xNivir8PM+UkYb06mX+f4PuDgEVbXoV1wLRT/4ZRog0jgtpgoOOfDyQjPefFL1R/0JXcBMM+z6zQCXNDJIfTrgG+nqCi3761Tkjifu0ly1AafuQl0vDERApBMHAKmVDWdjljGQMSnpo+mEdDCrJ6X5GZLIjMbtsSYusLi/KQ3E8AtiIcbA/50CM2zbq0MeAeu7G3Na9huS6P+lCSG77drV6Oj0vBr1vYWaWRmzjuq55mzdyx2Olhxzjf5ivNVPadjGvEZ8H5pvZvc0RldaaD64Lz16KfMqLwsiPcbkzCmuWgHZu9pFBrVC0tX7eQXLsrnYgdnvdRYGwbi/CKGlsn5c6OuMxnCuo0GOqN39OzZs/jkJz+58eef+MQncPo0fc72tUzMLNZmHC5tQgjiT3Q5HCoU3aN7ra+DlThPkTcPkBRYFee8K7QPYpaGwQJAW9s5b9/9BygMrHV0E54pI7LInOexOTKlqul0xDGAmEesTRzLmTgPAJBO3fOZ2bCDi6VdaGgNbrM0gJ7GOT8wfggrsTYAmfFEq6aXdLG931LoKivZNQ8AncyBsK6c825ibbQud7IIGqV9vnSo6jkds1gbHvWcQ6xNfxxioDIrpYQ5Mtri/PH9pOvI7fIvikPtJROCLr1llAfCOjhncY61WZjpmJw/N8x9TBz9wgMkg/erCEar/Y7v+A686lWvws7ODr7hG74BAPDhD38Yt912G77927+dZIEnhVgyy6ddds6PLjtz2mXmzc8pQZzXcmADJLlx65AMjwOMBBopJQaDAXq9Pqbj7EItpcR/efdbcEv/v6Nd97Nd0EEd+JZfAQDjPLtMx1gWlvLmgZyDchYlifO1vFgbRwdRL/Uj434Yq+cBkDw2IaZUNZ2OiFFNH6x3W42PnAwylFJm11IZz5x2O9dbX4tWNuzgPHmXnpRyU2zRITQXBqxdthM5TneaGuKqxZiUZfZUhN8SInbWaWfF2lgelJtGZnyexfcr1QWpS3Of9vGIqeo5HdNYsoipG4fRZoSLAxFXufOMWABPQluc7583dqcvo6RXFGF8DLRPaf+4b+tsFBKL81kzSJZxYMRKPbMzEJ4X53XHZ+A5XAfCzpzzPFz8qhi9cj/1Uz+Fu+++G0996lMRBLOHiuMYL3jBC6o8u4LEzDLnR8vO+TicubIsipZpXFIpusf3WV+HUfEnvtgYUDnnw9HM3ukVv3UdDAbodtXFnf945f/23nErOlkHpHoLMIiyWSYzazULiwPbOjpr8uulFd9c57yjzPnUCx0GrWrXknO+qul0zObIMBHn1weIO2qD70+iJQd/Csf3WxfnJ2GMyzoH6R59J9w4jGlEcZJYm3znvJQS4WSEfm0KqWoUOD5Cu3VgnFGrJa6WdKGsJM6XdMm+TGa3nqMDa2Z8nm/ROU8uzu/RPh4xVT2nI4xiCLiv54l1a1S+OK98ud2zL87rO9Yl0H8Q2L2RZB2HzJzzGkd7Ncid86qxNuWL0Jyd83VmzvmNS3drH8BieAJbNxDW6NNVr9fxnve8B69//evxl3/5l2i1Wnjc4x6HRzzCbv73tUgkJZuWOQCbB+jhoRNx/lDFUXZ0r3Vnv9YkeGB2sTE+It3E96mc88DsQG9RjC4MYcFLLap5WIy10VpTib93QbpF/cpfKL8tH8j41WZQcGfivPt1UFDVdDpm3XA8Ltz747V67uAwD+Tkzc8pIXeeU0wdmdsu0h9EN++Eu3x0nNsJNx0P8eu3PRN3FnqGu4xnyAC64nw5gvhuS2FtDsT5Vs2fzUVJundxdNneyHJKWnyNlC5QitDap308Yqp6Tsc0iiGEe+d8YpeVg1ibB44UL4OP7dfzi32DGnp0L5k4T9IBt8zELKousCWOOou1Kf+cxXogrM9LnBdCrHbfM7jAAAABAQgea1GFZLVf+ZVfia/8yq9EFEX41Kc+hd3dXRwcHFA89Ilhlk/rvvDPWRkIC8wmh+/dVPo6lNxt0wG5AL7Oxayc3DwGF2nF+XUXpAma4ny73Uav18Mv/N6n8fmH8uNxvvXgs/jG3u9mt1gDpMVXW5yv2busaAQear6HqcIg3QUlHujnxTV1fQ7a8oErxTUJBq1qvicAuV2FP4+qppvDqRtuwzk/uuxkHUoD5ErohOMkzpPl1Ib6e5SinXCu2GnoDIQtSZxXidyxuLdIQwiBdj1INnW4irXJHAhr0zlPLc5vR02s6rk500hCeO4NdImGtUmfNJ5FBXVx3n49V+qwT+PoSwCeSLKOI6phsHMM58jkdkLrYrDXSELZOe9A7K0nntndx6kCQC1wv4Z1Vj5zXMT5k+ac/1f/6l/hcY97HF7yNb6GHgABAABJREFUkpcgiiLccsst+OM//mO02238zu/8Dv7xP/7HRMu89gnjeJZnV3KBTWMl1gYAhhedrEP54Hp0nzVxXkqJSybutsEF4NSXka2HbBo8oF1khRAzF1ytiVojX2huNZvohAqfa8Kbfv2BsPac6kIIdJtBsc1kyR0rgS+QmjzhQFwAtsE5z8O5YEpV0+mIJICIuM1Zk40otNGhk3VcUBHFjziL8/TD7TiI87o88NbnqF2C/98/azxDBgC6Os75ktzhSnn4jsTwdt1PFucdOedrftbcoS0aCMs8c76q53TMxDn3l+2phrXJcamXRcqxNv3zs9k7vj0jzUWTGnr5S2Tr6I2IL28Mo+oy48NMIIjQW0Y91sbNOavd8HF5sKRzMDh3AkudEQwuCuasOOeZdJRvY3e7kQr83ve+F89//vMBAL/927+Nv/mbv8GnP/1p3HnnnXjta1+LP/qjPyJZ5EkgnvecxlMe4vy6c97CYFMVlNvEju8Dzj7ayhqOhuHMBakL8WtHKs4bikeFHOAqEBZfjuI8MGutZi3OZ0XblDRcb53UJTFwznsCbDZrplQ1nQYpJaSUbLrheus1w5E4f14l1ubY/oD388e6M2QsOOdNWvKXMRDn551wf/q3F/DOP7pb6Wd+sfufcAYX1XLk2y2SA6RWTWcVa+OmfrbrAYCEz4ejAe+B70EIMfuOXMfihUGz5qNZ8/PnXqjC3Dlf1XM6ZrE2ofacLipSz8Sjo1I/j8rOeUig96C1znsppZlz/vIXydbSp5oHN8fwwl1Z9C5KRHsJoRxr4+ic1W0Eq5diTM57Vy+5+Yjz9WU3PyPnPJf3TBWjCnP+/Hlcf/1scNf73/9+fNu3fRse9ahH4cUvfjE+9alPkSzwpBBGVzapTJx2w/XNqyNxXilzHrDaOmfsbLuGxfnF55YMuiKjH2tj9wBduLW6ZLd6sD7UZRlHznmvGghbClVNpyGaX+YSH2J02YhCG15ysg4lt13vIeuv23ndmLrxETCldY0Zuf6WCfVzYOedcEGjjVqjpfRPp1krMOCVZp9QD7zirfoludXVnPNuxPDUobAWh6/mUU9rybd8mbLXJrzQZ545X9VzOqZMzuipZ+ISh7wPJ1Gxc6jF8/lgEpkNVD/6YspADp21EHdWGH7WlEXvohjMt0mixTjWBkjQEpi4sDPP6o646pwXbAbCCgCSyXumitErd+7cOfzVX/0VoijCBz7wATz96U8HMMuv9P3teiFcw+0wP94Q593E2hRyzluCmzh/RNk6Z5iJPKUW5wnbs7SGxwElOOcLrqtRbhZwZst5Y6e8hSyRKgI5EjuW8T2xdYU/jaqm0xDGS51wDNhwzg8PnaxDLU5GWh8Ke0HFwZ/GgDbaRmlIrgoElwaTyFanh8NL95Kc8528eTqAs86zVpo477B+1tOEBctrOiAV50/RPZYFqnpOx6JL2HFNT3fOlzdHRjnSZs7x/XYWAuCiiWsemLnTieLqNhIHTDE8n8+GgVtwVRPPUkqtT+u4irVZvzxg4gi/2uVObZLUZyHOMzDOzRFCsFqPCkaf9Be96EX4tm/7Njz2sY+FEAJPe9rTAAB/8id/gkc/2k7EyLXK4jDPxTm/3p7lwDk/DiOMVNvELObUHprkzQP04jzlRHjDIhvFxJsRQvSd83bF+f12QSdfvVxBPMi67XbmnE/7H9zH2vieYLNZM6Wq6TRwu2zfaLd24JyXUqpnvVs8zAOGB3riaJsLJsPmlyHIgTVyIGZBKBAUjrYpKVe9EfireatJOBLDU52JDsX5VNefdXGe8PPAPNamqud0LMT50O0Z/XKaWaxMcb5XsNb07NVz4/M5AFy+x/wxALq4rDmG+0chhNqlcVGIz/3cnfPt9T0HE6HXnx+MiTo/KGApzgNb191u9En/8R//cTz2sY/FF77wBXzrt34rGo3Zpsr3ffzwD/8wyQJPChEzcX4URqsfDguD0PJQds0DM+d8HFn5QjgcGr4nxK9dodclD0NxPjTJ4k+CsMh06v5sSnfRh6zbFaALt1WX7Jz3szLnS17LnNRYGwbzOXxPAHK7Cn8aVU2nIWQmzic656UsdZDU8ThUF38tdsJNwtjsgrtHJ85LKc1c/MtM9WNt5kzIY+roKSw2lDj0NHfYe0ku/nVSY20cZeADSL/IsPx+FTZHpOEFzjoJVanqOQ1SyqXo2fIHby+T7pw/LG0NDxWd2WLxsp1koPrhF4CbnmD8MGPqy21p/njdRrC5/zNF0l5CKGfju4q1Wa+fTDqleTrnBRCBzWsEzI4529bdbvxJ/xf/4l9s/NkLX/hC04c9cXA7zA8nMVa2nZPerG26Vt7hInUqfRJxOBs6s3sD+TqMb+aHl8iEkNE02uxqMMFwYGHM6MZ2nZlrQGNjYjnWprBzq7FrZyEpZMfalLuWOakXBg4zc+ds4yT4LKqabk44d9k5PsjP6Y3XapiMZk67EjOTC7XCW4y1OTQ9zBM65/uTiM5tNx0YP4Q95zzd5eVsuGkBShTEO3Ufl/o81rJMamxAifv5depJ+wwvsO64O90hEudb+6VebupS1XNzVkxIhkM6TZhGcXrWe4ndcIU7zzjHzgJkQ2EX+z4qCETwnWYN91+mnZND7dRuBBkDwpcp8aJ9mco5rw5L57wQEEzy71XZrtVewyziQRwW/mU2BsIC5FmreSgPg51z9CU76zAV5+OQrOWQ1DUPGIvzEblznvZGvnALPGA9uuVUUXG+Wa4gnjlkps7NOe9ms7ayBO/aGQhbQQO3y/beOOFAX3JU3fki4rxFp90F04za/oM0CwFwQTXmRwXO4jxl5rxqPu0cv7zos41D/DquxPnUWBt3zvnEfUYJ9XyfKnOeeaRNBR3TZdHVYXd75vmvxLlwF4tGsfUvWNsLGWfOA2TaAfkMNgLRVXv2WokIIdK7u5Zx1Cm96Zzncd67Ks7ziRdeiPPMDGuC0WWBCjw+YRVY1H4msTbDpKnjxFmreRQuuoc0uXEbD0txMz+k2TiRbESWMfxSp9bmqdlpahzELDvn9ztFY21Kds6nudTrXWebo9T7AhYDYXHNZM5X0LA4zDOp54luu5Jz5x8qIkTbbIM3raGElxrKGfwqTIfGh/lxaGkgLKVznulAWEDh4sCRUz3dOe9SnE/YZ5RQz093iJ6D+TDYCjqmIQ/nfOZZlOiMqcKlwoY1aU0/IMmcP/oSiRBOblYjgOwychkL4rSSOO9oxthGtx6T8x5HcX4RtcNNDGdyoaLKdq32GmbhnGdwmJdSYpAUnVJy7nxhUZyoNW2d4huRBIhcDcbCwjqGmfO5bWjFH5D04bRcA5ad8zuNIH9w3DLNPXuLSSDVOe8wX5Wzc34Wa1OV0oqrLPJpGXTChVGcHIVWcj0v5Jzvnwci4pzUK1w0jrWhe93OU+XNA7MDmuFQ2G2ItekUjbUp0zmftzZHTvXUTF+HsTa1pDbzEur5qS5VrE3lnD8pTJad8w5reuZZtMROOC1B3FK0DYl5LhyTnNEj6vOwYWc7AOy1tkOcV8qdd3Te25hzw0R49gVDcX5+6c7sTLwFCXQr8Hr1TjAho4Gw4zBOvgEu2TlfuP3cQqzNJIzRT4oEKAqRS9G4JX8dwxgZ8qgz4iJTWJwPmtYLrxACB0Xc8yU75xMdbUDplwTLeGlufofiwpxKnK9YJ1xctruPtTlKy6gtO9amkBAtSeNjljHuPhsekl0ckA2DnTMxi7YhH/A+h/Bk1C46ELZE53yu+89RvWoGaeK8XSNCFon7jBLEl07dRz0gqNft0+aPUbEVrMbaOBTns2pXOAYmWQMvaIhjqRevemxnjkzh+Ns0CC4PyM1qBBSecaaChQ5qpUHvgaPMeaYDYRfOeYJLHCoW5j4m3QVzBHi8Z6oUfvWOjo6U/+7urpvhgdvI4kwUuhfnE13zQPmxNkUPrvPWNMKD4OGQ6P0gEufJY20YfakDIF9P4cz5kjLVT3XqePBIYZPvIEom0dEGOBXngzRx3lGG7zK+J9g4KXSoajo9i+xRBgNhj9IOryXPkCk0EBaYHZZ3byRfh3kNlbN63r3OeC0kw+yWmfSAjr5oaM05T/j92FZx2C1TYvRZanzMHEfO+VY9paa7jLVJqukliPMzc0QdD5gOSWQqzlf1nJ5xyMM5n+tYH1ywHst5PAr1RGgLQ97DKEYvzXxQlOP7gesfa/QQ9Nq8+QNaibWxEC+jNOjd1UDYjVgbHuc9IcTMPU88q8+ERV1nZlgTaRoCUwqrPvv7+xCK4mcU8fnAcOdqrI37w3xqsevZcbKlUfgQHU1nLecEh+Y5JHl2wMxtRwC5c56dOE8bZbBbNHO+Xo6T7FSnAeA4/y86EMQ5Ouf9VHHefea8EIBkthEpQlXT6bkaa+P+sv1olFLDSrxsD6NYoxPuXuCmJ5CvhSQabniRZJ9Bftlu6JxccYhSQpk5zzjWJrc131G9aqQ65+0KeVkkxueV5Iw8fQ2L81U9p2fKJNYmt4YOLgD7D7e6Bm3DmgVxXsvBn4aF9RlD0EluxzlP/5hqA2GZOOeZiPPAlbMxsW5igs82c/4aF+c/9KEPLf773XffjR/+4R/Gd3/3d+PJT34yAOBjH/sY3v3ud+P222+nW+UJYFH7GWTU9tJiXEpsg5+EsWbr3H2k4jxZxvvoMsnDXKAcIAcYf6kLQewWIL4BLhxrY9l1Mud0R3GT4SDXNDVznqVz3p3zb47vbXesTVXT6QkZXbYfDVO+40vMnL84mBR32x3ZyaglueAm6oQju/yfMzWLtbE20I6wDZxzrE2uOO8oRiZxXX7N2YB3wJ1zHiiw/8qCqThf1XN6VjqKXMba5HValXBG175QtjDkPTWyTwcCswL9edhcnD9F8V23joVLZqVZMo46pTc64pjE2gBXIl8ZmSyvOuf5vEYArn1x/pZbbln895/8yZ/EG9/4RnzHd3zH4s+e85zn4HGPexze9ra34YUvfCHNKk8AseSTOZ8qzg8vzZyAJbhbjDYAN3w12TpIhsECwOjQ+CGklBZibdxnIq9AXGR2iw7DKclJprxhau1bXUcS9VTn/H6p61jGT4vaYZA572955nxV0+lZxNoYDuikIPWSe3g46zYrwVlcONIGsDJDZjSNkofjFoVAnNfO7s1i0jP68cXnlhpS53zBQ1+JbrtmZpa5cOecryWsy6FrHkjphiupy+F01/R9EGwHwlb1nB4usTa5M0oIhprmoR3F1n8IiGMgbS+vQWpknw4lx/wpQSDON2s+WnWfZt8zx4JIrnTp7qh+1n0PnicQz80LjFzhHsDTOc/sTOwxW08eRqv92Mc+hic+8Ykbf/7EJz4RH//4x00e+sSxcCwxcM5nDkAtqRX+vK5DnHiIHFkm7FghwiSH/iSiz4Q1HFjoUd9Guh4IW5JzXlmcdyCI19Kc8w4Po7W0CwOHA+3mzFrIt6vwp1HVdBrCaGkgrOMhYekCsCytG05PnL+XfB1kl9sEnXC9iWZ2bxaGA2FjW59VwuFg7VqBx/LrpTqmMp3zQcOZeytxIGxJEX5pJHbobYtzvn3KadeBKlU9p2HCQJwPoxiX8yJlSuiGu9jXPDPG4SwOjpDUyD4dCC7cyc/DRGY1kk6hZSzUDs7OeSEEWsu1nZErnJtznmuszXb55g0VhZtvvhlvf/vbN/78V37lV3DzzTebPPSJYyHOM3DOH2eK8+XkzuuL87SXB5zE+cIDclUgiLUhRcakYlZh53xZsTZdxs75NPefg7XMSc2cZyHOY+ta5tKoajoNEyYZtUBORmxpl+0atWt8RFI3l2ElzlO25M9hG2tDd3mZO3R1mZKddokO9TkOh6/WfLFZohzXTj+pZpYkzp9S3X+l0TlDsxDLVPWcBg6Z87NouJy/VMJlu1G0KnG0zTFlDR1dNj570p+HaUTX2YwzQizUDqWOOEfiPLC2PkbC8yxznk8CwqKuM3OqC8KOnTIwuvr/t//23+JbvuVb8Lu/+7t40pOeBAD4+Mc/jr/+67/G+973PpIFnhQWjiXuzvke48M8QL4+skzY6WB2u2nwpX6hb+GzYXgZ5Hvi6vBDKuKIzJW00wgghFB3KJYkzisP6eHknHeZOZ+2JoeCxxxPiGtGnK9qOg0r34nR2Gn80uWsGlaSOK/lnAdmufPX7ZCtg+yyfXRk/BCp8YEmGMbaWHPOE34/1gMPvifULhLKFufTBq8CToUFIQTqgYfxdElkLGmvk0Zy5nxJsTamYlWHbq6VTap6TsOqc95NVF1upA1QSj03mtnSux/AY8nWQlpDo+nsvTU4U5CfhyOafz/jy8h1LNSOTiPnzO8FTruVVrriGInznhBknxMKuMbabNsJ3ejVe9aznoXPfvazePazn42LFy/i4sWLePazn43PfvazeNaznkW1xhPBIuuTgXM+09FV0mFe+3aeODeObCAsAEz6Rj9OPjwOMP68kbfxAaRDYYUQ2G0VKOj1LtlzZ9Gs+WjnbUYAJ1Eyqc55h+J8jbFzftsz55epajoNi4GwgPML98xc85Iu27XF+WPaobBkzvmxuTg/oMyAnTMdGv24FeO88MkvL5Vz5/1yxfnU2gk4r1X19Qtux+J8cuZ8Oe/XQbtm9pHsnCVbi02qek4Dh044JXF+cMF6jJ52VzsA9Gg77zONhDqMzS636WNtaM789LE29OfkXHHe4eU2sNaxxyjWxheCVDMxZfE7wOgCA5jHz24PxtdQN998M97whjdQrOVEE8VLGbWOybyN5h5rM7pMNuROSuKBbZMe0NzV/nEy198yoblznpw4BEB3SNtt1rLdo8uUeGA91a5hkLe5dBAl00hyqdfapbnakkh18zNwzs9q/nYV/iyqmm7OymBNh0NhpZTZl7rc6zmxOE922U4QtzOY8Iu1ASwIOxYORe1GoBZpUPKBfkMAX8bRMLs5jcDHMZZeM9exNg6d84HvYbdVYF+4Tnc7nPNAVc8p4OCcV6qhcTjLTm+fsrKGaRTr/84A5PWc/IJ70gOg/7tNfh4m0oOUZ5yp0qDrZpzTyRsI61icX5nbwkh49jzBQjecc9U5z+c1AgCxZWd0Y7vfRz7yETz/+c/H133d1+FLX/oSAODOO+/ERz/6UePFnSSuOufdx9pkivMlOe2UXAJpEOXu9ScRbQar4bA20ouCOYafNyviPPFQ2L0iufONcpzzgGIOoINYm0T3n8O8eQCopTkSHbv/gCuboy27lc+iqunmcHHOD6fRal7uOsROtsQ1TCJ9hxu1OE/VfWYYHwMAY+rh7oDxHsMKFg6y7azBq8uULIhnivOOL5I36npJXYJpJIvz5WTOA4Zu0u71dAuxTFXPzVl1zjMW5wGg94C1NRhfbhPrB+QX3IadZ/TiPI2Z4Ax1rI2ByTCNbp5z3mE0JAA0l+fJMBKePQFWzvnFUZhbNzmz5eRhtNz3ve99eMYznoFWq4U///M/x3g8Kx6XL1+ubuoLsjjMM4i1Oc6agG6x8M+ZRrGZEE00sZ5cDDd0tVkR5w2d84m5oaYYDqldZ79dQJwv8cB60MlZlxc4EZ8Tc3MdRtoAKbE2fs2pm3/OtSPLVzWdimnIwzmfG4VWQj03aoMnHiB3SDbgvWccH7CS/02FobhgBQsHWeWhsGU757NibRw75zfF+ZPrnAcMhyR2z9EtxCJVPadh5YLb0Xescta7xZquPQtuDvHahhPiGhoaivPUJh0iYwf5QNgGvTjfqvnZ0SOcYm0YOed9ts55Xmr4tp3TjV6917/+9bjjjjvw9re/HbXa1U3VU57yFPz5n/+58eJOEoshIgx+yTKd85OecXZ6Hsa380S580fk4rxZ4SedTD/HMNPOtzEBO3bonC9RDM8dCtvcc+LIbtQS3lPX4nxi1I571zwwHwjLayOiS1XTaQiXO64cOudzo9BGl4Gp3csDo5z34/tJM3TpLril8WV7SFznABivyUoupw3nfF0xkbNkQbzm8xUXNsV5t/UzMZ/ZK0+c13bOe4G12BBqqnpOw2qsjavMeVXnvL1uOONo1enAONd9mVFI7Bg2Natlff/rQGTs2G/VZh2+FDR2rNR0IQS6WdE2jjvPVmNt3A2mXUcIkKcNmLCo68y6ybctc95IUfjMZz6Db/iGb9j48729PRweHpo89IljEWvjeHjcaBrlO7osu+2MpsEDZM55cnHe8FY+c1CvLoafNyvOeeJCkyuCL8NKnN8vZR0bT5vknLeQMViEREcig0gb4Epb4TVCVdNpmDIYIAcoDhG3Xs8N/v3DEcnwVQAIozjbeFAUQ5NCGFnIdzc8zFv5LrNwcZmbTzunZEHc90T6mdSxOL9xwe041iZx7xiUF2ujncPcPctOeEijquc0rIrz5Tvn41iqR7IRd5stY3w2B0jn3IymxOI8t5hXoi4NzxM4VeQcnIXFc2k7K9rGsTi/Ylrj5JwXgjxtwITFbwCj1wg4Yc7566+/Hp/73Oc2/vyjH/0ovvzLv9zkoU8cXGJtjrIibeYc2z3MGzntADJxntypblho+zYGyBl+qdvJnKfdcO0VibUp0Y2dG7djIddPhUTnfIOjc979MFhgfiO/baU/maqm0zBlkFELKDrdLOfOG82PAcj2G71xSGnCNx4KG1LOs5lj6Jwnb8sH7MTaqGbOl1wjhBDpw8tdx9qsuzo5DoQt0ZF4SjeHeYvy5qt6TsOKOD8dkXZzqXA4nCJWrRcWL9tJYuEIc+fJ57ZoOuellOj3+4gnI0zHQ6V/+qMQMu9zFI7IOsm1v+/WsTh/bCdLnHd8ub0S98opc94TQMwpc34ROu90HetsyX36AqOd0Etf+lLcdttteOc73wkhBO6991587GMfww/8wA/gR3/0R6nWeCKYRvHs3XDsnFdyixMPaVvH3DlPIzaQi+EGIo2UUsklIKXEZDREH4pr7w+Afh/tdlur7SezjVsX4o3vvmqsTdAE/PIOh7lxOxZy/VRoJLnUHV0UzEl2zrt1/s3ZtqKfRVXTaZhGXGJtVJzz9px2AMFle+9+4LpHGa+D/LLdcChsbEPgCcez+qn5pRRkDTTVxUL0nXKsjYML3MD3VsW8xf/gVpzfdM677TxL/Ij65b1G2rE2O9sjzlf1nIaVgbAympmaSpyPUChOxmasTZ+gm5zQOZ/4PWuCZszrYDBAt1vsPHIngN47bkWnmVPLwiHJd7XRAOxlWgc0j5NAh7M4z9Q57wlRDYRVQGzZRFgjJeqHf/iHEccxnvrUp2IwGOAbvuEb0Gg08AM/8AP43u/9Xqo1nggWLc4OXXaAYiarbXHeZIAcQLY5IW2BB4xEmkkUK2nW4WSEn3/ZM/HzhR79l9Dr9dDpFN8AWDnME8fa7Ku285Us9h7kbZYcRck0k1yJrmNtkj5njgfazUnMzt1SqppOAxfn/EUV17rlWJuLpm47ojZ98npumJ2b65rTZTrU/m60ElNnwWXWVo61KV8QTzUslCg8J8FNnA+SLm1KNEfk7r/S2LmBdiEWqeo5DRsi8HRYqjhf6IJ7fDRbn4WLSZKZLUT1PIxi9W4C5Qd1a45MZNIn+a4+0yWqPydVnF82iDHKnJ/F2jAS5xf/hZcYvm3HdKNPmBACr33ta/GDP/iD+NznPoder4fHPOYxhW8QTzpxLBHNi0wczn7RHN3MKWXUWhbnjZ12gwskr2GfkTg/tZFPS0DNSkgt7b/rbjOAEAqG/Ea531udug/fE1d/99dxFWsTeJuvl2NxPjFqh0nmvAC2r/KnUNV0GrhkznOItTEe8E50eTCkzqc1dM5bIxxpi/OpkSwmWDjIdpSd8+XXiNQLDtfOeW4DYZM+aiVeYOw0AtR8b/W7WukHt8c5X9VzGjbiU8IRgPL254VraO9B4OAR5OtQir3Ng6ieT4r+3qqgGfPabrfR6/Xwpt//DD5zv9q+4K21N6tdMk96AM5qrWsZ7Rkb61gU5zNjbWqMxHlGwrPwVMSN8lgkMDB6jQBs3RndaNf84he/GG9+85uxs7ODxzzmMYs/7/f7+N7v/V68853vNF7gSWCjyIQjZxtnNee83Tb486YZtTKeCfRds4LWHxMf5jVb5gAgUhTng3oTP/iOD+B1eJvaA7dPAc9+E9ptTaedjcM8MYHvYbdZy/9sl+ycF0Jgt1VL33g7irURQqBR8zGaLH3+HUfIJDvn3V4YzJnV/O0q/GlUNZ2GCRPnvFJEnEXnvJRS7cI/C6L9xnBCXM8NM+etYfB5syLOW3CYtus8M+eBjD2Ra3F+/dLAdeZ80mG5RDeyEAIHnToePCr4+7JFzvmqntMwDtdqB9GgTlUK19D+Q+TivJRSLfY2D6J6Th5pA2jP/BNCoNPpoN3uotZQ22d06oHaqcGwQ28OnTh/iuZxEuDsnK/7S3sOVs55kKcNmMA21mbLxHmjV+/d7343hsPNIjUcDvGrv/qrJg99otgYDDZ1OUBOofiOj8kKxjpSSvNYG4DEDTggz5zXHzZz3OspDZkJJ1c/O+2Gj04zyP6n4aPT6Wh/cQU2MuctoNTCXLJzHsjJnXfoVt8YuOdanE/MnOcRayOuEWEeqGo6FSGDzPnRNMJApfur95C1ttijYZjeGaQKkbN/Q2AxfkCm4ryBcJT4PWuKDed81iF+GQduu9RuQp9IHNFk5eIlaDjPzfWSXqcSxXlAI4fZC4DOGTuLsUBVz2lIds6XR6HMecDKhftwGpnXcgDonwci87O1FXHecK9mZQbbpE/yMGTifNumOJ9RkxxfbnPNnPc9sBLnF3AT510voCBau+ajoyNIKWei4fExms2rG+AoivD+978fZ8+at+GcFMIN53y5t/LLKGfKHd8PNP4e+fMfDqY0G4DBeeOH6FM77QyGzdx8rlhB/HkoDpsxbIeqWRj2ZuNLXWkorAMBeifr/XEozjfXY2Qct8AnDqnlEmvjYeta5tapajotKwdHR8555QO9jEg6zZIwzpsHrmTojoyFVvLDvGGsjbVLPQOBIfF71hQrsTaqznkHsTZpzvmShed1VmJtHLvmgZRZLSVfYBQWrHau34paX9VzWjbF+XIv3A+LOtYtRNXRzWyRs6GwuzcaPcrGe0KBpnN+jpVOciITwDY457uZzvnyu+CWWbncZuScnxkr+cTaLNiCOskZrU/Y/v4+hBAQQuBRj3rUxv8uhMBP/MRPGC/upLARa1Nyy9wyl1UP08f3AmfoxfnzFK55YHY7b4iS67AImnl2VjG8cd0W5/y+ysbEiTifcWB3KD6vOOeF5yQeYJlER6cD4SUJsfSf20pV02mZRjEw/xVyJM4Xmt1yfL8dcd40b35O/yFg/2ajhyA/zI+OzH5e4StDSolwMsJgNEZ/pLh/OLqE9lmp1Q2XOAzcFAuitLpz3kGsDVPn/Mq6HNdzAEh8mUoemnu6W1Sc345Im6qe0yGlxHjq1jl/WPSSu/8Q+RpIY16P7yMQ5y10+xm+r1YGqk9oxPlmzUer7hvG+wmgtU+yniSyY20cO+dXMuf5OOc98MqcX8BMnGe2nFy0xPkPfehDkFLiG7/xG/G+970Pp05dvUmr1+t4xCMegRtvNPviPUmE63niLsX5Is55CzxEJc4PLxn9uJQSA2rnvGYrX7vdxn3nL+E17/2k0t+/vjbE6/A2tWEzht9YVpwCFgrfqbZKrE35TvVM57xDd9uKOF9rO69sjSDhM8HFOS+E89fHlKqm0zJhIM4Xyqm1lDtvPAx2zuC8sThP0pG3jKZzXkqJwWCAyWgWRZfFdDzEr9/2TNxZ6BnuQq/XQ6dT/Ptxo2OKAguidLvuqw15d3DhnmpYYCXOc3XOl9tdUNhNaigolkVVz+mYRrMOhBVKrOlSSlzqa2TOE9OnNKv1zNdnxTmvGT07RyUWbn7h3o9DNUvP5YtAv492u22cm326U8cXJwb6UmvfaqRLpnOeUywco1ibxHg4DnCLtdmyM7qWOH/LLbcAAP72b/8WD3/4w7fuX5obUybO+TCK1VvXju+zsgbjYbBzhodGPz6NJP1hXjPWRgiBvZ0d1Bpqbqd6Hego/2qb/e6m5quaYKHwHbQVDn0OMuczp9M7zHlv1ZfW5SC3d52aLzbFGDaZ89tPVdNpmYQxMD9LOKrnxZzzduo5nXP+gvFDbJggTBnrOecHgwG6XbczPNLYFue8EALtepAvGDlxzqccSh23wq/G2rh3zvssMucLujG3xDlf1XM6Eh3aJc6F60+iTY0g94e4i/PmZoDRejcDBYaRwqnf/ctPMRnhzpfdUuDC/S4A/1L7wn2Z/XYdX7xkIs7bi7QBeDvnV+YJMBLnE+soC7iti9t6sjHaLf7BH/wBut0uvvVbv3Xlz3/jN34Dg8EAL3zhC40Wd1LYFOcHTtZxNArVu2MsOedJhsEC2gfnOeTDYAGjWBsrg2YAns55C4Vvn6lzvpvmnPdrQODOKbDioGQQHyOEQD3wVtuLHQ+pnSMEtt45P6eq6TSs5Js7EucLtcIfW3LOU2TOA8DwovFDRNStv2OzzPmiPPDW56jFuTzxRWi39S4uW6pZ7kWwFFXSaeSI847i2NJjbRxnzi8LR4H7C/fEksk91mZLnPNzqnpuTqJDu0TnvFb32XQ4GyRK2F1K2klOMBPOSqyN4aWLtXM6EUomtSw6p2kWkvbwWd3+jp3zK10RjDLnEzvQHLLYZlfOeSOMPmG33347/v2///cbf3727Fn8y3/5L6vCr8h0I9bGjThf7DB/3+y3kPgDf4HKOW844Xw4tVD4DcT5wPfge4LezW/4BWolY89CEVZqX66XL86nii2O287byyINA+c8ANT9NXGeQWs+MB/suF2FP42qppsTxWtdV47E+UsMYm1IBsICwIBAnKeuneFoFlXnF9tGt9tt9Ho9/M4n78V/+6Rax8IrTv0prpv8idoBo1nX3pe1bTjnLV0ydxs+MsceOopj89MEGs+tOL8St8PAOb8pKojSHYnXaqzNnKqem+NanNfuPus/RCrO9ykNawSdcFZibQy1F5VYm6DexHfd8WG8tfZmtVPDdY8GvvFfa1+4L3NgOhTWsnO+nqVzOHbOr3RFMMqcFwArIXwRAcZoTduIkTh/zz334Mu+7Ms2/vwRj3gE7rnnHpOHPlFwyZwvNBF+Opy505t7pGu4QNUGbyjOk+fNA0CkF2szp1nzaVsLAQLnvI1YG/pD7D7TWJvUjD3HwvNKvEHg/iAPzHLnj7H0+ecizgsg3rJb+TSqmm4Ol064Qq713v1WLtsPi2blpjE6NH6I2MbQrMkx0Doo9CNCCHQ6HbTaHeWounazATFVfG804/MAoF234Aiz5NLOXaujmSSphgXXsTbLezXHQgeQ0I7v10q/TKn5HvbaNVxWucisd510V5pQ1XNzRklGrRLP6NrdZ/3zwMEjydYxoBwIS9AJtzGklwLDS5eaQie5EAK1RgudeqAmzgchYBhnM0epgzyLtl3nvBAC3UaQPPuw5K6qdVZjbRg55z2mM8+4rYnbenIwuto4e/YsPvnJzSGVn/jEJ3D6tN1f4muJabxWZAyFZV0KT4TvZfqWCjMbfEMkzhtmxyVuyEwxFOdbNlxthjfAKhl7xRBW2r+bNR/NvJZ9BwevdtqaHAvPK581Bgd5YM2VEjQA8s+eCZzWok9V083ZODTGofGgMR0K1dJoajxEfR0pJQ6HRP/eI7OYOsCCcx4w2qtZiwqV+qKF0hD5olgS5zOHqQPOhNTUPVHBDgtqVtbF4GJ7wznvaJ9xWtVNumWueaCq5xQkisAlivOFBrsvQ3w+J3XODw+Bdd2jIHZibcze17qNmFfCz9p+y/BsbVmcBzL2II5j4YQQVy+UGZ09PQFWLvWYa6zNlnW3G7163/Ed34FXvepV+NCHPoQoihBFEf7gD/4At912G77927+dao3XPNP19ixXTruiLjfiIXJag2/SCMdGxd+OOG8mVFjJg+UWaxPot+TnkZ23J5xkmKe6/xxHyax81hjk0wJAI+AlLsy5ljLnq5puTvIAuXJr+jSKcTwqeJi2UM/JhrCSOOfNl7GBkThv6TvDQJzv2HDOW4pQYeucT+smdNwKv7Iux/m9QMLllKM1nekqXgpsoThf1XNzXNdz/VgbYnGetGtbAuPLRo9gxTkfTYx0g5pCrE1hCCOUlDrIsyjYJahD6h6EQc1a1FBGznlu50+JhTrvdB3bjtEn7Kd+6qdw991346lPfSqCYPZQcRzjBS94Ad7whjeQLPAkEK6fGrehDR4gv5knc83PCYfaBzQrk+AJYm3IMRXnqWNtLIquB+067jtM2eg0uk4KXLpz3m2UzMq6HA6mXaaxMqSWR9QOcOVGnplLQJeqppuTmIU6HQCt/dLWoFVLew8A5/4vsjUcFYnJy8NwwDsARIZOvUQm+kNhuQ3yAiw55y2J5KnD1BfP62ZgeKpz3vGBftU5775+bsbauNlnnL6GxfmqnpuTeBYs1TmveS4md84TG9ZGR0Zi75jKyLdOOALqeudQK855Q91gmT1T53znDM1CMki9dOcgzs9rKKfMeSFYrQdcnfPWWlXtYLRbrNfreM973oOf+qmfwic+8Qm0Wi087nGPwyMe8Qiq9Z0INtzijmJtCovz/YdIn1+7fS+N6chAnLfhnB8b/bidWBuzL1CVjL1iD2hPnM/cmDhqgW8EHoQQV4eozHGc875yEcRgUwQAdX95SC0f5/yM7Sr8aVQ13ZxEcb7kmq41u4X4MJ+YG6pLNJ11wxlEX1g5y3OMtTH4LrLjnLckzuddJDiYIQPwzZxfdc67j6rjEmtzpqsaa3OT3YVYoKrn5rh2zmvXUeIh7wPqeWfjY6Mf30gcoMJEnLfhnDfsuF9mt1mDELPxQlqU4JxPNKx5AYsoGY7OeU+A1XoWHy2G5pNtguQdfdSjHoVHPepRFA91Itlo/Z64cs4X3AQQH+bJ8mnnGOTOW5kEHxqK83ULxYlbrI3mpkiFg6xhOI5cdkIINGsehuuuFNexNgzF+RXnPJOoHWCe+XdtbUSqmq5P4mHewGGtg1Yr/PH9pGsgdc4DM6dd9zrtH7fjnGcYa2NQ031PoFX3N+uRCZZqeu5FgivnfFo3oefW3VZbFjcYdMN5SQNhHaAca7Nzvd2FWKSq5/okO+fLO6MfmojzhEPeh9SGNUNxPrRRzwGjGJmGDXE+prsU8TyBnWZNb2/W2C3lOzoxvpeJ+Bx4AojhvJYv40GwWk8sq1gbCgp/4l/96lfjp37qp9DpdPDqV7868+++8Y1v1F7YSWLDOe8g1kZrGCuxc57UaQcYtR5ObIjzMgaiUHsw2ImItanbc7BnO+d3rT1vHs1aghji2Bm+8lljsjFaaRl1fHmxAbMWviJUNZ2WxNpRYhs8oOmcJ67nhTPv85gcA9AX56dU+fcrD6q/V7N2n2f4fb3TDIjFeTsieafBdCBsUjeh8Jxf4PLPnHfjnL9uR+F5hQd0t0Ocr+o5LcnO+XLqeRjF6OnW0fmQ9/YpkrWQR70aGhY24oCpMHCqN2uWzgGElyy7zUBPnCf6HOU+TZI4z6BeAVei2JiJ84KZcz6a/16yM6xxW082hd/Rv/iLv8B0Ol389zQEuzeGL1MGmfODSVRckB5cmA1PIWo3oo+10X8drUyCB2a38r7eYdWOOG/2e5qar6qLxQP1QYdfrA2QsqFzvBlZdc67cbSts9Iyysg5P6t121vvqppOC4tYm55GlxZxG/zxmLiec3TaGXQ5WnPOGx7Uuo0AD8Ksy28FS+J8N0+cdzQQtp5kWGBwmF8V593XdH/98+9oz3OqU8+Peuhcp22qKZuqntOSXM/L6YQzvuDuPUAozvNyzkc2LtsBINQX5xuBpe/5OCL7/tlt1YBLGpdLrXLE+cT4Xgb1Cliak8Ip453ZzLOrxvmqvphQ+Lf9Qx/6UOJ/r9BnQxSfDklvSlXQaoOX8exmvnOaZA30znn99rSJrcJvdCtvoyAYivPUzvmmPQd7tnPeTQs8ADSTNnSOB7axdM5zFeeBrd6IVDWdFg7ivFY9Hx/PaiZRV4q24y+NsZkgYqUbzqAF3tp9nmFkSbdBfBC2dPHN1jmfZFhgcJhfibVhkDm/IQ47ypyv+R72WvXswZs7N5S3IEOqek5LYt2IpkZd0KpoR9rM6T0InP0q43WEUXzVEUuFo9l6ucT6r7mdMzpIzxfaQ2FLcs4nxtpwcc7P3wcGl+1zhACbywtgKdaG0YUBgK07ozN79U4mG7E2cUg6BESFwsNg5wzOk63haMTHOW/lIA8Yva9W8uy4Oeebe7SPt8Ru1qbEUT4tsJalPsfRQXXOipufiTi/8vl3/PosI67BzPkKfcZJDrNtiLUBgD7dHJlj8gFyR0Y/zk2ct/aNYXiQ3WkSft8HDWv55p28gbAWI/KySDQsMKih7JzzG5nz7gSY3KGwW5w3X2FG6vyxqX1x2XhuC1E3nJUZbIbOeWtE+q95orBMAt1uQbu+lzAMFuDunL9yBmVQz+dwi7W5eodXnYlNKPyO3nrrrcp/96677ir68CeSDXEemAnLJQpQWk47AOifB677+yRrIM+oNRjAGia9J2tIKRFORuiPJghU1350Ge3uOa2WUiuT4A0hd85bbJ3bbWYUeIuO/TzqfsJmJHDrnBdCXL00YOD6A2YOtwWMnPMztncjUtV0WhIPsiVG1UkpcbGnW88fAvYfTrIOeue82WGe25D3DXGSCsN9Y+YldlEsXra3an52HImjbrh6UuY8A6fdSv1kcJjf+Pg7HFJ7ptvA5x7M6MzZIud8Vc9pSb3UnfStfr8BBM55ojkyiRqFKYbO+Y2B0lRI/fiepq0zOqH5ZyfrHJxFSc75xO4Dj4s4f+W/MDkTL2Dy+gBLznkGe55tpvAObW/vajGSUuK//tf/ir29PTzxiU8EAPzZn/0ZDg8PC20QTjqpA+RKuqkEDPLehxfJ1tCndtoZuNRVhs2EkxHufNktuLPQI9+FXq+HTqd4Hmoj6dBniulAWOoNksXPfLPmo1HzME4abOTQOZ946cLAGb7IT6TujtBkRfRg0uYIXGnP32LnfFXTaUkW58tzzvcnkf5hukc3FJa8no8uG/24FXHewGUnbF3oGV7s5ma5F8GieCWEQLsepH/OHNX0WlI9Z3CYX7kM4iDObzjn3e15Tuc658+VsxACqnpOS+r8sRJiWYyjXonEeStdZ4a5/dZmtmQOn8gm8L30M6YuwicW53k75xPFeSbzPhafOSZn4gVMXh9gVnNmbO+ZmAOF39F3vetdi//+Qz/0Q/i2b/s23HHHHfCvuD+jKMLLX/5y7O66c6JuG1ubUQvMhsISIKXkJc7bypw3gKdznnhNlm/nd5s1PDRNcDs6Gh4H8BXnF9E2TLLjVkQPJm2OV9nejUhV02lJPMyXWM+1XfMA2WEe4BdrY2XIu0E+rbXzneHMgN3WdojzwCx3PnnfKJzV9ETDAoPD/Mq6GNTPjYGwDvc8p7s5z93dnlibqp7TknqpW8JQ2Mu6cbNzqJzz1HnzgHYnnJQSg8EA0XiI6VjN9DAYTSClVOtYj832Ce16gPGUMJaYuHZoX76XNBC2mRTzysQZ7jEcCCsg2Lw+ALDwBW2xYY0DRrvwd77znfjoRz+6KPoA4Ps+Xv3qV+Prvu7r8PM///PGCzwJJDvny2uDB5A9DCmLAY1zfhzGJhfWyUj92+swzv/ZoN7Ed93xYfzM7vuwN/yi2gM/9XVot9taa6rZcM4bth7VSJ3zAmifIXy8TXaaAR46ThLnHTrnk6KBGMS2NAIfiMFHnF/JzHV/ebGMYPIamVLVdHMSXVMl5NPOuWhyoCeaIWPlst3ggkNKacf9Z7DH2BAnqaiZidKZ8W9Fsey266Rl/Da6zg6HiZftDA7zQoir4gID5zynzPnTnaznFkDH7r7UFlU9N2eUNEMGACb2z+jaHe1zBpeAODYWeK3UTk2NYzAYoNstdl67E0DvHbeiQzlPJYVuI8AlXbNjEsTfi9yd84uO7WWYdEovShajyBZuA2EjrgNht8xAZ/TqhWGIT3/60xt//ulPfxqxgrhZMSPZOV+yOK/bPkcUazOcWHC1GRycVSbTCyFQa7TQadbRaQZq/3RaWnnzgCXnvGmsDeWFQWvfentWt5FSxByK84mXLgyK7WIAK5Miuxpr415cWOZaMQlUNd0c151wRgfDPo04Pw5j+u4zg8z5aSTpL/8BI5edtczcut7l/5w9ysx5ywf6dpoLkFs9Z3KYX1xwM7gs2IilcOqczxB/2qdY7Md0qOq5OenivP2abpw5LyNgeMl4HSoz2ArDdSAszDYKuYPKi0LsitbKnPcCoFHOgPVGks7B5Ly3qFkM6uccKcHisn2OtLLRPnkYvaMvetGL8JKXvASf//zn8bVf+7UAgD/5kz/Bz/zMz+BFL3oRyQJPAonFv+RYG/3MefPCDwAjGy3nBkS2vmAMhE474rxZkSEdCNs9S/dYKaS6BhzG2iRecDBwztcDD5gAXG6ca0wz5wGwucAwparp5riu59oRdQCZOD+wcdlu0E04sSEuGEI+rwUAIICaoTjf3iJxPss574jkgbA8Ds/+3EHL4LJgcyCsO3H+VJZzfktd80BVzylINY6VEWtjKs4Ds+jZzmmjh5jaiHmNQyAKCwuv7XYbvV4P/+9ffgkf+N/3K/3Mjx78D7Snn1F8BrO6TNp5BpAPytaKtWnuleZAaiTG2vCon1cz53msZwGj8/DC2MrsTLxt3e1Gn7Bf+IVfwPXXX49f/MVfxH333QcAuOGGG/CDP/iD+P7v/36SBZ4EkjNq7Rf+OdMo1m9BHx7Oru4Mv7hJB6gQENvI2AOMDkWJhz5TDItMjTIPr2t/6FbixkR4Tg+GtaQLDgbFNtHB4JAVcZ7b5ugaoarp5iQ758ur55dMYm1Gh7Mhp4ZOUfJIG8BoqK5qW76UEuPRSH39oxDo99Futwt3xAU2csjrbeO9WLceQAhB44CynFObGmvDbcA7k3q1uBBiIM5vxtq424M1Aj99foHlqEWbVPXcnFHa2dRyTZdS4ohCnO8/BFz3KKOHUIl51XvgIeAXc2QLIdDpdLC/u4NaQ21IfLtZhwgV66Jh/dRypmdBfBZs130IUXDubUmRNkDa5TaPzqXFR4NB/ZwjIVl1dl1NneBh6ttWjHaMnufhNa95DV7zmtfg6Gg2rKsaMlOcxJv5Ep12RhuAOARGl2eRJAakDt1xhC1t3uSQZsU5b9guligs61KGOJ/knK+ZCxomJIo0DAbCLrL/mGS2rHzWGG2OriWqmm5OonM+mgLhuJTfayNxHpjNkdkx+y6245wfaf+oqjgfTkZ41Yu/Ba8q9Og/g16vh06nWPcVadfZHIIOMM8T2G0FuGyadwwAbTPHZh6tOr9OuOTMeR4X3T6jzHkhxOqFlmNDwqlOPVmc32LnfFXPzQijGNO0rquxXXF+OI2U4lVzIZgjE9o6EIcTQHNL1KrlnwGklAgnIwxGE/RHihfugxHaqsNjEyAdqA6QX1oKIdBpBOipvh4A0NwnXUMWge/B88SqQZKJ+CwgZudhJmfiBQzq+Zx4kTnP7DXaMozf0TAM8Yd/+If4/Oc/j+/8zu8EANx7773Y3d0tPLTjpJJ4M1+iOG/cOje8SCDO24i10f9ysBabZRBXYsXJbFj4fU8Uv4VPY+d6ggfJJrENvtay/rxZbDjIABYDT61cBhkQMHbO627kOVLVdDNSL5rHvVLE+Yt9gnpuLM5bcM7LSLtLL1VgcYiV79c6TS7sXqu2FeJ8aqwNN+c8E3Fh4Zxnkpm7YpJ0bEg4aNfxhYsJ0VmWuz9sU9VzfQZpefOA9TN6j6r7bHDB+CFILgmSiPVrTCvtu3+JcDLCnS+7BXcWeuS7tC7b5+y3iC8ZLXwvFhbnDfWdotR9D6PleT5MznueAJvaOYdb5vxiq30NnYldYPSO/t3f/R2e+cxn4p577sF4PMbTn/507Ozs4Gd/9mcxHo9xxx13UK3zmiWOZfLBcVLesJSjIl/SSQwuAqe+3OghrEyDN/hykIZDYVIxcAdZOcwbFn4hBOqBRxNLtHOD+WPk0EmKtXEszm9kDwuPxQCcxeeNyYCXldeJ2QZJXCMtfFVNNyf1onl8ZJz9moeU0mwgLDCr54YMs0QNE6KpVgarqvMvqDfxS//xfXjx+NfUHvhhTwCechva7eI571Zi6gyHwc7Zb9VxD/Qz/gHM6irRetJIdU+6dM4zzpxfDCHm5uQHnDvnDzopFyjt7RXnq3puRmrePDCr5xYpJJ5m0Wcszkf6exUV57wLSAeqA1bmj3UbAR4o8gPNPfI1ZFHzBUbL9zZMLreFAJvaOUdKsHl9gOV5jczOxFt2WWC0Y7ztttvwxCc+EZ/4xCdw+vTVQ+c//+f/HC996UuNF3cSSB2EWqJz/pCiDd6Q1Fy/kpFSYjAYYDIaYDpWy7jtjybYVW2DMxCCm4GFzQjBIbbub484n7ih4+acZ5A3D6xlvDOAdeb8lhX+NKqabk7qd2EJufP9SWTuEicY8m5thozUe9xY8YJRCIFGs4mOUPx+2dkDNB12Ks6/wjRonPOpQmURSsjqTn0NHYrziR2OTDJzA08AEmxi4XzPA+a/0sSDD4uSKqqVmLdMTVXPzciMZ7N8Ru+PiS64Ceq5av0s/sD6FxCpXVNLBPUmvuuOD+NHD/4Hbuz9ldoDf8MPaF22z9mnHKgOADV6cb6TFgeXRomxNkDC2ZNJ/RRCsBPnAcnm9QGW5jWye52264xupHB85CMfwR//8R+jXl/dVD3ykY/El770JaOFnRRSRWnLeXbLGMfaELTNJeb0OmAwGBRu9bwTQO8dt6KTlGe+jBcY3XB6nkCjRiSEz6mZO9tIHP2tA+suOyBlQxdU4nwS9cDD7JuBh3N+5XViV/ivDaqaboaUMsM5b7+mG7vmAZLDvLV6rivO23L+mVy223D+EYnSJO6/znXmj5EDR+e8EAI131u9JGMihntCcCnnAICVsQuOo/xSP/MlC1OUVPXcjMx4NsviPFk0HMH53FrzbKT/76hSP4UQqDVaaDfr6ISKclenY2S2IRfnLTjnO42C9ahZ7pyK2rqmwKR+AoBkdvacOef5mNUWXTbXiGHNFUbvaBzHiKLNQ9gXv/hF7OzQOHiudVIPsSW47OaYt8GbF//MbL9rBQIhvFnzacV5gmzWBoWjf/dG88dQINFp5zjrdEOcd+wgm1P3xUyc1xTEqAkqcd46VU03YxzG6QfZEmr6IUVOOIVz3tqA9+IqgZQSvX5fuRNuXBuhPw7Rbvj53XAGNd1KWz5R1vp+m6AGWY5wAjIEmpo7cR64crG9Is7zODzPnPMem4Nz4HvAvNw4NiWki/PbO0C1qudmZF4yT3raM1BUIBuqPro8E8ENBDxrznmDWBsV57wWhu9ntxEg8AXCiOg1sxRrU4hGyeL8+pmYSf0EwO7sGUOyen2sfVecMIze0X/6T/8p3vSmN+Ftb3sbgNktZa/Xw+te9zo861nPIlngtU7qIbZMcd70QD86NF7DgGr4jSHtdhu9Xg+/8N8/jc8/qOaM+Jnd96Ed35f/FwncXJ16QDOobQ7BwaNZIyhWJYnzidFAFjY/ReDqnOcWa+Mv2+yYiAtzmC1Hm6qmm5F5cTq2P0fmcEjgnCeo5xNGA1gHgwH+wVeoR6bdCeBVUOyGM6jpvifQqvvZucZFIRLnDyjcf52z5o+RQ7pz3n4XXhaNwEN/vPQHTDJhfU9Axnzqui/EItXGtSkhUZwPGs7NGyZU9dyMzLjVOJyJy5Y+H3RzW+RMoC/hsrQw0Tj/76RgJRYOMBZfhRA4aNfx0LH+v9sKBKa+ddpFxfmSM+d9b+09YFI/AfAT5yWvWJuIbazNdmEkzv/CL/wCnvnMZ+Ixj3kMRqMRvvM7vxN//dd/jTNnzuA//+f/TLXGa5rUFvhwbHzbrcpFBgPk+pQHVAOEEOh0Omi3O6g11ASGTrMOMVTJmzcvsu2i7Wh5EBRdkvb83YeZP4YCiWt1fChkL84zcc77gq9zXlwjboGqppuRWs+BE+WcN869T4Pb75nhhftOMyAW52kc4wckznn7sTapxgALgkYRGuvrYnJ4ng2E5XOT7PtL4rzjWJvdJHGeaIaDK6p6bkbud/Okb02c71Ma1oYXeYrz05H2j1qZwQaA4vtxn1Scp49d7TKPtQn8dec8j/oJgJ0TS0qwcs5zHQgrIZmtKBujd/Tmm2/GJz7xCbznPe/BJz7xCfR6PbzkJS/B8573PLRabnOct4VMp920D/h2byyllLjQNywio8vG67DinDcQ8Oo+zzzYnaI33nm0zTdsJA6GvXLE+cRhbY4Phd56sWcnzvMQxDYuMSrIqWq6GZlOuxIy5w9N58cAJPWcrKV7g+KP22638Refvw9v/B+fVfr7/7B7Hi8e/5raRbhhu/dus4YHj4gO8QDhQFiCGtS175xvpu09XIvz68IRE+efz2ygXeBdic4DnO97dpK6ZOrbLc5X9dyMUdZlOwBMBwBOWXluslgbgKSmWyFUi5pLwsoMNiJIOs/mWOgCaxcZCCs8so48VTbOekwy54UAq/oJAHEMXpnzEdfMeW7ryUb7HZ1Op3j0ox+N3/md38Hznvc8PO95z6Nc14khs/173LPeTjSYRObFbdIzdvlbyZw3+HKoBRZ+kQnE+cJZcZkIkmFXbQrnfEnivOclDGtzfCgMNpzzPA7ytbl7gYtz3uMba3MtUNV0cyYJ+b5X/0f74vzlAUGszaRvXM85OeeFEOh2O6g11MSoRrOJjlD8dzc8tCa6dU0gOkR36v5mnSz8ICU459PckxbchkXYcPQzcbZ5nmB1Rl0YE7wAWI8yKJlG4G+KfY1yRSlKqnpuTuZlO2B1KGyfaiAsAAwPjX58w0BExVRfnAcszGAjgqTzbI6F+SmdIuJ8vVv6eWvjTMxEnJ9tPxkVUFxxqgserw8AxIstOq/XadvQ3g3VajWMRvotSRUzMguL5WnwAHChR3CYB2YufwNIW/jmGHxhkQw5XYdAnCc9zLdPkdy4Fs6vW6feLTXTrr7unncshm8653lknAbcnPOMBfncwZFbQFXTzck8zJchzlM45wFgfGT041Nrznk9NjJMqTAU70gP8QCZmCiEMHPP11qlRILUfJGsG7BzzvPohptpHnycfwsDABNDwob5hSgmygVVPTcnM6YOuOKct8NgTOmcPzT6cWu7W4NYG4AoUtUC+6TOefrvoELxuA6ivTbOxKxibfjUTwCzZlIml//AUqwNtzMxt/XkYPQpe8UrXoGf/dmfRRjyGOa5jWQ77ewPkDOOtJljOOyuT7kRmWNw25oYf2IKhTjfJCxSRM42Yzf/3sNK/eLcGHTqWpxf/6gxaVG76l7gIbRtWW3dSqqabkbmYb6EWBsycd6wDT5kNBAWSHBiUWF4cD1FER+zDGH7+amOQV3snivlC1sIgca6QOPXnNfQjf0jE/HZFwKSkbiwuDRjIr50G2vrKDnOgZqqnpuR75y3J873KA1rhpft1tT50EycTx0I7hiSWLg5FsT5Qs55B+L8ZqwNjzMxAHYH0UhKVq9PHPPQC7Ydo13an/7pn+Kuu+7Cwx/+cDzjGc/ArbfeuvKPDr/8y7+MRz7ykWg2m3jSk56Ej3/840o/9+u//usQQuCbv/mbtZ7XFZnOeUPBWwXjYbBzDF3+w2n+RkRKieFojP4oVP+n34fUcP5auZEncHPtUd7Id8+RPEyHQpwvkfp6ZJHjg+HmRoTHQZXbQFghBFuHumBygWEKdU2v6vny/2i3nkspCZ3zZmud2tqga/7+bwwYo8JQvOMszhu5+kuItJmzIYQH7rO0N/aPTMR5j9nclsWlGZPOgo1BiY7jkUypzuhmTEJ3sTYDylibkZk4by3WxlCcTx0IboT53mWfssPdgjhezDlf/gUla3GeWVxLFEs2sT8A34Gw/NaTjdEnfn9/H9/yLd9CtRa85z3vwatf/WrccccdeNKTnoQ3velNeMYznoHPfOYzOHs2fbjU3XffjR/4gR/A13/915OtpSzGWcW/BKcdmThv0N4XxVIpNy6cjPBPvu17CzzyXQCAXq+HTqfY7bOVok9wcCYt+jvX0zxM0iCtIuzdTLIOVbg55zcz53kcVANmmfPAvC0fbKJ25jC9MygMZU2v6vkak/7sc2vpwzKcRnSDWE3FeVvOeU3X78Z3PgW1tvGh6EyX8Ls+aJA6xo0uDogu/lXYEOdrzdKeO41N5zyPms5toN1ChGFyebFhNHEcj2RKdUY3Y5Q3C80wzjULUue8YaSete1tbNYxz9U5T2qisyHOF3ndHAzF3tgiMxKfuR32Ymbi/MI5z+x12jaMdvLvete7qNYBAHjjG9+Il770pXjRi14EALjjjjvw3/7bf8M73/lO/PAP/3Diz0RRhOc973n4iZ/4CXzkIx/B4eEh6Zpsk90Gv0XOeYPsuKGNYbCGpA4aM4HAhUNa9HduIHmYXWNx/iaSdaiyIdQ4vpXfcIMzibWpebyc88Cyg4eXOH+tQFnTT2I9zzzMy2g2AK1uR/A5HlG2wRuK83mOQ230Nvwbc0YoIHgfr9shnC9CfIg2as0vVZxf26sFDMR5pgNhBTP3WMBMnN+Yn7TlzvnqjG7GJO+S2ZJzPoxi2kGnhvXcWseNNDv7s82cbxFdxvq12aU7MYHvoR54+Z0hgBPn/GbmPI/6OUth4FVDI3AbCMvVOb9daH3i4zjGz//8z+O3fuu3MJlM8NSnPhWve93r0Grpb2Qmkwn+7M/+DD/yIz+y+DPP8/C0pz0NH/vYx1J/7id/8idx9uxZvOQlL8FHPvKR3OcZj8cYj6/mrB8dGWaxGZLttLPvnL80IGqDj/Sz68eK4nxQb+JD/+X/wT88/pDaA3/dK4GbvxbtdvFDdKvOcyAsWdEHgN0baR7GNAe/5FibTac6M+c8k1gba1EQBizy+Zk557cd6pp+Uuv5KG+A3KRvUZwnquWA8d7DmnNe85BmZ4aM+aG12wjQrPsYTQgMCsQOu1MmsTY7Dp3zHMR5zgNhGTnnF0Pnmbw+nfV9/5aK89UZnYZh3veyJXF+QG1YK8Hop4fZGcPKOZ1AVKwHHlp1P//zk0djz3gtabTqvpo472DuxsZdEBNnuJRgNbMF4Oecj2JUujwBWp+yn/7pn8a//tf/Gt1uFzfddBPe/OY34xWveIXRQs6fP48oinDu3Oqm/ty5c7j//vsTf+ajH/0o3vGOd+Dtb3+78vPcfvvt2NvbW/xz883lRmqsk+m0Mx3iosAlKud8qP84mRcUSwgh0Go20GkGav/s7KLT6WjlVHONtakHnnmMDABAkDnnjdz89Q7Q3CdZhyoborPjW/kNlwATF9ni0oCRc55r5vy2Q13TT249z/ldsVjTLw/5tMFby5zX/K6u+x59ly3BZbsQAmep3PPEDjezWBuayDwVNroiGIjzG/tHJuLzbK/Bp4bWfGbO+fVBiQzmF+hQndFpyOxsB6wZ6IxF3XUMxXlr3xiGQqeVWBsi8XWXIoK2uWv+GCm0VS82Kuc8a2ZiOJ8Lg7gyzpGg9Y7+6q/+Kt761rfi937v9/Cbv/mb+O3f/m382q/9GuK4PBHn+PgY3/Vd34W3v/3tOHPmjPLP/ciP/AguX768+OcLX/iCxVXmkz1Azq5zXkqJSwMicd7EOW+rBd7XP/RaaZcjckySTILvnAECmgNjI/D1HQy7N5aeTeZ76y3nbm+dN2N2eBxUA2YDYYGlQwKjNV0LuK7p10o9z82otThAjtQ5b7j3CK0458VS60zBnxQCrXXhzRQi4e7cLpGYTO2c191nCH+2vyiJ+nr9tBADUJRN5zwPcYHb3fYic56J+LIhWG2pc951PQeujZqef9lux5E+oBbno4mRgc7a94bh2YuzOL9HIc437Inzyq+dA+f8BozEZ25FNI4lq9enEudp0NoR3XPPPXjWs561+P+f9rSnQQiBe++9Fw97mF5ExZkzZ+D7Ph544IGVP3/ggQdw/fWbLpzPf/7zuPvuu/HsZz978WfzjUcQBPjMZz6Dr/iKr9j4uUajgUbD/eZ9jstYm944nE16piDSL/xKrVU6GGys7Yjz5k47ADjTbeCeC/oDeAGQR8kctOsYTobFf3C33EgbIClGxu3BcGMyPZOD/GJdjIrtwlFRifOkUNf0k1rPcw/zFsX5I8rM+anGd/kSocK+QkqJcDJCX0xRixTW7teAfh/tdlurg6Zd8zGgHLJHJNxdTybO0x7k23UfjZpXPPu4c6bUC2+OzvnNgbA8vqOEEJCMxIXFXoyJIWHDZLKl4nx1RqfBVaxN7iW/DpMeEJwq/GNSSoyGA0zHanuC0WiE/ihEu+Hn12nDjqKmjVgbovOXcdwrADRtxtoo/nsS6RZF2PjcMLm8nW1r+dRPAIgk08x5RvsMAPzWk4PWJz4MQzSbqxvgWq2G6VTfuVWv1/GEJzwBH/zgB/HN3/zNAGaF/IMf/CBe+cpXbvz9Rz/60fjUpz618mf/5t/8GxwfH+PNb36z8/Z2VVwOhD2kypsHjG7lrYnzBg4qKxm1NZoid5rCOb9LO4T1oFPHvYc64jxNtE4RNsRwx4WNbea8rSFQFFTiPCnUNf2k1vN857y9C3dOmfMql/7hZIQ7X3YL7izywN/9HvR6PXQ6xWtpu+EDlC9/jUYE5uqcF0LgoF3H/ZdHxX6wxGGwQJI4717Y23TO86jp3Cr6oouRiSFhwznPJI6oKNUZ3ZwwivNnp1g6o1vpJh8fAe3i4vxgMMA/+ir192pez3vvuBWdvAhWw9+vtg0THdH5a7dF8J3W2jd/jLSHVnbOOxDn1/+AUaY6N7g5522NmzppaH17SCnx3d/93Su326PRCC972ctWDk133XVXocd99atfjRe+8IV44hOfiK/92q/Fm970JvT7/cVk+Be84AW46aabcPvtt6PZbOKxj33sys/v7+8DwMafc8alc/7ykFKcL3iAW2Ji67fZoO2c3DkfNMgOIKe7BIfPPVpx/kxXc5NFfEmgAreM940MfCYH+Y1LDAYs3jp24jy/16oINmr6SaznLsX5I8rM+alZZ5atyHkTug2esTbX7xGJ8xZmt5zqaIjzJQ6DBRJi4RgIqo2NzHkeNX0WOc+nVi32PkyckRuCVc3O8G7bVGd0c0YqAvn4eNZZSvw7NbbinDfsti5IX6VLbRIbdcNZGQhL1Hm1w905rzpXr0576a/CxkeBiTNcgv533ZQw5ram+eaf05q2D60d0Qtf+MKNP3v+859vvJjnPve5eOihh/BjP/ZjuP/++/E1X/M1+MAHPrAYQHPPPffA08wc5UrmYT6azhzpRNng69CK8/qZ83byacHLOU+Y26YthC9DLIqf7mi+1jvlDY6bs+EId+6c55WBP+fq68RHaVu8c7GFw8sJxkZNr+p5AhbnyJA65w3FeakQhRXUm/iuOz6MX2q+DbVIoeuqtQ8855fQbusJZvTiPI1D+9wukdPbwvC4/bbGXqPEYbAA18x5fhcGAL+BsNxibTYHwvJ434pSndHNURrKGoczYxpx/JEVw5pmBE+73cbHPvNFvPVDn8/9u9PxEL9+2zMBAOde/lsKjz67HNLuhrMiztP8zu/mdQ2oYOHCffHQys55BheUTC5vJcNYm1hyc87z0QuWsXCHahWtT/y73vUu6nUseOUrX5nYIgcAf/iHf5j5s//hP/wH+gVZJj+jVi8nTgVScd5gIOw0svTLbHADTu6cJ2w5P0PhnCcW5/UuDETpLfAA4G3EyDgW59ed80wOqled8wwrGjPnvGC0OdLBVk2v6vkaVjPnKcV5s8x5FYQQqDVa6DRrqEUKa2+3AI0D/JwuxWF5GSKHXbseYLdVw5HpfsyKc16jFpV84V4L+HWebcba8BB5Z4dTPvV8MXSeifjSXHeTMphfoEN1RjdHSZwHZhfu1OK8jVgbzQt3IQS6nS5qDX7zF8iHvANk3TK7FANhLcbaKGkcwnPSPVTF2qgTMou1YTSibqvhsSM6weQ77Y61cuJUMD4MLmOQOT+NLWxEvMAoRqbme/A9QXcLyEmcb+4BDdoJ7NftaKypc9rJQXojrcXxwZDbgNo5QojNiwwuMBPnKyoAYJQ1QwawGmvTox4Iy81qYlgrSNrMlyEU7q7fa5rvxywc5A+0nPNnydeRBcdYm40cfCY1HQCr3+mrznke4stqTIZg8VmqcMNQNVpmfAR0ryN9bjvivP6Fu2rEZVBv4n3veRee0ftttQf+hy8BvvwW7W445dx0Vfwa2Zn0mnDO1zs86gUj8ZkbMbNYG6bG+a2D0Y7x5BFGcb74a3Eo7CET53xkwzlPcHBu1X06wYNQnG/VfXSbgf7adm8kW8ucszpD7Ry45oGEjabjg6EQAjXfQzRvZWV0kF839bOhEucrGOIqc15Kid6YMOpJxkA0YRERssCwo2iHaawNAJzbaeCz9xvu9Sxlzhem7IGw6+I8g/q5Ic4zEXnF0n9y4Ko47/49A2afpUX2dVBnJXpUlMtgoni+slDTrcTaGMyF2zAQpSCEQLPZRCdU/H3e2TPqhiOPtanRDT/lnjm/0SWUBGEcbxE25g8wubzlqDtHUoJTTZeLV4nXq8VrNflU11EOURo4Y9FpRxprE+uL2JGNPhiCgzNp4ScusteZuOctDGHtNgK0i4ofnXIddnM2C7/7g+FKtA3R4GAKhBA8D6iVOF/BjDCKEeZdNFu6bB9MIqWc90IYzJHRGe6Wi2FN36GOtSHMYj2nc7m9TL1jJR+7sHO+dVD6hc6mc959rM3GhQETcR5CQDKq54v3jon4IoS4Klr5jC4mK0pH2TlvIapubMM5H+l3twfr32dUGH5XNwKPdq9B2FFuvN/wglldt4SSc564w16VjbeUwRkdAMvMFm6Z81ungjOF0Tt68sh12QF2nfMDSue8/mPFNvpgCA5DG8OhTGjQDmvTipGZY8E5D8zcf4Uouf19jr9e+RlMgl9x2jHJnAfmXQZ8DvMLqoGwFcxQumy3VM/7Y8JImzkm4jzhMhYYi/PUsTZ0GbxanWfLtA5oFrLGQVHnvIOaznFmS80XqwIDgwsDgJ9z3mfmnAeWojK2dBhsBQ3jvPkxi79owTlvQ5w3qOeqzvnCGP7eCyFoTXSUHe41XzkOKJHmvlVj1MbQ8iTqdK+HEQzO6ABP3TmKuMXacHyVQG9eskwlzjtESZwfXbb2/JeH+jfpGxh88K1kVBEMMelStsET58FyFOev3ysoMHRocxpV2dgvMXCqr2yUGB1UPWbO+cVXBTfnPKPXqMINSgPkxsdW3Dc9G+K8gdPOCoZD9+id83SutrMm9RwA2qdpFrJGp+5vOtOz6JY7DBbg6ZyfR9UBmNVzJvXBY7KOOQvRj4n4Aizlzm/pMNgKGtSd8/Ti/NRGrI1Bd/vGBSgVBCa6ToOnOC+EMBsKa3EYLKDqnGcizjM6E3Op5XNm+hmvNQFg2WWwTVTivEOUWtcsOe1G00jdGaCE/i+itHEfSdBaTXqYJ86DNRLnd26gW8gS2yPO84u1WWmDZyAuzJmJ8wzLhKyc8xW8ULpsl7GVNvg+Zd78HG7ivKFTvUstzhMeXI3qOWBNnBdC4KBToB45cM7XNpzz7us5sHRpwKieA2AlLlx1zvPZYyxEqyrW5kSjdNkOANMB+XNbcc6biPO2fj8Jvhs5d7gbaQgWh8ECis55V7E26/8fo/rAjVmsDZ+aXkFD9Yl3iJpz/tDKc5PmzQNGTlYrF2yGLjuA2jlP23aufZj3Amui+A1FxXlLgkIe3rp1noFrazXWxv165sxeKj6Ff/Fdwcw5v20tcxX0KNVzABgfkT93X3V4XRG4ifOGNb1bD2jPMITD0po13+wgb7GWFsqddxFrs35wZ1I/FzWdQczOHFvpFLosHLmMDADN+ftWxdqcaNSd8/TifGijndwgCnLjApQKEnGe0jlPK87vmkTpWRwGCwANFee8o4GwKzCp51zhNhC2ggYeFpMTikvnPLk4b4AVWYsg1saoJW2d9im6x4LBQNjOddaK3U37RV5zYS0nN4+NAyoDpx3XzHkhwHNzxEycr5wLFcqHeQs13UrmvMYcGSklBoMBpqMhpooXBn0xxV4g8we7GcbIeJ5Aux7QvFa1Nrl4d6bbwPFIc22dM6RrWaaYOH/O2jrS2IhcYHDZDvB0zgsBVkL44mKFyXsGXBGthqhibU44ypftU/pOOCuxNgbdpnUVl7UOBL9jpM55YkHc6MLdcqyNknO+SXtZoczyXpDd+ZPXWW+mzfNaU4U57lWpE4xS8R8eWnlueuc8M+cogXPe6NZ7GS8gv5E/aNfhewJRUYeFpUgbYObmV15TY8dZ1vtmrI374l/3l9bA4LJgDrdYm8UnK2YmzleceJTb4C3U9L7qcxchLr5HGAwG6HaLua3uBNB7x63o5B1kKebINInEeQuuttPdBv72vKbQ07YozhcZCusgqo6tc35+acCongvwGvC+iLVhtMe4GmtTOedPMsrivA3nfGTDOa9f9+pF5o4UgSA6ijRznriuGxn8WMTaOBLnl2F0ccuROObqnOelCfJaTT58dkQnEJfO+Z6uQysVZh99gjzYPSrnfGuf/GbT8wRO67jnd+wNbPM9oR5tQ9xJUISVWBsmw9pWnCmMnHaeB1YH50V8DDfnPMvNUUWZuHTOD2w45w3a4K1QNxfnO1Qt8Bbq1+kiIvg6FkXxfdV9kBc46YbbiFxgcpgPGDrnuZWpq5nzPN4zAGjNxXmCuVUV28tIdSabhcx5brE2ge9d/V2lhKCmc3bOGxn8rDvnFb5zLUfrpLHySWN0uc2RmJsxdg7XdW0J1afeIWOVw/z4eFZUiTevR6Nr3Dlv2AIPEIrzlvJgr9tp4MGjUbEfstx2fuN+C1+8NMz/i5ZdAVms7DGZCM/NGs/MeQFezvkF7MT5ipOO8mHewhwZK855jVibdruNXq+HV7/nL5UvK36p+Ta0A4V8+5p5Te9QzZFp0YvzhRzqKwi7mfOq6+pc5+Siu7bu6mRSP2u+B4RgFVPnCQHJwIwwp8Ywc37hKK2c8ycadec8faxNZKMz1MA5D8w6Smjj8wRJrA3ZhTtALojvtvgOhK35AkKI7HlZhEPvi+CxjrXhg5RyZotlVNO5wk2izKMS5x2idpiXM4GeuGhoZ5sSI6XEaDjAdKwg6AIYjsboj0K0G352Ri0n57wtcb6rcXjYsSvO33TQAv5W4S86upEH1gs/j6/AFRcDo8O8EILV5uhqgd2ySltxzTMOFQ/zFsT5oY2BsBqHeSEEOp0Oas0WQk/t9eg0axAqFwEEF+5kQ94tZLwftDW/99unrEbEnSoizjtgM3Oeh9Bb88VMnHcU35cEtwHvi70Yk/cMABq1SpyvAEaq9XyqdnYtwtRGrI3hgPd2nVicr7VIREWyC3eA/FxqpCFY7kITQqBZ87LjGB2e0xcwOn/Oyief+nk1QpjPmnJnR1UowWfXeAJRboMfXSYX560MkNNgMBjguU/+SuW/f+eV/5ubUUsgzu80g/ybZRUs5cGe0Ym1seycv2lfMeu/EudXWMn/Y7Im4MphnklMAABIcI21qTjpKDvtRkfkzz2w4Zw3+B2zskEnEOfJDvIWavq+rjjfPUu7kDWULw0sryONjbgFJkLvIgufUT3ndm6+Oi+Az8IagY8IqGJtTjjqsTb0znnjM2cShuJ8pxHgoeMx0WJAMkMGIMyc92tka5rDOdYGmH3XpYrzXkD+eqiyUtIZmdW4wdGixqeSbzc8drEnFOUBcqPL5M/dIxfnmX1NEAwy8TxB45635Jw/s6NxeLA4OA4AHnagWMwdtcsBa4d5JgfnRo2nOC8EWDkXFjAT57etZa6CHvVYG/p6bkec139MKxv0erFBs0mQifMd+pq+19J06nbsiuK7zdrqnJY0LEbrZFFbHwjLRIEOqoGwuTDU5qtYmwoABS7boykQmgnf61jJnNeIqVumTRkfA5DkzQOEmfPNPfLaoT0Qtt4tZVbJyrlz43/ccVZLV56W4/mTCYtzJ5M9D4ClWl4dik3gs2s8gQxUW9EtDJCjj7XR+3Jot9v4Lx/7a/z2J+5V+vsvPvVJ/MPjD6Gdd1tOlNd20K7hcGC48bLQAg9oOOdbB0Bg98BxpltHo+ZhnCdUORTnV3QGJi3nq7E2fDYjAmAlLiw2I5UaXsEMdec8vTivfNFfEoGV4XEUsTZE360WIly082ktO9Y9T2C/VcPFfs4+yNI+Jw+faazN4neAkfNPCLA6yC+c80zeM2Amzg+ASpw/wUgpMQ4LGEAmPSCgm0NiZcijoXOeLBJuTk2xyzoHsnVZiJHZaQQQQuO4YmHgfBLNrKGwDrvbxbKexKh+XimgrlexQDIUwBfJBNUZ3Qg+O6ITiPIQNwsZteTOec0NvxACrXYbtUZL6Z9Ws4HOlbiZTJrmznnAZEjbEpYK7ZmimfMlONuEEGrueZfi/LJwxCSyZTEQ1gtYHZ65DYS9Ks4zc867XkCFc5QP8zbEedWLgZLYiBoxRXgkh/kOlcvOQgdaI/CznWxp7FxPvpZ1lHLnLXflpbHhnGdyeA7mg2o5XbYz2lsAS855RjRqV96vSpw/sUwjibiIe514KGxkxTlvKM5nxcjqENCI83RD3unFec8T2NGJtrEwcD6JZqZznkZD0WHljM6pfrpewBocnfN8VrLdMNwanRwGqgK5hYzaHrVz3kDAI3fZ1TtkLWEHbQpx3o4o3m0ExQ7zJTnbHnagsOkiiCjQxV8uZCW0DqpQ9/nl0y5gtKarToFKDq/ghfJA2OnAuMV8HWXXfhEManotIN5aErVYkwgM9Q5Qa5o/TgI7DY161LUvzu+r7IMcOec3BsIyOcxfdc7zqZ8eM+e8z3Ag7GIvZrnLtIIvysNg50x6pM9vQ5tHbLZHIHfOk8XaEH3fWxrAuquz5yiplrZqPJ3zq93tPM7oAL/Osxm81uOxe322Ez47ohOIunOe1mk3DiNMI2LnqcEBxKe2zxBF2gAFhqGl4QXWbqCFEDjdKRBtU5KzTWkoLNHGTIfVzHkehb853yQxOsgD/DLnq1ibCq7kRnktQ3jhHkaxHaedwaZ/IXBRQXRQ1BK/17HYgbajc5DfsTvkHQBOdRReN0viRh6b5g4eh0OfpTjPqy2fvMOGgHqVOX/iKXzZPaYW5y3Uc8NuU6PhpkkQDRttBB7N94glt7pW7nxJ81uaTMV5jnPhgLW4nYpElOYTVeRSifMO6Ss752nFeXLXPGDmnF93PplCWFSU2rmzaJ+yetNaKHe+pBw7pVibmnl+sC4rN7tMhOdGrXLOqyAT/hsHZHVZcOIp5LYjjKqbUF+0zzGo6Q0bznkCSJzzFlvOC7fA1zulRMTldhA2dp053IQQq4d5Js4tns55Hq/NnKsHeT7rWlwsVuL8iWU0KVhTx8Td7Vac82bnfrL4mDlEmfNCCKK6budyeU9HnC/JOd/M6jpwOheOX/QsMN9a8KlVACCZrefqVqw6E5tQifOOCKMYE+WM2kPS5+6PLbTAGxzMyF12rX2yhzpdNNd9Hcs34KeKrK+k2/ibVGJtiDZmOqwc5Jm0zC0GwjIZULsCI3GBrXOemehRUT6FnPOEh/lpaOl3weDispHlyNKB6OBM0ppv0SHeKTqwducGOwtZI3f2TkkX/2msGjx4fBdfdc7zERc8weswfzXWhs+arjrneewNK8qn8AyX8THp81up6IZ75sK1KQ+CAe9zdijquqUaptVx0LE75H0O11ib1TM6n/MnnyrFF47dcNtIJc47QjnSBgCGl0if+3hMm3cLwCgehDxznvDwfKpIbEwSlgXx00Wc/SUNmek2AuzlxQE5FeeX/h8mwvPCacrIJQBcGSDHKA+2uo2v4IryQFiANNZmGvNzzmcOGtN6wH2Sh6kHnt7Q1WUIL//XKRxr07UfaQModBCWtLdII1iORmQi9LKNtWHy+gA8D/K1+UUPk8jDivIpHmtD65y30olpGGtDEgm3DGH3NGfnvFasTec6+oUkkC3OOxwIu9Ldzqd+Vpnz+SzeO24Gui2Dk+pyohhMCrSYjS4DhAfwYZGLAVVMnPPULfCERXavVZsJlLpwEudLdLdl5s4HDacFbrXw8ziALX4HGG1EgCtln9GaqnpfwRXlgbAAqdOOfH7MHIOLwmZAfMlIWLuM3fMWHWWFowPKcs7nxdpYvLBQYdXgweOwylKcZ3biY6dzYGmYdRVrc2Ip7JwnvGwHeFpQ2tTOeULxt0txcWDpgrlwrI0XlHZWzxyma2lWngoBw7lwwBWzGpP9BTDTMiSzIsrxwn0bYbZVOzkMigjkMgbGdLnzhTceKhiI8zXyWBs6cd73hNlQWMtDWAvF7hAOys3jxkxx3p1rHuDZMtfg3ErNqC2/ooIjYRQjjAocqSd0A+SsxdoYbPpbWYc+HQhrl/FB3qI4X/jiYLcccX5mUsj4C46Gwc5ZibVhcljlGGvDUVzgxtXMeYZ7sYpSKGxgI3fOkz4cCZ068VmJUPzdbRmuLWgAdZoBtesUXlv3bGk1LFucd5c57y/Xc0aX29yc84yWsqAS52moxHlHDIrmvg8ukj23Fee8we0m+fA4YhG6UK77OpZvwHMdbXMaO6UK0dnivGFUkCEcW+YWmfPMYm0AsHmNOMPwLFVRIqMikTYArXPeWqyNgThPnjm/T/ZQxi3wFh1lhWNtSnLO+57AXitjr+EwnxYA/BVLOI/DYTUQNh+f2XqApfetEudPLMWd83TmOXuY7VKbNY9WDCSs6Vq57stYjGUr7JzvXm9nIQm0sy5cHIrzq855PmdiwWRvMYdbPQcqcZ6KSpx3RKFYG4B0KGyhbFxVODnniQXxU6oCeBKWxfn9dl1tw1Sys+3GvWb6/xhk/G8l4DNsmav5YvY+MtqILGByYWAlh7OigoDiGbV04rzyYPnC6G+ym0wHwgIEw+MsHloLu/p3yjvIZ3YQOhbnayvOeR7HmsXBmcl6gNlAWE52O0ZLWeB7870Yj71hRfkU6mwHyGNtYit7XbNfNiEEbV0njp81wuI5vXDmfIk1PbXDUXhAzU4ngQorM2QYXZJ6ApDM6rlkJuNyHPK+jfCxdJwwChd/wqGwKuK8lBLj0RD9UPESYRwB/T7a7XbhjHbOmfOAwjC0LCxnzs8dbYeDSfZfLDHSBgBuyHTOu83y5BhrI4SYtVMzctkt4BZUW1HBjMLdaISxNoXidIpgsLmmHwhLJ/4WznVfx6I4X8g539gB6nRD9fI46NTxt+f7KWtxl08L8HRrLaJ2GF24e8xibYQQzKSF2Zp8z2P1vlWUS2Hn/PholkVDJEhx9aG0aj5N533QBGp08abG4rxF5/xOI4AQQt1ctHujtbWskxprU+84FVdXL9v5fA9zc6oLIdgdzznuxbYRhkrQyaBf2DlPdzOvsvEIJyP8yMv+GX5E+VHvAvA96PV66HSKHRhJxfmgQVr0gQLRMet4QSmH1lOdWr44X7JzvtsI0GkE6I8TPue+21ibVXGez9CvesDvQDhzkPEoEyt7W2abpIqTTeHLdkrnvLWBsPp1ObNduiheQOriMo+1sSfOF3LZlRRpM2eftXN+6bPKpDYsDvJM6ifAT1wAeK7JF2Dl2Kwol0HSuSWLOJxduBPVhii2oM4TOH4bVJfuxKY17TP6HIvOeSEEdpsBLg+naj9QojifOkeg3i1tDUms1HNG38OzUsWsXjFy8gPLkUS8XieG24xMeL2rJ4jCmfOEA2fGNgbCGkAaa2PBIX7Q0SwOrf1SvhH2VTYmhPl+qtyQFm3jWBBfyTll1Lpc8wWrgzxwRRBnVvxnbFmlrbimKe60I8yctyXOG/yOZQ4aK0pzj7SOdhsGawsaVg+Lnbqv3nlYYvs7AOxnZs67dc4HDN1ai9Z8RvXTYykugJ1V2PMEq71hRbkUrucAMDwke34rsTYE30Nks2SIxfm9rItjFSyb17heurfT9kIlduQlETB1zgsh2Km8HqP9BbA2zLdCG15K0AmisHOe8DCvko8b1Ju4/d3/L743fLfagz7mm4HH3Yp2u7jDjXQgrAUROnMQWhYWW+WWUXINlOycB4Czu0187sGE+AZWsTZ8DmD1wGO1EVnAck3VBqCCD4VnyIyPydrg7cXa6Nfl1CxTHYgv3FPdYipYHpImhMBuK8DlgYLLrmRxPj1zXjh32gWewKJ3kEn3meddGcHI6MLd8wQ7bZ6nc56fUaKiPLSiW0aXAdxM8vxWxHmC78VGwFOc320WjI6xvJ51lMX5WrvUs3rN91DzvU2Dh+N6Xl9xzvP5Hp7dbfMSwz1mxoSFUYJZXee1mnz4fOpPGL1R0cM8XUatiitACIFGs4VOqPgR6XaAgnE2c+qkznn69mrtPDvLw2DnZLabzyk5cx4Azu6kxNc4jrXxmMbaBFxzTpltRmZsW6mtuJYp3AkXh8B0QOJQUnHOSykRTkYYjCboq+49hhO0pSw8QwYwFMDXIXZlG8Xa1O2K88Bsv6EkznfLFedT3YmO82kBIPC9q+I8k9rgC4EQYHW5zewcD2D+bvFyzguf6V6sohT0nPM0c+GklHYaSShibaiMdMQCtBAC++0aLvVz4l3TsC3Oq+459m4qvZa2Gz4uD9b2kMTRwEUJljUhRpeks5EtvM7Dgtl6rg7zZbjZ2CL4fOpPGL2imXaEA+SGEwtt8AYOZM8TCHxB4wC0IM7vtjR/TUpyzu+rXB6UdFGwTLo479atvhJrw805z/FAyKz4A3AuBlVULFO4Ew6YzZEpSZwPJyPc+bJbcGehR75La4YMkNEurQPx3JauyUBYy855YBYfcw8G+X+xe876WpZJNSk4boEH1tvgedQGj6GDjKNLneGS4HkBz4VVlELhGTIAMDokeW4refMAyeeZLHPegjv8wESc5xJrs/swq+tIolNP6NRzXNNXBsIyEuc51k9ukX61KtaGBIaqy8ng2KU4byNz3jCfkaxdzoJDvBH4ern4JQniSnl7JV0ULHMmVZx361ZfmW7OyjnPtJW6ujCoqMhE7zB/meS5rbTAG9KmyqYFyKPqjMT5EgafKs+42SlXnE+dbVPChUUeKwdUJrVhEZ/HqH5yFBcAwS5zXjCKUqgoH5fOeVvaPEUHD905nb6OKs1eS8IL7IvzTcWavle+OJ9opHDsnK8HlXNeFY8yeYIAn+GsHQBaHcAu4fOpP2Ecu4y10XH55WG4mW3WPPTHBOuwdHjuNoPit/IlZccpxe44GAh7XeWcL0TN55o5z7CoMSv8FSebftHLdoDMaRcqnOaDehPfdceH8ZpTH8GXHf+52gM/83atGTLArC25UfMwnhJ06RHXdKNYmxLEeaV6XmuVngvbqfvwPbHp7KzpfUYoCVZu3HnUq8U+g1GtEgKQTF6fObO3jpk4z0gQqigXKSXGTsV5td8FKSWmoyH6nuLeoxYB/T7a7ba2UEUWa2PhQtcoftbyGUe5+96BOJ8YQehanF+JteFzJuZVOWf4zJzzQeWcJ6HagTggjGIMCjvn+2QD5CYKbfCFCZpGP042Bd7S4blT93GpX/CHShLnc1vmGjtOROidRpA8bMaxW311IKzb/PtlZs55PhsRAJCMLgtWvvoYCR4VFf2imfMAmXNepQ1eCIFao4V2s47OVHHbt7NntN9o1QKMp5pt5isPRNv1Ne+EU4kD2lzLPulakjjVUaiP3XOlX5oKIbDTrOFwsPaeMoi1WWmlZlIbFvsMRjXUE4LnZTszBLN9WEV5DKeRXiPH8JDk+VWfO5yM8JaXPR1vKfTob9WOqgOAJtU53YI4rzR7LYnOWdqFJKDunKcZKFyEdj3hPQ0YOecZ1U8IwWs9uBLBxojFXozJPmzOtu16eL2rJ4TCefMAADkT6BtmbikpJSahBZeKqThPNUDOljiv0wpfkji/08iZVG952E0aQggcdOp48Gi0+j84dquvDoTl45z3fcGuoLGF2Qap4mTTGysM8FyHSJy3lghh+N3Yafg4VIhOz8VCHd1pBriok09bQk1XEuc711lfRxI7zYClOL8yQI5JDV1EyDASennq8lWsTQUfhjoRdQCJc15KiV6/h+l4mPt3Vf4ONU2qzHkLNUPbOV9CLd1R6dartZzMhmsnOufN9BxTWDvnmRVRbhnviy5GZq/TtlHtQBxQONJmzvjYWJyPJdJFXBMM3dCJt7c6WBLntdZXkjgvhMBuM8DlYYpA5CBvfs6pTm1TnDecT2DKaqwNn8x5XzAU57kWWG6vU8WJpu8wc97aADlDcb5FVdMtuNV3WzVNcd5+LeUszid26TlugQeYZ84zWQ/AM3NeCLAT57m5ESvKQ2t+DAAML5o/92CAs6f2C//cA299Tr6B7ODhwD99vXZUHUDonLcQybbf0jzLde0753dUnPN7D3Ny3uokZc47ds4Hvnc1eomREYtj/fSYxdpcdc7zWte2Ue1AHKAvzh8BuMHoua0d5A0PaHTi/D7N46xR2NkfNEs9tO5kifMObuPnHCQN6WEVa8NHnA98j9XwGwCQjGaGr+RkenzWBWxfy1wFLS4z5yNbwpZh5Fe7RvRdZuGSW7nNfJ0SutASa+Y6rsT5JAcgh8z5ZfcYE6fdwvzHZD0AT3GBI8Ln855VlMtIJ28eAMIxMB05cR13GgE6ee7sVgvQjLNZfh5j/BoQ0J+7tJ3zXfuD1ZWG0DuItAFSIoUdO+eBpfkGjM7EQvCKegUAn1mtCtgOhHW9gmLw+dSfIPRibQCMjoyfW3XYTGEMRU6Soh80rBWVwpcHJQvis5v5lDbHkhz8SewnCQ0WNmZFWPmS5hRrw3ASPFuq16mCCVJKp5nzsZULd2H83Uhy4V5rzeo6MXuqA9rWKaGuN2s+Oo0g+8LHYazNBiyc8/N6wCdTfSGEM6pVsyXxeH3mzFbDyzkvmF3+V5SHtnMemLnnazdq/3i73cZ95y/hNe/9pNLf3/Mn+Bnv36Gd5H5eh+B7MXF4aFEsDTLnLM7XAw+NmofxNGPOjYNhsEBKrI1hTDEFi9x5TvUTYLO/mOMzuvwHlo0SvF6nbaMS5x2g5bIDgLH5Yd6ac97wAE0izltyzQMaQkPJUTLdLNdE50x5C1kjccPk2jnPNdbG91i57ACw2hgBS/siZu6FipPLaBrrRcWNj0me30pJD+rGh5AmhThvqaYnXhrn0Too7TL3VKeeI867qendBs9Ym0UrNaP6edVBxmdNngA7cYFjrI1g5NasKBczcf4Q2NUX54UQ6HQ6qDXUvlNrgY+O6meV4DOtlJ2eh4VhsMBsbUJnfMWOfXEemF1sjKcZUXoGnxsTWvWEMx4HcX7eesbtopTZmThgljlf8/ldqmwj1avngP7EnXPeXgu8mcjZVbn5z8NS3jyQ0vqVRenO+YxNk6OBsEBKG7xjtzrXWBuOmfO8jsxL0TbMXqeKk4vLeg4AUZzhxtLFMNIGADoU4ryFvHlA02VXoiB+Oi933lFNTzQBMDjI+/MDPCNx/upFMp9aJZgJ81fhtdPwGH2OKsplONWs5wBJVJ2131GC7yHtOLhlLInznieKr6/WtraedXINiLuVc36Zq855Pt/FHDvPfGaZ8wHDWTvbSPXqOWCg0wIPkDjtooirOE9Q9C3GtxR29pd8eM7MtHMpzic65+ljCooghLh6cGYUa+NVsTa5LLYhzJxtfEWPCtto1/PpAIgMhIArWOmGI4iSIYm1aeyaP0YC+20dcd7+4Lg5B1nivF8rTVBYJ9FEweAgv3CPMTrIX10Tt5pe1ao8RCXOn1jMnPOXjJ/fmtZG8JnuXnGnG2HRRJd43sxi53o7C0kgcfDqHL/mrBsu0XhoIUqwKDxjbQQrAwAA+MzOwoHPzyixjfD51J8gBrpOu/G17Jwn+ILhJM6XnPOe6ZwvOWJnmcT3lYFbfZEHy0qc5+ec58biYFAV/gomDEycdhPzC3crsTYEUSWFh6gn0bQlzmvUoG554vypLHG+fdpZNEniPohBrM3CrcXooLqIz2NUq9gOhGUXa1Ptw04q2pftAMkcGWu/o///9v48Tq6q3P7H1zmnpp6nQBJCQlAQEDIAkRkZhXABL/hD+DAkoIJeEUT5KoMXGT6RGxDhiqKCkIAyiXwwDIKooKBguDLIeJnHMIQECOkknaQ73fv3R6UrXd3VVafqDHtV1Xq/Xijd1d212HXOXmc/+9nPE8Lc6LlO+QHw4US04Q5UcCIuRk8vmKE+SMsm1jy9oVASBYGnMwbns3B5qEfmVUnapITqQqNngZ5Ku8GHkDkfTfM4BA5yhlLLLsId+bLL7sQcEB+1UU+y0Wrn9YKfK0FAPPcA7NrXMojjEgbnyRbzzuCDEZkuUb9U1Ax2ENYm7yFsoDJnzneSB+eLZvZb3Gwv+JxBsNmeO9rNFAgnPN7N65pcwXmVtalfKi5TB4SSOZ+IKnU+pFNF7Q0B5/sIT32VH5yPp948UKLMX+v42HQMJ1Moc97y6XZgaM15srmYbO3pkdWc39Brh+e5pxrR6FlgdaXH5tauDPzekWTOu4nAE1YomfMR1nkvu+xOzDXnR20Ia7GkDTBKph3BMXjPdWAch2KjYBDXAd+DCNlyPpckQJQhCbCNkoiTiv0cCK0pbOiEcKx51A3jcohow721IbEheOqXOMvaFNs8iPnZYiismfNJwoW8S9gfhbFmLpea9RB9ZiJeApW1CSFz3nOdaOJ/XjjPzBX1axlKhEl0ZZero8mctxmcHzbXJdIUTVgpM+cJ/ZOvISzfc081otGzQMWZdiEcgY+kPm0ImVOjBpfLIdMe/G+MQtmZ/XGXtRlt88DiQh7ILprTw82fINPOcZDNJCHaBVdZm9JsaAjLE4QR9U2gTDvW4HwImVOhbLhHlDnvOA7aibPsigbnY362GErB0xAEfr6hCRmPL3iMwXk4VM88OUwETa0DUPbGnagZKi47C4SSOe84zobs0zAJKaGloyloWZvoMufLbggb44Z7Y7HT9zE+Wwwn5bn5PbMIkucAIM0YnAfo9LgEGylDydWcJxunaoMr/bBOqNj8e1cFfu9ISjuGMJkPBnHX9gV4SI8wEN2Y8uC5jr/NDceNdKOgEKM2m7F4BD4nITlsmrFYZmcQz3VgiEraAEM2DJggW8hvaAjLNU5kwyRiJNBiPkAfGWMMenp6sHZ1D/rWrvb1Oz1remGMKd3AOMGy4R5dll1nUwofrer198OOG+tGd/GyNvaC8+mEC9d18ssjEizmc8EsopNwuePmRF7F6FOMmpg+MxEvtjPnASCZcNHXH/KGVUibqGUHwIeTag5FRyHaiDPnCzZezemwF5x3HCc/e57Az4GhmfM8BpFNnOcKOkeykRcAxv4/1YhGzwIr11YanO8BBgYCHTmKJDgfUrC1KZXA2j6fi+VCRLhodRwHbQ1Jf4v5TFvsx8JGbVjb0B6rjoISUsMz5+3Xs/McB4ZsAcaYOU/0XARgaK8AWZfgIFjN+coX8z09PWhuLm+hez2AlfO+gKZSgfMwTsOF0uS9PfjfGIWymsI2dsYasEsnXKQSLnrXFQjQWAzOO46DxpSHlWuGPMOGUAIpKB7hgjCXfE3m6YbsWD4jLluShIiNYA1huwOv0YEhpSHCJKSNy8BlbWhqzjtA45jItAynYOPVQWLcJChEXt15gjJ1wJBSdWz+SaaHryEsX4nBaoTrU60D+gdMgBq1BugLnj0fOolwJvNRA8x+cBOR7sgDZRi/hcVzOuFumBQtaxlOXq09NxFa7cMguK4D49jXMZRs5rym5GKwBjxE/RIocz6kTLvQCSE4n0oUKGlWLhH6V2c5x/ObNopMRyEGkwEKEvOpvOHklbZxExSLsFzdVargPGFZG9a4fCRZQ5Xj0g6UiJpAZepgQik/m4wi4BZSUlTZJV6Hw1LWpqE91rXoqJnzjmf9hHt+5rz9zXZgSJCXbTOZyM+BCBtIV0hCNedDgedJtk7IyziqhN5VkZpbRaQaQ/kzo5Zm8UNjZ+QrD9/NZiwExB3HQWPaw/KeYZl2lhfyAJBJuMDgZU9yZM51AEO0kAfWLwjJdsEdsv1blzBDUtQ3wTLnP674VxsbG7Fy5Ur86h9v4JHXPvT1O2d0/h2NvU+V/sEQj8Av7Vtb2S8nGyLN4ipa1304FhqrtzYksXRFgbGzfBouL8uOoN48MCRznqmsDWEdfMdx6GIdWciC82RN9kQ8DAwYrOkL4OdAto9MwHJskTR5DCno2hK0rE1EfWSAbKN33zTFlzUPFGkI29hhfd2XTnhA3/ovkk1WtQySK2tDsPk/iEN5up1LD2P/n2pEEY6Y6V7TV/qHirF2BdAyLhwxYRHSArpoN/NSxBAQ930M3lK2eks6geU9w66vCGv2+iWd9IA1678g2ZX3XIfK9IH1WeFkRsvGhrI2XJ+dqF9WVVqmDgBWf1zxrzqOg6amJmQaGpFM9/j6ncZMCk6fj4V/SEHXlkyicIDZDxFnk3U0cQfnW0Y7SWjZ0/My50n8nHFByNkQlg+ypPn18HxmIj56+vqDX49rlgNtmwb6EwVPQQcltOB8gHV6sjHSbPWGZBm94WL29FEz52MsrTMa6YQ7JDjPkUCXItwgNcbQHT/z6DLnOcsRVRsKzsdMKMH5CjHGYHXPKt/N49a6q7Fq3To0pr3iDeSS4WTONxaryVaKWILzPjMGLGWrNxZazBME5/My7Wgy5x0YooV8Fr5debbVfO45RJnzgoRAx+ADBOcHiWStENJCPlDzuIgXzx3lNI+zsOFeMAjiJkJ73qqUTIIvOJ/btCXKnM8lqdNtJJOZOiEO2QlGEQ+BNtoHWbsy8J+IpOY8Q/nZiNejjuOgOVMgSa0QMQfFR605H2Oj+dFID/V0y88Xg7DWnGfTQxecdzkT6IrGMAlRhCNmlq+2F5zv6enB1pPKazxyNnw0kAtpMm8iz5z3fQze0rHzgg34MtEdIfRL7ngaQNNsxnMdwrI2oMr8A8CbJUD22Yn6ZWXQsjYBs3EiqY8c0iZqWzkB8OFEHJwvqyGsleB8gbHLtFmfk/OCDCRlbRibkDmOs97TeTw0K4VHTw6y9HmH6DoS8bEylOB8d+A/kYgicz6kjOimQEl07aFoKEbBE+SFiDtzfrRxs1xvHhhec54jgU415/0RxVQRBNbgfLVB9rHWPt2rA5p/gOB8ZISVOR+k5nwM2erMNeeBAicPCLLsACDt8Rm/S1g7ziXMnGfbbXYISwUAfOMk4sEYg9VBMucH1gX2dDeKzJmQFvKBMucjrgnb7rfBO8CTOR9hvV6/pAlrzucSnV2ezHlg8DmDZ5HqwKHaLGDFYQsIiVgIJ3M++Bo9FUlZm3A8PeG5+aehyyGGQHSz37I7NGVt7AfnGRPoaBuLkulhW3sqgS4cNHoxY7OsTWNjI154awkuvOd5Xz//6YaPceq6X5UOmoc0mQfLnG8PRUMxOv3WqLVUSmbEccN0C8VCLN/4OYLznstX1sYhrDlv/+rJR2VtBBM9vSHUqF29LNAJJ484c76sBm3DiXjRmvBctGQSWLHGR0DGgqcXDDIQnITLCzKQlLVJDEbnGX1BGWRVh6uyNnVJOJnzIQTnExFcfyFupDanE5U1zo2hCavvsjsxB8U910E66WJt30D+CwSlZxnL2qRYy9qQrYojOTkbAMdxsmsSsvhKtUH4JFvbdAcua1P5kTnHcdDY1IRk2l8wPZ1Zi6Z1Pi6RkILzvne8CxGDwfkua2Op5vyIzY1UsxUdw8l70Ayp7mFQPJcvS50xm5/M9zcEIhXwEAQEqjc/yOplQMdmFf96JMdaGWrOx9TknTY4XyjIkG6JXcdw8o7AexzB+Q2+wLWkccmeMxwHMGTBBUbYnntEPKwKUqJukBDK2qSjCM6HuJHanEngg5UVNHqPI3Peb3DeQjmZhmQCa/t6879JEJxPJdxcP1ikOILzkZR2CogBqPwcGJKwRgRjLKPa0OjFTLefhWAx1gQz/kgeOlNNofwZ36ZaiBgC4pmkh3TSxy1jyWxHnHBIcwTnkx5f5nzWPMhczcGQ8/kcsC3kc0f4yMoXiPpkdW8Ii/k1ywP9uhfFnBHSJmrBuul+iWHx3OantI2XtFKOreDzEMGGe15DWJIGrN7gEXiPLDhP1kcmW3KezdNtKygApSgRNSwNYdkz5yvedI8jc97XCXzHSm+4EaVnAY5SdXmn20mC84NRZ7IgryHTw5Y5D6zXpAS6QHBdZXVA8Mz5YAv5SG7kkCbzgjVW/RJTQLxkE7lkI5CwU4e1cXhNu5A2TYKS9IZccyTG77ngK2sD0D2IsBm/p2YzgohQMu0CBucTzDXnicvaAD77yKRbrQTrWDPn8xIUWGrO58qdcWwWDMLo6Wwb7pSQPfeIeAjlJBxrzfkQN1IrXqvHUOfdV1mbhnYra4jCwXn7ns5Ycz5JmDkPQN7gA9dzNU4BIb36a5fANecDZs5HEmwLKQjcWk6Dtjyc2ILzJTPtLOzGD9I4oqyNfdMHhpksSUNYj/DYFedRMC49nutkgwsKzgsCVvfZX8xH0hA2pMz5ijPs3EQsWeK+MuctnYQrWOaPYCGfIaw5z9qEzGHLIHPAuWgm0+TQPYeJOAgncz54WZtIApNhZs5XulaPITjva+PAQkkboMAaHVDm/CjkPJ3MG+hqzhNalUP2HFaNEH6stYsxBiuDlrUJvJAP9vYFCWnB2JJOVBZoyLTGtgBqLxmcj75O7miMKGvDUjtu6GdKsivvuS4M2fTH2BCWDddxYJiCHaKu6QmjrE1vsGPwzJnzjSmvMk9vaI9lUeY7c94CDUlvQxmvQejK2nBkzrPWnIfDmEGmZ4xSjLjvRF2wMoyTcL2rAv+JaMrahJc572tTu9D7x7C57CtzvtHOOr1p+Bo9kaYoxZYiDM6zlrVh08PoVK6nNXpQuK6yGmdN3wD6B0ywP9LXA/RXHuD3iMvaOI5TmenHGBAvqc9ic5eG4WVtWEx2aFkblsx5F3SLZsrMebJuM9nPTcYvOAilrE3A4Hwkp+FCypx3HKeyI/AxZbb5y5y3E5x3HAfNhBvueQt5lprzpOXOHLK0NgcO52qeDLJHQxEToWTO9/UAA8GeC9gz5ytbp3fGcmP56l3HkjlPsNkODM+cZ0mgI82cJ9PDpSaLMueDw/XkWOOsCFrSZpDeyrPnw88IcUKtbd7hJ5NtODEabengfHssOgoxop4dSc35hMto/HwZbYz1aR0y63cdB8aR8QsOesKoUdvbE+jXQ8+cd9xQy5VUVNomhnrzgI8eMoDVDfcRWYAEi/m8hTxJ5rzjONl9ZLJFIVt5FJ3O84ejpXFdEoqfA9kAfQAiOQ0X0oY7UGFwPiZPb/WTDBCTluGMyJwnWaOnhmY6syX1sfkVmR6uFXoWh+A0SLXDdZXVOCvC2JUHApW2Cd3zk5lQg5y+FsvDUeZ89q1HZM6zBMKHZs6z1KgF2GzNIcycZzverbI2golVYZS16Vsd6Ne9sE092RCqp1dUnzYmT2euOQ8UyAIkWMznPWeQBOeB9adCyYLzbJn8wh8O2YlBEQ+hnIQDApe2Cd3TvWSoNW2ZT7gX7NUyHEuZ86yn23On4dwEz2m43DMo2VxMt0a3rWAkKmsTHK6rrMYJXG9+kADGH3qwLWRz6agkOB/jLnjJQIPFhrDphJs/UZMYf96DJo0mF4bMZF0HfCVbyMbIcx0YtjESdUtox+ADEPrDecgB4IqPwMcAe3B+5DF4+8H5FGHmPLA+oMoWDCfzTweg809nyP+ywKVGxMHAgAkxc55twz1c32ht4C1V15D0So9fDI1pCzHiJBzJejg5mKWeCDfZMggeYc15ytPtJmCp7AhwdLo9MFxXWY0TykIeCBScDz0hJOQa4pWVtWHKnG+PRUchHMfJz2ojqe+e9Agz5x2AbfpzXYfmwWgQtsx5BecFE6FsuK9bE+jXE2HXtQ65dErJJuqFiGnDPem5pRvIWfT0EcfgCRbzjDXnAc7MeYdssyDr51yeDoDuuYdyjESk9PT1I7Q4F11wPtxT1L4C4CN+qT1UDaPhOE7pJDpLZW1GlJ4lOd2e63FAogfgDM5nYfMGwuC8ytoEhu2qr2lWEgTn2U2/srI27aFqKEbJ+rkWs+yAYUfOSYw2r2EhyYaBF0XDpYAw7sqzLZpV1kYwEUofmYCZ86F7OkPmfIwB8fZSCQEsNee9JEUwPM0anFfmvD/IPJ0RtqQEET09Ya3PAb6a8yF7uq8A+HCYesOxNIQlWaPnPJ1ED7A+WQ3g81A2byDMnHcJ4yvVhkYwRkILzgcwfjf0sjbhTubMR+CBbD27okOYbo1NSyEyySG3NEmWel5mJ8kx+ARrlroeRIpCWfpH1C2h9JFZtzbQA3beyaQwCHkhXzL4XYgYM9tKJgRYLFWXl2lHkDUPZL0zZwsexzMGwOkNbA1hAdB5OmM2v2rO1x+hrc+B4MH5sD09gqBr2Y3eWU64JxuyvfIsMCJzniRZjTJz3uELzjN6FWXmPNkJxmqE56qvA0JpHgcEMv7Qd+RDz5znLmvjuU7xY/Dplti0FKKBsKxNXtUFEk2e48CQLVKzwQWyKZlMj8raCBaMMVgRVh+ZAMfgk2FnqYRd1qYST48zc77YQt7xrG64Nw3NtCNZODuOg3Ri/RxMdHzZccCXOc+mB4BhXPaRPYuJ+iO0ZrBA4IawoXt6BN7R4qfx6lBi3XAv4umWsuYBoIE1OD+YOZ/geMYAhiSSkq1B2eIGhjBznq2cXzXCddXXOKHVnF+3tuJf9YZmPYVB6I1myl3IO7EvnkfNGEikgYTdzPDcohngzJxn0eTx7YC7hJnzbMe7FZwXLPT09mNgIKQH4wDB+dCz7EIOzpedYeclYw1EF1/It1sNHOZnzttvBjtIru48yUk4YNA/ybyBzc8BOk2MOGTPhiJ6VqwNoUTdIHTB+fBPXZUdnGcpa2Op3jxQKHOeYz2cO31p6URBIVwXMOA64Z71T7JnDEJcT2MUFD2lxUhowfkAmfOO44Rr/CEvoptSZTaaSbcMS82OnlE71Ycc1KiEvLI2JEfOcx+Pm6DJJMteYzymD6x/BiF6EMnCZRHZ4DyXJlGfhJY1DwTy9Lwa4GEQclmbsjfcM22xzoMdxcraxHgqrxCNQ0/pES2cU4PPkC5PzXnOzW02PaAbI5ewVABbUoKInlAz59euCPTroQfnI8jQLis4n2qONXGtaHDeoqdnEpyZ8zk/J4kZAIN+Thac55GSw5gB2xJG4GoDIzBcT2k1Tmjmv6430K+HavzpcAPSZTeaycR/5LxltExAiuA8X+Z8rnYcUZad5/It5BmDC3FvfJXCcRwY1bMTBITSDHaQAJnzqbCzVEIOzqcTbnnZ/TGWtAF8ZM5bJK9MHUnNeQBIJgY9nSc4n20Iy+UNlKe8yJ4xGDcMCGMwImJCS54DgLXdgX49VQVlbZrTZcz9MWers264u66DdF5fOI41seM4Wf8kSgBwHb5T0oxeRQlZ3KAa0QjGSE9vWGVt1gT69VSYmXap8Gusl7Ujb6HG+6jH9EMOalRCenAx7yZotnlzJyGIFvIJwgxsGX9pPMYNDFGXdIcanK88cz5UPwdCDwI7jlPeQj7TFur7l4J1IQ/wBudz5fOIguGMJ8/csINsIcBWMxcA3edGJkfEQCjN3QdZEyw4n9v8DIsIEqPKWqfHXOe9+Ia7XU/PS6AjylRPuC5NJj/A2YPNISuzAwCgrDnP81xYrXBd+TVOaN3gA9ScB8IOzocfkC6rRq2F4PyoDyUp+4vnNGMtWMLgvMtmsNDxbj+4Dmk2oqg7ukMta1N55nxeKbMwiCTLrox7NuYeMkUX8jFn8Y94+9SQz5akISww5PQlm6eTLebZmq9ma6lzaco+HnI9Z6jmfP2xMkw/D5g5X3MNYWMOiLc3FFn/2vb0vNPtPOt0z3Wo4gYOoZ87hF4FEJa1UeZ8YDSCMdE/YLC6N6SyNv0Bg/PEZW2AMmvURpC5X4rRg/NEZW2ITDYxGJwnqk+b9Fy6HXDOzHmuMVJDWMFCqDXnA5yGy1vshUEUwflyFvIxZ863ZpKjb0IyLeSJgvMpj8/TPceh6WmTg83PKZ8xwPaYQTlEIlpCO9kOBM+cr7Wa8zGXtWnJJIp4erzPF8NpYM2c9xyqzHkAMA5XBjbjhgFh4jwctuewKoTrKqthVoVp/AFrzqfDzLSLICBdtJnLiPePv5TMqA8lBMfONzR24TG1XJY6U5ZdOU2HYyLXAIcKLj3ZXgFcmkR9EmqmXYDgfOgNYSNYoDWlywnOx5s577rO6E3emWrOEy2cc8EjokWYS9hHxiF7zqAs/UN4YlDUH6E3hA0QNUuW06PF1x+0XHM+5sx5Zk/Pe14jSqLzHJdKD4DBY1U0uIT+yRidV3A+OFxPsjVMqAv5gJnzIzqGByGCsjKtZdWcjz9bfdSGsATB+dzGC5HJbqg5z6MpQZiBzVhjjyn4Agw2CeLZeBL1S6gNYQOUqnMcJ9zs+Qg2vFvKCc7HXNYGKHIM3oKWoSRcZ8NGMsHzxSC50ohEG+7ZrDYuv2IrawOArtcOYzY/WfhFxMDqvhDX6KY/kKeH3xA2fO9oKqdUnYU676N6uuXM+fTQ+AuRf3ouqMrsAHy1y7PlzrjcwYAxOM/l59UI15Vfw4R6BL4/WOY8e8359mIN2oajmvN5MNac9wZ3mokCvZzZWg6dJLqa84SNfEV9wuTp6aSLNX0hZf5ZL2sTf0B81NN6FrQMxXGcDZ6e5Mmczz1DEpW1odzcJtsscIb8LwuMTfbYnntE9BTLnDfGYF3vGqxa04u0X99ftgSNG02s6FpKeC5c18HAQEiBtyiC86kEHMdn4q6F8nAFPd1NWN/kzou/JIjK2pDVnAf4+ouRJfLTouB8cBScj4mVa8PMsgu4kA8tOO9EYnTllbUhypwnOHae+2yJdpydwZgzkaaEx7ggBNgWzmx6KAMwoi5hyZwHsuVPliMMPQ6QiKB5XDlH4C1kq496BN7C5v8ICYOZdkT1aTeUteHxdI+wrA2bHsaauWSPYQDYnnpE1Bhjim5ur+tdg+v/Yy9cX9Zf/R1WrlyJpqbKEtjSCTe8PnURJI65roPGVAKr1vrYrLCQrV7Q09Mt1iecvPgL0+Y2YXCeKaEPGPRPLncwA4wNYbk+t2qE58m6xuleHWKW3UCwRXhowflUUyQTVUcj90K+KeUVzhggOHae8vgawjqOA9eFFvIlcAkXzobsQcRz+MoRifqkWOa8MQZr1qzBKr9ZdqtWAatWobGxsaJMu9DK2qQagQiyXtrK8XQLC/mCCQGOR+HpuVJ1VA1hCWvOM9aDJUy1Y/NPh3DDne0yEtHS12/QH1aWekhkkl6IwfloerM1Z4iD84WS6Bg224f2/CMqI5OtOc+zWQDwlWCjTA4jrDnPdmKwGuGJltU4y1ePHlDPHZlL9GHAT+PYvp5AC/nQytpEZPit5WTOWzBbx3HQnE6MDM4QHDtnrAULAC646sF6hKuvbOI8my4uPa4awgoCjDHoLhJ4X9e7BrO+fFwZf/F3AL5ecaZdJqwm71F5+minzQphI3O+kL6Ikg/KJRcIZzoC7zkwrkcxPoNky6OQLZzJ/DP7jME1Rpyfm6gn1qwrHgRPpDKYdeWDuLDtdnT2vOnvj+53LhobK9/cDc3Tgcg2mVsyCby/3Md7WwhCFyw/SxCcz+snQJREl02g44obMMUMgMFyZ1yezojK2gRHwfmY6C5yBL6iI3Mn3FLxQj4ZVrOZiIwuk/SQSXr+auhaqgnbnCkUnI8msFEOjLVgAcBxHaosO4+wdjljHXy22qvKnBcMrOrthyHKWGkIK3M+osC471J1yUbAi/+xtOBCPqKNinJJJ11gHaiC80nPpVs4U2a1kelxCE8XMH5uDpkeES1rSmSoO46DZLoBTZkUmgZ8+lNjJtC91pgKyQfdRGTJWr423S01YC3Y58Zyg3dgWHIkUXDecx26pD6m0/ZA1qvYTpMzZs7LP4PDdeXXMMt7QqxPGxD2zHkgewx+zfISwXnHs1JzHsjWnX8Pa/K/SXDsPOmtNw4LAY5iuOALzrPBuHBmM9ns58alSdQfK0uUq0mkMrj+5htweM/v/P3BSbsAu55ccaZdeGVtovHTdr9lbSxuto+AJTjvrQ/OEy3kk55L5ecA66kqPq9iS0pwwJiUYFuBiJM1fRHUbR4IVpImNE+PsDSbr1PulrLVC/a5SduJF+RJGIy/OB6Vh3qEZVXZ9DAm0DElCQ2izPngcEXwaphimfODR+Yua/o1GnqX+fuD/79rKl7IJ8K6cSIMjLc3JPH+8jXFfyjTau0pujld4NYhqE+bOzJH1DwOWH9kjshoE64DQ7hwpjN+Mj0OY+aCqDtKNXh3HAeZTMZ/ll0mAVTYOA4IcSEf4Wm4dNLF2lJBEEuZbQUX8pY2/oeTSrjAWlB5etLjKlMHsAYXuLyKsYQMY815UV+s9nNKO2ZCOw0XQTPYQXydiFPmfB655Eiik3DAYENYZc4XgzGBjhG2pL5qhOvKr2GKNYTdcGQuiQa/k1GAI3OpREiTS4S70MymD4xyDF4150cl2+yUZzHvug7WMRoImyayBxFXZW0EAcWawVaECZa5F9pCPkJPbW9M+dhwJ1rIE5yEA4aUISRqHpfwXL4MbLIEAIB0I5lsjLIbBlzjRCZHREyPn15vMRNazfkIy622FvLN4Vjy9KZUgWcigg33nJ8zrtHJguFsa73syW2ZQ0mUOR8YjWBMrCiRaVc2A5U/TIRWcz5Co/MVnLe4C14wOJ+wv5hnNX627CjKhrB8ksD2IOK5fJl/ov5YuZYrOM+eOQ9kT8OVxFZwvtBJOJKyNhs23HmC80nCeZjxyDmbnuwzBtvnBtCNE5keES2RZM4HfKAPr1Sd5cx5S+v0xkKeTlDWJne6nawvXLZiKFdwnqnsDzCYZMjlDQZ8ZW1EcLie0mqUtev6Sx/nLpcAi/lUaMH5CHfkiRfyANA0vFlPIk2xW7ghOM+zkAcGTc3++AziEdandQiNn0zO+rI2PNeRqE9K1Zwvm4DP1+mw+shEWPO9o9GHJ1mqT5tKuCOTFgjK1AGcwfkEYc15yrI2hEFetmx+tsQNUX/0rI0iOB/smg7N020n0VnqI9NYaHODYMOdtqyNA6rT7QDonjGcIf8rRscl+9yqEbonop/97GeYPHkyMpkMdt55Z/zzn/8c9Wevvvpq7Lnnnujo6EBHRwf233//oj9vi9CPwAOBms2kQ9uRj7AhLLHpAwWOwRNkzQNDGsIy7soTLcAYj6dRGj/ZQp7xxIMYnVr0cyCCzPmApMM6Ap+OsqwNb5YdUMDTicraGDdBNRcnPL7G3IxZbWx6GGvmZpMSbKvIh2yIqKhFT18VRVmbgOudVGjB+ejW6e2+NtzteLrrOiNPH6iszai4rkORYDgUtrI2jAl0nGiMgkJ1J95yyy04/fTTcd555+GJJ57AtGnTcOCBB2LJkiUFf/6BBx7A0Ucfjb/+9a9YuHAhJk6ciAMOOADvvPNOzMqLE3qWHQAE6NAcWi27VHRZbq0Z8oX88CNzBPXmgax5uA5fYxe2hXM2OC9K4RB9ZsD6B0iiTR4xOrXq5wCwiiw4H1qpuggz15kz54ECNWpZMuc9FwNkx82TLmHNecLMeboxImwIy3aqElBoYTRqydONMVi1ahVWrVqFj5avQN/a1SX/WbWmF8bv2jtocD4sT4/whDlzzXkAaBju6UyZ8y7PSThgvTeQPWcYh0sPYwk2Ssj8vBqhuvIvu+wynHTSSfjSl74EALjyyitx9913Y/78+TjrrLNG/PyNN96Y9/U111yD2267Dffffz9mz54di2Y/RJNlV3lwvjEZ0sduuyGszYX88OA8SeY8sP7IOWVwnmfCZtssADiz2thwHaCfa09ZjEKt+jkArOoN+Rh8wNs+d2IqKFEG55u4PX1E5nyEtXrLIem5dBlkjL0/OBfObHoAspwsulOVYnRqydN7enrQ3FzeGvZ6ACvnfQFNfoLSAQktcz5CT014LprSieLJCg3tkb1/KRqSHpYN/QbBhjtv6VnQzcOcyWFcmhhxyE5gVCM0I9jb24vHH38c+++/f+57ruti//33x8KFC339jZ6eHvT19aGzs3PUn1m7di26u7vz/omaSMraBKg535iukbI2VrPsODPngfWBGrKyNmyBZ8ayNlnYNHHpYdvkEYWpZT8HothwD3afpbywGsJGt+FOnzk/4jSc/YU8kPVztgyyhMdXioRxw52tVVu2ZwvXGDmUmypiOLXu6aHDUtYm4hPmJcvVZdojff9ijMicJ/D0wc/VkCXQZU+ecSUBsCUl6MC9iAuaJ/4PPvgA/f39GDt2bN73x44dixdeeMHX3zjzzDOxySab5D08DGfu3Lm44IILAmktl0iOwAcoazOiJEulRLiQbskksguJYv+ZFo+ojTB9qsx5B/Bobm0AfEFVz3H4FqngCy7QZS4Qfm5iJLXs50AEnh5wbkwmwrgnnEhrsnY2kQfnR2y421/IA9nFPFt5FMbMec8FnX+y6cnGwbk+N8ZNFbbnHgZqzdMbGxuxcuVKAMCP/vgCXl26quTvXNh2Oxr73/b3BiwNYSPuzdbWkMQ7y1YXftFNWF2n59Wcd1yKJqy5U44EWobCtkYHAISVdBISLuEGBiMO23VUhdTMCF500UX4zW9+gwULFiCTGT2L+eyzz8by5ctz/yxatChybZE0mwmQk5NOuOHUqI1wIe+6DlpK1Z23uHhuHLEjz5M5n3D5asexZUdlT13x6KGFbJHKe+JBhAmznwMRZM4HvM9CqU+baor0fm9rSGaPBRfVYK9h28jMeY4N9xRhWZsE4TzM9owBAMZw6WFETfbqAzZPdxwHTU1NaGpqQp+bQjLdUPKfpkzK/8aNG2zOTifCOg0XfXB+VBrard7bef31kg0U80zuWY1xjU4XVOXSo01bERc0s8OYMWPgeR7ef//9vO+///77GDduXNHf/dGPfoSLLroI9913H6ZOnVr0Z9PpNNLpeHcsV60NuT4tgCCLEMdx0JxJYNmq3srfPtUU+OGjFG0NSXSv7hv9BywG55OeC8910D+wfpOEKXPeJS1rQwRtQ1iygSKTQ/oAKYZTy35ujAk/cz7gQi0vQ6xSIs5adxwHHY1JfLhylOcOx7MaEGdtCJtM8AXnXTZjAGuzcK7CNoxNcxk9ne/qtk8te3pP2D1kgMAZtiNOZ1dKxL5ePDjfEel7l6Jh6HMRy2b74IkI1ZwviSHbwHDJyvMCnFnqDmt8pYqg+VRTqRR23HFH3H///bnvDQwM4P7778euu+466u/98Ic/xJw5c3DvvfdixowZcUgtm55IMueD0VoqK70UMRw/by1Vd95ytnrewxPREbWE50a+cVIubEeXs8EFHj0AYMgW8sD6UjtE0G6qiDxq2c/XrhvAuv6Q79WAC/lQgvMxHD8vWnc+3WzVIxpHZM5znIZLEAadEx6ff7qEZeHY9DA2zaUspyBGUMuevjqK4HzAwGJDGJ4O2M2ct1hvHhg2hiQJdCnShrBsfeEAviQAxrgBnx6AU1N1QbUtdfrpp+P444/HjBkzsNNOO+HHP/4xVq1alesMP3v2bEyYMAFz584FAFx88cU499xzcdNNN2Hy5MlYvHgxAKC5ubnsLuxREs2ufLCLvyVot/mIDR/w0RTWcmZbOuFi5eAXJLvywPpu8GRrHTZTY9ssAMCnBwDTZwYM1pwnu7hFQWrVzyPpIROwOVgoC/kYPL2jWN35GN6/GLSZ856LPrI5zyP0T8YMbEPmnwDo/JMxY5Ps0qahFj29f8Cgd91A+H84YIJUKJ6eao68/1jRJLqG9kjfuxR5SQskm+2e62TnF7K+cI7r0U18bCcGGTcwyOQAUPmfMKCaHY466igsXboU5557LhYvXozp06fj3nvvzTWgeeutt+C6Gx7ifvGLX6C3txdHHHFE3t8577zzcP7558cpvSjR1Jyv8+C8mwgc0AhKnvEzZc4TZtqxzdVKwPYH2zgxZv6JwtSsn0dRpi6gf2SSLhzHgQnQKD6O03AdjUU822IzWGDYSTiC54tBkh5fQ9iSvQMs4CkDuySU9d3JEjfE6NSip69dF0XZWQTfcA+jrE3EzWCBEuv0dFvk71+M/DU6R3DecZxsaRu60rN83ukwVgAg8yrGBAC2MapGqILzAHDKKafglFNOKfjaAw88kPf1G2+8Eb2gEOiJYjEfcCJtHn6Eu1xiWEgXNX0Co00nhnwGBHoG8VTWpiSM5VGCxNWigq12nHbkq4ta8nNjDHp6erBkWTf61q4u+fNr1qzBqjXr0Jj2Sl+3AY84O46DprSHlWsCJAKko89kbC9V1sYijakhz0REfp4tIcO1cPYI52GHsEktYaU6UF5LhNeTKEwteToArOmLIGseCOzp6cSwvmaVkIk+OF40cz6G9y9GXkNYogS6lOfSbP4PQrm2ItPkOIAh08Q2RgA4NVUZdMH5WiSSzHk32AN2U9DgfAym214sy46gjEwqwWn8SdcJXMM4bNgynh3CHXA+PQDbQp5tk0fUDz09PWUdxb9+/f+vnPcFNJU6qRZCMLg5nQgYnI+hrE3R4LzdsjaNKb4j8ACQdJU57wdGbxgg9HS2IAxjWRtRP0SXOR/GhnsC3av7Kv8jMazTi57Ct30ajrDmPDBYepYr/MaYOc+WZOiA73Qem58DnJqqDa6rrEaJJHM+4MQeOHPe9nE5gnqw6QTfkTkASLgup4GQTdgB97fqArKPDJ7jkB7jEyIAIQXnAxGDpxfdcI+hIW0x8soIeDyb7dnMeS4IY/N0CQAA+AwUfJLYmv6J+mJtFJnzXiqUG60pHTA4GUND1uZUYvT/VNvB+RRn6dkkYVkbOmMA4FAmGXJBuanC9hxWhXBt3dUga9f1o68/CvMPZjTBM+fbg/2+D9qLBedT9oPzrJnzlDXnbQsogBaF1QdjQx5RHzQ2NmLlypX447OLcfuT75T8+QPaFuHwnt+h0c8CO4RM7eDB+eiz7IrXpyXKsiM4mTdIwnPoGqO5joOIikFUDGPmPCNsi3nGRr7K/Ksf1kbRDDakZK2mVEBPj6Ehq+s6aEwlsGptgVN7TJ5OlECXIsycdymz1bg0OYQJAIxWJf8MDtfsUEMM1qf9aFWvr/q0ALAq0YeMY/xd2AHrlQXfkY+jrE2RY4FJu1l2wHqDHYToyJzngW7GZpysFZwvDdsYeYx1hUVd4DgOmpqasM5LIZkuPd9nMhk0Dfh8xArhJFg1bLgXDc5bzpxPeu6GXiQBSxKESfYkHNecRxucJ/MGyj4ytgUMw1EjX2GR3kiC8+H4R2BPb+gIRUcpmjOjBOdTdvvI5DeE5UmgyzaEJQu/Mc7BZJIY4xiiNiGbHWqHcuvTAtkatf7q06YDL9YCZ9nFsCOfSrhoSo9i+gSZbbSZ8x5fWRvG42BsmgjX8XQLecYAjKgvCvpRUCjK2rQH1lDyLZIeUgm3cECEYMM9dwyeKTjv8ZXyYlyjMp6qMoTReYcsQ9Il/NxE/dDbH0HZ2bAy56slOJ9O4P1CL1hu8k5b1sZz+OqpE87BbF7FN0J8J+FEOOhTrUZC2I2uFtPvbBplkWw5yw4A0qTB+SRb1Bl8gXAAdBsYjLBlzrsOYMg0ifqipzeCxXwIm82+yucUI4bTcADQmhkle57A0zODfWSI/DxBaJ6u49ClhbvKwPYJ1/XkEjbZE/UDc1mb5qCe3tAZio5S5DVTH8RNWN/kztNFdLqdsyEsly8AgEtWzo/ylBfh58b2jFGNcM0ONcRgfdrH3/wI1/z9dV+/c1nTr9HodJf+wRCOwAfKsvOSsTVk7WhMYdFHPSNfIFjI52XOEzWQ8wiPwTuUi3nbCvKhzLIjGyPGB0hRX/T0RpA5H8IiNlB92nQL4MXzONjakMAHK9eOfIGgj0wm6QLrYD2oMBTGWupZ7+TyKzY/Z4XsUoKjUnXCItGUtQlnPRg8ia49FB2lKBhPSDZan2xym+1AaKWGwiAbnGcLPJMFnWH98hkBmRwAfAl0APg+uCpEwfmIGKxPO+Ct8lWfFgCaMkk4vT4u6hCarKQT2fqq/QMVLLAy7bHdfJ1No2TZxbQ5UAzWzPmEx7fYyQZVuRbzdA8jMrSSKAAjbEObOV8oe80vMZ2EA4pkzhN4eibpZYPzTH7uOjBkh1wZN0ldl28Tg+05DOD77FzChrCifujrj2BdElKWdqANdzcRW833xkI6CRLoXNfZkERH1BA2TVhz3mFcXNFlzoPvGYNNDyglVR16IoqYSBbymdbAf8JxnMp35WPajQeAjlHL2thfyOfXnOcx/gThIpVLTRbKHWcy2IZINeeFbVZH4ekhZGo3l+pVU4yYjr8DQMtoOlmC8wDVwtkj9HPGzHm2oDMrbHV8FZwXNunrjyBzPhlSWZtAnt4em280FSq/QxCcB4CGJGmpOrbAM2E4kM3THfCtP+mSDEUo6FONmEiC8yFkzgOjGKofYqpNC2TL2hSEoH5cyls/fg5XcxePcAc8u5ZnW8zbVpAP2fAA4NvAYNMj6o/VfVE0kAu+cAxUqi7GzPkW4sz53Ia7N4pGCziOw7kAMxEEtQLgEo4RoaWThRbW65GvC0tEEpwPKVkr0Gm4GBq8D1Iww5/AzwEgPRicJyo9m0wwlrWxrWAkZPvIlGPEtoEhwoHs0q891kSxkA/JdJvTFS5AYzT9tobRFvL2M9UHF/LGS1HN2gnPBdsSjGh4cjBqYoMtGO640AcnrBKJp4ewcBw16O2HRsuZ814ytpr3xchlzhPVnAcAl24h75A9YfBlhAOg9Co6T2dssifqhkhqzoeUNR6orE2sG+6cZW2A9X1kAKrMecaa85RzMNsYAXSe7jI+99A9HVYfjJ9qTRFJJ/iQMtcr7gQfY+b8qMF5gjIyueC8y5NlB/AtvgDOmvOM4ySKo89M2KR/wGBd2DVq3UQoKULVkjnfWsjTCU7CAUMy54nK2gDr66mLonAuUkUpdGkLm0RTcz6kzPlK1+hArOVnC5bfoQnOD5a1sR8zGCTluXRlbRjjqXwpAADbQBkyPSIc9DQbMZHsyocWnK9wARpSWR0/tDXyBueTXnZSNETH5QDOmvOuA7q6LZSlAshgCwp5jqOHEWGNSPw8JC9LJVw0VHoM3nqWHckReMKyNgBpVjjZZruOd/uDbYPbVea8sMi6gQg8PaQ1crAN9xhPwxU6hR9TM9pSZBJ8wfmExzfnsZ3OA/g8nbEhLJufA6Abo2qEa3aoQSJZzKeDN4QFAhyDjzE435xKFL7PCY6opXOZ81xZdh5l00y+zHnK7vRskA2RPF/YJJrgfHheVjAr3Q9xZs4Xeu5gqU+by5znCs4z1lN3yPxcdu4TsoFiDHiI+oG5rE1D0qv81lDmPIAhp+ESPKXqkp5LF5xnnIHZAs9smwUApyYRHK7ZoQbpj2JXPhNOcL7ihXxImwN+cF2ncPYAQbZ6riEsWZadS1iXmzJznvJxhAu2hyOXcuNJ1Au9xM3jgCJl4EoRYx+ZgpnzSY6yNhsy57k23BlPeTlsfk6Y+ccI23OPPF3YZN1ABPNYSAlsjuOgsdK68zFmzhdco6c5Muc3eLr9mMEgSc+hq6fO6J9szz3OkP+lgWyNnoVRU3XBdeXXIMzGX/FCPsbMeWCUXXmCzHnWmvMeWWYUwLm7y6iJDbYRIry0RR3RF0VwPsTAdMGsdD/E2Eem4EKeruY8l6e7nia+UsjO/cE2TsqcFzZZF4Wnh5jAVnD964cYm7ynEu6G2u65b8YbJxiNVMLNzi9ESXQJ1yWc89j0EA4RIaoAUJsoOB8x/aFnFzmh1XKrPMsuvoU8MEr5HYL6cbTN42gzkcgy7eT8JWEbI8aNJ1E/hN4MFgg1OF+Rp6eaY80UT3guGocH6Gky59cHGMg8nS2DDOB7wmDzKlZ0Gk6IDTAn0AEB6s43doWmwQ8jTsSR9JFJeS6Mk6CK9DLWnGcM8hJ9ZAA4N5LZTsKJcOCaHWqQgbCNP90c2uTQ0cRf1gYo8HDieBTHzlOsNecJG8I6AF1ZG7YxYoRthBzHURBGWCOS5nEh1ltvH62BejFi3mwHCizkk/Y324H1tWABuuA8Y815ts12xjHidCouVdmAB99nJ+qD/iiC8yF6akslwflEOvY+LiOD8zw15weIsuaBwcx5rjnPIQwHsnk6YyDccbnGSISDPtWICd33Q+yA3tFYQYOUVFPsgfERwXmCkjZAdkceQHazgIjsBjiXiWTjqVyLeQV5S8M4RPrchC0iWciHuIiuKHM+xsZxg4wIONCVteHydFAuwLj8nC0jnBW2BEnX4UsmEfVD6J6eag7VPyrqDdfYFfs91ZwepjPJEZxPeA5d6dmER3haiEwOIFvwg4aoNmF84q8pwjf+8Awv6bnlL+ZjbBw3yMjgPEuWnQPH4cuczy52uG5tRpMllEQHY6aAgjDCFuxH4Kslc36Ep9Nkzq+fW8iC84xzHpsizk1bRk1kKHNeWCR0Sw/ZTyvacG8cE6oGP4yojU9S1ibhunzBeZdwjU6YAMBYzo/N0xmfexg1VRuMV35NMRB2KY8QM+cBoKu5zOx5C1l2I0yfJHPecRykEx5dcB6EddEYYTMQMjlZCDW5bKl/om6IJHM+HZ6nV7SQt7HhPsLTOTLn0wnO03BsXsUI5RgRamLbcFfNeWETE/YaPRNu2de2SjbcmywE59PDPJPE0xOuk605TwRj6Vm2zQKAcIjI9AAgFSWCwnc31hjhZ86Huxvd2VRmoLuhI9T398OIWnYkmfPA+sU82UKecbHjwKGrOc+5K88Fo+8rNi9sEU1wPszM+QpK1Vnx9OFH4Dk8PeWt93Iyb2CrvZqFy885S//wwXYpyc5FTRFyT7aKys/G3AwWAJqGnobzUjTzccJz6E7CJVwXbDOfTudVJ4yfmwgOx+xZw4SeOR9yk5eupnIz5ztDfX8/tIyoZcexkAeAdNKFITN+lbXxCZkoxsw/RuNnHCdRH4Tu50Cox+CbUt76eqZl0Bi/p48sa8NxBD6Z4Cxrw3jknG3pzKUmi0O4k8ymyCUs8SBExYRc1qazkuB800ahavBDY2qIZ5L4OTBY1oYtcx50609DOAezJdBl2/lxfW6iNuG68muQgYGQ/2Ay3KNinWUH5wky5z2OsjYAkE54fJnzgAzEB2wLZy41vGichC2iSJwPM9POcZzyS9tY2HDn7SMzWNaG7NFYk15JtGnrD7ZxInsME3VG6PdDyMH5jqbqKGuTSQ4NznP4OZBN4Ddka3SPMHOerdwZoDCGPzRItQjZCqT2MGEf/Q05OF+28VvIshvRrZ6k5jyw/oGELMtONef9ojEqBeMIMWbzi3ohisz59lD/XNnBeStH4Id5ZsjPNZWSSnAG5xnL2rAFedn0AJz+yYZDeNJT1A9e2JdeyMH55nRiw6axXyxkzjcMDc6T1JsH1mfOswXnHb6a82zJagDhWs/J/Q8NjJ+bCI6eiCIm9FPwIZte2TVqLWTOj8yy4wnOpxMu3XEwxprzAPgeRuj02FYwEkZNdA9sQgQh5MV82Z7OUNaGJHM+RZo5rzmvNIxrVMaPjW2cVCpA2MQLu2RYyDXnHcdBZ3M5nu5YOQ3XkOLMnM+WkCHzcy45AACHMBwoVygN44kHERy+u1EUJ+SGsGU3m7Fg+qmEi3RyyKVKFJzPJPnK2gCgexhhC4QD4BsjQpNl1MT4YCtERSQbgUQFNWWL0N5YRuZ8uhXwKjg2H5BG0uA8a1kbRvukg3KQuK4jRlzwZZGK+iEZ9m5VQ3u4fw9l1p1v6AC8+GusZxKcmfOu4xDWnOdLoGPsa8NmC4zrYbbrSIQD391YY4Q+uYTcaKV1eD33UoSc5eeXvKawRDXnM0mXbyEP8LkaAJlI9cF4GXE+IIn6IOyFfPgn0coqa2OhpA2QbVybB8mGu+c68BzwebqmvJIw+gJbljojOhUibJIrZRYWIWfOA0BHOb3hLNSbB4bVnCfxc2D9/EI2x1DOeZSSCEUJEQNcK5AaJPRJOOTgfMJzR9Z0L/beIWf5+aV56CaCZ0dDIdIJD2yuRlvDk+yBhEsN3fDQQpjgIeqE0INtEWTZlXUazkKZOgBoIi5Vl/BcusmYMauNDcbTeQ7hcxhbwEM1c4VN0mEH5zMRBOfLOQ1nLTg/9HQ7x0k4ILvhbshCXZTBeTJfAPjCGIwfG6MmERyyS7/2cMN+8Ew3h/v3UMZi3lLWPDBsMW9pg6AQmaQLwzg7krlaNpufTBPZ58alhhe24IKoH0JfVEVQJq6ssjYW6s0D2fIxeU3uiE7DJT2+03CcDWH5NLHB6FRkjz2EgkQ9kZfxHRQvGXoCHVDmhruFZrAAe+Y8V+nZbFs4snmPTY/wBVscA4CupRDQ03XEJMIOzqdawv17KGNXPh3+e/ulOT3EXIky51MJvoW8M+R/uSDTxGYgZHIAviEC1jd4EsICXjXUpy3nCLylzHlgyIa7l6A6DuO5fCfPCKdhOnOgzMAmlMSGNtuFTfL6mQUl0xbJvNhW1oa7ncz5dMLd8J9OFJz3XAeGzM8ZM+cZg7yM4yREHHDNWDVI6Iv5CALkvjvBs2TOE2XZpRN8DWEdwpq5AACXbJzIpj/GRSqjJsprW9QF4Qfnw89cLyvLLtMe+vv7JbfhTuTnwPoGgWRzDGOWuiHzBjY/Bzg/N7ZNDMagkKgf8hqZBv5j7eH9rSGUlzlvJzjvOA7Sg9nzVMF50G0kk03BAEjXemQwjhDjM4YIjj7ViAk1cz6RjqSki+9O8DaD86mhwfkysggiJvRmQmFB9jACgG4Tgw3Gj4wRxgdbUR+EfhIugsz1TNIbWdN91PdvD/39/dK4XqMh8nMASDCehmOc9NgMi00POAPPbEGY0Et/ClEGmeHNyYMQ0Um09ipo8g4ADUm+DXfX4cucZ5uDAXD6p20Bw2D0c1GbcM1YNYgX5nHtCLrAA2Ucg7dY1qYxxVnWJuk5nDXnyWzNAHQBDza4PjFe9IAkbBF65nxENd99e7rFzPmmQU93yYLzhA3VOWc8TlVMyKpKIz8XNmkMs+Z8RJvdrQ1J//eJxeB8riks0Ro94bpgC3WRPV6sh28eljWIeoVyiqglQs20iyhzvd1v5nxEmwN+yC9rw2P86QSh8YMvuACAr6wNmfEzLlIJJSlzXlgjGXbDgwjK2gDARi0+M9coas7z+DkAeB6ff1JmF5OZA5kcAKqZ6wfGS1vUD40pn6fM/BCRn3uu46/Ru5cCUk2RaPBDbiwjOOFfKZ7nwBD1tAE4+8Ixns6jLNlC5ulcakRYEF75tUWomXZR1bNr4m8Im585z5Npl3BduskajJ3gAbrgPBuEnxipJkZVoh5Ih13GLKJMuy7fmfMWN9xTQxrCEsGYOU8Z5CUbI0ZfoPzYyDSxlZwQ9UVjmr+sDQB0+AnON3RYvcEzSb7TcF62CZttGXlQJmKRjRHA51VkckQNo6eiiAk3OB9R5nxDFSzkSTPnE57D5yAuoSYAcLmCMGwwfmSMaJyELdJhHoFPt0S20dzV7CNzPtVsdaN7MChiXB4/Bzgz5xkX82xjxGgMLlnGJsAXYKC8tkXd0OK3P4sfIipTBwCdTT483eJJOGBozXkeT/cIG7wDYJyIbSugh3GIGDWJ4BDOWLVFuMH5aILjqYTrr4FcqjmS9/dDQ5Izcz5b5oBtdmTTsx6yhrDG2FaQD2PmAiN6GBG2yNVUDYMIF9JdzT4WxxYbvANDTsORZc5TLuYZJz1GTWRoiEpDeSpE1A3NGf6yNoDP03AWG7wDQEOu5jyPpydcB4Ys1MVY1kYIwQPXjFWDVEPmPOCzgZzFxXze5gFRBrZHmKVOG+Ql2lShhPBjY8xq02Je2CLlueF5eoR+OsZXll17ZO/vB8Yj8ADgOS5dCTbKOY9sA4OxPi2jJjZon1dFXdCQ9MLz9Agz58e0+Fmjt0f2/n5g9HTOk3C2FYzEIXvmAfjGSV4l4oJrxqpBwm0I2x7e3xqGr115iw1h82vO8xyZy+7Kk03YbI42CKH5M8H6sbFBGagSdYHjOP5OmfkhQj/v9JU5H937+4HxCDwAZA/DcXkVY3kUtoAHI3Kq0sjPhU0cx0FbQwjB5EQaSDYG/zuj0FVVG+5ECXQOXwKdqFJ0GYmY0NN1xFRN5nypxXwibbUDezrhbsjiJcrAzn6+XDM2Y7azMYbqgQ0ADMjq2ghfEF7eoo5oCquBXIR+3pTykC5Vgsd6WZusHxgyX3AJy9pwBjC5xogRxmcxNjREwjbtfpqtlqKhM9KLeUyLj+C8ZU/PBefJ1uiUTac18ZWELVNdH5mIC8IZq7bI1iQPiQh3xUvuylvMmgeyi5xc9jzRYp6zPi2ZnkGIjjoywhiA4VPEqUnUDy2ZkOaxCBusO45TulSd9YX8YH1aLl9wHYfvlBehN7BpYgyEU3o6myY2PaLuaG8MIfEswpI2gM/T7dbL2qz3dKI1eoJwjZ4NOnPNe2yBcCHqGa4ZqwZJhRmcj3AxXbKBnOWFPADO4DzhkTkuNUMgC8KIKoX2Ahf1QHNYZW0i3vDuLBVwiHBzwA8bjsBzBcJdwj4ybMEFAHSfGyOMwXk23DBPFwtRAb56rpUiwmawQNYvW0o1r7Vc1iadIFyjMzaE1ZTnC7ZxIpMjahiuGasGKXm03C9eMuJ6dvzBecYjc4wLeYd1sUP0wMYI46dGdmkDUMBD2CWU+rRA5MHxjlKenracOT+4kHe4fMElqzcPEGY7A3QbBpxjxKeJTZEyNoVtQgnON3YF/xsl2KhUaRvLmfNpwtNwjuPAZdxIJvMGxrgBmyLKZwxRk3A9XdcguaZnQcm0RTqZs5e1AYCGwcx5osVzNlDINmGz6VkP0QMbI/J9IfgJLTgfcXC8gzxzPk14BB4AvDBPO4YEZXYx0XMYK5SfGxl67hG28VUyphQxBOe7mout0x37wfkEp6e7HqNXaeITQhSGbxVSYww2PQtMxAv51oZE8ea1lhfywGCmHVc9WNcBDJvJsq52yGrOG/WDrUqUOS9sEl5wviWcvzMKpTPno33/UmSbvIPKzwHQBRayEM55ZJnzjBB+anQoc17YJpzM+WjL2gAlNhEybYBrd07eEJzn8nRHXiVCQE4l4kIzVsSUrBHnl4jLypRsIGd5IQ8ADSk323WdKDjngK/ZDC0eY9CDBwWdheCnWsralKw5T9DkPZVwYcgW8q7lAEchKDOw2caJ0D9dwmdDtmFiLKcg6oviGek+aegI/jdKMKaYzhg2B0qR8vhqzgOkmfN03sA3D7OVkSGTA4BvjEQ4sM0ONUd4wfnoF9JFM+1STZG/fykakh4MWX1axwXdjE2bicSWOW9bgKgIreWFTUqWi/GDmwASmeB/pwgdTUXmWzcBJBsifX8/ZBIeXXkUxoAq48JZy4fSkD0aUqIhErZpzSSQ8AJeiTEE54sm0FkuaQMAKdayNmQJANlquJr5qg3a2IqoOfR0HTHtYSzkgViy3IoemSOoOZ9OeNnMeSJchy9znnYnVTXnhRBVTltjCPNYxD1kgBJ9ZNItFIvDdNKlW8g7bBnh4DyWb8g+N0Zon8WY0BAJy5Q8OV76L8QSHC+qMYbNgVIkBzc42DbcyfrIKMjrD7ZRkp2LuOCasWqQ1kwinBs6hrIyRTcSko2Rv38pMimP7wi8A9BZCKuDkGXOs8H6sbGhB1thk9ZMif4sfohhId+Q8jY0UR8OwWY7AKQ8l24hzxmcJ5zzyJ7FGKH83MiQnwsGim5ml6KhPZYyX8WD8+2Rv38pkrQ157n0ZNG8J4QoDN8qpMZIeC5aw6hRm24O/jdKUDRznqCsTSbhwoDLZLMLC5lsKYyBas6LUCBMIhV1hOM4aA+aPR9Tg/VRPT2G5wk/pJMeXe1yyvrujEFeTcQlYfzY2NAYCQYCZc7HlLXemPKyp80KQbDhnnRdAFx94QDAIdssENWJ+sKJuNDTdQyUbMzmhxiMt3jNefuL+UzSwwCZyToOYDRh+0PH4IuiDDIhqoPAdedjqg87aqM7ggbvAJBOMGbOc+nJwucNfOPEN0ZazJeGs8eDqDe6mvmD847jjP7skWmLRUMxEp5DV3oW0IZ7taIhEvUK3yxagxQNevslhsz1opsIBJnz6YQLQ7aQz9ac53IQMjkbICtrY4xawlYjrJe3qB8C95KJ6Qj6mNGC8wSb7cBgWRuux1DGgKpDGVzg+twYIfzUhBAFqIbMeaCITobgvMsZnGcra+MwlsMVJSF8NBQ1Ct8sWoMULRfjlxgW052jZQ44HpAIUI8vJNJJD4bskmWsOU+bgc3WEJbMacnkAOCsmcuoSdQXnU0B57KYFvOjZgMSbLYDQCrh0jUWZWy+SunpZJnzjL7AqIkNjZBgIFjN+fiC86Nnztsva+M4DlyPyxcA0JXOy4YNyGY+Nj2EUI6QPreahGvGqlECZ9kBsSymm1IeUokCl0SygWICyB6B57pks4sv+2NDj+tRXENDUd58dcJ1FYl6pFrK2oypguA8p6ezwaeJ7RQjI5zXEhcaIsHAqF7pB4bMeZJSdR7Zpi0AuGTBeVGdyM9FXGjGioFAtewGiSFz3nGcwsZPtJBnWxC6DuhWF2RyAIAuO5IRwo+NEsbrW9QXgUvVxZU5P1o2IImnJz2XLgObsT4tY1kbR55eEkavYgswUJ4KEXVHR1Oq8vu1sTNULcUo3IzeAVIcwXnGzHnOOYZRkyiGPjERFwrOx0BHQTMtAzcBJEII8PugYAO5ZEMs712KlOfS1bNT5rxPtJAXQtQIgTPnYwrOj1qqLskRnM/WnOdazHMu5Akh+9wY0ZXkAw2SICDpuWhrqNDXG+ILzhd89kg10ZRu8Ty+tZ4y50UYkO1rixpGM1YMdAapZQfE2ryt4NE+kuB8kjA4D8j4faGFvBCiRgjUPA6IrXlbSzqRzU4fDomnJzyHLnOeLbMY4NwwcAgzJNlgvJbY0BAJFioubdPYFa6QIhQMzpOUtAEAj9AX2OYYx3H4RAkhaFBUMQbaGpLBHtJjPIJeMOiQbIzt/YuRSrh0DWEBvgUYl5r1EGZTiOqEMVAl6ov2IJ6eyMR2Es5xHHQUal4b44Z/MRKeS3eqyim0mWEbsmcMQKXq/MD3qQkhRmOjlgoS6RLpWNfo7YX8nKAZ7CAu2WY7wLdmcIb8LwtscQxGspsqtlWIeoBwFVJ7eK4zSp04n8SY5VYwOJ/IxPb+xUh4nLvNjDVq2WDrFQCA8loSpdHHJmzjug46Cy2S/RBT1vwghY/Bc2y4J12H7jScphefaCIuiYaoNBoiwcKYQmVdS9HQGeuN3pJOwBu+5iSpNw8AXoJv09ZwPWLIF6oYfXYiDsimrNolUFPYGLPcCjeEZVnIK3PeD1xqsjiUmfNcI8V2HQkhRqdgfxY/xJzlNmqNWgISnguQBeddwnnYIRsjACpVJ0JBzz2ChYoy55vGhC+kCI7joK1hWGIAUVkbxoawLtlaT/iD0Rv4FIlahPCJvzbZqNKFPBBv5nyhhXyCoz5tkjRznnLhzIYW8kKIGqKiLDsASMebOV/w1B5JQ9iE69B5A+OCkBGHsHwBG2zlFIQQo1ORpzfGG5wHCng6yWY7QFpz3rYAUTPo+VDEgaKKMVHxQh6It55doeB8kqOsjedyBuf5Mu2MbQEj0QaGEKKGqCjLDog9y21E5ryXoukBkvV0Lm9gc3NayD43RugeDQnREAkWxrZWEpyPrxnsICPW6UTBecqa85qIhRBVhJ6uY6LihTwQq/GmEi6a0sMW7iQNYR3HoTR+tmN8lI8hhJ8b28qZSw0vZB+bqFPGVurpMZe1GZllx9EMFhjMnOd6DGU8Ccc45TlsDWEZjYFRExmOejYJEtoakkiW2xA85rI2ALenu2y+AL6+cNosqF70yYk44FuF1CiBMudjLGsDAB3DjT/m9y+G5/Jdsi6hJjYMY3BeNluVqFSAYGDj1gpPlMWcOT8yy45jsx1YnzlP5g0KFvqEcBNDCCEqxXGc8rPnbWTON/B6ulvu5kYMaM0gwkL7KiIO+GbRGmXjQJnzlhfzJPVpAcAjOY4/FLbJ2mEsa0M41bBlL5DJEUIUoaIj8ACQsVxznuQkHAAkCPvI8JWpoxsiAKwb7lwwfm5sKHAmmCh7071po2iEFGGkpzMl0PH5AuM+srxBCDEahFNWbdLeWMFxuUFiric3MnOeo+Y8AHgen6M5dLcRYXCe8IFNCCEqpTGVQEumgs3iTHvoWorR1sDbPM51+Mra6ESVPxjL/7ChwHNpFKQSTIwrNzhvIXN+hKcneILzDuFJck0xIiwYkzdE7cE3i9YojuNUXnc+9hq1wzPneTLtXMbMed1FJWFcyDNqEqXRs5FgYWwlpW0a2kPXUYykN6yPDFGWXcJ16QyU7UQVK4423IUQNca4tjI8PdMGJFKlfy5kRm64a41eDMq1np4zhBCjQDhj1S4Vl7ZJW24gR7SYp6w5z2ayhjBznm2MhBAiIGUt5Adp6AxfSAnyFvNMm+0uwJbXpuC8TxgDHkIIEYCyPN1CSRsAaCXOnHflC0IIEQjNojGyccU1am1nzhMZv0eYraXFfEmUZSeEqDXGlxuc95JWysrkbbgneMrUJVwXhs0/2fSwoiBMSXQpCVFdlOXpTWOiE1KElnQif25hKj1LmECnaVgIUU3wzaI1zMYtFRio48aeOT/iyBxTQ1jCIC9d5jwhhnAhrwzJ6kSfmmBhXFuZG9eNXVYidvmZ8zyb7R5l5rxtBVUCoaezoUupNLrfBBONqcTIzPTRaNo4WjGj4LoOmvNK1fGchnNcvhuaUZOoTuRXIg70dB0jFdWcz7TFPhu0D30wcbxsth8JbqVNdesKwrI2WqYKIWqMamgeBwCtmaGZ8xWe4IuAbJYdlzdo09YnCs4LIWoQ36VtLJW1AYDmwWb0boJrja5AuKhh1ORdxIGermOkorI2DR3hCylBS2ZY8ziixarr8GXOUzabIYNxjBSEEUIEYaOWNLxyFqMMNWqJ6tN6jkP1fAHIq/yiUnUiDBTsEGz4Lm3TbC843zK44U5Upg4AXEJfYJxjGD1dCMEB3yqkhulqKnMhD1gJzic8F02DR+aIatkBQCJBaPzy2JI4hHUI9cEJIYLguU55m+62gvN5G+48nq6GsFUM4SaGEEIEZazfE3GWytoA2FDWhi04T+gLsnQhRDXBN4vWMJ7rYEy5pW0sBOcBoLVhMDjPU8sO0JG56kWfmxCi9hhfTt15S2VtWjK8DWHZVs5kcnghDMIIIURQfJers9QQFhhywj2RsqahEPJPIYQIhp6uY2Z8uTVqM+2R6CgF65G5hMeXOc8WeGY8ls/4xMZ41FGURpmtggnfR+ABa5nzeaXqPJ7FfHavnet+dvRY7AvK5wwy5FWl0RAJNnx5errVav8W1sx5T2t0UcPIr0Qc6Ok6ZsaWs5AHrGXOsxo/ZT07TdYlYQyE63MTQgTFd/M4gCM4T+TpnuvQrZsdxtN5hrDJuwxUCFGDdDX7KEFrsRksgA2lZ4mawQKAy+gLjJpEVaIrScSBgvMxM6G9zGZstsraZDhrznuUwXndRqWgrDkvm61K9KkJJjbxXdbGsVbWpjkvOM+TOZ8NgOiOrkYoNzHI0AiVRmMk2PDVS8ZiSRtgSAKdZy97vxCMwXlCSUIIMSqMEbOapqwj8ADQ0B6JjlLkFvNkxu95umRLoycRIYSIA9+Z842dgJco/XMRkE54SA56J1NZG8IAr0qR+EXjJISoTUr2krG00T7IhuA8j58DGKxVRwajJiGEKIwinTGzSXtDebu4tjLt0oM158mC8zL+0jAGFwhPFyjzTwgRlEzSQ1ezjwVy88bRiyn29oQb7q7j0HmDTsL5RONUEsZHMSFEaUom0rGUtSE6CQewZs7Lq0Q4KHlDxIFmrJjJJD1s7LcprJsAMm3RChqFpvT68jFku/KaGKsUfW5CiBplYkdj6R9qshycJ6xRq/3RKkaeLoSoUUpmztOUteFaozMG54UQoppQcN4Cm3c1+fvB5o2tLYBaBjPn2YyfsHY537MInSDCMeJsUiuEqD4mdvoIzreMjV5IERg33LWQr2b02QkhapNNO0oF521nzg/6Oc9mO6AEOiGECApfpLMO+NS4Fn8/2D4pWiFF2HAEnsv4OYPzehgphePwNfJVcKE60e0m2PjERj423Fs3jV5IEXLH4F07de8LwVnWxraCKkEDJUJAz8+CkfFtGSS8Itdms93N9sZUIjsFu2RrdPWFEzWM3ErEgWZRC2xdBcF51l15Zdr5QGMkhBCx8YmNmkv/UPvE6IUUoSk1GJzn2Sh1HdD5lU5U+UXjJISoTRKei01HK1eXbgVSPk7LRYjnOmhIuFqjCyFEjaHgvAXGtmYwvt1H3fmNtolezChsKGvD0zwOABzCzHm+RSqbHs7sKEJJQogqpDmdKJ49n2wEWsbHJ6gAzekEjJukmvgoF/LS5A9GTUIIERKfGjvKprvljfZBmtJJqjJ1AOPqk3P9KaoTXUoiDuginT/72c8wefJkZDIZ7LzzzvjnP/9Z9OdvvfVWbL311shkMpgyZQruueeemJQGY6fNu4r/QCINjPlUPGIKkEm68DyXsBO8bQUF0GxdlehTEyJa6sXPAWDaxPbRXxyzpXWfaM6sD84TkR0SzcRCCFEN1JOnbzWutfALFk+1D6U5k+ALzjOuhxk1CSHEKFAF52+55RacfvrpOO+88/DEE09g2rRpOPDAA7FkyZKCP/+Pf/wDRx99NL7yla/gX//6Fw477DAcdthhePbZZ2NWXj67fqKruF+MnwZ49mrDOo6D5rQHJHxk+McIpfGzoTESQlimnvwcAGZs1jn6ixvbOwU3SEsmgQHChbxLueMuSqPPTYh6ot48fauxLfAK+VPH5Ni1FKI5lcgm8hFBeRpOCCGqCKrg/GWXXYaTTjoJX/rSl/DpT38aV155JRobGzF//vyCP3/55Zdj5syZ+O53v4ttttkGc+bMwQ477IArrrgiZuXls1FLGttP6hj9BzbbIz4xo9CSSQDJEh3rY4bR+Pk2DNj0MI4RHxojUUvUk58DwLi2DLbYeJRj8OOnx6qlEG0NSfQn7NbJLYRL1xCWSw8t8isRArqKqod68/SGlIetCvWIs3iqfSgtmQRdAh2jL6iPjBCimrCXmj2M3t5ePP744zj77LNz33NdF/vvvz8WLlxY8HcWLlyI008/Pe97Bx54IG6//fZR32ft2rVYu3Zt7uvu7u5gwgNw8JTxaEoNac6W2g0Y6AEcF9hke2u6Btly03FAc5E6uhZIWDxNMBp0GwZsesAXgAEIPzchaoR69HMAOHTaJnjsjY8AABNSAAb2BdwERabduNYMVo3bwraMEXgeT4NaQMFC3xB6uqg+9BhWHdSrp++/zVh0NWVPnLnpHQGzhfX+MYN8anw70MpVqo7NzwElPgkhqguaSOcHH3yA/v5+jB07Nu/7Y8eOxQsvvFDwdxYvXlzw5xcvXjzq+8ydOxcXXHBBcMEhMHlME04Ys/mQ72w+6s/aYPrWfAv5VIrrQQQAHLYNA5fv4chNkI0RANdTcKEa0YM2P/Xo5wCw3YQ2bDehbf1XmwP4jE05eXQ1p9H16Z1tyxhBMsnl6ZS+QDjnOR7X58aIvKo0GqPqoF49fdrE9iH9ZLjW6FM3H2dbwgiSSa7SeQCy/fOECAH5lYiDupuxzj77bCxfvjz3z6JFi2xLEkIIIUSZyM+FEEKI2kCeLoQQop6hSWcdM2YMPM/D+++/n/f9999/H+PGFd4dHjduXFk/DwDpdBrpNFcDFSGEEKJWkJ8LIYQQtYE8XQghhIgemsz5VCqFHXfcEffff3/uewMDA7j//vux6667FvydXXfdNe/nAeDPf/7zqD8vhBBCiGiRnwshhBC1gTxdCCGEiB6azHkAOP3003H88cdjxowZ2GmnnfDjH/8Yq1atwpe+9CUAwOzZszFhwgTMnTsXAHDaaadhr732wqWXXoqDDz4Yv/nNb/DYY4/hl7/8pc3/DCGEEKKukZ8LIYQQtYE8XQghhIgWquD8UUcdhaVLl+Lcc8/F4sWLMX36dNx77725hjJvvfUWXHdDsv9uu+2Gm266Ceeccw6+973vYcstt8Ttt9+O7bbbztZ/ghBCCFH3yM+FEEKI2kCeLoQQQkSLY4wxtkXYpLu7G21tbVi+fDlaW1ttyxFCCCEqot79rN7/+4UQQtQG8jONgRBCiNrAr5/R1JwXQgghhBBCCCGEEEIIIeoFBeeFEEIIIYQQQgghhBBCiJhRcF4IIYQQQgghhBBCCCGEiBkF54UQQgghhBBCCCGEEEKImFFwXgghhBBCCCGEEEIIIYSIGQXnhRBCCCGEEEIIIYQQQoiYUXBeCCGEEEIIIYQQQgghhIgZBeeFEEIIIYQQQgghhBBCiJhRcF4IIYQQQgghhBBCCCGEiBkF54UQQgghhBBCCCGEEEKImFFwXgghhBBCCCGEEEIIIYSIGQXnhRBCCCGEEEIIIYQQQoiYUXBeCCGEEEIIIYQQQgghhIgZBeeFEEIIIYQQQgghhBBCiJhRcF4IIYQQQgghhBBCCCGEiBkF54UQQgghhBBCCCGEEEKImEnYFmAbYwwAoLu727ISIYQQonIGfWzQ1+oN+bkQQohaoN79HJCnCyGEqA38enrdB+dXrFgBAJg4caJlJUIIIURwVqxYgba2NtsyYkd+LoQQopaoVz8H5OlCCCFqi1Ke7ph63pIHMDAwgHfffRctLS1wHMeaju7ubkycOBGLFi1Ca2urNR1Dkabq0wNIUzXqAaSpGvUAXJqMMVixYgU22WQTuG79Va1j8XOA67pg1ANIUzXqAaTJL2ya2PQA0lSMevdzgMfTWa6JoUhT9ekBpKka9QDSVI16AC5Nfj297jPnXdfFpptualtGjtbWVusXz3CkqTRsegBp8gObHkCa/MCmB+DRVK8ZdgCfnwM818UgbHoAafIDmx5AmvzCpolNDyBNo1HPfg7weTrDNTEcaSoNmx5AmvzApgeQJj+w6QF4NPnx9PrcihdCCCGEEEIIIYQQQgghLKLgvBBCCCGEEEIIIYQQQggRMwrOk5BOp3HeeechnU7blpJDmkrDpgeQJj+w6QGkyQ9segBOTcI+bNcFmx5AmvzApgeQJr+waWLTA0iTqA4YrwlpKg2bHkCa/MCmB5AmP7DpATg1laLuG8IKIYQQQgghhBBCCCGEEHGjzHkhhBBCCCGEEEIIIYQQImYUnBdCCCGEEEIIIYQQQgghYkbBeSGEEEIIIYQQQgghhBAiZhScF0IIIYQQQgghhBBCCCFiRsF5IYQQQgghhBBCCCGEECJmErYFCB6MMXjzzTcxadIkuG7+vs3DDz+M3XffPXZNb775Jt544w3sueeecF0XN910E15++WUccMAB2HXXXWPXMxq77LIL7rzzTmy88ca2pQhRU3R3d2PBggV4+umnsWLFCrS0tGDKlCk4/PDD0dbWZlueELSwebr8XIj6Rn4uRGWw+TkgTxei3pGnh49jjDG2RYgNrF27Fo2Njejv74/1fZ9//nnMnDkTixYtQkdHBy677DIcf/zxuddbW1vR3d0dq6bbb78dxx13HAYGBrD77rvjoIMOwl/+8hf09fXhgQcewC233ILDDjssVk2nn356we9feeWVOO6449Dc3IzLLrssVk1Defvtt3HPPfcAAA488EBsttlmsWt49NFHMWnSJIwdOxbr1q3DxRdfjLvuugsAcNhhh+G73/0uPM+LVdOPf/xjHHnkkdhkk01ifd9iXH311XjmmWdwyCGH4IADDsBZZ52Fe+65BzvssAMuu+wydHZ2xq7pjTfewK9//Ws8/fTT6O7uRldXF/baay8cf/zxaGhoiF3PI488gkMOOQTjxo3D9OnT0d7ejuXLl+PJJ5/E4sWL8fvf/x4777xz7Lr0MCL8Ik/PIj+vDHn6SBj9HJCnl0J+Lqod+fkG5OnlIz8vDKOny89LI0+PCCOoWLNmjXEcJ/b3nTlzpvn+979vuru7zZ133mk22WQTc9FFF+Veb25ujl3T9ttvb/7xj3+YhQsXGsdxzN1335177dprrzW77LJL7JocxzF77LGHOeGEE/L+yWQy5sgjjzQnnHBCrHqmT5+e+/dHHnnEtLa2mp122snssssuprW11SxcuDBWPcYYs9VWW5m33nrLGGPMmWeeaaZOnWrmzZtn5s2bZ6ZPn27OPvvs2DU5jmMSiYT5t3/7N7NgwQKzbt262DUM5eyzzzabb765Oemkk8zmm29uvvnNb5r999/fXH/99eaAAw4ws2fPjl3Tvffea5qamsw+++xj9tprL5NIJMysWbPMzjvvbD71qU+Zd955J3ZN22+/vbn66qsLvnbNNdfkXf9xsXDhQtPV1WW23XZbc+yxx5pvfOMb5rjjjjPbbbedGTNmjHnkkUdi1yR4kadnkZ/7Q55eGjY/N0ae7gf5uah25OcbkKeXRn7uDzZPl5/7Q54eDQrOW6Cjo2PUf9rb243rurFr6urqMn19fbmv33zzTbPllluaOXPmGGPsGH97e3vu31OpVJ6+3t5e09nZGbumP/7xj2brrbc2c+bMydMzZswYKxPj0M9l//33N5deemnu68svv9zss88+sWtqamrK/fvmm29uFi1alPv6nXfeMZtuumnsmpqbm83jjz9uvv71r5v29nYzduxYc8YZZ5iXXnopdi3GGLPpppuaN954wxhjzCuvvGJc1zWLFy82xhizZMkSM27cuNg1bbvttub3v/997usFCxaYz3/+88YYYy644ALzxS9+MXZNjY2Npre3t+Brvb29prGxMWZFnA8jwi7y9NLIz/0hTy8Nm58bI0/3g/xcVAPyc3/I00sjP/cHm6fLz/0hT48GBect0NbWZubPn28eeOCBEf/88Y9/tGL8Y8aMMcuXL8/73qJFi8yWW25pvv/975uWlpbYNXV1deX+ferUqXmv9fb2mtbW1rglGWOMWbVqlfnWt75lpkyZYh566CFjjD3jH/q5bLzxxqanpyf39Zo1a8yYMWNi1zR58mTz2muvGWOM2WSTTfI09fT0WLmWhr7n6tWrza9//Wuz9957G9d1zR577GF+9atfxaqnra3NDAwMGGOM6evrM4lEwvT39xtjjBkYGMh76I2L4Z/LunXrcvdgd3e3FU3Tpk0z8+bNK/ja/PnzR8wLccD4MCLsIk8vjfzcH/L00rD5uTHydD/Iz0U1ID/3hzy9NPJzf7B5uvzcH/L0aFBw3gL77LOPueGGGwq+ZuvI3IEHHmhuueWWEd9ftGiR2WKLLaw8jOy2227m5ZdfLvjawoULrdz0Q3nkkUfM1KlTzUknnWTa29utGH9TU5N5+umnzVNPPWUmTJiQNyH19fVZyab4r//6L7Pnnnual19+2VxwwQVm1qxZ5vXXXzevvfaaOeGEE8xhhx0Wu6bRHjZefvllc9ZZZ5kJEybEqmeXXXYxF198sXnvvffMnDlzzMSJE83vfvc7Y4wxd9xxh9lhhx1i1WOMMTvssIO55557cl/fcccdZvvttzfGZB8CbDxoP/TQQ6ajo8NMmTLFzJo1y5x66qlm9uzZZurUqaazs9M8/PDDsWtifBgRdpGnl0Z+7g95emnY/NwYebof5OeiGpCf+0OeXhr5uT/YPF1+7g95ejQoOG+Bv/71r+af//xnwdcGBgbMAw88ELOirIn94Q9/KPjau+++ay644IKYFRnz9ttvm1WrVhV87cEHH8ybpGzR19dn5syZY/bee2/zwQcfxP7+juMY13WN4zjGcZy8a+fxxx8322yzTeyajMnWa8tkMqa9vT2n0XVdc8ABB5ilS5fGrqfUA9Dgjnhc/P3vfzddXV3GdV1z4oknmj/+8Y8mk8mYLbfc0jQ1NZm77rorVj3GGPOnP/3JNDc3m7322st89rOfNQ0NDebOO+80xhjz2GOPmc997nOxazLGmI8++sjMmzfPnHbaaeYrX/mKOe2008y8efPMRx99ZEUP48OIsIs8vTTyc3/I00vD5ufGyNP9Ij8X7MjP/SFPL4383B9sni4/9488PXwcY4yx3ZRWCBE+zz33HJYuXYq9997byvsvW7YMjzzyCD766CM0Nzdj2rRpmDx5shUtN910E4455hgr7z0aAwMDWL58OTo6OgAAL730Ep555hnsuOOO1sbp9ddfx7333ouBgQF87nOfw6c+9SkrOthZtmxZrhP8ypUr0dzcjKlTp+Lwww/PfZ5CCBEm8vQsjH4OyNOrFfm5ECJu5OcbYPR0+Xn1Uu2eruC88MXDDz+M3Xff3baMPKRJiPrm7bffxqabbmpbhhBVB5tXsekBODUJUavIz4WoDEavkiYh6ht5emW4tgWIkUyZMsW2hBHMnDnTtoQRSFNpHn74YdsSRnDzzTfbljACtnFiHCNGTZ/+9KdtSxjB22+/bVuCIEOeXho2PQCnJjavAvi8QWPkDzZN8nNRDcjP/SFNpZFX+YNtnBjHiFGTPL0ylDlPCOPxHlGdtLS0YMWKFbZl5LHddtvh2WeftS0jD7ZxYhwjRk2LFi3CxIkTbcvIo7W1Fd3d3bZlCCLk6SIs2LwK4PMGjZE/2DTJz0U1ID8XYSGv8gfbODGOEaMmeXplKDgvhBBChATjw4gQQgghykN+LoQQQtQG1eDpCs5b5I033sAzzzyDFStWoKWlBVOmTLHWZAIAXnvtNVx//fV4+umn8zTNnj0bn/jEJ6SJWJMQ9cbAwABuuOEGzJ4927YUIQDI06tND6smIeoN+blgQ34uTUKIypCnV46C8xZYvHgxjj32WDz44IMYN24c2tvbsXz5crz33nvYZ599cMMNN2Ds2LGxarrrrrtwzDHHYK+99sL06dNzmp588kk8+OCDuOmmm3DIIYdIE6EmxgeRv//975g/f/4ITSeeeCL22GMPK5rYxolxjBg1jcbatWvR2NiI/v5+21Jy6GGkPpGnV58eVk0An1cBfN6gMapeTYWQnwsW5OfSFCbyKn+wjRPjGDFqGg15euUoOG+BQw89FBtvvDHmzp2LjTfeOPf9JUuW4Hvf+x4WL16M3//+97Fq2nrrrXHppZfi4IMPHvHaPffcg29/+9t48cUXpYlME+ODyDXXXIPvfve7OPbYY0douummm3DJJZfgy1/+cqya2MaJcYwYNd15552jvtbX14cjjzySyvgZH0ZE9MjTq08PqyY2rwL4vEFjVJ2a5OeiGpCfS1NYyKv8wTZOjGPEqEmeHg0KzlugpaUFS5YsQUNDw4jXenp6MHbs2NgbXzQ1NWHZsmVIpVIjXuvt7UV7ezt6enqkiUwT24MIAEyePBm//e1vsdNOO4147Z///Ce++MUv4s0334xVE9s4MY4RoybXdTFx4kS4rjviNWMMFi1aFLvJVtvDiIgeeXr16WHVxOZVAJ83aIyqU5P8XFQD8nNpCgt5lT/YxolxjBg1ydMjwojYmTRpknn00UcLvvboo4+aTTfdNGZFxuy+++7m/PPPN+vWrcv7/rp168wFF1xgdt99d2ki1NTY2GjWrl1b8LW1a9eahoaGWPUYY0xTU5NZvXp1wdd6enpMU1NTzIr4xolxjBg1TZ482Tz00EMFX1u9erVxXTdmRcY4jmMmTZpkJk+ePOKfzTbbzIomYRd5evXpYdXE5lXG8HmDxsgfbJrk56IakJ9LU1jIq/zBNk6MY8SoSZ4eDQrOW+DKK6807e3t5tRTTzXz5s0zt912m5k/f7755je/aTo7O81VV10Vu6bnn3/ebLHFFqazs9Psueee5tBDDzWf/exnTVdXl9lyyy3N888/L02EmtgeRIwx5t/+7d/MiSeeaJYuXZr3/aVLl5qTTjrJHHTQQbFrYhsnxjFi1HTEEUeYyy+/vOBra9euNZMnT45ZEefDiLCLPL369LBqYvMqY/i8QWNUnZrk56IakJ9LU1jIq/zBNk6MY8SoSZ4eDSprY4m//OUvuO666/D0009j5cqVaG5uxtSpU/GlL30J++yzjxVN69atw4MPPohnnnkmT9NnP/tZJBIJaSLU9MILL+DQQw/FRx99hG233TZXg+y5555DZ2cn7rzzTmy99daxanrvvffwf/7P/8E//vEPjB8/Pqfp3Xffxe67746bb74Z48ePj1UT2zgxjhGjpr6+PgBAMpmM9X2L8cUvfhF77rknvvnNb454rbe3F1tttRVef/11C8qETeTp1aeHURObVwF83qAxqk5N8nNRLcjPpSkM5FX+YBsnxjFi1CRPjwYF54WocpgeRIby6quvjtBkqzM9wDlObGPEqokJxocRIUTtwOhVAJc3aIyqWxML8nMhRJTIq/zBOE5sY8SqiYla8HQF50WO5cuX43/+539wwAEH5H1/4cKFmDRpEiZMmCBNpJqEqFV0vwlRGWz3DpseVk1C1Cq634SoDMZ7R5qEqG90v4XPyPa6Ihbef/99TJw4EatXr877/k477YR//OMfVjQ1NDTg6KOPzjvusXbtWhx88MG5nShp4tO0fPly/OlPfxrx/YULF+Kdd96JXQ8AfPjhh7j66qtHfP+2227DK6+8YkER3zgxjhGjJrb7DeC7loR95OnVp4dVE+P8wuYNGiN/sGnS/SaqAfm5NIUF4/zC5gsA3zgxjhGjJrb7DeC7lsrGZsH7eme//fYzN954Y+7rf/3rX1a6wA/lq1/9qpkzZ07u69tuu83sscceFhVJUynWrl1rOjs7zWuvvZb73po1a0xHR4d5/fXXrWjq7+8348aNM88880zueytWrDCtra3m/ffft6KJbZwYx4hRkzFc95sxfNeS4ECeXn16jOHTxDi/sHmDxqh6Nel+E9WA/Nwf0lQcxvmF0RfYxolxjBg1GcN1vxnDdy2VizLnLTJr1izccMMNua9vuukmHHPMMRYVZTXdeOONua9vvvlmzJo1y6IiaSpFKpXCEUcckafn7rvvxrbbbovJkydb0eS6Lo4++ui863vBggXYddddsfHGG1vRxDZOjGPEqAngut8AvmtJcCBPrz49AJ8mxvmFzRs0RtWrSfebqAbk5/6QpuIwzi+MvsA2ToxjxKgJ4LrfAL5rqWxs7w7UM4O7XR988IEZGBgwEydONM8++6xtWWbzzTc3jz32mFm+fLlpa2szH3/8sW1J0lSCv//972brrbfOfX3EEUeYq666ypoeY4x54oknzKRJk3Jfz5w5My8LxQZs48Q4RoyajOG634zhu5aEfeTp1amHURPj/MLmDRojfzBq0v0m2JGfS1NYMM4vjL7ANk6MY8SoyRiu+80YvmupHNQQ1jLHHnssdtllF0ydOhWnnXYannzySduScO6556K7uxvTp0/HXXfdhdtuu822JGnywSc+8Qnceuut2HLLLTFp0iS8+eabaGtrs6ppypQp+NnPfoatt94aW221Fd599100NDRY1cQ2ToxjxKiJ7X4D+K4lYR95evXpYdXEOL+weYPGqDo16X4T1YD8XJrCgnF+YfMFgG+cGMeIURPb/QbwXUt+SdgWUO/MmjUL5513HqZPn279GNggs2bNwmc/+1k89dRTOO2002zLASBNfjjuuONw/fXXY/r06dhvv/0oJqBZs2bh+uuvx7Rp03DooYdaNw+Ab5wYx4hVE9P9BvBdS8I+8vTq0wNwamKcX9i8QWNUnZp0v4lqQH7uD2kqDeP8wuYLAN84MY4Rqyam+w3gu5Z8Yzt1v97p7+8348ePN21tbea9996zLSfHzjvvbLq6ukxfX59tKTmkqTgvvfSSGTdunNl7773NggULbMsxxhjz9ttvm87OTrPjjjuaP//5z7blGGP4xolxjBg1GcN1vxnDdy0J+8jT/cGmxxg+TYzzC5s3aIz8wahJ95tgR37uH2kqDuP8wugLbOPEOEaMmozhut+M4buW/KKyNgScd955eOmll3DzzTfblpLjgQcewJIlS3DkkUfalpJDmkqzyy674JVXXsHixYuRSHAcjNlvv/3w4osvYtGiRXAcx7YcAHzjxDhGjJrY7jeA71oS9pGnl4ZND8CpiXF+YfMGjZE/2DTpfhPVgPzcH9JUGsb5hc0XAL5xYhwjRk1s9xvAdy35QcF5IWoIxonxmWeewbJly/DZz37WtpQcbOPEOEaMmhhhu5aEELUD4/zC5g0aI38wamKD8VoSQtQGjPMLoy+wjRPjGDFqYoTtWvKDgvMkvPHGG5g8ebJtGXlceOGF+M///E/bMvKQJiHqG91vohqQp5eGTQ/AqUmIWkX3m6gG5Of+kCYh6hvdb8FxbQsQWaZOnWpbwgguvvhi2xJGIE2lufDCC21LGMFXv/pV2xJGwDZOjGPEqIntfgP4riVhH3l6adj0AJyaGOcXNm/QGPmDTZPuN1ENyM/9IU2lYZxf2HwB4BsnxjFi1MR2vwF811IplDlPQktLC1asWGFbRh7S5A82Ta2treju7rYtIw9pKg2bHoBTE9v9BnCOk7AL43XKpolND8CpiXF+YdPEpgeQJj/ofhPVAON1Kk3+YNPEOL9IU2nY9ACcmtjuN4BznIqhzHkSWJo5DEWa/MGmiXG/TZpKw6YH4NTEdr8BnOMk7MJ4nbJpYtMDcGpinF/YNLHpAaTJD7rfRDXAeJ1Kkz/YNDHOL9JUGjY9AKcmtvsN4BynYig4L0aF8WKWptIwTozSVBo2PQCnJrb7DeAcJyGGw3bvsOkBODUxzi9smtj0ANLkB91vQlQG470jTaVhnF+kqTRsegBOTWz3G8A5TsVQcJ6ETTbZxLaEEZx55pm2JYxAmkrDODFKU2nY9ACcmtjuN4BznIRd5OmlYdMDcGpinF/YNLHpAaTJD7rfRDUgP/eHNJWGcX6RptKw6QE4NbHdbwDnOBVDwXkSXnjhBdsSRnDOOefYljACaSoN48T4s5/9zLaEEbCNE+MYMWpiu98AvmtJ2EeeXho2PQCnJsb5hc0bNEb+YNOk+01UA/Jzf0hTaRjnFzZfAPjGiXGMGDWx3W8A37VUCjWEtUR3dzcWLFiAp59+GitWrEBLSwumTJmCww8/HG1tbbbl5dHX14cDDzwQf/nLX2J/7/vuuw8PPfQQpk2bhsMPPzzvtZNPPhk///nPY9Wzbt06/PCHP8TLL7+MU089FWPHjsUJJ5yAV155BTNnzsR///d/I5PJxKqpEI8++ii23357JBIJ21JoWb16NYwxaGxstC2Fmo8++gidnZ3W3v83v/kNfvnLX+Lpp59Gd3c3urq6sNdee+H73/8+tt12W2u6hBiKPL008vPKkacXR37uH5ueLj8X1YD83B/y9MqQn5dGnu4PrdFrD2XOW+CRRx7BJz7xCVxyySV4//33kUqlsGTJElx66aXYYost8D//8z+2JeYxMDCABx98MPb3vfbaa3HkkUfimWeewTe/+U3sv//+eR2gb7jhhtg1nXHGGbjvvvuwaNEizJw5E1dddRW+9rWv4cILL8Tf/vY3nH/++bFrKsTBBx+MpUuXWnnv4dfv5Zdfjt122w277bYbLrvsMiuafvnLX+Kll14CACxevBj7778/Wlpa0NraipkzZ2LJkiWx6pk+fTouvvhiLF68ONb3LcYHH3yAY489FlOnTsU555yDlStXYtddd8WYMWOw2Wab4amnnopd0yWXXIIzzzwTe++9N77xjW9g3Lhx+NrXvobJkydj3333xSOPPBK7JiA79+yzzz7o6upCKpVCV1cX9t57b9x4441W9Ai7yNNLIz8Phjx9A2x+DsjT/SA/F9WA/Nwf8vTKkZ/nw+bp8nN/yNMjwojY2X777c3VV19d8LVrrrnGTJ8+PWZFxhx++OGj/vPv//7vxnXd2DVts8025pFHHjHGGLN69Wpz7LHHmp122sksX77cGGNMc3Nz7Jo23XRT8+GHH5olS5YYx3HMCy+8kHvt6aefNp/85Cdj1dPR0VHwH9d1TXt7u+no6IhVjzHGtLS05P790ksvNZMmTTI///nPzS9+8QszefJk86Mf/Sh2TRMmTDAff/yxMcaYI4880hx33HHm1VdfNa+88oqZPXu2OfLII2PVk0wmzdSpU00ymTSf//znzV133WX6+/tj1TCco446ynz+8583N954oznkkEPMHnvsYc444wyzaNEic+aZZ5r99tsvdk2bbrqpefHFF3NfP/vss2b77bc3xhhz2223md133z12TXPmzDGTJk0yl1xyifnzn/9sHn30UXPfffeZSy65xGy22WbmBz/4QeyahF3k6aWRn/tDnl4aNj83Rp7uB/m5qAbk5/6Qp5dGfu4PNk+Xn/tDnh4NKmtjgaamJnz88cdIJpMjXuvr60N7eztWrVoVq6ZMJoOvfvWr6OrqKqhp7ty56O/vj1VTW1sbli9fnve9r3/963j00Ufx5z//GZttthm6u7utaWppacnLEgCA1tbWWDVtscUW2HzzzXHmmWfmridjDA477DBcc8012GijjbDXXnvFpgfIH5cpU6bgmmuuwc477wwAeOyxxzBr1iw8//zzsWpqbm7GihUr4DgONtlkE7zyyiu5o3KrV6/GZpttFuvO/OB18s9//hPz58/Hb37zGzQ1NeGEE07Al7/8ZXzyk5+MTcsgG2+8MV5//fXc/DRmzBisXLkSmUwGa9aswYQJE/Dhhx/GqqmzsxMffPABXDd7yKu3txcTJkzA0qVL0dfXh87OzhH3YNSMHz8ef/vb37DllluOeO3ll1/GnnvuSZVtIaJHnl4a+bk/5OmlYfNzQJ7uB/m5qAbk5/6Qp5dGfu4PNk+Xn/tDnh4RVrcG6pRp06aZefPmFXxt/vz5ZurUqTErMmbGjBnmjjvuKPja6tWrjeM4MSsyZvPNNzevv/76iO9//etfN9OnTzcNDQ2xa5o4caLp6ekxxhhz0UUX5b328ccfmzFjxsSqZ/Xq1ea73/2umTp1qnnooYdy3x8zZox55513YtUyyNBd+a6uLjMwMDDq63Exffp087e//c0YY8ynPvUps2jRotxr77zzTuyf2/AxWLVqlbn22mvNHnvsYVzXNfvss0+seozJflaD1/by5cuN67pmzZo1xhhj1qxZY7q6umLXdNBBB5nvf//7Zt26daa3t9ecddZZ5oADDjDGZMfMhqbW1lbT3d1d8LXly5dbub6FXeTppZGf+0OeXho2PzdGnu4H+bmoBuTn/pCnl0Z+7g82T5ef+0OeHg0KzlvgoYceMh0dHWbKlClm1qxZ5tRTTzWzZ882U6dONZ2dnebhhx+OXdMVV1xhFixYUPC1devWmfPPPz9eQcaYk046yZx33nkFXzv55JOtPIyceuqpecfkhnLttdeaz33uczEryvLYY4+ZHXbYwZx00klm2bJlZqONNrJm/KlUynz729823/72t01XV5f54IMPcq91d3dbOcZ3xx13mIkTJ5qrr77anHfeeWb69Onm2muvNfPnzzfbb7+9+c53vhOrnmLm8MILL5gzzjgjRjVZDjvsMHP44Yebm2++2Rx++OFm9913N2eddZZ55513zPe+9z1z4IEHxq7p1VdfNdtuu61JJpMmmUyaLbfc0vzv//6vMSZ7RPWcc86JXdPRRx9tDjroIPPEE0/kjjn29/ebJ554whx88MHm6KOPjl2TsIs8vTTy8/KQp48Om58bI0/3g/xcVAPyc3/I0/0jPy8Om6fLz/0hT48GlbWxxLJly3Kd4FeuXInm5mZMnToVhx9+ODo6OmzLo6C3txfr1q0btVP3W2+9hUmTJsWsanRWrVoFx3GsdRbv7+/HJZdcgiuvvBJLlizBK6+8gk022SR2HV/60pfyvj7ttNMwffp0AMCtt96KK664wkrzonvvvRfnnnsuHn/8cQxOe5MmTcJXv/pVnHXWWbljWXFQ6Lilbd59912cfPLJeP311/H//X//H3bbbTd87nOfw5tvvoktt9wSd9xxB7beeuvYdQ0MDODFF1/EwMAAttpqKyQSidg1DGXFihU45ZRTcMstt6Cvrw9NTU3o6elBMpnEUUcdhZ/+9KdoaWmxqlHEjzy9OPLz8pGnjw6TnwPydL/Iz0U1ID8vjTy9POTnxWHydPm5f+Tp4aPgvBA1xquvvoqHHnoIRx11FDKZjG05dPT09GDZsmVobm5GW1ubbTnUGGPw0UcfFaxzWe+sXr0aL774Ym7httVWW6GhocG2LCFEjSFPHx35eXnI0wsjPxdCxIH8vDjydP/Iz0enmj093tQS4Yu3337btoQRnHzyybYljECaCvPJT34Sxx9/vEx/FBobGzFhwgSZvg8cx6E2fZv3W0NDA6ZPn4499tgD06dPrxrTF/EjTy8Nmx6AR5M8fXTk5+XB7Onyc1ENyM/9IU2FkZ8XR57uH2Y/B+TplaLgPCGf/vSnbUsYAeMBC2kqDcODyHCkqTRsegBOTWz3GwBcdNFFtiUIMuTppWHTA3BqYpyH2TSx6QGkyQ+M95v8XAxHfu4PaSoN2xwMSJMf2PQAnJrY7jegOjxdwXlCnnvuOavvv2LFCrz77rt59bZ+8YtfWFQkTZXCODFKU2nY9AD2NVXD/QYAf/vb32xLEGTI07n1AJyaCmF7Hi4EmyY2PYA0Dada7jf5uRiO/Hwk0lQZ8gV/sGli0wPY11QN9xtQJZ4eabtZUTX09/ebOXPmmMmTJxvXdXP/bLbZZuYHP/hBruOxNPFpGqS7u9u88847pru725qG4UhTadj0GMOjifl+E4IZtnuHTQ+rpqGwzMNDYdPEpscYaRoN9vtNCFYY7x1pKg+GOXg40lQaNj3G8Ghivt+qGQXnLbF8+XJz3XXXmdNPP92cdNJJ5vTTTzfXXnut+fjjj63oOfXUU8306dPNrbfeal5++WWzdOlS88orr5hbb73V7LDDDua0006TJkJNjBOjNFWfHlZNbPdbKXp7e80+++xjW4awgDy9uvSwamKch9k0semRJn8w3m/FkJ/XL/JzaQoDtjlYmqpTD6smtvutFNXi6QrOW2DhwoWmq6vLbLvttubYY4813/jGN8xxxx1ntttuOzNmzBjzyCOPxK6pq6vLLF68uOBr7733nuns7IxZkTT5gXFilKbq08Oqie1+K8WaNWuM67q2ZYiYkadXnx5jODUxzsNsmtj0SJM/GO+3YsjP6xP5uT+kqTRsc7A0VaceVk1s91spqsXTHWMICyfVODvssANOPvlknHjiiSNemzdvHq644gr861//ilVTZ2cnXnjhBWy88cYjXnv//fexzTbb4KOPPpImMk1jxozBc889h7Fjx454bfHixdh2223x4YcfxqZHmqpTD6smtvsNAL7whS+M+trAwADuuusu9Pf3x6hI2EaeXn16WDUxzsNsmtj0SJM/GO83+bkYjvxcmsKCbQ6WpurUw6qJ7X4DasPT1RDWAi+++CKOP/74gq/Nnj0bL730UsyKgGOOOQYzZ87EggUL8Nprr+Gjjz7C66+/jgULFuDggw/GscceK02EmgYGBuA4TsHXHMex0iBEmqpPD8Cpie1+A4B77rkHm266KaZNmzbin+222y52PcI+8vTq08OqiXEeZtPEpgeQJj8w3m/yczEc+bk0hQXbHAxIUzXqATg1sd1vQI14euy5+sJMmzbNzJs3r+Br8+fPN1OnTo1ZkTF9fX3m3HPPNRMnTjSO4xjXdY3jOGbixInm3HPPNX19fdJEqOkb3/iG2X777c3vfvc78+qrr5oPP/zQvPbaa+Z3v/ud2XHHHc0pp5wSqx5pqk49rJrY7jdjjJkxY4a54447Cr62evVq4zhOzIqEbeTp1aeHVRPjPMymiU2PNPmD8X6Tn4vhyM+lKSzY5mBpqk49rJrY7jdjasPTFZy3wEMPPWQ6OjrMlClTzKxZs8ypp55qZs+ebaZOnWo6OzvNww8/bFXfsmXLzKJFi8yyZcus6hiKNBWGcWKUpurTw6ppKAz3mzHGXHHFFWbBggUFX1u3bp05//zz4xUkrCNPLw82PcbwaGKch9k0semRpvJhud/k52I48vPykabCMM7B0lR9elg1DYXhfjOmNjxdNectsWzZMixYsABPP/00Vq5ciZaWFkyZMgWHH344Ojo6bMsTVcjHH3+MlStXorm5Ge3t7bblAJCmatQDcGoSghl5uggbxnmYTRObHkCahKh25OcibBjnYGmqPj0ApyYRHgrOkzBlyhQ888wztmUIIYQQIiDydCGEEKL6kZ8LIYSIAwXnSWhtbUV3d7dtGUIIIYQIiDxdCCGEqH7k50IIIeLAtS1AZNEeiRBCCFEbyNOFEEKI6kd+LoQQIg4UnCfhqquusi1BCCGEECEgTxdCCCGqH/m5EEKIOFBZGyGEEEIIIYQQQgghhBAiZpQ5L4QQQgghhBBCCCGEEELEjILzQgghhBBCCCGEEEIIIUTMKDgvhBBCCCGEEEIIIYQQQsSMgvNCCCGEEEIIIYQQQgghRMwoOC9EDfDAAw/AcRx8/PHHsb/3+eefj+nTp4f296677jq0t7dX/PuO4+D2228PpOGEE07AYYcdlvt67733xre+9a1AfxMIf6yEEELUHvL0DcjThRBCVCvy8w3Iz4UojoLzQhTBcZyi/5x//vm2JVrnO9/5Du6//37bMnK89957OOiggwL9jcsvvxzXXXddOIKGMHyshj9gCCGEiA55emnk6f6RpwshhB3k56WRn/tHfi4YSNgWIAQz7733Xu7fb7nlFpx77rl48cUXc99rbm7O/bsxBv39/Ugk6uu2am5uzhsH24wbNy7w32hrawtByQYGrw22sRJCiHpCnl4aNp+SpwshhBiO/Lw0bB4lPxeiOMqcF6II48aNy/3T1tYGx3FyX7/wwgtoaWnBH/7wB+y4445Ip9N46KGHCu60futb38Lee++d+3pgYABz587F5ptvjoaGBkybNg3/7//9v6Ja1q5dizPPPBMTJ05EOp3GFltsgXnz5uX9zOOPP44ZM2agsbERu+22W95DCgDccccd2GGHHZDJZPCJT3wCF1xwAdatW5d73XEcXHXVVTjkkEPQ2NiIbbbZBgsXLsQrr7yCvffeG01NTdhtt93w6quv5n6n0DGw+fPnY9ttt0U6ncb48eNxyimn5F677LLLMGXKFDQ1NWHixIk4+eSTsXLlyqL/7UPp7e3FKaecgvHjxyOTyWCzzTbD3Llz8/4bBo/MvfHGG3AcB7/97W+x5557oqGhAZ/5zGfw0ksv4dFHH8WMGTPQ3NyMgw46CEuXLs39jVK75ddffz1mzJiBlpYWjBs3DscccwyWLFmSe33wCOPwa2PoWJ1//vn41a9+hTvuuCOX5fHAAw9g3333zRsvAFi6dClSqRRV9oMQQlQb8nR5eiHk6UIIUV3Iz+XnhZCfi2pGwXkhAnLWWWfhoosuwvPPP4+pU6f6+p25c+fi17/+Na688ko899xz+Pa3v43jjjsODz744Ki/M3v2bNx88834yU9+gueffx5XXXXViB3e//zP/8Sll16Kxx57DIlEAl/+8pdzr/3973/H7Nmzcdppp+F///d/cdVVV+G6667DhRdemPc35syZg9mzZ+PJJ5/E1ltvjWOOOQZf+9rXcPbZZ+Oxxx6DMWaEMQ3lF7/4Bb7xjW/gq1/9Kp555hnceeed2GKLLXKvu66Ln/zkJ3juuefwq1/9Cn/5y19wxhln+Bo3APjJT36CO++8E7/97W/x4osv4sYbb8TkyZOL/s55552Hc845B0888QQSiQSOOeYYnHHGGbj88svx97//Ha+88grOPfdc3xr6+vowZ84cPPXUU7j99tvxxhtv4IQTThjxc8Wuje985zs48sgjMXPmTLz33nt47733sNtuu+HEE0/ETTfdhLVr1+Z+9oYbbsCECROw7777+tYohBCifOTp+cjTNyBPF0KI6kF+no/8fAPyc0GJEUL44tprrzVtbW25r//6178aAOb222/P+7njjz/e/Pu//3ve90477TSz1157GWOMWbNmjWlsbDT/+Mc/8n7mK1/5ijn66KMLvveLL75oAJg///nPBV8f1HLfffflvnf33XcbAGb16tXGGGP2228/81//9V95v3f99deb8ePH574GYM4555zc1wsXLjQAzLx583Lfu/nmm00mk8l9fd5555lp06blvt5kk03Mf/7nfxbUWYhbb73VdHV15b4ePs7DOfXUU82+++5rBgYGCr4OwCxYsMAYY8zrr79uAJhrrrkmTz8Ac//99+e+N3fuXLPVVlvlvh7+Ge61117mtNNOG1XTo48+agCYFStWGGNGvzaGj1Wha2X16tWmo6PD3HLLLbnvTZ061Zx//vmjvr8QQojykKdnkaePRJ4uhBDVg/w8i/x8JPJzUU0oc16IgMyYMaOsn3/llVfQ09ODz33uc7n6Zs3Nzfj1r3+ddxRtKE8++SQ8z8Nee+1V9G8P3fkdP348AOSOcj311FP4v//3/+a950knnYT33nsPPT09Bf/G2LFjAQBTpkzJ+96aNWvQ3d094v2XLFmCd999F/vtt9+oGu+77z7st99+mDBhAlpaWjBr1ix8+OGHeRqKccIJJ+DJJ5/EVltthW9+85v405/+VPJ3/Pw3DT3yVorHH38chx56KCZNmoSWlpbc5/LWW2/l/Vy51wYAZDIZzJo1C/PnzwcAPPHEE3j22WcL7voLIYQIF3n6BuTp8nQhhKhW5OcbkJ/LzwU/9dUVQ4gIaGpqyvvadV0YY/K+19fXl/v3wdptd999NyZMmJD3c+l0uuB7NDQ0+NKSTCZz/+44DoBs7bzB973gggvwhS98YcTvZTKZon+j2N8tR+cbb7yBQw45BF//+tdx4YUXorOzEw899BC+8pWvoLe3F42NjSX/G3fYYQe8/vrr+MMf/oD77rsPRx55JPbff/+i9QD9/DcV+u8pxKpVq3DggQfiwAMPxI033oiNNtoIb731Fg488ED09vbm/ezwa8MvJ554IqZPn463334b1157Lfbdd19sttlmFf0tIYQQ/pGn+9cpT/eHPF0IIeJHfu5fp/zcH/JzESUKzgsRMhtttBGeffbZvO89+eSTOaP59Kc/jXQ6jbfeeqvkLvsgU6ZMwcDAAB588EHsv//+FenaYYcd8OKLL+bVlgublpYWTJ48Gffffz/22WefEa8//vjjGBgYwKWXXgrXzR7c+e1vf1v2+7S2tuKoo47CUUcdhSOOOAIzZ87ERx99hM7OzsD/DaV44YUX8OGHH+Kiiy7CxIkTAQCPPfZYRX8rlUqhv79/xPenTJmCGTNm4Oqrr8ZNN92EK664IpBmIYQQlSFPl6f7RZ4uhBC8yM/l536RnwsbKDgvRMjsu+++uOSSS/DrX/8au+66K2644QY8++yz2H777QFkzfE73/kOvv3tb2NgYAB77LEHlnnNkXwAAANGSURBVC9fjocffhitra04/vjjR/zNyZMn4/jjj8eXv/xl/OQnP8G0adPw5ptvYsmSJTjyyCN96Tr33HNxyCGHYNKkSTjiiCPgui6eeuopPPvss/jBD34Q2n//+eefj//4j//AxhtvjIMOOggrVqzAww8/jFNPPRVbbLEF+vr68NOf/hSHHnooHn74YVx55ZVl/f3LLrsM48ePx/bbbw/XdXHrrbdi3LhxaG9vD+2/oRiTJk1CKpXCT3/6U/zHf/wHnn32WcyZM6eivzV58mT88Y9/xIsvvoiuri60tbXlHhBPPPFEnHLKKWhqasLhhx8e5n+CEEIIn8jT5el+kacLIQQv8nP5uV/k58IGqjkvRMgceOCB+P73v48zzjgDn/nMZ7BixQrMnj0772fmzJmD73//+5g7dy622WYbzJw5E3fffTc233zzUf/uL37xCxxxxBE4+eSTsfXWW+Okk07CqlWrytL1+9//Hn/605/wmc98Brvssgv++7//O/SjWMcffzx+/OMf4+c//zm23XZbHHLIIXj55ZcBANOmTcNll12Giy++GNtttx1uvPFGzJ07t6y/39LSgh/+8IeYMWMGPvOZz+CNN97APffck9vlj5qNNtoI1113HW699VZ8+tOfxkUXXYQf/ehHFf2tk046CVtttRVmzJiBjTbaCA8//HDutaOPPhqJRAJHH3103pFGIYQQ8SFPl6f7RZ4uhBC8yM/l536RnwsbOGZ44S0hhBDWeeONN/DJT34Sjz76KHbYYQfbcoQQQghRIfJ0IYQQovqRn4uoUHBeCCGI6Ovrw4cffojvfOc7eP311/N26oUQQghRPcjThRBCiOpHfi6iRmVthBCCiIcffhjjx4/Ho48+WnatPyGEEELwIE8XQgghqh/5uYgaZc4LIYQQQgghhBBCCCGEEDGjzHkhhBBCCCGEEEIIIYQQImYUnBdCCCGEEEIIIYQQQgghYkbBeSGEEEIIIYQQQgghhBAiZhScF0IIIYQQQgghhBBCCCFiRsF5IYQQQgghhBBCCCGEECJmFJwXQgghhBBCCCGEEEIIIWJGwXkhhBBCCCGEEEIIIYQQImYUnBdCCCGEEEIIIYQQQgghYub/D9HfK673WNhTAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1500x800 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_comparison_violinplot_three_panels([scores_normal_model_both_modes_ionizing.pos_vs_pos_scores, scores_normal_model_both_modes_ionizing.pos_vs_neg_scores, scores_normal_model_both_modes_ionizing.neg_vs_neg_scores],\n",
+    "                                              [scores_balanced_model_both_modes_ionizing.pos_vs_pos_scores, scores_balanced_model_both_modes_ionizing.pos_vs_neg_scores, scores_balanced_model_both_modes_ionizing.neg_vs_neg_scores],\n",
+    "                                              bins)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
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