diff --git a/.gitignore b/.gitignore index 988b9c61..c9ce5eb0 100644 --- a/.gitignore +++ b/.gitignore @@ -5,3 +5,4 @@ /dev_scripts/ /recipe/ /tutorials/.ipynb_checkpoints/ +/aaanalysis/_archive/ diff --git a/aaanalysis/__pycache__/utils.cpython-39.pyc b/aaanalysis/__pycache__/utils.cpython-39.pyc index 8e27d44b..c101441e 100644 Binary files a/aaanalysis/__pycache__/utils.cpython-39.pyc and b/aaanalysis/__pycache__/utils.cpython-39.pyc differ diff --git a/aaanalysis/_utils/__pycache__/_check_type.cpython-39.pyc b/aaanalysis/_utils/__pycache__/_check_type.cpython-39.pyc index 71841fa1..eeabce26 100644 Binary files a/aaanalysis/_utils/__pycache__/_check_type.cpython-39.pyc and b/aaanalysis/_utils/__pycache__/_check_type.cpython-39.pyc differ diff --git a/aaanalysis/_utils/_check_type.py b/aaanalysis/_utils/_check_type.py index 5cd73ff0..6ff80bbc 100644 --- a/aaanalysis/_utils/_check_type.py +++ b/aaanalysis/_utils/_check_type.py @@ -70,7 +70,7 @@ def check_tuple(name=None, val=None, n=None, check_n=True, accept_none=False): raise ValueError(f"'{name}' ({val}) should be a tuple with {n} elements.") -def check_list(name=None, val=None, accept_none=False, convert=True): +def check_list_like(name=None, val=None, accept_none=False, convert=True): """""" if accept_none and val is None: return None diff --git a/aaanalysis/aaclust/__pycache__/aaclust.cpython-39.pyc b/aaanalysis/aaclust/__pycache__/aaclust.cpython-39.pyc index c0a20c47..1b275e4a 100644 Binary files a/aaanalysis/aaclust/__pycache__/aaclust.cpython-39.pyc and b/aaanalysis/aaclust/__pycache__/aaclust.cpython-39.pyc differ diff --git a/aaanalysis/aaclust/aaclust.py b/aaanalysis/aaclust/aaclust.py index d40b1933..dfb550cf 100644 --- a/aaanalysis/aaclust/aaclust.py +++ b/aaanalysis/aaclust/aaclust.py @@ -248,7 +248,7 @@ def fit(self, # Check input X = ut.check_X(X=X) ut.check_X_unique_samples(X=X) - names = ut.check_list(name="names", val=names, accept_none=True) + names = ut.check_list_like(name="names", val=names, accept_none=True) ut.check_number_range(name="mint_th", val=min_th, min_val=0, max_val=1, just_int=False, accept_none=False) ut.check_number_range(name="n_clusters", val=n_clusters, min_val=1, just_int=True, accept_none=True) check_merge_metric(merge_metric=merge_metric) @@ -391,7 +391,7 @@ def name_clusters(X: ut.ArrayLike2D, X = ut.check_X(X=X) ut.check_X_unique_samples(X=X) labels = ut.check_labels(labels=labels) - names = ut.check_list(name="names", val=names, accept_none=False) + names = ut.check_list_like(name="names", val=names, accept_none=False) ut.check_bool(name="shorten_names", val=shorten_names) ut.check_match_X_labels(X=X, labels=labels) check_match_X_names(X=X, names=names, accept_none=False) @@ -487,7 +487,7 @@ def comp_correlation(X: ut.ArrayLike2D, Returns ------- - df_corr + df_corr : pd.DataFrame DataFrame with correlation either for each pair in ``X`` of shape (n_samples, n_samples) or for each pair between ``X`` and ``X_ref`` of shape (n_samples, n_samples_ref). @@ -535,15 +535,15 @@ def comp_coverage(names : [List[str]] =None, names List of sample names. Should be subset of ``names_ref``. names_ref - List of reference sample names. Should superset of ``names``. + List of reference sample names. Should be superset of ``names``. Returns ------- - coverage + coverage : float Percentage of unique names from ``names`` that are found in ``names_ref``. """ - names = ut.check_list(name="names", val=names, accept_none=False) - names_ref = ut.check_list(name="names_ref", val=names_ref, accept_none=False) + names = ut.check_list_like(name="names", val=names, accept_none=False) + names_ref = ut.check_list_like(name="names_ref", val=names_ref, accept_none=False) ut.check_superset_subset(subset=names, name_subset="names", superset=names_ref, name_superset="names_ref") # Compute coverage diff --git a/aaanalysis/cpp/__pycache__/cpp.cpython-39.pyc b/aaanalysis/cpp/__pycache__/cpp.cpython-39.pyc index 25752c1e..2d02ee5c 100644 Binary files a/aaanalysis/cpp/__pycache__/cpp.cpython-39.pyc and b/aaanalysis/cpp/__pycache__/cpp.cpython-39.pyc differ diff --git a/aaanalysis/cpp/__pycache__/cpp_plot.cpython-39.pyc b/aaanalysis/cpp/__pycache__/cpp_plot.cpython-39.pyc index 2239c8b6..77973308 100644 Binary files a/aaanalysis/cpp/__pycache__/cpp_plot.cpython-39.pyc and b/aaanalysis/cpp/__pycache__/cpp_plot.cpython-39.pyc differ diff --git a/aaanalysis/cpp/__pycache__/feature.cpython-39.pyc b/aaanalysis/cpp/__pycache__/feature.cpython-39.pyc index 80662d8d..ce10b1ba 100644 Binary files a/aaanalysis/cpp/__pycache__/feature.cpython-39.pyc and b/aaanalysis/cpp/__pycache__/feature.cpython-39.pyc differ diff --git a/aaanalysis/cpp/cpp.py b/aaanalysis/cpp/cpp.py index 747d11dd..6635d302 100644 --- a/aaanalysis/cpp/cpp.py +++ b/aaanalysis/cpp/cpp.py @@ -3,7 +3,6 @@ """ import pandas as pd -import aaanalysis.data_handling.load_scales_ from aaanalysis.cpp.feature import SequenceFeature from aaanalysis.cpp._feature_stat import SequenceFeatureStatistics @@ -60,9 +59,9 @@ def __init__(self, df_scales=None, df_cat=None, df_parts=None, split_kws=None, # Load default scales if not specified sf = SequenceFeature() if df_cat is None: - df_cat = aaanalysis.data_loader.load_scales_.load_scales(name=ut.STR_SCALE_CAT) + df_cat = aa.load_scales(name=ut.STR_SCALE_CAT) if df_scales is None: - df_scales = aaanalysis.data_loader.load_scales_.load_scales() + df_scales = aa.load_scales() if split_kws is None: split_kws = sf.get_split_kws() ut.check_bool(name="verbose", val=verbose) diff --git a/aaanalysis/cpp/cpp_plot.py b/aaanalysis/cpp/cpp_plot.py index cbc9ffdc..e3e2f288 100644 --- a/aaanalysis/cpp/cpp_plot.py +++ b/aaanalysis/cpp/cpp_plot.py @@ -6,9 +6,8 @@ import seaborn as sns import inspect -import aaanalysis -import aaanalysis.data_handling.load_scales_ from aaanalysis.cpp._cpp import CPPPlots, get_optimal_fontsize +import aaanalysis as aa import aaanalysis.utils as ut @@ -230,7 +229,7 @@ def __init__(self, df_cat=None, accept_gaps=False, jmd_n_len=10, jmd_c_len=10, e ut.check_bool(name="verbose", val=verbose) if df_cat is None: - df_cat = aaanalysis.data_loader.load_scales_.load_scales(name=ut.COL_SCALE_ID) + df_cat = aa.load_scales(name=ut.COL_SCALE_ID) self.df_cat = df_cat self._verbose = verbose self._accept_gaps = accept_gaps diff --git a/aaanalysis/cpp/feature.py b/aaanalysis/cpp/feature.py index 07ab5e8b..fbd41a50 100644 --- a/aaanalysis/cpp/feature.py +++ b/aaanalysis/cpp/feature.py @@ -10,7 +10,6 @@ import multiprocessing as mp import warnings -import aaanalysis.data_handling.load_scales_ from aaanalysis.cpp._feature_pos import SequenceFeaturePositions from aaanalysis.cpp._split import Split, SplitRange from aaanalysis.cpp._part import Parts @@ -343,7 +342,7 @@ def get_features(self, list_parts=None, split_kws=None, df_scales=None, all_part ut.check_split_kws(split_kws=split_kws) ut.check_df_scales(df_scales=df_scales, accept_none=True) if df_scales is None: - df_scales = aaanalysis.data_loader.load_scales_.load_scales() + df_scales = aa.load_scales() if split_kws is None: split_kws = self.get_split_kws() scales = list(df_scales) @@ -387,7 +386,7 @@ def feat_matrix(features=None, df_parts=None, df_scales=None, accept_gaps=False, """ ut.check_number_range(name="j_jobs", val=n_jobs, accept_none=True, min_val=1, just_int=True) if df_scales is None: - df_scales = aaanalysis.data_loader.load_scales_.load_scales() + df_scales = aa.load_scales() ut.check_df_scales(df_scales=df_scales) ut.check_df_parts(df_parts=df_parts) features = ut.check_features(features=features, parts=df_parts, df_scales=df_scales) @@ -459,7 +458,7 @@ def feat_names(features=None, df_cat=None, tmd_len=20, jmd_c_len=10, jmd_n_len=1 features = ut.check_features(features=features) ut.check_df_cat(df_cat=df_cat) if df_cat is None: - df_cat = aaanalysis.data_loader.load_scales_.load_scales(name=ut.STR_SCALE_CAT) + df_cat = aa.load_scales(name=ut.STR_SCALE_CAT) # Get feature names sfp = SequenceFeaturePositions() dict_part_pos = sfp.get_dict_part_pos(tmd_len=tmd_len, jmd_n_len=jmd_n_len, jmd_c_len=jmd_c_len, diff --git a/aaanalysis/utils.py b/aaanalysis/utils.py index f458a729..4b9e886e 100644 --- a/aaanalysis/utils.py +++ b/aaanalysis/utils.py @@ -11,7 +11,7 @@ # Import utility functions explicitly from aaanalysis._utils._check_type import (check_number_range, check_number_val, check_str, check_bool, - check_dict, check_tuple, check_list, + check_dict, check_tuple, check_list_like, check_ax) from aaanalysis._utils._check_data import (check_X, check_X_unique_samples, check_labels, check_match_X_labels, check_superset_subset, diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index e8c9925f..b2a85a51 100644 Binary files a/docs/build/doctrees/environment.pickle and b/docs/build/doctrees/environment.pickle differ diff --git a/docs/build/doctrees/generated/aaanalysis.AAclust.doctree b/docs/build/doctrees/generated/aaanalysis.AAclust.doctree index a00769c9..1a0be0de 100644 Binary files a/docs/build/doctrees/generated/aaanalysis.AAclust.doctree and b/docs/build/doctrees/generated/aaanalysis.AAclust.doctree differ diff --git a/docs/build/doctrees/generated/aaanalysis.CPP.doctree b/docs/build/doctrees/generated/aaanalysis.CPP.doctree index fa5c083d..6997db1f 100644 Binary files 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100644 Binary files a/docs/build/html/_downloads/fb29bffb69140db4f68c4eb913c6f7d3/aaanalysis-plot_settings-1.pdf and b/docs/build/html/_downloads/fb29bffb69140db4f68c4eb913c6f7d3/aaanalysis-plot_settings-1.pdf differ diff --git a/docs/build/html/_images/output_13_1.png b/docs/build/html/_images/output_13_1.png index 815e3a39..6e2513d5 100644 Binary files a/docs/build/html/_images/output_13_1.png and b/docs/build/html/_images/output_13_1.png differ diff --git a/docs/build/html/_sources/generated/tutorial1_quick_start.rst.txt b/docs/build/html/_sources/generated/tutorial1_quick_start.rst.txt index 75442ba7..c9cc5aa9 100644 --- a/docs/build/html/_sources/generated/tutorial1_quick_start.rst.txt +++ b/docs/build/html/_sources/generated/tutorial1_quick_start.rst.txt @@ -69,9 +69,9 @@ set of 100 scales, as defined by the ``n_clusters`` parameters: from sklearn.cluster import AgglomerativeClustering - aac = aa.AAclust(model=AgglomerativeClustering) - X = np.array(df_scales) - scales = aac.fit(X, names=list(df_scales), n_clusters=100) + aac = aa.AAclust(model_class=AgglomerativeClustering) + X = np.array(df_scales).T + scales = aac.fit(X, names=list(df_scales), n_clusters=100).medoid_names_ df_scales = df_scales[scales] Comparative Physicochemical Profiling (CPP) @@ -131,10 +131,10 @@ A feature matrix from a given set of CPP features can be created using .. parsed-literal:: - Mean accuracy of 0.6 + Mean accuracy of 0.58 -Creating more features with CPP will take some more time, but improve +Creating more features with CPP will take a little time, but improve prediction performance: .. code:: ipython3 @@ -153,7 +153,7 @@ prediction performance: sns.barplot(pd.DataFrame({"Baseline": cv_base, "CPP": cv}), palette=["tab:blue", "tab:red"]) plt.ylabel("Mean accuracy", size=aa.plot_gcfs()+1) plt.ylim(0, 1) - plt.title("Comparison of Feature Engineering Methods") + plt.title("Comparison of Feature Engineering Methods", size=aa.plot_gcfs()-1) sns.despine() plt.show() diff --git a/docs/build/html/generated/aaanalysis.AAclust.html b/docs/build/html/generated/aaanalysis.AAclust.html index 48d50661..3e1e9520 100644 --- a/docs/build/html/generated/aaanalysis.AAclust.html +++ b/docs/build/html/generated/aaanalysis.AAclust.html @@ -498,11 +498,11 @@

aaanalysis.AAclustReturns: -

DataFrame with correlation either for each pair in X of shape (n_samples, n_samples) or +

df_corr – DataFrame with correlation either for each pair in X of shape (n_samples, n_samples) or for each pair between X and X_ref of shape (n_samples, n_samples_ref).

Return type:
-

df_corr

+

pd.DataFrame

@@ -532,14 +532,14 @@

aaanalysis.AAclustParameters:

  • names – List of sample names. Should be subset of names_ref.

    • -

  • names_ref – List of reference sample names. Should superset of names.

    • +

  • names_ref – List of reference sample names. Should be superset of names.

Returns:
-

Percentage of unique names from names that are found in names_ref.

+

coverage – Percentage of unique names from names that are found in names_ref.

Return type:
-

coverage

+

float

diff --git a/docs/build/html/generated/aaanalysis.CPP.html b/docs/build/html/generated/aaanalysis.CPP.html index 4d24b4f1..62c7e260 100644 --- a/docs/build/html/generated/aaanalysis.CPP.html +++ b/docs/build/html/generated/aaanalysis.CPP.html @@ -128,7 +128,7 @@

aaanalysis.CPP

-class aaanalysis.CPP(df_scales=None, df_cat=None, df_parts=None, split_kws=None, accept_gaps=False, verbose=True)[source]
+class aaanalysis.CPP(df_scales=None, df_cat=None, df_parts=None, split_kws=None, accept_gaps=False, verbose=True)[source]

Bases: Tool

Create and filter features that are most discriminant between two sets of sequences.

@@ -149,7 +149,7 @@

aaanalysis.CPP
-__init__(df_scales=None, df_cat=None, df_parts=None, split_kws=None, accept_gaps=False, verbose=True)[source]
+__init__(df_scales=None, df_cat=None, df_parts=None, split_kws=None, accept_gaps=False, verbose=True)[source]

Methods

@@ -168,7 +168,7 @@

aaanalysis.CPP
-run(labels=None, parametric=False, n_filter=100, tmd_len=20, jmd_n_len=10, jmd_c_len=10, ext_len=4, start=1, check_cat=True, n_pre_filter=None, pct_pre_filter=5, max_std_test=0.2, max_overlap=0.5, max_cor=0.5, n_processes=None)[source]
+run(labels=None, parametric=False, n_filter=100, tmd_len=20, jmd_n_len=10, jmd_c_len=10, ext_len=4, start=1, check_cat=True, n_pre_filter=None, pct_pre_filter=5, max_std_test=0.2, max_overlap=0.5, max_cor=0.5, n_processes=None)[source]

Perform CPP pipeline by creation and two-step filtering of features. CPP aims to identify a collection of non-redundant features that are most discriminant between a test and a reference group of sequences.

@@ -223,7 +223,7 @@

aaanalysis.CPP
-eval()[source]
+eval()[source]

Evaluate the output generated by the tool.

diff --git a/docs/build/html/generated/aaanalysis.CPPPlot.html b/docs/build/html/generated/aaanalysis.CPPPlot.html index 1ce88964..0faf6742 100644 --- a/docs/build/html/generated/aaanalysis.CPPPlot.html +++ b/docs/build/html/generated/aaanalysis.CPPPlot.html @@ -128,7 +128,7 @@

aaanalysis.CPPPlot

-class aaanalysis.CPPPlot(df_cat=None, accept_gaps=False, jmd_n_len=10, jmd_c_len=10, ext_len=4, verbose=True)[source]
+class aaanalysis.CPPPlot(df_cat=None, accept_gaps=False, jmd_n_len=10, jmd_c_len=10, ext_len=4, verbose=True)[source]

Bases: object

Create and filter features that are most discriminant between two sets of sequences.

@@ -145,7 +145,7 @@

aaanalysis.CPPPlot
-__init__(df_cat=None, accept_gaps=False, jmd_n_len=10, jmd_c_len=10, ext_len=4, verbose=True)[source]
+__init__(df_cat=None, accept_gaps=False, jmd_n_len=10, jmd_c_len=10, ext_len=4, verbose=True)[source]

Methods

@@ -167,7 +167,7 @@

aaanalysis.CPPPlot
-profile(df_feat=None, y='category', val_col='mean_dif', val_type='count', normalize=False, figsize=(7, 5), title=None, title_kws=None, dict_color=None, edge_color='none', bar_width=0.75, add_jmd_tmd=True, tmd_len=20, start=1, jmd_n_seq=None, tmd_seq=None, jmd_c_seq=None, tmd_color='mediumspringgreen', jmd_color='blue', tmd_seq_color='black', jmd_seq_color='white', seq_size=None, tmd_jmd_fontsize=None, xtick_size=11.0, xtick_width=2.0, xtick_length=5.0, xticks_pos=False, ytick_size=None, ytick_width=2.0, ytick_length=5.0, ylim=None, highlight_tmd_area=True, highlight_alpha=0.15, grid=False, grid_axis='both', add_legend_cat=True, legend_kws=None, shap_plot=False, **kwargs)[source]
+profile(df_feat=None, y='category', val_col='mean_dif', val_type='count', normalize=False, figsize=(7, 5), title=None, title_kws=None, dict_color=None, edge_color='none', bar_width=0.75, add_jmd_tmd=True, tmd_len=20, start=1, jmd_n_seq=None, tmd_seq=None, jmd_c_seq=None, tmd_color='mediumspringgreen', jmd_color='blue', tmd_seq_color='black', jmd_seq_color='white', seq_size=None, tmd_jmd_fontsize=None, xtick_size=11.0, xtick_width=2.0, xtick_length=5.0, xticks_pos=False, ytick_size=None, ytick_width=2.0, ytick_length=5.0, ylim=None, highlight_tmd_area=True, highlight_alpha=0.15, grid=False, grid_axis='both', add_legend_cat=True, legend_kws=None, shap_plot=False, **kwargs)[source]

Plot feature profile for given features from ‘df_feat’.

Parameters:
@@ -224,7 +224,7 @@

aaanalysis.CPPPlot
-heatmap(df_feat=None, y='subcategory', val_col='mean_dif', val_type='mean', normalize=False, figsize=(8, 5), title=None, title_kws=None, vmin=None, vmax=None, grid_on=True, cmap='RdBu_r', cmap_n_colors=None, dict_color=None, cbar_kws=None, facecolor_dark=False, add_jmd_tmd=True, tmd_len=20, start=1, jmd_n_seq=None, tmd_seq=None, jmd_c_seq=None, tmd_color='mediumspringgreen', jmd_color='blue', tmd_seq_color='black', jmd_seq_color='white', seq_size=None, tmd_jmd_fontsize=None, xticks_pos=False, xtick_size=11.0, xtick_width=2.0, xtick_length=5.0, ytick_size=None, add_legend_cat=True, legend_kws=None, add_importance_map=False, cbar_pct=False, **kwargs)[source]
+heatmap(df_feat=None, y='subcategory', val_col='mean_dif', val_type='mean', normalize=False, figsize=(8, 5), title=None, title_kws=None, vmin=None, vmax=None, grid_on=True, cmap='RdBu_r', cmap_n_colors=None, dict_color=None, cbar_kws=None, facecolor_dark=False, add_jmd_tmd=True, tmd_len=20, start=1, jmd_n_seq=None, tmd_seq=None, jmd_c_seq=None, tmd_color='mediumspringgreen', jmd_color='blue', tmd_seq_color='black', jmd_seq_color='white', seq_size=None, tmd_jmd_fontsize=None, xticks_pos=False, xtick_size=11.0, xtick_width=2.0, xtick_length=5.0, ytick_size=None, add_legend_cat=True, legend_kws=None, add_importance_map=False, cbar_pct=False, **kwargs)[source]

Plot a featuremap of the selected value column with scale information (y-axis) versus sequence position (x-axis).

This is a wrapper function for seaborn.heatmap(), designed to highlight differences between two sets of sequences at the positional level (e.g., amino acid level for protein sequences).

@@ -309,7 +309,7 @@

aaanalysis.CPPPlot
-update_seq_size()[source]
+update_seq_size()[source]

diff --git a/docs/build/html/generated/aaanalysis.SequenceFeature.html b/docs/build/html/generated/aaanalysis.SequenceFeature.html index fe01f963..e3c2ed08 100644 --- a/docs/build/html/generated/aaanalysis.SequenceFeature.html +++ b/docs/build/html/generated/aaanalysis.SequenceFeature.html @@ -128,7 +128,7 @@

aaanalysis.SequenceFeature

-class aaanalysis.SequenceFeature[source]
+class aaanalysis.SequenceFeature[source]

Bases: object

Retrieve and create sequence feature components (Part, Split, and Scale).

@@ -191,7 +191,7 @@

aaanalysis.SequenceFeature
-static get_df_parts(df_seq=None, list_parts=None, jmd_n_len=None, jmd_c_len=None, ext_len=None, all_parts=False)[source]
+static get_df_parts(df_seq=None, list_parts=None, jmd_n_len=None, jmd_c_len=None, ext_len=None, all_parts=False)[source]

Create DataFrane with sequence parts.

Parameters:
@@ -230,7 +230,7 @@

aaanalysis.SequenceFeature
-static get_split_kws(n_split_min=1, n_split_max=15, steps_pattern=None, n_min=2, n_max=4, len_max=15, steps_periodicpattern=None, split_types=None)[source]
+static get_split_kws(n_split_min=1, n_split_max=15, steps_pattern=None, n_min=2, n_max=4, len_max=15, steps_periodicpattern=None, split_types=None)[source]

Create dictionary with kwargs for three split types: Segment, Pattern, PeriodicPattern

Parameters:
@@ -277,7 +277,7 @@

aaanalysis.SequenceFeature
-get_features(list_parts=None, split_kws=None, df_scales=None, all_parts=False)[source]
+get_features(list_parts=None, split_kws=None, df_scales=None, all_parts=False)[source]

Create list of all feature ids for given Parts, Splits, and Scales

Parameters:
@@ -299,7 +299,7 @@

aaanalysis.SequenceFeature
-static feat_matrix(features=None, df_parts=None, df_scales=None, accept_gaps=False, n_jobs=None, verbose=False, return_labels=False)[source]
+static feat_matrix(features=None, df_parts=None, df_scales=None, accept_gaps=False, n_jobs=None, verbose=False, return_labels=False)[source]

Create feature matrix for given feature ids and sequence parts.

Parameters:
@@ -324,7 +324,7 @@

aaanalysis.SequenceFeature
-static feat_names(features=None, df_cat=None, tmd_len=20, jmd_c_len=10, jmd_n_len=10, ext_len=0, start=1)[source]
+static feat_names(features=None, df_cat=None, tmd_len=20, jmd_c_len=10, jmd_n_len=10, ext_len=0, start=1)[source]

Convert feature ids (PART-SPLIT-SCALE) into feature names (scale name [positions]).

Parameters:
@@ -361,7 +361,7 @@

aaanalysis.SequenceFeature
-static add_feat_value(df_parts=None, split=None, dict_scale=None, accept_gaps=False)[source]
+static add_feat_value(df_parts=None, split=None, dict_scale=None, accept_gaps=False)[source]

Create feature values for all sequence parts by combining Part, Split, and Scale.

Parameters:
@@ -410,7 +410,7 @@

aaanalysis.SequenceFeature
-static add_dif(df_feat=None, df_seq=None, labels=None, sample_name=<class 'str'>, ref_group=0, accept_gaps=False, jmd_n_len=10, jmd_c_len=10, df_parts=None, df_scales=None)[source]
+static add_dif(df_feat=None, df_seq=None, labels=None, sample_name=<class 'str'>, ref_group=0, accept_gaps=False, jmd_n_len=10, jmd_c_len=10, df_parts=None, df_scales=None)[source]

Add feature value difference between sample and reference group to DataFrame.

Parameters:
@@ -434,7 +434,7 @@

aaanalysis.SequenceFeature
-static add_position(df_feat=None, features=None, start=1, tmd_len=20, jmd_n_len=10, jmd_c_len=10, ext_len=4, part_split=False)[source]
+static add_position(df_feat=None, features=None, start=1, tmd_len=20, jmd_n_len=10, jmd_c_len=10, ext_len=4, part_split=False)[source]

Create list with positions for given feature names

Parameters:
diff --git a/docs/build/html/generated/tutorial1_quick_start.html b/docs/build/html/generated/tutorial1_quick_start.html index 017b0e88..6c23346f 100644 --- a/docs/build/html/generated/tutorial1_quick_start.html +++ b/docs/build/html/generated/tutorial1_quick_start.html @@ -180,9 +180,9 @@

AAclustn_clusters parameters:

from sklearn.cluster import AgglomerativeClustering
 
-aac = aa.AAclust(model=AgglomerativeClustering)
-X = np.array(df_scales)
-scales = aac.fit(X, names=list(df_scales), n_clusters=100)
+aac = aa.AAclust(model_class=AgglomerativeClustering)
+X = np.array(df_scales).T
+scales = aac.fit(X, names=list(df_scales), n_clusters=100).medoid_names_
 df_scales = df_scales[scales]
@@ -235,10 +235,10 @@

3. Protein Predictionprint(f"Mean accuracy of {round(np.mean(cv_base), 2)}")

-
Mean accuracy of 0.6
+
Mean accuracy of 0.58
 

 

-
Creating more features with CPP will take some more time, but improve
+
Creating more features with CPP will take a little time, but improve
 prediction performance:

 
# CPP features with default splits (around 100.000 features)
 df_parts = sf.get_df_parts(df_seq=df_seq)
@@ -254,7 +254,7 @@ 
3. Protein Predictionsns.barplot(pd.DataFrame({"Baseline": cv_base, "CPP": cv}), palette=["tab:blue", "tab:red"])
 plt.ylabel("Mean accuracy", size=aa.plot_gcfs()+1)
 plt.ylim(0, 1)
-plt.title("Comparison of Feature Engineering Methods")
+plt.title("Comparison of Feature Engineering Methods", size=aa.plot_gcfs()-1)
 sns.despine()
 plt.show()
 

diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 94d0e30a..168405de 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
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"Class Templates": [[19, "class-templates"]], "Function and Method Naming": [[19, "function-and-method-naming"]], "Code Philosophy": [[19, "code-philosophy"]], "Documentation Style": [[19, "documentation-style"]], "Documentation Layers": [[19, "documentation-layers"]], "Building the Docs": [[19, "building-the-docs"]], "Test with ChatGPT": [[19, "test-with-chatgpt"]], "Test Guided Development (TGD)": [[19, "test-guided-development-tgd"]], "Workflow": [[22, "workflow"]], "Overview of documentation": [[22, "overview-of-documentation"]], "References": [[24, "references"]], "Algorithms": [[24, "algorithms"]], "Datasets and Benchmarks": [[24, "datasets-and-benchmarks"]], "Use Cases": [[24, "use-cases"]], "Further Information": [[24, "further-information"]], "Tables": [[25, "tables"]], "Overview Table": [[25, "overview-table"]], "Protein Benchmark Datasets": [[25, "protein-benchmark-datasets"]], "Amino Acid Scale Datasets": [[25, "amino-acid-scale-datasets"]], "Categories": [[25, "categories"]], "Subcategories": [[25, "subcategories"]], "Usage Principles": [[26, "usage-principles"]], "AAontology: Classification of amino acid scales": [[27, "aaontology-classification-of-amino-acid-scales"]], "Data Flow and Enry Points": [[28, "data-flow-and-enry-points"]], "Data Flow: Components of AAanalysis": [[28, "data-flow-components-of-aaanalysis"]], "Entry Points: Bridges to External Libraries": [[28, "entry-points-bridges-to-external-libraries"]], "Identifying Physicochemical Signatures using CPP": [[29, "identifying-physicochemical-signatures-using-cpp"]], "Learning from unbalanced and small data": [[30, "learning-from-unbalanced-and-small-data"]], "What is PU learning?": [[30, "what-is-pu-learning"]], "Explainable AI at Sequence Level": [[31, "explainable-ai-at-sequence-level"]], "What is explainable AI?": [[31, "what-is-explainable-ai"]], "Tutorials": [[32, "tutorials"]], "Quick start": [[32, "quick-start"]], "Further Tutorials": [[32, "further-tutorials"]]}, "indexentries": {"aaclust (class in aaanalysis)": [[1, "aaanalysis.AAclust"]], "__init__() (aaanalysis.aaclust method)": [[1, "aaanalysis.AAclust.__init__"]], "center_labels_ (aaanalysis.aaclust attribute)": [[1, "aaanalysis.AAclust.center_labels_"]], "centers_ (aaanalysis.aaclust attribute)": [[1, "aaanalysis.AAclust.centers_"]], "comp_centers() (aaanalysis.aaclust static method)": [[1, "aaanalysis.AAclust.comp_centers"]], "comp_correlation() (aaanalysis.aaclust static method)": [[1, "aaanalysis.AAclust.comp_correlation"]], "comp_coverage() (aaanalysis.aaclust static method)": [[1, "aaanalysis.AAclust.comp_coverage"]], "comp_medoids() (aaanalysis.aaclust static method)": [[1, "aaanalysis.AAclust.comp_medoids"]], "eval() (aaanalysis.aaclust static method)": [[1, "aaanalysis.AAclust.eval"]], "fit() (aaanalysis.aaclust method)": [[1, "aaanalysis.AAclust.fit"]], "is_medoid_ (aaanalysis.aaclust attribute)": [[1, "aaanalysis.AAclust.is_medoid_"]], "labels_ (aaanalysis.aaclust attribute)": [[1, "aaanalysis.AAclust.labels_"]], "medoid_labels_ (aaanalysis.aaclust attribute)": [[1, "aaanalysis.AAclust.medoid_labels_"]], "medoid_names_ (aaanalysis.aaclust attribute)": [[1, "aaanalysis.AAclust.medoid_names_"]], "medoids_ (aaanalysis.aaclust attribute)": [[1, "aaanalysis.AAclust.medoids_"]], "model (aaanalysis.aaclust attribute)": [[1, "aaanalysis.AAclust.model"]], "n_clusters (aaanalysis.aaclust attribute)": [[1, "aaanalysis.AAclust.n_clusters"]], "name_clusters() (aaanalysis.aaclust static method)": [[1, "aaanalysis.AAclust.name_clusters"]], "cpp (class in aaanalysis)": [[2, "aaanalysis.CPP"]], "__init__() (aaanalysis.cpp method)": [[2, "aaanalysis.CPP.__init__"]], "eval() (aaanalysis.cpp method)": [[2, "aaanalysis.CPP.eval"]], "run() (aaanalysis.cpp method)": [[2, "aaanalysis.CPP.run"]], "cppplot (class in aaanalysis)": [[3, "aaanalysis.CPPPlot"]], "__init__() (aaanalysis.cppplot method)": [[3, "aaanalysis.CPPPlot.__init__"]], "heatmap() (aaanalysis.cppplot method)": [[3, "aaanalysis.CPPPlot.heatmap"]], "profile() (aaanalysis.cppplot method)": [[3, "aaanalysis.CPPPlot.profile"]], "update_seq_size() (aaanalysis.cppplot method)": [[3, "aaanalysis.CPPPlot.update_seq_size"]], "sequencefeature (class in aaanalysis)": [[4, "aaanalysis.SequenceFeature"]], "__init__() (aaanalysis.sequencefeature method)": [[4, "aaanalysis.SequenceFeature.__init__"]], "add_dif() (aaanalysis.sequencefeature static method)": [[4, "aaanalysis.SequenceFeature.add_dif"]], "add_feat_value() (aaanalysis.sequencefeature static method)": [[4, "aaanalysis.SequenceFeature.add_feat_value"]], "add_position() (aaanalysis.sequencefeature static method)": [[4, "aaanalysis.SequenceFeature.add_position"]], "feat_matrix() (aaanalysis.sequencefeature static method)": [[4, "aaanalysis.SequenceFeature.feat_matrix"]], "feat_names() (aaanalysis.sequencefeature static method)": [[4, "aaanalysis.SequenceFeature.feat_names"]], "get_df_parts() (aaanalysis.sequencefeature static method)": [[4, "aaanalysis.SequenceFeature.get_df_parts"]], "get_features() (aaanalysis.sequencefeature method)": [[4, "aaanalysis.SequenceFeature.get_features"]], "get_split_kws() (aaanalysis.sequencefeature static method)": [[4, "aaanalysis.SequenceFeature.get_split_kws"]], "__init__() (aaanalysis.dpulearn method)": [[5, "aaanalysis.dPULearn.__init__"]], "dpulearn (class in aaanalysis)": [[5, "aaanalysis.dPULearn"]], "eval() (aaanalysis.dpulearn method)": [[5, "aaanalysis.dPULearn.eval"]], "fit() (aaanalysis.dpulearn method)": [[5, "aaanalysis.dPULearn.fit"]], "labels_ (aaanalysis.dpulearn attribute)": [[5, "aaanalysis.dPULearn.labels_"]], "load_dataset() (in module aaanalysis)": [[6, "aaanalysis.load_dataset"]], "load_scales() (in module aaanalysis)": [[7, "aaanalysis.load_scales"]], "plot_gcfs() (in module aaanalysis)": [[8, "aaanalysis.plot_gcfs"]], "plot_get_cdict() (in module aaanalysis)": [[9, "aaanalysis.plot_get_cdict"]], "plot_get_clist() (in module aaanalysis)": [[10, "aaanalysis.plot_get_clist"]], "plot_get_cmap() (in module aaanalysis)": [[11, "aaanalysis.plot_get_cmap"]], "plot_legend() (in module aaanalysis)": [[12, "aaanalysis.plot_legend"]], "plot_settings() (in module aaanalysis)": [[13, "aaanalysis.plot_settings"]]}})
\ No newline at end of file
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index e6ddea7f..c2c1a032 100644
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diff --git a/docs/build/plot_directive/generated/aaanalysis-plot_settings-2.pdf b/docs/build/plot_directive/generated/aaanalysis-plot_settings-2.pdf
index b5ebd506..034540bd 100644
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diff --git a/docs/source/generated/output_13_1.png b/docs/source/generated/output_13_1.png
index 815e3a39..6e2513d5 100644
Binary files a/docs/source/generated/output_13_1.png and b/docs/source/generated/output_13_1.png differ
diff --git a/docs/source/generated/tutorial1_quick_start.rst b/docs/source/generated/tutorial1_quick_start.rst
index 75442ba7..c9cc5aa9 100644
--- a/docs/source/generated/tutorial1_quick_start.rst
+++ b/docs/source/generated/tutorial1_quick_start.rst
@@ -69,9 +69,9 @@ set of 100 scales, as defined by the ``n_clusters`` parameters:
 
     from sklearn.cluster import AgglomerativeClustering
     
-    aac = aa.AAclust(model=AgglomerativeClustering)
-    X = np.array(df_scales)
-    scales = aac.fit(X, names=list(df_scales), n_clusters=100) 
+    aac = aa.AAclust(model_class=AgglomerativeClustering)
+    X = np.array(df_scales).T
+    scales = aac.fit(X, names=list(df_scales), n_clusters=100).medoid_names_ 
     df_scales = df_scales[scales]
 
 Comparative Physicochemical Profiling (CPP)
@@ -131,10 +131,10 @@ A feature matrix from a given set of CPP features can be created using
 
 .. parsed-literal::
 
-    Mean accuracy of 0.6
+    Mean accuracy of 0.58
 
 
-Creating more features with CPP will take some more time, but improve
+Creating more features with CPP will take a little time, but improve
 prediction performance:
 
 .. code:: ipython3
@@ -153,7 +153,7 @@ prediction performance:
     sns.barplot(pd.DataFrame({"Baseline": cv_base, "CPP": cv}), palette=["tab:blue", "tab:red"])
     plt.ylabel("Mean accuracy", size=aa.plot_gcfs()+1)
     plt.ylim(0, 1)
-    plt.title("Comparison of Feature Engineering Methods")
+    plt.title("Comparison of Feature Engineering Methods", size=aa.plot_gcfs()-1)
     sns.despine()
     plt.show()
 
diff --git a/tutorials/tutorial1_quick_start.ipynb b/tutorials/tutorial1_quick_start.ipynb
index c3195c34..03993f2e 100644
--- a/tutorials/tutorial1_quick_start.ipynb
+++ b/tutorials/tutorial1_quick_start.ipynb
@@ -30,8 +30,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-09-28T08:06:29.545799916Z",
-     "start_time": "2023-09-28T08:06:29.522441946Z"
+     "end_time": "2023-10-02T20:32:53.039469001Z",
+     "start_time": "2023-10-02T20:32:53.019487708Z"
     }
    }
   },
@@ -55,8 +55,8 @@
      "name": "#%%\n"
     },
     "ExecuteTime": {
-     "end_time": "2023-09-28T08:06:30.369962978Z",
-     "start_time": "2023-09-28T08:06:29.532292243Z"
+     "end_time": "2023-10-02T20:32:54.531812229Z",
+     "start_time": "2023-10-02T20:32:53.027672866Z"
     }
    },
    "outputs": [],
@@ -89,16 +89,16 @@
    "source": [
     "from sklearn.cluster import AgglomerativeClustering\n",
     "\n",
-    "aac = aa.AAclust(model=AgglomerativeClustering)\n",
-    "X = np.array(df_scales)\n",
-    "scales = aac.fit(X, names=list(df_scales), n_clusters=100) \n",
+    "aac = aa.AAclust(model_class=AgglomerativeClustering)\n",
+    "X = np.array(df_scales).T\n",
+    "scales = aac.fit(X, names=list(df_scales), n_clusters=100).medoid_names_ \n",
     "df_scales = df_scales[scales]"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-09-28T08:06:30.451850383Z",
-     "start_time": "2023-09-28T08:06:30.376700053Z"
+     "end_time": "2023-10-02T20:32:54.532997978Z",
+     "start_time": "2023-10-02T20:32:54.250325038Z"
     }
    }
   },
@@ -126,8 +126,8 @@
      "name": "#%%\n"
     },
     "ExecuteTime": {
-     "end_time": "2023-09-28T08:06:30.578306544Z",
-     "start_time": "2023-09-28T08:06:30.532859763Z"
+     "end_time": "2023-10-02T20:32:54.533610828Z",
+     "start_time": "2023-10-02T20:32:54.388921950Z"
     }
    },
    "outputs": [],
@@ -160,8 +160,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-09-28T08:06:39.221976283Z",
-     "start_time": "2023-09-28T08:06:30.576931617Z"
+     "end_time": "2023-10-02T20:33:05.471265655Z",
+     "start_time": "2023-10-02T20:32:54.511627903Z"
     }
    }
   },
@@ -185,7 +185,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean accuracy of 0.6\n"
+      "Mean accuracy of 0.58\n"
      ]
     }
    ],
@@ -201,15 +201,15 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-09-28T08:06:43.613234709Z",
-     "start_time": "2023-09-28T08:06:39.228528460Z"
+     "end_time": "2023-10-02T20:33:10.066404589Z",
+     "start_time": "2023-10-02T20:33:05.478739730Z"
     }
    }
   },
   {
    "cell_type": "markdown",
    "source": [
-    "Creating more features with CPP will take some more time, but improve prediction performance: "
+    "Creating more features with CPP will take a little time, but improve prediction performance: "
    ],
    "metadata": {
     "collapsed": false
@@ -229,7 +229,7 @@
     {
      "data": {
       "text/plain": "
",
-      "image/png": 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J1a9fX+92+vXrJ9qpU6eOdO/ePa15IiIixDwKhUK6efOmJEnaX2g///yzbLn//ve/sulbtmyRTe/Tp4+Y1r17d9k0zQ9gq1atxIdK7ezZs7JgMnHiRNn0wsJC6fz587If05ycHOmjjz4SyyiVStkyml9mDg4OUm5urs7nrKQf1W7duonxnTt3ln7//XdxuEiSJPEBLM6Q16B4PYMGDdJaVnN3eGhoqM4aiouJiZG1q2vXdUJCQok/lKYKYZp7HadOnarVtuYXVO/evbWmX79+Xbp165Z4XFhYKG3evFm2Ts0w+iRDWIsWLXQ+X4Zusy6VEcKKH4aPjY2VTd+/f7+YVr9+fTE+MDBQq77t27eXuO6KhLB58+bJ2s3Ly5MsLCzE9M8//1xM0+d5zsnJke1xWrRokdZyvXr1EtPffPPNEp+/Q4cOaS0rSeUPYf7+/lrLau5tatq0aYnP75IlS7SWHTp0qF6f8eIhrPhz8NNPP0nnz58Xj+3s7KSCgoISPyczZswQ411dXbXWl5iYKKtTc+9PRUNYSkpKqW0fOHBATNPcS96iRQsRfjVpBkQHBwcxz0svvSQbf/fuXdlyjx8/lgVozdo//fRTMV59yF3z6El594brwhPzNdSuXVsMG3LSrWYvwd27d0etWrW05hkwYIAYlkroWRiA1omTxdvq0KGD7LHmNqi70NAlODgYZmbyl9/b2xsNGzYUj8+ePSubrlAoULNmTfzxxx+YMGECnn/+eTg4OMiuFC1tna1bt4alpWWJ03WZPHmyqDMxMRFDhgxBnTp14OPjg9dffx0bN27UeSGBMV+Djh07ao3T7GOrtJNAS6pJoVDgxRdf1JpHfXcGtUOHDpWr7Yry8fFBUFCQzj/NPseys7NlJ9R+8cUXWj1tL1y4UEzX1QmknZ0dDh8+jKioKISFhcHZ2Rnh4eGyeUo6Ybuy+fn5aY0zxjaXx/Tp0yEV/Ue4xL/SurCwtbWVXbkNaPexp35v3r9/H9evXxfjdXVt0qNHDyiVSr22RVPxz4uFhQWcnJy0atL3eU5NTZWddB8REaG1nGZ/eiW9PmZmZmjfvr2+mwmgYt8Nxb9Pdb0GPXv2NKgeTZp9dP3111+yE/QDAwNLfa1TUlLE8LVr17Se386dO8vm1/czoO7rUJPmewUo6rVA/e+JEyfE+PDwcJ3boPm9fvfuXdHZ8/nz58X49u3bw97eXractbU1AgICdNb5xhtvwNnZGcD/Tuxv0qQJXFxcMGjQIERHRyM9Pb3M7dWF/YRpaNq0KQ4cOAAAePz4MdLS0uDh4VHi/EuXLkVAQIDWZb6aV8w1aNBA57LFx9+9e1fnfMXDQ/E3XfFOE4sHq5Jo/tAXr0t9taFmuLlx4wbGjh2LDRs2yIJW3bp14eXlJftwlET9Jq6I3r17Y8eOHfjkk0+QmJgIoCgwnTt3DufOncPSpUtRq1YtTJs2DR988IFYzpivgaOjo9Y4KysrMSxJUrm2RbMmR0dH2NjYlFiX+urWkmoyVHn7Cavof0YyMzPFcEFBAaZNm4b58+fL3ku2trbo1KkTDh48WKG2S6Lr+S9vMNb1njRkm58kXR2mar4vgf89N/fv35eN19VRr7m5OZydnQ3uo628nxd9n+eKLlfSFcz29vYwNzfsJ7Ai3w3leQ2M2dek5lXBe/fuld2KrKwrqo31HJdF13vYwsJC9ljdsWxWVpask9mKfq8/evRIjCvp90/X6wkArq6uOHDgAD788ENs3rwZeXl5AIDr169j/fr1WL9+Pd599128+uqrmD9/PmrUqKGzHV24J0xDt27dZI+3bdtW4rz//vsvxowZg5YtW8Lb2xs///yzmKb54SopHf/777+yx5p7sDSVFaqKv2HLq6QvWs0Pn/p/CpIkoU+fPli/fj1UKhU6dOiAVatWIS0tDTdu3MB7771XrnVW5I2pKSQkBAkJCbhy5QqWLFmCV199VXTfARR9uU2ePBkrVqwQ44z5Ghj6Ra2rpqysLDx+/LjMukqq6Ukp/pp9+umnyMrKKvFP8301efJkfPXVV8jOzoaLiwt++OEHpKSk4P79+6JrEmPQFbhK6malOF3vSUO2+UmqyPvS1tZW9ljXj2xBQQFu3779xOrS93kuvlx0dHSpy5W0h1vf7yNNxn4NNLtIMpS/v79Y5507d7BhwwYxrawQpvnc+Pj4lPr8ZmVlYdCgQXrVWJE9r3Xq1JHNX9Hvdc3upkr6j1Npz3+TJk2wbt063Lx5E+vWrcO4cePg6+sralKpVFi2bBnGjRtXvg36/xjCNAwaNEiWzL/++usSv8xnzpwpUnlqaqosLGnuXt2zZ4/W/4AA4I8//hDDSqVS527tyrR9+3atQ4c3b96U7TJX7+GLj4+X9YK8ZcsWDB8+HO7u7gDK/8VR0TCjUqlw4cIFbNmyBf/9739Ro0YNvP7661ixYgVSU1Nx8eJF1KlTR8y/fv16Mfw0vgaaNUmSJPtSVEtISMDNmzfF4+K7/Z80R0dHWXg8duwYHBwcZH+rV6/GunXrcOrUKTFffn4+fvrpJ/H4hx9+wDvvvINWrVrBzMysQj82uvZ0ad7KR9d9MzUPPZRG13tS321+mjk6Osr+96/ZF5razp07Sz2doDJq0ud59vb2lv0Yp6SkaC23aNEibNq0Seu7WZOx/nNVXsVvf6TrNSjtP/4VZWFhgS5duojH6v+s2NnZ6TwMr0nz6M7ly5eRn58ve37Pnj2Ln3/+GYmJibh7926JOwPKe5SgPJRKpew53Lx5s873q+b3urOzM5o2bQoAaN68uRh/6NAhrf8E3717V/S/WVxGRgb27t2LBQsW4Pz58xg4cCDmzp2LpKQk3LlzBz169BDzav4OlQdDmAY7OztZj/mXLl1Cz549ZTcQfvToET744ANZD/keHh4YPny4eDxixAgxfOvWLbzyyiuyH4rFixfL9pwNGTKkxN2gleX8+fOIjIwUx9vv3buHUaNGyc7NUd9zrPj/LDQ70YuPj8cPP/wgm26s83sOHDgALy8v9O3bFxMnTsR7770nOxdEqVTKPuSaX7ZP42sQHh4uW8f7778v6+Dv1KlTGD16tHhsZ2cne1+ZSv/+/cVwTEyMrHPUuLg4jBs3DmPGjMFzzz2Hzz//HABw+/Zt2Wu1d+9e8VpduXIFEydOlK1D8z1T/LxB9d4ZzUMRmuH7zz//lH0ZR0dHGxyO9Nnmp53mPQR37tyJuXPnisenTp3C+PHjn3hN+jzPtra2CAkJEfPNnz9fdg7YypUr8eGHH2LEiBHo2LEjli9fXvkbUg5BQUGyvTFTpkyRncaxcOFCrF271ujrLO65554rcw+U5uuSm5uL119/HQ8ePABQdOTh7bffxqRJkxAWFobAwEBxeA6Qf34196waI5Bpfq+fOnUKb731lghThYWF+PTTT7Fr1y4xz5gxY8TvguZ5qHfv3kVERIT4/Xv06BHGjBmjc+/k48eP4ePjg+7du+Ott97CqFGjZP+JNDc3l303lfeUIEHvU/qrqcLCQunll1+WXZ2hUCikFi1aSH5+fpKtra1smrm5uRQbGytrQ6VSSaGhobL51H1UaV6hBBT1vaLZtUJZV8AV76+kuIpcmg9AqlWrluTn5ye7nBmA1LNnT7Fc8cuVgaIr6Ir396T+0+wKorSrjDRpLq95tZtm53n4/1e1dOrUSda3kfpv9erVRnkNSqunPM9zaVasWKH13mrSpInUokULrf7Hli5dKlvWmFdHVqSfsEuXLml1Luvh4SH5+vrKrqatVauWuEqqoKBA1v8TUNTPmq+vr+xKOfVfTEyMWN+pU6dk09R9+Wh2BfKf//xHNk+7du2kyMhIqWvXrhIg72+ptM9BSR2m6rPN5Xkei7dXVj9hxfsK03UVXGnr0LyK8ezZs5KlpaVsupubm9S6dWut917x56YiV0fqel+W9Jzr+zwnJiZq9anVtGlTqVWrVrJx7u7usqu5y3r+1Mp7daSu909p69C86hAo+v1o06aNrOsafT7jJa0zPj5eq03NzpFL+x4r3jmpnZ2d1KFDB60Obot30TJkyBDZ9BYtWkiurq7iKkhD3sMPHz6UWrduLZtes2ZNyc/PT6pdu7ZsfMuWLWUdlufn52stW7t2baljx46inzDNz4Hm8/HZZ5/JlrO0tJTat2+v1W8aIL8atzwYwnRQqVTShx9+WGLP0uq/OnXqiI7qisvMzJTCw8NLXd7Ly0s6deqUbLknFcIGDx4subi46KyrdevWWj8sr732WonbUTzcJSYmiuUMDWE3b96U/P39S30eAe1OEQ15DUqrpzzPc1nmzJlTak/fFhYW0vz587WWM1UIkyRJ2rlzp9Z/QDT/bG1tZd1MSFLRnQFKm1+z52rNjlgLCwulxo0bay3ToUMHMc/Zs2d1drAIFP3wfvzxx+X6HJTWa70+21wafbqoKF6jIT9gklTUY76uwAVAmjx5suzOEDNmzChXu4aEMEOe52XLlukM9Oq/evXqSadPn5YtY+oQVlBQUOJ3Us2aNWXd/QCQLl++XGKN5Vlnbm6uVsjV7PKhtO+xrKwsqVOnTqW+N3V97y5btkznvOrulAx9D6elpZV4pxjN74rid0uQJElKTU3V6hBW83vjk08+0fl8qFQqrX72dP09//zzWneqKQsPR+pgZmaGWbNm4fTp05g0aZK4v5/66qHg4GDMnj0b586dQ69evXS24ejoiE2bNmHLli0YNmwYPD09YWVlBQcHBzz33HP4/vvvcfz4cdlx6iepRYsWSE5OxujRo1GvXj1YWVnBx8cHM2bMQGJiotbVI0uXLsW3336LVq1awdLSEtbW1mjevDmmTJmCo0ePom3btmLe33//3Wh11qlTB4mJiViyZAlCQ0NRt25dmJubi3uYDRs2DHv27MH333+vtezT+hqMHz8eJ06cEPeOtLe3h7W1NZo2bYq3334bJ0+exNtvv/1EaypLaGgozp07h4kTJ6Jly5aws7ODhYUFPD09ERERgaNHj8ruVwcAo0ePxqZNm9C1a1fY2trCwsICDRs2xGuvvYakpCRx70NA/p5RKBTYunUrwsPDYW9vL+7p+Nxzz4l5vL298c8//2D48OGy9+/HH3+MlJQUeHp6mmSbn3bDhw8X93WsXbs2bGxs0LFjR6xevRpfffWVbN7iV1pWFn2f5xEjRiAlJQVvvvkmmjZtiho1aoj3wfvvv4+jR49qnYdlakqlEhs2bMCcOXPQtm1b2NjYwNnZGUOHDsWRI0e0zks19DWwtLSUnVdas2bNMs8HU3NwcMD+/fuxaNEidO/eHXXq1IG5uTlq166Nnj17Yv369Tq/d0eMGIHZs2ejadOmsLCwgKOjIwIDA/W6Ol4Xd3d37Nu3D7/99hv69++Phg0bwtLSEk5OTujRoweWLl2KhIQEuLq6ai3r5eWF5ORkTJw4EZ6enrC0tISnpycmTZqEY8eOybpo0mRmZoalS5di586dePnll8X9Ki0sLFC/fn306tUL0dHR2Lt3b4n3my6JQpKMeObcEyJJEvz9/cVJdBXdhPPnz+PTTz8VN1+tX78++vfvj6ioKNm5JtWNp6en6H5i+vTppfZBRETVy5kzZ2Bubg5XV1edVwaqVCrY2NiIE7gXL16MN95440mXWW0VFBTg6NGjcHV1Rb169XSel7VixQpx3pOZmRkeP35c4b4VqWqpknvCvv766xKvYijLsWPH0KFDB6xcuRLXr19HXl4erly5grlz56JTp05GvUSYiOhp8dlnn6Fp06awtbWFo6OjrEsXSZKwYMECWXcfxTvRJMMUFhaiY8eOaNCgAaysrNCkSRNZVwkPHjxAdHS0eNy0aVMGsGdAlQphKpUK06dPx5QpU/RaXpIkjBw5UnTe1qZNG4wfP17sgrx06RImTJhgpGqJiJ4emv0g3r17FyNHjkSbNm3QpUsXuLm5yfo3at68OTp16mSKMqstS0tL0WWESqXCxYsX4enpic6dO8PPzw/16tVDbGysmL88nSlT1VdlDkfu3LkTH3/8sc6O98q7CX///be4ZNfFxQWpqamwtbXFhQsX4OPjA5VKBaVSiX///Vdnb8ZVHQ9HEj27VCoV+vXrh61bt5Y6n6OjI3bu3Cm7fRUZR0pKCrp161ZmD/M9evTA1q1buSfsGVAl9oQdO3YMvXr1EgFM8yTwitDsCK9fv36iN+EmTZqIEz9VKpWszxkioupAqVRi8+bN+OWXXxAeHg53d3fY2NhAqVTCwcEBfn5++Oijj3Dy5EkGsErSunVrnDx5EtOmTUNAQACcnJxgbm4OS0tLuLi4oHfv3li+fDl27NjBAPaMqBL3jlTv6bKyskJUVBSGDRuGJk2aVLgdzZuSent7y6b5+PggLi4OAMp1H8SqSPNmuUT07DEzM8Mrr7wiuzKVnqx69erhs88+w2effWbqUugpUCX2hNWsWRPTp09HWloaPv7444r3SPv/ad5rqvg9+TR7MufJ+URERFTZqsSeMC8vL6Ocv/Tw4UMxXLz/Fc1dv+rbM5Rk4cKFWLRokc5ply5dgp2dHQIDA7Fq1SoDqiUiIqLqrEqEsKdNRkZGqV1kZGVl4cyZM0+wIiIiIqpqnqkQpj4RH4DshqMAxI08AZTZ462Liwt8fX11Tjt9+rTW3dmJiIiIinumQpiLiwuOHj0KoGhvlSbNTvN03e5AU2RkJCIjI3VO8/Pz07sjWSIiInp2VIkT841F8x6B586dk01LTU0Vw+wpmoiIiCrbMxXCQkNDxXBMTAyys7MBAGlpadi/fz+Aor50SropNxEREZGxVMvDkWvXrkVSUhIAICQkBCEhIWLYx8cHZ8+exY0bNxAYGIiuXbti48aNKCgoAAAMHjwY9erVM1ntRERE9GyoliFsy5YtWL58OQDA2tpahDBzc3OsXLkSISEhuHfvHo4dO4Zjx46J5Tw9PfHdd9+ZpGYiIiJ6tjxThyMBwN/fH0ePHsXIkSPh4uICCwsLNGzYEOPGjcPBgwer5T0jiYiI6OlTZW7gXVWor4709fUVh0SJiIiIinvm9oQRERERPQ0YwoiIiIhMgCGMiIiIyAQYwoiIiIhMgCGMiIiIyAQYwoiIiIhMgCGMiIiIyASqZY/5RERE+vrss89w9epV2biGDRsiKirKRBVRdcUQRkREpOHq1au4cOGCqcugZwAPRxIRERGZAEMYERERkQkwhBERERGZAEMYERERkQkwhBERERGZAEMYERERkQkwhBERERGZAEMYERERkQkwhBERERGZAEMYERERkQkwhBERERGZAEMYERERkQkwhBERERGZAEMYERERkQkwhBERERGZAEMYERERkQkwhBERERGZAEMYERERkQkwhBERERGZgLmpCyAiIrm4rkGmLuGZlm2uBBQK+bjz5/m6mFjQ33GmLsHouCeMiIiIyAQYwoiIiIhMgCGMiIiIyAQYwoiIiIhMgCGMiIiIyAQYwoiIiIhMgCGMiIiIyAQYwoiIiIhMgCGMiIiIyAQYwoiIiIhMgCGMiIiIyAQYwoiIiIhMgCGMiIiIyAQYwoiIiIhMgCGMiIiIyAQYwoiIiIhMgCGMiIiIyAQYwoiIiIhMgCGMiIiIyAQYwoiIiIhMgCGMiIiIyAQYwoiIiIhMgCGMiIiIyATMTV0AERHR08RJkso1jshQDGFEREQaBqkKTV0CPSN4OJKIiIjIBBjCiIiIiEyAIYyIiIjIBKpUCEtOTsaAAQPg7OwMa2treHt7Y9q0acjOzq5QO5IkYcmSJXjuuedgb28PS0tLNGjQAEOHDkVycnIlVU9ERET0PwpJqhqXfOzcuRPh4eHIy8vTmubn54e4uDjY2tqWq62hQ4dizZo1OqeZm5tj/fr16Nevn151+vn5ITk5Gb6+vkhKStKrDSJ6tsV1DTJ1CURPnaC/40xdgtFViT1h2dnZGDVqlAhggYGBGDt2LJycnAAASUlJmDlzZrna2rx5syyAvfDCCxg/fjyaNm0KACgoKMCYMWOQn59v5K0gIiIi+p8qEcJiYmJw7do1AED79u0RGxuLefPmYf369WKexYsX69xLVtyuXbvEcGRkJLZu3Yo5c+YgKSkJderUAQDcvHkTx48fN/JWEBEREf1PlQhh27ZtE8NDhgyBUqkEAAQFBcHDwwMAkJWVhcTExDLbUi8LAC1atBDDdnZ2cHNzE4+trKwMrpuIiIioJFUihKWkpIhhb29v2TQfHx8xfOLEiTLb6tOnjxheuHAhTp06hUePHmHFihU4duwYgKJwphnQiIiIiIytSoSw9PR0MVy7dm3ZNEdHRzGckZFRZlshISH45JNPYGZmhlOnTqFly5awtbXFiBEjUFhYiKZNmyImJgZmZlXiqSEiIqIqqkrctujhw4diuPhhQktLSzH84MGDcrX39ttvIyEhAbt379aaNmDAANlhSV0WLlyIRYsW6Zx2+vTpctVAREREz7YqEcKM6dq1awgICMDVq1cBAN27d0fz5s2xe/dunD17Fl9//TUSExOxe/duWcDTlJGRwf7EiIiIyCBVIoTZ2tri7t27AKB1BWRubq4YtrOzK7OtTz75RASwt99+G/PnzxftdO7cGUeOHMG+ffsQHR2NyMhInW24uLjA19dX57TTp0/j8ePHZdZBREREz7YqceKTi4uLGM7KypJNy8zMFMOurq5ltrV161YxPGbMGDFsZWWFV155RTz+66+/SmwjMjISSUlJOv+aN29eZg1EREREVSKEaQabc+fOyaalpqaK4VatWpXZlmaI09yLBgAqlUoMa56HRkRERGRsVSKEhYaGiuFVq1aJsJSQkIC0tDQARVdNBgQElNmWl5eXGF64cKEYzsvLw2+//SYes4sKIiIiqkxV4pywl19+GVFRUbh9+zaOHz+O4OBgtG3bFqtXrxbzREREyE6kX7t2rbh3Y0hICEJCQsR877zzDgAgOjoaqampaNu2Lfbs2SOubFQqlRg9evST2jwiIiJ6BlWJEObo6Ijly5djwIAByMvLQ3x8POLj48V0Pz8/REVFyZbZsmULli9fDgCwtrYWIWzs2LH4559/sGLFCgDQasvMzAw//vgjz+0iIiKiSlUlDkcCQFhYGA4fPoxBgwbB2dkZlpaW8PLywtSpUxEbG4saNWqUqx0zMzMsX74cMTEx6NWrF5ycnGBubo66devipZdeQnx8PCIiIip5a4iIiOhZp5AkSTJ1EdWJn58fkpOT4evrKw6HEhFVRFzXIFOXQPTUCfo7ztQlGF2V2RNGREREVJ0whBERERGZAEMYERERkQkwhBERERGZAEMYERERkQkwhBERERGZAEMYERERkQkwhBERERGZgN4hTH0TbSIiIiKqOL1DWIMGDTBx4kQcO3bMmPUQERERPRP0DmE3b97E999/D19fX7Rr1w7ff/89bt68aczaiIiIiKotg88JkyQJKSkpmDhxItzc3BAeHo5169YhLy/PGPURERERVUt6hzA/Pz+o7/2t/regoABbt27Fyy+/DBcXF4wdOxYHDx40TqVERERE1YjeIezw4cO4ePEivvrqK52BLCsrCwsWLECXLl3QvHlzzJo1C//++69xqiYiIiKq4gw6HOnp6YkPPvgAhw8fxoULFzBr1iz4+vrKApkkSTh79iw+/vhjeHp6IjQ0FGvWrOHVlURERPRMM1o/YY0aNcLkyZPxzz//4MKFC/jyyy/h5uYGhUIBhUIBSZJQWFiIPXv2YPjw4WjevDkOHTpkrNUTERERVSlG76z16tWrWL9+PdatW4f09HQxXh3GgKI9ZOfPn0doaChSUlKMXQIRERHRU8/cGI1cv34dv//+O9asWYMDBw5oTZckCQqFAi1atMDp06fF4crs7Gx89dVXWLlypTHKICIiIqoy9A5ht2/fxrp167BmzRrEx8ejsLAQwP8ClzpoNWzYECNGjMCoUaPQqFEjpKam4tVXX8WhQ4cgSRIPSVKV9Nlnn+Hq1auycQ0bNkRUVJSJKiIioqpG7xDm4uJSYvCysLBAv3798Prrr6Nnz57iMCQANG3aFOvWrYO7uzsAyA5ZElUVV69exYULF0xdBhERVWF6hzCVSiULV5IkoXXr1hg9ejReeeUVODk5lbism5sbnJyckJmZCTs7O31LICIiIqqyDDonTJIk2NvbY9iwYRg9ejT8/f3LtVxhYSHu378PV1dXdO3a1ZASiIiIiKokvUNYcHAwXn/9dQwcOBDW1tYVWtbMzAyPHj2CUqnUd/VEREREVZreIWzPnj2yx+rzwjRdvXoVDRs21Lk8AxgRERE9ywzuJywuLg5du3bF7NmzZeMLCwvh7e0Nf39/7Nq1y9DVEBEREVUrBoWwuXPnokePHti/fz+OHz8um3bx4kXk5ubiyJEj6N27N+bNm2dQoURERETVid4hLDk5Ge+9957opqJ4CDt16pQYliQJ77//PnvHJyIiIvr/9A5hc+bMQWFhIRQKBczMzODn5yeb3qJFC0RERMDMzAwKhQIqlQpz5841uGAiIiKi6kDvEBYfHy+GFy5ciKVLl8qme3l5YcGCBVi6dKnoxDU2Nlbf1RERERFVK3qHMHVP92ZmZnjttddKnG/o0KEwMzODJEn4999/9V0dERERUbWidwhTdzFRWFiInJycEud7+PChOG+seBcWRERERM8qvUOYm5ubGP71119LnG/JkiUAigJYgwYN9F0dERERUbWid2et3bp1w7lz5yBJEt577z3cvn0bffr0gYuLCwoKCnD58mXExMTITsbv0aOHUYomIiIiqur0DmETJkzAkiVLUFhYiMePHyMqKgpRUVFa86lPylcqlXj33Xf1r5SIiIioGtH7cKSPjw++/fZb2e2KJEmS/Wn67rvv0KxZM8OqJSIiIqomDOox/5133sGvv/4KFxcXrdAFFIWyBg0aYM2aNRg7dqwhqyIiIiKqVvQ+HKk2dOhQDBw4EH/99RcOHjyIGzduAADq1q2LgIAA9OjRAxYWFgYXSkRERFSdGBzCAMDCwgK9e/dG7969jdEcERERUbVn0OHIimJnrURERERFDN4TduLECRw8eBC3bt1Cfn6+1rlheXl5yMzMFPPl5uYaukoiIiKiKs+gEPbee+9hzpw55ZpX8ypKIiIiomed3iFs48aN+OGHH8RjhUIh9oJphi1dV00SERERPev0PidM83ZEAGQBTLOvMIVCAYVCAW9vb8ybN88IJRMRERFVfXqHsCNHjojhmTNn4tGjR2jdujUAYOfOncjJyUF8fDzc3d0hSRIyMjLQs2dPwysmIiIiqgb0DmF37twRwxMnToS1tbUIWfv374elpSW6dOki9n5lZ2dj9uzZBpZLREREVD3oHcJUKhUAwNLSEjY2NgCALl26QJIkHDp0SMzXq1cvKJVKAMDu3bsNqZWIiIio2tA7hDk4OAAo6oLi6tWrAAA/Pz8AQEJCgghp9+/fh0qlgiRJSE9PN7BcIiIioupB7xDWqlUrMTxkyBCcOXMG7u7ucHV1xf379zFp0iSkpKRgzJgxYj52UUFERERURO8QNnjwYDF86NAhbN++HUDR4UdJkjBnzhy0a9cOGzZsAFAUwFq0aGFYtURERETVhN4hbMyYMQgMDBRdU7Rp0wYAEBkZqbPbCvUyRERERGRACDM3N8euXbvw4YcfolatWmjbti0AoGPHjvj666/FfOr+wkaPHo2IiAjDKyYiIiKqBgy6bZGVlRW+/PJLzJw5ExYWFmL8xIkT0a9fP+zYsQM5OTno2rUrOnbsaHCxRERERNWF3iHsvffeQ7169TB8+HC4u7trTW/atCmaNm1qUHFERERE1ZXehyN/++03fPzxx2jcuDHWr19vzJqIiIiIqj29Q9jdu3fFvSFfeOEFY9ZEREREVO3pHcK8vb0BFJ14r+6YlYiIiIjKR+8Q9s0338Dc3BySJOH111/HjRs3jFkXERERUbWm94n5N27cwNChQ7Fy5UqsX78eGzZsQIsWLdCgQQPUqlULVlZWMDOTZzyFQoGff/5Z72KTk5Px2WefYd++fcjOzoa7uzuGDBmCKVOmoGbNmhVqq7CwEEuXLsWyZctw4sQJFBYWomnTphg9ejTGjBkDS0tLveskIiIiKoveIWzkyJFQKBRQKBSQJAkFBQU4fvw4UlJSdM6vPn9M3xC2c+dOhIeHIy8vT4xLTU3F559/ju3btyMuLg62trblauvhw4cYMGAAdu3aJRufnJyM5ORk7Nq1CzExMVohkoiIiMhYjJIy1GGssu4NmZ2djVGjRokAFhgYiLFjx8LJyQkAkJSUhJkzZ5a7vXfeeUcEMHt7e4waNQqvvvoqrKysAAAbN27Eb7/9ZuStICIiIvofgzprVd+WqLLFxMTg2rVrAID27dsjNjYWSqUSgwcPRnBwMABg8eLF+Oyzz8o8jHjx4kVER0cDAGrUqIFDhw6JiwwCAgLw7bffwsXFhee4ERERUaXSO4QVFhYas45Sbdu2TQwPGTIESqUSABAUFAQPDw+kpaUhKysLiYmJCAoKKrWttWvXivAYGhoqAhgAvP3223j77bcrYQuIiIiI5KrESU+a55lphiYA8PHxEcMnTpwos62kpCQx7OXlhYULF8Lb2xvW1tZo2bIlvv/+e3a5QURERJXOoMORT0p6eroYrl27tmyao6OjGM7IyCizrUuXLonhtWvX4sqVK+LxqVOn8N577yEhIQFr1qyptHPciIiIiPQOYStWrNBruddee63Cyzx8+FAMq0+eV9M8B+zBgwdltqU5z5UrV2Bvb4/Bgwfj2rVr2Lp1K4CicNa9e3e8+eabOttYuHAhFi1apHPa6dOny6yBiIiIyOAuKipKnxBmTJqHGpVKJWJjY9GuXTsAwIcffoivv/4aADB37twSQ1hGRgaSk5MrvdbS+H2gXwgm47C7lgllsXHnrmXydTGxpG9M+/1CRFQRBh+OrMgVkvoe3rO1tcXdu3cBQNZPGADk5uaKYTs7uzLb0pynQ4cOIoABQGRkpAhhp06dQnZ2ts5OYF1cXODr66uz/dOnT+Px48dl1kFERETPtkrvokKzDzF9Q5iLi4sIYVlZWbJpmZmZYtjV1bXMttzc3HDkyBEA2ueXNWjQQPb4/v37OkNYZGQkIiMjdbbv5+dn8r1kRERE9PTTO4Tt3btX53iVSoXc3FxkZmYiMTERixcvhqWlJdauXYvevXvrta7mzZuLc63OnTsnm5aamiqGW7VqVWZb7dq1w+bNmwEA58+fl027c+eOGDYzM9MKaURERETGoncIK6s/LgD4z3/+gy5duuCVV17B0KFDkZKSgoYNG1Z4XaGhoYiJiQEArFq1ChMnToRSqURCQgLS0tIAFO3VCggIKLOtAQMG4LPPPgNQFOjWrl2LwYMHA4Csl3x/f39YW1tXuFYiIiKi8qj0fsKGDx8Od3d3PHjwQJxvVVEvv/wynJ2dAQDHjx9HcHAwxo0bh379+ol5IiIiZFdKrl27FlOmTMGUKVOwe/duMb59+/bo27evrL7hw4dj2LBhmDx5shg/adIkvWolIiIiKo8n0lmrk5MTJEkSXUBUlKOjI5YvXy5CVnx8PObPny8OH/r5+SEqKkq2zJYtW/DVV1/hq6++Qnx8vGza0qVLxQn5BQUF+O2337B69WpxF4D33ntP7B0jIiIiqgyVHsJOnDgherwvT2eqJQkLC8Phw4cxaNAgODs7w9LSEl5eXpg6dSpiY2NRo0aNcrdVp04dJCQk4PPPP0erVq1gbW2NWrVqoVu3boiJicF///tfveskIiIiKg+9zwmbOXNmidNUKhUeP36Ma9euYdOmTSgoKABQFH4M0aZNG6xdu7Zc8y5btgzLli0rcbqNjQ2mTp2KqVOnGlQTERERkT70DmEzZswoV5cT6m4sFAoFXnjhBX1XR0RERFStVPrhSHVQc3JywrRp0yp7dURERERVgkEhTJKkMv9sbW0xcOBAJCYmws3NzVh1ExEREVVpeh+OvHTpUqnTzczMYGdnBwcHB31XQURERFRt6R3CPDw8jFkHERER0TPFaOeEPXjwQGtcQkIC8vPzjbUKIiIiomrD4BD2yy+/wNPTE3PmzJGNLygoQHBwMDw9PbFkyRJDV0NERERUrRgUwiZPnoyRI0fi6tWrOH78uGxaamoqCgoKkJGRgcjISHz44YcGFUpERERUnegdwvbu3YvZs2eLfsCKh7BTp04BKOqiQpIkzJ49W+v2QURERETPKr1D2E8//QSgKGTZ2NhgyJAhsumBgYH44osvULNmTdFXmHoZIiIiomed3ldHHjx4UAyvXr0affv2lU2vV68epkyZgrZt26JPnz5QKBRISEjQv1IiIiKiakTvPWE3btwAACiVSoSFhZU4X69evaBUKiFJEq5fv67v6oiIiIiqFb1DmJWVFYCim3Xfu3evxPlu374NlUoFADA313vHGxEREVG1oncI8/T0FMM//vhjifN98803AIrOHWMHr0RERERF9N41FRoaipSUFADAjBkzkJaWhj59+sDFxQUFBQW4fPkyYmJi8Mcff4hlevfubXjFRERERNWA3iFswoQJWLBgAR4/fgyVSoWff/4ZP//8s9Z86i4sbGxs8N577+lfKREREVE1ovfhSDc3N0RHR0OpVIouKCRJkv2pmZub45dffkGDBg0Mr5iIiIioGjCox/zBgwdjz549aN++vSx0qUmShA4dOiAuLg4DBgwwZFVERERE1YrBlysGBgbin3/+wZkzZ3Dw4EHRdUXdunUREBCAZs2aGVwkERERUXVjtD4jmjVrxsBFREREVE4GHY5U27NnDw4dOqQ1fuzYsfjzzz+NsQoiIiKiasWgEHbz5k306NEDoaGhWLNmjWzavXv38NNPP6F///7o2rWrOExJRERERAaEsNzcXISEhCA2NhaSJIk+w9ROnTolhuPj49G7d28UFBToXykRERFRNaJ3CFuwYAFOnDghHhcPYdevX4eZ2f+aP378OBYvXqzv6oiIiIiqFb1D2Lp168RwYGAg/v77b9n0AQMGIC0tDUFBQWLc77//ru/qiIiIiKoVvUOY5uHGZcuWoWnTplrzNGjQAEuXLgUAnYcsiYiIiJ5VendR8eDBAwCAUqlE48aNS5zP3d0dSqUSKpUK9+/f13d1RE8VlZV9ucYRERGVRO8Q5ujoiFu3bkGlUuH06dNo3ry5zvmOHTsGlUoFALC3548UVQ+PGgeVPRMREVEp9D4c2aZNGzE8duxYPHz4UGue27dvY8yYMQAAhUIhW4aIiIjoWab3nrAhQ4bgr7/+AgDExcWhYcOGCAoKgouLCwoKCnD58mXs378fOTk5YpmhQ4caXjERERFRNaB3CBs1ahR++uknHD16FAqFAnfv3sWmTZtk80iSBIVCAQBo164dRo8ebVi1RERERNWE3ocjzc3NsXnzZrRp00YWtoqTJAnt27fH5s2boVQq9S6UiIiIqDox6LZFDRo0wMGDBzFnzhx06NABSqUSkiRBkiQolUp07NgR8+fPR2JiIlxdXY1VMxEREVGVp/fhSDUrKyuMGzcO48aNQ0FBATIzMwEAtWvXhrm5wc0TERERVUtGTUnm5uaoW7euMZskIiIiqpYMOhxZHgUFBbh58yb27NmDyMjIyl4dERERUZVg0J6w8+fPY8qUKTh48CBu3bqF/Pz8MpdZuHChIaskIiIiqhb0DmG3b99Gly5dcOfOHUiSVK5lSrqCkoiIiOhZo/fhyFmzZuH27dsA/heuFAqFVtDSHFevXj19V0dERERUregdwnbv3i2GGzVqhMmTJ6NmzZoAgDFjxmDKlCkIDQ0Ve8n8/Pzw77//GlguERERUfWgdwi7fPmyGN68eTNmzZqF0NBQAEDHjh3xxRdfYMeOHXjrrbcgSRKSk5OxfPlygwsmIiIiqg70DmHqG3YrlUo0b94cABAUFARJknDgwAEx39SpU8UwQxgRERFREb1DmJWVFYCi2xKpVCoAQIcOHQAABw8eFPO5urqK2xWdOHFC70KJiIiIqhO9Q5ibmxsAoLCwUOzhateuHSwtLXHy5EmcOnUKALB9+3aoVCpIkoQHDx4YoWQiIiKiqk/vEPb8888DKNoTNmbMGCxatAjW1tbw9/eHJEno3r07Bg4ciMGDB4urI52dnY1TNREREVEVp3cImzBhAiwsLETAsrOzAwAMHjwYAHDr1i1s2LABjx49giRJUCgU6NGjhxFKJiIiIqr69A5hLVu2xOrVq2FrawsAaNu2LQAgIiICTZs2FV1TqENarVq1MH36dEPrJSIiIqoWDLp35IABA3Dx4kXMmjULzZo1AwDY2Nhg3759GD58OBwdHWFjY4PevXsjPj4eTZo0MUrRRERERFWdQfeOBIrO8/rggw9k4+rWrYuVK1ca2jQRERFRtWXQnjAiIiIi0g9DGBEREZEJMIQRERERmQBDGBEREZEJMIQRERERmQBDGBEREZEJMIQRERERmQBDGBEREZEJVKkQlpycjAEDBsDZ2RnW1tbw9vbGtGnTkJ2dbXDbYWFhUCgUUCgUiI2NNbxYIiIiolIY1GP+48ePMW/ePOzatQvXrl1DTk4OCgsLS5xfoVDgwoULeq1r586dCA8PR15enhiXmpqKzz//HNu3b0dcXJy4j2VFzZkzB9u2bdNrWSIiIiJ96B3CHjx4gMDAQJw4cQIAxA27S6O+mXdFZWdnY9SoUSKABQYGom3btli9ejXu3LmDpKQkzJw5E1999VWF205JScGHH36oV11ERERE+tL7cOTs2bORkpIiwpe+Aas8YmJicO3aNQBA+/btERsbi3nz5mH9+vVinsWLF8v2kpVHTk4Ohg8fjpycHKPWS0RERFQWvfeE/f777yJ4SZIEBwcHNG7cGLa2tjAzM+6pZpqHCocMGQKlUgkACAoKgoeHB9LS0pCVlYXExEQEBQWVu93JkyeLPXl2dnZ48OCBUesmIiIiKoneIezy5ctiePz48fj2229hbm7QKWYlSklJEcPe3t6yaT4+PkhLSwMAnDhxotwhbNu2bZg3bx4AYOTIkbh06RLi4uKMVDERERFR6fTeZWVvbw9JkmBmZoZZs2ZVWgADgPT0dDFcu3Zt2TRHR0cxnJGRUa72bt68iVGjRkGSJDRu3Bhz5swxTqFERERE5aR3curatSvWrVsnglhlevjwoRi2srKSTbO0tBTD5T2cOHr0aNy4cQNKpRIrV66EnZ1dhepZuHAhFi1apHPa6dOnK9QWERERPZv0DmGTJk3CH3/8gcLCQqxcuRKvv/66MeuqNPPmzcOWLVsAAFOnTkXnzp0r3EZGRgaSk5ONXRoRERE9Q/QOYR07dsTcuXMxduxYjB8/Hg8ePED//v3h6uqqtbfKULa2trh79y4AaF0BmZubK4bL2qN18uRJfPDBB6L+Tz75RK96XFxc4Ovrq3Pa6dOn8fjxY73aJSIiomeH3iGse/fuAAAHBwdkZWVh4sSJmDhxYqnLKBQKFBQUVHhdLi4uIoRlZWXJpmVmZophV1fXUttZt26d6I7i0KFDsLCw0Dlft27dAAB79+5FcHCw1vTIyEhERkbqXNbPz497yYiIiKhMeoew2NhY0UWFQqEoV2et+mrevLk41+rcuXOyaampqWK4VatWpbZTmTUSERERVYRBlzQ+qVATGhqKmJgYAMCqVaswceJEKJVKJCQkiO4pateujYCAgFLbCQwMLLF3/F9//RVXr14FAAwbNgzu7u5wd3c34lYQERER/Y/eIWzEiBHGrKNUL7/8MqKionD79m0cP34cwcHB4rZFahEREbIrJdeuXYukpCQAQEhIiOxPlwMHDogQFhERofMwJBEREZGx6B3CoqOjjVlHqRwdHbF8+XIMGDAAeXl5iI+PR3x8vJju5+eHqKgo2TJbtmzB8uXLAQDW1tYlhi8iIiIiU6jcDr6MKCwsDIcPH8agQYPg7OwMS0tLeHl5YerUqYiNjUWNGjVMXSIRERFRuVVeN/fF5OTk4I8//sCwYcP0bqNNmzZYu3ZtueZdtmwZli1bVu62Y2Nj9SuKiIiISA8GhbDc3FwsXrwYBw8exK1bt5Cfn691sn5eXh4yMzNx6dIl5OXlGRTCiIiIiKoLvUNYQUEBunXrhoMHD5Y5rzqYqbu0ICIiInrW6X1O2Pz583HgwAERsDT/Vf+pMXwRERERyekdwtTnZikUClhYWKBjx44wMzODQqGAv78/AgIC4ODgIMKYr68vTpw4YZyqiYiIiKo4vUOYZs/1MTExOHDgAJ5//nkAwOTJk5GQkIArV64gODgYkiTh6NGjuHnzpuEVExEREVUDeocw9b0cFQoFevfuDQDo0aMHJElCYmIigKIbb3/77bcAig5T/vDDDwaWS0RERFQ96B3ClEql+NfMrKiZTp06ASi6ObZa+/btxbzlOYmfiIiI6FmgdwirU6cOgKKrJNV7vvz8/KBQKHD48GHcu3cPAHDmzBmoVCpIkoTbt28boWQiIiKiqk/vEObv7w+g6DBj//79sW3bNjg6OqJZs2bIz89HeHg45s6diwEDBohlbG1tDa+YiIiIqBrQO4S9/vrrAIrOCbtz5w7S09MBAP369YMkSdi/fz8mTJiAs2fPivk6duxohJKJiIiIqj69Q1ifPn0wceJE0QVFmzZtAABjx46FnZ2dmE/dR5hCocDkyZMNqZWIiIio2jDoBt7ffPMNtm3bht69e6N169YAADc3N2zatAlubm6i01YHBwdER0eje/fuRimaiIiIqKoz+AbevXr1Qq9evWTjgoKCcOnSJZw6dQo5OTlo3bo1rKysDF0VERERUbVhcAgriZmZGVq1alVZzRMRERFVaUYLYfv27cOBAweQmpqKe/fuYc2aNbh58yYePHiAJk2aGGs1RERERNWCwSFsy5YtmDBhAi5evAigqMsK9cn4f//9N4YPH4433ngD8+bNE526EhERET3rDEpF8+fPR79+/XDx4kVxlaSmkydPoqCgAAsXLsTQoUMNWRURERFRtaJ3CDt9+jQmTJggC1/Fg9jJkyfF+PXr1+O3337Td3VERERE1YreIey7776DSqWCQqGAm5sbNm/ejAMHDsjmeeutt+Dt7S0eR0dH618pERERUTWidwjbs2ePGF6xYgX69OkDe3t72TzdunXD+vXrARTtDTty5Ii+qyMiIiKqVvQOYdeuXQMAKJVKdO3atcT5WrZsCaVSCQC4f/++vqsjIiIiqlb0DmHqzldVKpW4b6QuZ86cgUqlAgA4OjrquzoiIiKiakXvENaiRQsxPGHCBOTm5mrN8/DhQ7z77rsAiu4dqbkMERER0bNM737Chg0bhsTERADAH3/8AS8vLzRr1kxMHz58OPbt2ycOWwLASy+9ZECpRERERNWH3nvCIiMj0a5dO9EtRXp6ujhZX5IkrFmzRnaY0tvbG5GRkQaWS0RERFQ96B3CLCwssHXrVvj7+8v6B1MoFKLHfKAokPn4+GDnzp2wsLAwrFoiIiKiasKgHvPr16+P/fv3Y8GCBejSpQssLCwgSRIkSYKZmRnat2+Pb7/9FkePHkXDhg2NVTMRERFRlWfwvSMtLCwQERGBiIgIqFQqZGZmQqVSoXbt2rC0tDRGjURERETVjsEhTJNSqUSdOnWM2SQRERFRtVSuEHblyhWjrdDd3d1obRERERFVVeUKYZ6enrKT7fWlUChQUFBgcDtEREREVV25D0dqXgFJRERERIYpdwgr3u0EEREREemvQifmS5IEhUIBd3d3dOvWDZ07dxb3kCQiIiKi8qtQCFPvDbt69SpWrFiBNWvWICAgAN26dUO3bt0QEBAAc3OjXnBJREREVC2Vq7PWvn37ws7OTnTEqj4cmZOTg7i4OMyYMQNBQUFwcHBAaGgovvjiCyQmJkKlUlVq8URERERVVbl2W23atAkqlQqHDx/G7t27sXv3bhw4cAB5eXmy88MePXqEPXv2iHtI2traokuXLmJPmb+/P8zMDOqkn4iIiKhaKPexQ6VSiYCAAAQEBGDatGl4/Pgx/v77b+zevRt//fUXjh07JttLBgDZ2dnYtWsXdu3aBQCws7PD3bt3jb4RRERERFWN3idw2djYoFevXujVqxcA4M6dO9izZ48IZRcvXoRCoZCFsgcPHhheMREREVE1YLRjgyqVCvn5+cjPz0dubq4Yr1AojNLRKxEREVF1oveesEePHiEuLk6cI3bixAnZdHXw0twTZmNjo+/qiIiIiKqVcoewwsJCHDp0SHZifn5+vpiuqwNXa2trdO7cGd26dUNwcDA6depknKqJiIiIqrhyhbAXX3wRcXFxuH//vhinK3RZWVmJfsOCg4MREBAAS0tL41VLREREVE2Uu4uK4ifZA4ClpSU6deokQhd70CciIiIqH716zG/YsCGCgoLQpUsXcZ7XlStXcOXKlTLbeO211/Qok4iIiKh6qfC9I4Gi2xatXLkSK1eurPAKGcKIiIiI9NwTpqZ5eLKsbijUN/8mIiIiogqEMF0n4ldkOhERERH9T7lC2KVLlyq7DiIiIqJnSrlCmIeHR2XXQURERPRMMdpti4iIiIio/BjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBKpUCEtOTsaAAQPg7OwMa2treHt7Y9q0acjOzq5wWxs3bkRYWBjq1KkDCwsL1K1bF+Hh4di9e3clVE5EREQkV2VC2M6dO9G5c2ds2LABd+7cQW5uLlJTU/H5558jODgYDx8+LHdbERERePHFF7Ft2zbcvn0bBQUFuHXrFv7880+Ehobiyy+/rMQtISIiIqoiISw7OxujRo1CXl4eACAwMBBjx46Fk5MTACApKQkzZ84sV1vr1q3D4sWLxeOePXti3LhxaNWqlRj38ccf4+DBg0bcAiIiIiK5KhHCYmJicO3aNQBA+/btERsbi3nz5mH9+vVinsWLF4uQVpolS5aI4XfeeQc7duzA3LlzceTIEbRt2xZA0c3If/nlFyNvBREREdH/VIkQtm3bNjE8ZMgQKJVKAEBQUJC4r2VWVhYSExPLbOvhw4dwdHQEAPTv31+MNzc3R2hoqHh89epVo9ROREREpEuVCGEpKSli2NvbWzbNx8dHDJ84caLMtvbt24fMzEzk5OSga9eusmmXLl0Sw3Xq1NG3XCIiIqIymZu6gPJIT08Xw7Vr15ZNU+/VAoCMjIxyt2llZSV7nJKSgo0bN4rHvXr1qmiZREREROVWJUKY5pWPxcOTpaWlGH7w4IFe7Z8/fx5hYWEoKCgAADRr1gwvvfRSifMvXLgQixYt0jnt9OnTetVAREREz5YqEcIq04kTJ9CzZ0+xF83S0hLLly8X553pkpGRgeTk5CdVIhEREVVDVSKE2dra4u7duwCgdQVkbm6uGLazs6tQuwcOHEBYWBiysrIAFJ2cv3LlSnTs2LHU5VxcXODr66tz2unTp/H48eMK1UFERETPnioRwlxcXEQIUwcmtczMTDHs6upa7jbj4+PxwgsviN72rayssGbNGtkVkyWJjIxEZGSkzml+fn7cS0ZERERlqhJXRzZv3lwMnzt3TjYtNTVVDGt2uFqaI0eOICwsTASwWrVqYfv27eUKYERERETGUCVCmGb/XatWrYJKpQIAJCQkIC0tDUDRVZMBAQFltpWVlYX+/fuLk/htbGywa9cuBAcHG79wIiIiohJUicORL7/8MqKionD79m0cP34cwcHBaNu2LVavXi3miYiIkF0puXbtWiQlJQEAQkJCEBISAgD4/PPPZR2x+vj4ICYmBjExMbJ1+vj4YNSoUZW5WURERPQMqxIhzNHREcuXL8eAAQOQl5eH+Ph4xMfHi+l+fn6IioqSLbNlyxYsX74cAGBtbY2QkBA8fvwYCxYskM139OhRHD16VGudvXr1YggjIiKiSlMlDkcCQFhYGA4fPoxBgwbB2dkZlpaW8PLywtSpUxEbG4saNWqU2cbhw4dlfY4RERERmUqV2BOm1qZNG6xdu7Zc8y5btgzLli2TjevatSskSaqEyoiIiIgqpsrsCSMiIiKqThjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBBjCiIiIiEyAIYyIiIjIBKpUCEtOTsaAAQPg7OwMa2treHt7Y9q0acjOzq5wW+fPn8err74KFxcXWFlZwcPDA++88w5u3bpVCZUTERERyZmbuoDy2rlzJ8LDw5GXlyfGpaam4vPPP8f27dsRFxcHW1vbcrV17NgxBAcH4+7du2LclStXMHfuXPz555/Yv38/XFxcjL0JREREREKV2BOWnZ2NUaNGiQAWGBiIsWPHwsnJCQCQlJSEmTNnlqstSZIwcuRIEcDatGmD8ePHo2HDhgCAS5cuYcKECUbfBiIiIiJNVSKExcTE4Nq1awCA9u3bIzY2FvPmzcP69evFPIsXL5btJSvJvn37cPToUQCAi4sLEhISMGfOHOzduxdKpRIAsH79ely/ft34G0JERET0/1WJELZt2zYxPGTIEBGWgoKC4OHhAQDIyspCYmJihdrq16+fOITZpEkTBAYGAgBUKhV27NhhtPqJiIiIiqsSISwlJUUMe3t7y6b5+PiI4RMnTjzRtoiIiIj0VSVCWHp6uhiuXbu2bJqjo6MYzsjIeKJtEREREemrSlwd+fDhQzFsZWUlm2ZpaSmGHzx48ETaWrhwIRYtWqRz2rFjxwAAp0+fhp+fX5n16Ot0+p1Ka5uoqvLb84OpSzCK7HPnTF0C0VOnZiX+pmpq1qwZVq1a9UTWVSVC2NMmIyMDycnJpc7z+PHjMuchIuNKvpFm6hKIqLJUw9/UKhHCbG1tRZcSxa+AzM3NFcN2dnblaktN37ZcXFzg6+urc9qJEycgSRJq1qyJRo0alVkPVW2nT5/G48ePYWNjg+bNm5u6HCIyIn6+n03NmjV7YuuqEiHMxcVFhLCsrCzZtMzMTDHs6uparrbUXVTo21ZkZCQiIyPLXBdVf35+fkhOTkbz5s2RlJRk6nKIyIj4+abKViVOzNf8H8i5YudKpKamiuFWrVo90baIiIiI9FUlQlhoaKgYXrVqFVQqFQAgISEBaWlF54DUrl0bAQEBFWorJiZG3HcyLS0N+/fvBwAolUr06tXLaPUTERERFVclDke+/PLLiIqKwu3bt3H8+HEEBwejbdu2WL16tZgnIiJCdnXj2rVrxe7jkJAQhISEiGEfHx+cPXsWN27cQGBgILp27YqNGzeioKAAADB48GDUq1fvCW4hERERPWuqRAhzdHTE8uXLMWDAAOTl5SE+Ph7x8fFiup+fH6KiomTLbNmyBcuXLwcAWFtbixBmbm6OlStXIiQkBPfu3cOxY8dEtxIA4Onpie++++4JbBURERE9y6rE4UgACAsLw+HDhzFo0CA4OzvD0tISXl5emDp1KmJjY1GjRo1yt+Xv74+jR49i5MiRcHFxgYWFBRo2bIhx48bh4MGDqF+/fiVuCRERERGgkCRJMnURRFXVwoULkZGRARcXF14xS1TN8PNNlY0hjIiIiMgEqszhSCIiIqLqhCGMiIiIyAQYwoiIiIhMgCGMnhrLli2DQqHQ+adUKmFjY4MGDRqgd+/e2LJli6nLLbcZM2aI7Rg5cqQYf/nyZdk2EpHhVCoVVq5ciRdffBFubm6wtraGvb092rZti4kTJ+LixYtay3h6eur83jE3N0fNmjXh7u6OQYMG4eDBg1rLBgcHl7hsjRo14OrqihdeeAE7d+58EptPVQxDGFUJhYWFyMnJwbVr17Bjxw707dsXX3/9tanLIqKnSGpqKtq1a4dXX30VGzduRHp6OnJzc3H//n0cP34c//3vf9GiRQv8+OOP5WpPpVLh4cOHuHr1KtavX4/AwEBZJ+FlLfv48WNkZGRg+/bt6NWrF7755htDNo+qoSrRWSs9e2rVqoW33noLACBJEgoLC3Hv3j1s374dV69eBQB89NFHeOmll+Dl5WXKUvVmb2+PDz/80NRlEFULly9fRpcuXXD79m0AgJWVFXr37o1GjRrh6tWr2LJlC3JycpCbm4uxY8eiTp06GDx4sFY7YWFhaN26NSRJQk5ODlJTU7Fr1y4UFBSgoKAAERERCA0NhZOTk9ayzz33HAIDAwEAOTk5uHLlCrZt24acnBwARd9Z4eHhaNasWSU+E1SlSERPiejoaAmABEDy8PDQOU9mZqbk5OQk5pszZ86TLVIP06dPF/WOGDHC1OUQVUuBgYHic+bj4yOdP39eNj0tLU3y8vIS8zRs2FDKz8+XJEmSPDw8xPjo6Gittv/8808xHYA0b948MS0oKEiMnz59utayycnJkqWlpZhn0qRJRt1uqtp4OJKqFEdHR7Ro0UI8Vt/MXS02Nhb9+vUTd0KwsrKCp6cnXn31VaSmpmq1d/36dbz//vto2bIlbG1tYW5uDmdnZ3Tv3h0rVqwosY79+/ejf//+cHZ2FusYM2YMLly4UO5tKe2cMM3zTM6cOYMDBw6gT58+cHBwQM2aNREUFITt27frbPf27duYNGkSvLy8YGVlBScnJ/Ts2RMxMTHlro2oKvn7779lt7JbvXo1mjRpIpvH3d0dS5cuBQB4eXmhe/fuuHHjRrna79OnD1q2bCkenz9/vty1tW/fHqGhoXotS9UfD0dSlXLt2jXZvT6Dg4PF8MaNGzFw4ECtYJaWloa0tDRs2LABhw4dQvPmzQEAV69eRadOnZCRkSGb/86dO9i7dy/27t2LlJQUrfM45s6di3fffReSRj/HaWlpWLJkCX777Tds2LBB3KvUGNasWYPPPvtMtl1///039u3bhw0bNqBfv35i/JkzZxAaGop///1XjMvMzMSuXbuwa9cuREZGYsGCBUarjehpsGnTJjHcoUMHtGvXTud8zz//PG7evIk6deoYtL7CwkKTLEvVD0MYPZWysrIwZcoU8VilUuH27dv4888/cf/+fQBAZGSk+LLNz8/HW2+9JYJK586dERAQgKysLPzxxx+4d+8esrOzsWDBAvzwww8AgFmzZokA1rBhQ7zwwguwtbVFcnIy4uLiAACzZ8/GsGHD4OvrC6BoD9iECRNEAAsICIC/vz8SExORlJSEhw8fYujQoThz5gycnZ2N8lzMmDEDTk5OeOmll/Do0SOsWbMGBQUFkCQJX3/9tQhhKpUKAwcOFAGsbt26ePHFF3H37l2sX78eKpUKCxcuREBAgOwqTaKq7p9//hHD6s9qSfQJYH/++SdOnjwpHmvujS9LcnIydu3apdeyVP0xhNFT6f79+/jqq69KnB4SEiLCFFC0t+e1115DSkoKbGxs8Pvvv8PMrOhoe7du3TBixAgAkB0u1BxeunSpbO/Vhx9+iPT0dDRr1gzm5v/7mMyaNUv8T3bUqFHi8EZhYSEGDRqEP/74A3fu3MGSJUtkIdIQ9evXR1JSElxdXQEUHd6YNGkSAODEiRNivg0bNuDUqVMAgHr16uH48eOoW7eumDZgwACxDQxhVJ3cunVLDOs6Yb4i1q5dizNnzkCSJDx69EicmK/m6Oio84R+ANi9e7c4CT8nJweXL1/Gtm3bkJeXBwCwtLTkZ49kGMKoStq9ezdatmyJrVu3wtvbG/Xq1cOsWbNk86SnpyMhIQEbNmwQ4x49eiSG/fz8sGPHDgDASy+9hLCwMAQHB6NLly748ssvRYhTU6lU2Lt3r3isvnoTAMzMzDB69Gj88ccfAIAdO3YYLYSNGDFCBDAA6N69uxh+8OCBGNb8oRg4cKAIYADw4osvwsnJCXfu3MHZs2eRlpYGDw8Po9RHZGqah+oNPdy3detWbN26Vec0Gxsb/Prrr6hdu7bO6fv378f+/ft1TlMqlViwYAF8fHwMqo+qF56YT08lDw8PSJIk/vLz83Hnzh3s2bMHbdq0AVC0J2vAgAHiC7iwsBC//fYbBg4cCBcXF7i5uWHIkCEiGKnnUfvoo4/QtWtXAEVhZs2aNXjrrbfQtm1b1KlTB6NHj8bx48fF/Hfu3MHDhw/F444dO8pOrA8PDxfT1HukjPVcaKpVq5bO7bly5YoY/vHHH7U6j7xz506l1Edkapp7v9RdVBiDUqmEvb09mjdvjjfffBPHjx9H7969y7WsmZkZ7Ozs0LRpU7z66qs4dOgQRo0aZbTaqHrgnjCqEszNzVG7dm1069YNv/76K1q1agWgKEwcOHAAHTp0wAsvvIA9e/YAANzc3DBy5Eh07NgRkiRh7NixWm3WrFkTsbGx2LZtG37//Xfs2rUL165dA1B0eDM6OhorVqzApk2bEBYWpnXCf506dbT2lqlZWVkZbdttbGxkj5VKpc75NOurWbMmbG1tS2wzNzfXOMURPQXatm2LhIQEAEBSUlKJ8z169Ai9e/dGz5498eKLL4rvEU3R0dF6HzKcPn06ZsyYodey9GxiCKMqx9PTU/b433//xalTp0QACw0NxdatW8W5XH/++WeJbSkUCvj6+iIsLAwAcOnSJSQkJOCnn37C/v37oVKp8PHHHyMsLEx0R6EOMDt27ED79u1FWyqVqsSA9CQ0aNBADI8ePVp2zhxg+vqIKkvfvn3x008/AQCOHDmCo0eP6rxCcvXq1di3bx/27duHqKgoJCcnyz7DRE8aD0dSlbN27VrZ4yZNmiA5OVk8dnJyEgFMpVJh+fLlYpr68N2jR48QGBgIBwcHuLi4YOPGjQCARo0a4T//+Q/efvttsYz6MJ+FhQWee+45Mf7777+XdVMxevRoODs7o2vXruIH4Unq1q2bGF69erWs6429e/fC1tYWbdq0wX/+8x/uCaNq5YUXXpCFrmHDhuHy5cuyeZKTk/H++++Lxx07dmQAI5PjnjB6KhXvogIA8vLykJqaim3btolxrVq1gr+/v+xE2tWrV+PBgwdo1KgRdu3ahbNnz4ppjx8/BgDUqFEDbm5u4iTaIUOGIDw8HO7u7sjIyMDmzZvFMs8//7wYnjRpktjjtmLFCqSkpCAwMBBnz54VN+jdt2+fSQ5JDBo0CFFRUUhLS8PNmzfRqlUrvPjiizAzM8OaNWuQm5uLlJQUtGvXzqiHS4lMTaFQ4Ndff0VAQADu37+PM2fOoEWLFggLC0PDhg2RmpqK7du3i0P2VlZWmDdvnomrJgJvW0RPD83bFpXnz8HBQfrnn38kSZKk9PR0ydnZWed8tWrVkhQKhQRAcnJykgoLCyVJkqSsrCzJ39+/1HV4eHhIaWlpsjo1b0Ok62/mzJklzq9526JLly7JltOkeSuU4rdRKW25pKQkydHRscTa/Pz8pMzMTENeJqKn1tGjR6XGjRuX+vmsVauW9Oeff8qWK+u2RaUp67ZFRKXh4UiqMszNzVGzZk34+PjgrbfeQnJyMvz8/AAArq6u+Oeff/DKK6+gYcOGsLCwgKurK1555RUkJSWhc+fOAIqucNy3bx8AwMHBAXFxcfjmm2/QuXNn2NvbiyuafH19MX36dBw7dgzu7u6yOmbMmIG//voLL730EurXrw9LS0s0aNAAYWFh2LFjB6Kiop7sE6PB19cXp06dwvvvv49mzZqhRo0asLe3R/v27TF79mzExcXB0dHRZPURVaa2bdvi9OnTWLBgAUJDQ1G3bl2Ym5ujVq1a8Pf3xyeffIJz586hT58+pi6VCACgkCSNk1qIiIiI6IngnjAiIiIiE2AIIyIiIjIBhjAiIiIiE2AIIyIiIjIBhjAiIiIiE2AIIyIiIjIBhjAiIiIiE2AIIyIiIjIBhjAiIiIiE2AIIyIiIjIBhjAiIiIiE2AIIyIiIjIBhjAiIiIiE/h/Ic3M7KjvgtAAAAAASUVORK5CYII="
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -250,15 +250,15 @@
     "sns.barplot(pd.DataFrame({\"Baseline\": cv_base, \"CPP\": cv}), palette=[\"tab:blue\", \"tab:red\"])\n",
     "plt.ylabel(\"Mean accuracy\", size=aa.plot_gcfs()+1)\n",
     "plt.ylim(0, 1)\n",
-    "plt.title(\"Comparison of Feature Engineering Methods\")\n",
+    "plt.title(\"Comparison of Feature Engineering Methods\", size=aa.plot_gcfs()-1)\n",
     "sns.despine()\n",
     "plt.show()"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-09-28T08:10:29.342215861Z",
-     "start_time": "2023-09-28T08:09:02.264702356Z"
+     "end_time": "2023-10-02T20:37:12.095658200Z",
+     "start_time": "2023-10-02T20:35:34.799826700Z"
     }
    }
   },