From d272d96009ecad9a3f287e649db7e09b565ce967 Mon Sep 17 00:00:00 2001
From: demian1 <demian1@users.noreply.github.com>
Date: Wed, 13 Jul 2022 16:05:21 +0200
Subject: [PATCH 01/33] added benchmark module

Implemented a whole-dataset benchmark module (not individual target performances) and associated plotting functions. Currently only roc and prc AUCs are implemented as performance metrics.
---
 decoupler/utils_benchmark.py | 371 +++++++++++++++++++++++++++++++++++
 setup.py                     |   3 +-
 2 files changed, 373 insertions(+), 1 deletion(-)
 create mode 100644 decoupler/utils_benchmark.py

diff --git a/decoupler/utils_benchmark.py b/decoupler/utils_benchmark.py
new file mode 100644
index 0000000..94b38a9
--- /dev/null
+++ b/decoupler/utils_benchmark.py
@@ -0,0 +1,371 @@
+"""
+Utility functions to benchmark resources on known data
+"""
+
+import numpy as np
+import pandas as pd
+import decoupler as dc
+
+from sklearn.metrics import roc_auc_score, average_precision_score
+from numpy.random import default_rng
+from tqdm import tqdm
+import matplotlib.pyplot as plt
+
+def random_scores_GT(nexp=50, ncol = 4):
+    """
+    Generate random scores and groud-truth matrix, for testing
+
+    Args:
+        nexp (int, optional): Number of rows/experiments. Defaults to 50.
+        ncol (int, optional): Number of classes/TFs/pathways. Defaults to 4.
+
+    Returns:
+        _type_: (DataFrame): Dataframe with scores and associated ground truth
+    """
+    df = np.random.randn(nexp, ncol)
+    ind = np.random.randint(0, df.shape[1] ,size=df.shape[0])
+    gt = np.zeros(df.shape)
+    gt[range(gt.shape[0]),ind] = 1
+
+    return pd.DataFrame(np.column_stack((df.flatten(), gt.flatten())), columns = ['score', 'GT'])
+
+"""
+Downsample ground truth vector 
+"""
+
+def down_sampling(y, seed=7, n = 100):
+    """
+    Downsampling of ground truth
+    
+    Parameters
+    ----------
+    
+    y: array
+        binary groundtruth vector 
+        
+    seed: arbitrary seed for random sampling
+    
+    n: number of iterations
+        
+    Returns
+    -------
+    ds_msk: list of downsampling masks for input vectors
+    """
+    
+    msk = []
+    rng = default_rng(seed)
+    
+    # Downsampling
+    for i in range(n):
+        tn_index = np.where(y == 0)[0]
+        tp_index = np.where(y != 0)[0]
+        ds_array = rng.choice(tn_index, size=len(tp_index), replace=True)
+        ds_msk = np.hstack([ds_array, tp_index])
+
+        msk.append(ds_msk)
+
+    return msk
+
+"""
+Compute AUC of ROC or PRC
+"""
+
+def get_auc(x, y, mode):
+    """
+    Computes AUROC for each label
+    
+    Parameters
+    ----------
+    
+    x: array
+        binary groundtruth vector
+        
+    y: array (flattened)
+        vector of continuous values
+        
+        
+    Returns
+    -------
+    auc: value of auc
+    """
+
+    if mode == "roc":
+        auc = roc_auc_score(x, y) 
+    elif mode == "prc":
+        auc = average_precision_score(x, y)
+    else: 
+        raise ValueError("mode can only be roc or prc")
+    return auc
+
+def get_target_masks(long_data, targets, subset = None):
+    """
+    Generates a list of indices of a DataFrame that correspond to prediction scores and associated ground-truth for each target
+
+    Args:
+        long_data (DataFrame): DataFrame with a 'score' and 'GT' column.
+        targets (list): List of targets (have to be in correct order) for which there are entries in long_data.
+        subset (list, optional): A subset of the targets for which to make masks. If None, then the masks will be made for all targets. Defaults to None.
+
+    Returns:
+        target_ind: List of data indices for each target in the targets object
+        target_names : Target name corresponding to the elements in target_ind. Elements in subset are filtered and put in same order as in targets.
+    """
+
+    if long_data.shape[0] < len(targets):
+        raise ValueError('The data given is smaller than the number of targets')
+    elif long_data.shape[0] % len(targets) != 0:
+        raise ValueError('The data is likely misshapen: the number of rows cannot be divided by the number of targets')
+
+    if subset is not None:
+        iterateover = np.argwhere(np.in1d(targets, subset)).flatten().tolist()
+        target_names = [targets[i] for i in iterateover]
+    else:
+        iterateover = range(len(targets))
+        target_names = targets
+
+    target_ind = []
+    for target in iterateover:
+        target_ind.append(np.arange(target, long_data.shape[0], len(targets)))
+
+    return target_ind, target_names
+
+def get_performance(data, metric = 'mcroc', n_iter = 100, seed = 42, method_name = None):
+    """
+    Compute binary classifier performance
+
+    Args:
+        data (DataFrame): DataFrame with a 'score' and 'GT' column. 'GT' column contains groud-truth ( e.g. 0, 1). 'score' can be continuous or same as 'GT'
+        metric (str or list of str, optional): Which metric(s) to use. Currently implemeted methods are: 'mcroc', 'mcprc', 'roc', 'prc'. Defaults to 'mcroc'.
+        n_iter (int, optional): Number of iterations for the undersampling procedures for the 'mcroc' and 'mcprc' metrics. Defaults to 100.
+        seed (int, optional): Seed used to generate the undersampling for the 'mcroc' and 'mcprc' metrics. Defaults to 42.
+        method_name (str, optional): Name of the decoupler method used to do activity prediction. Added as prefix to the output dictionary: e.g. 'mlm_roc'. Defaults to 100.
+
+    Returns:
+        perf: Dict of prediction performance(s) on the given data. 'mcroc' and 'mcprc' metrics will return the values for each sampling. Other methods return a single value.
+    """
+
+    available_metrics = ['mcroc', 'mcprc', 'roc', 'prc']
+    metrics = [available_metrics[i] for i in np.argwhere(np.in1d(available_metrics, metric)).flatten().tolist()]
+
+    if len(metrics) == 0:
+        raise ValueError('None of the performance metrics given as parameter have been implemented')
+
+    if 'mcroc' in metrics or 'mcprc' in metrics:
+        masks = down_sampling(y = data['GT'].values, seed=seed, n=n_iter)
+
+    perf = {}
+    for met in metrics:
+        if met == 'mcroc' or met == 'mcprc':
+            # Compute AUC for each mask (with equalised class priors)
+            aucs = []
+            for mask in tqdm(masks):
+                auc = get_auc(x = data['GT'][mask],
+                            y = data['score'][mask],
+                            mode = met[2:])
+                aucs.append(auc)
+
+        elif met == 'roc' or met == 'prc':
+            # Compute AUC on the whole (unequalised class priors) data
+            aucs = get_auc(x = data['GT'], y = data['score'], mode = met)
+        
+        if method_name is None:
+            perf[met] = aucs
+        else:
+            perf[method_name + '_' + met] = aucs
+
+    return perf
+
+def get_target_performance(data, targets, metric='mcroc', subset = None, n_iter = 100, seed = 42):
+    """
+    Compute binary classifier performance for each target or subet of targets
+
+    Args:
+        data (DataFrame): DataFrame with a 'score' and 'GT' column. 'GT' column contains groud-truth ( e.g. 0, 1). 'score' can be continuous or same as 'GT'
+        targets (list of str): List of targets (have to be in correct order) for which there are entries in data.
+        metric (str, or list of str optional): Which metrics to use. Currently implemeted methods are: 'mcroc', 'mcprc', 'roc', 'prc'. Defaults to 'mcroc'.
+        subset (list of str, optional): A subset of the targets for which to compute performance. If None, then the performance will be calculated for all targets. Defaults to None.
+        n_iter (int, optional): Number of iterations for the undersampling procedures for the 'mcroc' and 'mcprc' metrics. Defaults to 100.
+        seed (int, optional):  Seed used to generate the undersampling for the 'mcroc' and 'mcprc' metrics. Defaults to 42.
+
+    Returns:
+        perf : Dict of prediction performance(s) for each target or subset of targets. 'mcroc' and 'mcprc' metrics will return the values for each sampling. Other methods return a single value.
+    """
+
+    masks, target_names = get_target_masks(data, targets, subset = subset)
+
+    perf = {}
+    for trgt, name in zip(masks, target_names):
+        perf[name] = get_performance(data.iloc[trgt.tolist()].reset_index(), metric, n_iter, seed)
+
+    return perf
+
+def get_scores_GT(decoupler_results, metadata, meta_target_col = 'target'):
+    """
+
+    Convert decouple output to flattenend vectors and combine with GT information
+
+    Args:
+        decoupler_results (dict): Output of decouple
+        metadata (DataFrame): Metadata of the perturbation experiment containing the activated/inhibited targets and the sign of the perturbation
+        meta_target_col (str, optional): Column name in the metadata with perturbation targets. Defaults to 'target'.
+
+    Returns:
+        scores_gt: dict of flattenend score,gt dataframes for each method
+    """
+    computed_methods = list(set([i.split('_')[0] for i in decoupler_results.keys()])) # get the methods that were able to be computed (filtering of methods done by decouple)
+    scores_gt = {}
+    for m in computed_methods:
+        # estimates = res[m + 'estimate']
+        # pvals = res[m + 'pvals']
+
+        # remove experiments with no prediction for the perturbed TF
+        missing = list(set( metadata[meta_target_col]) - set(decoupler_results[m + '_estimate'].columns))
+        keep = [trgt not in missing for trgt in metadata[meta_target_col].to_list()]
+        meta = metadata[keep]
+        estimates = decoupler_results[m + '_estimate'][keep]
+        # pvals = res[m + '_pvals'][keep]
+
+        # mirror estimates
+        estimates = estimates.mul(meta['sign'], axis = 0)
+        gt = meta.pivot(columns = meta_target_col, values = 'sign').fillna(0)
+
+        # add 0s in the ground-truth array for targets predicted by decoupler
+        # for which there is no ground truth in the provided metadata (assumed 0)
+        missing = list(set(estimates.columns) - set(meta[meta_target_col]))
+        gt = pd.concat([gt, pd.DataFrame(0, index= gt.index, columns=missing)], axis = 1, join = 'inner').sort_index(axis=1)
+
+        # flatten and then combine estimates and GT vectors
+        # set ground truth to be either 0 or 1
+        df_scores = pd.DataFrame({'score': estimates.to_numpy().flatten(), 'GT': gt.to_numpy().flatten()})
+        df_scores['GT'] = abs(df_scores['GT'])
+
+        scores_gt[m] = df_scores
+
+    return scores_gt
+
+def run_benchmark(data, metadata, network, methods = None, metric = 'roc', meta_target_col = 'target', net_source_col = 'source', net_target_col = 'target', filter_experiments= True, filter_sources = False, **kwargs):
+    """
+    Benchmark methods or networks on a given set of perturbation experiments using activity inference with decoupler.
+
+    Args:
+        data (DataFrame): Gene expression data where each row is a perturbation experiment and each column a gene
+        metadata (DataFrame): Metadata of the perturbation experiment containing the activated/inhibited targets and the sign of the perturbation
+        network (DataFrame): Network in long format passed on to the decouple function
+        methods (str or list of str, optional): List of methods to run. If none are provided use weighted top performers (mlm, ulm and wsum). To benchmark all methods set to "all". Defaults to None.
+        metric (str or list of str, optional): Performance metric(s) to compute. See the description of get_performance for more details. Defaults to 'roc'.
+        meta_target_col (str, optional): Column name in the metadata with perturbation targets. Defaults to 'target'.
+        net_source_col (str, optional): Column name in network with source nodes. Defaults to 'source'.
+        net_target_col (str, optional): Column name in net with target nodes. Defaults to 'target'.
+        filter_experiments (bool, optional): Whether to filter out experiments whose perturbed targets cannot be infered from the given network. Defaults to True.
+        filter_sources (bool, optional): Whether to fitler out sources in the network for which there are not perturbation experiments (reduces the number of predictions made by decouple). Defaults to False.
+        **kwargs: other arguments to pass on to get_performance (e.g. n_iter etc)
+
+    Returns:
+        mean_perf: DataFrame containing the mean performance for each metric and for each method (mean has to be done for the mcroc and mcprc metrics)
+        bench: dict containing the whole data for each method and metric. Useful if you want to see the distribution for each subsampling for the mcroc and mcprc methods
+    """
+
+    #subset by TFs with GT available
+    if filter_sources:
+        keep = [src in metadata[meta_target_col].to_list() for src in network[net_source_col].to_list()]
+        network = network[keep]
+
+    # filter out experiments without predictions available
+    if filter_experiments:
+        keep = [trgt in network[net_target_col].to_list() for trgt in metadata[meta_target_col].to_list()]
+        data = data[keep]
+        metadata = metadata[keep]
+
+    # run prediction
+    res = dc.decouple(data, network, methods=methods)
+
+    scores_gt = get_scores_GT(res, metadata, meta_target_col)
+
+    bench = {}
+    for method in scores_gt.keys():
+        print('Calculating performance metrics for', method)
+        perf = get_performance(scores_gt[method], metric, method_name= method, **kwargs)
+        bench.update(perf)
+    
+    #make dataframe with mean perfomances
+    mean_perfs = {}
+    for key, value in bench.items():
+        mean_perfs[key]=np.mean(value)
+    mean_perfs = pd.DataFrame.from_dict(mean_perfs, orient='index').reset_index(level=0)
+    mean_perfs.columns = ['id','value']
+    mean_perfs[['method','metric']] = mean_perfs['id'].str.split('_', expand=True)
+    mean_perfs = mean_perfs.pivot(index='method', columns='metric', values='value')
+
+    return mean_perfs, bench
+
+def benchmark_scatterplot(mean_perf, x = 'mcroc', y = 'mcprc'):
+    """
+    Creates a scatter plot for each given method for two performance metrics
+
+    Args:
+        mean_perf (DataFrame): Mean performance of each method output by run_benchmark()
+        x (str, optional): Which metric to plot on the x axis. Defaults to 'mcroc'.
+        y (str, optional): Which metric to plot on the y axis. Defaults to 'mcprc'.
+
+    Returns:
+        ax: Axes of a scatter plot
+    """
+
+    ax = plt.subplot(111)
+    ax.scatter(x = mean_perf[x], y = mean_perf[y])
+    ax.set_aspect('equal')
+
+    min_v = mean_perf[[x,y]].min().min()
+    max_v = mean_perf[[x,y]].max().max()
+    border = (max_v - min_v)/15
+
+    ax.set_xlim(min_v - border, max_v + border)
+    ax.set_ylim(min_v - border, max_v + border)
+
+    if (x in ['roc','mcroc'] and y in ['roc','mcroc']) or (x in ['prc','mcprc'] and y in ['prc','mcprc']):
+        ax.axline((0,0),slope=1, color = 'black', linestyle = ':')
+
+    for i, label in enumerate(mean_perf.index):
+        ax.annotate(label.capitalize(), (mean_perf[x][i], mean_perf[y][i]))
+
+    if x in ['mcroc', 'mcprc', 'roc', 'prc']:
+        x = x + ' AUC'
+
+    if y in ['mcroc', 'mcprc', 'roc', 'prc']:
+        y = y + ' AUC'
+
+    ax.set_xlabel(x.upper())
+    ax.set_ylabel(y.upper())
+
+    return ax
+
+def benchmark_boxplot(benchmark_data, metric = 'mcroc'):
+    """
+    Creates boxplots for an iterative performance metric (i.e. mcroc and mcprc)
+
+    Args:
+        benchmark_data (dict): dict containing complete output from run_benchmark()
+        metric (str, optional): Metric to plot a distribution for. Either mcroc or mcprc. Defaults to 'mcroc'.
+
+    Returns:
+        ax: Axes of a boxplot
+    """
+
+    if not (metric == 'mcprc' or metric == 'mcroc'):
+        raise ValueError('Plotting of boxplots only possible for the \'mcprc\' and \'mcroc\' methods')
+
+    keys = [key for key in benchmark_data.keys() if metric in key.split('_')[1]]
+    methods = [key.split('_')[0] for key in keys]
+
+    if len(keys) == 0:
+        raise ValueError('The given metric was not found in the benchmark data')
+
+    fig = plt.figure()
+    ax = plt.subplot(111)
+    for i, key in enumerate(keys):
+        ax.boxplot(benchmark_data[key], positions = [i])
+    ax.set_xlim(-0.5, len(keys) - 0.5)
+    ax.set_ylabel(metric.upper() + ' AUC')
+    ax.set_xticklabels([m.capitalize() for m in methods])
+    
+    return ax
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 92adc58..83e6ed1 100644
--- a/setup.py
+++ b/setup.py
@@ -33,7 +33,8 @@ def get_version(rel_path: str) -> str:
     },
     install_requires=["numba",
                       "tqdm",
-                      "anndata"],
+                      "anndata",
+                      "matplotlib"],
     packages=["decoupler"],
     python_requires=">=3.8",
     classifiers=[

From 7111fd9f394c18640dd78f90bf72d42a3d94baff Mon Sep 17 00:00:00 2001
From: demian1 <demian1@users.noreply.github.com>
Date: Wed, 13 Jul 2022 16:07:07 +0200
Subject: [PATCH 02/33] Update .gitignore

---
 .gitignore | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.gitignore b/.gitignore
index b6e4761..432c6dc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -127,3 +127,6 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+.DS_Store
+benchmark-dev.ipynb

From 2394c0aac9808542802059d79600ecd235d9d778 Mon Sep 17 00:00:00 2001
From: demian1 <demian1@users.noreply.github.com>
Date: Thu, 14 Jul 2022 16:23:36 +0200
Subject: [PATCH 03/33] Update utils_benchmark.py

Added per target performance estimation
---
 decoupler/utils_benchmark.py | 78 ++++++++++++++++++++++--------------
 1 file changed, 48 insertions(+), 30 deletions(-)

diff --git a/decoupler/utils_benchmark.py b/decoupler/utils_benchmark.py
index 94b38a9..4e4ae7e 100644
--- a/decoupler/utils_benchmark.py
+++ b/decoupler/utils_benchmark.py
@@ -2,8 +2,10 @@
 Utility functions to benchmark resources on known data
 """
 
+from statistics import mean
 import numpy as np
 import pandas as pd
+from sklearn.preprocessing import label_binarize
 import decoupler as dc
 
 from sklearn.metrics import roc_auc_score, average_precision_score
@@ -129,7 +131,7 @@ def get_target_masks(long_data, targets, subset = None):
 
     return target_ind, target_names
 
-def get_performance(data, metric = 'mcroc', n_iter = 100, seed = 42, method_name = None):
+def get_performance(data, metric = 'mcroc', n_iter = 100, seed = 42, prefix = None, **kwargs):
     """
     Compute binary classifier performance
 
@@ -138,7 +140,7 @@ def get_performance(data, metric = 'mcroc', n_iter = 100, seed = 42, method_name
         metric (str or list of str, optional): Which metric(s) to use. Currently implemeted methods are: 'mcroc', 'mcprc', 'roc', 'prc'. Defaults to 'mcroc'.
         n_iter (int, optional): Number of iterations for the undersampling procedures for the 'mcroc' and 'mcprc' metrics. Defaults to 100.
         seed (int, optional): Seed used to generate the undersampling for the 'mcroc' and 'mcprc' metrics. Defaults to 42.
-        method_name (str, optional): Name of the decoupler method used to do activity prediction. Added as prefix to the output dictionary: e.g. 'mlm_roc'. Defaults to 100.
+        prefix (str, optional): Added as prefix to the performance metric key in the output dictionary e.g. 'mlm_roc' if equal to 'mlm'. Defaults to 100.
 
     Returns:
         perf: Dict of prediction performance(s) on the given data. 'mcroc' and 'mcprc' metrics will return the values for each sampling. Other methods return a single value.
@@ -158,7 +160,7 @@ def get_performance(data, metric = 'mcroc', n_iter = 100, seed = 42, method_name
         if met == 'mcroc' or met == 'mcprc':
             # Compute AUC for each mask (with equalised class priors)
             aucs = []
-            for mask in tqdm(masks):
+            for mask in tqdm(masks, disable= not kwargs.get('verbose', True)):
                 auc = get_auc(x = data['GT'][mask],
                             y = data['score'][mask],
                             mode = met[2:])
@@ -168,14 +170,14 @@ def get_performance(data, metric = 'mcroc', n_iter = 100, seed = 42, method_name
             # Compute AUC on the whole (unequalised class priors) data
             aucs = get_auc(x = data['GT'], y = data['score'], mode = met)
         
-        if method_name is None:
+        if prefix is None:
             perf[met] = aucs
         else:
-            perf[method_name + '_' + met] = aucs
+            perf[prefix + '_' + met] = aucs
 
     return perf
 
-def get_target_performance(data, targets, metric='mcroc', subset = None, n_iter = 100, seed = 42):
+def get_target_performance(data, targets, metric='mcroc', subset = None, n_iter = 100, seed = 42, prefix = None, **kwargs):
     """
     Compute binary classifier performance for each target or subet of targets
 
@@ -186,6 +188,7 @@ def get_target_performance(data, targets, metric='mcroc', subset = None, n_iter
         subset (list of str, optional): A subset of the targets for which to compute performance. If None, then the performance will be calculated for all targets. Defaults to None.
         n_iter (int, optional): Number of iterations for the undersampling procedures for the 'mcroc' and 'mcprc' metrics. Defaults to 100.
         seed (int, optional):  Seed used to generate the undersampling for the 'mcroc' and 'mcprc' metrics. Defaults to 42.
+        prefix (str, optional):Added as prefix to the output dictionary. Defaults to 100.
 
     Returns:
         perf : Dict of prediction performance(s) for each target or subset of targets. 'mcroc' and 'mcprc' metrics will return the values for each sampling. Other methods return a single value.
@@ -195,11 +198,16 @@ def get_target_performance(data, targets, metric='mcroc', subset = None, n_iter
 
     perf = {}
     for trgt, name in zip(masks, target_names):
-        perf[name] = get_performance(data.iloc[trgt.tolist()].reset_index(), metric, n_iter, seed)
+        if prefix is None:
+            p = name
+        else:
+            p = prefix  + '_' + name
+
+        perf.update(get_performance(data.iloc[trgt.tolist()].reset_index(), metric, n_iter, seed, prefix = p, **kwargs))
 
     return perf
 
-def get_scores_GT(decoupler_results, metadata, meta_target_col = 'target'):
+def get_scores_GT(decoupler_results, metadata, meta_perturbation_col = 'target'):
     """
 
     Convert decouple output to flattenend vectors and combine with GT information
@@ -207,31 +215,33 @@ def get_scores_GT(decoupler_results, metadata, meta_target_col = 'target'):
     Args:
         decoupler_results (dict): Output of decouple
         metadata (DataFrame): Metadata of the perturbation experiment containing the activated/inhibited targets and the sign of the perturbation
-        meta_target_col (str, optional): Column name in the metadata with perturbation targets. Defaults to 'target'.
+        meta_perturbation_col (str, optional): Column name in the metadata with perturbation targets. Defaults to 'target'.
 
     Returns:
-        scores_gt: dict of flattenend score,gt dataframes for each method
+        scores_gt: dict of flattenend dataframes for each method
+        targets: dict with target names for which activities were inferred for each method respectively
     """
     computed_methods = list(set([i.split('_')[0] for i in decoupler_results.keys()])) # get the methods that were able to be computed (filtering of methods done by decouple)
     scores_gt = {}
+    targets = {}
     for m in computed_methods:
         # estimates = res[m + 'estimate']
         # pvals = res[m + 'pvals']
 
         # remove experiments with no prediction for the perturbed TF
-        missing = list(set( metadata[meta_target_col]) - set(decoupler_results[m + '_estimate'].columns))
-        keep = [trgt not in missing for trgt in metadata[meta_target_col].to_list()]
+        missing = list(set( metadata[meta_perturbation_col]) - set(decoupler_results[m + '_estimate'].columns))
+        keep = [trgt not in missing for trgt in metadata[meta_perturbation_col].to_list()]
         meta = metadata[keep]
         estimates = decoupler_results[m + '_estimate'][keep]
         # pvals = res[m + '_pvals'][keep]
 
         # mirror estimates
         estimates = estimates.mul(meta['sign'], axis = 0)
-        gt = meta.pivot(columns = meta_target_col, values = 'sign').fillna(0)
+        gt = meta.pivot(columns = meta_perturbation_col, values = 'sign').fillna(0)
 
         # add 0s in the ground-truth array for targets predicted by decoupler
         # for which there is no ground truth in the provided metadata (assumed 0)
-        missing = list(set(estimates.columns) - set(meta[meta_target_col]))
+        missing = list(set(estimates.columns) - set(meta[meta_perturbation_col]))
         gt = pd.concat([gt, pd.DataFrame(0, index= gt.index, columns=missing)], axis = 1, join = 'inner').sort_index(axis=1)
 
         # flatten and then combine estimates and GT vectors
@@ -240,10 +250,11 @@ def get_scores_GT(decoupler_results, metadata, meta_target_col = 'target'):
         df_scores['GT'] = abs(df_scores['GT'])
 
         scores_gt[m] = df_scores
+        targets[m] = list(estimates.columns)
 
-    return scores_gt
+    return scores_gt, targets
 
-def run_benchmark(data, metadata, network, methods = None, metric = 'roc', meta_target_col = 'target', net_source_col = 'source', net_target_col = 'target', filter_experiments= True, filter_sources = False, **kwargs):
+def run_benchmark(data, metadata, network, methods = None, metric = 'roc', meta_perturbation_col = 'target', net_source_col = 'source', filter_experiments= True, filter_sources = False, **kwargs):
     """
     Benchmark methods or networks on a given set of perturbation experiments using activity inference with decoupler.
 
@@ -253,9 +264,8 @@ def run_benchmark(data, metadata, network, methods = None, metric = 'roc', meta_
         network (DataFrame): Network in long format passed on to the decouple function
         methods (str or list of str, optional): List of methods to run. If none are provided use weighted top performers (mlm, ulm and wsum). To benchmark all methods set to "all". Defaults to None.
         metric (str or list of str, optional): Performance metric(s) to compute. See the description of get_performance for more details. Defaults to 'roc'.
-        meta_target_col (str, optional): Column name in the metadata with perturbation targets. Defaults to 'target'.
+        meta_perturbation_col (str, optional): Column name in the metadata with perturbation targets. Defaults to 'target'.
         net_source_col (str, optional): Column name in network with source nodes. Defaults to 'source'.
-        net_target_col (str, optional): Column name in net with target nodes. Defaults to 'target'.
         filter_experiments (bool, optional): Whether to filter out experiments whose perturbed targets cannot be infered from the given network. Defaults to True.
         filter_sources (bool, optional): Whether to fitler out sources in the network for which there are not perturbation experiments (reduces the number of predictions made by decouple). Defaults to False.
         **kwargs: other arguments to pass on to get_performance (e.g. n_iter etc)
@@ -267,24 +277,27 @@ def run_benchmark(data, metadata, network, methods = None, metric = 'roc', meta_
 
     #subset by TFs with GT available
     if filter_sources:
-        keep = [src in metadata[meta_target_col].to_list() for src in network[net_source_col].to_list()]
+        keep = [src in metadata[meta_perturbation_col].to_list() for src in network[net_source_col].to_list()]
         network = network[keep]
 
     # filter out experiments without predictions available
     if filter_experiments:
-        keep = [trgt in network[net_target_col].to_list() for trgt in metadata[meta_target_col].to_list()]
+        keep = [trgt in network[net_source_col].to_list() for trgt in metadata[meta_perturbation_col].to_list()]
         data = data[keep]
         metadata = metadata[keep]
 
     # run prediction
-    res = dc.decouple(data, network, methods=methods)
+    res = dc.decouple(data, network, methods=methods, verbose = kwargs.get('verbose', True))
 
-    scores_gt = get_scores_GT(res, metadata, meta_target_col)
+    scores_gt, targets = get_scores_GT(res, metadata, meta_perturbation_col)
 
     bench = {}
     for method in scores_gt.keys():
-        print('Calculating performance metrics for', method)
-        perf = get_performance(scores_gt[method], metric, method_name= method, **kwargs)
+        if kwargs.get('verbose', True): print('Calculating performance metrics for', method)
+        if kwargs.get('by_target', False):
+            perf = get_target_performance(scores_gt[method], targets[method], metric, prefix = method, **kwargs)
+        else:
+            perf = get_performance(scores_gt[method], metric, prefix= method, **kwargs)
         bench.update(perf)
     
     #make dataframe with mean perfomances
@@ -293,12 +306,16 @@ def run_benchmark(data, metadata, network, methods = None, metric = 'roc', meta_
         mean_perfs[key]=np.mean(value)
     mean_perfs = pd.DataFrame.from_dict(mean_perfs, orient='index').reset_index(level=0)
     mean_perfs.columns = ['id','value']
-    mean_perfs[['method','metric']] = mean_perfs['id'].str.split('_', expand=True)
-    mean_perfs = mean_perfs.pivot(index='method', columns='metric', values='value')
+    if kwargs.get('by_target', False):
+        mean_perfs[['method','target','metric']] = mean_perfs['id'].str.split('_', expand=True)
+        mean_perfs = mean_perfs.pivot(index=['method','target'], columns='metric', values='value')
+    else:
+        mean_perfs[['method','metric']] = mean_perfs['id'].str.split('_', expand=True)
+        mean_perfs = mean_perfs.pivot(index='method', columns='metric', values='value')
 
-    return mean_perfs, bench
+    return mean_perfs.reset_index(), bench
 
-def benchmark_scatterplot(mean_perf, x = 'mcroc', y = 'mcprc'):
+def benchmark_scatterplot(mean_perf, x = 'mcroc', y = 'mcprc', label_col=None):
     """
     Creates a scatter plot for each given method for two performance metrics
 
@@ -325,8 +342,9 @@ def benchmark_scatterplot(mean_perf, x = 'mcroc', y = 'mcprc'):
     if (x in ['roc','mcroc'] and y in ['roc','mcroc']) or (x in ['prc','mcprc'] and y in ['prc','mcprc']):
         ax.axline((0,0),slope=1, color = 'black', linestyle = ':')
 
-    for i, label in enumerate(mean_perf.index):
-        ax.annotate(label.capitalize(), (mean_perf[x][i], mean_perf[y][i]))
+    if label_col is not None and label_col in mean_perf.columns:
+        for i, label in enumerate(mean_perf[label_col]):
+            ax.annotate(label.capitalize(), (mean_perf[x][i], mean_perf[y][i]))
 
     if x in ['mcroc', 'mcprc', 'roc', 'prc']:
         x = x + ' AUC'

From 4a43effc839db601871d5775c6912cb05210f2ae Mon Sep 17 00:00:00 2001
From: demian1 <demian1@users.noreply.github.com>
Date: Fri, 15 Jul 2022 14:25:42 +0200
Subject: [PATCH 04/33] Update utils_benchmark.py

changed some plotting functions
---
 decoupler/utils_benchmark.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/decoupler/utils_benchmark.py b/decoupler/utils_benchmark.py
index 4e4ae7e..1ec28ea 100644
--- a/decoupler/utils_benchmark.py
+++ b/decoupler/utils_benchmark.py
@@ -315,7 +315,7 @@ def run_benchmark(data, metadata, network, methods = None, metric = 'roc', meta_
 
     return mean_perfs.reset_index(), bench
 
-def benchmark_scatterplot(mean_perf, x = 'mcroc', y = 'mcprc', label_col=None):
+def benchmark_scatterplot(mean_perf, x = 'mcroc', y = 'mcprc', ax = None, label_col=None):
     """
     Creates a scatter plot for each given method for two performance metrics
 
@@ -327,8 +327,9 @@ def benchmark_scatterplot(mean_perf, x = 'mcroc', y = 'mcprc', label_col=None):
     Returns:
         ax: Axes of a scatter plot
     """
+    mean_perf = mean_perf.reset_index()
 
-    ax = plt.subplot(111)
+    if ax is None: ax = plt.subplot(111)
     ax.scatter(x = mean_perf[x], y = mean_perf[y])
     ax.set_aspect('equal')
 
@@ -357,7 +358,7 @@ def benchmark_scatterplot(mean_perf, x = 'mcroc', y = 'mcprc', label_col=None):
 
     return ax
 
-def benchmark_boxplot(benchmark_data, metric = 'mcroc'):
+def benchmark_boxplot(benchmark_data, metric = 'mcroc', ax = None):
     """
     Creates boxplots for an iterative performance metric (i.e. mcroc and mcprc)
 
@@ -378,8 +379,7 @@ def benchmark_boxplot(benchmark_data, metric = 'mcroc'):
     if len(keys) == 0:
         raise ValueError('The given metric was not found in the benchmark data')
 
-    fig = plt.figure()
-    ax = plt.subplot(111)
+    if ax is None: ax = plt.subplot(111)
     for i, key in enumerate(keys):
         ax.boxplot(benchmark_data[key], positions = [i])
     ax.set_xlim(-0.5, len(keys) - 0.5)

From 633f66024dec5cc0d6f0d0cfaf00361dc15aca5e Mon Sep 17 00:00:00 2001
From: demian1 <demian1@users.noreply.github.com>
Date: Mon, 18 Jul 2022 15:45:53 +0200
Subject: [PATCH 05/33] modularised and added calibrated PR AUC

---
 decoupler/utils_benchmark.py          | 148 ++++++++++++++++++--------
 decoupler/utils_calibrated_metrics.py | 122 +++++++++++++++++++++
 2 files changed, 223 insertions(+), 47 deletions(-)
 create mode 100644 decoupler/utils_calibrated_metrics.py

diff --git a/decoupler/utils_benchmark.py b/decoupler/utils_benchmark.py
index 1ec28ea..0114c25 100644
--- a/decoupler/utils_benchmark.py
+++ b/decoupler/utils_benchmark.py
@@ -7,6 +7,7 @@
 import pandas as pd
 from sklearn.preprocessing import label_binarize
 import decoupler as dc
+from .utils_calibrated_metrics import average_precision as calibrated_average_precision
 
 from sklearn.metrics import roc_auc_score, average_precision_score
 from numpy.random import default_rng
@@ -72,7 +73,7 @@ def down_sampling(y, seed=7, n = 100):
 Compute AUC of ROC or PRC
 """
 
-def get_auc(x, y, mode):
+def get_auc(x, y, mode, pi0 = None):
     """
     Computes AUROC for each label
     
@@ -95,6 +96,8 @@ def get_auc(x, y, mode):
         auc = roc_auc_score(x, y) 
     elif mode == "prc":
         auc = average_precision_score(x, y)
+    elif mode == 'calprc':
+        auc = calibrated_average_precision(x, y_pred= y, pi0=pi0)
     else: 
         raise ValueError("mode can only be roc or prc")
     return auc
@@ -137,7 +140,7 @@ def get_performance(data, metric = 'mcroc', n_iter = 100, seed = 42, prefix = No
 
     Args:
         data (DataFrame): DataFrame with a 'score' and 'GT' column. 'GT' column contains groud-truth ( e.g. 0, 1). 'score' can be continuous or same as 'GT'
-        metric (str or list of str, optional): Which metric(s) to use. Currently implemeted methods are: 'mcroc', 'mcprc', 'roc', 'prc'. Defaults to 'mcroc'.
+        metric (str or list of str, optional): Which metric(s) to use. Currently implemeted methods are: 'mcroc', 'mcprc', 'roc', 'prc', 'calprc'. Defaults to 'mcroc'.
         n_iter (int, optional): Number of iterations for the undersampling procedures for the 'mcroc' and 'mcprc' metrics. Defaults to 100.
         seed (int, optional): Seed used to generate the undersampling for the 'mcroc' and 'mcprc' metrics. Defaults to 42.
         prefix (str, optional): Added as prefix to the performance metric key in the output dictionary e.g. 'mlm_roc' if equal to 'mlm'. Defaults to 100.
@@ -146,7 +149,7 @@ def get_performance(data, metric = 'mcroc', n_iter = 100, seed = 42, prefix = No
         perf: Dict of prediction performance(s) on the given data. 'mcroc' and 'mcprc' metrics will return the values for each sampling. Other methods return a single value.
     """
 
-    available_metrics = ['mcroc', 'mcprc', 'roc', 'prc']
+    available_metrics = ['mcroc', 'mcprc', 'roc', 'prc', 'calprc']
     metrics = [available_metrics[i] for i in np.argwhere(np.in1d(available_metrics, metric)).flatten().tolist()]
 
     if len(metrics) == 0:
@@ -166,9 +169,9 @@ def get_performance(data, metric = 'mcroc', n_iter = 100, seed = 42, prefix = No
                             mode = met[2:])
                 aucs.append(auc)
 
-        elif met == 'roc' or met == 'prc':
+        elif met == 'roc' or met == 'prc' or met == 'calprc':
             # Compute AUC on the whole (unequalised class priors) data
-            aucs = get_auc(x = data['GT'], y = data['score'], mode = met)
+            aucs = get_auc(x = data['GT'], y = data['score'], mode = met, pi0 = kwargs.get('pi0', None))
         
         if prefix is None:
             perf[met] = aucs
@@ -207,7 +210,7 @@ def get_target_performance(data, targets, metric='mcroc', subset = None, n_iter
 
     return perf
 
-def get_scores_GT(decoupler_results, metadata, meta_perturbation_col = 'target'):
+def get_scores_GT(decoupler_results, metadata):
     """
 
     Convert decouple output to flattenend vectors and combine with GT information
@@ -229,19 +232,19 @@ def get_scores_GT(decoupler_results, metadata, meta_perturbation_col = 'target')
         # pvals = res[m + 'pvals']
 
         # remove experiments with no prediction for the perturbed TF
-        missing = list(set( metadata[meta_perturbation_col]) - set(decoupler_results[m + '_estimate'].columns))
-        keep = [trgt not in missing for trgt in metadata[meta_perturbation_col].to_list()]
+        missing = list(set(metadata['source']) - set(decoupler_results[m + '_estimate'].columns))
+        keep = [trgt not in missing for trgt in metadata['source'].to_list()]
         meta = metadata[keep]
         estimates = decoupler_results[m + '_estimate'][keep]
         # pvals = res[m + '_pvals'][keep]
 
         # mirror estimates
         estimates = estimates.mul(meta['sign'], axis = 0)
-        gt = meta.pivot(columns = meta_perturbation_col, values = 'sign').fillna(0)
+        gt = meta.pivot(columns = 'source', values = 'sign').fillna(0)
 
         # add 0s in the ground-truth array for targets predicted by decoupler
         # for which there is no ground truth in the provided metadata (assumed 0)
-        missing = list(set(estimates.columns) - set(meta[meta_perturbation_col]))
+        missing = list(set(estimates.columns) - set(meta['source']))
         gt = pd.concat([gt, pd.DataFrame(0, index= gt.index, columns=missing)], axis = 1, join = 'inner').sort_index(axis=1)
 
         # flatten and then combine estimates and GT vectors
@@ -254,6 +257,81 @@ def get_scores_GT(decoupler_results, metadata, meta_perturbation_col = 'target')
 
     return scores_gt, targets
 
+def format_benchmark_data(data, metadata, network, meta_perturbation_col = 'treatment', metadata_sign_col = 'sign', net_source_col = 'source', net_weight_col = 'weight', filter_experiments = True, filter_sources = False):
+
+    if not all(item in network.columns for item in [net_source_col, net_weight_col]):
+        missing = list(set([net_source_col, net_weight_col]) - set(network.columns))
+        raise ValueError('{0} column(s) are missing from the input network'.format(str(missing)))
+
+    if not all(item in metadata.columns for item in [meta_perturbation_col, metadata_sign_col]):
+        missing = list(set([meta_perturbation_col, metadata_sign_col]) - set(metadata.columns))
+        raise ValueError('{0} column(s) are missing from the input metadata'.format(str(missing)))
+
+    network = network.rename(columns={net_source_col:'source', net_weight_col:'weight'})
+    metadata = metadata.rename(columns={meta_perturbation_col:'source', metadata_sign_col:'sign'})
+
+    #subset by TFs with GT available
+    if filter_sources:
+        keep = [src in metadata['source'].to_list() for src in network['source'].to_list()]
+        network = network[keep]
+
+    # filter out experiments without predictions available
+    if filter_experiments:
+        keep = [src in network['source'].to_list() for src in metadata['source'].to_list()]
+        data = data[keep]
+        metadata = metadata[keep]
+
+    return data, metadata, network
+
+def performances(flat_data, sources, metric = 'roc', by = 'method', verbose = True, **kwargs):
+    bench = {}
+    kwargs['verbose'] = verbose
+
+    for method in flat_data.keys():
+        if verbose: print('Calculating performance metrics for', method)
+        if 'method' in by or 'all' in by:
+            perf = get_performance(flat_data[method], metric, prefix= method, **kwargs)
+            bench.update(perf)
+        if 'source' in by or 'all' in by:
+            perf = get_target_performance(flat_data[method], sources[method], metric, prefix = method, **kwargs)
+            bench[method + '_bySource'] = perf
+
+    return bench
+
+def get_mean_performances(benchmark_dict):
+    #make dataframes with mean perfomances
+    perf_method = {}
+    perf_bySource = {}
+    for topkey, topvalue in benchmark_dict.items():
+        if '_bySource' in topkey:
+            for key, value in topvalue.items():
+                perf_bySource[key] = np.mean(value)
+        else:
+            perf_method[topkey] = np.mean(topvalue)
+
+    perfs = []
+
+    if len(perf_method) > 0:
+        perf_method = pd.DataFrame.from_dict(perf_method, orient='index').reset_index()
+        perf_method.columns = ['id','value']
+        perf_method[['method','metric']] = perf_method['id'].str.split('_', expand=True)
+        perf_method = perf_method.pivot(index='method', columns='metric', values='value').reset_index()
+        perfs.append(perf_method)
+
+    if len(perf_bySource) > 0:
+        perf_bySource = pd.DataFrame.from_dict(perf_bySource, orient='index').reset_index()
+        perf_bySource.columns = ['id','value']
+        perf_bySource[['method','source','metric']] = perf_bySource['id'].str.split('_', expand=True)
+        perf_bySource = perf_bySource.pivot(index=['method','source'], columns='metric', values='value').reset_index()
+        perfs.append(perf_bySource)
+
+    mean_perf = pd.concat(perfs)
+    if 'source' in mean_perf.columns:
+        mean_perf['source'] = mean_perf['source'].fillna('overall')
+
+    return mean_perf.reset_index()
+
+
 def run_benchmark(data, metadata, network, methods = None, metric = 'roc', meta_perturbation_col = 'target', net_source_col = 'source', filter_experiments= True, filter_sources = False, **kwargs):
     """
     Benchmark methods or networks on a given set of perturbation experiments using activity inference with decoupler.
@@ -275,45 +353,21 @@ def run_benchmark(data, metadata, network, methods = None, metric = 'roc', meta_
         bench: dict containing the whole data for each method and metric. Useful if you want to see the distribution for each subsampling for the mcroc and mcprc methods
     """
 
-    #subset by TFs with GT available
-    if filter_sources:
-        keep = [src in metadata[meta_perturbation_col].to_list() for src in network[net_source_col].to_list()]
-        network = network[keep]
+    #format and filter the data, metadata and networks
+    data, metadata, network = format_benchmark_data(data, metadata, network, meta_perturbation_col=meta_perturbation_col,
+                                                    net_source_col=net_source_col, filter_experiments=filter_experiments, filter_sources=filter_sources)
 
-    # filter out experiments without predictions available
-    if filter_experiments:
-        keep = [trgt in network[net_source_col].to_list() for trgt in metadata[meta_perturbation_col].to_list()]
-        data = data[keep]
-        metadata = metadata[keep]
+    #run prediction
+    res = dc.decouple(data, network, methods=methods, args = kwargs.get('args', {}), verbose = kwargs.get('verbose', True))
 
-    # run prediction
-    res = dc.decouple(data, network, methods=methods, verbose = kwargs.get('verbose', True))
+    #
+    scores_gt, sources = get_scores_GT(res, metadata)
 
-    scores_gt, targets = get_scores_GT(res, metadata, meta_perturbation_col)
-
-    bench = {}
-    for method in scores_gt.keys():
-        if kwargs.get('verbose', True): print('Calculating performance metrics for', method)
-        if kwargs.get('by_target', False):
-            perf = get_target_performance(scores_gt[method], targets[method], metric, prefix = method, **kwargs)
-        else:
-            perf = get_performance(scores_gt[method], metric, prefix= method, **kwargs)
-        bench.update(perf)
+    bench = performances(scores_gt, sources, metric = metric, **kwargs)
     
-    #make dataframe with mean perfomances
-    mean_perfs = {}
-    for key, value in bench.items():
-        mean_perfs[key]=np.mean(value)
-    mean_perfs = pd.DataFrame.from_dict(mean_perfs, orient='index').reset_index(level=0)
-    mean_perfs.columns = ['id','value']
-    if kwargs.get('by_target', False):
-        mean_perfs[['method','target','metric']] = mean_perfs['id'].str.split('_', expand=True)
-        mean_perfs = mean_perfs.pivot(index=['method','target'], columns='metric', values='value')
-    else:
-        mean_perfs[['method','metric']] = mean_perfs['id'].str.split('_', expand=True)
-        mean_perfs = mean_perfs.pivot(index='method', columns='metric', values='value')
+    mean_perfs = get_mean_performances(bench)
 
-    return mean_perfs.reset_index(), bench
+    return mean_perfs, bench
 
 def benchmark_scatterplot(mean_perf, x = 'mcroc', y = 'mcprc', ax = None, label_col=None):
     """
@@ -340,17 +394,17 @@ def benchmark_scatterplot(mean_perf, x = 'mcroc', y = 'mcprc', ax = None, label_
     ax.set_xlim(min_v - border, max_v + border)
     ax.set_ylim(min_v - border, max_v + border)
 
-    if (x in ['roc','mcroc'] and y in ['roc','mcroc']) or (x in ['prc','mcprc'] and y in ['prc','mcprc']):
+    if (x in ['roc','mcroc'] and y in ['roc','mcroc']) or (x in ['prc','mcprc', 'calprc'] and y in ['prc','mcprc', 'calprc']):
         ax.axline((0,0),slope=1, color = 'black', linestyle = ':')
 
     if label_col is not None and label_col in mean_perf.columns:
         for i, label in enumerate(mean_perf[label_col]):
             ax.annotate(label.capitalize(), (mean_perf[x][i], mean_perf[y][i]))
 
-    if x in ['mcroc', 'mcprc', 'roc', 'prc']:
+    if x in ['mcroc', 'mcprc', 'roc', 'prc', 'calprc']:
         x = x + ' AUC'
 
-    if y in ['mcroc', 'mcprc', 'roc', 'prc']:
+    if y in ['mcroc', 'mcprc', 'roc', 'prc', 'calprc']:
         y = y + ' AUC'
 
     ax.set_xlabel(x.upper())
diff --git a/decoupler/utils_calibrated_metrics.py b/decoupler/utils_calibrated_metrics.py
new file mode 100644
index 0000000..4c39e26
--- /dev/null
+++ b/decoupler/utils_calibrated_metrics.py
@@ -0,0 +1,122 @@
+# taken directly from: https://github.com/wissam-sib/calibrated_metrics/blob/f1c262d0aaac16356a143596a832030abb87d48c/calibrated_metrics.py
+# Siblini W., Fréry J., He-Guelton L., Oblé F., Wang YQ. (2020) Master Your Metrics with Calibration. In: Berthold M., Feelders A., Krempl G. (eds) Advances in Intelligent Data Analysis XVIII. IDA 2020. Lecture Notes in Computer Science, vol 12080. Springer, Cham
+
+import numpy as np
+from sklearn.metrics._ranking import _binary_clf_curve
+from sklearn.metrics import confusion_matrix
+
+def precision_recall_curve(y_true, y_pred, pos_label=None,
+                           sample_weight=None,pi0=None):
+    """Compute precision-recall (with optional calibration) pairs for different probability thresholds
+    This implementation is a modification of scikit-learn "precision_recall_curve" function that adds calibration
+    ----------
+    y_true : array, shape = [n_samples]
+        True binary labels. If labels are not either {-1, 1} or {0, 1}, then
+        pos_label should be explicitly given.
+    probas_pred : array, shape = [n_samples]
+        Estimated probabilities or decision function.
+    pos_label : int or str, default=None
+        The label of the positive class.
+        When ``pos_label=None``, if y_true is in {-1, 1} or {0, 1},
+        ``pos_label`` is set to 1, otherwise an error will be raised.
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+    Returns
+    -------
+    calib_precision : array, shape = [n_thresholds + 1]
+        Calibrated Precision values such that element i is the calibrated precision of
+        predictions with score >= thresholds[i] and the last element is 1.
+    recall : array, shape = [n_thresholds + 1]
+        Decreasing recall values such that element i is the recall of
+        predictions with score >= thresholds[i] and the last element is 0.
+    thresholds : array, shape = [n_thresholds <= len(np.unique(probas_pred))]
+        Increasing thresholds on the decision function used to compute
+        precision and recall.
+    """
+    
+    fps, tps, thresholds = _binary_clf_curve(y_true, y_pred,
+                                             pos_label=pos_label,
+                                             sample_weight=sample_weight)
+    
+   
+    
+    
+    if pi0 is not None:
+        pi = np.sum(y_true)/float(np.array(y_true).shape[0])
+        ratio = pi*(1-pi0)/(pi0*(1-pi))
+        precision = tps / (tps + ratio*fps)
+    else:
+        precision = tps / (tps + fps)
+    
+    precision[np.isnan(precision)] = 0
+        
+    recall = tps / tps[-1]
+
+    # stop when full recall attained
+    # and reverse the outputs so recall is decreasing
+    last_ind = tps.searchsorted(tps[-1])
+    sl = slice(last_ind, None, -1)
+    return np.r_[precision[sl], 1], np.r_[recall[sl], 0], thresholds[sl]
+
+
+def average_precision(y_true, y_pred, pos_label=1, sample_weight=None,pi0=None):
+        precision, recall, _ = precision_recall_curve(y_true, y_pred, pos_label=pos_label, sample_weight=sample_weight, pi0=pi0)
+        return -np.sum(np.diff(recall) * np.array(precision)[:-1])
+    
+    
+def f1score(y_true, y_pred, pi0=None):
+    """
+    ----------
+    y_true : 1d array-like, or label indicator array / sparse matrix
+        Ground truth (correct) target values.
+    y_pred : 1d array-like, or label indicator array / sparse matrix
+        Estimated targets as returned by a classifier. (must be binary)
+    pi0 : float, None by default
+        The reference ratio for calibration
+    """
+    CM = confusion_matrix(y_true, y_pred)
+    
+    tn = CM[0][0]
+    fn = CM[1][0]
+    tp = CM[1][1]
+    fp = CM[0][1] 
+        
+    pos = fn + tp
+    
+    recall = tp / float(pos)
+    
+    if pi0 is not None:
+        pi = pos/float(tn + fn + tp + fp)
+        ratio = pi*(1-pi0)/(pi0*(1-pi))
+        precision = tp / float(tp + ratio*fp)
+    else:
+        precision = tp / float(tp + fp)
+    
+    if np.isnan(precision):
+        precision = 0
+    
+    if (precision+recall)==0.0:
+        f=0.0
+    else:
+        f = (2*precision*recall)/(precision+recall)
+
+    return f
+
+
+def bestf1score(y_true, y_pred, pi0=None):
+    """
+    ----------
+    y_true : 1d array-like, or label indicator array / sparse matrix
+        Ground truth (correct) target values.
+    y_pred : 1d array-like, or label indicator array / sparse matrix
+        Estimated targets as returned by a classifier.
+    pi0 : float, None by default
+        The reference ratio for calibration
+    """
+    
+    precision, recall, _ = precision_recall_curve(y_true, y_pred, pi0=pi0)
+    
+    fscores = (2*precision*recall)/(precision+recall)
+    fscores = np.nan_to_num(fscores,nan=0, posinf=0, neginf=0)
+    
+    return np.max(fscores)
\ No newline at end of file

From 67914448a2249298e26ba7456adc423dc708900d Mon Sep 17 00:00:00 2001
From: demian1 <demian1@users.noreply.github.com>
Date: Thu, 21 Jul 2022 10:34:22 +0200
Subject: [PATCH 06/33] Update utils_benchmark.py

added performance measure on positive and negative perturbations separately
added class imbalance as an output
---
 decoupler/utils_benchmark.py | 115 ++++++++++++++++++++++++-----------
 1 file changed, 80 insertions(+), 35 deletions(-)

diff --git a/decoupler/utils_benchmark.py b/decoupler/utils_benchmark.py
index 0114c25..8d2e43b 100644
--- a/decoupler/utils_benchmark.py
+++ b/decoupler/utils_benchmark.py
@@ -98,49 +98,58 @@ def get_auc(x, y, mode, pi0 = None):
         auc = average_precision_score(x, y)
     elif mode == 'calprc':
         auc = calibrated_average_precision(x, y_pred= y, pi0=pi0)
+    elif mode == 'ci':
+        auc = x.sum()/x.size
     else: 
         raise ValueError("mode can only be roc or prc")
     return auc
 
-def get_target_masks(long_data, targets, subset = None):
+def get_source_masks(long_data, sources, subset = None, min_exp = 5):
     """
-    Generates a list of indices of a DataFrame that correspond to prediction scores and associated ground-truth for each target
+    Generates a list of indices of a DataFrame that correspond to prediction scores and associated ground-truth for each source
 
     Args:
         long_data (DataFrame): DataFrame with a 'score' and 'GT' column.
-        targets (list): List of targets (have to be in correct order) for which there are entries in long_data.
-        subset (list, optional): A subset of the targets for which to make masks. If None, then the masks will be made for all targets. Defaults to None.
+        sources (list): List of sources (have to be in correct order) for which there are entries in long_data.
+        subset (list, optional): A subset of the sources for which to make masks. If None, then the masks will be made for all targets. Defaults to None.
+        min_exp (int, optional): The minimum number of perturbation experiments required to compute an individual source performance score. Defaults to 5.
 
     Returns:
         target_ind: List of data indices for each target in the targets object
         target_names : Target name corresponding to the elements in target_ind. Elements in subset are filtered and put in same order as in targets.
     """
 
-    if long_data.shape[0] < len(targets):
+    if long_data.shape[0] < len(sources):
         raise ValueError('The data given is smaller than the number of targets')
-    elif long_data.shape[0] % len(targets) != 0:
+    elif long_data.shape[0] % len(sources) != 0:
         raise ValueError('The data is likely misshapen: the number of rows cannot be divided by the number of targets')
 
     if subset is not None:
-        iterateover = np.argwhere(np.in1d(targets, subset)).flatten().tolist()
-        target_names = [targets[i] for i in iterateover]
+        iterateover = np.argwhere(np.in1d(sources, subset)).flatten().tolist()
+        source_names = [sources[i] for i in iterateover]
     else:
-        iterateover = range(len(targets))
-        target_names = targets
-
-    target_ind = []
-    for target in iterateover:
-        target_ind.append(np.arange(target, long_data.shape[0], len(targets)))
-
-    return target_ind, target_names
-
-def get_performance(data, metric = 'mcroc', n_iter = 100, seed = 42, prefix = None, **kwargs):
+        iterateover = range(len(sources))
+        source_names = sources
+
+    source_ind = []
+    names = []
+    #create indexes
+    for id, name in zip(iterateover, source_names):
+        ind = np.arange(id, long_data.shape[0], len(sources))
+        # check that there is at least min_exp perturbations
+        if long_data.iloc[ind,:]['GT'].sum() >= min_exp:
+            source_ind.append(ind)
+            names.append(name)
+
+    return source_ind, names
+
+def get_performance(data, metric = 'roc', n_iter = 100, seed = 42, prefix = None, **kwargs):
     """
     Compute binary classifier performance
 
     Args:
         data (DataFrame): DataFrame with a 'score' and 'GT' column. 'GT' column contains groud-truth ( e.g. 0, 1). 'score' can be continuous or same as 'GT'
-        metric (str or list of str, optional): Which metric(s) to use. Currently implemeted methods are: 'mcroc', 'mcprc', 'roc', 'prc', 'calprc'. Defaults to 'mcroc'.
+        metric (str or list of str, optional): Which metric(s) to use. Currently implemeted methods are: 'mcroc', 'mcprc', 'roc', 'prc', 'calprc' and 'ci'. Defaults to 'roc'.
         n_iter (int, optional): Number of iterations for the undersampling procedures for the 'mcroc' and 'mcprc' metrics. Defaults to 100.
         seed (int, optional): Seed used to generate the undersampling for the 'mcroc' and 'mcprc' metrics. Defaults to 42.
         prefix (str, optional): Added as prefix to the performance metric key in the output dictionary e.g. 'mlm_roc' if equal to 'mlm'. Defaults to 100.
@@ -149,7 +158,7 @@ def get_performance(data, metric = 'mcroc', n_iter = 100, seed = 42, prefix = No
         perf: Dict of prediction performance(s) on the given data. 'mcroc' and 'mcprc' metrics will return the values for each sampling. Other methods return a single value.
     """
 
-    available_metrics = ['mcroc', 'mcprc', 'roc', 'prc', 'calprc']
+    available_metrics = ['mcroc', 'mcprc', 'roc', 'prc', 'calprc', 'ci']
     metrics = [available_metrics[i] for i in np.argwhere(np.in1d(available_metrics, metric)).flatten().tolist()]
 
     if len(metrics) == 0:
@@ -169,7 +178,7 @@ def get_performance(data, metric = 'mcroc', n_iter = 100, seed = 42, prefix = No
                             mode = met[2:])
                 aucs.append(auc)
 
-        elif met == 'roc' or met == 'prc' or met == 'calprc':
+        elif met == 'roc' or met == 'prc' or met == 'calprc' or met == 'ci':
             # Compute AUC on the whole (unequalised class priors) data
             aucs = get_auc(x = data['GT'], y = data['score'], mode = met, pi0 = kwargs.get('pi0', None))
         
@@ -180,9 +189,9 @@ def get_performance(data, metric = 'mcroc', n_iter = 100, seed = 42, prefix = No
 
     return perf
 
-def get_target_performance(data, targets, metric='mcroc', subset = None, n_iter = 100, seed = 42, prefix = None, **kwargs):
+def get_source_performance(data, sources, metric='mcroc', subset = None, n_iter = 100, seed = 42, prefix = None, **kwargs):
     """
-    Compute binary classifier performance for each target or subet of targets
+    Compute binary classifier performance for each source or susbet of sources
 
     Args:
         data (DataFrame): DataFrame with a 'score' and 'GT' column. 'GT' column contains groud-truth ( e.g. 0, 1). 'score' can be continuous or same as 'GT'
@@ -197,10 +206,10 @@ def get_target_performance(data, targets, metric='mcroc', subset = None, n_iter
         perf : Dict of prediction performance(s) for each target or subset of targets. 'mcroc' and 'mcprc' metrics will return the values for each sampling. Other methods return a single value.
     """
 
-    masks, target_names = get_target_masks(data, targets, subset = subset)
+    masks, source_names = get_source_masks(data, sources, subset = subset, min_exp = kwargs.get('min_exp', 5))
 
     perf = {}
-    for trgt, name in zip(masks, target_names):
+    for trgt, name in zip(masks, source_names):
         if prefix is None:
             p = name
         else:
@@ -210,7 +219,7 @@ def get_target_performance(data, targets, metric='mcroc', subset = None, n_iter
 
     return perf
 
-def get_scores_GT(decoupler_results, metadata):
+def get_scores_GT(decoupler_results, metadata, by = None, min_exp = 5):
     """
 
     Convert decouple output to flattenend vectors and combine with GT information
@@ -219,6 +228,9 @@ def get_scores_GT(decoupler_results, metadata):
         decoupler_results (dict): Output of decouple
         metadata (DataFrame): Metadata of the perturbation experiment containing the activated/inhibited targets and the sign of the perturbation
         meta_perturbation_col (str, optional): Column name in the metadata with perturbation targets. Defaults to 'target'.
+        by (str or list of str, optional): How to decompose performance. By 'sign' will also subselect scores of activating/inhibiting perturbations specifically, in addition to the overall scores. Defaults to None.
+        min_exp (int, optional): Min number of perturbation experiments in order to compute score. Defaults to 5.
+
 
     Returns:
         scores_gt: dict of flattenend dataframes for each method
@@ -227,6 +239,7 @@ def get_scores_GT(decoupler_results, metadata):
     computed_methods = list(set([i.split('_')[0] for i in decoupler_results.keys()])) # get the methods that were able to be computed (filtering of methods done by decouple)
     scores_gt = {}
     targets = {}
+
     for m in computed_methods:
         # estimates = res[m + 'estimate']
         # pvals = res[m + 'pvals']
@@ -247,18 +260,34 @@ def get_scores_GT(decoupler_results, metadata):
         missing = list(set(estimates.columns) - set(meta['source']))
         gt = pd.concat([gt, pd.DataFrame(0, index= gt.index, columns=missing)], axis = 1, join = 'inner').sort_index(axis=1)
 
+        flat_scores = []
+        scores_names = [m]
+
         # flatten and then combine estimates and GT vectors
         # set ground truth to be either 0 or 1
         df_scores = pd.DataFrame({'score': estimates.to_numpy().flatten(), 'GT': gt.to_numpy().flatten()})
-        df_scores['GT'] = abs(df_scores['GT'])
+        flat_scores.append(df_scores)
+
+        if by is not None and 'sign' in by:
+            pos_scores = df_scores[df_scores['GT'] >= 0].copy()
+            scores_names.append(m + '_positive')
+            flat_scores.append(pos_scores)
+            neg_scores = df_scores[df_scores['GT'] <= 0].copy()
+            flat_scores.append(neg_scores)
+            scores_names.append(m + '_negative')
+
+        for score, name in zip(flat_scores, scores_names):
+            score['GT'] = abs(score['GT'])
+            if score['GT'].sum() > min_exp:
+                scores_gt[name] = score.reset_index()
+                targets[name] = list(estimates.columns)
 
-        scores_gt[m] = df_scores
-        targets[m] = list(estimates.columns)
 
     return scores_gt, targets
 
 def format_benchmark_data(data, metadata, network, meta_perturbation_col = 'treatment', metadata_sign_col = 'sign', net_source_col = 'source', net_weight_col = 'weight', filter_experiments = True, filter_sources = False):
 
+
     if not all(item in network.columns for item in [net_source_col, net_weight_col]):
         missing = list(set([net_source_col, net_weight_col]) - set(network.columns))
         raise ValueError('{0} column(s) are missing from the input network'.format(str(missing)))
@@ -289,11 +318,11 @@ def performances(flat_data, sources, metric = 'roc', by = 'method', verbose = Tr
 
     for method in flat_data.keys():
         if verbose: print('Calculating performance metrics for', method)
-        if 'method' in by or 'all' in by:
+        if 'method' in by or 'sign' in by or 'all' in by:
             perf = get_performance(flat_data[method], metric, prefix= method, **kwargs)
             bench.update(perf)
-        if 'source' in by or 'all' in by:
-            perf = get_target_performance(flat_data[method], sources[method], metric, prefix = method, **kwargs)
+        if ('source' in by or 'all' in by) and ('_positive' not in method and '_negative' not in method):
+            perf = get_source_performance(flat_data[method], sources[method], metric, prefix = method, **kwargs)
             bench[method + '_bySource'] = perf
 
     return bench
@@ -302,10 +331,13 @@ def get_mean_performances(benchmark_dict):
     #make dataframes with mean perfomances
     perf_method = {}
     perf_bySource = {}
+    perf_bySign = {}
     for topkey, topvalue in benchmark_dict.items():
         if '_bySource' in topkey:
             for key, value in topvalue.items():
                 perf_bySource[key] = np.mean(value)
+        elif '_negative' in topkey or '_positive' in topkey:
+            perf_bySign[topkey] = np.mean(topvalue)
         else:
             perf_method[topkey] = np.mean(topvalue)
 
@@ -317,6 +349,13 @@ def get_mean_performances(benchmark_dict):
         perf_method[['method','metric']] = perf_method['id'].str.split('_', expand=True)
         perf_method = perf_method.pivot(index='method', columns='metric', values='value').reset_index()
         perfs.append(perf_method)
+    
+    if len(perf_bySign) > 0:
+        perf_bySign = pd.DataFrame.from_dict(perf_bySign, orient='index').reset_index()
+        perf_bySign.columns = ['id','value']
+        perf_bySign[['method', 'sign','metric']] = perf_bySign['id'].str.split('_', expand=True)
+        perf_bySign = perf_bySign.pivot(index=['method','sign'], columns='metric', values='value').reset_index()
+        perfs.append(perf_bySign)
 
     if len(perf_bySource) > 0:
         perf_bySource = pd.DataFrame.from_dict(perf_bySource, orient='index').reset_index()
@@ -328,6 +367,8 @@ def get_mean_performances(benchmark_dict):
     mean_perf = pd.concat(perfs)
     if 'source' in mean_perf.columns:
         mean_perf['source'] = mean_perf['source'].fillna('overall')
+    if 'sign' in mean_perf.columns:
+        mean_perf['sign'] = mean_perf['sign'].fillna('overall')
 
     return mean_perf.reset_index()
 
@@ -361,7 +402,7 @@ def run_benchmark(data, metadata, network, methods = None, metric = 'roc', meta_
     res = dc.decouple(data, network, methods=methods, args = kwargs.get('args', {}), verbose = kwargs.get('verbose', True))
 
     #
-    scores_gt, sources = get_scores_GT(res, metadata)
+    scores_gt, sources = get_scores_GT(res, metadata, by=kwargs.get('by', None), min_exp = kwargs.get('min_exp', 5))
 
     bench = performances(scores_gt, sources, metric = metric, **kwargs)
     
@@ -369,7 +410,7 @@ def run_benchmark(data, metadata, network, methods = None, metric = 'roc', meta_
 
     return mean_perfs, bench
 
-def benchmark_scatterplot(mean_perf, x = 'mcroc', y = 'mcprc', ax = None, label_col=None):
+def benchmark_scatterplot(mean_perf, x = 'mcroc', y = 'mcprc', ax = None, label_col=None, ann_fontsize = None):
     """
     Creates a scatter plot for each given method for two performance metrics
 
@@ -399,7 +440,10 @@ def benchmark_scatterplot(mean_perf, x = 'mcroc', y = 'mcprc', ax = None, label_
 
     if label_col is not None and label_col in mean_perf.columns:
         for i, label in enumerate(mean_perf[label_col]):
-            ax.annotate(label.capitalize(), (mean_perf[x][i], mean_perf[y][i]))
+            if ann_fontsize is None:
+                ax.annotate(label.capitalize(), (mean_perf[x][i], mean_perf[y][i]))
+            else:
+                ax.annotate(label.capitalize(), (mean_perf[x][i], mean_perf[y][i]), fontsize = ann_fontsize)
 
     if x in ['mcroc', 'mcprc', 'roc', 'prc', 'calprc']:
         x = x + ' AUC'
@@ -427,6 +471,7 @@ def benchmark_boxplot(benchmark_data, metric = 'mcroc', ax = None):
     if not (metric == 'mcprc' or metric == 'mcroc'):
         raise ValueError('Plotting of boxplots only possible for the \'mcprc\' and \'mcroc\' methods')
 
+    #TODO: change so that input format corresponds again. Since mc is not that useful anymore, repurpose for target by target boxplots ?
     keys = [key for key in benchmark_data.keys() if metric in key.split('_')[1]]
     methods = [key.split('_')[0] for key in keys]
 

From 0683a16004fedbe3485109d3e8e3df234f70d469 Mon Sep 17 00:00:00 2001
From: demian1 <demian1@users.noreply.github.com>
Date: Thu, 21 Jul 2022 16:59:46 +0200
Subject: [PATCH 07/33] Update utils_benchmark.py

corrected how positive and negative performances were computed
---
 decoupler/utils_benchmark.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/decoupler/utils_benchmark.py b/decoupler/utils_benchmark.py
index 8d2e43b..8ef0bc6 100644
--- a/decoupler/utils_benchmark.py
+++ b/decoupler/utils_benchmark.py
@@ -269,12 +269,13 @@ def get_scores_GT(decoupler_results, metadata, by = None, min_exp = 5):
         flat_scores.append(df_scores)
 
         if by is not None and 'sign' in by:
-            pos_scores = df_scores[df_scores['GT'] >= 0].copy()
-            scores_names.append(m + '_positive')
-            flat_scores.append(pos_scores)
-            neg_scores = df_scores[df_scores['GT'] <= 0].copy()
-            flat_scores.append(neg_scores)
-            scores_names.append(m + '_negative')
+            keep = [meta['sign'] == 1, meta['sign'] == - 1]
+            sign = ['_positive', '_negative']
+
+            for ind, s in zip(keep, sign):
+                df = pd.DataFrame({'score': estimates[ind].to_numpy().flatten(), 'GT': gt[ind].to_numpy().flatten()})
+                flat_scores.append(df)
+                scores_names.append(m + s)
 
         for score, name in zip(flat_scores, scores_names):
             score['GT'] = abs(score['GT'])

From dcb5e5334935081885443dde7ecd943eec7e673a Mon Sep 17 00:00:00 2001
From: demian1 <demian1@users.noreply.github.com>
Date: Thu, 21 Jul 2022 22:24:50 +0200
Subject: [PATCH 08/33] benchmark module first version

- added arguments of all functions wrapped by run_benchmark
- added run_benchmark to decouple import
---
 decoupler/__init__.py        |  1 +
 decoupler/utils_benchmark.py | 69 ++++++++++++++++++++++--------------
 2 files changed, 43 insertions(+), 27 deletions(-)

diff --git a/decoupler/__init__.py b/decoupler/__init__.py
index e597a71..7f41686 100644
--- a/decoupler/__init__.py
+++ b/decoupler/__init__.py
@@ -18,6 +18,7 @@
 from .consensus import run_consensus  # noqa: F401
 from .omnip import show_resources, get_resource, get_progeny, get_dorothea  # noqa: F401
 from .plotting import plot_volcano  # noqa: F401
+from .utils_benchmark import format_benchmark_data, get_scores_GT, performances, get_mean_performances, run_benchmark
 
 # External libraries go out of main setup
 try:
diff --git a/decoupler/utils_benchmark.py b/decoupler/utils_benchmark.py
index 8ef0bc6..e49d469 100644
--- a/decoupler/utils_benchmark.py
+++ b/decoupler/utils_benchmark.py
@@ -85,6 +85,9 @@ def get_auc(x, y, mode, pi0 = None):
         
     y: array (flattened)
         vector of continuous values
+
+    pi0: float
+        Reference ratio for calibrated metrics. Corresponds to the baseline/reference class inbalance to which to set the metric. Defaults to None.
         
         
     Returns
@@ -143,7 +146,7 @@ def get_source_masks(long_data, sources, subset = None, min_exp = 5):
 
     return source_ind, names
 
-def get_performance(data, metric = 'roc', n_iter = 100, seed = 42, prefix = None, **kwargs):
+def get_performance(data, metric = 'roc', n_iter = 100, seed = 42, prefix = None, pi0 = 0.5):
     """
     Compute binary classifier performance
 
@@ -152,7 +155,8 @@ def get_performance(data, metric = 'roc', n_iter = 100, seed = 42, prefix = None
         metric (str or list of str, optional): Which metric(s) to use. Currently implemeted methods are: 'mcroc', 'mcprc', 'roc', 'prc', 'calprc' and 'ci'. Defaults to 'roc'.
         n_iter (int, optional): Number of iterations for the undersampling procedures for the 'mcroc' and 'mcprc' metrics. Defaults to 100.
         seed (int, optional): Seed used to generate the undersampling for the 'mcroc' and 'mcprc' metrics. Defaults to 42.
-        prefix (str, optional): Added as prefix to the performance metric key in the output dictionary e.g. 'mlm_roc' if equal to 'mlm'. Defaults to 100.
+        prefix (str, optional): Added as prefix to the performance metric key in the output dictionary e.g. 'mlm_roc' if equal to 'mlm'. Defaults to None.
+        pi0 (float, optional): Reference ratio used to calculate calibrated metrics. Must be between 0 and 1. Defaults to 0.5.
 
     Returns:
         perf: Dict of prediction performance(s) on the given data. 'mcroc' and 'mcprc' metrics will return the values for each sampling. Other methods return a single value.
@@ -172,7 +176,7 @@ def get_performance(data, metric = 'roc', n_iter = 100, seed = 42, prefix = None
         if met == 'mcroc' or met == 'mcprc':
             # Compute AUC for each mask (with equalised class priors)
             aucs = []
-            for mask in tqdm(masks, disable= not kwargs.get('verbose', True)):
+            for mask in masks:
                 auc = get_auc(x = data['GT'][mask],
                             y = data['score'][mask],
                             mode = met[2:])
@@ -180,7 +184,7 @@ def get_performance(data, metric = 'roc', n_iter = 100, seed = 42, prefix = None
 
         elif met == 'roc' or met == 'prc' or met == 'calprc' or met == 'ci':
             # Compute AUC on the whole (unequalised class priors) data
-            aucs = get_auc(x = data['GT'], y = data['score'], mode = met, pi0 = kwargs.get('pi0', None))
+            aucs = get_auc(x = data['GT'], y = data['score'], mode = met, pi0 = pi0)
         
         if prefix is None:
             perf[met] = aucs
@@ -189,7 +193,7 @@ def get_performance(data, metric = 'roc', n_iter = 100, seed = 42, prefix = None
 
     return perf
 
-def get_source_performance(data, sources, metric='mcroc', subset = None, n_iter = 100, seed = 42, prefix = None, **kwargs):
+def get_source_performance(data, sources, metric='roc', subset = None, n_iter = 100, seed = 42, prefix = None, pi0 = 0.5, min_exp = 5):
     """
     Compute binary classifier performance for each source or susbet of sources
 
@@ -200,13 +204,15 @@ def get_source_performance(data, sources, metric='mcroc', subset = None, n_iter
         subset (list of str, optional): A subset of the targets for which to compute performance. If None, then the performance will be calculated for all targets. Defaults to None.
         n_iter (int, optional): Number of iterations for the undersampling procedures for the 'mcroc' and 'mcprc' metrics. Defaults to 100.
         seed (int, optional):  Seed used to generate the undersampling for the 'mcroc' and 'mcprc' metrics. Defaults to 42.
-        prefix (str, optional):Added as prefix to the output dictionary. Defaults to 100.
+        prefix (str, optional): Added as prefix to the output dictionary. Defaults to 100.
+        pi0 (float, optional): Reference ratio for calibrated metrics. Corresponds to the baseline/reference class inbalance to which to set the metric. Should be between 0 and 1. Defaults to 0.5.
+        min_exp (int, optional): Minium number of perturbation experiments required to compute performance scores. Defaults to 5.
 
     Returns:
         perf : Dict of prediction performance(s) for each target or subset of targets. 'mcroc' and 'mcprc' metrics will return the values for each sampling. Other methods return a single value.
     """
 
-    masks, source_names = get_source_masks(data, sources, subset = subset, min_exp = kwargs.get('min_exp', 5))
+    masks, source_names = get_source_masks(data, sources, subset = subset, min_exp = min_exp)
 
     perf = {}
     for trgt, name in zip(masks, source_names):
@@ -215,7 +221,7 @@ def get_source_performance(data, sources, metric='mcroc', subset = None, n_iter
         else:
             p = prefix  + '_' + name
 
-        perf.update(get_performance(data.iloc[trgt.tolist()].reset_index(), metric, n_iter, seed, prefix = p, **kwargs))
+        perf.update(get_performance(data.iloc[trgt.tolist()].reset_index(), metric, n_iter, seed, prefix = p, pi0=pi0))
 
     return perf
 
@@ -286,19 +292,18 @@ def get_scores_GT(decoupler_results, metadata, by = None, min_exp = 5):
 
     return scores_gt, targets
 
-def format_benchmark_data(data, metadata, network, meta_perturbation_col = 'treatment', metadata_sign_col = 'sign', net_source_col = 'source', net_weight_col = 'weight', filter_experiments = True, filter_sources = False):
-
+def format_benchmark_data(data, metadata, network, meta_perturbation_col = 'treatment', meta_sign_col = 'sign', net_source_col = 'source', net_weight_col = 'weight', filter_experiments = True, filter_sources = False):
 
     if not all(item in network.columns for item in [net_source_col, net_weight_col]):
         missing = list(set([net_source_col, net_weight_col]) - set(network.columns))
         raise ValueError('{0} column(s) are missing from the input network'.format(str(missing)))
 
-    if not all(item in metadata.columns for item in [meta_perturbation_col, metadata_sign_col]):
-        missing = list(set([meta_perturbation_col, metadata_sign_col]) - set(metadata.columns))
+    if not all(item in metadata.columns for item in [meta_perturbation_col, meta_sign_col]):
+        missing = list(set([meta_perturbation_col, meta_sign_col]) - set(metadata.columns))
         raise ValueError('{0} column(s) are missing from the input metadata'.format(str(missing)))
 
     network = network.rename(columns={net_source_col:'source', net_weight_col:'weight'})
-    metadata = metadata.rename(columns={meta_perturbation_col:'source', metadata_sign_col:'sign'})
+    metadata = metadata.rename(columns={meta_perturbation_col:'source', meta_sign_col:'sign'})
 
     #subset by TFs with GT available
     if filter_sources:
@@ -313,17 +318,16 @@ def format_benchmark_data(data, metadata, network, meta_perturbation_col = 'trea
 
     return data, metadata, network
 
-def performances(flat_data, sources, metric = 'roc', by = 'method', verbose = True, **kwargs):
-    bench = {}
-    kwargs['verbose'] = verbose
+def performances(flat_data, sources, metric = 'roc', by = 'method', subset = None, n_iter = 100, seed = 7, pi0 = 0.5, min_exp = 5, verbose = True):
 
+    bench = {}
     for method in flat_data.keys():
         if verbose: print('Calculating performance metrics for', method)
         if 'method' in by or 'sign' in by or 'all' in by:
-            perf = get_performance(flat_data[method], metric, prefix= method, **kwargs)
+            perf = get_performance(flat_data[method], metric, n_iter = n_iter, seed = seed, prefix= method, pi0=pi0)
             bench.update(perf)
         if ('source' in by or 'all' in by) and ('_positive' not in method and '_negative' not in method):
-            perf = get_source_performance(flat_data[method], sources[method], metric, prefix = method, **kwargs)
+            perf = get_source_performance(flat_data[method], sources[method], metric, subset = subset, n_iter = n_iter, seed = seed, prefix = method, pi0 = pi0, min_exp = min_exp)
             bench[method + '_bySource'] = perf
 
     return bench
@@ -374,7 +378,8 @@ def get_mean_performances(benchmark_dict):
     return mean_perf.reset_index()
 
 
-def run_benchmark(data, metadata, network, methods = None, metric = 'roc', meta_perturbation_col = 'target', net_source_col = 'source', filter_experiments= True, filter_sources = False, **kwargs):
+def run_benchmark(data, metadata, network, methods = None, metric = ['roc', 'calprc'], meta_perturbation_col = 'target', meta_sign_col = 'sign', net_source_col = 'source', net_weight_col = 'weight',
+filter_experiments= True, filter_sources = False, by = 'method', subset = None, min_exp = 5, pi0 = 0.5, n_iter = 100, seed = 7, verbose = True, **kwargs):
     """
     Benchmark methods or networks on a given set of perturbation experiments using activity inference with decoupler.
 
@@ -383,12 +388,21 @@ def run_benchmark(data, metadata, network, methods = None, metric = 'roc', meta_
         metadata (DataFrame): Metadata of the perturbation experiment containing the activated/inhibited targets and the sign of the perturbation
         network (DataFrame): Network in long format passed on to the decouple function
         methods (str or list of str, optional): List of methods to run. If none are provided use weighted top performers (mlm, ulm and wsum). To benchmark all methods set to "all". Defaults to None.
-        metric (str or list of str, optional): Performance metric(s) to compute. See the description of get_performance for more details. Defaults to 'roc'.
+        metric (str or list of str, optional): Performance metric(s) to compute. See the description of get_performance for more details. Defaults to ['roc', 'calprc'].
         meta_perturbation_col (str, optional): Column name in the metadata with perturbation targets. Defaults to 'target'.
+        meta_sign_col (str, optional): Column name in the metadata with sign of perturbation. Defaults to 'sign'.
         net_source_col (str, optional): Column name in network with source nodes. Defaults to 'source'.
+        net_weight_col (str, optional): Column name in network with interaction weight. Defaults to 'weight'.
         filter_experiments (bool, optional): Whether to filter out experiments whose perturbed targets cannot be infered from the given network. Defaults to True.
         filter_sources (bool, optional): Whether to fitler out sources in the network for which there are not perturbation experiments (reduces the number of predictions made by decouple). Defaults to False.
-        **kwargs: other arguments to pass on to get_performance (e.g. n_iter etc)
+        by (str or list of str, optional): How to compute/decompose the performance score: any of 'method', 'sign' or 'source'. Defaults to 'method'.
+        subset (str or list of str, optional): Subset of sources for which to compute an individual performance score. Requires by to contain 'source'. Sources with fewer than 'min_exp' experiments in the dataset are ignored Defaults to None
+        min_exp (int, optional): Minium number of perturbation experiments required to compute performance scores. Defaults to 5.
+        pi0 (float, optional): Reference ratio for calibrated metrics. Corresponds to the baseline/reference class inbalance to which to set the metric. Defaults to 0.5.
+        n_iter (int, optional): Number of iterations/subsampling used for the 'mcroc' and 'mcprc' metrics. Defaults to 100.
+        seed (int, otional): Random seed to use for subsampling for the 'mcroc' and 'mcprc' metrics. Defaults to 7.
+        verbose (bool, optional): Whether to print progession. Defaults to True.
+        **kwargs: Other arguments to pass on to decouple
 
     Returns:
         mean_perf: DataFrame containing the mean performance for each metric and for each method (mean has to be done for the mcroc and mcprc metrics)
@@ -396,16 +410,17 @@ def run_benchmark(data, metadata, network, methods = None, metric = 'roc', meta_
     """
 
     #format and filter the data, metadata and networks
-    data, metadata, network = format_benchmark_data(data, metadata, network, meta_perturbation_col=meta_perturbation_col,
-                                                    net_source_col=net_source_col, filter_experiments=filter_experiments, filter_sources=filter_sources)
+    data, metadata, network = format_benchmark_data(data, metadata, network, meta_perturbation_col=meta_perturbation_col, meta_sign_col = meta_sign_col,
+                                                    net_source_col=net_source_col, net_weight_col = net_weight_col, filter_experiments=filter_experiments, filter_sources=filter_sources)
 
+    
     #run prediction
-    res = dc.decouple(data, network, methods=methods, args = kwargs.get('args', {}), verbose = kwargs.get('verbose', True))
+    res = dc.decouple(data, network, methods=methods, verbose = verbose, **kwargs)
 
-    #
-    scores_gt, sources = get_scores_GT(res, metadata, by=kwargs.get('by', None), min_exp = kwargs.get('min_exp', 5))
+    #flatten and select predicitons before computing performance measures
+    scores_gt, sources = get_scores_GT(res, metadata, by= by, min_exp = min_exp)
 
-    bench = performances(scores_gt, sources, metric = metric, **kwargs)
+    bench = performances(scores_gt, sources, metric = metric, by = by, subset = subset, n_iter = n_iter, seed = seed, pi0 = pi0, min_exp = min_exp, verbose = verbose)
     
     mean_perfs = get_mean_performances(bench)
 

From 649da6cd769f770f20b6d3852be927cf0a1585fb Mon Sep 17 00:00:00 2001
From: demian1 <demian1@users.noreply.github.com>
Date: Tue, 26 Jul 2022 10:16:35 +0200
Subject: [PATCH 09/33] Update utils_benchmark.py

added functionality to group and compute performance by any column in the metadata (group experiments)
---
 decoupler/utils_benchmark.py | 103 +++++++++++++++++++++++++++++------
 1 file changed, 86 insertions(+), 17 deletions(-)

diff --git a/decoupler/utils_benchmark.py b/decoupler/utils_benchmark.py
index e49d469..8fcba41 100644
--- a/decoupler/utils_benchmark.py
+++ b/decoupler/utils_benchmark.py
@@ -215,13 +215,57 @@ def get_source_performance(data, sources, metric='roc', subset = None, n_iter =
     masks, source_names = get_source_masks(data, sources, subset = subset, min_exp = min_exp)
 
     perf = {}
-    for trgt, name in zip(masks, source_names):
+    for src, name in zip(masks, source_names):
         if prefix is None:
             p = name
         else:
             p = prefix  + '_' + name
 
-        perf.update(get_performance(data.iloc[trgt.tolist()].reset_index(), metric, n_iter, seed, prefix = p, pi0=pi0))
+        perf.update(get_performance(data.iloc[src.tolist()].reset_index(), metric, n_iter, seed, prefix = p, pi0=pi0))
+
+    return perf
+
+def get_meta_masks(flat_data, metadata, column, min_exp = 5):
+
+    items = np.sort(metadata[column].unique())
+    n_features = flat_data.shape[0] / metadata.shape[0]
+
+    indexes = []
+    names = []
+    for item in items:
+        #get row numbers of all experiments corresponding to this level
+        ids = np.flatnonzero(metadata[column] == item)
+        if len(ids) >= min_exp:
+            #find indexes of flattened array from row number in original array
+            indexes.append(np.concatenate([np.arange(id * n_features, (id * n_features) + n_features, step = 1) for id in ids]))
+            names.append(str(item))
+        
+    return indexes, names
+
+def get_meta_performance(flat_data, metadata, columns, metric= 'roc', n_iter = 100, seed = 7, prefix= None, pi0=0.5, min_exp = 5):
+
+    if type(columns) != list:
+        columns = [columns]
+
+    #check that columns are in metadata
+    columns = list(set(metadata.columns).intersection(set(columns)))
+
+    if len(columns) == 0:
+        raise ValueError('None of the columns are in the metadata')
+
+    perf = {}
+    for column in columns:
+
+        masks, names = get_meta_masks(flat_data, metadata, column = column, min_exp = min_exp)
+
+        for slice, name in zip(masks, names):
+            name = column + ':' + name
+            if prefix is None:
+                p = name
+            else:
+                p = prefix  + '_' + name
+
+            perf.update(get_performance(flat_data.iloc[slice.tolist()].reset_index(), metric, n_iter, seed, prefix = p, pi0=pi0))
 
     return perf
 
@@ -241,16 +285,18 @@ def get_scores_GT(decoupler_results, metadata, by = None, min_exp = 5):
     Returns:
         scores_gt: dict of flattenend dataframes for each method
         targets: dict with target names for which activities were inferred for each method respectively
+        meta: filtered metadata, based on decoupler output
     """
     computed_methods = list(set([i.split('_')[0] for i in decoupler_results.keys()])) # get the methods that were able to be computed (filtering of methods done by decouple)
     scores_gt = {}
     targets = {}
+    metadatas = {}
 
     for m in computed_methods:
         # estimates = res[m + 'estimate']
         # pvals = res[m + 'pvals']
 
-        # remove experiments with no prediction for the perturbed TF
+        # remove experiments with no prediction for the perturbed TFs
         missing = list(set(metadata['source']) - set(decoupler_results[m + '_estimate'].columns))
         keep = [trgt not in missing for trgt in metadata['source'].to_list()]
         meta = metadata[keep]
@@ -288,9 +334,10 @@ def get_scores_GT(decoupler_results, metadata, by = None, min_exp = 5):
             if score['GT'].sum() > min_exp:
                 scores_gt[name] = score.reset_index()
                 targets[name] = list(estimates.columns)
+                metadatas[name] = meta
 
 
-    return scores_gt, targets
+    return scores_gt, targets, metadatas
 
 def format_benchmark_data(data, metadata, network, meta_perturbation_col = 'treatment', meta_sign_col = 'sign', net_source_col = 'source', net_weight_col = 'weight', filter_experiments = True, filter_sources = False):
 
@@ -318,7 +365,7 @@ def format_benchmark_data(data, metadata, network, meta_perturbation_col = 'trea
 
     return data, metadata, network
 
-def performances(flat_data, sources, metric = 'roc', by = 'method', subset = None, n_iter = 100, seed = 7, pi0 = 0.5, min_exp = 5, verbose = True):
+def performances(flat_data, sources, metadatas, columns = None, metric = 'roc', by = 'method', subset = None, n_iter = 100, seed = 7, pi0 = 0.5, min_exp = 5, verbose = True):
 
     bench = {}
     for method in flat_data.keys():
@@ -326,9 +373,13 @@ def performances(flat_data, sources, metric = 'roc', by = 'method', subset = Non
         if 'method' in by or 'sign' in by or 'all' in by:
             perf = get_performance(flat_data[method], metric, n_iter = n_iter, seed = seed, prefix= method, pi0=pi0)
             bench.update(perf)
-        if ('source' in by or 'all' in by) and ('_positive' not in method and '_negative' not in method):
+        if('source' in by or 'all' in by) and ('_positive' not in method and '_negative' not in method):
             perf = get_source_performance(flat_data[method], sources[method], metric, subset = subset, n_iter = n_iter, seed = seed, prefix = method, pi0 = pi0, min_exp = min_exp)
             bench[method + '_bySource'] = perf
+        if(columns is not None) and ('_positive' not in method and '_negative' not in method):
+            perf = get_meta_performance(flat_data[method], metadatas[method], columns, metric, n_iter = n_iter, seed = seed, prefix= method, pi0=pi0, min_exp = min_exp)
+            bench[method + '_byMeta'] = perf
+
 
     return bench
 
@@ -337,10 +388,14 @@ def get_mean_performances(benchmark_dict):
     perf_method = {}
     perf_bySource = {}
     perf_bySign = {}
+    perf_byMeta = {}
     for topkey, topvalue in benchmark_dict.items():
         if '_bySource' in topkey:
             for key, value in topvalue.items():
                 perf_bySource[key] = np.mean(value)
+        elif '_byMeta' in topkey:
+            for key, value in topvalue.items():
+                perf_byMeta[key] = np.mean(value)
         elif '_negative' in topkey or '_positive' in topkey:
             perf_bySign[topkey] = np.mean(topvalue)
         else:
@@ -354,7 +409,7 @@ def get_mean_performances(benchmark_dict):
         perf_method[['method','metric']] = perf_method['id'].str.split('_', expand=True)
         perf_method = perf_method.pivot(index='method', columns='metric', values='value').reset_index()
         perfs.append(perf_method)
-    
+
     if len(perf_bySign) > 0:
         perf_bySign = pd.DataFrame.from_dict(perf_bySign, orient='index').reset_index()
         perf_bySign.columns = ['id','value']
@@ -369,16 +424,29 @@ def get_mean_performances(benchmark_dict):
         perf_bySource = perf_bySource.pivot(index=['method','source'], columns='metric', values='value').reset_index()
         perfs.append(perf_bySource)
 
+    if len(perf_byMeta) > 0:
+        perf_byMeta = pd.DataFrame.from_dict(perf_byMeta, orient='index').reset_index()
+        perf_byMeta.columns = ['id','value']
+        perf_byMeta[['method','meta','metric']] = perf_byMeta['id'].str.split('_', expand=True)
+
+        perf_byMeta = perf_byMeta.pivot(index = ['method', 'meta'], columns = 'metric', values = 'value').reset_index()
+
+        perf_byMeta[['meta', 'level']] = perf_byMeta['meta'].str.split(':', expand=True)
+        factors = perf_byMeta['meta'].unique()
+
+        perf_byMeta = perf_byMeta.pivot(index=list(set(perf_byMeta.columns) -set(['meta', 'level'])), columns='meta', values='level').reset_index()
+
+        perf_byMeta = perf_byMeta.rename(dict(zip(factors ,'meta_' + factors)), axis = 1)
+        perfs.append(perf_byMeta)
+
+
     mean_perf = pd.concat(perfs)
-    if 'source' in mean_perf.columns:
-        mean_perf['source'] = mean_perf['source'].fillna('overall')
-    if 'sign' in mean_perf.columns:
-        mean_perf['sign'] = mean_perf['sign'].fillna('overall')
+    mean_perf = mean_perf.fillna('').reset_index()
 
-    return mean_perf.reset_index()
+    return mean_perf.drop(labels='index', axis = 1)
 
 
-def run_benchmark(data, metadata, network, methods = None, metric = ['roc', 'calprc'], meta_perturbation_col = 'target', meta_sign_col = 'sign', net_source_col = 'source', net_weight_col = 'weight',
+def run_benchmark(data, metadata, network, methods = None, metric = ['roc', 'calprc'], columns = None, meta_perturbation_col = 'target', meta_sign_col = 'sign', net_source_col = 'source', net_weight_col = 'weight',
 filter_experiments= True, filter_sources = False, by = 'method', subset = None, min_exp = 5, pi0 = 0.5, n_iter = 100, seed = 7, verbose = True, **kwargs):
     """
     Benchmark methods or networks on a given set of perturbation experiments using activity inference with decoupler.
@@ -389,13 +457,14 @@ def run_benchmark(data, metadata, network, methods = None, metric = ['roc', 'cal
         network (DataFrame): Network in long format passed on to the decouple function
         methods (str or list of str, optional): List of methods to run. If none are provided use weighted top performers (mlm, ulm and wsum). To benchmark all methods set to "all". Defaults to None.
         metric (str or list of str, optional): Performance metric(s) to compute. See the description of get_performance for more details. Defaults to ['roc', 'calprc'].
+        column (str or list of str, optional): Metadata columns that contain the levels for which you want individual performance decomposition. Defaults to None.
         meta_perturbation_col (str, optional): Column name in the metadata with perturbation targets. Defaults to 'target'.
         meta_sign_col (str, optional): Column name in the metadata with sign of perturbation. Defaults to 'sign'.
         net_source_col (str, optional): Column name in network with source nodes. Defaults to 'source'.
         net_weight_col (str, optional): Column name in network with interaction weight. Defaults to 'weight'.
         filter_experiments (bool, optional): Whether to filter out experiments whose perturbed targets cannot be infered from the given network. Defaults to True.
         filter_sources (bool, optional): Whether to fitler out sources in the network for which there are not perturbation experiments (reduces the number of predictions made by decouple). Defaults to False.
-        by (str or list of str, optional): How to compute/decompose the performance score: any of 'method', 'sign' or 'source'. Defaults to 'method'.
+        by (str or list of str, optional): How to compute/decompose the performance score: any of 'method' and/or 'source'. Defaults to 'method'.
         subset (str or list of str, optional): Subset of sources for which to compute an individual performance score. Requires by to contain 'source'. Sources with fewer than 'min_exp' experiments in the dataset are ignored Defaults to None
         min_exp (int, optional): Minium number of perturbation experiments required to compute performance scores. Defaults to 5.
         pi0 (float, optional): Reference ratio for calibrated metrics. Corresponds to the baseline/reference class inbalance to which to set the metric. Defaults to 0.5.
@@ -418,10 +487,10 @@ def run_benchmark(data, metadata, network, methods = None, metric = ['roc', 'cal
     res = dc.decouple(data, network, methods=methods, verbose = verbose, **kwargs)
 
     #flatten and select predicitons before computing performance measures
-    scores_gt, sources = get_scores_GT(res, metadata, by= by, min_exp = min_exp)
+    scores_gt, sources, metadatas = get_scores_GT(res, metadata, by= by, min_exp = min_exp)
+
+    bench = performances(scores_gt, sources, metadatas, columns = columns, metric = metric, by = by, subset = subset, n_iter = n_iter, seed = seed, pi0 = pi0, min_exp = min_exp, verbose = verbose)
 
-    bench = performances(scores_gt, sources, metric = metric, by = by, subset = subset, n_iter = n_iter, seed = seed, pi0 = pi0, min_exp = min_exp, verbose = verbose)
-    
     mean_perfs = get_mean_performances(bench)
 
     return mean_perfs, bench

From f798021c2c616f90f963d97fe0aaa3270ada3ad0 Mon Sep 17 00:00:00 2001
From: demian1 <demian1@users.noreply.github.com>
Date: Tue, 26 Jul 2022 15:40:25 +0200
Subject: [PATCH 10/33] Update utils_benchmark.py

---
 decoupler/utils_benchmark.py | 29 ++++++++++++++++++++++++++---
 1 file changed, 26 insertions(+), 3 deletions(-)

diff --git a/decoupler/utils_benchmark.py b/decoupler/utils_benchmark.py
index 8fcba41..3bff87c 100644
--- a/decoupler/utils_benchmark.py
+++ b/decoupler/utils_benchmark.py
@@ -339,7 +339,10 @@ def get_scores_GT(decoupler_results, metadata, by = None, min_exp = 5):
 
     return scores_gt, targets, metadatas
 
-def format_benchmark_data(data, metadata, network, meta_perturbation_col = 'treatment', meta_sign_col = 'sign', net_source_col = 'source', net_weight_col = 'weight', filter_experiments = True, filter_sources = False):
+def format_benchmark_data(data, metadata, network, columns = None, meta_perturbation_col = 'treatment', meta_sign_col = 'sign', net_source_col = 'source', net_weight_col = 'weight', filter_experiments = True, filter_sources = False):
+
+    if metadata.shape[0] != data.shape[0]:
+        raise ValueError('The data and metadata do not have the same number of rows! ({0} vs. {1})\n'.format(data.shape[0], metadata.shape[0]))
 
     if not all(item in network.columns for item in [net_source_col, net_weight_col]):
         missing = list(set([net_source_col, net_weight_col]) - set(network.columns))
@@ -363,7 +366,27 @@ def format_benchmark_data(data, metadata, network, meta_perturbation_col = 'trea
         data = data[keep]
         metadata = metadata[keep]
 
-    return data, metadata, network
+    if columns is not None:
+        if type(columns) != list:
+            columns = [columns]
+
+        if meta_perturbation_col in columns:
+            columns.append('source')
+
+        if meta_sign_col in columns:
+            columns.append('sign')
+
+        #check that columns are in metadata
+        columns = list(set(metadata.columns).intersection(set(columns)))
+
+        if len(columns) == 0:
+            raise ValueError('None of the columns are in the metadata')
+
+        for c in columns:
+            if type(metadata[c][0]) == 'str':
+                metadata[c] = metadata[c].str.replace('[_,:]', ' ')
+
+    return data, metadata, network, columns
 
 def performances(flat_data, sources, metadatas, columns = None, metric = 'roc', by = 'method', subset = None, n_iter = 100, seed = 7, pi0 = 0.5, min_exp = 5, verbose = True):
 
@@ -479,7 +502,7 @@ def run_benchmark(data, metadata, network, methods = None, metric = ['roc', 'cal
     """
 
     #format and filter the data, metadata and networks
-    data, metadata, network = format_benchmark_data(data, metadata, network, meta_perturbation_col=meta_perturbation_col, meta_sign_col = meta_sign_col,
+    data, metadata, network, columns = format_benchmark_data(data, metadata, network, columns, meta_perturbation_col=meta_perturbation_col, meta_sign_col = meta_sign_col,
                                                     net_source_col=net_source_col, net_weight_col = net_weight_col, filter_experiments=filter_experiments, filter_sources=filter_sources)
 
     

From c47cff7d18c1ad5e94a78ec51e4d091012138eb6 Mon Sep 17 00:00:00 2001
From: demian1 <demian1@users.noreply.github.com>
Date: Tue, 26 Jul 2022 15:51:26 +0200
Subject: [PATCH 11/33] Update utils_benchmark.py

---
 decoupler/utils_benchmark.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/decoupler/utils_benchmark.py b/decoupler/utils_benchmark.py
index 3bff87c..c7e2203 100644
--- a/decoupler/utils_benchmark.py
+++ b/decoupler/utils_benchmark.py
@@ -383,7 +383,7 @@ def format_benchmark_data(data, metadata, network, columns = None, meta_perturba
             raise ValueError('None of the columns are in the metadata')
 
         for c in columns:
-            if type(metadata[c][0]) == 'str':
+            if isinstance(metadata[c][0],str):
                 metadata[c] = metadata[c].str.replace('[_,:]', ' ')
 
     return data, metadata, network, columns

From 1f82927f2f3afb26b794c9513008e85f11d47f9a Mon Sep 17 00:00:00 2001
From: Pau Badia i Mompel <44896790+PauBadiaM@users.noreply.github.com>
Date: Wed, 17 Aug 2022 13:55:22 +0200
Subject: [PATCH 12/33] Removed limit python>3.8

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 83e6ed1..75bc701 100644
--- a/setup.py
+++ b/setup.py
@@ -36,7 +36,7 @@ def get_version(rel_path: str) -> str:
                       "anndata",
                       "matplotlib"],
     packages=["decoupler"],
-    python_requires=">=3.8",
+    python_requires=">=3.6",
     classifiers=[
         "Programming Language :: Python :: 3",
         "Operating System :: OS Independent"]

From 187e79989f759b8afc9e756fe3ac4e8b83c64edb Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 14:20:15 +0200
Subject: [PATCH 13/33] Removed 3.8 limitation

---
 setup.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 75bc701..e42cf5a 100644
--- a/setup.py
+++ b/setup.py
@@ -33,8 +33,7 @@ def get_version(rel_path: str) -> str:
     },
     install_requires=["numba",
                       "tqdm",
-                      "anndata",
-                      "matplotlib"],
+                      "anndata"],
     packages=["decoupler"],
     python_requires=">=3.6",
     classifiers=[

From 1b9a19f0e280addaf8dce3ab18552d48227a5de8 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 14:21:09 +0200
Subject: [PATCH 14/33] Fixed devel github CI name

---
 .github/workflows/devel.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/devel.yml b/.github/workflows/devel.yml
index 883bab8..dee862c 100644
--- a/.github/workflows/devel.yml
+++ b/.github/workflows/devel.yml
@@ -2,7 +2,7 @@ name: devel
 
 on:
   push:
-    branches: [ development ]
+    branches: [ devel ]
   pull_request:
     branches: [ main ]
 

From 6b002839630031c130e457e25d7f613cc1a987e6 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 14:22:03 +0200
Subject: [PATCH 15/33] Deleted calibrated metrics

---
 decoupler/utils_calibrated_metrics.py | 122 --------------------------
 1 file changed, 122 deletions(-)
 delete mode 100644 decoupler/utils_calibrated_metrics.py

diff --git a/decoupler/utils_calibrated_metrics.py b/decoupler/utils_calibrated_metrics.py
deleted file mode 100644
index 4c39e26..0000000
--- a/decoupler/utils_calibrated_metrics.py
+++ /dev/null
@@ -1,122 +0,0 @@
-# taken directly from: https://github.com/wissam-sib/calibrated_metrics/blob/f1c262d0aaac16356a143596a832030abb87d48c/calibrated_metrics.py
-# Siblini W., Fréry J., He-Guelton L., Oblé F., Wang YQ. (2020) Master Your Metrics with Calibration. In: Berthold M., Feelders A., Krempl G. (eds) Advances in Intelligent Data Analysis XVIII. IDA 2020. Lecture Notes in Computer Science, vol 12080. Springer, Cham
-
-import numpy as np
-from sklearn.metrics._ranking import _binary_clf_curve
-from sklearn.metrics import confusion_matrix
-
-def precision_recall_curve(y_true, y_pred, pos_label=None,
-                           sample_weight=None,pi0=None):
-    """Compute precision-recall (with optional calibration) pairs for different probability thresholds
-    This implementation is a modification of scikit-learn "precision_recall_curve" function that adds calibration
-    ----------
-    y_true : array, shape = [n_samples]
-        True binary labels. If labels are not either {-1, 1} or {0, 1}, then
-        pos_label should be explicitly given.
-    probas_pred : array, shape = [n_samples]
-        Estimated probabilities or decision function.
-    pos_label : int or str, default=None
-        The label of the positive class.
-        When ``pos_label=None``, if y_true is in {-1, 1} or {0, 1},
-        ``pos_label`` is set to 1, otherwise an error will be raised.
-    sample_weight : array-like of shape (n_samples,), default=None
-        Sample weights.
-    Returns
-    -------
-    calib_precision : array, shape = [n_thresholds + 1]
-        Calibrated Precision values such that element i is the calibrated precision of
-        predictions with score >= thresholds[i] and the last element is 1.
-    recall : array, shape = [n_thresholds + 1]
-        Decreasing recall values such that element i is the recall of
-        predictions with score >= thresholds[i] and the last element is 0.
-    thresholds : array, shape = [n_thresholds <= len(np.unique(probas_pred))]
-        Increasing thresholds on the decision function used to compute
-        precision and recall.
-    """
-    
-    fps, tps, thresholds = _binary_clf_curve(y_true, y_pred,
-                                             pos_label=pos_label,
-                                             sample_weight=sample_weight)
-    
-   
-    
-    
-    if pi0 is not None:
-        pi = np.sum(y_true)/float(np.array(y_true).shape[0])
-        ratio = pi*(1-pi0)/(pi0*(1-pi))
-        precision = tps / (tps + ratio*fps)
-    else:
-        precision = tps / (tps + fps)
-    
-    precision[np.isnan(precision)] = 0
-        
-    recall = tps / tps[-1]
-
-    # stop when full recall attained
-    # and reverse the outputs so recall is decreasing
-    last_ind = tps.searchsorted(tps[-1])
-    sl = slice(last_ind, None, -1)
-    return np.r_[precision[sl], 1], np.r_[recall[sl], 0], thresholds[sl]
-
-
-def average_precision(y_true, y_pred, pos_label=1, sample_weight=None,pi0=None):
-        precision, recall, _ = precision_recall_curve(y_true, y_pred, pos_label=pos_label, sample_weight=sample_weight, pi0=pi0)
-        return -np.sum(np.diff(recall) * np.array(precision)[:-1])
-    
-    
-def f1score(y_true, y_pred, pi0=None):
-    """
-    ----------
-    y_true : 1d array-like, or label indicator array / sparse matrix
-        Ground truth (correct) target values.
-    y_pred : 1d array-like, or label indicator array / sparse matrix
-        Estimated targets as returned by a classifier. (must be binary)
-    pi0 : float, None by default
-        The reference ratio for calibration
-    """
-    CM = confusion_matrix(y_true, y_pred)
-    
-    tn = CM[0][0]
-    fn = CM[1][0]
-    tp = CM[1][1]
-    fp = CM[0][1] 
-        
-    pos = fn + tp
-    
-    recall = tp / float(pos)
-    
-    if pi0 is not None:
-        pi = pos/float(tn + fn + tp + fp)
-        ratio = pi*(1-pi0)/(pi0*(1-pi))
-        precision = tp / float(tp + ratio*fp)
-    else:
-        precision = tp / float(tp + fp)
-    
-    if np.isnan(precision):
-        precision = 0
-    
-    if (precision+recall)==0.0:
-        f=0.0
-    else:
-        f = (2*precision*recall)/(precision+recall)
-
-    return f
-
-
-def bestf1score(y_true, y_pred, pi0=None):
-    """
-    ----------
-    y_true : 1d array-like, or label indicator array / sparse matrix
-        Ground truth (correct) target values.
-    y_pred : 1d array-like, or label indicator array / sparse matrix
-        Estimated targets as returned by a classifier.
-    pi0 : float, None by default
-        The reference ratio for calibration
-    """
-    
-    precision, recall, _ = precision_recall_curve(y_true, y_pred, pi0=pi0)
-    
-    fscores = (2*precision*recall)/(precision+recall)
-    fscores = np.nan_to_num(fscores,nan=0, posinf=0, neginf=0)
-    
-    return np.max(fscores)
\ No newline at end of file

From 9440bfedacd4c01f6df665f33fa2c44a023c0315 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 17:54:45 +0200
Subject: [PATCH 16/33] Updated decouple docs

---
 decoupler/decouple.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/decoupler/decouple.py b/decoupler/decouple.py
index 6b71926..510c191 100644
--- a/decoupler/decouple.py
+++ b/decoupler/decouple.py
@@ -104,7 +104,7 @@ def decouple(mat, net, source='source', target='target', weight='weight', method
         Column name in net with target nodes.
     weight : str
         Column name in net with weights.
-    methods : list, str
+    methods : list, str, None
         List of methods to run. If none are provided use weighted top performers (mlm, ulm and wsum). To run all methods set to
         "all".
     args : dict

From ca87454214c2101c4c87f3264fcfe72228678351 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 17:56:46 +0200
Subject: [PATCH 17/33] Readed cache to gsva funcs

---
 decoupler/method_gsva.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/decoupler/method_gsva.py b/decoupler/method_gsva.py
index bccefac..2c895af 100644
--- a/decoupler/method_gsva.py
+++ b/decoupler/method_gsva.py
@@ -79,7 +79,7 @@ def density(mat, kcdf=False):
     return mat
 
 
-@nb.njit(nb.types.Tuple((nb.f4[:, :], nb.i4[:, :]))(nb.f4[:, :]), parallel=True)
+@nb.njit(nb.types.Tuple((nb.f4[:, :], nb.i4[:, :]))(nb.f4[:, :]), parallel=True, cache=True)
 def nb_get_D_I(mat):
     n = mat.shape[1]
     rev_idx = np.abs(np.arange(start=n, stop=0, step=-1, dtype=nb.f4) - n / 2)
@@ -92,7 +92,7 @@ def nb_get_D_I(mat):
     return mat, Idx
 
 
-@nb.njit(nb.f4(nb.f4[:], nb.i4[:], nb.i4, nb.i4[:], nb.i4[:], nb.i4, nb.f4))
+@nb.njit(nb.f4(nb.f4[:], nb.i4[:], nb.i4, nb.i4[:], nb.i4[:], nb.i4, nb.f4), cache=True)
 def ks_sample(D, Idx, n_genes, geneset_mask, fset, n_geneset, dec):
 
     sum_gset = 0.0
@@ -121,7 +121,7 @@ def ks_sample(D, Idx, n_genes, geneset_mask, fset, n_geneset, dec):
     return mx_value_sign
 
 
-@nb.njit(nb.f4[:](nb.f4[:, :], nb.i4[:, :], nb.i4[:]), parallel=True)
+@nb.njit(nb.f4[:](nb.f4[:, :], nb.i4[:, :], nb.i4[:]), parallel=True, cache=True)
 def ks_matrix(D, Idx, fset):
     n_samples, n_genes = D.shape
     n_geneset = fset.shape[0]

From 4d49c7fd5a8f92918ef7d24ee630bbe8c86b0b4c Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 17:59:31 +0200
Subject: [PATCH 18/33] Made mdt check if skranger is installed

---
 decoupler/__init__.py   | 24 +++++++++---------------
 decoupler/method_mdt.py | 19 +++++++++++++++----
 2 files changed, 24 insertions(+), 19 deletions(-)

diff --git a/decoupler/__init__.py b/decoupler/__init__.py
index 73d7f39..f1987b3 100644
--- a/decoupler/__init__.py
+++ b/decoupler/__init__.py
@@ -1,15 +1,17 @@
-__version__ = '1.1.13'  # noqa: F401
+__version__ = '1.1.14'  # noqa: F401
 __version_info__ = tuple([int(num) for num in __version__.split('.')])  # noqa: F401
 
 from .pre import extract, match, rename_net, get_net_mat, filt_min_n, mask_features  # noqa: F401
 from .utils import melt, show_methods, check_corr, get_toy_data, summarize_acts, assign_groups  # noqa: F401
-from .utils import dense_run, p_adjust_fdr  # noqa: F401
+from .utils import dense_run, p_adjust_fdr, shuffle_net  # noqa: F401
 from .utils_anndata import get_acts, get_pseudobulk, get_contrast, get_top_targets, format_contrast_results  # noqa: F401
 from .method_wmean import run_wmean  # noqa: F401
 from .method_wsum import run_wsum  # noqa: F401
 from .method_ulm import run_ulm  # noqa: F401
+from .method_mdt import run_mdt  # noqa: F401
 from .method_mlm import run_mlm  # noqa: F401
-from .method_ora import run_ora, test1r  # noqa: F401
+from .method_udt import run_udt  # noqa: F401
+from .method_ora import run_ora, test1r, get_ora_df  # noqa: F401
 from .method_gsva import run_gsva  # noqa: F401
 from .method_gsea import run_gsea  # noqa: F401
 from .method_viper import run_viper  # noqa: F401
@@ -18,15 +20,7 @@
 from .consensus import cons  # noqa: F401
 from .omnip import show_resources, get_resource, get_progeny, get_dorothea  # noqa: F401
 from .plotting import plot_volcano, plot_violins, plot_barplot  # noqa: F401
-from .utils_benchmark import format_benchmark_data, get_scores_GT, performances, get_mean_performances, run_benchmark # noqa: F401
-
-# External libraries go out of main setup
-try:
-    from .method_mdt import run_mdt  # noqa: F401
-except Exception:
-    pass
-
-try:
-    from .method_udt import run_udt  # noqa: F401
-except Exception:
-    pass
+from .plotting import plot_metrics_scatter, plot_metrics_boxplot, plot_metrics_scatter_cols  # noqa: F401
+from .benchmark import benchmark, format_benchmark_inputs, get_performances  # noqa: F401
+from .utils_benchmark import get_toy_benchmark_data, show_metrics  # noqa: F401
+from .metrics import metric_auroc, metric_auprc, metric_mcauroc, metric_mcauprc  # noqa: F401
diff --git a/decoupler/method_mdt.py b/decoupler/method_mdt.py
index ed819c7..e5e23b1 100644
--- a/decoupler/method_mdt.py
+++ b/decoupler/method_mdt.py
@@ -9,14 +9,22 @@
 from .pre import extract, match, rename_net, get_net_mat, filt_min_n
 
 from anndata import AnnData
-from skranger.ensemble import RangerForestRegressor
 from tqdm import tqdm
 
 
-def fit_rf(net, sample, trees=100, min_leaf=5, n_jobs=-1, seed=42):
+def check_if_skranger():
+    try:
+        from skranger import ensemble
+    except Exception:
+        raise ImportError('skranger is not installed. Please install it with: pip install skranger')
+    return ensemble
+
+
+def fit_rf(sr, net, sample, trees=100, min_leaf=5, n_jobs=-1, seed=42):
 
     # Fit Random Forest
-    regr = RangerForestRegressor(n_estimators=trees, min_node_size=min_leaf, n_jobs=n_jobs, seed=seed, importance='impurity')
+    regr = sr.RangerForestRegressor(n_estimators=trees, min_node_size=min_leaf, n_jobs=n_jobs,
+                                    seed=seed, importance='impurity')
     regr.fit(net, sample)
 
     # Extract importances
@@ -25,13 +33,16 @@ def fit_rf(net, sample, trees=100, min_leaf=5, n_jobs=-1, seed=42):
 
 def mdt(mat, net, trees=10, min_leaf=5, n_jobs=4, seed=42, verbose=False):
 
+    # Check if skranger is installed
+    sr = check_if_skranger()
+
     # Init empty acts
     acts = np.zeros((mat.shape[0], net.shape[1]), dtype=np.float32)
 
     # For each sample
     for i in tqdm(range(mat.shape[0]), disable=not verbose):
         sample = mat[i].A[0]
-        acts[i] = fit_rf(net, sample, trees=trees, min_leaf=min_leaf, n_jobs=n_jobs, seed=seed)
+        acts[i] = fit_rf(sr, net, sample, trees=trees, min_leaf=min_leaf, n_jobs=n_jobs, seed=seed)
 
     return acts
 

From 8ea6d0e741b3377c1f8ea0011af55c9aab792410 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 18:01:13 +0200
Subject: [PATCH 19/33] Added ora function to work with list of genes instead
 of mat

---
 decoupler/__init__.py   |  2 +-
 decoupler/method_ora.py | 81 +++++++++++++++++++++++++++++++++++++++++
 2 files changed, 82 insertions(+), 1 deletion(-)

diff --git a/decoupler/__init__.py b/decoupler/__init__.py
index f1987b3..477666d 100644
--- a/decoupler/__init__.py
+++ b/decoupler/__init__.py
@@ -1,4 +1,4 @@
-__version__ = '1.1.14'  # noqa: F401
+__version__ = '1.1.15'  # noqa: F401
 __version_info__ = tuple([int(num) for num in __version__.split('.')])  # noqa: F401
 
 from .pre import extract, match, rename_net, get_net_mat, filt_min_n, mask_features  # noqa: F401
diff --git a/decoupler/method_ora.py b/decoupler/method_ora.py
index 724e4ae..1684167 100644
--- a/decoupler/method_ora.py
+++ b/decoupler/method_ora.py
@@ -108,6 +108,87 @@ def ora(mat, net, n_up_msk, n_bt_msk, n_background=20000, verbose=False):
     return pvls
 
 
+def get_ora_df(df, net, groupby, features, source='source', target='target', n_background=20000, min_n=5, verbose=False):
+    """
+    Wrapper to run ORA without an input matrix, instead it uses an input DataFrame in long format with one group columns
+    and the significant features in another. Useful for results of differential analysis where no mat is available.
+
+    Parameters
+    ----------
+    df : DataFrame
+        Long format DataFrame with groups and significant features.
+    net : DataFrame
+        Network in long format.
+    groupby : str
+        Column name in df with groups.
+    features : str
+        Column name in df with significant features.
+    source : str
+        Column name in net with source nodes.
+    target : str
+        Column name in net with target nodes.
+    n_background : int
+        Integer indicating the background size.
+    min_n : int
+        Minimum of targets per source. If less, sources are removed.
+    verbose : bool
+        Whether to show progress.
+
+    Returns
+    -------
+    pvals : DataFrame
+        Obtained p-values per group and source.
+    """
+
+    # Extract
+    df = df.copy()
+    cols = df.columns
+    if groupby not in cols:
+        raise ValueError('Column name "{0}" for groupby not found in df. Please specify a valid column.'.format(groupby))
+    if features not in cols:
+        raise ValueError('Column name "{0}" for features not found in df. Please specify a valid column.'.format(features))
+    c = np.unique(df[features].values)
+
+    # Transform net
+    net = rename_net(net, source=source, target=target, weight=None)
+    net = filt_min_n(c, net, min_n=min_n)
+
+    # Transform targets to indxs
+    table = {name: i for i, name in enumerate(c)}
+    net['target'] = [table[target] for target in net['target']]
+    df[features] = [table[target] for target in df[features]]
+
+    # Transform to groups
+    net = net.groupby('source')['target'].apply(lambda x: np.array(x, dtype=np.int32))
+    df = df.groupby(groupby)[features].apply(lambda x: np.array(x, dtype=np.int32))
+    srcs = net.index.values
+    grps = df.index.values
+
+    # Flatten net and get offsets
+    offsets = net.apply(lambda x: len(x)).values.astype(np.int32)
+    net = np.concatenate(net.values)
+
+    # Define starts to subset offsets
+    starts = np.zeros(offsets.shape[0], dtype=np.int32)
+    starts[1:] = np.cumsum(offsets)[:-1]
+
+    # Init empty
+    pvals = np.zeros((grps.size, srcs.size), dtype=np.float32)
+    for i in tqdm(range(grps.size), disable=not verbose):
+
+        # Extract idxs per group
+        idxs = df.iloc[i]
+
+        # Estimate pvals
+        pvals[i] = get_pvals(idxs, net, starts, offsets, n_background)
+
+    # Transform to df
+    pvals = pd.DataFrame(pvals, index=grps, columns=srcs)
+    pvals.name = 'ora_pvals'
+
+    return pvals
+
+
 def run_ora(mat, net, source='source', target='target', n_up=None, n_bottom=0, n_background=20000, min_n=5, seed=42,
             verbose=False, use_raw=True):
     """

From dcf04d6cd5ebf86fb50477f686ce0e669fe85c07 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 18:04:24 +0200
Subject: [PATCH 20/33] Made udt check if sklearn is installed

---
 decoupler/__init__.py   |  2 +-
 decoupler/method_udt.py | 18 ++++++++++++++----
 2 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/decoupler/__init__.py b/decoupler/__init__.py
index 477666d..a1431be 100644
--- a/decoupler/__init__.py
+++ b/decoupler/__init__.py
@@ -1,4 +1,4 @@
-__version__ = '1.1.15'  # noqa: F401
+__version__ = '1.1.16'  # noqa: F401
 __version_info__ = tuple([int(num) for num in __version__.split('.')])  # noqa: F401
 
 from .pre import extract, match, rename_net, get_net_mat, filt_min_n, mask_features  # noqa: F401
diff --git a/decoupler/method_udt.py b/decoupler/method_udt.py
index 53def8d..6a97dcf 100644
--- a/decoupler/method_udt.py
+++ b/decoupler/method_udt.py
@@ -9,14 +9,21 @@
 from .pre import extract, match, rename_net, get_net_mat, filt_min_n
 
 from anndata import AnnData
-from sklearn import tree
 from tqdm import tqdm
 
 
-def fit_dt(regulator, sample, min_leaf=5, seed=42):
+def check_if_sklearn():
+    try:
+        import sklearn as sk
+    except Exception:
+        raise ImportError('sklearn is not installed. Please install it with: pip install scikit-learn')
+    return sk
+
+
+def fit_dt(sk, regulator, sample, min_leaf=5, seed=42):
     # Fit DT
     x, y = regulator.reshape(-1, 1), sample.reshape(-1, 1)
-    regr = tree.DecisionTreeRegressor(min_samples_leaf=min_leaf, random_state=seed)
+    regr = sk.tree.DecisionTreeRegressor(min_samples_leaf=min_leaf, random_state=seed)
     regr.fit(x, y)
 
     # Get importance
@@ -25,13 +32,16 @@ def fit_dt(regulator, sample, min_leaf=5, seed=42):
 
 def udt(mat, net, min_leaf=5, seed=42, verbose=False):
 
+    # Check if sklearn is installed
+    sk = check_if_sklearn()
+
     # Init empty acts
     acts = np.zeros((mat.shape[0], net.shape[1]))
 
     # For each sample and regulator fit dt
     for i in tqdm(range(mat.shape[0]), disable=not verbose):
         for j in range(net.shape[1]):
-            acts[i, j] = fit_dt(net[:, j], mat[i], min_leaf=min_leaf, seed=seed)
+            acts[i, j] = fit_dt(sk, net[:, j], mat[i], min_leaf=min_leaf, seed=seed)
 
     return acts
 

From 72883fadaada167bbac52b86a082a29fda3a7861 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 18:05:00 +0200
Subject: [PATCH 21/33] Added doc description to omnip

---
 decoupler/omnip.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/decoupler/omnip.py b/decoupler/omnip.py
index 4b1bd45..d752ad7 100644
--- a/decoupler/omnip.py
+++ b/decoupler/omnip.py
@@ -1,3 +1,8 @@
+"""
+Utility functions to query OmniPath.
+Functions to retrieve resources from the meta-database OmniPath.
+"""
+
 import numpy as np
 
 

From 95b5e7c7e3963fad2c01c19e127e3b03c4a2061c Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 18:14:28 +0200
Subject: [PATCH 22/33] Added benchmarking plots

---
 decoupler/__init__.py |   2 +-
 decoupler/plotting.py | 399 ++++++++++++++++++++++++++++++++++++++++--
 2 files changed, 387 insertions(+), 14 deletions(-)

diff --git a/decoupler/__init__.py b/decoupler/__init__.py
index a1431be..bbc804f 100644
--- a/decoupler/__init__.py
+++ b/decoupler/__init__.py
@@ -1,4 +1,4 @@
-__version__ = '1.1.16'  # noqa: F401
+__version__ = '1.1.17'  # noqa: F401
 __version_info__ = tuple([int(num) for num in __version__.split('.')])  # noqa: F401
 
 from .pre import extract, match, rename_net, get_net_mat, filt_min_n, mask_features  # noqa: F401
diff --git a/decoupler/plotting.py b/decoupler/plotting.py
index 7af2941..269bf07 100644
--- a/decoupler/plotting.py
+++ b/decoupler/plotting.py
@@ -27,31 +27,41 @@ def check_if_seaborn():
     return sns
 
 
+def check_if_adjustText():
+    try:
+        import adjustText as at
+    except Exception:
+        raise ImportError('adjustText is not installed. Please install it with: pip install adjustText')
+    return at
+
+
 def save_plot(fig, ax, save):
     if save is not None:
         if ax is not None:
-            fig.savefig(save)
+            fig.savefig(save, bbox_inches='tight')
         else:
             raise ValueError("ax is not None, cannot save figure.")
 
 
-def set_limits(vmin, vcenter, vmax, values):
-
-    if vmin is None:
-        vmin = values.min()
+def filter_limits(df, sign_limit=None, lFCs_limit=None):
 
-    if vmax is None:
-        vmax = values.max()
+    # Define limits if not defined
+    if sign_limit is None:
+        sign_limit = np.inf
+    if lFCs_limit is None:
+        lFCs_limit = np.inf
 
-    if vcenter is None:
-        vcenter = values.mean()
+    # Filter by absolute value limits
+    msk_sign = df['pvals'] < np.abs(sign_limit)
+    msk_lFCs = np.abs(df['logFCs']) < np.abs(lFCs_limit)
+    df = df.loc[msk_sign & msk_lFCs]
 
-    return vmin, vcenter, vmax
+    return df
 
 
 def plot_volcano(logFCs, pvals, contrast, name=None, net=None, top=5, source='source', target='target',
-                 weight='weight', sign_thr=0.05, lFCs_thr=0.5, figsize=(7, 5), dpi=100, ax=None,
-                 return_fig=False, save=None):
+                 weight='weight', sign_thr=0.05, lFCs_thr=0.5, sign_limit=None, lFCs_limit=None, figsize=(7, 5),
+                 dpi=100, ax=None, return_fig=False, save=None):
     """
     Plot logFC and p-values. If name and net are provided, it does the same for the targets of a selected source.
 
@@ -79,6 +89,10 @@ def plot_volcano(logFCs, pvals, contrast, name=None, net=None, top=5, source='so
         Significance threshold for p-values.
     lFCs_thr : float
         Significance threshold for logFCs.
+    sign_limit : float
+        Limit of p-values to plot in -log10.
+    lFCs_limit : float
+        Limit of logFCs to plot in absolute value.
     figsize : tuple
         Figure size.
     dpi : int
@@ -89,10 +103,22 @@ def plot_volcano(logFCs, pvals, contrast, name=None, net=None, top=5, source='so
         Whether to return a Figure object or not.
     save : str, None
         Path to where to save the plot. Infer the filetype if ending on {`.pdf`, `.png`, `.svg`}.
+
+    Returns
+    -------
+    fig : Figure, None
+        If return_fig, returns Figure object.
     """
 
     # Load plotting packages
     plt = check_if_matplotlib()
+    at = check_if_adjustText()
+
+    # Match dfs
+    index = logFCs.index.intersection(pvals.index)
+    columns = logFCs.columns.intersection(pvals.columns)
+    logFCs = logFCs.loc[index, columns]
+    pvals = pvals.loc[index, columns]
 
     # Transform sign_thr
     sign_thr = -np.log10(sign_thr)
@@ -118,6 +144,7 @@ def plot_volcano(logFCs, pvals, contrast, name=None, net=None, top=5, source='so
         df['logFCs'] = logFCs.loc[[contrast]].T
         df['pvals'] = -np.log10(pvals.loc[[contrast]].T)
         df = df[~np.any(pd.isnull(df), axis=1)]
+        df = filter_limits(df, sign_limit=sign_limit, lFCs_limit=lFCs_limit)
 
         if has_neg:
             vmin = -max_n
@@ -130,6 +157,7 @@ def plot_volcano(logFCs, pvals, contrast, name=None, net=None, top=5, source='so
         df = logFCs.loc[[contrast]].T.rename({contrast: 'logFCs'}, axis=1)
         df['pvals'] = -np.log10(pvals.loc[[contrast]].T)
         df = df[~np.any(pd.isnull(df), axis=1)]
+        df = filter_limits(df, sign_limit=sign_limit, lFCs_limit=lFCs_limit)
         df['weight'] = 'gray'
         df.loc[(df['logFCs'] >= lFCs_thr) & (df['pvals'] >= sign_thr), 'weight'] = '#D62728'
         df.loc[(df['logFCs'] <= -lFCs_thr) & (df['pvals'] >= sign_thr), 'weight'] = '#1F77B4'
@@ -150,6 +178,7 @@ def plot_volcano(logFCs, pvals, contrast, name=None, net=None, top=5, source='so
     texts = []
     for x, y, s in zip(signs['logFCs'], signs['pvals'], signs.index):
         texts.append(ax.text(x, y, s))
+    at.adjust_text(texts, arrowprops=dict(arrowstyle='-', color='black'), ax=ax)
 
     save_plot(fig, ax, save)
 
@@ -188,6 +217,11 @@ def plot_violins(mat, thr=None, log=False, use_raw=False, figsize=(7, 5), dpi=10
         Whether to return a Figure object or not.
     save : str, None
         Path to where to save the plot. Infer the filetype if ending on {`.pdf`, `.png`, `.svg`}.
+
+    Returns
+    -------
+    fig : Figure, None
+        If return_fig, returns Figure object.
     """
 
     # Load plotting packages
@@ -227,6 +261,26 @@ def plot_violins(mat, thr=None, log=False, use_raw=False, figsize=(7, 5), dpi=10
         return fig
 
 
+def set_limits(vmin, vcenter, vmax, values):
+
+    if vmin is None:
+        vmin = values.min()
+
+    if vmax is None:
+        vmax = values.max()
+
+    if vcenter is None:
+        vcenter = values.mean()
+
+    if vmin >= vcenter:
+        vmin = -vmax
+
+    if vcenter >= vmax:
+        vmax = -vmin
+
+    return vmin, vcenter, vmax
+
+
 def plot_barplot(acts, contrast, top=25, vertical=False, cmap='coolwarm', vmin=None, vcenter=0, vmax=None,
                  figsize=(7, 5), dpi=100, return_fig=False, save=None):
     """
@@ -258,15 +312,26 @@ def plot_barplot(acts, contrast, top=25, vertical=False, cmap='coolwarm', vmin=N
         Whether to return a Figure object or not.
     save : str, None
         Path to where to save the plot. Infer the filetype if ending on {`.pdf`, `.png`, `.svg`}.
+
+    Returns
+    -------
+    fig : Figure, None
+        If return_fig, returns Figure object.
     """
+
     # Load plotting packages
     sns = check_if_seaborn()
     plt = check_if_matplotlib()
     mpl = check_if_matplotlib(return_mpl=True)
 
+    # Check for non finite values
+    if np.any(~np.isfinite(acts)):
+        raise ValueError('Input acts contains non finite values.')
+
     # Process df
     df = acts.loc[[contrast]]
-
+    df.index.name = None
+    df.columns.name = None
     df = (df
 
           # Sort by absolute value and transpose
@@ -319,3 +384,311 @@ def plot_barplot(acts, contrast, top=25, vertical=False, cmap='coolwarm', vmin=N
 
     if return_fig and ax is None:
         return fig
+
+
+def build_msks(df, groupby):
+
+    # Process groupby
+    if type(groupby) is str:
+        groupby = [groupby]
+
+    # Build msks
+    if groupby is None:
+        msks = [np.full(df.shape[0], True)]
+        cats = [None]
+    else:
+        msks = []
+        sub = df[groupby].values
+        cats = np.unique(sub)
+        for cat in cats:
+            msk = sub == cat
+            msks.append(msk)
+    return msks, cats
+
+
+def write_labels(ax, title, xlabel, ylabel, x, y):
+
+    if title is not None:
+        ax.set_title(title)
+    if xlabel is None:
+        xlabel = x.upper()
+    ax.set_xlabel(xlabel)
+    if ylabel is None:
+        ylabel = y.upper()
+    ax.set_ylabel(ylabel)
+
+
+def plot_metrics_scatter(df, x='auroc', y='auprc', groupby=None, show_text=True, show_legend=True, mirror_xy=True,
+                         figsize=(5, 5), dpi=100, ax=None, title=None, xlabel=None, ylabel=None, color='black',
+                         return_fig=False, save=None):
+    """
+    Plot scatter plot of metrics across two different axes.
+
+    Parameters
+    ----------
+    df : DataFrame
+        Performance metrics per method, obtained by running run_benchmark.
+    x : str
+        Name of the metric to plot in the x axis.
+    y : str
+        Name of the metric to plot in the y axis.
+    groupby : str
+        Metrics can be gruped by an extra categorical column.
+    show_text : bool
+        Whether to plot text labels.
+    show_legend : bool
+        Whether to plot the legend.
+    mirror_xy : bool
+        Whether to make x and y axis have the same values.
+    figsize : tuple
+        Figure size.
+    dpi : int
+        DPI resolution of figure.
+    ax : Axes, None
+        A matplotlib axes object. If None returns new figure.
+    title : str
+        Text to write as title of the plot.
+    xlabel : str
+        Text to write as xlabel of the plot.
+    ylabel : str
+        Text to write as ylabel of the plot.
+    color : str
+        Color to plot the dots.
+    return_fig : bool
+        Whether to return a Figure object or not.
+    save : str, None
+        Path to where to save the plot. Infer the filetype if ending on {`.pdf`, `.png`, `.svg`}.
+
+    Returns
+    -------
+    fig : Figure, None
+        If return_fig, returns Figure object.
+    """
+
+    # Load plotting packages
+    plt = check_if_matplotlib()
+    at = check_if_adjustText()
+
+    # Build msks and cats
+    msks, cats = build_msks(df, groupby)
+
+    # Plot for each group in groupby
+    fig = None
+    texts = []
+    for cat, msk in zip(cats, msks):
+
+        # Reformat df
+        sub = (
+            df[msk]
+            .groupby(['method', 'metric'])
+            .mean().reset_index()
+            .pivot(index='method', columns='metric', values='score').reset_index()
+        )
+
+        # Extract values
+        x_vals = sub[x].values
+        y_vals = sub[y].values
+        s_vals = sub['method'].values
+
+        # Plot
+        if ax is None:
+            fig, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi)
+        ax.set_axisbelow(True)
+        ax.grid(zorder=0)
+        ax.scatter(x_vals, y_vals, zorder=1, label=cat)
+        if show_text:
+            text = [ax.text(x_vals[i], y_vals[i], s_vals[i], zorder=2) for i in range(len(x_vals))]
+            texts.extend(text)
+
+    # Add legend
+    if groupby is not None and show_legend:
+        ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+    # Adjust limits
+    if mirror_xy:
+        max_n = np.max([np.max(ax.get_xticks()), np.max(ax.get_yticks())])
+        min_n = np.min([np.min(ax.get_xticks()), np.min(ax.get_yticks())])
+        ax.set_xlim(min_n, max_n)
+        ax.set_ylim(min_n, max_n)
+    if show_text:
+        at.adjust_text(texts, arrowprops=dict(arrowstyle='-', color='gray'), ax=ax)
+
+    # Write labels
+    write_labels(ax, title, xlabel, ylabel, x, y)
+
+    save_plot(fig, ax, save)
+
+    if return_fig and ax is None:
+        return fig
+
+
+def plot_metrics_scatter_cols(df, col, x='auroc', y='auprc', groupby=None, n_cols=4, figsize=(10, 12), dpi=100,
+                              return_fig=False, save=None):
+    """
+    Extension of the function plot_metrics_scatter to group metrics by two categories at the same time.
+
+    Parameters
+    ----------
+    df : DataFrame
+        Performance metrics per method, obtained by running run_benchmark.
+    col : str
+        Name of the group column to group by. Each of its categories will become a subplot.
+    x : str
+        Name of the metric to plot in the x axis.
+    y : str
+        Name of the metric to plot in the y axis.
+    groupby : str
+        Name of the group column to additionaly group by. Each of its categories will appear in the legend.
+    n_cols : int
+        Number of columns per row.
+    figsize : tuple
+        Figure size.
+    dpi : int
+        DPI resolution of figure.
+    return_fig : bool
+        Whether to return a Figure object or not.
+    save : str, None
+        Path to where to save the plot. Infer the filetype if ending on {`.pdf`, `.png`, `.svg`}.
+
+    Returns
+    -------
+    fig : Figure, None
+        If return_fig, returns Figure object.
+    """
+
+    # Load plotting packages
+    plt = check_if_matplotlib()
+
+    # Get unique cats
+    cats = np.unique(df[col].values)
+    n_rows = int(np.ceil(cats.size / n_cols))
+
+    # Start figure
+    fig, axes = plt.subplots(n_rows, n_cols, figsize=figsize, dpi=dpi, tight_layout=True, sharex=True, sharey=True)
+    axes = axes.ravel()
+
+    # Draw a subplot per cat
+    for i in range(axes.size):
+        ax = axes[i]
+        if i < len(cats):
+            cat = cats[i]
+            plot_metrics_scatter(df[df[col] == cat], x=x, y=y, groupby=groupby, show_text=False, show_legend=False,
+                                 mirror_xy=False, ax=ax, title=cat, xlabel='', ylabel='')
+        else:
+            ax.axis('off')
+
+    # Format legend
+    handles, labels = axes[len(cats) - 1].get_legend_handles_labels()
+    fig.legend(handles, labels, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+    save_plot(fig, ax, save)
+
+    if return_fig and ax is None:
+        return fig
+
+
+def plot_metrics_boxplot(df, metric, groupby=None, figsize=(5, 5), dpi=100, ax=None, title=None,
+                         xlabel=None, ylabel=None, return_fig=False, save=None, **kwargs):
+    """
+    Plot boxplots showing the distribution of scores between methods for a metric.
+
+    Parameters
+    ----------
+    df : DataFrame
+        Performance metrics per method, obtained by running run_benchmark.
+    metric : str
+        Name of metric to plot, must be either "mcauroc" or "mcauprc".
+    groupby : str
+        Metrics can be gruped by an extra categorical column.
+    figsize : tuple
+        Figure size.
+    dpi : int
+        DPI resolution of figure.
+    ax : Axes, None
+        A matplotlib axes object. If None returns new figure.
+    title : str
+        Text to write as title of the plot.
+    xlabel : str
+        Text to write as xlabel of the plot.
+    ylabel : str
+        Text to write as ylabel of the plot.
+    return_fig : bool
+        Whether to return a Figure object or not.
+    save : str, None
+        Path to where to save the plot. Infer the filetype if ending on {`.pdf`, `.png`, `.svg`}.
+    kwargs : dict
+        Other keyword arguments are passed through to seaborn.boxplot().
+
+    Returns
+    -------
+    fig : Figure, None
+        If return_fig, returns Figure object.
+    """
+
+    # Load plotting packages
+    sns = check_if_seaborn()
+    plt = check_if_matplotlib()
+
+    if metric not in ['mcauroc', 'mcauprc']:
+        raise ValueError('Argument metric must be either "mcauroc" or "mcauprc".')
+
+    # Subset metric
+    df = df[df['metric'] == metric]
+
+    # Plot
+    fig = None
+    if ax is None:
+        fig, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi)
+    ax.set_axisbelow(True)
+    ax.grid(zorder=0)
+
+    if type(groupby) is str and groupby != 'method':
+
+        # Compute order
+        order = (
+            df
+            .groupby(['method', groupby])
+            .mean()
+            .reset_index()
+            .groupby('method')
+            .max()
+            .sort_values('score')
+            .index
+        )
+
+        sns.boxplot(x='method', y='score', hue=groupby, data=df, ax=ax, order=order, **kwargs)
+        ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+    elif groupby is None:
+
+        # Compute order
+        order = (
+            df
+            .groupby(['method'])
+            .mean()
+            .sort_values('score')
+            .index
+        )
+
+        sns.boxplot(x='method', y='score', data=df, ax=ax, order=order, **kwargs)
+
+    else:
+        raise ValueError('Argument groupby must be a string and not be "method".')
+
+    # Rotate xticks
+    ax.tick_params(axis='x', rotation=90)
+
+    # Write labels
+    if title is not None:
+        ax.set_title(title)
+    if xlabel is None:
+        xlabel = 'Methods'
+    ax.set_xlabel(xlabel)
+    if ylabel is None:
+        ylabel = metric.upper()
+    ax.set_ylabel(ylabel)
+
+    save_plot(fig, ax, save)
+
+    if return_fig and ax is None:
+        return fig

From 7803821dee2ca72e0b3122783bec36cc5b01b8b3 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 18:19:47 +0200
Subject: [PATCH 23/33] Added shuffle_net func

---
 decoupler/__init__.py |  2 +-
 decoupler/utils.py    | 92 +++++++++++++++++++++++++++++++++++--------
 2 files changed, 76 insertions(+), 18 deletions(-)

diff --git a/decoupler/__init__.py b/decoupler/__init__.py
index bbc804f..3f4993b 100644
--- a/decoupler/__init__.py
+++ b/decoupler/__init__.py
@@ -1,4 +1,4 @@
-__version__ = '1.1.17'  # noqa: F401
+__version__ = '1.1.18'  # noqa: F401
 __version_info__ = tuple([int(num) for num in __version__.split('.')])  # noqa: F401
 
 from .pre import extract, match, rename_net, get_net_mat, filt_min_n, mask_features  # noqa: F401
diff --git a/decoupler/utils.py b/decoupler/utils.py
index 66a62fc..2dad415 100644
--- a/decoupler/utils.py
+++ b/decoupler/utils.py
@@ -4,6 +4,7 @@
 """
 
 import numpy as np
+from numpy.random import default_rng
 import pandas as pd
 
 from .pre import extract, rename_net, get_net_mat, filt_min_n
@@ -17,8 +18,8 @@ def m_rename(m, name):
     m = m.rename({'index': 'sample', 'variable': 'source'}, axis=1)
 
     # Assign score or pval
-    if 'pval' in name:
-        m = m.rename({'value': 'pval'}, axis=1)
+    if 'pvals' in name:
+        m = m.rename({'value': 'pvals'}, axis=1)
     else:
         m = m.rename({'value': 'score'}, axis=1)
 
@@ -64,7 +65,7 @@ def melt(df):
                         pvals = df[methd+'_pvals'].reset_index().melt(id_vars='index')['value'].values
                     else:
                         pvals = np.full(m.shape[0], np.nan)
-                    m['pval'] = pvals
+                    m['pvals'] = pvals
 
                     res.append(m)
 
@@ -169,7 +170,7 @@ def check_corr(net, source='source', target='target', weight='weight', mat=None,
 
 def get_toy_data(n_samples=24, seed=42):
     """
-    Generate a toy `mat` and `net` for testing.
+    Generate a toy mat and net for testing.
 
     Parameters
     ----------
@@ -181,17 +182,19 @@ def get_toy_data(n_samples=24, seed=42):
     Returns
     -------
     mat : DataFrame
-        `mat` example.
+        mat example.
     net : DataFrame
-        `net` example.
+        net example.
     """
 
-    from numpy.random import default_rng
-
     # Network model
-    net = pd.DataFrame([['T1', 'G01', 1], ['T1', 'G02', 1], ['T1', 'G03', 0.7], ['T2', 'G06', 1], ['T2', 'G07', 0.5],
-                        ['T2', 'G08', 1], ['T3', 'G06', -0.5], ['T3', 'G07', -3], ['T3', 'G08', -1], ['T3', 'G11', 1]],
-                       columns=['source', 'target', 'weight'])
+    net = pd.DataFrame([
+        ['T1', 'G01', 1], ['T1', 'G02', 1], ['T1', 'G03', 0.7],
+        ['T2', 'G04', 1], ['T2', 'G06', -0.5], ['T2', 'G07', -3], ['T2', 'G08', -1],
+        ['T3', 'G06', 1], ['T3', 'G07', 0.5], ['T3', 'G08', 1],
+        ['T4', 'G05', 1.9], ['T4', 'G10', -1.5], ['T4', 'G11', -2], ['T4', 'G09', 3.1],
+        ['T5', 'G09', 0.7], ['T5', 'G10', 1.1], ['T5', 'G11', 0.1],
+    ], columns=['source', 'target', 'weight'])
 
     # Simulate two population of samples with different molecular values
     rng = default_rng(seed=seed)
@@ -258,12 +261,18 @@ def summarize_acts(acts, groupby, obs=None, mode='mean', min_std=1.0):
     for i in range(n_groups):
         msk = obs == groups[i]
         f_msk = np.isfinite(acts[msk])
-        if mode == 'mean':
-            summary[i] = np.mean(acts[msk][f_msk], axis=0)
-        elif mode == 'median':
-            summary[i] = np.median(acts[msk][f_msk], axis=0)
-        else:
-            raise ValueError('mode can only be either mean or median.')
+        for i in range(n_groups):
+            msk = obs == groups[i]
+            grp_acts = acts[msk]
+            for j in range(n_features):
+                ftr_acts = grp_acts[:, j]
+                f_msk = np.isfinite(ftr_acts)
+                if mode == 'mean':
+                    summary[i, j] = np.mean(ftr_acts[f_msk])
+                elif mode == 'median':
+                    summary[i, j] = np.median(ftr_acts[f_msk])
+                else:
+                    raise ValueError('mode can only be either mean or median.')
 
     # Filter by min_std
     min_std = np.abs(min_std)
@@ -454,3 +463,52 @@ def dense_run(func, mat, net, source='source', target='target', weight='weight',
         mat.obsm[pvals.name] = pvals
     else:
         return acts, pvals
+
+
+def shuffle_net(net, target=None, weight=None, seed=42, same_seed=True):
+    """
+    Shuffle network to make it random.
+
+    Shuffle a given net by targets, weight or both at the same time.
+    If only targets are shuffled, targets will change but the distirbution of weights for each footprint will be preserved.
+    If only weights are shuffled, targets will be the same but the distirbution of weights for each footprint will change.
+    If targets and weights are shuffled at the same time, both targets and weight distirbtion will change for each footprint.
+
+    Parameters
+    ----------
+    net : DataFrame
+        Network in long format.
+    target : str
+        Column name in net with target nodes.
+    weight : str
+        Column name in net with weights.
+    seed : int
+        Random seed to use.
+    same_seed : bool
+        Whether to share seed between targets and weights if both are not None.
+    """
+
+    # Make copy of net
+    rnet = net.copy()
+
+    # Shuffle
+    if target is None and weight is None:
+        raise ValueError('If target and weight are None, nothing is shuffled.')
+
+    if target is not None:
+        if target not in rnet.columns:
+            raise ValueError('Colum target="{0}" not in rnet. Specify a valid column name.'.format(target))
+        else:
+            rng = default_rng(seed=seed)
+            rng.shuffle(rnet[target].values)
+
+    if weight is not None:
+        if weight not in rnet.columns:
+            raise ValueError('Colum weight="{0}" not in rnet. Specify a valid column name.'.format(weight))
+        else:
+            if not same_seed:
+                seed = seed + 1
+            rng = default_rng(seed=seed)
+            rng.shuffle(rnet[weight].values)
+
+    return rnet

From 2db6d63ddc99da799567891a15e3dc31643d0261 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 18:23:41 +0200
Subject: [PATCH 24/33] Added matching check for extract_psbulk_inputs

---
 decoupler/__init__.py      |  2 +-
 decoupler/utils_anndata.py | 20 +++++++++++++++-----
 2 files changed, 16 insertions(+), 6 deletions(-)

diff --git a/decoupler/__init__.py b/decoupler/__init__.py
index 3f4993b..f634fa2 100644
--- a/decoupler/__init__.py
+++ b/decoupler/__init__.py
@@ -1,4 +1,4 @@
-__version__ = '1.1.18'  # noqa: F401
+__version__ = '1.1.19'  # noqa: F401
 __version_info__ = tuple([int(num) for num in __version__.split('.')])  # noqa: F401
 
 from .pre import extract, match, rename_net, get_net_mat, filt_min_n, mask_features  # noqa: F401
diff --git a/decoupler/utils_anndata.py b/decoupler/utils_anndata.py
index 427547a..eeb3758 100644
--- a/decoupler/utils_anndata.py
+++ b/decoupler/utils_anndata.py
@@ -66,10 +66,20 @@ def extract_psbulk_inputs(adata, obs, layer, use_raw):
         if obs is None:
             raise ValueError('If adata is a pd.DataFrame, obs cannot be None.')
 
+        # Match indexes of X with obs if DataFrame
+        idxs = adata.index
+        try:
+            obs = obs.loc[idxs]
+        except KeyError:
+            raise KeyError('Indices in obs do not match with mat\'s.')
+
     if type(X) is not csr_matrix:
         X = csr_matrix(X)
 
-    return X, obs, var
+    # Sort genes
+    msk = np.argsort(var.index)
+
+    return X[:, msk], obs, var.iloc[msk]
 
 
 def check_for_raw_counts(X):
@@ -189,7 +199,7 @@ def get_pseudobulk(adata, sample_col, groups_col, obs=None, layer=None, use_raw=
     groups_col : str
         Column of `obs` where to extract the groups names.
     obs : DataFrame, None
-        If provided, meta-data dataframe.
+        If provided, metadata dataframe.
     layer : str
         If provided, which element of layers to use.
     use_raw : bool
@@ -456,7 +466,7 @@ def get_top_targets(logFCs, pvals, contrast, name=None, net=None, source='source
         pval_col = 'adj_pvals'
     else:
         pval_col = 'pvals'
-        
+
     # Filter by thresholds
     df = df[(np.abs(df['logFCs']) >= lFCs_thr) & (np.abs(df[pval_col]) <= sign_thr)]
 
@@ -478,7 +488,7 @@ def format_contrast_results(logFCs, pvals):
         DataFrame in long format.
     """
 
-    df = melt([logFCs, pvals]).rename({'sample': 'contrast', 'source': 'name', 'score': 'logFC'}, axis=1)
-    df = df[['contrast', 'name', 'logFC', 'pval']].sort_values('pval').reset_index(drop=True)
+    df = melt([logFCs, pvals]).rename({'sample': 'contrast', 'source': 'name', 'score': 'logFCs'}, axis=1)
+    df = df[['contrast', 'name', 'logFCs', 'pvals']].sort_values('pvals').reset_index(drop=True)
 
     return df

From 21373d37b2d54633a70b7c0813179ac58bda5834 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 18:29:59 +0200
Subject: [PATCH 25/33] Added metrics

---
 decoupler/__init__.py |   2 +-
 decoupler/metrics.py  | 247 ++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 248 insertions(+), 1 deletion(-)
 create mode 100644 decoupler/metrics.py

diff --git a/decoupler/__init__.py b/decoupler/__init__.py
index f634fa2..f5a6e02 100644
--- a/decoupler/__init__.py
+++ b/decoupler/__init__.py
@@ -1,4 +1,4 @@
-__version__ = '1.1.19'  # noqa: F401
+__version__ = '1.1.20'  # noqa: F401
 __version_info__ = tuple([int(num) for num in __version__.split('.')])  # noqa: F401
 
 from .pre import extract, match, rename_net, get_net_mat, filt_min_n, mask_features  # noqa: F401
diff --git a/decoupler/metrics.py b/decoupler/metrics.py
new file mode 100644
index 0000000..c5f6a34
--- /dev/null
+++ b/decoupler/metrics.py
@@ -0,0 +1,247 @@
+"""
+Metric functions to evaluate benchamrk performance.
+Functions to compute metrics for evaluating methods and networks.
+"""
+
+import numpy as np
+import numba as nb
+
+
+@nb.njit(nb.types.UniTuple(nb.f4[:], 3)(nb.f4[:], nb.f4[:]), cache=True)
+def binary_clf_curve(y_true, y_score):
+
+    # Sort scores
+    idx = np.flip(np.argsort(y_score))
+    y_score = y_score[idx]
+    y_true = y_true[idx]
+
+    # Find unique value idxs
+    idx = np.where(np.diff(y_score))[0]
+
+    # Append a value for the end of the curve
+    idx = np.append(idx, y_true.size - 1)
+
+    # Acucmulate TP with decreasing threshold
+    tps = np.cumsum(y_true)[idx]
+    fps = 1 + idx - tps
+
+    return fps, tps, y_score[idx]
+
+
+@nb.njit(nb.types.UniTuple(nb.f4[:], 3)(nb.f4[:], nb.f4[:]), cache=True)
+def roc_curve(y_true, y_score):
+
+    # Compute binary curve
+    fps, tps, thr = binary_clf_curve(y_true, y_score)
+
+    # Add limits
+    fps = np.append(nb.float32(0), fps)
+    tps = np.append(nb.float32(0), tps)
+    thr = np.append(thr[0] + nb.float32(1), thr)
+
+    # Compute ratios
+    fpr = fps / fps[-1]
+    tpr = tps / tps[-1]
+
+    return fpr, tpr, thr
+
+
+@nb.njit(nb.types.UniTuple(nb.f4[:], 3)(nb.f4[:], nb.f4[:], nb.f4), cache=True)
+def prc_curve(y_true, y_score, pi0):
+
+    # Compute binary curve
+    fps, tps, thr = binary_clf_curve(y_true, y_score)
+
+    # Compute prc
+    ps = tps + fps
+    msk = ps != 0
+    if pi0 > 0 and pi0 < 1:
+        # Siblini W., Fréry J., He-Guelton L., Oblé F., Wang YQ. (2020) Master
+        # Your Metrics with Calibration. In: Berthold M., Feelders A., Krempl G.
+        # (eds) Advances in Intelligent Data Analysis XVIII. IDA 2020. Lecture
+        # Notes in Computer Science, vol 12080. Springer, Cham
+        pi = np.sum(y_true) / nb.float32(y_true.size)
+        ratio = pi * (nb.float32(1) - pi0) / (pi0 * (nb.float32(1) - pi))
+        prc = tps[msk] / (tps[msk] + ratio * fps[msk])
+    else:
+        prc = np.divide(tps[msk], ps[msk])
+
+    # Compute rcl
+    rcl = tps / tps[-1]
+
+    # Flip and add limits
+    prc = np.append(np.flip(prc), nb.float32(1))
+    rcl = np.append(np.flip(rcl), nb.float32(0))
+    thr = np.flip(thr)
+
+    return prc, rcl, thr
+
+
+@nb.njit(nb.f4(nb.f4[:], nb.f4[:]), cache=True)
+def auc(x, y):
+
+    # Compute diff
+    dx = np.diff(np.ascontiguousarray(x))
+
+    # Get direction slope
+    if np.all(dx <= 0):
+        d = nb.float32(-1)
+    else:
+        d = nb.float32(1)
+
+    # Compute area
+    ret = np.sum((dx * (y[1:] + y[:-1]) / nb.float32(2.0)))
+    area = d * ret
+
+    return area
+
+
+@nb.njit(nb.f4(nb.f4[:], nb.f4[:]), cache=True)
+def roc_auc(y_true, y_score):
+
+    # Compute roc curve
+    fpr, tpr, _ = roc_curve(y_true, y_score)
+
+    # Compute auc
+    return auc(fpr, tpr)
+
+
+@nb.njit(nb.f4(nb.f4[:], nb.f4[:], nb.f4), cache=True)
+def prc_auc(y_true, y_score, pi0):
+
+    # Compute prc curve
+    prc, rcl, _ = prc_curve(y_true, y_score, pi0)
+
+    # Compute auc
+    dx = np.diff(np.ascontiguousarray(rcl))
+    return -np.sum(dx * prc[:-1])
+
+
+def check_m_inputs(y_true, y_score):
+    unq = np.sort(np.unique(y_true))
+    lbl = np.array([0, 1])
+    if unq.size > 2:
+        raise ValueError("""Ground truth vector contains more than one
+        class. Only binary classes are supported.""")
+    else:
+        if not np.all(unq == lbl):
+            raise ValueError("""Ground truth binary classes must be 0 and 1.""")
+    assert y_true.size == y_score.size, 'y_true and y_score must have the same length.'
+
+
+def metric_auroc(y_true, y_score):
+    """
+    Area Under the Receiver Operating characteristic Curve (AUROC)
+    """
+
+    # Flatten
+    y_true = np.asarray(y_true, dtype=np.float32).flatten()
+    y_score = np.asarray(y_score, dtype=np.float32).flatten()
+
+    # Check inputs
+    check_m_inputs(y_true, y_score)
+
+    return roc_auc(y_true, y_score)
+
+
+def metric_auprc(y_true, y_score, pi0=None):
+    """
+    Area Under the Precision-Recall Curve (AUPRC)
+    """
+
+    # Flatten
+    y_true = np.asarray(y_true, dtype=np.float32).flatten()
+    y_score = np.asarray(y_score, dtype=np.float32).flatten()
+
+    # Check inputs
+    check_m_inputs(y_true, y_score)
+
+    if pi0 is None:
+        pi0 = 0.0
+
+    return prc_auc(y_true, y_score, pi0)
+
+
+@nb.njit(nb.types.Tuple((nb.f4[:], nb.f4[:, :]))(nb.f4[:], nb.f4[:], nb.i4, nb.i4), cache=True)
+def mc_perm(y_true, y_score, n_iter, seed):
+
+    # Separate TPs from TNs
+    msk = y_true != 0
+
+    # Split TP and TN
+    tp_score = y_score[msk]
+    tn_score = y_score[~msk]
+    tp_true = y_true[msk]
+    tn_true = y_true[~msk]
+    n_tp = tp_score.size
+
+    # Init y_true
+    y_true = np.append(tp_true, tn_true[:n_tp])
+
+    # Set seed
+    np.random.seed(seed)
+
+    # Generate random shuffling matrix
+    idx = np.arange(tn_score.size, dtype=nb.i4)
+    scores = np.zeros((n_iter, n_tp * 2), dtype=nb.f4)
+    for i in range(n_iter):
+        r_i = np.random.choice(idx, n_tp)
+        scores[i] = np.append(tp_score, tn_score[r_i])
+
+    return y_true, scores
+
+
+@nb.njit(nb.f4[:](nb.f4[:], nb.f4[:, :]), parallel=True, cache=True)
+def mcauroc(y_true, y_score):
+    n_iter = y_score.shape[0]
+    total = np.zeros(n_iter, dtype=nb.f4)
+    for i in nb.prange(n_iter):
+        total[i] = roc_auc(y_true, y_score[i])
+    return total
+
+
+@nb.njit(nb.f4[:](nb.f4[:], nb.f4[:, :]), parallel=True, cache=True)
+def mcauprc(y_true, y_score):
+    n_iter = y_score.shape[0]
+    total = np.zeros(n_iter, dtype=nb.f4)
+    for i in nb.prange(n_iter):
+        total[i] = prc_auc(y_true, y_score[i], nb.f4(0.0))
+    return total
+
+
+def metric_mcauroc(y_true, y_score, n_iter=1000, seed=42):
+    """
+    Monte-Carlo Area Under the Receiver Operating characteristic Curve (AUROC)
+    """
+
+    # Flatten
+    y_true = np.asarray(y_true, dtype=np.float32).flatten()
+    y_score = np.asarray(y_score, dtype=np.float32).flatten()
+
+    # Check inputs
+    check_m_inputs(y_true, y_score)
+
+    # Perform MC permutations
+    y_true, y_score = mc_perm(y_true, y_score, n_iter, seed)
+
+    # Compute AUC per permutation
+    return mcauroc(y_true, y_score)
+
+
+def metric_mcauprc(y_true, y_score, n_iter=1000, seed=42):
+    """
+    Monte-Carlo Area Under the Precision-Recall Curve (AUPRC)
+    """
+
+    # Flatten
+    y_true = np.asarray(y_true, dtype=np.float32).flatten()
+    y_score = np.asarray(y_score, dtype=np.float32).flatten()
+
+    # Check inputs
+    check_m_inputs(y_true, y_score)
+
+    # Perform MC permutations
+    y_true, y_score = mc_perm(y_true, y_score, n_iter, seed)
+
+    # Compute AUC per permutation
+    return mcauprc(y_true, y_score)

From 0888d6b443b5ab3a09aedba2fabff9df301f90f0 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 18:34:09 +0200
Subject: [PATCH 26/33] Added bench utils

---
 decoupler/utils_benchmark.py | 782 +++++++++++++----------------------
 1 file changed, 280 insertions(+), 502 deletions(-)

diff --git a/decoupler/utils_benchmark.py b/decoupler/utils_benchmark.py
index c7e2203..44941ba 100644
--- a/decoupler/utils_benchmark.py
+++ b/decoupler/utils_benchmark.py
@@ -1,596 +1,374 @@
 """
-Utility functions to benchmark resources on known data
+Utility functions to benchmark methods and nets.
+Functions to benchmark methods and nets using perturbation experiments.
 """
 
-from statistics import mean
 import numpy as np
-import pandas as pd
-from sklearn.preprocessing import label_binarize
-import decoupler as dc
-from .utils_calibrated_metrics import average_precision as calibrated_average_precision
-
-from sklearn.metrics import roc_auc_score, average_precision_score
 from numpy.random import default_rng
-from tqdm import tqdm
-import matplotlib.pyplot as plt
+import pandas as pd
 
-def random_scores_GT(nexp=50, ncol = 4):
-    """
-    Generate random scores and groud-truth matrix, for testing
+from .utils import get_toy_data
+from .pre import match
+from .metrics import metric_auroc, metric_auprc, metric_mcauroc, metric_mcauprc
 
-    Args:
-        nexp (int, optional): Number of rows/experiments. Defaults to 50.
-        ncol (int, optional): Number of classes/TFs/pathways. Defaults to 4.
 
-    Returns:
-        _type_: (DataFrame): Dataframe with scores and associated ground truth
+def get_toy_benchmark_data(n_samples=24, seed=42, shuffle_perc=0.25):
     """
-    df = np.random.randn(nexp, ncol)
-    ind = np.random.randint(0, df.shape[1] ,size=df.shape[0])
-    gt = np.zeros(df.shape)
-    gt[range(gt.shape[0]),ind] = 1
-
-    return pd.DataFrame(np.column_stack((df.flatten(), gt.flatten())), columns = ['score', 'GT'])
-
-"""
-Downsample ground truth vector 
-"""
+    Generate a toy mat, net and obs for testing the benchmark pipeline.
 
-def down_sampling(y, seed=7, n = 100):
-    """
-    Downsampling of ground truth
-    
     Parameters
     ----------
-    
-    y: array
-        binary groundtruth vector 
-        
-    seed: arbitrary seed for random sampling
-    
-    n: number of iterations
-        
+    n_samples : int
+        Number of samples to generate.
+    seed : int
+        Random seed to use.
+    shuffle_perc : float
+        Percentage of the ground truth to randomize.
+
     Returns
     -------
-    ds_msk: list of downsampling masks for input vectors
+    mat : DataFrame
+        mat example.
+    net : DataFrame
+        net example.
+    obs : DataFrame
+        obs example.
     """
-    
-    msk = []
-    rng = default_rng(seed)
-    
-    # Downsampling
-    for i in range(n):
-        tn_index = np.where(y == 0)[0]
-        tp_index = np.where(y != 0)[0]
-        ds_array = rng.choice(tn_index, size=len(tp_index), replace=True)
-        ds_msk = np.hstack([ds_array, tp_index])
 
-        msk.append(ds_msk)
+    # Get toy data
+    mat, net = get_toy_data(n_samples=n_samples, seed=seed)
 
-    return msk
+    # Simulate 2 populations of perturbations
+    obs = pd.DataFrame(columns=['group'])
+    n_samples = mat.shape[0]
+    n = int(n_samples/2)
+    res = n_samples % 2
+    obs['perturb'] = [['T1', 'T2'] for _ in range(n)] + [['T3', 'T4'] for _ in range(n+res)]
+    obs['group'] = np.tile(['CA', 'CB'], n+res)[: obs['perturb'].size]
+    obs['sign'] = 1
+    obs.index = mat.index.copy()
+
+    # Shuffle a percentage of the samples
+    idxs = np.arange(mat.shape[0])
+    rng = default_rng(seed=seed)
+    idxs = rng.choice(idxs, int(idxs.size * shuffle_perc), replace=False)
+    r_idxs = rng.choice(idxs, idxs.size, replace=False)
+    mat.iloc[r_idxs] = mat.iloc[idxs].values
+
+    return mat, net, obs
 
-"""
-Compute AUC of ROC or PRC
-"""
 
-def get_auc(x, y, mode, pi0 = None):
+def show_metrics():
     """
-    Computes AUROC for each label
-    
-    Parameters
-    ----------
-    
-    x: array
-        binary groundtruth vector
-        
-    y: array (flattened)
-        vector of continuous values
-
-    pi0: float
-        Reference ratio for calibrated metrics. Corresponds to the baseline/reference class inbalance to which to set the metric. Defaults to None.
-        
-        
+    Shows available evaluation metrics.
+    The first column correspond to the function name in decoupler and the second to the metrics's full name.
+
     Returns
     -------
-    auc: value of auc
+    df : DataFrame
+        Dataframe with the available metrics.
     """
 
-    if mode == "roc":
-        auc = roc_auc_score(x, y) 
-    elif mode == "prc":
-        auc = average_precision_score(x, y)
-    elif mode == 'calprc':
-        auc = calibrated_average_precision(x, y_pred= y, pi0=pi0)
-    elif mode == 'ci':
-        auc = x.sum()/x.size
-    else: 
-        raise ValueError("mode can only be roc or prc")
-    return auc
-
-def get_source_masks(long_data, sources, subset = None, min_exp = 5):
-    """
-    Generates a list of indices of a DataFrame that correspond to prediction scores and associated ground-truth for each source
+    import decoupler
 
-    Args:
-        long_data (DataFrame): DataFrame with a 'score' and 'GT' column.
-        sources (list): List of sources (have to be in correct order) for which there are entries in long_data.
-        subset (list, optional): A subset of the sources for which to make masks. If None, then the masks will be made for all targets. Defaults to None.
-        min_exp (int, optional): The minimum number of perturbation experiments required to compute an individual source performance score. Defaults to 5.
+    df = []
+    lst = dir(decoupler)
+    for m in lst:
+        if m.startswith('metric_'):
+            name = getattr(decoupler, m).__doc__.split('\n')[1].lstrip()
+            df.append([m, name])
+    df = pd.DataFrame(df, columns=['Function', 'Name'])
 
-    Returns:
-        target_ind: List of data indices for each target in the targets object
-        target_names : Target name corresponding to the elements in target_ind. Elements in subset are filtered and put in same order as in targets.
-    """
+    return df
 
-    if long_data.shape[0] < len(sources):
-        raise ValueError('The data given is smaller than the number of targets')
-    elif long_data.shape[0] % len(sources) != 0:
-        raise ValueError('The data is likely misshapen: the number of rows cannot be divided by the number of targets')
 
-    if subset is not None:
-        iterateover = np.argwhere(np.in1d(sources, subset)).flatten().tolist()
-        source_names = [sources[i] for i in iterateover]
-    else:
-        iterateover = range(len(sources))
-        source_names = sources
-
-    source_ind = []
-    names = []
-    #create indexes
-    for id, name in zip(iterateover, source_names):
-        ind = np.arange(id, long_data.shape[0], len(sources))
-        # check that there is at least min_exp perturbations
-        if long_data.iloc[ind,:]['GT'].sum() >= min_exp:
-            source_ind.append(ind)
-            names.append(name)
-
-    return source_ind, names
-
-def get_performance(data, metric = 'roc', n_iter = 100, seed = 42, prefix = None, pi0 = 0.5):
-    """
-    Compute binary classifier performance
-
-    Args:
-        data (DataFrame): DataFrame with a 'score' and 'GT' column. 'GT' column contains groud-truth ( e.g. 0, 1). 'score' can be continuous or same as 'GT'
-        metric (str or list of str, optional): Which metric(s) to use. Currently implemeted methods are: 'mcroc', 'mcprc', 'roc', 'prc', 'calprc' and 'ci'. Defaults to 'roc'.
-        n_iter (int, optional): Number of iterations for the undersampling procedures for the 'mcroc' and 'mcprc' metrics. Defaults to 100.
-        seed (int, optional): Seed used to generate the undersampling for the 'mcroc' and 'mcprc' metrics. Defaults to 42.
-        prefix (str, optional): Added as prefix to the performance metric key in the output dictionary e.g. 'mlm_roc' if equal to 'mlm'. Defaults to None.
-        pi0 (float, optional): Reference ratio used to calculate calibrated metrics. Must be between 0 and 1. Defaults to 0.5.
-
-    Returns:
-        perf: Dict of prediction performance(s) on the given data. 'mcroc' and 'mcprc' metrics will return the values for each sampling. Other methods return a single value.
-    """
+def validate_metrics(metrics):
 
-    available_metrics = ['mcroc', 'mcprc', 'roc', 'prc', 'calprc', 'ci']
-    metrics = [available_metrics[i] for i in np.argwhere(np.in1d(available_metrics, metric)).flatten().tolist()]
-
-    if len(metrics) == 0:
-        raise ValueError('None of the performance metrics given as parameter have been implemented')
-
-    if 'mcroc' in metrics or 'mcprc' in metrics:
-        masks = down_sampling(y = data['GT'].values, seed=seed, n=n_iter)
-
-    perf = {}
-    for met in metrics:
-        if met == 'mcroc' or met == 'mcprc':
-            # Compute AUC for each mask (with equalised class priors)
-            aucs = []
-            for mask in masks:
-                auc = get_auc(x = data['GT'][mask],
-                            y = data['score'][mask],
-                            mode = met[2:])
-                aucs.append(auc)
-
-        elif met == 'roc' or met == 'prc' or met == 'calprc' or met == 'ci':
-            # Compute AUC on the whole (unequalised class priors) data
-            aucs = get_auc(x = data['GT'], y = data['score'], mode = met, pi0 = pi0)
-        
-        if prefix is None:
-            perf[met] = aucs
-        else:
-            perf[prefix + '_' + met] = aucs
+    # Check if not list
+    if type(metrics) is str:
+        metrics = [metrics]
 
-    return perf
+    # Retrieve available metrics
+    a_metrics = [metric.split('metric_')[1] for metric in show_metrics()['Function']]
 
-def get_source_performance(data, sources, metric='roc', subset = None, n_iter = 100, seed = 42, prefix = None, pi0 = 0.5, min_exp = 5):
-    """
-    Compute binary classifier performance for each source or susbet of sources
-
-    Args:
-        data (DataFrame): DataFrame with a 'score' and 'GT' column. 'GT' column contains groud-truth ( e.g. 0, 1). 'score' can be continuous or same as 'GT'
-        targets (list of str): List of targets (have to be in correct order) for which there are entries in data.
-        metric (str, or list of str optional): Which metrics to use. Currently implemeted methods are: 'mcroc', 'mcprc', 'roc', 'prc'. Defaults to 'mcroc'.
-        subset (list of str, optional): A subset of the targets for which to compute performance. If None, then the performance will be calculated for all targets. Defaults to None.
-        n_iter (int, optional): Number of iterations for the undersampling procedures for the 'mcroc' and 'mcprc' metrics. Defaults to 100.
-        seed (int, optional):  Seed used to generate the undersampling for the 'mcroc' and 'mcprc' metrics. Defaults to 42.
-        prefix (str, optional): Added as prefix to the output dictionary. Defaults to 100.
-        pi0 (float, optional): Reference ratio for calibrated metrics. Corresponds to the baseline/reference class inbalance to which to set the metric. Should be between 0 and 1. Defaults to 0.5.
-        min_exp (int, optional): Minium number of perturbation experiments required to compute performance scores. Defaults to 5.
-
-    Returns:
-        perf : Dict of prediction performance(s) for each target or subset of targets. 'mcroc' and 'mcprc' metrics will return the values for each sampling. Other methods return a single value.
-    """
+    # Check if given metrics exist
+    for metric in metrics:
+        if metric not in a_metrics:
+            raise ValueError("""Metric {0} not available, please run show_metrics() to see the list of available
+            metrics.""".format(metric))
 
-    masks, source_names = get_source_masks(data, sources, subset = subset, min_exp = min_exp)
 
-    perf = {}
-    for src, name in zip(masks, source_names):
-        if prefix is None:
-            p = name
-        else:
-            p = prefix  + '_' + name
+def compute_metric(act, grt, metric, pi0=0.5, n_iter=1000, seed=42):
 
-        perf.update(get_performance(data.iloc[src.tolist()].reset_index(), metric, n_iter, seed, prefix = p, pi0=pi0))
+    if metric == 'auroc':
+        scores = metric_auroc(grt, act)
+    elif metric == 'auprc':
+        scores = metric_auprc(grt, act, pi0=pi0)
+    elif metric == 'mcauroc':
+        scores = metric_mcauroc(grt, act, n_iter=n_iter, seed=seed)
+    elif metric == 'mcauprc':
+        scores = metric_mcauprc(grt, act, n_iter=n_iter, seed=seed)
 
-    return perf
+    # Output must be list
+    if type(scores) is not np.ndarray:
+        scores = np.array([scores])
 
-def get_meta_masks(flat_data, metadata, column, min_exp = 5):
+    return scores
 
-    items = np.sort(metadata[column].unique())
-    n_features = flat_data.shape[0] / metadata.shape[0]
 
-    indexes = []
-    names = []
-    for item in items:
-        #get row numbers of all experiments corresponding to this level
-        ids = np.flatnonzero(metadata[column] == item)
-        if len(ids) >= min_exp:
-            #find indexes of flattened array from row number in original array
-            indexes.append(np.concatenate([np.arange(id * n_features, (id * n_features) + n_features, step = 1) for id in ids]))
-            names.append(str(item))
-        
-    return indexes, names
+def append_by_experiment(df, grpby_i, grp, act, grt, srcs, mthds, metrics, min_exp=5, pi0=0.5,
+                         n_iter=1000, seed=42):
 
-def get_meta_performance(flat_data, metadata, columns, metric= 'roc', n_iter = 100, seed = 7, prefix= None, pi0=0.5, min_exp = 5):
+    # Flatten act by method
+    act, grt = act.reshape(-1, act.shape[-1]).T, grt.flatten()
 
-    if type(columns) != list:
-        columns = [columns]
+    # Compute Class Imbalance
+    ci = np.sum(grt) / len(grt)
 
-    #check that columns are in metadata
-    columns = list(set(metadata.columns).intersection(set(columns)))
+    # Compute per method and metric
+    for m in range(len(mthds)):
+        mth = mthds[m]
+        for metric in metrics:
+            scores = compute_metric(act[m], grt, metric, pi0=pi0, n_iter=n_iter, seed=seed)
+            for score in scores:
+                row = [grpby_i, grp, None, mth, metric, score, ci]
+                df.append(row)
 
-    if len(columns) == 0:
-        raise ValueError('None of the columns are in the metadata')
 
-    perf = {}
-    for column in columns:
+def append_by_source(df, grpby_i, grp, act, grt, srcs, mthds, metrics, min_exp=5, pi0=0.5,
+                     n_iter=1000, seed=42):
 
-        masks, names = get_meta_masks(flat_data, metadata, column = column, min_exp = min_exp)
+    # Remove sources with less than min_exp
+    src_msk = np.sum(grt > 0., axis=0) >= min_exp
+    act, grt = act[:, src_msk, :], grt[:, src_msk]
+    srcs = srcs[src_msk]
 
-        for slice, name in zip(masks, names):
-            name = column + ':' + name
-            if prefix is None:
-                p = name
-            else:
-                p = prefix  + '_' + name
+    # Compute per source, method and metric
+    for s in range(len(srcs)):
+        src = srcs[s]
+        tmp_grt = grt[:, s]
 
-            perf.update(get_performance(flat_data.iloc[slice.tolist()].reset_index(), metric, n_iter, seed, prefix = p, pi0=pi0))
+        # Compute Class Imbalance
+        ci = np.sum(tmp_grt) / len(tmp_grt)
 
-    return perf
+        for m in range(len(mthds)):
+            mth = mthds[m]
+            tmp_act = act[:, s, m]
+            for metric in metrics:
+                scores = compute_metric(tmp_act, tmp_grt, metric, pi0=pi0, n_iter=n_iter, seed=seed)
+                for score in scores:
+                    row = [grpby_i, grp, src, mth, metric, score, ci]
+                    df.append(row)
 
-def get_scores_GT(decoupler_results, metadata, by = None, min_exp = 5):
-    """
 
-    Convert decouple output to flattenend vectors and combine with GT information
+def append_metrics_scores(df, grpby_i, grp, act, grt, srcs, mthds, metrics, by, min_exp=5, pi0=0.5,
+                          n_iter=1000, seed=42):
 
-    Args:
-        decoupler_results (dict): Output of decouple
-        metadata (DataFrame): Metadata of the perturbation experiment containing the activated/inhibited targets and the sign of the perturbation
-        meta_perturbation_col (str, optional): Column name in the metadata with perturbation targets. Defaults to 'target'.
-        by (str or list of str, optional): How to decompose performance. By 'sign' will also subselect scores of activating/inhibiting perturbations specifically, in addition to the overall scores. Defaults to None.
-        min_exp (int, optional): Min number of perturbation experiments in order to compute score. Defaults to 5.
+    if not min_exp > 0:
+        raise ValueError('Argument min_exp must be bigger than 0.')
 
+    if by == 'experiment':
+        append_by_experiment(df, grpby_i, grp, act, grt, srcs, mthds, metrics, min_exp=min_exp, pi0=pi0,
+                             n_iter=n_iter, seed=seed)
 
-    Returns:
-        scores_gt: dict of flattenend dataframes for each method
-        targets: dict with target names for which activities were inferred for each method respectively
-        meta: filtered metadata, based on decoupler output
-    """
-    computed_methods = list(set([i.split('_')[0] for i in decoupler_results.keys()])) # get the methods that were able to be computed (filtering of methods done by decouple)
-    scores_gt = {}
-    targets = {}
-    metadatas = {}
+    elif by == 'source':
+        append_by_source(df, grpby_i, grp, act, grt, srcs, mthds, metrics, min_exp=min_exp, pi0=pi0,
+                         n_iter=n_iter, seed=seed)
 
-    for m in computed_methods:
-        # estimates = res[m + 'estimate']
-        # pvals = res[m + 'pvals']
 
-        # remove experiments with no prediction for the perturbed TFs
-        missing = list(set(metadata['source']) - set(decoupler_results[m + '_estimate'].columns))
-        keep = [trgt not in missing for trgt in metadata['source'].to_list()]
-        meta = metadata[keep]
-        estimates = decoupler_results[m + '_estimate'][keep]
-        # pvals = res[m + '_pvals'][keep]
-
-        # mirror estimates
-        estimates = estimates.mul(meta['sign'], axis = 0)
-        gt = meta.pivot(columns = 'source', values = 'sign').fillna(0)
+def mirror_acts(acts, grts):
 
-        # add 0s in the ground-truth array for targets predicted by decoupler
-        # for which there is no ground truth in the provided metadata (assumed 0)
-        missing = list(set(estimates.columns) - set(meta['source']))
-        gt = pd.concat([gt, pd.DataFrame(0, index= gt.index, columns=missing)], axis = 1, join = 'inner').sort_index(axis=1)
-
-        flat_scores = []
-        scores_names = [m]
-
-        # flatten and then combine estimates and GT vectors
-        # set ground truth to be either 0 or 1
-        df_scores = pd.DataFrame({'score': estimates.to_numpy().flatten(), 'GT': gt.to_numpy().flatten()})
-        flat_scores.append(df_scores)
-
-        if by is not None and 'sign' in by:
-            keep = [meta['sign'] == 1, meta['sign'] == - 1]
-            sign = ['_positive', '_negative']
-
-            for ind, s in zip(keep, sign):
-                df = pd.DataFrame({'score': estimates[ind].to_numpy().flatten(), 'GT': gt[ind].to_numpy().flatten()})
-                flat_scores.append(df)
-                scores_names.append(m + s)
-
-        for score, name in zip(flat_scores, scores_names):
-            score['GT'] = abs(score['GT'])
-            if score['GT'].sum() > min_exp:
-                scores_gt[name] = score.reset_index()
-                targets[name] = list(estimates.columns)
-                metadatas[name] = meta
-
-
-    return scores_gt, targets, metadatas
-
-def format_benchmark_data(data, metadata, network, columns = None, meta_perturbation_col = 'treatment', meta_sign_col = 'sign', net_source_col = 'source', net_weight_col = 'weight', filter_experiments = True, filter_sources = False):
-
-    if metadata.shape[0] != data.shape[0]:
-        raise ValueError('The data and metadata do not have the same number of rows! ({0} vs. {1})\n'.format(data.shape[0], metadata.shape[0]))
-
-    if not all(item in network.columns for item in [net_source_col, net_weight_col]):
-        missing = list(set([net_source_col, net_weight_col]) - set(network.columns))
-        raise ValueError('{0} column(s) are missing from the input network'.format(str(missing)))
-
-    if not all(item in metadata.columns for item in [meta_perturbation_col, meta_sign_col]):
-        missing = list(set([meta_perturbation_col, meta_sign_col]) - set(metadata.columns))
-        raise ValueError('{0} column(s) are missing from the input metadata'.format(str(missing)))
-
-    network = network.rename(columns={net_source_col:'source', net_weight_col:'weight'})
-    metadata = metadata.rename(columns={meta_perturbation_col:'source', meta_sign_col:'sign'})
-
-    #subset by TFs with GT available
-    if filter_sources:
-        keep = [src in metadata['source'].to_list() for src in network['source'].to_list()]
-        network = network[keep]
-
-    # filter out experiments without predictions available
-    if filter_experiments:
-        keep = [src in network['source'].to_list() for src in metadata['source'].to_list()]
-        data = data[keep]
-        metadata = metadata[keep]
-
-    if columns is not None:
-        if type(columns) != list:
-            columns = [columns]
-
-        if meta_perturbation_col in columns:
-            columns.append('source')
-
-        if meta_sign_col in columns:
-            columns.append('sign')
-
-        #check that columns are in metadata
-        columns = list(set(metadata.columns).intersection(set(columns)))
-
-        if len(columns) == 0:
-            raise ValueError('None of the columns are in the metadata')
-
-        for c in columns:
-            if isinstance(metadata[c][0],str):
-                metadata[c] = metadata[c].str.replace('[_,:]', ' ')
-
-    return data, metadata, network, columns
-
-def performances(flat_data, sources, metadatas, columns = None, metric = 'roc', by = 'method', subset = None, n_iter = 100, seed = 7, pi0 = 0.5, min_exp = 5, verbose = True):
-
-    bench = {}
-    for method in flat_data.keys():
-        if verbose: print('Calculating performance metrics for', method)
-        if 'method' in by or 'sign' in by or 'all' in by:
-            perf = get_performance(flat_data[method], metric, n_iter = n_iter, seed = seed, prefix= method, pi0=pi0)
-            bench.update(perf)
-        if('source' in by or 'all' in by) and ('_positive' not in method and '_negative' not in method):
-            perf = get_source_performance(flat_data[method], sources[method], metric, subset = subset, n_iter = n_iter, seed = seed, prefix = method, pi0 = pi0, min_exp = min_exp)
-            bench[method + '_bySource'] = perf
-        if(columns is not None) and ('_positive' not in method and '_negative' not in method):
-            perf = get_meta_performance(flat_data[method], metadatas[method], columns, metric, n_iter = n_iter, seed = seed, prefix= method, pi0=pi0, min_exp = min_exp)
-            bench[method + '_byMeta'] = perf
-
-
-    return bench
-
-def get_mean_performances(benchmark_dict):
-    #make dataframes with mean perfomances
-    perf_method = {}
-    perf_bySource = {}
-    perf_bySign = {}
-    perf_byMeta = {}
-    for topkey, topvalue in benchmark_dict.items():
-        if '_bySource' in topkey:
-            for key, value in topvalue.items():
-                perf_bySource[key] = np.mean(value)
-        elif '_byMeta' in topkey:
-            for key, value in topvalue.items():
-                perf_byMeta[key] = np.mean(value)
-        elif '_negative' in topkey or '_positive' in topkey:
-            perf_bySign[topkey] = np.mean(topvalue)
-        else:
-            perf_method[topkey] = np.mean(topvalue)
+    for i in range(acts.shape[0]):
+        grt_i = grts[i]
 
-    perfs = []
+        # Check that exps don't have both signs of perturbations
+        sign = np.unique(grt_i[grt_i != 0])
+        if sign.size > 1:
+            raise ValueError('Experiments with sources perturbed in both signs (-1 and +1) are not supported.')
 
-    if len(perf_method) > 0:
-        perf_method = pd.DataFrame.from_dict(perf_method, orient='index').reset_index()
-        perf_method.columns = ['id','value']
-        perf_method[['method','metric']] = perf_method['id'].str.split('_', expand=True)
-        perf_method = perf_method.pivot(index='method', columns='metric', values='value').reset_index()
-        perfs.append(perf_method)
+        # Mirror acts
+        acts[i] = acts[i] * sign
 
-    if len(perf_bySign) > 0:
-        perf_bySign = pd.DataFrame.from_dict(perf_bySign, orient='index').reset_index()
-        perf_bySign.columns = ['id','value']
-        perf_bySign[['method', 'sign','metric']] = perf_bySign['id'].str.split('_', expand=True)
-        perf_bySign = perf_bySign.pivot(index=['method','sign'], columns='metric', values='value').reset_index()
-        perfs.append(perf_bySign)
+    # Set grts to 1
+    grts[grts != 0.] = 1.
 
-    if len(perf_bySource) > 0:
-        perf_bySource = pd.DataFrame.from_dict(perf_bySource, orient='index').reset_index()
-        perf_bySource.columns = ['id','value']
-        perf_bySource[['method','source','metric']] = perf_bySource['id'].str.split('_', expand=True)
-        perf_bySource = perf_bySource.pivot(index=['method','source'], columns='metric', values='value').reset_index()
-        perfs.append(perf_bySource)
 
-    if len(perf_byMeta) > 0:
-        perf_byMeta = pd.DataFrame.from_dict(perf_byMeta, orient='index').reset_index()
-        perf_byMeta.columns = ['id','value']
-        perf_byMeta[['method','meta','metric']] = perf_byMeta['id'].str.split('_', expand=True)
+def build_acts_tensor(res):
 
-        perf_byMeta = perf_byMeta.pivot(index = ['method', 'meta'], columns = 'metric', values = 'value').reset_index()
+    # Get unique methods
+    mthds = [m for m in res.keys() if '_pvals' not in m]
 
-        perf_byMeta[['meta', 'level']] = perf_byMeta['meta'].str.split(':', expand=True)
-        factors = perf_byMeta['meta'].unique()
+    # Extract dimensions
+    exps = res[mthds[0]].index.values
+    srcs = res[mthds[0]].columns.values
 
-        perf_byMeta = perf_byMeta.pivot(index=list(set(perf_byMeta.columns) -set(['meta', 'level'])), columns='meta', values='level').reset_index()
+    # Build acts tensor and sort by exps and srcs
+    n_exp, n_src, n_mth = len(exps), len(srcs), len(mthds)
+    acts = np.zeros((n_exp, n_src, n_mth))
+    for i, m in enumerate(mthds):
+        acts[:, :, i] = res[m].values
+    msk = np.argsort(srcs)
+    acts = acts[:, msk]
+    srcs = srcs[msk]
+    msk = np.argsort(exps)
+    exps = exps[msk]
 
-        perf_byMeta = perf_byMeta.rename(dict(zip(factors ,'meta_' + factors)), axis = 1)
-        perfs.append(perf_byMeta)
+    return acts, exps, srcs, mthds
 
 
-    mean_perf = pd.concat(perfs)
-    mean_perf = mean_perf.fillna('').reset_index()
+def build_grts_mat(obs, exps, srcs):
 
-    return mean_perf.drop(labels='index', axis = 1)
+    # Explode nested perturbs and pivot into mat
+    grts = obs.explode('perturb').pivot(columns='perturb', values='sign').fillna(0.)
 
+    # Sort by columns (srcs) and by rows (exps)
+    msk = np.argsort(grts.columns)
+    grts = grts.loc[exps].iloc[:, msk]
 
-def run_benchmark(data, metadata, network, methods = None, metric = ['roc', 'calprc'], columns = None, meta_perturbation_col = 'target', meta_sign_col = 'sign', net_source_col = 'source', net_weight_col = 'weight',
-filter_experiments= True, filter_sources = False, by = 'method', subset = None, min_exp = 5, pi0 = 0.5, n_iter = 100, seed = 7, verbose = True, **kwargs):
-    """
-    Benchmark methods or networks on a given set of perturbation experiments using activity inference with decoupler.
-
-    Args:
-        data (DataFrame): Gene expression data where each row is a perturbation experiment and each column a gene
-        metadata (DataFrame): Metadata of the perturbation experiment containing the activated/inhibited targets and the sign of the perturbation
-        network (DataFrame): Network in long format passed on to the decouple function
-        methods (str or list of str, optional): List of methods to run. If none are provided use weighted top performers (mlm, ulm and wsum). To benchmark all methods set to "all". Defaults to None.
-        metric (str or list of str, optional): Performance metric(s) to compute. See the description of get_performance for more details. Defaults to ['roc', 'calprc'].
-        column (str or list of str, optional): Metadata columns that contain the levels for which you want individual performance decomposition. Defaults to None.
-        meta_perturbation_col (str, optional): Column name in the metadata with perturbation targets. Defaults to 'target'.
-        meta_sign_col (str, optional): Column name in the metadata with sign of perturbation. Defaults to 'sign'.
-        net_source_col (str, optional): Column name in network with source nodes. Defaults to 'source'.
-        net_weight_col (str, optional): Column name in network with interaction weight. Defaults to 'weight'.
-        filter_experiments (bool, optional): Whether to filter out experiments whose perturbed targets cannot be infered from the given network. Defaults to True.
-        filter_sources (bool, optional): Whether to fitler out sources in the network for which there are not perturbation experiments (reduces the number of predictions made by decouple). Defaults to False.
-        by (str or list of str, optional): How to compute/decompose the performance score: any of 'method' and/or 'source'. Defaults to 'method'.
-        subset (str or list of str, optional): Subset of sources for which to compute an individual performance score. Requires by to contain 'source'. Sources with fewer than 'min_exp' experiments in the dataset are ignored Defaults to None
-        min_exp (int, optional): Minium number of perturbation experiments required to compute performance scores. Defaults to 5.
-        pi0 (float, optional): Reference ratio for calibrated metrics. Corresponds to the baseline/reference class inbalance to which to set the metric. Defaults to 0.5.
-        n_iter (int, optional): Number of iterations/subsampling used for the 'mcroc' and 'mcprc' metrics. Defaults to 100.
-        seed (int, otional): Random seed to use for subsampling for the 'mcroc' and 'mcprc' metrics. Defaults to 7.
-        verbose (bool, optional): Whether to print progession. Defaults to True.
-        **kwargs: Other arguments to pass on to decouple
-
-    Returns:
-        mean_perf: DataFrame containing the mean performance for each metric and for each method (mean has to be done for the mcroc and mcprc metrics)
-        bench: dict containing the whole data for each method and metric. Useful if you want to see the distribution for each subsampling for the mcroc and mcprc methods
-    """
+    # Remove cols that are not in res srcs
+    msk = np.isin(grts.columns.values, srcs)
+    grts = grts.loc[:, msk]
 
-    #format and filter the data, metadata and networks
-    data, metadata, network, columns = format_benchmark_data(data, metadata, network, columns, meta_perturbation_col=meta_perturbation_col, meta_sign_col = meta_sign_col,
-                                                    net_source_col=net_source_col, net_weight_col = net_weight_col, filter_experiments=filter_experiments, filter_sources=filter_sources)
+    return grts
 
-    
-    #run prediction
-    res = dc.decouple(data, network, methods=methods, verbose = verbose, **kwargs)
 
-    #flatten and select predicitons before computing performance measures
-    scores_gt, sources, metadatas = get_scores_GT(res, metadata, by= by, min_exp = min_exp)
+def unique_obs(col):
 
-    bench = performances(scores_gt, sources, metadatas, columns = columns, metric = metric, by = by, subset = subset, n_iter = n_iter, seed = seed, pi0 = pi0, min_exp = min_exp, verbose = verbose)
+    # Gets unique categories from a column with both lists and elements.
 
-    mean_perfs = get_mean_performances(bench)
+    # Init empty cats
+    cats = set()
 
-    return mean_perfs, bench
+    for row in col:
 
-def benchmark_scatterplot(mean_perf, x = 'mcroc', y = 'mcprc', ax = None, label_col=None, ann_fontsize = None):
-    """
-    Creates a scatter plot for each given method for two performance metrics
+        # Check if col elements are lists
+        if type(row) is list:
+            for r in row:
+                if r not in cats:
+                    cats.add(r)
+        else:
+            if row not in cats:
+                cats.add(row)
 
-    Args:
-        mean_perf (DataFrame): Mean performance of each method output by run_benchmark()
-        x (str, optional): Which metric to plot on the x axis. Defaults to 'mcroc'.
-        y (str, optional): Which metric to plot on the y axis. Defaults to 'mcprc'.
+    return np.sort(list(cats))
 
-    Returns:
-        ax: Axes of a scatter plot
-    """
-    mean_perf = mean_perf.reset_index()
 
-    if ax is None: ax = plt.subplot(111)
-    ax.scatter(x = mean_perf[x], y = mean_perf[y])
-    ax.set_aspect('equal')
+def build_msks_tensor(obs, groupby):
 
-    min_v = mean_perf[[x,y]].min().min()
-    max_v = mean_perf[[x,y]].max().max()
-    border = (max_v - min_v)/15
+    # If groupby
+    if groupby is not None:
 
-    ax.set_xlim(min_v - border, max_v + border)
-    ax.set_ylim(min_v - border, max_v + border)
+        # Init empty lsts
+        msks = []
+        grps = []
+        grpbys = []
+        for grpby_i in groupby:
 
-    if (x in ['roc','mcroc'] and y in ['roc','mcroc']) or (x in ['prc','mcprc', 'calprc'] and y in ['prc','mcprc', 'calprc']):
-        ax.axline((0,0),slope=1, color = 'black', linestyle = ':')
+            # Handle nested groupbys
+            if type(grpby_i) is list:
+                grpby_i = np.sort(grpby_i)
+                grpby_name = '|'.join(grpby_i)
+                if grpby_i.size > 1:
+                    obs[grpby_name] = obs[grpby_i[0]].str.cat(obs[grpby_i[1:]], sep='|')
+                grpby_i = grpby_name
 
-    if label_col is not None and label_col in mean_perf.columns:
-        for i, label in enumerate(mean_perf[label_col]):
-            if ann_fontsize is None:
-                ax.annotate(label.capitalize(), (mean_perf[x][i], mean_perf[y][i]))
-            else:
-                ax.annotate(label.capitalize(), (mean_perf[x][i], mean_perf[y][i]), fontsize = ann_fontsize)
+            # Find msk in obs based on groupby
+            grps_j = unique_obs(obs[grpby_i].values)
+            msk_i = []
+            grps_i = []
+            for grp in grps_j:
+                m = np.array([grp in lst for lst in obs[grpby_i]])
+                msk_i.append(m)
+                grps_i.append(grp)
 
-    if x in ['mcroc', 'mcprc', 'roc', 'prc', 'calprc']:
-        x = x + ' AUC'
+            # Append
+            msks.append(msk_i)
+            grpbys.append(grpby_i)
+            grps.append(grps_i)
 
-    if y in ['mcroc', 'mcprc', 'roc', 'prc', 'calprc']:
-        y = y + ' AUC'
+    else:
+        msks = None
+        grpbys = None
+        grps = None
 
-    ax.set_xlabel(x.upper())
-    ax.set_ylabel(y.upper())
+    return msks, grpbys, grps
 
-    return ax
 
-def benchmark_boxplot(benchmark_data, metric = 'mcroc', ax = None):
-    """
-    Creates boxplots for an iterative performance metric (i.e. mcroc and mcprc)
+def format_acts_grts(res, obs, groupby):
 
-    Args:
-        benchmark_data (dict): dict containing complete output from run_benchmark()
-        metric (str, optional): Metric to plot a distribution for. Either mcroc or mcprc. Defaults to 'mcroc'.
+    # Build acts tensor and sort by exps and srcs
+    acts, exps, srcs, mthds = build_acts_tensor(res)
 
-    Returns:
-        ax: Axes of a boxplot
-    """
+    # Make sure obs and acts match by exps idxs
+    obs = obs.loc[exps]
+
+    # Build sorted and filtered grts mat
+    grts = build_grts_mat(obs, exps, srcs)
+
+    # Match to same srcs between acts and grts
+    grts = match(srcs, grts.columns, grts.T).T
+
+    # Mirror acts
+    mirror_acts(acts, grts)
+
+    # Build msks tensor
+    msks, grpbys, grps = build_msks_tensor(obs, groupby)
+
+    return acts, grts, msks, srcs, mthds, grpbys, grps
+
+
+def rename_obs(obs, perturb, sign):
+
+    # Check if names are in columns
+    msg = 'Column name "{0}" not found in obs. Please specify a valid column.'
+    assert perturb in obs.columns, msg.format(perturb)
+
+    # Check that they are not the same
+    if perturb == sign:
+        raise ValueError("perturb={0} and sign={1} cannot have the same value.".format(perturb, sign))
+
+    # Validate sign
+    if type(sign) is str:
+        assert sign in obs.columns, msg.format(sign)
+        unq = np.sort(np.unique(obs[sign].values))
+        lbl = np.array([-1, 1])
+        msg = '`sign` values can only be -1 or 1, got {0}.'
+        assert np.all(np.isin(unq, lbl)), msg.format(list(unq))
+    elif sign == 1 or sign == -1:
+        obs = obs.copy()
+        obs['sign'] = sign
+        sign = 'sign'
+    else:
+        raise ValueError("If sign is not a column name, it must be 1 or -1.")
+
+    # Rename
+    obs = obs.rename(columns={perturb: 'perturb', sign: 'sign'})
+
+    return obs
+
+
+def check_groupby(obs, groupby, perturb, by):
+
+    if groupby is not None:
+        if type(groupby) is str:
+            groupby = [groupby]
+
+        for grp_i in groupby:
+            if type(grp_i) is str:
+                grp_i = [grp_i]
+            # For each group inside each groupby
+            for grp_j in grp_i:
 
-    if not (metric == 'mcprc' or metric == 'mcroc'):
-        raise ValueError('Plotting of boxplots only possible for the \'mcprc\' and \'mcroc\' methods')
+                # Check if perturb is in groupby when by=source
+                msg = 'perturb="{0}" column cannot be in groupby if by="source". Please remove it.'
+                assert not (perturb == grp_j and by == 'source'), msg.format(perturb)
 
-    #TODO: change so that input format corresponds again. Since mc is not that useful anymore, repurpose for target by target boxplots ?
-    keys = [key for key in benchmark_data.keys() if metric in key.split('_')[1]]
-    methods = [key.split('_')[0] for key in keys]
+                # Assert that columns exist in obs
+                msg = 'Column name "{0}" not found in obs. Please specify a valid column.'
+                assert grp_j in obs.columns, msg.format(grp_j)
 
-    if len(keys) == 0:
-        raise ValueError('The given metric was not found in the benchmark data')
+                # Assert that column doesn't contain "|"
+                msg = "Column names cannot contain the character \"|\", please rename column {0}.".format(grp_j)
+                assert '|' not in grp_j
 
-    if ax is None: ax = plt.subplot(111)
-    for i, key in enumerate(keys):
-        ax.boxplot(benchmark_data[key], positions = [i])
-    ax.set_xlim(-0.5, len(keys) - 0.5)
-    ax.set_ylabel(metric.upper() + ' AUC')
-    ax.set_xticklabels([m.capitalize() for m in methods])
-    
-    return ax
\ No newline at end of file
+    return groupby

From eb465f4613dcc6e278f0fde9a847bf932728b0cd Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 18:37:17 +0200
Subject: [PATCH 27/33] Added benchmarking pipeline

---
 decoupler/__init__.py  |   2 +-
 decoupler/benchmark.py | 236 +++++++++++++++++++++++++++++++++++++++++
 2 files changed, 237 insertions(+), 1 deletion(-)
 create mode 100644 decoupler/benchmark.py

diff --git a/decoupler/__init__.py b/decoupler/__init__.py
index f5a6e02..fb8eb2f 100644
--- a/decoupler/__init__.py
+++ b/decoupler/__init__.py
@@ -1,4 +1,4 @@
-__version__ = '1.1.20'  # noqa: F401
+__version__ = '1.1.22'  # noqa: F401
 __version_info__ = tuple([int(num) for num in __version__.split('.')])  # noqa: F401
 
 from .pre import extract, match, rename_net, get_net_mat, filt_min_n, mask_features  # noqa: F401
diff --git a/decoupler/benchmark.py b/decoupler/benchmark.py
new file mode 100644
index 0000000..e9bcb3d
--- /dev/null
+++ b/decoupler/benchmark.py
@@ -0,0 +1,236 @@
+"""
+Functions to benchmark methods and nets.
+Functions to benchmark methods and nets using perturbation experiments.
+"""
+
+import numpy as np
+import pandas as pd
+
+from .decouple import decouple
+from .utils_anndata import extract_psbulk_inputs
+from .pre import rename_net, filt_min_n
+from .utils_benchmark import format_acts_grts, append_metrics_scores
+from .utils_benchmark import validate_metrics, check_groupby, rename_obs
+
+
+def get_performances(res, obs, groupby, by, metrics, min_exp=5, pi0=0.5, n_iter=1000,
+                     seed=42, verbose=False):
+
+    # Return acts, grts and msks tensors
+    acts, grts, msks, srcs, mthds, grpbys, grps = format_acts_grts(res, obs, groupby)
+
+    # Init empty df
+    df = []
+    if msks is not None:
+        n_grpbys = len(msks)
+        for i in range(n_grpbys):
+            msk_i = msks[i]
+            grpby_i = grpbys[i]
+            grps_i = grps[i]
+            n_grps = len(grps_i)
+            if verbose:
+                print('Computing metrics for groupby {0}...'.format(grpby_i))
+            for j in range(n_grps):
+                msk = msk_i[j]
+                grp = grps_i[j]
+                n = np.sum(msk)
+
+                # If enough exps, subset by group
+                if n >= min_exp:
+                    act, grt = acts[msk, :, :], grts[msk, :]
+
+                    # Special case when groupby == perturb, remove extra grts
+                    if grp in srcs:
+                        m = grp == srcs
+                        grt[:, ~m] = 0.
+
+                    # Compute and append scores to df
+                    append_metrics_scores(df, grpby_i, grp, act, grt, srcs, mthds, metrics, by, min_exp=min_exp,
+                                          pi0=pi0, n_iter=n_iter, seed=seed)
+    else:
+        n_exp = acts.shape[0]
+        if n_exp >= min_exp:
+
+            # Compute and append scores to df
+            if verbose:
+                print('Computing metrics...')
+            append_metrics_scores(df, None, None, acts, grts, srcs, mthds, metrics, by, min_exp=min_exp,
+                                  pi0=pi0, n_iter=n_iter, seed=seed)
+
+    # Format df
+    df = pd.DataFrame(df, columns=['groupby', 'group', 'source', 'method', 'metric', 'score', 'ci'])
+
+    return df
+
+
+def format_benchmark_inputs(mat, obs, perturb, sign, net, groupby, by, f_expr=True, f_srcs=False,
+                            source='source', target='target', weight='weight', min_n=5,
+                            verbose=False, use_raw=True, decouple_kws={}):
+
+    # Extract inputs
+    if verbose:
+        print("Extracting inputs...")
+    mat, obs, var = extract_psbulk_inputs(mat, obs, layer=None, use_raw=use_raw)
+
+    # Format groupby
+    groupby = check_groupby(obs, groupby, perturb, by)
+
+    # Rename obs
+    obs = rename_obs(obs, perturb, sign)
+
+    # Rename net
+    if verbose:
+        print("Formating net...")
+    net = rename_net(net, source=decouple_kws['source'], target=decouple_kws['target'], weight=decouple_kws['weight'])
+    net = filt_min_n(var.index.values.astype('U'), net, min_n=decouple_kws['min_n'])
+
+    # Remove experiments without sources in net
+    if f_expr:
+        msk = np.full((obs['perturb'].size, ), False)
+        srcs = net['source'].values.astype('U')
+        for i, src in enumerate(obs['perturb']):
+            msk[i] = np.any(np.isin(src, srcs))
+        if verbose:
+            n = np.sum(~msk)
+            print("{0} experiments without sources in net, they will be removed.".format(n))
+        mat, obs = mat[msk], obs.loc[msk]
+
+    # Remove sources without experiments in obs
+    if f_srcs:
+        msk = np.isin(net['source'].values, obs['perturb'].values.ravel())
+        if verbose:
+            n = np.sum(~msk)
+            print("{0} sources without experiments in obs, they will be removed.".format(n))
+        net = net.loc[msk]
+
+    return mat, obs, var, net, groupby
+
+
+def _benchmark(mat, obs, net, perturb, sign, metrics=['auroc', 'auprc'], groupby=None, by='experiment', f_expr=True,
+               f_srcs=False, min_exp=5, pi0=0.5, n_iter=1000, seed=42, verbose=True, use_raw=True, decouple_kws={}):
+
+    # Format inputs
+    mat, obs, var, net, groupby = format_benchmark_inputs(mat, obs, perturb, sign, net, groupby, by, f_expr=f_expr,
+                                                          f_srcs=f_srcs, verbose=verbose, use_raw=use_raw,
+                                                          decouple_kws=decouple_kws)
+
+    # Reset net names args
+    decouple_kws['source'] = 'source'
+    decouple_kws['target'] = 'target'
+    decouple_kws['weight'] = 'weight'
+
+    # Run prediction
+    if verbose:
+        print('Running methods...')
+    res = decouple([mat, obs.index, var.index], net, verbose=verbose, **decouple_kws)
+
+    # Compute metrics
+    if verbose:
+        print('Calculating metrics...')
+    df = get_performances(res, obs, groupby, by, metrics, min_exp=min_exp, pi0=pi0,
+                          n_iter=n_iter, seed=seed, verbose=verbose)
+    if verbose:
+        print('Done.')
+
+    return df
+
+
+def benchmark(mat, obs, net, perturb, sign, metrics=['auroc', 'auprc', 'mcauroc', 'mcauprc'], groupby=None,
+              by='experiment', f_expr=True, f_srcs=False, min_exp=5, pi0=0.5, n_iter=1000, seed=42,
+              verbose=True, use_raw=True, decouple_kws={}):
+    """
+    Benchmark methods or networks on a given set of perturbation experiments using activity inference with decoupler.
+
+    Parameters
+    ----------
+    mat : list, DataFrame or AnnData
+        List of [features, matrix], dataframe (samples x features) or an AnnData instance.
+    obs : DataFrame or None
+        Metadata containing the perturbed targets and the sign of the perturbation. If mat is AnnData, use mat.obs
+        attribute instead.
+    net : DataFrame, dict
+        Network in long format. Can be dictionary of nets, where key is the name and value is the long format DataFrame.
+    perturb : str
+        Column name in obs with perturbed sources.
+    sign : str, int
+        Column name in obs with sign of the perturbation. Can be set to 1 or -1 if all experiments are overexpression or
+        knockouts, respectively.
+    metrics : list, str
+        Performance metric(s) to compute. See the description of get_performance for more details. Defaults
+        to ['roc', 'calprc'].
+    groupby : list, str, None
+        Performance metrics(s) can be computed per groups if enough experiments are available.
+    by : str
+        Whether to evaluate performances at the "experiment" or at the "source" level.
+    f_expr : bool
+        Whether to filter out experiments whose perturbed sources are not in the given net. Defaults to True.
+    f_srcs : bool
+        Whether to fitler out sources in net for which there are not perturbation data. Defaults to False.
+    min_exp : int
+        Minimum of perturbation experiments per group.
+    pi0 : float
+        Reference ratio for calibrated metrics. Corresponds to the baseline/reference class inbalance to which
+        to set the metric.
+    n_iter : int
+        Number of downsampling iterations used for the 'mcroc' and 'mcprc' metrics.
+    seed : int
+        Random seed to use.
+    verbose : bool
+        Whether to show progress.
+    use_raw : bool
+        Use raw attribute of mat if present.
+    decouple_kws : dict
+        Parameters for the decoupler.decouple function. If more than one net, use a nested dictionary where the main
+        key is the network name and the value is a dictionary with the requiered arguments.
+
+    Returns
+    -------
+    df : DataFrame
+        DataFrame containing the metrics' scores.
+    """
+
+    # Init default args
+    default_kws = {'source': 'source', 'target': 'target', 'weight': 'weight', 'min_n': 5}
+
+    # Validate by
+    if by not in ['experiment', 'source']:
+        raise ValueError('Argument `by` has to be either "experiment" or "source".')
+
+    # Validate metrics
+    validate_metrics(metrics)
+
+    # Validate pi0
+    if pi0 is not None:
+        if pi0 < 0 or pi0 > 1:
+            raise ValueError('Argument `pi0` needs to be between 0 and 1.')
+
+    # Run benchmark per net
+    if type(net) is not dict:
+
+        # Update decouple args
+        decouple_kws = {**default_kws, **decouple_kws}
+
+        # Run benchmark
+        df = _benchmark(mat, obs, net, perturb, sign, metrics, groupby, by, f_expr, f_srcs, min_exp, pi0,
+                        n_iter, seed, verbose, use_raw, decouple_kws)
+    else:
+        df = []
+        for net_name in net:
+
+            if verbose:
+                print('Using {0} network...'.format(net_name))
+
+            # Update decouple args
+            decouple_kws.setdefault(net_name, {})
+            decouple_kws[net_name] = {**default_kws, **decouple_kws[net_name]}
+
+            # Run benchmark
+            tmp = _benchmark(mat, obs, net[net_name], perturb, sign, metrics, groupby, by, f_expr, f_srcs,
+                             min_exp, pi0, n_iter, seed, verbose, use_raw, decouple_kws[net_name])
+            tmp['net'] = net_name
+            df.append(tmp)
+
+        # Merge all results
+        df = pd.concat(df)
+
+    return df

From 7071d45d437561348bdb2e41a19173bd828e4934 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Wed, 31 Aug 2022 18:38:28 +0200
Subject: [PATCH 28/33] Updated CI tests

---
 decoupler/tests/test_aucell.py         |   4 +-
 decoupler/tests/test_benchmark.py      |  89 +++++++
 decoupler/tests/test_consensus.py      |   3 +-
 decoupler/tests/test_decouple.py       |  11 +-
 decoupler/tests/test_gsea.py           |   4 +-
 decoupler/tests/test_gsva.py           |   8 +-
 decoupler/tests/test_mdt.py            |  21 +-
 decoupler/tests/test_metrics.py        |  94 +++++++
 decoupler/tests/test_mlm.py            |   6 +-
 decoupler/tests/test_omnip.py          |  10 +-
 decoupler/tests/test_ora.py            |  28 +-
 decoupler/tests/test_plotting.py       | 105 +++++++-
 decoupler/tests/test_pre.py            |  18 +-
 decoupler/tests/test_udt.py            |  20 +-
 decoupler/tests/test_ulm.py            |   6 +-
 decoupler/tests/test_utils.py          |  44 ++-
 decoupler/tests/test_utilsanndata.py   |  28 +-
 decoupler/tests/test_utilsbenchmark.py | 354 +++++++++++++++++++++++++
 decoupler/tests/test_viper.py          |  17 +-
 decoupler/tests/test_wmean.py          |   6 +-
 decoupler/tests/test_wsum.py           |   6 +-
 21 files changed, 806 insertions(+), 76 deletions(-)
 create mode 100644 decoupler/tests/test_benchmark.py
 create mode 100644 decoupler/tests/test_metrics.py
 create mode 100644 decoupler/tests/test_utilsbenchmark.py

diff --git a/decoupler/tests/test_aucell.py b/decoupler/tests/test_aucell.py
index 81161da..8adc35a 100644
--- a/decoupler/tests/test_aucell.py
+++ b/decoupler/tests/test_aucell.py
@@ -14,12 +14,14 @@ def test_nb_aucell():
     n_up = np.array([1], dtype=np.int32)[0]
     nb_aucell(n_samples, n_features, m.data, m.indptr, m.indices, net, offsets, n_up)
 
+
 def test_aucell():
     m = csr_matrix(np.array([[1, 0, 2], [1., 0, 3], [0, 0, 0]], dtype=np.float32))
     net = pd.Series([np.array([1, 3], dtype=np.int32), np.array([1, 3], dtype=np.int32)], index=['T1', 'T2'])
     n_up = np.array([1], dtype=np.int32)[0]
     aucell(m, net, n_up)
 
+
 def test_run_aucell():
     m = np.array([[7., 1., 1.], [4., 2., 1.], [1., 2., 5.], [1., 1., 6.]])
     r = np.array(['S1', 'S2', 'S3', 'S4'])
@@ -27,7 +29,7 @@ def test_run_aucell():
     df = pd.DataFrame(m, index=r, columns=c)
     adata = AnnData(df)
     net = pd.DataFrame([['T1', 'G2'], ['T1', 'G4'], ['T2', 'G3'], ['T2', 'G1']],
-                   columns=['source', 'target'])
+                       columns=['source', 'target'])
     run_aucell(adata, net, n_up=1.2, min_n=0, verbose=True, use_raw=False)
     with pytest.raises(ValueError):
         run_aucell(adata, net, n_up=-3, min_n=0, verbose=True, use_raw=False)
diff --git a/decoupler/tests/test_benchmark.py b/decoupler/tests/test_benchmark.py
new file mode 100644
index 0000000..7b9a575
--- /dev/null
+++ b/decoupler/tests/test_benchmark.py
@@ -0,0 +1,89 @@
+import pytest
+import numpy as np
+import pandas as pd
+from ..utils import shuffle_net
+from ..utils_benchmark import get_toy_benchmark_data
+from ..benchmark import get_performances, format_benchmark_inputs, _benchmark, benchmark
+
+
+def test_get_performances():
+    exps = np.array(['S1', 'S2', 'S3', 'S4', 'S5', 'S6'])
+    srcs = np.array(['T1', 'T2', 'T3'])
+    mthds = np.array(['m_a', 'm_b'])
+    m_a = pd.DataFrame([
+        [4., 6., 5.],
+        [1., 3., 2.],
+        [4., 6., 5.],
+        [1., 3., 2.],
+        [4., 6., 5.],
+        [1., 3., 2.]
+    ], index=exps, columns=srcs)
+    m_b = pd.DataFrame([
+        [7., 9., 8.],
+        [1., 3., 2.],
+        [7., 9., 8.],
+        [1., 3., 2.],
+        [7., 9., 8.],
+        [1., 3., 2.]
+    ], index=exps, columns=srcs)
+    res = {mthds[0]: m_a, mthds[1]: m_b}
+    obs = pd.DataFrame([
+            ['A1', 'B1', 'C1', ['T3', 'T2'], 1],
+            ['A1', 'B2', 'C1', ['T3', 'T2'], -1],
+            ['A1', 'B2', 'C2', ['T3', 'T2'], 1],
+            ['A2', 'B1', 'C1', 'T1', -1],
+            ['A2', 'B1', 'C2', 'T2', 1],
+            ['A2', 'B2', 'C2', 'T3', -1],
+        ], columns=['col_A', 'col_B', 'col_C', 'perturb', 'sign'], index=exps)
+
+    get_performances(res, obs, groupby=['col_B'], by='experiment', metrics=['auroc'], min_exp=1)
+    get_performances(res, obs, groupby=None, by='source', metrics=['auroc'], min_exp=1)
+
+
+def test_format_benchmark_inputs():
+    mat, net, obs = get_toy_benchmark_data(n_samples=6, shuffle_perc=0)
+    decouple_kws = {'source': 'source', 'target': 'target', 'weight': 'weight', 'min_n': 1}
+
+    out = format_benchmark_inputs(mat, obs, perturb='perturb', sign='sign', net=net, groupby='group',
+                                  by='experiment', f_expr=True, f_srcs=True, min_n=1, verbose=True, decouple_kws=decouple_kws)
+    assert out is not None
+
+
+def test__benchmark():
+    mat, net, obs = get_toy_benchmark_data(n_samples=6, shuffle_perc=0)
+    decouple_kws = {'source': 'source', 'target': 'target', 'weight': 'weight', 'min_n': 1}
+
+    out = _benchmark(mat, obs, net, perturb='perturb', sign='sign', metrics=['auroc'], groupby='group',
+                     by='experiment', min_exp=1, verbose=True, decouple_kws=decouple_kws)
+    assert type(out) is pd.DataFrame
+
+
+def test_benchmark():
+    mat, net, obs = get_toy_benchmark_data(n_samples=6, shuffle_perc=0)
+
+    with pytest.raises(ValueError):
+        benchmark(mat, obs, net, perturb='perturb', sign='sign', by='asd')
+    with pytest.raises(ValueError):
+        benchmark(mat, obs, net, perturb='perturb', sign='sign', pi0=-1)
+    df = benchmark(mat, obs, net, metrics=['auroc'], perturb='perturb',
+                   sign='sign', decouple_kws={'min_n': 0})
+    assert np.all(df.score.values > 0.75)
+    df = benchmark(mat, obs, net, metrics=['auroc'], perturb='perturb', sign='sign',
+                   groupby='group', decouple_kws={'min_n': 0}, min_exp=1)
+    assert np.all(df.score.values > 0.75)
+    df = benchmark(mat, obs, net, metrics=['auroc'], perturb='perturb', sign='sign',
+                   groupby='perturb', decouple_kws={'min_n': 0}, min_exp=1)
+    assert df['group'].unique().size == 4
+    assert df['ci'].unique() == 0.2
+
+    rnet = shuffle_net(net, target='target',
+                       weight='weight').drop_duplicates(['source', 'target'])
+    nets = {'net': net, 'rnet': rnet}
+    decouple_kws = {
+        'net': {'min_n': 0},
+        'rnet': {'min_n': 0}
+    }
+    df = benchmark(mat, obs, nets, metrics=['auroc'], perturb='perturb', sign='sign',
+                   decouple_kws=decouple_kws, min_exp=1)
+    msk = df['net'].values == 'net'
+    assert np.all(df['score'].values[msk] > df['score'].values[~msk])
diff --git a/decoupler/tests/test_consensus.py b/decoupler/tests/test_consensus.py
index da56d5c..6c38170 100644
--- a/decoupler/tests/test_consensus.py
+++ b/decoupler/tests/test_consensus.py
@@ -1,4 +1,3 @@
-import pytest
 import numpy as np
 import pandas as pd
 from ..consensus import z_score, mean_z_scores, cons
@@ -8,10 +7,12 @@ def test_z_score():
     arr = np.array([1., 2., 6.], dtype=np.float32)
     z_score(arr)
 
+
 def test_mean_z_scores():
     arr = np.array([[[1., 2., 3.], [4., 5., 6.]], [[7., 8., 9.], [0., 1., 2.]]], dtype=np.float32)
     mean_z_scores(arr)
 
+
 def test_cons():
     mlm_estimate = pd.DataFrame([[3.5, -0.5, 0.3], [3.6, -0.6, 0.04], [-1, 2, -1.8]],
                                 columns=['T1', 'T2', 'T3'], index=['C1', 'C2', 'C3'])
diff --git a/decoupler/tests/test_decouple.py b/decoupler/tests/test_decouple.py
index 30b292c..8714733 100644
--- a/decoupler/tests/test_decouple.py
+++ b/decoupler/tests/test_decouple.py
@@ -1,7 +1,6 @@
 import pytest
 import numpy as np
 import pandas as pd
-from scipy.sparse import csr_matrix
 from anndata import AnnData
 from ..decouple import get_wrappers, run_methods, parse_methods, decouple, run_consensus
 
@@ -9,20 +8,23 @@
 def test_get_wrappers():
     get_wrappers(['mlm', 'ulm'])
 
+
 def test_run_methods():
     m = np.array([[7., 1., 1., 1.], [4., 2., 1., 2.], [1., 2., 5., 1.], [1., 1., 6., 2.]])
     r = np.array(['S1', 'S2', 'S3', 'S4'])
     c = np.array(['G1', 'G2', 'G3', 'G4'])
     df = pd.DataFrame(m, index=r, columns=c)
     net = pd.DataFrame([['T1', 'G1', 1], ['T1', 'G2', 2], ['T2', 'G3', -3], ['T2', 'G4', 4]],
-                   columns=['source', 'target', 'weight'])
+                       columns=['source', 'target', 'weight'])
     run_methods(df, net, 'source', 'target', 'weight', ['mlm', 'ulm'], {}, 0, True, False)
 
+
 def test_parse_methods():
     parse_methods(None, None)
     parse_methods('all', None)
     parse_methods(['mlm', 'ulm'], None)
 
+
 def test_decouple():
     m = np.array([[7., 1., 1., 1.], [4., 2., 1., 2.], [1., 2., 5., 1.], [1., 1., 6., 2.]])
     r = np.array(['S1', 'S2', 'S3', 'S4'])
@@ -30,11 +32,12 @@ def test_decouple():
     df = pd.DataFrame(m, index=r, columns=c)
     adata = AnnData(df)
     net = pd.DataFrame([['T1', 'G1', 1], ['T1', 'G2', 2], ['T2', 'G3', -3], ['T2', 'G4', 4]],
-                   columns=['source', 'target', 'weight'])
+                       columns=['source', 'target', 'weight'])
     decouple(adata, net, methods=['mlm', 'ulm'], min_n=0, verbose=True, use_raw=False)
     with pytest.raises(ValueError):
         decouple(adata, net, methods=['mlm', 'ulm', 'asd'], min_n=0, verbose=True, use_raw=False)
 
+
 def test_run_consensus():
     m = np.array([[7., 1., 1., 1.], [4., 2., 1., 2.], [1., 2., 5., 1.], [1., 1., 6., 2.]])
     r = np.array(['S1', 'S2', 'S3', 'S4'])
@@ -42,5 +45,5 @@ def test_run_consensus():
     df = pd.DataFrame(m, index=r, columns=c)
     adata = AnnData(df)
     net = pd.DataFrame([['T1', 'G1', 1], ['T1', 'G2', 2], ['T2', 'G3', -3], ['T2', 'G4', 4]],
-                   columns=['source', 'target', 'weight'])
+                       columns=['source', 'target', 'weight'])
     run_consensus(adata, net, min_n=0, verbose=True, use_raw=False)
diff --git a/decoupler/tests/test_gsea.py b/decoupler/tests/test_gsea.py
index b8b7204..b4b0e72 100644
--- a/decoupler/tests/test_gsea.py
+++ b/decoupler/tests/test_gsea.py
@@ -1,4 +1,3 @@
-import pytest
 import numpy as np
 import pandas as pd
 from scipy.sparse import csr_matrix
@@ -12,6 +11,7 @@ def test_gsea():
     gsea(m, net, times=2)
     gsea(m, net, times=0)
 
+
 def test_run_gsea():
     m = np.array([[7., 1., 1.], [4., 2., 1.], [1., 2., 5.], [1., 1., 6.]])
     r = np.array(['S1', 'S2', 'S3', 'S4'])
@@ -19,6 +19,6 @@ def test_run_gsea():
     df = pd.DataFrame(m, index=r, columns=c)
     adata = AnnData(df)
     net = pd.DataFrame([['T1', 'G2'], ['T1', 'G4'], ['T2', 'G3'], ['T2', 'G1']],
-                   columns=['source', 'target'])
+                       columns=['source', 'target'])
     run_gsea(adata, net, min_n=0, use_raw=False, times=2, verbose=True)
     run_gsea(df, net, min_n=0, use_raw=False, times=2, verbose=True)
diff --git a/decoupler/tests/test_gsva.py b/decoupler/tests/test_gsva.py
index 4f98408..a54d764 100644
--- a/decoupler/tests/test_gsva.py
+++ b/decoupler/tests/test_gsva.py
@@ -1,4 +1,3 @@
-import pytest
 import numpy as np
 import pandas as pd
 from scipy.sparse import csr_matrix
@@ -9,16 +8,19 @@
 def test_init_cdfs():
     init_cdfs(pre_res=10000, max_pre=10)
 
+
 def test_density():
     m = csr_matrix(np.array([[1, 0, 2], [1., 0, 3], [0, 0, 0]], dtype=np.float32)).A
     density(m, kcdf=True)
     density(m, kcdf=False)
-    
+
+
 def test_gsva():
     m = csr_matrix(np.array([[1, 0, 2], [1., 0, 3], [0, 0, 0]], dtype=np.float32)).A
     net = pd.Series([np.array([1, 3], dtype=np.int32), np.array([1, 3], dtype=np.int32)], index=['T1', 'T2'])
     gsva(m, net)
 
+
 def test_run_gsva():
     m = np.array([[7., 1., 1.], [4., 2., 1.], [1., 2., 5.], [1., 1., 6.]])
     r = np.array(['S1', 'S2', 'S3', 'S4'])
@@ -26,5 +28,5 @@ def test_run_gsva():
     df = pd.DataFrame(m, index=r, columns=c)
     adata = AnnData(df)
     net = pd.DataFrame([['T1', 'G2'], ['T1', 'G4'], ['T2', 'G3'], ['T2', 'G1']],
-                   columns=['source', 'target'])
+                       columns=['source', 'target'])
     run_gsva(adata, net, min_n=0, use_raw=False, verbose=True)
diff --git a/decoupler/tests/test_mdt.py b/decoupler/tests/test_mdt.py
index 7139b8c..6fd5b20 100644
--- a/decoupler/tests/test_mdt.py
+++ b/decoupler/tests/test_mdt.py
@@ -1,21 +1,28 @@
-import pytest
 import numpy as np
 import pandas as pd
 from scipy.sparse import csr_matrix
 from anndata import AnnData
-from ..method_mdt import fit_rf, mdt, run_mdt
+from ..method_mdt import check_if_skranger, fit_rf, mdt, run_mdt
+
+
+def test_check_if_skranger():
+    sr = check_if_skranger()
+    assert sr is not None
 
 
 def test_fit_rf():
-    net = np.array([[1. , 0. ], [1. , 0. ], [0. , 1. ]])
-    sample = np.array([1. , 0. , 3.])
-    fit_rf(net, sample)
+    net = np.array([[1., 0.], [1., 0.], [0., 1.]])
+    sample = np.array([1., 0., 3.])
+    sr = check_if_skranger()
+    fit_rf(sr, net, sample)
+
 
 def test_mdt():
     m = csr_matrix(np.array([[7., 1., 1.], [4., 2., 1.], [1., 2., 5.], [1., 1., 6.]]))
-    net = np.array([[1. , 0. ], [1. , 0. ], [0. , 1. ]])
+    net = np.array([[1., 0.], [1., 0.], [0., 1.]])
     mdt(m, net)
 
+
 def test_run_mdt():
     m = np.array([[7., 1., 1.], [4., 2., 1.], [1., 2., 5.], [1., 1., 6.]])
     r = np.array(['S1', 'S2', 'S3', 'S4'])
@@ -23,5 +30,5 @@ def test_run_mdt():
     df = pd.DataFrame(m, index=r, columns=c)
     adata = AnnData(df)
     net = pd.DataFrame([['T1', 'G2', 1], ['T1', 'G4', 2], ['T2', 'G3', 3], ['T2', 'G1', 1]],
-                   columns=['source', 'target', 'weight'])
+                       columns=['source', 'target', 'weight'])
     run_mdt(adata, net, verbose=True, use_raw=False, min_n=0)
diff --git a/decoupler/tests/test_metrics.py b/decoupler/tests/test_metrics.py
new file mode 100644
index 0000000..4da143d
--- /dev/null
+++ b/decoupler/tests/test_metrics.py
@@ -0,0 +1,94 @@
+import pytest
+import numpy as np
+from sklearn.metrics import roc_auc_score, average_precision_score
+from sklearn.metrics._ranking import _binary_clf_curve
+from ..metrics import check_m_inputs, binary_clf_curve, mc_perm, metric_auroc, metric_auprc, metric_mcauroc, metric_mcauprc
+
+
+def test_check_m_inputs():
+    act = np.array([7, 6, 5, 5, 4, 3, 2, 1, 0])
+    grt = np.array([1, 0, -1, 0, 1, 1, 0, 0, 0])
+    with pytest.raises(ValueError):
+        check_m_inputs(y_true=grt, y_score=act)
+
+    grt = np.array([-1, 0, -1, 0, -1, -1, 0, 0, 0])
+    with pytest.raises(ValueError):
+        check_m_inputs(y_true=grt, y_score=act)
+
+    act = np.array([7, 6, 5, 5, 4, 3, 2, 1])
+    grt = np.array([1, 0, 1, 0, 1, 1, 0, 0, 0])
+    with pytest.raises(AssertionError):
+        check_m_inputs(y_true=grt, y_score=act)
+
+
+def test_binary_clf_curve():
+    act = np.array([7, 6, 5, 5, 4, 3, 2, 1, 0], dtype=np.float32)
+    grt = np.array([1, 0, 0, 0, 1, 1, 0, 0, 0], dtype=np.float32)
+
+    a = _binary_clf_curve(y_true=grt, y_score=act)
+    b = binary_clf_curve(y_true=grt, y_score=act)
+    assert np.allclose(a, b)
+
+
+def test_mc_perm():
+    act = np.array([7, 6, 5, 5, 4, 3, 2, 1, 0], dtype=np.float32)
+    grt = np.array([1, 0, 0, 0, 1, 1, 0, 0, 0], dtype=np.float32)
+
+    y_true, y_score = mc_perm(y_true=grt, y_score=act, n_iter=100, seed=42)
+    assert np.all(y_score[:, 0] == 7.)
+    assert np.all(y_score[:, 1] == 4.)
+    assert np.all(y_score[:, 2] == 3.)
+    _, counts = np.unique(y_score[:, 3:], axis=0, return_counts=True)
+    assert np.all(counts < 15)
+
+
+def test_metric_auroc():
+    act = np.array([7, 6, 5, 5, 4, 3, 2, 1, 0])
+    grt = np.array([1, 0, 0, 0, 1, 1, 0, 0, 0])
+
+    a = roc_auc_score(y_true=grt, y_score=act)
+    b = metric_auroc(y_true=grt, y_score=act)
+    assert np.isclose(a, b)
+
+    act = np.array([7, -6, 5, 5, -4, 3, 2, 1, 0])
+    grt = np.array([1, 0, 0, 0, 1, 1, 0, 0, 0])
+    a = roc_auc_score(y_true=grt, y_score=act)
+    b = metric_auroc(y_true=grt, y_score=act)
+    assert np.isclose(a, b)
+
+
+def test_metric_auprc():
+    act = np.array([7, 6, 5, 5, 4, 3, 2, 1, 0])
+    grt = np.array([1, 0, 0, 0, 1, 1, 0, 0, 0])
+
+    a = average_precision_score(y_true=grt, y_score=act)
+    b = metric_auprc(y_true=grt, y_score=act)
+    assert np.isclose(a, b)
+    b = metric_auprc(y_true=grt, y_score=act, pi0=0.5)
+    assert np.isclose(0.7460317, b)
+
+    act = np.array([7, -6, 5, 5, -4, 3, 2, 1, 0])
+    grt = np.array([1, 0, 0, 0, 1, 1, 0, 0, 0])
+    a = average_precision_score(y_true=grt, y_score=act)
+    b = metric_auprc(y_true=grt, y_score=act)
+    assert np.isclose(a, b)
+    b = metric_auprc(y_true=grt, y_score=act, pi0=0.5)
+    assert np.isclose(0.7373737, b)
+
+
+def test_metric_mcauroc():
+    act = np.array([7, 6, 5, 5, 4, 3, 2, 1, 0])
+    grt = np.array([1, 0, 0, 0, 1, 1, 0, 0, 0])
+
+    a = metric_auroc(y_true=grt, y_score=act)
+    b = metric_mcauroc(y_true=grt, y_score=act)
+    assert np.isclose(a, np.mean(b), rtol=1e-01)
+
+
+def test_metric_mcauprc():
+    act = np.array([7, 6, 5, 5, 4, 3, 2, 1, 0])
+    grt = np.array([1, 0, 0, 0, 1, 1, 0, 0, 0])
+
+    a = metric_auprc(y_true=grt, y_score=act, pi0=0.5)
+    b = metric_mcauprc(y_true=grt, y_score=act)
+    assert np.isclose(a, np.mean(b), rtol=1e-01)
diff --git a/decoupler/tests/test_mlm.py b/decoupler/tests/test_mlm.py
index 9afce0e..689bbeb 100644
--- a/decoupler/tests/test_mlm.py
+++ b/decoupler/tests/test_mlm.py
@@ -1,4 +1,3 @@
-import pytest
 import numpy as np
 import pandas as pd
 from scipy.sparse import csr_matrix
@@ -8,9 +7,10 @@
 
 def test_mlm():
     m = csr_matrix(np.array([[7., 1., 1., 1.], [4., 2., 1., 2.], [1., 2., 5., 1.], [1., 1., 6., 2.]], dtype=np.float32))
-    net = np.array([[1. , 0. ], [2 , 0. ], [0. , -3. ], [0. , 4. ]], dtype=np.float32)
+    net = np.array([[1., 0.], [2, 0.], [0., -3.], [0., 4.]], dtype=np.float32)
     mlm(m, net)
 
+
 def test_run_mlm():
     m = np.array([[7., 1., 1., 1.], [4., 2., 1., 2.], [1., 2., 5., 1.], [1., 1., 6., 2.]])
     r = np.array(['S1', 'S2', 'S3', 'S4'])
@@ -18,5 +18,5 @@ def test_run_mlm():
     df = pd.DataFrame(m, index=r, columns=c)
     adata = AnnData(df)
     net = pd.DataFrame([['T1', 'G1', 1], ['T1', 'G2', 2], ['T2', 'G3', -3], ['T2', 'G4', 4]],
-                   columns=['source', 'target', 'weight'])
+                       columns=['source', 'target', 'weight'])
     run_mlm(adata, net, verbose=True, use_raw=False, min_n=0)
diff --git a/decoupler/tests/test_omnip.py b/decoupler/tests/test_omnip.py
index 0623dfe..1bf8b70 100644
--- a/decoupler/tests/test_omnip.py
+++ b/decoupler/tests/test_omnip.py
@@ -1,35 +1,39 @@
 import pytest
 import pandas as pd
-from ..omnip import check_if_omnipath, get_progeny, get_resource, show_resources, get_resource, get_dorothea
+from ..omnip import check_if_omnipath, get_progeny, get_resource, show_resources, get_dorothea
 
 
 def test_check_if_omnipath():
     check_if_omnipath()
 
+
 def test_get_progeny():
     df = get_progeny(organism='human', top=100)
     n_paths = len(df['source'].unique())
     n_rows = (n_paths * 100)
     assert type(df) is pd.DataFrame
     assert df.shape[0] == n_rows
-    with pytest.raises(ValueError):   
+    with pytest.raises(ValueError):
         get_progeny(organism='asdfgh')
     get_progeny(organism='mouse')
 
+
 def test_get_resource():
     res = get_resource('TFcensus')
     assert type(res) is pd.DataFrame
     assert res.shape[0] > 0
 
+
 def test_show_resources():
     lst = show_resources()
     assert type(lst) is list
     assert len(lst) > 0
 
+
 def test_get_dorothea():
     df = get_dorothea(organism='human')
     assert type(df) is pd.DataFrame
     assert df.shape[0] > 0
-    with pytest.raises(ValueError):   
+    with pytest.raises(ValueError):
         get_dorothea(organism='asdfgh')
     get_dorothea(organism='mouse')
diff --git a/decoupler/tests/test_ora.py b/decoupler/tests/test_ora.py
index af5e342..6ef4469 100644
--- a/decoupler/tests/test_ora.py
+++ b/decoupler/tests/test_ora.py
@@ -3,7 +3,7 @@
 import pandas as pd
 from scipy.sparse import csr_matrix
 from anndata import AnnData
-from ..method_ora import ora, run_ora
+from ..method_ora import ora, get_ora_df, run_ora
 
 
 def test_ora():
@@ -11,6 +11,30 @@ def test_ora():
     net = pd.Series([np.array([1, 3], dtype=np.int32), np.array([1, 3], dtype=np.int32)], index=['T1', 'T2'])
     ora(m, net, 1, 0)
 
+
+def test_get_ora_df():
+    df = pd.DataFrame([
+        ['GA', 'FA'],
+        ['GB', 'FB'],
+    ], columns=['groupby', 'features'])
+    net = pd.DataFrame([
+        ['SA', 'FA'],
+        ['SA', 'FC'],
+        ['SB', 'FB'],
+        ['SB', 'FC'],
+    ], columns=['source', 'target'])
+
+    with pytest.raises(ValueError):
+        get_ora_df(df, net, groupby='asd', features='features')
+    with pytest.raises(ValueError):
+        get_ora_df(df, net, groupby='groupby', features='asd')
+    res = get_ora_df(df, net, groupby='groupby', features='features', min_n=0)
+    assert res.loc['GA', 'SA'] < 0.05
+    assert res.loc['GA', 'SB'] > 0.05
+    assert res.loc['GB', 'SA'] > 0.05
+    assert res.loc['GB', 'SB'] < 0.05
+
+
 def test_run_ora():
     m = np.array([[7., 1., 1.], [4., 2., 1.], [1., 2., 5.], [1., 1., 6.]])
     r = np.array(['S1', 'S2', 'S3', 'S4'])
@@ -18,5 +42,5 @@ def test_run_ora():
     df = pd.DataFrame(m, index=r, columns=c)
     adata = AnnData(df)
     net = pd.DataFrame([['T1', 'G2'], ['T1', 'G4'], ['T2', 'G3'], ['T2', 'G1']],
-                   columns=['source', 'target'])
+                       columns=['source', 'target'])
     run_ora(adata, net, min_n=0, verbose=True, use_raw=False)
diff --git a/decoupler/tests/test_plotting.py b/decoupler/tests/test_plotting.py
index 44583ee..0109ef4 100644
--- a/decoupler/tests/test_plotting.py
+++ b/decoupler/tests/test_plotting.py
@@ -1,25 +1,32 @@
 import pytest
 import matplotlib.pyplot as plt
 import pandas as pd
-from ..plotting import check_if_matplotlib, check_if_seaborn, save_plot, set_limits, plot_volcano, plot_violins, plot_barplot
+import numpy as np
+from ..plotting import check_if_matplotlib, check_if_seaborn, save_plot, set_limits, plot_volcano, plot_violins
+from ..plotting import plot_barplot, build_msks, write_labels, plot_metrics_scatter, plot_metrics_scatter_cols
+from ..plotting import plot_metrics_boxplot
 
 
 def test_check_if_matplotlib():
     check_if_matplotlib(return_mpl=False)
     check_if_matplotlib(return_mpl=True)
 
+
 def test_check_if_seaborn():
     check_if_seaborn()
 
+
 def test_save_plot():
-    fig, ax = plt.subplots(1,1)
+    fig, ax = plt.subplots(1, 1)
     with pytest.raises(AttributeError):
         save_plot(fig, ax, True)
 
+
 def test_set_limits():
-    values = pd.Series([1,2,3])
+    values = pd.Series([1, 2, 3])
     set_limits(None, None, None, values)
 
+
 def test_plot_volcano():
     logFCs = pd.DataFrame([[3, 0, -3], [1, 2, -5]], index=['C1', 'C2'], columns=['G1', 'G2', 'G3'])
     logFCs.name = 'contrast_logFCs'
@@ -27,11 +34,12 @@ def test_plot_volcano():
     pvals.name = 'contrast_pvals'
     net = pd.DataFrame([['T1', 'G1', 1], ['T1', 'G2', 1], ['T2', 'G3', 1], ['T2', 'G4', 0.5]],
                        columns=['source', 'target', 'weight'])
-    fig, ax = plt.subplots(1,1)
+    fig, ax = plt.subplots(1, 1)
     plot_volcano(logFCs, pvals, 'C1', name=None, net=None, ax=None, return_fig=True)
     plot_volcano(logFCs, pvals, 'C1', name=None, net=None, ax=ax, return_fig=False)
     plot_volcano(logFCs, pvals, 'C1', name='T1', net=net, ax=None, return_fig=True)
 
+
 def test_plot_violins():
     m = [[1, 2, 3], [4, 5, 6]]
     c = ['G1', 'G2', 'G3']
@@ -39,8 +47,95 @@ def test_plot_violins():
     mat = pd.DataFrame(m, index=r, columns=c)
     plot_violins(mat, thr=1, log=True, use_raw=False, ax=None, title='Title', ylabel='Ylabel', return_fig=True)
 
+
 def test_plot_barplot():
     estimate = pd.DataFrame([[3.5, -0.5, 0.3], [3.6, -0.6, 0.04], [-1, 2, -1.8]],
-                    columns=['T1', 'T2', 'T3'], index=['C1', 'C2', 'C3'])
+                            columns=['T1', 'T2', 'T3'], index=['C1', 'C2', 'C3'])
     plot_barplot(estimate, 'C1', vertical=False, return_fig=False)
     plot_barplot(estimate, 'C1', vertical=True, return_fig=True)
+
+
+def test_build_msks():
+    df = pd.DataFrame([
+        ['mlm_estimate', 'mcauroc', 0.7, 'A'],
+        ['mlm_estimate', 'mcauprc', 0.7, 'A'],
+        ['ulm_estimate', 'mcauroc', 0.6, 'A'],
+        ['ulm_estimate', 'mcauprc', 0.6, 'A'],
+        ['mlm_estimate', 'mcauroc', 0.5, 'B'],
+        ['mlm_estimate', 'mcauprc', 0.5, 'B'],
+        ['ulm_estimate', 'mcauroc', 0.4, 'B'],
+        ['ulm_estimate', 'mcauprc', 0.4, 'B'],
+    ], columns=['method', 'metric', 'score', 'net'])
+
+    msks, cats = build_msks(df, groupby=None)
+    assert np.all(msks)
+    assert msks[0].size == df.shape[0]
+    assert cats[0] is None
+    msks, cats = build_msks(df, groupby='net')
+    assert len(msks) == 2
+    assert not np.all(msks[0])
+    assert not np.all(msks[1])
+    assert not np.any(msks[0] * msks[1])
+    assert cats.size == 2
+
+
+def test_write_labels():
+    fig, ax = plt.subplots(1, 1)
+    assert ax.title.get_text() == ''
+    assert ax.get_xlabel() == ''
+    assert ax.get_ylabel() == ''
+    write_labels(ax, title='title', xlabel=None, ylabel=None, x='x', y='y')
+    assert ax.title.get_text() == 'title'
+    assert ax.get_xlabel() == 'X'
+    assert ax.get_ylabel() == 'Y'
+
+
+def test_plot_metrics_scatter():
+    df = pd.DataFrame([
+        ['mlm_estimate', 'mcauroc', 0.7, 'A'],
+        ['mlm_estimate', 'mcauprc', 0.7, 'A'],
+        ['ulm_estimate', 'mcauroc', 0.6, 'A'],
+        ['ulm_estimate', 'mcauprc', 0.6, 'A'],
+        ['mlm_estimate', 'mcauroc', 0.5, 'B'],
+        ['mlm_estimate', 'mcauprc', 0.5, 'B'],
+        ['ulm_estimate', 'mcauroc', 0.4, 'B'],
+        ['ulm_estimate', 'mcauprc', 0.4, 'B'],
+    ], columns=['method', 'metric', 'score', 'net'])
+
+    plot_metrics_scatter(df, x='mcauroc', y='mcauprc', groupby=None, title='title', return_fig=False)
+    plot_metrics_scatter(df, x='mcauroc', y='mcauprc', groupby='net', title='title', return_fig=True)
+
+
+def test_plot_metrics_scatter_cols():
+    df = pd.DataFrame([
+        ['mlm_estimate', 'mcauroc', 0.7, 'A'],
+        ['mlm_estimate', 'mcauprc', 0.7, 'A'],
+        ['ulm_estimate', 'mcauroc', 0.6, 'A'],
+        ['ulm_estimate', 'mcauprc', 0.6, 'A'],
+        ['mlm_estimate', 'mcauroc', 0.5, 'B'],
+        ['mlm_estimate', 'mcauprc', 0.5, 'B'],
+        ['ulm_estimate', 'mcauroc', 0.4, 'B'],
+        ['ulm_estimate', 'mcauprc', 0.4, 'B'],
+    ], columns=['method', 'metric', 'score', 'net'])
+
+    plot_metrics_scatter_cols(df, col='net', x='mcauroc', y='mcauprc', groupby='method', return_fig=True)
+
+
+def test_plot_metrics_boxplot():
+    df = pd.DataFrame([
+        ['mlm_estimate', 'mcauroc', 0.7, 'A'],
+        ['mlm_estimate', 'mcauroc', 0.6, 'A'],
+        ['ulm_estimate', 'mcauroc', 0.5, 'A'],
+        ['ulm_estimate', 'mcauroc', 0.6, 'A'],
+        ['mlm_estimate', 'mcauroc', 0.5, 'B'],
+        ['mlm_estimate', 'mcauroc', 0.4, 'B'],
+        ['ulm_estimate', 'mcauroc', 0.5, 'B'],
+        ['ulm_estimate', 'mcauroc', 0.4, 'B'],
+    ], columns=['method', 'metric', 'score', 'net'])
+
+    with pytest.raises(ValueError):
+        plot_metrics_boxplot(df, 'auroc')
+    plot_metrics_boxplot(df, 'mcauroc', groupby='net', title='title', return_fig=True)
+    plot_metrics_boxplot(df, 'mcauroc', groupby=None)
+    with pytest.raises(ValueError):
+        plot_metrics_boxplot(df, 'mcauroc', groupby='method')
diff --git a/decoupler/tests/test_pre.py b/decoupler/tests/test_pre.py
index a0bed77..beef5f1 100644
--- a/decoupler/tests/test_pre.py
+++ b/decoupler/tests/test_pre.py
@@ -7,16 +7,17 @@
 
 
 def test_check_mat():
-    m = csr_matrix(np.array([[1,0,2], [1, 0, 3], [0, 0, 0]]))
+    m = csr_matrix(np.array([[1, 0, 2], [1, 0, 3], [0, 0, 0]]))
     r = np.array(['S1', 'S2', 'S3'])
     c = np.array(['G1', 'G2', 'G3'])
     check_mat(m, r, c, verbose=True)
-    m = csr_matrix(np.array([[1,0,2], [np.nan, 0, 3], [0, 0, 0]]))
+    m = csr_matrix(np.array([[1, 0, 2], [np.nan, 0, 3], [0, 0, 0]]))
     with pytest.raises(ValueError):
         check_mat(m, r, c)
 
+
 def test_extract():
-    m = np.array([[1,0,2], [1, 0, 3], [0, 0, 0]])
+    m = np.array([[1, 0, 2], [1, 0, 3], [0, 0, 0]])
     r = np.array(['S1', 'S2', 'S3'])
     c = np.array(['G1', 'G2', 'G3'])
     df = pd.DataFrame(m, index=r, columns=c)
@@ -27,11 +28,12 @@ def test_extract():
     extract(df)
     extract(adata, use_raw=False)
     extract(adata_raw, use_raw=True)
-    with pytest.raises(ValueError):   
+    with pytest.raises(ValueError):
         extract('asdfg')
     with pytest.raises(ValueError):
         extract(adata, use_raw=True)
 
+
 def test_filt_min_n():
     net = pd.DataFrame([['T1', 'G1', 1], ['T1', 'G2', 1], ['T2', 'G3', 1], ['T2', 'G4', 0.5]],
                        columns=['source', 'target', 'weight'])
@@ -40,12 +42,14 @@ def test_filt_min_n():
     with pytest.raises(ValueError):
         filt_min_n(c, net, min_n=5)
 
+
 def test_match():
     c = np.array(['G1', 'G2', 'G3', 'G4'])
     targets = np.array(['G2', 'G1', 'G4', 'G3'])
-    net = np.array([[1. , 0. ], [1. , 0. ], [0. , 1. ], [0. , 0.5]])
+    net = np.array([[1., 0.], [1., 0.], [0., 1.], [0., 0.5]])
     match(c, targets, net)
 
+
 def test_rename_net():
     net = pd.DataFrame([['T1', 'G1', 1], ['T1', 'G2', 1], ['T2', 'G3', 1], ['T2', 'G4', 0.5]],
                        columns=['source', 'target', 'weight'])
@@ -56,13 +60,15 @@ def test_rename_net():
     with pytest.raises(ValueError):
         rename_net(net)
 
+
 def test_get_net_mat():
     net = pd.DataFrame([['T1', 'G1', 1], ['T1', 'G2', 1], ['T2', 'G3', 1], ['T2', 'G4', 0.5]],
                        columns=['source', 'target', 'weight'])
     get_net_mat(net)
 
+
 def test_mask_features():
-    m = np.array([[1,0,2], [1, 0, 3], [0, 0, 0]])
+    m = np.array([[1, 0, 2], [1, 0, 3], [0, 0, 0]])
     r = np.array(['S1', 'S2', 'S3'])
     c = np.array(['G1', 'G2', 'G3'])
     df = pd.DataFrame(m, index=r, columns=c)
diff --git a/decoupler/tests/test_udt.py b/decoupler/tests/test_udt.py
index 2198d25..0a2bfe1 100644
--- a/decoupler/tests/test_udt.py
+++ b/decoupler/tests/test_udt.py
@@ -1,21 +1,27 @@
-import pytest
 import numpy as np
 import pandas as pd
-from scipy.sparse import csr_matrix
 from anndata import AnnData
-from ..method_udt import fit_dt, udt, run_udt
+from ..method_udt import check_if_sklearn, fit_dt, udt, run_udt
+
+
+def test_check_if_sklearn():
+    sk = check_if_sklearn()
+    assert sk is not None
 
 
 def test_fit_dt():
     sample = np.array([7., 1., 1.])
-    net = np.array([1. , 1., 0.])
-    fit_dt(net, sample)
+    net = np.array([1., 1., 0.])
+    sk = check_if_sklearn()
+    fit_dt(sk, net, sample)
+
 
 def test_udt():
     m = np.array([[7., 1., 1.], [4., 2., 1.], [1., 2., 5.], [1., 1., 6.]])
-    net = np.array([[1. , 0. ], [1. , 0. ], [0. , 1. ]])
+    net = np.array([[1., 0.], [1., 0.], [0., 1.]])
     udt(m, net)
 
+
 def test_run_mdt():
     m = np.array([[7., 1., 1.], [4., 2., 1.], [1., 2., 5.], [1., 1., 6.]])
     r = np.array(['S1', 'S2', 'S3', 'S4'])
@@ -23,5 +29,5 @@ def test_run_mdt():
     df = pd.DataFrame(m, index=r, columns=c)
     adata = AnnData(df)
     net = pd.DataFrame([['T1', 'G2', 1], ['T1', 'G4', 2], ['T2', 'G3', 3], ['T2', 'G1', 1]],
-                   columns=['source', 'target', 'weight'])
+                       columns=['source', 'target', 'weight'])
     run_udt(adata, net, verbose=True, use_raw=False, min_n=0)
diff --git a/decoupler/tests/test_ulm.py b/decoupler/tests/test_ulm.py
index 5ffb309..3af5755 100644
--- a/decoupler/tests/test_ulm.py
+++ b/decoupler/tests/test_ulm.py
@@ -1,4 +1,3 @@
-import pytest
 import numpy as np
 import pandas as pd
 from scipy.sparse import csr_matrix
@@ -8,9 +7,10 @@
 
 def test_ulm():
     m = csr_matrix(np.array([[7., 1., 1., 1.], [4., 2., 1., 2.], [1., 2., 5., 1.], [1., 1., 6., 2.]], dtype=np.float32))
-    net = np.array([[1. , 0. ], [2 , 0. ], [0. , -3. ], [0. , 4. ]], dtype=np.float32)
+    net = np.array([[1., 0.], [2, 0.], [0., -3.], [0., 4.]], dtype=np.float32)
     ulm(m, net)
 
+
 def test_run_ulm():
     m = np.array([[7., 1., 1., 1.], [4., 2., 1., 2.], [1., 2., 5., 1.], [1., 1., 6., 2.]])
     r = np.array(['S1', 'S2', 'S3', 'S4'])
@@ -18,5 +18,5 @@ def test_run_ulm():
     df = pd.DataFrame(m, index=r, columns=c)
     adata = AnnData(df)
     net = pd.DataFrame([['T1', 'G1', 1], ['T1', 'G2', 2], ['T2', 'G3', -3], ['T2', 'G4', 4]],
-                   columns=['source', 'target', 'weight'])
+                       columns=['source', 'target', 'weight'])
     run_ulm(adata, net, verbose=True, use_raw=False, min_n=0)
diff --git a/decoupler/tests/test_utils.py b/decoupler/tests/test_utils.py
index 997cfaa..807a1af 100644
--- a/decoupler/tests/test_utils.py
+++ b/decoupler/tests/test_utils.py
@@ -1,8 +1,9 @@
 import pytest
+import numpy as np
 import pandas as pd
 from anndata import AnnData
 from ..utils import m_rename, melt, show_methods, check_corr, get_toy_data, summarize_acts
-from ..utils import assign_groups, p_adjust_fdr, dense_run
+from ..utils import assign_groups, p_adjust_fdr, dense_run, shuffle_net
 from ..method_mlm import run_mlm
 
 
@@ -17,10 +18,10 @@ def test_m_rename():
 
 def test_melt():
     estimate = pd.DataFrame([[3.5, -0.5, 0.3], [3.6, -0.6, 0.04], [-1, 2, -1.8]],
-                    columns=['T1', 'T2', 'T3'], index=['S01', 'S02', 'S03'])
+                            columns=['T1', 'T2', 'T3'], index=['S01', 'S02', 'S03'])
     estimate.name = 'mlm_estimate'
     pvals = pd.DataFrame([[.005, .5, .7], [.006, .6, .9], [.004, .3, .7]],
-                            columns=['T1', 'T2', 'T3'], index=['S01', 'S02', 'S03'])
+                         columns=['T1', 'T2', 'T3'], index=['S01', 'S02', 'S03'])
     pvals.name = 'mlm_pvals'
     pvals
     res_dict = {estimate.name: estimate, pvals.name: pvals}
@@ -29,7 +30,7 @@ def test_melt():
     melt(pvals)
     melt(res_dict)
     melt(res_list)
-    with pytest.raises(ValueError):   
+    with pytest.raises(ValueError):
         melt({0, 1, 2, 3})
 
 
@@ -42,7 +43,7 @@ def test_show_methods():
 
 def test_check_corr():
 
-    mat = pd.DataFrame([[1,2,3,4,5,6]], columns=['G01','G02','G03','G06','G07','G08'])
+    mat = pd.DataFrame([[1, 2, 3, 4, 5, 6]], columns=['G01', 'G02', 'G03', 'G06', 'G07', 'G08'])
     net = pd.DataFrame([['T1', 'G01', 1], ['T1', 'G02', 1], ['T2', 'G06', 1], ['T2', 'G07', 0.5],
                         ['T3', 'G06', -0.5], ['T3', 'G07', -3]], columns=['source', 'target', 'weight'])
     check_corr(net, min_n=2)
@@ -57,18 +58,19 @@ def test_get_toy_data():
 def test_summarize_acts():
 
     estimate = pd.DataFrame([[3.5, -0.5, 0.3], [3.6, -0.6, 0.04], [-1, 2, -1.8]],
-                    columns=['T1', 'T2', 'T3'], index=['S01', 'S02', 'S03'])
+                            columns=['T1', 'T2', 'T3'], index=['S01', 'S02', 'S03'])
     obs = pd.DataFrame([['C01', 'C01', 'C02']], columns=estimate.index, index=['celltype']).T
     adata = AnnData(estimate, obs=obs)
     summarize_acts(estimate, obs=obs, groupby='celltype', mode='median', min_std=0)
-    summarize_acts(adata, groupby='celltype', mode='mean', min_std=0)
+    acts = summarize_acts(adata, groupby='celltype', mode='mean', min_std=0)
+    assert np.unique(acts.values).size > 2
     with pytest.raises(ValueError):
         summarize_acts(adata, 'celltype', obs, 'mean', 0)
 
 
 def test_assign_groups():
     estimate = pd.DataFrame([[3.5, -0.5, 0.3], [3.6, -0.6, 0.04], [-1, 2, -1.8]],
-                    columns=['T1', 'T2', 'T3'], index=['S01', 'S02', 'S03'])
+                            columns=['T1', 'T2', 'T3'], index=['S01', 'S02', 'S03'])
     obs = pd.DataFrame([['C01', 'C01', 'C02']], columns=estimate.index, index=['celltype']).T
     sum_acts = summarize_acts(estimate, obs=obs, groupby='celltype', min_std=0)
     assign_groups(sum_acts)
@@ -79,7 +81,31 @@ def test_p_adjust_fdr():
 
 
 def test_denserun():
-    mat = pd.DataFrame([[1,2,3,4,5,6]], columns=['G01','G02','G03','G06','G07','G08'])
+    mat = pd.DataFrame([[1, 2, 3, 4, 5, 6]], columns=['G01', 'G02', 'G03', 'G06', 'G07', 'G08'])
     net = pd.DataFrame([['T1', 'G01', 1], ['T1', 'G02', 1], ['T2', 'G06', 1], ['T2', 'G07', 0.5],
                         ['T3', 'G06', -0.5], ['T3', 'G07', -3]], columns=['source', 'target', 'weight'])
     dense_run(run_mlm, mat, net, min_n=0)
+
+
+def test_shuffle_net():
+    net = pd.DataFrame([['T1', 'G01', 1], ['T1', 'G02', 1], ['T2', 'G03', 1], ['T2', 'G04', 0.5],
+                        ['T3', 'G05', -0.5], ['T3', 'G06', -3]], columns=['source', 'target', 'weight'])
+
+    with pytest.raises(ValueError):
+        shuffle_net(net, target=None, weight=None, seed=42, same_seed=True)
+    with pytest.raises(ValueError):
+        shuffle_net(net, target='asd', weight=None, seed=42, same_seed=True)
+    rnet = shuffle_net(net, target='target', weight=None, seed=42, same_seed=True)
+    assert np.any(net.target.values != rnet.target.values)
+    with pytest.raises(ValueError):
+        shuffle_net(net, target=None, weight='asd', seed=42, same_seed=True)
+    rnet = shuffle_net(net, target=None, weight='weight', seed=42, same_seed=True)
+    assert np.any(net.weight.values != rnet.weight.values)
+    rnet = shuffle_net(net, target='target', weight='weight', seed=42, same_seed=True)
+    net_dict = {k: v for k, v in zip(net.target, net.weight)}
+    rnet_dict = {k: v for k, v in zip(rnet.target, rnet.weight)}
+    assert net_dict == rnet_dict
+    rnet = shuffle_net(net, target='target', weight='weight', seed=42, same_seed=False)
+    net_dict = {k: v for k, v in zip(net.target, net.weight)}
+    rnet_dict = {k: v for k, v in zip(rnet.target, rnet.weight)}
+    assert net_dict != rnet_dict
diff --git a/decoupler/tests/test_utilsanndata.py b/decoupler/tests/test_utilsanndata.py
index 8790239..cf0eb69 100644
--- a/decoupler/tests/test_utilsanndata.py
+++ b/decoupler/tests/test_utilsanndata.py
@@ -3,22 +3,25 @@
 import pandas as pd
 from scipy.sparse import csr_matrix
 from anndata import AnnData
-from ..utils_anndata import get_acts, extract_psbulk_inputs, check_for_raw_counts, format_psbulk_inputs, compute_psbulk, get_unq_dict, get_pseudobulk, check_if_skip, get_contrast, get_top_targets, format_contrast_results
+from ..utils_anndata import get_acts, extract_psbulk_inputs, check_for_raw_counts, format_psbulk_inputs
+from ..utils_anndata import compute_psbulk, get_unq_dict, get_pseudobulk, check_if_skip, get_contrast
+from ..utils_anndata import get_top_targets, format_contrast_results
 
 
 def test_get_acts():
-    m = np.array([[1,0,2], [1, 0, 3], [0, 0, 0]])
+    m = np.array([[1, 0, 2], [1, 0, 3], [0, 0, 0]])
     r = np.array(['S1', 'S2', 'S3'])
     c = np.array(['G1', 'G2', 'G3'])
     df = pd.DataFrame(m, index=r, columns=c)
     estimate = pd.DataFrame([[3.5, -0.5, 0.3], [3.6, -0.6, 0.04], [-1, 2, -1.8]],
-                    columns=['T1', 'T2', 'T3'], index=r)
+                            columns=['T1', 'T2', 'T3'], index=r)
     adata = AnnData(df)
     adata.obsm['estimate'] = estimate
     get_acts(adata, 'estimate')
 
+
 def test_extract_psbulk_inputs():
-    m = np.array([[1,0,2], [1, 0, 3], [0, 0, 0]])
+    m = np.array([[1, 0, 2], [1, 0, 3], [0, 0, 0]])
     r = np.array(['S1', 'S2', 'S3'])
     c = np.array(['G1', 'G2', 'G3'])
     df = pd.DataFrame(m, index=r, columns=c)
@@ -36,6 +39,7 @@ def test_extract_psbulk_inputs():
     with pytest.raises(ValueError):
         extract_psbulk_inputs(df, obs=None, layer=None, use_raw=False)
 
+
 def test_check_for_raw_counts():
     X = csr_matrix(np.array([[1, 0, 2], [1, 0, 3], [0, 0, 0]]))
     X_float = csr_matrix(np.array([[1.3, 0, 2.1], [1.48, 0.123, 3.33], [0, 0, 0]]))
@@ -49,12 +53,14 @@ def test_check_for_raw_counts():
     with pytest.raises(ValueError):
         check_for_raw_counts(X_inf)
 
+
 def test_format_psbulk_inputs():
     sample_col, groups_col = 'sample_id', 'celltype'
     obs = pd.DataFrame([['S1', 'S2', 'S3'], ['C1', 'C1', 'C2']], columns=['S1', 'S2', 'S3'], index=[sample_col, groups_col]).T
     format_psbulk_inputs(sample_col, groups_col, obs)
     format_psbulk_inputs(sample_col, None, obs)
 
+
 def test_compute_psbulk():
     sample_col, groups_col = 'sample_id', 'celltype'
     min_cells, min_counts, min_prop = 0., 0., 0.
@@ -67,12 +73,15 @@ def test_compute_psbulk():
     props = np.full((n_rows, n_cols), False)
     obs = pd.DataFrame([smples, groups], columns=smples, index=[sample_col, groups_col]).T
     new_obs = pd.DataFrame(columns=obs.columns)
-    compute_psbulk(psbulk, props, X, sample_col, groups_col, np.unique(smples), np.unique(groups), obs, new_obs, min_cells, min_counts, min_prop)
-    compute_psbulk(psbulk, props, X, sample_col, None, np.unique(smples), np.unique(groups), obs, new_obs, min_cells, min_counts, min_prop)
+    compute_psbulk(psbulk, props, X, sample_col, groups_col, np.unique(smples),
+                   np.unique(groups), obs, new_obs, min_cells, min_counts, min_prop)
+    compute_psbulk(psbulk, props, X, sample_col, None, np.unique(smples),
+                   np.unique(groups), obs, new_obs, min_cells, min_counts, min_prop)
+
 
 def test_get_pseudobulk():
     sample_col, groups_col = 'sample_id', 'celltype'
-    m = np.array([[1,0,2], [1, 0, 3], [0, 0, 0]])
+    m = np.array([[1, 0, 2], [1, 0, 3], [0, 0, 0]])
     r = np.array(['S1', 'S2', 'S3'])
     c = np.array(['G1', 'G2', 'G3'])
     df = pd.DataFrame(m, index=r, columns=c)
@@ -83,6 +92,7 @@ def test_get_pseudobulk():
     get_pseudobulk(adata, sample_col, sample_col, min_prop=0, min_cells=0, min_counts=0, min_smpls=0)
     get_pseudobulk(adata, sample_col, groups_col, min_prop=0, min_cells=0, min_counts=0, min_smpls=0)
 
+
 def test_get_unq_dict():
     col = pd.Series(['C1', 'C1', 'C2', 'C3'], index=['S1', 'S2', 'S3', 'S4'])
     condition = 'C1'
@@ -90,6 +100,7 @@ def test_get_unq_dict():
     get_unq_dict(col, condition, reference)
     get_unq_dict(col, condition, 'rest')
 
+
 def test_check_if_skip():
     grp = 'Group'
     condition_col = 'celltype'
@@ -104,6 +115,7 @@ def test_check_if_skip():
     unq_dict = {'C1': 1, 'C2': 2}
     check_if_skip(grp, condition_col, condition, reference, unq_dict)
 
+
 def test_get_contrast():
     groups_col, condition_col = 'celltype', 'condition'
     m = np.array([[7., 1., 1.], [4., 2., 1.], [1., 2., 5.], [1., 1., 6.]])
@@ -124,6 +136,7 @@ def test_get_contrast():
                        columns=r, index=[groups_col, condition_col]).T
     get_contrast(adata, groups_col, condition_col, condition, reference)
 
+
 def test_get_top_targets():
     logFCs = pd.DataFrame([[3, 0, -3], [1, 2, -5]], index=['C1', 'C2'], columns=['G1', 'G2', 'G3'])
     pvals = pd.DataFrame([[.3, .02, .01], [.9, .1, .003]], index=['C1', 'C2'], columns=['G1', 'G2', 'G3'])
@@ -137,6 +150,7 @@ def test_get_top_targets():
     get_top_targets(logFCs, pvals, contrast, name=None, net=None, sign_thr=1, lFCs_thr=0.0, fdr_corr=True)
     get_top_targets(logFCs, pvals, contrast, name=None, net=None, sign_thr=1, lFCs_thr=0.0, fdr_corr=False)
 
+
 def test_format_contrast_results():
     logFCs = pd.DataFrame([[3, 0, -3], [1, 2, -5]], index=['C1', 'C2'], columns=['G1', 'G2', 'G3'])
     logFCs.name = 'contrast_logFCs'
diff --git a/decoupler/tests/test_utilsbenchmark.py b/decoupler/tests/test_utilsbenchmark.py
new file mode 100644
index 0000000..baf2cac
--- /dev/null
+++ b/decoupler/tests/test_utilsbenchmark.py
@@ -0,0 +1,354 @@
+import pytest
+import numpy as np
+from numpy.random import default_rng
+import pandas as pd
+from ..utils_benchmark import get_toy_benchmark_data, show_metrics, validate_metrics
+from ..utils_benchmark import compute_metric, mirror_acts, build_acts_tensor, build_grts_mat
+from ..utils_benchmark import unique_obs, build_msks_tensor, append_by_experiment
+from ..utils_benchmark import append_by_source, append_metrics_scores, check_groupby
+from ..utils_benchmark import rename_obs, format_acts_grts
+
+
+def test_get_toy_benchmark_data():
+
+    n = 24
+    mat, net, obs = get_toy_benchmark_data(n_samples=n, shuffle_perc=0)
+    assert mat.shape[0] == n and obs.shape[0] == n
+    mean_pos_unsh = np.mean(mat.values[:12, 0])
+    mean_neg_unsh = np.mean(mat.values[12:, 0])
+    assert mean_pos_unsh > mean_neg_unsh
+    mat, net, obs = get_toy_benchmark_data(n_samples=n, shuffle_perc=0.5)
+    mean_pos_05 = np.mean(mat.values[:12, 0])
+    mean_neg_05 = np.mean(mat.values[12:, 0])
+    assert mean_pos_unsh > mean_pos_05
+    assert mean_neg_unsh < mean_neg_05
+    assert mean_pos_05 > mean_neg_05
+
+
+def test_show_metrics():
+    m = show_metrics()
+    r, c = m.shape
+    assert r > 0 and c > 0
+
+
+def test_validate_metrics():
+    metrics = 'auroc'
+    validate_metrics(metrics)
+    metrics = ['auroc', 'auprc', 'mcauroc', 'mcauprc']
+    validate_metrics(metrics)
+    metrics = ['auroc', 'asd', 'mcauroc', 'mcauprc']
+    with pytest.raises(ValueError):
+        validate_metrics(metrics)
+
+
+def test_compute_metric():
+    act = [6., 5., 4., 3., 2., 1., 0.]
+    grt = [1., 0., 1., 1., 0., 0., 0.]
+    metric = 'auroc'
+    res = compute_metric(act, grt, metric)
+    assert type(res) is np.ndarray
+    metric = 'auprc'
+    res = compute_metric(act, grt, metric)
+    assert type(res) is np.ndarray
+    metric = 'mcauroc'
+    res = compute_metric(act, grt, metric)
+    assert type(res) is np.ndarray
+    metric = 'mcauprc'
+    res = compute_metric(act, grt, metric)
+    assert type(res) is np.ndarray
+
+
+def test_mirror_acts():
+    rng = default_rng(seed=42)
+    acts = rng.normal(size=(3, 5, 2))
+    grts = np.array([
+        [-1., 0., 0., 0., -1.],
+        [0., 0., 0., 0., 1.],
+        [1., 1., 0., 0., 0.]
+    ])
+    m_acts = acts.copy()
+    m_grts = grts.copy()
+
+    mirror_acts(m_acts, m_grts)
+    assert np.all(np.isin(m_grts, [0., 1.]))
+    assert np.all(acts[0] == m_acts[0] * -1)
+    assert np.all(acts[1] == m_acts[1])
+    assert np.all(acts[2] == m_acts[2])
+    grts[0, 0] = 1.
+    with pytest.raises(ValueError):
+        mirror_acts(acts, grts)
+
+
+def test_build_acts_tensor():
+    exps = np.array(['S2', 'S1'])
+    srcs = np.array(['T1', 'T3', 'T2'])
+    mthds = np.array(['m_a', 'm_b'])
+
+    m_a = pd.DataFrame([
+        [4., 6., 5.],
+        [1., 3., 2.]
+    ], index=exps, columns=srcs)
+    m_b = pd.DataFrame([
+        [7., 9., 8.],
+        [1., 3., 2.]
+    ], index=exps, columns=srcs)
+    res = {mthds[0]: m_a, mthds[1]: m_b}
+
+    racts, rexps, rsrcs, rmthds = build_acts_tensor(res)
+    assert np.all(racts[0, :, 0] == np.array([4., 5., 6.]))
+    assert np.all(racts[1, :, 0] == np.array([1., 2., 3.]))
+    assert np.all(racts[0, :, 1] == np.array([7., 8., 9.]))
+    assert np.all(rexps == np.sort(exps))
+    assert np.all(rsrcs == np.sort(srcs))
+    assert np.all(rmthds == mthds)
+
+
+def test_build_grts_mat():
+    exps = np.array(['S1', 'S2'])
+    srcs = np.array(['T1', 'T2', 'T3'])
+    obs = pd.DataFrame([
+        [['T3', 'T2'], 1],
+        ['T4', -1],
+    ], columns=['perturb', 'sign'], index=['S2', 'S1'])
+
+    grts = build_grts_mat(obs, exps, srcs)
+    assert np.all(grts.columns == np.array(['T2', 'T3']))
+    assert np.all(grts.index == np.array(['S1', 'S2']))
+    assert np.all(grts.values == np.array([[0., 0.], [1., 1.]]))
+
+
+def test_unique_obs():
+    col = ['T1', 'T1', 'T2', 'T2']
+    assert len(unique_obs(col)) == 2
+
+    col = ['T1', ['T1', 'T2'], 'T3', 'T2']
+    assert len(unique_obs(col)) == 3
+
+    col = [['T1', 'T2'], ['T1', 'T2'], ['T3', 'T4'], ['T3', 'T4']]
+    assert len(unique_obs(col)) == 4
+
+
+def test_build_msks_tensor():
+    obs = pd.DataFrame([
+            ['A1', 'B1', 'C1'],
+            ['A1', 'B2', 'C1'],
+            ['A1', 'B2', 'C2'],
+            ['A2', 'B1', 'C1'],
+            ['A2', 'B1', 'C2'],
+            ['A2', 'B2', 'C2'],
+        ], columns=['col_A', 'col_B', 'col_C'])
+
+    msks, grpbys, grps = build_msks_tensor(obs, groupby=['col_C'])
+    assert np.all(msks[0][0] == np.array([True, True, False, True, False, False]))
+    assert grpbys[0] == 'col_C'
+    assert np.all(grps == np.array(['C1', 'C2']))
+    msks, grpbys, grps = build_msks_tensor(obs, groupby=[['col_A']])
+    assert np.all(msks[0][0] == np.array([True, True, True, False, False, False]))
+    assert grpbys[0] == 'col_A'
+    assert np.all(grps == np.array(['A1', 'A2']))
+    msks, grpbys, grps = build_msks_tensor(obs, groupby=[['col_A', 'col_B']])
+    assert np.all(msks[0][0] == np.array([True, False, False, False, False, False]))
+    assert np.all(msks[0][1] == np.array([False, True, True, False, False, False]))
+    assert grpbys[0] == 'col_A|col_B'
+    assert np.all(grps == np.array(['A1|B1', 'A1|B2', 'A2|B1', 'A2|B2']))
+    msks, grpbys, grps = build_msks_tensor(obs, groupby=None)
+    assert msks is None and grpbys is None and grps is None
+
+
+def test_append_by_experiment():
+    act = np.array([
+        [[2, 5],
+         [1, 4],
+         [2, 3],
+         [4, 2],
+         [5, 1]],
+        [[5, 1],
+         [4, 2],
+         [3, 3],
+         [1, 5],
+         [2, 4]],
+        [[2, 5],
+         [1, 4],
+         [2, 3],
+         [4, 2],
+         [5, 1]]
+    ])
+    grt = np.array([
+            [1., 0., 0., 0., 0.],
+            [0., 0., 0., 0., 1.],
+            [1., 0., 0., 0., 0.]
+    ])
+    srcs = np.array(['T1', 'T2', 'T3', 'T4', 'T5'])
+    mthds = np.array(['M1', 'M2'])
+    metrics = ['auroc']
+    df = []
+
+    append_by_experiment(df, grpby_i=None, grp=None, act=act, grt=grt, srcs=srcs,
+                         mthds=mthds, metrics=metrics, min_exp=1)
+    assert len(df) == 2
+    assert df[0][5] < df[1][5]
+
+
+def test_append_by_source():
+    act = np.array([
+        [[2, 5],
+         [1, 4],
+         [2, 3],
+         [4, 2],
+         [5, 1]],
+        [[5, 1],
+         [4, 2],
+         [3, 3],
+         [1, 5],
+         [2, 4]],
+        [[2, 5],
+         [1, 4],
+         [2, 3],
+         [4, 2],
+         [5, 1]]
+    ])
+    grt = np.array([
+            [1., 0., 0., 0., 0.],
+            [0., 0., 0., 0., 1.],
+            [1., 0., 0., 0., 0.]
+    ])
+    srcs = np.array(['T1', 'T2', 'T3', 'T4', 'T5'])
+    mthds = np.array(['M1', 'M2'])
+    metrics = ['auroc']
+    df = []
+    append_by_source(df, grpby_i=None, grp=None, act=act, grt=grt, srcs=srcs,
+                     mthds=mthds, metrics=metrics, min_exp=1)
+    assert len(df) == 4
+    assert df[0][5] < df[1][5]
+
+
+def test_append_metrics_scores():
+    act = np.array([
+        [[2, 5],
+         [1, 4],
+         [2, 3],
+         [4, 2],
+         [5, 1]],
+        [[5, 1],
+         [4, 2],
+         [3, 3],
+         [1, 5],
+         [2, 4]],
+        [[2, 5],
+         [1, 4],
+         [2, 3],
+         [4, 2],
+         [5, 1]]
+    ])
+    grt = np.array([
+            [1., 0., 0., 0., 0.],
+            [0., 0., 0., 0., 1.],
+            [1., 0., 0., 0., 0.]
+    ])
+    srcs = np.array(['T1', 'T2', 'T3', 'T4', 'T5'])
+    mthds = np.array(['M1', 'M2'])
+    metrics = ['auroc']
+    by = 'experiment'
+    df = []
+
+    with pytest.raises(ValueError):
+        append_metrics_scores(df, grpby_i=None, grp=None, act=act, grt=grt, srcs=srcs,
+                              mthds=mthds, metrics=metrics, by=by, min_exp=0)
+    df = []
+    append_metrics_scores(df, grpby_i=None, grp=None, act=act, grt=grt, srcs=srcs,
+                          mthds=mthds, metrics=metrics, by=by, min_exp=1)
+    assert len(df) == 2
+    assert df[0][5] < df[1][5]
+    df = []
+    append_metrics_scores(df, grpby_i=None, grp=None, act=act, grt=grt, srcs=srcs,
+                          mthds=mthds, metrics=metrics, by='source', min_exp=1)
+    assert len(df) == 4
+    assert df[0][5] < df[1][5]
+
+
+def test_check_groupby():
+
+    obs = pd.DataFrame([
+        ['A1', 'B1', 'C1'],
+        ['A1', 'B2', 'C1'],
+        ['A1', 'B2', 'C2'],
+        ['A2', 'B1', 'C1'],
+        ['A2', 'B1', 'C2'],
+        ['A2', 'B2', 'C2'],
+    ], columns=['A', 'B', 'C'])
+    perturb = 'A'
+
+    check_groupby(obs=obs, groupby='A', perturb=perturb, by='experiment')
+    with pytest.raises(AssertionError):
+        check_groupby(obs=obs, groupby='A', perturb=perturb, by='source')
+    with pytest.raises(AssertionError):
+        check_groupby(obs=obs, groupby='D', perturb=perturb, by='experiment')
+
+    check_groupby(obs=obs, groupby=['A', 'B'], perturb=perturb, by='experiment')
+    with pytest.raises(AssertionError):
+        check_groupby(obs=obs, groupby=['A', 'B'], perturb=perturb, by='source')
+    with pytest.raises(AssertionError):
+        check_groupby(obs=obs, groupby=['D', 'B'], perturb=perturb, by='experiment')
+
+    check_groupby(obs=obs, groupby=['A', ['B', 'C']], perturb=perturb, by='experiment')
+    with pytest.raises(AssertionError):
+        check_groupby(obs=obs, groupby=['A', ['B', 'C']], perturb=perturb, by='source')
+    with pytest.raises(AssertionError):
+        check_groupby(obs=obs, groupby=['D', ['B', 'C']], perturb=perturb, by='experiment')
+    obs = obs.rename({'A': 'A|Z'}, axis=1)
+    with pytest.raises(AssertionError):
+        check_groupby(obs=obs, groupby=['A|Z'], perturb=perturb, by='experiment')
+
+
+def test_rename_obs():
+    meta = pd.DataFrame([
+        ['TF1', -1],
+        [np.array(['TF1', 'TF2']), 0],
+        ['TF2', 1]
+    ], columns=['perturb', 'sign'])
+
+    with pytest.raises(AssertionError):
+        rename_obs(meta, 'asd', 'sign')
+    with pytest.raises(ValueError):
+        rename_obs(meta, 'perturb', 'perturb')
+    with pytest.raises(AssertionError):
+        rename_obs(meta, 'perturb', 'asd')
+    with pytest.raises(AssertionError):
+        rename_obs(meta, 'perturb', 'sign')
+    rename_obs(meta, 'perturb', -1)
+    with pytest.raises(ValueError):
+        rename_obs(meta, 'perturb', 0)
+
+
+def test_format_acts_grts():
+    exps = np.array(['S1', 'S2', 'S3', 'S4', 'S5', 'S6'])
+    srcs = np.array(['T1', 'T2', 'T3'])
+    mthds = np.array(['m_a', 'm_b'])
+    m_a = pd.DataFrame([
+        [4., 6., 5.],
+        [1., 3., 2.],
+        [4., 6., 5.],
+        [1., 3., 2.],
+        [4., 6., 5.],
+        [1., 3., 2.]
+    ], index=exps, columns=srcs)
+    m_b = pd.DataFrame([
+        [7., 9., 8.],
+        [1., 3., 2.],
+        [7., 9., 8.],
+        [1., 3., 2.],
+        [7., 9., 8.],
+        [1., 3., 2.]
+    ], index=exps, columns=srcs)
+    res = {mthds[0]: m_a, mthds[1]: m_b}
+    obs = pd.DataFrame([
+            ['A1', 'B1', 'C1', ['T3', 'T2'], 1],
+            ['A1', 'B2', 'C1', ['T3', 'T2'], -1],
+            ['A1', 'B2', 'C2', ['T3', 'T2'], 1],
+            ['A2', 'B1', 'C1', 'T1', -1],
+            ['A2', 'B1', 'C2', 'T2', 1],
+            ['A2', 'B2', 'C2', 'T3', -1],
+        ], columns=['col_A', 'col_B', 'col_C', 'perturb', 'sign'], index=exps)
+
+    out = format_acts_grts(res, obs, groupby=['col_C'])
+    assert out is not None
diff --git a/decoupler/tests/test_viper.py b/decoupler/tests/test_viper.py
index 32582a9..bdfc54f 100644
--- a/decoupler/tests/test_viper.py
+++ b/decoupler/tests/test_viper.py
@@ -1,4 +1,3 @@
-import pytest
 import numpy as np
 import pandas as pd
 from scipy.sparse import csr_matrix
@@ -9,26 +8,30 @@
 def test_get_inter_pvals():
     nes_i = np.array([1, 2, 3, 4])
     ss_i = np.array([5, 4, 3, 2])
-    sub_net = np.array([[1. , 0. ], [2 , 0. ], [0. , -3. ], [0. , 4. ]], dtype=np.float32)
+    sub_net = np.array([[1., 0.], [2, 0.], [0., -3.], [0., 4.]], dtype=np.float32)
     n_targets = 2
     get_inter_pvals(nes_i, ss_i, sub_net, n_targets)
 
+
 def test_shadow_regulon():
     nes_i = np.array([1, 2])
     ss_i = np.array([5, 4])
-    net = np.array([[1. , 0. ], [2 , 2. ], [1. , -3. ], [1. , 4. ]], dtype=np.float32)
+    net = np.array([[1., 0.], [2, 2.], [1., -3.], [1., 4.]], dtype=np.float32)
     shadow_regulon(nes_i, ss_i, net, reg_sign=1.96, n_targets=2, penalty=20)
 
+
 def test_aREA():
     m = np.array([[7., 1., 1., 1.], [4., 2., 1., 2.], [1., 2., 5., 1.], [1., 1., 6., 2.]])
-    net = np.array([[1. , 0. ], [2 , 0. ], [0. , -3. ], [0. , 4. ]])
+    net = np.array([[1., 0.], [2, 0.], [0., -3.], [0., 4.]])
     aREA(m, net)
 
+
 def test_viper():
     m = csr_matrix(np.array([[7., 1., 1., 1.], [4., 2., 1., 2.], [1., 2., 5., 1.], [1., 1., 6., 2.]]))
-    net = np.array([[1. , 0. ], [2 , 0. ], [0. , -3. ], [0. , 4. ]])
+    net = np.array([[1., 0.], [2, 0.], [0., -3.], [0., 4.]])
     viper(m, net, pleiotropy=True, reg_sign=0.95, n_targets=1, penalty=20, verbose=True)
 
+
 def test_run_viper():
     m = np.array([[7., 1., 1., 1.], [4., 2., 1., 2.], [1., 2., 5., 1.], [1., 1., 6., 2.]])
     r = np.array(['S1', 'S2', 'S3', 'S4'])
@@ -36,5 +39,5 @@ def test_run_viper():
     df = pd.DataFrame(m, index=r, columns=c)
     adata = AnnData(df)
     net = pd.DataFrame([['T1', 'G1', 1], ['T1', 'G2', 2], ['T2', 'G3', -3], ['T2', 'G4', 4]],
-                   columns=['source', 'target', 'weight'])
-    run_viper(adata, net, verbose=True, use_raw=False, min_n=0)
\ No newline at end of file
+                       columns=['source', 'target', 'weight'])
+    run_viper(adata, net, verbose=True, use_raw=False, min_n=0)
diff --git a/decoupler/tests/test_wmean.py b/decoupler/tests/test_wmean.py
index 2362511..68480a2 100644
--- a/decoupler/tests/test_wmean.py
+++ b/decoupler/tests/test_wmean.py
@@ -1,4 +1,3 @@
-import pytest
 import numpy as np
 import pandas as pd
 from scipy.sparse import csr_matrix
@@ -8,9 +7,10 @@
 
 def test_wmean():
     m = csr_matrix(np.array([[7., 1., 1., 1.], [4., 2., 1., 2.], [1., 2., 5., 1.], [1., 1., 6., 2.]], dtype=np.float32))
-    net = np.array([[1. , 0. ], [2 , 0. ], [0. , -3. ], [0. , 4. ]], dtype=np.float32)
+    net = np.array([[1., 0.], [2, 0.], [0., -3.], [0., 4.]], dtype=np.float32)
     wmean(m, net, 2, 10000, 42, True)
 
+
 def test_run_wmean():
     m = np.array([[7., 1., 1., 1.], [4., 2., 1., 2.], [1., 2., 5., 1.], [1., 1., 6., 2.]])
     r = np.array(['S1', 'S2', 'S3', 'S4'])
@@ -18,5 +18,5 @@ def test_run_wmean():
     df = pd.DataFrame(m, index=r, columns=c)
     adata = AnnData(df)
     net = pd.DataFrame([['T1', 'G1', 1], ['T1', 'G2', 2], ['T2', 'G3', -3], ['T2', 'G4', 4]],
-                   columns=['source', 'target', 'weight'])
+                       columns=['source', 'target', 'weight'])
     run_wmean(adata, net, verbose=True, use_raw=False, min_n=0, times=2)
diff --git a/decoupler/tests/test_wsum.py b/decoupler/tests/test_wsum.py
index e70ca7d..ce8bf89 100644
--- a/decoupler/tests/test_wsum.py
+++ b/decoupler/tests/test_wsum.py
@@ -1,4 +1,3 @@
-import pytest
 import numpy as np
 import pandas as pd
 from scipy.sparse import csr_matrix
@@ -8,9 +7,10 @@
 
 def test_wsum():
     m = csr_matrix(np.array([[7., 1., 1., 1.], [4., 2., 1., 2.], [1., 2., 5., 1.], [1., 1., 6., 2.]], dtype=np.float32))
-    net = np.array([[1. , 0. ], [2 , 0. ], [0. , -3. ], [0. , 4. ]], dtype=np.float32)
+    net = np.array([[1., 0.], [2, 0.], [0., -3.], [0., 4.]], dtype=np.float32)
     wsum(m, net, 2, 10000, 42, True)
 
+
 def test_run_wmean():
     m = np.array([[7., 1., 1., 1.], [4., 2., 1., 2.], [1., 2., 5., 1.], [1., 1., 6., 2.]])
     r = np.array(['S1', 'S2', 'S3', 'S4'])
@@ -18,5 +18,5 @@ def test_run_wmean():
     df = pd.DataFrame(m, index=r, columns=c)
     adata = AnnData(df)
     net = pd.DataFrame([['T1', 'G1', 1], ['T1', 'G2', 2], ['T2', 'G3', -3], ['T2', 'G4', 4]],
-                   columns=['source', 'target', 'weight'])
+                       columns=['source', 'target', 'weight'])
     run_wsum(adata, net, verbose=True, use_raw=False, min_n=0, times=2)

From 2fa61725d537cf5d3c137b05a3d590283b14b45b Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Thu, 1 Sep 2022 14:00:44 +0200
Subject: [PATCH 29/33] Fixed volcano_plot bug where nets with other names
 would not work

---
 decoupler/__init__.py | 2 +-
 decoupler/plotting.py | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/decoupler/__init__.py b/decoupler/__init__.py
index fb8eb2f..abb45b2 100644
--- a/decoupler/__init__.py
+++ b/decoupler/__init__.py
@@ -1,4 +1,4 @@
-__version__ = '1.1.22'  # noqa: F401
+__version__ = '1.1.23'  # noqa: F401
 __version_info__ = tuple([int(num) for num in __version__.split('.')])  # noqa: F401
 
 from .pre import extract, match, rename_net, get_net_mat, filt_min_n, mask_features  # noqa: F401
diff --git a/decoupler/plotting.py b/decoupler/plotting.py
index 269bf07..8cd1df5 100644
--- a/decoupler/plotting.py
+++ b/decoupler/plotting.py
@@ -83,8 +83,8 @@ def plot_volcano(logFCs, pvals, contrast, name=None, net=None, top=5, source='so
         Column name in net with source nodes.
     target : str
         Column name in net with target nodes.
-    weight : str
-        Column name in net with weights.
+    weight : str, None
+        Column name in net with weights. If none, set to None.
     sign_thr : float
         Significance threshold for p-values.
     lFCs_thr : float
@@ -140,7 +140,7 @@ def plot_volcano(logFCs, pvals, contrast, name=None, net=None, top=5, source='so
         # Filter by shared targets
         if name is None:
             raise ValueError('If net is given, name cannot be None.')
-        df = net[net[source] == name].set_index('target')
+        df = net[net['source'] == name].set_index('target')
         df['logFCs'] = logFCs.loc[[contrast]].T
         df['pvals'] = -np.log10(pvals.loc[[contrast]].T)
         df = df[~np.any(pd.isnull(df), axis=1)]

From 977c1d85f9e5bc2dfb121cbeec802ef379934094 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Thu, 1 Sep 2022 14:02:20 +0200
Subject: [PATCH 30/33] Added adj_pval to format_contrast_results

---
 decoupler/__init__.py      |  2 +-
 decoupler/utils_anndata.py | 16 ++++++++++++++--
 2 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/decoupler/__init__.py b/decoupler/__init__.py
index abb45b2..f8fea5d 100644
--- a/decoupler/__init__.py
+++ b/decoupler/__init__.py
@@ -1,4 +1,4 @@
-__version__ = '1.1.23'  # noqa: F401
+__version__ = '1.1.24'  # noqa: F401
 __version_info__ = tuple([int(num) for num in __version__.split('.')])  # noqa: F401
 
 from .pre import extract, match, rename_net, get_net_mat, filt_min_n, mask_features  # noqa: F401
diff --git a/decoupler/utils_anndata.py b/decoupler/utils_anndata.py
index eeb3758..c478ccf 100644
--- a/decoupler/utils_anndata.py
+++ b/decoupler/utils_anndata.py
@@ -470,6 +470,16 @@ def get_top_targets(logFCs, pvals, contrast, name=None, net=None, source='source
     # Filter by thresholds
     df = df[(np.abs(df['logFCs']) >= lFCs_thr) & (np.abs(df[pval_col]) <= sign_thr)]
 
+    # Format names
+    df = df.reset_index().rename({'index': 'name'}, axis=1)
+    df['contrast'] = contrast
+
+    # Order columns
+    if fdr_corr:
+        df = df[['contrast', 'name', 'logFCs', 'pvals', 'adj_pvals']].sort_values('pvals')
+    else:
+        df = df[['contrast', 'name', 'logFCs', 'pvals']].sort_values('pvals')
+
     return df
 
 
@@ -487,8 +497,10 @@ def format_contrast_results(logFCs, pvals):
     df : DataFrame
         DataFrame in long format.
     """
-
+    
     df = melt([logFCs, pvals]).rename({'sample': 'contrast', 'source': 'name', 'score': 'logFCs'}, axis=1)
-    df = df[['contrast', 'name', 'logFCs', 'pvals']].sort_values('pvals').reset_index(drop=True)
+    df = df[['contrast', 'name', 'logFCs', 'pvals']].sort_values('contrast').reset_index(drop=True)
+    df['adj_pvals'] = df.groupby('contrast').apply(lambda x: p_adjust_fdr(x['pvals'])).explode().values
+    df = df.sort_values(['contrast', 'pvals']).reset_index(drop=True)
 
     return df

From bf4a9678d838b92e21eead8c262efc9d1d0263c4 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Thu, 1 Sep 2022 14:47:55 +0200
Subject: [PATCH 31/33] Bumped version to 1.2.0

---
 decoupler/__init__.py      | 2 +-
 decoupler/utils_anndata.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/decoupler/__init__.py b/decoupler/__init__.py
index f8fea5d..6e373ef 100644
--- a/decoupler/__init__.py
+++ b/decoupler/__init__.py
@@ -1,4 +1,4 @@
-__version__ = '1.1.24'  # noqa: F401
+__version__ = '1.2.0'  # noqa: F401
 __version_info__ = tuple([int(num) for num in __version__.split('.')])  # noqa: F401
 
 from .pre import extract, match, rename_net, get_net_mat, filt_min_n, mask_features  # noqa: F401
diff --git a/decoupler/utils_anndata.py b/decoupler/utils_anndata.py
index c478ccf..c0bf930 100644
--- a/decoupler/utils_anndata.py
+++ b/decoupler/utils_anndata.py
@@ -497,7 +497,7 @@ def format_contrast_results(logFCs, pvals):
     df : DataFrame
         DataFrame in long format.
     """
-    
+
     df = melt([logFCs, pvals]).rename({'sample': 'contrast', 'source': 'name', 'score': 'logFCs'}, axis=1)
     df = df[['contrast', 'name', 'logFCs', 'pvals']].sort_values('contrast').reset_index(drop=True)
     df['adj_pvals'] = df.groupby('contrast').apply(lambda x: p_adjust_fdr(x['pvals'])).explode().values

From 1fb076edf6c14841fb643542355988a02c593c07 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Thu, 1 Sep 2022 14:50:13 +0200
Subject: [PATCH 32/33] Updated docs

---
 docs/source/api.rst                         |   48 +-
 docs/source/index.rst                       |    1 +
 docs/source/net_plot.png                    |  Bin 26522 -> 49386 bytes
 docs/source/notebooks/benchmark.ipynb       | 2008 +++++++++++++++++++
 docs/source/notebooks/bulk.ipynb            |  408 +++-
 docs/source/notebooks/cell_annotation.ipynb |  899 ++++-----
 docs/source/notebooks/dorothea.ipynb        |  953 ++++-----
 docs/source/notebooks/msigdb.ipynb          | 1187 ++++++-----
 docs/source/notebooks/progeny.ipynb         |  697 +++----
 docs/source/notebooks/pseudobulk.ipynb      |  869 +++++---
 docs/source/notebooks/usage.ipynb           |  263 ++-
 docs/source/release_notes.rst               |   21 +-
 12 files changed, 5070 insertions(+), 2284 deletions(-)
 create mode 100644 docs/source/notebooks/benchmark.ipynb

diff --git a/docs/source/api.rst b/docs/source/api.rst
index dc323df..bc87e78 100644
--- a/docs/source/api.rst
+++ b/docs/source/api.rst
@@ -47,23 +47,31 @@ Running multiple methods:
    cons
    dense_run
    
-Utils:
-------
+General utils:
+--------------
 .. autosummary::
    :toctree: generated
 
+   melt
    show_methods
    check_corr
-   melt
-   get_acts
    get_toy_data
    summarize_acts
    assign_groups
+   dense_run
+   p_adjust_fdr
+   shuffle_net
+
+AnnData utils:
+--------------
+.. autosummary::
+   :toctree: generated
+   
+   get_acts
    get_pseudobulk
    get_contrast
    get_top_targets
    format_contrast_results
-   p_adjust_fdr
    
 Omnipath wrappers:
 ------------------
@@ -83,3 +91,33 @@ Plotting
    plot_volcano
    plot_violins
    plot_barplot
+   plot_metrics_scatter
+   plot_metrics_scatter_cols
+   plot_metrics_boxplot
+
+Metrics
+-------
+.. autosummary::
+   :toctree: generated
+   
+   metric_auroc
+   metric_auprc
+   metric_mcauroc
+   metric_mcauprc
+
+Benchmark utils
+---------------
+.. autosummary::
+   :toctree: generated
+   
+   get_toy_benchmark_data
+   show_metrics
+
+Benchmark
+---------
+.. autosummary::
+   :toctree: generated
+   
+   benchmark
+   format_benchmark_inputs
+   get_performances
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 8cde53e..4ee727b 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -75,3 +75,4 @@ from omics data. Bioinformatics Advances. https://doi.org/10.1093/bioadv/vbac016
    notebooks/msigdb
    notebooks/pseudobulk
    notebooks/bulk
+   notebooks/benchmark
diff --git a/docs/source/net_plot.png b/docs/source/net_plot.png
index a5190547bcb00f243c90d2c2eaadc3936982f297..3b5ac5e5b4d719d7b95b10451bd97cde23637528 100644
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diff --git a/docs/source/notebooks/benchmark.ipynb b/docs/source/notebooks/benchmark.ipynb
new file mode 100644
index 0000000..b1ebad6
--- /dev/null
+++ b/docs/source/notebooks/benchmark.ipynb
@@ -0,0 +1,2008 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9be010e3",
+   "metadata": {},
+   "source": [
+    "# Benchmarking pipeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88056c0f",
+   "metadata": {},
+   "source": [
+    "Biological activities can be estimated by coupling a statistical method with a prior knowledge network. However, it is not clear which method or network performs better. When perturbation experiments are available, one can benchmark these methods by evaluating how good are they at recovering perturbed regulators. In our original `decoupler` [publication](https://doi.org/10.1093/bioadv/vbac016), we observed that `wsum`, `mlm`, `ulm` and `consensus` where the top performer methods. \n",
+    "\n",
+    "In this notebook we showcase how to use `decoupler` for the benchmarking of methods and prior knowledge networks. The data used here consist of single-gene perturbation experiments from the [KnockTF2](http://www.licpathway.net/KnockTFv2/index.php) database, an extensive dataset of literature curated perturbation experiments. If you use these data for your resarch, please cite the original publication of the resource:\n",
+    "\n",
+    ">Chenchen Feng, Chao Song, Yuejuan Liu, Fengcui Qian, Yu Gao, Ziyu Ning, Qiuyu Wang, Yong Jiang, Yanyu Li, Meng Li, Jiaxin Chen, Jian Zhang, Chunquan Li, KnockTF: a comprehensive human gene expression profile database with knockdown/knockout of transcription factors, Nucleic Acids Research, Volume 48, Issue D1, 08 January 2020, Pages D93–D100, https://doi.org/10.1093/nar/gkz881\n",
+    "\n",
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    "**Note**\n",
+    "    \n",
+    "This tutorial assumes that you already know the basics of `decoupler`. Else, check out the [Usage](https://decoupler-py.readthedocs.io/en/latest/notebooks/usage.html) tutorial first.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a2fe17a",
+   "metadata": {},
+   "source": [
+    "## Loading packages\n",
+    "\n",
+    "First, we need to load the relevant packages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8c610d39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import decoupler as dc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1552661f",
+   "metadata": {},
+   "source": [
+    "## Loading the data\n",
+    "\n",
+    "We can download the data easily from Zenodo:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7654e58f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2022-09-01 14:40:45--  https://zenodo.org/record/7035528/files/knockTF_expr.csv?download=1\n",
+      "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
+      "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 146086808 (139M) [text/plain]\n",
+      "Saving to: ‘knockTF_expr.csv’\n",
+      "\n",
+      "knockTF_expr.csv    100%[===================>] 139,32M  4,62MB/s    in 31s     \n",
+      "\n",
+      "2022-09-01 14:41:18 (4,43 MB/s) - ‘knockTF_expr.csv’ saved [146086808/146086808]\n",
+      "\n",
+      "--2022-09-01 14:41:18--  https://zenodo.org/record/7035528/files/knockTF_meta.csv?download=1\n",
+      "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
+      "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 144861 (141K) [text/plain]\n",
+      "Saving to: ‘knockTF_meta.csv’\n",
+      "\n",
+      "knockTF_meta.csv    100%[===================>] 141,47K  --.-KB/s    in 0,03s   \n",
+      "\n",
+      "2022-09-01 14:41:19 (4,25 MB/s) - ‘knockTF_meta.csv’ saved [144861/144861]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget 'https://zenodo.org/record/7035528/files/knockTF_expr.csv?download=1' -O knockTF_expr.csv\n",
+    "!wget 'https://zenodo.org/record/7035528/files/knockTF_meta.csv?download=1' -O knockTF_meta.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2e11366",
+   "metadata": {},
+   "source": [
+    "We can then read it using `pandas`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "25c778f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read data\n",
+    "mat = pd.read_csv('knockTF_expr.csv', index_col=0)\n",
+    "obs = pd.read_csv('knockTF_meta.csv', index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70634b2b",
+   "metadata": {},
+   "source": [
+    "`mat` consist of a matrix of logFCs between the perturbed and the control samples. Positive values mean that these genes were\n",
+    "overexpressed after the perturbation, negative values mean the opposite."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8fc3a459",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A1BG</th>\n",
+       "      <th>A1BG-AS1</th>\n",
+       "      <th>A1CF</th>\n",
+       "      <th>A2LD1</th>\n",
+       "      <th>A2M</th>\n",
+       "      <th>A2ML1</th>\n",
+       "      <th>A4GALT</th>\n",
+       "      <th>A4GNT</th>\n",
+       "      <th>AA06</th>\n",
+       "      <th>AAA1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ZWINT</th>\n",
+       "      <th>ZXDA</th>\n",
+       "      <th>ZXDB</th>\n",
+       "      <th>ZXDC</th>\n",
+       "      <th>ZYG11A</th>\n",
+       "      <th>ZYG11B</th>\n",
+       "      <th>ZYX</th>\n",
+       "      <th>ZZEF1</th>\n",
+       "      <th>ZZZ3</th>\n",
+       "      <th>psiTPTE22</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DataSet_01_001</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.033220</td>\n",
+       "      <td>-0.046900</td>\n",
+       "      <td>-0.143830</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_01_002</th>\n",
+       "      <td>-0.173750</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.537790</td>\n",
+       "      <td>-0.4424</td>\n",
+       "      <td>-1.596640</td>\n",
+       "      <td>-0.00010</td>\n",
+       "      <td>-0.08260</td>\n",
+       "      <td>-3.64590</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.39579</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.086110</td>\n",
+       "      <td>0.28574</td>\n",
+       "      <td>-0.130210</td>\n",
+       "      <td>-0.276640</td>\n",
+       "      <td>-0.881580</td>\n",
+       "      <td>0.812950</td>\n",
+       "      <td>0.47896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_01_003</th>\n",
+       "      <td>-0.216130</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.220270</td>\n",
+       "      <td>-0.0008</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.15580</td>\n",
+       "      <td>-0.35802</td>\n",
+       "      <td>-0.32025</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.82964</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.064470</td>\n",
+       "      <td>0.59323</td>\n",
+       "      <td>0.313950</td>\n",
+       "      <td>-0.232600</td>\n",
+       "      <td>0.065510</td>\n",
+       "      <td>-0.147140</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_01_004</th>\n",
+       "      <td>-0.255680</td>\n",
+       "      <td>0.111050</td>\n",
+       "      <td>-0.285270</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>-0.035860</td>\n",
+       "      <td>-0.46970</td>\n",
+       "      <td>0.18559</td>\n",
+       "      <td>-0.25601</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.39888</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.104440</td>\n",
+       "      <td>-0.16434</td>\n",
+       "      <td>0.191460</td>\n",
+       "      <td>0.415610</td>\n",
+       "      <td>0.393840</td>\n",
+       "      <td>0.127900</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_01_005</th>\n",
+       "      <td>0.478500</td>\n",
+       "      <td>-0.375710</td>\n",
+       "      <td>-0.847180</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>3.354450</td>\n",
+       "      <td>0.17104</td>\n",
+       "      <td>-0.34852</td>\n",
+       "      <td>-0.95517</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.24849</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.286920</td>\n",
+       "      <td>-0.01815</td>\n",
+       "      <td>0.119410</td>\n",
+       "      <td>0.077850</td>\n",
+       "      <td>0.234740</td>\n",
+       "      <td>0.228690</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_04_039</th>\n",
+       "      <td>0.668721</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.120893</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.263564</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.324808</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>-0.370491</td>\n",
+       "      <td>0.092874</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.014075</td>\n",
+       "      <td>-0.215169</td>\n",
+       "      <td>0.141374</td>\n",
+       "      <td>-0.076054</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_04_040</th>\n",
+       "      <td>1.816027</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.139721</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.459498</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.321726</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>-0.144934</td>\n",
+       "      <td>-0.238343</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>-0.134995</td>\n",
+       "      <td>0.577406</td>\n",
+       "      <td>0.082028</td>\n",
+       "      <td>-0.171424</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_04_041</th>\n",
+       "      <td>-0.174250</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.084499</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.112492</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.405369</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.272526</td>\n",
+       "      <td>-0.125475</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.019763</td>\n",
+       "      <td>-0.098584</td>\n",
+       "      <td>0.072675</td>\n",
+       "      <td>0.199813</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_04_042</th>\n",
+       "      <td>0.976681</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.099845</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.366973</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.397698</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>-0.398350</td>\n",
+       "      <td>0.037134</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.409704</td>\n",
+       "      <td>-0.056978</td>\n",
+       "      <td>0.308122</td>\n",
+       "      <td>-0.058794</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_04_043</th>\n",
+       "      <td>0.677944</td>\n",
+       "      <td>0.584963</td>\n",
+       "      <td>0.271215</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.104090</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.357371</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>-0.152622</td>\n",
+       "      <td>-0.076498</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.047949</td>\n",
+       "      <td>-0.169766</td>\n",
+       "      <td>0.178053</td>\n",
+       "      <td>0.199003</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>907 rows × 21985 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    A1BG  A1BG-AS1      A1CF   A2LD1       A2M    A2ML1  \\\n",
+       "DataSet_01_001  0.000000  0.000000  0.000000  0.0000  0.000000  0.00000   \n",
+       "DataSet_01_002 -0.173750  0.000000 -0.537790 -0.4424 -1.596640 -0.00010   \n",
+       "DataSet_01_003 -0.216130  0.000000 -0.220270 -0.0008  0.000000  0.15580   \n",
+       "DataSet_01_004 -0.255680  0.111050 -0.285270  0.0000 -0.035860 -0.46970   \n",
+       "DataSet_01_005  0.478500 -0.375710 -0.847180  0.0000  3.354450  0.17104   \n",
+       "...                  ...       ...       ...     ...       ...      ...   \n",
+       "DataSet_04_039  0.668721  0.000000  0.120893  0.0000  0.263564  0.00000   \n",
+       "DataSet_04_040  1.816027  0.000000  0.139721  0.0000  0.459498  0.00000   \n",
+       "DataSet_04_041 -0.174250  0.000000 -0.084499  0.0000  0.112492  0.00000   \n",
+       "DataSet_04_042  0.976681  0.000000  0.099845  0.0000  0.366973  0.00000   \n",
+       "DataSet_04_043  0.677944  0.584963  0.271215  0.0000  0.104090  0.00000   \n",
+       "\n",
+       "                 A4GALT    A4GNT  AA06  AAA1  ...     ZWINT     ZXDA  \\\n",
+       "DataSet_01_001  0.00000  0.00000   0.0   0.0  ...  0.000000  0.00000   \n",
+       "DataSet_01_002 -0.08260 -3.64590   0.0   0.0  ...  0.000000  0.39579   \n",
+       "DataSet_01_003 -0.35802 -0.32025   0.0   0.0  ...  0.000000 -0.82964   \n",
+       "DataSet_01_004  0.18559 -0.25601   0.0   0.0  ...  0.000000 -0.39888   \n",
+       "DataSet_01_005 -0.34852 -0.95517   0.0   0.0  ...  0.000000  0.24849   \n",
+       "...                 ...      ...   ...   ...  ...       ...      ...   \n",
+       "DataSet_04_039  0.00000  0.00000   0.0   0.0  ... -0.324808  0.00000   \n",
+       "DataSet_04_040  0.00000  0.00000   0.0   0.0  ... -0.321726  0.00000   \n",
+       "DataSet_04_041  0.00000  0.00000   0.0   0.0  ...  0.405369  0.00000   \n",
+       "DataSet_04_042  0.00000  0.00000   0.0   0.0  ...  0.397698  0.00000   \n",
+       "DataSet_04_043  0.00000  0.00000   0.0   0.0  ... -0.357371  0.00000   \n",
+       "\n",
+       "                    ZXDB      ZXDC   ZYG11A    ZYG11B       ZYX     ZZEF1  \\\n",
+       "DataSet_01_001  0.000000  0.000000  0.00000  0.000000 -0.033220 -0.046900   \n",
+       "DataSet_01_002  0.000000  0.086110  0.28574 -0.130210 -0.276640 -0.881580   \n",
+       "DataSet_01_003  0.000000  0.064470  0.59323  0.313950 -0.232600  0.065510   \n",
+       "DataSet_01_004  0.000000  0.104440 -0.16434  0.191460  0.415610  0.393840   \n",
+       "DataSet_01_005  0.000000 -0.286920 -0.01815  0.119410  0.077850  0.234740   \n",
+       "...                  ...       ...      ...       ...       ...       ...   \n",
+       "DataSet_04_039 -0.370491  0.092874  0.00000  0.014075 -0.215169  0.141374   \n",
+       "DataSet_04_040 -0.144934 -0.238343  0.00000 -0.134995  0.577406  0.082028   \n",
+       "DataSet_04_041  0.272526 -0.125475  0.00000  0.019763 -0.098584  0.072675   \n",
+       "DataSet_04_042 -0.398350  0.037134  0.00000  0.409704 -0.056978  0.308122   \n",
+       "DataSet_04_043 -0.152622 -0.076498  0.00000  0.047949 -0.169766  0.178053   \n",
+       "\n",
+       "                    ZZZ3  psiTPTE22  \n",
+       "DataSet_01_001 -0.143830    0.00000  \n",
+       "DataSet_01_002  0.812950    0.47896  \n",
+       "DataSet_01_003 -0.147140    0.00000  \n",
+       "DataSet_01_004  0.127900    0.00000  \n",
+       "DataSet_01_005  0.228690    0.00000  \n",
+       "...                  ...        ...  \n",
+       "DataSet_04_039 -0.076054    0.00000  \n",
+       "DataSet_04_040 -0.171424    0.00000  \n",
+       "DataSet_04_041  0.199813    0.00000  \n",
+       "DataSet_04_042 -0.058794    0.00000  \n",
+       "DataSet_04_043  0.199003    0.00000  \n",
+       "\n",
+       "[907 rows x 21985 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c420680b",
+   "metadata": {},
+   "source": [
+    "On the other hand, `obs` contains the meta-data information of each perturbation experiment.\n",
+    "Here one can find which TF was perturbed (`TF` column), and many other useful information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fa5396dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>TF</th>\n",
+       "      <th>Species</th>\n",
+       "      <th>Knock.Method</th>\n",
+       "      <th>Biosample.Name</th>\n",
+       "      <th>Profile.ID</th>\n",
+       "      <th>Platform</th>\n",
+       "      <th>TF.Class</th>\n",
+       "      <th>TF.Superclass</th>\n",
+       "      <th>Tissue.Type</th>\n",
+       "      <th>Biosample.Type</th>\n",
+       "      <th>Data.Source</th>\n",
+       "      <th>Pubmed.ID</th>\n",
+       "      <th>logFC</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DataSet_01_001</th>\n",
+       "      <td>ESR1</td>\n",
+       "      <td>Homo sapiens</td>\n",
+       "      <td>siRNA</td>\n",
+       "      <td>MCF7</td>\n",
+       "      <td>GSE10061</td>\n",
+       "      <td>GPL3921</td>\n",
+       "      <td>Nuclear receptors with C4 zinc fingers</td>\n",
+       "      <td>Zinc-coordinating DNA-binding domains</td>\n",
+       "      <td>Mammary_gland</td>\n",
+       "      <td>Cell line</td>\n",
+       "      <td>GEO</td>\n",
+       "      <td>18631401</td>\n",
+       "      <td>-0.713850</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_01_002</th>\n",
+       "      <td>HNF1A</td>\n",
+       "      <td>Homo sapiens</td>\n",
+       "      <td>shRNA</td>\n",
+       "      <td>HuH7</td>\n",
+       "      <td>GSE103128</td>\n",
+       "      <td>GPL18180</td>\n",
+       "      <td>Homeo domain factors</td>\n",
+       "      <td>Helix-turn-helix domains</td>\n",
+       "      <td>Liver</td>\n",
+       "      <td>Cell line</td>\n",
+       "      <td>GEO</td>\n",
+       "      <td>29466992</td>\n",
+       "      <td>0.164280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_01_003</th>\n",
+       "      <td>MLXIP</td>\n",
+       "      <td>Homo sapiens</td>\n",
+       "      <td>shRNA</td>\n",
+       "      <td>HA1ER</td>\n",
+       "      <td>GSE11242</td>\n",
+       "      <td>GPL4133</td>\n",
+       "      <td>Basic helix-loop-helix factors (bHLH)</td>\n",
+       "      <td>Basic domains</td>\n",
+       "      <td>Embryo_kidney</td>\n",
+       "      <td>Stem cell</td>\n",
+       "      <td>GEO</td>\n",
+       "      <td>18458340</td>\n",
+       "      <td>0.262150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_01_004</th>\n",
+       "      <td>CREB1</td>\n",
+       "      <td>Homo sapiens</td>\n",
+       "      <td>shRNA</td>\n",
+       "      <td>K562</td>\n",
+       "      <td>GSE12056</td>\n",
+       "      <td>GPL570</td>\n",
+       "      <td>Basic leucine zipper factors (bZIP)</td>\n",
+       "      <td>Basic domains</td>\n",
+       "      <td>Haematopoietic_and_lymphoid_tissue</td>\n",
+       "      <td>Cell line</td>\n",
+       "      <td>GEO</td>\n",
+       "      <td>18801183</td>\n",
+       "      <td>-0.950180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_01_005</th>\n",
+       "      <td>POU5F1</td>\n",
+       "      <td>Homo sapiens</td>\n",
+       "      <td>siRNA</td>\n",
+       "      <td>GBS6</td>\n",
+       "      <td>GSE12320</td>\n",
+       "      <td>GPL570</td>\n",
+       "      <td>Homeo domain factors</td>\n",
+       "      <td>Helix-turn-helix domains</td>\n",
+       "      <td>Bone_marrow</td>\n",
+       "      <td>Cell line</td>\n",
+       "      <td>GEO</td>\n",
+       "      <td>20203285</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_04_039</th>\n",
+       "      <td>SRPK2</td>\n",
+       "      <td>Homo sapiens</td>\n",
+       "      <td>CRISPR</td>\n",
+       "      <td>HepG2</td>\n",
+       "      <td>ENCSR929EIP</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>ENCODE_TF</td>\n",
+       "      <td>Liver</td>\n",
+       "      <td>Cell line</td>\n",
+       "      <td>ENCODE</td>\n",
+       "      <td>22955616</td>\n",
+       "      <td>-1.392551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_04_040</th>\n",
+       "      <td>WRN</td>\n",
+       "      <td>Homo sapiens</td>\n",
+       "      <td>shRNA</td>\n",
+       "      <td>HepG2</td>\n",
+       "      <td>ENCSR093FHC</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>ENCODE_TF</td>\n",
+       "      <td>Liver</td>\n",
+       "      <td>Cell line</td>\n",
+       "      <td>ENCODE</td>\n",
+       "      <td>22955616</td>\n",
+       "      <td>-0.173964</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_04_041</th>\n",
+       "      <td>YBX1</td>\n",
+       "      <td>Homo sapiens</td>\n",
+       "      <td>CRISPR</td>\n",
+       "      <td>HepG2</td>\n",
+       "      <td>ENCSR548OTL</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Cold-shock domain factors</td>\n",
+       "      <td>beta-Barrel DNA-binding domains</td>\n",
+       "      <td>Liver</td>\n",
+       "      <td>Cell line</td>\n",
+       "      <td>ENCODE</td>\n",
+       "      <td>22955616</td>\n",
+       "      <td>-2.025170</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_04_042</th>\n",
+       "      <td>ZC3H8</td>\n",
+       "      <td>Homo sapiens</td>\n",
+       "      <td>shRNA</td>\n",
+       "      <td>HepG2</td>\n",
+       "      <td>ENCSR184YDW</td>\n",
+       "      <td>-</td>\n",
+       "      <td>C3H zinc finger factors</td>\n",
+       "      <td>Zinc-coordinating DNA-binding domains</td>\n",
+       "      <td>Liver</td>\n",
+       "      <td>Cell line</td>\n",
+       "      <td>ENCODE</td>\n",
+       "      <td>22955616</td>\n",
+       "      <td>-0.027152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DataSet_04_043</th>\n",
+       "      <td>ZNF574</td>\n",
+       "      <td>Homo sapiens</td>\n",
+       "      <td>CRISPR</td>\n",
+       "      <td>HepG2</td>\n",
+       "      <td>ENCSR821GQN</td>\n",
+       "      <td>-</td>\n",
+       "      <td>C2H2 zinc finger factors</td>\n",
+       "      <td>Zinc-coordinating DNA-binding domains</td>\n",
+       "      <td>Liver</td>\n",
+       "      <td>Cell line</td>\n",
+       "      <td>ENCODE</td>\n",
+       "      <td>22955616</td>\n",
+       "      <td>0.990298</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>907 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    TF       Species Knock.Method Biosample.Name   Profile.ID  \\\n",
+       "DataSet_01_001    ESR1  Homo sapiens        siRNA           MCF7     GSE10061   \n",
+       "DataSet_01_002   HNF1A  Homo sapiens        shRNA           HuH7    GSE103128   \n",
+       "DataSet_01_003   MLXIP  Homo sapiens        shRNA          HA1ER     GSE11242   \n",
+       "DataSet_01_004   CREB1  Homo sapiens        shRNA           K562     GSE12056   \n",
+       "DataSet_01_005  POU5F1  Homo sapiens        siRNA           GBS6     GSE12320   \n",
+       "...                ...           ...          ...            ...          ...   \n",
+       "DataSet_04_039   SRPK2  Homo sapiens       CRISPR          HepG2  ENCSR929EIP   \n",
+       "DataSet_04_040     WRN  Homo sapiens        shRNA          HepG2  ENCSR093FHC   \n",
+       "DataSet_04_041    YBX1  Homo sapiens       CRISPR          HepG2  ENCSR548OTL   \n",
+       "DataSet_04_042   ZC3H8  Homo sapiens        shRNA          HepG2  ENCSR184YDW   \n",
+       "DataSet_04_043  ZNF574  Homo sapiens       CRISPR          HepG2  ENCSR821GQN   \n",
+       "\n",
+       "                Platform                                TF.Class  \\\n",
+       "DataSet_01_001   GPL3921  Nuclear receptors with C4 zinc fingers   \n",
+       "DataSet_01_002  GPL18180                    Homeo domain factors   \n",
+       "DataSet_01_003   GPL4133   Basic helix-loop-helix factors (bHLH)   \n",
+       "DataSet_01_004    GPL570     Basic leucine zipper factors (bZIP)   \n",
+       "DataSet_01_005    GPL570                    Homeo domain factors   \n",
+       "...                  ...                                     ...   \n",
+       "DataSet_04_039         -                                  Others   \n",
+       "DataSet_04_040         -                                  Others   \n",
+       "DataSet_04_041         -               Cold-shock domain factors   \n",
+       "DataSet_04_042         -                 C3H zinc finger factors   \n",
+       "DataSet_04_043         -                C2H2 zinc finger factors   \n",
+       "\n",
+       "                                        TF.Superclass  \\\n",
+       "DataSet_01_001  Zinc-coordinating DNA-binding domains   \n",
+       "DataSet_01_002               Helix-turn-helix domains   \n",
+       "DataSet_01_003                          Basic domains   \n",
+       "DataSet_01_004                          Basic domains   \n",
+       "DataSet_01_005               Helix-turn-helix domains   \n",
+       "...                                               ...   \n",
+       "DataSet_04_039                              ENCODE_TF   \n",
+       "DataSet_04_040                              ENCODE_TF   \n",
+       "DataSet_04_041        beta-Barrel DNA-binding domains   \n",
+       "DataSet_04_042  Zinc-coordinating DNA-binding domains   \n",
+       "DataSet_04_043  Zinc-coordinating DNA-binding domains   \n",
+       "\n",
+       "                                       Tissue.Type Biosample.Type Data.Source  \\\n",
+       "DataSet_01_001                       Mammary_gland      Cell line         GEO   \n",
+       "DataSet_01_002                               Liver      Cell line         GEO   \n",
+       "DataSet_01_003                       Embryo_kidney      Stem cell         GEO   \n",
+       "DataSet_01_004  Haematopoietic_and_lymphoid_tissue      Cell line         GEO   \n",
+       "DataSet_01_005                         Bone_marrow      Cell line         GEO   \n",
+       "...                                            ...            ...         ...   \n",
+       "DataSet_04_039                               Liver      Cell line      ENCODE   \n",
+       "DataSet_04_040                               Liver      Cell line      ENCODE   \n",
+       "DataSet_04_041                               Liver      Cell line      ENCODE   \n",
+       "DataSet_04_042                               Liver      Cell line      ENCODE   \n",
+       "DataSet_04_043                               Liver      Cell line      ENCODE   \n",
+       "\n",
+       "               Pubmed.ID     logFC  \n",
+       "DataSet_01_001  18631401 -0.713850  \n",
+       "DataSet_01_002  29466992  0.164280  \n",
+       "DataSet_01_003  18458340  0.262150  \n",
+       "DataSet_01_004  18801183 -0.950180  \n",
+       "DataSet_01_005  20203285  0.000000  \n",
+       "...                  ...       ...  \n",
+       "DataSet_04_039  22955616 -1.392551  \n",
+       "DataSet_04_040  22955616 -0.173964  \n",
+       "DataSet_04_041  22955616 -2.025170  \n",
+       "DataSet_04_042  22955616 -0.027152  \n",
+       "DataSet_04_043  22955616  0.990298  \n",
+       "\n",
+       "[907 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "obs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6d02df0",
+   "metadata": {},
+   "source": [
+    "## Filtering\n",
+    "\n",
+    "Since some of the experiments collected inside KnockTF are of low quality, it is best to remove them. We can do this by filtering\n",
+    "perturbation experiments where the knocked TF does not show a strong negative logFC (meaning that the knock method did not work).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b5450177",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((388, 21985), (388, 13))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Filter out low quality experiments (positive logFCs in knockout experiments)\n",
+    "msk = obs['logFC'] < -1\n",
+    "mat = mat[msk]\n",
+    "obs = obs[msk]\n",
+    "mat.shape, obs.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af388fa0",
+   "metadata": {},
+   "source": [
+    "## Evaluation\n",
+    "\n",
+    "### Single network\n",
+    "\n",
+    "As an example, we will evaluate the gene regulatory network\n",
+    "[DoRothEA](https://saezlab.github.io/dorothea/), for more information you can visit this\n",
+    "[other vignette](https://decoupler-py.readthedocs.io/en/latest/notebooks/dorothea.html).\n",
+    "\n",
+    "We can retrieve this network by running:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "39cc00e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source</th>\n",
+       "      <th>confidence</th>\n",
+       "      <th>target</th>\n",
+       "      <th>weight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TP53</td>\n",
+       "      <td>A</td>\n",
+       "      <td>CCNG1</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>FOS</td>\n",
+       "      <td>A</td>\n",
+       "      <td>IL1A</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>FOS</td>\n",
+       "      <td>A</td>\n",
+       "      <td>IL1B</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>HIF1A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>NOS3</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>MYC</td>\n",
+       "      <td>A</td>\n",
+       "      <td>RBM8A</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276829</th>\n",
+       "      <td>FOXA1</td>\n",
+       "      <td>D</td>\n",
+       "      <td>ARHGDIB</td>\n",
+       "      <td>0.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276830</th>\n",
+       "      <td>FOXA1</td>\n",
+       "      <td>D</td>\n",
+       "      <td>ARHGEF1</td>\n",
+       "      <td>0.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276831</th>\n",
+       "      <td>FOXA1</td>\n",
+       "      <td>D</td>\n",
+       "      <td>ARHGEF10L</td>\n",
+       "      <td>0.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276832</th>\n",
+       "      <td>FOXA1</td>\n",
+       "      <td>D</td>\n",
+       "      <td>ARHGAP25</td>\n",
+       "      <td>0.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276833</th>\n",
+       "      <td>ZZZ3</td>\n",
+       "      <td>D</td>\n",
+       "      <td>ZP3</td>\n",
+       "      <td>0.25</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>276834 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       source confidence     target  weight\n",
+       "0        TP53          A      CCNG1    1.00\n",
+       "1         FOS          A       IL1A    1.00\n",
+       "2         FOS          A       IL1B    1.00\n",
+       "3       HIF1A          A       NOS3    1.00\n",
+       "4         MYC          A      RBM8A    1.00\n",
+       "...       ...        ...        ...     ...\n",
+       "276829  FOXA1          D    ARHGDIB    0.25\n",
+       "276830  FOXA1          D    ARHGEF1    0.25\n",
+       "276831  FOXA1          D  ARHGEF10L    0.25\n",
+       "276832  FOXA1          D   ARHGAP25    0.25\n",
+       "276833   ZZZ3          D        ZP3    0.25\n",
+       "\n",
+       "[276834 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net = dc.get_dorothea(levels=['A', 'B', 'C', 'D'])\n",
+    "net"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c906e41",
+   "metadata": {},
+   "source": [
+    "We will run the benchmark pipeline using the top performer methods from `decoupler`. If we would like, we could use any other,\n",
+    "and we can optionaly change their parametters too (for more information check the `decouple` function).\n",
+    "\n",
+    "To run the benchmark pipeline, we need input molecular data (`mat`), its associated metadata (`obs`) with the name of the\n",
+    "column where the perturbed regulators are (`perturb`), and which direction the pertrubations are (`sign`, positive or negative)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "737759f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting inputs...\n",
+      "Formating net...\n",
+      "146 experiments without sources in net, they will be removed.\n",
+      "Running methods...\n",
+      "52 features of mat are empty, they will be removed.\n",
+      "Running mlm on mat with 242 samples and 21933 targets for 411 sources.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.61it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "52 features of mat are empty, they will be removed.\n",
+      "Running ulm on mat with 242 samples and 21933 targets for 411 sources.\n",
+      "52 features of mat are empty, they will be removed.\n",
+      "Running wsum on mat with 242 samples and 21933 targets for 411 sources.\n",
+      "Infering activities on 1 batches.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:11<00:00, 11.45s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating metrics...\n",
+      "Computing metrics...\n",
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example on how to set up decouple arguments\n",
+    "decouple_kws={\n",
+    "    'args' : {\n",
+    "        'wsum' : {'times': 100}\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# Run benchmark pipeline\n",
+    "df = dc.benchmark(mat, obs, net, perturb='TF', sign=-1, verbose=True, decouple_kws=decouple_kws)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d0c141a",
+   "metadata": {},
+   "source": [
+    "The result of running the pipeline is a long-format dataframe containing different performance metrics across methods:\n",
+    "\n",
+    "- `auroc`: Area Under the Receiver Operating characteristic Curve (AUROC)\n",
+    "- `auprc`: Area Under the Precision-Recall Curve (AUPRC), which by default uses a calibrated version where 0.5 indicates a random classifier (adapted from [this publication](https://doi.org/10.1007/978-3-030-44584-3_36)).\n",
+    "- `mauroc`: Monte-Carlo Area Under the Precision-Recall Curve (AUPRC)\n",
+    "- `mauprc`: Monte-Carlo Area Under the Receiver Operating characteristic Curve (AUROC)\n",
+    "\n",
+    "The Monte-Carlo metrics perform random permutations where each one samples the same number of true positives and true negatives to have balanced sets, returning one value per iteration done (10k by default)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ddba0c37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>groupby</th>\n",
+       "      <th>group</th>\n",
+       "      <th>source</th>\n",
+       "      <th>method</th>\n",
+       "      <th>metric</th>\n",
+       "      <th>score</th>\n",
+       "      <th>ci</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>mlm_estimate</td>\n",
+       "      <td>auroc</td>\n",
+       "      <td>0.620085</td>\n",
+       "      <td>0.002433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>mlm_estimate</td>\n",
+       "      <td>auprc</td>\n",
+       "      <td>0.685880</td>\n",
+       "      <td>0.002433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>mlm_estimate</td>\n",
+       "      <td>mcauroc</td>\n",
+       "      <td>0.617035</td>\n",
+       "      <td>0.002433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>mlm_estimate</td>\n",
+       "      <td>mcauroc</td>\n",
+       "      <td>0.615669</td>\n",
+       "      <td>0.002433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>mlm_estimate</td>\n",
+       "      <td>mcauroc</td>\n",
+       "      <td>0.626153</td>\n",
+       "      <td>0.002433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12007</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>consensus_estimate</td>\n",
+       "      <td>mcauprc</td>\n",
+       "      <td>0.696939</td>\n",
+       "      <td>0.002433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12008</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>consensus_estimate</td>\n",
+       "      <td>mcauprc</td>\n",
+       "      <td>0.683277</td>\n",
+       "      <td>0.002433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12009</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>consensus_estimate</td>\n",
+       "      <td>mcauprc</td>\n",
+       "      <td>0.647504</td>\n",
+       "      <td>0.002433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12010</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>consensus_estimate</td>\n",
+       "      <td>mcauprc</td>\n",
+       "      <td>0.673727</td>\n",
+       "      <td>0.002433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12011</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>consensus_estimate</td>\n",
+       "      <td>mcauprc</td>\n",
+       "      <td>0.681427</td>\n",
+       "      <td>0.002433</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>12012 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      groupby group source              method   metric     score        ci\n",
+       "0        None  None   None        mlm_estimate    auroc  0.620085  0.002433\n",
+       "1        None  None   None        mlm_estimate    auprc  0.685880  0.002433\n",
+       "2        None  None   None        mlm_estimate  mcauroc  0.617035  0.002433\n",
+       "3        None  None   None        mlm_estimate  mcauroc  0.615669  0.002433\n",
+       "4        None  None   None        mlm_estimate  mcauroc  0.626153  0.002433\n",
+       "...       ...   ...    ...                 ...      ...       ...       ...\n",
+       "12007    None  None   None  consensus_estimate  mcauprc  0.696939  0.002433\n",
+       "12008    None  None   None  consensus_estimate  mcauprc  0.683277  0.002433\n",
+       "12009    None  None   None  consensus_estimate  mcauprc  0.647504  0.002433\n",
+       "12010    None  None   None  consensus_estimate  mcauprc  0.673727  0.002433\n",
+       "12011    None  None   None  consensus_estimate  mcauprc  0.681427  0.002433\n",
+       "\n",
+       "[12012 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14098ba7",
+   "metadata": {},
+   "source": [
+    "We can easily visualize the results in a scatterplot: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ab8d2569",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dc.plot_metrics_scatter(df, x='mcauroc', y='mcauprc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9c647ca",
+   "metadata": {},
+   "source": [
+    "It looks like `mlm` and the `consensus` methods are the best performers at recovering perturbed regulators.\n",
+    "We can also visualise the distirbutions of the obtained Monte-Carlo metrics, for example `mcauroc`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "480692da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dc.plot_metrics_boxplot(df, metric='mcauroc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11d7c52e",
+   "metadata": {},
+   "source": [
+    "### Multiple networks\n",
+    "\n",
+    "This pipeline can evaluate multiple networks at the same time. Now we will evaluate different levels of confidence of DoRothEA (before we were using all of them, ABCD) and we will also add a randomized version as control. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a34abe69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Filter net into different confidence levels\n",
+    "doro_A = net[net['confidence'] == 'A']\n",
+    "doro_AB = net[np.isin(net['confidence'], ['A', 'B'])] \n",
+    "doro_ABC = net[np.isin(net['confidence'], ['A', 'B', 'C'])]\n",
+    "doro_ABCD = net\n",
+    "doro_rand = dc.shuffle_net(doro_ABC, target='target', weight='weight').drop_duplicates(['source', 'target'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65089089",
+   "metadata": {},
+   "source": [
+    "We can run the same pipeline but now using a dictionary of networks (and of extra arguments if needed):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "029d1161",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using A network...\n",
+      "Extracting inputs...\n",
+      "Formating net...\n",
+      "232 experiments without sources in net, they will be removed.\n",
+      "Running methods...\n",
+      "70 features of mat are empty, they will be removed.\n",
+      "Running mlm on mat with 156 samples and 21915 targets for 146 sources.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  3.24it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "70 features of mat are empty, they will be removed.\n",
+      "Running ulm on mat with 156 samples and 21915 targets for 146 sources.\n",
+      "70 features of mat are empty, they will be removed.\n",
+      "Running wsum on mat with 156 samples and 21915 targets for 146 sources.\n",
+      "Infering activities on 1 batches.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00,  1.90s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating metrics...\n",
+      "Computing metrics...\n",
+      "Done.\n",
+      "Using AB network...\n",
+      "Extracting inputs...\n",
+      "Formating net...\n",
+      "218 experiments without sources in net, they will be removed.\n",
+      "Running methods...\n",
+      "70 features of mat are empty, they will be removed.\n",
+      "Running mlm on mat with 170 samples and 21915 targets for 172 sources.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  3.68it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "70 features of mat are empty, they will be removed.\n",
+      "Running ulm on mat with 170 samples and 21915 targets for 172 sources.\n",
+      "70 features of mat are empty, they will be removed.\n",
+      "Running wsum on mat with 170 samples and 21915 targets for 172 sources.\n",
+      "Infering activities on 1 batches.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00,  2.94s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating metrics...\n",
+      "Computing metrics...\n",
+      "Done.\n",
+      "Using ABC network...\n",
+      "Extracting inputs...\n",
+      "Formating net...\n",
+      "174 experiments without sources in net, they will be removed.\n",
+      "Running methods...\n",
+      "55 features of mat are empty, they will be removed.\n",
+      "Running mlm on mat with 214 samples and 21930 targets for 297 sources.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.48it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55 features of mat are empty, they will be removed.\n",
+      "Running ulm on mat with 214 samples and 21930 targets for 297 sources.\n",
+      "55 features of mat are empty, they will be removed.\n",
+      "Running wsum on mat with 214 samples and 21930 targets for 297 sources.\n",
+      "Infering activities on 1 batches.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00,  4.72s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating metrics...\n",
+      "Computing metrics...\n",
+      "Done.\n",
+      "Using ABCD network...\n",
+      "Extracting inputs...\n",
+      "Formating net...\n",
+      "146 experiments without sources in net, they will be removed.\n",
+      "Running methods...\n",
+      "52 features of mat are empty, they will be removed.\n",
+      "Running mlm on mat with 242 samples and 21933 targets for 411 sources.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.50it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "52 features of mat are empty, they will be removed.\n",
+      "Running ulm on mat with 242 samples and 21933 targets for 411 sources.\n",
+      "52 features of mat are empty, they will be removed.\n",
+      "Running wsum on mat with 242 samples and 21933 targets for 411 sources.\n",
+      "Infering activities on 1 batches.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:09<00:00,  9.21s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating metrics...\n",
+      "Computing metrics...\n",
+      "Done.\n",
+      "Using rand network...\n",
+      "Extracting inputs...\n",
+      "Formating net...\n",
+      "177 experiments without sources in net, they will be removed.\n",
+      "Running methods...\n",
+      "55 features of mat are empty, they will be removed.\n",
+      "Running mlm on mat with 211 samples and 21930 targets for 296 sources.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.69it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55 features of mat are empty, they will be removed.\n",
+      "Running ulm on mat with 211 samples and 21930 targets for 296 sources.\n",
+      "55 features of mat are empty, they will be removed.\n",
+      "Running wsum on mat with 211 samples and 21930 targets for 296 sources.\n",
+      "Infering activities on 1 batches.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00,  4.98s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating metrics...\n",
+      "Computing metrics...\n",
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Build dictionary of networks to test\n",
+    "nets = {\n",
+    "    'A': doro_A,\n",
+    "    'AB': doro_AB,\n",
+    "    'ABC': doro_ABC,\n",
+    "    'ABCD': doro_ABCD,\n",
+    "    'rand': doro_rand\n",
+    "}\n",
+    "\n",
+    "# Example extra arguments\n",
+    "decouple_kws = {\n",
+    "    'A': {'args' : {'wsum' : {'times': 100}}},\n",
+    "    'AB': {'args' : {'wsum' : {'times': 100}}},\n",
+    "    'ABC': {'args' : {'wsum' : {'times': 100}}},\n",
+    "    'ABCD': {'args' : {'wsum' : {'times': 100}}},\n",
+    "    'rand': {'args' : {'wsum' : {'times': 100}}}\n",
+    "    \n",
+    "}\n",
+    "\n",
+    "# Run benchmark pipeline\n",
+    "df = dc.benchmark(mat, obs, nets, perturb='TF', sign=-1, verbose=True, decouple_kws=decouple_kws)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c37c44e",
+   "metadata": {},
+   "source": [
+    "Like before we can try to visualize the results in a scatterplot, this time grouping by the network used:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1a78ebba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dc.plot_metrics_scatter(df, groupby='net', x='mcauroc', y='mcauprc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68d852c7",
+   "metadata": {},
+   "source": [
+    "Since this plot is too crowded, we can plot it again separating the methods by columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "1a446ffd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x500 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dc.plot_metrics_scatter_cols(df, col='method', figsize=(9, 5), groupby='net')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a71056c",
+   "metadata": {},
+   "source": [
+    "Or by selecting only one of the methods, in this case `consensus` since it is the most reliable method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b1851c3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dc.plot_metrics_scatter(df[df['method'] == 'consensus_estimate'], groupby='net', x='mcauroc', y='mcauprc', show_text=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16dc3380",
+   "metadata": {},
+   "source": [
+    "As expected, the random network has a performance close to 0.5 while the rest show higher predictive performance. The confidence levels ABC seem to give the best tradeoff between TF coverage and predictability.\n",
+    "\n",
+    "If needed, we can also plot the distributions of the Monte-Carlo metrics, now grouped by network:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "36aedb27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dc.plot_metrics_boxplot(df, metric='mcauroc', groupby='net')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ccaa001",
+   "metadata": {},
+   "source": [
+    "### By source\n",
+    "\n",
+    "The benchmark pipeline also allows to test the performance of individual regulators using the argument `by='source'`. For simplicity let us just use the best performing network:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "389c20fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting inputs...\n",
+      "Formating net...\n",
+      "174 experiments without sources in net, they will be removed.\n",
+      "Running methods...\n",
+      "55 features of mat are empty, they will be removed.\n",
+      "Running mlm on mat with 214 samples and 21930 targets for 297 sources.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.87it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55 features of mat are empty, they will be removed.\n",
+      "Running ulm on mat with 214 samples and 21930 targets for 297 sources.\n",
+      "55 features of mat are empty, they will be removed.\n",
+      "Running wsum on mat with 214 samples and 21930 targets for 297 sources.\n",
+      "Infering activities on 1 batches.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:03<00:00,  3.80s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating metrics...\n",
+      "Computing metrics...\n",
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example on how to set up decouple arguments\n",
+    "decouple_kws={\n",
+    "    'args' : {\n",
+    "        'wsum' : {'times': 100}\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# Run benchmark pipeline\n",
+    "df = dc.benchmark(mat, obs, doro_ABC, perturb='TF', sign=-1, by='source', verbose=True, decouple_kws=decouple_kws)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21a76e8b",
+   "metadata": {},
+   "source": [
+    "We can plot the results with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d3ff2d43",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x500 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dc.plot_metrics_scatter_cols(df, col='source', figsize=(9, 5), groupby='method')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fc4336d",
+   "metadata": {},
+   "source": [
+    "It looks like we get good predictions for FXM1, HIF1A and RELA among others. On the other side, we get bad predictions for YBX1, STAT3 and NR2F2 indicating that the topology of the network is not informative for these TFs. REST is an interesting case, we always missclassify it, meaning that the weights in the network for it should be reversed but the topology is good."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b3e1bbe",
+   "metadata": {},
+   "source": [
+    "### By meta-data groups\n",
+    "\n",
+    "The benchmarking pipeline can also be run by grouping the input experiments using the `groupby` argument. Multiple groups can be selected at the same time but for simplicity, we will just test which knockout method seems to perform the best inhibitions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0ad8a428",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting inputs...\n",
+      "Formating net...\n",
+      "174 experiments without sources in net, they will be removed.\n",
+      "Running methods...\n",
+      "55 features of mat are empty, they will be removed.\n",
+      "Running mlm on mat with 214 samples and 21930 targets for 297 sources.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.28it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55 features of mat are empty, they will be removed.\n",
+      "Running ulm on mat with 214 samples and 21930 targets for 297 sources.\n",
+      "55 features of mat are empty, they will be removed.\n",
+      "Running wsum on mat with 214 samples and 21930 targets for 297 sources.\n",
+      "Infering activities on 1 batches.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00,  4.21s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating metrics...\n",
+      "Computing metrics for groupby Knock.Method...\n",
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example on how to set up decouple arguments\n",
+    "decouple_kws={\n",
+    "    'args' : {\n",
+    "        'wsum' : {'times': 100}\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# Run benchmark pipeline\n",
+    "df = dc.benchmark(mat, obs, doro_ABC, perturb='TF', sign=-1, groupby='Knock.Method', verbose=True, decouple_kws=decouple_kws)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82448007",
+   "metadata": {},
+   "source": [
+    "Again, we can visualize the results in a collection of scatterplots:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "80030b93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x500 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dc.plot_metrics_scatter_cols(df, col='method', figsize=(9, 5), groupby='group')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f2f04d5",
+   "metadata": {},
+   "source": [
+    "From our limited data-set, we can see that apparently shRNA works better than other knockout protocols."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "decoupler",
+   "language": "python",
+   "name": "decoupler"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/notebooks/bulk.ipynb b/docs/source/notebooks/bulk.ipynb
index f25fb6c..7f0a198 100644
--- a/docs/source/notebooks/bulk.ipynb
+++ b/docs/source/notebooks/bulk.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c028b5ad",
+   "id": "a6b025d6",
    "metadata": {},
    "source": [
     "# Bulk functional analysis"
@@ -10,10 +10,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "53a5fdda",
+   "id": "41d9ebac",
    "metadata": {},
    "source": [
-    "Bulk RNA-seq yields many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by inferring pathway and trasncription factor activities from prior knowledge.\n",
+    "Bulk RNA-seq yields many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by inferring pathway and transcription factor activities from prior knowledge.\n",
     "\n",
     "In this notebook we showcase how to use `decoupler` for transcription factor (TF) and pathway activity inference from a human data-set. The data consists of 6 samples of hepatic stellate cells (HSC) where three of them were activated by the cytokine Transforming growth factor (TGF-β), it is available at GEO [here](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151251).\n",
     "\n",
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a53581ea",
+   "id": "36d324eb",
    "metadata": {},
    "source": [
     "## Loading packages\n",
@@ -40,7 +40,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "bc764e4c",
+   "id": "20d55bbb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25748fb7",
+   "id": "47b662e1",
    "metadata": {},
    "source": [
     "## Loading the data\n",
@@ -66,23 +66,23 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "fea9e3e7",
+   "id": "4651c698",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "--2022-07-14 11:21:25--  https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE151251&format=file&file=GSE151251%5FHSCs%5FCtrl%2Evs%2EHSCs%5FTGFb%2Ecounts%2Etsv%2Egz\n",
-      "Resolving www.ncbi.nlm.nih.gov (www.ncbi.nlm.nih.gov)... 2607:f220:41e:4290::110, 130.14.29.110\n",
-      "Connecting to www.ncbi.nlm.nih.gov (www.ncbi.nlm.nih.gov)|2607:f220:41e:4290::110|:443... connected.\n",
+      "--2022-09-01 12:46:55--  https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE151251&format=file&file=GSE151251%5FHSCs%5FCtrl%2Evs%2EHSCs%5FTGFb%2Ecounts%2Etsv%2Egz\n",
+      "Resolving www.ncbi.nlm.nih.gov (www.ncbi.nlm.nih.gov)... 130.14.29.110, 2607:f220:41e:4290::110\n",
+      "Connecting to www.ncbi.nlm.nih.gov (www.ncbi.nlm.nih.gov)|130.14.29.110|:443... connected.\n",
       "HTTP request sent, awaiting response... 200 OK\n",
       "Length: 1578642 (1,5M) [application/octet-stream]\n",
       "Saving to: ‘counts.txt.gz’\n",
       "\n",
-      "counts.txt.gz       100%[===================>]   1,50M  1,28MB/s    in 1,2s    \n",
+      "counts.txt.gz       100%[===================>]   1,50M   349KB/s    in 4,6s    \n",
       "\n",
-      "2022-07-14 11:21:27 (1,28 MB/s) - ‘counts.txt.gz’ saved [1578642/1578642]\n",
+      "2022-09-01 12:47:00 (336 KB/s) - ‘counts.txt.gz’ saved [1578642/1578642]\n",
       "\n"
      ]
     }
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3761d923",
+   "id": "01794f8c",
    "metadata": {},
    "source": [
     "We can then read it using `pandas`:"
@@ -103,7 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "cc1bc4c0",
+   "id": "43f47b5b",
    "metadata": {},
    "outputs": [
     {
@@ -341,7 +341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e479f12b",
+   "id": "212f7c7c",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\">\n",
@@ -357,7 +357,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c893315f",
+   "id": "6335a2fe",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\">\n",
@@ -373,7 +373,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1cdaa972",
+   "id": "6bf58d9c",
    "metadata": {},
    "source": [
     "The obtained data consist of raw read counts for six different samples (three controls, three treatments) for ~60k genes.\n",
@@ -385,7 +385,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "12f46f49",
+   "id": "6d5ab0b2",
    "metadata": {},
    "outputs": [
     {
@@ -417,7 +417,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61f7e8f8",
+   "id": "42c4244a",
    "metadata": {},
    "source": [
     "Inside an `AnnData` object, there is the `.obs` attribute where we can store the metadata of our samples.\n",
@@ -427,7 +427,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "792e4365",
+   "id": "836b0d69",
    "metadata": {},
    "outputs": [
     {
@@ -518,7 +518,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abaeae0c",
+   "id": "dc5f2a9b",
    "metadata": {},
    "source": [
     "## Quality control\n",
@@ -531,7 +531,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "8442fad9",
+   "id": "43938df8",
    "metadata": {},
    "outputs": [
     {
@@ -553,7 +553,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0e2ad20",
+   "id": "1459ab50",
    "metadata": {},
    "source": [
     "It can be observed that across samples there seems to be a bimodal distribution of lowly and highly expressed genes. \n",
@@ -564,7 +564,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "eff0072d",
+   "id": "ca9686da",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -574,7 +574,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "385714dc",
+   "id": "add32932",
    "metadata": {},
    "source": [
     "Then we can remove samples with less than 200 highly expressed genes and genes that are not expressed across all samples.\n",
@@ -593,7 +593,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "9b38b88c",
+   "id": "84ceb51f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -606,7 +606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80d73be2",
+   "id": "cca0eb31",
    "metadata": {},
    "source": [
     "Now we can visualize again how the distributions look like after filtering:"
@@ -615,7 +615,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "673b4999",
+   "id": "a7252c81",
    "metadata": {},
    "outputs": [
     {
@@ -637,7 +637,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ebc9333",
+   "id": "af445d25",
    "metadata": {},
    "source": [
     "## Normalization\n",
@@ -648,7 +648,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "09fa211f",
+   "id": "ebc7cee2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -659,7 +659,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4ca72483",
+   "id": "21aa3d06",
    "metadata": {},
    "source": [
     "Now we visualize how the samples look like after normalization"
@@ -668,7 +668,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "b86cd013",
+   "id": "c24bf8a8",
    "metadata": {},
    "outputs": [
     {
@@ -690,7 +690,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73efb37e",
+   "id": "3a25f8c0",
    "metadata": {},
    "source": [
     "## Differential expression analysis\n",
@@ -703,7 +703,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "aaf630b0",
+   "id": "2ac0c912",
    "metadata": {},
    "outputs": [
     {
@@ -818,7 +818,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e8bc0de",
+   "id": "c722f6e2",
    "metadata": {},
    "source": [
     "After running DEA, we obtain how much each gene is changing in treatment compared to controls (`logFCs`), and how statistically \n",
@@ -830,12 +830,12 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "83a75246",
+   "id": "330524e0",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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WiUpIo+0nf6M3msj+7hPAy51rMv6RGiVlmkKhUCjyoaAOlvqqrFDcZVbuv4rBZHauTPo04nf+zI3fv8AkTSzdEV7S5ikwl59JTk4mOTm51JefKUu2KhT3E8rBUijuMpHxqWgyJRZSz+0lfvtyhEYHUnIjKUM9JEsBKSkpuLu74+7uXurlK8qSrQrF/URJryJUKO476lT0xGAyO1FutdviWqsNQmgQQPXy7lb6VgqFQqEom6gIlkJxl+nduBIBns5oNWZHSgjz21AC41T+lUKhUNwTKAdLobjLuDrq+HHMAzQPKWdp83F1ZGq/BvRuVKkELVMoFApFcaGmCBWKEiDY15UVYx4gMj6NpHQ9Ib5uSp5BoVAo7iGUg6VQlCABXs6Ac0mboVAoFIpiRn1lVigUCoVCoShmVARLoVAocqDVann88cctv5dmypKtCsX9hFJyVygUCoVCoSggSsldoVAoFAqFooRQDpZCoVAoFApFMaMcLIVCochBcnIyQgiEECQnJ5e0OXlSlmxVKO4nlIOlUCgUCoVCUcwoB0uhUCgUCoWimFEOlkKhUCgUCkUxoxwshUKhUCgUimJGOVgKhUKhUCgUxYxysBQKhUKhUCiKGVUqR6FQKHKg1Wrp0aOH5ffSTFmyVaG4n1ClchQKhUKhUCgKiCqVo1AoFAqFQlFCKAdLoVAoFAqFophRDpZCoVDkIDk5GTc3N9zc3Ep9+ZmyZKtCcT+hktwVCoXCDikpKSVtQoEpS7YqFPcLKoKlUCgUCoVCUcwoB0uhUCgUCoWimFEOlkKhUCgUCkUxoxwshUJhw/DhwxFCFHpbvHgxW7ZsQQhBXFyczbihoaF88cUXltfz58+nQ4cOeHp65nqMQqFQlEVUkrtCobBLt27dWLRokeV1RkYGWq3WohY+YcIEEhISrPp4eXmxa9euAp8jJSWFbt260a1bN958883iM16hUChKGOVgKRQKuzg5OREQEJDrfhcXF9LT0/Pskx8vvfQSAFu2bCnyGHcCjUZD+/btLb+XZsqSrQrF/YRysBQKhSIHLi4upc7py42yZKtCcT+hHCyFQmGXdevW4e7ubnndvXt3fvrppwIfX7lyZZs2pdekUCjuF0rUwRJChAMhdnbNkVKOvcvmKBSKbHTs2JG5c+daXru5uRXq+H///RcPDw+rtg4dOhSHaQqFQlHqKekIVgtAm+11feAvoOBfkxUKxR3Bzc2N6tWrF/n4KlWq4O3tbdWm05X0R07BSE5OJjQ0FIDw8PBCO5d3k7Jkq0JxP1Gin3ZSyujsr4UQ/wPOAVtLxiKFQqEwc+PGjZI2ocCUJVsVivuFUvN1UgjhCAwG/k9KKXPp4wQ4ZWvysNdPoVCUDSIjI4mMjOTs2bMAHDlyBA8PD4KDg/Hx8Slh6xQKhaLolKY1vX0Bb2BxHn3eBOKzbVfutFEKheLOMW/ePJo0acLo0aMBaNeuHU2aNGHNmjUlbJlCoVDcHiKXYNFdRwjxJ5AhpeyVRx97Eawr8fHxeHp63mkTFYp7mtjkDP46fp2kdANtqvtSO+D+fU8lJydbVlAmJSWV6rymsmSrQnEvkJCQgJeXF4CXlDIht36lYopQCBECdAIey6uflDIdSM923B22TKG4P/j14FVe/ekQeqNECJAS+jSuxGcDGqHTlqZAt0KhUJQNSssn5wggCvitpA1RKO43wm8k88oKs3MFZucKYM3BCL7590IJWqZQKBRllxKPYAkhNJgdrCVSSkNJ26NQ3G/8tO8yCECCMTWRhN2rcK/3MA5+QXy38yLPd6hW0ibedTQaDc2bN7f8XpopS7YqFPcTJe5gYZ4aDAYWlrQhCsX9SHSiZdad1PN7ST66GdeabQC4kZSe22H3NC4uLuzZs6ekzSgQZclWheJ+osS/7kgpN0gphZTydEnbolDcj9QP9MJkMs8LutfrSOWxS3CqWAONgHqVym6i+/DhwxFC8PHHH1u1r169GiGEZX9eW0HGyY6Ukm+++YYHHngAT09P3N3dqVevHhMmTLBIUQB88803PPTQQ5QrV45y5crRqVMndu/efYfuhEKhKAlK3MFSKBQlS78mgfh7OKHVWDsLJgnjHqlRQlYVD87OznzyySfExsba7Js5cybXrl2zbACLFi2yactvnCyklDz11FOMHz+eHj16sGHDBg4fPsysWbNwcXHho48+svTdsmULTz75JH///Tc7duwgODiYLl26cPXq1WK8eoVCUZKUhilChUJRgng4O/DTcw/w1i9H2HY2BoBAbxfe7FGbjrXKl7B1t0enTp04e/Ys06ZNY/r06Vb7vLy8spZaW/D29iYgIICUlBTq1q0LQNu2bfMcJ4sVK1bwww8/8Ouvv9K7d29Le9WqVXnkkUfILomzbNkyq2O/+eYbfv75ZzZt2sTQoUMLdY3ZbT1+/Diurq6FOl6hUNwZlIOlUCgI8XVj2ajW3EzOICXDQCUvFzSasi+DotVqmTp1qiWyVLly5QIdJ6Xk4sWLgNnBKsg4y5cvp1atWlbOVXbykpVJSUlBr9cXSb0+u62lRddQoVCoKUKFQpENHzdHKpdzZeTIEfnmJ9nbFi9ezJYtWxBCEBcXV9KXA0C/fv1o3LgxkydPvqPjnD59mlq1alm1vfTSS7i7u+Pu7p6nc/e///2PwMBAOnXqdFs2KhSK0oNysBQKhV26detmlY908eJFrly5Ynk9cOBAmz5PPPFESZttl08++YQlS5Zw/PjxOzpOzijV22+/zcGDB5k0aRJJSUl2j5k+fTrLly9n1apVODs735Z9CoWi9KCmCBUKhV2cnJwICAjIdb+Liwvp6el59ikttGvXjq5du/LWW28xfPjwOzJOjRo1OHnypFWbv78//v7+lC9vP5dtxowZTJ06lY0bN9KwYcMi26VQKEofKoKlUCjuCz7++GPWrl3L9u3b78g4Tz75JKdOneLXX38t0DiffvopH374IevXr7cIhSruLbZv345Wq6Vbt25W7eHh4VZT6+XKlaNdu3Zs3brVql9kZCTjxo2jatWqODk5ERQURK9evdi0aZOlT2hoqGUcFxcXQkNDGThwIJs3b87znB4eHtSrV4+xY8dy5swZq77Xrl3jqaeeolatWmg0Gl566SWba3vvvfdo3Ljx7d2gexzlYCkUCrusW7fOkj/k7u7OgAEDStqk26JBgwY8/fTTfPnll3dknEGDBvH4448zaNAgPvjgA3bt2kV4eDhbt25lxYoVaLVaS9/p06fzzjvvsHDhQkJDQ4mMjCQyMjLXaURF2WThwoWMGzeO//77j0uXLtns37hxI9euXWPr1q14enrSo0cPLlwwl6cKDw+nWbNmbN68menTp3PkyBHWr19Px44dGTt2rNU4H3zwAdeuXePUqVMsXboUb29vOnXqxJQpU3I956FDh5g6dSonTpygUaNGVk5beno6/v7+vP322zRq1KiY78p9hJSyzG6AJyDj4+OlQqEoPoYNGyY7deokz5w5Y9kiIiJs+vTp08fm2L///lsCMjY29u4Ymwv27AsPD5dOTk7S/NFnDSB/+eUXKaWUycnJsm7durJu3bry6aefLvA4RqNRzps3T7Zq1Uq6ublJR0dHWbVqVTl69Gh5/PhxS7+QkBAJ2GyTJ08u9HVmtzU5OblQxw4bNkwCcsyYMTb7nn/+eQnIYcOGWfXN2nx8fGTXrl3loUOHrI4DpJOTkwwPD7dq79Onj2WsrPHs/f9k8fXXX8v27dtLDw+PQv8/paSkSG9vb1muXDmZkpJis3///v2yZ8+e0t/fXzo5OcmQkBA5cOBAGR0dbekzfvx42bRpU+no6CgbNWpU4HNnkZSUJD08POTJkyflE088Id9//33LvgsXLkhAHjhwwNJ25coVCch58+ZJKaXs3r27DAwMlElJSTZjZ78XISEh8vPPP7fpM2nSJKnRaOTJkydzPaeU5v/ZDh06yJCQEGkwGGzGad++vZwwYYJN++TJk4t0X+4F4uPjs94HnjIPH0VFsBQKhV3c3NyoXr26ZatYsWJJm1QgpJSYTJLFixezevVqq30hISGkpaXZlTOQUtK3b18AXF1dOXbsGMeOHeO7774r8DgajYYxY8awc+dOkpKSSE9P59y5c8yfP586depY+oWHh9v9QH7vvfcKfb3ZbS2KBlZQUBA//PADqamplra0tDSWL19OcHCwVd/sixo2bdqETqfj0UcftRlTCMGkSZMKbUt2UlJS6NatG2+99Vahj125ciX169enbt26rFq1ympfVFQUnTp1ws/Pjz///JMTJ06wcOFCKlasSEpKiqWflJKRI0cWeeHGihUrqFWrFrVq1WLw4MEsWrQoTxmNrL+dXq/n5s2brF+/nrFjx+Lm5mbT19vbO9/zT5gwASllvlPWGo2GCRMmcPHiRfbt25fvuIqCo5LcFQrFPUF0YjrT159kzaEIMowm2lTz5fWutWkU5F3SppVqmjZtyvnz51m1ahVPP/00AKtWrSIoKIiqVata9c2+8CEgIIA33niDdu3aER0djb+/v6XfuHHj+Oyzz3j11Vdp0KBBkezKyvvZsmVLoY8NCwtj8ODBSCkJCwuzXBeY86ISEhJYsGABOp35EVilShUefvhhqzFmzZoFQHR0NIcPHy6yDWB2TJOSkti0aZNdKY7k5GTefPNNtFot7du35+zZs0gpqV27dqHPm4WPjw/ly5cnPDw8375Z5wkPD6dly5ZFPqfCGhXBUtwWkZGRTJgwgerVq+Ps7EyFChVo27Yt8+bNs/o2CDB16lS0Wq1NTTeAxYsX5/mtrCD14ApaW+6ff/6hV69eVKpUCSGETXQit7Fat25dyLtzf3PkyBEOHjxotd0pktMNPD53O6sOXCXdYEJK2HEuhgHzdnAsIv6OnfdeYcSIESxatMjyeuHChYwcOTLPY5KSkli2bBnVq1fH19fXal+bNm149NFHefPNN++IvXlx7tw5duzYwcCBAxk4cCDbt2/n/Pnzlv0BAQEYDAZ++eWXOybMeurUKXbv3s2gQYMA0Ol0PPHEEyxcuNCqX5s2bXB3d8fDw4O1a9eyePFiGjRoYLErL3HagiClLNAYxXU+hTXKwVIUmfPnz9OkSRM2bNjA1KlTOXDgABs3buTll19m7dq1bNy40ar/okWLeP31120+ZApKfvXgClpbLjk5mUaNGvHVV1/leb6cGk+///57key+X2nXrh1NmjSx2u4Uq/Zf4dLNFIyZRauNSbEkndqBwaDny81n8znalpSUFOrVq0e9evVsviiUNorD1iFDhvDff/8RHh7OxYsX2bZtmyX6kp3sCx88PDxYs2YNK1asQKOxfZRMmzaN9evX8++//xbJpqKycOFCunfvTrly5fDx8aFbt25WnzmtW7fmrbfe4qmnnsLPz4/u3bvz6aefcv369WKzISwsDIPBQGBgIDqdDp1Ox9y5c1m1apXV59eKFSs4dOgQ0dHRXL161XLPa9SogRCCEydOFNmGmJgYoqOjqVKlSr59s85TkL6KgqMcLEWReeGFF9DpdOzdu5eBAwdSp04dGjRoQP/+/fntt9/o1auXpe/WrVtJTU3lgw8+IDk5mX/++afQ5+vUqRMBAQFMmzbN7n4vLy8CAgIsG9yqLZe9rXv37nz00Uc89thjeZ4vazokaytKGZOyir38pYL26dChQ14LU+4Iu8NjyfrynXx8K1fmDOPG2hlkJN5k5/mYQo8npeT48eMcP3681JefKQ5b/fz86NmzJ0uWLGHRokX07NkTPz8/m34dO3a0RCN37dpFly5d6N69u6VUT3bq1q3L0KFDeeONN4pkU350797d4uzVq1cPAKPRyJIlS6ycw8GDB7NkyRKMRqOlbcqUKURGRjJv3jzq1q3LvHnzqF27NkeOHLltuwwGA0uXLuWzzz6zit4eOnSIkJAQqzqUQUFBVKtWzSYC6OPjQ9euXZk9ezbJyck25yhIlYSZM2ei0WgseYW5YTKZmDVrFlWqVLmjX4LuR1QOlqJIxMTEWCJX9pIwwTrcHBYWxpNPPomDgwNPPvkkYWFhtGvXrlDnLGpduaKyZcsWypcvj7e3N+3bt2fKlCm5CkbeL1yJTWHzySgAHq5dnsrlSkdhYU9nnfn/TUocK9XCt+fLuFRtjtbFAw8n9TFXEEaOHMmLL74IwOzZs+32yVr4kEWzZs3w8vLim2++4aOPPrLp//7771OzZs18nfWisGDBAktivoODAwB//vknV69etUlMNxqNbNiwge7du1vafH19GTBgAAMGDGDatGk0adKEGTNmsGTJktuya926dcTGxvLMM8/YFBN//PHHCQsLs7swICdz5syhTZs2tGzZkg8++ICGDRtiMBj466+/mDt3rlV0KzExkcjISPR6PRcuXOC7775jwYIFTJs2zervBebP7sjISFJSUjh69ChffPEFu3fv5rfffrOSEsma0k9KSiI6OpqDBw/i6OhoKSwOkJqaajP17+7ubnPO+xX1yaMoEllJmDlrr/n5+ZGWlgbA2LFj+eSTT0hISGDlypUWYcbBgwfz4IMP8uWXX+Lp6Vmo82avBxcWFlY8F2OH7t27M2DAAEJCQrhw4QLvvvsuDz/8MPv27cPJyemOnbc088XG08zcdAYkxO9ehWvN1rz2eAcmdKpR0qbxWNNAlu0y6ww5eAfg4G2OVgoBA5sHlaRpZYZu3bqRkZEBQNeuXQt0jBACjUZjtQIxO0FBQbz44ou89dZbVKtWrdhsBQgMDLRpCwsLY9CgQbz99ttW7R9//DFhYWFWDlZ2HB0dqVatmt1oUWEJCwujU6dONs4VQP/+/Zk6dSo3b97Md5wqVaqwf/9+pkyZwsSJE7l27Rr+/v40a9aMuXPnWvWdNGkSkyZNwtHRkYCAAFq3bs2mTZvo2LGjzbhZSfaurq6EhITQsWNH5s+fb+MUZY9m7du3j++//56QkBCrpPnTp0/bRL3at29fpIUJ9yLKwVLcFjmTInfv3o3JZOLpp58mPT0dgO+//56qVataBOsaN25M1apV+eGHH3j22WcLfc5PPvmEhx9+mIkTJ97+BeRC9m/A9evXp3nz5oSEhPDbb7/lO7V4L7L55HW+2GhWe9bHRhD790J05Srx+cbTNKjsycO1K5Sofc1CfJjwSA1mbjqDViMQgMEkaVPNl9HtquZ7vMIcIc6KimSPZGQnPT2dyMhIAGJjY/nqq69ISkqySgfIyZtvvsk333zDhQsXbCJL8fHxNhEQHx8fgoODLeKrZ8+ac+iOHDmCh4cHwcHBdqfro6OjWbt2LWvWrKF+/fpW+4YNG0bPnj2Jjo5m165d/PDDDwwaNIiaNWsipWTt2rX8/vvvVon+Z8+eJSkpicjISKtITd26dXF0dARAGo0kbd1K8rbtCGcnPLv3YO3atbnei6ZNm1qmcQsynVuxYkW++uqrPPNFC7JKEMyK74WZQs6v73vvvVckWZH7CeVgKYpE9erVEULY1F7LWtbt4uJiaVu4cCHHjh2zLIkG87x/WFhYkRys4qorVxgqVqxISEiITUmJ+4VlOy+hEWCSkHJ6O0LnhHNIY7RCsGzXpRJ3sABe7lyTbvUDWHc4gjS9ibY1/Ghfwx+NRq2MKij5RZTXr19v0UPz8PCgdu3a/PTTT3To0CHXY3x8fHjjjTfs6llt2bLFJgIybNgwFi9ezLx583j//fct7VkpBYsWLbL7vl+6dClubm488sgjNvs6duyIh4cH3377LX379sXV1ZWJEydy+fJlnJycqFGjBgsWLGDIkCGWY0aNGmVVuibLzgsXLhAaGoopLY3Lz44hZfdu0OlASm6GLcT3uTGUt1NaRnEfkpcKaWnfUEruJUqXLl1yVRrOUv89fPiwFELIrVu3yiNHjli2f//9Vwoh5JEjR6SUUi5atEh6eXnleq6cqs+HDx+WGo1Gvvbaa3ZVuaW0VubOjYL0kVLKGzduSCcnJ7lkyZJ8++bk+vXr8tlnn5VBQUHS0dFRVqhQQXbp0kVu375dSmmtxHz16lVZrlw5OXPmTKsxdu7cKXU6ndywYYOUUsqIiAj55JNPypo1a0ohhF2l5eKkx8x/ZMgb62TIG+ukZ+vHpXujbpbXPWb+c0fPXVi2bdsmNRqN7Nq1q1V7lpJ11ubu7i7r1q0rX3jhBXn69GmrvklJSZZ+9evXly4uLtLX11e2adNGLly4UEZFRckKFSrIKVOm2Jx/wIABsnnz5lKv18uVK1fKZs2aSS8vL+nq6iobNWokly5dWqzXm91We+/FLFKOHpWRU6fJq2++JWN/XimNqanFasf9RtTs2fJ4nbryeK3aVtvGqtVk9L//lrR5ijtIQZXcVQRLUWTmzJnDgw8+SPPmzXnvvfdo2LAhGo2GPXv2cPLkSZo1a0ZYWBgtW7a0m9D+wAMPEBYWxueffw6YE1FzThfkTKrM4nbqyiUlJVmmHcD8jfTgwYOWqYmkpCTee+89+vfvT8WKFQkPD+ett97Cz8+Pfv36Ffp8/fv3R6/Xs2TJEqpWrcr169fZtGmT3TyMSpUqMWvWLMaMGUP37t2pUaMGqampDBs2jFGjRtG5c2fAulZY1v27XU5cS+DI1XjKezjRtrofOu2tRcaNg7w5GZmI0SQp1364ZfpAqxE0LmVCnln13xYsWMClS5ds1Mg3btxokTQ4cuQIM2fOpFGjRqxdu9YS/dDr9Tg5OaHX6xk5ciQdO3bE09OTnTt3MmPGDJo0acL8+fMZMGAAvXr1sohp/vzzz6xdu5b9+/ej0+nw8fHh7bffpnbt2jg6OrJu3TpGjBhB+fLlC5znlB9CCEJCQiy/2+PGN98Q/dn/QebUX/yqVcSEhRGy7Dt05coVix33G/ErV4HJZNWWYTLx6IXzvDRpEtNyFFtW3Ifk5X2V9g0VwSpxIiIi5IsvviirVKkiHRwcpLu7u2zZsqX89NNPZXx8vPT19ZXTp0+3e+xnn30m/fz8ZHp6uly0aJHd2mwhISFSysLXlZMy9+hUVq28nFtWnbSUlBTZpUsX6e/vLx0cHGRwcLAcNmyYvHTpUqHvT2xsrATkli1bcu1jr5ZYv379ZJs2baTRaJQTJkyQVapUkYmJiXaPz61WWEFJStPLYQt3WSJSIW+sk62nbpTHrt56X52NSpQ13/5dVvnfrT5V/rdO1nz7d3k2yr5dJUFh679Jab8W2yeffCI1Go3cv3+/zTkyMjIskaLhw4fLxo0by4yMDBkVFSX9/f3t1oXLTpMmTeQ777xzexdaCNJOn7aJshyvVVseqlNX/vHMKLl27VppNBrvmj33CidbtbZ7X/+sVl2eHz+hpM1T3EEKGsESspRrvOSFEMITiI+Pjy/0ajSFoqjcSEpn8bZw/j4VhZNOQ98mgQxqEYyjzlZWzmAwUK5cOUaNGsXHH39sdwViaGgoL730kqU0CJjrpdWvX59HHnmEn376ic2bN+cqa9GhQwcaN27MF198UaTr+d/Kw/y49zKmbB8FWgHl3BzZ9r+HcdKZox77Lt7k3V+PcTwiAYC6lTz5sE89moWUHn2whQsXMnfuXPbs2cO6desYN24c58+fRwhBeHg4VapU4cCBAzRu3NjquNWrV9OvXz927dpFy5YtadSoEQEBAfz55595ni8hIYEGDRowbNgwTpw4QUxMDJs2bbIbSZJSsnnzZnr37s3q1ast0cg7TdTMmcTM/way6UABTI6M5Kf4ODw9PTl//ryNFpMib65MeInEjRtt7itAwHuTKZep4q6490hISMhaJeolpUzIrZ+aIlQoCsH1hDT6fLWNqMQ0TBIEsP9SHBuOXWfxiBZW02pgLpGxePFiRo8ezbx582jatCnt27dn0KBBNGzYMNfzlC9fng8//JDnnnuO559/vtCaYTmRUqLX60lPTyctLc3yMzYxmWW/b0XjE4LQ3vo4MEq4kZTBphNR9GhgTmpuFuLD7+Mf4nqCWYajgqfzbdl0JyhM/bfs5KzFdubMmTwTt7Pw9PRk0aJFdOnSBTc3Nw4fPmzjXMXHxxMYGEh6ejparZY5c+bcNecKQKakgB2H7wVfXx7z9qb3saO45qJlp8gdvxeeJ2nLFiTccrK0WhwqV8ard++SNE1RSlBK7gpFIXg3bB2XTx+xRHuygj7/nb3B+mORdo/p378/ERERrFmzhq5du7JlyxaaNm3K4sWLcz1PliK1q6srO3fuxGAwFMneCRMm4OLigkajwcnJCU9PT8qXL09QUBA1atSgZdPGXFk4gcR9tkvLNQIi4mz1jSp4OpdK56qg9d/skRXJz3KOpJT88MMPtGjRIleNpywefvhhWrduzZAhQyy5UNnx8PDg4MGD7NmzhylTpvDKK68Uq05QamoqLVq0yNVW11atwc7/TwUnJx546CHlXBUR51q1CPl+GW4PtgGdDuHqivfjjxO6/Hs0rqVDgFdRsqgIlkJRCNas/IGEqKuUf+wdq3atRrDx+HUebVjJ7nHOzs507tyZzp07M2nSJEaNGsXkyZNzlZmYMWMGZ86cYc+ePTz88MNMnTqVSZMmFdrewYMHWwpxOzk5WX5m/S60OkZ+ewiDt60Yp0lCrQCPQp+zpMhe/y0LKSUODg651q/MImctturVq3P06FGioqIw5UhktkdWvTl7aDQai4hj48aNOXHiBNOmTStQhKwgmEwm9u7da/k9J+7t2+HSrBmpBw7cSsrWaECjwf/ll4vFhvsVl3r1CJ4/v6TNUJRSlIOlUBSCkB7PcT0h3XaHLFjV+izq1q2ba/mQY8eOMXnyZL777jtLnbSBAwfSt2/fPKcV7ZEV2ciL8ekV+GLjabJnY2o1gprl3Xmwmm09utJI9vpvXbp0sdrXv39/li1blmt5Enu12AYOHMjRo0dzPVd6enquJaLyQ0ppEeG9GwitluAF33Bj3tfErVqFKSkJ1xbN8R87FpdM8V+FQlH8KAdLoSgE3etX5NsdFzHmWBxilNClrq3YZkxMDAMGDGDkyJE0bNgQDw8P9u7dy/Tp0+nTp49Nf4PBwLBhw+jXrx+PP/44AH379mXAgAEMHz6c3bt3WyIlBakVVhDGPVwdvdHEgv/Ok6Y3RzjaVvdjxoBGZUakszD13wpSi23s2LGWiOHXX3/NI488YvnbffLJJ4SFhdkkyttj2rRpNG/enGrVqpGRkcHvv//O0qVLbUqd3Gk0Li6Uf/klyr/80l09r0JxP6McLIWiEIztWJ2NJ64TEZdqTnI31xfm4drl6VIvwKa/u7s7rVq14vPPP+fcuXPo9XqCgoIYPXq0XWXrqVOncvXqVZvVa19++SX16tWzmiosSK2wgqDRCF7tWovnOlQj/EYyfu5OBHiVvhyrvChM/beC1GLLvtpz4cKFvP3227i6ulKnTh3Gjx9vU4olN5KTk3nhhRe4cuUKLi4u1K5dm++++86mZIxCobj3UDINijvC2ahEjkUk4O/uRKuqvmjLSCSkIMSlZPDtjov8fSoKF0ctfRoF8ljTQJsVhIo7g8Fo4ut/zvPtjotEJ6VTJ8CDcY/UoKsdB7eoJCcn4+7uDpgjhEWdDrwblCVbc5J66BDRs74kZc8eNK6uePXti9+LY9FmXo9CURopqEyDcrAUxUpqhpGXVhzgz2PXLW2Vy7kQNqxFmUqYVuSOyST57+wNdl+4iYezjl6NKlHJ2yX/A4uJl1ccZPWBq5acsawo4v8NbMRjTSsXyznKktNSlmzNTurBg4QPHmJOvM+WfO9cry6h33+PcHAoWQMVilwoqIOlvnIripUpvx3nr+PXrdquxaUyJGwXGYb8V2MpSjcpGQYGfbOToQt3M2/rOT5Zf5K2n2zmxz2X78r5z1xP5JdszpU0ZJByfj/GlHg+/uMkRlPeXxiNJklqhpGCfLH08/PDz69sJPmXJVuziJo5y9q5AjCZSDtylMRNm0rOMIWimFA5WIpiIzndwIpsiuD62GtonN3AxZOoxHQ2nbhO90zRSkXZ5IuNZ9gbbs5lMmRzZt5YdZhWVX0I8b2z0ZOd52MQmPXH9DevErn0FUzpyfj1eo0o1/ZcuplCFT9bG1IyDExff4oVey6TqjdSxc+NVzrXpFcj+7Iabm5uREdH39FrKS7Kkq3ZSdm92+JcvR8ZyVW9nlZurrT39KLc7t14dutWwhYqFLdHiUewhBCBQojvhBAxQogUIcRBIUSzkrZLUXhuJmegN5ofulJKrn//P67MepprS14m+dhmrtoRrVSULX7YfcniQKdeOIAxJR4ADYJfDly94+d3d9ZZoldat3J4tx9GpVFzcavbHgA3J63NMVJKnlm8l6U7wknVmxW3w28kM275AVbfBZsV9skuxvmAm/n32TduMOD8OfR2SkopFGWNEnWwhBDlgG2AHugO1AUmAnElaJaiiPh7OFkecEIIAoZ9jm/38ejKVUIfd53q5VXiallGSklimlkR3KRPI3r1VNIjTgHmPKjY5Iw7bkOnOhVwdjB/bGmcXPFo0gMH3yC0GkHrqr6U97Bd/bj7wk12nI+xqrWY9eunf57ClM+0ouLO4NW3L2TKYnTx8GR+UBA7q9fgz6pV8e/bt0RtKwmklCT88QfhTz3FmXbtufTssyTv2l3SZilug5KOYL0BXJZSjpBS7pZShkspN0kpz5WwXYoi4Oyg5Zm2VS2vde4+uDfsTEDf12nRbzTtaviXoHWK20UIQaMgbzQCNA7OBL30I67VWwLm6cKmIeXuuA0ezg588UQTdBqBRoBOIxCAr5sjHz/WwO4xey/Gos0UgU2POMXVb8YQ+/dCpNHA1bhUbiTbin6mpqbSoUMHOnTokG+pnJKmLNmaHf9xL+JUo4b5hU4HWi2OGg31Xn+DhHLlGDNmDMHBwTg5OREQEEDXrl3ZsWMHYC6Qnl9x85UrV9KhQwe8vLxwd3enYcOGfPDBBxa5jv/++48HH3wQX19fi4TG559/bjXG4sWLEULYbGlpaZY+w4cPt9vn7NmzgFkLrUWLFnh4eFC+fHn69u3LqVOnbOy9MWcOV19+hdSDhzBERZG8bTuXhg8n4fffi3qLFSVMSTtYvYG9QoifhBBRQogDQojRJWyT4jaY8EgNxrSrimM2yYJWVXzw2bcArVbDc889Z3PMCy+8gBDCpmzM9u3b0Wq1dLOTixEeHo4QgvLly5OYmGi1r3Hjxrz33nuW16tWraJr1674+fkhhLAIdObkwIEDDBgwgAoVKuDs7EzNmjUZPXo0p0+fvnV9EybQrFkznJycCiQ0ea/xSueaSMwRqyzleq1GUKO8O93r3538um71A9jyWgcmPFKTgS2C+LBvfTa/2oFQPzeklJyKTGTfxZukZJijbd6uDpgyk9od/EJwqdqctMtHMaUlodUI3BxtU1FNJhNbt25l69atBSqVU5KUJVuzo/X0pMqPK6g0Ywbejz2Gz/BhVFn9C37PjqZ///4cOnSIJUuWcPr0adasWUOHDh0szlF+vP322zzxxBO0aNGCP/74g6NHj/LZZ59x6NAhvv32W8Ccu/biiy/yzz//cOLECd555x3eeecd5ucofePp6cm1a9esNmdn60hpt27dbPpklV3aunUrY8eOZefOnfz1118YDAa6dOlCcnKy5XhDdDQ35mSKz2b9DY1GkJLIqdOQRaxFqihhpJQltgFpmdtUoAkwBkgFhubS3wnwzLYFAjI+Pl4qShdxKRly38Wb8lJMspRSymHDhsmgoCDp5eUlU1JSLP1SU1Olt7e3DA4OlsOGDbMa45lnnpETJkyQbm5u8uLFi1b7Lly4IAHp7OwsJ02aZLWvUaNGcvLkyZbXS5cule+//7785ptvJCAPHDhgY+/atWulo6Oj7NWrl/zrr7/k+fPn5c6dO+XEiRPlwIEDLf3GjRsnv/rqKzlkyBDZqFGjot2cYmLYsGGyT58+dveFhITIzz//3KZ98uTJVnZPnjxZYp4xs9r++usvm/0ajUZWrlxZ9uj/lOz44a8y5I11svpbv8lXVhyQMUnpd+AKC8fxiHjZ+f+2yJA31smQN9bJOu/+IedvPSdjk9Nlzbd/l6GZ7Vlb1Td/ky8u2yevXbsmX3zxRVmlShXp6OgoK1euLLt372657uDgYMvvzs7OMiQkRA4YMEBu2rTJxoaff/5ZtmzZUnp6ekp3d3dZt25d+corr1j2r1y5Unbq1En6+flJDw8P2bp1a7l+/frbuu6kpCSLfUlJSbc1VmkgNjZWAnLLli259snt/1tKKXft2iUB+cUXX+Q6fm7069dPDh482PJ60aJF0svLK09783of2iMqKkoCcuvWrZa2uDVr5fFateXxWrXlP9Wqy07u7vJ//uXloZq15PFatWXq8eMFHl9x54mPj896z3nKPHyckl5FqAH2SimzJK0PCCHqAc8DS+30fxOYfLeMUxQdLxcHmgZbTxk1bdqU8+fPs2rVKp5++mnAHF0KCgqiatWqVn2Tk5P58ccf2bNnD5GRkSxevNhuseNx48bxf//3f4wdO5by5cvbtWXIkCEAuSqcp6SkMGLECHr06MEvv/xiaa9SpQqtWrUiLi7O0jZr1iwAoqOjOXz4cN43oYxQr149Nm7caNXm4+Njs99oNHLgwAGeeeYZmibf5Piva3HQanAoBQKr8Sl6Bs3fSWKa3tKWkmFkyu8nKOfmyKwnm/Di9/sxmiQaITCYJFX93BjRyI1mzZrh7e3N9OnTadiwIXq9nrVr1/LHH39Yxvrggw8YPXo0GRkZhIeH891339GpUyc+/PBD3n77bQA2btzIoEGDmDp1Kr1790YIwfHjx9mUTXLgn3/+oXPnzkydOhVvb28WLVpEr1692LVrl5Uy//2Mu7s77u7urF69mtatW1up6heEZcuW4e7uzgsvvGB3v7e3t932AwcOsH37dj766COr9qSkJEJCQjAajTRu3JgPP/zwtv5W8fHmhSHZ32PZNb+chUCD4PMb0dRxdqaFq6vSBCujlLSDdQ04nqPtBNA/l/7TgP/L9toDuHIH7FLcIUaMGMGiRYssDtbChQsZOXIkW7Zsseq3YsUKatWqRa1atRg8eDDjxo3j3XfftSmo/OSTT/LXX3/xwQcf8NVXXxXJpj///JMbN27w+uuv292f2wfyvYJOpyMgIHcV9Oz7AwMDGT9+PJMmTUIY9Tg43j2B0bz45cAVElL15ExXF8C8LefYOLE92/73MGsORnAjKYPGQV48UqcCfXo9ihCC3bt3Wwl0hoaG8uabb1pee3h4WO5BcHAw7dq1o2LFikyaNInHH3+cWrVqsW7dOtq2bctrr71mOa5mzZr0zZawnTNvaOrUqfz666+sXbtWOViZ6HQ6Fi9ezOjRo5k3bx5Nmzalffv2DBo0qEDFzs+cOUPVqlVxKKBTUrlyZaKjozEYDLz33nuMGjXKsq927dosXryYBg0akJCQwMyZM3nwwQc5dOgQNbLyxzDXwnTPpj7fvXt3fvrpJ5tzSSl55ZVXaNu2rVW5Jbe2bREuLsjUVNy1Wr4IDMQkJRqtFsfgYByrVSvQtShKFyX91XMbUCtHW03gor3OUsp0KWVC1gYk2uunKL0MGTKE//77j/DwcC5evMi2bdsYPHiwTb+wsDBLe7du3UhKSrKKBGQhhODjjz9m/vz5nDtXtLURZ86cAcwfpgrQG038fuQaU38/wZ7wm+iN1nk9Li4umEwmDKUoL+RMVJLdckwSOH8jCYDyHs6Meqgq/+tem271K5IYH8f69esZO3ZskdTPJ0yYgJSSX3/9FYCAgACOHTvG0aNHCzyGyWQiMTHRKpqhMNePjIiIYM2aNXTt2pUtW7bQtGlTFi9enO+xUkqbL2J58e+//7J3717mzZvHF198wfLlyy37WrduzeDBg2nUqBEPPfQQP/74IzVr1uTLL7+0GqNjx44cPHjQsmVFunPy4osvcvjwYatzAGjd3aj4wfvm5MbMlZUanQ7h6MhkBx0ajQYhBA4ODlStWpVXX32V5ORkSy5q1ubl5UXr1q1Zu3at1fg5k/UrVKhAr169OHbsmFW/9957zyZZP+eXrw4dOvDSSy8V+P7ez5S0g/U50FoI8ZYQoroQ4ingWWB2CduluEP4+fnRs2dPlixZwqJFi+jZs6eNAvWpU6fYvXs3gwYNAszfaJ944gkWLlxod8yuXbvStm1b3n333SLZJMtwuai8eOONNyzTLVnb1KlTbfodOXLEqo9flXq8sGw/C/+7wPazNzgblcSaQxEAnDx5krlz59KyZUs8PEpP6aMgH1dLIntOKnnZj7KdPXsWKWWRHWsfHx/Kly9vmXoeN24cLVq0oEGDBoSGhjJo0CAWLlxIerrtKsUsPvvsM5KTkxk4cGCRbLiXcXZ2pnPnzkyaNInt27czfPhwJk/OP0OkZs2alsLqBaFKlSo0aNCA0aNH8/LLL1stkMmJRqOhRYsWli9lWbi5uVG9enXLVrGi7YKPcePGsWbNGv7++28qV7Yt6eTVqxdVfllFuScG4ta+Pb4jR1B13Tp0vr6WJPrz58/z0UcfMWfOHF599VXLsRs3buTatWvs2rWLli1b0r9/fxtHPytZPyIigt9++43k5GR69uxJRoa1vEq9evWskvWPHDlSkNuosEOJOlhSyj1AP+BJ4CjwLvCSlHJZSdqluLOMHDmSxYsXs2TJEkaOHGmzPywsDIPBQGBgIDqdDp1Ox9y5c1m1ahWxsbF2x/z4449ZsWIFBw4cKLQ9NWvWBMzOw73Ea6+9ZvWt+uDBg3ZXcdaqVcuyf+CU7/Hu/T/ALL0ggYzoi/RtUQ0XFxfq1q1LUFAQy5aVrrfoY00DST+3k6vzn0UarR+sI9pWsXtMlmOdW7TD1dUV10wxTL1ebzdilz1a4ubmxm+//cbZs2d55513cHd3Z+LEibRs2ZKUlBSbY5cvX857773HihUrcs0fLCjZbb1XqVu3rtXKu9x46qmnSEpKYs6cOXb3Z8+pzImUMk+HWErJwYMH7TpQeR3z4osvsmrVKjZv3mxZXWgP59q1CZg0ieCv51F+4kQcKwcCWKQqgoKCeOqpp3j66adZvXq15ThfX18CAgKoXbs2U6ZMQa/X8/fff1uNnRWNqlixIs2bN+fll1/m4sWLNpIRWSkBWZu/v5LXKSolnYOFlHIdsK6k7VDcPbp162b51tS1a1erfQaDgaVLl/LZZ5/RpUsXq339+/dn2bJlvPjiizZjtmzZkscee4z//e9/hbanS5cu+Pn5MX36dKsk9yzi4uJKJA9LSsmV2FRMUhLs42p5kF+LT+XEtQRikzPIayLEz8+P6tWrW7XZm4pydHSkevXqpOmN/HP9NMLdOqLo4BNIhcffZdwjtZjQu1Whk47vBhHnTnJz3f/hXq0FQmvOvdEIGN4mlBFtQu0eU6NGDYQQnDhxwipPCszOUnJyMmfPnqVVq1Zs2rSJ1NRU6tSpQ9euXfH09CQmJobo6GibB2a1atWoVq0ao0aN4u2336ZmzZqsWLGCESNGWPqsWLGCZ555hp9++olOnTrd1rVn2VqakFKSevAg+ogInGrUwDnzS0xBiImJYcCAAYwcOZKGDRvi4eHB3r17mT59On369LH0u3r1qo3kSnBwMK1ateL1119n4sSJXL16lX79+lGpUiXOnj3LvHnzaNu2LRMmTGD27NkEBwdbIpj//fcfM2bMYNy4cZbx3n//fVq3bk2NGjVISEhg1qxZHDx4kNmzCz7JMnbsWL7//nt+/fVXPDw8iIyMBMDLywsXl6LlMLq4uNiN0On1er755huAPHPQ4uLi+P777+32O3PmDJUqVcLJyYlWrVoxdepUm0VIioJR4g6W4v5Dq9Vy4sQJy+/ZWbduHbGxsTzzzDNZ1cotPP7444SFhdl1sACmTJlCvXr10Oms/61v3rzJpUuXiIgwT3NlfWPL+obm5ubGggULGDBgAL1792b8+PFUr16dGzdu8OOPP3Lp0iV++OEHwDytlJSURGRkJKmpqZYP+Lp16+Lo6Hh7NyYb+y7G8taqw5y6bs4fCvV1ZdKjdfn9aCQr919BSrhxMgpX0gm/kUyonfp7hSVdb7KqL2hBq8PRJxDHcgHF5lxFxqfxw55LnI9OJsTXlSdaBFG5XNEiMFFRUfTp04e6dWqz6e91HIpMJSnNQLOQcgR42Sq7Z+Hj40PXrl2ZPXs248ePt8nDOnz4MKtXr8ZoNJfXkVJy4sQJIiIieP7555k5cyYajcbGOctOaGgorq6uVg7Q8uXLGTlyJMuXL6dnz55FuubSTMaVq1x54QXSs+nHubVpQ+DML9AWYFrZ3d2dVq1a8fnnn1um+oKCghg9ejRvvfWWpd+MGTOYMWOG1bGLFi1i+PDhfPLJJzRr1ozZs2czb948TCYT1apV4/HHH2fYsGGAOf/tzTff5MKFC+h0OqpVq8bHH3/MmDFjLOPFxcXx7LPPEhkZiZeXF02aNOGff/6hZcuWBb4fc+ea9a06dOhg19bCsnv3br7//nseeeQRS1ubNm3QaDSkpqZiMpkIDQ21mXaOj4/H3d0dKaUlotq7d2+rKfJWrVqxdOlSatasyfXr1/noo49o06YNx44dw9fXt9C23u8oB0tRInh6etptDwsLo1OnTjbOFZgjWFOnTmX//v12IzE1a9Zk5MiRNkKBa9assYoeZOV2TZ482ZJv0adPH7Zv3860adN46qmnSEhIICgoiIcffthq2faoUaPYunWr5XXWyq8LFy4QGhpasIvPh4sxyTz1zU6r5PKLN1N4ZulecqYZJSbE0/fDZSwY2hxdplxCUROmPV10VPFzI/xGss1qPKNJ0iK0eBKxd1+4ydCFu8gwmK9PIPh663kWDm9B2xp++RxtTXp6Oo899hgZGRn8+uuveHu6096z4CWZ5syZQ5s2bWjZsiUffPABDRs2xGAw8Ndff/Hxxx9bplQzMjJITEzEZDJx4cIFBg0axOrVq5k2bZolSvjee++RkpJCjx49CAkJIS4ujlmzZqHX6+ncuTNgdq6GDh3KzJkzad26tSWa4eLiYvd/vqwhpeTyc8+RceGCVXvyrl1ce+cdKs+cme8YTk5OTJs2jWnTpuXaJzfJlewMHDgwz9y2cePGWUWr7PH555/bqLvnJL/E++LI8cxapWgwGNDr9fTp04cvv/zS4iitWLGC2rVrc/r0aV566SXmzZtn8zng4eHB/v37MRgMbN26lU8//ZR58+ZZ9enevbvl9wYNGvDAAw9QrVo1lixZwiuvvHLb13G/oRwsxV0hvw+h7PkEudG0aVOrDyt7H1xff/01X3/9tVXb8OHDC/RNsXnz5qxcuTLPPjnlJO4Ei7eHYzBJ69p5uXxGp106wqFZY2iRbdFS1jf0wiKE4PWutXh+2X6EuHVOAbQILUebarf/DdZokkz44QAZBlO265OYTJIJKw6w881HCqyrJaXk+eefZ+/evWzZssVu4nB+VKlShf379zNlyhQmTpzItWvX8Pf3p0mTJmi1Wr777jsA/v77b/7++2+0Wi3u7u7Ur1+fTZs20bFjR8tY7du3Z/bs2QwdOpTr169Trlw5mjRpwoYNG6hVy7xY+uuvv8ZgMDB27FjGjh1rOXbYsGEFWiFnj7S0NPr3NyvbrFy50kZl/G6Sum8fGZklYqwwGknc8BeG6Gh0Kqen0HTs2JG5c+fi4OBApUqVLNN6WY5mUFAQNWrUoEaNGri7u9O/f3+OHz9uldun0WgsXwZq165NZGQkTzzxBP/880+u53Vzc6NBgwY2Sf2KgiHK8goqIYQnEB8fH59rREShKGsMmLedPeH2k/ntoRHwZvc6jG5XPHkSG45F8tlfpzkVmYiro5YnWgTxapdauDnd3vcxg9HED3su887q3GUMvh/dijbVChbF+vzzz3nllVdYsmQJQ4cOvS3bcpKcnGzRNXrrrbespn81Gg2tW7e2RKVKmuy2JiUlFUlyoqhIKTElJaFxdUVotcT/+isRb+SeBxn604+4NLBfM1Jhn+HDhxMXF2f3S2h4eDhVqlThwIEDVqW7OnToQKNGjZiZGTFcvHgxL730klWCf1aUfvHixfTr18/uudPT06lWrRrPPvusRei5Q4cONG7cON9akPcyCQkJWRFnr0zJKLuoCJZCUcqo6OWCVhOH0V4+lB1MEqoUQw5WFl3qBdClXgAZBhMOWlEoTaHc2HIqild/OsSNpIw8+6XpjQUa748//uDVV1/l9ddfL3bnKj9MJhONGjW6q+csbUgpiV32PTHz52OIikLj5ka5p57E/eGHcz9Ip8PBTpRRGgxIkwlNMeYw3imkyUTKzp1kXLqMY2gIri1bIjQlrXZky8SJExkwYACvv/46gYGBdvt4enoyatQoJk+eTN++fRFC8Oqrr9KrVy+Cg4OJiorio48+IiEhwSYqHh0dbbPAICunVXGL0vefoVDc5zzdKrjAzpVWIwj2caVDreKfdnHUaYrFuTofncTopXuJyce50mng7PUkfjlwlZik3JfKnzhxgkGDBtGjRw+7ul53giyhRyEEPXv2vG1ZhbJOzIIFXP/oIwxRUQCYkpOJWRDGzSVLcWnaxCKWaUEIvB97DF25W+Wz9NejuPrqa5xs3IRTDRsR/tTTpOzbdzcvo1Dor17lfK9eXBr5DJHvvcel4SM437sP+mvXSto0Gx599FFCQ0OZMmVKnv0mTJjAiRMnLKrzV65c4cknn6RWrVo89thjODo6snPnTkJCQqyO+/7772nSpInVljOfS6GmCBWKUsmibRf4cN1xcvpZLo5a0jKMliT0epU8mft0M4J9S68G0ofrjrN4e3iuTqMAm6R6jYBXOtfkxYdr2PSfMGECmzdvZtu2bXfsfZ992u348eNcv34dR0dH6tWrV+qS0e/2FKEpLY0zD7bFlIs0RMgPy7kxdy7JWzNzezQavB57jIB330GTuQrVmJTMhT590EdGQuYqTTQa0GgIXf59qZtGlFIS/vgA0k6evGUvgFaLc/16VFmxouSMU9x11BShQlGGebpVCF9uOkNsinV9vQyDifqBXrzatRb+7k7UqehRLFEme5yMTODT9afYejoanVbwaMOKvNa1NhU8C5dEfeFGcp7OlaNOkG6w3m+SMGPDaaqXd6dbfWtRx48//hitVlusshh5ERwcTJ06de7KucoCGefP5+pcmfdfIPjrr9FHRKCPjMQxJARdjiX+8b+uRh8RYb16w2QCIbgxZy5Bc+2LhJYU6SdPkpajrAwARiNphw6Tdvp0obS+FPcHysFSKEoh287e4GaKrZCg0SQ5cjWeKr5udyxqFZucwdIdF/nq7zMYM1czGkySXw5EsOPcTf546SE8nQtWSBcg1NcNrUbYOFlaAbUDPDl2LdcvgIT9F27jYBVVnFFRPGjzEd3N2u9QqRIOlSrZ7ZO6/wBWS1WzMBpJ2bu3GKwsXvTXr+e533A9CpSDVSwYExKI+eYb4teuQ2Zk4N6+HX7PP49jcHBJm1ZoVA6WQlEKiUvNO18pNiXv/UVl/dFrtJq2ic83nkZvtJaKMJokEXGprNh9uVBjPt06GAE2qvNGCfUD857iuxhTuhTKFWbHybVFC9s8K40GrY8P7m0fzHcMrZeX2cHKbV8pw7lGjVztRQicalS3v09RKEypqVwcPISYsIUYIiMx3rxJ/Jq1XHh8ABmXC/e5UxpQESyFohTSJKhcrvtcHbXUqFBwMc2CEhmfxovfH7Cv5p6JBHZdiLGRhIiKiuLdd9/ljz/+sGhANWrUiA4dOlipb+dkOuDb4yV0XuW5vvxWP42LJ44BNag/7CWr/mfPnmXq1Kls3LiR69ev4+fnR+3atRk5ciRPPPGERcV/ypQp/Pbbbxw8eBBHR8c868/Zw83NrcwUAS8JWyt9PI2LQ4ehv3oVdDowGtG4ulL5qy8RBZi69erXl9jMUi1WCIH344/fAYtvD4fAQDx79CDhjz/MU5lZaDR49noUB7V6rliIX7PWqgIAAEYjpuRkbnw9n0offVgyhhUR5WApFKWQUD83HmsSyC8Hr9rMooztWB1Xx+J/6/5y4CqmfB7UWo3Aw870YP/+/dHr9SxZsoSqVaty/fp1Nm3aRN26dbmWucoqw2BixJgXSEpM5Ptvl+DmpCNNb6TnvH3EXDDnt1Qa/TUaR1cMCVHc3DifnbMnEv9SL7y8vNi9ezedOnWiXr16zJ49m9q1a5OUlMTx48eZN28e9evXt8gnZGRkMGDAAB544AHCwsKK+U4pHAIDqfbH7yRu3Eja6dM4VKyEZ4/uBSqFA+DSoAHlX51I1IzPbkXCjEbc2rbFZ+SIvA8uISpO+QiNmxtxq1aBwYBwcMCrf38qvFn4+qcK+yRv357r1HFytgoaZQW1ilChKKXojSa+3HSGxdvDSUgzEODlzNgO1RjcOuSOJLZ/lLnaL68IFsCSkS1pX/OWLERcXBzlypVjy5YttG/fPs9j7Ykm7r8US/93viH82zcImvADGmd3dBrB0KppTB7dn/Xr19OlSxfq1auHq6sru3fvRmNHe0hKaXNf7AksKkoP6ecvkLD+D2RqGm5tHsC1des7tmijuDAmJKCPjMShYsUCO5SKghHxxhvEr/vNeqVmJg7BwVTf8GcJWGWLWkWoUJQiEtL0hP17gXWHr2E0mehaL4DR7ari55578WQHrYZXutTipU41SdUbcXXU3tGHT8Mg73ydq1oV3GmXo16gu7s77u7urF69mtatWxe6IHSTIG96NarEl9/eaqvg6US1iuYkfr1ez8GDBzlx4gTLly+361wBxXpv0tLSGDJkCADffvttiZafyY+yZGtOnKpWwf+FF0rajEKh9fREq77Q3xE8uncn/tc1tjs0Grx69br7Bt0mKsldobjDJKcbGDB3B19uPsO56CTCY1JY8O95en/1X56CmlloNAI3J90d/2bftV4Fqvi52SSjZ+fU9SQiE9Ks2nQ6HYsXL2bJkiV4e3vz4IMP8tZbb3H48OECnfeXA1f5cZ91AuuVyCiee+VN3D08aNmyJacz8zKyavqBOe8ry7lzd3dn1pdf8fO+K4xbfoCJPx5i88nrRc5NMhqN/Pzzz/z8888Y7XybLk2UJVsVirxwb98er8cyy/ZotWZtNMC5bl18S+nUcV6oCJZCcYdZsecyp68nWulZGaU5qTzsvwu83q12idmWHSedlsGtg/lw3Yk8+3365yn+b2Bjq7b+/fvTs2dP/v33X3bs2MH69euZPn06CxYsyLfQ9ryt5yy/X5lj7iv1aejKVWLMOzOtVNOzO5m+vr6Wch3tO3Rg/pbTJF09hEaY+63cf4VaCWVv5ZFCcb8ihKDilCl4du9Owh/rkenpuD3UFs8ePcpEKaWcKAdLobjDbD553UapHMximn8dv37XHazwG8lExKVS1d+dAK9b00lJ6QZm/Hk6jyPNrNp/ladaBtM81Meq3dnZmc6dO9O5c2cmTZpkqXOWn4N1MSbF8nvA058gHF3Runrh4OKGcxVzHbUaNcyK7idPnrQUtdVqtVSvbl4en2qAtPg0PDDf16wk2X0XY62mPfVGE/P/Oc93Oy8Sk5RBvUBPxj9Sg4617u/SNwpFaUEIgftDD+H+0EMlbcpto6YIFYo7jE6ryVVCx0F7996CN5LSefqbnXSYsYWnFuzigY838dIPBywFlrefvUFqAYotazWCn/Zeybdf3bp1Sc5D8TuLIJ9bgqk6rwo4lKuIxskVKSWhmWKqTZo0oXbt2syYMQNT9mXymaRkGGwWHoH5A05vvNX/lRUHmfHnKa7Fp5FhNHHochwjFu3ht8Olr56cQqEo2ygHS6G4w/RoUNHuw18IeLRRRdsddwApJaOX7mXnhZvZ2mDNoQjeXX3U/LqAYxlNkpvZhE5jYmJ4+OGH+e677zh8+DAXLlxg+Q8rmPbxJ3TrmX9i6rMPVbVp0whzsemBzYMA87faRYsWcerUKR588EHWrFnDmTNnLBIN+qR4S74GgCEhiozr59EnRCNNJg4ePMjKDf/y697zVteZFdya9scJTAUssK1QKBQFQU0R5oKUkgOX49h1/ibuTlq61a+Iv0fhVkcpFAD9mgSy7lAE/5y5gSZT4kUCDQK9GN4m9K7YcPhKPAcuxdm0mySsOnCV/3WvzQPVfHHSaUg32EaIsqMR0DjI2/La3d2dVq1a8fnnn3Pu3DlS0zMQbr441+zIv/69GL10L1P7Ncj1/TOgeWX+aVaZ2ctvtfm5OzHrySaUz1b3sHXr1uzbt4+pU6cyduxYIiMjcXNzo1GjRnR55nVOeTUny/K4f5eRfHST5dgmTZoAEPDkVJyCG9rYcCU2lciENCp525bhSU9PR6vVlqnVeQqFouRROlh2SDcYGbtsPxtPRFkeiFqN4NMBDenXpHKxnUdx/6A3mlh7KIL1RyMxmCSd6lTgsaaBODto8z+4GFi1/wqv/Hgo1/2zn2pCOVdHDl6OY/qfp+zWDgTz+8DbxYG/XmmPj5tt0um3Oy9aImLZj6nm78YfE9qh1eS+RjE2OYMDl2NxddTRPKQcukJMn16+mUKvr/4jMc1gsVsjoEZ5D1aPfRAXRy0/7L7E/1YdsXu8AA5M6oy3q/makpOTcXc3q+W/9dZbODo6EhgYSLdu3ahcuXR9BmS3NSkpCTc3txK2SKG4tymoDpZysOzwxcbTzNx0xmZaRyNg88QOhPqpDzBF2WLfxZv0n7sj334aAQ9V90On1XA5NhV/d0euJ6ZxNioZAXSo5c/kXvXsvgdMJknraZuISrQvPbFgaHM61a1wu5eSK5dvpvDl5jNsOhmFk1ZD78aBPN+hGl4uZuX52OQMWk7diN6Yo+i0RvBgNV+WPtPK0hYXF8esWbPIyMhApzNLZAgh0Gq1PPvss/j7+1NakFKSkmJeKODq6lrqhToVirKOEhq9DZbtvGQ/ZwZhjgR0qWW7U1HinL6eyL6LsXi7ONCxdvm7Fh0qCzQNLkedih6cvp5kNzKVhUnCv2dv8GTLYMKGt7C0x6fqcdCKPEv0xKZk5Opc6TSCYxEJd9TBCvJxZfrjjXLdX87NkRkDGvHKikPIzEwskzTXdvxfd+uVnHv27AHAweFWWSApJSaTiR07dtC7d+87cAVFQwiholb5kBEezs2l35J66BC68uXxHjgAj44dS9osxT2OcrDsEJstgTc7QsCNZPv7FCVHusHISz8c5I+jkZY2LxcH5g5uSptqfnkcWbxExKWy+8JNXB21tKvpX6ocPCEEC4e3YMzSfRy+Gp9nX5OEFXsv83rX2ni5mh2MrChQXrg763LN4TKYJBU8Sz6HsU/jQJy0Gsb/cJAMowmNgKQ0A4Pm72TZqNY0qOwFwNWrV+2KlJpMJi5dunS3zVbcBqmHD3Nx6DCkXm8uwXLyJEl//43fC8/jP358SZunuIdRDpYdGgR6cehKHDm/6BtMkoaBXiVjlCJXvth4hj+PRVq1JaTpeWbxXna8+bAlr+ZOYTJJPvrtOIu2h1sinx7OOmYNakLH2qVHX6milwvfjWrJtrMxpOlNXIlN4bO/7OteGYySq3GpFgerICSnG+lctwK/Hb5mtVJPI8DFQUuPhnduxeTuCzeZ/895Tl1PILicKyMerGI3WmYyST787QSGTKmHrPd4UrqBscv28V7vepy6nsT5RGc2/LIagaRXr17odOaPSiGEJd+ptJCens6YMWMA+PrrrwtdquheJ/KDD5EZGZAl75Gpdn9j7jy8HnsMxwLk1Bnj49FfvYouIACdj0++/RUKUA6WXSZ0qsGIRXsQ3Fq6rtUIAjyd6d24UkmapsiByST5budFG2dYSkjTG/n1YATD7vBKvW93XmThtnCrtqQ0A89+u5ctr3Uk0M7KtLtNmt7IlN9O8MOeS+iNEgetoEOt3POItBpBRa+CrZpLTNPz9i9HWXc4ApPEptSOu5OO+UOb4+lcMGdNSkmq3oiTTptnUnwWaw9FMH75ATSZiflXY1PZdi6Gt3rU5tl21az67r8Uy9W4VJsxTBIuxaYycsletEKgT3fg8qGDAPTs2dPKtqZNmxboOu4WBoOBJUuWADB79mzlYGXDEBND2tGjue5P2rwZn6FDc91vysjg+sefEPfjj2AwgEaDZ/fuBLz/HtpS5mgrSh/KwbJDh1rlmTu4GR//cYLwmBSEgIdrlef9PvXyzEFR3H3SDEYS0wx292k1gmvxaXb3FSeLtl2wcsbJ/N1okvy45zIvd655x23IjzdXHeHXg1ctjqjeKNl0IgoPJx0peqNVXpZGmKUlytlZJWiPF78/wH9noi1jS8xOVrOQcgxrE0qnOhVwcSzYdOlvh6/x2YZTnL+RjLODhgHNgnisaSBbTkWTYTTRoaY/Lav4WBK59UYT7605ZrnfcCsq9emfpxjQLMjqOnL7X8mOMcfUoMSc4C6lpEWLFjRo0KBA16Io+1z/aApxP/98K/plMpHwxx8YExMJnv91yRqnKPUobyEXutUPoGu9CtxIysDFUYu7k7pVpREXBy2B3i52oxIGk6RORY87bsO1+DS7Ip1CCLt2FQSjSXIsIh6jSVI/0Ou2FN8j4lJZfeCqjY0maZ4aq17enTNRSZb2znUr8EGfegUa+1RkIltPR9u0S+Dg5TjmV/crsHO19lAE45YfsETA0vQmvt15kW93XrREsuZuOUd5DyfiUjLQaTU8WN2PmFzyIvVGyY7zMfRocGtqslGQNzqNsCqfkx8+9R6kdbAHNWvWtKqLqCj96Hx9ca5fj7TjJ245SdlwzyPR3XDzJnErV9oeZzKR/M8/pJ89i1NmqSaFwh7Ka8gDIYQSFy3lCCF48eHqvJlD3yhriqtb/QBLm95oYtOJKM5FJ1G5nAtd6wUUSyJ6jfLuHLuWYLPy1CQltSoU3sHbciqK/608TGSCeUWej5sjH/apT88i5jCdikzMVaVdAm/3qIOvuxMR8anUquBBqJ8bF2OS+fTPw2w4dh2ALvUq8HrX2gT7ulodfzIy1xXKGEySCzeS8HHLP2dFSsmnf56y2JST7BG2rJWKGUYjm05cz3PcnFOMPm6OPNuuKnO2nMvlCFv8AqvQtm3pKMidE2N8PJHTp1tehw8dRuhrr+LWunUJWlW6CHjnHS4OG440GMz5VxoNmEz4jnkWx6CgXI/LuHDBkq9lj/QzZ+6Yg2XKyCD91CmEkxNONWoo6Y0yinKwFGWeQS2CSM0w8sXG0yRkTgG1ruLD9AGNcNKZHagrsSk89c0uLt1MsYho+ro58u0zrahb6fY01F7oWJ0Xlu23atMIc6L7480KJ0p5+noio5bstXIobiZn8OLy/QR4PUCzkMIn2FbwzDuXqoKXM3UqelpW0F1PSKPv7G0kZBPt/ONoJNvO3mD9S+2sxrOnfF6Yc2cRn6rn0s2U/DvmIOs25ZyiBbP8QtvqtqtIX+tai/M3kll/NNJmnz3qBRafxl5xIvV6Lg4fQdzJk5a2tGPHuDTyGYIXLsStdas8jr5/cGncmCqrf+Hm0qWkHjTLNJR7YmCe0SsAXYWAvPcH5L2/MEgpMcbFoXFxIX7tWqI+nYEpwfzlxbFqFSp98gkuamq6zKEcLEWZRwjByLZVeLp1MOE3UvB2dbB5sI9ffsAyXZflNMSlZDB66V7+eb1jgZKpc6NHg4pM6Vef6etPEZ+qB6BmBQ/+b2DjAucxZbFkezgSW2dBIwQL/r1QJAerTkUPGgR6cfxagpXjptUI6lX0pE5Fawdi4bYLJKTqya7HaTRJElL1LNoWbqUZ1TykHNXLu3PhRrLN2G2r++Ht6sjG4+YoU+tqvrlOtTs7aAs9dWeFAK0wO85ajcAkJVP61cfNzvmEEMSl6O06ZfaoW7F0rhxO3LiR9BM5pr5MJtBoiJ41C7fWy0rELiklKXv2kH7qNA4VA3Bv1w7heGdX8uaHU5UqVJw8uVDHOFYOxK1tW5J37LCOZGm1OFWtikvjxvmOEb92HTFhYWSEh+NQuTK+w4fh1b+/VUQq4fffifr8C/SXL1uia9nJCL/IpeEjqLb+D3SlSOBWkT/KwVLcMzjptNQKsJ2Su3Ajmf126vAZJVyNS2XXhZjb1st6ulUIjzerzOnIJFydtFT1cytSWP/U9US7QqBGk+RUZGKBxzl4OY45W85y8FIc5T2c6NGwIsnpBs7fSLb0CfV1Ze6QZjbHbj8bg9GO52GUsO3sDas2IQRhw5ozfNEeLmQbu0GgF+1q+tHio42k6s0PJxcHLe/3rsfAFrbTMs4OWvo0rsTqgxF5CqHaQ6sRdKjpT7CvK6cjEwn2deWhGv6kG4z8d+YGD1TzLbQD7XgbOW93i5R9+0Gng4wcOWgmE6kHDiClvOtTS4bYWC6Pfta8ck+Y64xp/fwIXvANzrVvOebGuDji16xFf/UqjlWr4tmzJ1r30ieWWmn6J1x+7nnSDh+2tDkGB1N57px8723MosVEffKJ5T5knDvHtXfeRR9xDf/x4wBIWL+eq69MvHWQnTwxTCZMqanErVyJ33PPFct1Ke4OysFS3PPEJNlXF7+1v3jEY510Wss0W1EJ9XXlwKU4GydDqxGE5Mh/yo1/z0QzfJFZidxokkQnpnM0IoEnWwTxfp96hN9IJtTPjQer+aGx43ikG+znnQjA08X2IyPE141Nr7TnnzPR/HnsOml6I94uOj5cd8KqX6reyOsrD+Pv4YS/hxPlPZ0QCOZuOccfR68hJXi56LiZrEeXOY0rMU+35uVzGU2Swa1D6Fi7PCkZBiYsP2g1ZRvo7cLC4S2snO+u9Sqw63yM3Wsc074q4x+uQfLrUYC5/ExpROvpAVLiIgT/VTPnArlkPvQ1bkVz8G+Xa++8S9qJzL97ZlKi8eZNLo95juob/0I4OJCyfz+XRo1GpqaCVgsGA9EzZ1Jp2lSc6tTBoRQtJND5+BC64gdSDx4k4/wFHAIDcW3ZAqHJ2wE3JScTPWum+UVWcmbmzxvz51NuyGC03t5Ez5plccDyI/3M2du6lruNlBKZkYFwdLxvc8hK1MESQrwH5IzbXpdSFt/ktuK+p0YFDxy1GjKMdr4dAg1v0ykqTga3DmXlvqs27UaTZPiDVfI9XkrJB2uPYzJJy/RX1s/ley4zsm0VHqqR+zRDhsHE1Vj7Kx8l0L2+/UT7yIQ0Jv16jEs3U/Kd6hu5ZI/leeLsoEFvMFkiZloh8HN3pHejSlTydqFFlXJ8vfUCfx6LxGiSeLk4EJ9qdsDAnEg/9IEQi6bXR+uOs+mkdeJ7ZHwqQ8J28d8bD+OoMz8YB7UI5pcDVzlyJd5yf4SAmuU9GNuxOq5OOlxL+XSM56O9uDF3HkIIfHTZPso1Grwe63fX7THcuEHS5s22zoLJhOH6dZK2bcO9bVuujJ+ATEsz9zOYcyaznDAA1+bNqfjRhziGht7lK7CPEALXJk1wbdKkwMekHjuGTM1FIsZgIPXAAdweeICM8xcKagQOgYEFPn9JIqUkftUqbsydh/7KFTSenpR7+in8n3++xKeK7zalIYJ1DOiU7XXuyzYUiiLg5eLAMw9VYW6OlWMaAb0aVSLEt3inJi7fTOHbnRc5dNk8Pfdky2Da2Em2tkfjIG8+G9iId1YfJSXD/FZw0ml4s3tt2tfM/4EfnZhuJbmQHY2AraejqWFnZePlmyks23WJnedjSM7I/S3ok0tO2RsrD1ty3PLLo8r+/E3TWzu9Rim5mZyBr7sTox6qCsCcp8thMJowSXMkb+vpKP4+GY2DVkPPhgGWvLTkdAM/7btiE+0ySvPKw80nr9Mt00F0cdSy4tkHWLbrIr8fMSvPd6sXwNOtQ8qMJItT1SpUePcdrn/4kTl3RwgwGHCuV69ESsAYYmLyjMQYoqJI3rUL440bufYBSDlwgPDBg6m2fr2NmGfSf9uICVtA+ukzOFQOxGfoUDx79Ch1ERKNS95RT42rqzmy4+xsdjbzQggQAu/H+xejhXeO2G+/4/rUqZbXpoQEYuZ9jf7SZQI/m1GClt19SsMniUFKWbDlPApFEXmtSy3cHLXM/+c8CWkGXB21PN0qmFe7Fm/h7iNX4nli/g7SDSZLwvXaw9d4o1ttnu9QLf8BgMeaVqZrvQC2n4vBaJI8UM23QLUAgTz1sqTEEsHJzs7zMQxbuBuD0WQ39yo79nKTohLT+PdM3g/NwmCSsOnEdcZ2vLUEXpftvA/XrsDDtW3L4NxMzkCfywVoBFzJEZlzcdQy6qGqFkcuO+np6bzyyisA/N///V+pVUf3eeopHJo3Z8KoUUi9nk/ffRffLl0Qurv/0e4YFJSnw+Bcpy76q1fyH8hoxBhzk/hff8Xn6actzXGrV3Ptf29aEsGNsbFETHyVjPBw/MeOLa7LKBac69XFISgI/dWr1nlVQqD18cG1WTOEVot3//7ELl9uP/cqE42bG5WmT8cxOPguWH57mDIyiP7qK9sdUpLw22/4vfA8TtUK9jl4L1AaHKwaQogIIB3YBbwlpTxfwjYp7jE0GsGLD9dgTPtqxKZk4OXiYJFwKE7e/fUoaXqjJYqSlUs1/c+T9GlcKV9ZgyzcnHR0tlNLLz/KuTnSqooPe8Nv2jhLQkCXutaz7yaTZOKPh8gwmvJNA3Fz1NKmuq9Ne0LmysniQgiKpE/m7+GEm5OW5HTbCJxJmld22iM53YAQWFVpMBgMzJkzB4Dp06eXWgcLQBMYyKJ//wXgq/btS8S5AnNUxmfEcGLmzsuxQ4Nryxa4NKiPzs+3YDlHWi3p2eQnZEYGUR9/Yn6RTVUdzDUFyz35ZKFqBBpiYjDGJ+BYOfCOTFsJjYbAGZ9yccRIs8OZec3CQUfgZzMQDuYvTP4vv0zayROk7ttvzkczmRA6HRXefRetuxvC2QW3Ng+gcS6Y3ElJk3Eh3CIvYY/UAweK5GAZ4+NJPXoUrZsbzg0b5psDV1ooaQdrFzAUOA1UAN4Btgsh6kkpbTJQhRBOQPZPujsv0624p3DQaijvcWc+rKIT0zl4Oc7+TgkbT1xn6AOhd+Tc2fmwb30GzNtBYpreMq1mNEnefbQuATnqCx6LSCiw2vzUxxrYLRUV7OOGo05DhiH3b+GFQUrz1G1hcXbQ8kzbqszadMaqXasRVPd3s9HEOnIlng/WHWNPeCxgXmCQlG4gNllPqJedSF1UFKdOmcVQa9WqpVTd7eA/bhxCoyVm0SJkSgpotXj27EnApHcBcKhYEe8nBxH3/fK8BzKZ0Pnfur9pp05hjIuz39dgIGXXLjy7d8/XPv3161x7512S//sPpETj5YX/uHGUe/qpYp9mdGnUiOob/iRu1S9knD+PQ3AQ3v3741Dh1hcnrbsbId99R8rOnaTs34/W2xvP7t3LbEFprVfemnEaz8JpykkpuTFnDjFfzzcX7AYcKlcm8P8+w6VhwyLbebcoUQdLSvlHtpdHhBA7gHPAMOD/7BzyJrZJ8QrFXUFKyYHLcWw7cwMXRy3dG1S0KuRsyutbuaDQ8gNZxKfoWX3wKhdjUqji50qfJoF5Fk6uWcGDja+05/tdlzhyNQ5/DycGNg+iSXA5m765rRjMSTV/N/o0tp9k66jT4OaozdXBah5Sjrd61mFo2C6S7ESXsshaLdi+pn+hBVqzmPBIDTIMJhZtu0B6pj0PVPXl/wY2sloxeT46iYFf77C6/vCYW0KnZ6JuSU5IKfnzzz/ZuXOn5SG8efNmWrVqRdeuXUtd/s+dImX/AW5++y0ZF87jWKUqPkOG4NrUOvFbaDT4j3sR39GjMERGovXxQZvjoRrw9ts4VKzIzcVLMMbYruQ0DySsEvXzizIVJAolMzK4OGSoedou871qio/n+kcfoXF2wvvxx/Mdo7Do/Pzwe3Z0nn2EELg98ABuDzxQ7Oe/2zgEBODaogUp+/dba4cJgcbDA/f27Qs1XtxPP3HjS+spR31EBJdGPkO1vzagK2f+TJOZf8/S9l4s6QiWFVLKZCHEEaBGLl2mYe14eQAFmNRXKG4PvdHEuOUHWH80Eq0QSCRTfz/Be73rWaJS5T2cqB3gwenriTaJ1lLCw7ULH/E4dDmOIWG7SEwzoNUKjEbJjA2n+X50K+pVyn31o7+HExM6Wb+NohLSMEpJgKez5YMoJY+E9uw83Sokz/0+ro7EptifKoxKTMPFQYuTTpurg1XB04kWoT50qRdAj/oBVjlX9oiISyXsvwtsPxeDl4uOXo0q4e3igFHCqIeq8ELHapyPTsbfw8nKCc4i7L8LZGQmzufH0eMn2LlzJ3Drgxxg165dBAcHU7du3fwHKePEr11LxOtvmPOfjEbSz5wlcf16Kk3/BK9evWz6a5ydc10FKLRa/EaPxm/0aEypqUS+/wHxq1ff2u/sTKVPPsGx8i0n26lmTRyCg9FfuWKTr6Rxc8OtTZt8ryFx0yb0ly7Z3Rc9e46NAGhe6K9dwxAVhWNoKFqv0rMKuaSRBgNe/R8j/cwZc8RRpwOjEeHkROVZM9EUcqr95sJFto0mE6bkZOJX/4prixZEf/45yTt3Ihwd8ezRg/KvvIzO1zaVoSQoVQ5W5hRgHeBfe/ullOmYc7Wy+t8lyxRlhZvJGWw/dwOdRkPbGn7FtiIs7L8L/JlZWsWY7SE76ddjNAspR71KXggheK93PYaE7UJIc8QqKyozpl3VQq9WNJkkLyzbT1K6AQkYMpOqEtP0jF22n79f7VCg98DhK3G8s/ooh6/EA1Czgjvv967PA9V8+eNoZL4pMV4uDgxunbeD1bdJIJ9vPG3XYbkcm0qPWf/SINCLuFS93UheFT83vniicb6OFZijT/3mbCMpzYhRSgSw8/xNy36dRvBy55pWSfJZSCnZdjaGNYcKLmi6e98BhBBWzhWYP3/2799fph0s/bVrGGJumh2FXIQ+TWlpRL7/gfmfJCsqkfkz8oMP8ejSpdAPziw0Li5U+ngavqNHkbJ7Nxo3N9wffthm9aAQgkofT+PSyGfMU0UmkzlnSUoqTpmCxuWWE5165AjRs+dgSkjArd1D+I4ahUanI+3ESfMDP1MaIjuGa9cwJafkK3ZqiIkh4n9vkpyZ84ZOR7knn6TC669Z8qruV1IPHuTKuPEYom8Vf3eqWhWvfv3w6tvHEm0qDBmX7TvEaLWkHjxA9Oefm2tMmkzI1FTiV68mZc8eqv6yCo1byQvXlrQO1gxgLXAJKI85B8sTWFKSdinKJl9vPceMDacsK8lcHLRM6Vefx5oWbbopO9/vumS3rIpWI/h53xVLNKl1VV/WvNiWb/45z4HLcVTwdOLpViE8WoRCzfsvxdrNjzJJ83TW4SvxNAryznOMyzdTGDR/J2n6W5GjM1FJDF24i1/HtiU1w4CGvLVRPh/YiAOXYrmRlEH9QE+7juLItlXYcPw6R67G2+zL8ktOXItHYN8h3Hn+Jp9uOMWb3esAZidyxZ7LbDkVhYNWQ69GlejdqBI6rYZP1p+0OFdgW+7GYDIXjq7q50b3Btb3ffqfp2zkOvJDn5Zq41yZr0uSnJxs54jSjz4qimv/e5Pk7dsBc9TIZ8TwzBwqayc39cABTEn2pT9MiYmk7t9/29NbTtWq5Zv87Nq0KdV+/43YFT+SfvYsDoGVKPfEE1bHRUyaRPyPP92yff9+Yr5ZQPU/fkdXoXyuxZuFqysaF/u5mVJKEv/8k7iVK0nZsxeZnk242GAg9rvvzInpb7xeiCu+tzAmJHBp1GhMKdb1RNPPnMGUmlIk5wrAIbCyuYRQzvef0UjGhXCk0Wgd0TQa0V++TNyvv+Lz1FNFOmdxUtIRrMrAcsAPiAZ2Aq2llBdL1CpFnmw8fp05W85y+noSgeVcGNEmlCdaBJVoRPGv49eZ9sdJq7ZUvZGJPx2iRnmP21ZYj03JRe1dSmKTrffVqejJ/z3R+LbOB5CQlvfqvPz2AyzdEU66wXoqTEqzk7bg3/O0qebH6oMRdo911JojQe+vPc7FbIWYezWqyKePN7Ja6efmpOOn5x7gm3/P89mG03bH0xuhX5OK/HLA/vkWbwvnxY7VMZok/edu53x0MhKzuvrfp6L5cN1xmgR7s+VUdL5TexoBi7eHWzlYxyLiC+1cAVQNDebIkXi7EazgElo6bzKZiI6ORqPR4OJSsJWpWUijkUsjnyHjwi2RS5mWRszceWgcHfF7/nnrA/J7X9/F971DpUqUf/klu/uS9+61cq6ykMnJXBozhpBFi4ia8dktkdMsNBrKDRqE0NpfuXp96lRiv/0u99WPUhL7/ff4jR1bKsv93A0SfvsNU3Ky7f2Rktil3+L3/PNFej74jhxB5HvvWzdqNAhnZ/SRkfYdZiFI3bsPSoGDVeS1jkKIICHEQ0KIrkKIppnTe4VCSjlISllJSukopQyUUvaXUh4vqk2KO8+Pey4zauleDl6OIyndwOnIRP636gifrD9VonYt3nYBrZ33r0YIlu26fX+9SZC33fFNknyjSLmRlG7g663neGzONh6fu50F/54nNVtOVMPK3rnW0HPQChoEmp1Gg9HE/H/O0faTzdR+9w8en7edrafNYfpDl23L7oB5+vLA5Th6N65E7QAPq2vTCLO46U9jHmDRtnAb/ajfDl9jup2/t7ODlk518paW2H3hZq770g0mLsakMG/reS7cSLZRoo9N0fP3yfydKzD/XS7dtP42/fuRawWuSSgcHAl8LowNOw/Rrl07HBwcrB4QQggcHBxo3bp1gcYrTk6dOsXMmTOZN28ec+bMYcGCBfz7779cuHChQM5W8vbtZJw9a/fhFLNwkWW1VhYuTZrkuvpL4+qKIfoGGbnkNt1Nbsyek+u+9BMn0bi7EzRnNiLHPdJVqEC5p560e1zaqVNm5wrynEeX6enoI2wrMNwvZFy5Yp6ytYMxLs466lcIvJ94Ar8XnjdP7WaiK1+e4LAFNosnLAiBJscUc0lRKAdLCBEihJgmhAgHwoGtwB/AXiBeCPGXEGKAEKJsiFQoCkWGwcTU3811xrIeclkfOfP/Ocf1hHwUie8gl2JT7BcoNkku53jQFoXxj9SwRFOy0GoEFTydi7TiLTYlg36zt/Hx+pPsvxTH3ouxTPntBE/M32FxsvzcnRjV1n55nGfaVsHb1bxy6tWfDjHt95NciU0lTW9iX3gswxbu5vcj16jg6WzXqdAIqODhxJZT0bSp5kezEB/KuTjg5qilS70A1rzYlitxaUQlplvlnIH5b79s10WraccsapR3x8s598D41bi8/0duJmew7nBErk5UQddhajWCOhWtP4AzDKZcJihtEULDu4Pa07lVQ3x9fRkxYgQhIbfy0EJCQhgxYgTlijj1UVSuXr3KihUrSMimNRQfH8/ff/+Nu7s7mgLoA6WfPm1OVreDKTERQw6ldY2TEwGTJ5kjOFkP0cyfppQUIl57jXNdunL1lYmYivggLQ6M8bbT09mR6enoAgLMkchszrIhKopLw4ZjTLQtpp64aVOujoMVWm2pqqF4t3GqVt1ubhuALqACoog5ekII/MePp8a//xD09TxCln1H9U0bcW3SxLzK1N7/sdGIV5/eRTpfcVNgR0gIMRPIWuE3CagHeAGOQADQA/gP+BA4LIRoUezWKkqUU5GJxOUiKmmSZkXwkqJ2gKddR0KrEdQMuH25tOahPiwZ2ZLamWMJAQ/XKs/Pzz+ARx6SCTlJSNPz5qrDNP9oI2eikqy+FEvM2kw/7LkVDXijW23e6VmHCp7mD6isS/zm3wuMX36ADcciWX0wwsrxyPr91Z8O8XjzynYjWCYJl2NTeO67fSzdEc7eizdJyjDw6YBGzBvcjFoBHlyOTbEbtQNztOlmsu20qU6roVuDvEuJVvB0IrdA0nPf7SsWPS2TSTKmnbVCe4da5e2W8dEICPZxsajlB/u48unjDRnT/lZuT0BAAMOGDePNN9/kzTffZNiwYQQE3P2SqTt27LDbbjKZWLlyJaY8FMGzcKhYMVflcOHoiNbb26bdq2dPQlf8gGePHjjVrWtO6M7xcEtYv56oGZ/lfxF3CNcWuT9yhJMTwtWVmG8WQEaGdTTKaER/9So3l31ftBNrNHj27Gn3vt0veHbvhtbPz64z6jtq9G2nj+jKlcO9fXuLAj6A7zPP4NqyZWYHneXcfi88j2uzZrd1vuKiMDlYGUA1KWW0nX1RwObM7X0hRA8gBNhz+yYqSgsujnn74y5FUN8uLp5tV5WNJ6yL/GqEuXhwcYl7PlTDnz9e8ic+RY+DTtgV3cwLk0kyLGw3h6/E5VmS5s+jkYzILOys0QhGPVQVX3dHXl5xyEohft3hCNYesp/PBGYJhvVHI3mze20+WX/S6plSo4I75zK1nrKcDmmUTPjhAK2q+ODr7kR1f/dc7XR30uHrbl976KmWIazYY189xd1Jx4KhzRk0f6fdmofpBhPBPo7EJGcUaJWft4sDFb2dOR2ZaGVrvUBP/NydeP3nQ2w4fh2tEHSvH8BDNfz4L7Osj8TsgDtoBXOebka9Sp7ojRJHnYYbCcmMf2kiTg4apkyZgmOmzpJjCRerjYyMtMkFMxgMbN68GYCqVavSuXPnPMdwf+QRtL6+GGNjrR0tjQavxx5D42q/jp5Lw4YEfjqdhL/+4uo4O7UOTSbiVqyg/MsvWcaQUpLw++/E//ILxrg4XFu2wmfoEBzugHPqP2G8uexMhq3j7zNyJEIIc1J/LonuMbNn49m1C05VbkWNPR55hBuzvszzvG4PtiFg0iQywsPJuHIVx9BQHCuXjcLMxYXGxYWQpUuJePVV0o6bs3yEszN+Y56l3NN3JhdK4+REcNgCkv79l+Tt29E4OePZvRvOpWhVb4GfEFLK1wrR9/eimaMozVTzd6dGeXfORSdZTeEIzEnOD9XIuxjxtfhULsakEOTjaleb6HZoEerDl082YfKaY8QkmT9gK3m7MP3xhlTxK97EUy/Xoi3H/vfsDQ7kpvSeicR2GkxKyed/nbHpW5BcpJ/2XuHo+13p0ziQv05cx2SStKnmS/eZ/9pM/WVJQfx25BpDHwilQy1/Qn1duXQzxeZcIx8MzbXUUMPKXjxYzZcd52Nsjnu+QzUaVPamqr8bR67altQwmiTxqXp83Ry4kZhBfvEYiTRPqeZIQD56NYHOn29FCGFx1JbvuUwFDyde6VKTtYciSEjV80A1P57vUM1SRic8JonJvx5j28mrXJ5pltzrM3ICbevc/krU4sDb25uYHOKcJpOJ7ZmrAXfs2EHHjh3R5VEuR+PkhM+QwUR/MdOqXefvT/mJr+Rrg/7yFUs9wJzIjAwMN2NxdHVFSknkpMnE/fSTpX/aiZPE/fwzoT/8gFNV+9PfRUXr6krVNb9y+dkxFr0r4eCAz4gRlJ9gdgi1Hh4YIu2XvpUGA9fefofQ75dZ2pxr1aLckMHmPKysa9ZqEQ4O+D3/PO4dOqAr78/V8eMtKzIRAvdOnag0bVq+Se+pR44Q/eWXpOzeg8bNDe/H+uH33HOlQmKgsDhVrUKVVStJP38eY1w8TjVr3vGkf6HV4tGhAx4dOtzR8xSVIq0iFEK4AEJKmZL5OgToB5yQUv5ZjPYpShFCCP5vYGOe/GYnKRkGS5sA/m9gI1wc7T9wk9MNvP7zYX4/cs2Sx9S5bgVmDGyUpyJ5YXm0YSW61QvgxLVEdFpBrQoeVurdJc3BS3GWsjW5IYBu9a2/3SemG2wStguKwSSJSc4g0NuFIZlaVrHJGXanysAcMYtNNk8D67Qa+jerbLMq0EEr6Fy3AhkGEw5agRCCdIORiLg0fNwc8XJx4Ouhzflw3XF+3ncZY+ZzWKcRXItLJT5VT3kPZwQJdnOqrsWb87QCvZ3zzdly0GqsFNizY5JYOV1GkyQyIQ2NEGx42VZROjI+jcfnbic53TqXZPjCPfw20YtaxTDVfLu0bNmSc+dyXwmZnp5OUlIS3nlMV6Wfv0C0naiMISqKuB9/wveZkTb7jPHxJP37H5iM6Pz9cp9idHU17wfSDh82O1dwq7/RiCk5magZMwiaMztXG4uKU2go1Tf8iTElBVNiIrry5a2mp7z69iHq0xn2D5aS1P370UdGWkXYKrz1Fq7NmhO3ciWGmBhcmzfDZ+gwS5QqfMhQUvfvtxonadMmrr3zNpW/+CJXW1MPHSL86cHme2MyYUxLIyZsIcm79xC67LsSqyl5uzhVtS2efr9S1L/gr8AqYJ4QwhtzTUE94CeEeEVKObeY7FOUMhpU9uLvVzvw497LnIpMpHI5F55oEZSniObEnw6x4Vik1aqwTSeiGP/9ARaPbFms9um0mtuWZLhTlHNzwJSXcyWgfiUvBrWwXvpvVkHXWEq/FBZfN+tpLW9XB0J8zJGpnNYYTZLmoebE7ajENL7YaBs5MxglfWZvwyTN07C+7o4kpRlJ1RvRCHMdwQ/61OfRhhX5cc9lS3DJYJIs332JA5fj6NckkE0no/K0+2pcGg5aYdE1y4kAGgZ6s/lU3uNkxyRh66kouyKkS3eEk5RusIm6GaXkm3/PMWNA4wKf505Rs2ZNGjRowJEjR+zu12q1uOYyxZdF/C+r7EsrSEns8uU2Dlbsih+J/Ogj0GfmX2o0CC8vZEKCzco6nyFDLKKjiZs2m/Nick7JGY0kbdmCNBpzlUa4XbSurmjt3IdyQ4YQt2oVGefO53psTi0nIQSe3bri2a2rTd+0U6dI3WMnE8ZkIvHPDTbOWnaiZs6yOFfZj0s7dIjEzZvx7NIlVxsVZYOirvZryi219ceB65hzroYCdibnFfcS/h5OjO1YnVlPNuH1brXzdK6uxKbw59FIuw+tLaejORdtX8DwXuTRhpVw0GnsrmTzdXPk7R51WDGmtU0k0EGrYWDzoFwTw/Oiip+blV4VmB8Yr3atZbMqUiOgRWg52lQzl5nYdCLKbrRNcmt60iQhOjGD1MwVhSYJaw5F0Pn/tvL6z4cgh3SQUZoLTG87e6NAK/r0Romzg/2PqabB3sQkF27VmkaAqx11/4i4VFYfuGp32tVokuzNLAhdGujduzduuUwhNWrUKN88MUP0jdz35VhBmLL/AJGTJ99yrsCsmh0fb+NcCUdHvPr2ycf6W2Mk/LmhYH2LEY2jI4EzZ+a6X+vri2MhtM0ywvOQgJGSjIu5y1ek7NplPxKo05Gyc1eBbVCUXorqYLkCWWtauwCrpJQmzEKhedfUUNxXhN+wjZJk53x02VPCNhhN/Hb4GhN/PMTrPx9iU2ZuU374uDny1ZNN0GkFQpinzADqV/Jk88QOjHqoaq6J8290r02zEHNkSZs5Leus09CrUe4K8RoBs55sbHdfr0aVmPN0U6r6mx/Uzg4ahrQOYdGIlpYpFb2x4LIG2ZESohLTuRafblc6SKcRhfq7p+lNNAspx+tda9G7UUWGtg7mm6HNcXXS2VWOzwuThD6NK1m1nb6eSNcv/iEi3v50pBBmyYzSgk6nY8iQIXjZqYH3yCOP5Hu8c9269h/sGo1VgrAhNpaoWbMKbJc0GomZP9/y2uPhjrkmlANETJxI8u7dBR6/uHCuXt28xD97FC9zRWT5iRMLNTXnGByU+04hcAzKPXdP42xfOR4p0bjlHYVUlA2KOkV4FugrhPgF6Ap8ntleHrDNXFXct1Qul3cye5BP8Sa73wnS9EZW7b/Cn8eu4+ak5UpsKoevxFtkIX7ce4UeDQL48smm+QpZdqkXwI43H2HdoQhuJmfQONib9jXL53ucu5OOH8c8wM7zN9l/KRYfN0d61K+Il6sDI9rE8sXG0xy+EkdimrluYeuqvrzds06eBaF7NKhIjwYVSc0w4qjT2NjwUA3/AutOFQYpJQFezlyOTcmzBmJ29l2MZfrjDXkhc2rvbFQS/57JPRKTnazrMpokPRoE0LuR9QqvD9YeJyWXItRme+HJliWj2J4bFSpUYPz48YSHhxMVFcXUqVMBcLBTD88YH0/8r2vIuHgRx+Ag3B5+GO38+eZVhNkdICnxe/55c+3BDz8yF2DOw0GyPZGR+F9Wk7D+Tzy6dMZ//Hi8+vcnfuVK+/01GmLCFuLWsnjTBApCxQ8/xKlaNW5+twxDdDTONWvi+/xzeOazAtOYlETG2bNofXxwDA7GuU4dXJo0IfXwYet7pdXi3qEDDpUq5TqWV58+xK5YYXcK1bNnz9u5vHsCU3o6iRs2kH72HA6VKuHZs4dNjcrSTlEdrA+A7zE7VpuklFniLF2AA8VhmOLeINTPjYdq+LH9XIzVdJNWI2gS5E3tgFzUeEsJN5Mz6Pr5P0Qn2U5FZb+e349E0rVeBH0a578828/dieEPFn4FlRCCB6r58kA160rxTUPKsfSZVoUeL4vcFidU8XNjUIsgfthzuchj28MEPNEiiH0XYzEV1MMCLsYkU83f/AF7vgBTyzXLuzH8wSrsvxSHRpgXD3SoWd5q4UNqhpH/zubtqD1ax5uHq5a+VV0ajYaqVatSoULu6vmpR49xacQIcy3BzHwozawvqTh1CrHLfyBl504AHAIDKf/667g/1Jarr0wk4Y8/8q4AngcyNZWEdb+RvG0bVVatIu3YMdJPnrTtaDSSfuKE7fEGA2mnTiG0Wpxq1rSpjVgcCK0W32eewfeZZwrUX5pM3PjqK2LCFlpUyV2aNqHS9E+p/OUsrkx4idR9+yz93R58kEofT8tzTP/x40jZs4f0M2du6UcZjfi/9BLOtWsX7cLuETIuXuTisOHmFZ86HRiNRM2YQfCCb3Bp1KikzSswwl4R0wIdKEQAUBE4lDk9iBCiJZAgpbTzbip+hBCeQHx8fDyeucnmK0qcm8kZvLBsHzvP3yqT0jTYm6+HNMffo/RMvdhjaNgu/ilApEQjzEKWC4cXn75uusGIg0Zz11dC6o0m/jwWyfLdl9h+NqZYolhZlyCBR2qXZ+OJKDSiYFITWWya2N7iYJ2KNE/r5cbMQY3p3ahSvgKHKRkG6k6yXfgspQnDjUtU0d7kkYoGNBoNNWvWpG/fvoWu/VecSCltrslkMnEi01GpU6eORc1dmkyc69YN/ZWrNnpXugoVqL5po7mMSWoquooVERoN6Zcuc764kqs1GvyeG4MhNpa4H3+yjdRoNLg0bkTo97cEPhP+3EDkBx9gzJSicAgMxPfZ0QgHR3T+/rg90LpEVtfFLFhgK6Kq1eJQsSLV/vgd4eBA2unT6K9cxbFKqJWWVl6Y0tNJXL+e5N270bp74Nm7Fy716t2BKyhbXHhiEGlHj1r/z2g06Hx9qf735hJfYZmQkJA1Re8lpcx11q7IDlZpQDlYZYsT1xK4cCOZYB9X6geWzpV+2ZFSUu2t3wvsBFTwdOLpViH0axJIkE/Rcyg2nbjOjA2nOHEtEWcHDQOaBfFat1rFKmmRG7HJGfSZva3IshC5Ud3fjYuZeloFERC1R+9GFZnSr4FFOX/g1zvYdzHWajyNgLbV/awiepdiUvh532WuJ6RTP9CTvk0CrdT3n/h6B3vCb9r9O/dxOoaPxlyLUQhB1apVGTx4cJHsLyomk4n//vuPPXv2kJSUhL+/P+3ataN+/fp5Hpd6+DDhA5/IdX/I98twbdrUqu3Sc8+TvGVLwY3LRQ8rC5fGjQiYNIkL/R+3GxGr9NkMvDKnw1L2H+Di00/nGTnTVQwgaN7XONeqWXAbbxNpNHLmwbYY4+Ls7g+cORPPrndmxZ/U60k9ehQhBM716pkV9O9xMsLDOdete677gxYswL3tg3fRIlsK6mAV2A0UQqwqaF8p5WMF7au4f6hT0dOmPlxpIS4lg3SDifIeTpYIgZSFi7BEJ6bzxcbTzNx4hv97olGBpgtzsvH4dUYv3WtZ3pemN7Fs10UOX4kjbHgLftp7hX0XY/F2deDxZpVpXdU37wELyYQfDhS7c+XupOVCTEqRHassfjt8jdgUPd9mOk8fP9aAwQt2WSWnm0sApXI1LpVAbxfWHY5gwvKDgDmn+ce9ki83n+Wn5x6wrH5999G6DJi3gwyjCaNJWiJrtbVRFucKzA73uXPnuHHjBn5+frd1LYXh119/5fDhw5bX0dHRrFy5kvT0dJrlURLEXm297Jhy7JdG4y2xzIKg0aBxcQGtFlOCnWeMRoPGwxPnunWp9PE0rk2afKvor0aD33PP4dmjh6X7zUWLzA5bHnlfhmuRXOjfH5+hQ/AZPtxS/8+Umsr/s3fW4VFcbR++z6zEXUkICe4Ud2tpS53SFlrq7u72vXVX6i01ShUKLaUFqkgp7u4JIe6e1TnfH5tsErK72U02BNm7V66yI2fOrMw888jvqVixAlldTeCQIbZ2QF6g9NffKPjkE6fGFRoNhj27W8XAKvv9D3KeeQZrkc3zr4mKot0zTxNy+uleP9axhNP32s31xxKe+Nk8K9fx4eM4IK2gkid/3m7Pw0mJCuTJc3txeq84FEUQ4qel3Oi4iemR1NkPkgfnbGF0l2iiPKw+e+2PPY2kDVQJWzJKmfDGUsoMFqS05bD9uCGDe0/vyr2ne+dp3mJV3U4c94QKFwnknmCV8O++ArZllBIeqGPqx6soqGjcFuVQYSUXf7CSSf3b8fl/aXWK9TX/K6gw8ti8bdx7ejd+2pRBucHCTWM7kVNazabDJQRpVIJyNpH53y8sAcaMGdNAGf1oGlgFBQUNjKv6/PPPP/Tv3x+r1WpPcn/88cftMg0Bffog9Hqkg9YxaLX49+vXYJH58GGHbWacoqqolZWEX3YpJd//4HB98LixgC2hO/i006j891+k2Uzg8BHo4ho2Rzbs3u1eUr3FQtEXX1L608+kzP4B4/79ZD30sC3PDEAIIq6+mrhHHnaZv6UajVRv2YLQ6gjo17dR2Knws8/Je+0113OxWin6/Au00dFEXn45loICDLv3oI2KxK9Hj2b34KveupXM++5r4B20FhWRcfc9pMyZfUKHEf26dkX4+yMNDqp6hSCg//GTg+VJq5zrWnMiPnwcbUqrzVzy0Uq7cjnAocIqbpq1nm9uHMbIztHcdmpnXl28x+H+IX4ayp0YDxarZOG2bK7yoA+iwWxlT45zr0NZtcWeD1XrDXr7r32c168dXWLrVMYrjRZmrz/Mkt15aDUK5/VrxwWnJKDVuE4Wzi6rbla+lUYRqKpslYpDR1z6ySpC/LQOjSuwGaQ5ZQY+Xp7qdP3KA4WsPLAKjSKQUiKlrcnzj7eNxFiaz4cf/sW3y5YBMGpUw3BERESEd0/IBYcOOddZqqqqoqioiMDAQJ555hkAHnroIbuBpQkLI+qmmyh4v7FietT116M94jxEc3LLtFqUoCBCzjyT8j8a61rlvv4GAX364Ne9O1U1kgxBo0ahdWCgetQeRkqsZWXkvvgSFf/+26gasnjmTPTJHYi83HEfvJJ5P5H74ot2o0wTHU3Ciy8QPNZmEForKsh3U6JCGgzkPvscFX//Q+Xq1fa5+HXvTuLbb7mdj1Wfoq++aiwGKyUoCkWzZpH48ssej3m8oAQFEXXzTY17QApB2KRJ6NsfG22r3MH75Rk+fHgJg9nKgi1ZfPFfKuvTiho1uW0pc9YfprDC1KAnX6345nv/7Afg9vFduG5USgM9KH+twodXDuCfB091OrYiBGUG9zxfteg0Cn5a5z9JR2evEYKF2+p6q5UZzEz+4D+eXbCT5fsKWLonj/tnb+GmWeuxWOuehqtMFn7ZksWs1YfYlW0L74T6ed7IWKMIhnWMRNeE8eZNqkxWcss9Exh1hlWVqNL23maUVPPa77tZuNB5K1U/Pz8WLlzI8uXLqarybijV2fFasj76zjuIf+p/6GpuStqEBOKefJKY++5ttK0uLg7//v09m6DVii42lnYvvQiO5mI0cui669kzcBAZd9xJ5v0PsG/sOPLfebfR71l7hEfLnWNXLFtm8/IceW0QwtY/0AGVq1aR/fjjdR4vwFpYyOHb78BY04aoesuWunCmm1T+918DQ8+4fz/p113v2IPYBMb9+x1786xWTPv3ezze8Ub0bbcR9/hjaGJs/W2VkBCib7uVds8928Yz84xmp+ILIS4BpgIdgAZXZinlQIc7+fDhJuvTirhh5npKq832VivDOkby6TWDGyQot4TtmaUowhZ6qo8qYWtGif31U+f35r4zurEhrRi9VmFISiR6rYKU0nnLGSkZ1jHSo/loFMHkAYnMXn/Y/dwvAaZ6LXRmLD/I/rwK+3xqx1myO5/ftmUzqX8iy/fmc9s3G6g0WhHYjIuz+8Tz9mX96RYbxN4890RAFWH7e/ycnmzNKOHxn7a7e6rHJFZVsmBLJpdps51uYzQaSU9P5/Dhw2zYsIEbb7yRkJDW61HYrVs39Ho9piNu0kIIOnToQGhoKJWVzj8vIQQR06YRMW2awwrEI0l4/jnSrrgCtbReTpUQBI4YYZN0qJ/QLgRCpyP0vPNsuVuODBIpkdXVDZepKgUffIAuMYGg0aPRRkUhtFr8UlKoXP6vZ/IQquq07Y85u/HnKK1Wct94s1FzcKQEq5WMO+8i7vHHEYFeqBS1WrHk5Nja3px1lke76pOTMe5zYGRpNEiLlQPnnIu1rIygoUOJuuVm/Lt3b/l8jyGEEERefTURV12FWlmJEhDQam2VWpNmPXYKIe4GvgDygAHAWqAQ6AQs8trsfJyUVJksXP/lOsoNttBd7XVwfVoRzy7Y6bXjRAf7Ob4401i5O9Rfx6k9YhnVJRp9jZdJCMEjZ/dAQoM2NoqAcd1i7MrrnpAcGejQuIoO1jtslWNVJeO7x9hfL9ia7XB/RcDi7TkUVBi56av1dmHN2k0X78jhnb/3MaZbTOOdj6B2GoNTIvj+5hH0SQzjkkFJdIwKbFIw9VjH5Ga/Rykl5eXlLKsJI7YWer2eSy65BI1GgxDCLsEQHBzMBRdc4NFY7uQD+XXpQufFi4l96EFCzzuPyOuvp9Nvv5L0/nsEjRhh26hmDsLPj8R3pqONikIaPffSZD/xJPvHjWffmLEUfvElwRMnNk97y9E+ioJfl4b9Jg1797L/9DMwbt/ueB8pMaWmcvimmyhbuAhtXJz9XO14mlOl0WBKdRyqdkXklVc69WAZd+/GdPAg1oICyn7/nbQpU6l20pvyeMe4ezfFs2ZR+OlnGA867x95rNJcv/7twM1SyjsBE/CqlPIM4B3g2K+/93FM8/uOHMoMjpruws+bM6kyeRZ6c8bUIUlOW9xcOdy9jk/n9G3Hp1cPple7UAQQEajj1nGd+fiqQR4nuJYbzLz1d+PmymALi2mVxmrr5/SNb2DIWa2ODQSJzRj7eVMmZqvayOMmJcxadYi8sqbDIrX7FlWY6BZn06XSaxW+v2UEY7sdveo6b6MRglFdYtz+3KSU7NzpPYPfGV27duWee+7htNNOY9CgQZx33nnceeedREZ65iF1F21EBFE33EDi668R9/BD+HXqhBIQQNKnM0j+7ltiH3yQdi++SNd/lxMyfjwAgUOHNDZG3MRaXEzeK69Q+uOPhE2dYluo0diMGSHQJiQQMMRDfTlVJeqWm+0vpdnM4ZtuxpLnXmPwkm++IeqmG22yCIpSZ1h5agBareg86G1YS+CQIbR7/rkGeXHCUQjWakVaLOS9+abHxziWkapK9v+eInXyReS/9z7506dz8JxzyXv77baemkc0N0TYAait560Gan3ks7D1I7yzhfPycRKTW2Z0KkJptkpKqsxOe/Z5Qre4EF6+uC9P/LQdqyoRNce8cEAi145McXuc03vFcXqvOLdCMM5QVcnd321y6kGpMll57ZJ+LN+bz9rUIiKC9Fw6JImrhic3OOaZveP5cmVaI0kEKeG0HrEcLKhEEcKhgnqZwUKFmxWTAPvzK3lwzhY+vmowAHGh/nxx7VCmfLSSdcdQc2R30WoE04YmsXBBLCEG99Trj5aOYEhICKNHjz4qx3KGEILAAQMIHDCg0TpdbCzRt95CwQcfNg6/uUnpvJ9ApyN00gVIVUVWVhI4dCjhF18MQrBvzNjG4cbauel0yJqG1EpYGLEP3N+g7U3Fvyuw5Oa6PxmNBlN6Op1/X0z++x/UtftxdF5C4NezJ8YjjW2NBm1kJCFNtN9xRvgllxB69tm2fo1CULVqFUXffAOWIzxbqkrV6jVIi6XNBTi9Ren8XyiZPdv2op4nr/CjjwkcNIjgMWPaaGae0dxPIweIAg7V/A0HtgAdoVn9YX34sNM7IdRpDlJkoJ7YI9TfiypNHMyvIC7U32OBz0uHdOC0HnH8viMHg9nKyM7R9EpoqNW1LaOUXTllJIQFMKJzlNMwWHONK4Bv16azZE++y206xwYzZbCL5rLALeM68dvWbPLKDfb3UAjolxjGhQMSWbQ9G4uDN1cAHaIC3Q6R1fLnzlwqjRaC/GyXktJqM+ubYVxpaozbtpI97hQdxKuX9OOmmespNSQxQHUvgb0186+ON6Lvugt9cjJFs77GnJmJ8POzGTWeGFtmM2Xzf6Hdyy8RfuGF9sU5zz3v1LhCoyF4wgRi7rkbtaoKv65dUfQNCzbMOc7z6hwiQJpM6OLjsWRl2ZY5OI/Ia2x5Qrq4OHKef4GSOXPseWr65GTav/uOzfhr5sOXEhREyKm2YhrD1q3OfyAaTbM9iLVIi4XKVasw5+Tg36MH/n36tOia1hJK5sxxbKhrNJTMnXfCG1j/AOcDG4HPgLdqkt4HA24Lkvrw4YhRnaPpmxjGzuyyRp6Y20/tbJcbMFlUnlmwg+/XHbZvN6ZrNG9d2r9RDpUzMkuqWbAli3KDmWEdo+gRX3fDLK02c+usDaw6WGhflhQRwBfXDaVLrHebjs5a5bwcHyA2xI9T2oc3OU50kB+jukQxd2OmfZkApg5Owl+n4ew+7Xjjj71klxoavLcSuPu0rrz1116P5q1K2/tUa2DNWX+4WUbSkYUGR5vnJvXmpUW7Ka62eUHWyy60u/oNIkQlq6wJjCPD4X5FRUUYjcYmq/laE39/f9bWSCD4+/u32TxETRl92KRJAFiKi9l/6mmO9YxcD0TRl1/aDSzVZKLkxx+db6+qRF13rUs5BP+uXT2bg8VK8JgxSClt0guO1Oq1WqQq7bIB7Z55mujbb8ewayfayEh0KSkUvPMupT/9hFpVRUD//sTccw9Bw5vXNzRk4lk2D+GRaDSEnnVWA80v1WikbOEiqtavQxMcTOh55xPQ17nyv2HvXg7fciuWeoUBgUOH0v7999C0wUOEtajIsWFutWIt9L5WX2vRXJP3ZuAFACnlR8C1wC7gKeA2r8zMx0mLogi+un4oZ/WOtyd2hwXoeOKcntwwuu4i+uLCXXy7Nr2BobDyQCE3fLnOrdDN92vTGfPKP7y6eDcfLzvI1Z+vZdqM1fYcr8d/2sba1KIG+2SVGrj2i7UeqZLnlRvYcriE4krnicC5Zc5vQgJ49ZJ+biWQf7M2vYFxBTYj6Imft/PWn3t5bN42hqZE0qeely4yUM/zF/bh4kHtsbpoe+IMf52tonJjejE/eLkx9NEgPEDHjxttCvm1CEVDTEIHBiaFM8I/y+m+VquVNWvWHLVQoSM0Gg1DhgxhyJAhaNystKrauIlD19/A7v4D2DtmLHlvv43qzEPUBJbiYnJfepm9Y8awd/gIsh5/AlNGBtqICHvYziOkxJRe9z1Sy8tdSiaEnHN2kw2AAwYPxr9v37qmyvXRahsuF4LAYcMIHj8eIQRC70S+REqUIwxaXVwsIePH49+rF4evv57ib79FrawEKanesoX066+n/J8lWPLzPf7O+HfvRvTtt9te1M9Ri4kh9sEH7NtZS0pIu2QK2Y89RunP8yn6+hvSpkyh4KOPHJ+GxcLhm29plJ9WtWEDOU8/7dEcvUXAoEGOPytFIWCg8+4FxxrN8mDVNHdW672eDcz21qR8+IgI0vP+FQMprTJTXGUiITzAXr0HNr2nb9YcavSQY1UlWzJK2ZhezKBk50nAqQWVPPbTNvv+tTlJ69KKePuvfdwythOLtjWuyLOqkozialbsL2BcExV3ZQYzj87dyqLtOUgJWkUwZXB7njq/N/66hhePPolhrDxQ4DA0et2oFMZ3d08jaNaqNKfrpv+9D40AhMCqSm4c3ZFpwzqQFBFAUaWZggojnWNCyC71TP/nlcW7qTJaWbDVwzDMMUJJtZmfNjU0osJFNef57UKD6rB6sz5LlixBp9MxorbK7hhCSok502Zw6xITEUJQuXYt6dddX9MLSsVqMFD4yQyqN26iw5dfuFQ/PxJrRSWHpl2O6fBhe65M6fz5VPzzDx3nzUUJCGigN+UWQqBPScGwdy9F33yDJTcPERiIdKI7FupGjpMQgqRPPib7iSeoWLIUpET4+RF59VWEXjCJws8+o2rlSpSgIMImTybymqtBUSj+/nuEojj2ylqthJ7juGde+T//YNi+o+HCmoeXjBojSZecTNwjDxNy2mlNzr+WmLvvInD4MErnz0ctKyNg4CDCL74ITb1evPnvvFtXcVcvfyn/7ekEjx+Pf48eDcasWLECS04OjbBaKVu0mLgnn2wkStvaRN1wPWW//mozQmsf+jQalKAgIi6fdlTn0hKaZWAJIVKBr4FvpJS7vTslHz7qCAvUERbYWPcqo6gas4u40r7cCpcG1k+bMlEQWI+4dKrS5tmaPCDRpRZVdknTT/t3fLORlfsL7UacRZX8sM6mcfXKxQ3blNx+amf+O1Bg16UCW1VbsL+W20+tKzc3Wqws31tASZWJgckRdI5pGKrMKHY9L6vE7nr/dEUqO7PLyC41kFpg01KKCvJcbHTO+oxmVdcfy/RTDrHmv38RAoYNG9agVY4jli5dyuDBg9G1QTNek8nE9OnTAbjnnnvsSu5V69aR/fQzmGrEM/WdOhH/1FPkv/223biyo6pUrV1L5X//NchvUY1Gir76itL5v6BWVRE8ZgxRN91oD4uVzv0R06FDDcM5VivW8nIKP/2MsEmTKP7uO5cNoR1pUukS2pF6wSS3zr9o5leEnHaac09TDdqICJI++ABzXh7WggJ0HZLRBNvU4xNffqnR9vnvvudQBb92vtF33IF/z54Oj1W1fr3NM2ZxXjRiTk8n44476fDZpwSNHOly7vUJGjqUoKFDna4vnT/fscSDRkPZb781MrAcGle1qCrWwsKjbmD5depE8tezyH35Fao3bAAhCBo+nLjHHkUXF3dU59ISmpuD9S4wDXhCCLEJW/XgD1LK4/MR1sdxR3yYv9NKQ6DJZPfSKpOt8trB/uUGC4u2ZeFkNQDd4l3nJezOKXPY10+VtjylhyZ2b5AnNrJzNB9eMYjnft1JZo3x1q99KC9ffIp9uzUHC7n16w0UV9WFXS44JYHXp5xi9+7ZFNXd7/238kBhg9dFLsKYzmhhD+djknhZwqy//gRgiBsSASaTiby8PBITPW/w3VLMZjMPP/wwAJOzsggKCyNgwABy/u9/yHo3eFNqKuk33OD8pq/VUrl6td3AkhYLh2+6map16+wGUMmPP1K2cCEd58xGn5JCxcqVTnNlKpYvp+PcH6lavx7jnj22kE+N6roICCB8yiUEjRxF7rPPYq5JJBcB/oRPmULxV7PcPv/qTZso+vZb1KoqTKlp6JOSCL/kYnQJCQ6318XG2ptEO8NaUkLhJ584XikE7T/+iJCatjqO0ASHNJ3cX9P6puCDDz0ysJpCdZbzJgRqZWMvoF835yKlwt/f6fvY2gT07UvKN1/bQqxCoAR6VsB0LNDcEOGbwJtCiG7AFdjyrl4TQiwBvpZSfuXFOfrw0YjIID3nn5LAgi1ZDW7wGkXQISKAEZ2iXO4/MDmCmU4SyyXwzj8HHK7TKIL+SeEMSAp3Of6+XOdhEVXaQpRHJuKf1Sceo9nKa3/sIaO4mp3Z5cxancYjZ/VAVeH6L9dRbW5oPC3YmkWHyEAenGi7SLpqteMOJ6Ct1CzUZhRDH+1Ed4vFwt69e8nPqEvAL138O2ZFsTVfdqRWrqq2ajNHHiVVRfGv010q/+tve/9AO1YramUl+e9/QOJrr9pykJyMpwQEoAkLI2XObMoXL6Zq3TqUoGBCzz+vQbPi4LF/Yti+HbWqmoC+fch+9jmP34u8V161/aOm6q3w009J+vijOnFUD6nevsN5/piqNlldF3r+eRR86CAh3cFY1dub7oBQvmQJRZ99jvHAAXQdkoi65hpCzznH4bba6GjHkhQWC4EOEuwDBvQnoP8pVG/b3tDzJQSR11zT5oaNRz0qjzFadDWWUu6VUj4lpewOjAFisCm8+/DR6rwwuW+jPKhO0UF8ef1QlCYSZ87qE0/X2GCPlcdP6x7Dp1cPbrrlSLjrVhvtwhpXe83fnMk9P2wmsybMZ7SofLsmneu+WMfPmzOoMlkbeYukhK9WpdkFU/u1D28yZ+h45WieV5rV/ZCIEIL4+HiiHTQw9iZSStLS0tiyZQubNm3izTffZM6cOfxRT03eAnU3SUcelNrWMo7yrFSV0vnz7S1mKv5d7jjRWFUp/9Pm3Qs951zHxpoQhE2yKc0rej1hF1xAu+eeI+7RRxoYVwDSaEQbH0/gkMEoQUFui4E2HETWGZCqijSbyXzwoeYl2YM9dOgMJch1FbFfx47EPfmE7b2uTUh3dqwmBGOLZ88m47bbqdq4EWtxMYZt28m8/wEKPm7sYTPs2eNU70sEBjrM9xJC0P7DD21yEDXzFP7+RN14IzF33+Vybj5c02JVMiHEUOBy4FJsKu4u6ml9+PAewX5avrhuKPtyy9mdU067MH8GJUe41xJEq+H7m4fz0qJdzN+c5TKfqz4PndWDCDfylAZ2CKd7fAj78yoaVBxqFMHYrtG0j2j4VCil5PXf99j+XW+5KmH9oWLiw/zRKMKhhlWZwUK12UKQn46bxnTk710eCCoeBXSKwOyFOKKfVsFgUY9KvldAB+cl7Y22DQjgoosuasXZ2OQgvv32WwoLC11ut+2UfozavMX1YI7yc2owZ2Vx8PwLbG1iXAiGSoOBssWLCTnzTELPP5+yBQvqjDGrlYCBA4m48krX06ioIPell205QxYLmqgoou+4ncDhw6latcr1OTSFlFgLC6nasLFZsgj+/fqhS0y0GZv1DUhFQRsXR0B/11WLAJFXXEHQyJGULfgVU0aG7T1y0JQ64rLLnI6hGgzkvfpazQu1wf8L3nuPiEunogkPt29fsXSZU4+irKpCra52KLugjYig/XvvYikowFJYiL59++Pac3Ss0NxehN2EEM8IIfYB/wG9gEeBOCnlpd6coA8fTdE1LoTzT0lgcEqkU+Oq3GCm2tTwxhIV7MfrU/qz89mziHCQSO8IRwrojhBC8OnVg+kU3fAi1T8pnDem9m+0fXGVmcNOEtQ1iq3qz5FxVcsbNcbZsE5RvH1Z/wbn09YerRlXD+bjKwcSH+rXIhXiavPRMa4AVhwsa3ojoH///tx9993ExDTdw7G5SCn59ttvKSoqanLbQykpWFsiOKmqqBUVmA4cwHTwoMvk9Ly33gYhSHj1FZI+/oiwCycRdt65JLzxOslfftFIwqA+UkoO33wLpT/9ZM8JsxYWkvvscyg6HcJLYamKZUuxFHsufCsUhcS33rSFx4SwJawLgRIQQPu333K70tKvY0di7r6LxFdfod0LL9jHqfUghpx5JlHXXet0f8OOHU6rMKXZbEumrz9vTRPzEq7Xa6Oj8e/e3WdceYnmerB2A+uB94HvpZQuyhB8+Gg71qUV8fyvO9mSUYoQcGr3WP53Xi9S6hk+Oo1Cj/hQ1qQWOk3YFtgS67vGui+6lxQZyB/3jWVdWjGHi6roGhdM38Qwh0ZgoF6D1omHSlUlQ1Ii2ZJRQlaJ4wTWz1ceIrPUQEJ4AJP6J7Lm8dPZnlWKVhF8teoQP23KdKndVZvwLwRMGdSeORsyEDVyDrXJ/lePSGZISiT/N387JVVNh14EMKBDOOO62/r7Bfppuemr9RjMjW/argoK2gJ3HJp+fn4oisKff/5J9+7d6dKlS6soXx86dKhJz1UtqkaDSa8nwFNxT4eDudZEMx86hFpejiY0lOBx4wgeN87toavXr6d640aH6wo//ZSOc38k/aabsdTLL/Pv0wdTTg5qYaHb6vBFX3xJ0ayvCZ8yhfCLJnukTh7Qrx+d//yD0vnzMR06hL5DMqEXnI9aWoYxNRV9SopHn3f4RZMJHjuGsj/+QFZVETh8BAF9ervcR+hd5/Ud2Z8w5PTTyXv9jcYbajQEDh3aZOjTh3dproHVQ0rpmeSzDx9Hme2ZpVw+Y7XdsJASlu3N5+IPV/Ln/eOIrBfqu2VcpwaK7fXRKAIpJc9N6uNxzpYQgqEdIxna0XWehb9OwwWnJDB/S1YjQ0inVbhwQCKjukQx8e1/nY7xx85cFCH44r807ji1Mw9NtJVj33mqjsXbc6g2W+1jCyDQT8PT5/emf1I4P27IIK/cyNhuMZzfrx2XDE7ig6X72ZZRSlyoP1cNT+bSIUm280mJZOQr/zQpthrsp+Xli/qRUVxNakEld3yz0WErHlfVoMcEGseXSaPRyObNmwHYsGED3bt3Z+rUqSgtbFlyJKWlpW5v62cw4OdClNOraLWIZirHV2/b7jSUZS0qQvHzo+tff2LKycGUmoo+KQl9+/aYc/Mo+OADSn/+2aX4aAMsFkq++46S775D16EDiW+84VLVvD7aiAiirr0WgIoV/3HossswH7YZffqUFOKfeYagYc4lExqNFx1N5OWXu729f+9e6BISMOfkNHyvhEAJCSFwWMPwpz4lhejbb6fggw9sIVurFRQFJTCQ+Mcfc/u4PryDaIkCsRBiMNAT28Pnbinl+iZ28SpCiFCgtLS0lNB6Qms+fIBNh2rxjpxGhoAi4IEzu3NHPX0pgLkbMnj+t512GYQAnUJkkB+DkiO4cUxH+rnRqqYlFFeauPzT1ezKLker2BoyaxTBe5cPZGLveCqNFno/9bvb413YP4FXL7FJOBzIr2D6X/v4Z3ceeq3CBackcM+Erui0Crd9vaGBpERCuD+fXzuEHvGhSClZfbCItMJKUqKCGN7JFoadtSqN/5u/w6XnSRE2I6vM4H4D6WMFqVoxZtiEInt0aMepAa5bGdUyYcIErzdlzs7O5hNnkgGAqqocOmSb3wVFRfTcu6/pQTUaW7VZfn6Tnipn+4dNuoCEF19sclNpMmEpKkITEYFS43EpnT+frEcedTp2tzWrUfz8qPj3X8zZ2fh17UrgkCF2j5E5N48DZ51lM7I8mX+NsdHlrz8b5C41hWHXLlKnTLUZLLX3TCEQOh0df/4Jv06d3J+Dh1StW0f6jTfZEvZrpB0QgvbvvEPIaac63KdixX+UzJ2LtaiQgFP6E3HF5ceVftSxTllZGWFhYQBhUkqn+QTNMrCEEO2B74BRQEnN4nBgJTBNSulxvwwhxGPAi8B0KeW9bu7jM7B8OGXYC3+RW+74Kff0nnF8es3gRstNFpXdOWX46zR0jQ0+6s1Orarkn915bD5cTFSQHxf0T7DLORgtVqZ9sobN6cW4e0uZOjiJVy/p53T9g3O28NPGjAYhMQHoNIKByRGk5lc2eA+7xwXz5fVDaRcWwMZDxVz04cpmnOXxgR4L3bX5JCvFxGjca/4McO2115KcnOzVucycOZNDhw45ba+iM5nouWMn3XfvdivPTZuYQOyDD1E2/2cqV65CmtzUP6sRz/Tr2ZPkLz53aaRIq5WCDz6k6MsvUSsrEf7+RFw6lZgHHgCzmX3jxqNWVTU0kGr66kXfeostRJiTY0+29+/dm6RPPkYbZZNgqd68mcyHH8Zc01ZHCQ62VSE6qaKzIwRxjz5C5DXXuHfOQOajj1K24NfGBQIaDeFTp9DuqafcHqs5mDMzKZ49B9PBg+jatyd86hSXvRd9tC6tbWD9AYQC10gp99Qs6w58DlRKKc/0cLwh2FrtlAFLfAaWD29w9vTl7M4ub+Rh0SiCiwcm8uolTVcCHQuYLCpv/LGHWasPUVWTqO+iwKsBAji7bzsiAnWc268dIzpF2Y3GPTllnD39X4/CcxpF0DcxjJ/vGIXZotLtyUXHVO6Ut9Bi5UK/HQQLm+HhiZ0dFhbGPffc41XjvLq6mgULFrBr165G61L272fQho1oPPTk2A0bIQi94HxEUBCl337nfB8h0ERFEf/UU4ScOh5Ro25vraig6KuvKF/8O1KqhJ5xBpFXX03BJ59Q9PkRqj2KQujZZ5P4xutUrllLxu2324Qka8JZ/r170f7jjzk09VLMubkNDRqNhqDRo+nwcV1PPSklxt27UasN+PfqSc6zzzlXMq9FqyX8kotp52afPWmxcOCsszFnOG747d+vHx1n/+DWWD5ODNw1sJqbgzUGGFlrXAFIKfcIIe7CVlXoNkKIYOAb4CbgyWbOx4ePRlw+tAP/m7+j0XKrKpkyOKkNZtQ8Hp27lZ82ZzbSjIwK0iGlrQLRmZEjgcXbs1GE4Js16Vw9IplnLujNh8sO8NriPR4bR1ZVsvlwCf/szuXZBTtPSOMKQGM1sWPdChQBgwYNcruJMthyprKysryq6h4QEMDUqVMpKyvjnXfewVpjQMRlZzNg7Tq+LylBAFPCw9G5Y9jVN8akpGz+L01bkVJiLShAaDV240qtrOTQ5Zdj3H+gTj7gwEFKf1lgyxtycNyy334j5u67CBo2lC7LllH+++9YCgrw792boJEjqFyxwq7s3gCrlcrlyzHn5trDXUKIBu1qIq+5mtJffnH9BGK1ou/gnoexcvVqsh5+xLk2l0aDLvHoK51LiwVrcTFKWBhKEy2CfLQdzTWw0gFHde1aINPDsd4HfpNS/iWE8BlYPrzG5cOS2ZBews+bMu2J6lLCQ2d1Z0iK66TzIympMrEmtQg/rcLwTlH46zSYrSp7csrRKILucSFNips2hVWVfPFfKrNWHyKvzEjvhFAuG5LET5syHRoyZQYLX143hGu/WOdSx0uVdfISX606xD+7c8koblmV2Q1fHtV0y6NKR00Rg5V9vL5oIWCTYvDEwAJb+5rWIDQ0lAEDBrBhwwaklPTcvgOjlLyQZwuLXRgW1tDAqv23O+5OdyvzZn1N0PDhFH76GYWff46sPkJeRFVtDaZdjGfYuRN9sq0XYPjFdRpi5UuXkvXQwy7naMkvcJpP5N+9O0kffUTO00879jgpCsLfn7ALm+5zaMrI5PDNt7gWK7Vaibjs6DUfllJS9PkXFM6YgbWkBOFvay0U+8D9LmUxfLQNzTWwHgbeFULcAWyQUsqahPfpwIPuDiKEuAwYCDTd7Mu2vR9Qvy7V/Zp5HycdGkXw9qX9uX5UCsv25KPTKpzdJ57kKM9KlT9ceoA3/9xjN2JCA7RcNqQDczdkUFjTu69DZCBPnNuTw0VVHC6qonNsMJP6JxIW4H7z38fmbbU1Tq55vf5QMesPOdfwMVslOo2GubeN5PIZa6gwupdM3lLjClomqRARqGvQT/FYQiBJUYrQuQoxNYFeryehFfu3TZgwgaysLLKysogsKsJZ9pSuY0cCBw2iev16TOnpzUtmd0DVypXsG38qalmZcyOqCWNNsYVXGmDYs4eMO+50Gd4Tej36FNfep+DRo+j8x+8YDx6kaOZXlM6bZx9TGxtL4ptvom1CPR2gZM4cWy9HZ+ei1RL38MMeVRG2lIIPP6TgnXftr6XBQPE332DOyiLp/feO2jx8uEdzDawvgUBgDWCpyTXQYuvU8LkQ4vPaDaWUDr/JQogkbAbZmVJKd6/4jwGtm03o44SjX/twtysALVaVMoOFUH8tWo3Cb1uzeWXx7gbblFVb+GT5wQbL0ouquGXWBltnjBr9qDf/3Mt3Nw2nZ7um8wP351Uwe73jHA9XRAbp6RIbzMK7x3DFp6s5XFx9zMseHKvGFdgMx6Xmzpwtyps9xoQJE9C3YtjG39+fG264gX27d2P93nnuj1/XLiQ8/xx506dT+MkMr85BdUM6QgQEOK3yy3vrbcyHDhE4bDh+nWzJ2kWzvm5iQEHk1VehCXbcpsacmUnxd99RvWMHurh4wqdOIeG5Z4m59x7Kfv0VtbKSkAkT8O/uvLlxLdaKCiqWLHFqlIqAALr8/Zdbhpq3UKuqKJzxqYMVKhV//41x3z78unY9avPx0TTNNbDu9cKxBwGxwIZ6yaAaYKwQ4k7AT0p55KPMS8Cb9V6HAJ7flXz4OAKrKvlgyX4+XZFKabWZED8t143uyL/78j0yWKQES80Tb3m1mbu+28Sf941tMuF51YECj8Q2a5PNu8TabjYdogJZ8uB4lu7JJ62wkr925bIurbhJrar6CGyCp9Xmup6Hx5oAaOtjO+M9Fs/7CiYmJjJq1Ch61ssJai0URaFTZCT7XXiK9DU5YGGTJ9sMLHcrI7yEdCF2aty2jZxt2wAInzKF+Kefwrh3r3PvlRBE3XwzMXffhZSS0vnzKfz0M8zp6ejatydk4pkUfTnTVg1ptYJGQ+nPPxN+6aWU//031gKbDEnBO+8SOHw47ae/jSYsDNVopOKffzBn5+DXtStBo0aiVlaSNu1yTPv3O56LouDXretRNa4ATGlpjcOx9ajeus1nYB1jNMvAklLO9MKx/wb6HrHsC2wq8a84MK6QUhoBe8340S6hP9nYllHK3I0ZlFSZGJgcwUUD2xPs1+L2lcckLy7cxWcrUu2vy40W3v17H3qt0mxvkFXaPFO7c8qb9GIF6LVuGTK198iEMH/enTagwTqtRuH0XrbclFN7xHLBeyswmFW3jCxF2Iy2L64bwor9hSzalo0QcHafdnSOCeLu7ze7MbsTA4mgTLpW0D6Svn37tno/wiPRhIUh9HpwYsgYdu9hz6DBqNXVKKGhqCUlR3V+7hpzJXPm4Ne1C/rkZAw7djQ2shSFoLFjib3vXgAKZswg/4037T8GU2oqhR9+1NCArBmj5IfGHr6qtWvJevRRYu6+m/Qbb8JaWGivqvTr2pWgsWMwHTjgfMKqStR117l1bt6kqabQmqija/D5aBq3JYeFEB4lrjS1vZSyXEq5vf4fUAkU1vzbRxvy6b8HOf+9FcxafYhftmTx1PwdTHxrOdmlzp+gjgVUVbJ0Tx6vLt7NB0v3k1HctH5RUaWJmSvTGi2XgNGitriXX1l10yGxM3rG4adt+ufYITKQUZ2juOPULsSEODcCOscEM/+OUZzTJ96tcbvHh/D1DcOoMFpYl1pEabWZiEA9fRPD+Ge3kwqqExSBJEzUaX81ZSfExsZy1llntfKsGlNpsVA6aCDyyAdNRQFFoWrNGpsEgqo2NK6EQJeURIdvvsbPjXDZ0aDom2+JuHya45CcqhJ1zdWALXRX8N77tuW1H8yR/28KVaViyVLSb74Fa22fwprjGg8coOiLL52PJQSxDz9MaBt83rr4eAJHjqhrql2LoqCJjiZ41KijPicfrvGkp8N+IcTjQgin2ZvCxhlCiEXA3S2fno+2IK2gkhd+s+ntWFWJKm3GRk6Zwb68LWjKE1NptHDpJ6u49ot1fLL8IK//voexry7h+7XpLvfbmVXmspHykas8sbf8dQq9Exsn9Nanwmjh3X/2oXHDI3u4qIo1qUU8Om8bZ09fTv4RQqplBjPbM0vJLzfSJTaEdy8fyLanznRqZAlg4T2jWXTPWPbmVXD9l+tZk1pIXrmRtalF3PjV+gYq7yc+tg+7uzbfvsTRxxIUFER4eDhRUVEkJSVRWVl5tCYIQGVlJTNmzOCvpCRyj6ioE4GBNoPBWVK7lJgzMjBs3oLUNlEdKUSjfnetgSU/n8ABA2j34ouIgIC6w/v5Ef/0UwSNGAGAYft291vkNIHVkYq9q/cNCBg0iKjrj773qpaEl15C36GD7UWNoaWEhJD0wfsInfsFNT6ODp7Ee8YDzwNPCSE2Y2v2nAUYgAigFzACMGPLlXLe28EJUsrxnu7jw/v8VhMeOvIhzqpKFm3PwWxV0TXVtd1LSCn5Yd1hPlh6gPSiKmKC/bh+dEduHtupUV/At//ay4aaqrv6BtNjP21jeKeoBg2e61O/J6EjbhvfmW9WH7K3fEkID2Bst2jmbMjAUlNZqBE0UkOXwF2ndXUZVrVYVa6YsZptmaUOQ5GKAIHAWvNh1JdcSC+q5qWFu3jz0v6YrSovL9zFrNXpmKwqQsDEXvG8cnE/wgJ1vDNtALd+vaGuy0fN/B44sxu92oVRbjDz3IId9mNAXe5VbaXkyYAeK2P0qURh5vKannEajQYpGxpalZWVCGGT/iguLmbz5s1cfvnldGrFlin1WbNmDRUVFUiNhjXjx3FTRDjB5eWs69SZIVISuGYNWFxUlUpJ+d9/oxaXNHmsxOlvkz/9HYwORE69gqLg39PWNzN88oWEnnkGlWvWglQJHDasQVK7EhjonWM2Jx9NCELPOds7x28murg4Oi34hYrlyzHu3Ys2Pp7QM8/03vviw6t4rORe0yZnCjAWSAECgAJgE/A7sFBK6Z164Kbn4lNybwVe/30PHy474NRjtOvZswjQe6YL1Fw+WnaAlxftbpRsfcWwDrwwuWEKX7+nf3fY906jCO48tQv3ndHN4TGklJw9/V/25VU0OGeNIugQGcg/D4zDaFHZnlmKXqvQJyEMRREUVZpYeaAAraIwsnMUs9cf5rMVqWSXGugQGcht4ztzWU2DZGcs3p7NrV9vbLRcAHGh/qhSkuek3Q/YWtps+d+ZvPb7br5cdajBPUMjBINSIvjh5uEIIdhyuIRP/z3I9qwyEsMDuHpEMmf2jgfgqk9X8+9+x82uTybO1u0kXts4rHykgXUkQgjCw8O56667jkpu6CeffEJ2drbDdSP376f9xk2u1cwBbWIiQSOGU/rjXKfbxD70IOV//U31li1ek3lwhD4lhZi77yL0nHNcbidVlQNnnIk5O7tR82OkrGtw3IQBFTR2LJXLl3s0R/9+/Uj+aqZPb8pH6ym5SykzgLdq/nycgIzqEs17SxpX0CjCJnlwtIyrKpOFd/62Na498lL57dp0bj+1C4nhtnCClJJyJzpQAptQqDOEELx/xUCumLGGnDIDWkVgUSURgTo+unKQTS1ap2HwEeKkkUF6zutXFzG/cUwnbhzTCSml2zfZ1QeL7MerT21Itik/odkq6eWkAbRVStamFrE1o5RTksI5JSmcdy8f2Gi7bRmlPuOqhpXmjlyo2dEo765pkXObJys/P5/Y2FivzcdsNlNUVERgYCAhIXWyfzoX4aDivn1pv65pIVhLXh5Bo0ZROneeQ2MkcNgwtLGxVG/a1LzJe4ApPZ3M+x/AnJPrMgQnFIXEN17n0PU32KoUawwpodcT99ijVK5ajWH7drRxcVjy8mxio0ecW9Do0bR/+y1Sp07FtN9FMns9tO3akTzzS59x5cMjTsySMB8tYninSE7tEcPSPfn2a5NGCBDw6Nk9jto8dueU23vvHYmUsOFQsd3AEkIwICmczYdLGoXaLKpkUBPK7Z1jgln60Hh+35FDakElyVGBnN2nHf66OmPSYLayaHs2qQVVdIxuvL4WTzwYwX6uqwe94TPYk1vOKUnhTtcv35eP4qVjHe+U4k+BGkikLGfbtq0A9O3bD41G41Y/wub0dnU2zooVK1ixYgWmmkbMnTt3ZtKkSYSEhNC3b1/S0225hRHZ2WQsXUpwRSUT4uIInjyZmAfub1Bp5xCzmaz77ne4KnD4cJLef4/sp5+p8wq1JjXeqPx33iF86hSnWlcAAf370+XPPyiZNw9Tahr6Dh0Iu2gyuthYIi691L6dcf9+8t54k6r165EmE349exJ9x+2EjBkDgD6pg9sGVsydd6DUyw3z4cMdPE6kEUK0F0K8IIRYIoTYJYTYWfPvF2rEQ30c5wgh+OjKQTx4ZneSIgII8dfaco5uHcHwTlFHbR6h/q6TNkP9Gz4f3H9GdyQNk9A1iqBbXDBn1YTCXOGv0zCpfyL3nt6NyQPaNzCe9uWWM+bVJdz3wxY+WLKf+37YwuhX/mFvbvMFKQEm9U/wSKuqOSSEub4x6DTCs8z9ExrBH6ZurDfGMX/+fObPn4/VanXLuAoLC/Oa92r16tX8888/duMK4ODBg8ycORNVVRkwYAAdOnQgsqCAocv/5bU9e3gqMwO/8nL0X3+Nce8+Oi34haCxYz0/uBCgCJSgIFvi9FHWzqrevKXJ7bRRUURddx2BQ4dgzs2h7LeF9t6H0mol+5lnOHj+BVQsWYJaXo4SEkzcQw/ajSuwyVw0qsirT82HHj5tGmGTJ7fsxHyclHhkYAkhRgO7gMnAFuAr4Ouaf18I7BBC+GpFTwD8tBruOLUL/z5yGtuensgX1w1lYIeIozqHzjFB9GoX2iiZXREQFaRnVJeGYpCju0bzxbVD6Jlgy8fTaQQXD0zkh5tHoHdDqsAZUkpu+2YjRTXJ3rXhvOJKc03iePNvQF3jQnjiHJswpUYRaGvO1R1phabQKILkyEBGdnZtFE/sHX8076HHPGa07LDUGeTu6ZMJzjnnHK/kX6mqyooVKxotl1JSWFjIxo0b+eyzz0hPT6coOprfzj+vbh41/y9bsABpsdB++tvoOnRwbUg0PhBVK1dx+I470UZFup975aXcM8XfedWi8cABKletouSnn9gzaDDZjz5GyXffk/fKK+w/9TSKZn1N4WefU/Ld9w0MQ2tRMek33oSlVpYBCJt0gWvPnKKQ9MnHtHvqfwjl6BT1+Dix8DRE+BbwqZTyPkcrhRBvAW/jZm9BHz5cIYRg+mX9ueyT1RRWmtAqtmo6f52GD68c5LCScXz3WMZ3j8VgtqJVBFovVDtuzyxjf15Fo+VWKTmYX2nPcWouN43txNhuMfy0KZNyg5mhHSPJLzfywm+7WqSi3j48gC+uG9JkE+r4MH/untCV6TX5bj4kYaKawzWvmjIbhBDExcWRn59PUlISAS0MJVVVVVFV5Vi/TQjBn3/+2aCZtKp1fBk/fMut6FNSiLjicqo2baZi8WKP5lHx999U/A1ota4rEmvQd+qE0Okw7t7d5LYOEQJtdDQB/fs3WmXOyiLjvvsxbHHh3ZKS3BdecNjnEFVFGgyULVhA5NU2Ta3A4cOJuvEGCj/9zOmQlf+tJLg5XkAfPvDcwOoDXOli/cfArc2fjg8fDekaF8Kyh0/ll81Z7Msrp31EIJMHJDYpreAoN6q5FLlIkAfsnq2W0D0+pEF+m9mqsvpgIX/tykOrCCQNdcBctbDRKoJz+sbzxpT+6Op5wqyqZOWBArJKqukWF0KnmCBeWribeRszMVm9m4F1/LbYkQzTpdNFk0mt2rE7Ce45OTnk5OSwZs0arr/+esLDw5s9A39/f7RaLRYHRo2UskHYsPHs67Dk5WHJz6dq7VpCzz2X6HvuoeCddzwP+blhXAGgtEAzS1EQWi0Jr72KOMJglFYr6dffgOnwYSc7N8Rpn0SNBnNmpv2lEIKIq66iZO68OsHR+litGF0puvvw0QSeGljZwEhgj5P1I2q28eHDawT7abl8WIc2O37vhFCXBkPvhKYlQv7bX8C3a9LJLq3mlKRwrh2ZQnKU82YHOo3CJ1cN5r8DBSzZnY9OIzirTzxaRaGg0kiPuBBeXLiLBVsb/9wsqmTBlmziwwJ4vCb8eDC/gmu/WEd6UZ1nJEivodpkbZXk9uPTuIIumkJ6avLJsTRPV6iyspI///yTKVOmNHsOWq2WgQMHsm7dugbhZyEEiqIgpUR1ErZrpOpes3/Zb7+RMP1tRGAgspVEUYVGi2GPs1uDc3QdUwg96ywipkxBl9BYx7ryv/8wpaW5P48Af2S1g/ZBFgv6Tp0BqN6xg5ynnsaw3UXTEI0GfQdfWrGP5uOpgfU68JEQYhDwJ5CL7VoaD5wB3Ih3GkH78HHMoGsi/yKtsIrYUOfl27VaXhpFYFUlWzJK+W5tOt/dNJwBR+S1SSkprjLjr1MI1GsZ0zWGMV1jHI77zrQBjO8ewwNztjZaJ4Ev/0vjztO6EKzXcv2X68gsadjmqNJJhebJTHulhJ+NvSgy1n3mK4wpjNVloxVNm42qqrJ7925UVUVpQd7O6aefTklJCXv37rUvCwgIYODAgQ7zs2oRLgS7cp951hbuayVCzz6L/HfebXrDWmkFf38ir7qKmHvubuS1qo/RWdNlJwSNGk3FX381XKjRoAkLI/TcczFnZ5N+9TWoLhonAyAl4Zde5tGxffioj0e/NinlB0KIQuA+4BagNg5jBTYAV0spZ3t3ij58tC0ZJVUuPTKpBRUM7ehYBiKn1MCri205KbUhPqsqkVLy5M/b+e3uuqqmP3bk8NKi3aQWVKIIOLN3PM9c0Js4J8abLaHaefzKZFVJza+k0mghrbDpnow+YJ05iSr01OspT6oaQYBZywi965ZLtaiq2mK5Bp1Ox7Rp08jNzSUzM5OgoCC6dOmCxWJh/fr1GI1Gh8dwFc20FhW1bE6dO2N2FjLTaIi88koqli5rWjdLSpTgIGLuuRe/rl0x7NyJf58+ThPJLUUOwndOJ6kj9onHMaWlYapnmAm9nsQ330QTHEThJ5/YjCsXyftKcDDtnn8O/+6OxYl9HJtIi8XWMFwI/Hv1cmm4Hw2aIzT6A/CDEEIH1JZxFUgpm+5o68PHcUhieIDd++SIDpHOQ31/7851mPKiStiRVUZOqYH4MH+W7snjllkbGqz/fUcOK/YV8NjZPbhoYPsGAq9SSrJKDQQ0kWsWE+LnMEHfh2MqseUQCa2O6EmP1vxbz15rNINlBjrRdEA1PDzca3pYcXFxxNXrNajRaLjyyiuZPXs2ZWVl9mUPP/wwfePj0X/0sVeO6whZ6fx7FDxuHEpQEDEPPED6la7SdG2oFZXkvvCC/bUuMZGE118jcMAA+zJLcTHlf/xJpQuP3ZEIReHQZdOw5uY2nLvJRO5LL9Hx559sN2AnxpUSEUHiyy8ROGyYT1T0OKP8r7/IfupprIU20WRNdDTtnn2GkNNOa7M5Ndu8qzGofPlWPk54wgP1XDQgkbkbMxqImAoBnaODGd7JuYhpbaNs5+tta9/+q7FivZS2RtBP/LydD5YeYPatI0gMD+DnTRm8vHg3OaU2L4siGjekVgSM6RpDQngA3eND8OEuNiU1oWgI6jHavlQFqqSOMNF0o+GSkhK+++47rrzySq+2zcnLyyMnJ4fCwkICAgKorKwkKCiIoUOHMnLkSEz793PwY49bwLqNJSfXqXBp1ebNlC5YQOjEieDvDwYHOVAuMGdnk379DXT543e0MTGULVxI5sOPuJ9gX4M0GhsZV4AtYX3PHoq/+aZO/+pIiQZFwa9jCsHjxnl0TB9tT/W27WTcfU9DaY7CQjLuvIuOP87Bv1evNpmXV/1nQojOwAwpZduZjD58tAJPntuLv3fnUlRZ56iV0tbOp7jK7LCqcXtmKQfynAuRJkUE0C7M9pS8NaPEpSGWU2bgsblb0SiCJXvyG6xzplP69Pm9AeiTGMbIzlGsOVhkbxp9tEiODCC7zIjJcrzoxDs2iDSoBAr3nfQHDx7kwIEDdOnSpcUzMplM/Pjjj+zb11hGo6ysjL/++guTycT48ePRJSVhdrParlk4+f6oRUVkPfQwxd99T8Tl0yj+/AvPxlVVZHU1aZdNI/K6a8l98aVW6X2Y+/wLztXtVZWIy3w5V8cjRbNm2T7X+t8ZKUFRKPpqFgkvv9Qm8/K2elow4DP/fZxw/LWroXFVS26ZgY+XN8xLKagwctEH/3Heuyv4cuUhp2OWGyx0f3IxfZ76vcnKeasqWb6voJFx5QwpYcWBAvvrj64axFl94+099vQahWtGJBPUyn0lkyKD+P2eMU1veIwhVSuVu1dQuXsFUrXQXZPvVniwPgcPHvTKXBYtWsR+F4neVquVjz76iG+++YaYp57yTFTUy1Rv3IgmOJiA2lCfh4n+5sxMmxHUio2l7Z27a72LNf+OvP56Qs8/v/WO66PVMO7d61g01mrF6ODB5GjhkQdLCHF3E5sktmAuPny0KVUmCz+sO8yS3XloNQrn9m3HpP4JaDUKf+3KdRiKs0pYtC2Hx87uaV92xzcb2ZLhRIunHiXVNoPN2xpUYFNx35Vd1+Q91F/H+5cPpLDCSE6ZgbgQf75cmeYt8W2HKAKGdYykY0wwFw9MZO7GzKZ3OkaQFjMF818GoM/9XzE4MMPjMZyJhXqCwWBg69atLnO6rFYrs2fPZvbs2VRUVJD81UwOXdF0HlRrUb5oMUmff8bhW27FuHNnqxwjcORIhL8flUuXgaqiiYoi9NxzKP5qlnsD1LyfSmgo+qQkou+8g5BTT23RnGo/I2+GhX24hz65g82QOtLI0mjQJye3zaTwPET4Nra8K2dKd67VH334OEYpM5iZ8uGqut6CAv7ZncevW7OYcfVgt8fZl1vOmtSWVWt5A4sq+XZNOtszS3nmgt52OYioYD8ig/Rc/fla/ttf4DS86A3CA3RcMdx2cXvpon7EhPgzc2Uq1ebjJVxoY4TuMBo3JBqOpLy8ZX0qASoqKpxqXjni4MGD9B00CIKCoJX0rppCra4m4447MTZDE8tdqlauRBMVRfz//R9BY8agi48DISj7/Q/HOVjO5lpWhmH3bjLuupsOn31G0LChHs/FnJVF3ptvUf7HH0irleBx44i9/z78vBAe9uEeEVdcQfnvfzReYbUSccXlR39CNXgaIjwE3Cel7OjoDzi3Febow0er8+nyg+zLK0diS3OudRgs2ZPPNV+s5ZT24Q6NEY2As/vW9a07XHxsySFszyxl2ozVpBZUoqqSWasPMfqVf/h3X+saVwDDOkUxd0MG/+7LBynRaYTdY3bsd3are3OileYZKt6oJAwNDUXrQan5kk8+Yd9pp3mcZO5NtLGxGLZudd3nzwtYCwvJeeYZDDt2ILRaLAWFqGVlTe/YaCArqCq5zcjTsRQXk3bpZZQtWoQ0mcBqpWLZMtIum+a28nwtUkqq1q0j95VXyX31Nao2bvRaNeqJTtDQocQ/+wyiXuWnCAig3fPPEThoUJvNy1MP1gZgEOBM68pWguPDx3HGgq3ZTg2OlfsL2XiohL6JYWzLLLWruisCEiMCuWVsZ/u2nWOCvT63Xu1CuXtCV75alcaa1CKHchGOwpdgW2a2Sj5fkQrArNXOc8K8zaLtOSzenoMEAvUaquoJmx77Pqy6y1hzIz7dujVfQ6mkpIT09HT8/f0ZMmQIq1atcmu/Up0OS1bbFndXb9x49A4mBAUffkjoxDMpmTMHaWy6ytMhqopx124sRUVoI51XBR9J8XffYSksbJgzZrWiVlaS9fjjdJgxwy25B6mqZD3yKGULFoBWAxKKPv+csIsvpt1zzzrVCJNmM6ULfrUZeGYTwWPHET51Kppg59IxJyoRU6cSes65VK1ZDUIQOHRYm78PnhpY/wNc9ZDYCXRs/nR8+Dh6WKwqFtXWPNqZxhXYjCmjxUqATuG5Sb35ZUsWZqtkQo9Yrh6RQligzr5tclQQp/eM5Z/deQ0MHkXA4JRIHju7B8//upMN6SUu56bTCC4e2J4RnSJZnVrMf/sLuGpYMlVGC5vr5XfFh/rzyVUDmbMhg1mrHQthWlXJ6oOF7GsDPazat6DqJFON12g0DBw40OP9VFVl0aJFrF+/3r7M39+fkJAQt0KOgW0UFmwzpMS4Zw9SSlsOjjOPj6K4lTgvnBQIWPLzyZv+DuWLFiHNZoLGjCH23nuoWrvW8bhSUr1uPWlTLyX561loQl230yqd/4vNuAKw1P1WSufOJWjkCMLObRwckhYLh++4k8rly+3nV7VmLSU//kjKd9/a5ChOMjTBQYRMmNDW07DjqZK7y4zFGm2so/eI7MNHMyisMPLiwl12Q6lPQii9E0LJLKl2amipEtamFfPFdUO5akSKy/HfurQ/D/+41e69EQLO6tOOVy/pR7CfltennsJpbyxzei8Y3jGCYZ2j+GjpQb5fZwszKMLmfZoyKJH/nd+L1IIqkqMCGZQcwSuL9/C1E+Oqdl+N4nMsH20qKiqI9MAbArBq1aoGxhXYEt0Nbob8urRhxVQj3DRqWoomKgohBLp27WzHPDI0KQR+3bvT7qn/Ub1zJ7nPPd/YENNoCBw0yKFRYq2oIG3a5Zizs+1jVyxdSuWqVQT07+/yPI3791M4YwaxDzzg8hxKf/rJsXyEolD683yHBlbZ4t9txhXUHV9KTGlpZD/9DKETzyRgwEB0cbEuj+2j9WhbHXkfPo4yRouVqR+vIq2wym5M7cguY0dWGRFBeoorTS71qNwJF4X46/jwykFkl1ZzuKiaDpGBxIfVhQk6Rgdz5bAOTj1Oq1OLWZ3asD1Ird03Z0MmE3rGc/Gg9gBUGi18uTK1CTFTGNUlit05LU+69uEeVquVTz/9lFtvvZXQJrwX9VmzZk2LjhvQVH+9luKB0aRERKDWqGq3GkIQMc2mXRU+5RKKvvqqsaEiJZFXXklA//4E9O+PrK4m77XX68RGFQUlKIj4/3vS4SFKfvwRc2ZmwzGtVls4UrW6fj9UldIFvzZpYFnLypxqc1lLHVckl//5p+PPQ1UpX7SI8kWLQFGIvPZaYh960Ffd2AY0K9dUCLFJCLHRwd8GIcR/QoiZQoiW1bz68NEKLNyWzYH8ygaeqlpZnA6RAUwZ3N7pvjpFsHh7jtvHahcWwNCOkQ2Mq1piQ/yb9eMTwPzNdXIHmSXVGJqoyksI98dgPrlCdC1FaLREnXMvUefcyzprSpM6ZY4wGAweGUxSymZVHmo0GiZNmsSkSZOoiIzyeH+3qM0BctO4Enq9zbhqTsNrB/toohyfV+jZZxF9880A+HXuTOIbrzdIdEZRiLrpRsIummxfFHXDDSR/PYuwCy4gaPQoom+9lU6/LsCva1eHx6hau86x8WO1YjyYSvi0aS5Px528sKDhwx3rlykKQcOGOd5JVZ2HROttU/T55xR/+22Tc/DhfZrrwVoM3AZsA9Ziu+4PBvoBXwK9gL+EEBdJKed7YZ4+fHiF9WnFaBWB5YhQoCphW2YZP98xmoggPR8vaywSaVYl98/eQkSQnlO7t8ztPrZbDG/8udfj/SQ2r1UtsSF+ThPca8kqMfDNmlZU9z7hkAiNluC+pwOwF0mE1UwvrXsir/ZRpCQ1NdXt7YUQREVFUeih10ej0TCgRtgz5N+m+/Zp4+NBSjTh4e5LKXgY6pMmk+f7abVE3XA91Zu3UFVjmGrj49DGxNr6Bx5B+GWX0e7ppxosCz37bILHjqXi3xVIo4HAYcPQxcc32jdw8GACBzctv2I6dIjqbducrteEhNDuqf+hjQin4IMPHWygIXh809rbkddcTclPP6FWVNSFODUaNCEhRDjp7Rg8fpzNi9UUQlA08ysir7ii6W19eJXmVktHA29IKcdIKR+QUt4vpRwLvA4ESSnPBJ4H/s9bE/XhwxuEB+qchtNC/W3PG4+e1YPJAxxr5ioCPlx6wOE6TzglKZxT2jcvCXVUl2j7v8MD9Zx/SgKuUqx8gQFPcFwIvcvSPIPa382Gwaqqsm/fvgaNnT1CVfEzGGiXlWVfFDh2rMNNLXl5WHJzW1WnyhNCzj2Hdi+9RMrsHxB+fug7JBFzz92kzJtL0iefYNi2zaGhVvrzz6gOkvqVoCBCz5pI2KRJDo0rd7GWlpJ2+RX25sGNEILwGs9Y1K234t+nT0Pvm0aDEhhI9K23NnksXbt2pHz/HSETTgOtFrRaQk4/nZTZPzjNoQo9/3xbDlhToT8psWT72ga3Bc31YE3FJtdwJN9jk3K4CfgOuL+Z4/vw0SpMHtCeD5Y0NpAUAZcO6QDYPAkWVTr0DKkSdmc719pJK6jkp02ZlFabGZQcwcTe8ei1CmarSn65kYhAPQE17WlemNyX895t2uNQn/YRAVw2tEODZc9e0JvN6cUcKnKcf+NT0vEcqVqpTt2IBpUhXRPopitueicH9O3bt8ltysvL+eqrrygoKGhWnozVaiVtzx76bN2KVKX9hmtJT3eao3MsUb5oMcGjR3Po0suQNTlRWK02r8t11zrdTxoMGA+mEtC3T6vMq2TuPKxFRU7DcIEjRhBx1VUAKHo9yTO/pPCzzyn95RdUg4HgsWOJvvUW9B06ONz/SPw6dqT9O++4rQiv6PV0+MIW/iv9bSGWvDysBQWNNxQCfUdfcX9b0FwDywCMBI5skDWyZh3YvGPNFCXx4aN16BIbzHMX9uH/5m9HAEqNMTUkJZJ7JtTlYCSE+dsucEdcXAW23CpHfLc2ncd/2oaCTVDzy5VpdI8P4cxeccxcmUaZwYJeo3DJoESePK8XfRLD6BoTxL78pkvrBdAjPoRgfy0PzN7MpP6JnNu3HYoieH/pAafGlX1/J/1tfThGWszk//gMAL0fexx/pXlNKvbu3WsP3znj559/tocFmyMsabVYmDXbJk04oWs3tDU3ZlNamsdjtQlSkvPU00iLxfYlrTEAreXllMyd53JXbWSEw+WW/HyKv/uOqk2b0EREEj55MsFjRns0LcP27U5/OCIggA4zPmkg66AEBRFz913E3H2XR8dpNLYHRrYSEEDUDTcQdcMNWIqLOXDmRJtX74imx1E33dSiOfloHs01sN4FPhJCDALWYXtIHgrcCLxYs81EYFOLZ+jDh5e5cngy47rFsGBrFhUGC8M6RTGmSzRKvTjbpUOS+PTfVLuoaC0SuHZUSqMxN6UX89g8W66GFWnfaW9OOXvqVe+ZrCrfrztMdqmBL64bytzbRzHipb+pdKITJYD7zujG9+vS2ZNbjipt3ra/duXx585c/ndeT7uIqCu6xQazJ/fo62Adb+ixYqZhsnFLFC52795NXl4esbGOwzxlZWUtbwpd74Z8XNrQUtblbNXHasWcno4mKgprcXFDo0GjIXDwYHSJjUP5xtRUDk2bhrWs3LaPRkP5woVE3XoLsffe6/a0NNFRTp9KtJGRTjWz2gptRATJM78k86GHMB2wfaeUoCBi7rmbsPPPa+PZnZw0KwdLSvk8tjDgUOAdbAbXUOAmKeULNZt9BPhak/s4JkmKDOT28V14+KwejOsW08C4AugUE8y7lw+wh/PAdh+7aUxHLhuS1GDbb9ekc9EHKx0ex9HlWZW2Fjw7s8oIDdCx6X9n0DXWsQL8q1P6kV9uJLfMaA9X1v7/ly1ZfLfucKOEfUf4jCv36KgpIkp4V6wzM9N5k+tKLwuDGv39vDresUDcY4/WCXXWGDW69u1JcNLaJvell7GWV9QZZDVJ44UffYxx/5FBF+cEDR/u3O2r0zle3sb49+pFp19/peMv80n+7lu6rviXyKuvbutpnbQ0WwdLSvkN8I2L9a0syOLDR+tyTt92jOsWw9I9+RjMVoZ3jiIxvGF4cFd2GU/8tK1ZnoNtmSX0SghFr9Xw5/3j+HdvPm/+sZe8CgOdYoJ54tye9IgP5cXfdjkUQBUC3vn7GBKWPAHYb43iUv8tVKLyjpfGDAx03PzCZDK53QLHXTSWE0uOQwQFEnLmmYRMmED5H39gzsrCr2tXgsePRzjo0agajVT++69jw0hRKP/rL7eaMFvy8yn77Ten681paZgyMtC3dy7r0lYIIfBvQZsmH96jRUKjNSHCntge1HdKKX0hwZMcqyrZn1eBRrH15Tvexe2C/LSc26+d0/Wz1x9GUYTLVjvOeGzeNl5etJupQ5K4d0I3xnSLYUy3mEbbORta1vQZ9OE9rGj40dCX8TSWBfAUIQQBAQF0cXJD//nnn9m9e3eLj1MfncXSPO2pY5TAQYMp//13zJlZ+HXpTNRNN9kNK2NqKuW//4E0GQkaPYaAAf1da0MJgWzCAJVSUvD+BxR8+GGTzaqzHn6ElG+d+hjaFGtpKUKrRQk6+XoSHks0y8ASQsRiqxgcD5RgSxUJE0IsAS6TUnomGOPjhODPnbn8b/52skttdQ4pUYG8fHE/hndqJfHDo4DJorJ8bz7pRVW0jwhgXPcY/LR1YcOCClOTicmuGjEXV5mZsfwgWw6X8O2NwxuFKgHO6BXLT5uymmXEucOReWYnOyZ0LDZ1b/E4Op2Oyy67DI2DXJ2ioiJ27drV4mMciVmrPeaqBFtC1bp1tnYwNarrug4dSJ75JaXzfyH/7bftxmTBBx8SPGEC7ae/TeDgwVRt3Nj4fbBaCR4/3uXxyhYupOC999yaW/XGjRj37XMqUNoWVK5eQ+6rr2DcuQuEIGjMGOKffMLtSkYf3qW5jzrvAqFAbyllpJQyAuhTs8xbnnUfxxGbD5dwy6z15JTW9UxLL6ri6s/WcjD/+Mz/2ZZRysiX/+bGr9bz7K87uXnWBnr+32Le+Xuf3ajqlxjm1DgJ8dfy7rT+dI0NAZz/2FQJqw8W8d8BByXWwD0TuhEWoPN6P8FO0UHceWoXVj52Gg+c6QspNKTl7/WECRNISkpyuC4/v3WeQbef0u+YNpY18XEe5S/ZVdBrvEnmzEwO33a7zbgCmxFVY0hV/P03qVOmEnXrrQidrrEyuqKQ8/TTlLkQ5yyeNcsjD6Ap3XkP0KNN9bZtpN94I8bdNfpmUlL533+kTbscS3HzZEZUk4nqzZsx7NyJPIEM96NFcw2ss4DbpJT2R7CaRtB3AGd7Y2I+ji9mLD+IEKLBxV2VYJWSr1Ydf/2/DWYr13y+loKKhtVNqoQ3/9zLR8tsWlpTBrcnMkjfwPixyT/AZ9cM4fxTEnl2Um86xwTh6vKkVQRrDhY5XJcUGchvd4/mmhEphAd4J7m2e3wI/zw4ngcndqddWAB3ndaV9hGO5SdORoRGQ+wZN3HW2ec49EC5w9KlS9m8eTOqgxuTJ/0Jm0Kj0XDOOedwzjnnkNGpE1aN5pg1sqyFRbaGyu4aMQ68UMbdu53ub9y1i7xXXyVlzmzCJk1CBNcrHlFVDDt3knnX3ZT8+KPD/U2ZWR55APUpKW5v29oUfPxxA5kLAKxWrEVFlM6d6/F4JXPnsW/0GNIum0bqRRdz4MyJVB3RiNyHa5prYCmA2cFysydjCiFuE0JsFUKU1fytEkL4DLTjkB1ZpQ5DWFZVssuFMOexyp87cymqclA6XsPbf+3DZFEJD9Tz460jGdm5LgzaKSaIz64ZwtCOkWzLKOXKz9aQWuC6WkyVkmB/5xH7dmEB3D2hC5Umi9NtPGFPTjlmq4qqSv7YkcPd323CZPE9odqQCI2O3kNGM3zY0GYbWNXV1cyfP5+ff/65URg5Pj6edu3aeSVHUaPRMHToUIYOHYrQ6zHpm6fZdVSwWGximC31hrjY37h3L+aMTKJvvx15ZJVmzX55r7/hUBrCv1s3xz0BnXD4llsonjOnWfpl3qZ60yaneWPVW7Z6NFbFv/+S/cQTqGV1125zVhbpN9xoa3ztwy2am+T+DzBdCDFNSpkFIIRIBN4C/vZgnAzgUeoES68B5gshBkgpW55l6uOokRQZSHpRVaNcI40iGlXeHQ/UD3U6wmhRyS0zkBQZSMfoIGbdMIzSKjNGi5WYED/7jfPdJftQpetegbWcf0qCy/U7s8u8ltQepNegEfDgj1uYtzGzyX6GJxfeDcVu27aNQYMGkZycXHcEIejevTvZbrQwCQwMpKqqyq1jKRYLfgbDsdseyU1DxL9/fwybNzteqdWCxcWDhhAYtm/HWlbq9HjWkhKMBw/i36NHg+VRN91I5cojJFdcNLo2Z2aR83//Qy2vIOr665zP6SigjYzCWlTc+JwVBU1kpEdjFX76WeMuAKqKNJsp/mE2sfff54UZn/g014N1JxACpAkhDggh9gOpNcvclrGVUi6QUi6UUu6t+XsCqACGN3NePtqIa0akOLxBW1XJFcOTG6/wAjuzynhozhbOfedfbvt6A6sOeNYk1xU92oU0uU14YMNwXVigjthQ/wZeifVpxS6T02sjiy9M7tukIRoZ5D3PxNQhSSzbV8C8jbanUZ9x1RCpWtmfms7B1FSHIT5PObJasKSkhGXLlrm1b1PGlaqqpKamkpqaikVRKIxpXIl6XKEohJ1/PiFnTXQYCoy88kpw1eNRSjSRkShO5DHsh3GwPmj4cBJeew1NVJ1HWp+cTPLXX9Phq5kOjwVQ8P77qAbXD2WtTfjUqY4NSqvV3jPRXYz79jn2EqoqxoMt78V6stBcodHDUsqBwLnA29gS28+RUg6SUmY0Z0whhEYIcRkQBHhXHMZHq3N6rzgePqt7g1wkvUbhpYv6MijZcTuLlrBkTx7nv7eCeZsy2ZFVxh87c5k2YzWzVqV5ZfxRnaOJC3Uu2qjTCLYcLkWtZ5lYrColVaYGBlVEoOOcqdo8rVpl9jUHCymtdhR1r6N7XAg94kPQtDCspFUE/lqFe7/f3KJxTmSkxczh7/6Pr2bOxGKxtNgAPTIUuGOH9xz0FouFmTNnMnPmTCwmE4eSW+eB5qihquQ+9xwRU6YQedVViBpjSgkLJXzaZZQvWwZNGDOGPXsoeO99x7laGg3+vXqhjY2l6OtvOHT1NRy6+mqKZs5Eraoi7Lxz6bp0CR3nzaXTrwvotPA3AgcOcNkwWa2sxLi/bQ2PiMunEXpejWK7RmM7dyGIfeQRAk45xaOx9ElJTt87vQP1fB+OaZEOlpTyT8B5SYYbCCH6YjOo/LF5rybXJMw72tYPqH/Xa9rNcBwhpWTB1my+WplGVkk1vRLCuHVcJwaneObebStuH9+FqYOTWLGvAEURjOsaQ5gTA6MlWFXJ4/O2oarSnsxba9Q89+suLuifSFgLk8EVRfDDzSM4/c1lDpXSLarkys/WMKZrNB9dMZCP/03ly/9SKTNYiAjUceOYTtw2rjNTByfx0qLGWkeSuodNVcKCLdlklRr44ebhTvNyhBC8d/lALp+xmrzy5rf5tKiSj5YdPGYToY81/jF2or+mmHhN81XXexwRijKZTLaiEG/n7giBRdeiy/qxgUZD8ezZtJ8+nZgH7sdaUoI0mzl43vl1lYXOEMKW1O3E8yj8/Ih/5mnSr72O6i1bbAulpGrdeop/+pn4hx+ieutWlMBAQiZOtP8eTVmuw7maMO8VLjQHodGQ+PprRF57LZUrViD8/Ag54wz07T03iCKvuZrM+x844gC29yH80ku9Md2TArd/iUKIu93dVkrpiVTDHqA/EA5cDMwUQoxzYmQ9BjzlwdjHFW/9uZd3/tlv92zklhv5Z3cuH145iIm949t6em4RHezHhQNa9wlnT065XWvrSExWlRX7ClyKg7pLSnQQC+8Zw+3fbGR/XkOpidr74n/7C7hsxhq2Z5baDZbiKjOv/b6HTenFbDzUsDzaWa6TVUrWphax6XAJAzs49/h1iQ1m+cOncu/3m1i8I7fZ5+YzrtwnUw1HmgM5W7O3Wfv379+/kVxDx44dWb58uTem14jY3OZ/L44ZrFZMqWkAKHo9Smws+e++ZzOu3AnZOttGCGRVFfnvvW8zruobuFJi2r2b9OtvsHmAVJXcl1+h3bPP4N+7t3N9LEUhoF8/m9fnGCCgT28C+vS2vzbn5lH+559Io4GgkSPx79mzyTFCzj6bmPTD5L/3nj3fTQkKot1LL+LXqVOrzf1Ew5NHHXez2iQeaGFJKU3UJbmvF0IMAe4BbnGw+UvAm/Veh2BLlD/uyS6t5r0ltreh9gZsVSUCePqXHZzeM87rOkgnKtKL5kO3uBD+vG8s98/ezPzNWY2MI1XCtsxSh/v+tSvP4+PtyCpzaWAB+Os0TptDtxSNEGgUgcnqqyisT7Bonsdw8uTJ9O3bt5FXMjk5mS5dunDgwIFGXiytVovFVRK3C/yrq0lOO/5kURqh0TQS8DTu3+9ekryrbWrWVTaV/1ZbjScl2f/3P5cCpUKnc9oXsa0p/v57cp59znbeQsBrrxN6wfkkvPSSy2bVQgiib72F8EunUrV2HcJPT9Dw4Siuct98NMJtA0tK2dHRciHEaGC9lNJbGX6ChmHA+nMwAvYr3fHehqU+K/YVOPRsSCC71MDB/Aq6xp1QEdFm0z0+hHZh/uSUGhqZUnqNwpgu3k3yFUJQ+19r+37iQtxr1hvr5nb1aWr2eo3gksFJ3DauMzNXpfHpv6keH+NEZJT2ID383Kviq0/nzp3p16+fw3VCCC699FL+++8/Nm7cSHV1NfHx8YSGhnqen1XPoBix4j80TbR4OS5QVSKvvqrBIl27dra8oKN9fopC1YYNTo8rjcZjsiWNYdcucp5+pm5Bzfek7JcFBPTp41YTaG1EBKETz2ytKZ7weKNp1ULAdX25E4QQLwohxgghUoQQfYUQL2Brv3NsNnhqRXQa1x9FU+tPJjSK4MXJfVEUYffq1f7/yfN6tkre16CUCKwOnow9dSqq0lYNeOR+QkB0sJ7x3WPdGic52nWFVC0hflpiQ/zw0yr0aBfCuX3bOZ3z0xf04cXJfUmKDGTKoPYkRx5/8hqtQUdtSbP2KywspKzMuQacVqtl3Lhx3HfffTz++OOce+65zUt+r/egGV5aeuxKNLiJJiKCxLfebJSYHT51itsyD95GNKEtVvLzz0dnIh5QMu8np5pexd99f5Rnc3LijWzIlvye44BZQDugFNgKnFWTPH9ScWr3WPQapVFoRhG2vJvkKPduqCcLp/aI5Zc7R/H5ilR2ZZfTISqQq0ckM7JzdKscb/KARD5edpDMkmp7Qr0iQBGCmBA9BeVG3JGo0iiChDB/iiobihxKCeO6xaLXumdIr9jXtCTFNzcOZXinaLvxmVVSzf/mb2/gKa31ag3tGMnFgxKxWFUenrvVLt/go/mUlpbyxRdfcMcdd6DVur7USimZPXt2s47jX0/G4bjMrRMCpCRw6FCi77qTwFNOcWjQ+HXqROKbb5L1+ONIN3XBXKFt1w5LTk7TRltND8NSJ+rvKArmw8depoq1sMDpuVmKHHeN8OFd2rTcREp5Q1se/1giLFDH8xf24ZG5W1EUgVWVaIRAr1V4+eJ+J1Q41Fv0Tgjjjan9j8qxAvVafrx1BC/8totft2VhVaFbXDDPTupLsJ+WKz9bQ1GlCa0isKiS8AAtJdWN82hUVTZKmK9l7sYM7jy1Cx1jmg43WJrIkUqKCGBk52j796bcYObiD1c2qj6UwC1jO3H/md3QKQr3z97Mz5uzmjz+iY7QKISPv45+2mw0zfQeSykpKSlhx44dnOKiTD4nJ4c5c+ZQ1IybXmBFBRN+/52SmFgEEt1xeJ1QQkJIePUVgseNa/I6F3rWRILHjqHiv/+o/G8lFcuWuZRPcIUlOxttQgKWLNv3XQkLQy0ttRt8AGg06OLiiHv0Ecr//NO2/kikxK/zsZf47d+7D2WLFjdeodEQ0K/v0Z/QSYg34k63ACdA2UrbM3VIEvPvHMXUwe0Z1y2Gm8Z24s/7xzaZ9Ozj6JBZUs2KAwXU2ja7cyr4YOl+UqID+e+R03h9yincPr4z0y/rz+rHT+eJc3qi09TdMAJ0Gu46rQsGFy1pHvpxi1tzmdAzzqXr+N7TuzW4Wc3dkEFOqaGR6KlGCPbmlqPXKDwyd6vPuKpBq9HSZ8RpdB4+EY2m+c+hiqK4VGs3GAzMnDmT4uY045WSU//6Gz9VcmNkJDdERuF3HBpYakUF1evXN/i+qtXVyJqcJ2tFBUWzvibzgQfJefY5jAcOEHrGGbR7+im6LvmH0EkXNAiT1uLfty9BY8fi16uX02PXGleArS2MoqBNqKlAVhSCTx1P8rffoAkOJu7RRxsPoCgowcGETZrUvJNvRcIvvsim4F4/TFhjPEbfemvbTewkosUeLCnlt96YiA8b/dqH0699eFtPw8cRVJksXPvFOsoNDcVAl+3J5/9+3s6ADhF8s+YQeWVG+ieF0z4igJvGdmLK4PasPFCIRhGM7hLNocIq3vlnv5OjwIb0YooqTU2qtl81Ipm5GzNIza9sEBZSBDx5bi8uHtS+wfabDpc0eDCvxSol6w8Vsza1iDkbjr0wR1sQr5Rxqv4A/qLlydSqqhLkIgF6y5YtGJqpAB6bm0dQdXVzp3bsoKqULVpM7IMPUvbHH+RPn47pwEGEvz+hZ02kctVqLHl5NuNACIq//ZaQiRPRp6Tg16Uz8U8+iTYqmuLvvkVWGxCBgQQNHYI5JxfD9u22cKNGgaYqY2sq7fTJyXT88UcUP78Gau9hF07CWlRE/nvvIWved31KMomvv44mPLwV3yDXmNLSMKamoU/u0EBCQRMeTsq335Dz7HO29j9Sou/UkbhHHiFw0KA2m+/JxAmgSOfDR+uzeHuOQ6V1CczdmMncjZn2fKale/JZsiePz68dwvjusZzTt06Tq2e7EKKD9RRUOG4kLSXklhnIKqnmq1Vp7M+roHNMMNeMTKFPYljNNpIAnYafbhvFZysOsnB7DharZFy3aO6a0IXo4Mal1BGBepuHwEFORrCflj925trDmyczfpg5Xb8foVrIzLF5ntq1a4fiSNXaTcxm5wr9BQUFKIrSrHY82hopB6uU7Kwx0nr5+7dY6b8tUKuqSL3oYgw76+QPpcFA6c/z6zaq990t//13m2fGakUbF0fyzC+JuedurMXFFHzyCSXfflcX6nPyvXc8EZXqTZvRRjSOGgghiLrheiIuuxTD7t0owcH4devWZukb1Tt2cPjW27Dm59uX+fXuTYePPkRb0y5Jn5xMh88+xVpWhjSZ0ERF+dJNjiI+A8vHCUVplRmj1UpMsJ9XLyTZpYYmGyLbVeWlRAh47tedjOsW02AeQggemdiDh+Y67m7vp1XYlV3Gg3O2IIQtF29LRilzN2bw1qX92ZNTzjdrDlFabSElKpC7TuvKX/ePa3L+lwxqz5cr0xyuyykzkFrQfJXyE4kwYcAkFXRWCzNmzADg8ccfR99EFZkrtmzZwmmnneZwXURERLPV3AtiolGFwKiqXJpu075a37UbgcfhDdRaXIzV0zBpTQjRUlBA5oMP0fHHOViMRptxBXVGlYfvryYi3OV6JSiozT1A5pwc0i6bBkcY78YdOzh00810+mleg+uOJrRtVeZPVny1/z5OCFILKrny0zWc8uwfDH3hb854azlL93gu9OmM+DB/j/rRSQkH8isdtrS5ZHB7+iaGNUobEcC0oR149tedqLKu/Y9VlagS7v1+Mx8sPUBpTfJ8WmEVD8zZ4lb/xT6JYTx5rhMFZwmrDxSc9N4rgDwZzFxjX3KswV4bs7y83KkRdcopp6DVapv1MGDy82OXi/yi1kLbruVdEryK1Yph+3aMB1OpXOVGG1tX3kghiJgyxSvTUg0GShf8SsFHH1P2xx9IF55MTyn85JNGxlUtpt27MWzb1uQY0mzGePAg5lzX10nVZMKcm4tqcux19+Ecn4Hl47inuNLEJR+uZNXBOumCA/kVXP/lOtanuV+ZtSennId/3MLEt5dz3Rdr+WtnXe3Gj+sPN2tujvTLhBDMvH4op3WPtSeq6zSC60alcHqvWEqqHF84nZk/b/yxF5OLxPlahnZ03NNSAlVmlcGt0JT7+ENgReFfk0Nd5WYRGRnp1IAKCgpiypQpTco4OGNH3z5sHDSwJdPzmOZW7bU2hh07EDrXnsaYBx4g9sEHSfnxR+L+70lb+FBR7Ingft26YTqcQd7rr2PY27zWSGBrNr1/wulkPfQQ+e++S+bd93Dg7HMwZXhH/qRy7VqX601paS7XF8+Zw75x4zl4zrnsHzeOQ1ddjSk9vcE20mIh76232Tt8BPvHjWfvsOHkvf66Vw3FEx1fiNDHcc8P6w9TXGVq4GGS0taw+f0l+/niuqFNjrHqQCFXf77G7jnan1vOkj353H9GNyYPSGTVQc9K6DVCMDglwmmyemSQns+uHUJOqYHcMgMpUUGEBepYub/Ao+MAlFSbSSusJCkikPeX7OeH9YcpN5gZkhLJvad3Y1CN4WRuIsn3ulEdSYwIYP5JX0koMOA9sdoxY8Y4XWcymfj777+b3RoHIUg73nvDKYp7/QWbIOuhhwgYPNiem3XkMfz79iX6phvtiwL69CZk/HjKFi/GUlBI2aJFGPfssbXkAQo//Yy4J58k8sorPJqHVFUy7rizLuRZMxdzdjaZDzxAxx+aFvlUTSbKFiyg/M+/kFISMuE0wiZNQvGzdXDQRsdg2n/A6f76Dh2critbuJCc//tfg2VVGzdy6Kqr6bxooT2xP+fFFyn57nt7iFVWV1P42edYiktIeOH5Js/Bh8+D5eMEYFuG416AVlWyKb2kyf2llPzf/O1YVFkXlqsx1t7+ay+7c5yrcUNdhbhGsTXTUQSEBGh5YXKfJo8dH+bPKUnhdvX5gckRhPh7/tyj1ypc+8VaPli6n/xyIwazyn/7C5j68SrW1Hj2eieEEepi7NgQPf5a5/3JTlZaIh4eEBDgtF0OwKZNm8jNzW12HtZxiRB2j1HohZO8YlzVUr1+fZ1xVVN1iKLYGhU/+0yj7XWJiUTdcAOWokIstcniVqt9jNwXXsB02DPvddX69ZgzMhqfl9WKYcsWjAddt6BSjUYOX38D2U88ScWyZVQuX07O/54i/drrUGuKGSKmTXO6vy4xEX8XumsFH37UWNbCasWSm0vpr78Ctry2ku9/aPzll5LSefMw5+S4PAcfNnwGlo82wWRR+WtnLt+uSWdrRkmLxooO1jsMwQgg2o2efRnF1ezPq3B4I1UlHC6qRu9CbPKDKway4M7RTBuaxJm947j/jG78df84usR63jvSX6fh6fN7I6hr/+NOds5nK1JZk1rUwIunSpvx+NrvewCbB8tVR4BpM9awN7fc4zmf6LQkZ7y6upr0I0Iv9dnbgjDU8YjQ6Qg69VRCJkyg/SefkPDSSwSNa7pIo1lIiRIaStRtt9J54W/4d+/ueDOLhbLfFjruNSgEZQsXeXRYaxOCsdYi110YSubOtfU+BJuBU3Nhqt68mZIffgAg5MwzCJ86tdG+mqhIOnz7jdOQtJTSedNsrRbjbtu1wrB7j3PDV0oMu3a5PAcfNnwhQh9Hne2ZpVz35Try6yWAj+ocxcdXDybYz/Ov5JTBScxcdcjhuiuHOXeV19LUDTRAr+H60R35aFlDl7wi4PSecZzdx5b027e9d9SRLx7UnqTIQD7/L5X9uRWEBWrZcKjE5T5/7MhxKLOgSlh/qBiLVWXajNVsz3TujbOokk2HXR/nZKSlzqVSR+rfNZxsJfOauDgqly4FVaVi6VLCp0yhetPGVjueWlqKPrG9XbbAEdJiAWchWkVBrfSswta/d2+n0hBCp8Ova1eX+5c7Ul8HkJKyRYuJvOYahBC0e/YZwi+dSum8eViKigk5fQJh557rcmwhBJrISKyFDow8VUUbFweANjrK5Tja6NZpSXai4TOwfBxVDGYr13yxluIjevGtOljI07/s4PUpzl3bzuiTGMZzF/bh6fk7UGskElQJFw1M5KoRKfbtzFaVf3bnsT+vgvYRAUzsHY+/TkNieADd44LZl1fRqFJQEXBaj1iig20Nkz9dcZBKoxW9RmHq4PY8cW7rVHEN7RjZICn9rT/3Mv3vfU63t6rSqSGg1yjc98Nml8aVj4YIjYawUbYwTIUIwI/mi47G1dy0HNGrVy8OHHCeS+MOiqIwbtw49CYTmsJju8ecJaNOzFaaTBR/843ng3iia6UoVCxbRvhFk51v4u+Pf58+Ng2uI702FguBw5rO4ayPPimJ0PPPp2zBgkbzjLj6ajRhYS73lxaL0/OTRxiCAb17E9C7t0fzi7zyCvLfebfhMYSwfecvtCnS+3Xvjl/PHhj37mvo2dNo0HdMwb9P0+kPPnwGlo+jzN+78ih0ILKpSvh5UyZPnd+LEH/PE4yvGp7Mmb3i+H1HDgazldFdYuiVUKf9klFcxRUz1nCoqApNTa/HqCA9X90wlN4JYbwwuS9XfLrGnodVu83DZ/UgLtQm3HnfGd24bXxn8suNRAXrCdQ7/vlYVUmF0UKInxZF8Y6H4r4zunG4uMppE+Z2Yf4OxUs1imBYp0gWbD02K7+ORUKEgT7+ubSb0A+j1KIXZmimgaUoiksDKyUlpdlCo7VotVpOPfVUkBLzLwvw80Ij5GMaKVEiI1Hd7N0oNE3nFcY+cD/pN9zYMOFeUQgcOpSgESM8nmLC88+hjY6m+LvvkNXVtlDlddcSdfPNTe4bfNqpVG/e3NjIUhRCJjjWU/OEqBtvxHgw1WYA1iACAkh88w10Nd9VIQTt336b9Ouux5yVVSfqGhND+3fePek8r81FHM/JlUKIUKC0tLSUUJ+Q2nHBZytSeeG3nU41pZY/dCodXOQJNZdLPlrJpvSSBr34FGFLMv/34dPQKIKD+RV88V8aWzNKaBcewBXDOjCmq/PQwpGYLCrT/97LVysPUW60EB2s59ZxnblhdEevXJBe+G0nM/51nCBbe6znf9tlz92yqpJOMUH0iAth4XZfUqo7RIgqzvXbjQYVpZ6jpLkfn1ar5dprryUxMdHh+nnz5rHNDc2ipojJy2PImjUEVxwdwVih1yNNJrcqAJWwMNTycq8ms3tC4ttvE3rWxCa3q1q3jvz33qd60yaU0FDCL7mY6FtvRfFv3BnBXVSTCWtJCdqICITOvQdHa0UFaVMvtUkt1DP2dO3b03HO7CY9YO5i3L+fqvUbUEKCCRk/HsVBSydpNlO+dCmm1DT0HToQctqpttZDJzllZWWE2T6HMCml09CAz4Pl46jSMz7EqXEV4qclLqzppHRPOVRYyfq0xirRqoSsEgNrDhYysks0nWKCee7C5ru+H527lZ82Z9pvygUVJp7/bReVRiv3nO467+JIckoNfLs2nV3ZZbQL8+eyIR2oMFqdtrMpqTJz45hOjOwczU+bMiitNjM4JZILTkngzm83NfucTjYG6TLQoIJUycu3SWZER0cjRPPqgaxWK19//TX33XefQzX4nfVawzSXwNJSEhYvJluVdNbrUY6CdyHuyScIHDyYssWLKflxboOmyUeiushBa22CJ0wg5IzT3do2cMgQkmd+6dXjK3o9SmysR/togoNJ+e5bimbOpGzx74AtqT3q2mu9ZlwB+HXpgl+XLi63ETodoWecYSsEWPw7mQ8+hLRaCB47jrBJF7TI+DwZ8BlYPo4qwztF0SchlF055Q28SQA3j+2EXyvIBDjr+2dfX9lyheJDhZXM2+Q4fPfhsv3cMKajywR+k0Xl69WH+HFDBgUVRgorjaiqTQRUowhmrT7EJQPbOzSuFIG9T2GvhFB6JTTMCxveKZK/duU22q8+MSF+DYoOTkYEkvZKGUKAyWzhgw8+AFrWKkdKicFgYNeuXZzioHTeGxGE5F27mHzwIHCUWuVoteQ88yxKYAAhZ55Jx5/mkXH7HVTXVr55Cb++fTE207sXNG4sYeedT+g5ZzsMEcoauYHib7/DnJODf58+RN98U5u3wKlFExZGzN13E3P33W09FaTFQsZdd1OxZInNYyklFX//Q8mc2STPnOnQ8+XDhk+mwcdRRVEEX90wjNN6xNrDLoF6Dfee3pU7TnX9NNVcusYF46d1/lXvm9jyp8LNLqrvDGbVpfyBqkpu/XoDz/26k13ZZeSVG7Gq9Xob1iSwz9+cSXyoXyPZBlXC5UMdV0vmlBoYlBxBiJ9zw/W28Z355Y5RTtefLEicq+W3BEVRKCtzHEXo1q1b8weWEq3RSLuso5xfZ7GA1YpaXkHp3HnsGzW6Vcr2g8c6F2h1ihDoO3Yk8bXXCDv/PKf5V7kvvUz2E09i2LkTa2EhlStWcOiqqyn/5x8A1MpKrG3oeTuWKFu0yGZcgS1kWfNQYNi5i6JZs9pwZsc+Pg+Wj6NOZJCeGVcPtnlqKkx0iAwkQN96Apeh/jpuGN2RD5Y2llk4p287Oka3/AksItC1hyPSxfp/9xfwz+6m+yaarJKkiAByyhp6mhQBHy7dz8WD2tvzr9ILq3joxy2sSbUlAof6a9FrBCZrwxy0uFB/bhrTiUC9pslm1ic+gkPWcJI1JV4dVVVVYh2EiXJycsjNde1ZbIqJi3+H6uoWjdFiLJZG1W0tQqPBv7dNZb3w/Q+a3r5+VaGUmFJT2TdmLAmvv0boGWc02tyUnk5xrWFQu5/VCkKQ8+xzFH33PVUrVoCU+PXsSdwjjxA0fJiXTu74o2zx745z7VSVst8WEn3rrW0zseMAnwfLR5sRHexH9/iQVjWuannwzO48NLE7YQG2RNMAnYbrR3Xkjamey0I4YmTnKGJD/DiyaFCjCAZ0CCfFhRG3ZHceWjerDTemN36qViWkFlbx7z6bEnW1ycqln6xi/aG6vLMygwWTVTK2awzxof7Eh/pzzcgUfrpjJFsySpj+9z66NkMY9URjvSUJs5cvi1FRUXQ9QvuoqKiIzz//nOLixrmB9QkICKhNpm2MEGx1oRJ/vKIJCyPhpRfx79MHjYsKTAAlPBxdUlKj5dJkIvO++x0qjleuXOlYBkFKLDk5VP33n329cc8e0m+4geqtW5t3MicA0mRyLhvhawDtEp+B5eOkQFEEd5zahfVPns7aJyaw+akzePK8Xl7L+dJqFD6pJ5RaazDFhfox/dIBLvfVaQSqG7k4ArA62U4AaQW2CrIFW7PILjU0ynEDWL4vn8ggPU9f0IsHz+zOXd9u4rov1jFj+UH25TkOY2pPoorsCulHtjW0xeKi9bnmmmtQlIaX2tWrV2N2o2ludXW1S6HSDmmpbin9Hy+ETZpE598X49e5M0IIEt943eX2yV/PQq2oaLxCSlBVSn+e32hVUw2hG3hqav5d8PEnTc79RCXYmdK+RkPwhAlHdzLHGT4Dy8dJhU6jEBvi3yrJ9P2Twln5jaDMjwAAPQBJREFU2ARevqgvt43vzPTL+rPkwfFNyk5Um60uQ3O1uWrXjkpxmksmwe4l25Vdhlbj/La7M7uMW7/eyO3fbGRDjZfLokqHc9BrFCwnVdhQIr1osoSEhDhs5OyqfY4nVIWEtEreWFtR+ssvlC1YgGq0hcEDBw0icJTj/MDQyRfi17mz89Y0ilLXX7AewaedCloPsmOsViqWLqVk3k8nV8/IGsIvmoxft262MGEtGg3aqCiirru2zeZ1POAzsHz48CLBflouG9qBB87szqT+iU0acqsPFvL1asc3W60iiAjU0TchjNennML/zuvFtKEdHIYhO0YH2TW72oX5o7qRTLV8b36TOVcma9toF7UdAn9hblH/wfqUl5czY8YMSkpKGiwP8lLllSoU9vTs4ZWxjgmkJOfZ59g3ZiwVK1aQ9cijtpBd/Q9ECCKuvpqEZ55BCIFf9+6OhcosFvx7Ne60oI2IIP6p/zVoOo2igE7X0Iioj9VK9uOPU/jpp144yeMLJTCQ5G++Jvq229CnpKBLak/kVVeR8uMcly2IfPiS3H34aFNmrzvsNLm8V0Iov9w5usGyx87pQYXRwtyNGfYwVq92oXxwxUB7gvvkAe1544+9mCyqS+/Gyfcs7h6hwoCiKIwcORKgUXjPU6qrq/nvv/84t16fuIEDB3KwRlqh2UjJloEDsFosjCwowM9gQDWabEnnuNck/FhFLS/n8K231fUIPMJzpI2OtgteRt9+O5n33NNwAI0GbWwsoeec7XD8iClTCOjdm+I5c7Dk5ePfowf+/fqScYvrhO2C9z8gYtrlaIJPLmkCTXAwMXfdScxdd7b1VI4rfEruPny0EaoqGf7S3+Q50Z9qHxHAikcct8bIKTWwO6eMuFB/erZr/N1ftjefO77ZSIXRdXWXr3KwIUHCyBS/bV7zYNUSHh7OPfWMACklL7/8MiYvJgkLVaV9+mFGrFrltTHbHCd9B3Xt2xNy+umo1dUEDR+GWl1N/vR3sOTmghAEjR5Fu2eeQZeQ4NHhCr/8krxXXnXZ67DDl1+e1FWFPnxK7j58HPPM3Zjh1LhSBAzsEOF03/gwf+LDnKsoj+sWw7onTue3bVk8Nm8bZmvDG4ZGEXSMCuRAfqVHvXNPdDooJa0yrvaInB8hBIMGDWKVF40hqShkdEjCvG4dOm/KJrQlTr6Y5owMmwaTEJT88AP+ffvS8ZdfUMvLUYIC0UY4/+24Iuraa9HGxJL1wANOt1GCg5s1to+TD18Olg8fbcTs9YedrpMSbhnXqUXjB+g1XDIoifcuH4hWESjCltclsBlw+/MrfWHCI9hnjWa2oS9p5hCKi4spLi5uUSPmWvr0adiCSUpJYWFhi8cFm85W7VytgPlY6BXnSRJ5c7Fa7SFEw44dFH06A337xGYbV7WETjzTllt0ZGhYUdB16IB/78Z5XT58OMJnYPnw4QYGs5Uf1qVz/+zNPP3LDjalu9YvcofiSudl+oqAzjHeeVKe2DueZQ+fyr2nd2PKoPZEBukaSDjUOgmCXKi9g01O4kTHgkIVerZWRzJ9+nSmT5/usArQU2rzuWo5dOgQe/fubfG4ABaLxT5XTXk5/m0tPApE3XhjywYQAiUyEl1ycl0ies1yh6gqJT/93LJj1h5CqyXxzTcQfn42I0urtc0nMJDEN173SuN2HycHvhChDx9NUFRpYspHKzmQX2lPJP9yZRr3n9GNuyd41sS5PoNTItif70DDB7BK+HNnLuef0jiHRFUlP6w/zPdr0ymoMDE4JYJbxnamV4LzPMTE8ADuntCVxdtz+G6dY89ZpdHqdP+BHcIZ3imKj5YdOMFztgR+WDjN7yAbvTRiSEgIOp2uwbJ9+/ahKIpXvGP16b5rN6Kt472KgrWsDCU0FNVJi6AmkZKk6W+j79KFwo8+pnThQjCbUUJDMWdkNFYVB9SqqhZOvI7AIUPo8ucflPz0E+b0dPQpHQm7aHKLvWO1mA4fpvyPP5FmE0FjxhDQu7dXxvVxbOEzsHz4aII3/thDWoHt4l3f8/Pmn3s5vWecS8PGFRf2T+R7J8aOAHLLDA7XPTpvK7PXZyCwVQLmbDWwcFs23900nMEpkY22X5dWxHdr0sktM2C0qGgU4VCE1BWpBZV8cvUgft2aTUZx1QltZHXWFKCTzo1NT6murkZK2cDz0dLKRGd0OXAA0Upju42qUvLtt869TW5izs4mcMgQ4h57lLjHHgWg5OefyX70scYbazQEDfNu4rk2Oprom27y6pgABTNmkP/mW7b3Rwjy355O6KRJJLz4gtPeiT6OT3whQh8+muCnTZkOFdQ1imDB1qxmjzswOYIgJ22CJDisDtyeWcrs9Rn2bcBm9FlVyXO/7my0/ecrUpny0Srmb8nivwOFbDhU7LFxBVBcZWZrRik/3zGKW8d1pmN0EJ1igpjU37MqreMBMxq86VeyWCz8+uuvDRTZe/bs6R3vlapy5m8LWz5Oa9BCT5oICGi0LPTss22il/UNEY0GodEcFxICVRs2kP/Gm3aleaw2Q75s/nxKZs9u49n58DY+A8uHDxdIKTGaHd8IBba+f81Fr1W447QujZZrFEHfxDBGdo5qtO55B0YU2KQWtmSUUlpdl9eVV27ghd92AXWeN2e3PHd8DfvzKgjy09AuzJ/YED+klOzJKScx3Hk14/HIPmsM6dZwr465ceNG3nnnHfbs2QNAQkICw7zgcUlKTyfYi6GxYwIhUIKCCB49Gmt5OcVz5pD//vuU/7MEodWSPOsrIi6/HCU0FKHXEzRqJMnffutQVPRYo2TevIbGYS1CUPyDz8A60fCFCH34cIEQghGdo1h5oKBRWMyiSodGkCfcOrYzqir5cNkBKo1WhIDTe8by0kX9GiXTbssoZXWqk7Yg2Iyk+k2j/96V57R34ZGEBegoqXbdGy+7pJorP13DurSWJ/gf20i2Wdp5fVRVVZk3bx4PPPAAer2eiRMn0rlzZxYvXkyRs3YvTVAVGEj5MS4boAQFoVba+mQqwcGg1aLWKtvrdAghkLWFBDXf+YRXXqZ66zYO33YbsrralmxuteLXtSsdvvyC+CceJ/6Jx9vgbFqGtbjY7rVqgJTOW/74OG7xGVg+fDTBQxO7c8lHhQi1rtmyImBQcgQTesa1aGxFEdx5WlduHNOJw0VVRAbpiQr2c7jtgq1ZLoVBx3SNJsiv7idtsar2PC1HvHpJX0DQPymcG2eub9LASiusPAmMKwBBqWwcnvIGJpOJffv20bt3b4QQdO3alUWLFjV7vMLYWHb26Q2bvJWS712U0FC6LltK9aZNoNESOKA/aDRUb9mKWlVFwCn9kEYjJT/OxbhvH7qEBMIvuRhtbCz7xo5DGgy2cFqNUWI8eJCcp5+m/bvvtu2JNZOAfv2oWLqscZK+RkPAANdN4X0cf/gMLB8+muCUpHDm3TaKd/7ex6qDBQT76ZgyuD23j+9irypsKf46DV3jQlxuYzBbUYRAdeKVmjK4fYPX47rFAjsabScEdIwOYsqgJLuXbFByBJkl1S7zs7JLjSeNKKlQFHoOGkmwYvZ6QrrR2FBctrqFsgpVISFcGhGBkPKYu6BH3XwTakUFgcOGNUjgDhxYz5gICSH61lsa7Ff662+o5eWNB7RaKf/rb6wlJWjCw1tp1q1H+NSpFM38CmtZWZ0nS1FACKJvubltJ+fD67Tp71EI8RhwEdADqAZWAo9IKfe05bx8+DiSvu3DmHHN4Dadw6gu0Xy16pDT9fd8vxmdRsNZfeIB6BAVyM3jOvHxsoN2z1etQfjMBb0bhCBvGdeJX7dmodLY4yWAyCA90cF+7MlxcNM7ARFaPabTH+Yc/81eb5uTkpLS4HVAQAAGg+OK0aYILi/njGXLOTs27thKqBUCvx49KJj+Dvmvv4EmKoroO27Hv3dvij79lKpNm9FGRxNx2aWET53aqPLRWlzstE0OUmItK3NqYEmLhYqlSzHs3GnrR3j22WhsbU3aHG1kJCnffUvOCy9SuWIFSIl/r17EPfLwcZFD5sMz2rQXoRBiMfA9sA6bsfcC0BfoJaWsdGN/Xy9CHycNFqvKtBmr2XCo2GGYUAAxIX6semyC3ZCSUvLLliy+WZNOdmk1p7QP55axnenbvvENZ11aEc8s2Mn2zNIGyztGBfLhVYNYn1bMkz9vb41TO+YQSDophfTR5RIqDGiFd66TAwcO5Pzzz7e/NhqNvPLKKzT3Ojx4zRpSUtNQvHAdF8HByArHumwejxUYaAvvHRkKE8KeT1VrQIVdfDEJLzzfYLPqzZtJu2yaw7E14eF0/Xc54ghtMQBLfj6Hrr0O04EDNoFQqxXh70/Shx8QNHy4V87NW6iVlUirFY3v3nXc4W4vwmOq2bMQIgbIA8ZJKZe7sb3PwPJxUlFtsvLA7M0s3J7jdJtf7xpNn8TmP7EXVZoorjJyqLCKiEA9/ZPCEUJgMFu57JPVbDlcckK32FFQmaDbS4QxF1VCYGCgV0LBXbp0Ydq0afaQo5SSL774gsOHnbdMaopzf/mFwIpKimvCTREaTbOVxiNvvJHiH35AOgrNtTId58/Hv3s3+2spJek33EDV6jWNjLSQM8/EnJODWlVJ8KjRRF53Lbp4m9f28O23U7FsecNE8pqqxK7Ll6EEBh6V8/FxYuOugXVMeZWB2ruCr5zChw8HBOg1XNCE9lRLQ1qRQXo6x4RwWo84BnSIsN+w/XUavrp+aLOFVY8XemjyiLEW8dprr/HG669htbhO/neXoqKiBvlcK1asaJFxBVDt50+VlIw+sJ/RB/ZT3YIH5qqNG/Hv3r1F82kWikLlvw2fp4UQJL33HhGXXWprWQNoYmLQd+lC+R9/YNi2DdP+AxR9/TWpky/ClJGBpbiYiiVLG1fpSYlaUUH5P0uO0gn58GHjmDGwhO0q/iawQkrpMA4hhPATQoTW/gGus4J9+DjGySs3sDe3HIPZfT2tUV2i8dM2/ukKAQnh/vSMbx0DyGC28uyvO9mZ1cz2J8cJXbSOmzC31NlfVFTEDz/8gNVqxWq1smRJy2/4aZ06tniMWgybN1O9fr3XxnMbKRH6xpWzSmAg8f/7H93WraXrqpXEP/U/TPv32/cBwGrFWlZGwfvv29ryOPuQhLDldfnwcRQ5Zgws4D2gH+A48G7jMaC03l/GUZiXDx9eJ7fMwDWfr2XoC39z5lvLGfz8X3y49IBbuTgh/jqem9QHqEta1ygCrSJ4+aJ+KF6qbKzP4u3ZDH3hL37ckHFChwcBdKheT2yvZffu3axcuZJ9+/Y1O++qPgc7dyanXZ1ml1ozcXNzWq40V1m+pW+WEISccbrT1YpejzYigsplyx2LdFqtlP/5F7qEBDTOegVKSUD/U1o2Tx8+POSYqOoVQrwLXACMlVK6MppewublqiUEn5Hl4zjDYlW5fMZq0grrFLgrjBZeWbwbP63C9aOb9kpMHZJE59hgvl59iPSiKnrEh3DtyJQmpR6aw56ccm7/ZuMJ3X+wPplqKB1l4zwkbxlda9asIdhb4qCKwqpRI2H5MgD2d+1KQUoyiZmZ9Ny12zvHcIVWa3tjzE2HUZXQUFSjEUwmm6dJowGrlbhHHkbXzg1hV1dyGYqC0OmIvvMOcp97vtG6oJEjCejbt+lj+PDhRdpapkEA7wKTgfFSylRX20spjYBdRKa5yZw+fLQl/+zO40C+4yLZ95fs55qRKW4lVQ9KjmBQspMndi/yzZpDtt/aMVQQ05pst8TTXjQsIpDSewZWVVUVlZVNFkm7T72JbT+lH3qdjqgCx2FObxN60WTKZs9xa1u1rMxmJNW8mYFDhhB7370EnOKeZynk9NMp+eGHxis0GkLPPhuAiMsvx5KfT9GXM21VjIogaPRoEt9+y+1z8uHDW7R1iPB94ErgcqBcCBFf89c6Mso+fBwD7M4pb9DSpj6FlSZKqkxHeUauOVRY1awG0ccrFdKPxca6ijbVi8YV2KoSW/XhUAhUzdG5tJfNcc+4slMbhpSSqnXr0MbGur1r0OhRhJ57ru1FrTdLUdDGxhB9x+0AVK74j8IZnyJrPWoSKpcvp/CTTzybpw8fXqCtDazbsFUOLgWy6/1d2oZz8uGjVYkP9cfixGDx1ykE+zt3LG/PLOWmmevp89TvjHzpb976c69HCfLNoUtsMK2Q1nVMU1avVY63zz0uLq51DSwpMfodpQbcLbG7paT0lwVuby6EIOG1V0l4/XWCx44hcMhgYu66k47z5qGLjUVKSc5zz9mMuNpKwhqva+FHH2PKzGzBZH348Jw2DRFKKU+yy7YPH3BOv3Y899tOKo2WBnlNioDLhnTAT+s4QXnz4RKmfLQSVYJVlVQYLbz7zz5WHyzk25uGe61tz5Gc1Seez1a4jN6fcAQoKt36DSZAeL9VTlxcHAcPHvTaeIqicEpNmE1RFISU6GqbJx/LKIrHDY6FohB23rmEnXduo3XmjAzM6elO981/6y0SX3/d42n68NFcjokkdx8+TiaC/bR8ed0Qbpy5nuKquuTg8d1jefTsHk73e3XxbqyqbGCUqRLWpBaxbG8ep/VoWeNpZ6xNLTppehDakJwZlEbk5PNaxXPn7+9Px44dSU31jtGq1WqZPHmy/bUEktKdt1Q6qigKmqgorAUFjb9AFgv+/byXeC6aqJysWLYcqaqN2vL48NFa+AwsHz7agEHJkax+fAJL9+RTVGnilPbhLgU8pZSsOljo0MjRKoJ/9xW0moGVVVKNRggsJ4mFFatUEq1UNb1hM9myZQsTJ070moEFEJObS68dO4koKsKs0xFY1Xrz9whVJeLyyymYPr1hb0GNBn1yMqFnnOG1Q2nbtbMZc4WOE/zV8nJUFz0MffjwNj5T3oePNsJPq2Fi73imDe3gljq6I3FRsHksAvXN0D1ykx7tQk+qJPdwUYWUEpPJhMlk8opeVX2KiooICgpCq/XO8218RgZD//qboJwcdCYTgVVVHPXcC0feI0XBv39/NKGh6Lt0Qej19uWhEyf+f3t3Hh51ee99/P2dmUw2mEQStggEkLCEXQNYFfEAtgdcTt1bV7RH2/q0Kn20PdoF23Nqn8P1XLVW21OP7WOLPdpqK/WqSusKWhUREIWCgEBYQtkJIZB15n7+mGTIOmSZyWSSz+u65mLym+/85js/Jr98577v332T/9SSU9tiwMzIuvLK1h9PT8eTmRmz1xM5HRVYIknAzPiXKWe2OM4qGHJcOin68jmd8fkpefTr48fbS6ZFGe45Qk1NDQ899BAPPfQQNW2Y46m9qqurOeecczq3E+fAOcauXsO0rVuYvmUzFc51fXEF9PvSlxi06Ht4c3PDG3w+ApddBs6x/9//nept23BVVWCGf+RIBv3g+/hycmKeR+4dt2PpLVyE7vFwxrXXtLhAtEi8qMASSRLf/NwYhvULL1brNSJTPXzj4tGMGxy/9QH7pqXwu389l0FZp5Yz8RiM7J/Z464uzKCaPG95XF/D6/UyYMAANm/e3LkdmZF+8iR9YjmnVgelFozijC9+kYIVyxn11grGfLCKjKlTqfzoo3BAfSugc1Rv387Rp56KSx7evn0Z9sR/48lqvNh5n1kX0n/hwri8pkhrNAZLJEnk9Enl5btm8qd1JazacYRAmo/PTz2TqcNiM9lo6clqjlXUkJedTkqTeZSWrNxJSWll5OeQg+2tTJaazAKeyrgtk1Nv+vTpFBcXU1pa2ul9BbvJgO193/kupX/4I/5Bg8i68koyZ0zn+Kuv0uLVEaEQZX/5K7lf/WpccskoKqLgrRWUL19B8OgR0idPJm3cuLi8lkg0KrBEkki638sXpw/ji9OHxWyfh8qr+PbS9byycT/OwRkZKdw1p4AF5w3HzNh95CS/XdnyVWlej+Gc6zHL6JS75osOx1qfPn3Yv38/Ztbp8V3VaWkcysmBrTFKroNcVRUV779PhcfDsRdeIPeur0d/bx1d97CNPKmpBD732bi+hsjpdI+vPyKSEMGQ48Zfvs9rmw5EGhqOnqzh+3/eyG/fD88p9EHxkVbnkwyGHF+cPoxh/TLIzkhhSHYaPk/0yTnT6gbrd8fuxXKXSpXzNmt0ieU4971799KnT5/YDJ43Y820os7vJ1bqCqdDP300vFRNS+/R46HvZ1X8SM+nFiyRXmz55gN8sq/5wsYAj76+lRumDyMzNfpp4vaZI/nhFafmM3LO4Rw8sHQ9v/tgd2S7Eb7i8YdXTCAzNYXn1+6hrLKG8spaNuwti8Xb6bSAVZJqQaqbFH+x7DYsLy+nX79+Mdvfib6xX+A7Fio+/LD5Ro8H/7Bh9Lv5pq5PSKSLqcAS6cX+vrcMr8danIbhwPEqjp6sZtbo/gTSfByvrG3UkuU1Y9zgvgztl8HyzQfYur+cM89IZ864AaT6vMwtHMgL60qoqDnVHXTDjGFcdc5QIDxD/AvrSrj7d+vi/C7bLt1if8VgU2lpaZ0f4N6KbtVT26D1ypeXhzc7m8DFcznjhhvwBuJ3UYZId6ECS6QXG9A3tdU5rlJ94XURU31efvrFqdyxZA21oRAeCxdkgXQf375kHPMeeYst+8vxWHjwe/++qTx0xQS+8tQagg127YD/eX8Xe0sr2LTvOH6vUXoy/gVNe5SG0uoWdzYKCwsBYr5uYG5uLu+9917M9mdmTB41ioH79sfuhF7/np0jbcoUKjduhOoOLkJuhi8nhxHPPduhp7uaGk6sfJ9g2TEypkwh5cwzO5aHSBezWE+i15XMLAAcO3bsGAF9IxJpt7LKGj7z0OtU1ASbrYt4/Yxh/MfnT3X97TtWyR/W7KaktJIxA/twxdlDuPO3a1i540ijIs1r4E/xUFEd34HM8XJd2jrSqY3b1YRz587ltddei+k++5aVMe+ll2OzMzP63XE7Kf36kTlzJr7cXLacfwF0Yj6wlCFDGPXaq+1+3sm1a9lz110EDx2O5JZ99dUMWvQ9LEYTtYq0V1lZGVnhqUCynHOtjm/QIHeRXiyQlsKvFkyLjLOqryk+MzKH++c1vrR9UFYaX5tdwI+unMiC80dwvLKGd7YdbtYCFnQkbXEF4MXFdaqGE3GYtyq9bp8x+brsHEf++wlOrl6DNyuL6uLiThVXAOkdmFQ1WFrKrn+9neCRo41yK/3DHzj8y192Kh+RrqCvACK93Lkjc3j/gTm8unF/eF3EodlMHZp92q6xQ+Ud7DLqxowg+4KZBKya7DjNiTVkyJCY73P05i2EiOE3Zuc4/sYbVBXvYOh//Vend+c9o/FcbS4UwtXUYH5/q5+zY39+EVdR0fxKROc4suQpcr785Zh334rEkgosESHD7+Nfppx+bEtVbZCX1/+DD4qPkubzkOI1aoLxG2bgqZuxvjqOr9GQAT6DzGAZ3//3hwB44IEH8Mdwzbx+/foxceJE1q9fH5P9VVdXM3f5mwCsLhhNRqwmHw0Gqd76KVVbtpB54YWcePvtDs9XUf7GG/Bv3yJUWcnBnzxC6XPPETpxAv/w4eR+7WtkXXpJs+fUlJSE1zisrW2e2pEj4Va1GP6/iMSaCiwRaZOjJ6q57r/fY8v+cnwew0GLA+Q9BtOH92PljiOdfs2Qg7zsdIoPn+z0vtoi31PKYE8ZtcFT22I9TPXNN9/k2muvZfv27THpLkyP51I5Ph8n135I+uTJnFi5ssMD3Wt27WLnzbfggsHw9A1182VV79zJ3nvvxVVXk33lFY2ekzrqrBaLKwhflRjLhaJF4kFjsESkTf7zL5+w7UD4j3ltyDUqrjJSvAD4vR6unz6MJ2+dzuKrJ+FvsOSOx6B/n/AfRbNTE42mp7R+GkpP8XDzecNj/E5aN9BT3mwC1Fj3Qm3ZsoVnnnkmJsVV39JSPrfsLzHIqhXBIKXPPsuhxx7r+FWEdU6uXk3FmjWNZ3Gvq14PPvIILhhsFB+YPz+8eLTX22xfuXfc3qlcRLqCWrBE5LScc/zpwxKCLTTneD3Gl2aO4NqioeT08ZPhD59Wri0ayucKB7F8ywGqa0PMLOhPdkYKL6wrYfnmg6R4PcwdN5C7ftfChJR1KmpC9E31cfbQbNbuLo3X26vjGOaL92uEbdu2LSb76XusDIvXleBm4PEQKi+PTTNelOVxavfvp/bAAVIGD45s82RkkL9kCXvvvTc8TQRg6enkfuUrZF93XefzEYkzFVgiclohB5W1Lf+BNKCyJsjQfhnNHsvKSGk2tuu6acO4blp4LcVQyPHtP63neGXLXUEegxc//gcfxr24AjCqnJcMutfcXNHszR/GwOIhsHVL7HZat0CzJyODUEVF3NcNrBeqrGy2LXXkCEY8/0eqtu8gVHaM1IICPJmZXZKPSGepi1BETsvrMc4elt3i+oG1Ice5I3M6tF+Px7jp3PxWHw85eOfTQ102Q/m6mjPr64sI52I/DitmnGPzmNEx251v0CBSx40j84LzGfLYo6cvrhoOqK/rSw1cdhneBi1RbVX6u9+3+ljqyBGkT5mi4kqSigosEWmTb/7zWAxrVGR5DIryz+CiMQM6vN975o6mYECfZtsN8Ps81LYy03w87Apls7x6JCfcqQHUZjQrurqT6tTURj93Js3affuo2rSJE++tZNett+HLy4v+hCbjqfp96Tby/vP/kHPD9c0Hr51mMNvxGE++KpJoKrBEpE3OHZnDM3ecy/QROaR4jTMyUrj9wpH85rbpeFtq2mojv8/Dy3fPZM64xkVadkYKX7lwZGfTbpc8O8Zo70GqzUNBQQEFBQWRuZa65ZRLZniAmZmZXJiZiYdTk8V2mHNQN+C8du/eduVyvG7Afb8FCwhcfnlkO4D5/aRPmdy41atO0ONh2+DBPPfcc/zxj39k48aNhLqoa1IkXnrEUjl79+5tcakcr9dLWlpa5OdoV+14PB7S09M7FHvy5ElaO45mRkZGRodiKyoqop5kMhs0l7cntrKykmCTK3Y6GpuRkRH5A1RVVUVtK5dVtzc2PT0dT92JuLq6mpooM0m3JzYtLQ1v3VVJ7YmtqamhOspVVKmpqfjqlu5oT2xtbS1VVVWtxvr9flJSUtodGwwGqWxhTEu9lJSUyNxO7YkNhUJUVFTEJLasrIySkhJ8Ph+jR4/GOceOg+Ws23OMrDQf5xfkcrTsJHMe/hvO48N84ffmnMPVVNJaKWEeD+Y71QIVqm743hyp1BLEQy3eFmNHeo9wTspu0qzx597MIscXiPp/3J5YoNE8W+2JrampwYVCFGzezMT1G3A0PioN58SqDIWIVrI0jK0KhQgCeL2kFRZS2WTOrhZj64x84U/4h4XH2FV9+ilu/Xq8ffrQd/ZsDr/xBru/+a1G+6r1enn7olkc7dcPf93Eo845Ro0axeWXXx753W5K54iwnnyO8Pl8pNa10DrnOHmy9ela2hPbntqgpdiysjLywq27UZfKCZ+skvQGBAi3iLd4mz9/vmsoIyOj1dhZs2Y1is3NzW01tqioqFFsfn5+q7GFhYWNYgsLC1uNzc/PbxRbVFTUamxubm6j2FmzZrUam5GR0Sh2/vz5rcaGPxKnXH311VFjy8vLI7G33HJL1NgDBw5EYu+8886osTt27IjE3nvvvVFjN2zYEIldtGhR1NhVq1ZFYhcvXhw19s0334zEPvbYY1FjX3zxxUjsk08+GTX22WefjcQ+++yzUWOffPLJSOyLL74YNfaxxx6LxL755ptRYxcvXhyJXbVqVdTYRYsWRWI3bNgQNfbee++NxO7YsSNq7LRp09yDDz7oHnzwQfdv//ZvUWMzJ8xxw7/1Z5f/rRfdsIXPRY3NGHO+y//Wi5Fb1P2OPNvl1+03/1svOktJbTU2Pz8/ku+DDz4Y9XySl5fXKDYrK6vV2P79+zeK7d+/f6uxWVlZjWLz8vJajT3D63Ubx4yN3Kalp7cam27WKPbCzMyox61h7Gf79I0a2+gccdNNUWPvu+++yHubNm1a1FidI8K3nnyOuPPOOyOxBw4ciBp7yy23RGLLy8ujxl599dWuoWixp6kjAi5KjaIuQhFJuGitpACDPWUM9pTR1yoZ6imN2esO8JzAi4vZ/rqTGp+Pgznhiw9CbejfbM9RsAatc+0Sq1nmRZKAugjrqIuwY7HqIgxT83/bY5944gkOHjyIx+OJHAfnXNT/i5Zia5zxXOVEMq2WMhf+PfcRJOjxYb5TA79D1ZXM82+mn+dEC5OIGpsYwpra8PqA3uoTnJuyk8HBg/z0p48AcPfdd0e6rrpjF2FVVRWPPNI410hsSgo5Bw9SsPVTaqur2DdwILuGDmV48U4K//531k+axM4Rw8kuLeWfV7yFAeZcuNvP4yFj6hSGPv441mSyz5q/vsI/vv1t8Hioqq0laIYnM5Nhv3yCtNGNr2o83e/9z3/+c0pLS4Hw56c+tra2lhEjRnBdK3Ne6RwR1hPPEfWSvYuwR8yDlZmZ2agoiBbXnn22VcOiKJaxDYu4WMY2/LDEMjY1NTXyAY9lrN/vb/NacPGKTUlJafQHM1axPp8vciKNZazX623zZ7il2FAoRG1tbaM/eBAudNq639Ziq6qqmh13M2vz/0W4ePGTAtzo34xzjgpSCWGkU8Nr1aM44FIxHA7w+FMZlFaDx5rv3zmYwH58FmJbbQ7T++xmgKeCmhp/5A9ra5+T9qxPGK/YlJSURsVpS7keHjCAwwNOXUDgAfYUjmP/qLOoSkvDZ0Z5ejpvXDyXwg0bGHDwEJ70dNxFF5H/wP1ktPDllSuvIG38eEr/8AdqDxwgbdxYsq+5Bl9O9Ok6Wvq9nzhxIqtWrWr2xdPn8zFhwoQ2fd50jmh/bGfPEa2JxTmiJWYWl1hof21wuhb3ej2iwBKR2KitrWX58uWsXr2aqqoqAoEAM2fO5JxzzmlUaHXG4MGDKS4ubrUlty3qU/HhwCCFU9/a56duZmcoi4PBPpzhqWSo52jUK+s8BoW+gxT6DnY4n2QT8nqpavKl7EhODn+bNavRtqxf/5rbbrutxR6CtDGjGfTtBzqdy3nnnceGDRsate6bGbm5uUyZMqXT+xdJFHWIi0jE0qVLeffddyPdDGVlZbz00kusXLkyZq8xc+bMZsVVrIq3enmeMopSShjlO0yqJ9TqFAvdcuqFbqSsrIxXX301rq8RCAS44447KCoqIhAIkJ2dzfnnn89tt93WrtY8ke5GBZaIAHDgwAE2btzYYsvSihUroo6Za48RI0ZwzTXXkJWVFdmWl5fHqFGjmhVa7W3qDz8H/OZUPLXCzJgzZ06bYp1zbNiwgX379sU1p0AgwPz581m4cCF33303c+bMadfwBJHuSF2EIgLAnj17Wn2sqqqKw4cPM3DgwJi8VmFhIePGjePIkSOkpKQQCASoqqri2WefZfv27ZG47OxsRowYwdq1a2PyuhIumvbs2UMgEOD48eNt6qp9/PHHCQQCVFRUkJ2dzbnnnsvUqVNj3vIo0pOowBIR4PQXSrTnQoq2MDNyGgyKTk1N5aabbmLfvn3s37+fQCDA8OHDWbp0aWTySYmNLVu2MGLECEKhEOXl5W06vmVl4YulDh48yJ///GdKS0uZPXt2V6QrkpRUYIkIAAUFBaSlpVFVVdXoj62ZkZ+f3+JA53gYNGgQgwYNivw8cOBANmzY0CWvXa/+Pdff7846kqtzju3bt/P1r3+dffv2cfToUTZu3MjediyN88477zBjxox2d+GK9BYJnQfLzC4E7gPOAQYDVzjn/tSO5weAY8eOHeuyk79IT1ZcXMzTTz9NTU0NHo+HUChEdnY2CxYsaDRmqiudPHmSRx99tFnhJ5136623MqxuWZtgMMh7773H66+/3ubnX3vttYwbNy5e6Yl0S2VlZfXnw249D1Ym8BHwJPDHBOci0usNHz6chQsXsmHDBsrKyhg4cCBjx45t89w68ZCRkcGCBQt44YUX+Mc//gGE52eLNvGhtE12dnbkvtfr5YILLmDz5s2UlJS0qZht61xOIr1RQgss59wyYBl0/2Z4kd4iPT2dadOmJTqNRgYOHMgdd9xBaWkpNTU1bN26lddee00tWp1QUFDQYsv/nDlzWLJkyWnHZaWnpzNixIh4piiS1DRNg4gkjezsbPr3709WVlZci6vq6moWL17M4sWLT7t0TaJ1NNcBDWZ3b2j48OHccsst5Ofn4/F4SE1NjSwzY2aYGV6vl6uuuiqyTIyINJfoLsJ2MbNUoOE6C30TlYuIJM6YMWPIyMigoqIiboVWtLXMupv25urxeFp9jnOOgQMHctNNN0UKq/LyclavXs2mTZsoLS3FOceqVatIT0+vX5NNRJpIqgILuB9YlOgkRCSxfD4fN9xwA08//XTUhVqlZaFQqNGVmhAurD744APefvttysvL8fv9nHPOOcyePZuMjAy2b9/OwYMHIwXt1q1b2bZtG7feeitnnnlmIt6GSLeWbF2EPwKyGtyGJDYdEUmUvLw8Fi5cyDXXXENubm6i00kqfr+fSZMmNdr27rvvsmzZMsrLy4Fw1+PKlSt5/vnn2bJlC7t3727UWuicIxQK8cYbb3Rp7iLJIqlasJxzVXBqVVcNjBfp3bxeL4WFhYwdO5YlS5awc+fORKeUFK6//vpGS9HU1NTw9ttvN4tzzrFp0ya8Xm9k2o6mj+/YsQPnnM7HIk0ktAXLzPqY2RQzm1K3aUTdz8MSmZeIJBePx8Oll16a6DSSRtMWvyNHjkQW+G5JtCkxUlJSVFyJtCDRXYRFwId1N4Af193/QcIyEpGklJuby4wZMxKdRlIoLS1t9PPplkE666yzmrVeQbgXoWlXo4iEJXoerOWAvvqISEx87nOf48wzz2TNmjXs27cvaqtMNGYWuTquu7fOdCTXpvNf1a/7uHPnzmbLJKWnp1NUVERVVRXLly/H4/HgnMM5R05ODv/0T/8Uuzcj0oMkdKmcztJSOSLSmpdeeom1a9e22PLS282fP7/ZZLLHjh3jN7/5DUePHo2Mt/L7/dxwww2R5XRKSkr4+OOPqaqqIj8/nwkTJmg2d+l12rpUjgosEemRPvnkE37/+98nOo1uqW/fvixcuLBZi1cwGOSTTz7hwIEDBAIBJkyYQGpqait7EemdkmUtQhGRTgkGg/ztb39jzZo1VFRUkJeXx6xZsxg9ejTDhw+nuLg40Sl2O8ePH6eysrLZ2Cuv18v48eMZP358gjIT6TkSPchdRKTDnHM899xzLF++nOPHj1NbW8vu3bt56qmn+PTTT7nuuus61IVVXV3Nww8/zMMPP5wUS+W0N9eUlBT8fn+cMxPp3VRgiUjS2rNnD5s3b260rX7Yw6uvvkpJSQk1NTUd2vexY8c4duxYp3PsCu3J1cw4++yztY6gSJypwBKRpFVcXNzqlXOHDh1KqvUE48Hr9TYbQ1VQUMCcOXMSlJFI76ExWCKStKJ1c5kZw4YNw+v1EgwGuzCr7mP48OF84QtfYMuWLZw8eZIhQ4Y0W4NQROJDLVgikrQKCwtb3G5mjB49mqysLGbNmhXZ1tsUFhbi8/koLCykqKhIxZVIF1KBJSJJq2/fvlx22WVAeLkcjyd8SgsEAsybNw+AmTNncuWVVzJ48OBeNeVA//79mTx5cqLTEOm11EUoIklt6tSpDB06lHXr1nHy5Eny8vKYNGlSo+7DiRMnMnHiRACeeeYZtmzZkqh0u8zs2bM1kF0kgVRgiUjSy83NZe7cuW2Kveqqq3j88cc5cuRI1Lj+/fvHIrUu0TRXv9/PWWedlaBsRAQ0k7uI9EK1tbVs3LiR9957j3379iU6nZi78sorIy12IhJbWipHRKQNPvroI1566aUOz5fVHfXp0wePx8P48eO54IILyMjISHRKIj2GCiwRkSiKi4t55ZVXOHr0KH6/n2AwyIkTJxKdVkyZGWeccQa33347aWlpiU5HpEfQWoQiIq1YvXo1L730UuTnysrKRo9XV1fzxBNPAHD77bd362VlouXqnOPo0aOsXbuW8847L1EpivRKmqZBRHqVYDDIsmXLTht38OBBDh482AUZdV60XJ1zfPrpp12ckYiowBKRXqW4uJhQKJToNLqMmeHzqbNCpKvpt05EerQdO3awYsUKSkpKSE9PJz8/P9EpxUxKSkqj4qml2eqdc4wfP74r0xIR1IIlIj3Y1q1bWbJkCbt27aK2tpbjx4+zYcOGRKcVE8OHD+eyyy7jxhtvjGwbMmQIEC606outMWPGaMoGkQRQC5aI9EjOOV599dXI/Z6muLiY4uJiqqurI9uuv/56iouL2bJlCx6Ph7FjxzJ27NjIEkIi0nVUYIlIj1RZWdnqwG8zo3///hw4cKDFx5N1SgOv18ukSZOYNGlSolMR6fX0tUZEeiSv19vimKR6kydPJjc3t8XHKisryc3NJTs7O07ZxVZWVhbZ2dk9arJUkWSnAktEeiS/38+YMWNaLbImTJhAnz59Wn3u1772NbZv305eXl480+w0v9/PwoULueeee3pkV6hIslKBJSI91rx58yKrPHg8nshYpEsuuYRAIHDa6Rr+8pe/cOjQobjnGQt+v7/VglFEup7GYIlIjxUIBLjzzjv5+OOPKSkpISMjgylTptC/f38ARowYwa5du1p9/p49e7oq1U6bMWMGKSkpiU5DROpoLUIR6bUqKipYvHhxs+01NTU8+eSTeL1ebr755m5duIRCIZ555hmysrJ46623SE9PT3RKIj2a1iIUETmN9PR0hg4dyu7duxttd86xd+/eyP3uavbs2YwbN44f/OAHAL1qhnqR7k5jsESkV7v55pvJyclptK3hwPiMjIyuTqlNvF4vRUVFarES6abURSgiAuzatYtNmzbRp08fJkyYEJmiYdeuXaxYsYJt27YB4cLG6/U2muAzEebOncv555/PiRMnIoPby8vLyczMTGheIj1dW7sIVWCJiDTRUtFSXV1NKBQiLS2Nn/3sZwm9uvDqq6+OrC+oAkuka7W1wFIXoYhIG/j9/sgM70OGDIk6iWk8mRljxoxJyGuLSNupwBIRaafPfOYzCVvfb+zYsfh8uj5JpLtTgSUi0oLc3NxWl9IZMGAAN954Y2Q+ra7St29fPvvZzzbbHi1XEUmMhI/BMrM7gfuAwcDfgXucc2+38bkagyUiCeOcY/369SxdujQu+8/JySE/P5+KigqGDBnC1KlTddWgSIIlxTxYZnYd8BPgTuAd4MvAMjMrdM61Pr2yiEg3YGYUFhaybNkyKisrY77/adOmMWPGjJjvV0TiL9FdhN8AfuWc+6VzbpNz7h5gN/DVxKYlItI2Pp+Pyy67DDOLybis+sHzo0ePpqioqNP7E5HESFgXoZn5gZPANc65pQ22PwJMcc7NasM+1EUoIjFXUVHBvHnzAFi2bFmbuuX279/PBx98wOHDhwkEAnz88cftft1AIMDYsWMpKCjgrLPOatOVih3JVUQ6Lhm6CHMBL7C/yfb9wKCWnmBmqUBqg01945OaiPRmoVCIFStWRO63xcCBA7n00ksjPx8+fJiSkpI2v+ZFF13EBRdcgNfrjXuuIhJ/ie4iBGjahGYtbKt3P3CswS15lroXkV5lwYIFbS6WvvOd7zBr1qx2F1ci0n0lssA6BARp3lo1gOatWvV+BGQ1uA2JW3YiIp3g8/m4//77Txt33333qbAS6YESVmA556qBNcDFTR66GHi3ledUOefK6m/A8TinKSLSYV6vl+9+97utPj5r1qxuu5i0iHROoqcD/jHwlJmtBt4D7gCGAb9IaFYiIjHi8XhYtGgRZWVlPPzww0D4SsHvfe97Cc5MROIpoQWWc+73ZpYDfI/wRKMbgPnOuZ2JzEtEJNYCgQCLFi1KdBoi0kUS3YKFc+7nwM8TnYeISEPJ1HWXTLmK9BYJL7BERLqbzMxMTpw4keg02iSZchXpTbrDNA0iIiIiPYoKLBEREZEYU4ElItJEZWUll1xyCZdccklcFnGOpWTKVaQ30RgsEZEmgsEgL7/8cuR+d5ZMuYr0JmrBEhEREYkxFVgiIiIiMaYCS0RERCTGVGCJiIiIxJgKLBEREZEY6xFXEZaVlSU6BRHpQRrOjF5WVtatr85LplxFeoK21hzmnItzKvFjZmcCexKdh4iIiPQ6Q5xzJa09mOwFlgF5wPFE5xJFX8JF4BC6d57JSMc2PnRc40fHNn50bONHx7a5vsBeF6WISuouwro31mr12B2Ea0AAjjvn1JcZQzq28aHjGj86tvGjYxs/OrYtOu1x0CB3ERERkRhTgSUiIiISYyqw4q8K+H7dvxJbOrbxoeMaPzq28aNjGz86th2Q1IPcRURERLojtWCJiIiIxJgKLBEREZEYU4ElIiIiEmMqsERERERiTAVWFzOzS8zsfTOrMLNDZvZ8onPqScws1czWmZkzsymJzifZmdlwM/uVme2o+8xuM7Pvm5k/0bklIzO7s+5YVprZGjObmeickp2Z3W9mH5jZcTM7YGZ/MrMxic6rp6k7zs7MfpLoXJKFCqwuZGZXAU8BTwKTgfOBpxOaVM+zGNib6CR6kLGEzxNfBsYDC4GvAA8lMqlkZGbXAT8BfghMBd4GlpnZsETm1QPMAn4GnAtcTHiFklfMLDOhWfUgZjYNuAP4ONG5JBNN09BFzMwHFAOLnHO/SnA6PZKZzQN+DFwF/B2Y6pxbl9CkeiAzuw/4qnNuZKJzSSZm9j6w1jn31QbbNgF/cs7dn7jMehYz6w8cAGY5595KdD7Jzsz6AGuBO4HvAOucc/ckNKkkoRasrnM2cCYQMrMPzewfZrbMzMYnOrGewMwGAk8ANwEnE5xOT5cFHEl0Esmkrkv1HOCVJg+9ApzX9Rn1aFl1/+ozGhs/A15yzr2W6ESSjQqsrlP/bf9B4D+AS4GjwAoz65eopHoCC69E+mvgF8651QlOp0czs7OArwO/SHQuSSYX8AL7m2zfDwzq+nR6prpzwY+BvznnNiQ6n2RnZl8g3DigFtYOUIHVSWb2YN3Av2i3Ik4d6x865/7onFsD3Ao44JqEvYFurB3H9utAAPhRglNOGu04tg2fkwf8BXjOOffLxGSe9JqOybAWtknHPQZMAr6Y6ESSnZkNBR4BbnTOVSY6n2TkS3QCPcBjwO9OE1MM9K27v7F+o3Ouysy2Axrk2rK2HtvvEB7gWhX+Ahux2sz+xzl3S3zSS2ptPbZApLh6E3iP8GBXaZ9DQJDmrVUDaN6qJR1gZo8ClwMXOuf2JDqfHuAcwp/PNQ3Oq17gQjP7GpDqnAsmKrlkoAKrk5xzhwifPKMyszWEF8ocA/ytblsKMBzYGccUk1Y7ju1dhIusennAX4HrgPfjk11ya+uxBTCzMwkXV2uAW51zoXjm1hM556rrzgEXA0sbPHQx8EJisuoZ6roFHwWuAC5yzu1IcEo9xevAxCbbngQ+Af5TxdXpqcDqIs65MjP7BfB9M9tNuKi6r+7h5xKXWfJzzu1q+LOZldfd3aZvsp1T13K1HNgF3Av0r/8265zbl7jMktKPgafMbDWnWgKHofFsnfUz4HrgX4DjZlbfSnjMOVeRuLSSm3PuONBoHJuZnQAOa3xb26jA6lr3AbWE58JKJ9y6Mts5dzShWYm07rPAqLpb02LVmodLa5xzvzezHOB7wGDCf7zmO+fUgt059dNeLG+y/VbCF7+IJITmwRIRERGJMV1FKCIiIhJjKrBEREREYkwFloiIiEiMqcASERERiTEVWCIiIiIxpgJLREREJMZUYImIiIjEmAosEen2zGy5mf0k0XmIiLSVCiwR6XXMbIGZuRZu/9ogxm9m3zSzj8zspJkdMrN3zOzWunVERURapaVyRKS3KiO8+HpDxyBcXBFeMHwy8F3gnbr4cwmvyfghsK6rEhWR5KMWLBFJKmZ2hpktMbOjdS1Ly8ysoEnM7Wa2u+7xpWb2DTMrbbIr55zb1+RWvzjwPcCFwBzn3M+cc+ucc9udc08DM4Ctda9ztZmtN7MKMztsZq+ZWWZ8j4CIJAMVWCKSbH4NFAGXA58hvOj0y/XddmZ2PvAL4BFgCvAq8O12vsYNwGvOuQ+bPuCcq3HOnTCzwcAzwP8DxgEXAc+jRbBFBHURikgSqWupuhw43zn3bt22G4DdwOeB54CvA8ucc/+37mlbzOw84NImu8sys/IGP5c75wbV3S8Alp8mncGEz6HPO+d21m1b3+43JSI9kgosEUkm44Ba4P36Dc65w2a2ue4xCI+rWtrkeatoXmAdB85u8HOowX0D3Gly+Qh4HVhvZn8FXgH+4Jw72ob3ISI9nLoIRSSZtNb91rAgaqk4aul5Iefcpw1u2xs8toVTBVuLnHNB4GJgHrCRcMvZZjMbcZr3ICK9gAosEUkmGwm3vM+o32BmOcBoYFPdpk+A6U2eV9TO13kamGtmU5s+YGa++oHsLuwd59wiYCpQDVzRztcSkR5IBZaIJA3n3FbgBeAJM7vAzCYDvwVK6rYDPArMr7tysMDMvky4lel0XX4N/YTw1Ayvm9n/MrPJZjbSzK4l3D1ZYGYzzOwBMysys2HAlUB/ThV6ItKLqcASkWRzK7AGeBF4j3D333znXA2Ac+4d4CvANwiPk/pn4GGgsq0v4JyrItz9txj4MrAS+AC4C/gpsIHwvFgXAi8T7lL8D+B/O+eWdfodikjSM+fa86VORCT5mNkTwFjn3MxE5yIivYOuIhSRHsfM7iU8/9UJwt2DtwB3JjQpEelV1IIlIj2OmT1LeOLPvsB24FHn3C8SmpSI9CoqsERERERiTIPcRURERGJMBZaIiIhIjKnAEhEREYkxFVgiIiIiMaYCS0RERCTGVGCJiIiIxJgKLBEREZEYU4ElIiIiEmMqsERERERi7P8DyhvVx/VMupAAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 700x500 with 1 Axes>"
       ]
@@ -852,7 +852,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "475af7be",
+   "id": "54a07439",
    "metadata": {},
    "source": [
     "To extract the top significant genes after FDR correction (`adj_pvals`), we can run this:"
@@ -861,7 +861,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "580937d1",
+   "id": "d40f9478",
    "metadata": {},
    "outputs": [
     {
@@ -885,6 +885,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>contrast</th>\n",
+       "      <th>name</th>\n",
        "      <th>logFCs</th>\n",
        "      <th>pvals</th>\n",
        "      <th>adj_pvals</th>\n",
@@ -892,31 +894,41 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>LPAR1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>treatment.vs.control</td>\n",
+       "      <td>LPAR1</td>\n",
        "      <td>-2.856200</td>\n",
        "      <td>1.555664e-08</td>\n",
        "      <td>0.000222</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>FTH1</th>\n",
+       "      <th>1</th>\n",
+       "      <td>treatment.vs.control</td>\n",
+       "      <td>FTH1</td>\n",
        "      <td>-2.028517</td>\n",
        "      <td>1.330181e-07</td>\n",
        "      <td>0.000948</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>NTNG2</th>\n",
+       "      <th>2</th>\n",
+       "      <td>treatment.vs.control</td>\n",
+       "      <td>NTNG2</td>\n",
        "      <td>-0.901692</td>\n",
        "      <td>2.986280e-07</td>\n",
        "      <td>0.000957</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>FTL</th>\n",
+       "      <th>3</th>\n",
+       "      <td>treatment.vs.control</td>\n",
+       "      <td>FTL</td>\n",
        "      <td>-1.825337</td>\n",
        "      <td>3.792117e-07</td>\n",
        "      <td>0.000957</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>APCDD1L</th>\n",
+       "      <th>4</th>\n",
+       "      <td>treatment.vs.control</td>\n",
+       "      <td>APCDD1L</td>\n",
        "      <td>2.706221</td>\n",
        "      <td>4.179862e-07</td>\n",
        "      <td>0.000957</td>\n",
@@ -926,57 +938,69 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>VAMP1</th>\n",
+       "      <th>3766</th>\n",
+       "      <td>treatment.vs.control</td>\n",
+       "      <td>VAMP1</td>\n",
        "      <td>-0.892178</td>\n",
        "      <td>2.064460e-02</td>\n",
        "      <td>0.049842</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>RP11-214K3.25</th>\n",
+       "      <th>3767</th>\n",
+       "      <td>treatment.vs.control</td>\n",
+       "      <td>RP11-214K3.25</td>\n",
        "      <td>-0.652381</td>\n",
        "      <td>2.065199e-02</td>\n",
        "      <td>0.049852</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>SLC26A6</th>\n",
+       "      <th>3768</th>\n",
+       "      <td>treatment.vs.control</td>\n",
+       "      <td>SLC26A6</td>\n",
        "      <td>-0.787473</td>\n",
        "      <td>2.071529e-02</td>\n",
        "      <td>0.049963</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ANKDD1A</th>\n",
+       "      <th>3769</th>\n",
+       "      <td>treatment.vs.control</td>\n",
+       "      <td>ANKDD1A</td>\n",
        "      <td>-1.015187</td>\n",
        "      <td>2.071575e-02</td>\n",
        "      <td>0.049963</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>BCKDHB</th>\n",
+       "      <th>3770</th>\n",
+       "      <td>treatment.vs.control</td>\n",
+       "      <td>BCKDHB</td>\n",
        "      <td>-0.627068</td>\n",
        "      <td>2.072337e-02</td>\n",
        "      <td>0.049965</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>3771 rows × 3 columns</p>\n",
+       "<p>3771 rows × 5 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                 logFCs         pvals  adj_pvals\n",
-       "LPAR1         -2.856200  1.555664e-08   0.000222\n",
-       "FTH1          -2.028517  1.330181e-07   0.000948\n",
-       "NTNG2         -0.901692  2.986280e-07   0.000957\n",
-       "FTL           -1.825337  3.792117e-07   0.000957\n",
-       "APCDD1L        2.706221  4.179862e-07   0.000957\n",
-       "...                 ...           ...        ...\n",
-       "VAMP1         -0.892178  2.064460e-02   0.049842\n",
-       "RP11-214K3.25 -0.652381  2.065199e-02   0.049852\n",
-       "SLC26A6       -0.787473  2.071529e-02   0.049963\n",
-       "ANKDD1A       -1.015187  2.071575e-02   0.049963\n",
-       "BCKDHB        -0.627068  2.072337e-02   0.049965\n",
+       "                  contrast           name    logFCs         pvals  adj_pvals\n",
+       "0     treatment.vs.control          LPAR1 -2.856200  1.555664e-08   0.000222\n",
+       "1     treatment.vs.control           FTH1 -2.028517  1.330181e-07   0.000948\n",
+       "2     treatment.vs.control          NTNG2 -0.901692  2.986280e-07   0.000957\n",
+       "3     treatment.vs.control            FTL -1.825337  3.792117e-07   0.000957\n",
+       "4     treatment.vs.control        APCDD1L  2.706221  4.179862e-07   0.000957\n",
+       "...                    ...            ...       ...           ...        ...\n",
+       "3766  treatment.vs.control          VAMP1 -0.892178  2.064460e-02   0.049842\n",
+       "3767  treatment.vs.control  RP11-214K3.25 -0.652381  2.065199e-02   0.049852\n",
+       "3768  treatment.vs.control        SLC26A6 -0.787473  2.071529e-02   0.049963\n",
+       "3769  treatment.vs.control        ANKDD1A -1.015187  2.071575e-02   0.049963\n",
+       "3770  treatment.vs.control         BCKDHB -0.627068  2.072337e-02   0.049965\n",
        "\n",
-       "[3771 rows x 3 columns]"
+       "[3771 rows x 5 columns]"
       ]
      },
      "execution_count": 14,
@@ -991,7 +1015,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b7add23f",
+   "id": "bd430c3a",
    "metadata": {},
    "source": [
     "## Pathway activity inference\n",
@@ -1003,7 +1027,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "74d4b0bd",
+   "id": "056bcf0b",
    "metadata": {},
    "outputs": [
     {
@@ -1092,7 +1116,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "413ec82e",
+   "id": "56cde814",
    "metadata": {},
    "source": [
     "After running the `consensus` method, we obtained activities and p-values for each pathway.\n",
@@ -1103,7 +1127,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "c1ce8f9a",
+   "id": "efe727bd",
    "metadata": {},
    "outputs": [
     {
@@ -1125,7 +1149,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f1f51a36",
+   "id": "7d0a85e3",
    "metadata": {},
    "source": [
     "As expected, after treating cells with the cytokine TGFb we see an increase of activity for this pathway.\n",
@@ -1138,12 +1162,12 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "09ab0d6a",
+   "id": "e00e5fd2",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x500 with 2 Axes>"
       ]
@@ -1160,7 +1184,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cde703ef",
+   "id": "49c87ac5",
    "metadata": {},
    "source": [
     "In this plot we can see that genes with positive weights towards TFGb have positive logFCs, while genes with negative weights have\n",
@@ -1172,12 +1196,12 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "b1e852c5",
+   "id": "9ea2cd8c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 700x500 with 2 Axes>"
       ]
@@ -1194,7 +1218,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c5becd38",
+   "id": "6b959638",
    "metadata": {},
    "source": [
     "Here most of the downstream genes with positive weights of JAK-STAT seem to have negative logFCs, indicating that the pathway\n",
@@ -1203,7 +1227,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f1d00a75",
+   "id": "3fe9dee7",
    "metadata": {},
    "source": [
     "## Transcription factor activity inference\n",
@@ -1215,7 +1239,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "6cbda2c5",
+   "id": "3b8386a0",
    "metadata": {},
    "outputs": [
     {
@@ -1317,14 +1341,14 @@
     "# Retrieve DoRothEA gene regulatory network\n",
     "dorothea = dc.get_dorothea()\n",
     "\n",
-    "# Infer pathway activities with mlm\n",
+    "# Infer pathway activities with consensus\n",
     "tf_acts, tf_pvals = dc.run_consensus(mat=logFCs, net=dorothea)\n",
     "tf_acts"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0325ad4d",
+   "id": "a3834731",
    "metadata": {},
    "source": [
     "After running the `consensus` method, we obtained activities and p-values for each transcription factor.\n",
@@ -1335,7 +1359,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "5e20be0e",
+   "id": "f810e11d",
    "metadata": {},
    "outputs": [
     {
@@ -1357,7 +1381,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd507021",
+   "id": "2b4cf5b1",
    "metadata": {},
    "source": [
     "EPAS1, SRF, and TP63 seem to be the most activated in this treatment while STAT2, HNF4A AND SOX10 seem to be inactivated.\n",
@@ -1368,12 +1392,12 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "42bec403",
+   "id": "92d55a88",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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zcuRIlixZwoIFC7juuusYPXo01113XY1yrVq14uabb+buu++mtLS0QdcwxmDN81rzunPnzmXMmDGcddZZ7NixI6h7UUopFXkSLyHZVP00yWpiUlNT6dmzJwcccABPPfUUJSUl3HvvvT7L3njjjRQVFfHcc8816BorVqygW7duPq976KGHMmXKFOLi4pgyZUrA96GUUsoeQXcVejZVP02ymrh77rmHRx55hC1bttQ4lpaWxl133cX9999Pbm6uX/WtXLmSL7/8kjPOOKPOcsaYGlM9KKWUUuovmmQ1cSNGjGDAgAE88MADPo9fccUVZGZm8s4779Q4Vl5ezrZt29iyZQtLly7l6aefZvjw4QwaNIhbbrkFsGYCv/POO5k/fz4bNmzg559/5vLLL2fz5s2MGzcurPemlFIq9LS7MHI0yYoCN954Iy+//DKbNm2qcSw+Pp7/+7//o7i45qoHv//+O+3ataNz586MGDGC6dOnc8cdd/DDDz+QlpYGgNPpZOXKlZxxxhn07t2bE088kZ07d/LDDz8wYMCAsN+bUkqp0NKnCyNHmvKcOCKSAeTk5ORE5bI6pryckpVLcOflEN+xG/GdutsdUswoKSnhyiuvBODFF1+M+lnQY+1+lWoscnNzyczMBMg0xvg3riNAFb8zv+i7P6nO4JbVKXC5GLtyKUQg7qYsKtYujEZlm9ay99VHcef/9b2b0Gs/ml10PY6kZBsjiw2JiYlMmzbN7jAiJtbuVymlIkG7CxshU1bKnlf+jbug6qLspWuWk/dZw+e9UkoppSo4nBKSTdVPk6xGqHjZIkxhPhhDudvNy4tX8v7v68C4KVo8B3dJzfFVKrSMMRQUFFBQUBATy8zE2v0qFcvEISHZVP00yWqE3Dl7Qawvza/b9vDAD0vYlFNgHXSVWwmYCqvCwkLS0tJIS0uLiWVmYu1+lVIqEnRMViMU164zGGvNwYPbZ7Hu+rMrZ2CX5FQc6c1sjE4ppVRTJk4H4gyujUXQFm9/aJLVCCX0GkBc+y6Ub9sEbneVJW5SR56ExOmXTSmlVGBCMabKgXYX+kO7CxshcThoPuFWEvsdCBUtWInJpI0ZR+qIE2yOTimllFL+0CaRRsqZlknz8X/HlZ+DOz+PuJatkfgEu8NSSinVxIkEP3Bd3NqS5Q9Nsho5Z1omzrRMu8NQSikVJcRJ0N2FokOy/KLdhUoppZRSYaAtWUr54HQ6OfPMMytfR7tYu1+lYpk4BQm6JUu7C/2hSZZSPiQlJfH+++/bHUbExNr9KhXLxOFAHEFO4RDk+bFCPyWllFJKqTDQliyllFIqhoRiWRxdVsc/2pKllA8FBQXWY84iFBQU2B1O2MXa/SoVy3SB6MjRJEsppZRSKgy0u1AppZSKIdpdGDmaZCmllFIxRCQETxeKdoT5Qz8lpZRSSqkwsDXJEpFJImKqbdvsjEkppZSKZhXdhcFuAV1b5GoRWS8ixSKyWESO9PO8w0WkXESWBHRhmzSG7sLfgWO93rvsCkQppZSKdqF4OtARwALRInI28ARwNfAjcCUwU0T6G2M21nFeJvA68C3QJpB47dIYkqxyY4y2XqlGxel0Mnbs2MrX0S7W7lcpZYsbgSnGmFc8728QkdHAROCOOs57EXgbqxHm1LBGGGKNIcnqJSJbgBJgAXCnMWadr4Iikggkeu1Kj0B8KgYlJSXx+eef2x1GxMTa/SoVy0L8dGG6SJW6SowxJTXKiyQABwMPVjv0FXBYrdcRuQToAVwA/DOIkG1h98D3BcBFwGhgAtAWmCsiLWspfweQ47VtjkSQSimlVLSoWLsw2M1jM1V/L9fWIpUFOIHt1fZvx/rdXzNOkV5YSdn5xpjyIG/bFra2ZBljZnq9XSoi84C1wMXAYz5OmVxtfzqaaCmllFJ26Qjkeb2v0YpVjan2XnzsQ0ScWF2E9xhjVgcVoY3sbsmqwhhTACwFetVyvMQYk1uxUfUL26iMHz++cpmS+Ph42rRpw6hRo3j11Vdxu92V5bp27VpZznt78EGrRTU7OxsRoXXr1uTlVb3dQYMGMWnSpMr3I0aMQER49913q5R74okn6Nq1a+X7adOmVV7H6XTSvHlzhg4dyn333UdOTk6Vc59//nkOOOAAMjIyyMjIYNiwYcycOZNoV1BQQGpqKqmpqTGxzEys3a9SsSzETxfmef9e9tVV6LELa0xV9Var1tRs3QKrEWUw8IznqcJy4G5goOf90cF/EuHXqJIsz5irfsBWu2MJhTFjxrB161ays7OZOXMmI0eO5Prrr+fEE0+kvPyvls/77ruPrVu3Vtmuu+66KnXl5eXxyCOP1HvNpKQk/vnPf1JWVlZnuYyMDLZu3crmzZuZO3cuV1xxBa+//jqDBg1iy5YtleU6duzIgw8+yKJFi1i0aBFHH300p5xyCr///nsDP42mp7CwkMLCQrvDiJhYu1+lYpUdUzgYY0qBxcCoaodGAXN9nJIL7A8M8tpeAFZ5Xi9oUAA2sXuerEdEZLiIdBORocB/gAzgNTvjCpXExETatm1Lhw4dOOigg7jzzjv55JNPmDlzJtOmTassl56eTtu2batsqampVeq67rrreOyxx9ixY0ed1zz33HPJycnh5ZdfrrOciNC2bVvatWtHv379uOyyy5g7dy75+fnceuutleVOOukkxo4dS+/evenduzf3338/aWlpzJ8/v+EfiFJKqVj2GHC5iFwqIv1E5HGgM1byhIhMFpHXAYwxbmPMMu8N2AEUe943iSZ3u1uyOgLvYGWmHwKlwKHGmA22RhVGRx99NAMHDuTDDz9s0HnnnnsuPXv25L777quzXEZGBnfeeSf33Xdfg7t9Wrduzfnnn8+nn36Ky1VzujKXy8W7775LQUEBw4YNa1DdSimlGge7JiM1xrwH3IDV7bcEOAoY6/U7vx1W0hU1bE2yjDHnGGPaG2MSjDEdjDFnGGOW2xlTJPTt25fs7OzK97fddhtpaWlVttmzZ1c5p2Kc1ksvvcTatWvrrP/qq68mKSmJxx7z9exA/bHl5eWxe/fuyn1Lly4lLS2NxMRErrrqKj766CP69+/f4LqVUkrZz0qSgn26MLApIIwxzxljuhpjEo0xBxtj/ud1bLwxZkQd504yxgwK6MI2sbslKyYZY/CeV+SWW25hyZIlVbahQ4fWOG/06NEcccQR3HXXXXXWn5iYyH333ce///1vdu3a1eDYgCrx9enThyVLljB//nwmTpzIxRdfzPLlUZ8LK6VUVBKHVM76HugW7DxbsUKTLBusWLGCbt26Vb7PysqiZ8+eVbbk5GSf5z744IO89957/PLLL3Ve44ILLqBr167861//anBsGRkZtGz511RlCQkJ9OzZk8GDBzN58mQGDhzIk08+2aB6lVJKqVjTGGZ8jynfffcdS5cu5e9//3tA5x9yyCGcfvrp3H777XWWczgcTJ48mdNPP52JEyf6VfeOHTt4++23OfXUU3E4as+/jTGUlNQ3FUrT5nA4GD58eOXraBdr96tULAvxjO+qDppkhVFJSQnbtm3D5XKxfft2vvzySyZPnsyJJ57IRRddVFkuLy+PbduqLt+YkpJCRkaGz3rvv/9+BgwYQFxc3V++E044gaFDh/Liiy/Spk3VNTWNMWzbtg1jDPv27WPevHk88MADZGZmVs7RBXDnnXdy/PHH06lTJ/Ly8nj33XeZPXs2X375ZUM/jiYlOTm5xri4aBZr96tULKs2Y3vAdaj66acURl9++SXt2rWja9eujBkzhlmzZvHUU0/xySefVFmE9+6776Zdu3ZVNu9pFKrr3bs3l156KcXFxfXG8NBDD/ksl5ubS7t27ejQoQPDhg3jxRdf5OKLL+aXX36hXbt2leW2b9/OhRdeSJ8+fTjmmGNYsGABX375JaNGVZ/qRCmllFLepGKgc1MkIhlATk5OTq2tPuFm3G6KfvqeokU/YIoKSOg5gNThY3E2z7IlHqWUUk1Hbm4umZmZAJmelUzCpuJ35m8XHk96QnxQdeWVlnHAGzMhAnE3ZdpdGARjDPvefpaSXxeACBhD+c6tFC2eQ8vrJhHXur3dIaoAFRQUVC5FlJ2dXWNy2GgTa/erVCzTMVmRo92FQShdu8JKsAA8LYLbcvP5ZsVadn7ylo2RqVDYtWtXg6fAaMpi7X6VUirctCUrCCUrfgGHE9wuft22m+tnzmPV7hziHMI3mWm0vfzmKvNNKaWUUnbTge+Ro0lWMLwSqKQ4J0d0bsvfh+3HEZ3b0jI9zcbAlFJKKd+0uzByNMkKQtKAwRR+/wUAfbKa8a9jBlsHHA6S9h+srVhKKaVUDNP2viDEd+1F8iEjrDfiqPzXkZJG2vFn2RaXUkopVZvg1y0MvrsxVmhLVhBEhIwzLyOx9/4ULZ6Du6iAhJ79STlsFM70TLvDU0oppWoSqTLcJeA6VL00yQqSiJA0cChJA2su6KyaLofDweDBgytfR7tYu1+llIoETbKU8iE5OZmffvrJ7jAiJtbuV6lYJhKCge/akuUXTbKUUkqpGKJTOESOfkpKKaWUUmGgSVYMGD9+vNU8LEJ8fDzdu3fn5ptvpqCggOzsbESEJUuW+Dx32rRpNGvWrMp7EWHMmDFVyu3btw8RYfbs2YC1NMtll11Gt27dSE5OpkePHtxzzz2UlpaG6S5Dq7CwkK5du9K1a1cKCwvtDifsYu1+lYplFfNkBbup+ml3YYwYM2YMU6dOpaysjB9++IHLL7+cgoICbrvttgbXFRcXx7fffsusWbMYOXKkzzIrV67E7Xbz4osv0rNnT5YtW8aECRMoKCjgkUceCfZ2ws4Yw4YNGypfR7tYu1+lYpl2F0aOJlkxIjExkbZt2wJw3nnnMWvWLD7++OOAkqzU1FTOOussbr/9dhYsWOCzzJgxY6q0dnXv3p1Vq1bx/PPPN4kkSymllAqWpqIxKjk5mbKysoDPnzRpEkuXLuU///mP3+fk5OTQokWLgK8ZCnPnzsXpdNbo7gT44IMPGDp0KJmZmZUJaW2OO+44nE4n8+fPr3Gsru7ZCtdffz0HH3wwiYmJDBo0KOj7Ukopf4kjFF2Gdt9F06AfUwxauHAhb7/9Nsccc0zAdbRv357rr7+ef/zjH5SXl9dbfu3atTz99NNcddVVAV8zFF599VWuu+465syZw8aNGyv3f/PNN5xzzjmceeaZLFy4kB9++KHWOjZu3Mi8efO49tprmTJlis8yY8aMYevWraxbt45//etfPPfcc9x8882Vx40xXHrppZx99tmhuzmllPKDjsmKHE2yYsSMGTNIS0sjKSmJYcOGcdRRR/H0008HVedtt93Gzp07efXVV+sst2XLFsaMGcO4ceO4/PLLg7pmMAoKCpg+fToTJ07kxBNPZNq0aZXHZsyYwRFHHMEtt9xCnz596NWrV631TJ06lRNPPJGJEyfy3nvvVWmhqlDRPdupUyfOO+88zj//fD7++OPK40899RTXXHMN3bt3D+UtKqWUakQ0yYoRI0eOZMmSJaxatYri4mI+/PBDWrduHVSdzZo144477uDee++t9Ym0LVu2MHLkSIYNG8ZLL70U1PWC9d5779GnTx/69OnDBRdcwNSpUysHebdt25bff/+dZcuW1VmHMYapU6dywQUX0LdvX3r37s306dPrvXaw3bNKKRUyDkdoNlUv/ZRiRGpqKj179qRLly7Ex8eHrN7rrrsOh8PBk08+WePYn3/+yYgRIzjooIOYOnWq7cu1TJkyhQsuuACwuvPy8/P59ttvAes+hgwZwv7770/Xrl0ZP3487dq1o2/fvlVmNv7mm28oLCxk9OjRAFxwwQW1dhlWCEX3bLiJCP3796d///46k7OKuB07dnDllVfSuXPnylbg0aNHM2/ePAC6du1aOc4xJSWF/fbbjxdffLHy/OpTzXgTkSqtyNXfl5WVcc4559CuXTt+++03AF566SVGjBhBRkYGIsK+fftCfcu2qvgsg91U/fTpQgXAqlWrauzr379/veclJSVx7733cs0111TZv2XLFkaMGEHnzp155JFH2LlzZ+Wx+gaVh8OqVatYuHAhH374IWBNQ3H22Wfz6quvcuyxx5Kamsrnn3/O2rVrmTVrFvPnz6eoqIhWrVpVqWfKlCmcffbZxMVZ/3XOPfdcbrnlFlatWkWfPn0qy1V0z5aXl1NWVsYpp5wSdPdsOKWkpPD777/bHYaKUWeccQZlZWW89tprdO/ene3bt/Ptt9+yZ8+eyjL33XcfEyZMID8/n2nTpnHVVVfRrFmzoMY1FhYWcsYZZ7B69WrmzJlDjx49KvdXPCF9xx13BH1/KnZpkqUAOOecc2rsW79+vV/nXnzxxTz66KMsX768ct9XX33FmjVrWLNmDR07dqxS3o55mKZMmUJ5eTkdOnSoEkd8fDx79+6lefPmAPTo0YMePXpw+eWX849//IPevXvz3nvvcckll7Bnzx4+/vhjysrKeP755yvrcblcvPrqqzz00EOV+0aOHMnzzz9PfHw87du3D2nroVLRZN++fcyZM4fZs2czfPhwALp06cIhhxxSpVx6enrlH2j/+te/mD59Oh9//HHASda+ffs48cQTyc3NZc6cObRr167y2A033ABQOblytNF5siJHk6wY4D3Au7quXbvWmfSMHz+e8ePH1/oewOl01mgF8VWuQtmmdeR9OZ3SNcuRuHiSDjqc9DHjcKSm13crASkvL+f111/n0Ucf5bjjjqty7IwzzuCtt97i2muvrXFe165dSUlJqRzY/tZbb9GxY8cqXQ0A3377LZMnT+b++++vbOGq6J5VStUtLS2NtLQ0Pv74Yw499FASExP9Oi8pKSngcY7btm1j+PDhpKam8v3331f+kRUrQvF0oD5d6B9NslRElf2Zze7n7gOXG4wbU1pC0YLZlK5dQdYN/0IS/PsB2xAzZsxg7969XHbZZWRmZlY5duaZZzJlyhR27dpFYWEhY8eOpUuXLmzdupWxY8eSl5fHEUccAVitYWeeeSb77bdflTq6dOnCbbfdxueff84pp5ziV0xr1qwhPz+fbdu2UVRUVLmsUf/+/UlISAj+phuosLCQIUOGAPDTTz+RkpIS8RhUbIqLi2PatGlMmDCBF154gYMOOojhw4dzzjnncMABB9QoX15ezptvvsnSpUuZOHFi5f6cnBzS0tL8uub1119P9+7dmTdvXmx+r0sIBq7rRFl+0U9JRVT+1x+B201hSSm7CoqtncaNa+dWin6ZG5ZrTpkyhWOPPbZGggVWS9aSJUtIT09n3bp1XHTRRfTt25fTTjuNnJwcjDH06tWLxYsX8+uvv3LGGWfUqCM9PZ3jjjuu3gHw3i6//HIOPPBAXnzxRVavXs2BBx7IgQceyJYtW4K610AZY1i+fDnLly/XZXVUxJ1xxhls2bKFTz/9lNGjRzN79mwOOuigKq3wt912G2lpaSQnJ3PNNddwyy23cOWVV1YeT09PZ8mSJTU2X0466SRWr15dZfC8UuGgLVkqokrXLge3m+cXLeexect44cTDOalPFxAHpetWkjLU91qIDWGMoXTVbxT9/CPu4iLevekqkoeO8Fn2oIMO8plUFBQUVPmr+OCDD64z+fj0008rX9fVPVshWsd6KBWopKQkRo0axahRo7j77ru5/PLLueeeeyqHHdxyyy2MHz+elJQU2rVrV+PpNofD4XcX/QUXXMDJJ5/MpZdeisvlqjJRcEwIxWSi2l3oF02yVERJYjKmuIiJQ/rTpVk6A9u09BwAR1Jomu3zPn2Twjn/tZqzjZvSlUso+PErWl47CWdmbI29UKqp6t+/f5Xxj1lZWSEd53jRRRfhdDq5+OKLcbvd3HrrrSGru7ETcSBBdvcFe36s0CRLRVTykKMo+PYTUuLjOLN/t78OuN0kHXR40PWXblhjJVgAxu35x407dy95/32fZmddEfQ1lFKhs3v3bsaNG8ell17KAQccQHp6OosWLeLhhx/2e4xjoM4//3wcDgcXXnghbreb22+/HbAGxm/bto01a9YAsHTpUtLT0+ncubPt66+qpkWTLBVRaSNPonTtCsrWrwKHEzDgdpM66jQSugT/V2rxbwutet0uVu/O4ZavFrBy1z5+uep0WDIfNMlSqlFJS0tj6NChPP7446xdu5aysjI6derEhAkTuPPOO8N+/XPPPRen08n555+P2+3mzjvv5IUXXuDee++tLHPUUUcB1pJatT013aQ4JPjuPu0u9Is05UGuIpIB5OTk5JCRkWF3OMpPxu2mZMUvlP7xO5KQQNLAYcR36BKSunM/fp3Ced+C28VPf+7klZ9XcnKfLpzQuzM4nbR98DW/6vEek5Wfn09qampI4musYu1+lQqEq6SUHZ99S9GmraT160GrUUcgTmdQdebm5lY8lJNpjMkNSaC1qPiduWHSBDKSgnuKObe4lC6TXoYIxN2UaUuWijhxOEgacDBJAw4Oed2JfQdS+ONXAAzp0IohHTwztjscJPYd5H+MInTp0qXydbSLtftVqqFyf1vJgrGXUbp9Fzgd4HKT1q8nQ798laT2bewOTzVSOnJNRZWE3vuT0OcAwCtRcDiQuATSxpzpdz0pKSlkZ2eTnZ0dE/PoxNr9KtUQxuXip1OvonTXXmuHyxrvWfDHepZccpuNkQWmYjLSYDdVP23JUlFFHA6aj7+Rwh+/omjRD7iLC0noNYC0kScR16pd/RUopVQ1u2cvoHjTVn52F/C52cdIyeAIRzqm3MXu7+ZRuOFPUrp0qL+ixkIk+MlEtcXbL5pkqagjcXGkDh9L6vCxdoeilIoCxdusBe5bShyFxs2L7h0MlVTiPYlK6fbdTSvJUhGjSZZSPhQVFVU+UfS///2P5ORkmyMKr1i7X6UaIvPAAQB0kUQmOztVOeZITCC1b3c7wgqYrl0YOZpkKeWD2+1m0aJFla+jXazdr1INkd6/J21OPpbtM74D7/8fAt2uH098hn9rJjYajhCsXRjs+TFCPyWllFKqHge+8QidJ5yNI9Ga+iAuPZVed11Hn/tusDcw1ahpS5ZSSilVD2dKMvs/M4l+D91K6c49JLZrjTMxuLmm7CIiQU/VolO9+EeTLKWUUspPcakpxKU28WlOJATdhbp2oV/0U1JKKaWUCgNtyVJKKaViiD5dGDmaZClVi6ysLLtDiKhYu1+lYpY4QjAZqXaE+UOTLKV8SE1NZefOnXaHETGxdr9KKRUJmmSpiDLGsHfuz+yePR9nairtzhxDcse2doellFKxwyHWFmwdql6aZKmIcZWU8vPZ17Hj89mI04kxhhW3PcT+z0yi84Sz7Q5PKaVigogDCbK7L9jzY4V+Sipi1j3yMjtmfg9Yq9rjdoPbzdJr7iFv+Rqbo6uqqKiIESNGMGLECIqKiuwOJ+xi7X6VUioStCVLRczGl98Dt6mxX5wONr/xEf0m32JDVL653W6+//77ytfRLtbuV6mYpt2FEaNJloqY0j05tR/buSeCkSgVGON2s/WDL9ky/QvcRcVkHXsEnS49s+mtXadimjgcSJCTkQZ7fqzQJEtFTLMhB7BnzqKqC6xidR02HzrInqCU8pNxu/nlwpvYOv0La7Zst5udX81hw0vvcPgP75LQsrndISqlGplGk4qKyB0iYkTkCbtjUeHR665rrBdea15JnJPkzu1pf95JNkWlAEp3/dWSOPuAE1hx+8NV9inYMfN7K8GCv/5QMIbCdZtYM/kF+wJTqqFEQrOpejWKJEtEhgBXAL/ZHYsKn6wRhzLkkxdI69/T2uFw0OakYxg2+52mvxZYE1a2N4d5oy6ufF+84U/WPzGNOcPGUbpnn32BNTLbPvwKiXPWPOBysWX655EPSKlAOcRqjQ1q0yTLH7Z3F4pIGvAWMAH4p83hqDBrPWY4rUYfRfm+XByJCThTku0OKeZlP/8WRdl/VtlnXC6KNm5hw3Nv0uuf19oUWePiLiuDms9tAGDKXZENRqlghKIlSluy/NIYWrKeBT43xnxTX0ERSRSRjIoNSA9/eCrURIT45pmNPsFKSUkhJSX6W9h2zPwe3G4SERLx+sHpdrP989m2xdXYtB47wpp6pBpxOmlz8rE2RKSUauxsbckSkXOAg4Ahfp5yB3BP+CJSypKamkpBQYHdYUSEIyGBJIeTD6RX1QMiOBIT7AmqEWp3xmg2vPgOe39cDMZq0hKnk/jmGfS84yqbo1PKf/p0YeTY9imJSCfgSeACY0yxn6dNBjK9to5hCk+pmNF+3PHU1g/WftzYyAbTiDni4xn6xRT6Tr6Z9P37kNKjM10mnscRCz8ipUsHu8NTyn8VC0QHu6l62dmSdTDQGlgsf/XtOoGjRORaINEYU6Vt3hhTApRUvBftE1YqaB0vOZOtH/6X3bPmWwNaAdxuWhw1hE6Xn2VvcI2MMzmJHjddTo+bLrc7FKVUE2BnkvUtsH+1fVOBlcBD1RMspSKpuLiYM844A4APPviApKQkmyMKH2diAgd88AwnHTmSkh27ePiwsXQ+9TjanzUWR4J2FyoVdSQEM75rI4dfbEuyjDF5wDLvfSJSAOw2xizzfZZSkeFyufjiiy8qX0c743Dw3a+LARj42r9JTU21OSLlj9zfVrJ3/hISWjaj9dgROJOj948BFTq6QHTk2D6Fg1JKqYZxFZfwy/k3sv3Tvx7Kjm+WwcH/eYaWw4faGJlSylujSkWNMSOMMTfYHYdSSjVmq+95gu0zvquyryw3n59OuYqyfbk2RaWajIoFooPdVL0aVZKllFKqbu7ycja89G6NNUBxu3EVFrHl3Rn2BKaaDhufLhSRq0VkvYgUi8hiETmyjrKni8jXIrJTRHJFZJ6IjA74vm2gSZZSSjUhrsJiXPmFPo9JnJPiLdsjHJFS/hGRs4EngPuBA4EfgJki0rmWU44CvgbGYs1IMAv4TEQODH+0oaFJllJKNSFx6akkdWjr85gpKyfjgL4Rjkg1OfYtEH0jMMUY84oxZoVneNAmYKKvwsaYG4wxDxtjfjLG/GGMuRP4Azgp0FuPNE2ylFKqCRERet5Z83eSxDlJ6dGZNicfY0NUqkkJenFox19z6kG693J3IpLo65IikoDVGvVVtUNfAYf5E7ZYjzSmA3sCu/HI0yRLKR9SU1MxxmCMiYnpDGLtfpu6zhPOpv8jdxDXLKNyX8uRwzj0mzd0bjMVaZuBHK/tjlrKZWFNOF69P3s74LtptqabgFRgesPDtIdO4aCUUk2MiNDt+vF0vuo8CtduJL5FJkltW9kdlmoqQrEszl/ndwTyvI6U1CxcRfU1vMTHvpqXEzkXmAScYozZ4V+Q9tMkSymlmihnYgLp/XvaHYZqakIxBcNf5+cZY/yZN2QX4KJmq1VrarZuVeEZMD8FGGeM+aauso2Ndhcq5UNxcTHjxo1j3LhxFBf7u3550xVr96uUiixjTCmwGBhV7dAoYG5t53lasKYB5xljPg9bgGGiLVlK+eByufjPf/4DwLRp0+wNJgJi7X6VimkiIeguDKgl7DHgDRFZBMwDrgA6Ay9YVcpkoIMx5iLP+3OB14HrgfkiUtEKVmSMyQnuBiJDkyyllFIqlgQ+BUPVOhrIGPOeiLQE7gbaYa1fPNYYs8FTpB1W0lXhSqw85VnPVuE1YHzDg448TbKUUkopFRHGmOeA52o5Nr7a+xERCCmsNMlSSimlYknVea4Cr0PVS5MspZRSKpbY1F0YizQVVUoppZQKA23JUkoppWJJaCcjVXXQJEspH1JSUsjPz698He1i7X6VimkSgjFZmmT5RZMspXwQkZhawy/W7lcppSJBkyyllLJReUEh6x6dwuY3P8ZVUESr446g5x0TSevdze7QVLTSge8Ro0mWUj6UlJRw5ZVXAvDiiy+SmJhoc0ThFWv321i4y8pYMOYS9i38DdxuALa8M4Ntn3zDEXPfJ61vD5sjVFFJx2RFjH5KHkWbtrL59Y/YMv0LynLz7Q5H2ay8vJzXXnuN1157jfLycrvDCbtYu9/GYtuHX7Fv/pLKBAvAuFy4C4tZ/X/P1n6iUqpJiPmWLGMMK29/mHWPTwVjAHAkJzHw5Qdof/YJNkenlIpmO7+eg8Q5MeWuKvuNy8XOL7+3KSoV9bS7MGJiviVr87QPWPfYq5UJFoC7qJhfLrqZvBVrbYxMKRXtHEm1d8s6EhMiGImKKRUzvge7qXrF/Ke0/pk3fGbk4hA2vfq+DREppWJF+3HH12jFAhCnkw7nnmRDRCoWGJGQbKp+MZ9kFW/eWqUVq4Jxu61jSikVJi2OOoQuE88DQOKcVuuAQGqfbvT8x9U2R6eUClbMj8nKOKAvu3/4CVzuakeE9P162xKTUio2iAgDnrybNicfy5Z3ZuAqLKTlyGF0vOAUnCnJdoenopVICJ4u1JYsf8R8ktXj1ivYPXtB1Z1OB3GpKXS67Cx7glJKxQwRodWxh9Pq2MPtDkXFCp3CIWJi/lNqNeoIBr3xKIltsyr3pQ/oxaFfv05S21Y2RqbslJKSwo4dO9ixY0dMLDMTa/erlFKREPMtWQAdzjmRdmeOoWDlOhzJSaR074RoU2hMExFatYqdJDvW7lepWBaKges68N0/mmR5OOLidAyWUkqp6KfdhRGjn5JSPpSUlHDNNddwzTXXUFJSYnc4YRdr96uUUpEgxsf0BU2FiGQAOTk5OWRkZNgdjooiBQUFpKWlAZCfn09qaqrNEYVXrN2vUo1Fbm4umZmZAJnGmNxwXqvid+bWmdPISA1u7GVuQSHtjh8PEYi7KdPuQqWUUiqWhGLGdp3x3S/6KSmllFJKhYG2ZCmllFIxRJ8ujBxNspRSSqlYok8XRowmWUqpsMhZsoINz79FwR/ZpPXpTperzydj/z52h6WUUhGjSZZSKuS2/mcmP59/I+IQTLmLvfN+ZtPU9zn4/Wdoc9IxdoenVEwz4sAE2RIV7PmxIuAkS0Q6AV2BFGAn8LsxRifYUVEhOTmZ9evXV76OdqG8X1dxCb9ddRe43RjPuuum3AUi/HblPzlmzFE44uODDVkpFSiR4Bd41jFZfmlQkiUiXYCrgHOBToD3p1wqIj8ALwEfGFPx41WppsfhcNC1a1e7w4iYUN7v3h8XU56TV/OAMZTu3MO+Bb/S4ojBIbmWUko1Zn6394nIk8BSoBdwNzAAyAQSgLbAWGAO8H/AbyIyJOTRKqUaPXd5eZ3HjcsVoUiUUr4YHJVdhgFvOgOUXxrSklUK9DDG7PRxbAfwnWe7V0TGAl2An4IPUYWLMYay7NW49u0mrk0H4tt3sTukRqO0tJR//OMfANx///0kJCTYHFF4hfJ+WxwxGGdqMq6CohrH4jLTaTZ0UMB1K6VCQLsLI0aX1YlR5Xt2svfVR3Bt/7NyX3yP/jS/6HocKbqkSqwtMxPq+934ynSWTrwLnE5wuRCnE+NyMfDVh+h44akhiFip6GDHsjqbv3ufjLQgl9XJL6Tj0eNAl9WpU0AD30UkGStBK/S87wKcBqwwxvw3hPGpMDDGWAnWzq1V9petX0nO+y/T/OIb7AksgowxlK5dQdn6VUhyKkkDh+JMz7Q7rKjR+fKzSOneifVPTSN/1XrS+/ek2/XjaXnUIXaHppQSCcE8WdqS5Y9Any78BPgQeEFEmgELgDIgS0RuNMY8H6L4VBiUrV9VpQWrkttNybJFuHL24sxsHvnAIsRdWszeVx+lbO0Ka/0tY8j77C0yz72K5EHD7A4vamQdPYyso/XzVKqx0RnfIyfQVPYg4AfP6zOB7VhjsC4C/haCuFQYufbuqvt4zu4IRWKP/P9+QNm6VdYbtxuMAbeLnHeex7Uvuu9dKaVU5ASaZKUAFc9oHwd86JmyYT5WsqUasbg2HWo/6HAS17JN5IKJMGMMRQtmga8ZRoyh6OcfIx+UUkpFUsWyOsFuql6BfkprgFM9E5KOBr7y7G8N6AC4Ri6+Yzfiu/e1usq8iZB8yHAcqen2BBYJbjempNj3MXHgLvAxv5NSSkURg4RkU/ULNMm6D3gEyAYWGGPmefYfB/wSgrhUmDW/+AYS+w36a4fDQfIhI8g45ULbYooEcTqJa9fJ96BNt4v4zj0iH5SKWqa8nPLtf+LK2Wt3KEopGwQ08N0Y8x8RmQO0A371OvQt8FEoAlPh5UhJo/n4G3Ht240rZy9xWW2iuwXLS9pxZ7Lvtcer7nQ4cGa1JWmANRN5cnIyy5Ytq3wd7WLtfiOhcN635H35PqYwH4D4Hv3IPGsCcS1a2xyZinW6dmHk6DxZKiYV/7aQvJnTce3aBg4nSQOHknHyBTjS9PtIBa9o8Rxy3n2h6k6HA0dGc1rd8jCSkGhPYKrRsWOerA0/zCAjLbi58HLzC+hy5Img82TVye+WLBH50N+yxpjTAwtHqchIOuAQEvcfgikqQOITkPjontFdRVb+Nx/X3Ol24963m6JfF5Ay5KiIx6RUBZ3CIXIa0l2YE7YolLKBiCApaT6PlZaW8sADDwBw5513xsSyOrF0v+FkXC6rhdQXp5PyrRsjG5BSyjbaXaiUD7qsTnTfb3XG5WLPnEWU7tpL5kEDSOnWKfC6jGHHpImVY7GqECH9hHNJHT42iGhVNLGju3D9jzND0l3Y7fDjQbsL6xTojO9KKRUVcpasYPEZV1O0cYu1Q4QOF5zCAS/8H44AWvREhJTDRlHw7cfWRLd/HQFnHEkHHR6SuJUKmC4QHTEBPx4gImeKyHQRmS8iP3tvDahjooj8JiK5nm2eiBwfaExKKdUQrsIiFh5/CUV/enXvGcOfb37C6nufDrjetGNOIWng0Cr7JDGJ5uP/rmtkKhVDAl0g+m/A/cBrwCnAVKAHMAR4tgFVbQZux5rcFOBi4BMROdAY83sgsSmllL+2fvhfSnf5mMPKGLKff4vek/6GIz6+wfVKXBzNzr+WsmNOpSx7NZKcQmK/QTgSkkIQtVJBCsEUDjrju38C7S68GrjCGPOOiFwMPGyMWSci9wEt/K3EGPNZtV3/EJGJwKGAJllKqbAqyt6MxDkx5a4ax1x5BZTtyyOxld8/0mqIb9uR+LYdgwlRqZALxYztOuO7fwJNRTsDcz2vi4CKWSzfAM4NpEIRcYrIOUAqMK+WMokiklGxeV1XKaUaLK1vD58JFkB8i0zim+sDNUqpwAWaZG0DWnpeb8BqeQLoBg1Lb0VkfxHJB0qAF4DTjDHLayl+B9ZUEhXb5gbGrZRSldqcfAxJndsjcc4ax7rfdDmOOH02SEWfihnfg91U/QL9lL4DTvK8ngI8LiJfA+/R8GV1VgGDsBK154HXRKR/LWUnA5lem7bDq7BISkpi4cKFLFy4kKSk6B9HE2v3W8GRkMCwr18n48C/fuQ4EhPocesV9Lj5chsjUyqMhL+eMAx4s/smQk9E7haRFB/7k0Xk7oDqDGSeLBFxAA5jTLnn/VnAEVgD2F8wxpQGEoynrm+AtcaYK/0oq/NkKaVCIn/lWkp37SV9v97EN9OfJyoy7Jgna+2Cb0lP8z0Rs7/y8vPpMfQYiKJ5skTEBbQzxuyotr8lsMMYU7PJux6BLhDtBtxe76cD0wOpywcBdGEvpRoJYwx75/5M4dqNpPTsQvNhByJROEdOWt8edoegVEQYHJjAZ3CqrCMKCeCr5WkgsCeQCgOdwmE98CbwljFmZSB1eOp5AJgJbMIaxH4OMAIYE2idSoVCaWkpTz75JADXX3991C8zU9v9Fv+5nYUnX0Heb3/9N884cABDPnmBpHatbYlVKRUcXbuwKhHZi5VcGWC1iHgnWk4gDWvMeMPrDrC78EaspwgPBn7BeqrwPWPM1gbWMwU4BmiHNZD9N+AhY8zXfp6v3YUqLGJtmZna7vfHw88i5+dlVZ7AkzgnzQ4dxGGz3rYlVqWiiR3dhX8snB2S7sJeh4yAKOgu9ExFJcCrwA1UXau5FMg2xvic9aA+gXYXPgY8JiK9gfOBicC/RWQW8KYx5nU/67kskOsrpcIv97eV7Fv4a439ptzF3jmLyVu+hvT+PW2ITCkVjFA8HRhNTxcaY16Dyl66ucaYslDVHdSnZIxZbYy5xxjTBzgSaIU1+7tSqokr2ryt7uObtkQoEqVUKFVMRhrsFm2MMd8DLhHpLSJHiMhR3lsgdQY9CYyIHAKcB5yNNa3Cf4KtUyllv/R+dQwEFyG9n7ZiKaWih4gcCrwNdKHmJBUGa3xWgwTUkuXJ8u4VkT+AH4H+WGsQtjHGnB1InUqpxiWlWyfanj4aHNV+TDgctDtrLMmd29sTmFIqKDoZaa1eABYB+2EtEdjcawtofa1AW7JWegJ5FnjXGFN3v4JSqkka+OqDOFOS+POdz8DlRuKcdDjvZPZ7+h67Q1NKBUifLqxVL+BMY8yaUFUYaJLV1xizOlRBKKVCzxhD0U//o/DHr3Dl7CG+fRdSR55IYq/9/K4jLjWFQVMfpt/Dt1O04U9SunYgISvwBZOVUqoRWwD0xJpYPSQCfbpwNYCIDAb6YfVVrjTGLApVYErZKSkpiVmzZlW+boryZrxD4f++oGJ+vdI1yyn9YxmZF1xL8sBDq5St734TW7UgsZUmV0pFg1AMXI+Wge8icoDX26eBR0WkLbAUqPKUoTHmt4bWH+hkpB2Bd4DDgX2e3c1EZC5wrjFmUyD1KtVYOJ1ORowYYXcYASvfs9OTYEHlBMbGWqQh79M3Sdr/EMRrrFVTv1+llP90CocqlmD9kPTOGl/1el1xLKCB74F2F74KxAP9jDGrAESkj2f/FOC4AOtVSoVA6ZrltR5z5+7DtXMrcW06RDAipZRqlLqFs/JAk6wjgcMqEiwAY8wqEbkO62lDpZq0srIyXnrpJQCuuOIK4uPjbY6oYaS+eKsdb+r3q5Tyn3YX/sUYsyGc9QeaZG3EasnyVd+fgYejVONQWlrKtddeC8D48eObXNKR2HcgxMVDebWJi8VBXLtOxLWouu5gU79fpZT/DCHoLozCBaJF5ORaDhmgGFhjjFnfkDoDTbJuBZ4WkWuAxcYY4xkE/yRwc4B1KqVCxJGcSua4y8l59wUQAbcBAUlIJHPc5XaHp5RSjdHH1Byfhdc+IyJzgFONMXv9qTDQJGsakIL1uGO5WPNlxAHlwKsiUjlozBijjySpsDLl5RTO/47in3/ElBaT0GcgqUcdjzOzud2h2Sr5oMOJa9+ZogWzrSkc2nUmeegInBmx/bkoFevs7C4UkauBW4B2wO/ADcaYH+ooPxx4DBgAbAEeNsa8ENDF6zcKuB/4B7DQs+8Q4F/A/2EtHP0i8Ajg19rLgSZZNwR4nlIhZdxu9k59lNLVSyv3le/cSvHiH2h5/f/hbJ7V8DqNoejX+ZXv9779LPGjzyChcx3LzDRS8W07EX/KhXaHoZRqRKzJSIN9urDhSZaInA08AVyNNX77SmCmiPQ3xmz0Ub4b8AXwMnAB1owGz4nITmPMB4FHX6sngSuMMXO99n0rIsXAS8aYASJyA1WfPqxToPNkvRbIeUqFWsnvi6skWAC43biLCsj7+iOanTWhwXXmz5xO7n8/qnxfuvwX9vyxjOaX3mSNdVJKhU15QSE7//sDroIiWhw1hJQu+hRsqNnYknUjMMUY84rn/Q0iMhqYCNzho/xVwEZjzA2e9ys8Q5NuBsKRZPUAcn3szwW6e17/Afj917vfqayIpPpbNpDySgWiZMUvNdfWA3C7KVnW8Llxy/fspGDWZ9XqMmAMuR+/hjEmwEiVUvXZ/tm3fNPpCH4++2/8eultzOp1DL/f9ADG7bY7NFW7dBHJ8NoSfRUSkQTgYOCraoe+Ag6rpe5hPsr/FxgsIuF4Omcx8G8RaVWxw/P6YeAnz65ewGZ/K2xIe+EaEblTRGpdFVYso0RkJvC3BtStVGDqavIOoDm7RqtYJYNr9w5cu3c0uE6lVP0K129i8Vl/w5Vf+NdOY8h+6jU2vPiOfYFFoYq1C4PdPDZjjVWq2Hy1SIHV+uMEtlfbvx1oW8s5bWspH0cDWpMa4DKsebM2i8gaEfkD6/66AhVPDKVhjc/yS0O6C0dgDf66R0SWYC0QvQXrscbmQH+srLMMmAy81IC6lQpI4n4HU7Rwds0DDkeNpWP84rAm9E2Ic/DGaSMqX1cQZ4Mn/G0SEhMTmTFjRuVrpSJt07QPwFitxlWIkP3MG3SdeL49gUUhYwRjguwu/Ov8jkCe16GS+k6t9l587KuvvK/9QfPM99kPGA309lxrJfC1MdaSGcaYjxtSp99Jlmfi0XGeJXXGAUdhNfElA7uAX4AJwBcVwSgVbol9BpI4cCglvy6wWq6MAREcmS1IPfbUhtfXfxA4nMQBx/bwGgsigrNNRza9/SW7v1+AMzWFDueeSNaoI5AoWI0+Li6OE044oUHnGGPIW7aasr05ZOzfh/jmmWGKTsWCoo1bfR8whuLNtRxTjUGeMcbXOKbqdgEuarZataZma1WFbbWULwd2NyRIfxlrTMiXni1oDR74bozZDDzu2ZSylTgcNDvvGooHHEzRzz9CaQkJvfcnZdgxOFLSGlyfMy2TjFMuIPej16yxXm631SUZF8+a939lz88fVV73zzc/pss1FzDg8X9GRaLVEPkr1/Lz+TeS99tKAByJCXS/8VJ6T7q+ypqISvkrfb9e+Pz73OEgfUDvyAcU1RwhmEy0YecbY0pFZDHWNAkfeR0aBXxSy2nzgJOq7TsOWGSMKfNRvsFE5G9YTw4We17XyhjzVIPrb8oDeUUkA8jJyckhIyPD7nBUFClYs4LXHnsId1EB5512Ctu+WUX2Sx9gXK4aZYfNeosWRwy2IcrQKSsr46233gLg/PPPr3PGd1dhEbP6jKJ0554an0e/h2+j+98vDWusKjqV7trDrH6jKc/LB1fVZOvgD56l7cnH2hRZeOXm5pKZmQmQ6WeLUMAqfmcu/mUZaenpQdWVn5fHwQfuBw2I2zOFwxtYTw3OA67A6gEbYIzZICKTgQ7GmIs85bsBy7DmpnoZa0jSC8C5oZrCQUTWA4ONMbs9r2tjjDHd6zjuu/6GJlme7sKJWF2FbbH6RbcDc4EXjDGbGhpEoDTJUuFSUFBAWprVEpafn8+P3UZQtrfmzxGJc9LlyvMY8MQ/IxxhaFW/39TU2h8O3vzGx/x66W0+jyW0acmxG+doa5YKSO6vK/n1stvJ/XUFAPEtm9Fv8i10uuRMmyMLn1hKsjwxXI21akw7rATq78aY/3mOTQO6GmNGeJUfjtVzVjEZ6UNhnIw05BrUXSgiRwAzgU1Yj1V+hTUwrDVwKnCdiBxvjNFFolVUcZfW3jLtKq5vnGd0yV+1DomPw5SV1zhWun03roJC4tIb3lWrVMbAvhy56GMK1mygPL+Q9P49cCQk2B1W1LFzxndjzHPAc7UcG+9j3/fAQQFdLECe6Sa6AWuNMTV/0DVAQ8dkPQ68Yoz5ey2BPY41m+uQYIJSqrFpNfootn/yTY3uMVPuotXoI22Kyh4p3Tphyn3/3IlvkYkzNSXCEalok9qzi90hRDU7k6zGTERSgKeBiz27egPrROQpYIsx5sGG1tnQNv39sPpDa/Oip4xSUaX3PX/DkZJUdQoHh9DiyCG0Oelo+wKzQfuzjie+WSY4q/34EKHbdRdpV6FSqqmaDAzEmrKq2Gv/N8DZgVTY0J+GW6l9ZlawBqXps7ZNRNm2zRT/uoCyTWt1JvN6pPfvyRHzP6D9+SeT2LYVqb260nvS9Rzy+Ss44gJdArRpiktPY+iXU0nu4PVktcNB5wln0+P2q+wLTCnll4qWrGC3KHQqcK0xZg5V5+FajrXkToM19LfDI8ALInIw8DXWgHeDNQB+FNaMqDcEEoiKHHdRAfvefJrS1csq98V16Erz8X/H2ayljZE1bmm9uzFoSoNbi6NS5kEDGLn6G/b8uJjS3ftoNuQAkjvWNmmzUqoxCfFkpNGkFeBrWY9UApz8tEFJljHmORHZDfwda/Xsir4TF9aaPxcZY6YHEoiKnJzpL1O6ZnmVfeVbN7J36mO0vOFfMTfnkwqMOJ20POoQu8NQSqlQ+Qk4AWtcFvyVWE3AmnKiwQKZjPQ94D3P4owVawftCtXEYCq8XDl7fC+c7HZTvmUDZRvXktClZ+QDa2QSExOZPn165etoF2v3q1Qs04HvtboD+FJE+mPlR9eLyACsoVDDA6kw4MEknqRKx181Ma69u+o+vmcHaJJFXFwc48aNszuMiIm1+1UqlmmS5ZsxZq6IHAbcAqzFml3+Z2CYMWZpIHWGdMSuiPQAXjbGxNbjVk2IM6uNtUxMLctLxrVuH+GIlFJKKfuJyFvAbOB+Y8zqUNQZ6met0wiwSU1FhjMtk6SDDrcWU/bmcBDfrQ/xHbraEldjU15ezvvvv8/7779PeS1zQkWTpny/BX9ks/WDL9k77xd9SlYpP+jThbXKB24CVojIFhF5R0SuEpG+gVbY0Bnf61w8EegQaCAqcjLPuMRa2f6XH8HzSymh9/40O0cfv69QUlLCWWedBVjLzMRF+TQNTfF+ywsKWTL+VrZ//HXlvrR+PRn84XM6maVSdTCE4OnCKEyyjDFXAohIW6y5skYA1wPPisgOY0y7htbZ0J+kT2CNwyqt5biuf9AESHwCzc69CtcJZ+PatR1Hs5bEtWhld1hKNcjyG+9n+2ffVtlXsHo9C0+4nBHLv6w6caxSSvkvD9jr2fYB5cC2QCpqaJK1AbittmkaRGQQ1lQOqglwZjTHmdHc7jCUarCyfblsfv1jcFUdW2hcLgrXbWTnNz/SevRR9gSnVCPnRnAH2RIV7PmNkYg8hDXkaSDW4tX/w5oF/n/GmH2B1NnQJGsxcDBQ21xYBqLwk1dKNSrFW3fWun4iQOG6TRGMRqmmRZ8urNUtwE7gXuATY8yKYCtsaJJ1N1DX6q/LsVauVkqpsEnu1BZHYgLuEt8jF9L6BbQChlIqth2I1ZI1ArhJRFzA91hPHM4OJOlq0NOFxpjlxhgfM1lWHi8zxmxoaBBKKdUQcWmpdLnqvBpPyUqck/T9+9By+FCbIlOq8atYVifYLdoYY341xjxljDndGNMKGA0UAk9hdR82WON/hEgppXzo+8BNuIpL2DRlOqbcBUDzww/mwNcf0aWhlKqDIfjuvmidLEVEDuSvJwuPBDKAJcCsQOoLKMkSkV/w/RkboBhYA0wzxgQUlFLBchWXsPWDL8lZvIzENll0OO9kkjv5//RtQkICU6dOrXwd7Zri/ToSEtj/mUn0vudv5K9cS1K71jp1g1IqYCKyF2u+z1+xughfxhr0nhtwnYFM3icik4GJwFJgIdZg98HAAcA0oD9wDHC6MeaTQIPzI44MICcnJ4eMjIxwXUY1McVbtjPv6AsoXLsRiY/DuNyIw8GBbz5KuzPG2B2eUkpVys3NJTMzEyAzmF/m/qj4nTlrUTZpacH9zszPz2Xk4K4QgbgjRUROJMikqrpAuwuzgEeNMf/nvVNE/gl0McYcJyL3AncBYUuylPJl2d/uoyj7TwBMmfUEmjGGJRffQssRQ0loqdNWKKVilz5d6JsxZkao6wx0WZ2zgHd87H/XcwzP8T4B1q9UQMpy89n+2bcYl6vqAWNwl5ax7cOv/KqnvLyczz//nM8//7zJLTMTiFi7X6WUioRAW7KKgcOwxl55O8xzDKwEriTA+pUKiCu/ANy1dIE7hLIc/1qBS0pKOPHEE4Gms8xMMGLtfpWKZaF4OjAany4Mh0B/kj4NvCAiBwM/YQ14PwS4HHjAU2Y08EvQESrVAIltW5HcuT1FG7fUPOhy0+LwwZEPSimlGhEDuOstVX8dqn4BdRcaY/4FTMBKrJ7CSroOASYYY+73FHsBOCkUQSrlL3E46HP/TZ43Xn9pORy0GnMUzQ4dZEtcSinVWOg8WZETcJ+AMeYt4K06jhcFWrdSwehwzok4kxJZfe9T5C1bTVyzDLpccQ697rpW509SSikVMUENvPB0F/bDajlcbozR7kHVKLQ9dRRtTx2FcVvTNyillLLo04WRE+hkpK2xniQcAezDmicrU0RmAecYY3aGKkClgqEJVviYslKKFs+hePkviMNBeWIW69/5HzmLfyepXWu6XHUenS49U78GSjUyOvA9coIZ+J4BDKhYMFFE+gOvYY3ROjc04SmlGiN3aTF7X5hM2aa11tg3z6TGmUk57Ny1h9Kde1g68S5yl65kvyfvtjlapZSyR6BJ1hjgWO8VqY0xy0XkGsC/iYiUasQSEhJ45plnKl9Hu4beb+GP31C2eZ31xmvViIzOGbTs25Ldy3cBsOG5t+h27UWk9uoa8piVUoHR7sLICTTJcgBlPvaXEfgEp0o1GvHx8VxzzTV1lnEVl7Dl3Rns+nYuztQU2p89lpYjDm2Sg+v9uV9vxUvmVUmuKhlo3qt5ZZKFCDu//lGTLKUaEbepfTrBhtSh6hdokvUd8KSInGuM2QIgIh2Ax4FvQxWcUr6Y8nJcu7cjSck4M1vYEkNZTh7zjj6fvN9WgdOBiLBpynS6Xnsh/R/7R5NMtBqk+oz6FQTE6XXvxuBIiv6WQKWU8iXQJOtarDUJs0VkE9bThZ2xFoy+IESxKVVD4bxvyfvyfUxhPgDx3fuSedYVxLVsHdLruFwufvjhBwCOPPJInE5nleNrJj9P3u9/eAq7Kyfmy37mDdqeOoqWw4eGNJ5AmfIyTEkxkpxa5wD0+u63usQBB1G+YwuYmlMa5qzPqXwtcXG0OemYAKNXSoWDdhdGTkBJljFmE3CQiIwC+mI9XbjcGPNNKINTylvRzz+S++HUKvvKslez5/n/I+vWf+NISArZtYqLixk5ciRgLTOTmppa5fifb30KrpoJhsQ52TL9C1uSrKJf51P4w39x7d6Bs3U7JCGJ0jW/Q3kZjozmpI06jeShI322stV3v9WlHjmGol/m4t63pzLRMm5D8d5idv2+C4lzYlxu9ntmEomt7GltVEr5pk8XRk5Q82QZY74Gvg70fBG5AzgdK1ErAuYCtxljVgUTl4pO+d98XHOn2407Zy/FSxaQcsjwiMXiKir2fcCAq7CWY2GU/92n5M+cXvmknzs/p8pxd+5ecj94FdwuUg4bFfT1HGkZtPzbvRR+P5PipT+Bw0F8zwPIX76HVmPbkdSuNZ0uOZPMA/sHfS2llGqq/E6yRORv/pY1xjzlZ9HhwLNY6x/GAfcDX4lIf2NMgb/XU9HPuN24dm71fdDhpHzrhojG0+q4I9n24X8x1cYmGZeLrGOGRTQWd0Ee+V994Amg7tGo+V9/RPLQo5F6ugP94UzLJP2Ec0g/4ZzKfc1OD7papVSYGVPvjwq/6lD1a0hL1t/9LGew5sqqv6AxY7zfi8glwA7gYOB/DYhNRTlxOHCkpuMuyKt50LgjPgC+113XsmPmbNxFJX8lWg4HmQf2p924sRGNpXTdytoHolfjzs/FlbOHuBatwhyVUqqxciO4gxxTFez5scLvJMsY083XfhE5AlhkjAlFH0mm5989tVwrEUj02pUegmuqJiLl8OPI/+pDqq7/LuCMI+mgI0J2nYI/stmTvanOMun9enD43P+w5oHn2PnVHJwpSXS44FR63joBZ6L1NJ2rqJjC9ZtJbN2ChKzwJYESF9+AwoIjue7xVkoppUIjqDFZHl8Ag4B1wVQi1mjcx4A5xphltRS7A7gnmOuopiv16JMo37mV4l/mVu6TxCSaXXAtzoxmQddftHELv1x4E3vn/kyx11Nz1bsEK6T368GBbzxaY79xu1kz+XnWPjIFV34BOBy0PXUU+z93LwktmwcdZ3UJPfsjScmY4nrWZHc4SNxvMI7klJDHoJRqOnTge+SEIskK1Sf9DHAAUFeTxGSsRKxCOrA5RNdXjZw442h23tWUH3MKpdmrcSSlkNhvEJKQWP/J9XCXlzN/9HiK1tf8dlrz0IsMuv8Wv+ta89CLrJ7k1WPudrP9k28o2rSFw398P+RzaEl8AplnXcG+N5+uvJ73Ujc4HOB2E9e6A5mnjceUlVK+cxuO1HScmaFP+pRSjZuOyYqcUCRZQRORp4GTgaOMMbUmTcaYEqDE67wIRKcam7g2HYhr0yGkde788n8Urvlr8LwT4RJHFgCbn3uL/e++vrIbsC6uklLWPfJKjf3G5SLnp6Xs+d/CsEzvkLT/ELJunEzhglm49uwkrnV7kgYNo2zTWty5+4jr2JWE3gdQ+P3nFHz3KabE6t1P6L0/mWdNID45jYcffhiwZn9XSikVvFAkWVcC2wM50dNF+DRwGjDCGLM+BPEo1WD5K9ciTmdl12C8CGeIZxxVfhGlO3aT3KldvfUUb95GeW6+74MOIffXlWGbQyuuTQcyTq46F3B8+86Vrwv+N9Oa5sFL6Zrf2fPiZLJuepBbbvG/tU4p1XTpZKSRE/Q6g8aYt4OYbuFZrBnizwPyRKStZ0sONi6lGiKla8dax145khJJyPKvWy0hqzkSV8v0CG5DUoc2gYYYFON2U/DdpzUPeKbGKFmxJOIxKaXsUbF2YbCbqp/dizlPxHqicDaw1Ws728aYVAxqfdIxJLZrVTl/lMsYVptiVlNCh8vG4Uz2bzb5+Mx02p99Ijir/ddyOkho1YLWJx4d6tD94i7M8z39BYDDQcmf2fz000/89NNPuPycDkIppVTdbB2TZfTxBNVIOBMTGDpzKotOn0jhuk2UYbjRtRGAnLuuaVBdA568i6LNW9nz/cLKfQlZLTjkkxf9GtcVDo6kFIhPgLLSmgfdbkqT0zjkkEMA/5bVUUo1YSF4uhD99e2XRjHwXanGIH1AL0as+Io9Py5mb/YmuOgMAJxJDXt6MT4znUO/fp19C38j95ffSWzfmtZjjsKRYE+CBdZcWilDR1D449dVHwsSQRKTSdpvsG2xKaUiS58ujBxNspTyIg4HLY8cQtJB/eGiIOoRofnQgTQfOjB0wQUpfew5uPbsomT5z5X7HCnpNLvkRsoSQ7e4tlJKKYsmWUrFCIlPoPklN1K2ZSNlm9fhSE0nsc9AJC6OsgJdKlSpWKHL6kSOJllKxZj49p2rTO2glIot2l0YOXY/XaiUUkopFZW0JUsppZSKIbp2YeRokqWUD/Hx8dxzzz2Vr6NdrN2vUrEsFJOJ6mSk/hHThDtWRSQDyMnJySEjI8PucJRqElw5e3Hn7sHZsg2OlDS7w1EqpuXm5pKZmQmQaYzJDee1Kn5nvvntHlLSgvudWZifywXHtIAIxN2UaUuWUjHCXVhAzvsvU7JskbXD4SR56AgyTr4AiQtt65W7sABEcCSnhLRepZRqSjTJUsoHt9vNihUrAOjXrx8Oh/WMSMEf2ax7Yip7/vcTCa2a0+mScXQ4/2TE0fifIdn7+hOUrV/11w63i6L53wGQfurFPu+3oUqz/yDv0zcp27QWgPge/cg45SLi23UKLnilVMjoAtGRo0mWUj4UFRWx3377AX8tM5Pzy3LmjTwPd0kpptwFqx3s+WERe+Ys4oAX/2VzxHUr+zObsrUrah4whqIFs3EccXyN+22o8u1/sufFB8BV/td1161iz3P3kXXTgzibtQw4fqVU6LgJwZiskEQS/Rr/n99KNRIrbn0QV3GJlWABuK0fM5tefZ+cxctsjKx+5ds2137Q7aJ8z46gr5E/+3Nwu6pOoGPcmJISCud+E3T9SinV1GiSpZQfXMUl7J69AFw1/34Tp5Ptn8+yISr/OZtn1X08o0XQ1yjb8Edl4lmFcVO64Y+g61dKhUbFZKTBbqp+mmQp5Q8Ra6vtsLNx/1eK79YHZ+v2UH2slcNB4oCDcWY2D/oajrRM35+Rw2EdU0o1CppkRU7j/s2gVCPhTEyg1egjEaezxjHjctHm5GNtiMp/IkLzS2/GmdW2yv74rr3JPGtCSK6RMnSE75+8bjcph4wIyTWUUqop0YHvSvmp/79v58cjz6E8Lx9cbsTpxLhcdL/xUjL272N3ePWKa9marJsfomz9Klx7dhLXtiPxHbtZB0OwQHTSQYdTunEtRXO/BnEA1p+7aaNOJ7HP/kHXr5QKDbcR3EHO2B7s+bFCkyyl/JTWtwfDl8wg+7k32fPjYhKymtNp/Bm0PmEk5QWFbHn7M/bM/ZmEFpl0uOBUMg/sb3fINYgICd37Qve+Yak787SLSRl2DCUrliCersi4rDYhv5ZSKnC6QHTkaJKllA/x8fHcfPPNla8rJHVoQ9/7b6pStnjbTuaNOI/CdRsRhxME1j/1Gv0evo3uf780onHXxlVcQunOPSS0aoEzKbHG8druNxDxbTsS37ZjUHUopVQ00GV1lArSkvG3sOW9z/+a2qGCCCN+/5LUXl1tiQvAXVrKyn8+zoYX3sZdVIwzLYWu11xI70l/wxGnf2MpZTc7ltV5+ct9pKQGuaxOQS4TxjQDXVanTjrwXakgGJeLLdNn1kywABzClumfRz4oL79NvJv1T07FXVQMgCu/kLUPv8Tyv99va1xKKfsY89ci0YFuTbh9JqI0yVLKizGGjS+/x6z9xjAtrR/TDziOze/OqL28y4UpK/N5TMSBq6AoXKHWq2jjFv584+OaUzsbw8ZX3qNkx+7KXW63m+zsbLKzs3H7mutKKaVUg2mSpZSXVXc9ztKr72bPqnVcUrCSs5d+zYIL/s76p1/3Wd6RkECzQwfVnH8KMOXltDx6WJgjrl3uryvAGBwJDjK7NyOzRzMcCQ5PbC7ylv61jmFRURHdunWjW7duFBXZlxgqpcLPGAnJFi4i0lxE3hCRHM/2hog0q6N8vIg8JCJLRaRARLaIyOsi0j5sQfpJkyylPEq272Ldo69Yb6q1ha+6+wlchb6Tj74P3Iw4BLwnJHU4yDrmMLJsTLISWmfRckAWB0wYRM+Te9HzpF4MnHAgWfu3AiCxTd2zwCulolMTmIz0bWAQMMazDQLeqKN8CnAQ8H+ef08HegOfhjNIf+jIV6U89vy42PfYKsCVX0DOL8tpcfjBNY61PHIIw757iz/uf449cxYR3zyDTpeOo8ctExAfLVyRktIqka6juuH9cIsj3kGXY7oSl9WW9P162xabUkr5IiL9sBKrQ40xCzz7JgDzRKSPMWZV9XOMMTnAqGr1XAcsFJHOxpiNEQjdJ02ylPJwpibXeTwuLbXWY82HHcghM14OdUhBKZr3DYgDoeoYK+M2dDmtZrKolIoNFYPXg60jTIYBORUJFoAxZr6I5ACHATWSrFpkAgbYF/IIG0CTLKU8skYeSnzLZpTtybH+a1ZwOkjt0ZX0Axr/rO7eyndtB+NjQWuHQFGeDREppRqDEE9Gmi5V1ywtMcaUBFF1W2CHj/07PMfqJSJJwIPA23ZPL6FjspTycCQkcOCbj+FIiK8yviouNZlBr/0bqWOB6MYorm0nnwPycTiIa9cp8gEppaLRZiDHa7vDVyERmSQipp5tsKe4rxRQatlf/TrxwLtY+c3VgdxQKGlLllJeWh17OCNWfMWql96Gf1kzoB+5+FOadetsc2QNl3rEaIoXz6l5wFjHlFKxKcQtWR0B76bx2lqxnsFKfuqSDRwA+FqLqxWwva6TPQnWdKAbcLTdrVigSZZSNSR3ake/f17L1XvWAZDavmmuvRffoQvNxt9A7n9exZ23DwBHeiYZp19CfKfuVcrGxcVx9dVXV75WSkWvEI/JyvMnmTHG7AJ21VdOROYBmSJyiDFmoWffUKwxVnPrOK8iweoFjDTG7K6tbCTpsjpKRTnjclG+JRuAuPZdEafT3oCUUpXsWFbnqU9ySA5yWZ2iglz+dkp44haRmUB74ErPrpeADcaYk7zKrATuMMZ8JCJxwAdY0zecSNUWrz3GmNJQxtcQ+ierUlFOnE7iO/WwOwylVCMR4u7CcDgfeAr4yvP+U+DaamX6YLVugdVlebLn9ZJq5UYCs0MeoZ80yVLKB2MMu3ZZLdtZWVlNbtB7Q8Xa/SoVy9xuawu2jnAxxuwBLqinjHi9zsYaGN/oaJKllA+FhYW0bt0agPz8fFJTa58jKxrE2v0qpVQkaJKllFJKxZAm0F0YNTTJUipM3GVlbHnvc3Z8PgtEaHPSMbQbdzwOfXpPKWUjTbIiR3/aKxUGrpJSFp5wGXu+X1g5IejW92ey+fWPGPLJCzgSEmyOUCkVq9yEYAqHkEQS/TTJUspP7oI8ChfMonTtciQpleSDDyex34E+B4lvfOld9vzvJ8+Jf/042vXNj2x+/WM6X35WpMJWSillk6hIsgoKCnD6mPvH6XSSlJRUpVxtHA4HycnJAZUtLCyktvnGRISUlJSAyhYVFeGu4xEO78HJDSlbXFyMy+UKSdmUlJTKJKOkpITy8vKQlE1OTsbhaQEqLS2lrKwsJGWTkpIqv1fqKlv961+8cxtbn5qEycux2slF2LvoR5IOPZrMk88nMTGxchLPsrIy1r7zCcXGXbNNXYSN735WmWSVl5dTUlL7Ml8JCQnEx8c3uKzL5aK4uLjWsvHx8SR4WtNcLleV+61+795l3W43RUVFftVbX9m4uDgSExMB6+nGwsLCkJRtyP97/Rnhu6z+jKj/Z0T1smVlZZSW1j4dU/WfERVl6/o+ChdjTK3fYw2pQ/mh4sNuihuQgbWWkc9t7NixxltKSkqtZYcPH16lbFZWVq1lBw8eXKVsly5dai3bv3//KmX79+9fa9kuXbpUKTt48OBay2ZlZVUpO3z48FrLpqSkVCk7duzYWsta3xJ/OfPMM+ssm5+fX1n24osvrrPsjh07KsteffXVdZZdv359Zdmbb765zrLLli2rLHvPPffUWXbhwoWVZR9++OE6y3rf48Pjz66zzIwZMyrrnTp1ap1l/6/vYZVlp0+fXmfZqVOnVpadMWNGnWWfeeaZyrKzZs2qs+zDDz9cWXbhwoV1lr3nnnsqyy5btqzOsjfffHNl2fXr19dZ9uqrr64su2PHjjrLXnzxxZVl8/Pz6yx75plnVvkerqus/oywNv0Z8dfWkJ8Rs2bNqiz7zDPP1FnWj58RGSZCvzMfmr7PPDXDHdT20PR9EYu7KW9R0ZKlVDiMHj2atm3b4nQ6Kf8zO2T1Zh40IGR1KaWUaryiYlmdLVu2+FxWR7sCfJfVroCGdQUYY9h8y0W+yzocJB96NK3OGF+lKyB/yzbmjjifki07MJ7PT5xOkrq0Z+Tc/5Cc1QJoPN2F/pbV7kL9GdHQsrHwMwIC7y7Mzc2lffv2EMFldR58dx9JKcEtq1NcmMvt5zSDCMTdlEVFkqVrF6pw2zv1MUpWLvE5zXHzCbeT2Hu/GvtLduxm7b9fZusHXyIOB+3OHEOPmy8nwZNgKaWUHWsXTn4nNEnWHec2A02y6qTdhUr54N1akpKSQtrxZ1G6djmmrLRKouVo0Zr8bz6i6Oc5pBx6NAlde1ceS2zdkv7/vp3+/7494vE3VPX71WV1lFIqeJpkKeVDYWEhaWlpgGeZmbYdaXnDvyiY/Tmlf/wODgeuPTtw792Je88Oyjb8QfHiOWScfgkpw46xOfqGq3G/uqyOUlHLbUIwT1bT7QSLKIfdASjVVMRltSXzzMvIuv1REAFjrA0qW7dyP3kDd0GejVEqpVTdKn50Bbup+mlLlrKVMYbds+ezdfpMXMUltBp1OO3OHNOoZ0R37dqOa+fWWg6WU7LqN5IPOjyyQSmllGp0NMlStjHGsOy6e9n44jtInBMM/Pnmx6x/5g0O/WoacWmNtMvKXfuTVNWPl+3LxbjdJLRoFt6YlFLKT8ZtMEH29wV7fqzQ7kJlm13f/MjGF98BwJS7Kqc6yFm8jHWPTrEztDo5W7XD0TzL90FxkNB7f3J/W8m8oy/gq1ZD+LrNUOYcdiZ75/0S2UCVUsqHijFZwW6qfppkKdtsee9zqwWrOrebzW9+HPF4/CUOBxmnXQziqFz8GbH+TTvudEr3FTFv5Pnsnbu48pycxb8z/7iLyFu+Jmxx7fx6DvOOvZD/Zg3m+0EnsuHFdzB1zIuklFIqvDTJUrZxFRXX2uTsKqx9cszGIKnfgbS49h4S9xuCs2UbEnr0o9lF15N27KlkP/8WroIijMsrwXG7MeUu1j3+alji2TL9CxaOvYw9cxZRnpNH/vI1LLt2EstveiAs11NKNV068D1ydEyWsk3WyGFsnf5Fjf0S56TV6CNtiOgvTqeTM888s/K1Lwmde5Bw4XU19u+dv6Sy69ObKXexd+7PoQ0UMC4Xy2+ebL2pSOw8PwGzn32TbtddTEr3TnXW4c/9KqWig9ttcAfZ3xfs+bFCkyxlmw7nn0z2s6+Tt2JtZXIgTifOlGR63n6VrbElJSXx/vvvB3Zu2yzE6ayZaDkcJLatZSxXEHJ/XUnJ1h2+DxrDrlnz6VxPkhXM/SqllPJNuwuVbZzJSQyb9Tbdrx9PYttWxDVLp91ZYzl87vuk9e5md3gB63TpOJ8tWbjddL787JBea+Mr05k74rw6yziTE0N6TaVU06bdhZGjLVnKVvHNMuj30G30e+g2u0MJmVajjqDX3dfxx31P/zUw3u2m67UX0v6cE0N2nV3fzmXpxLvqLONISqT1CSNDdk2lVNMXiiRJkyz/2JpkichRwC3AwUA74DRjzMd2xqQUQEFBQVDLzPS+61o6nHcy2z/9BuNy0XrsSNL79wxpjOueeg2cTvDVaiYCIhzw8gPEZ6bXW1ew96uUUqomu1uyUoFfganABzbHolRIpfboTPe/Xxq2+gtWrfOdYAFJHdpwyBevkt6vR9iur5RqmtzG4A6yKSrY82OFrUmWMWYmMBNAROwMRakmJ61fD4qy/6wx/kvinLQeO0ITLKWUT8ZtbcHWoerXpAa+i0iiiGRUbED9/SBKRaluf7u45gB7zx8rXa6qezC8Ukqp8GtSSRZwB5DjtW22Nxyl7JM1chgHvPwAcelplfsSWjbj4OlPk7F/HxsjU0o1ZgaDMUFuaHehP+wek9VQk4HHvN6no4mWimGdxp9B+7NPYO/8X5C4OJoPHYgjIcHusJRSjZhxQ7Arbml3oX+aVJJljCkBSire6zgu1ZS4CoswbjdxaaF9cs+ZnETWyGEhrVMppVTwmlSSpVSkOJ1Oxo4dW/k6UKa8jL0zP2PPZ59QuH4Le1buJqFbb/o9fDvNDx0UomiDF6r7VUo1fhVdfsHWoepn9zxZaYD35EHdRGQQsMcYs9GeqJSylpn5/PPPg6rDlbuPXY/cjinKJy0TUge2pvWgNmxduJX5x17IEfM/IH2/3iGKODihuF+lVNPgNtYWbB2qfnYPfB8M/OLZwBpv9Qtwn20RKRUie567D3dRfuX7iu7tdoe0IzkrkbWPvmJXaEoppSLA7nmyZgM6sEpFnbItG3Dt3uHzm9sYQ/Oezdk9Z1HE41JKKeM2mCCbooI9P1bY3ZKlVKNUUFBAamoqqampFBQUNPj8sg1raj0mIiSkxZPYqmUwIYZUsPerlGo6dIHoyNGB70rVorCwMOBzHSlpdR7P3ZhLp2suCbj+cAjmfpVSTYfbbXAH2RIV7PmxQluylAqDxH6DICGpxn5jDOUl5SQdeDidLjkj8oEppZSKGE2ylAoDSUik+SU3Qlx8tQNxpJ56FQOnPIQ49L+fUirygp7tPQRTQMQK7S5UKkwSe/an9V3PULxkLq6cvcS170zSgMFIXGj/25WuX03+Nx9Slv0HkpJGyiEjSB15IlI9wVNKKXSB6EjSJEupMHKkpJJy2Kiw1V/yxzL2vvyw9ca4MaUl5H/9IaUb19D80pt1VQSllLKR9lco1YTlff4uYKr+WWkMpSt/pWzdStviUko1Xm5jQrKp+mlLllI+OBwOhg8fXvm6MXKXFFP+Z7bvgw4HJWt+J6FHP7/qagr3q5QKDV1WJ3I0yVLKh+TkZGbPnm13GHUSpxMcDnD7GBxhDJKQ6HddTeF+lVKqqdE/WZVqoiQunsT9hliJlg9JAw+NcERKqaagYp6sYDdVP02ylGrCMk4+H2dmC+uNw1GZcKWffCFxLVrZGJlSqrHSGd8jR7sLlfKhoKCArl27ApCdnU1qaqq9AdXCmdmCljc9SPHPP1K64Q8cqekkH3wE8e27NKiepnK/SinVlGiSpVQtdu3aZXcIABSu28Sub+fiSEqk9QkjSGjRrMpxR2ISKcOOIWXYMUFdp7Hcr1IqvIwJwQLR2pTlF02ylGqkjDEsv3ky2U+/Xtk270hMYP/n7qPjRafZHJ1SqqkyIZiCQZMs/+iYLKUiJHfpKnb8938UbdrqV/lNU/9D9lOvVRn84C4p5dfL7yD3N50DSymlGjtNspQKs6JNW/nxsHH8cNDJ/HTiBL7rMZJfLr4ZV3FJnedteP4t8DFjuzgdbHr1P+EKVykV5YzbhGRT9dPuQqXCyLjdLDxxAgWr13ntNGx593PiUlPY/7n7aj23+M/tPh/hMS43xVu2hyNcpVQMCEWSpEmWf7QlS6kw2v39AvKX/4Epd1U94Haz8ZXp7Pjyf7Wem3Fgf3A6ax4QIeOAPiGOVCmlVKhpkqWUDw6Hg8GDBzN48OCglpkp+GND7QeN4aeTJpD93Fs+D/e89QprNnfvLkOng7i0FDpdfnbAMfkSqvtVSjV+bhOaTdVPf5oq5UNycjI//fQTP/30E8nJyQHXk9qr/vmqlt/0ACU799TY33L4UA567ymSOrat3Jd54AAO/fZNktqGdqLRUN2vUqrx0zFZkaNjspQKo5bDh5LWryf5q9eBy8cag4ApL2fnzO99TsvQ7rTjaHvKsRSs2YAzKZHkzu0BcBUVs/OrObgKCmlx5BCSO7UL630opZRqOE2ylAojcTg4ZMbLLDz5CvJ//6PWcsblqvWYa+dWyhfNpPCPZeTFJ+B2OynesBHjcrP3jz38dsUOOl91Pv0fvh3Rrj6lVD2MMUHPc6XzZPlHkyylfCgsLKR///4ALF++nJSUlIDrSu7cnqN+/pTvuo+geMuOmk8MOhy0Ou5In+eWbdvEnqcnYcrLrPFZWD/ckpolApDUvD3NezZn5XNvkNqjC10nnh9QjKG8X6VU4+Z2E/QCz27fDfOqGv2zVykfjDFs2LCBDRs2hOQvNnE42P/FfyFOBxLneWLQaf336/XPa0jq0Mbnefn//aBKggUgXgPhxSEktUwma//WZD/zRsDxhfp+lVJKaUuWUhHTevRRHD5nOusef5Wcn38nqVM7uk48n7anjqr1nJJVv/n1J2Nmt0x2fVHHk4xKKeWh3YWRo0mWUhGUefB+HPjmY36Xl7h4TFlp3YWM9bRQev+eQUanlIoFOhlp5Gh3oVKNWNLAQ30urVOFwL4/9tDjtisjE5RSSim/aEuWUo1U6frVFC9d4HtpHWPAWGOy8rcW0m7itbQ77TgbolRKNTXakhU5mmQp1Qi5iwrYO+VhTGnNRaTjOvdAklJxF5fgaNeTbsefQlyqPg2olPKPG4M7yDFVbjTJ8ocmWUr5ICKVUxpIfd11YVD8y1xMSQn4+EHm2rGVpDP/Tkrn9sQ3zwzJ9ey+X6VU5GhLVuRokqWUDykpKfz++++2Xd+1b7c1xYOPSUpNcSFzDz0dg4OOF53KgMf/iTMluKVw7L5fpZSKRppkKdUIxbXu4DvBMoay/DLc5W7AzaZpH1KeV8BBbz8R8RiVUk2TTuEQOfp0oVKNUNLAoTgym0O1ZXJEhG0/bflrh9vN1v98SeH6TRGOUCnVVBm3wR3kFs7uQhFpLiJviEiOZ3tDRJo14PwXRcSIyA1hC9JPmmQp5UNhYSEDBgxgwIABFBYWRvz6Ep9Ai6v+QVyHrpX7XGUuNs/ZxM7fdlYtbAx5y9cEdT2771cppby8DQwCxni2QYBfS1qIyKnAUGBLPUUjQrsLlfLBGMPy5csrX4dL2Z8byP/uU8qyV+FIzSB56AhShh2LOBxIXDyUlVWWdcY7adm3JXtW7qYsv6xKPUkd2gYVR6TuVyllv8Y88F1E+mElVocaYxZ49k0A5olIH2PMqjrO7QA8A4wGPg9LgA2kLVlK2aR0/Wp2P30PJct+wp27j/KtG8n7+HVy3nkeYwz73nia8h1/VjknqUUy3U/4a2Z3cTrJPHg/Mgb2jXT4SqkmqmJMVrBbmAwDcioSLE+884Ec4LDaThIRB1Zr17+NMY3mKR5tyVLKJnmfvw1uV43JRouXzCOh/4GUbazZBSgOIa1dGslZyRTtKiK1T3cOnv60TruglLJLerWfPyXGmJoT/PmvLbDDx/4dnmO1uQ0oB54K4tohp0mWUjZwlxZTtqGWcVQOB6UrltR5fq/bLiN50BBaHnUI4tAGaaWU/4zbjfFj4fn66vDYXO3QvcCk6uVFZBJwTz3VDqmo3scxqWU/InIwcD1wkGlk4x00yVLKBiIOa01CXz8PDNaThXVof/G5OJtnhSk6pVQ0q3hCMNg6PDoCeV6HamvFegZ4t55qs4EDgDY+jrUCttdy3pFAa2CjV6uaE3hURG4wxnSt57pho0mWUjaQ+AQS+w2iZOWvUP0vSuMm5ZCRuPftofjX+VUTMRGSBg7VBEsp1VjkGWNy6ytkjNkF7KqvnIjMAzJF5BBjzELPvqFAJjC3ltPeAL6ptu+/nv1T67tmOGmSpZQPIkKXLl0qX4dD+kkXULZxLe6CPCuRcjjA7SZtzDjiWrUl86wJSEIiRYt+sMZuOZwkH3wEGadeFPJYInG/SqnGoTFPRmqMWSEiXwIvi8iVnt0vATO8nywUkZXAHcaYj4wxu4Hd3vWISBmwra6nESNBkyylfEhJSSE7Ozus14jLakPWzQ9RuGAWRb//Sv6aP9m5ZCtm6cd03ptA2zPGkDnuctJPOBfXvl04m7XEkZIWllgicb9KqcahMU/h4HE+1gD2rzzvPwWurVamD1brVqOmSZZSNnKkplOa1JFFd/4DU+7ClLvA4WDXt3PpftNl9HvwVhwpqThSUu0OVSmlIsIYswe4oJ4ydTa52zkOy5s+lqSUzZbfeD/u0nIrwYLKMVrrHp1CwR/Z9gWmlIpKFS1ZwW6qfppkKeVDUVERQ4YMYciQIRQVFYXtOiU795CzeFnNwe8ADgfbP58dtmt7i9T9KqXs58aN2wS5EdwUELFCuwuV8sHtdrNo0aLK1+FS9xhzgzgiMwg9UverlFKxRFuylLJRQlYLmh06CJw+/isaaHPS0RGPSSkV3Yw7FF2Gdt9F06BJllI22+/Ju3EmJSJOJ0Dlv73vvpaUbp3sDE0pFYV0TFbkaHehUjbLPGgAR/3yGdnPvsneBUtIateaTpeNo/Xoo9j59Rw2vvIexVt20GzIAXS79iJSumvipZRSTYEmWUo1AindOtH/kTuq7Ptj8vOsvvsJxOnEuFzk/LSUTVPe59BvXqfZkANsilQp1dQ15slIo412FyrVCBVu+JPV9zwJgHG5Kv91FZew7G/32RmaUqqJc7vdIdlU/bQlS6laZGXZtz7gjtqmbnC7yVm0lOJtO0lq2yqk17TzfpVSKhppkqWUD6mpqezcudOWaxdu+JPCdRvrLhTipno771cpFVlNYFmdqKFJllKNRMn2XSwZfyu7vvmx9kIOBxn79yGpXevIBaaUiirGuDFBzsEQ7PmxwvYxWSJytYisF5FiEVksIkfaHZNSkWaMYeGJE9g9a37Ng54ZSyXOiSM+jgFP/DPC0SmllAqErUmWiJwNPAHcDxwI/ADMFJHOdsalVFFRESNGjGDEiBERWWZmzw8/kbtkeeUg9+rS+nanw4WnccSCD2lxxOCQXz/S96uUso/OkxU5dncX3ghMMca84nl/g4iMBiYCd9R+mlLh5Xa7+f777ytfh1v+8jW1HzSGg6Y/Q3q/HmG7fqTvVyllo1AkSZpk+cW2liwRSQAOBr6qdugr4LBazkkUkYyKDUgPc5hKRURy1w61HpM4J0ntQvskoVJKqfCzs7swC3AC26vt3w60reWcO4Acr21z2KJTKoKyjj2c5K4dKpfUqeR00P68k4lvlmFPYEqpqOM27pBsqn62D3wHqrc5io99FSYDmV5bxzDGpVTEOOLiOGTGKzWWzGk9+ij2e/Ium6JSSkUjHZMVOXaOydoFuKjZatWamq1bABhjSoCSivfieepKqWiQ1qc7w5fNZM+Piyn+czsZB/QlvX9Pu8NSSkUZY9yYIMde6hQO/rEtyTLGlIrIYmAU8JHXoVHAJ/ZEpZS9xOGg5ZFD7A5DKaVUCNj9dOFjwBsisgiYB1wBdAZesDUqpYCUlBS7Q4ioWLtfpWKVzvgeObYmWcaY90SkJXA30A5YBow1xmywMy6lUlNTKSgosDuMiIm1+1UqlumM75Fjd0sWxpjngOfsjkMppZRSKpRsT7KUUkopFTluN7iD7O7TOYv90ximcFCq0SkuLuaEE07ghBNOoLi42O5wwi7W7lepWGbc7pBsqn7akqWUDy6Xiy+++KLydbSLtftVSqlI0CRLKaWUiiH6dGHkaJKllFJKxRB9ujBydEyWUkoppVQYaEuWUkopFUO0uzByNMlSSimlYkgong7Upwv9ExVJVm5urt0hqCjjPft5bm5u1D9xF2v3q1RjYcfvL1d58Ks7hKKOWCDGNN0mPxHpAGy2Ow6llFIqSB2NMX+G8wIikgSsB9qGqMptQDdjjE6uV4umnmQJ0BtYCXQE8uyNKGLSsZLLWLlnvd/opvcb3fR+/Ttni4nAL2RPopUQoupKNcGqW5PuLjTGGBHZ6nmbZ4yJiX5DK7cEYuSe9X6jm95vdNP79UvEPhdPUqSJUYToFA5KKaWUUmGgSZZSSimlVBhEQ5JVAtzr+TdWxNo96/1GN73f6Kb3q2JWkx74rpRSSinVWEVDS5ZSSimlVKOjSZZSSimlVBhokqWUUkopFQaaZCmllFJKhUHUJVki8qmIbBSRYhHZKiJviEh7u+MKBxHpKiJTRGS9iBSJyFoRuVdEQjWbb6MjIv8QkbkiUigi++yOJ9RE5GrP17NYRBaLyJF2xxQuInKUiHwmIltExIjIqXbHFE4icoeI/CQieSKyQ0Q+FpE+dscVLiIyUUR+E5FczzZPRI63O65I8Xy9jYg8YXcsyj5Rl2QBs4CzgD7AGUAP4D+2RhQ+fbG+hlcCA4C/A1cBD9gZVJglAO8Dz9sdSKiJyNnAE8D9wIHAD8BMEelsZ1xhlAr8ClxrdyARMhx4FjgUGIW14sZXIpJqa1Thsxm4HRjs2b4DPhGRAbZGFQEiMgS4AvjN7liUvaJ+CgcRORn4GEg0xpTZHE7YicgtwERjTHe7YwknERkPPGGMaWZzKCEjIguAn40xE732rQA+NsbcYV9k4SciBjjNGPOx3bFEioi0AnYAw40x/7M7nkgQkT3ALcaYKXbHEi4ikgb8DFwN/BNYYoy5wdaglG2isSWrkoi0AM4H5sZCguWRCeyxOwjVMJ4u3oOBr6od+go4LPIRqQjI9Pwb9f9fRcQpIudgtV7OszueMHsW+NwY843dgSj7RWWSJSIPiUgBsBvoDJxic0gRISI9gOuAF+yORTVYFuAEtlfbvx1oG/lwVDiJtYrwY8AcY8wyu+MJFxHZX0TysWY/fwGrtXK5zWGFjSeRPAiI6pZn5b8mkWSJyCTPAMK6tsFep/wba0zLcYALeF28lkZv7AK4XzyD+78E3jfGvGJP5IEJ5H6jWPX+e/GxTzV9zwAHAOfaHUiYrQIGYY1Dex54TUT62xpRmIhIJ+BJ4AJjTLHd8ajGoUmMyRKRLKy/9OuS7esbW0Q6ApuAw4wxTaKZuqH360mwZgELgPHGGHeYQwypQL6+0TYmy9NdWAiMM8Z85LX/SWCQMWa4bcFFQCyNyRKRp4FTgaOMMettDieiROQbYK0x5kq7Ywk1z9OxH2H9YV/BifVHkhtrXLDLx6kqisXZHYA/jDG7gF0Bnl7RgpUYonDCriH3KyIdsBKsxcAlTS3BgqC/vlHBGFMqIouxnjr7yOvQKOATe6JSoeRpTX8aOA0YEWsJlofQhH4WN9C3wP7V9k0FVgIPaYIVm5pEkuUvETkEOASYA+wFugP3AWuJwsGWnhas2cBG4GagVUWvqDFmm32RhY9nOoMWWGPtnCIyyHNojTEm37bAQuMx4A0RWYT1/XoF1n1G5Rg7z1NYPb12dfN8PfcYYzbaE1VYPQuchzVGNE9EKsba5RhjiuwLKzxE5AFgJlZPQjpwDjACGGNjWGFjjMkDqoyvqxgbHM3j7lTdoirJAoqA04F7sZ5i2Yo1TukcY0yJnYGFyXFYv6R6Ys1J463JjEFroPuAi73e/+L5dyRWwtlkGWPeE5GWwN1AO6wf2GONMRvsjSxsBmO1wlZ4zPPva8D4iEcTfhVTc8yutv8SYFpEI4mMNsAbWN/LOVhzRo0xxnxta1RKRVCTGJOllFJKKdXUNImnC5VSSimlmhpNspRSSimlwkCTLKWUUkqpMNAkSymllFIqDDTJUkoppZQKA02ylFJKKaXCQJMspZRSSqkw0CRLqSghIrNF5Am741BKKWXRJEsp5ZOIjBcR42O73KtMgojcKiK/ikihiOwSkR9F5BIRibczfqWUslu0LaujlAqtXKBPtX05YCVYwH+BgcBdwI+e8odiraX5C7AkUoEqpVRjoy1ZSkUhEWkuIq+LyF5PC9NMEelVrcwEEdnkOf6RiNwoIvuqVWWMMduqbRWLGd8AHAUcY4x51hizxBizzhjzNjAU+MNznTNFZKmIFInIbhH5RkRSw/sJKKWU/TTJUio6TcNagPlkYBjWguFfVHThicjhwAvAk8Ag4GvgHw28xvnAN8aYX6ofMMaUGWMKRKQd8A7wKtAPGAF8SPQuYK6UUpW0u1CpKONpsToZONwYM9ez73xgE3Aq8D5wHTDTGPOI57TVInIYcGK16jJFJN/rfb4xpq3ndS9gdj3htMP6OfOhMWaDZ9/SBt+UUko1QZpkKRV9+gHlwIKKHcaY3SKyynMMrHFWH1U7byE1k6w84CCv926v1wKYemL5FfgWWCoi/wW+Av5jjNnrx30opVSTpt2FSkWf2rrivJMiXwmSr/Pcxpg1Xts6r2Or+Stp88kY4wJGAccDy7Fa0FaJSLd67kEppZo8TbKUij7LsVqph1bsEJGWQG9ghWfXSuCQaucNbuB13gaOFZEDqx8QkbiKwe3G8qMx5h7gQKAUOK2B11JKqSZHkyyloowx5g/gE+BlETlCRAYCbwJ/evYDPA2M9TxR2EtErsRqbaqv+8/bE1jTNnwrIteIyEAR6S4iZ2F1VfYSkaEicqeIDBaRzsDpQCv+SvaUUipqaZKlVHS6BFgMzADmYXUFjjXGlAEYY34ErgJuxBo3NQZ4HCj29wLGmBKsrsCHgSuB+cBPwN+Ap4BlWPNmHQV8gdW9+C/gJmPMzKDvUCmlGjkxpiF/uCqlopWIvAz0NcYcaXcsSikVDfTpQqVilIjcjDU/VgFWV+HFwNW2BqWUUlFEW7KUilEiMh1rctB0YB3wtDHmBVuDUkqpKKJJllJKKaVUGOjAd6WUUkqpMNAkSymllFIqDDTJUkoppZQKA02ylFJKKaXCQJMspZRSSqkw0CRLKaWUUioMNMlSSimllAoDTbKUUkoppcJAkyyllFJKqTD4f3SG7vZYL0tXAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 700x500 with 2 Axes>"
       ]
@@ -1391,12 +1415,12 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "2d98a9bc",
+   "id": "da422813",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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NoPam+P1+ZsyYwVlnnVWz76yzzmLGjBn4/fo4klJOkxSJyaaap0mWUiqotLQ0Jk6cyPXXX8/s2bOZOnUqN9xwQ7PnffDBB6xfv55TTz0Vj8eDx+PhtNNOY926dcycOTMBkSulmhJ1V2FgU83TJEspFZLBgwdTUlLSbLknnniC0047jYULF9bZzjzzTJ544okERKqUUi2DPl2olKpj+/btnHzyyZx//vkMGzaM7Oxs5s+fz+23387xxx/f5Llbt27lrbfe4s0332To0KF1jp177rkcffTRbN26lU6dOjUbx8KFCwEoLi5m69atLFy4kNTUVAYPHhzxZ1NKBboLXdG1RImlLVmh0CRLKVVHVlYW++67L3fddRcrVqygqqqKnj17ctFFF3Httdc2ee5TTz1FZmYmhx12WINjhxxyCNnZ2Tz99NP88Y9/bDaOESNG1LxesGABzz33HL1792bVqlVhfyal1G4ud/RPB7r8mmSFQhKw6HfciEgOUFBQUKDL6igVhcLFP7NzznekdmhH58kTcKenOR1Si1ZRUcEll1wCwCOPPKIzl4dB666uwsLC6gl8c40xhfG8V/V35rt77U2mO7pldUr8fib/tBgSEHcy05Yspdowf0UlC8/5E5te2z0g3dMum1Ev3UfHQ/Z3MLKWzev1hvSkpWpI6061JTrwXak27Jd/3MumNz6qs89XWMz8E35L5Y5dzgSllIqr6u7CaDfVPE2ylGqjjGWx+uHnwbIA+NmU8YB/M2v95fjLytnwwjsOR9hyGWMoKSmhpKSEZB5y4QStO+eJS2KyqeZpkqVUG+UvK8dXVFzz/iH/FpaYMgDE46Z8/SanQmvxSktLa5Yf0qVhwqN1p9oSHZOlVBvlzkgnvVc3ytZsAOAudy8ARART5SNn2F5OhqeUihNxuxB3lAtEo62QodCWLKXaKBFhwLWX1XkvIojbTUa/nnQ5YaKD0Sml4kXHZCWOJllKtWE9zz+JwXdeR0r73Jp9HSbsy34fPY3bm+pgZEoplfy0u1CpNkxE6HvFOfS+5DRKlq8hpX0OaV07Ox2WUiqORHTG90TRJEsphSs1lezBA5wOQymVAOIm6u4+0SFZIdHuQqWUUkqpONCWLKWUCpPb7eakk06qea1Cp3XnPHELEnVLlnYXhkKTLKWUClNaWhovv/yy02EkJa0754nLhbiinMIhyvPbCq0lpZRSSqk40JYspZRSqg2JxbI4uqxOaLQlSymlwlRSUlIzeWtJSYnT4SQVrTvn6WSkiaNJllJKKaVUHGh3oVJKKdWGaHdh4miSpZRSSrUhIjF4ulC0IywUWktKKaWUUnGgSZZSSinVhlR3F0a7RXRvkctEZKWIlIvIAhE5KMTzDhARn4gsjOjGDtEkSymllGpDnHq6UEROBe4GbgFGAF8A74lIr2bOywWeAj4O+6YO0zFZSikVJrfbzeTJk2teA1g+H+VrN+LJzSY1r52D0bVswepOtRl/BJ4wxjweeH+ViBwJ/Ba4ponzHgGeA/zACXGNMMY0yVJKqTClpaXxzjvv1Lxf8/hL/Hzj3VRu3g4idDn2UIbefyNpXTs7GGXLVL/uVOLF+OnCbJE616owxlQ0KC+SCowC/lXv0ExgXKP3ETkP6A+cBfwtipAdod2FSikVhXVPvc7i3/7dTrAAjGHLO7P4+vBzsKqqnA1OqSCq1y6MdgtYBxTU2hprkeoIuIHN9fZvBvKDximyB3ZSdqYxxhflx3aEJllKKRUhYwzL/nl/w/1+PyXLVrL5zaQbQqJUuHoAubW225opb+q9lyD7EBE3dhfhDcaYZTGI0xGaZCmlVJhKSkrIzMwkKyuLnSvXBC0jKR4Kvlua4Mhavuq6y8zM1GV1HBLjpwuLjDGFtbYGXYUB27DHVNVvtepMw9YtgGxgNHB/4KlCH3A9MDzw/tDoayL+dEyWUkpFoLS0FAB3ThqUVjY4bnx+vPmdEh1WUqiuO+UMJ2Z8N8ZUisgCYCLweq1DE4H/BTmlENi73r7LgEOBk4CVYQXgEG3JUkqpKPSYehLUnz1bBFdaKt1PO9qZoJRqme4ELhSR80VkkIjcBfQCHgYQkdtE5CkAY4xljFlSewO2AOWB90nRDKotWUopFYU9/3Y51vK1bJ35BbgELIM7I51RL91Lasc8p8NTqgGn1i40xrwoIh2wu/26AkuAycaY1YEiXbGTrlZDkyyllIqCOyOdMW8/RsG8xeyc8y0pHdqRf/zheLKznA5NqaDsJCvKtQsjTNKMMQ8CDzZybGoz594I3BjRjR2iSZZSSkVJRGg3dhjtxg5zOhSlmiWuyGZsr3MNf3TntxU6JksppZRSKg60JUsppcLkcrkYP358zWsVOq075zk1Jqst0iRLKaXClJ6ezqxZs5wOIylp3Tmv3oztEV9DNU9rSSmllFIqDrQlSymllGpDtLswcbQlSymlwlRSUkKnTp3o1KmTLg0TJq0758V4WR3VBG3JUkqpCGzbts3pEJKW1p1qKzTJUkoppdoQHfieOJpkKaWUUm2IjslKHE1FlVJKtWpTp07lhBNOqHktIg225cuXNygb7H21WbNmISLs2rULgPLycqZOncree++Nx+MJeo5qexxNskTkRhEx9bZNTsaklFKqdZs0aRIbN26ss/Xt2zeqa/r9ftLT07nyyis5/PDDYxRpfFR3F0a7qea1hO7CH4Da/yL9TgWilFKq9fN6veTn58f0mpmZmTz00EMAfPXVVzUtXC2SiL1Few3VrJaQZPmMMdp6pZRKGi6Xi9GjR9e8VqHTulNtSUtIsvYQkQ1ABfANcK0x5tdgBUXEC3hr7cpOQHxKKVVHeno68+bNczqMpNQS6u7tt98mKyur5v1RRx3Fyy+/HHJ5sLsHk5VIDAa+a0tWSJxOsr4BzgGWAV2AvwGzRWSIMWZ7kPLXADckMD6llFKtzCGHHFLTtQd2V1845QG++eYbzjrrrLjEF286hUPiOJpkGWPeq/V2sYjMAVYA5wJ3Bjnltnr7s4F18YtQKaVUa5OZmcmAAQOiKr9unX71qOa1qFTUGFMCLAb2aOR4hTGmsHoDihIaoFJKAaWlpfTp04c+ffpQWlrqdDhJRevOebqsTuI43V1YR2DM1SDgC6djUUqpxhhjWL16dc1rFbrWXHdLly6lsrKSHTt2UFRUxMKFCwHYZ599HI2rPu0uTByn58n6j4iMF5G+IrIv8AqQA8xwMi6lWorZs2fjdruZNGlSnf2rVq2qM5Fi+/btOfjgg/nss8/qlFu7di0XXHAB3bp1IzU1ld69e/P73/+e7dvrDnmcMGECV111VdAYVq1axQUXXEDfvn1JT0+nf//+3HDDDVRWVsb0syqV7CZPnsyIESN46623mDVrFiNGjGDEiBFOh6Uc5HRLVg/geaAjsBX4GtjPGLPa0aiUaiGefPJJrrjiCh5//HHWrFlDr1696hz/6KOPGDJkCFu2bOHaa69l8uTJLFmyhL59+/Lrr7+y//77s+eee/L888/Tt29ffvjhB66++mree+89vv76a/Ly8pqN4aeffsKyLB555BEGDBjAkiVLuOiiiygpKeE///lPvD66UhEzfh8VPy/CKtiJp3tvpk2bVvM03PTp05s8t/7xxspPmDChQUvcqlWrIow4scQV/bI4og1ZIXF64PtpTt5fqZaspKSEl156iXnz5rFp0yamT5/O9ddfX6dMhw4dyM/PJz8/n0ceeYQePXowc+ZMLrnkEi6//HJSU1OZOXMm6enpAPTq1YsRI0bQv39/rrvuugZPTAUzadKkOi1p/fr14+eff+ahhx7SJEu1OFXrV7Hzif9gFe2q2ZfSby/aT/0jrvQM5wJrQXTtwsTRXFSpFurFF19k4MCBDBw4kLPOOotp06Y1OYYlI8P+AqmqqmLHjh188MEHXHbZZTUJVrX8/HzOPPNMXnzxxYjHxBQUFITUCqZUIhmfj52P34FVXFhnf9XKZRS+oaNQVOJpkqVUC/XEE0/UzMMzadIkiouL+fjjj4OWLSkp4ZprrsHtdjN+/Hh++eUXjDEMGjQoaPlBgwaxc+dOtm7dGnZcK1as4L777uPSSy8N+1yl4qnix++wigvAWPgti2cXLWflziIwFuUL52CVlTgdYsvgcsVmU83SWlKqBfr555+ZO3cup51m96h7PB5OPfVUnnzyyTrlxo0bR1ZWFtnZ2bz11ltMnz6dvffeu9nrV7dghTtr84YNG5g0aRInn3wyF154YVjntiYiwuDBgxk8eLDOfB2meNadv2BnzZp6S7fu4m+fzOeXHQX2QcvCKtZZf4A6D81Es6nmOT3wXSkVxBNPPIHP56N79+41+4wxpKSksHPnzpp9L774IoMHD6Zdu3Z06NChZv+AAQMQEZYuXcoJJ5zQ4Po//fQT7du3p2PHjiHHtGHDBg455BD2339/Hn300cg+WCuRkZHBDz/84HQYSSmedZfSrRcEfoHYu0sev/7+1JpkQLxpuNtpF7dKLG3JUqqF8fl8PPXUU/z3v/9l4cKFNdv3339P7969efbZZ2vK9uzZk/79+9dJsMAeED9x4kQefPBBysrK6hzbtGkTzz77LKeeemrIv42uX7+eCRMmMHLkSKZNm6YL+6oWKaXvQFJ69q/pyqr97ztz/NFISqpTobUo1fNkRbup5mlLllItzNtvv83OnTu54IILyM3NrXPspJNO4oknnuCYY45p9jr3338/48aN48gjj+Tmm2+uM4VD9+7dueWWW+qU37p1a83kidXy8/OxLIsJEybQq1cv/vOf/9QZx5Wfnx/5B1UqxkSE9hf8mYJXn6RiyXwwBkn1kjF+MpmHHe90eC2GPl2YOJpkKdXCPPHEExx++OENEiyAKVOmcOutt7Jjx45mr7PHHnswf/58brzxRk499VS2b99Ofn4+J5xwAjfccEODpwOfe+45nnvuuTr7brjhBvr06cPy5ctZvnw5PXr0qHO8tc3YHarS0lLGjBkDwLx582qe7FTNi3fduTKzaX/O77GKC/EXF+LJ64SkemN6j6QnMRi4rhNlhUSS+YekiOQABQUFBeTk5DgdjlIRMcZQtWYFVSt/QtIySNt7DK7MbKfDUk0oKSkhKysLgOLiYjIzMx2OKHlo3dVVWFhY/QtVbmBN3rip/s5cec25ZKdF13VaVF5J39tmQALiTmbakqWUg0xVJTufupfKnxbavxkai8L/PUW70y8jbdhYp8NTSrVGsVjgWbsLQ6LtfUo5qPij16n8+Xv7jbHsP30+dj17P/5d2xs/USmlIiTiismmmqe1pJSDSr/+pOaR82/WbeHiN7/giW9/BgNlC750ODqllFLR0CRLKYcYy8KU7p6B+p6vl7ByVxF922eDiD1ztVJKxZpLYrOpZumYLKUcIi4Xnq698G1aC8bw3EmH7j5o+Unp0c+54JRSrVYs5rnSebJCo7WklIOyjjixpruwhsuFu0NnHfjegokIvXv3pnfv3rq8SJi07lRboi1ZSjkobehocs+8nOJ3X8S/cxuI4B00gpzfnKuzU7dgGRkZrFq1yukwkpLWnfN0MtLE0SRLKYel77M/acP2xSoqQFK9uNJ1YkulVByJRD+ZqLZChkSTLKVaAHG5cOe2dzoMpZRSMaRjspRSKkxlZWWMGTOGMWPGNFiAWzVN68551d2F0W6qedqSpZRSYbIsi/nz59e8VqHTumsBXDFYu1CfLgyJ1pJSSimlVBxokqWUSjoi0uQ2derUBuWysrIYPnw406dPr3OtWbNmISLs2rWrzvv27dtTXl5ep+zcuXNrrlXt888/5/jjj6dr165kZmayzz778Oyzz9Y5b+PGjZxxxhkMHDgQl8vFVVddFesqUSpkzf3/CXVTzdMkS6kksGXLFi655BJ69eqF1+slPz+fI488kjlz5gDQp0+fmh98GRkZDB06lEceeaTm/FC+5H/44QemTJlSc6277747aCwPPvggffv2JS0tjVGjRvHFF1/E4yM3aePGjTXb3XffTU5OTp1999xzT03ZadOmsXHjRr7//ntOPfVUzjvvPD744INm75Gdnc3rr79eZ9+TTz5Jr1696uz75ptvGDZsGK+++iqLFi3i/PPP55xzzuGtt96qKVNRUUGnTp247rrrGD58eJSfXqkoiWt3l2Gkm65dGBKtJaWSwJQpU/j++++ZMWMGy5Yt480332TChAns2LGjpsxNN93Exo0bWbRoESeccAKXXnopL774IhDal3xpaSn9+vXjX//6F/n5+UHLvPjii1x11VVcd911fPfddxx00EEcddRRrFmzJvYfugn5+fk1W25uLiLSYF+1du3akZ+fT//+/bn22mvJy8tj5syZzd7j3HPP5cknn6x5X1ZWxgsvvMC5555bp9zVV1/NP//5T8aNG0f//v258sormTRpUp0ErU+fPtxzzz2cc845dWJTSrVummQp1cLt2rWLL7/8kn//+98ccsgh9O7dm7Fjx3LNNddw9NFH15TLzs4mPz+fAQMGcPPNN7PHHnvwxhtvAKF9yY8ZM4Y77riD0047Da/XG7TMnXfeyQUXXMCFF17IoEGDuPvuu+nZsycPPfRQzD93rPn9fl566SV27NhBSkpKs+XPPvtsvvjii5oE8tVXX6VPnz6MHDmy2XMLCgrIy8uLOmal4kGfLkwcTbKUauGysrLIysrijTfeoKKiIuTz0tLSqKqqilkclZWVLFiwgCOOOKLO/iOOOILZs2fH7D6xdvrpp5OVlYXX6+XUU08lLy+PCy+8sNnzOnfuzFFHHVUzhuvJJ5/k/PPPrznesWNHOnbs2OC8V155hXnz5nHeeefF7DO0No3VnUoQccVmU83SWlKqhfN4PEyfPp0ZM2bQrl07DjjgAK699loWLVoUtLzP52P69OksXryYww47LGZxbNu2Db/fT5cuXers79KlC5s2bYrZfWLtrrvuYuHChXz44Yfss88+3HXXXQwYMCCkc88//3ymT5/Or7/+ypw5czjzzDNrjm3dupWtW7eSmZlZs2/WrFlMnTqVxx57jCFDhsT8s7QGmZmZQetOqdZIkyylksCUKVPYsGEDb775JkceeSSzZs1i5MiRdZ6U++tf/0pWVhbp6elcfvnlXH311VxyySUxj6X+U0XGmBb9pFF1F+ohhxzCyy+/zOWXX87SpUtDOnfy5MmUl5dzwQUXcOyxx9KhQ4dGy3722Wcce+yx3HnnnZxzzjmxCl+p2HNJbDbVLE2ylEoSaWlpTJw4keuvv57Zs2czdepUbrjhhprjV199NQsXLmT16tUUFxdz++2344rhhIEdO3bE7XY3aLXasmVLg9atlmrAgAFMmTKFa665JqTybrebs88+m1mzZtXpKqxv1qxZHH300fzrX//i4osvjlW4SsWFiCsmm2qe1pJSSWrw4MGUlJTUvO/YsSMDBgygW7ducWlZSk1NZdSoUXz44Yd19n/44YeMGzcu5veLlz/96U+89dZbNbOON+ef//wnW7du5cgjj6yzf8KECUyYMIEPPviAo48+miuvvJIpU6awadMmNm3aVOfJT4CFCxeycOFCiouL2bp1KwsXLgy5Ra01KSsrq6k7XVZHtXa6rI5SLdz27ds5+eSTOf/88xk2bBjZ2dnMnz+f22+/neOPPz7k6yxcuBCgzpd8amoqgwcPBuyB7dVf+pWVlaxfv56FCxeSlZVVM4bpj3/8I2effTajR49m//3359FHH2XNmjVceumlsf3QcbT33ntz+OGHc/311/Puu+82Wz41NTXoIO3PPvsMgB49elBaWsptt93GbbfdVnN8/PjxzJo1q+b9iBEjal4vWLCA5557jt69e7Nq1arIP0wSsiyrpu50WR2HxKK7T7sLQyLGGKdjiJiI5AAFBQUF5OTkOB2OUnFRUVHBjTfeyMyZM1mxYgVVVVX07NmTk08+mWuvvZb09HT69OnDVVdd1eRM4sFat2p/ya9atYq+ffs2KFM/WXjwwQe5/fbb2bhxI0OHDuWuu+7i4IMPjvZjNslXUsrKe2ew4bk38ZdV0HnyBPr/+ULSe3WL630bU1JSUjPre3FxsQ7gDoPWXV2FhYXV06rkGmMK43mv6u/Mjff9hZz04NO0hKqwrIKuV9wOCYg7mWmSFeDbsoGKX35AUlJIGzIKV2Z2bIJUKgz+ikrWP/0Gm96YibEsuhx7GD2nTsGdnuZ0aI7xV1Ty9WFnsWveYgi0fIjHTUq7HA74+lUyendPeEyaKERO664uTbJatzbfXWgsi8LXp1P29SeAAIbC16aRc+L5ZIwd73R4qg3xV1TyzaTz2PnlfAi0Om378CuW//sRxrz2ELkj2+aUABtfepdd33xf895vDMurium6w8eKfz3C3g/d5GB0SiUhkZqfMVFdQzWrzQ98L/v6k0CCBRBo1fP7KXz5Mao2rnUsLtX2rJv2Cju/CgzGNgZjWRhjqFi/mS/3PZGfb7jb0ficsvWDz8Ft/6iaZxVztv9X/uRfy6e+XWx662OHo1MqCbkk+rULdUxWSNp8klU656PgB1wuyuZ+mthgVJu28bUPsFtT7WTiQv8qbrY21BxffutDbP3wS4eic46kpNSMJ2svHo6WXP7j7smx0g5XavPL4yil6qluyYp2U81q80mWv3BX8APGYDV2TKk4MD4/BMZIVmIYIRmc7dr9VJu43ayd/qpT4Tmm65Qj7boBBkgaZ7o7spek4/K46Xbq0c2cHT8ZGRlkZGQ4dv9kpnWn2oo2PyYrpUcfKn9ZCqbho8Se7n0SH5Bqs7oceyg7vpwPxnCAK5sDqPvwhfH7qdy6o5GzW6/OkyfQ7bRj2PDC23a3oWUnolkD+zHgL85M/JmZmVlnjjIVumSpO1NVSemcjyn//muM30/a4JFkHHgErowsp0OLmrhcSJQTFUd7flvR5pOsrEOPZ8cvP9TdKS4kPYOMsRMciUm1Tb0uPIV1T71G0Q+/1AwPrMPtov24kQmPy2nicrHPjDvIP/FINr78Lv7ScjodcRA9zv0NnkxtDVGxZ3xV7HjkNqpWL6f6P2PxhtWUffslHa74R/I/fR6LBZ51xveQtPlaSu0/iHbnXIU7r1PNvpTeA8j77d9wZencWypxPNlZ7P/ZC/S+7KwG4x3E7SYlN4fel5zuUHTOEpeLrr85gpHP3c2YNx6mz2VnaoKl4qZswZdUrf6FOr/tGIN/xzZKPn/PsbhU8mnzLVkAaUNH4R08Av+u7YgnBXdOO6dDUm1USk4WQ+/+Oz3OPJ4f//pvdnxhT+fQceIBDP7PNaR17ex0iAooLy9nypQpALz66qukpbXdeczClQx1V/7Dt/YvOvXnkTQW5Yvnkn3UKc4EFisSgxnfdeB7SDTJChCXC0+t1iylnNRuzDD2/+RZfCWliMvVpicjbYn8fn/Nkjx+v9/haJJLMtSd2FMmNnY0gZHERywWeNYFokOjtaRUC+bJzNAES6kE8w4dTdAsS4S0YWMTHo9KXppkKaWUUrWkjziAlP6DAu8CLVciuDt1JfPgyY7FFTPVC0RHu6lmaZKllFJK1SIeD3kX/oWcE88jdcAgUvrsSdZRp9LhihtxZbSCtRarny6Mdovk1iKXichKESkXkQUiclATZU8UkQ9FZKuIFIrIHBE5MuLP7QAdk6VatbI1GyjfsIWsgX1JaZ/rdDhKqSQhnhQy9j+MjP0PczqUVkNETgXuBi4DvgIuAd4TkcHGmDVBTjkY+BC4FtgFnAe8JSL7GmO+S0jQUdIkS7VKFVu28/15f2XrzC8AkBQPvS89g0H//guuFF2KRSnVhjm3QPQfgSeMMY8H3l8VaJn6LXBN/cLGmKvq7bpWRI4HjgU0yVLKCcYY5h13MYULf9y9r8rHqvufxldUwpC7/6ZzLCml2q7qRZ6jvYYtW+omXBXGmIr6xUUkFRgF/KveoZnAuFBuKfYjjdlA0ix9oWOyVKuz86sFFCxYgqn/eLgxrJv+KjPz9+On6/6L5fM5E6BKepmZmRhjMMaQmdkKxugkkNZdq7MOKKi1NWiRCugIuIHN9fZvBvJDvNefgEzgpfDDdIa2ZKlWp+iHX5o8bsorWHHHY1g+H4P//dcERaWUUi1EbJfV6QEU1TrSoBWrnvpzYzQ5K1lNIZHTgRuB440xW0IL0nktpiVLRK4RESMidzsdi0pu6b27NV/IGFY/8AxVBUXNl1VKqdYktlM4FBljCmttjSVZ2wA/DVutOtOwdauOwID5J4BTjDEfRfHJE65FJFkiMga4GFjkdCwq+XU8/ADSe3dH3O4my1kVlZT8sioxQalWpby8nJNPPpmTTz6Z8vJyp8NJKlp3bZMxphJYAEysd2giMLux8wItWNOBM4wx78QtwDhxPMkSkSzgWeAiYKfD4ahWwOXxMPadx0nv073pgiK6FqCKiN/v55VXXuGVV15psUvDtFRady2ASAzmyYro6cI7gQtF5HwRGSQidwG9gIftsOQ2EXlqd5hyOvAU9lisr0UkP7AlzXw8jidZwAPAO8nWBKhatqyB/Ziw9AP2/WA6KR3zGjxJIx43nSYdTFr3Lg5FqJRSDqmewiHaLUzGmBeBq4DrgYXY82BNNsasDhTpip10VbsEe+z4A8DGWts9EX7yhHN04LuInAaMBMaEWN4LeGvtyo5HXKp1EJeLjofuz/4fP83cYy6kfO3GmiGWOcMHMfzx25wOUSml2hRjzIPAg40cm1rv/YQEhBRXjiVZItITOxs9whgTasf8NcAN8YtKtUbZgwdwyLKP2PrBF5St3kD20D3IO2gMEu1kfEoplYxiO0+WaoKTLVmjsJ8qWFDry84NHCwivwO8xpj6Hfa3YffpVsvGnqNDqSa5PB66HH2I02EopZTznJvxvc1xMsn6GNi73r5pwE/Av4MkWAQeDa15PFRbIpRSSinVUjmWZBljioAltfeJSAmw3RizJPhZSqnG+LZuouy7rzBlJaT2GYh36CjErfMNK6Xqie1kpKoJ+hNYqVagdM7HFL42rebR6tIvZ+Lp1ou8S67DlaFLl8RaRkYGxcXFNa9V6LTuWgCJwZgsTbJC0qJqyRgzIciq20qpJvi2babw9en2G2OBZfe0+zauo+i9pFniK6mICJmZmWRmZuqwhTBp3am2pEUlWUqp8JV/Nxt7bop6jEXZgi8wptllwZRSbYlD82S1RdpdqFSSs8rL7B94wXKpqkqwLHC7sSrKKfnsHcoXfIWpqiB14HCyDjsOT8f6S4mp5lRUVHDJJZcA8Mgjj+D1eps5Q1XTumsBdExWwmgtKZXkUvsNrOkirEOElJ79Ebcb4/Ox45FbKfnoDfw7tmAVFVD+7Zdsv+fv+LZuSnzQSc7n8zFjxgxmzJiBz+dzOpykonWn2hJNspRKct5BI0jp2b/ub5aBpvysSScDUP791/jW/gq1uw4tC1NZQfFHrycyXKWU07S7MGE0yVIqyYnLRfuL/0rGARMRbzoAKb360/6iv+LdcygAFcsWB2/etywqfvo+keEqpZxWPeN7tJtqlo7JUqoVcKVlkHP82eQcfzbGmAZPbYknpWbdxvrEk5KYIJVSLYIRwUTZEhXt+W2FpqJKtTLBHotPG76vPQC+QWEX6SPGJSAqpZRqezTJUqoNSN1jKOljA2s3ulw14yk8XbqRedhxDkamlEo4kd1PGEa8aUtWKLS7UKk2QETIOel80vYeTdnCr6Gqwk68Rh6ApOoj9Eq1KTqFQ8JokqVUGyEiePcajnev4U6HkvQyMjLYsmVLzWsVOq071ZZokqWUUmESETp16uR0GElJ6855OvA9cTTJUkoppdoS7S5MGK0lpZQKU0VFBZdffjmXX345FRUVToeTVLTuVFsiybx4rIjkAAUFBQXk5OQ4HY5Sqo0oKSkhKysLgOLiYjIzMx2OKHlo3dVVWFhIbm4uQK4xpjCe96r+ztz43nRyMqMbD1dYUkrXo6ZCAuJOZtpdqJRSSrUlsZixXWd8D4kmWcoRxhh8m9dDVQWerr101nGllFKtjiZZKuGq1q5g1wuP4N+yAQBJzyT7mNPJGDvB2cCUUqoN0KcLE0eTLJVQ/sJd7HjkNkzl7gGvpqyEwpcfx5WVQ9rgkQ5Gp5RSbYA+XZgwWksqocrmzrITrPoPXIhQ8ulbzgSllFJKxYG2ZKmE8m1eF/yAMfg2NXJMKaVUzBhxYaJsiYr2/LYi4iRLRHoCfYAMYCvwgzFGJz1RTXK372gvLNpg6hCxjykVhZLlq6nYsp3sIXuQkpsdt/ukp6ezcuXKmtcqdFp3LYBI9As865iskISVZIlIb+BS4HSgJ1C7litF5AvgUeBVY4wVsyhVq5E+dgIln70X5Igh48BJCY9HtQ6lq9bx3Tl/Ztec7wBweVPpe9V5DLzpKiQOj5q7XC769OkT8+u2BVp3qi0J+aePiNwDLAb2AK4HhgC5QCqQD0wGvgT+CSwSkTExj1YlPU/HfNqdfSXirfUbrLjIPPRY0scc7FxgKmlZVVV8c+RUCuYu2r2vopIV/36EFXc85mBkSrVMBldNl2HEmw7pDkk4LVmVQH9jzNYgx7YAnwS2f4jIZKA3MC/6EFVrkzZ0FN4bHqBi2WJMVSWp/fbCndPe6bBUktr89qeU/ro26LFf//sE/f50AS5PbIefVlZWct111wFwyy23kJqaGtPrt2Zady2AdhcmjC6ro5RKar/c+iC//PN+jM8f9Pjh677C2yW24/10aZjIad3V5cSyOus+eZmcrCiX1SkupcehJ4Muq9OkiH69E5F07AStNPC+N/Ab4EdjzAcxjE8ppZqU3rt7owmWOzOdlPb6C5hSdYjEYJ4sbckKRaS1/D/gHAARaQd8A/wJeENEfhub0JRSqnldTzyS1I7twV3vx5nLRe9LTsel3VFK1VE943u0m2pepEnWSOCLwOuTgM3YY7DOAa6MQVxKKRUSd3oaY9+bRlq3LnX2dzt1Mnve9AeHolJKqcjnycoAigKvjwBeM8ZYIvI1drKllFIJk7vPIA795WO2fz6Xyq07yR05hMwB+qNIqaB0WZ2EiTTJWg6cICKvA0cCdwX2dwZ0AJxSKuHE7abjIfs7HYZSLZ5BMES5QHSU57cVkaaiNwH/AVYB3xhj5gT2HwF8F4O4lFJKKaWSWkQtWcaYV0TkS6Ar8H2tQx8Dr8ciMKWUaqnS09NZsmRJzetIlC/9lpJZ7+LfugF3xy5kHnQUacPGxjLMFikWdaeio2sXJk7EM/QZYzYBm+rtmxt1REop1cK5XC6GDBkS8fmlsz+i8PXpNet4WiVF7Fp1L9nHnEHm+MmxC7QFirbuVAzomKyECTnJEpHXQi1rjDkxsnCUUqrl8hcVgLFwZbdDInyE3aosp+id5+031ZNBB/4sev9l0sdOwJUe3USRSjUlFlMw6BQOoQmnJasgblEopVQLVrV2BQWvTce3biUAVqfuPLS+BHf7jlx77bVhLQ1TteZXTGVF8IO+KqpWLcM7aJ8YRN0yVVZWcuuttwKEXXdKJZuQkyxjzHnxDEQppVoi3/YtbH/oFvBV1ewr37CGm+99AYCrr746rERBPClNF4jxOostTVVVFf/4xz+A8OtOxYaOyUocrSWllGpC6Vczwe/b3bUHdV+HKaVXf1w57YMsSyJIZjapffeK+NpKhaR6gehoN9WsiJMsETlJRF4Ska9F5NvaWywDVEopJ1WtWQGWFbPrictF7mmXgNsNrsCPYJcL3C7anXoJ0spbspRqSyJdIPpK4BZgBnA8MA3oD4wBHohZdEopFYLK7TtZO+M1ihb9TFr3LvScOoXMPfrE5NqunPZ2EhTDRMu7x1A6Xn07ZV9/gm/LRtwdu5Cx76F4OuXH7B5KNSoG3YX6dGFoIv2V6TLgYmPM8yJyLnC7MeZXEbkJyItdeEop1bSipcuZc+iZVO0sAHEhwK//fZwRz9xJ15OOivr6GfseQsXi2M9O48nrTPbk02J+XaWaozO+J06kqWgvYHbgdRmQHXj9NHB6tEEppVSoFl10Db5dRWAZ8Psxfj/GMiw8//+oKiyO+vregXuTNenkwBgUHYuilApdpC1Zm4AOwOrAth/2zO99QdNbpVob3+b1lM79DKtoFynd+5A+ZjyujEynw6Js7UZ2zV3U8IAxWGXlbHnnU7qffmzU98k67HjSRx5A+dJvwRjSe+0J974Y9XWVcoI+XZg4kSZZnwDHAt8CTwB3ichJwGgg5ElLlYqVHV8tYPm/H2HX3O/x5nei98Wn0/uS0xC32+nQkl7pvM8pfPmxwOzkUL5wDiWz3ibvsr/j6dTV0dj8JaXNHC+L2b3c7TuSecAR9nX9fubOtbsQ09LSYnaPtiAtLU3rzmlC9C2yrbA5RUSuB/5jjCmttz8duNoYc1PY1zQRPIosIi7AZYzxBd6fAhwILAceNsZUhn3RCIhIDlBQUFBATk5OIm6pmlC8bCUbXngbX0EReQePpfPRE3Al4EmpLe9/xrzjL0VEMH5/zVIlPc6bwvBHb437/Vszq7iQLTdfAX5/3QMuF6n99iLvkmudCSzA8vn4pO94KjZtC3p8wo8zyRzQO8FRKRW6wsJCcnNzAXKNMYXxvFf1d+aKbz4mOysrqmsVFRfTf9/DIAFxJ4qI+IGuxpgt9fZ3ALYYY8L+rT2i9j5jjFWdYAXev2SMudIYc2+iEizVsqy87yk+G3oUy299iFUPPcuCky5n9sGnx2RMTFOMMSz9021gjJ1g2TsBWDftVYqWLIvr/Vu7su9mN0ywACyLyuVLsUqKEh9ULS6Ph0H//mvgTeDHWeA39F4Xn6YJllJBGFwx2VohAYK1PA0HdkRywYhqSURWisg/RURnzVMU/biCpX+8pSbRMVV2/l3w7RJ+uem+uN67YuMWSpatDD45pMvF1g+/iuv9WzOrvJTiT95ssoypcv53qu5nHMeYNx+l/X774M7MIHNAbwbf9TeG3ndD3O5ZWVnJHXfcwR133EFlpfN1kEy07pxXvXZhtFtrISI7RWQHdoK1TER21NoKgA+BlyK5dqR9OfdhP0V4nYh8h/1U4YvGmI0RXk8lsQ3Pv4l43BhfvRYPv8Xa6a8y+D/XxO3eLm8TS3IYgzvdG7d7t3ZlX3+CKW68F8DdoTOu3JYxY0vno8bT+ajxCbtfVVUVf/nLXwC47LLLdGmYMGjdqRboKuxWrCeBG6i7VnMlsMoYMyeSC0eUZBlj7gTuFJE9gTOB3wJ3iMinwDPGmKciua5KTlW7ihodROkrKonrvVM7tKfDhH3Z8cX83d2FAeJ20eWEiXG9f2tWvvS7Jo9nH3Mm0op+m1WqrdCnC+syxswAu5cOmG2MqWrmlJBFVUvGmGXGmBuMMQOBg4BO2LO/qzYk78DRNV2EdbhctB83Mu73H3r/jXja5dhjckQQjz02ccjdfyctv1Pc799a2U9mBk+i3J26kjZ0VGIDUkrFRPVkpNFurY0x5jPALyJ7isiBInJw7S2Sa0b96JeIjAXOAE4FcoFXor2mSi75JxxO9vC9KF7yy+7WJJcLBAb+4/dxv3/WwH5MWPIua6e9wq4FS/B26UTPqVPIHTE47veuZlVVsfHVD9jyzqeIy25Byz/usKSeQiJt2L5ULl8a5IiQsd+hCY9HKaXiSUT2A54DetPwN0wDhP0DPdK1C6u7Cc8A+gCfAv8HvGaMCflxIxH5LXZXY5/Arh+Am4wx70USl3KGKzWV/T98ip/+fhfrn34Df1k57fcdzsB//oEOB49NSAypHfPof/XFCblXff7yCuYecyE7PpsLbhcgrH/uTTofcyijXr4vIdNYxEP6mIMp+/5rqlb8uLs72BhSevcnY//DnA1OKRUx7S5s1MPAfOBoYCPBnzQMS6Q//X8KBPIA8IIxZlOE11mHnZwtD7w/F/ifiIwwxvwQ4TWVA1La57L3/TfaT3QZg7ha5X/AoNY8+gI7Pp9nv/HvXkR4y9ufsOH5t+lx9gnOBBYl8aSQd+FfKftuNhVL5mOMIW3ISNJHHYh4UpwOTykVoVg8Hdiani6sZQ/gJGPM8mZLhijSJGsvY0zUExAZY96qt+u6QOvWftitWirJiLS9td02vPB2o1NIbHgxeZMsAPF4yBhzMBljIhqOoJRSyeQbYAC7G36iFunThcsARGQ0MAi7Se0nY8z8SAMRETdwMpAJBH1UUkS8QO1n8rODlVMqnip+WULZ/C+xSotI7bMn+MqDF7Qs/KWNHFNJLS0tjU8//bTmdX3+gh1Urf0VV0YWKX32bFMtu81pru5U/MVi4HprGfguIsNqvb0P+K+I5AOLgTpPGRpjgiyU2rRIx2T1AJ4HDgB2BXa3E5HZwOnGmLVhXGtv7KQqDSgGfmOMCTbaFuAa7DkslHJE0QevUvLR6/bAfsui8udF9B3fgR9WpVO5s946eS6h81HRtwAZy6Js/heUzfsMq7QE74DBZBx8FJ4OnaO+toqM2+1mwoQJDfYby6Lwf09RNufjmtZNV7sOtD/n96T07JfgKFumxupOJY6OyapjIXZDUe2s8clar6uPRTTwPdJaehJIAQYZY/KMMXnYLVqCvWB0OH4G9sHuInwImCEijT0Wdhv2E4zVW4/wQ1cqMr7N6+0EC8AKjL0yBpdY9DqsX50nCcXtJqNPT3pddFpU9zTGUPDCwxS+/BhVq3/Bv2U9pV9/zPa7rqNq07qorq1ir+TTtyib/VGd7mOrYCc7Hvs3VnnTi1krpRzRF+gX+DPY1q/Wn2GLdEzWQcA4Y8zP1TuMMT+LyBVAWOuYBNY6rO7/nC8iY4DfA5cEKVsBVFS/14kQVSKVL54H4gJj1T1gLHJ7ZtLr4lPY9MZHiMtF15OOYsD/XUJKu+gWLq9atYzy72YH7hP44rYsTFUFxe++SPvz/xTV9VVkqqqqePTRRwG4+OKLSUlJwVgWJV+837CwsTBlJZQv/FqnviB43anE0u7C3Ywxq+N5/UiTrDXYLVnBrrc+8nAAuzVM10JRLY7x+xpfPtQYhvz3Wobee2NM71mx9Luarsk6LIuKnxZiLEvH+zigsrKS3/3udwBMnTrVTrJ8lZjGFsx2u/Fv35LACFuuYHWnEssQg+7CVrhAtIgc18ghA5QDy40xK8O5ZqRJ1l+A+0TkcmCBMcYEBsHfA/w51IuIyK3Ae8Ba7EHspwETgEkRxqVU3HgHDqfkozcaHhAhpc9AJCUOa7C5mvhtUVtyWxRJ8eLKbodVtKvhQb8fd+euCY9JKRWWN2g4Pota+4yIfAmcYIzZGcoFI01Fp2OPo/oGKBeRisDrkcCTtVewbuY6XbAXl/4Z+BjYF5hkjPkwwriUipuU3gNI22f/ujtdLnB7yD7m9LjcM23o6IatWIH7eoeM0lasGLEqyima+Spb//VHttz0O3a99Bi+bZvDuoaIkDnh6CAHXLiyckgbvm+MolUqOk4uqyMil4nIShEpF5EFInJQM+XHB8qVi8ivInJpRDcOzURgXuDP6rHfE4G5wDHAwUAH4D+hXjDSlqyrIjyvDmPMBbG4jlKJICLknv5bUvsOpHTuLKySIlL77UXmIceQkt8zLvdM6dmf9HGH24Opq8eDiQtJzyR7cnSD6pXN+HzseORWfOtW1ox7K1/wBRWL59Hh9zfh6Zgf8rUyDpqEVVpMyax3wG+v5+np0o12Z12BK1WnK1Atgz0ZabRPF4afZInIqcDdwGXY47cvAd4TkcHGmDVByvcF3gUeA87CntHgQRHZaox5NfLoG3UPcLExZnatfR+LSDnwqDFmiIhcRd2nD5sU6TxZMyI5T6lkJy4XGeMOJ2Pc4Qm7Z84J5+IdMISy+V9glRaT2n8QGeMm4s5pl7AYWrPy77/Gt/bXujstC1NZTvFHb9DutNB/cRYRsiedTObBk6nauBpXehaerj31IR3Vojg48P2PwBPGmMcD768SkSOxl9e7Jkj5S4E1xpirAu9/DAxN+jMQjySrP1AYZH8hu58u/AXoGOoFQ06yRCTTGFMSr/Kq9bCqqljz+Eusf/oNqgqK6DTxQPr+4Twyend3OrSkJCKk7T2GtL3HOB1KUFXrVlK24AusslJS+w4kfcQ4JDU+z674tm+h9KuZ9kSfuXlk7Hco3gHRLQRe8fOi4E+NWhYVPy6M6JqujEy8/RO3QLlSDsqu90tERWAmgDpEJBUYBfyr3qGZwLhGrr1/4HhtHwAXiEiKMaYqyDnRWADcISLnGGO2AohIJ+B27G5EsJfeCXn+nHBaspaLyH3AdGPMhmAFxK7pw7Gz1c+x57VSbYixLBacciVb3vkEEDCG0hVrWPfs/zjgq5fI2rOv0yGqGCr+5E2K33vJHpsGlC/4kpJZ75B3+d9xZ+XG9F6Va5az4+Fb7W44ywKXi4rvvyb7mDPIHD854utKSkqjT43qGo2qNYrx2oX1E45/ADcGOaUj9mSe9Qc7bgYa65PPb6S8J3C9jaFFG7ILgP8B60RkLfZPhV7Ar8DxgTJZwD9DvWA4SdYE4GbgBhFZiL1A9AbsxxrbA4Oxs84q7OTq0TCurVqJrTO/YMvbnwTe2d9axu/HX1zCshvvZeRzdzkXnIop3+b1doIFdQbn+3dsoejdF2l3ysUxvV/hK0+Cr6rOfGEARe+8QNqI/XHntI/oumnD9qVs7mcND4iL9BHBf8H2er28/fbbNa9V6LTunGeMYEyUSdbu83sAtecuadCKVf/Ueu8bmxinqfLB9kctMN/nIOBIYM/AvX4CPjTGbuo2xrwRzjVDTrICE4+eHFhS52TsUfbjgHRgG/AdcBHwbnUwqu3Z8u5niMeD8fnq7Dc+P5trki/VGpQtnNPoHF7l383BnHxRzMYi+Xdtx7exwbhYm7GoWPpdxBN9pu65N+ljxlM277PA5zGAwdOlG5mHBZ82x+PxcPTRQZ4kVM3Sumt1iowxwcYx1bcN8NOw1aozDVurqm1qpLwP2B5OkKEyxhjg/cAWtbAHvhtj1gF3BTal6hC3i8Z+wbCPqdbCVFbScDqZgOoWp1gN+DbN/NLa3PEmiAg5J19I2t5jKFv4NcZXiXfAENJHHRi3sWVKOcsVg8lEwzvfGFMpIguwp0R4vdahidhddMHMAY6tt+8IYH6sxmOJyJXYTw6WB143yhhzb7jXj3QKB6WCyv/NEay6/+kG+8XjpusUnWO2NfEOGEzp5+82PCBCSt+BMZ3Dy9WuA+7O3fBv2UiDJF4E717Do7q+iOAdtA/eQfuEVL6qqopnn30WgDPPPFNnLQ+D1p3zHHy68E7gaRGZj51AXYw95ulhABG5DehujDknUP5h4Hcicif2NA77Y4+biuXEhH8AnsUe+vSHJsoZIP5JVqC78LfYXYX5gRtvBmYDDxtj1oZ7TdV65B00hh5Tp7Bu+qvgdoHfHqDs7dKRPW/8vdPhqRhKHTiM1P6DqPz1p90tSeICl5B91CkxvZeIkPObc9n5+O32vSzLbiUzhsxDj8Pdvu4T1aayAoOJ29xUlZWVnHfeeQCcfPLJmiiEQeuu7TLGvCgiHYDrga7AEmByrfUDu2InXdXlV4rIZOyes8uxx4FfGcs5sowxfYO9jhUxYTSzi8iB7F4GZyZ2ciXYfaQTgZ7AUcaYsBaJjpSI5AAFBQUF5OREtxCvih1jWWx640PWP/cmvoIiOhy6P70vPo3UDpENTFYtl6mqpPjTtyib+xmmvJSUvgPJmngiqb36x+V+VRtWU/LZu1St/RV3uw5k7H8Y3qGja8Z++Tavp/DNZ6hcthiAlH57kXPsmaT0CO1np6mqpPz7b6jasBp3TjvSRh4QdEB9SUkJWVlZABQXF5OZmRmjT9j6ad3VVVhYSG5uLkBuiGObIlb9nTn/ux/Iys6O6lrFRUWMHjEEEhB3ogWmm+gLrDDG+Jor3+S1wkyy5gFfGmOCNqmJyF3AgcaYhEzoo0mWUqqaf9d2tv33Gkxl+e7B+OICj4eOf7gVT6emZ27379zG9oduxtq5DVxue94st5v251zVoBtRE4XIad3V5USSNe+7pTFJssaMGAytKMkSkQzgPuDcwK49jTG/isi9wAZjTP05vpoV7qCJoQT6ThvxSKCMUkolVOlXH2Iqyus+7Wgs8PsoCTZ2rJ6CVx7HKggst2r57W5Jn49dz9yPVVEep6iVUi3IbcBw7Cmrav+n/wg4NZILhptkbaTxmVnBHpQW68nBlFKqWZWrljWctR3Asqj89ecmz/UXFVC5bEnQxbhNZTkbHngIf0VlrEJVylFOLhDdwp0A/M4Y8yV1n7BZir3kTtjCHfj+H+BhERkFfIg9JstgD4CfCFxIjBaPVkopf+FOqlYuQ7xppA4Y3OQM7K7MnODL44jgymq6a8SUlzV+zBg2PPsaa99ZwNh3n8TtTQ3rMyjV0sR4MtLWpBOwJcj+TCKc/DSsJMsY86CIbMd+zPES7CnywZ5gbAFwjjHmpUgCUUqpasYYit99kZLP3q1JmiQ9k3ZnXo534LCg56SPPZiKH+YHuxgZYyc0eT93XieMx4v4Gk5WLSIUbyiibPE81s14jd4Xnxb251FKJYV5wNHY47Jgd2J1EfaUE2GLZDLSF4EXRSSF3StRb4vDQo1KqTgxfj/4fS12ss2yrz+hZNbbdfaZ8lJ2Tr+TTn/9L+52HRqc4x00gswJx9jnict+7tmySN/vUNJGHtDovayyEsrmfkZlYQXeDDvBq35a0ViGnct3UratDETY+NoH9L74NLxeLy+9ZP8+qUvDhEfrznkOzpPV0l0DvC8ig7Hzo9+LyBDsoVDjI7lgxJORBpIqHX+lVBKxSooofOcFyr/7Cnw+PF17knXUKaQNGhHWdXxFxax+5Hk2vfkx4nLRdcokel14Cu702MxLVfJFkBUtjAG/Rdncz8g64sQGh0WE7KNPI23UgVT8sAAsC+/gkaR0793offw7t7H9gZuwCneSmm4wFogrMB1EmY8t329m09yNNfc3VfbT3B6Ph5NPPjn6D9oGad05T5Os4Iwxs0VkHHA1sAJ7dvlvgf2NMYsjuWZMZ3wXkf7AY8aYyBYRU0rFjfH72PHIrfg2r68Z4O3btI5dT/6X9hdcHfKs6VUFRcw++HSKf1pur/Mnws7Z37LhxbfZ76OncadF3zrh39XIsmQC/p1bmzw3Jb8HKfk9QrpP4dvPYxXtgurWq1rfGz+/9CPlO2s9YCRCl2MPC+m6SqnkIyLPArOAW4wxy2JxzVgvJpdFhE1qSqn4qliyAN/GtfWmOLCTpKIPXgn5Oqvuf4rin1YEFlIOXMMYds393p7pPwY8nboGX/fQGNydu0d0Tf/ObZQtnEPFT99jfD6M30/FknnBnyi0DO32qDUJqQjZQwbQ60K7Bcbn8/Hyyy/z8ssv4/NFNVdhm6N15zx9urBRxcCfgB9FZIOIPC8il4rIXpFeMKyWrOYWTwQi++mnlIq7ytW/2JNsWv66B4zBt24lxu9H3O7gJ9ey6fWZQRMTEDb97yN6X3pG1LFmHnosBc/cX+/yLsTrJWPMwWFdy1gWha9Pp+zrT2r2uTJzyD3zskY+B2DA5XHhykjD27UzPc86nj5XTsWTZU+cWVFRwSmn2EsHFRcX4/HoMrCh0rpzniEGTxe2wiTLGHMJgIjkY8+VNQH4PfCAiGwxxnQN95rh/uu+G3scVmMTxuizzUoFUbVuJaVff4J/13ZS8nuSPu5wPHmdEhqDZYmdSAX52SipaRDigs5NLRJRvYKE8VXh27IRV3pGg3UFQ5E+fD+s4kKK33sZU2FPr+DulE+703+LKyu81R1KZr1dJ8ECsEqL2DX9LlJ6DaBq7YoGH0rcwsD7/sOIYSPDjl0plfSKgJ2BbRfgAzZFcqFwk6zVwF8bm6ZBRPbBnspBKRVQNv9zCl581E5iLIvKX5ZQMvtD8i7+P1L77JmQGIwx/Hjna/QZk4aBmqfn7GOQMXZ8nX1N6fqbiRQtWRa0FSj/+MMp+fIDime+iikrBSClz57knnoJno5dwoo584AjyBg7gaoNaxCvF0+XHiHHWFvpFx803GkMpqqSlF79qVq/yp4motbn8Q4ZRfre4T0MoFSysBCsKFuioj2/JRKRf2MPeRqOvXj159izwH9ujNkVyTXDHZO1ABjVxHEDrbDmlYqQVVZKwavTAm+s3X/6qih4+XHCWTs0Gjtnf8uOOUtY/dFKMPaYI8tvx1OyqRjv2IkhX6vPFeeSNbAvuGr9Vxeh3Zi96TSsC0X/e7omwQKoWrOcHQ/fgqlsOAdVcyQlldTeA0jJ7xlRgmX8fqziguAHXS4QocMV/8A7dDSurFzcnbuRfcwZtDv7iojup1Qy0DFZjboae2Hof2DP+/knY8ybkSZYEH5L1vVARhPHl2IHqJQCKn5eBL4gU8gZg3/LBvxbN+Lp3C3ucZT8sgqA7Uu3U7imiLyBebi9boo3FFO4qoBOf9yOt2vTCyhXS8nNZtyXL7H64efY/MaH4HHTbcokel10Kjvuva7hCZaFVbCDsu+/CXs8VbTE7cad1wn/jiBPJPr9eLp0J6V7b9qf3dxwU6VUGzACuyVrAvAnEfEDn2E/cTjLGPNjuBcMd8b3pc0cr8LuUlRKAfibfnrK+P1NHo+VjH49a15XFVeyeUGt4QVuF+m9whvPmZKTxYC/XMyAv1xcs8/4ffi3B1uRAnC58W1aG9Y9YiXz0OMofOWJevG4cGVkk7bP/o7EpJSTdFmd4Iwx3wPfA/cCiMhw7KUC78Xu+Wv+yaB69LEOpeIodY8hwdfTA1w57fF0ScwDuXkHjSFr8B6ULPsV46uV2LlddD/9WFI75kV/E5cbycjClBY3PGasoLO0J0L62AlYpSWUfPR6TZelp2tP2p1xOS5vbCZPVSqZGKJ/OjAxAx0ST0RGsPvJwoOAHGAh8Gkk14soyRKR7whexwYoB5YD040xEQWlVGvhzmlP1uEnUPzha/a8T8bYY4GMIef4s5FGnugzVZX4C3fhysqJSSIgIox961Hmn/Q7Cr/7oWZ/l2MOY+j9N0Z9/ep7ZBwwkZIP36DOjwcR8KSQPmJcTO4TSVxZhxxDxgGH49u0DldaRtRdtKmpqUybNq3mtQqd1p1qqURkJ/Z8n99jdxE+hj3ovTDia0Yy8FZEbgN+CywG5mIPdh8NDAOmA4OBw4ATjTH/izS4EOLIAQoKCgrIyQnvsW6lEsUYQ8WiuZR8NRP/zm2kdOtF5vijSe3XcH47Y1kUf/gapV+8j6koB7eH9NEHk33cGbhSo0+2jDEUfreUsnUbyR68B5kDGl9yJqLr+30UvPgo5d/Nrtkn6Zm0O/sKvHsMjem9lGoNCgsLyc3NBciN5ss8FNXfmZ/OX0VWmFOh1FdcXMgho/tAAuJOFBE5hiiTqgbXjDDJegxYY4z5Z739fwN6G2MuEpF/AEcbY0bHJtSgcWiSpRxnLIvSOR9R+tWHWIU78XTrTdZhx+MdOCzsaxW+8zyls96pu1ME76ARtD/vjzGKOP58WzZQueoXXOkZePcajqRoi4VSwTiRZH0yf3VMkqxDR/eGVpRkxUOky+qcAjwfZP8LgWMEjg+M8PpKJY3C16dT9MZT+LduxFSUU7VqGTsfv52yhV+HdR2rrLTROZ0qln5rrzmYJDydu5Exdjxpe49plQmWz+fjnXfe4Z133tGlYcKkdafakkgHvpcD47DHXtU2LnAM7AQu/IlxlEoivq2bGswmXj17eNHbz5E2bGyj464aXmtDk08jVq1bmbCB8qppFRUVHHPMMYAuDRMurTvn6dOFiRPpv+77gIdFZBQwD3uU61jgQuDWQJkjge+ijlCpFqxy+Q+NHrMKduDfvtle7DgE7ux2TR535TR9XCmlQmGARlbtDOsaqnkRJVnGmJtFZCXwO+DswO6fgYuMMc8F3j8MPBR9iEq1XJLqjep4be72HUndYwiVK36su2SNuHC3yyO1/+BIw2wzqjaupeTzd6laswJ3bnsy9jsM795jdPZ2pWrRlqzEibid1hjzLPBsE8fLIr22UsnCO3gEeFIazurucpHSsz/u3PDmn8o97VJ2PvZvfJvW1Uz54MrOod35fwq527Gtqvz1J3Y8+q+adQj9WzdS+csPZB56LNlHnep0eEqpNiiqzvBAd+Eg7JbDpcYY7R5UbYorPZPcUy6i4PmHAkkRgEHSMsg9+cKwr+fOaU+HP9xK5S8/4Nu8Dnf7jngHjUB03EoNf8FOqtYsR9IzSO27F+J221NTvD4DLH/NmLjqP0s+eYv0fQ/Bk9fZwaiVajlisfZgK127MOYinYy0M/aThBOAXdjzZOWKyKfAacaYIAuFKdU6pY8YR0r3PpTN+xx/wQ5SuvUmfczBuDKzI7qeuFx4B+6Nd+DeMY40uRnLouitZyn9amZNAuXKbU+7s3+Pu11eE8v2CBU/LsRzwBGJC1apFky7CxMnmoHvOcCQ6gUTRWQwMAN7jZ/TYxOeai38xQWUL/wGq7SI1F4DSN1z71bV/eXp3I3so09zOoxWrfTzdyn9su4UF1bhLnY+9m/yrrixiTONjslSSjki0iRrEnB47RWpjTFLReRyYGZMIlOtRvnieex69gG7K0eEEssipVd/2l/4V1zpGU6Hp5JEyefvN9xpDKaynMpffsDToy++9at2dxdWE8E7eGRY9zI+H+UL51C+9FswhrQhI0nbZ1xNt21qair3339/zWsVOq0752l3YeJEmmS5gKog+6uIfIJT1Qr5iwrY9ez94A8sShz4Aqxau5Kid18gd8r5DkankoXx+7GKdgU/KC78O7aQe+JUtj90iz3XmGXZa0RaFlmTTg5rcWrjq2LH47dTteJHe5wdULFkPqXzvyDvwr8gnhRSUlK4/PLLY/DJ2h6tO+dZxt6ivYZqXqQJ0SfAPSJSs8qqiHQH7gI+jkVgqnUoXzin7nQE1YxF2fwvME1MvqlUNXG7cbXrGPyg5cfTqSspPfvT8U//IuOAI0jpswfeoWNof/H/kXXocWHdq2zuLDvBAvuXgupfDFb8SOk3s6L4FEqptibSlqzfAf8DVonIWuxnqnphLxh9VoxiU62AVVIE4gLjb3jQV4WpqkLc+uScal7WIUfbTxDW5nIh6ZmkjRgHgKdDZ3KOa/pHUMWW7WAM3i7Bk7ayhd80em75wjlkHjARv9/PF198AcBBBx2E2+0O45O0bVp3ztPuwsSJdDLStcBIEZkI7IX9dOFSY8xHsQxOJb+UXgPssVhBuDvmI960BEekklX6/odjlRRT/MmbNfOSeTp1I/esy3GlpTd7/q75i/nh9zexa+4iAHL2GcyQu/9G3gGj6hb0BxsJYatueS0vL+eQQw4B7KVhMjMzI/lIbZLWnfP06cLEiaoJwRjzIfBhjGJRrZB3r+F4uvfBt2GNPUlkLVmTTtKnvlTIRISsib8h46Aj8W1Yg6Rn4snvEdK/odJf1/L1YWfjL9+9nGrhop/45sipHDj3dbIHD6jZ7x00gqq1vwYdQJ82aETMPo9SqvULOckSkStDLWuMuTeycFRrIy4XeRdfQ+Hbz1L+7Wzw+3B36ELWpJNJH76f0+GpWgoX/UTJL6vI6NuTnBGDHUmAfVs3UvzJm1QuW4KkpZM++iAyDzwSSdn9FJorLYPUfnuFdd2V9z+FVVFZd3ygZWH8flbeM51hj9xcsztj3ETK5n+Of8e23b8YiAt3+45k6FxbqhWoNdQwqmuo5oXTkvWHEMsZ7LmylALAlZFJu1Muxpx4HqayEknP0BasFqRy+04WnHIFOz6fV7Ov3b77MOqV+0nL75SwOKo2rWPHfTdifIFkqHAnxe+9ROWyxbS/6P+imldt19xFGH/Dbmvj87Pz64V19rkyMulwxT8o+ewdyhfNBQNpw8aSOeFoXBnRd20Zy8K/bRN4UvDkJa5+lapmIVhRjqmK9vy2IuQkyxjTN9h+ETkQmG+MKY9ZVKpVEk8K4klxOgxVz3dn/4mdX31bZ1/BgsV8e8qVjPv8+YTFUfzBK7sTrGrGULl8KRU/fkfakFGNn9wMb9dO4HbvnkqkmttFWreGy+24MrPJnnwa2ZNjO8Fs+fffUPjmM1iFOwHwdO9N7skXk9K9d0zvo5RqGWIxp9W7QLdmSymlWpySFWvY9uFXDVp5jM/PzjnfUrjop4TFUvHT98Gn+3C5qfhpUVTX7n3RqQ0TLAC/Ra+LErN4dMXyH9j1zH01CRaAb8Nadjx8M/7CXQmJQSnYPfA92k01LxZJlta0UkmqbPX6Jo+XrlwXl/say6J8w2Z8RcU1+5paBDvaBbI7HXEQe954pT25qAi47B9b/f9yMfm/Scw4q5JP3rKnM6nNWJiKcsq++TQhMSgFu8dkRbup5ukERUq1YZkDettJRyM/MbMGBh0lEJW1019l2Y33UL5+M+J2kT/lKIbc/TfS9tmfsrmzGrZmWX7Shu8b9X33uO5yup95PJvf/hQsi85HH0Jm/14RXSslJYXbb7+95nUoqjasbvCELQAGqjauiSiOZBRJ3SmVrGKRZF0CbI7BdZRSCZbeqxtdpxzJxtdngn93AiBuNx0PH0fWXv1jer91z/6PRRddW/Pe+C02vfo+xUt/YdynM6hctgT/jq2AqVkWJ/2AI0jts2dM7p/Rpwd9f3d21NdJTU3l6quvDuscd24evpJi7GeDanEJ7ty8qGNKFpHUnYotnYw0caJOsowxz0V6rohcA5yIPaFpGTAb+Ksx5udo41JKhWbYY7eCCBtfed9u0RKh89GHMPyJ22J6H2MMv/zjvob7/X6Klixj2+ff0vkPN1M273MqV/yIeNNJHzmO1D33jmkc9VVu38mm12ZSVVRMhwPHkDtm77g8/ZpxwEQKX3684QFjSN93Qszvp1RjdO3CxHG6u3A88AAwLxDLLcBMERlsjClxNDKl2ghPViYjn7ub8js2U/LrGjJ6dye9V+yfZfEVlVC6cm3QY+LxULBgCfnHHU7mQZPIPGhSzO8fzIaX3+X78/6KVVlld5taFp2POYSRL9yL25va6Hl+v59vv7WfyBw5cmRIS8OkjxmPb9N6Sr94b/dOTwq5J11ASn7PqD9Lsoik7pRKVo4mWcaYOj9JReQ8YAswCvjckaCUaqPSunchrXuXuF3fnZGGKz0Nq6zhbC/G7ye1U4e43TuY0tXrWXjOnzG+wFOHgXFpW96dxcqbbyevu4vKVcuQ9Awyxown6/ATkFQvYC8NM3bsWCD0pWFEhJzjziTjwCOoXP4D4knBO2gfXOlta1mZSOquKVZFOeLx6Bqo4YjF04H6dGFIWtq/ytzAnzuCHRQRL+CttSs77hEppWLC5fHQc+oUVj/6fJ3xX4jg8qbS/bSjExrP+mfewAQZ8J/VLYvM0h+p/NVtP/1XXEjJrLepXP0LeZdcG9WkqACevE54xk6I6hoKypcsoOj9l/BvXg9uD94hI8k57qw2Nb4tUjrje+LEYgqHmBB7EMSdwJfGmCWNFLsGKKi1xef5cqVUXOx165/IO3C0/cbtAgFXmpeRL95LasfEfjlWbN4eNGHqfkAP+4WpOylq1a8/UblscYKiU00pX/odu2bchX/zBnuH30fForlsvflKCt58BuPzORugUgEtqSXrfmAYcGATZW7DTsSqZaOJllJJw5OVyX4fPsXOrxawc863pHRoT9cTjySlXU7CY8kdNRTz0LN19olLyOqaFfwEl5vKFT/i3Wt4AqJTTSl+/+VGpx4p++J9THkZ7U65yIHIkoMuq5M4LSLJEpH7gOOAg40xjSZNxpgKoKLWeQmITikVSyJC3oGjd7doOaTbKZP55eYHKF+7sWbGe2MMlt/C5Q7SyG8M4k2L6F7GGHZ+tYDNb30MInQ59jDajxupP8MiYPw+fM3MK1Y+/3P8R5yIu11ix/klC+0uTBxHuwvFdj/2NA6HGmNWOhmPUqrtcKensf+nz9J58ni7VQRI79Ud6dzfnqOrPmORts9+Yd/HWBbfn/9/zDnkTFbe+xQr75nOnAlnsOiS6zDBlhFSTXO5ax5AaJQxVG1oOxO8qpbL6ZasB4AzgOOBIhHJD+wvMMaUOReWUqotSO+Rz+jXHqKqoAh/cSnerp0wpcVsf/Cf+Ldu3J1sWRbZx5+Np2N+0xcMYv2zb7L+mTcA6owVWjftVToeOo7upx0Ti4/SZogI6WPHU/rVh002p7izcxs91tbFYu1BXbswNE4nWb8N/Dmr3v7zgOkJjUQp1Wal5GaTkms/rCxZOXT8wy2Uf/81lat+wZWeSdqoA+rMZZWSksINN9xQ8zoYU1VJ+aK5lH7yKl337872pduoLKjYXcDlYv0zb7S5JCuUumtO1pEnU7V2JVWrf2l40OXC06kbnh6xXxKqtdDJSBNHgj3CnCxEJAcoKCgoICcn8QNnlVIqGH9RATseuhn/1o2YWt9Gqz5cyY4ft9e8bzdmGAfMftmJEJOesSzK5n9B0VvPYMrLAAEMrnYdyLv4//B06up0iCEpLCwkNzcXINcYUxjPe1V/Zz7z8Q4ysqL7ziwtLuSsw/IgAXEnM6dbspRSqtUpevMZ/NvtJV3FZXerGGPoM7EvRWsKqSqpArebDhOiX/i6rRKXi4yx40kfdQAVP36Pf9tG3B264B08QicmVS2G/ktUSqkQVO0sYOtHXyEitNt3KEtmvY8pK2HvA8eTttfwmjm37G7Cb6DeoHYRwRhD+z3z2LpoG5522fS5PPrFqpONZVn8+OOPAAwaNAhXlJO7ittD2tBRsQitzdAFohNHkyyllGrGqgeeYelf/oWprCK3XzvyJ/Zm7AN2N9+KK08lp98e5F38f7jSM+3B7U08NehJT6HL8Yez1y1/iusyRi1VWVkZQ4cOBWKzrI4Kn0UMxmTFJJLWr8XM+K6UUi3Rtk/m8MNV/8RUVuFO89Bvcn/EXfe3eN+G1RS+ZU9sKmnpuLt0hyC/6YtLGPz4vYx68V4yB/RORPhKKQdpkqWUUk1Y9eAziNsNQN6e7RG3IPUTKMui/NvZGF8VIkL25FMBUzP/FgAipA4YTOqAwYkLXqkgqicjjXZTzdMkSymlmlC6cl3NjPCe9JTG+0n8PkylPUVD2uCRtL/galJ69AMRJCOTzAlH0/68P+ks78pxmmQljo7JUkqpJuQMG0jx0l/AskjvlGF3FfoblnPndULSd48v8u41HO9ewzHGaGKlVBulSZZSqk2oXLmMks/ewbdxDe68zqSPnYApL7UnHM3MIn3UgaR079PgvL5XTmXDC2/T4+CetOvfrtHrZx0xJWgypQmWamksI1hRztge7flthSZZSqlWr3zRXHY9c589Rsqy8O/YRuXyH+yD4gKB0i/eJ/vYM8k8+Kg65+aOGMzIF+7EfP50owlT1qSTSR91YLw/hlIxoQtEJ44mWUqpVs34/RS+MaPeN0utbwhj1bwteutZvINH4ulYd2qFvBF7sOPL3QmWxy38dvSgmtfudh2ij9Oy7PFbrbzlKyUlhT//+c81r5VqzTTJUkq1ar6Na7CKCkIrLC7KF31D1qHH1dntzmlX532q2831E0buPh5FklW1dgVF771E5fKl4HaTNnw/siefijunfcTXbMlSU1O54447nA6jTdOWrMTRpwuVUq1bOEusiNQ8IVjnEu064B00AurPTu5y4e7UlZS+AyMKrWrDarY/+E8ql/9of2v5fJR/N5sdD9yEVV4W0TWVao4xuxeJjnTTJCs0mmQppVo1T5fuuPM61Z2zqjGWn9QBQ4Ieyj31YlJ69reLGcPagmLWk0ru1D/ULKkTruKP3rBnhze15oWwLPw7tlK24IuIrtnSWZbFqlWrWLVqFVYTM+Mr1RpokqWUatXE5SLnlIvA7d7dElWdcNWfLHTgMFL7Dwp6HVdmNnmXX0/eFTeScuzZjH3sf4y+/QmqMnMjjq1yxY/Bl+ARoWrlzxFftyUrKyujb9++9O3bl7Iyba1zgjESky1eRKS9iDwtIgWB7WkRaddE+RQR+beILBaREhHZICJPiUi3uAUZIh2TpZRq9bz9B9Pxj/+idM5HgSkcOpHSew/KF82latUyJD2TjLETyDzkmCYHnosIqb0GkNGha0zicqVn4C8tDnYjJC0jJvdQqr4kGJP1HNADmBR4/yjwNHBsI+UzgJHAP4HvgfbA3cCbwOh4BtocTbKUUm2Cp1M+OcedVWdfxtgJzgQTkD52AsXvv9zwG8uydEqIFsq3ZQP+ndvwdO6Gu31Hp8NpdURkEHZytZ8x5pvAvouAOSIy0BjToInXGFMATKx3nSuAuSLSyxizJgGhB6VJllJKOSTz4KOoXPEjlcsWg8sNGLAsMif+htQIB9Or+PAX7mLXM/fV6cb1DtuX3FMvwpWa5mBk4asevB7tNeJkf6CgOsECMMZ8LSIFwDgg1H70XOzJWXbFPMIwaJKllFIOEU8K7S+4mspfllCxbDGSkkrasH1J6dbL6dBULcYYdk6/E9/6VXX2VyyeS6EnhXanX+pMYBGKcXdhdr0u9gpjTMNHdEOXD2wJsn9L4FizRCQN+BfwnDGmMIpYoqYD35VSykHicuEdOIycY88ke9LJmmC1QFVrV+Bb+2vDhxSMofy72fiLQ5yHrXVaBxTU2q4JVkhEbhQR08xWPX4qWAoojeyvf58U4AXs/OaySD5QLGlLllJKKdUE/9ZNjR809jJN7qzInzJNtBi3ZPUAimodaqwV637s5Kcpq4BhQJcgxzoBm5s6OZBgvQT0BQ51uhULNMlSSqmweTweLrvssprXKnTJWHfuDsG+8wNEkm4AfIzHZBWFkswYY7YB25orJyJzgFwRGWuMmRvYty/2GKvZTZxXnWDtARxijNne3L0SITn+hSulVAvi9Xp54IEHnA4jKSVj3aX0HoCnex98G9fU7TIUF2n77Ic7O3lasVo6Y8yPIvI+8JiIXBLY/Sjwdu0nC0XkJ+AaY8zrIuIBXsGexuEYwC0i1eO3dhhjKhP4EerQMVlKKaVUE0SE9uf9qWbG/2reISPJmXK+Q1FFrrq7MNotjs4EFgMzA9si4Ox6ZQZit26B3WV5XODPhcDGWtu4uEbaDG3JUkqpMBlj2LbN7vno2LFjkxOYNsYqL6Vs/hdU/voTrrQM0kYdgLf/4FiH2uLEou6c4M5tT4ff3UDVxrX2PFlduuPp0NnpsCJiWcEXGgj3GvFijNkBnNVMGan1ehX2wPgWR5MspZQKU2lpKZ0721+wxcXFZGZmNihjfD7Kl8ynavUyXOlZpI0ch6ej3YPhL9zJjvv/gX/ndvurQYSyeZ+ReehxZB91SiI/SsKFUnctWUrXnqR07el0GCpJaJKllFIxZpUUsePhW/BtWmdPMmoMxR+9Ts6U88nY9xCK3nsJf8EOwNgPpQf6Xko+eZO0YWNJ6d7HyfBVK5cEy+q0GjomSymlYqzwnRfwbdlgv7H8YCwwhsJXn8S3YyvlC79udGHo0nmfJzZY1eYkwZisVkOTLKWUiiFjWZR/91Wjg1bKvpsNfl8jJxvKvppJ6bzP4hihaussdk/jEPHm9IdIEppkKaVULPl94GskiRIXVJSTusdQ+3UjCl9+HN/m9XEKUCmVKJpkKaVUDElKKp7uvSHYU3OWn9R+e9mD293uJi4ilGm3oYoTY0xMNtU8TbKUUirGsicFnhCsnWiJkNJ7D1L33JuUHn3pcOVNjbdmGdr6engqjnRMVuLo04VKKRUmj8fDueeeW/O6Pu9ew2l/wV8onvkKVWtWIGnppI8ZT9aRJyEuO7FK6dqTlH4Dqfr1Z3tgfG3GkNKjX9w/hxOaqzulWhP9F66UUmHyer1Mnz696TID98Y7cG+MZdUkVvVlHX4COx/9V92dLheurFzSRx0Yo2hbllDqTsWXicFkpPV/L1DBaXehUkrFUWMJFoB3wBDaTf0D7o75NftS99ibvMv+jis9IxHhqTZIuwsTp1W0ZJWUlOAOMojU7XaTlpZWp1xjXC4X6enpEZUtLS1tdBCgiJCRkRFR2bKyMqwmft2oPVNyOGXLy8vx+/0xKZuRkVGzLEZFRQW+xp6qCrNseno6rsCXU2VlJVVVVTEpm5aWVvNvJZyyVVVVVFY2vsao1+ut6foIp6zP56OioqLRsqmpqaSkpIRd1u/3U15e3mjZlJQUUlNTwy5rWRZlZWUxKevxePB6vYA9ELe0tDQmZcP5fx/pzwhjDFu3bgXq/rsOVhaa+X/fZy86/uUOrKJdSEoq5UaoMIaKILG0hp8RVVVVNX9/9euuLf6MaOrfnGoFYvWUgRMbkENgvuRg2+TJk01tGRkZjZYdP358nbIdO3ZstOzo0aPrlO3du3ejZQcPHlyn7ODBgxst27t37zplR48e3WjZjh071ik7fvz4RstmZGTUKTt58uRGy9r/JHY76aSTmixbXFxcU/bcc89tsuyWLVtqyl522WVNll25cmVN2T//+c9Nll2yZElN2RtuuKHJsnPnzq0pe/vttzdZ9tNPP60pe//99zdZ9u23364pO23atCbLvvTSSzVlX3rppSbLTps2rabs22+/3WTZ+++/v6bsp59+2mTZ22+/vabs3Llzmyx7ww031JRdsmRJk2X//Oc/15RduXJlk2Uvu+yymrJbtmxpsuy5555bU7a4uLjJsieddFKdf8NNlY30Z0RzMejPiN2b/oywtxB+RuSYBH1n3vLcLvPfN6yotlue25WwuJN50+5CpZRq40xVJQWvPol/13anQ1EJYExsNtU8MUlcUyKSAxRs2LCBnJycBse1uzB4We0u1O5C7S4Mv2zt//clJSVkZWUBsHnz5gaLHLf0nxFWeSnbH7wZa/uWmm/LjLRUXBlZdLjqZqq8GXH7GVFQUECXLl2AhnXXFn9GFBYW0q1bN4BcY0xhoxeIgervzJuf3UVaRsPvzHCUlxbytzPbQQLiTmatIskqKCgImmQppVQ81E6yiouLGyRZLV3JZ+9S9M7zDZsjXC4yDjiCnOPOit+9k7zuYq2wsJDc3FxIYJL1z6d3xiTJ+vvZ7UGTrCZpd6FSSrUxFT8vCt7fY1lU/Lgw4fGoxIp63cLApprXKp4uVEqpSFhVVRQs+AGA3FFDcAW6W1s7SfXas9EHSbQk0N2rlIqeJllKqTZp4+szWXL5DVRu3QFAaucO7P3AP8g/YaLDkcVf2vB9qfhhQcMDIqSPOCDxAamEisXA9SQeaZRQmmQppdqcXfMX8+1pv6/zTVG5dQcLTruSA796mdxRQ5s83+12c9JJJ9W8TjZpw/ejfMl8KhbNBZcrMImARUqfPck4IL5JZrLXXWtgWQYryv6+aM9vKzTJUkq1OSvvm4G4BOOr9WSeMYi4WPnA0+zz5L+bPD8tLY2XX345zlHGj7hctDvzd1SM+I7yJfPAsvAO2oe0vccicV5PMNnrTqlwaJKllGpzin9YjvE1nHbA+PwULVnmQESJJy4XaUNHkTZ0lNOhqATT7sLE0acLlVJtTsaA3oinYVeVuN1kDuiT+IBaAKu4kNJ5n1P6zac6KWkrp5ORJo62ZCml2py+vzubTa++32C/8fvpc9mZzZ7f2uZ6KvnqQ4refAasQOueCJmHHU/WEVMarMsY9b1aWd0p1RRtyVJKtTl5B45m70duxp2xe1Z2d2Y6wx69hbwDRzsYWeJVrlxG0RszdidYAMZQ8tEbVCye51xgKm4sY2KyqeZpS5ZSqk3qdf7JdDtlMttnfQMidBg/Fk9W22tVKf3mE/sJw/rL84hQMvtD0oaNdSYwFTfGsrdor6Gap0mWUqrN8mRl0uWYQ50Ow1HWru0NEywAY+xjSqmIOdpdKCIHi8hbIrJBRIyInOBkPEop1dZ4uve1W7Lqc7nw9OiX+IBU3BkMxkS5od2FoXC6JSsT+B6YBrzqcCxKKRVTvi0bKPtuNqa8jNR+e+EdPBJpYRNwZhxwOKVzPgJTVeuRMXuwe9b4yc4FpuLGWMEbL8O9hmqeo0mWMeY94D0g5k+wKKWUk0q+/ICi/z0daCUSSr/8AE+PvuRdfA2u9Aynw6vhyetM3iXXUvjKE/g2rQXA3aETOSecS0pPbclSKhpOt2SFRUS8gLfWrmynYlFKtV1ut5vJkyfXvK7Pt3m9nWBBnSYD3/pVbH/mUTqc9ztccZ5ZPRypvQfQ4Y+34t+5DSw/7g5d4vaLb3N1p+Kvussv2muo5rWc/+WhuQa4wekglFJtW1paGu+8806jx8u+mx38iT1jqFj8NR/3fomh9/ydricdFedIQyciePI6xf0+zdWdij/L2Fu011DNS7Yk6zbgzlrvs4F1DsWilFJBmfJSqsc11edOdVO5ZTvfnvEH9s/vFPK8XP6CnZR8+ibli+eDS0gbth9Zhx6LK1Mb9JVqqZJqMlJjTIUxprB6A4qcjkkppepL7btX3ck9A4xlKF5fDNhrB/5697SQrucvKmD7vddTOudjrMKdWLt2UPrF+2y/70asstKYxq5aP2OZmGyqeUmVZCmlVLxY5aVUrV+Nv3BXs2VLSkrIzMwkMzOTkpKSBse9Q0fh6d4HZPeP2OoxLBu/2WC/9/spXPRTSLGVfvEeVlFB3e5HY+HfsYWybz4J6RotRXN1p+JP1y5MHEe7C0UkCxhQa1dfEdkH2GGMWeNMVEqptsT4/RS/9xIlX80EXxWI4B0yityTLmiyK660tPEWJHF7yLvkGnY9ez8VPy2G6jmFBHof3oefX/6RqjI/mf161ZxjlZeBMUGfPCz/4dvgz8wbQ8VP35M54ZiQP29L0FTdqfizLIMVZUtUtOe3FU6PyRoNfFrrffV4qxnA1IRHo5Rqc4rff5mSz2oNxDaGiqXfsnNaAXmXXx/5U3aWReWKH7FP332N1OxUeh3SmxVvLafP786mauNaiv73NJUrlgKQ0nsPco4/i5Se/QHwFxfg374l+D1EwJMaWXxKqbhztLvQGDPLGCNBtqlOxqWUahusinJKvvwgyAGLqtW/ULV6ecTXLl8yH3y+BvvFJeT2a8det/2BjgcMY8cDN1H56+5uw6o1y9n+0C34ttjdiiUfvhF0fBcAxpC+z34Rx6japqhne4/BFBBthY7JUkq1Wf4dW+0uwkZUT84ZCVNWCo20gokIfS46hdKvPsRUVdTtCjQG/D5KPn8PgLKFcxodAONq14G0EeMijlG1TdULREe7qeZpkqWUarPcOe0aTYQA3O06RHzt1P57NZocudt3xJXdjsrVvwRf38SyqFy5rOZ1UCKkDhjc4pbpUUrtpkmWUqrNcmVmkzZs3zpPAQIgLlztOpC6x9CIr53Ssz/ewSPrJnGB11mTT0NcLlxZOcEXZxbBlZ0DgHfwiOBljCFt0IiI41Ntl2VMTDbVPKcHviullKNyppyHv6iAql9/rNnnapdH3gVXN9pK5HK5GD9+fM3rxrQ7+wqKP/4fpXM+xpQW4+nWi6yJJ5I2ZBQAGWMnULFkfsMTjSFj30MAyJp4IhU/LsRUlO1u1RIhtd8gvIHrJJNQ607Fjy6rkziSzBUlIjlAQUFBATk5OU6Ho5RKYlVrf6Vq41rcuXmk7jEEiXECYIxp8KSiMYbiD16h5OP/BVq5BIxF+gETyTn+nJry/p3bKPn0bSqWLUJS00gfdSAZ4w5HUvTJwmRXWFhIbm4uQG5gku24qf7OvPw/G/CmR/edWVFWyAN/7gYJiDuZaUuWUkoBKT37kdKzX9yuH2wqCBEhe9LJpI86kPIlC8BYeAePICW/Z51y7vYdyTlxatxiU22LzpOVOJpkKaWUwzydupJ1SHJNKKqSVyxmbE/iTrCE0g5xpZQKU0lJCZ06daJTp066NEyYtO5UW6ItWUopFYFt27Y5HULS0rpzljHRL/CczOO5E0mTLKWUipOKn76ndM5H+Hduw9OjL5kHHUVK157NnmeMofLnRZQt+BKrvJTUfnuRse8huDKyEhC1au1MDKZg0CQrNJpkKaVUHBR/+hbF775oz3FlWfg2r6f8269of+Ff8A4Y0uS5RW89S+kX79ecW/nzIkq/+pAOv7shqglSlVKJpWOylFIqxvyFuyh+72X7TfXcVpYFfovCV6c12QpQtXaFnWDVPtcYrKJdFL33UhyjVm2FsUxMNtU8TbKUUirGKn7+vpHF3Qz+bZvwb9/c6Lnli+YFn+HdsihfNFe7aVTUNMlKHE2ylFIq5hpfD7E5xu9r/HzLH/F1lVKJp2OylFIqTC6Xi9GjR9e8rs+71/Ca8VR1iODumI+7Q5dGr+3da/ju7sK6N8U7cHjQSU2TSXN1p+LPMvYW7TVU8zTJUkqpMKWnpzNv3rxGj7uzc8mefBpFbz+3O9lyuUBc5Ew5r8lEKXXAELyDR1Kx9NvdO10uxJOCd9gYij98HfGmkTZsbFIOgm+u7lT8xaK7T7sLQ6NJllJKxUHm+Ml4uvai9OuP8e/cRkqPvmQeeCSeLt2bPE9cLtqdcyWlcz6mbN5nWKUlpPYfhH/nNgpffNRO1oyh6O3nyZlyXs1C0kqplkeTLKWUihPvnkPx7jk07PPE7SHzwCPJPPBIAIpnvkb5t1/ZB2u6IA2FrzxBap89m03clKrNGBP1AxT6AEZotENcKaXCVFpaSp8+fejTpw+lpaXxv983nwZfLM7lomzBl3G/fywluu5UQ5a1e5HoyDenP0Vy0JYspZQKkzGG1atX17yON6u0uPFjJYVxv38sJbrulHKStmQppVQLl9KzPwQbLG9ZpPQakPiAVFKr7i6MdlPN0yRLKaVauKwjfmO/qJ1ouVy423ckbcT+zgSlkpZORpo4mmQppVQL5x0whPbn/QlP58AAdxG8Q0aRd/n1uFLTnA1OKdUoHZOllFJJwDtoH1L3Go4pK0VSUpCUVKdDUklK58lKHE2ylFIqSYgIkpHpdBgqyVkYrCjHVFlokhUKTbKUUipMIsLgwYNrXqvQad05T1uyEkeTLKWUClNGRgY//PCD02EkJa071ZZokqWUUkq1ITrje+Lo04VKKaVUG2Kinu09vlM4iEh7EXlaRAoC29Mi0i6M8x8RESMiV8UtyBBpkqWUUmEqLS1lyJAhDBkyRJeGCZPWnQrBc8A+wKTAtg/wdCgnisgJwL7AhviEFh7tLlRKqTAZY1i6dGnNaxU6rTvnteSB7yIyCDux2s8Y801g30XAHBEZaIz5uYlzuwP3A0cC78QlwDBpS5ZSSinVhrTwZXX2BwqqE6xAvF8DBcC4xk4SERd2a9cdxpgW82SFtmQppZRSKlLZ9abiqDDGVERxvXxgS5D9WwLHGvNXwAfcG8W9Y05bspRSSqk2xFhWTLaAdditTNXbNcHuKSI3BgajN7WNrg4x2CUa2Y+IjAJ+D0w1LawPWluylFIqCZWt3cjWD7/ElZpC58kTSM1r53RIKklUPyEY7TUCegBFtQ411op1P/BCM5ddBQwDugQ51gnY3Mh5BwGdgTW1WtXcwH9F5CpjTJ9m7hs3mmQppVQSMcbw89/uZMUdj0Hgl3ZJTWHofTfQ6/yTHY5OtUFFxpjC5goZY7YB25orJyJzgFwRGWuMmRvYty+QC8xu5LSngY/q7fsgsH9ac/eMJ02ylFIqTCJC7969a14n0obn32LF7Y/W2Wcqq1h86d/JHT6I3FFDExpPuJysO2VryZORGmN+FJH3gcdE5JLA7keBt2s/WSgiPwHXGGNeN8ZsB7bXvo6IVAGbmnoaMRE0yVJKqTBlZGSwatUqR+696sFnwSVQr7tH3C7WPPESe7fwJMvJulO2ljyFQ8CZ2APYZwbevwn8rl6ZgditWy2aJllKKZVEytZubJBgARifn7L1mxyISKnYMsbsAM5qpkyTzaBOjsOqTZ8uVEqpJJI7cjDidjc84HaRM2xQ4gNSSae6JSvaTTVPkyyllApTWVkZY8aMYcyYMZSVlSX03v3/fJE9Hqb2eCa3C3d6Gr0vPi2hsUTCybpTNgsLy0S5YTV/I6VJllJKhcuyLObPn8/8+fOxrMR+2eQdMIpRL99HWo/d8zJmD9mD/WbOIL1n14TGEgkn606pRNMxWUoplWTyjzucLsccSskvq3ClppDep4c+qadCZqzoB64bzY9DokmWUkolIXG5yBrYz+kwVBJKgqcLWw1NspRSqhWwSooo/foTKn/9EUnLJH3UgXgH7aMtXEo5SJMspZRKcv6d29h+/z+winbZs8CLi4pF35B+wBHknnCO0+GpFqYlT0ba2ujAd6WUSnJF772EVVxQs8xO9YCZsq9mUrlmhYORqZbIsqyYbKp52pKllFIR6Nixo9MhAHaLQvniuRDsS8/lomLxPFJ79U98YE1oKXWnVLxpkqWUUmHKzMxk69atToexm7+RVgUDxu9LbCzNaHF11wbpwPfEcby7UEQuE5GVIlIuIgtE5CCnY1JKqWQhInj3Gg6uID/OjcXyR96hdNW6xAemWixjrJhsqnmOJlkicipwN3ALMAL4AnhPRHo5GZdSSiWTrMmnIJ5UqtsWqgc271y+k02ffM/Xh5+DVVnpaIxKtUVOt2T9EXjCGPO4MeZHY8xVwFrgt86GpZRSjSsrK2PChAlMmDChRSwNk5Lfk7wrb2LHLwVU7CqndEspaz9dw6/vLLcXjl69ns1vfeJ0mEDLq7u2SNcuTBzHxmSJSCowCvhXvUMzgXGNnOMFvLV2ZccnOqWUapxlWXz22Wc1r1sC405j1TvLgh4Tj4fiH5cnOKLgWmLdtTmxSJI0yQqJky1ZHQE3sLne/s1AfsPiAFwDFNTadKCBUkoBntxs3FkZQY8Zn4/03j0SHJFSyunuQoD66bAE2VftNiC31qY/NZRSCnClpND70jPAVW+Gd7eLlLx25J94hDOBqRbHMlZMNtU8J6dw2Ab4adhq1ZmGrVsAGGMqgIrq97pchFJK7TbwH7+nfP0mNjz/ds2+tK6dGf3qg3gyg7dyqbZHp3BIHMeSLGNMpYgsACYCr9c6NBH4nzNRKaVU8nKlpjLiqf+y5/VXsmv+YrydO9Bh/FjE7XY6NNWCGGNhohwPp1M4hMbpyUjvBJ4WkfnAHOBioBfwsKNRKaVUEssc0JvMAb2dDkOpNs/RJMsY86KIdACuB7oCS4DJxpjVTsallFLNycjQ7rdIad05S7sLE8fpliyMMQ8CDzodh1JKhSozM5OSkhKnw0hKWnfOi8WM7dpdGJqW8HShUkoppVSr43hLllJKKaUSx7LAirK7T+eRDY22ZCmlVJjKy8s5+uijOfrooykvL3c6nKSidec8Y1kx2VTztCVLKaXC5Pf7effdd2teq9Bp3am2RJMspZRSqg3RpwsTR5MspZRSqg3RpwsTR8dkKaWUUkrFgbZkKaWUUm2IdhcmjiZZSimlVBsSi6cD9enC0LSKJKuwsNDpEJRSbUjtGcsLCwv1KbkwaN3V5cT3l98X/Yz7sbhGWyDGJG+Tn4h0B9Y5HYdSSikVpR7GmPXxvIGIpAErgfwYXXIT0NcYoxOeNSLZkywBugFFYZ6ajZ2c9Yjg3LZO6y4yWm+R0XqLjNZb5Jyou2xgg0nAF3Ig0UqN0eUqNcFqWlJ3Fwb+QYad+du5GQBFxhjtawyD1l1ktN4io/UWGa23yDlUdwn7OwokRZoYJYhO4aCUUkopFQeaZCmllFJKxUFbTbIqgH8E/lTh0bqLjNZbZLTeIqP1FjmtOxUzST3wXSmllFKqpWqrLVlKKaWUUnGlSZZSSimlVBxokqWUUkopFQeaZCmllFJKxYEmWbWIiFdEFoqIEZF9nI6npRORN0VkjYiUi8hGEXlaRLo5HVdLJiJ9ROQJEVkpImUiskJE/iEisZqBudUSketEZLaIlIrILqfjaclE5LLAv7FyEVkgIgc5HVNLJyIHi8hbIrIh8B1wgtMxqeSnSVZdtwMbnA4iiXwKnAIMBKYA/YFXHI2o5dsL+//dJcAQ4A/ApcCtTgaVJFKBl4GHnA6kJRORU4G7gVuAEcAXwHsi0svJuJJAJvA98DunA1Gth07hECAiRwF3YicLPwAjjDELHQ0qyYjIccAbgNcYU+VwOElDRK4GfmuM6ed0LMlARKYCdxtj2jkcSoskIt8A3xpjfltr34/AG8aYa5yLLHmIiAF+Y4x5w+lYVHLTlixARLoAjwFnA6UOh5OURCQPOBOYrQlW2HKBHU4HoZJfoNt5FDCz3qGZwLjER6RU29bmkyyxVwOdDjxsjJnvcDhJR0T+LSIlwHagF3C8wyElFRHpD1wBPOx0LKpV6Ai4gc319m8G8hMfjlJtW6tNskTkxsDgxaa20dhfcDnAbQ6H3CKEUW/V7sAe93EE4AeeklrL2LcVEdQbgYcE3gdeNsY87kzkzoqk3lRI6o8DkSD7lFJx1mrHZIlIR+zf6pqyCngBOJa6P4Dc2AnDs8aYc+MSYAsVar0ZY8qDnNsDWAuMM8bMiUd8LVW49RZIsD4FvgGmGmOsOIfYIkXy703HZDUu0F1YCpxsjHm91v57gH2MMeMdCy6J6JgsFSsepwOIF2PMNmBbc+VE5Ergb7V2dQM+AE7F/gJsU0Ktt0ZUt2B5YxRO0gin3kSkO3aCtQA4r60mWBD1vzdVjzGmUkQWABOB12sdmgj8z5molGq7Wm2SFSpjzJra70WkOPByhTFmnQMhJQURGQuMBb4EdgL9gJuAFUCbasUKR6AFaxawBvgz0Km6d9UYs8m5yFq+wBQEedhj/9y15rJbbowpbvTEtudO4GkRmY/9f/Fi7DrTcX9NEJEsYECtXX0D/8Z21P+eUCpUbT7JUhErA04E/oE9v8xG7PFFpxljKpwMrIU7AvsH+QCgfhLf5sayhekmoHb3/XeBPw/BTlwVYIx5UUQ6ANcDXYElwGRjzGpnI2vxRmO3MFe7M/DnDGBqwqNRrUKrHZOllFJKKeWkVvt0oVJKKaWUkzTJUkoppZSKA02ylFJKKaXiQJMspZRSSqk40CRLKaWUUioONMlSSimllIoDTbKUUkoppeJAkyylWgkRmSUidzsdh1JKKZsmWUqpoERkqoiYINuFtcqkishfROR7ESkVkW0i8pWInCciKU7Gr5RSTtNldZRSTSkEBtbbVwB2goW9mPpw4O/AV4Hy+2Gvy/gdsDBRgSqlVEujLVlKtUIi0l5EnhKRnYEWpvdEZI96ZS4SkbWB46+LyB9FZFe9SxljzKZ6W1ng2FXAwcBhxpgHjDELjTG/GmOeA/YFfgnc5yQRWSwiZSKyXUQ+EpHM+NaAUko5T5MspVqn6dgL3h4H7I+9+PS71V14InIA8DBwD7AP8CFwXZj3OBP4yBjzXf0DxpgqY0yJiHQFngeeBAYBE4DX0MWwlVJtgHYXKtXKBFqsjgP+v537B62rjMM4/n2ga+kgBbsILUTqFCLBQK1OCm3ooA5dOkiniMUO6uTSpZMULM0iZC0OtrQIYlEodEn/4iCV2sQSkOIoDlaw1fbncE7I5Rq5CXKI9/T7gQvnvIdzz/sul+e+v/e8L1fV1bbtCHAfeAM4B7wHXKqqU+1ty0n2AYeGvm5HkgcD5w+q6tn2eAK4MqI7u2h+Zy5U1U9t2+1ND0qSxpAhS+qfF4C/gBurDVX1S5Kl9ho066wuDt13k3+GrN+AFwfOnwwcB6gRffkOuAzcTvI18A1wvqp+3cA4JGmsWS6U+uffSnGDoWi9gLTefU+q6t7AZ2Xg2jJroW1dVfUYeB04CNyhmUFbSrJ7xBgkaewZsqT+uUMzSz2z2pDkGeB54Ie26S7w0tB905t8zmfAa0mmhi8k2ba6uL0ai1V1ApgCHgFvbvJZkjR2DFlSz1TVj8AXwEKS/UkmgbPAz207wDww275ROJFkjma2aVT5b9Bpmm0bLic5lmQyyZ4kh2lKlRNJZpJ8lGQ6yXPAW8BO1sKeJPWWIUvqp6PAt8CXwDWaUuBsVf0JUFWLwDvA+zTrpg4AnwB/bPQBVfWQphT4MTAHXAduAceBM8D3NPtmvQp8RVNePAl8UFWX/vMIJel/LlWb+eMqqa+SLAB7q+qVre6LJPWBbxdKT6kkH9Lsj/U7TanwbeDdLe2UJPWIM1nSUyrJ5zSbg24HVoD5qvp0SzslST1iyJIkSeqAC98lSZI6YMiSJEnqgCFLkiSpA4YsSZKkDhiyJEmSOmDIkiRJ6oAhS5IkqQOGLEmSpA4YsiRJkjrwN3V2X0vWdulfAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 700x500 with 2 Axes>"
       ]
@@ -1410,6 +1434,222 @@
    "source": [
     "dc.plot_volcano(logFCs, pvals, 'treatment.vs.control', name='STAT2', net=dorothea, top=5, sign_thr=0.05, lFCs_thr=0.5)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bf0d157",
+   "metadata": {},
+   "source": [
+    "## Functional enrichment of biological terms\n",
+    "We can also assign the obtained DEG biological terms using the resource MSigDB and the `ora` method.\n",
+    "\n",
+    "For another example on functional enrichment of biological terms please visit this other notebook: [Functional enrichment of biological terms](https://decoupler-py.readthedocs.io/en/latest/notebooks/msigdb.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "372e0583",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>HALLMARK_ADIPOGENESIS</th>\n",
+       "      <th>HALLMARK_ALLOGRAFT_REJECTION</th>\n",
+       "      <th>HALLMARK_ANDROGEN_RESPONSE</th>\n",
+       "      <th>HALLMARK_ANGIOGENESIS</th>\n",
+       "      <th>HALLMARK_APICAL_JUNCTION</th>\n",
+       "      <th>HALLMARK_APOPTOSIS</th>\n",
+       "      <th>HALLMARK_COAGULATION</th>\n",
+       "      <th>HALLMARK_COMPLEMENT</th>\n",
+       "      <th>HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION</th>\n",
+       "      <th>HALLMARK_ESTROGEN_RESPONSE_EARLY</th>\n",
+       "      <th>...</th>\n",
+       "      <th>HALLMARK_MITOTIC_SPINDLE</th>\n",
+       "      <th>HALLMARK_MTORC1_SIGNALING</th>\n",
+       "      <th>HALLMARK_MYOGENESIS</th>\n",
+       "      <th>HALLMARK_P53_PATHWAY</th>\n",
+       "      <th>HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY</th>\n",
+       "      <th>HALLMARK_TGF_BETA_SIGNALING</th>\n",
+       "      <th>HALLMARK_TNFA_SIGNALING_VIA_NFKB</th>\n",
+       "      <th>HALLMARK_UV_RESPONSE_DN</th>\n",
+       "      <th>HALLMARK_UV_RESPONSE_UP</th>\n",
+       "      <th>HALLMARK_XENOBIOTIC_METABOLISM</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>treatment.vs.control</th>\n",
+       "      <td>5.764578e-11</td>\n",
+       "      <td>7.876590e-17</td>\n",
+       "      <td>7.876590e-17</td>\n",
+       "      <td>4.889991e-08</td>\n",
+       "      <td>1.373635e-28</td>\n",
+       "      <td>1.373635e-28</td>\n",
+       "      <td>3.564059e-24</td>\n",
+       "      <td>1.205612e-25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.072404e-27</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.680132e-09</td>\n",
+       "      <td>9.129221e-20</td>\n",
+       "      <td>1.052105e-22</td>\n",
+       "      <td>1.373635e-28</td>\n",
+       "      <td>2.308692e-15</td>\n",
+       "      <td>1.975060e-12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.275542e-42</td>\n",
+       "      <td>7.876590e-17</td>\n",
+       "      <td>3.564059e-24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      HALLMARK_ADIPOGENESIS  HALLMARK_ALLOGRAFT_REJECTION  \\\n",
+       "treatment.vs.control           5.764578e-11                  7.876590e-17   \n",
+       "\n",
+       "                      HALLMARK_ANDROGEN_RESPONSE  HALLMARK_ANGIOGENESIS  \\\n",
+       "treatment.vs.control                7.876590e-17           4.889991e-08   \n",
+       "\n",
+       "                      HALLMARK_APICAL_JUNCTION  HALLMARK_APOPTOSIS  \\\n",
+       "treatment.vs.control              1.373635e-28        1.373635e-28   \n",
+       "\n",
+       "                      HALLMARK_COAGULATION  HALLMARK_COMPLEMENT  \\\n",
+       "treatment.vs.control          3.564059e-24         1.205612e-25   \n",
+       "\n",
+       "                      HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION  \\\n",
+       "treatment.vs.control                                         0.0   \n",
+       "\n",
+       "                      HALLMARK_ESTROGEN_RESPONSE_EARLY  ...  \\\n",
+       "treatment.vs.control                      4.072404e-27  ...   \n",
+       "\n",
+       "                      HALLMARK_MITOTIC_SPINDLE  HALLMARK_MTORC1_SIGNALING  \\\n",
+       "treatment.vs.control              1.680132e-09               9.129221e-20   \n",
+       "\n",
+       "                      HALLMARK_MYOGENESIS  HALLMARK_P53_PATHWAY  \\\n",
+       "treatment.vs.control         1.052105e-22          1.373635e-28   \n",
+       "\n",
+       "                      HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY  \\\n",
+       "treatment.vs.control                              2.308692e-15   \n",
+       "\n",
+       "                      HALLMARK_TGF_BETA_SIGNALING  \\\n",
+       "treatment.vs.control                 1.975060e-12   \n",
+       "\n",
+       "                      HALLMARK_TNFA_SIGNALING_VIA_NFKB  \\\n",
+       "treatment.vs.control                               0.0   \n",
+       "\n",
+       "                      HALLMARK_UV_RESPONSE_DN  HALLMARK_UV_RESPONSE_UP  \\\n",
+       "treatment.vs.control             7.275542e-42             7.876590e-17   \n",
+       "\n",
+       "                      HALLMARK_XENOBIOTIC_METABOLISM  \n",
+       "treatment.vs.control                    3.564059e-24  \n",
+       "\n",
+       "[1 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Retrieve MSigDB resource\n",
+    "msigdb = dc.get_resource('MSigDB')\n",
+    "msigdb\n",
+    "\n",
+    "# Filter by a desired geneset collection, for example hallmarks\n",
+    "msigdb = msigdb[msigdb['collection']=='hallmark']\n",
+    "msigdb = msigdb.drop_duplicates(['geneset', 'genesymbol'])\n",
+    "\n",
+    "# Infer enrichment with ora using significant deg\n",
+    "top_genes = dc.get_top_targets(logFCs, pvals, 'treatment.vs.control', sign_thr=0.05, lFCs_thr=1.5)\n",
+    "enr_pvals = dc.get_ora_df(top_genes, msigdb, groupby='contrast', features='name', source='geneset', target='genesymbol')\n",
+    "enr_pvals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73c5d319",
+   "metadata": {},
+   "source": [
+    " We can then transform these p-values to their -log10 (so that the higher the value, the more significant the p-value).\n",
+    " We will also set 0s to a minimum p-value so that we do not get infinites."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b7f8e504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set 0s to min p-value\n",
+    "enr_pvals.values[enr_pvals.values == 0] = np.min(enr_pvals.values[enr_pvals.values != 0])\n",
+    "\n",
+    "# Log-transform\n",
+    "enr_pvals = -np.log10(enr_pvals)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c66b649",
+   "metadata": {},
+   "source": [
+    "Then we can visualize the most enriched terms:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "bd4a91d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dc.plot_barplot(enr_pvals, 'treatment.vs.control', top=25, vertical=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c34d80d",
+   "metadata": {},
+   "source": [
+    "TNFa and interferons response (JAK-STAT) processes seem to be enriched. We previously observed a similar result with the PROGENy pathways, were they were significantly downregulated. Therefore, one of the limitations of using a prior knowledge resource without weights is that it doesn't provide direction."
+   ]
   }
  ],
  "metadata": {
diff --git a/docs/source/notebooks/cell_annotation.ipynb b/docs/source/notebooks/cell_annotation.ipynb
index ee6f0db..e421be0 100644
--- a/docs/source/notebooks/cell_annotation.ipynb
+++ b/docs/source/notebooks/cell_annotation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "4bfa785b",
+   "id": "6fcfd8d8",
    "metadata": {
     "tags": []
    },
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44586160",
+   "id": "0a8f39d3",
    "metadata": {
     "tags": []
    },
@@ -45,7 +45,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "1a8f5ba9",
+   "id": "2219728f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c05a2266",
+   "id": "b5ae34f6",
    "metadata": {
     "tags": []
    },
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c946e69",
+   "id": "dbbf988d",
    "metadata": {},
    "source": [
     "### Loading the data-set\n",
@@ -80,7 +80,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "2f8ddda9",
+   "id": "b631de8e",
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d87069fb",
+   "id": "36ea6229",
    "metadata": {},
    "source": [
     "### QC, projection and clustering\n",
@@ -125,7 +125,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d92fd5ab",
+   "id": "680f0f61",
    "metadata": {},
    "outputs": [
     {
@@ -168,7 +168,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36df8b1c",
+   "id": "2caa07b4",
    "metadata": {},
    "source": [
     "Then we group cells based on the similarity of their transcription profiles. \n",
@@ -178,7 +178,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "3f3377e1",
+   "id": "971ec53b",
    "metadata": {},
    "outputs": [
     {
@@ -212,7 +212,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a760a18",
+   "id": "9aac4a30",
    "metadata": {},
    "source": [
     "At this stage, we have identified communities of cells that show a similar \n",
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d898d21",
+   "id": "99351823",
    "metadata": {
     "tags": []
    },
@@ -243,7 +243,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0019785e",
+   "id": "5dcea279",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\">\n",
@@ -258,7 +258,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "9c17b093",
+   "id": "daa3e1da",
    "metadata": {},
    "outputs": [
     {
@@ -281,7 +281,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>label</th>\n",
+       "      <th></th>\n",
        "      <th>genesymbol</th>\n",
        "      <th>canonical_marker</th>\n",
        "      <th>cell_type</th>\n",
@@ -480,44 +480,44 @@
        "</div>"
       ],
       "text/plain": [
-       "label genesymbol canonical_marker         cell_type germ_layer human  \\\n",
-       "0          CTRB1            False       Enterocytes   Endoderm  True   \n",
-       "1          CTRB1             True      Acinar cells   Endoderm  True   \n",
-       "2           KLK1             True      Acinar cells   Endoderm  True   \n",
-       "3           KLK1            False      Goblet cells   Endoderm  True   \n",
-       "4           KLK1            False  Epithelial cells   Mesoderm  True   \n",
-       "...          ...              ...               ...        ...   ...   \n",
-       "8472     SLC14A1             True  Urothelial cells   Mesoderm  True   \n",
-       "8473       UPK3A             True  Urothelial cells   Mesoderm  True   \n",
-       "8474       UPK1A             True  Urothelial cells   Mesoderm  True   \n",
-       "8475        UPK2             True  Urothelial cells   Mesoderm  True   \n",
-       "8476       UPK3B             True  Urothelial cells   Mesoderm  True   \n",
+       "     genesymbol canonical_marker         cell_type germ_layer human  \\\n",
+       "0         CTRB1            False       Enterocytes   Endoderm  True   \n",
+       "1         CTRB1             True      Acinar cells   Endoderm  True   \n",
+       "2          KLK1             True      Acinar cells   Endoderm  True   \n",
+       "3          KLK1            False      Goblet cells   Endoderm  True   \n",
+       "4          KLK1            False  Epithelial cells   Mesoderm  True   \n",
+       "...         ...              ...               ...        ...   ...   \n",
+       "8472    SLC14A1             True  Urothelial cells   Mesoderm  True   \n",
+       "8473      UPK3A             True  Urothelial cells   Mesoderm  True   \n",
+       "8474      UPK1A             True  Urothelial cells   Mesoderm  True   \n",
+       "8475       UPK2             True  Urothelial cells   Mesoderm  True   \n",
+       "8476      UPK3B             True  Urothelial cells   Mesoderm  True   \n",
        "\n",
-       "label human_sensitivity human_specificity mouse mouse_sensitivity  \\\n",
-       "0                   0.0        0.00439422  True        0.00331126   \n",
-       "1                   1.0       0.000628931  True          0.957143   \n",
-       "2              0.833333        0.00503145  True          0.314286   \n",
-       "3              0.588235        0.00503937  True          0.903226   \n",
-       "4                   0.0        0.00823306  True          0.225806   \n",
-       "...                 ...               ...   ...               ...   \n",
-       "8472                0.0         0.0181704  True               0.0   \n",
-       "8473                0.0               0.0  True               0.0   \n",
-       "8474                0.0               0.0  True               0.0   \n",
-       "8475                0.0               0.0  True               0.0   \n",
-       "8476                0.0               0.0  True               0.0   \n",
+       "     human_sensitivity human_specificity mouse mouse_sensitivity  \\\n",
+       "0                  0.0        0.00439422  True        0.00331126   \n",
+       "1                  1.0       0.000628931  True          0.957143   \n",
+       "2             0.833333        0.00503145  True          0.314286   \n",
+       "3             0.588235        0.00503937  True          0.903226   \n",
+       "4                  0.0        0.00823306  True          0.225806   \n",
+       "...                ...               ...   ...               ...   \n",
+       "8472               0.0         0.0181704  True               0.0   \n",
+       "8473               0.0               0.0  True               0.0   \n",
+       "8474               0.0               0.0  True               0.0   \n",
+       "8475               0.0               0.0  True               0.0   \n",
+       "8476               0.0               0.0  True               0.0   \n",
        "\n",
-       "label mouse_specificity ncbi_tax_id            organ ubiquitiousness  \n",
-       "0             0.0204803        9606         GI tract           0.017  \n",
-       "1             0.0159201        9606         Pancreas           0.017  \n",
-       "2             0.0128263        9606         Pancreas           0.013  \n",
-       "3             0.0124084        9606         GI tract           0.013  \n",
-       "4             0.0137585        9606       Epithelium           0.013  \n",
-       "...                 ...         ...              ...             ...  \n",
-       "8472                0.0        9606  Urinary bladder           0.008  \n",
-       "8473                0.0        9606  Urinary bladder             0.0  \n",
-       "8474                0.0        9606  Urinary bladder             0.0  \n",
-       "8475                0.0        9606  Urinary bladder             0.0  \n",
-       "8476                0.0        9606  Urinary bladder             0.0  \n",
+       "     mouse_specificity ncbi_tax_id            organ ubiquitiousness  \n",
+       "0            0.0204803        9606         GI tract           0.017  \n",
+       "1            0.0159201        9606         Pancreas           0.017  \n",
+       "2            0.0128263        9606         Pancreas           0.013  \n",
+       "3            0.0124084        9606         GI tract           0.013  \n",
+       "4            0.0137585        9606       Epithelium           0.013  \n",
+       "...                ...         ...              ...             ...  \n",
+       "8472               0.0        9606  Urinary bladder           0.008  \n",
+       "8473               0.0        9606  Urinary bladder             0.0  \n",
+       "8474               0.0        9606  Urinary bladder             0.0  \n",
+       "8475               0.0        9606  Urinary bladder             0.0  \n",
+       "8476               0.0        9606  Urinary bladder             0.0  \n",
        "\n",
        "[8477 rows x 13 columns]"
       ]
@@ -535,7 +535,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5facbe30",
+   "id": "c8f53332",
    "metadata": {},
    "source": [
     "Since our data-set is from human cells, and we want best quality of the markers, we can filter by `canonical_marker` and `human`:"
@@ -544,7 +544,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "3deaf56d",
+   "id": "40428a6a",
    "metadata": {},
    "outputs": [
     {
@@ -567,7 +567,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>label</th>\n",
+       "      <th></th>\n",
        "      <th>genesymbol</th>\n",
        "      <th>canonical_marker</th>\n",
        "      <th>cell_type</th>\n",
@@ -766,44 +766,44 @@
        "</div>"
       ],
       "text/plain": [
-       "label genesymbol canonical_marker                     cell_type germ_layer  \\\n",
-       "1          CTRB1             True                  Acinar cells   Endoderm   \n",
-       "2           KLK1             True                  Acinar cells   Endoderm   \n",
-       "5           KLK1             True               Principal cells   Mesoderm   \n",
-       "7           KLK1             True  Plasmacytoid dendritic cells   Mesoderm   \n",
-       "8           KLK1             True             Endothelial cells   Mesoderm   \n",
-       "...          ...              ...                           ...        ...   \n",
-       "8472     SLC14A1             True              Urothelial cells   Mesoderm   \n",
-       "8473       UPK3A             True              Urothelial cells   Mesoderm   \n",
-       "8474       UPK1A             True              Urothelial cells   Mesoderm   \n",
-       "8475        UPK2             True              Urothelial cells   Mesoderm   \n",
-       "8476       UPK3B             True              Urothelial cells   Mesoderm   \n",
+       "     genesymbol canonical_marker                     cell_type germ_layer  \\\n",
+       "1         CTRB1             True                  Acinar cells   Endoderm   \n",
+       "2          KLK1             True                  Acinar cells   Endoderm   \n",
+       "5          KLK1             True               Principal cells   Mesoderm   \n",
+       "7          KLK1             True  Plasmacytoid dendritic cells   Mesoderm   \n",
+       "8          KLK1             True             Endothelial cells   Mesoderm   \n",
+       "...         ...              ...                           ...        ...   \n",
+       "8472    SLC14A1             True              Urothelial cells   Mesoderm   \n",
+       "8473      UPK3A             True              Urothelial cells   Mesoderm   \n",
+       "8474      UPK1A             True              Urothelial cells   Mesoderm   \n",
+       "8475       UPK2             True              Urothelial cells   Mesoderm   \n",
+       "8476      UPK3B             True              Urothelial cells   Mesoderm   \n",
        "\n",
-       "label human human_sensitivity human_specificity mouse mouse_sensitivity  \\\n",
-       "1      True               1.0       0.000628931  True          0.957143   \n",
-       "2      True          0.833333        0.00503145  True          0.314286   \n",
-       "5      True               0.0        0.00814536  True          0.285714   \n",
-       "7      True               0.0        0.00820189  True               1.0   \n",
-       "8      True               0.0        0.00841969  True               0.0   \n",
-       "...     ...               ...               ...   ...               ...   \n",
-       "8472   True               0.0         0.0181704  True               0.0   \n",
-       "8473   True               0.0               0.0  True               0.0   \n",
-       "8474   True               0.0               0.0  True               0.0   \n",
-       "8475   True               0.0               0.0  True               0.0   \n",
-       "8476   True               0.0               0.0  True               0.0   \n",
+       "     human human_sensitivity human_specificity mouse mouse_sensitivity  \\\n",
+       "1     True               1.0       0.000628931  True          0.957143   \n",
+       "2     True          0.833333        0.00503145  True          0.314286   \n",
+       "5     True               0.0        0.00814536  True          0.285714   \n",
+       "7     True               0.0        0.00820189  True               1.0   \n",
+       "8     True               0.0        0.00841969  True               0.0   \n",
+       "...    ...               ...               ...   ...               ...   \n",
+       "8472  True               0.0         0.0181704  True               0.0   \n",
+       "8473  True               0.0               0.0  True               0.0   \n",
+       "8474  True               0.0               0.0  True               0.0   \n",
+       "8475  True               0.0               0.0  True               0.0   \n",
+       "8476  True               0.0               0.0  True               0.0   \n",
        "\n",
-       "label mouse_specificity ncbi_tax_id            organ ubiquitiousness  \n",
-       "1             0.0159201        9606         Pancreas           0.017  \n",
-       "2             0.0128263        9606         Pancreas           0.013  \n",
-       "5             0.0140583        9606           Kidney           0.013  \n",
-       "7             0.0129136        9606    Immune system           0.013  \n",
-       "8             0.0149153        9606      Vasculature           0.013  \n",
-       "...                 ...         ...              ...             ...  \n",
-       "8472                0.0        9606  Urinary bladder           0.008  \n",
-       "8473                0.0        9606  Urinary bladder             0.0  \n",
-       "8474                0.0        9606  Urinary bladder             0.0  \n",
-       "8475                0.0        9606  Urinary bladder             0.0  \n",
-       "8476                0.0        9606  Urinary bladder             0.0  \n",
+       "     mouse_specificity ncbi_tax_id            organ ubiquitiousness  \n",
+       "1            0.0159201        9606         Pancreas           0.017  \n",
+       "2            0.0128263        9606         Pancreas           0.013  \n",
+       "5            0.0140583        9606           Kidney           0.013  \n",
+       "7            0.0129136        9606    Immune system           0.013  \n",
+       "8            0.0149153        9606      Vasculature           0.013  \n",
+       "...                ...         ...              ...             ...  \n",
+       "8472               0.0        9606  Urinary bladder           0.008  \n",
+       "8473               0.0        9606  Urinary bladder             0.0  \n",
+       "8474               0.0        9606  Urinary bladder             0.0  \n",
+       "8475               0.0        9606  Urinary bladder             0.0  \n",
+       "8476               0.0        9606  Urinary bladder             0.0  \n",
        "\n",
        "[5180 rows x 13 columns]"
       ]
@@ -824,7 +824,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31c8defd",
+   "id": "311d3b4b",
    "metadata": {},
    "source": [
     "For this example we will use these markers, but any collection of genes could be used. To see the list of available resources inside `Omnipath`, run `dc.show_resources()`"
@@ -832,7 +832,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92f9accc",
+   "id": "693620e9",
    "metadata": {},
    "source": [
     "## Enrichment with Over Representation Analysis\n",
@@ -851,22 +851,22 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "d58a1ea1",
+   "id": "a8e28bd7",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "58 features of mat are empty in 2635 samples, they will be ignored.\n",
-      "Running ora on mat with 2638 samples and 13656 targets for 114 sources.\n"
+      "1 features of mat are empty, they will be removed.\n",
+      "Running ora on mat with 2638 samples and 13713 targets for 114 sources.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2638/2638 [00:02<00:00, 1055.62it/s]\n"
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2638/2638 [00:03<00:00, 862.79it/s]\n"
      ]
     }
    ],
@@ -876,7 +876,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "139b138c",
+   "id": "55278a6b",
    "metadata": {},
    "source": [
     "The obtained scores (-log10(p-value))(`ora_estimate`) and p-values (`ora_pvals`) are stored in the `.obsm` key:"
@@ -885,7 +885,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "883acc70",
+   "id": "ce2df87f",
    "metadata": {},
    "outputs": [
     {
@@ -935,122 +935,122 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AAACATACAACCAC-1</th>\n",
-       "      <td>0.497959</td>\n",
-       "      <td>0.094177</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>1.446730</td>\n",
-       "      <td>0.886826</td>\n",
-       "      <td>0.584060</td>\n",
-       "      <td>2.196676</td>\n",
-       "      <td>1.466532</td>\n",
-       "      <td>1.406718</td>\n",
+       "      <td>0.496369</td>\n",
+       "      <td>0.093412</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>1.443226</td>\n",
+       "      <td>0.885021</td>\n",
+       "      <td>0.581353</td>\n",
+       "      <td>4.624325</td>\n",
+       "      <td>1.459548</td>\n",
+       "      <td>1.399835</td>\n",
        "      <td>-0.000000</td>\n",
        "      <td>...</td>\n",
-       "      <td>13.554789</td>\n",
+       "      <td>13.523759</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>1.040001</td>\n",
+       "      <td>1.036748</td>\n",
        "      <td>-0.000000</td>\n",
        "      <td>-0.000000</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>2.374529</td>\n",
+       "      <td>3.325696</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>0.391150</td>\n",
+       "      <td>1.036748</td>\n",
        "      <td>-0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACATTGAGCTAC-1</th>\n",
-       "      <td>0.497959</td>\n",
-       "      <td>0.665563</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>0.570906</td>\n",
+       "      <td>0.496369</td>\n",
+       "      <td>1.114303</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.569257</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>0.584060</td>\n",
-       "      <td>5.719522</td>\n",
-       "      <td>8.744392</td>\n",
-       "      <td>14.024753</td>\n",
-       "      <td>0.090592</td>\n",
+       "      <td>1.119542</td>\n",
+       "      <td>5.592250</td>\n",
+       "      <td>8.719924</td>\n",
+       "      <td>15.198236</td>\n",
+       "      <td>0.306499</td>\n",
        "      <td>...</td>\n",
-       "      <td>2.440514</td>\n",
+       "      <td>2.431181</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>0.391150</td>\n",
+       "      <td>0.389675</td>\n",
        "      <td>-0.000000</td>\n",
        "      <td>-0.000000</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>0.300110</td>\n",
-       "      <td>0.886826</td>\n",
-       "      <td>2.865804</td>\n",
+       "      <td>0.827013</td>\n",
+       "      <td>0.885021</td>\n",
+       "      <td>2.858756</td>\n",
        "      <td>-0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACATTGATCAGC-1</th>\n",
-       "      <td>0.497959</td>\n",
-       "      <td>0.094177</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>0.570906</td>\n",
-       "      <td>0.886826</td>\n",
-       "      <td>0.584060</td>\n",
-       "      <td>0.934609</td>\n",
-       "      <td>2.040227</td>\n",
-       "      <td>4.029775</td>\n",
-       "      <td>1.093264</td>\n",
+       "      <td>0.496369</td>\n",
+       "      <td>0.316389</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>1.443226</td>\n",
+       "      <td>0.885021</td>\n",
+       "      <td>1.119542</td>\n",
+       "      <td>0.903197</td>\n",
+       "      <td>0.966789</td>\n",
+       "      <td>2.580070</td>\n",
+       "      <td>1.087856</td>\n",
        "      <td>...</td>\n",
-       "      <td>8.823756</td>\n",
+       "      <td>8.800720</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>0.391150</td>\n",
-       "      <td>-0.000000</td>\n",
+       "      <td>1.036748</td>\n",
        "      <td>-0.000000</td>\n",
+       "      <td>0.663965</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>1.532791</td>\n",
+       "      <td>2.367740</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>1.876995</td>\n",
+       "      <td>1.871866</td>\n",
        "      <td>-0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACCGTGCTTCCG-1</th>\n",
-       "      <td>1.282176</td>\n",
-       "      <td>2.295738</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>0.570906</td>\n",
-       "      <td>0.886826</td>\n",
-       "      <td>0.584060</td>\n",
-       "      <td>1.515196</td>\n",
-       "      <td>0.263925</td>\n",
-       "      <td>0.928107</td>\n",
-       "      <td>0.647792</td>\n",
+       "      <td>1.278757</td>\n",
+       "      <td>1.659624</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>1.443226</td>\n",
+       "      <td>0.885021</td>\n",
+       "      <td>0.195977</td>\n",
+       "      <td>0.903197</td>\n",
+       "      <td>0.074112</td>\n",
+       "      <td>0.923023</td>\n",
+       "      <td>0.306499</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.204336</td>\n",
+       "      <td>0.718715</td>\n",
        "      <td>-0.0</td>\n",
        "      <td>-0.000000</td>\n",
        "      <td>-0.000000</td>\n",
        "      <td>-0.000000</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>0.300110</td>\n",
-       "      <td>0.886826</td>\n",
-       "      <td>1.040001</td>\n",
+       "      <td>0.298770</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.389675</td>\n",
        "      <td>-0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACCGTGTATGCG-1</th>\n",
-       "      <td>0.497959</td>\n",
-       "      <td>0.094177</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>0.570906</td>\n",
-       "      <td>0.886826</td>\n",
-       "      <td>0.197098</td>\n",
-       "      <td>1.515196</td>\n",
-       "      <td>0.074781</td>\n",
-       "      <td>0.069323</td>\n",
-       "      <td>1.631113</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.885021</td>\n",
+       "      <td>0.569257</td>\n",
+       "      <td>0.885021</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.903197</td>\n",
+       "      <td>0.074112</td>\n",
+       "      <td>0.246287</td>\n",
+       "      <td>1.623875</td>\n",
        "      <td>...</td>\n",
-       "      <td>3.174191</td>\n",
+       "      <td>3.162941</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>1.040001</td>\n",
+       "      <td>1.036748</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>0.665675</td>\n",
+       "      <td>0.663965</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>0.300110</td>\n",
+       "      <td>0.298770</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>0.391150</td>\n",
+       "      <td>0.389675</td>\n",
        "      <td>-0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1079,48 +1079,48 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTCGAACTCTCAT-1</th>\n",
-       "      <td>1.282176</td>\n",
-       "      <td>1.666912</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>0.570906</td>\n",
-       "      <td>0.886826</td>\n",
-       "      <td>0.584060</td>\n",
-       "      <td>2.196676</td>\n",
-       "      <td>0.971964</td>\n",
-       "      <td>1.963666</td>\n",
-       "      <td>1.093264</td>\n",
+       "      <td>1.278757</td>\n",
+       "      <td>0.661862</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>1.443226</td>\n",
+       "      <td>0.885021</td>\n",
+       "      <td>0.581353</td>\n",
+       "      <td>1.469558</td>\n",
+       "      <td>0.262017</td>\n",
+       "      <td>1.399835</td>\n",
+       "      <td>1.087856</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.204336</td>\n",
+       "      <td>1.198738</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>0.391150</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
+       "      <td>0.389675</td>\n",
+       "      <td>0.569257</td>\n",
+       "      <td>0.663965</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>0.300110</td>\n",
+       "      <td>2.367740</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>1.040001</td>\n",
+       "      <td>1.036748</td>\n",
        "      <td>-0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTCTACTGAGGCA-1</th>\n",
        "      <td>-0.000000</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>1.446730</td>\n",
-       "      <td>0.886826</td>\n",
-       "      <td>0.197098</td>\n",
-       "      <td>2.196676</td>\n",
-       "      <td>1.466532</td>\n",
-       "      <td>1.963666</td>\n",
-       "      <td>0.308558</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>1.443226</td>\n",
+       "      <td>0.885021</td>\n",
+       "      <td>0.195977</td>\n",
+       "      <td>2.135997</td>\n",
+       "      <td>1.459548</td>\n",
+       "      <td>1.954926</td>\n",
+       "      <td>1.087856</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.722540</td>\n",
+       "      <td>1.198738</td>\n",
        "      <td>-0.0</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>0.570906</td>\n",
-       "      <td>-0.000000</td>\n",
+       "      <td>0.569257</td>\n",
+       "      <td>0.663965</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>0.300110</td>\n",
+       "      <td>0.827013</td>\n",
        "      <td>-0.000000</td>\n",
        "      <td>-0.000000</td>\n",
        "      <td>-0.0</td>\n",
@@ -1129,72 +1129,72 @@
        "      <th>TTTCTACTTCCTCG-1</th>\n",
        "      <td>-0.000000</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>1.446730</td>\n",
+       "      <td>0.885021</td>\n",
+       "      <td>1.443226</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>0.197098</td>\n",
-       "      <td>10.246682</td>\n",
-       "      <td>10.880681</td>\n",
-       "      <td>14.024753</td>\n",
-       "      <td>0.090592</td>\n",
+       "      <td>0.195977</td>\n",
+       "      <td>10.042270</td>\n",
+       "      <td>10.852223</td>\n",
+       "      <td>15.198236</td>\n",
+       "      <td>0.089844</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.780892</td>\n",
+       "      <td>1.773449</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>0.391150</td>\n",
+       "      <td>0.389675</td>\n",
        "      <td>-0.000000</td>\n",
        "      <td>-0.000000</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>3.334413</td>\n",
+       "      <td>3.325696</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>1.040001</td>\n",
+       "      <td>1.036748</td>\n",
        "      <td>-0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTGCATGAGAGGC-1</th>\n",
-       "      <td>0.497959</td>\n",
-       "      <td>0.318480</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>0.570906</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>0.584060</td>\n",
-       "      <td>6.766600</td>\n",
-       "      <td>5.853269</td>\n",
-       "      <td>9.540698</td>\n",
+       "      <td>0.316389</td>\n",
+       "      <td>0.885021</td>\n",
+       "      <td>0.569257</td>\n",
        "      <td>-0.000000</td>\n",
+       "      <td>0.195977</td>\n",
+       "      <td>6.621109</td>\n",
+       "      <td>7.714603</td>\n",
+       "      <td>12.817177</td>\n",
+       "      <td>0.089844</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.722540</td>\n",
+       "      <td>1.198738</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>0.391150</td>\n",
-       "      <td>0.570906</td>\n",
+       "      <td>0.389675</td>\n",
+       "      <td>0.569257</td>\n",
        "      <td>-0.000000</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>1.532791</td>\n",
+       "      <td>1.527899</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>1.040001</td>\n",
+       "      <td>1.036748</td>\n",
        "      <td>-0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTGCATGCCTCAC-1</th>\n",
-       "      <td>0.497959</td>\n",
-       "      <td>0.094177</td>\n",
-       "      <td>-0.0</td>\n",
+       "      <td>0.496369</td>\n",
+       "      <td>0.093412</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.885021</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>0.886826</td>\n",
-       "      <td>0.197098</td>\n",
-       "      <td>0.472481</td>\n",
-       "      <td>0.971964</td>\n",
-       "      <td>1.963666</td>\n",
-       "      <td>0.090592</td>\n",
+       "      <td>0.454158</td>\n",
+       "      <td>0.966789</td>\n",
+       "      <td>1.399835</td>\n",
+       "      <td>0.306499</td>\n",
        "      <td>...</td>\n",
-       "      <td>7.752409</td>\n",
+       "      <td>7.731358</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>1.040001</td>\n",
+       "      <td>1.036748</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>1.661189</td>\n",
+       "      <td>1.657599</td>\n",
        "      <td>-0.0</td>\n",
-       "      <td>0.830065</td>\n",
+       "      <td>1.527899</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>1.040001</td>\n",
+       "      <td>1.871866</td>\n",
        "      <td>-0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1204,82 +1204,82 @@
       ],
       "text/plain": [
        "source            Acinar cells  Adipocytes  Airway goblet cells  Alpha cells  \\\n",
-       "AAACATACAACCAC-1      0.497959    0.094177                 -0.0     1.446730   \n",
-       "AAACATTGAGCTAC-1      0.497959    0.665563                 -0.0     0.570906   \n",
-       "AAACATTGATCAGC-1      0.497959    0.094177                 -0.0     0.570906   \n",
-       "AAACCGTGCTTCCG-1      1.282176    2.295738                 -0.0     0.570906   \n",
-       "AAACCGTGTATGCG-1      0.497959    0.094177                 -0.0     0.570906   \n",
+       "AAACATACAACCAC-1      0.496369    0.093412            -0.000000     1.443226   \n",
+       "AAACATTGAGCTAC-1      0.496369    1.114303            -0.000000     0.569257   \n",
+       "AAACATTGATCAGC-1      0.496369    0.316389            -0.000000     1.443226   \n",
+       "AAACCGTGCTTCCG-1      1.278757    1.659624            -0.000000     1.443226   \n",
+       "AAACCGTGTATGCG-1     -0.000000   -0.000000             0.885021     0.569257   \n",
        "...                        ...         ...                  ...          ...   \n",
-       "TTTCGAACTCTCAT-1      1.282176    1.666912                 -0.0     0.570906   \n",
-       "TTTCTACTGAGGCA-1     -0.000000   -0.000000                 -0.0     1.446730   \n",
-       "TTTCTACTTCCTCG-1     -0.000000   -0.000000                 -0.0     1.446730   \n",
-       "TTTGCATGAGAGGC-1      0.497959    0.318480                 -0.0     0.570906   \n",
-       "TTTGCATGCCTCAC-1      0.497959    0.094177                 -0.0    -0.000000   \n",
+       "TTTCGAACTCTCAT-1      1.278757    0.661862            -0.000000     1.443226   \n",
+       "TTTCTACTGAGGCA-1     -0.000000   -0.000000            -0.000000     1.443226   \n",
+       "TTTCTACTTCCTCG-1     -0.000000   -0.000000             0.885021     1.443226   \n",
+       "TTTGCATGAGAGGC-1     -0.000000    0.316389             0.885021     0.569257   \n",
+       "TTTGCATGCCTCAC-1      0.496369    0.093412            -0.000000    -0.000000   \n",
        "\n",
        "source            Alveolar macrophages  Astrocytes    B cells  B cells memory  \\\n",
-       "AAACATACAACCAC-1              0.886826    0.584060   2.196676        1.466532   \n",
-       "AAACATTGAGCTAC-1             -0.000000    0.584060   5.719522        8.744392   \n",
-       "AAACATTGATCAGC-1              0.886826    0.584060   0.934609        2.040227   \n",
-       "AAACCGTGCTTCCG-1              0.886826    0.584060   1.515196        0.263925   \n",
-       "AAACCGTGTATGCG-1              0.886826    0.197098   1.515196        0.074781   \n",
+       "AAACATACAACCAC-1              0.885021    0.581353   4.624325        1.459548   \n",
+       "AAACATTGAGCTAC-1             -0.000000    1.119542   5.592250        8.719924   \n",
+       "AAACATTGATCAGC-1              0.885021    1.119542   0.903197        0.966789   \n",
+       "AAACCGTGCTTCCG-1              0.885021    0.195977   0.903197        0.074112   \n",
+       "AAACCGTGTATGCG-1              0.885021   -0.000000   0.903197        0.074112   \n",
        "...                                ...         ...        ...             ...   \n",
-       "TTTCGAACTCTCAT-1              0.886826    0.584060   2.196676        0.971964   \n",
-       "TTTCTACTGAGGCA-1              0.886826    0.197098   2.196676        1.466532   \n",
-       "TTTCTACTTCCTCG-1             -0.000000    0.197098  10.246682       10.880681   \n",
-       "TTTGCATGAGAGGC-1             -0.000000    0.584060   6.766600        5.853269   \n",
-       "TTTGCATGCCTCAC-1              0.886826    0.197098   0.472481        0.971964   \n",
+       "TTTCGAACTCTCAT-1              0.885021    0.581353   1.469558        0.262017   \n",
+       "TTTCTACTGAGGCA-1              0.885021    0.195977   2.135997        1.459548   \n",
+       "TTTCTACTTCCTCG-1             -0.000000    0.195977  10.042270       10.852223   \n",
+       "TTTGCATGAGAGGC-1             -0.000000    0.195977   6.621109        7.714603   \n",
+       "TTTGCATGCCTCAC-1              0.885021   -0.000000   0.454158        0.966789   \n",
        "\n",
        "source            B cells naive  Basophils  ...    T cells  \\\n",
-       "AAACATACAACCAC-1       1.406718  -0.000000  ...  13.554789   \n",
-       "AAACATTGAGCTAC-1      14.024753   0.090592  ...   2.440514   \n",
-       "AAACATTGATCAGC-1       4.029775   1.093264  ...   8.823756   \n",
-       "AAACCGTGCTTCCG-1       0.928107   0.647792  ...   1.204336   \n",
-       "AAACCGTGTATGCG-1       0.069323   1.631113  ...   3.174191   \n",
+       "AAACATACAACCAC-1       1.399835  -0.000000  ...  13.523759   \n",
+       "AAACATTGAGCTAC-1      15.198236   0.306499  ...   2.431181   \n",
+       "AAACATTGATCAGC-1       2.580070   1.087856  ...   8.800720   \n",
+       "AAACCGTGCTTCCG-1       0.923023   0.306499  ...   0.718715   \n",
+       "AAACCGTGTATGCG-1       0.246287   1.623875  ...   3.162941   \n",
        "...                         ...        ...  ...        ...   \n",
-       "TTTCGAACTCTCAT-1       1.963666   1.093264  ...   1.204336   \n",
-       "TTTCTACTGAGGCA-1       1.963666   0.308558  ...   0.722540   \n",
-       "TTTCTACTTCCTCG-1      14.024753   0.090592  ...   1.780892   \n",
-       "TTTGCATGAGAGGC-1       9.540698  -0.000000  ...   0.722540   \n",
-       "TTTGCATGCCTCAC-1       1.963666   0.090592  ...   7.752409   \n",
+       "TTTCGAACTCTCAT-1       1.399835   1.087856  ...   1.198738   \n",
+       "TTTCTACTGAGGCA-1       1.954926   1.087856  ...   1.198738   \n",
+       "TTTCTACTTCCTCG-1      15.198236   0.089844  ...   1.773449   \n",
+       "TTTGCATGAGAGGC-1      12.817177   0.089844  ...   1.198738   \n",
+       "TTTGCATGCCTCAC-1       1.399835   0.306499  ...   7.731358   \n",
        "\n",
        "source            T follicular helper cells  T helper cells  \\\n",
-       "AAACATACAACCAC-1                       -0.0        1.040001   \n",
-       "AAACATTGAGCTAC-1                       -0.0        0.391150   \n",
-       "AAACATTGATCAGC-1                       -0.0        0.391150   \n",
+       "AAACATACAACCAC-1                       -0.0        1.036748   \n",
+       "AAACATTGAGCTAC-1                       -0.0        0.389675   \n",
+       "AAACATTGATCAGC-1                       -0.0        1.036748   \n",
        "AAACCGTGCTTCCG-1                       -0.0       -0.000000   \n",
-       "AAACCGTGTATGCG-1                       -0.0        1.040001   \n",
+       "AAACCGTGTATGCG-1                       -0.0        1.036748   \n",
        "...                                     ...             ...   \n",
-       "TTTCGAACTCTCAT-1                       -0.0        0.391150   \n",
+       "TTTCGAACTCTCAT-1                       -0.0        0.389675   \n",
        "TTTCTACTGAGGCA-1                       -0.0       -0.000000   \n",
-       "TTTCTACTTCCTCG-1                       -0.0        0.391150   \n",
-       "TTTGCATGAGAGGC-1                       -0.0        0.391150   \n",
-       "TTTGCATGCCTCAC-1                       -0.0        1.040001   \n",
+       "TTTCTACTTCCTCG-1                       -0.0        0.389675   \n",
+       "TTTGCATGAGAGGC-1                       -0.0        0.389675   \n",
+       "TTTGCATGCCTCAC-1                       -0.0        1.036748   \n",
        "\n",
        "source            T regulatory cells  Tanycytes  Taste receptor cells  \\\n",
        "AAACATACAACCAC-1           -0.000000  -0.000000                  -0.0   \n",
        "AAACATTGAGCTAC-1           -0.000000  -0.000000                  -0.0   \n",
-       "AAACATTGATCAGC-1           -0.000000  -0.000000                  -0.0   \n",
+       "AAACATTGATCAGC-1           -0.000000   0.663965                  -0.0   \n",
        "AAACCGTGCTTCCG-1           -0.000000  -0.000000                  -0.0   \n",
-       "AAACCGTGTATGCG-1           -0.000000   0.665675                  -0.0   \n",
+       "AAACCGTGTATGCG-1           -0.000000   0.663965                  -0.0   \n",
        "...                              ...        ...                   ...   \n",
-       "TTTCGAACTCTCAT-1           -0.000000  -0.000000                  -0.0   \n",
-       "TTTCTACTGAGGCA-1            0.570906  -0.000000                  -0.0   \n",
+       "TTTCGAACTCTCAT-1            0.569257   0.663965                  -0.0   \n",
+       "TTTCTACTGAGGCA-1            0.569257   0.663965                  -0.0   \n",
        "TTTCTACTTCCTCG-1           -0.000000  -0.000000                  -0.0   \n",
-       "TTTGCATGAGAGGC-1            0.570906  -0.000000                  -0.0   \n",
-       "TTTGCATGCCTCAC-1           -0.000000   1.661189                  -0.0   \n",
+       "TTTGCATGAGAGGC-1            0.569257  -0.000000                  -0.0   \n",
+       "TTTGCATGCCTCAC-1           -0.000000   1.657599                  -0.0   \n",
        "\n",
        "source            Thymocytes  Trophoblast cells  Tuft cells  Urothelial cells  \n",
-       "AAACATACAACCAC-1    2.374529          -0.000000    0.391150              -0.0  \n",
-       "AAACATTGAGCTAC-1    0.300110           0.886826    2.865804              -0.0  \n",
-       "AAACATTGATCAGC-1    1.532791          -0.000000    1.876995              -0.0  \n",
-       "AAACCGTGCTTCCG-1    0.300110           0.886826    1.040001              -0.0  \n",
-       "AAACCGTGTATGCG-1    0.300110          -0.000000    0.391150              -0.0  \n",
+       "AAACATACAACCAC-1    3.325696          -0.000000    1.036748              -0.0  \n",
+       "AAACATTGAGCTAC-1    0.827013           0.885021    2.858756              -0.0  \n",
+       "AAACATTGATCAGC-1    2.367740          -0.000000    1.871866              -0.0  \n",
+       "AAACCGTGCTTCCG-1    0.298770          -0.000000    0.389675              -0.0  \n",
+       "AAACCGTGTATGCG-1    0.298770          -0.000000    0.389675              -0.0  \n",
        "...                      ...                ...         ...               ...  \n",
-       "TTTCGAACTCTCAT-1    0.300110          -0.000000    1.040001              -0.0  \n",
-       "TTTCTACTGAGGCA-1    0.300110          -0.000000   -0.000000              -0.0  \n",
-       "TTTCTACTTCCTCG-1    3.334413          -0.000000    1.040001              -0.0  \n",
-       "TTTGCATGAGAGGC-1    1.532791          -0.000000    1.040001              -0.0  \n",
-       "TTTGCATGCCTCAC-1    0.830065          -0.000000    1.040001              -0.0  \n",
+       "TTTCGAACTCTCAT-1    2.367740          -0.000000    1.036748              -0.0  \n",
+       "TTTCTACTGAGGCA-1    0.827013          -0.000000   -0.000000              -0.0  \n",
+       "TTTCTACTTCCTCG-1    3.325696          -0.000000    1.036748              -0.0  \n",
+       "TTTGCATGAGAGGC-1    1.527899          -0.000000    1.036748              -0.0  \n",
+       "TTTGCATGCCTCAC-1    1.527899          -0.000000    1.871866              -0.0  \n",
        "\n",
        "[2638 rows x 114 columns]"
       ]
@@ -1295,7 +1295,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f8f76bd",
+   "id": "d6e7859a",
    "metadata": {},
    "source": [
     "## Visualization\n",
@@ -1307,7 +1307,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "a4e776b0",
+   "id": "15525515",
    "metadata": {},
    "outputs": [
     {
@@ -1331,7 +1331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "437803a0",
+   "id": "fa8b0db0",
    "metadata": {},
    "source": [
     "`dc.get_acts` returns a new `AnnData` object which holds the obtained activities in its `.X` attribute, allowing us to re-use many `scanpy` functions, for example:"
@@ -1340,12 +1340,12 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "5fd81ce8",
+   "id": "d454794f",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -1362,7 +1362,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "316f6d21",
+   "id": "da7e40e2",
    "metadata": {},
    "source": [
     "The cells highlighted seem to be enriched by NK cell marker genes."
@@ -1370,7 +1370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "940ee060",
+   "id": "80801590",
    "metadata": {},
    "source": [
     "## Annotation\n",
@@ -1381,7 +1381,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "e7d396c5",
+   "id": "bf73958d",
    "metadata": {},
    "outputs": [
     {
@@ -1414,7 +1414,6 @@
        "      <th>Megakaryocytes</th>\n",
        "      <th>Microglia</th>\n",
        "      <th>Monocytes</th>\n",
-       "      <th>Müller cells</th>\n",
        "      <th>NK cells</th>\n",
        "      <th>Neutrophils</th>\n",
        "      <th>Plasma cells</th>\n",
@@ -1425,147 +1424,139 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.633783</td>\n",
-       "      <td>1.302419</td>\n",
-       "      <td>2.164898</td>\n",
-       "      <td>2.849073</td>\n",
-       "      <td>0.822652</td>\n",
-       "      <td>0.762983</td>\n",
-       "      <td>0.618432</td>\n",
-       "      <td>0.910687</td>\n",
-       "      <td>1.861758</td>\n",
-       "      <td>1.453609</td>\n",
-       "      <td>2.208548</td>\n",
-       "      <td>0.609505</td>\n",
-       "      <td>1.298526</td>\n",
-       "      <td>2.255408</td>\n",
-       "      <td>8.471022</td>\n",
+       "      <td>1.674612</td>\n",
+       "      <td>1.206118</td>\n",
+       "      <td>2.126226</td>\n",
+       "      <td>3.089706</td>\n",
+       "      <td>1.282204</td>\n",
+       "      <td>0.656916</td>\n",
+       "      <td>0.558249</td>\n",
+       "      <td>0.824813</td>\n",
+       "      <td>1.969143</td>\n",
+       "      <td>2.502024</td>\n",
+       "      <td>0.733604</td>\n",
+       "      <td>1.098518</td>\n",
+       "      <td>2.097416</td>\n",
+       "      <td>8.560976</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.584988</td>\n",
-       "      <td>0.626344</td>\n",
-       "      <td>1.302631</td>\n",
-       "      <td>11.734138</td>\n",
-       "      <td>0.247835</td>\n",
-       "      <td>5.543829</td>\n",
-       "      <td>1.060080</td>\n",
-       "      <td>3.647915</td>\n",
-       "      <td>7.179501</td>\n",
-       "      <td>2.043805</td>\n",
-       "      <td>0.685154</td>\n",
-       "      <td>4.044910</td>\n",
-       "      <td>1.095181</td>\n",
-       "      <td>2.961859</td>\n",
-       "      <td>1.399721</td>\n",
+       "      <td>1.491951</td>\n",
+       "      <td>0.537041</td>\n",
+       "      <td>1.358659</td>\n",
+       "      <td>12.386445</td>\n",
+       "      <td>0.392409</td>\n",
+       "      <td>5.205774</td>\n",
+       "      <td>0.994419</td>\n",
+       "      <td>3.438604</td>\n",
+       "      <td>6.913848</td>\n",
+       "      <td>0.876738</td>\n",
+       "      <td>4.510537</td>\n",
+       "      <td>1.040281</td>\n",
+       "      <td>2.978631</td>\n",
+       "      <td>1.319387</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>7.219003</td>\n",
-       "      <td>7.447044</td>\n",
-       "      <td>11.987724</td>\n",
-       "      <td>7.595209</td>\n",
-       "      <td>0.347243</td>\n",
-       "      <td>0.705144</td>\n",
-       "      <td>0.477132</td>\n",
-       "      <td>1.232237</td>\n",
-       "      <td>1.828144</td>\n",
-       "      <td>1.943184</td>\n",
-       "      <td>0.636506</td>\n",
-       "      <td>0.580695</td>\n",
-       "      <td>4.343903</td>\n",
-       "      <td>2.175789</td>\n",
-       "      <td>2.399092</td>\n",
+       "      <td>6.927173</td>\n",
+       "      <td>7.202431</td>\n",
+       "      <td>12.459538</td>\n",
+       "      <td>7.860511</td>\n",
+       "      <td>0.424341</td>\n",
+       "      <td>0.726596</td>\n",
+       "      <td>0.453553</td>\n",
+       "      <td>1.126456</td>\n",
+       "      <td>1.839738</td>\n",
+       "      <td>1.016634</td>\n",
+       "      <td>0.781631</td>\n",
+       "      <td>4.108602</td>\n",
+       "      <td>2.022158</td>\n",
+       "      <td>2.391670</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1.397204</td>\n",
-       "      <td>1.042255</td>\n",
-       "      <td>1.669261</td>\n",
-       "      <td>3.576214</td>\n",
-       "      <td>3.984392</td>\n",
-       "      <td>1.288712</td>\n",
-       "      <td>0.808170</td>\n",
-       "      <td>1.192696</td>\n",
-       "      <td>1.840238</td>\n",
-       "      <td>1.554080</td>\n",
-       "      <td>7.810662</td>\n",
-       "      <td>0.617790</td>\n",
-       "      <td>1.303970</td>\n",
-       "      <td>2.897434</td>\n",
-       "      <td>10.295679</td>\n",
+       "      <td>1.528638</td>\n",
+       "      <td>1.003478</td>\n",
+       "      <td>1.688443</td>\n",
+       "      <td>3.882292</td>\n",
+       "      <td>5.015036</td>\n",
+       "      <td>1.082678</td>\n",
+       "      <td>0.747354</td>\n",
+       "      <td>1.013499</td>\n",
+       "      <td>1.990285</td>\n",
+       "      <td>8.912252</td>\n",
+       "      <td>0.738189</td>\n",
+       "      <td>1.171692</td>\n",
+       "      <td>2.690642</td>\n",
+       "      <td>10.510677</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1.022450</td>\n",
-       "      <td>0.752051</td>\n",
-       "      <td>1.299743</td>\n",
-       "      <td>3.324744</td>\n",
-       "      <td>10.616033</td>\n",
-       "      <td>1.878505</td>\n",
-       "      <td>1.139605</td>\n",
-       "      <td>1.717801</td>\n",
-       "      <td>2.244920</td>\n",
-       "      <td>1.200979</td>\n",
-       "      <td>15.050570</td>\n",
-       "      <td>1.110789</td>\n",
-       "      <td>1.445903</td>\n",
-       "      <td>3.134182</td>\n",
-       "      <td>6.561923</td>\n",
+       "      <td>1.058014</td>\n",
+       "      <td>0.690444</td>\n",
+       "      <td>1.214375</td>\n",
+       "      <td>3.754120</td>\n",
+       "      <td>12.026393</td>\n",
+       "      <td>1.798656</td>\n",
+       "      <td>1.006925</td>\n",
+       "      <td>1.552309</td>\n",
+       "      <td>2.541472</td>\n",
+       "      <td>16.074585</td>\n",
+       "      <td>1.316749</td>\n",
+       "      <td>1.296880</td>\n",
+       "      <td>2.992357</td>\n",
+       "      <td>6.312927</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>2.102229</td>\n",
-       "      <td>0.950778</td>\n",
-       "      <td>1.663549</td>\n",
-       "      <td>12.496845</td>\n",
-       "      <td>0.478900</td>\n",
-       "      <td>6.009893</td>\n",
-       "      <td>0.724680</td>\n",
-       "      <td>3.601496</td>\n",
-       "      <td>7.016633</td>\n",
-       "      <td>2.000416</td>\n",
-       "      <td>0.821554</td>\n",
-       "      <td>2.504922</td>\n",
-       "      <td>0.953425</td>\n",
-       "      <td>3.422372</td>\n",
-       "      <td>1.610119</td>\n",
+       "      <td>2.046917</td>\n",
+       "      <td>0.715149</td>\n",
+       "      <td>1.472404</td>\n",
+       "      <td>12.163185</td>\n",
+       "      <td>0.667618</td>\n",
+       "      <td>5.303366</td>\n",
+       "      <td>0.701650</td>\n",
+       "      <td>3.465839</td>\n",
+       "      <td>6.493895</td>\n",
+       "      <td>0.895001</td>\n",
+       "      <td>2.756551</td>\n",
+       "      <td>0.970993</td>\n",
+       "      <td>3.152135</td>\n",
+       "      <td>1.381764</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>1.898036</td>\n",
-       "      <td>1.033753</td>\n",
-       "      <td>2.702941</td>\n",
-       "      <td>13.872005</td>\n",
-       "      <td>0.602729</td>\n",
-       "      <td>3.876826</td>\n",
-       "      <td>0.665819</td>\n",
-       "      <td>2.979970</td>\n",
-       "      <td>4.631779</td>\n",
-       "      <td>1.348061</td>\n",
-       "      <td>0.731783</td>\n",
-       "      <td>2.192446</td>\n",
-       "      <td>2.045006</td>\n",
-       "      <td>2.844497</td>\n",
-       "      <td>1.691185</td>\n",
+       "      <td>1.753542</td>\n",
+       "      <td>0.827478</td>\n",
+       "      <td>2.325148</td>\n",
+       "      <td>13.919129</td>\n",
+       "      <td>0.783363</td>\n",
+       "      <td>3.639544</td>\n",
+       "      <td>0.616080</td>\n",
+       "      <td>2.822582</td>\n",
+       "      <td>4.251034</td>\n",
+       "      <td>0.765348</td>\n",
+       "      <td>2.401234</td>\n",
+       "      <td>1.879233</td>\n",
+       "      <td>2.796829</td>\n",
+       "      <td>1.497420</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>0.873026</td>\n",
-       "      <td>0.304118</td>\n",
-       "      <td>0.603805</td>\n",
-       "      <td>2.546866</td>\n",
-       "      <td>0.748023</td>\n",
-       "      <td>1.413074</td>\n",
-       "      <td>5.883221</td>\n",
-       "      <td>1.449488</td>\n",
-       "      <td>1.338380</td>\n",
-       "      <td>5.847957</td>\n",
-       "      <td>0.546658</td>\n",
-       "      <td>0.726656</td>\n",
-       "      <td>1.239490</td>\n",
-       "      <td>25.584738</td>\n",
-       "      <td>0.745019</td>\n",
+       "      <td>0.801249</td>\n",
+       "      <td>0.592690</td>\n",
+       "      <td>1.186940</td>\n",
+       "      <td>3.111120</td>\n",
+       "      <td>0.701021</td>\n",
+       "      <td>1.905558</td>\n",
+       "      <td>5.641654</td>\n",
+       "      <td>1.086733</td>\n",
+       "      <td>0.595006</td>\n",
+       "      <td>0.993758</td>\n",
+       "      <td>1.055535</td>\n",
+       "      <td>1.049253</td>\n",
+       "      <td>23.893217</td>\n",
+       "      <td>1.044401</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1573,34 +1564,34 @@
       ],
       "text/plain": [
        "    B cells  B cells memory  B cells naive  Dendritic cells  \\\n",
-       "0  1.633783        1.302419       2.164898         2.849073   \n",
-       "1  1.584988        0.626344       1.302631        11.734138   \n",
-       "2  7.219003        7.447044      11.987724         7.595209   \n",
-       "3  1.397204        1.042255       1.669261         3.576214   \n",
-       "4  1.022450        0.752051       1.299743         3.324744   \n",
-       "5  2.102229        0.950778       1.663549        12.496845   \n",
-       "6  1.898036        1.033753       2.702941        13.872005   \n",
-       "7  0.873026        0.304118       0.603805         2.546866   \n",
+       "0  1.674612        1.206118       2.126226         3.089706   \n",
+       "1  1.491951        0.537041       1.358659        12.386445   \n",
+       "2  6.927173        7.202431      12.459538         7.860511   \n",
+       "3  1.528638        1.003478       1.688443         3.882292   \n",
+       "4  1.058014        0.690444       1.214375         3.754120   \n",
+       "5  2.046917        0.715149       1.472404        12.163185   \n",
+       "6  1.753542        0.827478       2.325148        13.919129   \n",
+       "7  0.801249        0.592690       1.186940         3.111120   \n",
        "\n",
        "   Gamma delta T cells  Macrophages  Megakaryocytes  Microglia  Monocytes  \\\n",
-       "0             0.822652     0.762983        0.618432   0.910687   1.861758   \n",
-       "1             0.247835     5.543829        1.060080   3.647915   7.179501   \n",
-       "2             0.347243     0.705144        0.477132   1.232237   1.828144   \n",
-       "3             3.984392     1.288712        0.808170   1.192696   1.840238   \n",
-       "4            10.616033     1.878505        1.139605   1.717801   2.244920   \n",
-       "5             0.478900     6.009893        0.724680   3.601496   7.016633   \n",
-       "6             0.602729     3.876826        0.665819   2.979970   4.631779   \n",
-       "7             0.748023     1.413074        5.883221   1.449488   1.338380   \n",
+       "0             1.282204     0.656916        0.558249   0.824813   1.969143   \n",
+       "1             0.392409     5.205774        0.994419   3.438604   6.913848   \n",
+       "2             0.424341     0.726596        0.453553   1.126456   1.839738   \n",
+       "3             5.015036     1.082678        0.747354   1.013499   1.990285   \n",
+       "4            12.026393     1.798656        1.006925   1.552309   2.541472   \n",
+       "5             0.667618     5.303366        0.701650   3.465839   6.493895   \n",
+       "6             0.783363     3.639544        0.616080   2.822582   4.251034   \n",
+       "7             0.701021     1.905558        5.641654   1.086733   0.595006   \n",
        "\n",
-       "   Müller cells   NK cells  Neutrophils  Plasma cells  Platelets    T cells  \n",
-       "0      1.453609   2.208548     0.609505      1.298526   2.255408   8.471022  \n",
-       "1      2.043805   0.685154     4.044910      1.095181   2.961859   1.399721  \n",
-       "2      1.943184   0.636506     0.580695      4.343903   2.175789   2.399092  \n",
-       "3      1.554080   7.810662     0.617790      1.303970   2.897434  10.295679  \n",
-       "4      1.200979  15.050570     1.110789      1.445903   3.134182   6.561923  \n",
-       "5      2.000416   0.821554     2.504922      0.953425   3.422372   1.610119  \n",
-       "6      1.348061   0.731783     2.192446      2.045006   2.844497   1.691185  \n",
-       "7      5.847957   0.546658     0.726656      1.239490  25.584738   0.745019  "
+       "    NK cells  Neutrophils  Plasma cells  Platelets    T cells  \n",
+       "0   2.502024     0.733604      1.098518   2.097416   8.560976  \n",
+       "1   0.876738     4.510537      1.040281   2.978631   1.319387  \n",
+       "2   1.016634     0.781631      4.108602   2.022158   2.391670  \n",
+       "3   8.912252     0.738189      1.171692   2.690642  10.510677  \n",
+       "4  16.074585     1.316749      1.296880   2.992357   6.312927  \n",
+       "5   0.895001     2.756551      0.970993   3.152135   1.381764  \n",
+       "6   0.765348     2.401234      1.879233   2.796829   1.497420  \n",
+       "7   0.993758     1.055535      1.049253  23.893217   1.044401  "
       ]
      },
      "execution_count": 11,
@@ -1615,7 +1606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a49e48b",
+   "id": "dc5c5590",
    "metadata": {},
    "source": [
     "We can visualize which cell types are more enriched for each cluster using `seaborn`:"
@@ -1624,12 +1615,12 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "6c318b1b",
+   "id": "9438b736",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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h1tbWzkpyyugcm1lfXz97dAYA4MRUVWcmOXN2aF9376uqRya5f5JnJXnt6rovTvLMqnpWd59zqNtWkJfvFEUUAODKuntfkn2bXPWgJF/Z3ZfMD1bVE5O8LckhC7I5yAAA7Cb7k3zhJsdvtLrukIwgAwCwmzw8yUuq6t1J3rc6duMkX57kpw7nBhRkAAB2je5+QVXdPMmdMp2kV0nen+R13X3Z4dyGggwAwK7S3fuTvPpIf94cZAAAmFGQAQBgRkEGAIAZBRkAAGYUZAAAmFGQAQBgRkEGAIAZBRkAAGYUZAAAmFGQAQBgRkEGAIAZBRkAAGYUZAAAmFGQAQBgRkEGAIAZBRkAAGYUZAAAmFGQAQBgRkEGAIAZBRkAAGYUZAAAmFGQAQBgRkEGAIAZBRkAAGb2jg6wcBetra2dPTjDGYP//YM79ZTRCba0/5pXHx1hU3s++rHREbZUe5b7mrl7/+gIW+rLLhsdYUt10kmjI+xIdfVlPn8kSbpHJ9ja/ktHJ9jSRZ8/OsHWLl67+egIW/roVy34d+EYUpAPYn19/ZzRGRZQ0AEATijLHS4CAIABFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABm9o4OAAAAR1NV3SlJd/frquqWSb4lyTu7+y8P5+cVZAAAdo2qelSSeyXZW1UvTvLVSV6a5Kyqul13/9KhbkNBXr6L1tbWzh4dYjPr6+tnj84AAJyYqurMJGfODu3r7n1J7pfktkmunuSDSb64uy+oqscleU0SBXmnW19fP2d0BgCApVmV4X2bXHVpd1+W5NNV9Y/dfcHq+z9TVfsP57adpAcAwG5ycVVdY/X1HTYOVtW1kxxWQTaCDADAbnLX7v5sknT3vBCfnOQBh3MDCjIAALvGRjne5PhHknzkcG7DFAsAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYGbvVfjZi9bW1s4+WkGOkjNGBwAAYGc74oK8vr5+ztEMcjQssLADALDDmGIBAAAzCjIAAMwoyAAAMKMgAwDAjIIMAAAzCjIAAMwoyAAAMKMgAwDAjIIMAAAzV2WraUg+/ZnRCbb0kbvdaHSETf3HA04bHWFLN3v6p0ZH2NIFX3bN0RG21CfV6AhbOv3prxodYWuXXTY6wZb64ktGR9hSXe3k0RG2VHuW+7tw06f9y+gIW7r0X94/OsKWvuDVp46OsLVfO3Y3bQQZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEA2FWq6hZV9Q1VddoBx7/lcH5eQQYAYNeoqocm+fMkP53krVV139nV/+twbmPvsQi2dGtra2clOWV0jp1ufX397NEZAIATU1WdmeTM2aF93b0vyY8nuUN3f7KqzkjynKo6o7uflKQO57ZPyIKc5BTlDgBg51qV4X2bXHVSd39y9T3vraq7ZSrJN8lhFmRTLAAA2E0+WFW33biwKsvfluT6Sb7qcG5AQQYAYDf54SQfnB/o7ku7+4eT3PVwbuBEnWIBAMAu1N3vP8h1f384t2EEGQAAZhRkAACYUZABAGBGQQYAgBkFGQAAZhRkAACYUZABAGBGQQYAgBkFGQAAZhRkAACYUZABAGBGQQYAgBkFGQAAZhRkAACYUZABAGBGQQYAgBkFGQAAZhRkAACYUZABAGBGQQYAgBkFGQAAZhRkAACYUZABAGBGQQYAgBkFGQAAZvaODsAOd81rjE6wpY/e9bOjI2zqP/3PD42OsLXPXjw6wZau+0/7R0fY0v5PXDA6wpb6pJNGR9jSpV/6haMjbGnPjT5/dIQt7fnURaMjbO3i5T6H9Cc/NTrClvZc7WqjI2ztsstGJxjCCDIAAMwoyAAAMKMgAwDAjIIMAAAzCjIAAMwoyAAAMKMgAwDAjIIMAAAzCjIAAMwoyAAAMKMgAwDAjIIMAAAzCjIAAMwoyAAAMKMgAwDAjIIMAAAzCjIAAMwoyAAAMKMgAwDAjIIMAAAzCjIAAMzsHR3gKLtobW3t7MP4vjOOcQ4AAHaoXVWQ19fXzzmc7zvMEg0AwAnIFAsAAJhRkAEAYEZBBgCAGQUZAIBdo6q+uqpOX319alU9uqqeV1W/XFXXPpzbUJABANhNnprk06uvn5Tk2kl+eXXsaYdzA7tqFQsObm1t7awkpxyt21tfXz/7aN0WAMB2VNWZSc6cHdrX3fuS7OnuS1fH1rr79quvX1FVbzyc21aQTyynKLUAwG6wKsP7NrnqrVX1wO5+WpI3VdVad69X1c2TXHI4t22KBQAAu8mPJfn6qvrHJLdM8qqqek+S311dd0hGkAEA2DW6+xNJfqSqrpXkSzP13fd394cO9zYUZAAAdp3uvjDJm47kZ02xAACAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYGbv6ADscCct9zVWffzk0RE21Z+4cHSELfUll4yOsKW62tVGR9jS/ksuHR1hS3tOufroCFu64Kanjo6wpVP/fZnPH0ly9f37R0fY0mU3OH10hC2d9LblPvfuv/ji0RG2tOfqp4yOMMRy2w0AAAygIAMAwIyCDAAAMwoyAADMKMgAADCjIAMAwIyCDAAAMwoyAADMKMgAADCjIAMAwIyCDAAAMwoyAADMKMgAADCjIAMAwMze0QEGuWhtbe3s0SEGOGN0AACApTshC/L6+vo5ozOMcIK+KAAA2BZTLAAAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAOGFU1QMP9T17j0cQdpa1tbWzkpxyqO9bX18/+9inAQD4XFV1ZpIzZ4f2dfe+w/jRRyd52sG+QUFmM6covwDAkq3K8KaFuKrevMWPVZIbHuq2FWQAAHabGyb55iQfO+B4JXnloX5YQQYAYLd5fpLTuvuNB15RVS891A8ryAAA7Crd/aCDXPdfDvXzVrEAAIAZBRkAAGYUZAAAmFGQAQBgRkEGAIAZBRkAAGYUZAAAmFGQAQBgRkEGAIAZBRkAAGYUZAAAmFGQAQBgRkEGAIAZBRkAAGYUZAAAmFGQAQBgRkEGAIAZBRkAAGYUZAAAmFGQAQBgRkEGAIAZBRkAAGYUZAAAmFGQAQBgZu/oABxXF62trZ19GN93xuHeYO9Z7mus6tEJtlA1OsHWeql3WlLXOHV0hC3VJz81OsKW+pJLR0fY0kkXj05wEAv+NV2yvR/6xOgIW1rus1tSJ500OgIHUJBPIOvr6+cczvcdZokGANiVljv8BwAAAyjIAAAwoyADAMCMggwAADMKMgAAzCjIAAAwoyADAMCMggwAADMKMgAAzCjIAAAwoyADAMCMggwAADMKMgAAzCjIAAAwoyADAMCMggwAADMKMgAAzCjIAAAwoyADAMCMggwAADMKMgAAzCjIAAAwoyADAMCMggwAADMKMgAAzCjIAAAws3d0AAAAOFaq6uuS3CnJW7v7RYfzM0aQAQDYNarqtbOvfzzJk5NcK8mjquqsw7kNI8hs5qK1tbWzD/VN6+vrh/weAIBjoarOTHLm7NC+7t6X5OTZsTOT3LO7/72qHp/k1UnOOdRtK8h8jvX19UP+jwMAMNKqDO/b5Ko9VXXdTDMlqrv/ffX9n6qqSw/nthVkAAB2k2snOT9JJemq+oLu/mBVnbY6dkgKMgAAu0Z3n7HFVfuTfOfh3IaCDADArtfdn07yT4fzvVaxAACAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYEZBBgCAGQUZAABmFGQAAJhRkAEAYKa6e3QGSJJU1ZndvW90js3IdmRkOzKyHRnZtm+puRLZjpRsR4cRZJbkzNEBDkK2IyPbkZHtyMi2fUvNlch2pGQ7ChRkAACYUZABAGBGQWZJljwvSbYjI9uRke3IyLZ9S82VyHakZDsKnKQHAAAzRpABAGBGQQYAgBkFGQAAZhRkOISq2lNVp4/OsaEmP1hVv7i6fOOqutPoXAeqqutW1a1H59jMAh/TP6mqe1eV52RYOM9tR2bJ99tmPBkzVFXdpaquufr6B6vqiVV1kwXk+sOqOn2V7e1J3lVVPzs618pvJvmaJPdfXb4wyf83Ls4Vquqlq/vt85K8KcnTquqJo3Mli39MfyvJf0ny7qo6p6puMTrQVhb+B1i2w7QqK3eqqrtufIzOlCRV9bDV72lV1VOq6vVV9U0LyOW57Qgs+X47FAWZ0X4ryaer6jZJ/nuSf07y+2MjJUlu2d0XJPmOJH+Z5MZJfmhooit8dXc/JMlFSdLdH0tytbGRLnft1f32XUme1t13SPKNgzNtWOxj2t1/3d0/kOT2Sd6b5MVV9cqqemBVnTw23eL/AMu2/Vw/luTlSV6Y5NGrz2ePzDTzo6vf029K8vlJHpjknLGRknhuO1JLvt8OSkFmtEt7Wmvwvkme1N1PSnKtwZmS5ORVMfmOJH/e3ZckWcqaiJdU1UlZ5amqz0+yf2yky+2tqhsl+d4kzx8d5gBLfkxTVddL8iNJfizJG5I8KVNhfvHAWBuW/AdYtu17WJI7Jvnn7r57ktsl+fexkS5Xq8/fmqlQvWl2bCTPbUdmyffbQSnIjHZhVf2PJD+Y5C9WxW/4iFmS38k0knfNJC9fTfu4YGiiK/x6kj9NcoOq+qUkr0jyv8ZGutxjMo1G/UN3v66qvjTJuwdn2rDYx7Sqnpvk75JcI8m3d/d9uvuPuvunk5w2Nl2SZf8Blm37Lurui5Kkqq7e3e9M8hWDM204v6pelKkgv7CqrpVlDAB4bjsyS77fDspGIQxVVV+Qae7l67r776rqxknu1t1LmGZxJVW1t7svHZ0jSVZzVL8h08jKS7r7HYMj7UhLeExXJ+b9Qnc/ZmSOg6mqhyZ5ZKY5hPfONBL69O7+z0ODRbYjzPWnmaYuPDzJPZJ8LMnJ3f2tI3Mll/8+3DbJe7r746t3Vr6ou988NtnOsoTntp1OQWaoqvrl7n7koY4dxzyPONj13T385IKqelKSP+ruV47OsqGqfiMHGRnr7ocexzhXskMe01d199eMzrEdS/4DLNvhq6qvT3LtJC/o7osH5rj9wa7v7tcfryxzntuOzJLvt8O1d3QATnj3zDTCMnevTY4dL0uY/3wor0/yC1V180xTLf6ou9cHZxr97x/MTnhMX1RV353kub3AUYuqumGmaTxf2N33qqpbZlpJ5Sljk8m2zTynd/cFqxUFNrxl9fm0JP8xINaGJxzkus400j2C57Yjs+T77bAYQWaIqvqvSR6c5EuT/OPsqmsleeXqjH4OYvVH7ruTfH+SG3f3zQZH4ghV1YWZ5g9eluQzmabOdHcvYlmwqvqrJE9L8vPdfZuq2pvkDd39VYOjyba9PM/v7m+rqn/KVDrnJ791d3/piFywREaQGeUPk/xVkv+d5KzZ8Qu7e9goRlX9+sGuX9jbQl+e5BZJzsi0hNQwVfW8HPzttPscxzhXshMe0+5e8khQkly/u5+9OqE23X1pVV02OtSKbIepu79t9fmmozJsparu0d1/U1Xftdn13f3c450p8dx2pJZ8vx0uBZkhuvsTST6R5P5V9XVJbtbdT6uq61fVTbv7nwZFO3/Qv3vYquqXM60p+Y9Jnp3ksd398aGhkscP/vcPZvGPaZJU1X2SbGzW8NLuXtKSSJ9anSy1sbTgnTP9/i6BbIdpqfN8V74+yd8k+fZNruskQwpyPLcdqSXfb4fFFAuGqqpHJVlL8hXdffOq+sIkf9zddxkcLUlSVdfs7k+NzjFXVT+Z5Dnd/ZHRWTZTVadmmvLxrtFZNrPQx/ScTOvSPmN16P5Jzu/us7b+qeNnVax+I8mtkrw10wYO37Nao3aoHZjtfqNWZKiqvz3I1d3do+b57gie247M0u+3rSjIDFVVb8y0SP3ru/t2q2Nv7u6h+7VX1caJNKd1941r2unvJ7r7wQMz3aK737nVKNDg0Z8kSVV9e6aRg6t1902r6rZJHrOEt9OW+JhuqKo3J7ltd+9fXT4p01zVob8HG6rq6pnmR39Fpnmr70qyp7s/OzRYlp0tmVatyCzbai1ktrB6PL8709Sxy9/lHr0Moue2I7Pk++1QTLFgtIu7u6tq4y3Ia44OtPJrSb45yXlJ0t1vqqq7HvQnjr1HJDkzm5/tPfIs77mzk9wpyUuTpLvfWFVnDMwz92tZ3mM6d51csYrAtQfm2Myruvv2Sd62caCqXp9pp7/RFpttk/m0N6+qTyR5S3d/eESmZNNcyTT1Y2iulT9fZTk/ySJe5KycHc9tR+LsLPd+OygFmdGeXVW/k+Q6VfXjSX40ye8OzpQk6e73VV1ph9OhJ/5095mrz3cfmeMQLu3uTxxwvy3G0h7Tmf+d5A2rt8Ar01zk/zE20uUb+XxRklOr6na5YtWD0zPt+jfMkrPNPCjTsm4bUxvuluTVmYryY7r7D+T6HF/c3d8y8N/fiue2I7Po++1gFGSG6u7HV9U9M22L+RVJfrG7Xzw4VpK8r6q+NklX1dWSPDTJYnarq6pbJbllklM2jvUydh98a1X9lyQnVdXNMt1vS9nQZLGPaXc/s6pemmkeciV5ZHd/cGyqJNOo1I8k+eIk800HLkjycyMCzSw524b9Sf5Td38ouXxd5N9K8tVJXp5kVBFdaq4keWVVfVV3v+XQ33pceW47Mku+3w7KHGTYRFVdP8mTknxjpsLyoiQP6+6PDg2Wy09svFumgvyXmTZWeUV3329kriSpqmsk+fkk37Q69MIk/293XzQu1WTJj2mSVNWt87nzLkeduX8lVfXd3f0no3NsZuHZ3jJf87imYbS3dPetquoNG+ddyDVlyjRVbG+SmyV5T6YpFhtrgo8+L8Vz2xFY8v12KAoyQ9S0McJm//MtaoOEJVr9IblNppO4brMa/fm97t5seSR2gKp6apJbZ5pHu391uLv7R8elukJNG0s8J8lTu3spI1NJFp/tN5PcOMkfrw59d5L3J/nZJM8fNV1qk1z3S/K+kbmq6iYHu767//l4ZYEk2TM6ACem7r5Wd5++yce1llCOq+rcqrrO7PJ1VyVmCT6zWu3g0qo6PcmHM+1IOFxVvXiT++2FAyNdbuGP6Z27e627H9DdD1x9LKIcr9w6yf9N8pSqenVVnbn6f28JlpztIUn+T5LbZlqt5/eTPKS7PzX4XIKHZNrhbyPXuaNzdfc/b3wkuV6S+ya5T5LrLaEce247Mku+3w5FQWa4qvq6qnrg6uvrV9USdnm6dc823+juj2X6Q7IE66snnN/NdKb365O8dmiiK1x/k/vtBuPiXMmSH9NXVdUtR4fYSndf2N2/291fm+S/J3lUkg+s/jB/uWxbZuvufk53/0x3P3z19fC3bVcZXpFpY46/TvLyJeRKkqr6xUyF/XpJrp/kaVX1C2NTJfHcdqSWfL8dlILMUKv5tI/MFWfsXy3J08clutyeqrruxoWq+rws5KTW7n5wd3+8u387yT2TPKC7Hzg618r+qrrxxoXV26aL+MObBT+mmQrBq6rqXVX15qp6S01rIy9CVZ1UVfepqj/NNNfxCZnetXhepnnwsm2e7c5V9bqq+mRVXVxVl1XVBSMzrXJ9b6YX1fdL8r1JXlNVw89hWLl/kjt296O6+1FJ7pzkBwZnSjy3Hakl328HtZQ7kBPXd2a1UUiSdPe/VdW1xkZKMv2RfWVVPSfTL/P3JvmlsZGuUFVflOQmWf0OV9Vdu/vlY1MlmU7GeEVVvWx1+a6Z1m5egiU/pk9N8kNJ3pIr5iAvybszLQn2uO6en4H+nBq/3uqSsz05yfdnmuu7luSHkwwd1V75+Uwl9MNJUlWfn2kk+TlDU03em2l1no2TuK6e5B+HpbmC57Yjs+T77aCcpMdQVfXa7r5TVb2+u29f00Yhrxp9xvIq2y0zbb5RSV7S3W8fHClJUlW/nOT7krw9V6x12b2QnYlWZ1TfOdP99qpe0JbYC35M/6YXvM1vVZ3W3Z8cnWMzC8+23t1rNdsdtKpeuZoOMjLXgatY7EnypvmxUarqzzItd/jiTGXvnpmmg3w4Sbr7oQOzeW47Aku+3w5GQWaoqvp/Mi3pc89MmyX8aJJndvevDw22YFX1rkxzzpa0yxRXwWpVgetkmhZw+eO6oGXezs20bNTHV5evm+QJSziRcOHZXp5p6a3fS/LBJB9I8iPdfZvBuR6X6eTGZ64OfV+SN3f3I8elmlTVAw52fXefe7yycGJTkBmupo1CvinTq8sX9jI2ClmsqvqrJN+z1FEztq+qnrbJ4SUt8/Y5a+NudmyEhWe7SZIPZTq34mcybSH+m939DwMzVabNVe6Y5OsyPe++vLv/dFSmA9W02cXNVxff1d2XDMyyt7svHfXvM445yAxVVb+8GrV48SbH2Nynk7yxql6SK482DnvrkatmQSdZbmVPVV13dQb60k4CWmy22fJkFyV59MgsG7q7q+rPuvsOSRbxDsVcVd0t00mr781U3r+kqh4w8ByL1ya5/aB/e1eqqqt198WjcxyKVSwY7Z6bHLvXcU9xgKq6aVWdMrt8alWdMTDS3HlJHptpu87zZx+LUVU3qKobb3yMznOg1XKCNTrHhqq6eVW9pKreurp864UsbbVh4ySgx1bVxv97vzI404bFZququ9S0Duz/rar3bHyMzpXk1VV1x9EhtvCEJN/U3V/f3XfNtKX4rw7Ms5jniQNV1YtGZ9hKVf3PLY5fO9NOf4tnigVDVNV/TfLgTMsxzc9QvlaSv+/uHxwSbKWq1pN87car3NVbfn/f3Uv9o7IIVXWfTH/gvjDTSTU3SfKO7v7KgZnunOScJP+R6YXFH2RaX3VPkh/u7heMyrZhdYb3zyb5nY2pAVX11u6+1dhkV5idBJQkf7Owk4AWma2q3plpasX5ueKE2vTgLYCr6u2ZpjD8c5JPZSHbOSfJ/ITGgx07jnnen+SJW13f3Vted6wtZSrRZlbl/XXd/fOzY1+QaavpP+nuxwwLd5gW8TYUJ6Q/TPJXmU7MO2t2/MLu/o8xka5k7/wtoO6+eFWSObjHZjpb+a+7+3ZVdfdM65qO9OQkP5dp/uffJLlXd7+6qm6R6SSl4QU5yTW6+7UHDGovbd7jyVkVqdXXS7LUbJ/o7r8aHWITw9+lO4jzq+opmV7IJtMayCPfITspyWlZ5kjytavqu7a6cvBJvvfJtNTiE7v7EVV1s0x/8x/X3b8zMNdhU5AZors/keQTWZWnqrpBprUvT1st2/QvI/Ml+fequk93n5ckVXXfJDtiaZrBLunuj1bVnqra091/u1qWbqS93f2iJKmqx3T3q5Oku9+5oFkWH6mqL8tqAf2aNm34wNhIV6iqhyX58SR/kqkoPL2q9nX3b4xNtsxsVbUxZ/VvVytGPDdXPl/g9UOCXeFGSd7W3RcmSU1rz98y04jyaD+ZaSvsh2Z1AmGS3xyY5wMLHu28dpJvy+blvTNwjnl3X1RV35nkWVX1rCRfk+ThSzoZ9FBMsWCoqvr2TG9fLeYt+VWuL0vyjFWuSvK+TG/HDzv7fDOr9UtP6+7hu3MlSVX9dZLvyPTOwPUzPaZ3HLnua63W2D7w680uj1JVX5pkX5KvTfKxJP+U5Ae7+70jc22oaVe/r+nuT60uL2m98sVlq6q/PcjVPXrN66p6Q5Lb96oArJ5H1kf/LqxyvHlhU4uWPI1hEc9fm6mqR6y+PDnTFvB/l+nFTpKxU1MOlxFkRvt/s7y35NPd/5jkzlV1WqYXkheOzrShqv4w0yjLZZneerz26m2sx41NliS5b6Yz9n8m01uj104yevTlNjVt71tJTq0rtvqtTO9aDNfd70nyjatyt2dJ/7+tVGZzaFdfL2X4fXHZuvvuI//9w1A9Gx3r7v1VNbwPrHK8qapuvIB3ETd8w+gAB7GU38HNzHfE/fVNji3e8F8ITniLeku+qn6wu58+e/W7cTzJYl713rK7L6iqH0jyl0kemakoDy/Is1G80zNtejFcd580OsOhVNV1Mm1DfEaSvbP/35aydN/TkrymqjbeHv2OJE8ZF+dKFputqq6X5FGZ1hvuTDvCPWb0SXpJ3lNVD03yW6vLD06yhNU1ktX0j6p6baYTCJMkPWin0IWcE7OVHxodYCvdvYhlDa8KBZnRPr4apX15kmdU1Ycz9uSka64+b/ZKdynzkU6uqpMzFYEnd/clVbWIbFX1E5lGjD+TZH+uOHHqS0fm2gH+Msmrk7wl0/22KN39xKp6aa7YWOKB3f2Gsakmq2wvS3KXLCxbkmdlem777tXlH0jyR5l21xvpJzON6v1Cpt/PlyQ5c2iiK+z4YnW8dPdbR2fYSlX94kGu7u5+7HELc4TMQWao1VvKF2X6w7bxlvwzRo+wVNVduvvvD3VshNXIzyOTvCnJvZPcOMnTu/s/Dw2WpKrenWk+qBMat2HJcwmTpKoen+Rp3f220Vk2U1UnJblhZoM+S3iLvqrOX23IMT+23t1rozItXW2yUdRmx1i2qvpvmxy+ZpIHJbled592nCNtm4IMm9issCy5xNRCtkOtqhck+a7u/vToLDtJVf1Mkk8meX6uvNrBIt7eraofS/LATAX0aUmeuVqJZriq+ulM0xg+lCvmHy9lTd/HJ1lP8uzVofsl+cruftSgPP+9u3+lqn4jm7wjtoQpPVs89w5bB5mrbrVKysMyleNnJ3lCd394bKpDU5AZoqouzPQEvXGSwcb/iBt/3E4flOtrMq0k8PBcefem05N8Z3ffZkSu5EpnBW9qCfOjq+p2Wc0JjW2wD1tVPSTJLyX5eK74XejuXtTUlKr6ikxF+f5J/j7J73b3wVZsOB6Z/iHJV49+12kzq+e5a+aKaTN7csW82uP+PFdV397dz6uqB2x2fXefezzzzNUVm0d9WZL5akHXSvLK7v6BIcF2gNUaw/8701J9l594PPr5o6Zt3x+R6d3hc5M8qVdbwu8E5iAzRHcv9WzWq2VaFH5vrjwP+YJMoz8jLfU+m/udTJtxLHIu7YI9IsmXL3lqymoawy1WHx/JNMXnEVX1E939/QOjvS/TmuqLs7Tnue5+3urzsCJ8EEvfPGrJnpbpXZRfTXL3TC9ih65wsVr/+7syLV/5Vd39yZF5joQRZIaoqlMynSjy5UnenOSpS5gisKGqbtLdS1g0f0epqleOXPN4p6qq85J8/1KnplTVE5N8e6YXP0/p7tfOrntXd3/FwGxPSfIVSf4iV37XYvg7Ksnl26/fdXXxpd39/IFZzjvY9aNWipirqhtvdnwJc8qXamOue1W9pbu/anXs70ael1JV+zP9Pl6aK0/nGfou8XYYQWaUc5Nckmnx8G9N8pWZ5igNVVW/1t0PT/LkzVaGGPkHpKp+/WDXL2Qaw99W1ZmZlnhb3FzaBbssyRtXG0wsampKTWvOfSzJbbYo8Hc6zpEO9C+rj6utPhajqs5JcsdMmw4lycOq6uu6+6yD/Nix9DWZRtyfmWka1BLX0f2LXDH97pQkN03yrkx/I9jcRatNVt5dVT+V5F+T3GBkoO7eM/LfPxqMIDPEAa909yZ57RJOgKuqO3T3+VX19Ztd390vO96ZNmw1b3DDEt42rap/2uTw4ubSLs0S54TObbYaw9KsTgTqJb2Vu9rl77bdvX91+aQkbxh1wtnq379npjnkt85URp+51NVJksu37f6J7v6J0VmWqqrumOQdSa6T5LGZzpn5le5+zchcO50RZEa5ZOOL7r50Y2OE0Vbl+KQkP97dPzg6z9yBZamqrrmxMcdSdPdNR2fYiZZShA/i1VV1x+5+3eggB6qqWyX5gySft7r8kUzbwi+l9F0nycY7KNcemCPdfVmSFyR5QVVdPVNRfmlVPaa7f2Nktq109+tXBZCtnbH63fxkpvnHqarvyfQuAUfICDJDVNVlueJs7kpyapJPZyHzk6rqhUm+vbsvHpljM6uVNp6S5LTuvnFV3SbTCMuDB0fbeFJ+QXdfWFW/kOT2SR67oI0bOAJV9fYkN0/yz5l+b5e0lNork/z8xmoaVXW3JP9rCXPhq+r7k/xykr/NdJ/dNcn/6O5nDcx09Uzrp98/086N52U6B+RfR2WaO2C1nj2ZnkOu193fPCjS4u20ZUl3CiPIDNHL3/73vUn+fnVSy3y70yWc+PNrSb450x+2dPebququB/2J4+d/dvcfV9XXZcr4+CS/neSrx8biKrrX6AAHcc35UnPd/dLVBkRDreaE7k9y50zzkCvJI7v7gwMznZvkVplWi3h0L3MntvnKH5dmmgbyJ4OyLFpV3SvTOTxfdMA5Kqdn7I60u4KCDJv7t9XHnixwebXuft8B01IuG5XlABs57p3kt7r7z6vq7IF5dpxVsTqtuy8YnWXDxoouVXWDzNZZXYj3VNX/zDTNIkl+MMlmc+GPq+7eX1U/1d3PzurF7AL8UKYX/DdP8tDZc8gi3rnLFOLRyTKnkC3QvyU5P8l9Vp83XJjkZ4Yk2kVMsYAdpqqek+SJSZ6caXTqoUnWBq9FmySpqudnOoP6G5PcIclnMp2AOWyDlZ2gqv4w07KHl2X6Q3ftJE/s7scNDbayWqrsCUm+MMmHk9wkyTu6e/jKAlV13SSPTvJ1mYrey5OcvYQNCVbF/TNJ/ihXfifKqi5bWPIUsqVayk6qu42CDDNV9bxssgXrhoWsE3r9JE/KVEIryYuSPGwJO4lV1TWSfEuSt3T3u6vqRpkWiX/R4GiLVlVv7O7bVtUPZHph8cgk5y9hjm+SVNWbktwjyV939+2q6u5J7t/dZw6OtmhWddm+qnpNpk2Zzuvu262OvbW7bzU22fJU1Vty8L9Xi3j+2KlMsYAre/zq83cl+YIkT19dvn+mecnDrXZbW+S2q6t1cp9bVTeYLfj/zpGZdoiTq+rkJN+R5Mndfclm63APdEl3f7Sq9lTVnu7+26r65ZGBdsKmF1Z1OTILnkK2NN82OsBupiDDzMY6x1X12O6en/j2vKp6+aBYV7I60eZh3f3x1eXrJnlCd//o0GDZ9K34G2cqyMPfil+438n0AuxNSV5eVTfJtL35Uny8qk7LNH3hGVX14Yw/CWjxm15U1UOSPOOA39X7d/dvDg22bO+rqq9N0lV1tUxTyN4xONMi9Wy319Vzxs26+6+r6tTod1eZKRawiap6R5J7d/d7VpdvmuQvu/s/jU2WVNUbNt56PNixEbwVf/QsaV7halWIizKV0B/INEf66SPn0u6ETS82ps4ccGwRv6tLteQpZEtVVT+e5Mwkn9fdX1ZVN0vy2939DYOj7WheYcDmfibTAvrvWV0+I8lSdnLaU1XX3TgJqao+L8v5XV7cW/FLdsCar5tZwrKCSfL93f2U1dfnJpdvozxqy+SdsunFnqqqXo1ErUr9orbDXpolTyFbsIdk2vL9NUmyOv9j6FbTu8FS/qjConT3C1avwm+xOvTO7v7syEwzT0jyytVqFp3ke5P80thIl1viW/FLtrglBLdwv6q6qLufkSRV9f9lAcu9bbLpxa8nee7ITAd4YZJnV9VvZ/pd/clMpZ4DVNUvHuTq7u7HHrcwO89nu/vijXnbVbU3Bzl5j8NjigVsYTUP7ozMXkh29+8PCzRTVbfMNJWhkryku98+OFKSLd+Kf4a3R3e21ZzG85I8NdOmIf/R3Q8fnGm+6cWzlrjpxWpN659I8g25YrrA761Gv5mpqv+2yeFrJnlQpp30TjvOkXaMqvqVJB9P8sNJfjrJg5O8vbt/fmSunU5Bhk1U1R8k+bIkb8wVZ1B3dz90WCh2nQN2v/oco/9/W03f2XCtJH+W5O+T/GIydj3fqtqfK9YWnv8hW8ymFxyZqrpWkodlKsfPznQS8ofHplqu1QuxByX5pkz//78w0wsxBe8qUJBhE6uT9G7pCebwVNWFmUrKxkoCG/ebsnIQVfWAg13f3eceryybWa3ju/G4zh/fxHq+h7SapvW/k9wysykp7rfNrV6QPSLTu0/nJnnSEjZ84cRkDjJs7q2Z1kH+wOggO0F375S5tItyYAFe2va61vG9yp6W5FFJfjXJ3ZM8MAtcjm4Jqupxmdaf35dpc6FPDo60eDYKObaMIMMmqupvk9w2yWuTXH5y3hI2H5hbLYn00dEj3VV1SqYTkL48yZuTPHUpS5TtBDthe92qulU+dyR0EXPyl6qqzu/uO1TVW7r7q1bH/q67//PobEuzmjLz2Uwn9ZoycxhWax8n0yoWSfIHq88/kOTT3f2Y459q9zCCDJs7e3SAA1XVnZOck+Q/kjw205Ph9TMtJfXD3T3y7Phzk1yS5O+SfGumjUEeNjDPTvNrSb4504lw6e43VdVdD/oTx1FVPSrJ3TIV5L/MdKLeK5IoyAd30Wp+6Lur6qeS/GsSy29torv3jM6w02xsFFJVd+nuu8yuOquq/j6JgnwVKMiwie5+2QE7E10jyUmDYz05yc9lWhnib5Lcq7tfXVW3yLSb2MiCfMvZCNlTMo28sw0L3173fkluk+QN3f3Aqrphkt8bnGkneHiSa2TaDe6xmVaeOei8czgC16yqr+vuVySXr8B0zcGZdjwFGTYx35ko02oWX5TktzMt1zTK3u5+UZKsNkN4dZJ09zsPKFYjXLLxRXdfuoA8O83St9f9THfvr6pLq+r0TNuIO9HsELr7dasvP5lp/jEcCw9K8tSqunam6SmfSPKjYyPtfAoybG6JOxPtn339mQOuG30ywW2q6oLV15Xk1NVl8wcPz09m2l73i5K8P9N6uQ856E8cX+tVdZ0kv5vk/EyFz7sEW6iq8w52/dLOZWBn6+7zMz0Hn57p3LJPjM60GzhJDzZRVa/p7q+uqjd09+1WOxO9fuRZwVV1WaZ1XyvJqUk+vXFVklO6++RR2ThxVNUZSU7v7jePzrJUVfXvSd6XaerTa3LAyhXd/bIRuYDDZ1I8bO5lVfVzmUZC75nkj5M8b2Sg7j6pu0/v7mt1997V1xuXleMdrKrOXY3Qbly+blU9dWCkK6mq2298ZJp2tLeqvmz1wpHP9QWZzhe4VaZ3Bu6Z5CPd/TLlGHYGI8iwCTsTcTxtvFNxqGOjVNWrk9w+0xJ+lan4vTnJ9ZL85MbceD5XVV09yf2TPC7JY7r7NwZHAg6DV/+widUJSX+W5M+6+99H52HX21NV193YNWy1o9iSnp/fm+RB3f22JKmqWyb52UwrMzw305xpZlbF+N6ZyvEZSX49030FR511yo++JT0Bw3A1Lb/wqCQ/lWmkrFZzf3/DouscQ09I8sqqek6mEy6/N8kvjY10JbfYKMdJ0t1vr6rbdfd7rFjyuarq3Eyj7H+V5NHd/dbBkdjFrFN+bJhiATNV9TOZNro4s7v/aXXsS5P8VpIXdPevjszH7rUalb1HphdmL+nutw+OdLmq+qNMG9Q8a3Xo+zJtUvNDSV7R3XcclW2JVrvCbWwZblc4jqnVltMb65TfZmOd8u7+9sHRdjQFGWaq6g1J7tndHzng+OcnedFS5oTC8VRVpyZ5cJKvy1TyXpHkN5NclOQa3f3JgfHghFZVr+3uO1XV+UnunuTCJG/t7q8cHG1HM8UCruzkA8txknT3v1eVlSI4IXX3Z6rqN5M8v7vfdcDVyjGMZZ3yY8AIMsxU1eu7+/bbvQ52s6q6T6ZVGK7W3TetqttmWpHBhhewINYpP3oUZJiZbcbxOVfFZhwcB1V1/SQfXdKSgqu3bu+R5KUb04yq6s0jN84BrlBVt860WsrlMwO626opV4EpFjDT3SeNzsCJo6runOScTCfAPTbJH2Q6+W1PVf1wd79gZL6ZS7v7E1asgOVZbSp06yRvS7J/dbhjWcGrREEGGOfJmXZcu3aSv0lyr+5+dVXdItM2xUspyG+tqv+S5KSqulmShyZ55eBMwOTO3X3L0SF2G1tNA4yzt7tf1N1/nOSD3f3qJOnudw7OdaCfTvKVST6bqbhfkOThIwMBl3vVaplIjiIjyADj7J99/ZkDrlvMHOTu/nSSn199AMtybqaS/MFML2I31tt2jsBV4CQ9gEFmJ4VWklOTfHrjqizgpNCqOu9g11vFAsarqn9I8ogkb8nsRXd3//OwULuAEWSAQXbASaFfk+R9maZVvCZTcQeW5V+6+6AvZtk+I8gAbKqqTkpyzyT3z3SW/F8keWZ3v21oMOByq018rpPkeZmmWCSxzNtVpSADcEhVdfVMRflxmTYJ+Y3BkYAkVfW0TQ53d//ocQ+ziyjIAGxpVYzvnakcn5HkvCRP7e5/HZkL4FhSkAHYVFWdm+RWSf4qybO6+62DIwEHqKqbZlqK8YxceSc9J9FeBQoyAJuqqv25Yuv1+R+LjWWkTj/+qYC5qnpTkqfkc1exeNmwULuAggwAsENV1Wu6+6tH59htFGQAgB1qtQ38zZK8KFdexeL1w0LtAtZBBgDYub4qyQ8luUeumGLRq8scISPIAAA7VFW9M8mtu/vi0Vl2kz2jAwAAcMTelGmjEI4iUywAAHauGyZ5Z1W9Lleeg2yZt6tAQQYA2LkeNTrAbmQOMgAAzJiDDACwQ1XVnavqdVX1yaq6uKouq6oLRufa6RRkAICd68lJ7p/k3UlOTfJjq2NcBeYgAwDsYN39D1V1UndfluRpVfXK0Zl2OgUZAGDn+nRVXS3JG6vqV5J8IMk1B2fa8UyxAADYuX4oU5/7qSSfSvIlSb57aKJdwCoWAAAwYwQZAGCHqar7VtVDZpdfU1XvWX3cb2S23UBBBgDYef57kvNml6+e5I5J7pbkv44ItJs4SQ8AYOe5Wne/b3b5Fd390SQfrSon6V1FRpABAHae684vdPdPzS5+/nHOsusoyAAAO89rqurHDzxYVT+R5LUD8uwqVrEAANhhquoGSf4syWeTvH51+A6Z5iJ/R3d/aFC0XUFBBgDYoarqHkm+cnXxbd39NyPz7BYKMgAAzJiDDAAAMwoyAADMKMgAADCjIAMAwMz/D/jhPnI463bVAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 720x720 with 4 Axes>"
       ]
@@ -1647,7 +1638,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "00b067f3",
+   "id": "3d7c2e81",
    "metadata": {},
    "source": [
     "From this plot we see that cluster 7 belongs to Platelets, cluster 4 appear to be NK cells, custers 0 and 3 might be T-cells, cluster 2 should be some sort of B cells and that clusters 6,5 and 1 are Dendritic cells or Monocytes\n",
@@ -1658,7 +1649,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "136d9f7b",
+   "id": "638c8099",
    "metadata": {},
    "outputs": [
     {
@@ -1686,7 +1677,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "454b4e22",
+   "id": "2a57852d",
    "metadata": {},
    "source": [
     "Which we can use to annotate our data-set:"
@@ -1695,14 +1686,14 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "54b29cbe",
+   "id": "cf1d85c6",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/badi/miniconda3/envs/decoupler/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.\n",
+      "/home/badi/miniconda3/envs/decoupler/lib/python3.8/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.\n",
       "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
       "... storing 'cell_type' as categorical\n"
      ]
@@ -1728,7 +1719,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7916b1fe",
+   "id": "e3ce49a8",
    "metadata": {},
    "source": [
     "Compared to the annotation obtained by the `scanpy` tutorial, it is very simmilar."
@@ -1736,7 +1727,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b007725c",
+   "id": "7200a94a",
    "metadata": {},
    "source": [
     "![](https://scanpy-tutorials.readthedocs.io/en/latest/_images/pbmc3k_100_1.png)"
@@ -1744,7 +1735,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae11816d",
+   "id": "0663794b",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\">\n",
diff --git a/docs/source/notebooks/dorothea.ipynb b/docs/source/notebooks/dorothea.ipynb
index aa687ef..74dd8ed 100644
--- a/docs/source/notebooks/dorothea.ipynb
+++ b/docs/source/notebooks/dorothea.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "ab96d2dd",
+   "id": "d657ee44",
    "metadata": {},
    "source": [
     "# Transcription factor activity inference"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "da908a79",
+   "id": "4d2389b5",
    "metadata": {},
    "source": [
     "scRNA-seq yield many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by infering transcription factor (TF) activities from prior knowledge.\n",
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d9a67e15",
+   "id": "86cebce3",
    "metadata": {},
    "source": [
     "## Loading packages\n",
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "4af4a4b3",
+   "id": "fc556afe",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81f1af9b",
+   "id": "b38db994",
    "metadata": {},
    "source": [
     "## Loading the data\n",
@@ -67,7 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "b876c357",
+   "id": "3233832b",
    "metadata": {},
    "outputs": [
     {
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "867a4183",
+   "id": "d19e3b06",
    "metadata": {},
    "source": [
     "We can visualize the different cell types in it:"
@@ -103,12 +103,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "a4b3427c",
+   "id": "af1aac17",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e41e52c0",
+   "id": "19e29567",
    "metadata": {
     "tags": []
    },
@@ -138,7 +138,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "b4aac667",
+   "id": "95aa5212",
    "metadata": {},
    "outputs": [
     {
@@ -280,7 +280,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8aa2349d",
+   "id": "01abde14",
    "metadata": {},
    "source": [
     "## Activity inference with Multivariate Linear Model\n",
@@ -295,22 +295,22 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "038518b9",
+   "id": "39951f88",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "58 features of mat are empty in 2635 samples, they will be ignored.\n",
-      "Running mlm on mat with 2638 samples and 13656 targets for 281 sources.\n"
+      "1 features of mat are empty, they will be removed.\n",
+      "Running mlm on mat with 2638 samples and 13713 targets for 281 sources.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00,  2.11s/it]\n"
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00,  2.78s/it]\n"
      ]
     }
    ],
@@ -320,7 +320,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20aceaa6",
+   "id": "ac298392",
    "metadata": {},
    "source": [
     "The obtained scores (t-values)(`mlm_estimate`) and p-values (`mlm_pvals`) are stored in the `.obsm` key:"
@@ -329,7 +329,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "ea1ddb5a",
+   "id": "20f2259d",
    "metadata": {},
    "outputs": [
     {
@@ -379,123 +379,123 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AAACATACAACCAC-1</th>\n",
-       "      <td>1.378150</td>\n",
-       "      <td>2.264298</td>\n",
-       "      <td>-0.873331</td>\n",
-       "      <td>-0.393862</td>\n",
-       "      <td>0.138301</td>\n",
-       "      <td>-0.672725</td>\n",
-       "      <td>-0.517485</td>\n",
-       "      <td>-0.248989</td>\n",
-       "      <td>3.503303</td>\n",
-       "      <td>2.835994</td>\n",
+       "      <td>1.378355</td>\n",
+       "      <td>2.274427</td>\n",
+       "      <td>-0.874242</td>\n",
+       "      <td>-0.387186</td>\n",
+       "      <td>0.148249</td>\n",
+       "      <td>-0.675334</td>\n",
+       "      <td>-0.517506</td>\n",
+       "      <td>-0.244091</td>\n",
+       "      <td>3.509188</td>\n",
+       "      <td>2.846779</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.184589</td>\n",
-       "      <td>-0.368378</td>\n",
-       "      <td>-2.079983</td>\n",
-       "      <td>-0.819110</td>\n",
-       "      <td>-1.070388</td>\n",
-       "      <td>-0.513945</td>\n",
-       "      <td>-0.168149</td>\n",
-       "      <td>1.016380</td>\n",
-       "      <td>-0.426697</td>\n",
-       "      <td>0.321187</td>\n",
+       "      <td>0.186219</td>\n",
+       "      <td>-0.365014</td>\n",
+       "      <td>-2.079993</td>\n",
+       "      <td>-0.819782</td>\n",
+       "      <td>-1.074077</td>\n",
+       "      <td>-0.510540</td>\n",
+       "      <td>-0.165862</td>\n",
+       "      <td>1.016863</td>\n",
+       "      <td>-0.426996</td>\n",
+       "      <td>0.328519</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACATTGAGCTAC-1</th>\n",
-       "      <td>-0.771588</td>\n",
-       "      <td>1.791014</td>\n",
-       "      <td>-0.843765</td>\n",
-       "      <td>-2.423640</td>\n",
-       "      <td>1.121624</td>\n",
-       "      <td>-1.710007</td>\n",
-       "      <td>-0.098785</td>\n",
-       "      <td>-0.437353</td>\n",
-       "      <td>1.153923</td>\n",
-       "      <td>0.325599</td>\n",
+       "      <td>-0.777049</td>\n",
+       "      <td>1.804605</td>\n",
+       "      <td>-0.846151</td>\n",
+       "      <td>-2.420458</td>\n",
+       "      <td>1.131415</td>\n",
+       "      <td>-1.712532</td>\n",
+       "      <td>-0.096642</td>\n",
+       "      <td>-0.433711</td>\n",
+       "      <td>1.155111</td>\n",
+       "      <td>0.332896</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.975830</td>\n",
-       "      <td>-1.015501</td>\n",
-       "      <td>-1.298656</td>\n",
-       "      <td>-0.695448</td>\n",
-       "      <td>-0.717212</td>\n",
-       "      <td>-0.854894</td>\n",
-       "      <td>-0.634031</td>\n",
-       "      <td>-0.014624</td>\n",
-       "      <td>0.189120</td>\n",
-       "      <td>0.082494</td>\n",
+       "      <td>0.976601</td>\n",
+       "      <td>-1.012220</td>\n",
+       "      <td>-1.297776</td>\n",
+       "      <td>-0.695343</td>\n",
+       "      <td>-0.721801</td>\n",
+       "      <td>-0.848833</td>\n",
+       "      <td>-0.629448</td>\n",
+       "      <td>-0.015790</td>\n",
+       "      <td>0.189198</td>\n",
+       "      <td>0.088182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACATTGATCAGC-1</th>\n",
-       "      <td>1.692741</td>\n",
-       "      <td>2.992088</td>\n",
-       "      <td>-0.532625</td>\n",
-       "      <td>-1.705129</td>\n",
-       "      <td>0.845746</td>\n",
-       "      <td>-1.167037</td>\n",
-       "      <td>-0.788174</td>\n",
-       "      <td>1.828790</td>\n",
-       "      <td>3.575234</td>\n",
-       "      <td>2.071524</td>\n",
+       "      <td>1.697553</td>\n",
+       "      <td>3.003373</td>\n",
+       "      <td>-0.532198</td>\n",
+       "      <td>-1.707768</td>\n",
+       "      <td>0.860097</td>\n",
+       "      <td>-1.167976</td>\n",
+       "      <td>-0.792977</td>\n",
+       "      <td>1.835434</td>\n",
+       "      <td>3.586673</td>\n",
+       "      <td>2.078914</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.001384</td>\n",
-       "      <td>-0.424550</td>\n",
-       "      <td>-2.065619</td>\n",
-       "      <td>-0.535101</td>\n",
-       "      <td>-0.751276</td>\n",
-       "      <td>0.057936</td>\n",
-       "      <td>0.295797</td>\n",
-       "      <td>1.095647</td>\n",
-       "      <td>1.089456</td>\n",
-       "      <td>1.264060</td>\n",
+       "      <td>0.011449</td>\n",
+       "      <td>-0.423068</td>\n",
+       "      <td>-2.063008</td>\n",
+       "      <td>-0.533873</td>\n",
+       "      <td>-0.761211</td>\n",
+       "      <td>0.064575</td>\n",
+       "      <td>0.296225</td>\n",
+       "      <td>1.087730</td>\n",
+       "      <td>1.092369</td>\n",
+       "      <td>1.271436</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACCGTGCTTCCG-1</th>\n",
-       "      <td>0.950014</td>\n",
-       "      <td>2.362606</td>\n",
-       "      <td>-0.064996</td>\n",
-       "      <td>-2.908076</td>\n",
-       "      <td>0.925004</td>\n",
-       "      <td>-1.022275</td>\n",
-       "      <td>-0.007307</td>\n",
-       "      <td>0.157358</td>\n",
-       "      <td>2.789861</td>\n",
-       "      <td>0.088665</td>\n",
+       "      <td>0.955585</td>\n",
+       "      <td>2.368041</td>\n",
+       "      <td>-0.058802</td>\n",
+       "      <td>-2.913917</td>\n",
+       "      <td>0.934386</td>\n",
+       "      <td>-1.021379</td>\n",
+       "      <td>-0.009797</td>\n",
+       "      <td>0.163788</td>\n",
+       "      <td>2.798030</td>\n",
+       "      <td>0.091727</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.729626</td>\n",
-       "      <td>-0.744912</td>\n",
-       "      <td>-1.341288</td>\n",
-       "      <td>-1.054425</td>\n",
-       "      <td>-1.698280</td>\n",
-       "      <td>-0.830635</td>\n",
-       "      <td>-0.641135</td>\n",
-       "      <td>-0.139098</td>\n",
-       "      <td>-0.632303</td>\n",
-       "      <td>-0.595420</td>\n",
+       "      <td>0.738507</td>\n",
+       "      <td>-0.743231</td>\n",
+       "      <td>-1.333049</td>\n",
+       "      <td>-1.056086</td>\n",
+       "      <td>-1.692437</td>\n",
+       "      <td>-0.824906</td>\n",
+       "      <td>-0.641627</td>\n",
+       "      <td>-0.136234</td>\n",
+       "      <td>-0.631287</td>\n",
+       "      <td>-0.588915</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACCGTGTATGCG-1</th>\n",
-       "      <td>1.064510</td>\n",
-       "      <td>1.690478</td>\n",
-       "      <td>-0.638209</td>\n",
-       "      <td>-1.559541</td>\n",
-       "      <td>0.021607</td>\n",
-       "      <td>-1.932919</td>\n",
-       "      <td>-0.375869</td>\n",
-       "      <td>-1.908948</td>\n",
-       "      <td>-2.098662</td>\n",
-       "      <td>2.609694</td>\n",
+       "      <td>1.067031</td>\n",
+       "      <td>1.692220</td>\n",
+       "      <td>-0.635391</td>\n",
+       "      <td>-1.558621</td>\n",
+       "      <td>0.028015</td>\n",
+       "      <td>-1.936304</td>\n",
+       "      <td>-0.378153</td>\n",
+       "      <td>-1.907514</td>\n",
+       "      <td>-2.097300</td>\n",
+       "      <td>2.618122</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.399450</td>\n",
-       "      <td>-0.787965</td>\n",
-       "      <td>-1.913958</td>\n",
-       "      <td>0.275981</td>\n",
-       "      <td>-0.631440</td>\n",
-       "      <td>-0.596082</td>\n",
-       "      <td>0.775437</td>\n",
-       "      <td>0.925863</td>\n",
-       "      <td>-0.350852</td>\n",
-       "      <td>0.218543</td>\n",
+       "      <td>1.405655</td>\n",
+       "      <td>-0.784626</td>\n",
+       "      <td>-1.912015</td>\n",
+       "      <td>0.277506</td>\n",
+       "      <td>-0.626883</td>\n",
+       "      <td>-0.592061</td>\n",
+       "      <td>0.778218</td>\n",
+       "      <td>0.930766</td>\n",
+       "      <td>-0.350260</td>\n",
+       "      <td>0.224808</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -523,123 +523,123 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTCGAACTCTCAT-1</th>\n",
-       "      <td>0.318669</td>\n",
-       "      <td>3.333388</td>\n",
-       "      <td>-0.411639</td>\n",
-       "      <td>-1.804543</td>\n",
-       "      <td>-0.024563</td>\n",
-       "      <td>-1.427912</td>\n",
-       "      <td>-0.363295</td>\n",
-       "      <td>-0.074911</td>\n",
-       "      <td>1.548207</td>\n",
-       "      <td>-1.499464</td>\n",
+       "      <td>0.318478</td>\n",
+       "      <td>3.343055</td>\n",
+       "      <td>-0.411779</td>\n",
+       "      <td>-1.802754</td>\n",
+       "      <td>-0.014591</td>\n",
+       "      <td>-1.429359</td>\n",
+       "      <td>-0.362832</td>\n",
+       "      <td>-0.070588</td>\n",
+       "      <td>1.551657</td>\n",
+       "      <td>-1.498392</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.924315</td>\n",
-       "      <td>0.169201</td>\n",
-       "      <td>-1.506775</td>\n",
-       "      <td>-1.248257</td>\n",
-       "      <td>-0.712768</td>\n",
-       "      <td>-0.823545</td>\n",
-       "      <td>-0.452053</td>\n",
-       "      <td>-0.257264</td>\n",
-       "      <td>0.458856</td>\n",
-       "      <td>-0.125243</td>\n",
+       "      <td>0.928078</td>\n",
+       "      <td>0.173974</td>\n",
+       "      <td>-1.504168</td>\n",
+       "      <td>-1.249793</td>\n",
+       "      <td>-0.711441</td>\n",
+       "      <td>-0.818099</td>\n",
+       "      <td>-0.448721</td>\n",
+       "      <td>-0.257337</td>\n",
+       "      <td>0.460690</td>\n",
+       "      <td>-0.117471</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTCTACTGAGGCA-1</th>\n",
-       "      <td>0.030769</td>\n",
-       "      <td>3.491299</td>\n",
-       "      <td>-0.003791</td>\n",
-       "      <td>-1.533298</td>\n",
-       "      <td>1.548915</td>\n",
-       "      <td>-1.666713</td>\n",
-       "      <td>-0.371389</td>\n",
-       "      <td>0.731881</td>\n",
-       "      <td>2.436971</td>\n",
-       "      <td>-0.304424</td>\n",
+       "      <td>0.033183</td>\n",
+       "      <td>3.501705</td>\n",
+       "      <td>-0.000555</td>\n",
+       "      <td>-1.534433</td>\n",
+       "      <td>1.561845</td>\n",
+       "      <td>-1.666364</td>\n",
+       "      <td>-0.373773</td>\n",
+       "      <td>0.738644</td>\n",
+       "      <td>2.446522</td>\n",
+       "      <td>-0.298816</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.801170</td>\n",
-       "      <td>-0.535308</td>\n",
-       "      <td>-1.734625</td>\n",
-       "      <td>-0.584285</td>\n",
-       "      <td>-1.229417</td>\n",
-       "      <td>-1.455677</td>\n",
-       "      <td>0.495567</td>\n",
-       "      <td>1.191070</td>\n",
-       "      <td>-0.316076</td>\n",
-       "      <td>-0.160704</td>\n",
+       "      <td>0.809919</td>\n",
+       "      <td>-0.533287</td>\n",
+       "      <td>-1.729188</td>\n",
+       "      <td>-0.584088</td>\n",
+       "      <td>-1.228989</td>\n",
+       "      <td>-1.447585</td>\n",
+       "      <td>0.498228</td>\n",
+       "      <td>1.190310</td>\n",
+       "      <td>-0.315665</td>\n",
+       "      <td>-0.154943</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTCTACTTCCTCG-1</th>\n",
-       "      <td>0.832451</td>\n",
-       "      <td>1.216455</td>\n",
-       "      <td>-0.077293</td>\n",
-       "      <td>-2.480380</td>\n",
-       "      <td>1.021595</td>\n",
-       "      <td>-1.059853</td>\n",
-       "      <td>-0.544001</td>\n",
-       "      <td>0.110235</td>\n",
-       "      <td>4.672344</td>\n",
-       "      <td>0.357117</td>\n",
+       "      <td>0.830120</td>\n",
+       "      <td>1.227889</td>\n",
+       "      <td>-0.081503</td>\n",
+       "      <td>-2.478240</td>\n",
+       "      <td>1.034375</td>\n",
+       "      <td>-1.065700</td>\n",
+       "      <td>-0.542246</td>\n",
+       "      <td>0.113498</td>\n",
+       "      <td>4.678612</td>\n",
+       "      <td>0.359061</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.063307</td>\n",
-       "      <td>-0.711507</td>\n",
-       "      <td>-0.494090</td>\n",
-       "      <td>-0.123923</td>\n",
-       "      <td>0.307083</td>\n",
-       "      <td>-0.587551</td>\n",
-       "      <td>-0.336608</td>\n",
-       "      <td>-0.489183</td>\n",
-       "      <td>-0.105758</td>\n",
-       "      <td>0.136212</td>\n",
+       "      <td>0.061982</td>\n",
+       "      <td>-0.709467</td>\n",
+       "      <td>-0.495299</td>\n",
+       "      <td>-0.122368</td>\n",
+       "      <td>0.295769</td>\n",
+       "      <td>-0.587837</td>\n",
+       "      <td>-0.334145</td>\n",
+       "      <td>-0.498106</td>\n",
+       "      <td>-0.106478</td>\n",
+       "      <td>0.140377</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTGCATGAGAGGC-1</th>\n",
-       "      <td>1.639109</td>\n",
-       "      <td>-1.020677</td>\n",
-       "      <td>-0.213234</td>\n",
-       "      <td>-1.343528</td>\n",
-       "      <td>-1.097390</td>\n",
-       "      <td>-1.058503</td>\n",
-       "      <td>-0.473822</td>\n",
-       "      <td>-1.477212</td>\n",
-       "      <td>3.461018</td>\n",
-       "      <td>-0.166352</td>\n",
+       "      <td>1.640406</td>\n",
+       "      <td>-1.019982</td>\n",
+       "      <td>-0.213989</td>\n",
+       "      <td>-1.342614</td>\n",
+       "      <td>-1.092845</td>\n",
+       "      <td>-1.062122</td>\n",
+       "      <td>-0.474243</td>\n",
+       "      <td>-1.477003</td>\n",
+       "      <td>3.468400</td>\n",
+       "      <td>-0.166772</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.090235</td>\n",
-       "      <td>-0.554029</td>\n",
-       "      <td>-1.828934</td>\n",
-       "      <td>-0.717332</td>\n",
-       "      <td>-1.356414</td>\n",
-       "      <td>0.781463</td>\n",
-       "      <td>-0.567849</td>\n",
-       "      <td>1.205468</td>\n",
-       "      <td>-0.513302</td>\n",
-       "      <td>-0.485558</td>\n",
+       "      <td>0.090998</td>\n",
+       "      <td>-0.551890</td>\n",
+       "      <td>-1.831205</td>\n",
+       "      <td>-0.717355</td>\n",
+       "      <td>-1.362649</td>\n",
+       "      <td>0.784528</td>\n",
+       "      <td>-0.567212</td>\n",
+       "      <td>1.206249</td>\n",
+       "      <td>-0.513655</td>\n",
+       "      <td>-0.483068</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTGCATGCCTCAC-1</th>\n",
-       "      <td>0.603507</td>\n",
-       "      <td>1.357398</td>\n",
-       "      <td>-0.001079</td>\n",
-       "      <td>-0.808254</td>\n",
-       "      <td>0.574953</td>\n",
-       "      <td>-0.918306</td>\n",
-       "      <td>0.057768</td>\n",
-       "      <td>-0.138383</td>\n",
-       "      <td>3.882593</td>\n",
-       "      <td>0.035894</td>\n",
+       "      <td>0.609625</td>\n",
+       "      <td>1.360696</td>\n",
+       "      <td>0.000145</td>\n",
+       "      <td>-0.807661</td>\n",
+       "      <td>0.589305</td>\n",
+       "      <td>-0.917798</td>\n",
+       "      <td>0.055635</td>\n",
+       "      <td>-0.133276</td>\n",
+       "      <td>3.894962</td>\n",
+       "      <td>0.038308</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.123599</td>\n",
-       "      <td>-0.818726</td>\n",
-       "      <td>-1.486402</td>\n",
-       "      <td>-0.168509</td>\n",
-       "      <td>-0.349769</td>\n",
-       "      <td>-0.609659</td>\n",
-       "      <td>-0.524839</td>\n",
-       "      <td>0.367849</td>\n",
-       "      <td>-0.639759</td>\n",
-       "      <td>-1.074101</td>\n",
+       "      <td>0.133564</td>\n",
+       "      <td>-0.816513</td>\n",
+       "      <td>-1.479075</td>\n",
+       "      <td>-0.166308</td>\n",
+       "      <td>-0.351758</td>\n",
+       "      <td>-0.602639</td>\n",
+       "      <td>-0.524562</td>\n",
+       "      <td>0.361360</td>\n",
+       "      <td>-0.639278</td>\n",
+       "      <td>-1.067313</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -648,56 +648,56 @@
       ],
       "text/plain": [
        "                       AHR        AR     ARID2    ARID3A      ARNT     ARNTL  \\\n",
-       "AAACATACAACCAC-1  1.378150  2.264298 -0.873331 -0.393862  0.138301 -0.672725   \n",
-       "AAACATTGAGCTAC-1 -0.771588  1.791014 -0.843765 -2.423640  1.121624 -1.710007   \n",
-       "AAACATTGATCAGC-1  1.692741  2.992088 -0.532625 -1.705129  0.845746 -1.167037   \n",
-       "AAACCGTGCTTCCG-1  0.950014  2.362606 -0.064996 -2.908076  0.925004 -1.022275   \n",
-       "AAACCGTGTATGCG-1  1.064510  1.690478 -0.638209 -1.559541  0.021607 -1.932919   \n",
+       "AAACATACAACCAC-1  1.378355  2.274427 -0.874242 -0.387186  0.148249 -0.675334   \n",
+       "AAACATTGAGCTAC-1 -0.777049  1.804605 -0.846151 -2.420458  1.131415 -1.712532   \n",
+       "AAACATTGATCAGC-1  1.697553  3.003373 -0.532198 -1.707768  0.860097 -1.167976   \n",
+       "AAACCGTGCTTCCG-1  0.955585  2.368041 -0.058802 -2.913917  0.934386 -1.021379   \n",
+       "AAACCGTGTATGCG-1  1.067031  1.692220 -0.635391 -1.558621  0.028015 -1.936304   \n",
        "...                    ...       ...       ...       ...       ...       ...   \n",
-       "TTTCGAACTCTCAT-1  0.318669  3.333388 -0.411639 -1.804543 -0.024563 -1.427912   \n",
-       "TTTCTACTGAGGCA-1  0.030769  3.491299 -0.003791 -1.533298  1.548915 -1.666713   \n",
-       "TTTCTACTTCCTCG-1  0.832451  1.216455 -0.077293 -2.480380  1.021595 -1.059853   \n",
-       "TTTGCATGAGAGGC-1  1.639109 -1.020677 -0.213234 -1.343528 -1.097390 -1.058503   \n",
-       "TTTGCATGCCTCAC-1  0.603507  1.357398 -0.001079 -0.808254  0.574953 -0.918306   \n",
+       "TTTCGAACTCTCAT-1  0.318478  3.343055 -0.411779 -1.802754 -0.014591 -1.429359   \n",
+       "TTTCTACTGAGGCA-1  0.033183  3.501705 -0.000555 -1.534433  1.561845 -1.666364   \n",
+       "TTTCTACTTCCTCG-1  0.830120  1.227889 -0.081503 -2.478240  1.034375 -1.065700   \n",
+       "TTTGCATGAGAGGC-1  1.640406 -1.019982 -0.213989 -1.342614 -1.092845 -1.062122   \n",
+       "TTTGCATGCCTCAC-1  0.609625  1.360696  0.000145 -0.807661  0.589305 -0.917798   \n",
        "\n",
        "                     ASCL1      ATF1      ATF2      ATF3  ...    ZNF217  \\\n",
-       "AAACATACAACCAC-1 -0.517485 -0.248989  3.503303  2.835994  ...  0.184589   \n",
-       "AAACATTGAGCTAC-1 -0.098785 -0.437353  1.153923  0.325599  ...  0.975830   \n",
-       "AAACATTGATCAGC-1 -0.788174  1.828790  3.575234  2.071524  ...  0.001384   \n",
-       "AAACCGTGCTTCCG-1 -0.007307  0.157358  2.789861  0.088665  ...  0.729626   \n",
-       "AAACCGTGTATGCG-1 -0.375869 -1.908948 -2.098662  2.609694  ...  1.399450   \n",
+       "AAACATACAACCAC-1 -0.517506 -0.244091  3.509188  2.846779  ...  0.186219   \n",
+       "AAACATTGAGCTAC-1 -0.096642 -0.433711  1.155111  0.332896  ...  0.976601   \n",
+       "AAACATTGATCAGC-1 -0.792977  1.835434  3.586673  2.078914  ...  0.011449   \n",
+       "AAACCGTGCTTCCG-1 -0.009797  0.163788  2.798030  0.091727  ...  0.738507   \n",
+       "AAACCGTGTATGCG-1 -0.378153 -1.907514 -2.097300  2.618122  ...  1.405655   \n",
        "...                    ...       ...       ...       ...  ...       ...   \n",
-       "TTTCGAACTCTCAT-1 -0.363295 -0.074911  1.548207 -1.499464  ...  0.924315   \n",
-       "TTTCTACTGAGGCA-1 -0.371389  0.731881  2.436971 -0.304424  ...  0.801170   \n",
-       "TTTCTACTTCCTCG-1 -0.544001  0.110235  4.672344  0.357117  ...  0.063307   \n",
-       "TTTGCATGAGAGGC-1 -0.473822 -1.477212  3.461018 -0.166352  ...  0.090235   \n",
-       "TTTGCATGCCTCAC-1  0.057768 -0.138383  3.882593  0.035894  ...  0.123599   \n",
+       "TTTCGAACTCTCAT-1 -0.362832 -0.070588  1.551657 -1.498392  ...  0.928078   \n",
+       "TTTCTACTGAGGCA-1 -0.373773  0.738644  2.446522 -0.298816  ...  0.809919   \n",
+       "TTTCTACTTCCTCG-1 -0.542246  0.113498  4.678612  0.359061  ...  0.061982   \n",
+       "TTTGCATGAGAGGC-1 -0.474243 -1.477003  3.468400 -0.166772  ...  0.090998   \n",
+       "TTTGCATGCCTCAC-1  0.055635 -0.133276  3.894962  0.038308  ...  0.133564   \n",
        "\n",
        "                     ZNF24    ZNF263    ZNF274    ZNF384    ZNF584    ZNF592  \\\n",
-       "AAACATACAACCAC-1 -0.368378 -2.079983 -0.819110 -1.070388 -0.513945 -0.168149   \n",
-       "AAACATTGAGCTAC-1 -1.015501 -1.298656 -0.695448 -0.717212 -0.854894 -0.634031   \n",
-       "AAACATTGATCAGC-1 -0.424550 -2.065619 -0.535101 -0.751276  0.057936  0.295797   \n",
-       "AAACCGTGCTTCCG-1 -0.744912 -1.341288 -1.054425 -1.698280 -0.830635 -0.641135   \n",
-       "AAACCGTGTATGCG-1 -0.787965 -1.913958  0.275981 -0.631440 -0.596082  0.775437   \n",
+       "AAACATACAACCAC-1 -0.365014 -2.079993 -0.819782 -1.074077 -0.510540 -0.165862   \n",
+       "AAACATTGAGCTAC-1 -1.012220 -1.297776 -0.695343 -0.721801 -0.848833 -0.629448   \n",
+       "AAACATTGATCAGC-1 -0.423068 -2.063008 -0.533873 -0.761211  0.064575  0.296225   \n",
+       "AAACCGTGCTTCCG-1 -0.743231 -1.333049 -1.056086 -1.692437 -0.824906 -0.641627   \n",
+       "AAACCGTGTATGCG-1 -0.784626 -1.912015  0.277506 -0.626883 -0.592061  0.778218   \n",
        "...                    ...       ...       ...       ...       ...       ...   \n",
-       "TTTCGAACTCTCAT-1  0.169201 -1.506775 -1.248257 -0.712768 -0.823545 -0.452053   \n",
-       "TTTCTACTGAGGCA-1 -0.535308 -1.734625 -0.584285 -1.229417 -1.455677  0.495567   \n",
-       "TTTCTACTTCCTCG-1 -0.711507 -0.494090 -0.123923  0.307083 -0.587551 -0.336608   \n",
-       "TTTGCATGAGAGGC-1 -0.554029 -1.828934 -0.717332 -1.356414  0.781463 -0.567849   \n",
-       "TTTGCATGCCTCAC-1 -0.818726 -1.486402 -0.168509 -0.349769 -0.609659 -0.524839   \n",
+       "TTTCGAACTCTCAT-1  0.173974 -1.504168 -1.249793 -0.711441 -0.818099 -0.448721   \n",
+       "TTTCTACTGAGGCA-1 -0.533287 -1.729188 -0.584088 -1.228989 -1.447585  0.498228   \n",
+       "TTTCTACTTCCTCG-1 -0.709467 -0.495299 -0.122368  0.295769 -0.587837 -0.334145   \n",
+       "TTTGCATGAGAGGC-1 -0.551890 -1.831205 -0.717355 -1.362649  0.784528 -0.567212   \n",
+       "TTTGCATGCCTCAC-1 -0.816513 -1.479075 -0.166308 -0.351758 -0.602639 -0.524562   \n",
        "\n",
        "                    ZNF639    ZNF644    ZNF740  \n",
-       "AAACATACAACCAC-1  1.016380 -0.426697  0.321187  \n",
-       "AAACATTGAGCTAC-1 -0.014624  0.189120  0.082494  \n",
-       "AAACATTGATCAGC-1  1.095647  1.089456  1.264060  \n",
-       "AAACCGTGCTTCCG-1 -0.139098 -0.632303 -0.595420  \n",
-       "AAACCGTGTATGCG-1  0.925863 -0.350852  0.218543  \n",
+       "AAACATACAACCAC-1  1.016863 -0.426996  0.328519  \n",
+       "AAACATTGAGCTAC-1 -0.015790  0.189198  0.088182  \n",
+       "AAACATTGATCAGC-1  1.087730  1.092369  1.271436  \n",
+       "AAACCGTGCTTCCG-1 -0.136234 -0.631287 -0.588915  \n",
+       "AAACCGTGTATGCG-1  0.930766 -0.350260  0.224808  \n",
        "...                    ...       ...       ...  \n",
-       "TTTCGAACTCTCAT-1 -0.257264  0.458856 -0.125243  \n",
-       "TTTCTACTGAGGCA-1  1.191070 -0.316076 -0.160704  \n",
-       "TTTCTACTTCCTCG-1 -0.489183 -0.105758  0.136212  \n",
-       "TTTGCATGAGAGGC-1  1.205468 -0.513302 -0.485558  \n",
-       "TTTGCATGCCTCAC-1  0.367849 -0.639759 -1.074101  \n",
+       "TTTCGAACTCTCAT-1 -0.257337  0.460690 -0.117471  \n",
+       "TTTCTACTGAGGCA-1  1.190310 -0.315665 -0.154943  \n",
+       "TTTCTACTTCCTCG-1 -0.498106 -0.106478  0.140377  \n",
+       "TTTGCATGAGAGGC-1  1.206249 -0.513655 -0.483068  \n",
+       "TTTGCATGCCTCAC-1  0.361360 -0.639278 -1.067313  \n",
        "\n",
        "[2638 rows x 281 columns]"
       ]
@@ -713,7 +713,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc4c4579",
+   "id": "a801ef4f",
    "metadata": {},
    "source": [
     "**Note**: Each run of `run_mlm` overwrites what is inside of `mlm_estimate` and `mlm_pvals`. if you want to run `mlm` with other resources and still keep the activities inside the same `AnnData` object, you can store the results in any other key in `.obsm` with different names, for example:"
@@ -722,7 +722,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "d1712883",
+   "id": "c54fd6cf",
    "metadata": {},
    "outputs": [
     {
@@ -750,7 +750,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1843fcc9",
+   "id": "ae0adc05",
    "metadata": {},
    "source": [
     "## Visualization\n",
@@ -762,7 +762,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "aa7f8064",
+   "id": "582030be",
    "metadata": {},
    "outputs": [
     {
@@ -786,7 +786,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "75f7c763",
+   "id": "14a114f1",
    "metadata": {},
    "source": [
     "`dc.get_acts` returns a new `AnnData` object which holds the obtained activities in its `.X` attribute, allowing us to re-use many `scanpy` functions, for example:"
@@ -795,12 +795,12 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "9b1e01fe",
+   "id": "fe2ad142",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 989.28x288 with 3 Axes>"
       ]
@@ -817,7 +817,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3c72d954",
+   "id": "c291edea",
    "metadata": {},
    "source": [
     "Here we observe the activity infered for PAX5 across cells, which it is particulary active in B cells. Interestingly, PAX5 is a known TF crucial for B cell identity and function.\n",
@@ -827,12 +827,12 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "47c1397b",
+   "id": "66fe8bdd",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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OwI0Tt9n281/P4A69+mQtTj5trpLngbI4qaDwDLh69IbVdkmSyEjPNm0kxxsXHOOGFSK9rFH79lkfi/eyUFTJBqJCYszGfzuzEzeSf+KM5MOJRfYIyRgyX7VpRZxcHdm9dD8h98MBeKfyR6SnmXucCCHoObYzTm5ONO/dkD3LDyGpQVfCaFaRHNTUe78J497pg0dBd6b0nMPRP08Zz6u4FwqZSaZeolnEGorgVlB4BnQa3paT285h0BsoWKIA9y4GgIAhM/rhVcSd+aOWyAIWQGWSSCr9USIx96ykbhVQpfzXVJUieAN/RoWU4TZGYf/gxiN2/3bArHtaDg8RMD44Pmk7lVLV/KjcuAI2dlrSU3W4/BlJco8ClJU8mDx+KInhCSTEJDLut5FUbFCOAkU9ada74bO5Of8CXuZCpDUUwa2g8AyoWL8siy5/w8lt51j2+VpjowRIEnXb16T/5wl8PWi+3N/th0ckdfRAHaXD4ZD1fNtIEhlpcWgzEwzaOxmFffcxHdk4b4fVIQWKehEbEYetoy0JUYnyeWQVTBAqgY+fN6MadaFDp1bEhMUxse0MLh26hr2THbP3TabX2M7P4pb8a5Ak/pE74PNAEdwKCs8AfYaeMU0mEeofjtZWi72zHSUqF6NprwYANOxahypNKnDj1G0y0vWoE/W4rDUG12hsNFRsUJaarapw6dB1zly6CQI0ETq+GFKE7u9EcO2MI7ExFZiyqS8OLvZmgtvOyZbURKPvdkRQFKuDF/Fm4XesnqdkkAgLiOCHkYv5beJqHFzsCQ+MBIxeL8c2naZcndLP81a9chgXJ59ZePszQRHcCgrPgNSkVEL9jaYQXZqO2fsmU6lhOQwGA2u+2kTo/TDGLR2JnYMtHzSYQGhABM4eTugz9Ixd/B5NexoFfIHB19l2ci0IcNgdzdlf4Oz+zAVLEUREUBTz319iduyUxDTSajkhkg3Y3khmWOUxPElq7cSYJNlXHECjVVOvY61nc0P+ZbzMhUhrKIJbQeEZ4OjqyKAv+7D5x53UbledCvWNIeN7lx9iyacrAQi4/pDvjkxjyY15xEcl4FXYw2KeA6G35GCb5Pae2F9Oxu5UZii8BOvmbLEYE9+vAIk9jGXI3OcGwRHrphe1RoU+w2DRXrZ2KSSDgRZvNqZig7J//+L/5Uj8raIJL4T89RjJ59w4dYf+pUYytNJoHtx89LJPRyGf0W9iD/4IWUy7wS25ctjoZWIanZiRnsFfKw6z8KNlJEQlWJ2jbcGKmKrLplVsAMIfRFqM0ZXOjs7MKJ/tJ+7gYk+5utlmD1OhXbi0D75lCzHiu0EUq1CEO+f9WTTud7b9vPdJL/c/heIO+Aqzeuaf8uvwhrnbcPdx5d6lAN78tDsV6yuaigJsXbCb70cuBuC9uYN4fWQ7gu+FEeIfRuOudZn51vcAnNx6llUPFsrj0tN0HFl/kpun7+B57AHxb3ijfZiGw4FYwBiSLhkkq3UUnDZGoithh0g2YL89Sm7P0Om5dfqu1fMMvmsMzd+z7BAZJjUtox7lVajlv4kEGJTFyZfPw1uPOLbxNDVaVflbCzElqxbnxBZjsQtJklg5bQMA/pcfsML/p+dyrgqvFtdO3JI/b/t5DxEPo+g3sQc/f7yc74ZnF0ZJSTTmAomNiOPq0Zvs/f0QxzedAcAOsDufiFqtws3HjRJVijFo2puMbzuVxJgkVCqRXbIMsLucRKFBt3BwtqNmmxpGP2wPG2Kr2KO9A9rgdGzttaRlZhUUaoGkN46/e8EfgFI1/ChQ1Ivuo/PO9/3f5MnLlL0o/nOCOz01nY+afk5sRDy29jYsvf09XkU8AUhJTAHA3sl67b+BU96gVDU/bOxtMOgN7Fy8D4AMXYbV/gr/PV4f2Z5zey6TnJDMw5vBPLwZTPiDCA6vPyn3qda8En0ndCctJY13qnxEbHg8KrWlRqfXG/jt1vfYO9oRcj9MXkg0GCQKFPOSzSZqrZo3xr1Or4+74OTmSGxEHM3WzkLna4tI0VPs04e0bteA7YuMkZCVG5anRJVibF+0VzafjFn4ruJNkgsS5Duvkvyl/78AUpPSiIs02hfTUtK5dOg6qclpnNt7iV4+b9PDeyindpwnPTWdHb/8xYmt2eXkhBA0fL0Ozh5OxEVm505OTkh54dehkD+pUK8M60IX0+Ht1nJbyP1wNFrjH763ryfTt39KzdZVuXX2HrHhxt9RVih8TkLuh/Fp+2l83vUrmvdphFcRD3p+1JlvDk7BzdvobdJhaCsGT3sTJzdHAOw8HND5GvOfSPZqBv42mLYDm+PgYo/WRkOPMZ0YNf9tvvjzf9TtUIN35wxQhHYeSJLAIKmsbi+L/5zG7eLpzPBvBrJt0R6iQ2KZ9db3eBf1pFLj8nLk2bTe3+JawIWwAKOfrVsBVyo2KMv5vZexd7YjJiwOocp+dSpSuhCXDl0j8FoQLfs2lv+AFP67DPiiN6mJqcRExHNq2zkA1Bo1s/dPxtbeKFS9M9/0wGjDbtm3CftWHJbbNFo1B1Yd5ezuSwDY2Nmw+uHP8v6lt78nOjSWouWKAMZKPJO7fk10SAzdFrTgL/yp712SnnUao1GpWRe2BIPegJ2D8fj1O9WififF/e9JUAJwXjKSJFGkbCHsHO1IijPmjYh4GIWjc7Z5JDU5jdRMoQ0QGx7H8c1n5H2QnRZTpRbcvejPxy2+AOCvlYcY+MUbVKhfFgdn6yYXhX8/zu5OjF0ygsAbQZzZcR6DQcLWwQbvotn5PwqV9GH87x9wfMsZWvVrQvUWlXH1dObR3RAcXR1o/kYj0pLTTPqb5952dHXE0TVbSdix6C9un70HgOfUi5w6Ncusv42tMZlUTFoS6wLPU9zRg9eKVHrm1/5vw5iPW7FxvzBCA8KZP2oJNnZaRv34Du4FXFk6aQ2rZvxp0dfOyZ535wzg54+XW53Lxt7GoloIgEFvvsZ/89Rdxr82jRJVirHg3NeoNWpC/MNwcLbH1cvFYrzCv5viFXz5bM0Yzu+9TJuBzWXhmUWrfk1o1a+J/P29uYMs5lBrNUQ9iua1IS3yPFaJqsXlzyWr+uXa76Mz6zkVaVyU/EmtoXnBck9wJf9l/lkFnOfBKyu4o0JiWDDmN1RqFSO+G4ybt6tFnyWfruTU9vMAeBbyoPZr1di/2jJBvLOHIxu+3YqtvY1Ze8dhbWjVrwn3LgZQp311NszdxtYFe3I9pyyXLQD/Kw+4cvQGF/dfZeW0Ddg52jJ732TK11Vq+f3XaNqzgRwZ+TQ06f5kldebdK/H7H2TiQ6JkUPtrRGemr0+E5Zi3Z9cIRujO6CicT8Tfv1sFYf+OAGAk6sjH/xkmZvB2cNZ/rz390Nsmr/Too/WTktCtHG1Pi0lHZValZ0zOS2dig3KEhoQzoMbj+gxphO7ft2PLs26F4lkkHD2cCIhOhEEjGs5Rd6XmpTGya3nFMGt8Fyp3qLyY/tMqtaRb67tpbijJ68Xs8zfrWCOkqvkGWLvmF2myT4XW3KtNlW4fzmAm6fuyvbsLFQaFa5eLsSExsptao2adkNasn2RMXqsWLkiLPxomSzw63WoaV1om0RFJEQnWg0ttrW3oUEX8yKssRFxODjbY2Nnrukr/Ld4dDcEeyc7PAq6v5Dj1fcuybrm776QY/1bUNK6PiOGzuyLk5sjKrWKzu+1YUKH6dy7FIiTmwPVmlWi84jX+LLXtxj0Bqs+soYMA0VKFzQT3PoMPd1Gt6dqs4ro0nQ06lqHL3t9K+8/teO89ZORwMXLmfhMN8OcQtvBxZ4eozvx26Q1tB3YnJZvNmbNV5tY8ulK3H1cmXtkKkVKF/rnN0XhlWPD3G0sHLsMGzsts3ZPokqTCi/7lBRyYEzrmr9MJfnrMZIH6anpfD14Ph82nsiVIzewd7Jn0NQ+tB3UnLVfb+HMrotEh8Tw4MYjti7cw+RuX5n4xkrU71SLFm82xt45W1MXKlBrzW/B/UsPcPZw4vsRv9C70DDqvFYNxydw7/Mp7kXFBmVxdDWvKdisd0O+3PQ/fv9yHef2XOKrAT8QH53Ajl+MWn1MWBwnt577ZzdH4ZXlyJ/GwJz0VB2ntiu/g/yKQRJWt5fFK6Nx71l2iL3LDgEwpcds5p2YwVf9v+fGyTtW+4fcM+YU0dpoqNCgLCczfWnL1CrJnfP3jcnlLz1ArzPXju0cbNm34jDpqcbw4PP7rpCWnPrY87tzzrhKX7Jace5fCgSMppePfhlulhhICIFGq0GfkZ0fwv/qgye6Bwr/Plr1a8r147exc7SlUbcnW4TMSUJMIo/uhFC6Rgk02lfmT/qVwZgdMH/puK/M/3J6arYrXlxkAu/XHW+WSzg3dOkZcqY2gDvn7sufk3PYvQEig6KoUL8sB1YfxWCQ5OCHLNQ2KgwZkuw9kpPUpDSz7/oMPSH3w8y+Xzl6k4SY7GPv/u0AnYe3VaLX/oN0Ht6Whq/Xwdbe5qkCt2LC43iv5jiigmOo1bYas3ZNfA5n+d/GGPKevwR3/jqbPMhpX06KtRS6WeS0aZtGOVolc7dPcW++H7mYhR8tZfSid3H1tvS71qcbKFahCLYONgiRPW/p6n6Uq1ua4Luh8jHHLR2JEELOx5zFqukb6P95T7M2bQ7/XoX/Dp6F3J862vbu+ftEBRsLDJ/bc0nJm/NcyH8h7/lWcKen6Qi6HYw+Q0/4gwiLPMSSJGHraGvW5urtgrOHk0Xeh6Lli2Bjn4fnRpZHSIyxRl+GTs+9CwE07VHfavfEmCS2Ja7ENJgqKT7FvJqIjZp6HWpweucFAq8HmY0vXd2PXmO7MO/YNNoPbcWnKz6gpEnwhILCk1KxYTn8KhcFoO2g5oqp5DlhQFjdXhb58n85NTmNDxt+xv3LgVRrXonge6FEPIyy6KfRqDE1TLh4uyAkyehHbULgtYcAFukwc1KsfBFuZuYvLliiAOXrlibwZhDXj982S4jf//NeAHgVdiciyJi/uEBxby4duAqA3l1D5AdF6Lh5HhPKtpUDczwKuTPiu8E07l4XgIoNylGxgRK1pvD0OLo4sPD8bOKjE3EvYBmEpvDPUbxKnpAj609y/7Jxge/SwWsWQtvJzZE3/vc6BYp5mbU/vB7EgxvGyjS2DubaOBjTYZaoUkz+nmVSsXWwYfTP7xJucpxrx27y+etfcfngdTOhXbFBGToOawPAwKl9KFmtOE17NmDGzk8pW7skAIldPEmr5kSEq47JB/+U7eHRITFUblwetVrNo7shfNZpBjP6fSdr+goKT4Nao1aE9nMmv5lK8p3GfXTjKWYP/tGi3cXLmfqdavHxkhGybbndkJa8XWWMhWcIYJacxxT/KyYeHCKrbzrfvfuzWb+CJX24dPCaxfje414nKT6ZhR8tY9ev+wFo0acxNjY2zDkwhQntpnMmOtusow80X0AdWmk0n60azZYFuzm94wIAPsW8eXNCd+5fDuDhrWBKVin+r16o1GfoUWvyVySagkJu5Meak/lGcB/ffIa0lHRObT+HZKVEdVJcMnuWHsTJ1ZH35g4iNDCc74YvwsHFgYQoKxqrtRpPOTBYKZyaxbk9lzAYJFy9XahQvww3TtxGrVXz1cAfScmRf/vepQDAGM1ZrEIRriy+SXGtHbEpyTj+FWN+HbHJTOszl0Zd68ptGlst71YbS2hWRkIBX+2ZRM1WVfO+gFeM1OQ0Pmk7lRsnbtNjTCfenTPgZZ+SgsJjkYCMf6BdCyHaAfMANbBYkqRZOfa7AiuAYhhl8hxJkn7La858Ibg3zd/Jjx/8mt0gjCkoKzcuz6VD19Hr9Ogz6+JtWbCbK0euc+eCv5lg1tpp0WX6XgO06tuEE1vPos/Q07p/Mxp0rsW0Pt+Rmvh4n2ww18zdCrgSF5Vg9UGg1qh5ePMRD24+okAxL3Ys3ocAMv4MwimXuYUQnN5xHt+yhXhtcEvK1CzBii/XZXeQYNvCvU8kuP2vBHJ2z2XqdaxJsfJFnujaXhZXDl/n+nFjaa8Nc7fx9qx+eWre0aExCJVKMQMovHSe1iwihFADPwJtgCDgjBBiiyRJ1026jQSuS5LUWQjhDdwSQqyUJMkyHWkm+cLGnbV4KCPB27PeYsaOzyhbq6TZroIlCnDnvL+FENWl6szc/g6vO0FyfAoOLvZc2HeFTfN3mQttkzeflNpOJLVyQ9IItDaWz7LTO85bFdoOLnboM/TcuxjAxy0mc+v0XSpkFg12L+iGs6el6C5eyVeuwhN0O4R7FwMoVd0Pv0pFzfrValuVpZ+vYfH4FSTFW3d9jIuMZ3STSSwat5zRjSe+lEo84Q8juX7ilsVb0qVD1/j9y3XcOpNdrNavcjGc3Y1ub6Vq+FlNRZDFoT+O82bR4fQrNlyJKFR4ueQSNfmE5pO6wF1Jku5nCuI1wOs5jwA4C6MN2AmIBvL068wXGne9TrXY9vNe+btnYXcadKnNL//7XY6M9C7mRaESBbh86Hpu05ilVdVlLijGhMYRQ5zsXy2TKWeSm7oSM9oXgPRyDgzQV2L74n2yS6FQCbliSU4KlSzIvYsBxuOExfFxyy9kt8PS1f0IfxhpYcap2aaamXvgwbXHKFa+CD+d+4qwwAjO7r5EgaJe3Dx9h9UzNwJG98PRP1smBYqPSiA53iisE6ITSYpLzrN4gy5dh9Ymb3/xpLgkVk3/E5VGTWpSKjFhsQye9iY+xb05sOYYj+6Esuu3/aQmplC0gi/+lwNJS06n47A2jF44DIC1szez+JMVAKycvoFfr39H4VIF8fb15NvDU/m0/TTunvdnWp+5TFr7EXuWHeTyoet0eKeV7GVzYO0xDHoDBr2BwxtOUq+jUqlF4eXwmEIKXkKIsybfF0mStMjkexHAVDMNAnKGyM4HtgDBgDPwhiRJudtxySeC29s3u4STjb0N845PY/0329i7/JDcHvEgkogcvtw5KVi8AJJkIOR+uMU+Od1qDrTls1/DMwrbsHXSXjPtWjJIhAYa53NwsZcFJSALbVOyii2c2XXRrN3eyY5+E3uw5afdFtp7alIqWhstzu5OlK1dirK1SnIx07UQjLbhpLgk7pz3p0zNEnLVk6LlitBnfDeO/nmS1m81M7uPOfnh/cVs+Wk3lRuXZ9buibk+jCZ2nsXVozfN2o5vPkOjrvU4uPaYWftNk3QDe5cf5K/fD/Ha4BbcNolO1ev0hD+IpHCpggBEBUcTmelCeXjdCa59eEtejD6++TTrI35FpVLRtGcDTmw+g0qtMlsPUFB4GeShXUdKklQ7t51gVeLnfH9/DbgItARKAXuFEEckSYrPOTCLfCG4S1Xz44Of3uHc3kt0eLs1K77cIHts5CSnL3aLPo1IjEvmwY0ggu+FWh1jY2/DT2e/4ujGU/w81ljhRq1Ro8/Qo1oXjE1xX/SuGlxWhlvcUtP83Lo0He4F3cwyCj4pLfo04trxWxaBRDVaVeHNCd2JDI5meI1xxEXEU7dDDT5ZPorEuCR0aRnUaV+DPr7vkpqURtHyRVh4/ms5FezQGX0ZOqNvnse+sP+K8YEBXD16kx2L97H1p924eDnz6coPWf7FH9w+c4+uH7S3ENrG687g0iFLDxtTsnK7bPlpN4On9eHm6TsIBA0616Zqs4pyvzK1SlKwRAFC/cOp36mWmXkrQ6eXTS4t32xMlSYVUKlVeBZ6MelOFRSs8Q8LKQQBpnZQX4yatSmDgVmS8cd/VwjhD5QHTuc2ab4Q3GDM2dB5eFsAC83OFI2thvqdanPj5G2KlivMgC/fwP/yA34anb24KYQws7l+vGQEiTFJdHynNad3XOD6iVukJRs1Y3VMBt6fBeR6PMmQ/cbi4GyPt68n8ZHxFqlb88KriAdNezdgfNtp2XO52NPzo85yMM/3IxcTF2F8wJ7ZeQFndyfe+ao/yfHJvFfzf3IOlIc3HxF0J4S1X20i+F4Y787uT+XG2alAz0QGEJOWTKvC5VELow15s0kBCRs7LQfXHOPhrWC4BQtGL+XYJuPv45f/rbB6/rb2Njg42xGT+Vx0cneUo0Trd6yJSqPi+Gbj26Ktgw3dR3ei/dutM8eZm25cPJxZdGkOoQERFKtQBLVazYjvBnPp0DW6vPcaanX2YmVebxAKCi8KCUGG4amXA88AZYQQJYBHQB8gp6b1AGgFHBFC+ADlgPvkQb4R3Ka883V/hEqgtdFSq21VAq4+ZPkXfwBG7c/R1Z6oR9EULV+E6W/M5e4Ff7PxkiTRpEc9Ih9FU6ZmKWb1/x6D3sDQmf14/4ehjGkySRbc1iheyZfAa0Y7tJ2jHSmJqQiVQGWn4WSjVNKHlsJ5Q6SFqx8YBWN6mk7W3F08nYl8FM2U7nMoUaUY/lceUKRMIRac+wqhUnHt+C38Khfl5NZsM1n1FlW4efou/2s1RS5OnEXFhmW5dPAa+1cZS7DNG/ELv1w25gzf/egaY84YvVPaqEvyYOA+bB1szcpm9Z/cm9D7YVw/cRsAv8pFOb75DJIk4ePnjVqjNkvEZe9kx/cnprP95794dMf4ABi9YBh+VYqRHJ9MhXpluXP+PrfP3ic9Vcf4FR9g52ArVxK3hr2TPSUqZwdCdfugA90+6JBrfwWFl83ThrdLkpQhhHgf2I3RHfBXSZKuCSGGZ+5fCEwFlgohrmA0rXwiSVKeduF8KbhdPJx4e2Y/3H3cAGjSvT5ndl7gxqk7SAaJnYuNZpTzey/nOseRDacAuH32nmzqOLv7Incv3Cc+Ku86e1lCG6BQKR88CroRERTNLbsYUpoZzyn2nYI4/BVj8d+ZbuKSqNaoiI82HislMRWVWvDpyg+o36k2NnY2jKr/KXfO+1O4lA9Vmlbg9I4L2DnaMurHoexbecRCaBcsWYD/LX2fz1//Wm7zMDEjXI8LkT8fv3MTt9hkEmOTuXrsJlO3jEdrq6FWm2ro0nWUq1saF09nGnWtS5UmFbhz3p82A5rh5ObA6R0XcPFyJvhuKFWaVMC3bGGGzx1I1WYVcfFyploz88rgZWqWZPVD8wAmBYV/DdI/qzkpSdIOYEeOtoUmn4OBtn9nznwnuG+fu8f/2nxJUmwyrw1uwcdLRhAfncBNE7cyjY2ajHS9mRdJFlobjexRAsZqNFltkiRRoKh5mPzjuH8pMDu/tq8t6AygVaEJSUerVZOR6V9epUkFrh69aWaiyWlOuXcxkFXT/6Tlm02IfBRldGsEgu+FMX7FB3T/sCO+ZQvjU9ybhq/XYeP3O0iOT8GzsDuSBO2HtGRIhdHyg6ho+SJ8tmq0PH/v4rU4EHKLmPRkfI4mEZfZrlarqN8p2ytDa6Ol/dBW8vdabapRq0127cEmmcm1TAW0Wq2W218mRzac5MDaYzTpXp8WfRq97NNR+A+gFAt+Amb0/U5O2br7twMMndkXrY0WtVpFhsEoJDPSjf9ay4lt62iHLj3be6TL++3YMn8XAJcPXWfglDdIiElk168H/va5aYPS8J4YQHppe+xPxFOwZAGc3Z0pU7MEI+YNZtvCPfz22Zpc/a4BHtx4RFJ8Mp6FPWjUtQ7HNp2hSpMKlK5RwsxVr2ytUqwKXEBKYipeRYy23sndvjbLfFi7bTVcPLMLIhdxdGdLq5EARFSJ4vPAr5AkiU9+/+BvX2t+JCYslulvfoc+Q8+xjaep1Kjc334QKyg8DYrgfgw506+OqjeBziNee1z0OmA0TSSaJGxydHNg8Jd9OLX1HGGBEdg52lKopA9jFg3HxdOFIxtOmhU5sKbBW5zfnRRs7hhdAiPSogi6FcKNk7c5sOYYQghZaGtsNWRkFhb2LVuYoNvGhWSVWqBSqxBC8MWf/yM+KgFnDyez3N7y+bs6yq5/ADVbV+X45jMAvDaoOe98/Vau5+nt68mCc1/nuv+VRAiybpMQAtXj8qwrKDwDJAT6p1+cfC7kr7MBPls9Bu+i2d4EYYERLPt8rRzybkrj7tl+7K37N2PskhFm+21stRxef5JCpXyo3Lg8s/d/gbevJyqVine+eovoEPPFRd+yuRfsValVuPuYh16npWQvcCZEJ5rZzju+3ZpqzSvR7cMOtOrXRG538XQ2q1Dv4ulsVWhb4/WR7Vhw7msWXf6Gj38d+dhgmn8b7gVc+Xz9x7R6qwkT146R30QUFJ43Sj7ux1C8gi+t+jbhz+93yMEs7j6usv+zq7cLqUmpFC5Z0EyYB157wGuDmpvNFRMWx9xh8hoAkzrPotO7bRg45Q1iwmLN7NHOHk78cGIGh/44wd4Vh7h6xNyf2aA3WJQls4aLpzPvzhlAmwHNZIHsfyWQFVPXo8/QExMWx/EtZ2jYpc7fuzGZlK5R4qnG/Vto0Lk2DTrnFe+goPBskf7h4uTzIN9p3NsX7WXNV5tkod1rbBcWnPuKgiUKABAXEU+GLgP/qw84sfUs9k52CCFITU5jXKspaG21lKlZ0urcseFxrJi6nsAbQaz7ZqvsAeLk7kiLPo3Yv+ooHd5pTed3rS/wmnqM5MTB1R7Pwu7M2DGBtgObm2nRqcnpqDTZtzo8MO8IUAUFhfyFJAmr28si32nch9afkD+rtWq6fdieC/uv4eLpRKi/MfTcNP/28G8G4F3MiwntZwDG6Ma6HWty73KAedpWkzSvXw/8QfboAKMnSlZkoYuXi1lBBVMadK7Fsc1nrNrBfYp6s+jyN1bHLRy7TM5cWKyCL68Nbp7nPVBQUMhP5L983PlO484yIQiVoHDJAgwu9yHT3viW22fvY+9kZ9G/ZDU/bp66a9ZWu01VuV6kWqNCqDALZb999r4sfDU2GuxNovvCAiPMiy1k4uTuyNGNp3NdvKzWvJLVdsCsUk+fT7pi75R7IigFBYX8h6JxP4au77enRsvK7Px1Pxu+3Wa2T61VU6C4N+GBxoIDNnZaytUpLYeKg1GAVm5cAY2NhnJ1SuPo4sC3JnbunGSkZ+BRyI2UhBRiwuJYNX0Db3/Vj/2rjhiPqVFRspofdV6rxqoZxmx9WflLilf0pUGX2vgUL0Cnd9vkeoyPFr1Ludql8CjoRsu+TXLtp6CgkP+QJNAb8pfGne8Etz5Dz4HVx7h82Hr61uV3f+DLnt9w7fgtqjevxJyhP3H77D15v09xb87/dZlP203DYJDM8murNCrZfGLq+leohA/3LgQAxko79o72jPx+CLfP3cO3TGFunb4jC+0iZQrx7jcDcHZ3olR1P9lD5MqRG8x8ax4OzvZ8sfF/+JbJ9lCxdzLmJVFQUHg1eZkeJNbIV4I7PTWdRf/7nc2ZATP2TsY8IVlUaFCWzfN34X/lAXER8Rxad8JiDq2NxhjmniMvNxhLlbl6uaBL1/HO1/1JSUglKTaJouULm6WQXTx+BXMOfEGR0gWZ0GGG2fx+lYvSoFNtwh9GcmT9ScrVLc36b7ZyYutZWfPf/MNORn4/5NndGAUFhZeGBC/VLGKNfCW4P+s4g4sHstOHpiSm4u3rSURQFDZ2Ws7suMCZHRdw9XK2GOvu40rzNxrR46NOJEQl8PuX64xeIDlqT3b7oAP9JvYwG/t516/Mvkc+imb3bwcoalIKTAiBWqMiKTaZGf2+4+yeSyREJaKx0ZhVgQcoVd3v6W+CgoJCPiP/LU7mG8EtSRKXrFS38fL1ZMwvw9m7/BAHVhsz4sVFJZjVmFSpBOOWjiQuIoH+JUei1qizhamJ0C5ftzQ9x3Yymz81Oc1qQYQHNx4x4Ive7FtxhDvn7+FZxAP/yw/MChwAZkLbs7A7H/86ktptq+WcTkFB4RXGSv3yl0q+EdxCCIqWLcyDm4/M2vpP7kmd16pTvKIvt07fIfheGEjIQtu3bCG8iniw69cDBN4MQjJIFhpwFrq0DIs6h7t/O2BR3AAgKiSG5PgULh28SoZOT7y1SvI5eOfr/orQVlD4F6KYSvKgWEVfM8GNgF8+WcGdc/70ndCdD356h0/bTzdzyYsOjSXotjGdaV7FZwHuXQog6FYwJaoUl9u8injIn4uUKcSjO8a5Wr7Z2JiDW60CnTETYeHSBclIz6Bk1eK4ermw69f9FClTkN7jXqdYBV8qNyr/LG6DgoJCPsLoVZK/PKfzleAe8d1gAI5tMvpLSwYJ/8sP8L/8gCY96lGwRAFs7LRyEYSqzSpy80x23UPTzHlg9BwRQsjt9s52FMqsfZhFo651+Wz1aILvhdHp3TbEhMUS/iCS6i0ro7XRMmXTJyz5dCV3L/iTkZbB3CNT5cosw78diL2THSpV/vpPVVBQeLbkN1NJvpI43r6eTF7/MV/8OY6WfRtjm1lFxdbeBlt7Gz5qNtmsck3xir6kJ1sPQ9faavkz6jd269YyeuEwurzXlsXX5spaeXqacVxkcDQVG5aj74TuuHg6s/brzUzoMIN3q48jISYRJzcHOSAnLDCCU9vPy8dwdHFQhLaCwn8AJQDnCWjYpQ4Nu9Th1tm77F91lCY96uPo6mCWza/NgKZ4+1rmYm4zoBm69Axav9VULqjbcVgbQvzDGN1oIlHBMZSrW4obJ+5QsmpxAq8/RDJIjFv6Pi36NJLdAh/efMT147dY+/Vm9BnGZFYarZrKjRVziILCfwmJlyukrZEvBTcYNeKfPvyN6yduc++iP0Nn9kOtUclVZZr0aGCRltXR1YHCpQoihGD+qCWE+odTsmpxpu+YwLzhvxCRmYPkxgmjeeX+5UB57L6Vhwn1D5ft3FpbDYVLF8THz5srR24AMHRWP/wqFUVBQeG/RT6zlORfwf3gepBc0PbSwet80OAzTIOXNv+40yLnR2pSKssmrzVru385kM+7fMWd89kFcB1dHEiKT8bF05mkuCT0GQZunLzD2d2X5D66tAxObDnLhwuGUaZmSdwKuNLyzcbP4UoVFPIn8dEJpKfq8CrswW8TV7P390M07dmA4d8MfNmn9mKRQMpnIe/51kBbpGwhilUoYt5o8tg7t8eyUHDOGo9ZmAptlVowa+9Epm4dz1uf95SL7SbFWZYb8/Hz5utB89m+aK/VBFcKCvmBDF0GFw9cJfKR9ayWWes5AFeP3eT8vivy95SkVAwGy7+bK0du0LfocPoWG86Ypp+zasafRDyMYsPcbeaeX/8RFBv3E2LvaMf807NYNnkt2xbuMVuUzCL4fiid3m3D1WM35crsRcoWIj0lnejQGDLS9VSoX4YbJ009TyTGtZxCtaYVObXzgsWcVZtWpFnvBhQqVZCoR9EcWX8SgB/eX6wk8Fd4aUiSxK8TVnH12E16je1CiSrF+LzrVyTFJuNdzIvrx2/h4GLPwvOzKVTSh1tn7hLiH2bMP38tiAFf9EZrp2XJ+JUAFK/kS1x4PLER8RQuXZDvjkzF3cdNPt6RDSflCk9Xj96Q253cHHEr4PJCrz0/kN+8SvKt4Ab4YeRisxwiObl/MZACRb345fK3XDx4lYiHkSwYs4yE6ERUahUOLvYMnvYmcRHxzBvxC4kxSQCkJqVx9q9sjd3Udu7o7kCXEe0AzKIkC5X0eR6XqKDwRFzYd4U1X20C4NrxW9RpV52Aqw8Bo2cUQHJ8CrfP3sP/ygO+6D7brMLTyqnrcfHOThWRpegABN8N5dSOC7Qb3IKk+GRunbmL1lZjls5BCEH/L3rRrFdDXDwsU078m1FylfwNbp6+k6fQziIxNpmbp+8wseNMsxqQBr2B5PgUNs/fScdhbShXuxSXDl2TK8Sblj2r0bIKZ/cY7dsh98Ll9uotKjNjxwQCrwfRNkdZNAWFF4mzh5P8WTJInN6R/bZYrnYpbp6+i1/lorgWcOazjrPMhDaAXm+QE6/lRKgEpWuWIPJRFCPqjCcmNBYwmhWzcC3gwtE/T1m8dWboMpg/6lce3nrE4Kl9qNy4wj+91PyHBOQzwZ1vbdymWQEBEMYK8E5ujjTpUR/PQu6UrFacMT+/y/5VR2WhLXJU/pYkmNBhBuf2XiYjXY+jq4PFsdoNbSmPC7j6gHuXAuR9ddrVoOdHnf9zWobCiyM+OoEln67kj9nZrqc5KVOzpEWxjrodajBx7UfMOz6drYkrWHTpG74e+KNc9g9hzJ8Dxr8dg8ncFRuUkz9LBglbOy1ndl2UhTYYzYpZxIbFcf9SID99+JvsZQXw14ojbF+0l8uHrjNn6II8r/P6ydssm7yWuxeM1adyPlzyM5JkfXtZ5EuNe9ev+1n40TLzRgnSU9KxsdNSvJIvn68bK++6cSrbhm0aDv/RL8M5/5f5IqZpQfViFYrQ55NuNOhcGwdne5LiklFr1MpCpMIL5Yf3l3BwzTHAmLah83tteXgzmKLlC2NrbwxCO//XZS4dvGY27sbJ2zTv3YjN83exYe42qreoTHJCirzfydURGzstNdtU5fzey7JAFypB34k9WDd7M5cOXqNa80pcPnydpRPXyGNN89WbcuXIDT5q9jkt32xM9ZaVzd4EhEowpeccfIp50ap/U/Q6PeXrlgEgJiyW/7WaQlpKOqtnbqRGqyqc/+syrl7O9B73ej7PVy/ynVeJeB5Pvdq1a0tnz559qrEpSal0cx+Yq4dIFnXb12D69gkAfNFjNsc2ngaMVdbjoxKwsdMybulIvAp7MLb5ZKuvibVfq8bMnRMBuHXGGOxTs01V6nWo+VTnrqDwd/G/+oCZb83D/7IxOtfWwQY7RzviIuIpXaMEkzd8zPaf93L/SqCZecQUIbK1v2ZvNOTI+pMW6R9y4uzpxJeb/kdBvwKc33uF2UN+NNtftFxhHt4Kfuz5tx/aimsnbvHgepDV/cO+7k/XD9oTdCuYYdU+znWeqVvGU79TrcceD8D/SiC/fLICz8IejPx+CHaZEdbPEiHEOUmSagPYlvSVCn850mq/gP4T5H4vknyncaclp5kJ7d6fvM7pHRcIyFEH8vz+KxxYc5T7lx+g12VQpExBHt0JJT4qATBWZF8yfiW/3/+JJTe+48cPfjXz0wZ4eCuYK0dusGrGBkpW9ePdbwYoIewKz5yUxBRs7GxQa9Rm7eu+2cqiccsRJq+BacnpsgfV3Qv+TOgwnYc3zQWoawFX4sLj5O8qdfbi+onNZ6wKbTtHW1KT0uTvCVGJzOr/A0Om9yXEP9yiv3cxT8IfRJqtG4GxUIlOlyG75h7bdJrE2KRcr33Xb/tZPH4FTiaauTVyHicv5o34hWvHbgHG4tu9xj5nbV3Kf4uT+U5KuXm7MnLeEAqWKEDzNxrSsHMdKjUoa9EvIy2DGX3nsWbWRk5uO098tGXa1ayEUr5lCqPRWj6jHF3smfnWPM7uvsQfszdz9M9Tz/6CFP7TbJq/k9ddB9K3+HtmJfYATm0/B+Ru61WphIXQBqjatIKZsDdVdNJTLXP3CJUwE9pZhAVEMLPfPFKTUsza7RxtUanU9Pu8J67e5q5/TXo1oHQ1P/l7fFRCrtq9RqsmPioRg0EiPjJBbnfxdGLIjL4M+7o/NVtXod9nPWjas77VOazh5OZo8tlyzeq5IOWyvSTyncYN0HVUe2Ij4lg1/U8Orj1usd/WwZa0ZPMfoo2d1qKfg0t2ZGXVZhU5ue2c2f6gO6E4mvRxcHlBPwKFfzWSJPH7lHX4X33AnfP3kSSJ6JAY3q83njcn9ODI+hPYOdnR8s3GXDlyAztHW15/vz2rZ/xpNk9uXiAX91/NVdh7+XqgS8vAp5gXkcHRRIfEWtiqs8yJWaz/dhtNezXgcGYpwNSkNM7uvsi5PZfMjuPi5cz+lcYi2raONqQl5a0l1+1QE2d3J3YvPYDGRkO3Dzpw6cA1bp+7x6ppG/jm0BS6fdiBaX3m0s1jED0/6sxbk3panSs0IJylk9bgVsCVUT++A/yCwSDR8PU6eZ7DsyN/adyPFdxCCBfAW5Kkeznaq0qSZBm++AyIi4xn5fQNFk8072KeaLUaYzEFExxdHUi3EqBjY6clPjqBldM2kJ6aTqFSPoTcC5MXXtJT0klPSad5n0bUalNNKYKg8Ew4suEkv3+5DsDMi0mSYMtPu+R4gkoNy7E+fAlaWy0Z6RkcXHOMkPvG37ajmwO29jZEh8RSq201SlYpxsWD17hz7j4JVt4uweg5Ehlk9OmOi4jHztHGar/4qASLknvJCSmotWozN9mcDwdTrVmlUlGhfhmC74USF2FsV2vUZl4xVZpW4MGNR5Sq7sfr77ej/ZBWDK00GjBWnrqw7ypJccny+tSyyWvpPa4LNnY2pKems2bWJnTpGbz5aTe+G76Ic5kuuxf2XeZ+5prAzLe+Z9auiVav85mS95LBCydPU4kQojdwE9gghLgmhDB9vC19XicVExZnIbRVaoHWRmshtDU2GiRJIiEm287m6OpA5/deY8R3g/lu+CL+/G472xbuJSwg3NjfIKHJrP6uUgnqvFad8MAILh26RmiApb1PQeHvkPXbAqPw6/tZdzRao33btIhHsQq+OLs7YWOnxcnNkSXX57LqwULentWPpNhkokNjGbd0JCWrFOPPeTtkgZ+FWmtuM0/PYScuU6uU2XcHF7vMf+1Ra1Wk1HEmpYELahs1Z3ddlIW2Z2F3vHw9yAsXD2e6fdCBpr0aym2mJhOhFhxYfYydi/dx72IAP4xYTFpKGu2HtjKO93Si4eu1KVK6IFrbzL9FtYqg2yEkJ6Tw4we/8vuX61gzayOLP1mBWpMtqrKENhi9XH6fss7ibfqZkuXHbW17AoQQ7YQQt4QQd4UQ43Pp01wIcTFTzj42gOVxGvcEoJYkSSFCiLrA70KICZIk/clzfHfwq1SU199vx45f/kKXZtQKDHqJ4LuhFn0z0jPI0JmXKktNSqNpz/q4ebsSeO2h3G7QSxj0xr42tlraDW5B8Yq+fPvOAvQZBn7/ch1CCMYueY/XBrXI8xwlSSI5PhlHV8c8+yn893BwtqdykwpcPXKD5PgUzu25xLK78wn1D2fPbwfQ2mh4bXBzAq8H0cW1P4YMAxNWjaZBl9rsXLyP1bM2GieSYM6Qn2RTR8j9MIqVL0KIfxgGg4QhD88rZ08npmz6hO6eg2QlqGmvhrR+qynn9lzml0sHiXu7EABiSyx2S7Pzj0QFx1iZ0ZywwAhm9J1nFjdhqqFLesnCpm9a1CQtOZ34qEQqNSxH4dKFCLz2EIPewJimkxAqQVJsdu6glKRUxvz8LpO7fs3tc/fN5kxPSWf5lD8AmHv4y+cWAPS0zndCCDXwI9AGCALOCCG2SJJ03aSPG/AT0E6SpAdCiAKPm/dxglstSVKI8cSl00KIFsA2IYQvz8k0H3DtIbMH/4jWVkO/iT1ZOmnN4wdJma9l14OIi0xAn6Hn7O6LVGpUjiJlCvLghmVSHDsnOz5cMIyrR2+YLe5IksTRjafyFNxpKWmMazWFGyfv0Lp/Uz5ZNuqprlXh38dvk9awavoGs7bUpDQ8C7mz5NOV7F9lLHit1qo5Y5IrZ+HYZayetZGbJjEJYB6XIFSCxLgkWZmxRvM+jQj2SCMmKZE/520z+ys9te0cY395j/RUHbqYE9nzls3d48Peyc4yGC6X8+s6qj37Vx0lPTXdbDHUxdOZcnVL816t/8lvDWkp6fz22SoCrj9Epc5+c0iON1koFdC0R32GzuyHV2EPnNyzlSQbexuLN4xQ//DnF7n59NKuLnBXkqT7AEKINcDrgGll9L7An5IkPQCQJOmxr/2P8ypJEELI71uZQrx55oEr5Tboadm99ADjWk3h9tl7XDt2izvn7pn9Z1VtVpHWbzU1G2Njp6XX2M4Mmd6XOBMb3ME/jvN+3U85scX6K1R0SAzpqelUblzBbIFSqAQ1Wlbm6MZTVrOmAdw+e19OXPXX74dJisvdHUrhv8Xxzaflz1o7LVpbDYHXg/iy1zdm2mlGmrn3R8j9MAuhnRPJIBEdEptnn79ibnOgdQoXX1fzo/8Rs31CCPQZeuq8Vp322jJoHqSiDk7DdkUIw+YMwNXLGdccCaRSElMpWMJSAbR1sLSfa200bIj4lRHzBsttfpWLMvbX9ziz8wIPbjwi2iSc/tKh68RFJBATGmtmXsqi9VtNmfTHWLwKG8023T7oiJ2DrXFNKzXd4p1/w7ztzBnyk1k2xGdG7qYSLyHEWZNtWI6RRYCHJt+DMttMKQu4CyEOCiHOCSEGPO50Hqdxv0eO2yNJUoIQoh3Q+3GT/x0iH0XxzdsLzJ7gxzadke1fAGH+EUQ+ijYbtzJwAW7erhzdaO7KFxYQkefxmvVuIFfIESa+290+6MDCj5YhSVCguBcr/S3DeItX8sWriAeRj6IpX6+M4o2iINP1/fZ8N3wRANWaVZRjB07vOE+/iT0pU6sklRqUpd/nvVgyfiU3Tt4m+F6omRbt5euJT3Ev7l8KtKrtFirlg2chD7qP6UBBvwJM7zOXR3eMZsSkQtm/ZV1J8wjg6NBYZvSbx6S1H6F6kILPD9mmjDI1SvD7/R953dVSZoRZWfexlq1z3TdbeXQvlM9WjcanuDfbf/6Lw+tPMLXnt6hUwsxLxjScHpAXSlVqgbOHMx3ebsXgaW8SGhBOdGgsLp7O3L8SyGuDW3Lur0tmaZiFSmDvZMfd8/7cPe9PpUblZFv6s0LkrnFHPiYAx5pJOedsGqAW0AqwB04IIU5KknQ7t0nzFNySJF0SQnQVQnQFrkiStDuzXQeszGvs3+XRnVCrIbamP+iwB+bCeOCU3rLQPr/3Mk17NeDasZuyjU6lVln1MR3wRS/emtRL/u5bthA3T99FpRIE3wuV7VnhgZH88P5iRs1/22y8i4czP1+cQ+D1IMrWLmnmU6vw36bjsDY06laXlMRUHF0dGNNkkmyqyyry4e3riZuXC4On9eGtEiPNfuN12lfnzM6LRAaZ59ZWaVSUqWHMV9Lr485EhcTwSesviYtMwMndkUqNynPt2E0cd8WQWsMJg5Mapw2RFud3cf9VdOk6SlYtzvHNZ4yNAio2KEtKUiqOro4WATV/x757fNMZRtYdT/M3GvHghlHRzFqDcnCxNzeFYPwbtXeykwWxQS8RFxHP6pkbSU/TseXH3ejSdLn+LYOxXGHw3VCuHr0JYJae9pkgCXj6kPcgwLRsli+Q0zk/COMDIAlIEkIcBqoBTye4hRA/YTSJHAemCiHqSpI09SlO/rEE5hIy6+PnTeSjaDM3JTAK23XfbmPTDzuJi0oAyWiPK1q+sCy4s/6jVWqR/YQX0Pm918yE7bRtn7JvxRFKVffDzsmWU9vOywstp7afZ9R8y/Ny8XSmSpN/YSY0hX+Mm7crbt6uACy6/A1zBv/IXyuyzRYntpzh9M4LFKtQxMwlD+D2mXtmIewAHoXcAEiITqDNgGYc33yW70f8IrveJcYkkZKYglAJbCIyaLfFnqBbIUQEWZrw4qMS6Oo+0MxrxLOwBz0LDCUlMRU7R1s0thoyctjRbe1tSEtJR2urRZfDFOHgYkdqUrr89xZw9SFLr1quTeUU2kJl/Bu1VsQE4Pzey/Kxcgrtmm2q4FXEE41GzZAZfdFn6Nm6YA9FyxV+4tD5v8XT27jPAGWEECWAR0AfjDZtUzYD84UQGsAGqAfMzWvSx9m4mwItJUn6FKNtu+vfPu0npEGX2rh6mdvXbB1smLXrM3p9bBnSGhsRT3JcstGunXlTUxJTuX32vkVfs9cyCYsoSlcvF7qP7ki15pUoV7s0P56ZhYunccGm/dvP9pVL4b+FWq2mlEmkIRiF8m8TV7Pp+x0M/2YARUoXlG3GcZEJ+FUuZhYeHx0aS3RILMH3wpjYaSZzhy20yCJ4/1IgkkHCoDdw89Rdpm0bT9Hyha2eU3qKjuA72W61UY+iZZNMalIajbvVw8Y+24bt4GzPj2dm8d63gyhZtbjZXGVqlWRTzHLmn5qJZxGPv/X2KT3GNzoiKAqXHDLBx8+big3LUahEQdoOaM6YRcNx9XLBo6A7A6e8Qcu+TZ74+H+Lp4yclCQpA3gf2A3cAP6QJOmaEGK4EGJ4Zp8bwC7gMnAaWCxJ0tXc5oTHC+50SZL0mZMn8xxdABNjEtHrjT/GsrVK0uGdVrw7ZyDv1fyENTM3WaRrzRk5aYp3UU9UauuX5uLpbBZRaY0yNUuyOmgR68OX0O+zHn/zShQUzOk5tguNu9U1a7t7wZ8N320n6HYIS2//wIjvhsj7okNjzAWziYAIC8x77QaMCsye5Yeshss/CQfXHDPz2EhNSmXx+JX4VS3GrTN35fYCxbyYf2omBoOBmf3mEfUo2mpEp+MThqU7ezjx+vvt5O+JMUn0GttJ/u7i6cwvV74l8NpDti/ay6ftpxP+0NIc9Fz4ByHvkiTtkCSprCRJpSRJmp7ZtlCSpIUmfWZLklRRkqTKkiR997g5H7c4WV4IkRUdKYBSmd+F8VhS1Sc79cdzfPNZ2VUoQ6dnzM/DWTx+BamZAtrU/m3tdU0IKOhXAF16BimJqRavVh3eaU2ttlWp3ba61URSer2eE1vO4lbAlcqNymNjq8XG1jKMXkHhaShZ3Y+jG09btGf9jju83Qrvop5cOnCVtV9v/sfHq9asEjdO3JYLbv8TDAaJk9vOcf9ygFl7+INIVk7bwJqvNlm45mUhhLlPNhgFtJObA/bOdlSsX46mvRpw/cRtStcowZwhP5n1fXDjkWw6MhgMhAdGkpKZulaXppPNL7uXHuDigat0fKf1s3cJfAULKVQAOmdunUy+d8r895lR+7VqcnrGRl2N2knD1+tg52iZsjGn0Abjf2yd9jVYfm8+Rcvn9LaByo3L07RHAxycrWvbC8csY0qPOYxpMsnCQ0VBIS8uHrjK14Pms2/lkVz75PzdVWxQljYDmzF0Vj+5rVrzStg42KLNyruTi6zQ2mrJyyJRuXF59q8+yuDpb1K6Roknvo4shBWpIIQg/IFlMeK1eQhtMA/KadKzPouuzGHKxv8RFRzD/UsPKFymEG7eLnQZ8Rpndl4g1iTrIcDe5Ydkc0liTBJ7lh1k1E/vULFhOYbO7IdfpaLcOX+fOUN+4q/fDzOx8yz5zT0iKOqZaeRCsr69LB7nVRJorV0I0Qijgd16ktqnoFyd0iy/N5/46ESKV/AFjFU6VgYsYOMPO1gxdb3Zq4m1VWb3Aq6Mqj+BexcD8CzsQVRmLT4hBHU71Mjz+LfPZbtG3Tl3n8bd6j2jK1P4N5OWksakLrNITUrjr98PU6q6Hye3nePRnRD6jO9KkdLG6MS4iHh5jIuXM9EhMdw570/NVlXl2ITJr38ll9DT2GjQaNVWs/plKS4OrvYkx6VY7M/yrji94wI1W1chOiRG9p9+EuydHUg2WTC0c7IjNZcgnCdNxypUgiPrTxIXHkfFBuXkLIZrv9rEoo+XY+doy6CpfayOTYzNzs3yx+zNaLRqHN0cZXOSqRww6A0gwdGNp5j2xlwkg4FPfv+Alm82fqLzzJV8VqznidO6CiGqCyG+FkIEANMw5jB5prj7uMlCOwsXT2cGfvEGC85+zeiF71ColA/uBV3NnuSvDW7B6IXDqNG6CvcuBgAQFRwt27ntne0em2y972c9cPF0pliFIsqCpMITI0nZgkOSJE5sOcOST1ey69f9fNnrG87/dZnYiDgOrD4qj9HYaAgNiECXpuPXiasZUft/LJmwysx+nJGeQWpSGvZOdmYLlaaBKn4Vi9K6f1OrwSsAyfHJHP3z1N8S2oCZ0AZyFdpPSr+JPWRT5+XDN7h6LFt0xEUaH2ipSWn8PNZY9apAMS+Gze4v28b1OnMFLUOnJy4inqWT1pAUl0S5OqUZNf9tmvSszxcb/4dao+bYptPoM/QYDBJH/zz5j84f8p/GnWcFHCFEWYzuK28CUcBa4GNJkornOoh/VgHnSdm+aC87l+yjXoda9J9s9MlOSUxhVH1jcd9qzSvR86POnNt7iZZ9m1ChXpnnej4K/12O7DjDpj8P0KJ+TbQatWynFUIgSRKVGpWTE//nxWuDWrB76QGLdr9KRQnIzLlj5toKdBrelgZdajPvvUWEBz4bs4CrlzPuPm7yMVHxRNnxbB1sLAJz7Bxsmb1/Mh82mmjinpv9tpxbetgl178jOiSGca2m5Ho8RxcHvHw9eG1wS4tiCqe2n2NKjzkYDBKfrR5Nkx5Pnu8bclTAKVZUKjJ2jNV+/qPHvpQKOI8T3AbgCDBUkqS7mW33JUkqmdekL0Jw50ZaShoh98PxLVvIavEEBYVnSWx6Mr0PLiIoOZbWhcozt3Yvlk/+g7N7LsquqS6ezqQmpVotcmCKs4eT1ZStDTrX5sTW3P+eChT1JPyhpf05C7VWha2dLSqNCvcCrnmWJMt6SxUqIcdOqNQqqjSpYFHz0hquXs7ERSbg5etB+6GtqNexFmd3XWTp55Z+3XZOtvT+uAvLv1hnsa9W22q4eDkRFhBJ1KNokhNSqNW2GgnRiaQkpFCmVkk2z98l95+2/VPqtTcvORgbEYdkkJ4qIMdMcBfNQ3CPeTmC+3Gmkh5AKHBACPGLEKIV+S2jeA5s7W3xq1RUEdoKL4TLMY8ISo4F4K+QmxgEDJ72JtO3T6Bo+SIIIeg1tjPzT8+iUqNycnpXAIOdivje3iR09kRSkWuebd9c/LGziLSWzc/kr1SvM5CckEKRMoUeW0fSoDdg0BvMAt6cPRyfSGgDZGToqd6iMhPXjOHGidt8PfAHwgKt50wqVbU4XUd1oGKjchb7zu25xIFVx7h+/BZhgRGUru7Ho9vB2NrbMGPHBNoNaWmW1vaLbrPlXOZZuHm7PrsoylepAo4kSRuBjUIIR4zBN2MAHyHEAmCjJEl7nv8pKijkX6q6F6GYowcPkqJpV6QSWpVRmDi4OFCsfGHCAsJZMmEVV4/d5JsDU9i97CBz3zG678YNKUhya3fjRGpw3pSpNQtQZ9aRVGvUNOxSB2c3R5ZPWYc+Q2+RGsLVy4WUxBTzhUzJMsT81um7PA1ZhRIsEFgIr6TYZC4euMrHrW7K0ZeP7oZYdSa4dvw2A0q9bxFMZI0L+43xKHfO+7N14V6ObDhp9nDJSM/gxqk7qNQqfIp7P/nFPSEinxVSeCK1NDOGfiWwUgjhAfQCxgOK4Fb4T+Nm48Cmlu8RnppAUQd3uf30jvMc23RG/n5q+3lWTNvAii+zzQIGl2yN0btKIcS+JOyd7IgOiUWfYcCvcjEmrPyAElWKU6lhObb9vJfwB5Z27JiwWKvnljPE/HHY2ttQp0MNdOkZRAVFc+9iQLYTgBUhnZfGaRoyn3Nx0d7JDo2NhoToxDwLDefGqe3nCLmXnZvfo5A7RcsVZma/eag1aiZv+JgGnZ+x9eJV8ioRQnjk3DJ3rQOsF4f7j2IwGEhJ+mer7wqvJnZqLcUczcO9i5QphEqd/V2lVrHlx11m41yXhmF7PgH7Y3HY/xFGeorOLG1ryL1Q7l4I4M75++jSMywST/0dHF3zjhYGo2ufLlVHsx4NuHvB3zwK0orgsrG3HqCWM8rZFPeCbkzb9ikVMwuAW6sVmxuuBVwQQnD16E0SYpPwq1yUdkNasjLgJzneQ5+h59QzroaTm0fJy/QqeZyNOxK4CJzN3M6ZbC9n9TEfEhkczcAyo+ji3P+ZRL0pvPpotGq5OlJW5RfTAr0AmtB0vKY9oNTKBGJuRViYDNJS0vl60HxG1P6E0Y0n0ihHDpG/Q5IVf29rSJKEj1+2qcFgpyJ2cEFihxbEvrCzWV+vIp4W472KePBn5G94Fna32Afwv6Xv8/3IxZzaft6YFMve5olXzXSpuuyHiQSRj6LpObYzGq2GNv2bodaosXOwpcU/9dm2xj8oXfY8eJzg/gGIwZgAZSBQUpKkEplbnp4l/yVObD5DqL9xAWbTDzte8tko5AcOrzspLzbm5bkF4FHQXMiZaupZ3Dl3nyMbTuYZpfgk2Dvb8frIdhQu7WOxT2ujAQkOrDlKqep+qDQq4t8sQFJnT5I6ehLSybxSTvDdUAut+/VR7XFyc2TW7klyjcsshs7sS4V6peVygpJBMqa5MLk9QiUo6OeN1kq6iZzBSIkxSRzPNEc16laX4d8OZPSiYVRr/sxrvLxyi5MfCuP7X3OgP/CDEGIPsECSJP8XcH6vBJUalcfOwZbU5DRqtnlm6VsUXhBbftpN8N0QenzUGW9fSy3yaajWolKuOaQ1WjUZJgtr9y4G0KZ/M/b+bqwRm7PIwBORwwattdOiy+F+WKZWSQZM7k39TrXY9vNe5r23yGy/Lj2D0ybl1IQQOHk4kmWFlqyoeekpOlwLuFC4hA+O7o7sXXYIGzst+1YcQWtrA2SbD7f8tJv6nWthY6e1cI0UKoFkkJAMEqVqlCA0IDuvi1+looz68W0+f/0rsxSwtvY21GhVGYAFY5ay5afd8jm9wEIKL4XHLk5KRnXhgBDiAsZgnKnAHeCX53xurwwlqxZnyfW5hAVGUsmKa5NC/uXAmmP88P5iAG6fu8+3h7584rGSJHFkgzEqr0mP+mY27sqNyjP8m4H8NPo3szHN3mjImIXDEGoVI2p9wqM7Iag1avb+fgi1RmVW/zQLi/YcQtrdx5WUxDRSM9dYKjUqD0gWQT+B1x8yqcssytYuSXqqTg4Qyuv6VAv88ZSKkZKSjssK6259CVGJdJzZjzlDjdWiFoxearVfxMMoJnX5ykJo125bjZun78oLlcdyJONKiEmkatOKdBnxGqtnbgSgz/iuvP5+e7msWdDtbDfHhzcta8z+I6T851XyuMVJRyFEXyHEZmAH4ATUlCRJEdo5KFDMmypNKljNPKiQf0mOz9bgkuKtJ/TPjbVfbWJq72+Z2vtbWaCY0u2DDny+/mM5sb+dgy09x3Tii+6z6eY+EI1WzUeLh8u27dyEdvFKRVFrTH5XEmZ24ZiwOFloA4T6hxEeGIGTu6NZPEN6ilFg3j57n4CrDx9rwgGwM6jZOfELhmiroU607rZn0BusZj60hjWvmHJ1SjN9+6e0G9wC37KWPusehdyJjYhjyPS+TNv2KY271eXYxtMcWZ8dyj7wyz4ULVeYCvXL0PWDDk90Ln+LV8lUAoRj1K5XA3cxnmodIUQdAEmS/ny+p6eg8HxpO6g5/lceEHw/jMG5JDnKjYDr2TVgA00+m9Kkez0avl6bL3t/w61Td1k+ZR0XD1zLHBPEb5+tQa1VW1R4sneyQ61VkxiTxP1LgRSv6EvhUj6c2Gr0mPAo6JZr4eAoawE5T8knv4/Cyc3RzLQDMOL7QSz6eIVcwedkpidHzpD8nBhyPJxcPJ156/OeJMencOPUHYJuBxu9UqTstYE75+7Tv+RINkT+hpu3i/yQ+Gn0b1RsUIZydcpQsX5Zfr0x75ldtwWvmKnkj8x/y2VukP2slwBFcCu80mhttLz/w9CnGvvG/7py97zRba7PJ11z7Xfl8A2ObzQuouUUqqY+2LaOtqRlLsAVq1iElPhUOUd94PUgCpX0QagEarXKTGg7ujlg72hL5KO/IbCt+WVb4ciGk1RpUpHWbzXl+OYz8vkvn7wOO0dbdBqVWX4SrY3WasZAra3GrLamjZ2WtgOb029iD64cucG1Y7fk8oX2znY4uzmZFY1ITUoj5F4Y7gXd0NioyUg3PkjGNJtM635Nef+HIXLx7+fBq2bjvor5i5kERABHlcVJSxJjk/ht4mp2/rof9wKuLLwwG2d3p8cPVHglKVG5GIuv5lkaEACf4t7ygpzWToteZ4x+LFOzBLfPZZfaSzPxmrh1+p7FPFlabYYhW/v1KuJB3wnd8SrmyRevf43BIGHvZEdqUlqeppCc9m0XTyfioyxD7g+uOU5MSCxXj90yc1fMeqCAedIoM6Et4MMF71DQrwDpqTq+7PlN9hxC8OGCYfw2cTWrZvyJEEIW7hnpeotKP1pbLXaOthQo6sWw2QP46UPj2oEuVcfOJfsoUaUY3Z6HiSSf8jiDrBPgnPlv1ufawE4hxN97r/yXExEUxZAKH7Llp93oUnWEP4i0qOah8N+kUEkfpm37FCGMgsagN7Dg/FfMOz6dju+0+UdzRz6K5vuRi5nW61sMmaHwKYmpj7Vfm4bNFylTkNn7vsDNx1VuM/XlvnHqjpnQzhlgY+o5Y5o/BAl+GLGEzzrMIDUxFZWJnV6SDKyasYELB65kfpdo3K0evcZ1MXN5tMusxalL07H405UAdH2/PX3Gd8PJ3VHuZ/r5ufAq2bglSbKaUzEzgvIvwDLl13+Ua8duEhNmXr3j+OYzvF/vU77+6/NcK+8o/DcI9Q83q9w+qcvXCGHMR53TjGCKSqOysAtbI8VRIqOUAzY3kmUPiCwXu8cRG5FA0fKFWReymPN/XeazTjMJC8jWeHU5KtHnNae7j5sc4SmEwGAwnsyf83bg4u5EVIjR1KJLzeC3iWtw9sh+Iz2w9hht+jc1my/VxAyjUqtYOmmNnKwrKS4Zz8Lu9BrbRS5G8VzIh14lT5VCT5KkaPF3yjn/B6jWvBIF/bwJDTB/xbt15i6rZ25k6Iy+L+nMFPIDv3yywux7xBOW1MpLaL//w1CWTFhJvJOBiNklkRzU2J2Iw3O20VastdE8NpUsQGpiChk6PVobLad3nJcXHLN4EuGfhWlYviRJxoeHJJkViTDFLCOiBHuXH7Zqf3f2cOLywWvsf3QElUpA5kMpKjgGHz/vv1Vd/qnIZzbup/JdE0K0xBhRqZCJu48bn6//2Kof9+kd51/CGSnkJ0w1S2uoTMwPpvk7hErw9sy+qG3UFmMOrz/ButDF2DcogORg3J9WNfs46ak6bJ8gRL5c7VLYOxqjHBu8XkfOyZ0bLfo0pmTVYnnmJMlCMkiPFXq2OcPerfRvM6CZnDLAYJDwq1QUMNbyLFXN77Hn8U8Q5L9cJXlq3EKIK1jeRg8gGBjwvE7qVSQiKIqxzSeTYqXMU8lqeRYMUvgPMGPHBDbM3cbWBdkJNdsPbUnR8kWo2LAcC0YvlbVSWUsW0PvjLrQe0JxSNUryedevzKIhA64+ICE6kXLpbjwKSiXD1xanreaJqNJS0h8baHP95B3+WnGY1m815beJq9GrJWJG+6LztcX19zDszpsvWh5YczSXmZ6O4pWLUr9jLQKuPeTon6csok01thr6TuhOxfplWT93G5UblWfgl29w6cBVilcqSkG/As/0fKySzzTux1XAySlxJCAqM81rrrzMCjgvg8jgaIZV+YiEGPPbUq5uaVq+2ZiOw1pja593zUuFfz8Gg4GBZUbJeW2GzOjLm+O7cXj9Cab2/taiv629jSx4KzepwL0L/iQnZCeLMrVhS4BkI1ClP52EKVahCK3fasqvn60mqbUbsSOKAKAOS6fge3dyHZf1UChftzT1u9Rh6cTVjz1WTr91Fy9n4iMTLK7JlKW3v5cLL78ITCvg2BcqKpUY+pHVfjemf/RSKuA8VZV3BXMeXA+yENpCJRgwuTd12+ddXV7h309yQgo/vL+YmLA4Ih9Fy+2/TlhFWEAElRqVtTouy7VOkiSuHL5usd9UwAlAi5oMrC9ymiLUggLFvEmJT5ZdAFMSU/n1M6PQVYdna/XqcJ2cMjUryVOW+5+jmwMlKhcjMTaJsYuH4+zp8ljB7VHIDV1aBgnRifI8CSZZE60J7TrtqlOwxAvQqvPi37A4qWBO5cblqda8EpcPXadm6yo07d2QcrVLPXfbm8Krwcbvd/DX74cBLBbRti/ay8G12aaHLGFm/D1d4wmi0mUqNijL3Qv+ZgUUclbBAaNtNsw/nH6TeuDo4khCdAIXD1wlIrNupd3lJDynB6LztcVhXyy6dMw05I+XvMfV47c4uuEkV48aK7aPbTmFxVfnorXRGL1QVCCw1J5NA4ey3vbzusZydUszffsEhBCEP4zE2d0Re6cX76H1qgXgKDwBNnY2zNn/BXq9HrXachFJ4b+Ni8nCpCRJ2DvbkZqUipSpxZnmys6y7wZcfWAh0Kq3rMzFzBJe1khNTjMT0kIlLDxEANnfe+XUDTTqWocSVYrT7cMO+F9ZaHxotKjEmZ0XsTtn1MZNg91t7W349p2FFiHw8ZEJ7Fqyn4lrxzDzre+NAUCPMQzn5a3Sb1JP1CoVHd9tjRCCXz9bxeqZG3HzdmHe8ekULlUwz7mfOflMcCsZkZ4hitBWsEbHd9tQo1UV+XtKQiqDp76Jd9HcU8jGRSZYeIS8Nakn845NY+T3Q+SCCraZASpqrZpEE9c6tVaNRqt+rDvgsU1nWDF1PcsmrSU1KY30VB2RQdE06Vnfan9nDycLoZ3F0s9XU6xCEYu82U+Dh48b/Sf3knOV/7XC+MYSGxHP/tXPdnH0seQWfJOPK+AoKCj8Q8IfROLgbI+DS/Yr/m8TV5vllrZGzpwfCdGJVGxQjhvHb8vRhVl5QvQ6PcH3squcG/SGXIN6NHaWL9qmmQkNBgM9xnSyuj5jaqPPiUEv8U7Vj2nYtQ4qjQqhEtTtUAOPwm65X6QVbB1sCLz2gJXTNpCeary+Jt2zHyTr52whOvTFeiO/Uu6ACgoK/5yFY5dxbJMxo12xCr48uBGEJEFa8uM1UzsnW1IT03D2cMK3XGEO/nGM/U/gjmdqhlBr1BgMBrnN1s6GjFRzoZ6cmEKTXvU59ucpAq8F8VGzz58oYjMnGekZPLoTKo89veMC1VtVITo4Vj6X8nVLU79TLXRpOpZPWWc2vtuHHUhNTGVLpttkSmIKb896i4FfvsGf87YDkBSfQtDtEIvKQc8VxVSioPDfwjTdQbXmleSyXHU71KTjO63x8vWQ9zft2QA3bxf5e2qiUbgnRCfyTuWPmN7nu799fH2G3kyQJ8VaavrxkQkcWXdSTsmaU2hrbZ5cx3twM0j2RAG4uO8KPcd2pvuHHfn+xHS+OzoNg0Hi9y/Xm42bsPIDRswdjN7EjzsrmZWDsz29xnZGa6OhfqdacrHhF4UwWN+eaKwQ7YQQt4QQd4UQ4/PoV0cIoRdCPLYQu6JxKyg8Z0Z8NxiPgm44uDjQ6+POdB/dkfDACKq1qIRarWbrwj38MPIXJAmuHL1BbET8Pzpe9RaV5JzfOXlcME5u6K2UYMsNjUZtYeeOehTDhwvfwdbehs86zeD0jgsW4+YOX0TdjrUYPO1NkuKSUakEA6b0lvcPmz2AYbNfQtzfP7BnCyHUwI9AGyAIOCOE2CJJ0nUr/b4Cdj/JvIrGraDwnHFyc+TtWW/Rd0J3tDZafMsUombrqvJidufhbeWK8DGhsRQo5gUYa1OWrlmSGi0rP/GxVGrByPlDKVXdz2JfVt4QawiVoHbbamhsNPgU97bYbxrNaOdoi3dRT8rVLoVXEQ9a9W3MrN0TaTe0JYBV2/qBNUcZ324a147fsiq0wbhom5KQghCCDm+35uNfRxIbHs+6OVtyLVTxIhB5bE9AXeCuJEn3JUlKx5iY73Ur/UYBGzAWr3ksisatoJAPqNexJvtWHsHBxZ4vN39CWko6fpWK4uBsT0x4HL0Lvm3W38HFnvL1ynB+72WzdoNeYuPcHdy7GGBxjLzc7ySDxNk9lwBjCbc+E7qxZf4uCx9wMAbipCalERMWx1uTehAWEIkkSZzZaV0gZxF0KxjfsoVxdnckISYJG3utXE7N2cOJN8d3Q63VMKzKR8RGxFOiSjHCAiNIjk9h9cw/+d3/JxxdHPI8xnPj6W3cRQDTp04QUM+0gxCiCNANaAnUeZJJFcGtoPACiQiK4s75+1RrXslMCP1v2ft0GfEaPn4FcCvgwh9fb2HfisO8+Wk3i3TBAMnxKUahbSWT3r5Vh+XPzh5OcgY+tUaFWvN4F8HEmCR8inpZFdqm6DP0LJ20FoCDa4/JeXocXOyNofkm56XWqHl39gDCAiNo9VZTvIt6olKp+Pnj5QgBA77oTdf323P58HXZVOR/5YE8PiEmiaS45JcmuPPwIPESQpjm91gkSdIi06FWxuSc7TvgE0mS9E+a5VAR3AoKL4jo0BiG1xhHfFQCpar7seDc13IkpUqlomIDY2bJrQv38OtnqwB4dDeU6ds+pWztUtw+a1kVB8nSbp3lIli4lA9jfhnOV/1/IPJRNB3ebk3pmiX48cPfzIoVAPiWK0zQrWDUGjXdPuhAjVZVzCrbmFKolA8h98LMNHjT5Gqt32rK3YsBXD9urDJfu201KjYsx9WjN/lu+M/oMww4ujqw6sFC4iLjWTNrEwvGLKVAMS9qv1ZdttF3Ht4WZw8nDq8/Qeu3mlGgqNfT3PZnQ+6CO/IxuUqCgKIm330xJukzpTawJvO34AV0EEJkSJK0KbdJFcGtoPCCeHgzWE5Neu9iAAHXH1KiUjGLfqYZANNT0omLjJcTU2ntNOhyuPJJkoStow1qtRpXL2dC7hv7hgZE8Olr07BzskWtVbN/9VG2LtxjNrZm6yqM+vFtCpXw4eGtYAqWKICdgy1Bt4OtCm1bBxurdnInNwdGzX+b1TM3suWn3ZSsWpxiFY2uj2f3XJLNMFkkx6eQkpgqm3QMegNnd12kYZc6zN73BfoMPWqNcQ1g8LQ387yvz51/VkjhDFBGCFECeAT0AcyS80uSVCLrsxBiKbAtL6ENiuBWUHhhVGhQ1lidPTQWgA3fbOPjX0dY9Ov0XltCA8KJfBTFkOl9eXDjkSzwdakZsvZduJQPkiTRok9jWbjFRsTRv8RIUpPTMOgNGPSQGGN0/8sZ8KPWqhnwxRv4likMQNHyhZnWey5n91ykdI0SlK9bmpunzQsgpCWnU7NVFXbc3wdAiarFSUlIofuHHXEv6EbANaM59/7l3PPTuXo5029iTzwLudO6fzPO7r4EwlhCTT43TT6LQn5KG7ckSRlCiPcxeouogV8lSbomhBieuX/h08yrCG4FhReEja2WtoNasGbWRgCzkPfIR1EcXn+SCvXLolKrqNWmKnXa10ClUmHvbEeJqsUIuPqQbqM6MGx2f6JCYvAq4oFKZe4Y5ubtyqCpfVg8fgUaG02u4ect+zam38SepKemM7LueByc7WjYtS5HN54C4OrRmwyb/Ra3ztwz07Ar1C/DqPlv06x3IyIfRTF70I+AscJPhfpl5H5Obo4kxmZnzNTaaWjaowG29jYMndUPFw9nwBjxKUnGYgsbv99JjzGd/8ktfm78kyhJSZJ2ADtytFkV2JIkDXqSORXBraDwAhk4pTfevp4IlaDD263k9rEtviD4bihqjVouzNt1VHvenTOAcS2n8PBWMO4+rrz1eU/UGjXxkQmo1CoWjl3GoT+Oo7XRUL1FZT5f/zE9xnSi++iOJCek0M1jkGyLzsoUaO9kx8Apb1C4VEEmdJwh284Tos1TE//+5XozoV20QmHG/ToSjVZDzVZV8L/6QLava201uHg6y337f96T+1cecGD1UXxKFGDalvFWE0O5F3STP3sUcrPYn2/IZ5GTiuBWUHiBaLQauox4zaxNn6GXi/OaVlO/cfI28VEJPLxlXMuKCYsj5H4Yq1b/yfpvt6K10aJLN9rDdWkZnNl1keObz9DyzcYIIXB0caBOu+qc3nEBjY2Gd77uj09xb4pXKEKBYkZf7RSTwgz3LgUg1ILS1Uvg6uXC2d0Xzc7z4Y1g5gz9iXnHpgNQonIxuo/uyIa520iOTyHE35grxbWAKyWr++Hl68lfvx8mzD+czfN3olKr6T66I96+2W8atdtWY9IfHxF4LYjXhrYgLDACryIe+c5Ukt/SuioBOAoKLxm1Rs3I74dQqKQPTXrUo3Dpgtg72dFnfDc8CrrTcVgbVGoVpar5kRCdyKnt5wBkoW06T3pKOuu+2UrkI2Nu7RotjVkJM9IzmDd8ET+MXCwvXkaFxsj5tLOQ9BIJ0Yn4+HlbrT2pzxEKbyb4LwQAEBcex7iWU/j1s9XoM/Skp+r4c94O1n+7lWl95lrM2bRnA/pO7M7sQT/yVokRjG4yifS0xxc5fmFIGAspWNteEorgVlDIB3Qe3pbld+fz+bqPWXb7BxZf/ZaarasCMHrhMKq3rMS9SwGMf22aLFBNiwoD1Glfne+GL2LRuOV83HIKAIHXzCMOQ+6HMW/EL+jSdayfs9X8JDJdiEP9w9n+816mbv2EcnVLy7tdvV34bPVosyFtB7XAwcUejY3GorZqWnIaQgizosLBd0KsXn9kULSca/zmqTs8up3TY+7lkR+LBSuCW0EhH3H3oj8z35pHP78RDCg1koe3HnFi61nO770i9wm8HgRgEUhzcus52dQSFhhBXFQ8Pcd2xruoJ2pN9p/6w1uP+Hrgj6z/NofgNhVEAkpULs7ohcMoX68MddrX4Jcr31KopI/c5erRG7h6OfPBT++AJBHxMIqmvRrI+338CrD09ve0G9RCbouNiGfXr/strturiIe8uFmyanEKl37BhRIeRz7Lx63YuBUU8glndl9kYscZcoWauMgEzuy8yNXjN3Mdo7XVUqRsQQKuZGvWjq4OJMUl07/ESGbtmcSqwIXERsTRyyczbF6Co3+etJjLNOCmw9ut+PnjZZzbcxlbBxtKVS2Oi6cT10/cYnK3r0lJSCUtJR2NVo1fpaJk6PQkRCcSFpCdakOtUVG4VEE6vdeWfauOyA+ajT/soN2QlmbHVmvUzDkwhUe3gylcumC+K64tniIx1/NEEdwKCvmEGyduy0IbjAK4VtuqOHs4cXTDKRBQuHRBgu+GgiQhSaBL0xF49SFuBVyIDTeGitva25AUl0xKYionNp9Br9Nz6I/j1GhVmQv7jOYIfYaBBl1qAQI3b1f8rwTS86POlK1TCr1Oz6O7oUzsNBOAxNgktv/yF0ItOLzuhFxgGCBDp5e9SVRqFV1GtGPH4r+Ij0pkyHRjnEnZWqX4cvMnTOgwHYNeokK9bLdBU2xstZSoUtzqvpfKS9auraEIbgWFfEKzNxqyfdFeEmOTGDqzH637N8XFw5niFYtSoUFZVCpB4VIFSU/TcXLrGab1+Q7JYBTgpgphlg3c1t6Gqs0rMaHDdFKT0tBo1WhsNGSkZyBJEsF3wyhV3Y/h3w40yxkuSRKH1p2wCHnftnCvxTk7uNjT/4veDJnRF3tne4qVL0Lbgc0t+tVqU41KDctz5cgN9q08QtdRHfCrVNSiX35F8SpRUFCwynfv/kx0aCxObo60eLOxHKQC4FumkOwHbWOrZdG4FWa5QuIi4tHYaFBrVEQ+ikZro2He8emUrVVSNlFk6PQ0790QMJpYAq8HsX/VUTbP32V2HjsX72PppDUWIe9qrbmLnlAJkuNT+H7EL5SrU5pi5Yvkem33Lwdy5cgNwJhd8NIh6/nC8yv/pJDC80AR3AoK+YC0lDTZNS86NNYsM5417J3tLNoy0jNkYatLz0Bjo8HVy4Vxv42kVttqfPDj2wye/iaFShVEZ+Jul1XXESDw+kP2LD9kNq9PcW/8KhWlp0lUo0qtkh8cof7hXDxwlXeqfsT/2nzJ+rnb5ND3LHKG29d5rXqe15fvyGeLk4rgVlDIB9ja29J+qDGSslydUmbh41mkpaQRE25M8Tpl4//o+G4bs8hDAFsH46KeRqsm6NYj/vxuOxXql2HWromkJqUxoNT7hNwLNRuz9qtNBF5/yImtZxlWdSzXjpkvhjq4OvDLlW8JC8xeeMx6QDi42NNvYg8WjFlKwNWHXNh3hZ/HLuODBhOIDM4uLFylSQUadKmNUAkKliyAk5vjU96pl0AuroBKsWAFBQU++mU4734zAAdne3LmZX50N4QxTSYRExbH4Glv0ndCd0YvGEZiTCKH/jgh98vKTZKh0zOt91wyMvT8PnUd5euV4eyui1aPq0vP4MGNR1w7dtNscTSLkHuh7PjlLw6uPQ6Yp5FNjk9h2eQ/qNSwnNmYlMRUIh5G4VXYgyMbTnLz1B1unbmLZJAIvR/OuFZTcHJ3ZOS8IZSsmg8XJHOi2LgVFBRyw9HFwUJoA5zadl4uqLD+263s/f0QKYkp3Dnvn+tcGZk+3YkxSZZCWxhTtGq0aio1Ks+eZQfZvngfWlsNarUKe6dsU0yp6n7cOX9f/l61eUUcXLIXM3VpOloPaMrQGf3QZNrB1Ro1PsW9uHn6Dl/2+oY/5myRvV7AaPO+fOg6P37465PfnJdEfgzAUTRuBYVXgCpNKsheHgnRiXw9cD7zR9lbVKnJma87p2eIu4+r8QEgZRdcuH78JjndlFMSUxEqgU8xLz748R1UahXn/7pMWko6Xd9vz5Qec+S+5eqUpl7HWpzccpYMnfFhoc/QkxibLFffAazm93b3cX36m/ICEXmUfXsZKIJbQeEVICY8zkLw5RTaDi72DJjcm52/7ifw2kNcvV2Iy1Ex3loZtNxiSySDxOfrP0aSJHzLFmLZnfkApCSmyEE+Lp5OfHvkSya0m86lg9meIjZ2NviWLUTRcoV543+vc/nwde6c9ycj3fhQsXOypdfYLgghGFH7f7Ts24SeH+XPlK4veyHSGorgVlB4BShcygetrRZdmg4bWy0ZGXpZkKs1KvQZBpLjU/j54+WsDVlEWnI667/ZwuYfd5vNY2OnzbPmpL2LHSkJqbKgmtBxBrFhcVRsUJbRP7/LjL7fkZacJnuJxEclEh4QzvUTt83mSU9N55uhPzHut/d5e9ZbgDHU/s/vtmPvYk/fT7uTEJ3IgNLvA3DnvD9NezV4ueXJ8uBluv5ZQxHcCgqvAL5lC/P9iencPnuf+1cC2fzDTnmfacY+lUaFja0W9wJuFCpdSG5Xa9VUa1aJ4XMHMqrup6TlqDmZRUp8KrXaViMmNBYfP29ObDHWwb1+4jY/j11GwNWHFmMO/nGClm825q8Vh3Ar4EpUcAwAe5YdwqOQB4fXHadex1qM+G4wHy4YBsC6b7ey9qtNqFQCg0HCwdneLAgo35HPNG5lcVJB4RWhdPUSdHi7Fcc3ns61j0aj5qNmk7l/OZDQ+2Fye5UmFfhqzyRKVCrGoKl9UKkEhUr6ULhUQTwKuVPYpGzYnXP3+fniHN6c0M1s7kuHrls95ukdF9i99AD6DAM9x3bG2cMJgIIlfVgzayPB98LY+P0Orp+4xa7fDnB04ykWfbycuIh4DAaJMrVKMnv/5HztIpjfFicVwa2g8Irh4OogfzZNmQqQlpLO/cuBLPl0BUnx2UEvF/dfZdvPe4gIiqLnR53ZnrKK1wa1IPheKNEhMQTfCUVra3wBb9K9HhcPXCXkXriZn3hGegZVGlewOB+VOvscdv16gIToRIRKMHjqG3hkjrdztGX1rI18M/Qns4VNMHqtlK1V6qnvx3NHAjmvQM7tJaGYShQUXiHSU9MJupWdq1rKxdshNDCS0T8P5+TWc7Jnx/cjFyO99wt+lYuRFJdE7RzRi7q0DAr6FeDC/its/+UvizlVasGVozfM2pzcHHDJ1LABuaixZJAIuhXC3CNTObn1HNVaVOKrAT+YjdXaaChTu+TLr+L+BOQ3G7eicSsovEKotWrZFJGFd1FPnN3NzQyVG5XH29fTzN86S8gHXH1AxMModi3ZT4Mutc3GhQaEE3wvDGs06VEfZ0/zYyfGJnNi6zn5u0olsLHT4lPcm/qda/HbpDWc3XMRW3sbypsUZQBj4M/147f5oP4Ewh9GPuEdePEoftwKCgr/CLVazZz9k9myYA8xYbEkxSRxfl92kQUbOy2t+zfl3TkDAMwCaXIiSRLRITE4eziZ+VvnxMnNkTGL3qVRt7pcO3aTsc2/yLVvo651Gfn9EFQqFb9+toqDa44BMKPvPO5fDrQ6Jiwwgj/nbWf4nIF5XfrL4yWbRayhaNwKCq8YxSsWZdQPQ/n8j7FUqF/WbF96qo7B095Ea6shJiyWj38dSYX6ZajS1NI2DVC6Rgma9KiPUBnNHjkZ99tIOrzTij/nbWfltA1UbFCOGTsmUKRMIbN+VZtV5LM1Y3j/h6GoVCpiwmLZv+qovP/O+fsY9Pqc08ts+XEX+1YeRpIkTmw9y5pZG+W8LPkBReNWUFB4ZvSd2IOIoGj2LDsAGDXslMQ0PmjwGSH3w+j+YUe+Pz6DxNgkunkMshjv5uPKyqkbAKPZw2yftwvpqen8MXsLANeO3WLtV5toM6A5ibFJcj/fsoV4bVBzVkxdz3fv/kz1lpUpUbkYYYERZvOpNWo5srJy4/K4eDpzfPMZwGhfn9X/By4euCaXNtv2815+OveVWXrbl0b+UrgVwa2g8CpjY6tl3G8jaD+0BWf3XKJx93rcOHmbkExXwK0Ld6NLz+D1ka/R8PU6sqDMwjR/CECVphWM3iNNKtLhnVZcOmjuApieqmP7ouyCCq5ezoz7bSRjmn4uBwQd23ialPhUs3FuBVzo9XEXVny5ntI1SzB926fYO9mzbs4W1s7eLEd4ntqRbS8PC4xgaq9vmL3vi392k54B+a2QgiK4FRT+BVRuXIHKma56zu5OuHo5ExeZgC4tg60LdnN290WW3fmBW2fukpyYyqrpG7B3tKP/571wcLZjw9zt+JYpxGerx+BZyB2Auxf8+f3LP9DaaY1Vc6x4sMRFJrDsiz/Q2mjMgnrO77uMjb2W9BQdjm4OzDs+HfcCrhzffIabJ++w+cddVG5UnmotKhH5KIp9q44SFxFPTGgcQiXMcn2/dCRAn78ktyK4FRT+ZfgU92bJ9e/Yt+oIC0YvBTKTRglBalIak7t8hV5vYOJao5B+439d2bvsEA9uPmJcqyn8cvkb1Bo1m+fvJDIoOu+DAef3XqbZGw0x6PUc33RGjuRMTzGG1ifFJjOs6scIFaQmGtPO/jZxNQa9ZJYiNgshBPW71CLkfhjvfNX/Gd6Zp0fRuBUUFJ47rl4udBvVgYSoRG6dvccb415HkiS+H/ELqclG4bln2UEadqlD8L0wYjNNFQ9vPiI6NJYf3l/M5cPZZhK1Vo1ep8e3bCEig2NITTQ3hRxae5ymvRowbdsEzuy8wKF1J4gyKaSQlnnMLAyZGmxOoe1brjD9J/WkZd8mz+5mPAvymVeJIrgVFP6lCCEYOOUN+fvVYzd5aBK8U6NlFQDK1ipJ2VoluX3OmHN77Veb5BwlWeh1egZMeYO+E7px8/RdxjSZhGSQcC/oRkxoLACH153gzvn71G5TjXodaqBSq9j7+yE5fazVczQxiwCM+XkYVZtW+sfX/qxRNG4FBYWXglcRDzk7oHtBVzoNbwMYvT18yxWWBffD28GoNWr0GXocnO1JTjCmj63atAJqtZrQ++GysI2LMHfZC7kXxtZ7ewDw8fOWhXaRMoXQpekIf2AMtFGpBGqtGl1ahtn4Xb8eoFT1Eji6mLsmntt7ib2/H6Jeh1q06NPoWd6Wx6OkdVVQUHhZFPQrwNwjU7l65CaNutVFrc6u2t6qX1OObDgFkkS3UR0Y9GUf7l8KoGabqpzYcpbiFX2p1syoCTfsWofKTSpw9cgN2eSRhanNOiwg2x0wMTZJ9hzxLVeYQiW9sbG15ezui2aLmnuXHyLodjDfH58htyUnpDC569ekpaRzYNVRytQqiW8OP/LniQCEsjipoKDwsihbq5TVhE4lqxZj7OLhVGxYjkIlfACoUM9YsLj7hx3N+to72vHetwMZWWe83Kax0TB9+6f8+OFvPLgeZDF/Vg4TgPDACDnfSpUmFbhyxDz/Sc6Qe0mS5FqYkgSS4cUnDhH/wMYthGgHzAPUwGJJkmbl2N8P+CTzayLwniRJl/KaUxHcCgr/ItJT09Fn6LF3evLc1vHRCYyo/QkxYXEULVeYRZe/QaPNWzTMeut7+bNQCaZuGU/NVlUZv3wUI2p/YtE/t2RYcZHZfuRaWw029jYM/3YgQbeDcfF0xsXTGUcXByavH8ue5Yeo16EmRcsVeeJreyb8A1OJEEIN/Ai0AYKAM0KILZIkmTrI+wPNJEmKEUK0BxYB9fKaVxHcCgr/Em6cusMnbb8kNTEVRzdHRs1/m5ZvNn7suJD74XJJs4e3gkmITsTdxy3X/ofWHTdb5Hzv24HUblsNgDI1S1KiclH8rRRcyMK0Ao+doy0OzvboM/QULFGAwOtBfPvOQnSpOhxc7PnuyFRKVClOvY61qNex1mOv5fnwj3KV1AXuSpJ0H0AIsQZ4HZAFtyRJx036nwR8HzepkqtEQeFfwv6VR0hJSEWSjJXdF360lA8bfca4Vl8QERSV67jSNfyo17EmKpWK1m81zVNoA9y/lJ0sytHVgY7vtjXb32Zgc/mzi6cTo38eRoFiXkZjcQ5cPJ1ZH7GEqVvHE5hpYtFlCvbk+BTO7s7TYvDCyCNXiZcQ4qzJNizH0CKA6VMsKLMtN4YCO/PYDyiCW0HhX0PNNlUtCitcP3GbiweusXrmxlzHqVQqooJjMBgMnNl1gZiw2DyP0/7tVnINzDb9m2Fjq5X3paems+UnY51LGzst847PoOM7bVgZsID1YUvo+1l3bB1s5f59xnfjyPqT/K/1lxbHcfZwom7Hmk9y6c+f3AspREqSVNtkW5RjpJXHlXXDixCiBUbBbWlryoFiKlFQeMU5s+sCK6dvoEyNkiy++i03T93FYJC4dvwmu5YYEzb5FPfOdXxqUip3L/gDxhD2BzceWWjdUSExTO8zl4ToRDqPeI3QgAgMegOb5u+kbO1StBnQDDBWkc8KU09P1bH8iz+YsPJDwBgUNHjqm7To05idi/dRsUFZqjWrxPcjfjE7lpevB/OOTcfR1cHCLfClIP0jr5IgoKjJd18gOGcnIURVYDHQXpKk3F+PMlE0bgWFV5yvB/3ItWO32DR/J4HXgmg7sDlNutfl/N7LABQo5kX3MR1yHW/vZE/X99sjhKB6y8pUaFDWos+m73dw5cgNAq49ZNX0DXJCKYBYk/SrPsW98S1bWP5u52RLTvwqFeW9uYNo1rshAB2GtcbFMzsD4Osj2lGgqFf+ENpZSLlsj+cMUEYIUUIIYQP0AbaYdhBCFAP+BPpLknT7SSZVBLeCwiuOm7eL/Dn4XihgXGTMCnYJfxBJYnSS1bFZjPx+CDvTVzP7r8lmpo8sTPNvl6hcTDbJlKtbWg7kyeLbw1Nw83EF4N4Ff3TpRpt1XGQ8Y5pOop/fe5zanp0FsHT1EmyI+JVNsctY/XAhfcZ3Q5IkZr41j/a2ffhm6E9PfC+eF0KSrG6PQ5KkDOB9YDdwA/hDkqRrQojhQojhmd0+BzyBn4QQF4UQZ3OZTkYxlSgovOL0Gd+VWf2N9RxXTf+T3uNep1R1P6o0rcCVwzdo1rsBbgVcHzuPaUAOQHqajoNrjlGgmBfthrTE0dWB+KgEWg9oRnRIDBnpGVZd80LuhxOb6aVy++x9Htx4RKlqfiwcu5yrR28C8Otnq2UvkZun7wBQvm4ZWct+cCNILsSw67cD9J3YQ/Yvfyn8Az9uSZJ2ADtytC00+fw28PbfmVMR3AoKrzi+JsLTwcUeIQQqtYruH3ZkwBe9qd688lPN+83Qn2ThOXXLeMrVLc0nbb7kp9G/8dEv79GqnzERVFxkPBk6vZwONjbH4qZrAVdCA8LZt/Kw3Obs4YQ+Q8/kbl9zavt5AD5cMIxO77bh0LoThNwPw6OQO9EhMfgU936sp8tzRQLyWbFgRXArKLzilKtdiolrP+L68Vu0f7sVf608wuxB8zHoDQghmL79U+q0q/G35w24lu3Ftnf5QRyc7Qm6HQLAwrHLqNKkPBcPXOObdxYgGSTe+3YQzh5OlKtbmvL1ynDz1B3snezo6/sufSf2MAvCSU/VsWPxPlloA1w+fA0HZztmZgb3NOhSmzb9m1G5SQXsHCxt5S8KwZOZRV4kiuBWUPgX0KxXA5r1agDABw0myIuHkiRx++z9pxLcQ6a9ybz3fiE9Tcfh9SfN9sWGxzG+3TSC74ZhyMy/vWjccjJ0ejwKufPbzXlsXbCbxeNXAnD4j+O0HdSCPUuNJdaigqNJjMm2u6vUKjq929Ys/D0mNJYmPer/7fN+LryEMPu8UBYnFRT+ZRhMNFtnDyfaDmputn/1zI0MLPM+C8YszXOeeh1rserBwlxdCcMCItBnZBcAzqonGR0SQ9DtYKo1r4RGa7SbV21WkXG/jqBWG2OEZfiDSPyvPaDvhO606teExde+pWrTinR+ry112tegZLXivPtNPqn6nmUqsba9JBTBraDwL2P0z8Nkk8UK/5/w9vWU98WEx/HrZ6sIvhfGn/O2c+9SgMX40zsvcHDtMfSZVdkHT3vTGPlogkqjMgtd19hoaDe0JXZOttg62DCyznh2LtkvC/OwTA+XsrVLymNcPZ1p3qcRQ2f2Q2ujZdP8nUQFxzBj+wR+vjCHyo3KP7N78k95Wq+S54ViKlFQ+JfRul9TWvdranWfg7Md7j6uxITFYedoi7uPubfJziX7+PYdo8NDzzOdeXfOAGq3rcbcw1/Sz2+E3C/LPJKFZDAw6oehFK/gy88fLweMRYOzuHzwGp2d3qLvxB70/awHkiSh0aoZVnUsWlst9k62xEclYutgS+FSBXByd+LTFR+aPXReKoqNW0FB4WUQFxnP512/xqA38NrgFnT7oAMeBd3N+gSYJIfyv/pA/uxkUoAYgUXwiZObI2qtmvL1y2DrYEtachpNetYn/EEEd877y1Vylk1ei6Q34ODigIuXMehGl6ZDl2bU3tOS0/C/YjyHP2ZvZuS8Ic/4LjwN/yjJ1HNBMZUoKPxH2Lv8ENeP3yIuMoEbJ29TqpqfRZ+uo9pTslpxCpYoQJcRr3Hz9B0kScLB2Z45B6dQqkYJM6GtUgmqt6jE7P1fsGLqesY2/RwbOy0T14zhw5/eYfq2CXxz4Ats7IxBPTZ2WgwGicTYJEJM8m63HdQcryIelKhSTG4rVPIl+m2bklXl3dr2klA0bgWF/wglqhSTK9SUrFqcEP8wJnf9msTYJD5ZPopqzSpRqKQPP1+YQ+D1h3zQ4DOSE1JoN6QlYxe/R8SDSO5l5jQBKFahCN1GdWDjDzv47t2fuX7CGK2dEJ3I3Yv+NOvdkOsnbnH16E26jmqPRqvhzvn7nNl1ETAvFLxvxREMegO9Pu6MjZ0t9k52tOz7+JS0LwrFHVBBQeGlUKtNNb49NIUQ/3Ca9W7Ib5+txv+K0RyyYup6fMsWZt+Kw5StXYqg2yFyrcnTO4y+1hu/zw7+c3J3+H97dx4dVXUHcPz7m8lkI0iYgCRhJ0QMBqSCRpQGFWjBBdGCiArVllK1bj1C63awKj3W44JoRcVTtNpTsCxuFKFFrCxBG6g5CGIwKHtYQlYYIJnM7R9vMpmZTBYbMjOpv885c868++7cee+f39y57977Y8HWZ7kq4SZq3Q2nV+SvKuAfb/yLsiMVjSZR8Fc3O2XNX9YzP/+pVt/rGaeBWykVKdkjssgekQVAv8G9feUZg3vz4Ng5fPvFXmw24YkPHsSZlkxpcblvM6i6vU8Ajpe5uLn3nSGDNsCugt1NXodI6FjoqnTh8XiYM3ku2/MKmXD3WKY8cP13vMszzAAt+PEJJw3cSn1PjZk2ks6pyZwoP8GIn+QwvuNUwJoHXphf5MvQ/vGiDdw250Ym3D2OeXfUb8F67GBpyHZ7DewRkHeyo7MDIKRldONk5UlOn6z2/QgkdkokxmGjsuQ4AAe+PsSy51ewfpm14GfhQ4uoOeXmltkTsdki9Ugu+h5OauBW6nusLuUYWDsA1g2dLJ27AleFC4Dyo5XcOuBeKv3yQ9ZJz+hGyYEyMi/oizMtmbLD5RzeUxJQx3XyNDw4gC2JbgZt7UL85vqAX/cddc7NyWywL8lbjy9B7DYuu2F4+PNN1omywK2zSpRSAOROql9e7qpwISK+2SClxWW+xTTO1GQm3n8N97w0nazh51B9qprteYUUFexm24ZCju4LzANQOTyJvdlQ2S+Gz4a5QmaBB+jSI4V+g3szdMz5XDVjNA6/7WXffPRtpp/3a19PPKwMUOsJ/YoQDdxKKYsJzLJljCFjSB/fcV2yA0ecgy/zCtmz4wAFa7f5zvtP77P5pVCzVdUvi7cdr38PkNAxnu79U8kY0oeS/cdY+doaHp/4DPe98kte+fxpRt4wnLQMa1qgx2PYvLqg1bf53RkwntCvCNHArZQCrPRkwTy1Hn63fBazl87k0usuAuDwnqN8uWkn7/3xQ8qPBA6fxCXEcsdzt/Lo8ll09Ab6hPwqkl88QNKyozif2ReQhfHxd3/La9ueo8959dm9dnxm7c/9yd/yKC0uY/QtuThiY0hIimf01JFn+rZbpvGckxGhY9xKKQD6/6Bvg7L9O4tZu2gD7mq3b9jEn/8mUwDO7k5GT8vlwNeHqDpW5Svv8HG5dT41mZ++eiPLn19B5tB+vPPiSmaNeoyU7vUrOG02G9c5b+V4ubV74Pa8nbx9cAEJSfHEJURge9conFWiPW6lFAADLszAHmPt5peQFE9svIO+g3qxbskm8t7LZ99XDXLcEuOwM3jkQN9xcdEhJqfPYPvGr+jYOalBfWd6Z66cPop5G+eweVUBee/mA3DsQH1vv+Z0jS9og9XrP1HhikzQrqM9bqVUNOo/pC9z1z/BroLd5E66mLOcHZk9oX4xTPHuIw0+Izabb8VkHXe1m1dnvkl6/1SqyqxpfvYYG2eldGT6kzcDUHKwjPKjDWep+LM77NTW1DJ0zGDSM1Jbe3utE2WzSjRwK6V8snIyycrJZNXCtewrPBiQzd1V4cJmt5HUORFX5Snc1W7f5lChHCw65Htf6/ZQdriCR8b/gaGjB/PN1j1NXsfw8cO464WfkZLu9P0LiBhjoLa2+XphpIFbKRVg47v/5tnpLwPQK6sHMbF23NVW4PLUenyLZfx17ZnC1NmTKC+pZOFDfw3YiMoR5/AFePdpd0C6MrBmoPgnf8gc2o9Hl86MfMD2F2U9bh3jVkoFqCqtD8zGGJYeWciYaU3P5uh+ThqfrtjC6w8twhEb+BDTXR3YKxe74Iiz+owxsTEBQXvUTT9kfv5T0RW0IerGuDVwK6UCjJ6ayzW3/4ghV2Qz80930OGsRGa9/iuyR1gZaWLjHZzdqwsPvHU3uZOGY3fYKfhoG3nv5WOMCRg+GZQ7MCC+2R12nlz5MAt3zCPnygtwV7sDvrumxk31qeqw3GfLGWtWSahXhOhQiVIqQIwjhnvm/yKgTER46p+z2VWwm54D0klK7gDAiYqTrFuyCagf8ujQKZHTJ6u5bPIlbP3ky4B2amtq6da7K6l9zub8y7P5zLvzoM0ueGoN65Zs4rxLBnD9vVeF4U5byICJ4GKbUDRwK6VaJDbOQVZOZkDZheOG4ExNpvRQOdffdzWDcrPIHnEuZzmtxTe/nzKXI3tLEJvQtUcKY6aOpMc56QBMuv8auvXugrvazao3PubzNV8A0KFTYnhvrCUiuLw9FDFtME4zbNgws3nz5jPerlIq+pw+eZrj5S5S0jo3OOeusR5GpvXrFrCNbLDSQ2UsffYDzu7VlWvvGouINFo3HERkizFmGEAnexczPGl8yHqrK1/31Qsn7XErpVolLiGu0cUxMY4YLp1wUbNtOFM7M+PpaWf60s6cKJtVooFbKaWaYTzRNVSigVsppZqkiRSUUqp9icJNpjRwK6VUEwxgomzJuy7AUUqpppjWJVIQkbEiUigiRSLyQIjzIiIveM9vFZELmmtTA7dSSjXDeEzIV3NExA68BIwDBgJTRGRgULVxQKb3NQN4ubl2NXArpVRz/vce90VAkTHmG2NMNbAYuDaozrXAm8byKZAsImlNNdomY9xbtmwpEZGm921USqno5VstVEXZ6jVmaZdG6sWLiP9qwwXGmAV+x92BfX7H+4GcoDZC1ekOFDd2cW0SuI0xXduiXaWUCjdjzNhWfDzUEtDgMZaW1AmgQyVKKdV29gM9/Y57AME54FpSJ4AGbqWUajv5QKaI9BWRWOBG4P2gOu8D07yzSy4GKowxjQ6TgM7jVkqpNmOMcYvIXcBqwA4sNMZsF5HbvedfAVYCVwJFgAu4rbl222R3QKWaIiJ9gBXGmGy/st8Bx4Fs4AagmzGmyntuHnAP0NUYU+Ituw5YDmQZY77ya3cHUAjEAuuAO40xHhFZBVwMbDDGXB2G21SqzehQiYpGRXinTImIDbgcOBBUZwqwAeuvp79dxpghwGCsebMTvOVPA1Pb5nKVCi8N3CoaLQIme99fBmwEfDmuRCQJuBT4OQ0DN2D9RQXygP7e44+Aqja7YqXCSAO3ikZfA11FpDNWz3px0PkJwCpjzE6gNNQSYRFJBEYBX7TxtSoVdhq4VSQ09mDFv3w5Vm86B1gfVM8/mC/2HtfJEJECrF76340xH7b6apWKMjqrREXCMSA4z5UT+NbveDHwH+DP3oeLAIhICnAFkC0iButJvRGR33g/VzfGrdT/Le1xq7AzxhwHikVkFICIOIGxWA8b6+rsBR4G5gd9fCLWvg69jTF9jDE9sQL+iLBcvFJRQAO3ipRpwCPeYY21wGPGmF3+FYwxrwaXYQ2LvBNUtgy4qakvE5H1wBJglIjsF5Eft+bilYokncetlFLtjPa4lVKqndHArZRS7YwGbqWUamc0cCulVDujgVsppdoZDdxKKdXOaOBWSql25r9zrP5jXF/khQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -849,7 +849,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "34adad2e",
+   "id": "67688f02",
    "metadata": {},
    "source": [
     "## Exploration\n",
@@ -860,7 +860,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "bef35e70",
+   "id": "d4d06723",
    "metadata": {},
    "outputs": [
     {
@@ -910,195 +910,195 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>B cells</th>\n",
-       "      <td>2.862964</td>\n",
-       "      <td>0.019416</td>\n",
-       "      <td>1.465129</td>\n",
-       "      <td>-1.228271</td>\n",
-       "      <td>0.766851</td>\n",
-       "      <td>-1.464808</td>\n",
-       "      <td>2.403027</td>\n",
-       "      <td>2.052576</td>\n",
-       "      <td>3.010636</td>\n",
-       "      <td>-1.225934</td>\n",
+       "      <td>2.868587</td>\n",
+       "      <td>0.022602</td>\n",
+       "      <td>1.464726</td>\n",
+       "      <td>-1.234619</td>\n",
+       "      <td>0.767511</td>\n",
+       "      <td>-1.476407</td>\n",
+       "      <td>2.415689</td>\n",
+       "      <td>2.066459</td>\n",
+       "      <td>3.021957</td>\n",
+       "      <td>-1.223385</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.235808</td>\n",
-       "      <td>13.664436</td>\n",
-       "      <td>1.983893</td>\n",
-       "      <td>1.009390</td>\n",
-       "      <td>0.446209</td>\n",
-       "      <td>-0.634329</td>\n",
-       "      <td>-0.379898</td>\n",
-       "      <td>1.135776</td>\n",
-       "      <td>3.559168</td>\n",
-       "      <td>1.004977</td>\n",
+       "      <td>-0.240303</td>\n",
+       "      <td>13.696566</td>\n",
+       "      <td>1.991853</td>\n",
+       "      <td>1.016835</td>\n",
+       "      <td>0.459040</td>\n",
+       "      <td>-0.632491</td>\n",
+       "      <td>-0.404400</td>\n",
+       "      <td>1.144343</td>\n",
+       "      <td>3.564371</td>\n",
+       "      <td>1.000175</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CD14+ Monocytes</th>\n",
-       "      <td>1.999010</td>\n",
-       "      <td>0.171994</td>\n",
-       "      <td>2.853401</td>\n",
-       "      <td>0.873312</td>\n",
-       "      <td>-1.068735</td>\n",
-       "      <td>-1.807254</td>\n",
-       "      <td>3.214971</td>\n",
-       "      <td>1.698302</td>\n",
-       "      <td>5.127343</td>\n",
-       "      <td>-0.795039</td>\n",
+       "      <td>2.007714</td>\n",
+       "      <td>0.175447</td>\n",
+       "      <td>2.859986</td>\n",
+       "      <td>0.866449</td>\n",
+       "      <td>-1.072762</td>\n",
+       "      <td>-1.804330</td>\n",
+       "      <td>3.222964</td>\n",
+       "      <td>1.711882</td>\n",
+       "      <td>5.142670</td>\n",
+       "      <td>-0.796957</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.484025</td>\n",
-       "      <td>6.790131</td>\n",
-       "      <td>3.596484</td>\n",
-       "      <td>2.012941</td>\n",
-       "      <td>1.400468</td>\n",
-       "      <td>2.490372</td>\n",
-       "      <td>-0.830063</td>\n",
-       "      <td>0.463730</td>\n",
-       "      <td>3.634529</td>\n",
-       "      <td>2.253693</td>\n",
+       "      <td>-0.485268</td>\n",
+       "      <td>6.818030</td>\n",
+       "      <td>3.603954</td>\n",
+       "      <td>2.031029</td>\n",
+       "      <td>1.407858</td>\n",
+       "      <td>2.494360</td>\n",
+       "      <td>-0.855818</td>\n",
+       "      <td>0.471007</td>\n",
+       "      <td>3.636787</td>\n",
+       "      <td>2.269021</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CD4 T cells</th>\n",
-       "      <td>2.803933</td>\n",
-       "      <td>0.874976</td>\n",
-       "      <td>1.300079</td>\n",
-       "      <td>-1.456408</td>\n",
-       "      <td>-0.922026</td>\n",
-       "      <td>-1.801688</td>\n",
-       "      <td>2.631588</td>\n",
-       "      <td>2.954416</td>\n",
-       "      <td>3.755872</td>\n",
-       "      <td>-0.997119</td>\n",
+       "      <td>2.811583</td>\n",
+       "      <td>0.879623</td>\n",
+       "      <td>1.298020</td>\n",
+       "      <td>-1.463941</td>\n",
+       "      <td>-0.925841</td>\n",
+       "      <td>-1.807085</td>\n",
+       "      <td>2.640772</td>\n",
+       "      <td>2.972541</td>\n",
+       "      <td>3.769373</td>\n",
+       "      <td>-0.997432</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.048044</td>\n",
-       "      <td>1.297521</td>\n",
-       "      <td>1.478310</td>\n",
-       "      <td>1.341562</td>\n",
-       "      <td>1.228791</td>\n",
-       "      <td>0.390741</td>\n",
-       "      <td>-0.088309</td>\n",
-       "      <td>1.126816</td>\n",
-       "      <td>3.673022</td>\n",
-       "      <td>1.261324</td>\n",
+       "      <td>-0.050020</td>\n",
+       "      <td>1.309786</td>\n",
+       "      <td>1.484332</td>\n",
+       "      <td>1.357391</td>\n",
+       "      <td>1.239065</td>\n",
+       "      <td>0.394266</td>\n",
+       "      <td>-0.115241</td>\n",
+       "      <td>1.138940</td>\n",
+       "      <td>3.678631</td>\n",
+       "      <td>1.265142</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CD8 T cells</th>\n",
-       "      <td>1.982264</td>\n",
-       "      <td>2.649553</td>\n",
-       "      <td>1.488398</td>\n",
-       "      <td>-1.810813</td>\n",
-       "      <td>-0.213379</td>\n",
-       "      <td>-1.688924</td>\n",
-       "      <td>2.406077</td>\n",
-       "      <td>2.480685</td>\n",
-       "      <td>4.022677</td>\n",
-       "      <td>-0.927006</td>\n",
+       "      <td>1.989253</td>\n",
+       "      <td>2.657882</td>\n",
+       "      <td>1.488735</td>\n",
+       "      <td>-1.821871</td>\n",
+       "      <td>-0.215796</td>\n",
+       "      <td>-1.692385</td>\n",
+       "      <td>2.414712</td>\n",
+       "      <td>2.496823</td>\n",
+       "      <td>4.036488</td>\n",
+       "      <td>-0.926765</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.185045</td>\n",
-       "      <td>2.509188</td>\n",
-       "      <td>2.047695</td>\n",
-       "      <td>1.809774</td>\n",
-       "      <td>0.565882</td>\n",
-       "      <td>1.710762</td>\n",
-       "      <td>-0.155667</td>\n",
-       "      <td>1.347121</td>\n",
-       "      <td>3.513260</td>\n",
-       "      <td>0.990922</td>\n",
+       "      <td>1.185035</td>\n",
+       "      <td>2.524118</td>\n",
+       "      <td>2.053695</td>\n",
+       "      <td>1.825817</td>\n",
+       "      <td>0.574987</td>\n",
+       "      <td>1.715534</td>\n",
+       "      <td>-0.182004</td>\n",
+       "      <td>1.357577</td>\n",
+       "      <td>3.518316</td>\n",
+       "      <td>0.995989</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dendritic cells</th>\n",
-       "      <td>2.866896</td>\n",
-       "      <td>0.180116</td>\n",
-       "      <td>1.889387</td>\n",
-       "      <td>-0.742983</td>\n",
-       "      <td>-0.417720</td>\n",
-       "      <td>-1.442676</td>\n",
-       "      <td>3.151718</td>\n",
-       "      <td>1.870627</td>\n",
-       "      <td>3.726094</td>\n",
-       "      <td>-0.932661</td>\n",
+       "      <td>2.875063</td>\n",
+       "      <td>0.184359</td>\n",
+       "      <td>1.892848</td>\n",
+       "      <td>-0.751902</td>\n",
+       "      <td>-0.419758</td>\n",
+       "      <td>-1.445644</td>\n",
+       "      <td>3.163058</td>\n",
+       "      <td>1.887027</td>\n",
+       "      <td>3.739679</td>\n",
+       "      <td>-0.930901</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.012765</td>\n",
-       "      <td>15.064636</td>\n",
-       "      <td>3.585419</td>\n",
-       "      <td>2.635822</td>\n",
-       "      <td>1.193597</td>\n",
-       "      <td>1.011139</td>\n",
-       "      <td>-0.950940</td>\n",
-       "      <td>0.472480</td>\n",
-       "      <td>4.095103</td>\n",
-       "      <td>2.617512</td>\n",
+       "      <td>0.011379</td>\n",
+       "      <td>15.106863</td>\n",
+       "      <td>3.594913</td>\n",
+       "      <td>2.652861</td>\n",
+       "      <td>1.206054</td>\n",
+       "      <td>1.013332</td>\n",
+       "      <td>-0.983827</td>\n",
+       "      <td>0.482137</td>\n",
+       "      <td>4.096582</td>\n",
+       "      <td>2.625957</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FCGR3A+ Monocytes</th>\n",
-       "      <td>2.293198</td>\n",
-       "      <td>0.446685</td>\n",
-       "      <td>2.491488</td>\n",
-       "      <td>-0.908603</td>\n",
-       "      <td>-1.255402</td>\n",
-       "      <td>-1.755676</td>\n",
-       "      <td>3.188233</td>\n",
-       "      <td>1.914761</td>\n",
-       "      <td>4.890978</td>\n",
-       "      <td>-0.538166</td>\n",
+       "      <td>2.303873</td>\n",
+       "      <td>0.451908</td>\n",
+       "      <td>2.498814</td>\n",
+       "      <td>-0.921232</td>\n",
+       "      <td>-1.259868</td>\n",
+       "      <td>-1.750411</td>\n",
+       "      <td>3.196807</td>\n",
+       "      <td>1.929357</td>\n",
+       "      <td>4.906310</td>\n",
+       "      <td>-0.540120</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.813959</td>\n",
-       "      <td>7.569202</td>\n",
-       "      <td>3.998404</td>\n",
-       "      <td>2.627059</td>\n",
-       "      <td>2.216238</td>\n",
-       "      <td>2.493336</td>\n",
-       "      <td>-1.909961</td>\n",
-       "      <td>0.895054</td>\n",
-       "      <td>3.707708</td>\n",
-       "      <td>1.937340</td>\n",
+       "      <td>-0.815608</td>\n",
+       "      <td>7.600002</td>\n",
+       "      <td>4.006298</td>\n",
+       "      <td>2.647358</td>\n",
+       "      <td>2.224453</td>\n",
+       "      <td>2.496363</td>\n",
+       "      <td>-1.937861</td>\n",
+       "      <td>0.902676</td>\n",
+       "      <td>3.707245</td>\n",
+       "      <td>1.954463</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Megakaryocytes</th>\n",
-       "      <td>0.040962</td>\n",
-       "      <td>2.745303</td>\n",
-       "      <td>4.102540</td>\n",
-       "      <td>-2.279636</td>\n",
-       "      <td>0.885389</td>\n",
-       "      <td>0.852653</td>\n",
-       "      <td>0.671578</td>\n",
-       "      <td>-0.539585</td>\n",
-       "      <td>5.077672</td>\n",
-       "      <td>2.363797</td>\n",
+       "      <td>0.050993</td>\n",
+       "      <td>2.751533</td>\n",
+       "      <td>4.110503</td>\n",
+       "      <td>-2.296985</td>\n",
+       "      <td>0.882954</td>\n",
+       "      <td>0.872765</td>\n",
+       "      <td>0.667223</td>\n",
+       "      <td>-0.532625</td>\n",
+       "      <td>5.092278</td>\n",
+       "      <td>2.361103</td>\n",
        "      <td>...</td>\n",
-       "      <td>2.611817</td>\n",
-       "      <td>2.147261</td>\n",
-       "      <td>3.802499</td>\n",
-       "      <td>7.186048</td>\n",
-       "      <td>-1.160673</td>\n",
-       "      <td>-1.038153</td>\n",
-       "      <td>-2.471840</td>\n",
-       "      <td>3.480007</td>\n",
-       "      <td>1.433540</td>\n",
-       "      <td>0.918447</td>\n",
+       "      <td>2.617590</td>\n",
+       "      <td>2.172133</td>\n",
+       "      <td>3.807284</td>\n",
+       "      <td>7.223926</td>\n",
+       "      <td>-1.168846</td>\n",
+       "      <td>-1.041995</td>\n",
+       "      <td>-2.491852</td>\n",
+       "      <td>3.492409</td>\n",
+       "      <td>1.438276</td>\n",
+       "      <td>0.944554</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NK cells</th>\n",
-       "      <td>1.108534</td>\n",
-       "      <td>2.085616</td>\n",
-       "      <td>1.788463</td>\n",
-       "      <td>-1.807453</td>\n",
-       "      <td>-0.826893</td>\n",
-       "      <td>-2.283551</td>\n",
-       "      <td>2.114203</td>\n",
-       "      <td>1.565360</td>\n",
-       "      <td>4.122890</td>\n",
-       "      <td>-1.014566</td>\n",
+       "      <td>1.114531</td>\n",
+       "      <td>2.093307</td>\n",
+       "      <td>1.790295</td>\n",
+       "      <td>-1.819632</td>\n",
+       "      <td>-0.830680</td>\n",
+       "      <td>-2.286488</td>\n",
+       "      <td>2.121871</td>\n",
+       "      <td>1.579840</td>\n",
+       "      <td>4.136767</td>\n",
+       "      <td>-1.014884</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.035408</td>\n",
-       "      <td>2.060028</td>\n",
-       "      <td>2.794987</td>\n",
-       "      <td>2.362190</td>\n",
-       "      <td>0.249298</td>\n",
-       "      <td>3.375603</td>\n",
-       "      <td>-0.687441</td>\n",
-       "      <td>1.471111</td>\n",
-       "      <td>3.432891</td>\n",
-       "      <td>0.413902</td>\n",
+       "      <td>-0.037481</td>\n",
+       "      <td>2.075289</td>\n",
+       "      <td>2.802213</td>\n",
+       "      <td>2.380026</td>\n",
+       "      <td>0.256690</td>\n",
+       "      <td>3.383240</td>\n",
+       "      <td>-0.715219</td>\n",
+       "      <td>1.481768</td>\n",
+       "      <td>3.433856</td>\n",
+       "      <td>0.419311</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1107,44 +1107,44 @@
       ],
       "text/plain": [
        "                       ATF2      ATF3     BACH2     CEBPB     CREB1      CREM  \\\n",
-       "B cells            2.862964  0.019416  1.465129 -1.228271  0.766851 -1.464808   \n",
-       "CD14+ Monocytes    1.999010  0.171994  2.853401  0.873312 -1.068735 -1.807254   \n",
-       "CD4 T cells        2.803933  0.874976  1.300079 -1.456408 -0.922026 -1.801688   \n",
-       "CD8 T cells        1.982264  2.649553  1.488398 -1.810813 -0.213379 -1.688924   \n",
-       "Dendritic cells    2.866896  0.180116  1.889387 -0.742983 -0.417720 -1.442676   \n",
-       "FCGR3A+ Monocytes  2.293198  0.446685  2.491488 -0.908603 -1.255402 -1.755676   \n",
-       "Megakaryocytes     0.040962  2.745303  4.102540 -2.279636  0.885389  0.852653   \n",
-       "NK cells           1.108534  2.085616  1.788463 -1.807453 -0.826893 -2.283551   \n",
+       "B cells            2.868587  0.022602  1.464726 -1.234619  0.767511 -1.476407   \n",
+       "CD14+ Monocytes    2.007714  0.175447  2.859986  0.866449 -1.072762 -1.804330   \n",
+       "CD4 T cells        2.811583  0.879623  1.298020 -1.463941 -0.925841 -1.807085   \n",
+       "CD8 T cells        1.989253  2.657882  1.488735 -1.821871 -0.215796 -1.692385   \n",
+       "Dendritic cells    2.875063  0.184359  1.892848 -0.751902 -0.419758 -1.445644   \n",
+       "FCGR3A+ Monocytes  2.303873  0.451908  2.498814 -0.921232 -1.259868 -1.750411   \n",
+       "Megakaryocytes     0.050993  2.751533  4.110503 -2.296985  0.882954  0.872765   \n",
+       "NK cells           1.114531  2.093307  1.790295 -1.819632 -0.830680 -2.286488   \n",
        "\n",
        "                       E2F7      ELK1      ESR2      FLI1  ...       REL  \\\n",
-       "B cells            2.403027  2.052576  3.010636 -1.225934  ... -0.235808   \n",
-       "CD14+ Monocytes    3.214971  1.698302  5.127343 -0.795039  ... -0.484025   \n",
-       "CD4 T cells        2.631588  2.954416  3.755872 -0.997119  ... -0.048044   \n",
-       "CD8 T cells        2.406077  2.480685  4.022677 -0.927006  ...  1.185045   \n",
-       "Dendritic cells    3.151718  1.870627  3.726094 -0.932661  ...  0.012765   \n",
-       "FCGR3A+ Monocytes  3.188233  1.914761  4.890978 -0.538166  ... -0.813959   \n",
-       "Megakaryocytes     0.671578 -0.539585  5.077672  2.363797  ...  2.611817   \n",
-       "NK cells           2.114203  1.565360  4.122890 -1.014566  ... -0.035408   \n",
+       "B cells            2.415689  2.066459  3.021957 -1.223385  ... -0.240303   \n",
+       "CD14+ Monocytes    3.222964  1.711882  5.142670 -0.796957  ... -0.485268   \n",
+       "CD4 T cells        2.640772  2.972541  3.769373 -0.997432  ... -0.050020   \n",
+       "CD8 T cells        2.414712  2.496823  4.036488 -0.926765  ...  1.185035   \n",
+       "Dendritic cells    3.163058  1.887027  3.739679 -0.930901  ...  0.011379   \n",
+       "FCGR3A+ Monocytes  3.196807  1.929357  4.906310 -0.540120  ... -0.815608   \n",
+       "Megakaryocytes     0.667223 -0.532625  5.092278  2.361103  ...  2.617590   \n",
+       "NK cells           2.121871  1.579840  4.136767 -1.014884  ... -0.037481   \n",
        "\n",
        "                        RFX5      SPI1       SRF     STAT3     STAT4  \\\n",
-       "B cells            13.664436  1.983893  1.009390  0.446209 -0.634329   \n",
-       "CD14+ Monocytes     6.790131  3.596484  2.012941  1.400468  2.490372   \n",
-       "CD4 T cells         1.297521  1.478310  1.341562  1.228791  0.390741   \n",
-       "CD8 T cells         2.509188  2.047695  1.809774  0.565882  1.710762   \n",
-       "Dendritic cells    15.064636  3.585419  2.635822  1.193597  1.011139   \n",
-       "FCGR3A+ Monocytes   7.569202  3.998404  2.627059  2.216238  2.493336   \n",
-       "Megakaryocytes      2.147261  3.802499  7.186048 -1.160673 -1.038153   \n",
-       "NK cells            2.060028  2.794987  2.362190  0.249298  3.375603   \n",
+       "B cells            13.696566  1.991853  1.016835  0.459040 -0.632491   \n",
+       "CD14+ Monocytes     6.818030  3.603954  2.031029  1.407858  2.494360   \n",
+       "CD4 T cells         1.309786  1.484332  1.357391  1.239065  0.394266   \n",
+       "CD8 T cells         2.524118  2.053695  1.825817  0.574987  1.715534   \n",
+       "Dendritic cells    15.106863  3.594913  2.652861  1.206054  1.013332   \n",
+       "FCGR3A+ Monocytes   7.600002  4.006298  2.647358  2.224453  2.496363   \n",
+       "Megakaryocytes      2.172133  3.807284  7.223926 -1.168846 -1.041995   \n",
+       "NK cells            2.075289  2.802213  2.380026  0.256690  3.383240   \n",
        "\n",
        "                     TWIST1      USF2       VDR      ZEB2  \n",
-       "B cells           -0.379898  1.135776  3.559168  1.004977  \n",
-       "CD14+ Monocytes   -0.830063  0.463730  3.634529  2.253693  \n",
-       "CD4 T cells       -0.088309  1.126816  3.673022  1.261324  \n",
-       "CD8 T cells       -0.155667  1.347121  3.513260  0.990922  \n",
-       "Dendritic cells   -0.950940  0.472480  4.095103  2.617512  \n",
-       "FCGR3A+ Monocytes -1.909961  0.895054  3.707708  1.937340  \n",
-       "Megakaryocytes    -2.471840  3.480007  1.433540  0.918447  \n",
-       "NK cells          -0.687441  1.471111  3.432891  0.413902  \n",
+       "B cells           -0.404400  1.144343  3.564371  1.000175  \n",
+       "CD14+ Monocytes   -0.855818  0.471007  3.636787  2.269021  \n",
+       "CD4 T cells       -0.115241  1.138940  3.678631  1.265142  \n",
+       "CD8 T cells       -0.182004  1.357577  3.518316  0.995989  \n",
+       "Dendritic cells   -0.983827  0.482137  4.096582  2.625957  \n",
+       "FCGR3A+ Monocytes -1.937861  0.902676  3.707245  1.954463  \n",
+       "Megakaryocytes    -2.491852  3.492409  1.438276  0.944554  \n",
+       "NK cells          -0.715219  1.481768  3.433856  0.419311  \n",
        "\n",
        "[8 rows x 29 columns]"
       ]
@@ -1161,7 +1161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69e343ad",
+   "id": "60397620",
    "metadata": {},
    "source": [
     "We can visualize which TF is more active across cell types using `seaborn`:"
@@ -1170,12 +1170,12 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "85a90f06",
+   "id": "a7f7ace2",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x720 with 4 Axes>"
       ]
@@ -1193,18 +1193,33 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09d8ced7",
+   "id": "0a1dd517",
    "metadata": {},
    "source": [
     "Here we can observe other known marker TFs appering, GATA1 for megakaryocytes, RFX5 for the myeloid lineage and JUND for the lymphoid."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e0a159e",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    "**Note**\n",
+    "    \n",
+    "If your data consist of different conditions with enough samples, we recommend to work with pseudo-bulk profiles instead. Check this\n",
+    "[vignette](https://decoupler-py.readthedocs.io/en/latest/notebooks/pseudobulk.html) for more informatin.\n",
+    "\n",
+    "</div>"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "decoupler",
+   "display_name": "test_decoupler",
    "language": "python",
-   "name": "decoupler"
+   "name": "test_decoupler"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1216,7 +1231,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/msigdb.ipynb b/docs/source/notebooks/msigdb.ipynb
index 6845b98..d99dc4b 100644
--- a/docs/source/notebooks/msigdb.ipynb
+++ b/docs/source/notebooks/msigdb.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "dcfcf0d0",
+   "id": "6ab59a1c",
    "metadata": {},
    "source": [
     "# Functional enrichment of biological terms"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2887ee4",
+   "id": "51c6a2a7",
    "metadata": {},
    "source": [
     "scRNA-seq yield many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by assigning them to biological terms from prior knowledge.\n",
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f5d56a8",
+   "id": "9e65d5aa",
    "metadata": {},
    "source": [
     "## Loading packages\n",
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "9d3b37e7",
+   "id": "4a74e503",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "052e9d3a",
+   "id": "d7911e03",
    "metadata": {},
    "source": [
     "## Loading the data\n",
@@ -67,7 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "899f2293",
+   "id": "caeb9307",
    "metadata": {},
    "outputs": [
     {
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d18f6228",
+   "id": "063875b8",
    "metadata": {},
    "source": [
     "We can visualize the different cell types in it:"
@@ -103,7 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "e9d21029",
+   "id": "5830fc61",
    "metadata": {},
    "outputs": [
     {
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "beaaa26e",
+   "id": "3434d155",
    "metadata": {},
    "source": [
     "## MSigDB gene sets\n",
@@ -134,7 +134,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "ccc580d9",
+   "id": "e23e276f",
    "metadata": {},
    "outputs": [
     {
@@ -157,7 +157,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>label</th>\n",
+       "      <th></th>\n",
        "      <th>genesymbol</th>\n",
        "      <th>collection</th>\n",
        "      <th>geneset</th>\n",
@@ -166,33 +166,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>F2RL1</td>\n",
-       "      <td>immunologic_signatures</td>\n",
-       "      <td>GSE28237_FOLLICULAR_VS_EARLY_GC_BCELL_DN</td>\n",
+       "      <td>MSC</td>\n",
+       "      <td>oncogenic_signatures</td>\n",
+       "      <td>PKCA_DN.V1_DN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>F2RL1</td>\n",
-       "      <td>oncogenic_signatures</td>\n",
-       "      <td>MEK_UP.V1_UP</td>\n",
+       "      <td>MSC</td>\n",
+       "      <td>mirna_targets</td>\n",
+       "      <td>MIR12123</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>F2RL1</td>\n",
-       "      <td>tf_targets</td>\n",
-       "      <td>ZNF92_TARGET_GENES</td>\n",
+       "      <td>MSC</td>\n",
+       "      <td>chemical_and_genetic_perturbations</td>\n",
+       "      <td>NIKOLSKY_BREAST_CANCER_8Q12_Q22_AMPLICON</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>F2RL1</td>\n",
+       "      <td>MSC</td>\n",
        "      <td>immunologic_signatures</td>\n",
-       "      <td>GSE30083_SP1_VS_SP3_THYMOCYTE_DN</td>\n",
+       "      <td>GSE32986_UNSTIM_VS_GMCSF_AND_CURDLAN_LOWDOSE_S...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>F2RL1</td>\n",
-       "      <td>immunologic_signatures</td>\n",
-       "      <td>GSE15930_NAIVE_VS_48H_IN_VITRO_STIM_IL12_CD8_T...</td>\n",
+       "      <td>MSC</td>\n",
+       "      <td>chemical_and_genetic_perturbations</td>\n",
+       "      <td>BENPORATH_PRC2_TARGETS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -201,68 +201,68 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2407284</th>\n",
+       "      <th>2407729</th>\n",
        "      <td>OR2W5P</td>\n",
        "      <td>immunologic_signatures</td>\n",
        "      <td>GSE22601_DOUBLE_NEGATIVE_VS_CD8_SINGLE_POSITIV...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2407285</th>\n",
+       "      <th>2407730</th>\n",
        "      <td>OR2W5P</td>\n",
        "      <td>immunologic_signatures</td>\n",
        "      <td>KANNAN_BLOOD_2012_2013_TIV_AGE_65PLS_REVACCINA...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2407286</th>\n",
+       "      <th>2407731</th>\n",
        "      <td>OR52L2P</td>\n",
        "      <td>immunologic_signatures</td>\n",
        "      <td>GSE22342_CD11C_HIGH_VS_LOW_DECIDUAL_MACROPHAGE...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2407287</th>\n",
+       "      <th>2407732</th>\n",
        "      <td>CSNK2A3</td>\n",
        "      <td>immunologic_signatures</td>\n",
        "      <td>OCONNOR_PBMC_MENVEO_ACWYVAX_AGE_30_70YO_7DY_AF...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2407288</th>\n",
+       "      <th>2407733</th>\n",
        "      <td>AQP12B</td>\n",
        "      <td>immunologic_signatures</td>\n",
        "      <td>MATSUMIYA_PBMC_MODIFIED_VACCINIA_ANKARA_VACCIN...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>2407289 rows × 3 columns</p>\n",
+       "<p>2407734 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "label   genesymbol              collection  \\\n",
-       "0            F2RL1  immunologic_signatures   \n",
-       "1            F2RL1    oncogenic_signatures   \n",
-       "2            F2RL1              tf_targets   \n",
-       "3            F2RL1  immunologic_signatures   \n",
-       "4            F2RL1  immunologic_signatures   \n",
-       "...            ...                     ...   \n",
-       "2407284     OR2W5P  immunologic_signatures   \n",
-       "2407285     OR2W5P  immunologic_signatures   \n",
-       "2407286    OR52L2P  immunologic_signatures   \n",
-       "2407287    CSNK2A3  immunologic_signatures   \n",
-       "2407288     AQP12B  immunologic_signatures   \n",
+       "        genesymbol                          collection  \\\n",
+       "0              MSC                oncogenic_signatures   \n",
+       "1              MSC                       mirna_targets   \n",
+       "2              MSC  chemical_and_genetic_perturbations   \n",
+       "3              MSC              immunologic_signatures   \n",
+       "4              MSC  chemical_and_genetic_perturbations   \n",
+       "...            ...                                 ...   \n",
+       "2407729     OR2W5P              immunologic_signatures   \n",
+       "2407730     OR2W5P              immunologic_signatures   \n",
+       "2407731    OR52L2P              immunologic_signatures   \n",
+       "2407732    CSNK2A3              immunologic_signatures   \n",
+       "2407733     AQP12B              immunologic_signatures   \n",
        "\n",
-       "label                                              geneset  \n",
-       "0                 GSE28237_FOLLICULAR_VS_EARLY_GC_BCELL_DN  \n",
-       "1                                             MEK_UP.V1_UP  \n",
-       "2                                       ZNF92_TARGET_GENES  \n",
-       "3                         GSE30083_SP1_VS_SP3_THYMOCYTE_DN  \n",
-       "4        GSE15930_NAIVE_VS_48H_IN_VITRO_STIM_IL12_CD8_T...  \n",
+       "                                                   geneset  \n",
+       "0                                            PKCA_DN.V1_DN  \n",
+       "1                                                 MIR12123  \n",
+       "2                 NIKOLSKY_BREAST_CANCER_8Q12_Q22_AMPLICON  \n",
+       "3        GSE32986_UNSTIM_VS_GMCSF_AND_CURDLAN_LOWDOSE_S...  \n",
+       "4                                   BENPORATH_PRC2_TARGETS  \n",
        "...                                                    ...  \n",
-       "2407284  GSE22601_DOUBLE_NEGATIVE_VS_CD8_SINGLE_POSITIV...  \n",
-       "2407285  KANNAN_BLOOD_2012_2013_TIV_AGE_65PLS_REVACCINA...  \n",
-       "2407286  GSE22342_CD11C_HIGH_VS_LOW_DECIDUAL_MACROPHAGE...  \n",
-       "2407287  OCONNOR_PBMC_MENVEO_ACWYVAX_AGE_30_70YO_7DY_AF...  \n",
-       "2407288  MATSUMIYA_PBMC_MODIFIED_VACCINIA_ANKARA_VACCIN...  \n",
+       "2407729  GSE22601_DOUBLE_NEGATIVE_VS_CD8_SINGLE_POSITIV...  \n",
+       "2407730  KANNAN_BLOOD_2012_2013_TIV_AGE_65PLS_REVACCINA...  \n",
+       "2407731  GSE22342_CD11C_HIGH_VS_LOW_DECIDUAL_MACROPHAGE...  \n",
+       "2407732  OCONNOR_PBMC_MENVEO_ACWYVAX_AGE_30_70YO_7DY_AF...  \n",
+       "2407733  MATSUMIYA_PBMC_MODIFIED_VACCINIA_ANKARA_VACCIN...  \n",
        "\n",
-       "[2407289 rows x 3 columns]"
+       "[2407734 rows x 3 columns]"
       ]
      },
      "execution_count": 4,
@@ -277,7 +277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f83fbc36",
+   "id": "ba58ae14",
    "metadata": {},
    "source": [
     "As an example, we will use the hallmark gene sets, but we could have used any other. \n",
@@ -296,7 +296,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "7a4fb121",
+   "id": "8aae33ad",
    "metadata": {},
    "outputs": [
     {
@@ -319,7 +319,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>label</th>\n",
+       "      <th></th>\n",
        "      <th>genesymbol</th>\n",
        "      <th>collection</th>\n",
        "      <th>geneset</th>\n",
@@ -327,34 +327,34 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>89</th>\n",
-       "      <td>F2RL1</td>\n",
+       "      <th>11</th>\n",
+       "      <td>MSC</td>\n",
        "      <td>hallmark</td>\n",
-       "      <td>HALLMARK_KRAS_SIGNALING_UP</td>\n",
+       "      <td>HALLMARK_TNFA_SIGNALING_VIA_NFKB</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>F2RL1</td>\n",
+       "      <th>149</th>\n",
+       "      <td>ICOSLG</td>\n",
        "      <td>hallmark</td>\n",
        "      <td>HALLMARK_TNFA_SIGNALING_VIA_NFKB</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>536</th>\n",
-       "      <td>IL7R</td>\n",
+       "      <th>223</th>\n",
+       "      <td>ICOSLG</td>\n",
        "      <td>hallmark</td>\n",
-       "      <td>HALLMARK_KRAS_SIGNALING_UP</td>\n",
+       "      <td>HALLMARK_INFLAMMATORY_RESPONSE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>538</th>\n",
-       "      <td>IL7R</td>\n",
+       "      <th>270</th>\n",
+       "      <td>ICOSLG</td>\n",
        "      <td>hallmark</td>\n",
-       "      <td>HALLMARK_TNFA_SIGNALING_VIA_NFKB</td>\n",
+       "      <td>HALLMARK_ALLOGRAFT_REJECTION</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>611</th>\n",
-       "      <td>IL7R</td>\n",
+       "      <th>398</th>\n",
+       "      <td>FOSL2</td>\n",
        "      <td>hallmark</td>\n",
-       "      <td>HALLMARK_INFLAMMATORY_RESPONSE</td>\n",
+       "      <td>HALLMARK_HYPOXIA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -363,32 +363,32 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>878053</th>\n",
-       "      <td>NKX2-2</td>\n",
+       "      <th>878342</th>\n",
+       "      <td>FOXO1</td>\n",
        "      <td>hallmark</td>\n",
        "      <td>HALLMARK_PANCREAS_BETA_CELLS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>878114</th>\n",
-       "      <td>PDX1</td>\n",
+       "      <th>878418</th>\n",
+       "      <td>GCG</td>\n",
        "      <td>hallmark</td>\n",
        "      <td>HALLMARK_PANCREAS_BETA_CELLS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>878266</th>\n",
-       "      <td>PCSK2</td>\n",
+       "      <th>878512</th>\n",
+       "      <td>PDX1</td>\n",
        "      <td>hallmark</td>\n",
        "      <td>HALLMARK_PANCREAS_BETA_CELLS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>878428</th>\n",
-       "      <td>FOXO1</td>\n",
+       "      <th>878605</th>\n",
+       "      <td>INS</td>\n",
        "      <td>hallmark</td>\n",
        "      <td>HALLMARK_PANCREAS_BETA_CELLS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>878828</th>\n",
-       "      <td>ISL1</td>\n",
+       "      <th>878785</th>\n",
+       "      <td>SRP9</td>\n",
        "      <td>hallmark</td>\n",
        "      <td>HALLMARK_PANCREAS_BETA_CELLS</td>\n",
        "    </tr>\n",
@@ -398,18 +398,18 @@
        "</div>"
       ],
       "text/plain": [
-       "label  genesymbol collection                           geneset\n",
-       "89          F2RL1   hallmark        HALLMARK_KRAS_SIGNALING_UP\n",
-       "93          F2RL1   hallmark  HALLMARK_TNFA_SIGNALING_VIA_NFKB\n",
-       "536          IL7R   hallmark        HALLMARK_KRAS_SIGNALING_UP\n",
-       "538          IL7R   hallmark  HALLMARK_TNFA_SIGNALING_VIA_NFKB\n",
-       "611          IL7R   hallmark    HALLMARK_INFLAMMATORY_RESPONSE\n",
+       "       genesymbol collection                           geneset\n",
+       "11            MSC   hallmark  HALLMARK_TNFA_SIGNALING_VIA_NFKB\n",
+       "149        ICOSLG   hallmark  HALLMARK_TNFA_SIGNALING_VIA_NFKB\n",
+       "223        ICOSLG   hallmark    HALLMARK_INFLAMMATORY_RESPONSE\n",
+       "270        ICOSLG   hallmark      HALLMARK_ALLOGRAFT_REJECTION\n",
+       "398         FOSL2   hallmark                  HALLMARK_HYPOXIA\n",
        "...           ...        ...                               ...\n",
-       "878053     NKX2-2   hallmark      HALLMARK_PANCREAS_BETA_CELLS\n",
-       "878114       PDX1   hallmark      HALLMARK_PANCREAS_BETA_CELLS\n",
-       "878266      PCSK2   hallmark      HALLMARK_PANCREAS_BETA_CELLS\n",
-       "878428      FOXO1   hallmark      HALLMARK_PANCREAS_BETA_CELLS\n",
-       "878828       ISL1   hallmark      HALLMARK_PANCREAS_BETA_CELLS\n",
+       "878342      FOXO1   hallmark      HALLMARK_PANCREAS_BETA_CELLS\n",
+       "878418        GCG   hallmark      HALLMARK_PANCREAS_BETA_CELLS\n",
+       "878512       PDX1   hallmark      HALLMARK_PANCREAS_BETA_CELLS\n",
+       "878605        INS   hallmark      HALLMARK_PANCREAS_BETA_CELLS\n",
+       "878785       SRP9   hallmark      HALLMARK_PANCREAS_BETA_CELLS\n",
        "\n",
        "[7318 rows x 3 columns]"
       ]
@@ -430,7 +430,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a209e54",
+   "id": "263733e6",
    "metadata": {},
    "source": [
     "For this example we will use the resource MSigDB, but we could have used any other such as GO. To see the list of available resources inside `Omnipath`, run `dc.show_resources()`"
@@ -438,7 +438,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a060983e",
+   "id": "730beda7",
    "metadata": {},
    "source": [
     "## Enrichment with Over Representation Analysis\n",
@@ -457,22 +457,22 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "a4d1eed5",
+   "id": "f18b018f",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "58 features of mat are empty in 2635 samples, they will be ignored.\n",
-      "Running ora on mat with 2638 samples and 13656 targets for 50 sources.\n"
+      "1 features of mat are empty, they will be removed.\n",
+      "Running ora on mat with 2638 samples and 13713 targets for 50 sources.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2638/2638 [00:02<00:00, 1292.43it/s]\n"
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2638/2638 [00:02<00:00, 1088.99it/s]\n"
      ]
     }
    ],
@@ -482,7 +482,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9bd14c1",
+   "id": "e6e928d2",
    "metadata": {},
    "source": [
     "The obtained scores (-log10(p-value))(`ora_estimate`) and p-values (`ora_pvals`) are stored in the `.obsm` key:"
@@ -491,7 +491,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "a046061d",
+   "id": "a0b76012",
    "metadata": {},
    "outputs": [
     {
@@ -541,123 +541,123 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AAACATACAACCAC-1</th>\n",
-       "      <td>1.317500</td>\n",
-       "      <td>10.071074</td>\n",
-       "      <td>1.658673</td>\n",
-       "      <td>0.391150</td>\n",
-       "      <td>0.946285</td>\n",
-       "      <td>0.653064</td>\n",
-       "      <td>3.435109</td>\n",
-       "      <td>0.621999</td>\n",
-       "      <td>0.460666</td>\n",
-       "      <td>0.033139</td>\n",
+       "      <td>1.307006</td>\n",
+       "      <td>10.029702</td>\n",
+       "      <td>0.822139</td>\n",
+       "      <td>0.389675</td>\n",
+       "      <td>1.670031</td>\n",
+       "      <td>0.650245</td>\n",
+       "      <td>2.441094</td>\n",
+       "      <td>0.337563</td>\n",
+       "      <td>0.805206</td>\n",
+       "      <td>0.032740</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.451100</td>\n",
-       "      <td>2.997417</td>\n",
-       "      <td>0.376115</td>\n",
-       "      <td>2.997417</td>\n",
-       "      <td>11.149969</td>\n",
-       "      <td>5.664834</td>\n",
-       "      <td>0.179224</td>\n",
-       "      <td>1.996328</td>\n",
-       "      <td>0.165837</td>\n",
-       "      <td>0.181499</td>\n",
+       "      <td>0.733962</td>\n",
+       "      <td>2.286568</td>\n",
+       "      <td>0.354931</td>\n",
+       "      <td>2.986336</td>\n",
+       "      <td>8.220117</td>\n",
+       "      <td>6.365243</td>\n",
+       "      <td>0.586324</td>\n",
+       "      <td>1.983873</td>\n",
+       "      <td>0.164805</td>\n",
+       "      <td>0.080157</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACATTGAGCTAC-1</th>\n",
-       "      <td>1.317500</td>\n",
-       "      <td>10.825559</td>\n",
-       "      <td>3.326873</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>2.116838</td>\n",
+       "      <td>1.307006</td>\n",
+       "      <td>14.824230</td>\n",
+       "      <td>2.152116</td>\n",
+       "      <td>0.389675</td>\n",
+       "      <td>2.104104</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>0.270934</td>\n",
-       "      <td>0.340281</td>\n",
-       "      <td>0.810018</td>\n",
-       "      <td>0.033139</td>\n",
+       "      <td>0.442704</td>\n",
+       "      <td>0.337563</td>\n",
+       "      <td>0.457516</td>\n",
+       "      <td>0.321093</td>\n",
        "      <td>...</td>\n",
-       "      <td>2.508805</td>\n",
-       "      <td>1.666912</td>\n",
-       "      <td>0.678600</td>\n",
-       "      <td>3.765025</td>\n",
-       "      <td>2.599805</td>\n",
-       "      <td>1.606474</td>\n",
-       "      <td>0.179224</td>\n",
-       "      <td>1.578525</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.181499</td>\n",
+       "      <td>2.496213</td>\n",
+       "      <td>1.659624</td>\n",
+       "      <td>1.015676</td>\n",
+       "      <td>1.659624</td>\n",
+       "      <td>3.027820</td>\n",
+       "      <td>2.547763</td>\n",
+       "      <td>0.346751</td>\n",
+       "      <td>0.871448</td>\n",
+       "      <td>0.503794</td>\n",
+       "      <td>0.080157</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACATTGATCAGC-1</th>\n",
-       "      <td>2.919346</td>\n",
-       "      <td>7.257330</td>\n",
-       "      <td>2.163150</td>\n",
-       "      <td>0.391150</td>\n",
-       "      <td>2.116838</td>\n",
-       "      <td>0.225447</td>\n",
-       "      <td>5.766530</td>\n",
-       "      <td>0.340281</td>\n",
-       "      <td>0.460666</td>\n",
-       "      <td>0.136104</td>\n",
+       "      <td>3.893118</td>\n",
+       "      <td>7.893567</td>\n",
+       "      <td>3.312021</td>\n",
+       "      <td>0.389675</td>\n",
+       "      <td>3.095283</td>\n",
+       "      <td>0.650245</td>\n",
+       "      <td>5.115631</td>\n",
+       "      <td>0.976973</td>\n",
+       "      <td>0.805206</td>\n",
+       "      <td>0.321093</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.983812</td>\n",
-       "      <td>5.477067</td>\n",
-       "      <td>0.041229</td>\n",
-       "      <td>2.295738</td>\n",
-       "      <td>9.664157</td>\n",
-       "      <td>2.060727</td>\n",
-       "      <td>0.357451</td>\n",
-       "      <td>3.488813</td>\n",
-       "      <td>0.165837</td>\n",
-       "      <td>0.536606</td>\n",
+       "      <td>1.087271</td>\n",
+       "      <td>3.752012</td>\n",
+       "      <td>0.037885</td>\n",
+       "      <td>1.114303</td>\n",
+       "      <td>7.549513</td>\n",
+       "      <td>2.048977</td>\n",
+       "      <td>0.888675</td>\n",
+       "      <td>4.038435</td>\n",
+       "      <td>0.164805</td>\n",
+       "      <td>1.087420</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACCGTGCTTCCG-1</th>\n",
-       "      <td>2.919346</td>\n",
-       "      <td>7.928895</td>\n",
-       "      <td>1.658673</td>\n",
-       "      <td>1.040001</td>\n",
-       "      <td>2.594660</td>\n",
+       "      <td>5.591584</td>\n",
+       "      <td>7.893567</td>\n",
+       "      <td>2.152116</td>\n",
+       "      <td>1.036748</td>\n",
+       "      <td>1.670031</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>3.435109</td>\n",
-       "      <td>0.621999</td>\n",
-       "      <td>0.206674</td>\n",
-       "      <td>0.595661</td>\n",
+       "      <td>2.911700</td>\n",
+       "      <td>0.143019</td>\n",
+       "      <td>1.747025</td>\n",
+       "      <td>0.591452</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.983812</td>\n",
-       "      <td>4.593055</td>\n",
-       "      <td>0.376115</td>\n",
-       "      <td>1.666912</td>\n",
-       "      <td>7.584396</td>\n",
-       "      <td>2.561403</td>\n",
-       "      <td>0.602180</td>\n",
-       "      <td>1.204925</td>\n",
-       "      <td>0.506356</td>\n",
-       "      <td>0.536606</td>\n",
+       "      <td>1.501986</td>\n",
+       "      <td>3.752012</td>\n",
+       "      <td>0.354931</td>\n",
+       "      <td>1.659624</td>\n",
+       "      <td>5.669689</td>\n",
+       "      <td>4.950302</td>\n",
+       "      <td>0.346751</td>\n",
+       "      <td>1.567924</td>\n",
+       "      <td>0.164805</td>\n",
+       "      <td>1.087420</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACCGTGTATGCG-1</th>\n",
-       "      <td>0.513621</td>\n",
-       "      <td>10.071074</td>\n",
-       "      <td>2.163150</td>\n",
-       "      <td>1.040001</td>\n",
-       "      <td>1.680902</td>\n",
-       "      <td>1.239647</td>\n",
-       "      <td>0.672315</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.460666</td>\n",
-       "      <td>0.595661</td>\n",
+       "      <td>0.999733</td>\n",
+       "      <td>10.029702</td>\n",
+       "      <td>2.707608</td>\n",
+       "      <td>1.871866</td>\n",
+       "      <td>1.670031</td>\n",
+       "      <td>1.948869</td>\n",
+       "      <td>0.666121</td>\n",
+       "      <td>0.337563</td>\n",
+       "      <td>0.805206</td>\n",
+       "      <td>0.591452</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.510836</td>\n",
-       "      <td>1.666912</td>\n",
-       "      <td>0.041229</td>\n",
-       "      <td>1.119758</td>\n",
-       "      <td>1.799358</td>\n",
-       "      <td>1.202151</td>\n",
-       "      <td>0.357451</td>\n",
-       "      <td>0.379035</td>\n",
-       "      <td>0.165837</td>\n",
-       "      <td>0.081326</td>\n",
+       "      <td>1.973108</td>\n",
+       "      <td>1.659624</td>\n",
+       "      <td>0.151790</td>\n",
+       "      <td>1.114303</td>\n",
+       "      <td>1.785917</td>\n",
+       "      <td>1.596582</td>\n",
+       "      <td>0.586324</td>\n",
+       "      <td>0.375221</td>\n",
+       "      <td>0.164805</td>\n",
+       "      <td>0.179270</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -685,123 +685,123 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTCGAACTCTCAT-1</th>\n",
-       "      <td>3.402233</td>\n",
-       "      <td>5.982209</td>\n",
-       "      <td>1.211746</td>\n",
-       "      <td>1.040001</td>\n",
-       "      <td>3.111822</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>7.097779</td>\n",
-       "      <td>0.144395</td>\n",
-       "      <td>2.334496</td>\n",
-       "      <td>0.595661</td>\n",
+       "      <td>3.382339</td>\n",
+       "      <td>9.297059</td>\n",
+       "      <td>1.204403</td>\n",
+       "      <td>1.036748</td>\n",
+       "      <td>2.580035</td>\n",
+       "      <td>0.224257</td>\n",
+       "      <td>10.804691</td>\n",
+       "      <td>0.143019</td>\n",
+       "      <td>2.324194</td>\n",
+       "      <td>1.359530</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.983812</td>\n",
-       "      <td>2.295738</td>\n",
-       "      <td>0.162598</td>\n",
-       "      <td>2.997417</td>\n",
-       "      <td>6.933300</td>\n",
-       "      <td>0.851656</td>\n",
-       "      <td>0.357451</td>\n",
-       "      <td>5.295349</td>\n",
-       "      <td>0.165837</td>\n",
-       "      <td>0.791739</td>\n",
+       "      <td>3.683543</td>\n",
+       "      <td>3.752012</td>\n",
+       "      <td>0.151790</td>\n",
+       "      <td>2.286568</td>\n",
+       "      <td>5.669689</td>\n",
+       "      <td>2.048977</td>\n",
+       "      <td>0.586324</td>\n",
+       "      <td>4.639190</td>\n",
+       "      <td>0.164805</td>\n",
+       "      <td>1.087420</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTCTACTGAGGCA-1</th>\n",
-       "      <td>2.919346</td>\n",
-       "      <td>7.257330</td>\n",
-       "      <td>6.180487</td>\n",
-       "      <td>1.876995</td>\n",
-       "      <td>1.680902</td>\n",
-       "      <td>0.653064</td>\n",
-       "      <td>0.944344</td>\n",
-       "      <td>0.144395</td>\n",
-       "      <td>0.460666</td>\n",
-       "      <td>1.367227</td>\n",
+       "      <td>6.851412</td>\n",
+       "      <td>4.777953</td>\n",
+       "      <td>6.964458</td>\n",
+       "      <td>1.036748</td>\n",
+       "      <td>1.670031</td>\n",
+       "      <td>0.650245</td>\n",
+       "      <td>2.006125</td>\n",
+       "      <td>0.035215</td>\n",
+       "      <td>0.457516</td>\n",
+       "      <td>1.359530</td>\n",
        "      <td>...</td>\n",
-       "      <td>5.796310</td>\n",
-       "      <td>2.295738</td>\n",
-       "      <td>0.376115</td>\n",
-       "      <td>2.997417</td>\n",
-       "      <td>2.599805</td>\n",
-       "      <td>8.749528</td>\n",
-       "      <td>0.179224</td>\n",
-       "      <td>2.455590</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.081326</td>\n",
+       "      <td>3.683543</td>\n",
+       "      <td>1.659624</td>\n",
+       "      <td>0.151790</td>\n",
+       "      <td>2.286568</td>\n",
+       "      <td>2.168128</td>\n",
+       "      <td>8.718220</td>\n",
+       "      <td>0.172992</td>\n",
+       "      <td>2.441252</td>\n",
+       "      <td>0.164805</td>\n",
+       "      <td>0.080157</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTCTACTTCCTCG-1</th>\n",
-       "      <td>1.008455</td>\n",
-       "      <td>7.257330</td>\n",
-       "      <td>2.163150</td>\n",
+       "      <td>1.652714</td>\n",
+       "      <td>7.224004</td>\n",
+       "      <td>2.152116</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>2.116838</td>\n",
-       "      <td>0.653064</td>\n",
-       "      <td>2.456295</td>\n",
-       "      <td>0.035634</td>\n",
-       "      <td>0.810018</td>\n",
-       "      <td>0.136104</td>\n",
+       "      <td>1.670031</td>\n",
+       "      <td>1.235034</td>\n",
+       "      <td>2.441094</td>\n",
+       "      <td>0.337563</td>\n",
+       "      <td>1.238111</td>\n",
+       "      <td>0.134773</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.094316</td>\n",
-       "      <td>0.665563</td>\n",
-       "      <td>0.041229</td>\n",
-       "      <td>1.666912</td>\n",
-       "      <td>8.950467</td>\n",
-       "      <td>3.105400</td>\n",
-       "      <td>0.067346</td>\n",
-       "      <td>4.661261</td>\n",
+       "      <td>1.087271</td>\n",
+       "      <td>0.661862</td>\n",
+       "      <td>0.151790</td>\n",
+       "      <td>1.659624</td>\n",
+       "      <td>8.911564</td>\n",
+       "      <td>3.089847</td>\n",
+       "      <td>0.064563</td>\n",
+       "      <td>4.639190</td>\n",
        "      <td>-0.000000</td>\n",
-       "      <td>0.332800</td>\n",
+       "      <td>0.329247</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTGCATGAGAGGC-1</th>\n",
-       "      <td>1.008455</td>\n",
-       "      <td>5.380289</td>\n",
-       "      <td>0.272201</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.653957</td>\n",
-       "      <td>0.653064</td>\n",
-       "      <td>0.143311</td>\n",
-       "      <td>0.144395</td>\n",
-       "      <td>0.206674</td>\n",
-       "      <td>0.033139</td>\n",
+       "      <td>2.034899</td>\n",
+       "      <td>5.352930</td>\n",
+       "      <td>1.204403</td>\n",
+       "      <td>1.036748</td>\n",
+       "      <td>0.939005</td>\n",
+       "      <td>1.235034</td>\n",
+       "      <td>0.267770</td>\n",
+       "      <td>0.617706</td>\n",
+       "      <td>0.805206</td>\n",
+       "      <td>0.134773</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.234800</td>\n",
-       "      <td>2.295738</td>\n",
-       "      <td>0.041229</td>\n",
-       "      <td>0.318480</td>\n",
-       "      <td>1.449199</td>\n",
-       "      <td>4.971691</td>\n",
-       "      <td>0.067346</td>\n",
-       "      <td>1.996328</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.081326</td>\n",
+       "      <td>0.733962</td>\n",
+       "      <td>2.286568</td>\n",
+       "      <td>0.151790</td>\n",
+       "      <td>0.661862</td>\n",
+       "      <td>3.502378</td>\n",
+       "      <td>5.641478</td>\n",
+       "      <td>0.346751</td>\n",
+       "      <td>2.441252</td>\n",
+       "      <td>0.164805</td>\n",
+       "      <td>0.329247</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTGCATGCCTCAC-1</th>\n",
-       "      <td>1.665028</td>\n",
-       "      <td>10.825559</td>\n",
-       "      <td>0.512554</td>\n",
-       "      <td>0.391150</td>\n",
-       "      <td>1.289685</td>\n",
-       "      <td>0.653064</td>\n",
-       "      <td>3.435109</td>\n",
-       "      <td>0.621999</td>\n",
-       "      <td>0.460666</td>\n",
-       "      <td>0.033139</td>\n",
+       "      <td>1.307006</td>\n",
+       "      <td>10.029702</td>\n",
+       "      <td>0.508632</td>\n",
+       "      <td>0.389675</td>\n",
+       "      <td>1.670031</td>\n",
+       "      <td>0.650245</td>\n",
+       "      <td>3.416073</td>\n",
+       "      <td>0.976973</td>\n",
+       "      <td>0.457516</td>\n",
+       "      <td>0.134773</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.020760</td>\n",
-       "      <td>2.295738</td>\n",
-       "      <td>0.162598</td>\n",
-       "      <td>2.295738</td>\n",
-       "      <td>4.027978</td>\n",
-       "      <td>4.971691</td>\n",
-       "      <td>0.910149</td>\n",
-       "      <td>4.058551</td>\n",
-       "      <td>0.165837</td>\n",
-       "      <td>0.081326</td>\n",
+       "      <td>0.232568</td>\n",
+       "      <td>2.286568</td>\n",
+       "      <td>0.037885</td>\n",
+       "      <td>1.659624</td>\n",
+       "      <td>3.502378</td>\n",
+       "      <td>4.950302</td>\n",
+       "      <td>0.888675</td>\n",
+       "      <td>4.038435</td>\n",
+       "      <td>0.164805</td>\n",
+       "      <td>0.080157</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -810,173 +810,173 @@
       ],
       "text/plain": [
        "source            HALLMARK_ADIPOGENESIS  HALLMARK_ALLOGRAFT_REJECTION  \\\n",
-       "AAACATACAACCAC-1               1.317500                     10.071074   \n",
-       "AAACATTGAGCTAC-1               1.317500                     10.825559   \n",
-       "AAACATTGATCAGC-1               2.919346                      7.257330   \n",
-       "AAACCGTGCTTCCG-1               2.919346                      7.928895   \n",
-       "AAACCGTGTATGCG-1               0.513621                     10.071074   \n",
+       "AAACATACAACCAC-1               1.307006                     10.029702   \n",
+       "AAACATTGAGCTAC-1               1.307006                     14.824230   \n",
+       "AAACATTGATCAGC-1               3.893118                      7.893567   \n",
+       "AAACCGTGCTTCCG-1               5.591584                      7.893567   \n",
+       "AAACCGTGTATGCG-1               0.999733                     10.029702   \n",
        "...                                 ...                           ...   \n",
-       "TTTCGAACTCTCAT-1               3.402233                      5.982209   \n",
-       "TTTCTACTGAGGCA-1               2.919346                      7.257330   \n",
-       "TTTCTACTTCCTCG-1               1.008455                      7.257330   \n",
-       "TTTGCATGAGAGGC-1               1.008455                      5.380289   \n",
-       "TTTGCATGCCTCAC-1               1.665028                     10.825559   \n",
+       "TTTCGAACTCTCAT-1               3.382339                      9.297059   \n",
+       "TTTCTACTGAGGCA-1               6.851412                      4.777953   \n",
+       "TTTCTACTTCCTCG-1               1.652714                      7.224004   \n",
+       "TTTGCATGAGAGGC-1               2.034899                      5.352930   \n",
+       "TTTGCATGCCTCAC-1               1.307006                     10.029702   \n",
        "\n",
        "source            HALLMARK_ANDROGEN_RESPONSE  HALLMARK_ANGIOGENESIS  \\\n",
-       "AAACATACAACCAC-1                    1.658673               0.391150   \n",
-       "AAACATTGAGCTAC-1                    3.326873              -0.000000   \n",
-       "AAACATTGATCAGC-1                    2.163150               0.391150   \n",
-       "AAACCGTGCTTCCG-1                    1.658673               1.040001   \n",
-       "AAACCGTGTATGCG-1                    2.163150               1.040001   \n",
+       "AAACATACAACCAC-1                    0.822139               0.389675   \n",
+       "AAACATTGAGCTAC-1                    2.152116               0.389675   \n",
+       "AAACATTGATCAGC-1                    3.312021               0.389675   \n",
+       "AAACCGTGCTTCCG-1                    2.152116               1.036748   \n",
+       "AAACCGTGTATGCG-1                    2.707608               1.871866   \n",
        "...                                      ...                    ...   \n",
-       "TTTCGAACTCTCAT-1                    1.211746               1.040001   \n",
-       "TTTCTACTGAGGCA-1                    6.180487               1.876995   \n",
-       "TTTCTACTTCCTCG-1                    2.163150              -0.000000   \n",
-       "TTTGCATGAGAGGC-1                    0.272201              -0.000000   \n",
-       "TTTGCATGCCTCAC-1                    0.512554               0.391150   \n",
+       "TTTCGAACTCTCAT-1                    1.204403               1.036748   \n",
+       "TTTCTACTGAGGCA-1                    6.964458               1.036748   \n",
+       "TTTCTACTTCCTCG-1                    2.152116              -0.000000   \n",
+       "TTTGCATGAGAGGC-1                    1.204403               1.036748   \n",
+       "TTTGCATGCCTCAC-1                    0.508632               0.389675   \n",
        "\n",
        "source            HALLMARK_APICAL_JUNCTION  HALLMARK_APICAL_SURFACE  \\\n",
-       "AAACATACAACCAC-1                  0.946285                 0.653064   \n",
-       "AAACATTGAGCTAC-1                  2.116838                -0.000000   \n",
-       "AAACATTGATCAGC-1                  2.116838                 0.225447   \n",
-       "AAACCGTGCTTCCG-1                  2.594660                -0.000000   \n",
-       "AAACCGTGTATGCG-1                  1.680902                 1.239647   \n",
+       "AAACATACAACCAC-1                  1.670031                 0.650245   \n",
+       "AAACATTGAGCTAC-1                  2.104104                -0.000000   \n",
+       "AAACATTGATCAGC-1                  3.095283                 0.650245   \n",
+       "AAACCGTGCTTCCG-1                  1.670031                -0.000000   \n",
+       "AAACCGTGTATGCG-1                  1.670031                 1.948869   \n",
        "...                                    ...                      ...   \n",
-       "TTTCGAACTCTCAT-1                  3.111822                -0.000000   \n",
-       "TTTCTACTGAGGCA-1                  1.680902                 0.653064   \n",
-       "TTTCTACTTCCTCG-1                  2.116838                 0.653064   \n",
-       "TTTGCATGAGAGGC-1                  0.653957                 0.653064   \n",
-       "TTTGCATGCCTCAC-1                  1.289685                 0.653064   \n",
+       "TTTCGAACTCTCAT-1                  2.580035                 0.224257   \n",
+       "TTTCTACTGAGGCA-1                  1.670031                 0.650245   \n",
+       "TTTCTACTTCCTCG-1                  1.670031                 1.235034   \n",
+       "TTTGCATGAGAGGC-1                  0.939005                 1.235034   \n",
+       "TTTGCATGCCTCAC-1                  1.670031                 0.650245   \n",
        "\n",
        "source            HALLMARK_APOPTOSIS  HALLMARK_BILE_ACID_METABOLISM  \\\n",
-       "AAACATACAACCAC-1            3.435109                       0.621999   \n",
-       "AAACATTGAGCTAC-1            0.270934                       0.340281   \n",
-       "AAACATTGATCAGC-1            5.766530                       0.340281   \n",
-       "AAACCGTGCTTCCG-1            3.435109                       0.621999   \n",
-       "AAACCGTGTATGCG-1            0.672315                      -0.000000   \n",
+       "AAACATACAACCAC-1            2.441094                       0.337563   \n",
+       "AAACATTGAGCTAC-1            0.442704                       0.337563   \n",
+       "AAACATTGATCAGC-1            5.115631                       0.976973   \n",
+       "AAACCGTGCTTCCG-1            2.911700                       0.143019   \n",
+       "AAACCGTGTATGCG-1            0.666121                       0.337563   \n",
        "...                              ...                            ...   \n",
-       "TTTCGAACTCTCAT-1            7.097779                       0.144395   \n",
-       "TTTCTACTGAGGCA-1            0.944344                       0.144395   \n",
-       "TTTCTACTTCCTCG-1            2.456295                       0.035634   \n",
-       "TTTGCATGAGAGGC-1            0.143311                       0.144395   \n",
-       "TTTGCATGCCTCAC-1            3.435109                       0.621999   \n",
+       "TTTCGAACTCTCAT-1           10.804691                       0.143019   \n",
+       "TTTCTACTGAGGCA-1            2.006125                       0.035215   \n",
+       "TTTCTACTTCCTCG-1            2.441094                       0.337563   \n",
+       "TTTGCATGAGAGGC-1            0.267770                       0.617706   \n",
+       "TTTGCATGCCTCAC-1            3.416073                       0.976973   \n",
        "\n",
        "source            HALLMARK_CHOLESTEROL_HOMEOSTASIS  HALLMARK_COAGULATION  ...  \\\n",
-       "AAACATACAACCAC-1                          0.460666              0.033139  ...   \n",
-       "AAACATTGAGCTAC-1                          0.810018              0.033139  ...   \n",
-       "AAACATTGATCAGC-1                          0.460666              0.136104  ...   \n",
-       "AAACCGTGCTTCCG-1                          0.206674              0.595661  ...   \n",
-       "AAACCGTGTATGCG-1                          0.460666              0.595661  ...   \n",
+       "AAACATACAACCAC-1                          0.805206              0.032740  ...   \n",
+       "AAACATTGAGCTAC-1                          0.457516              0.321093  ...   \n",
+       "AAACATTGATCAGC-1                          0.805206              0.321093  ...   \n",
+       "AAACCGTGCTTCCG-1                          1.747025              0.591452  ...   \n",
+       "AAACCGTGTATGCG-1                          0.805206              0.591452  ...   \n",
        "...                                            ...                   ...  ...   \n",
-       "TTTCGAACTCTCAT-1                          2.334496              0.595661  ...   \n",
-       "TTTCTACTGAGGCA-1                          0.460666              1.367227  ...   \n",
-       "TTTCTACTTCCTCG-1                          0.810018              0.136104  ...   \n",
-       "TTTGCATGAGAGGC-1                          0.206674              0.033139  ...   \n",
-       "TTTGCATGCCTCAC-1                          0.460666              0.033139  ...   \n",
+       "TTTCGAACTCTCAT-1                          2.324194              1.359530  ...   \n",
+       "TTTCTACTGAGGCA-1                          0.457516              1.359530  ...   \n",
+       "TTTCTACTTCCTCG-1                          1.238111              0.134773  ...   \n",
+       "TTTGCATGAGAGGC-1                          0.805206              0.134773  ...   \n",
+       "TTTGCATGCCTCAC-1                          0.457516              0.134773  ...   \n",
        "\n",
        "source            HALLMARK_PROTEIN_SECRETION  \\\n",
-       "AAACATACAACCAC-1                    0.451100   \n",
-       "AAACATTGAGCTAC-1                    2.508805   \n",
-       "AAACATTGATCAGC-1                    1.983812   \n",
-       "AAACCGTGCTTCCG-1                    1.983812   \n",
-       "AAACCGTGTATGCG-1                    1.510836   \n",
+       "AAACATACAACCAC-1                    0.733962   \n",
+       "AAACATTGAGCTAC-1                    2.496213   \n",
+       "AAACATTGATCAGC-1                    1.087271   \n",
+       "AAACCGTGCTTCCG-1                    1.501986   \n",
+       "AAACCGTGTATGCG-1                    1.973108   \n",
        "...                                      ...   \n",
-       "TTTCGAACTCTCAT-1                    1.983812   \n",
-       "TTTCTACTGAGGCA-1                    5.796310   \n",
-       "TTTCTACTTCCTCG-1                    1.094316   \n",
-       "TTTGCATGAGAGGC-1                    0.234800   \n",
-       "TTTGCATGCCTCAC-1                    0.020760   \n",
+       "TTTCGAACTCTCAT-1                    3.683543   \n",
+       "TTTCTACTGAGGCA-1                    3.683543   \n",
+       "TTTCTACTTCCTCG-1                    1.087271   \n",
+       "TTTGCATGAGAGGC-1                    0.733962   \n",
+       "TTTGCATGCCTCAC-1                    0.232568   \n",
        "\n",
        "source            HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY  \\\n",
-       "AAACATACAACCAC-1                                  2.997417   \n",
-       "AAACATTGAGCTAC-1                                  1.666912   \n",
-       "AAACATTGATCAGC-1                                  5.477067   \n",
-       "AAACCGTGCTTCCG-1                                  4.593055   \n",
-       "AAACCGTGTATGCG-1                                  1.666912   \n",
+       "AAACATACAACCAC-1                                  2.286568   \n",
+       "AAACATTGAGCTAC-1                                  1.659624   \n",
+       "AAACATTGATCAGC-1                                  3.752012   \n",
+       "AAACCGTGCTTCCG-1                                  3.752012   \n",
+       "AAACCGTGTATGCG-1                                  1.659624   \n",
        "...                                                    ...   \n",
-       "TTTCGAACTCTCAT-1                                  2.295738   \n",
-       "TTTCTACTGAGGCA-1                                  2.295738   \n",
-       "TTTCTACTTCCTCG-1                                  0.665563   \n",
-       "TTTGCATGAGAGGC-1                                  2.295738   \n",
-       "TTTGCATGCCTCAC-1                                  2.295738   \n",
+       "TTTCGAACTCTCAT-1                                  3.752012   \n",
+       "TTTCTACTGAGGCA-1                                  1.659624   \n",
+       "TTTCTACTTCCTCG-1                                  0.661862   \n",
+       "TTTGCATGAGAGGC-1                                  2.286568   \n",
+       "TTTGCATGCCTCAC-1                                  2.286568   \n",
        "\n",
        "source            HALLMARK_SPERMATOGENESIS  HALLMARK_TGF_BETA_SIGNALING  \\\n",
-       "AAACATACAACCAC-1                  0.376115                     2.997417   \n",
-       "AAACATTGAGCTAC-1                  0.678600                     3.765025   \n",
-       "AAACATTGATCAGC-1                  0.041229                     2.295738   \n",
-       "AAACCGTGCTTCCG-1                  0.376115                     1.666912   \n",
-       "AAACCGTGTATGCG-1                  0.041229                     1.119758   \n",
+       "AAACATACAACCAC-1                  0.354931                     2.986336   \n",
+       "AAACATTGAGCTAC-1                  1.015676                     1.659624   \n",
+       "AAACATTGATCAGC-1                  0.037885                     1.114303   \n",
+       "AAACCGTGCTTCCG-1                  0.354931                     1.659624   \n",
+       "AAACCGTGTATGCG-1                  0.151790                     1.114303   \n",
        "...                                    ...                          ...   \n",
-       "TTTCGAACTCTCAT-1                  0.162598                     2.997417   \n",
-       "TTTCTACTGAGGCA-1                  0.376115                     2.997417   \n",
-       "TTTCTACTTCCTCG-1                  0.041229                     1.666912   \n",
-       "TTTGCATGAGAGGC-1                  0.041229                     0.318480   \n",
-       "TTTGCATGCCTCAC-1                  0.162598                     2.295738   \n",
+       "TTTCGAACTCTCAT-1                  0.151790                     2.286568   \n",
+       "TTTCTACTGAGGCA-1                  0.151790                     2.286568   \n",
+       "TTTCTACTTCCTCG-1                  0.151790                     1.659624   \n",
+       "TTTGCATGAGAGGC-1                  0.151790                     0.661862   \n",
+       "TTTGCATGCCTCAC-1                  0.037885                     1.659624   \n",
        "\n",
        "source            HALLMARK_TNFA_SIGNALING_VIA_NFKB  \\\n",
-       "AAACATACAACCAC-1                         11.149969   \n",
-       "AAACATTGAGCTAC-1                          2.599805   \n",
-       "AAACATTGATCAGC-1                          9.664157   \n",
-       "AAACCGTGCTTCCG-1                          7.584396   \n",
-       "AAACCGTGTATGCG-1                          1.799358   \n",
+       "AAACATACAACCAC-1                          8.220117   \n",
+       "AAACATTGAGCTAC-1                          3.027820   \n",
+       "AAACATTGATCAGC-1                          7.549513   \n",
+       "AAACCGTGCTTCCG-1                          5.669689   \n",
+       "AAACCGTGTATGCG-1                          1.785917   \n",
        "...                                            ...   \n",
-       "TTTCGAACTCTCAT-1                          6.933300   \n",
-       "TTTCTACTGAGGCA-1                          2.599805   \n",
-       "TTTCTACTTCCTCG-1                          8.950467   \n",
-       "TTTGCATGAGAGGC-1                          1.449199   \n",
-       "TTTGCATGCCTCAC-1                          4.027978   \n",
+       "TTTCGAACTCTCAT-1                          5.669689   \n",
+       "TTTCTACTGAGGCA-1                          2.168128   \n",
+       "TTTCTACTTCCTCG-1                          8.911564   \n",
+       "TTTGCATGAGAGGC-1                          3.502378   \n",
+       "TTTGCATGCCTCAC-1                          3.502378   \n",
        "\n",
        "source            HALLMARK_UNFOLDED_PROTEIN_RESPONSE  HALLMARK_UV_RESPONSE_DN  \\\n",
-       "AAACATACAACCAC-1                            5.664834                 0.179224   \n",
-       "AAACATTGAGCTAC-1                            1.606474                 0.179224   \n",
-       "AAACATTGATCAGC-1                            2.060727                 0.357451   \n",
-       "AAACCGTGCTTCCG-1                            2.561403                 0.602180   \n",
-       "AAACCGTGTATGCG-1                            1.202151                 0.357451   \n",
+       "AAACATACAACCAC-1                            6.365243                 0.586324   \n",
+       "AAACATTGAGCTAC-1                            2.547763                 0.346751   \n",
+       "AAACATTGATCAGC-1                            2.048977                 0.888675   \n",
+       "AAACCGTGCTTCCG-1                            4.950302                 0.346751   \n",
+       "AAACCGTGTATGCG-1                            1.596582                 0.586324   \n",
        "...                                              ...                      ...   \n",
-       "TTTCGAACTCTCAT-1                            0.851656                 0.357451   \n",
-       "TTTCTACTGAGGCA-1                            8.749528                 0.179224   \n",
-       "TTTCTACTTCCTCG-1                            3.105400                 0.067346   \n",
-       "TTTGCATGAGAGGC-1                            4.971691                 0.067346   \n",
-       "TTTGCATGCCTCAC-1                            4.971691                 0.910149   \n",
+       "TTTCGAACTCTCAT-1                            2.048977                 0.586324   \n",
+       "TTTCTACTGAGGCA-1                            8.718220                 0.172992   \n",
+       "TTTCTACTTCCTCG-1                            3.089847                 0.064563   \n",
+       "TTTGCATGAGAGGC-1                            5.641478                 0.346751   \n",
+       "TTTGCATGCCTCAC-1                            4.950302                 0.888675   \n",
        "\n",
        "source            HALLMARK_UV_RESPONSE_UP  \\\n",
-       "AAACATACAACCAC-1                 1.996328   \n",
-       "AAACATTGAGCTAC-1                 1.578525   \n",
-       "AAACATTGATCAGC-1                 3.488813   \n",
-       "AAACCGTGCTTCCG-1                 1.204925   \n",
-       "AAACCGTGTATGCG-1                 0.379035   \n",
+       "AAACATACAACCAC-1                 1.983873   \n",
+       "AAACATTGAGCTAC-1                 0.871448   \n",
+       "AAACATTGATCAGC-1                 4.038435   \n",
+       "AAACCGTGCTTCCG-1                 1.567924   \n",
+       "AAACCGTGTATGCG-1                 0.375221   \n",
        "...                                   ...   \n",
-       "TTTCGAACTCTCAT-1                 5.295349   \n",
-       "TTTCTACTGAGGCA-1                 2.455590   \n",
-       "TTTCTACTTCCTCG-1                 4.661261   \n",
-       "TTTGCATGAGAGGC-1                 1.996328   \n",
-       "TTTGCATGCCTCAC-1                 4.058551   \n",
+       "TTTCGAACTCTCAT-1                 4.639190   \n",
+       "TTTCTACTGAGGCA-1                 2.441252   \n",
+       "TTTCTACTTCCTCG-1                 4.639190   \n",
+       "TTTGCATGAGAGGC-1                 2.441252   \n",
+       "TTTGCATGCCTCAC-1                 4.038435   \n",
        "\n",
        "source            HALLMARK_WNT_BETA_CATENIN_SIGNALING  \\\n",
-       "AAACATACAACCAC-1                             0.165837   \n",
-       "AAACATTGAGCTAC-1                            -0.000000   \n",
-       "AAACATTGATCAGC-1                             0.165837   \n",
-       "AAACCGTGCTTCCG-1                             0.506356   \n",
-       "AAACCGTGTATGCG-1                             0.165837   \n",
+       "AAACATACAACCAC-1                             0.164805   \n",
+       "AAACATTGAGCTAC-1                             0.503794   \n",
+       "AAACATTGATCAGC-1                             0.164805   \n",
+       "AAACCGTGCTTCCG-1                             0.164805   \n",
+       "AAACCGTGTATGCG-1                             0.164805   \n",
        "...                                               ...   \n",
-       "TTTCGAACTCTCAT-1                             0.165837   \n",
-       "TTTCTACTGAGGCA-1                            -0.000000   \n",
+       "TTTCGAACTCTCAT-1                             0.164805   \n",
+       "TTTCTACTGAGGCA-1                             0.164805   \n",
        "TTTCTACTTCCTCG-1                            -0.000000   \n",
-       "TTTGCATGAGAGGC-1                            -0.000000   \n",
-       "TTTGCATGCCTCAC-1                             0.165837   \n",
+       "TTTGCATGAGAGGC-1                             0.164805   \n",
+       "TTTGCATGCCTCAC-1                             0.164805   \n",
        "\n",
        "source            HALLMARK_XENOBIOTIC_METABOLISM  \n",
-       "AAACATACAACCAC-1                        0.181499  \n",
-       "AAACATTGAGCTAC-1                        0.181499  \n",
-       "AAACATTGATCAGC-1                        0.536606  \n",
-       "AAACCGTGCTTCCG-1                        0.536606  \n",
-       "AAACCGTGTATGCG-1                        0.081326  \n",
+       "AAACATACAACCAC-1                        0.080157  \n",
+       "AAACATTGAGCTAC-1                        0.080157  \n",
+       "AAACATTGATCAGC-1                        1.087420  \n",
+       "AAACCGTGCTTCCG-1                        1.087420  \n",
+       "AAACCGTGTATGCG-1                        0.179270  \n",
        "...                                          ...  \n",
-       "TTTCGAACTCTCAT-1                        0.791739  \n",
-       "TTTCTACTGAGGCA-1                        0.081326  \n",
-       "TTTCTACTTCCTCG-1                        0.332800  \n",
-       "TTTGCATGAGAGGC-1                        0.081326  \n",
-       "TTTGCATGCCTCAC-1                        0.081326  \n",
+       "TTTCGAACTCTCAT-1                        1.087420  \n",
+       "TTTCTACTGAGGCA-1                        0.080157  \n",
+       "TTTCTACTTCCTCG-1                        0.329247  \n",
+       "TTTGCATGAGAGGC-1                        0.329247  \n",
+       "TTTGCATGCCTCAC-1                        0.080157  \n",
        "\n",
        "[2638 rows x 50 columns]"
       ]
@@ -992,7 +992,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1569d3c4",
+   "id": "9d5e9a20",
    "metadata": {},
    "source": [
     "## Visualization\n",
@@ -1004,7 +1004,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "774708e5",
+   "id": "dcb61861",
    "metadata": {},
    "outputs": [
     {
@@ -1028,7 +1028,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e6a3d69",
+   "id": "35f062a9",
    "metadata": {},
    "source": [
     "`dc.get_acts` returns a new `AnnData` object which holds the obtained activities in its `.X` attribute, allowing us to re-use many `scanpy` functions, for example:"
@@ -1037,12 +1037,12 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "93fa8ade",
+   "id": "6b1a8a78",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -1059,7 +1059,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81550b59",
+   "id": "f3bcc01f",
    "metadata": {},
    "source": [
     "The cells highlighted seem to be enriched by coagulation."
@@ -1067,7 +1067,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ccb5260d",
+   "id": "3613244f",
    "metadata": {},
    "source": [
     "## Exploration\n",
@@ -1078,7 +1078,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "4afb5dd3",
+   "id": "1280b444",
    "metadata": {},
    "outputs": [
     {
@@ -1109,133 +1109,115 @@
        "      <th>HALLMARK_HYPOXIA</th>\n",
        "      <th>HALLMARK_INTERFERON_ALPHA_RESPONSE</th>\n",
        "      <th>HALLMARK_INTERFERON_GAMMA_RESPONSE</th>\n",
-       "      <th>HALLMARK_MTORC1_SIGNALING</th>\n",
        "      <th>HALLMARK_MYC_TARGETS_V1</th>\n",
        "      <th>HALLMARK_OXIDATIVE_PHOSPHORYLATION</th>\n",
-       "      <th>HALLMARK_P53_PATHWAY</th>\n",
        "      <th>HALLMARK_TNFA_SIGNALING_VIA_NFKB</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>B cells</th>\n",
-       "      <td>8.079591</td>\n",
-       "      <td>1.749463</td>\n",
-       "      <td>0.286118</td>\n",
-       "      <td>1.054397</td>\n",
-       "      <td>3.981507</td>\n",
-       "      <td>5.315256</td>\n",
-       "      <td>7.487265</td>\n",
-       "      <td>2.784845</td>\n",
-       "      <td>19.096043</td>\n",
-       "      <td>6.167998</td>\n",
-       "      <td>3.122765</td>\n",
-       "      <td>3.910282</td>\n",
+       "      <td>8.354994</td>\n",
+       "      <td>1.917664</td>\n",
+       "      <td>0.290295</td>\n",
+       "      <td>1.229005</td>\n",
+       "      <td>4.229028</td>\n",
+       "      <td>5.219356</td>\n",
+       "      <td>7.148639</td>\n",
+       "      <td>18.921244</td>\n",
+       "      <td>7.434455</td>\n",
+       "      <td>3.780326</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CD14+ Monocytes</th>\n",
-       "      <td>6.893857</td>\n",
-       "      <td>4.157110</td>\n",
-       "      <td>1.551010</td>\n",
-       "      <td>5.085937</td>\n",
-       "      <td>5.504116</td>\n",
-       "      <td>6.724769</td>\n",
-       "      <td>9.910409</td>\n",
-       "      <td>3.744251</td>\n",
-       "      <td>15.245032</td>\n",
-       "      <td>8.739642</td>\n",
-       "      <td>4.488810</td>\n",
-       "      <td>6.554947</td>\n",
+       "      <td>7.548974</td>\n",
+       "      <td>4.732542</td>\n",
+       "      <td>1.753238</td>\n",
+       "      <td>6.105103</td>\n",
+       "      <td>5.718626</td>\n",
+       "      <td>6.597623</td>\n",
+       "      <td>9.203345</td>\n",
+       "      <td>15.001544</td>\n",
+       "      <td>10.768515</td>\n",
+       "      <td>6.447618</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CD4 T cells</th>\n",
-       "      <td>6.738094</td>\n",
-       "      <td>2.542511</td>\n",
-       "      <td>0.385318</td>\n",
-       "      <td>1.387077</td>\n",
-       "      <td>5.477199</td>\n",
-       "      <td>5.183809</td>\n",
-       "      <td>6.133497</td>\n",
-       "      <td>3.825152</td>\n",
-       "      <td>21.438015</td>\n",
-       "      <td>7.835927</td>\n",
-       "      <td>4.455711</td>\n",
-       "      <td>5.101736</td>\n",
+       "      <td>7.367119</td>\n",
+       "      <td>2.989820</td>\n",
+       "      <td>0.404844</td>\n",
+       "      <td>1.667583</td>\n",
+       "      <td>5.704934</td>\n",
+       "      <td>5.119762</td>\n",
+       "      <td>5.761981</td>\n",
+       "      <td>20.898235</td>\n",
+       "      <td>8.849491</td>\n",
+       "      <td>4.792259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CD8 T cells</th>\n",
-       "      <td>9.274370</td>\n",
-       "      <td>2.505335</td>\n",
-       "      <td>0.521227</td>\n",
-       "      <td>2.662215</td>\n",
-       "      <td>5.422219</td>\n",
-       "      <td>5.628405</td>\n",
-       "      <td>7.875100</td>\n",
-       "      <td>4.289899</td>\n",
-       "      <td>20.455368</td>\n",
-       "      <td>7.498560</td>\n",
-       "      <td>4.230061</td>\n",
-       "      <td>5.271203</td>\n",
+       "      <td>10.050341</td>\n",
+       "      <td>2.916126</td>\n",
+       "      <td>0.551194</td>\n",
+       "      <td>3.018337</td>\n",
+       "      <td>5.510883</td>\n",
+       "      <td>5.461279</td>\n",
+       "      <td>7.221680</td>\n",
+       "      <td>19.887226</td>\n",
+       "      <td>8.511161</td>\n",
+       "      <td>4.978559</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dendritic cells</th>\n",
-       "      <td>9.610945</td>\n",
-       "      <td>4.332600</td>\n",
-       "      <td>1.328719</td>\n",
-       "      <td>3.415317</td>\n",
-       "      <td>6.171379</td>\n",
-       "      <td>8.055038</td>\n",
-       "      <td>11.730546</td>\n",
-       "      <td>5.031778</td>\n",
-       "      <td>24.268009</td>\n",
-       "      <td>13.322536</td>\n",
-       "      <td>5.185035</td>\n",
-       "      <td>6.472893</td>\n",
+       "      <td>11.001875</td>\n",
+       "      <td>4.818395</td>\n",
+       "      <td>1.361634</td>\n",
+       "      <td>4.210152</td>\n",
+       "      <td>6.341395</td>\n",
+       "      <td>8.200227</td>\n",
+       "      <td>11.063760</td>\n",
+       "      <td>25.464518</td>\n",
+       "      <td>15.929270</td>\n",
+       "      <td>6.571718</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FCGR3A+ Monocytes</th>\n",
-       "      <td>8.127956</td>\n",
-       "      <td>4.315158</td>\n",
-       "      <td>1.711685</td>\n",
-       "      <td>6.117206</td>\n",
-       "      <td>5.982983</td>\n",
-       "      <td>9.113420</td>\n",
-       "      <td>12.319559</td>\n",
-       "      <td>5.003101</td>\n",
-       "      <td>17.339090</td>\n",
-       "      <td>9.935337</td>\n",
-       "      <td>4.955063</td>\n",
-       "      <td>5.566297</td>\n",
+       "      <td>9.142790</td>\n",
+       "      <td>4.914899</td>\n",
+       "      <td>2.015463</td>\n",
+       "      <td>7.896366</td>\n",
+       "      <td>6.478059</td>\n",
+       "      <td>9.304629</td>\n",
+       "      <td>11.687559</td>\n",
+       "      <td>17.928917</td>\n",
+       "      <td>13.115271</td>\n",
+       "      <td>5.791608</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Megakaryocytes</th>\n",
-       "      <td>3.524540</td>\n",
-       "      <td>2.138839</td>\n",
-       "      <td>3.315179</td>\n",
-       "      <td>2.527668</td>\n",
-       "      <td>1.780706</td>\n",
-       "      <td>1.790546</td>\n",
-       "      <td>1.843046</td>\n",
-       "      <td>2.255984</td>\n",
-       "      <td>4.896301</td>\n",
-       "      <td>2.319902</td>\n",
-       "      <td>1.679166</td>\n",
-       "      <td>1.696074</td>\n",
+       "      <td>4.484497</td>\n",
+       "      <td>3.401895</td>\n",
+       "      <td>4.256961</td>\n",
+       "      <td>3.466092</td>\n",
+       "      <td>2.935619</td>\n",
+       "      <td>1.846708</td>\n",
+       "      <td>1.656383</td>\n",
+       "      <td>5.570364</td>\n",
+       "      <td>5.332106</td>\n",
+       "      <td>3.127663</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NK cells</th>\n",
-       "      <td>9.808369</td>\n",
-       "      <td>2.257473</td>\n",
-       "      <td>0.700178</td>\n",
-       "      <td>3.285920</td>\n",
-       "      <td>5.401470</td>\n",
-       "      <td>7.382476</td>\n",
-       "      <td>9.604524</td>\n",
-       "      <td>4.653162</td>\n",
-       "      <td>18.678684</td>\n",
-       "      <td>7.388927</td>\n",
-       "      <td>4.184835</td>\n",
-       "      <td>4.102460</td>\n",
+       "      <td>10.658255</td>\n",
+       "      <td>2.931217</td>\n",
+       "      <td>0.644769</td>\n",
+       "      <td>3.999646</td>\n",
+       "      <td>5.708977</td>\n",
+       "      <td>7.291569</td>\n",
+       "      <td>9.032887</td>\n",
+       "      <td>18.066849</td>\n",
+       "      <td>8.723539</td>\n",
+       "      <td>3.751579</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1243,74 +1225,74 @@
       ],
       "text/plain": [
        "                   HALLMARK_ALLOGRAFT_REJECTION  HALLMARK_APOPTOSIS  \\\n",
-       "B cells                                8.079591            1.749463   \n",
-       "CD14+ Monocytes                        6.893857            4.157110   \n",
-       "CD4 T cells                            6.738094            2.542511   \n",
-       "CD8 T cells                            9.274370            2.505335   \n",
-       "Dendritic cells                        9.610945            4.332600   \n",
-       "FCGR3A+ Monocytes                      8.127956            4.315158   \n",
-       "Megakaryocytes                         3.524540            2.138839   \n",
-       "NK cells                               9.808369            2.257473   \n",
+       "B cells                                8.354994            1.917664   \n",
+       "CD14+ Monocytes                        7.548974            4.732542   \n",
+       "CD4 T cells                            7.367119            2.989820   \n",
+       "CD8 T cells                           10.050341            2.916126   \n",
+       "Dendritic cells                       11.001875            4.818395   \n",
+       "FCGR3A+ Monocytes                      9.142790            4.914899   \n",
+       "Megakaryocytes                         4.484497            3.401895   \n",
+       "NK cells                              10.658255            2.931217   \n",
        "\n",
        "                   HALLMARK_COAGULATION  HALLMARK_COMPLEMENT  \\\n",
-       "B cells                        0.286118             1.054397   \n",
-       "CD14+ Monocytes                1.551010             5.085937   \n",
-       "CD4 T cells                    0.385318             1.387077   \n",
-       "CD8 T cells                    0.521227             2.662215   \n",
-       "Dendritic cells                1.328719             3.415317   \n",
-       "FCGR3A+ Monocytes              1.711685             6.117206   \n",
-       "Megakaryocytes                 3.315179             2.527668   \n",
-       "NK cells                       0.700178             3.285920   \n",
+       "B cells                        0.290295             1.229005   \n",
+       "CD14+ Monocytes                1.753238             6.105103   \n",
+       "CD4 T cells                    0.404844             1.667583   \n",
+       "CD8 T cells                    0.551194             3.018337   \n",
+       "Dendritic cells                1.361634             4.210152   \n",
+       "FCGR3A+ Monocytes              2.015463             7.896366   \n",
+       "Megakaryocytes                 4.256961             3.466092   \n",
+       "NK cells                       0.644769             3.999646   \n",
        "\n",
        "                   HALLMARK_HYPOXIA  HALLMARK_INTERFERON_ALPHA_RESPONSE  \\\n",
-       "B cells                    3.981507                            5.315256   \n",
-       "CD14+ Monocytes            5.504116                            6.724769   \n",
-       "CD4 T cells                5.477199                            5.183809   \n",
-       "CD8 T cells                5.422219                            5.628405   \n",
-       "Dendritic cells            6.171379                            8.055038   \n",
-       "FCGR3A+ Monocytes          5.982983                            9.113420   \n",
-       "Megakaryocytes             1.780706                            1.790546   \n",
-       "NK cells                   5.401470                            7.382476   \n",
+       "B cells                    4.229028                            5.219356   \n",
+       "CD14+ Monocytes            5.718626                            6.597623   \n",
+       "CD4 T cells                5.704934                            5.119762   \n",
+       "CD8 T cells                5.510883                            5.461279   \n",
+       "Dendritic cells            6.341395                            8.200227   \n",
+       "FCGR3A+ Monocytes          6.478059                            9.304629   \n",
+       "Megakaryocytes             2.935619                            1.846708   \n",
+       "NK cells                   5.708977                            7.291569   \n",
        "\n",
        "                   HALLMARK_INTERFERON_GAMMA_RESPONSE  \\\n",
-       "B cells                                      7.487265   \n",
-       "CD14+ Monocytes                              9.910409   \n",
-       "CD4 T cells                                  6.133497   \n",
-       "CD8 T cells                                  7.875100   \n",
-       "Dendritic cells                             11.730546   \n",
-       "FCGR3A+ Monocytes                           12.319559   \n",
-       "Megakaryocytes                               1.843046   \n",
-       "NK cells                                     9.604524   \n",
+       "B cells                                      7.148639   \n",
+       "CD14+ Monocytes                              9.203345   \n",
+       "CD4 T cells                                  5.761981   \n",
+       "CD8 T cells                                  7.221680   \n",
+       "Dendritic cells                             11.063760   \n",
+       "FCGR3A+ Monocytes                           11.687559   \n",
+       "Megakaryocytes                               1.656383   \n",
+       "NK cells                                     9.032887   \n",
        "\n",
-       "                   HALLMARK_MTORC1_SIGNALING  HALLMARK_MYC_TARGETS_V1  \\\n",
-       "B cells                             2.784845                19.096043   \n",
-       "CD14+ Monocytes                     3.744251                15.245032   \n",
-       "CD4 T cells                         3.825152                21.438015   \n",
-       "CD8 T cells                         4.289899                20.455368   \n",
-       "Dendritic cells                     5.031778                24.268009   \n",
-       "FCGR3A+ Monocytes                   5.003101                17.339090   \n",
-       "Megakaryocytes                      2.255984                 4.896301   \n",
-       "NK cells                            4.653162                18.678684   \n",
+       "                   HALLMARK_MYC_TARGETS_V1  \\\n",
+       "B cells                          18.921244   \n",
+       "CD14+ Monocytes                  15.001544   \n",
+       "CD4 T cells                      20.898235   \n",
+       "CD8 T cells                      19.887226   \n",
+       "Dendritic cells                  25.464518   \n",
+       "FCGR3A+ Monocytes                17.928917   \n",
+       "Megakaryocytes                    5.570364   \n",
+       "NK cells                         18.066849   \n",
        "\n",
-       "                   HALLMARK_OXIDATIVE_PHOSPHORYLATION  HALLMARK_P53_PATHWAY  \\\n",
-       "B cells                                      6.167998              3.122765   \n",
-       "CD14+ Monocytes                              8.739642              4.488810   \n",
-       "CD4 T cells                                  7.835927              4.455711   \n",
-       "CD8 T cells                                  7.498560              4.230061   \n",
-       "Dendritic cells                             13.322536              5.185035   \n",
-       "FCGR3A+ Monocytes                            9.935337              4.955063   \n",
-       "Megakaryocytes                               2.319902              1.679166   \n",
-       "NK cells                                     7.388927              4.184835   \n",
+       "                   HALLMARK_OXIDATIVE_PHOSPHORYLATION  \\\n",
+       "B cells                                      7.434455   \n",
+       "CD14+ Monocytes                             10.768515   \n",
+       "CD4 T cells                                  8.849491   \n",
+       "CD8 T cells                                  8.511161   \n",
+       "Dendritic cells                             15.929270   \n",
+       "FCGR3A+ Monocytes                           13.115271   \n",
+       "Megakaryocytes                               5.332106   \n",
+       "NK cells                                     8.723539   \n",
        "\n",
        "                   HALLMARK_TNFA_SIGNALING_VIA_NFKB  \n",
-       "B cells                                    3.910282  \n",
-       "CD14+ Monocytes                            6.554947  \n",
-       "CD4 T cells                                5.101736  \n",
-       "CD8 T cells                                5.271203  \n",
-       "Dendritic cells                            6.472893  \n",
-       "FCGR3A+ Monocytes                          5.566297  \n",
-       "Megakaryocytes                             1.696074  \n",
-       "NK cells                                   4.102460  "
+       "B cells                                    3.780326  \n",
+       "CD14+ Monocytes                            6.447618  \n",
+       "CD4 T cells                                4.792259  \n",
+       "CD8 T cells                                4.978559  \n",
+       "Dendritic cells                            6.571718  \n",
+       "FCGR3A+ Monocytes                          5.791608  \n",
+       "Megakaryocytes                             3.127663  \n",
+       "NK cells                                   3.751579  "
       ]
      },
      "execution_count": 10,
@@ -1325,7 +1307,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23b1679e",
+   "id": "9488a871",
    "metadata": {},
    "source": [
     "We can visualize which group is more enriched using `seaborn`:"
@@ -1334,12 +1316,12 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "15f66690",
+   "id": "e39e730c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x720 with 4 Axes>"
       ]
@@ -1351,17 +1333,32 @@
     }
    ],
    "source": [
-    "sns.clustermap(mean_enr, xticklabels=mean_enr.columns, cmap='viridis')\n",
+    "sns.clustermap(mean_enr, xticklabels=mean_enr.columns, vmax=20, cmap='viridis')\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f54484a9",
+   "id": "937b7931",
    "metadata": {},
    "source": [
     "In this specific example, we can observe that most cell types are enriched by targets of MYC, a global regulator of the immune system, and megakaryocytes are enriched by coagulation genes."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e534fff1",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    "**Note**\n",
+    "    \n",
+    "If your data consist of different conditions with enough samples, we recommend to work with pseudo-bulk profiles instead. Check this\n",
+    "[vignette](https://decoupler-py.readthedocs.io/en/latest/notebooks/pseudobulk.html) for more informatin.\n",
+    "\n",
+    "</div>"
+   ]
   }
  ],
  "metadata": {
diff --git a/docs/source/notebooks/progeny.ipynb b/docs/source/notebooks/progeny.ipynb
index a7627dd..7330810 100644
--- a/docs/source/notebooks/progeny.ipynb
+++ b/docs/source/notebooks/progeny.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "4481ae0a",
+   "id": "fbdce904",
    "metadata": {
     "tags": []
    },
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce0ada79",
+   "id": "6dd654ed",
    "metadata": {},
    "source": [
     "scRNA-seq yield many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by infering pathway activities from prior knowledge.\n",
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12c24d39",
+   "id": "7d95d630",
    "metadata": {},
    "source": [
     "## Loading packages\n",
@@ -44,7 +44,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "b8213959",
+   "id": "0f88b43c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "610c196d",
+   "id": "b95e99ac",
    "metadata": {},
    "source": [
     "## Loading the data\n",
@@ -69,7 +69,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "7b19f671",
+   "id": "012f2afe",
    "metadata": {},
    "outputs": [
     {
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ba66713",
+   "id": "c61c64b2",
    "metadata": {},
    "source": [
     "We can visualize the different cell types in it:"
@@ -105,12 +105,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "7090ed30",
+   "id": "f88ca737",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e1a7b70",
+   "id": "8023fff1",
    "metadata": {},
    "source": [
     "## PROGENy model\n",
@@ -137,7 +137,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "2257d876",
+   "id": "8c524d5f",
    "metadata": {},
    "outputs": [
     {
@@ -279,7 +279,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc1f77aa",
+   "id": "b34fde9e",
    "metadata": {},
    "source": [
     "## Activity inference with Multivariate Linear Model\n",
@@ -294,22 +294,22 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "d222405a",
+   "id": "9b8213fd",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "58 features of mat are empty in 2635 samples, they will be ignored.\n",
-      "Running mlm on mat with 2638 samples and 13656 targets for 14 sources.\n"
+      "1 features of mat are empty, they will be removed.\n",
+      "Running mlm on mat with 2638 samples and 13713 targets for 14 sources.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00,  1.90s/it]\n"
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00,  1.98s/it]\n"
      ]
     }
    ],
@@ -319,7 +319,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68e5729c",
+   "id": "1d708716",
    "metadata": {},
    "source": [
     "The obtained scores (t-values)(`mlm_estimate`) and p-values (`mlm_pvals`) are stored in the `.obsm` key:"
@@ -328,7 +328,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "fc356b28",
+   "id": "58ab2ddb",
    "metadata": {},
    "outputs": [
     {
@@ -371,88 +371,88 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AAACATACAACCAC-1</th>\n",
-       "      <td>-0.254249</td>\n",
-       "      <td>1.079791</td>\n",
-       "      <td>-0.285906</td>\n",
-       "      <td>-0.028353</td>\n",
-       "      <td>-1.106584</td>\n",
-       "      <td>-1.415070</td>\n",
-       "      <td>-0.082578</td>\n",
-       "      <td>-0.799087</td>\n",
-       "      <td>-1.149092</td>\n",
-       "      <td>0.683372</td>\n",
-       "      <td>-0.574785</td>\n",
-       "      <td>0.116901</td>\n",
-       "      <td>0.123429</td>\n",
-       "      <td>0.051064</td>\n",
+       "      <td>-0.250004</td>\n",
+       "      <td>1.082446</td>\n",
+       "      <td>-0.283717</td>\n",
+       "      <td>0.018847</td>\n",
+       "      <td>-1.101526</td>\n",
+       "      <td>-1.425420</td>\n",
+       "      <td>-0.079885</td>\n",
+       "      <td>-0.806441</td>\n",
+       "      <td>-1.146634</td>\n",
+       "      <td>0.685259</td>\n",
+       "      <td>-0.574002</td>\n",
+       "      <td>0.138756</td>\n",
+       "      <td>0.127025</td>\n",
+       "      <td>0.056324</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACATTGAGCTAC-1</th>\n",
-       "      <td>0.049356</td>\n",
-       "      <td>1.810619</td>\n",
-       "      <td>-0.642226</td>\n",
-       "      <td>0.473845</td>\n",
-       "      <td>0.044039</td>\n",
-       "      <td>-2.655564</td>\n",
-       "      <td>0.499077</td>\n",
-       "      <td>-0.048742</td>\n",
-       "      <td>-1.256668</td>\n",
-       "      <td>-0.668830</td>\n",
-       "      <td>0.662161</td>\n",
-       "      <td>0.320902</td>\n",
-       "      <td>-0.886260</td>\n",
-       "      <td>-1.080493</td>\n",
+       "      <td>0.055699</td>\n",
+       "      <td>1.808353</td>\n",
+       "      <td>-0.639859</td>\n",
+       "      <td>0.547163</td>\n",
+       "      <td>0.053690</td>\n",
+       "      <td>-2.664019</td>\n",
+       "      <td>0.503815</td>\n",
+       "      <td>-0.056263</td>\n",
+       "      <td>-1.252882</td>\n",
+       "      <td>-0.669461</td>\n",
+       "      <td>0.665907</td>\n",
+       "      <td>0.348851</td>\n",
+       "      <td>-0.883580</td>\n",
+       "      <td>-1.075813</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACATTGATCAGC-1</th>\n",
-       "      <td>-1.303848</td>\n",
-       "      <td>0.994644</td>\n",
-       "      <td>-1.424479</td>\n",
-       "      <td>0.786483</td>\n",
-       "      <td>0.760535</td>\n",
-       "      <td>-1.422900</td>\n",
-       "      <td>-0.092575</td>\n",
-       "      <td>-0.283805</td>\n",
-       "      <td>-0.821637</td>\n",
-       "      <td>0.893210</td>\n",
-       "      <td>0.002381</td>\n",
-       "      <td>0.186219</td>\n",
-       "      <td>-0.380405</td>\n",
-       "      <td>-0.004127</td>\n",
+       "      <td>-1.300669</td>\n",
+       "      <td>1.093902</td>\n",
+       "      <td>-1.423962</td>\n",
+       "      <td>0.862988</td>\n",
+       "      <td>0.770645</td>\n",
+       "      <td>-1.604121</td>\n",
+       "      <td>-0.089351</td>\n",
+       "      <td>-0.293437</td>\n",
+       "      <td>-0.817603</td>\n",
+       "      <td>0.895554</td>\n",
+       "      <td>0.004694</td>\n",
+       "      <td>0.213210</td>\n",
+       "      <td>-0.377153</td>\n",
+       "      <td>0.001996</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACCGTGCTTCCG-1</th>\n",
-       "      <td>-1.112302</td>\n",
-       "      <td>0.619356</td>\n",
-       "      <td>-0.535364</td>\n",
-       "      <td>0.393064</td>\n",
-       "      <td>5.351178</td>\n",
-       "      <td>-1.143144</td>\n",
-       "      <td>-1.695277</td>\n",
-       "      <td>-0.320915</td>\n",
-       "      <td>-0.656491</td>\n",
-       "      <td>1.709201</td>\n",
-       "      <td>-0.192290</td>\n",
-       "      <td>-0.466818</td>\n",
-       "      <td>-0.293868</td>\n",
-       "      <td>-1.363734</td>\n",
+       "      <td>-1.108973</td>\n",
+       "      <td>0.623916</td>\n",
+       "      <td>-0.533218</td>\n",
+       "      <td>0.456434</td>\n",
+       "      <td>5.369927</td>\n",
+       "      <td>-1.157837</td>\n",
+       "      <td>-1.695246</td>\n",
+       "      <td>-0.328195</td>\n",
+       "      <td>-0.652306</td>\n",
+       "      <td>1.713096</td>\n",
+       "      <td>-0.190242</td>\n",
+       "      <td>-0.439180</td>\n",
+       "      <td>-0.290556</td>\n",
+       "      <td>-1.360429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAACCGTGTATGCG-1</th>\n",
-       "      <td>-0.024978</td>\n",
-       "      <td>-0.952474</td>\n",
-       "      <td>0.111260</td>\n",
-       "      <td>0.429574</td>\n",
-       "      <td>1.997764</td>\n",
-       "      <td>1.380687</td>\n",
-       "      <td>-0.279586</td>\n",
-       "      <td>0.023845</td>\n",
-       "      <td>-0.366084</td>\n",
-       "      <td>-0.005061</td>\n",
-       "      <td>0.697570</td>\n",
-       "      <td>-0.186564</td>\n",
-       "      <td>-1.324416</td>\n",
-       "      <td>-0.745281</td>\n",
+       "      <td>-0.020885</td>\n",
+       "      <td>-0.934698</td>\n",
+       "      <td>0.113905</td>\n",
+       "      <td>0.480329</td>\n",
+       "      <td>2.008395</td>\n",
+       "      <td>1.355708</td>\n",
+       "      <td>-0.277678</td>\n",
+       "      <td>0.018336</td>\n",
+       "      <td>-0.362728</td>\n",
+       "      <td>-0.004614</td>\n",
+       "      <td>0.700825</td>\n",
+       "      <td>-0.166572</td>\n",
+       "      <td>-1.324292</td>\n",
+       "      <td>-0.742359</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -473,88 +473,88 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTCGAACTCTCAT-1</th>\n",
-       "      <td>-1.313532</td>\n",
-       "      <td>1.637261</td>\n",
-       "      <td>-0.521595</td>\n",
-       "      <td>0.790193</td>\n",
-       "      <td>5.909053</td>\n",
-       "      <td>-1.263014</td>\n",
-       "      <td>-2.046195</td>\n",
-       "      <td>0.868877</td>\n",
-       "      <td>-0.129425</td>\n",
-       "      <td>2.279451</td>\n",
-       "      <td>-0.395455</td>\n",
-       "      <td>0.120465</td>\n",
-       "      <td>0.379417</td>\n",
-       "      <td>-0.726687</td>\n",
+       "      <td>-1.310079</td>\n",
+       "      <td>1.646397</td>\n",
+       "      <td>-0.519146</td>\n",
+       "      <td>0.867002</td>\n",
+       "      <td>5.929371</td>\n",
+       "      <td>-1.278792</td>\n",
+       "      <td>-2.046493</td>\n",
+       "      <td>0.863211</td>\n",
+       "      <td>-0.123756</td>\n",
+       "      <td>2.284416</td>\n",
+       "      <td>-0.393778</td>\n",
+       "      <td>0.147630</td>\n",
+       "      <td>0.384334</td>\n",
+       "      <td>-0.721629</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTCTACTGAGGCA-1</th>\n",
-       "      <td>-0.089881</td>\n",
-       "      <td>-0.277900</td>\n",
-       "      <td>-0.336497</td>\n",
-       "      <td>0.483560</td>\n",
-       "      <td>1.307610</td>\n",
-       "      <td>-0.530299</td>\n",
-       "      <td>-0.635616</td>\n",
-       "      <td>0.279144</td>\n",
-       "      <td>-1.501107</td>\n",
-       "      <td>0.174172</td>\n",
-       "      <td>0.219880</td>\n",
-       "      <td>0.288029</td>\n",
-       "      <td>-0.357245</td>\n",
-       "      <td>-0.364742</td>\n",
+       "      <td>-0.084679</td>\n",
+       "      <td>-0.269608</td>\n",
+       "      <td>-0.333985</td>\n",
+       "      <td>0.554965</td>\n",
+       "      <td>1.318137</td>\n",
+       "      <td>-0.552446</td>\n",
+       "      <td>-0.633511</td>\n",
+       "      <td>0.272859</td>\n",
+       "      <td>-1.498412</td>\n",
+       "      <td>0.175075</td>\n",
+       "      <td>0.222500</td>\n",
+       "      <td>0.315759</td>\n",
+       "      <td>-0.354096</td>\n",
+       "      <td>-0.359570</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTCTACTTCCTCG-1</th>\n",
-       "      <td>-0.384818</td>\n",
-       "      <td>0.291916</td>\n",
-       "      <td>-0.584801</td>\n",
-       "      <td>-0.506899</td>\n",
-       "      <td>0.398995</td>\n",
-       "      <td>-0.762126</td>\n",
-       "      <td>-1.212282</td>\n",
-       "      <td>-1.737221</td>\n",
-       "      <td>-0.691521</td>\n",
-       "      <td>1.493315</td>\n",
-       "      <td>1.031305</td>\n",
-       "      <td>0.233905</td>\n",
-       "      <td>-0.592073</td>\n",
-       "      <td>-0.910929</td>\n",
+       "      <td>-0.381286</td>\n",
+       "      <td>0.296119</td>\n",
+       "      <td>-0.583498</td>\n",
+       "      <td>-0.474597</td>\n",
+       "      <td>0.406432</td>\n",
+       "      <td>-0.774971</td>\n",
+       "      <td>-1.212176</td>\n",
+       "      <td>-1.746175</td>\n",
+       "      <td>-0.688630</td>\n",
+       "      <td>1.496862</td>\n",
+       "      <td>1.035139</td>\n",
+       "      <td>0.253294</td>\n",
+       "      <td>-0.590249</td>\n",
+       "      <td>-0.908120</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTGCATGAGAGGC-1</th>\n",
-       "      <td>-0.081163</td>\n",
-       "      <td>1.160554</td>\n",
-       "      <td>-0.053387</td>\n",
-       "      <td>-0.944438</td>\n",
-       "      <td>-1.008870</td>\n",
-       "      <td>-1.162704</td>\n",
-       "      <td>-0.632125</td>\n",
-       "      <td>0.766052</td>\n",
-       "      <td>-0.935325</td>\n",
-       "      <td>0.303947</td>\n",
-       "      <td>2.430733</td>\n",
-       "      <td>-0.152342</td>\n",
-       "      <td>0.621712</td>\n",
-       "      <td>-0.182315</td>\n",
+       "      <td>-0.077524</td>\n",
+       "      <td>1.164005</td>\n",
+       "      <td>-0.051290</td>\n",
+       "      <td>-0.926941</td>\n",
+       "      <td>-1.005181</td>\n",
+       "      <td>-1.171102</td>\n",
+       "      <td>-0.631157</td>\n",
+       "      <td>0.763098</td>\n",
+       "      <td>-0.933493</td>\n",
+       "      <td>0.305002</td>\n",
+       "      <td>2.437439</td>\n",
+       "      <td>-0.133605</td>\n",
+       "      <td>0.625714</td>\n",
+       "      <td>-0.178558</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TTTGCATGCCTCAC-1</th>\n",
-       "      <td>-0.215233</td>\n",
-       "      <td>0.495030</td>\n",
-       "      <td>0.697128</td>\n",
-       "      <td>0.182926</td>\n",
-       "      <td>1.069097</td>\n",
-       "      <td>-0.965002</td>\n",
-       "      <td>-0.787809</td>\n",
-       "      <td>-1.315707</td>\n",
-       "      <td>-0.759915</td>\n",
-       "      <td>1.247356</td>\n",
-       "      <td>0.310475</td>\n",
-       "      <td>0.139115</td>\n",
-       "      <td>0.036928</td>\n",
-       "      <td>-1.155394</td>\n",
+       "      <td>-0.211075</td>\n",
+       "      <td>0.498874</td>\n",
+       "      <td>0.701253</td>\n",
+       "      <td>0.233277</td>\n",
+       "      <td>1.078380</td>\n",
+       "      <td>-0.977642</td>\n",
+       "      <td>-0.786710</td>\n",
+       "      <td>-1.324148</td>\n",
+       "      <td>-0.756899</td>\n",
+       "      <td>1.250499</td>\n",
+       "      <td>0.312962</td>\n",
+       "      <td>0.160146</td>\n",
+       "      <td>0.040263</td>\n",
+       "      <td>-1.152789</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -563,43 +563,43 @@
       ],
       "text/plain": [
        "                  Androgen      EGFR  Estrogen   Hypoxia  JAK-STAT      MAPK  \\\n",
-       "AAACATACAACCAC-1 -0.254249  1.079791 -0.285906 -0.028353 -1.106584 -1.415070   \n",
-       "AAACATTGAGCTAC-1  0.049356  1.810619 -0.642226  0.473845  0.044039 -2.655564   \n",
-       "AAACATTGATCAGC-1 -1.303848  0.994644 -1.424479  0.786483  0.760535 -1.422900   \n",
-       "AAACCGTGCTTCCG-1 -1.112302  0.619356 -0.535364  0.393064  5.351178 -1.143144   \n",
-       "AAACCGTGTATGCG-1 -0.024978 -0.952474  0.111260  0.429574  1.997764  1.380687   \n",
+       "AAACATACAACCAC-1 -0.250004  1.082446 -0.283717  0.018847 -1.101526 -1.425420   \n",
+       "AAACATTGAGCTAC-1  0.055699  1.808353 -0.639859  0.547163  0.053690 -2.664019   \n",
+       "AAACATTGATCAGC-1 -1.300669  1.093902 -1.423962  0.862988  0.770645 -1.604121   \n",
+       "AAACCGTGCTTCCG-1 -1.108973  0.623916 -0.533218  0.456434  5.369927 -1.157837   \n",
+       "AAACCGTGTATGCG-1 -0.020885 -0.934698  0.113905  0.480329  2.008395  1.355708   \n",
        "...                    ...       ...       ...       ...       ...       ...   \n",
-       "TTTCGAACTCTCAT-1 -1.313532  1.637261 -0.521595  0.790193  5.909053 -1.263014   \n",
-       "TTTCTACTGAGGCA-1 -0.089881 -0.277900 -0.336497  0.483560  1.307610 -0.530299   \n",
-       "TTTCTACTTCCTCG-1 -0.384818  0.291916 -0.584801 -0.506899  0.398995 -0.762126   \n",
-       "TTTGCATGAGAGGC-1 -0.081163  1.160554 -0.053387 -0.944438 -1.008870 -1.162704   \n",
-       "TTTGCATGCCTCAC-1 -0.215233  0.495030  0.697128  0.182926  1.069097 -0.965002   \n",
+       "TTTCGAACTCTCAT-1 -1.310079  1.646397 -0.519146  0.867002  5.929371 -1.278792   \n",
+       "TTTCTACTGAGGCA-1 -0.084679 -0.269608 -0.333985  0.554965  1.318137 -0.552446   \n",
+       "TTTCTACTTCCTCG-1 -0.381286  0.296119 -0.583498 -0.474597  0.406432 -0.774971   \n",
+       "TTTGCATGAGAGGC-1 -0.077524  1.164005 -0.051290 -0.926941 -1.005181 -1.171102   \n",
+       "TTTGCATGCCTCAC-1 -0.211075  0.498874  0.701253  0.233277  1.078380 -0.977642   \n",
        "\n",
        "                      NFkB      PI3K      TGFb      TNFa     Trail      VEGF  \\\n",
-       "AAACATACAACCAC-1 -0.082578 -0.799087 -1.149092  0.683372 -0.574785  0.116901   \n",
-       "AAACATTGAGCTAC-1  0.499077 -0.048742 -1.256668 -0.668830  0.662161  0.320902   \n",
-       "AAACATTGATCAGC-1 -0.092575 -0.283805 -0.821637  0.893210  0.002381  0.186219   \n",
-       "AAACCGTGCTTCCG-1 -1.695277 -0.320915 -0.656491  1.709201 -0.192290 -0.466818   \n",
-       "AAACCGTGTATGCG-1 -0.279586  0.023845 -0.366084 -0.005061  0.697570 -0.186564   \n",
+       "AAACATACAACCAC-1 -0.079885 -0.806441 -1.146634  0.685259 -0.574002  0.138756   \n",
+       "AAACATTGAGCTAC-1  0.503815 -0.056263 -1.252882 -0.669461  0.665907  0.348851   \n",
+       "AAACATTGATCAGC-1 -0.089351 -0.293437 -0.817603  0.895554  0.004694  0.213210   \n",
+       "AAACCGTGCTTCCG-1 -1.695246 -0.328195 -0.652306  1.713096 -0.190242 -0.439180   \n",
+       "AAACCGTGTATGCG-1 -0.277678  0.018336 -0.362728 -0.004614  0.700825 -0.166572   \n",
        "...                    ...       ...       ...       ...       ...       ...   \n",
-       "TTTCGAACTCTCAT-1 -2.046195  0.868877 -0.129425  2.279451 -0.395455  0.120465   \n",
-       "TTTCTACTGAGGCA-1 -0.635616  0.279144 -1.501107  0.174172  0.219880  0.288029   \n",
-       "TTTCTACTTCCTCG-1 -1.212282 -1.737221 -0.691521  1.493315  1.031305  0.233905   \n",
-       "TTTGCATGAGAGGC-1 -0.632125  0.766052 -0.935325  0.303947  2.430733 -0.152342   \n",
-       "TTTGCATGCCTCAC-1 -0.787809 -1.315707 -0.759915  1.247356  0.310475  0.139115   \n",
+       "TTTCGAACTCTCAT-1 -2.046493  0.863211 -0.123756  2.284416 -0.393778  0.147630   \n",
+       "TTTCTACTGAGGCA-1 -0.633511  0.272859 -1.498412  0.175075  0.222500  0.315759   \n",
+       "TTTCTACTTCCTCG-1 -1.212176 -1.746175 -0.688630  1.496862  1.035139  0.253294   \n",
+       "TTTGCATGAGAGGC-1 -0.631157  0.763098 -0.933493  0.305002  2.437439 -0.133605   \n",
+       "TTTGCATGCCTCAC-1 -0.786710 -1.324148 -0.756899  1.250499  0.312962  0.160146   \n",
        "\n",
        "                       WNT       p53  \n",
-       "AAACATACAACCAC-1  0.123429  0.051064  \n",
-       "AAACATTGAGCTAC-1 -0.886260 -1.080493  \n",
-       "AAACATTGATCAGC-1 -0.380405 -0.004127  \n",
-       "AAACCGTGCTTCCG-1 -0.293868 -1.363734  \n",
-       "AAACCGTGTATGCG-1 -1.324416 -0.745281  \n",
+       "AAACATACAACCAC-1  0.127025  0.056324  \n",
+       "AAACATTGAGCTAC-1 -0.883580 -1.075813  \n",
+       "AAACATTGATCAGC-1 -0.377153  0.001996  \n",
+       "AAACCGTGCTTCCG-1 -0.290556 -1.360429  \n",
+       "AAACCGTGTATGCG-1 -1.324292 -0.742359  \n",
        "...                    ...       ...  \n",
-       "TTTCGAACTCTCAT-1  0.379417 -0.726687  \n",
-       "TTTCTACTGAGGCA-1 -0.357245 -0.364742  \n",
-       "TTTCTACTTCCTCG-1 -0.592073 -0.910929  \n",
-       "TTTGCATGAGAGGC-1  0.621712 -0.182315  \n",
-       "TTTGCATGCCTCAC-1  0.036928 -1.155394  \n",
+       "TTTCGAACTCTCAT-1  0.384334 -0.721629  \n",
+       "TTTCTACTGAGGCA-1 -0.354096 -0.359570  \n",
+       "TTTCTACTTCCTCG-1 -0.590249 -0.908120  \n",
+       "TTTGCATGAGAGGC-1  0.625714 -0.178558  \n",
+       "TTTGCATGCCTCAC-1  0.040263 -1.152789  \n",
        "\n",
        "[2638 rows x 14 columns]"
       ]
@@ -615,7 +615,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5715bd82",
+   "id": "63b3bae2",
    "metadata": {},
    "source": [
     "**Note**: Each run of `run_mlm` overwrites what is inside of `mlm_estimate` and `mlm_pvals`. if you want to run `mlm` with other resources and still keep the activities inside the same `AnnData` object, you can store the results in any other key in `.obsm` with different names, for example:"
@@ -624,7 +624,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "12d609fe",
+   "id": "90246e91",
    "metadata": {},
    "outputs": [
     {
@@ -652,7 +652,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "083f88e5",
+   "id": "9854a72e",
    "metadata": {},
    "source": [
     "## Visualization\n",
@@ -664,7 +664,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "0aca492b",
+   "id": "722a7a19",
    "metadata": {},
    "outputs": [
     {
@@ -688,7 +688,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ddd68505",
+   "id": "45f41214",
    "metadata": {},
    "source": [
     "`dc.get_acts` returns a new `AnnData` object which holds the obtained activities in its `.X` attribute, allowing us to re-use many `scanpy` functions, for example let's visualise the Trail pathway:"
@@ -697,12 +697,12 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "c7f606d4",
+   "id": "e511cba5",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -719,7 +719,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18cc564d",
+   "id": "c7500018",
    "metadata": {},
    "source": [
     "It seem that in B and NK cells, the pathway Trail, associated with apoptosis, is more active."
@@ -727,7 +727,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f6746a4",
+   "id": "260da270",
    "metadata": {},
    "source": [
     "## Exploration\n",
@@ -738,7 +738,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "71018fa6",
+   "id": "8e4f8008",
    "metadata": {},
    "outputs": [
     {
@@ -781,139 +781,139 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>B cells</th>\n",
-       "      <td>-0.468620</td>\n",
-       "      <td>0.331232</td>\n",
-       "      <td>-0.417349</td>\n",
-       "      <td>-0.158265</td>\n",
-       "      <td>0.814644</td>\n",
-       "      <td>-0.850410</td>\n",
-       "      <td>-0.588561</td>\n",
-       "      <td>-0.209756</td>\n",
-       "      <td>-0.882239</td>\n",
-       "      <td>0.643023</td>\n",
-       "      <td>1.154483</td>\n",
-       "      <td>-0.204471</td>\n",
-       "      <td>-0.220058</td>\n",
-       "      <td>-0.571772</td>\n",
+       "      <td>-0.465011</td>\n",
+       "      <td>0.342595</td>\n",
+       "      <td>-0.415550</td>\n",
+       "      <td>-0.124931</td>\n",
+       "      <td>0.823214</td>\n",
+       "      <td>-0.876431</td>\n",
+       "      <td>-0.587007</td>\n",
+       "      <td>-0.215912</td>\n",
+       "      <td>-0.879469</td>\n",
+       "      <td>0.644808</td>\n",
+       "      <td>1.158729</td>\n",
+       "      <td>-0.182334</td>\n",
+       "      <td>-0.217285</td>\n",
+       "      <td>-0.567988</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CD14+ Monocytes</th>\n",
-       "      <td>-0.757303</td>\n",
-       "      <td>1.041441</td>\n",
-       "      <td>-0.491022</td>\n",
-       "      <td>0.747027</td>\n",
-       "      <td>2.380898</td>\n",
-       "      <td>-1.354828</td>\n",
-       "      <td>-0.985024</td>\n",
-       "      <td>-0.224320</td>\n",
-       "      <td>-0.775338</td>\n",
-       "      <td>1.474258</td>\n",
-       "      <td>-0.269537</td>\n",
-       "      <td>-0.301565</td>\n",
-       "      <td>0.006913</td>\n",
-       "      <td>-0.576483</td>\n",
+       "      <td>-0.753841</td>\n",
+       "      <td>1.052265</td>\n",
+       "      <td>-0.489119</td>\n",
+       "      <td>0.773133</td>\n",
+       "      <td>2.393195</td>\n",
+       "      <td>-1.379257</td>\n",
+       "      <td>-0.983988</td>\n",
+       "      <td>-0.230894</td>\n",
+       "      <td>-0.771927</td>\n",
+       "      <td>1.477719</td>\n",
+       "      <td>-0.267905</td>\n",
+       "      <td>-0.276274</td>\n",
+       "      <td>0.010432</td>\n",
+       "      <td>-0.572246</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CD4 T cells</th>\n",
-       "      <td>-0.739280</td>\n",
-       "      <td>0.398468</td>\n",
-       "      <td>-0.442663</td>\n",
-       "      <td>0.255920</td>\n",
-       "      <td>0.944010</td>\n",
-       "      <td>-0.813422</td>\n",
-       "      <td>-0.495894</td>\n",
-       "      <td>-0.644337</td>\n",
-       "      <td>-0.891932</td>\n",
-       "      <td>0.913994</td>\n",
-       "      <td>-0.273603</td>\n",
-       "      <td>0.058538</td>\n",
-       "      <td>-0.229640</td>\n",
-       "      <td>-0.631877</td>\n",
+       "      <td>-0.735975</td>\n",
+       "      <td>0.412647</td>\n",
+       "      <td>-0.440776</td>\n",
+       "      <td>0.292130</td>\n",
+       "      <td>0.953193</td>\n",
+       "      <td>-0.843468</td>\n",
+       "      <td>-0.494001</td>\n",
+       "      <td>-0.651749</td>\n",
+       "      <td>-0.888945</td>\n",
+       "      <td>0.916335</td>\n",
+       "      <td>-0.272181</td>\n",
+       "      <td>0.081154</td>\n",
+       "      <td>-0.226721</td>\n",
+       "      <td>-0.627953</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CD8 T cells</th>\n",
-       "      <td>-0.729590</td>\n",
-       "      <td>0.443396</td>\n",
-       "      <td>-0.409557</td>\n",
-       "      <td>0.302901</td>\n",
-       "      <td>1.102024</td>\n",
-       "      <td>-0.853103</td>\n",
-       "      <td>-0.754845</td>\n",
-       "      <td>-0.347034</td>\n",
-       "      <td>-0.993045</td>\n",
-       "      <td>1.228682</td>\n",
-       "      <td>0.293810</td>\n",
-       "      <td>0.072460</td>\n",
-       "      <td>-0.133140</td>\n",
-       "      <td>-0.677323</td>\n",
+       "      <td>-0.726172</td>\n",
+       "      <td>0.455278</td>\n",
+       "      <td>-0.407548</td>\n",
+       "      <td>0.322529</td>\n",
+       "      <td>1.111638</td>\n",
+       "      <td>-0.879143</td>\n",
+       "      <td>-0.753404</td>\n",
+       "      <td>-0.353885</td>\n",
+       "      <td>-0.990159</td>\n",
+       "      <td>1.231635</td>\n",
+       "      <td>0.296404</td>\n",
+       "      <td>0.095871</td>\n",
+       "      <td>-0.129972</td>\n",
+       "      <td>-0.673387</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dendritic cells</th>\n",
-       "      <td>-0.689356</td>\n",
-       "      <td>1.080140</td>\n",
-       "      <td>-0.639010</td>\n",
-       "      <td>0.825671</td>\n",
-       "      <td>3.072018</td>\n",
-       "      <td>-1.557169</td>\n",
-       "      <td>-0.519228</td>\n",
-       "      <td>-0.528199</td>\n",
-       "      <td>-0.769999</td>\n",
-       "      <td>0.858208</td>\n",
-       "      <td>-0.339825</td>\n",
-       "      <td>-0.484119</td>\n",
-       "      <td>0.182676</td>\n",
-       "      <td>-0.613223</td>\n",
+       "      <td>-0.684471</td>\n",
+       "      <td>1.089961</td>\n",
+       "      <td>-0.636636</td>\n",
+       "      <td>0.789944</td>\n",
+       "      <td>3.087430</td>\n",
+       "      <td>-1.581596</td>\n",
+       "      <td>-0.516469</td>\n",
+       "      <td>-0.536889</td>\n",
+       "      <td>-0.765263</td>\n",
+       "      <td>0.860534</td>\n",
+       "      <td>-0.337834</td>\n",
+       "      <td>-0.456041</td>\n",
+       "      <td>0.187420</td>\n",
+       "      <td>-0.607646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FCGR3A+ Monocytes</th>\n",
-       "      <td>-0.869172</td>\n",
-       "      <td>1.303017</td>\n",
-       "      <td>-0.634341</td>\n",
-       "      <td>0.868262</td>\n",
-       "      <td>3.638630</td>\n",
-       "      <td>-1.599229</td>\n",
-       "      <td>-1.155024</td>\n",
-       "      <td>-0.105529</td>\n",
-       "      <td>-1.160473</td>\n",
-       "      <td>1.756404</td>\n",
-       "      <td>-0.231644</td>\n",
-       "      <td>-0.363514</td>\n",
-       "      <td>-0.361645</td>\n",
-       "      <td>-0.638332</td>\n",
+       "      <td>-0.864837</td>\n",
+       "      <td>1.320364</td>\n",
+       "      <td>-0.632086</td>\n",
+       "      <td>0.927257</td>\n",
+       "      <td>3.654879</td>\n",
+       "      <td>-1.635532</td>\n",
+       "      <td>-1.153630</td>\n",
+       "      <td>-0.113269</td>\n",
+       "      <td>-1.156720</td>\n",
+       "      <td>1.760448</td>\n",
+       "      <td>-0.229468</td>\n",
+       "      <td>-0.333119</td>\n",
+       "      <td>-0.358097</td>\n",
+       "      <td>-0.633038</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Megakaryocytes</th>\n",
-       "      <td>-0.539583</td>\n",
-       "      <td>2.361184</td>\n",
-       "      <td>-0.379758</td>\n",
-       "      <td>1.339222</td>\n",
-       "      <td>0.074742</td>\n",
-       "      <td>-2.248966</td>\n",
-       "      <td>-0.628353</td>\n",
-       "      <td>-0.999684</td>\n",
-       "      <td>0.006636</td>\n",
-       "      <td>0.659785</td>\n",
-       "      <td>-0.047438</td>\n",
-       "      <td>-0.015523</td>\n",
-       "      <td>0.957471</td>\n",
-       "      <td>-0.315496</td>\n",
+       "      <td>-0.536795</td>\n",
+       "      <td>2.360259</td>\n",
+       "      <td>-0.378290</td>\n",
+       "      <td>1.376318</td>\n",
+       "      <td>0.080829</td>\n",
+       "      <td>-2.250954</td>\n",
+       "      <td>-0.627291</td>\n",
+       "      <td>-1.006189</td>\n",
+       "      <td>0.010520</td>\n",
+       "      <td>0.661547</td>\n",
+       "      <td>-0.045912</td>\n",
+       "      <td>0.003110</td>\n",
+       "      <td>0.962199</td>\n",
+       "      <td>-0.311917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NK cells</th>\n",
-       "      <td>-0.695226</td>\n",
-       "      <td>0.543965</td>\n",
-       "      <td>-0.211977</td>\n",
-       "      <td>0.382988</td>\n",
-       "      <td>2.203089</td>\n",
-       "      <td>-0.974522</td>\n",
-       "      <td>-1.161676</td>\n",
-       "      <td>-0.094563</td>\n",
-       "      <td>-1.086584</td>\n",
-       "      <td>1.237731</td>\n",
-       "      <td>0.729650</td>\n",
-       "      <td>0.240034</td>\n",
-       "      <td>-0.098274</td>\n",
-       "      <td>-0.713052</td>\n",
+       "      <td>-0.691467</td>\n",
+       "      <td>0.563583</td>\n",
+       "      <td>-0.209429</td>\n",
+       "      <td>0.389797</td>\n",
+       "      <td>2.215324</td>\n",
+       "      <td>-1.014608</td>\n",
+       "      <td>-1.160875</td>\n",
+       "      <td>-0.101439</td>\n",
+       "      <td>-1.083613</td>\n",
+       "      <td>1.240734</td>\n",
+       "      <td>0.733171</td>\n",
+       "      <td>0.264418</td>\n",
+       "      <td>-0.094835</td>\n",
+       "      <td>-0.708889</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -921,34 +921,34 @@
       ],
       "text/plain": [
        "                   Androgen      EGFR  Estrogen   Hypoxia  JAK-STAT      MAPK  \\\n",
-       "B cells           -0.468620  0.331232 -0.417349 -0.158265  0.814644 -0.850410   \n",
-       "CD14+ Monocytes   -0.757303  1.041441 -0.491022  0.747027  2.380898 -1.354828   \n",
-       "CD4 T cells       -0.739280  0.398468 -0.442663  0.255920  0.944010 -0.813422   \n",
-       "CD8 T cells       -0.729590  0.443396 -0.409557  0.302901  1.102024 -0.853103   \n",
-       "Dendritic cells   -0.689356  1.080140 -0.639010  0.825671  3.072018 -1.557169   \n",
-       "FCGR3A+ Monocytes -0.869172  1.303017 -0.634341  0.868262  3.638630 -1.599229   \n",
-       "Megakaryocytes    -0.539583  2.361184 -0.379758  1.339222  0.074742 -2.248966   \n",
-       "NK cells          -0.695226  0.543965 -0.211977  0.382988  2.203089 -0.974522   \n",
+       "B cells           -0.465011  0.342595 -0.415550 -0.124931  0.823214 -0.876431   \n",
+       "CD14+ Monocytes   -0.753841  1.052265 -0.489119  0.773133  2.393195 -1.379257   \n",
+       "CD4 T cells       -0.735975  0.412647 -0.440776  0.292130  0.953193 -0.843468   \n",
+       "CD8 T cells       -0.726172  0.455278 -0.407548  0.322529  1.111638 -0.879143   \n",
+       "Dendritic cells   -0.684471  1.089961 -0.636636  0.789944  3.087430 -1.581596   \n",
+       "FCGR3A+ Monocytes -0.864837  1.320364 -0.632086  0.927257  3.654879 -1.635532   \n",
+       "Megakaryocytes    -0.536795  2.360259 -0.378290  1.376318  0.080829 -2.250954   \n",
+       "NK cells          -0.691467  0.563583 -0.209429  0.389797  2.215324 -1.014608   \n",
        "\n",
        "                       NFkB      PI3K      TGFb      TNFa     Trail      VEGF  \\\n",
-       "B cells           -0.588561 -0.209756 -0.882239  0.643023  1.154483 -0.204471   \n",
-       "CD14+ Monocytes   -0.985024 -0.224320 -0.775338  1.474258 -0.269537 -0.301565   \n",
-       "CD4 T cells       -0.495894 -0.644337 -0.891932  0.913994 -0.273603  0.058538   \n",
-       "CD8 T cells       -0.754845 -0.347034 -0.993045  1.228682  0.293810  0.072460   \n",
-       "Dendritic cells   -0.519228 -0.528199 -0.769999  0.858208 -0.339825 -0.484119   \n",
-       "FCGR3A+ Monocytes -1.155024 -0.105529 -1.160473  1.756404 -0.231644 -0.363514   \n",
-       "Megakaryocytes    -0.628353 -0.999684  0.006636  0.659785 -0.047438 -0.015523   \n",
-       "NK cells          -1.161676 -0.094563 -1.086584  1.237731  0.729650  0.240034   \n",
+       "B cells           -0.587007 -0.215912 -0.879469  0.644808  1.158729 -0.182334   \n",
+       "CD14+ Monocytes   -0.983988 -0.230894 -0.771927  1.477719 -0.267905 -0.276274   \n",
+       "CD4 T cells       -0.494001 -0.651749 -0.888945  0.916335 -0.272181  0.081154   \n",
+       "CD8 T cells       -0.753404 -0.353885 -0.990159  1.231635  0.296404  0.095871   \n",
+       "Dendritic cells   -0.516469 -0.536889 -0.765263  0.860534 -0.337834 -0.456041   \n",
+       "FCGR3A+ Monocytes -1.153630 -0.113269 -1.156720  1.760448 -0.229468 -0.333119   \n",
+       "Megakaryocytes    -0.627291 -1.006189  0.010520  0.661547 -0.045912  0.003110   \n",
+       "NK cells          -1.160875 -0.101439 -1.083613  1.240734  0.733171  0.264418   \n",
        "\n",
        "                        WNT       p53  \n",
-       "B cells           -0.220058 -0.571772  \n",
-       "CD14+ Monocytes    0.006913 -0.576483  \n",
-       "CD4 T cells       -0.229640 -0.631877  \n",
-       "CD8 T cells       -0.133140 -0.677323  \n",
-       "Dendritic cells    0.182676 -0.613223  \n",
-       "FCGR3A+ Monocytes -0.361645 -0.638332  \n",
-       "Megakaryocytes     0.957471 -0.315496  \n",
-       "NK cells          -0.098274 -0.713052  "
+       "B cells           -0.217285 -0.567988  \n",
+       "CD14+ Monocytes    0.010432 -0.572246  \n",
+       "CD4 T cells       -0.226721 -0.627953  \n",
+       "CD8 T cells       -0.129972 -0.673387  \n",
+       "Dendritic cells    0.187420 -0.607646  \n",
+       "FCGR3A+ Monocytes -0.358097 -0.633038  \n",
+       "Megakaryocytes     0.962199 -0.311917  \n",
+       "NK cells          -0.094835 -0.708889  "
       ]
      },
      "execution_count": 10,
@@ -963,7 +963,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e3b6c74",
+   "id": "c7d72e13",
    "metadata": {},
    "source": [
     "We can visualize which group is more active using `seaborn`:"
@@ -972,12 +972,12 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "0f57e395",
+   "id": "f9851055",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x720 with 4 Axes>"
       ]
@@ -995,18 +995,33 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e644901f",
+   "id": "4839d081",
    "metadata": {},
    "source": [
     "In this specific example, we can observe that WNT to be more active in Megakaryocytes, and that Trail is more active in B and NK cells."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db57adbb",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    "**Note**\n",
+    "    \n",
+    "If your data consist of different conditions with enough samples, we recommend to work with pseudo-bulk profiles instead. Check this\n",
+    "[vignette](https://decoupler-py.readthedocs.io/en/latest/notebooks/pseudobulk.html) for more informatin.\n",
+    "\n",
+    "</div>"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "decoupler",
+   "display_name": "test_decoupler",
    "language": "python",
-   "name": "decoupler"
+   "name": "test_decoupler"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1018,7 +1033,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/pseudobulk.ipynb b/docs/source/notebooks/pseudobulk.ipynb
index 026f2b5..c2831d5 100644
--- a/docs/source/notebooks/pseudobulk.ipynb
+++ b/docs/source/notebooks/pseudobulk.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "687d0eb3",
+   "id": "18739291",
    "metadata": {},
    "source": [
     "# Pseudo-bulk functional analysis"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d25a71f",
+   "id": "438be8ce",
    "metadata": {},
    "source": [
     "When cell lineage is clear (there are clear cell identity clusters), it might be beneficial to perform functional analyses at the pseudo-bulk level instead of the single-cell.\n",
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "04aeadad",
+   "id": "44c4bc71",
    "metadata": {},
    "source": [
     "## Loading packages\n",
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "7688827d",
+   "id": "90250f85",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7eeedb1",
+   "id": "02c04508",
    "metadata": {},
    "source": [
     "## Loading the data\n",
@@ -69,7 +69,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "c7a59673",
+   "id": "5a9fad31",
    "metadata": {},
    "outputs": [
     {
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2649c59f",
+   "id": "6c1645cd",
    "metadata": {
     "tags": []
    },
@@ -114,7 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "86437bce",
+   "id": "84729a4e",
    "metadata": {},
    "outputs": [
     {
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d38493a0",
+   "id": "63816f60",
    "metadata": {},
    "source": [
     "Since the meta-data of this data-set is available, we can filter cells that were not annotated:"
@@ -158,7 +158,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "185e8511",
+   "id": "dfa28500",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,7 +168,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e8c07ec",
+   "id": "354f2dc7",
    "metadata": {},
    "source": [
     "We will store the raw counts in the `.layers` attribute so that we can use them\n",
@@ -178,7 +178,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "234549d9",
+   "id": "3a72f6ea",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,7 +193,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fef52a1f",
+   "id": "3e48e0cc",
    "metadata": {},
    "source": [
     "We can also look how cells cluster by cell identity:"
@@ -202,7 +202,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "2672b031",
+   "id": "e75a6fd4",
    "metadata": {},
    "outputs": [
     {
@@ -238,7 +238,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d54378ef",
+   "id": "1f765ae1",
    "metadata": {},
    "source": [
     "In this data-set we have two condition, `COVID-19` and `healthy`, across 6 different cell types."
@@ -246,7 +246,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "90d1f35c",
+   "id": "2d794c94",
    "metadata": {},
    "source": [
     "## Generation of pseudo-bulk profiles\n",
@@ -268,7 +268,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "d344bee5",
+   "id": "4573ce61",
    "metadata": {},
    "outputs": [
     {
@@ -304,7 +304,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2a2874dc",
+   "id": "75b34858",
    "metadata": {},
    "source": [
     "## Contrast between conditions\n",
@@ -315,7 +315,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "1660b5db",
+   "id": "eb469734",
    "metadata": {},
    "outputs": [
     {
@@ -339,125 +339,125 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>DPM1</th>\n",
-       "      <th>FGR</th>\n",
-       "      <th>FUCA2</th>\n",
-       "      <th>GCLC</th>\n",
-       "      <th>STPG1</th>\n",
-       "      <th>ANKIB1</th>\n",
-       "      <th>CYP51A1</th>\n",
-       "      <th>KRIT1</th>\n",
-       "      <th>BAD</th>\n",
-       "      <th>LAP3</th>\n",
+       "      <th>A1BG</th>\n",
+       "      <th>AAGAB</th>\n",
+       "      <th>AAK1</th>\n",
+       "      <th>AAMP</th>\n",
+       "      <th>AARS2</th>\n",
+       "      <th>AATF</th>\n",
+       "      <th>ABCA7</th>\n",
+       "      <th>ABCB11</th>\n",
+       "      <th>ABCB7</th>\n",
+       "      <th>ABCC1</th>\n",
        "      <th>...</th>\n",
-       "      <th>FRG1CP</th>\n",
-       "      <th>GTF2IP12</th>\n",
-       "      <th>SCO2</th>\n",
-       "      <th>CCDC39</th>\n",
-       "      <th>TBCE-1</th>\n",
-       "      <th>POLR2J3-1</th>\n",
-       "      <th>MKKS-1</th>\n",
-       "      <th>DERPC</th>\n",
-       "      <th>NOTCH2NLC</th>\n",
-       "      <th>DUS4L-BCAP29</th>\n",
+       "      <th>ZRANB2</th>\n",
+       "      <th>ZRSR2</th>\n",
+       "      <th>ZSCAN22</th>\n",
+       "      <th>ZSWIM6</th>\n",
+       "      <th>ZSWIM7</th>\n",
+       "      <th>ZSWIM8</th>\n",
+       "      <th>ZXDC</th>\n",
+       "      <th>ZYG11B</th>\n",
+       "      <th>ZYX</th>\n",
+       "      <th>ZZEF1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>B cell</th>\n",
+       "      <td>-0.352016</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>-0.720511</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.300538</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>-0.191943</td>\n",
+       "      <td>-0.333673</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.308509</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>...</td>\n",
+       "      <td>-0.275637</td>\n",
+       "      <td>0.031170</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.562253</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.806585</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>-0.282937</td>\n",
-       "      <td>-0.339039</td>\n",
-       "      <td>0.147279</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.492214</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.090038</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T cell</th>\n",
+       "      <td>-0.174568</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.614993</td>\n",
+       "      <td>-0.176686</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.076531</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.144981</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.062462</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.334564</td>\n",
-       "      <td>0.066740</td>\n",
-       "      <td>0.152962</td>\n",
+       "      <td>0.019278</td>\n",
+       "      <td>0.072665</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.140914</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>-0.331029</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.281399</td>\n",
+       "      <td>0.376813</td>\n",
+       "      <td>0.020089</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>monocyte</th>\n",
-       "      <td>0.037376</td>\n",
-       "      <td>-0.323548</td>\n",
-       "      <td>0.25692</td>\n",
-       "      <td>-0.473073</td>\n",
-       "      <td>-0.579108</td>\n",
-       "      <td>0.182966</td>\n",
-       "      <td>0.614051</td>\n",
-       "      <td>-0.278829</td>\n",
-       "      <td>0.218976</td>\n",
-       "      <td>-0.150524</td>\n",
+       "      <td>-0.188902</td>\n",
+       "      <td>-0.192955</td>\n",
+       "      <td>0.134138</td>\n",
+       "      <td>-0.071034</td>\n",
+       "      <td>0.472479</td>\n",
+       "      <td>-0.092681</td>\n",
+       "      <td>-0.146258</td>\n",
+       "      <td>0.157249</td>\n",
+       "      <td>-0.160763</td>\n",
+       "      <td>-0.747298</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.585409</td>\n",
-       "      <td>-0.000777</td>\n",
-       "      <td>0.008957</td>\n",
-       "      <td>0.155732</td>\n",
-       "      <td>0.060410</td>\n",
-       "      <td>0.358192</td>\n",
-       "      <td>-0.303077</td>\n",
-       "      <td>0.006159</td>\n",
-       "      <td>-0.462152</td>\n",
-       "      <td>0.658816</td>\n",
+       "      <td>-0.123918</td>\n",
+       "      <td>0.383032</td>\n",
+       "      <td>-0.158732</td>\n",
+       "      <td>0.36222</td>\n",
+       "      <td>-0.153625</td>\n",
+       "      <td>0.496824</td>\n",
+       "      <td>0.0142</td>\n",
+       "      <td>0.131526</td>\n",
+       "      <td>0.445066</td>\n",
+       "      <td>-0.162685</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>neutrophil</th>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>-0.057455</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>-0.368335</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>-0.182189</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -465,29 +465,29 @@
        "</div>"
       ],
       "text/plain": [
-       "                DPM1       FGR    FUCA2      GCLC     STPG1    ANKIB1  \\\n",
-       "B cell      0.000000  0.000000  0.00000  0.000000 -0.300538  0.000000   \n",
-       "T cell      0.000000  0.000000  0.00000  0.000000 -0.614993  0.000000   \n",
-       "monocyte    0.037376 -0.323548  0.25692 -0.473073 -0.579108  0.182966   \n",
-       "neutrophil  0.000000  0.000000  0.00000  0.000000  0.000000  0.000000   \n",
+       "                A1BG     AAGAB      AAK1      AAMP     AARS2      AATF  \\\n",
+       "B cell     -0.352016  0.000000 -0.720511  0.000000 -0.191943 -0.333673   \n",
+       "T cell     -0.174568  0.000000 -0.176686  0.000000  0.000000  0.076531   \n",
+       "monocyte   -0.188902 -0.192955  0.134138 -0.071034  0.472479 -0.092681   \n",
+       "neutrophil  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
        "\n",
-       "             CYP51A1     KRIT1       BAD      LAP3  ...    FRG1CP  GTF2IP12  \\\n",
-       "B cell      0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
-       "T cell      0.000000  0.000000  0.000000  0.000000  ... -0.062462  0.000000   \n",
-       "monocyte    0.614051 -0.278829  0.218976 -0.150524  ...  0.585409 -0.000777   \n",
-       "neutrophil  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
+       "               ABCA7    ABCB11     ABCB7     ABCC1  ...    ZRANB2     ZRSR2  \\\n",
+       "B cell      0.000000  0.308509  0.000000  0.000000  ... -0.275637  0.031170   \n",
+       "T cell      0.000000  0.144981  0.000000  0.000000  ...  0.019278  0.072665   \n",
+       "monocyte   -0.146258  0.157249 -0.160763 -0.747298  ... -0.123918  0.383032   \n",
+       "neutrophil  0.000000 -0.057455  0.000000  0.000000  ...  0.000000  0.000000   \n",
        "\n",
-       "                SCO2    CCDC39    TBCE-1  POLR2J3-1    MKKS-1     DERPC  \\\n",
-       "B cell      0.000000 -0.282937 -0.339039   0.147279  0.000000  0.000000   \n",
-       "T cell      0.000000 -0.334564  0.066740   0.152962  0.000000  0.000000   \n",
-       "monocyte    0.008957  0.155732  0.060410   0.358192 -0.303077  0.006159   \n",
-       "neutrophil  0.000000  0.000000  0.000000  -0.368335  0.000000  0.000000   \n",
+       "             ZSCAN22   ZSWIM6    ZSWIM7    ZSWIM8    ZXDC    ZYG11B       ZYX  \\\n",
+       "B cell      0.000000  0.00000  0.562253  0.000000  0.0000  0.806585  0.000000   \n",
+       "T cell      0.000000  0.00000  0.140914  0.000000  0.0000  0.281399  0.376813   \n",
+       "monocyte   -0.158732  0.36222 -0.153625  0.496824  0.0142  0.131526  0.445066   \n",
+       "neutrophil  0.000000  0.00000  0.000000  0.000000  0.0000  0.000000  0.000000   \n",
        "\n",
-       "            NOTCH2NLC  DUS4L-BCAP29  \n",
-       "B cell      -0.492214      0.000000  \n",
-       "T cell      -0.331029      0.000000  \n",
-       "monocyte    -0.462152      0.658816  \n",
-       "neutrophil   0.000000      0.000000  \n",
+       "               ZZEF1  \n",
+       "B cell      0.090038  \n",
+       "T cell      0.020089  \n",
+       "monocyte   -0.162685  \n",
+       "neutrophil -0.182189  \n",
        "\n",
        "[4 rows x 6963 columns]"
       ]
@@ -510,7 +510,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed4358fc",
+   "id": "7e5aadef",
    "metadata": {},
    "source": [
     "For each cell type, we will obtain a log fold change (`logFC`) between conditions and their associated p-value (`pvals`).\n",
@@ -519,7 +519,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f153a449",
+   "id": "c9f8f2a0",
    "metadata": {},
    "source": [
     "We can plot the results of individual cell types in a volcano plot:"
@@ -528,12 +528,12 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "342fafe3",
+   "id": "c31fb2a2",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x500 with 1 Axes>"
       ]
@@ -550,7 +550,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "308e0856",
+   "id": "44fc832a",
    "metadata": {},
    "source": [
     "Optionaly, these results can also be transformed into a long format dataframe:"
@@ -559,7 +559,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "abf7e0b1",
+   "id": "a1b84f40",
    "metadata": {},
    "outputs": [
     {
@@ -585,8 +585,9 @@
        "      <th></th>\n",
        "      <th>contrast</th>\n",
        "      <th>name</th>\n",
-       "      <th>logFC</th>\n",
-       "      <th>pval</th>\n",
+       "      <th>logFCs</th>\n",
+       "      <th>pvals</th>\n",
+       "      <th>adj_pvals</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -596,6 +597,7 @@
        "      <td>MPEG1</td>\n",
        "      <td>-1.739905</td>\n",
        "      <td>0.000027</td>\n",
+       "      <td>0.18849</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -603,6 +605,7 @@
        "      <td>PSMG4</td>\n",
        "      <td>-0.895388</td>\n",
        "      <td>0.000156</td>\n",
+       "      <td>0.54155</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -610,20 +613,23 @@
        "      <td>ANKMY1</td>\n",
        "      <td>-1.356016</td>\n",
        "      <td>0.000326</td>\n",
+       "      <td>0.755645</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>T cell</td>\n",
-       "      <td>NDUFA7</td>\n",
-       "      <td>0.507570</td>\n",
-       "      <td>0.000465</td>\n",
+       "      <td>B cell</td>\n",
+       "      <td>KDM5C</td>\n",
+       "      <td>-0.972460</td>\n",
+       "      <td>0.000818</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>T cell</td>\n",
-       "      <td>SERBP1</td>\n",
-       "      <td>0.424462</td>\n",
-       "      <td>0.000584</td>\n",
+       "      <td>B cell</td>\n",
+       "      <td>PSMC6</td>\n",
+       "      <td>0.556979</td>\n",
+       "      <td>0.000861</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -631,62 +637,68 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27847</th>\n",
        "      <td>neutrophil</td>\n",
-       "      <td>APH1B</td>\n",
+       "      <td>NSD3</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27848</th>\n",
-       "      <td>B cell</td>\n",
-       "      <td>PPCDC</td>\n",
+       "      <td>neutrophil</td>\n",
+       "      <td>SLX9</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27849</th>\n",
-       "      <td>T cell</td>\n",
-       "      <td>PPCDC</td>\n",
+       "      <td>neutrophil</td>\n",
+       "      <td>CEP290</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27850</th>\n",
-       "      <td>T cell</td>\n",
-       "      <td>SLC49A4</td>\n",
+       "      <td>neutrophil</td>\n",
+       "      <td>SLX1B</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27851</th>\n",
        "      <td>neutrophil</td>\n",
-       "      <td>DUS4L-BCAP29</td>\n",
+       "      <td>SMAD5</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>27852 rows × 4 columns</p>\n",
+       "<p>27852 rows × 5 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "         contrast          name     logFC      pval\n",
-       "0          B cell         MPEG1 -1.739905  0.000027\n",
-       "1          B cell         PSMG4 -0.895388  0.000156\n",
-       "2          B cell        ANKMY1 -1.356016  0.000326\n",
-       "3          T cell        NDUFA7  0.507570  0.000465\n",
-       "4          T cell        SERBP1  0.424462  0.000584\n",
-       "...           ...           ...       ...       ...\n",
-       "27847  neutrophil         APH1B  0.000000  1.000000\n",
-       "27848      B cell         PPCDC  0.000000  1.000000\n",
-       "27849      T cell         PPCDC  0.000000  1.000000\n",
-       "27850      T cell       SLC49A4  0.000000  1.000000\n",
-       "27851  neutrophil  DUS4L-BCAP29  0.000000  1.000000\n",
+       "         contrast    name    logFCs     pvals adj_pvals\n",
+       "0          B cell   MPEG1 -1.739905  0.000027   0.18849\n",
+       "1          B cell   PSMG4 -0.895388  0.000156   0.54155\n",
+       "2          B cell  ANKMY1 -1.356016  0.000326  0.755645\n",
+       "3          B cell   KDM5C -0.972460  0.000818       1.0\n",
+       "4          B cell   PSMC6  0.556979  0.000861       1.0\n",
+       "...           ...     ...       ...       ...       ...\n",
+       "27847  neutrophil    NSD3  0.000000  1.000000       1.0\n",
+       "27848  neutrophil    SLX9  0.000000  1.000000       1.0\n",
+       "27849  neutrophil  CEP290  0.000000  1.000000       1.0\n",
+       "27850  neutrophil   SLX1B  0.000000  1.000000       1.0\n",
+       "27851  neutrophil   SMAD5  0.000000  1.000000       1.0\n",
        "\n",
-       "[27852 rows x 4 columns]"
+       "[27852 rows x 5 columns]"
       ]
      },
      "execution_count": 10,
@@ -701,16 +713,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fce8cddd",
+   "id": "d2bc41b1",
    "metadata": {},
    "source": [
-    "To get and explore the list of significant genes of this plot we can use this line:"
+    "To get and explore the list of significant genes of this plot we can additionaly use this line:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "feacab58",
+   "id": "55d84bad",
    "metadata": {},
    "outputs": [
     {
@@ -734,6 +746,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>contrast</th>\n",
+       "      <th>name</th>\n",
        "      <th>logFCs</th>\n",
        "      <th>pvals</th>\n",
        "      <th>adj_pvals</th>\n",
@@ -746,7 +760,7 @@
       ],
       "text/plain": [
        "Empty DataFrame\n",
-       "Columns: [logFCs, pvals, adj_pvals]\n",
+       "Columns: [contrast, name, logFCs, pvals, adj_pvals]\n",
        "Index: []"
       ]
      },
@@ -762,16 +776,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "83cdb548",
+   "id": "1dd3a00c",
    "metadata": {},
    "source": [
-    "Unfortunately, after FDR correction none of the genes are significant. Nonetheless, we can still work with them in downstream\n",
-    "analyses."
+    "Unfortunately, after FDR correction none of the genes are significant. Nonetheless, this level of significance is not requiered for footprint analysis so we will keep working with them."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f3383ab9",
+   "id": "325fc36f",
    "metadata": {},
    "source": [
     "## Pathway activity inference\n",
@@ -784,7 +797,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "b3a9d45b",
+   "id": "b16ba03a",
    "metadata": {},
    "outputs": [
     {
@@ -798,7 +811,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:01<00:00,  2.07it/s]\n"
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:02<00:00,  1.91it/s]\n"
      ]
     },
     {
@@ -850,17 +863,17 @@
        "      <td>-0.618957</td>\n",
        "      <td>-0.873149</td>\n",
        "      <td>-1.344327</td>\n",
-       "      <td>-0.240278</td>\n",
+       "      <td>-0.240277</td>\n",
        "      <td>-0.237212</td>\n",
        "      <td>-0.270574</td>\n",
-       "      <td>-1.992414</td>\n",
+       "      <td>-1.992413</td>\n",
        "      <td>0.146785</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T cell</th>\n",
-       "      <td>0.366330</td>\n",
-       "      <td>0.250878</td>\n",
-       "      <td>0.681461</td>\n",
+       "      <td>0.366329</td>\n",
+       "      <td>0.250877</td>\n",
+       "      <td>0.681462</td>\n",
        "      <td>-0.256431</td>\n",
        "      <td>2.035453</td>\n",
        "      <td>-0.372100</td>\n",
@@ -875,15 +888,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>monocyte</th>\n",
-       "      <td>-0.722186</td>\n",
+       "      <td>-0.722187</td>\n",
        "      <td>0.271530</td>\n",
        "      <td>0.198237</td>\n",
        "      <td>-0.950822</td>\n",
        "      <td>2.282387</td>\n",
        "      <td>-0.380224</td>\n",
-       "      <td>-1.427934</td>\n",
+       "      <td>-1.427933</td>\n",
        "      <td>0.212970</td>\n",
-       "      <td>-0.460575</td>\n",
+       "      <td>-0.460576</td>\n",
        "      <td>-1.226618</td>\n",
        "      <td>0.211401</td>\n",
        "      <td>0.292549</td>\n",
@@ -914,18 +927,18 @@
       "text/plain": [
        "            Androgen      EGFR  Estrogen   Hypoxia  JAK-STAT      MAPK  \\\n",
        "B cell     -1.098919  1.237139  0.416324 -0.413475  1.445572 -0.700278   \n",
-       "T cell      0.366330  0.250878  0.681461 -0.256431  2.035453 -0.372100   \n",
-       "monocyte   -0.722186  0.271530  0.198237 -0.950822  2.282387 -0.380224   \n",
+       "T cell      0.366329  0.250877  0.681462 -0.256431  2.035453 -0.372100   \n",
+       "monocyte   -0.722187  0.271530  0.198237 -0.950822  2.282387 -0.380224   \n",
        "neutrophil  0.488334 -0.341408  0.115238  1.193122  0.531162 -0.591123   \n",
        "\n",
        "                NFkB      PI3K      TGFb      TNFa     Trail      VEGF  \\\n",
-       "B cell     -0.618957 -0.873149 -1.344327 -0.240278 -0.237212 -0.270574   \n",
+       "B cell     -0.618957 -0.873149 -1.344327 -0.240277 -0.237212 -0.270574   \n",
        "T cell     -2.178579  0.011292 -0.826486 -0.875604 -0.011300 -0.256430   \n",
-       "monocyte   -1.427934  0.212970 -0.460575 -1.226618  0.211401  0.292549   \n",
+       "monocyte   -1.427933  0.212970 -0.460576 -1.226618  0.211401  0.292549   \n",
        "neutrophil -1.618794 -0.768376 -0.470676 -0.644394  1.926585 -1.041501   \n",
        "\n",
        "                 WNT       p53  \n",
-       "B cell     -1.992414  0.146785  \n",
+       "B cell     -1.992413  0.146785  \n",
        "T cell      0.732481 -0.101372  \n",
        "monocyte   -0.448675 -1.140018  \n",
        "neutrophil  0.099447       NaN  "
@@ -947,7 +960,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3031eaea",
+   "id": "f2a4bd5d",
    "metadata": {},
    "source": [
     "Let us plot the obtained scores:"
@@ -956,7 +969,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "51e4a787",
+   "id": "a5a07e2a",
    "metadata": {},
    "outputs": [
     {
@@ -982,7 +995,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "30c29826",
+   "id": "39d0b5a9",
    "metadata": {},
    "source": [
     "It looks like JAK-STAT is active across cell types in COVID-19 compared to healthy. To further explore how the target genes behave, we can plot them in a volcano plot:"
@@ -991,12 +1004,12 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "032c8243",
+   "id": "7575b2a0",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x500 with 2 Axes>"
       ]
@@ -1013,7 +1026,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98494074",
+   "id": "794228c0",
    "metadata": {},
    "source": [
     "As an example, we can see that most genes that belong to the JAK-STAT pathway appear to be unregulated in COVID-19 monocytes compared to healthy samples.\n",
@@ -1026,7 +1039,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "48ac53e0",
+   "id": "c2963d1d",
    "metadata": {},
    "outputs": [
     {
@@ -1050,43 +1063,59 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>contrast</th>\n",
+       "      <th>name</th>\n",
        "      <th>logFCs</th>\n",
        "      <th>pvals</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>SECTM1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>SECTM1</td>\n",
        "      <td>0.537977</td>\n",
        "      <td>0.001906</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>WARS1</th>\n",
+       "      <th>1</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>WARS1</td>\n",
        "      <td>-0.523153</td>\n",
        "      <td>0.002031</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>NMI</th>\n",
+       "      <th>2</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>NMI</td>\n",
        "      <td>0.582018</td>\n",
        "      <td>0.012736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>CHMP5</th>\n",
+       "      <th>3</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>CHMP5</td>\n",
        "      <td>1.056818</td>\n",
        "      <td>0.013749</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>IFITM2</th>\n",
+       "      <th>4</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>IFITM2</td>\n",
        "      <td>1.655442</td>\n",
        "      <td>0.033603</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>SPATS2L</th>\n",
+       "      <th>5</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>SPATS2L</td>\n",
        "      <td>0.950594</td>\n",
        "      <td>0.036179</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>OAS3</th>\n",
+       "      <th>6</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>OAS3</td>\n",
        "      <td>0.992000</td>\n",
        "      <td>0.039488</td>\n",
        "    </tr>\n",
@@ -1095,14 +1124,14 @@
        "</div>"
       ],
       "text/plain": [
-       "           logFCs     pvals\n",
-       "SECTM1   0.537977  0.001906\n",
-       "WARS1   -0.523153  0.002031\n",
-       "NMI      0.582018  0.012736\n",
-       "CHMP5    1.056818  0.013749\n",
-       "IFITM2   1.655442  0.033603\n",
-       "SPATS2L  0.950594  0.036179\n",
-       "OAS3     0.992000  0.039488"
+       "   contrast     name    logFCs     pvals\n",
+       "0  monocyte   SECTM1  0.537977  0.001906\n",
+       "1  monocyte    WARS1 -0.523153  0.002031\n",
+       "2  monocyte      NMI  0.582018  0.012736\n",
+       "3  monocyte    CHMP5  1.056818  0.013749\n",
+       "4  monocyte   IFITM2  1.655442  0.033603\n",
+       "5  monocyte  SPATS2L  0.950594  0.036179\n",
+       "6  monocyte     OAS3  0.992000  0.039488"
       ]
      },
      "execution_count": 15,
@@ -1116,7 +1145,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ee0a630",
+   "id": "0626acc6",
    "metadata": {},
    "source": [
     "Another example is the NFkB pathway:"
@@ -1125,12 +1154,12 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "4ca11e9e",
+   "id": "7dc73609",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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BeMzuAWN9sqJnNVnGGGOMMUlgNVnGGGOM6ZDVZEXPkixjjDHGdMhNsuJ5utCSLGOMMcaYFsSJb8R3CWVekmV9sowxxhhjksBqsowx7XIchylTpjS8NpnH7gED1icrFpZkGWPalZuby6xZs7wOw3jI7gEDNuJ7LDLvio0xxhhjUsBqsowxxhjTIWsujJ7VZBlj2lVRUUGfPn3o06ePTamSoeweMGDT6sTCarKMMR3atm2b1yEYj9k9YEz0LMkyxhhjTIes43v0LMkyxhhjTIesT1b0Mi+tNMYYY4xJAavJMsYYY0yHrLkwepZkGWOMMaZjIu4Sz/4ZxpIsY0y7HMdh8uTJDa9N5rF7wJjYWJJljGlXbm4uH330kddhGA/ZPWAAROLs+G41WcYYY4wxLVmfrOhl3hUbY4wxxqSAJVnGmHZVVlYyfPhwhg8fTmVlpdfhGA/YPWDAptWJhTUXGmPapaqsXr264bXJPHYPGLDmwlhk3hUbY4wxxqSA1WQZY4wxpkPixDc1jmRgtY4lWcYYY4zpkM1dGL0MzCuNMcYYY5LParKMMcYY0zHHcZd49s8wlmQZY9olIowbN67htck8dg8YiIz4HsfvPxPvHUuyjDHtysvLY+HChV6HYTxk94BJByJyLfAb4C5VvcrjcDrFkixjjDHGdMjLcbJE5CDgEuDTmA/igcxrIDXGGGNM1Lwa8V1ECoBHgIuBnYm8pmSzJMsY067KykrGjx/P+PHjbUqVDGX3gAHcga6cOJY9A2UVikhRoyW7gzPfC7yoqv9L7gUmniVZSbBp0ya+973vMXLkSLKzsxkyZAhf+9rXeP311wEYPnx4QwfC3Nxchg8fzje/+U3eeOONFsf66KOPOPbYY+nRowc9e/bkuOOOY8GCBQ3blyxZwrRp0+jXrx85OTmMHDmSn/3sZwSDwVRdrunmVJVFixaxaNEim1IlQ9k9YBJsHVDaaLm2rYIichYwqb0y6cySrARbtWoVBx54IG+88Qa33norn332Ga+88grTpk3jiiuuaCj3y1/+ko0bN7JkyRL+/ve/06NHD77yla9w0003NZTZvXs3M2bMYOjQoXzwwQe88847FBUVMWPGjIYkKhAIcN555/Haa6+xZMkS7rzzTv76179yww03pPzajTHGdGPxNhXuaS4cDBQ3Wm5u7XQiMgS4CzhXVatTcYmJZh3fE+zyyy9HRPjwww/Jz89vWD9+/HguvPDChu8LCwvp378/AEOHDuXoo49mwIABXH/99Zx++umMGTOGJUuWsHPnTn75y18yZMgQAG644Qb2228/1qxZw6hRoxg5ciQjR45sOO6wYcOYNWsWb7/9doqu2BhjTCYQcZA45sZptO9uVS3rxC4HAn2BeY2Gf/ABR4vIlUC2qoZiDigFrCYrgXbs2MErr7zCFVdc0STBqtejR4929//BD36AqvLss88CMGbMGEpKSrj//vupra2lqqqK+++/n/HjxzNs2LBWj7Fs2TJeeeUVpkyZEvf1GGOMMR56HdgXOKDRMhe3E/wB6Z5ggSVZCbVs2TJUlbFjx8a0f69evejbty+rVq0C3NquWbNm8c9//pPc3FwKCgp49dVXeemll/D7m1ZCHn744eTk5DB69GiOOuoofvnLX8Z7OcYYY8we9U1+8SxRUNXdqvp54wWoALZHXqc9S7ISqL5DaDyj2qpqw/5VVVVceOGFHHHEEbz//vu8++67jB8/nhNPPJGqqqom+/373/9m/vz5PProo7z44ovcdtttsV+IMcYY00z9OFnxLJnG+mQl0OjRoxERFi9ezCmnnBL1/tu3b2fr1q2MGDECgEcffZRVq1YxZ84cnMjN+eijj9KzZ0+effZZzjrrrIZ96/tsjRs3jlAoxCWXXMKPfvQjfD5f/BdmMpqINDRPZ+K0GMbuAZM+VHWq1zFEI/PSyiTq1asXM2bM4N5776WioqLF9l27drW7/1133YXjOA0JWmVlJY7jNHlTq/8+HA63eRxVJRgM2qPWJiHy8vJYtWoVq1atIi8vz+twjAfsHjDg3WCkXZklWQn2xz/+kVAoxMEHH8yTTz7Jl19+yeLFi/nDH/7AYYcd1lBu9+7dbNq0ibVr1zJ79mwuueQSfv3rX3PTTTex1157ATB9+nR27tzJFVdcweLFi1m4cCEXXHABfr+fadOmAfDII4/w+OOPs3jxYlasWMETTzzBtddey5lnntmi35YxxhgTMxF3QNGYl8xLsuy/cIKNGDGC+fPnc9NNN/GjH/2IjRs30qdPHw488ED+9Kc/NZS7/vrruf7668nKyqJ///4ceuihvP766w3JE8DYsWN5/vnnufHGGznssMNwHIeJEyfyyiuvMGDAAAD8fj+//e1vWbp0KarKsGHDuOKKK/jhD3+Y8ms3xhhjzB6SaU1KIlIElJaWllJUVBTXsXbO+ZgNj79IqKqGPl85nH4nfwUnEEhMoMakiaqqKo4++mgAZs+eTW5urscRmVSzeyC9lJWVUVxcDFDcyfGm4lL/f3PtTZdRlNPRDDhtK6uuYchP74MUxZ0OrCYrBqrKF9f+jhW/vx/xux3L197/OD0OPYBDXnkQf771WTDdRzgcZu7cuQ2vTeaxe8AAe+YgjGf/DJN5V5wAO97+iBW/vx8ArQuhde54aLs+/JQVt/3Ny9CMMcYYkyYsyYrB+kef31ODpbX8OrSe+eEKCIdZ+/enPY4us8ycObNhsu3Gy7Jly9rcdvzxxwPwi1/8otXt9cuNN94IwMaNGzn77LMZM2YMjuNw1VVXeXjFxhjjjfbeLzu7ZBpLsmIQqqhsGB5hjdZQg9JH3JbXUHmll6FlpOOPP56NGzc2WerHGmtt27/+9S8ArrnmmhbbNm7cyMyZM+nRowdnn302ADU1NfTp04ef/vSn7L///p5dpzHGeEqcPU2GsSxxzHvYVXnaJ0tErgW+AYwFqoD3gJ+o6pIO9psC3A6MBzYAt6rqfUkOt0HvKYew4bEXADjCKeQICt24fD5Kjj08VWGYiOzs7IbJtqPZVlBQQEFBQZN1jzzyCP/4xz948cUXGT16NADDhw/nrrvuAuCBBx5IYOTGGGO6M6/TyinAvcChwHTcpO81EWk5u3KEiIwAXgLeBiYCvwH+ICKnJT9c16Czv0bBPnshjUZTF58PJzuL0T+9PFVhmASbN28eF198MbfccgszZszwOhxjjEkrNhhp9DxNslT1eFV9SFUXquonwAXAUODAdna7DFijqlep6mJV/RvwAHBNCkIGwJeXy2GzHmHYd88m0KsYX14Ofb86jcPf+TeF40enKgwT8cILLzTUShUUFHDGGWe0ua2goIBf/epXLY6xZcsWTj31VL7xjW9wzTUpu5W6jJKSEkpKSrwOw3jI7gET30Ck1lyYDoojX3e0U+Yw4LVm614FviMiAVUNJiWyZrJ69WD8HT9j/B0/S8XpTDumTZvWZKDX/Pz8NreBO/1RY8FgkNNPP51+/frxt7/Z06HN5efns3XrVq/DMB6ye8CY2KRNkiXuYwe3A++o6uftFO0PbG62bjPutZQAG5sdNxtoPHpaYfzRmnSSn5/fMBVRNNvqff/732fp0qXMnTuXnJycZIRojDFdnyPuEs/+GSZtkizgHmA/4MhOlG0+TL20sR7gWuCGOOIy3dhf/vIXHnjgAd58800GDx7sdTjGGJO2RBwkjia/ePbtqtLiikXkbuDrwDRVXddB8U24tVmN9QXqgO2tlL8ZtxmyfrH/pBmkpqaGTZs2NVm2bdsGwLvvvsv3vvc9rr/+ekaOHNmiXGlpacNxFixYwIIFCygvL2fr1q0sWLCARYsWtXvuLVu2cOmllzJ06NCGpxxnzJjBnDlzGsq89957nHjiifTs2ZOcnBz23Xdffv/73xMKhZoc680332TatGn06tWLvLw8Ro8ezfnnn09dXV2b44E1H5umvtxll13WItbLL78cEWHmzJkN62bPns3XvvY1BgwYgIgwYcIEqqqqov4dmK6vqqqKqVOnMnXqVLsHjImCp0mWuO7BHcbhGFVd2Ynd5uA+idjYccDc1vpjqWqNqpbVL8DuuAM3XUb9ZNqNlyOPdCtL//a3v1FbW8vPfvazFmUGDBjAD37wg4bjTJw4kYkTJzJv3jweffRRJk6cyIknntjuuU877TQ++eQTHn74YZYuXcpzzz3H1KlT2bHD7XL49NNPM2XKFAYPHsybb77JF198wQ9+8ANuuukmzjrrrIax2BYuXMgJJ5zAQQcdxOzZs/nss8+4++67CQQChMNh7rrrribjfAE8+OCDLdYBDBkyhMcee6zJP8rq6mr+9a9/MXTo0CbxV1RUsP/++/P73/++IQ6bUiUzhcNh3nrrLd566y27BzJZfXNhPEuG8XSCaBH5I3A2cDLQeGysUlWtipS5GRikqudFvh8BfA78Gfgrbkf4+4BvqeqTnThnwiaINt4Il+1Aq8txCnshuQUd7+CBXbt20bNnT2bNmsWUKVNabK+oqGDYsGFMmTKFJ59sets+//zzfP3rX+exxx7jzDPP5M477+Suu+5i5crOfAZxR2V++umnOeWUU5qsnzlzJrt27WLFihX85Cc/4ZxzzgHg0Ucf5ZZbbmHkyJH06NGDhx56qEWs9eOJlZeXN3mwwGSGaO+B+nvtmWeeYcuWLfz85z/n5ZdfZvPmzfTs2ZP999+fX/ziFxx22GEN+3z88cfccsstzJ49mx07dtC/f3/23XdfLr30Ur761a8iInzyySfccsstvPPOO2zbto3hw4dz2WWXNflABPDZZ59x5ZVX8uGHH9KrVy8uvfRSfv7zn3ebEce9miB6493/R1FuHBNEV9Uw4Hu3QgZNEO11c+F3cZvwZuF2WK9fzmxUZgDusA4ARGq7TgSmAguAnwPf70yCZbo2rSqn5t2nqX3nPwTnvkLNm49S+/HraKjO69BaqB8u4plnnqGmpqbF9tdee43t27e3OlzE1772Nfbee++Gken79+/Pxo0bmT17dkJiu+CCC3jwwQcbvn/ggQe48MILE3JsY5rrqEYX4Nlnn+XQQw+lvLychx9+mEWLFvHEE09wyimn8LOf/ayh6X7evHn06dOHf/7znyxcuJCf/vSnXHvttdxzzz0NxyorK2P69OkMHDiQjz76iLvvvpvbbruN22+/PeXXboynHd9VtcOPFao6s5V1bwGTkhGTSU+qSu1HL6MVu5qsD29cQdAfIGvfo70JrA1+v5+HHnqIiy++mPvuu49JkyYxZcoUzjrrLPbbbz+WLl0KwD777NPq/mPHjm0oc8YZZ/Dqq68yZcoU+vfvz6GHHsqxxx7LeeedF1Nt7Le//W2uvfZaVq1ahYjw7rvv8thjjzFr1qyYr9eY1uzatYt33nmnSY3usGHDOPjggxvKVFRU8J3vfIeTTjqJp556qmH9qFGjOPjgg7nooosams6bfxgYOXIkc+bM4amnnuLKK68E3Fkbqqureeihh8jOzmbChAksXbqU22+/nauvvrrb1GZ5QsRd4tk/w3hdk2VMp4R3bETLd0LkzfbNBYvZuKMUUMLrlqDBlrVFXjvttNPYsGEDzz33HDNmzGDWrFlMmjSpSXNcW831qkpWVhYAPp+PBx98kHXr1nHrrbcycOBAbrrpJsaPH9+kv1VnlZSUcNJJJ/Hwww/z4IMPctJJJ9kgkyYpOqrRhT21uv/3f//X5nHaS4xKS0ubjH03Z84cpkyZQnb2nmatGTNmsGHDBlatWhX9RZg9HIlv7sIM7JNlSZbpErRiz5N+i9ds4MSf3snr8yNP96miVRUeRda+nJwcpk+fzvXXX897773HzJkzueGGGxrmRVy8eHGr+33xxRfsvffeTdYNGjSIb3/729x7770sWrSI6upq7rsvtik7L7zwQh566CEefvhhayo0SVNfo/vwww/To0cPjjjiCK677jo+/fTThjL1NbZjxoxpWPfRRx81maXhhRdeaPX4c+bM4fHHH+fSSy9tWLdp0yb69evXpFz995s2bUrYtWWk+pqseJYMY0mW6RIkv7jh9dghA1j9yO849yuRTrMiSG7X6Iw9btw4KioqmDFjBr169Wp4cq+x5557ji+//LLJcArN9ezZkwEDBlBREVtyefzxx1NbW0ttbW2n52msr1kzmSkvL4+8vLyo9+tMjW5z++23X8OwKRUVFdTVtex3uXDhQk4++WSuv/56pk9v+sB585qv+hpjayo0qZZOg5Ea0yan1wCkoCdasQsB+vao74skOIPHIIHYn3hJhu3bt3PGGWdw4YUXst9++1FYWMjcuXO59dZbOfnkk8nPz+fPf/4zZ511FpdccglXXnklRUVFvP766/z4xz/moosuahgi4s9//jMLFizg1FNPZdSoUVRXV/P3v/+dhQsXcvfdd8cUn8/na6hF8zWa6Lyx8vJyli1b1vD9LbfcwpdffkmvXr1aDPdgurf8/PyYE3rYU6NbX6t70UUXccMNNzBz5syGWt0lS5Zw6KGHApCdnd3uTA2LFi3imGOO4eKLL+ZnP2s6tVn//v1b1Fht2bIFoEUNl4mOOA7ixDEYaRz7dlWZd8WmSxIRsg46ASnu02S9M3AUgXGHexRV2woKCjjkkEO44447OProo5kwYQI///nPufjiixuehDr99NN58803WbNmDUcddRQjRozgoosu4ic/+Ql//etfG4518MEHU15ezmWXXcb48eOZMmUK77//Ps8880yrw0N0VlFRUbsd5+fOndswPhjA1VdfzcSJE7n++utjPqcxsKdGF+C4446jV69e/Pa3v+3UvgsXLmTatGmcf/753HTTTS22H3bYYcyePZva2tqGda+99hoDBw5k+PDhCYk/Y9kE0VHzdJwsL9g4WV1fOo6TFS7bQWjNYgjV4fQfjtN3aNRNE9XV1Zx88smsXbuWt956iz59+nS8kzFpqH6crPvvv7/VGt3vfe97nHTSSdx///2AOzDvmWeeyfTp0/n+97/P6NGjKS8v55VXXuEnP/kJzz33HF/72tcaEqzjjjuO2267reF8Pp+v4e+ltLSUMWPGcMwxx3Ddddc1NL1ff/31/OhHP/Lk55FoXo2TtekvP6coN/b5Xcuqqul/ya8gg8bJsuZC0+U4Rb2gqFfHBVMkuPgD6j5+I9KpU2DhezgDR5F11DcQX+f/xHJycnj22We58847mT17Nqeddlrygo5CdXV1QyxPPvmkTaKdgWK9BxrX6C5fvpxgMMiQIUO4+OKLue666xrKnXrqqbz33nv89re/5bzzzmPHjh0UFxczefJkHnvsMb761a8C8MQTT7B161YeeeQRHnnkkYb9hw0b1vDkYHFxMf/973+54oormDx5Mj179uTqq6/m6quvTtBPI4NJnKO2Z2CfOKvJMiYO4R0bqXnloZbrw2GyJk4jMD79mjKjZSO+m47ugfIlK9jy0luIz6Hf144hb8QQL8LMGF7VZG3+2y8oyoujJquymn4X/QKsJssY0xl1Kz5z+xlomPU7Srnjhdn899Mv2bG7ktUPF3eLJMuYtqgqi//vt6y880F3HCSURdfczN6/+D6jr7vc6/CM8ZwlWcbEo7YacGuD31m8ktc+WcrR40byjUP2xQml3wCpxiTSxsdfchMsgEYTRy+94S56HLw/fb5yhEeRmaSId5LnDByM1JIsY+LglAwitGohAGcecQBnHnGAu0EEp481mZjubc3f/u3WYIXDrNVang7v4Egp5MBAEWvvf9ySrO4m3icEM/Dpwsy7YmMSyDdiAuQXNevQ6XaAD0ywfzCme6veuLWhBuu/4VIWaRVZImgoRPWmbR5HZ4z3LMkyJg4SyCZn+nk4Q8Y2fEqT3gPIOvZbOCWDPI7OmOTqeegBiN8dzPZCXx/u849gguQhPh89D97P4+hMwtm0OlGz5kJj4iR5hWQfeQoaDkE4jPgDnsQRqqmlYskKAj2KyB060JMYTGYZ+cML2fDYC2hY9/TJ8jk4eTkMu/xcb4MziVc/0XM8+2eYzLtiY5JEHJ9nCdbKu//O/wYfwdsHnswbo6bx3tSzqVi+JiHHzs/PR1VRVRu+IUO1dQ8Ujh/NIa8+RPHEcQ3reh5yAIe98Qh5w6wm1xiryTKmi1v70JMsurrp9CK73l/A+1/5NlMXvoIvL9ejyEwm6HXEgRz5/pPUbN2BOEJW755eh2SSxTq+Ry3zrtiYbmbZzX9qsU5DIarXbWLDEy97EJHJRNl9elmC1d3VD+EQz5JhLMkypgsLB4NUrljb6jYJ+Nn9+dK4z1FdXc0ZZ5zBGWecQXV1ddzHM12P3QPGxMaSLGO6MPH7CfTu0eo2DYXIHdw/7nOEQiH+85//8J///IdQKBT38UzXY/eAASJPCDpxLFaTZYzpQkSE4Zef2/LNyxGc7GwGnv11bwIzxnQ/NoRD1CzJMqaL2+vayxj4ra81WecvKuSgZ+4ju08vj6IyxhhjTxca08U5gQATH/4do6/7Ljvfm0+gZzF9jj8aX06216EZY7oTGycrapZkGdNNFIwZScGYkV6HYYzpruJt8rPmQmOMMcYYkwhWk2WMMcaYjtlgpFGzJMsY0668vDzKy8sbXpvMY/eAAdwkKZ5+VZZkGWNMUyJicxZmOLsHjImNJVnGGGOM6Zh1fI9a5tXdGWOiUlNTw8yZM5k5cyY1NTVeh2M8YPeAAeIc7T36/lwicq2IfCQiu0Vki4g8IyJjknR1SSGq6nUMKSUiRUBpaWkpRUVFXodjTNqrqKigoKAAgPLycms2ykB2D6SXsrIyiouLAYpVtSzZ56v/v7n5qXspys+N+ThlFVX0+8YV0Mm4ReQV4DHgI9yWt5uAfYFxqloRcyApZM2F3VTVuk1Ur9tE/l5DySqxUb+NMcbEKcXNhap6fNPd5QJgC3AgMDv2QFLHkqxupnb7Tj75zrVseWkWqCJ+H0MuOJ1xd/wMX3aW1+EZY4zpqhI34nuhNE24alS1M+3QxZGvO2IPIrWsT1Y3oqrMPf0Ktr4yGyLNwFoXYs39j7PoR7/xODpjjDFdmYrEvUSsA0obLdd2dG5xs7LbgXdU9fNkXWOiWZLVjZTO+5yd78xDQ6GmG8LK2geeoHbHLk/iMsYYYxoZjFsrVb/c3Il97gH2A76VxLgSzpoLu5Hyxcvb3KbBOiqXryGrV4/UBWSMMab7EIlzxPeGmqzd0XTYF5G7ga8DR6vqutgDSD1LsrqR3GED294oQs6QAakLxhhjTPeS4ml1Ik2EdwOnAlNVdWXsJ/eGJVndSK8jJ5M/ZiSVy1ejdXuaDMXno9/Jx5LTv4+H0ZmuKi8vjy1btjS8NpnH7gHjkXuBs4GTgd0i0j+yvlRVq7wLq/OsT1Y3Io7Dwc/9hfzRI5qs7zXlYPb7800eRWW6OhGhT58+9OnTB8nAEZuN3QPGlcCO7531Xdw+W7OAjY2WMxN6YUlkNVndTN7IIRz9yQvsfHceVWs2UDh+b4r2H+t1WMYYY7q6FDcXqmqXz+gtyeqGRIReR072OgzTTdTU1HD11VcDcPvtt5Odne1xRCbV7B4wJjY2rY4xpl02pYqxeyC9eDWtzsaXH6IoP/Y+eWUVlQw4YSakKO50YDVZxhhjjOlY4kZ8zxiZd8XGGGPatPW1t3lv2tm80msSs8Yfz8p7/oGGw16HZUyXZDVZxhhjANjw+Et8fM4P3RqHcJiK8lUs+uGvKflkodehmTQQ4xOCTfbPNJZkGWOMQUMhFv34Fveb+pqrSJ/ddQ8+6VFUJq2k+OnC7iDzrtgYY0wLlSvWUrNhs9dhGNOtWE2WMcYYnNwcr0MwaU7FQeOojYpn364q5iRLRIYAw4E8YCuwUFVrEhSXMSZN5ObmsnLlyobXpnvKHdyfHocewK6PPoVQ047uOTk5LH3vUwJFhXYPZDKRxpM8x7Z/hokqyRKRYcBlwLeAIUDjn1itiLwN/AV4UlXtcRRjugHHcRg+fLjXYZgU2O/PNzHnmHMI7iwFEUQEDSsH/O1mBu23r9fhGdPldDrJEpG7gAuA14DrgQ+B9UAV0AuYABwF/Aq4QUQuUNWPEh6xMcaYpCgctxdTF77C2oefomzBYnIG9WPIzNMoGDPS69BMGlDibC7MwG7g0dRk1QKjVHVrK9u2AG9ElhtF5ERgGGBJljFdXG1tLT/96U8BuOmmm8jKyvI4IpNMWb17Murq7zRZZ/eAAay5MAY2rY4xpl02pYqxeyC9eDWtzro3nqCoII5pdcorGXzMGWDT6rRPRHJxE7TKyPfDgFOBxar6agLjM8YYY0w6EIlznKzMq8mK9enCZ4GngPtEpAfwARAESkTkalX9U4LiM8YYY0wasBHfoxdrSjoJeDvy+nRgM24frPOA7ycgLmOMMcaYLi3Wmqw8YHfk9XHAU6oaFpH3cZMtY4wxxnQnNq1O1GK94mXAKZEBSWfgDusA0BfodGc2ETlaRJ4XkQ0ioiJySgflp0bKNV/GxngdxpgUqPhyFav+9AhrHniCmq07vA7HGBMDReJeMk2sNVm/BB4F7gBeV9U5kfXHAR9HcZx84BPgQSCaGUjH0DSZa21YCWOMx1SVhT/8Navv/afb6VWVz6/4BRPuvoGhF33T6/CMMSapYkqyVPU/IvIOMAA3Sar3OvB0FMd5GXgZQKLrELdFVXdFs4MxJja5ubl8/vnnDa+jsfaB/7gJFkBkuBitq+Ozy6+neNJ4iieNT2isJjniuQdM92FzF0Yv5rkLVXUTsKnZug/jjqhzPhaRHGAR8GtVfTNF5zUm4ziOw/jxsSVDq+97pKEGqzHxOax98D+WZHUR8dwDphuxPllRi2Zanac6W1ZVvxFbOB3aCFwCzAOygW8Dr4vIVFWd3doOIpIdKVuvMEmxGWOaqd6wpUWCBaChMNUbt3gQkTEmVjaEQ/SiqckqTVoUnaSqS4AljVbNiXS+vwZoNckCrgVuSHZsxnRXtbW1/OY3vwHguuuui2pKleLJ+7Lt1bfRUKjpBhGKD9gnkWGaJIrnHjAmk6XNtDoiosCpqvpMlPv9FDhXVVt9x26jJmudTatjADQcBkCc7l+NHQor67eH2VLmPuPTv6fDgJ6C08Gny3imVNnxzlzmHPtttzYr8l4jPgdfYQFTPnuJnP59Yr4ekzo2rU568WpanZXvvkxRQey/+7LyCkYccQLYtDpdykTcZsRWqWoNUFP/fZQd7E0nlS9ezLZXXiZUWUnRxEn0/spXcNL40275F8v54rrb2PLSWyDQ96vHsM9vriF/9HCvQ4vL1v+9xfp/PkH1xs0UT9yXoRedS/5eI6kLKe8vraOsak/ZzaUhNuwQDhzl6zDRilWvIycz+cl7WfSjm6lcsQaA4oP2Y997b7QEy5iuxiaIjlrMSZaInA58ExgKNPlvqqqTOnmMAmCvRqtGiMgBwA5VXSMiNwODVPW8SPmrgFXAwsg5zwVOiyzGI+sffIC1990HPh8AW597jg3//Afj/3QffvfTVlqpXL2ed488k1B5ZUMz1pbn32DH7I84+uPnyBnYz+MIY7Pijj+x4vd/BJ8DoTAVX3zJxieeZdJjf2Pb4P2aJFj1tpYpG3cqg3ol782v31ePoe9J06hatQ4nO6vL/nyNMSZaMbWRiMj3cce22oJbk/QhsB0YSWRIhk6ajDuuVv3YWrdHXv8y8v0A3CSuXhZwG/Ap7rQ+RwInqWqnO+WbxKpYutRNsABCIXcBqlauZO1f/+JhZG1beddDTRIsAA2FCJbuZlX9cANdTPX6jay4PTJlaMhtAtVQiHCwji+u+zUbdobb3HdTO9sSRUTIGzHEEixjurLIEA6xLpn4dGGsV3w5cImqXgnUAreq6nTgD0Cnqy5UdZaqSivLzMj2mao6tVH5W1V1L1XNVdVeqnqUqr4U4zWYBNj22qsNNVhNhMNseyk9fzXbZ33QsiM2QCjE9lkfpD6gBNj2ehvPfYTDlC9eSijYyvXWF0mPbpnGmDRnI75HL9YkayjwXuR1FXuGRfgH8K14gzJdR7iylTao+m01NW1u81KgVzG01tHd57jbuqGS7No23976FGfeG58xxqRCrEnWJqB35PVq4NDI6xGQgalqBiuaPLmhibAJx6HowANTH1AnDDn/NAi30kQWCjP4vFNTH1AClBx7dOsbHIeCcWMYPbKArEDLP87CXBjcy30b0HCY6iWfU/7u69Qs/4J0efLYGJMe4mkqjHe0+K4q1o7vbwBfA+YD9wN3RDrCTwasf1QG6XX00eSPH0/F4sV7EhfHQXw+hlx6mbfBtWHQOV9n2xtzWP/PZxC/29SpdSGGfOebDDj9BI+ji03OoAGM/NHlrLjtXsTnQ0MhxOdD/D7G3vRTcrIcjhwrrNgcZtOuMI7AgJ4OI/o5+H1C3bYtbLn319RtXNdwzKxho+hzxU/JyS/kww/dyRxycnK8ukTjoZycHLsHjPspLa6nCxMWSVKIyPXAbapa2Wx9LvBjVf1l63u2c8xYPq2KiAM4qloX+f6buJ3QlwH3qWpt1AdNkfrxPmycrMQJVVay/sEH2PrCC+4QDpMmMfjiSygYN87r0Nqkquyc8zGbn38dEaHf179Cj0P27/JDfGx7fTbrHvkPNZEhHIZ85xzyR41odx9VZdNNPyK4YU3TGj7HIXvv8fS76sYkR22MiYZX42Qt/+B1CiPjpcVid3k5ow45FtJ0nCwRCQEDVHVLs/W9cedMbqUDcgfHzLQmAUuyjGmqZtWXbL7lJ21uH/CrPxLo0z+FERlj2uNVkrXsgzfjTrL2OmQapG+SFQb6qerWZuuPAf6tqlEP7hdTc6GIrAT+CTyiql/EcgxjTHoI7djW5jYn4Kdi2SL+8uDfERF+8IMf2JQqGai2tpa77roLwO6BDNZd5y4UkZ2ARpalkRlo6vmAAuC+mI4dY3Ph1bhPER6IO67VP3CzvDZHXk8XVpNlkmHbrPdZ++CT1G7eRo9D9mfYpd/qMmNCBTetZ+MvvtdknS8ni5ySHvgCfipqgwy+3h3zzKZUyUw2rU568aom68sPZ8VdkzX64KmQZjVZInI+bo+xB4CraDpXcy2wSlXnxHLsmGqyVPV24HYR2Rs4B/gu8DsReRP4p6r+PZbjGtMVLbvlPpb8/I6GDufbZr3Pqj8+wuGzHqVw/OiknDO4fRvVq1YSKCkhZ1j7fa46Eug/iNx9J1P1+XzQME7AT17/3q2WDW1ZDyP2jut8xpiuKd4nBNP16UJVfRgaWuneU9Vgoo4d1xWr6lJVvUFVxwBHAX1wR4I3JiNUrljLkuvvBNgzwGkoTN3uChZe9auEny9cW8ua237DwrNPY/m1V/PFxeex9AeXUbt1S8c7t6P3d35I3kFHguOQVeTWUrT2EED1/DYGPTXGdHvdfTBSVX0LCInI3iJypIgc3XiJ5Zhxp5UicrCI3Ak8DYwB/hPvMY3pKjY9+7/WH2mOjB4fLN2d0POt/8u97PjvK6B7ngKsXPoFy6/9Edra2F+d5OTkUnLhVQz67f3kDB/Z5lOWoS3rWl1vjDFdnYgcijtKwmJgNjCr0fJmLMeMde7CvUXkRhH5EngXGAf8P9xe+WfGckxjuiIN1bU79ovW1SXsXKGKCna89Bw070cZClGzZhXln8yP+xy+wmJ8fQa2OceYU9Qr7nMYY7qmDBiM9D5gLjAB6AX0bLTE9OYX6xV/AZwA3AsMUdXjVPVhVU3sx3Zj0lyfGUc3TMjchONQPHlfsnr3TNi5gtu2tJu01axbm5DzZE08qklNWWPZBxyRkHMYY7qe+qcL41nS3GjgOlVdrKq7VLW08RLLAWNNssaq6sGqeqeqborxGMZ0eUX7jmHoJWe530TeQMTvjrQ+7nf/L6HnCpT0QfxtP6uSNWBQYs4zbG9yjz0dnJbj7gWGj03IOYwxJg19AOyVyAPG+nThUgARmQzsgzu2xBeqOjeBsRnTJUy4+wZ6HLw/a/76b2q2bKPnYRMZ9aOLKNovsQmJL7+AXjNOZPtLLzStaXJ8ZA8YSOGkyQk7V/bkqQTGTaZu5SKyQyFeP+hknJy8VqdUUVXCOzYQ3rYOxMHXdxhS3KfLj55v9sjJyeHNN99seG0yU7yd19Ox47uI7Nfo27uB34tIf+AzoMlThqr6adTHj3GcrMHAv4AjgF2R1T2A94BvqWpi2i2SwMbJMl1ZuKaGNb+/mV2zXm9YlztqNMNvuIns/gMSei4NhQgu/ZS6DSuRvAKy9z0Ep6C4aZlwmOCnbxLeunbPAwCqOIPHEBh7qCVaHiitVL7cFGZnuZLlh6ElDsP6CI79LroNr8bJWjj/g7jHyRo/6RBIo3GyIqO8K233rq3fprFMqxPrBNEPAAFgH1VdEgl0TGT9/cBxMR7XGNMOJzub4df9gpoLL6V65QoCJSXk7rV3wpOZcGU5ZQ/fRmjzOrfZUMNU/u8pCk+/lKx9JjaUC61b4iZY0KRDfnjdEsK9B+HrOzShcZn27ShX5nwZAnX/M9TUwcJ1YXZWCBOHO5b0GtNSfAMNdiDWJOso4PD6BAtAVZeIyPdwnzY0xiRRdv8BCa+5aqzilccIbdkAQDAY5O/z3Nmzzgv9kb7/dwdOnvtpNrRhWRtHEEIbl1uSlWKL1oVaPHwKsGGnMqIv9IxxoPZgMMhf/uKO+n/JJZcQCATiiNJ0Vd2xuVBVVyfz+LEmWWtwa7JaO9762MMxxnhNg7XUfv5RQ7+v2lCIn7zszihx1gGjqfn4bfwF+WhNJeGdW8EnrdSQKFqXsEGTTSfUhZRdla1vE2BrmdIzP7Z/crW1tVx55ZUAzJw505KsDKXEOeJ7/ENzJpWIfL2NTQpUA8tUdWU0x4w1yfo/4G4RuQKYp6oa6QR/F3BNjMdMqYqKCny+ls2rPp+vScfOioqKNo/hOA65ubkxla2srKSt/nAiQl5eXkxlq6qqCLczKGXjOceiKVtdXU2ofkTzOMvm5eU1/FOuqamhrp1hCaIpm5ubi+O4f8S1tbUEg23/k4+mbE5OTsO9Ek3ZYDBIbW1tm2Wzs7PxR54WjKZsXV0dNTU1bZbNyspq+CcYTdlQKER1dTXhit1UVFc3lKms3XO9voICdNlHVKtSHQy5iZjPj/TuizR6GjHg95HXqz8A4XCYqqqqNmMIBAINEw53VNbv95OdnQ24He4rK9vIKqIsG83ffbq+R9SFwlRXtfZ3J+Tk5uFE8qtY3iMax934tb1HuFL9HtHefWTi8gyt989q6JclIu8Ap6jqzk4dUVWjXoCdQA0Qinxt/HpH4yWW4ydzAYrYM9t2i+XEE0/UxvLy8tosO2XKlCZlS0pK2iw7efLkJmWHDRvWZtlx48Y1KTtu3Lg2yw4bNqxJ2cmTJ7dZtqSkpEnZKVOmtFk2Ly+vSdkTTzyxzbLubbTH6aef3m7Z8vLyhrLnn39+u2W3bNnSUPbyyy9vt+zKlSsbyl5zzTXtlv38888byt5www3tlv3www8byt56663tln3zzTcbyt5zzz3tln3hhRcayj744IPtln388ccbyj7++OPtln3wwQcbyr7wwgvtlr3nnnsayr755pvtlgV0833XacVDN+rs6y9pt9xPZ56u4ZoqVVX9/PPP2y17zTXXNMSwcuXKdstefvnlDWW3bNnSbtnzzz+/oWx5eXm7ZU8//fQm93B7Zbvae0TfAcP0+XlBLa8Kq6q9R9TrJu8RRZrC/5ufzp+nK79cEvPy6fx5McUNXA6sxK1NmgcclaTrPBZ4P/K1MLIcC8wBTsR92O9z4P7OHjPWmqyrYtzPGNOlda65yTdoNJJlj/qni7EDHfJz0q8/jOla3AFF45kgOvp7UETOBO7ETbTeBS4FXhaRcaq6JuZgWncXcImqvtdo3esiUg38RVXHi8hVuA/5dUpMQzh0ZfWPom7YsKHVIRzStSmgs2WtubDrNwV0VDbZzYX1ahbOpXL2C5RvWs8+v/8XAFv+8jPyswKEwmGqg01/FzmnX4WT7d7j0TQBWnNh62VjfY+orVPWbld2VbhDOAzu7WNw39j+7hs3F/br1w+AzZs3N5Sx9whXqt8jysrKGDhwIKR4CIcFH39MYWFhzMfZvXs3B0ycCFHELSIfAPNV9buN1i0GnlHVa2MOpvVzVQEHqernzdbvC3yoqrkiMgxYrKp5rR6k+TE7m2SJSL6qdrohONryqWLjZBnTeapKxc4dFPYuAWDLn39KfnZWi3JSXELOyd+1IQK6qYqKCgoi4yOVl5c3SZhM6nk1TlYCk6zBQONp+GpUtcWnQRHJAiqBM1T16Ubr7wIOUNUpMQfTikh/q93Aeaq6NbKuD/B3IF9VjxaRrwB/VNW9O3PMaOr9lonIdSIysJ0ARUSmi8jLwPejOLYxJg2JCJK9p9bG6Tt0z6CjjQQmHWsJljHdXALnLlwHlDZa2qqRKgF8wOZm6zcD/ZNwid/BHTdrnYgsE5EvI7EOBy6KlCkAftXZA0bTJ2sq8GvgBhFZgDtT9Qbcjmg9gXHAYbjD0N8M/CWKYxtj0lR2djYvvPACAEXHHoN+/g51S+dDsAbpPYCsA6biGzza4yhNMjW+B+qbYE3mURVU4xgna8++LWqyOtq12ffSyrq4qTve5z7ADGDvyHm+AP6r6o5po6rPRHPMqPtkRabUOQM4Gje7ywW2AR8DrwIv1QeTjqy50JjEUNWU1l6FS7cR3r4BycrFGTAC8cX63I4xXZtXzYXzP/4k7ubCSRP3h07GnermwmSI+l1KVdcBd0QWY0yGSlWCpaE6gh+8SHjtkj0rs3LIOuJUnL5DUhKDMQbAIb4BRaPbV1VrRWQeMB14utGm6cCzcQTSQES+j/vkYHXkdXvx/CHa49tHQWNMu4LBII888ggA55xzTspH+677/F3Ca5c2XVlbQ+3b/yH7a9+1oSJSwOt7wKQHj6bVuR34h4jMxR2v6hJgKHBfzIE09UPgEdyuTz9sp5wCUSdZsTYXfhc4HLfjmeJ2QnsPuE9V10YbRCpZc6HJBFW1ys5yJeCH3oWCE0etk5dPlmk4TM3Tf4C61h9z9x84Hf9eE1vdZhLHni5ML141F877+HMK4mguLN+9mwMnToAo4xaRy3FnmhmAOxjoD1V1dsyBpFBUNVkiciTwMrAWeC2yCNAXOAX4noicoKo2SbQxHlBVFq4NsXb7ng9PWX6YOMJHr4L0njesVaFgmwkW4qCVu1vfZoxJOK8miFbVPwJ/jPnEUYr0BRsBLFfVtgde64RomwvvAP6mqq1WqYnIHbgjsx4UT1DGmNis2BxukmAB1NbB3OUhpo4XsvxdbJgFfxbkFkJVK8mUhnF69El9TMZkKK+SrFQRkTzgbuD8yKq9gRUi8gdgg6reEu0xo/1oO4H220H/HCljjEkxVWXV1tYf7A2FYcOOtH3ot00ign/coa1tQPKLcQbZ0BHGmIS5Gdgfd8iq6kbr/wecGcsBo63J2ojbF2tJG9sPi5QxxqSY4tZatUYEqtqevSOt+UYdAHVB6ha9B0H3IqTPELIOPsGGcTAmhbp7TRZut6czVfV9EWncJLAIGBXLAaN9h7oNuE9EDgT+i9vhXXE7wE/HHRH1qlgCMcbExxEhN6v1ZEoVCrroBMEign/swfhGT0LLdiDZuUhe7J1vjTGxSeBgpOmqD7CllfX5xDj4aVRJlqr+UUS24z7meCnucPcAIWAe7nw/j8cSiDEmfiP7+Vi4tuXEu1l+GNAz7d/g2iU+P9Kzr9dhZJxgWTkbnnrF6zCMSYWPgJNw+2XBnsTqYtzhI6IWy2Ck/wb+LSIB3HmFALapatvTjhvPhaprWPPnf7H+sRcIV1fT94SpjPjBTLL7lXS8s+kyhvQW6kIOyzaFCUW6YBXlwv7D/fh9sSVZ2dnZPP744w2vTebY+NSrfHLBT6itqOT/OQMAWHrNLRxw742I0wWfVjVxyYDmwmuBV0RkHG5+9AMRGY/bFSqm0eWjHierq8vEcbLCwSAfnHAhO2Z/5LYbAeLzkdWvN0fO+Q85A/t5HKFJtLqQUl6tBHxCfhdtJjTeqli+hrcmHI+Gwg3vG/XG3f5TRnzvPI8iM16NkzVn/tK4x8k6bNLekKK4YyEiE4AfAwfiPhw4H/itqn4Wy/ES+lFEREaJyBuJPKaJ38b/vMKOtz5s8kapoRC1m7ez7Lc2j3d35PcJPfIdS7BMzNY+9KTbWNL8g7gIq//0iCcxGZNMIvIIbq3VTao6QVXHqeq5sSZYkPhpdQqIsUrNJM+Wl2aBz6Gh/ShCQyE2Pf0qE+76uTeBZajQ9s1U/O9papcsABGy9plE/ldOxdcjPZtu6+rqePppd9qwU089Fb/fnujLBNXrNjW8DqkyR8sBOEwKqN6w2auwjIcyoLmwHPgR7gN+m4G3IsssVf0ilgNGO+J7u5MnAoNiCcIkmQi0cXOLz9fqepMcodId7PrLTWhNFYTdpLf2sw8JrlhMz8t/gVOQfk3YNTU1fPOb3wTcKVUsycoMRfuNZf2j7hy8QZRbwu7oPP8J7E3vfcd4GZrxiBLn04VpnmSp6qUAItIfd6ysqcAPgHtFZIuqDoj2mNG+W96JOw5WWyPuZEUbgEm+/qdMZ8O/nm+xXnw+Bpx+ggcRZa6q915Dq6tAG9UqahitKKPqwzfIP+YUz2IzprHB55/Kst/eR92u3VDX6H4Nh9nr/13mXWDGJN9uYGdk2QXUAZva26Et0fbJWo07MeOI1hbcRx9Nmul/8lfo+9Vp7jf1TwQ5Qu7wQez1k0u8CywDBZcvappg1VMluGJx6gMypg1ZvXpw2BuPUHzg+Cbr973vV/Q7aZpHURkvhZG4l3QmIr8VkfeBbcCvcSuObgb6qWpMM9FHW5M1D7fHfVtjYSlttUsZz4jPx4FP3MOGR59nw+MvEqqspu8JRzP04rMI9Ei/5qnuTLJzcf9EWnYmlpw8L0Iypk2F4/biiHefYOviL2Hc3gAM+tbXPI7KeCUD+mT9GNgK3Ag8q6pxf/KNNsm6HmjvP8Ei3JmrTZpx/H4Gn3cqg8871etQMlrOxMMpX7us5QZVcvY/LPUBGdMJeUMHeh2CMakwEffhvanAj0QkRKTjO27n96iTrmhHfF/UwfYgbpOiMaYV2ROPpHbZQmoXzdvTdBsOk33AEWSNn+xtcMYY047uPq2Oqn4CfAL8AUBE9sedKvAPuN2ron5SzB4TMiaFxOej8MzvElyxmNovFoAI2ftMwj98b0TS+w3IGJPZlPia/LrC0OciMpE9TxYeBRQBC4A3YzleTEmWiHxM6z8vBaqBZcBDqhpTUMZ0ZyJC1qhxZI0a53UonZKVlcWDDz7Y8NpkHrsHTCYQkZ24431+gttE+Fdgdjyj08dak/UK8F3gM+BD3J68k4H9gIeAccD/ROQbqvpsrMEZY7wXCASYOXOm12EYD9k9YKD7NxcC3ybOpKq5WJOsEuD3qvqrxitF5GfAMFU9TkRuBH4OWJJljDHGdHHd/elCVX0h0ceMNcn6Ju5QDs09hjvMw8XAv4CrYzy+MXGpWbuSig/fQeuC5E6YRO64/a3PU4zq6up49dVXAZgxY4aN+J6B7B4wJjax/qVUA4fj9r1q7PDINnB74tfEeHxjYrbjqX+y67nH3Kf3RCh9+Sny9j+Ift+7DvEHvA6vy6mpqeGrX/0qkPhpdbQuSHjzSsKVu3EKeuD0G444NtVTuknmPWC6jgxoLky4WP9S7sadQPFA4CPcDu8HAxcBv4mUmQF8HHeExkSh6ovP3AQLGuYGBKj8dC6l/32eHid8w6PITHPhsm3UfvQyBGtAhJAq5OSTddCJOPnFXodnjGlGgVbmq4hq/0wT7bQ6AKjqr3GbBA/GHT/i7sjri1X1pkix+wAbGtikVPm7b0BrNSGq7J79WuoDMq1SDVM7/38QrK1f4X6tqST4iT2UbEw6qq/JimfJNDHX+arqI8Aj7WyvivXYxsQqVFnR+tyAQLiiPMXRmLbozs1Q3crvQxUt20a4fCdOQc/UB2aMMQkUV8N6pLlwH9xawEWqas2DxlM5e4+jcv6clhsch5yx+6Y+INMqra1uv0BH240xKdfdny5MhlgHI+2L+yThVGAX7jhZxSLyJnCWqm5NVIDGRKPoqOmUvvYsoZ3b9/TJEgfx+ejxtW96G5xp4BSXtLPRQQp7pS4YY0ynWMf36MXUJwu3D1YRMF5Ve6lqT2BCZN0fOnsQETlaRJ4XkQ0ioiJySif2mSIi80SkWkRWiMhlMV6D6YacvHwG/ex35B9yNPjczxA5++zLwOtuJXuIzV2eLiS3EGfQ3q1u8w3fFwlkpzgikywaqiO0ehF1i+YQWrcUDYe8DsmYlIm1ufB44CuNZ6RW1UUicgUQTe/ifNzh6x8EnuyosIiMAF7CHer+XOAI4I8islVVO9zfZAZ/zxL6XXoNevHVgNqQAHHKysrinnvuaXidKIHxR1KXnUdozUKoC0IgG/+I/fCN2C9h5zCJEes9EN61hdpZj0NNJYgDGkbyiwlMO8ueIO2CrLkweqIa/UOVIrIbOEpVFzRbPxF4S1WLYjimAqeq6jPtlPkt8HVV3afRuvuA/VX1sE6epwgoLS0tpago6jCNMQmm4TDU1UIgC5FYK9dNulFVal/8C1pZtufpUQARpNcAsr9yrnfBdXFlZWUUFxcDFCdyCpi21P/ffOn9DeQXxP5/s6K8jBMPHQgpijsdxPqO9gZwl4gMrF8hIoOAO4DXExFYGw6jZU3Zq8BkEbFRJo3pgsRxkKwcS7C6Gd26Fq0obZpggfsE6fYNhHfv8CYwY1Io1ubCK3HnJFwlImtxny4cijthdDI/nvQHNjdbtxn3OkqAjc13EJFsoHEHj8KkRWdMNxQKhXj77bcBOOqoo/D5rPk108RyD2h1ZfsFqivBHnDoUqy5MHoxJVmquhaYJCLTgbG4TxcuUtX/JTK4tk7f7HtpY329a4EbkheOMd1bdXU106ZNA9wpVfLz8z2OyKRaLPeA9Orf9kbHh7T3hKlJS/Z0YfTiGidLVf8L/DdBsXTGJtzarMb6AnXA9jb2uRm4vdH3hcC6xIdmjDGmnlPQA2fYOMKrF9P8M7Bv9IFIVo43gRmTQp1OskTk+50tq6qdHsYhSnNoOVXPccBcVQ22EUsNjSaqFsm8TNoYY7wQOOh46nLyCS1bAKHIE6RjDsK3z6Feh2ZioNqyi120+2eaaGqyftjJckonx8oSkQJgr0arRojIAcAOVV0jIjcDg1T1vMj2+4ArReR23GEcDgO+A3yrk7EZY4xJEfH5CRwwDf++R7mj+Gfn2pAqXVgYIRxHv6p49u2qOp1kqWqrIzmKyJG4NUmxzIMxGWg8G2x9s97DwExgAG6H+voYVorIibhPMV4BbAC+b2NkGWNM+hKfH3ILvA7DmJSLq09WxEvAAcCKaHdU1VnQdmqrqjNbWfcWMCnacxljjDEmdtbxPXqJSLIy76dmjEHDIcJb16LVlThFvZEefa3PozHdmPXJil4ikixjTDcWCAS49dZbG14DhEu3UTv3ZaipaignvQaQdeAMJJC4qXdMemjtHjDGdCymaXWaHEDkbOBZVa1ITEjJZdPqGBMfDYWoefNRtyNzk0fzBWfgKLIOOMar0IzJCF5Nq/PUO1vjnlbnG0f2AZtWp/NU9dGukmAZY+IX3roGaqtoOf6vEt64HA3WtLabMaaLC2v8S6ax5kJjTLtCoRDz588HYNKkSWh1O5+pVNHaaiSQ3XYZ0+U0vwdsaiVjOseSLGNMu6qrqzn44IMBd0qV3KLebRf2B5Cc9Jx2R6srqFu7mHDpNpzsPHyD98bp2c7UL6ZB83vAplbKUHE+XYg9XWiMMe2Tnv2RHn3R0q0tHhfyj9jfHRMpzYTLtlP74YsQqgOUkAihDV/iH3MI/uETvA7Pc+GyHQQ/f4/Q1vVIfjGBfQ7GP2ik12GZNGNPF0Yv7j5ZxpjMIiJkTT4ep99wGkZw8fnxjz4Q314TvQytTcFF7zUkWEDDu33dkg/bb/7MAKHNa6l8/E62LFvDPP9BvClH8d6CUjbNX+B1aMZ0een3kdMYk/YkK4esSdPR2mq0pgrJK0zLGiwAralCS7e0tZXQltX4h45LaUzppObtZ9hQsA/zh3wTQVHxsTunHxuqHA5ZV8mIwXleh2jShE2rEz2ryTLGxEyycnAKe6ZtggWAhuPb3o2Fy0sJbt/MZwO/Dggqbof2+q/z1gSoC2VgG49pVX1zYTxLsojIcBG5X0RWikiViCwXkRtFxNOB+9L4ndEYYxIgOw/J74FW7Gp1s1MyJLXxpBMNszNvKEF/K7VVItThZ9tuKLTxR036G4tbcXQpsAyYAPwVyAeu8SooS7KMMd2aiODf5zCC815xVzT6OO0bOg4nv9ijyLwnBT2Qgh5eh2G6iHSeu1BVXwFeabRqhYiMAb6LJVnGmHQVCAS44YYbGl53Rb7eA5FDvk7dqk8J79qKZOfhHzIWZ+BeXofmKRGh/+RJBFZVEXRyoMnck4rfJ5QUQjjU9e8BE794BxT1YDDSYmBHys/aSNzT6nQ1Nq2OMcY0tXLFDj7Y2AMhjIrP7QCPcOhoGN6nc7UPqkqovAJfXi5ig5UmlVfT6vzz9R3kxTGtTmV5Gece2wtgMLC70aYaVU3oVBEiMgqYD/xIVf+WyGNHwzq+G2NMhhsxshfT9xOG9PHRIw+G9Ba+sm/nEixVZc3fHueNkVN5tdeBvNrnIBb/5LeEqm16JdOmdUBpo+XatgqKyC9ERDtYJjfbZyBu0+ETXiZYYM2FxpgOhMNhFi9eDMA+++yD49hns+6od6FweGHr29q7B1bd8w8WXX1Tw/eh3RWsuPMhKpatYfKT9yY1ZpNaiqBxDMPQaN8WNVnt7HYP8FgHh15V/yKSYL0JzAEuiTrIBLMkyxjTrqqqKiZMcEdFtylVMlNb90C4tpYvf3VPyx3CYTY/9z/KPvmCov3HpjJUk0Rh4uyTtefl7s42c6rqNmBbZ8qKyCDcBGsecIGq9+Oz2EdSY4wxMalYvpbgztI2t+98/+MURmMyWaQGaxawFvdpwj4i0l9EPJ2g1GqyjDHGxCTQs/1O0FklPVMUiUmFNJ+78Dhgr8iyrtk2z4aat5osY4wxMcnp34eS6Uci/mZPEzqCv0chfU+c6klcJjnSecR3VX1IVaW1JXln7ZglWcZ0M+G6OjY//zor7niQzS+8QbiuzuuQTDe2319uInfYIAAk4AcRfDk5HPjEPfhyczyOzhhvWXOhMd1IxbLVfHD8BVStXg8+B0Jh8kYO4ZCXHyRvZPpNH1O3ewe1m1cRqtyNk51LVt+hBHp62oXCRCl3cH+mfPoim579H2WfLiFnYF8GnfVVAj0zdyT97iqsQjiOiqF49u2qLMkypptQVeadcSXV6za5K0LugzVVqzcw78zvc+SHTyGSPm9ywZ2bqFrxScP3oWA1VeU7CQ+sIHvAKA8jM9FysrIYeMaJDDzjRK9DMUmU5n2y0pIlWcZ0E2XzF7L786Ut1msoRNmCRez+dElMj9MHAgGuueaahteJoBqmes3iVrfVbFhOoGQwTiA7IefqyrSmmtDqL9BgNb6BI3F69vUkjmTcA8ZkAkuyjOkmqje3P5RMzeatuBPVRycrK4vf/e53MUbVunBVOVpX28ZWJbR7B06vAQk9Z1dTt2IhNW88AaFgwzrf6IlkTz0VcVI7bU0y7gHT9VhNVvSs47sx3UTRfmObTfDbiONQuO+Y1AbUHungrSeNmjW9EN69k5rXH2uSYAGEvvyY4KfvehSVyXSqeyaJjmWxJMsY02XlDu7P4PNOBadZgiLCkAtOI2dAbE1N4XCYVatWsWrVKsLhxAyg7OTkI9l5rW8UB39RSULO01XVLZnf5n+kus/fT3E0ybkHjMkE1lxoTDey7x9vJNCzmNV//hfhqmp8eTkMu+xsxvz66piPWVVVxYgRI4DETasjIuQOm0Dll3MjyYTijheo5Awbj/gy+61JK8qo/3m02FZVnvJ4knEPmK5HVYhn2CmPh6zyRGa/kxnTzThZWYz73f9jzI0/oGbLdrL7lSRtrCLVMMEln1C78CM0WEtg+FiyDzgCycnt1P7+wp4UjD+C2q1rCVXtxsnKJavPEHx57Y8ingmckgHwRSs1RiI4vWyIC+MN65MVPUuyjOmGfHm55A0fnLTjqyoVz/+d4GcfuP2nVKlbsYia+bMpPP8anPzOJUpOdh45g9Oor1ia8I8+gOC8N9HqSmg8x60qgUlTPYvLGBMd65NljIla3crFboIFez6eqhLetZ3qd172LrBuQrJyyDn5Epz+w/asyyska9rp+EeM8zAyk8ni6fRev2Qaq8kyxkSt9ouPwXGgeSdoDVO7aC55M85MynlVldpt66jduo5wsAZfXiE5/UbgL+qVlPN5ySnuTe7XLyJcUQZ1tUhhz5QP3ZAsqkpw7QrqNq3D16uErJH7II595k931lwYPUuyjDHRC4Vi2xanqrVfENy2fs+pdu+kYvdO8kbsS6Bnv6Sd10udbXrtKsKV5ex44PcEVy5pWOcr6U+vi/8Pf4n1NzPdi310MMZELbDXhJa1WOAOv7D3fkk5Z6i6okmC1VjV+i/RTPyY3AXt+vdfCK7+ssm60I4t7PjbragND5HW6muy4lkyjdVkGWPa5ff7ufzyyxteAwTG7I9/2N7Urf6ShmEGxEGyc8g96qSkxFG3e2eb27S2mnBtFb62xt4ycWntHohFqHQnNZ/PbbkhHCa0dRO1K5eQPWqfmI9vkiveflXWJ8sYY5rJzs7m3nvvbbJOHB8FZ11JzdxZ1Hz2AdTWEBg1nuzDpuMr7p2UODrqsyPSPforpaPW7oFYhEp3tL99Z/tTQ3VVqopWViDZOUgcSarpeuy3bTotXFfHlhdnUbZgEdkD+jLwmycS6NG9+oukQl1FJeLz4cvp2hMgiz9AzqHTyTl0ekrO5y/u0zBcRHO+/GKcrK7988wE/pJ+4PO12W8v0H9IiiNKvor332T3K/9xE0h/gPxDplL09XNwOjmeXDqxju/RsyTLdEr1pq18cNxMyhcvQ/x+NBRi8f/dwuSn/0TJtMO8Dq9L2PHefL74f7eyc477ZF6/r05j3G3Xkjcivf+xqCrbtrk1DCUlJUgr8wpqqI7w5lVodSVS1Bun98BWy8XD8QfIHboPVasX0Xg0dPH5yR1qTUzJ1Jl7oDOcvALyDj2Wyvf+2/Q/ruOQNXIsgcHDExBt+qiY8zq7HvvLnhV1QSree53g5vWUXHl9wv9Gki0cbr0rZjT7ZxpLskynfH7FDVQsXQmA1tUBEKqqZt7pV3Lsmrfx51tfmPaULljM+9PPQ+sin+DDYba8OIudH3zClE9eIKt3T28DbEdlZSV9+7rzHrY2pUpo52Zq5ryAQ6hhchwp6k3WwSchWfGNNl9XVkbtli1k9e+Pv6CArN4D8eUVUbt9AxqswcktJKv3QJxAVlznSQcaDoOG03JKoY7ugWgUff1cEKFyzusQqgMRcsZPpvjMixMVblrQcJiylx5vbQO1yxZRu3wx2XvZmGfdXfr9NZu0U7t9J5uff6NlXW9YqSsrZ/PzbzDorK96E1wXsfzWP7f4GKihELVbt7P2gScY9eNLPIwudpUrVhOa+xz+3AD4HOo/l2vZDoKfzSbrwONiOm6oqopVt93G1pdfglAICQToe/LJDPvBVfhyC8gdvHfiLsJjWl1JcMkHhDetAFWkuA/+vQ/C13ug16Elhfj9FJ96PoXHn05o22ac4l74inp4HVbChct2Ei7b1fpGcahdvazLJVnWXBg9G8LBdCi4a3fbfx0i1G5tvzOrgR3vzNtTi9VYWNn5/oKUx5MIqsryG35DoCAb8TV/K1G3+bC2OqZjL7/xF2x96cWGvjsaDLL5ySdZeetv44w6vWhdkNoPnm9IsAC0dBvBuS8T3rnZ4+iSy8nNJzBkZLdMsAAkJw+kjX+xGsbJL0xtQAlgQzhEz5Is06HcoQPIKmmjOUuVnocckNJ4uqK2fn7i9xHo1SO1wSRI6bzPCe3a2e74VFpbFfVxq9euZcebb7Yymryy9cUXqd3WfZ5AC21YhlY1/xCjoFC3/GPP4jLxc3JyyZ14qDszQhOCZGWTe8AhnsQVjzBxTqvj9QV4wJIs0yEnEGD0z7/XcoPPoWT6kfQ4ODmDT3YnQy48A1rp46p1IYac/43UB5QA1es3s3v55jY77yoOkhv9p/XKFcvb3hgOU7VqZdTHTFfhXVvcJyZb0G5fk5UJik+7AH//yETtjg8QCATodeHVODnWjzUTWJ8s0ynDvns2TsDPlzf9ker1m3Bycxh64RmM/c2PvA6tSxh22bfY+d48Nj7xMuL3ubU/oTB7//Iqeh052evwYlI4fjRlX25ixyer6TlhSIsmQxkwJqZO3FmRDtYqghb2RGqqkJqqFtu7Awm0M+xEkjvzl33yBTvf/5hAr2L6nTQNX17XG1Ig3fkKiuj7499SvehjgmtX4BQWkzfxcJz8Aq9Di4mqxjWzQibOymBJlukUEWHoxWcy5DtnUFe6G19BHk4g4HVYXYbj9zPxkTsY8b3z2fra2zjZWfT/xgwK9h7hdWgxy99rGP1Omc6ntzzP3t+ZQv+p43D8PoK7q9m1upLBJxwZ23HH7oNzynmUH/F1tGdfCIfJWzkf58HfUdC/P7lDhyX4SrzjG7QXodWft7rNP3hsUs4Zqq7h47N/yObnX99zruJCDvzPPZRMPTQp58xk4jjkTjiQ3AkHeh1K3Kzje/QsyTJREcch0LPY6zC6JBGh52ET6XnYRK9DiYrf7+f8889veN3YAQ/dysLv/5Iv/vQcS//6Jlk9C+hz8vGM+921MY8BtLk0zO6vXrTnHdlxqBw5idzr72Nkr91xXUu6cYpK8I89hLovPgDEbVIOh5FeA3CSNPbX0hvuZPOLbzZZV7e7nLknX8YxK2eR1UofwfbuAWNM2yTTqu9EpAgoLS0tpajIRis3JhFqd+yieu0mcocNjGsWAFVlztIaqmq01b5KY3d/yMDDjupygzh2JFxRSnjTSsI7NhLetBoqywDBGbQXgUlfQfIS8ySahkK8WjKZUHlly40ijL/r5wz/7jkJOZdJnrKyMoqLiwGKVbUs2eer/795y2O7yMmL/e+7urKM/3dWD0hR3OnAPpIYY+KW1atHqzUgHQlvW0fdsgXo7h1IQQ8YeQBVtX1bTbBEw5STh5ZuRXp0n35ZAE5+MZpfTHjua43WKuENy6jdtZms4y9E/PH30QpVVrWeYOE+6Vq9YUvc5zDdlzUXRs+eLjTGtEtVqaiooKKiIqEdV0NrFlP7zlOEN69EK3YR3rKa0JxncWh9XjtFCISq0brahMWQTuoWvkeLR1BV0YoyQqsXJ+QcvoJ8coYMaHWbBuso2m9M69uSdA8Y091ZkmWMaVdlZSUFBQUUFBRQWdl6LUi0NFRH8NO3It9ow1dB6Vf2BWjLMbKEMCU1a3CK+yQkhnSjOzZRPx9jE+KgOzYm5BwiwujrLm+53ucjb9RQ+p/8lVb3S8Y9YLqeuMbIiiyZxpoLjTEppzs3QRs1UsO2fUBlwSBKpUck2RKEEGN2vEPe8LHtD3vQlWXlQHVFKxsUcjqeKzBcWUZwwzLCFbuQnDwC/UfhayUhHfKdMwhVVfPlr+4huLMURCg59nD2/fOvcbK6/hyQJnmsuTB6lmQZY1KvrelGAJ/WsX+/akorN7Br6y78NWWUyE5yRo7FNyQ5wxqkA9+o/QktnEOL2ixVfMPHt7tvaNcWqhe/G9lVoaKU0PYNZI3Yj8CAvZqUFRFGfO88hl16FpUr1hLoWUx2v5KEXosxxmVJljEm5aRnP8jKhdam3fFn4e8zmBKfn5KRqY/NK/59DkV3bCa8cbnb8V/dJyz9Bx2PU9irzf1UlZrlH7ecmgeoXfUZ/pIhrdb+OVlZFIwdlejLMN2YhhWNo80vnn27KkuyjDEppbVVhFZ8huQWEq4oA7/fHZJBBBQCE4+NaaT4rk58fgJHfQPdvoHwljXgz8I3ZAyS2/7o4FpVjta01swIqBLatRl/n6FJiNhkmnj7VWVgjmVJljEmdcK7tlLzv39CbbXbZKgKwSCUDMDpMwj/iP1wutnwDNEQEaRkEE7JoCj2av8/Vyb2gzEmXdjThcaYlKmd8zwEa9xvNIybIChauo3AvlMyOsGKleQWItltTDYsgt9+piZB6ju+x7NkGs9rskTkcuDHwABgIXCVqr7dRtmpwJutbNpHVb9IVozGZDKfz8fpp5/e8DpW4d070J2bW98YrCW8cQW+IXvGadJgLTWffUBw+ULwB8geN5nA3vt1u9He4yUiZI2aSM3i9/Z0fEcAJWvoBCQrJ+5zJOoeMF1bOKyE42jzi2ffrsrTJEtEzgTuBC4H3gUuBV4WkXGquqadXccAjYfk35q0II3JcDk5OTzxxBPxH6gu2O5mDe4Z0iFcU0XZQ78jtHkd7px+Qu1nH5C136EUnHyBJVrN+Hv0Q/ab1jCEg5Odj7//SPw9+yXk+Am7B4zJMF7XZF0N3K+qf4t8f5WIzAC+C1zbzn5bVHVXsoMzxiSOFJW4Y0HVVre63ek7pOF19buvEtqyPvLdnnaG2k/fJzhuMll775fscLscX34PfKMnex2G6cZsnKzoeZZkiUgWcCBwS7NNrwGHd7D7xyKSAywCfq2qrTUhGmPSiPh8BA6YSvDDV6hvzqrnGz0JyS0gvGsrEsim5vMPWn9HFoeaRXO7fZJVu24VZa8+Re3yL3AKi8g//CsUHDUdcaypznjHkqzoeVmTVQL4gOadNDYD/dvYZyNwCTAPyAa+DbwuIlNVdXZrO4hIdqRsvcRMZ29MhqioqKCgwB1GoLy8nPz8jkcfb4t/r4lIVi7Bhe+hpduQ3EL8Yyajjo+a5+6DoFvLldWzkJqKMjTYvIlRoa4u5vN3BTXLv2DLnTe4DwaEw4R2bWfXv/9K7col9J75A09iSuQ9YEwm8bq5EFo+fyytrHMLqi4BljRaNUdEhgDXAK0mWbjNjjfEG6QxJjF8Q8fiG7pn5PbQqoXUffhykzJOdhY5w4ZQtXxl04+/qgRGjUtVqJ7Y9eTDEA61+Nhf+eFsCqaeRPbwvdrY05jkCqsSjqM6Kp59uyovh3DYBoRoWWvVl5a1W+15HxjdzvabgeJGy+Aojm2MSbK6Re83vA6WV1G9vRQNhRCfD39x0Z6CIvj6DyV730M8iDI1wtVV1K5a2nq7iuNQvWh+6oMyJkLD8S+ZxrOaLFWtFZF5wHTg6UabpgPPRnGoibjNiG2dpwaoqf/enkoyJn1oOIyW76S2rIL1b8yjYr37oLCTHaDvwePpOXECobpViD9A9r6HkHv4DMQf8DjqJHKcPVPqNKdk5Ej4xnRlXv/F3g78Q0TmAnNw+1sNBe4DEJGbgUGqel7k+6uAVbjjaWUB5wKnRRZjTBcjjkPYl8XKZ14mWL5nHsNwTZBNby8gMHwf+l39Ow8jTC0nK5uc8ZOoXvQxhJt97NcwOeMnehOYMYCiaBxNftrB7ATdkacjvqvqv4GrgOuBBcDRwImqujpSZABu0lUvC7gN+BR4GzgSOElVn0pRyMaYBCsvcwjurmy19mbza2/E9abeFfU4/QKcvJbzFWb3LKL6xYcIl5e2u394+0Zq5/6X2vdfJrTuSzQT22hMUkSexYh5ycRb0euaLFT1j8Af29g2s9n3twK3piAsY0yK1FSr20zWvOYGqN28Ga2tQbLjH7U83dXt2kHlp3MREXqefSnlT9xHXXUtjs8hUJCHPzcb3b2L6refJ++Ec1s9Ru3c/1H3yWx3XkigbuEcnEF7kT39bGtqNMYD9ldnjGmXz+fjxBNPbHidaFl9+7c5gI6voAAJZCX8nOlm1wtPsOOpf+xJNEXI69eL/P69mxbUMMEv5kMrSVZo40o3wYqUqxdev5y6hXMI7HdUzPEl+x4wXYNqnM2FKaqVjgzd9AGwPzBRVRek5MStsCTLGNOunJwcXnzxxaQdv/dXjmP9X/5IuKamabIlQp9TTkOctns1qCqhLevQqgp8fQe32syW7io/ncuO/zzcdKUqlZu248sOkFWQ23RbKISqtniIp27ZJ24NVos2GaVu6cdxJVnJvgdM1xBWd4ln/xS5FdiAm2R5ypIsY4yn/MXF7HXzbSz/+bWEync3rO859RgGzvxOm/uFtm2i4tn7CW+LPFzsOGQdOI3caScj0jQx09oqQlvXQjiE07M/TkHPpFxLLMreeLHN5tLqHWVNkyxx8I/Yp/WnpGur2+z0om1MZWRMdyMiJwDH4T4Qd4LH4ViSZYzxXtGkA9n/qeco/eB96srKKJiwL7nDR7RZXoO1lD/2B7SyfM/KcJjaj17Hyc0j57AZDavrNiwjuPTDJrVkvv4jCIw9tEUy5oW67VtbTbAAwsFGo9uLAz4/OUd9rdWyTr+hhFYtarlBHHwD2v5ZGtNZGlY0juqoRvsWNvugUBMZbikuItIP+CtwClAZ7/ESwft3GGNMWquoqCA/P5/8/HwqKiqSdh4nO4eeR0+lz1e/3m6CBRBcsgCtKGu15qbmozfQSNISLt9JcEnLeRBDm1YSWr80ccHHIWv4aGhtTkLHIWfUWJze/ZGCYgLjJlNw3o/x9R3U6nH8e09CCnq442zVEwHHIbD/0XHFmKp7wKS3+rkL41ki1gGljZZr441N3KztIeA+VZ0b7/ESxWqyjDEdqqxMiw+FDUI7triJSTjUYptWVaC11UhOHnUbl7c5uGfd+i/xDx7bYn2q9TjuZMrfe7NpnCLg+Oh5znfJGjikU8eRrByyv3oRwbn/I7TiM9AwzsBRZE3+Ck6vfnHHmW73gEm9cFgJx1GT1WjfwcDuRpvarMUSkV/Q8dR4BwGHA0W4s7ykDUuyjDFdjq9nSasJFoDk5CFZkSEfaqvbfHIxXfopZQ0ZzoAf3ci2v/+R4Kb1AAT6D6Lk/Cs6nWDVc/KLyJ7yDfToUwFNi+ZQY1qxW1XLOln2HuCxDsqsAn4GHArUNGuKnCsij6jq+VFHmQCWZBljupzAmInIm8+g1RUtkqjsydMankiUwl6wZXUrRxCcwl4piLRzcsftz+Cb76Nuy0YQwd+nf7tTgKlCaaVb4VWU27SFEOqnD7MpxExieTGEg6puw53ruF0i8n3cRKveQOBV4Ezc4Rw8YUmWMabLkaxsCs76HhVP/43wrq2RlULWxKPIbtTp3T9gFHVrFkGwFppM6aH4h09IacwdEREC/QZ2WG79DpizFMojFXFFuXDY3jAgfR6YNN1UvJM8J3PEd1Vd0/h7Eal/Kma5qq5L3pnbZ0mWMaZL8vUdROEl1xPasNIdJ6v/UJyC4iZlJJBN9qTjCC75kPCuze663EICe03C1yP+fkqptn03/O+zppV3ZVXw30/h65OhR753sRljWrIkyxjTZYkI/kEj2y3j5BWRPfEraLDG7ceVldtuU1w6W9TG53EFFq93a7SMSZawKuE4mgvj2TdaqrqKNGgztyTLGNMux3GYMmVKw+uuSgLZXocQt+3lrffjV3VruZKlu9wDJj5dZVqddGJJljGmXbm5ucyaNSvp59FwGK2pRPyBuBIiVSW0/ktqP3mbujXL0epqnB69yT54OoFxB3XZWiyAghworWjauwzcj+v5SZxDO1X3gDHdjSVZxhjPBTetoHbNIqirBcDXsz9ZIyfiZOd2sGcrx/riA4KfvEPd2j1PFYa3baLqpX8Q3rWNnCNOTFjcqTZ2IKzb3nK9RrYZk0wJHCcrY1i9rzHGU8HNq6hdsaAhwQII7dxM9cLZDSO3d1a4oozQmkXUbWi981LN+68Srkhiu1qSDe4Nk0e2HNT94L3s6UKTfAkc8T1jWE2WMaZdFRUVDB8+HIBVq1aRn5+4R9hUleC6L1rbglZXENqxAX/J4JZba6oI7dqM+Pw4PfsjPvetLLxjA1pRAaHWByolHCa0bhnOmIkJu4ZUmzAURvV3h3IQYFAvyMlK7jmTeQ8Y051ZkmWM6dC2bR2OBRibcAitaWO6FhHCFbugUZKlqtSt/JS61Qtp6JnkC5C1z2H4+gxBxYHqDqZ/8QcSErqXcrNgr/6pPWfS7gHTZajGOUF0BlZlWXOhMcY7jg+cNj7rqSJZTftkhTavpG715zTp+h0KUrvwbcKVZThZOYjPAX8bx8zKwT9sTGJiNybDaGQIh1iXTEyyrCbLGOMZESHQfwTBDV8CEFZhbd0A1tf1wyfK3s5gBumePkh1a1trWgQUQhuW4WTnu8csKSG4eXOLTiC5R5yAdIOaLGNM12BJljHGU4Eh4whX7aZ2xxZerzycTaG+CGFAWPKxsM9gmDLeTbS0uqKNo7h9uKTEnVDZyc4ma+BAQuXlaDCI+P04BQUExh+csusyprvRcJzNhRn4dKElWcYYT4nPR84+h7Pwi0o27XabB7VRT4bF62B4X3dx8osJl25t7ShIfjFOjz5I3yHo1nWI34+/R4+G7c7QMUhuQdKvx5juypKs6FmfLGNMWli2LY/WZsEQ4MsN7mv/0PGt7Cng+PAP3IuKD2ez/Y23qNnadDApZ8gYApOPS3jMxhjTHqvJMsa0y3EcJk+e3PA6WWrrWl+vQDAyIoOvZBCBsYcSXDa/YVwtyS0ka5/DqPx0PtsfuBOA0i2bcHKy8eXlIkW96Xf6DxHHl7TYY6HBGjRYi+TkI2k+VU2q7gGT3sLqLvHsn2ksyTLGtCs3N5ePPvoo6ecZWuJOctzaA0iDeu957R8wCl+/4Wj5LnB8SH4xAKXP/wuAUFYuZWOnUjlwH5xgFUVL36Xws3nk758e/bG0tpqaL+cR3lFfPRcgMHQc/oGj03bKn1TdAya9WXNh9CzJMsakhYkjYdkmt9aqPtESgcIc2KfZeKTi+JCiPZlXuLqKui0bqcstZs1pv6SusMTdoMruMUcTLP2S/VJ0He1RVao/ewutbDTqfF2Q4IpPQBwCA/fyLjhjTMJZva8xJi0U5cFph7mjmQd8kB2A8UPg1EMhq4OPgxLIQgJZbDvkm9QV9AZx3CXSRLileDSlldFN0ZMMoR0b0coyWk7xDME1i1sdR0hra6hdNJea+bMJbVmfgiiNaZ1GxrqKZ8k0VpNljGlXZWUl48aNA2DRokXk5eUl7Vw98mH6/tHvJz4f+YdNY/fowxsSqybbUTbvClOc5+3nSq3YFRmLopV/NsFqCNZCVvaeVSsWUf7036C2umFdYMxE8k++oGEqoVRI5T1g0lc4HN8kz1FORdotWE2WMaZdqsrq1atZvXp1Wn8S7XHqt6HNxEMIp0HskpXb5iy5Gg5TPX8WGkmowhW7KX/yz1Bb06RccOkCqt95KemxNomti9wDxqQbS7KMMd2Ck5tH7+IArTXFKVBS6P3bna9kcKuJoKoS3rKR2ndepPzRO90mwoUfQqiOFtejSs38tyzZMSlnzYXR8/5dxxhjEmSvAX58jrQYbatXgdA7DZIs8QfIHn9kwyTV9f90tHQn4TUrQZXw1vXUfvoe4d273H5lrdDqKgiHUhW2McCepwvjWTKN9ckyxnQpGqxxh25opUaoIMfh4L0CrNoaYsfuMH6fMKCnw9ASX8KHR9BQHaGVCwlvXgPZOfhH7Y/Ts2+H+/mK+5B78NeoePpPaGUZWr4brShvUib45acE9j20zUTK6dknpX2yjDGxsb9SY0yXEFz5BRWvP0low2pwHLL2OZD8487AKezRpFx+jsP4IcmttdLqCqpfehDdtbWhtqnu03cIHHoCgXGHdri/+HxoZRXhzRvbKCBkjZ1E9VvPuzVa2rTHcM4RJ8R7CcZEzcbJip739efGGNOB4JpllP3zTkIb17grwmFqF8+j9MFb0doaQtXV1G7dita1MWx8gtV+9F+0dJv7jYYbkqDg+y8TLt3ezp57ZI05gNamEQII7H0A4g9QeO7V+Ift3bBecgvIm3EW2ft2nMgZk2hhlLDGsbTSX7K7s5osY0y7RKTh8X2vRiSvmv2C+6Jxx9lwmLptm1lyzdVsf28uGgziz89lwBHj6Tf1IPxjDsI3av/ENxNqmNCKz1p/SlCE0MrPcA6Y2uFxsiYeTe0X8wlvWddkvW/gCLL2OwwAp7gXhd/6PuHyUrSmyp0A25f66YHS4R4w3rOarOhZkmWMaVdeXh4LFy70NIbg2uUtmsxUlbVzllO5dXdDwlNXUcXa1+aioRD9K0rRqt0E9j0qscGoRp76a42gwdpOHUaysin41lXUfjaH4JefggiBvfcna9/DkEjH+HpOQTEUFMcZeOzS4R4wpiuyJMsYk/ac3DzCwabjRVXvrKByS1mr5Te++zl9J+8NX3yEf/QkJCefUFU1OA6+7KxW91FVgisWU7t4PqiSNWZ/AntNaDF5szg+nD6DCW9dT8vhFcL4+g/v9HVJVjbZB04l+8Cpnd7HGK/EOwyDDeFgjDFpKHviUe5I6Y1U7axss3yoOkhtaQVomO2vvc57U77FK0X780rR/sw9/QoqV65tUl7DYXY/+TfKHv491XPfonrebMr+eRdl/7oHbaXWKjD5K253qsYxieD0G4ozyOYfNN2ThpVwHEsmNhdakmWMaVdlZSXjx49n/PjxVFa2ndgkU+4RMwiMdPsE4Tgggj+7/Yp4f242u1dv56NvXcfO9xdATi7O1Glsq8vhvVOuoHb7zoaytZ9/RO2n77vfuHOHABBc8gnV899pcWzfgBFknzATp98wN57sPPwTDif7uG+3qPnqDtLhHjCmK7LmQmNMu1SVRYsWNbz2gvgDFJ79fepWLSG4agkSyKJwr33ZfMa51JWXN50UTYQeowfhz8th1Qtz0HAYZ+o0Ajf+CsnPd68jFGLh2wuYMNpBd24muGopkp2N1tS0OHfNgjnkHjS1xXpf/+H4TrwgWZecVtLhHjDes47v0bMkyxjTJYgIgRFjCYwY27Bu3B/uZOGV3ydUXt4w8XJev54MP/UoyM4m0KsYZ9hQ/Lfc6tY4ReQFSxlV8xHBT6oRwBEld/hgajdtIVRbR2CvvfH17QcC4bJywhVlOPlFHly1MenD+mRFz5IsY0yXVTTxAA7+7yts+9/r1G7cQG6PAAXF6o6ULsLYi6cwokb5PFhKdW7vhv1Gbngdf6gaiXRcj+RnZA0cgPYdBP5AQ7OfU1RI9SevkzNxOk5ugSfXaYzpmrpf5wFjTEbx5eXR7+tfY/All1A4qHjPUA+RT82BLGGfnbMbvvfXVVJcsa4hwaonIlBYDIGsJv2qRARCIerWL03NBRmTpjQcjnvJNFaTZYzpFnT3TrSs5WjrjkBeaDeFtdvYnd0Hp72JlbNz2xhsUwnt2pK4YI3pguqfEoxn/0xjNVnGmG5Ba6va3R6oc5+Kqw0UUJVV3PoEH+Fw6yO5A+JvfXwtY4xpi9VkGWPaJSIMGzas4XW6cgp77elc1YwC4cI+EAZEWDtoCqNXPteyfHUl5LXsd6WAP4pBRrubrnIPmOSyju/RsyTLGNOuvLw8Vq1a5XUYHZLsXHzDxhNa9XmLbb7BY5g0rieVNUpYIT9nHDqykLqF7xLevgl8jttZHnUTrZy8PcmXCOXL1pN7xGmpvaA00lXuAZNcNoRD9CzJMsZ0G/59DgV/FqFVn0FdEHx+fMMm4N97MiJCfk6jWph+Q/H1GwpA8IsPqFvyEY4IVJWjtdVoVjYAEqxlye2PkHfQcRROGOfFZRljuihLsowx3YaIQ2Dvyfj3mgTBaghkI46vw/2c4j5NmsEkVIdU1aGq1O6qIFRdS9WatZZkmYxmNVnRs47vxph2VVVVcdBBB3HQQQdRVdV+5/J0IY6DZOd1KsECcPoNh9wiNLTnEXNVRURY++pcAHKHD0tGqF1CV7wHTOKFCRPWOBZsCAdjjGkiHA4zd+7chtfdkTgO2Uefzra/301Bn1zEcQiWVbL21Xlsnb+c4smTKBw3tuMDdVOZcA8YkwyWZBljDG7H+Z5nX8nSn93Aznffo+de/Rl46GhGHDcBye9B3Zcf49vrAHu6zmQsDcfX5KcZmJ9bkmWMMRH+/DzG3fE7qmY/B6s/RQEBqCwl+P6LaHUFgX2P9DhKY7xhfbKiZ32yjDGmEa2qgDXuMBDN66zqPnsHra1OfVDGmC7JkixjjGkkvG192+0aoTrCOzalNiBj0kT9YKTxLJnGmguNMaaxyPhYbQlvWYNWluEbMALJLUxRUMZ4LxwOx/XgQyY+NGFJljGmQyUlJV6HkDJOnyGQWwhV5dB8hkMR6r74EBGhDsG/75H4xx7sSZyplkn3gDGJ4nlzoYhcLiIrRaRaROaJyFEdlJ8SKVctIitE5LJUxWpMJsrPz2fr1q1s3bqV/Px8r8NJOnEcso7+Bvj9gIA4qEJYHEqzehMqLSUcDAJK3WdvE9q0yuOIky/T7gHTuvqO7/EsmcbTmiwRORO4E7gceBe4FHhZRMap6ppWyo8AXgL+CpwLHAH8UUS2quqTKQvcGNOt+foOIefUK9nw1MP4C4rYmT+ULb0mEPLnUhDayeAvnqe4ehtObi6hFZ/gy+DJo03mUA2jcYzDEM++XZXXNVlXA/er6t9UdbGqXgWsBb7bRvnLgDWqelWk/N+AB4BrUhOuMSZTSE4+u0ccyqLh32Bj34MI+XMBKHeKWbHP6dRWVKHhMFq52+NIjTH1ROQkEflARKpEZJuIPOVlPJ4lWSKSBRwIvNZs02vA4W3sdlgr5V8FJotIILERGmPAnVJl6tSpTJ06NaOmVPn0rtvYkr8XNB98VBxqnTx2jJmGButwevX3JsAUytR7wDSV7s2FInIa8A/gQWB/3NauR5N60g542VxYAviAzc3Wbwbaetfq30Z5f+R4G5vvICLZQOPHhexxIGOiEA6HeeuttxpeZ4qcrCxCbX120zA1ub3AEXyjJ6U2MA9k6j1gmok3UUpikiUifuAu4Meqen+jTUuSdtJO8Lq5EFo8voO0sq6j8q2tr3ctUNpoWRdtgMaYzFO2axeOhlrfKA5ZNeVkTTsLp7BXagMzpusrFJGiRkv746Z0ziRgEBAWkY9FZKOIvCwi4xNw7Jh5mWRtA0K0rLXqS8vaqnqb2ihfB2xvY5+bgeJGy+BYgjXGZJbJ115PSfVqaD6Aoobxay06bxb+/sO8Cc4YD4Q1HPcSsY6mlR/XJiC8kZGvvwB+DXwV2Am8JSKefRLyLMlS1VpgHjC92abpwHtt7DanlfLHAXNVNdjGeWpUtax+AayXqjGmU0L/e4aewaa9EAJay6Blr7DX1T/3KCpjvJHAPlmDaVr5cXNb5xSRX4iIdrBMZk8+c5OqPqmq84ALcFu5zkjaD6UDXg9GejvwDxGZi5tAXQIMBe4DEJGbgUGqel6k/H3AlSJyO+4wDocB3wG+lerAjTHd337X/RSA5f94AB0yCqksp3b9OkZefKnHkRmTeqphNI4+eY2GcNgdqfTojHuAxzoos4o9/a0X7Tmf1ojICty8whOeJlmq+m8R6Q1cDwwAPgdOVNXVkSIDaPTDUdWVInIicAdwBbAB+L6NkWWMSaZR377Q6xCMyUiqug23e1G7RGQeUAOMAd6JrAsAw4HVbe+ZXF7XZKGqfwT+2Ma2ma2sewu3g5sxJkXy8vK8DsF4zO4BE+8wDMkcwkFVy0TkPuBGEVmLm1j9OLL5iaSduAOeJ1nGmPSWn59PRUWF12EYD9k9YKBLjPj+Y9wH4f4B5AIfAMeo6s5kn7gtlmQZY4wxpsuLPAB3DWk0C4wlWcYYY4zpUDgM4Tia/DJxHNt0GIzUGJPGqqurOemkkzjppJOorq72OhzjAbsHDODO1RnnkmmsJssY065QKMRLL73U8NpkHrsHjImNJVnGGGOM6VA6P12YrizJMsYYY0yHusDThWnH+mQZY4wxxiSB1WQZY4wxpkPWXBg9S7KMMcYY06F4nxC0pwszSFlZZ+emNCazNR7pu6yszJ4uy0B2D6QXr/5/heriG/U/3v27IlHNrOo7ERkErPM6DmOMMSZOg1V1fbJPIiI5wEqgfwIOtwkYoaoZMeBaJiZZAgwEdkexWyFuYjY4yv26i0y/frCfQaZfP9jPINOvH9LrZ1AIbNAU/ROPJFpZCThUbaYkWJCBzYWRGzKqzN/NywDYraoZ186Y6dcP9jPI9OsH+xlk+vVD2v0MUnr+SGKUMclRotgQDsYYY4wxSWBJljHGGGNMEliS1Tk1wI2Rr5ko068f7GeQ6dcP9jPI9OsH+xmYKGVcx3djjDHGmFSwmixjjDHGmCSwJMsYY4wxJgksyTLGGGOMSQJLsowxxhhjksCSrGZEZLiI3C8iK0WkSkSWi8iNItLuSLfi+oWIbIjsN0tExqcq7kQTkZ+KyHsiUikiuzq5z0Mios2W95McalLEeP3d7R7oKSL/EJHSyPIPEenRwT5d+h4Qkcsjf/vVIjJPRI7qoPyUSLlqEVkhIpelKtZkiOb6RWRqK79rFZGxqYw5UUTkaBF5PvL3qyJySif26Va/f5N4lmS1NBb353IpMB74IXAZ8JsO9vs/4GrgSuAg3PmZ/isihckLNamygCeAP0W53yvAgEbLiQmOK1Viuf7udg88ChwAHB9ZDgD+0Yn9uuQ9ICJnAncCNwETgbeBl0VkaBvlRwAvRcpNxH2P+IOInJaSgBMs2utvZAxNf99fJjHMZMoHPsH9++1Qd/v9myRRVVs6WIAfAyva2S7ARuAnjdZlA7uAS72OP85rnwns6mTZh4BnvI7Zi+vvbvcAsA+gwCGN1h0aWTemO94DwAfAn5qtWwzc3Eb53wKLm627D5jj9bWk6PqnRu6HHl7HnoSfhQKndFCmW/3+bUnOYjVZnVMM7Ghn+wjc2clfq1+hqjXAW8DhyQ0t7UwVkS0islRE/ioifb0OKEW62z1wGFCqqh/Ur1DV94FSOr6eLncPRLoDHEij31/Ea7R9vYe1Uv5VYLKIBBIbYXLFeP31PhaRjSLyuohMS0qA6anb/P5N8liS1QERGQV8D/cTSlv6R75ubrZ+c6NtmeBl4BzgGOBHuE1mb4hItqdRpUZ3uwf6A1taWb+F9q+nq94DJYCP6H5//dso748cryuJ5fo3ApcApwHfAJYAr4vI0ckKMs10p9+/SZKMSbIiHZJb66TZeJncbJ+BuP1LnlDVv3XiNM2Hz5dW1nkmlp9BNFT136r6oqp+rqrPAycAewMnJewi4pDs64/oTvdAa3G3ez3pfg90QrS/v9bKt7a+q+j09avqElX9q6rOV9U5qno58CJwTbKDTCPd7fdvEszvdQApdA/wWAdlVtW/iCRYbwJzcD+ttWdT5Gt/3E939frS8pOOl6L6GcRLVTeKyGpgdKKOGadkXn93uwf2A/q1sq0PUVxPGt4DbdkGhGhZa9Pe729TG+XrgO0JjS75Yrn+1rwPnJuooNJcd/r9myTJmCRLVbfhvpF0SEQG4SZY84ALVDXcwS4rcf/gpgMfR46RBUwBfhJrzIkWzc8gEUSkNzCEpkmHZ5J8/d3qHhCROUCxiBysqh9G1h2C2z/xvc6eL93ugbaoaq2IzMP9/T3daNN04Nk2dpsDfK3ZuuOAuaoaTHyUyRPj9bdmImn+u06gbvP7N0nkdc/7dFuAgbiPIL8ODML9pNIf6N+s3BfAqY2+/wnuk2SnAhNwH3/fABR6fU0x/hyG4j6yfz2wO/L6AKCgtZ8BUADchtsZdDjuk0fvAeu64s8g2uvvpvfAy7iPtB8aWT4Fnm9WptvcA8CZQC1wIe7TlXcA5cCwyPabgb83Kj8CqABuj5S/MLL/aV5fS4qu/yrgFNxayvGR7Qp8w+trifH6Cxr9nSvu8D0HAEMz4fdvS3IWzwNItwX3kX1tbWlWToGZjb4X4Be4n+KqcZ8qm+D19cTxc3iojZ/D1NZ+BkAu7pM1WyJvNKsjxxji9bWk4vq76T3QC/gnUBZZ/kmzx/W72z0AXI7bXFqDW5N9dLN7Ylaz8lOA+ZHyK4HLvL6GVF0/7rhwy4Aq3Kev3wZO9Poa4rj2qW38zT+UKb9/WxK/iKr1zzPGGGOMSbSMebrQGGOMMSaVLMkyxhhjjEkCS7KMMcYYY5LAkixjjDHGmCSwJMsYY4wxJgksyTLGGGOMSQJLsowxxhhjksCSLGMynIjMEpE7vY7DGGO6G0uyjDEJJSIzRURbWS5qVCZLRP5PRD4RkUoR2SYi74rIBSIS8DJ+Y4xJlIyZINoYk1JlwJhm60qhYeLsV4H9gZ8D70bKHwpcgzvB9oJUBWqMMcliNVnGmAYi0lNE/i4iOyM1TC+LyOhmZS4WkbWR7U+LyNUisqvZoVRVNzVbqiLbrgKOBo5V1XtVdYGqrlDVR4FDcCdoR0ROF5HPRKRKRLaLyP9EJD+5PwFjjEkcS7KMMY09BEwGvg4chjvp9Uv1TXgicgRwH3AXcADwX+CnUZ7jHOB/qvpx8w2qGlTVChEZAPwLeADYB3fy3qci8RhjTJdgzYXGGID/387dg9ZVxnEc//7AVRxE0KXQQsROIRIaqC+TQg2laAcXB3FKUeygnVxcnERQzCJkLR3a0lIQg4WAS1pfcJCUaGIJiDiKgynUt/wdzrnkeE1JL+X03uH7gQvnPIfnnudZLr/7/J9zaFesTgBPVdW1tu0V4GfgReAC8CawXFUftN02kxwFjg993UNJtjvn21X1aHs8BXyxz3Aeo/l9ulRVP7VtayNPSpLGyJAlaeAw8Dfw1aChqn5NstFeg2af1eWhfl/z/5D1O/Bk53yncxyg9hnLd8AKsJbkc+AqcLGqfruLeUjSRLBcKGngTqW4bijaKyDt1W+nqm52Pluda5vshrY9VdU/wPPAC8A6zQraRpKD+8xBkiaGIUvSwDrN6vbcoCHJw8DjwPdt0w/AkaF+syPe5xzwXJKZ4QtJHhhsbq/GalW9C8wAfwIvjXgvSRobQ5YkAKrqR+AKsJTk6STTwFngl7YdYBGYb58onEqyQLPatF/5r+sjmtc2rCR5I8l0kkNJXqYpVU4lmUvyTpLZJAeAk8Aj7IY9SZp4hixJXa8B3wKfAtdpSoHzVfUXQFWtAqeAt2j2TR0DPgRu3+0NquoPmlLg+8AC8CXwDXAa+Bi4QfPerGeBz2jKi+8Bb1fV8j3PUJLuk1SN8gdUkv4ryRLwRFU9M+6xSNIk8elCSSNJcobm/Vi3aEqFrwKvj3VQkjSBXMmSNJIk52leDvogsAUsVtUnYx2UJE0gQ5YkSVIP3PguSZLUA0OWJElSDwxZkiRJPTBkSZIk9cCQJUmS1ANDliRJUg8MWZIkST0wZEmSJPXAkCVJktSDfwG0jPJsswUR+AAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 700x500 with 2 Axes>"
       ]
@@ -1147,7 +1176,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7d81061",
+   "id": "bbfd7947",
    "metadata": {},
    "source": [
     "Contrary to JAK-STAT, NFkB seems to be downregulated in T cells since most of its target genes are downregulated in COVID-19 samples compared to healthy ones."
@@ -1155,7 +1184,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6f49750",
+   "id": "4edc0996",
    "metadata": {},
    "source": [
     "## Transcription factor activity inference\n",
@@ -1168,7 +1197,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "3685e3a5",
+   "id": "8b637a55",
    "metadata": {},
    "outputs": [
     {
@@ -1182,7 +1211,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:12<00:00,  3.03s/it]\n"
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:11<00:00,  2.82s/it]\n"
      ]
     },
     {
@@ -1233,14 +1262,14 @@
        "    <tr>\n",
        "      <th>B cell</th>\n",
        "      <td>-0.291513</td>\n",
-       "      <td>-1.223752</td>\n",
+       "      <td>-1.223751</td>\n",
        "      <td>0.076004</td>\n",
        "      <td>-0.096457</td>\n",
-       "      <td>0.024239</td>\n",
-       "      <td>-1.126017</td>\n",
-       "      <td>-0.734805</td>\n",
+       "      <td>0.024240</td>\n",
+       "      <td>-1.126016</td>\n",
+       "      <td>-0.734804</td>\n",
        "      <td>0.139087</td>\n",
-       "      <td>-2.183874</td>\n",
+       "      <td>-2.183873</td>\n",
        "      <td>0.130307</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
@@ -1257,11 +1286,11 @@
        "    <tr>\n",
        "      <th>T cell</th>\n",
        "      <td>-0.184412</td>\n",
-       "      <td>0.710939</td>\n",
+       "      <td>0.710940</td>\n",
        "      <td>-1.238269</td>\n",
        "      <td>0.621122</td>\n",
        "      <td>0.491515</td>\n",
-       "      <td>-1.802897</td>\n",
+       "      <td>-1.802898</td>\n",
        "      <td>-0.523567</td>\n",
        "      <td>-0.573921</td>\n",
        "      <td>-2.350350</td>\n",
@@ -1281,12 +1310,12 @@
        "    <tr>\n",
        "      <th>monocyte</th>\n",
        "      <td>2.120646</td>\n",
-       "      <td>0.866576</td>\n",
-       "      <td>-0.633622</td>\n",
+       "      <td>0.866575</td>\n",
+       "      <td>-0.633623</td>\n",
        "      <td>-0.155740</td>\n",
        "      <td>0.699399</td>\n",
        "      <td>-0.383546</td>\n",
-       "      <td>-0.473023</td>\n",
+       "      <td>-0.473022</td>\n",
        "      <td>-0.313946</td>\n",
        "      <td>-1.981669</td>\n",
        "      <td>0.375956</td>\n",
@@ -1305,20 +1334,20 @@
        "    <tr>\n",
        "      <th>neutrophil</th>\n",
        "      <td>NaN</td>\n",
-       "      <td>1.414254</td>\n",
+       "      <td>1.414255</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.074941</td>\n",
-       "      <td>-0.506288</td>\n",
-       "      <td>0.675884</td>\n",
+       "      <td>0.074940</td>\n",
+       "      <td>-0.506289</td>\n",
+       "      <td>0.675885</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.326010</td>\n",
+       "      <td>-0.326009</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-2.034403</td>\n",
+       "      <td>-2.034404</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -1333,22 +1362,22 @@
       ],
       "text/plain": [
        "                 AHR        AR     ARID2    ARID3A      ARNT     ARNTL  \\\n",
-       "B cell     -0.291513 -1.223752  0.076004 -0.096457  0.024239 -1.126017   \n",
-       "T cell     -0.184412  0.710939 -1.238269  0.621122  0.491515 -1.802897   \n",
-       "monocyte    2.120646  0.866576 -0.633622 -0.155740  0.699399 -0.383546   \n",
-       "neutrophil       NaN  1.414254       NaN       NaN       NaN       NaN   \n",
+       "B cell     -0.291513 -1.223751  0.076004 -0.096457  0.024240 -1.126016   \n",
+       "T cell     -0.184412  0.710940 -1.238269  0.621122  0.491515 -1.802898   \n",
+       "monocyte    2.120646  0.866575 -0.633623 -0.155740  0.699399 -0.383546   \n",
+       "neutrophil       NaN  1.414255       NaN       NaN       NaN       NaN   \n",
        "\n",
        "               ASCL1      ATF1      ATF2      ATF3  ...     FOXL2      IRF8  \\\n",
-       "B cell     -0.734805  0.139087 -2.183874  0.130307  ...       NaN       NaN   \n",
+       "B cell     -0.734804  0.139087 -2.183873  0.130307  ...       NaN       NaN   \n",
        "T cell     -0.523567 -0.573921 -2.350350 -0.802317  ...       NaN       NaN   \n",
-       "monocyte   -0.473023 -0.313946 -1.981669  0.375956  ... -0.733783  0.207622   \n",
-       "neutrophil       NaN  0.074941 -0.506288  0.675884  ... -0.326010       NaN   \n",
+       "monocyte   -0.473022 -0.313946 -1.981669  0.375956  ... -0.733783  0.207622   \n",
+       "neutrophil       NaN  0.074940 -0.506289  0.675885  ... -0.326009       NaN   \n",
        "\n",
        "                MBD2       REL      RFX2      SIX5    ZNF197     ZNF24  \\\n",
        "B cell           NaN       NaN       NaN       NaN       NaN       NaN   \n",
        "T cell           NaN       NaN       NaN       NaN       NaN       NaN   \n",
        "monocyte    1.363607 -2.175412  0.087953 -0.225582  0.158962 -0.792434   \n",
-       "neutrophil       NaN -2.034403       NaN       NaN       NaN       NaN   \n",
+       "neutrophil       NaN -2.034404       NaN       NaN       NaN       NaN   \n",
        "\n",
        "              ZNF584   ZNF644  \n",
        "B cell           NaN      NaN  \n",
@@ -1375,7 +1404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e20bb16",
+   "id": "b3edd3b8",
    "metadata": {},
    "source": [
     "Let us plot the obtained scores for the top active/inactive transcription factors:"
@@ -1384,7 +1413,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "8ebe871c",
+   "id": "0960f9df",
    "metadata": {},
    "outputs": [
     {
@@ -1404,7 +1433,7 @@
     "# Set nans to zero to be able to plot\n",
     "tf_acts.fillna(0, inplace=True)\n",
     "\n",
-    "# Get top 5 most active/inactive sources\n",
+    "# Get top 10 most active/inactive sources\n",
     "top = 10\n",
     "top_idxs = set()\n",
     "for row in tf_acts.values:\n",
@@ -1422,7 +1451,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb70408f",
+   "id": "56cf7cfe",
    "metadata": {},
    "source": [
     "In accordance to the previous pathway results, monocytes seem to activate the transcription factor STAT2."
@@ -1430,7 +1459,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "339e5c02",
+   "id": "1b3243b4",
    "metadata": {},
    "source": [
     "Like with pathways, we can explore how the target genes look like:"
@@ -1439,12 +1468,12 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "a5a64bb9",
+   "id": "1cfebcd2",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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aoKzqqYgkqg+fyizvMHVPY2zO0Q3LmxUAOwG/6XDoCWDPLu9j9g1gM4LptH6edMBZosRMZADKGz6Kod/4Id7chEcihIqKsx2SiPRzfbhWZscVfi4GLopzygggDCzrsH8ZMKZzcTCzLQgSuX3cvaU/zt2oxExkALP8Aiw/21GIiGxgPND+SaROrWUdeIf3FmcfZhYm6L680N0/SCnCLFJilia1tbWMGjUKgOXLl2upG8lJqqcikqg+7Mpc5+7VCZyykmCMWMfWsVF0bkUDKAd2BnYws2tj+0IEK0C2AAe5+397HXiGKTFLo7q6umyHINIj1VMRSUSmZ/539yYzewM4EHig3aEDgX/HOaUa2KbDvrOAA4BjgWTX484oJWYiIiKSq/4I3GFmrwMzgTMIlmm8ETZcT9vdo8Cs9ieb2XKgwd1n0U8oMRMREZEeZWOtTHe/x8yGA78gWDt7FnCouy+MFem4nna/p8RMREREehQkZqk8lZlcUufu1wPXd3HslB7OvYj4T3zmLCVmIiIi0iMLJTd7f9v5kf43dUU2aEkmERERkRyhFrM0CYVC7Lfffm2vRXKR6qmIJCobY8wGIyVmaVJcXMyzzz6b7TBEuqV6KiKJ6sOZ/6Ub+pREREREcoRazERERKRH6srMDLWYpUltbS0jR45k5MiR1NbWZjsckbhUT0UkUa2JWSqb9EwtZmm0cuXKbIcg0iPVUxGR3KHETERERHqkwf+ZocRMREREeqQxZpmh9FVEREQkR6jFTERERHqkrszMUGImIiIiPTMLtlTOlx4pMUuTUCjEzjvv3PZaJBepnoqI5BYlZmlSXFzMa6+9lu0wRLqleioiiTJLcfC/WswSosRMREREeqQxZpmhT0lEREQkRygxS5O6ujomTpzIxIkTqaury3Y4InGpnopIorQkU2aoKzNN3J2FCxe2vRbJRaqnIpIodWVmhj4lERERkRyhFjMRERHpkYVSW1bJ1BSUECVmIiIi0iOtlZkZyl9FREREcoRazERERKRnoVCwpXK+9EiJWZqYGVtttVXba5FcpHoqIokys5R+TuhnTGKUmKVJSUkJ7733XrbDEOmW6qmISG5RYiYiIiI90jxmmaHETERERHqkpzIzQ+lrmtTV1TFt2jSmTZumpW4kZ6meikjCLLT+AYBkNk1klhC1mKWJuzN79uy21yK5SPVURCS3KDETERGRnqW6ELm6MhOixExERER6ZBbCUuiOTOXcwUSfkoiIiEiOUIuZiIiI9CxkqXVHqiszIUrMREREpEeaxywzlJiliZkxYcKEttciuUj1VEQktygxS5OSkhIWLFiQ7TBEuqV6KiKJ0gSzmaHETERERHpmltoksWqVT4g6fEVERERyhBKzNKmvr2eXXXZhl112ob6+PtvhiMSleioiiWrtykxlk56pKzNNotEor7/+ettrkVykeioiCWtd8zKV86VH+pREREREcoRazERERKRHZpbStDqakicxSsxERESkZ5ZiV6bWykxIVj8lM7vAzF4zs3VmttzM/mVmWyZw3n5m9oaZNZjZx2Z2ZibiFREREUmnbKev+wHXAbsDBxK04D1hZqVdnWBmk4BHgBeAHYBfA1eb2THpD1dERGRw0lOZmZHVrkx3P6T9ezP7BrAc2Al4vovTzgQ+cfdzYu/nmNnOwHnAP9IUalJGjBiR7RBEeqR6KiIJsVCKE8xmuy2of8i1MWaVsa+ruymzB/BEh32PA6eZWb67N6clsl4qLS1lxYoV2Q5DpFuqpyIiuSVnEjMLHtf4I/Ciu8/qpugYYFmHfcsIvpcRwGcdrlsIFLbbVZ56tCIiIoNMyIItlfOlRzmTmAHXAtsCeydQ1ju8ty72A1wAXJhCXCIiIoOeWQhLoTsylXMHk5z4lMzsGuAIYH93X9xD8aUErWbtjQJagFVxyl9G0EXauo1PLdrE1NfXM336dKZPn66lbiRnqZ6KiOSWrLaYxbovrwGOAqa7+/wETpsJfLHDvoOA1+ONL3P3RqCx3T2TD7gXotEozz33XNtrkVykeioiCVNXZkZkuyvzOuCrwJeAdWbW2hJW5e71AGZ2GTDO3U+KHbsR+I6Z/RG4meBhgNOAr2Q0chERkUHEQiEshQlmUzl3MMn2p/Rtgu7FZwkG7bduJ7QrMxbYpPVNrFXtUGA68Dbwf8D33D2npsoQEemNpUuX8t3vfpdNN92UwsJCNt54Y774xS/y9NNPAzBx4kSuvPLKTudddNFFbL/99hu8NzMOOeSQTmUvv/xyzIzp06d3Km9mhMNhNt54Y775zW9u8LTuxIkT28q0bj/5yU/67HsXkfWyPY9Zj+2a7n5KnH3PATumIyYRkUxbsGABe+21F0OGDOHyyy9n2223pbm5mccff5yzzz6buXPn9up6Y8eO5ZlnnmHx4sWMH79+WO2tt97KJpts0qn8tGnTeOqpp4hEIrz11lucdtppfPrppzz66KNtZX75y19y+umnt70vKytL4juVfs0s2FI5X3qU7a5MEZFB76yzzsLMePXVVyktXb/wybRp0zj11FN7fb1Ro0ax0047cfvtt/Ozn/0MgBkzZrBy5UqOO+44Zs+evUH5vLw8xowJRpKMGzeO733ve/ziF7+gvr6e4uJiAMrLy9vKyCAVstTWytQYs4RkuytTRGRQW716NY899hhnn332BklZqyFDhiR13VNPPZXbbrut7f1f/vIXTjzxRAoKCno8t7i4mGg0SktLS9u+3/72twwfPpztt9+eSy+9lKampqTikn6stcUslU16pMQsjUpKSigpKcl2GCLdUj3Nrg8//BB3Z8qUKT2W/fGPf0xZWdkG269//eu4ZQ8//HCqq6t5/vnnqa2t5d57702o9W3u3LnccMMN7LrrrpSXB/Nxf//73+fuu+/mmWee4Tvf+Q5XXnklZ511Vu++URFJiLoy06S0tJTa2tpshyHSLdXT7HMP5sVOZCqf888/n1NOOWWDfVdffTXPP995aeH8/Hy+9rWvceutt/Lxxx8zefJktt1227jXfffddykrKyMSidDY2Mj06dO56aab2o6fe+65ba+33XZbhg4dyrHHHtvWiiaDg57KzAwlZiIiWbTFFltgZsyZM4cjjzyy27IjRoxg880332DfsGHDuix/6qmnsttuuzFr1qxuW8u23HJLHnzwQcLhMBtttBGFhYVdlgXYfffdgaC1T4nZIKJFzDNCn5KISBYNGzaMgw8+mOuuuy5u6+XatWuTvva0adOYNm0as2bN4qtf/WqX5QoKCth8882ZNGlSj0kZwFtvvQUET3+KSN9SYpYmDQ0NHHbYYRx22GE0NDRkOxyRuFRPc8P1119PJBJh11135R//+Afz5s1jzpw5XH311eyxxx4pXfu///0vn332WdIPEcycOZMrrriCt99+m/nz53PvvffyrW99iyOOOCLu1BsygJmtn/0/mU2D/xOirsw0iUQiPPLII22vRXKR6mlumDRpEm+++SaXXnopP/zhD/nss88YOXIkO+20EzfccENK1473pGdvFBYWcs8993DxxRfT2NjIhAkTOP300/nRj36U0nWl/9Ei5plhrQNPBwszqwCqqqqqqKioSNt9amtr2yZgrKmpSfmHo0g6qJ5mXrShnvrXnqPpozlYYRHF2+9BwZTtMraOr/R/1dXVVFZWAlS6e3W679f6e3PZny+ioqQo6etU1zUw+psXQYbi7q/UYiYikiGRmipWX3sxkVUrAAcL0fDmSxTv+XkqjjxZyZnkNi1inhFqVxQRyZCaJ/5JZM1KIOip8GiExpYI9TOeonn++9kNTqQnrU9lprIlc1uzs8xsvpk1mNkbZrZPN2WPNrMnzWyFmVWb2UwzOzjp7zkLlJiJiGRIw9szIRoF4M7/fcietzzI1tffTwvQ8L9XsxucSA4ysxOAK4FLgR2AF4BHzayrJ0/2BZ4EDgV2Ap4B/mNmO6Q/2r6hrkwRkQzxdkscLaupZ69NxnDsVpPIC4XxluYsRiaSgOwsYv4D4BZ3/3Ps/TmxFrBvAxd0LOzu53TY9VMz+xLwReCtZALINCVmIiIZUjhlOxrfewOiUX6w5zbrD0QjFG4Zf1Z+kZwRCqW4iHnbueUdxlM2untjx+JmVkDQ6vWbDoeeAPZM5JYWPApaDqzubbjZoq7MNCktLcXdcXc96SY5S/U0s8oPPhbLL9jwl5sZ+ZtNpXCrHbMXmEhmLQaq2m2dWr5iRgBhYFmH/cuAMQne64dAKXBv78PMDrWYiYhkSN7ocQz//q+oeeZBmj6YFUyXsdPelO5zCBYOZzs8ke713ZJM44F17Y50ai3roOO8XhZnX+fbmX0FuAj4krsvTyzI7FNiJiKSQXkjxzDk+DOyHYZI7/XddBnrEpzHbCUQoXPr2Cg6t6JtIPbQwC3Ace7+VC8jzSp1ZaZJQ0MDxx13HMcdd5yWupGcpXoqIrnK3ZuAN4ADOxw6EJjR1XmxlrLbgK+6+8NpCzBN1GKWJpFIhPvvvx+A2267LbvBiHRB9VREEmaWYldmUq1tfwTuMLPXgZnAGcAmwI3BJe0yYJy7nxR7/xXgr8D3gZfNrLW1rd7dq5IPPnOUmImIiEjPsjBdhrvfY2bDgV8AY4FZwKHuvjBWZCxBotbqWwS5zXWxrdXtwCm9DzrzlJiJiIhIznL364Hruzh2Sof30zMQUlopMRvkPBqlZfkSLJxHeMRordUnIiLx9d08ZtINJWaDWMO7r1H9r78SrV4DQHj0OCqPO52CCZtnOTIREck52Zn5f9BR+jpINX08l7V3XN2WlAFEli9h9Z9+HVtkWURERDJNidkgVfvcw53/enGHlhbqZvarKV9ERCQTWieYTWWTHqkrM01KSkqoqalpe51rmj9dCNFo5wMepfmzRZkPSLIi1+upiOQQS3GMmRKzhCgxSxMzy+m1B8NDhwfdmN5hVYtQiPCQ4dkJSjIu1+upiMhgo/R1kCrZ66DOSRlANErJbvtnPiAREcltrYP/U9mkR0rM0qSxsZFTTjmFU045hcbGntZnzbyi7Xan9PNHbvgfJS+fiuPPIH/8pKzFJZmV6/VURHKIxphlhHm8VpMBzMwqgKqqqioqKirSdp/a2lrKysoAqKmpydnuokjVaprmzYJwHoVTtiNUnJtxSnr0l3oqIutVV1dTWVkJUJngYuApaf29ueyf11FRWpz0dapr6xl99NmQobj7K40xG+TClcMo3nnfbIchIiK5TvOYZYQSMxEREemZZv7PCCVmIiIi0iM3w1No9Url3MFE6auIiIhIjlCLmYiIiPTMLLUnK9VilhAlZiIiItKzVKe80HQZCVFiliYlJSUsX7687bVILlI9FRHJLUrM0sTMGDlyZLbDEOmW6qmIJEqD/zNDiZmIiIj0TF2ZGaFPKU0aGxs5++yzOfvss7XUjeQs1VMRkdyiJZnSREvdSH+geirS/2RrSabPHr2NitLkx6JW19Yx9gungJZk6pa6MkVERKRnmvk/I/QpiYiIiOQItZiJiIhIj/RUZmYoMRMREZGe6anMjNCnJCIiIpIj1GImIiIiPXIL4Sm0eqVy7mCSdGJmZhsDE4ESYAXwnrtrIqSY4uJi5s+f3/ZaJBepnopIwsxSW4hcY8wS0qvEzMwmAGcCXwE2Btp/yk1m9gJwE/APd4/2WZT9UCgUYuLEidkOQ6RbqqciIrkl4XZFM7sKeBfYAvgFMA2oBAqAMcChwIvAJcD/zGyXPo9WREREssIJtXVnJrVpWHtCetNi1gRs5u4r4hxbDvw3tl1sZocCE4DXUg+xf2pqauJnP/sZAJdeeikFBQVZjkikM9VTEUmYujIzQksypYmWupH+QPVUpP/J1pJMi/97HxVlKSzJVFPH+AOOAy3J1K2kBv+bWTFBUlcXez8BOAqY4+6P92F8IiIikgvMUpzHTC1miUj2qcx/A/8EbjSzIcArQDMwwsx+4O439FF8IiIikgM0839mJJv67gi8EHt9LLCMYEzZScD3+iAuERERkUEn2RazEmBd7PVBwD/dPWpmLxMkaCIiIjKQaEmmjEj2U/oQODI2yezBwBOx/aOAhAf0mdm+ZvYfM1tiZm5mR/ZQfnqsXMdtSpLfh4iIiCTAsZQ36Vmyidkvgd8DC4BX3H1mbP9BwFu9uE4p8A7wnV7ef0tgbLttXi/PFxEREck5SXVluvv9ZvYiQVL0TrtDTwMP9OI6jwKPAljvBgUud/e1vTkh04qLi5k1a1bba5FcpHoqIonSWpmZkfSn5O5L3f2t9ksvufur7j63b0Lr1ltm9pmZPW1m+2fgfr0WCoWYNm0a06ZNIxRSZZTcpHqaXosWLeK0005jo402oqCggAkTJvD973+fVatWdSp7xhlnEA6Hufvuuzsdq62t5cc//jGbbropRUVFjBw5kunTp/PQQw+1lbnooouYMmUKpaWlDB06lM9//vO88soraf3+ZJBpHWOWyiY9SrjFzMz+mWhZdz86uXB69BlwBvAGUAh8HXjazKa7+/PxTjCzwljZVuVpik1EpM3HH3/MHnvsweTJk/n73//OpEmTeO+99zj//PN59NFHefnllxk2bBgAdXV13HPPPZx//vnccsstfPnLX97gWmeeeSavvvoq1157LVtttRWrVq1ixowZGyR4kydP5tprr2XTTTelvr6eK664goMOOogPP/yQkSNHZvR7l4FJ02VkRsIz/5vZrYle1N2/0etAzBw4yt3/1cvz/hPc0o/o4vhFwIUd96d75v+mpiZ+/etfA/DTn/5US91ITlI9TZ8vfOELzJo1iw8++GCDbuKlS5ey2WabcdJJJ3HDDcGUj7fffjs33ngjjz32GGPHjmX27NkbLC4/ZMgQrrrqKk4++eSE7986O/xTTz3F5z73uT77viT7sjXz/4IXH6aiLPnVQaprapm492Ggmf+7lTNLMqWQmP0M+Jq7T+3ieLwWs8VakklE9TRdVq9ezYgRI7j00ku54IILOh0/44wzuP/++1m1ahVmxr777ssJJ5zA2WefzbHHHsu0adO4+OKL28pPmTKF7bbbjj//+c+Ul/fc6N/U1MTVV1/Nr371Kz788ENGjBjRp9+fZFe2ErP5Lz2acmI2aa8vgBKzbg2EDt8dCLo443L3Rnevbt1YP/+aiEhazJs3D3dn6tS4fy8ydepU1qxZw4oVK5g3bx4vv/wyJ5xwAgBf+9rXuPXWW4lG24bvctNNNzFjxgyGDx/OLrvswrnnnstLL73U6boPPfQQZWVlFBUVccUVV/Dkk08qKZO+07qIeSqb9CjpxMzMjjWze83sZTN7s/3Wi2uUmdn2ZrZ9bNek2PtNYscvM7O/tit/jpkdaWZbmNk0M7sMOAa4NtnvQ0Qk01p7KsyMW265hYMPPrgtgTr00EOpra3lqaeeaiu/77778vHHH/P0009zzDHH8N5777HPPvtwySWXbHDd/fffn7fffpsZM2ZwyCGHcPzxx7N8+fLMfWMikrKkEjMz+x5wK7CcoMXqVWAVsCmx6S8StDPBvGetc5/9Mfb6l7H3Y4FN2pUvIJg/7X8ES0LtDRzm7gk/mCAikm6bb745Zsbs2bPjHp87dy5Dhw5l2LBh/PWvf+Xhhx8mLy+PvLw8SkpKWL16NbfccssG5+Tn57PPPvvwk5/8hCeeeIJf/vKXXHLJJTQ1NbWVKS0tZfPNN2f33XfnlltuIS8vr9N1RJIWmy4j2U1PZSYm2SWZzgLOcPe/m9nJwOXu/rGZ/RIYluhF3P1Z6HoqYHc/pcP7y4HLk4pYRCRDhg8fzoEHHsj111/Pueee22nw/5133slJJ53EI488wrp163jrrbcIh8NtZebOncuJJ57IqlWrGD58eNx7bLXVVrS0tNDQ0NDlQxvuTmNjY99+czJopTp7v2b+T0yy6esmwIzY63rWT0FxB/CVVIMSEenvrr32WhobGzn44IN5/vnnWbRoEY899hgHHngg48aN49JLL+WWW27hsMMOY7vttmPrrbdu24455hhGjhzJ3/72NwCmT5/On/70J9544w0WLFjAI488wk9/+lP2339/KioqqK2t5ac//Skvv/wyCxcu5M033+Sb3/wmixcv5rjjjsvyJyEivZFsYrYUaP0zbiGwe+z1JLppARMRGSy22GILXn/9dTbbbDNOOOEENttsM8444wz2339/Zs6cSXNzMw8//DDHHHNMp3PNjKOPPrqtG/Lggw/m9ttv56CDDmLq1Kl897vf5eCDD+bee+8FIBwOM3fuXI455hgmT57M4YcfzooVK3jhhReYNm1aRr9vGbhS6cZMddWAwSSp6TLM7M/AIne/2MzOJBgb9hLBmLF/uvtpfRtm32l97Dfd02VEIhHefDN4DmLHHXfcoJtCJFeonvad5rXVrHn5LcLFRQzdaydCecmOFBHpXramy/jolacpj02vk4x1NTVsttvnYABNl2FmvwB+7+51HfYXA+e7+y/jn9nNNZNMzEJAyN1bYu+PJxiI/yFwo7s3dXd+NmUqMRORwcHd+eh3NzPvl9cQbQx+9BWOGcH2t/+eEQfskeXoZCBSYpY7zCwCjHX35R32DydY17vXf+0mu4h5FGi/Rua9wL3JXEtEpD9bcvdDvP+zP2ywr3H5Kl770hlMf+9xijfZKEuRifQtJ4SnMP1pKufmMAPitXBtB6xO5oLJTpcx38wuMbMpyZw/GDQ1NfG73/2O3/3udxs8zi6SS1RPU/fxFX+BUDC0dqU380S0ikgkSrS5hU9u0d+rMnC0rpWZyjZQmNkaM1tNkJR9YGar221VwJMk2WCV7CCIawievvyZmb1F8DTmPe7e5Qz8g01zczM/+tGPADjrrLO0BqHkJNXT1NV99AlEgz+Yb4guZ643sFu4jEoPU/fxoixHJyJpcg5Ba9lfCNbjrmp3rAlY4O4zk7lwsl2ZfwT+aGaTgROBbwO/M7NngL+5+1+7vYCIyABRusVEqt56D6LO+aGxGFBoIbDgmMhAkeqTlQPpqUx3vx2CHkRghrs399W1U/qU3P0Dd7/Q3bcE9gFGEqwIICIyKGz6w9PaWsyKLBQkZaEQofx8Nj5Vc4jJwNE6wWwq20Dj7s8BETObbGZ7m9m+7bdkrpny89xmtivwVeAEoBK4P9Vrioj0FxsddygNi5fxwUVXEqlrAKBoo1Fsf/vvKB4/JsvRiUg6mdnuwF3ABDrP4+pAZp7KbNeF+VVgIvAM8BOCOczWJXNNEZH+atNzv8Em3zyOta+9S7i4iCG7botpTjgZYNSVGdeNwOvAYcBnxH9Cs1eSbTGbGwvkOuBud1+aaiAiIv1ZXnmZ5i2TAS3VJysH0lOZ7WwBHOvuH/bVBZNNzKa4+wd9FYSIiIhIP/QKsDnBBPt9ItmnMj8AMLOdgakETXdz3f31vgqsvysqKuKZZ55pey2Si1RPRSRRqQ7gHyiD/81s23ZvrwH+YGZjgHeBDZ7OdPf/9fb6yY4xGw/8HdgLWBvbPcTMZgBfcfdBP3lPOBxm+vTp2Q5DpFuqpyKSKI0xa/M2QYNU+0zzL+1etx7L3OD/WAD5wFR3fx/AzLaM7b8FOCjJ64qIiIjksknpvHiyidk+wJ6tSRmAu79vZt8FXuqTyPq55uZmbrrpJgDOOOMM8vPzsxyRSGeqpyKSKHVlBtx9YTqvb+69f7LTzN4Hvu7ur3bYvytwl7tv3kfx9TkzqwCqqqqqqKioSNt9amtrKSsrA6CmpobS0tK03UskWaqnIv1PdXU1lZWVAJXuXp3u+7X+3nz3zdcpLy9L+jrr1tWwzY47Q4bizgQzO6KLQw40AB+6+/zeXDPZFrMfAdeY2dnAG+7usQcBrgLOS/KaGVVbW0s4zjxD4XB4g0HQtbW1XV4jFApRXFwct2zH1x3L1tXV0VVSbGaUlJQkVba+vp5oNNplzO1/8fambENDA5FIpE/KlpSUYLHHphsbG2lpaemTssXFxYRCwRiGpqYmmpu7XiGjN2WLiora6kpvyjY3N3e7MHhhYSF5eXm9LtvS0kJjY2OXZQsKCtpavnoq2/57iUQi3db3/Pz8trU0I5EIDQ0NCZWNRqPU19f3Sdm8vDwKCwsBcHfq6ur6pGxv/t/31c+InsrqZ4R+RnT1M6K7eiQZ9y86jzej3T43sxeBI919TUJXdPdeb8AaoBGIxL62f726/ZbM9dO5ARWxDyzuduihh3p7JSUlXZbdb7/9Nig7YsSILsvuvPPOG5SdMGFCl2W32mqrDcputdVWXZadMGHCBmV33nnnLsuOGDFig7L77bdfl2VLSko2KHvooYd2WTaoRusde+yx3ZatqalpK3vyySd3W3b58uVtZc8666xuy86fP7+t7Hnnnddt2VmzZrWVvfDCC7st++qrr7aVvfzyy7st+8wzz7SVvfbaa7st+9BDD7WVvfXWW7ste++997aVvffee7ste+utt7aVfeihh7ot+4c//KHt9SOPPNJt2csvv7ztuq+++mq3ZS+88MK2srNmzeq27HnnnddWdv78+d2WPeuss9rKLl++vNuyJ598clvZFe9/1G3ZY489doM63F1Z/YwINv2MWL9l8WdEhWfw9+b/3nzD5897P+ntf2++kVTcwFnAfIIWqDeAfXoov1+sXAPwMXBmGj+bzwEvx76Wx7bPATOBQwkekpwF3JLoNZNtMTsnyfNERDJq6YNPMeOE72Y7DJF+L5hgNpWnMns/xszMTgCuJEjOXgK+BTxqZlu5+ydxyk8CHgFuBr5GkBhdb2Yr3P0fSQfftauAM9x9Rrt9T5tZA3CTu08zs3PY8KnNbiU1xqw/a+0rX7JkSdwxZn3ZlTl69GgA3rz6FkZsvinjD94XizWNq5tC3RS50pU5dOhQAKqqquJ277fqj12ZoYYmntp4byL1DTT6+rpu4TDD9tmZXf79JyD+//tIQyMf/eHPLLr1H7SsqaZih6ls9pNvMfbg/dSVGaesfkZk7mdEdXU1G220EWR4jNnbb71FeXl50tdZt24d2++wA/QibjN7BXjT3b/dbt8c4F/ufkGc8r8FjnD3qe323Qhs5+59vjSHmdUDu7j7rA77twFedfdiM5sAzHH3krgX6XjNRBMzMyt194Q7tntbPlMyNfh/7ZKlDB03FoD7w5tTZCFKNt2EXR78E2Vbbpq2+4r0xkAf/L/4b//inW/8uMvjn18yk8KRwzrtd3de+9K3WPH4C9CamIRC4M5O91/LmCM+n66QRXqUrcH/fZiYjQfar6vd6O6d/oI0swKgDjjO3R9ot/8qYHt33y/OOc8Db7n799vtOwq4Fyhx966z5iTExo+tA05y9xWxfSOBvwKl7r6vmX0euN7dJydyzd60SX5oZj81s426CdDM7EAzexT4Xi+uPeC8d84lnfbVL/yUVw8/He/mL0UR6TstVTXQTfdJS3VN3P1rZrzJikefW5+UQdvruRf8vsvWKZGBrHWtzFS2mMVAVbutU8tXzAiCCVqXddi/DBjTxTljuiifF7teXzuNYF6zxWb2oZnNI/j+JgLfjJUpAzonBV3ozRiz6cCvgAvN7G2CRcyXEAyuGwpsBexBsBzBZcBNvbj2gNK4YjWr/vU0F4aCHDY/9rCGRyLUL1jMyv/OZOSBe2czRBEg6Cp56KGH2l4PNMP22Rm6SKIKx4ykZOK4uMdWv/AaFg53/iPKndoP5tO0ck3cljaRgczdcE9hHrP153ZqMevp1A7vLc6+nsrH258yD+ZwnQocDEyO3Wsu8KR7MH7C3f/Vm2smnJh5MJnscbHlmI4D9gX2BIqBlcBbwOnAI63BDFaNny0n7M4uofjzvdQvXJLhiETiy8vL47DDDst2GGlTse0Uxh57CJ/94/H1CZoZuDPl0h9iXYypC5eV0uWPsVCIcInWFRVJwboEu2BXEsz40LF1bBSdW8VaLe2ifAuwqjdBJsqDJvTHYlvKev1UprsvBq6IbRJH8cTxhAoLiDbGH9RZvnVC3cwi0ge2v/13lG21BQtvvIumFasp33oyW/zsLMYec0iX54w99hDmnH8ZHt2wxczCYUYf8TnyShMawysywITwXo2A6nx+b7h7k5m9ARwIPNDu0IHAv7s4bSbwxQ77DgJe76vxZWb2PYInLhtir7vk7lf3+vqDbaxEpgb/v/PDS7ntqmvBnelWQZ4ZFg5Tucu27Pn839ueIhLJpubmZu68804ATjzxxAG/JJO7J/x/b/Ed/+Kdb14QlDfDW1oonjiePZ+9i6Jxo9McqUjXsjX4/423ZlGWwuD/mnXr2GmHraF3T2WeANwBnEmQdJ1B0Ds3zd0XmtllwDh3PylWfhLBvGF/IpgyYw/gRuArfTVdhpnNB3Z291Wx111xd+/10369TsxiXZnfJujGHEPQZ7sMmAHc6O6LehtEJmUqMVu3di0VsWkI7g9vTlEozOjDD2Dbmy+lYPjQtN1XpDcG+lOZqar9cCGL//YvmlasZsgu27LR8YcSLinu+USRNBpMiVns/mcRrDg0liDpOtfdn48duw2Y6O7T25Xfj6BXbxrBWPjfuvuNSQeeYb1KzMxsb+BRYBHwBEFCZgT9twcCGwNfcPecXcg8G2tlfvrCKwzbbCJFY0el7X4iyVBiJtL/ZCsxe/2t91JOzHbeYRoMoLUyW8Wm9pgEfOTuXU+ml4DejjG7Avizu5/bRWBXEMzQu0sqQQ00lTtMo0i/8EREpB9zDO+0JGTvzh9ozKwEuAY4ObZrMvCxmV0NLHH33/T2mr0dxbc1QV9tV/4UKyMiIiIy0F0GbEcwpVj75VCeAk5I5oK9bTH7jGBs2ftdHN8jVkZEREQGELWYxXUkcIK7v2xm7ceGzQY2S+aCvU3Mfg/caGY7AU8SjDFzgocADiSY5facZAIRERGR3NWHE8wOJCOB5XH2l5LkhLa9Sszc/XozWwWcS7DCe+vsjBHgDYK1ou5NJhARERGRfuY14DCCcWawPhk7nWB6j15LZoLZe4B7zCyf9etOrezrhUH7u8LCQu6999621yK5SPVURBKlrsy4LgAeM7OtCHKq75vZNIKhXZ0WWU9ErxOzVrFETOPJupCXl8dxxx2X7TBEuqV6KiKJUmLWmbvPMLM9gfOBjwhWGXgT2MPd303mmkknZvGY2WbAze5+QF9eV0RERCTXmNmdwLPApe7+QV9cs08TM6CMJJvuBpqWlhYeeCBY2uuoo44iL6+vP2qR1Kmeikii1GIWVw3wQ4IHI5cBz8W2Z919bjIX7NVP4Z4W6wTGJRPEQNTY2Mjxxx8PBDOq6xee5CLVUxFJlJPiU5kDMDFz928BmNkYgrnMpgPfB64zs+XuPra31+ztT+ErCcaVNXVxvKC3AYiIiIj0c+uANbFtLdACLE3mQr1NzBYCP+5qSgwz255g2gwREREZQKIY0RRavVI5N1eZ2W8JhnBtR7DA+vMEqwE87+5rk7lmbxOzN4CdgK7mKnMYgJ+8iIjIIKcxZnGdD6wALgb+7e5zUr1gbxOzXwAl3RyfTbC6uojIoFT38SLqFy2hdPIkisaOynY4IpJeOxC0mE0HfmhmEWKD/wkeAOh1otbbmf9n93C8maC7U0QGsep33+eDi65ixZMvES4qYKOvHMHkX3yH5tVVfPjbP7HiiRfJKy9l/NePZNL3TyFcXJTtkFPWuGI1b598HiuffCnYEQox7sQj2Oa6iwfE9yeiJZk6c/d3gHeAqwHMbDuCpSmvBkKsXyEpYXoES2SQal66iNX33NL2ftUNvyL/uNMomLB5Stdd9948Zux9AtHGJjwSIVrfwCd/+jvLH32WphWriTY04i0RGj+D9y+8ihVPvMhuT9xGqB8/EeruvH70t6l6vd18ktEon975IKG8PLa96dLsBTdIRBvqafjfK0RWryBv5FiKtt0Vy9fzaH3JSa07MqmFI/sBM9uB9U9k7gNUAG8DzyRzvaR+EprZW8T/jB1oAD4EbnP3pIIaCAoKCrj11lvbXovkksjaVay+7pdQV8+Vh+wOgH32CatvvJQR5/yKvNHJz3wz79c3tCVlrTwSoX7+YgiFIBpdXzgaZfULr7HsP/9l7FEHJX3PbKt6/V3Wvvx25wPRKIvveIApv/4hBSOGZTyuwaJ58XxW3/QbvL4WQmGIRlj3yD0MO/On5I3s9WwFIgkzszUEc7i+Q9B9eTPBwP/qZK8ZSvK8x4BNgVqCjPBZgknWNiNY0HMs8JSZfSnZwPq7/Px8TjnlFE455RTy8/OzHY7IBupmPIk3NpIfghO23owTtt6M/JBBNELNcw+ndO2VT7+0QVK2gfZJWYzlhVnx2PMp3TPbat7/uMtj3hKh7uPFGYxmcPFolDW3X4k31Ac7okHdi9ZUsfbOa7MY2cDT2pWZyjYAfR0Y7u47u/t57v5QKkkZJN+VOQL4g7tf0n6nmf0cmODuB5nZxcD/Af9OJUAR6XvNCz8E75wkEY3SPD+1VUXyykppXrW2V+eEivr3Auolkzbu+qAZRZuo1SZdmue/T3Ttqs4HolFaPl1Iy7JPU2oBlvX0VGZn7v5QX18z2Raz44G/x9l/d+wYseNbJnn9fq+lpYWHH36Yhx9+mJaWlmyHI7IBK6uAUIiWaJSnPvqUpz76lJZoFMwIlVemdO3xJx0VdFnGE2e/t0QYe+whKd0z24buuSPl22yJ5XUY5xsOM/bYQygaMzI7gQ0C0dp1KR0XyTXJJmYNwJ5x9u8ZO9Z67cYkr9/vNTY2cvjhh3P44YfT2DhoPwbJUSW77g/RKE0tUb7+wLN8/YFnaWqJgjslu+2f0rU3O++bDN19eyDopmxNVjY586uUbrYJmIEZFg72T/j2Vxm2984p3TPbzIxd/v0nyrfe8G/RkQftzTY3/ipLUQ0O+RtvGtSpeMJ55I3tpjUzQ9yd5jVVRJu6WjSnf1BXZmYk25V5DcGCnTsRjClzYFfgm8CvY2UOBt5KOUIR6XOFW25D6YFHUffwfRvsL97z8xTtuFdK1w6XFLP703ew7MGnWfnkS4SKC9nouEMZuscOtNTUsvj2B1j57MvklZaw0VcOZ+RB+2Bd/WLtR4o3Hsver/6TqjdmUf/JEsq32pyyKZtlO6wBLzx0BMW7TKf+1Wfp+Exa6fTDCBWXZiWuVotu/ycfXHw1DYs+I1RUyPiTj2bqZeeRV16W1biS4UCcARC9Ol96Zu7JfVRmdiLwHdZ3V74PXOPud8WOFwPu7g1dXCIrzKwCqKqqqqKioiJt96mtraWsLPiPV1NTQ2lpdn84iMRTteBjhkwKkoc1H81lyKaDdvSB9GMeiVDz9L+oe/FxvL6OUFkFpfsdRsm+X8C66lbPgE9uuY93z/z5hjvDIYbttRO7P3VH0n+QVFdXU1lZCVCZ6kDzRLT+3nzy1UWUliX/e7O2ppoDd90YMhR3f5X0xEHufidwZzfH65O9tohkRt7I0W2v80ePz2IkIsmzcJjyg46h7PNH4Y0NWGFRVhMyCJLFDy66qvOBSJTVz7/Gmpfe6Pdd+JIeKc3oGOvKnErQQjnb3dV1KSIiWWGhEFbc3aqBmdPw2Qoal66IfzAcYs0rb/e7xExPZWZGshPMjiJ4AnM6sJZg4fJKM3sG+LK7d1EbRUREBr78yjIsHMIj8aelKRg+NPNBpUhLMmVGsm291xAsOTDN3Ye5+1Bg69i+qxO9iJnta2b/MbMlZuZmdmQC5+xnZm+YWYOZfWxmZyb5PYiIiKRFXnkZY44+uO3p4zZmhIuLGdOPV7qQ9Eq2K/MQ4PPtV01399lmdjbwRC+uU0qwjMGtwD96Kmxmk4BHCJY8+BqwF3C9ma1w9x7Pz6SCggKuvfbattciuUj1VCR9tr76F9TM/Zh1776P5eXhkQihogJ2vPdq8ivLsx1er6krMzOSeirTzNYB+7j72x327wA85+69fmzDzBw4yt3/1U2Z3wJHuPvUdvtuBLZz9z0SvE9GnsoUERHxSITljz5H1RuzKBg1nI1OOIyCYUNSuma2nsp85OUlKT+VeejuG4GeyuxWsi1m/wWuMrOvuPsSADMbB1wBPN1XwcWxB51b5B4HTjOzfHdvTuO9RUREesXCYUYffgCjDz8g26FIP5FsYvYdgjUwF5jZIoKnMjcB3iXoYkyXMcCyDvuWEXwfI4DPOp5gZoVA+4X4MtJ+HIlEeOGFFwDYZ599CHccZyCSA1RPRSRR6srMjKQSM3dfBOxoZgcCUwieypzt7k/1ZXBd3b7De+tif6sLgAvTF058DQ0N7L9/sLSNJpiVXKV6KiKJ0lOZmZHSPGbu/iTwZB/FkoilBK1m7Y0CWoBVXZxzGfDHdu/LgcV9H5pI6jwaJbJ2FaHCIkKl/W9wsIiIpCbhxMzMvpdoWXdPeMqMXpoJfLHDvoOA17saX+bujbRbTH0grMknA1P9Gy+y7pG7iVavBaBgy+2oPPZUwkOGZzcwERHAPdhSOV961psWs3MTLOckOJeZmZUBm7fbNcnMtgdWu/snZnYZMM7dT4odvxH4jpn9kWDKjD2A04CvJBibSE5q+N+rVN194wb7mua9y+rrL2HE+Zdj+ZrKQkSyK4oRTWGcWCrnDiYJJ2buPinefjPbm6DFKpnFyncGnmn3vrXL8XbgFGAswUMFrTHMN7NDCZ7+PBtYAnwv1+YwE+mtmif/STBcst2flNEokTUraXjnZYp33rfLc92dT+/4F/Ovvp26+Ysom7IZm51/OmOOPDDtcYuISN9KaYxZzCPA9sDHvT3R3Z+FrlNodz8lzr7ngB17ey+RXOXRKC1Luxj2GArT/OmCbhOzeZdcy7xLrgUzcGft6+/yxnHfYetrLmTCmV9NU9QiMtho8H9mJLskU3v6pEVSYKEQVlIW/6BHCZUP6fLcxhWr+fCyWBdo6wCOaLA239yf/oFIfTIN2SIinbWOMUtlk571RYuZxJGfn8/ll1/e9lqkOyV7fI7a/z7Y+SdXKETxTnt3ed7qF1/HW1riHmtZV0PVm+8xbK+dujxf9VREJLf0RWL2LTpP+jroFRQUcP7552c7DOknyj5/JC3LPqVx1uttXZLk5zPkq98hXDmsy/PCJUXdXjdcUtztcdVTEUmUJpjNjJQTM3e/qy8CERnMLC+foSefQ/Pi+TTNf59QcQmF03YmVFzS7XnDp+9O/tBKmtdWb9jaFgpRMnE8FdtP7fpkEZFeiHqwpXK+9ExdmWkSiUR48803Adhxxx211I0kJH/8JPLHx30AOq5wYQHb//V3vH70WRB1PJachYsL2f62y3uct0/1VEQktygxS5OGhgZ23XVXQEvdSHqNOmQ/pr/3GJ/85X7qFyymbMtJbHzqcRRtNLrHc1VPRSRhKT6ViZ7KTIgSM5EBoGTSxky5JNE5oEVEek8z/2dGX0yXISIiIiJ9QC1mIiIi0iMtyZQZSsxERESkR+rKzAx1ZYqIiIjkCCVmGdAw522aP13YNpWBiIhIf9O6VmYqm/RMXZlpkudRfnTkIUSWfUrtXdfSHA6TN34SQ08+h/CQ4dkOTwQIlmG68MIL216LiHRFE8xmhhKzNKl/5G7O3WIkbL4+CWtZspDVf76cET/8TY8Tf4pkQkFBARdddFG2wxCRfkBjzDJDXZlpEK2roeGNF8CjHQ5EiSz7lKaP52QnMBEREclpajFLg8jaVUQjEeatqgJgi+GVhNq1kDXPf5+6F5+gce7bYCGKtt2V8i8c3+1i1SLpEI1GmTMn+ENh6tSphEL6W01E4tMi5pmhxCwNwkNG0BBxpt/2MAAffe8ESgrWf9Q1z/wHWlogGrSoNbw1g6YP32PEub8mVFqelZhlcKqvr2frrbcGtCSTiHQvSopjzPoskoFNfx6nQaiklKId945zIISVlEFzc1tSBkA0SrR6LXWvPJO5IEUkq6JNTXzyl/t49fBv8sqhp7Lg+juJ1NVnOywRyTK1mKVJxRe/Apy9wb78TTYjUrUGr6vpfII7TR/OhgOOyEyAIpI10aYmXjn0NFY/9yrEhjmsfGoGi27/B3s8fQd5ZWq5lNyjwf+ZoRazNLH8grbXQ045lxHn/ZbhZ1/YdVelhbCi4gxFJyLZtPj2B4KkDNb/tnOn+u05LLzhzuwGJ9KFdlU16U16psQsAwonb03e6HEAFO+0T/xCHqU4XveniAw4nz3weFtL2QaiUZbc92jmAxKRnKHELMNK9jiAwq12CN6EwsEGFO9+AIXTdsxiZCKSKd4S6bL5wFsiGY5GJDFRt5Q36ZnGmGWYhfMYcvK5NM2bReOctyAUpmibXcmfuIUmnRUZJEYftj+rnn2lc3IWDjHmS5/PTlAiPdAYs8xQYpYm+fn5nHfeeW2v27NQiMItt6Vwy22zEZpIm+7qqaTPxt88nkV//SfrZs1b/4R2OETJhPFM/O5J2Q1ORLLKBtvC2mZWAVRVVVVRUVGR7XBEZJBqWVfD/Gvv4LP7HsUjEcYcdRCTvnsSBcOHZjs0yXHV1dVUVlYCVLp7dbrv1/p78+bH1lJSmvzvzbraak4/ZAhkKO7+Si1mIiJZkFdexhYXfJstLvh2tkMRSYinuIj5IGsHSpoSszSJRqN88sknAGyyySZa6kZykuqpiEhu0U/hNKmvr2fSpElMmjSJ+nrN5i25SfVURBLlbilv6WRmQ83sDjOrim13mNmQbsrnm9lvzexdM6s1syVm9lcz2yitgfZAiZmIiIj0qB9MMHsXsD1wSGzbHrijm/IlwI7AJbGvRwOTgQfTGWRP1JUpIiIi/ZqZTSVIxnZ391di+04HZprZlu7+fsdz3L0KOLDDdb4LvGpmm7j7JxkIvRO1mKUoUr2WltXL8faLkouIiAwwUU99S6M9gKrWpAzA3V8GqoA9e3GdSsCBtX0aXS+oxSxJLcs+per+W2he8AEA4aEjKP/iiRRts0uWIxMRyRx3Z/WLr1Mz+0OKN9mIEQfuRShPv1oGoj6cYLa8w4Tqje7emPyVARgDLI+zf3nsWI/MrAj4DXBXNqfz0P+eJERr17Hq+kvw+rq2fZE1K1l7x9UMPeMCCjffKovRSX/lzU14NEqosCjboYgkpHH5Kl474gyq3pjVtq94wjh2fehmyqZslsXIJMct7vD+YuCieAXN7CLgwh6u19oiEi9ttC72d7xPPnA3QU/iWT2VTyclZkmoe/VZvL42zp8ORu3T/1ZiJr3SsnoF6x78G42z3wR38jbelIrDv4qXj6L67dkUjBhGxfZTtWSX5Jx3Tv0x1W/P2WBfw+KlvHbkmUyf/Tim6VcGlD5sMRsPrGt3qLvWsmsJEqbuLAC2BUbHOTYSWNbdybGk7F5gEnBAtie/VWKWhJZPF8Y/4FGaP50PQF5eHmeddVbba5F4onW1rL72YqK11W0/tVoWz2fV9b/i/fvmUrsk+NlVttUW7HTPVX3eCqF6KsmqX/QZKx5/odN+j0So++gTVr/wGsP32y0LkUm6pDpOrN256xJNftx9JbCyp3JmNhOoNLNd3f3V2L7dCMaMzejmvNakbAtgf3dflUhc6aSfxEkIVQwBs7h/OoTKhwBQWFjIddddl9nAUuTRKI3vv0PTvPewgkKKtt+D/DHjsx3WgFb/6rNEa6o2rEuxP0tH7ziaj2OJWe37H/PyQSez/wdPEy4q7LP798d6KrmhYUm84Tzr1S9emqFIRMDd55jZY8DNZvat2O6bgIfaP5FpZnOBC9z9ATPLA+4nmCrjcCBsZq3j0Va7e1MGv4U2SsySULzrftS9+HjcYyV7Hhh3f67zpkZW3/I7mj+eC6Ew4NQ+/W/KvnA8ZQccke3wBqymRR/F3W8ho2yjsrb3HonQ+NkKlv7zccZ9Vf8ekn1lkydiBfl4U3Pc45XbTc1wRJJufdiVmS4nAlcDT8TePwh8p0OZLQla0SDoUm39gfp2h3L7A8/2eYQJ0ACAJOSP2ZjK40+HcHiD/cW77U/JHp8DgieVVqxYwYoVK+gPC8XXPPMfmufH/qiIRiA2/UfNo/fSvOjjLEY2sIVKyoLW1w7cnZb6lg32WV4etfMW9On9+1s9ldyRP7SSCWd+tXP9DYcYefA+lG89OTuBSdpEo6lv6eTuq939a+5eEdu+5u5rO5Qxd78t9npB7H287dn0Rts1tZglqXjnfSmcugMN772JNzdRuMU08katX8Whrq6OUaNGAVBTU0NpaWm2Qk1I/WvPxf9zJhSi/s0Xyd9408wHNQgU77wv9S//N+6xlbNWbPDeW1oo2WyTTuXcPekHA/pbPZXcMvW3PyKUn8eC6/5GtKERywuz0Ze/yNZX/1+2QxPpt5SYpSBUWk7JrvtlO4w+4Q1dr5MYbTctiPStggmbU37Yl1n38N1By4MZRKOs+XANy99e/yCRhcPkjxjK2GMOadvX8L9XqXnqAVo+W0SorILiPT5H2QFfwgboIH6PRvGGeqywCOvQWi3ZEcrLY+pvfsQWPz+b+k8+o3DMCAqGDcl2WJIm/aArc0AYmD/BpdfyN51C0/v/69zWHI1SMGnL7AQ1SJROP5zCrXeh4d1XobmZ/M23YuUVd0NoPkSCf4+SzTZmp3uuIVwczHFW/8aLVN19Y1s3UrSmmtqn/kXLsk8Z+vXvZe17SQd3p37mU9Q8/W+i1WuxgkKKdz+A8kOOw/ILsh2eAHllpZRvtXm2w5A0U2KWGUrMBIDyA49i1Qfvbvi0aShEeNgoinfozWoWkoy8EaMp2/+Lbe+3vuYiNv/pWVS9NZvCEcOo3GWbtu5Kj0ZZ90hsWp8OT3M2/u9Vmj9dSP64CZkMP63qnn+UdQ/d1fbemxqpe+ExIquWMfSUH2QxMpHBJUqK02X0WSQDmwb/CwD5G2/GsDN/TsGmU4IdefkU77wvw8/+BVbQd9MzSOKKxo5i9KHTGbLrthuMIYusXUm0em38k8xomj83MwHG4e40V9cQbWnpuXAi12tppuapf8W7EY3vvUnzkqysMSwikjZqMZM2BRO3YNiZPwsWZDfTTPM5KlRY3PVBd0JF3RxPo8V/fYAPfnkN9Qs/JVRcxCanHseWl/6AvNKSpK/ZsnIZ3tD1GMfmTz4kf6POD0SISN9z95Se3taT34lRYiadaBmVzGhZvoTaFx+n5dOFhIaOoHSPz1GwWc9zP4VKyymYvA1NH77XeUxgfgGF03ZOU8Rd++SW+3j3zJ+3vY/WN7DgxjtZN+dDdnvs1qST/FBJ90+JhkrLuz0eqatnzavvEMrLY8ju22txbZEUaIxZZuinVJrk5eVx8sknt70WaFmxlLoXHqNpwQeEyisp2W1/CrfZZVC2zDV+NJs1N18OHpvcZ/HHNL7zMuVfOonSvQ/q/txlK6muGUKB5xGiKZgQ2KNgIYZ89SxCxYm3UPVFPfVIhA8uurLzgUiUVf+dyZqZbzFszx2Tuna4Ymj8JNQMKy6lcMp2XZ77yS33Mef8y2hZVwtAwejhbHfzZYz6wsB4klpEBiZlDGlSWFjIbbfdlu0wckbzoo9ZdcOvINIS/IJdajR98C4l+x1KxeFfzXZ4GeXuVN//l2Ai39Y/IWNJx7qH7qR4hz26bAlact8jvH3y+XgkQqggzNBNhzB02wmM/cpRlO55AOEhw3sVS1/U04ZPl9G4tIul7EIh1r7ydtKJGUDl8aez+sZfE1m5NEhCoxGsoJChJ5/T5VOZK558cYMWPICm5at5/eiz2PetB/t8zVGRwcBTnCTWNfo/IUrMJCOq/30HtDSvT0RiX+uee4SSXadvMDnvQBdZuTRIMuIejNA49x2Kd9q706GGJcuCpKw5GFgfbWhh1eyVrHp/Nc1Dp7D1ocelM+wu5VWWQygU/yd2NErB8KEpXT9cOYwR5/0mGOz/2SLCQ4ZRtO1u3bYMfnzFrRAOtU03AsTqnLPwT39n2hU/7/LcTKqdt4AFN9xJ9TtzKZ44jgmnf5mhu2+f7bBE4lJXZmZoMFGauDu1tbXU1tYO+gGP0fpamhfOi/+/0oyG997MfFDZ1FN96OL4p3c/hEfiJD+RKItu+wceiSQRSur1NL+ynDFf+nznSV/NCJcUM/rI1NePtXAeRdvuSvnBx1Cy2/49dtfWzP1ow6Qsxlsi1HwwP+V4+sKqF17j+R2/yMIb7mT186+y5K4HmbHPCXzy53uzHZqIZJESszSpq6ujrKyMsrIy6uo0c353BtsQs/CIMYSHjQTifOOhEAVbbhv3vOaVa7Bw/P+y0foGol0sJt2dvqqnW197EaWTJwHBmp6YESoqYMd7rya/oqyHs/te2eRJQYtZB5YXpnSz7M/x5u7874yfEW1qwVuChLr163vnXELz2upshicSV9RT36RnSswk7ULFpeRPnAwWp7q5Uzhtp8wHlUUWClFx1CkQsqALMNgJQNkhxxEur4x73pDdtm/rxtzwgkbZ1M3bVgXIhsJRw9nnzX+z033Xstn532TaFT/nc/OfY9TB+2YlnknfP6Vzi1nsL4AJ3/pK5gPqoPb9j6n7cGHc7t9oYxMrHn8hC1GJdK+1KzOVTXqmMWaSERVHnsTq6y/BW5qDX0YWAo9Suv8XyRs5NtvhZVzhlO0Y/r1fUvv8o7R8uiCYLmPPAymcun2X54w6bDrl202hZta89d2WsZUaJl+U+jJMzdU1LP7HEzStWsOQXbZl6F479eqJ2VBeHmOOPJAxfdB1mapRX9iPaVf9grkX/I5IXbAObP7QSra9+VLKp22R5ejWt451pa8m6BWR/scG2/gnM6sAqqqqqqioqEjbfWpraykrC7pwampqKC3tfj6mwaBl1XLqXnycpgXzCFdUUrzrdAq32nFQTpeRrKbVa5l9/m9Y8vf/4M0tlG4xkS1/eQ5jj/1CUtdrX08fqNyG/NrGtoH8w/bdhV3+dSN55ZnviuwrLetqWD3jTUL5+QzbeydCBbmxtqZHIvx38wNo+HRZp2YEywvzuQXPUzh6RJaik1xXXV1NZWUlQKW7p73fu/X35iV3rKGoJPnfmw111fzf14dChuLur9RiJhmTN3wUFV/6erbD6NcKhg1h+1t+wzbX/5JoXT15Qyr6LLGN1DaQj7V1r61+6Q3m/OR3bHPdxX1y/WzIKy/LWndqdywcZuurf8Hrx34HC4eCFrTYU6STL/q+kjLJSamOE9MYs8RojJlIPxQuLCB/aGXftjZ2bD2PRFn8138SaWzqu3tIm9Ff/Bx7Pn83o790ICWbbcKI6buz0z+uY/MffyvboYlIFqnFTES6FG1oIrKuhnDhsGyHMiAN3W07drr7qmyHIZIQzWOWGVlvMTOzs8xsvpk1mNkbZrZPN2Wnm5nH2aZkMuZEhMNhjj32WI499ljCHed3EskR4XCYw/faj72sLO4Pg6JxY8gfNiTTYYlIDopGPeVNepbVFjMzOwG4EjgLeAn4FvComW3l7p90c+qWQPuBgyvSFmSSioqKuO+++7Idhki3ioqKePC5p3lh16OpeW9ep0lqt/i/72hRexGRDMr2T9wfALe4+5/dfY67nwMsAr7dw3nL3X1pu633U56LCBAMRN/98VsZc/RBbRPYFo4ewdbXXcwmp2VnmScRyT2axywzstZiZmYFwE7AbzocegLYs4fT3zKzImA28Ct3fyYNIfY73tJCw6zXaV44j1BJGUU77UXesFHZDkv6gYIRw9jxritprq6hZW01hRuNIpSnIagisp7GmGVGNn/yjgDCwLIO+5cBY7o45zPgDOANoBD4OvC0mU139+fjnWBmhbGyrcpTCTpRmZ7HLFq7jtU3XErLssUQCoM7NU/+k8rjT6d459ybLkByQ6d6WlGWlSWUREQkkAt/EnfMoS3OvqCg+/vA++12zTSzjYHzgLiJGXABcGGqQea66v/cScuKJcGb6Pqe3ap7/0zB5tMIDxmepchERGQgiLoTTaHZK5VzB5NsjjFbCUTo3Do2is6taN15GehujZXLgMp22/heXLtf8EgLDW/PjLvuHjgN77yS8ZhERGRg8Wjqm/Qsa4mZuzcRdEl2XFjvQGBGLy61A0EXZ1f3aXT36tYNWNfrYHOct7RApIvnHyxEtL42swGJiIhIUrLdlflH4A4zex2YSTB+bBPgRgAzuwwY5+4nxd6fAywA3gMKgK8Bx8S2QStUWETe6PG0LP+08+jKaISCSTk3zZukkbuz9J+Ps/hv/6Z5bTUj9t+dCWd+lcJR6s4WkeQ5Tirra3v8UUrSQVYTM3e/x8yGA78AxgKzgEPdfWGsyFiCRK1VAfB7YBxQT5CgHebuj2Qu6txUdugJrL31D2C2PjkzI3/iZAq2mJbd4CRj3J13z/w5i/5yf9ti5GtmvMnCm+5mr5fupWTCuGyHKCL9lEe7GDHTi/OlZ9mexwx3v97dJ7p7obvv1P7pSnc/xd2nt3t/ubtv7u7F7j7M3fdRUhYo2moHhp52PnnjJgJgRcWU7H0wQ795viYIHUTWvPRGkJTB+p+g0ShNK1fz/v9dkb3AREQkIdnuyhywwuEwhx56aNvrTCicsh2FU7bDo1ElY4PU0n8/heXlBeMO24tEWXL3Q0w462sUNi+l9oXHiFatoWXoKA7eew/CFUO1dJiIdMs9xa5MPZWZECVmaVJUVMTDDz+clXsrKRu8Oi6ptOFBZ/FFP2bUNiPbduWtWsptu29K+ZEnU1RUlIEIRaS/inqwpXK+9Ey/wUUGkNGHH9C5tSwmv7yAkVuP6LA3+ElZ89h9eHNTmqMTEZGeKDETGUCG7787Y44+OO6x8o3KMLO4x7yhjpZln6Ylpua11ax6/lWq331fXRki/ZhHPeVNeqbELE1qa2spLS2ltLSU2lrNIyaZYWbscOcfqdxp607HIk2duznrmlrY9Mq72fTKu6lr6dtHptyd9y+6iqfG78XLn/s6L+x4BC/seATr5nzUp/cRkczQIuaZocQsjerq6qirq8t2GDLIhPLy2PKSczvtr/6kmpaGlk4zCdW3RKhviZA3sqslapOz4Orb+fDS64k2ru8irZnzES8feBIttfp/IdLfRKOe8iY9U2ImkkHuTuNHs6mb+TSNc9/BU5kUqBsjD9ybLX5+dvAmFIJwCI84NYzH8vKC+e5C4eBrGrg7H/3+z533RyI0LVvJZ/c9mpb7ioj0d3oqUyRDItVrWXPL72hZsrBtX3jYKIae/iPyRvRtaxXA5Au/x0Zf+SLLHnwKb4kw6gvTqdhuCpGq1dS//gKRNSsJVY6Aq+7u83tH6uppXLoi7jHLz6Pm/Y/7/J4ikl6aLiMzlJiJZMjav19Py9JFG+yLrF3Jmlv/yIjzftvlwPxUlE2eRNl5p2+wL1w5jLLPfQkgbeMfw8VF5A8fQvOqtZ2OeUuE0k036XySiOS0VBci18z/iVFXpkiK6uYvYs6Pf8srh53G/878P6remNWpTMuq5TR/OLvzeibRKJHlS2heMC9D0WaGhUJM+v4pnbtKwyHyKssYe8JhWYlLRCTXqcVMJAWrX3qDVw75Bt7cgkciWF6YRX+5j+3+fBnjTzqqrVy0anW314lUd3+8P9r8R2fQuGQ5C2+6uy0hLdpoNDvdew35FWVZjk5EeivqTjSF7shUzh1MlJilSSgUYr/99mt7LQOPu/O/M35GtKm5LfHwlmBKinfPvpDRX/o8+ZXlAOSNHte2qHg8+WOz07WXznpq4TBbX3Mhm//kTNa8+g4Fw4cwbK+dMC39JNIvaYxZZigxS5Pi4mKeffbZbIchaVQz92NqP5gf91i0oZEVT7zARscF66WGSssp3m1/6l/+74aT+ViIwqnbkzdqo0yE3Ekm6mnRuNGMPeqgtN5DRGSgUGImkiRvbu7h+IZLI1V86etYXj51M5+GlmYIhSnacS8qjjopnWGKiPSJVOci0zxmiVFiJpKk8mlbkD9iGM0rO48Ps7wwww/YY8N94TwqjvgaZQcdQ7RqNaHyIYRKSjMVrohISlKdvV89mYnR4Kc0qa2tZeTIkYwcOVJLMg1QLTV1eFP8hb9Hf+nzFI0ZGfdYqKiYvNHjciIpUz0VEcktajFLo5UrV2Y7BEmjT+98kJZ1cZIZC8af9ReqpyKSCPfUFiLX4P/EKDGTQa/50wXUvvAYLZ8tIjx8FCV7Hkjh5lv1eN669z7A8sLkFRj55QU0rm0g0hABD9aEFBEZSDzF6TKUmCVGiZkMag2z32LtbVeAAdEoLUsX0fjua1QcdQole36+23OLx41k0sETGbLZEMwMjzorZ61g0XOfUDh2VGa+ARERGVCUmMmg5dEo1f/4S2ydkdjO2Dxj1f+5k6Id9iBUvOE4MG9phnAeZsaQ0pU0bzqkbSklCxkjth4JBiUHHp/Jb0VEJO08mmJXpp7KTIgSMxm0Wj77hGj1mi4ONtM07z2Ktt0VgPq3Z1LzxD+JrPgMKyymcOudaVn8IRbacMmhIDkbxajTj013+CIiGaXELDOUmIn0oP7156m656a2995YT8MbL3ZZ3gwiq1cQrhjS6Zg3NVL/zsu0LP2U8NDhFO+wJ6HS8nSELSIi/ZASszQJhULsvPPOba8l9+SN3YRQxdD4rWZ5+RRssTUejbLu0XvjnN39X37hIcM67WtZsZTVN/yK6Lq1EApDNErNo/cy9LTzKdh0SnLfRIpUT0UkUVEPtlTOl57pJ3GaFBcX89prr/Haa69RXFyc9vt5SzPNSxcTqeqia046sVCIimNPDdawbE1KYl8rjvgaoeISolWriVav7eYiG3ZlEgpROG1HwkOGdypa9fcbiNZWB2+iEcDx5ibW/PUqvKWlU/lMyHQ9FZH+q7UrM5VNeqYWs37O3al78XFqnvwnXl8HQMHm06g8/nTCQ0dkObrcVzR1B4Z//xJqX3icls8+ITx8FKV7HkjBZlMBsMLiIPnq4jFvK6/E2yVu+RMnU3n8GZ3KtaxcRvOiOFNouOO162ic9y5FU3fok+9JRET6LyVm/Vz9q8+y7sG/bbCv6eM5rL7xUkacfzmWl5+lyPqP/I0mMOSEzskUQKiklMKpO9A49+22JzbXn1jAiB/+lsiyxURWryBvzHjyx02Me52Gt2Z0G4PXadZ9Eclt7p7SXGSaxywxSszSpK6ujq22CiYpnT17NiUlJX1+D3en5ql/dT4QjRJZvYKGd1+jeIc9+/y+iWhZvoR1j91H45y3wEIUbbsr5YccF7eLL9dVHP0NVt94KZGVS4OxYR6FUJghX/sO4ZJSwpO2hElbdnm+NzdR+/wj3d4jf+Lkvg47IZmopyIyMESjqS1E3vFvW4lPiVmauDsLFy5se52WezQ1El27Kv7BUJiWzxZBFnrHWlYvZ9U1F+JNjW3/ExvemkHTvPcY8YNf97unEMOVQxnxw9/QMOs1mhfPJ1w+hKId9oz71GU8TZ98iDfUd3k8f4utyRuenQlpM1FPRUQkcUrM+jHLL8AKi/HGOL/0o1FClZ2fDMyEumcf2SApa40num4tdS//l7LPfSkrcaXC8vIo3n4Pirffo/fnYt0eL933C8mGJSKSMerKzAw9ldmPWShEyZ6f6/xkoBmWX0DxDr1PIvpC47xZ8dus3Wn68L3MB5Rl+RM2x0rK4h6zohIKYw8aiIjkMj2VmRlKzPq5soOOoXDrXTbYZ0UlDDnth4S6SAbSzYq6mHbBDAqKMhtMDrC8fCqPOTX4/ttPy2FGxTHfwPILshugiIjkDHVl9nOWl8/Qk75H89JFNC/8kFBxKYVTt8/qL/vinfdl3eL5nQ+407LoIyI1VYTLKjMfWBYVbbsrw7/3S2pfepLI8k8JjxhD6d4Hkb/xZtkOTUQkIVqSKTOUmA0Q+WM2Jn/MxtkOAwAbN5XalY2UjijsdCxaU03N4/8IWpAGmfzxk7qclkNEJNdFcaIpjBOL9rBiigSUmKWJmbVNQ2Adx4ANcO+e+X8UNa+leOhIQuEOveXuNLw1Y1AmZrloMNdTEekdtZhlhhKzNCkpKeG99wbfQPeGz5az8ukZbDx9ky7LeEtzBiOS7gzWeioikqs0+F/6VPPqKgCqP6nu3FoGOEbh5G0zHZaIiKSodbqMVDbpmRIz6VMlm08gb0gFVfPXUv1J9Qb/GT3qWDhM2cHHZjlKERHpLY860RS2dHdlmtlQM7vDzKpi2x1mNqQX5//JzNzMzklflD1TYpYmdXV1TJs2jWnTplFXV5ftcDImXFjA5P/7Djh8+O8PWDLjUxqrGmmua6a+voDh51xC/rgJbeU9GqVx7juse/Reap99iEhXKxlIWgzWeioiA9JdwPbAIbFte+CORE40syOB3YAl6QktcRpjlibuzuzZs9teDyYTv3sS4eIi5l12A0tf+4yVc9exybe+zOSLvk+4cP00HtGGetb8+XKaF85rW4Ny3SP3Unn8Nyneed8sfgeDx2CupyLSO7k8+N/MphIkY7u7+yuxfacDM81sS3d/v5tzxwHXAgcDD6ctyAQpMZM+Z2ZscvoJbPzN44nU1BIuKcbC4U7lah6/n+ZPPgreRCOxvU7VvTeTv+lU8oaNTDqGNa+8w8Kb/k79gk+p2HZLJnz7RMomT0r6eiIig12OL8m0B1DVmpTF7veymVUBewJxEzMzCxG0qv3O3d/LhafT1ZUpaWNm5JWXxU3K3J36V58Dj7N0E0bDWzOSvu8nf7mPGfucwJK7HmT186+y8Ma7eGHHI1j13Cs9nywiIulWbmYV7bbOk1723hhgeZz9y2PHuvJjoAW4ug9i6BNKzCRl62Z9wIe/uZF5l93AulkfJHZSNIo3NcQ/Zka0dl1SsTSvrea97/8S3PGWoBXOWyJEG5t457QL1F0nIpIkj0ZT3mIWA1Xttgu6uqeZXRQbkN/dtnNriPEu0cV+zGwn4PvAKZ5DvxzUlSlJc3fm/Oi3zL/yVohNjfHBL65k4ndPYqs//BSiURpnv0nj+//D8vIp2nZX8idtiZlh4TB54ybSsmQhdPz/EI1QMHFyr2KJVK2meclCVr/yHtGGprhl6hd+yqrnX2XEfrsl9f2KiAxmrU9XpnJ+zHig/V/fjd2cdi1wdw+XXgBsC4yOc2wksKyL8/YBRgGftOvCDAN/MLNz3H1iD/dNCyVmkrSl/3oySMoAIuu7JBdc81eG7b4tBaveofnjuW0Ld9e99ATFex1IxZdOwswoP/hY1vzl98Hi3q3JmYXIGz2Owmk7JhSDt7RQ9c9baXj9eXAnBEw9cRofP/QhjVXB//XCoUWM22s8QzYdQvN/rmHNgpnk73gAyx57maZVaxm6+w6MOnx/Qnn67yAikgHr3L06kYLuvhJY2VM5M5sJVJrZru7+amzfbkAl0NXYmDuApzrsezy2/9ZE4ksH/SZKEzNjwoQJba8HokW33h+0lLUmZa0NxuEQax++n6GjY/vXN19T/9KTFE3dkcItt6Fw6vYM+cYPqXn0XlqWLoJwHkU77kXFYV/GwolVzXWP3duWlLUqHl7MFkdvyfzHP2bcXuMp26gsCC/279Aw6w1qX3uZj+6aTUtDhI//cAvl201h9ydup2DYkFQ/ln5lMNRTEekbuTz4393nmNljwM1m9q3Y7puAh9o/kWlmc4EL3P0Bd18FbDBHk5k1A0u7e4oz3ZSYpUlJSQkLFizIdhhp1bR8FUSiDN9qBGN2HUvRkCKa65pZ/vYySsuaweMMYQyFqH9rBoVbbgNA0VY7ULTVDkSbGrBwftwHBbrizU3UzXiqU1eohYzCykImH7MlFrJOCYfhhAvDjNpuJJ++uBiAmlnzmPPjy9nu5l/38lPo3wZDPRWRvpHL02XEnEgwiP+J2PsHge90KLMlQStazlJiJkkbttdOFLGK8fuMb/tLKL8kn432GEfQfBaHO97YedB/qKCo1/ePrKuC5vjjydw9GMvWRSuQhYzy8eXry0cifHrXg2xz/cWE8vN7HYuIiGSXu68GvtZDmW67BrI1rqw9JWaStAnf/gprW94FNuwGa3vdfuxYK3cKNp/aJ/dvnP1ml8fMrMvcEIK/3FoaIhvua2om2tCoxExEJI5+0GI2IGi6jDSpr69nl112YZdddqG+vj7b4aRFQRGEC7rpegzltQ38D96HCI8Y02ez+te9+ETc/e5dPBvdjoWMVXPajSc1o2zKZoTLSvsktv5iMNRTEekbUaJEPYWNePNWSkdqMUuTaDTK66+/3vZ6ILLi7pOY8iO+StNHc2ic8zaWl0/xDntSduBRhAp7320ZT2R1vLkEAZyGVQ0UDS3CQvGbzVa8t4I1768O3sRa9rb81Q8G3QD4wVBPRUT6EyVmkrS84aPI32QzmhfP3+DJS8yw4lJKdp1O6Z4Hdjov0tDIqmdfJlLXwLC9d6Zw1PBOZdydaG01oYIirCD+pNChymFEu1j0fNWclQzdfBilY0rBQoCDO/mbTqFkvy+y8k//JvzSMiJ19ZRvPZktL/4+o7/4uaQ+BxGRwcCjqXVHxl3oRTpRYiYpqfzyt1l9w6+IrlvbthA5efkM+fr3sLzOY7WWP/oc/zvtRwydVMzQycNY8vBN2OhN2fin/0d0zQpqnvoXjfNmQ6Q5SPbMKNpudyq+9HVCZRUbXMtCnXviWx9CWP3+apa9sZQhmw1lyx9+mcJxYynaYU/yx2wMwNZXbce0K35OtLllg4XVRUQkPo0xywwlZpKSvJFjGPGT39Pw9iu0LF1EeOgIinfYs1MSBVA3fxFvnvAdJh+9BcXDi8GCQfreuITlvzoX8+YNW94A3Gl452Vali5i+DmXtk2nEVm7isjqFZ3u0doVWTa2jDUfrGbth2uwyXtSvu+uncuGQkrKREQkpygxk5SFCooo2XW/Hst98pf7GT5lGMUjijd8ijNkeEtDMNYrHndali6mcfabFG2zS7ArzpQb7YULgta0UGEBFdv1zVOgIiKDWS5PMDuQ6KlMyZj6BYupmNi5JQ0SmHU+FKZ50Udtb8MjxhAq63qOwHWLg2XYNvvxt8ivLO+ynIiIJCYajaa8Sc+UmKXRiBEjGDFiRLbDyBllUzYN5rFI5o8mj2LFpXg0Ssuq5bQsXUTZIccEx2JJnRP8Rbbi3RVY2VCmXfULtvj52X0V/oCleioikjuy3pVpZmcB5wNjgfeAc9z9hW7K7wf8EZgGLAEud/cbMxFrb5SWlrJiRecxUIPZxqcex/+OvJ+KCfFbzbrlTs0j97Du4XvaejytqITiXafTvGQhLZ8tIlwxlJI9P8+oSz/PNoXxn+SUDameikiiNPg/M7KamJnZCcCVwFnAS8C3gEfNbCt3/yRO+UnAI8DNBMsu7AVcb2Yr3P0fGQtcklI0dhSbX/F7Vt/0G0pHFARNXEYwvUYP565fYqndvoY66l99lsoTvkXxzvukMXIREXGP4inMeZHKuYNJtrsyfwDc4u5/dvc57n4OsAj4dhflzwQ+cfdzYuX/DPwFOC8z4Uqqhu25E5vefBeFe36R0NhNKdhiayqOPJmyQ46NW96KSnDvegyaAzX/fTCNEYuIiGRO1lrMzKwA2An4TYdDTwB7dnHaHqxfNb7V48BpZpbv7s19G2Xy6uvr+cIXvgDAo48+SnFxcZYjyh2h/HyGHf3lDfa5O6GKYdQ+8x8iK5cSHjqCkn2+wLoH/9blw5oQNLhFVnzW1qImvaN6KiKJUldmZmSzK3MEEAaWddi/DBjTxTljuiifF7veZx1PMLNCoP2Ao4w8oheNRnnuuefaXkv3zIySXfalZJcN19GseeIfeH1tt+eGyiuVlCVJ9VREEpZiYoYSs4RkuysTOj+jZ3H29VQ+3v5WFwBV7bbFvQ1Qsqd4l/26nfvG3SnZ++AMRiQiIpI+2UzMVgIROreOjaJzq1irpV2UbwHiL5oIlwGV7bbxyQQr2VF20NEQDho84yZo+UWU7ndYhqMSERl8oh5NeZOeZS0xc/cm4A2g4yrXBwIzujhtZpzyBwGvdzW+zN0b3b26dQPWpRC2ZFiosIgxl/2ZSIded3cnSpixl93StkyTiIikT+sYs1Q26Vm25zH7I3CHmb1OkHSdAWwC3AhgZpcB49z9pFj5G4HvmNkfCabM2AM4DfhKpgOXzLFQiHG/u43692ez5q4/4USp/NLXKNtxt2yHJiIyaLhH8RTGomq6jMRkNTFz93vMbDjwC4IJZmcBh7r7wliRsQSJWmv5+WZ2KHAFcDbBBLPf0xxmg0PxlltRfPFV2Q5DREQkbbLdYoa7Xw9c38WxU+Lsew7YMc1h9YmSkpJshyDSI9VTEUmEpsvIjKwnZgNVaWkptbXdT/Mgkm2qpyKSKM38nxm5MF2GiIiIiKAWMxEREUlANArRFLojNYd1YtRiliYNDQ0cdthhHHbYYTQ0NGQ7HJG4VE9FJFEejaa8Sc/UYpYmkUiERx55pO21SC5SPRURyS1KzERERKRHeiozM5SYiYiISI/0VGZmaIyZiIiISI5Qi5mIiIj0SF2ZmaHETERERHqU6pOVeiozMYM2Mauurk7r9dvPpl5dXa0n3iQnqZ6K9D/p/v3VlUhLaquEpHr+YGHug6tp0czGAYuzHYeIiEiKxrv7p+m+iZkVAfOBMX1wuaXAJHfXxIldGIyJmQEbAesycLtygiRwfIbuNxjoM+17+kz7lj7PvqfPtLNyYIln6Jd4LDkr6INLNSkp696g68qMVeK0/4UBEOSAAKxz9+y0PQ8w+kz7nj7TvqXPs+/pM40ro59DLJlSQpUBmi5DREREJEcoMRMRERHJEUrM0qsRuDj2VfqGPtO+p8+0b+nz7Hv6TGXQGHSD/0VERERylVrMRERERHKEEjMRERGRHKHETERERCRHKDETERERyRFKzPqYmQ01szvMrCq23WFmQ3o45zYz8w7byxkKOeeY2VlmNt/MGszsDTPbp4fy+8XKNZjZx2Z2ZqZi7Q9683ma2fQ4ddHNbEomY85lZravmf3HzJbEPpsjEzhHdbQLvf08VUdloFNi1vfuArYHDolt2wN3JHDeY8DYdtuh6Qkvt5nZCcCVwKXADsALwKNmtkkX5ScBj8TK7QD8GrjazI7JSMA5rrefZztbsmF9nJfGMPubUuAd4DuJFFYd7VGvPs92VEdlQNJ0GX3IzKYCs4Hd3f2V2L7dgZnAFHd/v4vzbgOGuPuRGQo1Z5nZK8Cb7v7tdvvmAP9y9wvilP8tcIS7T22370ZgO3ffIxMx57IkPs/pwDPAUHdfm6Ew+y0zc+Aod/9XN2VURxOU4Oc5HdVRGcDUYta39gCqWpMyAHd/GagC9uzh3OlmttzMPjCzm81sVDoDzUVmVgDsBDzR4dATdP357RGn/OPAzmaW37cR9i9Jfp6t3jKzz8zsaTPbPy0BDh6qo+mhOioDkhKzvjUGWB5n//LYsa48CpwIHAD8ENgF+K+ZFfZ5hLltBBAGlnXYv4yuP78xXZTPi11vMEvm8/wMOAM4BjgaeB942sz2TVeQg4DqaN9SHZUBLS/bAfQHZnYRcGEPxXaJfY3XN2xd7A9OcL+n3dtZZvY6sBA4DPhn4pEOGB0/q24/vy7Kx9s/WCX8eca629t3uc80s42B84Dn0xPeoKA62kdUR2WgU2KWmGuBu3soswDYFhgd59hIOv/F3CV3/8zMFgJbJHrOALESiNC5NWcUXX9+S7so3wKs6tPo+p9kPs94Xga+1ldBDUKqo+mnOioDhhKzBLj7SoJfct0ys5lApZnt6u6vxvbtBlQCMxK9n5kNBzYmaLIfNNy9yczeAA4EHmh36EDg312cNhP4Yod9BwGvu3tz30fZfyT5ecazA4OsLvYx1dH0Ux2VAUOJWR9y9zlm9hhws5l9K7b7JuCh9k9kmtlc4AJ3f8DMyoCLgH8Q/GCZSPA4/Uo2/GU6WPwRuCPWnTuTYCzJJsCNAGZ2GTDO3U+Klb8R+I6Z/RG4mWCg9WnAVzIdeI7q1edpZucQtP6+BxQQtEIcE9sEiP2f3bzdrklmtj2w2t0/UR3tnd5+nqqjMtApMet7JwJXs/4prAfpPD/PlgStaBB0NW0DnAQMIUjOngFOcPd16Q4217j7PbEWw18QzE00CzjU3RfGiowlSCxay883s0OBK4CzgSXA99z9H5mNPDf19vMk+EX3e2AcUE/wy+8wd38kc1HnvJ0J/o+2+mPs6+3AKaiO9lavPk9UR2WA0zxmIiIiIjlC02WIiIiI5AglZiIiIiI5QomZiIiISI5QYiYiIiKSI5SYiYiIiOQIJWYiIiIiOUKJmYiIiEiOUGImMsiZ2bNmdmW24xARESVmItLHzOwUM/M42zfblSkwsx+Z2TtmVmdmK83sJTP7hpnlZzN+EZFs0pJMIpIO1QRLj7VXBUFSBjwObAf8H/BSrPzuwHnAW8DbmQpURCSXqMVMRNqY2VAz+6uZrYm1ZD1qZlt0KHO6mS2KHX/AzH5gZms7XMrdfWmHrT527BxgX+Bz7n6du7/t7h+7+13AbsC82H2ONbN3zazezFaZ2VNmVpreT0BEJLuUmIlIe7cRLCp9BLAHYMAjrd2LZrYXcCNwFbA98CTws17e40TgKXd/q+MBd29291ozGwv8HfgLMBWYDvwzFo+IyIClrkwRASDWMnYEsJe7z4jtOxFYBBwJ3Ad8F3jU3X8fO+0DM9sTOLzD5SrNrKbd+xp3HxN7vQXwbA/hjCX4+fRPd18Y2/dur78pEZF+RomZiLSaCrQAr7TucPdVZvZ+7BgE48Ye6HDeq3ROzNYBO7Z7H2332gDvIZZ3gKeBd83sceAJ4H53X5PA9yEi0m+pK1NEWnXVTdg+kYqXVMU7L+ruH7bbPm537APWJ3pxuXsEOBD4AjCboKXufTOb1MP3ICLSrykxE5FWswla0Xdr3WFmw4HJwJzYrrnArh3O27mX97kL+LyZ7dDxgJnltQ7w98BL7n4hsAPQBBzVy3uJiPQrSsxEBAB3nwf8G7jZzPY2s+2AvwGfxvYDXAMcGnsScwsz+xZBq1ZPXZPtXUkwRcbTZna2mW1nZpua2fEE3ahbmNluZvZTM9vZzDYBjgZGsj5BFBEZkJSYiUh73wDeAB4CZhJ0Ux7q7s0A7v4ScCbwA4JxYIcAVwANid7A3RsJuikvB74FvAy8BnwPuBqYRTCv2b7AIwRdn78Cfujuj6b8HYqI5DBz780fuiIiGzKzm4Ep7r5PtmMREenv9FSmiPSKmZ1HMH9ZLUE35snAWVkNSkRkgFCLmYj0ipndSzDhaznwMXCNu9+Y1aBERAYIJWYiIiIiOUKD/0VERERyhBIzERERkRyhxExEREQkRygxExEREckRSsxEREREcoQSMxEREZEcocRMREREJEcoMRMRERHJEUrMRERERHLE/wO9R2i/Tv3cbAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 700x500 with 2 Axes>"
       ]
@@ -1461,7 +1490,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc343646",
+   "id": "abae59df",
    "metadata": {},
    "source": [
     "STAT2 seems to be active in monocytes since their positive targets are up-regulated in COVID-19."
@@ -1470,12 +1499,12 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "4fa502ec",
+   "id": "0e66a8a1",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x500 with 2 Axes>"
       ]
@@ -1492,7 +1521,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9631b2e3",
+   "id": "9d76ee13",
    "metadata": {},
    "source": [
     "On the other hand, RELB seems to be inactive in T cells since their positive targets are down-regulated in COVID-19.\n",
@@ -1503,7 +1532,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "2f1a2920",
+   "id": "d0893dba",
    "metadata": {},
    "outputs": [
     {
@@ -1527,23 +1556,31 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>contrast</th>\n",
+       "      <th>name</th>\n",
        "      <th>logFCs</th>\n",
        "      <th>pvals</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>MIDN</th>\n",
+       "      <th>0</th>\n",
+       "      <td>T cell</td>\n",
+       "      <td>MIDN</td>\n",
        "      <td>-0.745112</td>\n",
        "      <td>0.015619</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>NFKBIA</th>\n",
+       "      <th>1</th>\n",
+       "      <td>T cell</td>\n",
+       "      <td>NFKBIA</td>\n",
        "      <td>-1.646185</td>\n",
        "      <td>0.020168</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>IRF1</th>\n",
+       "      <th>2</th>\n",
+       "      <td>T cell</td>\n",
+       "      <td>IRF1</td>\n",
        "      <td>-0.925865</td>\n",
        "      <td>0.041884</td>\n",
        "    </tr>\n",
@@ -1552,10 +1589,10 @@
        "</div>"
       ],
       "text/plain": [
-       "          logFCs     pvals\n",
-       "MIDN   -0.745112  0.015619\n",
-       "NFKBIA -1.646185  0.020168\n",
-       "IRF1   -0.925865  0.041884"
+       "  contrast    name    logFCs     pvals\n",
+       "0   T cell    MIDN -0.745112  0.015619\n",
+       "1   T cell  NFKBIA -1.646185  0.020168\n",
+       "2   T cell    IRF1 -0.925865  0.041884"
       ]
      },
      "execution_count": 21,
@@ -1566,6 +1603,358 @@
    "source": [
     "dc.get_top_targets(logFCs, pvals, 'T cell', name='RELB', net=dorothea, sign_thr=0.05, lFCs_thr=0.5, fdr_corr=False)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fc171c8",
+   "metadata": {},
+   "source": [
+    "## Functional enrichment of biological terms\n",
+    "We can also assign the obtained DEG biological terms using the resource MSigDB and the `ora` method.\n",
+    "\n",
+    "For another example on functional enrichment of biological terms please visit this other notebook: [Functional enrichment of biological terms](https://decoupler-py.readthedocs.io/en/latest/notebooks/msigdb.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "d005de65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>HALLMARK_ADIPOGENESIS</th>\n",
+       "      <th>HALLMARK_ALLOGRAFT_REJECTION</th>\n",
+       "      <th>HALLMARK_APICAL_JUNCTION</th>\n",
+       "      <th>HALLMARK_APOPTOSIS</th>\n",
+       "      <th>HALLMARK_COMPLEMENT</th>\n",
+       "      <th>HALLMARK_DNA_REPAIR</th>\n",
+       "      <th>HALLMARK_E2F_TARGETS</th>\n",
+       "      <th>HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION</th>\n",
+       "      <th>HALLMARK_ESTROGEN_RESPONSE_EARLY</th>\n",
+       "      <th>HALLMARK_ESTROGEN_RESPONSE_LATE</th>\n",
+       "      <th>...</th>\n",
+       "      <th>HALLMARK_P53_PATHWAY</th>\n",
+       "      <th>HALLMARK_PEROXISOME</th>\n",
+       "      <th>HALLMARK_PI3K_AKT_MTOR_SIGNALING</th>\n",
+       "      <th>HALLMARK_PROTEIN_SECRETION</th>\n",
+       "      <th>HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY</th>\n",
+       "      <th>HALLMARK_SPERMATOGENESIS</th>\n",
+       "      <th>HALLMARK_TNFA_SIGNALING_VIA_NFKB</th>\n",
+       "      <th>HALLMARK_UV_RESPONSE_DN</th>\n",
+       "      <th>HALLMARK_UV_RESPONSE_UP</th>\n",
+       "      <th>HALLMARK_XENOBIOTIC_METABOLISM</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>B cell</th>\n",
+       "      <td>7.990353e-07</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>0.053091</td>\n",
+       "      <td>2.578191e-04</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>4.190804e-06</td>\n",
+       "      <td>7.248452e-10</td>\n",
+       "      <td>2.374115e-03</td>\n",
+       "      <td>0.053091</td>\n",
+       "      <td>8.358841e-02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.068993e-07</td>\n",
+       "      <td>0.073533</td>\n",
+       "      <td>0.000068</td>\n",
+       "      <td>1.988086e-07</td>\n",
+       "      <td>0.073533</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>7.272781e-07</td>\n",
+       "      <td>7.353273e-02</td>\n",
+       "      <td>3.324869e-04</td>\n",
+       "      <td>4.012057e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>T cell</th>\n",
+       "      <td>4.192186e-18</td>\n",
+       "      <td>7.744216e-10</td>\n",
+       "      <td>0.000053</td>\n",
+       "      <td>3.619727e-10</td>\n",
+       "      <td>0.000011</td>\n",
+       "      <td>1.221818e-08</td>\n",
+       "      <td>2.015566e-13</td>\n",
+       "      <td>1.965194e-10</td>\n",
+       "      <td>0.000053</td>\n",
+       "      <td>7.744216e-10</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.619727e-10</td>\n",
+       "      <td>0.000003</td>\n",
+       "      <td>0.000283</td>\n",
+       "      <td>4.421389e-03</td>\n",
+       "      <td>0.000003</td>\n",
+       "      <td>9.709890e-09</td>\n",
+       "      <td>9.756507e-31</td>\n",
+       "      <td>3.349332e-08</td>\n",
+       "      <td>1.013992e-11</td>\n",
+       "      <td>2.288820e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>monocyte</th>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>6.111180e-02</td>\n",
+       "      <td>0.000603</td>\n",
+       "      <td>3.837896e-03</td>\n",
+       "      <td>0.002126</td>\n",
+       "      <td>7.476772e-05</td>\n",
+       "      <td>9.592298e-03</td>\n",
+       "      <td>5.368059e-02</td>\n",
+       "      <td>0.038642</td>\n",
+       "      <td>6.111180e-02</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>0.053681</td>\n",
+       "      <td>0.061112</td>\n",
+       "      <td>4.619094e-02</td>\n",
+       "      <td>0.001253</td>\n",
+       "      <td>4.619094e-02</td>\n",
+       "      <td>1.649093e-03</td>\n",
+       "      <td>5.368059e-02</td>\n",
+       "      <td>1.281091e-04</td>\n",
+       "      <td>2.126024e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>neutrophil</th>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>7.178615e-03</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>7.178615e-03</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.074964e-02</td>\n",
+       "      <td>0.006284</td>\n",
+       "      <td>0.007179</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>2.666968e-02</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            HALLMARK_ADIPOGENESIS  HALLMARK_ALLOGRAFT_REJECTION  \\\n",
+       "B cell               7.990353e-07                  1.000000e+00   \n",
+       "T cell               4.192186e-18                  7.744216e-10   \n",
+       "monocyte             1.000000e+00                  6.111180e-02   \n",
+       "neutrophil           1.000000e+00                  7.178615e-03   \n",
+       "\n",
+       "            HALLMARK_APICAL_JUNCTION  HALLMARK_APOPTOSIS  HALLMARK_COMPLEMENT  \\\n",
+       "B cell                      0.053091        2.578191e-04             0.000002   \n",
+       "T cell                      0.000053        3.619727e-10             0.000011   \n",
+       "monocyte                    0.000603        3.837896e-03             0.002126   \n",
+       "neutrophil                  1.000000        1.000000e+00             1.000000   \n",
+       "\n",
+       "            HALLMARK_DNA_REPAIR  HALLMARK_E2F_TARGETS  \\\n",
+       "B cell             4.190804e-06          7.248452e-10   \n",
+       "T cell             1.221818e-08          2.015566e-13   \n",
+       "monocyte           7.476772e-05          9.592298e-03   \n",
+       "neutrophil         1.000000e+00          1.000000e+00   \n",
+       "\n",
+       "            HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION  \\\n",
+       "B cell                                    2.374115e-03   \n",
+       "T cell                                    1.965194e-10   \n",
+       "monocyte                                  5.368059e-02   \n",
+       "neutrophil                                1.000000e+00   \n",
+       "\n",
+       "            HALLMARK_ESTROGEN_RESPONSE_EARLY  HALLMARK_ESTROGEN_RESPONSE_LATE  \\\n",
+       "B cell                              0.053091                     8.358841e-02   \n",
+       "T cell                              0.000053                     7.744216e-10   \n",
+       "monocyte                            0.038642                     6.111180e-02   \n",
+       "neutrophil                          1.000000                     7.178615e-03   \n",
+       "\n",
+       "            ...  HALLMARK_P53_PATHWAY  HALLMARK_PEROXISOME  \\\n",
+       "B cell      ...          1.068993e-07             0.073533   \n",
+       "T cell      ...          3.619727e-10             0.000003   \n",
+       "monocyte    ...          1.000000e+00             0.053681   \n",
+       "neutrophil  ...          1.074964e-02             0.006284   \n",
+       "\n",
+       "            HALLMARK_PI3K_AKT_MTOR_SIGNALING  HALLMARK_PROTEIN_SECRETION  \\\n",
+       "B cell                              0.000068                1.988086e-07   \n",
+       "T cell                              0.000283                4.421389e-03   \n",
+       "monocyte                            0.061112                4.619094e-02   \n",
+       "neutrophil                          0.007179                1.000000e+00   \n",
+       "\n",
+       "            HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY  \\\n",
+       "B cell                                      0.073533   \n",
+       "T cell                                      0.000003   \n",
+       "monocyte                                    0.001253   \n",
+       "neutrophil                                  1.000000   \n",
+       "\n",
+       "            HALLMARK_SPERMATOGENESIS  HALLMARK_TNFA_SIGNALING_VIA_NFKB  \\\n",
+       "B cell                  1.000000e+00                      7.272781e-07   \n",
+       "T cell                  9.709890e-09                      9.756507e-31   \n",
+       "monocyte                4.619094e-02                      1.649093e-03   \n",
+       "neutrophil              1.000000e+00                      2.666968e-02   \n",
+       "\n",
+       "            HALLMARK_UV_RESPONSE_DN  HALLMARK_UV_RESPONSE_UP  \\\n",
+       "B cell                 7.353273e-02             3.324869e-04   \n",
+       "T cell                 3.349332e-08             1.013992e-11   \n",
+       "monocyte               5.368059e-02             1.281091e-04   \n",
+       "neutrophil             1.000000e+00             1.000000e+00   \n",
+       "\n",
+       "            HALLMARK_XENOBIOTIC_METABOLISM  \n",
+       "B cell                        4.012057e-03  \n",
+       "T cell                        2.288820e-09  \n",
+       "monocyte                      2.126024e-03  \n",
+       "neutrophil                    1.000000e+00  \n",
+       "\n",
+       "[4 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Retrieve MSigDB resource\n",
+    "msigdb = dc.get_resource('MSigDB')\n",
+    "msigdb\n",
+    "\n",
+    "# Filter by a desired geneset collection, for example hallmarks\n",
+    "msigdb = msigdb[msigdb['collection']=='hallmark']\n",
+    "msigdb = msigdb.drop_duplicates(['geneset', 'genesymbol'])\n",
+    "\n",
+    "# Infer enrichment with ora using significant deg\n",
+    "top_genes = dc.format_contrast_results(logFCs, pvals)\n",
+    "top_genes = top_genes[(np.abs(top_genes['logFCs']) > 0.5) & (top_genes['pvals'] < 0.05)]\n",
+    "enr_pvals = dc.get_ora_df(top_genes, msigdb, groupby='contrast', features='name', source='geneset', target='genesymbol')\n",
+    "enr_pvals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03908bac",
+   "metadata": {},
+   "source": [
+    " We can then transform these p-values to their -log10 (so that the higher the value, the more significant the p-value).\n",
+    " We will also set 0s to a minimum p-value so that we do not get infinites."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "0adf39bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set 0s to min p-value\n",
+    "enr_pvals.values[enr_pvals.values == 0] = np.min(enr_pvals.values[enr_pvals.values != 0])\n",
+    "\n",
+    "# Log-transform\n",
+    "enr_pvals = -np.log10(enr_pvals)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fde3053c",
+   "metadata": {},
+   "source": [
+    "Then we can visualize the most enriched terms:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "bd000eb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get top 10 most active/inactive sources\n",
+    "top = 10\n",
+    "top_idxs = set()\n",
+    "for row in enr_pvals.values:\n",
+    "    sort_idxs = np.argsort(-np.abs(row))\n",
+    "    top_idxs.update(sort_idxs[:top])\n",
+    "top_idxs = np.array(list(top_idxs))\n",
+    "top_names = enr_pvals.columns[top_idxs]\n",
+    "names = enr_pvals.index.values\n",
+    "top = pd.DataFrame(enr_pvals.values[:, top_idxs], columns=top_names, index=names)\n",
+    "\n",
+    "# Plot\n",
+    "sns.clustermap(top, cmap='viridis', vmax=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7d22990",
+   "metadata": {},
+   "source": [
+    "We can also visualize the downstream targets of a specific term:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "2f6e0d4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dc.plot_volcano(logFCs, pvals, 'T cell', name='HALLMARK_TNFA_SIGNALING_VIA_NFKB', net=msigdb, top=5, sign_thr=0.05, lFCs_thr=0.5, source='geneset', target='genesymbol', weight=None)"
+   ]
   }
  ],
  "metadata": {
diff --git a/docs/source/notebooks/usage.ipynb b/docs/source/notebooks/usage.ipynb
index fcc9f0a..b7e50c2 100644
--- a/docs/source/notebooks/usage.ipynb
+++ b/docs/source/notebooks/usage.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "5bc4c7f6-b861-45bd-99a7-c8ec26f7426b",
+   "id": "713d9b4a",
    "metadata": {},
    "source": [
     "# Usage\n",
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "207dffcc-3692-496f-b1eb-7387f366aac5",
+   "id": "4fc236eb",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\">\n",
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "166e9fc6-1255-4176-a050-9cadbc5c348d",
+   "id": "2337c9d3",
    "metadata": {},
    "source": [
     "## Loading packages\n",
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "4ffe6b9b-e4ac-4072-a3f0-11264012d493",
+   "id": "c6c36a9e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f8d46073-a5c4-41b7-8693-029b9cace80a",
+   "id": "f069cafc",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\">\n",
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3fc1e20-0ce0-41f8-bdea-d4eb6408d105",
+   "id": "3316c8ad",
    "metadata": {},
    "source": [
     "## Loading toy data\n",
@@ -77,7 +77,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "c9ed553c-5314-49d1-9554-fa0f3536dd28",
+   "id": "1a85cb79",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf6ee382-2c1e-4f5f-ad4b-dac7d9f7aae2",
+   "id": "59856674",
    "metadata": {},
    "source": [
     "This example consists of two small populations of samples (S, rows) with different gene expression patterns (G, columns):"
@@ -95,7 +95,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "8c33ac19-15c6-4725-9f5a-f12bd5d6720c",
+   "id": "0996787b",
    "metadata": {},
    "outputs": [
     {
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df79d354-e05f-49b3-aee1-47fec4d5f7ac",
+   "id": "6898f90c",
    "metadata": {},
    "source": [
     "Here we can see that some genes seem to be more expressed in one group of samples than in the other and vice-versa. Ideally, we would like to capture these differences in gene programs into interpretable biological entities. In this example we will do it by summarizing gene expression into transcription factor activities."
@@ -126,16 +126,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8da1021e-a264-48ee-8537-55be40d30ae3",
+   "id": "ab820efd",
    "metadata": {},
    "source": [
-    "The toy data also contains a simple `net` consisting of 3 transcription factors (Ts) with specific regulation to target genes (either positive or negative):"
+    "The toy data also contains a simple `net` consisting of 5 transcription factors (Ts) with specific regulation to target genes (Gs), either positive or negative:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "eb8f4aec-7dda-4fd5-9573-4848d291f8b9",
+   "id": "184c02ee",
    "metadata": {},
    "outputs": [
     {
@@ -186,61 +186,110 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>T2</td>\n",
-       "      <td>G06</td>\n",
+       "      <td>G04</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>T2</td>\n",
-       "      <td>G07</td>\n",
-       "      <td>0.5</td>\n",
+       "      <td>G06</td>\n",
+       "      <td>-0.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>T2</td>\n",
-       "      <td>G08</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>G07</td>\n",
+       "      <td>-3.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>T3</td>\n",
-       "      <td>G06</td>\n",
-       "      <td>-0.5</td>\n",
+       "      <td>T2</td>\n",
+       "      <td>G08</td>\n",
+       "      <td>-1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>T3</td>\n",
-       "      <td>G07</td>\n",
-       "      <td>-3.0</td>\n",
+       "      <td>G06</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>T3</td>\n",
-       "      <td>G08</td>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>G07</td>\n",
+       "      <td>0.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>T3</td>\n",
-       "      <td>G11</td>\n",
+       "      <td>G08</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>T4</td>\n",
+       "      <td>G05</td>\n",
+       "      <td>1.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>T4</td>\n",
+       "      <td>G10</td>\n",
+       "      <td>-1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>T4</td>\n",
+       "      <td>G11</td>\n",
+       "      <td>-2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>T4</td>\n",
+       "      <td>G09</td>\n",
+       "      <td>3.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>T5</td>\n",
+       "      <td>G09</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>T5</td>\n",
+       "      <td>G10</td>\n",
+       "      <td>1.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>T5</td>\n",
+       "      <td>G11</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "  source target  weight\n",
-       "0     T1    G01     1.0\n",
-       "1     T1    G02     1.0\n",
-       "2     T1    G03     0.7\n",
-       "3     T2    G06     1.0\n",
-       "4     T2    G07     0.5\n",
-       "5     T2    G08     1.0\n",
-       "6     T3    G06    -0.5\n",
-       "7     T3    G07    -3.0\n",
-       "8     T3    G08    -1.0\n",
-       "9     T3    G11     1.0"
+       "   source target  weight\n",
+       "0      T1    G01     1.0\n",
+       "1      T1    G02     1.0\n",
+       "2      T1    G03     0.7\n",
+       "3      T2    G04     1.0\n",
+       "4      T2    G06    -0.5\n",
+       "5      T2    G07    -3.0\n",
+       "6      T2    G08    -1.0\n",
+       "7      T3    G06     1.0\n",
+       "8      T3    G07     0.5\n",
+       "9      T3    G08     1.0\n",
+       "10     T4    G05     1.9\n",
+       "11     T4    G10    -1.5\n",
+       "12     T4    G11    -2.0\n",
+       "13     T4    G09     3.1\n",
+       "14     T5    G09     0.7\n",
+       "15     T5    G10     1.1\n",
+       "16     T5    G11     0.1"
       ]
      },
      "execution_count": 4,
@@ -254,7 +303,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e740d32-e08b-4daf-98a1-3c8266858762",
+   "id": "2f5a37e7",
    "metadata": {},
    "source": [
     "This network can be visualized like a graph. Green edges are positive regulation (activation), red edges are negative regulation (inactivation):\n",
@@ -264,15 +313,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e22de618-6e75-4b72-9077-bb0c045414b8",
+   "id": "e2eba9a7",
    "metadata": {},
    "source": [
-    "According to this network, the first population of samples should show high activity for T1 and T3, while the second one only for T2."
+    "According to this network, the first population of samples should show high activity for T1 and T2, while the second one for T3 and T4. T5 should have no activity in all samples."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "636de684-c374-4374-832a-438602e893df",
+   "id": "0adc4371",
    "metadata": {},
    "source": [
     "## Methods\n",
@@ -283,7 +332,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "b7be778b-2f0e-4287-a551-6b8dab34c98f",
+   "id": "8d022899",
    "metadata": {},
    "outputs": [
     {
@@ -320,7 +369,7 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>run_consensus</td>\n",
-       "      <td>Consensus.</td>\n",
+       "      <td>Consensus score from top methods.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -379,7 +428,7 @@
       "text/plain": [
        "         Function                                               Name\n",
        "0      run_aucell                                            AUCell.\n",
-       "1   run_consensus                                         Consensus.\n",
+       "1   run_consensus                  Consensus score from top methods.\n",
        "2        run_gsea               Gene Set Enrichment Analysis (GSEA).\n",
        "3        run_gsva                Gene Set Variation Analysis (GSVA).\n",
        "4         run_mdt                  Multivariate Decision Tree (MDT).\n",
@@ -403,7 +452,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c84c44ac-f8a8-4516-8b97-c49839d9262b",
+   "id": "f64f1bdc",
    "metadata": {},
    "source": [
     "Each method models biological activities in a different manner, sometimes returning more than one estimate or providing significance of the estimation. To know what each method returns, please check their documentation like this `?run_mlm`."
@@ -411,7 +460,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "83a30925-1f19-4a69-bae7-af5bbcc55faf",
+   "id": "590e371b",
    "metadata": {},
    "source": [
     "To have a unified framework, methods inside `decoupler` have these shared arguments:\n",
@@ -429,7 +478,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73ee401f-2f10-48c1-bda6-8d76eee30486",
+   "id": "0f85f343",
    "metadata": {},
    "source": [
     "## Running methods\n",
@@ -442,7 +491,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "09d8dade-b64a-4d87-9de9-de9b447d3820",
+   "id": "75ccd460",
    "metadata": {},
    "outputs": [
     {
@@ -469,50 +518,62 @@
        "      <th>T1</th>\n",
        "      <th>T2</th>\n",
        "      <th>T3</th>\n",
+       "      <th>T4</th>\n",
+       "      <th>T5</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>S01</th>\n",
        "      <td>0.888889</td>\n",
+       "      <td>0.711597</td>\n",
        "      <td>-0.555556</td>\n",
-       "      <td>-0.625</td>\n",
+       "      <td>-0.50</td>\n",
+       "      <td>-0.666667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S02</th>\n",
        "      <td>0.888889</td>\n",
+       "      <td>0.699385</td>\n",
        "      <td>-0.555556</td>\n",
-       "      <td>-0.500</td>\n",
+       "      <td>-0.50</td>\n",
+       "      <td>-0.444444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S03</th>\n",
        "      <td>0.888889</td>\n",
+       "      <td>0.551198</td>\n",
        "      <td>-0.444444</td>\n",
-       "      <td>-0.500</td>\n",
+       "      <td>-0.75</td>\n",
+       "      <td>-0.666667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S04</th>\n",
        "      <td>0.888889</td>\n",
+       "      <td>0.607223</td>\n",
        "      <td>-0.666667</td>\n",
-       "      <td>-0.500</td>\n",
+       "      <td>-0.50</td>\n",
+       "      <td>-0.444444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S05</th>\n",
        "      <td>0.888889</td>\n",
+       "      <td>0.732963</td>\n",
        "      <td>-0.888889</td>\n",
-       "      <td>-0.875</td>\n",
+       "      <td>-0.50</td>\n",
+       "      <td>-0.444444</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "source        T1        T2     T3\n",
-       "S01     0.888889 -0.555556 -0.625\n",
-       "S02     0.888889 -0.555556 -0.500\n",
-       "S03     0.888889 -0.444444 -0.500\n",
-       "S04     0.888889 -0.666667 -0.500\n",
-       "S05     0.888889 -0.888889 -0.875"
+       "source        T1        T2        T3    T4        T5\n",
+       "S01     0.888889  0.711597 -0.555556 -0.50 -0.666667\n",
+       "S02     0.888889  0.699385 -0.555556 -0.50 -0.444444\n",
+       "S03     0.888889  0.551198 -0.444444 -0.75 -0.666667\n",
+       "S04     0.888889  0.607223 -0.666667 -0.50 -0.444444\n",
+       "S05     0.888889  0.732963 -0.888889 -0.50 -0.444444"
       ]
      },
      "execution_count": 6,
@@ -528,7 +589,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b496dde9-1fb8-446f-9026-2f1b7ca8d8bc",
+   "id": "ffc2fd1f",
    "metadata": {},
    "source": [
     "In the case of `gsea`, it returns a simple estimate of activities (`acts`), a normalised estimate (`norm_acts`) and `pvals` data-frames after doing permutations."
@@ -536,7 +597,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0aa9d6ee-c731-4f33-be09-feb487610f45",
+   "id": "44598ded",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\">\n",
@@ -550,7 +611,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5159a3cd-fee4-4f36-b33d-c97060fb6766",
+   "id": "4f11b1d4",
    "metadata": {},
    "source": [
     "Let us plot the obtained results:"
@@ -559,12 +620,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "4b4a7ff6-2433-4ff4-825e-ffa16fd6b580",
+   "id": "dd2b1fb4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 648x216 with 6 Axes>"
       ]
@@ -583,17 +644,17 @@
     "axes[1].set_title('norm_acts')\n",
     "sns.heatmap(norm_acts, cmap='coolwarm', vmin=-1, vmax=1, ax=axes[1])\n",
     "axes[2].set_title('pvals')\n",
-    "sns.heatmap(pvals, cmap='viridis_r', ax=axes[2], vmax=0.05)\n",
+    "sns.heatmap(pvals, cmap='viridis_r', ax=axes[2], vmax=1)\n",
     "\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c518bb98-58ca-4db9-b9c2-9871b0a0e970",
+   "id": "c476bc3a",
    "metadata": {},
    "source": [
-    "We can observe that for transcription factors T1 and T2, the obtained activities correctly distinguish the two sample populations. T3, on the other hand, should be down for the second population of samples since it is a repressor. This mislabeling of activities happens because `gsea` cannot model weights when inferring biological activities.\n",
+    "We can observe that for transcription factors T1 and T3, the obtained activities correctly distinguish the two sample populations. T2, on the other hand, should be down for the second population of samples since it is a repressor. This mislabeling of activities happens because `gsea` cannot model weights when inferring biological activities.\n",
     "\n",
     "When weights are available in the prior knowledge, we definitely recommend using any of the methods that take them into account to get better estimates, one example is the Univariate Linear Model method `ulm`:"
    ]
@@ -601,7 +662,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "2c5bfc1e-1613-464c-b35a-be37b5af2f58",
+   "id": "20525e79",
    "metadata": {},
    "outputs": [
     {
@@ -628,50 +689,62 @@
        "      <th>T1</th>\n",
        "      <th>T2</th>\n",
        "      <th>T3</th>\n",
+       "      <th>T4</th>\n",
+       "      <th>T5</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>S01</th>\n",
        "      <td>4.020364</td>\n",
+       "      <td>1.667426</td>\n",
        "      <td>-1.378734</td>\n",
-       "      <td>0.903866</td>\n",
+       "      <td>-0.243605</td>\n",
+       "      <td>-1.188728</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S02</th>\n",
        "      <td>4.060294</td>\n",
+       "      <td>1.392356</td>\n",
        "      <td>-1.313069</td>\n",
-       "      <td>0.722529</td>\n",
+       "      <td>-0.387175</td>\n",
+       "      <td>-1.102454</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S03</th>\n",
        "      <td>4.189780</td>\n",
+       "      <td>1.086979</td>\n",
        "      <td>-1.346471</td>\n",
-       "      <td>0.420075</td>\n",
+       "      <td>-0.294281</td>\n",
+       "      <td>-1.139018</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S04</th>\n",
        "      <td>4.149534</td>\n",
+       "      <td>1.395950</td>\n",
        "      <td>-1.332407</td>\n",
-       "      <td>0.718914</td>\n",
+       "      <td>-0.273508</td>\n",
+       "      <td>-1.197696</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S05</th>\n",
        "      <td>4.052074</td>\n",
+       "      <td>1.610522</td>\n",
        "      <td>-1.534533</td>\n",
-       "      <td>0.849667</td>\n",
+       "      <td>-0.311155</td>\n",
+       "      <td>-0.990895</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "           T1        T2        T3\n",
-       "S01  4.020364 -1.378734  0.903866\n",
-       "S02  4.060294 -1.313069  0.722529\n",
-       "S03  4.189780 -1.346471  0.420075\n",
-       "S04  4.149534 -1.332407  0.718914\n",
-       "S05  4.052074 -1.534533  0.849667"
+       "           T1        T2        T3        T4        T5\n",
+       "S01  4.020364  1.667426 -1.378734 -0.243605 -1.188728\n",
+       "S02  4.060294  1.392356 -1.313069 -0.387175 -1.102454\n",
+       "S03  4.189780  1.086979 -1.346471 -0.294281 -1.139018\n",
+       "S04  4.149534  1.395950 -1.332407 -0.273508 -1.197696\n",
+       "S05  4.052074  1.610522 -1.534533 -0.311155 -0.990895"
       ]
      },
      "execution_count": 8,
@@ -687,7 +760,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "459c8bbb-3306-4442-a4d2-d893f53da111",
+   "id": "5ecc8fe7",
    "metadata": {},
    "source": [
     "In this case, `ulm` only returns infered activities and their associated p-value.\n",
@@ -698,12 +771,12 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "47f2a3d2-d6f4-4870-8982-96c825787cba",
+   "id": "9c61d04b",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x216 with 4 Axes>"
       ]
@@ -720,35 +793,35 @@
     "axes[0].set_title('acts')\n",
     "sns.heatmap(acts, cmap='coolwarm', vmin=-1, vmax=1, ax=axes[0])\n",
     "axes[1].set_title('pvals')\n",
-    "sns.heatmap(pvals, cmap='viridis_r', ax=axes[1], vmax=0.05)\n",
+    "sns.heatmap(pvals, cmap='viridis_r', ax=axes[1], vmax=1)\n",
     "\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ca01be69-da1b-4e1a-a4c2-7ea4d1d35743",
+   "id": "4c21c032",
    "metadata": {},
    "source": [
-    "Since `ulm` models weights when estimating biological activities, it correctly assigns T3 as inactive in the second population of samples."
+    "Since `ulm` models weights when estimating biological activities, it correctly assigns T2 as inactive in the second population of samples."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3471bb88-81c5-426e-900e-5101a002bede",
+   "id": "7e00a68b",
    "metadata": {},
    "source": [
     "### Multiple methods\n",
     "\n",
     "`decoupler` also allows to run multiple methods at the same time. Moreover, it computes a consensus score based on the obtained activities across methods, called `consensus`.\n",
     "\n",
-    "By default, `deocuple` runs only the top performer methods in our benchmark (`mlm`, `ulm` and `wsum`), and estimates a consensus score across them. Specific arguments to specific methods can be passed using the variable `args`. For more information check `?decouple`."
+    "By default, `deocuple` runs only the top performer methods in our benchmark (`mlm`, `ulm` and `wsum`), and estimates a consensus score across them. Specific arguments to specific methods can be passed using the variable `args`. For more information check `?decouple`. If we wanted to only obtain the consensus score, we could also have used `run_consensus`, check `?run_consensus` for more information."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "5c340349-4e2c-434c-b948-1e7c3d5033e9",
+   "id": "e3825926",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -758,7 +831,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "191d24ef-0cda-44c2-ac3f-61786bb3fd03",
+   "id": "0fb3db1b",
    "metadata": {},
    "source": [
     "`decouple` either returns a dictionary of activities and p-values, or stores them in the `AnnData` instance provided. \n",
@@ -769,7 +842,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "e30097c4-fe70-4d22-92d3-7dac316ff05e",
+   "id": "ddedb9dd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -781,12 +854,12 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "6e4887ea-095c-4da1-bf77-1d582fc9f39d",
+   "id": "ca700b0f",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x216 with 4 Axes>"
       ]
@@ -803,17 +876,17 @@
     "axes[0].set_title('acts')\n",
     "sns.heatmap(acts, cmap='coolwarm', vmin=-1, vmax=1, ax=axes[0])\n",
     "axes[1].set_title('pvals')\n",
-    "sns.heatmap(pvals, cmap='viridis_r', ax=axes[1], vmax=0.05)\n",
+    "sns.heatmap(pvals, cmap='viridis_r', ax=axes[1], vmax=1)\n",
     "\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "19ba9ebb-d361-429f-af7b-b5b067cb865b",
+   "id": "65e14da3",
    "metadata": {},
    "source": [
-    "We can observe that the consensus score correctly predicts that T1 and T3 should be active in the first population of samples while T2 in the second one."
+    "We can observe that the consensus score correctly predicts that T1 and T2 should be active in the first population of samples while T3 and T4 in the second one. T5 is inactive everywhere as expected."
    ]
   }
  ],
diff --git a/docs/source/release_notes.rst b/docs/source/release_notes.rst
index acdbfd7..6c137b9 100644
--- a/docs/source/release_notes.rst
+++ b/docs/source/release_notes.rst
@@ -8,21 +8,40 @@ Bug fixes
 ~~~~~~~~~
 - Removed ``python <3.10`` limitation.
 - Forced ``np.float32`` type to output of ``get_contrast``.
+- Made ``summarize_acts`` compatible with older versions of numpy.
+- ``extract_psbulk_inputs`` now checks if mat and obs have matching indexes.
+- ``plot_volcano`` now correctly can plot networks with different source names.
 
 Changes
 ~~~~~~~~
 - ``extract`` now removes empty samples and features.
 - ``run_consensus`` now follows the same format as other methods, old function is now called ``cons``.
 - ``get_pseudobulk`` now checks if input are raw integer counts.
-- ``plot_volcano`` now can also plot without subsetting features by a network and can save plots to disk.
+- ``plot_volcano`` now can plot without subsetting features by a network and can save plots to disk.
+- ``plot_volcano`` now uses ``adjustText`` to better plot text labels.
+- ``plot_volcano`` now can set logFCs and p-value limits for outliers.
 - ``get_top_targets`` now can also work without subsetting features by a network and returns significant adjusted p-values.
 - ``get_contrast`` now can also work without needing to group.
+- ``udt`` and ``mdt`` now check if ``skranger`` and ``sklearn`` are installed, respectively.
+- ``get_toy_data`` now contains more example TFs.
+- ``get_top_targets`` now returns ``logFCs`` and ``pvals`` as column names instead of ``logFC`` and ``pval``.
+- ``format_contrast_results`` now returns also the adjusted p-value.
 
 Additions
 ~~~~~~~~~
 - Added ``dense_run`` util function which runs methods ignoring zeros in the data.
 - Added ``plot_violins`` and ``plot_barplot`` functions.
 - Added ``p_adjust_fdr`` util function to correct p-values for FDR.
+- Added ``get_ora_df`` function to infer ora from lists of genes instead of an input mat.
+- Added ``shuffle_net`` function to randomize networks.
+- Added benchmarking metrics ``metric_auroc``, ``metric_auprc``, ``metric_mcauroc`` and ``metric_mcauprc``.
+- Added ``get_toy_benchmark_data`` function to generate a toy example for benchmarking.
+- Added ``show_metrics`` function to show available metrics.
+- Added  ``benchmark``, ``format_benchmark_inputs`` and ``get_performances`` functions to benchmark methods and nets.
+- Added ``plot_metrics_scatter`` function to plot the results of running the benchmarking pipeline.
+- Added ``plot_metrics_scatter_cols`` function to plot the results of running the benchmarking pipeline grouped by two levels.
+- Added ``plot_metrics_scatter`` function to plot the results of running the benchmarking pipeline.
+- Added ``plot_metrics_boxplot`` function to plot the distributions of Monte-Carlo benchmarking metrics.
 
 1.1.0
 -----

From 1e45694ea753e1dd33a5cb989002d4f50c8d9f19 Mon Sep 17 00:00:00 2001
From: PauBadiaM <pau.badia@uni-heidelberg.de>
Date: Thu, 1 Sep 2022 14:57:26 +0200
Subject: [PATCH 33/33] Updated CI

---
 .github/workflows/devel.yml | 4 ++--
 .github/workflows/main.yml  | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/devel.yml b/.github/workflows/devel.yml
index dee862c..bb85d09 100644
--- a/.github/workflows/devel.yml
+++ b/.github/workflows/devel.yml
@@ -21,7 +21,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install wheel
-        pip install pytest flake8 sklearn skranger omnipath scanpy .
+        pip install pytest flake8 sklearn skranger omnipath scanpy adjustText .
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
@@ -46,7 +46,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install wheel
-        pip install pytest flake8 sklearn skranger omnipath scanpy .
+        pip install pytest flake8 sklearn skranger omnipath scanpy adjustText .
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 13add10..159c4f7 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -19,7 +19,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install wheel
-        pip install pytest-cov flake8 sklearn skranger omnipath scanpy .
+        pip install pytest-cov flake8 sklearn skranger omnipath scanpy adjustText .
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
@@ -49,7 +49,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install wheel
-        pip install pytest flake8 sklearn skranger omnipath scanpy .
+        pip install pytest flake8 sklearn skranger omnipath scanpy adjustText .
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names