diff --git a/LICENSE b/LICENSE
index a012b08..a1ba3ea 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 The MIT License (MIT)
 
-Copyright (c) 2023 Dinithi Sumanaweera, Teichmann Lab
+Copyright (c) 2024 Dinithi Sumanaweera, Teichmann Lab
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
+THE SOFTWARE.
\ No newline at end of file
diff --git a/README.md b/README.md
index 62d1396..1640b35 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-<p align="left"><img src="G2G_logo.png"></p>
+<p align="left"><img src="images/G2G_logo_new.png" style="max-width: 15%; max-height: 15%;"></p>
 
 # Genes2Genes
 Project page: https://teichlab.github.io/Genes2Genes
@@ -30,7 +30,6 @@ conda create --name g2g_env python=3.8
 conda activate g2g_env
 pip install git+https://github.com/Teichlab/Genes2Genes.git
 ```
-
 The package will be made available on PyPi soon.
 
 ### **Input to G2G**
@@ -40,13 +39,26 @@ The package will be made available on PyPi soon.
 
 **Note:** Please ensure that you have reasonable pseudotime estimates that fairly represent the trajectories, as the accuracy and reliability of trajectory alignment entirely depend on the accuracy and reliability of your pseudotime estimation. We recommend users to inspect whether the cell density distribution along estimated pseudotime (in terms of the meta attributes such as the annotated cell type, sampling time points, etc. where applicable) well-represents each trajectory of focus. Users can choose the best pseudotime estimates to compare after testing several different pseudotime estimation tools on their datasets. 
 
-### Tutorial
+### **Tutorial**
 
 Please refer to the notebook [`notebooks/Tutorial.ipynb`](https://github.com/Teichlab/Genes2Genes/blob/main/notebooks/Tutorial.ipynb) which gives an example analysis between a reference and query dataset from literature. 
 Please also refer https://teichlab.github.io/Genes2Genes on how to read a trajectory alignment output generated by G2G. <br>
 
-**Note**: The runtime of the G2G algorithm depends on the number of cells in the reference and query datasets, the number of interpolation time points, and the number of genes to align. 
-G2G currently utilizes concurrency through Python multiprocessing to speed up the gene-level alignment process. It creates a number of processes equal to the number of cores in the system, and each process performs a single gene-level alignment at one time. 
+### **Runtime**
+
+The runtime of the G2G algorithm depends on the number of cells in the reference and query datasets, the number of interpolation time points, and the number of genes to align. 
+For an idea, please see below a simple run-time analysis of G2G for 89 genes of the reference (N<sub>R</sub> = 179 cells) and query (N<sub>Q</sub> = 290 cells) from literature used in our tutorial. (Note: the number of interpolation points = 14 for the middle plot). Reference: [`notebooks/Supplementary_notebook1.ipynb`].
+
+<div style="display: flex; justify-content: space-between;">
+    <img src="images/n_interpolation_points_vs_time_PAM_LPS_G2G_alignment.png" style="max-width: 30%; max-height: 30%;">
+    <img src="images/cell_numbers_vs_approx_time_PAM_LPS_G2G_alignment.png" style="max-width: 80%; max-height: 80%;">
+</div><br>
+
+**Further examples from the case studies of our manuscript:** Reference: [`notebooks/Supplementary_notebook2.ipynb`].
+
+It took approximately 12min to align 1371 gene trajectories of 20,327 reference cells & 17,176 query cells under 14 interpolation time points; and approximately 4.5min to align 994 gene trajectories of 3157 reference cells & 890 query cells under 13 interpolation time points. 
+
+G2G can also utilize concurrency through Python multiprocessing by creating a number of processes equal to the number of cores in the system where each process performs a single gene-level alignment at one time. However we note that sequential processing (the default setting of G2G) seems to be more efficient than parallel processing, as multiprocessing seems to add an overhead when allocating and sharing resources amongst processes, thus doubling up the runtime. 
 
 
 ### Funding Acknowledgement
diff --git a/genes2genes/.ipynb_checkpoints/AlignmentDistMan-checkpoint.py b/genes2genes/.ipynb_checkpoints/AlignmentDistMan-checkpoint.py
new file mode 100644
index 0000000..74144f7
--- /dev/null
+++ b/genes2genes/.ipynb_checkpoints/AlignmentDistMan-checkpoint.py
@@ -0,0 +1,239 @@
+import regex 
+import numpy as np 
+from tqdm import tqdm 
+import pandas as pd
+from tabulate import tabulate
+from scipy.spatial import distance
+
+"""
+This script defines complementary classes and functions for alignment result analysis
+"""
+
+class _TempFSMObj:
+    
+    def __init__(self, al_str, gene):
+        self.al_str = al_str
+        self.fsm, self.counts = self._get_transition_probs(al_str)
+        self.al_length = len(al_str)
+        self.gene = gene
+        
+    def _get_transition_probs(self, al_str):
+        transition_counts =  {'MM':0,'MI':0, 'MD':0,'MW':0,'MV':0, 'II':0, 'IM':0,'ID':0, 'IW':0, 'IV':0,'DD':0, 'DM':0,'DI':0, 'DW':0, 'DV':0, 
+                             'WM':0,'WW':0,'WV':0,'WI':0,'WV':0, 'VM':0,'VW':0,'VV':0,'VI':0,'VV':0}
+        sum_transitions = 0
+        for key in transition_counts.keys():
+            transition_counts[key] = len(regex.findall(key,al_str, overlapped=True)) + 1 # Adding pseudocount to avoid log(0)
+            sum_transitions += transition_counts[key]
+
+        transition_probs = transition_counts.copy()
+
+        for key in transition_counts.keys():
+            transition_probs[key] = transition_counts[key]/sum_transitions
+        return transition_probs, transition_counts
+
+def _compute_msg_len(transition_counts, fsm, al_len):
+    
+    msg_len = 0.0
+    for key in transition_counts.keys():
+        msg_len = -np.log(fsm[key])*transition_counts[key]
+    msg_len = msg_len/al_len
+    return msg_len
+
+def _pairwise_alignment_dist_v2(a1,a2):
+    
+    x1 = _compute_msg_len(a1.counts, a1.fsm, a1.al_length)
+    y1 = _compute_msg_len(a2.counts, a1.fsm, a2.al_length)
+    x2 = _compute_msg_len(a1.counts, a2.fsm, a1.al_length)
+    y2 = _compute_msg_len(a2.counts, a2.fsm, a2.al_length)
+
+    return (np.abs(x1-y1) + np.abs(x2-y2))/2
+
+def _get_region_str(al_str):
+        prev = ''
+        i=0
+        regions = ''
+        for i in range(len(al_str)):
+            if(i==0):
+                regions += al_str[i]
+                continue
+            if(al_str[i-1]==al_str[i]):
+                continue
+            else:
+                regions += al_str[i]
+                continue
+        return regions
+
+def _test_unique_index_sums(a):
+    index_sum = 0
+    m = {'M':0,'I':0,'D':0,'W':0,'V':0}
+
+    l = 0
+    for i in range(len(a)):
+        if(i==len(a)-1):
+            if(a[i-1]==a[i]):
+                m[a[i]] += (index_sum + i)/(l+1)
+            else:
+                m[a[i-1]] += index_sum/l
+                index_sum = 0
+                m[a[i]] += (index_sum + i)
+            break
+            
+        if(i==0 or a[i-1]==a[i]):
+            index_sum += i
+            l+=1
+        else:
+            m[a[i-1]] = m[a[i-1]] +  (index_sum/l)
+            index_sum = 0
+            index_sum += i
+            l=1  
+    return m 
+
+class AlignmentDist:
+    
+    def __init__(self, aligner_obj):
+        self.alignments = aligner_obj.results
+        self.gene_list = aligner_obj.gene_list
+        self.results_map = aligner_obj.results_map
+        self.results = aligner_obj.results
+        
+     # computing pairwise polygon based distance between each pair of alignments in the set of all gene ref-query alignments
+    def compute_polygon_area_alignment_dist(self):
+        
+        DistMat = []
+        for i in range(len(self.alignments)):
+            DistMat.append(np.repeat(-1,len(self.alignments)))
+        for i in tqdm(range(len(self.alignments))):
+            for j in range(len(self.alignments)):
+                if(DistMat[i][j] < 0):
+                    DistMat[i][j] = Utils.compute_alignment_area_diff_distance(self.alignments[i].alignment_str, self.alignments[j].alignment_str
+                                                    ,self.alignments[i].fwd_DP.S_len, self.alignments[i].fwd_DP.T_len )
+                else:
+                    DistMat[i][j] = DistMat[j][i]
+        DistMat = pd.DataFrame(DistMat)
+        DistMat.index = self.gene_list
+        DistMat.columns = self.gene_list
+        
+        DistMat/np.max(np.asarray(DistMat).flatten())
+        
+        return DistMat
+
+    def compute_alignment_ensemble_distance_matrix(self, scheme):
+
+        PolygonDistMat = self.compute_polygon_area_alignment_dist()
+        if(scheme==1):
+            return PolygonDistMat
+
+        FSA_objects = []
+        FSA_objects_regionwise = []
+
+        for i in range(len(self.alignments)):
+            FSA_objects.append(_TempFSMObj(self.alignments[i].alignment_str,self.alignments[i].gene ) )
+            region_str = _get_region_str(self.alignments[i].alignment_str)
+            FSA_objects_regionwise.append(_TempFSMObj(region_str,self.alignments[i].gene ))
+            self.alignments[i].unique_index_sums = list(_test_unique_index_sums(self.alignments[i].alignment_str).values())
+            self.alignments[i].region_str = region_str
+
+        Mat = []; Mat_ui = []
+        for i in range(len(self.alignments)):
+            Mat.append(np.repeat(-1.0,len(self.alignments)))
+            Mat_ui.append(np.repeat(-1.0,len(self.alignments)))
+
+        for i in range(len(self.alignments)):
+            for j in range(len(self.alignments)):
+                if(i==j):
+                    Mat[i][j] = 0.0; Mat_ui[i][j] = 0.0
+                if(Mat[i][j]<0):
+                    Mat[i][j] = _pairwise_alignment_dist_v2(FSA_objects[i],FSA_objects[j])
+                    Mat_ui[i][j] = distance.euclidean(self.alignments[i].unique_index_sums,self.alignments[j].unique_index_sums)
+
+        LikelihoodDistMat = pd.DataFrame(Mat)
+        LikelihoodDistMat.columns = self.gene_list
+        LikelihoodDistMat.index = self.gene_list
+        LikelihoodDistMat = (LikelihoodDistMat/np.max(np.max(LikelihoodDistMat )))
+        IndexSumDistMat = pd.DataFrame(Mat_ui)
+        IndexSumDistMat.columns = self.gene_list
+        IndexSumDistMat.index = self.gene_list
+        IndexSumDistMat = IndexSumDistMat /np.max(np.max(IndexSumDistMat))
+
+        if(scheme==2):
+            return LikelihoodDistMat 
+        elif(scheme==3):
+            return  IndexSumDistMat
+        elif(scheme==0):
+            joint_mat = PolygonDistMat + LikelihoodDistMat + IndexSumDistMat
+            return joint_mat/3
+        elif(scheme==4):
+            joint_mat = PolygonDistMat + LikelihoodDistMat
+            return joint_mat/2
+        elif(scheme==5):
+            joint_mat = LikelihoodDistMat  + IndexSumDistMat
+            return joint_mat/2
+        elif(scheme==6):
+            joint_mat = PolygonDistMat  + IndexSumDistMat
+            return joint_mat/2
+        
+    def order_genes_by_alignments(self):
+    
+        indices = []
+        genes = []
+        gene_strs = []
+        first_lengths= [] 
+        
+        for a in self.results:
+            gene_strs.append(a.alignment_str)
+            genes.append(a.gene_pair)
+            w_index = a.alignment_str.find('W')
+            m_index = a.alignment_str.find('M')
+            v_index = a.alignment_str.find('V')
+            if(w_index<0):
+                w_index = np.inf
+            if(m_index<0):
+                m_index = np.inf
+            if(v_index<0):
+                v_index = np.inf
+
+            if(m_index<0):
+                if(w_index >=0 and (w_index<v_index)):
+                    first_index = w_index
+                elif(v_index >=0 and (v_index<w_index)):
+                    first_index = v_index
+                else:
+                    first_index = -1
+            else:
+                if((m_index<w_index) and (m_index<v_index)):
+                    first_index = m_index
+                elif((w_index<m_index) and (w_index<v_index)):
+                    first_index = w_index
+                elif((v_index<m_index) and (v_index<w_index)):
+                    first_index = v_index
+                else:
+                    first_index = -1
+            indices.append(first_index)
+
+            l = 1
+            r = first_index
+            while(True):
+                if(r+1==len( a.alignment_str)):
+                    break
+                curr_state =  a.alignment_str[r]
+                next_state =  a.alignment_str[r+1]
+                #print('',curr_state, next_state)
+                if(next_state in ['M','W','V']):
+                    l+=1
+                    r+=1
+                else:
+                    break
+
+            first_lengths.append(l)
+
+        df = pd.DataFrame([genes, gene_strs, indices, first_lengths]).transpose()
+        df.columns = ['genes','alignment','start_m_index','first_lengths']
+        df = df.sort_values(['start_m_index','first_lengths'], ascending=[True,False])
+
+        sorted_gene_list = df.genes
+        table = []
+        for gene in sorted_gene_list :
+            table.append([gene,self.results_map[gene].colored_alignment_str]) 
+
+        print(tabulate(table, headers=['Gene','Alignment']))
+        return sorted_gene_list
\ No newline at end of file
diff --git a/genes2genes/.ipynb_checkpoints/ClusterUtils-checkpoint.py b/genes2genes/.ipynb_checkpoints/ClusterUtils-checkpoint.py
new file mode 100644
index 0000000..938dff7
--- /dev/null
+++ b/genes2genes/.ipynb_checkpoints/ClusterUtils-checkpoint.py
@@ -0,0 +1,521 @@
+import numpy as np
+import pandas as pd
+import seaborn as sb
+import scipy
+import sklearn 
+from tqdm import tqdm
+import matplotlib.pyplot as plt
+from leven import levenshtein  
+from scipy.spatial import distance
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.metrics.pairwise import euclidean_distances
+
+from . import VisualUtils
+from . import PathwayAnalyser
+from . import Utils
+
+"""
+This script defines all methods required for finding clusters of gene alignments
+"""
+
+def compute_levenshtein_dist_matrix(set_of_strings):
+    print('- using levenshtein distance metric')
+    E = []
+    for s1 in set_of_strings:
+        row = []
+        for s2 in set_of_strings:
+            row.append(levenshtein(s1,s2)/np.max([len(s1),len(s2)]))
+        E.append(row)
+    return E
+
+def compute_hamming_dist_matrix(set_of_strings):
+    
+    print('- using hamming distance metric')
+    al_binary_encodings= []
+    for a in set_of_strings:
+        al_binary_encodings.append(binary_encode_alignment_path(a))
+    E = distance.cdist(al_binary_encodings, al_binary_encodings, 'hamming')
+    return E
+
+def run_agglomerative_clustering(E, gene_list, DIST_THRESHOLD, linkage = "average"):
+    model = sklearn.cluster.AgglomerativeClustering(n_clusters=None, affinity='precomputed', linkage=linkage, distance_threshold = DIST_THRESHOLD) 
+    model.fit(E)
+    
+    cluster_map = {}
+    for i in range(len(gene_list)):
+        cid = model.labels_[i]
+        if(cid not in cluster_map.keys()):
+            cluster_map[cid] = []
+        cluster_map[cid].append(gene_list[i]) 
+        
+    if(len(cluster_map.keys())==1):
+        return cluster_map, None, None, None, None
+    silhouette_score = sklearn.metrics.silhouette_score(X=E , labels = model.labels_, metric='precomputed') 
+    silhouette_score_samples = sklearn.metrics.silhouette_samples(X=E , labels = model.labels_, metric='precomputed') 
+    silhouette_score_mode = scipy.stats.mode(silhouette_score_samples)[0][0]
+    
+    n_clusters_less_members = []
+    for cid in cluster_map.keys():
+        if(len(cluster_map[cid])<10):
+            n_clusters_less_members.append(cid)
+    n_small_clusters = len(n_clusters_less_members)
+    
+    #print('n clusters:', len(cluster_map.keys()), 'for','Dist threshold:', DIST_THRESHOLD, ' | silhouette score:', silhouette_score, 
+    #      '| mode: ',silhouette_score_mode, ' | n small clusters: ', n_small_clusters )
+    cluster_ids =  model.labels_
+    return cluster_map, cluster_ids, silhouette_score, silhouette_score_mode, n_small_clusters
+
+def run_clustering_for_subset(aligner, metric, MIN_DIST = 0.001):
+    
+    # extract the alignment strings to cluster
+    alignment_strings = []
+    for i in range(len(aligner.gene_list)):
+        alignment_strings.append(aligner.results[i].alignment_str)  
+        
+    dist_mat_functions = {'hamming': compute_hamming_dist_matrix, 'levenshtein': compute_levenshtein_dist_matrix}
+    n_iter = 50 
+    np.random.seed(0)
+    n_unique = []; n_clusters = []; equal=[]
+    for i in range(n_iter):
+        rand_indices = np.random.choice(np.arange(len(alignment_strings)), 200)
+        string_subset = []; gene_subset = []
+        for r in rand_indices:
+            string_subset.append(alignment_strings[r])
+            gene_subset.append(aligner.gene_list[r])
+        
+        compute_dist_matrix = dist_mat_functions[metric]
+        E = compute_dist_matrix(string_subset)
+        gene_clusters, cluster_ids, silhouette_score, silhouette_score_mode, n_small_cluster = run_agglomerative_clustering(E, gene_subset, MIN_DIST)
+        
+        n_clusters.append(len(gene_clusters.keys()))
+        n_unique.append(len(np.unique(string_subset)))
+        
+    df = pd.DataFrame([n_unique, n_clusters]).transpose()
+    df.columns = ['n_unique_strings','n_identified_clusters']
+    df['equal'] = df['n_unique_strings'] == df['n_identified_clusters']
+    return df
+
+# main clustering function 
+def run_clustering(aligner, metric,DIST_THRESHOLD=0.2, experiment_mode=False):
+    
+    # extract the alignment strings to cluster
+    alignment_strings = []
+    for i in range(len(aligner.gene_list)):
+        alignment_strings.append(aligner.results[i].alignment_str)  
+
+    # compute distance matrix 
+    print('Compute distance matrix')
+    dist_mat_functions = {'hamming': compute_hamming_dist_matrix, 'levenshtein': compute_levenshtein_dist_matrix}
+    compute_dist_matrix = dist_mat_functions[metric]
+    E = compute_dist_matrix(alignment_strings)
+    
+    if(experiment_mode):
+        print('Experimental mode: exploring different thresholds')
+        scores = [];  n_clusters = []; dist_thresholds = np.arange(0.01,1.0,0.01); score_modes = []; n_small_clusters = [] 
+        eval_dists = []
+        for D_THRESH in tqdm(dist_thresholds): 
+            gene_clusters, cluster_ids, silhouette_score, silhouette_score_mode, n_small_cluster = run_agglomerative_clustering(E, aligner.gene_list, D_THRESH)
+            
+            if(len(gene_clusters.keys())==1):
+                break
+            scores.append(silhouette_score)
+            n_clusters.append(len(gene_clusters.keys()))
+            score_modes.append(silhouette_score_mode)
+            n_small_clusters.append(n_small_cluster)
+            eval_dists.append(D_THRESH)
+        print('-- Cluster diagnostic plots')
+        plt.rcParams.update({'font.size': 14})
+        plt.subplots(1,3,figsize=(10,5))
+        plt.subplot(1,3,1)
+        sb.lineplot(x=eval_dists, y=scores, color = 'blue', marker='o') 
+        plt.xlabel('Distnace threshold')
+        plt.ylabel('Mean Silhouette Score')
+        plt.subplot(1,3,2)
+        sb.lineplot(x=n_clusters, y=scores, color='red', marker='o')
+        plt.xlabel('Number of clusters')
+        plt.ylabel('Mean Silhouette Score')
+        plt.subplot(1,3,3)
+        sb.lineplot(x=eval_dists, y=n_clusters, color='green', marker='o')
+        plt.xlabel('Distance threshold')
+        plt.ylabel('Number of clusters')
+        plt.tight_layout() 
+        df = pd.DataFrame([eval_dists,scores,n_clusters]).transpose()
+        df.columns = ['Distance threshold', 'Mean Silhouette Score','Number of clusters']
+        
+        print('Potential candidates for distance threshold: a locally optimal thresholds that gives a good trade-off between high mean Silhouette score and low number of clusters ')
+        return df
+    else:
+        print('run agglomerative clustering | distance threshold =', np.round(DIST_THRESHOLD,2) )
+        gene_clusters, cluster_ids, silhouette_score, silhouette_score_samples, n_small_cluster  = run_agglomerative_clustering(E, aligner.gene_list, DIST_THRESHOLD)
+        print('silhouette_score: ', silhouette_score)
+        aligner.cluster_ids = cluster_ids
+        aligner.gene_clusters = gene_clusters 
+        aligner.DistMat = pd.DataFrame(E)
+        aligner.DistMat.columns = aligner.gene_list
+        aligner.DistMat.index = aligner.gene_list
+        aligner.E = E
+        
+        for i in range(len(aligner.gene_clusters)):
+            for g in aligner.gene_clusters[i]:
+                aligner.results_map[g].cluster_id = i
+        
+
+# plotting function of all gene alignment paths grouped by clusters
+def visualise_clusters(aligner, n_cols = 5, figsize= (10,6)): 
+    
+        print('Cluster ID | Number of genes in the cluster')
+    
+        cluster_ids = aligner.cluster_ids
+        alignments = aligner.results 
+        cluster_map = aligner.gene_clusters
+        n_clusters = len(cluster_map.keys())
+
+        S_len = alignments[0].fwd_DP.S_len
+        T_len = alignments[0].fwd_DP.T_len 
+        unique_cluster_ids = cluster_map.keys() #np.unique(cluster_ids)
+        n_rows = int(np.ceil(n_clusters/n_cols)) 
+        fig, axs = plt.subplots(n_rows,n_cols, figsize = (12,n_rows*3)) # custom -- only for 20 clusters -- TODO change later
+        axs = axs.flatten() 
+        i = 0; k=1
+
+        for cid in range(n_clusters): 
+            cluster_alignments = cluster_map[cid]
+            paths = []
+            for g in cluster_alignments:
+                    a = aligner.results_map[g]
+                    a.fwd_DP.alignment_path.append([0,0])
+                    paths.append(a.fwd_DP.alignment_path)
+            plot_different_alignments(paths, S_len, T_len, axs[cid])
+            axs[cid].set_title('Cluster-'+str(i) + ' | '+str(len(cluster_alignments)))
+            i=i+1; k=k+1
+        fig.tight_layout()
+        n = n_cols * n_rows; i = 1
+        while(k<=n):
+            axs.flat[-1*i].set_visible(False) 
+            k = k+1
+            i=i+1  
+
+def plot_different_alignments(paths, S_len, T_len, ax, mat=[]): # pass alignment path coordinates
+        mat=[]
+        for i in range(T_len+1):
+            mat.append(np.repeat(0,S_len+1)) 
+        sb.heatmap(mat, square=True,  cmap='viridis', ax=ax, vmin=0, vmax=0, cbar=False,xticklabels=False,yticklabels=False)
+        path_color = "orange"
+        
+        for path in paths: 
+            path_x = [p[0]+0.5 for p in path]
+            path_y = [p[1]+0.5 for p in path]
+            ax.plot(path_y, path_x, color=path_color, linewidth=3, alpha=0.5, linestyle='dashed') # path plot
+
+
+def get_significant_pathways_for_clusters(aligner, PVAL_THRESHOLD=0.01):
+    results = {}
+    cluster_map = aligner.gene_clusters
+    for cid in tqdm(cluster_map.keys()):
+        glist = []
+        for g in cluster_map[cid]:
+            glist.append(g)
+
+        df = PathwayAnalyser.run_overrepresentation_analysis(glist)
+        results[cid] = df[df['Adjusted P-value']< PVAL_THRESHOLD]
+
+    return results
+
+
+def print_cluster_average_alignments(aligner):
+    
+    aligner.gene_clusters_average_alignments = {}
+    for i in range(len(aligner.gene_clusters)):
+        average_alignment, avg_path =  get_cluster_average_alignments(aligner, aligner.gene_clusters[i] )
+        print('cluster: ', i, VisualUtils.color_al_str(average_alignment), '(',len(aligner.gene_clusters[i]), 'genes)')
+        aligner.gene_clusters_average_alignments[i] = average_alignment
+
+
+
+# G2G Version 1 ---- PREVIOUS CLUSTERING UTILS
+def check_alignment_clusters(cluster_ids, alignments, n_cols = 5, figsize= (10,6), n_clusters=20): 
+        clusters = [] 
+        S_len = alignments[0].fwd_DP.S_len
+        T_len = alignments[0].fwd_DP.T_len
+        unique_cluster_ids = np.unique(cluster_ids)
+        n_rows = int(np.ceil(n_clusters/n_cols)) 
+        fig, axs = plt.subplots(n_rows,n_cols, figsize = (20,n_rows*3)) # custom -- only for 20 clusters -- TODO change later
+        axs = axs.flatten() 
+        i = 0
+        k=1
+        for cluster_id in range(n_clusters): 
+            paths = []
+            cluster_genes = [] 
+            cluster_alignments = np.asarray(alignments)[cluster_ids == unique_cluster_ids[cluster_id]]
+            for a in cluster_alignments:
+                    a.fwd_DP.alignment_path.append([0,0])
+                    paths.append(a.fwd_DP.alignment_path)
+                    #print(a.gene)
+                    cluster_genes.append(a.gene);# cluster_genes.append(a.gene)
+            clusters.append(list(np.unique(cluster_genes)) ) 
+            plot_different_alignments(paths, S_len, T_len, axs[cluster_id])
+            axs[cluster_id].set_title('Cluster-'+str(i) + ' | '+str(len(cluster_alignments)))
+            i=i+1
+            k=k+1
+        fig.tight_layout()
+        n = n_cols * n_rows
+        i = 1
+        while(k<=n):
+            axs.flat[-1*i].set_visible(False) 
+            k = k+1
+            i=i+1        
+        return clusters
+
+
+def one_hot_encode(al_string):
+    #print('One hot encoding for alignment string clustering')
+    # Modified code in https://machinelearningmastery.com/how-to-one-hot-encode-sequence-data-in-python/ 
+    alphabet = 'MWVID'
+    char_to_int = dict((c, i) for i, c in enumerate(alphabet))
+    int_encoding = [char_to_int[char] for char in al_string]
+    onehot_encoding = []
+    for value in int_encoding:
+        letter = [0 for _ in range(len(alphabet))]
+        letter[value] = 1
+        onehot_encoding.append(letter)
+    
+    return onehot_encoding
+
+def run_hierarchical_clustering_with_one_hot_encoding(aligner,DIST_THRESHOLD=25):
+    
+    alignment_strings = []
+    for i in range(len(aligner.gene_list)):
+        alignment_strings.append(aligner.results[i].alignment_str)
+    
+    E = [] # alignment encoding matrix
+    longestL = np.max([len(a) for a in alignment_strings]) 
+    for alignment_str in alignment_strings:
+        encoding = one_hot_encode(alignment_str)
+        max_len = longestL
+        for i in range(max_len - len(alignment_str)):
+            encoding.append([0,0,0,0,0]) 
+        E.append(list(np.asarray(encoding).flatten())) 
+    E = pd.DataFrame(E)
+
+    model = AgglomerativeClustering(n_clusters=None, affinity='euclidean', linkage='ward',distance_threshold = DIST_THRESHOLD)
+    model.fit(E)
+    cluster_ids = model.labels_
+    
+    gene_clusters = check_alignment_clusters(cluster_ids, aligner.results , n_clusters=len(np.unique(cluster_ids) ) )
+
+    aligner.cluster_ids = cluster_ids
+    aligner.gene_clusters = gene_clusters 
+    aligner.DistMat = pd.DataFrame(euclidean_distances(E, E)) 
+    aligner.DistMat.columns = aligner.gene_list
+    aligner.DistMat.index = aligner.gene_list
+    aligner.E = E
+
+
+# As ordinal data - numerical encoding 
+def run_hierarchical_clustering_with_int_encoding_scheme(aligner,DIST_THRESHOLD=25, 
+                                          codes = [1,2,3,4,5]  ):
+    
+    aligment_strings = []
+    for i in range(len(aligner.gene_list)):
+        aligment_strings.append(aligner.results[i].alignment_str)
+    
+    longestL = np.max([len(a) for a in aligment_strings]) # 30
+    
+    # Numerical Encoding of 5-state Alignment strings
+    # None - 0; M - 1; W - 2; V - 3; D - 5; I - 6 
+    E = [] # alignment encoding matrix
+    for i in range(len(aligment_strings)):
+        E.append(np.repeat(0, longestL)) 
+    E = np.matrix(E) 
+
+    for i in range(len(aligment_strings)):
+        for j in range(len(aligment_strings[i])):
+            al_state = aligment_strings[i][j]
+            if(al_state=='M'):
+                E[i,j] = codes[0]
+            elif(al_state=='W'):
+                E[i,j] = codes[1]
+            elif(al_state=='V'):
+                E[i,j] = codes[2]
+            elif(al_state=='D'):
+                E[i,j] = codes[3]
+            elif(al_state=='I'):
+                E[i,j] = codes[4]
+    E = pd.DataFrame(E)
+    model = AgglomerativeClustering(n_clusters=None, affinity='euclidean', linkage='ward',distance_threshold = DIST_THRESHOLD)
+    model.fit(E)
+    cluster_ids = model.labels_
+    
+    gene_clusters = check_alignment_clusters(cluster_ids, aligner.results , n_clusters=len(np.unique(cluster_ids) ) )
+
+    aligner.cluster_ids = cluster_ids
+    aligner.gene_clusters = gene_clusters 
+    aligner.DistMat = pd.DataFrame(euclidean_distances(E, E)) 
+    aligner.DistMat.columns = aligner.gene_list
+    aligner.DistMat.index = aligner.gene_list
+    aligner.E = E
+   # return cluster_ids, gene_clusters
+
+
+# ==============================================================
+def binary_encode_alignment_path(al_str):
+    S = ''
+    T = ''
+    for i in range(len(al_str)):
+        if(al_str[i]=='M'):
+            S+='1'
+            T+='1'
+        elif(al_str[i]=='W'):
+            S+='1'
+        elif(al_str[i]=='V'):
+            T+='1'
+        elif(al_str[i]=='D'):
+            S+='0'
+        elif(al_str[i]=='I'):
+            T+='0'
+    binary_encoding = S+T
+    return np.asarray([int(c) for c in binary_encoding]  ) 
+
+
+def run_hierarchical_clustering_with_binary_encode_alignment_path_euclidean(aligner,DIST_THRESHOLD=25):
+    
+    alignment_strings = []
+    for i in range(len(aligner.gene_list)):
+        alignment_strings.append(aligner.results[i].alignment_str)
+    
+    E = [] # alignment encoding matrix
+    longestL = np.max([len(a) for a in alignment_strings]) 
+    for alignment_str in alignment_strings:
+        encoding = binary_encode_alignment_path(alignment_str)
+        E.append(list(np.asarray(encoding).flatten())) 
+    E = pd.DataFrame(E)
+
+    model = AgglomerativeClustering(n_clusters=None, affinity='euclidean', linkage='ward',distance_threshold = DIST_THRESHOLD)
+    model.fit(E)
+    cluster_ids = model.labels_
+    
+    gene_clusters = check_alignment_clusters(cluster_ids, aligner.results , n_clusters=len(np.unique(cluster_ids) ) )
+
+    aligner.cluster_ids = cluster_ids
+    aligner.gene_clusters = gene_clusters 
+    aligner.DistMat = pd.DataFrame(euclidean_distances(E, E)) 
+    aligner.DistMat.columns = aligner.gene_list
+    aligner.DistMat.index = aligner.gene_list
+    aligner.E = E
+
+def run_hierarchical_clustering_with_binary_encode_alignment_path_hamming(aligner,DIST_THRESHOLD=25):
+    
+    alignment_strings = []
+    for i in range(len(aligner.gene_list)):
+        alignment_strings.append(aligner.results[i].alignment_str)
+    
+    E = [] # alignment encoding matrix
+
+    al_binary_encodings= []
+    for a in aligner.results:
+        al_binary_encodings.append(binary_encode_alignment_path(a.alignment_str))
+    E = distance.cdist(al_binary_encodings, al_binary_encodings, 'hamming')
+
+    model = AgglomerativeClustering(n_clusters=None, affinity='precomputed', linkage='average',distance_threshold = DIST_THRESHOLD)
+    #model = AgglomerativeClustering(n_clusters=None, affinity='euclidean', linkage='ward',distance_threshold = DIST_THRESHOLD)
+    model.fit(E)
+    cluster_ids = model.labels_
+    print('run')
+    gene_clusters = check_alignment_clusters(cluster_ids, aligner.results , n_clusters=len(np.unique(cluster_ids) ) )
+
+    aligner.cluster_ids = cluster_ids
+    aligner.gene_clusters = gene_clusters 
+    aligner.DistMat = pd.DataFrame(E)
+    aligner.DistMat.columns = aligner.gene_list
+    aligner.DistMat.index = aligner.gene_list
+    aligner.E = E
+
+# ==============================================================
+
+def get_pairwise_match_count_mat(aligner, gene_set):
+            mat = []
+            nT_points = len(aligner.results[0].T.time_points)
+            nS_points = len(aligner.results[0].S.time_points)
+            for i in range(nT_points + 1):
+                mat.append(np.repeat(0.0, nS_points+1))
+
+            # counts of total matches between the each pair of ref and query timepoints across all alignments 
+            for g in gene_set:
+                a = aligner.results_map[g]
+                matchS = a.match_points_S+1
+                matchT = a.match_points_T+1
+                for i in range(len(matchS)):
+                    mat[matchT[i]][matchS[i]] = mat[matchT[i]][matchS[i]] + 1
+
+            return pd.DataFrame(mat) 
+
+# Cluster Average Util
+
+# This computes an average alignment across all the alignments for a given set of gene alignments 
+def get_cluster_average_alignments(aligner, gene_set, deterministic=True):
+    
+            cluster_alobjs = []
+            for g in gene_set:
+                cluster_alobjs.append(aligner.results_map[g])
+            i = aligner.results[0].fwd_DP.T_len
+            j = aligner.results[0].fwd_DP.S_len
+            
+            avg_alignment = ''
+            tracked_path = []
+            tracked_path.append([i,j])
+            
+            while(True):
+                if(i==0 and j==0):
+                    break
+                backtrack_states_probs = {}
+                backtrack_states_probs['M'] = 0 
+                backtrack_states_probs['W'] = 0 
+                backtrack_states_probs['V'] = 0 
+                backtrack_states_probs['D'] = 0 
+                backtrack_states_probs['I'] = 0 
+                # record the count of each state at this [i,j] cell across all alignments 
+                for a in cluster_alobjs:
+                    backtract_state = a.landscape_obj.L_matrix_states[i,j]
+                    if(backtract_state=='0'):
+                        backtrack_states_probs['M']+=1 
+                    elif(backtract_state=='1'):
+                        backtrack_states_probs['W']+=1 
+                    elif(backtract_state=='2'):
+                        backtrack_states_probs['V']+=1 
+                    elif(backtract_state=='3'):
+                        backtrack_states_probs['D']+=1 
+                    elif(backtract_state=='4'):
+                        backtrack_states_probs['I']+=1 
+                # compute the proportion of the state for the [i,j] cell
+                for state in backtrack_states_probs.keys(): 
+                    backtrack_states_probs[state] = backtrack_states_probs[state]/len(cluster_alobjs) 
+
+                if(deterministic):
+                    # take the most probable state based on max frequent state of this [i,j] cell
+                    cs = np.argmax(np.asarray(list(backtrack_states_probs.values())) )
+                else:
+                    # sample a state from the state frequency distribution
+                    cs = Utils.sample_state(np.asarray(list(backtrack_states_probs.values()) ) )
+                if(cs==0):
+                    i = i-1
+                    j = j-1
+                    avg_alignment = 'M' + avg_alignment 
+                elif(cs==1 or cs==3):
+                    j= j-1
+                    if(cs==1):
+                        avg_alignment = 'W' + avg_alignment
+                    else:
+                        avg_alignment = 'D' + avg_alignment
+                elif(cs==2 or cs==4):
+                    i=i-1
+                    if(cs==2):
+                        avg_alignment = 'V' + avg_alignment
+                    else:
+                        avg_alignment = 'I' + avg_alignment
+            
+                tracked_path.append([i,j])
+            
+            return avg_alignment, tracked_path
+
+
+        
\ No newline at end of file
diff --git a/genes2genes/.ipynb_checkpoints/Main-checkpoint.py b/genes2genes/.ipynb_checkpoints/Main-checkpoint.py
new file mode 100644
index 0000000..e2ee9a2
--- /dev/null
+++ b/genes2genes/.ipynb_checkpoints/Main-checkpoint.py
@@ -0,0 +1,842 @@
+import regex 
+import copy
+import scipy
+import anndata
+import scipy.sparse
+import pandas as pd
+import numpy as np
+import seaborn as sb
+from tqdm import tqdm
+import multiprocessing
+import scipy.stats as stats
+import matplotlib.pyplot as plt
+from multiprocessing import Pool
+from tqdm.notebook import tqdm_notebook
+
+from . import OrgAlign as orgalign
+from . import MyFunctions 
+from . import TimeSeriesPreprocessor
+from . import AlignmentDistMan
+from . import VisualUtils
+from . import ClusterUtils
+from . import Utils
+
+__version__ = 'v0.2.0'
+
+class hcolors:
+    MATCH = '\033[92m'
+    INSERT = '\033[91m'
+    DELETE = '\033[91m'
+    STOP = '\033[0m'    
+
+    
+class AligmentObj:
+    
+    """
+    This class defines an aligned object of a reference and query gene expression time series,
+    to carry all related results. 
+    """
+    
+    def __init__(self,gene, S,T, fwd_DP_obj,bwd_DP_obj, landscape_obj):
+        self.gene = gene
+        self.S = S
+        self.T = T
+        self.alignment_str = fwd_DP_obj.alignment_str
+        self.landscape_obj = landscape_obj
+        self.fwd_DP = fwd_DP_obj
+        self.bwd_DP = bwd_DP_obj
+        
+        out = self.fwd_DP.get_matched_regions()
+        self.match_regions_S = out[0]
+        self.match_regions_T = out[1]
+        self.non_match_regions_S = out[2]
+        self.non_match_regions_T = out[3]
+        
+        self.compute_series_match_percentage()
+        try:
+            self.run_DEAnalyser() 
+        except Exception as e:
+            print(str(e),gene)
+
+    # Printing details about the optimal alignment object
+    def print(self):
+        print('Fwd opt cost', self.fwd_DP.opt_cost)
+        print('5-state alignment string: ', self.fwd_DP.alignment_str) 
+        print('Ref matched time points ranges: ', self.match_regions_S)
+        print('Query matched time points ranges: ', self.match_regions_T)
+        print('Ref mismatched time points ranges: ',self.non_match_regions_S)
+        print('Query mismatched time points ranges: ',self.non_match_regions_T)
+        print('Alignment landscape plot: ')
+        self.landscape_obj.plot_alignment_landscape() 
+        
+    def plotTimeSeries(self, refQueryAlignerObj, plot_cells = False, plot_mean_trend= False):
+        sb.scatterplot(x=self.S.X, y=self.S.Y, color = 'forestgreen' ,alpha=0.05)#, label='Ref') 
+        sb.scatterplot(x=self.T.X, y=self.T.Y, color = 'midnightblue' ,alpha=0.05)#, label ='Query')
+      # plt.legend(loc='upper left')
+        if(plot_cells):
+            sb.scatterplot(x=refQueryAlignerObj.ref_time, y=np.asarray(refQueryAlignerObj.ref_mat[self.gene]), color = 'forestgreen' ) 
+            sb.scatterplot(x=refQueryAlignerObj.query_time, y=np.asarray(refQueryAlignerObj.query_mat[self.gene]), color = 'midnightblue' )
+        plt.title(self.gene)
+        plt.xlabel('Pseudotime')
+        plt.ylabel('Gene expression')
+        
+        if(plot_mean_trend):
+            self.plot_mean_trends() 
+            
+    def plotTimeSeriesAlignment(self):  
+        sb.scatterplot(x=self.S.X, y=self.S.Y, color = 'forestgreen' ,alpha=0.05)#, label='Ref') 
+        sb.scatterplot(x=self.T.X, y=self.T.Y, color = 'midnightblue' ,alpha=0.05)#, label ='Query') 
+      #  plt.legend(loc='upper left')
+        self.plot_mean_trends() 
+        plt.title(self.gene)
+        plt.xlabel('Pseudotime')
+        plt.ylabel('Gene expression')
+        
+        for i in range(self.matched_region_DE_info.shape[0]):
+            S_timebin = int(self.matched_region_DE_info.iloc[i]['ref_bin'])
+            T_timebin = int(self.matched_region_DE_info.iloc[i]['query_bin']) 
+            x_vals = [self.matched_region_DE_info.iloc[i]['ref_pseudotime'],self.matched_region_DE_info.iloc[i]['query_pseudotime']] 
+            y_vals = [self.S.mean_trend[S_timebin ], self.T.mean_trend[T_timebin]] 
+            plt.plot(x_vals, y_vals, color='black', linestyle='dashed', linewidth=0.6)
+    
+    
+    def compute_series_match_percentage(self):
+        x = np.unique(list(self.alignment_str), return_counts=True) 
+        x = dict(zip(x[0],x[1]))
+        p1 = 0
+        p2 = 0 
+        p3 = 0
+        for state in ['M','V','W']:
+            if(state in x.keys()):
+                if(state=='M'):
+                    p1 = p1 + x[state] 
+                    p2 = p2 + x[state] 
+                    p3 = p3 +  x[state] 
+                if(state == 'V'):
+                    p1 = p1 + x[state] 
+                    p2 = p2 +  x[state] 
+                if(state == 'W'):
+                    p1 = p1 + x[state] 
+                    p3 = p3 +  x[state] 
+                
+        self.match_percentage = round((p1/len(self.alignment_str))*100,2)
+        self.match_percentage_T = round((p2/len(self.T.data_bins))*100,2)
+        self.match_percentage_S = round((p3/len(self.S.data_bins))*100,2)
+        
+    def get_series_match_percentage(self):
+        return self.match_percentage , self.match_percentage_S, self.match_percentage_T  
+    
+    def run_DEAnalyser(self):
+        #print('DEAnalyser: started')
+        DE_analyser = DEAnalyser(self)
+        #print('DEAnalyser: get matched regions')
+        DE_analyser.get_matched_regions()
+        #print('DEAnalyser: get matched timepoints')
+        DE_analyser.get_matched_time_points()
+
+        if(isinstance(self.S,TimeSeriesPreprocessor.SummaryTimeSeries)):
+            DE_analyser.get_DE_info_for_matched_regions() 
+        
+    def plot_matched_region_dists(self):
+        
+        n_cols = 10
+        n_rows = int(np.ceil(len(self.match_points_S)/n_cols))
+        fig, axs = plt.subplots(n_rows,n_cols,figsize=(20,n_rows*3))
+
+        for i in range(len(self.match_points_S)):
+            plt.subplot(n_rows,n_cols,i+1)
+            S_bin = self.S.data_bins[self.match_points_S[i]]  
+            T_bin = self.T.data_bins[self.match_points_T[i]] 
+            sb.kdeplot(S_bin, fill=True)
+            sb.kdeplot(T_bin, fill=True)  
+            
+        fig.tight_layout()
+        n = n_cols * n_rows
+        k = i
+        i = 1
+        while(k<=n):
+            axs.flat[-1*i].set_visible(False) 
+            k = k+1
+            i=i+1
+            
+    def print_alignment(self):
+
+        print(self.al_visual)
+        print('Matched percentages: ')
+        p1,p2,p3 = self.get_series_match_percentage()
+        print('w.r.t alignment: ', p1,'%')
+        print('w.r.t ref: ', p2,'%')
+        print('w.r.t query: ', p3,'%')
+        
+    def plot_mean_trends(self):
+        # mean trend plot
+        self.S.plot_mean_trend(color='forestgreen')
+        self.T.plot_mean_trend(color='midnightblue')
+        
+    def get_opt_alignment_cost(self):
+        return self.fwd_DP.opt_cost
+
+
+class RefQueryAligner:
+    
+    """
+    This class defines the main aligner class of genes2genes alignment, acting as entry point to initialise alignment parameters and interpolation. 
+    It contains all methods for running genes2genes alignment between the specified genes of the reference and query datasets.  
+    
+    Parameters
+    ----------
+    *args
+        adata_ref: anndata 
+        adata_query: anndata
+        gene_list: list
+        n_interpolation_points: int
+        adaptive_kernel: boolean
+    """
+    
+    def __init__(self, *args):
+        print('===============================================================================================================')
+        print('Genes2Genes ('+ __version__ +')')
+        print('Dynamic programming alignment of gene pseudotime trajectories using a bayesian information-theoretic framework')
+        print('===============================================================================================================')
+        
+        if(len(args) == 4 ):
+            self.run_init1(args[0], args[1], args[2], args[3])
+            adaptive_kernel = False
+        elif(len(args) == 5 ):
+            print('Running in adaptive interpolation mode')
+            self.run_init1(args[0], args[1], args[2], args[3])
+            adaptive_kernel = args[4]
+        else:
+            print('pls pass the required number of args')
+            
+        k=1;
+        self.TrajInt_R = TimeSeriesPreprocessor.TrajectoryInterpolator(self.adata_ref, n_bins=self.n_artificial_time_points, adaptive_kernel=adaptive_kernel,raising_degree = k)
+        self.TrajInt_R.run() 
+        self.TrajInt_Q = TimeSeriesPreprocessor.TrajectoryInterpolator(self.adata_query, n_bins=self.n_artificial_time_points, adaptive_kernel=adaptive_kernel,raising_degree = k)
+        self.TrajInt_Q.run() 
+        print('Interpolator initialization completed')
+        self.state_params = [0.99,0.1,0.7] # parameters empirically found over our simulated dataset
+        self.no_extreme_cases =False
+        
+        print('Aligner initialised to align trajectories of', self.adata_ref.shape[0], 'reference cells &',self.adata_query.shape[0], 'query cells in terms of', len(self.gene_list), 'genes')
+    
+    # converts ref and query anndata objects to pd.DataFrames 
+    def run_init1(self, adata_ref, adata_query, gene_list, n_artificial_time_points):
+        
+        self.adata_ref = adata_ref[:, gene_list] 
+        self.adata_query = adata_query[:, gene_list]
+        
+        if(isinstance(adata_ref.X, scipy.sparse.csr.csr_matrix) 
+           or isinstance(adata_ref.X,anndata._core.views.SparseCSCView)
+           or isinstance(adata_ref.X,scipy.sparse.csc.csc_matrix)):
+            ref_mat = pd.DataFrame(adata_ref.X.todense()) 
+        else:
+            ref_mat = pd.DataFrame(adata_ref.X) 
+        if(isinstance(adata_query.X, scipy.sparse.csr.csr_matrix) 
+           or isinstance(adata_query.X,anndata._core.views.SparseCSCView)
+           or isinstance(adata_query.X,scipy.sparse.csc.csc_matrix)):
+            query_mat = pd.DataFrame(adata_query.X.todense()) 
+        else:
+            query_mat = pd.DataFrame(adata_query.X)     
+            
+        ref_mat.columns = adata_ref.var_names
+        ref_mat = ref_mat.set_index(adata_ref.obs_names)
+        ref_time = np.asarray(adata_ref.obs['time']) 
+        query_mat.columns = adata_query.var_names
+        query_mat = query_mat.set_index(adata_query.obs_names)
+        query_time = np.asarray(adata_query.obs['time']) 
+        
+        self.run_init2(ref_mat, ref_time, query_mat, query_time, gene_list, n_artificial_time_points)
+    
+    def run_init2(self, ref_mat, ref_time, query_mat, query_time, gene_list, n_artificial_time_points):
+        self.ref_mat = ref_mat
+        self.query_mat = query_mat
+        self.ref_time = ref_time
+        self.query_time = query_time
+        self.gene_list = gene_list
+        self.pairs = {}
+        self.n_artificial_time_points = n_artificial_time_points
+    
+    # util functions 
+    def extract_significant_regions_only(self, regions):
+        if(len(regions)==0): 
+            return []
+        adjacent_region_start = regions[0][0]
+        filtered_regions = np.asarray([], dtype=np.float64)
+        adjacent_region_indices = np.asarray([], dtype=np.float64)
+        filtered_region_indices = np.asarray([], dtype=np.float64)
+        for k in range(len(regions)):
+            if(k!=len(regions)-1):
+                if(regions[k][1] != regions[k+1][0]):
+                    ended_adjacent_region_len = regions[k][1]- adjacent_region_start
+                    if(ended_adjacent_region_len>0.2):
+                        adjacent_region_indices = np.append(adjacent_region_indices,regions[k][0])
+                        adjacent_region_indices = np.append(adjacent_region_indices, regions[k][1])
+                        filtered_regions=np.append(filtered_regions,[adjacent_region_start,regions[k][1] ])
+                        filtered_region_indices = np.append(filtered_region_indices,adjacent_region_indices)
+                    adjacent_region_start = regions[k+1][0]
+                    adjacent_region_indices = []
+                else:
+                    adjacent_region_indices=np.append(adjacent_region_indices,regions[k][0])
+                    continue
+            else:
+                if(len(adjacent_region_indices)>0): # check if there is a continuing adjacent region
+                    ended_adjacent_region_len = regions[k][1]- adjacent_region_start
+                    if(ended_adjacent_region_len>0.2):
+                        adjacent_region_indices = np.append(adjacent_region_indices,regions[k][0])
+                        adjacent_region_indices=np.append(adjacent_region_indices,regions[k][1])
+                        filtered_regions=np.append(filtered_regions,[adjacent_region_start,regions[k][1] ])
+                        filtered_region_indices = np.append(filtered_region_indices, adjacent_region_indices)
+                        
+        return list(filtered_region_indices)
+
+    def check_inconsistent_zero_region(self, gex_arr, pseudotime_arr, trajInterpolator):
+        
+        regions = []
+        window_range = trajInterpolator.interpolation_points
+        
+        for i in range(1,len(window_range)):
+            sliding_region = np.logical_and(pseudotime_arr>=window_range[i-1], pseudotime_arr<window_range[i]) 
+            n_pos = np.count_nonzero(gex_arr[sliding_region])
+            if(n_pos<=3): #almost 0 expression 
+                regions.append([window_range[i-1], window_range[i]])
+        regions = self.extract_significant_regions_only(regions)
+        return regions
+ 
+        
+    def run_interpolation(self, gene_idx):
+
+        gex_r = Utils.csr_mat_col_densify(self.TrajInt_R.mat , gene_idx)
+        gex_q = Utils.csr_mat_col_densify(self.TrajInt_Q.mat , gene_idx)
+        USER_GIVEN_STD = np.repeat(-1.0, self.TrajInt_R.n_bins)
+        
+        if(self.no_extreme_cases):
+            
+            
+            if((not gex_r.any()) and (not gex_q.any()) ):# both are 0 expressed
+                #print('both', g)    # complete match     
+                S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
+                T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
+            elif( (not gex_r.any()) or (np.count_nonzero(gex_r)<=50) ):# only ref is 0 expressed or only small number of cells <=50
+                #print('ref 0 expressed')
+                # get query mean trend and std trend estimated 
+                T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
+                                                                             ESTIMATE=True, user_given_std=[])
+                # assigning query std trend to ref
+                S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
+                                                                            ESTIMATE = False, user_given_std = T.intpl_stds)
+            elif( (not gex_q.any()) or (np.count_nonzero(gex_q)<=50)  ): # only query is 0 expressed or only small number of cells <=50
+                #print('query 0 expressed')
+                # get ref mean trend and std trend estimated 
+                S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
+                                                                               ESTIMATE=True, user_given_std=[])
+                # assigning ref std trend to query
+                T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
+                                                                            ESTIMATE = False, user_given_std = S.intpl_stds)
+            else: # both are expressed
+                #print('both expressed')
+                S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
+                T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
+                # get both ref and query mean trend and std trend estimated 
+
+            return [S,T]
+
+        if(np.count_nonzero(gex_r)<=3 and np.count_nonzero(gex_q)<=3): # if both are almost 0 (less than 3 counts overall)
+
+            USER_GIVEN_STD = np.repeat(0.01, self.TrajInt_R.n_bins) # use a very low constant std for both cases 
+            S = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_R, user_given_std = USER_GIVEN_STD)
+            T = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_Q, user_given_std = USER_GIVEN_STD)  
+
+        elif(np.count_nonzero(gex_r)<=3):  # if only ref is almost 0 expressed
+
+            T_temp = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_Q, user_given_std = USER_GIVEN_STD)
+            regions = self.check_inconsistent_zero_region(gex_q, self.TrajInt_Q.cell_pseudotimes, self.TrajInt_Q)
+            common_std = min(T_temp.intpl_stds)/10
+            if(len(regions)!=0):
+                USER_GIVEN_STD[np.in1d(self.TrajInt_Q.interpolation_points, regions)] = common_std 
+                T = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_Q, user_given_std = USER_GIVEN_STD)  
+            else:
+                T = T_temp
+            USER_GIVEN_STD = np.repeat(common_std, self.TrajInt_R.n_bins)
+            S = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_R, user_given_std = USER_GIVEN_STD)
+
+        elif(np.count_nonzero(gex_q)<=3): # if only query is almost 0 expressed
+
+            S_temp = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_R, user_given_std = USER_GIVEN_STD)
+            regions = self.check_inconsistent_zero_region(gex_r, self.TrajInt_R.cell_pseudotimes, self.TrajInt_R)
+            common_std = min(S_temp.intpl_stds)/10
+            if(len(regions)!=0):
+                USER_GIVEN_STD[np.in1d(self.TrajInt_R.interpolation_points, regions)] = common_std 
+                S = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_R, user_given_std = USER_GIVEN_STD)  
+            else:
+                S = S_temp
+            USER_GIVEN_STD = np.repeat(common_std, self.TrajInt_Q.n_bins)
+            T = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_Q, user_given_std = USER_GIVEN_STD)
+
+        else:
+            S_temp = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_R, user_given_std = USER_GIVEN_STD)
+            T_temp = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_Q, user_given_std = USER_GIVEN_STD)
+            
+            regions_S = self.check_inconsistent_zero_region(gex_r, self.TrajInt_R.cell_pseudotimes, self.TrajInt_R)
+            regions_T = self.check_inconsistent_zero_region(gex_q, self.TrajInt_Q.cell_pseudotimes, self.TrajInt_Q)
+
+            if(len(regions_S) == 0 and len(regions_T)==0):
+                S = S_temp; T = T_temp 
+
+            elif(len(regions_S) != 0 and len(regions_T)!=0):
+                
+                common_std = min(min(S_temp.intpl_stds), min(T_temp.intpl_stds))/10
+                USER_GIVEN_STD[np.in1d(self.TrajInt_R.interpolation_points, regions_S)] = common_std 
+                
+                S = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_R, user_given_std = USER_GIVEN_STD)
+                USER_GIVEN_STD = np.repeat(-1.0, self.TrajInt_R.n_bins)
+                USER_GIVEN_STD[np.in1d(self.TrajInt_Q.interpolation_points, regions_T)] = common_std
+                T = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_Q, user_given_std = USER_GIVEN_STD)  
+
+            elif(len(regions_S) != 0):         
+                common_std = min(min(S_temp.intpl_stds), min(T_temp.intpl_stds))/10
+                USER_GIVEN_STD[np.in1d(self.TrajInt_R.interpolation_points, regions_S)] = common_std 
+                S = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_R, user_given_std = USER_GIVEN_STD)
+                T = T_temp
+
+            elif(len(regions_T) != 0):
+                common_std = min(min(S_temp.intpl_stds), min(T_temp.intpl_stds))/10
+                USER_GIVEN_STD[np.in1d(self.TrajInt_Q.interpolation_points, regions_T)] = common_std 
+                T = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_Q, user_given_std = USER_GIVEN_STD)  
+                S = S_temp
+                
+        del gex_r
+        del gex_q
+        return [S,T]
+    
+    
+    def align_single_pair(self, gene_idx):
+        
+        gene = self.gene_list[gene_idx]
+        
+        if(not(gene in self.pairs.keys())):
+            self.pairs[gene] = self.run_interpolation(gene_idx)
+        
+        S = self.pairs[gene][0]
+        T = self.pairs[gene][1]
+        
+        fwd_DP = orgalign.DP5(S,T,  free_params = self.state_params, backward_run=False,  zero_transition_costs= False, prohibit_case = False) 
+        fwd_opt_cost = fwd_DP.run_optimal_alignment() 
+        alignment_path = fwd_DP.backtrack() 
+        fwd_DP.alignment_path = alignment_path
+
+        landscapeObj = orgalign.AlignmentLandscape(fwd_DP, None, len(S.mean_trend), len(T.mean_trend), alignment_path, the_5_state_machine = True)
+        landscapeObj.collate_fwd() #landscapeObj.plot_alignment_landscape()
+
+        a = AligmentObj(gene, S,T,fwd_DP,None, landscapeObj)
+        return a
+    
+    def align_all_pairs(self, concurrent=False, n_processes = None):
+        
+        print('Running gene-level alignment: \U0001F9EC')
+        
+        if(concurrent):
+            if(n_processes is None):
+                n_processes = multiprocessing.cpu_count()
+                print('concurrent mode, running with',n_processes, 'processes')
+            with Pool(n_processes) as p:
+                results = list(tqdm(p.imap(self.align_single_pair, range(0,len(self.gene_list))), total=len(self.gene_list))) 
+            p.close(); p.terminate(); p.join(); 
+            self.results = results 
+            self.results_map = {}
+            for a in self.results:
+                self.results_map[a.gene] = a
+        else:
+            self.results_map = {}
+            self.results = []
+            for gene_idx in tqdm(range(len(self.gene_list))):
+                a = self.align_single_pair(gene_idx)
+                self.results.append(a)
+                self.results_map[a.gene] = a
+                
+        print('Alignment completed! \u2705')
+        
+    def get_stat_df(self):
+
+        opt_alignment_costs = [] 
+        match_percentages =[] 
+        l2fc = []
+        for g in self.gene_list:
+            opt_alignment_costs.append(self.results_map[g].fwd_DP.opt_cost)
+            match_percentages.append(self.results_map[g].match_percentage/100)
+            rgex = np.asarray(self.ref_mat.loc[:,g])
+            qgex = np.asarray(self.query_mat.loc[:,g])        
+            l2fc.append(np.log2(np.mean(rgex)/np.mean(qgex))) 
+
+        df = pd.DataFrame([self.gene_list, match_percentages, opt_alignment_costs, l2fc]).transpose()
+        df.columns = ['Gene','alignment_similarity_percentage', 'opt_alignment_cost','l2fc']
+        df.set_index('Gene')
+        df['color'] = np.repeat('green',df.shape[0])
+        df.loc[df['alignment_similarity_percentage']<=0.5,'color'] = 'red'
+        df['abs_l2fc'] = np.abs(df['l2fc']) 
+        df = df.sort_values(['alignment_similarity_percentage','abs_l2fc'],ascending=[True, False])
+        
+        plt.subplots(1,2,figsize=(10,4))
+        plt.subplot(1,2,1)
+        print('Mean alignment similarity percentage (matched %): ')
+        print(round(np.mean(df['alignment_similarity_percentage']),4)*100,'%' )
+        temp = np.asarray([x*100 for x in df['alignment_similarity_percentage']]) 
+        p = sb.kdeplot(temp, fill=True,label='Alignment Similarity %')
+        plt.xlabel('Alignment Similarity Percentage')
+        plt.xlim([0,100])
+        p.set_yticklabels(p.get_yticks(), size = 12)
+        p.set_xticklabels(p.get_xticks(), size = 12)
+        plt.xlabel('Alignment similarity percentage', fontsize='12')
+        plt.ylabel('Density', fontsize='12')
+
+        plt.subplot(1,2,2)
+        VisualUtils.plot_alignmentSim_vs_optCost(df)
+        
+        return df
+        
+        
+    def get_aggregate_alignment(self):
+        average_alignment, alignment_path =  ClusterUtils.get_cluster_average_alignments(self, self.gene_list)
+        mat = ClusterUtils.get_pairwise_match_count_mat(self, self.gene_list )
+        print('Average Alignment: ', VisualUtils.color_al_str(average_alignment), '(cell-level)')
+        print( '% similarity:', np.round(len(regex.findall("M|W|V",average_alignment))*100/len(average_alignment),2) )
+        self.average_alignment = average_alignment
+        VisualUtils.plot_alignment_path_on_given_matrix(paths = [alignment_path], mat=mat)
+        
+    def get_aggregate_alignment_for_subset(self, gene_subset):
+        average_alignment, alignment_path =  ClusterUtils.get_cluster_average_alignments(self, gene_subset)
+        mat = ClusterUtils.get_pairwise_match_count_mat(self, gene_subset )
+        print('Average Alignment: ', VisualUtils.color_al_str(average_alignment), '(cell-level)')
+        VisualUtils.plot_alignment_path_on_given_matrix(paths = [alignment_path], mat=mat)
+   
+    def show_cluster_alignment_strings(self,cluster_id):
+        for i in range(len(self.cluster_ids)):
+            if(self.cluster_ids[i]==cluster_id):
+                print(VisualUtils.color_al_str(self.results[i].alignment_str))
+                self.results[i].cluster_id = cluster_id
+                
+    def get_cluster_alignment_objects(self, cluster_id):
+        cluster_al_objects = []
+        for i in range(len(self.cluster_ids)):
+            if(self.cluster_ids[i]==cluster_id):
+                #print(self.results[i].alignment_str)
+                self.results[i].cluster_id = cluster_id
+                cluster_al_objects.append(self.results[i])
+        return cluster_al_objects 
+
+    def show_ordered_alignments(self):
+        
+        print('In the order of the first match occurrence along pseudotime')
+        
+        for a in self.results:
+            a.gene_pair = a.gene
+        return AlignmentDistMan.AlignmentDist(self).order_genes_by_alignments()
+    
+    
+    def show_pairwise_distance_matrix(self, al_obj): # pairwise log compression matrix 
+        
+        # check compression of each matched pair
+        temp_mat = al_obj.fwd_DP.DP_util_matrix
+        compression_dist_mat = [] 
+        for i in range(1,temp_mat.shape[0]):
+            row = []
+            for j in range(1,temp_mat.shape[1]):
+                x = np.abs(temp_mat[i,j][2])
+                row.append(float(x))
+            compression_dist_mat.append(row)   
+
+        x = pd.DataFrame(np.log10(np.asarray(compression_dist_mat) ))
+        min_x =  np.nanmin(np.asarray(x).flatten())
+        x = x.fillna(min_x) 
+        sb.heatmap(x, cmap='jet')  
+    
+    def get_match_stat_for_all_genes(self):
+        m_p = []
+        m_ps = []
+        m_pt = []
+        for a in self.results:
+            m_p.append(a.get_series_match_percentage()[0])
+            m_ps.append(a.get_series_match_percentage()[1])
+            m_pt.append(a.get_series_match_percentage()[2])
+            
+        df = pd.DataFrame([m_p,m_ps,m_pt,self.cluster_ids]).transpose() 
+        df.columns = ['match %', 'match % S', 'match % T', 'cluster_id']
+        return df 
+    
+
+
+class DEAnalyser:
+    
+    """
+    This class defines complementary functions for alignment results analysis. 
+    """
+    
+    def __init__(self, al_obj):
+        self.al_obj = al_obj
+        self.alignment_str = al_obj.alignment_str
+        self.al_visual = None
+
+    def _util_1(self, a):
+        ind = ""
+        i = 0
+        for c in range(len(a)):
+            if(a[c]=='-'):
+                ind = ind + ' '
+            elif(a[c]=='^'):
+                ind = ind + str(i-1)
+            elif(a[c] == '*'):
+                if(i<10):
+                    ind = ind + str(i)
+                    i=i+1
+                else:
+                    i=0
+                    ind = ind + str(i)
+                    i=i+1
+        return ind
+
+    def resolve(self, regions):
+        for i in range(len(regions)):
+            x = list(regions[i]); x[1] = x[1]-1; regions[i] = x
+        return regions
+
+    def get_matched_regions(self):
+        
+        if(self.al_visual == None):
+           # print('retrieving matched regions')
+            D_regions = [(m.start(0), m.end(0)) for m in regex.finditer("D+", self.alignment_str)]
+            I_regions = [(m.start(0), m.end(0)) for m in regex.finditer("I+", self.alignment_str)]
+            M_regions = [(m.start(0), m.end(0)) for m in regex.finditer("M+", self.alignment_str)] 
+            W_regions = [(m.start(0), m.end(0)) for m in regex.finditer("W+", self.alignment_str)]
+            V_regions = [(m.start(0), m.end(0)) for m in regex.finditer("V+", self.alignment_str)]
+
+            M_regions = self.resolve(M_regions); D_regions = self.resolve(D_regions); 
+            I_regions = self.resolve(I_regions)
+            W_regions = self.resolve(W_regions); V_regions =self. resolve(V_regions)
+            i = 0; j = 0; m_id = 0; i_id = 0; d_id = 0; v_id = 0; w_id = 0; c = 0
+            S_match_regions = []; T_match_regions = []
+            S_non_match_regions = []; T_non_match_regions = []
+            a1 = ""; a2 = ""
+
+            abstract_regions = []
+            colored_string=''
+            
+            while(c<len(self.alignment_str)):
+                #print(self.alignment_str[c])
+                if(self.alignment_str[c]=='M'):
+                    #print('match region')
+                    step = (M_regions[m_id][1] - M_regions[m_id][0] + 1)
+                    S_match_regions.append([j,j+step-1]); T_match_regions.append([i,i+step-1])
+                    i = i + step; j = j + step; m_id = m_id + 1
+                    a1 = a1 + hcolors.MATCH; a2 = a2 + hcolors.MATCH;
+                    a1 = a1 + "*"*(step); a2 = a2 + "*"*(step)
+                    a1 = a1 + hcolors.STOP; a2 = a2 + hcolors.STOP
+                    abstract_regions.append('M')
+                    colored_string += (hcolors.MATCH + "M"*(step) + hcolors.STOP)
+                    
+                    # process W,V separately 
+                if(self.alignment_str[c]=='V'):
+                    #print('warp V region')
+                    step = (V_regions[v_id][1] - V_regions[v_id][0] + 1)
+                    T_match_regions.append([i,i+step-1])
+                    i = i + step; v_id = v_id + 1
+                    a2 = a2 + hcolors.MATCH;
+                    a1 = a1 + "^"*(step); a2 = a2 + "*"*(step)
+                    a2 = a2 + hcolors.STOP
+                    abstract_regions.append('V')
+                    
+                    colored_string += (hcolors.MATCH + "V"*(step) + hcolors.STOP)
+                    
+                if(self.alignment_str[c]=='W'):
+                    #print('warp W region')
+                    step = (W_regions[w_id][1] - W_regions[w_id][0] + 1)
+                    S_match_regions.append([j,j+step-1])
+                    j = j + step; w_id = w_id + 1
+                    a1 = a1 + hcolors.MATCH;
+                    a1 = a1 + "*"*(step); a2 = a2 + "^"*(step)
+                    a1 = a1 + hcolors.STOP;
+                    abstract_regions.append('W')
+                    
+                    colored_string += (hcolors.MATCH + "W"*(step) + hcolors.STOP)
+                    
+                if(self.alignment_str[c]=='I'):
+                    #print('insert region')
+                    step = (I_regions[i_id][1] - I_regions[i_id][0] + 1)
+                    T_non_match_regions.append([i,i+step-1])
+                    i = i + step; i_id = i_id + 1
+                    a2 = a2+ hcolors.INSERT
+                    a1 = a1 + "-"*(step); a2 = a2 + "*"*(step)
+                    a2 = a2 + hcolors.STOP
+                    
+                    colored_string += (hcolors.INSERT + "I"*(step) + hcolors.STOP)
+                    #abstract_regions.append('I')
+                if(self.alignment_str[c]=='D'):
+                    #print('delete region')
+                    step = (D_regions[d_id][1] - D_regions[d_id][0] + 1)
+                    S_non_match_regions.append([j,j+step-1])
+                    j = j + step; d_id = d_id + 1
+                    a1 = a1 + hcolors.DELETE
+                    a1 = a1 + "*"*(step); a2 = a2 + "-"*(step)
+                    a1 = a1 + hcolors.STOP;
+                    
+                    colored_string += (hcolors.DELETE + "D"*(step) + hcolors.STOP)
+                    #abstract_regions.append('D')
+                c = c + step 
+                #print(step)
+            #print(abstract_regions)
+            # ----- to get index line along the alignment -----
+            i=0; index_line = ""
+            k = 0
+            while(k<len(self.alignment_str)):
+                if(i<10):
+                    index_line = index_line + str(i)
+                    i=i+1
+                else:
+                    i=0
+                    index_line = index_line + str(i)
+                    i=i+1
+                k=k+1
+            # ----- to get index line along the alignment -----
+
+            index_line_S =  self._util_1(a1) + ' Reference index'
+            index_line_T =  self._util_1(a2) + ' Query index'
+            
+            self.al_visual = index_line_S + '\n' + a1 + '\n' + a2 + '\n' + index_line_T
+            self.S_match_regions = S_match_regions
+            self.T_match_regions = T_match_regions
+            self.S_non_match_regions = S_non_match_regions
+            self.T_non_match_regions = T_non_match_regions 
+            self.abstract_regions = abstract_regions 
+            self.index_line = index_line
+            self.al_obj.colored_alignment_str = colored_string
+            
+        self.al_obj.al_visual = self.index_line + ' Alignment index \n'  + self.al_visual  + '\n'+ self.alignment_str + ' 5-state string '
+            
+  
+    # returns each 1-1 matching of time bins matched through M,W,V
+    def get_matched_time_points(self):
+        j = 0
+        i = 0
+        FLAG = False
+        matched_points_S = [] 
+        matched_points_T = [] 
+        prev_c = ''
+        for c in self.alignment_str:
+            if(c=='M'):
+                #print(c,i,j)
+                if(prev_c=='W'):
+                    i=i+1
+                if(prev_c=='V'):
+                    j=j+1
+                matched_points_T.append(i)
+                matched_points_S.append(j)
+                i=i+1
+                j=j+1
+            elif(c=='W'):
+                #print(prev_c,i,j)
+                if(prev_c not in ['W','V']):
+                    i=i-1
+                if(prev_c=='V'):
+                    i=i-1
+                    j=j+1
+                if(prev_c=='D' and not FLAG):
+                    FLAG = True
+                matched_points_T.append(i)
+                matched_points_S.append(j)
+                j=j+1
+            elif(c=='V'):
+                if(prev_c not in ['W','V']):
+                    j=j-1
+                if(prev_c=='W'):
+                    j=j-1
+                    i=i+1
+                if(prev_c=='I' and not FLAG):
+                    FLAG = True
+                matched_points_T.append(i)
+                matched_points_S.append(j)
+                i=i+1
+            elif(c=='I'):
+                if(prev_c=='W'):
+                    i=i+1
+                if(prev_c=='V'):
+                    j=j+1
+                i=i+1
+            elif(c=='D'):
+                if(prev_c=='W'):
+                    i=i+1
+                if(prev_c=='V'):
+                    j=j+1
+                j=j+1
+            prev_c = c
+        assert(len(matched_points_S) == len(matched_points_T))  
+ 
+        self.match_points_S = np.array(matched_points_S)
+        self.match_points_T = np.array(matched_points_T)        
+        self.l2fold_changes = [] 
+        self.l2fold_changes_in_matches = []
+
+        for i in range(len(self.match_points_S)):
+            
+            S_bin = self.al_obj.S.data_bins[self.match_points_S[i]] 
+            T_bin = self.al_obj.T.data_bins[self.match_points_T[i]] 
+            S_bin_mean = np.mean(S_bin)
+            T_bin_mean = np.mean(T_bin )
+            self.l2fold_changes.append([np.log2(S_bin_mean/T_bin_mean),self.match_points_S[i],self.match_points_T[i]])
+             # converting time bin ids to interpolated pseudotime values
+            self.l2fold_changes_in_matches.append([np.log2(S_bin_mean/T_bin_mean), self.al_obj.fwd_DP.S.time_points[self.match_points_S[i]],
+                                                 self.al_obj.fwd_DP.T.time_points[self.match_points_T[i]] ])
+            
+               
+    
+    # Sanity checker for non-significant DE in matched regions
+    def get_DE_info_for_matched_regions(self):
+        
+        s = self.match_points_S
+        t = self.match_points_T
+
+        matched_S_time = []
+        matched_T_time = []
+        compression_statistic = []
+        l2fc = []
+        wilcox_p = []
+        ks2_p = []
+        ttest_p = []
+
+        for i in range(len(s)):
+            l2fc.append(self.l2fold_changes_in_matches[i][0])
+            matched_S_time.append(self.l2fold_changes_in_matches[i][1]) 
+            matched_T_time.append(self.l2fold_changes_in_matches[i][2]) 
+            S_bin = self.al_obj.S.data_bins[s[i]] 
+            T_bin = self.al_obj.T.data_bins[t[i]] 
+            
+            self.al_obj.S.data_bins[s[i]] = np.asarray(self.al_obj.S.data_bins[s[i]])
+            self.al_obj.S.data_bins[t[i]] = np.asarray(self.al_obj.S.data_bins[t[i]])
+            
+            if(not np.any(self.al_obj.S.data_bins[s[i]] - self.al_obj.S.data_bins[t[i]] )):
+                wilcox_p.append(0.0)
+                ks2_p.append(0.0) 
+                ttest_p.append(0.0)
+            else:
+                wilcox = stats.wilcoxon(self.al_obj.S.data_bins[s[i]], self.al_obj.T.data_bins[t[i]])
+                ks2 =  stats.ks_2samp(self.al_obj.S.data_bins[s[i]], self.al_obj.T.data_bins[t[i]])
+                ttest = stats.ttest_ind(self.al_obj.S.data_bins[s[i]], self.al_obj.T.data_bins[t[i]])
+
+                wilcox_p.append(np.round(wilcox[1],6))
+                ks2_p.append(np.round(ks2[1],6)) 
+                ttest_p.append(np.round(ttest[1],6))
+
+            delta = np.round(self.al_obj.fwd_DP.DP_util_matrix[t[i]+1,s[i]+1][2],7)
+            compression_statistic.append(delta)
+
+        df = pd.DataFrame([s,t,matched_S_time, matched_T_time, compression_statistic, l2fc, wilcox_p, ks2_p,ttest_p]).transpose()
+        df.columns = [ 'ref_bin','query_bin','ref_pseudotime','query_pseudotime','Delta','l2fc', 'wilcox','ks2','ttest'] 
+                  
+        self.al_obj.matched_region_DE_info = df 
+        self.al_obj.match_points_S = s
+        self.al_obj.match_points_T = t
+
+    
+
+    
+    
+    
+    
diff --git a/genes2genes/.ipynb_checkpoints/MyFunctions-checkpoint.py b/genes2genes/.ipynb_checkpoints/MyFunctions-checkpoint.py
new file mode 100644
index 0000000..36e6f5c
--- /dev/null
+++ b/genes2genes/.ipynb_checkpoints/MyFunctions-checkpoint.py
@@ -0,0 +1,94 @@
+import torch
+import numpy as np
+
+torch.set_default_dtype(torch.float64)
+
+"""
+This script defines all methods required for computing mml distance between two gene expression distributions as Gaussian
+"""
+
+def negative_log_likelihood(μ,σ,N,data):
+    data = torch.tensor(data)
+    #opt_mode = True
+    #if(opt_mode):
+    sum_term = torch.sum(((data - μ)/σ)**2.0)/2.0
+    return ((N/2.0)* torch.log(2*torch.tensor(np.pi))) + (N*torch.log(σ)) + sum_term 
+
+def compute_expected_Fisher_matrix(μ,σ,N):
+    return torch.tensor([[N/(σ**2),0],[0,(2*N)/(σ**2)]]) # depends on σ
+    ####  ---- expected_Fisher = compute_expected_Fisher_matrix(μ_base,σ_base,N) # compute the closed form of matrix determinant instead 
+
+def I_prior(μ,σ): 
+    R_μ = torch.tensor(15.0) # uniform prior for mean over region R_μ 
+    R_σ = torch.tensor(3.0) # log σ has a uniform prior
+    return torch.log(σ) + torch.log(R_μ * R_σ)  # depends on σ
+
+def I_conway_const(d):
+    #if(d==2): # check withdrawn for optimisation (we know this is n=2 for Gaussian!)
+    c_2 = torch.tensor(5/(36 * np.sqrt(3)))
+    return torch.log(c_2)  # a constant
+
+def run_dist_compute_v3(data_to_model,μ_base, σ_base, print_stat=False):
+
+    if(len(data_to_model)==0):
+        return 
+    μ_base = torch.tensor(μ_base); σ_base=torch.tensor(σ_base) 
+    data = data_to_model
+    N = torch.tensor(len(data_to_model), requires_grad=False)
+    
+    # MODEL1 - using base model to encode data
+
+    determinant_of_the_expected_fisher = (2*(N**2))/(σ_base**4)   #torch.det(expected_Fisher) CLOSED FORM
+
+    L_θ = negative_log_likelihood(μ_base,σ_base,N,data) - (N*np.log(0.001)) # Accuracy of Measurement epsilon = 0.001
+    
+    #I_base_model = (I_conway_const(d=2) + I_prior(μ_base,σ_base) + (0.5*torch.log(torch.det(expected_Fisher)))) 
+    I_base_model = (I_conway_const(d=2) + I_prior(μ_base,σ_base) + (0.5*torch.log(determinant_of_the_expected_fisher))) 
+    # compute the I(data|base_model)
+    I_data_g_base_model = L_θ  + torch.tensor(1.0)
+    
+    return I_base_model, I_data_g_base_model
+
+
+# random gaussian distributed data generation
+def generate_random_dataset(N_datapoints, mean, variance):  
+    
+    μ = torch.tensor(mean); σ = torch.tensor(variance) 
+    if(variance<0):
+        μ = torch.distributions.Uniform(0,10.0).rsample() # random μ sampling
+        σ = torch.distributions.Uniform(0.8,3.0).rsample() # random σ sampling
+        #σ = torch.distributions.HalfCauchy(1).rsample() # random σ sampling   
+    
+    μ.requires_grad = True
+    σ.requires_grad = True
+    NormalDist = torch.distributions.Normal(μ,σ)
+    D = []
+    for n in range(N_datapoints):
+        D.append(float(NormalDist.rsample().detach().numpy()))
+    #print('True params: [ μ=',μ.data.numpy(), ' , σ=', σ.data.numpy(),']' )
+    return D,μ,σ
+
+    
+
+    
+
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
diff --git a/genes2genes/.ipynb_checkpoints/OrgAlign-checkpoint.py b/genes2genes/.ipynb_checkpoints/OrgAlign-checkpoint.py
new file mode 100644
index 0000000..c6a215d
--- /dev/null
+++ b/genes2genes/.ipynb_checkpoints/OrgAlign-checkpoint.py
@@ -0,0 +1,764 @@
+import torch
+import regex
+import numpy as np
+import seaborn as sb
+import matplotlib.pyplot as plt
+
+from . import MyFunctions
+from . import TimeSeriesPreprocessor
+
+torch.set_default_dtype(torch.float64)
+
+class FiveStateMachine:
+    """
+    This class represents a symmetric and probabilistic finite state machine with 5 alignment states (M,W,V,I,D) 
+    to define transition probabilities between each pair of states. 
+    """
+    # M,I,D as usual
+    # Additional states: W(Wd) , V(Wi) (representing insert direction warps and delete direction warps)
+    
+    def __init__(self, P_mm, P_ii, P_mi, PROHIBIT_CASE):
+        
+        # ====== M STATE
+        self.P_mm = P_mm
+        k = (1.0 - self.P_mm)/4.0
+        self.P_wm = k
+        self.P_vm = k
+        self.P_im = k
+        self.P_dm = k
+        
+        # ====== W and V STATES as equivalent to M STATE        
+        self.P_ww = self.P_mm
+        self.P_mw = self.P_wm
+        self.P_vw = self.P_vm
+        self.P_iw = self.P_im
+        self.P_dw = self.P_dm
+              
+        self.P_vv = self.P_mm
+        self.P_mv = self.P_vm
+        self.P_wv = self.P_wm
+        self.P_iv = self.P_im
+        self.P_dv = self.P_dm
+        
+        # ====== I STATE
+        # prohibit any transition from I or D to a warp state
+        
+        if(PROHIBIT_CASE):
+            self.P_ii = P_ii   
+            self.P_mi = P_mi
+            self.P_wi = 0.0
+            self.P_vi = 0.0 
+        else:
+            self.P_ii = P_ii # USE P_II for prohibitive case
+            self.P_mi = P_mi
+            self.P_wi = 0.0
+            self.P_vi = P_ii # USE 0 for prohibitive case
+
+        self.P_di = 1.0 - self.P_ii - self.P_mi - self.P_wi - self.P_vi
+
+        
+        # ====== D STATE as equivalent to I STATE
+        self.P_md = self.P_mi; 
+        self.P_dd = self.P_ii; 
+        self.P_id = self.P_di
+        self.P_wd = self.P_vi #self.P_wi; 
+        self.P_vd = 0.0 #self.P_vi
+        
+        # =====================================================
+        # encoding length terms 
+        self.I_mm = -np.log(self.P_mm)
+        self.I_wm = -np.log(self.P_wm)
+        self.I_vm = -np.log(self.P_vm)
+        self.I_im = -np.log(self.P_im)
+        self.I_dm = -np.log(self.P_dm)
+        
+        self.I_ww = self.I_mm; 
+        self.I_mw = self.I_wm; 
+        self.I_vw = self.I_vm; 
+        self.I_dw = self.I_dm;  
+        self.I_iw = self.I_im;
+        
+        self.I_vv = self.I_mm; 
+        self.I_mv = self.I_vm; 
+        self.I_wv = self.I_wm; 
+        self.I_dv = self.I_dm;  
+        self.I_iv = self.I_im;
+        
+        self.I_ii = -np.log(self.P_ii)
+        self.I_mi = -np.log(self.P_mi)
+        self.I_wi = -np.log(self.P_wi)
+        self.I_vi = -np.log(self.P_vi)
+        self.I_di = -np.log(self.P_di)
+        
+        self.I_dd = -np.log(self.P_dd)
+        self.I_md = -np.log(self.P_md)
+        self.I_wd = -np.log(self.P_wd) 
+        self.I_vd = -np.log(self.P_vd)
+        self.I_id = -np.log(self.P_id); 
+
+    def print(self):
+        print('M state outgoing: ')
+        print('Pmm = ', self.I_mm, self.P_mm)
+        print('Pwm = ', self.I_wm, self.P_wm)
+        print('Pvm = ', self.I_vm, self.P_vm)
+        print('Pim = ', self.I_im, self.P_im)
+        print('Pdm = ', self.I_dm, self.P_dm)
+        print(self.P_mm + self.P_wm + self.P_vm + self.P_im + self.P_dm)
+        
+        print('====================')
+        print('W state outgoing: ')
+        print('Pww = ', self.I_ww, self.P_ww)
+        print('Pmw = ', self.I_mw, self.P_mw)
+        print('Pvw = ', self.I_vw, self.P_vw)
+        print('Piw = ', self.I_iw, self.P_iw)
+        print('Pdw = ', self.I_dw, self.P_dw) 
+        print(self.P_ww + self.P_mw + self.P_vw + self.P_iw + self.P_dw )
+        
+        print('====================')
+        print('V state outgoing: ')
+        print('Pvv = ', self.I_vv, self.P_vv)
+        print('Pmv = ', self.I_mv, self.P_mv)
+        print('Pwv = ', self.I_wv, self.P_wv)
+        print('Piv = ', self.I_iv, self.P_iv)
+        print('Pdv = ', self.I_dv, self.P_dv) 
+        print(self.P_vv + self.P_mv + self.P_wv + self.P_iv + self.P_dv )
+        
+        print('====================')
+        print('I state outgoing: ')
+        print('Pii = ', self.I_ii, self.P_ii)
+        print('Pmi = ', self.I_mi, self.P_mi)
+        print('Pwi = ', self.I_wi, self.P_wi)   
+        print('Pvi = ', self.I_vi, self.P_vi)    
+        print('Pdi = ', self.I_di, self.P_di)
+        print(self.P_ii + self.P_mi + self.P_wi + self.P_vi + self.P_di)
+            
+        print('====================')
+        print('D state outgoing: ')
+        print('Pdd = ', self.I_dd, self.P_dd)
+        print('Pmd = ', self.I_md, self.P_md)
+        print('Pwd = ', self.I_wd, self.P_wd)
+        print('Pvd = ', self.I_vd, self.P_vd)
+        print('Pid = ', self.I_id, self.P_id) 
+        print(self.P_dd + self.P_md + self.P_wd + self.P_vd + self.P_id )
+        
+    def _set_all_zero_costs(self):
+            self.I_mm = 0.0
+            self.I_wm = 0.0
+            self.I_vm = 0.0
+            self.I_im = 0.0
+            self.I_dm = 0.0
+
+            self.I_ww = 0.0 
+            self.I_mw = 0.0 
+            self.I_vw = 0.0 
+            self.I_dw = 0.0
+            self.I_iw = 0.0
+
+            self.I_vv = 0.0
+            self.I_mv = 0.0
+            self.I_wv = 0.0
+            self.I_dv = 0.0
+            self.I_iv = 0.0
+
+            self.I_ii = 0.0
+            self.I_mi = 0.0
+            self.I_wi = 0.0
+            self.I_vi = 0.0
+            self.I_di = 0.0
+
+            self.I_dd = 0.0
+            self.I_md = 0.0
+            self.I_wd = 0.0 
+            self.I_vd = 0.0
+            self.I_id = 0.0
+            
+    def reverse(self):
+        P_mm = self.P_mm; P_mi = self.P_im; P_md = self.P_dm; P_mw = self.P_wm; P_mv = self.P_vm
+        P_im = self.P_mi; P_ii = self.P_ii; P_id = self.P_di; P_iw = self.P_wi; P_iv = self.P_vi 
+        P_dd = self.P_dd; P_dm = self.P_md; P_di = self.P_id; P_dw = self.P_wd; P_dv = self.P_vd
+        P_ww = self.P_ww; P_wm = self.P_mw; P_wi = self.P_iw; P_wd = self.P_dw; P_wv = self.P_vw 
+        P_vm = self.P_mv; P_vv = self.P_vv; P_vd = self.P_dv; P_vw = self.P_wv; P_vi = self.P_iv 
+
+        self.P_mm = P_mm; self.P_mi = P_mi; self.P_md = P_md; self.P_mw = P_mw; self.P_mv = P_mv
+        self.P_im = P_im; self.P_ii = P_ii; self.P_id = P_id; self.P_iw = P_iw; self.P_iv = P_iv
+        self.P_dd = P_dd; self.P_dm = P_dm; self.P_di = P_di; self.P_dw = P_dw; self.P_dv = P_dv
+        self.P_ww = P_ww; self.P_wm = P_wm; self.P_wi = P_wi; self.P_wd = P_wd; self.P_wv = P_wv 
+        self.P_vm = P_vm; self.P_vv = P_vv; self.P_vd = P_vd; self.P_vw = P_vw; self.P_vi = P_vi 
+        
+        # encoding length terms 
+        self.I_mm =  -np.log(self.P_mm); self.I_im =  -np.log(self.P_im); self.I_dm =  -np.log(self.P_dm);
+        self.I_wm =  -np.log(self.P_wm); self.I_vm =  -np.log(self.P_vm);
+
+        self.I_ii =  -np.log(self.P_ii); self.I_mi =  -np.log(self.P_mi); self.I_di =  -np.log(self.P_di)
+        self.I_wi =  -np.log(self.P_wi); self.I_vi =  -np.log(self.P_vi);
+        
+        self.I_md = self.I_mi; self.I_dd = self.I_ii; self.I_id = self.I_di
+        self.I_wd = self.I_wi; self.I_vd = self.I_vi
+        
+        self.I_vv =  -np.log(self.P_vv); self.I_mv =  -np.log(self.P_mv); self.I_dv =  -np.log(self.P_dv)
+        self.I_wv =  -np.log(self.P_wv); self.I_iv =  -np.log(self.P_iv);
+        
+        self.I_ww = self.I_vv; self.I_iw = self.I_iv; self.I_dw = self.I_dv; 
+        self.I_vw = self.I_wv; self.I_mw = self.I_mv; 
+
+        
+
+class DP5:
+    
+    """
+    This class defines the dynamic programming algorithm and related functions 
+    used to find the optimal alignment between two gene expression time series
+    """
+
+    def __init__(self, S,T, backward_run, free_params, zero_transition_costs = False, prohibit_case=True):
+        self.S = S
+        self.T = T
+        self.S_len = len(S.data_bins)
+        self.T_len = len(T.data_bins) 
+        self.FSA = FiveStateMachine(free_params[0], free_params[1], free_params[2], PROHIBIT_CASE= prohibit_case)
+        self.backward_run = backward_run
+        if(backward_run):
+            self.FSA.reverse() 
+        if(zero_transition_costs):
+            self.FSA._set_all_zero_costs() 
+        self.init_DP_matrices()  
+        self.init_backtrackers() 
+        self.alignment_str = "" 
+        self.init()     
+        
+    def init_DP_matrices(self):    
+        
+        self.DP_M_matrix = []
+        self.DP_I_matrix = []
+        self.DP_D_matrix = []
+        self.DP_W_matrix = []
+        self.DP_V_matrix = []
+        self.DP_util_matrix = [] # to store match and null lengths that make the amount of compression 
+        
+        for i in range(self.T_len+1):
+            self.DP_M_matrix.append(np.repeat(0.0,self.S_len+1))
+            self.DP_I_matrix.append(np.repeat(0.0,self.S_len+1))
+            self.DP_D_matrix.append(np.repeat(0.0,self.S_len+1))
+            self.DP_W_matrix.append(np.repeat(0.0,self.S_len+1))
+            self.DP_V_matrix.append(np.repeat(0.0,self.S_len+1))
+            
+            self.DP_util_matrix.append(np.repeat(None,self.S_len+1))
+            
+        self.DP_M_matrix = np.matrix(self.DP_M_matrix) 
+        self.DP_I_matrix = np.matrix(self.DP_I_matrix)
+        self.DP_D_matrix = np.matrix(self.DP_D_matrix) 
+        self.DP_W_matrix = np.matrix(self.DP_W_matrix) 
+        self.DP_V_matrix = np.matrix(self.DP_V_matrix) 
+        
+        self.DP_util_matrix = np.matrix(self.DP_util_matrix) 
+        
+    def init_backtrackers(self):
+        self.backtrackers_M = []
+        self.backtrackers_I = []
+        self.backtrackers_D = []
+        self.backtrackers_W = []
+        self.backtrackers_V = []
+        
+        for i in range(self.T_len+1):
+            row = []
+            for j in range(self.S_len+1):
+                row.append([0,0,0]) # record <i,j,state> backtracker pointer info
+            self.backtrackers_M.append(row.copy())
+            self.backtrackers_I.append(row.copy())
+            self.backtrackers_D.append(row.copy())
+            self.backtrackers_W.append(row.copy())
+            self.backtrackers_V.append(row.copy())
+            
+    def init(self): 
+        
+        ProbM = 0.9999
+        ProbI = (1.0 - ProbM)/2.0
+        ProbD = ProbI
+        
+        # DP_M --- first row and first col --- np.inf
+        for j in range(1,self.S_len+1):
+            self.DP_M_matrix[0,j] = np.inf
+            self.backtrackers_M[0][j] = [np.inf,np.inf,np.inf]
+            self.DP_W_matrix[0,j] = np.inf
+            self.backtrackers_W[0][j] = [np.inf,np.inf,np.inf]
+            self.DP_V_matrix[0,j] = np.inf
+            self.backtrackers_V[0][j] = [np.inf,np.inf,np.inf]
+            
+        for i in range(1,self.T_len+1):
+            self.DP_M_matrix[i,0] = np.inf
+            self.backtrackers_M[i][0] = [np.inf,np.inf,np.inf]
+            self.DP_W_matrix[i,0] = np.inf
+            self.backtrackers_W[i][0] = [np.inf,np.inf,np.inf]
+            self.DP_V_matrix[i,0] = np.inf
+            self.backtrackers_V[i][0] = [np.inf,np.inf,np.inf]
+        
+        # DP_I --- first row np.inf
+        for j in range(1,self.S_len+1):
+            self.DP_I_matrix[0,j] = np.inf
+            self.backtrackers_I[0][j] = [np.inf,np.inf,np.inf]
+            
+        for i in range(1,self.T_len+1):
+            cost_D, cost_I = self.compute_cell(i-1,0, only_non_match=True)
+
+            if(i==1):
+                self.DP_I_matrix[i,0] = self.DP_I_matrix[i-1,0] + cost_I -np.log(ProbI) 
+            else:
+                self.DP_I_matrix[i,0] = self.DP_I_matrix[i-1,0] + cost_I + self.FSA.I_ii 
+
+            
+            #self.backtrackers_I[i][0] = [i-1,0,4] 
+            if(not self.backward_run):
+                self.backtrackers_I[i][0] = [i-1,0,4] 
+            else:
+                self.backtrackers_I[i][0] = [i-1,0,0] 
+
+        # DP_D --- first col np.inf
+        for i in range(1,self.T_len+1):
+            self.DP_D_matrix[i,0] = np.inf
+            self.backtrackers_D[i][0] = [np.inf,np.inf,np.inf]
+            
+        for j in range(1,self.S_len+1):
+            cost_D, cost_I =self.compute_cell(0,j-1, only_non_match=True)
+        
+            if(j==1):
+                self.DP_D_matrix[0,j] = self.DP_D_matrix[0,j-1] + cost_D -np.log(ProbD)#-np.log(1/3) 
+            else:
+                self.DP_D_matrix[0,j] = self.DP_D_matrix[0,j-1] + cost_D + self.FSA.I_dd
+            
+            if(not self.backward_run):
+                self.backtrackers_D[0][j] = [0,j-1,3]  
+            else:
+                self.backtrackers_D[0][j] = [0,j-1,1]  
+        
+    def run_optimal_alignment(self):
+
+        # initial state probabilities 
+        ProbM = 0.99
+    
+        for i in range(1,self.T_len+1):
+            for j in range(1,self.S_len+1):
+                match_len,non_match_len_D,non_match_len_I = self.compute_cell(i-1,j-1) # here we use i-1 and j-1 to correctly call the time bin to use 
+                             
+                if(not self.backward_run):
+                    # filling M matrix
+                    if(i==1 and j==1):
+                        temp_m = [  self.DP_M_matrix[i-1,j-1] + match_len - np.log(ProbM), # 0
+                               np.inf, # 1
+                               np.inf, # 2
+                               np.inf, # 3
+                               np.inf  # 4
+                             ]  
+                    else:      
+                        temp_m = [  self.DP_M_matrix[i-1,j-1] + match_len + self.FSA.I_mm, # 0
+                                self.DP_W_matrix[i-1,j-1] + match_len  + self.FSA.I_mw, # 1
+                                self.DP_V_matrix[i-1,j-1] + match_len + self.FSA.I_mv, # 2
+                                self.DP_D_matrix[i-1,j-1]  + match_len + self.FSA.I_md, # 3
+                                self.DP_I_matrix[i-1,j-1] + match_len  + self.FSA.I_mi  # 4
+                             ] 
+                    
+                    # [END] NEW TEST 21122022 ====
+                    
+                    # filling W matrix 
+                    temp_w = [  self.DP_M_matrix[i,j-1] + match_len + self.FSA.I_wm, 
+                                self.DP_W_matrix[i,j-1]  + match_len  + self.FSA.I_ww, 
+                                self.DP_V_matrix[i,j-1]  + match_len  + self.FSA.I_wv, 
+                                self.DP_D_matrix[i,j-1]  + match_len + self.FSA.I_wd, 
+                                self.DP_I_matrix[i,j-1]  + match_len +  self.FSA.I_wi
+                             ]
+                    # filling V matrix  
+                    temp_v = [ self.DP_M_matrix[i-1,j]  + match_len+ self.FSA.I_vm, 
+                                self.DP_W_matrix[i-1,j]   + match_len + self.FSA.I_vw, 
+                                self.DP_V_matrix[i-1,j]   + match_len + self.FSA.I_vv, 
+                                self.DP_D_matrix[i-1,j]  + match_len+ self.FSA.I_vd, 
+                                self.DP_I_matrix[i-1,j]  + match_len+ self.FSA.I_vi
+                             ]
+
+                    # filling D matrix
+                    temp_d = [  self.DP_M_matrix[i,j-1]  + non_match_len_D + self.FSA.I_dm, 
+                                self.DP_W_matrix[i,j-1]  + non_match_len_D + self.FSA.I_dw,
+                                self.DP_V_matrix[i,j-1]  + non_match_len_D + self.FSA.I_dv, 
+                                self.DP_D_matrix[i,j-1]  + non_match_len_D + self.FSA.I_dd, 
+                                self.DP_I_matrix[i,j-1]  + non_match_len_D + self.FSA.I_di
+                             ]
+                    # filling I matrix
+                    temp_i = [  self.DP_M_matrix[i-1,j]  + non_match_len_I + self.FSA.I_im, 
+                                self.DP_W_matrix[i-1,j]  + non_match_len_I + self.FSA.I_iw, 
+                                self.DP_V_matrix[i-1,j]  + non_match_len_I + self.FSA.I_iv, 
+                                self.DP_D_matrix[i-1,j]  + non_match_len_I + self.FSA.I_id, 
+                                self.DP_I_matrix[i-1,j]  + non_match_len_I + self.FSA.I_ii
+                             ]
+                else:
+                    # filling M matrix
+                    temp_m = [  self.DP_I_matrix[i-1,j-1] + match_len + self.FSA.I_mi, # 0
+                                self.DP_D_matrix[i-1,j-1] + match_len + self.FSA.I_md, # 1
+                                self.DP_V_matrix[i-1,j-1] + match_len + self.FSA.I_mv, # 2
+                                self.DP_W_matrix[i-1,j-1] + match_len + self.FSA.I_mw, # 3
+                                self.DP_M_matrix[i-1,j-1] + match_len + self.FSA.I_mm  # 4
+                             ] 
+                    # filling W matrix 
+                    temp_w = [  self.DP_I_matrix[i,j-1] + match_len +  self.FSA.I_wi,
+                                self.DP_D_matrix[i,j-1] + match_len+ self.FSA.I_wd, 
+                                self.DP_V_matrix[i,j-1]  + match_len + self.FSA.I_wv, 
+                                self.DP_W_matrix[i,j-1]  + match_len + self.FSA.I_ww,
+                                self.DP_M_matrix[i,j-1] + match_len+ self.FSA.I_wm
+                             ]
+                    # filling V matrix  
+                    temp_v = [  self.DP_I_matrix[i-1,j] + match_len + self.FSA.I_vi,
+                                self.DP_D_matrix[i-1,j] + match_len + self.FSA.I_vd,
+                                self.DP_V_matrix[i-1,j] + match_len + self.FSA.I_vv,
+                                self.DP_W_matrix[i-1,j] + match_len + self.FSA.I_vw, 
+                                self.DP_M_matrix[i-1,j] + match_len + self.FSA.I_vm
+                             ]
+
+                    # filling D matrix
+                    temp_d = [  self.DP_I_matrix[i,j-1] + non_match_len_D + self.FSA.I_di,
+                                self.DP_D_matrix[i,j-1] + non_match_len_D + self.FSA.I_dd,
+                                self.DP_V_matrix[i,j-1] + non_match_len_D + self.FSA.I_dv,
+                                self.DP_W_matrix[i,j-1] + non_match_len_D + self.FSA.I_dw,
+                                self.DP_M_matrix[i,j-1] + non_match_len_D + self.FSA.I_dm
+                             ]
+                    # filling I matrix
+                    temp_i = [  self.DP_I_matrix[i-1,j] + non_match_len_I + self.FSA.I_ii,
+                                self.DP_D_matrix[i-1,j] + non_match_len_I + self.FSA.I_id, 
+                                self.DP_V_matrix[i-1,j] + non_match_len_I + self.FSA.I_iv,
+                                self.DP_W_matrix[i-1,j] + non_match_len_I + self.FSA.I_iw,
+                                self.DP_M_matrix[i-1,j] + non_match_len_I + self.FSA.I_im
+                             ]
+
+                tot_m = min(temp_m) 
+                min_idx_m = temp_m.index(tot_m)
+                tot_d = min(temp_d) 
+                min_idx_d = temp_d.index(tot_d)
+                tot_i = min(temp_i) 
+                min_idx_i = temp_i.index(tot_i)
+                tot_w = min(temp_w)
+                min_idx_w = temp_w.index(tot_w)
+                tot_v = min(temp_v)
+                min_idx_v = temp_v.index(tot_v)
+            
+                self.DP_M_matrix[i,j] =  tot_m #+ match_len
+                self.DP_I_matrix[i,j] =  tot_i #+ non_match_len_I
+                self.DP_D_matrix[i,j] =  tot_d #+ non_match_len_D
+                self.DP_W_matrix[i,j] =  tot_w #+ match_len
+                self.DP_V_matrix[i,j] =  tot_v #+ match_len
+                     
+                # save backtracker info
+                self.backtrackers_M[i][j] = [i-1,j-1,min_idx_m]
+                self.backtrackers_W[i][j] = [i,j-1,min_idx_w]
+                self.backtrackers_V[i][j] = [i-1,j,min_idx_v]
+                self.backtrackers_D[i][j] = [i,j-1,min_idx_d]
+                self.backtrackers_I[i][j] = [i-1,j,min_idx_i]
+                             
+        
+        # RETURN TOT MESSAGE LENGTH OF THE OPTIMAL ALIGNMENT    
+        if(not self.backward_run):
+            self.opt_cost = min([self.DP_M_matrix[self.T_len,self.S_len], 
+                               self.DP_W_matrix[self.T_len,self.S_len],
+                               self.DP_V_matrix[self.T_len,self.S_len], 
+                               self.DP_D_matrix[self.T_len,self.S_len], 
+                               self.DP_I_matrix[self.T_len,self.S_len]])
+            return 
+        else:
+            self.opt_cost = min([self.DP_I_matrix[self.T_len,self.S_len],
+                        self.DP_D_matrix[self.T_len,self.S_len], 
+                        self.DP_V_matrix[self.T_len,self.S_len],
+                        self.DP_W_matrix[self.T_len,self.S_len],
+                        self.DP_M_matrix[self.T_len,self.S_len]
+                    ])
+            return 
+    
+        
+    def compute_cell(self,i,j,only_non_match=False):
+        # Maximising the COMPRESSION ============
+        if(only_non_match):
+            return 0.0,0.0
+        
+        μ_S = self.S.mean_trend[j]; σ_S = self.S.std_trend[j]; 
+        ref_data = self.S.data_bins[j]
+        μ_T = self.T.mean_trend[i]; σ_T = self.T.std_trend[i]; 
+        query_data = self.T.data_bins[i]
+
+        I_ref_model, I_refdata_g_ref_model = MyFunctions.run_dist_compute_v3(ref_data, μ_S, σ_S) 
+        I_query_model, I_querydata_g_query_model = MyFunctions.run_dist_compute_v3(query_data, μ_T, σ_T) 
+        I_ref_model, I_querydata_g_ref_model = MyFunctions.run_dist_compute_v3(query_data, μ_S, σ_S) 
+        I_query_model, I_refdata_g_query_model = MyFunctions.run_dist_compute_v3(ref_data, μ_T, σ_T) 
+        
+        match_encoding_len1 = I_ref_model + I_querydata_g_ref_model + I_refdata_g_ref_model
+        match_encoding_len1 = match_encoding_len1/(len(query_data)+len(ref_data))
+        match_encoding_len2 = I_query_model + I_refdata_g_query_model + I_querydata_g_query_model
+        match_encoding_len2 = match_encoding_len2/(len(query_data)+len(ref_data))
+        match_encoding_len = (match_encoding_len1 + match_encoding_len2 )/2.0 
+        #match_encoding_len = torch.min(torch.tensor([match_encoding_len, match_encoding_len2]))
+        
+        null = (I_ref_model + I_refdata_g_ref_model + I_query_model + I_querydata_g_query_model)/(len(query_data)+len(ref_data))
+        match_compression =   match_encoding_len - null 
+        
+        #match_compression = match_compression - self.mean_batch_effect # [POSSIBLE METHOD] 
+        # constant adjustment for accounting for batch effect
+        
+        non_match_encoding_len_D = 0.0
+        non_match_encoding_len_I = 0.0 
+        self.DP_util_matrix[i+1,j+1] = [null.numpy(),match_encoding_len.numpy(),match_compression.numpy()]
+
+        return match_compression.numpy(), non_match_encoding_len_D, non_match_encoding_len_I
+
+
+
+    def _backtrack_util(self,backtracker_pointer):
+        
+        prev_i = backtracker_pointer[0]
+        prev_j = backtracker_pointer[1]
+        prev_state = '-'
+
+        if(not self.backward_run):
+            if(backtracker_pointer[2]==0):
+                prev_state = 'M'
+            elif(backtracker_pointer[2]==1): 
+                prev_state = 'W'
+            elif(backtracker_pointer[2]==2):
+                prev_state = 'V'
+            elif(backtracker_pointer[2]==3):
+                prev_state = 'D'
+            elif(backtracker_pointer[2]==4):
+                prev_state = 'I'
+
+        else:
+            if(backtracker_pointer[2]==0):
+                prev_state = 'I'
+            elif(backtracker_pointer[2]==1):
+                prev_state = 'D'
+            elif(backtracker_pointer[2]==2):
+                prev_state = 'V'
+            elif(backtracker_pointer[2]==3): 
+                prev_state = 'W'
+            elif(backtracker_pointer[2]==4):
+                prev_state = 'M'
+
+        return prev_i, prev_j, prev_state
+    
+    def backtrack(self):
+        self.alignment_str = ""
+        j = self.S_len ; i = self.T_len
+        self.S_str = "" ; self.T_str = ""
+        tracked_path = [] 
+        # seek backtrack starting point 
+        if(not self.backward_run):
+            last_cell_costs = [self.DP_M_matrix[i,j], 
+                               self.DP_W_matrix[i,j],
+                               self.DP_V_matrix[i,j], 
+                               self.DP_D_matrix[i,j], 
+                               self.DP_I_matrix[i,j]]
+            min_idx = last_cell_costs.index(min(last_cell_costs))  
+
+            #print('tot_msg_len_of_alignment = ', min(last_cell_costs)) 
+            if(min_idx==0): # match
+                state = 'M'
+            elif(min_idx==1):
+                state = 'W'
+            elif(min_idx==2):
+                state = 'V'
+            elif(min_idx==3): # delete
+                state = 'D'
+            elif(min_idx==4): # insert
+                state = 'I'
+        else:
+            last_cell_costs = [self.DP_I_matrix[i,j], 
+                               self.DP_D_matrix[i,j],
+                               self.DP_V_matrix[i,j], 
+                               self.DP_W_matrix[i,j], 
+                               self.DP_M_matrix[i,j]]
+            min_idx = last_cell_costs.index(min(last_cell_costs))  
+
+            if(min_idx==0): # match
+                state = 'I'
+            elif(min_idx==1):
+                state = 'D'
+            elif(min_idx==2):
+                state = 'V'
+            elif(min_idx==3): # delete
+                state = 'W'
+            elif(min_idx==4): # insert
+                state = 'M'
+            
+        while(True): 
+            if(i==0 and j==0):
+                break
+            #print(i,j,state)
+            if(state=='M'): # match
+                prev_i, prev_j, prev_state = self._backtrack_util(self.backtrackers_M[i][j])
+            elif(state=='D'): # delete
+                prev_i, prev_j, prev_state = self._backtrack_util(self.backtrackers_D[i][j])
+            elif(state=='I'): 
+                prev_i, prev_j, prev_state = self._backtrack_util(self.backtrackers_I[i][j]) 
+            elif(state=='W'):
+                prev_i, prev_j, prev_state = self._backtrack_util(self.backtrackers_W[i][j]) 
+            elif(state=='V'):
+                prev_i, prev_j, prev_state = self._backtrack_util(self.backtrackers_V[i][j]) 
+            #self._align_str_util(state)
+            self.alignment_str = state + self.alignment_str
+            #print(i,j, state)
+            tracked_path.append([i,j])
+            #print('goto --> ', prev_i, prev_j, prev_state )
+            i = prev_i
+            j = prev_j
+            state = prev_state
+            #self.alignment_str = state + self.alignment_str
+
+        return tracked_path
+                
+            
+     
+    def get_matched_regions(self):
+        D_regions = [(m.start(0), m.end(0)) for m in regex.finditer("D+", self.alignment_str)]
+        I_regions = [(m.start(0), m.end(0)) for m in regex.finditer("I+", self.alignment_str)]
+        M_regions = [(m.start(0), m.end(0)) for m in regex.finditer("M+", self.alignment_str)] 
+        W_regions = [(m.start(0), m.end(0)) for m in regex.finditer("W+", self.alignment_str)]
+        V_regions = [(m.start(0), m.end(0)) for m in regex.finditer("V+", self.alignment_str)]
+        def resolve(regions):
+            for i in range(len(regions)):
+                x = list(regions[i]); x[1] = x[1]-1; regions[i] = x
+            return regions
+        M_regions = resolve(M_regions); D_regions = resolve(D_regions); I_regions = resolve(I_regions)
+        i = 0; j = 0; m_id = 0; i_id = 0; d_id = 0; c = 0
+        S_match_regions = []; T_match_regions = []
+        S_non_match_regions = []; T_non_match_regions = []
+        a1 = ""; a2 = ""
+
+        while(c<len(self.alignment_str)):
+            if(self.alignment_str[c]=='M'):
+                step = (M_regions[m_id][1] - M_regions[m_id][0] + 1)
+                S_match_regions.append([j,j+step-1]); T_match_regions.append([i,i+step-1])
+                i = i + step; j = j + step; m_id = m_id + 1
+                a1 = a1 + "*"*(step); a2 = a2 + "*"*(step)
+                # process W,V separately 
+            if(self.alignment_str[c]=='I'):
+                step = (I_regions[i_id][1] - I_regions[i_id][0] + 1)
+                T_non_match_regions.append([i,i+step-1])
+                i = i + step; i_id = i_id + 1
+                a1 = a1 + "-"*(step); a2 = a2 + "*"*(step)
+            if(self.alignment_str[c]=='D'):
+                step = (D_regions[d_id][1] - D_regions[d_id][0] + 1)
+                S_non_match_regions.append([j,j+step-1])
+                j = j + step; d_id = d_id + 1
+                a1 = a1 + "*"*(step); a2 = a2 + "-"*(step)
+            c = c + step 
+
+        return [S_match_regions, T_match_regions, S_non_match_regions, T_non_match_regions]
+
+
+class AlignmentLandscape:
+    
+    def __init__(self, fwd_DP, bwd_DP, S_len, T_len, alignment_path, the_5_state_machine = False):
+        self.the_5_state_machine = the_5_state_machine
+        self.fwd_DP_M = fwd_DP.DP_M_matrix
+        self.fwd_DP_I = fwd_DP.DP_I_matrix
+        self.fwd_DP_D = fwd_DP.DP_D_matrix 
+        if(the_5_state_machine):
+            self.fwd_DP_W = fwd_DP.DP_W_matrix
+            self.fwd_DP_V = fwd_DP.DP_V_matrix
+        if(bwd_DP!=None): 
+            self.bwd_DP_M = bwd_DP.DP_M_matrix
+            self.bwd_DP_I = bwd_DP.DP_I_matrix
+            self.bwd_DP_D = bwd_DP.DP_D_matrix
+            if(the_5_state_machine):
+                self.bwd_DP_W = bwd_DP.DP_W_matrix
+                self.bwd_DP_V = bwd_DP.DP_V_matrix
+        
+        self.FSA = fwd_DP.FSA
+        self.S_len = S_len
+        self.T_len = T_len
+        
+        self.L_matrix = []
+        for i in range(T_len+1):
+            self.L_matrix.append(np.repeat(0.0,S_len+1))
+        self.L_matrix = np.matrix(self.L_matrix) 
+        
+        self.L_matrix_states = []
+        for i in range(T_len+1):
+            self.L_matrix_states.append(np.repeat('',S_len+1))
+        self.L_matrix_states = np.matrix(self.L_matrix_states) 
+        self.alignment_path = alignment_path
+        
+    def collate(self):
+        
+        #for i in tqdm(range(0,self.T_len+1)):
+        for i in range(0,self.T_len+1):
+            for j in range(0,self.S_len+1):
+                # considering all possible state transitions (cartesian product of {m,i,d})
+                _i = self.T_len-i
+                _j = self.S_len-j
+                
+                if(not self.the_5_state_machine):
+                    temp = [ self.fwd_DP_M[i,j] + self.bwd_DP_M[_i, _j] + self.FSA.I_mm, 
+                             self.fwd_DP_M[i,j] + self.bwd_DP_I[_i, _j] + self.FSA.I_im,
+                             self.fwd_DP_M[i,j] + self.bwd_DP_D[_i, _j] + self.FSA.I_dm,
+                             self.fwd_DP_I[i,j] + self.bwd_DP_M[_i, _j] + self.FSA.I_mi, 
+                             self.fwd_DP_I[i,j] + self.bwd_DP_I[_i, _j] + self.FSA.I_ii, 
+                             self.fwd_DP_I[i,j] + self.bwd_DP_D[_i, _j] + self.FSA.I_di, 
+                             self.fwd_DP_D[i,j] + self.bwd_DP_M[_i, _j] + self.FSA.I_md, 
+                             self.fwd_DP_D[i,j] + self.bwd_DP_I[_i, _j] + self.FSA.I_id, 
+                             self.fwd_DP_D[i,j] + self.bwd_DP_D[_i, _j] + self.FSA.I_dd ] 
+                else:
+                    temp = [ self.fwd_DP_M[i,j] + self.bwd_DP_M[_i, _j] + self.FSA.I_mm, 
+                         self.fwd_DP_M[i,j] + self.bwd_DP_W[_i, _j] + self.FSA.I_wm,
+                         self.fwd_DP_M[i,j] + self.bwd_DP_V[_i, _j] + self.FSA.I_vm,
+                         self.fwd_DP_M[i,j] + self.bwd_DP_D[_i, _j] + self.FSA.I_dm,
+                         self.fwd_DP_M[i,j] + self.bwd_DP_I[_i, _j] + self.FSA.I_im,
+                         self.fwd_DP_W[i,j] + self.bwd_DP_M[_i, _j] + self.FSA.I_mw,
+                         self.fwd_DP_W[i,j] + self.bwd_DP_W[_i, _j] + self.FSA.I_ww,
+                         self.fwd_DP_W[i,j] + self.bwd_DP_V[_i, _j] + self.FSA.I_vw,
+                         self.fwd_DP_W[i,j] + self.bwd_DP_D[_i, _j] + self.FSA.I_dw,
+                         self.fwd_DP_W[i,j] + self.bwd_DP_I[_i, _j] + self.FSA.I_iw,
+                         self.fwd_DP_V[i,j] + self.bwd_DP_M[_i, _j] + self.FSA.I_mv, 
+                         self.fwd_DP_V[i,j] + self.bwd_DP_W[_i, _j] + self.FSA.I_wv,
+                         self.fwd_DP_V[i,j] + self.bwd_DP_V[_i, _j] + self.FSA.I_vv,
+                         self.fwd_DP_V[i,j] + self.bwd_DP_D[_i, _j] + self.FSA.I_dv,
+                         self.fwd_DP_V[i,j] + self.bwd_DP_I[_i, _j] + self.FSA.I_iv,
+                         self.fwd_DP_D[i,j] + self.bwd_DP_M[_i, _j] + self.FSA.I_md, 
+                         self.fwd_DP_D[i,j] + self.bwd_DP_W[_i, _j] + self.FSA.I_wd, 
+                         self.fwd_DP_D[i,j] + self.bwd_DP_V[_i, _j] + self.FSA.I_vd,
+                         self.fwd_DP_D[i,j] + self.bwd_DP_D[_i, _j] + self.FSA.I_dd,
+                         self.fwd_DP_D[i,j] + self.bwd_DP_I[_i, _j] + self.FSA.I_id, 
+                         self.fwd_DP_I[i,j] + self.bwd_DP_M[_i, _j] + self.FSA.I_mi, 
+                         self.fwd_DP_I[i,j] + self.bwd_DP_W[_i, _j] + self.FSA.I_wi, 
+                         self.fwd_DP_I[i,j] + self.bwd_DP_V[_i, _j] + self.FSA.I_vi, 
+                         self.fwd_DP_I[i,j] + self.bwd_DP_D[_i, _j] + self.FSA.I_di, 
+                         self.fwd_DP_I[i,j] + self.bwd_DP_I[_i, _j] + self.FSA.I_ii
+                       ]
+                    #print(min(temp))
+                self.L_matrix[i,j] = min(temp)
+        
+        
+    def collate_fwd(self):
+        
+        #for i in tqdm(range(0,self.T_len+1)):
+        for i in range(0,self.T_len+1):
+            for j in range(0,self.S_len+1):
+                _i = self.T_len-i
+                _j = self.S_len-j
+                
+                if(not self.the_5_state_machine):
+                    temp = [ self.fwd_DP_M[i,j], self.fwd_DP_D[i,j], self.fwd_DP_I[i,j]]
+                else:
+                    temp = [ self.fwd_DP_M[i,j],self.fwd_DP_W[i,j] ,self.fwd_DP_V[i,j], 
+                           self.fwd_DP_D[i,j], self.fwd_DP_I[i,j]]
+                    #print(min(temp))
+                self.L_matrix[i,j] = min(temp)
+                self.L_matrix_states[i,j] = np.argmin(temp) 
+                
+        
+    def plot_alignment_landscape(self): # pass alignment path coordinates
+        fig, ax = plt.subplots(figsize=(5, 5))
+        ax = sb.heatmap(self.L_matrix, square=True,  cmap="jet", ax=ax)
+        path_x = [p[0]+0.5 for p in self.alignment_path]
+        path_y = [p[1]+0.5 for p in self.alignment_path]
+        ax.plot(path_y, path_x, color='black', linewidth=3, alpha=0.5, linestyle='dashed') # path plot
+        plt.xlabel("Reference",fontweight='bold')
+        plt.ylabel("Query",fontweight='bold')  
+        plt.title('Alignment cost landscape')
+
+
+
+    
+
+    
diff --git a/genes2genes/.ipynb_checkpoints/PathwayAnalyser-checkpoint.py b/genes2genes/.ipynb_checkpoints/PathwayAnalyser-checkpoint.py
new file mode 100644
index 0000000..c539455
--- /dev/null
+++ b/genes2genes/.ipynb_checkpoints/PathwayAnalyser-checkpoint.py
@@ -0,0 +1,129 @@
+import gseapy as gp
+from gseapy import barplot, dotplot
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+from tabulate import tabulate
+from gsea_api.molecular_signatures_db import MolecularSignaturesDatabase
+
+from . import ClusterUtils
+from . import VisualUtils
+
+"""
+This script defines Wrappers for GSEAPY enrichr and other functions related to analysing pathway gene sets. 
+"""
+
+def run_overrepresentation_analysis(gene_set, TARGET_GENESETS=['MSigDB_Hallmark_2020','KEGG_2021_Human']):
+    enr = gp.enrichr(gene_list=gene_set,
+                     gene_sets=TARGET_GENESETS,
+                     organism='human',
+                     outdir=None,
+                       )
+    df = enr.results[enr.results['Adjusted P-value']<0.05]
+    if(df.shape[0]==0):
+        return df
+    df = df.sort_values('Adjusted P-value')
+    df['-log10 Adjusted P-value'] = [-np.log10(q) for q in df['Adjusted P-value']]
+    max_q = max(df['-log10 Adjusted P-value'][df['-log10 Adjusted P-value']!=np.inf])
+    #df.columns = ['Gene_set']+list(df.columns[1:len(df.columns)])
+    qvals = []
+    for q in df['-log10 Adjusted P-value']:
+        if(q==np.inf):
+            q = -np.log10(0.00000000001) # NOTE: For -log10(p=0.0) we replace p with a very small p-val to avoid inf
+        qvals.append(q)
+    df['-log10 FDR q-val'] = qvals 
+    df = df.sort_values('Adjusted P-value',ascending=True)
+    return df
+
+def plot_overrep_results(df):
+    height = df.shape[0]*(1/(np.log2(df.shape[0])+1)) 
+    ax = barplot(df,
+                  column="Adjusted P-value",
+                  group='Gene_set', # set group, so you could do a multi-sample/library comparsion
+                  size=10,
+                  top_term=20,
+                  figsize=(5,height),
+                  color=['darkred', 'darkblue'], # set colors for group
+                 )
+
+def plot_gsea_dotplot(df, size=100, figsize=(3,4), n_top_terms = 5):
+        ax = dotplot(df,
+                  column="P-value",
+                  x='-log10 Adjusted P-value', # set x axis, so you could do a multi-sample/library comparsion
+                  size=size,
+                  top_term=n_top_terms,
+                  figsize=figsize,
+                  xticklabels_rot=45, # rotate xtick labels
+                  show_ring=False, # set to False to revmove outer ring
+                  marker='o',
+                 )   
+
+def run_cluster_overrepresentation_analysis(aligner):
+
+    overrep_cluster_results = {}
+    cluster_overrepresentation_results = [] 
+
+    for cluster_id in tqdm(range(len(aligner.gene_clusters))):
+        df = run_overrepresentation_analysis(aligner.gene_clusters[cluster_id])
+        if(df.shape[0]==0):
+            continue
+        n_genes = len(aligner.gene_clusters[cluster_id])
+        pathways = list(df.Term) 
+        pathway_specific_genes = list(df.Genes) 
+        sources = [str(s).split('_')[0] for s in list(df.Gene_set)] 
+
+        if(n_genes<15):
+            genes = aligner.gene_clusters[cluster_id]
+        else:
+            genes = aligner.gene_clusters[cluster_id][1:7] + [' ... '] +  aligner.gene_clusters[cluster_id][n_genes-7:n_genes]
+
+        cluster_overrepresentation_results.append([cluster_id,n_genes,genes,pathways, pathway_specific_genes, sources]) 
+        overrep_cluster_results[cluster_id] = df 
+
+    results= pd.DataFrame(cluster_overrepresentation_results)
+    print(tabulate(results,  headers=['cluster_id','n_genes', 'Cluster genes', 'Pathways','Pathway genes','Source'],tablefmt="grid",maxcolwidths=[3, 3, 3,30,40,40,10])) 
+
+
+def get_pathway_alignment_stat(aligner, GENE_LIST, pathway_name, cluster=False, FIGSIZE = (14,7)):
+    
+    print('Gene set: ======= ', pathway_name)
+    perct_A = []
+    perct_S = []
+    perct_T = []
+    for gene in GENE_LIST:
+        series_match_percent = aligner.results_map[gene].get_series_match_percentage()
+        perct_A.append(series_match_percent[0])
+        perct_S.append(series_match_percent[1])
+        perct_T.append(series_match_percent[2])
+
+    print('mean matched percentage: ', round(np.mean(perct_A),2),'%' )
+    #print('mean matched percentage wrt ref: ',round(np.mean(perct_S),2),'%'  )
+    #print('mean matched percentage wrt query: ', round(np.mean(perct_T),2),'%' )
+    average_alignment, alignment_path =  ClusterUtils.get_cluster_average_alignments(aligner, GENE_LIST)
+    mat = ClusterUtils.get_pairwise_match_count_mat(aligner,GENE_LIST )
+    print('Average Alignment: ', VisualUtils.color_al_str(average_alignment), '(cell-level)')
+    print('- Plotting average alignment path')
+    VisualUtils.plot_alignment_path_on_given_matrix(paths = [alignment_path], mat=mat) 
+    VisualUtils.plot_mean_trend_heatmaps(aligner,GENE_LIST, pathway_name,cluster=cluster, FIGSIZE=FIGSIZE) 
+        
+
+class InterestingGeneSets:
+    
+    def __init__(self, MSIGDB_PATH, version):        
+        self.SETS = {}
+        self.dbs = {}
+        self.msigdb = MolecularSignaturesDatabase(MSIGDB_PATH , version=version)
+        self.dbs['kegg'] = self.msigdb.load('c2.cp.kegg', 'symbols')
+        self.dbs['hallmark'] = self.msigdb.load('h.all', 'symbols')
+        #self.dbs['gobp'] = self.msigdb.load('c5.go.bp', 'symbols')
+        #self.dbs['gocc'] = self.msigdb.load('c5.go.cc', 'symbols')
+        #self.dbs['reac'] = self.msigdb.load('c2.cp.reactome', 'symbols')
+        
+    def add_new_set_from_msigdb(self, db_name, dbsetname, avail_genes, usersetname):
+        self.SETS[usersetname] = np.intersect1d(list(self.dbs[db_name].gene_sets_by_name[dbsetname].genes), avail_genes)
+
+    def add_new_set(self, geneset, usersetname, avail_genes):
+        geneset = np.asarray(geneset)
+        self.SETS[usersetname] = geneset[np.where([g in avail_genes for g in geneset])]
+
+        
diff --git a/genes2genes/.ipynb_checkpoints/TimeSeriesPreprocessor-checkpoint.py b/genes2genes/.ipynb_checkpoints/TimeSeriesPreprocessor-checkpoint.py
new file mode 100644
index 0000000..dac1f80
--- /dev/null
+++ b/genes2genes/.ipynb_checkpoints/TimeSeriesPreprocessor-checkpoint.py
@@ -0,0 +1,221 @@
+import numpy as np
+import seaborn as sb
+import pandas as pd
+import torch
+import multiprocessing
+from scipy.sparse import csr_matrix
+from sklearn.preprocessing import MinMaxScaler
+
+from . import MyFunctions
+from . import Utils
+
+class TrajectoryInterpolator:
+    
+    """
+    This class defines an interpolator function for a given gene expression time series, which prepares required summary statistics for interpolation
+    """
+    
+    def __init__(self, adata, n_bins, adaptive_kernel = True, kernel_WINDOW_SIZE=0.1, raising_degree = 1):
+        self.n_bins = n_bins
+        self.adata = adata[np.argsort(adata.obs['time'])] 
+
+        self.cell_pseudotimes = np.asarray(self.adata.obs.time)
+        self.interpolation_points = np.linspace(0,1,n_bins) 
+        self.kernel_WINDOW_SIZE = kernel_WINDOW_SIZE
+        self.adaptive_kernel = adaptive_kernel
+        self.k = raising_degree # the degree of stretch imposed for the window sizes from baseline kernel_WINDOW_SIZE  = 0.1
+        
+        self.mat = csr_matrix(self.adata.X.todense().transpose())
+        self.N_PROCESSES = multiprocessing.cpu_count() 
+        self.gene_list = self.adata.var_names
+        
+    def run(self):
+        #print('computing absolute time diffs')
+        self.abs_timediff_mat = self.compute_abs_timediff_mat()
+        if(self.adaptive_kernel):
+            #print('Running in adaptive interpolation mode')
+            #print('computing an cell densities for adaptive interpolation')
+            self.reciprocal_cell_density_estimates = self.compute_cell_densities()
+            #print('computing adaptive win denomes')
+            self.adaptive_win_denoms =  self.compute_adaptive_window_denominator()
+        #print('computing cell weight matrix')
+        self.cell_weight_mat = self.compute_Weight_matrix() 
+        
+    def compute_abs_timediff_mat(self): # interpolation time points x cells matrix
+        df = [] 
+        for i in self.interpolation_points:
+            # absolute difference between actual pseudotime point of a cell and interpolation time point (needed to compute gaussian kernel later on)
+            abs_dist = np.abs(np.asarray(self.cell_pseudotimes) - i) #np.repeat(i,len(self.cell_pseudotimes)) 
+            df.append(abs_dist)
+        df = pd.DataFrame(df); df.columns = self.adata.obs_names; df.index = self.interpolation_points
+        return df
+        
+    def compute_cell_densities(self): # cell density vector across interpolation time points
+        # compute cell density estimate for each interpolation point
+        cell_density_estimates = [] 
+        interpolation_points = self.interpolation_points
+        cell_pseudotimes = self.cell_pseudotimes
+        range_length_mid = interpolation_points[2] - interpolation_points[0] # constant across 
+        range_length_corner = interpolation_points[1] - interpolation_points[0] # constant across 
+        for i in range(len(interpolation_points)):
+            prime_point = interpolation_points[i] 
+            cell_density = 0.0 # per discrete point cell density = # of cells falling within interpolation time points [i-1,i+1] range window / window length
+            if(i==0):
+                logic = cell_pseudotimes <= interpolation_points[i+1]; range_length = range_length_corner
+            elif(i==len(interpolation_points)-1):
+                logic = cell_pseudotimes >= interpolation_points[i-1]; range_length = range_length_corner
+            else:
+                logic = np.logical_and(cell_pseudotimes <= interpolation_points[i+1], cell_pseudotimes >= interpolation_points[i-1])
+                range_length = range_length_mid
+
+            density_stat = np.count_nonzero(logic)
+            density_stat = density_stat/range_length
+            cell_density_estimates.append(density_stat)
+        #print('** per unit cell density: ', cell_density_estimates)
+        self.cell_density_estimates = cell_density_estimates
+        cell_density_estimates  = [1/x for x in cell_density_estimates] # taking reciprocal for weighing
+        
+        #print('reciprocals: ', cell_density_estimates)
+        # if this has inf values, use the max weight for them (otherwise it becomes inf resulting same weights 1.0 for all cells)
+        arr = cell_density_estimates
+        if(np.any(np.isinf(arr))):
+            max_w = max(np.asarray(arr)[np.isfinite(arr)] ) 
+            cell_density_estimates = np.where(np.isinf(arr), max_w, arr)
+        #print('** adaptive weights -- ', cell_density_estimates)
+        
+        return cell_density_estimates
+        
+    def compute_adaptive_window_denominator(self): # for each interpolation time point
+
+        cell_density_adaptive_weights  = self.reciprocal_cell_density_estimates
+
+        # using min-max to stretch the range (for highly adapted window sizes having high window sizes)
+        cell_density_adaptive_weights =np.asarray(cell_density_adaptive_weights) 
+        scaler = MinMaxScaler()
+        cell_density_adaptive_weights = scaler.fit_transform(cell_density_adaptive_weights.reshape(-1, 1)).flatten()
+        cell_density_adaptive_weights = cell_density_adaptive_weights * self.k
+        
+        # ======= enforcing the same window_size = kernel_WINDOW_SIZE for the interpolation with the least weighted kernel window size
+        adaptive_window_sizes = []
+        for cd in cell_density_adaptive_weights:
+            adaptive_window_sizes.append(cd*self.kernel_WINDOW_SIZE)  #weighing stadard window size
+            
+        # find the interpolation point for which the window_size weighted to be lowest -- furthest to kernel_WINDOW_SIZE
+        temp = list(np.abs(adaptive_window_sizes - np.repeat(self.kernel_WINDOW_SIZE,self.n_bins)))
+        least_affected_interpolation_point = temp.index(max(temp))
+        residue = np.abs(self.kernel_WINDOW_SIZE - adaptive_window_sizes[least_affected_interpolation_point])
+        if(self.k>1): # linear scaling to stretch the range of window size from 0.1 base line. 
+            adaptive_window_sizes = adaptive_window_sizes + (residue/(self.k-1)) 
+        else: 
+            adaptive_window_sizes = adaptive_window_sizes + residue 
+  
+        # compute adaptive window size based denominator of Gaussian kernel for each cell for each interpolation time point
+        W = [] 
+        for adw in adaptive_window_sizes:
+            adaptive_W_size = adw**2
+            W.append(adaptive_W_size)
+        self.adaptive_window_sizes = adaptive_window_sizes
+            
+        return W
+        
+    # compute Gaussian weights for each interpolation time point and cell
+    def compute_Weight_matrix(self):
+        if(self.adaptive_kernel):
+            adaptive_win_denoms_mat = np.asarray([np.repeat(a, len(self.cell_pseudotimes)) for a in self.adaptive_win_denoms])
+            W_matrix = pd.DataFrame(np.exp(-np.divide(np.array(self.abs_timediff_mat**2), adaptive_win_denoms_mat)))
+        else:
+            W_matrix = pd.DataFrame(np.exp(-np.array(self.abs_timediff_mat**2)/self.kernel_WINDOW_SIZE**2))
+        W_matrix.columns = self.adata.obs_names
+        self._real_intpl = self.interpolation_points
+        #self.interpolation_points = [np.round(i,2) for i in  self.interpolation_points]
+        W_matrix.index = self.interpolation_points
+        #sb.heatmap(W_matrix)
+        return W_matrix
+    
+    def get_effective_cell_pseudotime_range(self, i, effective_weight_threshold):
+        effective_weights = self.cell_weight_mat.loc[self.interpolation_points[i]]
+        cell_names = np.asarray(effective_weights.index)
+        effective_weights = np.asarray(effective_weights)
+        cell_ids = np.where(effective_weights>effective_weight_threshold)[0]
+        effective_cell_names = cell_names[cell_ids]
+        effective_cell_pseudotimes = self.cell_pseudotimes[cell_ids]
+        return effective_cell_pseudotimes
+
+    # plotting highly effective cell_contribution regions for given interpolation points based on adaptive weighted gaussian kernel
+    def plot_effective_regions_for_interpolation_points(self, intpointsIdx2plots, effective_weight_threshold=0.5, plot=True):
+        
+        cmap = sb.color_palette("viridis", as_cmap=True)
+        self.n_effective_cells = []
+        for i in intpointsIdx2plots:
+            x = self.get_effective_cell_pseudotime_range(i, effective_weight_threshold= effective_weight_threshold)
+            self.n_effective_cells.append(len(x))
+            if(plot):
+                sb.kdeplot(x, fill=True, color=cmap(i/self.n_bins), clip=(0.0,1.0))
+
+    
+"""
+The below functions define interpolation functions used by the above Interpolator object 
+(defined outside class for time efficiency)
+"""
+# ====================== interpolation process of genes
+def compute_stat(row, x, cell_densities, user_given_std):
+            idx = row.name
+            if(user_given_std[idx] < 0):
+                cell_weights_sum = np.sum(row)
+
+                # estimate weighted mean
+                weighted_mean = np.dot(row, x)/cell_weights_sum
+                #print(weighted_mean)
+
+                # estimate weighted variance
+                real_mean = np.mean(x); n = len(row)
+                weighted_sum_std = np.dot(row, (x - real_mean) ** 2 )
+                weighted_std = np.sqrt(weighted_sum_std/(cell_weights_sum * (n-1)/n)) 
+                weighted_std = weighted_std * cell_densities[idx] # weighting according to cell density 
+            else:
+                weighted_mean = 0.0 
+                weighted_std =  user_given_std[idx] #
+            
+            D,_,_ = MyFunctions.generate_random_dataset(50, weighted_mean, weighted_std)
+            return np.asarray([weighted_mean, weighted_std, D] )
+
+#row = list(trajInterpolator.cell_weight_mat.loc[intpl_i])
+def interpolate_gene_v2(i, trajInterpolator, user_given_std):
+        torch.manual_seed(1)
+        GENE = trajInterpolator.gene_list[i]
+        #print(GENE)
+        x = Utils.csr_mat_col_densify(trajInterpolator.mat ,i)
+        N_cells= len(trajInterpolator.cell_pseudotimes)
+        
+        trajInterpolator.cell_weight_mat.index = range(0,len(trajInterpolator.cell_weight_mat))
+        cell_densities = list(trajInterpolator.cell_weight_mat.apply(np.sum, axis=1)/N_cells)
+        
+        results = trajInterpolator.cell_weight_mat.apply(compute_stat, axis=1, args = ([x,cell_densities, user_given_std]), result_type='expand')
+        results = pd.DataFrame(results)
+
+        return SummaryTimeSeries(GENE, results[0], results[1], results[2], trajInterpolator.interpolation_points) 
+
+class SummaryTimeSeries:
+    """
+    This class defines an interpolated time series object that carries the interpolated result of a gene expression time series
+    """
+    
+    def __init__(self, gene_name, mean_trend, std_trend, intpl_gex, time_points):
+        self.gene_name = gene_name
+        self.mean_trend = np.asarray([np.mean(data_bin) for data_bin in intpl_gex]) # interpolated dist mean
+        self.std_trend = np.asarray([np.std(data_bin) for data_bin in intpl_gex]) # interpolated dist std
+        self.data_bins = list(intpl_gex)
+        self.intpl_means = list(mean_trend) # actual weighted means
+        self.intpl_stds = list(std_trend) # actual weighted stds
+        self.time_points = np.asarray(time_points)
+        
+        self.Y = np.asarray([np.asarray(x) for x in self.data_bins]).flatten() 
+        self.X = np.asarray([np.repeat(t,50) for t in self.time_points]).flatten() 
+        
+    def plot_mean_trend(self, color='midnightblue'):
+        sb.lineplot(x= self.time_points, y=self.mean_trend, color=color, linewidth=4) 
+        
+    def plot_std_trend(self, color='midnightblue'):
+        sb.lineplot(x= self.time_points, y=self.std_trend, color=color, linewidth=4) 
+    
+    
\ No newline at end of file
diff --git a/genes2genes/.ipynb_checkpoints/Utils-checkpoint.py b/genes2genes/.ipynb_checkpoints/Utils-checkpoint.py
new file mode 100644
index 0000000..7959f86
--- /dev/null
+++ b/genes2genes/.ipynb_checkpoints/Utils-checkpoint.py
@@ -0,0 +1,198 @@
+import numpy as np
+from scipy.sparse import csr_matrix
+from . import MyFunctions
+
+# UTIL FUNCTIONS
+def csr_mat_col_densify(csr_matrix, j): 
+        start_ptr = csr_matrix.indptr[j]
+        end_ptr = csr_matrix.indptr[j + 1]
+        data = csr_matrix.data[start_ptr:end_ptr]
+        dense_column = np.zeros(csr_matrix.shape[1])
+        dense_column[csr_matrix.indices[start_ptr:end_ptr]] = data
+        return dense_column
+
+    
+def minmax_normalise(arr):
+        
+        norm_arr = []
+        arr = np.asarray(arr)
+        arr_max = np.max(arr)
+        arr_min = np.min(arr)
+        for i in range(len(arr)):
+            norm_arr.append((arr[i] - arr_min )/(arr_max  - arr_min )) 
+        return norm_arr
+    
+
+# computes distributional distance under the MML framework
+def compute_mml_dist(ref_adata_subset,query_adata_subset, gene):
+
+        ref_data = np.asarray(ref_adata_subset[:,gene].X.todense()).flatten()
+        query_data = np.asarray(query_adata_subset[:,gene].X.todense()).flatten()
+        μ_S = np.mean(ref_data)
+        μ_T = np.mean(query_data)
+        σ_S =np.std(ref_data)
+        σ_T =np.std(query_data)
+        #print(μ_S,μ_T)
+        if(not np.any(ref_data)):
+            σ_S = 0.1
+        if(not np.any(query_data)):
+            σ_T = 0.1    
+
+        I_ref_model, I_refdata_g_ref_model = MyFunctions.run_dist_compute_v3(ref_data, μ_S, σ_S) 
+        I_query_model, I_querydata_g_query_model = MyFunctions.run_dist_compute_v3(query_data, μ_T, σ_T) 
+        I_ref_model, I_querydata_g_ref_model = MyFunctions.run_dist_compute_v3(query_data, μ_S, σ_S) 
+        I_query_model, I_refdata_g_query_model = MyFunctions.run_dist_compute_v3(ref_data, μ_T, σ_T) 
+
+        match_encoding_len1 = I_ref_model + I_querydata_g_ref_model + I_refdata_g_ref_model
+        match_encoding_len1 = match_encoding_len1/(len(query_data)+len(ref_data))
+        match_encoding_len2 = I_query_model + I_refdata_g_query_model + I_querydata_g_query_model
+        match_encoding_len2 = match_encoding_len2/(len(query_data)+len(ref_data))
+        match_encoding_len = (match_encoding_len1 + match_encoding_len2 )/2.0 
+
+        null = (I_ref_model + I_refdata_g_ref_model + I_query_model + I_querydata_g_query_model)/(len(query_data)+len(ref_data))
+        match_compression =   match_encoding_len - null 
+
+        return match_compression
+    
+    
+def sample_state(x):
+    x = np.cumsum(x)
+    rand_num = np.random.rand(1)
+   # print(rand_num)
+    if(rand_num<=x[0]):
+        return 0#'M'
+    elif(rand_num>x[0] and rand_num<=x[1]):
+        return 1#'W'
+    elif(rand_num>x[1] and rand_num<=x[2]):
+        return 2#'V'
+    elif(rand_num>x[2] and rand_num<=x[3]):
+        return 3#'D'
+    elif(rand_num>x[3] and rand_num<=x[4]):
+        return 4#'I'
+    
+
+def compute_alignment_area_diff_distance(A1, A2, S_len, T_len):
+
+        pi = np.arange(1, S_len+T_len+1) # skew diagonal indices 
+        A1_ = ""
+        for c in A1:
+            A1_ = A1_ + c
+            if(c=='M'):
+                A1_ = A1_ + 'X'
+        A2_ = ""
+        for c in A2:
+            A2_ = A2_ + c 
+            if(c=='M'):
+                A2_ = A2_ + 'X'
+
+        pi_1_k = 0
+        pi_2_k = 0
+        #print(0, pi_1_k , pi_2_k )
+        A1_al_index = 0
+        A2_al_index = 0
+        absolute_dist_sum = 0.0
+        for k in pi:
+            #print('k=',k, A1_al_index, A2_al_index)
+            A1_state = A1_[A1_al_index]
+            A2_state = A2_[A2_al_index]
+            if(A1_state=='I' or A1_state=='V'):
+                pi_1_k = pi_1_k - 1
+            elif(A1_state=='D' or  A1_state=='W'):
+                pi_1_k = pi_1_k + 1 
+            if(A2_state=='I' or A2_state=='V'):
+                pi_2_k = pi_2_k - 1   
+            elif(A2_state=='D' or  A2_state=='W'):
+                pi_2_k = pi_2_k + 1   
+  
+            absolute_dist_sum = absolute_dist_sum + np.abs(pi_1_k - pi_2_k)
+            #print('-----')
+            A1_al_index = A1_al_index + 1
+            A2_al_index = A2_al_index + 1 
+
+        return absolute_dist_sum
+
+def compute_chattergi_coefficient(y1,y2):
+        df = pd.DataFrame({'S':y1, 'T':y2})
+        df['rankS'] = df['S'].rank() 
+        df['rankT'] = df['T'].rank() 
+        # sort df by the S variable first
+        df = df.sort_values(by='rankS')
+        return 1 - ((3.0 * df['rankT'].diff().abs().sum())/((len(df)**2)-1)) 
+    
+    
+def plot_different_alignments(paths, S_len, T_len, ax, mat=[]): # pass alignment path coordinates
+        mat=[]
+      #  if(len(mat)==0):
+        for i in range(T_len+1):
+            mat.append(np.repeat(0,S_len+1)) 
+        sb.heatmap(mat, square=True,  cmap='viridis', ax=ax, vmin=0, vmax=0, cbar=False,xticklabels=False,yticklabels=False)
+        path_color = "orange"
+        
+        for path in paths: 
+            path_x = [p[0]+0.5 for p in path]
+            path_y = [p[1]+0.5 for p in path]
+            ax.plot(path_y, path_x, color=path_color, linewidth=3, alpha=0.5, linestyle='dashed') # path plot
+        plt.xlabel("S",fontweight='bold')
+        plt.ylabel("T",fontweight='bold')
+        
+    
+def check_alignment_clusters(n_clusters , cluster_ids, alignments, n_cols = 5, figsize= (10,6)): 
+        
+        clusters = [] 
+        S_len = alignments[0].fwd_DP.S_len
+        T_len = alignments[0].fwd_DP.T_len
+        
+        unique_cluster_ids = np.unique(cluster_ids)
+        n_rows = int(np.ceil(n_clusters/n_cols)) 
+        
+
+        fig, axs = plt.subplots(n_rows,n_cols, figsize = (20,n_rows*3)) # custom -- only for 20 clusters -- TODO change later
+        axs = axs.flatten() 
+        i = 0
+        k=1
+        for cluster_id in range(n_clusters): 
+            paths = []
+            cluster_genes = [] 
+            cluster_alignments = np.asarray(alignments)[cluster_ids == unique_cluster_ids[cluster_id]]
+            for a in cluster_alignments:
+                    paths.append(a.fwd_DP.alignment_path)
+                    #print(a.gene)
+                    cluster_genes.append(a.gene);# cluster_genes.append(a.gene)
+            clusters.append(list(np.unique(cluster_genes)) ) 
+
+            plot_different_alignments(paths, S_len, T_len, axs[cluster_id])
+            axs[cluster_id].set_title('Cluster-'+str(i) + ' | '+str(len(cluster_alignments)))
+
+            i=i+1
+            k=k+1
+        
+        fig.tight_layout()
+        n = n_cols * n_rows
+        i = 1
+        while(k<=n):
+            axs.flat[-1*i].set_visible(False) 
+            k = k+1
+            i=i+1        
+            
+        return clusters
+    
+
+# input: log1p gene expression vectors
+def compute_KLDivBasedDist(x,y):
+
+        # convert to probabilities
+        x = x.numpy()
+        y = y.numpy()
+        # convering backto counts+1
+        x = np.exp(x)
+        y = np.exp(y)
+        x = x/np.sum(x)
+        y = y/np.sum(y)
+
+        sum_term = 0.0
+        for i in range(len(x)):
+            sum_term += x[i]*(np.log(x[i]) - np.log(y[i]))
+    
+        return sum_term
+
+    
\ No newline at end of file
diff --git a/genes2genes/.ipynb_checkpoints/VisualUtils-checkpoint.py b/genes2genes/.ipynb_checkpoints/VisualUtils-checkpoint.py
new file mode 100644
index 0000000..7e55a0e
--- /dev/null
+++ b/genes2genes/.ipynb_checkpoints/VisualUtils-checkpoint.py
@@ -0,0 +1,419 @@
+import pandas as pd
+import seaborn as sb
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.stats import zscore
+import matplotlib.colors as mcolors
+import matplotlib
+import matplotlib.patches as mpatches
+import regex
+
+from . import Main
+
+
+vega_20 = ['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c', '#98df8a', '#d62728',
+            '#ff9896', '#9467bd', '#c5b0d5', '#8c564b', '#c49c94', '#e377c2', '#f7b6d2',
+            '#7f7f7f', '#c7c7c7', '#bcbd22', '#dbdb8d', '#17becf', '#9edae5',]
+    
+def plot_celltype_barplot(adata, n_bins, annotation_colname, joint_cmap, plot_cell_counts = False, legend=False):
+
+        if(plot_cell_counts):
+            normalize = False
+        else:
+            normalize = 'columns'
+
+        vec = adata.obs.time
+        bin_edges = np.linspace(0, 1, num=n_bins)  
+        bin_ids = np.digitize(vec, bin_edges, right=False) # use right=True if we don't need 1.0 cell to always be a single last bin 
+        adata.obs['bin_ids'] = bin_ids
+        tmp = pd.crosstab(adata.obs[annotation_colname],adata.obs['bin_ids'], normalize=normalize).T.plot(kind='bar', stacked=True,        
+                                                                                                 color=joint_cmap,grid = False, legend=False, width=0.7,align='edge',figsize=(9,1))
+        if(legend):    
+            tmp.legend(title='Cell-type annotations', bbox_to_anchor=(1.5, 1.02),loc='upper right')
+        plt.axis('off')
+    
+def visualize_gene_alignment(alignment, adata_ref, adata_query, annotation_colname, cmap=None):
+
+            if(isinstance(alignment,Main.AligmentObj )):
+                alignment = alignment.alignment_str
+
+            matched_points_S, matched_points_T = get_matched_time_points(alignment)
+
+            fig = plt.figure(figsize=(4,2))
+            heights = [1, 1, 1] 
+            gs = plt.GridSpec(3, 1, height_ratios=heights)
+            ax1 = fig.add_subplot(gs[0, 0])
+            ax2 = fig.add_subplot(gs[1, 0],sharex=ax1)
+            ax3 = fig.add_subplot(gs[2, 0],sharex=ax1)
+            
+            if(cmap is None):
+                cmap = vega_20
+
+            plt.subplot(3,1,1)
+            
+            metaS = pd.crosstab(adata_ref.obs.bin_ids, adata_ref.obs[annotation_colname])
+            metaS.apply(lambda x: x*100/sum(x), axis=1).plot(kind='bar',stacked=True,color=cmap, grid = False, legend=False, width=0.7, ax=ax1)
+            
+            metaT = pd.crosstab(adata_query.obs.bin_ids, adata_query.obs[annotation_colname])
+            metaT.apply(lambda x: x*100/sum(x), axis=1).plot(kind='bar',stacked=True,color=cmap, grid = False, legend=False, width=0.7,ax=ax3)
+            
+            plt.subplot(3,1,2)
+            for i in range(len(matched_points_S)):
+                        S_timebin = matched_points_S[i]
+                        T_timebin = matched_points_T[i]
+                        x_vals = [T_timebin+1, S_timebin+1]
+                        y_vals = [0,1]
+                        plt.plot(x_vals, y_vals, marker='.', color='black', linewidth=0.5)
+
+            def set_grid_off(ax):
+                ax.spines['top'].set_visible(False)
+                ax.spines['bottom'].set_visible(False)
+                ax.spines['left'].set_visible(False)
+                ax.spines['right'].set_visible(False)
+                ax.set_xticks([])
+                ax.xaxis.set_ticks_position('none') 
+                ax.set_yticks([])
+                ax.figure.tight_layout()
+                ax.grid(False)
+
+            set_grid_off(ax1); set_grid_off(ax2); set_grid_off(ax3); 
+            ax1.set_ylabel('Ref', rotation=90)
+            ax3.set_ylabel('Query',rotation=90)
+            fig.text(0.5, -0.05, 'Pseudotime bins with cell type composition', ha='center')
+            ax1.set_title('Alignment w.r.t cell type compositions')
+
+
+def get_matched_time_points(alignment_str):
+        j = 0; i = 0
+        FLAG = False
+        matched_points_S = [] 
+        matched_points_T = [] 
+        prev_c = ''
+        for c in alignment_str:
+            if(c=='M'):
+                if(prev_c=='W'):
+                    i=i+1
+                if(prev_c=='V'):
+                    j=j+1
+                matched_points_T.append(i)
+                matched_points_S.append(j)
+                i=i+1
+                j=j+1
+            elif(c=='W'):
+                if(prev_c not in ['W','V']):
+                    i=i-1
+                if(prev_c=='V'):
+                    i=i-1
+                    j=j+1
+                if(prev_c=='D' and not FLAG):
+                    FLAG = True
+                matched_points_T.append(i)
+                matched_points_S.append(j)
+                j=j+1
+            elif(c=='V'):
+                if(prev_c not in ['W','V']):
+                    j=j-1
+                if(prev_c=='W'):
+                    j=j-1
+                    i=i+1
+                if(prev_c=='I' and not FLAG):
+                    FLAG = True
+                matched_points_T.append(i)
+                matched_points_S.append(j)
+                i=i+1
+            elif(c=='I'):
+                if(prev_c=='W'):
+                    i=i+1
+                if(prev_c=='V'):
+                    j=j+1
+                i=i+1
+            elif(c=='D'):
+                if(prev_c=='W'):
+                    i=i+1
+                if(prev_c=='V'):
+                    j=j+1
+                j=j+1
+            prev_c = c
+        assert(len(matched_points_S) == len(matched_points_T)) 
+        return matched_points_S, matched_points_T
+    
+
+def plotTimeSeries(gene, aligner, plot_cells = False, plot_mean_trend= False):
+    
+        al_obj = aligner.results_map[gene]
+        plt.subplots(1,3,figsize=(15,3))
+        plt.subplot(1,3,1)
+        plotTimeSeriesAlignment(gene, aligner) 
+        plt.subplot(1,3,2)
+        max_val = np.max([np.max(np.asarray(aligner.ref_mat[al_obj.gene])), np.max(np.asarray(aligner.query_mat[al_obj.gene]))])
+        min_val = np.min([np.min(np.asarray(aligner.ref_mat[al_obj.gene])), np.min(np.asarray(aligner.query_mat[al_obj.gene]))])
+        g = sb.scatterplot(x=aligner.query_time, y=np.asarray(aligner.query_mat[al_obj.gene]), alpha=0.7, color = 'midnightblue', legend=False,linewidth=0.3, s=20)  
+        plt.title('Query')
+        plt.ylim([min_val-0.5,max_val+0.5])
+        plt.xlabel('Pseudotime')
+        plt.ylabel('Gene expression')
+        plt.subplot(1,3,3)
+        g = sb.scatterplot(x=aligner.ref_time, y=np.asarray(aligner.ref_mat[al_obj.gene]), color = 'forestgreen', alpha=0.7, legend=False,linewidth=0.3,s=20 ) 
+        plt.title('Reference')
+        plt.ylim([min_val-0.5,max_val+0.5])
+        plt.xlabel('Pseudotime')
+        plt.ylabel('Gene expression')
+
+def plotTimeSeriesAlignment(gene, aligner):  
+    
+        al_obj = aligner.results_map[gene]
+        sb.scatterplot(x=al_obj.S.X, y=al_obj.S.Y, color = 'forestgreen' ,alpha=0.05, legend=False)#, label='Ref') 
+        sb.scatterplot(x=al_obj.T.X, y=al_obj.T.Y, color = 'midnightblue' ,alpha=0.05, legend=False)#, label ='Query') 
+        al_obj.plot_mean_trends() 
+        plt.title(al_obj.gene)
+        plt.xlabel('Pseudotime')
+        plt.ylabel('Gene expression')
+        plt.axis('off')
+        
+        for i in range(al_obj.matched_region_DE_info.shape[0]):
+            S_timebin = int(al_obj.matched_region_DE_info.iloc[i]['ref_bin'])
+            T_timebin = int(al_obj.matched_region_DE_info.iloc[i]['query_bin']) 
+            x_vals = [al_obj.matched_region_DE_info.iloc[i]['ref_pseudotime'],al_obj.matched_region_DE_info.iloc[i]['query_pseudotime']] 
+            y_vals = [al_obj.S.mean_trend[S_timebin ], al_obj.T.mean_trend[T_timebin]] 
+            plt.plot(x_vals, y_vals, color='black', linestyle='dashed', linewidth=1.5)
+
+
+def plot_alignmentSim_vs_l2fc(x):
+    ax=sb.scatterplot(x=x['l2fc'],y=x['alignment_similarity_percentage']*100,s=120, legend=False, hue =x['alignment_similarity_percentage'] ,
+                   palette=sb.diverging_palette(0, 255, s=150, as_cmap=True),edgecolor='k',linewidth=0.3)
+    plt.yticks(fontsize=15)
+    plt.xticks(fontsize=15)
+    plt.ylabel('Alignment Similarity %', fontsize=15, fontweight='bold')
+    plt.xlabel('Log2 fold change of mean expression', fontsize = 15, fontweight='bold')
+    plt.grid(False)
+    plt.axhline(50, color='black')
+    plt.axvline(0, color='black', linestyle='dashed')
+
+
+def plot_alignmentSim_vs_optCost(x, opt_cost_cut=0):
+    sb.scatterplot(x=x['opt_alignment_cost'],y=x['alignment_similarity_percentage']*100,s=120, legend=False, hue =x['alignment_similarity_percentage'] ,
+                   palette=sb.diverging_palette(0, 255, s=150, as_cmap=True),edgecolor='k',linewidth=0.3)
+    plt.yticks(fontsize=15)
+    plt.xticks(fontsize=15)
+    plt.ylabel('Alignment Similarity %', fontsize=15, fontweight='bold')
+    plt.xlabel('Optimal alignment cost (nits)', fontsize = 15, fontweight='bold')
+    plt.grid(False)
+    plt.axhline(50, color='black')
+    plt.axvline(opt_cost_cut, color='black', linestyle='dashed')
+    plt.tight_layout()
+
+
+def plot_alignment_path_on_given_matrix(mat, paths, cmap='viridis'):
+        fig,ax = plt.subplots(1,1, figsize=(7,7))
+        sb.heatmap(mat, square=True,  cmap='viridis', ax=ax, cbar=True)  
+        for path in paths: 
+            path_x = [p[0]+0.5 for p in path]
+            path_y = [p[1]+0.5 for p in path]
+            ax.plot(path_y, path_x, color='white', linewidth=6)
+        plt.xlabel("Reference",fontweight='bold')
+        plt.ylabel("Query",fontweight='bold')
+        ax.xaxis.tick_top() # x axis on top
+        ax.xaxis.set_label_position('top')
+
+def plot_distmap_with_clusters(aligner, cmap=None, vmin = 0.0, vmax = 1.0, genes2highlight=None):
+    
+    godsnot_64 = [
+    # "#000000",  # remove the black, as often, we have black colored annotation, 
+        '#0173b2', '#de8f05', '#029e73', '#d55e00', '#cc78bc', '#ca9161',
+       '#fbafe4', '#949494', '#ece133', '#56b4e9', # <--added colorblind palette to this
+    "#FFFF00", "#1CE6FF", "#FF34FF", "#FF4A46", "#008941", "#006FA6", "#A30059",
+    "#FFDBE5", "#7A4900", "#0000A6", "#63FFAC", "#B79762", "#004D43", "#8FB0FF", "#997D87",
+    "#5A0007", "#809693", "#FEFFE6", "#1B4400", "#4FC601", "#3B5DFF", "#4A3B53", "#FF2F80",
+    "#61615A", "#BA0900", "#6B7900", "#00C2A0", "#FFAA92", "#FF90C9", "#B903AA", "#D16100",
+    "#DDEFFF", "#000035", "#7B4F4B", "#A1C299", "#300018", "#0AA6D8", "#013349", "#00846F",
+    "#372101", "#FFB500", "#C2FFED", "#A079BF", "#CC0744", "#C0B9B2", "#C2FF99", "#001E09",
+    "#00489C", "#6F0062", "#0CBD66", "#EEC3FF", "#456D75", "#B77B68", "#7A87A1", "#788D66",
+    "#885578", "#FAD09F", "#FF8A9A", "#D157A0", "#BEC459", "#456648", "#0086ED", "#886F4C",
+    "#34362D", "#B4A8BD", "#00A6AA", "#452C2C", "#636375", "#A3C8C9", "#FF913F", "#938A81",
+    "#575329", "#00FECF", "#B05B6F", "#8CD0FF", "#3B9700", "#04F757", "#C8A1A1", "#1E6E00",
+    "#7900D7", "#A77500", "#6367A9", "#A05837", "#6B002C", "#772600", "#D790FF", "#9B9700",
+    "#549E79", "#FFF69F", "#201625", "#72418F", "#BC23FF", "#99ADC0", "#3A2465", "#922329",
+    "#5B4534", "#FDE8DC", "#404E55", "#0089A3", "#CB7E98", "#A4E804", "#324E72", "#6A3A4C"]
+    
+    # ordering genes by packing them into their clusters
+    cluster_ordered_genes = []
+    cluster_ids = []
+    
+    cluster_lens = []
+    for i in aligner.gene_clusters.keys():
+        cluster_lens.append(len(aligner.gene_clusters[i]))
+    c_keys = np.asarray(list(aligner.gene_clusters.keys()) ) [np.argsort(cluster_lens)[::-1]] # ordered according to cluster size
+    for i in c_keys:
+        cluster_ordered_genes  += aligner.gene_clusters[i]
+        cluster_ids += list(np.repeat(i,len(aligner.gene_clusters[i]))) 
+    temp = pd.DataFrame([cluster_ordered_genes, cluster_ids]).transpose() 
+    temp.columns = ['Gene','cluster_id']
+
+    n_clusters = len(aligner.gene_clusters.keys())
+    if(n_clusters<=20):
+        color_list = list(sb.color_palette('colorblind'))[0:n_clusters] 
+    else:
+        if(cmap is not None):
+            orig_cmap = plt.cm.get_cmap(cmap)
+            custom_cmap = orig_cmap(np.linspace(vmin, vmax, n_clusters))
+            color_list = [mcolors.rgb2hex(custom_cmap[i]) for i in range(n_clusters)]
+        else:
+            color_list = godsnot_64[0:n_clusters]
+        #np.random.seed(3); np.random.shuffle(color_list)
+
+    x = dict(zip(temp['cluster_id'].unique(), color_list )) 
+    rcolors = pd.Series(temp['cluster_id']).map(x)
+    rcolors.name = ''
+    x = aligner.DistMat[cluster_ordered_genes].loc[cluster_ordered_genes]   
+    p = sb.clustermap(x.reset_index(drop=True), cmap='viridis', 
+                      square=True, row_cluster=False, col_cluster=False, row_colors=rcolors, figsize=(10,10), xticklabels=False,
+                      cbar_pos=(1.05, 0.54, 0.02, 0.25))
+    if(genes2highlight is None):
+        gene_labels = []
+        for tick_label in p.ax_heatmap.axes.get_yticklabels():
+            tick_text = tick_label.get_text()
+            gene = temp.Gene.loc[int(tick_text)]
+            tick_label.set_color(rcolors[int(tick_text)])
+            gene_labels.append(gene)
+        p.ax_heatmap.axes.set_yticklabels(gene_labels, rotation = 0) 
+    else:
+        tick_indices = []
+        for g in genes2highlight:
+            tick_indices.append(temp.index[temp['Gene']==g][0]) 
+        p.ax_heatmap.axes.set_yticks(tick_indices) 
+        p.ax_heatmap.axes.set_yticklabels(genes2highlight, rotation = 0) 
+        
+        k=0
+        for tick_label in p.ax_heatmap.axes.get_yticklabels():
+            tick_label.set_color(rcolors[tick_indices[k]])
+            k+=1
+
+    # plotting the legend of clusters
+    legend_labels = ['Cluster-'+str(k) for k in c_keys] 
+    legend_patches = [mpatches.Patch(color=color_list[i], label=legend_labels[i]) for i in range(len(color_list))]
+    ax = p.ax_row_dendrogram
+    ax.legend(handles=legend_patches, loc='center')
+    ax.axis('off'); ax.set_xticks([]); ax.set_yticks([]); 
+
+    
+def resolve(regions):
+    for i in range(len(regions)):
+        x = list(regions[i]); x[1] = x[1]-1; regions[i] = x
+    return regions
+
+def color_al_str(alignment_str):
+        
+        D_regions = [(m.start(0), m.end(0)) for m in regex.finditer("D+", alignment_str)]
+        I_regions = [(m.start(0), m.end(0)) for m in regex.finditer("I+", alignment_str)]
+        M_regions = [(m.start(0), m.end(0)) for m in regex.finditer("M+", alignment_str)] 
+        W_regions = [(m.start(0), m.end(0)) for m in regex.finditer("W+", alignment_str)]
+        V_regions = [(m.start(0), m.end(0)) for m in regex.finditer("V+", alignment_str)]
+        M_regions = resolve(M_regions); D_regions = resolve(D_regions); 
+        I_regions = resolve(I_regions)
+        W_regions = resolve(W_regions); V_regions = resolve(V_regions)
+        i = 0; j = 0; m_id = 0; i_id = 0; d_id = 0; v_id = 0; w_id = 0; c = 0
+        colored_string=''
+            
+        while(c<len(alignment_str)):
+            if(alignment_str[c]=='M'):
+                step = (M_regions[m_id][1] - M_regions[m_id][0] + 1)
+                i = i + step; j = j + step; m_id = m_id + 1
+                colored_string += (Main.hcolors.MATCH + "M"*(step) + Main.hcolors.STOP)
+                # process W,V separately 
+            if(alignment_str[c]=='V'):
+                step = (V_regions[v_id][1] - V_regions[v_id][0] + 1)
+                i = i + step; v_id = v_id + 1
+                colored_string += (Main.hcolors.MATCH + "V"*(step) + Main.hcolors.STOP)
+            if(alignment_str[c]=='W'):
+                step = (W_regions[w_id][1] - W_regions[w_id][0] + 1)
+                j = j + step; w_id = w_id + 1
+                colored_string += (Main.hcolors.MATCH + "W"*(step) + Main.hcolors.STOP)
+            if(alignment_str[c]=='I'):
+                step = (I_regions[i_id][1] - I_regions[i_id][0] + 1)
+                i = i + step; i_id = i_id + 1
+                colored_string += (Main.hcolors.INSERT + "I"*(step) + Main.hcolors.STOP)
+            if(alignment_str[c]=='D'):
+                step = (D_regions[d_id][1] - D_regions[d_id][0] + 1)
+                j = j + step; d_id = d_id + 1
+                colored_string += (Main.hcolors.DELETE + "D"*(step) + Main.hcolors.STOP)
+            c = c + step 
+            
+        return colored_string
+
+
+def plot_any_legend(text2color_map):
+    legend_labels= list(text2color_map.keys())
+    color_list = list(text2color_map.values())
+    legend_patches = [mpatches.Patch(color=color_list[i], label=legend_labels[i]) for i in range(len(color_list))]
+    fig, ax = plt.subplots()
+    ax.legend(handles=legend_patches, loc='center')
+    ax.axis('off'); ax.set_xticks([]); ax.set_yticks([]); 
+
+def show_gene_alignment(gene, aligner,  adata_ref, adata_query, annotation_colname, cmap=None):
+    visualize_gene_alignment(aligner.results_map[gene].alignment_str,  adata_ref, adata_query, annotation_colname, cmap=cmap)
+    plotTimeSeries(gene, aligner, plot_cells=True)
+    aligner.results_map[gene].alignment_str
+    print(color_al_str(aligner.results_map[gene].alignment_str)) 
+    print('Optimal alignment cost:', round(aligner.results_map[gene].fwd_DP.opt_cost,3),'nits')
+    print('Alignment similarity percentage:', aligner.results_map[gene].match_percentage,'%' )
+
+    
+# smoothened/interpolated mean trends + Z normalisation 
+def plot_mean_trend_heatmaps(aligner, GENE_LIST, pathway_name, cluster=False, FIGSIZE=(14,7)):
+    S_mat = []
+    T_mat = []
+    S_zmat = []
+    T_zmat = []
+
+    for gene in GENE_LIST:
+
+        fS = pd.DataFrame([aligner.results_map[gene].S.mean_trend, np.repeat('Ref', len(aligner.results_map[gene].S.mean_trend))]).transpose()
+        fT = pd.DataFrame([aligner.results_map[gene].T.mean_trend, np.repeat('Organoid', len(aligner.results_map[gene].T.mean_trend))]).transpose()
+        f = pd.concat([fS,fT])
+        f[0] = np.asarray(f[0], dtype=np.float64)
+        f['z_normalised'] = zscore(f[0])
+        S_mat.append(np.asarray(f[f[1]=='Ref'][0]))
+        T_mat.append(np.asarray(f[f[1]=='Organoid'][0]))    
+        S_zmat.append(np.asarray(f[f[1]=='Ref']['z_normalised']))
+        T_zmat.append(np.asarray(f[f[1]=='Organoid']['z_normalised']))  
+    S_mat = pd.DataFrame(S_mat)
+    T_mat = pd.DataFrame(T_mat)
+    S_zmat = pd.DataFrame(S_zmat)
+    T_zmat = pd.DataFrame(T_zmat)
+    
+    S_mat.index = GENE_LIST  
+    T_mat.index = GENE_LIST  
+    S_zmat.index = GENE_LIST 
+    T_zmat.index = GENE_LIST 
+    
+    print('- Plotting z-normalised interpolated mean trends')
+    plot_heatmaps(S_zmat, T_zmat, GENE_LIST, pathway_name,cluster=cluster, FIGSIZE=FIGSIZE)
+
+def plot_heatmaps(mat_ref,mat_query,GENE_LIST, pathway_name, cluster=False, FIGSIZE=(14,7), write_file=False):
+    
+    if(cluster):
+        g=sb.clustermap(mat_ref, figsize=(0.4,0.4), col_cluster=False, cbar_pos=None) 
+        gene_order = g.dendrogram_row.reordered_ind
+        df = pd.DataFrame(g.data2d) 
+        df.index = GENE_LIST[gene_order]
+    else:
+        df=mat_ref
+    plt.close()
+    
+    plt.subplots(1,2) #8,14/7 ******************************************************
+    max_val = np.max([np.max(mat_ref),np.max(mat_query)]) 
+    min_val = np.min([np.min(mat_ref),np.min(mat_query)]) 
+    plt.subplot(1,2,1)
+    ax=sb.heatmap(df, vmax=max_val,vmin=min_val, cbar_kws = dict(use_gridspec=False,location="top")) 
+    plt.title('Reference')
+    ax.yaxis.set_label_position("left")
+    for tick in ax.get_yticklabels():
+        tick.set_rotation(360)
+    plt.subplot(1,2,2)
+    if(cluster):
+        mat_query = mat_query.loc[GENE_LIST[gene_order]] 
+    ax = sb.heatmap(mat_query,vmax=max_val,  vmin=min_val,cbar_kws = dict(use_gridspec=False,location="top"), xticklabels=True, yticklabels=False) 
+    plt.title('Query')
+    if(write_file):
+        plt.savefig(pathway_name+'_heatmap.png', bbox_inches='tight')
+    plt.show()
\ No newline at end of file
diff --git a/genes2genes/.ipynb_checkpoints/__init__-checkpoint.py b/genes2genes/.ipynb_checkpoints/__init__-checkpoint.py
new file mode 100644
index 0000000..63cd49d
--- /dev/null
+++ b/genes2genes/.ipynb_checkpoints/__init__-checkpoint.py
@@ -0,0 +1,12 @@
+"""Genes2Genes: Aligning transcriptomic trajectories of single-cell reference and query systems at gene-level"""
+__version__ = "0.2.0"
+
+from . import AlignmentDistMan
+from . import ClusterUtils
+from . import Main
+from . import MyFunctions
+from . import OrgAlign 
+from . import PathwayAnalyser
+from . import TimeSeriesPreprocessor
+from . import Utils
+from . import VisualUtils
\ No newline at end of file
diff --git a/genes2genes/AlignmentDistMan.py b/genes2genes/AlignmentDistMan.py
index 224d80d..74144f7 100644
--- a/genes2genes/AlignmentDistMan.py
+++ b/genes2genes/AlignmentDistMan.py
@@ -1,25 +1,28 @@
-import regex as re
-from . import OrgAlign as orgalign 
+import regex 
 import numpy as np 
 from tqdm import tqdm 
 import pandas as pd
 from tabulate import tabulate
 from scipy.spatial import distance
 
-class TempFSMObj:
+"""
+This script defines complementary classes and functions for alignment result analysis
+"""
+
+class _TempFSMObj:
     
     def __init__(self, al_str, gene):
         self.al_str = al_str
-        self.fsm, self.counts = self.get_transition_probs(al_str)
+        self.fsm, self.counts = self._get_transition_probs(al_str)
         self.al_length = len(al_str)
         self.gene = gene
         
-    def get_transition_probs(self, al_str):
+    def _get_transition_probs(self, al_str):
         transition_counts =  {'MM':0,'MI':0, 'MD':0,'MW':0,'MV':0, 'II':0, 'IM':0,'ID':0, 'IW':0, 'IV':0,'DD':0, 'DM':0,'DI':0, 'DW':0, 'DV':0, 
                              'WM':0,'WW':0,'WV':0,'WI':0,'WV':0, 'VM':0,'VW':0,'VV':0,'VI':0,'VV':0}
         sum_transitions = 0
         for key in transition_counts.keys():
-            transition_counts[key] = len(re.findall(key,al_str, overlapped=True)) + 1 # Adding pseudocount to avoid log(0)
+            transition_counts[key] = len(regex.findall(key,al_str, overlapped=True)) + 1 # Adding pseudocount to avoid log(0)
             sum_transitions += transition_counts[key]
 
         transition_probs = transition_counts.copy()
@@ -28,7 +31,7 @@ def get_transition_probs(self, al_str):
             transition_probs[key] = transition_counts[key]/sum_transitions
         return transition_probs, transition_counts
 
-def compute_msg_len(transition_counts, fsm, al_len):
+def _compute_msg_len(transition_counts, fsm, al_len):
     
     msg_len = 0.0
     for key in transition_counts.keys():
@@ -36,16 +39,16 @@ def compute_msg_len(transition_counts, fsm, al_len):
     msg_len = msg_len/al_len
     return msg_len
 
-def pairwise_alignment_dist_v2(a1,a2):
+def _pairwise_alignment_dist_v2(a1,a2):
     
-    x1 = compute_msg_len(a1.counts, a1.fsm, a1.al_length)
-    y1 = compute_msg_len(a2.counts, a1.fsm, a2.al_length)
-    x2 = compute_msg_len(a1.counts, a2.fsm, a1.al_length)
-    y2 = compute_msg_len(a2.counts, a2.fsm, a2.al_length)
+    x1 = _compute_msg_len(a1.counts, a1.fsm, a1.al_length)
+    y1 = _compute_msg_len(a2.counts, a1.fsm, a2.al_length)
+    x2 = _compute_msg_len(a1.counts, a2.fsm, a1.al_length)
+    y2 = _compute_msg_len(a2.counts, a2.fsm, a2.al_length)
 
     return (np.abs(x1-y1) + np.abs(x2-y2))/2
 
-def get_region_str(al_str):
+def _get_region_str(al_str):
         prev = ''
         i=0
         regions = ''
@@ -60,7 +63,7 @@ def get_region_str(al_str):
                 continue
         return regions
 
-def test_unique_index_sums(a):
+def _test_unique_index_sums(a):
     index_sum = 0
     m = {'M':0,'I':0,'D':0,'W':0,'V':0}
 
@@ -102,7 +105,7 @@ def compute_polygon_area_alignment_dist(self):
         for i in tqdm(range(len(self.alignments))):
             for j in range(len(self.alignments)):
                 if(DistMat[i][j] < 0):
-                    DistMat[i][j] = orgalign.Utils().compute_alignment_area_diff_distance(self.alignments[i].alignment_str, self.alignments[j].alignment_str
+                    DistMat[i][j] = Utils.compute_alignment_area_diff_distance(self.alignments[i].alignment_str, self.alignments[j].alignment_str
                                                     ,self.alignments[i].fwd_DP.S_len, self.alignments[i].fwd_DP.T_len )
                 else:
                     DistMat[i][j] = DistMat[j][i]
@@ -124,10 +127,10 @@ def compute_alignment_ensemble_distance_matrix(self, scheme):
         FSA_objects_regionwise = []
 
         for i in range(len(self.alignments)):
-            FSA_objects.append( TempFSMObj(self.alignments[i].alignment_str,self.alignments[i].gene ) )
-            region_str = get_region_str(self.alignments[i].alignment_str)
-            FSA_objects_regionwise.append(TempFSMObj(region_str,self.alignments[i].gene ))
-            self.alignments[i].unique_index_sums = list(test_unique_index_sums(self.alignments[i].alignment_str).values())
+            FSA_objects.append(_TempFSMObj(self.alignments[i].alignment_str,self.alignments[i].gene ) )
+            region_str = _get_region_str(self.alignments[i].alignment_str)
+            FSA_objects_regionwise.append(_TempFSMObj(region_str,self.alignments[i].gene ))
+            self.alignments[i].unique_index_sums = list(_test_unique_index_sums(self.alignments[i].alignment_str).values())
             self.alignments[i].region_str = region_str
 
         Mat = []; Mat_ui = []
@@ -140,7 +143,7 @@ def compute_alignment_ensemble_distance_matrix(self, scheme):
                 if(i==j):
                     Mat[i][j] = 0.0; Mat_ui[i][j] = 0.0
                 if(Mat[i][j]<0):
-                    Mat[i][j] = pairwise_alignment_dist_v2(FSA_objects[i],FSA_objects[j])
+                    Mat[i][j] = _pairwise_alignment_dist_v2(FSA_objects[i],FSA_objects[j])
                     Mat_ui[i][j] = distance.euclidean(self.alignments[i].unique_index_sums,self.alignments[j].unique_index_sums)
 
         LikelihoodDistMat = pd.DataFrame(Mat)
@@ -233,5 +236,4 @@ def order_genes_by_alignments(self):
             table.append([gene,self.results_map[gene].colored_alignment_str]) 
 
         print(tabulate(table, headers=['Gene','Alignment']))
-        return sorted_gene_list
-
+        return sorted_gene_list
\ No newline at end of file
diff --git a/genes2genes/BatchAnalyser.py b/genes2genes/BatchAnalyser.py
deleted file mode 100644
index afc476f..0000000
--- a/genes2genes/BatchAnalyser.py
+++ /dev/null
@@ -1,104 +0,0 @@
-from tqdm.notebook import tqdm
-import numpy as np
-from . import MyFunctions
-import scipy
-import scipy.sparse
-import seaborn as sb
-import matplotlib.pyplot as plt
-import pandas as pd
-
-class BatchAnalyser:
-    
-    def __init__(self):
-        # HOUSEKEEPING GENES LIST FROM  https://www.genomics-online.com/resources/16/5049/housekeeping-genes/
-        self.housekeeping_genes = ['RRN18S','ACTB','GAPDH','PGK1','PPIA','RPL13A','RPLP0','ARBP','B2M','YWHAZ','SDHA','TFRC','GUSB','HMBS','HPRT1','TBP']
-        
-        hg1 = pd.read_csv('/home/jovyan/OrgAlign/source/HousekeepingGeneLists/MostStable.csv',sep=';')
-        hg1 = list(hg1['Gene name'])
-        self.housekeeping_genes = np.unique(hg1 + self.housekeeping_genes) 
-        
-    def eval_between_system_batch_effect(self, adata_ref, adata_query, plot=False):
-        return self.eval_batch_effect(adata_ref, adata_query, plot=plot)
-
-    def eval_batch_effect(self, adata_ref, adata_query,plot):
-        
-        housekeeping_genes  = np.intersect1d(self.housekeeping_genes,adata_ref.var_names)
-        mean_batch_dist = 0.0
-        ncols = 5
-        nrows = int(np.ceil(len(housekeeping_genes)/ncols))
-        if(plot):
-            plt.subplots(nrows,ncols,figsize=(20,nrows*2.5))
-            k=1
-        for gene in housekeeping_genes:
-            if(plot):
-                plt.subplot(nrows,ncols,k)
-                k+=1
-            delta = self.compute_batch_effect(adata_ref, adata_query,gene, plot=plot)
-            mean_batch_dist = mean_batch_dist + delta
-        mean_batch_dist = mean_batch_dist/len(housekeeping_genes)
-        return mean_batch_dist
-        
-    def eval_within_system_batch_effect(self, adata, batch_key):
-
-        batch_adata = [] 
-        batches = []
-        for batch in np.unique(adata.obs[batch_key]):
-            batch_adata.append(adata[adata.obs[batch_key]==batch])  #if(batch_key =='donor' and (batch.startswith('T') or batch.startswith('P'))):
-            batches.append(batch)
-        
-        housekeeping_genes  = np.intersect1d(self.housekeeping_genes,adata.var_names)
-        mean_batch_dists = []
-        for i in tqdm(range(len(batch_adata))):
-            for j in range(i, len(batch_adata)):
-                if(i==j):
-                    continue
-                mean_batch_dist = self.eval_batch_effect(batch_adata[i],batch_adata[j],plot=False)
-                mean_batch_dists.append(mean_batch_dist) 
-                #print('mean batch dist = ', mean_batch_dist, ' | ', batches[i], batches[j], '|', i,j)
-               # plt.show() 
-        print('List of batches: ', batches)
-        return np.mean(mean_batch_dists)
-        
-    def compute_batch_effect(self, adata_ref, adata_query, housekeeping_gene, plot=False):
-
-            if(isinstance(adata_ref.X, scipy.sparse.csr.csr_matrix)):
-                ref_data = np.asarray(adata_ref[:,housekeeping_gene].X.todense().flatten())[0] 
-            else:
-                ref_data = np.asarray(adata_ref[:,housekeeping_gene].X.flatten()) 
-            if(isinstance(adata_query.X, scipy.sparse.csr.csr_matrix)):
-                query_data = np.asarray(adata_query[:,housekeeping_gene].X.todense().flatten())[0] 
-            else:
-                query_data = np.asarray(adata_query[:,housekeeping_gene].X.flatten())    
-            
-            μ_S = np.mean(ref_data); σ_S = np.std(ref_data); 
-            μ_T = np.mean(query_data); σ_T = np.std(query_data); 
-
-            dist_ref = MyFunctions.generate_random_dataset(100, μ_S, σ_S)
-            dist_query = MyFunctions.generate_random_dataset( 100, μ_T, σ_T)
-            if(plot):
-                sb.kdeplot(dist_ref[0])
-                sb.kdeplot(dist_query[0])
-                plt.title(housekeeping_gene)
-
-            I_ref_model, I_refdata_g_ref_model = MyFunctions.run_dist_compute_v3(ref_data, μ_S, σ_S) 
-            I_query_model, I_querydata_g_query_model = MyFunctions.run_dist_compute_v3(query_data, μ_T, σ_T) 
-            I_ref_model, I_querydata_g_ref_model = MyFunctions.run_dist_compute_v3(query_data, μ_S, σ_S) 
-            I_query_model, I_refdata_g_query_model = MyFunctions.run_dist_compute_v3(ref_data, μ_T, σ_T) 
-
-            match_encoding_len1 = I_ref_model + I_querydata_g_ref_model + I_refdata_g_ref_model
-            match_encoding_len1 = match_encoding_len1/(len(query_data)+len(ref_data))
-            match_encoding_len2 = I_query_model + I_refdata_g_query_model + I_querydata_g_query_model
-            match_encoding_len2 = match_encoding_len2/(len(query_data)+len(ref_data))
-            match_encoding_len = (match_encoding_len1 + match_encoding_len2 )/2.0 
-
-            null = (I_ref_model + I_refdata_g_ref_model + I_query_model + I_querydata_g_query_model)/(len(query_data)+len(ref_data))
-            match_compression =   match_encoding_len - null 
-            return match_compression.numpy()
-
-        
-
-        
-
-        
-
-        
diff --git a/genes2genes/ClusterUtils.py b/genes2genes/ClusterUtils.py
index 10dab34..938dff7 100644
--- a/genes2genes/ClusterUtils.py
+++ b/genes2genes/ClusterUtils.py
@@ -1,23 +1,25 @@
-from sklearn.cluster import AgglomerativeClustering
-from sklearn.metrics.pairwise import euclidean_distances
-from scipy.spatial import distance
-import scipy.cluster.hierarchy as sch
-import matplotlib.pyplot as plt
-from numpy import argmax
 import numpy as np
 import pandas as pd
 import seaborn as sb
 import scipy
-from leven import levenshtein   
 import sklearn 
 from tqdm import tqdm
+import matplotlib.pyplot as plt
+from leven import levenshtein  
+from scipy.spatial import distance
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.metrics.pairwise import euclidean_distances
 
 from . import VisualUtils
-from . import PathwayAnalyserV2
+from . import PathwayAnalyser
+from . import Utils
 
+"""
+This script defines all methods required for finding clusters of gene alignments
+"""
 
 def compute_levenshtein_dist_matrix(set_of_strings):
-    print('using levenshtein distance metric')
+    print('- using levenshtein distance metric')
     E = []
     for s1 in set_of_strings:
         row = []
@@ -28,7 +30,7 @@ def compute_levenshtein_dist_matrix(set_of_strings):
 
 def compute_hamming_dist_matrix(set_of_strings):
     
-    print('using hamming distance metric')
+    print('- using hamming distance metric')
     al_binary_encodings= []
     for a in set_of_strings:
         al_binary_encodings.append(binary_encode_alignment_path(a))
@@ -93,6 +95,7 @@ def run_clustering_for_subset(aligner, metric, MIN_DIST = 0.001):
     df['equal'] = df['n_unique_strings'] == df['n_identified_clusters']
     return df
 
+# main clustering function 
 def run_clustering(aligner, metric,DIST_THRESHOLD=0.2, experiment_mode=False):
     
     # extract the alignment strings to cluster
@@ -101,12 +104,13 @@ def run_clustering(aligner, metric,DIST_THRESHOLD=0.2, experiment_mode=False):
         alignment_strings.append(aligner.results[i].alignment_str)  
 
     # compute distance matrix 
-    print('compute distance matrix')
+    print('Compute distance matrix')
     dist_mat_functions = {'hamming': compute_hamming_dist_matrix, 'levenshtein': compute_levenshtein_dist_matrix}
     compute_dist_matrix = dist_mat_functions[metric]
     E = compute_dist_matrix(alignment_strings)
     
     if(experiment_mode):
+        print('Experimental mode: exploring different thresholds')
         scores = [];  n_clusters = []; dist_thresholds = np.arange(0.01,1.0,0.01); score_modes = []; n_small_clusters = [] 
         eval_dists = []
         for D_THRESH in tqdm(dist_thresholds): 
@@ -119,7 +123,7 @@ def run_clustering(aligner, metric,DIST_THRESHOLD=0.2, experiment_mode=False):
             score_modes.append(silhouette_score_mode)
             n_small_clusters.append(n_small_cluster)
             eval_dists.append(D_THRESH)
-            
+        print('-- Cluster diagnostic plots')
         plt.rcParams.update({'font.size': 14})
         plt.subplots(1,3,figsize=(10,5))
         plt.subplot(1,3,1)
@@ -137,9 +141,11 @@ def run_clustering(aligner, metric,DIST_THRESHOLD=0.2, experiment_mode=False):
         plt.tight_layout() 
         df = pd.DataFrame([eval_dists,scores,n_clusters]).transpose()
         df.columns = ['Distance threshold', 'Mean Silhouette Score','Number of clusters']
+        
+        print('Potential candidates for distance threshold: a locally optimal thresholds that gives a good trade-off between high mean Silhouette score and low number of clusters ')
         return df
     else:
-        print('run agglomerative clustering | ', np.round(DIST_THRESHOLD,2) )
+        print('run agglomerative clustering | distance threshold =', np.round(DIST_THRESHOLD,2) )
         gene_clusters, cluster_ids, silhouette_score, silhouette_score_samples, n_small_cluster  = run_agglomerative_clustering(E, aligner.gene_list, DIST_THRESHOLD)
         print('silhouette_score: ', silhouette_score)
         aligner.cluster_ids = cluster_ids
@@ -153,11 +159,12 @@ def run_clustering(aligner, metric,DIST_THRESHOLD=0.2, experiment_mode=False):
             for g in aligner.gene_clusters[i]:
                 aligner.results_map[g].cluster_id = i
         
-        #return aligner
-
 
+# plotting function of all gene alignment paths grouped by clusters
 def visualise_clusters(aligner, n_cols = 5, figsize= (10,6)): 
     
+        print('Cluster ID | Number of genes in the cluster')
+    
         cluster_ids = aligner.cluster_ids
         alignments = aligner.results 
         cluster_map = aligner.gene_clusters
@@ -188,7 +195,6 @@ def visualise_clusters(aligner, n_cols = 5, figsize= (10,6)):
             k = k+1
             i=i+1  
 
-
 def plot_different_alignments(paths, S_len, T_len, ax, mat=[]): # pass alignment path coordinates
         mat=[]
         for i in range(T_len+1):
@@ -210,26 +216,23 @@ def get_significant_pathways_for_clusters(aligner, PVAL_THRESHOLD=0.01):
         for g in cluster_map[cid]:
             glist.append(g)
 
-        df = PathwayAnalyserV2.run_overrepresentation_analysis(glist)
+        df = PathwayAnalyser.run_overrepresentation_analysis(glist)
         results[cid] = df[df['Adjusted P-value']< PVAL_THRESHOLD]
 
     return results
 
 
-
 def print_cluster_average_alignments(aligner):
     
     aligner.gene_clusters_average_alignments = {}
     for i in range(len(aligner.gene_clusters)):
         average_alignment, avg_path =  get_cluster_average_alignments(aligner, aligner.gene_clusters[i] )
-        print('cluster: ', i, VisualUtils.color_al_str(average_alignment), len(aligner.gene_clusters[i]))
+        print('cluster: ', i, VisualUtils.color_al_str(average_alignment), '(',len(aligner.gene_clusters[i]), 'genes)')
         aligner.gene_clusters_average_alignments[i] = average_alignment
 
 
 
 # G2G Version 1 ---- PREVIOUS CLUSTERING UTILS
-
-
 def check_alignment_clusters(cluster_ids, alignments, n_cols = 5, figsize= (10,6), n_clusters=20): 
         clusters = [] 
         S_len = alignments[0].fwd_DP.S_len
@@ -354,7 +357,7 @@ def run_hierarchical_clustering_with_int_encoding_scheme(aligner,DIST_THRESHOLD=
    # return cluster_ids, gene_clusters
 
 
-# ========== BEST VERSION ====================================================
+# ==============================================================
 def binary_encode_alignment_path(al_str):
     S = ''
     T = ''
@@ -427,36 +430,27 @@ def run_hierarchical_clustering_with_binary_encode_alignment_path_hamming(aligne
     aligner.DistMat.index = aligner.gene_list
     aligner.E = E
 
-# ========== BEST VERSION ====================================================
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+# ==============================================================
 
+def get_pairwise_match_count_mat(aligner, gene_set):
+            mat = []
+            nT_points = len(aligner.results[0].T.time_points)
+            nS_points = len(aligner.results[0].S.time_points)
+            for i in range(nT_points + 1):
+                mat.append(np.repeat(0.0, nS_points+1))
 
+            # counts of total matches between the each pair of ref and query timepoints across all alignments 
+            for g in gene_set:
+                a = aligner.results_map[g]
+                matchS = a.match_points_S+1
+                matchT = a.match_points_T+1
+                for i in range(len(matchS)):
+                    mat[matchT[i]][matchS[i]] = mat[matchT[i]][matchS[i]] + 1
 
+            return pd.DataFrame(mat) 
 
 # Cluster Average Util
 
-
-
 # This computes an average alignment across all the alignments for a given set of gene alignments 
 def get_cluster_average_alignments(aligner, gene_set, deterministic=True):
     
@@ -501,7 +495,7 @@ def get_cluster_average_alignments(aligner, gene_set, deterministic=True):
                     cs = np.argmax(np.asarray(list(backtrack_states_probs.values())) )
                 else:
                     # sample a state from the state frequency distribution
-                    cs = MyFunctions.sample_state(np.asarray(list(backtrack_states_probs.values()) ) )
+                    cs = Utils.sample_state(np.asarray(list(backtrack_states_probs.values()) ) )
                 if(cs==0):
                     i = i-1
                     j = j-1
@@ -523,106 +517,5 @@ def get_cluster_average_alignments(aligner, gene_set, deterministic=True):
             
             return avg_alignment, tracked_path
 
-def get_pairwise_match_count_mat(aligner, gene_set):
-            mat = []
-            nT_points = len(aligner.results[0].T.time_points)
-            nS_points = len(aligner.results[0].S.time_points)
-            for i in range(nT_points + 1):
-                mat.append(np.repeat(0.0, nS_points+1))
-
-            # counts of total matches between the each pair of ref and query timepoints across all alignments 
-            for g in gene_set:
-                a = aligner.results_map[g]
-                matchS = a.match_points_S+1
-                matchT = a.match_points_T+1
-                for i in range(len(matchS)):
-                    mat[matchT[i]][matchS[i]] = mat[matchT[i]][matchS[i]] + 1
-
-            return pd.DataFrame(mat) 
-
-        
-
-        
-
-        
-
-        
 
-# computes simple DP alignment (using match score = pairwise total match count frequency) across all gene-level alignments 
-    # gap score is taken as penalising 8% of the total number of tested genes => so that it controls the matching based on the number of 
-    # total matches (i.e. it controls the degree of significant matching) 
-def compute_overall_alignment(aligner,mat, plot=False, GAP_SCORE = None):
-                
-                if(GAP_SCORE==None):
-                    GAP_SCORE= -len(aligner.gene_list)*0.08
-
-                if(plot):
-                    sb.heatmap(mat, cmap='viridis', square=True)
-
-                # DP matrix initialisation 
-                opt_cost_M = []
-                for i in range(mat.shape[0]):
-                    opt_cost_M.append(np.repeat(0.0, mat.shape[1]))
-                opt_cost_M = np.matrix(opt_cost_M) 
-                # backtracker matrix initialisation 
-                tracker_M = []
-                for i in range(mat.shape[0]):
-                    tracker_M.append(np.repeat(0.0, mat.shape[1]))
-                tracker_M = np.matrix(tracker_M) 
-                for i in range(1,mat.shape[0]):
-                    tracker_M[i,0] = 2
-                for j in range(1,mat.shape[1]):
-                    tracker_M[0,j] = 1
-
-                # running DP
-                for j in range(1,mat.shape[1]):
-                    for i in range(1,mat.shape[0]):
-                        m_dir = opt_cost_M[i-1,j-1] + mat.loc[i,j]
-                        d_dir = opt_cost_M[i,j-1] +  GAP_SCORE
-                        i_dir = opt_cost_M[i-1,j] +  GAP_SCORE
-
-                        a = max([m_dir, d_dir, i_dir])
-                    
-                        if(a==d_dir):
-                            opt = d_dir
-                            dir_tracker = 1
-                        elif(a==i_dir):
-                            opt =i_dir
-                            dir_tracker = 2
-                        elif(a==m_dir):
-                            opt = m_dir
-                            dir_tracker = 0
-
-                        opt_cost_M[i,j] = opt
-                        tracker_M[i,j] = dir_tracker     
-
-                # backtracking
-                i = mat.shape[0]-1
-                j = mat.shape[1]-1
-                alignment_str = ''
-                tracked_path = []
-                while(True):
-                    tracked_path.append([i,j])
-                    if(tracker_M[i,j]==0):
-                        alignment_str = 'M' + alignment_str
-                        i = i-1
-                        j = j-1
-                    elif(tracker_M[i,j]==1):
-                        if(mat.loc[i,j]>0):
-                            alignment_str = 'W' + alignment_str
-                        else:
-                            alignment_str = 'D' + alignment_str
-                        j = j-1
-                    elif(tracker_M[i,j]==2):
-                        if(mat.loc[i,j]>0):
-                            alignment_str = 'V' + alignment_str
-                        else:
-                            alignment_str = 'I' + alignment_str
-                        i = i-1 
-
-                    if(i==0 and j==0) :
-                        break
-                tracked_path.append([0,0])
-                # NOTE: This alignment string does not have the same interpretation as of the 5-state gene alignment string we get.
-                # Here we are only interested in the path 
-                return alignment_str, tracked_path#, opt_cost_M, tracker_M
+        
\ No newline at end of file
diff --git a/genes2genes/MVG.py b/genes2genes/MVG.py
deleted file mode 100644
index 3db0a17..0000000
--- a/genes2genes/MVG.py
+++ /dev/null
@@ -1,185 +0,0 @@
-import torch
-import seaborn as sb
-import torch.nn as nn
-import numpy as np
-import pandas as pd
-import time
-import gpytorch
-import matplotlib.pyplot as plt
-import torch.distributions as td
-import scipy
-import warnings
-warnings.filterwarnings("ignore")
-
-torch.set_default_dtype(torch.float64)
-
-def generate_random_MVG_dataset(d,N,DIST_SEED=1,use_zero_mean=False,MEAN_SEED=1,):
-    #d = n_dimensions
-    #N = n_data_points
-    input_dims = []
-    for i in range(d):
-        input_dims.append(i)
-    X = torch.tensor(input_dims) # input points on x axis (dims) as in GP
-    kernel = gpytorch.kernels.RBFKernel()
-    C = kernel(X).evaluate()
-    μ = torch.zeros(d) # zero mean case for dimensions
-    if(not use_zero_mean):
-        # difference mean for all cases
-        torch.manual_seed(MEAN_SEED)
-        for i in range(d):
-            μ[i] = torch.distributions.Uniform(5,10).rsample() 
-    D = torch.empty(N,d) # Data matrix
-    torch.manual_seed(DIST_SEED)
-    for i in range(N):
-        D[i] = torch.distributions.MultivariateNormal(μ, C).rsample().detach() 
-    return μ,C,D
-
-# As p (n free dimensions) increases, the lower and upper bounds converge [Ref: Wallace book]
-#def conway_constant_upper_bound(p):
-#    return ((scipy.special.gamma( (p/2)+1  )**(2/p))*scipy.special.gamma( (2/p)+1))/(np.pi*p)
-# Test case: p = 100 ----- 2**log2_conway_constant_upper_bound(p) #0.0613252739213439
-def log_factorial(x):
-    #return scipy.special.gammaln((x+1))/np.log(2)
-    return scipy.special.gammaln((x+1))
-def log_conway_constant_upper_bound(p):
-    #return ((2/p)*log2_factorial(p/2)) +  log2_factorial(2/p) -np.log2(np.pi) -np.log2(p)
-    return ((2/p)*log_factorial(p/2)) +  log_factorial(2/p) -np.log(np.pi) -np.log(p)
-
-def negative_log_likelihood(μ,C,N,data, d, det_C):
-    #print('det_C -- ', det_C)
-    term1 = ((N*d)/2.0)*np.log(2*np.pi) 
-    #term2 = (N/2.0)*np.log(det_C)
-    term2 = 0.0 # bcz det_C =1 due to C=I
-    term3 = 0.0
-    #inverse_C = torch.linalg.inv(C)
-    inverse_C = C # inverse of the I is itself (since we use Identity matrix)
-    
-    for i in range(N):
-        temp = np.matrix(data[i] - μ)
-        x_i = torch.tensor(temp)
-        x_it =torch.tensor(temp.transpose())
-        #term3 = term3 + torch.matmul(torch.matmul(x_i , inverse_C ), x_it).flatten()[0]
-        term3 = term3 + torch.matmul(x_i, x_it).flatten()[0] # because C=I
-    term3 = term3 * 0.5
-    #print('NEG LOG:2 ', term1,term2, term3)
-    return (term1 + term2 + term3).detach().item() 
-
-def I_first_part(p,d,N,det_C):
-    return (0.5*p*log_conway_constant_upper_bound(p))  + ((p/2)*np.log(N)) - (d/2) - (0.5*np.log(det_C))
-
-def compute_mml_estimates(data,d,N):
-    μ_mml = torch.mean(data,axis=0)
-    term = 0.0
-    for i in range(N):
-        temp = data[i] - μ_mml   
-        temp = np.matrix(temp) 
-        temp_C = np.matmul(temp.transpose(),temp)
-        term = term + temp_C
-    C_mml = torch.tensor(term/(N-1)) 
-    #print(np.linalg.det(temp_C))
-    
-    if(torch.linalg.det(C_mml)<=0): # (adding a small perturbation) regularisation to avoid numerical instability --- then it will have only positive eigenvalues and it will have the exact same eigenvectors
-        C_mml = C_mml + (0.001*torch.eye(len(C_mml)))
-    
-    return  μ_mml,C_mml
-
-def compute_MML_msg_len(data):
-    
-    d = data.shape[1]; N = len(data)
-    μ,C = compute_mml_estimates(data,d,N)
-    d = len(C)
-    p = d*(d+3)/2
-    det_C = torch.linalg.det(C).detach().numpy()  # determinant of the covariance matrix 
-
-    I_model = I_first_part(p,d,N,det_C)
-    I_data_g_model = negative_log_likelihood(μ,C,N,data,d, det_C) + p/2 
-    #print('NEG LOG: ', I_data_g_model)
-    return I_model, I_data_g_model, C
-
-def run_dist_compute_v3(data_to_model, μ_base, C_base):
-    data = data_to_model
-    d = data.shape[1]; N = len(data)
-    μ = torch.tensor(μ_base); C = torch.tensor(C_base) 
-    d = len(C)
-    p = d*(d+3)/2
-    #det_C = torch.linalg.det(C).detach().numpy()  # determinant of the covariance matrix 
-    det_C = 1.0 #(if we are using C=Identity matrix)
-    
-   # I_model = I_first_part(p,d,N,det_C)
-    I_model = 0.0 # (because we consider same C for both)
-    I_data_g_model = negative_log_likelihood(μ,C,N,data,d, det_C) + p/2 
-    #print('NEG LOG:2 ', p/2)
-    
-    #print('msg len entropy: ', (I_model + I_data_g_model)/len(data_to_model)  )
-    return I_model, I_data_g_model
-
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diff --git a/genes2genes/Main.py b/genes2genes/Main.py
index be58066..e2ee9a2 100644
--- a/genes2genes/Main.py
+++ b/genes2genes/Main.py
@@ -1,25 +1,17 @@
-import multiprocessing
-from multiprocessing import Pool
-from tqdm.notebook import tqdm_notebook
-import pandas as pd
-import numpy as np
-import seaborn as sb
-import matplotlib.pyplot as plt
-import textdistance 
-from Levenshtein import distance
-import time
+import regex 
 import copy
-from sklearn.cluster import AgglomerativeClustering
 import scipy
+import anndata
 import scipy.sparse
+import pandas as pd
+import numpy as np
+import seaborn as sb
+from tqdm import tqdm
+import multiprocessing
 import scipy.stats as stats
-from scipy.cluster.hierarchy import dendrogram, linkage
-from scipy.spatial.distance import squareform
-from scipy.cluster.hierarchy import fcluster
-from scipy.stats import zscore
-from tabulate import tabulate
-import regex 
-import anndata
+import matplotlib.pyplot as plt
+from multiprocessing import Pool
+from tqdm.notebook import tqdm_notebook
 
 from . import OrgAlign as orgalign
 from . import MyFunctions 
@@ -27,11 +19,25 @@
 from . import AlignmentDistMan
 from . import VisualUtils
 from . import ClusterUtils
+from . import Utils
+
+__version__ = 'v0.2.0'
+
+class hcolors:
+    MATCH = '\033[92m'
+    INSERT = '\033[91m'
+    DELETE = '\033[91m'
+    STOP = '\033[0m'    
 
+    
 class AligmentObj:
     
+    """
+    This class defines an aligned object of a reference and query gene expression time series,
+    to carry all related results. 
+    """
+    
     def __init__(self,gene, S,T, fwd_DP_obj,bwd_DP_obj, landscape_obj):
-        
         self.gene = gene
         self.S = S
         self.T = T
@@ -47,33 +53,32 @@ def __init__(self,gene, S,T, fwd_DP_obj,bwd_DP_obj, landscape_obj):
         self.non_match_regions_T = out[3]
         
         self.compute_series_match_percentage()
-        #if(isinstance(S,TimeSeriesPreprocessor.SummaryTimeSeries)):
         try:
             self.run_DEAnalyser() 
         except Exception as e:
             print(str(e),gene)
 
+    # Printing details about the optimal alignment object
     def print(self):
         print('Fwd opt cost', self.fwd_DP.opt_cost)
-        print(self.fwd_DP.alignment_str) 
-        #print('Bwd opt cost: ', self.bwd_DP_obj.opt_cost)
-        #print(bwd_DP.alignment_str[::-1])
-        print(self.match_regions_S)
-        print(self.match_regions_T)
-        print(self.non_match_regions_S)
-        print(self.non_match_regions_T)
+        print('5-state alignment string: ', self.fwd_DP.alignment_str) 
+        print('Ref matched time points ranges: ', self.match_regions_S)
+        print('Query matched time points ranges: ', self.match_regions_T)
+        print('Ref mismatched time points ranges: ',self.non_match_regions_S)
+        print('Query mismatched time points ranges: ',self.non_match_regions_T)
+        print('Alignment landscape plot: ')
         self.landscape_obj.plot_alignment_landscape() 
         
     def plotTimeSeries(self, refQueryAlignerObj, plot_cells = False, plot_mean_trend= False):
         sb.scatterplot(x=self.S.X, y=self.S.Y, color = 'forestgreen' ,alpha=0.05)#, label='Ref') 
         sb.scatterplot(x=self.T.X, y=self.T.Y, color = 'midnightblue' ,alpha=0.05)#, label ='Query')
-      #  plt.legend(loc='upper left')
+      # plt.legend(loc='upper left')
         if(plot_cells):
             sb.scatterplot(x=refQueryAlignerObj.ref_time, y=np.asarray(refQueryAlignerObj.ref_mat[self.gene]), color = 'forestgreen' ) 
             sb.scatterplot(x=refQueryAlignerObj.query_time, y=np.asarray(refQueryAlignerObj.query_mat[self.gene]), color = 'midnightblue' )
         plt.title(self.gene)
-        plt.xlabel('pseudotime')
-        plt.ylabel('log1p expression')
+        plt.xlabel('Pseudotime')
+        plt.ylabel('Gene expression')
         
         if(plot_mean_trend):
             self.plot_mean_trends() 
@@ -84,8 +89,8 @@ def plotTimeSeriesAlignment(self):
       #  plt.legend(loc='upper left')
         self.plot_mean_trends() 
         plt.title(self.gene)
-        plt.xlabel('pseudotime')
-        plt.ylabel('log1p expression')
+        plt.xlabel('Pseudotime')
+        plt.ylabel('Gene expression')
         
         for i in range(self.matched_region_DE_info.shape[0]):
             S_timebin = int(self.matched_region_DE_info.iloc[i]['ref_bin'])
@@ -93,26 +98,6 @@ def plotTimeSeriesAlignment(self):
             x_vals = [self.matched_region_DE_info.iloc[i]['ref_pseudotime'],self.matched_region_DE_info.iloc[i]['query_pseudotime']] 
             y_vals = [self.S.mean_trend[S_timebin ], self.T.mean_trend[T_timebin]] 
             plt.plot(x_vals, y_vals, color='black', linestyle='dashed', linewidth=0.6)
-            
-    def plotTimeSeries_for_gene_pair(al_obj, aligner, plot_cells = False, plot_mean_trend= False):
-        sb.scatterplot(x=al_obj.S.X, y=al_obj.S.Y, color = 'forestgreen' ,alpha=0.05)#, label='Ref') 
-        sb.scatterplot(x=al_obj.T.X, y=al_obj.T.Y, color = 'midnightblue' ,alpha=0.05)#, label ='Query')
-        if(plot_cells):
-            sb.scatterplot(x=aligner.ref_time, y=np.asarray(aligner.ref_mat[al_obj.gene_pair[0]]), color = 'forestgreen' ) 
-            sb.scatterplot(x=aligner.ref_time, y=np.asarray(aligner.ref_mat[al_obj.gene_pair[1]]), color = 'midnightblue' )
-        plt.title(al_obj.gene)
-        plt.xlabel('pseudotime')
-        plt.ylabel('log1p expression')
-        
-        if(plot_mean_trend):
-            self.plot_mean_trends() 
-        
-        
-    def get_ref_timeseries_obj(self):
-        return self.fwd_DP.S
-    
-    def get_query_timeseries_obj(self):
-        return self.fwd_DP.T
     
     
     def compute_series_match_percentage(self):
@@ -150,7 +135,6 @@ def run_DEAnalyser(self):
         DE_analyser.get_matched_time_points()
 
         if(isinstance(self.S,TimeSeriesPreprocessor.SummaryTimeSeries)):
-            #print('DEAnalyser: get DE info')
             DE_analyser.get_DE_info_for_matched_regions() 
         
     def plot_matched_region_dists(self):
@@ -177,11 +161,6 @@ def plot_matched_region_dists(self):
             
     def print_alignment(self):
 
-       # print('S matched : ', self.S_match_regions)
-       # print('T matched : ', self.T_match_regions)
-       # print('S not matched : ', self.S_non_match_regions)
-       # print('T not matched : ',  self.T_non_match_regions)
-       # print('')
         print(self.al_visual)
         print('Matched percentages: ')
         p1,p2,p3 = self.get_series_match_percentage()
@@ -200,22 +179,52 @@ def get_opt_alignment_cost(self):
 
 class RefQueryAligner:
     
+    """
+    This class defines the main aligner class of genes2genes alignment, acting as entry point to initialise alignment parameters and interpolation. 
+    It contains all methods for running genes2genes alignment between the specified genes of the reference and query datasets.  
+    
+    Parameters
+    ----------
+    *args
+        adata_ref: anndata 
+        adata_query: anndata
+        gene_list: list
+        n_interpolation_points: int
+        adaptive_kernel: boolean
+    """
+    
     def __init__(self, *args):
-        if(len(args) ==4 ):
+        print('===============================================================================================================')
+        print('Genes2Genes ('+ __version__ +')')
+        print('Dynamic programming alignment of gene pseudotime trajectories using a bayesian information-theoretic framework')
+        print('===============================================================================================================')
+        
+        if(len(args) == 4 ):
+            self.run_init1(args[0], args[1], args[2], args[3])
+            adaptive_kernel = False
+        elif(len(args) == 5 ):
+            print('Running in adaptive interpolation mode')
             self.run_init1(args[0], args[1], args[2], args[3])
-        elif(len(args) == 6 ): 
-            self.run_init2(args[0], args[1], args[2], args[3], args[4], args[5])
+            adaptive_kernel = args[4]
         else:
             print('pls pass the required number of args')
-    
-    def set_n_threads(self,n):
-        self.n_threads = n
+            
+        k=1;
+        self.TrajInt_R = TimeSeriesPreprocessor.TrajectoryInterpolator(self.adata_ref, n_bins=self.n_artificial_time_points, adaptive_kernel=adaptive_kernel,raising_degree = k)
+        self.TrajInt_R.run() 
+        self.TrajInt_Q = TimeSeriesPreprocessor.TrajectoryInterpolator(self.adata_query, n_bins=self.n_artificial_time_points, adaptive_kernel=adaptive_kernel,raising_degree = k)
+        self.TrajInt_Q.run() 
+        print('Interpolator initialization completed')
+        self.state_params = [0.99,0.1,0.7] # parameters empirically found over our simulated dataset
+        self.no_extreme_cases =False
+        
+        print('Aligner initialised to align trajectories of', self.adata_ref.shape[0], 'reference cells &',self.adata_query.shape[0], 'query cells in terms of', len(self.gene_list), 'genes')
     
     # converts ref and query anndata objects to pd.DataFrames 
     def run_init1(self, adata_ref, adata_query, gene_list, n_artificial_time_points):
-       
-        #if(not hasattr(self, 'mean_batch_effect' )):
-            #self.mean_batch_effect =  BatchAnalyser.BatchAnalyser().eval_between_system_batch_effect(adata_ref, adata_query)
+        
+        self.adata_ref = adata_ref[:, gene_list] 
+        self.adata_query = adata_query[:, gene_list]
         
         if(isinstance(adata_ref.X, scipy.sparse.csr.csr_matrix) 
            or isinstance(adata_ref.X,anndata._core.views.SparseCSCView)
@@ -239,33 +248,18 @@ def run_init1(self, adata_ref, adata_query, gene_list, n_artificial_time_points)
         
         self.run_init2(ref_mat, ref_time, query_mat, query_time, gene_list, n_artificial_time_points)
     
-    def run_init2(self, ref_mat, ref_time, query_mat, query_time, gene_list, n_artificial_time_points, CONST_STD=False):
+    def run_init2(self, ref_mat, ref_time, query_mat, query_time, gene_list, n_artificial_time_points):
         self.ref_mat = ref_mat
         self.query_mat = query_mat
         self.ref_time = ref_time
         self.query_time = query_time
         self.gene_list = gene_list
         self.pairs = {}
-        self.n_threads = multiprocessing.cpu_count()
-        self.CONST_STD = CONST_STD
-        
-        # to preserve the number of time points ratio
-        time_lens = [len(self.ref_time), len(self.query_time)]  
         self.n_artificial_time_points = n_artificial_time_points
-        #self.n_q_points = int(n_artificial_time_points * time_lens[0]/time_lens[1])
-        self.n_q_points = n_artificial_time_points
-        
-       # self.ref_processor = TimeSeriesPreprocessor.Prepocessor(self.ref_mat, self.ref_time, 50)
-       # self.query_processor =  TimeSeriesPreprocessor.Prepocessor(self.query_mat, self.query_time, n_q_points)
-        
-    
-
-    
-    
-    
     
+    # util functions 
     def extract_significant_regions_only(self, regions):
-        if(len(regions)==0):
+        if(len(regions)==0): 
             return []
         adjacent_region_start = regions[0][0]
         filtered_regions = np.asarray([], dtype=np.float64)
@@ -275,7 +269,6 @@ def extract_significant_regions_only(self, regions):
             if(k!=len(regions)-1):
                 if(regions[k][1] != regions[k+1][0]):
                     ended_adjacent_region_len = regions[k][1]- adjacent_region_start
-                    #print(ended_adjacent_region_len)
                     if(ended_adjacent_region_len>0.2):
                         adjacent_region_indices = np.append(adjacent_region_indices,regions[k][0])
                         adjacent_region_indices = np.append(adjacent_region_indices, regions[k][1])
@@ -287,10 +280,8 @@ def extract_significant_regions_only(self, regions):
                     adjacent_region_indices=np.append(adjacent_region_indices,regions[k][0])
                     continue
             else:
-                #print(regions[k][1]- adjacent_region_start)
                 if(len(adjacent_region_indices)>0): # check if there is a continuing adjacent region
                     ended_adjacent_region_len = regions[k][1]- adjacent_region_start
-                    #print(ended_adjacent_region_len)
                     if(ended_adjacent_region_len>0.2):
                         adjacent_region_indices = np.append(adjacent_region_indices,regions[k][0])
                         adjacent_region_indices=np.append(adjacent_region_indices,regions[k][1])
@@ -298,44 +289,30 @@ def extract_significant_regions_only(self, regions):
                         filtered_region_indices = np.append(filtered_region_indices, adjacent_region_indices)
                         
         return list(filtered_region_indices)
-    
-    def check_inconsistent_zero_region(self, mat, time_arr, g):
+
+    def check_inconsistent_zero_region(self, gex_arr, pseudotime_arr, trajInterpolator):
         
         regions = []
-        window_range = np.linspace(0,1, self.n_artificial_time_points)
+        window_range = trajInterpolator.interpolation_points
+        
         for i in range(1,len(window_range)):
-            logic = np.logical_and(time_arr>=window_range[i-1], time_arr<window_range[i]) 
-            n_pos = np.count_nonzero(np.asarray(mat[g][logic]))
+            sliding_region = np.logical_and(pseudotime_arr>=window_range[i-1], pseudotime_arr<window_range[i]) 
+            n_pos = np.count_nonzero(gex_arr[sliding_region])
             if(n_pos<=3): #almost 0 expression 
                 regions.append([window_range[i-1], window_range[i]])
         regions = self.extract_significant_regions_only(regions)
         return regions
-    
-        
-    def run_interpolation(self, gene):
-        #print('***** ', self.n_artificial_time_points)
-        
-        if not (hasattr(self, 'opt_binning_S') and hasattr(self, 'opt_binning_T')):
-            self.opt_binning_S = []
-            self.opt_binning_T = []
-            self.optimal_binning = False
-        
-        ref_processor = TimeSeriesPreprocessor.Prepocessor(self.ref_mat, self.ref_time, self.n_artificial_time_points, self.WINDOW_SIZE, self.optimal_binning,self.opt_binning_S)
-        query_processor =  TimeSeriesPreprocessor.Prepocessor(self.query_mat, self.query_time, self.n_q_points, self.WINDOW_SIZE, self.optimal_binning, self.opt_binning_T)
-        
-        
-        # Run interpolation 
-        #S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
-        #T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
-        
-        gex_r = np.asarray(self.ref_mat[gene] )
-        gex_q = np.asarray(self.query_mat[gene] )
-        
-        
+ 
         
+    def run_interpolation(self, gene_idx):
+
+        gex_r = Utils.csr_mat_col_densify(self.TrajInt_R.mat , gene_idx)
+        gex_q = Utils.csr_mat_col_densify(self.TrajInt_Q.mat , gene_idx)
+        USER_GIVEN_STD = np.repeat(-1.0, self.TrajInt_R.n_bins)
         
-        if(False):
-            # ================================================================================================================================
+        if(self.no_extreme_cases):
+            
+            
             if((not gex_r.any()) and (not gex_q.any()) ):# both are 0 expressed
                 #print('both', g)    # complete match     
                 S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
@@ -361,180 +338,89 @@ def run_interpolation(self, gene):
                 S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
                 T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
                 # get both ref and query mean trend and std trend estimated 
-            # ================================================================================================================================
-            
+
+            return [S,T]
+
+        if(np.count_nonzero(gex_r)<=3 and np.count_nonzero(gex_q)<=3): # if both are almost 0 (less than 3 counts overall)
+
+            USER_GIVEN_STD = np.repeat(0.01, self.TrajInt_R.n_bins) # use a very low constant std for both cases 
+            S = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_R, user_given_std = USER_GIVEN_STD)
+            T = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_Q, user_given_std = USER_GIVEN_STD)  
+
+        elif(np.count_nonzero(gex_r)<=3):  # if only ref is almost 0 expressed
+
+            T_temp = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_Q, user_given_std = USER_GIVEN_STD)
+            regions = self.check_inconsistent_zero_region(gex_q, self.TrajInt_Q.cell_pseudotimes, self.TrajInt_Q)
+            common_std = min(T_temp.intpl_stds)/10
+            if(len(regions)!=0):
+                USER_GIVEN_STD[np.in1d(self.TrajInt_Q.interpolation_points, regions)] = common_std 
+                T = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_Q, user_given_std = USER_GIVEN_STD)  
+            else:
+                T = T_temp
+            USER_GIVEN_STD = np.repeat(common_std, self.TrajInt_R.n_bins)
+            S = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_R, user_given_std = USER_GIVEN_STD)
+
+        elif(np.count_nonzero(gex_q)<=3): # if only query is almost 0 expressed
+
+            S_temp = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_R, user_given_std = USER_GIVEN_STD)
+            regions = self.check_inconsistent_zero_region(gex_r, self.TrajInt_R.cell_pseudotimes, self.TrajInt_R)
+            common_std = min(S_temp.intpl_stds)/10
+            if(len(regions)!=0):
+                USER_GIVEN_STD[np.in1d(self.TrajInt_R.interpolation_points, regions)] = common_std 
+                S = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_R, user_given_std = USER_GIVEN_STD)  
+            else:
+                S = S_temp
+            USER_GIVEN_STD = np.repeat(common_std, self.TrajInt_Q.n_bins)
+            T = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_Q, user_given_std = USER_GIVEN_STD)
+
         else:
+            S_temp = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_R, user_given_std = USER_GIVEN_STD)
+            T_temp = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_Q, user_given_std = USER_GIVEN_STD)
             
-            USER_GIVEN_STD = np.repeat(-1.0, self.n_artificial_time_points )
-            
-            if(np.count_nonzero(gex_r)<=3 and np.count_nonzero(gex_q)<=3): # if both are almost 0 (less than 3 counts overall)
-               # S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
-               # T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
-                # use a very low constant std for both cases 
-                S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
-                                                                                    ESTIMATE = False, user_given_std = np.repeat(0.01, self.n_artificial_time_points))
-                T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
-                                                                                ESTIMATE = False, user_given_std = np.repeat(0.01, self.n_artificial_time_points))
-            elif(np.count_nonzero(gex_r)<=3): # if only ref is almost 0 expressed
-                
-                T_temp = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY, user_given_std= USER_GIVEN_STD)
-                regions_T = self.check_inconsistent_zero_region(self.query_mat, self.query_time, gene)
-                common_std = min(T_temp.intpl_stds)/10
-                std_S = np.repeat(common_std, self.n_artificial_time_points)
-                
-                S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
-                                                                                    ESTIMATE = False, user_given_std = std_S)
-                if(len(regions_T)!=0):
-                    user_given_std_arr_T = np.repeat(-1.0, self.n_artificial_time_points )
-                    for subregion_t in np.unique(np.asarray(regions_T).flatten() ):
-                        l = np.where(query_processor.artificial_time_points==  subregion_t)
-                        user_given_std_arr_T[l[0][0]] = common_std 
-                    T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
-                                                                          ESTIMATE = False, user_given_std = user_given_std_arr_T)
-                else: 
-                    T= T_temp
-                
-        
-                # get query mean trend and std trend estimated
-             #   T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
-                                                         #                    ESTIMATE=True, user_given_std=USER_GIVEN_STD) 
-                # check if query has a sub region almost 0 expressed
-            #    inconsistent_traj_Q_regions = self.check_inconsistent_zero_region(self.query_mat, self.query_time, gene)
-            #    if(len(inconsistent_traj_Q_regions)==0): 
-            #        std_T = np.repeat((np.min(T.intpl_stds))/10, self.n_artificial_time_points) # if there is no such sub region, always use 10% of query's min std for ref std
-                    
-            #    else:
-                    #std_T = T.intpl_stds  # if there is such sub region, use the min std of query for ref std
-            #        std_T = np.repeat((np.min(T.intpl_stds)), self.n_artificial_time_points) 
-            #    S = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
-                                                  #                                  ESTIMATE = False, user_given_std = std_T)
-                                                   
-            elif(np.count_nonzero(gex_q)<=3): # if only query is almost 0 expressed
-                
-                
-                S_temp = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY, user_given_std= USER_GIVEN_STD)
-                regions_S = self.check_inconsistent_zero_region(self.ref_mat, self.ref_time, gene)
-                common_std = min(S_temp.intpl_stds)/10
-                std_T = np.repeat(common_std, self.n_artificial_time_points)
-                
-                T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
-                                                                                    ESTIMATE = False, user_given_std = std_T)
-                if(len(regions_S)!=0):
-                    user_given_std_arr_S = np.repeat(-1.0, self.n_artificial_time_points )
-                    for subregion_t in np.unique(np.asarray(regions_S).flatten() ):
-                        l = np.where(ref_processor.artificial_time_points ==  subregion_t)
-                        user_given_std_arr_S[l[0][0]] = common_std 
-                    S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
-                                                                          ESTIMATE = False, user_given_std = user_given_std_arr_S)
-                else: 
-                    S= S_temp
-                
-                
-                # get ref mean trend and std trend estimated 
-               # S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
-                                         #                                          ESTIMATE=True, user_given_std=USER_GIVEN_STD )
-                
-                # check if ref has a sub region almost 0 expressed
-               # inconsistent_traj_R_regions = self.check_inconsistent_zero_region(self.ref_mat, self.ref_time, gene)
-               # if(len(inconsistent_traj_R_regions)==0):
-               #     std_S = np.repeat((np.min(S.intpl_stds))/10, self.n_artificial_time_points) #  if there is no such sub region, always use 10% of ref's min std for query std
-              #  else:
-               #     std_S = np.repeat((np.min(S.intpl_stds)), self.n_artificial_time_points) 
-                
-               # T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,                                                     
-                                                    #                            ESTIMATE = False, user_given_std = std_S)
-            else: 
+            regions_S = self.check_inconsistent_zero_region(gex_r, self.TrajInt_R.cell_pseudotimes, self.TrajInt_R)
+            regions_T = self.check_inconsistent_zero_region(gex_q, self.TrajInt_Q.cell_pseudotimes, self.TrajInt_Q)
 
-                S_temp = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY, user_given_std= USER_GIVEN_STD)
-                T_temp = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY, user_given_std= USER_GIVEN_STD)
-                regions_S = self.check_inconsistent_zero_region(self.ref_mat, self.ref_time, gene)
-                regions_T = self.check_inconsistent_zero_region(self.query_mat, self.query_time, gene)
-                
-                if(len(regions_S) == 0 and len(regions_T)==0):
-                    S = S_temp
-                    T = T_temp 
-                elif(len(regions_S) != 0 and len(regions_T)!=0):
-                    common_std = min(min(S_temp.intpl_stds), min(T_temp.intpl_stds))/10
-                    user_given_std_arr_S = np.repeat(-1.0,self.n_artificial_time_points )
-                    for subregion_t in np.unique(np.asarray(regions_S).flatten() ):
-                        l = np.where(ref_processor.artificial_time_points==  subregion_t)
-                        user_given_std_arr_S[l[0][0]] = common_std
-                    S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
-                                                                                    ESTIMATE = False, user_given_std = user_given_std_arr_S)
-                   # print('S --', user_given_std_arr_S)
-                    user_given_std_arr_T = np.repeat(-1.0, self.n_artificial_time_points )
-                    for subregion_t in np.unique(np.asarray(regions_T).flatten() ):
-                        l = np.where(query_processor.artificial_time_points==  subregion_t)
-                        user_given_std_arr_T[l[0][0]] = common_std 
-                    T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
-                                                                                ESTIMATE = False, user_given_std = user_given_std_arr_T)
-                   # print('T --', user_given_std_arr_T)
-                elif(len(regions_S) != 0):
-                    common_std = min(min(S_temp.intpl_stds), min(T_temp.intpl_stds))/10
-                    
-                    user_given_std_arr_S = np.repeat(-1.0,self.n_artificial_time_points )
-                    for subregion_t in np.unique(np.asarray(regions_S).flatten() ):
-                        l = np.where(ref_processor.artificial_time_points==  subregion_t)
-                        user_given_std_arr_S[l[0][0]] = common_std
-                    S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
-                                                                                    ESTIMATE = False, user_given_std = user_given_std_arr_S)
-                    T = T_temp
-                        
-                elif(len(regions_T) != 0):
-                    common_std = min(min(S_temp.intpl_stds), min(T_temp.intpl_stds))/10
-                    user_given_std_arr_T = np.repeat(-1.0, self.n_artificial_time_points )
-                    for subregion_t in np.unique(np.asarray(regions_T).flatten() ):
-                        l = np.where(query_processor.artificial_time_points==  subregion_t)
-                        user_given_std_arr_T[l[0][0]] = common_std 
-                    T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
-                                                                                ESTIMATE = False, user_given_std = user_given_std_arr_T)
-                    S = S_temp
-            
+            if(len(regions_S) == 0 and len(regions_T)==0):
+                S = S_temp; T = T_temp 
+
+            elif(len(regions_S) != 0 and len(regions_T)!=0):
                 
+                common_std = min(min(S_temp.intpl_stds), min(T_temp.intpl_stds))/10
+                USER_GIVEN_STD[np.in1d(self.TrajInt_R.interpolation_points, regions_S)] = common_std 
                 
-              #  S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
-                                #                                            ESTIMATE=True, user_given_std=[])
-             #   inconsistent_traj_R = self.check_inconsistent_zero_region(self.ref_mat, self.ref_time, gene)
-             #   inconsistent_traj_Q = self.check_inconsistent_zero_region(self.query_mat, self.query_time, gene)
+                S = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_R, user_given_std = USER_GIVEN_STD)
+                USER_GIVEN_STD = np.repeat(-1.0, self.TrajInt_R.n_bins)
+                USER_GIVEN_STD[np.in1d(self.TrajInt_Q.interpolation_points, regions_T)] = common_std
+                T = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_Q, user_given_std = USER_GIVEN_STD)  
+
+            elif(len(regions_S) != 0):         
+                common_std = min(min(S_temp.intpl_stds), min(T_temp.intpl_stds))/10
+                USER_GIVEN_STD[np.in1d(self.TrajInt_R.interpolation_points, regions_S)] = common_std 
+                S = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_R, user_given_std = USER_GIVEN_STD)
+                T = T_temp
+
+            elif(len(regions_T) != 0):
+                common_std = min(min(S_temp.intpl_stds), min(T_temp.intpl_stds))/10
+                USER_GIVEN_STD[np.in1d(self.TrajInt_Q.interpolation_points, regions_T)] = common_std 
+                T = TimeSeriesPreprocessor.interpolate_gene_v2(gene_idx, self.TrajInt_Q, user_given_std = USER_GIVEN_STD)  
+                S = S_temp
                 
-              #  if(inconsistent_traj_R and inconsistent_traj_Q): # if both have 0 expressed sub regions
-              #      std_S = np.repeat((np.min(S.intpl_stds)), self.n_artificial_time_points) 
-              #      T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY,
-                  #                                                              ESTIMATE = False, user_given_std = std_S)
-              #  else:
-                    # normal estimation
-  #                  S = ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY, USER_GIVEN_STD)
-  #                  T = query_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
-            
-            
-            
+        del gex_r
+        del gex_q
         return [S,T]
     
-    def interpolate_genes(self):
-        with Pool(self.n_threads) as p:
-            results = list(tqdm_notebook(p.imap(self.run_interpolation, self.gene_list), total=len(self.gene_list)))
-        #print(len(results))
-        p.close()
-        p.join()
-        for i in range(len(self.gene_list)):
-            self.pairs[self.gene_list[i]] = results[i]
-            
-    def align_single_pair(self, gene, state_params = [0.99,0.5,0.4],zero_transition_costs=False, prohibit_case = False): #[0.9,0.5,0.4]):
+    
+    def align_single_pair(self, gene_idx):
+        
+        gene = self.gene_list[gene_idx]
         
-       # if(prohibit_case):
-       #     state_params = [0.95,0.3,0.6]
-       # else:
-       #     state_params=[0.95,0.5,0.4]
- 
         if(not(gene in self.pairs.keys())):
-            #print('interpolating sequences')
-            self.pairs[gene] = self.run_interpolation(gene)
-            #print(self.pairs[gene], gene)
-            
-        S = self.pairs[gene][0] 
-        T = self.pairs[gene][1] 
+            self.pairs[gene] = self.run_interpolation(gene_idx)
+        
+        S = self.pairs[gene][0]
+        T = self.pairs[gene][1]
         
-        fwd_DP = orgalign.DP5(S,T,  free_params = self.state_params, backward_run=False,  zero_transition_costs= zero_transition_costs, prohibit_case = prohibit_case) #,mean_batch_effect=self.mean_batch_effect) 
+        fwd_DP = orgalign.DP5(S,T,  free_params = self.state_params, backward_run=False,  zero_transition_costs= False, prohibit_case = False) 
         fwd_opt_cost = fwd_DP.run_optimal_alignment() 
         alignment_path = fwd_DP.backtrack() 
         fwd_DP.alignment_path = alignment_path
@@ -542,40 +428,33 @@ def align_single_pair(self, gene, state_params = [0.99,0.5,0.4],zero_transition_
         landscapeObj = orgalign.AlignmentLandscape(fwd_DP, None, len(S.mean_trend), len(T.mean_trend), alignment_path, the_5_state_machine = True)
         landscapeObj.collate_fwd() #landscapeObj.plot_alignment_landscape()
 
-        return AligmentObj(gene, S,T,fwd_DP,None, landscapeObj)
-        
-        #return AligmentObj(gene, S,T,fwd_DP,None, None)
-        # Backward alignment =========== 
-        S_rev = copy.deepcopy(S)
-        T_rev = copy.deepcopy(T)
-       # artificial_time_points1 = S.time_points
-       # artificial_time_points2 = T.time_points
-       # X1 = S.X; Y1 = S.Y; 
-       # X2 = T.X; Y2 = T.Y; 
-       # proc1 = TimeSeriesPreprocessor.Prepocessor(); proc1.artificial_time_points = artificial_time_points1
-       # proc2 = TimeSeriesPreprocessor.Prepocessor(); proc2.artificial_time_points = artificial_time_points2
-       # S = proc1.create_summary_trends(X1[::-1], Y1[::-1])
-       # T = proc2.create_summary_trends(X2[::-1], Y2[::-1])
-        bwd_DP = orgalign.DP5(S_rev,T_rev, free_params= self.state_params, backward_run=True, zero_transition_costs=zero_transition_costs)#,mean_batch_effect=self.mean_batch_effect) 
-        bwd_opt_cost = bwd_DP.run_optimal_alignment() 
-        temp_path = bwd_DP.backtrack() 
-        landscapeObj = orgalign.AlignmentLandscape(fwd_DP, bwd_DP, len(S.mean_trend), len(T.mean_trend), alignment_path, the_5_state_machine = True)
-        landscapeObj.collate() #landscapeObj.plot_alignment_landscape()
-        return AligmentObj(gene, S,T,fwd_DP, bwd_DP, landscapeObj)
-            
-    def align_all_pairs(self):
-        print('WINDOW_SIZE=',self.WINDOW_SIZE)
-        with Pool(self.n_threads) as p:
-            results = list(tqdm_notebook(p.imap(self.align_single_pair, self.gene_list), total=len(self.gene_list)))
-        self.results = results 
-        
-        self.results_map = {}
-        for a in self.results:
-            self.results_map[a.gene] = a
-        #print(self.pairs)
+        a = AligmentObj(gene, S,T,fwd_DP,None, landscapeObj)
+        return a
+    
+    def align_all_pairs(self, concurrent=False, n_processes = None):
         
+        print('Running gene-level alignment: \U0001F9EC')
         
-
+        if(concurrent):
+            if(n_processes is None):
+                n_processes = multiprocessing.cpu_count()
+                print('concurrent mode, running with',n_processes, 'processes')
+            with Pool(n_processes) as p:
+                results = list(tqdm(p.imap(self.align_single_pair, range(0,len(self.gene_list))), total=len(self.gene_list))) 
+            p.close(); p.terminate(); p.join(); 
+            self.results = results 
+            self.results_map = {}
+            for a in self.results:
+                self.results_map[a.gene] = a
+        else:
+            self.results_map = {}
+            self.results = []
+            for gene_idx in tqdm(range(len(self.gene_list))):
+                a = self.align_single_pair(gene_idx)
+                self.results.append(a)
+                self.results_map[a.gene] = a
+                
+        print('Alignment completed! \u2705')
         
     def get_stat_df(self):
 
@@ -599,12 +478,8 @@ def get_stat_df(self):
         
         plt.subplots(1,2,figsize=(10,4))
         plt.subplot(1,2,1)
-        #sb.kdeplot(df['alignment_similarity_percentage'])
-        print('mean matched percentage: ')
+        print('Mean alignment similarity percentage (matched %): ')
         print(round(np.mean(df['alignment_similarity_percentage']),4)*100,'%' )
-        #sb.kdeplot(df['alignment_similarity_percentage'], fill=True,label='Alignment Similarity %')
-        #plt.xlabel('Alignment Similarity Percentage')
-        #plt.xlim([0,1])
         temp = np.asarray([x*100 for x in df['alignment_similarity_percentage']]) 
         p = sb.kdeplot(temp, fill=True,label='Alignment Similarity %')
         plt.xlabel('Alignment Similarity Percentage')
@@ -623,81 +498,21 @@ def get_stat_df(self):
     def get_aggregate_alignment(self):
         average_alignment, alignment_path =  ClusterUtils.get_cluster_average_alignments(self, self.gene_list)
         mat = ClusterUtils.get_pairwise_match_count_mat(self, self.gene_list )
-        print('Average Alignment: ', average_alignment)
+        print('Average Alignment: ', VisualUtils.color_al_str(average_alignment), '(cell-level)')
+        print( '% similarity:', np.round(len(regex.findall("M|W|V",average_alignment))*100/len(average_alignment),2) )
+        self.average_alignment = average_alignment
         VisualUtils.plot_alignment_path_on_given_matrix(paths = [alignment_path], mat=mat)
         
     def get_aggregate_alignment_for_subset(self, gene_subset):
         average_alignment, alignment_path =  ClusterUtils.get_cluster_average_alignments(self, gene_subset)
         mat = ClusterUtils.get_pairwise_match_count_mat(self, gene_subset )
-        print('Average Alignment: ', average_alignment)
+        print('Average Alignment: ', VisualUtils.color_al_str(average_alignment), '(cell-level)')
         VisualUtils.plot_alignment_path_on_given_matrix(paths = [alignment_path], mat=mat)
-        
-    def align_all_pairs_no_thread_version(self):
-        
-        self.results = [] 
-        for gene in self.gene_list:
-            self.results.append(self.align_single_pair(gene))
-        
-        self.results_map = {}
-        for a in self.results:
-            self.results_map[a.gene] = a  
    
-        
-    def cluster_all_alignments(self, n_clusters=None, possible_dist_threshold=None, linkage_method='complete', scheme=0):
-            
-        # compute the pairwise alignment distance matrix 
-        if(not hasattr(self, 'DistMat' )):
-            print('computing the Distance matrix')
-            DistMat = AlignmentDistMan.AlignmentDist(self).compute_alignment_ensemble_distance_matrix(scheme=scheme)
-            #c = sb.clustermap(DistMat,figsize=(10,30)) 
-            self.DistMat = DistMat
-        if(n_clusters!=None):
-            gene_clusters, cluster_ids = self.cluster_alignments_v1(n_clusters=n_clusters, linkage_method= linkage_method)
-        else:
-            gene_clusters, cluster_ids = self.cluster_alignments_v2(linkage_method, possible_dist_threshold=possible_dist_threshold)
-        self.gene_clusters = gene_clusters
-        self.cluster_ids = cluster_ids
-        
-    
-    def cluster_alignments_v1(self, n_clusters, linkage_method):
-        
-        cluster = AgglomerativeClustering(n_clusters=n_clusters, affinity='precomputed', linkage=linkage_method) 
-        x = cluster.fit_predict(self.DistMat)
-        gene_clusters = orgalign.Utils().check_alignment_clusters(n_clusters, x,
-                                                                self.results, n_cols=4, figsize=(10,10)) 
-        return gene_clusters, x   
-    
-    def cluster_alignments_v2(self, linkage_method, possible_dist_threshold = None):
-
-        X = squareform(self.DistMat)
-        #print(X)
-        Z = linkage(X, linkage_method)
-        if(possible_dist_threshold==None):
-            possible_dist_threshold = np.quantile(squareform(self.DistMat),0.25)
-        x = fcluster(Z, possible_dist_threshold , criterion='distance') # cluster ids
-        n_clusters = len(np.unique(x))
-        gene_clusters = orgalign.Utils().check_alignment_clusters(n_clusters, x,
-                                                                self.results, n_cols=4, figsize=(10,10)) 
-        x = x-1 # to make cluster ids 0-indexed
-        return gene_clusters, x   
-    
- 
-    
-    def show_cluster(self, cluster_id):
-
-        for i in range(len(self.cluster_ids)):
-            if(self.cluster_ids[i]==cluster_id):
-                print('Gene: ', self.results[i].gene)
-                print(self.results[i].al_visual)
-                self.results[i].plotTimeSeries(self, plot_cells=True) 
-                plt.show() 
-                print('----------------------------------------------')
-                
-      
     def show_cluster_alignment_strings(self,cluster_id):
         for i in range(len(self.cluster_ids)):
             if(self.cluster_ids[i]==cluster_id):
-                print(self.results[i].alignment_str)
+                print(VisualUtils.color_al_str(self.results[i].alignment_str))
                 self.results[i].cluster_id = cluster_id
                 
     def get_cluster_alignment_objects(self, cluster_id):
@@ -707,64 +522,17 @@ def get_cluster_alignment_objects(self, cluster_id):
                 #print(self.results[i].alignment_str)
                 self.results[i].cluster_id = cluster_id
                 cluster_al_objects.append(self.results[i])
-        return cluster_al_objects
+        return cluster_al_objects 
 
-    def show_cluster_plots(self, cluster_id, show_alignment = False):
-
-        temp = np.unique(self.cluster_ids == cluster_id, return_counts=True)[1][1]
-        n_cols = 4
-        n_rows = int(np.ceil(temp/n_cols))
-        fig,axs = plt.subplots(n_rows,n_cols,figsize=(20,n_rows*3))
-
-        k = 1
-        for i in range(len(self.cluster_ids)):
-            if(self.cluster_ids[i]==cluster_id):
-                plt.subplot(n_rows, n_cols, k )
-                if(show_alignment):
-                    self.results[i].plotTimeSeriesAlignment()
-                else:
-                    self.results[i].plotTimeSeries(self, plot_cells=True, plot_mean_trend=True) 
-                plt.title(self.results[i].gene)
-                k = k+1
-        fig.tight_layout()
-        n = n_cols * n_rows
-        i = 1
-        while(k<=n):
-            axs.flat[-1*i].set_visible(False) 
-            k = k+1
-            i=i+1         
-
-            
-
-    def show_cluster_table(self):
-        
-        info = []
-        for cluster_id in range(len(self.mvg_cluster_average_alignments)):
-            mvg_obj = self.mvg_cluster_average_alignments[cluster_id]
-            al_str = mvg_obj.al_visual
-            al_str = al_str.replace('5-state string','')
-            al_str = al_str.replace('Alignment index','')
-            al_str = al_str.replace('Reference index','')
-            al_str = al_str.replace('Query index','')
-
-            n_genes = len(self.gene_clusters[cluster_id])
-            if(n_genes<15):
-                genes = self.gene_clusters[cluster_id]
-            else:
-                genes = self.gene_clusters[cluster_id][1:7] + [' ... '] +  self.gene_clusters[cluster_id][n_genes-7:n_genes]
-            info.append((cluster_id, n_genes, genes, mvg_obj.get_series_match_percentage()[0],mvg_obj.get_series_match_percentage()[1],mvg_obj.get_series_match_percentage()[2], al_str))
-
-        print(tabulate(pd.DataFrame(info),  headers=['cluster_id','n_genes','gene_set','A%','S%','T%','cell-level alignment'],
-                               tablefmt="grid",maxcolwidths=[None,None,None,30,None,None,None]))   
-            
-            
     def show_ordered_alignments(self):
         
+        print('In the order of the first match occurrence along pseudotime')
+        
         for a in self.results:
             a.gene_pair = a.gene
         return AlignmentDistMan.AlignmentDist(self).order_genes_by_alignments()
-                
-                
+    
+    
     def show_pairwise_distance_matrix(self, al_obj): # pairwise log compression matrix 
         
         # check compression of each matched pair
@@ -781,45 +549,6 @@ def show_pairwise_distance_matrix(self, al_obj): # pairwise log compression matr
         min_x =  np.nanmin(np.asarray(x).flatten())
         x = x.fillna(min_x) 
         sb.heatmap(x, cmap='jet')  
-        
-        
-    # separately get correlation coefficient of ref and query mean trends along the trajectory by 
-    # first doing distributional interpolation with number of time bins and then take sliding window to compute 
-    # pearson correlation coefficient 
-    def get_correlation_coefficient_trend(self, gene, SLIDING_WINDOW = 10, n_bins = 50):
-        
-        # correlation coefficient trend over sliding window of 10 bins
-        rp = TimeSeriesPreprocessor.Prepocessor(self.ref_mat, self.ref_time, n_bins)
-        qp =  TimeSeriesPreprocessor.Prepocessor(self.query_mat, self.query_time, n_bins)
-        S = rp.prepare_interpolated_gene_expression_series(gene,WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
-        T = qp.prepare_interpolated_gene_expression_series(gene,WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
-        Y1 = S.Y; Y2 = T.Y
-        X1 = S.X; X2 = T.X
-        bin_times = np.unique(X1) 
-        correlation_coefficients = []
-        for i in range(len(bin_times)):
-            if(i+SLIDING_WINDOW>=len(bin_times)):
-                break
-            s = []
-            t = []
-            for k in range(SLIDING_WINDOW):
-                s.append(S.mean_trend[i+k])
-                t.append(T.mean_trend[i+k])
-            cc = stats.pearsonr(s,t)[0]
-            #print('Pearson correlation: ', stats.pearsonr(s,t)[0])
-            correlation_coefficients.append(cc)
-        return correlation_coefficients
-    
-    def get_correlation_coefficient_trend_for_all_genes(self):
-
-        cc = []
-        for gene in tqdm_notebook(self.gene_list):
-            pcc = self.get_correlation_coefficient_trend(gene, SLIDING_WINDOW=10)
-            cc.append(pcc)
-        df = pd.DataFrame(cc) 
-        df.index = self.gene_list
-        return df
-    
     
     def get_match_stat_for_all_genes(self):
         m_p = []
@@ -834,307 +563,14 @@ def get_match_stat_for_all_genes(self):
         df.columns = ['match %', 'match % S', 'match % T', 'cluster_id']
         return df 
     
-    
-    #interpolated gene expression heat matrix 
-    def __prepare_intpl_df(self,intpl_df, intpl_time):
-        intpl_df = pd.DataFrame(intpl_df)
-        intpl_df = intpl_df.transpose()
-        intpl_df['time'] = intpl_time
-        intpl_df = intpl_df.sort_values(by='time')
-        intpl_df = intpl_df.iloc[:,intpl_df.columns!='time']
-        intpl_df.columns = self.gene_list
-        df_zscore = intpl_df.apply(zscore)
-        return df_zscore
-
-    def __get_zscore_expr_mat(self,expr_mat, expr_time):
-        expr_mat['time'] = expr_time
-        expr_mat = expr_mat.sort_values(by='time')
-        df = expr_mat
-        df  = df.iloc[:,df.columns!='time']
-        df_zscore = df.apply(zscore)
-        #df_zscore  = df_zscore.iloc[:,df_zscore.columns!='time']
-        return df_zscore
-
-    # z normalised for plotting purposes
-    def __save_interpolated_and_noninterpolated_mats(self):
-        ref_intpl_df = [] 
-        query_intpl_df = [] 
-        for gene in self.gene_list:
-            ref_intpl_df.append(self.pairs[gene][0].Y)
-            query_intpl_df.append(self.pairs[gene][1].Y) 
-        self.ref_intpl_df = self.__prepare_intpl_df(ref_intpl_df, self.pairs[gene][0].X)
-        self.query_intpl_df = self.__prepare_intpl_df(query_intpl_df, self.pairs[gene][1].X)
-        self.ref_expr_df = self.__get_zscore_expr_mat(self.ref_mat[self.gene_list], self.ref_time )
-        self.query_expr_df = self.__get_zscore_expr_mat(self.query_mat[self.gene_list], self.query_time )
-        #return ref_intpl_df, query_intpl_df, ref_expr_df, query_expr_df 
-
-    def __plot_comparative_heatmap(self, ref_df, query_df, cluster_id = None):
-        
-        if(cluster_id!=None):
-            ref_df = ref_df[self.gene_clusters[cluster_id]]
-            query_df = query_df[self.gene_clusters[cluster_id]]
-            fig, axs = plt.subplots(1,2, figsize=(10,ref_df.shape[1]*0.5))
-        else:
-            fig, axs = plt.subplots(1,2, figsize=(10,10))
-
-       # plt.subplot(1,2,1)
-        sb.clustermap(ref_df.transpose(), xticklabels=False, vmin=-2, vmax=2, cbar=False,cmap = 'YlGnBu', col_cluster=False)#, ax=axs[0])
-       # plt.subplot(1,2,2)
-        sb.clustermap(query_df.transpose(), xticklabels=False, vmin=-2, vmax=2,cmap = 'YlGnBu',col_cluster=False)#,ax=axs[1])
-        fig.tight_layout()
-    
-    def prepare_interpolated_non_interpolated_mats(self):
-        self.__save_interpolated_and_noninterpolated_mats() 
-    
-    def plot_comparative_heatmap_intpl(self, cluster_id = None):
-        if(not hasattr(self, 'ref_intpl_df' )):
-            self.__save_interpolated_and_noninterpolated_mats() 
-        self.__plot_comparative_heatmap(self.ref_intpl_df, self.query_intpl_df,cluster_id=cluster_id)
-    
-    def plot_comparative_heatmap_expr(self, cluster_id = None):
-        if(not hasattr(self, 'ref_expr_df' )):
-            self.__save_interpolated_and_noninterpolated_mats() 
-        self.__plot_comparative_heatmap(self.ref_expr_df, self.query_expr_df,cluster_id=cluster_id)
-    
-    
-    def run_MVG_alignment(self, mvg_genes,MVG_MODE_KL=True):
-        
-        D_ref = []
-        D_query = [] 
-        i = 0
-        for gene in mvg_genes:
-            S = self.pairs[gene][0]
-            T = self.pairs[gene][1]
-
-            if(i==0):
-                for bin_id in range(len(S.data_bins)):
-                    D_ref.append(pd.DataFrame(S.data_bins[bin_id]))
-            else:
-                for bin_id in range(len(S.data_bins)):
-                    D_ref[bin_id] = pd.concat([D_ref[bin_id], pd.Series(S.data_bins[bin_id]) ], axis=1) 
-
-            if(i==0):
-                for bin_id in range(len(T.data_bins)):
-                    D_query.append(pd.DataFrame(T.data_bins[bin_id]))
-            else:
-                for bin_id in range(len(T.data_bins)):
-                    D_query[bin_id] = pd.concat([D_query[bin_id], pd.Series(T.data_bins[bin_id]) ], axis=1) 
-
-            if(i==0):
-                S_time = S.X  # no need to do this at every iteration since it is the same artificial time points for all genes
-                T_time = T.X
-
-            i=i+1
-
-        S = TimeSeriesPreprocessor.SummaryTimeSeriesMVG(S_time, D_ref)
-        T = TimeSeriesPreprocessor.SummaryTimeSeriesMVG(T_time, D_query)
-        state_params=[0.99,0.5,0.4] #[0.95,0.5,0.4]
-        fwd_DP = orgalign.DP5(S,T,  free_params = self.state_params, backward_run=False,  zero_transition_costs= False, prohibit_case = False, MVG_MODE_KL = MVG_MODE_KL)#,mean_batch_effect=self.mean_batch_effect) 
-        fwd_opt_cost = fwd_DP.run_optimal_alignment() 
-        alignment_path = fwd_DP.backtrack() 
-        fwd_DP.alignment_path = alignment_path
-        landscapeObj = orgalign.AlignmentLandscape(fwd_DP, None,len(S.data_bins), len(T.data_bins), alignment_path, the_5_state_machine = True)
-        landscapeObj.collate_fwd() #landscapeObj.plot_alignment_landscape()
-        #return fwd_DP.alignment_str, fwdlandscapeObj
-        return AligmentObj(str(mvg_genes), S,T,fwd_DP,None, landscapeObj)
-        
-    
-    
-    def compute_cluster_MVG_alignments(self,MVG_MODE_KL=True, RECOMPUTE=False):
-        
-        if((not hasattr(self, 'mvg_cluster_average_alignments')) or RECOMPUTE):
-            print('run MVG alignment')
-            self.mvg_cluster_average_alignments = []
-
-            for cluster_id in tqdm_notebook(range(len(self.gene_clusters))):
-                group = self.gene_clusters[cluster_id]
-                if(len(group)>1):
-                    al_obj = self.run_MVG_alignment(group,MVG_MODE_KL=MVG_MODE_KL)
-                    self.mvg_cluster_average_alignments.append(al_obj)
-                else: # don't run MVG because there is only one gene in this cluster
-                    self.mvg_cluster_average_alignments.append(self.get_cluster_alignment_objects(cluster_id)[0])
-                         
-        return             
-
-        n_col = 5; n_row = int(np.ceil(len(self.mvg_cluster_average_alignments)/n_col)) 
-        fig,axs =plt.subplots(n_row,n_col,figsize=(20,n_row*3))
-        i=1
-        for a in self.mvg_cluster_average_alignments:
-           # plt.subplot(4,5,i)
-           # plot_alignment_landscape(a.landscape_obj,i)
-            plt.subplot(n_row,n_col,i)
-            ax = sb.heatmap(a.landscape_obj.L_matrix, square=True,  cmap="jet")
-            path_x = [p[0]+0.5 for p in a.landscape_obj.alignment_path]
-            path_y = [p[1]+0.5 for p in a.landscape_obj.alignment_path]
-            ax.plot(path_y, path_x, color='black', linewidth=3, alpha=0.5, linestyle='dashed') # path plot
-            plt.xlabel("S",fontweight='bold')
-            plt.ylabel("T",fontweight='bold')    
-            i=i+1
-            
-            
-    def plot_mvg_alignment(self, cluster_id):
-            
-        mvg_path = None
-        if(len(self.gene_clusters[cluster_id])>1):
-            mvg_path = self.mvg_cluster_average_alignments[cluster_id].landscape_obj.alignment_path
-            avg_alignment, path = self.get_cluster_average_alignments(cluster_id)
-        else: 
-            path = self.get_cluster_alignment_objects(cluster_id)[0].landscape_obj.alignment_path
-            mvg_path = path
-            avg_alignment = self.get_cluster_alignment_objects(cluster_id)[0].alignment_str
-        self.__plot_avg_alignment_landscape_in_cluster(cluster_id, path, mvg_path)
-
-
-    def __plot_avg_alignment_landscape_in_cluster(self,cluster_id, path, mvg_path=None): 
-
-            avg_DP_M_matrix = None 
-            avg_DP_W_matrix = None 
-            avg_DP_V_matrix = None 
-            avg_DP_D_matrix = None 
-            avg_DP_I_matrix = None 
-
-            cluster_al_objects = self.get_cluster_alignment_objects(cluster_id) 
-            for a in cluster_al_objects:
-                if(avg_DP_M_matrix is None):
-                    avg_DP_M_matrix = a.fwd_DP.DP_M_matrix
-                    avg_DP_W_matrix = a.fwd_DP.DP_W_matrix
-                    avg_DP_V_matrix = a.fwd_DP.DP_V_matrix
-                    avg_DP_D_matrix = a.fwd_DP.DP_D_matrix
-                    avg_DP_I_matrix = a.fwd_DP.DP_I_matrix
-                else:
-                    avg_DP_M_matrix = avg_DP_M_matrix + a.fwd_DP.DP_M_matrix
-                    avg_DP_W_matrix = avg_DP_W_matrix + a.fwd_DP.DP_W_matrix
-                    avg_DP_V_matrix = avg_DP_V_matrix + a.fwd_DP.DP_V_matrix
-                    avg_DP_D_matrix = avg_DP_D_matrix + a.fwd_DP.DP_D_matrix
-                    avg_DP_I_matrix = avg_DP_I_matrix + a.fwd_DP.DP_I_matrix
-
-            avg_DP_M_matrix = avg_DP_M_matrix/len(cluster_al_objects)
-            avg_DP_W_matrix = avg_DP_W_matrix/len(cluster_al_objects)
-            avg_DP_V_matrix = avg_DP_V_matrix/len(cluster_al_objects)
-            avg_DP_D_matrix = avg_DP_D_matrix/len(cluster_al_objects)
-            avg_DP_I_matrix = avg_DP_I_matrix/len(cluster_al_objects)
-
-            L_matrix = []
-            T_len = self.results[0].fwd_DP.T_len
-            S_len = self.results[0].fwd_DP.S_len
-            for i in range(T_len+1):
-                L_matrix.append(np.repeat(0.0,S_len+1))
-            L_matrix = np.matrix(L_matrix) 
-
-            if(mvg_path != None):
-                paths = [path, mvg_path]
-            else:
-                paths = [path]
-            for a in cluster_al_objects:
-                paths.append(a.landscape_obj.alignment_path)
-
-            for i in range(0,T_len+1):
-                    for j in range(0,S_len+1):
-                        _i = T_len-i
-                        _j = S_len-j
-                        temp = [ avg_DP_M_matrix[i,j],avg_DP_W_matrix[i,j] ,avg_DP_V_matrix[i,j], avg_DP_D_matrix[i,j], avg_DP_I_matrix[i,j]]
-                        L_matrix[i,j] = min(temp)
-
-            mat = L_matrix
-            fig, ax = plt.subplots(1,1, figsize=(5,5))
-            sb.heatmap(mat, square=True,  cmap='jet', ax=ax, cbar=False,xticklabels=False,yticklabels=False)
-            path_color = "black"
-            alpha = 2.0; linewidth = 4
-            i=0
-            for path in paths: 
-                path_x = [p[0]+0.5 for p in path]
-                path_y = [p[1]+0.5 for p in path]
-                ax.plot(path_y, path_x, color=path_color, linewidth=linewidth, alpha=alpha, linestyle='dashed') # path plot
-                if((i>=1)):
-                    alpha = 0.5
-                    linewidth = 1
-                    path_color = 'black'
-                else:
-                    path_color = 'brown'
-                i=i+1
-
-            plt.xlabel("S",fontweight='bold')
-            plt.ylabel("T",fontweight='bold')
-            
-    def get_cluster_average_alignments(self, cluster_id, deterministic=True):
-        
-            cluster_alobjs = self.get_cluster_alignment_objects(cluster_id)
-            i = self.results[0].fwd_DP.T_len
-            j = self.results[0].fwd_DP.S_len
-            
-            avg_alignment = ''
-            tracked_path = []
-            tracked_path.append([i,j])
-            
-            while(True):
-                if(i==0 and j==0):
-                    break
-                backtrack_states_probs = {}
-                backtrack_states_probs['M'] = 0 
-                backtrack_states_probs['W'] = 0 
-                backtrack_states_probs['V'] = 0 
-                backtrack_states_probs['D'] = 0 
-                backtrack_states_probs['I'] = 0 
-                for a in cluster_alobjs:
-                    backtract_state = a.landscape_obj.L_matrix_states[i,j]
-                    if(backtract_state=='0'):
-                        backtrack_states_probs['M']+=1 
-                    elif(backtract_state=='1'):
-                        backtrack_states_probs['W']+=1 
-                    elif(backtract_state=='2'):
-                        backtrack_states_probs['V']+=1 
-                    elif(backtract_state=='3'):
-                        backtrack_states_probs['D']+=1 
-                    elif(backtract_state=='4'):
-                        backtrack_states_probs['I']+=1 
-                for state in backtrack_states_probs.keys(): 
-                    backtrack_states_probs[state] = backtrack_states_probs[state]/len(cluster_alobjs) 
-
-                if(deterministic):
-                    cs = np.argmax(np.asarray(list(backtrack_states_probs.values())) )
-                else:
-                    cs = MyFunctions.sample_state(np.asarray(list(backtrack_states_probs.values()) ) )
-                if(cs==0):
-                    i = i-1
-                    j = j-1
-                    avg_alignment = 'M' + avg_alignment 
-                elif(cs==1 or cs==3):
-                    j= j-1
-                    if(cs==1):
-                        avg_alignment = 'W' + avg_alignment
-                    else:
-                        avg_alignment = 'D' + avg_alignment
-                elif(cs==2 or cs==4):
-                    i=i-1
-                    if(cs==2):
-                        avg_alignment = 'V' + avg_alignment
-                    else:
-                        avg_alignment = 'I' + avg_alignment
-            
-                tracked_path.append([i,j])
-            
-            return avg_alignment, tracked_path
-        
-        
-    def get_pairwise_match_count_mat(self):
-            mat = []
-            nT_points = len(self.results[0].T.time_points)
-            nS_points = len(self.results[0].S.time_points)
-            for i in range(nT_points + 1):
-                mat.append(np.repeat(0.0, nS_points+1))
-
-            # counts of total matches between the each pair of ref and query timepoints across all alignments 
-            for a in self.results:
-                matchS = a.match_points_S+1
-                matchT = a.match_points_T+1
-                for i in range(len(matchS)):
-                    mat[matchT[i]][matchS[i]] = mat[matchT[i]][matchS[i]] + 1
-
-            return pd.DataFrame(mat) 
 
 
 class DEAnalyser:
     
+    """
+    This class defines complementary functions for alignment results analysis. 
+    """
+    
     def __init__(self, al_obj):
         self.al_obj = al_obj
         self.alignment_str = al_obj.alignment_str
@@ -1276,9 +712,8 @@ def get_matched_regions(self):
         self.al_obj.al_visual = self.index_line + ' Alignment index \n'  + self.al_visual  + '\n'+ self.alignment_str + ' 5-state string '
             
   
-        # returns each 1-1 matching of time bins matched through M,W,V
+    # returns each 1-1 matching of time bins matched through M,W,V
     def get_matched_time_points(self):
-        #print('alignment string: ', self.alignment_str)
         j = 0
         i = 0
         FLAG = False
@@ -1292,12 +727,6 @@ def get_matched_time_points(self):
                     i=i+1
                 if(prev_c=='V'):
                     j=j+1
-           #     if(FLAG):
-           #         if(prev_c=='I'):
-           #             j=j+1
-           #         if(prev_c=='D'):
-           #             i=i+1
-           #         FLAG=False
                 matched_points_T.append(i)
                 matched_points_S.append(j)
                 i=i+1
@@ -1356,7 +785,6 @@ def get_matched_time_points(self):
             self.l2fold_changes_in_matches.append([np.log2(S_bin_mean/T_bin_mean), self.al_obj.fwd_DP.S.time_points[self.match_points_S[i]],
                                                  self.al_obj.fwd_DP.T.time_points[self.match_points_T[i]] ])
             
-            #print('**** ', self.match_points_S[i], self.match_points_T[i], np.log2(S_bin_mean/T_bin_mean))
                
     
     # Sanity checker for non-significant DE in matched regions
@@ -1380,6 +808,9 @@ def get_DE_info_for_matched_regions(self):
             S_bin = self.al_obj.S.data_bins[s[i]] 
             T_bin = self.al_obj.T.data_bins[t[i]] 
             
+            self.al_obj.S.data_bins[s[i]] = np.asarray(self.al_obj.S.data_bins[s[i]])
+            self.al_obj.S.data_bins[t[i]] = np.asarray(self.al_obj.S.data_bins[t[i]])
+            
             if(not np.any(self.al_obj.S.data_bins[s[i]] - self.al_obj.S.data_bins[t[i]] )):
                 wilcox_p.append(0.0)
                 ks2_p.append(0.0) 
@@ -1405,461 +836,7 @@ def get_DE_info_for_matched_regions(self):
 
     
 
-class hcolors:
-    MATCH = '\033[92m'
-    INSERT = '\033[91m'
-    DELETE = '\033[91m'
-    STOP = '\033[0m'    
     
-    #### test cases for 1-1 match point retrieval:
-       # al_str = 'MMMVVVVVVWWWWWW'
-       # al_str = 'MMMWWWWWWVVVVVV'
-       # al_str = 'DDDIIIVVVIIIMMM'
-       # al_str = 'IIIDDDWWWIIIMMM'
-       # al_str  = 'MMMIIIDDWWDDDIIVVDDMM'
-
-
-
     
-
-# ======================================================== NEW CODE FOR GENE VS GENE WITHIN SYSTEM COMPARISON
-
-class GeneAligner:
     
-        def __init__(self, *args):
-            if(len(args) ==4 ):
-                self.run_init1(args[0], args[1], args[2], args[3])
-            elif(len(args) == 6 ): 
-                self.run_init2(args[0], args[1], args[2], args[3], args[4], args[5])
-            else:
-                print('pls pass the required number of args')
-                
-            self.init_gene_pairs()
-
-        def set_n_threads(self,n):
-            self.n_threads = n
-
-        # converts ref and query anndata objects to pd.DataFrames 
-        def run_init1(self, adata_ref, adata_query, gene_list, n_artificial_time_points):
-
-            if(isinstance(adata_ref.X, scipy.sparse.csr.csr_matrix) 
-               or isinstance(adata_ref.X,anndata._core.views.SparseCSCView)
-               or isinstance(adata_ref.X,scipy.sparse.csc.csc_matrix)):
-                ref_mat = pd.DataFrame(adata_ref.X.todense()) 
-            else:
-                ref_mat = pd.DataFrame(adata_ref.X)   
-
-            ref_mat.columns = adata_ref.var_names
-            ref_mat = ref_mat.set_index(adata_ref.obs_names)
-            ref_time = np.asarray(adata_ref.obs['time']) 
-
-            self.run_init2(ref_mat, ref_time, None, None, gene_list, n_artificial_time_points)
-
-        def run_init2(self, ref_mat, ref_time, query_mat, query_time, gene_list, n_artificial_time_points, CONST_STD=False):
-            self.ref_mat = ref_mat
-            self.ref_time = ref_time
-            self.gene_list = gene_list
-            self.pairs = {}
-            self.genes = {}
-            self.n_threads = multiprocessing.cpu_count()
-            self.CONST_STD = CONST_STD
-
-            # to preserve the number of time points ratio 
-            self.n_artificial_time_points = n_artificial_time_points
-
-        def init_gene_pairs(self):    
-            
-            #genes = {} 
-            #for g in tqdm(gene_list) :
-            #    genes[g] = self.run_interpolation(g)
-            
-            pairs = {}
-            self.GENE_PAIRS = []
-            for i in tqdm_notebook(range(len(self.gene_list))):
-                for j in range(i, len(self.gene_list)):
-                    if(i==j):
-                        continue
-                    gene1 = self.gene_list[i]
-                    gene2 = self.gene_list[j]
-                    self.GENE_PAIRS.append( (gene1, gene2) )
-                    #pairs[ (gene1, gene2) ]  = [ self.genes[gene1], self.genes[gene2] ] 
-
-            print('n_gene_pairs for comparison: ', len(self.GENE_PAIRS))
-
-
-        def run_interpolation(self, gene):
-            ref_processor = TimeSeriesPreprocessor.Prepocessor(self.ref_mat, self.ref_time, 15, 0.1, False)
-            return ref_processor.prepare_interpolated_gene_expression_series(gene, WEIGHT_BY_CELL_DENSITY = True)
-
-        def align_single_pair_within_system(self, KEY, state_params = [0.99,0.5,0.4], zero_transition_costs=False, prohibit_case = False): 
-
-          #  KEY = (gene1, gene2)
-            if( (KEY not in self.pairs.keys()) ):
-            
-                if( (KEY[1],KEY[0]) in self.pairs.keys() ):
-                    KEY = (KEY[1],KEY[0])
-                    self.pairs[KEY] = [ self.genes[KEY[0]], self.genes[KEY[1]] ] 
-                else:
-                    gene1 = KEY[0]
-                    gene2 = KEY[1]
-                    
-                    if(gene1 not in self.genes.keys()):
-                        self.genes[gene1] = self.run_interpolation(gene1)
-                    if(gene2 not in self.genes.keys()):
-                        self.genes[gene2] = self.run_interpolation(gene2)
-                    self.pairs[KEY] = [ self.genes[gene1], self.genes[gene2] ] 
-
-            S = self.pairs[KEY][0] 
-            T = self.pairs[KEY][1] 
-
-            fwd_DP = orgalign.DP5(S,T,  free_params = self.state_params, backward_run=False,  zero_transition_costs= zero_transition_costs, prohibit_case = prohibit_case) #,mean_batch_effect=self.mean_batch_effect) 
-            fwd_opt_cost = fwd_DP.run_optimal_alignment() 
-            alignment_path = fwd_DP.backtrack() 
-            fwd_DP.alignment_path = alignment_path
-
-            landscapeObj = orgalign.AlignmentLandscape(fwd_DP, None, len(S.mean_trend), len(T.mean_trend), alignment_path, the_5_state_machine = True)
-            landscapeObj.collate_fwd() 
-
-            return AligmentObj(KEY, S,T, fwd_DP, None, landscapeObj)
-
-
-        def align_all_pairs_within_system(self):
-
-            print('WINDOW_SIZE=',self.WINDOW_SIZE)
-            with Pool(self.n_threads) as p:
-                results = list(tqdm_notebook(p.imap(self.align_single_pair_within_system, self.GENE_PAIRS), total=len(self.GENE_PAIRS)))
-            self.results = results 
-
-            self.results_map = {}
-            for a in self.results:
-                self.results_map[a.gene] = a
-                
-
-        def get_cluster_average_alignments(self, cluster_id, deterministic=True):
-
-                cluster_alobjs = self.get_cluster_alignment_objects(cluster_id)
-                i = self.results[0].fwd_DP.T_len
-                j = self.results[0].fwd_DP.S_len
-
-                avg_alignment = ''
-                tracked_path = []
-                tracked_path.append([i,j])
-
-                while(True):
-                    if(i==0 and j==0):
-                        break
-                    backtrack_states_probs = {}
-                    backtrack_states_probs['M'] = 0 
-                    backtrack_states_probs['W'] = 0 
-                    backtrack_states_probs['V'] = 0 
-                    backtrack_states_probs['D'] = 0 
-                    backtrack_states_probs['I'] = 0 
-                    for a in cluster_alobjs:
-                        backtract_state = a.landscape_obj.L_matrix_states[i,j]
-                        if(backtract_state=='0'):
-                            backtrack_states_probs['M']+=1 
-                        elif(backtract_state=='1'):
-                            backtrack_states_probs['W']+=1 
-                        elif(backtract_state=='2'):
-                            backtrack_states_probs['V']+=1 
-                        elif(backtract_state=='3'):
-                            backtrack_states_probs['D']+=1 
-                        elif(backtract_state=='4'):
-                            backtrack_states_probs['I']+=1 
-                    for state in backtrack_states_probs.keys(): 
-                        backtrack_states_probs[state] = backtrack_states_probs[state]/len(cluster_alobjs) 
-
-                    if(deterministic):
-                        cs = np.argmax(np.asarray(list(backtrack_states_probs.values())) )
-                    else:
-                        cs = MyFunctions.sample_state(np.asarray(list(backtrack_states_probs.values()) ) )
-                    if(cs==0):
-                        i = i-1
-                        j = j-1
-                        avg_alignment = 'M' + avg_alignment 
-                    elif(cs==1 or cs==3):
-                        j= j-1
-                        if(cs==1):
-                            avg_alignment = 'W' + avg_alignment
-                        else:
-                            avg_alignment = 'D' + avg_alignment
-                    elif(cs==2 or cs==4):
-                        i=i-1
-                        if(cs==2):
-                            avg_alignment = 'V' + avg_alignment
-                        else:
-                            avg_alignment = 'I' + avg_alignment
-
-                    tracked_path.append([i,j])
-
-                return avg_alignment, tracked_path
-
-
-        def get_pairwise_match_count_mat(self):
-                mat = []
-                nT_points = len(self.results[0].T.time_points)
-                nS_points = len(self.results[0].S.time_points)
-                for i in range(nT_points + 1):
-                    mat.append(np.repeat(0.0, nS_points+1))
-
-                # counts of total matches between the each pair of ref and query timepoints across all alignments 
-                for a in self.results:
-                    matchS = a.match_points_S+1
-                    matchT = a.match_points_T+1
-                    for i in range(len(matchS)):
-                        mat[matchT[i]][matchS[i]] = mat[matchT[i]][matchS[i]] + 1
-
-                return pd.DataFrame(mat) 
-            
-            
-        def cluster_all_alignments(self, n_clusters=None, possible_dist_threshold=None, linkage_method='complete', scheme=0):
-            
-            # compute the pairwise alignment distance matrix 
-            if(not hasattr(self, 'DistMat' )):
-                print('computing the Distance matrix')
-                DistMat = AlignmentDistMan.AlignmentDist(self).compute_alignment_ensemble_distance_matrix(scheme=scheme)
-                #c = sb.clustermap(DistMat,figsize=(10,30)) 
-                self.DistMat = DistMat
-            if(n_clusters!=None):
-                gene_clusters, cluster_ids = self.cluster_alignments_v1(n_clusters=n_clusters, linkage_method= linkage_method)
-            else:
-                gene_clusters, cluster_ids = self.cluster_alignments_v2(linkage_method, possible_dist_threshold=possible_dist_threshold)
-            self.gene_clusters = gene_clusters
-            self.cluster_ids = cluster_ids
-        
     
-        def cluster_alignments_v1(self, n_clusters, linkage_method):
-
-            cluster = AgglomerativeClustering(n_clusters=n_clusters, affinity='precomputed', linkage=linkage_method) 
-            x = cluster.fit_predict(self.DistMat)
-            gene_clusters = orgalign.Utils().check_alignment_clusters(n_clusters, x,
-                                                                    self.results, n_cols=4, figsize=(10,10)) 
-            return gene_clusters, x   
-
-        def cluster_alignments_v2(self, linkage_method, possible_dist_threshold = None):
-
-            X = squareform(self.DistMat)
-            #print(X)
-            Z = linkage(X, linkage_method)
-            if(possible_dist_threshold==None):
-                possible_dist_threshold = np.quantile(squareform(self.DistMat),0.25)
-            x = fcluster(Z, possible_dist_threshold , criterion='distance') # cluster ids
-            n_clusters = len(np.unique(x))
-            gene_clusters = orgalign.Utils().check_alignment_clusters(n_clusters, x,
-                                                                    self.results, n_cols=4, figsize=(10,10)) 
-            x = x-1 # to make cluster ids 0-indexed
-            return gene_clusters, x   
-
-
-
-        def show_cluster(self, cluster_id):
-
-            for i in range(len(self.cluster_ids)):
-                if(self.cluster_ids[i]==cluster_id):
-                    print('Gene: ', self.results[i].gene)
-                    print(self.results[i].al_visual)
-                    self.results[i].plotTimeSeries(self, plot_cells=True) 
-                    plt.show() 
-                    print('----------------------------------------------')
-
-
-        def show_cluster_alignment_strings(self,cluster_id):
-            for i in range(len(self.cluster_ids)):
-                if(self.cluster_ids[i]==cluster_id):
-                    print(self.results[i].alignment_str)
-                    self.results[i].cluster_id = cluster_id
-
-        def get_cluster_alignment_objects(self, cluster_id):
-            cluster_al_objects = []
-            for i in range(len(self.cluster_ids)):
-                if(self.cluster_ids[i]==cluster_id):
-                    #print(self.results[i].alignment_str)
-                    self.results[i].cluster_id = cluster_id
-                    cluster_al_objects.append(self.results[i])
-            return cluster_al_objects
-
-        def show_cluster_plots(self, cluster_id, show_alignment = False):
-
-            temp = np.unique(self.cluster_ids == cluster_id, return_counts=True)[1][1]
-            n_cols = 4
-            n_rows = int(np.ceil(temp/n_cols))
-            fig,axs = plt.subplots(n_rows,n_cols,figsize=(20,n_rows*3))
-
-            k = 1
-            for i in range(len(self.cluster_ids)):
-                if(self.cluster_ids[i]==cluster_id):
-                    plt.subplot(n_rows, n_cols, k )
-                    if(show_alignment):
-                        self.results[i].plotTimeSeriesAlignment()
-                    else:
-                        self.results[i].plotTimeSeries(self, plot_cells=True, plot_mean_trend=True) 
-                    plt.title(self.results[i].gene)
-                    k = k+1
-            fig.tight_layout()
-            n = n_cols * n_rows
-            i = 1
-            while(k<=n):
-                axs.flat[-1*i].set_visible(False) 
-                k = k+1
-                i=i+1         
-
-
-
-        def show_cluster_table(self):
-
-            info = []
-            for cluster_id in range(len(self.mvg_cluster_average_alignments)):
-                mvg_obj = self.mvg_cluster_average_alignments[cluster_id]
-                al_str = mvg_obj.al_visual
-                al_str = al_str.replace('5-state string','')
-                al_str = al_str.replace('Alignment index','')
-                al_str = al_str.replace('Reference index','')
-                al_str = al_str.replace('Query index','')
-
-                n_genes = len(self.gene_clusters[cluster_id])
-                if(n_genes<15):
-                    genes = self.gene_clusters[cluster_id]
-                else:
-                    genes = self.gene_clusters[cluster_id][1:7] + [' ... '] +  self.gene_clusters[cluster_id][n_genes-7:n_genes]
-                info.append((cluster_id, n_genes, genes, mvg_obj.get_series_match_percentage()[0],mvg_obj.get_series_match_percentage()[1],mvg_obj.get_series_match_percentage()[2], al_str))
-
-            print(tabulate(pd.DataFrame(info),  headers=['cluster_id','n_genes','gene_set','A%','S%','T%','cell-level alignment'],
-                                   tablefmt="grid",maxcolwidths=[None,None,None,30,None,None,None]))   
-
-
-
-        def show_ordered_alignments(self):
-
-            return AlignmentDistMan.AlignmentDist(self).order_genes_by_alignments()
-
-
-        def show_pairwise_distance_matrix(self, al_obj): # pairwise log compression matrix 
-
-            # check compression of each matched pair
-            temp_mat = al_obj.fwd_DP.DP_util_matrix
-            compression_dist_mat = [] 
-            for i in range(1,temp_mat.shape[0]):
-                row = []
-                for j in range(1,temp_mat.shape[1]):
-                    x = np.abs(temp_mat[i,j][2])
-                    row.append(float(x))
-                compression_dist_mat.append(row)   
-
-            x = pd.DataFrame(np.log10(np.asarray(compression_dist_mat) ))
-            min_x =  np.nanmin(np.asarray(x).flatten())
-            x = x.fillna(min_x) 
-            sb.heatmap(x, cmap='jet')  
-
-
-        # separately get correlation coefficient of ref and query mean trends along the trajectory by 
-        # first doing distributional interpolation with number of time bins and then take sliding window to compute 
-        # pearson correlation coefficient 
-        def get_correlation_coefficient_trend(self, gene, SLIDING_WINDOW = 10, n_bins = 50):
-
-            # correlation coefficient trend over sliding window of 10 bins
-            rp = TimeSeriesPreprocessor.Prepocessor(self.ref_mat, self.ref_time, n_bins)
-            qp =  TimeSeriesPreprocessor.Prepocessor(self.query_mat, self.query_time, n_bins)
-            S = rp.prepare_interpolated_gene_expression_series(gene,WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
-            T = qp.prepare_interpolated_gene_expression_series(gene,WEIGHT_BY_CELL_DENSITY = self.WEIGHT_BY_CELL_DENSITY)
-            Y1 = S.Y; Y2 = T.Y
-            X1 = S.X; X2 = T.X
-            bin_times = np.unique(X1) 
-            correlation_coefficients = []
-            for i in range(len(bin_times)):
-                if(i+SLIDING_WINDOW>=len(bin_times)):
-                    break
-                s = []
-                t = []
-                for k in range(SLIDING_WINDOW):
-                    s.append(S.mean_trend[i+k])
-                    t.append(T.mean_trend[i+k])
-                cc = stats.pearsonr(s,t)[0]
-                #print('Pearson correlation: ', stats.pearsonr(s,t)[0])
-                correlation_coefficients.append(cc)
-            return correlation_coefficients
-
-        def get_correlation_coefficient_trend_for_all_genes(self):
-
-            cc = []
-            for gene in tqdm_notebook(self.gene_list):
-                pcc = self.get_correlation_coefficient_trend(gene, SLIDING_WINDOW=10)
-                cc.append(pcc)
-            df = pd.DataFrame(cc) 
-            df.index = self.gene_list
-            return df
-
-
-        def get_match_stat_for_all_genes(self):
-            m_p = []
-            m_ps = []
-            m_pt = []
-            for a in self.results:
-                m_p.append(a.get_series_match_percentage()[0])
-                m_ps.append(a.get_series_match_percentage()[1])
-                m_pt.append(a.get_series_match_percentage()[2])
-
-            df = pd.DataFrame([m_p,m_ps,m_pt,self.cluster_ids]).transpose() 
-            df.columns = ['match %', 'match % S', 'match % T', 'cluster_id']
-            return df 
-
-
-        #interpolated gene expression heat matrix 
-        def __prepare_intpl_df(self,intpl_df, intpl_time):
-            intpl_df = pd.DataFrame(intpl_df)
-            intpl_df = intpl_df.transpose()
-            intpl_df['time'] = intpl_time
-            intpl_df = intpl_df.sort_values(by='time')
-            intpl_df = intpl_df.iloc[:,intpl_df.columns!='time']
-            intpl_df.columns = self.gene_list
-            df_zscore = intpl_df.apply(zscore)
-            return df_zscore
-
-        def __get_zscore_expr_mat(self,expr_mat, expr_time):
-            expr_mat['time'] = expr_time
-            expr_mat = expr_mat.sort_values(by='time')
-            df = expr_mat
-            df  = df.iloc[:,df.columns!='time']
-            df_zscore = df.apply(zscore)
-            #df_zscore  = df_zscore.iloc[:,df_zscore.columns!='time']
-            return df_zscore
-
-        # z normalised for plotting purposes
-        def __save_interpolated_and_noninterpolated_mats(self):
-            ref_intpl_df = [] 
-            query_intpl_df = [] 
-            for gene in self.gene_list:
-                ref_intpl_df.append(self.pairs[gene][0].Y)
-                query_intpl_df.append(self.pairs[gene][1].Y) 
-            self.ref_intpl_df = self.__prepare_intpl_df(ref_intpl_df, self.pairs[gene][0].X)
-            self.query_intpl_df = self.__prepare_intpl_df(query_intpl_df, self.pairs[gene][1].X)
-            self.ref_expr_df = self.__get_zscore_expr_mat(self.ref_mat[self.gene_list], self.ref_time )
-            self.query_expr_df = self.__get_zscore_expr_mat(self.query_mat[self.gene_list], self.query_time )
-            #return ref_intpl_df, query_intpl_df, ref_expr_df, query_expr_df 
-
-        def __plot_comparative_heatmap(self, ref_df, query_df, cluster_id = None):
-
-            if(cluster_id!=None):
-                ref_df = ref_df[self.gene_clusters[cluster_id]]
-                query_df = query_df[self.gene_clusters[cluster_id]]
-                fig, axs = plt.subplots(1,2, figsize=(10,ref_df.shape[1]*0.5))
-            else:
-                fig, axs = plt.subplots(1,2, figsize=(10,10))
-
-           # plt.subplot(1,2,1)
-            sb.clustermap(ref_df.transpose(), xticklabels=False, vmin=-2, vmax=2, cbar=False,cmap = 'YlGnBu', col_cluster=False)#, ax=axs[0])
-           # plt.subplot(1,2,2)
-            sb.clustermap(query_df.transpose(), xticklabels=False, vmin=-2, vmax=2,cmap = 'YlGnBu',col_cluster=False)#,ax=axs[1])
-            fig.tight_layout()
-
-        def prepare_interpolated_non_interpolated_mats(self):
-            self.__save_interpolated_and_noninterpolated_mats() 
-
-        def plot_comparative_heatmap_intpl(self, cluster_id = None):
-            if(not hasattr(self, 'ref_intpl_df' )):
-                self.__save_interpolated_and_noninterpolated_mats() 
-            self.__plot_comparative_heatmap(self.ref_intpl_df, self.query_intpl_df,cluster_id=cluster_id)
-
-        def plot_comparative_heatmap_expr(self, cluster_id = None):
-            if(not hasattr(self, 'ref_expr_df' )):
-                self.__save_interpolated_and_noninterpolated_mats() 
-            self.__plot_comparative_heatmap(self.ref_expr_df, self.query_expr_df,cluster_id=cluster_id)
diff --git a/genes2genes/MyFunctions.py b/genes2genes/MyFunctions.py
index c7ff8ec..36e6f5c 100644
--- a/genes2genes/MyFunctions.py
+++ b/genes2genes/MyFunctions.py
@@ -1,14 +1,11 @@
 import torch
-import seaborn as sb
-import torch.nn as nn
 import numpy as np
-import pandas as pd
-import time
-import gpytorch
-import matplotlib.pyplot as plt
-import torch.distributions as td
+
 torch.set_default_dtype(torch.float64)
 
+"""
+This script defines all methods required for computing mml distance between two gene expression distributions as Gaussian
+"""
 
 def negative_log_likelihood(μ,σ,N,data):
     data = torch.tensor(data)
@@ -17,36 +14,9 @@ def negative_log_likelihood(μ,σ,N,data):
     sum_term = torch.sum(((data - μ)/σ)**2.0)/2.0
     return ((N/2.0)* torch.log(2*torch.tensor(np.pi))) + (N*torch.log(σ)) + sum_term 
 
-   # print('arr sum: ',torch.sum(((data - μ)/σ)**2.0))
-   # print('arr grad sum: ', torch.neg(torch.sum((data - μ)/(σ**2)))   )
-    reimplemented_mode = True
-    # Reimplementation =============================================================
-    if(reimplemented_mode):
-        ts = time.time()
-        sum_term = 0.0
-        #grad1_term = 0.0 
-        for n in range(N):
-            sum_term = sum_term + (((data[n] - μ)/σ)**2.0)
-            #grad1_term = grad1_term - ((data[n] - μ)/(σ**2))
-        sum_term = sum_term/2.0
-        
-        te = time.time()
-      #  print('TIME: ', te-ts)
-        return ((N/2.0)* torch.log(2*torch.tensor(np.pi))) + (N*torch.log(σ)) + sum_term 
-    # ================================================================================
-    else:
-        Gaussian_dist = torch.distributions.Normal(μ,σ)
-        sum_term = 0.0
-        for n in range(N):
-            sum_term = sum_term - Gaussian_dist.log_prob(torch.tensor(data[n])) 
-        return sum_term
-
 def compute_expected_Fisher_matrix(μ,σ,N):
     return torch.tensor([[N/(σ**2),0],[0,(2*N)/(σ**2)]]) # depends on σ
- ####  ---- expected_Fisher = compute_expected_Fisher_matrix(μ_base,σ_base,N) # compute the closed form of matrix determinant instead 
-
-# def compute_observed_Fisher_matrix(μ,σ):
-#    return torch.autograd.functional.hessian(negative_log_likelihood ,(μ,σ))
+    ####  ---- expected_Fisher = compute_expected_Fisher_matrix(μ_base,σ_base,N) # compute the closed form of matrix determinant instead 
 
 def I_prior(μ,σ): 
     R_μ = torch.tensor(15.0) # uniform prior for mean over region R_μ 
@@ -98,25 +68,27 @@ def generate_random_dataset(N_datapoints, mean, variance):
     #print('True params: [ μ=',μ.data.numpy(), ' , σ=', σ.data.numpy(),']' )
     return D,μ,σ
 
-
-def sample_state(x):
-    x = np.cumsum(x)
-    rand_num = np.random.rand(1)
-   # print(rand_num)
-    if(rand_num<=x[0]):
-        return 0#'M'
-    elif(rand_num>x[0] and rand_num<=x[1]):
-        return 1#'W'
-    elif(rand_num>x[1] and rand_num<=x[2]):
-        return 2#'V'
-    elif(rand_num>x[2] and rand_num<=x[3]):
-        return 3#'D'
-    elif(rand_num>x[3] and rand_num<=x[4]):
-        return 4#'I'
-
     
 
     
 
     
-
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
diff --git a/genes2genes/OrgAlign.py b/genes2genes/OrgAlign.py
index 154a610..c6a215d 100644
--- a/genes2genes/OrgAlign.py
+++ b/genes2genes/OrgAlign.py
@@ -1,41 +1,25 @@
 import torch
-import time
 import regex
 import numpy as np
-import pandas as pd
-import gpytorch
 import seaborn as sb
 import matplotlib.pyplot as plt
-import torch.distributions as td
-from tqdm import tqdm 
-from scipy.spatial import distance
-from scipy.special import softmax
-from scipy.special import kl_div
 
 from . import MyFunctions
 from . import TimeSeriesPreprocessor
-from . import MVG
-import pickle
-import scipy
 
 torch.set_default_dtype(torch.float64)
 
-# M,I,D as usual
-# Additional states: W(Wd) , V(Wi) (representing insert direction warps and delete direction warps)
-
 class FiveStateMachine:
+    """
+    This class represents a symmetric and probabilistic finite state machine with 5 alignment states (M,W,V,I,D) 
+    to define transition probabilities between each pair of states. 
+    """
+    # M,I,D as usual
+    # Additional states: W(Wd) , V(Wi) (representing insert direction warps and delete direction warps)
     
     def __init__(self, P_mm, P_ii, P_mi, PROHIBIT_CASE):
         
         # ====== M STATE
-        #self.P_mm = P_mm/3.0
-        #self.P_wm = self.P_mm
-        #self.P_vm = self.P_mm
-        #self.P_im = (1.0 - self.P_mm - self.P_wm - self.P_vm)/2.0
-        #self.P_dm = self.P_im
-        #print(self.P_mm + self.P_wm + self.P_vm + self.P_im + self.P_dm )
-        #assert(self.P_mm + self.P_wm + self.P_vm + self.P_im + self.P_dm == 1.0) 
-        
         self.P_mm = P_mm
         k = (1.0 - self.P_mm)/4.0
         self.P_wm = k
@@ -49,23 +33,15 @@ def __init__(self, P_mm, P_ii, P_mi, PROHIBIT_CASE):
         self.P_vw = self.P_vm
         self.P_iw = self.P_im
         self.P_dw = self.P_dm
-        #print(self.P_ww + self.P_mw + self.P_vw + self.P_iw + self.P_dw )        
-        #assert(self.P_ww + self.P_mw + self.P_vw + self.P_iw + self.P_dw == 1.0) 
               
         self.P_vv = self.P_mm
         self.P_mv = self.P_vm
         self.P_wv = self.P_wm
         self.P_iv = self.P_im
         self.P_dv = self.P_dm
-        #print(self.P_vv + self.P_mv + self.P_wv + self.P_iv + self.P_dv)
-        #assert(self.P_vv + self.P_mv + self.P_wv + self.P_iv + self.P_dv == 1.0)
         
         # ====== I STATE
         # prohibit any transition from I or D to a warp state
-       # self.P_ii = P_ii/2.0      # USE P_II for prohibitive case
-       # self.P_mi = P_mi
-       # self.P_wi = 0.0
-       # self.P_vi = self.P_ii # USE 0 for prohibitive case
         
         if(PROHIBIT_CASE):
             self.P_ii = P_ii   
@@ -73,23 +49,13 @@ def __init__(self, P_mm, P_ii, P_mi, PROHIBIT_CASE):
             self.P_wi = 0.0
             self.P_vi = 0.0 
         else:
-           # self.P_ii = P_ii/2.0      
-           # self.P_mi = P_mi
-           # self.P_wi = 0.0
-           # self.P_vi = self.P_ii 
-            
-            # NEW TEST =====+++++
-            self.P_ii = P_ii      # USE P_II for prohibitive case
+            self.P_ii = P_ii # USE P_II for prohibitive case
             self.P_mi = P_mi
             self.P_wi = 0.0
             self.P_vi = P_ii # USE 0 for prohibitive case
-            # NEW TEST =====+++++
-            
-            
-        
+
         self.P_di = 1.0 - self.P_ii - self.P_mi - self.P_wi - self.P_vi
-        #print(self.P_ii + self.P_mi + self.P_wi + self.P_vi + self.P_di)
-        #assert(self.P_ii + self.P_mi + self.P_wi + self.P_di == 1.0)
+
         
         # ====== D STATE as equivalent to I STATE
         self.P_md = self.P_mi; 
@@ -97,8 +63,6 @@ def __init__(self, P_mm, P_ii, P_mi, PROHIBIT_CASE):
         self.P_id = self.P_di
         self.P_wd = self.P_vi #self.P_wi; 
         self.P_vd = 0.0 #self.P_vi
-        #print(self.P_dd + self.P_md + self.P_wd + self.P_vd + self.P_id)
-        #assert(self.P_dd + self.P_md + self.P_wd + self.P_vd + self.P_id == 1.0)
         
         # =====================================================
         # encoding length terms 
@@ -240,15 +204,17 @@ def reverse(self):
         
 
 class DP5:
+    
+    """
+    This class defines the dynamic programming algorithm and related functions 
+    used to find the optimal alignment between two gene expression time series
+    """
 
-    def __init__(self, S,T, backward_run, free_params, zero_transition_costs = False, prohibit_case=True, MVG_MODE_KL=True):#, mean_batch_effect=0.0):
+    def __init__(self, S,T, backward_run, free_params, zero_transition_costs = False, prohibit_case=True):
         self.S = S
         self.T = T
         self.S_len = len(S.data_bins)
         self.T_len = len(T.data_bins) 
-        #self.mean_batch_effect = mean_batch_effect
-        self.MVG_MODE_KL = MVG_MODE_KL
-        #print('*** ', self.S_len, self.T_len)
         self.FSA = FiveStateMachine(free_params[0], free_params[1], free_params[2], PROHIBIT_CASE= prohibit_case)
         self.backward_run = backward_run
         if(backward_run):
@@ -332,19 +298,13 @@ def init(self):
             self.backtrackers_I[0][j] = [np.inf,np.inf,np.inf]
             
         for i in range(1,self.T_len+1):
-            #if(isinstance(self.S, TimeSeriesPreprocessor.SummaryTimeSeries)):
             cost_D, cost_I = self.compute_cell(i-1,0, only_non_match=True)
-            #elif(isinstance(self.S, TimeSeriesPreprocessor.SummaryTimeSeriesMVG)):
-            #    cost_D, cost_I = self.compute_cell_as_MVG(i-1,0, only_non_match=True)
-                
-            #self.DP_I_matrix[i,0] = self.DP_I_matrix[i-1,0] + cost_I + self.FSA.I_ii  
-            
-            #  [START] NEW TEST 21122022 ====
+
             if(i==1):
-                self.DP_I_matrix[i,0] = self.DP_I_matrix[i-1,0] + cost_I -np.log(ProbI)#-np.log(1/3)  
+                self.DP_I_matrix[i,0] = self.DP_I_matrix[i-1,0] + cost_I -np.log(ProbI) 
             else:
                 self.DP_I_matrix[i,0] = self.DP_I_matrix[i-1,0] + cost_I + self.FSA.I_ii 
-            # [END] NEW TEST 21122022 ====
+
             
             #self.backtrackers_I[i][0] = [i-1,0,4] 
             if(not self.backward_run):
@@ -358,21 +318,13 @@ def init(self):
             self.backtrackers_D[i][0] = [np.inf,np.inf,np.inf]
             
         for j in range(1,self.S_len+1):
-            #if(isinstance(self.S, TimeSeriesPreprocessor.SummaryTimeSeries)):
             cost_D, cost_I =self.compute_cell(0,j-1, only_non_match=True)
-            #elif(isinstance(self.S, TimeSeriesPreprocessor.SummaryTimeSeriesMVG)):
-            #    cost_D, cost_I =self.compute_cell_as_MVG(0,j-1, only_non_match=True)
-                
-            #self.DP_D_matrix[0,j] = self.DP_D_matrix[0,j-1] + cost_D + self.FSA.I_dd
-            
-            #  [START] NEW TEST 21122022 ====
+        
             if(j==1):
                 self.DP_D_matrix[0,j] = self.DP_D_matrix[0,j-1] + cost_D -np.log(ProbD)#-np.log(1/3) 
             else:
                 self.DP_D_matrix[0,j] = self.DP_D_matrix[0,j-1] + cost_D + self.FSA.I_dd
-            # [END] NEW TEST 21122022 ====
             
-            #self.backtrackers_D[0][j] = [0,j-1,3] 
             if(not self.backward_run):
                 self.backtrackers_D[0][j] = [0,j-1,3]  
             else:
@@ -382,31 +334,19 @@ def run_optimal_alignment(self):
 
         # initial state probabilities 
         ProbM = 0.99
-        
-        #for i in tqdm(range(1,self.T_len+1)):
+    
         for i in range(1,self.T_len+1):
             for j in range(1,self.S_len+1):
-                #if(isinstance(self.S, TimeSeriesPreprocessor.SummaryTimeSeries)):
                 match_len,non_match_len_D,non_match_len_I = self.compute_cell(i-1,j-1) # here we use i-1 and j-1 to correctly call the time bin to use 
-                #elif(isinstance(self.S, TimeSeriesPreprocessor.SummaryTimeSeriesMVG)):
-                #    match_len,non_match_len_D,non_match_len_I = self.compute_cell_as_MVG(i-1,j-1)
-                    
-                   
+                             
                 if(not self.backward_run):
                     # filling M matrix
-                   # temp_m = [  self.DP_M_matrix[i-1,j-1] + match_len + self.FSA.I_mm, # 0
-                   #             self.DP_W_matrix[i-1,j-1] + match_len  + self.FSA.I_mw, # 1
-                   #             self.DP_V_matrix[i-1,j-1] + match_len + self.FSA.I_mv, # 2
-                   #             self.DP_D_matrix[i-1,j-1]  + match_len + self.FSA.I_md, # 3
-                   #             self.DP_I_matrix[i-1,j-1] + match_len  + self.FSA.I_mi  # 4
-                   #          ] 
-                    #  [START] NEW TEST 21122022 ====
                     if(i==1 and j==1):
-                        temp_m = [  self.DP_M_matrix[i-1,j-1] + match_len - np.log(ProbM), #-np.log(1/3), #self.FSA.I_mm, # 0
-                               np.inf,# self.DP_W_matrix[i-1,j-1] + match_len + np.inf, # -np.log(1/5), #+ self.FSA.I_mw, # 1
-                               np.inf,# self.DP_V_matrix[i-1,j-1] + match_len + np.inf, # -np.log(1/5),  #+ self.FSA.I_mv, # 2
-                               np.inf,# self.DP_D_matrix[i-1,j-1]  + match_len -np.log(1/5), #+ self.FSA.I_md, # 3
-                               np.inf# self.DP_I_matrix[i-1,j-1] + match_len  -np.log(1/5) #+ self.FSA.I_mi  # 4
+                        temp_m = [  self.DP_M_matrix[i-1,j-1] + match_len - np.log(ProbM), # 0
+                               np.inf, # 1
+                               np.inf, # 2
+                               np.inf, # 3
+                               np.inf  # 4
                              ]  
                     else:      
                         temp_m = [  self.DP_M_matrix[i-1,j-1] + match_len + self.FSA.I_mm, # 0
@@ -501,10 +441,6 @@ def run_optimal_alignment(self):
                 self.DP_D_matrix[i,j] =  tot_d #+ non_match_len_D
                 self.DP_W_matrix[i,j] =  tot_w #+ match_len
                 self.DP_V_matrix[i,j] =  tot_v #+ match_len
-                
-               # print(i,j,match_len,non_match_len_I,non_match_len_D )
-               # print('** ', self.DP_M_matrix[i,j],self.DP_I_matrix[i,j],self.DP_D_matrix[i,j]   )
-               # print('temp ', temp_m, temp_i,temp_d  )
                      
                 # save backtracker info
                 self.backtrackers_M[i][j] = [i-1,j-1,min_idx_m]
@@ -532,8 +468,6 @@ def run_optimal_alignment(self):
             return 
     
         
-
-    #def criterion2_compute_cell(self,i,j,only_non_match=False):
     def compute_cell(self,i,j,only_non_match=False):
         # Maximising the COMPRESSION ============
         if(only_non_match):
@@ -559,7 +493,8 @@ def compute_cell(self,i,j,only_non_match=False):
         null = (I_ref_model + I_refdata_g_ref_model + I_query_model + I_querydata_g_query_model)/(len(query_data)+len(ref_data))
         match_compression =   match_encoding_len - null 
         
-        #match_compression = match_compression - self.mean_batch_effect # [POSSIBLE METHOD]constant adjustment for accounting for batch effect
+        #match_compression = match_compression - self.mean_batch_effect # [POSSIBLE METHOD] 
+        # constant adjustment for accounting for batch effect
         
         non_match_encoding_len_D = 0.0
         non_match_encoding_len_I = 0.0 
@@ -568,54 +503,6 @@ def compute_cell(self,i,j,only_non_match=False):
         return match_compression.numpy(), non_match_encoding_len_D, non_match_encoding_len_I
 
 
-    def compute_cell_as_MVG(self,i,j,only_non_match=False):
-        
-        if(only_non_match):
-            return 0.0,0.0
-        
-        ref_data = torch.tensor(np.asarray(self.S.data_bins[j]) )
-        query_data = torch.tensor(np.asarray(self.T.data_bins[i]) )
-
-       # μ_S, C_S = MVG.compute_mml_estimates(ref_data, ref_data.shape[1], ref_data.shape[0]) 
-       # C_S = torch.eye(ref_data.shape[1])
-       # μ_T, C_T = MVG.compute_mml_estimates(query_data, query_data.shape[1], query_data.shape[0]) 
-       # C_T = torch.eye(query_data.shape[1])
-        
-        μ_S = self.S.mean_trends[j]
-        μ_T = self.T.mean_trends[i]
-        
-        if(self.MVG_MODE_KL):
-            return Utils().compute_KLDivBasedDist(μ_S,μ_T), 0.0,0.0 
-        
-        #return distance.euclidean(μ_S,μ_T), 0.0,0.0 
-        
-        C_S = torch.eye(ref_data.shape[1])
-        C_T = torch.eye(query_data.shape[1])
-
-        I_ref_model, I_refdata_g_ref_model  = MVG.run_dist_compute_v3(ref_data, μ_S, C_S)
-        I_query_model, I_querydata_g_query_model  = MVG.run_dist_compute_v3(query_data,μ_T, C_T)
-        I_ref_model, I_querydata_g_ref_model = MVG.run_dist_compute_v3(query_data, μ_S, C_S) 
-        I_query_model, I_refdata_g_query_model = MVG.run_dist_compute_v3(ref_data, μ_T, C_T) 
-        
-        match_encoding_len1 = I_ref_model + I_querydata_g_ref_model + I_refdata_g_ref_model
-        match_encoding_len1 = match_encoding_len1/(len(query_data)+len(ref_data))
-        match_encoding_len2 = I_query_model + I_refdata_g_query_model + I_querydata_g_query_model
-        match_encoding_len2 = match_encoding_len2/(len(query_data)+len(ref_data))
-        match_encoding_len = (match_encoding_len1 + match_encoding_len2 )/2.0 
-        
-        null = (I_ref_model + I_refdata_g_ref_model + I_query_model + I_querydata_g_query_model)/(len(query_data)+len(ref_data))
-        
-        #n_dimensions = self.S.data_bins[0].shape[0]
-        match_compression =   (match_encoding_len - null)
-        non_match_encoding_len_D = 0.0
-        non_match_encoding_len_I = 0.0 
-        self.DP_util_matrix[i+1,j+1] = [null,match_encoding_len,match_compression]
-        
-        assert(null>=0.0)
-        assert(match_encoding_len>=0.0)
-        
-        return match_compression,non_match_encoding_len_D,non_match_encoding_len_I
-    
 
     def _backtrack_util(self,backtracker_pointer):
         
@@ -682,7 +569,6 @@ def backtrack(self):
                                self.DP_M_matrix[i,j]]
             min_idx = last_cell_costs.index(min(last_cell_costs))  
 
-            #print('tot_msg_len_of_alignment = ', min(last_cell_costs)) 
             if(min_idx==0): # match
                 state = 'I'
             elif(min_idx==1):
@@ -694,8 +580,6 @@ def backtrack(self):
             elif(min_idx==4): # insert
                 state = 'M'
             
-        #  self.alignment_str = state + self.alignment_str
-        #  self._align_str_util(state)
         while(True): 
             if(i==0 and j==0):
                 break
@@ -869,157 +753,9 @@ def plot_alignment_landscape(self): # pass alignment path coordinates
         path_x = [p[0]+0.5 for p in self.alignment_path]
         path_y = [p[1]+0.5 for p in self.alignment_path]
         ax.plot(path_y, path_x, color='black', linewidth=3, alpha=0.5, linestyle='dashed') # path plot
-        plt.xlabel("S",fontweight='bold')
-        plt.ylabel("T",fontweight='bold')            
-
-
-class Utils:
-
-    def compute_alignment_area_diff_distance(self, A1, A2, S_len, T_len):
-
-       # print(A1)
-       # print(A2)
-        pi = np.arange(1, S_len+T_len+1) # skew diagonal indices 
-        A1_ = ""
-        for c in A1:
-            A1_ = A1_ + c
-            if(c=='M'):
-                A1_ = A1_ + 'X'
-        A2_ = ""
-        for c in A2:
-            A2_ = A2_ + c 
-            if(c=='M'):
-                A2_ = A2_ + 'X'
-
-        pi_1_k = 0
-        pi_2_k = 0
-        #print(0, pi_1_k , pi_2_k )
-        A1_al_index = 0
-        A2_al_index = 0
-        absolute_dist_sum = 0.0
-        for k in pi:
-            #print('k=',k, A1_al_index, A2_al_index)
-            A1_state = A1_[A1_al_index]
-            A2_state = A2_[A2_al_index]
-            if(A1_state=='I' or A1_state=='V'):
-                pi_1_k = pi_1_k - 1
-            elif(A1_state=='D' or  A1_state=='W'):
-                pi_1_k = pi_1_k + 1 
-            if(A2_state=='I' or A2_state=='V'):
-                pi_2_k = pi_2_k - 1   
-            elif(A2_state=='D' or  A2_state=='W'):
-                pi_2_k = pi_2_k + 1   
-            #print(k, pi_1_k, pi_2_k)
-            #print(A1_state, A2_state)
-            absolute_dist_sum = absolute_dist_sum + np.abs(pi_1_k - pi_2_k)
-            #print('-----')
-            A1_al_index = A1_al_index + 1
-            A2_al_index = A2_al_index + 1 
-
-        return absolute_dist_sum
-
-    def compute_chattergi_coefficient(self, y1,y2):
-        df = pd.DataFrame({'S':y1, 'T':y2})
-        df['rankS'] = df['S'].rank() 
-        df['rankT'] = df['T'].rank() 
-        # sort df by the S variable first
-        df = df.sort_values(by='rankS')
-        return 1 - ((3.0 * df['rankT'].diff().abs().sum())/((len(df)**2)-1)) 
-    
-    
-    def plot_different_alignments(self, paths, S_len, T_len, ax, mat=[]): # pass alignment path coordinates
-        mat=[]
-      #  if(len(mat)==0):
-        for i in range(T_len+1):
-            mat.append(np.repeat(0,S_len+1)) 
-        sb.heatmap(mat, square=True,  cmap='viridis', ax=ax, vmin=0, vmax=0, cbar=False,xticklabels=False,yticklabels=False)
-        path_color = "orange"
-     #   else:
-     #       sb.heatmap(mat, square=True,  cmap='viridis', cbar=False,xticklabels=False,yticklabels=False, vmax=1)
-     #       path_color = "black"
-        
-        for path in paths: 
-            path_x = [p[0]+0.5 for p in path]
-            path_y = [p[1]+0.5 for p in path]
-            ax.plot(path_y, path_x, color=path_color, linewidth=3, alpha=0.5, linestyle='dashed') # path plot
-        plt.xlabel("S",fontweight='bold')
-        plt.ylabel("T",fontweight='bold')
-        
-    
-    def check_alignment_clusters(self, n_clusters , cluster_ids, alignments, n_cols = 5, figsize= (10,6)): 
-        
-        clusters = [] 
-        S_len = alignments[0].fwd_DP.S_len
-        T_len = alignments[0].fwd_DP.T_len
-        
-        unique_cluster_ids = np.unique(cluster_ids)
-        n_rows = int(np.ceil(n_clusters/n_cols)) 
-        
-
-        fig, axs = plt.subplots(n_rows,n_cols, figsize = (20,n_rows*3)) # custom -- only for 20 clusters -- TODO change later
-        axs = axs.flatten() 
-        i = 0
-        k=1
-        for cluster_id in range(n_clusters): 
-            paths = []
-            cluster_genes = [] 
-            cluster_alignments = np.asarray(alignments)[cluster_ids == unique_cluster_ids[cluster_id]]
-            for a in cluster_alignments:
-                    paths.append(a.fwd_DP.alignment_path)
-                    #print(a.gene)
-                    cluster_genes.append(a.gene);# cluster_genes.append(a.gene)
-            clusters.append(list(np.unique(cluster_genes)) ) 
-    #        self.plot_different_alignments(paths, S_len, T_len, ax=axs[cluster_id])
-            
-            ####
-   #         mat = []
-   #         for i in range(T_len+1):
-   #             mat.append(np.repeat(0,S_len+1)) 
-   #         sb.heatmap(mat, square=True,  cmap='viridis', ax=axs[cluster_id], vmin=0, vmax=0, cbar=False,xticklabels=False,yticklabels=False)
-   #         path_color = "orange"
-   #         for path in paths: 
-   #             path_x = [p[0]+0.5 for p in path]
-   #             path_y = [p[1]+0.5 for p in path]
-    #            axs[cluster_id].plot(path_y, path_x, color=path_color, linewidth=3, alpha=0.5, linestyle='dashed') # path plot
-    #        plt.xlabel("S",fontweight='bold')
-   #         plt.ylabel("T",fontweight='bold')
-            
-            self.plot_different_alignments(paths, S_len, T_len, axs[cluster_id])
-            axs[cluster_id].set_title('Cluster-'+str(i) + ' | '+str(len(cluster_alignments)))
-
-            i=i+1
-            k=k+1
-        
-        fig.tight_layout()
-        n = n_cols * n_rows
-        i = 1
-        while(k<=n):
-            axs.flat[-1*i].set_visible(False) 
-            k = k+1
-            i=i+1        
-            
-        return clusters
-    
-
-    # input: log1p gene expression vectors
-    def compute_KLDivBasedDist(self,x,y):
-
-        # convert to probabilities
-      #  x = softmax(x)
-      #  y = softmax(y)
-        x = x.numpy()
-        y = y.numpy()
-        # convering backto counts+1
-        x = np.exp(x)
-        y = np.exp(y)
-        x = x/np.sum(x)
-        y = y/np.sum(y)
-
-        sum_term = 0.0
-        for i in range(len(x)):
-            sum_term += x[i]*(np.log(x[i]) - np.log(y[i]))
-      #  print(x,y,' ---------- ',sum_term)
-        return sum_term
+        plt.xlabel("Reference",fontweight='bold')
+        plt.ylabel("Query",fontweight='bold')  
+        plt.title('Alignment cost landscape')
 
 
 
diff --git a/genes2genes/PathwayAnalyser.py b/genes2genes/PathwayAnalyser.py
new file mode 100644
index 0000000..c539455
--- /dev/null
+++ b/genes2genes/PathwayAnalyser.py
@@ -0,0 +1,129 @@
+import gseapy as gp
+from gseapy import barplot, dotplot
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+from tabulate import tabulate
+from gsea_api.molecular_signatures_db import MolecularSignaturesDatabase
+
+from . import ClusterUtils
+from . import VisualUtils
+
+"""
+This script defines Wrappers for GSEAPY enrichr and other functions related to analysing pathway gene sets. 
+"""
+
+def run_overrepresentation_analysis(gene_set, TARGET_GENESETS=['MSigDB_Hallmark_2020','KEGG_2021_Human']):
+    enr = gp.enrichr(gene_list=gene_set,
+                     gene_sets=TARGET_GENESETS,
+                     organism='human',
+                     outdir=None,
+                       )
+    df = enr.results[enr.results['Adjusted P-value']<0.05]
+    if(df.shape[0]==0):
+        return df
+    df = df.sort_values('Adjusted P-value')
+    df['-log10 Adjusted P-value'] = [-np.log10(q) for q in df['Adjusted P-value']]
+    max_q = max(df['-log10 Adjusted P-value'][df['-log10 Adjusted P-value']!=np.inf])
+    #df.columns = ['Gene_set']+list(df.columns[1:len(df.columns)])
+    qvals = []
+    for q in df['-log10 Adjusted P-value']:
+        if(q==np.inf):
+            q = -np.log10(0.00000000001) # NOTE: For -log10(p=0.0) we replace p with a very small p-val to avoid inf
+        qvals.append(q)
+    df['-log10 FDR q-val'] = qvals 
+    df = df.sort_values('Adjusted P-value',ascending=True)
+    return df
+
+def plot_overrep_results(df):
+    height = df.shape[0]*(1/(np.log2(df.shape[0])+1)) 
+    ax = barplot(df,
+                  column="Adjusted P-value",
+                  group='Gene_set', # set group, so you could do a multi-sample/library comparsion
+                  size=10,
+                  top_term=20,
+                  figsize=(5,height),
+                  color=['darkred', 'darkblue'], # set colors for group
+                 )
+
+def plot_gsea_dotplot(df, size=100, figsize=(3,4), n_top_terms = 5):
+        ax = dotplot(df,
+                  column="P-value",
+                  x='-log10 Adjusted P-value', # set x axis, so you could do a multi-sample/library comparsion
+                  size=size,
+                  top_term=n_top_terms,
+                  figsize=figsize,
+                  xticklabels_rot=45, # rotate xtick labels
+                  show_ring=False, # set to False to revmove outer ring
+                  marker='o',
+                 )   
+
+def run_cluster_overrepresentation_analysis(aligner):
+
+    overrep_cluster_results = {}
+    cluster_overrepresentation_results = [] 
+
+    for cluster_id in tqdm(range(len(aligner.gene_clusters))):
+        df = run_overrepresentation_analysis(aligner.gene_clusters[cluster_id])
+        if(df.shape[0]==0):
+            continue
+        n_genes = len(aligner.gene_clusters[cluster_id])
+        pathways = list(df.Term) 
+        pathway_specific_genes = list(df.Genes) 
+        sources = [str(s).split('_')[0] for s in list(df.Gene_set)] 
+
+        if(n_genes<15):
+            genes = aligner.gene_clusters[cluster_id]
+        else:
+            genes = aligner.gene_clusters[cluster_id][1:7] + [' ... '] +  aligner.gene_clusters[cluster_id][n_genes-7:n_genes]
+
+        cluster_overrepresentation_results.append([cluster_id,n_genes,genes,pathways, pathway_specific_genes, sources]) 
+        overrep_cluster_results[cluster_id] = df 
+
+    results= pd.DataFrame(cluster_overrepresentation_results)
+    print(tabulate(results,  headers=['cluster_id','n_genes', 'Cluster genes', 'Pathways','Pathway genes','Source'],tablefmt="grid",maxcolwidths=[3, 3, 3,30,40,40,10])) 
+
+
+def get_pathway_alignment_stat(aligner, GENE_LIST, pathway_name, cluster=False, FIGSIZE = (14,7)):
+    
+    print('Gene set: ======= ', pathway_name)
+    perct_A = []
+    perct_S = []
+    perct_T = []
+    for gene in GENE_LIST:
+        series_match_percent = aligner.results_map[gene].get_series_match_percentage()
+        perct_A.append(series_match_percent[0])
+        perct_S.append(series_match_percent[1])
+        perct_T.append(series_match_percent[2])
+
+    print('mean matched percentage: ', round(np.mean(perct_A),2),'%' )
+    #print('mean matched percentage wrt ref: ',round(np.mean(perct_S),2),'%'  )
+    #print('mean matched percentage wrt query: ', round(np.mean(perct_T),2),'%' )
+    average_alignment, alignment_path =  ClusterUtils.get_cluster_average_alignments(aligner, GENE_LIST)
+    mat = ClusterUtils.get_pairwise_match_count_mat(aligner,GENE_LIST )
+    print('Average Alignment: ', VisualUtils.color_al_str(average_alignment), '(cell-level)')
+    print('- Plotting average alignment path')
+    VisualUtils.plot_alignment_path_on_given_matrix(paths = [alignment_path], mat=mat) 
+    VisualUtils.plot_mean_trend_heatmaps(aligner,GENE_LIST, pathway_name,cluster=cluster, FIGSIZE=FIGSIZE) 
+        
+
+class InterestingGeneSets:
+    
+    def __init__(self, MSIGDB_PATH, version):        
+        self.SETS = {}
+        self.dbs = {}
+        self.msigdb = MolecularSignaturesDatabase(MSIGDB_PATH , version=version)
+        self.dbs['kegg'] = self.msigdb.load('c2.cp.kegg', 'symbols')
+        self.dbs['hallmark'] = self.msigdb.load('h.all', 'symbols')
+        #self.dbs['gobp'] = self.msigdb.load('c5.go.bp', 'symbols')
+        #self.dbs['gocc'] = self.msigdb.load('c5.go.cc', 'symbols')
+        #self.dbs['reac'] = self.msigdb.load('c2.cp.reactome', 'symbols')
+        
+    def add_new_set_from_msigdb(self, db_name, dbsetname, avail_genes, usersetname):
+        self.SETS[usersetname] = np.intersect1d(list(self.dbs[db_name].gene_sets_by_name[dbsetname].genes), avail_genes)
+
+    def add_new_set(self, geneset, usersetname, avail_genes):
+        geneset = np.asarray(geneset)
+        self.SETS[usersetname] = geneset[np.where([g in avail_genes for g in geneset])]
+
+        
diff --git a/genes2genes/PathwayAnalyserV2.py b/genes2genes/PathwayAnalyserV2.py
deleted file mode 100644
index 12265f0..0000000
--- a/genes2genes/PathwayAnalyserV2.py
+++ /dev/null
@@ -1,294 +0,0 @@
-import gseapy as gp
-from gseapy import barplot, dotplot
-import anndata
-import time 
-import numpy as np
-import pandas as pd
-import scanpy as sc
-import seaborn as sb
-import scipy.stats as stats
-import matplotlib.pyplot as plt
-import os,sys,inspect
-import pickle
-from tqdm import tqdm
-from tabulate import tabulate
-from gsea_api.molecular_signatures_db import MolecularSignaturesDatabase
-from adjustText import adjust_text
-from mpl_toolkits.axes_grid1.inset_locator import inset_axes
-from scipy.stats import zscore
-
-from . import ClusterUtils
-from . import VisualUtils
-
-
-def run_overrepresentation_analysis(gene_set, TARGET_GENESETS=['MSigDB_Hallmark_2020','KEGG_2021_Human']):
-    enr = gp.enrichr(gene_list=gene_set,
-                     gene_sets=TARGET_GENESETS,
-                     organism='human',
-                     outdir=None,
-                       )
-    df = enr.results[enr.results['Adjusted P-value']<0.05]
-    if(df.shape[0]==0):
-        return df
-    df = df.sort_values('Adjusted P-value')
-    df['-log10 Adjusted P-value'] = [-np.log10(q) for q in df['Adjusted P-value']]
-    max_q = max(df['-log10 Adjusted P-value'][df['-log10 Adjusted P-value']!=np.inf])
-    #df.columns = ['Gene_set']+list(df.columns[1:len(df.columns)])
-    qvals = []
-    for q in df['-log10 Adjusted P-value']:
-        if(q==np.inf):
-            q = -np.log10(0.00000000001) # NOTE: For -log10(p=0.0) we replace p with a very small p-val to avoid inf
-        qvals.append(q)
-    df['-log10 FDR q-val'] = qvals 
-    df = df.sort_values('Adjusted P-value',ascending=True)
-    return df
-
-def plot_overrep_results(df):
-    height = df.shape[0]*(1/(np.log2(df.shape[0])+1)) 
-    ax = barplot(df,
-                  column="Adjusted P-value",
-                  group='Gene_set', # set group, so you could do a multi-sample/library comparsion
-                  size=10,
-                  top_term=20,
-                  figsize=(5,height),
-                  color=['darkred', 'darkblue'], # set colors for group
-                 )
-
-def plot_gsea_dotplot(df, size=100, figsize=(3,4), n_top_terms = 5):
-        ax = dotplot(df,
-                  column="P-value",
-                  x='-log10 Adjusted P-value', # set x axis, so you could do a multi-sample/library comparsion
-                  size=size,
-                  top_term=n_top_terms,
-                  figsize=figsize,
-                  xticklabels_rot=45, # rotate xtick labels
-                  show_ring=False, # set to False to revmove outer ring
-                  marker='o',
-                 )   
-
-def run_cluster_overrepresentation_analysis(aligner):
-
-    overrep_cluster_results = {}
-    cluster_overrepresentation_results = [] 
-
-    for cluster_id in tqdm(range(len(aligner.gene_clusters))):
-        df = run_overrepresentation_analysis(aligner.gene_clusters[cluster_id])
-        if(df.shape[0]==0):
-            continue
-        n_genes = len(aligner.gene_clusters[cluster_id])
-        pathways = list(df.Term) 
-        pathway_specific_genes = list(df.Genes) 
-        sources = [str(s).split('_')[0] for s in list(df.Gene_set)] 
-
-        if(n_genes<15):
-            genes = aligner.gene_clusters[cluster_id]
-        else:
-            genes = aligner.gene_clusters[cluster_id][1:7] + [' ... '] +  aligner.gene_clusters[cluster_id][n_genes-7:n_genes]
-
-        cluster_overrepresentation_results.append([cluster_id,n_genes,genes,pathways, pathway_specific_genes, sources]) 
-        overrep_cluster_results[cluster_id] = df 
-
-    results= pd.DataFrame(cluster_overrepresentation_results)
-    print(tabulate(results,  headers=['cluster_id','n_genes', 'Cluster genes', 'Pathways','Pathway genes','Source'],tablefmt="grid",maxcolwidths=[3, 3, 3,30,40,40,10])) 
-
-        
-
-
-def get_pathway_alignment_stat(aligner, GENE_LIST, pathway_name, cluster=False, FIGSIZE = (14,7)):
-    
-   # print('PATHWAY ======= ',pathway_name)
-   # GENE_LIST = IGS.SETS[pathway_name]
-    perct_A = []
-    perct_S = []
-    perct_T = []
-    for gene in GENE_LIST:
-        series_match_percent = aligner.results_map[gene].get_series_match_percentage()
-        perct_A.append(series_match_percent[0])
-        perct_S.append(series_match_percent[1])
-        perct_T.append(series_match_percent[2])
-
-    print('mean matched percentage: ', round(np.mean(perct_A),2),'%' )
-    print('mean matched percentage wrt ref: ',round(np.mean(perct_S),2),'%'  )
-    print('mean matched percentage wrt query: ', round(np.mean(perct_T),2),'%' )
-    average_alignment, alignment_path =  ClusterUtils.get_cluster_average_alignments(aligner, GENE_LIST)
-    mat = ClusterUtils.get_pairwise_match_count_mat(aligner,GENE_LIST )
-    print('Average Alignment: ', average_alignment)
-    VisualUtils.plot_alignment_path_on_given_matrix(paths = [alignment_path], mat=mat) #AAAAAAAA
- #  plt.xlabel('Ref pseudotime')
-    # plt.ylabel('Organoid pseudotime')
-   # plt.savefig('Ref_organoid_'+pathway_name+'_overall_alignment.png')
-    plot_mean_trend_heatmaps(aligner,GENE_LIST, pathway_name,cluster=cluster, FIGSIZE=FIGSIZE) 
-
-def plot_DE_genes(pathway_name):
-    PATHWAY_SET = IGS.SETS[pathway]
-    ax=sb.scatterplot(x['l2fc'],x['sim']*100,s=50, legend=False, hue =x['sim'] ,palette=sb.diverging_palette(15, 133, s=50, as_cmap=True),edgecolor='k',linewidth=0.3)
-    plt.yticks(fontsize=12)
-    plt.xticks(fontsize=12)
-    plt.ylabel('Alignment Similarity %', fontsize=12, fontweight='bold')
-    plt.xlabel('L2FC mean expression', fontsize = 12, fontweight='bold')
-    plt.grid(False)
-    plt.tight_layout()
-
-    TEXTS = [] 
-    for label, a, b in zip(x.index, x['l2fc'],x['sim']*100):
-        if(label in PATHWAY_SET):# and b<=50):
-            TEXTS.append(ax.text(a, b, label, color='white', fontsize=9, fontweight='bold',bbox=dict(boxstyle='round,pad=0.1', fc='black', alpha=0.75)))
-    adjust_text(TEXTS, expand_points=(2, 2),arrowprops=dict(arrowstyle="->", color='black', lw=2))
-    plt.title(pathway_name,fontweight='bold', fontsize=15)
-
-# smoothened/interpolated mean trends + Z normalisation 
-def plot_mean_trend_heatmaps(aligner, GENE_LIST, pathway_name, cluster=False, FIGSIZE=(14,7)):
-    S_mat = []
-    T_mat = []
-    S_zmat = []
-    T_zmat = []
-
-    for gene in GENE_LIST:
-
-        fS = pd.DataFrame([aligner.results_map[gene].S.mean_trend, np.repeat('Ref', len(aligner.results_map[gene].S.mean_trend))]).transpose()
-        fT = pd.DataFrame([aligner.results_map[gene].T.mean_trend, np.repeat('Organoid', len(aligner.results_map[gene].T.mean_trend))]).transpose()
-        f = pd.concat([fS,fT])
-        f[0] = np.asarray(f[0], dtype=np.float64)
-        from scipy.stats import zscore
-        f['z_normalised'] = zscore(f[0])
-        S_mat.append(np.asarray(f[f[1]=='Ref'][0]))
-        T_mat.append(np.asarray(f[f[1]=='Organoid'][0]))    
-        S_zmat.append(np.asarray(f[f[1]=='Ref']['z_normalised']))
-        T_zmat.append(np.asarray(f[f[1]=='Organoid']['z_normalised']))  
-    S_mat = pd.DataFrame(S_mat)
-    T_mat = pd.DataFrame(T_mat)
-    S_zmat = pd.DataFrame(S_zmat)
-    T_zmat = pd.DataFrame(T_zmat)
-    
-    S_mat.index = GENE_LIST #IGS.SETS[pathway_name]
-    T_mat.index = GENE_LIST #IGS.SETS[pathway_name]
-    S_zmat.index = GENE_LIST#IGS.SETS[pathway_name]
-    T_zmat.index = GENE_LIST#IGS.SETS[pathway_name]
-    
-   # print('Interpolated mean trends')
-   # plot_heatmaps(S_mat, T_mat, pathway_name, cluster=cluster)
-    print('Z-normalised Interpolated mean trends')
-    plot_heatmaps(S_zmat, T_zmat, GENE_LIST, pathway_name,cluster=cluster, FIGSIZE=FIGSIZE)
-
-def plot_heatmaps(mat_ref,mat_query,GENE_LIST, pathway_name, cluster=False, FIGSIZE=(14,7)):
-    
-    if(cluster):
-        g=sb.clustermap(mat_ref, figsize=(0.4,0.4), col_cluster=False, cbar_pos=None) 
-        gene_order = g.dendrogram_row.reordered_ind
-        df = pd.DataFrame(g.data2d) 
-        df.index = GENE_LIST[gene_order]
-    else:
-        df=mat_ref
-    plt.close()
-    
-    plt.subplots(1,2,figsize=FIGSIZE) #8,14/7 ******************************************************
-    max_val = np.max([np.max(mat_ref),np.max(mat_query)]) 
-    min_val = np.min([np.min(mat_ref),np.min(mat_query)]) 
-    plt.subplot(1,2,1)
-    ax=sb.heatmap(df, vmax=max_val,vmin=min_val, cbar_kws = dict(use_gridspec=False,location="top")) 
-    plt.title('Reference')
-    ax.yaxis.set_label_position("left")
-    for tick in ax.get_yticklabels():
-        tick.set_rotation(360)
-    plt.subplot(1,2,2)
-    if(cluster):
-        mat_query = mat_query.loc[GENE_LIST[gene_order]] 
-    ax = sb.heatmap(mat_query,vmax=max_val,  vmin=min_val,cbar_kws = dict(use_gridspec=False,location="top"), yticklabels=False) 
-    plt.title('Query')
-    plt.savefig(pathway_name+'_heatmap.png', bbox_inches='tight')
-    plt.show()
-
-        
-
-class InterestingGeneSets:
-    
-    def __init__(self, MSIGDB_PATH ='../OrgAlign/msigdb/' ):        
-        self.SETS = {}
-        self.dbs = {}
-        self.msigdb = MolecularSignaturesDatabase(MSIGDB_PATH , version='7.5.1')
-        self.dbs['kegg'] = self.msigdb.load('c2.cp.kegg', 'symbols')
-        self.dbs['hallmark'] = self.msigdb.load('h.all', 'symbols')
-        #self.dbs['gobp'] = self.msigdb.load('c5.go.bp', 'symbols')
-        #self.dbs['gocc'] = self.msigdb.load('c5.go.cc', 'symbols')
-        #self.dbs['reac'] = self.msigdb.load('c2.cp.reactome', 'symbols')
-        
-    def add_new_set_from_msigdb(self, db_name, dbsetname, avail_genes, usersetname):
-        self.SETS[usersetname] = np.intersect1d(list(self.dbs[db_name].gene_sets_by_name[dbsetname].genes), avail_genes)
-
-    def add_new_set(self, geneset, usersetname, avail_genes):
-        geneset = np.asarray(geneset)
-        #print(geneset)
-        self.SETS[usersetname] = geneset[np.where([g in avail_genes for g in geneset])]
-
-        
-
-        
-
-        
-
-        
-
-        
-
-        
-
-        
-
-        
-
-        
-
-# ATTIC
-
-def get_ranked_genelist(aligner):
-        #print('make ranked gene list')
-        matched_percentages = {}
-        for al_obj in aligner.results:
-            matched_percentages[al_obj.gene] = (al_obj.get_series_match_percentage()[0]/100 )
-        x = sorted(matched_percentages.items(), key=lambda x: x[1], reverse=False)
-        x = pd.DataFrame(x)
-        x.columns = ['Gene','Alignment_Percentage']
-        x = x.set_index('Gene')
-        return x
-
-# get the top k DE genes (k decided on matched percentage threshold) 
-def topkDE(aligner, DIFF_THRESHOLD=0.5):
-        ranked_list = get_ranked_genelist(aligner)
-        top_k = np.unique(ranked_list ['Alignment_Percentage'] < DIFF_THRESHOLD , return_counts=True)[1][1]
-        print(top_k, ' # of DE genes to check')
-        clusters = pd.DataFrame([ranked_list[0:top_k].index, np.repeat(0,top_k)]).transpose()
-        clusters.columns = ['Gene','ClusterID']
-        clusters = clusters.set_index('Gene')
-        return list(clusters.index), ranked_list 
-
-def run_GSEA_on_rankedlist(rankedDEgenes):
-    pre_res = gp.prerank(rnk=rankedDEgenes, # or rnk = rnk,
-                         gene_sets=['MSigDB_Hallmark_2020','KEGG_2021_Human','Reactome_2022','GO_Biological_Process_2021'],#targets5,
-                         threads=4,
-                         min_size=5,
-                         max_size=1000,
-                         permutation_num=1000,
-                         outdir=None, 
-                         seed=6,
-                         verbose=True, 
-                        )
-    pre_res.res2d[pre_res.res2d['FDR q-val']<0.05]
-
-    df = pre_res.res2d[pre_res.res2d['FDR q-val']<0.05]
-    df['Name'] = [str(t).split('_')[0] for t in df.Term]  
-    df = df.sort_values('FDR q-val')
-    df['-log10 FDR q-val'] = [-np.log10(q) for q in df['FDR q-val']]
-    max_q = max(df['-log10 FDR q-val'][df['-log10 FDR q-val']!=np.inf])
-    #df.columns = ['Gene_set']+list(df.columns[1:len(df.columns)])
-    qvals = []
-    for q in df['-log10 FDR q-val']:
-        if(q==np.inf):
-            q = -np.log10(0.00000000001) # NOTE: For -log10(p=0.0) we replace p with a very small p-val to avoid inf
-        qvals.append(q)
-    df['-log10 FDR q-val'] = qvals 
-    #df['Name'] = df['Gene_set']
-    sb.set(rc={'figure.figsize':(10,15)})
-    sb.factorplot(y='Term', x='-log10 FDR q-val', data=df, kind='bar', hue='Name',dodge=False)
-    plt.xlim([0,max_q])
-    
-    return pre_res
diff --git a/genes2genes/SimulationExperimentAnalyser.py b/genes2genes/SimulationExperimentAnalyser.py
deleted file mode 100644
index 3a44f24..0000000
--- a/genes2genes/SimulationExperimentAnalyser.py
+++ /dev/null
@@ -1,452 +0,0 @@
-# NEW ALIGNMENT ACCURACY STATISTIC CODE
-import numpy as np
-import regex as re
-import pandas as pd
-import seaborn as sb
-import matplotlib.pyplot as plt
-from tqdm import tqdm
-
-from . import ClusterUtils
-
-class SimulationExperimenter:
-    
-    def __init__(self, adata_ref, adata_query, aligner_all, CP_25, CP_05, CP_75, pattern_map): 
-        self.adata_ref = adata_ref
-        self.adata_query = adata_query
-        self.aligner_all = aligner_all
-        self.CP_25 = CP_25
-        self.CP_05 = CP_05
-        self.CP_75 = CP_75
-        self.pattern_map = pattern_map
-
-    def compute_match_statistics(self, existing_method_df, existing_method=True, print_stat=True):
-
-        group_alignment_strings, gene_group = self.get_group_alignment_strings('AllMatch', existing_method, existing_method_df)
-        #print(gene_group)
-        n_false_mismatches = 0
-        n_false_mismatched_alignments = 0
-        alignment_state_count = 0 
-        false_mismatch_counts = []
-        for alignment_string in group_alignment_strings:
-            mismatch_count = alignment_string.count('I') + alignment_string.count('D') 
-            if(mismatch_count>0):
-                n_false_mismatched_alignments+=1
-                n_false_mismatches += mismatch_count
-                false_mismatch_counts.append(mismatch_count)
-            alignment_state_count += len(alignment_string)
-
-        if(print_stat):
-            print('Number of false mismatched alignments ', n_false_mismatched_alignments, ' = ',n_false_mismatched_alignments*100 / len(group_alignment_strings), '%' )
-            print('Number of false mismatches ', n_false_mismatches, ' = ', n_false_mismatches*100  / alignment_state_count, '%' )
-            print('mean false mismatch count for an alignment = ', np.mean(false_mismatch_counts))
-            print('*****')
-        return n_false_mismatched_alignments*100 / len(group_alignment_strings)
-
-    def get_group_alignment_strings(self, pattern, existing_method=False,  existing_method_df = None):
-
-        gene_group = list(self.adata_ref.var_names[self.adata_ref.var.gene_pattern == pattern]) 
-        #print(gene_group)
-        if(existing_method):
-            gene_group  =np.intersect1d(list(existing_method_df.index), gene_group)
-            df = existing_method_df.loc[gene_group]
-            group_alignment_strings = [] 
-            for i in range(df.shape[0]):
-                group_alignment_strings.append(df['alignment_string'][i])
-            group_alignment_strings = [a.replace(' ','')  for a in group_alignment_strings] 
-        else:
-            group_alignment_strings = []
-            for g in gene_group:
-                group_alignment_strings.append(self.aligner_all.results_map[g].alignment_str)
-
-        return group_alignment_strings, gene_group
-
-
-    def get_accuracy_stat_divergence(self, alignment_str,  divergence_mode = True):
-
-        if(not divergence_mode):
-            alignment_str = alignment_str[::-1]
-
-        expected_pattern = '^[M/W/V]+[I/D]+$'
-        swapped_pattern = '^[I/D]+[M/W/V]+$'
-
-        false_start_mismatch_len = 0
-        false_end_match_len = 0
-        n_matches = 0
-        n_false_intermediate_mismatches = 0
-        end_mismatch_len = 0
-        status = ''
-
-        if(alignment_str.count('M') +  alignment_str.count('W')  + alignment_str.count('V') == 0):
-            status = 'complete_mismatch'
-            false_start_mismatch_len = -1
-            false_end_match_len = -1
-            n_false_intermediate_mismatches = -1
-            n_matches = -1
-
-        elif(alignment_str.count('I') +  alignment_str.count('D')== 0 ):
-            status = 'complete_match'
-            false_start_mismatch_len = -1
-            false_end_match_len = -1
-            n_false_intermediate_mismatches = -1
-            n_matches = -1
-        else:
-            res = re.findall(expected_pattern, alignment_str)
-            res_alt = re.findall(swapped_pattern, alignment_str)
-            if(len(res)==1):
-                status = 'expected_pattern' 
-                n_matches = alignment_str.count('M') + alignment_str.count('W') + alignment_str.count('V')
-                end_mismatch_len =  alignment_str.count('I') + alignment_str.count('D') 
-            elif(len(res_alt)==1):
-                status = 'swapped_pattern'
-                false_start_mismatch_len = -1
-                false_end_match_len = -1
-                n_false_intermediate_mismatches = -1
-                n_matches = -1
-                end_mismatch_len = -1
-            else:
-                status = 'complex_pattern' 
-
-                # check for false start mismatches
-                false_start_mismatch_len = 0
-                c=0
-                while(alignment_str[c] in ['I','D']):
-                    false_start_mismatch_len+=1
-                    c+=1
-
-                false_end_match_len = 0
-                c=len(alignment_str)-1
-                while(alignment_str[c] in ['M','W','V']):
-                    false_end_match_len +=1 
-                    c-=1
-
-                # find intermediate number of false mismatches within matched region 
-                # by first extracting the region between the first match and the last match
-                match_regions = []
-                for m in re.finditer('[M/V/W]+', alignment_str):
-                    if(m.start(0) != m.end(0)):
-                        match_regions.append([m.start(0), m.end(0)-1]) 
-
-                if(false_end_match_len==0):
-                    first_match_region = match_regions[0]
-                    last_match_region = match_regions[len(match_regions)-1]
-                else:
-                    first_match_region = match_regions[0]
-                    last_match_region = match_regions[len(match_regions)-2]
-
-                intermediate_str = alignment_str[first_match_region[0]: last_match_region[1]+1] 
-
-                n_matches = intermediate_str.count('M') + intermediate_str.count('W') + intermediate_str.count('V')
-                n_false_intermediate_mismatches = intermediate_str.count('I') + intermediate_str.count('D')
-
-
-                indel_regions = []
-                for m in re.finditer('[I/D]+', alignment_str):
-                    if(m.start(0) != m.end(0)):
-                        indel_regions.append([m.start(0), m.end(0)-1]) 
-                last_indel_region = indel_regions[len(indel_regions)-1]
-                end_mismatch_len = len(alignment_str[last_indel_region[0]:last_indel_region[1]+1]) 
-
-                # main statistics 
-                #print('False start mismatch len: ', false_start_mismatch_len)
-                #print('False end match len: ', false_end_match_len)
-                #print('[start] Match length', n_matches)
-                #print('end mismatch length')
-                #print('# of false intermediate mismatches', n_false_intermediate_mismatches)
-                #print('End mismatch end', end_mismatch_len)
-
-        return status, false_start_mismatch_len, false_end_match_len, n_matches, n_false_intermediate_mismatches, end_mismatch_len
-
-
-    def plot_validation_stat(self, accuracy_results, n_bins = 15, divergence=True):
-
-       # plt.subplots(1,3, figsize=(15,3))
-       # plt.subplot(1,3,1)
-       # sb.heatmap(CP_25, square=True, cmap='jet') 
-       # plt.subplot(1,3,2)
-       # sb.heatmap(CP_05, square=True, cmap='jet') 
-       # plt.subplot(1,3,3)
-       # sb.heatmap(CP_75, square=True, cmap='jet') 
-
-        #plt.savefig('changepoint_kernels.pdf')
-
-
-       # plt.subplots(1,3, figsize=(15,3))
-       # plt.subplot(1,3,1)
-        a = pd.DataFrame(self.CP_25 > 0.01) 
-       # sb.heatmap(a, square=True) 
-        approx_bifurcation_start_point_25 = np.min(np.where(a.iloc[299]==True))
-        #approx_bifurcation_start_point_25  = np.round((0.5* approx_bifurcation_start_point_25/150) ,2)
-        approx_bifurcation_start_point_25  = np.round((approx_bifurcation_start_point_25/300) ,2)
-
-
-     #   plt.subplot(1,3,2)
-        a = pd.DataFrame(self.CP_05 > 0.01) 
-       # sb.heatmap(a, square=True) 
-        approx_bifurcation_start_point_05 = np.min(np.where(a.iloc[299]==True)) 
-        #approx_bifurcation_start_point_05 = np.round((0.5* approx_bifurcation_start_point_05/150),2)
-        approx_bifurcation_start_point_05 = np.round((approx_bifurcation_start_point_05/300),2)
-
-
-      #  plt.subplot(1,3,3)
-        a = pd.DataFrame(self.CP_75 > 0.01) 
-       # sb.heatmap(a, square=True) 
-        approx_bifurcation_start_point_75 = np.min(np.where(a.iloc[299]==True))
-        #approx_bifurcation_start_point_75  = np.round((0.5* approx_bifurcation_start_point_75/150),2)
-        approx_bifurcation_start_point_75  = np.round((approx_bifurcation_start_point_75/300),2)
-
-        if(divergence):
-            expected_len_25 = [n_bins*approx_bifurcation_start_point_25, n_bins*0.25]
-            expected_len_05 = [n_bins*approx_bifurcation_start_point_05, n_bins*0.5]
-            expected_len_75 = [n_bins*approx_bifurcation_start_point_75, n_bins*0.75]
-
-            expected_mismatch_len_25 = [n_bins*(1-0.25),n_bins*(1-approx_bifurcation_start_point_25)]
-            expected_mismatch_len_05 = [n_bins*(1-0.5), n_bins*(1-approx_bifurcation_start_point_05)]
-            expected_mismatch_len_75 = [n_bins*(1-0.75),n_bins*(1-approx_bifurcation_start_point_75)]
-
-            y1 = 'start_match_len'
-            y2 = 'end_mismatch_len'
-            y3 = 'false_start_mismatch_len'
-            y4 = 'n_false_intermediate_mismatches'
-
-            y1_ = 'Start match length'
-            y2_ = 'End mismatch length'
-            y3_ = 'False start mismatch length'
-            y4_ = 'Number of false intermediate mismatches'
-
-            print('Approx. bifurcation start i for cp=0.25 = ', approx_bifurcation_start_point_25 )
-            print('Approx. bifurcation start i for cp=0.5 = ', approx_bifurcation_start_point_05 )
-            print('Approx. bifurcation start i for cp=0.75 = ', approx_bifurcation_start_point_75 )
-
-                # divegence
-            df_025 = accuracy_results['Divergence_025']
-            df_05 = accuracy_results['Divergence_05']
-            df_075 = accuracy_results['Divergence_075']
-        else:
-            expected_len_25 = [n_bins*(approx_bifurcation_start_point_75),n_bins*0.75]
-            expected_len_75 = [n_bins*(approx_bifurcation_start_point_25),n_bins*0.25]
-            expected_len_05 = [n_bins*(approx_bifurcation_start_point_05),n_bins*0.5]
-
-            expected_mismatch_len_25 = [n_bins*(1-0.75),n_bins*(1-approx_bifurcation_start_point_75)]
-            expected_mismatch_len_75 = [n_bins*(1-0.25), n_bins*(1-approx_bifurcation_start_point_25)]
-            expected_mismatch_len_05 = [n_bins*(1-0.5), n_bins*(1-approx_bifurcation_start_point_05)]
-
-            y1='end_match_len'
-            y2= 'start_mismatch_len'
-            y3='false_end_mismatch_len'
-            y4 = 'n_false_intermediate_mismatches'
-
-            y1_='End match length'
-            y2_= 'Start mismatch length'
-            y3_='False end mismatch length'
-            y4_ = 'Number of false intermediate mismatches'
-
-            print('Approx. convergent start i for cp=0.25 = ', 1-approx_bifurcation_start_point_75 )
-            print('Approx. convergent start i for cp=0.5 = ', 1-approx_bifurcation_start_point_05 )
-            print('Approx. convergent start i for cp=0.75 = ', 1-approx_bifurcation_start_point_25 )
-
-            # divegence
-            df_025 = accuracy_results['Convergence_025']
-            df_05 = accuracy_results['Convergence_05']
-            df_075 = accuracy_results['Convergence_075']
-
-        print('Expected match len for cp=0.25 = ', expected_len_25 )
-        print('Expected match len for cp=0.5 = ', expected_len_05 )
-        print('Expected match len  for cp=0.75 = ', expected_len_75 )
-
-        print('Expected mismatch len for cp=0.25 = ', expected_mismatch_len_25 )
-        print('Expected mismatch len for cp=0.5 = ', expected_mismatch_len_05 )
-        print('Expected mismatch len  for cp=0.75 = ', expected_mismatch_len_75 )
-
-        df_075['BF_approx'] = np.repeat('0.75', len(df_075))  
-        df_025['BF_approx'] = np.repeat('0.25', len(df_025))  
-        df_05['BF_approx'] = np.repeat('0.5', len(df_05)) 
-        df = pd.concat( [df_025, df_05, df_075])
-        df = df[df.status!='complete_mismatch']
-        df = df[df.status!='swapped_pattern']
-        df = df[df.status!='complete_match']
-        
-        # get the max mismatch length (across Is and Ds segments)
-        mismatch_regions = []
-        for a in df['alignment_str']:
-            temp_reg = re.findall('[I/D]+',a)
-            if(not divergence): 
-                mismatch_regions.append(temp_reg[0]) # first mismatch region 
-            else:
-                mismatch_regions.append(temp_reg[len(temp_reg)-1]) # last mismatch region
-        mismatch_lengths = [] 
-        for a in mismatch_regions:
-             mismatch_lengths.append(np.max([a.count('I'),a.count('D')]))
-    
-        if(divergence): # because the indel length will always be twice the expected length (# of Is == # of Ds)
-            df['end_mismatch_len'] = mismatch_lengths
-        else:
-            df['start_mismatch_len'] = mismatch_lengths
-
-        plt.subplots(1,4, figsize=(15,4))
-        plt.subplot(1,4,1)
-        g = sb.violinplot(data=df,  y = y1, x='BF_approx', cut=0)
-        plt.xlabel('Approx bifurcation point')
-        plt.title(y1_)
-        plt.ylim([0,18])
-        g.axhspan(expected_len_25[0], expected_len_25[1], xmin=0, xmax=0.35, alpha=0.2)
-        g.axhspan(expected_len_05[0], expected_len_05[1], xmin=0.35, xmax=0.65,facecolor='orange', alpha=0.2)
-        g.axhspan(expected_len_75[0], expected_len_75[1], xmin=0.65, xmax=1.0,facecolor='green', alpha=0.2)
-        plt.ylabel(y1_)
-
-        plt.subplot(1,4,2)
-        g = sb.violinplot(data=df,  y = y2, x='BF_approx', cut=0)
-        plt.xlabel('Approx bifurcation point')
-        plt.ylabel('')
-        plt.ylim([0,18])
-        plt.title(y2_)
-        g.axhspan(expected_mismatch_len_25[0], expected_mismatch_len_25[1], xmin=0, xmax=0.35,alpha=0.2)
-        g.axhspan(expected_mismatch_len_05[0], expected_mismatch_len_05[1],xmin=0.35, xmax=0.65,facecolor='orange', alpha=0.2)
-        g.axhspan(expected_mismatch_len_75[0], expected_mismatch_len_75[1],xmin=0.65, xmax=1.0,facecolor='green', alpha=0.2)
-        plt.ylabel(y2_)
-
-        plt.subplot(1,4,3)
-        sb.violinplot(data=df,  y =  y3, x='BF_approx', cut=0)
-        plt.xlabel('Approx bifurcation point')
-        plt.ylabel('')
-        plt.ylim([0,18])
-        plt.title(y3_)
-        plt.ylabel(y3_)
-
-        plt.subplot(1,4,4)
-        sb.violinplot(data=df,  y =  y4, x='BF_approx', cut=0)
-        plt.xlabel('Approx bifurcation point')
-        plt.ylabel('')
-        plt.ylim([0,18])
-        plt.title(y4_)
-        plt.ylabel(y4_)
-
-        plt.tight_layout()
-
-        return df
-
-
-    def compute_divergence_convergence_statistics(self, existing_method = False, tr_df = None, print_stat=True):
-
-        divcov_alignment_accuracy_results = {}
-
-        for PATTERN in [ 'Convergence_025', 'Convergence_05', 'Convergence_075','Divergence_025', 'Divergence_05', 'Divergence_075']:
-
-            if(not existing_method): # G2G
-                group_alignment_strings, gene_group = self.get_group_alignment_strings(PATTERN)
-            else: # TrAGEDy
-                group_alignment_strings, gene_group = self.get_group_alignment_strings(PATTERN, existing_method=True, existing_method_df=tr_df)
-
-            accuracy_status = [] 
-            for al in group_alignment_strings:
-                status, false_start_mismatch_len, false_end_match_len, n_matches, n_false_intermediate_mismatches, end_mismatch_len  = self.get_accuracy_stat_divergence(al, divergence_mode=PATTERN.startswith('Div'))
-                accuracy_status.append([status, false_start_mismatch_len, false_end_match_len, n_matches, n_false_intermediate_mismatches, end_mismatch_len])
-
-            d = pd.DataFrame(accuracy_status)
-            if(PATTERN.startswith('Div')):
-                d.columns = ['status','false_start_mismatch_len','false_end_match_len','start_match_len','n_false_intermediate_mismatches','end_mismatch_len']
-            else:
-                d.columns = ['status','false_end_mismatch_len','false_start_match_len','end_match_len','n_false_intermediate_mismatches','start_mismatch_len']
-            d['alignment_str'] = group_alignment_strings
-            d['gene'] = gene_group
-            if(print_stat):
-                print(PATTERN, len(gene_group), np.unique(d['status'] , return_counts=True)) 
-
-            divcov_alignment_accuracy_results[PATTERN] = d
-
-        return divcov_alignment_accuracy_results 
-
-    # clustering related
-
-    def computeE(self, alignment_strings, metric):
-        # compute distance matrix 
-        print('compute distance matrix')
-        dist_mat_functions = {'hamming': ClusterUtils.compute_hamming_dist_matrix, 'levenshtein': ClusterUtils.compute_levenshtein_dist_matrix}
-        compute_dist_matrix = dist_mat_functions[metric]
-        E = compute_dist_matrix(alignment_strings)
-        return E
-
-    def run_clustering(self, alignment_strings, metric, gene_names, DIST_THRESHOLD=0.2, experiment_mode=False, E=None):
-
-        if(E is None):
-            # compute distance matrix 
-            E = self.computeE(alignment_strings, metric)
-
-        if(experiment_mode):
-            scores = [];  n_clusters = []; dist_thresholds = np.arange(0.01,1.0,0.01); score_modes = []; n_small_clusters = [] 
-            eval_dists = []
-            for D_THRESH in tqdm(dist_thresholds): 
-                gene_clusters, cluster_ids, silhouette_score, silhouette_score_mode, n_small_cluster = ClusterUtils.run_agglomerative_clustering(E, gene_names, D_THRESH)
-
-                if(len(gene_clusters.keys())==1):
-                    break
-                scores.append(silhouette_score)
-                n_clusters.append(len(gene_clusters.keys()))
-                score_modes.append(silhouette_score_mode)
-                n_small_clusters.append(n_small_cluster)
-                eval_dists.append(D_THRESH)
-
-            plt.rcParams.update({'font.size': 14})
-            plt.subplots(1,3,figsize=(10,5))
-            plt.subplot(1,3,1)
-            sb.lineplot(x=eval_dists, y=scores, color = 'blue', marker='o') 
-            plt.xlabel('Distnace threshold')
-            plt.ylabel('Mean Silhouette Score')
-            plt.subplot(1,3,2)
-            sb.lineplot(x=n_clusters, y=scores, color='red', marker='o')
-            plt.xlabel('Number of clusters')
-            plt.ylabel('Mean Silhouette Score')
-            plt.subplot(1,3,3)
-            sb.lineplot(x=eval_dists, y=n_clusters, color='green', marker='o')
-            plt.xlabel('Distance threshold')
-            plt.ylabel('Number of clusters')
-            plt.tight_layout() 
-            df = pd.DataFrame([eval_dists,scores,n_clusters]).transpose()
-            df.columns = ['Distance threshold', 'Mean Silhouette Score','Number of clusters']
-            return df
-
-        else:
-            print('run agglomerative clustering | ', np.round(DIST_THRESHOLD,2) )
-            gene_clusters, cluster_ids, silhouette_score, silhouette_score_samples, n_small_cluster  = ClusterUtils.run_agglomerative_clustering(E, gene_names, DIST_THRESHOLD)
-            print('silhouette_score: ', silhouette_score)
-            return gene_clusters 
-
-    def compute_misclustering_rate(self, gene_clusters, alignment_strings):
-        misclustered_count = 0
-        cid = 0
-        for i in range(len(gene_clusters)):
-            cluster = gene_clusters[i]
-            cluster_pattern =[]
-            for g in cluster:
-                cluster_pattern.append(self.pattern_map[g])
-
-            pattern_types = np.unique(cluster_pattern, return_counts=True)[0]
-            pattern_counts = np.unique(cluster_pattern, return_counts=True)[1]
-
-            if(len(pattern_types)>1):
-                max_count = np.max(pattern_counts)
-                # recording the number of outliers in a cl
-                for c in pattern_counts:
-                    if(c!=max_count):
-                        misclustered_count += c
-            #print(cid, pattern_types, pattern_counts, misclustered_count)#, ' || misclustered count = ',misclustered_count)
-            cid+=1
-        print('misclustered rate: ', misclustered_count*100/len(alignment_strings),'%')
-        return misclustered_count*100/len(alignment_strings)
-
-    def compute_cluster_diagnostics(self, alignment_strings, gene_names, distance_metric = 'levenshtein'):
-
-        E = self.computeE(alignment_strings, metric=distance_metric)
-        df = self.run_clustering(alignment_strings, metric=distance_metric, gene_names=gene_names, experiment_mode=True, E=E) 
-
-        print('computing misclustering rates for different distance thresholds') 
-        misclustering_rates = []
-        distance_thresholds = []
-        dist_range = list(df['Distance threshold'])
-        for dist_thresh in dist_range: 
-            gene_clusters = self.run_clustering(alignment_strings, metric=distance_metric, gene_names=gene_names, DIST_THRESHOLD=dist_thresh , experiment_mode=False, E=E) 
-            mc = self.compute_misclustering_rate(gene_clusters, alignment_strings) 
-            misclustering_rates.append(mc)
-            distance_thresholds.append(dist_thresh)
-        df['misclustering_rate']= misclustering_rates
-
-        return E, df
diff --git a/genes2genes/TimeSeriesPreprocessor.py b/genes2genes/TimeSeriesPreprocessor.py
index ae7e515..dac1f80 100644
--- a/genes2genes/TimeSeriesPreprocessor.py
+++ b/genes2genes/TimeSeriesPreprocessor.py
@@ -1,307 +1,221 @@
 import numpy as np
 import seaborn as sb
+import pandas as pd
 import torch
-from optbinning import ContinuousOptimalBinning
+import multiprocessing
+from scipy.sparse import csr_matrix
+from sklearn.preprocessing import MinMaxScaler
 
 from . import MyFunctions
-from . import MVG
+from . import Utils
 
-class SummaryTimeSeries:
+class TrajectoryInterpolator:
     
-    def __init__(self, time_points, mean_trend, std_trend, data_bins, X,Y, cell_densities):
-        self.time_points = np.asarray(time_points)
-        self.mean_trend = np.asarray(mean_trend)
-        self.std_trend = np.asarray(std_trend)
-        self.data_bins = data_bins
-        self.X = X
-        self.Y = Y
-        self.cell_densities = cell_densities
-        self.intpl_means = None
-        self.intpl_stds = None
+    """
+    This class defines an interpolator function for a given gene expression time series, which prepares required summary statistics for interpolation
+    """
+    
+    def __init__(self, adata, n_bins, adaptive_kernel = True, kernel_WINDOW_SIZE=0.1, raising_degree = 1):
+        self.n_bins = n_bins
+        self.adata = adata[np.argsort(adata.obs['time'])] 
+
+        self.cell_pseudotimes = np.asarray(self.adata.obs.time)
+        self.interpolation_points = np.linspace(0,1,n_bins) 
+        self.kernel_WINDOW_SIZE = kernel_WINDOW_SIZE
+        self.adaptive_kernel = adaptive_kernel
+        self.k = raising_degree # the degree of stretch imposed for the window sizes from baseline kernel_WINDOW_SIZE  = 0.1
         
-    def plot_mean_trend(self, color='blue'):
-        sb.lineplot(x=self.time_points, y=self.mean_trend, linewidth=3, color=color)
+        self.mat = csr_matrix(self.adata.X.todense().transpose())
+        self.N_PROCESSES = multiprocessing.cpu_count() 
+        self.gene_list = self.adata.var_names
         
+    def run(self):
+        #print('computing absolute time diffs')
+        self.abs_timediff_mat = self.compute_abs_timediff_mat()
+        if(self.adaptive_kernel):
+            #print('Running in adaptive interpolation mode')
+            #print('computing an cell densities for adaptive interpolation')
+            self.reciprocal_cell_density_estimates = self.compute_cell_densities()
+            #print('computing adaptive win denomes')
+            self.adaptive_win_denoms =  self.compute_adaptive_window_denominator()
+        #print('computing cell weight matrix')
+        self.cell_weight_mat = self.compute_Weight_matrix() 
         
-    def reverse_time_series(self):
+    def compute_abs_timediff_mat(self): # interpolation time points x cells matrix
+        df = [] 
+        for i in self.interpolation_points:
+            # absolute difference between actual pseudotime point of a cell and interpolation time point (needed to compute gaussian kernel later on)
+            abs_dist = np.abs(np.asarray(self.cell_pseudotimes) - i) #np.repeat(i,len(self.cell_pseudotimes)) 
+            df.append(abs_dist)
+        df = pd.DataFrame(df); df.columns = self.adata.obs_names; df.index = self.interpolation_points
+        return df
         
-        self.time_points = self.time_points[::-1]
-        self.mean_trend = self.mean_trend[::-1]
-        self.std_trend = self.std_trend[::-1]
-      #  self.data_bins = self.data_bins[::-1]
-        self.X = self.X [::-1]
-        self.Y = self.Y[::-1]
-        self.cell_densities = self.cell_densities[::-1]
-        self.intpl_means = self.intpl_means[::-1]
-        self.intpl_stds = self.intpl_stds[::-1]
-
-
-class Prepocessor:
-    
-    def __init__(self, *args):
-        if len(args)>1:
-            GEX_MAT =args[0] 
-            pseudotime_series = args[1]
-            m = args[2]
-            WINDOW_SIZE = args[3]
-            optimal_binning = args[4]
-            opt_binning = args[5]
-            self.GEX_MAT = GEX_MAT
-            self.pseudotime_series = pseudotime_series
-            self.compute_cell_density_trend(WINDOW_SIZE, m=m, optimal_binning=optimal_binning, opt_binning = opt_binning) 
-        else:
-            self.GEX_MAT = None
-            self.pseudotime_series = None
-            self.cell_densities = None
+    def compute_cell_densities(self): # cell density vector across interpolation time points
+        # compute cell density estimate for each interpolation point
+        cell_density_estimates = [] 
+        interpolation_points = self.interpolation_points
+        cell_pseudotimes = self.cell_pseudotimes
+        range_length_mid = interpolation_points[2] - interpolation_points[0] # constant across 
+        range_length_corner = interpolation_points[1] - interpolation_points[0] # constant across 
+        for i in range(len(interpolation_points)):
+            prime_point = interpolation_points[i] 
+            cell_density = 0.0 # per discrete point cell density = # of cells falling within interpolation time points [i-1,i+1] range window / window length
+            if(i==0):
+                logic = cell_pseudotimes <= interpolation_points[i+1]; range_length = range_length_corner
+            elif(i==len(interpolation_points)-1):
+                logic = cell_pseudotimes >= interpolation_points[i-1]; range_length = range_length_corner
+            else:
+                logic = np.logical_and(cell_pseudotimes <= interpolation_points[i+1], cell_pseudotimes >= interpolation_points[i-1])
+                range_length = range_length_mid
+
+            density_stat = np.count_nonzero(logic)
+            density_stat = density_stat/range_length
+            cell_density_estimates.append(density_stat)
+        #print('** per unit cell density: ', cell_density_estimates)
+        self.cell_density_estimates = cell_density_estimates
+        cell_density_estimates  = [1/x for x in cell_density_estimates] # taking reciprocal for weighing
         
-    
-    def create_summary_trends(self, X, Y):
-        # remember we have 100 synthetic cells per each time point
-        mean_trend = []
-        std_trend = [] 
-        data_bins = []
-        for t in range(len(self.artificial_time_points)):
-            data_points = Y[X== self.artificial_time_points[t]]
-            mean_trend.append(np.mean(data_points) ) 
-            std_trend.append(np.std(data_points) )
-            data_bins.append(data_points)
-
-        return SummaryTimeSeries(self.artificial_time_points, mean_trend, std_trend, data_bins,X,Y,self.cell_densities)
-
-
-    # data = dataframe, pseudotime series = array of pseudotimes for cells
-    def create_equal_len_time_bins(self, gene, N_BINS = 10): 
-        bins_indices = np.linspace(np.min(self.pseudotime_series), np.max(self.pseudotime_series), N_BINS+1 ) # bin margins
-        bins_indices[N_BINS] = np.max(self.pseudotime_series) + 0.00001 # small jitter added to consistently mark the bin boundaries as [), [), .... ]]
-        data_bins = []
-        time_bins = []
-        bin_compositions = []
-
-        for i in range(len(bins_indices)):
-            if(i==len(bins_indices)-1):
-                break
-            t = np.logical_and(self.pseudotime_series >= bins_indices[i], self.pseudotime_series < bins_indices[i+1])
-            data_bins = data_bins + list(self.GEX_MAT.loc[t,gene])
-            bin_compositions.append(len(self.GEX_MAT.loc[t,gene]))
-            time_bins = time_bins + list(np.repeat(bins_indices[i+1], len(self.GEX_MAT[t]))) 
-        return bins_indices[1:len(bins_indices)], time_bins, np.asarray(data_bins), bin_compositions
-
-    # **** CellAlign paper's interpolation method based on Gaussian Kernel
-    def interpolate_time_series(self, gene): # WINDOW_SIZE = 0.1 # default value used in CellAlign 
-
-        intpl_gex = []
-        for intpl_i in range(len(self.artificial_time_points)):
-            weights = self.cell_weights[intpl_i]
-            weighted_sum = 0.0
-            for cell_i in range(len(self.pseudotime_series)):
-                weighted_sum = weighted_sum + (weights[cell_i]*self.GEX_MAT[gene][cell_i]) 
-            weighted_sum = weighted_sum/np.sum(weights)
-            intpl_gex.append(weighted_sum)
-        intpl_gex = np.asarray(intpl_gex).flatten() 
-
-        # min max normalisation
-        scaled_intpl_gex = []
-        for i in range(len(intpl_gex)):
-            scaled_intpl_gex.append((intpl_gex[i] - np.min(intpl_gex))/(np.max(intpl_gex) - np.min(intpl_gex) )) 
-        return scaled_intpl_gex, self.artificial_time_points
-
-
-    # Interpolation of distributions based on Gaussian kernel (similar to above method but we get a distribution of artificial cells for interpolated time points now)
-    #  weighted mean and weighted std based dist interpolation
-    # Extending the CellAlign interpolation method based on Gaussian Kernel 
-    def interpolate_time_series_distributions(self, gene, N=50, CONST_STD= False,WEIGHT_BY_CELL_DENSITY=False, ESTIMATE = True, user_given_std =[]):
+        #print('reciprocals: ', cell_density_estimates)
+        # if this has inf values, use the max weight for them (otherwise it becomes inf resulting same weights 1.0 for all cells)
+        arr = cell_density_estimates
+        if(np.any(np.isinf(arr))):
+            max_w = max(np.asarray(arr)[np.isfinite(arr)] ) 
+            cell_density_estimates = np.where(np.isinf(arr), max_w, arr)
+        #print('** adaptive weights -- ', cell_density_estimates)
+        
+        return cell_density_estimates
+        
+    def compute_adaptive_window_denominator(self): # for each interpolation time point
 
-        torch.manual_seed(1)
-        intpl_gex = []
-        all_time_points = [] 
-        intpl_means = []
-        intpl_stds = []
+        cell_density_adaptive_weights  = self.reciprocal_cell_density_estimates
 
-        for intpl_i in range(len(self.artificial_time_points)):
+        # using min-max to stretch the range (for highly adapted window sizes having high window sizes)
+        cell_density_adaptive_weights =np.asarray(cell_density_adaptive_weights) 
+        scaler = MinMaxScaler()
+        cell_density_adaptive_weights = scaler.fit_transform(cell_density_adaptive_weights.reshape(-1, 1)).flatten()
+        cell_density_adaptive_weights = cell_density_adaptive_weights * self.k
+        
+        # ======= enforcing the same window_size = kernel_WINDOW_SIZE for the interpolation with the least weighted kernel window size
+        adaptive_window_sizes = []
+        for cd in cell_density_adaptive_weights:
+            adaptive_window_sizes.append(cd*self.kernel_WINDOW_SIZE)  #weighing stadard window size
             
-            # estimate weighted mean and weighted variance
-            #if(ESTIMATE):
-            if(user_given_std[intpl_i] <0):
-                weights = self.cell_weights[intpl_i]
-                weighted_sum = 0.0
-                for cell_i in range(len(self.pseudotime_series)):
-                    weighted_sum = weighted_sum + (weights[cell_i]*self.GEX_MAT[gene][cell_i]) 
-                weighted_sum = weighted_sum/np.sum(weights)
-                dist_mean = weighted_sum
-
-                if(CONST_STD): # for getting just the average trend across
-                    dist_std = 0.1
-                else: # tweighted standard deviation 
-                    real_mean = np.mean(self.GEX_MAT[gene]) 
-                    weighted_sum_std = 0.0
-                    for cell_i in range(len(self.pseudotime_series)):
-                        weighted_sum_std = weighted_sum_std + (weights[cell_i]*(( self.GEX_MAT[gene][cell_i] - real_mean) ** 2))
-                    n = len(self.pseudotime_series)
-                    weighted_std = np.sqrt(weighted_sum_std/(np.sum(weights) * (n-1)/n)) 
-                    if(WEIGHT_BY_CELL_DENSITY):
-                        weighted_std = weighted_std * self.cell_densities[intpl_i] # weighting according to cell density 
-                    dist_std = weighted_std
-                if(dist_std==0 or np.isnan(dist_std)): # case of single data point or no data points
-                    #print('!!!! ALERT ---- DIST STD =0 nan')
-                    dist_std = 0.01#0.1 #np.mean(summary_series_obj.std_trend)
-            else:
-                dist_mean = 0.0 
-                dist_std =  user_given_std[intpl_i] #
-            D,temp1,temp2 = MyFunctions.generate_random_dataset(N, dist_mean, dist_std)
+        # find the interpolation point for which the window_size weighted to be lowest -- furthest to kernel_WINDOW_SIZE
+        temp = list(np.abs(adaptive_window_sizes - np.repeat(self.kernel_WINDOW_SIZE,self.n_bins)))
+        least_affected_interpolation_point = temp.index(max(temp))
+        residue = np.abs(self.kernel_WINDOW_SIZE - adaptive_window_sizes[least_affected_interpolation_point])
+        if(self.k>1): # linear scaling to stretch the range of window size from 0.1 base line. 
+            adaptive_window_sizes = adaptive_window_sizes + (residue/(self.k-1)) 
+        else: 
+            adaptive_window_sizes = adaptive_window_sizes + residue 
+  
+        # compute adaptive window size based denominator of Gaussian kernel for each cell for each interpolation time point
+        W = [] 
+        for adw in adaptive_window_sizes:
+            adaptive_W_size = adw**2
+            W.append(adaptive_W_size)
+        self.adaptive_window_sizes = adaptive_window_sizes
             
-            intpl_gex.append(D)
-            intpl_means.append(dist_mean)
-            intpl_stds.append(dist_std)
-            all_time_points.append(np.repeat(self.artificial_time_points[intpl_i], N)) 
-
-        return [np.asarray(intpl_gex).flatten(), np.asarray(all_time_points).flatten(), self.artificial_time_points, intpl_means, intpl_stds]
-    
-
-    def prepare_interpolated_gene_expression_series(self, gene, CONST_STD=False, WEIGHT_BY_CELL_DENSITY=False,  ESTIMATE = True, user_given_std =[]):
-        intpl_out = self.interpolate_time_series_distributions(gene, CONST_STD=CONST_STD, WEIGHT_BY_CELL_DENSITY= WEIGHT_BY_CELL_DENSITY, ESTIMATE=ESTIMATE, user_given_std=user_given_std)
-        X = intpl_out[1]; Y =  intpl_out[0]; artificial_time_points = intpl_out[2]
-        obj = self.create_summary_trends(X,Y) 
-        obj.intpl_means = intpl_out[3]
-        obj.intpl_stds = intpl_out[4]
+        return W
         
-        self.all_time_points = X # [TODO] -- repeats the same thing! To be done efficiently later!!!!
-        
-        return obj
-
-    
-    def get_optimal_binning(self, time_var_arr, n_points):
-        x = time_var_arr
-        optb = ContinuousOptimalBinning(name='pseudotime', dtype="numerical", max_n_bins=n_points)
-        # this pacakge uses mixed integer programming based optimization to determine an optimal binning
-        optb.fit(x, x)
-        return optb.splits   
-
+    # compute Gaussian weights for each interpolation time point and cell
+    def compute_Weight_matrix(self):
+        if(self.adaptive_kernel):
+            adaptive_win_denoms_mat = np.asarray([np.repeat(a, len(self.cell_pseudotimes)) for a in self.adaptive_win_denoms])
+            W_matrix = pd.DataFrame(np.exp(-np.divide(np.array(self.abs_timediff_mat**2), adaptive_win_denoms_mat)))
+        else:
+            W_matrix = pd.DataFrame(np.exp(-np.array(self.abs_timediff_mat**2)/self.kernel_WINDOW_SIZE**2))
+        W_matrix.columns = self.adata.obs_names
+        self._real_intpl = self.interpolation_points
+        #self.interpolation_points = [np.round(i,2) for i in  self.interpolation_points]
+        W_matrix.index = self.interpolation_points
+        #sb.heatmap(W_matrix)
+        return W_matrix
     
-    def compute_cell_density_trend(self, WINDOW_SIZE = 0.1, m=50, optimal_binning = False, opt_binning = []): # TODO LATEST TEST 07/01/2023 earlier used 0.15 for early Jan runs
+    def get_effective_cell_pseudotime_range(self, i, effective_weight_threshold):
+        effective_weights = self.cell_weight_mat.loc[self.interpolation_points[i]]
+        cell_names = np.asarray(effective_weights.index)
+        effective_weights = np.asarray(effective_weights)
+        cell_ids = np.where(effective_weights>effective_weight_threshold)[0]
+        effective_cell_names = cell_names[cell_ids]
+        effective_cell_pseudotimes = self.cell_pseudotimes[cell_ids]
+        return effective_cell_pseudotimes
+
+    # plotting highly effective cell_contribution regions for given interpolation points based on adaptive weighted gaussian kernel
+    def plot_effective_regions_for_interpolation_points(self, intpointsIdx2plots, effective_weight_threshold=0.5, plot=True):
         
-        artificial_time_points = []
-        if(optimal_binning):
-            artificial_time_points = opt_binning
-        else:
-            for j in range(1,m):
-                artificial_time_points.append((j-1)/(m-1))    
-            artificial_time_points.append(1.0)
+        cmap = sb.color_palette("viridis", as_cmap=True)
+        self.n_effective_cells = []
+        for i in intpointsIdx2plots:
+            x = self.get_effective_cell_pseudotime_range(i, effective_weight_threshold= effective_weight_threshold)
+            self.n_effective_cells.append(len(x))
+            if(plot):
+                sb.kdeplot(x, fill=True, color=cmap(i/self.n_bins), clip=(0.0,1.0))
 
-            artificial_time_points = np.asarray(artificial_time_points)
-            artificial_time_points = artificial_time_points[artificial_time_points >= np.min(self.pseudotime_series)] 
-            artificial_time_points = artificial_time_points[artificial_time_points <= np.max(self.pseudotime_series)] 
-            if(artificial_time_points[0]!=0.0):
-                artificial_time_points = np.asarray([0] + list(artificial_time_points))     
+    
+"""
+The below functions define interpolation functions used by the above Interpolator object 
+(defined outside class for time efficiency)
+"""
+# ====================== interpolation process of genes
+def compute_stat(row, x, cell_densities, user_given_std):
+            idx = row.name
+            if(user_given_std[idx] < 0):
+                cell_weights_sum = np.sum(row)
+
+                # estimate weighted mean
+                weighted_mean = np.dot(row, x)/cell_weights_sum
+                #print(weighted_mean)
+
+                # estimate weighted variance
+                real_mean = np.mean(x); n = len(row)
+                weighted_sum_std = np.dot(row, (x - real_mean) ** 2 )
+                weighted_std = np.sqrt(weighted_sum_std/(cell_weights_sum * (n-1)/n)) 
+                weighted_std = weighted_std * cell_densities[idx] # weighting according to cell density 
+            else:
+                weighted_mean = 0.0 
+                weighted_std =  user_given_std[idx] #
+            
+            D,_,_ = MyFunctions.generate_random_dataset(50, weighted_mean, weighted_std)
+            return np.asarray([weighted_mean, weighted_std, D] )
 
-        cell_densities = [] 
-        cell_weights = {} 
+#row = list(trajInterpolator.cell_weight_mat.loc[intpl_i])
+def interpolate_gene_v2(i, trajInterpolator, user_given_std):
+        torch.manual_seed(1)
+        GENE = trajInterpolator.gene_list[i]
+        #print(GENE)
+        x = Utils.csr_mat_col_densify(trajInterpolator.mat ,i)
+        N_cells= len(trajInterpolator.cell_pseudotimes)
         
-        for intpl_i in range(len(artificial_time_points)):
-            weights = []
-            for cell_i in range(len(self.pseudotime_series)):
-                w_i = np.exp(-((self.pseudotime_series[cell_i] - artificial_time_points[intpl_i])**2)/(WINDOW_SIZE**2)) 
-                weights.append(w_i)
-            # weighted cell density
-            cell_densities.append(np.sum(weights))
-            cell_weights[intpl_i] = np.asarray(weights) 
-        cell_densities = np.asarray(cell_densities)
-        cell_densities = cell_densities/len(self.pseudotime_series)
+        trajInterpolator.cell_weight_mat.index = range(0,len(trajInterpolator.cell_weight_mat))
+        cell_densities = list(trajInterpolator.cell_weight_mat.apply(np.sum, axis=1)/N_cells)
         
-        self.cell_weights = cell_weights
-        self.artificial_time_points = artificial_time_points
-        self.cell_densities = cell_densities
-    
-        return cell_weights, artificial_time_points, cell_densities
+        results = trajInterpolator.cell_weight_mat.apply(compute_stat, axis=1, args = ([x,cell_densities, user_given_std]), result_type='expand')
+        results = pd.DataFrame(results)
 
-    
+        return SummaryTimeSeries(GENE, results[0], results[1], results[2], trajInterpolator.interpolation_points) 
 
-# Later TODO: make superclass SummaryTimeSeries for both univariate and multivariate cases
-class SummaryTimeSeriesMVG:
+class SummaryTimeSeries:
+    """
+    This class defines an interpolated time series object that carries the interpolated result of a gene expression time series
+    """
     
-    def __init__(self, time_points, data_bins):
+    def __init__(self, gene_name, mean_trend, std_trend, intpl_gex, time_points):
+        self.gene_name = gene_name
+        self.mean_trend = np.asarray([np.mean(data_bin) for data_bin in intpl_gex]) # interpolated dist mean
+        self.std_trend = np.asarray([np.std(data_bin) for data_bin in intpl_gex]) # interpolated dist std
+        self.data_bins = list(intpl_gex)
+        self.intpl_means = list(mean_trend) # actual weighted means
+        self.intpl_stds = list(std_trend) # actual weighted stds
         self.time_points = np.asarray(time_points)
-        self.data_bins = data_bins
         
-        self.mean_trends = [] 
-        for data_bin in data_bins:
-            data_bin = torch.tensor(np.asarray(data_bin) )
-            μ, C = MVG.compute_mml_estimates(data_bin, data_bin.shape[1], data_bin.shape[0]) 
-            self.mean_trends.append(μ)
-
-
-
-class Utils:
-    
-    def minmax_normalise(arr):
+        self.Y = np.asarray([np.asarray(x) for x in self.data_bins]).flatten() 
+        self.X = np.asarray([np.repeat(t,50) for t in self.time_points]).flatten() 
         
-        norm_arr = []
-        arr = np.asarray(arr)
-        arr_max = np.max(arr)
-        arr_min = np.min(arr)
-        for i in range(len(arr)):
-            norm_arr.append((arr[i] - arr_min )/(arr_max  - arr_min )) 
-        return norm_arr
-    
-
-    # computes distributional distance under the MML framework
-    def compute_mml_dist(ref_adata_subset,query_adata_subset, gene):
-
-        ref_data = np.asarray(ref_adata_subset[:,gene].X.todense()).flatten()
-        query_data = np.asarray(query_adata_subset[:,gene].X.todense()).flatten()
-        μ_S = np.mean(ref_data)
-        μ_T = np.mean(query_data)
-        σ_S =np.std(ref_data)
-        σ_T =np.std(query_data)
-        #print(μ_S,μ_T)
-        if(not np.any(ref_data)):
-            σ_S = 0.1
-        if(not np.any(query_data)):
-            σ_T = 0.1    
-
-        I_ref_model, I_refdata_g_ref_model = MyFunctions.run_dist_compute_v3(ref_data, μ_S, σ_S) 
-        I_query_model, I_querydata_g_query_model = MyFunctions.run_dist_compute_v3(query_data, μ_T, σ_T) 
-        I_ref_model, I_querydata_g_ref_model = MyFunctions.run_dist_compute_v3(query_data, μ_S, σ_S) 
-        I_query_model, I_refdata_g_query_model = MyFunctions.run_dist_compute_v3(ref_data, μ_T, σ_T) 
-
-        match_encoding_len1 = I_ref_model + I_querydata_g_ref_model + I_refdata_g_ref_model
-        match_encoding_len1 = match_encoding_len1/(len(query_data)+len(ref_data))
-        match_encoding_len2 = I_query_model + I_refdata_g_query_model + I_querydata_g_query_model
-        match_encoding_len2 = match_encoding_len2/(len(query_data)+len(ref_data))
-        match_encoding_len = (match_encoding_len1 + match_encoding_len2 )/2.0 
-
-        null = (I_ref_model + I_refdata_g_ref_model + I_query_model + I_querydata_g_query_model)/(len(query_data)+len(ref_data))
-        match_compression =   match_encoding_len - null 
-        #print(match_compression)
-        #sb.kdeplot(ref_data, fill=True)
-        #sb.kdeplot(query_data, fill=True)
-        return match_compression
-
-
-def refine_pseudotime(adata):
-    average_ctype_mean_times = {}
-
-    for ctype in np.unique(adata.obs.ANNOTATION_COMB):
-        average_ctype_mean_times[ctype] = np.mean(adata[adata.obs.ANNOTATION_COMB==ctype].obs.time)
-    
-    adata.obs['refined_time'] = adata.obs.time 
+    def plot_mean_trend(self, color='midnightblue'):
+        sb.lineplot(x= self.time_points, y=self.mean_trend, color=color, linewidth=4) 
+        
+    def plot_std_trend(self, color='midnightblue'):
+        sb.lineplot(x= self.time_points, y=self.std_trend, color=color, linewidth=4) 
     
-    ctype_Ls = {}
-    ctype_Us = {}
-
-    for ctype in np.unique(adata.obs.ANNOTATION_COMB):    
-        ctype_adata = adata[adata.obs.ANNOTATION_COMB==ctype]
-        Q1,Q3 = np.percentile( ctype_adata.obs.time, [25,75])
-        IQR = Q3-Q1
-        U = Q3+(1.5 * IQR)
-        L = Q1-(1.5 * IQR)
-        ctype_Ls[ctype] = L
-        ctype_Us[ctype] = U
-
-
-    for i in range(adata.shape[0]):
-        ctype = adata.obs.ANNOTATION_COMB[i]
-        if(adata.obs.time[i]<ctype_Ls[ctype] or adata.obs.time[i]>ctype_Us[ctype]):
-            adata.obs['refined_time'][i] = average_ctype_mean_times[ctype] 
-            
-    return Utils.minmax_normalise(np.asarray(adata.obs.refined_time))
-
+    
\ No newline at end of file
diff --git a/genes2genes/Utils.py b/genes2genes/Utils.py
new file mode 100644
index 0000000..7959f86
--- /dev/null
+++ b/genes2genes/Utils.py
@@ -0,0 +1,198 @@
+import numpy as np
+from scipy.sparse import csr_matrix
+from . import MyFunctions
+
+# UTIL FUNCTIONS
+def csr_mat_col_densify(csr_matrix, j): 
+        start_ptr = csr_matrix.indptr[j]
+        end_ptr = csr_matrix.indptr[j + 1]
+        data = csr_matrix.data[start_ptr:end_ptr]
+        dense_column = np.zeros(csr_matrix.shape[1])
+        dense_column[csr_matrix.indices[start_ptr:end_ptr]] = data
+        return dense_column
+
+    
+def minmax_normalise(arr):
+        
+        norm_arr = []
+        arr = np.asarray(arr)
+        arr_max = np.max(arr)
+        arr_min = np.min(arr)
+        for i in range(len(arr)):
+            norm_arr.append((arr[i] - arr_min )/(arr_max  - arr_min )) 
+        return norm_arr
+    
+
+# computes distributional distance under the MML framework
+def compute_mml_dist(ref_adata_subset,query_adata_subset, gene):
+
+        ref_data = np.asarray(ref_adata_subset[:,gene].X.todense()).flatten()
+        query_data = np.asarray(query_adata_subset[:,gene].X.todense()).flatten()
+        μ_S = np.mean(ref_data)
+        μ_T = np.mean(query_data)
+        σ_S =np.std(ref_data)
+        σ_T =np.std(query_data)
+        #print(μ_S,μ_T)
+        if(not np.any(ref_data)):
+            σ_S = 0.1
+        if(not np.any(query_data)):
+            σ_T = 0.1    
+
+        I_ref_model, I_refdata_g_ref_model = MyFunctions.run_dist_compute_v3(ref_data, μ_S, σ_S) 
+        I_query_model, I_querydata_g_query_model = MyFunctions.run_dist_compute_v3(query_data, μ_T, σ_T) 
+        I_ref_model, I_querydata_g_ref_model = MyFunctions.run_dist_compute_v3(query_data, μ_S, σ_S) 
+        I_query_model, I_refdata_g_query_model = MyFunctions.run_dist_compute_v3(ref_data, μ_T, σ_T) 
+
+        match_encoding_len1 = I_ref_model + I_querydata_g_ref_model + I_refdata_g_ref_model
+        match_encoding_len1 = match_encoding_len1/(len(query_data)+len(ref_data))
+        match_encoding_len2 = I_query_model + I_refdata_g_query_model + I_querydata_g_query_model
+        match_encoding_len2 = match_encoding_len2/(len(query_data)+len(ref_data))
+        match_encoding_len = (match_encoding_len1 + match_encoding_len2 )/2.0 
+
+        null = (I_ref_model + I_refdata_g_ref_model + I_query_model + I_querydata_g_query_model)/(len(query_data)+len(ref_data))
+        match_compression =   match_encoding_len - null 
+
+        return match_compression
+    
+    
+def sample_state(x):
+    x = np.cumsum(x)
+    rand_num = np.random.rand(1)
+   # print(rand_num)
+    if(rand_num<=x[0]):
+        return 0#'M'
+    elif(rand_num>x[0] and rand_num<=x[1]):
+        return 1#'W'
+    elif(rand_num>x[1] and rand_num<=x[2]):
+        return 2#'V'
+    elif(rand_num>x[2] and rand_num<=x[3]):
+        return 3#'D'
+    elif(rand_num>x[3] and rand_num<=x[4]):
+        return 4#'I'
+    
+
+def compute_alignment_area_diff_distance(A1, A2, S_len, T_len):
+
+        pi = np.arange(1, S_len+T_len+1) # skew diagonal indices 
+        A1_ = ""
+        for c in A1:
+            A1_ = A1_ + c
+            if(c=='M'):
+                A1_ = A1_ + 'X'
+        A2_ = ""
+        for c in A2:
+            A2_ = A2_ + c 
+            if(c=='M'):
+                A2_ = A2_ + 'X'
+
+        pi_1_k = 0
+        pi_2_k = 0
+        #print(0, pi_1_k , pi_2_k )
+        A1_al_index = 0
+        A2_al_index = 0
+        absolute_dist_sum = 0.0
+        for k in pi:
+            #print('k=',k, A1_al_index, A2_al_index)
+            A1_state = A1_[A1_al_index]
+            A2_state = A2_[A2_al_index]
+            if(A1_state=='I' or A1_state=='V'):
+                pi_1_k = pi_1_k - 1
+            elif(A1_state=='D' or  A1_state=='W'):
+                pi_1_k = pi_1_k + 1 
+            if(A2_state=='I' or A2_state=='V'):
+                pi_2_k = pi_2_k - 1   
+            elif(A2_state=='D' or  A2_state=='W'):
+                pi_2_k = pi_2_k + 1   
+  
+            absolute_dist_sum = absolute_dist_sum + np.abs(pi_1_k - pi_2_k)
+            #print('-----')
+            A1_al_index = A1_al_index + 1
+            A2_al_index = A2_al_index + 1 
+
+        return absolute_dist_sum
+
+def compute_chattergi_coefficient(y1,y2):
+        df = pd.DataFrame({'S':y1, 'T':y2})
+        df['rankS'] = df['S'].rank() 
+        df['rankT'] = df['T'].rank() 
+        # sort df by the S variable first
+        df = df.sort_values(by='rankS')
+        return 1 - ((3.0 * df['rankT'].diff().abs().sum())/((len(df)**2)-1)) 
+    
+    
+def plot_different_alignments(paths, S_len, T_len, ax, mat=[]): # pass alignment path coordinates
+        mat=[]
+      #  if(len(mat)==0):
+        for i in range(T_len+1):
+            mat.append(np.repeat(0,S_len+1)) 
+        sb.heatmap(mat, square=True,  cmap='viridis', ax=ax, vmin=0, vmax=0, cbar=False,xticklabels=False,yticklabels=False)
+        path_color = "orange"
+        
+        for path in paths: 
+            path_x = [p[0]+0.5 for p in path]
+            path_y = [p[1]+0.5 for p in path]
+            ax.plot(path_y, path_x, color=path_color, linewidth=3, alpha=0.5, linestyle='dashed') # path plot
+        plt.xlabel("S",fontweight='bold')
+        plt.ylabel("T",fontweight='bold')
+        
+    
+def check_alignment_clusters(n_clusters , cluster_ids, alignments, n_cols = 5, figsize= (10,6)): 
+        
+        clusters = [] 
+        S_len = alignments[0].fwd_DP.S_len
+        T_len = alignments[0].fwd_DP.T_len
+        
+        unique_cluster_ids = np.unique(cluster_ids)
+        n_rows = int(np.ceil(n_clusters/n_cols)) 
+        
+
+        fig, axs = plt.subplots(n_rows,n_cols, figsize = (20,n_rows*3)) # custom -- only for 20 clusters -- TODO change later
+        axs = axs.flatten() 
+        i = 0
+        k=1
+        for cluster_id in range(n_clusters): 
+            paths = []
+            cluster_genes = [] 
+            cluster_alignments = np.asarray(alignments)[cluster_ids == unique_cluster_ids[cluster_id]]
+            for a in cluster_alignments:
+                    paths.append(a.fwd_DP.alignment_path)
+                    #print(a.gene)
+                    cluster_genes.append(a.gene);# cluster_genes.append(a.gene)
+            clusters.append(list(np.unique(cluster_genes)) ) 
+
+            plot_different_alignments(paths, S_len, T_len, axs[cluster_id])
+            axs[cluster_id].set_title('Cluster-'+str(i) + ' | '+str(len(cluster_alignments)))
+
+            i=i+1
+            k=k+1
+        
+        fig.tight_layout()
+        n = n_cols * n_rows
+        i = 1
+        while(k<=n):
+            axs.flat[-1*i].set_visible(False) 
+            k = k+1
+            i=i+1        
+            
+        return clusters
+    
+
+# input: log1p gene expression vectors
+def compute_KLDivBasedDist(x,y):
+
+        # convert to probabilities
+        x = x.numpy()
+        y = y.numpy()
+        # convering backto counts+1
+        x = np.exp(x)
+        y = np.exp(y)
+        x = x/np.sum(x)
+        y = y/np.sum(y)
+
+        sum_term = 0.0
+        for i in range(len(x)):
+            sum_term += x[i]*(np.log(x[i]) - np.log(y[i]))
+    
+        return sum_term
+
+    
\ No newline at end of file
diff --git a/genes2genes/VisualUtils.py b/genes2genes/VisualUtils.py
index 2458c1a..7e55a0e 100644
--- a/genes2genes/VisualUtils.py
+++ b/genes2genes/VisualUtils.py
@@ -1,14 +1,8 @@
 import pandas as pd
 import seaborn as sb
 import matplotlib.pyplot as plt
-import anndata
 import numpy as np
-from adjustText import adjust_text
-from mpl_toolkits.axes_grid1.inset_locator import inset_axes
 from scipy.stats import zscore
-import colorcet as cc
-from optbinning import ContinuousOptimalBinning
-from scipy.stats import gaussian_kde
 import matplotlib.colors as mcolors
 import matplotlib
 import matplotlib.patches as mpatches
@@ -20,185 +14,30 @@
 vega_20 = ['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c', '#98df8a', '#d62728',
             '#ff9896', '#9467bd', '#c5b0d5', '#8c564b', '#c49c94', '#e377c2', '#f7b6d2',
             '#7f7f7f', '#c7c7c7', '#bcbd22', '#dbdb8d', '#17becf', '#9edae5',]
-
-class VisualUtils():
-    
-    def __init__(self, adata_ref, adata_query, cell_type_colname, S_len, T_len, titleS = 'Reference', titleT = 'Query', mode='comp', write_file=False, optimal_binning=True, PLOT=True):
-        self.write_file = write_file 
-        if(mode=='comp'):
-            self.titleS = titleS
-            self.titleT = titleT
-            
-            n_points = S_len
-            while(True):
-                # later to replace with a better optimal binning that gives exact number we request
-                print('# trying max n points for optimal binning =', n_points)
-                adata_ref, bm1 = self.pseudotime2bin_celltypes(adata_ref, n_points, optimal_binning=optimal_binning)
-                adata_query, bm2 = self.pseudotime2bin_celltypes(adata_query, n_points, optimal_binning=optimal_binning)
-                
-                if(not (len(bm1) == len(bm2))):
-                    n_points=n_points-1               
-                    if(n_points<=5):
-                        print('Consider equal length binning')
-                        break
-                else:
-                    print('====================================================')
-                    print('Optimal equal number of bins for R and Q = ',len(bm1))
-                    break
-                  
-            if(PLOT):
-                plt.subplots(1,2, figsize=(10,3))
-                x = list(adata_ref.obs.time)
-                plt.subplot(1,2,1)
-                sb.kdeplot(list(adata_ref.obs.time), color='ForestGreen' , fill=True)
-                for s in bm1: 
-                    plt.axvline(x=s, color='ForestGreen')
-                x = list(adata_query.obs.time)
-                plt.subplot(1,2,2)
-                sb.kdeplot(list(adata_query.obs.time),color='midnightblue', fill=True)
-                for s in bm2: 
-                    plt.axvline(x=s, color='midnightblue')
-                
-            meta1 = self.plot_cell_type_proportions(adata_ref, cell_type_colname, 'bin_ids',None,'tab20')
-            meta2 = self.plot_cell_type_proportions(adata_query, cell_type_colname, 'bin_ids',None,'tab20')
-            if(not optimal_binning):
-                meta1 = self.simple_interpolate(meta1,S_len)
-                meta2 = self.simple_interpolate(meta2,T_len)
-          #  meta1.loc[1] = meta1.loc[0] + meta1.loc[1]
-          #  meta2.loc[1] = meta2.loc[0] + meta2.loc[1]
-          #  meta1.loc[0] = np.repeat(0.0,len(np.unique(adata_ref.obs[cell_type_colname])) )
-          #  meta2.loc[0] = np.repeat(0.0,len(np.unique(adata_query.obs[cell_type_colname])))
-            
-            temp1 = pd.Series(np.repeat(0.0,len(np.unique(adata_ref.obs[cell_type_colname])) ))
-            temp1.index = meta1.columns
-            meta1 = pd.concat([pd.DataFrame(temp1).transpose(),meta1.loc[:]]).reset_index(drop=True)
-            
-            temp2 = pd.Series(np.repeat(0.0,len(np.unique(adata_query.obs[cell_type_colname])) ))
-            temp2.index = meta2.columns
-            meta2 = pd.concat([pd.DataFrame(temp2).transpose(),meta2.loc[:]]).reset_index(drop=True)
-            
-            self.metaS = meta1
-            self.metaT = meta2
-            
-            self.optimal_bining_S = bm1
-            self.optimal_bining_T = bm2
-         
-
-    def get_optimal_binning(self, time_var_arr, n_points):
-        x = time_var_arr
-        optb = ContinuousOptimalBinning(name='pseudotime', dtype="numerical", max_n_bins=n_points)
-        # this pacakge uses mixed integer programming based optimization to determine an optimal binning
-        #kde = gaussian_kde(x); #density_values = kde(x); #optb.fit(x, density_values)
-        optb.fit(x, x)
-        #sb.kdeplot(x, fill=True)
-        #for s in optb.splits: 
-        #    plt.axvline(x=s)
-        #print(len(optb.splits))
-        return optb.splits
-
-
-    # annotates cells with their respective bins based on interpolated pseudotime points
-    def pseudotime2bin_celltypes(self, adata, n_points, optimal_binning = True):
-
-            adata.obs['bin_ids'] = np.repeat(-1,adata.shape[0])
-            if(optimal_binning):
-                bin_margins = self.get_optimal_binning(np.asarray(adata.obs.time) , n_points)
-            else:
-                #bin_margins =  np.linspace(0,1,n_points+1)
-                bin_margins =  np.linspace(0,1,n_points)#[1:-1]
-            #print('computed the margins for ' + str(len(bin_margins)) +  ' bins')
-            #print(bin_margins)
-            bin_ids = []
-            k = 0 
-            for i in range(len(bin_margins)):
-                
-                if(i==0):
-                    logic = np.logical_and(adata.obs.time >= 0, adata.obs.time < bin_margins[i+1])
-                    #print('i==0', adata[logic].shape[0])
-                elif(i==len(bin_margins)-1):
-                    logic =  np.logical_and(adata.obs.time >= bin_margins[i], adata.obs.time <= 1.0)
-                else:
-                    logic =  np.logical_and(adata.obs.time >= bin_margins[i], adata.obs.time < bin_margins[i+1])
-                adata.obs['bin_ids'][logic] = i 
-            return adata, bin_margins
-
-    # for plotting or getting celltype freq counts per bin
-    def plot_cell_type_proportions(self, adata, cell_type_colname, covariate_colname, sorter, color_scheme_name="Spectral", plot=False):
-        meta = pd.DataFrame(np.vstack((adata.obs[cell_type_colname],adata.obs[covariate_colname])).transpose(),columns=[cell_type_colname,covariate_colname])
-        
-        meta['COUNTER'] = 1
-        meta = meta.groupby([covariate_colname,cell_type_colname])['COUNTER'].sum().unstack()
-        meta = meta.fillna(0)
-        #meta = meta.transpose()
-        #meta = meta.sort_values(by=covariate_colname, key=sorter)
-        if(plot):
-            p = meta.apply(lambda x: x*100/sum(x), axis=1).plot(kind='bar',stacked=True, color=sb.color_palette(color_scheme_name, 20), grid = False)
-            #p.legend(labels = ['not infected','infected'], loc='center left', bbox_to_anchor=(1.25, 0.5), ncol=1)
-            p.legend(loc='center left', bbox_to_anchor=(1.25, 0.5), ncol=1)
-        return meta
-    
     
-    def simple_interpolate(self,meta, n_points):
-        for i in range(n_points):
-            #print(k)
-            if(i not in meta.index):
-                k=i
-                while(k not in meta.index):
-                    k = k-1
-                _temp = meta.loc[k].copy()
-                _temp.name = i
-                meta = meta.append(_temp)
-        meta = meta.sort_index()
-        return meta
-    
-    
-    def get_celltype_composition_across_time(adata_ref, adata_query, n_points, ANNOTATION_COLNAME, optimal_binning=True
-                                        , order_S_legend=None, order_T_legend=None, PLOT=True, ref_cmap = None, query_cmap =None, plot_celltype_counts=False):
-        
-        a = sb.color_palette(cc.glasbey_hv, n_colors=3)
-        vega_20 = [
-            '#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c', '#98df8a', '#d62728',
-            '#ff9896', '#9467bd', '#c5b0d5', '#8c564b', '#c49c94', '#e377c2', '#f7b6d2',
-            '#7f7f7f', '#c7c7c7', '#bcbd22', '#dbdb8d', '#17becf', '#9edae5',
-        ]
-        
-        if(ref_cmap==None):
-            ref_cmap = vega_20
-        if(query_cmap==None):
-            query_cmap = vega_20
-        
-        vs = VisualUtils(adata_ref, adata_query, cell_type_colname = ANNOTATION_COLNAME, 
-                        S_len=n_points, T_len=n_points, titleS='Reference', titleT='Query',
-                        write_file=False, optimal_binning=optimal_binning, PLOT=PLOT)
-        
-        if(plot_celltype_counts):
-        
-            ax = vs.metaS.apply(lambda x: x, axis=1).plot(kind='bar',stacked=True,color=ref_cmap, grid = False, legend=True, width=0.7,align='edge',figsize=(10,3))
-            ax.legend(bbox_to_anchor=(1.1, 1.44))
-            if(order_S_legend is not None):
-                handles, labels = ax.get_legend_handles_labels()
-                ax.legend(handles=[handles[idx] for idx in order_S_legend],labels=[labels[idx] for idx in order_S_legend],bbox_to_anchor=(1.0, 1.0))
-
-            ax = vs.metaT.apply(lambda x: x, axis=1).plot(kind='bar',stacked=True,color=query_cmap, grid = False, legend=True, width=0.7,align='edge',figsize=(10,3))
-            ax.legend(bbox_to_anchor=(1.1, 1.05))
-            if(order_T_legend is not None):
-                handles, labels = ax.get_legend_handles_labels()
-                ax.legend(handles=[handles[idx] for idx in order_T_legend],labels=[labels[idx] for idx in order_T_legend],bbox_to_anchor=(1.0, 1.0))
-            
-        vs.metaS.apply(lambda x: x*100/sum(x), axis=1).plot(kind='bar',stacked=True,color=ref_cmap, grid = False, legend=False, width=0.7,align='edge',figsize=(10,1))
-        plt.axis('off')
-        vs.metaT.apply(lambda x: x*100/sum(x), axis=1).plot(kind='bar',stacked=True,color=query_cmap, grid = False, legend=False, width=0.7,align='edge',figsize=(10,1))
-        plt.axis('off')
+def plot_celltype_barplot(adata, n_bins, annotation_colname, joint_cmap, plot_cell_counts = False, legend=False):
 
-        return vs 
-    
+        if(plot_cell_counts):
+            normalize = False
+        else:
+            normalize = 'columns'
+
+        vec = adata.obs.time
+        bin_edges = np.linspace(0, 1, num=n_bins)  
+        bin_ids = np.digitize(vec, bin_edges, right=False) # use right=True if we don't need 1.0 cell to always be a single last bin 
+        adata.obs['bin_ids'] = bin_ids
+        tmp = pd.crosstab(adata.obs[annotation_colname],adata.obs['bin_ids'], normalize=normalize).T.plot(kind='bar', stacked=True,        
+                                                                                                 color=joint_cmap,grid = False, legend=False, width=0.7,align='edge',figsize=(9,1))
+        if(legend):    
+            tmp.legend(title='Cell-type annotations', bbox_to_anchor=(1.5, 1.02),loc='upper right')
+        plt.axis('off')
     
-    def visualize_gene_alignment(self, alignment, cmap=None):
+def visualize_gene_alignment(alignment, adata_ref, adata_query, annotation_colname, cmap=None):
 
             if(isinstance(alignment,Main.AligmentObj )):
                 alignment = alignment.alignment_str
 
-            matched_points_S, matched_points_T = self.get_matched_time_points(alignment)
+            matched_points_S, matched_points_T = get_matched_time_points(alignment)
 
             fig = plt.figure(figsize=(4,2))
             heights = [1, 1, 1] 
@@ -211,8 +50,13 @@ def visualize_gene_alignment(self, alignment, cmap=None):
                 cmap = vega_20
 
             plt.subplot(3,1,1)
-            self.metaS.apply(lambda x: x*100/sum(x), axis=1).plot(kind='bar',stacked=True,color=cmap, grid = False, legend=False, width=0.7, ax=ax1)
-            self.metaT.apply(lambda x: x*100/sum(x), axis=1).plot(kind='bar',stacked=True,color=cmap, grid = False, legend=False, width=0.7,ax=ax3)
+            
+            metaS = pd.crosstab(adata_ref.obs.bin_ids, adata_ref.obs[annotation_colname])
+            metaS.apply(lambda x: x*100/sum(x), axis=1).plot(kind='bar',stacked=True,color=cmap, grid = False, legend=False, width=0.7, ax=ax1)
+            
+            metaT = pd.crosstab(adata_query.obs.bin_ids, adata_query.obs[annotation_colname])
+            metaT.apply(lambda x: x*100/sum(x), axis=1).plot(kind='bar',stacked=True,color=cmap, grid = False, legend=False, width=0.7,ax=ax3)
+            
             plt.subplot(3,1,2)
             for i in range(len(matched_points_S)):
                         S_timebin = matched_points_S[i]
@@ -233,135 +77,13 @@ def set_grid_off(ax):
                 ax.grid(False)
 
             set_grid_off(ax1); set_grid_off(ax2); set_grid_off(ax3); 
-            ax1.set_ylabel('Ref', rotation=0)
-            ax3.set_ylabel('Query',rotation=0)
+            ax1.set_ylabel('Ref', rotation=90)
+            ax3.set_ylabel('Query',rotation=90)
             fig.text(0.5, -0.05, 'Pseudotime bins with cell type composition', ha='center')
             ax1.set_title('Alignment w.r.t cell type compositions')
 
-    
-    
-    def plot_comprehensive_alignment_landscape_plot(self, aligner, gene = None, order_S_legend=None, order_T_legend=None, paths_to_display=None, cmap='viridis'):
-
-        if(gene!=None):
-            al_obj = aligner.results_map[gene]
-            if(paths_to_display==None):
-                al_obj.landscape_obj.alignment_path.append([0,0])
-                paths_to_display=[al_obj.landscape_obj.alignment_path]
-            match_points_S = np.unique(al_obj.match_points_S) + 1
-            match_points_T = np.unique(al_obj.match_points_T) + 1
-            landscape_mat = pd.DataFrame(al_obj.landscape_obj.L_matrix)
-        else:
-            al_str, path = self.compute_overall_alignment(aligner)
-            match_points_S, match_points_T = self.get_matched_time_points(al_str)
-            match_points_S = np.unique(match_points_S) + 1
-            match_points_T = np.unique(match_points_T) + 1
-            if(paths_to_display==None):
-                paths_to_display=[path]
-            landscape_mat = aligner.get_pairwise_match_count_mat()
-                
-        nS_points=len(aligner.results[0].S.time_points)
-        nT_points=len(aligner.results[0].T.time_points)
-
-        fig, ((ax3, ax1, cbar_ax), (dummy_ax1, ax2, dummy_ax2)) = plt.subplots(nrows=2, ncols=3, figsize=(9*2, 6*2), sharex='col', sharey='row',
-                                                                               gridspec_kw={'height_ratios': [2,1], 'width_ratios': [0.5, 1, 0.5]})
-        g = sb.heatmap(landscape_mat.transpose(), xticklabels=True, yticklabels=True, cmap=cmap, cbar_ax=cbar_ax,  ax=ax1, cbar=False)
-        g.tick_params( labelsize=10, labelbottom = True, bottom=True, top = False)#, labeltop=True)
-        ax1.set_xlabel('pseudotime')
-        x_ticks = np.asarray(range(0,nS_points+1)) 
-        y_ticks = np.asarray(range(0,nT_points+1)) 
-
-        # first barplot (Reference) --- left horizontal barplot
-        p= self.metaS.apply(lambda x: x*100/sum(x), axis=1).plot(kind='barh',stacked=True, title=self.titleS ,color=sb.color_palette('deep', 20), grid = False, ax=ax3,legend=False, width=0.7,align='edge')
-        for patch in p.patches:
-            if(patch.get_y() in match_points_S):
-                p.annotate(str('M'), (100, patch.get_y() * 1.005)  )      
-        handles, labels = ax3.get_legend_handles_labels()
-        for spine in p.spines:
-            p.spines[spine].set_visible(False)
-        if(order_S_legend!=None):
-            dummy_ax1.legend(handles=[handles[idx] for idx in order_S_legend],labels=[labels[idx] for idx in order_S_legend])
-        else:
-            dummy_ax1.legend(handles,labels)
-        # second barplot (Query) --- bottom barplot
-        p = self.metaT.apply(lambda x: x*100/sum(x), axis=1).plot(kind='bar',stacked=True, title=self.titleT, color=sb.color_palette('deep', 20), grid = False, ax=ax2, legend=False,width=0.7,align='edge')
-        for patch in p.patches:
-           # print(patch.get_height())
-            if(patch.get_x() in match_points_T):
-                p.annotate(str('M'), (patch.get_x() * 1.005, 100) )
-        handles, labels = ax2.get_legend_handles_labels()
-        for spine in p.spines:
-            p.spines[spine].set_visible(False)
-        if(order_T_legend!=None):
-            dummy_ax2.legend(handles=[handles[idx] for idx in order_T_legend],labels=[labels[idx] for idx in order_T_legend],loc='upper left')
-        else:
-            dummy_ax2.legend(handles,labels, loc='upper left')
-        dummy_ax1.axis('off')
-        dummy_ax2.axis('off')
-        cbar_ax.axis('off')
-        if(paths_to_display!=None): # for max 2 paths
-            styles = ['solid', 'dashed']; i = 0 
-            for path in paths_to_display: 
-                path_x = [p[0]+0.5 for p in path]
-                path_y = [p[1]+0.5 for p in path]
-                ax1.plot(path_x, path_y, color='black', linewidth=9, alpha=1.0, linestyle=styles[i]) # path plot
-                i=i+1
-        ax1.axis(ymin=0, ymax=nS_points+1, xmin=0, xmax=nT_points+1)
-        plt.tight_layout()
-       # plt.show()
-        
-        if(self.write_file):
-            plt.savefig('comprehensive_alignment_landscape_plot.pdf',bbox_inches = 'tight')
-        
-    def plot_match_stat_across_all_alignments(self, aligner):
-            
-            nS_points = len(aligner.results[0].S.time_points)
-            nT_points = len(aligner.results[0].T.time_points)
-            S_line = np.repeat(0, nS_points+1)
-            T_line = np.repeat(0, nT_points+1)
-
-            for a in aligner.results:
-                matchS = a.match_points_S+1
-                matchT = a.match_points_T+1
-                for i in range(len(matchS)):
-                    S_line[matchS[i]] =  S_line[matchS[i]] + 1
-                    T_line[matchT[i]] =  T_line[matchT[i]] + 1
-
-            S_line = S_line/np.sum(S_line)*100
-            T_line = T_line/np.sum(T_line)*100
-
-            plt.subplots(2,2,figsize=(17,6))
-            plt.subplot(2,2,1)
-            sb.barplot(np.asarray(range(nS_points+1)) , np.cumsum(S_line), color='midnightblue') 
-            plt.ylabel('cumulative match percentage')
-            plt.subplot(2,2,3)
-            sb.barplot(np.asarray(range(nT_points+1)) , np.cumsum(T_line), color='forestgreen') 
-            plt.ylabel('cumulative match percentage')
-            plt.xlabel('pseudotime bin')
-            plt.subplot(2,2,2)
-            sb.barplot(np.asarray(range(nS_points+1)) , S_line, color='midnightblue') 
-            plt.ylabel('match percentage')
-            plt.subplot(2,2,4)
-            sb.barplot(np.asarray(range(nT_points+1)) , T_line, color='forestgreen') 
-            plt.ylabel('match percentage')
-            plt.xlabel('pseudotime bin')
-          #  plt.show()
-            
-            if(self.write_file):
-                plt.savefig('match_stat_plot_across_all_alignments.pdf',bbox_inches = 'tight')
-
-#    def plot_alignment_path_on_given_matrix(mat, paths, cmap='viridis',annot=True):
-#        fig,ax = plt.subplots(1,1, figsize=(7,7))
-#        sb.heatmap(mat, square=True,  cmap='viridis', ax=ax, cbar=True, annot=annot,fmt='g')  
-#        for path in paths: 
-#            path_x = [p[0]+0.5 for p in path]
-#            path_y = [p[1]+0.5 for p in path]
-#            ax.plot(path_y, path_x, color='black', linewidth=6) # path plot
-#        plt.xlabel("PAM (Reference)",fontweight='bold')
-#        plt.ylabel("LPS (Query)",fontweight='bold')
-#        ax.xaxis.tick_top() # x axis on top
-#        ax.xaxis.set_label_position('top')
-
-    def get_matched_time_points(self, alignment_str):
+
+def get_matched_time_points(alignment_str):
         j = 0; i = 0
         FLAG = False
         matched_points_S = [] 
@@ -414,160 +136,7 @@ def get_matched_time_points(self, alignment_str):
             prev_c = c
         assert(len(matched_points_S) == len(matched_points_T)) 
         return matched_points_S, matched_points_T
-        
-            # computes simple DP alignment (using match score = pairwise total match count frequency) across all gene-level alignments 
-    # gap score is taken as penalising 8% of the total number of tested genes => so that it controls the matching based on the number of 
-    # total matches (i.e. it controls the degree of significant matching) 
-    def compute_overall_alignment(self, aligner, plot=False, GAP_SCORE = None):
-                
-                if(GAP_SCORE==None):
-                    GAP_SCORE= -len(aligner.gene_list)*0.08
-
-                mat = aligner.get_pairwise_match_count_mat()
-                if(plot):
-                    sb.heatmap(mat, cmap='viridis', square=True)
-
-                # DP matrix initialisation 
-                opt_cost_M = []
-                for i in range(mat.shape[0]):
-                    opt_cost_M.append(np.repeat(0.0, mat.shape[1]))
-                opt_cost_M = np.matrix(opt_cost_M) 
-                # backtracker matrix initialisation 
-                tracker_M = []
-                for i in range(mat.shape[0]):
-                    tracker_M.append(np.repeat(0.0, mat.shape[1]))
-                tracker_M = np.matrix(tracker_M) 
-                for i in range(1,mat.shape[0]):
-                    tracker_M[i,0] = 2
-                for j in range(1,mat.shape[1]):
-                    tracker_M[0,j] = 1
-
-                # running DP
-                for j in range(1,mat.shape[1]):
-                    for i in range(1,mat.shape[0]):
-                        m_dir = opt_cost_M[i-1,j-1] + mat.loc[i,j]
-                        d_dir = opt_cost_M[i,j-1] +  GAP_SCORE
-                        i_dir = opt_cost_M[i-1,j] +  GAP_SCORE
-                       # w_dir = opt_cost_M[i,j-1] +  mat.loc[i,j]
-                       # v_dir = opt_cost_M[i-1,j] +  mat.loc[i,j]
-
-                        a = max([m_dir, d_dir, i_dir])  # ,w_dir, v_dir]) 
-                        if(a==d_dir):
-                            opt = d_dir
-                            dir_tracker = 1
-                        elif(a==i_dir):
-                            opt =i_dir
-                            dir_tracker = 2
-                        elif(a==m_dir):
-                            opt = m_dir
-                            dir_tracker = 0
-                       # elif(a==w_dir):
-                      #      opt = w_dir
-                       #     dir_tracker = 3
-                       # elif(a==v_dir):
-                       #     opt = v_dir
-                       #     dir_tracker = 4
-                        #if(i==1 and j==4):
-                        #    print(a, opt_cost_M[i-1,j-1], mat.loc[i,j], opt_cost_M[i,j-1] ,opt_cost_M[i-1,j]  )
-
-                        opt_cost_M[i,j] = opt
-                        tracker_M[i,j] = dir_tracker     
-              #  print(tracker_M)
-
-                # backtracking
-                i = mat.shape[0]-1
-                j = mat.shape[1]-1
-                alignment_str = ''
-                tracked_path = []
-                while(True):
-                  #  print([i,j])
-                    tracked_path.append([i,j])
-                    if(tracker_M[i,j]==0):
-                        alignment_str = 'M' + alignment_str
-                        i = i-1
-                        j = j-1
-                    elif(tracker_M[i,j]==1):
-                        alignment_str = 'D' + alignment_str
-                        j = j-1
-                    elif(tracker_M[i,j]==2):
-                        alignment_str = 'I' + alignment_str
-                        i = i-1 
-                 #   elif(tracker_M[i,j]==3):
-                 #       alignment_str = 'W' + alignment_str
-                 #       j = j-1
-                 #   elif(tracker_M[i,j]==4):
-                 #       alignment_str = 'V' + alignment_str
-                 #       i = i-1
-                    if(i==0 and j==0) :
-                        break
-                tracked_path.append([0,0])
-                return alignment_str, tracked_path#, opt_cost_M, tracker_M
-
-            
-
-            
-
-def plot_heatmaps(mat_ref,mat_query,pathway_name, IGS, cluster=False):
-    
-    if(cluster):
-        g=sb.clustermap(mat_ref, figsize=(0.4,0.4), col_cluster=False) 
-        gene_order = g.dendrogram_row.reordered_ind
-        df = pd.DataFrame(g.data2d) 
-        df.index = IGS.SETS[pathway_name][gene_order]
-    else:
-        df=mat_ref
     
-    plt.subplots(1,2,figsize=(8,12))
-    max_val = np.max([np.max(mat_ref),np.max(mat_query)]) 
-    min_val = np.min([np.min(mat_ref),np.min(mat_query)]) 
-    plt.subplot(1,2,1)
-    ax=sb.heatmap(df, vmax=max_val,vmin=min_val, cbar_kws = dict(use_gridspec=False,location="top")) 
-    plt.title('Reference')
-    ax.yaxis.set_label_position("left")
-    for tick in ax.get_yticklabels():
-        tick.set_rotation(360)
-    plt.subplot(1,2,2)
-    if(cluster):
-        mat_query = mat_query.loc[IGS.SETS[pathway_name][gene_order]] 
-    ax = sb.heatmap(mat_query,vmax=max_val,  vmin=min_val,cbar_kws = dict(use_gridspec=False,location="top"), yticklabels=False) 
-    plt.title('Query')
-    #plt.show()
-
-
-# smoothened/interpolated mean trends + Z normalisation 
-def plot_mean_trend_heatmaps(pathway_name, IGS, aligner, cluster=False):
-    S_mat = []
-    T_mat = []
-    S_zmat = []
-    T_zmat = []
-
-    for gene in IGS.SETS[pathway_name]:
-
-        fS = pd.DataFrame([aligner.results_map[gene].S.mean_trend, np.repeat('Ref', len(aligner.results_map[gene].S.mean_trend))]).transpose()
-        fT = pd.DataFrame([aligner.results_map[gene].T.mean_trend, np.repeat('ATO', len(aligner.results_map[gene].T.mean_trend))]).transpose()
-        f = pd.concat([fS,fT])
-        f[0] = np.asarray(f[0], dtype=np.float64)
-        from scipy.stats import zscore
-        f['z_normalised'] = zscore(f[0])
-        S_mat.append(np.asarray(f[f[1]=='Ref'][0]))
-        T_mat.append(np.asarray(f[f[1]=='ATO'][0]))    
-        S_zmat.append(np.asarray(f[f[1]=='Ref']['z_normalised']))
-        T_zmat.append(np.asarray(f[f[1]=='ATO']['z_normalised']))  
-    S_mat = pd.DataFrame(S_mat)
-    T_mat = pd.DataFrame(T_mat)
-    S_zmat = pd.DataFrame(S_zmat)
-    T_zmat = pd.DataFrame(T_zmat)
-    
-    S_mat.index = IGS.SETS[pathway_name]
-    T_mat.index = IGS.SETS[pathway_name]
-    S_zmat.index = IGS.SETS[pathway_name]
-    T_zmat.index = IGS.SETS[pathway_name]
-    
-    print('Interpolated mean trends')
-    plot_heatmaps(S_mat, T_mat, pathway_name, IGS, cluster=cluster)
-    print('Z-normalised Interpolated mean trends')
-    return plot_heatmaps(S_zmat, T_zmat, pathway_name, IGS, cluster=cluster)
-
 
 def plotTimeSeries(gene, aligner, plot_cells = False, plot_mean_trend= False):
     
@@ -581,10 +150,14 @@ def plotTimeSeries(gene, aligner, plot_cells = False, plot_mean_trend= False):
         g = sb.scatterplot(x=aligner.query_time, y=np.asarray(aligner.query_mat[al_obj.gene]), alpha=0.7, color = 'midnightblue', legend=False,linewidth=0.3, s=20)  
         plt.title('Query')
         plt.ylim([min_val-0.5,max_val+0.5])
+        plt.xlabel('Pseudotime')
+        plt.ylabel('Gene expression')
         plt.subplot(1,3,3)
         g = sb.scatterplot(x=aligner.ref_time, y=np.asarray(aligner.ref_mat[al_obj.gene]), color = 'forestgreen', alpha=0.7, legend=False,linewidth=0.3,s=20 ) 
         plt.title('Reference')
         plt.ylim([min_val-0.5,max_val+0.5])
+        plt.xlabel('Pseudotime')
+        plt.ylabel('Gene expression')
 
 def plotTimeSeriesAlignment(gene, aligner):  
     
@@ -593,7 +166,7 @@ def plotTimeSeriesAlignment(gene, aligner):
         sb.scatterplot(x=al_obj.T.X, y=al_obj.T.Y, color = 'midnightblue' ,alpha=0.05, legend=False)#, label ='Query') 
         al_obj.plot_mean_trends() 
         plt.title(al_obj.gene)
-        plt.xlabel('pseudotime')
+        plt.xlabel('Pseudotime')
         plt.ylabel('Gene expression')
         plt.axis('off')
         
@@ -642,14 +215,6 @@ def plot_alignment_path_on_given_matrix(mat, paths, cmap='viridis'):
         ax.xaxis.tick_top() # x axis on top
         ax.xaxis.set_label_position('top')
 
-def plot_alignment_clustermap():
-    
-    p = sb.clustermap(aligner.DistMat,cmap='viridis', figsize=(10,10))
-    p.ax_heatmap.set_xticklabels(p.ax_heatmap.get_xmajorticklabels(), fontsize = 12)
-    p.ax_heatmap.set_yticklabels(p.ax_heatmap.get_ymajorticklabels(), fontsize = 12)
-    p.ax_row_dendrogram.set_visible(False)
-
-
 def plot_distmap_with_clusters(aligner, cmap=None, vmin = 0.0, vmax = 1.0, genes2highlight=None):
     
     godsnot_64 = [
@@ -731,8 +296,6 @@ def plot_distmap_with_clusters(aligner, cmap=None, vmin = 0.0, vmax = 1.0, genes
     ax.axis('off'); ax.set_xticks([]); ax.set_yticks([]); 
 
     
-
-
 def resolve(regions):
     for i in range(len(regions)):
         x = list(regions[i]); x[1] = x[1]-1; regions[i] = x
@@ -786,10 +349,71 @@ def plot_any_legend(text2color_map):
     ax.legend(handles=legend_patches, loc='center')
     ax.axis('off'); ax.set_xticks([]); ax.set_yticks([]); 
 
-def show_gene_alignment(gene, aligner, vs, cmap=None):
-    vs.visualize_gene_alignment(aligner.results_map[gene].alignment_str, cmap=cmap)
+def show_gene_alignment(gene, aligner,  adata_ref, adata_query, annotation_colname, cmap=None):
+    visualize_gene_alignment(aligner.results_map[gene].alignment_str,  adata_ref, adata_query, annotation_colname, cmap=cmap)
     plotTimeSeries(gene, aligner, plot_cells=True)
     aligner.results_map[gene].alignment_str
     print(color_al_str(aligner.results_map[gene].alignment_str)) 
     print('Optimal alignment cost:', round(aligner.results_map[gene].fwd_DP.opt_cost,3),'nits')
     print('Alignment similarity percentage:', aligner.results_map[gene].match_percentage,'%' )
+
+    
+# smoothened/interpolated mean trends + Z normalisation 
+def plot_mean_trend_heatmaps(aligner, GENE_LIST, pathway_name, cluster=False, FIGSIZE=(14,7)):
+    S_mat = []
+    T_mat = []
+    S_zmat = []
+    T_zmat = []
+
+    for gene in GENE_LIST:
+
+        fS = pd.DataFrame([aligner.results_map[gene].S.mean_trend, np.repeat('Ref', len(aligner.results_map[gene].S.mean_trend))]).transpose()
+        fT = pd.DataFrame([aligner.results_map[gene].T.mean_trend, np.repeat('Organoid', len(aligner.results_map[gene].T.mean_trend))]).transpose()
+        f = pd.concat([fS,fT])
+        f[0] = np.asarray(f[0], dtype=np.float64)
+        f['z_normalised'] = zscore(f[0])
+        S_mat.append(np.asarray(f[f[1]=='Ref'][0]))
+        T_mat.append(np.asarray(f[f[1]=='Organoid'][0]))    
+        S_zmat.append(np.asarray(f[f[1]=='Ref']['z_normalised']))
+        T_zmat.append(np.asarray(f[f[1]=='Organoid']['z_normalised']))  
+    S_mat = pd.DataFrame(S_mat)
+    T_mat = pd.DataFrame(T_mat)
+    S_zmat = pd.DataFrame(S_zmat)
+    T_zmat = pd.DataFrame(T_zmat)
+    
+    S_mat.index = GENE_LIST  
+    T_mat.index = GENE_LIST  
+    S_zmat.index = GENE_LIST 
+    T_zmat.index = GENE_LIST 
+    
+    print('- Plotting z-normalised interpolated mean trends')
+    plot_heatmaps(S_zmat, T_zmat, GENE_LIST, pathway_name,cluster=cluster, FIGSIZE=FIGSIZE)
+
+def plot_heatmaps(mat_ref,mat_query,GENE_LIST, pathway_name, cluster=False, FIGSIZE=(14,7), write_file=False):
+    
+    if(cluster):
+        g=sb.clustermap(mat_ref, figsize=(0.4,0.4), col_cluster=False, cbar_pos=None) 
+        gene_order = g.dendrogram_row.reordered_ind
+        df = pd.DataFrame(g.data2d) 
+        df.index = GENE_LIST[gene_order]
+    else:
+        df=mat_ref
+    plt.close()
+    
+    plt.subplots(1,2) #8,14/7 ******************************************************
+    max_val = np.max([np.max(mat_ref),np.max(mat_query)]) 
+    min_val = np.min([np.min(mat_ref),np.min(mat_query)]) 
+    plt.subplot(1,2,1)
+    ax=sb.heatmap(df, vmax=max_val,vmin=min_val, cbar_kws = dict(use_gridspec=False,location="top")) 
+    plt.title('Reference')
+    ax.yaxis.set_label_position("left")
+    for tick in ax.get_yticklabels():
+        tick.set_rotation(360)
+    plt.subplot(1,2,2)
+    if(cluster):
+        mat_query = mat_query.loc[GENE_LIST[gene_order]] 
+    ax = sb.heatmap(mat_query,vmax=max_val,  vmin=min_val,cbar_kws = dict(use_gridspec=False,location="top"), xticklabels=True, yticklabels=False) 
+    plt.title('Query')
+    if(write_file):
+        plt.savefig(pathway_name+'_heatmap.png', bbox_inches='tight')
+    plt.show()
\ No newline at end of file
diff --git a/genes2genes/__init__.py b/genes2genes/__init__.py
index 79c9e1d..7f0c4b2 100644
--- a/genes2genes/__init__.py
+++ b/genes2genes/__init__.py
@@ -1,14 +1,12 @@
-"""Aligning transcriptomic trajectories of single-cell reference and query systems"""
-__version__ = "0.1.0"
+"""A tool for aligning gene expression trajectories of single-cell reference and query systems"""
+__version__ = "0.2.0"
 
 from . import AlignmentDistMan
-from . import BatchAnalyser
 from . import ClusterUtils
 from . import Main
-from . import MVG
 from . import MyFunctions
-from . import OrgAlign
-from . import PathwayAnalyserV2
-from . import SimulationExperimentAnalyser
+from . import OrgAlign 
+from . import PathwayAnalyser
 from . import TimeSeriesPreprocessor
-from . import VisualUtils
+from . import Utils
+from . import VisualUtils
\ No newline at end of file
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diff --git a/images/cell_numbers_vs_approx_time_PAM_LPS_G2G_alignment.png b/images/cell_numbers_vs_approx_time_PAM_LPS_G2G_alignment.png
new file mode 100644
index 0000000..7dd1e7b
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diff --git a/images/n_interpolation_points_vs_time_PAM_LPS_G2G_alignment.png b/images/n_interpolation_points_vs_time_PAM_LPS_G2G_alignment.png
new file mode 100644
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diff --git a/notebooks/.ipynb_checkpoints/Supplementary_notebook1-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Supplementary_notebook1-checkpoint.ipynb
new file mode 100644
index 0000000..3158549
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/Supplementary_notebook1-checkpoint.ipynb
@@ -0,0 +1,248 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "opening-punishment",
+   "metadata": {},
+   "source": [
+    "# Supplementary Notebook 1: Checking total time of alignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "humanitarian-billion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import anndata\n",
+    "import numpy as np\n",
+    "import seaborn as sb\n",
+    "import numpy as np\n",
+    "import platform\n",
+    "import warnings\n",
+    "import time\n",
+    "import matplotlib.pyplot as plt\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "from genes2genes import Main\n",
+    "from genes2genes import VisualUtils\n",
+    "from genes2genes import ClusterUtils\n",
+    "from genes2genes import TimeSeriesPreprocessor\n",
+    "from genes2genes import PathwayAnalyser\n",
+    "from genes2genes import VisualUtils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "virgin-peoples",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "89 genes\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_dir = 'data/'\n",
+    "adata_ref = anndata.read_h5ad(input_dir + 'adata_pam_local.h5ad') # Reference dataset\n",
+    "adata_query = anndata.read_h5ad(input_dir +'adata_lps_local.h5ad') # Query dataset\n",
+    "# define the gene list to align\n",
+    "gene_list = adata_ref.var_names \n",
+    "print(len(gene_list),'genes')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "damaged-israeli",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 179 × 89\n",
+       "    obs: 'time'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata_ref"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "noble-apparatus",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 290 × 89\n",
+       "    obs: 'time'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata_query"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "proof-battlefield",
+   "metadata": {},
+   "source": [
+    "### A simple experiment to check the number of interpolation time points vs. approximate time taken for alignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "loved-mistake",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "times = []\n",
+    "for n_bins in range(5,50):\n",
+    "    s = time.time()\n",
+    "    aligner = Main.RefQueryAligner(adata_ref, adata_query, gene_list, n_bins)\n",
+    "    aligner.align_all_pairs() \n",
+    "    t = time.time()\n",
+    "    times.append(t-s) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "worst-raleigh",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sb.lineplot(x=range(5,50), y=times, linewidth=3, marker = 'o')\n",
+    "plt.xlabel('Number of interpolation points')\n",
+    "plt.ylabel('Time (seconds)')\n",
+    "plt.title('PAM vs LPS alignment (179 reference cells & 290 query cells in terms of 89 genes)')\n",
+    "#plt.savefig('n_interpolation_points_vs_time_PAM_LPS_G2G_alignment.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hydraulic-arctic",
+   "metadata": {},
+   "source": [
+    "### A simple experiment to check the ref and query dataset size (number of cells) vs the approximate time taken for alignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "aggregate-brake",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata_ref_mult = [adata_ref]\n",
+    "\n",
+    "temp = adata_ref\n",
+    "for i in range(0,10):\n",
+    "    temp = anndata.concat([temp, temp])\n",
+    "    adata_ref_mult.append(temp)\n",
+    "adata_query_mult = [adata_query]\n",
+    "temp = adata_query\n",
+    "for i in range(0,10):\n",
+    "    temp = anndata.concat([temp, temp])\n",
+    "    adata_query_mult.append(temp)\n",
+    "\n",
+    "r_size = []\n",
+    "q_size = []\n",
+    "for a in  adata_ref_mult:\n",
+    "    r_size.append(a.shape[0])\n",
+    "for a in  adata_query_mult:\n",
+    "    q_size.append(a.shape[0])\n",
+    "\n",
+    "times = []\n",
+    "n_bins = 14\n",
+    "for i in range(len(adata_ref_mult)):\n",
+    "    R = adata_ref_mult[i]\n",
+    "    Q = adata_query_mult[i]\n",
+    "    s = time.time()\n",
+    "    aligner = Main.RefQueryAligner(R, Q, gene_list, n_bins)\n",
+    "    aligner.align_all_pairs() \n",
+    "    t = time.time()\n",
+    "    times.append(t-s) \n",
+    "\n",
+    "plt.subplots(1,2, figsize=(10,3))\n",
+    "plt.subplot(1,2,1)\n",
+    "sb.lineplot(x=range(len(adata_ref_mult)), y=times, linewidth=3, marker = 'o')\n",
+    "plt.xlabel('k (for aligning genes of $N_{R}.2^k$ cells and $N_{Q}.2^k$ cells)')\n",
+    "plt.ylabel('Time (seconds)')\n",
+    "plt.xticks(range(0,11))\n",
+    "plt.title('Total alignment time of 89 genes')\n",
+    "plt.subplot(1,2,2)\n",
+    "sb.lineplot(x=r_size, y=q_size, linewidth=3, marker = 'o', color='orange')\n",
+    "plt.title('# of cells')\n",
+    "plt.xlabel('Number of reference cells')\n",
+    "plt.ylabel('Number of query cells')\n",
+    "plt.xticks(rotation =10)\n",
+    "plt.tight_layout() \n",
+    "plt.savefig('cell_numbers_vs_approx_time_PAM_LPS_G2G_alignment.png')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "g2g_installed_env",
+   "language": "python",
+   "name": "g2g_installed_env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/.ipynb_checkpoints/Supplementary_notebook2-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Supplementary_notebook2-checkpoint.ipynb
new file mode 100644
index 0000000..8c537d1
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/Supplementary_notebook2-checkpoint.ipynb
@@ -0,0 +1,404 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "municipal-surgeon",
+   "metadata": {},
+   "source": [
+    "# Supplementary Notebook 2: Re-running T-cell case studyhealthy/IPF case study with G2G v0.2.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "studied-springfield",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import anndata\n",
+    "import numpy as np\n",
+    "import seaborn as sb\n",
+    "import numpy as np\n",
+    "import warnings\n",
+    "import scanpy as sc\n",
+    "import matplotlib.pyplot as plt\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "from genes2genes import Main\n",
+    "from genes2genes import ClusterUtils\n",
+    "from genes2genes import TimeSeriesPreprocessor\n",
+    "from genes2genes import PathwayAnalyser\n",
+    "from genes2genes import VisualUtils"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "collective-separation",
+   "metadata": {},
+   "source": [
+    "## in vitro vs in vivo T-cell trajectory alignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "sticky-oxygen",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.6547761  3.421763   3.27304    ... 0.95708966 0.95708966 1.4370381 ]\n",
+      "[1.0180244  1.0180244  1.0180244  ... 0.87026346 0.5269568  1.1253548 ]\n",
+      "1371\n"
+     ]
+    }
+   ],
+   "source": [
+    "adata_ref = anndata.read_h5ad('adata_ref_spt.h5ad')\n",
+    "adata_query = anndata.read_h5ad('adata_ato_spt.h5ad') \n",
+    "print(adata_ref.X.data) \n",
+    "print(adata_query.X.data)\n",
+    "gene_list = adata_ref.var_names\n",
+    "print(len(gene_list))\n",
+    "n_bins = 14"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "interesting-creativity",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===============================================================================================================\n",
+      "Genes2Genes (v0.2.0)\n",
+      "Dynamic programming alignment of gene pseudotime trajectories using a bayesian information-theoretic framework\n",
+      "===============================================================================================================\n",
+      "Interpolator initialization completed\n",
+      "Aligner initialised to align trajectories of 20327 reference cells & 17176 query cells in terms of 1371 genes\n",
+      "Running gene-level alignment: 🧬\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1371/1371 [11:45<00:00,  1.94it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alignment completed! ✅\n",
+      "Time taken: 709.1007261276245 sec\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "s = time.time()\n",
+    "aligner = Main.RefQueryAligner(adata_ref, adata_query, gene_list, n_bins)\n",
+    "aligner.align_all_pairs() \n",
+    "t = time.time()\n",
+    "print('Time taken:', t-s,'sec')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "earlier-basketball",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average Alignment:  \u001b[91mIII\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mD\u001b[0m (cell-level)\n",
+      "% similarity: 64.71\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "aligner.get_aggregate_alignment() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "residential-aurora",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean alignment similarity percentage (matched %): \n",
+      "65.67 %\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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sSAgcBC6VrPe+xFpU8p7XhaLi99Xm3JIjEX9yaYO5vUciyHOuYlS+Ku7hkHsBtF3B0Y8zF7Pr1o4gCI3KDTHSLQiCIFx7kiQRn2m85omQBOF6u/RvuKr9o6/+d16Tf/c1KfPWW2/h7Oxc+hMYWMVa4ivExsaWjGxWl8irKmrHkinVVzHbpFr9d51bUATKWk5vv0SpIyev9onbKmK2VXDQWgTIa9Y/W3HdX0eNVP6emq2Xz+UX/rv3E8vl99NS0XsiCMJNRwTdgiAIN6HotALu+e4Q/RduZ/aSw6Tl10+iKEG4Hi7tlVxVArFLGb0dHR3L1LnyXHV1KvLCCy+Qm5tb+nPx4sUa9bl79+6ABOb8Ctdl14gxvcy2YZfolVKVDyCuFuDtWrItVh3ITFn4e9dx5PgqemUF74PGFSRr9f1TqECpr75cXd9rKNm2rCJ2G47ay1+Z/b3q/n5SnHV5JF6S0CnFQ1FBaApE0C0IgnCT+XF/HCM+3MXZlDymdwvkSFw2wz/Yxa6o2q0DFYQbRVBQEECZxKdXKiwsJCcnBxcXl9Jg22Aw4OzsXGW9S8cvtV+Rum4HGhISAtZiKM6FuC01qlNOViSEjvnnFxkYmoGhGWHullo1M6BvD1qZdtWpC+G2/XS7pXwiurro1twRjFdtk6V1AZkS0qpJ2NZsOGScguxqkt65tYa047XvnCSBzVThKZeUDdw9eXDp72OH9yM4b3PtrwElfXMLB0CftptpI7rVrR1BEBoVEXQLgiDcRGIzCnltTQT9W3ryzpSOTOjkzztTOuDvouOZ305gsoq5jELjExYWhkajIT09vcIA+ujRowB06NChzPGOHTuWOX8li8XC6dOn0Wg0hIWFXfM+y2QynCwXwV4MyQdr30D6KbCZL68HV+nh/lj0E3/kiRmDatWUTqejV6C5JGlZbVhN9AspyfR+LTx1362EZi0vf8IpsOS1WqrYCs05GPLiQO1cmoisQs2GQPzW2ncu8a+SrcMq0FV/lvZtW1/uirMzPXzrMIPBXFAyvfyfzOqd5AcZ0Ld77fsqCEKjI4JuQRCEm4QkSby46hSuDiru7t0MtbLkT7xBp+LePiGk55tYfqTiET9BuJHpdDoGDy4ZaVy+vHzQdunY2LFjyxwfM2ZMpXXWrl1LcXExQ4YMQavVXusuA3D+0DqwSyUjtLUZfZUkOPQOdLz/quN2uti2Mahfj1r3Zd594/BJWlqrOv7JPzHvgYm1vlZlnJycGBRsKh80t5wEpjz4+/uqG/DqXJJl/O8qEujK5KDzhJwLNe+Y3QYXt4NP+QDYIesQdw0PL3f8mbuG45Fcy8zjEd+XbE0GqLNPM7VfUK2WCQiC0HiJoFsQBOEmsepYInvPZzKrdwgaZdkMwX4uOnqEuvH5jvNYbf9izaMgNJC5c+cC8Prrr3Pu3LnS4/v27ePLL7/EYDAwe/bsMnXuu+8+DAYDq1evZuXKy3swp6Wl8dxzz5Vptz54enoyuIMrmPLh6CLIjq6+kiTB7hdL1jqHXLG9lmQnPG4hP7wxq06BWuuwFrw82hHXtJpNi3ZPXc9/J3sTHFz51Pu6+PTVhxiY+2HJWvdLlNqSYDT/IpyrIpBtNgTy40HrDmereIDQ5k6IXAoFSdV3SLLD4Xeh3b3ltixT557lbr993HXbmHLVbunSnnkDrBjSazht/9zKkv3a9V7Ic88z3WUDc2ZPrVldQRAaPRF0C4Ig3AQKTFb+uzaC3s3d6RjoUmGZCZ38Scgu4o8TNfgiKgj1bN26dfTs2bP0B8BsNpc5tm7dutLyQ4cOZc6cOWRmZtKpUycmTpzI6NGj6d+/PxaLhW+//RY3N7cy13Bzc+Pbb79FLpdz6623MmjQIKZOnUpYWBjR0dE88cQTDBkypF5f59bfl9C3tSNkX4Dd8yDiR7BWvHaYrCjYeHdJlu4hH5UeVmb/zS3x/0EZ9TNTb51MUVFRnfry0F0TeXeUiZC4T6CokkRgxgyax33EhxMVzJpWPtj8tzQaDeu+fJ5xxYtwTNl6OXmZd5eS/bXjtsKBBZXumU3r2yFhV8n0+4MLKg6sZTLo+AjseRnO/FL5tPr0k7DvvyVtOl2xl7u1GO+En5kTspNP/vt4pa/l6Qen8caAdILivgBTbsWFClPh8PslD1EC+uF4/gce8lnPdwufFqPcgtCEyCSxl0y9ycvLw9nZmdzc3BonXhEEQaiLb/+K4Y31Z1g0rRPujpXv4btw01lyiyxsfmoAcrn4wnc9iXtCWYsXL2bWrFlVlvnuu++45557ytX75JNPOHPmDCqVip49ezJ//nz69q183+Q9e/bw+uuvs3//fsxmM+Hh4Tz66KPVXr8idf0ck5KS8A8fAFpDSUI09zbgHAIKLRQmQ+pRSDsJ/j3RBPZA5+iMEzm0c8vnzmFtGDu8b+n1CgoKSvcYr4vc3Fze+2o5G0/kcNHkiUnhhMaWTzNtGqM7e/DUfVPKZH+vL3sPHmXRT9s5kqwhV+4FyHCwJuOUe5yMPAuZMh+shuagdkFmykKeHYGaYhTurTGnRWA2FSHJlKDzAufQkqzgppySGQWm3JI14rZicPAGxwBw9AW5EvITSn4KElE5eaLz64DSEIhKKsZTkU7/5kpeeHASAQH+NXod2dnZvPPlcjadzOZcnjNGyQF7URbkxoIMFK7NcSGdMe0dePmxqYSGBtfjuyrUtyPHTvLHln0UFplx0KmZMKw3XTq1b+huCQ2kpvcEEXTXI/EFSxCE68FqszNg4Q5CPBx4dFCLKsueSc7jtbUR/PZQL7oFu1VZVri2xD3h5nAtPseVK1fy8MMPk5aWhlqtZsiQITz99NN07NgRg8FAfn4+JpMJNze30vXmhYWFpVub/dug+xJJksjPzyc/Px8nJyecnJwaZPTVYrGQnZ2NJEm4urqWJm4rKCggMTERq9WKn58fLi4uGI1GcnJy0Ov1ODo6kpOTQ3p6OhaLBa1Wi0wmIzMzk5SUFHQ6HW3atEEul3Px4kUslpKs72q1GoPBQGBgIE5OTqVt6nQ6nJ2dkcvrNhFUkiTy8vLIz89HLpdTWFiIzWbD09MTFxcXFApF9Y0INyRJkvhiyXJW743lrL09Jt+ByJQaJKsJTfJ2whWnmdgnhAfunCJmMDQxIui+AYgvWIIgXA/rTyXzyE9HeXNSe0I8qv4ibrdLPPzTEe7s1YxnR7SusqxwbYl7ws2hoT7H+gi6BUGonsVi4a6nFrBTMRXcWlVaTpZ1lgH2FSx5/3lUKtV17KHQkGp6TxBrugVBEBq5r3ZfoK2fodqAG0Aul9E+wIXtZ8We3ULtpKSksHjxYt5++22++OILzpw509BdEgRBqFeSJDHrmYVs1z9QZcANILm1Zpv2fmY/9y5iTFO4mrKhOyAIgiDU3ZG4bI7F5/DM8JrvM9w50IVPtkeTmleMt6F+tkoSbi6ffvopzzzzDGazuczxe+65h6+//lpMpxRqRJIkNm3dxdHT0RSbzPj7uDN94gicnZ0bumuCUKHVG7ay3TIMuZtnjcrLHb3YmjKYtZu2M27k4HrundCYiKBbEAShEft+byy+zlo6B7nUuE6HAGdkwM7IdG7rFlhvfRNuDjt37uTxxyvO4Lx48WJatGjBCy+8cJ17JTQmBQUFvPP5z+w6k0eswwAk9+nI5CpsZ9L5fPuvdHTPY86dI0QyKuGG88OG40jeT9eqjuTTne/XvSeCbqEMMb1cEAShkcouNLP+dDKDW3shr8VIo5NWRQtvR7ZHptVj74SbxZIlSwAYN24c27dvJyoqir179/LAAw8gSRLfffddA/ew6fDw8MDDw6Ohu1ErFxMSGfPwuyzOnUJc87nIfLoiV+mQKZQonXzJbXkfO12e5M6PT/O/H6vYo1sQrrOkpCRO5fnVqe6JXB9SUlKucY+ExkyMdAuCIFwDkiTx3Z5YTiflkpFvwtugZf6YNjjr6y+ZyoqjCQD0b1WzaW9X6hTgwobTKVhsdlQK8fy1qdu1axf9+/ev8FxcXBwymYwlS5bg4uICQIsWLejZsye///478fHx17GnTZeDgwPp6Y0rF0NOTg4z5n3DxbCXUMgrz9wtk8sxhk7jg71/4qjfwIzJo65jLwWhYoeP/02OU+c6BUvZTp04djKCUT4+17xfQuMkvmkJgiBcAws2RvLa2ghOJ+ZiNNvYcDqFsR/v5u+k3Hq5niRJ/HQgnm7Bbhi0tQ/sOwW6UGCyciQuux56JzQ2gwYN4o477iApKancOXd3dwC+/PLL0i2XAH799VfS09NxdXW9bv0UGpd573xHfItnkFURcF+p2H84H/5+BpPJVM89E4Tq5eQbkan0daorU+nJyTde4x4JjZkIugVBEP6lxXti+GLnee7s2YxXx7fj6eFhvDGxHUqFnMmf7WXf+cxrfs2DMVnEZBQyuLVXneoHezjgolexI7JxjZwJ9cPFxYVffvmF1q1bs3DhQqxWa+m5cePGIUkSL774Is7OzqV7G8+YMQOA8ePHN1S3hRtYUVERR5JUyFW1S9aY4nMrX4tp5sINwMfDGXtx3R5MS8U5+HiIBIHCZSLoFgRB+Bd2RaXz6poIxrT3ZXR739LjXgYtr4xrSwsvR+YsPUZWobmKVmrv5wPx+DpraeNbt32C5TIZrX2cOBKXdU37JTRO586d4/7778doNDJv3jzat2/Pn3/+CcDMmTOZOnUqkiRRXFxMYmIihYWFSJJE69atefPNNxu4901DUVERAwcOZODAgRQVFTV0d6r15fcrSfWZVOt6CucA1h1KqIceCULt9O3VnYCCv+pUN7BwDz2733KNeyQ0ZiLoFgRBqCNJknh/cxStfJyY0SOo3Hm1Us4jA1tQZLHx/PKT12zfzswCU2kCtX+zVVNzT0dOJeZitdmvSb+ExsvNzY0vv/ySAwcO0L17dyIjIxk1ahSTJ0/m4sWL/Prrr/z222/ceeedDB06lFtvvZVPPvmEw4cPl04/F+qX3W5n586d7Ny5E7v9xv9v9szFbBROvtUXrEBakdjKUGh4er2ern5mJJu1+sJXkGwWuvpb0Ol09dQzoTESQbcgCEIdHY7L5vjFHMZ38Ks0e7ibg5oH+oWy+UwqPx64Ngmnlh66iAwZA+qQQO1KLTwdKbbYiUotuCb9Ehq/rl27sm/fPr7++ms8PT35/fffCQ8P57XXXmPs2LEsWbKEP//8k2XLlvHII4+IL5VCpSy2utc127hmDykF4d+YO2sMTnG/1aqOU+wynrl3XD31SGisRNAtCIJQR//beYEAVx2dqtkj+5ZgN4aGe/HmujOk5Bb/q2tabXZ+2BdHnxbuONUhgdqVgj0ckMvgRELOv2pHuPnce++9REVF8dhjj2E2m3n11Vdp06YNq1evbuiuCY2EWlH3oFmjlP7VLB5BuFbatA7j0X4KNMk7a1Rek7yDJwZpCGvVsp57JjQ2IugWBEGog/PpBWw5k8ro9r412iN7ercg1Eo5b6yL+FfX3RyRSkpeMcPb/vttSLQqBUFueo7H5/zrtoSbj8Fg4KOPPuLIkSP07duXmJgYJk+ezKhRo4iKimro7gk3uEFdmyOln65T3WZO/+7hpCBcS3Pum8azPTNwPvcVdlPFM8PspgKcz/2P53tn89isqde5h0JjIIJuQRCEOvh69wVc9Cr6tvCoUXkHjZIZ3YNYczKZvdEZdb7u4r2xtPZxItjdoc5tXCnU05FjF8W2YQLs3LmTQYMG4eTkhJOTE4MHD2bXrl106NCBnTt38sMPP+Dj48OmTZvo0KED8+bNo7CwsKG7Ldygpk4YSVDu5lrXk1KPceeozvXQI0Gou0fuuZWd70/kLv0PBF94H23sKuQXt6CNXUVwzPvc7fAjuz6YzEN3TW7orgo3KBF0C4Ig1FKxxcaqY4kMCfdGpaj5n9F+LT1o7ePE/60+jdla+0RIZ1PyOBCTxfA23rWuW5kWno5EpxVQaKpdohjh5rJz506GDRvGrl27KCwspLCwkB07djB06FB2794NwB133EFUVBRPP/00kiSxcOFCWrduzdKlSxu498KNSC6XM7idM1JeYo3rSJJEs5z1jB81pB57Jgh14+XlxTvzH2H3t0+y+/UebHkmkN2v92D3N0+y4KWH8fT8d3lWhJubCLoFQRBq6a9zGRRb7PQMrV3WZplMxj29g4nJKOSr3Rdqfd2Pt0bj7qCmW4hbretWprmXI3YJTiXmXrM2hcbn1VdfxWq1EhoaylNPPcWTTz5J8+bNsVqtvPLKK6XlHBwcWLhwISdOnGDw4MEkJiYyc+bMhut4E6PX69Hr9Q3djRr7vydn0TX3W2zGmm1N6Br1BYuemSLWcws3NIVCgb+/P61bt8bf3x+FQtHQXRIaARF0C4Ig1NKfESn4u+jwd6l95uZm7g6Mbu/LR1vPEZdZ86m5pxJyWXcqmSldAlDKr92f7gAXHVqVnBMXc65Zm0Ljc+jQIZycnDh8+DDvvfce77//PocPH8bR0ZFDhw6VK9+6dWs2b97MsmXLCAgIaIAeNz0ODg6lsxAcHK7N8pL6plAo+HnRPPrmfYE8cVelGcmt+cl4n3mb/z3Zj04d2lznXgqCINQ/ZUN3QBAEoTGx2SU2R6TSr2Xdp5FN6RLAgZgsXlx1ih9n96jRqM6CjWfxd9HR/19uE3Y1uVxGqIcjx0TQ3aTZbDY0Gk2ZYE6v16NSqSgurjyp1a233sqYMWOuRxeFRkqj0fDTRy+xe+9Bvlz+AX9nG8hVh2CVqdFb0wlWxDKiiw8Pv/yU2IJOqBdms5nk5GSMRiPOzs74+vqK2RTCdSeCbkEQhFo4EpdNttHCLc1c69yGVqXg3j7BLNgYyapjiUzuUvVI4Z7oDP6KzuCpoa1QyK/9F4VQTwcOx4lkak1Z+/btOXz4MIMGDWLatGkALFu2jJycHLp161ZlXREoCdWRyWT079OD/n16kJeXR1JSEiaTCXf35gQE3NrQ3RNuUtHnz7No8R+cTpGTrwnGLtehtJ7GwxZH3zauPDZrKgaDoaG7KTQRIugWBEGohT//TsFVr6K5l+O/aqdToCt9W3jw0qrTtPYx0Mav4ht/scXG6+siaOHlSLfgugf6VWnh6cjak8mk55vwdNLUyzWEG9szzzzDtGnT2Lt3L3v37gVKklrJZDKeffbZBu6dAFBcXMyUKVMAWLFiBVqttoF7VDcGg6HKQEeSJJav3sB/P/qJrGIlcrUjSDb0ciNzZg7joXtnlhulzMvL47PFyzmTUIjJCiqlDB8nmDNrolj+0ARJksR/FnzBplgX5K0eRu6hQn3F+RxgVWEmG574imend2H8yEEN1FOhKRFBtyAIQg1JksSmv1PoEuRao725qzO7bwhJOUXMXnKI1Y/2wctQ9ku03S7x5K/HuZBeyH/Gtqm36XBBbiWJmSJT8kXQ3URNnTqVgoICXnnlFS5evAhAYGAgr776ammgJzQsm83G+vXrS///zeiLJct5+6s12J2Cce38OMHNys6y+Pzv/SwY8jh3jwjn1ecfpbCwkGff+JLTGXpMIZNRBVxObnneUsxfb/xOqDqB/z41ndDgZtf75QgNZN4bn7K9eBDK8NaVllE5uGPr/CRvr1kBbBeBt1DvRNAtCIJQQ5Gp+VzMLmJGj2vz5U2rUvD08DD+b/VpZi85zGd3dCHQ7XJm4rc2nGHT6RTmDmtFqOe/G1mvirdBi0Yp52xKHn1b1mzfceHmM2vWLGbNmkV6ejqA2P6miVu7bgPvLPofZ2MSsFhBIbPTzM+TOQ/fzV133F7r9o4cPcoHny/hfFwyGrWaof278cA9t+Pl5QXAc//9iKX7MnHpei+G5n0rbMMQ3BNDcE9WRm1n/fDbSc8H38lfoPBxRHVVWblKC+HTibXb6TPzCbIit6BU6ZAkK5JkR7JLIEnIVRpkSFjNxcjkChQqDZLdhtVcjEIuR6PT46DXIdmtqLSO5GVnYpfsKFV6HB11mIz5GE127MixmYuwWc0oNVoUCjU2SxHIFCjUOmymQpBsKDQOSHY7VksRcoUWmUyGzVqEQqFEpdIixwIKDUgSapUCD3dX9HodzgYDnTu0pW3rlpw/H82FhAwkoGPrYJ567AFkMhlbt+/mwsUUZDIZYc0D6d+nJwcPHeV0ZAxmq5UgX0+GDurLvoNHiLqQgCRJNA/yZcigftVm4E5KSmL7X4fIyM7DzcWJQX1uqdEsgsLCQjZu2UlSahYOei1d2ofRqWP7mvyTqZPf121me24HlAGVB9xltJrCwl8XMbB3VzHVXKhXMqmyVJLCv5aXl4ezszO5ubniP2RBuAn8b9d53vsziq/uuqVW+3NXJyajkIWbzpJfbGV690DcHTRsOZPK30l53N0rmJHtfK7ZtSoz//dTdAlyZeHUjvV+raZK3BNuDg31ORYWFuLoWPLwraCgoF4ymFssFu57fB5rth1GafDFObQP7m1HotS7YjcXkXP+LzJPr8OUGUvvLi1Z+cPnVU5zlySJ1xYs4tuVO7Hp/TGE9MK5eV8UGgdsxXlknvwdh7zTKOzFZOg7YwjpgSG0T436mhO9E2PSafz6P1ptWUmSiF39LMUZ0Xi0n4glLxlJsuHRaRIaJx/sdguFyRHkXdiLKScRlcEHjUsAltwkVAYf3NoMx5j0N+nHV2EpysYQ0hskGzLApdVAtG7BFCb/TfaZzSj1rnh2nvzPe2YkM2ITuef/QusaiEenyWhdApAkG8bUSHKj/0KhM+DSoj+ZERspiD+K0sENrVsQhpBeOHiHIZMrMeUmkXrwR4wZ51E7++HX6140rgEg2SlKO0/mieVYsy7g3uMenEL6YDMVkn7oe+TmXAzhY9B6t0EmV2LOSyLn5ArMFisenW5F4xqANfciLpk76Ryo4ekHp5V72LZx8w5+3nCIC8U+2Hz7otAasJnyUSTvIUSdxG0jujBuZPl91SPORPLx92s5m6nB6DsUlaMHdksxUvopfM2nGdLFn/vvvBW1Wl2u7r8xfc5C4kPn1qqOpTCTSQ5reHHO7GvaF6FpqOk9QQTd9Uh8wRKEm8u9iw+Rnm/ixdHh17ztYouNP/9OYc3JZCRJomOgCz1D3ekWfO325K7KFzvPk1loZu3jFY8wCf+euCfcHG7WoDsrK4uuI++hwKrFu+tteHScWOmSlsKUM1xY8x/UkpEjG7/D19e3XBmr1UrvsbNJMjnj3X0mhqCuFbZls5q5sO6/qBzc8B/0VK36HPvHCzQb+zoyefX7JNvMRtIPfIcMCa1HCB7tKs66b8pLIWn3F6gcvdG6BuAU1JWkPV8RMOAxVI7umHKTiVn7H5qNfAmdezAAKQd/RKk14N5+XJn3LPfCPgouHsOn970oVBU/nLAUZpG46zN8etyJxjWIjOMrMaado9mIeeXK2q1mUvYvRu8dhkvLAWXbMWYTu/41HPw6YM5NwK/vQ6idKp6tYreYSN6/GAef1qXt2EyFKCO+4o0HB9OrW2ckSeKZVxexz9gJTbN+lb6vxfH76KY6wIevPYX8n+0sF/+ymq9356EMn1bpZ2PKisf74mK+e+cpXFxcKm2/Ni7ExHD7woMowmqfnM8l4n3WffGMyGou1FpN7wlin25BEIQasNklDsZk0drHqV7a16oUjO/kz+czu/DFnV15fHDL6xZwQ8m67nOp+djs4jmsIDQ1ZrOZbqPupRAnAgc9gWenSVUGHw4+4YTP/Aar0pmuo+7HaDSWOS9JEn3G30eK3Zeg4c9XGnADZJ1ej9ajOS5hw2rdb6/ud5JxbEWNyirUeuw2K379HgK7neyo7RWW0xh8aDbyJazGDOx2CwVJJ2k24gUSdn6CzWxE4+xLq+mfkXrge2ymQlIPL0Xv1RKPDuPLvGf5CccpSjuH/4BHKg24AVQObjQb8SKph37BUpCBZ+cpuLQaRPy2D8qVlSvV+PV9AFNuCnmxB8q2o3elxZT3MaaewTmkT6UBN4BcpcG/34OYcpNL21FoHJA6P8mLX+3ldEQkz/33Y/YrxlQZcANog3pxVDeZp17+EIBfVqznm8MqVG1nVPkwROMWRGab57j7mQ8wmUxVXqOmfluzDYJH1aluhiKY5OTka9IPQaiICLoFQRBq4ExyHgUmK21863dkSymXo5Rf/z/NgW56TFY7cZmF1/3agiA0rBkPPk8BLri2HIBzaK8a1VHqDIROfAu7zpvR0x8qc+7Dz74ilWZ4dbkVjaHq5TFFmbHYi3Nx8GtX637rfdpQEH+wxuVdwoaSHbUdjw7jKLh4DLvVXGE5uUJF4JCnMWcnYEyNRLJbCRwyl+R935WeDxgyl4Sdn2C3FGMI7lGmviRJZEVswqfnXTXql0wuJ3Do06Qc+B4A55AeKFR6irMvVlje+5Zp5JzbiWS3l21HJiN0/JukHllKTSayet8yneyoHWXakTo8yJw3vmNPThhqt9Aa9V/lEsQhUxfWbNjM/zZEogwdUaN6CpWWzLDHef2Db2tUvjp5BUUoNHWbAWJVlYxUCkJ9EUG3IAhCDey/kIlaIf/XW4XdqAJdS/ZajkzJb+CeCIJwPUmSxNELuSjUWry7Tq9VXZ1bMxQqNWcTi7BaraXHv/39AAqNA06BnWvQigyZ4uo0aDUnU9Z8xwWNewim7AQAPDtPIePkH5WWVaj1IFfg3mE86cdXodQ6gWQvDdSVGkckiwmPjhPL1c29sAfXVgNr9TrkChUqB3csBZkA+Pa8h+S9lQejbm1GkX12cwXtKDEE9yArYlONruvedhTZkVtKf5fJZGQaemDWetWq/+qgvrz56a+Ym0+rVT2V3pVDMUXXJCO/Rq3EbrPUqa7CZqyXPAmCcIkIugVBEGpg/4VMWno7XtMEajcSF70aZ52KsyLoFoQbkoODA5IkIUnSNQ0Ovvh6CcU2FTrP5nUaJfToOBG53oPX3l4EwJkzZ8hXeqP9Z71z9f7tkpaar8GVyRUglYzqat2aYcqOr7K8Z8dJ5MccwJKfCoB7+3Fknlpbet671z0VBr75sQdxata9xv0qvV6nyaQfXwmUTAFHkiodjXf0b09h8t8VnvPudjvpx2s27d7RvwOFSafLHHNpM5acs1tr0fOSYD3N4ora4F2regB5PiP47fcNta53tf49OmBNqvnMhys5meLw8an/pKVC03VzfnsUBEG4huyl67lv7uRXga46zqbkNXQ3BEG4jlZu3INMoUDrVretEF1bDUKyWdiwo2Rt8PLf16PQuOAcUrNp6hIyJJu1+oKV1bfVfD2wOScRldPlEVy5svK11gAaFz+shVnI1Xokux2dezDmvJTS8zq3ICz56eXqyZXaOiXkUuoMSFeM1DoGdsKUk1Bp+cr6r1DrUepcanzdq9uRyeXIazGDAECy25AZ/GtV5xKNV2sO/x1bp7pXGtC3Fz4FB6oveBW71Ux7H+maZ1IXhCuJoFsQBKEaZ1PyySu20sbvJg+63fScTRYj3QJ88sknZGdnN3Q3hOug2GLDbrOWTKeuA5lcAXI5ZkvJ9OC0zFwk7DUeNdc4eSBXO1KcGVvraxdnx6P1aFXj8tkRG3ALvyJhm0yGZK9+WrNcpcNuKarxdaR/PXpfQqlzxmYqqFNdmaz6jO6XVNTf2r4Gm6kQhabuy68s/352OTKZjAHtPbHk1i4hmhS9midmjf/3HRCEKoigWxAEoRr7L2SiUsho4Xlzrue+JNBNT3yWEaO57qNOws3hiSeewM/Pj+nTp7Np06YaJWUS6ldxcTFTp05l6tSpFBcXX7N2dWolcoUKW3HdHrjZbVaQ7GjVSgD8vN1AJsNaXLNZM56dJmE1ppN1ek2tr52271u8e9xZw35akGErk0lckuw12m7MbipEXseHEv+GpTALhbZuD3vt0vX9O67QONb53xDAP/98/rU598/A/+L/sJmN1RcGLOlnGBGSQ/PQmiWNE4S6EkG3IAhCNQ7EZNLSywm18ub+kxnkpkcCzqXWbWRFuLmYzWZ+++03Ro8eTbNmzfi///s/zp8/39DdarJsNhvLly9n+fLl1yTp1CX33z4Gu81KcVZcnepnnFwNMiUzJw8HYOb0KdiNWeSc21Wj+gq1HrWDG5LNijH1bI2va0yJBLkKeRXbcV0pbd+3eHSYWOaY3VL1w4ui9POoXfywWYqQyWQUJkeg9QguPV+YHIHGNbBcPclqqtODKnNBBvIrZggUxB9BW0H7UJIAz1bJ6LvFmIOtqGYPPSRJKvc+2G0WJHvtgnaZXI6UV/Ua+cqYEg8zqHubOtW9mlqt5vv3nsE3amG1I97WhD0M1m7n1WcfvCbXFoSq3NzfIAVBEP4lSZI4FJtNK+/62Z/7RhLgqkOGyGAulLiUtEuSJBISEnjzzTdp1aoVAwcO5IcffqCoqObTbYUb19QpE3BU2jCmR2Mx1n5JQXbUNmTFmTz20L0ABAYG4iKlY8pNqnHg6dfnPuzmfNIP/0JRWlS15YtSI4n947mSQK8G68HTDv2Io18bdO5BpccKU87g4NO6ynoZp9fi4NMWnXsIAFln/sQt/PJ2WKmHl+LcvE+5ei5hg8mJ2lZtv66WfmwFnp2nAGA1FaDQGSodic+LPYBzSM8Kz6Ue/AHf3rNqdM28mP3ltonLObEc9za12+9akiQCHYyYsmJrVQ/ALWs3o4YPqnW9yhgMBpZ9Op9pHltxO/M+5ot7sFvNJQ8qTAXYI5cTHPMBLw0ys2D+43Vafy8ItSWCbkEQhCokZBeRVWim5U26VdiVNEoFPs5aIlNF0N3UHTt2jBdeeIEWLVqUHrsUgO/evZt77rkHHx8fHnjgAfbt29eAPRWuhYEdfZHsNlIP/FirevkXj2M3m+kW5lEmcHl61ihs5gJyzu2oUTsymYyQUfOxFKSSsGUhSTs+KpOw7BJzfhrx618l/fCPuIWPwKvjeBK3vkPa4Z/LBd+SJJEXs5fELQtw8muDa8t+Zc5nnPgdtzYjK+2TpSATuVJDxslVuHcYhzkvFYXaoTQINueno3JwJ+3w0nJ1nQI6kRdzoFaj3VZTAXZLEcp/1kUn7foc316zKywrSRLZkVsxhPYud85mNlKQcLxGiewkSSI7ahuGK8pKdhv+tggUWaerqFme6dw63nhuNvqY32pVz5yTQN82btc88FWr1TzzyN2s+fwZ3puoYgzfM9j4BVN0K/jluR78/OEzTBg99JpeUxCqIpPEQq16k5eXh7OzM7m5uRgMN3cCJkG4Wa05kcTjvxzjy5ldMejqvpdsY/HupkgctUqW3Fv77W6EqjXWe8KxY8f49ddfWb58ORcuXChz7tIX5bCwMJ566ilmz56NXH5zP89vqM+xsLAQR8eSgKygoOCabhtmt9vpOHA6KQUSPt3vxKP9mGrrFOckEL3iWXQYiTm0GoWi7IjsqOmPEZHthE+PO9F7h1XbXtbZrVgKMnBvP4aLOz4jL3Y/GpdAFBpHJLsVW1EeKkdXgoe/gFLnhN1q5vzqFwgZ+ypZEZtIPbwM59De8M+e35K1GOfQXjiH9CwX0CXvX4Kjf3ucArtU2BerqYCLW95F594cnXdLHHzacHHbBzQb+RJyhRJrcT4Xt75Ps5Evkn22ZI9r97ZlR4aLMmLIOPUHAQOfqDagtFtMxP35NoFDn0apcSTj5BosRTn4VrJePXHX57i0GlRupN5uNRP162P49XsIQ1DFr61MOzs/w7X1kNLPR7LbkR9ZyPdv3M1PKzezJqUNKr+u1bZjSTnJcJfD/Ofp+9myYy+vLY9BEV79nu+WwiyC4j7jh4/ml/v3IwiNRU3vCTf3nVEQBOFfOn4xB2+DpkkE3AB+LlrOpYmRbuGyzp078/bbbxMdHc3Bgwfp3r3kgYxMJisd/Y6MjOShhx6ib9++5ObmNnCPhdqSy+Uc2fIjPk6QvPdbEnd+hs1c8fIBSZLI/HsTUUsfx0Fu5OxfyyoMmNb/8jFt3Qu4uONj0k/+gWS3V9ie3WomcdfnWArS8b5lGkqNIyEjnqPtXYvROLrh2/12Wk58E0eflri27I9CW/LgQa5UEzr2NWLW/AetewiBAx+hMPE4LqE9CRr4CM2GzsUltFeZgNdizOH86hfRuARUGnAXJJ4kftMCFGoHtJ4hyJUaErZ/SNCw55ArlORfPErU0kfw6joNuUKFe9tRSHYbSXu+LvOe6TxCcAsfQfyfb2POS630vTemRhH351sEDHwcmVzJxW0fYs5PqzDgNhdkEPfnApyb9ykXcBvTzhH5y8N4d51O5qk1FCZHVHrN0nZa9CsNuM05iWiOvsn//u92Avz9ef7xexjvewZTxG8lyfIqINltmM6sZJTbUf5v7n0ADB3YmxcmBmI/9jHWKhKrFV88SPPEL/ju/Xki4BaaBDHSXY8a66iGIAiXTfpsD1qVgicGt2zorlwXO6PS+GLnBSJeG4H+WqWTFYDGfU9ITU3lu+++4+uvvyYmJqbMuSu/RshkMubMmcP7779/vbt43dyMI92XSJLEx198y1sf/4BFacDRvyMurQaicnDHZi4k9/weci/sRSrK4MGZY3l9/nPVzmxYs34j8976miyrAw5+HXEK7oZS44S1KIe8sxtp415Mq2BvzmYoSde2R9J5IdnMaAqiCVImkpscyek0DYbmfbFaLRRcPI7ayQuXFv1QaB0x5aeTsn8xktWEY7NuFKdHI0OGIbgHer+2KFRaTDlJZP69HnNOEu4dJ2HKjsdalINLy/5oXQKwWU0UpZ6lMPkMFmM2aoMvSo0D5oI09F5h6P3aUXDxKLnnd2M3GXFsdgtyhRK7xYSDb1t0nqGY8zPIOrMRuVyFa5vhqB09sZkKyYs9QH78UVROXrg074PWIwTsNgqTIzCmRqF29kXvHUZWxCaK0qPRerZAqXFA59kcvXcYcoWaosxYss9uxZSbhMrRA/c2I0sSutltGFOjKIrZibw4DddO08G1JZLdSmHkemRFGegDuyFzD0emUGPLS8B4fjtWFDiFj0WhcURZmICfFM3gTn7cN3MyWm3ZxHSRUef4+Pu1RKQpyTN0QqZ2QjLn45R3gnAPM4/cMYp2bcPLfe6ZmZks+uY3Dp8vJEPfAUnngWQpRlcQSYgunRmjuzNiSH+xnlpo9Gp6TxBBdz1qzF+wBEEAs9VOu5c3Ma1bIKPb+zZ0d66L6LR8/m/136x9vC/t/J0bujs3lcZ2T5AkiY0bN/LVV1+xbt06rFZrmXMA4eHhPPbYY6SmpvL2229jNpsJCgoiNja2gXpd/27moPtKMTEx/Pjjj2zZtpPk1HRcXZzo16c39866hzZtap9pOiMjg9WrV3MqIhK9XsfQQQPo3bt3aZAnSRJnz54lOTUdnVZNSHAwMpmMQfcupN3UtynKTqIw8yKSzYJcpcZmMZMRvY/CtAtIgFwhR6FyJD1yJxIKLAVpSMhQqPVIBSk46lUUmcFuK0KGkqLiAuySHKVShVIuIUkybDYLFpsMJBvIlSg0Dujdg9C5BoLdikzlQFHGeZBkIAe9R3NsFiOFadFYjDlYjNnI1XrkMjkaR3fsNguW4nzkSg1yGSi0ThTnpyMzF5Zs02a3IpOrwVKE3tkZbw8PXF2ciE9Iw2QDlUqFk5s3OrWSzuFBTJgwkdDgQDIzM4k8dx5JkujYvi39+pWsV4+OjiYxKQWFQk5QYABBQUEkJSURGxeP2WzB18eLVq1aER8fz8WERGw2OwH+vjRv3rzaz89oNBIZGUlWTh6uzk6EhYXV6N+g3W4nIiKCtIws9DoNzUND8fT0rPW/H0G4UYmg+wbQ2L5gCYJQ1smEHMZ/sofXxrelZRPIXg5gNFuZveQwH07rxMTO/g3dnZtKY7onvPrqq3z77bckJCQAJQHRpenkcrmcMWPG8MQTTzBkyJDSOo899hifffYZCoUCi8XSUF2vdw31OUqShNFYsvewXq9vEiOEU2c9ibXb/6HS1/wBoN1q4fy2z2k5/InSYwUpZ5nZ/Dwzpk6osq4kSUx67F1snZ6qdV8zI3eh0+twD+1WZbmc+ONEb/2MnrO/LHPcXJhDwp5vKciIo/XE/6LWl/23lR93GHXKTu6b1ItRQwfUun+CINSPm3JNd0FBAU8++SR+fn5otVo6derE0qXls0ZWJC0tjXvuuQcPDw/0ej29evVi69atZcrk5eXxxhtvMHDgQHx8fHB0dKR9+/YsWLCA4uKq93IUBOHmc/xiDkq5jGbu9TuidCPRq5W4OaiJThN7dTdlr776KgkJCaVrtgGcnZ2ZO3cu0dHRrF69ukzADRAcHAyUjGwJ155MJsPBwQEHB4cmEXDb7XYikm21CrgB5EoVMrkC2xV7Tzv6tOaLZTuqrSuTyejZyg1zYVZtu0te/JFqA24Al6BOKDVOGLOSyhxXO7gQOnwurca+ROSmRdis5jLnnZrdgqbH03y2OZWlK9fXun+CIDSsRhV0T548mSVLlvDyyy+zYcMGunXrxu23387PP/9cZT2TycSQIUPYunUrixYtYvXq1Xh7ezNy5Eh27txZWi4+Pp4PP/yQLl268L///Y8//viDW2+9lVdeeYWxY8fWausHQRAav+PxOQS761ErG9Wfyn/Nz0VLtEimJvyjbdu2fP755yQmJvLuu++WBtdX69OnDy+//DL/+c9/rm8HhZvS3r17cW9Xu72iL/EKH0RG1J4yx4z65vzw6+/V1n1k1lSKTyyp1fWMGTHoXX1qXN7RqzmJx9ZWeE5r8KLlkIc4t+WzCs87tZ3Ej3syOXz0ZK36KAhCw2o0WXLWr1/P5s2b+fnnn7n99tsBGDRoEHFxcTz77LNMmzat0uyH33zzDadPn2bv3r306tWrtG7Hjh157rnnOHDgAAAhISHExsaWWaMyePBgHBwcePbZZ9mzZw99+/at51cqCMKN4kh8NmFNZFr5lfycdZwTI91NmkwmY/z48Tz++OMMHjy4RnV69epVeo+tT/v372fhwoXs2bOHzMxMnJyc6Ny5Mw8//DC33nprhXW+//57PvnkEyIiIlCr1fTs2ZP58+fTu3f5fY5vVCaTiQcffBCAL7/8Eo1G08A9ql/nLsShc2lbp7oaJ09yE8ruM6118WP5poPcOW1ilXUNBgMDWyjZcGIN3h3HVXstc0EmyQd/pu24F2rcP7WjG0VZ8ZWe1xo80Rq8MGYnoXf1K3feqcM0vvp1Ebd06VDjawqC0LAazfDNqlWrcHR0ZOrUqWWOz5o1i6SkpNLAubK6YWFhZb4MKJVKZs6cycGDB0lMTAQonbZ1tUvbo1y8ePFavBRBEBqBHKOZuEwjLbwcG7or152/q464TCMWm5gm3FRduHCBVatW1Tjgvl5+++03+vTpw8qVKwkMDGTKlCm0a9eOHTt2MHXqVObNm1euzty5c7n77rs5ffo0Q4cOpXv37mzevJn+/fuzatWqBngVdWO1WlmyZAlLliwpk9TuZuWo12G31S03gN1mRq4q+1DCbjVTaOjAwUNHq60/cnAvzNlxXPzrO2wWU6XlcmIPk7D7f7QZ8yyyWuxPb7OakMur3oYy4JZJJBz+vcJzMpmMBJMbGRkZNb6mIAgNq9EE3adPnyY8PBylsuzgfIcOHUrPV1X3UrmK6v79999VXnvbtm1AyRQ7QRCahhMJJXsNN8mg20WH1S4Rl2ls6K4IDWTQoEGEhoby+OOPV1rm+++/Z+7cuTz99NPXpU9Wq5VHH30Uu93O0qVLOXToEEuXLmXXrl389ddfaLVa3nnnHc6fP19aZ9u2bXzwwQe4u7tz4sQJfv/9dzZu3MiuXbtQKBTMmjWL7Ozs69J/oXZu6dqZ7JjDdaqbl3QWR8/QMseKc1Nwbz+OX9ftrrZ+SEgw3p7u+LcfQtKer4jd9jE5sYcoTIsmL+EUiQd+IWLZ01hz4mgzbh5ypbpW/TPlpuLgFVJlGaVahyTZKj2vD5/Etz//XqvrCoLQcBpN0J2ZmYmbm1u545eOZWZm1kvdkydP8s477zBp0qQKA/crmUwm8vLyyvwIgtA4nUrIwUGtwMegrb7wTcbfRQcgkqk1YbGxscTFxZGamlppmTVr1vDhhx/y4YcfXpc+nT17lvT0dFq3bs20adPKnOvVqxcjRoxAkiSOHDlSevy9994DYP78+bRs2bJM+Yceeojc3Fy+/fbb69J/oXZCQkKwJR+pvmAFci6exDmwfenvkt2GZLeiUKkptlX/1dfHxwc/ZSoOHkG0GvYYYUMeRCWzYck8j8yUjX+7gXSa+jqmvNRaJ7WzWUwUpJ0noNPoasvKZBUvmwRQ6ZzIzhdJfgWhsWg0QTdQ5R+26v7o1aVubGwsY8eOJTAwkK+//rra/r311ls4OzuX/gQGBlZbRxCEG9OpxFxCPJpGluCrOetUOGgUnE8XQbdQueu9LVhN1zBfeqBeXFxcuktJRWu9Lx1bs2bNNeqhcK3dMeYWsmOrnw5+paLsJHTOZZOaJZ9cj1+HkQDU9C/6xEEdKEg8AYBcqcajRU98O4zAq3V/dC6+KNQ67FYzdmvt/jtIPLoaB/dA5Mqqp5fXxPW6PaWkpLBpyzZ+W7WWHTt3U1RUdH0uLAg3kUYTdLu7u1c4Ip2VVbKtQ0Uj2f+mblxcHIMGDUKpVLJ169Yq27/khRdeIDc3t/RHrAEXhMbrREJJ0N0UyWQy/F10YqS7iYmPjy/9ucRoNJY5fuln//79pblUrteDqdDQUEJDQzl79izLli0rc27fvn1s2rSJkJAQ+vfvD5SMjJtMJjw9PQkICCjXXpcuXYCSGW3CjWnuYw+QtP09rMU1+1tkt5q5sOMr/G+ZXHqsKDuRgpQonP3CsNtt6FQ124lm3KihuCevwVJU+U4OQT2nc27zRzXe3aYgPYaUkxtoOeShGpW32ytfu28qyMLbrf4SfUqSxPpNW3nwuXd45P0/+fakO78ntuLT/RrunPcVT7/8PlFR5+rt+oJws2k02cvbt2/PL7/8gtVqLbOu+9SpUwC0a9euyrqXyl2psrpxcXEMHDgQSZLYsWNHhTfrimg0mps+m6ggNAXp+SZScosJ9Wx667kv8XPWcS5VbBvWlAQHB5cJoCVJYsOGDYSEVL321MXFpZ57VkKhULB48WLGjRvHtGnTWLhwIc2bNyc5OZm//vqL7t2788MPP6BWl6yvvfTwoLJ7uIODAy4uLmRnZ5Ofn4+TU9PbqeBGJ5fL2bP6S3qMfYCw2xahNXhWWtZaXEDkxvdpMewxFP8kUStIPU/CwV9pM+45ABIOreTvC2fZPvl5pIIUHr59GPfePQN5BUnQ5HI5X77zHPfOfZuiNg+hMXiVK6N19sG342giN7xL2Mi5yOSVTwfPTTrD2bVv027CS2id3Kt97ZaiPJRqXaXn4/d8x05nGYee/oBAFzkP3jWR4GbNgJKHZd/+tIIjkWkU21XYJQm13EaQm5KH755MQEAAh48e5/sVW8gsVmKxyVAqZDgqzQzvFc7YkUN45uX3SXcdiNstT+F99cVDOlNgs/L8/35lTMfj3Hfn1KtLCIJwlUYTdE+aNImvvvqKFStWlFnLtWTJEvz8/OjRo0eVdR955BEOHDhQWs5qtfLjjz/So0cP/Pwub8cQHx/PwIEDsdls7Nixg2b//AETBKHpOJ1YkkStqY50Q0kG8wMxWUiS1CSn2DdlV47aVTWCJ5PJkMlk13UrzX79+rFz504mTZrE4cOHOXy4JNGWk5MTQ4cOLXM/LygoGR3V6/WVtufg4EBOTg4FBQWVBt0mkwmT6XIGa5Gv5fpycXHhxJbvGXHbQ0SZXWjWfzbOfuGl5wszYkk4vAq5QknYqKdRahzIPH+QzHO70Ro8aTt+XunfMHNOAv3vXVRad9OFo3w+7lHefnoGwwb3K3ftoqIiEpPiSTv7InK9O8363YvBN6z0vDEznrSIrUgWE8d+nIPeI5jQ/veidiyZHSlJEpnnD5JwcBl2SxGdpy9A71IuhK1Q/KGVBHabXOE5yW5HLlPgO+hJALKtZp754g+87L8QEuDJgfNGnNpORN/Dhyv/9WeYi3jsw5UkntpGULcpeHd4HJerHjisijnN62Puo9P0t3FzrryvcoUSn+53sDlqJ8qfV3LPjIr7KghCiUYTdI8aNYphw4bx8MMPk5eXR4sWLfjll1/YuHEjP/74Y+ke3bNnz2bJkiWcP3++NGC+9957+fTTT5k6dSpvv/02Xl5efPbZZ0RGRrJly5bSa6SlpTFo0CCSk5P55ptvSEtLIy0trfR8QEBAjUe9BUFovE4l5uKoUeLl1HRnrvi56Ciy2EjJK8bXufLRFuHmUtNpspfKent78+abb9Zjj8r65ZdfmDVrFj179mTp0qW0bduWpKQk3n33XV5//XW2bt3Kzp07UalUpa+lqodGNXm9b731Fq+++uo1ew11pdfrS7+TVPUg4Wak1WrZ+cdizGYzc557me0bzlNYZMFqs2IzGZHLlSj1BoyZ8Th4BOHi35bwUU+V+ewTj/6BX1jZARqv0C54hXbhzaX/o8BYxKSxw0vPpaWlMfrOZwgZ9CjuRXnkpl3gzO+v4N6yzz/tytAaPGg56L7SkXVTYQ5nNy7CVJAFMhmmvDScvJvj13EEdktRjQPuwow4bOYiNI4Vj4jH7PqWFt0v7yGuUKrx7nIrJ9d/TLprG7z73FJhPaVah3+PO/BoN4YT6xbhET4YpbpsstCMi5G0Hvs8uioC7iu5tRrA73u/ZsywdDw9K5+JIAhNXaMJugFWrlzJSy+9xH/+8x+ysrJo3bo1v/zyC9OnTy8tY7PZsNlsZW6kGo2GrVu38txzz/H4449jNBrp1KkTGzZsYMCAAaXlIiIiuHDhAgAzZ84sd/2XX36ZV155pf5eoCAIN4STCTmENtEkapf4/RNon08rFEF3E/Hyyy+X/v9XX30VmUxGeHg4U6eWnToqk8nQ6XS0aNGCESNGXLcA8Ny5c9x99914e3uzbt06HBxKZqK0bNmSL7/8kuTkZNasWcN3333HAw88UDpyXVhYWGmbRmPJtniOjpUvJXnhhReYO3du6e95eXkNkihVJpM1+aBGrVbz+YdvAWC323l03gJSfCaj8witsl7S8XVolRL+4b0rPN9m2AO8t/QdHLUKut3SFWdnZ267/wWC+t2P1smNjJhjOHk1I6zXRDKSLtBy5NMVJkLTOLjQccrL2K0Wjv3yDO0mzMM1sGTnm4Qjq0k8thb/zmOr7GtB2gXiDy4nfPQzFZ6P3LSIwLBuuPg2L3P8wsHVeLfqhVfzigPucv0cM4dTGz+l8/iyW/7lpccR2nNKtW1cyaPLdD777mdefu7RWtUThKakUQXdjo6OLFq0iEWLFlVaZvHixSxevLjccW9vb5YsWVJl+5fWcQuC0LSdTMilZ2j1a+5uZp5OGpQKGefTC+jb0qOhuyNcB1cH3ZIk0aZNmzLHG9LSpUuxWCyMHDmyNOC+0m233caaNWvYsWMHDzzwAEFBQQAkJCRU2F5hYSE5OTm4uLhUuZ5b5Gu5Mcnlcj5bMI8FH3/L72u/x6vHXWX25pYkiYzIv8iJPYRvy64EthtQRWvQbsxTPL/oeVqEx1KcdJxClTdtw3pyYt1HtBvxMCfXf0zn8XPxy0kjYssiVAZf/LtOQqm5/NDJaiok4fBKrPmp9Lz1eY6u+ZD0MwEE9ZxOQNcJpJ7ZyZn17+MWegteYf3KPNgtSL9AzO7vMRVk0XbCS8iumPZtt9tIPrGBgqS/yUs8g/uQsgNDkiSRm3Ke0O4Tavz+aRxccHTzpyAzAUf3klmcGXGncAtsW+M2LlFpHYlIMGKz2UpnngqCUFajCroFQRDqW1peMWn5JkI9m+56bgCFXIavs1ZsG9ZEfffdd0BJcrUbxaXg2WAwVHj+0vFLO5OEhYWh0WhIT08nISGh3PKwo0dLtqLq0KFDfXX5mjKZTKUj7u+//754EEDJ6P/TD9/FoagPKEg8TtqJ9WQnn0OuUOLs1YyA1r1pNWlu9Q0BCqUKpdYFny63sjviBM1uGYLVXIRcoSQ3OQq3gJJ15A4uXnSb9AzGnBTO7fsem11ComQrMoVCTljPiaXTyPve+RbHVr9HzplNFBXkIcnkaLU60k+tJ/XEWpQaB7QGL7DbcPbwo8+0l7BbzUTvX0nKP+VlAJKV4E7DcOs9lqOr3yV633LaDX+wtO8pkfvwbV3xKH5VQrpPJGLrN3QYVTJCnRp1gFYD7qx1OwAWx2BSU1PL5FUQBOEyEXQLgiBc4dQ/SdRCPZpu5vJLfJ3FtmFN1d13393QXSjHx6dk7+VLydOudujQIeDygwKdTsfgwYPZsGEDy5cv58knnyxTfvny5QCMHVv1dN8bhdVq5bPPPgPgnXfeEUH3P7bv+gt18GBaNOsIwJZPZhHcfQKh3SfWuq2W/W4n9vgWzKYi/Nr0I+7YBoI6DSfu2CbajSi7zZfexYeOo6re+ksuV6DU6AgfWHEge2L9x3Qc/XCZYwqlqtLyAD4texBzZG2ZY2kXjtBh1GNV9qUiSrUWSbKX/m6zmkvXp9eWTO1Efr7Y8UIQKiOCbkEQhCucTMjFoFXi4ahu6K40OD9nHfsuZDR0N4Tr4N577wWgW7duPPzww6W/14RMJuObb76pr66VmjBhAq+99hq7du3i888/5+GHLwcr+/fv54MPPgDg1ltvLT0+d+5cNmzYwOuvv86YMWNo2bIlULKv95dffonBYGD27Nn13neh/iQkpaN1aVX6u2S3o3fxrVNbDq5+JJ38E7XOgEwmozgvA32bAchkMuRVbAdWFYVChWS3l5kufolcXvuv4XpXH+xWy1XtKOqcg+TK16VQqrFZTHUKvCVzfpW5EQShqRNBtyAIwhVOJeYS3MSTqF3i56IlNc9EgcmKo0bcLm5mixcvRiaTUVBQwMMPP1z6e3UubSl3PYLuLl268Mwzz/Duu+/yyCOP8Omnn9KmTRuSkpLYt28fdrudBx54gKFDh5bWGTp0KHPmzGHRokV06tSJYcOGYTab2bx5M3a7nZ9++gk3N7d677sAFouFH39dxWffr0Hh5AMyGXabFWNOGkq1BgdXP8xFeeSkXOCW8CDe+e+8KhPWxcbGMu+/H3LqXBLd7riclVwmkyHZrXXqo91uRaHSlI7+yuRy7HVs6xJJssM1vJ/YbdZ6uz95t+pB0t87Cew0vPrCV4k5vY9pT5/EZrNQlJOMZEzH2ScUpd4DSSbHbinGkp9Cr/bBvPjMo/y17xAb/zpJvlWD1S5DKZMwqMwM7d2WiWNHVrh3elOQkZHBl0uWEZ1ShNmuwi6BSm4lwEXO/TMnEnIDLfkRakd8ixIEQbjCyYQc+rYQicOgZNswgAvpBXQIcGnYzggCsHDhQnr37s0XX3zBkSNHiIyMxMnJiQEDBnDfffcxY8aMcnU+/PBDOnXqxCeffMLmzZtRqVQMGTKE+fPnX9c9xpuytRu38u6362g5aDZDHptY5pwkSSSfO0zs8S10Hnk/Lj6hnPlrOWNmzadPpxAWvfkiavXlmUdms5m7H32BXG0L2ox6DU3bM+SknMPB7dJaYhm5qTH4tu5T637mJEbi6tuSpHOHsVnNOHkGk5scjVypxlJciEpb+1wflQXJkiRht1kqqFG1vJTzKDVl+yFJEjarBUUFGdVr0r9LPJq1J/7E5loH3ZbiAtz9Qrhl9OV15gVZyUTuW4Wjqy9hvSeVXivqwBr6T3mMziPvJ6D3kzhf1dbGixEsn/M2j8wYSt9e3Wv9ehoru93Ofxd+TmSWBu/O0/BuWTa5o8ViZv43a/CwLWXBf55Eq9VW0pJwo2qaj5EEQRAqkJpXTEaBWazn/kfptmEimVqTcPXuHZIkVfvTECZNmsSmTZvIyMjAYrGQlZXFtm3bKgy4L7nnnns4fPhwacbyjRs3ioD7Olm2ch3fbEmg58y3cfdvWe68TCbDr1U3ek2dR8yxzWQlnqPtgOkMfeBDjpzP457H5mOxlASnFouFSXc/iUe/p+gwbDZKtRaf5p3IunCktD2DdyjZCWfq1NcLh1YT0LYvjq4+xBz8A9+w3iSd2U3ILeO4cHBVrduzFBdWOlU7/thG/ML71brNjLhTeDRrX+ZYs84jiTu6rtZtFeVloHFwKXPM4BFETlJUrdo5v2cp4f8E1pc4uvnSdcwjOLj6cHr7TwDIFUpa957EyEc/J+b4NkzGvHJtuQW2IWjYM3y+JoLN23fX7gU1UpIkMff/3iHNfRhBfe5Coy+/m4JCpSaw2xRk7e7lwWfewGQyNUBPhX9DBN2CIAj/OJVQkkQtpIlnLr9Ep1bg7qDmfFrl+xwLNwe73Y7dbmfZsmVlfq/Jj81ma+DeCzeqv8+cZdmeBML6315tWZlMRpcxjxC1fzUWUxFqrQN9b/8/4vI1/OftTwF46qW3CRn5Ag4uXmXqOTh7UJSXDkCH0Y9RkJlIRuzxWvXVZMzFxSsQmVxOmwEzSLtwGJlcjlrrhEKtxZiTVusHTecPrCS0x6QKzyVG7ELnXLt91/PSYinMTiak2/gyx519mpOXGlOrtgAuHFhVrn/Ne03hwv4Vpe9ndZIjdmFwcSvzmVwpILwXDq7exJ/aWXpMpdHTc8ozHF33eaXtBvacwTd/HCUlJaVG/WjMPvxiMeZm43DyCq62rNbRBdfej/HiGx/Vf8eEa0oE3YIgCP84mViSRM3dQSRRu8TXRWwb1hSdPHmy9EcE1UJdffvLWkL73lXj8jKZjPZD7iJq3+8AqLUOuAeEcSImj7i4OM5lqXB09S5Xr02/KZzZ/CV2uw2toyuOHoGc3vw1luKaPTCU7HYOLn2F8H7TAXDzb4kMichdP9Ki91RObfiUZp1Hcnb7khq/lry0GCxFBeidywejsYfX4tGsAwd+faXM9O6q2KxmTm38DJ9WPSocPfdr059ze5fVuH9ZCRHIlWrUurKjqjKZjM4TnuHM1m9IPXew0vp2m5Xze5ZiK0wlvE/FDxYuCek0lORzZXcdUGn0eASFk5VY+ai6f++7+WLx8hq8msbLarVyJCoTg0/5WSCV0Tq6kmx2Iy0trR57JlxrIugWBEH4x+mEHEJEErUy/MS2YU1Sp06d6Ny5MxMmTEChqFvWZuHa0ul0xMTEEBMTg06na+juVCs/P5+kQi3yWv77cXL3ozDn8uhmWK9JGG1Knpj3Bq0H3VNhHZVGT+fh93B81dvYLCZ63v4akt3G3h/nYSrMqfJ6NquFv5Y8zS0TnkDjcHmFcf87Xyc1cj8XDv1Bq34ziDm8BievYM7u+L7a15CdFEn03rJ7aV8Sc3gN+VmJ5KXG0HP6f9n74zys5qIq27MUF7L/5/k4uPkR1v+OCst4Ne+KpbiQiK3fVtu/9JhjJJzcRuuBFT8QkcnlWIw5FKed5fCv/0fUzh/ITjxLfkY8mXEnidj8OZGbP6FZWGfa9ru1wjau5uwdQs5Vo/HNbxnN+cMbKq2j0ug5l1qM1frvktndyJb/vg6HsJG1rufTeRJfLvmtHnok1BeRSE0QBOEfJxNz6duidtP9bnZ+Ljq2R6ZhtdlRKsRz2qbCxcWF3Nxc2rVr19BdEf4hl8tL9yBvDP5YvxnXNrUPJgBcvEPJS7+IwTMQhVKFUq0lNUtOuFPlmeYNHv70GP8wx/78FLnGiZ4zXuf0hs/Y8b9H8AjuRFj/O3B09y8tbzLmErHla/JSoukz45Vy06PlCiVDH1zEvmVvcCxqP57Nu5B2/jBWUxFHVr2Ds08oId3Go1BenhmVEXucyF0/o3ZwpuukF0q3CZPsduJP/Ela9CGK8jIpzE5C6+jGhYO/027Ygxz67b+oHVxoO2Q2Wif30vaK8tKJ3PUTWRcjMHiFkJtynjPbl9Cq34wySdMy4k4S/ddSfEPb4+rbnFPr3sfRqznNuoxBrrj8VT/twhGST2+nMCsRg3coBZnxOHk0Kz1vLson5sBKzAUZeAe1IrjDANoPnsmeX9+kMEFJxsWzWIoLGHDna6g0+lp9pi27j+Xklu/oPOrygwi5Qomsmq3YVIG92bvvAP371T4xXmNw8HQsrl1G1bqeWudIfKa5Hnok1BcRdAuCIHBlEjWxnvtKfi46LDaJhOwigsV702R0796dP//8k/j4+IbuitBIJaWm4+DvU6e6ehdPivIzMXj+s2WYTAaK6veO1jq60mvyU5gKczmzZxWOLm7oDb24cGwTiae34+juj0yuQLLb0Dq60HPqC+iqCORlcjm9ps3nwK+voZQs5KeeB7mC4vws0s8fJnrvbyg1Dkh2K3abFZulGKXGAYsxhz3fPQkyGRaTEUtRAQqlCr3BA3f/EHqMfwi5QsWOH14mKWIXKr0TxtxUNn90FzqDJ/J/9ssuzs9ArtSg1hsoSI+l1/T/4+/NX3Fu2/+QkP2zxtyOR0AYrl6+tBtYMj3eP6wbmYnniNzyBcjkFGQlodI4ENimJ31unYtMJsNqMRG1fw0Xj6wBZMjkMpRKFW16T0Rv8CDhzD6MeZkYPIPQObni7t8KvcEdY25GrQNuKEkEZreXX6pS0f7lV9I7e5Gc+netr9dYWGwy6rqgzWIXs5AaExF0C4IgACdFErUK+TmXbEtyPr1ABN1NyH/+8x+2bt3K6dOnWbp0KdOnT2/oLjV5ZrOZl156CYA33nijzDZaNyK1Wo3NZkGhqn0/7VYLcv3V21/VPImZxsGZTsPvKf1dpZRj8AjAO7QzRQU5JJ07Qtsh91Ra/0pH13xIYNu+hHQaUuPrX5Jx8QwxxzajUKjoMubhcuc7DJqBJFfRrNPQCmqXJdntHFq5gO4TnqjwQcGR5HNlfnf3b1maLT5i9zIC2/TB6YqRfqVKQ5sqpobbbRYUisvrveVKFVC3bc7+DZvVglpd+63QGguZvO7L2f5FVaEBiKBbEAQBOJWYi7NOJZKoXcXNQY1OpSA6rYAh4eUTGAk3p+joaCZNmsTy5cu54447+OGHH+jXrx8+Pj7IKxiZuuuumifLEurGYrHw7rvvAvDKK6/c8EF327BQjh+NwLtlt1rXzU2LwyukY+nvks0K5vw69UOSJOxWCy4+zclKjCK40xAsxYWc2PAF7YbNrnRva5vFzMk//4ebTzBWk7FO185KiMRmMaM3eFKYk4qDS9m/oS26jSJy7+9E7V1By16TK80nYjLmcXztx3QYclelI/N2a+XBcEFmIjqDR636npsai1dwe6wWEzJZyUOLlOij1a6Rr0xhThraq7Yng6r7DVCYGknrAa3rdM3GQCe3Itnt1Y74V0Qjv74PQIR/RwTdgiAIXEqiphdJ1K4ik8nwd9WKZGpNzD333INMJkMmK5nCunHjRjZu3FhpeRF0C1cbPKAf3699D2oZdEuShDEvA62jC1ASrNntFgZ0bUFazIkywXhNJJ7Zi3/rXng2a0vMsT8J7jSEgPCeOLn7cXrTF0gyBaG3jMHRPQCQyEuPJ/bIBmQyifDekzB4+LPlq6dp2WNcra4LkJMag0wmo0WPcUTs+KnMeuZLwnpPJDXmFMfWfIhS40CLHhPRGTyw26xkJZzl4qmtqHWOdJ/wOBq9ocLrFGSnoK9k+zHJbgdkKCvZL7zCOqWfgStndv9Gi+5j0Tq6UpSfiUKpxmYx13oGQ+TelbQbfGeZY0V5meX2Cb+aruAc4a0n1upa18PhI8dYvm47ZrsSm11CKbMTHuLNzOmT0Wq1NW5n+oQhfLBuC37th9fq+rmpF+jdIbiWvRYakgi6BUEQEEnUquLrrCMqrW6jTELjd+WDqCv3Kb4UkIsHVUJF5HI5bQMNJBfklAbQNZEUdRD/sB6lv5/9azneDhKv/+c5xsz6v1oH3UlRB+k24UkAHF19yc9MwsndD2evILqNfxyLqYiYo38Sf3wTyGQ4uvrQZdR9pUFqZkIkar1jaWK3mspOPo+Ldyh6Zw8y4k5jNRdjt9kqzObuHdIe75D2FOVnsX3Ji3iHdkYuV+DqE0qPCXOqHQWN2vc77YdU/OArcv9q/K54P2siOeoQfq26A5CfmYDBIwAAv1bdKcrPIurAH4T3rVnWcgC7zYbVUoxaW3aJUuS+VYT3u63Sennp8fRu36zS8w1h51/7WLpmNwqvdgT1fLjMzJ+4jEQeffkLWnhreG7OAzXa+aFzpw6of9oI1C7ozjuznukLHq9t94UGJFLRCoLQ5IkkalULcNERnVpQJuASbn6SJJX7ufq8IFTlkdkzSN796T+jrdUzFeYSe2wL/uG9AUiOPkJRXhoTBnZApVJxx+hbiDmyvsbXj9j1K96hnUsfDLXqNZETf36D7YopzSqNjla9JtBl1AN0GXk/rXqMKw24LaYiInb9Sq8p8zi4+kNslppli7aaizm9/SdadB+Df3hvog+tJ7TLCI6s/aTKejonN7qOfhit3onOI+8juNPgagPulPPH0OidK0xuVpCdQtT+3zmz+9dafQYxxzYT0KYPp7Z+T0inYaXnAtr0IT3uNHmpceSlX6xRewBH1n1K6z5lg/T0uL+RK1SVjt5bLSbyji7hjmkTa3yd+rZyzUaW7oil+bA5BHccUm6pjbOHP+HDHqbIfwRPPP96jbc6mzm+H8lHV9W4Hxnn9jGkaxAq1c271v1mJIJuQRCavBMXcwAIFUnUKuTnqqPQbCM1z9TQXRGuk+3bt9f4Z9u2bQ3dXeEG5eTkxIJ5szmz+hWslqr/fhTmpHHoj4/oPqkku/bFv/dwdtev3D60DbffWjK1++4Zt9LJOYnoA39Ue+1zB/7Aai4i+tC60mBZqdbSZfRD7PvtbczFhVXWLy7IYf+KhXSfMIekqAO4+rZg3/IFmIuqXmpjKsxl3/J36Db+CeQKJXlpccgVSiJ2/4pXSCcO/fFRlQGwd2hHJGQcWftZta8xMfIAKecO03bg7eXO5aTFseXrp/EK6UiPSU+z97e3a/UZnNq6BGfvYDyDL28bKJPJ6D7xKcymQk78+S1ZSeeqaK1kavvhNR/TrMOg0tFygORzR4g/vbPS0XmTMZ+krQtZ9NqTN0zugoOHjrLpaDrNe06utqzB3R+PHrP4vzc/rFHbA/v1YmxHJ5KOrKi2bNrZXbTRnufeO2o+00C4Mcgk8ai63uTl5eHs7Exubi4GQ8VP8gRBaHgLN53l5wPxfDqji5gqW4GU3GKeWnacH2Z3p19LMQW/rsQ94ebQUJ9jYWEhjo6OABQUFODg0HgeEmZlZTH/zU84cbGYrmMfLbP+OCspmvOH16NUaWk/9B4SIv4iav9qZMVZvPzM/YwfVT6z968r/uDznzfh2rI/rXpOKB0NttusnD+8nqzEaKxmI7KiNO6fOpgTkfEkFerw6DQJvcEDc1EBJzZ/i2S3E95vapms3nnp8UTt/wOZTIajux/xp3aBTEan4bNx9g7m5OZvsdtstO57a5lAMjctnuiDawDoMOxespOjiT2xFa2DK+0H34kxL5OdS15ErlTh4OqDs1czwnpPRqO/nCE8OfoIcSe24+jmi3tgOGd3L8MtIIy2A2eUjr5LkkTs8a2c3bMChVJF/5mvonV0LW0jLfYUJ/78lpzUC7QdeAdt+t1Gbmosh9Z8TH5GAt6hnek0YnaZzyA7OZroQ+uRy5VoHJzJSYnBYjLi5t+S8H63lZkWnnzuMLEnt1OQmYzVbMTZqxnNbxmNd+jlaf8mYz6ntn6P1VxEeL+pOHs1Q5IkEs7sJfrAGnKSz+PXugfh/W7D4BlUWq8gM5GsU6sJdoOXnrr/hvo3/uT8hQQMfKJWdaL2/sb8e/oTFBRUfWFg976D/PT7Dgo0zfDtNLY0yZ9kt5NyZjvKzFOM6d+ByeNH1rr/Qv2p6T1BBN31SHzBEoTGYebXByiy2HhmeFhDd+WGZLdL3LP4IC+ODmdWn5CG7k6jJe4JNwcRdNddQUEBH325mF9Wb8Nqk2M2FWIpNuLg6gUyOebCXJoHuLPgtefp3LFDte0dOHiINxctJikjj2KTBYu5GK1KQaCfJ689/zCdO10OBPPz8/nu5xUkpOVjs8O56HMUKn2IOrQBR1dfJCRs5mKsZhM6Rxcs5mIKc9NBAgcXL8zF+Wj0BuQKJZLdjjEnDYVGj/afoNmYl4nGwQDIQbJTXJiNzskDhVKF3WbFmJeB2ZiPo4cfNlMRCqUGZBJqnQFJsmMuKsRSnI9ab0Cl1mOzmrBazdjMxcgVanROriArCWhtlmIUShUmYwEqjQ6dwQO5TI65KJ+CrBQkuQIXr0BkcjmWIiPm4nwkCXQGdyS7DVNBNhoHF5QqDTabBYu5CIVShbXYiEyuKC1nzE0vqefkhkwux2oqwm634eDshakon+KCLOw2G2qtAyq9Ewq5EpvNglZvoHXPMaRcOEFhXhYymRyb1YzOwUC30ffj6tMMq8XEgVWL8HUw4e7phUouo3mAO3fdXrtEZNdDQkICr369nbC+la8/r4jVbKLoxHe8+sKcWtU7f+EC3y9bj9EsYZckNAqYNKof3bp2rlU7wvUhgu4bgPiCJQg3PkmS6PDqn4xs68PkLgHVV2ii5q08SZ8WHrw5qX1Dd6XRaqz3hDNnzhAVFUVeXl6l67ibUvbyhvoc7XY7Z86cASA8PLzCrduEmjObzTz49H+RNxtCdsoFOg65o9o6kiSxf/XHdOg7ATefkgRfuelJfP/KdAwePox+5EMM7n7VtpOXkcipHb/Se8pTpbOr9q/+lLAeo3H1qf7BpjEvk30rP2Lw3a9UOTsrMymavSs+ZMwjiypM4Ha1yAPryEmOxr9FJ/LzcgjvPeHyNfOzOLT2S/pNex65XI7FVMRfv71Lv2nPo1SpkSSJPcvfo+OQO3By8632WvkZicjO/cY7rz53w88we/O9T5GFz0Slrv3DgLN/fsLX7zxdD70SbhQ1vSeIv9iCIDRpsZlG8outtPBybOiu3ND8XHScSxUZzJuSffv20bZtW9q1a8fkyZO55557mDVrVoU/Qv2Ty+W0bduWtm3bioD7GlCr1Sx641kid3yHWufAoXVflUmwdjVzsZF9Kz+gddehpQE3gLOnH7c9+yV5mcms+/QJUi6crPK6iZGHOfbnEnpNmlMm2Ow+7mHO7lvDxTMHqqyfFhdB+l8fY82OJjWm6muZjPnkZaWw5uNHKcrPrrSc3W7n2J/fkx57mv63zqF5pwHotDpObPkBu80GgN7JjU7D7mTX0rcoLsxFpdHRY8Kj7P51AQXZqchkMnpNfpLjW34kJeZUlf1Kjz2JFPkrb7/8zA0fcAMUFtvqFHADWCQl9homsRNubmKkux411lENQWhKfj+WyJO/HuerO2/BUSt2UazMiqMJbD2TyrH/1G5bE+GyxnRPiImJoUOHDhiNxmqzlMtkMmz/fDFvChrT5yhUb+OfW1m0bC/G/EJsditO7r606jYag6c/kt1OVvJ5ovavRe/kSocBk3F0qTivhTEvi6+eH41SrcMzMIwWXYcT2nkwGp0TpqJ8og9vIu70XxQV5GIy5uHk5kOnoXfi26ITCqUaY14G5w5uIDXmFFonVzyD2tC88xA0egPm4gJiT+wgLzWGzMRzdG/jT5GuGUaLjJgzR/Bv3YPmXYeXXuvC8a3EndqDTCbR/9anUCgVbP1pAUq1jrZ9J+MV0g6FUk1hbjoRu5ZhzjhHTm4+U+d9X+Y1pV88x5kDG1CodLTsPhJHVx+M+Vkc3fgtJmMebfpOxs03lLP711KQlUxgm174tuhCzIntpMdF4O4XSmiXy/1KPbMTnTGOgd1bM3XS2EYRcAO89MZHuPYov8d6TZze/D++efORGm0fJjRONb0niG+YgiA0aScScvAxaEXAXY0AFx3ZRguZBSbcHTUN3R2hni1atIjCwsLSL8WX/reifbqF68NsNvPmm28C8OKLL94wWZ0buxHDBrN882FaTHuOhKijJJ47xsE1nwF23H1DSb8YxfjH3kehqPoeoTe4Mefz/dhtNr58Zhixp/9Ct/w9JBlINhtWixmVWofGwYnOg6bTZdgMEiIPE7lnBRazCQeDG50GTkY39l4AspJjuXBkA6d2LqfzgEmkx0cydvarHN+1kmPHdzH2gcfQORjoMXwGf639hh3fv4zNZkWhVOEb2p6xD75ZZnR28pyPsNls7P/jC07u+AVzcSFeOjPffvYe/v7+PDjnRUxFBWh0l2d9eQa2xDOwJaaiAqIOb+FCbjpypYrQdr0Ibt+Xv1Z8xLbF8zG4uKNUqjmVcJrTf8qwWEy4ewchy5Vz+JcX0augX6+u3Dt9GK1a1W5d9I1Aq5Jjs1pKE5vVhkKyiIBbAMRId70ST8MF4cY36bM96FQKHh/csqG7ckO7mGXkuRUn+fWBnvQIdW/o7jRKjeme0LFjR06dOoVcLueTTz7hkUceQSaT0b9/fx566CHeeustTp8+zfz58xk0aBADBgxo6C5fNyKR2s1n3catrD2RR7Muo8udS75wiqToY3QdXrO8BdFHt2EryqXzgEnlzq3//i1adh+Ff4tONWprx9KFuLp702vUXSSeP0lBbgbN2/Vh27IP8G4WTsd+JWuu7XY7G75/k96T55TJhl4Zq9nE+Y0L+eLd+Wg0JQ9Rc3JymDDreUY89GGNli/kZiSx8esXuPeFz8lKvcjhrb/Rqd94oo7vwiugJRZzEdlpCQyc/DBWczERu3+ja6gT990zo0av/UZyLjqa9349SoseE6ovfAWbzUruwS958//m1lPPhBuBWNMtCIJQDYvNTkRSHs09xXru6vg6a5HL4Fxa1XvUCjeH2NhYZDIZ7dq146GHHio97unpybRp09i6dSsGg4EFCxaI4E9o9MaMHELfEBmxh8rv/+0b2h43nxAOb1xc7cyO88e2c3bv6goDboBRd87j9M5lxJ3eW2U7kiSxY+lC1EolvUaVBPueAS3JSolDqdagdXAmLzO5tLxcLmfY9LnsWfEBBdlpVbZdXJjHuY3v8t6rT5UG3FCyxt3LoGTNp09iNVe9n3dm0gU2fvUCtz36JmqNDp+gVoy++wVO799I5wETsVnNqLV6OvQew5ZfF6HS6Og07G5irQF8/tWSKtu+EbVs0QJVYVyt68Ue2cDdt42thx4JjZEIugVBaLIiU/IxWe0i6K4BpUKOr7OOaBF0NwlFRUUApfvLXhr5MplKvoy7u7vTo0cPTCYTL7/8csN0UhCuoTtum8jt/QNI2vER5/etwGoxl54LbN2d4sJcfv/ocQ5t+BZzUWHpObvNxpl96/hr+QeoZHZ8g8MrvYZMJmPSg2+SFnOCtZ8/TeShTWUC+eLCXPau+pRVHz6Cj38oQ6c/VXpOoVBit1lL27n6AYBaq2fUnS8Qtf8P9qxYRFr82TLns5Jj2LvqYw7+9AKfL5iHm5tbmfNvvPc5fWe8wpT75rPhy2fY9tOb5GYklSlzMfIQ6//3PHuXv8/dz32Mm+flTO1yuZzht89l3/of6Nh3LOmJF1Dr9LTtMZzju1aXvI9h3YnK0XPo8NFK36Mb1aThvYg7uqHG5YsKc9HkRxEW1qoeeyU0JmIRoyAITdbJhFzkMgj20Dd0VxoFPxct59JEBvOmwMXFhczMzNLfHRwcKCgo4O+//y49lpqaCpRkOReEm8GAvr0Y0LcXMTEx/LT8e/Itdo4dP4FZ5UHfsbNx8wkiLyuVkzt+wWotCYCRJMK6DKJrvzEApF4V7FZ4nUkPAxBx4E/++Phx5AoVkiSRl5HIiDtfYOjUR8vVyc9OxcHZA0mSMJuMOBjKL/NRKFX0HXcfdpuNiIN/Ent8O/yTj8HZzZv+4++j+NSP6PVl73k5OTlkW/QE6Z3Q6p2469mPKMzP4fsFj+Dg4olMrsBmteDm6cvUB19Brak4k7dcoSCkbXcunjtBzxF3cHDzL/SfcD8RBzeXlgnrMZblaz+h2y1dAMjNzSUrKwu1Wo2np+cNm6tg8MC+xCUs49iJzQR1HFZlWWN+NnHbP+WTBS9ep94JjYEIugVBaLKOX8wm0E2PRimSnNSEv4uev6LTG7obwnXg7u5ORkYG6ekln3ezZs04ffo0MTExTJw4EZ1Ox/HjxwEoLi5uwJ4KwrUXEhLC/GdLAt+srCxmPPE2bj4lsz4Mbt70HXdfpXXdvJuRGh+Fd1D1I5xtegynTY+SHSGKC/M4un0ZzcK6VFj29N71dBs+gwun9pCfnUb3EXdW2q5coaBdr1Hljh/btox5M8vvQPHdj8to2bPslPioYzu59eHX8fQPrfZ1XCmsy0C2/fYxgS07YjEVY7fZCGjRgYvnThDYsiMymYw8myNffbOEMzEp2DXuaJ3csdssGLOTcdFauX3SKNq2bVOr614Ps2behvOajazftAjXVoPxDmlf5nxxYR6xh37HQ5XLZwvnl5m+Lwgi6BYEock6HJtNS7E/d40FuenJKDCLDOZNQHh4OJGRkcTFlaxj7Nu3L6dPnwZgzZo1peVkMhkdO3ZskD4KwvXg5uaGrDidYmM+2hokKWvTcyS7Vn5Wo6D7Ssd3raRj/8kVnrPbbFjMRWh0jsSeOYhSoSQlNgKnTrVLYBh/dj+tWs0sdzwp00iLji5ljuWkJ9K5f+0Sh0HJNPNLWb79QtuSevEcYV0GsnPVlwS2LPlbEdB+MPuObWPQhDnl6tttNn7c8iey5Wt47aWnUSpvrFBl8riRTBwznLUbNrN958eYJTV2u4RSbifAXcuCp27D3V0kGxXKE2u6BUFokrILzVzIKKSVd/VfooQSQe4lUxIjU8QU85td165dgZIp5FFRUTz++OOVTvt88UUxhVK4uc17YjY7ly+qUVmFQomDsxvJMX9XX/gfBTnpFBfm42Bwq/D84S0/07bnaOLOHMJUmEe3wVOIOfVXtQnPrhRxcBNOTgYKCwvLnbNXEA7IZP8mRCiZ0q53cqGoIKdcNnQHJxeUqoof3MoVCtr0GIXfLdN4/j9v3ZDbEsrlcsaPGcEH/32GT19/gs/fnMPHrz/F8089LAJuoVIi6BYEoUk6djEbQATdteBr0KJWyIlIzmvorgj1bM6cOZw7d46oqCgCAwMJDw9n9erVtGzZEkmSkCSJoKAgfvrpJ8aNG9fQ3W0StFotBw8e5ODBg2i1Fa+pFerHgP596R3uwa6Vn9WofEFOJlt+eZe0i1HVl83NYPfvX9Bv4kMVnj/51x84uXpjNhk5tnMlwWGdCArrxOApD7Fl6cIaBd7RJ3aTdO4oXj5+6HS6cuflsupfU12Yi42oteVzppQcL9+PKzm7+xDQbSqff934sp0LQkVurDkbgiAI18nh2Gxc9Cq8nMQ06ZqSy2UEuuk4K0a6b3qOjo6le0JfMmLECM6ePUt2djYWiwUvL68G6l3TpFAo6NatW0N3o8l66bk5fPz516z85Ek6D7mD4PDyn0Xc2cMc2LgEu03Cw92F1EM/khYZTGiPSeidXMuUNRcb2b9+MVmp8YyZ/WrplOxLUuLO8ve+9XgGtCD1YiTHd67E0zeIzgNKpnw7GNwYOvURdiz/EO9mbWjTcxQKRdmv9TnpFzn51x/kpicycsZTHFzzCZFRUbQJL5thXTLlY7fZkCsu5zdRKFU1nlJ/Naul5EFASnwUtwy+lbSE87h6BVx+n6KOERTattp2PHyD2bhpMUrFElwMzvTueQsBAQHV1rukuLiYbTt2kZyShlqtonWrFtzStQsyWT09ZRCEKoigWxCEJulIXDatvJzEzbeWAl31nBEj3U2aq6tr9YUEoRGRJImvvv6OP3fsBZmCvNxsNBo1Lq6uXIxP4GJ8LCqNDp3eCZvNwvIPH8XdryWe/qEolGpsNiuZybEY8zJBBnZzIUadnvhzJuTsYdf6nzF4BuHo4oFMLsdUmEdm0nnMxUZQqFn82h14BbZErlAiAwrzsrGYi9Ho9Fw8d5TOfccydMpHbFv+aZl+Ozi5Muau50iJP8ee3z8DmRyZXIGMksDX1dOXAeNmsfP3L5EpFKj0rny58hCHdv6H7PRkiouNOBlc8QlsjtFxHR37ji9tu/OAiRzdsYreo++q1XuZlnAeT/9Q7HY7xYV56BwM7F23hIGTHy4tkx4fSc8Bo2vUXljPcURnJhPs1Za3v16L0pTOyMG98fb0YNPWXdjsJSP1QQE+qJQKzscmkpmVTeS58zi6B9Bt8BRcAttSZLWw/shZflq1gNYhPtx9x7QKR/2rk5uby0+/riAnrxAk0GqUTJsyHn9//1q3JTQtIugWBKHJsdjsnLiYw5SuNX9iLpRo5q5n7/lMrDY7SoVYoXSz2LVr17+q379//2vUE6EyZrOZRYtK1hXPmTPnht1aqTGxWq088fQLpORa6T54Cs27OpEcH01Yj9F07DmUwvwcdm9YSrPwHvQcMhFPv2YA2Gw2jv61geT4aPxDwujSZyRHdq/n1MHtNG/TlX6jpiOTySgqzGfLqm+JPHkAnVqOgxrcvHwZPOF5HBydSU+OZ8+fy5HL5eTmZHPrQ6+V6+Pq79+j+7BpAHgHtSIpJgK/kLKZvX2CWuIT1LLC1xh9cg8hbbuzZ823jJs2G72jM31GTOXbd5/G3dOPMXc8gVKpYsV37yNJUumDaEdndwpyM8uNgFfn5J61DJryKGcObyWsy0CKjQUolCoU/yRES70YjZd/cI3bC2ndla3LP+OWAWPoP/ZuAPYf3cWezz5k9ryPsFpM7Fz7I6fjIuk2YDRxuakYjWpuf/pjlFfNHnBx94auA8jJTOWpF9/klecfxcfHp0b9OHcumsW/rKIIBzr0m4iXoeTho6m4iEU/rEZWlM7EUQPp1bN7jV+b0LTIpBsxQ8FNIi8vD2dnZ3JzczEYDA3dHUEQ/nHiYg4TPt3Da+Pb0lKs6a6ViKRc/rvuDJuf6i/eu1q6ke8Jcrm8zrM+ZDLZ5T2Lm4CG+hwLCwtLp/wXFBTg4OBw3a59MyosLOS2ux9h4n0v4+Lhw/qln9G6Uy+ah5ds2ZWWFMfuDUsZf+eTqNSVL0M6d/oQ5yOOMGLqg8hkMuKj/+bY3j8ZN3NOaQKxv4/s5sKZo4y944nS/86i/z7MudOHGDH1QeRyOT999hqT73+5XPsXzhwl7kIkPYbfjt1uZ8MPCxg4+SEcnKqfcZKbmcL+jT/SomNfjm1dyv3zPgBgxTcLcPP0YdD4u0vLXjx/hhNH9tFn3OzSY9npiRzasoxh05+s0d+HU/s2olAo8WkWxtEdKxk05VE2/LCAodOeQKt3oqgwj+2/fczke+fVKpDfuuIzRk97uMyx9KQ41v70EU7Oroy94wn0jgbW/vQxjs7u9B8zo9o2bVYrO5e9yzuvPF3tf8c7d+1h9fZj9Bw9q1xSuCud+OsPwn2UzJg2pWYvTLgp1PSeIIYpBEFoco7EZaNSyAj2EF9aayvIreQ9OyPWdd+ULiVJq+2PIDQmNpuN6fc8xvQnFuLq6cvmFV/Tuffw0oA7PzeL3RuWMvne56oMuAFatutG21sGsG31YgCCWrSl97ApbPj1cwBiIk+QmnCBcTPnlAauSXHnOB9xhFHTHr4iiKs4qA0N74KPbyB71nwLksTw6U+xY8XnZCTHVdmv5Niz7NvwA14BLchJPk9ASBgA2Zkp5Genlwm4AQKbh9MqvB27Vn2O7Z+HaK6e/nToM4Y/f34Pi6m40mtJksSR7SuwWS0Y3L05vO03eo26iw0/LKDf+Nlo9U5kpsaz/bePGXfnU7UKuAFkFbw3nn7N6D5oPAY3L/SOBv4+sovc7IwaBdwACqWSvpPn8P4nX1dZ7sSp0/yx8yS9x86uMuAG6Nh3POcylaxZt6lGfRCaFjG9XBCEJudwXBbNPR1RienRteaoVeLuqOZsch7jO/o1dHeEa0gEz0JT8d2SHxh822PoHJzITE1A52jAr9nl6dmXRrhrOvsjMDScCxFHyclMxcXdG0/fIDx9A0mOj+bk/q1MuHtumfKHdqxh/F1PXdWKvcz07iuFd+mLq6cfu1Z+ilylpc/YWUQe3cnRHSsJbNWJVh37oVAqsVktnD2ynYToUxTmZaHRaMlNjaP74EnEnz0EwOrv3mXYrfdX+Dpate+Oq4cve//4HEmmpH3f8XgHtqTX6LvYtuJTzEVGegy/Hc+A5shkMoz5ORzbtZrCvExUGh1Ws4mstIuAjKM7VjJ46mMknj/F4c2/kJpwjnuf+7jWAXdeVhp6x4pHD9t07c/eP5cjSRJ7Nv3G6Nsfr1Xbaq2OHJMSo9GIXl8+yzrAD7+uoefE8vuJV6Ztj+FsXvERo0cORVHL1yrc3ETQLQhCkyJJEodjs+kRUvF+qEL1gkQytZvOd99919BdEITrZuPOw0x/omSd9MEdaxgycVbpOYvZhAxZtSPcV+s5dBI71vzAiKkPAtC172h+/eK/tOrQo0y5zLRE3L0DygXXXXuPIPL4X7Tu3K/C9n0CQxk94wmKjQUc3b0Oa1Euep2OY1t/46/VX+HpG4yxIAeDmxeefsEMGH0721b9jxHTHuWPJQuYMutp7HY7puIi/IPDKn0dnr6BjL/jMUzFRg7v2sDZ7HTysjPJSksgMzURS1EOekdnJLudiKO7CQgNx263U1iQS3jn/qjUGqxqJXabhePblhHcuhNjZz7J3o2/kJeTjot7zdZQX3Jo+0oGj59Z4TmZTEbztl1Z89NHGFw98AtuVau2Adr0GsfPv67gvll3ljsXHR2Nxi2k1ktvmncZxpp1G5k4fkyt+yPcvETQLQhCk5KQXURavolWPmI9cl0Fuuk5GJPV0N0QrqG77767+kKCcBM4f/48Aa26AmC327HbbKg1l/c9P7xrHd0G1n7veY1Wj9l0ec9shVJJfm4mnfuMKFPu8M615aZ2A4R17MHiD16qNOi+RKt3pPeIkgcGkiSx9JMXGHXHE/gHty5TrqgwH4OrJzarhdz0RFRqDXnZ6SXJxGr4evoML7s2efF7z3DbAy/haHBlw6+fE9KqLbOe/QC5XM7anz9lwLi7y7yXV7pl0ER2rlnM8KmP1Oj6AFarBavFhFZX+VKw1p368POn/0f7WwbWuN0ruXj4cPZEdoXnlq5cR/sBsyo8VxX/kHD2r9kqgm6hDDG3UhCEJmVPdAZyGYT73FiJrBqTZu56UvKKyTGaG7orgiAItXL02Amaty3ZY9tYkIvB1aPM+dysNDx86razhd7RgKm4qPR3ZzevclOMJUmqMDCVyWT0HDyObb9Xvcb4SltXfIlKrSsXcNusVpZ9/n/0HX0nG35+H5+g5gAUGwtrPb37SkqVhmJjIQARx/bg7OZVus554NgZbPj5fex2e4V1NVo9bp7+nD26u0bXkqT/Z++uo6M63gaOfze2cXcPUdydQHB3twKlQAu0pbSlRt9S+7XUKdJSirU4FHd3CO4ECSHuttkku5uV94+UbdMkRJAQmM85ObB7Z+6d3U323ufOzDM6dq38gXY9hj+0nKm5Baam5uio/PQYTSlVlWpJsQzo5d6nRKwuIBQlgm5BEF4oJyPTqeFogYVUDPSpLG/7wrlvNxNFMrXnRUxMDDExMaSnpxd5XN4fQaguZDlyTP4eOl6gUmBsUnLPbGWYSE0pUP2TcKyiAW7NBi1xdnZl58ofSw1eoTCo3rnqJ1IT7jNgwsdFtuXJs/nj+7foPuwN9q2bS8e+Y5CaFq5HbWZhhbqgoEJt+rcCpQIzi8IM+mq1qnCN8r+Trlla29G+92i2Lf8aZX5uifWbhPUlMy2BM4c2PTSHhEqRz/blswntOgQbe6eHtik/NwcDg0ebO11aepdHyXIhUmQI/yWuOgVBeGHodDpORKbR2t+x7MJCqdxszJAaGXA1PouW/g5V3RzhMfD19UUikTBo0CDWrl2rf1weL9qSYVXF1NSUQ4cO6f8vVI63lzuXktNwcPHEwsoOuaz4VBmtVltmpuqS5MmzMbP4ZxSVSplfrExZCQubtuvJnWufsvKHt7B39aZtrzFY2Tqi0+nISkvk+M6VRN+5jI29C0OnfImBgQE6nY7o25c5tGUJEokER1cvbpzdR68RU1Ep8rCwtAXA0saerIykCr8ugKS4e+Tn5WBuaQOAhaUdTq5eXD17iAYtOwPg5OpJr+GTObZrBUqlkjotOuPuE4JEIkGpyOPCka1kZyRhYWXD8m/fILBuCxq364upuSU6nY7kuLtcObkbYyMjug2ZiKV12cuiRVw6ibmlDWpV5UZeJcfdw9/brcRtBpLKR84SSek3TYQXkwi6BUF4YdxJkZMuV1HbXQwtfxSGBhL8nSy5EJNV1U0RHrP/BgQio/mzw9DQkLCwsKpuRrUX2qYNy6Z/iV9IA0ykpuTnFh2xU7txWy6f2ldsLnZ5qJSKIsPJc2WZZKYlYef4T/IwRxdPEmPu4uYdUOp+nFw96D92Olqtll8+ew21VoOxkQm58mxyczLRaDSkxEfx9dRuGBgaIkGCh28QPoG1cfH0p0GLTvpe9mO7VhPWuzBJmEQiwcbOmcgb5/Gv1bhCr+3s4W2E1G+lvxk34OV3OLpzNWbpNvqgG8Dc0oaugyei1Wi4dHo/ty8eAa0OIxMpDVt3xcHZHXl2BoYGEhq27MC5g+uRy7K4dvYQXQZOoMfQVzE0Kl94otPpyMlKQ6spwMM3iHsRF6kR0rBCryt872qWzim+PjqAv5czqUmxOLl6VWifBSolNmZiMLFQlAi6BUF4YZy4m4axoYRgkUTtkfk7WXDmvkim9jwRAbfwIjA1NcVEk4NaXYCRkTEunn4kxkbi5lU479nLvxaXKhF0x967WSQreHpKPEF1WxB+cDPdhryqf75Rm+7sXvcrPUdMLXVfLTr25/SBTbTv8xK9R71Bfn4uUbcuoS4oYMDL75V7FEqeXIZGrUZq+s9yWH1ems7KuR9VKOjOyUon5s5VXnn/J/1z7t6BxNy5RqvOg4m7dxPPGjWL1DEwNKRRKe/hsV1raNdrFOaW1nTsN5btK3/GN6guWq263AE3wMUTu1GrlPQc+QY3L54g6vYV/IIblPv9kWdnEOztgLFxyfO2hw0ewHtfzMep32vlbhPAlRPbeXVovwrVEZ5/IugWBOGFcTIynUBnK6RGYu3MRxXobMW2K4kkZufjZmNW1c0RHtEnnxT29NSqVavIY+HZUVBQwG+//QbAxIkTSw0UhLJ9+M4UZi9aRLehk2ncpgebl33HgPH/BLPuPoHcvX6OgNpNyrU/nU5H+MHN9B83Q//c0Z2r6Tl8Kgc2L9Wv3w2FWc1NzS0e2oNqZeuAXJZFnlxGUL3mbPj9K/xCGpIcd5/d636l25BXywwsVUoF21fOoe+Yt4s8b2ltS0Ctxmxe9i39xr5b5mtT5MlZvWAWrboMwszcssi2mo3akJmeSGpiNOZWNtg7uZe5v/PHduLuG6Rfe/vcke1kpyfRa+TrXDp1oNy98NF3rpISH4W5pQ1uXv6cPbyN+s3ac2DTEjoNGF/268rPZcMvH7Fp5cJSy0ilUjwcTMlMS8TOseQh6P+lVOSjSIvEx6fkZc6EF5dEJ25lPzEymQwbGxuys7OxthbDWQWhKqk1Whp+to9udVwZ0KhymWmFf2Tkqpiy6gK/jGxE97rluxh50YlzwvOhqj7H3NxcLC0Lgx65XI6FRenLKAllm7vgN6LlFrToOID4+7e4cvog3Yb+E8zuWvsLdZqG4fWfHtz/0ul07Fg1j8Zte+h7y/dtXExA7Sb4BddHrS5g4+LZ9Bg+RT9HWavVsmnJN3ToN7bI0PN/Uyry2bz8O/qMfou8nCyO7lqDnaMbsZE3MLO0plP/l7G2KznJWOy9m+xYNZfBE2fi4Fw8ENbpdKz8eSZqtYpBEz4sde70/dtX2L5yDs3CetOiQ78i2/Jzc/hryWxSE2JoGtab9OQ4atRqTK1GoSXeEFAq8ji2czWOrl40aNUFpSKPoztWkpOVTmCdptRv0RGAw9tXYmQspXmHfiX2ems1Gs4e2UZ2RjL3b1+lTbeh1GkcilKRx+bl31O/eUciroTToc8YLG3sS3xd0XeusnvVj6xfPh97+5LLPKBWq3n7w8+o3/UVrG0fntRNpVSwddk32Ei1fPLBm7i7l30TQqj+yntOEEH3EyQusATh2XE5Nou+808wq3dtMbz8MXl99QUGNvLkgx4PvygVColzwuOTlJTE7Nmz2bFjB7GxsZiZmeHn50fHjh355ptvipX/448/mDdvHjdu3MDExIQWLVowc+ZMWrVqVeFji6D7+fHLb0s4dO4OXQZNIjcni1P7N9Ky0wA8fIPR6XQc2LwUY2MpLTr1LzJE+4HYezc5fWATbboNxc3Ln8SYu5zc9xeNQ7vjG1QPKAywTx/cxNnD22ge1pem7ftgaGiIRq1mx+p5uHj40rhtz2JLU+l0Oq6eOcyRHSsJ7TEcd59ADm/9A1cvf84c3oaRkTHOnv4E1WmKvbM7arWauHs3iIm8ToFSgb2rJ6q8PIylUoLqNsfK1gFlfh6x926QJ8/G0tqWezcuIstOx9rWkUZtumPn5Ia6QEVcVAQ3Lx5Hp9UyfPKsIoG7Vqvl/LGdXDt7mOFTPsXUzII5M1/G0c2TkPqtiLsXgaWNHZ41amIiNSMnK53r54+i0+mo3bgtEomE6DtX0agLkGWmAtB3zHRs/nUDITEmkvPHdoEEatRsiLmlDbk52dy5dgZDA0NMLSyRZaTSa+TrnDu6k7ycLFp1GYSBoRE7V83DJ7Au2VmpyGXZeAfUxsbeGY26gKSYu0TdvkxafCTNmzfDydaCEUP64+vrW+Lvx4ULF9m6cz9ypYbTp8Np3W0Yjdr0KHYzQKfTEXHpBLcuHGHIS69hbmnN1j++48uP38HKSlxvPO9E0P0MEBdYgvDsWHD4Lj8fuMOil5pgVImstEJxcw7cRqPVsf7VigcuL6Lqek5Qq9Wkp6ejVCpLLePt7f3U2nPq1Cl69OhBVlYWtWrVok6dOuTk5HDjxg3i4uKKZVKfPn06P/74I2ZmZnTp0gWFQsGBAwfQ6XSsX7+e/v37V+j4Iuh+vshkMmbO+orYtFys7F3JTk9BpVRg4+CChZUNmWlJJMXdw87BBWcPP0ykpmSmJpKaFIvU1AyvGrWQZaURG3kTuSyTGjUb4ejqiUQC2ekpJMTeJTnuPnYOLphZWCKRGGDn5Iq9sweGBoYkRN8hNSkGJ1dv7J3dMTKRIs9OJys9BQsrW4yMjIm6fRkDA0O8A+qgyJNToFJQoFKRnhyHVqvF3NIaYxMTDAyNcHH3w8rOAWVeLjGR10lLisfE1BR3n0BMTEzRoUORm4OFtT2mZuakJcaQlZFKWnIcWq2GAkU+VrYOmFlYYWXniJtnDWwdXNDpdKQlxxIXeROJkQGqfAVObt64ePhhZWtPYuw9bl0+hVeNWtg5uaNWq8iVZZKdnkxKYgwSQwMCajXhQR+4RAKWNg6kJcWSmhSLi7svrl41sLS2Jy9XRnpSHGkpCeTLsjAxM8MnsC7mltZkpSagyJPhX8MbE0snTC1skGelc//uNaxs7HH1rIEsK5PkhBis7RxRazSo1Wq06gJsrCzp1GMANYIKp9EUqFSEH9mBRp7EB+9O0yfBy8nJ4fOvf8AzsDH1m4cRH32Pu3du4ONfk5OH96DWgoWVLRJDQ/JyskCjomGT1oTUa6zv5c/LzeHWyb+Y/sbkKvitFp6m8p4TxJxuQRBeCPtvJFPXw0YE3I9RgJMVG87HUqDRYlzaQqdCtXXkyBE+/fRTTp48ScFD1vZ9mkuGJSQk0KNHD5RKJRs3biwWMJ85c6bI44MHD/Ljjz/i4ODAqVOnCAwMBAoD97CwMMaNG0dYWBh2dmUvTSQ8n6ytrfn5h68AyMvLIyEhAScnJywsLMjKykIikZCfn09MTAxRUVEEBARgaWmJRqNBq9UilUpxdnYmNzcXY2Nj7t69i1qtxsfHB6VSiVKpxMPDA1NTU06fPs3Vq1fx8vJCp9Oh0+mYG76b4ZM+wdbBBUNDQwyNjDAztyJPLiMrIxWtVkPnfqNBq2PD0u8IqNmApm27o1arSU6IxsbGnqvnT7D3r8V07P8ypw9uxsnFg3rNO+Dq4YuBkSFJ0XdJTLiHvYMrpuZmSIyMibl9lZtXw1Hm5mJr54S5mQUNQ7sQENIInUbFqUM7qd2wJdF3b3B89xoMTaR06TuGFmE9kWdlYGBgwO3r5ylQ5iOVSmncsiNtOvTGWColNzeH+xGXSbgfwZAJ72FpZYW1jSO3r4Zz4fBmJFILegx9DSs7R04f3IZGq6F9j+EoFXlkpCRiYGyMkZERtvbOGJtIyZPLgMKRBakJUXj5BtC8hpTGjRuTnZ2Nubk5ZmaFuUUUCgW5ublotVo+/XoOnQdOQiqVYm5hVWzYu7GJCW069yctOZGZs/7Hl59+RH5+Ph99Opveo6ZhalY4uuHkkd30G1GYVd3bLxCdTkd+nhytRoO5pXWJy8uZW1iRlC6v9PJzwvPnmejplsvlzJw5k3Xr1pGRkUFISAjvv/8+w4YNK7NuSkoKM2bMYPv27eTl5VG/fn2++OILOnbsWKTc9u3bWbduHRcvXiQiIgK1Wl1iZtb79+/j5+dX4rFWr15drjY9UF17NQTheZOao6TZl/uZ2LYGYcHOVd2c58atpBxmbbvO9tfbUMfDpqqb88yrTueEffv20bNnTzQaTZlZzCUSCRqN5qm066WXXuLPP/9k7ty5TJ1aevbnB3r27MnOnTv58ccfmTZtWpFtb775Jj///DPfffcdb7/9dsk7KIHo6RYeJ7lczsuvTad+2wHUadK2xPnQd29cYNe6RTRs0YG23QeXuJ/L4UdY9/tsmrbrQVzULewcXek68GXsnYrn3ChQKTm8YzV5chmGxsZYWtvSoedw/fYzR3Zgam5JncahrF/8DT2GTCQlMYboO9fp0HtkuV7XiX2bsHN0pVbDloWvU5bFwq/eZNb3S7h35wZnTp2g57DCrO4XTu4nNuo2YT2HYVXCPGxFfi6Htq3Axs6B9t0HcfHkPgaEhRAUFFTq8d//+HM6DZ6CVFq+Ne0T4+6TfvcUiSlptO45DnMLy7/fKxWb1yym/6iKZTAHuH/nOs7GGfTsXvHl54Tqo1r1dA8YMICzZ8/y9ddfExQUxKpVqxg+fDharZYRI0aUWk+pVNKxY0eysrKYM2cOzs7OzJ8/n27durF//37atWunL7tp0yZOnz5Nw4YNkUqlnD9//qFtev3114sd+8EdckEQqpdDESkANPIWvVmPk5+jBYYGEi7GZomg+znzySefoFary730ztOQmZnJunXrsLGx4ZVXXimz/INh5ACDBg0qtn3QoEH8/PPPbNu2rUJBtyA8TpaWlqz9YyE7d+1m2Q/TsXL0wsndDwMDCekp8SRF38bH3Y7mDQJx9q1R6n7qN2+Hb1BtZk3uh6WtPYr8XH7/bgYOTu54+9fEytaBfLmMmHsRZKYlYWpmiZOrB2E9huiTuel0Oo7v3YjU1JSQes1Zv/gb2vccgZWNPWbmVuxct4i83Bx6Dp1U6neDTqfjyM61WNs56APu9OR4fv/uXd766BsMDAwICK5DamI8G5f/SL/Rb9KoVSeC6zbl+L5NyHOycHTxxNzCCkW+nPt3bqBW5TNk3DRsHQpvmqfFRRAQ0KvU9+LOnTvYuQeWO+AGcPP0JfzgZqztXfQBN0BWZhqOrh7l3s+/efvX5NK+ZSLoFoBnIOjeuXMn+/bt0wfaAO3btyc6Opp3332XoUOH6udY/NfixYu5du0aJ0+epGXLlvq69evXZ8aMGYSHh+vLLlq0SD+8Y+rUqWUG3d7e3rRo0eJxvERBEKrY3htJBLlaYW0mlth5nEyMDPB1MOdiTCajW/hUdXOEx+jy5ctIJBJ0Oh1NmzYlNDS0yhMCnThxAqVSSadOnTA2NmbDhg0cP36cgoICQkJCGDJkCC4uLvryERERKJVKnJyc8PQsvmJBo0aNALhy5cpTew2CUBKJRELPHt3p2aM7crmcW7duIZfL8fZuga+vrz7A/WvTNo5tnIuVSwB1W3TRXx8nRN8h4uwezp85RcOWYYx/6wtiom5xbM9GMtKSyMtJx87eAbUqD1V+Lt41gunU9yV9EKvIz+XEvk1kZ6bh6RtEYmwkyQkx9Bw6CWV+HtvX/IJOq6X/iFfZs/lPtq6Yi5mFFa069cfS2hYonMN8ZOca8vNyady6M97+tbh19Qw3Lp5ElpGCl7cfTv8KXluGdeXyhVNsWzkXUzNLWnXuT9cBY9HpdKQkRHNs70ZUilxCO/YisHZDfb1cuQxP55KHdD+wdsMWWveZWOHPoUHrbkRHRhR5TqVUYGIirfC+AAwMDNBotJWqKzx/qjzo3rRpE5aWlgweXHS4zLhx4xgxYgTh4eGlZhfdtGkTwcHB+oAbwMjIiFGjRvHhhx8SHx+Ph0fhH7iYTyEIL6Z8lYbjd9LEMmFPSKCzFafvpaPT6Z6pXlHh0VhYWKBQKKhfvz6nT59+Jj7b69evA+Di4kJoaCinTp0qsv2DDz5g6dKl+uuJmJgYgBIDbih8jba2tmRmZpKTk1PlNxXKIpVK2b59u/7/wvPJ0tKSxo1LXqd6YP/eDOzfm2vXr7Nr7zIKNFoMJRICavjwzSdvMXNWHhFRyWSkJeHtF8zIVz8ocT95uTmcPLCN3FwZOVkZXDl3nHpN2mBla09WehJGxkZIgKO712Jj60jPQeMwkZqy6c95uLh7EdZ9CMYmUk4e3EZ+fi7odEilZji7eZKcEM3180e5fuEYNQLr0m/ka6z97SvqNWxerB3tu/QlITGROo3bcPLgdvLycgCQmpjSfcBobO2LL9F1YtcqPnpzzEPfQ4XGEKMSlhsri69/COdPHirynLWNHdlZ6RXeF4AiPw9z8/L3tgvPt0oH3SqVChMTk0duwLVr16hZs2axP4569erpt5cWdF+7do3Q0NBizz+oe/36dX3QXVFff/01H374IUZGRjRq1IgZM2bQp0+fSu1LEISqc/xuGgq1lsY+Ymj5k1DP04bd15OITM0lwNmy7ApCtdClSxdWrVqFqanpMxFwQ+Hwcihc/ksqlbJ48WL69OmDXC5n7ty5/PDDD4waNYrg4GDq1auHXC4HwNy8+FJPDzxIliWXy0sNuh8kw3pAJpM9xldVfkZGRvTs2bNKji08W+rUrk2d2rWLPf/V5/9H/6FjWPLDx0x6b3aJ86OhMMlXpz4j0KjVLPruAwaNnUaz0C4PPebuv5ZSI7gudRq1ZtVvX9Nv5BS69Bv90DrqggJWLfySZs1DMbUsPtc1uE5DoiIjiLkXQee+Zc8VP3NwE11C65e5traOR/jO+s/3nZWNHVlpKZXa1dkjOxg3qHvl2yI8Vyrd/evh4cEHH3ygv5NcWenp6SX+8Tx4Lj299LtLj1K3NFKplAkTJvDLL79w8OBBfv/9dzQaDX379uX3339/aF2lUolMJivyIwhC1dp3IwkPWzPcbc2quinPpVru1hgbSjh8q3IXJcKz6X//+x8ODg6cOXOG77//HpVKVdVN0idrU6vV/PDDD7z88ss4Ojri6+vL999/z6BBg1CpVPp1uh8kgHvYTYPy5JL96quvsLGx0f94eXk9hlcjCI+fRCLhr9VLsbUyYe7nb3L68A602pKHN9+5cZEfP3kNqZmUzNQEFHm5JZbLSEtiw9IfsLd35OrZo0RH3mDoK++yY90izp3YV+r+I66cYcmPH2JuKsXe0QmVUlFiuW59h5ORFM3uv5aSnycvsUxmWjK7Vv9MyzrudO3UodTXHxMTw7ff/0RySmqpZcpS0veFi5snyQmxFd7X1QsnWbryLz778lv27NlXru8b4flV6Z7u3r1767N+9urVi6lTpxbLGF5eDzshlnWH/VHqlsTNzY3ffvutyHODBw+mefPmvP/++4wdO7bUIStfffUVn376aYWPKQjCk6HWaNl/M4XW/g5V3ZTnltTIkFpu1hy+lcoroaUn+RGqF29vbw4fPkyzZs2YMWMGn3/+Of7+/tjYFE+YJ5FI9AnLnqQHPdEGBgaMGVN8eOnLL7/Mhg0bOHz4cJHyubklBxNQuEQUoM8KXpIPPviA6dOn6x/LZLIqCbwLCgpYuXIlACNHjsTYWOSoEIozNDRk/colnDhxko9mfcm+LSvwC6qDq6cfRkbGpCXHcy/iKi7uHkz/5CdMzczJTE/h4PaVKJUKbB1cMDU1J1eeTVZGKtF3rvPmxz9ibmFJqw49OH/yINtPHcDG1oHYe7c4e2wPtvZOOLl4IDUzJzMtmXu3rmBoACG1G6DTanH18Obalc00aNqmxDZ37jWYrIxUDu1YTb4iHxt7Z0zNLMjLlZEYE4mviyVffjit1Iz92dnZfPfjPGydvWjX6yXilv9WYrmyqFTKEmOH0M69Wf37HEa+OqPc+zp/8gDdeg2kcfPWANy6fpmZn82mfZvmdOrYvlLtE6q3SgfdS5Ys4fvvv2fRokX8+uuvdOnShaCgIKZMmcKYMWPKPTfKwcGhxB7pjIwMgIcOIXmUuhVhbGzM0KFDef/997lz5w41a9YssdyzcmIWBKHQsTtpZOSqaFFDBN1PUn0vW1afiSFPpcbcpMpThQiPQUZGBiNGjEChUKDT6ZDJZFy8eLHYBenTnMvv6+sLgKura4lzmh9sT0kpHHXh7e0NQFxcXIn7y83NJSsrC1tb24des0il0mdiDrVKpWLcuHFAYWeACLqFh2nduhWjRwzBKaA5uXIZUXduYWllRZuWzfl9wX38g+ro16G2c3Cm3/AJaLVa5LIsFPl5WFhZY2FpzbZ1i1m39CdemvwhBgYGNG3TiaZtOqFU5CPLzsTIyAgzM0vy8nKIjbpDRnIMnl4+uLp7kicvnKNtaWWDXJb50O8LW3sn+o94Ba1WS25ONnl5uVhYWnF46zI+++T9UutlZGTw+dc/MPTlaZiaFo5o8/DwJDHuPm6evhV6z47s2oC3X0Cx501MpLTu2IMtaxbRZ+grZX7nXbtwCll6PJ06/jP8Prh2fYJr1+f4gR3It2yjX9/eFWqbUP09UnYxOzs7ZsyYQWRkJJs2bcLLy4s333wTDw8Ppk6dSkRERJn7qFu3Ljdv3kStVhd5/urVqwDUqVPnoXUflKto3Yp6MCTkYQnZpFIp1tbWRX4EQag6687F4m1vjp+jWM/2SWrgaUuBRsepyMolmxGePe+9957+XCqRSPQ/Valhw8IMxpmZmSUO03xwE/5Br3VwcDBSqZTU1NQSA+8LFy4A/+SBEYTniU6nIzI6AXcPbwKD69Cl10BateuCi7sXfv6BmJqZ8dv3H5ORmqSvY2BggLWtPc5unuTlylm3dA5evoGEdR3AnC/eIuLaPyv/SE3NcHJxx87BGR06zp3YT/z9O9Rv0pocWRatwrphbCLV/602bNqGy2ePl9luAwMDrGzscHHzRKnIJ8DH7aHLk3317U8MHz9dH3ADtO/ck5OHtlf4/dLkpZF473qJ2wOC69CgcStWLvyGmKjbJZaR52Szfd0Sjuxax4AhJc93b9OxJ5HxGVy4eKlC7ROqv8fSJSGRSOjTpw9eXl68/fbbHD58mAULFvDLL7/Qr18/fvnlF5ydnUus279/fxYtWsRff/3F0KFD9c8vX74cd3d3mjcvnu3w33UnT55MeHi4vpxarWbFihU0b94cd3f3x/HyKCgoYO3atTg6OhIQUPwOmCAIz57MXBX7byYzrKl3lQcLzztXG1NcrKUcvpVKx5ouZVcQnnlbt27VLxkG6HuDq3IlkLp16+Ln50dUVBTh4eHFlvV8MKz8wVJgZmZmdOjQgV27drFhwwamTZtWpPyGDRsA6NWr9PV+BeFJ0el0bNq0iZOnwomNi8PY2AS1SkmOPBcTUymuLs68+/Z0atSoQVZWFkePHufSpUvk5ufh7uZO3Tq1CQ1tU+KIh4iICI4cO4aVfcnXwT37D2Ppwp8YN+V9/lrxC/Gx0bh5+GBuaYU8R0ZGWhJajQZHF3dOHtxBXm4O/YZNQJadwbqlP2FsYoqFlTUqpZL83BykUlPCuvbF2ETKsvlfUbteYy6eOUZInQbcvlG4JF+teo35Y+F3+AaElJiV/L8KVCqWz51F7SA/3pw2nbz8PBwdHKhduzaOjo60axvKkSPHqNMkDJP/jEQxNjHB29uHS+FHaNC8Xbk+j30bl9K3Z2f27N3PyYPbaNWheE+0f3BtfANCOHVkD6cP78REaoaRsTFqlQq1WoWlpRU+vn7UDH54rNCh+0C2rVpAo4YNytU24fkg0T3irH61Ws369euZP38+p06dwsvLiylTpjB06FC2bNnC559/TqNGjdi9e3ep++jSpQvnzp1j9uzZBAQEsHr1ahYtWsSKFSsYObIwm+H48eNZvnw5kZGR+PgUrgerVCpp3LgxMpmMr7/+GmdnZxYsWMC2bdvYv38/7dr984cWHR3N2bNngcL1vXfv3s369euBwiFpTZo0AWD69OkUFBTQunVrXF1diY2NZe7cuZw9e5alS5cyduzYcr83MpkMGxsbsrOzRa+3IDxlf5y6z6fbbjB/RCNsxPrcT9zSE1HcSJRxbEZ7cZOjFNXpnGBlZUVeXh7Ozs4cOXKEoKCgqm4SAAsXLuTVV1+ladOm7Ny5E0dHRwDOnz9Pp06dyMrKYv369QwaNAiA/fv307lzZxwcHDh16hSBgYEAnDp1ivbt2yOVSomKiqrQdLSq+hxzc3P1vfhyubzU+a3Cs00ul/PRx5+QnJqFuYUlBgaGxMVG4e7hRfc+g3D38EKn0xEXc589OzYTeecmEokBDRo3p2mLNvj4BWBoaEBSYgLHD+7C3dWRkcOH4OTkxF8bNxFx+x6uXjUwMpKiNpDSuHnxVX4A4mLus/y3n5j6wWxMTKQUFBSQkZqEUpmPpbUd6gIlZhZW3Ll+kT1b1zBlxv+wti38O9FqteTnyTE2kerXsE5JimfV7z8QEFiLfsPHs2rxT5hbWNOp50AsrQpzQagLCvhj4fd07jMcN0+fUt+jXLmMP3+dza0rZ3F1d2fQsDH41Qgi4sYVbt28hqmpGbLsDKKiovjix8WlnnN2bF6HqaXdQzOz63Q6tq3+hbzMBDy8a9C4eVvOnwtHpTOmY88hD623Y8MynJzdaNa6A8Z/r+i0Zskcxk58vczz4K5NK5gweqD+O0yovsp7Tqh00B0fH8/ChQtZtGgRycnJhIaG8sYbb9C/f/8id8I3btzIqFGj9MlKSiKXy/noo49Yt24dGRkZhISE8MEHHzBs2DB9mbFjx7J8+XKioqL087YAkpOTmTFjBtu3bycvL48GDRrw+eef06lTpyLHWLZsmX4u1H+NGTOGZcuWAYVz1RcuXMjdu3eRyWRYWVnRrFkzpk+fTpcuD19O4b+q0wWWIDxves89jtTIgLe7BFd1U14IF2Iy+XbPLfZPb0uA87O93nFVqU7nhHbt2nH8+HE6derEnj17qro5elqtlmHDhrF+/Xrs7e1p1aoVcrmckydPolKpmDBhQrFkqNOmTWPOnDmYm5vTuXNnVCoV+/YVZl1et24dAwcOrFAbRNAtVNa9e/eYPuMjuvceROSdW7h5eHP10lmmTP8QC4uSk/nlyLKZ98OX5Ocp+OSrHzE0NCyyXZGfz8Y1S4i9f5fh417H27cwoeWiBT/gF1yfFqGdStotAAd2b+Hc6WNY2tjRa9BYnF0Ll9nVarWcP3WY8ycPkpmZRr48B3NLKzx8Agjt2BPfgH9yG924cpb929eRnyunY48BtAjtzNWL4WSkp5AYe59h414vckytVsvuLauJi46iaWgn6jRorg9QY6PucProbqQmJtSs0wB5RiLXLp8nMyODOvUbMnTUeADiY6NZv3oZtg7ODBvz2kPf83Phx7ly6QLObt607tALo79HBuTIsjh9cAuxkdfx8QtgyMjxReKXC+fCWbdmJQ2ahRLaua/+5kKuXMbhPZvIyc6kTVh3fAP+ucaIj7nHjUun6DNwxEPbBJCXK+fSsS1MfnVimWWFZ9sTD7qNjY0xMjJi2LBhvPnmmzRo0KDEcpcuXaJ///5ERUVV5jDVWnW6wBKE58mtpBy6/nSU6Z2DaOr7eBIqCg+nUmuZuvoCQ5p48XGvWlXdnGdSdTonHDx4kM6dO2NlZcWFCxeoUePZyUyv1Wr59ddf+f3337l16xYSiYT69evz6quvMnp0yfMoly1bxrx587h58ybGxsa0aNGCmTNn0qZNydmUH0YE3UJlpKWlMeHVNxgz8Q3OnzlBcM26nDp+iNffnllmXZ1Ox0+zZyHPyWXmF9+W2Iu6YfVy6tRvREituuzYvA57R2fuRt6j//DxD9332j8WUrthc+7cvMqta5dQqhTIMtPx8Q+h9+AxeHr7cfncSRLiojE2NuHS2eNkZRUmLDY0MMTeyZm+Q17G179wNMz9yFucPrIHeY6MUROn65O1/dv9uxGcP3WIoFr1uHXjCkgkSAA3dy9at++KkZEROzauxMRQR4/eA1k471vkOTnUqdeIPgMLO+RiY6I4dfIEfQaNKvP9g8Ke/WOH96LVFC5xduvqGXr36IqFoxeNmrQqsU7knVts2bIRa1tH/dJoUqkZ7Tr3wsauaILW9NQkdmxYxsTXZ5R7tNf+jb/zzvQ3y1VWeHaV95xQ6Tndn3zyCZMmTcLJ6eHzMho0aPBCBtyCIFSdP07dx8bMmIZetlXdlBeGiZEB7YOdWXc2lre7BIks5tVcXFwc3bp1Y9euXTRu3Jhhw4ZRq1atEpcMA3jppZeeWtsMDAyYPHkykydPLnedsWPHVmh6mCA8btPensHbH33JxrV/MGHK23z87hQ+/3Z+uepKJBLenPEJM9+dTPjJI7RoHVaszKDhY/ht3nfYOzihUCho2SaMnds2lrnCwJDRE9m4Zilm5la88cFXGJuYkBB7n12bV4GuMNCs/3dQev3yWca/8SF2JczJLlCpOLJvK/du30Cr1TB60tvFAu4HZXLl2Qwd8yoSiYRGzVqX2C5ZZipmpiao1QWMnfA6m9ev5PyZk7Tr2BUbWzssLCwx+XtId3l4evsy/KV/epXnfhVDZEwyY7sNLbWOf2AwvXv3Y+eOrXTpOwJXd+9iZbRaLWeO7+f+neuMn/x2BadXialYL5JKXxV5e3uXmlAlIyOD7du3P9WTsCAIAkCKTMG6c7EMbOSJkWHVJX16EXWq6cL2KwlsuhjPyOalz9cTnn1jx47VXzxmZ2cXG7L9X+J8/+RJpVLWrVun/79QfcjlcswsbcnMSCOkdj1OnzhCuw5dKxSgGRgY0KJ1GLu2bS4x6AZo1qotq5YvZNLr76JSKrGxtuZC+FEatyg9mZhEImHg8JeJj41my9rf0agLE6gF1WrA9r/+ICs9Df+Qunh4+eLg5MLvc77EwMCA4NoNcHB2RZGXR8z9O6QkxmFlY0vDpq1JS0li8dz/UatuY6zt7MnOzCArIxXQ0a5TLzy8Hn5+uHvrKsEhtahbvyF7dmxmwNDRaHU6AoJC2LZ5LaPGvoq1jR3ZmRnlfv/+LTMjjZiY+0ye/mGZZQODa/Kabw327dnO0b2bMDW3xs3DG5VKSWpiHHcirjB8zKu079i5wm2wtyv5JqbwfKr0Fem4ceOIjIwscVtUVFSp86cFQRCepEXH7mFiaEDnWiKL9tPmZCWlsY8dy07cL3FJJ6H6+e9SYTqdTv/z4LHwdBgZGTF48GAGDx6MkZEYSVKdfP7lV3TvPYgTRw/Spl0ndm5ZT4euFc+a363XAGTZGSQnJpS4vU69Rmi1WszMzDm4bwcjxk7ixqUzZKanlrlvDy8fhoyehFyWiVyWjkaRySsTXuPjz74iPTmGi2eOkZYSj72jE5Pemkmb9l1wcHDAwdGJDl378vp7XzDlnU+p16g5memp9BsyhqatQrl9/TxH926m76DRjBg3pcyAO1eew9G9W2nTriN29o5kZ2UCENapG9a2tty6cQ2dToeRkREadYF+2HdFHNi1GWcXl1Ln0f+XiVRKzz4DmfTaG/h4uIAyi2YNa/PS6FEEBQbhH1Sz7J38x/YNfzJ40IAK1xOqr0oH3Q870SoUimKJHgRBEJ60zFwVK07H0LmWixjeXEW61HLlToqc0/cq1wMhPDv+HWD/O9D+93ZBEMoWn5BErTr1MTY2/nstbNtKXSebmJjg5u7JjeuXS9yenppCcM06hf9PS8Xdw4vxr77B5tWLSIqPeei+FYp8liz4BkdnVzzd3ek/aAROzi5YWlox48PPaNasGdkZaWRnpLHwx8/Ysm45CoUCd08fzMwtuHPzCovmfMmBHRsZNHI8NQJDOHloNy2at6JBo8asWvIT8pzsh7YhMz2F1Yt/4pXX3tSPprWysUGpUODrF4A8JwcrK2vkOTIAWrYJ48yJwxV6DzUaDQVKBe4e3pUK2Nt26EpGehoent6YmpnRpm17wo8fqtA+tFotaSmJJCYmVvj4QvVVoavSmJgY7t+/r3988eJFFApFkTL5+fn89ttveHsXn/cgCILwJC07eR+tTke3Om5V3ZQXVm13azztzPhp/22a+bXA0EDMWauODh2q2EWk8OSp1Wo2bdoEQP/+/UVvdzUiMSgaYBtIKj/1ydDQkLxceYnb8vLkWFo9WD2i8KaYiYkJU6a9x8Z1K4mLj6N1++7UrNNQXyc5MZ5De7YQF3MPczMzpMaOdOras9gxu3TvQ5fufbgfFcm1yxc4eewQS+Yew8nVg4ZNW1MjsCYT3vgApSKfA7s2cufmFYaPepmAoBBuR1xj1NhXWbd6OaoCDW069sDT+5/kjPcjIzh1ZC+WFuZMmfZekbXHzczMycuTIzU1BXQYm5iQl5uLlbUNNWvX5fCBb6ldvxFW1rblev/+WrWYzt16c+zwXpRKBWYlJHp7GAMDA7S6f4L1mrXrcGDfLuo0aFzuNmxctZjRYyeyacs23nv37QodX6i+KvSNvXTpUj799FP9cLOSkpg8uPM9Z86cx9NCQRCEckiRKVh8PIr2wc5iXe4qJJFIGNvKl//tvMkP+27xbteQh5bPU6nZeCGe43fSkCvVKNUaetZ1Y1QLHzEnvwq1a1f6HFChaiiVSoYMKVw3WC6Xi6C7GtFq1H9fHxdeI1emh/WBAnUBllYlZ0i2sLAiOyvr70f/3PA0MDBg0LDRaLVaTh0/wp8LvyUlOQmNRkNGeipNm7bilUmvY2howPUrFx96fF8/f3z9/OnVbzAAd27d5NSJI9y8Es7+HWtRKRUMHfkyLo72BAQ9+P7XITU15aWXX6WgoICD+3Zy9the/T49PL15ecLkEnv/c3PlWFg+eL0SlEoFFpb/DAt/eeLrzPpwGq+/9zn2DqUnd9bpdGxas4x69Rvi6e1DrjwHU1Ozh77Wkmg0Ggz/cxNlwquvM2/OdwwaPanMNmxeu4z69evj5x/AmZMHUSgUmJqaVrgdQvVToW/sIUOGUKdOHXQ6HUOGDOF///sfgYGBRcpIpVLq1KlTZC1tQRCEJ+2z7TcwNJAwsJFnVTflhVfb3YahTbyYfyiSBl52Jc6vT8lRsPhYFKvOxJCrVFPTzRpLqRFaHXy67QZrz8Xy9YB61BcZ6AVBqOZCgv25eO504dDmggJycmQUFBQU6dEtj/z8PBLj46hbv1GJ2x2cnLl98xoAbu6eRN27g1+Nf67TDQwMaN22Pa3bttc/d+XiOVQqJZ5ehcOt9+zYXKE2BQbXJDC4Jgf37sDJ0REvHz8i70TQpl1HfZmatetz+eJZGjRqhrGxMV179C33/nNzcjAxMeH2zWvY2Npx68ZVLCyt9NtvR1ynd/8hHNjxF1lZWfQbNhYnZ1f99oKCAg7t2UpyQixhHbsSGFw4/zopKaGCmcYLnT5+mMZNWxZ5TmpqyhvT32PVn0vQaCGsa99ibTh+cBfxMffo2LkrQX+3wc3Dl4SEhGdqSUbhyalQ0F2zZk1q1iz8RVm6dCm9evXCwcGhjFqCIAhP1qFbKWy/ksjkMH8sTUXvz7Ogd3137qbKmbbmIuPb+DGkqRd25iZcjc9mz/UkVoXHYGggoUOIM11queJk9U825rspcpaeiGLYb6fZPS0UHwexHvGTdvToUQCcnJyoWbOm/nF5tW3b9kk0SxCeCzPeeYfJb85g6KhXOLx/F4NHjGXXtg30GTC8QvvZtmktzs5upfamnj19DDNzC+Q5Mtp26MqKpb8WCbpLUrdBY5Yu/JkmzVtjYGCAmZkZufKcIoFtedy/dxeJRELPvoO5cOYUDo7O+m0Nm7Rg2W9zadCoWYX2mZKUqA9eTxw9CBKo17BJkWD5XPgJxk58HTd3T25H3ODi6SNkZmbo54RHXL/CxClv49NvoL5ObHQUxkaGZGdlYmNrV6E23bl1nbAOHYs9b2xszJiXJ6HIz2f3jq1cuXIRT29fJBIJOq2GDp260adPnyJ1TM3MyM3NrdDxheqr0lenY8aMeZztEARBqJR8lYaZm65R18OGNgGOVd0c4W8SiYRX2/mz+kwsi45FMffgXSQS0OrAUmpE7/rudKvtioW0+GkowNmSmT1r8cGmK7y19hLrX20l5oY/YWFhYUgkEgYNGsTatWv1j8tDIpGgVqufcAsFofqSSqXo1EoMDAyIirxNhy49WbX8N3r0KX8mepVKxbnTxxk57tUSt+t0Oi6dP8PIsZPYunENI8ZMRCqVkpmRhp196edGiUSCk4srsdFRePn40aVHX7ZtWsuw0a+U+/VF3LiKtY0tUlNTzpw6SoPGRYNriUSCvaMT8XExeHiWP+fT9s3rGDFmIqkpyRgZG3Pr5jU+mDVbvz0pMR5bO3skEgn7dm1l/GtvFRmifj/qLg4ODvj4Fe1J3rNjE5PfeJctm9Ywatxr5W7Pvbu38PD0emgZUzMz+g0aiiw7g5fGl/xZPZAjy8bWtna5jy9UbxUKuj/77DNeeeUV3N3d+eyzzx5aViKR8PHHHz9S4wRBqB5uJMjYcimebVcSSJEpkUjAwsSIlv4OtA9xpmstV2zMH/88a51Ox3t/XSY1R8nbXYIqNVRMeHLMTYwY38aPkc29CY/KQKvV4e9siYetWZlBtJmJIa+1C+Cz7df59UgkU9oHPKVWv9hEhnJBeDLmz/2RYSPH8vJr01mycA7jJr3J7M8/4INPZut7ZUuj0WiY/dn7eHj5UL9h02LbdTodyxbNpUefQdja2ePu6c2xQ/sYOGwMv837lnGT3nzo8lg9+w5m4dxvGTJyHA6Ozri4unPiyAFatyveo/tfSYnxHNy7HZ0OuvXqz+kThxk5tniw2bv/UH79+RuGj5mAnV3Zo2T37txMnfqNKChQsWr5QrIy0+ncrY/+dWRlZvDXmuW8+sZ7bN6wilahHYoE3GmpyezetpFJr79bZL9bN66heYvW2Ds44u8fyOEDuwnr2K3M9qQkJ7F/91Ymv/FOmWWh8CZJWRLj7uPpOaRc+xOqP4muAmdUAwMDTp8+TbNmzcr8gpBIJGg0mkduYHUmk8mwsbEhOzsba+uSk14IQnWmKNDwxY4brDgdg7WpEc38HPC2N0Ong2xFAVfjsrmbIsfMxJCRzb0Z36YGrjaPL2HID3tv8fPBu0zrGEjzGmKqy/NozdkYtl9JZOcboQS7Vmy447PmWT4nPDinDxo0iHXr1pV5jv+3F+18X1WfY25uLpZ/J5CSy+VYWIhpF9VNeno6L0+YTMdufbl7+ya16zZg3+5tvD79I1zc3EusEx8Xw7wfvsTR0YXpH3xa7OZyRnoqm9cuI0+eTZtOvanfsBkSiYRD+3aSlpZC5669Wbl8IZ269dEvJ/ZfObJsFi34AUV+Pt37DKRBo2Yc3LuDrMwMevUb8nfm8KJ0Oh2nTxzh+JH9WFhY4uNXg6OH9jLzs++xsrYp8TiXzp9h0YLvmTDl7VKHmufl5bJ5/UpqBARhbWPH9s1ryUxLo2PX3nTq3huAq5fOc/zIfoaMHMe2Teuo36gpDRs317frwtlTXDh7inGT3tSPJMiV57B+1TLqN2xEsxat9cc7fGAv8fFx9BkwHFOz4onVdDod58JPcPXSWcZPer1cIxOuXbmELDuTVqHtSy2TlZnB2eO7eXXihDL3JzzbyntOqFDQLVTMs3yBJQiPKiY9j9dWnud2cg6jW/jQPsQZoxIu1DPzVOy9nsS+G8kUaHSMbe3LlLCAR+75Xn8ulnc3XGFYUy/6NvB4pH0Jzy61Rsv0dZdoF+zMd4PrV3VzHsmzfE5Yvnw5AL6+vrRr107/uLxepClnIugWHoVGo+H773/i/KUrGJmYYmZmQWx0FLb29rRp1wkvHz90Oh0xUZGcPH6I2Jh72NnZ4eUTQFBIHXxrBGBoaEhCXAy3b1ykdkggw4YOxsLCgiNHj3H0+CnMLO3x9Q9GLpdx6tghkEhwcHRClp2Nl48vgUG1MDY2Jjk5geOHDwDQvFVbIm5eJTE+FmsbO/z8A3FwdOL6lUsYGhnSpFkrbO0cyMvL5cbVy9y5dR2VSoW1tTXZ2VkYGRrh5eNHxI2rOLu40SasE45OLhSoVFy7epELZ07h6u5B994DuXLpHDeuXqJGQDANGjfD1NSMzIw0Lp4LR60uwNrGlriYaBITYpGamjFw6GgsLa25HXGd+LhorK1tSU1NIi0lma69+uPp5YtCkc+Fs6eJuH4Fd08v2nfqgdTUlLTUZM6eOoaziyu9+g7E3qH4UPukxERW/7kYQ0MjmrYMxdrGFqVCwd3bN7l5/TLNW7ama4++5RpNp9PpmP/TbKZMe++h5f9cPJ+3Xp+EjU3JNyiE6kME3c+AZ/kCSxAeRYpMQd/5J9DqdLzZMQg/x7Iv/vJUanZeTWLH1QRMDA14LSyAMa18MDepWGoJrVbHT/tv8/PBu3QIceaVNn5iWPlzbsuleDZeiOf0hx2xtzCp6uZUmjgnPB+q6nMsKChg5cqVAIwcObLCma+FZ8/Vq1c5efIk0TGxmJuZYmJiwr3795EamxAUFMhLL72ElZUVBQUFXL16lYiIW8jlObi7exAQ4E9wcHCJ57/U1FQiIyPJzc1DLs/B1MyM+Lg4zMzNsbaywtDQCI1GQ3Z2FgqFktt3bhMbG4uhoRGWFuaYm5sjy5GTmBBPdrYMmSyHtPQ0DAwMyZXnYGhYeN5WqVRIJGBtY4tarcHQqHB4tyI/D61Gh8RQgqGhETX8A1Eo8ilQqTA1syQ9NYm8vFzkOTLsHByxtrZFoymgQKXCy9sHZxcPagQG0rJ1W2JjoklKTODi+XBMpWa079IDXz9/AoNrodVquR1xgwtnT3Ph3CmcnFyQ52TRsWNnrl29Qla2jGGjxxMUUuuho3eiIu9w+OBeRo2dyJ1bN8nOysTUzBxfvxrY2tnzy9zvGDhkFC6ubg/9PHU6HcsXLyCsQ1d8a5Q+JWrvjk0E1/CkQ4ewcvyWPF7Xrl3j5MmTKBRKpFITWrVqRd26dZ96O54nTzzoVigU+jtcD6xbt44LFy7QqVMnOnXqVJndPlfEBZbwPMpXaRi68BRxWfl83rdOhYOgrDwVGy/GczAiBVtzYyaHBTC4iSfWpmVfQCZk5TNz8zUORaQwpKkXfeu7i4D7BSBTFDB11QXe6hzE5LDqO7dbnBOeD+JzFF5U27ZtJ/zCFTIzs+nYpQf1GjRGLs9h1/bN5MpzkEgMyM7KJD0thezsLHxrBGAgMSA5KQH/wCCMjU3Q6XRIgC7d++DuUThK7X7UXY4d3MuwESP5dcF8Jr3+DiYmxa8t8vPy2LVjCxkZGRgaGZKanISllRUeHl5069GHzX+tISQogJatCoePX7xwnsNHjtB30EicXVyL7U+lUrF/z3b27thCl5596dqjb4lrhcdG32fOd1/QrVd/2nfqVmKZmOgo/lzyC65uHowaO6nEIfkZ6Wls37SGsDYtaN++XUXf/krT6XT89ddf3L0bSWBgEKGhoZiamqJQKDh+/Di3bkXg71+DwYMHi2uqSnjiQffgwYVDWZYtWwbAzz//zLRp0wp3KpGwbds2evToUZldPzfEiVl43mi1OqauvsCBmyl80rt2uXq4S5MiU/DXxTiO30lDamTIgEYedKntShMfuyIZrQs0Wm4l5bAyPJr15+IwMzFkcpg/DbwqtsyHUL39eiSSO8k5HJ3RHiPD8s83fpZU93PC5cuXuX37NkZGRoSEhOiXEH3RVPfPURAeRWZmJqtWr+XS5atokODh6YOrmwfotCQlxBN17zb9+w+kdZtQdu3aQUpqOn0HDCsxCNVqtRzav4ukhDheHj8BiURCeloavy9aSO8BQ6jhH1RiGxIT4lm/5g8GDB6Bp5cPOTkyNqz+g2uXz/HLb0uLlM3Pz+f/Pv4IL28fnFzcsba1RanIJykhHgk6unXrgY+vL2tXr+TMmTMEBIfg7u6FiakpmRnppKWm4O7uQaeuPdm+ZQNnT5+kVt36uLp5YGJsTHx8LKnJCdStU5du3XuQmprKj9/PJjC4Js6unpiZmZOTk016SiLubk4MHzpEP03kaSgoKODrr7+mW7fuBAeHlFruzp3b7NixnQ8++ECMoqmgJx50+/j4MHv2bIYNGwZAQEAArVq1Yt68eYwfP5709HQOHjxYudY/J8SJWXjerDkTw/sbr/JWpyCa+dk/ln1m5Ko4GJHM4VuppOeqMDKQ4GlnhrmJERJJ4ZrNSrUWGzNjetRxpXMtV8xMit9lFp5vUWm5fLjpKr+Oaky3OsV7LKqDZ/mcEBsby6FDh4DCtbq7d++u35aSksKgQYM4ceJEkTqNGzfmzz//JDg4+Km2tapV1eeoVqvZs2cPAF27di33UlOC8CSo1Wree+89tFodEomEMWPG4OTkzMqVqxjx0sv638+EhAR27tiGqkBNYHCtv+eFy7kTcROlMo+wsI7UrFWr2L4P7NvDnbt3cfPwwtPLG5AQHR3FtcsXsLG1o1XrMHLz5Ny+eQ1LCwusrKxITojm5YlTMf1PgP/L/J+Z+NrrZGSkI8vOxtTMDEdHp2LBZXpaKps3bSQ1NRVvP3/c3DwxNDLk6uULxMbcp1adetSqXZ/kpAQuXzyLhbkZAwcNxtvbt8h+li9ZyPS33iQ1NZXc3FxsbGxwdHR86r3IOp2O//3vK4YOHYazs3OZ5VNTU1m1agUzZ84UPd4V8MSDbnNzc/bs2UNoaChRUVH4+/sTHh5O06ZN2b17Ny+99BIpKSmVfgHPg2f5AksQKiolR0Gn74/QwNuW19o9/iG+Op2OxGwF1xNkJMkUqNRaNFodnnZm1HCyoIajJSZG1bOHU3g8Zm29jpOVlBWvNK/qplTKs3xO+O6775gxYwYSiYRZs2YVWfKzS5cu7N+/v8R67u7uXLt2DVtb26fU0qonEqkJAmzcuBEPD08WL17Md999r3/+9u3b3Ii4Q9fuPYuULygo4Nq1K/z0w3e8Me1tgoJCsChHj29iYgJJiYks+f1XZrw/Eytra2JjYsjPz8fOzhb/gCAMDAxY8ts8Xho9kt17DtCn/6Ai+wg/fQqJgQFNmpbv3KHRaIi8e4d1a1bSoWMn6tarj0ajIS42FoVCgZ29HQEBJS9Tmp+fz5aNq3nzjTfKdawn6dChQ+h00KRJ8WXmSnPhwgUKCpR07tz5Cbbs+VLec0Klb5Oam5uTnZ0NwLFjx7C0tKRJkyYAmJqaIpfLK7trQRCeQZ9vuwHAyOY+T2T/EokEd1sz3G2LL9khCACtAxxYfjKa7LyCJ7Lu+4vs0qVL+v8PHz5c///Tp0+zf//+IheX/75Xn5iYyPz58/noo4+eSjsFQXg23Lp1m7t379K3b98izwcFBbF02fJiQbexsTENGzamVq063L17h4aNmpTrOG5u7shzcujcpTte3oXXH7a2RaeX3bkdQUhwIF5eXqSlJRfbR7PmLfj5p+/LHXQbGhriV8Of7KxM2oT+M/fa3r7spUl3bt/CkMGDy3WcJy08PJzJk6dWqE6jRo1YsGCeCLqfgEoH3XXr1mX+/Pn4+PiwYMEC2rdvrz8px8TE4OpaPYf/CYJQ3OFbKWy7ksjkMP9yJTwThCehkbcdS07c5/DtFLFM3GN240bhTTUPDw8CAv4ZybJhw4Yi5YKDg3nppZfYvXs3R48eBWDnzp0vZNCdm5tbYkKlJ3m8kv4vCE/b7du3cXd3Z9++fYwa9VKx30dFfh779+3VJzT7t7Evj+fTT2bi7eVN3XoNyjxWRmYGSxb/xkczZ5FXwu99enoae3Zu4YP33yM3N5emTZqwY/sWwtoXTejcqnVbli/9ncFDhhfbx39ptVp++3UuderU4uLFCwQFlW8KjUyWTfjpk4wZPbzK/0aTk5OxsrKuVDtsbW25e/cubm4Pz9YuFCrve1zp4eUHDx6kV69eKJVKTExM2L9/P61bF/5xDR06FI1GU+xk/aJ5locSCkJ5abQ6uvx4BFNjQz7qUVPM8xGq1Eebr1LLzZp5IxpVdVMq7Fk+J3h5eZGQkEDHjh3Zu3ev/vmmTZty4cKFwozDEglXrlyhdu3aKBQKvLy8SE9Px97enrS0tCps/dP14HMUBEEQhAee2PDyDh06cPPmTc6fP0+DBg2oUaNGkW0NGjSo7K4FQXiGbL0cT2RqLp/3rSMCbqHKNfK2Y/e1JFRqrZjj/xilp6cDFJknrFKpuHr1qv5xUFAQtWvXBgqnkTVu3Ji9e/eSk5PzdBsrCIIgCNXMI6W+9PHxwcen+PzOSZMmPcpuBUF4RhRotPyw9zZNfOwIcH56S1wIQmma+Nix4Xwc4VHphAY6VXVznhsPbqglJSXpnztz5gwqlQqJRIJEIiEsLKxInQeZf62srJ5aO58lCQkJTz2RmouLC1A4dFQkUnsxaLVaFv32GxSo6dm5G7a2RUdZyGQ5/LlmFZH3o/j43fexs7MFYPEfy8nKymL08OE4OxXNXJ2fr2DLjm2cOH0aWztb2rRoSXZWNgnJSbg4OZMty6Zb5y5s37mD9MxMBvfrT61/LRG4e/9+ajZswJ9//sGHHxafWrJ06WLGjRv/97Hy2blrF8nJKfrvDK1Wh1arpmOHDgQGBnLq1CmOHTtOviKfhPh4+vbti6WlBe3ahaHT6Vjw008EBwWRI5NjZm5GzaBgIu9Fcub8OZBIqFuzFt27dOX4yRMoC1R07VQ4H1mpVLFr/z6u3IzAztEBE2Nj/bFbtWyFXC4nNS0VQ0MDPNw9aNiwIRKJpEj7/3mfZWzbtg1ZjlyfmV2j1WJkaEC3rt3w9Cyc8vThh+8zb968Uj9PpVLJgnnzsLeyoXunLpibF81jk5qayqoN60lLT2fGm29hZVX02iszM4sd+3ZjJJUyfuKEUjtDClebMKBu3bqltqU0N27cQKnMp127p7eWeHUmk8lwd3cvs9wjrzeRkpJCdHQ0+fn5xba1bdv2UXcvCEIV2nA+jrjMfKa0f/zZygWhMrztzXGylLL/RrIIuh8jDw8P7t69y/nz57ly5Qr16tVjwYIFAPqh5f8NuuPi4gDKtRTN88jCwqLKAt+qPLbw9Oh0Ov73xRf069oDHy/vEstYmFswY9p04uLjWb1hHW+//ibfz/uZjm3DaFTKqFMLcwvGvzSWAX36MeurL4m6fx8jIyP8/fzw8vCkdYuWfDb7K0ICA3lv+jvF6rdr3ZrVWzeTlZWFkZERUqm0yHatVoe5uTkSiQQLCwteGj36oa+zc+fOdO7cmVu3Irh37x7nz59n+vS3MTc35+SJE6SlpfPetF5FAkxnRwdu3opg+tQ3MDExIVsmIyYujtcmTCzyOkcOHkL3zEy27t/DS2PGPrQdJbVfvy8LCyZOnPiQWnArIoJAHz/OhIfTvkOHYtuVSiVfffElE0ePxd7OroQ9gIWPBR++/S7Xblxn687tTPxP8G9hbsGkseO5dz+K+T/PZcYH75OVlcX9+/fJz8/H3t6eoKAg2rRpw6+/LqRFixbles3/duXKZSZOnCC+Y8pJo9GUq1ylx+YlJibSqVMn3NzcaNGiBe3bt9f/hIWF0b59+8ruWhCEZ4BSreHnA3do6e+Aj4P44hWeDRKJhIbetuy9kUwlU5IIJXhwk1yj0dCoUSMcHBxYu3at/qLTxMSELl266Mvn5ORw7do1JBIJHh4iqd3TYGJiwrx585g3bx4mJiZV3RzhKfj9t0X06dyt1ID73zw9PBg2cDDvz/qY0BYtSw24/83O1pZZH3xETFwsVlbWZGRm0rpFS1atW4uHmxtDBgwqsd623buQZ8sYMGAgf/1VPH9Ts2bNOHv2TJnH/6/9+/fToUNH0tJSMTc3B+DcmTNMnjCBP9es1pfT6XT8vnwZ77wxTf+3sHn7NoYNHlLifu3t7MhOzyj3OaOy7d+3dw9TJkziTHjJdef8+ONDA+5/q1OrNs0aN2XLjm0lbq/h60fn0HZMGPcy65b9SUZULLrMHG6fu8R3X37Fn8uWo1AoUKvVFXoNGo0GjUYtAu4noNJB99SpU7l48SKzZ89m165dHDx4UP9z6NAhDh48+DjbKQjCU7b1UgKJ2Qr6NxQX1MKzpbGPHYnZCm4mirnEj8ubb76pHzKp1WrJzMzUb5NIJIwZMwa7f10obtiwQX93v02bNk+3sS8oY2NjpkyZwpQpU/TDdIXnl1KpJCczCz8f33LX8fTwQKPW0KJZ+ZbGgsLAu1XzFiQnJ1FQUADAyTPhjB1Zcu90fn4+BSoV3Tp0Qi6Xc/z4MZRKZZEyzZu34PDhwxW6MZqYmIilpSUrV65k0qRXAYi8ewd3Zxdq+PqRl5enP86xkyfo0aWb/jtLp9ORLcvGzta21P23a92G3Tt3lastlWl/Qnw81uYWGBoa4mxnrx8J9EBSUhIO1rblCrgfaFCvHtGxsaW2IyggkEDfGgzt3Y8WjZvSoE49Ooa2Y8pLL9M7tAOylDTWrV1b7uMBbN68iV69elWojlA+lQ66jxw5wnfffcc777xDly5daNeuXbEfQRCqJ51Ox29H79HI2xZPO/Oqbo4gFBHiao2xoYRT99KruinPjbp167Jo0aIiPag6nQ6dTkfjxo359ttvi5T/95zFTp2KLs0jCMKj2/jXRnp06lJ2wX85eSacQf36V/hYfXv0IjEpCVdnF3bs3U1QQECpy+Ft27WT3t170rBePQ7u3UPHjp344IP39QE7FN6oGzx4CPPnzytX4JqRkc7y5ctwd/cgNTWFmjVrERcXx1/r1jG4bz8Aenbtxo49uwG4dPUKDerV09dPS0/D0/3hHQQN6tYjMzWV8+fPldmeByN4vvlmdrnb/+fyZYweWrgcWbvWbTh+7HiRMqtWrKB31+5l7uu/2rRsxYnTp0rd3r1LV3Yc2FfseRtraz5+6x1u37jBkSNHynWsU6dOYGoqJSQkpMLtFMpW6aBbIpHg5eX1ONsiCMIz4sjtVO6kyOlZr+zEEILwtJkYGRDobEW4CLofqzFjxnD79m2+/fZbJk2axNSpU1m1ahUnT54skiwtJSWFPn368Mknn/DJJ59Uas6gUHEajYbDhw9z+PDhcs8hFKqvhLg4PCs4dePUmXBaNG1W4WOZmpri5+uLVCpl9bp1DOpbeuCelpGOm6sriUlJdAhthzo3HxdnZ954YyrXrl3Tl/Pz86N79+7Mnv01UVH3StyXTqfjyJEj/PbbbxgZGXHmzBneeedd9uzexZ9Ll1AzIEg/xcXL05OklGSUSiUW5kU7A7JlOeVK6DhqyFAib0awauUKFApFiWVkMhmLFv2Gg4MDISEhTJ/+FlFRUaW3//Bhli5axNuTX9ffqLC2skYmy9aXS0xMJPpelH7IfEU0qt+AK9evlbq9hp8f8YkJpW7//J332bxhPcuWLUUul5dYJjc3l+XLl5Odnc3w4WWvYy5UTqUTqQ0ePJjt27eLO9yC8BxadOwe/k4W1HR9MbMSC8++EDcrDtxMQavVYWAglrJ7XLy8vHj77bcfWsbZ2ZlPPvnkKbVIeEChUOjz5cjlcjHn8jlnKKl4v5hOq6300p5mpmZkZmdhZGSIy0OSIxr9HVimpqXi4uxMq+YtiE9MZOO2rXwz+2usbaxp0KAB3t4+aDRazMzMeffddzEzMyM0NBQfHx/UajX370cTExNNZmYmqakp9O3Tl4KCAn6dO5eObdvRceqbrFi3psixDQ0MyZZl42DvUOR5SwsL8vJyy/U6hw0YRHxiIn8sXkyBVouHlxcW5uZky2TcuhWBUqnEztaWHdu2Y+/oQJcuXVi7ZjXZMhnBwcF4eHii02qIi4sjT5ZDmxatePf1aUWOIc/NxdKy8PopJyeHX+bMxdvds1ztK4mhQcmjDvTbjR6+/Z2JUzh29QIbN/5Fbm4u3t7eWFhYkpeXy/3797GwsGDIkMH61RGEJ6PSQfeQIUOYMGECWq2W3r174+DgUKxMo0aNHqlxgiA8fdcTsjlxN503OgSIdbmFZ1ZNV2s2XojndkoOIa5Pb9kmQRCEp0FSiZuJj3LO1qHDyNAInU6HRqMpdXj5A0ZGRmi1WgA83Nx4feIkXlOP5/u5c/B39yI5PoGklBSkUim/fPs95ubm3L57l+MnT3Lz9i2sLC0IC21Ho/r1keXIMTWV4uzohJlZ4RJa+fn5GBkWDVMkEjAwMCw20sPJ0ZHY+Phyv1YPNzdeHTcetVrN9t07ycnLo27NWtQPCMLY2AgnRyeMDA35beligr18CPH2xcnRCZVKRUZmJiYmxrRt3KzUnuvDx4/SuFXhCKDli5cyeeRYVm35q9zt+6+yPlYJDy/g7elJxoE9vP9/M9FoNCQmJpKTUzg6YPDgQWV+1sLjUemgu8PfqfDnzZvH/Pnzi2x7sLyIGP4kCNXPkuP3cbI0oZlf8RtpgvCsCHSxxMhAQvi9DBF0C4Lw3FFVMOs0gKGREWq1Wp9grCJkMhk+nl5IJAZERt2jZnDJ83pVf8/ddnVx5eKVy7Rp2Uq/zcjICCcnJ5o3aYKlhSWZWVns2LMbF+fCHtTGDRrSuEHDYvt0cy1+nMioe3h7Fu0dVhUUYGtjQ2paWpHnDQ0NMTE2Ji8vr0JDuI2MjIiOieGNVyeXeMPCwNAQX2+fIu+nYwmdjP8VFXmPAp2WWrVqkSfLwcrSElWBqtzt+rfCbOLaUrfrdLpy7dvMxET/u+HpWfled6HyKh10L1269HG2QxCEZ0BGroptlxMY2MgDQzFkV3iGSY0M8Xe25PS9dMa08q3q5giCIDxWjRo34tKVognDytKzSze27NzBwD59K3SstPR04uLjcXJ04v3p09mwZQsz3y056A4JCuL6zRvUrlmL9IyMYtt7d+vO1p07GDF4KHa2tqRnVC73xuHjx3ht/AT944tXLlOvdh2MjIxQa9TFeuP79uzFhs2beGnEyHIfIy4uDhdn51JHCDjY2bN521YG9R9Q7n3eiLiJRAcZySns3rWLTq1CAbC3tiU1LRUnR6dy7wtg78EDdGgXVur2s+fP0ahu/TL3Y2VhSU5OTpFVKISnq9KJ1MaMGVPmjyAI1cv6c7Ho0BEWXPp8LkF4VtR0tSI8qvxrrwqCIFQXHTp25PjZ0xWqExQQwKGjhyt8rE3bthASFEyOPIeQoBASEhPJy8sruV1twzh4tDAbdoO69bhw+VKR7U6OTqSlp+u/lxvWq1+sTFnkuXLMTE2LBNXHTp4gtFVrADqFtWf/4UNF6rg4u2AqlXLuwvlyHUOWk8Mfq1eWmjROqVQSGx9HTq6cmxE3y7XPtPR0dmzfgYmhEa0bNOXIwcME+wcA0K9bT7ZsL3nN7Ye5E3mX4MDAUrefCg+nVZOyk+fl5uVhaWlZ4eMLj0+lg+5/u3XrFidOnCA3t3xJDARBePZotDr+PB1NixoOWJuJNWCFZ19NN2syclVEppackVUQBKG6kkgk1G1QnyMnj5dd+G8nwk8THBjEH6tWlrvOjYibXLxyGYVKiVpdOC30pREj+d/335R4Q9PAwAB/vxocP3WS0Fat2b1vL7n/SWLWtWNn/li9CoDQVq3Zs39fsTKl0el0/LL4dwb06ad/7sjxY4QEBet7pEOCgrl24wZp6UV70YcMGMjtu3fYuWf3Q2/GRt6LZP7CX5g2eWqJQ/GVSiU/zPuZCWNfZuyIUZwMP83Bw4ceus9rN66zdOlSpo2fhKmpKQ1q1yHzXyMBLMzNMTMx5fqN6+V6HwC27NhG8yZNS91+4PAh6obULNdc/jylAmNjcW1XlR4p6P7jjz/w9PSkVq1atG3bllu3bgGFSdYWLVr0WBooCMLTceR2CnGZ+XSpJbJXCtVDkIsVBhI4fa/4EEdBEITqrmevXmTmyjl8/FiZZY+dPEF0TDSvT3oNG1sblpcj8L5y7RpzflmAm6sbOq2Wls2a8cfqlbRo0pS6tevy6ddflpifqUeXrsTGx3Hk+DHeeG0yP82fR2ZWln57cGAgAf7+/LlmNRKJhNdffa1YmZKo1Wp+nD+XAb37YGdrC8DBI4dJTkmhS4eORcq+PulV5v32K/EJRZfLGjF4KO6ubsz9dQHLV64gJjaWHLmclNQUdu/by7T33uX7eT8zbvRLxeZ/a7VatuzYzoyPP2TSy69gb2eHRCJh/EtjsbCw4Odf5rNy7WriExLIkctJSk5iy/ZtTJvxLtF3Inn31amYmJgAhTdNNFpNkfXLR/YfxLHjx7l05XJZHw1bdmxDKjUtNejef+gg2ZmZdAxtV+a+0jLScXJ3K7Oc8GRJdJUcl7d+/XqGDh1Kr1696N69O1OmTOHcuXM0atSIr7/+mgMHDrBvX/HF2l8kMpkMGxsbsrOzsbYWiX6EZ9vYpWeIycjji751RNZyodr4vy3XCHGzZu7w4sl5njXinPB8qKrPUaVSMWfOHADefPNN/cW98Pzbs3s3l85foG5ILUJbttKfo3U6HSfCT7Nj725S01IZNWQY7VqHIpFIOH7qJFt37qBF02b07dmryFDti1cus2nbVmLiYgmo4c/gfgO4fPUKZy6cx8fbG3VBAa7OLjg6OLBl5w7q1a7D8MFDsPrX8OS4+Hhm//QDEgl06dCpcCh2Tg4jBg/Vry9+/eYNdu/fh7ubG726dmflurUA9OnRE3e3f4LAHLmczdu3kpGZydABg3BxdubI8WPs2rcXS0tL3p76RpEAOSYulh27d2NoaEByaioFBQWMGTEKfz8/fZn8/HxWb1jP5atX8K/hT26unKjoaAwNDcnMysTC3ILatWphaW6OTqcjIyuT23fuYGZqSs9u3bl77x6+Pj707NKtSG/4kWNH2bhtK1qdFgOJAb4eXkwbPwkDg3/6MX/9YymvjBjNTysXE+jhQ98u3Yt8npt2bScmMYHQ1q1pWL+B/nmNRsPeA/s5FR5OcnoKY0e+RIsmTYt83vsOHuDa9es0rlefsFZtyvX78838n7FzdqR5y5a0aNVSXOM9ZuU9J1Q66G7UqBENGzZk8eLFaDQajI2N9UH3li1bmDx5MvEVSN//PBIXWEJ1EZuRR9tvDjGhbQ3ai/ncQjXy56n7XI7L5sT7Haq6KWWqTueEmJgYACwsLEpcEvRFVp0+R+H5cu7cOY4ePqxft1mj1dC6TSjNWzQHYPWqVRzYtx/zv5fdysvPx93Tg7TUVAoUKnQ6HXn5+cjkOVhbWREcHIytrS0ajYY69erSqXNn7ty5w87tO0hNSebunbsYGhiSkytHpVTi5OiEsbExBQUFKFQqmjVrirunB75+foSfPMXFixdJS03FzNQUE6kUQwMDcuRyCtRqNGoN9va2SJCQlJyCnZ0tNtY2qFRKYuLisDA3x87WDiMjQzIyM9HpdNjb2ZGVnY2qoAAHe3sMDQzJz89HnpeLra0tWo2G3Nxc1BoN+Xl5uLu7Y25qRo5cXriWvaUllhbmZGXLyMvLRVVQgAQJxsbGWFlZYmluAQYSsmUyjI2Nsba2RpYjw83ZlYKCAnJkORgbGVLDx5f0rCyUSgUmxiZ4e3jg4+5Ft7AOxYanazQaflm+hIAaNajVqim//PQzX73/cbHPUqfTcfT0Sa7djtAvjabRqGnfui1mUimL1q9i6PDhnDp5AkMDQyQSCWqNmpatW7Nj8xbeerlwGHtZDh07Sn5aFp1btuH8zeucvXMDF19PRo55Sdy4e0zKe06odPbymzdvMnv27BK32dvbk55euWyFgiA8fWvPxmJmYkjLGuLiWqheApwt2XktiTS5EkdLaVU357nh6+uLRCJh4MCBrFu3rsQyM2bMYP/+/UgkEs6fL1/yIkEQKq9JkyY0adKk1O3DR4xg+IgRj3SM4OBggoODK1wvNDS0wnUWLviF3i3aYWNV8ZtX81Yv473/m6l//PlHHzO591DMyhGIAkRERXIjO4lRY0tO/JyXl8dvP81lwtBRFWrXvqOH6dQ2jH3hxxkQMhp1QQEnz4bTqmnzIuUkEgntWramXcvWxfbx7dw5OFnb0qJlC1q2allse82aNfnuq68ZM2AI7q4lDxvXarVs2r4NQ6Wa/h26AtCyXkNa1mtIYmoKn70/kxmzZoobh09Rped0m5ubk52dXeK2+Ph4kZJeEKqJAo2WtediaR3giKmxYdkVBOEZ4u9UONzxcmxW1TbkBRQVFcWlS5e4dOlSVTflhaDRaDh79ixnz54tcZ6tIFQ3g4cNZcv+PRWuFxUTTY3gIP3j27dvE+TiWe6AGyDEz5/4u1GlJkczNzdHZ2xEZnZWufep0Wi4cvM6SpWKmvXqcOXKFXq26cD1a9e5FnGjXPtYvOIPurduR33/YK5evVpiGVtbW/7v8884HXGNucsXc+jkcVLT08mRy4mOjWXJn38wf8ECarp66wPuf3NzcuatQaP55tMvUKkqt364UHGVDrpbt27NvHnzSvxlXbZsGWFhYY/SLkEQnpKDESmk5ijpGCKGlQvVj5OVFBszYy6JoPupEyuWPF0KhYJmzZrRrFkzFApFVTdHEB6Zvb09MlV+hW8i7Th6kH4D/lnqa/GCX+nequyEYv/Vtm5j9u/dW+r2116fwq9r/iS3lOXT/k2r1fLjb78Q1rI1J65fpG+/fsTHxuLt5s4rg0dw4fwFVv21jrz8/BLrR0Xf55u5P9G8dn3qBNfE29Wd+NjYUo9nYmLC6DEv8f7/zcQjJIAdJw/z5Tdfc+3cRQa37cIbw8dS27/0pcbMTE15uWs/Vi7/o8zXJjwelR5e/n//93+0adOGZs2aMWLECCQSCRs3buSTTz7h6NGjnDlz5nG2UxCEJ2RVeAyBzpb4OFhUdVMEocIkEgn+ThZcjMmq6qZUe3/8UfziKzo6usTnExISOHz4MECRBE2CIAgVMWrcGOb9uog3Ro8vV4KvA6eOUadJQ4yMjPh81mdcOHYSf2/fEpf+Kkv94Jos3LWRzl2L9wYDmJqa8v7/zeTb/31NWOMWNGtQcsLOe9H3WbVpA94enly+f4d33psBFM7bliBBIpHwUr/BJKelsmL1KlQaNfb2dphKzcjKziJHJsPPw4u3Rr+iX9ZLIpGg05Yv7VbDhg05snc/n0+ejolx+edpuzo6kXwkrrCdIrnaE1fpoLtJkybs2rWLyZMn8/bbbwPwv//9j8DAQHbu3EmdOnUeWyMFQXgy4jLzOHo7lQlta1R1UwSh0vydLNl9LQmtVoeBgbhwqKyxY8cWufDS6XScO3eOcePGlVj+wUg3V1fXp9I+QRCePx4eHgwYMYyflv/O5BEvITUpOTeHTqdj64E9mDhY0697N0b0H0QtNx+Wf/INv2/fUOnjG5YRbFpaWjLry885evgIc/5cjAkG+Hv5YGRsREJSEneiIslTKPAJqEFY3x7UrFlTX9fd05PYy7fw8/IGwMXRiYlDR6HT6cjJlZOvUGBjZY2ptPhrjk1KxL1hzWLPl6SgoACNPL9CAfcDzQJrcfL4CVqHli8TulB5lQ66Adq3b8/NmzeJjIwkOTkZR0dHgoKCyq4oCMIzYd3ZWEyNRQI1oXoLcLYkR6kmKj1XP8dbqLx/Txsrbb6jRCLRB+g9e/Z8Ku0qSUZGBiEhIaSmphIcHExERESpZf/44w/mzZvHjRs3MDExoUWLFsycOZNWrVo9xRYLQvWTlpbG7FmfU5AtR2pkjE4H2Xly7L3c+XDWx1hZWRUpHx0dzaZVa0FRADodEgMDVBItnfv0pEHDhsTGxvLNp1+gkedjaWpGTl4u8cmJnDl9Cl9vX14Z+RI1vH2QSCTI5DksWb2Cy9eukpKcjIOVLT98+Q2DO3Vj0sDCpHGP0kt79OAhtm/agr+XLybGxuTkycnMz8XOxhYzQyPMjE1QqApQqQtIzcrA2NiIQ/sPYGZmhgQJGo2GApWK2Lv3uHbiDFbmFqg1GiRAfGoKTi7OtG1SPImataUV1pZWJTcK2HPsML5RkRzeuB0kEtSGEnoM7Eet2rWKlU1KSsLL0aVSr79xzTqsOnNQBN1PQaWC7tTUVBYuXMjRo0dJ+HtRend3d9q3b8/EiRPF8iKCUA1otDrWnoullb+DSKAmVGsPAu1LMVki6H5E5V1F9MFwxB49evD1118/4VaVbvr06aSlpZWr3I8//oiZmRldunRBoVCwb98+9u7dy/r16+nfv3+Z+xCEF41SqWTG5DdwM7fhrR5Dsf1PlvGLt67zyWvTkFhb8M3cH0lPT2fRj3PxsXJgTPNOSP+1JJVWq+XQkTP8+H9fYGpswsR+w3Cys2fZ9r/wdHTFxcaeGh7edG0RyqGz4ezfXZhgzczEBA9TG2JMLfAIqklyRjq1awToA26g0snAFEolQV5+SH2NSM/OoqZvDUZ370eWXMaGA7tRFRQwod9Qdp44zI2ou9haWNIgsCY924RhbFQ4DPzguVNcuRNBmwZNaVKz6CjfrBwZ/7foZ9KzMnGwLX+C6bTMDIKc3RnXuZ/+OY1Gw77dR9m4fCXDJowlIPCf+dp5eXmYScufRO7fjI2NKRDJ1J6KCq/TfeDAAQYOHIhMJsPQ0BBHR0d0Oh3p6eloNBrs7OzYtGkTbdu2fVJtrjbEWp7Cs+xgRDIvLzvHl/3qUEMEKkI19876y3QIcebzfs/u1KZn/Zxw5MgRoDCg7tChAxKJhLZt2zJr1qwi5SQSCWZmZvj7+2Nvb18FLS104MABOnXqxMSJE/ntt99K7ek+ePAgHTt2xMHBgVOnThH498XqqVOnCAsLw8zMjKioqHKvulJVn2Nubi6WloXf1XK5HAsLkYdDeHIUCgVTRr/MrDGTcbCxfWjZnScO89fpQwT7+PFWv5H6gLQkOp2OZdv/wt3JhYu3rjOiax9W7dnK9BHjMTF+eL1561dw4so5JvQfTsdm/4xQWbN3O+1atcbNuWK9vet2bqV1cD08nF3ZfeooscmJ5Obn8+awMUgkEtKyMvlg/rf0Du3A7Zj7TBs2tsjc8a1HDyA1MaFri9KXS8vJy+XnTSv5YMq0cvXI63Q6vv1lHpP7DsXKvPjfuE6n49cdG+gyYiB169cjMjKSPxctwcPClpE9+1Xo9UPhWu5ztq7m89lfVbiuUKi854QKZS9PTU1l6NCh2NjYsG7dOrKzs0lMTCQpKYns7GzWrFmDhYUFgwYNEut0C8Izbs2ZWHwdzPFzFBduQvVXw8mCizGZVd2Maq1du3a0a9dOv/qITqfDyclJ//yDn7Zt29K0adMqDbjz8/N59dVXqVWrFu+8885Dy37//fcAzJw5Ux9wA7Rs2ZJXX32V7OxslixZ8kTbKwjVzbuTX+fzcVPLDLgBOjdvja2hKW/3H/3QgBsKb9qN7t6P7ccOMm3YWP7YuYl3Rr7y0ID7Qb1hXXpiJjWlQ9Oia1f3bdepwkuP6XQ6EhIT8XAuzEnRrWVb3Byc8HRx5c9dmwE4ffUio7r35fq9O0wf8XKRgDv82iUMJJKHBtwAVuYWDGnblR9//wWtVvvQslqtlp+X/MaANh1LDLih8H14tecgNi7+g4/eeJsza3cwrUN/UlNSKvDq/7Hn+GFUadnExMRUqr5QfhUKuhcvXoxGo+HEiRMMGjQIc3Nz/TZzc3OGDBnC8ePHKSgoYPHixY+9sYIgPB4pMgUHbqbQPsRZZKwUngsBzpZEJOWgKBDrFz8OUVFRREVFsWDBgqpuSok+/fRTIiMj+eWXX/TZfkuiUCg4cOAAAIMGDSq2/cFz27ZtezINfYyMjY355JNP+OSTTx76mgXhUd25c4fG3kHFhpOXZtfJo0wZPKrcKxnsOnWEN4aNYf+Zkwzr3Kvcmcdv3b+Hp7NbsesWM6kpjlY2nLt6qVz7AVj211q6NSsaMPcK7UBcchK5+XnkKxTcjYshKT2NMT0HYGBQNGQ6dfUSvUI7lOtYgV4+DAntygdff86qrX9RUFBQZHtBQQGb9+zkh4XzGdSmE4FePg/dn0QiYVKPQbgYmtG3ZRgmxibYW1iTlplRrvb8W3RcLB+OmsCGFasrXFeomAoF3Xv37uXll1/G09Oz1DLe3t6MGzeO3bt3P3LjBEF4MjZciMPIUEJrf8eqboogPBb+TpaotTpuJMqquinPBR8fH3x8fHB0fPa+I65cucL333/PuHHjypzKFhERgVKpxMnJqcRrl0aNGun3+awzMTFh1qxZzJo1CxOTimcpFoTyWjLvFwa061zu8lEJcdTw8Cp3+XvxcQR6+RIZH0tAGQHmv+UpFRgYlhy6DOrYnesREZw4//Ali3U6HUvWr6amuy8hvsVXbvFxc6dRcG1+XLOUdo2akpSehrtT0WHr0Ynx+Lp5lLvdAF4urvxvwjROnw7n3S8+YeEfy5i/7Hdm/TCbN2d9RD2PGrw77GU8ncu3GoS1hSV5KqW+97x/aCcWrFpeoTaFX75AXb9AjI2M0WbJUSgUFaovVEyFgu6bN2/Spk3Z2e1CQ0O5efNmpRslCMKTo9XqWHMmlmZ+9lhIH2kBA0F4Znjbm2NkIOF6fHZVN+W5ERERwdixY6lRowZmZmYYGhqW+FOZ9XErS6vVMmHCBGxtbfnmm2/KLP9gyGRpnQUWFhbY2tqSmZlJTk7OY22rIFRXOrmi3H/XiWmpeFZgLnVhEOtMckYabo5OFWqXjYUlBaqCUreP6TmAPJmcOcsWsef44SLDuXNy5fz852LmLPmN0NoNaVG3QYn76Nm6PZfvRJCSno6NpTVB3r7Fyuw5fYyebcIq1HYAIyMjgn1roFVreLX3YKb2HQ4FGvq16YB/BW5aPNCqXkPO3Ci8YWhpZk5mZiYrtv5Vrrq3oyK5cOUyHZsWzo3v3rgVe3bsrHAbhPKr0JkyKysLZ2fnMss5OzuTlZVV2TYJgvAEnb6XTkxGHuNa+VZ1UwThsTE2NMDL3pyrIuh+LMLDw+nYsSP5+fnlzmj+NMydO5czZ86wdOnScq2UIpfLAYpMh/svCwsLsrKykMvlxZY+gsIMzkqlUv9YJqua0RRarVbfoVGzZs1iw10F4XHQaDRYmpqVu3xyRhoeTuXrnf2nvAvJGel4OFUs8Vn9wJrMWfsH+UpFqdm6OzdvQ+fmbbhx7y6/rfwDJBIkksIOhxsREfz6/ucPPYaJsTE6nQ4zU1OS0lOL9XJD4d9iWXPXSyM1NsHU5J+6CqWy3L3b/+Xh7Eb4lYsA5CrycbV3JDc7h7l/LmFE7/4lZkwvKChg68G9ZGZk8Gr/4frnPZ1dOXr1eKXaIZRPhYJupVJZrnlERkZGlU7f/zByuZyZM2eybt06/dqc77//PsOGDSuzbkpKCjNmzGD79u3k5eVRv359vvjiCzp27Fik3Pbt21m3bh0XL14kIiICtVr9TF1wCMKjWnM2BndbM4JdS18fUhCqI18HC67EiaD7cfjggw/Iy8vTr8f9LJwHY2NjmTlzJu3atWPs2LHlqvOg3Q/LXVHWa/vqq6/49NNPy93OJyU/P586dQqz84vs5cKTUvj3XsHylL/Cg+8TiaT8SxQ+YCqV4u7gxPp9O3mp14CHlq1VI4BaNQL0j+euWc7/jZ9agXaW/r3xqLlwDPhn7ruEir8P/9DxoCUP/g3x9adhcC027dtDZm4Ovp5e2FhZk6/IJzo+Dq1aQ4+WbfFrU7RnXacDCSLHz5NU4TFht27dKnPISUlLdjwOAwYM4OzZs3z99dcEBQWxatUqhg8fjlarZcSIEaXWUyqVdOzYkaysLObMmYOzszPz58+nW7du7N+/n3bt2unLbtq0idOnT9OwYUOkUinnz59/Iq9FEKpCZq6KXdeSGNLESyRQE547fo4WHLuTiqJAI9aef0RnzpzRf0e4urrSvHlzLC0ty50o6UmYPHkyKpWKX375pdx1HvRc5+bmllomLy8PQL8c13998MEHTJ8+Xf9YJpPh5VXxoaCCUB0YGBggV+SVu7yrgyNHLpyhZd2G5S5/494dOvq04lrk7Qq3b8qQUcz6fS5DOvfEVCotV53c/Dyu3r3F64NHl1lWoVRiYGBAbl4ebg5OXI28Rf3AkCJlDA0NUSiV5T7+vylVSnIV+frHZqamxCQlUMc/qML7ik6Mx92pcASyuakZEiAuJYl2jZoxpkd/dDodSempZMlzsHB0o1P95qVmib+fFI+nt3eF2yCUX4WD7vLcXS68g/V4L+h37tzJvn379IE2QPv27YmOjubdd99l6NChpV4MLF68mGvXrnHy5Elatmypr1u/fn1mzJhBeHi4vuyiRYv0Q7amTp0qgm7hubLpYjw6HYQGVmwelSBUB36OFqi1OiKScmjgZVvVzanWHtxcd3Nz4+rVq+Vew/pJ2r59O7a2trz22mtFnn+Q/CcmJka/3Nn27duxtLTE+++LyLi4uBL3mZubS1ZWFra2tiUOLQeQSqVIK3FxLQjVlbGdFaqCgjKX8QJwsXckMS213Pt2tnMgOSMdJ1t7UjIqnm3bz90LA+Cdn/7Hj2/PLHOYt1Kl4s3vPmP2lBnl2v+24wepFxBMtjyH5Iw07sUX/+7o3rIt244fZHDH7hVqu6qggIS0FP188mx5Dt6u7tyJja7Qfh4Iv3aZNwYV3kiQSCSYmkhJzfxnyWaJRIKbozNujmVPDd5z8TRvffl/lWqHUD4VCrqXLl36pNpRpk2bNmFpacngwYOLPD9u3DhGjBhBeHg4rVq1KrVucHCwPuCGwguKUaNG8eGHHxIfH4+HR2EWQjFHSnhe6XQ6Vp+JobGPHTZmYrkZ4fnjbW+OoYGEq/HZIuh+RM2aNePAgQPUqlXrmQi4H8jKyuLIkSMlbsvPz9dvU6vVAAQHByOVSklNTSUuLq5YQrULFy4AUK9evSfYakGoXl55fTLrF61iZLc+5Sof5O3Lreh7BPsUzwZekmAfP25GRRb+ez+Smr7+5W5bvlJB89oNUBUUMPGLj3h79Hjq+AeXWPbirRv8tHIJ/3tternWGwdISE0hNTOD14e8xK8bV+Pl4kpMUgLeru76Mh7OrsSnJJe7zQ9sP36QjKws/fu6+fA++oV1Zv+Zk8SnJOnXDC+PrBwZNuaWRTo5uzRrw9YTB7kWebtCPedKlQqpvbVYFeEJq1DQPWbMmCfVjjJdu3aNmjVrFhva/uBEee3atVKD7mvXrhEaWnzx+gd1r1+/rg+6BeF5dTE2izspct7vFlJ2YUGohkyMDPCyMxMZzB+DmTNncuDAAcLDw7l//z6+vr5V3aRS5z3ev38fPz8/goODi01vMzMzo0OHDuzatYsNGzYwbdq0Its3bNgAQK9evZ5ImwWhOvL19eVGWhwpmek425WdsLBL8zZM/+l/fPfm++VKMNa5WWumfvspP0z7kO9XLWbG6Inl6lUHWLR5LUM798TF3pG4lEQ+WvADpqamNK5ZB1+3wptqkXExnLl+iXyFkjlvz8TFvnxLH244sBt3Jxe0Wg1SExNq+hXeDPhz52beHzOpyIjado2asuHAbgZ17FaufadmZXDq6kVCavgjkUiIToxHWaDCzsqGPqEd+W7F78WOURqdTseiTWuY2GdokedDfGqw5fhB1h/YRaCXL9JyBtHzt67h5femlausUHnVpls3PT0de3v7Ys8/eC49Pb3YtsdRtyKUSiUymazIjyA8K1aHx+BkJaWuh01VN0UQnhiRTO3xGTRoEDk5ObRs2ZJZs2axZcsWjh49WuLPs+zBfOwvvviCO3fu6J8/deoUCxcuxNramvHjx1dV8wThmfTNvDl8sWoRiWkpZZbddGQfBVJDvt34J/nKh6/1rNPpWLhpDSO69eaHVUsY13sg3/65qFz1vl7+K2GNW+iDaE9nN5bP+pYF786ilm8A0QlxxCbEExl7n7lvf8Kfn37L4i3rSUpPK/M1bD68j8ycbNKzMhjauScAdf2D2HRoD7X8Avh2xSJUBf8sV9YwuDaW5uZsPryvzH0npqXyvyULMDQ0YtqwcUQlxLLuwC5e6TsEAKmJCeN6D+S7Fb8XOUZJNBoNP6xcTP+2nbGxLD4l5s3Bo9Go1Xy17Ffy/jV3vCQ6nY45f62gx6ghuLu7P7Ss8Oiq1SK9D5snXtYc8kepW17PSoZTQfgvmaKAbVcS6F3PHQMDkUBNeH75OVlw/G4aSrUGqZFIplZZYWFh+nNjcnIyn39e+jI7EolEP5z7WdSpUyfefPNN5syZQ4MGDejcuTMqlYp9+/ah1WpZuXJliTfmBeFFZmxszK8rlvHBm9MxK4CxPQbg6vBPj7FOp+PohTPsPH0UGy9Xlq5ZiUwmY+EPc7CVSOnbMgwr838y7BeoC9gZfowdJw5jamhE01p1mTpkNEu3/YWDjQ3z1q/Axd6B/mFditXbfHg/xy6dw8nWjhW7NtO6fmN6tW6v7xU2NDSkll8AJ6+c515cLFOGjMLy72UCZ4yewJJtG8hXKhnauWex13Ds0jmOXz6HTJ5D45p1GdyxO4lpKazbvxNzUzPmvTuLfWdOoNPBF4vnE+xbg37tOmFhZk63lm05dfUiP65aQr3AEDo0aVkkpkhKT2Ptvu1cuHWDOjWCGNKpO58vnoeBRMIHY18rOqVVp8NQYsDE2R/TrXU7+rQMw/xfS7cpVSq2hx/hXmYKcrUKf4+Sk56Zmkj5v3FTmb9xBe/N/YaGIbUZ1KEb1hb/JIpUq9X8dXgvESmxjJ82laDgkofnC4+XRPcsrANSDi1btkSj0XDmzJkiz1+/fp06deqwcOFCJk6cWGJdNzc3QkNDWbduXZHnd+zYQa9evdizZw9dunQpVm/q1KnMnz+/3Kn8S1rL08vLi+zsbKytrcu1D0F4Ev48Hc0nW64xd3gj7C3EnB3h+XU3JYePt1xn69TW1PO0rermFCGTybCxsakW5wQDA4NiN6RLOxdKJBI0Gs3TaFaJHja8/N+WLVvGvHnzuHnzJsbGxrRo0YKZM2fSpk2bCh2vqj5HlUrFRx99BMCXX34p5l8KT01ubi7f/e9r0qLjMDE0RoeOnLxcAurX5q0Z7xb7XUxLS2P9ilUoZXLQ6JAYGqA1NqTPkIH4+/uTmZnJN59/SUZcEhamZiiUSpIzUrGzsSU1LQ0nGzuQgFajJTUrA2WBCmc7BxLTUrG2KJzHbGAgwcLMHHWBGiSQl6/A1tKKxMxUtFodLnYOmJuaodFpyMrJISMrE41Wi7uTM1bmlkgMJGRkZ5OTJ6dArSbA2wdDA0PkynyUaHC0dUCjVGFqbEy+QoFCpSQjMwMbC2vUGjUufwfvKZkZyHNzkUgkmJmaYmFmhlarA3Qkp6djaWZO85p10enAzc6BXs3acvnebcJvX9VPmdVqtTha2dK3ZRh/3jzBkLGj2bBiNercfNAWvn+YGNF/xFC8vb25ef0G25auYnL3QQ/NRZUll/HWgtmYmppiZ2GNkaERGrRYONgy5rWJBAYGPrHfmRdJec8J1aanu27duqxevRq1Wl1kXvfVq1cB9GtXllb3Qbl/K0/dihAZToVnkU6nY+XpaBp524mAW3juedtbYCCBq/HZz1zQXd1Uk3vy+Pr6lqutY8eOLff63s8iExMTvv3226puhvACsrCw4JMvSx/t8l+Ojo68Nu2NUrfb2dnx1Q/fPY6mPVVbNmwk4dJNhoZ2wdSk+PW+Tqfj5PVLrNyzlXmvvl/q/OxmIXVoFlI89shTKDAxM8PV1ZWp77xVajtq1q6F+dQJzPllEf62LnRv0qrIXHqlSsWOs8eIlmfw05LfcHNzq8SrFR63ahN09+/fn0WLFvHXX38xdOg/iQOWL1+Ou7s7zZs3f2jdyZMnEx4eri+nVqtZsWIFzZs3F/MYhOfalbhsIpJymNFVDB8Snn8mRgZ42plzTSRTeyRVuVqJIAjCs6jvoAHENo/lz5VrUGfm0MDTHzsrG/KVCm4kRJOFitad2lMzsU65EqL919bzx+g3dWy5yvr4+vDR7C+4e/cui9duwEClAa0ODCRgakz/0UMZ5eNT4TYIT061Cbq7d+9O586dee2115DJZAQEBLB69Wp2797NihUr9L/c48ePZ/ny5URGRuLz9y/byy+/zPz58xk8eDBff/01zs7OLFiwgFu3brF///4ix4mOjubs2bMAREZGAv9kN/X19aVJkyZP6yULwmOx5mwMjpYm1Be9fsILwtfBnGvxIpHlo6jK1UqEkmm1WmJiYgDw9vYWS5wKQhXw8vLizfffRaVSceXKFTIyMrCwdGHAoM44OBRmelcoFFy4FUEj/4qtFpOmU+DqWv5lwwACAgKY/tH7FaojVI1qE3QDbNy4kY8++oj/+7//IyMjg5CQEFavXs2wYcP0ZTQaDRqNpshQM6lUyoEDB5gxYwavv/46eXl5NGjQgF27dtGuXbsixzh06BDjxo0r8tyDtcHHjBnDsmXLntwLFITHLEdRwOaLCfSo6yYSqAkvDD9HC06diaFAo8XYUAQmwvMhPz8fPz8/AORyORYWFmXUEITq4caNG/z0yZeYqyUYGRig1mnJM9DwxqyPKjQFNC4ujo3LV0KuErRaMDTExN6aIeNGY29vj06nY/nvSzm+ZSfWJmYYIEGhLUDq5oi9nS1RVyMwUuvQaTTkKxVk5eZgZWqOsaEROYp8ZIo8fD08sXWwJzEpCQtjKVam5mTlyolPTcLdwRk7KyvUOh1JsgwWvfUJRoblC7W2nj+OvZcbrw0ZRXpiMjZSC4wNDVEb6HD082LMG68RHBJCZmYm65b9iSI1C4lWCwYG6MxM6D9mBN7eJSdXe6CkuphL6ffS8DLrCo+u2iRSq46qU9Ic4fkkEqgJL6KIJBmfbrvBzjdCqeX+7Hz3VsdzQn5+Pr///ju7du0iJiaG/Px8IiMj2bp1K1lZWRgaGjJy5MiqbuZTVVWfY25uLpaWhRmIRdAtPA8iIiL45q0Pqefszag2XbH8V7buPKWC1Sf3cz7xHtO//eyhwXdcbBwr5vyCm1ZKn9rNMfvXfGtZfi6bb4Rz9PYVrDGmc81GdK3XXD9SJDM3h1/2bsTa3IIxbXtgZWaur5uvUrL53FESMtMY1rITl6PvcvjGBTJyZXzUbwy2FlYsPbwDJ2tb+jVpi5WZOTqdjhXH9xCTmkSBkYQPX3oVY6OHB947Th1h+a5NuFrbUcezBv2btcPZ2k6/Xa3RsOL4Ho5GXqNlSF2GNAjFxvyfbOTKAhVbr4cTTz7DX5+Ij69vkf3n5OTw69ffYynX0L9mc2wtiteN0+Ux4o1JxeoKZXvuEqkJglAxIoGa8KLysbdAAlxPyH6mgu7q5vr16/Tu3Zvo6Gig8DvlQUbz48eP8/333wPg5+dHq1atqqydgiBUP2fDz7D4/2bz49BXkRoXv0Yxl5oyvn0vxmg0fPDeZwx7/w1ahxZfaeB2xC02/Pgrb7bqhaFB8XnU1mYWREdH084jhNFtuxXZlpydwZJD25neczimJawGYGYiZXirzqg1Gn7atY4BTdvSo2FLNpw+xHfbV+NgZc1H/cbo26/T6fhh5xr6Ng5ldGg3UmRZ/LBqCXUCg+nWrE2xed534qLZfeoIQY4erHv9M+bv/YsWgXWKBNwA2Xly4tJTmDfyDcylpsXaKTU2YXCDULRaLfNmz6PX5LHUqlt4kyI9PZ2f3p/FtOY9sJCaVaiu8HiJcXeC8Jy6FJtFRFIOHWs6V3VTBOGpMjMxxN3WjOsJYl53ZWVmZtK9e3fu379f4vZRo0bpp3Ft3rz56TVMEIRqLzMzkwUffcH3IyaXGHD/m5GhIbOHvMofX/xAampqkW3Z2dms/eEX3mrdp8SAG+D3g1vxtHcqFnCr1AUsOrCVd3uPLDHg/m8b3u45jPXhh5Dl5zKoRXvq+wTQr0nbIu3//dA2BjRtR4CrJwDO1ra832M4/uYOLFj3Jwv+Wskvm1bzy6ZV/Lx2OWt3baWBqx/d6jZDIpEwtesgjty8SGJWun6fGq2GBfs2MqPPyBID7n8zMDDg9ZY92TxvMenp6Wg0Gn788FPebdWnxIC7xLrzl5Cenv7QskLliKBbEJ5Tq8JjcLaSUs/DtqqbIghPnbeDOVdFBvNK++mnn4iLi0MikaDT6Yol7apXrx7OzoU39E6ePFkVTRQEoZr6fMaHfNhrVKmB8n8ZGBjwcd8xfDHjoyLPr1m8nImNO+lH4PyXTqfjUvRdxoX1KrZt24UTjA7thlE5s4xLJBImduzLxjOHAZjYsS9Hbl7Sb8/KlWNkYIifc/EVkWp6+PBG54FMadeHyaG9mBzamzc7DuDj/mM5H3WrSNlJHfuy6cwR/eO9V84wuHkHTP61JFhZ7ZzUtAtrfl/Gri3bGOTfpGJ1m3Rmze/LylVeqBgRdAvCcyg7v4BtlxNoH+wsEqgJLyQ/BwtuJMjQaEXaksrYsmULUHixe/DgQfr161esTO3atdHpdNy5c+cpt04QhOpKp9NRkJiBUzYP4gAAh3JJREFUl0PFRuE5WtkgScvRj7DRarVkR8UXmdv8X9svnKBpjZIziMekJePjVLFM4XYWVmTl5erb4GbrQEJmGgCbzh6hf9N2D6teopoePtyMv69/LDU2QYcOlboAgFuJsYR4VGzpLwupGXmxKVw/Fk6IW8USpD2oq9FoKlRPKJsIugXhObTxQhwFWh1hwU5V3RRBqBJ+jhbkF2iISsut6qZUS5GRkUgkElq3bk1YWFiJZaysrADIysp6eg0TBKFa2759O20D6lWqbtfaTdiwfj0AJ48dp6VzjYeW33XpNENadiz2fHRqEjVK6JEuj+YBtTh77yYA/Zq0ZcfFwpE+ckV+kQRl5dW5blMOXb9Q5Lnu9Vuw98oZkrMzcLWxr1Q727oFIotPrlTddu5BHN5/sFJ1hdKJoFsQnjM6nY4Vp6Np6muHrblIoCa8mHwdCjM7X08QQ8wr40Evx8MyZCcmJgKFy3IKT56RkRGTJ09m8uTJGJWRDVkQnlU3L18h8O85zxXl7+LJrSvXAYiNisbP4eE91VIj4yKZzB+Iy0jFz9mtUm3wc3InPqNwbrmpiQkabeF3pbFR+Yap/5ehgWGxEYk+Tq4kZKaRmJle4d74B2o4uWNYyc5qP0c3YqPuV66yUCrxrS0Iz5nwqAwiU3MZ1lSsuSi8uCxNjXC2knI9QUbfBh5V3Zxqx83NjaioKM6cOUN+fn6x7devX+f8+fNIJBI8PSt3AS1UjFQqZf78+VXdDEF4JBq1Bolx5aa9SSQSNBo1UDi8vLKT53Q6LZJK1pZIQPuEV1uWUJhLQ/tI7ZRQ2VWhC99nMbz8cRM93YLwnFl5Ohp3G1Nqi6WShBecj4M5V+NET3dlhIaGApCRkUHnzp25d++eftvs2bPp1KkT2v9v777jmrreP4B/soGEvTfIVFHEvXHvPau1rlZba22tba1aFbWur63aZWtr3YrWvbXiXrg3ipM9BNkQCCQ5vz/4kRIIEBBI0Of9euWlufece597CNw89557jlIJAGjfvvQ0PoQQokk9Xx9EpyRWqW5cShLcvb0BAPbOjoj7/+epyyKTF6BALi+13NbUAnFpyRpqVCw+7TVs/7/Lt7xYYiqvYpLKGIPi//+WFklIT4G1iTlsTS1Uz4xXVlxaMpS8qiXs8enJcHChi6nVjZJuQt4iyVkyHH+YiK71bcsczZOQd4WbpRgP4zOqfLX/XTZ16lTV35DQ0FDcvXsXQOEXxDlz5uDVq8JnBTkcDj755BNdhflOYYwhOTkZycnJ9JkmddbwEcNx+vGdKtU9fD8U748dAwDo2LkTLiQ8Lbd8Gy8/HLh5odRyTzsnPI6LqlIMF8PvoY1XQwDA0TtX0KNRSwCAgMeHVJZX6e1denIPrTwbqi07eucKevq3hKOFNaJTqvZc9unoMIjtrapU91RUGLr27FGluqRslHQT8hbZfSsGXA4HHb1oADVC3K3EyMqTIya1dPdoUr4WLVpgzpw5askdh8NRJeJF/86aNQtNmzbVSYzvGqlUChsbG9jY2EAqleo6HEKqhMvlIt/UAK+zKtcLKTM3B1IjPnj/P8UXn8+HyMEKufmyMuu8374HLj+5X2o5h8OBrak5EtNTKxVDjiwXBgKhaqqzl0nxqGdb+PjSwOYdcfDmxUptDwBuvnyC5sVGWFcoFcgryFfNq+1saYOo5Mr1DJAV5INnaw6PFv6Iel21usIK5i4nlUdJNyFvCYWSYfvVaLSuZwGJAQ3XQIi7VeEgYDRfd9V8//33+PPPP2FrawvGmNrLysoKa9asweLFi3UdJiGkjvnuf4vxvyPbK9VjY8XRYHy7bKHashEfjsWGW6fLrMPlcuFoYaMxGR7UvCM2nT9aqRjWnz2imhZs77VzatOR2ZlZIDkrHUmZaVpv79KT+2jg6Ka2bMuFE+jftJ3qff+m7bDt0r+qx3m0senWGQydMAaDRw7H9kdXqlSXVD9Kugl5S5x/moS49Fx0b1C1kS4JeduYGQlhKRFS0v0GJk2ahNjYWFy7dg3//PMPdu7ciatXryI+Ph5TpkzRdXiEkDrI0dERvSaPwaL9mypMCBljWH54G9qNHgw3Nze1dba2tggcOxTrb5wqM3n+pv9oXHx8F8fvhqotNxIZYHjrLvj5xG6tYlh35hA6+jaBtYkZTj+8iXNht3Ex/J5a3SndBmPd6UN4lVHxHfSrz8IQkRSPnv6tVMt2XA6Bp50j3ItNZybkCzC2Y2+sPLpDq+fGt9w6g5Yj+8HJyQlCoRAfzf0Kqy4fqnTdqsjOzkZUVBTi4+ORn59fpW28zTiMHgyqMZmZmTA1NUVGRgZMTGhQK1KzJmy8jqgUKRYP8qPnuQn5f6tCnkDA4yJ4Umtdh0LnhLeErn6OOTk5kEgK5wHOzs4udzo3QuqCC2fPYfPyn9DDtxn6NW2r6rYNFI5OfuL+NRx7eB2jvvoUXXt0L3M7YfcfYP9fm9HMzAmdvP3VvgPJFQocf3wTO0NPwdXUGqNad0UjFw/V+piUJGy9eAI+Di4Y1LxDqRjOPrqNWy/DMbB5R8jk+Thw4yJiU5MwrecwmBpJsONKCBo6uaN3k9bgcXmQKxT48/QBGAiEGNKyE8zFxmqxPkuIwfF7V+FiaYtBLTqCMYaL4few6+ppgAHtff0xqHlHGJTo3n30zhUcuH0JPZu0xqAm7cDn/RcnYwwXnz/E9ZQo9J/4PvybBajVTUhIwPrlq+EpNEffBi0gKDblYEV1K6JQKHBs/0E8Pn8NpjlKWAnFkDMlEguykW8lRq/3h6ORf9XmZa8rtD0nUNJdg+gLFqktMalSdFxxFpM61ENnXxtdh0OI3th3OxYnwhJxP6iHzi9G1dVzglwuR0pKCmSysp+ddHF5d6YopKSbkOp1YO8+HNqwHdYiCXgcDhSMIVmWjb5j38PQkcO13s6t6zdwZv8RiAoYlHIFOHwe5IZ89Ht/BHx8fZGbm4ulcxfg9ZMImIqMwOEAMoUcKco8SEyMkZecDiOuADwOF7KCfKRmZ8KAJ4DEyAgZOdlIyc6EnaUVREaGkEqlMDUwgsTACK/SU5CSmQ4HC2uYiI3B4XIQk5kCKycHuFpYg6sAuFwO0qTZuP/kMVxNrCDi88EYQ2peDhwb+WD67Jk4uGsvzh04itzMLNgYm4HP5UGmVIBnaoQB40ejS4/ueP7sOQ5v/wfcbBmgUILD4yJfwEHgwD5o2ab8i8vPnj4rXVfIReCA3hXW1SQqMhLr5/8PQ+0bwde69N1xpVKJkKgHCDPIxdfLF0IgEFR6H3UBJd16oK5+wSJ1z4oT4dh8JRK/jW4KAwGv4gqEvCPuxqTjfyfCcf6bTnC11G2SUtfOCefPn8fChQtx5coVFBQUlFmOw+FArmFanrcVJd2EkHddTHQMts79H7706wYut/ynlZOzM7A++S7m/fpDhWXrIm3PCTTaEiF1nEyuwM4bMejgZU0JNyEl1Cs2mJquk+66JCQkBH379oVCoaDpqQghdZZCocCfv6xB2LVbyJfJYGVvi0++nQFXV9dKbSciIgKXTp5GVnomxCYSBLRtjbiIKNy7cQv3bt6CLE8GaxtruHp7IiU1FeE37yAnOxtKJWBsbQ4XZ2c4ODnBxMwMySnJyHyVgoKCAlg52cPUzBRQMEQ9fwmugI9GAf4QScS4F3oNGSlpMBAboWOfHhj63gi9SVo3LPoB3/h11Soea4kp3pc3xN+rfsHkr6fXfHB6ipJuQuq4Ew8TkZqTj271bXUdCiF6x8RQAGuJCA9iM9CvsUPFFQgAICgoCHK5XOdd8sl/+Hw+xo0bp/o/IaRsSUlJCPr0SwhfSzHQsxlGenWHiC9AijQLe6YuwuPcZPSdPBaDRw4rdzv/HjqCe/9egFMuH93svGEickJOVh5OLduCS5GPkC8vwKQmgfC3d8OlyEe4e+0pGpha4evuHyE6/TVOPr8DI4EIQ5zbwFpsgnyZHPezCnAhKQ4Rqa/AXsvQo1E7uJrZQFHPHRtvncK9ncfQyaMRvnVrCyNnEdLzcnB872V88sdWmNevh+9+XKLT3lI3rl5Fa0MHteffK+JsZoVn/57Ab3n5GPPZZJiZmdVcgHqKupfXoLrWlZDUTcP+uIK8AgW+69tA16EQopdWhTwBn8vFjsm6HUytLp0TxGIx8vLywBhDixYt0KFDBxgbG5dZPigoqBaj06269HMk5F109/ZtrP50FpZ0fg/WYlONZRhj2P3wCp5ZMSxas7rUeqVSidXzvkerTCO0sK1X5r7Ck2KxL+wqDAUCtHdrgBZOXgCAA2FXoWQMgxu2LvPiZYo0C79fPYbJLXvCWGiIVZcOYkxAJ7iZlz02z9kXD7D92VUs2vI73OuVHVdNWvHVd/jcrmWlL8qGJ0YjLDYSL/hSjA6aAQ8vzxqKsHZR93JC3gGPEzJxMyoN07t66ToUQvRWPSsJjj1IAGOM7txqqSjp9vf3x9WrV6ndCCF1QkxMDH76dBZ+6zURQn7ZA3dxOByMaNQOZ148wNKZ32HOiiVq638JWoq+Mhu425Y/OK2vjRMEj3kYUL8VPCwLp2w99uQmjA2M0NWj/FG7LY2MMTtwGJad2wOZogDfBg6Fsciw3DqdPRrBWmyCBeOm4sd9W2BtbV1u+ZogzJSBY1/5c4KvnQvOPLmLb1r2wsoFq/Dhyvmws3t3prnVjwcDCCFVsu1qFMyNBGjmZq7rUAjRW+5WYmTJ5IhKkeo6lDqjR48eYIzBwMCAEm49wRhDTk4OcnJy6Dl7Qsow/8Np+KHbB+Um3MV18WgE+f0opKb+N7f27es34flKAXezimeDuRv/Ek0c3FUJtzRfhojUVxUm3EX4PB5mdBgIY5FRhQl3ET87V3S198Xq7xZpVb66cRRV//vD4XDA4XAw3bsTtqz4pRqj0n+UdBNSR2XlFWDf7Th08bUBX08G1iBEH7lbFw6gdj8uQ8eR1B1Lly6FpaUlrl+/jpUrVyI/P1/XIb3zpFIpJBIJJBIJpFK6gERISWlpabCVC2BiYFSpeh817YalX3+nen965350c26oVd1zEQ/RzdNf9f7Ao2sY6te2UvsXCw0gEYqQLy97loiSBjdohdePXiIzM7NS+6oOHG7VL8QW1eTzeDBLzVe72PG2o2/qhNRR++/EQSZXoIsvDaBGSHlMDASwMRbhfky6rkOpM1xcXHDu3DkYGBhg5syZsLGxQbNmzdClS5dSr65du+o6XEIIwZJv5mJM446VrmdpZAx55CsAQEZGBoxT8rTq4ZORlwMTkZFa2aScdNgZV773Yf/6LXE4/IbW5YV8AbxMbLDjr42V3tebkomqlj5m5OZAyPuvB8JgJ3/s/ntLdYWl9+iZbkLqIMYYtoRGobmrBSzEQl2HQ4je87CW4A4l3VpLTU3F6NGjVYOpZWZm4s6dO6W+iNJz8oQQfZEZmwCv9l2qVNfe0BQFBQUIDw+Hn5F2z0k/fR2PRnb/TT3GGIOBlt3aS3Ixs8axJzcrVcfL0h53n0dUaX9vwqVFI0Q+ewU388rd9Nl37zIGN2ilem9iYIT81y+qOzy9RXe6CamDrkWk4nlSNro3oLvchGjD00aCh3EZyJcrdR1KnfDtt9/iwYMHAP57Bo+Sa0KIPuO+wZ93iUAEqVQKaVY2jPgirepIC2QwEvxXtkAhB59be/czjYQi5OXl1dr+igwePRJHEh5Vqo5CqUC6NAvmhhK15dnptd89XlfoTjchddDW0Cg4mBmioQNNV0OINrxsJJDJlQhPzERjJzNdh6P3Dh06BA6Hoxqwy8zMDMbGxuDS+BGEED0l57Aq975JzyscM8HUwhwZ+bla1TERGSEj77/xFYR8AQoU8krvu0hlx0fMyJXC0Lxyz69XB6FQCN8+HXD6Yhi6avHsO2MMv5w/iJF+7Uutiw1/hrB799HQX7uB5+oyOnsSUsckZebhRFgiute3oTtPhGjJzUoMPo+DO9Hpug6lTigaqMvW1hbh4eFITU1FVFQUIiIiNL4IIUTXvFoFIDQ6vEp14/IzwePx0LBhQ9zNS9KqTgMbZ9yJV+8enSev2qCTt+NewNfGqVJ1HifHoEHLZlXa35vqO2wIsgOccfDl7XJnU5DJC7DyzF709WwKBxMLtXWx6a/RysIVB5b9jpycnJoOWeco6Sakjtl5IwZ8LgcdvGp/bkZC6ioBjwt3SzHuRKfpOpQ6oWnTpgCAxo0bw9vbW8fREEJIxWbMm40DTyv3XDQAvEhJgGu7AACASCQCx8USMi1GEhf9//Pbxcv6WjvhcVJMpWM4H/EQndz9tC6fnpuDSFkGBo0eUel9VZcR48eg3tjeWBR2An+H/otsWWEPAcYYIlMSsebCIay/fBxj/TvB29qxVP1Dj65hkE8LjLNrgt0bttZ2+LWOkm5C6hC5Qont16LQ3tMKYhE9HUJIZXjYSHCb7nRrJSgoCABw7do1vHz5UsfREADg8XgYNmwYhg0bBh6Pp+twCNE7AoEABTbGeJGSUKl6f9wOwZfzZqveD/5oLHa91C5571+/Jf65f0n1vodXExx8dL1S+49KS0JuQX6lei9uvXMW9Tu0gEBQtYHbqkvz1q2wdNMfiBLmYdeNc1h74TD+ungU96KeY1LTbvi0VW/YSExL1UuVZoGjZDDgC2EjNkXynfBy75i/DehbOyF1SMijV3iVKcP0bjSAGiGV5WUjwYmHiUjJlsFSot1AOe+q2NhY9OrVC8ePH0ezZs3w3nvvoUGDBjA1Lf3lCQDGjh1byxG+ewwMDLB7925dh0GIXvtp+3qM7dQHy9uPhH2J7sya/H7tOLpMeR9C4X8zwbi6usKsawDOXXuCTg4+5dZ3MbNGRl4OTj69gx7eAeBxeejr2wzrb4bgw+bdK9x/ck4Gtt89D2ORIZ6nJMDT0r7COoceX0c4MvHHgu8qLFtbPl06Fxu+XIg5LfpVePEgW5aL3y4fwazWA1TLfORiPHv27K3uWcVhb/tlBR3KzMyEqakpMjIyYGJCA16RN/feX1eRJs3Hgv4VD1xBCFGXnJWHz3fexfpxzdG1fu1fuKpL5wQul6s2kFpFX6IUCkVthKUX6tLPkZB3UVZWFj7uMwyj67VAN88mGv9+vc7JxOqrR9BywmCMGDtG43b2bQlG0qmbGOnWHEbC0hdq8+UF2BsWigKFAqYGRkiRZuG9xh1gJBThbvxLnHp+D2ObdtF4p5cxhnMvH+J67FPMaD8QfC4Pf13/F3bG5ujr0xx8Db1ZMvOk2HDrNJ7zpfhl12YYGhpWoXVqzurFy5ESchPjmneFi7mNxjI3Y5/h3/Db+LJFb4iFBqrl915FIm9MW3ToFFhb4VYbbc8JdKebkDri2assXH2Zgs86e+o6FELqJCuJCGZGAtyJTtdJ0l0XaZqXu/g6mqebEKJvjI2Nsf3CCWxcuw5Td22Gt6ElGto4w4gvQmJ2Oq69egGusyXm7FoLa+uyx8cZMnY0Ent0wfY/NyHveQJ8+OYw5gmRkZ+L63HPEZGcACO+EA1snGBnbAZLQwnmntwGIV+Atq6+CHDwwG9XjiArPw8tnDzhbGYFaX4+wl/HIjk7Azn5eVAyYP2NEPhYO8HX2gnXYp5if9hVuJvbooWTF8wMjZAqzca1+Od4zS/AkE8nYuagAXr5d9fBwQEDGgXiakQ49j24AmdzG9hKzCBXKhCdnoyU7Aw0s62H79oOKhW/ginBE7zdaenbfXSEvEW2Xo2CqaEArdwr7i5FCCmNw+HA01qC2zSYmlYq6ghHHeVqV05ODiSSwjlus7OzIRaLdRwRIfqLw+Fg4pTJmDhlMmJiYnDnzh0kSaWo5+aGUa1aaZ202tnZ4bOgWZDL5YiMjERmZiYcJBL0cnVFVlYWYmJi8PTpUyRlZ8PKygpf1J8EmUyGs2fPIioqCoZODdDRzw/W1taQSCSwE4vhqlAgLi4OMpkM6SkpCPnnAC4+eIa8fBl4BiJ4tfOHmG+Ac6/ikJGeDQsHW3y+8Ue4ubmp4kpJScG+v7dAlpIBKBmUQh4Ch/ZH/UYNcSh4N26fu4jkyDgIeXxILM1h5eWKyPBnYGnZ4CiBfK4Sfj07wFQkRuLzSDy4dQe5mdkwkRjDyFgM9xaN8dHM6TA3Ny+3faRSKfZvDkZ6VDyiY2JgyXPA8PqtAQDxWWlIlmZAIDBAE3fHUnN0F/coPRHty7kA8jagpJuQOiBbJsfeW7Ho3sAOfB6Nf0hIVXnaSHDoXjzkCiX9LpXj7Nmzug6BEEKqhbOzM5ydnd9oG3w+H56e6j0NRSIRrKysEBAQUKp8o0aNyt3eo2u3cXLDDtSDBPPc28DT87/eV49S4rH9aSgcDCRY1qg7sgpk2PfVD5A6maDVkN4I3XUE5olSDDHzgYmwcJoxZbYSxxZsxrJn1+EgMkZPFz909h2ApxmvcDTyHsyTI/GRR2tIbAq7dCuUShw6cxdXX71AvlyOse5N0NnfV3UxIuOVFH8P+hSvbQzwyfJ5cPeopxZ/amoqNi9dDX5kCgaJPWBn5ABmbI/Vj8+go7MvAMDB2BwOxuUn7UViExPw7+zVOOxigQ9mfwEbG83d0+sySroJqQP2345FboEC3eq/fX+ECKlN9e1NsPNGDB7GZ6KJs5muw9FbgYF177k6QgjRd4wxLJ8xG6/O3sEPTfvBVGRUqkwDCwcsaT0UydJMfHdtH4JaDMRHNgG4Fv0SZ2b/jFmNe4Fjo36nnsvhIjY9GZ836IwWtu4AgCsJz/EoLR5f+vcodWefx+VicL2mGFyvKS7EP0Fk5mu1MqYiI0xv0BWMMfxv8nfo8/10NGvbCgAQFRGJLV8vxgzL5hBau6rqcDgcWPANkSLNgqWRsdZtEv46Do1MHdDPpjEKcuRY9clsjP7fbNTzersep6TL/IToOcYYNl2JRHNXCxpxmZA3VM9aDAMBF1dfpug6FEIIIe+Y379fgeRzd/B9i0EaE+7irI1MML/FACy5dRjP0hNxNyUas/17a+wavyX8MtrZe6kS7kep8XiSnoCPGnSssCt9Rwcf+JjbYc+L0tOkcTgczPLuihNBv+Dl8xdIT0/H5q8XY6Z1Swh5pe/dDnNvirXXTkDJlOXus4hMXoBd9y6ht3PhHOUCHh8zbVpj+7fLkJLydp2n6U43IXru8vMUvEjOweiWLroOhZA6j8/lwsfWGKEvUvBJoIeuw9F7UqkUN2/eREJCAmQyWZnlaMowQggpX3JyMp4du4DPGnaBAV+7+bUlAgOM9GyFvx9dxPI2wzSWSZdJUaBUoJGlk2rZiegH+NK/h9axtbL1wM2k08iTF2iM7RuPTvhp1VqIrc3xuXkAuBzN920lAgOM9WiNFRcOYEb7ARoT8yJZslysvnQInzfoDB73v+1xOBxMt2yOTav+wLQlc7U+Bn1HSTchem7TlQi4WBiivj1NTUNIdahvb4JD9+JRoFBCQM91l2nhwoX48ccfIZVKKyxbG0m3VCrFyZMncfjwYdy4cQORkZFQKBTw9PTE0KFDMWPGDNVAYyVt2bIFv/32Gx49egShUIjWrVtj7ty5aNu2bY3HTQghALD7jw2whSFcjS0rVc9ebAo3E8sy71jve3kLQz2aq97HZKfCSWJR6RHOB7oH4EDEbbzn1arUOg6HA9HL10iJfw2xfbtyt+MiscBHnm3x2+UjMBMbY2jDNjA1+O+uflJOBvaHXYVMJsPXft0hFpTuxWnAF6DgaRwKCgogEGh3gULfUdJNiB6LSZXi9OMkfNjeXS+nhyCkLmroUPhc94O4DDR10W6Ql3fNypUrsXDhwnLL1PaUYcHBwZg0aRIAoGHDhujVqxcyMzNx5coVBAUFYceOHTh//nypAXhmzJiB1atXw9DQED169EBeXh5CQkJw8uRJ7N69G4MHD66V+Akh77bIq3fRwdKx0vUORtzBWJ+yE92s/DyYFeuqfiTyHibWb1/p/ThJLJAozShz/RjHplgbdg6wr3hb1obG+MqvO9JkOdh75yIepSfA3dQaHAAmXBE+cG+hMdkurq/IFUd27cXg99+r3IHoKUq6CdFjW69GwUjEQztPK12HQshbw81KDEMBD6EvUijpLsNff/0F4L/EWpPanjJMKBRiypQp+PLLL+Hl5aVanpCQgL59++LOnTuYPn06goODVevOnDmD1atXw9LSEqGhoap6oaGh6NSpEyZMmIBOnTpVOC2OPuDxeOjTp4/q/4SQukOhUCA3LRMNPJpUvq5SWWaCyhgr1YWbA0DEq9rd4fLqmQgNIeFXbmwhc5EYH/q0x8GIO2hm7QonifbT3tYztkLI05eV2p8+o6SbED0lzZdj541odPK2gYGAvmARUl34XC587Ixx9WUKpnZ+u0ZHrS5RUVGqO9iTJ09Gnz59IJFIdJrsjR07VmM3dnt7e6xZswZt27bFvn37kJ+fD6FQCKDwjj0AzJ07Vy1Rb9OmDT755BP88ssv2LBhA7766qvaOYg3YGBggKNHj+o6DEJIFRQUFICjLJ0ga6O8y5tMw9o3uRyqaXvFVbVnk4jHh0whr3xFpXYDstUFlHQToqf23Y5Ddp4cPRrYVlyYEFIp9e1NsP9OLPLlSgj59Fx3SdbW1oiPj0eLFi2wdu1aXYdTIX9/fwCATCZDSkoK7O3tkZeXh9OnTwMAhg0rPQDRsGHD8Msvv+Dw4cN1IukmhNRdBgYGYEI+knOzYGdkWqm6CqYs81EeLocLRYmRwku+rwxlOT2YGGOQKQqqtN3k3Cy0tq3c4KV58gLwjQyrtD99RN80CNFDSiXDhssRaO5qARsTA12HQ8hbp6GDCfIKlHgQl67rUPTSoEGDwBiDso7cZXj5srALokAggIVFYffF8PBwyGQyWFtbw8nJqVSdpk2bAgDu379fe4ESQt5Z9vXr4Xz8k0rXa2LljNDEF2WuL1Aq1B738TazxYOU2Ervp0AhL/exoTMJT5AjrNqd7td52WrPnWvjUEo4+o4ZUaX96SNKugnRQxeeJeNlcg56N7LTdSiEvJXcLMUQC3k4//S1rkPRS0FBQXBwcMCtW7fw+++/6zqcCv38888AgF69ekEkKnzmMDo6GgA0JtwAIBaLYWZmhrS0NGRlZdVOoG8gJycHYrEYYrEYOTk5ug6HEFJJHd8bhKS8LORXspu1v6UzDkXeKXN9JwcftWS+m1MDnI59VOn4jkTdRx/XxmWuv2WYDSMf50pv90VmstZTpBUXb82Hs3Pl96evqHs5IXpow6UI1LMWw8fWWNehEPJW4nE58Hc2w8mwRMzo7q3rcPTOzJkz4e7ujvj4eEybNg0//vgj/P39NQ44xuFwsH79eh1EWejYsWNYv349BAIBvv/+e9Xy7OxsAICRUdl3V8RiMdLT05GdnQ1jY81/b2Uymdoc5ZmZmdUUeeVpM30bIUQ/tesciN0Of2Lb01BMrN9B63p/hJ2Fj5k9HqTEqs3FXSTA2hVLbh1BOztPCHh8cDlcWBua4Gl6IrzNtLt5kyvPx9P0RAyu11Tj+uuvI+Hbpz0c67nh2JpD6GOp3XmTMYad0mdQWAkrNdtFSOpztP6oj1Zl6wpKugnRM89eZeHCs9f4tJMHTRNGSA1q7mqOX848R0yqFM4Wlev29rbbtGkTOByOavTyyMhIREVFlSpX9CVKV0n348ePMWbMGDDG8MMPP6ie7S6KDSh/4B9tRmBftmxZhdOnEUJIRTgcDr79YyUWjpwEi5e3MaiMBLe4vx+dR1s7T7Sz88LP90Mg4vE1JtKTGwRixd0TmNmkFwQ8PkZ7tcLKu/9imEdzuJmUPwNOnrwAP9w5gen+3TWuf5Aai1B3Dr6eMAYAsP3BI1y6HIX2Zq7lbpcxht9e3cB7338FHp+Pn2f/iC9sW1b43fZqejSSWzujT/eu5Zara6h7OSF65u+LEbAQC9GmnqWuQyHkrebvbAY+l4NTj1/pOhS9VpR865vY2Fj06tULaWlpmDFjBr744gu19UV3rsvril1051gikZRZZvbs2cjIyFC9YmJiqiF6Qsi7yMHRAUH/rEOIPB7f3zqMiMxkjeWepidgdugeNLZ0Rnt7b2Tk50Jia4k/pGHY8uousgvy1MpbGxpjUv2O+PbqHpyMfggAmNGkB45HP8C2p6GQFshK7UPJlDgUcQdzr+3D5426wkSoPmhZZn4ufn58Bpd9hRj79TTk5RXuc+QnExHT1glrEq4jKVdzz5876bFYmnIN/ZbMgE/DBrCxt0PXOR9jSeo13ErT/Df0dW4W1sRfR0xHF4z7cmr5DVkHcVhtT7T5DsnMzISpqSkyMjJgYmKi63BIHZCUmYd2/zuDYc2cMcDfQdfhEPLWW378MYyEfOyY3LrG91WXzglcrvbX5DkcDhQKRQ1GU9rr16/RoUMHhIeHY8KECVi/fn2pCwN3795FQEAArK2tkZSUVGobOTk5kEgkque6taWrn2NRvEBh13mxWFxr+yaEVC/GGC6ePoetP/wCw5RcWAvEEHB5yFPKEZmTCr6hCI1tXAE+B/lGApg08sCITybAzMwMycnJ2P3HBuSGx0CYKweXAQV8DmS2EsBcjMS74ciKfgV7kTEMeHyk5uUgIvM17MVmsDYyhognQLYiH49S4yDi8mDCM4SCKWAnNoOQy4cCSuTwgVQx4GJgBgcpF2LGw520WEjl+XA1NIOjxALZLB+3MuJgKjaGg5klDLgCyPkc5FsZIaB/Nzi6ueD4n1vBef4K5vk88MDBa04+XoryIDEygp3SAByZHBlZGUjNzoK8oACNzR2RJ+BA5miChgO6oOfgAZU6H+mCtucE6l5OiB7ZeCUSfC4X3erb6DoUQt4JzVzNsflKFDKkBTA1qvxAL28rfR61PCsrC71790Z4eDiGDBmCdevWabwT7+PjA5FIhOTkZMTGxpYaUO327dsAgMaNyx44iBBCagKHw0HHbp3RsVtnAIBcXjhyuEDw33lILpeDx+OV+vtmbW2NT+d/C6AweVcoFODzC1O6+Lh4rP3sO4y0DUATw8IpZ3kcrmobMkUBlkZfAJ/Hx++e/WAtkhRugynB5XBQoFTgYPxDPM5JwVemrWHDkyCSl4qt0TcxxqERGpnYq8UyybQx8hQF2Cd9gbSWdvh8aRAKCgqw6vPZyH4uxVSxO3giK0D0Xx3GGK6kx2FrWhgCjGwx3aQJDM2F6g2UATz88woWbtqPcavmoZ6X55s3uo7pxaWD7OxsTJ8+HQ4ODjAwMECTJk2wc+dOreomJSVh/PjxsLKygpGREdq0aaOal7OkU6dOoU2bNjAyMoKVlRXGjx9f6up3ZGSkqitdyZe2MRFSFdkyObaFRqFrfRsYCel6GCG1oZmrBRSM4eyT0ndCif6RyWQYOHAgbt68iZ49e2LHjh3g8XgayxoaGqJLly4AgD179pRaX7SsX79+NRcwIYRogc/nqyXcRcsqerSHw+GoEu7EhESs/+hbzFH4ornYAXwuD3yuetJ+Mukp2omdMM8lENYiyX/b4PLA5XBx5vVzSAQGCPLqBhuRBFHSNOyNu4/vfLqVSriLGPAEGG3si153CvDj9DlYOvFzjI82QT+JB3ic0qkmh8NBO7ETfrbviqSkJOQzzT2l/Ixs8B23IXZPXYSI52VPmVZX6EXSPWTIEGzevBlBQUE4fvw4WrRogVGjRiE4OLjcejKZDF27dsXp06fx888/4+DBg7C1tUWvXr1w/vx5tbLnz59H7969YWtri4MHD+Lnn3/GqVOn0LVrV7VRSYtMmzYNoaGhaq/u3TUPMEBIddh5PRq5BQr09tP8R40QUv0sxEJ4Wotx8lGirkMhFVAoFBg1ahTOnj2LDh06YN++fRAKheXWmTFjBgBg8eLFePbsmWp5aGgo/vzzT5iYmODDDz+s0birC5fLRWBgIAIDA/W+uyUhpPatmz4f3wj8wOdqvhD5Iuc1MuR56GHjo3F9lDQNiXlZ6GNXH0DhHemtMTfxpWdHcDUkzyV5GJij0wMZHB+lwkpY8eMvIh4fX3t1xp8RoWWW4XK4+NLQD5u/XqzVwJf6TOe3044dO4aQkBAEBwdj1KhRAIDOnTsjKioK33zzDUaOHFnmVez169fj4cOHuHLlCtq0aaOq6+/vj5kzZ+LatWuqst988w28vb2xZ88e1RUhd3d3tGvXDhs2bMCUKVPUtu3i4oLWrWv+GT9CAEAmV2DdxZdo62kJC3H5XyIJIdWrhZsF9t+Joy7mxSxatEirckKhEPb29ggMDISbm1uNxvTbb79h//79AAArKyt8+umnGsv9+OOPsLIqHK23W7du+OKLL/Dzzz+jSZMm6N69O/Lz8xESEgKlUont27fDwsKiRuOuLoaGhjh37pyuwyCE6KE7N26iZYoQfInmnAkAjiQ+xlT3tmWuP5QQho/d/8t9Lqa8RC8bX60S7iIBJg44l/Ss4oL/z5AngLvYEtHSNLgYlZ6SEihMvHvnWODciRB07t1D623rG50n3fv374dEIsHw4cPVlk+YMAGjR4/GtWvX0Lat5g/I/v374ePjo0q4gcKuGGPGjMGcOXMQFxcHR0dHxMXF4caNG1i2bJkq4QaAtm3bwtvbG/v37y+VdBNSm/bcikVSpgzf9PTVdSiEvHM6eltj961Y7Lkdiw/bu+s6HL2wYMGCSo1YzuFw8MEHH+CPP/6AgYFBjcRUfLCzouRbkwULFqiSbgD46aef0KRJE/z2228ICQmBQCBA165dMXfuXLRv375GYiWEkNp0ev0/+FzsUuZ6qTwfAg63zLvgMoUcHA4g5P6XJ91Mj8EMz06VjqWZmRNupMWghbmzVuUH2DXEX5FXMc2j7L/HzcT2WL3raJ1OunXeP+nhw4eoX7++WjIM/DewycOHD8utq2kAlKJlYWFhatsoq6ymfSxfvhxCoRBGRkZo3749Dh06pOUREVI5BQol1px9jtb1LOFoZlhxBUJItTIzEqKluwW2Xo2s893XqhtjTGObFC0vWqdUKrFlyxaMGDGixmJZsGCB2n7Lemm64z5+/HjcvHkTOTk5SE9Px4kTJyjhJoS8NQTx6eXekb6WFo1AK48y199Ij0E7C/WLzsUT8MroYFkP19KitC4v4vGhzTVeQVxGleLRFzpPulNSUjR27SpalpKS8sZ1i/4tq2zxfYhEIkyaNAl//PEHzpw5g7///hsKhQIDBw7E33//Xe6xyGQyZGZmqr0Iqcj+23GIT8/DoABHXYdCyDurW31bRL6W4sqLss857xrGmGog0ZKJbfG5u4sSb8YYjh49imPHjuky7LdWTk4OrK2tYW1tXe7c44SQd5BMXu7qtAIpLIVGZa/PV18vU8hhwK3a41YcDgccaN9TCoBW5Q0VHNVc4XWRzpNuAOV2YdNm1D5t15VVtvhye3t7/PXXXxg+fDjat2+P0aNH48KFCwgICMCsWbMgl5f9oV62bBlMTU1VL2dn7bpVkHeXXKHEb2efo6WbBVwsyv5jSAipWb52xnA2N8TWq9pfndeGUsnw7FUW9t6KxdnwujNCekREBCZMmADGGBo2bIitW7fi7t27uHfvHrZt2wY/Pz8AwKefforTp0+jZ8+eqrrbt2/XVdhvvdevX+P169e6DoMQome4/LKf5QYAA64AecqycxgDHl9tvYDLRUEZo4pro3Ipt3bl8zmswsEz9ZnOn+m2tLTUeDc7NTUVgOa705Wta2lpCUDzXfPU1NQKB1ERCAQYOXIkZs2ahWfPnqF+/foay82ePVs1UipQOFk6Jd6kPPvvxCE6VYpPAsvu8kMIqXkcDgfd6ttiS2gUEjPyYGf6Zs8lp2TLsObsC+y+FYOsvMIvMkqZtDpCrRXXr1/Hxo0bYWNjg4sXL8LU1FS1rlGjRujduzd8fX3xxx9/oGfPnjh8+DA8PDwQExODGzdu6DByQgh598iMhUA5N4G9Jda4nxEPNyPNOY+3xAY302PgLbEGUDh4WZ6ioEqxvMrLgqmgcudQWTkXBFRljIV1euYGnUfeqFEjPH78uNQd5AcPHgCA6mp6WXWLypVXt+jfssqWt48iRd3nyvthi0QimJiYqL0IKYtMrsDqkKdo6W4Bd6uKp1YghNSs9l5WMBTy8OPJ8CpvQ65Q4rczz9BhxVnsvBGNLr42+K5Pfawf1xw/Dis9roi+WrVqFQAgICBALeEuYm5ujoCAADDG8L///Q98Ph+9evUCACQkJNRqrIQQ8q6za+eP+Lyyn3n2lFjheU7ZvWRcjcwRJU1VW2bMN0B6QW6lY9mX8ACD7BtpXT4sMxG+xjbllkkvyIVJM+9Kx6JPdJ50Dx48GNnZ2di7d6/a8s2bN8PBwQGtWrUqt254eLja1GByuRzbtm1Dq1at4ODgAABwdHREy5YtsW3bNigU/3WVuHr1Kp48eYIhQ4aUG2NBQQH++ecfWFlZwdPTsyqHSUgp265GIzEzDyOaU28IQvSBkZCP91o6Y8+tOFx9Wflnu6NScjBsbShWhTxFJx8b/DSyCd5r4QI/R1MYCfmwEItqIOqa8fDhQ3A4HDx8+FDjM3T5+fl49OgRAODevXsAADs7OwCF50xCCCG1Z+hHY7GfE1duGQ+xFZ5mJ5e53ldii7DMRNX7YY6NsTvuXqXiKFAqIFMUQMzXvhv4yaQnZc4dXmRPfgSGT/2wUrHoG50n3b1790b37t0xZcoUrFu3DmfPnsXkyZNx4sQJrFixQjVH94cffgg+n4+oqP+et5s4cSIaNmyI4cOHIzg4GKdOncKIESPw5MkT/O9//1Pbz//+9z+Eh4dj+PDhOHXqFIKDgzFixAj4+flhwoQJqnIzZszAtGnTsHPnTpw7dw5bt25Fu3btcPfuXfzwww9lzhlOSGVk5RXgtzPPEOhtQyOWE6JHOvvYwMfWGHP2P0C+XKl1vYN349D754tIyMjFgv4N8UFrVxgb1N05vwWCwtjj4+PRu3dvnDp1ComJiUhKSsL58+fRv39/xMbGqpVNT08HALXpugghhNQ8Q0NDOA3thEu58WWW6WfXADtj7yBbLtO4vpetD/bF30dmQeGFVjOBIYx4AtxOj9UqBsYYlmfdgtJK+56+J16Fo5GJPXjljLx+Jy8JRr2awdxc8zzedYXOk24A2LdvHz744APMnz8fvXr1wrVr17Bjxw68//77qjIKhQIKhUJt6hKRSITTp0+jc+fOmDZtGvr374+EhAQcP34cgYGBavvo1KkTjh07hoSEBPTv3x/Tpk1D586dcfr0aYhE/9198PPzw/Xr1zF16lR0794dX3zxBSwsLPDvv/9i/PjxNd4W5N2w7mIEcmQKDG1KI5YTok+4HA4mtndH5OscrDn7vMLy0nw5Zu65hy923kVTF3MsHdwIXrbGtRBpzQoMDFSdby9cuICePXvC0dER9vb26NKlC06dOgWg8Fn4zp07AwBevnwJAHB1ddVN0IQQ8g4b+tFYxPT0xEmp5gFBeRwuvvLshB+fnUNibukZlrj/v/6nFxcQl5sOAHjfuRlup8fh4uuX5e47XynHivwHGPP7InSYNxlrcx9Bycq/cL0l+iZy5DJ0sym72/il3HjcbmOJsV9NLXdbdQGH0aSkNSYzMxOmpqbIyMig57uJSlx6Lrr+eA7dG9hidCv6ckqIPtpzKwZ7b8fhs86e+KqHt8bZLy48Tcb8gw+RkJGHCe3c0NHLutwZNXKystA9wL1OnBMeP36MVq1aqaamKvlVoWgaMYlEguvXr8PFxQXW1tbIzc3FrFmzsHTpUl2EXSt0dW7Pzc1Fx44dARReCDE0pF5ShJDSLpw4hWvBh+AUl4eB4nqq+bZf5WVhP2KR52kFrsQQivA4BOaYoKnYHkDh3/nr0nhcFGchHlI4wQgdc0zQXOyAU0lP8SAzAS6G5hho3xB8bmHP39i8dBzkJIDVt8foWZ/Dxqbw2ewXT57hwM/rYPQiBUN5rrD4/+nIchUF2Bd7D0nZGWgktMRjWSoKBBwMd24CZ6PCO9lypQLHciLxwp6HxoN7oOfQAbXdhJWi7TmBku4aREk30eTT7bcQ+iIFPw73h5FQ5xMIEEI0YIzhyP0EBF+PRr/G9pjcsR7q25ugQKHEtZep+OdGNE6EvUIDexNMbOcOR/OKE6C6lHQDheOejB49GpGRkRrXu7u7Izg4GK1atUJqaipCQkIAAG3atIGLi0stRlq76NxOCKkLIl5G4PiWnVDmyAAeB+bODhg84X0YGRUmwIwxnDl6Ao8v3wTkCjA+F/5d2qFDty6qC6vnToQg7MI1QK4A+DyY1XNCRnQ8WF4BwOPAup4LBo4dBQMDzaOVZ2ZmYunUbyC7+gTuEEMALvqZesJB+F+PMJlSjoPpT/G0IB0x1jwEdO+I7u8Pg7dv+c956wtKuvUAnZhJSVeev8bov6/h004e6OBlretwCCEVuPYyBWsvvEBegRIGfC7kSga5ksHWRIThzZzR1sOy3LvbxdW1pBsoHBRt3759OHPmDGJiYsAYg4uLC7p06YIhQ4aonud+l9C5nRBCtHP1zHnEzNuMIcxJq/LP5ek4290OnyyZU8ORVR9tzwl0m42QWiJXKBF0KAw+tsZo70kDDRFSF7SqZ4kAF3NEpuTgeVI2eFwOGjuaws7UQOtkuy4TCAQYOXIkRo4cqetQCCGE1DGX1u7Al1om3ADgyTfD+fOPkJ6eDjMzs5oLTAf0YiA1Qt4Ff154iRfJ2RjX1u2d+LJOyNtCyOfC29YYfRrZo2dDO9ibGdLvMKl1UqkUbm5ucHNzg1Qq1XU4hBBSrqePw+ERWXrKyYoMzXfA7l/W1UBEukV3ugmpBU9fZeGnU0/Rr7ED3K3Eug6HEELUTJw4EQDQokULTJkyRfVeGxwOB+vXr6+p0Mj/Y4yppk2lJwMJIfruxF/b8DFP+7vcRcz4Bsi++aQGItItSroJqWFyhRJf7boHWxMDDG1a+T8+hBBS0zZt2gQOh4Ps7GxMmTJF9b4ijDFKugkhhJTCzZKBz6naLAv87Pxqjkb3qHs5ITXszwsvERafgY871oOQT79yhBBCCCHk7caU5c/TXX7dt683D93pJqQGXY9IxaqTTzHA3wGeNsYVVyCEEB0p2WWZujATQgipKoWhQNUbqrKURm/fzBiUdBNSQ5KzZJgafBs+dsYY1sxZ1+EQQkiZlCXuSJR8TwghhFRG2/cG4OL1jegosK9UvTylHLwGb9/3ZurrSkgNkCuU+GLnHRTIlfisiyd4XBrpmBBCCCGEvBtadmiLWw6V7zF1gBOLwdMn1UBEukV3ugmpZowxzDsYhqsvU/Bdn/owNxLqOiRCCCF1HIfDQYMGDVT/J4QQfVd/eA9c+u0M2nNttCqfWpCL+ABrODmVPfCwUqnE+X9PITrsKRRyOWzruaDrgD4wMDAoVTYyIgJXj59BVmoajM3N0LJnZ9Tz9Kjy8bwJSroJqWa/nXmOHdej8UlgPTRwMNV1OIQQUqFFixa9Uf358+dXUySkLEZGRggLC9N1GIQQorW+Y0bg7xeREByLQCuedbllUwqk+M05Bd/9ulbj+vT0dOxc8RtyLj9CYDQPA7nG4IGLOMU9/LVqF5TN3DBk5hQ4u7jg5N5DuL/jGJzD09FJagYTrhBZ7BkurDmFfb6maDCyB3qPGFKrFzA5jEZKqTGZmZkwNTVFRkYGTExMdB0OqQXB16IxZ/8DDG/mhCE0PRghpJicrCx0D3DXy3MCl8t9oy8fCoWiGqPRb3RuJ4SQytm1Zj3i9p7DgEwLuAnVb0hlK/KxlxeHtCb2mPbTYgiFpXuIPnsUjl2T5uGzRBsYczX3IM1nCmwwTcBTWy5GPROhBSzLjOc2S8Xpzhb4eu2P4PF4b3Rs2p4TKOmuQXRifrdsuhyBBYcfoUcDW4xv60bd/wghat7GpLtoZFpKugkhhJQnPz8fBzZtR9yZmxBkysAUCigNBRD6uWLYF5Nhba35Tnh8XBy2DPsC3yQ7aXWe2pwZBj+hFZoZ2JZbLk6Rg53dTfD12h/e6Du7tucE6l5OyBtijOGP8y+w4sQT9G1kj/dbuVDCTQipc+gavH6TSqVo0aIFAODGjRswMjLScUSEEKI9oVCIEZMnAJMnVKretm+WYkayo9bfrceZNMT/0m6gqcim3DqOPDHanEnE+RMh6NS7R6ViqgpKugl5AzK5AvMOPMSum7EY2tQRQ5tqdxWOEEL0SUREhK5DIBVgjOHRo0eq/xNCyNsuKSkJNmHJ4HMcK1Wvs6EzLuXFo4Nh+fXawhqrNh+gpJsQfZaYkYdPtt1CWHwGPgn0QKB3+QNEEEKIvnJ1ddV1CIQQQoiavav+xJhsm0pPct1CZIvV6bcrTLoBwCbsNRITE2FnZ1fFKLVD83QTUkmMMRy8G4ceP51HTKoUQf0bUsJNCHnnREREYOnSpfDz89N1KIQQQt5CipjXEHMFla7H4XBgwNHu3nLzTEM8uH670vuoLLrTTUglxKXn4vvDYTgR9gptPSwxoa07JAb0a0QIeTckJyfjn3/+QXBwMK5du6brcAghhLzN8uUANI9WXhEG7R7DkXAFyE5Nq9I+KoOyBUK0kJuvwJ8XXmDtuRcwEPDweRcvtPEoeyoCQgh5W2RnZ2Pfvn0IDg7GmTNnVCOVFz1XTONYEEIIqQlMVPOpaoYiHyY2Nf+dnpJuQsqRm6/A9mtR+OPcC2TkFqC3nx0GBTjCSEi/OoSQt1dBQQGOHj2K4OBgHD16FHl5eQA0D+D1pnOcEkIIIZpIGrrh9ZUnsOIZVqqekjHkM6VWZS9b52F0+7ZVCa9SKHMgRIOUbBm2Xo3CltAopEvzEehtjUFNHGFjYqDr0AghpEYwxnD27FkEBwdj3759yMjIUC0HCu9oczgc1dzcPXv2xNChQzFo0CAdRv3u4HA4qgHvqHcBIeRdMHzaJGzZNxGfpFdu9PKT0ih0N3KpsJySMeQ0dih3fu3qQkk3If+PMYZ7sRnYdjUKh+/FAwACva3Rp5E9bCnZJoS8xWbMmIF//vkHiYmJADR3HefxeGCMqbqXHzt2rPYDfYcZGRkhMjJS12EQQkitkUgkkDVzQ1ZIDoy52j3brWQMt2VJ6CV2q7DsQUECekyb/YZRaoeSbvLOy8gtwKF78dhxLRqPEjJhbSzC4ABHdPW1pUHSCCHvhJ9++kl1F7s4Pp+Prl27YtiwYRg0aBD8/f0RFxenoygJIYS8az5etQDLB0zErAhrGHDL/17OGMPi9OuYYNygwu3e5KQi64PWaNS0STVFWj7KKMg7SalkuBaRil03Y3D8QQLyFUo0dTHHNz180MTZDFwudd0jhLybBAIBevfujaFDh2LAgAEwNTXVdUiEEELeUUZGRpixZy3+9/40DA3nwY9rrrFcrDwbm5wzYDa0B84deYiRMiMIOaXHHMlnCuwRJUA5tj3Gf/tFTYevQkk3eackZuRhz60Y/HMjBjFpubA3NcCgAEd09LaGuVHVpiQghJC3iVwuR1RUFKKjo5GYmEhJt57Izc1Fx44dAQAXLlyAoWHlBhYihJC6yszMDEGHN+PfPQfx7z8nYPckDW5ZXPAYB/FGCkTUE8Opd1t8O3ksRCIRIj+JwJ8r/wJuRaBhEmCcz0GWkOGRNcCau2PAjP/BvV69Wj0GDtM0FCmpFpmZmTA1NUVGRkatPKBPNFMoGc49SULwtWicfZIEAY+Llu4W6OxjA187YxqQhhBSK3KystA9wF0vzwlcLrfMv4X169fH8OHDMXToUPTu3RtxcXHgcDiqZ7vfNbo6t+fk5EAikQAonMZNLBbX2r4JIUSfJCUlIS4uDnK5HLa2tnBx0Txomkwmw4sXL5CZkQETU1N4eHhAJBJVayzanhMo6a5BlHTrVnKWDP/ciMb2a9FIyMhDPSsxOvnYoJ2nJU35RQipdfqcdK9atQo7duzArVu3VMuKP+NdPCEvGr2ckm5Kugkh5F2n7TmBMg/y1rkXk44NlyNw9H4CeFwO2npYYmpnT3hYS3QdGiGE6KUZM2ZgxowZeP78ObZt24adO3fi6dOnAP5LuEteo/fz88PgwYMxePBgNG3atNZjJoQQQuoKutNdg+hOd+1RKBlOhiVi3cWXuB2dDlsTEbrXt0OgjzUkIrq2RAjRPX2+063JrVu3sH37duzatQvx8YXTKGq6483hcCCXy3UVZq2jO92EEEKK0J1u8k7IK1Bg980Y/HXhJWLSclHf3hhfdfdGUxdzGoGcEELeQLNmzdCsWTOsXLkS586dQ3BwMPbt24e0tDQA0DjFGCGEEEJKo6Sb1EmZeQXYGhqF9ZcikC7NR0t3C3wc6EFdyAkhpJpxOBx07twZnTt3xu+//45jx44hODgYR44cQW5urq7DI4QQQvQeJd2kTknLycf6SxHYdCUSMrkCHb2s0d/fAbYmBroOjRBC3noCgQADBw7EwIEDkZ2djX379mHnzp26DqtCeXl5WLZsGXbs2IHo6GhYWFigV69eWLRoEZycnHQdntasrKx0HQIhhNR5Hw0agWdnroEVFIAjNsSXf6/EoEGDanSf9Ex3DaJnuqtPSrYM6y5GYPOVSCgZQ7f6tujb2J7m1iaE1Bl17Znut0VeXh66du2KK1euwN7eHh06dEBkZCSuX78Oa2trhIaGwsPDQ+vt0bmdEELqnoSEBIzyaQ3vLAEGwwmtYAkD8JCGfOxBDC4gCeJuTbAl5HCltkvPdJO3QmpOPv688AKbr0SCAw66N7BF30b2MDEU6Do0QgghdcDSpUtx5coVtGnTBidPnlQNRrZq1Sp89dVXmDhxIs6fP6/jKAkhhNSUixcvYmXHUdiEpnCD+qOoRuDjC/jgc3hjz6kYdDd0QUhudLXHQHe6axBdDa+6tJx8rLv4EhsvRwIAeja0RZ9G9jA2oGSbEFI30Z3u2ldQUAAbGxukp6fj9u3bCAgIUFvv7++P+/fv4+bNm2jWrJlW26RzOyGE1B0JCQmY4tACwWgLIy3uN19CEhaJn+NkdqRW29f2nMDVNmBCakOGtAArTz5B+/+dwYbLEejewBY/vdcEI1u4UMJNCCGkUi5duoT09HR4eHiUSrgBYNiwYQCAw4cr151QF3Jzc9GpUyd06tSJBrAjhBAtve/eAmvRQquEGwDawwa9ciywffv2ao2DupcTvZCVV4CNlyOx7sJL5CuU6N7AFv0bO1A3ckIIIVV27949AEDTpk01ri9aXlROnymVSlU3eKVSqeNoCCFE/xUUFKChzAh2MKxUvcnwwKDxM/H+++9XWyyUdBOdypbJsflKJP688AK5+Qp0rW+Lgf4OMKMB0gghhLyh6OjC5/LKGqG8aHlROUIIIW+PoU07Yg7cKl1PAgEay40hlUphZGRULbFQ0k10oijZ/uvCS0jz5ejsY4OBTRxhIaZkmxBCSPXIzs4GgDK/NInFYrVymshkMshkMtX7zMzMaoyQEEJITcl6HIlW6FSlug1hipMnT1bbVGKUdJNalZlXgC1XIrHuYgSk+XJ08rHBQH8HWEpEug6NEELIW6ZorFgOh1Pu+vIsW7YMCxcurNa4CCGE1Dwh44IDzX//K2IKAcLDw6stFkq6Sa1IzcnHpssR2HglEnkFCnT2scEASrYJIYTUIGNjYwBATk6OxvVSqRQAVNOIaTJ79mzMmDFD9T4zMxPOzs7VGCUhhJCakMdVQqlk4FYh8X4NGfz9/astFkq6SY2KTZNi/aUI7LgeDcaArr426NvYgbqRE0IIqXEuLi4AgNjYWI3ri5YXldNEJBJBJKILxIQQUtd49e2A4wfj0ReOla57D+n4sFu3aouFkm5SI+5Ep2Hj5QgcvZ8IIyEPffzs0dPPDiY07RchhJBaUnSX4vbt2xrXFy1v3LhxrcX0JqprQB9CCHkX/H1gFyZzPCuddCchD+EGeRAIqi9voaSbVJu8AgWOP0zApiuRuBeTAVsTEca0dkUnH2sYCHi6Do8QQsg7pl27djA1NcWLFy9w586dUnN179mzBwDQr18/XYRXKWKxuMxu8oQQQjR7YabEw/R0+MFM6zqr8QQrLuyr1ji41bo18k56kpiFxUceofWy0/jyn3tQKoGvenhj1fAm6OVnRwk3IYQQnRAKhfjss88AAJ999pla0rpq1Srcv38f7du3R4sWLXQVIiGEkBp0IukJZnDuIBbaXbTchgiEexpW+3mB7nSTKknMyMPRBwk4cCcOD+IyYGLAR3sva3TztYG9WeUmoCeEEEJqyty5c3Hq1ClcuXIFXl5e6NChA6KionDt2jVYWlpi48aNug6REEJIDREIBNidFo5h5r74kvmgN+w1jmieiQKsRjhu1xPg4DPNjyS9CUq6idYiXucg5FEiToa9wq2oNPB5HDRxNsOM7t4IcDYDn0cdJwghhOgXAwMDnD17FsuWLUNwcDAOHDgAc3NzjBs3Dt9//32dGYk8Ly8PQ4cOBQDs3bsXBgYGOo6IEELqBlNTU4QoEzC2xwCsC7mEdrBCM1hADB5eIx8hSMBDfjY+2fkLgv7/72x14zBtJqkkVZKZmQlTU1NkZGTAxMRE1+FUWmJGHm5EpuLKixRcfJaM2LRcCHlcNHI0RXM3c7Rws4BYRNdtCCFEGzlZWege4F5nzwmkkK7O7Tk5OaqpzbKzsyEWi2tt34QQ8jY5e/YsNm/ejORXr+Dp7Y1ly5ZVeaBKbc8Jb23GlJ2djblz52LXrl1ITU2Fr68vZs2ahffee6/CuklJSZg5cyaOHDkCqVQKf39/LF68GF27dq1SLJl5BSjgyqD8/8sbPC4HAh4HQj4XQh4XHE7VJm2vLkolQ1x6Lp4nZeNRQiYexmXgfmwG4tJzAQCOZoZo6GCC91q4wM/RBCI+PaNNCCGEEEIIqXs6d+6Mzp071+o+39qke8iQIbhx4waWL18Ob29vBAcHY9SoUVAqlRg9enSZ9WQyGbp27Yr09HT8/PPPsLGxwZo1a9CrVy+cOnUKgYGBlY6l7bIz4Io0Xz3hcgADAQ8SER/GBnwYGwhgaiiAiaEApoZ8mPz/e2MDAUwM+RCL+JCI+DAS8mAgKHwJeBwIuFxwuYXJO2MMBQqGAoUSuQUK5OYrkJUnR0ZuPtKkBUjOkiEpKw/x6XmITpEiNk2KPLkSAGAo4MHdSgx/J1MMb+4EH1tjmBnRnNqEEEIIIYQQUhVvZdJ97NgxhISEqBJtoPCKRlRUFL755huMHDkSPJ7mu7Xr16/Hw4cPceXKFbRp00ZV19/fHzNnzsS1a9cqHc8nHT0gMTEGhwNwwIGSMcgVDHKlEjK5ErICJXIL5MjNVyAnXwFpvhyvs2WQ5iuQI5MjRyaHNF+B6ngOgAPA1FAAc7EQZkYCeNhI0MbDEg5mBnA0M4KlRAiuju+8E0IIIYQQQsjb4q1Muvfv3w+JRILhw4erLZ8wYQJGjx6Na9euoW3btmXW9fHxUSXcAMDn8zFmzBjMmTMHcXFxcHSs3ATrLd0tIDY2rvyBFKNkDHn/f9c6r0CJPLkCBfLCpF2hZFAoGZT/n5ZzwAGPW/gS8bmFd8T5XEgM+BAL+ao74oQQQgghhBBCatZbmXQ/fPgQ9evXB5+vfniNGzdWrS8r6X748CE6dOhQanlR3bCwsEon3dWBy+HASMiHkfCt/JERQgghhBBCyFvprczgUlJSUK9evVLLLSwsVOvLq1tUrrJ1ZTIZZDKZ6n1GRgYAYO/1ZxAZ0iijhBDyLpPl5gAoHHeD1F1FP7/MzMxa3W9OTo7q/5mZmVAoFLW6f0IIIaUVnQsqOre/lUk3gHJHBK9otPCq1l22bBkWLlxYavnaj3uUuz9CCCHvjpSUFJiamuo6DFJFWVlZAKDT+b0dHBx0tm9CCCGlZWVllXtufyuTbktLS413pFNTUwFA453s6qg7e/ZszJgxQ/U+PT0drq6uiI6Opi9YlZCZmQlnZ2fExMTQXLaVQO1WedRmVUPtVjUZGRlwcXEp9zxC9J+DgwNiYmJgbGxc61N+1uXfvbocO1C346fYdYNi153ajJ8xhqysrAovhr6VSXejRo2wY8cOyOVytee6Hzx4AADw8/Mrt25RueK0qSsSiSASiUotNzU1rZMfWF0zMTGhdqsCarfKozarGmq3quFyuboOgbwBLpcLJycnncZQl3/36nLsQN2On2LXDYpdd2orfm1urr6VZ/7BgwcjOzsbe/fuVVu+efNmODg4oFWrVuXWDQ8PV5saTC6XY9u2bWjVqhV16SKEEEIIIYQQorW38k5379690b17d0yZMgWZmZnw9PTEjh07cOLECWzbtk01R/eHH36IzZs348WLF3B1dQUATJw4EWvWrMHw4cOxfPly2NjY4Pfff8eTJ09w6tQpXR4WIYQQQgghhJA65q1MugFg3759+O677zB//nykpqbC19cXO3bswHvvvacqo1AooFAo1EabE4lEOH36NGbOnIlp06ZBKpWiSZMmOH78OAIDAysVg0gkQlBQkMYu56Rs1G5VQ+1WedRmVUPtVjXUbuRN1eXPUF2OHajb8VPsukGx644+xs9hNHcJIYQQQgghhBBSI97KZ7oJIYQQQgghhBB9QEk3IYQQQgghhBBSQyjproLs7GxMnz4dDg4OMDAwQJMmTbBz506t6iYlJWH8+PGwsrKCkZER2rRpg9OnT9dwxLXrzJkzmDhxInx9fSEWi+Ho6IiBAwfi1q1bpcrevn0b3bp1g0QigZmZGYYMGYKXL19qva9Tp06hTZs2MDIygpWVFcaPH4+kpKTqPByd+fvvv8HhcCCRSEqto3ZTd+nSJfTp0wfm5uYwNDSEl5cXvv/+e7Uy1Gbq7ty5g0GDBsHBwQFGRkbw9fXFokWLIJVK1cq9q+2WlZWFmTNnokePHrC2tgaHw8GCBQs0lq1MG/3666/w9fWFSCSCu7s7Fi5ciIKCAq1iKigowMKFC+Hm5gaRSARfX1/8+uuvVT1EUofk5eUhKCgI3t7eMDAwgIODAyZOnIjY2Fhdh4Zbt25h+fLlGDJkCBwdHcHhcGBgYFBhvS1btqBly5aQSCSwsLBAnz59cOXKlVqI+D9SqRQHDhzAhx9+iMaNG8PExARisRj+/v5YtGgRsrOzy6yrD/GvWrUKQ4YMgZeXF0xNTSESieDq6opx48YhLCyszHr6EHtJqampsLGxAYfDga+vb7ll9SH+Tp06gcPhlPk6ceKE3sZeJDExEV9++SW8vb1haGgICwsLNGvWDDNnztRYXtexnzt3rtw2L3otWrRI72IHADBSad27d2dmZmZs7dq17MyZM+yjjz5iANj27dvLrZeXl8f8/PyYk5MT27ZtGzt58iQbOHAg4/P57Ny5c7UUfc0bNmwY69y5M/v999/ZuXPn2O7du1nr1q0Zn89np0+fVpV7/PgxMzY2Zh06dGBHjx5le/fuZQ0bNmQODg4sKSmpwv2cO3eO8fl8NnDgQHby5Em2bds25ujoyPz8/FheXl5NHmKNi42NZaampszBwYGJxWK1ddRu6rZv3864XC5777332KFDh9iZM2fYunXr2MKFC1VlqM3UhYWFMQMDA+bv78/++ecfdvr0aRYUFMR4PB4bMGCAqty73G4RERHM1NSUdezYUfU3PigoqFS5yrTR4sWLGYfDYbNnz2Znz55lK1asYEKhkE2aNEmrmD766CMmEonYihUr2NmzZ9msWbMYh8NhS5YsqY5DJnoqNzeXtW3blgFg9vb2bMSIEaxly5YMALO2tmbPnz/XaXwDBw5kANReIpGo3DpffvklA8AMDQ3ZwIEDWc+ePRmfz2c8Ho/t27evliJnbN26daqYGzZsyIYPH8569uzJjI2NGQDm6+vLXr16pbfxW1paMgMDA9ayZUs2ePBgNnjwYObt7c0AMKFQyI4dO6a3sZc0btw4xuFwGADm4+NTZjl9iT8wMJABYEOHDmXjxo0r9bp//77exs4YY1euXGFmZmYMAGvQoAEbMWIE6927N3N1dWU8Hk8vY3/8+LHGth43bhwbM2aM6nf5zJkzehc7Y4xR0l1JR48eZQBYcHCw2vLu3bszBwcHJpfLy6y7Zs0aBoBduXJFtaygoIA1aNCAtWzZssZirm2aTlBZWVnM1taWde3aVbVs+PDhzMrKimVkZKiWRUZGMoFAwGbOnFnhflq0aMEaNGjACgoKVMsuX77MALDff//9DY9Ct/r168f69+/Pxo0bVyrppnb7T2xsLBOLxWzKlCnllqM2U/fdd98xAKW+rE+ePJkBYKmpqYyxd7vdlEolUyqVjDHGkpOTy0y6tW2j169fMwMDAzZ58mS1+kuWLGEcDoeFhYWVG8/Dhw8Zh8NhS5cuVVs+adIkZmhoyFJSUip7iKSOmDdvHgPA2rRpw7KyslTLV65cyQCwjh076jA6xpYvX87mz5/PDh8+zBITEytMuk+fPs0AMEtLS/b06VPV8itXrjChUMhMTU1Vf4Nq2ubNm9mUKVPU4mCMsfj4eBYQEMAAsFGjRult/JcuXWK5ubmllv/+++8MQKnvpfoUe3GnTp1iAFTnoLKSbn2KvyjpjoiI0Kq8PsUeFxfHzMzMmKGhocak89q1a2rv9Sn2shw7dowBYM7OzkyhUKiW61PslHRX0kcffcQkEonal0jGGAsODmYA2OXLl8us261bN41/SJYuXcoAsNjY2GqPV5907tyZeXt7M8YKLzYYGhqyjz/+uFS5Hj16MC8vr3K3FRsbywCwZcuWlVrn7e3NunfvXj1B68DWrVuZsbExi4mJKZV0U7upW7BgAQPAIiMjyyxDbVZaUbslJyerLZ85cybjcrksOzub2q2YspLuyrTRtm3bGAAWGhqqVi4+Pp4BqPBu9eLFixkAlpCQoLb8ypUrWvW0InVTfn6+6m7U7du3S61v3LgxA8Bu3rypg+g0qyjp7tOnDwPAVq9eXWrd559/zgCwH3/8sQYj1E7R75ZIJGIymUy1vK7E7+npyQCoXdDTx9ilUinz9PRkDRo0YE+fPi036dan+CubdOtT7B988AEDwH799VetyutT7GUZPXo0A8BmzZqltlyfYqdnuivp4cOHqF+/Pvh89SnOGzdurFpfXt2icprqlvf8TV2XkZGB27dvo2HDhgCAFy9eIDc3t8z2eP78OfLy8srcXlE7l1W/vJ+DPktKSsL06dOxfPlyODk5lVpP7abuwoULsLCwQHh4OJo0aQI+nw8bGxt88sknyMzMBEBtpsm4ceNgZmaGKVOm4OXLl8jKysKRI0fw559/YurUqRCLxdRuWqhMGxUdb6NGjdTK2dvbw8rKqsL2ePjwIaytrWFnZ1dqP8W3T94uly5dQnp6Ojw8PBAQEFBq/bBhwwAAhw8fru3QqiQvL081jk1R7MXp0/H4+/sDAGQyGVJSUgDUrfh5PB4AQCgUAtDf2BcuXIgXL17gjz/+gEAgKLOcvsavDX2KPS0tDbt27YKpqSk++uijCsvrU+xlycnJwcGDBwEAY8aMUS3Xt9j5FRchxaWkpKBevXqllltYWKjWl1e3qFxl69Z1U6dORU5ODr777jsA/x1rWe3BGENaWhrs7e01bq+i+nW1LT/99FP4+PhgypQpGtdTu6mLi4uDVCrF8OHDMXv2bPz000+4ceMGgoKC8PDhQ1y8eJHaTAM3NzeEhoZi8ODB8PDwUC3//PPP8dNPPwGgz5o2KtNGKSkpEIlEEIvFGstW1B5lnT/EYjGEQuFb0Z6ktHv37gEAmjZtqnF90fKicvouPDwcMpkM1tbWGi8sFx3P/fv3azu0UooGQxQIBKrfvboS/5YtW/DkyRN4e3urvrPqY+z379/HypUrMWHCBHTs2BGRkZFlltXH+AFg/fr1SElJAZfLhbe3NwYNGgQXFxe1MvoU++XLlyGTydCtWzcIBALs2bMHly5dQkFBAXx9fTFixAjY2trqZexl2bdvH3JychAQEKC6uQfoX+yUdFcBh8Op0ro3rVtXzZs3D9u3b8evv/6KZs2aqa170/Yoq0xdbMu9e/fi8OHDuHPnTo1/jt6WdlMqlapRfWfNmgWgcERRoVCI6dOn4/Tp0zAyMgJAbVZcZGQk+vfvD1tbW+zZswfW1ta4du0aFi9ejOzsbKxfv15VltqtYtq2UU21pbb1Sd0THR0NABq/MBZfXlRO31V0PGKxGGZmZkhLS0NWVhaMjY1rMzw1P//8MwCgV69eEIlEAPQ3/h9++AFhYWHIycnB48ePERYWBgcHBwQHB4PL5epl7EqlEpMmTYKZmRlWrFhRYXl9i7/I4sWL1d5//fXXmDdvHubNm6dapk+xF/WqtbW1RYcOHRAaGqq2fvbs2di4cSOGDx+ud7GXZdu2bQCADz74QG25vsVO3csrydLSUuMdhdTUVACa73hUR926auHChVi8eDGWLFmCzz77TLXc0tISgOa7+6mpqeBwODAzMytzuxXVr2ttmZ2djalTp2LatGlwcHBAeno60tPTkZ+fDwBIT09HTk4OtVsJRcfTs2dPteW9e/cGUDiVE7VZabNmzUJmZib+/fdfDB06FB07dsQ333yDn376CRs2bMD58+ep3bRQmTaytLREXl5eqSnZispW1B5lnT9ycnKQn5//VrQnKa1oyqqii4clFfWcKG9qK31S0fEA+nFMx44dw/r16yEQCNSmn9TX+P/9919s3rwZe/bsQVhYGJydnREcHKx2o0PfYv/1119x/fp1/PDDD6q/peXRt/g7duyIrVu34sWLF5BKpXjy5AmWLFkCPp+P+fPnqy7aFI9HH2JPS0sDUNgb4v79+1i/fj2Sk5MRERGBGTNmICcnB2PGjFHd/dWn2DVJTEzE6dOnwePxMGrUKLV1+hY7Jd2V1KhRIzx+/BhyuVxt+YMHDwAAfn5+5dYtKlfZunXRwoULsWDBAixYsABz5sxRW+fh4QFDQ8My28PT07PceT6L2qqs+nWtLV+/fo1Xr15h5cqVMDc3V7127NiBnJwcmJub4/3336d2K0HTs7QAwBgDAHC5XGozDe7evYsGDRqU6urcokULAIXPB1O7VawybVT0LHfJsomJiXj9+nWF7dGoUSMkJycjMTGx1H6At+/8QQoV/S0rqydD0fq6oqLjKV5GVx4/fowxY8aAMYYffvhB9Ww3oL/xnzp1SvU4y4ULF+Dj44NOnTphyZIlpeLSh9hjYmIwd+5cBAYGYvz48VrV0af4AWDRokUYM2YM6tWrB0NDQ3h7e2POnDk4cOAAACAoKAi5ublqcelD7AqFAgAgl8uxatUqTJw4EVZWVnBzc8PKlSsxbNgw5Ofnq3of6FPsmgQHB0OhUKB79+6lxjzRt9gp6a6kwYMHIzs7G3v37lVbvnnzZjg4OKBVq1bl1g0PD8e1a9dUy+RyObZt24ZWrVrBwcGhxuKubd9//z0WLFiAuXPnIigoqNR6Pp+P/v37Y9++fcjKylItj46OxtmzZzFkyJByt+/o6IiWLVti27Ztqj8gAHD16lU8efKkwvr6xs7ODmfPni316tmzJwwMDHD27FksXryY2q2EoUOHAgCOHz+utvzYsWMAgNatW1ObaeDg4ICwsLBSV3aLupk5OTlRu2mhMm3Uq1cvGBgYYNOmTWrb2LRpEzgcDgYNGlTuvgYOHAgOh4PNmzeXqm9oaIhevXq98fEQ/VPU3TEnJ0fj+qKeExKJpNZiehMVHQ+g22OKjY1Fr169kJaWhhkzZuCLL75QW6/v8ZuZmaFDhw44duwYmjVrhnnz5uHGjRsA9Cv2Tz/9FPn5+fjjjz+0rqNP8ZenR48eaN68OTIyMnD16lUA+hV7USxcLhfjxo0rtX7ixIkAgHPnzqmV14fYNSmrazmgh7HXyhjpb5nu3bszc3Nz9tdff7EzZ86wSZMmMQBs27ZtqjITJ05kPB5PbSqjvLw81rBhQ+bs7My2b9/OQkJC2ODBgxmfz2fnzp3TxaHUiB9//JEBYL169WKhoaGlXkUeP37MJBIJ69ixIzt27Bjbt28f8/PzYw4ODiwpKUltmzwej3Xp0kVt2dmzZxmfz2eDBw9mISEhbPv27czZ2Zn5+fmxvLy8WjnWmqZpnm5qN3X9+/dnIpGIff/99ywkJIQtW7aMGRgYsH79+qnKUJupO3jwIONwOKx169bsn3/+YadPn2ZLlixhEomENWjQQDU9zrvebseOHWO7d+9mGzZsYADY8OHD2e7du9nu3btZTk4OY6xybbR48WLG4XDYnDlz2Llz59gPP/zARCIRmzRpklq5zZs3Mx6PxzZv3qy2/KOPPmIikYj98MMP7Ny5c2zOnDmMw+FUON0YqbtWr16t+uxpcuTIEQaADRo0qJYjKxvKmTLszp07DACztrbWuD47O5sBYGZmZjUZokbJycnM19eXAWATJkxgSqWyVBl9jr+kFStWMABs3rx5jDH9ir1oP4GBgWqvVq1aMQDM0NBQtaxobnp9ir8io0aNUpvKUZ9i37x5s2oOd00ePXrEADCBQMAY06/YSyqKVSKRqM7Jxelb7JR0V0FWVhb7/PPPmZ2dHRMKhaxx48Zsx44damXGjRuncf6+xMRENnbsWGZhYcEMDAxY69atWUhISC1GX/OK5i4s61XczZs3WdeuXZmRkREzMTFhgwYNYs+fPy+1TQAsMDCw1PKTJ0+y1q1bMwMDA2ZhYcHGjh3LXr16VVOHVus0Jd2MUbsVJ5VK2bfffsucnZ0Zn89nLi4ubPbs2aWSOmozdWfOnGE9evRgdnZ2zNDQkHl7e7OvvvqKvX79Wq3cu9xurq6uZf4dK/63Xds2Yoyxn3/+mXl7ezOhUMhcXFxYUFAQy8/PVyuzceNGBoBt3LhRbXl+fj4LCgpiLi4uTCgUMm9vb/bLL79U92ETPXLmzBkGgHl4eGhcv2jRIgaAzZ8/v5YjK1t5SbdUKmUikYgBYDExMaXWX7hwgQFgHTt2rOkw1WRmZrLmzZszAGzIkCFMLpdrLKev8WtSdLHwk08+YYzpV+zlfUcs+UpLS9O7+CvSq1cvBoAdPHiQMaZfsd+/f191YUPThaWLFy8yAMzc3FzvYi9p9uzZDAAbO3asxvX6Fjsl3YQQQgghekgmkzFTU1MGgN2+fbvU+saNGzMA7Pr16zqITrPykm7GGOvduzcDwFavXl1q3eeff84AsBUrVtRghOry8vJY586dGQDWs2dPVU+fsuhb/GUpuvnzww8/qJbpe+wREREMAPPx8dG4Xt/jZ4yxpKQkJhaLSyV6+hS7u7s7A6DW+7TI999/zwCwrl27qpbpU+xFlEql6sJ4eTcv9Sl2SroJIYQQQvTUd999xwCwtm3bsuzsbNXylStXMgCsffv2OoyutIqS7pCQEAaAWVpasqdPn6qWX7lyhYlEImZiYsJSUlJqI1Qml8vZ4MGDGQDWoUMHjV1US9KX+C9cuMB27tzJCgoK1Jbn5+ezX375hXG5XGZoaMiio6P1LvayVJR060v8oaGh7MyZM6XuFEdERLB27doxAGzAgAF6GTtjjK1du5YBYC1atGDJycmq5Tdv3mRmZmYMANu9e7dexl7k/Pnzqm7yCoWizHL6FDsl3YQQQggheio3N1f1rKu9vT0bMWKE6r2lpSV79uyZTuM7cuQIa9WqleoFgHE4HLVlR44cUavzxRdfMADMyMiIDRw4kPXu3Zvx+XzG5XLZnj17ai32n376SdWNefDgwWzcuHEaX8UTE32Jv+gxFCsrK9azZ082evRo1qNHD2Zvb88AMAMDA/bPP/+UqqcPsZeloqSbMf2Iv6jt7e3tWWBgIBs5ciRr164dMzAwYABYw4YNNT5GpQ+xM8aYQqFgw4cPZwCYhYUF69evH+vUqRMTCoUMQKlxRvQp9iJF42l98803FZbVl9gp6SaEEEII0WNSqZTNmzePeXh4MKFQyGxtbdm4cePU7mLqSlECUt6r5PgERfWaNWvGjIyMmKmpKevZsye7ePFircYeFBSk1XPFJcfn0Yf4X758yebMmcPatWvH7O3tmUAgYGKxmDVs2JBNmzat3Isxuo69LNok3YzpPv5Hjx6xKVOmsKZNmzJra2vG5/OZqakpa926NVu5ciWTSqV6G3sRhULB1qxZwwICApiRkRETi8Wsbdu2bMuWLWXW0ZfY8/LymLm5OQPA7t27p1UdfYidw1gdm+SREEIIIYQQQgipI2iebkIIIYQQQgghpIZQ0k0IIYQQQgghhNQQSroJIYQQQgghhJAaQkk3IYQQQgghhBBSQyjpJjXql19+AYfDgZ+fX5llOBwOFixYoHp/7tw5cDgcnDt3ruYD1DPHjh1Ta4vq4ObmhvHjx9foNiMjI8HhcLBp06Zq3Y+mz0JNtNG7gtqOEEIIIaT2UdJNatSGDRsAAGFhYbh27ZpWdZo2bYrQ0FA0bdq0JkPTS8eOHcPChQurdZv79+/HvHnz9H6bmmj6LNREG70rqO0IIYQQQmofJd2kxty8eRP37t1D3759AQDr16/Xqp6JiQlat24NExOTmgzvnREQEAAPDw+932ZxBQUFkMvlb91ngTGG3NxcXYdBCCGEEEJqESXdpMYUJdnLly9H27ZtsXPnTkil0grrldW9fN26dfD29oZIJEKDBg0QHByM8ePHw83NTVWmqJvzjz/+iFWrVsHd3R0SiQRt2rTB1atX1bY3fvx4SCQShIeHo2fPnhCLxbC3t8fy5csBAFevXkX79u0hFovh7e2NzZs3l4o1MTERH3/8MZycnCAUCuHu7o6FCxdCLpdXOqbx48djzZo1AAq73Be9IiMjy2yrO3fuoF+/frCxsYFIJIKDgwP69u2L2NhYVZmSXcGL2jc4OBjffvst7O3tIZFI0L9/f7x69QpZWVmYPHkyrKysYGVlhQkTJiA7O1ttv9p0WX/+/DkmTJgALy8vGBkZwdHREf3798eDBw/UyhXFs3XrVnz11VdwdHSESCTC8+fPS30Wymujrl27wtfXF4wxte0zxuDp6am6+FMWNzc39OvXD/v370fjxo1hYGCAevXq4ZdffilVNjMzE19//TXc3d0hFArh6OiI6dOnIycnR60ch8PBZ599hrVr16J+/foQiUSqz1F4eDhGjRoFW1tbiEQiuLi4YOzYsZDJZKr6tfn5WrNmDTp27AgbGxuIxWI0atQIK1asQEFBQan2XLp0KVxdXWFgYIDmzZsjJCQEnTp1QqdOnarUToQQQgghbzO+rgMgb6fc3Fzs2LEDLVq0gJ+fHyZOnIiPPvoIu3fvxrhx4yq9vb/++gsff/wxhg4ditWrVyMjIwMLFy5US1CKW7NmDXx9ffHTTz8BAObNm4c+ffogIiICpqamqnIFBQUYMmQIPvnkE3zzzTcIDg7G7NmzkZmZib179+Lbb7+Fk5MTfv31V4wfPx5+fn5o1qwZgMKEqGXLluByuZg/fz48PDwQGhqKxYsXIzIyEhs3bqxUTPPmzUNOTg727NmD0NBQVT17e3uNx5iTk4Pu3bvD3d0da9asga2tLRITE3H27FlkZWVV2KZz5sxB586dsWnTJkRGRuLrr7/GqFGjwOfz4e/vjx07duDOnTuYM2cOjI2NNSaf5YmPj4elpSWWL18Oa2trpKamYvPmzWjVqhXu3LkDHx8ftfKzZ89GmzZtsHbtWnC5XNjY2CAxMVGtTHlt9MUXX2DgwIE4ffo0unXrplp3/PhxvHjxQqv47969i+nTp2PBggWws7PD9u3b8cUXXyA/Px9ff/01AEAqlSIwMBCxsbGYM2cOGjdujLCwMMyfPx8PHjzAqVOnwOFwVNs8cOAALl68iPnz58POzg42Nja4d+8e2rdvDysrKyxatAheXl5ISEjAoUOHkJ+fD5FIVOufrxcvXmD06NGqBPnevXtYsmQJwsPDVY+JAMB3332HZcuWYfLkyRgyZAhiYmLw0UcfoaCgAN7e3qpylW0nQgghhJC3FiOkBmzZsoUBYGvXrmWMMZaVlcUkEgnr0KFDqbIAWFBQkOr92bNnGQB29uxZxhhjCoWC2dnZsVatWqnVi4qKYgKBgLm6uqqWRUREMACsUaNGTC6Xq5Zfv36dAWA7duxQLRs3bhwDwPbu3ataVlBQwKytrRkAdvv2bdXylJQUxuPx2IwZM1TLPv74YyaRSFhUVJRaXD/++CMDwMLCwiod09SpU5m2v5Y3b95kANiBAwfKLefq6srGjRunel/Uvv3791crN336dAaAff7552rLBw0axCwsLMrdZtExbty4scw45HI5y8/PZ15eXuzLL78sFU/Hjh1L1Sn5WWCs7DZSKBSsXr16bODAgWrLe/fuzTw8PJhSqSwztqJj4nA47O7du2rLu3fvzkxMTFhOTg5jjLFly5YxLpfLbty4oVZuz549DAA7duyYahkAZmpqylJTU9XKdunShZmZmbGkpKQy49Hl50uhULCCggK2ZcsWxuPxVPGnpqYykUjERo4cqVY+NDSUAWCBgYGqZZVpJ0KIZlKplP3++++sT58+zMHBgYlEImZiYsJ8fHzYhx9+yM6dO6fT+IrOo0UvfRYYGKiKs/j3hpqyceNGtbYpfh4rOrcVvco7dxJS0/bu3av6LHbq1Knatuvq6qrabvHvBzXp+fPnjMfjMQDM3t5e9d1NH1D3clIj1q9fD0NDQ7z33nsAAIlEguHDh+PixYt49uxZpbb15MkTJCYmYsSIEWrLXVxc0K5dO411+vbtCx6Pp3rfuHFjAEBUVJRaOQ6Hgz59+qje8/l8eHp6wt7eHgEBAarlFhYWsLGxUat/5MgRdO7cGQ4ODpDL5apX7969AQDnz5+vUkza8vT0hLm5Ob799lusXbsWjx49qlT9fv36qb2vX7++Ks6Sy1NTU0t1Ma+IXC7H0qVL0aBBAwiFQvD5fAiFQjx79gyPHz8uVX7o0KGV2n5JXC4Xn332GY4cOYLo6GgAhXdvT5w4gU8//VSru6oNGzaEv7+/2rLRo0cjMzMTt2/fBlD4c/fz80OTJk3Ufu49e/bU+FhEly5dYG5urnovlUpx/vx5jBgxAtbW1mXGUtufrzt37mDAgAGwtLQEj8eDQCDA2LFjoVAo8PTpUwCFj1zIZLJSv4utW7dWe8yjKu1ECFF3+fJleHl54dNPP8WxY8cQHx8PmUyGzMxMPHnyBOvXr0enTp0waNAgZGZmVuu+ix7tKXpV98wUhNSkBQsWaP2YXl1V/PjeZIYauVyO2bNnq97PmjWrGqKr2KZNm9SOobq+E3h4eGDYsGEAgISEBKxatapatlsdKOkm1e758+e4cOEC+vbtC8YY0tPTkZ6ervolKN5VVRspKSkAAFtb21LrNC0DAEtLS7X3IpEIAEoNYmVkZAQDAwO1ZUKhEBYWFqW2KRQKkZeXp3r/6tUrHD58GAKBQO3VsGFDAMDr16+rFJO2TE1Ncf78eTRp0gRz5sxBw4YN4eDggKCgoFLP4WpS8hiFQmG5y4sfuzZmzJiBefPmYdCgQTh8+DCuXbuGGzduwN/fX+Mxl9WNvjImTpwIQ0NDrF27FkBhl2tDQ0NMnDhRq/p2dnZlLiv6HL569Qr3798v9XM3NjYGY6zUz73kcaWlpUGhUMDJyancWGrz8xUdHY0OHTogLi4OP//8My5evIgbN26ongEv2kZlfhcr206EkP+EhoaiS5cuiIuLUy2ztbVFnz590KFDBwgEAtXygwcPolu3bmU+blWTWrRogaFDh6peRDvW1tZq7VbyoiUhtWXTpk2qC+t+fn7o2bNntW27T58+qs94YGBgtW23Il999ZXq/ytWrEBaWlqt7bs89Ew3qXYbNmwAYwx79uzBnj17Sq3fvHkzFi9erHZXrjxFycSrV69KrSv5zG9tsrKyQuPGjbFkyRKN6x0cHGo8hkaNGmHnzp1gjOH+/fvYtGkTFi1aBENDw1q7WlmWbdu2YezYsVi6dKna8tevX8PMzKxU+ep4vtfU1BTjxo3D33//ja+//hobN27E6NGjNe5PE02fp6JlRZ9DKysrGBoalnnxyMrKSu19yeOysLAAj8dTG+yurO3U1ufrwIEDyMnJwb59++Dq6qpafvfuXbVyFf0uFv/iWNl2IoQUkslkGDlyJPLz81XLpk+fjhUrVqiS7cjISPTr1w9hYWEAgBs3biAoKEg1EGhtmTp1KqZOnVqr+3wbNGzYUOP3I0Jq22+//ab6//vvv1+t2/7999+rdXvaatGiBTw9PfH8+XNkZWVh69at+Pzzz3USS3F0p5tUK4VCgc2bN8PDwwNnz54t9frqq6+QkJCA48ePa71NHx8f2NnZYdeuXWrLo6OjceXKleo+BK3169cPDx8+hIeHB5o3b17qVZWkqKp3vzkcDvz9/bF69WqYmZmpukLrEofDUR1PkaNHj6rduamKitro888/x+vXrzFs2DCkp6fjs88+03rbYWFhuHfvntqy4OBgGBsbq+YK79evH168eAFLS0uNP/eK7lgYGhoiMDAQu3fvLvdub21+voouDBT/eTHGsG7dOrVyrVq1gkgkwj///KO2/OrVq6W6sb9pOxHyrtq2bRtiYmJU7zt06IDVq1er3d12c3PD3r17wef/d+9kzZo1yMjIUL0fP368WvdNxhj++usvBAQEwMjICNbW1hgzZoza727RbAidO3dWi2nChAkau+uW3EdxJbv4RkRE4I8//oCfnx8MDQ1Rr149LF26FAqFAgBw8eJFdO3aFcbGxjAzM8PgwYM1Po4WEhKCqVOnok2bNnBxcYFYLFbN3tG7d29s3bq11CwWbyI+Ph7z589H37594eXlBQsLCwgEApiZmaF58+aYM2eOxguR5dGm+/7hw4fRrl07iMViWFhYoH///rhz50653XI1bffevXsYMmQILCwsYGhoiJYtW+LYsWOl9ldTPy+g8Hyye/du9O/fH/b29hAKhTA3N0fHjh3x119/qc3IUUTT5/fvv/9Gs2bNYGhoCCsrK7z//vuIj49X1Slqm4ULF6pty93dXbWdyp57Ll++jHHjxsHT0xNisRhisRj16tXDqFGjSj3mBQAvX77E9OnT4efnB2NjYxgYGMDFxQUjRozA6dOnNe4jLS0N8+bNQ7NmzWBqagqBQAAbGxs0btwYH3zwAX777TdVb0M3N7dSv2ubN2+u0uMg165dU/vOM3LkyFJlqvJzKFIUK4fDUc1uUvQZnTBhglrZzp07a/xbkpubix9++AFt2rSBhYUF+Hw+LC0t0aBBA4wYMQIrV65U9cIrrvhjcCW/y+iMjp4lJ2+pw4cPMwDsf//7n8b1ycnJTCQSsUGDBqmWoYKB1Bhj7M8//2QA2NChQ9nRo0fZ9u3bmbe3N3NxcWHu7u6qckWDSv3www+l9l1yP+PGjWNisbhUucDAQNawYcNSy11dXVnfvn1V7+Pj45mrqyvz9fVlv//+Ozt9+jQ7evQoW7NmDevbty+LiYmpdExFA68EBQWxq1evshs3bjCZTFaqHmOFbd27d2/2559/spCQEHby5En2ySefMADsr7/+Uotb00Bqu3fvVtte0b5LDnwVFBTEALDk5OQyt6lpILWxY8cykUjEVq9ezU6fPs1WrFjBrK2tmZOTk9qAGmXFU3xd8c+CNm3Uu3dvBoC1b99eY9tp4urqyhwdHZmLiwvbsGEDO378OHv//fdLfZ6zs7NZQEAAc3JyYitXrmQhISHs33//ZevWrWPDhw9nV69eVZUFwKZOnVpqX3fv3mUSiYTVq1eP/fXXX+zMmTNsx44dbNSoUSwzM5MxVrufr8ePHzOhUMg6derEjh07xvbt28e6d+/OvLy8SrX/7NmzGQD28ccfsxMnTrC///6bOTs7M3t7e9a5c+cqtRMh5D8DBgxQG2Rr586dZZbt1auXWtniA2uWHOTsgw8+UHtf9LKxsWFPnjxhjP3396SiV0REhMZ9FFd07ih69e3bV+O2Jk2axHbt2qUa/Kj4y9bWttSAk0V/l8t7DRkyhCkUCrV6VR1ILSQkpML9FW/DIm8ykNqvv/6qcT9CobDU8Ze33UGDBjGhUFhqO1wul4WEhNTKzysnJ6fU57Tkq0OHDiwjI0OtXsnP1ogRIzTW9fHxYbm5uRrbXNNL25+9QqFgU6ZMKXdbX3zxhVqdnTt3MkNDw3LrfPzxx2oDu+bk5LD69etXGHfROb/44GRlvbQdmK/ofF5eu1Tl51BE00BqJT+jZb0YY0ypVLLOnTtXWPbixYul4i75e/vixQut2qQmUfdyUq3Wr18PoVBY6gpWESsrKwwePBh79uzBq1evynwmu6TJkyeDw+FgxYoVGDx4MNzc3DBr1iwcPHhQNWhWbbO3t8fNmzfx/fff44cffkBsbCyMjY3h7u6OXr16qQ2epa3Ro0fj8uXL+P3337Fo0SIwxhAREaHxyqyXlxfMzMywYsUKxMfHQygUwsfHB5s2barStGzV7eeff4ZAIMCyZcuQnZ2Npk2bYt++fZg7d+4bbVebNho5ciSOHz9eqbvcANCkSRNMmDABQUFBePbsGRwcHLBq1Sp8+eWXqjJisRgXL17E8uXL8ddffyEiIgKGhoZwcXFBt27dtLqK7u/vj+vXryMoKAizZ89GVlYW7Ozs0KVLF9Uz9LX5+fL19cXevXsxd+5cDBkyBJaWlhg9ejRmzJihGrityJIlSyAWi7F27Vps3LgRvr6++OOPP/Ddd9+pdeOvjnYi5F1UsqdSmzZtyizbpk0bnDhxQq3uwIEDNZbdunUrnJ2dUb9+fdy+fVvV0yYpKQnjxo1DaGgoxGIxhg4diuTkZFy4cEFVt3nz5mqPnojF4kof19GjR+Ho6IgGDRrg4sWLqjt369atw9atWyEQCNCxY0c8ffpU1SPq1atXWLNmDRYsWKC2LT6fDx8fH1hbW8PU1BSZmZm4c+cO0tPTAQD79u3Drl27VIO5VgdnZ2c4OjrCysoKCoUCL168UD0Lm5SUhM8//1ztZ1FVjx49wowZM9SWNWjQALa2trh69Sq2b9+u9bYOHDgAkUiEwMBAxMTE4OXLlwAApVKJRYsWqU2vWVJ1/bwmTZqk1i7u7u5o0KABoqOj8eDBAwCFd80//PBD7N69u8x4du3aBXt7ezRs2BChoaHIyckBUDjYbnBwMCZOnAg3NzcMHToUjx49UhuwtXfv3jAyMgIA2NjYaNV2CxcuxB9//KG2zMfHB/Xq1UNUVBSePHmitu727dv44IMP1MbUadKkCczNzXH16lVVD7M///wT7u7u+PbbbwEUflaLx+rp6Yn69esjIyMDMTExiIiIUNtPnz59kJSUhL1796qWubq6onnz5qr32p5fS/6Oa0Obn0N5isYziIqKws2bN1XLO3bsWGpw2cuXL+Ps2bOq946OjggICIBUKkVMTAxevHgBpVKpcT/NmzdX3ZkHCgefrVevnlbHWGN0m/MTUnVpaWnM2tqaTZo0SdehED0zZMgQ5uDgwPLz87WuU7InA9Hey5cvmVAoZEuWLNF1KITUeQYGBmp3aMrq7cTYf73Ail5TpkxRrSt5h6pHjx4sLy+PMcbY69evS91du3LliqqutlNaVeZOd/PmzVl2djZjjLG1a9eWuvNatP9Xr16ptUHJqYaePHnCsrKySsWSnZ3N6tWrp6o3bNgwtfVVvdP9+vVrFhkZqXHdzJkz1Y6h+N3aqt7p/vjjj9XWffXVV6p19+7dY0ZGRlpvVyQSqXqv5ebmssaNG6vWCQQCtc9WTfy8Hj58WOrOcPG7vPPnz1dbX3zKzpKfrQ4dOqimf7py5YrauuI97zQdS1HPDG29fv1a7Zh4PB7bs2ePWpno6Gh24sQJ1ftBgwap7fPnn39Wrbt//z4Ti8WqdSYmJkwqlTLGGFu+fLlqua+vb6keGrGxsezPP/9k6enpasvLO35tFf8szZ07V2OZN/k5lDdlWHm/H0V27typ1mYl76S/fv2abdmypcyfr6Ojo6r+Z599plWb1CR6ppvUCYmJiZg2bRr27duH8+fPY8uWLejcuTOysrLwxRdf6Do8ogdkMhlCQ0Px888/Y//+/fjmm2/UnoEk1ePevXuYNWsWDh06hHPnzuHPP/9Et27dYGJigg8//FDX4RHyTmElnl0ub0DKBQsWqMZtsLS0VOvBA0DtjlJNmDFjhuoOeevWrdXWde3aVXVH38bGBg0aNFCtS0hIUCvr7u6OHTt2oGfPnnBwcICBgQE4HA4kEonqTi4A1V3oN2VpaYmEhASMHz8evr6+kEgk4HK5qt53RZRKJV68ePHG+ztz5ozq/yKRCPPnz1e9b9y4MUaNGqX1tkaMGKG6g2lgYIAuXbqo1hUUFGh8FrZIdfy8jh49qlbv0aNHGD58OIYNG4Zhw4bh0qVLauvLG+9nwYIFqrvVbdq0UetZVfIz8qZOnz6tNmPLxIkTS43O7+zsrBrpW6FQICQkRLXOyclJraddo0aN1AYpy8zMRGhoKIDCO9tFXr58ifnz5+PAgQMIDw+HXC6Ho6MjJk+eDFNT02o9xpycHEilUtV7TbP2aFKbP4fibZOZmYmZM2diz549ePDgAWQyGSwtLfHBBx+UeWe/+DFVdtyFmkDdy0mdIBKJEBkZiU8//RSpqakwMjJC69atsXbtWtUUSuTdlpCQgLZt28LExAQff/wxpk2bpuuQ3kpisRg3b97E+vXrkZ6eDlNTU3Tq1AlLlizR+nERQkjZrKys1GY3SExMhIuLi8ayJb9IljcrQMlzZcn3xQdvqwnFEzNjY+NyYym+vvhUaAqFAr169VJLTMtSXXOXb9y4ER9++KFWg7NVxz6L/xxcXFxgYmKitr5Ro0ZabysgIEDtvUQiUXtf3jRz1fHzKjk/dvHEVJPyHhfUdCxFjxNU93R5JeNu165dueVTUlJU3awBoH79+uBy1e9rlmyzogEM+/XrB39/f9y7dw/5+flqM5YYGhqiQ4cOmDZtGvr161eVQylTUdsVKfkzLktt/hyaNm2K3r17qy7G/Prrr/j1118BAAKBAK1atcKkSZMwduxYjfWL/+6UPF5doKSb1Anm5uY4fPiwrsMgeszNze2NRqwteZIlmnl6euLUqVO6DoOQt1bTpk3Vku7Q0NAyk+6iu2XF61ZVdUzbWJ7id+pKJiTa3sXbvXu3WsItFArRunVrWFlZgcPh4Pz586pn1d/kfFBEJpNh+vTpatuqX78+vLy8IBAISj2XWh37LK5kOwGV+zmVHPtD26lager5eVW2PYrfeS3pTY7lTVXU5tr0OCmrLUQiEc6fP49ff/0Vhw4dwv3791XJa25uLk6ePImTJ0/i4MGDGDBgQBWPoLSSU6lmZWVpVa82fw4cDgf79+/H2rVrsXfvXty6dUv1GSkoKMClS5dw6dIlpKena5wSrPhsDtpOHVuTqHs5IYQQQoie6N+/v9r7koM5FXn69KnaBTCxWKyalkeTR48eqb0vPngTUNgltkhNJ+BVVXKa0CtXruD8+fPYu3cv9uzZA0tLy2rdX1hYmNrd688++wyPHj3CwYMHsWfPnlJdjquDs7Oz6v/R0dGlEtGiwcfqgpLdfq9cuQLGWJkvbae6qsibfn5Lxl2yG3xJVlZWqi7XQOHvVskBvkr+/hW/kGZqaoq5c+fi+vXryMnJQXR0NPbv36/2O1ndc16LxWK1mFNTU6t1+xXR9mckEonwxRdf4MKFC8jOzkZ8fDxOnDih1uOjrLYpfkwlB2nTBUq6CSGEEEL0xAcffKD2Zfv8+fP46quv1OYyjoqKwrBhw9SWffrpp+XegVy0aBHy8/MBFH4ZXbVqldr64gm7oaGh2jpNc/DqQvGRoQGoJQ3r168vNaJ0Te4vKioKv/32W7XuD4Dac9e5ublYtmyZ6v39+/cRHBxc7fusKSVnv/jmm29KJXdyuRxnzpzB6NGj1Xp4vIk3/fx27doVBgYGqvcbNmxQGy0cKHz+uuiZdR6Ph+7du6vWxcTEqCWCYWFhaqPOGxsbo23btgAKRz3fsGGDql14PB6cnZ0xcOBAtdG2i7qjazrGqv5+NmnSRPX/khcFapo2P6PIyEj89ttvqnUcDgf29vbo2bMn/P39VeVKtg1Q2J28+DPmJbvF6wIl3YQQQgghekIkEmHHjh1qA0GuWrUKzs7O6NevHzp16gQvLy+1O55NmzbFokWLyt3u8ePH4enpiZ49e8LX11ftS3bLli1VSQAAeHh4qN2J+v7779G7d28MGzYM33zzTXUcZpW0aNFC7X3r1q3Rt29fBAQE4KOPPqr2O/QNGzZUSw5WrFiBtm3bonv37mjQoEGNXIz4/PPP1X72ixcvRuPGjdGlSxe0bt263C7Y+qZx48YYPny46v3ly5fh7OyMwMBADBw4EG3btoW5uTm6du2KHTt2qF1EehPFB+ACgCFDhmDgwIEYNmyYVnfTLS0t8dVXX6neKxQKDBs2DPXr10efPn3QuHFj+Pj4qD2jPnfuXPD5/z21O23aNDRt2hRdunRBixYt1J75njVrlupz9fLlS3z44YewtbWFl5cXOnfujAEDBsDHx0dtSq+Sx1T8fUhICNq3b68aoE7bz0hgYKDq/8Ufk6gNJY/n008/Rb9+/TBs2DDVAIWvX7/GtGnT4OTkhHr16qFDhw4YOHAg/P39sW3btjK3BQA3btxQ69Jf/Fh1hZJuQgghhBA90r59e5w6dQr29vaqZYmJiTh69CjOnz+vdge2X79+OH36tNqdOU2mTZuGmJgYnDx5EsnJyarlVlZW2Lx5s1pZS0tL9OnTR/U+Ly8PJ06cwN69eyscDKsmvf/++2jcuLHqfWZmJo4dO4a7d++iZ8+eFQ54VVkSiQTz5s1TWxYaGopTp06Bz+fj66+/rtb9AYUDmK1cuVJt2YMHD1Sjy48bN05tnVAorPYYqtOGDRtUo3wDhc9tX7hwAYcOHUJoaCiys7NV66rr+eCePXuqDez56tUrHDp0CHv37sXdu3e12saiRYswadIktWXh4eE4fvw4Hjx4UOoCQfPmzbFp0ya138M7d+7g7Nmzqjm6AeDDDz/ErFmzSu1PLpfj+fPnOHfuHA4fPoxnz56p1kkkEgQFBamVLzl42OXLl7F3717s3btX1aOlIgMHDlT9Pzo6Wm30/5oWEBAAPz8/1fuMjAwcPXoUe/fuLfUYCWMMERERuHTpkuq59yICgUCtN0iR4rMx+Pr6wsvLqwaOonIo6SaEEEII0TMdO3bEixcv8Ntvv6FXr16wt7eHUCiERCKBl5cXJkyYgDNnzuDw4cNaDRL0yy+/YOPGjQgICICBgQEsLS0xevRo3LhxA76+vqXKb926FZMnT4ajo2OtDlpVHpFIhHPnzuHjjz+Gra0thEIhPDw8MH/+fBw6dKhG4pw9ezb+/vtv+Pn5QSAQwNLSEkOHDsWNGzfURviuTtOmTcOhQ4fQpk0bGBoawtzcHAMGDMC1a9dKDapX/MKMPpJIJDh+/Dj27duHwYMHw8nJCSKRCEKhEE5OTujevTuWLFmCp0+fqj3P/ibEYjFOnTqF/v37w9zcvEo9ILhcLv766y+cP38eY8aMQb169WBoaAhDQ0O4u7tj5MiRGDx4sFqd999/Hw8ePMC0adNQv359GBkZqY5z2LBh+Pfff/H333+rDUwXGBiI3377DSNHjkT9+vVhaWkJHo8HsViMhg0b4rPPPsPdu3dVU78VmTFjBpYvXw5fX98qX3hp1aqV2kWsf/75p0rbqQoOh4Njx47hvffeg42NjcZBA319ffH3339j3Lhx8PPzg42NDfh8PgwNDVV/A69fv65xZPddu3ap/v/xxx/X6LFoi8Oqe6hFQgghhBCiU+PHj1e7g01f9+qOuLg42Nvbl0pEXr16hWbNmiEuLg4A4OrqSjNvkDeybt06TJ48GUDh4xQPHz7UcURv7vr162jVqhWAwufnIyMjtZ6HvCbRlGGEEEIIIYToie+//x779+9H586d4eTkBKFQiKioKBw6dEitO/b8+fN1GCV5G0yYMAE//vgjnj59irCwMJw4cQK9evXSdVhvpPjjGd98841eJNwAJd2EEEIIIYTolaSkpDK7+3K5XHz33XeYOHFiLUdF3jZ8Ph/Lli1TTX+3fPnyOp10v3jxQjXSvL29PWbMmKHjiP5DSTchhBBCCCF64oMPPgBjDKGhoUhISEB6ejqMjIzg5uaGDh06YNKkSWpTJhHyJoYMGfLWPH7i4eFRbaPgVzd6ppsQQgghhBBCCKkhNHo5IYQQQgghhBBSQyjpJoQQQgghhBBCaggl3YQQQgghhBBCSA2hpJsQQgghhBBCCKkhlHQTQgghhBBCCCE1hJJuQgghhBBCCCGkhlDSTQghhBBCCCGE1BBKugkhhBBCCCGEkBpCSTchhBBCCCGEEFJD/g/A8nHCoEfOcQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = aligner.get_stat_df()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "balanced-acting",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===============================================================================================================\n",
+      "Genes2Genes (v0.2.0)\n",
+      "Dynamic programming alignment of gene pseudotime trajectories using a bayesian information-theoretic framework\n",
+      "===============================================================================================================\n",
+      "Interpolator initialization completed\n",
+      "Aligner initialised to align trajectories of 20327 reference cells & 17176 query cells in terms of 1371 genes\n",
+      "Running gene-level alignment: 🧬\n",
+      "concurrent mode, running with  26  processes\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1371/1371 [24:43<00:00,  1.08s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alignment completed! ✅\n",
+      "Time taken: 1491.372834444046 sec\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "s = time.time()\n",
+    "aligner = Main.RefQueryAligner(adata_ref, adata_query, gene_list, n_bins)\n",
+    "aligner.align_all_pairs(concurrent=True) \n",
+    "t = time.time()\n",
+    "print('Time taken:', t-s,'sec')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "worthy-harrison",
+   "metadata": {},
+   "source": [
+    "## Healthy vs. IPF trajectory alignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "healthy-fireplace",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1. 1. 4. ... 1. 1. 1.]\n",
+      "[1. 2. 1. ... 1. 1. 1.]\n",
+      "[1.7595813 1.7595813 3.0076618 ... 1.2661365 1.2661365 1.2661365]\n",
+      "[1.1192989 1.6342111 1.1192989 ... 1.284542  1.284542  1.284542 ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "adata_healthy = anndata.read_h5ad('adata_healthy_AT2_to_AT1.h5ad')\n",
+    "adata_disease = anndata.read_h5ad('adata_IPF_AT2_to_AberrantB.h5ad')\n",
+    "adata_healthy.obs['time'] = adata_healthy.obs['dpt_pseudotime']\n",
+    "adata_disease.obs['time'] = adata_disease.obs['dpt_pseudotime']\n",
+    "adata_ref = adata_healthy\n",
+    "adata_query = adata_disease\n",
+    "print(adata_ref.X.data) \n",
+    "print(adata_query.X.data)\n",
+    "sc.pp.normalize_per_cell(adata_ref, 10000) \n",
+    "sc.pp.log1p(adata_ref)\n",
+    "sc.pp.normalize_per_cell(adata_query, 10000) \n",
+    "sc.pp.log1p(adata_query)\n",
+    "print(adata_ref.X.data)\n",
+    "print(adata_query.X.data)\n",
+    "common_hvg_genes = np.intersect1d(adata_healthy.var_names[adata_healthy.var.HVG] , adata_disease.var_names[adata_disease.var.HVG] )\n",
+    "len(common_hvg_genes)\n",
+    "n_bins = 13"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "valuable-facing",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===============================================================================================================\n",
+      "Genes2Genes (v0.2.0)\n",
+      "Dynamic programming alignment of gene pseudotime trajectories using a bayesian information-theoretic framework\n",
+      "===============================================================================================================\n",
+      "Interpolator initialization completed\n",
+      "Aligner initialised to align trajectories of 3157 reference cells & 890 query cells in terms of 994 genes\n",
+      "Running gene-level alignment: 🧬\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 994/994 [04:29<00:00,  3.69it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alignment completed! ✅\n",
+      "Time taken: 272.8219108581543 sec\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "s = time.time()\n",
+    "aligner = Main.RefQueryAligner(adata_ref, adata_query, common_hvg_genes, n_bins)\n",
+    "aligner.align_all_pairs() \n",
+    "t = time.time()\n",
+    "print('Time taken:', t-s,'sec')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "heated-pacific",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average Alignment:  \u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[92mM\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m (cell-level)\n",
+      "% similarity: 73.33\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "aligner.get_aggregate_alignment() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "curious-tribune",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean alignment similarity percentage (matched %): \n",
+      "60.809999999999995 %\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = aligner.get_stat_df()"
+   ]
+  }
+ ],
+ "metadata": {
+  "environment": {
+   "kernel": "genes2genes",
+   "name": "pytorch-gpu.1-9.m82",
+   "type": "gcloud",
+   "uri": "gcr.io/deeplearning-platform-release/pytorch-gpu.1-9:m82"
+  },
+  "kernelspec": {
+   "display_name": "g2g_installed_env",
+   "language": "python",
+   "name": "g2g_installed_env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/.ipynb_checkpoints/Tutorial-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Tutorial-checkpoint.ipynb
new file mode 100644
index 0000000..d7a1e86
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/Tutorial-checkpoint.ipynb
@@ -0,0 +1,1443 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "insured-murray",
+   "metadata": {},
+   "source": [
+    "# Tutorial on single-cell trajectory alignment using Genes2Genes\n",
+    "\n",
+    "Genes2Genes (G2G) aims to guide downstream comparative analysis of single-cell reference and query systems along any axis of progression (e.g. differentiation pseudotime, disease/treatment response pseudotime etc.). This notebook describes how we can use G2G framework to infer and analyse gene-level trajectory alignments between a given reference and query dataset.\n",
+    "\n",
+    "In this tutorial, we are going to compare trajectories between two treatment groups (PAM and LPS) of mouse bone marrow-derived dendritic cells from Shalek et al (2014). The single cell datasets and their pseudotime estimates were downloaded from https://github.com/shenorrLab/cellAlign (Alpert et al 2018) and packaged into adata objects. There are 2 gene modules: global (core antiviral module) and local (peaked inflammatory module) considered by Alpert et al (2018), and we use the local module as an example comparison."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "gross-campus",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import anndata\n",
+    "import numpy as np\n",
+    "import seaborn as sb\n",
+    "import numpy as np\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "from genes2genes import Main\n",
+    "from genes2genes import ClusterUtils\n",
+    "from genes2genes import TimeSeriesPreprocessor\n",
+    "from genes2genes import PathwayAnalyser\n",
+    "from genes2genes import VisualUtils"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "involved-egypt",
+   "metadata": {},
+   "source": [
+    "### Load anndata reference and query objects\n",
+    "\n",
+    "Make sure that each adata object has: <br>\n",
+    "(1) log normalized gene expression in `adata.X` <br>\n",
+    "(2) pseudotime estimates in `adata.obs['time']`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "developed-breed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_dir = 'notebooks/data/'\n",
+    "adata_ref = anndata.read_h5ad(input_dir + 'adata_pam_local.h5ad') # Reference dataset\n",
+    "adata_query = anndata.read_h5ad(input_dir +'adata_lps_local.h5ad') # Query dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "favorite-pearl",
+   "metadata": {},
+   "source": [
+    "## 1. Preparing data for alignment "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "everyday-ratio",
+   "metadata": {},
+   "source": [
+    "### Pseudotime range check\n",
+    "Check whether the current range of pseudotime values are between 0 and 1. If not, run min max normalization. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "lightweight-management",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 1.0\n",
+      "0.0 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(min(adata_ref.obs['time']), max(adata_ref.obs['time']))\n",
+    "print(min(adata_query.obs['time']), max(adata_query.obs['time']))\n",
+    "\n",
+    "## uncomment below if the range is not [0,1] for any of the objects\n",
+    "#adata_ref.obs['time'] = TimeSeriesPreprocessor.Utils.minmax_normalise(np.asarray(adata_ref.obs['time']))\n",
+    "#adata_query.obs['time'] = TimeSeriesPreprocessor.Utils.minmax_normalise(np.asarray(adata_query.obs['time']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "beneficial-major",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize the pseudotime distributions\n",
+    "sb.kdeplot(adata_ref.obs['time'], fill=True, label='Reference - PAM', color='forestgreen') \n",
+    "sb.kdeplot(adata_query.obs['time'], fill=True, label='Query - LPS', color='midnightblue'); \n",
+    "plt.xlabel('pseudotime'); plt.legend(); plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "short-feature",
+   "metadata": {},
+   "source": [
+    "### Determine the number of discrete pseudotime points to align\n",
+    "\n",
+    "We can use optbinning package (https://gnpalencia.org/optbinning/installation.html) to get a heuristic estimate about the number of discrete time points to consider by running below on each dataset. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "coral-detective",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(CVXPY) Apr 17 06:27:42 PM: Encountered unexpected exception importing solver GLOP:\n",
+      "RuntimeError('Unrecognized new version of ortools (9.9.3963). Expected < 9.8.0. Please open a feature request on cvxpy to enable support for this version.')\n",
+      "(CVXPY) Apr 17 06:27:42 PM: Encountered unexpected exception importing solver PDLP:\n",
+      "RuntimeError('Unrecognized new version of ortools (9.9.3963). Expected < 9.8.0. Please open a feature request on cvxpy to enable support for this version.')\n",
+      "14\n",
+      "14\n"
+     ]
+    }
+   ],
+   "source": [
+    "from optbinning import ContinuousOptimalBinning\n",
+    "\n",
+    "x = np.asarray(adata_ref.obs.time)\n",
+    "optb = ContinuousOptimalBinning(name='pseudotime', dtype=\"numerical\")\n",
+    "optb.fit(x, x)\n",
+    "print(len(optb.splits))\n",
+    "\n",
+    "x = np.asarray(adata_query.obs.time)\n",
+    "optb = ContinuousOptimalBinning(name='pseudotime', dtype=\"numerical\")\n",
+    "optb.fit(x, x)\n",
+    "print(len(optb.splits))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "curious-visitor",
+   "metadata": {},
+   "source": [
+    "Accordingly, we go with `n_bins=14`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "selective-payroll",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_bins = 14"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stopped-shore",
+   "metadata": {},
+   "source": [
+    "### Define which cell type annotations and color scheme to use for visualization purposes\n",
+    "\n",
+    "`annotation_colname` and `joint_cmap`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "sexual-narrow",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define annotation column name in the adata obs\n",
+    "annotation_colname = 'annotation' \n",
+    "adata_ref.obs[annotation_colname] = [x.split('_')[1] for x in adata_ref.obs_names] \n",
+    "adata_query.obs[annotation_colname] = [x.split('_')[1] for x in adata_query.obs_names] \n",
+    "\n",
+    "# define the joint colormap to use for both reference and query\n",
+    "col = np.array(sb.color_palette('colorblind'))[range(4)]\n",
+    "joint_cmap={'1h':col[0], '2h':col[1] , '4h':col[2] , '6h':col[3]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "norman-hungarian",
+   "metadata": {},
+   "source": [
+    "Inspect the cell type compositions around each discrete pseudotime point (x-axis) to see if it reasonably represents the entire trajectory of interest. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "taken-opposition",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 900x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 900x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "VisualUtils.plot_celltype_barplot(adata_ref, n_bins, annotation_colname, joint_cmap)\n",
+    "VisualUtils.plot_celltype_barplot(adata_query, n_bins, annotation_colname, joint_cmap)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "failing-botswana",
+   "metadata": {},
+   "source": [
+    "## 2. G2G trajectory alignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "important-survivor",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "89 genes\n"
+     ]
+    }
+   ],
+   "source": [
+    "# define the gene list to align\n",
+    "gene_list = adata_ref.var_names \n",
+    "print(len(gene_list),'genes')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "experienced-serbia",
+   "metadata": {},
+   "source": [
+    "### Aligning all genes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "alpine-italian",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===============================================================================================================\n",
+      "Genes2Genes (v0.2.0)\n",
+      "Dynamic programming alignment of gene pseudotime trajectories using a bayesian information-theoretic framework\n",
+      "===============================================================================================================\n",
+      "Interpolator initialization completed\n",
+      "Aligner initialised to align trajectories of 179 reference cells & 290 query cells in terms of 89 genes\n",
+      "Running gene-level alignment: 🧬\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 89/89 [00:26<00:00,  3.41it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alignment completed! ✅\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "s = time.time()\n",
+    "aligner = Main.RefQueryAligner(adata_ref, adata_query, gene_list, n_bins) #\n",
+    "aligner.align_all_pairs() \n",
+    "t = time.time()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "suited-commander",
+   "metadata": {},
+   "source": [
+    "To access gene-level alignments, use the dictionary: `aligner.results_map` which carries all gene alignment objects. <br>\n",
+    "e.g. `aligner.results_map['TNF']`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "occupational-remains",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DDDIDIDIDDDMMMMMIIIIIID\n",
+      "\u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "\n",
+      "01234567890123456789012 Alignment index \n",
+      "012 3 4 56789012      3 Reference index\n",
+      "\u001b[91m***\u001b[0m-\u001b[91m*\u001b[0m-\u001b[91m*\u001b[0m-\u001b[91m***\u001b[0m\u001b[92m*****\u001b[0m------\u001b[91m*\u001b[0m\n",
+      "---\u001b[91m*\u001b[0m-\u001b[91m*\u001b[0m-\u001b[91m*\u001b[0m---\u001b[92m*****\u001b[0m\u001b[91m******\u001b[0m-\n",
+      "   0 1 2   34567890123  Query index\n",
+      "DDDIDIDIDDDMMMMMIIIIIID 5-state string \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gene_obj = aligner.results_map['TNF']\n",
+    "alignment_str = gene_obj.alignment_str\n",
+    "print(alignment_str)\n",
+    "print(VisualUtils.color_al_str(alignment_str)) \n",
+    "print()\n",
+    "print(gene_obj.al_visual)\n",
+    "# Alignment landscape of costs (Note: dashed black path is the optimal alignment)\n",
+    "gene_obj.landscape_obj.plot_alignment_landscape()\n",
+    "# Note: optimal path diagonals represent matches; \n",
+    "# vertical and horizontal paths could represent either warp matches or indels (mismatches)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "alert-silence",
+   "metadata": {},
+   "source": [
+    "Visualise alignment in terms of both the cell-type compositions, as well as actual and interpolated gene expression. <br>\n",
+    "Top left: Visualise alignmebt in terms of cell-type composition <br>\n",
+    "Bottom left: the mean trends and interpolated distributions of gene expression along pseudotime. <br>\n",
+    "Bottom right: the actual gene expression values along pseudotime. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "radio-mirror",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "Optimal alignment cost: 57.99 nits\n",
+      "Alignment similarity percentage: 21.74 %\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 400x200 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "VisualUtils.show_gene_alignment('TNF', aligner, adata_ref, adata_query, annotation_colname, joint_cmap)\n",
+    "\n",
+    "# Visualise gene-level alignment in terms of only the cell-type composition \n",
+    "# VisualUtils.visualize_gene_alignment(aligner.results_map['TNF'], adata_ref, adata_query, annotation_colname, cmap=joint_cmap)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "entitled-brass",
+   "metadata": {},
+   "source": [
+    "### Aggregate (average) cell-level alignment across all aligned genes\n",
+    "\n",
+    "This is an average alignment which is sampled based on the frequency distribution of alignment states between each pair of reference and query timepoints. The heatmap value gives the number of genes where the corresponding timepoints have been matched. Note: There can still be different patterns of alignment across these genes (100% mismatching, 100% matching, early mismatching, late mismatching gene groups) which we will find by clustering in the next section. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "spanish-bowling",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average Alignment:  \u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m (cell-level)\n",
+      "% similarity: 47.37\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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yl2EYGjFihLp27aqWLVuaWmvjxo3q1KmTfvrpJ1WtWlXz5s3TxRdfbGrNt956S+vWrQvqnI6idOzYUa+//rqaNm2q/fv364knnlDnzp21adMm1axZ05SaO3bs0IwZMzRixAg99NBDWrVqle677z653W7ddNNNptT8pffff19HjhzRzTffbGqdMWPGKD8/X82bN1d4eLi8Xq8mTJiggQMHmlYzJiZGnTp10uOPP64WLVooPj5eb775plauXKkmTZqYVvess987hX0n7dq1y/T6dvnpp5/04IMPatCgQYqNjTW11kcffaQBAwboxIkTSkxMVFZWlmrVqmVKrcmTJysiIkL33XefKdfHhXN8EDrr13+rNQzD1L/p2umee+7Rhg0bLPnbbbNmzbR+/XodOXJE7777roYMGaIlS5aYFob27Nmj+++/X5999tk5f6M3S9++ff3/3KpVK3Xq1EmNGjXS7NmzNWLECFNq+nw+tW/f3v8m9ssuu0ybNm3SjBkzLAlCr776qvr27Wv6fIe5c+fqjTfe0Jw5c3TJJZdo/fr1Gj58uJKSkkr9ZvvS+Pvf/66hQ4eqTp06Cg8PV9u2bTVo0CCtW7fOtJq/5qTvpNOnT2vAgAHy+XyaPn266fV69uyp9evX68CBA3r55Zd1/fXXa+XKlapdu3ZQ66xdu1bPPfec1q1bF9L/34XqSI1VHH9rrFatWgoPDz9n9CcvL6/Yt3aXR/fee6/mz5+vRYsWqW7duqbXi4yMVOPGjdW+fXtlZmaqdevWeu6550yrt3btWuXl5aldu3aKiIhQRESElixZoueff14RERHyer2m1T4rOjparVq10rZt20yrkZiYeE6YbNGihakT/M/atWuXFi5caMmb3keNGqUHH3xQAwYMUKtWrTR48GA98MADyszMNLVuo0aNtGTJEh07dkx79uzRqlWrdPr0aaWkpJhaV5L/iUOnfCedPn1a119/vXJycpSVlWX6aJD085/Rxo0b6ze/+Y1effVVRURE6NVXXw16nS+//FJ5eXmqX7++//to165d+r//+z81aNAg6PVQNo4PQpGRkWrXrp3/CZizsrKy1LlzZ5u6Cj7DMHTPPffovffe0xdffGHJF/r5+vB4PKZdv3fv3tq4caPWr1/v39q3b68bb7xR69evV3h4uGm1z/J4PNq8ebMSE817o3OXLl3OWf4gOztbycnJptU8a+bMmapdu7auuuoq02udOHFCYWGBX1Ph4eGmPz5/VnR0tBITE3X48GF9+umn6t+/v+k1U1JSlJCQEPCddOrUKS1ZsqRCfSdJ/wtB27Zt08KFC027lVwcs76XBg8erA0bNgR8HyUlJWnUqFH69NNPg16vrHyGy7ItFHFrTNKIESM0ePBgtW/fXp06ddJLL72k3bt368477zSt5rFjx7R9+3b/zzk5OVq/fr1q1Kih+vXrB73esGHDNGfOHH3wwQeKiYnx/20zLi5OUVFRQa8nSQ899JD69u2revXq6ejRo3rrrbe0ePFiLViwwJR60s/zO3497yk6Olo1a9Y0bT7UyJEj1a9fP9WvX195eXl64oknVFBQYOqtmwceeECdO3fWxIkTdf3112vVqlV66aWX9NJLL5lWU/r5ltzMmTM1ZMgQRUSY//XRr18/TZgwQfXr19cll1yir7/+WlOmTNHQoUNNrfvpp5/KMAw1a9ZM27dv16hRo9SsWTPdcsstQbl+cX/+hw8frokTJ6pJkyZq0qSJJk6cqCpVqmjQoEGm1Tx06JB2797tX8fnbNBOSEgo87pYRdVMSkrSH//4R61bt04fffSRvF6v/3upRo0aioyMDHrNmjVrasKECbr66quVmJiogwcPavr06dq7d2+Zl4Eo7n/XX4e7SpUqKSEhQc2aNStTPZjAxifWQsoLL7xgJCcnG5GRkUbbtm1Nf6x80aJFhqRztiFDhphSr7BakoyZM2eaUs8wDGPo0KH+/00vuugio3fv3sZnn31mWr3zMfvx+RtuuMFITEw0KlWqZCQlJRnXXnutsWnTJtPqnfXhhx8aLVu2NNxut9G8eXPjpZdeMr3mp59+akgytm7danotwzCMgoIC4/777zfq169vVK5c2WjYsKExduxYw+PxmFp37ty5RsOGDY3IyEgjISHBGDZsmHHkyJGgXb+4P/8+n88YN26ckZCQYLjdbqN79+7Gxo0bTa05c+bMQo+PGzfOlJpnH9MvbFu0aJEpNU+ePGlcc801RlJSkhEZGWkkJiYaV199tbFq1SpT6hUmFB+fX7+rrmVbKHIZhhGqSxwBAACTrdsd/LsQ59O2vvnzGEvL8XOEAACAczFHCAAAB/M6fEzE2Z8eAAA4GiNCAAA4WKg+1m4VRoQAAIBjMSIEAICD8YoN+Hk8Ho0fP97UlY+paU1NJ3xGalKTmtTEhWMdoV8oKChQXFyc8vPzLXnfDTUrTj1qUpOa1AyFmmXx5c7GltXq1mB78SdZjBEhAADgWMwRAgDAwXwOHxNx9qcHAACOViHnCPUJK9tbhH2GVznaohQ1V5grPMhdUdPKmk74jNSkJjUrbs0s39smdXWuL3Y2s6xWrwZbLatVUgQhAABCDEHIOswRAgDAwbyGs2fJOPvTAwAAR2NECAAAB/OxsjQAAIAzMSIEAICDeR0+JuLsTw8AAByNIAQAAByLW2MAADiY0x+fd3QQ6nhVW111e5/zHj9+5IQmD/mrhR0BAAArOToIJTSorU792p/3+OH9R6xrBgAAGzj9pau2BqG9e/dqxowZWr58uXJzc+VyuRQfH6/OnTvrzjvvVL169exsDwAAVHC2BaFly5apb9++qlevntLS0pSWlibDMJSXl6f3339ff/3rX/XJJ5+oS5cuRV7H4/HI4/EE7PMZXsteqgcAQHnmNZy9oKJtQeiBBx7Qn//8Zz377LPnPT58+HCtXr26yOtkZmbq0UcfDdiXohZqpEuC1isAAKiYbLsx+O233+rOO+887/E77rhD3377bbHXycjIUH5+fsCWoubBbBUAgArLqzDLtlBk24hQYmKili9frmbNmhV6/KuvvlJiYmKx13G73XK73QH7uC0GAABKwrYgNHLkSN15551au3at+vTpo/j4eLlcLuXm5iorK0uvvPKKpk6dald7AAA4go91hOxx9913q2bNmnr22Wf14osvyuv1SpLCw8PVrl07vf7667r++uvtag8AADiArY/P33DDDbrhhht0+vRpHThwQJJUq1YtVapUyc62AABwjFCdu2OVkFhQsVKlSiWaDwQAABBMIRGEAACAPZy+jpCzx8MAAICjMSIEAICDOf1dY87+9AAAwNEYEQIAwMG8Dl9HyNmfHgAAOBpBCAAAOBa3xgAAcDCfnP34PEEoSMJbOeON99tuqm55zch86/+QJk/dYHlN77FjltcEAKcjCAEA4GBMlgYAAHAoRoQAAHAwp7901dmfHgAAOBojQgAAOJiPl64CAAA4EyNCAAA4GHOEAAAAHIoRIQAAHMzn8HWECEJFiKlRVc8ufbxE57qio0zuJjScrF22f2XOeH3KPnhQH2zerPX79gW5KwAAyoYgVISIShFq2dUZr86wwm/q19ONrS/VtBUr9fxXX9ndDgBAktfh7xpz9ngYLBceFqb7O3fSmO7d7G4FAIDQDkJ79uzR0KFDizzH4/GooKAgYPMZXos6RFnd3qEDYQgAQoDPCLNsC0Wh2dV/HTp0SLNnzy7ynMzMTMXFxQVsOdpSout7Tp4KRpsoI8IQAMButs4Rmj9/fpHHd+zYUew1MjIyNGLEiIB918TdXKL6OzbsKtF5MM/tHTpIkiYv/dLmTgDAmZw+R8jWIJSeni6XyyXDMM57jstV9P9Bbrdbbrc7YF+YK7xE9bevy9G3y7YwIdpmhCEAgF1svTWWmJiod999Vz6fr9Bt3bp1ptb3+XyaNPh5Za8tfuQJ5uI2GQDYw+lzhGwdEWrXrp3WrVun9PT0Qo8XN1oUDPt3/aj7Oj2k3/y+nRq3TVF0bJUyXcdVs3qQOwtNR1q4iz/pFyLCwnRdy5ZyRxT/rxojQwAAq9kahEaNGqXjx4+f93jjxo21aNEi0/vwnvHq3++v0r/fX1Xma4S3csbttW03lT7wff6fHfpb/6sJQwCAEsvMzNR7772nLVu2KCoqSp07d9bkyZPVrFkz/zk333zzOQ9VdezYUStWrChxHVvHqbp166bf/va35z0eHR2t1NRUCzuCGZbu3Kk7P5gvz5kzJTqf22QAYB2vEWbZVhpLlizRsGHDtGLFCmVlZenMmTNKS0s7ZwDlt7/9rfbt2+ff/vWvf5WqDitLwxJnwxAjQwCAkliwYEHAzzNnzlTt2rW1du1ade/e3b/f7XYrISGhzHVCc+YSKiRGhgAg9PjksmwrbBFkj8dToj7z8/MlSTVq1AjYv3jxYtWuXVtNmzbVbbfdpry8vFJ9foIQLFWWMDQyjTAEABVBYYsgZ2ZmFvt7hmFoxIgR6tq1q1q2bOnf37dvX/3jH//QF198oWeeeUarV69Wr169ShyuJG6NwQalvU12a9f2kqSnP+M2GQAEW2nn7lyIhwtZBPnXawEW5p577tGGDRu0bNmygP033HCD/59btmyp9u3bKzk5WR9//LGuvfbaEvXEiBBsUdqRoVu7tmdkCADKObfbrdjY2ICtuCB07733av78+Vq0aJHq1q1b5LmJiYlKTk7Wtm3bStwTQQi2IQwBgP18hsuyrTQMw9A999yj9957T1988YVSUlKK/Z2DBw9qz549SkxMLHEdghBsRRgCABRm2LBheuONNzRnzhzFxMQoNzdXubm5OnnypCTp2LFjGjlypL766ivt3LlTixcvVr9+/VSrVi1dc801Ja5DEILtCEMAYB+vwizbSmPGjBnKz89Xjx49lJiY6N/mzp0rSQoPD9fGjRvVv39/NW3aVEOGDFHTpk311VdfKSYmpsR1mCwdLDl7LS/pPXbM8poJK35jynWzV+QoY8sHyhzVX+7Ikk2gTmv1tXYcfsqUft5J72DKdYvy3ci2ltestCbb8pq+SxpaXtNYucHymgAuTHGv2IqKitKnn356wXUYEULIWLl+pzKe+kCeUyUbGaoXd5saVh9lclcAULGF6hwhqxCEEFIIQwAAKxGEEHIIQwBgHZ/CLNtCUWh2BccjDAEArEAQQsg6G4Z8RsmWSicMAUDpeQ2XZVsoIgghpK1cv1Pf5t1NGAIAmIIghJB3+OSXhCEAMAlPjQHlAGEIAGAGghDKDcIQACDYCEIoVwhDABBcPiPMsi0UhWZXQBEIQwCAYCEIoVwqSxhKqT7S5K4AoPzxymXZFopsD0InT57UsmXL9N13351z7KefftLrr79e5O97PB4VFBQEbD7Da1a7CCGlDUP1424nDAEAAtgahLKzs9WiRQt1795drVq1Uo8ePbRv3z7/8fz8fN1yyy1FXiMzM1NxcXEBW462mN06QgRhCAAuDI/P22jMmDFq1aqV8vLytHXrVsXGxqpLly7avXt3ia+RkZGh/Pz8gC1FzU3sGqGGMAQAKKsIO4svX75cCxcuVK1atVSrVi3Nnz9fw4YNU7du3bRo0SJFR0cXew232y232x2wL8wVblbLCFFnw1DL2tMV5nIXe379uNslSTmHnza7NQAIaaH6NJdVbP30J0+eVEREYBZ74YUXdPXVVys1NVXZ2dk2dYbyiJEhAEBp2Toi1Lx5c61Zs0YtWrQI2P/Xv/5VhmHo6quvtqkzlFeMDAFA6fhC9Gkuq9g6InTNNdfozTffLPTYtGnTNHDgQBmGYXFXKO8YGQIAlJStQSgjI0P/+te/znt8+vTp8vl8FnaEioIwBAAl4zVclm2hyNkzpFChEYYAAMWxdY4QYLayzhnSoSUmdwYAoYGnxoAKriwjQ1cnDTC5KwBAKCAIwRFKG4b6JPQjDAFwBFaWBhyCMAQA+DWCEByFMAQAgXxyWbaFIoIQHIcwBAA4iyAERyIMAcDPnD5HiMfng8R77JjlNcOrVrW8Zsyn31pe8yHdYdq1O7b5SJmj+ssdWfwfhT4J/dQsap9pr+NY9Lz1/w4tGtPF8ppVtv5oeU1vx0str2ms3GB5TQClx4gQHG3l+p3KeOoDeU6dKdH5LLoIABULQQiORxgC4GQ+I8yyLRSFZleAxQhDAOBMBCHgvwhDAJzI6ZOlCULAL5wNQ7yoFQCcgSAE/MrK9Tt5az0Ax2BBRQDnKMuLWglDAFD+EISA8yAMAXAC5ggBOC/CEABUbAQhoBiEIQAVGSNCAIpFGAKAiokgBJQQYQhARcSIkM02b96smTNnasuWLZKkLVu26K677tLQoUP1xRdfFPv7Ho9HBQUFAZvP8JrdNhyKMAQAFYutQWjBggVq06aNRo4cqcsuu0wLFixQ9+7dtX37du3evVtXXnllsWEoMzNTcXFxAVuOtlj0CeBEhCEAFQkjQjZ67LHHNGrUKB08eFAzZ87UoEGDdNtttykrK0sLFy7U6NGjNWnSpCKvkZGRofz8/IAtRc0t+gRwKsIQAFQMtgahTZs26eabb5YkXX/99Tp69Kj+8Ic/+I8PHDhQGzZsKPIabrdbsbGxAVuYK9zMtgFJhCEAFQMrS4eIsLAwVa5cWdWqVfPvi4mJUX5+vn1NAcUgDAFA+WZrEGrQoIG2b9/u//mrr75S/fr1/T/v2bNHiYmJdrQGlBhhCEB5xhwhG911113yev/3hFfLli0VERHh//mTTz5Rr1697GgNKBXCEACUTxHFn2KeO++8s8jjEyZMsKgT4MKdDUMta09XmMtd7Pn1426XJOUcftrs1gAA5xEyc4SAioCRIQDlDbfGAAQVYQgAyg+CEGCCsoShK+JvMrkrADgXI0IATFHaMNT1oj8QhgDAYgQhwESEIQChjhEhAKYiDAFA6CIIARYgDAEIVYbhsmwLRQQhwCKEIQAIPbYuqIjyx3vsmOU1Yz791vKao6OKXuzzQvym1cd68v6r5Y4s/o9f14v+oPqRB01bdLHWM0dNuW5R/j7x95bXrLq3ZOEzmMI7Xmp5TWNl0S+pBgoTqi9DtQojQoDFVmzcqdHPzZfn1JkSnc86QwBgHoIQYAPCEIBQwVNjAGxBGAIA+xGEABsRhgDYjafGANjqbBji3WQAYD2CEBACVmzcyYtaAdiCOUIAQgJvrQcA6xGEgBBCGAJgNeYIAQgphCEAsA5BCAhBhCEAVmGOUIgxDMPuFoCQQBgCAPOFXBByu93avHmz3W0AIYEwBADmsu2lqyNGjCh0v9fr1aRJk1SzZk1J0pQpU4q8jsfjkccT+B8Jn+FVmCs8OI0CNjsbhlrWnq4wl7vY8+vH3S5Jpr2oFUDF4vQbMbYFoalTp6p169aqVq1awH7DMLR582ZFR0fL5Sr+fmJmZqYeffTRgH0paqFGuiSY7QK2IgwBgDlsC0ITJkzQyy+/rGeeeUa9evXy769UqZJmzZqliy++uETXycjIOGd06Zq4m4PZKhASCEMAzOBTaE5itoptc4QyMjI0d+5c3XXXXRo5cqROnz5dpuu43W7FxsYGbNwWQ0XFnCEACC5bJ0t36NBBa9eu1Y8//qj27dtr48aNJbodBjgZYQhAMLGgos2qVq2q2bNnKyMjQ3369JHX67W7JSDkEYYAIDhsD0JnDRgwQGvWrNF7772n5ORku9sBQh5hCEAwhOqCipmZmerQoYNiYmJUu3Ztpaena+vWrQHnGIah8ePHKykpSVFRUerRo4c2bdpUqjohE4QkqW7duurfv7+io6PtbgUoFwhDACqqJUuWaNiwYVqxYoWysrJ05swZpaWl6fjx4/5znnzySU2ZMkXTpk3T6tWrlZCQoD59+ujo0aMlrmPbU2MAgoOnyQBciFBdR2jBggUBP8+cOVO1a9fW2rVr1b17dxmGoalTp2rs2LG69tprJUmzZ89WfHy85syZozvuuKNEdUJqRAhA2TAyBKA88Hg8KigoCNh+vSjy+eTn50uSatSoIUnKyclRbm6u0tLS/Oe43W6lpqZq+fLlJe6JIARUEGUJQ50uus3krgCEOiufGsvMzFRcXFzAlpmZWYIeDY0YMUJdu3ZVy5YtJUm5ubmSpPj4+IBz4+Pj/cdKgltjQAVS2ttk7WoOlCR99ePLZrcGAIUugux2F/9ddc8992jDhg1atmzZOcd+veyOYRilWoqHIARUMIQhAKVh5fo+bre7RMHnl+69917Nnz9fS5cuVd26df37ExISJP08MpSYmOjfn5eXd84oUVG4NQZUQKW9Tdau5kBukwEIKYZh6J577tF7772nL774QikpKQHHU1JSlJCQoKysLP++U6dOacmSJercuXOJ6zAiBFRQjAwBKInSru9jlWHDhmnOnDn64IMPFBMT45/3ExcXp6ioKLlcLg0fPlwTJ05UkyZN1KRJE02cOFFVqlTRoEGDSlyHIARUYIQhAOXVjBkzJEk9evQI2D9z5kzdfPPNkqTRo0fr5MmTuvvuu3X48GF17NhRn332mWJiYkpcx2UYobqCQNn1CbvO7hYqrPCqVS2v6T12zPKaEfG1La9Z0LWhadfu2KaBMkf1lzuyZH/3OVDwgvLyJ5rSyxfHm5ly3aLMnNLP8prVtv9kec1KB09YWs+7cYul9Zwky/e2ZbUufn+8ZbW+S7euVkkxRwhwgJXrdyrjqQ/kOXWmROfXih2m2nEPmdwVANiPIAQ4BGEIAM5FEAIchDAE4NesXFAxFBGEAIchDAHA/xCEAAc6G4Z8Rskm9BKGgIqLESEAjrRy/U7tOXArYQiAoxGEAAc7/tNiwhDgcIaFWygiCAEORxgC4GQEIQCEIcDBmCMEACIMAXAmghAAP8IQ4EAOnyREEAIQgDAEwElC6u3zhw8f1uzZs7Vt2zYlJiZqyJAhqlevXpG/4/F45PF4Avb5DK/CXOFmtgpUaGfDUL1aryrMVbnY82vFDpMk017UCsA8oTp3xyq2jgglJSXp4MGDkqScnBxdfPHFmjx5srZt26YXX3xRrVq10pYtRb/dODMzU3FxcQFbjngjMnChGBkC4AS2BqHc3Fx5vV5J0kMPPaTmzZvrP//5jz777DNt375d3bp10yOPPFLkNTIyMpSfnx+wpai5Fe0DFR5hCKj4DMO6LRSFzByhlStX6pFHHlGVKlUkSW63Ww8//LBWrFhR5O+53W7FxsYGbNwWA4KHMASgIrM9CLlcP9+b9Hg8io+PDzgWHx+vH3/80Y62APwCYQiouFhHyGa9e/dW27ZtVVBQoOzs7IBju3fvVq1atWzqDMAvEYYAVES2PjU2bty4gJ/P3hY768MPP1S3bt2sbAlAEXiaDKiAQnSkxiohFYR+7amnnrKoEwAlRRgCUJHYfmsMQPnDbTKg4uCpMQAoA8IQgIqAIASgzMoShjpddJvJXQFAyRGEAFyQ0oahdjUHEoaAUMJLVwHgwhCGAJRXBCEAQUEYAsonFlQEgCAhDAEobwhCAIKKMASUMw6fI2Trgooof7zHjlleM7xqVctrntmfZ3nN2K+rFH9SkN115/2mXfvydh/qiYevkTuy+K+ZdjUHqkXkIR0/as6iixeNPmrKdYsy5bGBlteMO3nG0nrhrZpbWk+SvBu3WF4TFRsjQgBMsWptjh5+Yp48p0r2H+cqMcMUHcM6Q4DVmCMEACYhDAEIdQQhAKYiDAEhzuFzhAhCAExHGAIQqghCACxxNgwZJXyajDAEWMVl4RZ6CEIALLNqbY7yD91KGAIQMghCACx12rOYMASEEuYIAYC1CEMAQgVBCIAtCENAiGBECADsQRgCYDeCEABbEYYAmxku67YQRBACYDvCEAC72BqEvv76a+Xk5Ph/fuONN9SlSxfVq1dPXbt21VtvvVXsNTwejwoKCgI2n+E1s20AJiAMAbCDrUHo1ltv1c6dOyVJr7zyim6//Xa1b99eY8eOVYcOHXTbbbfptddeK/IamZmZiouLC9hyxNuJgfKIMARYzzCs20JRhJ3Ft27dqkaNGkmSpk+frqlTp+r222/3H+/QoYMmTJigoUOHnvcaGRkZGjFiRMC+a+JuNqVfAOY7G4biarwql6tysedXiRkmSTp+dKLZrQGogGwdEYqKitKPP/4oSfr+++/VsWPHgOMdO3YMuHVWGLfbrdjY2IAtzBVuWs8AzMfIEGAhHp+3T9++fTVjxgxJUmpqqt55552A4//85z/VuHFjO1oDYDPCEAAr2HprbPLkyerSpYtSU1PVvn17PfPMM1q8eLFatGihrVu3asWKFZo3b56dLQKwEbfJAAuE6GPtVrF1RCgpKUlff/21OnXqpAULFsgwDK1atUqfffaZ6tatq3//+9/63e9+Z2eLAGzGyBAAM9k6IiRJ1apV06RJkzRp0iS7WwEQohgZAszjCtG5O1ZhQUUA5QIjQwDMUKYgdPHFF+vJJ5/U999/H+x+AOC8CEOACXhqrPS2bNmijIwMNWjQQFdeeaXmzJmjkydPBrs3ADhHWcLQpTXvMbkrAOVVmYLQ8OHDlZycLK/Xq6ysLA0ePFjx8fG69dZbtXjx4iC3CACBShuGWlQfQhgCzoeXrpbelClTtGPHDq1Zs0YZGRlq2rSpjh07plmzZql3795q1KiRpk+fHuxeAcCPMAQgGC7oqbG2bduqSpUqOn36tPbu3asTJ07IMAzl5OTo3nvv1bZt2/Tss88Gq1cACFDap8laVB8iSdpwcJrZrQHlR4jO3bFKmUaEjh07pldeeUWdO3fWJZdcomeeeUbHjx9XQkKCHnnkEb355puqXr26Xn/99WD3CwABGBkCcCHKNCKUmJjoH/2RpJ49e+quu+5Senq6IiJ+vuS8efP09ttvB69TADgPRoaAC+DwEaEyBaHjx4+rWrVquummm3TXXXepWbNm55xzzz33qG/fvhfcIOA9dszymuFVq1pe08g7YHnNKpZXlH47fqRp1+50yb/0zN1Xy12p+K+2FtWHqHklj4xjT5vSy7Lha025blE2PNLa0npuS6v9LLxVc8trejdusbwmrFPqIOTz+fTyyy+rS5cuatasmVyuwmeBd+3aVV27dr3gBgGgpL7atEv/N31+icOQq+rtkmRaGALKBYePCJVpjtAdd9yhK6+88rwhCADscjYMeU6fKdH5rqq3y1XVvFEqAKGt1EEoLCxMycnJqlLFjkF1ACgeYQgoBdYRKr3x48dr27Zt+tvf/hbsfgAgKAhDAEqiTEHoL3/5iyIiIjRs2DBVrVpVKSkpatiwoRo2bKhGjRoFu0cAKJOzYcgwPCU6nzAEOE+ZnhrbtWuX/59PnDgR8DPzhgCEkq827ZJx+G6p+nS5XMU/58QEajiNy+GTpcsUhMaNGxfsPgDAPKe+JAwBKBRBCIAzEIaAwjEiVDYej0dz5szRihUrlJCQoFtvvVU7d+5Uy5YtVaNGjWD2CADBQRgC8Ctlmix98OBBtW/fXn/+85/1yiuvKCsrS5s3b1bPnj31/PPPB7tHAAie/4YhJlADkMoYhEaPHq1NmzapcuXK/veNXXHFFapSpYo++eSToDYIAEFHGALwX2UKQh999JHi4uL0n//8x78vPDxcycnJ2rFjR9CaAwDTEIYAST8/NWbVForKFITy8/PVoEEDJSQkBOz3er06evRoia9z77336ssvvyxLC34ej0cFBQUBm8/wXtA1ATgEYQhwvDIFofr162vTpk1atmyZf9+HH36orVu3qkGDBiW+zgsvvKAePXqoadOmmjx5snJzc0vdS2ZmpuLi4gK2HPGmYAAlRBiC0/GKjdIbOHCgzpw5o9TUVLlcLq1cuVLp6elyuVwaOHBgqa712Wef6Xe/+52efvpp1a9fX/3799dHH30kn89Xot/PyMhQfn5+wJai5mX5WACcijAEOFaZgtDYsWPVt29fGYYRsKWlpSkjI6NU12rVqpWmTp2qH374QW+88YY8Ho/S09NVr149jR07Vtu3by/y991ut2JjYwO2MFd4WT4WACcjDMGpDAu3EFSmdYQiIyP18ccfa+nSpVq1apUMw9Dll1+u1NTUMjdSqVIlXX/99br++uu1e/duvfbaa5o1a5YmTZokr5c5PwAswDpDgOOUeUFFSerevbu6d+8erF786tevr/Hjx2vcuHFauHBh0K8PAOdFGILThOhIjVXKFIR69ep13mMul0uff/55ia6TnJys8PDz38ZyuVzq06dPqfsDgAtCGAJCwtKlS/XUU09p7dq12rdvn+bNm6f09HT/8ZtvvlmzZ88O+J2OHTtqxYoVJa5RpiC0ePFiuVwu/2KKkvw/l+bt8zk5OWUpDwDmIwzBIUJ1fR9JOn78uFq3bq1bbrlFf/jDHwo957e//a1mzpzp/zkyMrJUNcoUhG666aaAwJOfn6/Fixfr6NGjGjBgQFkuCQChhzAE2Kpv377q27dvkee43e5z1jUsjTIFoVmzZp2z78CBA7r00ktVt27dMjcDACGnjGFIWmJuX0CwWDgi5PF45PEEPpnpdrvldhf/Z+t8Fi9erNq1a6tatWpKTU3VhAkTVLt27RL/fpkeny9MrVq11Lhx40JDEgCUa2V4tP7qJEbHgV8rbBHkzMzMMl+vb9+++sc//qEvvvhCzzzzjFavXq1evXqdE7aKUqYRocceeyzgZ6/Xq+zsbC1btkzVq1cvyyUBILSVcmSoT0I/SdL8H94yuzOg3MjIyNCIESMC9l3IaNANN9zg/+eWLVuqffv2Sk5O1scff6xrr722RNcoUxAaP378eSdF//73vy/LJQEg9BGGUBFZeGvsQm+DFScxMVHJycnatm1biX+nTEGofv36AUHI5XKpdu3a6t27d6lXlgaAcoUwBISsgwcPas+ePUpMTCzx75QpCO3cuVNHjhzRf/7zH1WqVEmNGjVSdHR0WS4FAOUPYQgVSCg/Pn/s2LGAV23l5ORo/fr1qlGjhmrUqKHx48frD3/4gxITE7Vz50499NBDqlWrlq655poS1yh1ENq3b5/uvvvugBejVqpUSQMHDtTUqVMVFxcnSdq7dy9PkKHc8h47ZnnNiPiSP+UQLGd27LS8ZrX6NSyv2fzVu0y5breGH2v6dVfLHVH8V6kVYajKmO9Nu3ZhTkyuY2k9STLvpsr52fFnEz9bs2aNevbs6f/57PyiIUOGaMaMGdq4caNef/11HTlyRImJierZs6fmzp2rmJiYEtcoVRA6cuSIunbtqp07dwYspnjq1Cm9/vrrWrt2rVavXq3Nmzfr97//vfbu3VuaywNAufLljl26++35IRWGgFIzSr4QstV69OgRkDd+7dNPP73gGqUKQk899ZR/Nehu3bqpbdu2crlcWrdunZYuXapNmzZp4MCB+vzzz3XMhr9RA4DVCENA+VaqIPTBBx/I5XLp9ddf14033hhw7O9//7uGDBmiDz74QNK5j9gDQEVFGEK5FsJzhKxQqgUVc3JyVK9evXNCkCQNHjxY9evXlyS99957Gjt2bHA6BIBy4GwY8pw5U6Lz+yT0Y9FFIAQEbWVpSQoLC1NUVJT69+8fzMsCQLlAGEJ55DKs20JRqYJQw4YNtWfPHs2ZM+ecY++++6527typhg0bBq05AChvzoah075TJTqfMATYq1RzhNLT07Vp0yYNHjxYL7/8sn+y9IYNG/T555/L5XLp6quvNqtXACgXvtyxSy/veFa3NXxAlcIiiz2fOUOwVYiO1FilVEFo5MiRevPNN7Vjxw4tXbpUS5cu9R8zDEPJyckaOXJk0JsEgPJmc8EGwhBQDpTq1lhcXJyWLVum/v37y+VyyTAMGYYhl8ul3//+9/ryyy9VrVo1k1oFgPLlbBjiNhlCmdPnCJV6ZemEhATNmzdP+fn5ys7OliQ1btyYt84DQCEYGQJCW5mfGouLi1OHDh3UoUMHQhAAFIGRIYQ0w8ItBAX18XkAQOEIQ0BoIggBgEUIQwhJjAjZ669//auGDBmif/7zn5J+flXHxRdfrObNm+uhhx7SmWIWJvN4PCooKAjYfIbXitYBoNQIQ0BosTUIPf744xo7dqyOHz+u+++/X5MnT9YDDzygG2+8UUOGDNErr7yixx9/vMhrZGZmKi4uLmDL0RaLPgEAlB5hCKHE6U+N2RqEZs2apVmzZumdd97RggULNHbsWD333HMaO3asMjIy9OKLLxa6ivUvZWRkKD8/P2BLUXOLPgEAlA1hCAgNtgahffv2qX379pKk1q1bKywsTG3atPEfb9u2rX744Ycir+F2uxUbGxuwhbnCzWwbAIKCMATYz9YglJCQoO+++06StG3bNnm9Xv/PkrRp0ybVrl3brvYAwHSEIcBepV5QMZgGDRqkm266Sf3799fnn3+uMWPGaOTIkTp48KBcLpcmTJigP/7xj3a2CACmY9FFwD62BqFHH31UUVFRWrFihe644w6NGTNGl156qUaPHq0TJ06oX79+xU6WBoCKgDAE24ToJGar2BqEwsPDNXbs2IB9AwYM0IABDPsCcB7CEGA929cRAgD8D3OGYDUenwcAhBTCEGAdghAAhKCyhKGhKf1M7goVEq/YAACEotKGoevqXUEYAkqJIAQAIYwwBNMxIgQACGWEIcA8BCEAKAcIQzCL058as3UdIQD/Yxw/YXnN8KpVLa8ZsfuQ5TWj9idZXnPDI62Df01JOe0+0BMPXyN3ZPFf39fVu0KH98Tp6UXLgt6LJOmWY+ZctwgNnrG8pC1/NmEdRoQAoBxZtTZHDz8xT55TZ0p0/u2dO2hkz64md4VyjTlCAIDyhDAEBA9BCADKIcIQgsXpc4QIQgBQThGGgAtHEAKAcowwhAvGHCEAQHnmD0NnCENAaRGEAKACWLU2R3e/PZ8wBJQSQQgAKogvd+wiDKH0uDUGAKgoCENA6RCEAKCCIQyhNHh8HgBQ4RCGgJIhCAFABUUYQokwR8g++/bt01/+8hf16tVLLVq0UMuWLdWvXz+9+uqr8nq9drYGABUCYQgomm1BaM2aNWrRooU+/PBD/fTTT8rOzlbbtm0VHR2tkSNHqlu3bjp69Gix1/F4PCooKAjYfAYhCgDOIgyhSIwI2WP48OF64IEH9PXXX2v58uWaPXu2srOz9dZbb2nHjh06efKkHn744WKvk5mZqbi4uIAtR1ss+AQAUH4QhoDC2RaE1q1bp8GDB/t/HjRokNatW6f9+/erevXqevLJJ/XOO+8Ue52MjAzl5+cHbClqbmbrAFAuEYZQGJ4as0nt2rW1b98+/8/79+/XmTNnFBsbK0lq0qSJDh06VOx13G63YmNjA7YwV7hpfQNAeUYYAgLZFoTS09N15513asGCBVq0aJFuvPFGpaamKioqSpK0detW1alTx672AKDCIgwhAHOE7PHEE0/o4osvVr9+/dS7d295PB699tpr/uMul0uZmZl2tQcAFRphCPhZhF2Fq1atqrlz5+qnn37SmTNnVLVq1YDjaWlpNnUGAM5wNgxNv+5quSOK/8/B7Z07SJKeXrTM7NZgoVCdu2MV2xdUrFy58jkhCABgDUaG4HS2ByEAgL0IQw7HHCEAgNOVKQy16m1yV4D5CEIAAEllCEPNOhOGKgJGhAAA+BlhCE5DEAIABCAMOYvLwi0UEYQAAOcgDMEpCEIAgEIRhuAEti2oCMB+3mPHLK8ZUbuW5TWr7vNZXjPipNfymg3/eTTo1/xeh/TY0qMaN3WQIt2Vij3/9madFf5DJT37iTmLLu7pY8pli5R8oq71Ra0UopOYrcKIEACgSGuXb9ejw+folOd0ic6/tWcHPdCXdYZQPhCEAADFIgxVXC7Dui0UEYQAACVCGEJFRBACAJQYYagCYkFFAABK7mwY8pwu2dNkhCGEMoIQAKDU1i7frvtmzycMVQSMCAEAUHr/zt5FGEK5RxACAJQZYaj846kxAAAuAGEI5ZntK0sfP35cc+bM0fLly5WbmyuXy6X4+Hh16dJFAwcOVHR0tN0tAgCKcTYMPT/karkrFf+fllt7dpAk01agRimE6EiNVWwdEfruu+/UtGlTjR49WocPH1b9+vVVt25dHT58WKNGjVKzZs303Xff2dkiAKCEGBlCeWTriNCwYcPUvXt3zZ49W5GRkQHHTp06pZtvvlnDhg3TokWLbOoQAFAaZRkZ2rgnVwu/3W5BdyhMqM7dsYqtI0IrV67UI488ck4IkqTIyEg99NBDWrlypQ2dAQDKqrQjQ/f9tovJHQHnZ2sQql69urZt23be49u3b1f16tWLvIbH41FBQUHA5jOsf+szAOB/ShOGGtauoWj3uX8hhkVYR8g+t912m4YMGaKnn35a33zzjXJzc7V//3598803evrppzV06FDdcccdRV4jMzNTcXFxAVuOtlj0CQAA51OaMFS1MkEI9rB1jtD48eMVFRWlKVOmaPTo0XK5XJIkwzCUkJCgBx98UKNHjy7yGhkZGRoxYkTAvmvibjarZQBAKfw7e5fGv7tQmQN+a3crOA+nzxGy/fH5MWPGaMyYMcrJyVFubq4kKSEhQSkpKSX6fbfbLbfbHbAvzBUe9D4BAGWz+8ARu1sAzitkFlRMSUlRp06d1KlTJ38I2rNnj4YOHWpzZwAAoKIKmSBUmEOHDmn27Nl2twEAQMXl8MnStt4amz9/fpHHd+zYYVEnAAAg1CxdulRPPfWU1q5dq3379mnevHlKT0/3HzcMQ48++qheeuklHT58WB07dtQLL7ygSy65pMQ1bA1C6enpcrlcMozzx8SzE6gBAIAJQnSkRvr5NVytW7fWLbfcoj/84Q/nHH/yySc1ZcoUzZo1S02bNtUTTzyhPn36aOvWrYqJiSlRDVtvjSUmJurdd9+Vz+crdFu3bp2d7QEAABv17dtXTzzxhK699tpzjhmGoalTp2rs2LG69tpr1bJlS82ePVsnTpzQnDlzSlzD1iDUrl27IsNOcaNFAADgwrgM67bCFkH2eDxl6vvs0+ZpaWn+fW63W6mpqVq+fHmJr2NrEBo1apQ6d+583uONGzfmPWMAAFQQhS2CnJmZWaZrnV1yJz4+PmB/fHy8/1hJ2DpHqFu3bkUej46OVmpqqkXdAADgQBbeeClsEeRfrwVYWr+eS2wYRqnmF9u+oCIAAHCGwhZBLquEhARJP48MJSYm+vfn5eWdM0pUlJBeRwgAAJjLZRiWbcGUkpKihIQEZWVl+fedOnVKS5YsKXLaza8xIgQAAELSsWPHtH37dv/POTk5Wr9+vWrUqKH69etr+PDhmjhxopo0aaImTZpo4sSJqlKligYNGlTiGgQhABVexAmv5TUrrcm2vOaPAy61tF7Sl8dKdF6tlieKPSd+5XGF/1j89X7oVrVENYPpaNNqlte0VAg/nL1mzRr17NnT//PZ+UVDhgzRrFmzNHr0aJ08eVJ33323f0HFzz77rMRrCEkEIQAAEKJ69OhR7KLL48eP1/jx48tcgyAEAICDuUJ4RMgKTJYGAACOxYgQAABOxogQAACAMzEiBACAgzFHCAAAwKEYEQIAwMkYEQIAAHCmkA5C+/fv12OPPWZ3GwAAoIIK6SCUm5urRx991O42AACosFyGdVsosnWO0IYNG4o8vnXrVos6AQAATmRrEGrTpo1cLleh7xE5u9/lctnQGQAADhGiIzVWsTUI1axZU5MnT1bv3r0LPb5p0yb169evyGt4PB55PJ6AfT7DqzBXeND6BAAAFZOtQahdu3b64YcflJycXOjxI0eOFPnWWUnKzMw8Zx5RilqokS4JWp8AAFRUoTp3xyq2Tpa+44471KBBg/Mer1+/vmbOnFnkNTIyMpSfnx+wpah5kDsFAAAVka0jQtdcc02Rx6tXr64hQ4YUeY7b7Zbb7Q7Yx20xAABKqJg7LxVdSD8+v2fPHg0dOtTuNgAAQAUV0kHo0KFDmj17tt1tAABQYbGOkI3mz59f5PEdO3ZY1AkAAHAiW4NQenr6edcROot1hAAAMFGIjtRYxdZbY4mJiXr33Xfl8/kK3datW2dnewAAoIKzNQi1a9euyLBT3GgRAAC4MC6fdVsosvXW2KhRo3T8+PHzHm/cuLEWLVpkYUcAAMBJbA1C3bp1K/J4dHS0UlNTLeoGAAAHcviNl5B+fB4AAMBMto4IAQAAe4Xq+j5WYUQIAAA4FiNCAFBB1HhluaX1Dv65c4nOy29YpdhzDjeP1sGE4ocm6n56qEQ1g+lo02qW14R1CEIAADiZw5ep4dYYAABwLEaEAABwMCZLAwAAOBQjQgAAOBkjQgAAAM7EiBAAAA7GHCEAAACHYkQIAAAnYx0h++3du1fHjh07Z//p06e1dOlSGzoCAABOYGsQ2rdvny6//HIlJyerWrVqGjJkSEAgOnTokHr27GljhwAAVGwuw7otFNkahB588EGFh4dr5cqVWrBggb777jv16NFDhw8f9p9jOHzIDgAAmMfWILRw4UI999xzat++va644gotW7ZMdevWVa9evXTo0M8v1nO5XHa2CABAxWZYuIUgW4NQfn6+qlev7v/Z7XbrnXfeUYMGDdSzZ0/l5eUVew2Px6OCgoKAzWd4zWwbAABUELYGoYYNG2rDhg0B+yIiIvT222+rYcOG+v3vf1/sNTIzMxUXFxew5WiLWS0DAFChMEfIRn379tVLL710zv6zYahNmzbFzhHKyMhQfn5+wJai5ma1DAAAKhBb1xGaMGGCTpw4UeixiIgIvffee9q7d2+R13C73XK73QH7wlzhQesRAIAKzReiQzUWsXVEKCIiQrGxsec9/sMPP+jRRx+1sCMAAOAkIbGg4vkcOnRIs2fPtrsNAAAqLoc/NWbrrbH58+cXeXzHjh0WdQIAAJzI1iCUnp4ul8tV5IRo1hECAABmsfXWWGJiot599135fL5Ct3Xr1tnZHgAAFR6Pz9uoXbt2RYad4kaLAAAALoStt8ZGjRql48ePn/d448aNtWjRIgs7AgDAYRw+4GBrEOrWrVuRx6Ojo5WammpRNwAAwGlsDUIAAMBeoTp3xyohvY4QAACAmRgRAgDAyRw+IkQQAmCt44W/X9BMESe9ltd0ghqvLC/RebG/aSo9OKDIc6q9uUZnvj9U7LV2Pdy5RDWDqV7WMctrwjoEIQAAHMzl8KfGmCMEAAAcixEhAACczGd3A/ZiRAgAADgWI0IAADgYc4QAAAAcihEhAACczNkDQowIAQAA52JECAAAJ3P4HCHbg9DBgwe1YcMGtW7dWjVq1NCBAwf06quvyuPx6LrrrlOLFi3sbhEAYLIhj92gk0d/Kva8ox0Sy1zjh/wCrdqxR5tzfyzzNVDx2BqEVq1apbS0NBUUFKhatWrKysrSddddp4iICBmGoUmTJmnZsmVq27atnW0CAEz221t6WVLH5zP0+op1mrxgqSX1EPpsnSM0duxYXXfddcrPz9dDDz2k9PR09e7dW9nZ2dq2bZsGDRqkxx9/3M4WAQAVSFiYSzd3bqc/tm1pdyshw2VYt4UiW4PQ2rVrNWLECMXExOj+++/XDz/8oNtuu81/fNiwYVq9erWNHQIAKqLBnS6zuwWECFtvjZ06dUpRUVGSpEqVKqlKlSqqVauW/3jNmjV18ODBIq/h8Xjk8XgC9vkMr8Jc4cFvGABQaj5v6L3DodFFNVQpPEynQ7A3yzl8srStI0L16tXTjh07/D+/9dZbSkz830S4ffv2BQSjwmRmZiouLi5gy9EW03oGAJTOni3f293COcLDwhQRxgoysDkIDRgwQHl5ef6fr7rqKv8IkSTNnz9fl19+eZHXyMjIUH5+fsCWouam9QwAKJ0TR0/qq/lr7G4D5+HyWbeFIltvjY0bN67I42PHjlV4eNG3uNxut9xud8A+bosBQGh57q6XVLdZkuo1S7K7FSCA7esIFeXgwYMaN26cXnvtNbtbAQBcgIP7DuvOy0bp6rvTdHGnZqoeX61M1/HUiynReXFRldW4ds0y1XAch88RCukgdOjQIc2ePZsgBAAVwKmfTumdKR9J+qjM19j7cOcSnde9SQO9OPiaMteBc9gahObPn1/k8V9OpAYAACYI0QGh8ePH69FHHw3YFx8fr9zc3KDWsTUIpaeny+VyyShiWM7lclnYEQAACBWXXHKJFi5c6P+5uHnDZWHrU2OJiYl699135fP5Ct3WrVtnZ3sAAFR4LsOwbCutiIgIJSQk+LeLLroo6J/f1iDUrl27IsNOcaNFAACg/PB4PCooKAjYfr0o8i9t27ZNSUlJSklJ0YABA0yZMmNrEBo1apQ6dz7/xLfGjRtr0aJFFnYEAIDDGIZlW2GLIGdmZhbaVseOHfX666/r008/1csvv6zc3Fx17ty52DdOlJatc4S6detW5PHo6GilpqZa1A0AADBTRkaGRowYEbDv12sBntW3b1//P7dq1UqdOnVSo0aNNHv27HOucSFC+vF5AABgMgtXfC5sEeSSio6OVqtWrbRt27ag9sSLVgAAQMjzeDzavHlzwDtJg4ERIQAAHKwsT3NZYeTIkerXr5/q16+vvLw8PfHEEyooKNCQIUOCWocgBIQI77FjdrdgiTP784o/KcjCbKjptbyiM9R9YnmJzqvV96RUzMrSdSav1E8nzv/E0llbX+5QopoIrr1792rgwIE6cOCALrroIv3mN7/RihUrlJycHNQ6BCEAAJwsREeE3nrrLUvqMEcIAAA4FkEIAAA4FrfGAABwshC9NWYVRoQAAIBjMSIEAICTWbigYihiRAgAADgWI0IAADhYqC6oaBVGhAAAgGOF5IhQw4YN9emnn6pJkyZ2twIAqKBGvHynzpwufg3wgk41g1LPaxga9eUnQblWUDl8RMjWIPT8888Xun/37t2aOXOmEhISJEn33XeflW0BAByg58CultY75fWGZhByOFuD0PDhw1WnTh1FRAS24fP59Prrr6tSpUpyuVwEIQAAzMKIkH1uu+02rVq1SnPmzFGLFi38+ytVqqTPPvtMF198sY3dAQCAis7WydIvvviixo0bpyuvvFLTpk0r0zU8Ho8KCgoCNp/Be58BwMlKMvcH/2UY1m0hyPanxtLT0/XVV19p3rx56tu3r3Jzc0v1+5mZmYqLiwvYcrTFpG4BAOXBrk177G4B5YTtQUiS6tSpo4ULF6p79+667LLLZJQiNWZkZCg/Pz9gS1FzE7sFAIS6g/sOa82n6+1uo3zwWbiFoJB5fN7lcikjI0NpaWlatmyZEhMTS/R7brdbbrc7YF+YK9yMFgEA5ciU219U5idjlXxxXbtbQQgLmSB0Vrt27dSuXTtJ0p49ezRu3Di99tprNncFAChvftxzQHe1G60rb+6hFh2bKqZm1TJd53jrakHp54wvNIdEnL6ydMgFoV86dOiQZs+eTRACAJTJac9pffRilj56MavM18h+uUMQO0KosTUIzZ8/v8jjO3bssKgTAAAcihEh+6Snp8vlchU5OdrlclnYEQAAcBJbnxpLTEzUu+++K5/PV+i2bt06O9sDAAAVnK1BqF27dkWGneJGiwAAwAXyGdZtIcjWW2OjRo3S8ePHz3u8cePGWrRokYUdAQAAJ7E1CHXr1q3I49HR0UpNTbWoGwAAHMjhd15CYmVpAAAAO4T0OkIAAMBkjAgBAAA4U4UcEcryvW13CwAAlA+MCAEAADhThRwRAgAAJRSi6/tYhREhAADgWIwIAQDgZIbP7g5sxYgQAABwLEaEAABwMp4aAwAAcCZGhAAAcDKeGgMAAHAmRoQAAHAy5ggBAAA4EyNCAAA4GSNCAAAAzkQQAgAAjsWtMQAAnIxbYwAAAM7EiBAAAE7m46WrAAAAjsSIEAAATsYcIQAAAGdiRAgAACdjRAgAAMCZGBECAMDJfIwIAQAAOBIjQgAAOJhhsI4QAACAIzEiBACAkzFHCAAAwJkYEQIAwMlYRwgAAMCZGBECAMDJePs8AACAMxGEAACAY3FrDAAAJ2OyNAAAgDMxIgQAgIMZTJYGAABwJkaEAABwMuYIAQAAOBMjQgAAOBkvXQUAAHAmRoQAAHAyg6fGAAAAHIkRIQAAHMxgjhAAAIAzMSIEAICTMUcIAADAmQhCAAA4mOEzLNvKYvr06UpJSVHlypXVrl07ffnll0H9/AQhAAAQkubOnavhw4dr7Nix+vrrr9WtWzf17dtXu3fvDloNl2E4/CUjAAA4WJ+w6yyrleV7u1Tnd+zYUW3bttWMGTP8+1q0aKH09HRlZmYGpSdGhAAAQMg5deqU1q5dq7S0tID9aWlpWr58edDq8NQYAACwhMfjkcfjCdjndrvldrvPOffAgQPyer2Kj48P2B8fH6/c3Nyg9UQQAgDAwUp7u+pCjB8/Xo8++mjAvnHjxmn8+PHn/R2XyxXws2EY5+y7EAQhAABgiYyMDI0YMSJgX2GjQZJUq1YthYeHnzP6k5eXd84o0YVgjhAAALCE2+1WbGxswHa+IBQZGal27dopKysrYH9WVpY6d+4ctJ4YEQIAACFpxIgRGjx4sNq3b69OnTrppZde0u7du3XnnXcGrQZBCAAAhKQbbrhBBw8e1GOPPaZ9+/apZcuW+te//qXk5OSg1WAdIQAA4FjMEQIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI71/5LqjovQemHbAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 700x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "aligner.get_aggregate_alignment() \n",
+    "# Note: White path represents the average alignment path where diagonals represent matches; \n",
+    "# vertical and horizontal paths could represent either warp matches or indels (mismatches)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "powered-wyoming",
+   "metadata": {},
+   "source": [
+    "## 3. Analysing gene-level alignments\n",
+    "\n",
+    "Ranking genes based on their alignment similarities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "chubby-stomach",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean alignment similarity percentage (matched %): \n",
+      "50.39 %\n"
+     ]
+    },
+    {
+     "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>Gene</th>\n",
+       "      <th>alignment_similarity_percentage</th>\n",
+       "      <th>opt_alignment_cost</th>\n",
+       "      <th>l2fc</th>\n",
+       "      <th>color</th>\n",
+       "      <th>abs_l2fc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>CCRL2</td>\n",
+       "      <td>0.2174</td>\n",
+       "      <td>55.943686</td>\n",
+       "      <td>-0.487688</td>\n",
+       "      <td>red</td>\n",
+       "      <td>0.487688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>NFKBIA</td>\n",
+       "      <td>0.2174</td>\n",
+       "      <td>54.673471</td>\n",
+       "      <td>-0.091748</td>\n",
+       "      <td>red</td>\n",
+       "      <td>0.091748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>NLRP3</td>\n",
+       "      <td>0.2174</td>\n",
+       "      <td>57.177548</td>\n",
+       "      <td>0.069058</td>\n",
+       "      <td>red</td>\n",
+       "      <td>0.069058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TNF</td>\n",
+       "      <td>0.2174</td>\n",
+       "      <td>57.990078</td>\n",
+       "      <td>-0.006439</td>\n",
+       "      <td>red</td>\n",
+       "      <td>0.006439</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>C5AR1</td>\n",
+       "      <td>0.2727</td>\n",
+       "      <td>57.858236</td>\n",
+       "      <td>0.8711</td>\n",
+       "      <td>red</td>\n",
+       "      <td>0.8711</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>NUP54</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>30.744062</td>\n",
+       "      <td>0.012993</td>\n",
+       "      <td>green</td>\n",
+       "      <td>0.012993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>CD44</td>\n",
+       "      <td>0.7647</td>\n",
+       "      <td>28.366715</td>\n",
+       "      <td>-0.021366</td>\n",
+       "      <td>green</td>\n",
+       "      <td>0.021366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>PLAGL2</td>\n",
+       "      <td>0.8235</td>\n",
+       "      <td>31.807955</td>\n",
+       "      <td>-0.051268</td>\n",
+       "      <td>green</td>\n",
+       "      <td>0.051268</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>ZSWIM4</td>\n",
+       "      <td>0.8235</td>\n",
+       "      <td>30.214576</td>\n",
+       "      <td>0.030379</td>\n",
+       "      <td>green</td>\n",
+       "      <td>0.030379</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>SGMS2</td>\n",
+       "      <td>0.8667</td>\n",
+       "      <td>37.626682</td>\n",
+       "      <td>-0.020399</td>\n",
+       "      <td>green</td>\n",
+       "      <td>0.020399</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>89 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Gene alignment_similarity_percentage opt_alignment_cost      l2fc  \\\n",
+       "63   CCRL2                          0.2174          55.943686 -0.487688   \n",
+       "77  NFKBIA                          0.2174          54.673471 -0.091748   \n",
+       "68   NLRP3                          0.2174          57.177548  0.069058   \n",
+       "3      TNF                          0.2174          57.990078 -0.006439   \n",
+       "45   C5AR1                          0.2727          57.858236    0.8711   \n",
+       "..     ...                             ...                ...       ...   \n",
+       "34   NUP54                            0.75          30.744062  0.012993   \n",
+       "15    CD44                          0.7647          28.366715 -0.021366   \n",
+       "19  PLAGL2                          0.8235          31.807955 -0.051268   \n",
+       "51  ZSWIM4                          0.8235          30.214576  0.030379   \n",
+       "26   SGMS2                          0.8667          37.626682 -0.020399   \n",
+       "\n",
+       "    color  abs_l2fc  \n",
+       "63    red  0.487688  \n",
+       "77    red  0.091748  \n",
+       "68    red  0.069058  \n",
+       "3     red  0.006439  \n",
+       "45    red    0.8711  \n",
+       "..    ...       ...  \n",
+       "34  green  0.012993  \n",
+       "15  green  0.021366  \n",
+       "19  green  0.051268  \n",
+       "51  green  0.030379  \n",
+       "26  green  0.020399  \n",
+       "\n",
+       "[89 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = aligner.get_stat_df() # ordered genes according to alignment similarity statistics \n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "controversial-calgary",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "VisualUtils.plot_alignmentSim_vs_l2fc(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "under-sponsorship",
+   "metadata": {},
+   "source": [
+    "A ranked list of genes based on their first match occurrence "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "cleared-jimmy",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In the order of the first match occurrence along pseudotime\n",
+      "Gene      Alignment\n",
+      "--------  ------------------------\n",
+      "PTAFR     \u001b[92mM\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mII\u001b[0m\n",
+      "OSBPL3    \u001b[92mM\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "RFFL      \u001b[92mM\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
+      "TNFAIP2   \u001b[92mM\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "SGMS2     \u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[92mM\u001b[0m\n",
+      "SLC16A10  \u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "FPR1      \u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "FAM20C    \u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMM\u001b[0m\u001b[91mIIII\u001b[0m\n",
+      "CLEC4D    \u001b[91mI\u001b[0m\u001b[92mMM\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "TSHZ1     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "IL1F9     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "PSTPIP2   \u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[92mVVVVV\u001b[0m\u001b[91mII\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "RELA      \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\n",
+      "NUP54     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\n",
+      "DDHD1     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\n",
+      "NRP2      \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "TREM1     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "GRAMD1B   \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMM\u001b[0m\n",
+      "TOP1      \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "ICOSL     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMMM\u001b[0m\n",
+      "DUSP16    \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMMM\u001b[0m\n",
+      "PTPRE     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "LDLR      \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "TNIP1     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "PLAGL2    \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[92mVVV\u001b[0m\n",
+      "ZSWIM4    \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[92mVVV\u001b[0m\n",
+      "ZC3H12C   \u001b[91mDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[92mVVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "AK150559  \u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[92mVVVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDDD\u001b[0m\n",
+      "F10       \u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMM\u001b[0m\u001b[92mVVVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDDDDD\u001b[0m\n",
+      "FAM108C   \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
+      "RBM7      \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
+      "RASA2     \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
+      "SLC25A37  \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
+      "IRAK-2    \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "PLEKHO2   \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "LCP2      \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "TRIM13    \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMM\u001b[0m\n",
+      "PTX3      \u001b[91mDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMMMMM\u001b[0m\n",
+      "SPATA13   \u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "BCL2L11   \u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMM\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\n",
+      "CD44      \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[92mVV\u001b[0m\n",
+      "AK163103  \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "LZTFL1    \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "IRAK3     \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mII\u001b[0m\u001b[91mDDDDDD\u001b[0m\n",
+      "ARG2      \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\n",
+      "ZEB2      \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "TLR2      \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "MCOLN2    \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "CPD       \u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "RCAN1     \u001b[91mDDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMMM\u001b[0m\u001b[91mI\u001b[0m\n",
+      "PILRA     \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "ARHGEF3   \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMMM\u001b[0m\n",
+      "C5AR1     \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDDDDDDD\u001b[0m\n",
+      "SLC39A14  \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMM\u001b[0m\u001b[91mIIII\u001b[0m\n",
+      "CLCN7     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "BC031781  \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "NUPR1     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "CDC42EP4  \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "NFKBIE    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMM\u001b[0m\n",
+      "PLSCR1    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "NCK1      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMM\u001b[0m\n",
+      "ADORA2B   \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "ORAI2     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "KLF7      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "NIACR1    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMM\u001b[0m\u001b[92mVVVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "PDE4B     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\n",
+      "SERTAD2   \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "CXCL1     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "MPP5      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "TGM2      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
+      "PIP5K1A   \u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "FLRT3     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\n",
+      "SOCS3     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMM\u001b[0m\u001b[92mVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[92mMMMM\u001b[0m\n",
+      "TNFAIP3   \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\n",
+      "RASGEF1B  \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\n",
+      "SLC25A25  \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[92mM\u001b[0m\n",
+      "INSIG1    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "CXCL2     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "MALT1     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "RALGDS    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "H1F0      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMMM\u001b[0m\n",
+      "IL1A      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\n",
+      "NLRP3     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "TNF       \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "NFKBIA    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "PLK2      \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\n",
+      "NFKBIZ    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[92mVVVVVVV\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMM\u001b[0m\n",
+      "NFKBID    \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\n",
+      "CCRL2     \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "earliest_match_sorted_genes_list = aligner.show_ordered_alignments() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "retired-legislation",
+   "metadata": {},
+   "source": [
+    "## Gene-set overrepresentation analysis on the top dissimilar genes \n",
+    "\n",
+    "Checking top dissimilar genes, i.e, only <=30% similarity along pseudotime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "bound-sheep",
+   "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>Gene_set</th>\n",
+       "      <th>Term</th>\n",
+       "      <th>Overlap</th>\n",
+       "      <th>P-value</th>\n",
+       "      <th>Adjusted P-value</th>\n",
+       "      <th>Old P-value</th>\n",
+       "      <th>Old Adjusted P-value</th>\n",
+       "      <th>Odds Ratio</th>\n",
+       "      <th>Combined Score</th>\n",
+       "      <th>Genes</th>\n",
+       "      <th>-log10 Adjusted P-value</th>\n",
+       "      <th>-log10 FDR q-val</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>KEGG_2021_Human</td>\n",
+       "      <td>C-type lectin receptor signaling pathway</td>\n",
+       "      <td>4/104</td>\n",
+       "      <td>1.414253e-07</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>132.600000</td>\n",
+       "      <td>2091.300110</td>\n",
+       "      <td>NFKBIA;NLRP3;TNF;MALT1</td>\n",
+       "      <td>5.182020</td>\n",
+       "      <td>5.182020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>KEGG_2021_Human</td>\n",
+       "      <td>NF-kappa B signaling pathway</td>\n",
+       "      <td>4/104</td>\n",
+       "      <td>1.414253e-07</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>132.600000</td>\n",
+       "      <td>2091.300110</td>\n",
+       "      <td>NFKBIA;TNF;CXCL2;MALT1</td>\n",
+       "      <td>5.182020</td>\n",
+       "      <td>5.182020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MSigDB_Hallmark_2020</td>\n",
+       "      <td>TNF-alpha Signaling via NF-kB</td>\n",
+       "      <td>4/200</td>\n",
+       "      <td>1.944057e-06</td>\n",
+       "      <td>0.000013</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>67.326531</td>\n",
+       "      <td>885.393278</td>\n",
+       "      <td>NFKBIA;CCRL2;TNF;CXCL2</td>\n",
+       "      <td>4.898378</td>\n",
+       "      <td>4.898378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MSigDB_Hallmark_2020</td>\n",
+       "      <td>Inflammatory Response</td>\n",
+       "      <td>4/200</td>\n",
+       "      <td>1.944057e-06</td>\n",
+       "      <td>0.000013</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>67.326531</td>\n",
+       "      <td>885.393278</td>\n",
+       "      <td>NFKBIA;C5AR1;CCRL2;NLRP3</td>\n",
+       "      <td>4.898378</td>\n",
+       "      <td>4.898378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>KEGG_2021_Human</td>\n",
+       "      <td>NOD-like receptor signaling pathway</td>\n",
+       "      <td>4/181</td>\n",
+       "      <td>1.305897e-06</td>\n",
+       "      <td>0.000040</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>74.625235</td>\n",
+       "      <td>1011.068962</td>\n",
+       "      <td>NFKBIA;NLRP3;TNF;CXCL2</td>\n",
+       "      <td>4.392729</td>\n",
+       "      <td>4.392729</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Gene_set                                      Term Overlap  \\\n",
+       "13       KEGG_2021_Human  C-type lectin receptor signaling pathway   4/104   \n",
+       "14       KEGG_2021_Human              NF-kappa B signaling pathway   4/104   \n",
+       "0   MSigDB_Hallmark_2020             TNF-alpha Signaling via NF-kB   4/200   \n",
+       "1   MSigDB_Hallmark_2020                     Inflammatory Response   4/200   \n",
+       "15       KEGG_2021_Human       NOD-like receptor signaling pathway   4/181   \n",
+       "\n",
+       "         P-value  Adjusted P-value  Old P-value  Old Adjusted P-value  \\\n",
+       "13  1.414253e-07          0.000007            0                     0   \n",
+       "14  1.414253e-07          0.000007            0                     0   \n",
+       "0   1.944057e-06          0.000013            0                     0   \n",
+       "1   1.944057e-06          0.000013            0                     0   \n",
+       "15  1.305897e-06          0.000040            0                     0   \n",
+       "\n",
+       "    Odds Ratio  Combined Score                     Genes  \\\n",
+       "13  132.600000     2091.300110    NFKBIA;NLRP3;TNF;MALT1   \n",
+       "14  132.600000     2091.300110    NFKBIA;TNF;CXCL2;MALT1   \n",
+       "0    67.326531      885.393278    NFKBIA;CCRL2;TNF;CXCL2   \n",
+       "1    67.326531      885.393278  NFKBIA;C5AR1;CCRL2;NLRP3   \n",
+       "15   74.625235     1011.068962    NFKBIA;NLRP3;TNF;CXCL2   \n",
+       "\n",
+       "    -log10 Adjusted P-value  -log10 FDR q-val  \n",
+       "13                 5.182020          5.182020  \n",
+       "14                 5.182020          5.182020  \n",
+       "0                  4.898378          4.898378  \n",
+       "1                  4.898378          4.898378  \n",
+       "15                 4.392729          4.392729  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "threshold_similarity = 0.3 \n",
+    "topDEgenes = df[list(df['alignment_similarity_percentage'] <=threshold_similarity)]['Gene']\n",
+    "# Calling wrapper function for GSEAPy enrichr inferface\n",
+    "pathway_df = PathwayAnalyser.run_overrepresentation_analysis(topDEgenes) \n",
+    "pathway_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "helpful-remove",
+   "metadata": {},
+   "source": [
+    "## Clustering alignments \n",
+    "\n",
+    "Running experiment to determine the distance threshold for alignment clusters from hierarchical clustering. We aim to select a locally optimal threshold that gives a good trade-off between high mean Silhouette score and low number of clusters which can be biologically meaningful. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "grand-pakistan",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compute distance matrix\n",
+      "- using levenshtein distance metric\n",
+      "Experimental mode: exploring different thresholds\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 68%|██████▊   | 67/99 [00:00<00:00, 243.98it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-- Cluster diagnostic plots\n",
+      "Potential candidates for distance threshold: a locally optimal thresholds that gives a good trade-off between high mean Silhouette score and low number of clusters \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = ClusterUtils.run_clustering(aligner, metric='levenshtein', experiment_mode=True) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "metallic-duncan",
+   "metadata": {},
+   "source": [
+    "Run clustering with the chosen distance threshold. In this case we select 0.37"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "equal-above",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compute distance matrix\n",
+      "- using levenshtein distance metric\n",
+      "run agglomerative clustering | distance threshold = 0.37\n",
+      "silhouette_score:  0.3757459494624326\n"
+     ]
+    }
+   ],
+   "source": [
+    "ClusterUtils.run_clustering(aligner, metric='levenshtein', DIST_THRESHOLD=0.37) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "forty-psychology",
+   "metadata": {},
+   "source": [
+    "Visualise gene alignment grouped together in each cluster <br>\n",
+    "Note: diagonals represent matches; vertical and horizontal paths could represent either warp matches or indels (mismatches)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "alert-story",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cluster ID | Number of genes in the cluster\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ClusterUtils.visualise_clusters(aligner,n_cols = 4, figsize= (10,6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fancy-meter",
+   "metadata": {},
+   "source": [
+    "Visualise the distance matrix used in the clustering "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "suspected-ordering",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "VisualUtils.plot_distmap_with_clusters(aligner)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "welcome-darwin",
+   "metadata": {},
+   "source": [
+    "Print the aggregate (average) cell-level alignments for each cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "swiss-marketplace",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cluster:  0 \u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDDD\u001b[0m ( 18 genes)\n",
+      "cluster:  1 \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m ( 3 genes)\n",
+      "cluster:  2 \u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m ( 36 genes)\n",
+      "cluster:  3 \u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[92mM\u001b[0m\u001b[92mVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m ( 12 genes)\n",
+      "cluster:  4 \u001b[91mIII\u001b[0m\u001b[91mDDDDDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mD\u001b[0m ( 12 genes)\n",
+      "cluster:  5 \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDDD\u001b[0m\u001b[92mMMMM\u001b[0m\u001b[92mV\u001b[0m\u001b[92mM\u001b[0m\u001b[92mVV\u001b[0m\u001b[92mM\u001b[0m ( 2 genes)\n",
+      "cluster:  6 \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMMM\u001b[0m ( 6 genes)\n"
+     ]
+    }
+   ],
+   "source": [
+    "ClusterUtils.print_cluster_average_alignments(aligner)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "continent-ancient",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['FPR1', 'TREM1', 'TOP1', 'CXCL1', 'ORAI2', 'PILRA', 'CLEC4D', 'C5AR1', 'PTPRE', 'LDLR', 'PLSCR1', 'LCP2', 'TNIP1', 'ADORA2B', 'KLF7', 'CPD', 'TNFAIP2', 'SPATA13']\n",
+      "\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "\u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "\u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "\u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "\u001b[91mI\u001b[0m\u001b[92mMM\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDDDDDDD\u001b[0m\n",
+      "\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "\u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "\u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "\u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "\u001b[92mM\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDDDDD\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "# To access the genes in a particular cluster\n",
+    "cluster_id = 0\n",
+    "print(aligner.gene_clusters[cluster_id]) \n",
+    "\n",
+    "# To print all gene alignments in the cluster\n",
+    "aligner.show_cluster_alignment_strings(cluster_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "neural-wales",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To get the cluster id of an alignment object, e.g. TNF, \n",
+    "aligner.results_map['TNF'].cluster_id"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "confirmed-nepal",
+   "metadata": {},
+   "source": [
+    "### Average alignment of any given subset of genes \n",
+    "e.g. `gene_list[40:60]` or a specific gene set in a specific pathway, e.g. EMT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "operational-wayne",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average Alignment:  \u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDDD\u001b[0m (cell-level)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "GENE_SUBSET = gene_list[40:60]\n",
+    "aligner.get_aggregate_alignment_for_subset(GENE_SUBSET )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "alone-maryland",
+   "metadata": {},
+   "source": [
+    "###  Exploring alignment and trends of a given gene set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "welcome-wilderness",
+   "metadata": {},
+   "source": [
+    "Following call extracts a specified pathway gene set from msigdb database. It requires downloading the database from https://www.gsea-msigdb.org/gsea/downloads.jsp and specifying path to the msigdb folder and its version. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "surprised-bryan",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['CD44', 'CXCL1', 'PTX3', 'TGM2', 'TNFAIP3'], dtype=object)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "IGS = PathwayAnalyser.InterestingGeneSets(MSIGDB_PATH='../../OrgAlign/msigdb', version='7.5.1') # need to create db folder and pass args\n",
+    "IGS.add_new_set_from_msigdb('hallmark', 'HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION', aligner.gene_list, 'EMT')   \n",
+    "IGS.SETS['EMT']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "aboriginal-dakota",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gene set: =======  EMT\n",
+      "mean matched percentage:  51.04 %\n",
+      "Average Alignment:  \u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[92mM\u001b[0m\u001b[91mI\u001b[0m (cell-level)\n",
+      "- Plotting average alignment path\n",
+      "- Plotting z-normalised interpolated mean trends\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "PathwayAnalyser.get_pathway_alignment_stat(aligner, IGS.SETS['EMT'], 'EMT', cluster=True, FIGSIZE=(3,6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "single-medium",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gene set: =======  EMT\n",
+      "mean matched percentage:  50.04 %\n",
+      "Average Alignment:  \u001b[91mII\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m (cell-level)\n",
+      "- Plotting average alignment path\n",
+      "- Plotting z-normalised interpolated mean trends\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "PathwayAnalyser.get_pathway_alignment_stat(aligner, gene_list[0:40], 'EMT', cluster=True, FIGSIZE=(3,6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "secret-terminology",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "g2g_installed_env",
+   "language": "python",
+   "name": "g2g_installed_env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/Supplementary_notebook1.ipynb b/notebooks/Supplementary_notebook1.ipynb
new file mode 100644
index 0000000..3158549
--- /dev/null
+++ b/notebooks/Supplementary_notebook1.ipynb
@@ -0,0 +1,248 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "opening-punishment",
+   "metadata": {},
+   "source": [
+    "# Supplementary Notebook 1: Checking total time of alignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "humanitarian-billion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import anndata\n",
+    "import numpy as np\n",
+    "import seaborn as sb\n",
+    "import numpy as np\n",
+    "import platform\n",
+    "import warnings\n",
+    "import time\n",
+    "import matplotlib.pyplot as plt\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "from genes2genes import Main\n",
+    "from genes2genes import VisualUtils\n",
+    "from genes2genes import ClusterUtils\n",
+    "from genes2genes import TimeSeriesPreprocessor\n",
+    "from genes2genes import PathwayAnalyser\n",
+    "from genes2genes import VisualUtils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "virgin-peoples",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "89 genes\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_dir = 'data/'\n",
+    "adata_ref = anndata.read_h5ad(input_dir + 'adata_pam_local.h5ad') # Reference dataset\n",
+    "adata_query = anndata.read_h5ad(input_dir +'adata_lps_local.h5ad') # Query dataset\n",
+    "# define the gene list to align\n",
+    "gene_list = adata_ref.var_names \n",
+    "print(len(gene_list),'genes')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "damaged-israeli",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 179 × 89\n",
+       "    obs: 'time'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata_ref"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "noble-apparatus",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 290 × 89\n",
+       "    obs: 'time'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata_query"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "proof-battlefield",
+   "metadata": {},
+   "source": [
+    "### A simple experiment to check the number of interpolation time points vs. approximate time taken for alignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "loved-mistake",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "times = []\n",
+    "for n_bins in range(5,50):\n",
+    "    s = time.time()\n",
+    "    aligner = Main.RefQueryAligner(adata_ref, adata_query, gene_list, n_bins)\n",
+    "    aligner.align_all_pairs() \n",
+    "    t = time.time()\n",
+    "    times.append(t-s) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "worst-raleigh",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sb.lineplot(x=range(5,50), y=times, linewidth=3, marker = 'o')\n",
+    "plt.xlabel('Number of interpolation points')\n",
+    "plt.ylabel('Time (seconds)')\n",
+    "plt.title('PAM vs LPS alignment (179 reference cells & 290 query cells in terms of 89 genes)')\n",
+    "#plt.savefig('n_interpolation_points_vs_time_PAM_LPS_G2G_alignment.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hydraulic-arctic",
+   "metadata": {},
+   "source": [
+    "### A simple experiment to check the ref and query dataset size (number of cells) vs the approximate time taken for alignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "aggregate-brake",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata_ref_mult = [adata_ref]\n",
+    "\n",
+    "temp = adata_ref\n",
+    "for i in range(0,10):\n",
+    "    temp = anndata.concat([temp, temp])\n",
+    "    adata_ref_mult.append(temp)\n",
+    "adata_query_mult = [adata_query]\n",
+    "temp = adata_query\n",
+    "for i in range(0,10):\n",
+    "    temp = anndata.concat([temp, temp])\n",
+    "    adata_query_mult.append(temp)\n",
+    "\n",
+    "r_size = []\n",
+    "q_size = []\n",
+    "for a in  adata_ref_mult:\n",
+    "    r_size.append(a.shape[0])\n",
+    "for a in  adata_query_mult:\n",
+    "    q_size.append(a.shape[0])\n",
+    "\n",
+    "times = []\n",
+    "n_bins = 14\n",
+    "for i in range(len(adata_ref_mult)):\n",
+    "    R = adata_ref_mult[i]\n",
+    "    Q = adata_query_mult[i]\n",
+    "    s = time.time()\n",
+    "    aligner = Main.RefQueryAligner(R, Q, gene_list, n_bins)\n",
+    "    aligner.align_all_pairs() \n",
+    "    t = time.time()\n",
+    "    times.append(t-s) \n",
+    "\n",
+    "plt.subplots(1,2, figsize=(10,3))\n",
+    "plt.subplot(1,2,1)\n",
+    "sb.lineplot(x=range(len(adata_ref_mult)), y=times, linewidth=3, marker = 'o')\n",
+    "plt.xlabel('k (for aligning genes of $N_{R}.2^k$ cells and $N_{Q}.2^k$ cells)')\n",
+    "plt.ylabel('Time (seconds)')\n",
+    "plt.xticks(range(0,11))\n",
+    "plt.title('Total alignment time of 89 genes')\n",
+    "plt.subplot(1,2,2)\n",
+    "sb.lineplot(x=r_size, y=q_size, linewidth=3, marker = 'o', color='orange')\n",
+    "plt.title('# of cells')\n",
+    "plt.xlabel('Number of reference cells')\n",
+    "plt.ylabel('Number of query cells')\n",
+    "plt.xticks(rotation =10)\n",
+    "plt.tight_layout() \n",
+    "plt.savefig('cell_numbers_vs_approx_time_PAM_LPS_G2G_alignment.png')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "g2g_installed_env",
+   "language": "python",
+   "name": "g2g_installed_env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/Supplementary_notebook2.ipynb b/notebooks/Supplementary_notebook2.ipynb
new file mode 100644
index 0000000..3754e00
--- /dev/null
+++ b/notebooks/Supplementary_notebook2.ipynb
@@ -0,0 +1,467 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "hungarian-metadata",
+   "metadata": {},
+   "source": [
+    "# Supplementary Notebook 2: Re-running T-cell case studyhealthy/IPF case study with G2G v0.2.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "golden-nicholas",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import anndata\n",
+    "import numpy as np\n",
+    "import seaborn as sb\n",
+    "import numpy as np\n",
+    "import warnings\n",
+    "import scanpy as sc\n",
+    "import matplotlib.pyplot as plt\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "from genes2genes import Main\n",
+    "from genes2genes import ClusterUtils\n",
+    "from genes2genes import TimeSeriesPreprocessor\n",
+    "from genes2genes import PathwayAnalyser\n",
+    "from genes2genes import VisualUtils"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "derived-chicago",
+   "metadata": {},
+   "source": [
+    "## in vitro vs in vivo T-cell trajectory alignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "civilian-image",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.6547761  3.421763   3.27304    ... 0.95708966 0.95708966 1.4370381 ]\n",
+      "[1.0180244  1.0180244  1.0180244  ... 0.87026346 0.5269568  1.1253548 ]\n",
+      "1371\n"
+     ]
+    }
+   ],
+   "source": [
+    "adata_ref = anndata.read_h5ad('adata_ref_spt.h5ad')\n",
+    "adata_query = anndata.read_h5ad('adata_ato_spt.h5ad') \n",
+    "print(adata_ref.X.data) \n",
+    "print(adata_query.X.data)\n",
+    "gene_list = adata_ref.var_names\n",
+    "print(len(gene_list))\n",
+    "n_bins = 14"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "native-swiss",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===============================================================================================================\n",
+      "Genes2Genes (v0.2.0)\n",
+      "Dynamic programming alignment of gene pseudotime trajectories using a bayesian information-theoretic framework\n",
+      "===============================================================================================================\n",
+      "Interpolator initialization completed\n",
+      "Aligner initialised to align trajectories of 20327 reference cells & 17176 query cells in terms of 1371 genes\n",
+      "Running gene-level alignment: 🧬\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1371/1371 [11:45<00:00,  1.94it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alignment completed! ✅\n",
+      "Time taken: 709.1007261276245 sec\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "s = time.time()\n",
+    "aligner = Main.RefQueryAligner(adata_ref, adata_query, gene_list, n_bins)\n",
+    "aligner.align_all_pairs() \n",
+    "t = time.time()\n",
+    "print('Time taken:', t-s,'sec')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "accredited-stone",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11.818345435460408"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "709.1007261276245/60"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "numerous-departure",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average Alignment:  \u001b[91mIII\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mD\u001b[0m (cell-level)\n",
+      "% similarity: 64.71\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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66Se9+uqrGjhwoFauXKm6dev6tc7q1av13HPPac2aNUH9/y6UR3zsEvaX4erUqaPIyMgzRpHy8/Mtn9Yeiu655x7Nnz9fS5YsUf369QNeLyYmRk2bNlV6erqys7PVrl07PffccwGrt3r1auXn56tTp06KiopSVFSUli5dqueff15RUVEqLi4OWO0ScXFxatOmjbZt2xawGikpKWcEzlatWgX0poQSO3fu1OLFi3XbbbcFvNaYMWP08MMPa9CgQWrTpo2GDh2q+++/X9nZ2QGt26RJEy1dulRHjx7V7t279dVXX+nkyZNKS0sLaF1JpXdShstn0smTJzVw4EDl5uYqJycn4KNK0i8/o02bNtUll1yi6dOnKyoqStOnT/d7nS+//FL5+flq0KBB6efRzp079cADD6hRo0Z+r4fACfuwFBMTo06dOpXe2VMiJydHXbt2dahX/meapu6++2598MEH+uyzz2z50C+rH263O2Dn79Wrl9avX69169aVtvT0dN14441at26dIiMjA1a7hNvt1qZNm5SSErgneXfr1u2MpR+2bt2qhg0bBqxmiRkzZqhu3bq66qqrAl7r+PHjiojw/piKjIwM+NIBJeLi4pSSkqKCggJ98sknuvbaawNeMy0tTcnJyV6fSUVFRVq6dGmV+kyS/huUtm3bpsWLFwfssrWVQH0uDR06VN9++63X51FqaqrGjBmjTz75xO/1KstjGra1UMVlOEmjRo3S0KFDlZ6eri5duuiVV17Rrl27NGLEiIDVPHr0qL7//vvSr3Nzc7Vu3Tqdd955atCggd/rjRw5UrNnz9Y//vEPxcfHl/7VmpiYqNjYWL/Xk6RHHnlE/fr10/nnn68jR45ozpw5+vzzz7Vw4cKA1JN+mW9y+jysuLg41a5dO2Dzs0aPHq3+/furQYMGys/P15NPPqnDhw8H9DLR/fffr65du2rixIkaOHCgvvrqK73yyit65ZVXAlZT+uXy34wZMzRs2DBFRQX+46N///6aMGGCGjRooAsvvFBr167VpEmTNHz48IDW/eSTT2Saplq0aKHvv/9eY8aMUYsWLfTHP/7RL+e3+vnPzMzUxIkT1axZMzVr1kwTJ05U9erVNWTIkIDVPHjwoHbt2lW6zlFJGE9OTq70umG+aqampur3v/+91qxZo48//ljFxcWln0vnnXeeYmJi/F6zdu3amjBhgq655hqlpKTowIEDeumll7Rnz55KL4Fh9X09PQBGR0crOTlZLVq0qFQ9OMTBO/GCyosvvmg2bNjQjImJMTt27BjwW+qXLFliSjqjDRs2LCD1zlZLkjljxoyA1DNN0xw+fHjp9/Q3v/mN2atXL3PRokUBq1eWQC8d8Ic//MFMSUkxo6OjzdTUVPP66683N27cGLB6JT766COzdevWpsvlMlu2bGm+8sorAa/5ySefmJLMLVu2BLyWaZrm4cOHzfvuu89s0KCBWa1aNbNx48bm2LFjTbfbHdC67777rtm4cWMzJibGTE5ONkeOHGkeOnTIb+e3+vn3eDzmuHHjzOTkZNPlcpk9evQw169fH9CaM2bMOOv+cePGBaRmyRIFZ2tLliwJSM0TJ06Y1113nZmammrGxMSYKSkp5jXXXGN+9dVXAal3NsG4dMC6nfVta6HKME0zlJeJAgAA52DNLv9fzShLxwaBn1cZCGE/ZwkAAMAX5iwBABDGihk3scR3CAAAwAdGlgAACGOhfEu/XRhZAgAA8IGRJQAAwhiPO7HGyNKvuN1ujR8/PqArTFPTnprh8B6pSU1qUhP2YJ2lXzl8+LASExNVWFhoy/OJqFl16lGTmtSkZjDUrIwvdzS1rVb3Rt9bHxSEGFkCAADwgTlLAACEMQ/jJpb4DgEAAPhQJecs9Ymo3NOjPWaxcrVZaWqpCCPSz72ipp01w+E9UpOa1Ky6NXM87wWoV2f6bEcL22pd3miLbbX8ibAEAECQISwFF+YsAQAQxopNZuRY4TsEAADgAyNLAACEMQ8reFtiZAkAAMAHRpYAAAhjxYybWOI7BAAA4ANhCQAAwAcuwwEAEMZYOsBaWIelzld11FX/06fM/ccOHdfTw16wsUcAACDYhHVYSm5UV136p5e5v2D/Ifs6AwCAA3iQrjVHw9KePXs0bdo0LV++XHl5eTIMQ0lJSeratatGjBih888/38nuAQAAOBeWli1bpn79+un8889X37591bdvX5mmqfz8fH344Yd64YUXtGDBAnXr1s3nedxut9xut9c2j1ls24MSAQAIZcUmi1JacSws3X///brttts0efLkMvdnZmZq1apVPs+TnZ2txx57zGtbmlqpiS70W18BAED4cuxC5YYNGzRixIgy999xxx3asGGD5XmysrJUWFjo1dLU0p9dBQCgyipWhG0tVDk2spSSkqLly5erRYsWZ93/r3/9SykpKZbncblccrlcXtu4BAcAAPzFsbA0evRojRgxQqtXr1afPn2UlJQkwzCUl5ennJwcvfbaa5oyZYpT3QMAICx4WGfJkmNh6a677lLt2rU1efJkvfzyyyouLpYkRUZGqlOnTnrjjTc0cOBAp7oHAAAgyeGlA/7whz/oD3/4g06ePKmffvpJklSnTh1FR0c72S0AAMJGKM8lsktQLEoZHR1drvlJAAAAdguKsAQAAJzBOkvWGHsDAADwgZElAADCGM+Gs8Z3CAAAwAdGlgAACGPFrLNkie8QAACAD4QlAAAAH7gMBwBAGPOIpQOsEJb85JO939he84p6HWyvaUQ48ENl2D8Aap46aXtNI9L+B0AbMTG219R/Hm1kqzD5N2T3+3TkPQIOICwBABDGmOBtje8QAAAIetnZ2TIMQ5mZmaXbTNPU+PHjlZqaqtjYWPXs2VMbN270ep3b7dY999yjOnXqKC4uTtdcc4327NlTodqEJQAAwlixImxrlbVq1Sq98soratu2rdf2Z555RpMmTdLUqVO1atUqJScnq0+fPjpy5EjpMZmZmZo3b57mzJmjZcuW6ejRo7r66qtVXIEpAYQlAAAQtI4ePaobb7xRr776qmrVqlW63TRNTZkyRWPHjtX111+v1q1ba9asWTp+/Lhmz54tSSosLNT06dP17LPPqnfv3urQoYPeeustrV+/XosXLy53HwhLAACEMY9p2NbcbrcOHz7s1dxut8/+jRw5UldddZV69+7ttT03N1d5eXnq27dv6TaXy6WMjAwtX75ckrR69WqdPHnS65jU1FS1bt269JjyICwBAABbZGdnKzEx0atlZ2eXefycOXO0Zs2asx6Tl5cnSUpKSvLanpSUVLovLy9PMTExXiNSpx9THtwNBwBAGDuXuUQVlZWVpVGjRnltc7lcZz129+7duu+++7Ro0SJVq1atzHMahveSNqZpnrHtdOU55tcYWQIAALZwuVxKSEjwamWFpdWrVys/P1+dOnVSVFSUoqKitHTpUj3//POKiooqHVE6fYQoPz+/dF9ycrKKiopUUFBQ5jHlQVgCACCMecwI21pF9OrVS+vXr9e6detKW3p6um688UatW7dOjRs3VnJysnJyckpfU1RUpKVLl6pr166SpE6dOik6OtrrmH379mnDhg2lx5QHl+EAAEDQiY+PV+vWrb22xcXFqXbt2qXbMzMzNXHiRDVr1kzNmjXTxIkTVb16dQ0ZMkSSlJiYqFtvvVUPPPCAateurfPOO0+jR49WmzZtzpgw7gthCQCAMFYcws+Ge/DBB3XixAndddddKigoUOfOnbVo0SLFx8eXHjN58mRFRUVp4MCBOnHihHr16qWZM2cqsgKPmCIsAQCAkPD55597fW0YhsaPH6/x48eX+Zpq1arphRde0AsvvFDpukE9Z2n37t0aPny4z2POtmaDx3TgQZ0AAISgYJ2zFEyCuucHDx7UrFmzfB5ztjUbcrXZph4CAICqztHLcPPnz/e5f/v27ZbnONuaDdcl3nIu3QIAIGyE8pwluzgalgYMGCDDMGSaZpnHWC0a5XK5zlijIcIo/6QtAAAAXxy9DJeSkqK5c+fK4/Gcta1Zs8bJ7gEAUOUxZ8maoz3v1KmTz0BkNeoEAAAQaI5ehhszZoyOHTtW5v6mTZtqyZIlNvYIAADAm6NhqXv37j73x8XFKSMjw6beAAAQfopD+PKYXfgOAQAA+MAK3gAAhDEPSwdYYmQJAADAB0aWAAAIY8xZssZ3CAAAwAdGlgAACGMekzlLVhhZAgAA8IGRJQAAwlgx4yaWCEt+clXG9bbXnL/nXdtrDkjrantNRdr/YGTDgZrmqZO215THgccJGQ58MJse20saUdG217T731A4vEdAIiwBABDWmLNkjbE3AAAAHxhZAgAgjHkYN7HEdwgAAMAHRpYAAAhjxcxZssTIEgAAgA+MLAEAEMa4G84aI0sAAAA+EJYAAAB84DIcAABhzGMybmKF7xAAAIAPjCwBABDGisUEbyuOjyydOHFCy5Yt03fffXfGvp9//llvvPGGz9e73W4dPnzYq3nM4kB1FwAAhBlHw9LWrVvVqlUr9ejRQ23atFHPnj21b9++0v2FhYX64x//6PMc2dnZSkxM9Gq52hzorgMAUCV4TMO2FqocDUsPPfSQ2rRpo/z8fG3ZskUJCQnq1q2bdu3aVe5zZGVlqbCw0KulqWUAew0AAMKJo3OWli9frsWLF6tOnTqqU6eO5s+fr5EjR6p79+5asmSJ4uLiLM/hcrnkcrm8tkUYkYHqMgAAVQp3w1lzNCydOHFCUVHeXXjxxRcVERGhjIwMzZ4926GeAQAA/MLRsNSyZUt9/fXXatWqldf2F154QaZp6pprrnGoZwAAhAcPd8NZcnTs7brrrtM777xz1n1Tp07V4MGDZZqmzb0CAAD4L0fDUlZWlv75z3+Wuf+ll16Sx+OxsUcAAISXYtOwrYUqZnUBAAD4wAreAACEMe6Gs8Z3CAAAwAdGlgAACGOhvLK2XRhZAgAA8IGRJQAAwhjrLFljZAkAAMAHRpYAAAhjzFmyRljyE091l/VBfnbJU/faXnPx9r/aXvOmZr1sr2lUs///p07aP9BrFp20vaYRGSYPujbtX1DXiIq2tZ55yoF/Pza/R8mZ94ngwmU4AAAAHxhZAgAgjLEopTW+QwAAAD4wsgQAQBhjgrc1RpYAAAB8YGQJAIAwxqKU1hhZAgAA8IGRJQAAwhhzlqwxsgQAAOADI0s+JP4mQXP2vFy+g6Pt/1YWx1Qu63o8pnL3H9SCNZv1j5Xf+blXAIBQwsiSNcKSDxEREaqdep7T3QiI5Frx6tKyoXq2bqKHZv1TRaeKne4SAABBictwYe7ytk016db+iokKk+d1AQC8eEzDthaqHA9LmzZt0owZM7R582ZJ0ubNm3XnnXdq+PDh+uyzzyxf73a7dfjwYa/mMRklqYjuF6QRmAAAKIOjYWnhwoVq3769Ro8erQ4dOmjhwoXq0aOHvv/+e+3atUtXXHGFZWDKzs5WYmKiV8vV5nLVP37khD/eRpVAYAKA8MTIkjVHw9Ljjz+uMWPG6MCBA5oxY4aGDBmi22+/XTk5OVq8eLEefPBBPfXUUz7PkZWVpcLCQq+Wppblqr9tTa4/3kaVQWACAOBMjoaljRs36pZbbpEkDRw4UEeOHNHvfve70v2DBw/Wt99+6/McLpdLCQkJXi3CKN8v+x0bdmnZvK8q3f+qiMAEAOHFI8O2Fqocn7NUIiIiQtWqVVPNmjVLt8XHx6uwsDCgdSfdPo3AdBoCEwAA/+Xo0gGNGjXS999/r6ZNm0qS/vWvf6lBgwal+3fv3q2UlJSA9uHIwaN67Hd/UeO2DdWsY5qqJ1Sv1HmM1GQ/98za4aaxFTo+wpCGXZ6uuok1LI8tCUyjpn/EsgIAUIWF8lwiuzgalu68804VF//3F3Hr1q299i9YsECXX365LX3Z/u1Obf92Z6VfH9HuAj/2pnzyetSs8Gu+/G6Hpt99g36TGGd5LIEJAACHL8ONGDFCV111VZn7J0yYoNdee83GHlV9O/MLdOvU9/Rj4bFyHc8lOQBAuAuaOUuwD4EJAFCCpQOsEZbCVGUDk+QKbMcAAAgyhKUwVpnAlHDeqyIwAUDVwciSNcJSmKtoYHJV60VgAgCEFcISCEwAEMYYWbJGWIIkAhMAAGUhLKEUgQkAwo9pGra1UEVYghcCEwAA3hxdwbsqMTdssb3mid91Dsh5N5sFuunt9/Tm0BtUN956pW9XtV7adORT3ft2YFb6fu27F/x+TiuPt7dn5fhfi4it2ONr/MGMjra9pufYcdtrOsGIcOCvaNNjazkjyv5/P+apk7bXdOJ92imUH3BrF0aWcFa5Bws09M33lH+kfCNMPVqk6fkbWbgSAFD1EJZQJgITAFR93A1njbAEnwhMAIBwR1iCJQITAFRd3A1njbCEcikJTD8eJjABAMILYQnllnuwQMNeIzABQFXCnCVrhCVUyI6fCEwAgPBCWEKFEZgAoOpgzpI1whIqhcAEAAgXhCVUGoEJAEIfc5asBV1YMk3T6S6gAghMAICqLujCksvl0qZNm5zuBiqAwAQAqMoce5DuqFGjzrq9uLhYTz31lGrXri1JmjRpks/zuN1uud1ur20es1gRBr+I7VQSmGbddoN+k2D98N2SwBSoh+8CAMqHCzrWHAtLU6ZMUbt27VSzZk2v7aZpatOmTYqLi5NhWF/fzM7O1mOPPea1LU2t1EQX+rO7KAcCEwCgKnLsMtyECRNUWFioRx99VEuWLCltkZGRmjlzppYsWaLPPvvM8jxZWVkqLCz0amlqacM7wNlwSQ4AQotHhm0tVDkWlrKysvTuu+/qzjvv1OjRo3Xy5MlKncflcikhIcGrcQnOWQQmAEBV4ugE74suukirV6/Wjz/+qPT0dK1fv75cl94Q/AhMABAaWJTSmuN3w9WoUUOzZs1SVlaW+vTpo+Ji5q5UFQQmAEBV4HhYKjFo0CB9/fXX+uCDD9SwYUOnuwM/ITABQHBjUUprQROWJKl+/fq69tprFRdnfScVQgeBCQAQyoIqLKHqIjABQHAyTftaqCIswTaVDUyGYgLcMwAAykZYgq0qE5ha1v0bgQkAAoS74awRlmC7igamWrE9CUwAAMcQluAIAhMABAdGlqwRluAYAhMAIBQQluAoAhMAOIt1lqwRluA4AhMA4HTTpk1T27ZtS5/72qVLFy1YsKB0v2maGj9+vFJTUxUbG6uePXtq48aNXudwu9265557VKdOHcXFxemaa67Rnj17KtyXqHN+N5AkGZH2rweUNu+Q7TW3DU0MyHk366AGzfu73vn9DaobV8Py+FqxPVV0coFGzv1IRQF4RE7mivl+P6eV+V2a2l7TqB5re80IBx5p5Dlxwvaapsf+v0Wd+ByymxEVbX9R02N/TRsF6/pH9evX11NPPaWmTX/5bJw1a5auvfZarV27VhdeeKGeeeYZTZo0STNnzlTz5s315JNPqk+fPtqyZYvi4+MlSZmZmfroo480Z84c1a5dWw888ICuvvpqrV69WpEV+HlhZAlBI7egQIPff0/5x46W6/iMpml68Xf9FRMGvyAAINz0799fV155pZo3b67mzZtrwoQJqlGjhlasWCHTNDVlyhSNHTtW119/vVq3bq1Zs2bp+PHjmj17tiSpsLBQ06dP17PPPqvevXurQ4cOeuutt7R+/XotXry4Qn0hLCGoEJgAoOpyu906fPiwV3O73ZavKy4u1pw5c3Ts2DF16dJFubm5ysvLU9++fUuPcblcysjI0PLlyyVJq1ev1smTJ72OSU1NVevWrUuPKS/CEoIOgQkA7GPn0gHZ2dlKTEz0atnZ2WX2bf369apRo4ZcLpdGjBihefPm6YILLlBeXp4kKSkpyev4pKSk0n15eXmKiYlRrVq1yjymvAhLCEoEJgCoerKyslRYWOjVsrKyyjy+RYsWWrdunVasWKE777xTw4YN03fffVe63zC877AzTfOMbacrzzGnIywhaJUGpqPlu0uOwAQAFWfnyJLL5Sq9u62kuVyuMvsWExOjpk2bKj09XdnZ2WrXrp2ee+45JScnS9IZI0T5+fmlo03JyckqKipSQUFBmceUF2EJQS23oEBD3yIwAQB+GRVyu91KS0tTcnKycnJySvcVFRVp6dKl6tq1qySpU6dOio6O9jpm37592rBhQ+kx5UVYQtDLPUhgAoBAMW1sFfHII4/oyy+/1I4dO7R+/XqNHTtWn3/+uW688UYZhqHMzExNnDhR8+bN04YNG3TLLbeoevXqGjJkiCQpMTFRt956qx544AF9+umnWrt2rW666Sa1adNGvXv3rlBfWGcJIaEkML150w2qWyPO8viMpmma+rurdffcjwOyDhMAILD279+voUOHat++fUpMTFTbtm21cOFC9enTR5L04IMP6sSJE7rrrrtUUFCgzp07a9GiRaVrLEnS5MmTFRUVpYEDB+rEiRPq1auXZs6cWaE1liTJMM1gXY6q8vpE3GB7zYgYB1aTvtD+RQwDtSilL1HH/zsRL+28WuUOTJL0+ffbKxWYMn8XJotSxlazvaZ5pHyT9v3JiUUpZbAoZZXhwKKUn/z8tm21ms99wrZaW3/3qG21/InLcAgpFb0k17NpY0393dVckgMAVBphCSGHwAQAfhSsk5aCCGEJIYnABACwS1CFpYKCAk2ZMkUjR47Uk08+qd27d1u+5mxLp3tMJvSGAwITAJw7O9dZClWOhqXU1FQdOHBAkpSbm6sLLrhATz/9tLZt26aXX35Zbdq00ebNm32e42xLp+fK92tQdRCYAACB5mhYysvLU/F/7lJ65JFH1LJlS/373//WokWL9P3336t79+569FHfM+fPtnR6mlra0X0ECQITAFSeadrXQlXQXIZbuXKlHn30UVWvXl3SL08P/tOf/qQVK1b4fN3Zlk6PMPglGG4ITACAQHE8LJU8zM7tdp/16cE//vijE91CCCIwAUDFMWfJmuNhqVevXurYsaMOHz6srVu3eu3btWuX6tSp41DPEIoITAAAf3P0cSfjxo3z+rrkElyJjz76SN27d7ezS6gCKvpolJLAxKNRAISlEB7xsUtQhaXT/eUvf7GpJ6hqCEwAAH9x/DIcECglgWl/OZ9FxiU5AOGIu+GsEZZQpeUeLNDNb79f4cAUaUQHuGcAgFBBWEKVV5nA9NvUxwlMAABJhCWEiYoGpoY1LiEwAQgPPEjXEmEJYYPABACoDMISwgqBCQC8sSilNcISwg6BCQBQEYQlhCUCEwD8B3OWLDm6KGVVEhEfb3/RPfm2l2z0UaztNX+870RAzrtXh3TbV69rerdbVLdaguXxDWtcohYRfwvYwpX9Pv3K7+e0sqWn/QtwGnHVrQ/yd00nFhp1omZE6F7mCGaGy/5/swgujCwhrO04dkC3/t9M5f98uFzHs3AlgKqGOUvWCEsIewQmAIAvhCVABCYAYYw5S5YIS8B/EJgAAGdDWAJ+pSQw8fBdAOHDsLGFJsIScJodxw5U6uG7BCYAqJoIS8BZVObhuwQmACGJOUuWCEtAGQhMAACJsAT4RGACUOUxsmSJsARYIDABQHgjLAHlQGACUGWZhn0tRBGWgHIiMAFAeHI0LK1du1a5ubmlX7/11lvq1q2bzj//fF166aWaM2eO5TncbrcOHz7s1TymAw+wRFggMAFA+HE0LN16663asWOHJOm1117T//zP/yg9PV1jx47VRRddpNtvv12vv/66z3NkZ2crMTHRq+Vqsw29R7giMAGoSkzTvhaqHA1LW7ZsUZMmTSRJL730kqZMmaLnnntOI0aM0OTJk/Xyyy/r2Wef9XmOrKwsFRYWerU0tbSj+whjBCYACB+OhqXY2Fj9+OOPkqQffvhBnTt39trfuXNnr8t0Z+NyuZSQkODVIgx+ISHwCEwAqgSWDrDkaFjq16+fpk2bJknKyMjQ+++/77X/73//u5o2bepE14ByITABQNUX5WTxp59+Wt26dVNGRobS09P17LPP6vPPP1erVq20ZcsWrVixQvPmzXOyi4ClksD0xo2/V1J8DcvjSwLT3XM/VlExNyMAcFgI39JvF0dHllJTU7V27Vp16dJFCxculGma+uqrr7Ro0SLVr19f//d//6crr7zSyS4C5cIIEwBUXY6vs1SzZk099dRT2rhxo06cOCG3260dO3bo7bffVnp6utPdA8qNwAQgFBmmfS1UOR6WgKqEwAQAVU+lwtIFF1ygZ555Rj/88IO/+wOEvMoGpigjOsA9A4Cz4G44S5UKS5s3b1ZWVpYaNWqkK664QrNnz9aJEyf83TcgZFUmMA1Pu5/ABABBqFJhKTMzUw0bNlRxcbFycnI0dOhQJSUl6dZbb9Xnn3/u5y4CoamigenCxA4EJgD240G6lioVliZNmqTt27fr66+/VlZWlpo3b66jR49q5syZ6tWrl5o0aaKXXnrJ330FQg6BCQBC3zlN8O7YsaOGDh2q/v37Ky4uTpJkmqZyc3N1zz336P777/dLJ4FQRmACENSYs2SpUmHp6NGjeu2119S1a1ddeOGFevbZZ3Xs2DElJyfr0Ucf1TvvvKNatWrpjTfe8Hd/gZBEYAKA0FWpFbxTUlJ0/Phxmf95hPBll12mO++8UwMGDFBU1C+nnDdvnt577z3/9RQIcRVd6bskML2eO1mnzJM29BBAWArhER+7VCosHTt2TDVr1tTNN9+sO++8Uy1atDjjmLvvvlv9+vU75w6GiuJDh2yvGXl+fdtrurbn216zxpx6ttesc9uegJy3SHs0ZmOeJnW4U3VciZbHX5jYQVcnjtedH38UkEejtPnf7X4/pxX3lcdsrxlRvbrtNT1Hjthe0zx5yvaa4cBwuZzuAhxW4ctwHo9Hr776qpYvX67JkyefNShJ0qWXXqphw4adcweBqmb38R81au00/eQuLNfxl6U11rSr+7NwJYDAYM6SpUrNWbrjjjt0xRVXyDBC9zZAwEkEJgAIHRUOSxEREWrYsKGqOzCsDVQlBCYAQYF1lixVamRp/Pjx2rZtm/72t7/5uz9AWCEwAUDwq1RY+vOf/6yoqCiNHDlSNWrUUFpamho3bqzGjRurSZMm/u4jUKWVBKb9R8u3rACBCQDsVamwtHPnThUVFck0TR0/flw7d+7Ujh07ShuAitl9/EcNmfsegQmA7QzTvhaqKrV0wLhx4/zdDyDs5RYUaMjc9zT7dzcoqYb1OkwlgSlQywoAAH5BWAKCCIEJgO1CeMTHLpV+Npzb7daMGTN0xx13aNy4cdq1a5e++OILHTx40J/9A8JOSWDikhwABIdKhaUDBw4oPT1dt912m1577TXl5ORo06ZNuuyyy/T888/7u49A2CEwAUDwqFRYevDBB7Vx40ZVq1at9PlwvXv3VvXq1bVgwQK/dhAIVwQmAAgOlQpLH3/8sRITE/Xvf/+7dFtkZKQaNmyo7dvtf9YUUFURmAAEGnfDWatUWCosLFSjRo2UnJzstb24uFhHKvDwyHvuuUdffvllZbpQyu126/Dhw17NYzLRFVUHgQkAnFWpsNSgQQNt3LhRy5YtK9320UcfacuWLWrUqFG5z/Piiy+qZ8+eat68uZ5++mnl5eVVuC/Z2dlKTEz0arnaXOHzAMGMwAQgYHjciaVKhaXBgwfr1KlTysjIkGEYWrlypQYMGCDDMDR48OAKnWvRokW68sor9de//lUNGjTQtddeq48//lgej6dcr8/KylJhYaFXS1PLyrwtIKgRmADAGZUKS2PHjlW/fv1kmqZX69u3r7Kysip0rjZt2mjKlCnau3ev3nrrLbndbg0YMEDnn3++xo4dq++//97n610ulxISErxahMEvB1RNBCYAfmfa2EJUpcJSTEyM/vd//1eff/65nnnmGT399NNasmSJFixYoJiYmEp1JDo6WgMHDtTChQu1fft23X777Xr77bfVokWLSp0PqKoITABgr0ovSilJPXr00OjRozVmzBhlZGT4q09q0KCBxo8fr9zcXC1cuNBv5wWqCgITAL9hZMlSpR53cvnll5e5zzAMffrpp+U6T8OGDRXp48PbMAz16dOnwv0DwgGPRgEAe1QqLH3++ecyDKN0QUpJpV8bRvlnu+fm5lamPID/IDABOFehvP6RXSoVlm6++WavUFRYWKjPP/9cR44c0aBBg/zWOQDWziUwAQCsVSoszZw584xtP/30k9q2bav69eufa58AVFBlA9OLe5/XSfOUDT0EELQYWbJ0ThO8f61OnTpq2rTpWYMUgMCrzKTvh1vdpmijUn8zAUDYqNSn5OOPP+71dXFxsbZu3aply5apVq1afukYgIqr6AhT+nmt9XCr2/TUptcYYQKAMlQqLI0fP77MidxXX331OXUIwLkhMAGoEC7DWapUWGrQoIFXWDIMQ3Xr1lWvXr0qvII3AP8jMAGA/1QqLO3YsUOHDh3Sv//9b0VHR6tJkyaKi4vzd98AnAMCE4DyYOkAaxUOS/v27dNdd93l9bDb6OhoDR48WFOmTFFiYqIkac+ePWF1Z5xRyce8nAtP/o+214xIrmt7zZqr9tte85BSba/Z6e5tATnvxK07Na51ps6LqWl5bPp5rTUy9d6ArsPUbsG/A3JeX05ce9L2mkZsrO01ddLm93kqTEJ1RPnXD0TVVKG74Q4dOqRLL71U8+fPV3FxcekDdIuKivTGG2+oe/fucrvdWrdunS655JJA9RlABez9eb8e2zBFB4sOlet4Ho0ChBnTsK+FqAqFpb/85S/Kzc2VaZrq3r277rvvPmVmZqpHjx4yTVMbN27U4MGDlZGRoX379gWqzwAqiMAEAJVXoctw//jHP2QYht544w3deOONXvvefPNNDRs2TP/4xz8knbm8AABnlQSm8l6S49EoQJhgzpKlCo0s5ebm6vzzzz8jKEnS0KFD1aBBA0nSBx98oLFjx/qnhwD8hhEmAKg4v63gLUkRERGKjY3Vtdde68/TAvAjAhOAXzNM+1qoqlBYaty4sXbv3q3Zs2efsW/u3LnasWOHGjdu7LfOAQiMksBUkUejEJgAhKsKhaUBAwbINE0NHTpUl112mR544AGNHj1affv21cCBA2UYhq655ppA9RWAH+39eX+FnyVHYAKqINPGFqIqFJZGjx6txo0byzRNffHFF5oyZYomT56sTz/9VKZpqkGDBho9enSg+grAzyrz8F0CE4BwU6GwlJiYqGXLlunaa6+VYRil6ywZhqGrr75aX375pWrWrBmgrgIIBAITEN6Ys2StwhO8k5OTNW/ePB04cEArV67UypUr9eOPP2r+/PlhtWI3UJUQmACgbJW+Gy4xMVEXXXSRLrroItWqVcuffQLgAAITEKaYs2TJr0sHAAhtBCYAOBNhCYAXAhMQZhhZsuR4WHrhhRc0bNgw/f3vf5f0y2NTLrjgArVs2VKPPPKITlk81drtduvw4cNezWPyaAbgXBCYAOC/HA1LTzzxhMaOHatjx47pvvvu09NPP637779fN954o4YNG6bXXntNTzzxhM9zZGdnKzEx0avlarNN7wCoughMQHjgbjhrjoalmTNnaubMmXr//fe1cOFCjR07Vs8995zGjh2rrKwsvfzyy2ddLfzXsrKyVFhY6NXS1NKmdwBUbQQmAHA4LO3bt0/p6emSpHbt2ikiIkLt27cv3d+xY0ft3bvX5zlcLpcSEhK8WoTBBzXgLwQmAOHO0bCUnJys7777TpK0bds2FRcXl34tSRs3blTdunWd6h6A/yAwAQhnUU4WHzJkiG6++WZde+21+vTTT/XQQw9p9OjROnDggAzD0IQJE/T73//eyS4C+I+SwDT7dzcoqUYNy+NLAtOdH3+komJuugAQuhwNS4899phiY2O1YsUK3XHHHXrooYfUtm1bPfjggzp+/Lj69+9vOcEbgH0ITEAVFMITr+3iaFiKjIzU2LFjvbYNGjRIgwYNcqhHAKwQmACEG8fXWQIQepjDBFQdLB1gjbAEoFIqG5iiDUcHtAGgwghLACqtMoHpwZa3E5iAYMLjTiwRlgCck4oGpvTzWhOYAIQUwhKAc0ZgAkJYkI4sZWdn66KLLlJ8fLzq1q2rAQMGaMuWLd5dN02NHz9eqampio2NVc+ePbVx40avY9xut+655x7VqVNHcXFxuuaaa7Rnz54K9YWwBMAvCEwA/Gnp0qUaOXKkVqxYoZycHJ06dUp9+/bVsWPHSo955plnNGnSJE2dOlWrVq1ScnKy+vTpoyNHjpQek5mZqXnz5mnOnDlatmyZjh49qquvvlrFFbg7l7AEwG8ITEDoCda74RYuXKhbbrlFF154odq1a6cZM2Zo165dWr16taRfRpWmTJmisWPH6vrrr1fr1q01a9YsHT9+vPS5soWFhZo+fbqeffZZ9e7dWx06dNBbb72l9evXa/HixeXuC59Q/uKxf+aaEWP//z4z/yfbaxrJ9j/ypuaq/bbX3PlyM9trXn7PtwE57/O5WzSmxUOqGVPL8tj081prROp9uvOf8wO2DtMVn6wLyHl9ye1h2F7TiLL3M8E8dcrWepLkOWl/zSibv69Vmdvtltvt9trmcrnkcrksX1tYWChJOu+88yRJubm5ysvLU9++fb3OlZGRoeXLl+uOO+7Q6tWrdfLkSa9jUlNT1bp1ay1fvlxXXHFFufrNyBIAv9vvztNftjytQ0UF5Tr+8kaNNe3Ka1iHCXCCjXOWsrOzlZiY6NWys7Otu2iaGjVqlC699FK1bt1akpSXlydJSkpK8jo2KSmpdF9eXp5iYmJUq1atMo8pD8ISgIAgMAE4XVZWlgoLC71aVlaW5evuvvtuffvtt3rnnXfO2GcY3qO4pmmese105Tnm1whLAAKGwAQEPzvnLLlcLiUkJHg1q0tw99xzj+bPn68lS5aofv36pduTk5Ml6YwRovz8/NLRpuTkZBUVFamgoKDMY8qDsAQgoAhMACrDNE3dfffd+uCDD/TZZ58pLS3Na39aWpqSk5OVk5NTuq2oqEhLly5V165dJUmdOnVSdHS01zH79u3Thg0bSo8pD8ISgIAjMAFBLEjXWRo5cqTeeustzZ49W/Hx8crLy1NeXp5OnDgh6ZfLb5mZmZo4caLmzZunDRs26JZbblH16tU1ZMgQSVJiYqJuvfVWPfDAA/r000+1du1a3XTTTWrTpo169+5d7r4QlgDYoiQwlXdZAQITEN6mTZumwsJC9ezZUykpKaXt3XffLT3mwQcfVGZmpu666y6lp6frhx9+0KJFixQfH196zOTJkzVgwAANHDhQ3bp1U/Xq1fXRRx8psgKfLYZpmvbf8x5gfSJusL1mhKua7TWNmGjba8qBfy5OLB3gxPs80DXZ9pptA7R0gC+5m3po9nUDlVSjRrmO/2zH9nNeVuCKrusq/drKCoulA067BdwOjiwd8JvattdckPeSbbUuGDvZtlrfTbjftlr+xMgSAFttP1SgIfP+zggTECyC9DJcMCEsAbAdgQlAKCEsAXAEgQkIDsH6uJNgQlgC4BgCE4BQQFgC4CgCE+Aw5ixZcjQs7du3T3/+8591+eWXq1WrVmrdurX69++v6dOnqzhAD9QEEHwITACCmWNh6euvv1arVq300Ucf6eeff9bWrVvVsWNHxcXFafTo0erevbuOHDlieR63263Dhw97NY9J0AJCDYEJcAgjS5YcC0uZmZm6//77tXbtWi1fvlyzZs3S1q1bNWfOHG3fvl0nTpzQn/70J8vznO0JxrnabMM7AOBvBCYAwcixsLRmzRoNHTq09OshQ4ZozZo12r9/v2rVqqVnnnlG77//vuV5zvYE4zS1DGTXAQQQgQmwF3fDWXMsLNWtW1f79u0r/Xr//v06deqUEhISJEnNmjXTwYMHLc9zticYRxh8aAKhjMAEIJg4FpYGDBigESNGaOHChVqyZIluvPFGZWRkKDY2VpK0ZcsW1atXz6nuAXAYgQmwCXOWLDkWlp588kldcMEF6t+/v3r16iW3263XX3+9dL9hGMrOznaqewCCAIEJQDCw96mLv1KjRg29++67+vnnn3Xq1CnVOO2hmn379nWoZwCCSUlgKu/Dd0sC07k+fBcIF6E8l8guji9KWa1atTOCEgD8GiNMAJzkeFgCgPIgMAEBwpwlS4QlACGjsoEpyogOcM8AVGWEJQAhpTKB6Y4m9xKYgLIwsmSJsAQg5FQ0MLVJbE9gAlBphCUAIYnABPiHYWMLVYQlACGLwATADoQlACGNwAQg0BxblLKq8RQV2V7TiaRrxNj/C8bMy7e9plGntu01a6+w/31+q7a217zi3rUBOe9LOzbp/uaPqGZMLctj2yS21/V1xmrEp/+QO0ALV/Zcsjkg5/Vlf2+PrfUMl8vWepJDf+FHVfFflSE88doujCwBqBL2u/M0eetEHSoqKNfxlzdoor/1ulYu1mECYIGwBKDKIDABFWeY9rVQRVgCUKUQmAD4G2EJQJVDYAIqgEUpLRGWAFRJBCYA/kJYAlBllQSm/cfK+WgUAhPCESNLlghLAKq0/e48DfrnHAITgEojLAGo8rYXFhCYgDJwN5w1whKAsEBgAlBZji9LeuzYMc2ePVvLly9XXl6eDMNQUlKSunXrpsGDBysuLs7pLgKoIkoC05wrBykprobl8SWBKZArfQOOC+ERH7s4OrL03XffqXnz5nrwwQdVUFCgBg0aqH79+iooKNCYMWPUokULfffdd052EUAVwwgTgIpydGRp5MiR6tGjh2bNmqWYmBivfUVFRbrllls0cuRILVmyxKEeAqiKGGEC/iuU5xLZxdGRpZUrV+rRRx89IyhJUkxMjB555BGtXLnSgZ4BqOoYYQJQXo6GpVq1amnbtm1l7v/+++9Vq5bvJ4i73W4dPnzYq3lM/vIDYI3ABIh1lsrB0bB0++23a9iwYfrrX/+qb775Rnl5edq/f7+++eYb/fWvf9Xw4cN1xx13+DxHdna2EhMTvVquNtv0DgCEOgITACuOhqXx48crKytLkyZNUocOHVSvXj2lpqaqQ4cOmjRpkh5++GH9+c9/9nmOrKwsFRYWerU0tbTpHQCoCghMCGess2TN8aUDHnroIT300EPKzc1VXl6eJCk5OVlpaWnler3L5ZLL5fLaFmHwAQagYpj0DaAsQbMoZVpamrp06aIuXbqUBqXdu3dr+PDhDvcMQLhghAnA2QRNWDqbgwcPatasWU53A0AYITAh7DDB25Kjl+Hmz5/vc//27dtt6gkA/BeX5AD8mqNhacCAATIMQ6ZZdtw0DMPGHgHALwhMCBshPOJjF0cvw6WkpGju3LnyeDxnbWvWrHGyewDCHJfkAEgOh6VOnTr5DERWo04AEGiVDUzRhuM3GwPlwtIB1hwNS2PGjFHXrl3L3N+0aVOeCwfAcZUJTHc3G0lgAqoIR8NS9+7d9dvf/rbM/XFxccrIyLCxRwBwdhUNTO1rtiUwITRwN5yloF46AACCCYEJCE+EJQCoAAITqhrDNG1roYqwBAAVRGACwgs/uf5iemwv6Skqsr2mI+k60oGqPx2wvaRRt47tNWsvz7O95lpPB9trXv/AVwE57/RdGzWy6aNKjD7P8tj2NdtqSPKDunPJhwFbh6nRvPyAnLcsrqv321rPMdFV/Fdl6A742IaRJQCopB/d+/Ti90+o8OTBch1/+flNNO2yAazDBIQYwhIAnAMCE0Id6yxZIywBwDkiMAFVG2EJAPyAwISQxTpLlghLAOAnBCagaiIsAYAflQSm/cfL+WgUAhMcxpwla4QlAPCzH937NHjBHAITUEUQlgAgALYfPkhgQmhgzpIlwhIABAiBCagagjos7d+/X48//rjT3QCASiMwAaEvqMNSXl6eHnvsMae7AQDnpDKB6W+XE5hgDyZ4W3P0gTfffvutz/1btmyxqScAEFglgemdfoOUVL2G5fGX1f8lMI34LHDPkgNQPo6Gpfbt28swDJnmmXGzZLthGA70DAD8j8CEoBTCIz52cfQyXO3atfXqq68qNzf3jLZ9+3Z9/PHHludwu906fPiwV/OYfKgACE4VvSRXEpi4JAc4x9Gw1KlTJ+3du1cNGzY8a6tXr95ZR51+LTs7W4mJiV4tV5ttegcAUHEEJgQT5ixZczQs3XHHHWrUqFGZ+xs0aKAZM2b4PEdWVpYKCwu9Wppa+rmnAOBfBCYgdDgalq677jrddNNNZe6vVauWhg0b5vMcLpdLCQkJXi3C4MMEQPAjMCEomKZ9LUQF9dIBu3fv1vDhw53uBgAEDIEJCH5BHZYOHjyoWbNmOd0NAAgoAhOcxJwla44uHTB//nyf+7dv325TTwDAWSwrAAQvR8PSgAEDylxnqQTrLAEIFwQmOCKER3zs4uhluJSUFM2dO1cej+esbc2aNU52DwBsxyU5IPg4vs6Sr0BkNeoEAFVRZQNTTISjFwsQogyPfS1UORqWxowZo65du5a5v2nTplqyZImNPQKA4FCZwPR4m2EEJiAAHA1L3bt3129/+9sy98fFxSkjI8PGHgFA8KhoYLqkTisCEyrOtLGFqKBeOgAAwh2BCXAeYQkAghyBCYHEOkvWCEsAEAIITIBz+CkKZab9txaYp07aXtNQtO01FWH/3xHmgQLbaxo1E2yved7K/bbXXPbcxbbXHDrqy4Ccd87utfpj4yeUEH2e5bGX1GmlR9Lu0IiP56soAOswucbU8/s5rZz/1Crba4plGcIeI0sAEEJ+KtqrGdsf1eGTB8t1fM9Gafrb1dcohl/4KAsP0rVEWAKAEENgAuxFWAKAEERggr8wwdsaYQkAQhSBCbAHYQkAQhiBCeeMRSktEZYAIMSVBKb9R8u3rACBCagYwhIAVAE/Fe3VkLnvEZhQYcxZskZYAoAqIvdQAYEJCADCEgBUIQQmVBjrLFkKirC0Z88eHT3LD/bJkyf1xRdfONAjAAhdBCbAvxwNS/v27dPFF1+shg0bqmbNmho2bJhXaDp48KAuu+wyB3sIAKGJwITyYs6SNUfD0sMPP6zIyEitXLlSCxcu1HfffaeePXuqoKCg9BgzhIftAMBJBCbAPxwNS4sXL9Zzzz2n9PR09e7dW8uWLVP9+vV1+eWX6+DBX9YMMQzDyS4CQEgjMMES6yxZcjQsFRYWqlatWqVfu1wuvf/++2rUqJEuu+wy5efnW57D7Xbr8OHDXs1j+v/p2gAQqghMwLlxNCw1btxY3377rde2qKgovffee2rcuLGuvvpqy3NkZ2crMTHRq+Vqc6C6DAAhicCEsjBnyZqjYalfv3565ZVXztheEpjat29vOWcpKytLhYWFXi1NLQPVZQAIWQQmoHIcDUsTJkzQe++9d9Z9UVFR+uCDD5Sbm+vzHC6XSwkJCV4twuAHGwDOhsCEM3hM+1qIcjQsRUVFKSEhocz9e/fu1WOPPWZjjwCg6iMwARUTFItSluXgwYOaNWuW090AgCqHwIRS3A1nKcrJ4vPnz/e5f/v27Tb1BADCT0lgmv27G5RUo4bl8SWBacTH81VUzF3HCB+OhqUBAwbIMAyfk7hZZwkAAudcApPkCXwHgSDg6GW4lJQUzZ07Vx6P56xtzZo1TnYPAMJCpS/JRXFJripg6QBrjoalTp06+QxEVqNOAAD/qExgemFQfwITAuqLL75Q//79lZqaKsMw9OGHH3rtN01T48ePV2pqqmJjY9WzZ09t3LjR6xi326177rlHderUUVxcnK655hrt2bOnQv1wNCyNGTNGXbt2LXN/06ZNtWTJEht7BADhq6KBqUdzAlOVYJr2tQo6duyY2rVrp6lTp551/zPPPKNJkyZp6tSpWrVqlZKTk9WnTx8dOXKk9JjMzEzNmzdPc+bM0bJly3T06FFdffXVKq7AvDtHw1L37t3129/+tsz9cXFxysjIsLFHABDeCEwIJv369dOTTz6p66+//ox9pmlqypQpGjt2rK6//nq1bt1as2bN0vHjxzV79mxJvzxWbfr06Xr22WfVu3dvdejQQW+99ZbWr1+vxYsXl7sfQb10AADAfgSm8GLnnKWzPc/V7XZXqt+5ubnKy8tT3759S7e5XC5lZGRo+fLlkqTVq1fr5MmTXsekpqaqdevWpceUB2EJAHAGAhMC4WzPc83Ozq7UufLy8iRJSUlJXtuTkpJK9+Xl5SkmJka1atUq85jyICwBAM6KwBQmbFyU8mzPc83Kyjqn7p++xJBpmpbLDpXnmF9zdJ0lhB6ThegCxnBgZWSz8Ij1QX5mnFfT9pq1vz5ge81/Pt/D9prdbttofVAl/PXf3+uRVmNUK6am5bE9mqfphf/pp3u/eldFnlN+78u+ey7y+zmt1J+/z/aaVZXL5ZLL5fLLuZKTkyX9MnqUkpJSuj0/P790tCk5OVlFRUUqKCjwGl3Kz8/3eYPZ6RhZAgD4lPfzfk3c9BcVFB0q1/E9kpvp+Yv/oJgI/h4PBYZp2tb8KS0tTcnJycrJySndVlRUpKVLl5YGoU6dOik6OtrrmH379mnDhg2EJQCAfxGY4ISjR49q3bp1WrdunaRfJnWvW7dOu3btkmEYyszM1MSJEzVv3jxt2LBBt9xyi6pXr64hQ4ZIkhITE3XrrbfqgQce0Keffqq1a9fqpptuUps2bdS7d+9y94N/xQCAcikJTOW+JPefwBSoS3LwkyB+as3XX3+tyy67rPTrUaNGSZKGDRummTNn6sEHH9SJEyd01113qaCgQJ07d9aiRYsUHx9f+prJkycrKipKAwcO1IkTJ9SrVy/NnDlTkRWY+mCYVXCJ7D4RNzjdBfiRE3N5jKho+2tW8891/IoVtf/Zi07MWTJjY2yv+eMldWyveUGA5iydLrlaUrkDkyR9kbfNb4Ep8svEcz5HRTkxZ2nBtmdsq3V5r6dsq/XZpw/bVsufuAwHAKiQkhGm/BPlu0GAS3LBLVTnLNmJsAQAqLC8n/frlv+bSWBCWCAsAQAqZcfRAwSmqsDGdZZCFWEJAFBpBCaEA8ISAOCcEJhCnGna10KU42HpwIEDWrJkiQ4ePChJ+umnn/T000/r8ccf16ZNmxzuHQCgPAhMqMocDUtfffWVmjRpol69eqlp06ZavXq1Lr74Yk2fPl1vvvmmOnXqpDVr1jjZRQBAORGYUFU5GpbGjh2rG264QYWFhXrkkUc0YMAA9erVS1u3btW2bds0ZMgQPfHEE052EQBQAQSm0GOY9rVQ5WhYWr16tUaNGqX4+Hjdd9992rt3r26//fbS/SNHjtSqVasc7CEAoKIITKhqHA1LRUVFio2NlSRFR0erevXqqlPnv6ve1q5dWwcO+H5auNvt1uHDh72axywOaL8BAL4RmEIIE7wtORqWzj//fG3fvr306zlz5iglJaX063379nmFp7PJzs5WYmKiV8vV5oD1GQBQPgQmVBWOhqVBgwYpPz+/9OurrrqqdKRJkubPn6+LL77Y5zmysrJUWFjo1dLUMmB9BgCUH4Ep+Bke+1qocvRf47hx43zuHzt2rOVTgV0ul1wu7weQRhj2P3gVAHB2JYFpZrdbVDc23vL4ksDkr4fvAufK8XWWfDlw4IDuvPNOp7sBADhHJYHpx58ZYQo6zFmyFNRh6eDBg5o1a5bT3QAA+MGOowc0bBmBCaHH0X+B8+fP97n/15O/AQChryQwzbr0Fv2mGpfkgkLoDvjYxtGwNGDAABmGIdPH0JxhGDb2CAAQaOcSmFgYBk5w9DJcSkqK5s6dK4/Hc9bGo04AoGqq9CW5KG7g8TfDNG1rocrRsGT17DerUScAQOiqTGCackt/AhNs52hYGjNmjLp27Vrm/qZNm2rJkiU29ggAYKeKBqburdIITP7G3XCWHA1L3bt3129/+9sy98fFxSkjI8PGHgEA7EZgQrAL6qUDAADhgcDkII+NLUQRlgAAQYHAhGBFWAIABA0Ck/24G84ay6Ii6JnF4bGyilHswI9jTLT9NX92217SrFXD9po19p60veb6Ny+0td5j978VsHN/nb9EPVNfUWxUHctju7dK05uPdNf03Mk6Zfr/+764Rwu/nxOhhZElAEDQOXpypz7f+z86ceqnch1/QWIH3Zp2v6IMB/4ACHXcDWeJsAQACEoEJgQLwhIAIGgRmBAMCEsAgKBGYAowLsNZIiwBAIJeSWAqPFlQruMJTPAnwhIAICQcPblTU7c9QWDyNxaltERYAgCEjHz3PgITbEdYAgCEFAKTf7EopTXCEgAg5BCYYKegXMG7cePG+uSTT9SsWTOnuwIACFIlgenuZo8qMbqW5fEXJHbQvc3+rP3uvRWqc3mdxMp20YvH9OhPa//hl3P5VQiP+NjF0bD0/PPPn3X7rl27NGPGDCUnJ0uS7r33Xju7BQAIERUNTA3jmqphXNOKFTmvkp07zUlPcXCGJVhyNCxlZmaqXr16iory7obH49Ebb7yh6OhoGYZBWAIAlKmigQmnYWTJkqNzlm6//XbVqVNH//znP5Wbm1vaIiMjtWjRIuXm5mr79u1OdhEAEAIqOocJqAhHw9LLL7+scePG6YorrtDUqVMrdQ63263Dhw97NY8ZHk+pBwD8F4GpkljB25Ljd8MNGDBA//rXvzRv3jz169dPeXl5FXp9dna2EhMTvVquNgeotwCAYEZgQiA4HpYkqV69elq8eLF69OihDh06yKxA+szKylJhYaFXS1PLAPYWABDMCEwVxAreloJm6QDDMJSVlaW+fftq2bJlSklJKdfrXC6XXC6X17YIIzIQXQQAhIiSwHRr2iglx9Z3ujsIcUETlkp06tRJnTp1kiTt3r1b48aN0+uvv+5wrwAAoSbfvU9/2fKIOp+XoYZxTRUXVaNS5/npROVed7pTZnAOrYTyytp2Cbqw9GsHDx7UrFmzCEsAgEo5ZZ7U/x1YrP87sLjS51i8q4Ufe4RQ5GhYmj9/vs/9LBsAAECAMbJkydGwNGDAABmG4XNCt2EYNvYIAADAm6N3w6WkpGju3LnyeDxnbWvWrHGyewAAAM6GpU6dOvkMRFajTgAA4Bx5TPtaiHL0MtyYMWN07NixMvc3bdpUS5YssbFHAAAA3hwNS927d/e5Py4uThkZGTb1BgCAMMQVHEtBsYI3AABAsArqdZYAAECAMbJkiZElAAAAH6rkyFKO5z2nuwAAYeB+2yveYHtFSR2cKGojRpYsMbIEAADgQ5UcWQIAAOUUwusf2YWRJQAAAB8YWQIAIJyZHqd7EPQYWQIAAPCBkSUAAMIZd8NZYmQJAADAB0aWAAAIZ9wNZ4mRJQAAAB8YWQIAIJwxZ8kSI0sAAAA+MLIEAEA4Y2TJEiNLAAAAPhCWAAAAfOAyHAAA4YzLcJYYWQIAAPCBkSUAAMKZhwfpWmFkCQAAwAdGlgAACGfMWbLEyBIAAIAPjCwBABDOGFmyxMgSAACAD4wsAQAQzjyMLFlhZAkAAMAHRpYAAAhjpsk6S1YYWQIAAPCBkSUAAMIZc5YsMbIEAADgAyNLAACEM9ZZssTIEgAAgA+MLAEAEM483A1nhZElAAAAHwhLAAAAPnAZDgCAcMYEb0uMLAEAAPjAyBIAAGHMZIK3JUaWAAAAfGBkCQCAcMacJUuMLAEAAPjAyBIAAOGMB+laYmQJAADAB0aWAAAIZyZ3w1lhZAkAAMAHRpYAAAhjJnOWLDGyBAAA4AMjSwAAhDPmLFliZAkAAMAHwhIAAGHM9Ji2tcp46aWXlJaWpmrVqqlTp0768ssv/fwdsEZYAgAAQendd99VZmamxo4dq7Vr16p79+7q16+fdu3aZWs/DNPkoTAAAISrPhE32FYrx/NehY7v3LmzOnbsqGnTppVua9WqlQYMGKDs7Gx/d69MjCwBAICgU1RUpNWrV6tv375e2/v27avly5fb2hfuhgMAALZwu91yu91e21wul1wu1xnH/vTTTyouLlZSUpLX9qSkJOXl5QW0n6cjLAEAEMYqemnsXIwfP16PPfaY17Zx48Zp/PjxZb7GMAyvr03TPGNboBGWAACALbKysjRq1CivbWcbVZKkOnXqKDIy8oxRpPz8/DNGmwKNOUsAAMAWLpdLCQkJXq2ssBQTE6NOnTopJyfHa3tOTo66du1qR3dLMbIEAACC0qhRozR06FClp6erS5cueuWVV7Rr1y6NGDHC1n4QlgAAQFD6wx/+oAMHDujxxx/Xvn371Lp1a/3zn/9Uw4YNbe0H6ywBAAD4wJwlAAAAHwhLAAAAPhCWAAAAfCAsAQAA+EBYAgAA8IGwBAAA4ANhCQAAwAfCEgAAgA+EJQAAAB8ISwAAAD4QlgAAAHwgLAEAAPjw/+EKRwXpQKHBAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 700x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "aligner.get_aggregate_alignment() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dominican-blackjack",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean alignment similarity percentage (matched %): \n",
+      "65.67 %\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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sSAgcBC6VrPe+xFpU8p7XhaLi99Xm3JIjEX9yaYO5vUciyHOuYlS+Ku7hkHsBtF3B0Y8zF7Pr1o4gCI3KDTHSLQiCIFx7kiQRn2m85omQBOF6u/RvuKr9o6/+d16Tf/c1KfPWW2/h7Oxc+hMYWMVa4ivExsaWjGxWl8irKmrHkinVVzHbpFr9d51bUATKWk5vv0SpIyev9onbKmK2VXDQWgTIa9Y/W3HdX0eNVP6emq2Xz+UX/rv3E8vl99NS0XsiCMJNRwTdgiAIN6HotALu+e4Q/RduZ/aSw6Tl10+iKEG4Hi7tlVxVArFLGb0dHR3L1LnyXHV1KvLCCy+Qm5tb+nPx4sUa9bl79+6ABOb8Ctdl14gxvcy2YZfolVKVDyCuFuDtWrItVh3ITFn4e9dx5PgqemUF74PGFSRr9f1TqECpr75cXd9rKNm2rCJ2G47ay1+Z/b3q/n5SnHV5JF6S0CnFQ1FBaApE0C0IgnCT+XF/HCM+3MXZlDymdwvkSFw2wz/Yxa6o2q0DFYQbRVBQEECZxKdXKiwsJCcnBxcXl9Jg22Aw4OzsXGW9S8cvtV+Rum4HGhISAtZiKM6FuC01qlNOViSEjvnnFxkYmoGhGWHullo1M6BvD1qZdtWpC+G2/XS7pXwiurro1twRjFdtk6V1AZkS0qpJ2NZsOGScguxqkt65tYa047XvnCSBzVThKZeUDdw9eXDp72OH9yM4b3PtrwElfXMLB0CftptpI7rVrR1BEBoVEXQLgiDcRGIzCnltTQT9W3ryzpSOTOjkzztTOuDvouOZ305gsoq5jELjExYWhkajIT09vcIA+ujRowB06NChzPGOHTuWOX8li8XC6dOn0Wg0hIWFXfM+y2QynCwXwV4MyQdr30D6KbCZL68HV+nh/lj0E3/kiRmDatWUTqejV6C5JGlZbVhN9AspyfR+LTx1362EZi0vf8IpsOS1WqrYCs05GPLiQO1cmoisQs2GQPzW2ncu8a+SrcMq0FV/lvZtW1/uirMzPXzrMIPBXFAyvfyfzOqd5AcZ0Ld77fsqCEKjI4JuQRCEm4QkSby46hSuDiru7t0MtbLkT7xBp+LePiGk55tYfqTiET9BuJHpdDoGDy4ZaVy+vHzQdunY2LFjyxwfM2ZMpXXWrl1LcXExQ4YMQavVXusuA3D+0DqwSyUjtLUZfZUkOPQOdLz/quN2uti2Mahfj1r3Zd594/BJWlqrOv7JPzHvgYm1vlZlnJycGBRsKh80t5wEpjz4+/uqG/DqXJJl/O8qEujK5KDzhJwLNe+Y3QYXt4NP+QDYIesQdw0PL3f8mbuG45Fcy8zjEd+XbE0GqLNPM7VfUK2WCQiC0HiJoFsQBOEmsepYInvPZzKrdwgaZdkMwX4uOnqEuvH5jvNYbf9izaMgNJC5c+cC8Prrr3Pu3LnS4/v27ePLL7/EYDAwe/bsMnXuu+8+DAYDq1evZuXKy3swp6Wl8dxzz5Vptz54enoyuIMrmPLh6CLIjq6+kiTB7hdL1jqHXLG9lmQnPG4hP7wxq06BWuuwFrw82hHXtJpNi3ZPXc9/J3sTHFz51Pu6+PTVhxiY+2HJWvdLlNqSYDT/IpyrIpBtNgTy40HrDmereIDQ5k6IXAoFSdV3SLLD4Xeh3b3ltixT557lbr993HXbmHLVbunSnnkDrBjSazht/9zKkv3a9V7Ic88z3WUDc2ZPrVldQRAaPRF0C4Ig3AQKTFb+uzaC3s3d6RjoUmGZCZ38Scgu4o8TNfgiKgj1bN26dfTs2bP0B8BsNpc5tm7dutLyQ4cOZc6cOWRmZtKpUycmTpzI6NGj6d+/PxaLhW+//RY3N7cy13Bzc+Pbb79FLpdz6623MmjQIKZOnUpYWBjR0dE88cQTDBkypF5f59bfl9C3tSNkX4Dd8yDiR7BWvHaYrCjYeHdJlu4hH5UeVmb/zS3x/0EZ9TNTb51MUVFRnfry0F0TeXeUiZC4T6CokkRgxgyax33EhxMVzJpWPtj8tzQaDeu+fJ5xxYtwTNl6OXmZd5eS/bXjtsKBBZXumU3r2yFhV8n0+4MLKg6sZTLo+AjseRnO/FL5tPr0k7DvvyVtOl2xl7u1GO+En5kTspNP/vt4pa/l6Qen8caAdILivgBTbsWFClPh8PslD1EC+uF4/gce8lnPdwufFqPcgtCEyCSxl0y9ycvLw9nZmdzc3BonXhEEQaiLb/+K4Y31Z1g0rRPujpXv4btw01lyiyxsfmoAcrn4wnc9iXtCWYsXL2bWrFlVlvnuu++45557ytX75JNPOHPmDCqVip49ezJ//nz69q183+Q9e/bw+uuvs3//fsxmM+Hh4Tz66KPVXr8idf0ck5KS8A8fAFpDSUI09zbgHAIKLRQmQ+pRSDsJ/j3RBPZA5+iMEzm0c8vnzmFtGDu8b+n1CgoKSvcYr4vc3Fze+2o5G0/kcNHkiUnhhMaWTzNtGqM7e/DUfVPKZH+vL3sPHmXRT9s5kqwhV+4FyHCwJuOUe5yMPAuZMh+shuagdkFmykKeHYGaYhTurTGnRWA2FSHJlKDzAufQkqzgppySGQWm3JI14rZicPAGxwBw9AW5EvITSn4KElE5eaLz64DSEIhKKsZTkU7/5kpeeHASAQH+NXod2dnZvPPlcjadzOZcnjNGyQF7URbkxoIMFK7NcSGdMe0dePmxqYSGBtfjuyrUtyPHTvLHln0UFplx0KmZMKw3XTq1b+huCQ2kpvcEEXTXI/EFSxCE68FqszNg4Q5CPBx4dFCLKsueSc7jtbUR/PZQL7oFu1VZVri2xD3h5nAtPseVK1fy8MMPk5aWhlqtZsiQITz99NN07NgRg8FAfn4+JpMJNze30vXmhYWFpVub/dug+xJJksjPzyc/Px8nJyecnJwaZPTVYrGQnZ2NJEm4urqWJm4rKCggMTERq9WKn58fLi4uGI1GcnJy0Ov1ODo6kpOTQ3p6OhaLBa1Wi0wmIzMzk5SUFHQ6HW3atEEul3Px4kUslpKs72q1GoPBQGBgIE5OTqVt6nQ6nJ2dkcvrNhFUkiTy8vLIz89HLpdTWFiIzWbD09MTFxcXFApF9Y0INyRJkvhiyXJW743lrL09Jt+ByJQaJKsJTfJ2whWnmdgnhAfunCJmMDQxIui+AYgvWIIgXA/rTyXzyE9HeXNSe0I8qv4ibrdLPPzTEe7s1YxnR7SusqxwbYl7ws2hoT7H+gi6BUGonsVi4a6nFrBTMRXcWlVaTpZ1lgH2FSx5/3lUKtV17KHQkGp6TxBrugVBEBq5r3ZfoK2fodqAG0Aul9E+wIXtZ8We3ULtpKSksHjxYt5++22++OILzpw509BdEgRBqFeSJDHrmYVs1z9QZcANILm1Zpv2fmY/9y5iTFO4mrKhOyAIgiDU3ZG4bI7F5/DM8JrvM9w50IVPtkeTmleMt6F+tkoSbi6ffvopzzzzDGazuczxe+65h6+//lpMpxRqRJIkNm3dxdHT0RSbzPj7uDN94gicnZ0bumuCUKHVG7ay3TIMuZtnjcrLHb3YmjKYtZu2M27k4HrundCYiKBbEAShEft+byy+zlo6B7nUuE6HAGdkwM7IdG7rFlhvfRNuDjt37uTxxyvO4Lx48WJatGjBCy+8cJ17JTQmBQUFvPP5z+w6k0eswwAk9+nI5CpsZ9L5fPuvdHTPY86dI0QyKuGG88OG40jeT9eqjuTTne/XvSeCbqEMMb1cEAShkcouNLP+dDKDW3shr8VIo5NWRQtvR7ZHptVj74SbxZIlSwAYN24c27dvJyoqir179/LAAw8gSRLfffddA/ew6fDw8MDDw6Ohu1ErFxMSGfPwuyzOnUJc87nIfLoiV+mQKZQonXzJbXkfO12e5M6PT/O/H6vYo1sQrrOkpCRO5fnVqe6JXB9SUlKucY+ExkyMdAuCIFwDkiTx3Z5YTiflkpFvwtugZf6YNjjr6y+ZyoqjCQD0b1WzaW9X6hTgwobTKVhsdlQK8fy1qdu1axf9+/ev8FxcXBwymYwlS5bg4uICQIsWLejZsye///478fHx17GnTZeDgwPp6Y0rF0NOTg4z5n3DxbCXUMgrz9wtk8sxhk7jg71/4qjfwIzJo65jLwWhYoeP/02OU+c6BUvZTp04djKCUT4+17xfQuMkvmkJgiBcAws2RvLa2ghOJ+ZiNNvYcDqFsR/v5u+k3Hq5niRJ/HQgnm7Bbhi0tQ/sOwW6UGCyciQuux56JzQ2gwYN4o477iApKancOXd3dwC+/PLL0i2XAH799VfS09NxdXW9bv0UGpd573xHfItnkFURcF+p2H84H/5+BpPJVM89E4Tq5eQbkan0daorU+nJyTde4x4JjZkIugVBEP6lxXti+GLnee7s2YxXx7fj6eFhvDGxHUqFnMmf7WXf+cxrfs2DMVnEZBQyuLVXneoHezjgolexI7JxjZwJ9cPFxYVffvmF1q1bs3DhQqxWa+m5cePGIUkSL774Is7OzqV7G8+YMQOA8ePHN1S3hRtYUVERR5JUyFW1S9aY4nMrX4tp5sINwMfDGXtx3R5MS8U5+HiIBIHCZSLoFgRB+Bd2RaXz6poIxrT3ZXR739LjXgYtr4xrSwsvR+YsPUZWobmKVmrv5wPx+DpraeNbt32C5TIZrX2cOBKXdU37JTRO586d4/7778doNDJv3jzat2/Pn3/+CcDMmTOZOnUqkiRRXFxMYmIihYWFSJJE69atefPNNxu4901DUVERAwcOZODAgRQVFTV0d6r15fcrSfWZVOt6CucA1h1KqIceCULt9O3VnYCCv+pUN7BwDz2733KNeyQ0ZiLoFgRBqCNJknh/cxStfJyY0SOo3Hm1Us4jA1tQZLHx/PKT12zfzswCU2kCtX+zVVNzT0dOJeZitdmvSb+ExsvNzY0vv/ySAwcO0L17dyIjIxk1ahSTJ0/m4sWL/Prrr/z222/ceeedDB06lFtvvZVPPvmEw4cPl04/F+qX3W5n586d7Ny5E7v9xv9v9szFbBROvtUXrEBakdjKUGh4er2ern5mJJu1+sJXkGwWuvpb0Ol09dQzoTESQbcgCEIdHY7L5vjFHMZ38Ks0e7ibg5oH+oWy+UwqPx64Ngmnlh66iAwZA+qQQO1KLTwdKbbYiUotuCb9Ehq/rl27sm/fPr7++ms8PT35/fffCQ8P57XXXmPs2LEsWbKEP//8k2XLlvHII4+IL5VCpSy2utc127hmDykF4d+YO2sMTnG/1aqOU+wynrl3XD31SGisRNAtCIJQR//beYEAVx2dqtkj+5ZgN4aGe/HmujOk5Bb/q2tabXZ+2BdHnxbuONUhgdqVgj0ckMvgRELOv2pHuPnce++9REVF8dhjj2E2m3n11Vdp06YNq1evbuiuCY2EWlH3oFmjlP7VLB5BuFbatA7j0X4KNMk7a1Rek7yDJwZpCGvVsp57JjQ2IugWBEGog/PpBWw5k8ro9r412iN7ercg1Eo5b6yL+FfX3RyRSkpeMcPb/vttSLQqBUFueo7H5/zrtoSbj8Fg4KOPPuLIkSP07duXmJgYJk+ezKhRo4iKimro7gk3uEFdmyOln65T3WZO/+7hpCBcS3Pum8azPTNwPvcVdlPFM8PspgKcz/2P53tn89isqde5h0JjIIJuQRCEOvh69wVc9Cr6tvCoUXkHjZIZ3YNYczKZvdEZdb7u4r2xtPZxItjdoc5tXCnU05FjF8W2YQLs3LmTQYMG4eTkhJOTE4MHD2bXrl106NCBnTt38sMPP+Dj48OmTZvo0KED8+bNo7CwsKG7Ldygpk4YSVDu5lrXk1KPceeozvXQI0Gou0fuuZWd70/kLv0PBF94H23sKuQXt6CNXUVwzPvc7fAjuz6YzEN3TW7orgo3KBF0C4Ig1FKxxcaqY4kMCfdGpaj5n9F+LT1o7ePE/60+jdla+0RIZ1PyOBCTxfA23rWuW5kWno5EpxVQaKpdohjh5rJz506GDRvGrl27KCwspLCwkB07djB06FB2794NwB133EFUVBRPP/00kiSxcOFCWrduzdKlSxu498KNSC6XM7idM1JeYo3rSJJEs5z1jB81pB57Jgh14+XlxTvzH2H3t0+y+/UebHkmkN2v92D3N0+y4KWH8fT8d3lWhJubCLoFQRBq6a9zGRRb7PQMrV3WZplMxj29g4nJKOSr3Rdqfd2Pt0bj7qCmW4hbretWprmXI3YJTiXmXrM2hcbn1VdfxWq1EhoaylNPPcWTTz5J8+bNsVqtvPLKK6XlHBwcWLhwISdOnGDw4MEkJiYyc+bMhut4E6PX69Hr9Q3djRr7vydn0TX3W2zGmm1N6Br1BYuemSLWcws3NIVCgb+/P61bt8bf3x+FQtHQXRIaARF0C4Ig1NKfESn4u+jwd6l95uZm7g6Mbu/LR1vPEZdZ86m5pxJyWXcqmSldAlDKr92f7gAXHVqVnBMXc65Zm0Ljc+jQIZycnDh8+DDvvfce77//PocPH8bR0ZFDhw6VK9+6dWs2b97MsmXLCAgIaIAeNz0ODg6lsxAcHK7N8pL6plAo+HnRPPrmfYE8cVelGcmt+cl4n3mb/z3Zj04d2lznXgqCINQ/ZUN3QBAEoTGx2SU2R6TSr2Xdp5FN6RLAgZgsXlx1ih9n96jRqM6CjWfxd9HR/19uE3Y1uVxGqIcjx0TQ3aTZbDY0Gk2ZYE6v16NSqSgurjyp1a233sqYMWOuRxeFRkqj0fDTRy+xe+9Bvlz+AX9nG8hVh2CVqdFb0wlWxDKiiw8Pv/yU2IJOqBdms5nk5GSMRiPOzs74+vqK2RTCdSeCbkEQhFo4EpdNttHCLc1c69yGVqXg3j7BLNgYyapjiUzuUvVI4Z7oDP6KzuCpoa1QyK/9F4VQTwcOx4lkak1Z+/btOXz4MIMGDWLatGkALFu2jJycHLp161ZlXREoCdWRyWT079OD/n16kJeXR1JSEiaTCXf35gQE3NrQ3RNuUtHnz7No8R+cTpGTrwnGLtehtJ7GwxZH3zauPDZrKgaDoaG7KTQRIugWBEGohT//TsFVr6K5l+O/aqdToCt9W3jw0qrTtPYx0Mav4ht/scXG6+siaOHlSLfgugf6VWnh6cjak8mk55vwdNLUyzWEG9szzzzDtGnT2Lt3L3v37gVKklrJZDKeffbZBu6dAFBcXMyUKVMAWLFiBVqttoF7VDcGg6HKQEeSJJav3sB/P/qJrGIlcrUjSDb0ciNzZg7joXtnlhulzMvL47PFyzmTUIjJCiqlDB8nmDNrolj+0ARJksR/FnzBplgX5K0eRu6hQn3F+RxgVWEmG574imend2H8yEEN1FOhKRFBtyAIQg1JksSmv1PoEuRao725qzO7bwhJOUXMXnKI1Y/2wctQ9ku03S7x5K/HuZBeyH/Gtqm36XBBbiWJmSJT8kXQ3URNnTqVgoICXnnlFS5evAhAYGAgr776ammgJzQsm83G+vXrS///zeiLJct5+6s12J2Cce38OMHNys6y+Pzv/SwY8jh3jwjn1ecfpbCwkGff+JLTGXpMIZNRBVxObnneUsxfb/xOqDqB/z41ndDgZtf75QgNZN4bn7K9eBDK8NaVllE5uGPr/CRvr1kBbBeBt1DvRNAtCIJQQ5Gp+VzMLmJGj2vz5U2rUvD08DD+b/VpZi85zGd3dCHQ7XJm4rc2nGHT6RTmDmtFqOe/G1mvirdBi0Yp52xKHn1b1mzfceHmM2vWLGbNmkV6ejqA2P6miVu7bgPvLPofZ2MSsFhBIbPTzM+TOQ/fzV133F7r9o4cPcoHny/hfFwyGrWaof278cA9t+Pl5QXAc//9iKX7MnHpei+G5n0rbMMQ3BNDcE9WRm1n/fDbSc8H38lfoPBxRHVVWblKC+HTibXb6TPzCbIit6BU6ZAkK5JkR7JLIEnIVRpkSFjNxcjkChQqDZLdhtVcjEIuR6PT46DXIdmtqLSO5GVnYpfsKFV6HB11mIz5GE127MixmYuwWc0oNVoUCjU2SxHIFCjUOmymQpBsKDQOSHY7VksRcoUWmUyGzVqEQqFEpdIixwIKDUgSapUCD3dX9HodzgYDnTu0pW3rlpw/H82FhAwkoGPrYJ567AFkMhlbt+/mwsUUZDIZYc0D6d+nJwcPHeV0ZAxmq5UgX0+GDurLvoNHiLqQgCRJNA/yZcigftVm4E5KSmL7X4fIyM7DzcWJQX1uqdEsgsLCQjZu2UlSahYOei1d2ofRqWP7mvyTqZPf121me24HlAGVB9xltJrCwl8XMbB3VzHVXKhXMqmyVJLCv5aXl4ezszO5ubniP2RBuAn8b9d53vsziq/uuqVW+3NXJyajkIWbzpJfbGV690DcHTRsOZPK30l53N0rmJHtfK7ZtSoz//dTdAlyZeHUjvV+raZK3BNuDg31ORYWFuLoWPLwraCgoF4ymFssFu57fB5rth1GafDFObQP7m1HotS7YjcXkXP+LzJPr8OUGUvvLi1Z+cPnVU5zlySJ1xYs4tuVO7Hp/TGE9MK5eV8UGgdsxXlknvwdh7zTKOzFZOg7YwjpgSG0T436mhO9E2PSafz6P1ptWUmSiF39LMUZ0Xi0n4glLxlJsuHRaRIaJx/sdguFyRHkXdiLKScRlcEHjUsAltwkVAYf3NoMx5j0N+nHV2EpysYQ0hskGzLApdVAtG7BFCb/TfaZzSj1rnh2nvzPe2YkM2ITuef/QusaiEenyWhdApAkG8bUSHKj/0KhM+DSoj+ZERspiD+K0sENrVsQhpBeOHiHIZMrMeUmkXrwR4wZ51E7++HX6140rgEg2SlKO0/mieVYsy7g3uMenEL6YDMVkn7oe+TmXAzhY9B6t0EmV2LOSyLn5ArMFisenW5F4xqANfciLpk76Ryo4ekHp5V72LZx8w5+3nCIC8U+2Hz7otAasJnyUSTvIUSdxG0jujBuZPl91SPORPLx92s5m6nB6DsUlaMHdksxUvopfM2nGdLFn/vvvBW1Wl2u7r8xfc5C4kPn1qqOpTCTSQ5reHHO7GvaF6FpqOk9QQTd9Uh8wRKEm8u9iw+Rnm/ixdHh17ztYouNP/9OYc3JZCRJomOgCz1D3ekWfO325K7KFzvPk1loZu3jFY8wCf+euCfcHG7WoDsrK4uuI++hwKrFu+tteHScWOmSlsKUM1xY8x/UkpEjG7/D19e3XBmr1UrvsbNJMjnj3X0mhqCuFbZls5q5sO6/qBzc8B/0VK36HPvHCzQb+zoyefX7JNvMRtIPfIcMCa1HCB7tKs66b8pLIWn3F6gcvdG6BuAU1JWkPV8RMOAxVI7umHKTiVn7H5qNfAmdezAAKQd/RKk14N5+XJn3LPfCPgouHsOn970oVBU/nLAUZpG46zN8etyJxjWIjOMrMaado9mIeeXK2q1mUvYvRu8dhkvLAWXbMWYTu/41HPw6YM5NwK/vQ6idKp6tYreYSN6/GAef1qXt2EyFKCO+4o0HB9OrW2ckSeKZVxexz9gJTbN+lb6vxfH76KY6wIevPYX8n+0sF/+ymq9356EMn1bpZ2PKisf74mK+e+cpXFxcKm2/Ni7ExHD7woMowmqfnM8l4n3WffGMyGou1FpN7wlin25BEIQasNklDsZk0drHqV7a16oUjO/kz+czu/DFnV15fHDL6xZwQ8m67nOp+djs4jmsIDQ1ZrOZbqPupRAnAgc9gWenSVUGHw4+4YTP/Aar0pmuo+7HaDSWOS9JEn3G30eK3Zeg4c9XGnADZJ1ej9ajOS5hw2rdb6/ud5JxbEWNyirUeuw2K379HgK7neyo7RWW0xh8aDbyJazGDOx2CwVJJ2k24gUSdn6CzWxE4+xLq+mfkXrge2ymQlIPL0Xv1RKPDuPLvGf5CccpSjuH/4BHKg24AVQObjQb8SKph37BUpCBZ+cpuLQaRPy2D8qVlSvV+PV9AFNuCnmxB8q2o3elxZT3MaaewTmkT6UBN4BcpcG/34OYcpNL21FoHJA6P8mLX+3ldEQkz/33Y/YrxlQZcANog3pxVDeZp17+EIBfVqznm8MqVG1nVPkwROMWRGab57j7mQ8wmUxVXqOmfluzDYJH1aluhiKY5OTka9IPQaiICLoFQRBq4ExyHgUmK21863dkSymXo5Rf/z/NgW56TFY7cZmF1/3agiA0rBkPPk8BLri2HIBzaK8a1VHqDIROfAu7zpvR0x8qc+7Dz74ilWZ4dbkVjaHq5TFFmbHYi3Nx8GtX637rfdpQEH+wxuVdwoaSHbUdjw7jKLh4DLvVXGE5uUJF4JCnMWcnYEyNRLJbCRwyl+R935WeDxgyl4Sdn2C3FGMI7lGmviRJZEVswqfnXTXql0wuJ3Do06Qc+B4A55AeKFR6irMvVlje+5Zp5JzbiWS3l21HJiN0/JukHllKTSayet8yneyoHWXakTo8yJw3vmNPThhqt9Aa9V/lEsQhUxfWbNjM/zZEogwdUaN6CpWWzLDHef2Db2tUvjp5BUUoNHWbAWJVlYxUCkJ9EUG3IAhCDey/kIlaIf/XW4XdqAJdS/ZajkzJb+CeCIJwPUmSxNELuSjUWry7Tq9VXZ1bMxQqNWcTi7BaraXHv/39AAqNA06BnWvQigyZ4uo0aDUnU9Z8xwWNewim7AQAPDtPIePkH5WWVaj1IFfg3mE86cdXodQ6gWQvDdSVGkckiwmPjhPL1c29sAfXVgNr9TrkChUqB3csBZkA+Pa8h+S9lQejbm1GkX12cwXtKDEE9yArYlONruvedhTZkVtKf5fJZGQaemDWetWq/+qgvrz56a+Ym0+rVT2V3pVDMUXXJCO/Rq3EbrPUqa7CZqyXPAmCcIkIugVBEGpg/4VMWno7XtMEajcSF70aZ52KsyLoFoQbkoODA5IkIUnSNQ0Ovvh6CcU2FTrP5nUaJfToOBG53oPX3l4EwJkzZ8hXeqP9Z71z9f7tkpaar8GVyRUglYzqat2aYcqOr7K8Z8dJ5MccwJKfCoB7+3Fknlpbet671z0VBr75sQdxata9xv0qvV6nyaQfXwmUTAFHkiodjXf0b09h8t8VnvPudjvpx2s27d7RvwOFSafLHHNpM5acs1tr0fOSYD3N4ora4F2regB5PiP47fcNta53tf49OmBNqvnMhys5meLw8an/pKVC03VzfnsUBEG4huyl67lv7uRXga46zqbkNXQ3BEG4jlZu3INMoUDrVretEF1bDUKyWdiwo2Rt8PLf16PQuOAcUrNp6hIyJJu1+oKV1bfVfD2wOScRldPlEVy5svK11gAaFz+shVnI1Xokux2dezDmvJTS8zq3ICz56eXqyZXaOiXkUuoMSFeM1DoGdsKUk1Bp+cr6r1DrUepcanzdq9uRyeXIazGDAECy25AZ/GtV5xKNV2sO/x1bp7pXGtC3Fz4FB6oveBW71Ux7H+maZ1IXhCuJoFsQBKEaZ1PyySu20sbvJg+63fScTRYj3QJ88sknZGdnN3Q3hOug2GLDbrOWTKeuA5lcAXI5ZkvJ9OC0zFwk7DUeNdc4eSBXO1KcGVvraxdnx6P1aFXj8tkRG3ALvyJhm0yGZK9+WrNcpcNuKarxdaR/PXpfQqlzxmYqqFNdmaz6jO6XVNTf2r4Gm6kQhabuy68s/352OTKZjAHtPbHk1i4hmhS9midmjf/3HRCEKoigWxAEoRr7L2SiUsho4Xlzrue+JNBNT3yWEaO57qNOws3hiSeewM/Pj+nTp7Np06YaJWUS6ldxcTFTp05l6tSpFBcXX7N2dWolcoUKW3HdHrjZbVaQ7GjVSgD8vN1AJsNaXLNZM56dJmE1ppN1ek2tr52271u8e9xZw35akGErk0lckuw12m7MbipEXseHEv+GpTALhbZuD3vt0vX9O67QONb53xDAP/98/rU598/A/+L/sJmN1RcGLOlnGBGSQ/PQmiWNE4S6EkG3IAhCNQ7EZNLSywm18ub+kxnkpkcCzqXWbWRFuLmYzWZ+++03Ro8eTbNmzfi///s/zp8/39DdarJsNhvLly9n+fLl1yTp1CX33z4Gu81KcVZcnepnnFwNMiUzJw8HYOb0KdiNWeSc21Wj+gq1HrWDG5LNijH1bI2va0yJBLkKeRXbcV0pbd+3eHSYWOaY3VL1w4ui9POoXfywWYqQyWQUJkeg9QguPV+YHIHGNbBcPclqqtODKnNBBvIrZggUxB9BW0H7UJIAz1bJ6LvFmIOtqGYPPSRJKvc+2G0WJHvtgnaZXI6UV/Ua+cqYEg8zqHubOtW9mlqt5vv3nsE3amG1I97WhD0M1m7n1WcfvCbXFoSq3NzfIAVBEP4lSZI4FJtNK+/62Z/7RhLgqkOGyGAulLiUtEuSJBISEnjzzTdp1aoVAwcO5IcffqCoqObTbYUb19QpE3BU2jCmR2Mx1n5JQXbUNmTFmTz20L0ABAYG4iKlY8pNqnHg6dfnPuzmfNIP/0JRWlS15YtSI4n947mSQK8G68HTDv2Io18bdO5BpccKU87g4NO6ynoZp9fi4NMWnXsIAFln/sQt/PJ2WKmHl+LcvE+5ei5hg8mJ2lZtv66WfmwFnp2nAGA1FaDQGSodic+LPYBzSM8Kz6Ue/AHf3rNqdM28mP3ltonLObEc9za12+9akiQCHYyYsmJrVQ/ALWs3o4YPqnW9yhgMBpZ9Op9pHltxO/M+5ot7sFvNJQ8qTAXYI5cTHPMBLw0ys2D+43Vafy8ItSWCbkEQhCokZBeRVWim5U26VdiVNEoFPs5aIlNF0N3UHTt2jBdeeIEWLVqUHrsUgO/evZt77rkHHx8fHnjgAfbt29eAPRWuhYEdfZHsNlIP/FirevkXj2M3m+kW5lEmcHl61ihs5gJyzu2oUTsymYyQUfOxFKSSsGUhSTs+KpOw7BJzfhrx618l/fCPuIWPwKvjeBK3vkPa4Z/LBd+SJJEXs5fELQtw8muDa8t+Zc5nnPgdtzYjK+2TpSATuVJDxslVuHcYhzkvFYXaoTQINueno3JwJ+3w0nJ1nQI6kRdzoFaj3VZTAXZLEcp/1kUn7foc316zKywrSRLZkVsxhPYud85mNlKQcLxGiewkSSI7ahuGK8pKdhv+tggUWaerqFme6dw63nhuNvqY32pVz5yTQN82btc88FWr1TzzyN2s+fwZ3puoYgzfM9j4BVN0K/jluR78/OEzTBg99JpeUxCqIpPEQq16k5eXh7OzM7m5uRgMN3cCJkG4Wa05kcTjvxzjy5ldMejqvpdsY/HupkgctUqW3Fv77W6EqjXWe8KxY8f49ddfWb58ORcuXChz7tIX5bCwMJ566ilmz56NXH5zP89vqM+xsLAQR8eSgKygoOCabhtmt9vpOHA6KQUSPt3vxKP9mGrrFOckEL3iWXQYiTm0GoWi7IjsqOmPEZHthE+PO9F7h1XbXtbZrVgKMnBvP4aLOz4jL3Y/GpdAFBpHJLsVW1EeKkdXgoe/gFLnhN1q5vzqFwgZ+ypZEZtIPbwM59De8M+e35K1GOfQXjiH9CwX0CXvX4Kjf3ucArtU2BerqYCLW95F594cnXdLHHzacHHbBzQb+RJyhRJrcT4Xt75Ps5Evkn22ZI9r97ZlR4aLMmLIOPUHAQOfqDagtFtMxP35NoFDn0apcSTj5BosRTn4VrJePXHX57i0GlRupN5uNRP162P49XsIQ1DFr61MOzs/w7X1kNLPR7LbkR9ZyPdv3M1PKzezJqUNKr+u1bZjSTnJcJfD/Ofp+9myYy+vLY9BEV79nu+WwiyC4j7jh4/ml/v3IwiNRU3vCTf3nVEQBOFfOn4xB2+DpkkE3AB+LlrOpYmRbuGyzp078/bbbxMdHc3Bgwfp3r3kgYxMJisd/Y6MjOShhx6ib9++5ObmNnCPhdqSy+Uc2fIjPk6QvPdbEnd+hs1c8fIBSZLI/HsTUUsfx0Fu5OxfyyoMmNb/8jFt3Qu4uONj0k/+gWS3V9ie3WomcdfnWArS8b5lGkqNIyEjnqPtXYvROLrh2/12Wk58E0eflri27I9CW/LgQa5UEzr2NWLW/AetewiBAx+hMPE4LqE9CRr4CM2GzsUltFeZgNdizOH86hfRuARUGnAXJJ4kftMCFGoHtJ4hyJUaErZ/SNCw55ArlORfPErU0kfw6joNuUKFe9tRSHYbSXu+LvOe6TxCcAsfQfyfb2POS630vTemRhH351sEDHwcmVzJxW0fYs5PqzDgNhdkEPfnApyb9ykXcBvTzhH5y8N4d51O5qk1FCZHVHrN0nZa9CsNuM05iWiOvsn//u92Avz9ef7xexjvewZTxG8lyfIqINltmM6sZJTbUf5v7n0ADB3YmxcmBmI/9jHWKhKrFV88SPPEL/ju/Xki4BaaBDHSXY8a66iGIAiXTfpsD1qVgicGt2zorlwXO6PS+GLnBSJeG4H+WqWTFYDGfU9ITU3lu+++4+uvvyYmJqbMuSu/RshkMubMmcP7779/vbt43dyMI92XSJLEx198y1sf/4BFacDRvyMurQaicnDHZi4k9/weci/sRSrK4MGZY3l9/nPVzmxYs34j8976miyrAw5+HXEK7oZS44S1KIe8sxtp415Mq2BvzmYoSde2R9J5IdnMaAqiCVImkpscyek0DYbmfbFaLRRcPI7ayQuXFv1QaB0x5aeTsn8xktWEY7NuFKdHI0OGIbgHer+2KFRaTDlJZP69HnNOEu4dJ2HKjsdalINLy/5oXQKwWU0UpZ6lMPkMFmM2aoMvSo0D5oI09F5h6P3aUXDxKLnnd2M3GXFsdgtyhRK7xYSDb1t0nqGY8zPIOrMRuVyFa5vhqB09sZkKyYs9QH78UVROXrg074PWIwTsNgqTIzCmRqF29kXvHUZWxCaK0qPRerZAqXFA59kcvXcYcoWaosxYss9uxZSbhMrRA/c2I0sSutltGFOjKIrZibw4DddO08G1JZLdSmHkemRFGegDuyFzD0emUGPLS8B4fjtWFDiFj0WhcURZmICfFM3gTn7cN3MyWm3ZxHSRUef4+Pu1RKQpyTN0QqZ2QjLn45R3gnAPM4/cMYp2bcPLfe6ZmZks+uY3Dp8vJEPfAUnngWQpRlcQSYgunRmjuzNiSH+xnlpo9Gp6TxBBdz1qzF+wBEEAs9VOu5c3Ma1bIKPb+zZ0d66L6LR8/m/136x9vC/t/J0bujs3lcZ2T5AkiY0bN/LVV1+xbt06rFZrmXMA4eHhPPbYY6SmpvL2229jNpsJCgoiNja2gXpd/27moPtKMTEx/Pjjj2zZtpPk1HRcXZzo16c39866hzZtap9pOiMjg9WrV3MqIhK9XsfQQQPo3bt3aZAnSRJnz54lOTUdnVZNSHAwMpmMQfcupN3UtynKTqIw8yKSzYJcpcZmMZMRvY/CtAtIgFwhR6FyJD1yJxIKLAVpSMhQqPVIBSk46lUUmcFuK0KGkqLiAuySHKVShVIuIUkybDYLFpsMJBvIlSg0Dujdg9C5BoLdikzlQFHGeZBkIAe9R3NsFiOFadFYjDlYjNnI1XrkMjkaR3fsNguW4nzkSg1yGSi0ThTnpyMzF5Zs02a3IpOrwVKE3tkZbw8PXF2ciE9Iw2QDlUqFk5s3OrWSzuFBTJgwkdDgQDIzM4k8dx5JkujYvi39+pWsV4+OjiYxKQWFQk5QYABBQUEkJSURGxeP2WzB18eLVq1aER8fz8WERGw2OwH+vjRv3rzaz89oNBIZGUlWTh6uzk6EhYXV6N+g3W4nIiKCtIws9DoNzUND8fT0rPW/H0G4UYmg+wbQ2L5gCYJQ1smEHMZ/sofXxrelZRPIXg5gNFuZveQwH07rxMTO/g3dnZtKY7onvPrqq3z77bckJCQAJQHRpenkcrmcMWPG8MQTTzBkyJDSOo899hifffYZCoUCi8XSUF2vdw31OUqShNFYsvewXq9vEiOEU2c9ibXb/6HS1/wBoN1q4fy2z2k5/InSYwUpZ5nZ/Dwzpk6osq4kSUx67F1snZ6qdV8zI3eh0+twD+1WZbmc+ONEb/2MnrO/LHPcXJhDwp5vKciIo/XE/6LWl/23lR93GHXKTu6b1ItRQwfUun+CINSPm3JNd0FBAU8++SR+fn5otVo6derE0qXls0ZWJC0tjXvuuQcPDw/0ej29evVi69atZcrk5eXxxhtvMHDgQHx8fHB0dKR9+/YsWLCA4uKq93IUBOHmc/xiDkq5jGbu9TuidCPRq5W4OaiJThN7dTdlr776KgkJCaVrtgGcnZ2ZO3cu0dHRrF69ukzADRAcHAyUjGwJ155MJsPBwQEHB4cmEXDb7XYikm21CrgB5EoVMrkC2xV7Tzv6tOaLZTuqrSuTyejZyg1zYVZtu0te/JFqA24Al6BOKDVOGLOSyhxXO7gQOnwurca+ROSmRdis5jLnnZrdgqbH03y2OZWlK9fXun+CIDSsRhV0T548mSVLlvDyyy+zYcMGunXrxu23387PP/9cZT2TycSQIUPYunUrixYtYvXq1Xh7ezNy5Eh27txZWi4+Pp4PP/yQLl268L///Y8//viDW2+9lVdeeYWxY8fWausHQRAav+PxOQS761ErG9Wfyn/Nz0VLtEimJvyjbdu2fP755yQmJvLuu++WBtdX69OnDy+//DL/+c9/rm8HhZvS3r17cW9Xu72iL/EKH0RG1J4yx4z65vzw6+/V1n1k1lSKTyyp1fWMGTHoXX1qXN7RqzmJx9ZWeE5r8KLlkIc4t+WzCs87tZ3Ej3syOXz0ZK36KAhCw2o0WXLWr1/P5s2b+fnnn7n99tsBGDRoEHFxcTz77LNMmzat0uyH33zzDadPn2bv3r306tWrtG7Hjh157rnnOHDgAAAhISHExsaWWaMyePBgHBwcePbZZ9mzZw99+/at51cqCMKN4kh8NmFNZFr5lfycdZwTI91NmkwmY/z48Tz++OMMHjy4RnV69epVeo+tT/v372fhwoXs2bOHzMxMnJyc6Ny5Mw8//DC33nprhXW+//57PvnkEyIiIlCr1fTs2ZP58+fTu3f5fY5vVCaTiQcffBCAL7/8Eo1G08A9ql/nLsShc2lbp7oaJ09yE8ruM6118WP5poPcOW1ilXUNBgMDWyjZcGIN3h3HVXstc0EmyQd/pu24F2rcP7WjG0VZ8ZWe1xo80Rq8MGYnoXf1K3feqcM0vvp1Ebd06VDjawqC0LAazfDNqlWrcHR0ZOrUqWWOz5o1i6SkpNLAubK6YWFhZb4MKJVKZs6cycGDB0lMTAQonbZ1tUvbo1y8ePFavBRBEBqBHKOZuEwjLbwcG7or152/q464TCMWm5gm3FRduHCBVatW1Tjgvl5+++03+vTpw8qVKwkMDGTKlCm0a9eOHTt2MHXqVObNm1euzty5c7n77rs5ffo0Q4cOpXv37mzevJn+/fuzatWqBngVdWO1WlmyZAlLliwpk9TuZuWo12G31S03gN1mRq4q+1DCbjVTaOjAwUNHq60/cnAvzNlxXPzrO2wWU6XlcmIPk7D7f7QZ8yyyWuxPb7OakMur3oYy4JZJJBz+vcJzMpmMBJMbGRkZNb6mIAgNq9EE3adPnyY8PBylsuzgfIcOHUrPV1X3UrmK6v79999VXnvbtm1AyRQ7QRCahhMJJXsNN8mg20WH1S4Rl2ls6K4IDWTQoEGEhoby+OOPV1rm+++/Z+7cuTz99NPXpU9Wq5VHH30Uu93O0qVLOXToEEuXLmXXrl389ddfaLVa3nnnHc6fP19aZ9u2bXzwwQe4u7tz4sQJfv/9dzZu3MiuXbtQKBTMmjWL7Ozs69J/oXZu6dqZ7JjDdaqbl3QWR8/QMseKc1Nwbz+OX9ftrrZ+SEgw3p7u+LcfQtKer4jd9jE5sYcoTIsmL+EUiQd+IWLZ01hz4mgzbh5ypbpW/TPlpuLgFVJlGaVahyTZKj2vD5/Etz//XqvrCoLQcBpN0J2ZmYmbm1u545eOZWZm1kvdkydP8s477zBp0qQKA/crmUwm8vLyyvwIgtA4nUrIwUGtwMegrb7wTcbfRQcgkqk1YbGxscTFxZGamlppmTVr1vDhhx/y4YcfXpc+nT17lvT0dFq3bs20adPKnOvVqxcjRoxAkiSOHDlSevy9994DYP78+bRs2bJM+Yceeojc3Fy+/fbb69J/oXZCQkKwJR+pvmAFci6exDmwfenvkt2GZLeiUKkptlX/1dfHxwc/ZSoOHkG0GvYYYUMeRCWzYck8j8yUjX+7gXSa+jqmvNRaJ7WzWUwUpJ0noNPoasvKZBUvmwRQ6ZzIzhdJfgWhsWg0QTdQ5R+26v7o1aVubGwsY8eOJTAwkK+//rra/r311ls4OzuX/gQGBlZbRxCEG9OpxFxCPJpGluCrOetUOGgUnE8XQbdQueu9LVhN1zBfeqBeXFxcuktJRWu9Lx1bs2bNNeqhcK3dMeYWsmOrnw5+paLsJHTOZZOaJZ9cj1+HkQDU9C/6xEEdKEg8AYBcqcajRU98O4zAq3V/dC6+KNQ67FYzdmvt/jtIPLoaB/dA5Mqqp5fXxPW6PaWkpLBpyzZ+W7WWHTt3U1RUdH0uLAg3kUYTdLu7u1c4Ip2VVbKtQ0Uj2f+mblxcHIMGDUKpVLJ169Yq27/khRdeIDc3t/RHrAEXhMbrREJJ0N0UyWQy/F10YqS7iYmPjy/9ucRoNJY5fuln//79pblUrteDqdDQUEJDQzl79izLli0rc27fvn1s2rSJkJAQ+vfvD5SMjJtMJjw9PQkICCjXXpcuXYCSGW3CjWnuYw+QtP09rMU1+1tkt5q5sOMr/G+ZXHqsKDuRgpQonP3CsNtt6FQ124lm3KihuCevwVJU+U4OQT2nc27zRzXe3aYgPYaUkxtoOeShGpW32ytfu28qyMLbrf4SfUqSxPpNW3nwuXd45P0/+fakO78ntuLT/RrunPcVT7/8PlFR5+rt+oJws2k02cvbt2/PL7/8gtVqLbOu+9SpUwC0a9euyrqXyl2psrpxcXEMHDgQSZLYsWNHhTfrimg0mps+m6ggNAXp+SZScosJ9Wx667kv8XPWcS5VbBvWlAQHB5cJoCVJYsOGDYSEVL321MXFpZ57VkKhULB48WLGjRvHtGnTWLhwIc2bNyc5OZm//vqL7t2788MPP6BWl6yvvfTwoLJ7uIODAy4uLmRnZ5Ofn4+TU9PbqeBGJ5fL2bP6S3qMfYCw2xahNXhWWtZaXEDkxvdpMewxFP8kUStIPU/CwV9pM+45ABIOreTvC2fZPvl5pIIUHr59GPfePQN5BUnQ5HI5X77zHPfOfZuiNg+hMXiVK6N19sG342giN7xL2Mi5yOSVTwfPTTrD2bVv027CS2id3Kt97ZaiPJRqXaXn4/d8x05nGYee/oBAFzkP3jWR4GbNgJKHZd/+tIIjkWkU21XYJQm13EaQm5KH755MQEAAh48e5/sVW8gsVmKxyVAqZDgqzQzvFc7YkUN45uX3SXcdiNstT+F99cVDOlNgs/L8/35lTMfj3Hfn1KtLCIJwlUYTdE+aNImvvvqKFStWlFnLtWTJEvz8/OjRo0eVdR955BEOHDhQWs5qtfLjjz/So0cP/Pwub8cQHx/PwIEDsdls7Nixg2b//AETBKHpOJ1YkkStqY50Q0kG8wMxWUiS1CSn2DdlV47aVTWCJ5PJkMlk13UrzX79+rFz504mTZrE4cOHOXy4JNGWk5MTQ4cOLXM/LygoGR3V6/WVtufg4EBOTg4FBQWVBt0mkwmT6XIGa5Gv5fpycXHhxJbvGXHbQ0SZXWjWfzbOfuGl5wszYkk4vAq5QknYqKdRahzIPH+QzHO70Ro8aTt+XunfMHNOAv3vXVRad9OFo3w+7lHefnoGwwb3K3ftoqIiEpPiSTv7InK9O8363YvBN6z0vDEznrSIrUgWE8d+nIPeI5jQ/veidiyZHSlJEpnnD5JwcBl2SxGdpy9A71IuhK1Q/KGVBHabXOE5yW5HLlPgO+hJALKtZp754g+87L8QEuDJgfNGnNpORN/Dhyv/9WeYi3jsw5UkntpGULcpeHd4HJerHjisijnN62Puo9P0t3FzrryvcoUSn+53sDlqJ8qfV3LPjIr7KghCiUYTdI8aNYphw4bx8MMPk5eXR4sWLfjll1/YuHEjP/74Y+ke3bNnz2bJkiWcP3++NGC+9957+fTTT5k6dSpvv/02Xl5efPbZZ0RGRrJly5bSa6SlpTFo0CCSk5P55ptvSEtLIy0trfR8QEBAjUe9BUFovE4l5uKoUeLl1HRnrvi56Ciy2EjJK8bXufLRFuHmUtNpspfKent78+abb9Zjj8r65ZdfmDVrFj179mTp0qW0bduWpKQk3n33XV5//XW2bt3Kzp07UalUpa+lqodGNXm9b731Fq+++uo1ew11pdfrS7+TVPUg4Wak1WrZ+cdizGYzc557me0bzlNYZMFqs2IzGZHLlSj1BoyZ8Th4BOHi35bwUU+V+ewTj/6BX1jZARqv0C54hXbhzaX/o8BYxKSxw0vPpaWlMfrOZwgZ9CjuRXnkpl3gzO+v4N6yzz/tytAaPGg56L7SkXVTYQ5nNy7CVJAFMhmmvDScvJvj13EEdktRjQPuwow4bOYiNI4Vj4jH7PqWFt0v7yGuUKrx7nIrJ9d/TLprG7z73FJhPaVah3+PO/BoN4YT6xbhET4YpbpsstCMi5G0Hvs8uioC7iu5tRrA73u/ZsywdDw9K5+JIAhNXaMJugFWrlzJSy+9xH/+8x+ysrJo3bo1v/zyC9OnTy8tY7PZsNlsZW6kGo2GrVu38txzz/H4449jNBrp1KkTGzZsYMCAAaXlIiIiuHDhAgAzZ84sd/2XX36ZV155pf5eoCAIN4STCTmENtEkapf4/RNon08rFEF3E/Hyyy+X/v9XX30VmUxGeHg4U6eWnToqk8nQ6XS0aNGCESNGXLcA8Ny5c9x99914e3uzbt06HBxKZqK0bNmSL7/8kuTkZNasWcN3333HAw88UDpyXVhYWGmbRmPJtniOjpUvJXnhhReYO3du6e95eXkNkihVJpM1+aBGrVbz+YdvAWC323l03gJSfCaj8witsl7S8XVolRL+4b0rPN9m2AO8t/QdHLUKut3SFWdnZ267/wWC+t2P1smNjJhjOHk1I6zXRDKSLtBy5NMVJkLTOLjQccrL2K0Wjv3yDO0mzMM1sGTnm4Qjq0k8thb/zmOr7GtB2gXiDy4nfPQzFZ6P3LSIwLBuuPg2L3P8wsHVeLfqhVfzigPucv0cM4dTGz+l8/iyW/7lpccR2nNKtW1cyaPLdD777mdefu7RWtUThKakUQXdjo6OLFq0iEWLFlVaZvHixSxevLjccW9vb5YsWVJl+5fWcQuC0LSdTMilZ2j1a+5uZp5OGpQKGefTC+jb0qOhuyNcB1cH3ZIk0aZNmzLHG9LSpUuxWCyMHDmyNOC+0m233caaNWvYsWMHDzzwAEFBQQAkJCRU2F5hYSE5OTm4uLhUuZ5b5Gu5Mcnlcj5bMI8FH3/L72u/x6vHXWX25pYkiYzIv8iJPYRvy64EthtQRWvQbsxTPL/oeVqEx1KcdJxClTdtw3pyYt1HtBvxMCfXf0zn8XPxy0kjYssiVAZf/LtOQqm5/NDJaiok4fBKrPmp9Lz1eY6u+ZD0MwEE9ZxOQNcJpJ7ZyZn17+MWegteYf3KPNgtSL9AzO7vMRVk0XbCS8iumPZtt9tIPrGBgqS/yUs8g/uQsgNDkiSRm3Ke0O4Tavz+aRxccHTzpyAzAUf3klmcGXGncAtsW+M2LlFpHYlIMGKz2UpnngqCUFajCroFQRDqW1peMWn5JkI9m+56bgCFXIavs1ZsG9ZEfffdd0BJcrUbxaXg2WAwVHj+0vFLO5OEhYWh0WhIT08nISGh3PKwo0dLtqLq0KFDfXX5mjKZTKUj7u+//754EEDJ6P/TD9/FoagPKEg8TtqJ9WQnn0OuUOLs1YyA1r1pNWlu9Q0BCqUKpdYFny63sjviBM1uGYLVXIRcoSQ3OQq3gJJ15A4uXnSb9AzGnBTO7fsem11ComQrMoVCTljPiaXTyPve+RbHVr9HzplNFBXkIcnkaLU60k+tJ/XEWpQaB7QGL7DbcPbwo8+0l7BbzUTvX0nKP+VlAJKV4E7DcOs9lqOr3yV633LaDX+wtO8pkfvwbV3xKH5VQrpPJGLrN3QYVTJCnRp1gFYD7qx1OwAWx2BSU1PL5FUQBOEyEXQLgiBc4dQ/SdRCPZpu5vJLfJ3FtmFN1d13393QXSjHx6dk7+VLydOudujQIeDygwKdTsfgwYPZsGEDy5cv58knnyxTfvny5QCMHVv1dN8bhdVq5bPPPgPgnXfeEUH3P7bv+gt18GBaNOsIwJZPZhHcfQKh3SfWuq2W/W4n9vgWzKYi/Nr0I+7YBoI6DSfu2CbajSi7zZfexYeOo6re+ksuV6DU6AgfWHEge2L9x3Qc/XCZYwqlqtLyAD4texBzZG2ZY2kXjtBh1GNV9qUiSrUWSbKX/m6zmkvXp9eWTO1Efr7Y8UIQKiOCbkEQhCucTMjFoFXi4ahu6K40OD9nHfsuZDR0N4Tr4N577wWgW7duPPzww6W/14RMJuObb76pr66VmjBhAq+99hq7du3i888/5+GHLwcr+/fv54MPPgDg1ltvLT0+d+5cNmzYwOuvv86YMWNo2bIlULKv95dffonBYGD27Nn13neh/iQkpaN1aVX6u2S3o3fxrVNbDq5+JJ38E7XOgEwmozgvA32bAchkMuRVbAdWFYVChWS3l5kufolcXvuv4XpXH+xWy1XtKOqcg+TK16VQqrFZTHUKvCVzfpW5EQShqRNBtyAIwhVOJeYS3MSTqF3i56IlNc9EgcmKo0bcLm5mixcvRiaTUVBQwMMPP1z6e3UubSl3PYLuLl268Mwzz/Duu+/yyCOP8Omnn9KmTRuSkpLYt28fdrudBx54gKFDh5bWGTp0KHPmzGHRokV06tSJYcOGYTab2bx5M3a7nZ9++gk3N7d677sAFouFH39dxWffr0Hh5AMyGXabFWNOGkq1BgdXP8xFeeSkXOCW8CDe+e+8KhPWxcbGMu+/H3LqXBLd7riclVwmkyHZrXXqo91uRaHSlI7+yuRy7HVs6xJJssM1vJ/YbdZ6uz95t+pB0t87Cew0vPrCV4k5vY9pT5/EZrNQlJOMZEzH2ScUpd4DSSbHbinGkp9Cr/bBvPjMo/y17xAb/zpJvlWD1S5DKZMwqMwM7d2WiWNHVrh3elOQkZHBl0uWEZ1ShNmuwi6BSm4lwEXO/TMnEnIDLfkRakd8ixIEQbjCyYQc+rYQicOgZNswgAvpBXQIcGnYzggCsHDhQnr37s0XX3zBkSNHiIyMxMnJiQEDBnDfffcxY8aMcnU+/PBDOnXqxCeffMLmzZtRqVQMGTKE+fPnX9c9xpuytRu38u6362g5aDZDHptY5pwkSSSfO0zs8S10Hnk/Lj6hnPlrOWNmzadPpxAWvfkiavXlmUdms5m7H32BXG0L2ox6DU3bM+SknMPB7dJaYhm5qTH4tu5T637mJEbi6tuSpHOHsVnNOHkGk5scjVypxlJciEpb+1wflQXJkiRht1kqqFG1vJTzKDVl+yFJEjarBUUFGdVr0r9LPJq1J/7E5loH3ZbiAtz9Qrhl9OV15gVZyUTuW4Wjqy9hvSeVXivqwBr6T3mMziPvJ6D3kzhf1dbGixEsn/M2j8wYSt9e3Wv9ehoru93Ofxd+TmSWBu/O0/BuWTa5o8ViZv43a/CwLWXBf55Eq9VW0pJwo2qaj5EEQRAqkJpXTEaBWazn/kfptmEimVqTcPXuHZIkVfvTECZNmsSmTZvIyMjAYrGQlZXFtm3bKgy4L7nnnns4fPhwacbyjRs3ioD7Olm2ch3fbEmg58y3cfdvWe68TCbDr1U3ek2dR8yxzWQlnqPtgOkMfeBDjpzP457H5mOxlASnFouFSXc/iUe/p+gwbDZKtRaf5p3IunCktD2DdyjZCWfq1NcLh1YT0LYvjq4+xBz8A9+w3iSd2U3ILeO4cHBVrduzFBdWOlU7/thG/ML71brNjLhTeDRrX+ZYs84jiTu6rtZtFeVloHFwKXPM4BFETlJUrdo5v2cp4f8E1pc4uvnSdcwjOLj6cHr7TwDIFUpa957EyEc/J+b4NkzGvHJtuQW2IWjYM3y+JoLN23fX7gU1UpIkMff/3iHNfRhBfe5Coy+/m4JCpSaw2xRk7e7lwWfewGQyNUBPhX9DBN2CIAj/OJVQkkQtpIlnLr9Ep1bg7qDmfFrl+xwLNwe73Y7dbmfZsmVlfq/Jj81ma+DeCzeqv8+cZdmeBML6315tWZlMRpcxjxC1fzUWUxFqrQN9b/8/4vI1/OftTwF46qW3CRn5Ag4uXmXqOTh7UJSXDkCH0Y9RkJlIRuzxWvXVZMzFxSsQmVxOmwEzSLtwGJlcjlrrhEKtxZiTVusHTecPrCS0x6QKzyVG7ELnXLt91/PSYinMTiak2/gyx519mpOXGlOrtgAuHFhVrn/Ne03hwv4Vpe9ndZIjdmFwcSvzmVwpILwXDq7exJ/aWXpMpdHTc8ozHF33eaXtBvacwTd/HCUlJaVG/WjMPvxiMeZm43DyCq62rNbRBdfej/HiGx/Vf8eEa0oE3YIgCP84mViSRM3dQSRRu8TXRWwb1hSdPHmy9EcE1UJdffvLWkL73lXj8jKZjPZD7iJq3+8AqLUOuAeEcSImj7i4OM5lqXB09S5Xr02/KZzZ/CV2uw2toyuOHoGc3vw1luKaPTCU7HYOLn2F8H7TAXDzb4kMichdP9Ki91RObfiUZp1Hcnb7khq/lry0GCxFBeidywejsYfX4tGsAwd+faXM9O6q2KxmTm38DJ9WPSocPfdr059ze5fVuH9ZCRHIlWrUurKjqjKZjM4TnuHM1m9IPXew0vp2m5Xze5ZiK0wlvE/FDxYuCek0lORzZXcdUGn0eASFk5VY+ai6f++7+WLx8hq8msbLarVyJCoTg0/5WSCV0Tq6kmx2Iy0trR57JlxrIugWBEH4x+mEHEJEErUy/MS2YU1Sp06d6Ny5MxMmTEChqFvWZuHa0ul0xMTEEBMTg06na+juVCs/P5+kQi3yWv77cXL3ozDn8uhmWK9JGG1Knpj3Bq0H3VNhHZVGT+fh93B81dvYLCZ63v4akt3G3h/nYSrMqfJ6NquFv5Y8zS0TnkDjcHmFcf87Xyc1cj8XDv1Bq34ziDm8BievYM7u+L7a15CdFEn03rJ7aV8Sc3gN+VmJ5KXG0HP6f9n74zys5qIq27MUF7L/5/k4uPkR1v+OCst4Ne+KpbiQiK3fVtu/9JhjJJzcRuuBFT8QkcnlWIw5FKed5fCv/0fUzh/ITjxLfkY8mXEnidj8OZGbP6FZWGfa9ru1wjau5uwdQs5Vo/HNbxnN+cMbKq2j0ug5l1qM1frvktndyJb/vg6HsJG1rufTeRJfLvmtHnok1BeRSE0QBOEfJxNz6duidtP9bnZ+Ljq2R6ZhtdlRKsRz2qbCxcWF3Nxc2rVr19BdEf4hl8tL9yBvDP5YvxnXNrUPJgBcvEPJS7+IwTMQhVKFUq0lNUtOuFPlmeYNHv70GP8wx/78FLnGiZ4zXuf0hs/Y8b9H8AjuRFj/O3B09y8tbzLmErHla/JSoukz45Vy06PlCiVDH1zEvmVvcCxqP57Nu5B2/jBWUxFHVr2Ds08oId3Go1BenhmVEXucyF0/o3ZwpuukF0q3CZPsduJP/Ela9CGK8jIpzE5C6+jGhYO/027Ygxz67b+oHVxoO2Q2Wif30vaK8tKJ3PUTWRcjMHiFkJtynjPbl9Cq34wySdMy4k4S/ddSfEPb4+rbnFPr3sfRqznNuoxBrrj8VT/twhGST2+nMCsRg3coBZnxOHk0Kz1vLson5sBKzAUZeAe1IrjDANoPnsmeX9+kMEFJxsWzWIoLGHDna6g0+lp9pi27j+Xklu/oPOrygwi5Qomsmq3YVIG92bvvAP371T4xXmNw8HQsrl1G1bqeWudIfKa5Hnok1BcRdAuCIHBlEjWxnvtKfi46LDaJhOwigsV702R0796dP//8k/j4+IbuitBIJaWm4+DvU6e6ehdPivIzMXj+s2WYTAaK6veO1jq60mvyU5gKczmzZxWOLm7oDb24cGwTiae34+juj0yuQLLb0Dq60HPqC+iqCORlcjm9ps3nwK+voZQs5KeeB7mC4vws0s8fJnrvbyg1Dkh2K3abFZulGKXGAYsxhz3fPQkyGRaTEUtRAQqlCr3BA3f/EHqMfwi5QsWOH14mKWIXKr0TxtxUNn90FzqDJ/J/9ssuzs9ArtSg1hsoSI+l1/T/4+/NX3Fu2/+QkP2zxtyOR0AYrl6+tBtYMj3eP6wbmYnniNzyBcjkFGQlodI4ENimJ31unYtMJsNqMRG1fw0Xj6wBZMjkMpRKFW16T0Rv8CDhzD6MeZkYPIPQObni7t8KvcEdY25GrQNuKEkEZreXX6pS0f7lV9I7e5Gc+netr9dYWGwy6rqgzWIXs5AaExF0C4IgACdFErUK+TmXbEtyPr1ABN1NyH/+8x+2bt3K6dOnWbp0KdOnT2/oLjV5ZrOZl156CYA33nijzDZaNyK1Wo3NZkGhqn0/7VYLcv3V21/VPImZxsGZTsPvKf1dpZRj8AjAO7QzRQU5JJ07Qtsh91Ra/0pH13xIYNu+hHQaUuPrX5Jx8QwxxzajUKjoMubhcuc7DJqBJFfRrNPQCmqXJdntHFq5gO4TnqjwQcGR5HNlfnf3b1maLT5i9zIC2/TB6YqRfqVKQ5sqpobbbRYUisvrveVKFVC3bc7+DZvVglpd+63QGguZvO7L2f5FVaEBiKBbEAQBOJWYi7NOJZKoXcXNQY1OpSA6rYAh4eUTGAk3p+joaCZNmsTy5cu54447+OGHH+jXrx8+Pj7IKxiZuuuumifLEurGYrHw7rvvAvDKK6/c8EF327BQjh+NwLtlt1rXzU2LwyukY+nvks0K5vw69UOSJOxWCy4+zclKjCK40xAsxYWc2PAF7YbNrnRva5vFzMk//4ebTzBWk7FO185KiMRmMaM3eFKYk4qDS9m/oS26jSJy7+9E7V1By16TK80nYjLmcXztx3QYclelI/N2a+XBcEFmIjqDR636npsai1dwe6wWEzJZyUOLlOij1a6Rr0xhThraq7Yng6r7DVCYGknrAa3rdM3GQCe3Itnt1Y74V0Qjv74PQIR/RwTdgiAIXEqiphdJ1K4ik8nwd9WKZGpNzD333INMJkMmK5nCunHjRjZu3FhpeRF0C1cbPKAf3699D2oZdEuShDEvA62jC1ASrNntFgZ0bUFazIkywXhNJJ7Zi3/rXng2a0vMsT8J7jSEgPCeOLn7cXrTF0gyBaG3jMHRPQCQyEuPJ/bIBmQyifDekzB4+LPlq6dp2WNcra4LkJMag0wmo0WPcUTs+KnMeuZLwnpPJDXmFMfWfIhS40CLHhPRGTyw26xkJZzl4qmtqHWOdJ/wOBq9ocLrFGSnoK9k+zHJbgdkKCvZL7zCOqWfgStndv9Gi+5j0Tq6UpSfiUKpxmYx13oGQ+TelbQbfGeZY0V5meX2Cb+aruAc4a0n1upa18PhI8dYvm47ZrsSm11CKbMTHuLNzOmT0Wq1NW5n+oQhfLBuC37th9fq+rmpF+jdIbiWvRYakgi6BUEQEEnUquLrrCMqrW6jTELjd+WDqCv3Kb4UkIsHVUJF5HI5bQMNJBfklAbQNZEUdRD/sB6lv5/9azneDhKv/+c5xsz6v1oH3UlRB+k24UkAHF19yc9MwsndD2evILqNfxyLqYiYo38Sf3wTyGQ4uvrQZdR9pUFqZkIkar1jaWK3mspOPo+Ldyh6Zw8y4k5jNRdjt9kqzObuHdIe75D2FOVnsX3Ji3iHdkYuV+DqE0qPCXOqHQWN2vc77YdU/OArcv9q/K54P2siOeoQfq26A5CfmYDBIwAAv1bdKcrPIurAH4T3rVnWcgC7zYbVUoxaW3aJUuS+VYT3u63Sennp8fRu36zS8w1h51/7WLpmNwqvdgT1fLjMzJ+4jEQeffkLWnhreG7OAzXa+aFzpw6of9oI1C7ozjuznukLHq9t94UGJFLRCoLQ5IkkalULcNERnVpQJuASbn6SJJX7ufq8IFTlkdkzSN796T+jrdUzFeYSe2wL/uG9AUiOPkJRXhoTBnZApVJxx+hbiDmyvsbXj9j1K96hnUsfDLXqNZETf36D7YopzSqNjla9JtBl1AN0GXk/rXqMKw24LaYiInb9Sq8p8zi4+kNslppli7aaizm9/SdadB+Df3hvog+tJ7TLCI6s/aTKejonN7qOfhit3onOI+8juNPgagPulPPH0OidK0xuVpCdQtT+3zmz+9dafQYxxzYT0KYPp7Z+T0inYaXnAtr0IT3uNHmpceSlX6xRewBH1n1K6z5lg/T0uL+RK1SVjt5bLSbyji7hjmkTa3yd+rZyzUaW7oil+bA5BHccUm6pjbOHP+HDHqbIfwRPPP96jbc6mzm+H8lHV9W4Hxnn9jGkaxAq1c271v1mJIJuQRCavBMXcwAIFUnUKuTnqqPQbCM1z9TQXRGuk+3bt9f4Z9u2bQ3dXeEG5eTkxIJ5szmz+hWslqr/fhTmpHHoj4/oPqkku/bFv/dwdtev3D60DbffWjK1++4Zt9LJOYnoA39Ue+1zB/7Aai4i+tC60mBZqdbSZfRD7PvtbczFhVXWLy7IYf+KhXSfMIekqAO4+rZg3/IFmIuqXmpjKsxl3/J36Db+CeQKJXlpccgVSiJ2/4pXSCcO/fFRlQGwd2hHJGQcWftZta8xMfIAKecO03bg7eXO5aTFseXrp/EK6UiPSU+z97e3a/UZnNq6BGfvYDyDL28bKJPJ6D7xKcymQk78+S1ZSeeqaK1kavvhNR/TrMOg0tFygORzR4g/vbPS0XmTMZ+krQtZ9NqTN0zugoOHjrLpaDrNe06utqzB3R+PHrP4vzc/rFHbA/v1YmxHJ5KOrKi2bNrZXbTRnufeO2o+00C4Mcgk8ai63uTl5eHs7Exubi4GQ8VP8gRBaHgLN53l5wPxfDqji5gqW4GU3GKeWnacH2Z3p19LMQW/rsQ94ebQUJ9jYWEhjo6OABQUFODg0HgeEmZlZTH/zU84cbGYrmMfLbP+OCspmvOH16NUaWk/9B4SIv4iav9qZMVZvPzM/YwfVT6z968r/uDznzfh2rI/rXpOKB0NttusnD+8nqzEaKxmI7KiNO6fOpgTkfEkFerw6DQJvcEDc1EBJzZ/i2S3E95vapms3nnp8UTt/wOZTIajux/xp3aBTEan4bNx9g7m5OZvsdtstO57a5lAMjctnuiDawDoMOxespOjiT2xFa2DK+0H34kxL5OdS15ErlTh4OqDs1czwnpPRqO/nCE8OfoIcSe24+jmi3tgOGd3L8MtIIy2A2eUjr5LkkTs8a2c3bMChVJF/5mvonV0LW0jLfYUJ/78lpzUC7QdeAdt+t1Gbmosh9Z8TH5GAt6hnek0YnaZzyA7OZroQ+uRy5VoHJzJSYnBYjLi5t+S8H63lZkWnnzuMLEnt1OQmYzVbMTZqxnNbxmNd+jlaf8mYz6ntn6P1VxEeL+pOHs1Q5IkEs7sJfrAGnKSz+PXugfh/W7D4BlUWq8gM5GsU6sJdoOXnrr/hvo3/uT8hQQMfKJWdaL2/sb8e/oTFBRUfWFg976D/PT7Dgo0zfDtNLY0yZ9kt5NyZjvKzFOM6d+ByeNH1rr/Qv2p6T1BBN31SHzBEoTGYebXByiy2HhmeFhDd+WGZLdL3LP4IC+ODmdWn5CG7k6jJe4JNwcRdNddQUEBH325mF9Wb8Nqk2M2FWIpNuLg6gUyOebCXJoHuLPgtefp3LFDte0dOHiINxctJikjj2KTBYu5GK1KQaCfJ689/zCdO10OBPPz8/nu5xUkpOVjs8O56HMUKn2IOrQBR1dfJCRs5mKsZhM6Rxcs5mIKc9NBAgcXL8zF+Wj0BuQKJZLdjjEnDYVGj/afoNmYl4nGwQDIQbJTXJiNzskDhVKF3WbFmJeB2ZiPo4cfNlMRCqUGZBJqnQFJsmMuKsRSnI9ab0Cl1mOzmrBazdjMxcgVanROriArCWhtlmIUShUmYwEqjQ6dwQO5TI65KJ+CrBQkuQIXr0BkcjmWIiPm4nwkCXQGdyS7DVNBNhoHF5QqDTabBYu5CIVShbXYiEyuKC1nzE0vqefkhkwux2oqwm634eDshakon+KCLOw2G2qtAyq9Ewq5EpvNglZvoHXPMaRcOEFhXhYymRyb1YzOwUC30ffj6tMMq8XEgVWL8HUw4e7phUouo3mAO3fdXrtEZNdDQkICr369nbC+la8/r4jVbKLoxHe8+sKcWtU7f+EC3y9bj9EsYZckNAqYNKof3bp2rlU7wvUhgu4bgPiCJQg3PkmS6PDqn4xs68PkLgHVV2ii5q08SZ8WHrw5qX1Dd6XRaqz3hDNnzhAVFUVeXl6l67ibUvbyhvoc7XY7Z86cASA8PLzCrduEmjObzTz49H+RNxtCdsoFOg65o9o6kiSxf/XHdOg7ATefkgRfuelJfP/KdAwePox+5EMM7n7VtpOXkcipHb/Se8pTpbOr9q/+lLAeo3H1qf7BpjEvk30rP2Lw3a9UOTsrMymavSs+ZMwjiypM4Ha1yAPryEmOxr9FJ/LzcgjvPeHyNfOzOLT2S/pNex65XI7FVMRfv71Lv2nPo1SpkSSJPcvfo+OQO3By8632WvkZicjO/cY7rz53w88we/O9T5GFz0Slrv3DgLN/fsLX7zxdD70SbhQ1vSeIv9iCIDRpsZlG8outtPBybOiu3ND8XHScSxUZzJuSffv20bZtW9q1a8fkyZO55557mDVrVoU/Qv2Ty+W0bduWtm3bioD7GlCr1Sx641kid3yHWufAoXVflUmwdjVzsZF9Kz+gddehpQE3gLOnH7c9+yV5mcms+/QJUi6crPK6iZGHOfbnEnpNmlMm2Ow+7mHO7lvDxTMHqqyfFhdB+l8fY82OJjWm6muZjPnkZaWw5uNHKcrPrrSc3W7n2J/fkx57mv63zqF5pwHotDpObPkBu80GgN7JjU7D7mTX0rcoLsxFpdHRY8Kj7P51AQXZqchkMnpNfpLjW34kJeZUlf1Kjz2JFPkrb7/8zA0fcAMUFtvqFHADWCQl9homsRNubmKkux411lENQWhKfj+WyJO/HuerO2/BUSt2UazMiqMJbD2TyrH/1G5bE+GyxnRPiImJoUOHDhiNxmqzlMtkMmz/fDFvChrT5yhUb+OfW1m0bC/G/EJsditO7r606jYag6c/kt1OVvJ5ovavRe/kSocBk3F0qTivhTEvi6+eH41SrcMzMIwWXYcT2nkwGp0TpqJ8og9vIu70XxQV5GIy5uHk5kOnoXfi26ITCqUaY14G5w5uIDXmFFonVzyD2tC88xA0egPm4gJiT+wgLzWGzMRzdG/jT5GuGUaLjJgzR/Bv3YPmXYeXXuvC8a3EndqDTCbR/9anUCgVbP1pAUq1jrZ9J+MV0g6FUk1hbjoRu5ZhzjhHTm4+U+d9X+Y1pV88x5kDG1CodLTsPhJHVx+M+Vkc3fgtJmMebfpOxs03lLP711KQlUxgm174tuhCzIntpMdF4O4XSmiXy/1KPbMTnTGOgd1bM3XS2EYRcAO89MZHuPYov8d6TZze/D++efORGm0fJjRONb0niG+YgiA0aScScvAxaEXAXY0AFx3ZRguZBSbcHTUN3R2hni1atIjCwsLSL8WX/reifbqF68NsNvPmm28C8OKLL94wWZ0buxHDBrN882FaTHuOhKijJJ47xsE1nwF23H1DSb8YxfjH3kehqPoeoTe4Mefz/dhtNr58Zhixp/9Ct/w9JBlINhtWixmVWofGwYnOg6bTZdgMEiIPE7lnBRazCQeDG50GTkY39l4AspJjuXBkA6d2LqfzgEmkx0cydvarHN+1kmPHdzH2gcfQORjoMXwGf639hh3fv4zNZkWhVOEb2p6xD75ZZnR28pyPsNls7P/jC07u+AVzcSFeOjPffvYe/v7+PDjnRUxFBWh0l2d9eQa2xDOwJaaiAqIOb+FCbjpypYrQdr0Ibt+Xv1Z8xLbF8zG4uKNUqjmVcJrTf8qwWEy4ewchy5Vz+JcX0augX6+u3Dt9GK1a1W5d9I1Aq5Jjs1pKE5vVhkKyiIBbAMRId70ST8MF4cY36bM96FQKHh/csqG7ckO7mGXkuRUn+fWBnvQIdW/o7jRKjeme0LFjR06dOoVcLueTTz7hkUceQSaT0b9/fx566CHeeustTp8+zfz58xk0aBADBgxo6C5fNyKR2s1n3catrD2RR7Muo8udS75wiqToY3QdXrO8BdFHt2EryqXzgEnlzq3//i1adh+Ff4tONWprx9KFuLp702vUXSSeP0lBbgbN2/Vh27IP8G4WTsd+JWuu7XY7G75/k96T55TJhl4Zq9nE+Y0L+eLd+Wg0JQ9Rc3JymDDreUY89GGNli/kZiSx8esXuPeFz8lKvcjhrb/Rqd94oo7vwiugJRZzEdlpCQyc/DBWczERu3+ja6gT990zo0av/UZyLjqa9349SoseE6ovfAWbzUruwS958//m1lPPhBuBWNMtCIJQDYvNTkRSHs09xXru6vg6a5HL4Fxa1XvUCjeH2NhYZDIZ7dq146GHHio97unpybRp09i6dSsGg4EFCxaI4E9o9MaMHELfEBmxh8rv/+0b2h43nxAOb1xc7cyO88e2c3bv6goDboBRd87j9M5lxJ3eW2U7kiSxY+lC1EolvUaVBPueAS3JSolDqdagdXAmLzO5tLxcLmfY9LnsWfEBBdlpVbZdXJjHuY3v8t6rT5UG3FCyxt3LoGTNp09iNVe9n3dm0gU2fvUCtz36JmqNDp+gVoy++wVO799I5wETsVnNqLV6OvQew5ZfF6HS6Og07G5irQF8/tWSKtu+EbVs0QJVYVyt68Ue2cDdt42thx4JjZEIugVBaLIiU/IxWe0i6K4BpUKOr7OOaBF0NwlFRUUApfvLXhr5MplKvoy7u7vTo0cPTCYTL7/8csN0UhCuoTtum8jt/QNI2vER5/etwGoxl54LbN2d4sJcfv/ocQ5t+BZzUWHpObvNxpl96/hr+QeoZHZ8g8MrvYZMJmPSg2+SFnOCtZ8/TeShTWUC+eLCXPau+pRVHz6Cj38oQ6c/VXpOoVBit1lL27n6AYBaq2fUnS8Qtf8P9qxYRFr82TLns5Jj2LvqYw7+9AKfL5iHm5tbmfNvvPc5fWe8wpT75rPhy2fY9tOb5GYklSlzMfIQ6//3PHuXv8/dz32Mm+flTO1yuZzht89l3/of6Nh3LOmJF1Dr9LTtMZzju1aXvI9h3YnK0XPo8NFK36Mb1aThvYg7uqHG5YsKc9HkRxEW1qoeeyU0JmIRoyAITdbJhFzkMgj20Dd0VxoFPxct59JEBvOmwMXFhczMzNLfHRwcKCgo4O+//y49lpqaCpRkOReEm8GAvr0Y0LcXMTEx/LT8e/Itdo4dP4FZ5UHfsbNx8wkiLyuVkzt+wWotCYCRJMK6DKJrvzEApF4V7FZ4nUkPAxBx4E/++Phx5AoVkiSRl5HIiDtfYOjUR8vVyc9OxcHZA0mSMJuMOBjKL/NRKFX0HXcfdpuNiIN/Ent8O/yTj8HZzZv+4++j+NSP6PVl73k5OTlkW/QE6Z3Q6p2469mPKMzP4fsFj+Dg4olMrsBmteDm6cvUB19Brak4k7dcoSCkbXcunjtBzxF3cHDzL/SfcD8RBzeXlgnrMZblaz+h2y1dAMjNzSUrKwu1Wo2np+cNm6tg8MC+xCUs49iJzQR1HFZlWWN+NnHbP+WTBS9ep94JjYEIugVBaLKOX8wm0E2PRimSnNSEv4uev6LTG7obwnXg7u5ORkYG6ekln3ezZs04ffo0MTExTJw4EZ1Ox/HjxwEoLi5uwJ4KwrUXEhLC/GdLAt+srCxmPPE2bj4lsz4Mbt70HXdfpXXdvJuRGh+Fd1D1I5xtegynTY+SHSGKC/M4un0ZzcK6VFj29N71dBs+gwun9pCfnUb3EXdW2q5coaBdr1Hljh/btox5M8vvQPHdj8to2bPslPioYzu59eHX8fQPrfZ1XCmsy0C2/fYxgS07YjEVY7fZCGjRgYvnThDYsiMymYw8myNffbOEMzEp2DXuaJ3csdssGLOTcdFauX3SKNq2bVOr614Ps2behvOajazftAjXVoPxDmlf5nxxYR6xh37HQ5XLZwvnl5m+Lwgi6BYEock6HJtNS7E/d40FuenJKDCLDOZNQHh4OJGRkcTFlaxj7Nu3L6dPnwZgzZo1peVkMhkdO3ZskD4KwvXg5uaGrDidYmM+2hokKWvTcyS7Vn5Wo6D7Ssd3raRj/8kVnrPbbFjMRWh0jsSeOYhSoSQlNgKnTrVLYBh/dj+tWs0sdzwp00iLji5ljuWkJ9K5f+0Sh0HJNPNLWb79QtuSevEcYV0GsnPVlwS2LPlbEdB+MPuObWPQhDnl6tttNn7c8iey5Wt47aWnUSpvrFBl8riRTBwznLUbNrN958eYJTV2u4RSbifAXcuCp27D3V0kGxXKE2u6BUFokrILzVzIKKSVd/VfooQSQe4lUxIjU8QU85td165dgZIp5FFRUTz++OOVTvt88UUxhVK4uc17YjY7ly+qUVmFQomDsxvJMX9XX/gfBTnpFBfm42Bwq/D84S0/07bnaOLOHMJUmEe3wVOIOfVXtQnPrhRxcBNOTgYKCwvLnbNXEA7IZP8mRCiZ0q53cqGoIKdcNnQHJxeUqoof3MoVCtr0GIXfLdN4/j9v3ZDbEsrlcsaPGcEH/32GT19/gs/fnMPHrz/F8089LAJuoVIi6BYEoUk6djEbQATdteBr0KJWyIlIzmvorgj1bM6cOZw7d46oqCgCAwMJDw9n9erVtGzZEkmSkCSJoKAgfvrpJ8aNG9fQ3W0StFotBw8e5ODBg2i1Fa+pFerHgP596R3uwa6Vn9WofEFOJlt+eZe0i1HVl83NYPfvX9Bv4kMVnj/51x84uXpjNhk5tnMlwWGdCArrxOApD7Fl6cIaBd7RJ3aTdO4oXj5+6HS6cuflsupfU12Yi42oteVzppQcL9+PKzm7+xDQbSqff934sp0LQkVurDkbgiAI18nh2Gxc9Cq8nMQ06ZqSy2UEuuk4K0a6b3qOjo6le0JfMmLECM6ePUt2djYWiwUvL68G6l3TpFAo6NatW0N3o8l66bk5fPz516z85Ek6D7mD4PDyn0Xc2cMc2LgEu03Cw92F1EM/khYZTGiPSeidXMuUNRcb2b9+MVmp8YyZ/WrplOxLUuLO8ve+9XgGtCD1YiTHd67E0zeIzgNKpnw7GNwYOvURdiz/EO9mbWjTcxQKRdmv9TnpFzn51x/kpicycsZTHFzzCZFRUbQJL5thXTLlY7fZkCsu5zdRKFU1nlJ/Naul5EFASnwUtwy+lbSE87h6BVx+n6KOERTattp2PHyD2bhpMUrFElwMzvTueQsBAQHV1rukuLiYbTt2kZyShlqtonWrFtzStQsyWT09ZRCEKoigWxCEJulIXDatvJzEzbeWAl31nBEj3U2aq6tr9YUEoRGRJImvvv6OP3fsBZmCvNxsNBo1Lq6uXIxP4GJ8LCqNDp3eCZvNwvIPH8XdryWe/qEolGpsNiuZybEY8zJBBnZzIUadnvhzJuTsYdf6nzF4BuHo4oFMLsdUmEdm0nnMxUZQqFn82h14BbZErlAiAwrzsrGYi9Ho9Fw8d5TOfccydMpHbFv+aZl+Ozi5Muau50iJP8ee3z8DmRyZXIGMksDX1dOXAeNmsfP3L5EpFKj0rny58hCHdv6H7PRkiouNOBlc8QlsjtFxHR37ji9tu/OAiRzdsYreo++q1XuZlnAeT/9Q7HY7xYV56BwM7F23hIGTHy4tkx4fSc8Bo2vUXljPcURnJhPs1Za3v16L0pTOyMG98fb0YNPWXdjsJSP1QQE+qJQKzscmkpmVTeS58zi6B9Bt8BRcAttSZLWw/shZflq1gNYhPtx9x7QKR/2rk5uby0+/riAnrxAk0GqUTJsyHn9//1q3JTQtIugWBKHJsdjsnLiYw5SuNX9iLpRo5q5n7/lMrDY7SoVYoXSz2LVr17+q379//2vUE6EyZrOZRYtK1hXPmTPnht1aqTGxWq088fQLpORa6T54Cs27OpEcH01Yj9F07DmUwvwcdm9YSrPwHvQcMhFPv2YA2Gw2jv61geT4aPxDwujSZyRHdq/n1MHtNG/TlX6jpiOTySgqzGfLqm+JPHkAnVqOgxrcvHwZPOF5HBydSU+OZ8+fy5HL5eTmZHPrQ6+V6+Pq79+j+7BpAHgHtSIpJgK/kLKZvX2CWuIT1LLC1xh9cg8hbbuzZ823jJs2G72jM31GTOXbd5/G3dOPMXc8gVKpYsV37yNJUumDaEdndwpyM8uNgFfn5J61DJryKGcObyWsy0CKjQUolCoU/yRES70YjZd/cI3bC2ndla3LP+OWAWPoP/ZuAPYf3cWezz5k9ryPsFpM7Fz7I6fjIuk2YDRxuakYjWpuf/pjlFfNHnBx94auA8jJTOWpF9/klecfxcfHp0b9OHcumsW/rKIIBzr0m4iXoeTho6m4iEU/rEZWlM7EUQPp1bN7jV+b0LTIpBsxQ8FNIi8vD2dnZ3JzczEYDA3dHUEQ/nHiYg4TPt3Da+Pb0lKs6a6ViKRc/rvuDJuf6i/eu1q6ke8Jcrm8zrM+ZDLZ5T2Lm4CG+hwLCwtLp/wXFBTg4OBw3a59MyosLOS2ux9h4n0v4+Lhw/qln9G6Uy+ah5ds2ZWWFMfuDUsZf+eTqNSVL0M6d/oQ5yOOMGLqg8hkMuKj/+bY3j8ZN3NOaQKxv4/s5sKZo4y944nS/86i/z7MudOHGDH1QeRyOT999hqT73+5XPsXzhwl7kIkPYbfjt1uZ8MPCxg4+SEcnKqfcZKbmcL+jT/SomNfjm1dyv3zPgBgxTcLcPP0YdD4u0vLXjx/hhNH9tFn3OzSY9npiRzasoxh05+s0d+HU/s2olAo8WkWxtEdKxk05VE2/LCAodOeQKt3oqgwj+2/fczke+fVKpDfuuIzRk97uMyx9KQ41v70EU7Oroy94wn0jgbW/vQxjs7u9B8zo9o2bVYrO5e9yzuvPF3tf8c7d+1h9fZj9Bw9q1xSuCud+OsPwn2UzJg2pWYvTLgp1PSeIIYpBEFoco7EZaNSyAj2EF9aayvIreQ9OyPWdd+ULiVJq+2PIDQmNpuN6fc8xvQnFuLq6cvmFV/Tuffw0oA7PzeL3RuWMvne56oMuAFatutG21sGsG31YgCCWrSl97ApbPj1cwBiIk+QmnCBcTPnlAauSXHnOB9xhFHTHr4iiKs4qA0N74KPbyB71nwLksTw6U+xY8XnZCTHVdmv5Niz7NvwA14BLchJPk9ASBgA2Zkp5Genlwm4AQKbh9MqvB27Vn2O7Z+HaK6e/nToM4Y/f34Pi6m40mtJksSR7SuwWS0Y3L05vO03eo26iw0/LKDf+Nlo9U5kpsaz/bePGXfnU7UKuAFkFbw3nn7N6D5oPAY3L/SOBv4+sovc7IwaBdwACqWSvpPn8P4nX1dZ7sSp0/yx8yS9x86uMuAG6Nh3POcylaxZt6lGfRCaFjG9XBCEJudwXBbNPR1RienRteaoVeLuqOZsch7jO/o1dHeEa0gEz0JT8d2SHxh822PoHJzITE1A52jAr9nl6dmXRrhrOvsjMDScCxFHyclMxcXdG0/fIDx9A0mOj+bk/q1MuHtumfKHdqxh/F1PXdWKvcz07iuFd+mLq6cfu1Z+ilylpc/YWUQe3cnRHSsJbNWJVh37oVAqsVktnD2ynYToUxTmZaHRaMlNjaP74EnEnz0EwOrv3mXYrfdX+Dpate+Oq4cve//4HEmmpH3f8XgHtqTX6LvYtuJTzEVGegy/Hc+A5shkMoz5ORzbtZrCvExUGh1Ws4mstIuAjKM7VjJ46mMknj/F4c2/kJpwjnuf+7jWAXdeVhp6x4pHD9t07c/eP5cjSRJ7Nv3G6Nsfr1Xbaq2OHJMSo9GIXl8+yzrAD7+uoefE8vuJV6Ztj+FsXvERo0cORVHL1yrc3ETQLQhCkyJJEodjs+kRUvF+qEL1gkQytZvOd99919BdEITrZuPOw0x/omSd9MEdaxgycVbpOYvZhAxZtSPcV+s5dBI71vzAiKkPAtC172h+/eK/tOrQo0y5zLRE3L0DygXXXXuPIPL4X7Tu3K/C9n0CQxk94wmKjQUc3b0Oa1Euep2OY1t/46/VX+HpG4yxIAeDmxeefsEMGH0721b9jxHTHuWPJQuYMutp7HY7puIi/IPDKn0dnr6BjL/jMUzFRg7v2sDZ7HTysjPJSksgMzURS1EOekdnJLudiKO7CQgNx263U1iQS3jn/qjUGqxqJXabhePblhHcuhNjZz7J3o2/kJeTjot7zdZQX3Jo+0oGj59Z4TmZTEbztl1Z89NHGFw98AtuVau2Adr0GsfPv67gvll3ljsXHR2Nxi2k1ktvmncZxpp1G5k4fkyt+yPcvETQLQhCk5KQXURavolWPmI9cl0Fuuk5GJPV0N0QrqG77767+kKCcBM4f/48Aa26AmC327HbbKg1l/c9P7xrHd0G1n7veY1Wj9l0ec9shVJJfm4mnfuMKFPu8M615aZ2A4R17MHiD16qNOi+RKt3pPeIkgcGkiSx9JMXGHXHE/gHty5TrqgwH4OrJzarhdz0RFRqDXnZ6SXJxGr4evoML7s2efF7z3DbAy/haHBlw6+fE9KqLbOe/QC5XM7anz9lwLi7y7yXV7pl0ER2rlnM8KmP1Oj6AFarBavFhFZX+VKw1p368POn/0f7WwbWuN0ruXj4cPZEdoXnlq5cR/sBsyo8VxX/kHD2r9kqgm6hDDG3UhCEJmVPdAZyGYT73FiJrBqTZu56UvKKyTGaG7orgiAItXL02Amaty3ZY9tYkIvB1aPM+dysNDx86razhd7RgKm4qPR3ZzevclOMJUmqMDCVyWT0HDyObb9Xvcb4SltXfIlKrSsXcNusVpZ9/n/0HX0nG35+H5+g5gAUGwtrPb37SkqVhmJjIQARx/bg7OZVus554NgZbPj5fex2e4V1NVo9bp7+nD26u0bXkqT/Z++uo6M63gaOfze2cXcPUdydQHB3twKlQAu0pbSlRt9S+7XUKdJSirU4FHd3CO4ECSHuttkku5uV94+UbdMkRJAQmM85ObB7Z+6d3U323ufOzDM6dq38gXY9hj+0nKm5Baam5uio/PQYTSlVlWpJsQzo5d6nRKwuIBQlgm5BEF4oJyPTqeFogYVUDPSpLG/7wrlvNxNFMrXnRUxMDDExMaSnpxd5XN4fQaguZDlyTP4eOl6gUmBsUnLPbGWYSE0pUP2TcKyiAW7NBi1xdnZl58ofSw1eoTCo3rnqJ1IT7jNgwsdFtuXJs/nj+7foPuwN9q2bS8e+Y5CaFq5HbWZhhbqgoEJt+rcCpQIzi8IM+mq1qnCN8r+Trlla29G+92i2Lf8aZX5uifWbhPUlMy2BM4c2PTSHhEqRz/blswntOgQbe6eHtik/NwcDg0ebO11aepdHyXIhUmQI/yWuOgVBeGHodDpORKbR2t+x7MJCqdxszJAaGXA1PouW/g5V3RzhMfD19UUikTBo0CDWrl2rf1weL9qSYVXF1NSUQ4cO6f8vVI63lzuXktNwcPHEwsoOuaz4VBmtVltmpuqS5MmzMbP4ZxSVSplfrExZCQubtuvJnWufsvKHt7B39aZtrzFY2Tqi0+nISkvk+M6VRN+5jI29C0OnfImBgQE6nY7o25c5tGUJEokER1cvbpzdR68RU1Ep8rCwtAXA0saerIykCr8ugKS4e+Tn5WBuaQOAhaUdTq5eXD17iAYtOwPg5OpJr+GTObZrBUqlkjotOuPuE4JEIkGpyOPCka1kZyRhYWXD8m/fILBuCxq364upuSU6nY7kuLtcObkbYyMjug2ZiKV12cuiRVw6ibmlDWpV5UZeJcfdw9/brcRtBpLKR84SSek3TYQXkwi6BUF4YdxJkZMuV1HbXQwtfxSGBhL8nSy5EJNV1U0RHrP/BgQio/mzw9DQkLCwsKpuRrUX2qYNy6Z/iV9IA0ykpuTnFh2xU7txWy6f2ldsLnZ5qJSKIsPJc2WZZKYlYef4T/IwRxdPEmPu4uYdUOp+nFw96D92Olqtll8+ew21VoOxkQm58mxyczLRaDSkxEfx9dRuGBgaIkGCh28QPoG1cfH0p0GLTvpe9mO7VhPWuzBJmEQiwcbOmcgb5/Gv1bhCr+3s4W2E1G+lvxk34OV3OLpzNWbpNvqgG8Dc0oaugyei1Wi4dHo/ty8eAa0OIxMpDVt3xcHZHXl2BoYGEhq27MC5g+uRy7K4dvYQXQZOoMfQVzE0Kl94otPpyMlKQ6spwMM3iHsRF6kR0rBCryt872qWzim+PjqAv5czqUmxOLl6VWifBSolNmZiMLFQlAi6BUF4YZy4m4axoYRgkUTtkfk7WXDmvkim9jwRAbfwIjA1NcVEk4NaXYCRkTEunn4kxkbi5lU479nLvxaXKhF0x967WSQreHpKPEF1WxB+cDPdhryqf75Rm+7sXvcrPUdMLXVfLTr25/SBTbTv8xK9R71Bfn4uUbcuoS4oYMDL75V7FEqeXIZGrUZq+s9yWH1ems7KuR9VKOjOyUon5s5VXnn/J/1z7t6BxNy5RqvOg4m7dxPPGjWL1DEwNKRRKe/hsV1raNdrFOaW1nTsN5btK3/GN6guWq263AE3wMUTu1GrlPQc+QY3L54g6vYV/IIblPv9kWdnEOztgLFxyfO2hw0ewHtfzMep32vlbhPAlRPbeXVovwrVEZ5/IugWBOGFcTIynUBnK6RGYu3MRxXobMW2K4kkZufjZmNW1c0RHtEnnxT29NSqVavIY+HZUVBQwG+//QbAxIkTSw0UhLJ9+M4UZi9aRLehk2ncpgebl33HgPH/BLPuPoHcvX6OgNpNyrU/nU5H+MHN9B83Q//c0Z2r6Tl8Kgc2L9Wv3w2FWc1NzS0e2oNqZeuAXJZFnlxGUL3mbPj9K/xCGpIcd5/d636l25BXywwsVUoF21fOoe+Yt4s8b2ltS0Ctxmxe9i39xr5b5mtT5MlZvWAWrboMwszcssi2mo3akJmeSGpiNOZWNtg7uZe5v/PHduLuG6Rfe/vcke1kpyfRa+TrXDp1oNy98NF3rpISH4W5pQ1uXv6cPbyN+s3ac2DTEjoNGF/268rPZcMvH7Fp5cJSy0ilUjwcTMlMS8TOseQh6P+lVOSjSIvEx6fkZc6EF5dEJ25lPzEymQwbGxuys7OxthbDWQWhKqk1Whp+to9udVwZ0KhymWmFf2Tkqpiy6gK/jGxE97rluxh50YlzwvOhqj7H3NxcLC0Lgx65XI6FRenLKAllm7vgN6LlFrToOID4+7e4cvog3Yb+E8zuWvsLdZqG4fWfHtz/0ul07Fg1j8Zte+h7y/dtXExA7Sb4BddHrS5g4+LZ9Bg+RT9HWavVsmnJN3ToN7bI0PN/Uyry2bz8O/qMfou8nCyO7lqDnaMbsZE3MLO0plP/l7G2KznJWOy9m+xYNZfBE2fi4Fw8ENbpdKz8eSZqtYpBEz4sde70/dtX2L5yDs3CetOiQ78i2/Jzc/hryWxSE2JoGtab9OQ4atRqTK1GoSXeEFAq8ji2czWOrl40aNUFpSKPoztWkpOVTmCdptRv0RGAw9tXYmQspXmHfiX2ems1Gs4e2UZ2RjL3b1+lTbeh1GkcilKRx+bl31O/eUciroTToc8YLG3sS3xd0XeusnvVj6xfPh97+5LLPKBWq3n7w8+o3/UVrG0fntRNpVSwddk32Ei1fPLBm7i7l30TQqj+yntOEEH3EyQusATh2XE5Nou+808wq3dtMbz8MXl99QUGNvLkgx4PvygVColzwuOTlJTE7Nmz2bFjB7GxsZiZmeHn50fHjh355ptvipX/448/mDdvHjdu3MDExIQWLVowc+ZMWrVqVeFji6D7+fHLb0s4dO4OXQZNIjcni1P7N9Ky0wA8fIPR6XQc2LwUY2MpLTr1LzJE+4HYezc5fWATbboNxc3Ln8SYu5zc9xeNQ7vjG1QPKAywTx/cxNnD22ge1pem7ftgaGiIRq1mx+p5uHj40rhtz2JLU+l0Oq6eOcyRHSsJ7TEcd59ADm/9A1cvf84c3oaRkTHOnv4E1WmKvbM7arWauHs3iIm8ToFSgb2rJ6q8PIylUoLqNsfK1gFlfh6x926QJ8/G0tqWezcuIstOx9rWkUZtumPn5Ia6QEVcVAQ3Lx5Hp9UyfPKsIoG7Vqvl/LGdXDt7mOFTPsXUzII5M1/G0c2TkPqtiLsXgaWNHZ41amIiNSMnK53r54+i0+mo3bgtEomE6DtX0agLkGWmAtB3zHRs/nUDITEmkvPHdoEEatRsiLmlDbk52dy5dgZDA0NMLSyRZaTSa+TrnDu6k7ycLFp1GYSBoRE7V83DJ7Au2VmpyGXZeAfUxsbeGY26gKSYu0TdvkxafCTNmzfDydaCEUP64+vrW+Lvx4ULF9m6cz9ypYbTp8Np3W0Yjdr0KHYzQKfTEXHpBLcuHGHIS69hbmnN1j++48uP38HKSlxvPO9E0P0MEBdYgvDsWHD4Lj8fuMOil5pgVImstEJxcw7cRqPVsf7VigcuL6Lqek5Qq9Wkp6ejVCpLLePt7f3U2nPq1Cl69OhBVlYWtWrVok6dOuTk5HDjxg3i4uKKZVKfPn06P/74I2ZmZnTp0gWFQsGBAwfQ6XSsX7+e/v37V+j4Iuh+vshkMmbO+orYtFys7F3JTk9BpVRg4+CChZUNmWlJJMXdw87BBWcPP0ykpmSmJpKaFIvU1AyvGrWQZaURG3kTuSyTGjUb4ejqiUQC2ekpJMTeJTnuPnYOLphZWCKRGGDn5Iq9sweGBoYkRN8hNSkGJ1dv7J3dMTKRIs9OJys9BQsrW4yMjIm6fRkDA0O8A+qgyJNToFJQoFKRnhyHVqvF3NIaYxMTDAyNcHH3w8rOAWVeLjGR10lLisfE1BR3n0BMTEzRoUORm4OFtT2mZuakJcaQlZFKWnIcWq2GAkU+VrYOmFlYYWXniJtnDWwdXNDpdKQlxxIXeROJkQGqfAVObt64ePhhZWtPYuw9bl0+hVeNWtg5uaNWq8iVZZKdnkxKYgwSQwMCajXhQR+4RAKWNg6kJcWSmhSLi7svrl41sLS2Jy9XRnpSHGkpCeTLsjAxM8MnsC7mltZkpSagyJPhX8MbE0snTC1skGelc//uNaxs7HH1rIEsK5PkhBis7RxRazSo1Wq06gJsrCzp1GMANYIKp9EUqFSEH9mBRp7EB+9O0yfBy8nJ4fOvf8AzsDH1m4cRH32Pu3du4ONfk5OH96DWgoWVLRJDQ/JyskCjomGT1oTUa6zv5c/LzeHWyb+Y/sbkKvitFp6m8p4TxJxuQRBeCPtvJFPXw0YE3I9RgJMVG87HUqDRYlzaQqdCtXXkyBE+/fRTTp48ScFD1vZ9mkuGJSQk0KNHD5RKJRs3biwWMJ85c6bI44MHD/Ljjz/i4ODAqVOnCAwMBAoD97CwMMaNG0dYWBh2dmUvTSQ8n6ytrfn5h68AyMvLIyEhAScnJywsLMjKykIikZCfn09MTAxRUVEEBARgaWmJRqNBq9UilUpxdnYmNzcXY2Nj7t69i1qtxsfHB6VSiVKpxMPDA1NTU06fPs3Vq1fx8vJCp9Oh0+mYG76b4ZM+wdbBBUNDQwyNjDAztyJPLiMrIxWtVkPnfqNBq2PD0u8IqNmApm27o1arSU6IxsbGnqvnT7D3r8V07P8ypw9uxsnFg3rNO+Dq4YuBkSFJ0XdJTLiHvYMrpuZmSIyMibl9lZtXw1Hm5mJr54S5mQUNQ7sQENIInUbFqUM7qd2wJdF3b3B89xoMTaR06TuGFmE9kWdlYGBgwO3r5ylQ5iOVSmncsiNtOvTGWColNzeH+xGXSbgfwZAJ72FpZYW1jSO3r4Zz4fBmJFILegx9DSs7R04f3IZGq6F9j+EoFXlkpCRiYGyMkZERtvbOGJtIyZPLgMKRBakJUXj5BtC8hpTGjRuTnZ2Nubk5ZmaFuUUUCgW5ublotVo+/XoOnQdOQiqVYm5hVWzYu7GJCW069yctOZGZs/7Hl59+RH5+Ph99Opveo6ZhalY4uuHkkd30G1GYVd3bLxCdTkd+nhytRoO5pXWJy8uZW1iRlC6v9PJzwvPnmejplsvlzJw5k3Xr1pGRkUFISAjvv/8+w4YNK7NuSkoKM2bMYPv27eTl5VG/fn2++OILOnbsWKTc9u3bWbduHRcvXiQiIgK1Wl1iZtb79+/j5+dX4rFWr15drjY9UF17NQTheZOao6TZl/uZ2LYGYcHOVd2c58atpBxmbbvO9tfbUMfDpqqb88yrTueEffv20bNnTzQaTZlZzCUSCRqN5qm066WXXuLPP/9k7ty5TJ1aevbnB3r27MnOnTv58ccfmTZtWpFtb775Jj///DPfffcdb7/9dsk7KIHo6RYeJ7lczsuvTad+2wHUadK2xPnQd29cYNe6RTRs0YG23QeXuJ/L4UdY9/tsmrbrQVzULewcXek68GXsnYrn3ChQKTm8YzV5chmGxsZYWtvSoedw/fYzR3Zgam5JncahrF/8DT2GTCQlMYboO9fp0HtkuV7XiX2bsHN0pVbDloWvU5bFwq/eZNb3S7h35wZnTp2g57DCrO4XTu4nNuo2YT2HYVXCPGxFfi6Htq3Axs6B9t0HcfHkPgaEhRAUFFTq8d//+HM6DZ6CVFq+Ne0T4+6TfvcUiSlptO45DnMLy7/fKxWb1yym/6iKZTAHuH/nOs7GGfTsXvHl54Tqo1r1dA8YMICzZ8/y9ddfExQUxKpVqxg+fDharZYRI0aUWk+pVNKxY0eysrKYM2cOzs7OzJ8/n27durF//37atWunL7tp0yZOnz5Nw4YNkUqlnD9//qFtev3114sd+8EdckEQqpdDESkANPIWvVmPk5+jBYYGEi7GZomg+znzySefoFary730ztOQmZnJunXrsLGx4ZVXXimz/INh5ACDBg0qtn3QoEH8/PPPbNu2rUJBtyA8TpaWlqz9YyE7d+1m2Q/TsXL0wsndDwMDCekp8SRF38bH3Y7mDQJx9q1R6n7qN2+Hb1BtZk3uh6WtPYr8XH7/bgYOTu54+9fEytaBfLmMmHsRZKYlYWpmiZOrB2E9huiTuel0Oo7v3YjU1JSQes1Zv/gb2vccgZWNPWbmVuxct4i83Bx6Dp1U6neDTqfjyM61WNs56APu9OR4fv/uXd766BsMDAwICK5DamI8G5f/SL/Rb9KoVSeC6zbl+L5NyHOycHTxxNzCCkW+nPt3bqBW5TNk3DRsHQpvmqfFRRAQ0KvU9+LOnTvYuQeWO+AGcPP0JfzgZqztXfQBN0BWZhqOrh7l3s+/efvX5NK+ZSLoFoBnIOjeuXMn+/bt0wfaAO3btyc6Opp3332XoUOH6udY/NfixYu5du0aJ0+epGXLlvq69evXZ8aMGYSHh+vLLlq0SD+8Y+rUqWUG3d7e3rRo0eJxvERBEKrY3htJBLlaYW0mlth5nEyMDPB1MOdiTCajW/hUdXOEx+jy5ctIJBJ0Oh1NmzYlNDS0yhMCnThxAqVSSadOnTA2NmbDhg0cP36cgoICQkJCGDJkCC4uLvryERERKJVKnJyc8PQsvmJBo0aNALhy5cpTew2CUBKJRELPHt3p2aM7crmcW7duIZfL8fZuga+vrz7A/WvTNo5tnIuVSwB1W3TRXx8nRN8h4uwezp85RcOWYYx/6wtiom5xbM9GMtKSyMtJx87eAbUqD1V+Lt41gunU9yV9EKvIz+XEvk1kZ6bh6RtEYmwkyQkx9Bw6CWV+HtvX/IJOq6X/iFfZs/lPtq6Yi5mFFa069cfS2hYonMN8ZOca8vNyady6M97+tbh19Qw3Lp5ElpGCl7cfTv8KXluGdeXyhVNsWzkXUzNLWnXuT9cBY9HpdKQkRHNs70ZUilxCO/YisHZDfb1cuQxP55KHdD+wdsMWWveZWOHPoUHrbkRHRhR5TqVUYGIirfC+AAwMDNBotJWqKzx/qjzo3rRpE5aWlgweXHS4zLhx4xgxYgTh4eGlZhfdtGkTwcHB+oAbwMjIiFGjRvHhhx8SHx+Ph0fhH7iYTyEIL6Z8lYbjd9LEMmFPSKCzFafvpaPT6Z6pXlHh0VhYWKBQKKhfvz6nT59+Jj7b69evA+Di4kJoaCinTp0qsv2DDz5g6dKl+uuJmJgYgBIDbih8jba2tmRmZpKTk1PlNxXKIpVK2b59u/7/wvPJ0tKSxo1LXqd6YP/eDOzfm2vXr7Nr7zIKNFoMJRICavjwzSdvMXNWHhFRyWSkJeHtF8zIVz8ocT95uTmcPLCN3FwZOVkZXDl3nHpN2mBla09WehJGxkZIgKO712Jj60jPQeMwkZqy6c95uLh7EdZ9CMYmUk4e3EZ+fi7odEilZji7eZKcEM3180e5fuEYNQLr0m/ka6z97SvqNWxerB3tu/QlITGROo3bcPLgdvLycgCQmpjSfcBobO2LL9F1YtcqPnpzzEPfQ4XGEKMSlhsri69/COdPHirynLWNHdlZ6RXeF4AiPw9z8/L3tgvPt0oH3SqVChMTk0duwLVr16hZs2axP4569erpt5cWdF+7do3Q0NBizz+oe/36dX3QXVFff/01H374IUZGRjRq1IgZM2bQp0+fSu1LEISqc/xuGgq1lsY+Ymj5k1DP04bd15OITM0lwNmy7ApCtdClSxdWrVqFqanpMxFwQ+Hwcihc/ksqlbJ48WL69OmDXC5n7ty5/PDDD4waNYrg4GDq1auHXC4HwNy8+FJPDzxIliWXy0sNuh8kw3pAJpM9xldVfkZGRvTs2bNKji08W+rUrk2d2rWLPf/V5/9H/6FjWPLDx0x6b3aJ86OhMMlXpz4j0KjVLPruAwaNnUaz0C4PPebuv5ZSI7gudRq1ZtVvX9Nv5BS69Bv90DrqggJWLfySZs1DMbUsPtc1uE5DoiIjiLkXQee+Zc8VP3NwE11C65e5traOR/jO+s/3nZWNHVlpKZXa1dkjOxg3qHvl2yI8Vyrd/evh4cEHH3ygv5NcWenp6SX+8Tx4Lj299LtLj1K3NFKplAkTJvDLL79w8OBBfv/9dzQaDX379uX3339/aF2lUolMJivyIwhC1dp3IwkPWzPcbc2quinPpVru1hgbSjh8q3IXJcKz6X//+x8ODg6cOXOG77//HpVKVdVN0idrU6vV/PDDD7z88ss4Ojri6+vL999/z6BBg1CpVPp1uh8kgHvYTYPy5JL96quvsLGx0f94eXk9hlcjCI+fRCLhr9VLsbUyYe7nb3L68A602pKHN9+5cZEfP3kNqZmUzNQEFHm5JZbLSEtiw9IfsLd35OrZo0RH3mDoK++yY90izp3YV+r+I66cYcmPH2JuKsXe0QmVUlFiuW59h5ORFM3uv5aSnycvsUxmWjK7Vv9MyzrudO3UodTXHxMTw7ff/0RySmqpZcpS0veFi5snyQmxFd7X1QsnWbryLz778lv27NlXru8b4flV6Z7u3r1767N+9urVi6lTpxbLGF5eDzshlnWH/VHqlsTNzY3ffvutyHODBw+mefPmvP/++4wdO7bUIStfffUVn376aYWPKQjCk6HWaNl/M4XW/g5V3ZTnltTIkFpu1hy+lcoroaUn+RGqF29vbw4fPkyzZs2YMWMGn3/+Of7+/tjYFE+YJ5FI9AnLnqQHPdEGBgaMGVN8eOnLL7/Mhg0bOHz4cJHyubklBxNQuEQUoM8KXpIPPviA6dOn6x/LZLIqCbwLCgpYuXIlACNHjsTYWOSoEIozNDRk/colnDhxko9mfcm+LSvwC6qDq6cfRkbGpCXHcy/iKi7uHkz/5CdMzczJTE/h4PaVKJUKbB1cMDU1J1eeTVZGKtF3rvPmxz9ibmFJqw49OH/yINtPHcDG1oHYe7c4e2wPtvZOOLl4IDUzJzMtmXu3rmBoACG1G6DTanH18Obalc00aNqmxDZ37jWYrIxUDu1YTb4iHxt7Z0zNLMjLlZEYE4mviyVffjit1Iz92dnZfPfjPGydvWjX6yXilv9WYrmyqFTKEmOH0M69Wf37HEa+OqPc+zp/8gDdeg2kcfPWANy6fpmZn82mfZvmdOrYvlLtE6q3SgfdS5Ys4fvvv2fRokX8+uuvdOnShaCgIKZMmcKYMWPKPTfKwcGhxB7pjIwMgIcOIXmUuhVhbGzM0KFDef/997lz5w41a9YssdyzcmIWBKHQsTtpZOSqaFFDBN1PUn0vW1afiSFPpcbcpMpThQiPQUZGBiNGjEChUKDT6ZDJZFy8eLHYBenTnMvv6+sLgKura4lzmh9sT0kpHHXh7e0NQFxcXIn7y83NJSsrC1tb24des0il0mdiDrVKpWLcuHFAYWeACLqFh2nduhWjRwzBKaA5uXIZUXduYWllRZuWzfl9wX38g+ro16G2c3Cm3/AJaLVa5LIsFPl5WFhZY2FpzbZ1i1m39CdemvwhBgYGNG3TiaZtOqFU5CPLzsTIyAgzM0vy8nKIjbpDRnIMnl4+uLp7kicvnKNtaWWDXJb50O8LW3sn+o94Ba1WS25ONnl5uVhYWnF46zI+++T9UutlZGTw+dc/MPTlaZiaFo5o8/DwJDHuPm6evhV6z47s2oC3X0Cx501MpLTu2IMtaxbRZ+grZX7nXbtwCll6PJ06/jP8Prh2fYJr1+f4gR3It2yjX9/eFWqbUP09UnYxOzs7ZsyYQWRkJJs2bcLLy4s333wTDw8Ppk6dSkRERJn7qFu3Ljdv3kStVhd5/urVqwDUqVPnoXUflKto3Yp6MCTkYQnZpFIp1tbWRX4EQag6687F4m1vjp+jWM/2SWrgaUuBRsepyMolmxGePe+9957+XCqRSPQ/Valhw8IMxpmZmSUO03xwE/5Br3VwcDBSqZTU1NQSA+8LFy4A/+SBEYTniU6nIzI6AXcPbwKD69Cl10BateuCi7sXfv6BmJqZ8dv3H5ORmqSvY2BggLWtPc5unuTlylm3dA5evoGEdR3AnC/eIuLaPyv/SE3NcHJxx87BGR06zp3YT/z9O9Rv0pocWRatwrphbCLV/602bNqGy2ePl9luAwMDrGzscHHzRKnIJ8DH7aHLk3317U8MHz9dH3ADtO/ck5OHtlf4/dLkpZF473qJ2wOC69CgcStWLvyGmKjbJZaR52Szfd0Sjuxax4AhJc93b9OxJ5HxGVy4eKlC7ROqv8fSJSGRSOjTpw9eXl68/fbbHD58mAULFvDLL7/Qr18/fvnlF5ydnUus279/fxYtWsRff/3F0KFD9c8vX74cd3d3mjcvnu3w33UnT55MeHi4vpxarWbFihU0b94cd3f3x/HyKCgoYO3atTg6OhIQUPwOmCAIz57MXBX7byYzrKl3lQcLzztXG1NcrKUcvpVKx5ouZVcQnnlbt27VLxkG6HuDq3IlkLp16+Ln50dUVBTh4eHFlvV8MKz8wVJgZmZmdOjQgV27drFhwwamTZtWpPyGDRsA6NWr9PV+BeFJ0el0bNq0iZOnwomNi8PY2AS1SkmOPBcTUymuLs68+/Z0atSoQVZWFkePHufSpUvk5ufh7uZO3Tq1CQ1tU+KIh4iICI4cO4aVfcnXwT37D2Ppwp8YN+V9/lrxC/Gx0bh5+GBuaYU8R0ZGWhJajQZHF3dOHtxBXm4O/YZNQJadwbqlP2FsYoqFlTUqpZL83BykUlPCuvbF2ETKsvlfUbteYy6eOUZInQbcvlG4JF+teo35Y+F3+AaElJiV/L8KVCqWz51F7SA/3pw2nbz8PBwdHKhduzaOjo60axvKkSPHqNMkDJP/jEQxNjHB29uHS+FHaNC8Xbk+j30bl9K3Z2f27N3PyYPbaNWheE+0f3BtfANCOHVkD6cP78REaoaRsTFqlQq1WoWlpRU+vn7UDH54rNCh+0C2rVpAo4YNytU24fkg0T3irH61Ws369euZP38+p06dwsvLiylTpjB06FC2bNnC559/TqNGjdi9e3ep++jSpQvnzp1j9uzZBAQEsHr1ahYtWsSKFSsYObIwm+H48eNZvnw5kZGR+PgUrgerVCpp3LgxMpmMr7/+GmdnZxYsWMC2bdvYv38/7dr984cWHR3N2bNngcL1vXfv3s369euBwiFpTZo0AWD69OkUFBTQunVrXF1diY2NZe7cuZw9e5alS5cyduzYcr83MpkMGxsbsrOzRa+3IDxlf5y6z6fbbjB/RCNsxPrcT9zSE1HcSJRxbEZ7cZOjFNXpnGBlZUVeXh7Ozs4cOXKEoKCgqm4SAAsXLuTVV1+ladOm7Ny5E0dHRwDOnz9Pp06dyMrKYv369QwaNAiA/fv307lzZxwcHDh16hSBgYEAnDp1ivbt2yOVSomKiqrQdLSq+hxzc3P1vfhyubzU+a3Cs00ul/PRx5+QnJqFuYUlBgaGxMVG4e7hRfc+g3D38EKn0xEXc589OzYTeecmEokBDRo3p2mLNvj4BWBoaEBSYgLHD+7C3dWRkcOH4OTkxF8bNxFx+x6uXjUwMpKiNpDSuHnxVX4A4mLus/y3n5j6wWxMTKQUFBSQkZqEUpmPpbUd6gIlZhZW3Ll+kT1b1zBlxv+wti38O9FqteTnyTE2kerXsE5JimfV7z8QEFiLfsPHs2rxT5hbWNOp50AsrQpzQagLCvhj4fd07jMcN0+fUt+jXLmMP3+dza0rZ3F1d2fQsDH41Qgi4sYVbt28hqmpGbLsDKKiovjix8WlnnN2bF6HqaXdQzOz63Q6tq3+hbzMBDy8a9C4eVvOnwtHpTOmY88hD623Y8MynJzdaNa6A8Z/r+i0Zskcxk58vczz4K5NK5gweqD+O0yovsp7Tqh00B0fH8/ChQtZtGgRycnJhIaG8sYbb9C/f/8id8I3btzIqFGj9MlKSiKXy/noo49Yt24dGRkZhISE8MEHHzBs2DB9mbFjx7J8+XKioqL087YAkpOTmTFjBtu3bycvL48GDRrw+eef06lTpyLHWLZsmX4u1H+NGTOGZcuWAYVz1RcuXMjdu3eRyWRYWVnRrFkzpk+fTpcuD19O4b+q0wWWIDxves89jtTIgLe7BFd1U14IF2Iy+XbPLfZPb0uA87O93nFVqU7nhHbt2nH8+HE6derEnj17qro5elqtlmHDhrF+/Xrs7e1p1aoVcrmckydPolKpmDBhQrFkqNOmTWPOnDmYm5vTuXNnVCoV+/YVZl1et24dAwcOrFAbRNAtVNa9e/eYPuMjuvceROSdW7h5eHP10lmmTP8QC4uSk/nlyLKZ98OX5Ocp+OSrHzE0NCyyXZGfz8Y1S4i9f5fh417H27cwoeWiBT/gF1yfFqGdStotAAd2b+Hc6WNY2tjRa9BYnF0Ll9nVarWcP3WY8ycPkpmZRr48B3NLKzx8Agjt2BPfgH9yG924cpb929eRnyunY48BtAjtzNWL4WSkp5AYe59h414vckytVsvuLauJi46iaWgn6jRorg9QY6PucProbqQmJtSs0wB5RiLXLp8nMyODOvUbMnTUeADiY6NZv3oZtg7ODBvz2kPf83Phx7ly6QLObt607tALo79HBuTIsjh9cAuxkdfx8QtgyMjxReKXC+fCWbdmJQ2ahRLaua/+5kKuXMbhPZvIyc6kTVh3fAP+ucaIj7nHjUun6DNwxEPbBJCXK+fSsS1MfnVimWWFZ9sTD7qNjY0xMjJi2LBhvPnmmzRo0KDEcpcuXaJ///5ERUVV5jDVWnW6wBKE58mtpBy6/nSU6Z2DaOr7eBIqCg+nUmuZuvoCQ5p48XGvWlXdnGdSdTonHDx4kM6dO2NlZcWFCxeoUePZyUyv1Wr59ddf+f3337l16xYSiYT69evz6quvMnp0yfMoly1bxrx587h58ybGxsa0aNGCmTNn0qZNydmUH0YE3UJlpKWlMeHVNxgz8Q3OnzlBcM26nDp+iNffnllmXZ1Ox0+zZyHPyWXmF9+W2Iu6YfVy6tRvREituuzYvA57R2fuRt6j//DxD9332j8WUrthc+7cvMqta5dQqhTIMtPx8Q+h9+AxeHr7cfncSRLiojE2NuHS2eNkZRUmLDY0MMTeyZm+Q17G179wNMz9yFucPrIHeY6MUROn65O1/dv9uxGcP3WIoFr1uHXjCkgkSAA3dy9at++KkZEROzauxMRQR4/eA1k471vkOTnUqdeIPgMLO+RiY6I4dfIEfQaNKvP9g8Ke/WOH96LVFC5xduvqGXr36IqFoxeNmrQqsU7knVts2bIRa1tH/dJoUqkZ7Tr3wsauaILW9NQkdmxYxsTXZ5R7tNf+jb/zzvQ3y1VWeHaV95xQ6Tndn3zyCZMmTcLJ6eHzMho0aPBCBtyCIFSdP07dx8bMmIZetlXdlBeGiZEB7YOdWXc2lre7BIks5tVcXFwc3bp1Y9euXTRu3Jhhw4ZRq1atEpcMA3jppZeeWtsMDAyYPHkykydPLnedsWPHVmh6mCA8btPensHbH33JxrV/MGHK23z87hQ+/3Z+uepKJBLenPEJM9+dTPjJI7RoHVaszKDhY/ht3nfYOzihUCho2SaMnds2lrnCwJDRE9m4Zilm5la88cFXGJuYkBB7n12bV4GuMNCs/3dQev3yWca/8SF2JczJLlCpOLJvK/du30Cr1TB60tvFAu4HZXLl2Qwd8yoSiYRGzVqX2C5ZZipmpiao1QWMnfA6m9ev5PyZk7Tr2BUbWzssLCwx+XtId3l4evsy/KV/epXnfhVDZEwyY7sNLbWOf2AwvXv3Y+eOrXTpOwJXd+9iZbRaLWeO7+f+neuMn/x2BadXialYL5JKXxV5e3uXmlAlIyOD7du3P9WTsCAIAkCKTMG6c7EMbOSJkWHVJX16EXWq6cL2KwlsuhjPyOalz9cTnn1jx47VXzxmZ2cXG7L9X+J8/+RJpVLWrVun/79QfcjlcswsbcnMSCOkdj1OnzhCuw5dKxSgGRgY0KJ1GLu2bS4x6AZo1qotq5YvZNLr76JSKrGxtuZC+FEatyg9mZhEImHg8JeJj41my9rf0agLE6gF1WrA9r/+ICs9Df+Qunh4+eLg5MLvc77EwMCA4NoNcHB2RZGXR8z9O6QkxmFlY0vDpq1JS0li8dz/UatuY6zt7MnOzCArIxXQ0a5TLzy8Hn5+uHvrKsEhtahbvyF7dmxmwNDRaHU6AoJC2LZ5LaPGvoq1jR3ZmRnlfv/+LTMjjZiY+0ye/mGZZQODa/Kabw327dnO0b2bMDW3xs3DG5VKSWpiHHcirjB8zKu079i5wm2wtyv5JqbwfKr0Fem4ceOIjIwscVtUVFSp86cFQRCepEXH7mFiaEDnWiKL9tPmZCWlsY8dy07cL3FJJ6H6+e9SYTqdTv/z4LHwdBgZGTF48GAGDx6MkZEYSVKdfP7lV3TvPYgTRw/Spl0ndm5ZT4euFc+a363XAGTZGSQnJpS4vU69Rmi1WszMzDm4bwcjxk7ixqUzZKanlrlvDy8fhoyehFyWiVyWjkaRySsTXuPjz74iPTmGi2eOkZYSj72jE5Pemkmb9l1wcHDAwdGJDl378vp7XzDlnU+p16g5memp9BsyhqatQrl9/TxH926m76DRjBg3pcyAO1eew9G9W2nTriN29o5kZ2UCENapG9a2tty6cQ2dToeRkREadYF+2HdFHNi1GWcXl1Ln0f+XiVRKzz4DmfTaG/h4uIAyi2YNa/PS6FEEBQbhH1Sz7J38x/YNfzJ40IAK1xOqr0oH3Q870SoUimKJHgRBEJ60zFwVK07H0LmWixjeXEW61HLlToqc0/cq1wMhPDv+HWD/O9D+93ZBEMoWn5BErTr1MTY2/nstbNtKXSebmJjg5u7JjeuXS9yenppCcM06hf9PS8Xdw4vxr77B5tWLSIqPeei+FYp8liz4BkdnVzzd3ek/aAROzi5YWlox48PPaNasGdkZaWRnpLHwx8/Ysm45CoUCd08fzMwtuHPzCovmfMmBHRsZNHI8NQJDOHloNy2at6JBo8asWvIT8pzsh7YhMz2F1Yt/4pXX3tSPprWysUGpUODrF4A8JwcrK2vkOTIAWrYJ48yJwxV6DzUaDQVKBe4e3pUK2Nt26EpGehoent6YmpnRpm17wo8fqtA+tFotaSmJJCYmVvj4QvVVoavSmJgY7t+/r3988eJFFApFkTL5+fn89ttveHsXn/cgCILwJC07eR+tTke3Om5V3ZQXVm13azztzPhp/22a+bXA0EDMWauODh2q2EWk8OSp1Wo2bdoEQP/+/UVvdzUiMSgaYBtIKj/1ydDQkLxceYnb8vLkWFo9WD2i8KaYiYkJU6a9x8Z1K4mLj6N1++7UrNNQXyc5MZ5De7YQF3MPczMzpMaOdOras9gxu3TvQ5fufbgfFcm1yxc4eewQS+Yew8nVg4ZNW1MjsCYT3vgApSKfA7s2cufmFYaPepmAoBBuR1xj1NhXWbd6OaoCDW069sDT+5/kjPcjIzh1ZC+WFuZMmfZekbXHzczMycuTIzU1BXQYm5iQl5uLlbUNNWvX5fCBb6ldvxFW1rblev/+WrWYzt16c+zwXpRKBWYlJHp7GAMDA7S6f4L1mrXrcGDfLuo0aFzuNmxctZjRYyeyacs23nv37QodX6i+KvSNvXTpUj799FP9cLOSkpg8uPM9Z86cx9NCQRCEckiRKVh8PIr2wc5iXe4qJJFIGNvKl//tvMkP+27xbteQh5bPU6nZeCGe43fSkCvVKNUaetZ1Y1QLHzEnvwq1a1f6HFChaiiVSoYMKVw3WC6Xi6C7GtFq1H9fHxdeI1emh/WBAnUBllYlZ0i2sLAiOyvr70f/3PA0MDBg0LDRaLVaTh0/wp8LvyUlOQmNRkNGeipNm7bilUmvY2howPUrFx96fF8/f3z9/OnVbzAAd27d5NSJI9y8Es7+HWtRKRUMHfkyLo72BAQ9+P7XITU15aWXX6WgoICD+3Zy9the/T49PL15ecLkEnv/c3PlWFg+eL0SlEoFFpb/DAt/eeLrzPpwGq+/9zn2DqUnd9bpdGxas4x69Rvi6e1DrjwHU1Ozh77Wkmg0Ggz/cxNlwquvM2/OdwwaPanMNmxeu4z69evj5x/AmZMHUSgUmJqaVrgdQvVToW/sIUOGUKdOHXQ6HUOGDOF///sfgYGBRcpIpVLq1KlTZC1tQRCEJ+2z7TcwNJAwsJFnVTflhVfb3YahTbyYfyiSBl52Jc6vT8lRsPhYFKvOxJCrVFPTzRpLqRFaHXy67QZrz8Xy9YB61BcZ6AVBqOZCgv25eO504dDmggJycmQUFBQU6dEtj/z8PBLj46hbv1GJ2x2cnLl98xoAbu6eRN27g1+Nf67TDQwMaN22Pa3bttc/d+XiOVQqJZ5ehcOt9+zYXKE2BQbXJDC4Jgf37sDJ0REvHz8i70TQpl1HfZmatetz+eJZGjRqhrGxMV179C33/nNzcjAxMeH2zWvY2Npx68ZVLCyt9NtvR1ynd/8hHNjxF1lZWfQbNhYnZ1f99oKCAg7t2UpyQixhHbsSGFw4/zopKaGCmcYLnT5+mMZNWxZ5TmpqyhvT32PVn0vQaCGsa99ibTh+cBfxMffo2LkrQX+3wc3Dl4SEhGdqSUbhyalQ0F2zZk1q1iz8RVm6dCm9evXCwcGhjFqCIAhP1qFbKWy/ksjkMH8sTUXvz7Ogd3137qbKmbbmIuPb+DGkqRd25iZcjc9mz/UkVoXHYGggoUOIM11queJk9U825rspcpaeiGLYb6fZPS0UHwexHvGTdvToUQCcnJyoWbOm/nF5tW3b9kk0SxCeCzPeeYfJb85g6KhXOLx/F4NHjGXXtg30GTC8QvvZtmktzs5upfamnj19DDNzC+Q5Mtp26MqKpb8WCbpLUrdBY5Yu/JkmzVtjYGCAmZkZufKcIoFtedy/dxeJRELPvoO5cOYUDo7O+m0Nm7Rg2W9zadCoWYX2mZKUqA9eTxw9CBKo17BJkWD5XPgJxk58HTd3T25H3ODi6SNkZmbo54RHXL/CxClv49NvoL5ObHQUxkaGZGdlYmNrV6E23bl1nbAOHYs9b2xszJiXJ6HIz2f3jq1cuXIRT29fJBIJOq2GDp260adPnyJ1TM3MyM3NrdDxheqr0lenY8aMeZztEARBqJR8lYaZm65R18OGNgGOVd0c4W8SiYRX2/mz+kwsi45FMffgXSQS0OrAUmpE7/rudKvtioW0+GkowNmSmT1r8cGmK7y19hLrX20l5oY/YWFhYUgkEgYNGsTatWv1j8tDIpGgVqufcAsFofqSSqXo1EoMDAyIirxNhy49WbX8N3r0KX8mepVKxbnTxxk57tUSt+t0Oi6dP8PIsZPYunENI8ZMRCqVkpmRhp196edGiUSCk4srsdFRePn40aVHX7ZtWsuw0a+U+/VF3LiKtY0tUlNTzpw6SoPGRYNriUSCvaMT8XExeHiWP+fT9s3rGDFmIqkpyRgZG3Pr5jU+mDVbvz0pMR5bO3skEgn7dm1l/GtvFRmifj/qLg4ODvj4Fe1J3rNjE5PfeJctm9Ywatxr5W7Pvbu38PD0emgZUzMz+g0aiiw7g5fGl/xZPZAjy8bWtna5jy9UbxUKuj/77DNeeeUV3N3d+eyzzx5aViKR8PHHHz9S4wRBqB5uJMjYcimebVcSSJEpkUjAwsSIlv4OtA9xpmstV2zMH/88a51Ox3t/XSY1R8nbXYIqNVRMeHLMTYwY38aPkc29CY/KQKvV4e9siYetWZlBtJmJIa+1C+Cz7df59UgkU9oHPKVWv9hEhnJBeDLmz/2RYSPH8vJr01mycA7jJr3J7M8/4INPZut7ZUuj0WiY/dn7eHj5UL9h02LbdTodyxbNpUefQdja2ePu6c2xQ/sYOGwMv837lnGT3nzo8lg9+w5m4dxvGTJyHA6Ozri4unPiyAFatyveo/tfSYnxHNy7HZ0OuvXqz+kThxk5tniw2bv/UH79+RuGj5mAnV3Zo2T37txMnfqNKChQsWr5QrIy0+ncrY/+dWRlZvDXmuW8+sZ7bN6wilahHYoE3GmpyezetpFJr79bZL9bN66heYvW2Ds44u8fyOEDuwnr2K3M9qQkJ7F/91Ymv/FOmWWh8CZJWRLj7uPpOaRc+xOqP4muAmdUAwMDTp8+TbNmzcr8gpBIJGg0mkduYHUmk8mwsbEhOzsba+uSk14IQnWmKNDwxY4brDgdg7WpEc38HPC2N0Ong2xFAVfjsrmbIsfMxJCRzb0Z36YGrjaPL2HID3tv8fPBu0zrGEjzGmKqy/NozdkYtl9JZOcboQS7Vmy447PmWT4nPDinDxo0iHXr1pV5jv+3F+18X1WfY25uLpZ/J5CSy+VYWIhpF9VNeno6L0+YTMdufbl7+ya16zZg3+5tvD79I1zc3EusEx8Xw7wfvsTR0YXpH3xa7OZyRnoqm9cuI0+eTZtOvanfsBkSiYRD+3aSlpZC5669Wbl8IZ269dEvJ/ZfObJsFi34AUV+Pt37DKRBo2Yc3LuDrMwMevUb8nfm8KJ0Oh2nTxzh+JH9WFhY4uNXg6OH9jLzs++xsrYp8TiXzp9h0YLvmTDl7VKHmufl5bJ5/UpqBARhbWPH9s1ryUxLo2PX3nTq3huAq5fOc/zIfoaMHMe2Teuo36gpDRs317frwtlTXDh7inGT3tSPJMiV57B+1TLqN2xEsxat9cc7fGAv8fFx9BkwHFOz4onVdDod58JPcPXSWcZPer1cIxOuXbmELDuTVqHtSy2TlZnB2eO7eXXihDL3JzzbyntOqFDQLVTMs3yBJQiPKiY9j9dWnud2cg6jW/jQPsQZoxIu1DPzVOy9nsS+G8kUaHSMbe3LlLCAR+75Xn8ulnc3XGFYUy/6NvB4pH0Jzy61Rsv0dZdoF+zMd4PrV3VzHsmzfE5Yvnw5AL6+vrRr107/uLxepClnIugWHoVGo+H773/i/KUrGJmYYmZmQWx0FLb29rRp1wkvHz90Oh0xUZGcPH6I2Jh72NnZ4eUTQFBIHXxrBGBoaEhCXAy3b1ykdkggw4YOxsLCgiNHj3H0+CnMLO3x9Q9GLpdx6tghkEhwcHRClp2Nl48vgUG1MDY2Jjk5geOHDwDQvFVbIm5eJTE+FmsbO/z8A3FwdOL6lUsYGhnSpFkrbO0cyMvL5cbVy9y5dR2VSoW1tTXZ2VkYGRrh5eNHxI2rOLu40SasE45OLhSoVFy7epELZ07h6u5B994DuXLpHDeuXqJGQDANGjfD1NSMzIw0Lp4LR60uwNrGlriYaBITYpGamjFw6GgsLa25HXGd+LhorK1tSU1NIi0lma69+uPp5YtCkc+Fs6eJuH4Fd08v2nfqgdTUlLTUZM6eOoaziyu9+g7E3qH4UPukxERW/7kYQ0MjmrYMxdrGFqVCwd3bN7l5/TLNW7ama4++5RpNp9PpmP/TbKZMe++h5f9cPJ+3Xp+EjU3JNyiE6kME3c+AZ/kCSxAeRYpMQd/5J9DqdLzZMQg/x7Iv/vJUanZeTWLH1QRMDA14LSyAMa18MDepWGoJrVbHT/tv8/PBu3QIceaVNn5iWPlzbsuleDZeiOf0hx2xtzCp6uZUmjgnPB+q6nMsKChg5cqVAIwcObLCma+FZ8/Vq1c5efIk0TGxmJuZYmJiwr3795EamxAUFMhLL72ElZUVBQUFXL16lYiIW8jlObi7exAQ4E9wcHCJ57/U1FQiIyPJzc1DLs/B1MyM+Lg4zMzNsbaywtDQCI1GQ3Z2FgqFktt3bhMbG4uhoRGWFuaYm5sjy5GTmBBPdrYMmSyHtPQ0DAwMyZXnYGhYeN5WqVRIJGBtY4tarcHQqHB4tyI/D61Gh8RQgqGhETX8A1Eo8ilQqTA1syQ9NYm8vFzkOTLsHByxtrZFoymgQKXCy9sHZxcPagQG0rJ1W2JjoklKTODi+XBMpWa079IDXz9/AoNrodVquR1xgwtnT3Ph3CmcnFyQ52TRsWNnrl29Qla2jGGjxxMUUuuho3eiIu9w+OBeRo2dyJ1bN8nOysTUzBxfvxrY2tnzy9zvGDhkFC6ubg/9PHU6HcsXLyCsQ1d8a5Q+JWrvjk0E1/CkQ4ewcvyWPF7Xrl3j5MmTKBRKpFITWrVqRd26dZ96O54nTzzoVigU+jtcD6xbt44LFy7QqVMnOnXqVJndPlfEBZbwPMpXaRi68BRxWfl83rdOhYOgrDwVGy/GczAiBVtzYyaHBTC4iSfWpmVfQCZk5TNz8zUORaQwpKkXfeu7i4D7BSBTFDB11QXe6hzE5LDqO7dbnBOeD+JzFF5U27ZtJ/zCFTIzs+nYpQf1GjRGLs9h1/bN5MpzkEgMyM7KJD0thezsLHxrBGAgMSA5KQH/wCCMjU3Q6XRIgC7d++DuUThK7X7UXY4d3MuwESP5dcF8Jr3+DiYmxa8t8vPy2LVjCxkZGRgaGZKanISllRUeHl5069GHzX+tISQogJatCoePX7xwnsNHjtB30EicXVyL7U+lUrF/z3b27thCl5596dqjb4lrhcdG32fOd1/QrVd/2nfqVmKZmOgo/lzyC65uHowaO6nEIfkZ6Wls37SGsDYtaN++XUXf/krT6XT89ddf3L0bSWBgEKGhoZiamqJQKDh+/Di3bkXg71+DwYMHi2uqSnjiQffgwYVDWZYtWwbAzz//zLRp0wp3KpGwbds2evToUZldPzfEiVl43mi1OqauvsCBmyl80rt2uXq4S5MiU/DXxTiO30lDamTIgEYedKntShMfuyIZrQs0Wm4l5bAyPJr15+IwMzFkcpg/DbwqtsyHUL39eiSSO8k5HJ3RHiPD8s83fpZU93PC5cuXuX37NkZGRoSEhOiXEH3RVPfPURAeRWZmJqtWr+XS5atokODh6YOrmwfotCQlxBN17zb9+w+kdZtQdu3aQUpqOn0HDCsxCNVqtRzav4ukhDheHj8BiURCeloavy9aSO8BQ6jhH1RiGxIT4lm/5g8GDB6Bp5cPOTkyNqz+g2uXz/HLb0uLlM3Pz+f/Pv4IL28fnFzcsba1RanIJykhHgk6unXrgY+vL2tXr+TMmTMEBIfg7u6FiakpmRnppKWm4O7uQaeuPdm+ZQNnT5+kVt36uLp5YGJsTHx8LKnJCdStU5du3XuQmprKj9/PJjC4Js6unpiZmZOTk016SiLubk4MHzpEP03kaSgoKODrr7+mW7fuBAeHlFruzp3b7NixnQ8++ECMoqmgJx50+/j4MHv2bIYNGwZAQEAArVq1Yt68eYwfP5709HQOHjxYudY/J8SJWXjerDkTw/sbr/JWpyCa+dk/ln1m5Ko4GJHM4VuppOeqMDKQ4GlnhrmJERJJ4ZrNSrUWGzNjetRxpXMtV8xMit9lFp5vUWm5fLjpKr+Oaky3OsV7LKqDZ/mcEBsby6FDh4DCtbq7d++u35aSksKgQYM4ceJEkTqNGzfmzz//JDg4+Km2tapV1eeoVqvZs2cPAF27di33UlOC8CSo1Wree+89tFodEomEMWPG4OTkzMqVqxjx0sv638+EhAR27tiGqkBNYHCtv+eFy7kTcROlMo+wsI7UrFWr2L4P7NvDnbt3cfPwwtPLG5AQHR3FtcsXsLG1o1XrMHLz5Ny+eQ1LCwusrKxITojm5YlTMf1PgP/L/J+Z+NrrZGSkI8vOxtTMDEdHp2LBZXpaKps3bSQ1NRVvP3/c3DwxNDLk6uULxMbcp1adetSqXZ/kpAQuXzyLhbkZAwcNxtvbt8h+li9ZyPS33iQ1NZXc3FxsbGxwdHR86r3IOp2O//3vK4YOHYazs3OZ5VNTU1m1agUzZ84UPd4V8MSDbnNzc/bs2UNoaChRUVH4+/sTHh5O06ZN2b17Ny+99BIpKSmVfgHPg2f5AksQKiolR0Gn74/QwNuW19o9/iG+Op2OxGwF1xNkJMkUqNRaNFodnnZm1HCyoIajJSZG1bOHU3g8Zm29jpOVlBWvNK/qplTKs3xO+O6775gxYwYSiYRZs2YVWfKzS5cu7N+/v8R67u7uXLt2DVtb26fU0qonEqkJAmzcuBEPD08WL17Md999r3/+9u3b3Ii4Q9fuPYuULygo4Nq1K/z0w3e8Me1tgoJCsChHj29iYgJJiYks+f1XZrw/Eytra2JjYsjPz8fOzhb/gCAMDAxY8ts8Xho9kt17DtCn/6Ai+wg/fQqJgQFNmpbv3KHRaIi8e4d1a1bSoWMn6tarj0ajIS42FoVCgZ29HQEBJS9Tmp+fz5aNq3nzjTfKdawn6dChQ+h00KRJ8WXmSnPhwgUKCpR07tz5Cbbs+VLec0Klb5Oam5uTnZ0NwLFjx7C0tKRJkyYAmJqaIpfLK7trQRCeQZ9vuwHAyOY+T2T/EokEd1sz3G2LL9khCACtAxxYfjKa7LyCJ7Lu+4vs0qVL+v8PHz5c///Tp0+zf//+IheX/75Xn5iYyPz58/noo4+eSjsFQXg23Lp1m7t379K3b98izwcFBbF02fJiQbexsTENGzamVq063L17h4aNmpTrOG5u7shzcujcpTte3oXXH7a2RaeX3bkdQUhwIF5eXqSlJRfbR7PmLfj5p+/LHXQbGhriV8Of7KxM2oT+M/fa3r7spUl3bt/CkMGDy3WcJy08PJzJk6dWqE6jRo1YsGCeCLqfgEoH3XXr1mX+/Pn4+PiwYMEC2rdvrz8px8TE4OpaPYf/CYJQ3OFbKWy7ksjkMP9yJTwThCehkbcdS07c5/DtFLFM3GN240bhTTUPDw8CAv4ZybJhw4Yi5YKDg3nppZfYvXs3R48eBWDnzp0vZNCdm5tbYkKlJ3m8kv4vCE/b7du3cXd3Z9++fYwa9VKx30dFfh779+3VJzT7t7Evj+fTT2bi7eVN3XoNyjxWRmYGSxb/xkczZ5FXwu99enoae3Zu4YP33yM3N5emTZqwY/sWwtoXTejcqnVbli/9ncFDhhfbx39ptVp++3UuderU4uLFCwQFlW8KjUyWTfjpk4wZPbzK/0aTk5OxsrKuVDtsbW25e/cubm4Pz9YuFCrve1zp4eUHDx6kV69eKJVKTExM2L9/P61bF/5xDR06FI1GU+xk/aJ5locSCkJ5abQ6uvx4BFNjQz7qUVPM8xGq1Eebr1LLzZp5IxpVdVMq7Fk+J3h5eZGQkEDHjh3Zu3ev/vmmTZty4cKFwozDEglXrlyhdu3aKBQKvLy8SE9Px97enrS0tCps/dP14HMUBEEQhAee2PDyDh06cPPmTc6fP0+DBg2oUaNGkW0NGjSo7K4FQXiGbL0cT2RqLp/3rSMCbqHKNfK2Y/e1JFRqrZjj/xilp6cDFJknrFKpuHr1qv5xUFAQtWvXBgqnkTVu3Ji9e/eSk5PzdBsrCIIgCNXMI6W+9PHxwcen+PzOSZMmPcpuBUF4RhRotPyw9zZNfOwIcH56S1wIQmma+Nix4Xwc4VHphAY6VXVznhsPbqglJSXpnztz5gwqlQqJRIJEIiEsLKxInQeZf62srJ5aO58lCQkJTz2RmouLC1A4dFQkUnsxaLVaFv32GxSo6dm5G7a2RUdZyGQ5/LlmFZH3o/j43fexs7MFYPEfy8nKymL08OE4OxXNXJ2fr2DLjm2cOH0aWztb2rRoSXZWNgnJSbg4OZMty6Zb5y5s37mD9MxMBvfrT61/LRG4e/9+ajZswJ9//sGHHxafWrJ06WLGjRv/97Hy2blrF8nJKfrvDK1Wh1arpmOHDgQGBnLq1CmOHTtOviKfhPh4+vbti6WlBe3ahaHT6Vjw008EBwWRI5NjZm5GzaBgIu9Fcub8OZBIqFuzFt27dOX4yRMoC1R07VQ4H1mpVLFr/z6u3IzAztEBE2Nj/bFbtWyFXC4nNS0VQ0MDPNw9aNiwIRKJpEj7/3mfZWzbtg1ZjlyfmV2j1WJkaEC3rt3w9Cyc8vThh+8zb968Uj9PpVLJgnnzsLeyoXunLpibF81jk5qayqoN60lLT2fGm29hZVX02iszM4sd+3ZjJJUyfuKEUjtDClebMKBu3bqltqU0N27cQKnMp127p7eWeHUmk8lwd3cvs9wjrzeRkpJCdHQ0+fn5xba1bdv2UXcvCEIV2nA+jrjMfKa0f/zZygWhMrztzXGylLL/RrIIuh8jDw8P7t69y/nz57ly5Qr16tVjwYIFAPqh5f8NuuPi4gDKtRTN88jCwqLKAt+qPLbw9Oh0Ov73xRf069oDHy/vEstYmFswY9p04uLjWb1hHW+//ibfz/uZjm3DaFTKqFMLcwvGvzSWAX36MeurL4m6fx8jIyP8/fzw8vCkdYuWfDb7K0ICA3lv+jvF6rdr3ZrVWzeTlZWFkZERUqm0yHatVoe5uTkSiQQLCwteGj36oa+zc+fOdO7cmVu3Irh37x7nz59n+vS3MTc35+SJE6SlpfPetF5FAkxnRwdu3opg+tQ3MDExIVsmIyYujtcmTCzyOkcOHkL3zEy27t/DS2PGPrQdJbVfvy8LCyZOnPiQWnArIoJAHz/OhIfTvkOHYtuVSiVfffElE0ePxd7OroQ9gIWPBR++/S7Xblxn687tTPxP8G9hbsGkseO5dz+K+T/PZcYH75OVlcX9+/fJz8/H3t6eoKAg2rRpw6+/LqRFixbles3/duXKZSZOnCC+Y8pJo9GUq1ylx+YlJibSqVMn3NzcaNGiBe3bt9f/hIWF0b59+8ruWhCEZ4BSreHnA3do6e+Aj4P44hWeDRKJhIbetuy9kUwlU5IIJXhwk1yj0dCoUSMcHBxYu3at/qLTxMSELl266Mvn5ORw7do1JBIJHh4iqd3TYGJiwrx585g3bx4mJiZV3RzhKfj9t0X06dyt1ID73zw9PBg2cDDvz/qY0BYtSw24/83O1pZZH3xETFwsVlbWZGRm0rpFS1atW4uHmxtDBgwqsd623buQZ8sYMGAgf/1VPH9Ts2bNOHv2TJnH/6/9+/fToUNH0tJSMTc3B+DcmTNMnjCBP9es1pfT6XT8vnwZ77wxTf+3sHn7NoYNHlLifu3t7MhOzyj3OaOy7d+3dw9TJkziTHjJdef8+ONDA+5/q1OrNs0aN2XLjm0lbq/h60fn0HZMGPcy65b9SUZULLrMHG6fu8R3X37Fn8uWo1AoUKvVFXoNGo0GjUYtAu4noNJB99SpU7l48SKzZ89m165dHDx4UP9z6NAhDh48+DjbKQjCU7b1UgKJ2Qr6NxQX1MKzpbGPHYnZCm4mirnEj8ubb76pHzKp1WrJzMzUb5NIJIwZMwa7f10obtiwQX93v02bNk+3sS8oY2NjpkyZwpQpU/TDdIXnl1KpJCczCz8f33LX8fTwQKPW0KJZ+ZbGgsLAu1XzFiQnJ1FQUADAyTPhjB1Zcu90fn4+BSoV3Tp0Qi6Xc/z4MZRKZZEyzZu34PDhwxW6MZqYmIilpSUrV65k0qRXAYi8ewd3Zxdq+PqRl5enP86xkyfo0aWb/jtLp9ORLcvGzta21P23a92G3Tt3lastlWl/Qnw81uYWGBoa4mxnrx8J9EBSUhIO1rblCrgfaFCvHtGxsaW2IyggkEDfGgzt3Y8WjZvSoE49Ooa2Y8pLL9M7tAOylDTWrV1b7uMBbN68iV69elWojlA+lQ66jxw5wnfffcc777xDly5daNeuXbEfQRCqJ51Ox29H79HI2xZPO/Oqbo4gFBHiao2xoYRT99KruinPjbp167Jo0aIiPag6nQ6dTkfjxo359ttvi5T/95zFTp2KLs0jCMKj2/jXRnp06lJ2wX85eSacQf36V/hYfXv0IjEpCVdnF3bs3U1QQECpy+Ft27WT3t170rBePQ7u3UPHjp344IP39QE7FN6oGzx4CPPnzytX4JqRkc7y5ctwd/cgNTWFmjVrERcXx1/r1jG4bz8Aenbtxo49uwG4dPUKDerV09dPS0/D0/3hHQQN6tYjMzWV8+fPldmeByN4vvlmdrnb/+fyZYweWrgcWbvWbTh+7HiRMqtWrKB31+5l7uu/2rRsxYnTp0rd3r1LV3Yc2FfseRtraz5+6x1u37jBkSNHynWsU6dOYGoqJSQkpMLtFMpW6aBbIpHg5eX1ONsiCMIz4sjtVO6kyOlZr+zEEILwtJkYGRDobEW4CLofqzFjxnD79m2+/fZbJk2axNSpU1m1ahUnT54skiwtJSWFPn368Mknn/DJJ59Uas6gUHEajYbDhw9z+PDhcs8hFKqvhLg4PCs4dePUmXBaNG1W4WOZmpri5+uLVCpl9bp1DOpbeuCelpGOm6sriUlJdAhthzo3HxdnZ954YyrXrl3Tl/Pz86N79+7Mnv01UVH3StyXTqfjyJEj/PbbbxgZGXHmzBneeedd9uzexZ9Ll1AzIEg/xcXL05OklGSUSiUW5kU7A7JlOeVK6DhqyFAib0awauUKFApFiWVkMhmLFv2Gg4MDISEhTJ/+FlFRUaW3//Bhli5axNuTX9ffqLC2skYmy9aXS0xMJPpelH7IfEU0qt+AK9evlbq9hp8f8YkJpW7//J332bxhPcuWLUUul5dYJjc3l+XLl5Odnc3w4WWvYy5UTqUTqQ0ePJjt27eLO9yC8BxadOwe/k4W1HR9MbMSC8++EDcrDtxMQavVYWAglrJ7XLy8vHj77bcfWsbZ2ZlPPvnkKbVIeEChUOjz5cjlcjHn8jlnKKl4v5hOq6300p5mpmZkZmdhZGSIy0OSIxr9HVimpqXi4uxMq+YtiE9MZOO2rXwz+2usbaxp0KAB3t4+aDRazMzMeffddzEzMyM0NBQfHx/UajX370cTExNNZmYmqakp9O3Tl4KCAn6dO5eObdvRceqbrFi3psixDQ0MyZZl42DvUOR5SwsL8vJyy/U6hw0YRHxiIn8sXkyBVouHlxcW5uZky2TcuhWBUqnEztaWHdu2Y+/oQJcuXVi7ZjXZMhnBwcF4eHii02qIi4sjT5ZDmxatePf1aUWOIc/NxdKy8PopJyeHX+bMxdvds1ztK4mhQcmjDvTbjR6+/Z2JUzh29QIbN/5Fbm4u3t7eWFhYkpeXy/3797GwsGDIkMH61RGEJ6PSQfeQIUOYMGECWq2W3r174+DgUKxMo0aNHqlxgiA8fdcTsjlxN503OgSIdbmFZ1ZNV2s2XojndkoOIa5Pb9kmQRCEp0FSiZuJj3LO1qHDyNAInU6HRqMpdXj5A0ZGRmi1WgA83Nx4feIkXlOP5/u5c/B39yI5PoGklBSkUim/fPs95ubm3L57l+MnT3Lz9i2sLC0IC21Ho/r1keXIMTWV4uzohJlZ4RJa+fn5GBkWDVMkEjAwMCw20sPJ0ZHY+Phyv1YPNzdeHTcetVrN9t07ycnLo27NWtQPCMLY2AgnRyeMDA35beligr18CPH2xcnRCZVKRUZmJiYmxrRt3KzUnuvDx4/SuFXhCKDli5cyeeRYVm35q9zt+6+yPlYJDy/g7elJxoE9vP9/M9FoNCQmJpKTUzg6YPDgQWV+1sLjUemgu8PfqfDnzZvH/Pnzi2x7sLyIGP4kCNXPkuP3cbI0oZlf8RtpgvCsCHSxxMhAQvi9DBF0C4Lw3FFVMOs0gKGREWq1Wp9grCJkMhk+nl5IJAZERt2jZnDJ83pVf8/ddnVx5eKVy7Rp2Uq/zcjICCcnJ5o3aYKlhSWZWVns2LMbF+fCHtTGDRrSuEHDYvt0cy1+nMioe3h7Fu0dVhUUYGtjQ2paWpHnDQ0NMTE2Ji8vr0JDuI2MjIiOieGNVyeXeMPCwNAQX2+fIu+nYwmdjP8VFXmPAp2WWrVqkSfLwcrSElWBqtzt+rfCbOLaUrfrdLpy7dvMxET/u+HpWfled6HyKh10L1269HG2QxCEZ0BGroptlxMY2MgDQzFkV3iGSY0M8Xe25PS9dMa08q3q5giCIDxWjRo34tKVognDytKzSze27NzBwD59K3SstPR04uLjcXJ04v3p09mwZQsz3y056A4JCuL6zRvUrlmL9IyMYtt7d+vO1p07GDF4KHa2tqRnVC73xuHjx3ht/AT944tXLlOvdh2MjIxQa9TFeuP79uzFhs2beGnEyHIfIy4uDhdn51JHCDjY2bN521YG9R9Q7n3eiLiJRAcZySns3rWLTq1CAbC3tiU1LRUnR6dy7wtg78EDdGgXVur2s+fP0ahu/TL3Y2VhSU5OTpFVKISnq9KJ1MaMGVPmjyAI1cv6c7Ho0BEWXPp8LkF4VtR0tSI8qvxrrwqCIFQXHTp25PjZ0xWqExQQwKGjhyt8rE3bthASFEyOPIeQoBASEhPJy8sruV1twzh4tDAbdoO69bhw+VKR7U6OTqSlp+u/lxvWq1+sTFnkuXLMTE2LBNXHTp4gtFVrADqFtWf/4UNF6rg4u2AqlXLuwvlyHUOWk8Mfq1eWmjROqVQSGx9HTq6cmxE3y7XPtPR0dmzfgYmhEa0bNOXIwcME+wcA0K9bT7ZsL3nN7Ye5E3mX4MDAUrefCg+nVZOyk+fl5uVhaWlZ4eMLj0+lg+5/u3XrFidOnCA3t3xJDARBePZotDr+PB1NixoOWJuJNWCFZ19NN2syclVEppackVUQBKG6kkgk1G1QnyMnj5dd+G8nwk8THBjEH6tWlrvOjYibXLxyGYVKiVpdOC30pREj+d/335R4Q9PAwAB/vxocP3WS0Fat2b1vL7n/SWLWtWNn/li9CoDQVq3Zs39fsTKl0el0/LL4dwb06ad/7sjxY4QEBet7pEOCgrl24wZp6UV70YcMGMjtu3fYuWf3Q2/GRt6LZP7CX5g2eWqJQ/GVSiU/zPuZCWNfZuyIUZwMP83Bw4ceus9rN66zdOlSpo2fhKmpKQ1q1yHzXyMBLMzNMTMx5fqN6+V6HwC27NhG8yZNS91+4PAh6obULNdc/jylAmNjcW1XlR4p6P7jjz/w9PSkVq1atG3bllu3bgGFSdYWLVr0WBooCMLTceR2CnGZ+XSpJbJXCtVDkIsVBhI4fa/4EEdBEITqrmevXmTmyjl8/FiZZY+dPEF0TDSvT3oNG1sblpcj8L5y7RpzflmAm6sbOq2Wls2a8cfqlbRo0pS6tevy6ddflpifqUeXrsTGx3Hk+DHeeG0yP82fR2ZWln57cGAgAf7+/LlmNRKJhNdffa1YmZKo1Wp+nD+XAb37YGdrC8DBI4dJTkmhS4eORcq+PulV5v32K/EJRZfLGjF4KO6ubsz9dQHLV64gJjaWHLmclNQUdu/by7T33uX7eT8zbvRLxeZ/a7VatuzYzoyPP2TSy69gb2eHRCJh/EtjsbCw4Odf5rNy7WriExLIkctJSk5iy/ZtTJvxLtF3Inn31amYmJgAhTdNNFpNkfXLR/YfxLHjx7l05XJZHw1bdmxDKjUtNejef+gg2ZmZdAxtV+a+0jLScXJ3K7Oc8GRJdJUcl7d+/XqGDh1Kr1696N69O1OmTOHcuXM0atSIr7/+mgMHDrBvX/HF2l8kMpkMGxsbsrOzsbYWiX6EZ9vYpWeIycjji751RNZyodr4vy3XCHGzZu7w4sl5njXinPB8qKrPUaVSMWfOHADefPNN/cW98Pzbs3s3l85foG5ILUJbttKfo3U6HSfCT7Nj725S01IZNWQY7VqHIpFIOH7qJFt37qBF02b07dmryFDti1cus2nbVmLiYgmo4c/gfgO4fPUKZy6cx8fbG3VBAa7OLjg6OLBl5w7q1a7D8MFDsPrX8OS4+Hhm//QDEgl06dCpcCh2Tg4jBg/Vry9+/eYNdu/fh7ubG726dmflurUA9OnRE3e3f4LAHLmczdu3kpGZydABg3BxdubI8WPs2rcXS0tL3p76RpEAOSYulh27d2NoaEByaioFBQWMGTEKfz8/fZn8/HxWb1jP5atX8K/hT26unKjoaAwNDcnMysTC3ILatWphaW6OTqcjIyuT23fuYGZqSs9u3bl77x6+Pj707NKtSG/4kWNH2bhtK1qdFgOJAb4eXkwbPwkDg3/6MX/9YymvjBjNTysXE+jhQ98u3Yt8npt2bScmMYHQ1q1pWL+B/nmNRsPeA/s5FR5OcnoKY0e+RIsmTYt83vsOHuDa9es0rlefsFZtyvX78838n7FzdqR5y5a0aNVSXOM9ZuU9J1Q66G7UqBENGzZk8eLFaDQajI2N9UH3li1bmDx5MvEVSN//PBIXWEJ1EZuRR9tvDjGhbQ3ai/ncQjXy56n7XI7L5sT7Haq6KWWqTueEmJgYACwsLEpcEvRFVp0+R+H5cu7cOY4ePqxft1mj1dC6TSjNWzQHYPWqVRzYtx/zv5fdysvPx93Tg7TUVAoUKnQ6HXn5+cjkOVhbWREcHIytrS0ajYY69erSqXNn7ty5w87tO0hNSebunbsYGhiSkytHpVTi5OiEsbExBQUFKFQqmjVrirunB75+foSfPMXFixdJS03FzNQUE6kUQwMDcuRyCtRqNGoN9va2SJCQlJyCnZ0tNtY2qFRKYuLisDA3x87WDiMjQzIyM9HpdNjb2ZGVnY2qoAAHe3sMDQzJz89HnpeLra0tWo2G3Nxc1BoN+Xl5uLu7Y25qRo5cXriWvaUllhbmZGXLyMvLRVVQgAQJxsbGWFlZYmluAQYSsmUyjI2Nsba2RpYjw83ZlYKCAnJkORgbGVLDx5f0rCyUSgUmxiZ4e3jg4+5Ft7AOxYanazQaflm+hIAaNajVqim//PQzX73/cbHPUqfTcfT0Sa7djtAvjabRqGnfui1mUimL1q9i6PDhnDp5AkMDQyQSCWqNmpatW7Nj8xbeerlwGHtZDh07Sn5aFp1btuH8zeucvXMDF19PRo55Sdy4e0zKe06odPbymzdvMnv27BK32dvbk55euWyFgiA8fWvPxmJmYkjLGuLiWqheApwt2XktiTS5EkdLaVU357nh6+uLRCJh4MCBrFu3rsQyM2bMYP/+/UgkEs6fL1/yIkEQKq9JkyY0adKk1O3DR4xg+IgRj3SM4OBggoODK1wvNDS0wnUWLviF3i3aYWNV8ZtX81Yv473/m6l//PlHHzO591DMyhGIAkRERXIjO4lRY0tO/JyXl8dvP81lwtBRFWrXvqOH6dQ2jH3hxxkQMhp1QQEnz4bTqmnzIuUkEgntWramXcvWxfbx7dw5OFnb0qJlC1q2allse82aNfnuq68ZM2AI7q4lDxvXarVs2r4NQ6Wa/h26AtCyXkNa1mtIYmoKn70/kxmzZoobh09Rped0m5ubk52dXeK2+Ph4kZJeEKqJAo2WtediaR3giKmxYdkVBOEZ4u9UONzxcmxW1TbkBRQVFcWlS5e4dOlSVTflhaDRaDh79ixnz54tcZ6tIFQ3g4cNZcv+PRWuFxUTTY3gIP3j27dvE+TiWe6AGyDEz5/4u1GlJkczNzdHZ2xEZnZWufep0Wi4cvM6SpWKmvXqcOXKFXq26cD1a9e5FnGjXPtYvOIPurduR33/YK5evVpiGVtbW/7v8884HXGNucsXc+jkcVLT08mRy4mOjWXJn38wf8ECarp66wPuf3NzcuatQaP55tMvUKkqt364UHGVDrpbt27NvHnzSvxlXbZsGWFhYY/SLkEQnpKDESmk5ijpGCKGlQvVj5OVFBszYy6JoPupEyuWPF0KhYJmzZrRrFkzFApFVTdHEB6Zvb09MlV+hW8i7Th6kH4D/lnqa/GCX+nequyEYv/Vtm5j9u/dW+r2116fwq9r/iS3lOXT/k2r1fLjb78Q1rI1J65fpG+/fsTHxuLt5s4rg0dw4fwFVv21jrz8/BLrR0Xf55u5P9G8dn3qBNfE29Wd+NjYUo9nYmLC6DEv8f7/zcQjJIAdJw/z5Tdfc+3cRQa37cIbw8dS27/0pcbMTE15uWs/Vi7/o8zXJjwelR5e/n//93+0adOGZs2aMWLECCQSCRs3buSTTz7h6NGjnDlz5nG2UxCEJ2RVeAyBzpb4OFhUdVMEocIkEgn+ThZcjMmq6qZUe3/8UfziKzo6usTnExISOHz4MECRBE2CIAgVMWrcGOb9uog3Ro8vV4KvA6eOUadJQ4yMjPh81mdcOHYSf2/fEpf+Kkv94Jos3LWRzl2L9wYDmJqa8v7/zeTb/31NWOMWNGtQcsLOe9H3WbVpA94enly+f4d33psBFM7bliBBIpHwUr/BJKelsmL1KlQaNfb2dphKzcjKziJHJsPPw4u3Rr+iX9ZLIpGg05Yv7VbDhg05snc/n0+ejolx+edpuzo6kXwkrrCdIrnaE1fpoLtJkybs2rWLyZMn8/bbbwPwv//9j8DAQHbu3EmdOnUeWyMFQXgy4jLzOHo7lQlta1R1UwSh0vydLNl9LQmtVoeBgbhwqKyxY8cWufDS6XScO3eOcePGlVj+wUg3V1fXp9I+QRCePx4eHgwYMYyflv/O5BEvITUpOTeHTqdj64E9mDhY0697N0b0H0QtNx+Wf/INv2/fUOnjG5YRbFpaWjLry885evgIc/5cjAkG+Hv5YGRsREJSEneiIslTKPAJqEFY3x7UrFlTX9fd05PYy7fw8/IGwMXRiYlDR6HT6cjJlZOvUGBjZY2ptPhrjk1KxL1hzWLPl6SgoACNPL9CAfcDzQJrcfL4CVqHli8TulB5lQ66Adq3b8/NmzeJjIwkOTkZR0dHgoKCyq4oCMIzYd3ZWEyNRQI1oXoLcLYkR6kmKj1XP8dbqLx/Txsrbb6jRCLRB+g9e/Z8Ku0qSUZGBiEhIaSmphIcHExERESpZf/44w/mzZvHjRs3MDExoUWLFsycOZNWrVo9xRYLQvWTlpbG7FmfU5AtR2pkjE4H2Xly7L3c+XDWx1hZWRUpHx0dzaZVa0FRADodEgMDVBItnfv0pEHDhsTGxvLNp1+gkedjaWpGTl4u8cmJnDl9Cl9vX14Z+RI1vH2QSCTI5DksWb2Cy9eukpKcjIOVLT98+Q2DO3Vj0sDCpHGP0kt79OAhtm/agr+XLybGxuTkycnMz8XOxhYzQyPMjE1QqApQqQtIzcrA2NiIQ/sPYGZmhgQJGo2GApWK2Lv3uHbiDFbmFqg1GiRAfGoKTi7OtG1SPImataUV1pZWJTcK2HPsML5RkRzeuB0kEtSGEnoM7Eet2rWKlU1KSsLL0aVSr79xzTqsOnNQBN1PQaWC7tTUVBYuXMjRo0dJ+HtRend3d9q3b8/EiRPF8iKCUA1otDrWnoullb+DSKAmVGsPAu1LMVki6H5E5V1F9MFwxB49evD1118/4VaVbvr06aSlpZWr3I8//oiZmRldunRBoVCwb98+9u7dy/r16+nfv3+Z+xCEF41SqWTG5DdwM7fhrR5Dsf1PlvGLt67zyWvTkFhb8M3cH0lPT2fRj3PxsXJgTPNOSP+1JJVWq+XQkTP8+H9fYGpswsR+w3Cys2fZ9r/wdHTFxcaeGh7edG0RyqGz4ezfXZhgzczEBA9TG2JMLfAIqklyRjq1awToA26g0snAFEolQV5+SH2NSM/OoqZvDUZ370eWXMaGA7tRFRQwod9Qdp44zI2ou9haWNIgsCY924RhbFQ4DPzguVNcuRNBmwZNaVKz6CjfrBwZ/7foZ9KzMnGwLX+C6bTMDIKc3RnXuZ/+OY1Gw77dR9m4fCXDJowlIPCf+dp5eXmYScufRO7fjI2NKRDJ1J6KCq/TfeDAAQYOHIhMJsPQ0BBHR0d0Oh3p6eloNBrs7OzYtGkTbdu2fVJtrjbEWp7Cs+xgRDIvLzvHl/3qUEMEKkI19876y3QIcebzfs/u1KZn/Zxw5MgRoDCg7tChAxKJhLZt2zJr1qwi5SQSCWZmZvj7+2Nvb18FLS104MABOnXqxMSJE/ntt99K7ek+ePAgHTt2xMHBgVOnThH498XqqVOnCAsLw8zMjKioqHKvulJVn2Nubi6WloXf1XK5HAsLkYdDeHIUCgVTRr/MrDGTcbCxfWjZnScO89fpQwT7+PFWv5H6gLQkOp2OZdv/wt3JhYu3rjOiax9W7dnK9BHjMTF+eL1561dw4so5JvQfTsdm/4xQWbN3O+1atcbNuWK9vet2bqV1cD08nF3ZfeooscmJ5Obn8+awMUgkEtKyMvlg/rf0Du3A7Zj7TBs2tsjc8a1HDyA1MaFri9KXS8vJy+XnTSv5YMq0cvXI63Q6vv1lHpP7DsXKvPjfuE6n49cdG+gyYiB169cjMjKSPxctwcPClpE9+1Xo9UPhWu5ztq7m89lfVbiuUKi854QKZS9PTU1l6NCh2NjYsG7dOrKzs0lMTCQpKYns7GzWrFmDhYUFgwYNEut0C8Izbs2ZWHwdzPFzFBduQvVXw8mCizGZVd2Maq1du3a0a9dOv/qITqfDyclJ//yDn7Zt29K0adMqDbjz8/N59dVXqVWrFu+8885Dy37//fcAzJw5Ux9wA7Rs2ZJXX32V7OxslixZ8kTbKwjVzbuTX+fzcVPLDLgBOjdvja2hKW/3H/3QgBsKb9qN7t6P7ccOMm3YWP7YuYl3Rr7y0ID7Qb1hXXpiJjWlQ9Oia1f3bdepwkuP6XQ6EhIT8XAuzEnRrWVb3Byc8HRx5c9dmwE4ffUio7r35fq9O0wf8XKRgDv82iUMJJKHBtwAVuYWDGnblR9//wWtVvvQslqtlp+X/MaANh1LDLih8H14tecgNi7+g4/eeJsza3cwrUN/UlNSKvDq/7Hn+GFUadnExMRUqr5QfhUKuhcvXoxGo+HEiRMMGjQIc3Nz/TZzc3OGDBnC8ePHKSgoYPHixY+9sYIgPB4pMgUHbqbQPsRZZKwUngsBzpZEJOWgKBDrFz8OUVFRREVFsWDBgqpuSok+/fRTIiMj+eWXX/TZfkuiUCg4cOAAAIMGDSq2/cFz27ZtezINfYyMjY355JNP+OSTTx76mgXhUd25c4fG3kHFhpOXZtfJo0wZPKrcKxnsOnWEN4aNYf+Zkwzr3Kvcmcdv3b+Hp7NbsesWM6kpjlY2nLt6qVz7AVj211q6NSsaMPcK7UBcchK5+XnkKxTcjYshKT2NMT0HYGBQNGQ6dfUSvUI7lOtYgV4+DAntygdff86qrX9RUFBQZHtBQQGb9+zkh4XzGdSmE4FePg/dn0QiYVKPQbgYmtG3ZRgmxibYW1iTlplRrvb8W3RcLB+OmsCGFasrXFeomAoF3Xv37uXll1/G09Oz1DLe3t6MGzeO3bt3P3LjBEF4MjZciMPIUEJrf8eqboogPBb+TpaotTpuJMqquinPBR8fH3x8fHB0fPa+I65cucL333/PuHHjypzKFhERgVKpxMnJqcRrl0aNGun3+awzMTFh1qxZzJo1CxOTimcpFoTyWjLvFwa061zu8lEJcdTw8Cp3+XvxcQR6+RIZH0tAGQHmv+UpFRgYlhy6DOrYnesREZw4//Ali3U6HUvWr6amuy8hvsVXbvFxc6dRcG1+XLOUdo2akpSehrtT0WHr0Ynx+Lp5lLvdAF4urvxvwjROnw7n3S8+YeEfy5i/7Hdm/TCbN2d9RD2PGrw77GU8ncu3GoS1hSV5KqW+97x/aCcWrFpeoTaFX75AXb9AjI2M0WbJUSgUFaovVEyFgu6bN2/Spk3Z2e1CQ0O5efNmpRslCMKTo9XqWHMmlmZ+9lhIH2kBA0F4Znjbm2NkIOF6fHZVN+W5ERERwdixY6lRowZmZmYYGhqW+FOZ9XErS6vVMmHCBGxtbfnmm2/KLP9gyGRpnQUWFhbY2tqSmZlJTk7OY22rIFRXOrmi3H/XiWmpeFZgLnVhEOtMckYabo5OFWqXjYUlBaqCUreP6TmAPJmcOcsWsef44SLDuXNy5fz852LmLPmN0NoNaVG3QYn76Nm6PZfvRJCSno6NpTVB3r7Fyuw5fYyebcIq1HYAIyMjgn1roFVreLX3YKb2HQ4FGvq16YB/BW5aPNCqXkPO3Ci8YWhpZk5mZiYrtv5Vrrq3oyK5cOUyHZsWzo3v3rgVe3bsrHAbhPKr0JkyKysLZ2fnMss5OzuTlZVV2TYJgvAEnb6XTkxGHuNa+VZ1UwThsTE2NMDL3pyrIuh+LMLDw+nYsSP5+fnlzmj+NMydO5czZ86wdOnScq2UIpfLAYpMh/svCwsLsrKykMvlxZY+gsIMzkqlUv9YJqua0RRarVbfoVGzZs1iw10F4XHQaDRYmpqVu3xyRhoeTuXrnf2nvAvJGel4OFUs8Vn9wJrMWfsH+UpFqdm6OzdvQ+fmbbhx7y6/rfwDJBIkksIOhxsREfz6/ucPPYaJsTE6nQ4zU1OS0lOL9XJD4d9iWXPXSyM1NsHU5J+6CqWy3L3b/+Xh7Eb4lYsA5CrycbV3JDc7h7l/LmFE7/4lZkwvKChg68G9ZGZk8Gr/4frnPZ1dOXr1eKXaIZRPhYJupVJZrnlERkZGlU7f/zByuZyZM2eybt06/dqc77//PsOGDSuzbkpKCjNmzGD79u3k5eVRv359vvjiCzp27Fik3Pbt21m3bh0XL14kIiICtVr9TF1wCMKjWnM2BndbM4JdS18fUhCqI18HC67EiaD7cfjggw/Iy8vTr8f9LJwHY2NjmTlzJu3atWPs2LHlqvOg3Q/LXVHWa/vqq6/49NNPy93OJyU/P586dQqz84vs5cKTUvj3XsHylL/Cg+8TiaT8SxQ+YCqV4u7gxPp9O3mp14CHlq1VI4BaNQL0j+euWc7/jZ9agXaW/r3xqLlwDPhn7ruEir8P/9DxoCUP/g3x9adhcC027dtDZm4Ovp5e2FhZk6/IJzo+Dq1aQ4+WbfFrU7RnXacDCSLHz5NU4TFht27dKnPISUlLdjwOAwYM4OzZs3z99dcEBQWxatUqhg8fjlarZcSIEaXWUyqVdOzYkaysLObMmYOzszPz58+nW7du7N+/n3bt2unLbtq0idOnT9OwYUOkUinnz59/Iq9FEKpCZq6KXdeSGNLESyRQE547fo4WHLuTiqJAI9aef0RnzpzRf0e4urrSvHlzLC0ty50o6UmYPHkyKpWKX375pdx1HvRc5+bmllomLy8PQL8c13998MEHTJ8+Xf9YJpPh5VXxoaCCUB0YGBggV+SVu7yrgyNHLpyhZd2G5S5/494dOvq04lrk7Qq3b8qQUcz6fS5DOvfEVCotV53c/Dyu3r3F64NHl1lWoVRiYGBAbl4ebg5OXI28Rf3AkCJlDA0NUSiV5T7+vylVSnIV+frHZqamxCQlUMc/qML7ik6Mx92pcASyuakZEiAuJYl2jZoxpkd/dDodSempZMlzsHB0o1P95qVmib+fFI+nt3eF2yCUX4WD7vLcXS68g/V4L+h37tzJvn379IE2QPv27YmOjubdd99l6NChpV4MLF68mGvXrnHy5Elatmypr1u/fn1mzJhBeHi4vuyiRYv0Q7amTp0qgm7hubLpYjw6HYQGVmwelSBUB36OFqi1OiKScmjgZVvVzanWHtxcd3Nz4+rVq+Vew/pJ2r59O7a2trz22mtFnn+Q/CcmJka/3Nn27duxtLTE+++LyLi4uBL3mZubS1ZWFra2tiUOLQeQSqVIK3FxLQjVlbGdFaqCgjKX8QJwsXckMS213Pt2tnMgOSMdJ1t7UjIqnm3bz90LA+Cdn/7Hj2/PLHOYt1Kl4s3vPmP2lBnl2v+24wepFxBMtjyH5Iw07sUX/+7o3rIt244fZHDH7hVqu6qggIS0FP188mx5Dt6u7tyJja7Qfh4Iv3aZNwYV3kiQSCSYmkhJzfxnyWaJRIKbozNujmVPDd5z8TRvffl/lWqHUD4VCrqXLl36pNpRpk2bNmFpacngwYOLPD9u3DhGjBhBeHg4rVq1KrVucHCwPuCGwguKUaNG8eGHHxIfH4+HR2EWQjFHSnhe6XQ6Vp+JobGPHTZmYrkZ4fnjbW+OoYGEq/HZIuh+RM2aNePAgQPUqlXrmQi4H8jKyuLIkSMlbsvPz9dvU6vVAAQHByOVSklNTSUuLq5YQrULFy4AUK9evSfYakGoXl55fTLrF61iZLc+5Sof5O3Lreh7BPsUzwZekmAfP25GRRb+ez+Smr7+5W5bvlJB89oNUBUUMPGLj3h79Hjq+AeXWPbirRv8tHIJ/3tternWGwdISE0hNTOD14e8xK8bV+Pl4kpMUgLeru76Mh7OrsSnJJe7zQ9sP36QjKws/fu6+fA++oV1Zv+Zk8SnJOnXDC+PrBwZNuaWRTo5uzRrw9YTB7kWebtCPedKlQqpvbVYFeEJq1DQPWbMmCfVjjJdu3aNmjVrFhva/uBEee3atVKD7mvXrhEaWnzx+gd1r1+/rg+6BeF5dTE2izspct7vFlJ2YUGohkyMDPCyMxMZzB+DmTNncuDAAcLDw7l//z6+vr5V3aRS5z3ev38fPz8/goODi01vMzMzo0OHDuzatYsNGzYwbdq0Its3bNgAQK9evZ5ImwWhOvL19eVGWhwpmek425WdsLBL8zZM/+l/fPfm++VKMNa5WWumfvspP0z7kO9XLWbG6Inl6lUHWLR5LUM798TF3pG4lEQ+WvADpqamNK5ZB1+3wptqkXExnLl+iXyFkjlvz8TFvnxLH244sBt3Jxe0Wg1SExNq+hXeDPhz52beHzOpyIjado2asuHAbgZ17FaufadmZXDq6kVCavgjkUiIToxHWaDCzsqGPqEd+W7F78WOURqdTseiTWuY2GdokedDfGqw5fhB1h/YRaCXL9JyBtHzt67h5femlausUHnVpls3PT0de3v7Ys8/eC49Pb3YtsdRtyKUSiUymazIjyA8K1aHx+BkJaWuh01VN0UQnhiRTO3xGTRoEDk5ObRs2ZJZs2axZcsWjh49WuLPs+zBfOwvvviCO3fu6J8/deoUCxcuxNramvHjx1dV8wThmfTNvDl8sWoRiWkpZZbddGQfBVJDvt34J/nKh6/1rNPpWLhpDSO69eaHVUsY13sg3/65qFz1vl7+K2GNW+iDaE9nN5bP+pYF786ilm8A0QlxxCbEExl7n7lvf8Kfn37L4i3rSUpPK/M1bD68j8ycbNKzMhjauScAdf2D2HRoD7X8Avh2xSJUBf8sV9YwuDaW5uZsPryvzH0npqXyvyULMDQ0YtqwcUQlxLLuwC5e6TsEAKmJCeN6D+S7Fb8XOUZJNBoNP6xcTP+2nbGxLD4l5s3Bo9Go1Xy17Ffy/jV3vCQ6nY45f62gx6ghuLu7P7Ss8Oiq1SK9D5snXtYc8kepW17PSoZTQfgvmaKAbVcS6F3PHQMDkUBNeH75OVlw/G4aSrUGqZFIplZZYWFh+nNjcnIyn39e+jI7EolEP5z7WdSpUyfefPNN5syZQ4MGDejcuTMqlYp9+/ah1WpZuXJliTfmBeFFZmxszK8rlvHBm9MxK4CxPQbg6vBPj7FOp+PohTPsPH0UGy9Xlq5ZiUwmY+EPc7CVSOnbMgwr838y7BeoC9gZfowdJw5jamhE01p1mTpkNEu3/YWDjQ3z1q/Axd6B/mFditXbfHg/xy6dw8nWjhW7NtO6fmN6tW6v7xU2NDSkll8AJ6+c515cLFOGjMLy72UCZ4yewJJtG8hXKhnauWex13Ds0jmOXz6HTJ5D45p1GdyxO4lpKazbvxNzUzPmvTuLfWdOoNPBF4vnE+xbg37tOmFhZk63lm05dfUiP65aQr3AEDo0aVkkpkhKT2Ptvu1cuHWDOjWCGNKpO58vnoeBRMIHY18rOqVVp8NQYsDE2R/TrXU7+rQMw/xfS7cpVSq2hx/hXmYKcrUKf4+Sk56Zmkj5v3FTmb9xBe/N/YaGIbUZ1KEb1hb/JIpUq9X8dXgvESmxjJ82laDgkofnC4+XRPcsrANSDi1btkSj0XDmzJkiz1+/fp06deqwcOFCJk6cWGJdNzc3QkNDWbduXZHnd+zYQa9evdizZw9dunQpVm/q1KnMnz+/3Kn8S1rL08vLi+zsbKytrcu1D0F4Ev48Hc0nW64xd3gj7C3EnB3h+XU3JYePt1xn69TW1PO0rermFCGTybCxsakW5wQDA4NiN6RLOxdKJBI0Gs3TaFaJHja8/N+WLVvGvHnzuHnzJsbGxrRo0YKZM2fSpk2bCh2vqj5HlUrFRx99BMCXX34p5l8KT01ubi7f/e9r0qLjMDE0RoeOnLxcAurX5q0Z7xb7XUxLS2P9ilUoZXLQ6JAYGqA1NqTPkIH4+/uTmZnJN59/SUZcEhamZiiUSpIzUrGzsSU1LQ0nGzuQgFajJTUrA2WBCmc7BxLTUrG2KJzHbGAgwcLMHHWBGiSQl6/A1tKKxMxUtFodLnYOmJuaodFpyMrJISMrE41Wi7uTM1bmlkgMJGRkZ5OTJ6dArSbA2wdDA0PkynyUaHC0dUCjVGFqbEy+QoFCpSQjMwMbC2vUGjUufwfvKZkZyHNzkUgkmJmaYmFmhlarA3Qkp6djaWZO85p10enAzc6BXs3acvnebcJvX9VPmdVqtTha2dK3ZRh/3jzBkLGj2bBiNercfNAWvn+YGNF/xFC8vb25ef0G25auYnL3QQ/NRZUll/HWgtmYmppiZ2GNkaERGrRYONgy5rWJBAYGPrHfmRdJec8J1aanu27duqxevRq1Wl1kXvfVq1cB9GtXllb3Qbl/K0/dihAZToVnkU6nY+XpaBp524mAW3juedtbYCCBq/HZz1zQXd1Uk3vy+Pr6lqutY8eOLff63s8iExMTvv3226puhvACsrCw4JMvSx/t8l+Ojo68Nu2NUrfb2dnx1Q/fPY6mPVVbNmwk4dJNhoZ2wdSk+PW+Tqfj5PVLrNyzlXmvvl/q/OxmIXVoFlI89shTKDAxM8PV1ZWp77xVajtq1q6F+dQJzPllEf62LnRv0qrIXHqlSsWOs8eIlmfw05LfcHNzq8SrFR63ahN09+/fn0WLFvHXX38xdOg/iQOWL1+Ou7s7zZs3f2jdyZMnEx4eri+nVqtZsWIFzZs3F/MYhOfalbhsIpJymNFVDB8Snn8mRgZ42plzTSRTeyRVuVqJIAjCs6jvoAHENo/lz5VrUGfm0MDTHzsrG/KVCm4kRJOFitad2lMzsU65EqL919bzx+g3dWy5yvr4+vDR7C+4e/cui9duwEClAa0ODCRgakz/0UMZ5eNT4TYIT061Cbq7d+9O586dee2115DJZAQEBLB69Wp2797NihUr9L/c48ePZ/ny5URGRuLz9y/byy+/zPz58xk8eDBff/01zs7OLFiwgFu3brF///4ix4mOjubs2bMAREZGAv9kN/X19aVJkyZP6yULwmOx5mwMjpYm1Be9fsILwtfBnGvxIpHlo6jK1UqEkmm1WmJiYgDw9vYWS5wKQhXw8vLizfffRaVSceXKFTIyMrCwdGHAoM44OBRmelcoFFy4FUEj/4qtFpOmU+DqWv5lwwACAgKY/tH7FaojVI1qE3QDbNy4kY8++oj/+7//IyMjg5CQEFavXs2wYcP0ZTQaDRqNpshQM6lUyoEDB5gxYwavv/46eXl5NGjQgF27dtGuXbsixzh06BDjxo0r8tyDtcHHjBnDsmXLntwLFITHLEdRwOaLCfSo6yYSqAkvDD9HC06diaFAo8XYUAQmwvMhPz8fPz8/AORyORYWFmXUEITq4caNG/z0yZeYqyUYGRig1mnJM9DwxqyPKjQFNC4ujo3LV0KuErRaMDTExN6aIeNGY29vj06nY/nvSzm+ZSfWJmYYIEGhLUDq5oi9nS1RVyMwUuvQaTTkKxVk5eZgZWqOsaEROYp8ZIo8fD08sXWwJzEpCQtjKVam5mTlyolPTcLdwRk7KyvUOh1JsgwWvfUJRoblC7W2nj+OvZcbrw0ZRXpiMjZSC4wNDVEb6HD082LMG68RHBJCZmYm65b9iSI1C4lWCwYG6MxM6D9mBN7eJSdXe6CkuphL6ffS8DLrCo+u2iRSq46qU9Ic4fkkEqgJL6KIJBmfbrvBzjdCqeX+7Hz3VsdzQn5+Pr///ju7du0iJiaG/Px8IiMj2bp1K1lZWRgaGjJy5MiqbuZTVVWfY25uLpaWhRmIRdAtPA8iIiL45q0Pqefszag2XbH8V7buPKWC1Sf3cz7xHtO//eyhwXdcbBwr5vyCm1ZKn9rNMfvXfGtZfi6bb4Rz9PYVrDGmc81GdK3XXD9SJDM3h1/2bsTa3IIxbXtgZWaur5uvUrL53FESMtMY1rITl6PvcvjGBTJyZXzUbwy2FlYsPbwDJ2tb+jVpi5WZOTqdjhXH9xCTmkSBkYQPX3oVY6OHB947Th1h+a5NuFrbUcezBv2btcPZ2k6/Xa3RsOL4Ho5GXqNlSF2GNAjFxvyfbOTKAhVbr4cTTz7DX5+Ij69vkf3n5OTw69ffYynX0L9mc2wtiteN0+Ux4o1JxeoKZXvuEqkJglAxIoGa8KLysbdAAlxPyH6mgu7q5vr16/Tu3Zvo6Gig8DvlQUbz48eP8/333wPg5+dHq1atqqydgiBUP2fDz7D4/2bz49BXkRoXv0Yxl5oyvn0vxmg0fPDeZwx7/w1ahxZfaeB2xC02/Pgrb7bqhaFB8XnU1mYWREdH084jhNFtuxXZlpydwZJD25neczimJawGYGYiZXirzqg1Gn7atY4BTdvSo2FLNpw+xHfbV+NgZc1H/cbo26/T6fhh5xr6Ng5ldGg3UmRZ/LBqCXUCg+nWrE2xed534qLZfeoIQY4erHv9M+bv/YsWgXWKBNwA2Xly4tJTmDfyDcylpsXaKTU2YXCDULRaLfNmz6PX5LHUqlt4kyI9PZ2f3p/FtOY9sJCaVaiu8HiJcXeC8Jy6FJtFRFIOHWs6V3VTBOGpMjMxxN3WjOsJYl53ZWVmZtK9e3fu379f4vZRo0bpp3Ft3rz56TVMEIRqLzMzkwUffcH3IyaXGHD/m5GhIbOHvMofX/xAampqkW3Z2dms/eEX3mrdp8SAG+D3g1vxtHcqFnCr1AUsOrCVd3uPLDHg/m8b3u45jPXhh5Dl5zKoRXvq+wTQr0nbIu3//dA2BjRtR4CrJwDO1ra832M4/uYOLFj3Jwv+Wskvm1bzy6ZV/Lx2OWt3baWBqx/d6jZDIpEwtesgjty8SGJWun6fGq2GBfs2MqPPyBID7n8zMDDg9ZY92TxvMenp6Wg0Gn788FPebdWnxIC7xLrzl5Cenv7QskLliKBbEJ5Tq8JjcLaSUs/DtqqbIghPnbeDOVdFBvNK++mnn4iLi0MikaDT6Yol7apXrx7OzoU39E6ePFkVTRQEoZr6fMaHfNhrVKmB8n8ZGBjwcd8xfDHjoyLPr1m8nImNO+lH4PyXTqfjUvRdxoX1KrZt24UTjA7thlE5s4xLJBImduzLxjOHAZjYsS9Hbl7Sb8/KlWNkYIifc/EVkWp6+PBG54FMadeHyaG9mBzamzc7DuDj/mM5H3WrSNlJHfuy6cwR/eO9V84wuHkHTP61JFhZ7ZzUtAtrfl/Gri3bGOTfpGJ1m3Rmze/LylVeqBgRdAvCcyg7v4BtlxNoH+wsEqgJLyQ/BwtuJMjQaEXaksrYsmULUHixe/DgQfr161esTO3atdHpdNy5c+cpt04QhOpKp9NRkJiBUzYP4gAAh3JJREFUl0PFRuE5WtkgScvRj7DRarVkR8UXmdv8X9svnKBpjZIziMekJePjVLFM4XYWVmTl5erb4GbrQEJmGgCbzh6hf9N2D6teopoePtyMv69/LDU2QYcOlboAgFuJsYR4VGzpLwupGXmxKVw/Fk6IW8USpD2oq9FoKlRPKJsIugXhObTxQhwFWh1hwU5V3RRBqBJ+jhbkF2iISsut6qZUS5GRkUgkElq3bk1YWFiJZaysrADIysp6eg0TBKFa2759O20D6lWqbtfaTdiwfj0AJ48dp6VzjYeW33XpNENadiz2fHRqEjVK6JEuj+YBtTh77yYA/Zq0ZcfFwpE+ckV+kQRl5dW5blMOXb9Q5Lnu9Vuw98oZkrMzcLWxr1Q727oFIotPrlTddu5BHN5/sFJ1hdKJoFsQnjM6nY4Vp6Np6muHrblIoCa8mHwdCjM7X08QQ8wr40Evx8MyZCcmJgKFy3IKT56RkRGTJ09m8uTJGJWRDVkQnlU3L18h8O85zxXl7+LJrSvXAYiNisbP4eE91VIj4yKZzB+Iy0jFz9mtUm3wc3InPqNwbrmpiQkabeF3pbFR+Yap/5ehgWGxEYk+Tq4kZKaRmJle4d74B2o4uWNYyc5qP0c3YqPuV66yUCrxrS0Iz5nwqAwiU3MZ1lSsuSi8uCxNjXC2knI9QUbfBh5V3Zxqx83NjaioKM6cOUN+fn6x7devX+f8+fNIJBI8PSt3AS1UjFQqZf78+VXdDEF4JBq1Bolx5aa9SSQSNBo1UDi8vLKT53Q6LZJK1pZIQPuEV1uWUJhLQ/tI7ZRQ2VWhC99nMbz8cRM93YLwnFl5Ohp3G1Nqi6WShBecj4M5V+NET3dlhIaGApCRkUHnzp25d++eftvs2bPp1KkT2v9v777jmrreP4B/soGEvTfIVFHEvXHvPau1rlZba22tba1aFbWur63aZWtr3YrWvbXiXrg3ipM9BNkQCCQ5vz/4kRIIEBBI0Of9euWlufece597CNw89557jlIJAGjfvvQ0PoQQokk9Xx9EpyRWqW5cShLcvb0BAPbOjoj7/+epyyKTF6BALi+13NbUAnFpyRpqVCw+7TVs/7/Lt7xYYiqvYpLKGIPi//+WFklIT4G1iTlsTS1Uz4xXVlxaMpS8qiXs8enJcHChi6nVjZJuQt4iyVkyHH+YiK71bcsczZOQd4WbpRgP4zOqfLX/XTZ16lTV35DQ0FDcvXsXQOEXxDlz5uDVq8JnBTkcDj755BNdhflOYYwhOTkZycnJ9JkmddbwEcNx+vGdKtU9fD8U748dAwDo2LkTLiQ8Lbd8Gy8/HLh5odRyTzsnPI6LqlIMF8PvoY1XQwDA0TtX0KNRSwCAgMeHVJZX6e1denIPrTwbqi07eucKevq3hKOFNaJTqvZc9unoMIjtrapU91RUGLr27FGluqRslHQT8hbZfSsGXA4HHb1oADVC3K3EyMqTIya1dPdoUr4WLVpgzpw5askdh8NRJeJF/86aNQtNmzbVSYzvGqlUChsbG9jY2EAqleo6HEKqhMvlIt/UAK+zKtcLKTM3B1IjPnj/P8UXn8+HyMEKufmyMuu8374HLj+5X2o5h8OBrak5EtNTKxVDjiwXBgKhaqqzl0nxqGdb+PjSwOYdcfDmxUptDwBuvnyC5sVGWFcoFcgryFfNq+1saYOo5Mr1DJAV5INnaw6PFv6Iel21usIK5i4nlUdJNyFvCYWSYfvVaLSuZwGJAQ3XQIi7VeEgYDRfd9V8//33+PPPP2FrawvGmNrLysoKa9asweLFi3UdJiGkjvnuf4vxvyPbK9VjY8XRYHy7bKHashEfjsWGW6fLrMPlcuFoYaMxGR7UvCM2nT9aqRjWnz2imhZs77VzatOR2ZlZIDkrHUmZaVpv79KT+2jg6Ka2bMuFE+jftJ3qff+m7bDt0r+qx3m0senWGQydMAaDRw7H9kdXqlSXVD9Kugl5S5x/moS49Fx0b1C1kS4JeduYGQlhKRFS0v0GJk2ahNjYWFy7dg3//PMPdu7ciatXryI+Ph5TpkzRdXiEkDrI0dERvSaPwaL9mypMCBljWH54G9qNHgw3Nze1dba2tggcOxTrb5wqM3n+pv9oXHx8F8fvhqotNxIZYHjrLvj5xG6tYlh35hA6+jaBtYkZTj+8iXNht3Ex/J5a3SndBmPd6UN4lVHxHfSrz8IQkRSPnv6tVMt2XA6Bp50j3ItNZybkCzC2Y2+sPLpDq+fGt9w6g5Yj+8HJyQlCoRAfzf0Kqy4fqnTdqsjOzkZUVBTi4+ORn59fpW28zTiMHgyqMZmZmTA1NUVGRgZMTGhQK1KzJmy8jqgUKRYP8qPnuQn5f6tCnkDA4yJ4Umtdh0LnhLeErn6OOTk5kEgK5wHOzs4udzo3QuqCC2fPYfPyn9DDtxn6NW2r6rYNFI5OfuL+NRx7eB2jvvoUXXt0L3M7YfcfYP9fm9HMzAmdvP3VvgPJFQocf3wTO0NPwdXUGqNad0UjFw/V+piUJGy9eAI+Di4Y1LxDqRjOPrqNWy/DMbB5R8jk+Thw4yJiU5MwrecwmBpJsONKCBo6uaN3k9bgcXmQKxT48/QBGAiEGNKyE8zFxmqxPkuIwfF7V+FiaYtBLTqCMYaL4few6+ppgAHtff0xqHlHGJTo3n30zhUcuH0JPZu0xqAm7cDn/RcnYwwXnz/E9ZQo9J/4PvybBajVTUhIwPrlq+EpNEffBi0gKDblYEV1K6JQKHBs/0E8Pn8NpjlKWAnFkDMlEguykW8lRq/3h6ORf9XmZa8rtD0nUNJdg+gLFqktMalSdFxxFpM61ENnXxtdh0OI3th3OxYnwhJxP6iHzi9G1dVzglwuR0pKCmSysp+ddHF5d6YopKSbkOp1YO8+HNqwHdYiCXgcDhSMIVmWjb5j38PQkcO13s6t6zdwZv8RiAoYlHIFOHwe5IZ89Ht/BHx8fZGbm4ulcxfg9ZMImIqMwOEAMoUcKco8SEyMkZecDiOuADwOF7KCfKRmZ8KAJ4DEyAgZOdlIyc6EnaUVREaGkEqlMDUwgsTACK/SU5CSmQ4HC2uYiI3B4XIQk5kCKycHuFpYg6sAuFwO0qTZuP/kMVxNrCDi88EYQ2peDhwb+WD67Jk4uGsvzh04itzMLNgYm4HP5UGmVIBnaoQB40ejS4/ueP7sOQ5v/wfcbBmgUILD4yJfwEHgwD5o2ab8i8vPnj4rXVfIReCA3hXW1SQqMhLr5/8PQ+0bwde69N1xpVKJkKgHCDPIxdfLF0IgEFR6H3UBJd16oK5+wSJ1z4oT4dh8JRK/jW4KAwGv4gqEvCPuxqTjfyfCcf6bTnC11G2SUtfOCefPn8fChQtx5coVFBQUlFmOw+FArmFanrcVJd2EkHddTHQMts79H7706wYut/ynlZOzM7A++S7m/fpDhWXrIm3PCTTaEiF1nEyuwM4bMejgZU0JNyEl1Cs2mJquk+66JCQkBH379oVCoaDpqQghdZZCocCfv6xB2LVbyJfJYGVvi0++nQFXV9dKbSciIgKXTp5GVnomxCYSBLRtjbiIKNy7cQv3bt6CLE8GaxtruHp7IiU1FeE37yAnOxtKJWBsbQ4XZ2c4ODnBxMwMySnJyHyVgoKCAlg52cPUzBRQMEQ9fwmugI9GAf4QScS4F3oNGSlpMBAboWOfHhj63gi9SVo3LPoB3/h11Soea4kp3pc3xN+rfsHkr6fXfHB6ipJuQuq4Ew8TkZqTj271bXUdCiF6x8RQAGuJCA9iM9CvsUPFFQgAICgoCHK5XOdd8sl/+Hw+xo0bp/o/IaRsSUlJCPr0SwhfSzHQsxlGenWHiC9AijQLe6YuwuPcZPSdPBaDRw4rdzv/HjqCe/9egFMuH93svGEickJOVh5OLduCS5GPkC8vwKQmgfC3d8OlyEe4e+0pGpha4evuHyE6/TVOPr8DI4EIQ5zbwFpsgnyZHPezCnAhKQ4Rqa/AXsvQo1E7uJrZQFHPHRtvncK9ncfQyaMRvnVrCyNnEdLzcnB872V88sdWmNevh+9+XKLT3lI3rl5Fa0MHteffK+JsZoVn/57Ab3n5GPPZZJiZmdVcgHqKupfXoLrWlZDUTcP+uIK8AgW+69tA16EQopdWhTwBn8vFjsm6HUytLp0TxGIx8vLywBhDixYt0KFDBxgbG5dZPigoqBaj06269HMk5F109/ZtrP50FpZ0fg/WYlONZRhj2P3wCp5ZMSxas7rUeqVSidXzvkerTCO0sK1X5r7Ck2KxL+wqDAUCtHdrgBZOXgCAA2FXoWQMgxu2LvPiZYo0C79fPYbJLXvCWGiIVZcOYkxAJ7iZlz02z9kXD7D92VUs2vI73OuVHVdNWvHVd/jcrmWlL8qGJ0YjLDYSL/hSjA6aAQ8vzxqKsHZR93JC3gGPEzJxMyoN07t66ToUQvRWPSsJjj1IAGOM7txqqSjp9vf3x9WrV6ndCCF1QkxMDH76dBZ+6zURQn7ZA3dxOByMaNQOZ148wNKZ32HOiiVq638JWoq+Mhu425Y/OK2vjRMEj3kYUL8VPCwLp2w99uQmjA2M0NWj/FG7LY2MMTtwGJad2wOZogDfBg6Fsciw3DqdPRrBWmyCBeOm4sd9W2BtbV1u+ZogzJSBY1/5c4KvnQvOPLmLb1r2wsoFq/Dhyvmws3t3prnVjwcDCCFVsu1qFMyNBGjmZq7rUAjRW+5WYmTJ5IhKkeo6lDqjR48eYIzBwMCAEm49wRhDTk4OcnJy6Dl7Qsow/8Np+KHbB+Um3MV18WgE+f0opKb+N7f27es34flKAXezimeDuRv/Ek0c3FUJtzRfhojUVxUm3EX4PB5mdBgIY5FRhQl3ET87V3S198Xq7xZpVb66cRRV//vD4XDA4XAw3bsTtqz4pRqj0n+UdBNSR2XlFWDf7Th08bUBX08G1iBEH7lbFw6gdj8uQ8eR1B1Lly6FpaUlrl+/jpUrVyI/P1/XIb3zpFIpJBIJJBIJpFK6gERISWlpabCVC2BiYFSpeh817YalX3+nen965350c26oVd1zEQ/RzdNf9f7Ao2sY6te2UvsXCw0gEYqQLy97loiSBjdohdePXiIzM7NS+6oOHG7VL8QW1eTzeDBLzVe72PG2o2/qhNRR++/EQSZXoIsvDaBGSHlMDASwMRbhfky6rkOpM1xcXHDu3DkYGBhg5syZsLGxQbNmzdClS5dSr65du+o6XEIIwZJv5mJM446VrmdpZAx55CsAQEZGBoxT8rTq4ZORlwMTkZFa2aScdNgZV773Yf/6LXE4/IbW5YV8AbxMbLDjr42V3tebkomqlj5m5OZAyPuvB8JgJ3/s/ntLdYWl9+iZbkLqIMYYtoRGobmrBSzEQl2HQ4je87CW4A4l3VpLTU3F6NGjVYOpZWZm4s6dO6W+iNJz8oQQfZEZmwCv9l2qVNfe0BQFBQUIDw+Hn5F2z0k/fR2PRnb/TT3GGIOBlt3aS3Ixs8axJzcrVcfL0h53n0dUaX9vwqVFI0Q+ewU388rd9Nl37zIGN2ilem9iYIT81y+qOzy9RXe6CamDrkWk4nlSNro3oLvchGjD00aCh3EZyJcrdR1KnfDtt9/iwYMHAP57Bo+Sa0KIPuO+wZ93iUAEqVQKaVY2jPgirepIC2QwEvxXtkAhB59be/czjYQi5OXl1dr+igwePRJHEh5Vqo5CqUC6NAvmhhK15dnptd89XlfoTjchddDW0Cg4mBmioQNNV0OINrxsJJDJlQhPzERjJzNdh6P3Dh06BA6Hoxqwy8zMDMbGxuDS+BGEED0l57Aq975JzyscM8HUwhwZ+bla1TERGSEj77/xFYR8AQoU8krvu0hlx0fMyJXC0Lxyz69XB6FQCN8+HXD6Yhi6avHsO2MMv5w/iJF+7Uutiw1/hrB799HQX7uB5+oyOnsSUsckZebhRFgiute3oTtPhGjJzUoMPo+DO9Hpug6lTigaqMvW1hbh4eFITU1FVFQUIiIiNL4IIUTXvFoFIDQ6vEp14/IzwePx0LBhQ9zNS9KqTgMbZ9yJV+8enSev2qCTt+NewNfGqVJ1HifHoEHLZlXa35vqO2wIsgOccfDl7XJnU5DJC7DyzF709WwKBxMLtXWx6a/RysIVB5b9jpycnJoOWeco6Sakjtl5IwZ8LgcdvGp/bkZC6ioBjwt3SzHuRKfpOpQ6oWnTpgCAxo0bw9vbW8fREEJIxWbMm40DTyv3XDQAvEhJgGu7AACASCQCx8USMi1GEhf9//Pbxcv6WjvhcVJMpWM4H/EQndz9tC6fnpuDSFkGBo0eUel9VZcR48eg3tjeWBR2An+H/otsWWEPAcYYIlMSsebCIay/fBxj/TvB29qxVP1Dj65hkE8LjLNrgt0bttZ2+LWOkm5C6hC5Qont16LQ3tMKYhE9HUJIZXjYSHCb7nRrJSgoCABw7do1vHz5UsfREADg8XgYNmwYhg0bBh6Pp+twCNE7AoEABTbGeJGSUKl6f9wOwZfzZqveD/5oLHa91C5571+/Jf65f0n1vodXExx8dL1S+49KS0JuQX6lei9uvXMW9Tu0gEBQtYHbqkvz1q2wdNMfiBLmYdeNc1h74TD+ungU96KeY1LTbvi0VW/YSExL1UuVZoGjZDDgC2EjNkXynfBy75i/DehbOyF1SMijV3iVKcP0bjSAGiGV5WUjwYmHiUjJlsFSot1AOe+q2NhY9OrVC8ePH0ezZs3w3nvvoUGDBjA1Lf3lCQDGjh1byxG+ewwMDLB7925dh0GIXvtp+3qM7dQHy9uPhH2J7sya/H7tOLpMeR9C4X8zwbi6usKsawDOXXuCTg4+5dZ3MbNGRl4OTj69gx7eAeBxeejr2wzrb4bgw+bdK9x/ck4Gtt89D2ORIZ6nJMDT0r7COoceX0c4MvHHgu8qLFtbPl06Fxu+XIg5LfpVePEgW5aL3y4fwazWA1TLfORiPHv27K3uWcVhb/tlBR3KzMyEqakpMjIyYGJCA16RN/feX1eRJs3Hgv4VD1xBCFGXnJWHz3fexfpxzdG1fu1fuKpL5wQul6s2kFpFX6IUCkVthKUX6tLPkZB3UVZWFj7uMwyj67VAN88mGv9+vc7JxOqrR9BywmCMGDtG43b2bQlG0qmbGOnWHEbC0hdq8+UF2BsWigKFAqYGRkiRZuG9xh1gJBThbvxLnHp+D2ObdtF4p5cxhnMvH+J67FPMaD8QfC4Pf13/F3bG5ujr0xx8Db1ZMvOk2HDrNJ7zpfhl12YYGhpWoXVqzurFy5ESchPjmneFi7mNxjI3Y5/h3/Db+LJFb4iFBqrl915FIm9MW3ToFFhb4VYbbc8JdKebkDri2assXH2Zgs86e+o6FELqJCuJCGZGAtyJTtdJ0l0XaZqXu/g6mqebEKJvjI2Nsf3CCWxcuw5Td22Gt6ElGto4w4gvQmJ2Oq69egGusyXm7FoLa+uyx8cZMnY0Ent0wfY/NyHveQJ8+OYw5gmRkZ+L63HPEZGcACO+EA1snGBnbAZLQwnmntwGIV+Atq6+CHDwwG9XjiArPw8tnDzhbGYFaX4+wl/HIjk7Azn5eVAyYP2NEPhYO8HX2gnXYp5if9hVuJvbooWTF8wMjZAqzca1+Od4zS/AkE8nYuagAXr5d9fBwQEDGgXiakQ49j24AmdzG9hKzCBXKhCdnoyU7Aw0s62H79oOKhW/ginBE7zdaenbfXSEvEW2Xo2CqaEArdwr7i5FCCmNw+HA01qC2zSYmlYq6ghHHeVqV05ODiSSwjlus7OzIRaLdRwRIfqLw+Fg4pTJmDhlMmJiYnDnzh0kSaWo5+aGUa1aaZ202tnZ4bOgWZDL5YiMjERmZiYcJBL0cnVFVlYWYmJi8PTpUyRlZ8PKygpf1J8EmUyGs2fPIioqCoZODdDRzw/W1taQSCSwE4vhqlAgLi4OMpkM6SkpCPnnAC4+eIa8fBl4BiJ4tfOHmG+Ac6/ikJGeDQsHW3y+8Ue4ubmp4kpJScG+v7dAlpIBKBmUQh4Ch/ZH/UYNcSh4N26fu4jkyDgIeXxILM1h5eWKyPBnYGnZ4CiBfK4Sfj07wFQkRuLzSDy4dQe5mdkwkRjDyFgM9xaN8dHM6TA3Ny+3faRSKfZvDkZ6VDyiY2JgyXPA8PqtAQDxWWlIlmZAIDBAE3fHUnN0F/coPRHty7kA8jagpJuQOiBbJsfeW7Ho3sAOfB6Nf0hIVXnaSHDoXjzkCiX9LpXj7Nmzug6BEEKqhbOzM5ydnd9oG3w+H56e6j0NRSIRrKysEBAQUKp8o0aNyt3eo2u3cXLDDtSDBPPc28DT87/eV49S4rH9aSgcDCRY1qg7sgpk2PfVD5A6maDVkN4I3XUE5olSDDHzgYmwcJoxZbYSxxZsxrJn1+EgMkZPFz909h2ApxmvcDTyHsyTI/GRR2tIbAq7dCuUShw6cxdXX71AvlyOse5N0NnfV3UxIuOVFH8P+hSvbQzwyfJ5cPeopxZ/amoqNi9dDX5kCgaJPWBn5ABmbI/Vj8+go7MvAMDB2BwOxuUn7UViExPw7+zVOOxigQ9mfwEbG83d0+sySroJqQP2345FboEC3eq/fX+ECKlN9e1NsPNGDB7GZ6KJs5muw9FbgYF177k6QgjRd4wxLJ8xG6/O3sEPTfvBVGRUqkwDCwcsaT0UydJMfHdtH4JaDMRHNgG4Fv0SZ2b/jFmNe4Fjo36nnsvhIjY9GZ836IwWtu4AgCsJz/EoLR5f+vcodWefx+VicL2mGFyvKS7EP0Fk5mu1MqYiI0xv0BWMMfxv8nfo8/10NGvbCgAQFRGJLV8vxgzL5hBau6rqcDgcWPANkSLNgqWRsdZtEv46Do1MHdDPpjEKcuRY9clsjP7fbNTzersep6TL/IToOcYYNl2JRHNXCxpxmZA3VM9aDAMBF1dfpug6FEIIIe+Y379fgeRzd/B9i0EaE+7irI1MML/FACy5dRjP0hNxNyUas/17a+wavyX8MtrZe6kS7kep8XiSnoCPGnSssCt9Rwcf+JjbYc+L0tOkcTgczPLuihNBv+Dl8xdIT0/H5q8XY6Z1Swh5pe/dDnNvirXXTkDJlOXus4hMXoBd9y6ht3PhHOUCHh8zbVpj+7fLkJLydp2n6U43IXru8vMUvEjOweiWLroOhZA6j8/lwsfWGKEvUvBJoIeuw9F7UqkUN2/eREJCAmQyWZnlaMowQggpX3JyMp4du4DPGnaBAV+7+bUlAgOM9GyFvx9dxPI2wzSWSZdJUaBUoJGlk2rZiegH+NK/h9axtbL1wM2k08iTF2iM7RuPTvhp1VqIrc3xuXkAuBzN920lAgOM9WiNFRcOYEb7ARoT8yJZslysvnQInzfoDB73v+1xOBxMt2yOTav+wLQlc7U+Bn1HSTchem7TlQi4WBiivj1NTUNIdahvb4JD9+JRoFBCQM91l2nhwoX48ccfIZVKKyxbG0m3VCrFyZMncfjwYdy4cQORkZFQKBTw9PTE0KFDMWPGDNVAYyVt2bIFv/32Gx49egShUIjWrVtj7ty5aNu2bY3HTQghALD7jw2whSFcjS0rVc9ebAo3E8sy71jve3kLQz2aq97HZKfCSWJR6RHOB7oH4EDEbbzn1arUOg6HA9HL10iJfw2xfbtyt+MiscBHnm3x2+UjMBMbY2jDNjA1+O+uflJOBvaHXYVMJsPXft0hFpTuxWnAF6DgaRwKCgogEGh3gULfUdJNiB6LSZXi9OMkfNjeXS+nhyCkLmroUPhc94O4DDR10W6Ql3fNypUrsXDhwnLL1PaUYcHBwZg0aRIAoGHDhujVqxcyMzNx5coVBAUFYceOHTh//nypAXhmzJiB1atXw9DQED169EBeXh5CQkJw8uRJ7N69G4MHD66V+Akh77bIq3fRwdKx0vUORtzBWJ+yE92s/DyYFeuqfiTyHibWb1/p/ThJLJAozShz/RjHplgbdg6wr3hb1obG+MqvO9JkOdh75yIepSfA3dQaHAAmXBE+cG+hMdkurq/IFUd27cXg99+r3IHoKUq6CdFjW69GwUjEQztPK12HQshbw81KDEMBD6EvUijpLsNff/0F4L/EWpPanjJMKBRiypQp+PLLL+Hl5aVanpCQgL59++LOnTuYPn06goODVevOnDmD1atXw9LSEqGhoap6oaGh6NSpEyZMmIBOnTpVOC2OPuDxeOjTp4/q/4SQukOhUCA3LRMNPJpUvq5SWWaCyhgr1YWbA0DEq9rd4fLqmQgNIeFXbmwhc5EYH/q0x8GIO2hm7QonifbT3tYztkLI05eV2p8+o6SbED0lzZdj541odPK2gYGAvmARUl34XC587Ixx9WUKpnZ+u0ZHrS5RUVGqO9iTJ09Gnz59IJFIdJrsjR07VmM3dnt7e6xZswZt27bFvn37kJ+fD6FQCKDwjj0AzJ07Vy1Rb9OmDT755BP88ssv2LBhA7766qvaOYg3YGBggKNHj+o6DEJIFRQUFICjLJ0ga6O8y5tMw9o3uRyqaXvFVbVnk4jHh0whr3xFpXYDstUFlHQToqf23Y5Ddp4cPRrYVlyYEFIp9e1NsP9OLPLlSgj59Fx3SdbW1oiPj0eLFi2wdu1aXYdTIX9/fwCATCZDSkoK7O3tkZeXh9OnTwMAhg0rPQDRsGHD8Msvv+Dw4cN1IukmhNRdBgYGYEI+knOzYGdkWqm6CqYs81EeLocLRYmRwku+rwxlOT2YGGOQKQqqtN3k3Cy0tq3c4KV58gLwjQyrtD99RN80CNFDSiXDhssRaO5qARsTA12HQ8hbp6GDCfIKlHgQl67rUPTSoEGDwBiDso7cZXj5srALokAggIVFYffF8PBwyGQyWFtbw8nJqVSdpk2bAgDu379fe4ESQt5Z9vXr4Xz8k0rXa2LljNDEF2WuL1Aq1B738TazxYOU2Ervp0AhL/exoTMJT5AjrNqd7td52WrPnWvjUEo4+o4ZUaX96SNKugnRQxeeJeNlcg56N7LTdSiEvJXcLMUQC3k4//S1rkPRS0FBQXBwcMCtW7fw+++/6zqcCv38888AgF69ekEkKnzmMDo6GgA0JtwAIBaLYWZmhrS0NGRlZdVOoG8gJycHYrEYYrEYOTk5ug6HEFJJHd8bhKS8LORXspu1v6UzDkXeKXN9JwcftWS+m1MDnI59VOn4jkTdRx/XxmWuv2WYDSMf50pv90VmstZTpBUXb82Hs3Pl96evqHs5IXpow6UI1LMWw8fWWNehEPJW4nE58Hc2w8mwRMzo7q3rcPTOzJkz4e7ujvj4eEybNg0//vgj/P39NQ44xuFwsH79eh1EWejYsWNYv349BAIBvv/+e9Xy7OxsAICRUdl3V8RiMdLT05GdnQ1jY81/b2Uymdoc5ZmZmdUUeeVpM30bIUQ/tesciN0Of2Lb01BMrN9B63p/hJ2Fj5k9HqTEqs3FXSTA2hVLbh1BOztPCHh8cDlcWBua4Gl6IrzNtLt5kyvPx9P0RAyu11Tj+uuvI+Hbpz0c67nh2JpD6GOp3XmTMYad0mdQWAkrNdtFSOpztP6oj1Zl6wpKugnRM89eZeHCs9f4tJMHTRNGSA1q7mqOX848R0yqFM4Wlev29rbbtGkTOByOavTyyMhIREVFlSpX9CVKV0n348ePMWbMGDDG8MMPP6ie7S6KDSh/4B9tRmBftmxZhdOnEUJIRTgcDr79YyUWjpwEi5e3MaiMBLe4vx+dR1s7T7Sz88LP90Mg4vE1JtKTGwRixd0TmNmkFwQ8PkZ7tcLKu/9imEdzuJmUPwNOnrwAP9w5gen+3TWuf5Aai1B3Dr6eMAYAsP3BI1y6HIX2Zq7lbpcxht9e3cB7338FHp+Pn2f/iC9sW1b43fZqejSSWzujT/eu5Zara6h7OSF65u+LEbAQC9GmnqWuQyHkrebvbAY+l4NTj1/pOhS9VpR865vY2Fj06tULaWlpmDFjBr744gu19UV3rsvril1051gikZRZZvbs2cjIyFC9YmJiqiF6Qsi7yMHRAUH/rEOIPB7f3zqMiMxkjeWepidgdugeNLZ0Rnt7b2Tk50Jia4k/pGHY8uousgvy1MpbGxpjUv2O+PbqHpyMfggAmNGkB45HP8C2p6GQFshK7UPJlDgUcQdzr+3D5426wkSoPmhZZn4ufn58Bpd9hRj79TTk5RXuc+QnExHT1glrEq4jKVdzz5876bFYmnIN/ZbMgE/DBrCxt0PXOR9jSeo13ErT/Df0dW4W1sRfR0xHF4z7cmr5DVkHcVhtT7T5DsnMzISpqSkyMjJgYmKi63BIHZCUmYd2/zuDYc2cMcDfQdfhEPLWW378MYyEfOyY3LrG91WXzglcrvbX5DkcDhQKRQ1GU9rr16/RoUMHhIeHY8KECVi/fn2pCwN3795FQEAArK2tkZSUVGobOTk5kEgkque6taWrn2NRvEBh13mxWFxr+yaEVC/GGC6ePoetP/wCw5RcWAvEEHB5yFPKEZmTCr6hCI1tXAE+B/lGApg08sCITybAzMwMycnJ2P3HBuSGx0CYKweXAQV8DmS2EsBcjMS74ciKfgV7kTEMeHyk5uUgIvM17MVmsDYyhognQLYiH49S4yDi8mDCM4SCKWAnNoOQy4cCSuTwgVQx4GJgBgcpF2LGw520WEjl+XA1NIOjxALZLB+3MuJgKjaGg5klDLgCyPkc5FsZIaB/Nzi6ueD4n1vBef4K5vk88MDBa04+XoryIDEygp3SAByZHBlZGUjNzoK8oACNzR2RJ+BA5miChgO6oOfgAZU6H+mCtucE6l5OiB7ZeCUSfC4X3erb6DoUQt4JzVzNsflKFDKkBTA1qvxAL28rfR61PCsrC71790Z4eDiGDBmCdevWabwT7+PjA5FIhOTkZMTGxpYaUO327dsAgMaNyx44iBBCagKHw0HHbp3RsVtnAIBcXjhyuEDw33lILpeDx+OV+vtmbW2NT+d/C6AweVcoFODzC1O6+Lh4rP3sO4y0DUATw8IpZ3kcrmobMkUBlkZfAJ/Hx++e/WAtkhRugynB5XBQoFTgYPxDPM5JwVemrWHDkyCSl4qt0TcxxqERGpnYq8UyybQx8hQF2Cd9gbSWdvh8aRAKCgqw6vPZyH4uxVSxO3giK0D0Xx3GGK6kx2FrWhgCjGwx3aQJDM2F6g2UATz88woWbtqPcavmoZ6X55s3uo7pxaWD7OxsTJ8+HQ4ODjAwMECTJk2wc+dOreomJSVh/PjxsLKygpGREdq0aaOal7OkU6dOoU2bNjAyMoKVlRXGjx9f6up3ZGSkqitdyZe2MRFSFdkyObaFRqFrfRsYCel6GCG1oZmrBRSM4eyT0ndCif6RyWQYOHAgbt68iZ49e2LHjh3g8XgayxoaGqJLly4AgD179pRaX7SsX79+NRcwIYRogc/nqyXcRcsqerSHw+GoEu7EhESs/+hbzFH4ornYAXwuD3yuetJ+Mukp2omdMM8lENYiyX/b4PLA5XBx5vVzSAQGCPLqBhuRBFHSNOyNu4/vfLqVSriLGPAEGG3si153CvDj9DlYOvFzjI82QT+JB3ic0qkmh8NBO7ETfrbviqSkJOQzzT2l/Ixs8B23IXZPXYSI52VPmVZX6EXSPWTIEGzevBlBQUE4fvw4WrRogVGjRiE4OLjcejKZDF27dsXp06fx888/4+DBg7C1tUWvXr1w/vx5tbLnz59H7969YWtri4MHD+Lnn3/GqVOn0LVrV7VRSYtMmzYNoaGhaq/u3TUPMEBIddh5PRq5BQr09tP8R40QUv0sxEJ4Wotx8lGirkMhFVAoFBg1ahTOnj2LDh06YN++fRAKheXWmTFjBgBg8eLFePbsmWp5aGgo/vzzT5iYmODDDz+s0birC5fLRWBgIAIDA/W+uyUhpPatmz4f3wj8wOdqvhD5Iuc1MuR56GHjo3F9lDQNiXlZ6GNXH0DhHemtMTfxpWdHcDUkzyV5GJij0wMZHB+lwkpY8eMvIh4fX3t1xp8RoWWW4XK4+NLQD5u/XqzVwJf6TOe3044dO4aQkBAEBwdj1KhRAIDOnTsjKioK33zzDUaOHFnmVez169fj4cOHuHLlCtq0aaOq6+/vj5kzZ+LatWuqst988w28vb2xZ88e1RUhd3d3tGvXDhs2bMCUKVPUtu3i4oLWrWv+GT9CAEAmV2DdxZdo62kJC3H5XyIJIdWrhZsF9t+Joy7mxSxatEirckKhEPb29ggMDISbm1uNxvTbb79h//79AAArKyt8+umnGsv9+OOPsLIqHK23W7du+OKLL/Dzzz+jSZMm6N69O/Lz8xESEgKlUont27fDwsKiRuOuLoaGhjh37pyuwyCE6KE7N26iZYoQfInmnAkAjiQ+xlT3tmWuP5QQho/d/8t9Lqa8RC8bX60S7iIBJg44l/Ss4oL/z5AngLvYEtHSNLgYlZ6SEihMvHvnWODciRB07t1D623rG50n3fv374dEIsHw4cPVlk+YMAGjR4/GtWvX0Lat5g/I/v374ePjo0q4gcKuGGPGjMGcOXMQFxcHR0dHxMXF4caNG1i2bJkq4QaAtm3bwtvbG/v37y+VdBNSm/bcikVSpgzf9PTVdSiEvHM6eltj961Y7Lkdiw/bu+s6HL2wYMGCSo1YzuFw8MEHH+CPP/6AgYFBjcRUfLCzouRbkwULFqiSbgD46aef0KRJE/z2228ICQmBQCBA165dMXfuXLRv375GYiWEkNp0ev0/+FzsUuZ6qTwfAg63zLvgMoUcHA4g5P6XJ91Mj8EMz06VjqWZmRNupMWghbmzVuUH2DXEX5FXMc2j7L/HzcT2WL3raJ1OunXeP+nhw4eoX7++WjIM/DewycOHD8utq2kAlKJlYWFhatsoq6ymfSxfvhxCoRBGRkZo3749Dh06pOUREVI5BQol1px9jtb1LOFoZlhxBUJItTIzEqKluwW2Xo2s893XqhtjTGObFC0vWqdUKrFlyxaMGDGixmJZsGCB2n7Lemm64z5+/HjcvHkTOTk5SE9Px4kTJyjhJoS8NQTx6eXekb6WFo1AK48y199Ij0E7C/WLzsUT8MroYFkP19KitC4v4vGhzTVeQVxGleLRFzpPulNSUjR27SpalpKS8sZ1i/4tq2zxfYhEIkyaNAl//PEHzpw5g7///hsKhQIDBw7E33//Xe6xyGQyZGZmqr0Iqcj+23GIT8/DoABHXYdCyDurW31bRL6W4sqLss857xrGmGog0ZKJbfG5u4sSb8YYjh49imPHjuky7LdWTk4OrK2tYW1tXe7c44SQd5BMXu7qtAIpLIVGZa/PV18vU8hhwK3a41YcDgccaN9TCoBW5Q0VHNVc4XWRzpNuAOV2YdNm1D5t15VVtvhye3t7/PXXXxg+fDjat2+P0aNH48KFCwgICMCsWbMgl5f9oV62bBlMTU1VL2dn7bpVkHeXXKHEb2efo6WbBVwsyv5jSAipWb52xnA2N8TWq9pfndeGUsnw7FUW9t6KxdnwujNCekREBCZMmADGGBo2bIitW7fi7t27uHfvHrZt2wY/Pz8AwKefforTp0+jZ8+eqrrbt2/XVdhvvdevX+P169e6DoMQome4/LKf5QYAA64AecqycxgDHl9tvYDLRUEZo4pro3Ipt3bl8zmswsEz9ZnOn+m2tLTUeDc7NTUVgOa705Wta2lpCUDzXfPU1NQKB1ERCAQYOXIkZs2ahWfPnqF+/foay82ePVs1UipQOFk6Jd6kPPvvxCE6VYpPAsvu8kMIqXkcDgfd6ttiS2gUEjPyYGf6Zs8lp2TLsObsC+y+FYOsvMIvMkqZtDpCrRXXr1/Hxo0bYWNjg4sXL8LU1FS1rlGjRujduzd8fX3xxx9/oGfPnjh8+DA8PDwQExODGzdu6DByQgh598iMhUA5N4G9Jda4nxEPNyPNOY+3xAY302PgLbEGUDh4WZ6ioEqxvMrLgqmgcudQWTkXBFRljIV1euYGnUfeqFEjPH78uNQd5AcPHgCA6mp6WXWLypVXt+jfssqWt48iRd3nyvthi0QimJiYqL0IKYtMrsDqkKdo6W4Bd6uKp1YghNSs9l5WMBTy8OPJ8CpvQ65Q4rczz9BhxVnsvBGNLr42+K5Pfawf1xw/Dis9roi+WrVqFQAgICBALeEuYm5ujoCAADDG8L///Q98Ph+9evUCACQkJNRqrIQQ8q6za+eP+Lyyn3n2lFjheU7ZvWRcjcwRJU1VW2bMN0B6QW6lY9mX8ACD7BtpXT4sMxG+xjbllkkvyIVJM+9Kx6JPdJ50Dx48GNnZ2di7d6/a8s2bN8PBwQGtWrUqt254eLja1GByuRzbtm1Dq1at4ODgAABwdHREy5YtsW3bNigU/3WVuHr1Kp48eYIhQ4aUG2NBQQH++ecfWFlZwdPTsyqHSUgp265GIzEzDyOaU28IQvSBkZCP91o6Y8+tOFx9Wflnu6NScjBsbShWhTxFJx8b/DSyCd5r4QI/R1MYCfmwEItqIOqa8fDhQ3A4HDx8+FDjM3T5+fl49OgRAODevXsAADs7OwCF50xCCCG1Z+hHY7GfE1duGQ+xFZ5mJ5e53ldii7DMRNX7YY6NsTvuXqXiKFAqIFMUQMzXvhv4yaQnZc4dXmRPfgSGT/2wUrHoG50n3b1790b37t0xZcoUrFu3DmfPnsXkyZNx4sQJrFixQjVH94cffgg+n4+oqP+et5s4cSIaNmyI4cOHIzg4GKdOncKIESPw5MkT/O9//1Pbz//+9z+Eh4dj+PDhOHXqFIKDgzFixAj4+flhwoQJqnIzZszAtGnTsHPnTpw7dw5bt25Fu3btcPfuXfzwww9lzhlOSGVk5RXgtzPPEOhtQyOWE6JHOvvYwMfWGHP2P0C+XKl1vYN349D754tIyMjFgv4N8UFrVxgb1N05vwWCwtjj4+PRu3dvnDp1ComJiUhKSsL58+fRv39/xMbGqpVNT08HALXpugghhNQ8Q0NDOA3thEu58WWW6WfXADtj7yBbLtO4vpetD/bF30dmQeGFVjOBIYx4AtxOj9UqBsYYlmfdgtJK+56+J16Fo5GJPXjljLx+Jy8JRr2awdxc8zzedYXOk24A2LdvHz744APMnz8fvXr1wrVr17Bjxw68//77qjIKhQIKhUJt6hKRSITTp0+jc+fOmDZtGvr374+EhAQcP34cgYGBavvo1KkTjh07hoSEBPTv3x/Tpk1D586dcfr0aYhE/9198PPzw/Xr1zF16lR0794dX3zxBSwsLPDvv/9i/PjxNd4W5N2w7mIEcmQKDG1KI5YTok+4HA4mtndH5OscrDn7vMLy0nw5Zu65hy923kVTF3MsHdwIXrbGtRBpzQoMDFSdby9cuICePXvC0dER9vb26NKlC06dOgWg8Fn4zp07AwBevnwJAHB1ddVN0IQQ8g4b+tFYxPT0xEmp5gFBeRwuvvLshB+fnUNibukZlrj/v/6nFxcQl5sOAHjfuRlup8fh4uuX5e47XynHivwHGPP7InSYNxlrcx9Bycq/cL0l+iZy5DJ0sym72/il3HjcbmOJsV9NLXdbdQGH0aSkNSYzMxOmpqbIyMig57uJSlx6Lrr+eA7dG9hidCv6ckqIPtpzKwZ7b8fhs86e+KqHt8bZLy48Tcb8gw+RkJGHCe3c0NHLutwZNXKystA9wL1OnBMeP36MVq1aqaamKvlVoWgaMYlEguvXr8PFxQXW1tbIzc3FrFmzsHTpUl2EXSt0dW7Pzc1Fx44dARReCDE0pF5ShJDSLpw4hWvBh+AUl4eB4nqq+bZf5WVhP2KR52kFrsQQivA4BOaYoKnYHkDh3/nr0nhcFGchHlI4wQgdc0zQXOyAU0lP8SAzAS6G5hho3xB8bmHP39i8dBzkJIDVt8foWZ/Dxqbw2ewXT57hwM/rYPQiBUN5rrD4/+nIchUF2Bd7D0nZGWgktMRjWSoKBBwMd24CZ6PCO9lypQLHciLxwp6HxoN7oOfQAbXdhJWi7TmBku4aREk30eTT7bcQ+iIFPw73h5FQ5xMIEEI0YIzhyP0EBF+PRr/G9pjcsR7q25ugQKHEtZep+OdGNE6EvUIDexNMbOcOR/OKE6C6lHQDheOejB49GpGRkRrXu7u7Izg4GK1atUJqaipCQkIAAG3atIGLi0stRlq76NxOCKkLIl5G4PiWnVDmyAAeB+bODhg84X0YGRUmwIwxnDl6Ao8v3wTkCjA+F/5d2qFDty6qC6vnToQg7MI1QK4A+DyY1XNCRnQ8WF4BwOPAup4LBo4dBQMDzaOVZ2ZmYunUbyC7+gTuEEMALvqZesJB+F+PMJlSjoPpT/G0IB0x1jwEdO+I7u8Pg7dv+c956wtKuvUAnZhJSVeev8bov6/h004e6OBlretwCCEVuPYyBWsvvEBegRIGfC7kSga5ksHWRIThzZzR1sOy3LvbxdW1pBsoHBRt3759OHPmDGJiYsAYg4uLC7p06YIhQ4aonud+l9C5nRBCtHP1zHnEzNuMIcxJq/LP5ek4290OnyyZU8ORVR9tzwl0m42QWiJXKBF0KAw+tsZo70kDDRFSF7SqZ4kAF3NEpuTgeVI2eFwOGjuaws7UQOtkuy4TCAQYOXIkRo4cqetQCCGE1DGX1u7Al1om3ADgyTfD+fOPkJ6eDjMzs5oLTAf0YiA1Qt4Ff154iRfJ2RjX1u2d+LJOyNtCyOfC29YYfRrZo2dDO9ibGdLvMKl1UqkUbm5ucHNzg1Qq1XU4hBBSrqePw+ERWXrKyYoMzXfA7l/W1UBEukV3ugmpBU9fZeGnU0/Rr7ED3K3Eug6HEELUTJw4EQDQokULTJkyRfVeGxwOB+vXr6+p0Mj/Y4yppk2lJwMJIfruxF/b8DFP+7vcRcz4Bsi++aQGItItSroJqWFyhRJf7boHWxMDDG1a+T8+hBBS0zZt2gQOh4Ps7GxMmTJF9b4ijDFKugkhhJTCzZKBz6naLAv87Pxqjkb3qHs5ITXszwsvERafgY871oOQT79yhBBCCCHk7caU5c/TXX7dt683D93pJqQGXY9IxaqTTzHA3wGeNsYVVyCEEB0p2WWZujATQgipKoWhQNUbqrKURm/fzBiUdBNSQ5KzZJgafBs+dsYY1sxZ1+EQQkiZlCXuSJR8TwghhFRG2/cG4OL1jegosK9UvTylHLwGb9/3ZurrSkgNkCuU+GLnHRTIlfisiyd4XBrpmBBCCCGEvBtadmiLWw6V7zF1gBOLwdMn1UBEukV3ugmpZowxzDsYhqsvU/Bdn/owNxLqOiRCCCF1HIfDQYMGDVT/J4QQfVd/eA9c+u0M2nNttCqfWpCL+ABrODmVPfCwUqnE+X9PITrsKRRyOWzruaDrgD4wMDAoVTYyIgJXj59BVmoajM3N0LJnZ9Tz9Kjy8bwJSroJqWa/nXmOHdej8UlgPTRwMNV1OIQQUqFFixa9Uf358+dXUySkLEZGRggLC9N1GIQQorW+Y0bg7xeREByLQCuedbllUwqk+M05Bd/9ulbj+vT0dOxc8RtyLj9CYDQPA7nG4IGLOMU9/LVqF5TN3DBk5hQ4u7jg5N5DuL/jGJzD09FJagYTrhBZ7BkurDmFfb6maDCyB3qPGFKrFzA5jEZKqTGZmZkwNTVFRkYGTExMdB0OqQXB16IxZ/8DDG/mhCE0PRghpJicrCx0D3DXy3MCl8t9oy8fCoWiGqPRb3RuJ4SQytm1Zj3i9p7DgEwLuAnVb0hlK/KxlxeHtCb2mPbTYgiFpXuIPnsUjl2T5uGzRBsYczX3IM1nCmwwTcBTWy5GPROhBSzLjOc2S8Xpzhb4eu2P4PF4b3Rs2p4TKOmuQXRifrdsuhyBBYcfoUcDW4xv60bd/wghat7GpLtoZFpKugkhhJQnPz8fBzZtR9yZmxBkysAUCigNBRD6uWLYF5Nhba35Tnh8XBy2DPsC3yQ7aXWe2pwZBj+hFZoZ2JZbLk6Rg53dTfD12h/e6Du7tucE6l5OyBtijOGP8y+w4sQT9G1kj/dbuVDCTQipc+gavH6TSqVo0aIFAODGjRswMjLScUSEEKI9oVCIEZMnAJMnVKretm+WYkayo9bfrceZNMT/0m6gqcim3DqOPDHanEnE+RMh6NS7R6ViqgpKugl5AzK5AvMOPMSum7EY2tQRQ5tqdxWOEEL0SUREhK5DIBVgjOHRo0eq/xNCyNsuKSkJNmHJ4HMcK1Wvs6EzLuXFo4Nh+fXawhqrNh+gpJsQfZaYkYdPtt1CWHwGPgn0QKB3+QNEEEKIvnJ1ddV1CIQQQoiavav+xJhsm0pPct1CZIvV6bcrTLoBwCbsNRITE2FnZ1fFKLVD83QTUkmMMRy8G4ceP51HTKoUQf0bUsJNCHnnREREYOnSpfDz89N1KIQQQt5CipjXEHMFla7H4XBgwNHu3nLzTEM8uH670vuoLLrTTUglxKXn4vvDYTgR9gptPSwxoa07JAb0a0QIeTckJyfjn3/+QXBwMK5du6brcAghhLzN8uUANI9WXhEG7R7DkXAFyE5Nq9I+KoOyBUK0kJuvwJ8XXmDtuRcwEPDweRcvtPEoeyoCQgh5W2RnZ2Pfvn0IDg7GmTNnVCOVFz1XTONYEEIIqQlMVPOpaoYiHyY2Nf+dnpJuQsqRm6/A9mtR+OPcC2TkFqC3nx0GBTjCSEi/OoSQt1dBQQGOHj2K4OBgHD16FHl5eQA0D+D1pnOcEkIIIZpIGrrh9ZUnsOIZVqqekjHkM6VWZS9b52F0+7ZVCa9SKHMgRIOUbBm2Xo3CltAopEvzEehtjUFNHGFjYqDr0AghpEYwxnD27FkEBwdj3759yMjIUC0HCu9oczgc1dzcPXv2xNChQzFo0CAdRv3u4HA4qgHvqHcBIeRdMHzaJGzZNxGfpFdu9PKT0ih0N3KpsJySMeQ0dih3fu3qQkk3If+PMYZ7sRnYdjUKh+/FAwACva3Rp5E9bCnZJoS8xWbMmIF//vkHiYmJADR3HefxeGCMqbqXHzt2rPYDfYcZGRkhMjJS12EQQkitkUgkkDVzQ1ZIDoy52j3brWQMt2VJ6CV2q7DsQUECekyb/YZRaoeSbvLOy8gtwKF78dhxLRqPEjJhbSzC4ABHdPW1pUHSCCHvhJ9++kl1F7s4Pp+Prl27YtiwYRg0aBD8/f0RFxenoygJIYS8az5etQDLB0zErAhrGHDL/17OGMPi9OuYYNygwu3e5KQi64PWaNS0STVFWj7KKMg7SalkuBaRil03Y3D8QQLyFUo0dTHHNz180MTZDFwudd0jhLybBAIBevfujaFDh2LAgAEwNTXVdUiEEELeUUZGRpixZy3+9/40DA3nwY9rrrFcrDwbm5wzYDa0B84deYiRMiMIOaXHHMlnCuwRJUA5tj3Gf/tFTYevQkk3eackZuRhz60Y/HMjBjFpubA3NcCgAEd09LaGuVHVpiQghJC3iVwuR1RUFKKjo5GYmEhJt57Izc1Fx44dAQAXLlyAoWHlBhYihJC6yszMDEGHN+PfPQfx7z8nYPckDW5ZXPAYB/FGCkTUE8Opd1t8O3ksRCIRIj+JwJ8r/wJuRaBhEmCcz0GWkOGRNcCau2PAjP/BvV69Wj0GDtM0FCmpFpmZmTA1NUVGRkatPKBPNFMoGc49SULwtWicfZIEAY+Llu4W6OxjA187YxqQhhBSK3KystA9wF0vzwlcLrfMv4X169fH8OHDMXToUPTu3RtxcXHgcDiqZ7vfNbo6t+fk5EAikQAonMZNLBbX2r4JIUSfJCUlIS4uDnK5HLa2tnBx0Txomkwmw4sXL5CZkQETU1N4eHhAJBJVayzanhMo6a5BlHTrVnKWDP/ciMb2a9FIyMhDPSsxOvnYoJ2nJU35RQipdfqcdK9atQo7duzArVu3VMuKP+NdPCEvGr2ckm5Kugkh5F2n7TmBMg/y1rkXk44NlyNw9H4CeFwO2npYYmpnT3hYS3QdGiGE6KUZM2ZgxowZeP78ObZt24adO3fi6dOnAP5LuEteo/fz88PgwYMxePBgNG3atNZjJoQQQuoKutNdg+hOd+1RKBlOhiVi3cWXuB2dDlsTEbrXt0OgjzUkIrq2RAjRPX2+063JrVu3sH37duzatQvx8YXTKGq6483hcCCXy3UVZq2jO92EEEKK0J1u8k7IK1Bg980Y/HXhJWLSclHf3hhfdfdGUxdzGoGcEELeQLNmzdCsWTOsXLkS586dQ3BwMPbt24e0tDQA0DjFGCGEEEJKo6Sb1EmZeQXYGhqF9ZcikC7NR0t3C3wc6EFdyAkhpJpxOBx07twZnTt3xu+//45jx44hODgYR44cQW5urq7DI4QQQvQeJd2kTknLycf6SxHYdCUSMrkCHb2s0d/fAbYmBroOjRBC3noCgQADBw7EwIEDkZ2djX379mHnzp26DqtCeXl5WLZsGXbs2IHo6GhYWFigV69eWLRoEZycnHQdntasrKx0HQIhhNR5Hw0agWdnroEVFIAjNsSXf6/EoEGDanSf9Ex3DaJnuqtPSrYM6y5GYPOVSCgZQ7f6tujb2J7m1iaE1Bl17Znut0VeXh66du2KK1euwN7eHh06dEBkZCSuX78Oa2trhIaGwsPDQ+vt0bmdEELqnoSEBIzyaQ3vLAEGwwmtYAkD8JCGfOxBDC4gCeJuTbAl5HCltkvPdJO3QmpOPv688AKbr0SCAw66N7BF30b2MDEU6Do0QgghdcDSpUtx5coVtGnTBidPnlQNRrZq1Sp89dVXmDhxIs6fP6/jKAkhhNSUixcvYmXHUdiEpnCD+qOoRuDjC/jgc3hjz6kYdDd0QUhudLXHQHe6axBdDa+6tJx8rLv4EhsvRwIAeja0RZ9G9jA2oGSbEFI30Z3u2ldQUAAbGxukp6fj9u3bCAgIUFvv7++P+/fv4+bNm2jWrJlW26RzOyGE1B0JCQmY4tACwWgLIy3uN19CEhaJn+NkdqRW29f2nMDVNmBCakOGtAArTz5B+/+dwYbLEejewBY/vdcEI1u4UMJNCCGkUi5duoT09HR4eHiUSrgBYNiwYQCAw4cr151QF3Jzc9GpUyd06tSJBrAjhBAtve/eAmvRQquEGwDawwa9ciywffv2ao2DupcTvZCVV4CNlyOx7sJL5CuU6N7AFv0bO1A3ckIIIVV27949AEDTpk01ri9aXlROnymVSlU3eKVSqeNoCCFE/xUUFKChzAh2MKxUvcnwwKDxM/H+++9XWyyUdBOdypbJsflKJP688AK5+Qp0rW+Lgf4OMKMB0gghhLyh6OjC5/LKGqG8aHlROUIIIW+PoU07Yg7cKl1PAgEay40hlUphZGRULbFQ0k10oijZ/uvCS0jz5ejsY4OBTRxhIaZkmxBCSPXIzs4GgDK/NInFYrVymshkMshkMtX7zMzMaoyQEEJITcl6HIlW6FSlug1hipMnT1bbVGKUdJNalZlXgC1XIrHuYgSk+XJ08rHBQH8HWEpEug6NEELIW6ZorFgOh1Pu+vIsW7YMCxcurNa4CCGE1Dwh44IDzX//K2IKAcLDw6stFkq6Sa1IzcnHpssR2HglEnkFCnT2scEASrYJIYTUIGNjYwBATk6OxvVSqRQAVNOIaTJ79mzMmDFD9T4zMxPOzs7VGCUhhJCakMdVQqlk4FYh8X4NGfz9/astFkq6SY2KTZNi/aUI7LgeDcaArr426NvYgbqRE0IIqXEuLi4AgNjYWI3ri5YXldNEJBJBJKILxIQQUtd49e2A4wfj0ReOla57D+n4sFu3aouFkm5SI+5Ep2Hj5QgcvZ8IIyEPffzs0dPPDiY07RchhJBaUnSX4vbt2xrXFy1v3LhxrcX0JqprQB9CCHkX/H1gFyZzPCuddCchD+EGeRAIqi9voaSbVJu8AgWOP0zApiuRuBeTAVsTEca0dkUnH2sYCHi6Do8QQsg7pl27djA1NcWLFy9w586dUnN179mzBwDQr18/XYRXKWKxuMxu8oQQQjR7YabEw/R0+MFM6zqr8QQrLuyr1ji41bo18k56kpiFxUceofWy0/jyn3tQKoGvenhj1fAm6OVnRwk3IYQQnRAKhfjss88AAJ999pla0rpq1Srcv38f7du3R4sWLXQVIiGEkBp0IukJZnDuIBbaXbTchgiEexpW+3mB7nSTKknMyMPRBwk4cCcOD+IyYGLAR3sva3TztYG9WeUmoCeEEEJqyty5c3Hq1ClcuXIFXl5e6NChA6KionDt2jVYWlpi48aNug6REEJIDREIBNidFo5h5r74kvmgN+w1jmieiQKsRjhu1xPg4DPNjyS9CUq6idYiXucg5FEiToa9wq2oNPB5HDRxNsOM7t4IcDYDn0cdJwghhOgXAwMDnD17FsuWLUNwcDAOHDgAc3NzjBs3Dt9//32dGYk8Ly8PQ4cOBQDs3bsXBgYGOo6IEELqBlNTU4QoEzC2xwCsC7mEdrBCM1hADB5eIx8hSMBDfjY+2fkLgv7/72x14zBtJqkkVZKZmQlTU1NkZGTAxMRE1+FUWmJGHm5EpuLKixRcfJaM2LRcCHlcNHI0RXM3c7Rws4BYRNdtCCFEGzlZWege4F5nzwmkkK7O7Tk5OaqpzbKzsyEWi2tt34QQ8jY5e/YsNm/ejORXr+Dp7Y1ly5ZVeaBKbc8Jb23GlJ2djblz52LXrl1ITU2Fr68vZs2ahffee6/CuklJSZg5cyaOHDkCqVQKf39/LF68GF27dq1SLJl5BSjgyqD8/8sbPC4HAh4HQj4XQh4XHE7VJm2vLkolQ1x6Lp4nZeNRQiYexmXgfmwG4tJzAQCOZoZo6GCC91q4wM/RBCI+PaNNCCGEEEIIqXs6d+6Mzp071+o+39qke8iQIbhx4waWL18Ob29vBAcHY9SoUVAqlRg9enSZ9WQyGbp27Yr09HT8/PPPsLGxwZo1a9CrVy+cOnUKgYGBlY6l7bIz4Io0Xz3hcgADAQ8SER/GBnwYGwhgaiiAiaEApoZ8mPz/e2MDAUwM+RCL+JCI+DAS8mAgKHwJeBwIuFxwuYXJO2MMBQqGAoUSuQUK5OYrkJUnR0ZuPtKkBUjOkiEpKw/x6XmITpEiNk2KPLkSAGAo4MHdSgx/J1MMb+4EH1tjmBnRnNqEEEIIIYQQUhVvZdJ97NgxhISEqBJtoPCKRlRUFL755huMHDkSPJ7mu7Xr16/Hw4cPceXKFbRp00ZV19/fHzNnzsS1a9cqHc8nHT0gMTEGhwNwwIGSMcgVDHKlEjK5ErICJXIL5MjNVyAnXwFpvhyvs2WQ5iuQI5MjRyaHNF+B6ngOgAPA1FAAc7EQZkYCeNhI0MbDEg5mBnA0M4KlRAiuju+8E0IIIYQQQsjb4q1Muvfv3w+JRILhw4erLZ8wYQJGjx6Na9euoW3btmXW9fHxUSXcAMDn8zFmzBjMmTMHcXFxcHSs3ATrLd0tIDY2rvyBFKNkDHn/f9c6r0CJPLkCBfLCpF2hZFAoGZT/n5ZzwAGPW/gS8bmFd8T5XEgM+BAL+ao74oQQQgghhBBCatZbmXQ/fPgQ9evXB5+vfniNGzdWrS8r6X748CE6dOhQanlR3bCwsEon3dWBy+HASMiHkfCt/JERQgghhBBCyFvprczgUlJSUK9evVLLLSwsVOvLq1tUrrJ1ZTIZZDKZ6n1GRgYAYO/1ZxAZ0iijhBDyLpPl5gAoHHeD1F1FP7/MzMxa3W9OTo7q/5mZmVAoFLW6f0IIIaUVnQsqOre/lUk3gHJHBK9otPCq1l22bBkWLlxYavnaj3uUuz9CCCHvjpSUFJiamuo6DFJFWVlZAKDT+b0dHBx0tm9CCCGlZWVllXtufyuTbktLS413pFNTUwFA453s6qg7e/ZszJgxQ/U+PT0drq6uiI6Opi9YlZCZmQlnZ2fExMTQXLaVQO1WedRmVUPtVjUZGRlwcXEp9zxC9J+DgwNiYmJgbGxc61N+1uXfvbocO1C346fYdYNi153ajJ8xhqysrAovhr6VSXejRo2wY8cOyOVytee6Hzx4AADw8/Mrt25RueK0qSsSiSASiUotNzU1rZMfWF0zMTGhdqsCarfKozarGmq3quFyuboOgbwBLpcLJycnncZQl3/36nLsQN2On2LXDYpdd2orfm1urr6VZ/7BgwcjOzsbe/fuVVu+efNmODg4oFWrVuXWDQ8PV5saTC6XY9u2bWjVqhV16SKEEEIIIYQQorW38k5379690b17d0yZMgWZmZnw9PTEjh07cOLECWzbtk01R/eHH36IzZs348WLF3B1dQUATJw4EWvWrMHw4cOxfPly2NjY4Pfff8eTJ09w6tQpXR4WIYQQQgghhJA65q1MugFg3759+O677zB//nykpqbC19cXO3bswHvvvacqo1AooFAo1EabE4lEOH36NGbOnIlp06ZBKpWiSZMmOH78OAIDAysVg0gkQlBQkMYu56Rs1G5VQ+1WedRmVUPtVjXUbuRN1eXPUF2OHajb8VPsukGx644+xs9hNHcJIYQQQgghhBBSI97KZ7oJIYQQQgghhBB9QEk3IYQQQgghhBBSQyjproLs7GxMnz4dDg4OMDAwQJMmTbBz506t6iYlJWH8+PGwsrKCkZER2rRpg9OnT9dwxLXrzJkzmDhxInx9fSEWi+Ho6IiBAwfi1q1bpcrevn0b3bp1g0QigZmZGYYMGYKXL19qva9Tp06hTZs2MDIygpWVFcaPH4+kpKTqPByd+fvvv8HhcCCRSEqto3ZTd+nSJfTp0wfm5uYwNDSEl5cXvv/+e7Uy1Gbq7ty5g0GDBsHBwQFGRkbw9fXFokWLIJVK1cq9q+2WlZWFmTNnokePHrC2tgaHw8GCBQs0lq1MG/3666/w9fWFSCSCu7s7Fi5ciIKCAq1iKigowMKFC+Hm5gaRSARfX1/8+uuvVT1EUofk5eUhKCgI3t7eMDAwgIODAyZOnIjY2Fhdh4Zbt25h+fLlGDJkCBwdHcHhcGBgYFBhvS1btqBly5aQSCSwsLBAnz59cOXKlVqI+D9SqRQHDhzAhx9+iMaNG8PExARisRj+/v5YtGgRsrOzy6yrD/GvWrUKQ4YMgZeXF0xNTSESieDq6opx48YhLCyszHr6EHtJqampsLGxAYfDga+vb7ll9SH+Tp06gcPhlPk6ceKE3sZeJDExEV9++SW8vb1haGgICwsLNGvWDDNnztRYXtexnzt3rtw2L3otWrRI72IHADBSad27d2dmZmZs7dq17MyZM+yjjz5iANj27dvLrZeXl8f8/PyYk5MT27ZtGzt58iQbOHAg4/P57Ny5c7UUfc0bNmwY69y5M/v999/ZuXPn2O7du1nr1q0Zn89np0+fVpV7/PgxMzY2Zh06dGBHjx5le/fuZQ0bNmQODg4sKSmpwv2cO3eO8fl8NnDgQHby5Em2bds25ujoyPz8/FheXl5NHmKNi42NZaampszBwYGJxWK1ddRu6rZv3864XC5777332KFDh9iZM2fYunXr2MKFC1VlqM3UhYWFMQMDA+bv78/++ecfdvr0aRYUFMR4PB4bMGCAqty73G4RERHM1NSUdezYUfU3PigoqFS5yrTR4sWLGYfDYbNnz2Znz55lK1asYEKhkE2aNEmrmD766CMmEonYihUr2NmzZ9msWbMYh8NhS5YsqY5DJnoqNzeXtW3blgFg9vb2bMSIEaxly5YMALO2tmbPnz/XaXwDBw5kANReIpGo3DpffvklA8AMDQ3ZwIEDWc+ePRmfz2c8Ho/t27evliJnbN26daqYGzZsyIYPH8569uzJjI2NGQDm6+vLXr16pbfxW1paMgMDA9ayZUs2ePBgNnjwYObt7c0AMKFQyI4dO6a3sZc0btw4xuFwGADm4+NTZjl9iT8wMJABYEOHDmXjxo0r9bp//77exs4YY1euXGFmZmYMAGvQoAEbMWIE6927N3N1dWU8Hk8vY3/8+LHGth43bhwbM2aM6nf5zJkzehc7Y4xR0l1JR48eZQBYcHCw2vLu3bszBwcHJpfLy6y7Zs0aBoBduXJFtaygoIA1aNCAtWzZssZirm2aTlBZWVnM1taWde3aVbVs+PDhzMrKimVkZKiWRUZGMoFAwGbOnFnhflq0aMEaNGjACgoKVMsuX77MALDff//9DY9Ct/r168f69+/Pxo0bVyrppnb7T2xsLBOLxWzKlCnllqM2U/fdd98xAKW+rE+ePJkBYKmpqYyxd7vdlEolUyqVjDHGkpOTy0y6tW2j169fMwMDAzZ58mS1+kuWLGEcDoeFhYWVG8/Dhw8Zh8NhS5cuVVs+adIkZmhoyFJSUip7iKSOmDdvHgPA2rRpw7KyslTLV65cyQCwjh076jA6xpYvX87mz5/PDh8+zBITEytMuk+fPs0AMEtLS/b06VPV8itXrjChUMhMTU1Vf4Nq2ubNm9mUKVPU4mCMsfj4eBYQEMAAsFGjRult/JcuXWK5ubmllv/+++8MQKnvpfoUe3GnTp1iAFTnoLKSbn2KvyjpjoiI0Kq8PsUeFxfHzMzMmKGhocak89q1a2rv9Sn2shw7dowBYM7OzkyhUKiW61PslHRX0kcffcQkEonal0jGGAsODmYA2OXLl8us261bN41/SJYuXcoAsNjY2GqPV5907tyZeXt7M8YKLzYYGhqyjz/+uFS5Hj16MC8vr3K3FRsbywCwZcuWlVrn7e3NunfvXj1B68DWrVuZsbExi4mJKZV0U7upW7BgAQPAIiMjyyxDbVZaUbslJyerLZ85cybjcrksOzub2q2YspLuyrTRtm3bGAAWGhqqVi4+Pp4BqPBu9eLFixkAlpCQoLb8ypUrWvW0InVTfn6+6m7U7du3S61v3LgxA8Bu3rypg+g0qyjp7tOnDwPAVq9eXWrd559/zgCwH3/8sQYj1E7R75ZIJGIymUy1vK7E7+npyQCoXdDTx9ilUinz9PRkDRo0YE+fPi036dan+CubdOtT7B988AEDwH799VetyutT7GUZPXo0A8BmzZqltlyfYqdnuivp4cOHqF+/Pvh89SnOGzdurFpfXt2icprqlvf8TV2XkZGB27dvo2HDhgCAFy9eIDc3t8z2eP78OfLy8srcXlE7l1W/vJ+DPktKSsL06dOxfPlyODk5lVpP7abuwoULsLCwQHh4OJo0aQI+nw8bGxt88sknyMzMBEBtpsm4ceNgZmaGKVOm4OXLl8jKysKRI0fw559/YurUqRCLxdRuWqhMGxUdb6NGjdTK2dvbw8rKqsL2ePjwIaytrWFnZ1dqP8W3T94uly5dQnp6Ojw8PBAQEFBq/bBhwwAAhw8fru3QqiQvL081jk1R7MXp0/H4+/sDAGQyGVJSUgDUrfh5PB4AQCgUAtDf2BcuXIgXL17gjz/+gEAgKLOcvsavDX2KPS0tDbt27YKpqSk++uijCsvrU+xlycnJwcGDBwEAY8aMUS3Xt9j5FRchxaWkpKBevXqllltYWKjWl1e3qFxl69Z1U6dORU5ODr777jsA/x1rWe3BGENaWhrs7e01bq+i+nW1LT/99FP4+PhgypQpGtdTu6mLi4uDVCrF8OHDMXv2bPz000+4ceMGgoKC8PDhQ1y8eJHaTAM3NzeEhoZi8ODB8PDwUC3//PPP8dNPPwGgz5o2KtNGKSkpEIlEEIvFGstW1B5lnT/EYjGEQuFb0Z6ktHv37gEAmjZtqnF90fKicvouPDwcMpkM1tbWGi8sFx3P/fv3azu0UooGQxQIBKrfvboS/5YtW/DkyRN4e3urvrPqY+z379/HypUrMWHCBHTs2BGRkZFlltXH+AFg/fr1SElJAZfLhbe3NwYNGgQXFxe1MvoU++XLlyGTydCtWzcIBALs2bMHly5dQkFBAXx9fTFixAjY2trqZexl2bdvH3JychAQEKC6uQfoX+yUdFcBh8Op0ro3rVtXzZs3D9u3b8evv/6KZs2aqa170/Yoq0xdbMu9e/fi8OHDuHPnTo1/jt6WdlMqlapRfWfNmgWgcERRoVCI6dOn4/Tp0zAyMgJAbVZcZGQk+vfvD1tbW+zZswfW1ta4du0aFi9ejOzsbKxfv15VltqtYtq2UU21pbb1Sd0THR0NABq/MBZfXlRO31V0PGKxGGZmZkhLS0NWVhaMjY1rMzw1P//8MwCgV69eEIlEAPQ3/h9++AFhYWHIycnB48ePERYWBgcHBwQHB4PL5epl7EqlEpMmTYKZmRlWrFhRYXl9i7/I4sWL1d5//fXXmDdvHubNm6dapk+xF/WqtbW1RYcOHRAaGqq2fvbs2di4cSOGDx+ud7GXZdu2bQCADz74QG25vsVO3csrydLSUuMdhdTUVACa73hUR926auHChVi8eDGWLFmCzz77TLXc0tISgOa7+6mpqeBwODAzMytzuxXVr2ttmZ2djalTp2LatGlwcHBAeno60tPTkZ+fDwBIT09HTk4OtVsJRcfTs2dPteW9e/cGUDiVE7VZabNmzUJmZib+/fdfDB06FB07dsQ333yDn376CRs2bMD58+ep3bRQmTaytLREXl5eqSnZispW1B5lnT9ycnKQn5//VrQnKa1oyqqii4clFfWcKG9qK31S0fEA+nFMx44dw/r16yEQCNSmn9TX+P/9919s3rwZe/bsQVhYGJydnREcHKx2o0PfYv/1119x/fp1/PDDD6q/peXRt/g7duyIrVu34sWLF5BKpXjy5AmWLFkCPp+P+fPnqy7aFI9HH2JPS0sDUNgb4v79+1i/fj2Sk5MRERGBGTNmICcnB2PGjFHd/dWn2DVJTEzE6dOnwePxMGrUKLV1+hY7Jd2V1KhRIzx+/BhyuVxt+YMHDwAAfn5+5dYtKlfZunXRwoULsWDBAixYsABz5sxRW+fh4QFDQ8My28PT07PceT6L2qqs+nWtLV+/fo1Xr15h5cqVMDc3V7127NiBnJwcmJub4/3336d2K0HTs7QAwBgDAHC5XGozDe7evYsGDRqU6urcokULAIXPB1O7VawybVT0LHfJsomJiXj9+nWF7dGoUSMkJycjMTGx1H6At+/8QQoV/S0rqydD0fq6oqLjKV5GVx4/fowxY8aAMYYffvhB9Ww3oL/xnzp1SvU4y4ULF+Dj44NOnTphyZIlpeLSh9hjYmIwd+5cBAYGYvz48VrV0af4AWDRokUYM2YM6tWrB0NDQ3h7e2POnDk4cOAAACAoKAi5ublqcelD7AqFAgAgl8uxatUqTJw4EVZWVnBzc8PKlSsxbNgw5Ofnq3of6FPsmgQHB0OhUKB79+6lxjzRt9gp6a6kwYMHIzs7G3v37lVbvnnzZjg4OKBVq1bl1g0PD8e1a9dUy+RyObZt24ZWrVrBwcGhxuKubd9//z0WLFiAuXPnIigoqNR6Pp+P/v37Y9++fcjKylItj46OxtmzZzFkyJByt+/o6IiWLVti27Ztqj8gAHD16lU8efKkwvr6xs7ODmfPni316tmzJwwMDHD27FksXryY2q2EoUOHAgCOHz+utvzYsWMAgNatW1ObaeDg4ICwsLBSV3aLupk5OTlRu2mhMm3Uq1cvGBgYYNOmTWrb2LRpEzgcDgYNGlTuvgYOHAgOh4PNmzeXqm9oaIhevXq98fEQ/VPU3TEnJ0fj+qKeExKJpNZiehMVHQ+g22OKjY1Fr169kJaWhhkzZuCLL75QW6/v8ZuZmaFDhw44duwYmjVrhnnz5uHGjRsA9Cv2Tz/9FPn5+fjjjz+0rqNP8ZenR48eaN68OTIyMnD16lUA+hV7USxcLhfjxo0rtX7ixIkAgHPnzqmV14fYNSmrazmgh7HXyhjpb5nu3bszc3Nz9tdff7EzZ86wSZMmMQBs27ZtqjITJ05kPB5PbSqjvLw81rBhQ+bs7My2b9/OQkJC2ODBgxmfz2fnzp3TxaHUiB9//JEBYL169WKhoaGlXkUeP37MJBIJ69ixIzt27Bjbt28f8/PzYw4ODiwpKUltmzwej3Xp0kVt2dmzZxmfz2eDBw9mISEhbPv27czZ2Zn5+fmxvLy8WjnWmqZpnm5qN3X9+/dnIpGIff/99ywkJIQtW7aMGRgYsH79+qnKUJupO3jwIONwOKx169bsn3/+YadPn2ZLlixhEomENWjQQDU9zrvebseOHWO7d+9mGzZsYADY8OHD2e7du9nu3btZTk4OY6xybbR48WLG4XDYnDlz2Llz59gPP/zARCIRmzRpklq5zZs3Mx6PxzZv3qy2/KOPPmIikYj98MMP7Ny5c2zOnDmMw+FUON0YqbtWr16t+uxpcuTIEQaADRo0qJYjKxvKmTLszp07DACztrbWuD47O5sBYGZmZjUZokbJycnM19eXAWATJkxgSqWyVBl9jr+kFStWMABs3rx5jDH9ir1oP4GBgWqvVq1aMQDM0NBQtaxobnp9ir8io0aNUpvKUZ9i37x5s2oOd00ePXrEADCBQMAY06/YSyqKVSKRqM7Jxelb7JR0V0FWVhb7/PPPmZ2dHRMKhaxx48Zsx44damXGjRuncf6+xMRENnbsWGZhYcEMDAxY69atWUhISC1GX/OK5i4s61XczZs3WdeuXZmRkREzMTFhgwYNYs+fPy+1TQAsMDCw1PKTJ0+y1q1bMwMDA2ZhYcHGjh3LXr16VVOHVus0Jd2MUbsVJ5VK2bfffsucnZ0Zn89nLi4ubPbs2aWSOmozdWfOnGE9evRgdnZ2zNDQkHl7e7OvvvqKvX79Wq3cu9xurq6uZf4dK/63Xds2Yoyxn3/+mXl7ezOhUMhcXFxYUFAQy8/PVyuzceNGBoBt3LhRbXl+fj4LCgpiLi4uTCgUMm9vb/bLL79U92ETPXLmzBkGgHl4eGhcv2jRIgaAzZ8/v5YjK1t5SbdUKmUikYgBYDExMaXWX7hwgQFgHTt2rOkw1WRmZrLmzZszAGzIkCFMLpdrLKev8WtSdLHwk08+YYzpV+zlfUcs+UpLS9O7+CvSq1cvBoAdPHiQMaZfsd+/f191YUPThaWLFy8yAMzc3FzvYi9p9uzZDAAbO3asxvX6Fjsl3YQQQgghekgmkzFTU1MGgN2+fbvU+saNGzMA7Pr16zqITrPykm7GGOvduzcDwFavXl1q3eeff84AsBUrVtRghOry8vJY586dGQDWs2dPVU+fsuhb/GUpuvnzww8/qJbpe+wREREMAPPx8dG4Xt/jZ4yxpKQkJhaLSyV6+hS7u7s7A6DW+7TI999/zwCwrl27qpbpU+xFlEql6sJ4eTcv9Sl2SroJIYQQQvTUd999xwCwtm3bsuzsbNXylStXMgCsffv2OoyutIqS7pCQEAaAWVpasqdPn6qWX7lyhYlEImZiYsJSUlJqI1Qml8vZ4MGDGQDWoUMHjV1US9KX+C9cuMB27tzJCgoK1Jbn5+ezX375hXG5XGZoaMiio6P1LvayVJR060v8oaGh7MyZM6XuFEdERLB27doxAGzAgAF6GTtjjK1du5YBYC1atGDJycmq5Tdv3mRmZmYMANu9e7dexl7k/Pnzqm7yCoWizHL6FDsl3YQQQggheio3N1f1rKu9vT0bMWKE6r2lpSV79uyZTuM7cuQIa9WqleoFgHE4HLVlR44cUavzxRdfMADMyMiIDRw4kPXu3Zvx+XzG5XLZnj17ai32n376SdWNefDgwWzcuHEaX8UTE32Jv+gxFCsrK9azZ082evRo1qNHD2Zvb88AMAMDA/bPP/+UqqcPsZeloqSbMf2Iv6jt7e3tWWBgIBs5ciRr164dMzAwYABYw4YNNT5GpQ+xM8aYQqFgw4cPZwCYhYUF69evH+vUqRMTCoUMQKlxRvQp9iJF42l98803FZbVl9gp6SaEEEII0WNSqZTNmzePeXh4MKFQyGxtbdm4cePU7mLqSlECUt6r5PgERfWaNWvGjIyMmKmpKevZsye7ePFircYeFBSk1XPFJcfn0Yf4X758yebMmcPatWvH7O3tmUAgYGKxmDVs2JBNmzat3Isxuo69LNok3YzpPv5Hjx6xKVOmsKZNmzJra2vG5/OZqakpa926NVu5ciWTSqV6G3sRhULB1qxZwwICApiRkRETi8Wsbdu2bMuWLWXW0ZfY8/LymLm5OQPA7t27p1UdfYidw1gdm+SREEIIIYQQQgipI2iebkIIIYQQQgghpIZQ0k0IIYQQQgghhNQQSroJIYQQQgghhJAaQkk3IYQQQgghhBBSQyjpJjXql19+AYfDgZ+fX5llOBwOFixYoHp/7tw5cDgcnDt3ruYD1DPHjh1Ta4vq4ObmhvHjx9foNiMjI8HhcLBp06Zq3Y+mz0JNtNG7gtqOEEIIIaT2UdJNatSGDRsAAGFhYbh27ZpWdZo2bYrQ0FA0bdq0JkPTS8eOHcPChQurdZv79+/HvHnz9H6bmmj6LNREG70rqO0IIYQQQmofJd2kxty8eRP37t1D3759AQDr16/Xqp6JiQlat24NExOTmgzvnREQEAAPDw+932ZxBQUFkMvlb91ngTGG3NxcXYdBCCGEEEJqESXdpMYUJdnLly9H27ZtsXPnTkil0grrldW9fN26dfD29oZIJEKDBg0QHByM8ePHw83NTVWmqJvzjz/+iFWrVsHd3R0SiQRt2rTB1atX1bY3fvx4SCQShIeHo2fPnhCLxbC3t8fy5csBAFevXkX79u0hFovh7e2NzZs3l4o1MTERH3/8MZycnCAUCuHu7o6FCxdCLpdXOqbx48djzZo1AAq73Be9IiMjy2yrO3fuoF+/frCxsYFIJIKDgwP69u2L2NhYVZmSXcGL2jc4OBjffvst7O3tIZFI0L9/f7x69QpZWVmYPHkyrKysYGVlhQkTJiA7O1ttv9p0WX/+/DkmTJgALy8vGBkZwdHREf3798eDBw/UyhXFs3XrVnz11VdwdHSESCTC8+fPS30Wymujrl27wtfXF4wxte0zxuDp6am6+FMWNzc39OvXD/v370fjxo1hYGCAevXq4ZdffilVNjMzE19//TXc3d0hFArh6OiI6dOnIycnR60ch8PBZ599hrVr16J+/foQiUSqz1F4eDhGjRoFW1tbiEQiuLi4YOzYsZDJZKr6tfn5WrNmDTp27AgbGxuIxWI0atQIK1asQEFBQan2XLp0KVxdXWFgYIDmzZsjJCQEnTp1QqdOnarUToQQQgghbzO+rgMgb6fc3Fzs2LEDLVq0gJ+fHyZOnIiPPvoIu3fvxrhx4yq9vb/++gsff/wxhg4ditWrVyMjIwMLFy5US1CKW7NmDXx9ffHTTz8BAObNm4c+ffogIiICpqamqnIFBQUYMmQIPvnkE3zzzTcIDg7G7NmzkZmZib179+Lbb7+Fk5MTfv31V4wfPx5+fn5o1qwZgMKEqGXLluByuZg/fz48PDwQGhqKxYsXIzIyEhs3bqxUTPPmzUNOTg727NmD0NBQVT17e3uNx5iTk4Pu3bvD3d0da9asga2tLRITE3H27FlkZWVV2KZz5sxB586dsWnTJkRGRuLrr7/GqFGjwOfz4e/vjx07duDOnTuYM2cOjI2NNSaf5YmPj4elpSWWL18Oa2trpKamYvPmzWjVqhXu3LkDHx8ftfKzZ89GmzZtsHbtWnC5XNjY2CAxMVGtTHlt9MUXX2DgwIE4ffo0unXrplp3/PhxvHjxQqv47969i+nTp2PBggWws7PD9u3b8cUXXyA/Px9ff/01AEAqlSIwMBCxsbGYM2cOGjdujLCwMMyfPx8PHjzAqVOnwOFwVNs8cOAALl68iPnz58POzg42Nja4d+8e2rdvDysrKyxatAheXl5ISEjAoUOHkJ+fD5FIVOufrxcvXmD06NGqBPnevXtYsmQJwsPDVY+JAMB3332HZcuWYfLkyRgyZAhiYmLw0UcfoaCgAN7e3qpylW0nQgghhJC3FiOkBmzZsoUBYGvXrmWMMZaVlcUkEgnr0KFDqbIAWFBQkOr92bNnGQB29uxZxhhjCoWC2dnZsVatWqnVi4qKYgKBgLm6uqqWRUREMACsUaNGTC6Xq5Zfv36dAWA7duxQLRs3bhwDwPbu3ataVlBQwKytrRkAdvv2bdXylJQUxuPx2IwZM1TLPv74YyaRSFhUVJRaXD/++CMDwMLCwiod09SpU5m2v5Y3b95kANiBAwfKLefq6srGjRunel/Uvv3791crN336dAaAff7552rLBw0axCwsLMrdZtExbty4scw45HI5y8/PZ15eXuzLL78sFU/Hjh1L1Sn5WWCs7DZSKBSsXr16bODAgWrLe/fuzTw8PJhSqSwztqJj4nA47O7du2rLu3fvzkxMTFhOTg5jjLFly5YxLpfLbty4oVZuz549DAA7duyYahkAZmpqylJTU9XKdunShZmZmbGkpKQy49Hl50uhULCCggK2ZcsWxuPxVPGnpqYykUjERo4cqVY+NDSUAWCBgYGqZZVpJ0KIZlKplP3++++sT58+zMHBgYlEImZiYsJ8fHzYhx9+yM6dO6fT+IrOo0UvfRYYGKiKs/j3hpqyceNGtbYpfh4rOrcVvco7dxJS0/bu3av6LHbq1Knatuvq6qrabvHvBzXp+fPnjMfjMQDM3t5e9d1NH1D3clIj1q9fD0NDQ7z33nsAAIlEguHDh+PixYt49uxZpbb15MkTJCYmYsSIEWrLXVxc0K5dO411+vbtCx6Pp3rfuHFjAEBUVJRaOQ6Hgz59+qje8/l8eHp6wt7eHgEBAarlFhYWsLGxUat/5MgRdO7cGQ4ODpDL5apX7969AQDnz5+vUkza8vT0hLm5Ob799lusXbsWjx49qlT9fv36qb2vX7++Ks6Sy1NTU0t1Ma+IXC7H0qVL0aBBAwiFQvD5fAiFQjx79gyPHz8uVX7o0KGV2n5JXC4Xn332GY4cOYLo6GgAhXdvT5w4gU8//VSru6oNGzaEv7+/2rLRo0cjMzMTt2/fBlD4c/fz80OTJk3Ufu49e/bU+FhEly5dYG5urnovlUpx/vx5jBgxAtbW1mXGUtufrzt37mDAgAGwtLQEj8eDQCDA2LFjoVAo8PTpUwCFj1zIZLJSv4utW7dWe8yjKu1ECFF3+fJleHl54dNPP8WxY8cQHx8PmUyGzMxMPHnyBOvXr0enTp0waNAgZGZmVuu+ix7tKXpV98wUhNSkBQsWaP2YXl1V/PjeZIYauVyO2bNnq97PmjWrGqKr2KZNm9SOobq+E3h4eGDYsGEAgISEBKxatapatlsdKOkm1e758+e4cOEC+vbtC8YY0tPTkZ6ervolKN5VVRspKSkAAFtb21LrNC0DAEtLS7X3IpEIAEoNYmVkZAQDAwO1ZUKhEBYWFqW2KRQKkZeXp3r/6tUrHD58GAKBQO3VsGFDAMDr16+rFJO2TE1Ncf78eTRp0gRz5sxBw4YN4eDggKCgoFLP4WpS8hiFQmG5y4sfuzZmzJiBefPmYdCgQTh8+DCuXbuGGzduwN/fX+Mxl9WNvjImTpwIQ0NDrF27FkBhl2tDQ0NMnDhRq/p2dnZlLiv6HL569Qr3798v9XM3NjYGY6zUz73kcaWlpUGhUMDJyancWGrz8xUdHY0OHTogLi4OP//8My5evIgbN26ongEv2kZlfhcr206EkP+EhoaiS5cuiIuLUy2ztbVFnz590KFDBwgEAtXygwcPolu3bmU+blWTWrRogaFDh6peRDvW1tZq7VbyoiUhtWXTpk2qC+t+fn7o2bNntW27T58+qs94YGBgtW23Il999ZXq/ytWrEBaWlqt7bs89Ew3qXYbNmwAYwx79uzBnj17Sq3fvHkzFi9erHZXrjxFycSrV69KrSv5zG9tsrKyQuPGjbFkyRKN6x0cHGo8hkaNGmHnzp1gjOH+/fvYtGkTFi1aBENDw1q7WlmWbdu2YezYsVi6dKna8tevX8PMzKxU+ep4vtfU1BTjxo3D33//ja+//hobN27E6NGjNe5PE02fp6JlRZ9DKysrGBoalnnxyMrKSu19yeOysLAAj8dTG+yurO3U1ufrwIEDyMnJwb59++Dq6qpafvfuXbVyFf0uFv/iWNl2IoQUkslkGDlyJPLz81XLpk+fjhUrVqiS7cjISPTr1w9hYWEAgBs3biAoKEg1EGhtmTp1KqZOnVqr+3wbNGzYUOP3I0Jq22+//ab6//vvv1+t2/7999+rdXvaatGiBTw9PfH8+XNkZWVh69at+Pzzz3USS3F0p5tUK4VCgc2bN8PDwwNnz54t9frqq6+QkJCA48ePa71NHx8f2NnZYdeuXWrLo6OjceXKleo+BK3169cPDx8+hIeHB5o3b17qVZWkqKp3vzkcDvz9/bF69WqYmZmpukLrEofDUR1PkaNHj6rduamKitro888/x+vXrzFs2DCkp6fjs88+03rbYWFhuHfvntqy4OBgGBsbq+YK79evH168eAFLS0uNP/eK7lgYGhoiMDAQu3fvLvdub21+voouDBT/eTHGsG7dOrVyrVq1gkgkwj///KO2/OrVq6W6sb9pOxHyrtq2bRtiYmJU7zt06IDVq1er3d12c3PD3r17wef/d+9kzZo1yMjIUL0fP368WvdNxhj++usvBAQEwMjICNbW1hgzZoza727RbAidO3dWi2nChAkau+uW3EdxJbv4RkRE4I8//oCfnx8MDQ1Rr149LF26FAqFAgBw8eJFdO3aFcbGxjAzM8PgwYM1Po4WEhKCqVOnok2bNnBxcYFYLFbN3tG7d29s3bq11CwWbyI+Ph7z589H37594eXlBQsLCwgEApiZmaF58+aYM2eOxguR5dGm+/7hw4fRrl07iMViWFhYoH///rhz50653XI1bffevXsYMmQILCwsYGhoiJYtW+LYsWOl9ldTPy+g8Hyye/du9O/fH/b29hAKhTA3N0fHjh3x119/qc3IUUTT5/fvv/9Gs2bNYGhoCCsrK7z//vuIj49X1Slqm4ULF6pty93dXbWdyp57Ll++jHHjxsHT0xNisRhisRj16tXDqFGjSj3mBQAvX77E9OnT4efnB2NjYxgYGMDFxQUjRozA6dOnNe4jLS0N8+bNQ7NmzWBqagqBQAAbGxs0btwYH3zwAX777TdVb0M3N7dSv2ubN2+u0uMg165dU/vOM3LkyFJlqvJzKFIUK4fDUc1uUvQZnTBhglrZzp07a/xbkpubix9++AFt2rSBhYUF+Hw+LC0t0aBBA4wYMQIrV65U9cIrrvhjcCW/y+iMjp4lJ2+pw4cPMwDsf//7n8b1ycnJTCQSsUGDBqmWoYKB1Bhj7M8//2QA2NChQ9nRo0fZ9u3bmbe3N3NxcWHu7u6qckWDSv3www+l9l1yP+PGjWNisbhUucDAQNawYcNSy11dXVnfvn1V7+Pj45mrqyvz9fVlv//+Ozt9+jQ7evQoW7NmDevbty+LiYmpdExFA68EBQWxq1evshs3bjCZTFaqHmOFbd27d2/2559/spCQEHby5En2ySefMADsr7/+Uotb00Bqu3fvVtte0b5LDnwVFBTEALDk5OQyt6lpILWxY8cykUjEVq9ezU6fPs1WrFjBrK2tmZOTk9qAGmXFU3xd8c+CNm3Uu3dvBoC1b99eY9tp4urqyhwdHZmLiwvbsGEDO378OHv//fdLfZ6zs7NZQEAAc3JyYitXrmQhISHs33//ZevWrWPDhw9nV69eVZUFwKZOnVpqX3fv3mUSiYTVq1eP/fXXX+zMmTNsx44dbNSoUSwzM5MxVrufr8ePHzOhUMg6derEjh07xvbt28e6d+/OvLy8SrX/7NmzGQD28ccfsxMnTrC///6bOTs7M3t7e9a5c+cqtRMh5D8DBgxQG2Rr586dZZbt1auXWtniA2uWHOTsgw8+UHtf9LKxsWFPnjxhjP3396SiV0REhMZ9FFd07ih69e3bV+O2Jk2axHbt2qUa/Kj4y9bWttSAk0V/l8t7DRkyhCkUCrV6VR1ILSQkpML9FW/DIm8ykNqvv/6qcT9CobDU8Ze33UGDBjGhUFhqO1wul4WEhNTKzysnJ6fU57Tkq0OHDiwjI0OtXsnP1ogRIzTW9fHxYbm5uRrbXNNL25+9QqFgU6ZMKXdbX3zxhVqdnTt3MkNDw3LrfPzxx2oDu+bk5LD69etXGHfROb/44GRlvbQdmK/ofF5eu1Tl51BE00BqJT+jZb0YY0ypVLLOnTtXWPbixYul4i75e/vixQut2qQmUfdyUq3Wr18PoVBY6gpWESsrKwwePBh79uzBq1evynwmu6TJkyeDw+FgxYoVGDx4MNzc3DBr1iwcPHhQNWhWbbO3t8fNmzfx/fff44cffkBsbCyMjY3h7u6OXr16qQ2epa3Ro0fj8uXL+P3337Fo0SIwxhAREaHxyqyXlxfMzMywYsUKxMfHQygUwsfHB5s2barStGzV7eeff4ZAIMCyZcuQnZ2Npk2bYt++fZg7d+4bbVebNho5ciSOHz9eqbvcANCkSRNMmDABQUFBePbsGRwcHLBq1Sp8+eWXqjJisRgXL17E8uXL8ddffyEiIgKGhoZwcXFBt27dtLqK7u/vj+vXryMoKAizZ89GVlYW7Ozs0KVLF9Uz9LX5+fL19cXevXsxd+5cDBkyBJaWlhg9ejRmzJihGrityJIlSyAWi7F27Vps3LgRvr6++OOPP/Ddd9+pdeOvjnYi5F1UsqdSmzZtyizbpk0bnDhxQq3uwIEDNZbdunUrnJ2dUb9+fdy+fVvV0yYpKQnjxo1DaGgoxGIxhg4diuTkZFy4cEFVt3nz5mqPnojF4kof19GjR+Ho6IgGDRrg4sWLqjt369atw9atWyEQCNCxY0c8ffpU1SPq1atXWLNmDRYsWKC2LT6fDx8fH1hbW8PU1BSZmZm4c+cO0tPTAQD79u3Drl27VIO5VgdnZ2c4OjrCysoKCoUCL168UD0Lm5SUhM8//1ztZ1FVjx49wowZM9SWNWjQALa2trh69Sq2b9+u9bYOHDgAkUiEwMBAxMTE4OXLlwAApVKJRYsWqU2vWVJ1/bwmTZqk1i7u7u5o0KABoqOj8eDBAwCFd80//PBD7N69u8x4du3aBXt7ezRs2BChoaHIyckBUDjYbnBwMCZOnAg3NzcMHToUjx49UhuwtXfv3jAyMgIA2NjYaNV2CxcuxB9//KG2zMfHB/Xq1UNUVBSePHmitu727dv44IMP1MbUadKkCczNzXH16lVVD7M///wT7u7u+PbbbwEUflaLx+rp6Yn69esjIyMDMTExiIiIUNtPnz59kJSUhL1796qWubq6onnz5qr32p5fS/6Oa0Obn0N5isYziIqKws2bN1XLO3bsWGpw2cuXL+Ps2bOq946OjggICIBUKkVMTAxevHgBpVKpcT/NmzdX3ZkHCgefrVevnlbHWGN0m/MTUnVpaWnM2tqaTZo0SdehED0zZMgQ5uDgwPLz87WuU7InA9Hey5cvmVAoZEuWLNF1KITUeQYGBmp3aMrq7cTYf73Ail5TpkxRrSt5h6pHjx4sLy+PMcbY69evS91du3LliqqutlNaVeZOd/PmzVl2djZjjLG1a9eWuvNatP9Xr16ptUHJqYaePHnCsrKySsWSnZ3N6tWrp6o3bNgwtfVVvdP9+vVrFhkZqXHdzJkz1Y6h+N3aqt7p/vjjj9XWffXVV6p19+7dY0ZGRlpvVyQSqXqv5ebmssaNG6vWCQQCtc9WTfy8Hj58WOrOcPG7vPPnz1dbX3zKzpKfrQ4dOqimf7py5YrauuI97zQdS1HPDG29fv1a7Zh4PB7bs2ePWpno6Gh24sQJ1ftBgwap7fPnn39Wrbt//z4Ti8WqdSYmJkwqlTLGGFu+fLlqua+vb6keGrGxsezPP/9k6enpasvLO35tFf8szZ07V2OZN/k5lDdlWHm/H0V27typ1mYl76S/fv2abdmypcyfr6Ojo6r+Z599plWb1CR6ppvUCYmJiZg2bRr27duH8+fPY8uWLejcuTOysrLwxRdf6Do8ogdkMhlCQ0Px888/Y//+/fjmm2/UnoEk1ePevXuYNWsWDh06hHPnzuHPP/9Et27dYGJigg8//FDX4RHyTmElnl0ub0DKBQsWqMZtsLS0VOvBA0DtjlJNmDFjhuoOeevWrdXWde3aVXVH38bGBg0aNFCtS0hIUCvr7u6OHTt2oGfPnnBwcICBgQE4HA4kEonqTi4A1V3oN2VpaYmEhASMHz8evr6+kEgk4HK5qt53RZRKJV68ePHG+ztz5ozq/yKRCPPnz1e9b9y4MUaNGqX1tkaMGKG6g2lgYIAuXbqo1hUUFGh8FrZIdfy8jh49qlbv0aNHGD58OIYNG4Zhw4bh0qVLauvLG+9nwYIFqrvVbdq0UetZVfIz8qZOnz6tNmPLxIkTS43O7+zsrBrpW6FQICQkRLXOyclJraddo0aN1AYpy8zMRGhoKIDCO9tFXr58ifnz5+PAgQMIDw+HXC6Ho6MjJk+eDFNT02o9xpycHEilUtV7TbP2aFKbP4fibZOZmYmZM2diz549ePDgAWQyGSwtLfHBBx+UeWe/+DFVdtyFmkDdy0mdIBKJEBkZiU8//RSpqakwMjJC69atsXbtWtUUSuTdlpCQgLZt28LExAQff/wxpk2bpuuQ3kpisRg3b97E+vXrkZ6eDlNTU3Tq1AlLlizR+nERQkjZrKys1GY3SExMhIuLi8ayJb9IljcrQMlzZcn3xQdvqwnFEzNjY+NyYym+vvhUaAqFAr169VJLTMtSXXOXb9y4ER9++KFWg7NVxz6L/xxcXFxgYmKitr5Ro0ZabysgIEDtvUQiUXtf3jRz1fHzKjk/dvHEVJPyHhfUdCxFjxNU93R5JeNu165dueVTUlJU3awBoH79+uBy1e9rlmyzogEM+/XrB39/f9y7dw/5+flqM5YYGhqiQ4cOmDZtGvr161eVQylTUdsVKfkzLktt/hyaNm2K3r17qy7G/Prrr/j1118BAAKBAK1atcKkSZMwduxYjfWL/+6UPF5doKSb1Anm5uY4fPiwrsMgeszNze2NRqwteZIlmnl6euLUqVO6DoOQt1bTpk3Vku7Q0NAyk+6iu2XF61ZVdUzbWJ7id+pKJiTa3sXbvXu3WsItFArRunVrWFlZgcPh4Pz586pn1d/kfFBEJpNh+vTpatuqX78+vLy8IBAISj2XWh37LK5kOwGV+zmVHPtD26lager5eVW2PYrfeS3pTY7lTVXU5tr0OCmrLUQiEc6fP49ff/0Vhw4dwv3791XJa25uLk6ePImTJ0/i4MGDGDBgQBWPoLSSU6lmZWVpVa82fw4cDgf79+/H2rVrsXfvXty6dUv1GSkoKMClS5dw6dIlpKena5wSrPhsDtpOHVuTqHs5IYQQQoie6N+/v9r7koM5FXn69KnaBTCxWKyalkeTR48eqb0vPngTUNgltkhNJ+BVVXKa0CtXruD8+fPYu3cv9uzZA0tLy2rdX1hYmNrd688++wyPHj3CwYMHsWfPnlJdjquDs7Oz6v/R0dGlEtGiwcfqgpLdfq9cuQLGWJkvbae6qsibfn5Lxl2yG3xJVlZWqi7XQOHvVskBvkr+/hW/kGZqaoq5c+fi+vXryMnJQXR0NPbv36/2O1ndc16LxWK1mFNTU6t1+xXR9mckEonwxRdf4MKFC8jOzkZ8fDxOnDih1uOjrLYpfkwlB2nTBUq6CSGEEEL0xAcffKD2Zfv8+fP46quv1OYyjoqKwrBhw9SWffrpp+XegVy0aBHy8/MBFH4ZXbVqldr64gm7oaGh2jpNc/DqQvGRoQGoJQ3r168vNaJ0Te4vKioKv/32W7XuD4Dac9e5ublYtmyZ6v39+/cRHBxc7fusKSVnv/jmm29KJXdyuRxnzpzB6NGj1Xp4vIk3/fx27doVBgYGqvcbNmxQGy0cKHz+uuiZdR6Ph+7du6vWxcTEqCWCYWFhaqPOGxsbo23btgAKRz3fsGGDql14PB6cnZ0xcOBAtdG2i7qjazrGqv5+NmnSRPX/khcFapo2P6PIyEj89ttvqnUcDgf29vbo2bMn/P39VeVKtg1Q2J28+DPmJbvF6wIl3YQQQgghekIkEmHHjh1qA0GuWrUKzs7O6NevHzp16gQvLy+1O55NmzbFokWLyt3u8ePH4enpiZ49e8LX11ftS3bLli1VSQAAeHh4qN2J+v7779G7d28MGzYM33zzTXUcZpW0aNFC7X3r1q3Rt29fBAQE4KOPPqr2O/QNGzZUSw5WrFiBtm3bonv37mjQoEGNXIz4/PPP1X72ixcvRuPGjdGlSxe0bt263C7Y+qZx48YYPny46v3ly5fh7OyMwMBADBw4EG3btoW5uTm6du2KHTt2qF1EehPFB+ACgCFDhmDgwIEYNmyYVnfTLS0t8dVXX6neKxQKDBs2DPXr10efPn3QuHFj+Pj4qD2jPnfuXPD5/z21O23aNDRt2hRdunRBixYt1J75njVrlupz9fLlS3z44YewtbWFl5cXOnfujAEDBsDHx0dtSq+Sx1T8fUhICNq3b68aoE7bz0hgYKDq/8Ufk6gNJY/n008/Rb9+/TBs2DDVAIWvX7/GtGnT4OTkhHr16qFDhw4YOHAg/P39sW3btjK3BQA3btxQ69Jf/Fh1hZJuQgghhBA90r59e5w6dQr29vaqZYmJiTh69CjOnz+vdge2X79+OH36tNqdOU2mTZuGmJgYnDx5EsnJyarlVlZW2Lx5s1pZS0tL9OnTR/U+Ly8PJ06cwN69eyscDKsmvf/++2jcuLHqfWZmJo4dO4a7d++iZ8+eFQ54VVkSiQTz5s1TWxYaGopTp06Bz+fj66+/rtb9AYUDmK1cuVJt2YMHD1Sjy48bN05tnVAorPYYqtOGDRtUo3wDhc9tX7hwAYcOHUJoaCiys7NV66rr+eCePXuqDez56tUrHDp0CHv37sXdu3e12saiRYswadIktWXh4eE4fvw4Hjx4UOoCQfPmzbFp0ya138M7d+7g7Nmzqjm6AeDDDz/ErFmzSu1PLpfj+fPnOHfuHA4fPoxnz56p1kkkEgQFBamVLzl42OXLl7F3717s3btX1aOlIgMHDlT9Pzo6Wm30/5oWEBAAPz8/1fuMjAwcPXoUe/fuLfUYCWMMERERuHTpkuq59yICgUCtN0iR4rMx+Pr6wsvLqwaOonIo6SaEEEII0TMdO3bEixcv8Ntvv6FXr16wt7eHUCiERCKBl5cXJkyYgDNnzuDw4cNaDRL0yy+/YOPGjQgICICBgQEsLS0xevRo3LhxA76+vqXKb926FZMnT4ajo2OtDlpVHpFIhHPnzuHjjz+Gra0thEIhPDw8MH/+fBw6dKhG4pw9ezb+/vtv+Pn5QSAQwNLSEkOHDsWNGzfURviuTtOmTcOhQ4fQpk0bGBoawtzcHAMGDMC1a9dKDapX/MKMPpJIJDh+/Dj27duHwYMHw8nJCSKRCEKhEE5OTujevTuWLFmCp0+fqj3P/ibEYjFOnTqF/v37w9zcvEo9ILhcLv766y+cP38eY8aMQb169WBoaAhDQ0O4u7tj5MiRGDx4sFqd999/Hw8ePMC0adNQv359GBkZqY5z2LBh+Pfff/H333+rDUwXGBiI3377DSNHjkT9+vVhaWkJHo8HsViMhg0b4rPPPsPdu3dVU78VmTFjBpYvXw5fX98qX3hp1aqV2kWsf/75p0rbqQoOh4Njx47hvffeg42NjcZBA319ffH3339j3Lhx8PPzg42NDfh8PgwNDVV/A69fv65xZPddu3ap/v/xxx/X6LFoi8Oqe6hFQgghhBCiU+PHj1e7g01f9+qOuLg42Nvbl0pEXr16hWbNmiEuLg4A4OrqSjNvkDeybt06TJ48GUDh4xQPHz7UcURv7vr162jVqhWAwufnIyMjtZ6HvCbRlGGEEEIIIYToie+//x779+9H586d4eTkBKFQiKioKBw6dEitO/b8+fN1GCV5G0yYMAE//vgjnj59irCwMJw4cQK9evXSdVhvpPjjGd98841eJNwAJd2EEEIIIYTolaSkpDK7+3K5XHz33XeYOHFiLUdF3jZ8Ph/Lli1TTX+3fPnyOp10v3jxQjXSvL29PWbMmKHjiP5DSTchhBBCCCF64oMPPgBjDKGhoUhISEB6ejqMjIzg5uaGDh06YNKkSWpTJhHyJoYMGfLWPH7i4eFRbaPgVzd6ppsQQgghhBBCCKkhNHo5IYQQQgghhBBSQyjpJoQQQgghhBBCaggl3YQQQgghhBBCSA2hpJsQQgghhBBCCKkhlHQTQgghhBBCCCE1hJJuQgghhBBCCCGkhlDSTQghhBBCCCGE1BBKugkhhBBCCCGEkBpCSTchhBBCCCGEEFJD/g/A8nHCoEfOcQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = aligner.get_stat_df()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "express-barrier",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===============================================================================================================\n",
+      "Genes2Genes (v0.2.0)\n",
+      "Dynamic programming alignment of gene pseudotime trajectories using a bayesian information-theoretic framework\n",
+      "===============================================================================================================\n",
+      "Interpolator initialization completed\n",
+      "Aligner initialised to align trajectories of 20327 reference cells & 17176 query cells in terms of 1371 genes\n",
+      "Running gene-level alignment: 🧬\n",
+      "concurrent mode, running with  26  processes\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1371/1371 [24:43<00:00,  1.08s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alignment completed! ✅\n",
+      "Time taken: 1491.372834444046 sec\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "s = time.time()\n",
+    "aligner = Main.RefQueryAligner(adata_ref, adata_query, gene_list, n_bins)\n",
+    "aligner.align_all_pairs(concurrent=True) \n",
+    "t = time.time()\n",
+    "print('Time taken:', t-s,'sec')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "charming-gospel",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24.856213907400768"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1491.372834444046/60"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "indonesian-taxation",
+   "metadata": {},
+   "source": [
+    "## Healthy vs. IPF trajectory alignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "vanilla-begin",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1. 1. 4. ... 1. 1. 1.]\n",
+      "[1. 2. 1. ... 1. 1. 1.]\n",
+      "[1.7595813 1.7595813 3.0076618 ... 1.2661365 1.2661365 1.2661365]\n",
+      "[1.1192989 1.6342111 1.1192989 ... 1.284542  1.284542  1.284542 ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "adata_healthy = anndata.read_h5ad('adata_healthy_AT2_to_AT1.h5ad')\n",
+    "adata_disease = anndata.read_h5ad('adata_IPF_AT2_to_AberrantB.h5ad')\n",
+    "adata_healthy.obs['time'] = adata_healthy.obs['dpt_pseudotime']\n",
+    "adata_disease.obs['time'] = adata_disease.obs['dpt_pseudotime']\n",
+    "adata_ref = adata_healthy\n",
+    "adata_query = adata_disease\n",
+    "print(adata_ref.X.data) \n",
+    "print(adata_query.X.data)\n",
+    "sc.pp.normalize_per_cell(adata_ref, 10000) \n",
+    "sc.pp.log1p(adata_ref)\n",
+    "sc.pp.normalize_per_cell(adata_query, 10000) \n",
+    "sc.pp.log1p(adata_query)\n",
+    "print(adata_ref.X.data)\n",
+    "print(adata_query.X.data)\n",
+    "common_hvg_genes = np.intersect1d(adata_healthy.var_names[adata_healthy.var.HVG] , adata_disease.var_names[adata_disease.var.HVG] )\n",
+    "len(common_hvg_genes)\n",
+    "n_bins = 13"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "compliant-germany",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===============================================================================================================\n",
+      "Genes2Genes (v0.2.0)\n",
+      "Dynamic programming alignment of gene pseudotime trajectories using a bayesian information-theoretic framework\n",
+      "===============================================================================================================\n",
+      "Interpolator initialization completed\n",
+      "Aligner initialised to align trajectories of 3157 reference cells & 890 query cells in terms of 994 genes\n",
+      "Running gene-level alignment: 🧬\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 994/994 [04:29<00:00,  3.69it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alignment completed! ✅\n",
+      "Time taken: 272.8219108581543 sec\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "s = time.time()\n",
+    "aligner = Main.RefQueryAligner(adata_ref, adata_query, common_hvg_genes, n_bins)\n",
+    "aligner.align_all_pairs() \n",
+    "t = time.time()\n",
+    "print('Time taken:', t-s,'sec')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cooperative-medium",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average Alignment:  \u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[92mM\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m (cell-level)\n",
+      "% similarity: 73.33\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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2VNeuXZWdna2OHTvqD3/4g9/73bZtm4qKitSlSxdFRUUpKipKGzZs0B//+EdFRUXJ4XD4fQxnxcXFKT09Xfv27fN7XykpKecF0Xbt2gXkJoazvv76a61bt06//vWvA9bno48+qscee0wjR45Uenq6Ro8erYcffljZ2dkB6b9FixbasGGDTpw4oW+++UaffPKJTp8+rbS0tID0L8l1x2W4vsedPn1aI0aMUH5+vnJzcwNWVZJ+fo23bNlSV111lZYsWaKoqCgtWbIkYP2jehCW9POHdpcuXVx36JyVm5urnj17mjQq/zMMQ/fff7/eeust/fOf/wzom/eFxmO32/3eT9++fbVz507t2LHDtXXt2lW33XabduzYocjISL+P4Sy73a7du3crJcX/T/3u1avXeUtD7N27V02bNvV732ctXbpUDRs21A033BCwPk+dOqWICPe3usjIyIAtHXBWXFycUlJSVFxcrPfff19DhgwJWN9paWmy2Wxu73Hl5eXasGFDjX6Pk/4TlPbt26d169b5/XK3N4F6n6sMp2EJ2BaquAz3bxMnTtTo0aPVtWtX9ejRQ4sXL9ahQ4c0fvx4v/d94sQJ7d+/3/V1fn6+duzYoXr16qlJkyZ+63fChAlauXKl/va3vyk+Pt71V2diYqJiYmL81q8kPf744xo4cKAaN26s48ePa9WqVfrwww+1Zs0av/Yr/TyP5Nx5WXFxcapfv77f52tNmjRJgwcPVpMmTVRUVKTf/e53Ki0tDcjloIcfflg9e/bUrFmzNGLECH3yySdavHixFi9e7Pe+pZ8vAy5dulRjxoxRVFTg3noGDx6smTNnqkmTJmrfvr0+/fRTzZ07V2PHjg1I/++//74Mw1CbNm20f/9+Pfroo2rTpo3uuuuuau3H2/tIZmamZs2apVatWqlVq1aaNWuWYmNjNWrUKL/3ffToUR06dMi1vtHZ0G6z2S56nTFPfaempurmm2/W9u3b9e6778rhcLje5+rVq6fo6Gi/9V2/fn3NnDlTN954o1JSUnTkyBEtXLhQhw8fDsiSGahmJt6JF3Sef/55o2nTpkZ0dLTRuXPngN1Cv379ekPSeduYMWP82m9FfUoyli5d6td+DcMwxo4d6/pdX3LJJUbfvn2NtWvX+r3fCwnU0gG33HKLkZKSYtSqVctITU01hg0bZuTl5fm937Peeecdo0OHDobVajXatm1rLF68OGB9v//++4YkY8+ePQHr0zAMo7S01HjooYeMJk2aGLVr1zaaN29uTJ061bDb7QHp/7XXXjOaN29uREdHGzabzZgwYYJx7Nixau/H2/uI0+k0pk2bZthsNsNqtRrXXHONsXPnzoD0vXTp0gqPT5s2za99n12qoKJt/fr1fu27rKzMuOmmm4zU1FQjOjraSElJMW688Ubjk08+ueh+q9uOrxsFbAtVFsMwQnmZKAAAcBG2H/LfFYxzdW4SuDmS1Yk5SwAAAB4wZwkAgDDmoG7iFb8hAAAAD6gsAQAQxkL5lv5AobIEAADgAZUlAADCGI878Y7K0jnsdrumT59uygqr9E3f9E3f9E3fCD6ss3SO0tJSJSYmqqSkJKDPD6Jv+qZv+qZv+jbDv75qGbC+ejfb771REKKyBAAA4AFzlgAACGNO6iZe8RsCAADwoEbOWeofUfUnOjsNh/L1pdLUVhGWyGocFX3TN33TN33Tt29yna/7YVQV++dXbQLW13XN9gSsr+pEWAIAIMgQloILc5YAAAhjDoMZOd7wGwIAAPCAyhIAAGHMyQreXlFZAgAA8IDKEgAAYcxB3cQrfkMAACDoNGvWTBaL5bxtwoQJkiTDMDR9+nSlpqYqJiZGffr0UV5ents57Ha7HnjgATVo0EBxcXG68cYbdfjw4UqPhbAEAACCzpYtW1RQUODacnNzJUnDh/+8PNCcOXM0d+5cLViwQFu2bJHNZlP//v11/Phx1zkyMzOVk5OjVatWaePGjTpx4oQGDRokh8NRqbGwztI5UlvYVPhVkZwOZzWOCAAA3wVynaV3Dl4esL4GN/+8yt+bmZmpd999V/v27ZMkpaamKjMzU1OmTJH0cxUpOTlZs2fP1j333KOSkhJdcskleuWVV3TLLbdIkr777js1btxY//jHP3T99df73DeVpV9ocUUzPbd5lh575UFFRPKrAQCgOtntdpWWlrptdrvd6/eVl5drxYoVGjt2rCwWi/Lz81VYWKgBAwa42litVmVkZGjTpk2SpG3btun06dNubVJTU9WhQwdXG1+RCP6txRXNNCf3t0qoH69rR/YiMAEAwoJTEQHbsrOzlZiY6LZlZ2d7HePbb7+tY8eO6c4775QkFRYWSpKSk5Pd2iUnJ7uOFRYWKjo6WklJSRds4yvuhpN7UDrr2pG9JElPj/4jl+QAAKgGWVlZmjhxots+q9Xq9fuWLFmigQMHKjU11W2/xeK+RpRhGOftO5cvbc5lalg6fPiwFi1apE2bNqmwsFAWi0XJycnq2bOnxo8fr8aNG/t9DBUFpbMITACAms5hBG5RSqvV6lM4+qWvv/5a69at01tvveXaZ7PZJP1cPUpJSXHtLyoqclWbbDabysvLVVxc7FZdKioqUs+ePSs1BtOuM23cuFHt2rVTTk6OOnbsqDvuuEO33367OnbsqLffflvt27fX//7v//p1DJ6C0llckgMAwDxLly5Vw4YNdcMNN7j2paWlyWazue6Qk36e17RhwwZXEOrSpYtq1arl1qagoEC7du2qdFgyrbL08MMP69e//rXmzZt3weOZmZnasmWLx/PY7fbzJoc5DYciLJEevy8iMkK/WfWwx6B0FhUmAEBNFcyLUjqdTi1dulRjxoxRVNR/IovFYlFmZqZmzZqlVq1aqVWrVpo1a5ZiY2M1atQoSVJiYqLGjRunRx55RPXr11e9evU0adIkpaenq1+/fpUah2m/oV27dmn8+PEXPH7PPfdo165dXs9T0WSxfH3p9fucDqd+N3KeSo8c99pWosIEAECgrVu3TocOHdLYsWPPOzZ58mRlZmbqvvvuU9euXfXtt99q7dq1io//TxFk3rx5Gjp0qEaMGKFevXopNjZW77zzjiIjPRdUzmXaOkvNmzfXE088obvuuqvC40uXLtVTTz2lgwcPejxPRZWlmxLv9FpZOsuXS3G/tH7V/1JhAgD4VSDXWXptf7eA9XVLS89Xi4KVaZfhJk2apPHjx2vbtm3q37+/kpOTZbFYVFhYqNzcXL344ouaP3++1/NUNFnM16AkSQd2fKXJ/Z/0OTBxSQ4AgPBiWli67777VL9+fc2bN09//vOfXUuPR0ZGqkuXLnr55Zc1YsSIgIyFwAQACFfBPGcpWATF405Onz6tH3/8UZLUoEED1apV66LOV9XHnXBJDgAQDAJ5GW7l/u4B62tUy48D1ld1Coo4WatWLaWkpCglJeWig9LFOFthYtI3ACBcOAxLwLZQxaf8OQhMAADgl/iErwCBCQAQLgL5bLhQFboj9zMCEwAAkAhLHhGYAAA1ncOICNgWqkJ35AFCYAIAILzxie4DAhMAAOGLT3MfEZgAADWRU5aAbaGKT/JKIDABABB++BSvJAITAKAmYYK3d6Y9Gy6UVeVZcn2GHJNRMkmSw2/juv7STn47t1eGeY98iUpuaFrfivT9oc3V7cx3Bab1DQDhJHRjnskqW2GyxNwgS+LvJZn34QoAwLkcigjYFqpCd+RBgMAEAEDNR1i6SAQmAEAocxqWgG2hirBUDQhMAADUXISlanI2MBnOYp/aE5gAAMGAOUvehe7Ig9CBHV/JOHongQkAgBqEpQOq25ndMo7eKdVbJktEktfmlpgbJMnvywoAAFARZwivfxQo/Ib84d+BiQoTAAChj7DkLwQmAEAIcMgSsC1UEZb8icAEAEDIY86SvzGHCQAQxJiz5B2/oUCgwgQAQMgiLAUKgQkAEISYs+RdUIelb775RmPHjjV7GNWHwAQAQMgJ6rB09OhRLV++3GMbu92u0tJSt81pBPFcHwITACCIOI2IgG2hytQJ3qtXr/Z4/ODBg17PkZ2drRkzZrjtS1M7tVD7ixqbXzHpGwCAkGFqWBo6dKgsFosMw7hgG4vF8zXOrKwsTZw40W3fTYl3Vsfw/IvABABASDC1JpaSkqI333xTTqezwm379u1ez2G1WpWQkOC2RVhC5JIVl+QAACZzGBEB20KVqSPv0qWLx0DkrepUIxCYAAAIaqZehnv00Ud18uTJCx5v2bKl1q9fH8ARmYRLcgAAkzhD+Jb+QDE1LPXu3dvj8bi4OGVkZARoNCYjMAEAEJR43EkwITABAAIslOcSBQq/oWDDHCYAAIIKlaVgVMUKU0TkCjkdTj8PDgBQkzgN5ix5Q2UpWFWhwvTYKw8oIpL/pQAAVCcqS8GskhWma2/pKUl6evRzVJgAAD5xUDfxit9QsKtkhenaW3pSYQIAoBrxiRoKCEwAAD9xGpaAbaGKT9NQQWACAMAUfJKGEgITAKCaORURsC1UMcG7mg1s1s3vfbTo+Iye/keWEurHe2177S09ddXgH1RSfL/8uXDlHc2u8du5vTnzfZFpfUd0vMy0vs188Z75rsDE3gEgsEI35oWxA599rcd+la3SI8d9ah8TO0SJSQvEwpUAgHM5DEvAtlBFWApRBCYAAAKDsBTCCEwAgIvF3XDeEZZCHIEJAAD/IizVAGcDk9N51Kf2BCYAAHzH3XA1xIHPvtbRH0eqXoNVioio57V9TOwQSfL7XXIAgODmNKibeMNvqAY5c3qXjv44kgoTAADViLBUwxCYAACV4ZAlYFuoIizVQAQmAACqD2GphiIwAQB8wdIB3hGWajACEwAAF4+74Wq4s4GJu+QAABXhbjjv+A2FASpMAIBQ9O233+r2229X/fr1FRsbqyuuuELbtm1zHTcMQ9OnT1dqaqpiYmLUp08f5eXluZ3DbrfrgQceUIMGDRQXF6cbb7xRhw8frtQ4TA9LZWVl2rhxo7744ovzjv300096+eWXTRhVzUNgAgBUxClLwLbKKC4uVq9evVSrVi299957+uKLL/Tss8+qbt26rjZz5szR3LlztWDBAm3ZskU2m039+/fX8eP/eapFZmamcnJytGrVKm3cuFEnTpzQoEGD5HD4fvXE1LC0d+9etWvXTtdcc43S09PVp08fFRQUuI6XlJTorrvu8ngOu92u0tJSt81pcPmoIgQmAEComD17tho3bqylS5fqyiuvVLNmzdS3b1+1aNFC0s9Vpfnz52vq1KkaNmyYOnTooOXLl+vUqVNauXKlpJ9zxJIlS/Tss8+qX79+6tSpk1asWKGdO3dq3bp1Po/F1LA0ZcoUpaenq6ioSHv27FFCQoJ69eqlQ4cO+XyO7OxsJSYmum35+tKPow5tBCYAwC85DEvAtooKHHa7vcJxrV69Wl27dtXw4cPVsGFDderUSS+88ILreH5+vgoLCzVgwADXPqvVqoyMDG3atEmStG3bNp0+fdqtTWpqqjp06OBq4wtTw9KmTZs0a9YsNWjQQC1bttTq1as1cOBA9e7dWwcPHvTpHFlZWSopKXHb0tTWzyMPbQQmAIAZKipwZGdnV9j24MGDWrRokVq1aqX3339f48eP14MPPuianlNYWChJSk5Odvu+5ORk17HCwkJFR0crKSnpgm18YerdcGVlZYqKch/C888/r4iICGVkZLjKaJ5YrVZZrVa3fREWPtS9+c9dcq8pIiLJa3vukgOAmimQd8NlZWVp4sSJbvvO/Qw/y+l0qmvXrpo1a5YkqVOnTsrLy9OiRYt0xx13uNpZLO5zoQzDOG/fuXxp80umVpbatm2rrVu3nrf/ueee05AhQ3TjjTeaMKrw8XNgukVOZ7FP7akwAQAuhtVqVUJCgtt2obCUkpKiyy67zG1fu3btXFN1bDabJJ1XISoqKnJVm2w2m8rLy1VcXHzBNr4wNSzddNNNevXVVys8tmDBAt16660yDCPAowovBCYACG/BuoJ3r169tGfPHrd9e/fuVdOmTSVJaWlpstlsys3NdR0vLy/Xhg0b1LNnT0lSly5dVKtWLbc2BQUF2rVrl6uNL0wNS1lZWfrHP/5xweMLFy6U0+kM4IjCE4EJABBsHn74YW3evFmzZs3S/v37tXLlSi1evFgTJkyQ9PPlt8zMTM2aNUs5OTnatWuX7rzzTsXGxmrUqFGSpMTERI0bN06PPPKIPvjgA3366ae6/fbblZ6ern79+vk8FlbwhqT/BCbmMAFAeKns+keB0q1bN+Xk5CgrK0tPPvmk0tLSNH/+fN12222uNpMnT1ZZWZnuu+8+FRcXq3v37lq7dq3i4+NdbebNm6eoqCiNGDFCZWVl6tu3r5YtW6bISN//4LcYNfA6V/+I4ab1HREdbVrfyw+sv+hzRNXq4HNgkqSyU39TSfH9uqNZr4vuu6qMSiwsVt0iOl7mvZG/+v7Bt0qgP5z5rsB7IwBVlut8PWB93fbx3QHr6y/dX/DeKAhRWYKbqlaYIiJfk9PBJVMACDWVnUsUjkx/3AmCT1XmMD22/H5FRPLPCQBQ8/DphgpVNjD1uaUngQkAUCNxGQ4XVNlLcn1u+fk2zKfHLOCSHACEiEAuShmq+A3BIypMAIBwxycavCIwAUDNFayLUgYTPs3gEwITACBcMWepBhnTsq/f+2jR8VnN/sdjiq9Xx2vbPrf0VKsbSvRp0eMy/Lhw5XNtzFvrSLv3m9a1s31L0/qOMnF5tjMFvj8pHIB3wbooZTDhz35UyoHPvtaUXz2t40dP+NT+0joD1anhLFl4NAoAIEQRllBpBCYAqDmYs+QdYQlVQmACAIQLwhKqjMAEAKGPypJ3hCVclLOBqdxR4lN7AhMAINQQlnDRDnz2tf6v4G4CEwCEICpL3hGWUC1Ky78kMAEAaiTCEqoNgQkAQg+VJe8IS6hWBCYAQE1DWEK1IzABQOhwyhKwLVQRluAXBCYAQE1BWILfEJgAIPgxZ8k7whL8isAEAAh1poel3bt3a+nSpfryyy8lSV9++aXuvfdejR07Vv/85z9NHh2qA4EJABDKoszsfM2aNRoyZIjq1KmjU6dOKScnR3fccYc6duwowzB0/fXX6/3339d11113wXPY7XbZ7Xa3fU7DoQgLH7TB5Gxg6pHygqIjE722v7TOQEnSp0WPy5DD38MDgLAVypfHAsXUytKTTz6pRx99VEeOHNHSpUs1atQo3X333crNzdW6des0efJkPf300x7PkZ2drcTERLctX18G6CdAZVBhAgCEIlPDUl5enu68805J0ogRI3T8+HH993//t+v4rbfeqs8//9zjObKyslRSUuK2pamtP4eNi0BgAoDgwgRv70yfs3RWRESEateurbp167r2xcfHq6TE84eq1WpVQkKC28YluOBGYAIAhBJTw1KzZs20f/9+19f/93//pyZNmri+/uabb5SSkmLG0OBnBCYACA5UlrwzNSzde++9cjj+M3m3Q4cOior6z5zz9957z+PkboQ2AhMAIBSYejfc+PHjPR6fOXNmgEYCs3CXHACYywjhik+gBM2cJYQvKkwAgGBGWEJQIDABgDl4kK53hCUEjaoGpohI/hkDAPyHTxkElaoEpseW309gAoAq4m447/iEQdCpbGDqc0tPAhMAwG/4dEFQIjABQGAYhiVgW6jikwVBi8AEAAgGfKogqBGYAMC/mLPkHZ8oCHoEJgCAmUxdwbsmMpyGeX2f+cm0vhf28f9jaVq0X6zsV+9TfN04r2373NJTKQPtev+7bBly+m1M/7gi2W/n9sZy8FvT+nY2u9S0vs1803IU/WBa34aDFevhH6E8lyhQ+NMbIeNA3rfKunWhjh876VP7NgnX6vrULFn4Zw4AuAh8iiCkEJgAoHoxZ8k7PkEQcghMAIBA4tMDIYnABAAIFD45ELLOBqafHKU+tScwAcD5DCNwW6jiUwMh7UDet3rr0GQCEwDAb/jEQMj7wb6fwAQAVeSUJWBbqOLTAjUCgQkA4C98UqDGIDABQOXxIF3v+JRAjUJgAgBUNz4hUOMQmADAdyxK6V3QfToYoXxvIYIGgQkAUF2C7pPBarVq9+7dZg8DNQCBCQC8Y50l70x7gPfEiRMr3O9wOPT000+rfv36kqS5c+cGclioYc4GpmFN5qh2ZILX9m0SrpUkvf9dtgw5/T08AEAIMC0szZ8/Xx07dlTdunXd9huGod27dysuLk4Wi/frm3a7XXa73W2f03AowhJZncNFCCMwAcCFhfJdaoFi2vWGmTNnqqSkRE888YTWr1/v2iIjI7Vs2TKtX79e//znP72eJzs7W4mJiW5bvr4MwE+AUMIlOQBAVZn2SZCVlaXXXntN9957ryZNmqTTp09X+TwlJSVuW5raVvNoURMQmADgfKyz5J2pnwLdunXTtm3b9MMPP6hr167auXOnT5fefslqtSohIcFt4xIcLuRsYCojMAEAfGT6J0CdOnW0fPlyZWVlqX///nI4HGYPCTXcD/b9yiEwAYCk4F1nafr06bJYLG6bzWZzHTcMQ9OnT1dqaqpiYmLUp08f5eXluZ3DbrfrgQceUIMGDRQXF6cbb7xRhw8frvTvKGje/UeOHKmtW7fqrbfeUtOmTc0eDmo4AhMABL/27duroKDAte3cudN1bM6cOZo7d64WLFigLVu2yGazqX///jp+/LirTWZmpnJycrRq1Spt3LhRJ06c0KBBgypdmDHtbriKNGrUSI0aNTJ7GAgTZwPTTU3mKIa75ACEqWBe/ygqKsqtmnSWYRiaP3++pk6dqmHDhkmSli9fruTkZK1cuVL33HOPSkpKtGTJEr3yyivq16+fJGnFihVq3Lix1q1bp+uvv97ncfBnMsIaFSYACBy73a7S0lK37dzlf35p3759Sk1NVVpamkaOHKmDBw9KkvLz81VYWKgBAwa42lqtVmVkZGjTpk2SpG3btun06dNubVJTU9WhQwdXG1/xjo+wR2ACgMCoaLmf7OzsCtt2795dL7/8st5//3298MILKiwsVM+ePXXkyBEVFhZKkpKTk92+Jzk52XWssLBQ0dHRSkpKumAbXwXVZTjALFW9JPde5EtyOrgkByB0BfKW/qysx857gofVaq2w7cCBA13/nZ6erh49eqhFixZavny5rrrqKkk67w56wzC83lXvS5tz8acx8G9VqTBNWXqfIiJ5GQGALypa7udCYelccXFxSk9P1759+1zzmM6tEBUVFbmqTTabTeXl5SouLr5gG1/xLg/8QmUDU58RPQhMAEJaqCxKabfbtXv3bqWkpCgtLU02m025ubmu4+Xl5dqwYYN69uwpSerSpYtq1arl1qagoEC7du1ytfEV7/DAOQhMAGC+SZMmacOGDcrPz9fHH3+sm2++WaWlpRozZowsFosyMzM1a9Ys5eTkaNeuXbrzzjsVGxurUaNGSZISExM1btw4PfLII/rggw/06aef6vbbb1d6errr7jhfMWcJqEBl5zD1GdFDkjT7roXMYQIQUoJ15YDDhw/r1ltv1Y8//qhLLrlEV111lTZv3uxai3Hy5MkqKyvTfffdp+LiYnXv3l1r165VfHy86xzz5s1TVFSURowYobKyMvXt21fLli1TZGTlnvRhMYxgXmGhavpHDDetb0tULdP6Ns5U7fl61SGq0aWm9f1fa3f57dyXWFv6HJgk6cO//l/AApMlJsbvfVyI0cy8/98RRUdN69tR9INpfRs83SCs5DpfD1hfbd56MmB97Rn224D1VZ24bgB4wCU5ADVdqMxZMhOX4VAtzhz+1rS+1/xXut/72NR+sWatuFcJSXFe2/YZ0UPHOycp84O/y+HHwm2bhz/z27m9sRw4ZFrfzlZNTOs7MtbEat735lW1HCdOmNY3EAz48xfwwYG8b/X47YtUWnzSp/aDW7bV/L43KLKSa3kAQMAZAdxCFGEJ8BGBCQDCE2EJqAQCE4CahjlL3hGWgEoiMAFAeCEsAVVwNjAV/1TmU3sCE4BgZRiB20IVYQmoogN53+r2d14nMAFADUdYAi5C3pEiAhOAkMacJe8IS8BFIjABQM1GWAKqAYEJQMgyLIHbQhRhCagmBCYAqJkIS0A1IjABCDXcDecdYQmoZgQmAKhZCEuAHxCYAKDmiDJ7AL9UXFys5cuXa9++fUpJSdGYMWPUuHFjs4cFVMnZwLRi8HAl1fb+tPrBLdtKkjI/+LscoVyvBhBaeLvxytTKUmpqqo4cOSJJys/P12WXXabZs2dr3759+vOf/6z09HR9+eWXHs9ht9tVWlrqtjkNRyCGD3hFhQkAQp+pYamwsFAOx8/B5vHHH1fbtm114MABrV27Vvv371fv3r31xBNPeDxHdna2EhMT3bZ8eQ5YQCARmAAEMxal9C5o5ix9/PHHeuKJJxQbGytJslqt+s1vfqPNmzd7/L6srCyVlJS4bWlqG4ghAz4jMAFA6DI9LFn+/WFgt9uVnJzsdiw5OVk//PCDx++3Wq1KSEhw2yIskX4bL1BVBCYAQckI4BaiTA9Lffv2VefOnVVaWqq9e/e6HTt06JAaNGhg0siA6kdgAoDQY+rdcNOmTXP7+uwluLPeeecd9e7dO5BDAvyOu+QABJNQnksUKEEVls71zDPPBGgkQGARmAAgdJh+GQ4IV1ySAxAUmLPkFWEJMBGBCQCCH2EJMFlVA1NEJC9fANXBEsAtNPFuCwSBqgSmKUvvJTABQADwTgsEicoGpj7DryIwAbh4zFnyindZIIgQmAAg+PAOCwQZAhOAgKKy5BXvrkAQIjABQPDgnRUIUgQmAAFhWAK3hShTV/AGqsOZQ4dN67vtwji/nt+hY/rN6y8pe/FdSqgb67V9n+FX6XjHunp47T/8utJ360e3++3c3lgOfmta345WTUzr28y1tSJ/MO/h5I7S46b1LcNpXt8IKvwJCgS5A18WKOt/lqr02Cmf2g9u3VbzBvyKhSsBoJoQloAQQGAC4C+GEbgtVBGWgBBBYAIAcxCWgBBCYAJQ7Vg6wCvCEhBizgam4jIfH41CYAKAi0JYAkLQgS8LdPvbbxCYAFw8lg7wirAEhKgvfiwiMAFAABCWgBBGYAJwsSxG4LZQRVgCQhyBCQD8q0ph6bLLLtOcOXP07bfmraQL4D8ITACqjLvhvKpSWPryyy+VlZWlZs2a6frrr9fKlStV5uObNAD/IDABgH9UKSxlZmaqadOmcjgcys3N1ejRo5WcnKxx48bpww8/rOYhAvAVgQlApXE3nFdVCktz587VwYMHtXXrVmVlZal169Y6ceKEli1bpr59+6pFixZauHBhdY8VgA8ITABQvS5qgnfnzp01evRoDR48WHFxPz993TAM5efn64EHHtDDDz/s8fs//fRT5efnu75esWKFevXqpcaNG+vqq6/WqlWrLmZ4QNgiMAHwGXOWvKpSWDpx4oRefPFF9ezZU+3bt9ezzz6rkydPymaz6YknntCrr76qpKQkvfzyyx7PM27cOH311VeSpBdffFH/8z//o65du2rq1Knq1q2b7r77br300ksez2G321VaWuq2OQ1HVX4soEYhMAFA9YiqyjelpKTo1KlTMv79COFrr71W9957r4YOHaqoqJ9PmZOTo9dff93jefbs2aMWLVpIkhYuXKj58+frf/7nf1zHu3XrppkzZ2rs2LEXPEd2drZmzJjhti9N7dRC7avyowE1ytnAtGLozUqKifHafnDrtpKkh9f+Q45QfkQ4AN/xUveqSpWlkydPKjExUQ8++KB2796tDz74QDfffLMrKEnS/fff77UqFBMTox9++EGS9O2336p79+5ux7t37+52ma4iWVlZKikpcdvS1LYqPxZQI1FhAoCLU+mw5HQ69cILL2jTpk2aN2+e2rRpU2G7q6++WmPGjPF4roEDB2rRokWSpIyMDL3xxhtux//617+qZcuWHs9htVqVkJDgtkVYIivxEwE1H4EJwAUxZ8mrKl2Gu+eee3TppZfq66+/vqjOZ8+erV69eikjI0Ndu3bVs88+qw8//FDt2rXTnj17tHnzZuXk5FxUHwB+xiU5AKiaSleWIiIi1LRpU8XGxl5056mpqfr000/Vo0cPrVmzRoZh6JNPPtHatWvVqFEj/e///q9+9atfXXQ/AH5GhQnAeVhnyasqzVmaPn269u3bpz/96U8XPYC6devq6aefVl5ensrKymS32/XVV1/pL3/5i7p27XrR5wfgjsAEAJVTpbD029/+VlFRUZowYYLq1KmjtLQ0NW/eXM2bN3fd3QYgeBGYAMB3VZqz9Mu5SqdOnXL72sKbKRASLmYOE4Caw8KURK+qFJamTZtW3eMAYIKqBqaFkZ/K6XD6e3gAEBQIS0CYq0pgil96n2bftZDABNQEIVJZys7O1uOPP66HHnpI8+fPl/TzI9ZmzJihxYsXq7i4WN27d9fzzz+v9u3/szC13W7XpEmT9Oqrr6qsrEx9+/bVwoUL1ahRI5/7rvKz4ex2u5YuXap77rlH06ZN06FDh/TRRx/p6NGjVT0lAJNUdg5TnxE9NGXpfYqIvKjHSwKAT7Zs2aLFixfr8ssvd9s/Z84czZ07VwsWLNCWLVtks9nUv39/HT9+3NUmMzNTOTk5WrVqlTZu3KgTJ05o0KBBcjh8fzRald7pjhw5oq5du+rXv/61XnzxReXm5mr37t269tpr9cc//rEqpwRgMgITgGB04sQJ3XbbbXrhhReUlJTk2m8YhubPn6+pU6dq2LBh6tChg5YvX65Tp05p5cqVkqSSkhItWbJEzz77rPr166dOnTppxYoV2rlzp9atW+fzGKr0Ljd58mTl5eWpdu3arufD9evXT7GxsXrvvfeqckoAQYDABMCf7Ha724PvS0tLZbfbPX7PhAkTdMMNN6hfv35u+/Pz81VYWKgBAwa49lmtVmVkZGjTpk2SpG3btun06dNubVJTU9WhQwdXG19U6R3u3XffVWJiog4cOODaFxkZqaZNm+rgwYNVOSWAIEFgAsKLxQjclp2drcTERLctOzv7gmNbtWqVtm/fXmGbwsJCSVJycrLb/uTkZNexwsJCRUdHu1Wkzm3jiyq9u5WUlKhZs2ay2Wxu+x0Oh9t1QgChicAEwB+ysrLcHnxfUlKirKysCtt+8803euihh7RixQrVrl37guc8d8kiwzC8LmPkS5tfqtLdcE2aNFFeXp42btzo2vfOO+9oz549at26dVVOWXMY3B0UThx5+0zru82Sih9iXV0cKtFv3l6q7D/fqYS63h9v1GdED528rJ4mv/WeX58l13TOp347tzcR+w6Z1rez+aWm9R3xUx3z+vYxsPuDs7zctL4DKoCPIbFarbJarT613bZtm4qKitSlSxfXPofDoY8++kgLFizQnj17JP1cPUpJSXG1KSoqclWbbDabysvLVVxc7FZdKioqUs+ePX0ed5X+DLz11lt15swZZWRkyGKx6OOPP9bQoUNlsVh06623VuWUAILQgT2FyrpnmUqPnfKp/Q0d2mjOsIGs9A3govXt21c7d+7Ujh07XFvXrl112223aceOHWrevLlsNptyc3Nd31NeXq4NGza4glCXLl1Uq1YttzYFBQXatWtXpcJSlSpLU6dO1datW8+bzH399ddfsJwGIDSdDUy+Vphu6PBzxcvfFSYA1SRIX6bx8fHq0KGD2764uDjVr1/ftT8zM1OzZs1Sq1at1KpVK82aNUuxsbEaNWqUJCkxMVHjxo3TI488ovr166tevXqaNGmS0tPTz5sw7kmVwlJ0dLT+/ve/66OPPtInn3wiwzB05ZVXKiMjoyqnAxDkCEwAgtHkyZNVVlam++67z7Uo5dq1axUfH+9qM2/ePEVFRWnEiBGuRSmXLVumyMhIn/uxGEbNeyfrHzHctL4tlfjlVzejEgtsoZpYzJvQHJnu3zlLFWnRxuZzYJKkv+/aU+2Bycw5S5boaNP6Nsycs/RDiWl9O4t+MK9vE+cs5TpfD1hfzefNDVhfBx+eGLC+qlOVKkvXXXfdBY9ZLBZ98MEHVR4QgOB1tsI0c+ldqhvr/dEoVJgA1ARVCksffvihLBaLflmUOvt1ZW7FAxB6Duwp1F2vvKmlo/+bwATUABZell5VKSzdcccdbqGopKREH374oY4fP66RI0dW2+AABKfdhT8QmACEjSqFpWXLlp2378cff9Tll19eqaf4AghdBCaghuDl6FW1zU5t0KCBWrZsWWGQAlAznQ1Mx075tnAg6zABCEVVqiw9+eSTbl87HA7t3btXGzduPO/5KwBqNipMAGq6KoWl6dOnX3Ai96BBgy5qQABCD4EJCGG8BL2q8rPhfhmWLBaLGjZsqL59+7KCNxCmCEwAaqoqhaWvvvpKx44d04EDB1SrVi21aNFCcXFx1T02ACGGwASEHpYO8K7SE7wLCgp000036ZJLLtGVV16pTp06qX79+rrrrrtUUvKfVV4PHz7s9VwPPPCA/vWvf1V2CACCGJO+AdQ0lQpLx44d09VXX63Vq1fL4XDIMAwZhqHy8nK9/PLL6t27t+x2u3bs2KGrrrrK6/mef/559enTR61bt9bs2bNVWFhY6R/AbrertLTUbXMaPPYDMBOBCQghhiVwW4iqVFh65plnlJ+fL8Mw1Lt3bz300EPKzMzUNddcI8MwlJeXp1tvvVUZGRkqKCjw6Zxr167Vr371K/3+979XkyZNNGTIEL377rtyOp0+fX92drYSExPdtnx9WZkfC4AfEJgA1BSVCkt/+9vfZLFY9Morr2jDhg2aN2+e5s6dqw8//FDLly+XYRj629/+phMnTpy3vMCFpKena/78+fruu++0YsUK2e12DR06VI0bN9bUqVO1f/9+j9+flZWlkpISty1NbSvzYwHwEwITEAKMAG4hqlJhKT8/X40bN9Ztt9123rHRo0erSZMmkqS33npLU6dOrdRAatWqpREjRmjNmjU6ePCg7r77bv3lL39Rmzaen6xutVqVkJDgtkVYIivVNwD/ITABCHXVtoK3JEVERCgmJkZDhgy5qPM0adJE06dPV35+vtasWVNNowNgFgITELwsRuC2UFWpsNS8eXN98803Wrly5XnH3nzzTX311Vdq3ry5z+dr2rSpIiMvXAWyWCzq379/ZYYIIEgRmACEqkqtszR06FDl5eVp9OjReuGFF9S5c2dZLBZ9/vnn+uCDD2SxWHTjjTf6fL78/PxKDxhA6GIdJiAI8dLyqlJhadKkSXr11Vd18OBBffTRR/roo49cxwzDUNOmTTVp0qRqHySAmoPABCDUVOoyXGJiojZu3KghQ4bIYrG41lmyWCwaNGiQ/vWvf6lu3bp+GiqAmoJLckDwYM6Sd5V+3InNZlNOTo5KSkq0d+9eSVLLli2VlJRU7YMDUHNVtcK04Pc75HT4tg4bAFSHKt8Nl5iYqG7duqlbt24EJQBVUpUK05Ql4xURWa038gLhjXWWvOIdB4CpKhuY+gy/isAEIKB4twFgOgITYCIqS17xTgMgKBCYAAQr3mUABA0CExB43A3nHe8wAIIKgQlAsKn00gHwwmLmG7bDxL7DlGHeLeyOnXtM67uppa1fz39Kx/T4+mV6+k9jlFA31mv7PsOv0sn2SZr0N/8uXNl89k6/ndsby9cFpvXtaNnItL4jTVyI1Pj+B9P6RnDhTzEAQenAnkI9Nn65So+d8qn9DZe10e+HsHAlgOpHWAIQtAhMAIIBYQlAUCMwAX7G0gFeEZYABD0CEwAzEZYAhISzganY10ejEJgAn7B0gHeEJQAh48CeQt356psEJgABRVgCEFJ2f/8DgQmoTsxZ8oqwBCDkEJgABBJhCUBIIjAB1YTKkleEJQAhi8AEIBAISwBCGoEJuDjcDecdYQlAyCMwAfAnwhKAGoHABFQRc5a8Mj0sPffccxozZoz++te/SpJeeeUVXXbZZWrbtq0ef/xxnTlzxuQRAggVBCYA/hBlZudPPfWUnnnmGQ0YMEAPPfSQ8vPz9cwzz+jhhx9WRESE5s2bp1q1amnGjBkXPIfdbpfdbnfb5zQcirBE+nv4AILQ2cC07Nb/VlJsjNf2N1zWRpI06W/vyWGE8J++QBWF8lyiQDG1srRs2TItW7ZMb7zxhtasWaOpU6fqD3/4g6ZOnaqsrCz9+c9/1sqVKz2eIzs7W4mJiW5bvr4M0E8AIBhRYQJQnUwNSwUFBerataskqWPHjoqIiNAVV1zhOt65c2d99913Hs+RlZWlkpISty1Nbf05bAAhgMAE+Ig5S16ZGpZsNpu++OILSdK+ffvkcDhcX0tSXl6eGjZs6PEcVqtVCQkJbhuX4ABIBCYA1cPUOUujRo3SHXfcoSFDhuiDDz7QlClTNGnSJB05ckQWi0UzZ87UzTffbOYQAYQ45jABuFimhqUZM2YoJiZGmzdv1j333KMpU6bo8ssv1+TJk3Xq1CkNHjxYTz31lJlDBFADEJgAD/gn7pWpYSkyMlJTp0512zdy5EiNHDnSpBEBqKkITACqyvR1lgAgUJjDBJyPx514R1gCEFYITAAqi7AEIOxUNTBFRPKWiRqIpQO84pUPICxVJTBNfuFuAhMQhnjVAwhblQ1Mff67O4EJNQ+VJa94xQMIawQmAN7wagcQ9ghMCGfcDecdr3QAEIEJwIXxKgeAfyMwISwxZ8krU1fwrpEMp2ldW6Jqmda3cea0aX2H689t5r815+dfmtZ3WmQ7v57/J5Vo6j+X6+nn71BC3Viv7fv8d3eduCxRj/zdvyt9t1hV4rdze2NEmfdw8mNXNzGt79iCZNP6hrRo0SItWrRIX331lSSpffv2+u1vf6uBAwdKkgzD0IwZM7R48WIVFxere/fuev7559W+fXvXOex2uyZNmqRXX31VZWVl6tu3rxYuXKhGjRpVaiz8OQQA5ziwp1CPTXhZpcdO+dR+ULu2evYGFq5EaArWOUuNGjXS008/ra1bt2rr1q267rrrNGTIEOXl5UmS5syZo7lz52rBggXasmWLbDab+vfvr+PHj7vOkZmZqZycHK1atUobN27UiRMnNGjQIDkcjkqNhbAEABUgMAHmGjx4sH71q1+pdevWat26tWbOnKk6depo8+bNMgxD8+fP19SpUzVs2DB16NBBy5cv16lTp7Ry5UpJUklJiZYsWaJnn31W/fr1U6dOnbRixQrt3LlT69atq9RYCEsAcAEEJoSFAM5ZstvtKi0tddvsdrvXITocDq1atUonT55Ujx49lJ+fr8LCQg0YMMDVxmq1KiMjQ5s2bZIkbdu2TadPn3Zrk5qaqg4dOrja+IqwBAAeEJiA6pOdna3ExES3LTs7+4Ltd+7cqTp16shqtWr8+PHKycnRZZddpsLCQklScrL7vLLk5GTXscLCQkVHRyspKemCbXzFBG8A8OJsYJr54hglxcR4bT+oXVtJ8vukb6BaBPCfaFZWliZOnOi2z2q1XrB9mzZttGPHDh07dkxvvvmmxowZow0bNriOW875o8QwjPP2ncuXNueisgQAPjiwp1Bj/vqGist8W1aAChNwPqvVqoSEBLfNU1iKjo5Wy5Yt1bVrV2VnZ6tjx476wx/+IJvNJknnVYiKiopc1Sabzaby8nIVFxdfsI2vCEsA4KMvin4gMKHGsQRwu1iGYchutystLU02m025ubmuY+Xl5dqwYYN69uwpSerSpYtq1arl1qagoEC7du1ytfEVl+EAoBLOBqblI27mkhzgR48//rgGDhyoxo0b6/jx41q1apU+/PBDrVmzRhaLRZmZmZo1a5ZatWqlVq1aadasWYqNjdWoUaMkSYmJiRo3bpweeeQR1a9fX/Xq1dOkSZOUnp6ufv36VWoshCUAqCQCE+B/33//vUaPHq2CggIlJibq8ssv15o1a9S/f39J0uTJk1VWVqb77rvPtSjl2rVrFR8f7zrHvHnzFBUVpREjRrgWpVy2bJkiIyu30KrFMGreK7d/xHDT+rZU8n9A9XZu3lVVVvAOMyb+W4vo5N8VvD05cEuC29eXNbzE58AkSe/u/rLKgcnMFbydMdGm9X2icW3T+o4t8H5Lu7988OHjAevr8ofnBayvz+c9HLC+qhNzlgCgipjDBIQHwhIAXAQCE0JdsD7uJJiYGpYKCgr029/+Vtddd53atWunDh06aPDgwVqyZEmln9sCAGYhMAE1m2lhaevWrWrXrp3eeecd/fTTT9q7d686d+6suLg4TZo0Sb1793Z7GB4ABDMCE0JWAB93EqpMC0uZmZl6+OGH9emnn2rTpk1avny59u7dq1WrVungwYMqKyvTb37zG6/nqeg5M06DqhSAwCMwATWTaWFp+/btGj16tOvrUaNGafv27fr++++VlJSkOXPm6I033vB6noqeM5OvL/05dAC4IAITQg6VJa9MC0sNGzZUQUGB6+vvv/9eZ86cUULCz7fmtmrVSkePHvV6nqysLJWUlLhtaWrrt3EDgDcEJqBmMS0sDR06VOPHj9eaNWu0fv163XbbbcrIyFDMv9cr2bNnjy699FKv56noOTMRFhPXOgIA/ScwHSv7yaf2BCaYhbvhvDMtLP3ud7/TZZddpsGDB6tv376y2+166aWXXMctFouys7PNGh4AXLQvin7QHX99ncAEhDjTHndSp04dvfbaa/rpp5905swZ1alTx+34gAEDTBoZAFSfs4Hp5RHDVTfG+2rUPBoFAcc/M69MX5Sydu3a5wUlAKhJqDABoc30sAQA4YDAhGDFnCXvCEsAECAEJiA0EZYAIICqGpgiInm7hp+wzpJXvPoAIMCqEpimPDWMwASYhFceAJigsoGpz/XpBCb4BXOWvONVBwAmITABoYFXHACYiMAEBD9ebQBgMgITTMUEb694pQFAECAwAcHLtMed1FSGw2Fe5ybOnouIjjatbzMZFhM/qAxnWPZtfPalaX03j7jMr+f/SSeUlfuyZj8/WgmJMV7b97k+XT81iNbUl9+Tw+m/13/KO4f8dm5v6n5n3oPRT7VPNq3vgArhik+g8CcJAASRA3sLNWXCKyotKfOp/X91aaOZdwxUZAQLVwL+QlgCgCBDYEIgsXSAd4QlAAhCBCYgeBCWACBIEZgQENwN5xVhCQCC2NnAVHLSt7vkCExA9SMsAUCQO7C3UP+z4A0CE/zCYhgB20IVYQkAQsCewz8QmACTEJYAIEQQmOAXzFnyirAEACGEwAQEHmEJAEIMgQnViXWWvDP9cScnT57UypUrtWnTJhUWFspisSg5OVm9evXSrbfeqri4OLOHCABB52xgWnz/zUqMq+21/X91aSNJfn80ClATmVpZ+uKLL9S6dWtNnjxZxcXFatKkiRo1aqTi4mI9+uijatOmjb744gszhwgAQasqFaYZt13v51Eh5DBnyStTK0sTJkzQNddco+XLlyv6nAexlpeX684779SECRO0fv16k0YIAMGtshWmQVe206bdX+kfW817IDEQakytLH388cd64oknzgtKkhQdHa3HH39cH3/8sQkjA4DQUdkK0009Ovh5RAglzFnyztSwlJSUpH379l3w+P79+5WUlOTxHHa7XaWlpW6b03BU91ABIKhVJjC1TG0QgBEBNYepYenuu+/WmDFj9Pvf/16fffaZCgsL9f333+uzzz7T73//e40dO1b33HOPx3NkZ2crMTHRbcsX5WUA4cfXwFQrkhuh8QvMWfLK1DlL06dPV0xMjObOnavJkyfLYvn5tlbDMGSz2fTYY49p8uTJHs+RlZWliRMnuu27KfFOfw0ZAILansM/aOHfNylrxHVmDwWoMUxfOmDKlCmaMmWK8vPzVVhYKEmy2WxKS0vz6futVqusVqvbvghLZLWPEwBCRfkZpiIA1cn0sHRWWlraeQHpm2++0bRp0/TSSy+ZNCoAAGq2UJ54HShBfeH66NGjWr58udnDAAAAYczUytLq1as9Hj948GCARgIAQJiisuSVqWFp6NChslgsMowL/586O+kbAADADKZehktJSdGbb74pp9NZ4bZ9+3YzhwcAQI3HopTemRqWunTp4jEQeas6AQAA+Jupl+EeffRRnTx58oLHW7ZsyXPhAADwJ4oSXpkalnr37u3xeFxcnDIyMgI0GgAAgPMFzTpLAAAg8EJ5LlGgBPU6SwAAAGajsgQAQDijsuQVlSUAAAAPqCzVJIbTtK6dp8+Y1ndkXKxpfcvEnztcGQ7zHhIb8cUB0/q+JKqVz23jU+0ej1uc0iXbLnwn8rmO9Wjkc9vqVue1zab1HVteblrfgWQx76MjZFBZAgAA8IDKEgAA4Yw5S15RWQIAAPCAyhIAAGGMdZa8o7IEAADgAWEJAAAEnezsbHXr1k3x8fFq2LChhg4dqj179ri1MQxD06dPV2pqqmJiYtSnTx/l5eW5tbHb7XrggQfUoEEDxcXF6cYbb9Thw4crNRbCEgAA4cwwArdVwoYNGzRhwgRt3rxZubm5OnPmjAYMGKCTJ/+z7MWcOXM0d+5cLViwQFu2bJHNZlP//v11/PhxV5vMzEzl5ORo1apV2rhxo06cOKFBgwbJUYllSJizBAAAgs6aNWvcvl66dKkaNmyobdu26ZprrpFhGJo/f76mTp2qYcOGSZKWL1+u5ORkrVy5Uvfcc49KSkq0ZMkSvfLKK+rXr58kacWKFWrcuLHWrVun66+/3qexUFkCACCMWYzAbXa7XaWlpW6b3e55EdWzSkpKJEn16tWTJOXn56uwsFADBgxwtbFarcrIyNCmTZskSdu2bdPp06fd2qSmpqpDhw6uNr4gLAEAgIDIzs5WYmKi25adne31+wzD0MSJE3X11VerQ4cOkqTCwkJJUnJyslvb5ORk17HCwkJFR0crKSnpgm18EdRh6fvvv9eTTz5p9jAAAKi5jMBtWVlZKikpcduysrK8DvH+++/X559/rldfffW8YxaLxf3HMYzz9p33I/vQ5peCOiwVFhZqxowZZg8DAABUA6vVqoSEBLfNarV6/J4HHnhAq1ev1vr169Wo0X+eU2iz2STpvApRUVGRq9pks9lUXl6u4uLiC7bxhakTvD///HOPx8+9RRAAcPEiIiyKiLTI6WA1QgTvopSGYeiBBx5QTk6OPvzwQ6WlpbkdT0tLk81mU25urjp16iRJKi8v14YNGzR79mxJUpcuXVSrVi3l5uZqxIgRkqSCggLt2rVLc+bM8XkspoalK664QhaLRUYFtxOe3V+ZMhkAwLuYmGhlTRuq7BlvE5gQtCZMmKCVK1fqb3/7m+Lj410VpMTERMXExMhisSgzM1OzZs1Sq1at1KpVK82aNUuxsbEaNWqUq+24ceP0yCOPqH79+qpXr54mTZqk9PR0191xvjA1LNWvX1+zZ89W3759Kzyel5enwYMHezyH3W4/bya903AowhJZbeMEgFBy+rT39WP69GsvSQQmVHr9o0BZtGiRJKlPnz5u+5cuXao777xTkjR58mSVlZXpvvvuU3Fxsbp37661a9cqPj7e1X7evHmKiorSiBEjVFZWpr59+2rZsmWKjPQ9J5galrp06aLvvvtOTZs2rfD4sWPHKqw6/VJ2dvZ585rS1E4t1L7axgkAoST/YJFP7QhMCGbePv+ln69CTZ8+XdOnT79gm9q1a+u5557Tc889V+WxmDrB+5577lGzZs0ueLxJkyZaunSpx3NUNLM+TW2reaQAEDry9xfpyy++9altn37tlTVtqCIimfIQrgK5zlKoMrWydNNNN3k8npSUpDFjxnhsY7Vaz5tJzyU4AOHM6TQ056nVmv+nMUpIjPXangoT4FlQLx3wzTffaOzYsWYPAwBCzjdfH9Hkh/6i0pJTPrWnwhTGArjOUqgK6rB09OhRLV++3OxhAEBIOrD3ewITUA1MvQy3evVqj8cPHjwYoJEAQM10NjDN+cNtXJJDhUJ5LlGgmBqWhg4desF1ls5inSUAuDgEJuDimHoZLiUlRW+++aacTmeF2/bt280cHgDUGFySwwU5jcBtIcrUsNSlSxePgchb1QkA4LuqBqbICAITwpupYenRRx9Vz549L3i8ZcuWWr9+fQBHBAA1W1UC0/T7byAw1WTcDeeVqWGpd+/e+q//+q8LHo+Li1NGRkYARwQANV9lA1O/Hm0ITAhrQb10AADAPwhMgO8ISwAQpghMkHjciS8ISwAQxghMgHeEJQAIcwSmMGcYgdtClKmLUqIGMZzmdX36jGl9W0z8sDAcpnUdtpxlP5nWd+SuA349/1e7Dihr/2Flr35UCfXqeG3fr0cbRZ08o6en5/h14Ur7DVf67dzeRG0/ZFrfCC5UlgAAkqQDnx9S1o3PqPToCZ/a9+nXXo9Nv4mFK0Mcc5a8IywBAFwITMD5CEsAADcEpjDDopReEZYAAOchMAH/QVgCAFTIFZgq8WgUAlPosRhGwLZQRVgCAFzQgc8PacqDlXuWHIEJNQ1hCQDg0YG9hQSmmswZwC1EEZYAAF4RmBDOCEsAAJ8QmGom5ix5R1gCAPiMwIRwFBSPOzl8+LDq1q2rOnXcl9g/ffq0/u///k/XXHONSSMDAJzrbGCa/cfblJAY67V9n37tlWxL1A9FpZXuyxkdWZUhSpIMGfq24Jg2bz2onV98W+Xz1HihW/AJGFPDUkFBgYYMGaJt27bJYrHotttu0/PPP+8KTUePHtW1114rh4OHYAFAMKlsYGrXoZHaBWBcFbl9+FV6Y/U2PffCP00aAUKdqZfhHnvsMUVGRurjjz/WmjVr9MUXX6hPnz4qLi52tTFC+BonANRklb0kZ6abb+yifhlmxbUgZxiB20KUqWFp3bp1+sMf/qCuXbuqX79+2rhxoxo1aqTrrrtOR48elSRZLFznBoBgFUqBadCAy80eAkKUqWGppKRESUlJrq+tVqveeOMNNWvWTNdee62Kioq8nsNut6u0tNRtcxpctgOAQAmVwJTWrIHZQ0CIMjUsNW/eXJ9//rnbvqioKL3++utq3ry5Bg0a5PUc2dnZSkxMdNvy9aW/hgwAqEAoBKZaUVWfLF6TWYzAbaHK1LA0cOBALV68+Lz9ZwPTFVdc4XXOUlZWlkpKSty2NLX115ABABcQCoEJqApT74abOXOmTp2q+EUVFRWlt956S4cPH/Z4DqvVKqvV6rYvwsJfDwBghgN7CzX5/hV6YtZ/69LG9c0eDnwRwhOvA8XUsBQVFaWEhIQLHv/uu+80Y8YMvfTSSwEcFQDgYhzc/73Gj16sfr+6XK3bpiqujtX7N11AZdZZSrElqk1LW5X7Ai4kKBalvJCjR49q+fLlhCUACDF2+xn9PWe7/q7tF3eeBrV9bnvDgHRNfuC/Lqq/cGQJ4QfcBoqpYWn16tUejx88eDBAIwEAAKiYqWFp6NChslgsHidxs84SAAB+xJwlr0y9Gy4lJUVvvvmmnE5nhdv27RdXvgUAALhYpoalLl26eAxE3qpOAADgIhkB3EKUqZfhHn30UZ08efKCx1u2bKn169cHcEQAAADuTA1LvXv39ng8Li5OGRkZARoNAADhx8IVHK9MvQwHAAAQ7IJ6nSUAAOBnVJa8orIEAADgAZUlAADCGSt4e0VYQshz2n8yewgIF4Z5nyqOEydM6zti537T+o6tl+Rz2+h2jT0etzgMxX72rc/nK+3R1Oe2qNkISwAAhDHuhvOOOUsAAAAeUFkCACCcUVnyisoSAACAB4QlAAAAD7gMBwBAOOMynFdUlgAAADygsgQAQDhjUUqvqCwBAAB4QGUJAIAwxqKU3pkelo4cOaLPP/9cHTt2VL169fTjjz9qyZIlstvtGj58uNq1a2f2EAEANYA1ppYeX3Snz+1P14uptr7t5Wf0u+feq7bzIbBMDUuffPKJBgwYoNLSUtWtW1e5ubkaPny4oqKiZBiGnn76aW3cuFGdO3c2c5gAgBogMipSvW+4wpS+T56ym9KvT4K4svTRRx/pmWee0bZt21RQUKCcnBwNHTrUddwwDM2YMUOLFy9WcXGxunfvrueff17t27d3tbHb7Zo0aZJeffVVlZWVqW/fvlq4cKEaNWrk8zhMnbM0depUDR8+XCUlJXr88cc1dOhQ9e3bV3v37tW+ffs0atQoPfXUU2YOEQAAmOTkyZPq2LGjFixYUOHxOXPmaO7cuVqwYIG2bNkim82m/v376/jx4642mZmZysnJ0apVq7Rx40adOHFCgwYNksPh8Hkcpoalbdu2aeLEiYqPj9dDDz2k7777Tnfffbfr+IQJE7RlyxYTRwgAQA1nGIHbKmngwIH63e9+p2HDhlUwbEPz58/X1KlTNWzYMHXo0EHLly/XqVOntHLlSklSSUmJlixZomeffVb9+vVTp06dtGLFCu3cuVPr1q3zeRymhqXy8nLFxPx8TbhWrVqKjY1VgwYNXMfr16+vI0eOeDyH3W5XaWmp2+Y0fE+LAICa4cxp3vuDXUWf2XZ71S5R5ufnq7CwUAMGDHDts1qtysjI0KZNmyT9XJQ5ffq0W5vU1FR16NDB1cYXpoalxo0b6+DBg66vV61apZSUFNfXBQUFbuGpItnZ2UpMTHTb8vWl38YMAAhOX335ndlDCE0BrCxV9JmdnZ1dpWEXFhZKkpKTk932Jycnu44VFhYqOjpaSUlJF2zjC1PD0siRI1VUVOT6+oYbbnBVmiRp9erVuvLKKz2eIysrSyUlJW5bmtr6bcwAgOCUv/s77dnxtdnDgAcVfWZnZWVd1DktFovb14ZhnLfvXL60+SVT74abNm2ax+NTp05VZGSkxzZWq1VWq9VtX4TF8/cAAGoep9PQM5l/0Yyld+vStEvMHk7oCOAK3hV9ZleVzWaT9HP16JdXpYqKilzVJpvNpvLychUXF7tVl4qKitSzZ0+f+zJ9nSVPjhw5omnTpumll14yeygAgBDw7cEiTbh+jvre3E2t0hsrLqHqayVV9zpLqF5paWmy2WzKzc1Vp06dJP08F3rDhg2aPXu2JKlLly6qVauWcnNzNWLECEk/T/HZtWuX5syZ43NfQR2Wjh49quXLlxOWAAA+s/90Wv9Y4fvk3QspvdL3dXhCWTCv4H3ixAnt37/f9XV+fr527NihevXqqUmTJsrMzNSsWbPUqlUrtWrVSrNmzVJsbKxGjRolSUpMTNS4ceP0yCOPqH79+qpXr54mTZqk9PR09evXz+dxmBqWVq9e7fH4Lyd/AwCA8LJ161Zde+21rq8nTpwoSRozZoyWLVumyZMnq6ysTPfdd59rUcq1a9cqPj7e9T3z5s1TVFSURowY4VqUctmyZV6n+fySxTDMi5QRERGyWCzyNASLxVKphaMkqX/E8IsdGgDg3yJiY83ru16S90Z+YmZl6X/fmBSwvga2u7gJ1pXx3u6q3flmNlPvhktJSdGbb74pp9NZ4bZ9+3YzhwcAAGBuWOrSpYvHQOSt6gQAAOBvps5ZevTRR3Xy5MkLHm/ZsqXWr18fwBEBABBmnBQlvDE1LPXu3dvj8bi4OGVkZARoNAAAAOcL6qUDAACAnzHdxStT5ywBAAAEOypLAACEMypLXlFZAgAA8KBGVpZyna+bPQQAAEIDlSWvqCwBAAB4UCMrSwAAwEess+QVlSUAAAAPqCwBABDODKfZIwh6VJYAAAA8oLIEAEA44244r6gsAQAAeEBlCQCAcMbdcF5RWQIAAPCAyhIAAOGMOUteUVkCAADwgMoSAADhjMqSV1SWAAAAPCAsAQAAeMBlOAAAwhmX4byisgQAAOABlSUAAMKZkwfpekNlCQAAwAMqSwAAhDPmLHlFZQkAAMADKksAAIQzKkteUVkCAADwgMoSAADhzEllyRsqSwAAAB5QWQIAIIwZBusseUNlCQAAwAMqSwAAhDPmLHlFZQkAAMADKksAAIQz1lnyisoSAACAB1SWAAAIZ07uhvOGyhIAAIAHhCUAAAAPuAwHAEA4Y4K3V1SWAAAAPKCyBABAGDOY4O0VlSUAAAAPqCwBABDOmLPkFZUlAAAAD6gsAQAQzniQrldUlgAAADygsgQAQDgzuBvOGypLAAAAHlBZAgAgjBnMWfKKyhIAAIAHVJYAAAhnzFnyisoSAACAB4QlAADCmOE0ArZVxcKFC5WWlqbatWurS5cu+te//lXNvwHvCEsAACAovfbaa8rMzNTUqVP16aefqnfv3ho4cKAOHToU0HFYDIOHwgAAEK76RwwPWF+5ztcr1b579+7q3LmzFi1a5NrXrl07DR06VNnZ2dU9vAuisgQAAIJOeXm5tm3bpgEDBrjtHzBggDZt2hTQsXA3HAAACAi73S673e62z2q1ymq1ntf2xx9/lMPhUHJystv+5ORkFRYW+nWc5yIsAQAQxip7aexiTJ8+XTNmzHDbN23aNE2fPv2C32OxWNy+NgzjvH3+RlgCAAABkZWVpYkTJ7rtq6iqJEkNGjRQZGTkeVWkoqKi86pN/sacJQAAEBBWq1UJCQlu24XCUnR0tLp06aLc3Fy3/bm5uerZs2cghutCZQkAAASliRMnavTo0eratat69OihxYsX69ChQxo/fnxAx0FYAgAAQemWW27RkSNH9OSTT6qgoEAdOnTQP/7xDzVt2jSg42CdJQAAAA+YswQAAOABYQkAAMADwhIAAIAHhCUAAAAPCEsAAAAeEJYAAAA8ICwBAAB4QFgCAADwgLAEAADgAWEJAADAA8ISAACAB4QlAAAAD/4fP9xDysV7wt8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 700x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "aligner.get_aggregate_alignment() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "retired-battlefield",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.547031847635905"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    " 272.8219108581543/60"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "regulation-turtle",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean alignment similarity percentage (matched %): \n",
+      "60.809999999999995 %\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = aligner.get_stat_df()"
+   ]
+  }
+ ],
+ "metadata": {
+  "environment": {
+   "kernel": "genes2genes",
+   "name": "pytorch-gpu.1-9.m82",
+   "type": "gcloud",
+   "uri": "gcr.io/deeplearning-platform-release/pytorch-gpu.1-9:m82"
+  },
+  "kernelspec": {
+   "display_name": "g2g_installed_env",
+   "language": "python",
+   "name": "g2g_installed_env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/Tutorial.ipynb b/notebooks/Tutorial.ipynb
index c264226..d7a1e86 100644
--- a/notebooks/Tutorial.ipynb
+++ b/notebooks/Tutorial.ipynb
@@ -2,12 +2,12 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e912a4e8-bea6-4b97-adaa-2921e3311a55",
+   "id": "insured-murray",
    "metadata": {},
    "source": [
     "# Tutorial on single-cell trajectory alignment using Genes2Genes\n",
     "\n",
-    "G2G aims to guide downstream comparative analysis of single-cell reference and query systems along any axis of progression (e.g. differentiation pseudotime, disease/treatment response pseudotime etc.). This notebook describes how we can use G2G framework to infer and analyse gene-level trajectory alignments between a given reference and query dataset.\n",
+    "Genes2Genes (G2G) aims to guide downstream comparative analysis of single-cell reference and query systems along any axis of progression (e.g. differentiation pseudotime, disease/treatment response pseudotime etc.). This notebook describes how we can use G2G framework to infer and analyse gene-level trajectory alignments between a given reference and query dataset.\n",
     "\n",
     "In this tutorial, we are going to compare trajectories between two treatment groups (PAM and LPS) of mouse bone marrow-derived dendritic cells from Shalek et al (2014). The single cell datasets and their pseudotime estimates were downloaded from https://github.com/shenorrLab/cellAlign (Alpert et al 2018) and packaged into adata objects. There are 2 gene modules: global (core antiviral module) and local (peaked inflammatory module) considered by Alpert et al (2018), and we use the local module as an example comparison."
    ]
@@ -15,92 +15,70 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "355c9f7b-716e-4e1c-91c6-61ecdf2e9753",
+   "id": "gross-campus",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(CVXPY) Oct 11 05:41:15 PM: Encountered unexpected exception importing solver GLOP:\n",
-      "RuntimeError('Unrecognized new version of ortools (9.6.2534). Expected < 9.5.0.Please open a feature request on cvxpy to enable support for this version.')\n",
-      "(CVXPY) Oct 11 05:41:15 PM: Encountered unexpected exception importing solver PDLP:\n",
-      "RuntimeError('Unrecognized new version of ortools (9.6.2534). Expected < 9.5.0.Please open a feature request on cvxpy to enable support for this version.')\n",
-      "3.9.16\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import anndata\n",
     "import numpy as np\n",
     "import seaborn as sb\n",
+    "import numpy as np\n",
+    "import warnings\n",
     "import matplotlib.pyplot as plt\n",
-    "import platform\n",
-    "from optbinning import ContinuousOptimalBinning\n",
+    "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "from genes2genes import Main\n",
-    "from genes2genes import VisualUtils\n",
     "from genes2genes import ClusterUtils\n",
     "from genes2genes import TimeSeriesPreprocessor\n",
-    "from genes2genes import PathwayAnalyserV2\n",
-    "\n",
-    "print(platform.python_version())"
+    "from genes2genes import PathwayAnalyser\n",
+    "from genes2genes import VisualUtils"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9b38b57b-ef6f-4339-8059-ea47900df570",
+   "id": "involved-egypt",
    "metadata": {},
    "source": [
-    "Load the reference and query anndata objects"
+    "### Load anndata reference and query objects\n",
+    "\n",
+    "Make sure that each adata object has: <br>\n",
+    "(1) log normalized gene expression in `adata.X` <br>\n",
+    "(2) pseudotime estimates in `adata.obs['time']`"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "d8aef058-0e71-4a84-bcfb-53e371517dfc",
+   "id": "developed-breed",
    "metadata": {},
    "outputs": [],
    "source": [
-    "input_dir = 'data/'\n",
-    "adata_ref = anndata.read_h5ad(input_dir + 'adata_pam_local.h5ad') # PAM dataset\n",
-    "adata_query = anndata.read_h5ad(input_dir +'adata_lps_local.h5ad') # LPS dataset"
+    "input_dir = 'notebooks/data/'\n",
+    "adata_ref = anndata.read_h5ad(input_dir + 'adata_pam_local.h5ad') # Reference dataset\n",
+    "adata_query = anndata.read_h5ad(input_dir +'adata_lps_local.h5ad') # Query dataset"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "45a6c0c9-31c0-427a-bfc3-5cf7251d0c84",
+   "cell_type": "markdown",
+   "id": "favorite-pearl",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AnnData object with n_obs × n_vars = 179 × 89\n",
-      "    obs: 'time'\n",
-      "AnnData object with n_obs × n_vars = 290 × 89\n",
-      "    obs: 'time'\n"
-     ]
-    }
-   ],
    "source": [
-    "print(adata_ref)\n",
-    "print(adata_query)"
+    "## 1. Preparing data for alignment "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a743108b-daa6-45ff-a07b-9dc948705824",
+   "id": "everyday-ratio",
    "metadata": {},
    "source": [
-    "### Min max normalize the pseudotime"
+    "### Pseudotime range check\n",
+    "Check whether the current range of pseudotime values are between 0 and 1. If not, run min max normalization. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "863a45e4-8723-48e4-ad5e-606dc97b582a",
+   "execution_count": 3,
+   "id": "lightweight-management",
    "metadata": {},
    "outputs": [
     {
@@ -113,24 +91,23 @@
     }
    ],
    "source": [
-    "# check the current range\n",
     "print(min(adata_ref.obs['time']), max(adata_ref.obs['time']))\n",
     "print(min(adata_query.obs['time']), max(adata_query.obs['time']))\n",
     "\n",
-    "# if it does not follow [0,1] range, run below\n",
-    "adata_ref.obs['time'] = TimeSeriesPreprocessor.Utils.minmax_normalise(np.asarray(adata_ref.obs['time']))\n",
-    "adata_query.obs['time'] = TimeSeriesPreprocessor.Utils.minmax_normalise(np.asarray(adata_query.obs['time']))"
+    "## uncomment below if the range is not [0,1] for any of the objects\n",
+    "#adata_ref.obs['time'] = TimeSeriesPreprocessor.Utils.minmax_normalise(np.asarray(adata_ref.obs['time']))\n",
+    "#adata_query.obs['time'] = TimeSeriesPreprocessor.Utils.minmax_normalise(np.asarray(adata_query.obs['time']))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "ed6d223e-4116-45f5-b718-1688f76e3a19",
+   "execution_count": 4,
+   "id": "beneficial-major",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -141,35 +118,43 @@
    ],
    "source": [
     "# Visualize the pseudotime distributions\n",
-    "sb.kdeplot(adata_ref.obs['time'], fill=True, label='PAM', color='forestgreen') \n",
-    "sb.kdeplot(adata_query.obs['time'], fill=True, label='LPS', color='midnightblue'); \n",
+    "sb.kdeplot(adata_ref.obs['time'], fill=True, label='Reference - PAM', color='forestgreen') \n",
+    "sb.kdeplot(adata_query.obs['time'], fill=True, label='Query - LPS', color='midnightblue'); \n",
     "plt.xlabel('pseudotime'); plt.legend(); plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e4e1a533-6596-4b86-a3c7-1a72a1660d33",
+   "id": "short-feature",
    "metadata": {},
    "source": [
-    "### Check the number of bins in the optimal binning structure using OptBinning package"
+    "### Determine the number of discrete pseudotime points to align\n",
+    "\n",
+    "We can use optbinning package (https://gnpalencia.org/optbinning/installation.html) to get a heuristic estimate about the number of discrete time points to consider by running below on each dataset. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "526c5d51-3b6a-4340-ad05-3185cde512c4",
+   "execution_count": 5,
+   "id": "coral-detective",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "(CVXPY) Apr 17 06:27:42 PM: Encountered unexpected exception importing solver GLOP:\n",
+      "RuntimeError('Unrecognized new version of ortools (9.9.3963). Expected < 9.8.0. Please open a feature request on cvxpy to enable support for this version.')\n",
+      "(CVXPY) Apr 17 06:27:42 PM: Encountered unexpected exception importing solver PDLP:\n",
+      "RuntimeError('Unrecognized new version of ortools (9.9.3963). Expected < 9.8.0. Please open a feature request on cvxpy to enable support for this version.')\n",
       "14\n",
       "14\n"
      ]
     }
    ],
    "source": [
+    "from optbinning import ContinuousOptimalBinning\n",
+    "\n",
     "x = np.asarray(adata_ref.obs.time)\n",
     "optb = ContinuousOptimalBinning(name='pseudotime', dtype=\"numerical\")\n",
     "optb.fit(x, x)\n",
@@ -183,71 +168,68 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c9ed718-e2e4-4dab-8f77-c1c355952189",
+   "id": "curious-visitor",
+   "metadata": {},
+   "source": [
+    "Accordingly, we go with `n_bins=14`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "selective-payroll",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "OptBinning estimates 14 optimal number of splits for both reference and query pseudotime distributions. Therefore we choose the same number of interpolation points."
+    "n_bins = 14"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9515a55f-e90b-44da-bd4c-ad1dcebe0861",
+   "id": "stopped-shore",
    "metadata": {},
    "source": [
-    "### Visualize the interpolation binning structure in terms of the cell type composition \n",
+    "### Define which cell type annotations and color scheme to use for visualization purposes\n",
     "\n",
-    "For this dataset, we use the author-given time annotations (1h,2h,4h,6h) as the cell-type annotations. "
+    "`annotation_colname` and `joint_cmap`"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "6c5d1d4f-ccbf-44f5-9c58-6211282e69d3",
+   "id": "sexual-narrow",
    "metadata": {},
    "outputs": [],
    "source": [
-    "adata_ref.obs['annotation'] = [x.split('_')[1] for x in adata_ref.obs_names] \n",
-    "adata_query.obs['annotation'] = [x.split('_')[1] for x in adata_query.obs_names] "
+    "# define annotation column name in the adata obs\n",
+    "annotation_colname = 'annotation' \n",
+    "adata_ref.obs[annotation_colname] = [x.split('_')[1] for x in adata_ref.obs_names] \n",
+    "adata_query.obs[annotation_colname] = [x.split('_')[1] for x in adata_query.obs_names] \n",
+    "\n",
+    "# define the joint colormap to use for both reference and query\n",
+    "col = np.array(sb.color_palette('colorblind'))[range(4)]\n",
+    "joint_cmap={'1h':col[0], '2h':col[1] , '4h':col[2] , '6h':col[3]}"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "39448682-08d4-4c17-9c2b-c12ac33464d0",
+   "id": "norman-hungarian",
    "metadata": {},
    "source": [
-    "Next we define a colormap of our choice for these annotations, and call the below function. "
+    "Inspect the cell type compositions around each discrete pseudotime point (x-axis) to see if it reasonably represents the entire trajectory of interest. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "11a9f623-0dd6-4044-8f33-123a7dcf0783",
+   "id": "taken-opposition",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# trying max n points for optimal binning = 14\n",
-      "====================================================\n",
-      "Optimal equal number of bins for R and Q =  14\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1000x300 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 1000x100 with 1 Axes>"
+       "<Figure size 900x100 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -255,9 +237,9 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 1000x100 with 1 Axes>"
+       "<Figure size 900x100 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -265,144 +247,129 @@
     }
    ],
    "source": [
-    "col = np.array(sb.color_palette('colorblind'))[range(4)]\n",
-    "joint_cmap={'1h':col[0], '2h':col[1] , '4h':col[2] , '6h':col[3]}\n",
-    "vs = VisualUtils.VisualUtils.get_celltype_composition_across_time(adata_ref, adata_query, n_points=14, \n",
-    "                                     ANNOTATION_COLNAME='annotation', optimal_binning=False, ref_cmap=joint_cmap, query_cmap=joint_cmap)"
+    "VisualUtils.plot_celltype_barplot(adata_ref, n_bins, annotation_colname, joint_cmap)\n",
+    "VisualUtils.plot_celltype_barplot(adata_query, n_bins, annotation_colname, joint_cmap)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4e0d2a46-7f6e-4468-b133-bd44e0d5fc6b",
+   "id": "failing-botswana",
    "metadata": {},
    "source": [
-    "### Run G2G alignment\n",
-    "\n",
-    "We can now run Gene-level alignment for all 89 genes in the PAM and LPS datasets"
+    "## 2. G2G trajectory alignment"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "ea5e0eca-f528-42f7-b0b6-43d9fdfe8bfe",
+   "id": "important-survivor",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "89\n"
+      "89 genes\n"
      ]
     }
    ],
    "source": [
+    "# define the gene list to align\n",
     "gene_list = adata_ref.var_names \n",
-    "print(len(gene_list))"
+    "print(len(gene_list),'genes')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "71ebac26-4337-48c5-885c-baa8fcfe2a28",
+   "id": "experienced-serbia",
    "metadata": {},
    "source": [
-    "This is done by first creating an aligner object, passing and setting all relevant parameters.\n",
-    "Next we align all gene pairs. (This step is parallelizing indepedenent gene-alignments to make the process time-efficient, however the computational time for an individual alignment will increase as the number of cells and/or the number of interpolation time points increase. "
+    "### Aligning all genes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "8d7657be-9703-4f32-9093-71b656b66878",
+   "execution_count": 15,
+   "id": "alpine-italian",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WINDOW_SIZE= 0.1\n"
+      "===============================================================================================================\n",
+      "Genes2Genes (v0.2.0)\n",
+      "Dynamic programming alignment of gene pseudotime trajectories using a bayesian information-theoretic framework\n",
+      "===============================================================================================================\n",
+      "Interpolator initialization completed\n",
+      "Aligner initialised to align trajectories of 179 reference cells & 290 query cells in terms of 89 genes\n",
+      "Running gene-level alignment: 🧬\n"
      ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "793a80e659c84f4783d1e40a698b6af5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/89 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "aligner = Main.RefQueryAligner(adata_ref, adata_query, gene_list, len(vs.optimal_bining_S))\n",
-    "aligner.WEIGHT_BY_CELL_DENSITY = True\n",
-    "aligner.WINDOW_SIZE=0.1\n",
-    "aligner.state_params = [0.99,0.1,0.7]\n",
-    "aligner.optimal_binning = True\n",
-    "aligner.opt_binning_S = vs.optimal_bining_S\n",
-    "aligner.opt_binning_T = vs.optimal_bining_T\n",
-    "aligner.align_all_pairs() "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c008dfb4-715a-4b13-94e2-a5612d74baff",
-   "metadata": {},
-   "source": [
-    "Now we can check the aggregate (average) alignment across all genes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "3fea4d54-63da-4ae1-bba2-a22883ca3ebb",
-   "metadata": {},
-   "outputs": [
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 89/89 [00:26<00:00,  3.41it/s]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average Alignment:  IDDDMMMMMMMMMIIIDID\n"
+      "Alignment completed! ✅\n"
      ]
     },
     {
-     "data": {
-      "image/png": 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ECHXo0EHNmjUztda3336rdu3a6dSpUypXrpzmzp2rJk2amFpz9uzZWr9+fUDndOSnbdu2mjFjhho2bKj9+/drwoQJ6tixozZt2qTo6GhTau7cuVPTpk3TqFGj9Mgjj2jt2rW6//77FRERUeQXnRbHhx9+qKNHj+qOO+4wtc7DDz+snJwcNWrUSKGhofJ6vZo0aZIGDhxoWs3o6Gi1a9dOEydOVOPGjVWtWjX9+9//1sqVK1WvXj3T6p5z4MABSbrg76Rzx0qjU6dO6aGHHtKAAQMUExNjaq358+erf//+OnHihOLj47Vo0SJVrlzZtHpTpkxRWFiY7r//ftNqoPgcH4ScKCUlRZs2bbLk/24bNmyojIwMZWdn64MPPtDgwYO1fPly08LQ3r179cADD2jRokXn/R+9WX47OtGiRQu1bdtWtWrV0nvvvae77rrLlJo+n09t2rTJexP75Zdfrk2bNumVV16xJAi98cYb6tmzp+lzHd577z298847mjVrlpo2baqMjAyNGDFCCQkJpn7Pt99+W0OGDFH16tUVGhqq1q1ba8CAAUpPTzetppOdOXNGt9xyiwzD0LRp00yv16VLF2VkZOjQoUN67bXXdMstt2j16tWqWrVqwGulp6fr+eef1/r1600dPb0UwTpSYxXH3xqrXLmyQkNDlZWV5bc/KytLcXFxNnVlnuHDh2v+/PlaunSpatSoYXq9iIgI1atXT0lJSUpLS1PLli31/PPPm1YvPT1dBw8eVOvWrRUWFqawsDAtX75cL7zwgsLCwuT1ek2rfU758uXVoEED7dixw7Qa8fHx54XJxo0bm35LTpJ2796txYsXW/Km9zFjxujhhx9W//791bx5c91+++0aOXKk0tLSTK1bt25dLV++XMePH9fevXu1Zs0anTlzRnXq1DG1rqS83ztO+Z10LgTt3r1bixYtMn00SJKioqJUr149XXnllXrjjTcUFhamN954w5RaX331lQ4ePKiaNWvm/U7avXu3/vrXv6p27dqm1ETROD4IRUREKCkpSUuWLMnb5/P5tGTJEkvmsljFMAwNHz5cc+fO1RdffKHExERb+vD5fPJ4PKZdv2vXrvr222+VkZGRt7Vp00YDBw5URkaGQkNDTat9zvHjx/XDDz8oPt68Nzp36NDhvOUPtm3bplq1aplW85zp06eratWquu6660yvdeLECYWE+P+aCg0Nlc9nzVq4UVFRio+P1y+//KKFCxeqd+/eptdMTExUXFyc3++knJwcrV69ulT9TpL+F4K2b9+uxYsXq1KlSrb0Yebvpdtvv10bN270+52UkJCgMWPGaOHChabULCqf4bJsC0bcGpM0atQoDR48WG3atNEVV1yh5557Trm5ubrzzjtNq3n8+HG/EYPMzExlZGSoYsWKqlmzZsDrpaSkaNasWfroo48UHR2dN9cgNjZWkZGRAa8nSampqerZs6dq1qypY8eOadasWVq2bJmpf/ijo6PPm/cUFRWlSpUqmTYfavTo0erVq5dq1aqlffv2ady4cQoNDdWAAQNMqSdJI0eOVPv27fXUU0/plltu0Zo1a/Tqq6/q1VdfNa2m9Ot/MKZPn67BgwcrLMz8Xx+9evXSpEmTVLNmTTVt2lQbNmzQs88+qyFDhphad+HChTIMQw0bNtSOHTs0ZswYNWrUKGC/Ewr68z9ixAg9+eSTql+/vhITE/XYY48pISFBffr0Ma3mkSNHtGfPnrx1fM4F7bi4uGKPROVXMz4+XjfffLPWr1+v+fPny+v15v1eqlixoiIiIgJes1KlSpo0aZJuuOEGxcfH69ChQ3rppZf0008/XdIyEAX9vf19wAsPD1dcXJwaNmxY7JoIIJufWgsa//jHP4yaNWsaERERxhVXXGGsWrXK1HpLly41JJ23DR482JR6F6olyZg+fbop9QzDMIYMGWLUqlXLiIiIMKpUqWJ07drV+Pzzz02rdzFmPz5/6623GvHx8UZERIRRvXp149ZbbzV27NhhWr1zPv74Y6NZs2aG2+02GjVqZLz66qum11y4cKEhydi6davptQzDMHJycowHHnjAqFmzplGmTBmjTp06xtixYw2Px2Nq3XfffdeoU6eOERERYcTFxRkpKSnG0aNHA3b9gv78+3w+47HHHjOqVatmuN1uo2vXrpf897ygmtOnT7/g8XHjxplS89xj+hfali5dakrNkydPGn379jUSEhKMiIgIIz4+3rjhhhuMNWvWFLteQTUvJNgen8/YXcOyLRi5DMPkJVoBAEDQWr8n8HchLqZ1TfPnMRaV4+cIAQAA52KOEAAADuZ1+JiIs789AABwNEaEAABwsGB9rN0qjAgBAADHYkQIAAAH4xUbyOPxeDR+/HhTVz6mpjU1nfAdqUlNalITl451hH4jJydHsbGxys7OtuR9N9QsPfWoSU1qUjMYahbHV7vqWVarY23z3sFYXIwIAQAAx2KOEAAADuZz+JiIs789AABwtFI5R+jakOK9RdhneJWpLUpUI4W4QgPcFTWtrOmE70hNalKz9NZc5HvfpK7O98WuhpbVuqb2VstqFRZBCACAIEMQsg5zhAAAcDCv4exZMs7+9gAAwNEYEQIAwMF8rCwNAADgTIwIAQDgYF6Hj4k4+9sDAABHIwgBAADH4tYYAAAO5vTH5x0dhNpe11rXDbv2osdzj57QlMH/sLAjAABgJUcHobjaVdWuV5uLHv8l66h1zQAAYAOnv3TV1iB06NAhvfnmm1q5cqUOHDggSYqLi1P79u11xx13qEqVKna2BwAASjnbgtDatWvVo0cPlS1bVt26dVODBg0kSVlZWXrhhRc0efJkLVy4UG3aXHzERpI8Ho88Ho/fPp/hteylegAAlGRew9kLKtoWhO677z7169dPr7zyilwu/38IhmHonnvu0X333aeVK1fme520tDRNmDDBb1+iGquumga8ZwAAULrYdmPwv//9r0aOHHleCJIkl8ulkSNHKiMjo8DrpKamKjs7229LVCMTOgYAoPTxKsSyLRjZNiIUFxenNWvWqFGjC4eWNWvWqFq1agVex+12y+12++3jthgAACgM24LQ6NGjNWzYMKWnp6tr1655oScrK0tLlizRa6+9pr/97W92tQcAgCP4WEfIHikpKapcubKmTp2ql19+WV6vV5IUGhqqpKQkzZgxQ7fccotd7QEAAAew9fH5W2+9VbfeeqvOnDmjQ4cOSZIqV66s8PBwO9sCAMAxgnXujlWCYkHF8PBwxcfH290GAABwmKAIQgAAwB5OX0fI2eNhAADA0RgRAgDAwZz+rjFnf3sAAOBojAgBAOBgXoevI+Tsbw8AAByNIAQAAByLW2MAADiYT85+fJ4gFCChzZ3xxvvtgypYXjMi2/o/pLWe22h5Te/x45bXBACnIwgBAOBgTJYGAABwKEaEAABwMKe/dNXZ3x4AADgaI0IAADiYj5euAgAAOBMjQgAAOBhzhAAAAByKESEAABzM5/B1hAhC+YiuWE5Tv5xYqHNdUZEmdxMcTlYt3r8yZ70+bTt8WB9t3qyM/fsD3BUAAMVDEMpHWHiYml3ljFdnWOHKmpdpYMsWenHVar2wcqXd7QAAJHkd/q4xZ4+HwXKhISF6oH07PdSpo92tAAAQ3EFo7969GjJkSL7neDwe5eTk+G0+w2tRhyiuYcnJhCEACAI+I8SyLRgFZ1f/78iRI5o5c2a+56SlpSk2NtZvy9SWQl3fc/J0INpEMRGGAAB2s3WO0Lx58/I9vnPnzgKvkZqaqlGjRvnt6xt7R6Hq79y4u1DnwTzDkpMlSVO+/MrmTgDAmZw+R8jWINSnTx+5XC4ZhnHRc1yu/P8Bud1uud1uv30hrtBC1d+xPlObVmxhQrTNCEMAALvYemssPj5ec+bMkc/nu+C2fv16U+v7fD5Nvv0FbUsveOQJ5uI2GQDYw+lzhGwdEUpKSlJ6erp69+59weMFjRYFQtbun3V/u0d05fVJqtc6UVExZYt1HVelCgHuLDgdbewu+KTfCAsJUb9mzeQOK/hfNUaGAABWszUIjRkzRrm5uRc9Xq9ePS1dutT0Prxnvfr6wzX6+sM1xb5GaHNn3F7bPqjogW/JDzv1Su8bCEMAgEJLS0vTnDlztGXLFkVGRqp9+/aaMmWKGjZsmHfO1VdfreXLl/v93N13361XXnml0HVsDUIdO+Z/KyQqKkqdO3e2qBuY5ctdu3TPR/MIQwAQhLxBestq+fLlSklJUXJyss6ePatHHnlE3bt31/fff6+oqKi884YOHaonnngi73PZskW7s8PK0rAEYQgAUBQLFizw+zxjxgxVrVpV6enp6tSpU97+smXLKi4urth1gjMGolQ6F4Y8Z88W6nwmUAOA+XxyWbZdaBFkj8dTqD6zs7MlSRUrVvTb/84776hy5cpq1qyZUlNTdeLEiSJ9f4IQLFWcMDS6O2EIAEqDCy2CnJaWVuDP+Xw+jRgxQh06dFCzZs3y9t92223617/+paVLlyo1NVVvv/22/vSnPxWpJ26NwXJFvU1211VtJEl/+5zbZAAQaFbOEXr0Aosg/34twAtJSUnRpk2btGLFCr/9w4YNy/vr5s2bKz4+Xl27dtUPP/ygunXrFqonRoRgi6KODN11VRtGhgCghHO73YqJifHbCgpCw4cP1/z587V06VLVqFEj33Pbtm0rSdqxY0eheyIIwTaEIQCwn89wWbYVhWEYGj58uObOnasvvvhCiYmJBf5MRkaGpF8XbC4sghBsRRgCAFxISkqK/vWvf2nWrFmKjo7WgQMHdODAAZ08eVKS9MMPP2jixIlKT0/Xrl27NG/ePA0aNEidOnVSixYtCl2HIATbEYYAwD5ehVi2FcW0adOUnZ2tq6++WvHx8Xnbu+++K0mKiIjQ4sWL1b17dzVq1Eh//etfddNNN+njjz8uUh2XYfY7LGxwbUg/y2uGlitneU3v8eOW1zxx05WmXbttq9pKG9Nb7ojCzeHfm/2adv7yjCm9fHAk2ZTr5uf70c0KPinAwtdts7ymr2kdy2saqzdaXhO4FIt871tW6+GNN1tWa3KLDyyrVViMCCForM7YpdRnPpLndOFGhi6LHao6FcaY3BUAlG7BOkfIKgQhBBXCEADASgQhBB3CEABYx6cQy7ZgFJxdwfEIQwAAKxCEELTOhSGfUbj30BCGAKDovIbLsi0YEYQQ1FZn7NKmg38hDAEATEEQQtD75eRXhCEAMAlPjQElAGEIAGAGghBKDMIQACDQCEIoUQhDABBYPiPEsi0YBWdXQD4IQwCAQCEIoUQqThhKrDDa5K4AoOTxymXZFoxsD0InT57UihUr9P3335937NSpU3rrrbfy/XmPx6OcnBy/zWd4zWoXQaSoYahm7DDCEADAj61BaNu2bWrcuLE6deqk5s2bq3Pnztq/f3/e8ezsbN155535XiMtLU2xsbF+W6a2mN06ggRhCAAuDY/P2+ihhx5Ss2bNdPDgQW3dulXR0dHq0KGD9uzZU+hrpKamKjs7229LVCMTu0awIQwBAIorzM7i33zzjRYvXqzKlSurcuXK+vjjj/WXv/xFHTt21NKlSxUVFVXgNdxut9xut9++EFeoWS0jSJ0LQ82qvqwQl7vA82vGDpMkZf7yN7NbA4CgFqxPc1nF1m9/8uRJhYX9L4u5XC5NmzZNvXr1UufOnbVt2zYbu0NJw8gQAKCobB0RatSokdatW6fGjRv77X/xxRclSTfccIMdbaEEY2QIAIrGF6RPc1nF1hGhvn376t///vcFj7344osaMGCADMOwuCuUdIwMAQAKy9YglJqaqk8//fSix19++WX5fD4LO0JpQRgCgMLxGi7LtmDk7BlSKNUIQwCAgtg6RwgwW3HnDOnIcpM7A4DgwFNjQClXnJGhGxL6m9wVACAYEITgCEUNQ9fG9SIMAXAEVpYGHIIwBAD4PYIQHIUwBAD+fHJZtgUjghAchzAEADiHIARHIgwBwK+cPkeIx+cDxHv8uOU1Q8uVs7xm9MJNltd8RHebdu22reYrbUxvuSMK/qNwbVwvNYzcb9rrOJa+YP2/Q0sf6mB5zbJbf7a8prdtC8trGqs3Wl4TQNExIgRHW52xS6nPfCTP6bOFOp9FFwGgdCEIwfEIQwCczGeEWLYFo+DsCrAYYQgAnIkgBPw/whAAJ3L6ZGmCEPAb58IQL2oFAGcgCAG/szpjF2+tB+AYLKgI4DzFeVErYQgASh6CEHARhCEATsAcIQAXRRgCgNKNIAQUgDAEoDRjRAhAgQhDAFA6EYSAQiIMASiNGBGy2ebNmzV9+nRt2bJFkrRlyxbde++9GjJkiL744osCf97j8SgnJ8dv8xles9uGQxGGAKB0sTUILViwQK1atdLo0aN1+eWXa8GCBerUqZN27Nih3bt3q3v37gWGobS0NMXGxvptmdpi0TeAExGGAJQmjAjZ6IknntCYMWN0+PBhTZ8+XbfddpuGDh2qRYsWacmSJRozZowmT56c7zVSU1OVnZ3ttyWqkUXfAE5FGAKA0sHWIPTdd9/pjjvukCTdcsstOnbsmG6++ea84wMHDtTGjRvzvYbb7VZMTIzfFuIKNbNtQBJhCEDpwMrSNnO5fv0bExISojJlyig2NjbvWHR0tLKzs+1qDSgQYQgASjZbg1Dt2rW1ffv2vM8rV65UzZo18z7v2bNH8fHxdrQGFBphCEBJxhwhG917773yev/3hFezZs0UFhaW9/mzzz7TNddcY0drQJEQhgCgZAor+BTz3HPPPfkef+qppyzqBLh058JQs6ovK8TlLvD8mrHDJEmZv/zN7NYAABdh+xwhoDRhZAhAScOtMQABRRgCgJKDIASYoDhhqFu1QSZ3BQDnY0QIgCmKGoauqnITYQgALEYQAkxEGAIQ7BgRAmAqwhAABC+CEGABwhCAYGUYLsu2YEQQAixCGAKA4GPrgoooebzHj1teM3rhJstrPhiZ/2Kfl+LK5p/o6QdukDui4D9+V1W5STUjDpu26GLlvx8z5br5efup6y2vWe7HwoXPQApt28Lymsbq/F9SDVxIsL4M1SqMCAEWW/XtLj34/Dx5Tp8t1PmsMwQA5iEIATYgDAEIFjw1BsAWhCEAsB9BCLARYQiA3XhqDICtzoUh3k0GANYjCAFBYNW3u3hRKwBbMEcIQFDgrfUAYD2CEBBECEMArMYcIQBBhTAEANYhCAFBiDAEwCrMEQoyhmHY3QIQFAhDAGC+oAtCbrdbmzdvtrsNICgQhgDAXLa9dHXUqFEX3O/1ejV58mRVqlRJkvTss8/mex2PxyOPx/8/Ej7DqxBXaGAaBWx2Lgw1q/qyQlzuAs+vGTtMkkx7USuA0sXpN2JsC0LPPfecWrZsqfLly/vtNwxDmzdvVlRUlFyugu8npqWlacKECX77EtVYddU0kO0CtiIMAYA5bAtCTz31lF599VX9/e9/1zXXXJO3Pzw8XDNmzFCTJk0KdZ3U1NTzRpf6xt4RyFaBoEAYAmAGn4JzErNVbJsj9PDDD+vdd9/Vvffeq9GjR+vMmTPFuo7b7VZMTIzfxm0xlFbMGQKAwLJ1snRycrLS09P1888/q02bNtq0aVOhbocBTkYYAhBILKhos3LlymnmzJlKTU1Vt27d5PV67W4JCHqEIQAIDNuD0Dn9+/fXunXrNGfOHNWqVcvudoCgRxgCEAjBuqBiWlqakpOTFR0drapVq6pPnz7aunWr3zmnTp1SSkqKKlWqpHLlyummm25SVlZWkeoETRCSpBo1aqh3796KioqyuxWgRCAMASitli9frpSUFK1atUqLFi3SmTNn1L17d+Xm5uadM3LkSH388cd6//33tXz5cu3bt0833nhjkerY9tQYgMDgaTIAlyJY1xFasGCB3+cZM2aoatWqSk9PV6dOnZSdna033nhDs2bNynv6fPr06WrcuLFWrVqlK6+8slB1gmpECEDxMDIEoCTweDzKycnx236/KPLFZGdnS5IqVqwoSUpPT9eZM2fUrVu3vHMaNWqkmjVrauXKlYXuiSAElBLFCUPtqgw1uSsAwc7Kp8bS0tIUGxvrt6WlpRXYo8/n04gRI9ShQwc1a9ZMknTgwAFFRESctzBztWrVdODAgUJ/f26NAaVIUW+TJVUaIEla+fNrZrcGABdcBNntLvh3VUpKijZt2qQVK1YEvCeCEFDKEIYAFIWV6/u43e5CBZ/fGj58uObPn68vv/xSNWrUyNsfFxen06dP6+jRo36jQllZWYqLiyv09bk1BpRCRb1NllRpALfJAAQVwzA0fPhwzZ07V1988YUSExP9jiclJSk8PFxLlizJ27d161bt2bNH7dq1K3QdRoSAUoqRIQCFUdT1faySkpKiWbNm6aOPPlJ0dHTevJ/Y2FhFRkYqNjZWd911l0aNGqWKFSsqJiZG9913n9q1a1foJ8YkRoSAUo2RIQAl1bRp05Sdna2rr75a8fHxedu7776bd87UqVN1/fXX66abblKnTp0UFxenOXPmFKmOyzCCdQWB4rs2pJ/dLZRaoeXKWV7Te/y45TXDqlW1vGbOVXVMu3bbVrWVNqa33BGFGwQ+lPOSDmY/ZUovX+Q2NOW6+Zn+bC/La5bfccrymuGHT1haz/vtFkvrOcki3/uW1Wry4XjLan3fx7pahcWIEOAAqzN2KfWZj+Q5fbZQ51eOSVHV2EdM7goA7EcQAhyCMAQA5yMIAQ5CGALwe1YuqBiMCEKAwxCGAOB/CEKAA50LQz6jcBN6CUNA6cWIEABHWp2xS3sP3UUYAuBoBCHAwXJPLSMMAQ5nWLgFI4IQ4HCEIQBORhACQBgCHIw5QgAgwhAAZyIIAchDGAIcyOGThAhCAPwQhgA4SeHewGiR3Nxcvffee9qxY4fi4+M1YMAAVapUKd+f8Xg88nj836ztM7wKcYWa2SpQqp0LQ5dVfkMhrjIFnl85JkWSTHtRKwDzBOvcHavYOiLUpEkTHTlyRJK0d+9eNWvWTCNHjtSiRYs0btw4NWnSRJmZmfleIy0tTbGxsX5bpngjMnCpGBkC4AS2BqEtW7bo7Nlfl/lPTU1VQkKCdu/erTVr1mj37t1q0aKFxo4dm+81UlNTlZ2d7bclqpEV7QOlHmEIKP0Mw7otGAXNHKGVK1dq/Pjxio2NlSSVK1dOEyZM0IoVK/L9ObfbrZiYGL+N22JA4BCGAJRmtgchl+vXe5OnTp1SfHy837Hq1avr559/tqMtAL9BGAJKL9YRslnXrl3VunVr5eTkaOvWrX7Hdu/eXeBkaQDWIAwBKI1sfWps3Lhxfp/LlSvn9/njjz9Wx44drWwJQD54mgwohYJ0pMYqQRWEfu+ZZ56xqBMAhUUYAlCa2H5rDEDJw20yoPTgqTEAKAbCEIDSgCAEoNiKE4baVRlqclcAUHgEIQCXpKhhKKnSAMIQEEx46SoAXBrCEICSiiAEICAIQ0DJxIKKABAghCEAJQ1BCEBAEYaAEsbhc4RsXVARJY/3+HHLa4b+bsVxK5zNOmh5zZgNZS2vee89D5h27SuSPtaTj/aVO6LgXzNJlQaoccQR5R4zZ9HFKg8eM+W6+Xn2iQGW14w9edbSeqHNG1laT5K8326xvCZKN0aEAJhiTXqmHn1yrjynC/cf57LRKYqKZp0hwGrMEQIAkxCGAAQ7ghAAUxGGgCDn8DlCBCEApiMMAQhWBCEAljgXhoxCPk1GGAKs4rJwCz4EIQCWWZOeqewjdxGGAAQNghAAS53xLCMMAcGEOUIAYC3CEIBgQRACYAvCEBAkGBECAHsQhgDYjSAEwFaEIcBmhsu6LQgRhADYjjAEwC62BqH169crMzMz7/Pbb7+tDh066LLLLtNVV12l2bNnF3gNj8ejnJwcv81neM1sG4AJCEMA7GBrELrzzjv1ww8/SJJef/113X333WrTpo3Gjh2r5ORkDR06VG+++Wa+10hLS1NsbKzflineTgyURIQhwHqGYd0WjMLsLL59+3bVr19fkvTyyy/r+eef19ChQ/OOJycna9KkSRoyZMhFr5GamqpRo0b57esbe4cp/QIw37kwFFvxDblcZQo8v2x0iiQp99hTZrcGoBSydUSobNmyOnTokCTpp59+0hVXXOF3vG3btn63zi7E7XYrJibGbwtxhZrWMwDzMTIEWIjH5+3Ts2dPTZs2TZLUuXNnffDBB37H33vvPdWrV8+O1gDYjDAEwAq23hqbMmWKOnTooM6dO6tNmzb6+9//rmXLlqlx48baunWrVq1apblz59rZIgAbcZsMsECQPtZuFVtHhBISErRhwwa1a9dOCxYskGEYWrNmjT7//HPVqFFDX3/9tf74xz/a2SIAmzEyBMBMto4ISVL58uU1efJkTZ482e5WAAQpRoYA87iCdO6OVVhQEUCJwMgQADMUKwj98Y9/1HvvvSePxxPofgDgoghDgAl4aqzoFixYoAEDBiguLk533323vv7660D3BQAXVJww1KLScJO7AlBSFSsI9e3bV5GRkcrOztZrr72mTp06qV69epo4caJ27doV4BYBwF9Rw1DjCoMJQ8DF8NLVovvPf/6jn3/+We+//7769++vcuXKaefOnRo/frzq1q2rLl266NNPPw10rwCQhzAEIBCKPVk6MjJSN910k6ZNm6bx48crKipKkmQYhpYvX65evXppypQpAWsUAH6PMAQEAHOEimfx4sW67bbbFB8fr9GjRys3N1cRERG6/fbblZaWpsjISE2dOjWQvQLAeQhDAC5FsdYRql27tvbu3Svj/18lW7duXd19990aMmSIKlasKElau3Ytq0IDsERR1xlqXGGwJGnj4RfNbg0IfkE6UmOVYgWhPXv2KDQ0VNddd53uvfde9ejR47xzBg4cqObNm19yg4D3+HHLa4aWK2d5TePgIctrlrW8ovSH8aNNu3a7pp/q73+5Qe7wgn+1Na4wWI3CPTKO/82UXlaMSDfluvnZ+FhLS+u5La32q9DmjSyv6f12i+U1YZ1iBaFHH31Ud999t6pXr37Rc/r27au+ffsWuzEAKKqV3+3WX1+eV+gw5Co3TJJMC0NAieDwEaEizxE6c+aMJk2apNatW+fdGgOAYHEuDHnOnC3U+a5yw+QqZ94oFYDgVuQgFB4ervj4eFWpUkUuV3CuCQDA2QhDQBGwjlDRPfDAA9q6das+//zzQPcDAAFBGAJQGMWaI/Tpp58qNDRUPXv2VMOGDVWtWrW80SGXy6UlS5YEtEkAKI5zYegf918nl6vgqb3MGQKcp1hBaPny5Xl/vWXLFm3Z8r8Z9dwuAxBMVn63W8Yvf5EqvEwYAi7A5fDpvsUKQoMGDSLwACg5Tn9FGAJwQcUKQjNmzAhwGwBgMsIQcGGMCBXf0qVLtWrVKlWoUEG33Xabjh49qmrVqsnttmOZLQAoAGEIwO8U66mxkydP6tprr1W3bt306KOP6q233tLixYuVmJio5557LsAtAkAA/X8YMgxPoU7naTKgdCtWEHr00Ue1ZMkSGYaRt6jiddddp4iICH3yyScBbRAAAo4wBOD/FSsIvffee4qMjFRGRkbePrfbrVq1amnbtm2B6g0AzEMYAiT9+tSYVVswKlYQOnjwoBo0aKAWLVr47Q8PD9fRo0cLfZ377rtPX331VXFayOPxeJSTk+O3+QzvJV0TgEMQhgDHK1YQio+P17Zt2/TDDz/k7cvIyNDmzZuVkJBQ6Ou89NJLuvrqq9WgQQNNmTJFBw4cKHIvaWlpio2N9dsyxZuCARQSYQhOxys2iq537946efKkmjVrJpfLpQ0bNuiKK66QYRjq3bt3ka71+eef649//KP+9re/qWbNmurdu7fmz58vn89XqJ9PTU1Vdna235aoRsX5WgCcijAEOFaxgtDEiRPVsmVLeTweGYYhj8ejs2fPqnnz5powYUKRrtW8eXM999xz2rdvn/71r3/J4/GoT58+uuyyyzR27Fjt2LEj3593u92KiYnx20JcocX5WgCcjDAEpzIs3IJQsdYRiomJ0Zo1azRr1iytXbtWkpScnKwBAwYoIiKiWI2Eh4frlltu0S233KI9e/bozTff1IwZMzR58mR5vcz5AWAB1hkCHKfYCyqGh4dr8ODBGjx4cCD7kSTVrFlT48eP17hx47R48eKAXx8ALoowBKcJ0pEaqxQrCA0ZMuSix1wul954441CXadWrVoKDb34bSyXy6Vrr722yP0BwCUhDAFB4csvv9Qzzzyj9PR07d+/X3PnzlWfPn3yjt9xxx2aOXOm38/06NFDCxYsKHSNYr9r7EIvXTUMo0hBKDMzszjlAcB8hCE4RLCu7yNJubm5atmypYYMGaIbb7zxguf84Q9/0PTp0/M+F/U1X8UKQp06dfILQtnZ2fr2229lGIY6duxYnEsCQPAhDAG26tmzp3r27JnvOW63W3FxccWuUawgtGzZsvP2bdmyRe3bt9f1119f7GYAIOgUMwxJy83tCwgUC0eEPB6PPB7/JzPdbvclvax92bJlqlq1qipUqKBrrrlGTz75pCpVqlTony/W4/MX0qhRI7Vq1Ur/+Mc/AnVJAAgOxXi0/oaE/iY3BZQ8F1oEOS0trdjX+8Mf/qC33npLS5Ys0ZQpU7R8+XL17NmzSE+bF2tE6K233vL77PV6tW3bNn311VcqW7ZscS4JAMGtiCND18b1kiTN2zfb7M6AEiM1NVWjRo3y23cpo0H9+//vfziaN2+uFi1aqG7dulq2bJm6du1aqGsUKwjdcccdF50s3blz5+JcEgCCH2EIpZGFt8Yu9TZYQerUqaPKlStrx44dhQ5Cxb41ZhiG31alShUNGDBAr732WnEvCQDBr4i3ya6N68VtMsAiP/74ow4fPqz4+PhC/0yxRoTOvQfs0KFDioiIUExMTHEuAwAlEyNDKEWC+fH548eP+71qKzMzUxkZGapYsaIqVqyoCRMm6KabblJcXJx++OEHPfjgg6pXr5569OhR6BpFDkLHjx/X2LFjNWvWLB05ckTSr2+jHzp0qB555BGFh4dLko4dO6bo6OiiXh4ICt7jxy2vGVatquU1z+7cZXnN8jUrWl6z0Rv3mnLdjnU+0cv9bpA7rOBfpVaEobIP/WTatS/kxJTqltaTJPNuqlycHX828at169apS5cueZ/PzS8aPHiwpk2bpo0bN2rmzJk6evSoEhIS1L17d02cOLFIt9+KFIROnjypzp07KyMjQ4bxvwi5b98+PfHEE/ryyy+1aNEi/fjjj7r++uu1cePGolweAEqUr3bu1l/enxdUYQgoMuP8Ob/B4uqrr/bLG7+3cOHCS65RpDlCzz77rDZs2CDDMJSYmKjevXurb9++qlOnjgzD0LJlyzR8+HC1a9dO33333SU3BwDB7lwY8pw9W6jzmTMEBJciBaEPPvhALpdLkydP1o4dOzR37lz95z//0fbt2/XUU0/JMAz985//1P79+3XPPfeY1TMABBXCEEo0w8ItCBUpCO3YsUMJCQl68MEH/R6fd7lcevjhh5WQkCBJeu211/TSSy8FtlMACGKEIaBkKlIQ8nq9ioiIuOjxMmXKqEyZMrrrrrsuuTEAKGkIQyiJXIZ1WzAqUhBKTEzUrl279P777593bN68edq5c6cSExMD1hwAlDTnwtAZ3+lCnU8YAuxVpKfGbrjhBm3evFn9+/fXK6+8olatWikkJEQbN27UkiVL5HK51KtXL7N6BYAS4audu/XazqkaWmekwkMuPop+Dk+TwVZBOlJjlSIFoYceekizZ8/W7t27tWzZMr+30BuGocsuu0xjxowJdI8AUOJsztlIGAJKgCLdGitfvry++eYb3XDDDXK5XHmv15Ck6667TitWrFDFitYvlgYAwehcGOI2GYKZ0+cIFXll6fj4eH344YfKzs7Wtm3bJEn16tVThQoVAt4cAJR0jAwBwa3YL12NjY1VcnKykpOTCUEAkA9GhhDUWEcIAGA2whAQnAhCAGARwhCCEiNC9nrxxRc1aNAgzZ796/3wt99+W02aNFGjRo30yCOP6GwBC5N5PB7l5OT4bT7Da0XrAFBkhCEguNgahJ588kk98sgjOnHihEaOHKkpU6Zo5MiRGjhwoAYPHqzXX39dEydOzPcaaWlpio2N9dsytcWibwAARUcYQjBx+lNjtgahGTNmaMaMGfrggw+0YMECjR07Vs8//7zGjh2r1NRU/fOf/9SsWbPyvUZqaqqys7P9tkQ1sugbAEDxEIaA4GBrENq3b5/atGkjSWrZsqVCQkLUqlWrvOOtW7fWvn378r2G2+1WTEyM3xbiCjWzbQAICMIQYD9bg1BcXJy+//57SdL27dvl9XrzPkvSd999p6pVq9rVHgCYjjAE2KvICyoG0sCBAzVo0CD17t1bS5Ys0YMPPqjRo0fr8OHDcrlcmjRpkm6++WY7WwQA07HoImAfW4PQhAkTFBkZqZUrV2ro0KF6+OGH1bJlSz344IM6ceKEevXqVeBkaQAoDQhDsE2QTmK2iq1BKCQkRI888ojfvv79+6t/f4Z9ATgPYQiwnu3rCAEA/oc5Q7Aaj88DAIIKYQiwDkEIAIJQccLQkMReJneFUolXbAAAglFRw1C/y7oRhoAiIggBQBAjDMF0jAgBAIIZYQgwD0EIAEoAwhDM4vSnxmxdRwjA/xi5JyyvGVqunOU1w/YcsbxmZFaC5TU3PtYy8NeUlJn0kZ58tK/cEQX/+u53WTf9sjdWf1u6IuC9SJLuPG7OdfNR+++Wl7Tlzyasw4gQAJQga9Iz9eiTc+U5fbZQ5w9rn6zRXa4yuSuUaMwRAgCUJIQhIHAIQgBQAhGGEChOnyNEEAKAEoowBFw6ghAAlGCEIVwy5ggBAEqyvDB0ljAEFBVBCABKgTXpmfrL+/MIQ0AREYQAoJT4auduwhCKjltjAIDSgjAEFA1BCABKGcIQioLH5wEApQ5hCCgcghAAlFKEIRQKc4Tss3//fj3++OO65ppr1LhxYzVt2lS9evXSG2+8Ia/Xa2drAFAqEIaA/NkWhNatW6fGjRvr008/1ZkzZ7R9+3YlJSUpKipKo0ePVqdOnXTs2LECr+PxeJSTk+O3+QxCFACcQxhCvhgRsseIESM0cuRIrVu3Tl999ZVmzJihbdu2afbs2dq5c6dOnDihRx99tMDrpKWlKTY21m/L1BYLvgEAlByEIeDCbAtC69ev1+233573+bbbbtP69euVlZWlChUq6Omnn9YHH3xQ4HVSU1OVnZ3ttyWqkZmtA0CJRBjChfDUmE2qVq2q/fv3533OysrS2bNnFRMTI0mqX7++jhw5UuB13G63YmJi/LYQV6hpfQNASUYYAvzZFoT69Omje+65RwsWLNDSpUs1cOBAde7cWZGRkZKkrVu3qnr16na1BwClFmEIfpgjZI8nn3xSTZo0Ua9evdS1a1d5PB69+eabecddLpfS0tLsag8ASjXCEPCrMLsKlytXTu+++65OnTqls2fPqly5cn7Hu3fvblNnAOAM58LQy/1ukDus4P8cDGufLEn629IVZrcGCwXr3B2r2L6gYpkyZc4LQQAAazAyBKezPQgBAOxFGHI45ggBAJyuWGGoeVeTuwLMRxACAEgqRhhq2J4wVBowIgQAwK8IQ3AaghAAwA9hyFlcFm7BiCAEADgPYQhOQRACAFwQYQhOYNuCigDs5z1+3PKaYVUrW16z3H6f5TXDTnotr1nnvWMBv+ZPOqInvjymcc/dpgh3eIHnD2vYXqH7wjX1M3MWXdx7rSmXzVetEzWsL2qlIJ3EbBVGhAAA+Ur/ZocmjJil054zhTr/ri7JGtmTdYZQMhCEAAAFIgyVXi7Dui0YEYQAAIVCGEJpRBACABQaYagUYkFFAAAK71wY8pwp3NNkhCEEM4IQAKDI0r/ZoftnziMMlQaMCAEAUHRfb9tNGEKJRxACABQbYajk46kxAAAuAWEIJZntK0ufPn1aH374oVauXKkDBw5IkuLi4tS+fXv17t1bERERNncIACjIuTD0wuAb5A4v+D8td3VJliTTVqBGEQTpSI1VbB0R2rFjhxo3bqzBgwdrw4YN8vl88vl82rBhgwYNGqSmTZtqx44ddrYIACgkRoZQEtk6InTvvfeqefPm2rBhg2JiYvyO5eTkaNCgQUpJSdHChQtt6hAAUBTFGRn6du8BLd7E//TaJVjn7ljF1hGhr7/+Wk8++eR5IUiSYmJiNHHiRH311Vc2dAYAKK6ijgzd/4cOJncEXJytQah8+fLatWvXRY/v2rVL5cuXz/caHo9HOTk5fpvPsP6tzwCA/ylKGKpTtaKi3MwHtQ3rCNnnz3/+swYNGqSpU6dq48aNysrKUlZWljZu3KipU6fqjjvu0LBhw/K9RlpammJjY/22TG2x6BsAAC6mKGGoXBmCEOxh6xyhJ554QlFRUXrmmWf017/+VS6XS5JkGIbi4uL00EMP6cEHH8z3GqmpqRo1apTfvr6xd5jVMgCgCL7etlvj/7NYaf3/YHcruAinzxGy/fH5hx56SA899JAyMzP9Hp9PTEws1M+73W653W6/fSGu0ID3CQAonj2HjtrdAnBRQbOgYmJiotq1a6d27drlhaC9e/dqyJAhNncGAABKq6AJQhdy5MgRzZw50+42AAAovRw+WdrWW2Pz5s3L9/jOnTst6gQAAASbL7/8Us8884zS09O1f/9+zZ07V3369Mk7bhiGxo0bp9dee01Hjx5Vhw4dNG3aNNWvX7/QNWwNQn369JHL5ZJhXDwmnptADQAATBCkIzWSlJubq5YtW2rIkCG68cYbzzv+9NNP64UXXtDMmTOVmJioxx57TD169ND333+vMmXKFKqGrbfG4uPjNWfOnLxXa/x+W79+vZ3tAQAAG/Xs2VNPPvmk+vbte94xwzD03HPP6dFHH1Xv3r3VokULvfXWW9q3b58+/PDDQtewNQglJSUpPT39oscLGi0CAACXxmVYt11oEWSPx1Osvs89bd6tW7e8fbGxsWrbtq1WrlxZ6OvYGoTGjBmj9u3bX/R4vXr1tHTpUgs7AgAAZrnQIshpaWnFuta5JXeqVavmt79atWp5xwrD1jlCHTt2zPd4VFSUOnfubFE3AAA4kIU3Xi60CPLv1wK0mu0LKgIAAGe40CLIxRUXFydJysrKUnx8fN7+rKwstWrVqtDXCep1hAAAgLlchmHZFkiJiYmKi4vTkiVL8vbl5ORo9erVateuXaGvw4gQAAAISsePH9eOHTvyPmdmZiojI0MVK1ZUzZo1NWLECD355JOqX79+3uPzCQkJfmsNFYQgBKDUCzvhtbxm+Lptltf8uX8LS+slfHW8UOdVbnaiwHOqrc5V6M8FX29fx3KFqhlIxxqUt7ympYL44ex169apS5cueZ/PzS8aPHiwZsyYoQcffFC5ubkaNmyYjh49qquuukoLFiwo9BpCEkEIAAAEqauvvrrARZefeOIJPfHEE8WuQRACAMDBXEE8ImQFJksDAADHYkQIAAAnY0QIAADAmRgRAgDAwZgjBAAA4FCMCAEA4GSMCAEAADhTUAehrKysS1okCQAAID9BHYQOHDigCRMm2N0GAACllsuwbgtGts4R2rhxY77Ht27dalEnAADAiWwNQq1atZLL5brge0TO7Xe5XDZ0BgCAQwTpSI1VbA1CFStW1NNPP62uXbte8Ph3332nXr165XsNj8cjj8fjt89neBXiCg1YnwAAoHSyNQglJSVp3759qlWr1gWPHz16NN+3zkpSWlraefOIEtVYddU0YH0CAFBaBevcHavYOln6nnvuUe3atS96vGbNmpo+fXq+10hNTVV2drbflqhGAe4UAACURraOCPXt2zff4xUqVNDgwYPzPcftdsvtdvvt47YYAACFVMCdl9IuqB+f37t3r4YMGWJ3GwAAoJQK6iB05MgRzZw50+42AAAotVhHyEbz5s3L9/jOnTst6gQAADiRrUGoT58+F11H6BzWEQIAwERBOlJjFVtvjcXHx2vOnDny+XwX3NavX29newAAoJSzNQglJSUpPT39oscLGi0CAACXxuWzbgtGtt4aGzNmjHJzcy96vF69elq6dKmFHQEAACexNQh17Ngx3+NRUVHq3LmzRd0AAOBADr/xEtSPzwMAAJjJ1hEhAABgr2Bd38cqjAgBAADHYkQIAEqJiq9/Y2m9w39uX6jzsuuULfCcXxpF6XBcwUMTNRYeKVTNQDrWoLzlNWEdghAAAE7m8GVquDUGAAAcixEhAAAcjMnSAAAADsWIEAAATsaIEAAAgDMxIgQAgIMxRwgAAMChGBECAMDJWEfIfj/++KOOHz9+3v4zZ87oyy+/tKEjAADgBLYGof379+uKK65QrVq1VL58eQ0aNMgvEB05ckRdunSxsUMAAEo3l2HdFoxsDUIPP/ywQkJCtHr1ai1YsEDff/+9unTpol9++SXvHMPhQ3YAAMA8tgahxYsX64UXXlCbNm3UrVs3ff3114qPj9c111yjI0d+fbGey+Wys0UAAEo3w8ItCNkahLKzs1WhQoW8z263W3PmzFHt2rXVpUsXHTx4sMBreDwe5eTk+G0+w2tm2wAAoJSwNQjVqVNHGzdu9NsXFham999/X3Xq1NH1119f4DXS0tIUGxvrt2Vqi1ktAwBQqjBHyEY9e/bUq6++et7+c2GoVatWBc4RSk1NVXZ2tt+WqEZmtQwAAEoRW9cRmjRpkk6cOHHBY2FhYfrPf/6jn376Kd9ruN1uud1uv30hrtCA9QgAQKnmC9KhGovYOiIUFhammJiYix7fv3+/JkyYYGFHAADASYJiQcWLOXLkiGbOnGl3GwAAlF4Of2rM1ltj8+bNy/f4zp07LeoEAAA4ka1BqE+fPnK5XPlOiGYdIQAAYBZbb43Fx8drzpw58vl8F9zWr19vZ3sAAJR6PD5vo6SkJKWnp1/0eEGjRQAAAJfC1ltjY8aMUW5u7kWP16tXT0uXLrWwIwAAHMbhAw62BqGOHTvmezwqKkqdO3e2qBsAAOA0tgYhAABgr2Cdu2OVoF5HCAAAwEyMCAEA4GQOHxEiCAGwVu6F3y9oprCTXstrOkHF178p1HkxVzaQHu6f7znl/71OZ386UuC1dj/avlA1A+myRcctrwnrEIQAAHAwl8OfGmOOEAAAcCxGhAAAcDKf3Q3YixEhAADgWIwIAQDgYMwRAgAAcChGhAAAcDJnDwgxIgQAAJyLESEAAJzM4XOEbA9Chw8f1saNG9WyZUtVrFhRhw4d0htvvCGPx6N+/fqpcePGdrcIADDZ4Cdu1cljpwo871hyfLFr7MvO0Zqde7X5wM/FvgZKH1uD0Jo1a9S9e3fl5OSofPnyWrRokfr166ewsDD5fD5NnjxZK1asUOvWre1sEwBgsj/ceY0ldXw+Q2+tWq8pC760pB6Cn61zhMaOHat+/fopOztbjzzyiPr06aOuXbtq27Zt2rFjh/r376+JEyfa2SIAoBQJCXHpjvZJurl1M7tbCRouw7otGNkahNLT0zVq1ChFR0frgQce0L59+zR06NC848OHD9fatWtt7BAAUBrd3u5yu1tAkLD11tjp06cVGRkpSQoPD1fZsmVVuXLlvOOVK1fW4cOH872Gx+ORx+Px2+czvApxhQa+YQBAkfm8wfcOh7pVKio8NERngrA3yzl8srStI0KXXXaZdu7cmfd59uzZio//30S4/fv3+wWjC0lLS1NsbKzflqktpvUMACiavVt+sruF84SGhCgshBVkYHMQ6t+/vw4ePJj3+brrrssbIZKkefPm6Yorrsj3GqmpqcrOzvbbEtXItJ4BAEVz4thJrZy3zu42cBEun3VbMLL11ti4cePyPT527FiFhuZ/i8vtdsvtdvvt47YYAASX5+99VTUaJuiyhgl2twL4sX0dofwcPnxY48aN05tvvml3KwCAS3B4/y+65/IxuuEv3dWkXUNVqFa+WNfxXBZdqPNiI8uoXtVKxarhOA6fIxTUQejIkSOaOXMmQQgASoHTp07rg2fnS5pf7Gv8+Gj7Qp3XqX5t/fP2vsWuA+ewNQjNmzcv3+O/nUgNAABMEKQDQuPHj9eECRP89jVs2FBbtgT2gShbg1CfPn3kcrlk5DMs53K5LOwIAAAEi6ZNm2rx4sV5n8PCAh9bbH1qLD4+XnPmzJHP57vgtn79ejvbAwCg1HMZhmVbUYWFhSkuLi5vK2hJneKwNQglJSUpPT39oscLGi0CAAAlh8fjUU5Ojt/2+0WRf2v79u1KSEhQnTp1NHDgQO3ZsyfgPdkahMaMGaP27S8+8a1evXpaunSphR0BAOAwhmHZdqFFkNPS0i7YVtu2bTVjxgwtWLBA06ZNU2Zmpjp27Khjx44F9OvbOkeoY8eO+R6PiopS586dLeoGAACYKTU1VaNGjfLb9/u1AM/p2bNn3l+3aNFCbdu2Va1atfTee+/prrvuClhPQf34PAAAMJmFKz5faBHkwipfvrwaNGigHTt2BLQnXrQCAACC3vHjx/XDDz/4vZM0EAhCAAA4WLA+NTZ69GgtX75cu3bt0jfffKO+ffsqNDRUAwYMCOj359YYECS8x4/b3YIlzmYdLPikAAuxoabX8orOUOPJbwp1XuWeJ6UCVpauPmW1Tp24+BNL52x9LblQNRFYP/74owYMGKDDhw+rSpUquuqqq7Rq1SpVqVIloHUIQgAAOFmQLlMze/ZsS+pwawwAADgWQQgAADgWt8YAAHCyIL01ZhVGhAAAgGMxIgQAgJNZuKBiMGJECAAAOBYjQgAAOFhRFzosbRgRAgAAjhWUI0J16tTRwoULVb9+fbtbAQCUUqNeu0dnzxS8BnhOu0oBqec1DI356rOAXCugHD4iZGsQeuGFFy64f8+ePZo+fbri4uIkSffff7+VbQEAHKDLgKssrXfa6w3OIORwtgahESNGqHr16goL82/D5/PprbfeUnh4uFwuF0EIAACzMCJkn2HDhmn16tWaNWuWGjdunLc/PDxcn3/+uZo0aWJjdwAAoLSzdbL0K6+8oscff1w9evTQiy++WKxreDwe5eTk+G0+g/c+A4CTFWbuD/6fYVi3BSHbnxrr27evVq5cqblz56pnz546cOBAkX4+LS1NsbGxflumtpjULQCgJNj93V67W0AJYXsQkqTq1atr8eLF6tSpky6//HIZRUiNqampys7O9tsS1cjEbgEAwe7w/l+0bmGG3W2UDD4LtyAUNI/Pu1wupaamqnv37lqxYoXi4+ML9XNut1tut9tvX4gr1IwWAQAlyLPD/qm0z8aqVpMadreCIBY0QeicpKQkJSUlSZL27t2rcePG6c0337S5KwBASfPz3kO6N+lB9bjjajVu20DRlcoV6zq5LcsHpJ+zvuAcEnH6ytJBF4R+68iRI5o5cyZBCABQLGc8ZzT/n4s0/5+Lin2Nba8lB7AjBBtbg9C8efPyPb5z506LOgEAwKEYEbJPnz595HK58p0c7XK5LOwIAAA4ia1PjcXHx2vOnDny+XwX3NavX29newAAoJSzNQglJSUpPT39oscLGi0CAACXyGdYtwUhW2+NjRkzRrm5uRc9Xq9ePS1dutTCjgAAgJPYGoQ6duyY7/GoqCh17tzZom4AAHAgh995CYqVpQEAAOwQ1OsIAQAAkzEiBAAA4EylckRoke99u1sAAKBkYEQIAADAmUrliBAAACikIF3fxyqMCAEAAMdiRAgAACczfHZ3YCtGhAAAgGMxIgQAgJPx1BgAAIAzMSIEAICT8dQYAACAMzEiBACAkzFHCAAAwJkYEQIAwMkYEQIAAHAmghAAAHAsbo0BAOBk3BoDAABwJkaEAABwMh8vXQUAAHAkRoQAAHAy5ggBAAA4EyNCAAA4GSNCAAAAzsSIEAAATuZjRAgAAMCRGBECAMDBDIN1hAAAAByJESEAAJyMOUIAAADOxIgQAABOxjpCAAAAzsSIEAAATsbb5wEAAJyJIAQAAByLW2MAADgZk6UBAACciREhAAAczGCyNAAAgDMxIgQAgJMxRwgAAMCZGBECAMDJeOkqAACAMzEiBACAkxk8NQYAAOBIjAgBAOBgBnOEAAAAnIkRIQAAnIw5QgAAAM5EEAIAwMEMn2HZVhwvvfSSateurTJlyqht27Zas2ZNQL8/QQgAAASld999V6NGjdK4ceO0fv16tWzZUj169NDBgwcDVsNlGA5/yQgAAA52bUg/y2ot8r1fpPPbtm2r5ORkvfjii5Ikn8+nyy67TPfdd58efvjhgPTEiBAAAAg6p0+fVnp6urp165a3LyQkRN26ddPKlSsDVoenxgAAgCU8Ho88Ho/fPrfbLbfbfd65hw4dktfrVbVq1fz2V6tWTVu2bAlYTwQhAAAcrKi3qy7F+PHjNWHCBL9948aN0/jx4y3r4fcIQgAAwBKpqakaNWqU374LjQZJUuXKlRUaGqqsrCy//VlZWYqLiwtYT8wRAgAAlnC73YqJifHbLhaEIiIilJSUpCVLluTt8/l8WrJkidq1axewnhgRAgAAQWnUqFEaPHiw2rRpoyuuuELPPfeccnNzdeeddwasBkEIAAAEpVtvvVU///yzHn/8cR04cECtWrXSggULzptAfSlYRwgAADgWc4QAAIBjEYQAAIBjEYQAAIBjEYQAAIBjEYQAAIBjEYQAAIBjEYQAAIBjEYQAAIBjEYQAAIBjEYQAAIBjEYQAAIBjEYQAAIBj/R+GJG55/qOnLQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 700x700 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
     }
    ],
    "source": [
-    "aligner.get_aggregate_alignment()"
+    "s = time.time()\n",
+    "aligner = Main.RefQueryAligner(adata_ref, adata_query, gene_list, n_bins) #\n",
+    "aligner.align_all_pairs() \n",
+    "t = time.time()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8a8ea807-5716-4b89-bc9f-1ef69f93f363",
+   "id": "suited-commander",
    "metadata": {},
    "source": [
-    "We can also visualize this alignment in terms of cell-type composition"
+    "To access gene-level alignments, use the dictionary: `aligner.results_map` which carries all gene alignment objects. <br>\n",
+    "e.g. `aligner.results_map['TNF']`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "68049aa6-be3a-4daf-8702-ba68b4044fee",
+   "execution_count": 11,
+   "id": "occupational-remains",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DDDIDIDIDDDMMMMMIIIIIID\n",
+      "\u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "\n",
+      "01234567890123456789012 Alignment index \n",
+      "012 3 4 56789012      3 Reference index\n",
+      "\u001b[91m***\u001b[0m-\u001b[91m*\u001b[0m-\u001b[91m*\u001b[0m-\u001b[91m***\u001b[0m\u001b[92m*****\u001b[0m------\u001b[91m*\u001b[0m\n",
+      "---\u001b[91m*\u001b[0m-\u001b[91m*\u001b[0m-\u001b[91m*\u001b[0m---\u001b[92m*****\u001b[0m\u001b[91m******\u001b[0m-\n",
+      "   0 1 2   34567890123  Query index\n",
+      "DDDIDIDIDDDMMMMMIIIIIID 5-state string \n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 400x200 with 3 Axes>"
+       "<Figure size 500x500 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -410,21 +377,33 @@
     }
    ],
    "source": [
-    "vs.visualize_gene_alignment(\"IDDDMMMMMMMMMIIIDID\", cmap=joint_cmap)"
+    "gene_obj = aligner.results_map['TNF']\n",
+    "alignment_str = gene_obj.alignment_str\n",
+    "print(alignment_str)\n",
+    "print(VisualUtils.color_al_str(alignment_str)) \n",
+    "print()\n",
+    "print(gene_obj.al_visual)\n",
+    "# Alignment landscape of costs (Note: dashed black path is the optimal alignment)\n",
+    "gene_obj.landscape_obj.plot_alignment_landscape()\n",
+    "# Note: optimal path diagonals represent matches; \n",
+    "# vertical and horizontal paths could represent either warp matches or indels (mismatches)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "60f221f9-e1c3-4894-8a52-335f8e8e52c9",
+   "id": "alert-silence",
    "metadata": {},
    "source": [
-    "We can also visualize an individual gene (e.g. JUNB), displaying its alignment statistics"
+    "Visualise alignment in terms of both the cell-type compositions, as well as actual and interpolated gene expression. <br>\n",
+    "Top left: Visualise alignmebt in terms of cell-type composition <br>\n",
+    "Bottom left: the mean trends and interpolated distributions of gene expression along pseudotime. <br>\n",
+    "Bottom right: the actual gene expression values along pseudotime. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "id": "9df2b2b2-83b9-4374-8ead-8f381bd8de16",
+   "execution_count": 12,
+   "id": "radio-mirror",
    "metadata": {},
    "outputs": [
     {
@@ -438,7 +417,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 400x200 with 3 Axes>"
       ]
@@ -448,36 +427,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1500x300 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "VisualUtils.show_gene_alignment('TNF', aligner, vs, joint_cmap)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d98f91f3-cab4-4c40-8f27-7e922421193e",
-   "metadata": {},
-   "source": [
-    "To check only the cell plots of a gene alignment (e.g. SERTAD2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "a06ae658-91a7-42d6-bacf-d4d4a37b8547",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
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gEODCCy/kvvvuIycnZ7inJBAIBAKBQDAkCMeTQCAQCAQCwXFGfO8nEAgEAoHgZEGkWAoEAoFAIBAIBAKBQCAQCIYEITwJBAKBQCAQCAQCgUAgEAiGBCE8CQQCgUAgEAgEAoFAIBAIhgSR8SQQCASCYce2bQ4ePEgoFEKSpOGejkAgEJzwOI5DOBymsrISWRbfNYNYawQCgWCw6etaI4QngUAgEAw7Bw8eZNy4ccM9DYFAIBh17Nu3j7Fjxw73NEYEYq0RCASCoaG3tUYITwKBQCAYdkKhEJBctHJzc4d5NgKBQHDi09bWxrhx49z3V4FYawQCgWCw6etaI4QngUAgEAw7qZKH3NxccTMgEAgEg4goKTuGWGsEAoFgaOhtrREF3wKBQCAQCAQCgUAgEAgEgiFBCE8CgUAgEAgEAoFAIBAIBIIhQQhPAoFAIOiRV155hUsuuYTKykokSeKJJ55wHzMMg69+9avMnj2bYDBIZWUlH/vYxzh48ODwTVggEAgEJxxirREITi50U2dj/UZWvL+CjfUb0U19uKckGEKE8CQQnKDYjo1u6sSNOLqpYzv2cE9JMEqJRCKccsop/PrXv+7yWDQaZc2aNXz7299mzZo1/PWvf2Xbtm1ceumlwzBTgUAgEJyoiLVGIDh50E2dRzc+yq9e+xUPrH+AX732Kx7d+KgQn0YxIlxcIDgBMSwD3erwxuyAYRtoioZH8QzfxASjkgsuuIALLrgg42N5eXn83//9X9q2O++8k9NOO429e/cyfvz44zFFgUAgEJzgiLVGIDh52N64nZU7V6ZtW7lzJadUnMKsslnDMynBkCKEJ4HgBMN27HTRqQO6paPICrIkzIyC4aO1tRVJksjPz+92H13X0fVjr+O2trbjMDOBQCAQjBbEWiMQnLgcaT+SeXsk83bBiY+4OxUITjAMy8jqcYFgKInH43zta1/jhhtu6LFV9Y9+9CPy8vLcn3Hjxh3HWQoEAoHgREasNQLBiU1JTknm7cHM2wUnPkJ4EghOMBzHGe4pCAQZMQyD6667Dtu2ufvuu3vc9+tf/zqtra3uz759+47TLAUCgUBwIiPWGoHgxGdq0VRqJ9WmbaudVMvUoqnDMyHBkCNK7QSCEwxJkkBoT4IRhmEYXHPNNezatYuXXnqpx2+gATRNQ9O04zQ7gUAgEIwGxFojEIwONFXjqllXcUrFKRyJHKEkWMLUoqloqvjvdbQihCeB4ATDo3gw7O7L6US4uOB4k7oR2L59OytWrKCoqGi4pyQQCASCUYZYawSC0YWmaid8kLhu6mxv3M6R9iOU5AjxrCeE8CQQnGDIkoymaBkDxjVFE8HigkGnvb2dHTt2uL/v2rWLdevWUVhYSGVlJVdddRVr1qzhqaeewrIsDh06BEBhYSFer3e4pi0QCASCEwix1ggEghMJ3dR5dOOjad35aifVctWsq4T4lAHJEYExAsEJie3YaUHiHsUjRCfBkLBy5UqWLl3aZfuNN97IHXfcwcSJEzM+b8WKFdTW1vbpGG1tbeTl5dHa2tpr6YRA0Bu6brJt2yHq61spK8ujurocTRPftQlOLk6091Wx1ggEgoEwXK6jjfUb+dVrv+qy/YtLvnjCO7n6Q1/fV8WnMIHgBEWWZKGmC44LtbW1PYbai+8vBCMJXTd56KG3eOGF99xty5bN4NprTxfik0AwghFrjUAwuhkKgWg4XUdH2o9k3h7JvP1kR3wCEwgEAoFAMGrYtu1QmugE8MIL7zFv3gRmzx47TLMSCAQCgeDkpT8CUXu8nVUHVnGg7QBjcsewcMxCcnw5Gcfd3rg9bUyAlTtXckrFKUPuOirJKcm8PZh5+8mOEJ4EAoFAIBCMGurrW7vZ3sbs2cd5MgKBQCAQCPosELXH2/n1W7/myc1P4lW8WI7FxdMu5jMLP0N+IL/LuMPpOppaNJXaSbVdxLSpRVOH/NgnIkJ4EggEAoFAMGooK8vrZrvIcxEIBAKBYDjoq0C06sAqntz8JAB14ToAfr/695SHyrl2zrVd3FHD6TrSVI2rZl3FKRWncCRyhJKg6GrXEyKJWCAQCAQCwaihurqcZctmpG1btmwG1dXlg3YMXTfZsGE/L7ywiQ0b9qPr5qCNLRAIBALBaKOvAtGBtgN4FS+N0ca07RvrN7K9cXuX56dcRx05nq4jTdWYVTaLpZOWMqtslhCdekA4ngQCwbCQ6srnOA6SJImufAKBYFDQNJVrrz2defPGU18fpqwsd1C72onwcoFAIBAI+kdfy9LG5I7Bcqwuzw95QxnL54Tr6MRBfEISCAQZGUphyLAMdEs/tsEBwzbQFA2P4hmUYwgEgpMXTVOZPXvckGQ6ifBygUAgEAj6R18FooVjFnLxtIv5/erfu9uWTVlGS7yl2/K5lOtoMBiKznuCJEJ4EggEXRhKYch27PSxO6BbOoqsCOeTQCAYsYjwcoFAIBAI+k9fBKIcXw6fWfgZynLK2HR4EyFviJZ4C6eOOXXIy+f603lP0H+E8CQQCNIYamHIsIxeHxdv7gKBYKQiwssFAoFAIBgadFNnf3g/EwsmMj5/PJZtUZ5bPijOo97cTH3tvCcYGEJ4EggEaQy1MOQ4zoCfKxAIBMNNKry8c8bTYIaXCwQCgUBwstGd4+isiWcNiujUk5tJN3X2NO+hIlRB0BukIdJAS7wF6Np5TzAwhPAkEJygDFUG01ALQ5IkgdCeBALBMKHrJtu2HaK+vpWysrx+B48PdXi5QCAQCAQnI0PpOOpp7KlFU3l046M8uflJdjbvBODsqrPJ9+X3mC0l6B/iU5JAcAIylBlMKWHItE3iiTiWY6FICj6vD1XO/i3Do3gw7O5dVSJcXCAQDBWD1ZFuKMPLBQKBQCA4GTnSntlZ1JPjqK9h4L2NvXLnSoLeIEWBIhqjjbyy+xWunHklcyvnUpVXxcb6jSJwPEuE8CQQnGAMdQaTR/HQFm+jTW/DtExsbGRkIkaEXC2XQDAw4LEBZElGUzRiZixtfFVR8at+ESwuEAiGDNGRTiAQCASC4UM3dbYc2cL2xu34VT8TCia4Qk5JTmZnUXeOo76EgaeEqcZYI1OKpqSV0KXGTolSsiRTGaokT8tDt3SmFE1h+ZTl/G3L30Tg+CAghCeB4ARjqDOYbMcmakSJmTF3m4WFYRqoskqBUzCo4pCMEJoEAsHxQXSkEwgEAoFgeNBNnYfffZgH3n2AxmgjkCxpWzRuEZdMv4SpRVM5b8p5bD2ylXAiTMgbYlrJtG672fVWmtdRmLIdm4Phg8wsnemW0NVOqu0ytizJhLQQIZLH3t26WwSODxJCeBIITjBSGUy2Y3dxDA2GIBTRI1iOhaZoJIwEtmMjSzJejxfLsYjoEfL8mbs69YWUY0uWZLyqN+2xwXBsCQQCQWdSuU7RqE44HCcY1JBlyX1cdKQTCAQCgWDg9KXkbXvjdp7e+rQrOgG8svsVSoIlbG/cTlVeFXua9/DSzpeORX2oPgwz85fqncvn8n35FAeLWXdwHQCWbbmiUcrNtL91Px+d99E0p9XUoqnUTqrt4mqaWjSV1/e8nvF8ByNwvK9lgqMFITwJBCcYkiRhmAYJK+Fus7AwbAOv4sUjZ5eRpJs6pmWSsBMgg4SEg4Nu6Xgdb6+Oq94YaseWQCAQdKRjrlNBQYBx4wrZuPEAlZX5yLIkOtIJBAKBQJAFfSl5g6RQlCkuJGJEOBI5wpH2Izyy6ZG0xx7Z9AgLxi5g6eSlXZ7XsTQv35ePg8Njmx5jUsEkXt71MgvGLHC/QIdjbqaAN5DmVtJUjatmXcUpFadwJHKEkmDJgMv/+kpfr9loQghPAsEJhiIraaJTRxJWgpAWymp8R3KSolOm8e0ENnZ24w9x1zyBQCDoSMdcp+bmKAUFAZYuraG6upwZMypFRzqBQCAQCLKgr93oSnJK0JSuokrQE6QkWOI6lTpzoO1Axu0dnUrFwWIe2/QYRYEigt4gAC3xFiKJSJd7o0yikaZqGUvnenJD9ZVMzqah7OA3UhGftASCEwzLtvAq3ozik1fxYtlWVt3nvLK3x8ezdVSluuYJBAJBJlJlcfX1rZSV5WUtDHXOdWpujtLcHOWMM6aIQHGBQCAQCLKkr93ophZN5aJpF3XJeJpcOJmpRVO7HWdM7piM2zs6ldYdXMekgkkEvUHX4dQQaeCM8WewsX6j+5z+ikY9uaH6QnfOpnG549L261wmOBrL7oTwJBCcYDiOg0dJij/heBjTMVEllZAv5G7PBk3VCHlDhBPhLo+FvCF8Hl9W43sUD4bdfbndYJyDQCAYWgZbHOo4bqoszrYdIhGdJUumcP75s5g2rWJAxygry5xJ15dcp6E6z+M1vkAgEAgEQ01fy9E0VeOaOdcwp2IOOxp34FN9aVlL8yrm8akFn2J93Xq8qpftDds5s+pMFo5Z2O2xOzqVXt71ctpjLfEWbpx/I+dOOXdAolGmY/SX7pxNN5xyg/t7pjLB0Vh2Jz7dCAQnGJIkEdNjacKQ6Zg0x5oJeUN4fNkJN4qskOvLRZZlonoU27aRZZmAFiDHm5N18LcsyWiKlrHGW1M0ESwuEIxwOopDKZYtm8G1156etWiSKouzbYeDB1toaAjz/vuHicUMJkzYy7XXnu7u11exprq6nGXLZnSZb2+5TkN5nsdj/JGMENwEAoFg9NCfcjRN1Til4hROqTglbbtu6jy97Wne2PsGLfEW4macD079IDfOu5EcX05WcxhO8aY7F5dlWz2WCY7GsjuxygsEJxiSJGV0IwGEE+GsOs7BUcdRB0NSqmte2uNZ4lE8KLKSFjTuUTxCdBIITgA6ZialeOGF95g3b0LWpWupsrhIRKeh4dj7XDSq88IL7zF37njWrdvbL7FG01SuvfZ05s0bT319mLKy3D4JHf05z4EIKUN5HUcyJ7PgJhAIBKORgZajdcw+Anjp/ZdQZZXiQDEAG+s3sj+8n/xAfr/msKd5DzEz1q+SuqGiOzdYeW45Z008q9syQRicznkjCbHCCwQnGLqh9+gY0g0dr9JzTlNvxBIxWuNHc1EkMByD1ngrCgp5vuyErc44jpPMfRIIBCcEnTOTjm1vY/bs7MZOlcXpeno5biCQ/PC6e3dDj2JNdwKQpqnMnj2uX/Pr63mmhJTVq3dTXBwiGtWZNWssl146j1Co+9LkobyOI5mTVXATCASC0UzncjTd1NlYvzEtULujENUeb+eprU/x3uH3CHqDmLbJwfBBKkOVWYkv6+vWu66n53hu2EvWenNidVcmCNl3zhtpCOFJIDjBsBwLVVFBgoSZcEvhvKoXVVZxskzujhtx4lYcv+rHtEwcHCQkVEUlbsWJG3EC3kBWxzAsg5gZw7RM11GlKip+1S8yngSCEU42mUm9kSqLe/zxNe622toa1/0UiyUFqYKCgCvyBAIaDQ3tg+6k6et5btt2iNWrd+M48MgjqwB4+ul3aWqK8JnP1HZ77KG8jiOZk1VwEwgEgpOF7gK1UwKQbur8ef2f+f3q37uP33DKDbTF28jT8tK60PVHfBmsTnGZutB1Fq4y7ZOaQ2pbVV4Vu1t3My53HDeccgOWbVGeW95lvMHonDfQ8zieCOFJIDjBUCQF0zJdx5MkSzg46KYOCkhqdu6hSCKSHFeS8KhdRaBoIpqV8GQ7Nu2J9rSufBYWhm1g2RZ5vjxRcicQjGD6k5nU3xK0VFnc7NljeeONHTQ2RmhoCNPcHGXZshlMm1bG668H0kQegPz8AEVFwUF10vT1POvrWykuDqXNB+CZZzZQW1vT7bEHmj11onOyCm4CgUBwstCbALS9cTvPbX8u7fEXdrzAeVPOY/2h9YRICk/9FV/62l2vJ3oTzbrb5+yqs/F7/O552Y7N1OKpNEYaaYm3uOOcNfGsLuJPtp3zBnoexxshPAkEJxiaR0OPdi2zA9AtHc2T3ZtJyjHlOE7S8XS0FE5V1GRJXJZVcbqpp4lOHUlYCXRTx+/xZ3cQgUAwZPQ1M2mgDiRNU5k3bwIzZoxh27a6tGMAnHbaJH7yk3+4+xcXh3jrrZ1MmFCYcbyBOmn6ep5lZXlEM7wna5ra47EHmj11onOyCm4CgUBwstCbAHSk/Qiakn6/cjhymIA3wL+e8a8g0W/xRTeT63BDtAFN0dLykgbbNZVpn6e3Ps28ynnu75FEhL9u+itXzrzSFZ56cl9l0zlvoOdxvBG2AoFgiLAdG93UiRtxdFPHduxBGddxHELeUMbHQt4QjpNdqZ2maJiWScyMYTgGJiaGc6w0ziNnVwpnmMlSGdM2adfbaYu10a63Y9pmcrtlZjW+YPB55ZVXuOSSS6isrESSJJ544om0xx3H4Y477qCyshK/309tbS2bNm0anskKhhRdN9mwYT///OdWQOKss6qZPXtsRrGkuyyfbdsO9elYqVymZctmuMfQNJXi4hCTJ5cydmwhkyeXUlmZjyxLxGKZ3zv666RJneNzz23k1Ve30dDQ3qMoVF1dzqxZ6a6m4uIQwaDW67E1TaW6uoKyslzq61vZtu0Qut71PFJzeuGFTWzYsD/jPicKKcHt9ts/yMc+toTbb79ABIsLALHWCASjhe4CtVMCUElOCUFvkKJAUdrj4/PGs3jCYpZOWsqssln9Ep0e3fgo/9j6DyYXTmZn804Ohg9iO3ZG11Qqf2rF+yvYWL/RFa2gb66pTPvolk7ESFaN2I5N1IjiU314ZA/5vvyM4wwlg+H+GmzEKi8QDAGGZaSHfztg2AaaomWdYeQ4Dn6vH4/qIZ6IuxlMPq8PVc7+P2m/199t2LckSfi92bmRHMkhlogRToSxbAvbsZElmagRJeQN4VeF22mkEYlEOOWUU/j4xz/OlVde2eXxn/70p/ziF7/g/vvvp7q6mu9///ucd955bN26lVAos0gqOPHor4NpqLJ8KivzCYV8dH5pTZtWhq5n56RJnePzz2/i4MEWGhrC1NbWIEkwf35VxnPVNJVLL51HU1OEZ57ZgKapBIMa558/s9dj9+WajsYucAMJexeMfsRaIxCMDnrLLJpaNJUPTP4AL73/EnlaHrqls3zqci6edvGAysA6unvyfflcMeMKomaUpROXsnjC4rQxeytB6000g8zCmqZoBD1BbMfmYPgg9e31RBNR9rXtQ5EU8n35tMRbjltgeF/O43hzYn5iEQhGMLZjZ+w4B0k1XJGVrDKMJEkCB2QpGSieKoUbrFwkx3HI9+XTqrdi2RY4gASKrJCn5WXtqFIkhVa9tYswh528dsXB4qzGFww+F1xwARdccEHGxxzH4Ze//CXf/OY3ueKKKwD4wx/+QFlZGQ888AA333zz8ZyqYAjprRtZ5zyn0tLMbp9ss3y6K9WaPn0M06ePYe7cceze3Yhl2eTkaKxcuYXKyvw+lbGlzjES0d1A85Urt3D11Qt7zIsKhXx85jO11NZO61fZXF86vIkucIKTBbHWCASjg94yiwY706iju6cl3uKWtiHRZczeStD6EvSdaZ+Lpl2E3+Nn7cG1NEYbUSWVZVOWsb1hO4cjh7ly5pXMrZybdWB4XxmqwPJsEMKTQDDIGJbR6+PZhLp5FA9RI5qek3TUUeVVvAQ82XWcSzmqFEUhokcwbRNVVglqQbyKN6uxIXn+juNg28lSxFRXPk3VcGSn1+vXF2zHdo8jSRIexSMCy4eIXbt2cejQIc4//3x3m6ZpnHPOObz++uvd3gzouo6uHxMf29rahnyuguzoycFUXd3VlbN06XQuumgOTz/9rrttMLJ8espG0nWTdev2uV3mVq7cQnFxiMrKfM4/f2avLqHUOep6+vtQKsOpt8ym/rp4+uIKE13gBAKx1ggEI5XuOqf1llk0mJlG/XH39FaC1hdRrLt9APweP2/tf4ugJ8jh9sPkeHPwKB6mFE3h4pq+OboGoxvdUASWZ4sQngSCQSZbR9BwI0kShmkQM2KYtolt25iYxIwYElL2GU+2gSIpxM2466iybRvHdAh6gliOld34Q1jmKOjKoUPJvJ6ysrK07WVlZezZs6fb5/3oRz/iu9/97pDOTTC49NSNLJMrZ8WKzdx22/nMmFE56OHZ3Yk8qXlMnVrmdplraAiTl+fvk0sodY6alv5eEQhoRx8f3M5rfenwJrrACQRirREIRiIjpXNaf9w9fRGp+iKKdbfPhIIJPPHeE+7vIS1EiBDTSqb1WXQarGs62IHl2SIsAALBINNdPtJgYVgGHsWD3+PHI3tQZRWPfPR3xZO1Y0iRFcLxMM2xZiJGhJgVI2JEaI41E46HUWQlq/Et2yJmxQh4AgQ8Afxev/vvMSuWVbh4qszRdmwSZgLd0EmYibTtgqGh8+s+5Tbrjq9//eu0tra6P/v27RvqKQqyJFXi1pGUg6mjK8e2HcLhOA0NYbZurae6uiItIHwoSc2jc5e5VBh3Q0N7jyHdqXMMBjWKi4+2c66toaEhPCSd13q6pv3ZRyA4WRBrjUAwcuiubG174/bjOo+Uu+eLS77IDXNv4ItLvtitUJMSqToymCVo2Y7f2zXtKRh9pCMcTwLBIONRPBh29+LPYISLp/1+NFx8sNBNnbgZx3EcTMt0P9SpikrcTHboU70Df+tIlbw5OCDhZkilziMb4c6wDAzLIG7Gu8zdp/ow5OzKHAVdKS9P3vweOnSIiooKd/vhw4e7fDPdEU3T0DTxtxhJdM5o6uxO6qnELeXKsW3HDeWGpAPpoYfe6lcQtq6bbN58gK1b6/H7VaqqSpg2raJPz0/NI+VQ6jj3goIAGzfuZ9WqXe72ziHdHc+xrq4VRZGQZYXi4pxu3Vq9Xbee6Oma9mcfgWC0I9YagWDk0V3Z2qG2Q+7jAy0VGzC9FJ4MVglaTyWG2YzfUyngSHGYDRTxqUUgGGRkSUZTtIwB45qiZZ01lCqF605cybYULpaIAUdzkuxjOUmynJx33IgT9AYHPL5H8RBQAjTGG7s8VuQryuqN07ItIomIW8aXmrtiKVi2hU/1DXhsQWYmTpxIeXk5//d//8e8efMASCQSvPzyy/zkJz8Z5tkJ+kpfO6d1V+KWcuU8/vgaV3RKOYW2b6/PWOKWSbABeOCBN/nTn15PG2fJkil86EPzexVbUvNYvXo3tbU1bsZTMKhx2mmTeOaZDQBEIjq6bvD442uYM2csc+dOyHCO4wbtuvVEX7KhRBc4wcmOWGsEgpFHprI127FpiDbw0IaH3G39FUf6m3HUX0EmmxK01Nxe3PEib+x9g6A3iCzJacfLZvyeSgF7C0Yf6QjhSSAYAjyKB0VW0sreBivgWpEVIolIxnBxy7YIadm1FDacZL6TbusgHbO165YODpj2wEvhINnVTlM18n35SXvoUcdT6o06G/eWYSXnnhLlbGxkZFRFxXEcEmYCv8ef1fxPRtrb29mxY4f7+65du1i3bh2FhYWMHz+eW2+9lR/+8IdMnTqVqVOn8sMf/pBAIMANN9wwjLMW9IeBdk7rKB7NnTue3Fw/b731PoGARkNDmObmKNA1CLujYFNQEKC4OERhYZCamnJefXWbKzpBMiC8pCTEtm2H+jSXceMKqawsQJLg0kvnYds2FRX5HDzYApDmyAJ4/fUdTJ8+ZkAOItFxTiAYPMRaIxCcWGTKVlo4diGrD6xO2++l91+iMlQJ0KuQNBBXz/ESZFJz29+6n8c2PQZAUaCIylDloB2vp7yq1/e8nvE5qWD0kY4QngSCIUKW5CGxPabELNux0109R7OXDMtAlbMohUNGt5Pd5hJmAsd2kGQJr+pFJ/s6Yo/iQVEUcuQc/Ko/bf6pDnQDxXTMpChnHxPlLCwMMynKWYHsgstPVt555x2WLl3q/n7bbbcBcOONN3L//ffzla98hVgsxmc/+1mam5s5/fTTef755wmFshNBBcePgXROy+T2Wb58Fvv2NSPL6QJy5yDslGBTUBDAceCRR1bhOMn9Jk0qprg4hyNH2klV3kajerdzSZXmvfbaDpqaIq7glcl5FInoaaITQGNjpFdRqztExzmBYPAQa41AcGKRqaysKdKUJjzZjs3B8EFW7FpBXVsd0LOQNBARqbdOdd3RX2dVam4VoWPlvo3RRvK0PEJaaFAEoJ5K9frTva83BqNzXn8RwpNAcIKRsBJISMkMJtPEciwUSUH2yEhIGJaRlavHI3vAhuZosxvMLUsyXtVLUaAIr+LNav6yJBPyhggnwqhK+ltQyBvKKrzcsiwkScK2bGJGDMu2UGQFv8ePpEjJLnqCflNbW9tjt0ZJkrjjjju44447jt+kBIPKQDqnZXL7vP32Tk4/fVKXHKXOQdgpwaa4OOSKTvG4QTSa4MUXN/OBD0xn375mfD4PkpTMbMo0l5T49fjja3j//cNAsjSvoCDQxXlUXV3OkiVT3P1S+zY0hKmra3Hn1Z+cJtFxTiAYPMRaIxCceHQuK9tYvzHt8UgiQmO0kaAnGdOR78tnf+t+ntzyJDUlNV0Ej4GISAMRZAbirErNrXPkiG7phAgNSADKRHelev3p3tcTw5UVJYQngeAEw7EdokaUqBElYSRc4ckkWQKX483JbnzJwcZOlqvZpis82aaNjZ0MBc8CSZLwe/0oikIkHnHHD/qCWYtaDg66qdMca07O/WipXdyMI8tyjx9oBYKTmVQ2Uuesop46p2Vy+zQ3R5k1ayy1tdN6DMJOCTap7nOWZZNImMgyFBQEkWWJRMLE41GOzqOsy1x03eTVV7fx4INvkUhYOA54vQqrVu3iuutOo60tTkNDu7u/pqmcf/4sYjGDaFR3ywEbGyMcORLmz39+I+3cu8tp6lheWFqay9Kl01mxYnOfr1vnMfobSC4QCAQCwUilsziiWzoXVV+EpmpUFVQR9AZ5YccLvLnvTYoDxV0Ej/6KSKmubrPKZnXJXOpJkMnkrOqpJDB1nIpQBUFPkHMnn8uL778IJDN8+yoA9eQ06s2FNFjB6MOVFSU+5QgEJxgODm3xNtribUlXz1Hhye/xY/pMigPFWY1vWAYxM4ZX8SIhueKNR/EQM2Pp2VIDwKN4iBpRDMvA6zkmNBmWgYREwBMY8NiSJBHWw+hWslQwJWpZjkVYDyPJg9f9TyAYTQykc1p3bp/i4hxmzx7bY6lZSujasyfZZMC2bYqLc4jFkqXE8+dXMXFiCWPHFjBnzrguXe1STqfNmw+yf38zwaCGbSeF5YMHW9i48QBvvbWT/PwAixdPcZ87bVoFEybsTRPYTj99Em+/vdPNmopGdfbsaWTr1jrmzEkPGM9UXnjRRXP40pfOp7Ex0qfrNhiB5AKBQCAQjEQ6iyMKCs9sf4an1z9N0BOkLlzH2RPPRpEUElaCJzc/Sb4vnwkFE5haNLVfrp6Ozp18Xz5zK+ZSEChg0bhF1JTU9CjIdHZW9VQSCPDoxkd56f2XOBg+SGO0kYuqL+KGOTfg9/r7dLzO8+14bh2P0ZsLKZvg8hQDLU3MFvEJRyA4wbAci7Z4G/WR+mTp2NFw7rZEGwA2dlbj66aOZVkk7ERSvLFtHNnBkRw0SyNhZic8pTBtk3gi7opDPq8va8dTwjw2Z93QXdFM82juNoFAkJlMndN6cuYMxCXV8VjXXns6W7cexO/38tJLm6mvb8UwLGpra9i06QDNzVEuuuiUjNlLqTK/qVOTbdQTCZP8fD/79jUhSRKaplJcHOKtt3ZSW1vjjpFJYGtoCLNjR72bNZXC7/d2EbwylRc+/fS7zJgxhmXLZvThKg9NILlwUAkEAoFgpNBRHFlft551deuA5D0MwJt73+T6Odfzyp5XaIw28tb+t3jivSdcoaWvrp6Ozp2WeAst8RZogiUTlvQqAnV2VnUuCYRjLqDUv8uSTGWokjwtj3WH1vGvZ/wriycs7rPjqCenUerfMz022C6kwcyK6g/iU4lAcIIR0SO0J5LlIxISjuS4neDaE+20x9spChQNeHzTMl2RRjd1HJLja6pGUA1mnZNkWAamZdKutxPRj5XamY6JV/ZiWMaA64tNO5l5pVs6hmO4wehYEHAC2E52opxAcDLRmzNnIC6pjmiaypw545k2rZJzz53OG2/soLExPRy8qqqYDRv2dxFUUmV+DQ1hamtrWLlyCx6Pgt/vZenS6cRiBpWV+ciy1CXou7PAtmHDfjdrqiOvvbad5ctnpYlBgxEmPtiB5MJBJRAIBIK+MNiB0n0Zryna5Io1USNKe6IdVVJp09tojCZdzymxp6PQ0hexJRvnTqaSwLOrzqYh0tB1rA5JHbIkE9JChAhh2Va/rmeP8+0mDWQwXEid/05VeVWDkhXVX8QnEsGIxbZtDCPpXpFlGY9HRpbl4Z7WsBM1olhYeGUvunVMGPLKXiwsYkYsq/ElSaI90Y4jOXhUjzu+Izmu4JUNhmVwJHKENr2NhJVw/74JO4FlWwS8ATQGtgjato1pmck5SxIoR8U5HEzLxLTMrOcvEAwHw+Fo6YszJ5NLqr9omsq8eROYMWMM27bVuSJWVVUxjz++OqOgkirza26OUlAQ4OqrF+LxKEQiOm1tMTRNoaqqmGhUR5KS16+7vCbLssjJ8REMaiQSJoZhUVwcIhjUuohBgxEmPtiB5EPhoBIIBALB6GKwA6X7Ol5JTokr1gS9QQzboDHaiFdNVjl0Fnv6I7Rk49zpXBKIA//Y+o+ka6oPY9mOTUO0gYc2PORu63z+nQWfgcw3WxdSd3+ny2ouyzorqr8I4UkwIjEMC1033N8ty8YwQNM8eDwD73o2KnCSYkqb3pbsaoeFgoJu6xQECpDJTpxTZAWv4iXhJJLiTQe8ijerrnMA0USU5lgzbXobjuPgOEmRSJKSXeeKA8VdukX0GRkMx0CWZJBwx5YlGcMxRMaT4IRkuBwtA3XmDFQky+RE6k5Q6Vjm19wcPVqWN4do1GDduj1u2VxxcYh9+5o5cKC5y/XqeF2nTi2jtTVGYWGQ8eOTopMsSxQXB9McV1VVxQMuL0yRTYliJgbbQSUQCASC0cdgB0r3dbyOzqJUqdpl0y9jTO4YAp4ADZGGNLGnP0JLtl3eOpYE6qbOwfDBbsfqfJyFYxey+sDqtPE6nn8mwefsqrNZPnU5z21/rk/HGAwX0nAFiWdCCE+CATNUjiTbttNEp47ouoGiSCe180nzaiSMBAkzgWEZrvBkY5MwEng8nqzGlyWZfH8+dswmFo+RsBN4ZS9+n598f35S1MmCSCJCm96GbnXIWzpqL23T24gmogOfOzJBNUg8EU/mPR0t40uVCSrSSS5aCk5IsnW0DFQIGogzpy8iWV/n07OgMjZjmR/A+PEF/PznzzN5cqkrIGW6Xh2va0NDmGXLZrBy5RZKSkLIssTSpdPZuvUQTz/9btq5XH75/AGXF8LAgtx7YrAdVAKBQCAYfQx2oHRv43V0+6REjqZYk+uugeTn/h2NO9zn9ldo6W+Xt84OJMu2aIo2uWVyPY3V+bGmSFMX4anj+WcSfF7Z/QqfP+Pz1JTW9OkYg+FCGq4g8UwI4UkwIAzDIhbTMU0b23aQZQlVlfH7tawdSYaRzOE5Jmwlx08JW4Zho2kjX3iyHRvDMlzXjUfxZC3aAHjlpDVVlmVUSUV1VJBwx/bIAxOeookoWxu28ta+t1i9fzXbG7fTFGvCo3jI0/Io8BcwIX8CNaU1VBdXUxGqoDK3kpAW6tdxUoJTyu3U0fEEkLAHHl6uKioexYPjOFi2hemYqJKK4zjJ638SC5aCE5dsHC3ZuKUG4szpTSTrz3w6Ciq27RCJ6Oi6kVY6l6nMz3GSnfU60/l6dbyuHUv2xo0rZP78KhzH4f/9v2e7OZfsywuzHSPFYDuoBAKBQDD6GOxA6Z7G62sZXkpo2dO8h5gZG5C7p69d3jp3wHNw2HR4E5WhSmRJdufX3ViaqrnzS4k5+b78bkvzuhN8mmJNLJ20tOfJdpP3NBCGK0g8E0J4EvQb27Zpb4+j6+l5OYmEhWU55OX5s7rBTwpOVtr4lpUUuzRNRVVHvmvFsIwujh7DNtAUDY+SnSPJdEzyfHkYjoFhGsccZ6qHPF9erwHajuNwOHKYzYc3s/nIZt47/B6bD29md/NunA7vdLZh4xgOSkChJd7CntY9rDu0jr9t+VvaeDneHFeESvtnqJKK3ArKc8rTFhlVSr7tWJZFRI+4jqqgFgQVFAb+91XlZFihiYmsyHhsD5IsYWLSprcNWJQTCIaTbBwt2bilBuLMOXiwhXA4jq4baJrHdRylRJ/+zCclqDz//CYOHmxxg8T//ve1GUvnUvT1enXeL1Wy96EPncrs2WN54YVNGccZaSVsg+2gEggEAsHoI9uytP6M15/yrvV16919n+O5NIFqMMPQO86pOFjMY5seAyBPyyOkhXotP+ssptmOzdTi5LVLiU8dr2d/BZ/BzuBKMdh/92wQn0pGMUNVCqfrJrqe7HxmWcccSYoiu4/5/d6sj2EYCSKRBIZh4fEoBIPJMX2+kS0e2I6dLjp1QLd0FFnJyvnkOA5BbxDTNkkoCWxsZGS8qpegN5gmPJm2yc6mna7ItPlwUmhqijWljWlFLBJ1CfQDOomDCfSDOsZhg9wzcin+UHGP82lPtLO9cTvbG7d3u09xoNgVpXK1XOJGHMMyCKgB/F4/OZ4cvHEvE/InENACA742CSuBLMnYlk3cjLtuKr/qR5bkbv8uo5Vt27axcuVKDh8+jG2nC5Lf+c53hmlWgv6SjaMl2/yf/jhzdN2koSHMjh2Hj64NNqWluYwbV+iKPv2ZT0pQqaws4MUXNxEIaG7Hu57Es75er+rqci66aA7bttUTjeoEAhrV1WXufmVleRQUBCguDrmPNzSER2QJ22A6qASC/iLWGoFg5NPfsrS+cErFKeT78ombcaYUTaGmpAZN1fpc3tWTQDW1aOqgCjEd5xRJRNx/1y2dEKGM8+tprrIks71hOx+Z+xGQ6HI9exN8OotqqXPvyGBkMQ3F332gCOFplDKU4dy6bmKaVhfHEyQdSYmEhd+f1SFoa4vR3BzpsMWgtTVOQUGQvLwsBx9iDCtzPlXHx7P5j11TNExMAloAzaMl7ZhSUnR5r+E91tat5UD4AJsPb2Zrw1YS1rHSNcdxcOIOsj8pfNm6zb7/tw+rxco814aez6WvNEQbaIg2sKF+Q8bHJSQCngAF/gKmbp/KxIKJlIfKKc8ppzxUTkWogtKcUlS557eseCKOZVtIcrLLX0p0leRkcHnCGHgZ34nGb3/7W2655RaKi4spLy9PC4qXJEncDJxAZONoOZ75P9u2HeLNN9/njDMm8dxzSbfQnj2NLF8+i6qq4gHNJ3mODgcOtHR5rDvxrD/XKxo1WLNmj1u6N3ZsoftYVVUxJSUhHn54lbvtmmsWuuciSGc4Oi8Khh+x1ggEJw59LUvrje7cOTUlNUDf3T69CVSDKcR0nFPHJkaa0qEDXw/lZ5nmmmpmlKl0rifBJ9P1m1U2K2Pp3mBkMQ3W3z1bxCeCUchQh3M7jpNBdEqNb5KT03OpV28kEgmi0czOlGhUxzCMrB1VQ4njJMvVbMfGtEzXdaMq6qBkPPk9fkLeEFsatrDmwBr2tuxlX+s+DkcOp8/DdEgcSrqXEgcTSSdTnY5vvI+KT1cAIGsyHP1zqYUq3kovWqWGd0zyn0re4Jc1Oo6D1Zp0WKkFKt5yLw4OESNCxIiwv20/K1jR5XmyJFMSLHHFqM7CVHlOOXEzjo2NYznEzTimbaLKKkEpiI2NZWcW2EYj3//+9/nBD37AV7/61eGeimAQGKijJeX+Wb16t+vcmTVr7JCIJ/X1rezf30x7e5yLLz6FRCIp5kgS7N7dwOzZY3t0I3UnXPRVrOr6/Apmzx7X7Xy3bTvEihWbCYV8hI5G1a1YsZkFC6qYPXssu3c3sGXLISZPLnWFqS1bDrnnIjjGcHVeFAw/Yq0RCE4+eiul62t5V08C1WCHYnecU0OkgbOrzmbT4U2uCNVb+VnnudqOTSQRIZqIsrF+Y0YXUXeCT6br98beN5hXOa/bzKjRgPg0MApJhXP39Hg24dwdv83qZo8Bjw0QiZgoiozf78E0j52LqspIkkR7u0HuyKt0cJEkCcM00pxGqYwnr+LNOmdIUzXe3v82P3/1525ZnW3YyJ5jf9ODdx8kvifuikodMQ6ni5IVn65AzVNdF1SKfF8+ZTllmLZJa7yV1ngrht0/B5RjOuj7dBJ1CRKHjv3YseTE8mrzKLqoqE9j2Y5NfXs99e31rD+0vtv9gp4gAU8AvydZwleaU8q0omlIstSH127fGKrg+MGkubmZq6++erinIRhmNE3l8svn09oa45lnNqBpKvv2NWNZ9qCLAmVleei6QX19G/X1be72q69e6LqTunMjAd0KF30pnRuI8NFb2V99fStFRUGmTStPK7UbaRlPI4FsOy8KTlzEWiMQnHz0Jgr1tbxrIPlDAxViMs3Jsq20Tns9VaR0nGvqnmTBmAVsqt/ExvqNTC6czCXTL+lTVUum6xf0Bsn356dtG64spqFCCE+jkM719Z1JOXIGiscjo2kqsVgiQ1c7L15vdi6Z1PwkScpYFijLgxj1PwQospIuOnUgYSX63QWuI47j8Nt//Jbv3vVdYjtj2DE7KeIoMP5r44/tKAE2yH7ZdS+l3Eye0nThK1gZpLq4mpqSGqaXTmds7lh0U6ddb8cwDSwsFBRURSWoBSkOFBM1oxxsO0hduI6D4YMcaD7A+9vfp+79OqSgRKA6mdNkhk0O3n2w64nI4CnxoAQG31GVck6leK/hPV7f9zqzS2czLn8c1aXVWY1vWAYRI5Is63MsFEnB5/UR9ASzDo4fTK6++mqef/55/uVf/mW4pyIYZnbvbmDVql1pnd6GQhSori7nggvm8Nvfvuxuq62t6ZKLlMm9tWHD/jThoqAgwJ49jTzxxBpmzKjk8svn91g6NxDhozcnVWlpLo4DjzySLLXzeBSWL59FOBxjw4b9J1wp2VCWwmWbJSY4cRFrjUBw8tGXUrq+lHf1JFBNLZrK2VVn8/re1ynNKSVhJphTPoeqvKoBzzubkrOOc916ZCv7Wvfxwo4X3IqTs6vOpqa0hlllszKGokPS6bSneQ/NsWZmls3kQOsB1+EkSzKLxi1iyYQlw57FNFScOJ+YBH1GlmUsq3vxKVvXh6qqeL0q0Wgc0zSxLBtFkVGU5HZFyU5M0DQPXq9KPJ7AspwO4eUSPp8XVR05N/eZsGwLr+LNKD55FS+WbfWaVQRgmiaKorBjxw7eeOMNfvzjH7N169bMwqKUzGuSjzrZii8vRtZklHwl7e+d78tneul0ZpTMoKa0hhmlM5hUOAmvcqx0sb69nj1Ne5AdmbgSx7EdJFnCp/gozilmbO5Y1r62loMbDrJhwwY2bNjAli1bMIykG+rCSy7ke//+PQ6GD3Kw9SDf/t9v4yvy4a3wYhfZJIoSeEu9SOrguI86YkWsZED6gWRQeui0EIHqAAkrweq61Xz6iU9z2tjTuP6U61k+dXm/38xtx6Yl1kI4EU7bHjWjGF6DomDRiHE+TZkyhW9/+9u8+eabzJ49G48n/b+bf/3Xfx2mmQmONwMRBVIiRUNDGNt2ME2bysr8HsUKTVO56aYzKSgIsmnTftchNH9+Va9B6B3nWFAQcAWfsWMLKS7Ocd1L3ZXO9XaOmUSX3pxUtm2zceMBAPdLkEceeQe/38vjj685oUrJhroU7nhmiQlGFmKtEQhOPjq6f/J9+RQHi8n35WPZFrqp9+vzdUoMSok1r+95ncJAIYqs4OBQEixhxc4VKJLChvoNmLbJNXOuGRZBJjXXPc17eGD9A2mPvbL7FZZOWpoxFH351OXYjs3b+9/mld2vgJN0OC0av4h8Xz5NsSYWjl1Ia6yVwmAhi8cvHlWCU4qR/2lJ0G88Hhmjh4oojye7G2NFgUTCxHEkQEKSZEDCcSQSCZMsdSdyc700NqaypCwcx0aSZPx+xX18JOM4Dh7FgyIryYwnHCR6znhqbm5m/fr1rFq1itdee43du3ezY8cOGhsbOfvsszl06FD6ExRQ81S0cRq5i3PxVnhd0QnAW+5lfP54ZpTMSApNpTOoKamhIlTRq/BoWRaWY2EnbPZu2cuurbvQfBoXXn0hlmPhOA5XXXUV8Xg87XmhUIjZs2dz2vzTmD9mPvOZD8DNu29O229Hww6e2/oc2xq30aa30Z5oJ2pEiRpR103UprfRF8w2k/CqMPr+pNhkNqdnj3mKPa77KsXb+9/m7f1vU+Av4IqZV3DdnOuYVDipT8eLG3HCiTCO42BapttRUFVUwokwQW+QgHfgXfkGk//+7/8mJyeHl19+mZdffjntMUmSxM3ASUR/RYGUSLF69W4cJ+kcKiwMUFKSy6WXzuOGGxZ1K1aEQj6uvnohp5wyts9B6LpuIkkSY8bkEwho+P0e/vjH1wHc52XjXupJdOkphLyhoZ3Kynzy8vxEown27WvC5/O4GYSpOVVXl4/4UO2hLIXTdRPLsjj99Ek0NUXczoN97bwoOLERa41AcPLR0f3z4o4XeWPvGwS9Qd458M6AOs91DNu2HZuD4YOcPvZ0HBwe3vAwXsWLT/URMSI8vfVp5o2ZN6xh2TEzlnF73IxnzG96v+l9EmYiKToBSMnOeluObOET8z9BJBFh9YHVrD6wGsiue99IZmR9MhIMCrIso2mejAHjmubJKlgckhlRlmV1cSTZto3Pp2IYNmoWryxVVfH7PbS0RElONTlfXbcoLPSgZjP4cUCSpGSnuaOkhCc42lWuQ6njt7/9be69916OHMlcK71582Y+8IEPsGXHFnbLu6EEgrODeArSv1EclzuOqoIqqourWTh2IYvGL+p3Sd/DDz/M2rVreXvN22zauIn6g/XuY5NrJnPF9VckXzsyXHbZZciyzOzZs92f8ePH98lNl+fPY2rJVPweP63xVrfzXJ4vj7H5Y5k3Zh453hwOtR/iUDj5s3X3VtavXc/2TduRSiWohqZYE7Zu0/xsc9r4apGKNkZDG6vhn9p9B8TmWDO/e+d3/O6d33H6uNO5/pTrOX/K+T2+yUcSEUzLJGbG0E0d27GRJRlN1fCrfqKJ6IgRnnbt2jXcUxCMEPqSkdSRlEgxdWoZf/zj6yQSJpGIjmU5/OlPrzNv3jjmzp3Q7fH6E4SeEoWef34TBw+20NAQ5oor5uPxKOTlBQgGj/332JNDq6dz7E106W6uZWV5yLJEKORD1w1UNbkWBQLH5lRX18LatXtGfKj2UJXCdRT1bNshEtFZsmQqn/zk2UybVjGiroFgaBBrjUBw8nKk/Qgrd61M6wy3cudKZpXNQpGVtFKznj5fdxRrIokIjdFGXtr5EpfUXEJ5Tjkzy2ZiOzY+j4+2eNugdHrLhqlFUykKFNEYbXS3FQWKmFI0JWN+UyQRIW6mf2GfikROWAlefP/FtIey6d43khGfCEYpHo+CJDlEoya2bSHLCoGAiqpmn6kTiyWIx5PfcIbDBqZpoKoeQiEP8bhJPJ5d1zldN/F6PVRU5BOLxbEsB0WR8Pt9eL0qum6O6K52HsVD1IjSHmtn+9btbHx3I5s2bHL/+fbbb/PA/z7A7373O/bv39/l+Tk5Odx1112cc845jB8/nh/++odc++C15EfyMx6vdmItl9dcniyBVLxMKZ6SUXRyHIe9e/e65XGxWIzvfe977uP//u//zpYtW9KeU1pRSlV1FdWzqtFNnRxvDn6PnwcffHDA18e0TGRHRpVVQlromCNMVpEdmVgsxusvvM6aNWtYvXo1q1evTnN8XXvttTz4qwfRTZ26tjo+9/7nKJ1YSsHEAmKFMfbF99EQbSCcCHd9k++Gt/a9xVv73qLQX8hVs67i2jnXUlVQ1WU/y7EI62Fa4i3EE3FMx0SVVHxeH/m+/C6hgCOFjrlpgpOP7gK9ewvdbm2NkkgccxFalk1DQ5itW+t7FJ76Q0oUkmXJdRclEiZlZXkUFgaR5WOv2Z7Ktno6x4GKLlVVxSxcOJFnnnmXpKsXli5NZlalUBT5hAjVHqpSuI6iXkqke/fdfSxfPmtYRaehzLMSdI9YawSCk4OUQ2nrka0caEuWpBcFiqgMVQLw5r43eWf/O+7+vTl4Ooo1upV0FSuSgiqrTCycyHPbn0OWZWRkLqi+YNg7vdWU1HDDnBt4euvT6JaOpmhcNO0iakpq2N64vcv+QW8wY3MpTdHwqb6MxxhucW0oEKvwKMUwLCKROPG4iWnaqKqMbasEg/6Mgd39Hbu5OUpjYxjLsnEckKQE7e0yRUUOBQXZOT4SiWTL+2RYuS8tvDx1fH/3RpZh569//Svf+9732Lx5M6Zhdnl83bp1tLe3s3//fmRZZsqUKdTW1lJbW8vixYvTnEOH2w9z06M3dfvms3DsQi6uuRhTMpOlX7ad5rZ64IEHeOWVV9iwYQMbN26kre1YCVswGOSOO+5wHXDXXHMNhw8fZsq0KZRWlZI/IR+v3+uWk9mOjU/xoUjZvX7iZpyEnSDoDdK8r5ldm3chyzJnnncmCTtB3Ixz1VVXYZrHrp0sy9TU1HDqqady/vnnA0mbb1VhFU8/+rS734G2A7y842Xq2+sxLZNDkUO8d+Q93m96H8uxep1bU6yJ/1713/z3qv9myYQlXDfnOpZNWeZmYDmOw5HwERpjjeiG7oaLax4NwzCozK3M6toMNn/84x/52c9+xvbtyUWwurqa22+/nY9+9KPDPDPB8aY/LqSUSOH1pn9EUJTke4XfP3g5ex1FoZRwEYnofOAD03n33X3uYx0dWt2JCt2dY+p8Uo4cXTfQNA/FxcFu56XrJo8/vpodO+o544wpWJbNxRfPYfPmOlpaYu6cPB6FqVPL0rreNTdHhzVUeyB5VgNlpIWK67rJ5s0HeO21HV3K/kaaC200IdYageDkIuVQmlI0xd3WGG0kT0uuty2xlrT9e3PwdAwrT7mnElaCwkAh7x1OrlsSEl7Fy5YjW7Ds3j/TDyWaqnHNnGuYN2ZexlD0zp36JhdOxnZsWvVWt9yuKFDERdMuYkJB5i/yhltcGwrECjwKsW2b5uYI4fAxt4euQySSIJGwKS7OyarcLpEwaGhoJZGwsW3bbSkvyzINDa2MHZv5m9W+4jgOpmkRiyWwLLtDuHiya16WTfmyntu+fftYt25d2s8999xDdXU1zz77LP/7wP+y4d0NXZ77hS99getvuJ5ZM2YxZ/YcLr74YhYuXEgwmPnmpzXeyo2P3sje1r0ZH59VNosPTfsQe7btYc/2PezZtoemg0384U9/cPd58MEHefLJJ93fVVWlpqaG2bNnM2fOHBKJBD5fUmn/7ne/C0BYD/N+4/u06+2E42HX1eORPRiOMeDXzs6dO1m9ejX/98r/8c6ad9jx3g7CLUnnwKQZkzh7+dlJwU2GK664gkAgwKmnnsr8+fM55ZRTur1OHXFsh4JAAbqlkzATBLUg1UXVWI7FntY9vHPgHXa37O7TfF/b8xqv7XmN4kCx64ICCMfCHG47jO50KLWTNLx4Me2uQuNw8Ytf/IJvf/vbfP7zn2fJkiU4jsNrr73Gv/zLv9DQ0MCXvvSl4Z6iYISSEilefnkrtbU1rFy5heLiHBIJk9raGqqqigftWJmcOM3NUT75ybNZvnxmF/fSQEKyq6vLWbp0On/60+uuW6m2toatWw8xffoYgC5CTUcnT3Nz9Og/I1x22alH551LVVUx99//qtv1LjVuQUFg2EK1B5pnNVBGUqh46twff3wN77+f7DKU+nuMRBfaaEGsNQLByUfKodQQaeDsqrNdMUW3dGon1rK/tWtFR08Ono5iTdAbpChQxMzSmdSH6wlpIQr8BSiyQo43hxxvDk2xpiE5r/7QXYe87jr1Acwom8HSSUuJm3GmFE2hpqQGoItQVTup1n3OaEIIT6OQWCyRJjp1JByOEwx6CQYz2/r6gq4bWBbE4wlMM+V4SjqUFMWXMVuqP3g8MpGITiymE4tZbqmg369g29k7qlLYto1h2G7GkMcjdyuqrFq1iq9+9ausW7eO5ubmLo+vXbuW999/n8997nNp26dOm8qCRQuYv3A+533wPMZVjsPr8VJTU0NNTU23c4smonzyr59kW8O2Lo+1r2tH3aGyvnE9T+9+GstMV/0bGhqYUJRUz6+++mpmzJjBnDlzmD17NtOmTcPr7blM0bIsTMukIdZwbJtjocd0fKovc1e9Dti2zY4dO9i3bx/nnnuuu33ZsmVdsiAUVWHclHFMnjU5adGXwKt6eeihh3o8RncYtoGmapQES4gbcVcY8nl81JTWcMtpt9AQa+Av7/6Ff2z9R8bOg51piDbwm7d/w71v38sp5acwKXcSmqwRt+OYtokqqwSUACFPiNZIK+PyM3fdOt7ceeed3HPPPXzsYx9zt1122WXMnDmTO+64Q9wMnKAcjxKiVNna7NljWbt2D6edNpEjR8Ioikx1dRnTplUM2tw7O3Fs2+H00yfR2NhOcXGIs86qTju/gYRka5rK/PkT2L+/Kc2Z9PTT7zJ9eiXr1u3tItSMG1eYNoZtO+zd28T+/Y3Mnz/RFafeemsnxcUhV9BauXILX/3qhcclVDvT9RxontVA6Y+Taqhfu6lz7/gZZOXKLVx99cJhd6GNZsRaIxCMLlLd5XrKZ0o5lFriLeT78rlixhVEzShLJy6lJKeEX732qy7j9uTg6SzWFPqTXe32NO9hzcE1BL1Bt0FTvi8fHFjx/oo+5Uf157wGi+5EqVllszJuzyRUjbZgcRDC06gkEkneTFuWia4fK7XTNBVFUYlGjayEJ9t2AAfDsDEMwx3fcTyAg21nX9sfj5s0NETQdQPTtFBVBU3zUFKSfUYVJMv1wuHI0Ywqk2g0yt69W9m+fSsbNrzLunXruPnmm/nMZz6D4zgcOHCAFStWdBnnmmuu4ZZbbmHu3Lns27eP8847j4WnL2T6vOnMnDuTQE7AdYSpikokEem2ljdFfUM9N959I++sfYfEoQSJugTlnyhHCSTPPdgQZNdbxwScQCjAhCkTmFA9gdmzZlOYc+yGaSA296gRJWJGyPXmEk/E3fn7vD4iZoRoIkqeP/ktt2VZbN261c1jWrNmDWvXriUcDpOfn09TU5NbNrh48WKKioqomV1DycQSiiYXUTGpAtWrIiERM2PkefPwKAMv45FJBn3HjTiyLCM5UtKNdzQAXJZlFoxdwIKxC/jW0m/x+KbH+cu7f2Fn085ex3ZwWHdoHesOrUNTNCqCFZQHywl6g8SlOKqi0hbrWze+40FdXR2LFy/usn3x4sXU1dUNw4wE2TIQt89AjtFRHLj66tPYvftI1i6ZTHO/6KI51NSUU1ycw4UXznHfa954YwerVu3KeH4DLe06fLiN7dvru2zfvbsho1Bz441L3N9t23GDz/fta+all7awbNkMN3w8lU2l6yaaplJSEhrykq7uXgudBbMUQyW69DU/7Hi8dlOvDU1LX0NSXQhTLiyR/zS4iLVGIBg9dOwulyJTPlNHh1JLvIWWeAu1k2pZPGGx+5y+OHg6ikGFgaTYpKDQFG3Csi3G5Y2jdlItz21/Dk3RGJM7hqJgEX9e92dXiOpLB7i+ntdw0Z1QNdoQK+0oxHGSrqdIRHe3JRLWUcFJIzd34KITJHM4LCspOum6hW07WFay/s2yNGQ5u1o4XTeIRmO0tUWx7WQXOMOw0HWDYNBz9J/ZCGc2hw+Hee+9bfzkJ3ewdesm9u/vWs729ttvM3/+fM4//3yamrpaOqdMmcKyZcuora0FID8/n+eff55IIsKB1gMk7ASGc/SbVwcM08CyLQr8BV3GeuKJJ/jv//5vNmzYkDFwPHEogX+SnymFU/jEFz7By3NfpryqnMopleSX5aNKKpqikRfIo7Ao841HX9FNHdu2SdgJZPWYAyyWiHF472HGLxzvbvvQhz7EU0891WUMn89HTU0Nzc3NFBYm5/OnP/0JSZJoijWx9uBaGiONxPU4ppF0DamqiqM4WXWF83l9xI04cTuOg+PmU8XtOJqh4fMce90U+Av4xIJP8PH5H+ft/W/zl/V/4bntz/XJBaVbOrvbdrO7bTf5vnwq/BXJ1qhGZMBzH2ymTJnCww8/zDe+8Y207Q899BBTp44+++7JwEDcPv2hJ3Fg9uzunXwdb+RLS3OxbZuGhva0m/rOcy8oCLBhw35eeWUr//xnMhcmPz/AaadN7Pb8dN1EkiTGjMlPy1OC3ku7uisJi8UyO3Qty3adPJGITkNDmNraY8HiHcWpVDZV6GhPh4qK/B7nMhh091roKJh1ZChL31LZWtXVydfBP/+5tYugM9SvXTj2Nw4GtTQXWiCguS6s4yGAnWyItUYgGD107C6XIlM+U3flZCkRpy8Ono5ikO3YHAwfZG7FXHyKj6e3PU1psBTLtlg0bhHnVJ1DU7yJmaUzeXbbs6iy2uP8OrubLNvq03kJhhaxyo5CVJU00akjkYhOFvFOAHi9ComEiWHYOE4yg0mSJAxDIpEw8Xiye1m1tsZpaYnh83lc4SmZISXR0hKjrS1OYWHXrm2dMQyDLVu2pGUxLVmyhNtv/xpNTWH8fj8vvviMu38gEESWZWbPns3tt/8bCxYsIBQK0dTUhM/n47TTTuOMM85g8eLFLFq0iNLS0szHNQ0kScJxHCzbwrIt6g/U8/7W99m1dRd7t+9l86bN/OlPf+LUU5N5IQcPHuSZZ47NRc1X8VZ48ZZ7k/8s9VIZquT+q+9HlVQmzpnIofAh9ISOntBxFAdZTnaKS5i9Cyc9IUkSRsJgx5YdbN+0nR2bdvD+e8m5J/QEa7evhaLkvrNmzWLFihXMnTuX+fPnu5lMNTU1qKraZVxIuqS8kjcpcMk2kiRhSza6qeOVvJjWwHOSVElFURQSZgLLstwySkVRUPxKxmB0SZI4fdzpnD7udBqjjfx101958N0H2d28u0/HTH3TczBykOVTlw947oPNd7/7Xa699lpeeeUVlixZgiRJvPrqq7z44os8/PDDwz09wQAY6iDngYgDHW/kCwoCOA5s3HiAysp8ZFli2bIZXH75fN577yANDWE0zeMKA/X1rbz44mY3xPzw4TZee20HF144xxWUUudXXZ08zvPPb3LdR6n8nvnzq3otbeuuJGzatDKefrrr/hUV+ZxzTg3z5o1n9erd7NvXnCZ0FRQEiEYTnH76pC4h1sejzK6710JHwSzF8ZhTb4JOT6/dlGCVrQOp49845UJbsmQq558/k2nTKtA0lQ0b9o+YLoSjxXkl1hqBYPTQsbtc2vYM+Uw9uXT64uDpKHJFEhEao428ufdN5lbOdbe16q08ve1prpx5JXVtdeBAzIh16d7dcX6Z3E0Lxixw4zd6O6/hortSwONZIjjUnHgrnKBXJEk+Kg51Tfz3ehUkKTvlybIcPB4Fw0ig6zaWBYoCmmbh8YR6zQDqjXhcd4+TSJhuqZ3Xq6KqErqeWVQDSCQSfPazn2XdunVs3Lixy76WZXHTTZ/jf/7nbtaufYdAIIdotB2AaDTpVrFtuPzyy93nrF+/npqaml6zkSAZPm7YyW/QX135Knf/7G52bttJtD3aZd/169e7wtN5553HFV++gjdjb+It9yL70/9GRYEi/nj1H6kIVbC/dT/tsXYiiYjrTjIcAxOT9lg7Zl7/hJt4PI6qqq5QdO+v7uXnP/45RqKrEyCQE2D/3v3MnTIXgG9961t8//vfR1H6XgIZM2NEzAhF/iISVsLtDOdVvETMiNtGdSAYloGMjCqpmI6JQ1K4VCUVGbnX8O+iQBGfXvhpPrngk7y5703+sv4v/N/2/3P/pj3hV/0knOxEv8Hkyiuv5K233uI///M/eeKJJ3AchxkzZvD2228zb968QT+eaZrccccd/O///i+HDh2ioqKCm266iW9961tZNTMQHGOog5wHImx1FKuKi0Nu0HZenp9QyMfzz29CUWR27jzC/v3N7n5jxxag62baa0OWZSzLdkujUpSV5brH6VjatmPHYb785eWceWZ1rzfs3ZWEAd0KNSknD0i89NIW9/GUwPb73/+TYFAjEtFZsmQqn/zk2a7AMdR091roKJgNZoh4b/QmWnY33+Li4KA5kPpS9jdSuvCNJufV8VxrxDojEAwtHbvLpW0fgg5rHUWu1Gd/y7HcL9Atx8J2kveUqYqCoDeIbumESBeeOs4vk2urJd5CJBHpIliNlM5x3ZUCXlZzGX/b8rcRWyLYX06s1U3QJyRJorAwSHNzhFjMwnEsJCkZzl1QEHRbYg+URMImkbBQVRXbNlGUZNc5VVVJJCwSieyEJ1VVsG2bI0dasSwH2wZZBkWRKC3NpaHhMNu3H3MxFRUVce+99wLg9Xp56qmnqK9PZnnk5OQwbtw4xo4dy3XXXceCBQtwHIf77/8NbW0tACiKyuTJNUyfPofZs+dx0UXnpc1nzpw5Xeao6zpbtmzh3XffZcOGDe7Pz3/+c867+DzCeph2vZ2NazYmz8mjMmHyBKqnV3PavNM4bf5pnHbaae54K1tWsq58HT66lhDmeHO478r7mFiYLEHRTZ3meDOGZbgin23bGJZBc7yZqNFV5EoRi8VYv359WibTxo0befXVVzn99NMBqCirwEgY5OTmMHnGZKbMmMLkGZOZOmsq06ZMY86YY9ejL53mOpMwEmiKho6OT0k/X03RiCcyB+P3Bd3SiSViSUHLtrCwUByFhJUgloihm30TtWRJZvH4xSwev5iGSAOPbXyMB999sNsOgwBT8qfg2MPYcjED8+fP589//vNxOdZPfvITfvOb3/CHP/yBmTNn8s477/Dxj3+cvLw8vvjFLx6XOYx2+hPk3F9SZWwdXUmynHQp9iRsdbyR7ygY6bpJKJR02W7cuJ9oNOF2yGtoCOPxKBQUBNLWI0WRmTix2M0pCgQ0qqvLqK4u55//3Oru17G0zXHo8416SkjqLDD0JlZ0vu7FxSFWrNjiurpCIR/vvruP5cuT3+5u2LB/yF0sPb0WujvPoaQ3Qae7+cqyPCAHUnduod7OfaR04TsepYfHk+O11oh1RiAYWjpmN6Xob4e1vjp0SnJKsB2bSCKCZVuYtoksyXjV5Bf9iqS4DqWgJ3m/0RBpYPnU5aw+sLrb+WVybTVEGjhj/BlsrN844PMaSrorcawMVY6qEkEhPI1CFEVBURQ0TcO2dWxbRpYlNE1DUbJ3PCUSxwQPxwGQjv4z1Skuu5byfr9KOKxj2xKJRDJc/Lnn/pfdu99j3773aW9P/4A7ZkyyHbZpmrz77rssW7aMPXv2sGvXLg4cOMDmzZsJh8N84hOfAGDnzkN86EMfRpIUpk6dzaRJ0/D5Ang8ylFxq8wd23EcEokEmpZ8w3znnXe48cYb2bp1K5bV1VH27rvvsuziZeimzrTZ0/j6L77OhOoJjJ84Hp/mw6f6qC6upihY5D7nkQ2P8OOXf5zxWmiqxv9c8T/MLJvpbjMMg4gZwZEcFPWY08jBIWJGMDNc/2effZbbb7+dzZs3Z5z3+vXrXeHp0ssuZeqpU/GX+LEd2y11VGSFfH8+ZKmtqIqKLMv48JGwEq711at4kWTJXXAGQsJM0G62k7AT2NjggCVZOLaT3D6AMsTiYDE3n34znz7t0/x85c95YssTHIocStsn15NLvi/fXRiHi7a2NnJzc91/74nUfoPFG2+8wWWXXcZFF10EQFVVFX/5y1945513BvU4JzN9DXLuLyn3xerVu5kypYyVK7dQXByisjKf88+f2aOw1fFGPhA49sEyNSddNwgENA4caKGgIMDVVy8kGtU544wptLREicdNVq5MuommT69g1qyx/N//bULXLTRNZezYpAhVXJxDOBxH1400YWwwBIPexIrO172xsZ2tW5PvAR3ndPhwG2vX7jkuLpahei0MlN4Ene7m21FQ7EhPDqRs3EJDKd72h5HivBoow7XWiHVGIBhaestu6g3d1Hly85O83/Q+kUSEoDfI5MLJXDL9ki5jVOVVMbV4Kn/d9Fc8sge/6mde5TzytKN5fd4gPtXHzLKZNESSnbbnVs7lsprLOLPqzG7nl8m11RJv4cb5N3LulHNHZOe47kocD7QdyLz/CCoR7A9CeBqF+Hwyhw7pGEayPOhotA6GYRCNQmlpTlbjSxKYpk0sZhztOmcedT85BIM20P+uduFwmHffTXaTe+21N9m5cz9XXvllN7T8vfdWs2fPZiAprE2fPp2ZM2eycOFC5s2bh+M4LF26lFdffTVt3FRm0+LFi0kkEni9Xrxeleuv/xQNDRESCYtIxCYWi5BIRGhrO8Dq1Q3s27eLDRs2sHHjRr7yla/wrW99C4DCwkLeey/5gTU/P5/Zs2cze/Zs5syZw+zZs5k1axYxJ4YiKXj8HhYuW4jlWMStOCSSir3lHBN+ntv2HN94Pj2QM4Uqq9x1yV0sHLswbbvlWORqubTqxz64xtpj7Nm6h/od9Ty842F2bt7Jt7/9ba6//nogGfa9cWNS5S8tLWX+/Pnuz6mnnsq4cceCg/ML8pnmn4ZhGUT1KA4OEhIBLZBVx7kUQW8Qv+ynKdGUzGE6GgBuOzaFvkL8Hv+Ax7YcC93Qk0JZp/I/3dBdy+5AkCWZ08eezt6mvRwMH6RBb+Bw9DAJK0F5sBzbsQlqwys8FRQUUFdXR2lpKfn5+W6uVkdSQmImATIbzjzzTH7zm9+wbds2qqurWb9+Pa+++iq//OUvM+6v63paKWxvNy+CJNm4WbpziXR0X3QUh5Ytm9lrGVvHG/nDh9s466yprF+/D0h2g7vggjns2JF0oDY3R2lujlJQEEDTVIqKcrjssrksXz6b9vY4hYUB/vCH1wmF/G5Q94oVm5k/fwJbtx5i3LhCV6QqLg7x0Y8uPm6CQcfrvmHDfp58cp2bNZWivr6N55/f5DrFYGhdLMPhbOqOvgg6meY7EAdSNm6hkSLYjRTn1UAZrrWmv+sMiLVGIOgv/emw1tndBPDmvjd5Zfcr7j5nV51NTWlNlzF3t+6mMdLIFTOuIGpGKfQXoqBQVVjFORPPwbItSnJKUGSli1g0y9f9/HpybQ2W0DTYuUvdlTiOyR2Tef8RUiLYX4TwNAqxrGQpQjgcxzAsLMtGUWQ8HoVAwEu2nwFUVSIajdPc3I5pOjgOSJKJquoEAh48fdQmfv/73/PMM8+wbt06duzYkfaYJElce+2X8Hg0HMfh7LMvo7FxAV6vArSzefMG/vrXv/K73/3OLfc69dRT2bBhgxsAvnjxYk477TRCofR6XsMwaG1tJZFI3hg1Ntbx4x//Ky0tDRnnuWnTJvffq6qq+Mc//sHs2bMZM2ZMxg9b7a3txMwYcSuObus4joMpmWAl841SbrHX9rzGrU/fmlEMkZD42QU/Y+nkpV0e09Rkd7aGfQ3c96v72PneTg7tPdRlv1WrVrnC04IFC/j73//OqaeeSmVlZcZ5u8eWJbyKF8dJdphLfXhUZTXpSurhuX0h5AslHXmqRoKEO75X9aIoCiFf78Hx3eJArpZLOBHGtExXNFMVlZA3i3GP4lf95Pvy0Q2dgBZgXHAcrUYr5b5y8n35BDwD78g3GLz00ktuF8EVK1Yc12N/9atfpbW1lZqaGhRFwbIsfvCDH7ivwc786Ec/4rvf/e5xneNopreg4p5cIh3dFylxCPpWxpa6kZ89eyxvvLEDXTepqiqhqamdWbPGcuGFc3jyyXVpwlZJSYj77nvVFWhS8/jnP7emiTYpdu9u4Omn300TxQIBjfnzJwyLw6e6upzTT5/Eb3/7sruttraGrVvraGqKAE6aK+tEcbFkw0AFnYE4kLJ1C40EwW6kOK8GynCtNf1dZ0CsNQLBUJEpl6imtIYtR7ak7ffK7ldYOmlpl65ze5r34Pf4kSSJWCLGprbk/dasilksndT1/qevZOva6o3u8piyyV3qTixbOGYhB8MHsyp9HEkI4WkUEosliMeT5VaS5CDLyX8CxOMm8biB3z/wcibLsonHTdradPbtayYeN/B4FHw+lebmGPn5QRIJiUBApb5+Hzt2bGHDhmQW0t///nc8R5WpV155hUcffdQdd8yYMcydO5fKyknk5Y2luDiPVav+yYoVf2fnzs1uCHhHNmzYwKJFiwD4wQ9+wH/+53+6AZO2bbN7925eeumltBymbdu2sXTphdxyy7eJROLk55cSDrcAUFpawZw5czjttPmum6m6uto9nizLXHDBBT1eH9MxiejJEDwZGUdKih8AET1Cwk6wrm4d//LEv5CwMpd+/fu5/86l0y+loaGBNWvWuJlMtbW1XPfx6/CFfSSUBK8/+7r7nOKKYqbPns6Zi87kzNPPZOHCY06pnJwcLrnkkh7nncKrJF8btmNjWqYb/p26rtm6nhzHocBfgOVY+FSfKw4pskKBvwDHGXgtn9/jx+fxYdgGBscCwT2qB58nWeqYFQpU5lfSGmvFcRxs1aZcK8cv+anMr0TOMj8tW84555yM/348eOihh/jzn//MAw88wMyZM1m3bh233norlZWV3HjjjV32//rXv85tt93m/t7W1pbmvBN0pTtxqS+lRz25RLJ1X2iaiqoqvPnmzrTtBw60MGfOuDRBQpKSodzRaMItUXv++U09ziMWS/633FEUAzh8OJxx/6FC1022bq1j9+4jBAJePv/5c2loCGNZDocPtxGLJWhoCBOPJ+ebKlc8UVws2TIQQWcggtWJ7haCkeO8GijDtdb0d50BsdYIBENFplyiFe+vYGrxVA5HDqdtj5vH8ltTws2Tm59kZ3Pyc8PZVWeT78unJd4yKG6e/ri2+kum8153cB2VoUqAATmgehLLhlJEO96cGCucoF/oukEkcqwzXMrxpKrJoFddTwADKwmKxw2ef/49HntsDbt2NdBZI9D13dx33w5M8wiG0QCk26sWLfo3ysurCIV8tLYWcfbZH2b8+CkUFhYQDh9m377NjBt3GpIU4sCBNvbu3cfGjckuSZrmY8qUGZx55hIuuWQ5ixYtoqgomZXU2NhIU1MTU6cmFeBEIkFpaSmtrZm/GT18uI6mpnbicQPDsPjyl3/F2LHjqamZwPTpFYwdWzyg6wNgWza2YxM3dBKGgW05yIqE5bHxyB52Nuzky89+OWMIuJ2wmbl7Jo+//jjfWv0t9u5ND7NOJBJ88uZPElACTBgzgZtuv4lxU8YxefpkSopLKA4UM2fMnKwEFo/iwbIt4mbc7ShhY2ObNl7Fm7XwlLASBLUgiqLQHmvHtm1kWSbHn4NP9WFYxoDL7UK+EH6PH9M20RTNFbVkWcbv8ZPjy67MVHZkEnaCicUT0S0d0zFRJRVN0UjYCcguV39QefbZZ8nJyeHMM88E4K677uK3v/0tM2bM4K677qKgoGBQj3f77bfzta99jeuuuw6A2bNns2fPHn70ox9lvCHQNM3NThP0Tk/iUl9Kj3pyiZx1VnXW7oueXShjXUHiuec2UlfXmlaiVlwcoq6uhXPOqck4j2nTynj66a5jH0+hQddNnnhiNa+9toOVK7dgmhY+n4czz6wmEtFpbGwnEtFZtmwGTz21HoCGhjCXX37qiHKx9OaMGw76K1id6G6hFCPBeTUYHM+1pr/rDIi1RiAYKjLlEgW9QWS6duaeUjTF/T0l3AS9QYoCRTRGG3ll9ytcOfNK5lbOHfFuns7nne/Lx8Hhv974L4oDyfvHgTiguhPLhlJEO94I4WkUYpo28bhOY2MU2z4WDi3LMkVFAUyzf3fHjuOwbt1eHn54FU88sYbW1liHx0xsO4aiJMuYEol6YrFjHQMkyYOqFuPxlODxlLB/f5yDB3diGPUkEnUkEgd59dW/YtvHxly1qp1AYObRc9HJza2lsHASU6ZUU1NTzrRpPg4dOsyPf/xjt6tcXV0dS5YscTOevF4vpaWlxGIxpk+fnpbFFAqVc+BAnIMHm9F1E8tyKCwch2UptLREMYzsahEtLCRLoT0cw7ANN8MoIZsYhsn33vkeDYcb0PfrJA4kkH0yeWclv8G9aeFN/PQHPyUaPSZKTZkyhVNPPZX58+ezZMkSDNOgKFCEiclHPv0R15Hk9/opChRhmEZWwpNhHXMKpdxHHV1IhmWgygN/65AcCdMyMS0Tj+pxX5+mZWJKJpI68FI+TdUo8BegyioJM+Fee6/qJaSFsv6GwOvxEvAGaE+041E8eDgmwgW8AbzegTsJB5vbb7+dn/zkJ0DSGXjbbbfx5S9/mZdeeonbbruN++67b1CPF41Gu7SzVhTFLS092RjsG/yexKW+lB715BIZDPdFX10oqiqniU6QFGgURe52HsCwCw3bth1i27Z6Vq7cguMk19nm5iivvrqNxYun0NQUYe7c8SiKxEc/uhjLsvH7PUyeXHrc5tgb2YRyjyROdLfQaON4rjVinREIRg6ZcolyvDksGLOAA20H0C0dTdG4aNpF1JTUuPukhBtZkqkMVZKn5aFbOlOKpnBxzcUj3s3T+byLg8U8tukxJhVMcredyJ3nhhKxSo9CHMeipSVOJBJ3u8KpqoLX60FRZJw+BiwfOtTKY4+9w8MPr2L79vqM+0iSim3HXeFJ0ybgOKYrNClKPrbdDsgoStJlFY/vpLn5751GUvB4SvF6K/B4jrmNVLUAVS1A12HTpkOsWPEDLCtzeUUkEkn7fcWKFZSWlrqlfSnee28fa9duJpGwsCwH206KKomERX19K7FY/zufpeFIhOPtqKoHx3Cwsdn4+gZ2bN7Bm2teo31vO1b4mLjlKfWQd1Yel8+4nG8v+zbO7Q6hUIj58+czd+5c8vPz04ZvijYhKRIlwRJ0TXeFG03VkGQpLbx8IOiG7pbBJZyEK2x5VS+WY5EwE1kFgCuKQjQRRbePhX3iAHayPDLfnz/wsSWFkmAJqqzSrrdjYaGgkKPluIJUNgS8ASpyKjgUOYTpmMfyrySV8mD5sGc8dWTXrl3MmDEDgMcee4xLLrmEH/7wh6xZs4YLL7xw0I93ySWX8IMf/IDx48czc+ZM1q5dyy9+8Qu3m+TJxFDc4PckLvVF9OnNJZKt+6IvLhRdN2lri7F48RReeWUbiiIjScl8pNTNZHfzGG6hob6+lWg05SS20XUTWZbw+byUl+czY0Ylq1fvQZKgtTXuhqBPnlxKQ0N4RIg72YRyDyaDIcqOFrfQaOB4rjVinREIRg6Zcok+MPkDXFJzCbMrZvep65wsyYS0ECFCTCuZNuJFJ+h63pFEhKJAEUFvejXRidp5bigRwtMoxDAgkTBoa4thGCnHk4XHY+LzeUgkus/QiccNnntuI488soqVK7e4okxPKMqxmxuPp+ToHOoIh3eQSNRh22Fyck4nFDrj6D7lyHLwqMhUiddbjsdTiiT1/nJU1UJs28DjKTrqpCpGVYtR1SISiQI+8pH/Zt688Zx66gTmzh3fRXSCZF6IJEm0t8fQdRvbdpBlCU2TCQY1dN3sdR6ZcByH3bt388yLz7J++3pOu2gRcTuObTs8eu/D1O/pEAAuJQUnbayGNlZj2eRl/PiDP0aWZO64445ejwPJN2uP7HFdPbKUvHGz7OyEJ8MxsGwL3dJBPppThZP85gINwzZ6H6QHJCQcMr+uUqVxAx5bSgaJ+71+bMfGsR0kWcLv9aMq2b/d5Wg5FOcU4/V4CethHMtBUiRCWohcLXfYu9p1xOv1us65F154gY997GNAsjPjUHT1ufPOO/n2t7/NZz/7WQ4fPkxlZSU333wz3/nOdwb9WCOdobjB70lc6mtHsaEUb3obPyXG7dnTyPvvH2bp0hpkWaK0NJeGhjDFxT2XwQ630FBWlkcgkPxAnHJX2LaD4yR/tm49xPTplcRiCR55JFkeXlwcIhjUhkXcyUS2odyDwWhxXQmOcTzXGrHOCAQjh57yhwbade5EoPN548CBtgPufViKE7Xz3FAiVvlRiGUlw8UVRYIOdbaKIh0NHU8XDhzHYe3avTz88Nv87W9r00rp+oIsa9h2jObmp0gk6oHOwo2EbR8rHVOUAKWlnxpQd7T8/IuRJDXjc5ubI7z00mZeemmzu23SpBLmzRvPvHkTOPXUCcyYUUkiYWKayW5/uAJIMgvLNC0Sib4JT7t27WLVqlWsXr3aDQBvamoCwKt5mXv+qViKjW7reGd6yCnNQRujoY3R8FZ6kb3Jv83pY0/nvy75rz67cTRVw4yaxMwYuqm74oqmavjVZLh2NsiSnBSdMqBbepc31v5i2AYBbwDFVLp0ntNUDdMemPAHoMgKkUQEwzLwqsfK3gzLIJKIENKy62xXECigMdqILCe/oUk5nhRZIcebQ0FgcHOTsuHMM8/ktttuY8mSJbz99ts89NBDAGzbto2xYwf/BjgUCvHLX/6yx7bWJwuZbvALCgLs2nVkwC6PnsSlvopKAxVvOoZqx2Im06aVMXlyGbt3N3Q5n+7GT4lxBQUBpk+vTHMEjbQcpExUVRUzfXoFF1wwm5Urt2LbEUIhH0uWTKGhIUxzc5RPfvJsNm8+yNixhWia6na1g6EVd/rqIBoJodwjxXUlGDyO51oj1hmBYGQxkPyh0RCY3fG8dVMfVZ3nhhIhPI1CEgkHj0dBVRVsO1lOpigSqqrg8SjEYkmxpa6uhcceW83DD7/Njh2HexkVHMfGNBtJJOowjDpCoXwuvfRTzJo1BkmCr33tz4BJTk4uU6fOZvz4GioqplJQMJ54XKK9PU5bW4xwOO7+tLXF+1XaJsv9C7beufMIO3ce4bHHVgPJG68JE4rw+1UKC4MUFgYJBDwoioKiyEQiOp01Ldu22bZtG+vXr+eaa65xRa9bb72Vv/89vWTQ4/FQXVNN6cQyIpEIttfh2T1P41nqoZSuWR8zy2Zy7+X39uvNVpEVsKEl3tJhkhAzY/gD/qyFIVVS8creZFh2J7yyF0VSshpfciQ8igdFTgpP7nEVFVmSs3I8GZbR7fMlpKzzqbyKlzG5YzgcOZzs0OEAEvhUH6XBUrcj4Ejg17/+NZ/97Gd59NFHueeeexgzZgwAzzzzDB/84AeHeXajm843+AUFARwH7rvvVUKhpDDcX5dHSlyaO3ccu3c3EosZTJtWlvb4UDiCOodqAxQV5XDeeTOpr291u8z1dj4pMa65OUpBQYCrr15INKpzxhlTWL589oh2u+i6yeOPr+b55zehaSpLl9ZQWBjA7/eyY8dhmpujLFs2g4kTSzhyJAwZHJ1DJe70x0E0EkK5R4LrSjC4iLVGIBD0l9EUmD0ahLTjxcj9pHcSYNs2hmG7Xb08HrlLaOJAUBQ6iCfO0X9PfhA2TYtXX93Kj370NC+/vLXXUjpd30MicfCo2HQIxzkmRhQVjecTnzjLLVX7f//vLsaOHc/ixfMpLu77h2zTtNLEqDVrdrFmzR5aW2PEYgYNDe0cOtRKY2Ok98F6IfnNcHpeVSDgZebMCmbNqsDn09izZydbtrzBmjVrWL16NevWraO9vR2AxYsXu214lyxZQl1dHfPnz2f+/PmceuqpzJo1i/ebdvLmjndoS7Tyj51PsS+8J+NcJhVO4r4r7+u3C8e0TRIkKPAVoJu6W2qnqRoJElk5hiDZ1S7gDSAZEnEz7rp6fKoPv8ef5iQa0Piqh5h1zFWXmn+KbEriElYiKWDJMpZtpTmSZEnOqmNeijx/Hn6vn3A8jO3Yyfp0X2hEiU4A48eP56mnnuqy/T//8z+HYTYnF51v8IuLQ6xYsYXKynx3n4G6PNat2+eO+/TTQ1+m1DFUO0V9fRtPPrmOCy+c4wpPvZ1PRzGuuTnqPm/ixJK0uY/Ermvbth3i+ec3EYnoNDW109QUIRDw8olPnMn06WMoK8ulqqqYxx9fzerVu5kypYyVK7dQXByisjKf88+f2Wdxp7/n318H0Zw5Y9E0Fb/fS1VVMdOmVRzX6zsSXFeCwUWsNQLB6EE3dbY3budI+xFKcoSA0ldGk5A2lAjhaZgwDIvW1jBtbXF03UTTVHJzfeTlhfB4snOU+HwaHo+CaVok44Ac9u9vYcOGA7z3Xh2xWNeMHsdxsKwWTLMZn+9YKn9b2z8xzQb395ycHBYsWEhNzRymTZuTzNRRk6LB/PlnEgpp+P39uwFXVYWCgiAFBcl8HNM0CIeTzqhEwsFxbCRJBizCYR2PR6Wuro21a/dQX59dfoBtx4lEDFat2sOhQ62EQpt48cVHuuzn9/uZO3cuzc3NrvD0la98ha985Std9nWw8flU/v7+S+xo2ZbxuBWhCv5w1R8oChT1e85xM46maMScGKqiuuIKEmiKhm7qWZWUKbKSHN+IITtyUlw5Kgxpipa1o0pTNWRdpj3Rniy1SwV0Kyq5Wm5WC5zkJBVXWZKRla7zzMZN1RGv4qUo2P+/3fFkzZo1eDweZh+1Efztb3/jvvvuY8aMGdxxxx0jqgPfaKNz6VtjYztbtx5yy65S9NflMdAypWzEnI6h2ils28ay7C7bezqfvoaPj6T8n9R1e+edXdTVtaR1PS0uDmFZDuefn+zAumHDfnfeHR1dy5bN5Mwzq/s0/4Gcf18dRN2NPW1aRa/zGkyOl+tqJAqYoxWx1ggEowPd1Hl046NdSsaumnWVEJ8Eg4JYhYcB27apq2umrq7jB0adI0ciVFSYjB1bmJXzyeNJltTZNrz55vusW7e/i1vItg0M4xCGUUcikfxxnDigUF5+C5KkMmlSCXPnXojfH2fZsloWL17MrFmziMcNDh8O09wcpr3dxHEsJEkhJ0fF79cGlN3UEVVV8Pm8RCI6yWzwpBCnKB7GjClk0aJJzJgxDsdxOHiwhbVr97BmzV7Wrt3D+vX7iMf7Hn5t2zEsqx1NG8e+fS1Iko7PF2DhwqSDKeVkqqmpQVH6JgjKyLxTv4p3j6zL+Hiulssfr/4jlbmVfZ5nR5yjLjXp6P+S/5eOiSq958H3iCIrJKwEsiQnha2jGUyyJJOwEslSv0HAwXHnjkS3geP9obObqjODETB+onDzzTfzta99jdmzZ7Nz506uu+46Lr/8ch555BGi0ajIyBhiOpa+bdiwn6efXt9ln/66PAZSppStmNMxVDuFLMsoiuxuLygIUFwcoqGhjRdffA9ZliguDqXd7Pclh6ovwtrxEhQ6XrcJE4rYseMwxcU5eDwKhmHR0BBG6SBud/zbdHR0OQ59dnQNRFjsq4NosEXLgf4dhjrkPjXnkSRgjnbEWiMQjA62N25PE50AVu5cySkVpwypm0e4rE4exAo8DITDUerqWjFNC103sCwbRZHRNA91da3k5fnIy+u5w0932LbNW2/t5P77X2Pt2r1u1x3AFYTa2v5JJLKGrgqFgt9fwYc+NJ1PfepCTj11QkYRybIMkoHhEo5jHs2QcrBtDyBhmtkJCKoqk5Pjw7YddN3o0HXOQ06OD49Hds9nzJgCxowp4OKL5wLJsr0tWw4dFaP2sHbtHrZvP+xeg84oSj6m2ej+btsTKCn5F2655Rquv/70Ps33ySef5MCBA9TX13Po0CF27N3Ba5tfw2gz8BR5qPj0sW+UfaqPn37wp0wqnNTDiD3jUTxp4d+u44lk+He2wpBhGUmxSZLwqOmZWg5O1jlJqfLAoDfYJVzcxkY39QGXw2mqhtf0krAy5FMp3pNqIdu2bRtz584F4JFHHuHss8/mgQce4LXXXuO6664TNwPHkcFyeQykTCnbMOfq6nKqq8uora1xy+3KynI577yZ1NW14PEo6LrJ889v4p//3MahQ63U1tYgSTB/flXazX5vOVS9CWvHU1DoGIYeCHi54or5vPzyFoJBjdbWGLW1NWlfEPXlb9Pb/AciLPb1tdVx7JRQGI3q7Np1pFvhp7v5Xn75fB5/fPWA/w5D3aFQBJgfX8RaIxCMDo60H8m8PZJ5+2AgXFYnF0J4GgaamqLE4zqNjRFM08ZxkplMqipTVBSkuTnWb+Hp8OE2Hnrobf73f99g794md7vjOBw5cj+FhVegqskPxrIcABxkOQevtwKvt5JFixbxqU9dzsUXz+tDqZxFY2Mb7e06ICHLEo4D7e1xwKGoKLsMHZ/PSyikHe3AB6kEZ6/XQyikoWndvxGpqsKsWWOYNWsMH/3oYgDa2mKsX7+PNWuSYtSqVbtoaUl+Gy1JEj7fFPf5kqRgmjaf+9wP+J//Keb00ytobGxwRaX6+nomT57MCy+84D7nc5/7HPv27ev1vBRJ4QsLv8j8MfP7f1E6oKkaCgphM3xsowPYEPKEsn6jNkwDj+JBkiTiRtztmufz+FBlNRkI3r+M9zRSopAsyRnzorLJYZIlmRxvDjEz1kXU8qvZB6+fSDiO47Z9f+GFF7j44osBGDduHA0NDT09VTDIDJbLYyACVrZhzpqm8qEPzWf69ArOPXcG8Xgy1HzChGKefvpdGhvDPPjg20SjCaJRHY9HYeXKLVx99cJ+3+z3Jt4cT0Ghvr7VDYX/05/eYMqUEq6+eiGSJFFYGGTfvkaKi4+t03352/Q2/4EIi319baXGTp3TI4+sAmDLlkO0tEQzikbdzXfMmIIRLeyIAPPji1hrBILRQUlOSebtwczb+0JvbqbhclkJjnE8HWdCeBoGYrEER460k0iYriNJkiRMU+LIkXbKy/P7NI5t2/zzn9v5859f57nnNmKadpd9JElCloMYRp0rPPn90/H7q6munsw11yzkyisXpIXe9kY0apBImFiWhWHYrmPL45FJJEyiUYPcLHJCAwEPfr8Xn09HkhxM00ZVZTcQNRDon+qRm+vnrLOqOXJkI5IUxettZdOm99m//yCHDx/GNCMoSh5FRVe5z2lrW8lLL4V56aWu43UugzzvvPNobGykrKyM4pJiHtjxAFFvFCVHQc099p/YVdXXMjZ3At2Yr/qF3+PHdMyu4orHn3WpoyMlXU0JK4EkSUjKUTeVqeMoDo6a3Qmkcpi6fTzLHKZUxzzDMtK2nUyiE8CCBQv4/ve/z7Jly3j55Ze55557ANi1axdlZWW9PFsw2AyGy2MgAtZghDlrmsqcOeOZM2e8u23Dhv08//xGxozJJxLRSSRMWloMKivzMQzLzX/qeLPfuTyrqqqY3bsb3N/Hji1g4cKJPPPMu2iah2BQSwvmPp6CQllZHsXFIR55ZBWVlfnk5gb49a9fxOfzUFqay0UXzaGqqjjtOb0Fd/c2/4E64/ry2kqNvWdPI488soqyslzmzRuPLMvs2dPI1q11zJkzrtO8Ms+345dbmc5juPOVRID58UWsNQLB6GBq0VRqJ9V2cR9NLZo6oPH64mYaDpeV4BjH23EmhKdhwLYtdN0kHk9gWTaWlexEpygyjgOG0XNG0ZEjYR588C0eeOBN9uxp7HFfgPz85ShKAAC/38N5553BZz6zlHnzxg9IpNB1E9O0icUSGIbdQTiT8XhUEgmr32N2RFFUfD4FWU66qTwe+aiAJqFpMnKHUrLHHnuMffv2UV9fn/Zz6NAhJk+ezMsvv+zu+6UvfYm9e/d2c9R0UcLrnYDjxJHlAIFAHh/+8FLOOWcuZWVllJen3wT87ne/c//90Y2P8uCzD5JDumNtTHAcVcFqHEsiHG2nKFg4wKsDlm3h9/rxqB7iibi73ecdHEeSR/ZkLFWDo13jsiizg+OTwyRL8klv0f3lL3/Jhz/8YZ544gm++c1vMmVK0tn36KOPsnjx4mGenWCg9FfAGqow55Qokcp5SgnylmWnbU/d7Hcu27Jth5qaco4cCdPcHHV/13WDM86YQjSqM2vWWC69dJ4rWhxPQaG6upzCwmTDi5qacp58cj1+vxdVlQkGNbZsOcTu3Q3Mnj22z8Hdvc1/KPOPUmM/8cRqTjllPLm5Gs88s9HtgOv3e7sIZd3Nd/z4zOtXWVnuiMhXOl4B5oIkYq0RCEYHmqpx1ayrOKXiFI5EjlASzM790hc3U0lOCfm+fIqDxUQSEYLeIA2RhqxcVoK+c7wdZ0J4Gga83mQ4aXu7nuZ4kmUJRVHw+bqqBrZt8+qr2/nzn9/g2Wc3ZHQ3dYeq5jFhQiFz5oxl8eLJLFo0hWnTBhZsDUlbdTxukEik5pAstUsk7KPB3n2fW2q8jgLYX//6KGvXbqau7hBNTQ20tDTS2tpMW1sTY8aM56UONqR/+7d/Y/fu3RnH9XjSr+MHP/hBmpqa8HpzSCQ8KIoPvz+fQCCXLVva2Lz5WOlafv6ytOc+/HAblZU+Lr10UVqgbEdsx+Z/Vv1PxsfmFS/Esi1a2lqJFesZ9+krqQynVKma+/o56ujJVriRJAmv0n1OUraOKpHDdHyYM2cOGzZs6LL9Zz/7WZ+D8gUjn97cJUMlZqREiYaGMLW1NaxYsQWvV0VRZGpra2hoCKfd7Hcu24pEdB5+eBVXX72Q5uZo2u/bt9cDcOBAC3PmjHPLt46noKBpKkuWTOGFF97D41EJBjUURUaSIBTyIcuS6/DpawlgX+Y/lPlHmqYyY8YYtm2r55FHVtHxrfy117azfPmsPs33tNMmceBAc8bzGAn5SscjwLwnhtvxdbwRa41AMHrQVG3QBIe+uJmq8qooChbx2KbH3G1XzLyCqryqQZmDoGeOt+Ns9K6EIxhFSVrxm5vbicdNTNM62sktuV3qUBJ05EiYhx56mwceeJPdu/tXK5+TozFr1hhmzx5DXp4fRZEwDJv+CkOdkSQHy7LRNA+maeI4NpIko6oqlmW79f4dS9IefPBB9u7d28WZVF9fz6RJk3jjjTfcfb/3vTvYt29PxmN7vd4O2U9w4YUXumVunX86O5PuvfdeALZvP8i77+6juTlKLGZiWTY1NTJz5hzmiSfWoesmmfjlL59n7do9/PrXH6GoqGsG14qdK9jeuL3L9kKtmIn+KViWiW4liCS6d/v0BUVWkJGJGB06FTpg2AYhbyjrkjIJiaA3iGIqXUr5fKov6/FFDtPxo6WlhUcffZT333+f22+/ncLCQt577z3KysoYM2bMcE9PkCV9dZcMhZhRXV3ORRfNYdu2eizL5pZbatE0laqqYmRZobg4J+2Gu3PZlq4nnb2pkrzOv6foWEZ3vAWF6dPHcPnlp7JnTyOqmnxfKi4OEQymu7n6WgI43IIIpDu5UqTOqT/z7W77SMlXGuoA8+4YCY6v4UCsNQKBoDN9yYza3bqb7Q3bmVQwCd3S0RSN7Q3b2d26m1k+kfE0VKRynaJGlLAeJugNpt1/DZXjbPSugiMaCds2MQyT1tYotm0hywqKEsC2TRwHXnllK//7v2/w7LMbMYy+l65JksTChVVUV5cxfnwBluW44eUej4KqqjDADB3LslAUBUVR8Ps9PP74oxw+fIiWliba2pI/4XALbW3NVFdP5c0333Sf+41vfINdu3ZlHNfn86X9vnTpuezbd4hgMI9QqIC8vELy8gopKipi7NhKN8QS4K677ur3eciyiqp6ABlFkVy32cyZY6mpqeQPf3i9W5Hv5Ze38sEP/oLf/vYm5s4dn/bYvW/fm/E5M3zziYQNfJqERAKfmkUdHEnhycZGU3zoegLLdlBkCU3zYmNn3dVOkiQ3J6mzMDRYopDIYRp63n33Xc4991zy8/PZvXs3n/70pyksLOTxxx9nz549/PGPfxzuKQqOMlCHxNatdezZ08iYMfkEAhoNDeHj6i6JRg3WrNmDrptomsqll87jzDOnZZx757ItTUu+D6ZK8jr/fux5uZ2ed/wEhZTAsnXrQfx+L6+9tp1gUEOWpTSnUn9KAIdLEOl4/JSTK/V3S51Tf+bb3faTPV9pJDi+jjdirREIBJnoS2bUkfYjyJJMSAsRInRsu8h4GjI65jrl+/IZmzeWTYc3URmqRJbkrHK9ekMITz1g2zaGYbvuHY9H7hIsPbBxTZqbI9TXh0kkjjmewuE47757gF/84gUOHGjp15hlZblcf/3pXH/9IiKRKDt2HOHgwUZaWw1s20SWVfx+heLiIB2FJ9M0j4pRSf785z+zZ8+ejM6kKVOm8Pbbb+PxqAQCHh5++L85dOhAxvnU1dWl/X7ppZfS1NTkupFKS0u7dSZ997s/ZOPGg13C12VZwutVcZzsHFuaJuE4Dh6PAtjYtnw0Syr5Lf3999/Ef/7ni/ztb2szPv/AgWY+9KH/4nvfu5yPfnQxkiTxzoF3WH1gdZd9/VKQPKOShpY2VFVhdmhi1uKKZVtItoIe0wEZBcAGPWbiCXixbCurHCaP4sGwjW67znmU7ISzFCKHaWi57bbb+PjHP85Pf/pTQqFji/kFF1zADTfcMIwzE3RkoA4JXTd5/vmNbncygNraGgoKAsfFXbJt2yFWrNhMKOQj9fJasWIzCxZUZbzB7ly2FQxqXHPNQo4cCWf8HUZGLk8qWH3atEqWL5+Z0anU3xLA4S7FSjm5hqJk8WTPVxopjq/jiVhrBAJBJvqSGTUUnfQEPdMx16kl3kK+L59zqs5havFUppVME13thgPDsIhEYkdL4ZJd1Xw+lWDQf1SwGDhtbTEaG6NIko0kQXNzOzt3NnHwYCu23feOYZIkUVs7jY985AyWLZvpzmvbtgjxuIFpOigKvPPOClpbjxCLtaLr7dh2lJaWJldMWr36mGDy3e9+lx07dmQ8XkpMUlUJ03RYsmQZra1N5OUVUVBQRHFxCeXlZZx66jRqaqakPfeXv/xlP85LweNRXOEJkjlQKXFIkv5/e/cd31Z99Q/8c7eGZXk78UickDiJs5pNEggJTRN2gTKeQtm08DyF8kChQOmvlBZKd0tbylNoCy0tLRBKgBJmSwYJZDiBLLK3kzjxljWu7vj+/pAlW7Zsa1qSfd40rzRX0r3fK8v66h6dc76J9gzgoSgCRJEDx0kwTYDnAUHgoCgC7HYLfve76zBzZhUeeeS1iP20/H4DDzywDLW1h/D441fi6Q1PRzzSJGUOnFw+JFGAYpHQ2OIFSzTwZBgwdQ4WyQqjS0aSIIgwdS4sIywePMdDERSoRs9eVIqgUFZSlti4cWOovLSr8vJynDx5Mg0jIpHEmyGxZ89JrF0b/l69cuUuXHnlrAHJLon1AltRRFx22QyUleXj2LFGVFYWYvr0kTh2rCkUzAmscnc6bWVo3fUMEA3H5MmVPe4XSwmdy+XD669vwfbtx0JZajNmVA1oKVZf4000KBbtczEQwbd0BPiGYsYXzTWEkN701zMq2Svpkf517+vU4mtBi68Fc0bMSUlD8a4o8BSBaZpobnajtdUDXTdhmoGghyjy8PtNFBXlxJ351NTkxrp1B7Bhw0GcONGKpiZPR0Pu3jHGABjguMCPq6TEgbFjXSgvF+DzbcHTT7+NH/ygMzNp1KjRuPfe33RkazH8+98vorHxeMR919fXh/37sssu67VnUueyuIHMozvuuL/H/nJyFIweXYyCAkeP26IlijxsNhl+v45AWWJnxpnNJkOSEgt8MMZgt1vg85nweDwAAqsK2mxW2O2WjtJEDrfcsgBTplTi9tv/jBMnIl9kvfzyJmz+9CCOzf8YfH74bTIsKPWOh5vp4DgDkqJjVPkw+L2Re0hFS9cDwTie48FHyEjSNBPWnptjQqVw2c9isaCtra3H9t27d6O4mL5JyhRdAzj5+TYUFTng8ag4ePB0nxfK9fWtsNsVFBU50NDQmSVUWGgfkOySWC+wVVXHq6/WhgXZ6uqacfXVc8KCOeksQ+uqv0y0SEGN/sauqjqee+5DPPNM52qrCxeOR23toQEvxYpUKtfXOQOIOojTXzlhKvogdf95VFUV9Xi9DUSvpaGY8UVzDSEkXsleSY/0L51ZZhR4isDr9aO52Y2mJhdOnWqHqvqhKDJKSnJgmgx2uwy73dLvfk6dasO2bcfC/hw71tzr/T2e7TCMNhiGB6bZ+ccwPJCkAlxxxffxla/MxRe+MBETJ07AsmU9G1kDwOnTDbBaFRw/3gyv10B19Sz4fG7k5eWjqqoS06aNx9Sp47oFkwJ+8pOf9HtehsHgcFjBcTy8XhWGYUIQeFitCnJyFBjRt6SKSJJEKIoMm82A32+EmpfLsgBFkSEkuGobzwswTROGoYMPrhDHA4ahwzTNsFXrZs0ahXfe+Sb++7//0iO7IGj/nkbg0ALI522BMKbz270xmAHJb4WpAZwE8JwMl8tAhMXcYiJwfZ9/ImV2XVEpXHb74he/iO9///t46aWXAASCqUeOHMEDDzyAL33pS2keHQkKBnDy821gDKHSuV27TqKlxdPrhXJpqRM8z6GsLA9OpzXUr2fevDEDkjkT6wV2vJld6SpLC463azDw8OFG7N59AuPGDY8rcLJnz0m89dbWsG3BLLVMKMXq7Wc0dWoFtm8/nrQsrWT3QYoUyJo1axT27Qv/Ym0gei1lQgP5gUZzDSEkEclcSY/0L51ZZoN3JkyAy6Xi6NHT2LmzDu3tfmiaBkmScPiwjJqacuTl2cICT4wx1NW1hIJL27cH/q6v7/kNUF/a22thGJEDUwUFPF544bbQv6+88speM5Pq61Xs2nUaHCdAEEwsXXo9gEDz1qKiHMyefQamTBkR8TjRCJb0cVx4WWDw34lmJFksAmQ50MCc43gwZoDjhNB2iyXR5tkMfr8R6hsFBDLaOI7rCHSFn1dRkQN///vt+MlP3sJvf/vvyDv1S/C/PhvirL0Q5++CyIsY4ZoGr8ZgGAyCn4OpGtAtDJovsVI4URCgCDLUCBEsRZATbi5OBoef/exnuOCCC1BSUgKv14tzzjkHJ0+exNy5c/HYY4+le3ikQzCAc/hwYyjoFFxlrK8L5a6Bn2CfpcWLazBhwsCsIBXrBXY8vW/SuUJYfX1rj2AgAFg70knjCZzU17eGmqh35fGoGVGKFelnlJ9vw8qVe7B378mO4KYPeXm2hLK0kt0HKVIg6623tmLu3DFobvYk5RixSHcD+YFGcw0hhGSPdGaZUeApgsbGNmzZcgT79p3uWOKZAeCgKBJ8Ph2McfB69S6ZTHVobnb3t9t+Wa1jYZo+8LwN48ePxtKlM7F06SyUl5f1yEzqazJvbNwLl8sHjuPDPuRyHA+XywevN7GUG1kW4PdraGvzhbbpuom2Nh8kSUi4BxYgwGazwOfTYLFwYEwIBYlsNguAxPbPGELBJY7jwHGBvwO3Re6xJYoCvv3tizB9+kjcddcLcLl8Ee+nbxwL82QeRp9ngrkt8GtaqIcUk3h42zXwLLFfO0niIWnBUrjOsj2pY9W5RAN/ZHDIzc3Fhx9+iP/85z/YvHkzTNPE9OnTsXjx4nQPjXQRDOAsX16LioqCsFXGgL57JqU7syKWC+x4et+kc4Ww0lInioocYUEnAFi7di+qq0siPqa/oEZpqTNieeSkSRVpL8VSVR0cx6GhwQVFkUKvwaqqIuzcWYe3394euu/ChePxuc9Vxh3ESXYfpEiBLEWR4PH07FOYCQG+wYbmGkIGJ1VXsbdxL063n0ZxDpXADSbpyjKjwFMEJ060Yt++U1BVP9rb/Whp8aC93Q+Xy4e33toesdl0MhQVLcD48SW4664lOO+8qXHvRxA6Aylct548HMdBFLlID4uaaZrQdRMWi9Rj1blAT6zEnh+OYxAEDnl5Vrjd/kDGkMDBbpchCBx4PvoG7JHougGLRUR7u6/jeMHng8FiEWEYvY//vPMm4+2378FXv/ocdu6M3DfLPFqMgy8IKDjTjVynFTAZwDiYvAlT42EaiY2f5wMBRVXVoHTr8aQoUlJWXiTZTdd1WCwWfPLJJzj33HNx7rnnpntIQ15fJWOKIqKmphxFRTk9Htf9Qrm/htfpXjGtt7EWFeXgwgun4M03O0vN+ut9k84Vwqqrh6GgwB62LZiJ5u2lT19/QY3q6mFYsmQi3n13R6g88vzzJ+OSS6alvYn6iy+uR23tIUyfPhItLR5wHIeSklyUlubimWdWh91/5cpdmD59RNxBnGT3QYoUyLLbFUyaVBG2QvBg77WUDjTXEDI4qbqKZduX9SjHumLSFVEFnyhoRSKhwFMEBw+ewr59p3D4cBM8nr4bfyeC4wCbTUZOjoKCAhvKywtQXV2EkpKeFx+xsFpllJQ4cPq0C0aXIIcgcCgudkRM9Y+Fz2fAZlM6Ak2dgSdR5GCxyFBVA474e4uDscC+2tv1joBWIDikqjqsVhmmmVjgjOMCx3A6c6Cq/lCPKkWRwVig9K4vo0YV4/XX78Jltz6KbStdEe/jdRtY+586jBlfhPKyHPCyAKsuAUwAi2Hlwt4EVvdj8Hh0mKYBnhdgs4kQRSqzI4Aoihg5ciSMRBuukaSIpmQsmovxaBpe93ecZAWm+ttPpLEsWjQB9967FKdOtUeVoZXOFcIURcT8+WPw/vs7Q/2zgllA48aVQlVjD5xkQpZaV8Gf4c6dx3H4cCNycy3wejXU1h6GYZgoKnJAEAJ9xA4fbuxY8CNAksS4gzjJfh4i/e4sWTIRl1wyDVOmVGTEcz1Y0VxDyOC0t3FvWNAJAFYeWImpw6f2mymTaNCKDF40A3doanLjzTc/xfLlm/Hxx/vRS8VV3ALfaJdh8uQKWCwCmppc4Diho6F1oMeQLAeyV/pb5a4/TqcNDocVoijC6/VD1xlEkYPVKsNqlZCXZ01o/4ZhQhQFWCyBleeCq87JciDwYSYYWOE4Bp9P78hEYqGSE47j4PPpCWc8iaIIQeDg9frB81yowbimaVAUBaLY/68FL5lonPcWJNEB7YNJgNEz4MMYsPezBrQ2+TC+phiqaYAzODAu8ReXphlQVR08j9D4fT4disIlodSRDAbf+c538OCDD+Kvf/0rCgoK0j2cIS2akrFoLsb7209/t8fTMylSgAlAv/uJNJYPPvgMM2dWYfHimqiet+rqYVi0aAJef31LIMNTkXDJJdMGLGtlwoRyXHbZ9B7nOWFCOSZMKI8rcJIp/X+6vhYaGlw4dqwZ1103DytWrIemdQYRNmw4iOnTR6C+vg2SFFiYo6QkF7Nnj0ooiJPM56Gv351MeK67yqSMxGShuYaQwed0++nI292Rt3eVSNCKDG7ZPdslqL3dh3fe2Y7lyzdj1ardSSuhs9lkTJpUjsmTK0J/xowpDQUE/vOf7Vi3bi/a273QNBmMBbJwJAkAAg2uE1FQ4MCIEfk4erQZosiHMnpkWUBlZT7y8xNIRwJgsUhoa/OEvv0Mlnb5/To4DpDlxF5WhsFCfZe6B1E4DtD1xAI3sizCapWhaQYMozNjSxACwbloxv/KjlfQ7GuCOKUJfEkr/G/MBHPZIt73VH072ttVzJ49EiYAlmCpnWmaUFUtVPIYDFyKIg9V1TrKEancbqj79a9/jX379qGsrAwjR46E3R5eNrR58+Y0jWzoibZkrL8L5f7209/tsfZM6i1QNWVKRcT9TJlSAUEQUF/fisZGd+i9qa9z7o/NJmH69JHweFTYbApstsQydmPRXzAwVUGNgQhOdH0tBLOg9+w5CVkWQ4GnYJbXiBGFGDOmJJT5dckl08LKOzNBf787mRDwSWez/FSiuYaQwac4pzjydnvk7V0lErQig1v2znRxUlUd//nPZ1i+fDPee29HErKLrJg8uQKTJnUGmUaNKoIg9H7hH/hgp4MxHjwfyCDieR6MAZqmJ/wBxGqVUVSUB03j4HZ7Q4Etu92KoiJnaFWeeNlsYq8ZYYwFbk+EaZoQBAEWCw/DMELjFwQBPM/12gA8WqIowOGwQhB4qKoRCjwpigCbTYEg9J0xpJs6/rDxD6F/88NaoHxlNfwrpsM8HLnprMetYeUH+3D6lAsLF0xMaPyaZoYynrry+w0oighNM6EoFHga6i699NJ0D4F0SFbJWH/76e/2WHsm9RaoilSubZoM69btw8cfHwAAjB1biuPHW1BWlhcWfIrlnPfsORnWEwoA9u6tR01NecqbiwcNdNbMQAUnur4Wgg3PFaWzx2GwnxXPczjrrLE455xxWVuylikBn3Q2y08lmmsIGXzGFo7FwtELe5TLjS0c2+9jEwlakcEtez45JEDXDaxduw/Ll2/GW29tDVuNLRY8zyE314IZM0bg6qvPxOTJFRgxorBLc+roOBxWlJY6cfRoU8d++Y6/AxcOgZXbEmOzBfo8eb1Sl1I7C2y2xIJOAMAYj/z8HLS2umEYZpeMIR5Opx2MJRb0EAQh1K+E58Nfoooi9hnUi4YkCaEP1BaL2aVHFd+R8dR34OntPW/jSOuRsG2c1Y+Z/+3B/Pol+OUv3+31sTt21OPu/30Rv/oVj89/Prpyk+50XYeq6jAMHT6fDl03IYo8LBYRqoqOD9JD4leb9OHhhx9O9xBIh2Q1U+5vP/3dXlKSi7FjS0PZQw0NLjQ3e3oNBvUWqLJaewae3G4VjY2dq7s2NLgwaVI5jh5tgsNhieuc09lcPF0GKjjRNUjJ84E+Tl6vhi99aSY+/nh/aI4MlhYGAnCR95UJ2UR9yZSAz2B9PdNcQ8jgo4gKrph0BaYOn4rT7tMotkffILxr0MpkJtx+N+aOmAsg0P+J+jwNXZnzySAObW1tqKtrg9erwWqVUF6ei9zcwAdoxhhqaw/j1Vdr8cYbn6ChoT2uYzgcCvLzbcjLs0GSTAiCjEsumYaLLvpc3ON2Oi3Iz3eAMQ4+nxYKDFksEvLzc5Cfn1gPJk0LfGMpCIAgiAACpXbBRJ5EM2JM04TNJkGWc+DxGABMADxsNgGiKCackSTLAkQxkN3UPbDF83zCPYxEsTPw1H3/VqvcZ8YTYwxPb3w64m23zfkazh93Pmx2AT/9ybu9lkw2NrbjuuueweWXz8Ajj1yKwsLYmskbBuD1+kOr8gGA3w94PH7k5Fhgs9EbOum0adMmfPbZZ+A4DhMmTMCMGTPSPaQhJ1nNlPvbT1+3q6qO2trD+OCDXWhoCCyKsHDheFx44ZReg0G9ZVBVVRX1CHDNnz8m9GUKADQ3e5Cfb8ONN54Fu12J65zT2Vw8XQYqONE9SMnzHM45Zxwuu2wGLrhgctSv00zJJupLpgR8BvvrmeYaQgYXRVTi6skUDFpNKp2Ej49+jBZvC461HsMTa5+gJuNDXGZ8KojDoUOn8dFHe3HiRGso8DRsWC5KSvKwYcNBvPbalrAPwbEYNaoI+fkWmGYg0BAITACCoGDMmCLk50fu5RMtm82KyspCCAIPn08L9WCyWCSUleXDYkks40nXdbjdKlTV6MjkCTafNsCYmnBGDM/zHQ3GReTm9txPrBlg3SmK2CXjiY94WyIkKRC8EgQFmtYZeJKkYGCr96DcuiPrsKN+R4/tVflVWDJ2CQCgZtJwnH/ROPzn3/vgavX3uq9//rMWq1btxqOPXo5LLvlcDM+bgfZ2Hxhj0PXwjK32dh8KCxN7fZLB4dixY/jyl7+MtWvXIi8vDwDQ0tKCefPm4e9//zsqK5Pfo6Wurg73338/3nrrLXi9XlRXV+OPf/wjXYAgeSVb/e2nt9v37DmJDz74DGVleXA6rVBVHUePNmHcuN4DC71lUI0bNxzjxg0PC3AxxvCzn70d9vjmZg9Gjy6OO6skWZli2aSv4EQyM4uS1ZA7U7KJ+pIpAZ/B+noe6LmG5hlCMp8iKhB4AZuObQrbTk3Gh7asDDy1tbXhvfc+waefHoHbbaC11YeGhnbU17fB5VLj2md1dSkuvXQ6vvjFaWhpcePjj/di375T8Pm0UINUi0XCGWeUoLg4sebcVquIggIbABNutx+6rkMURdjtMgoKbAk3T9U0s0f/nyBV1eH3G7AlEJuQJB5aH62x+grcRIPneeTkWCAIao/AkNWqJNw4m+d5KIrUsUpS98CW1Of+f7/h9xG33zrzVggdq8t5fB7YnDzO/sIIHNjdgj07G2H20lC8sbEd//3ff8Grr27G449/CcOH5/U7fr+fQRA4uFzhQS1NM+BwKPD7k7Mko2ma0DQztGphMDBHssPNN98MTdPw2WefYdy4cQCA3bt34+abb8Ytt9yCd9/tvSQ0Hs3NzZg/fz4WLVqEt956CyUlJdi/f3/oQoSkVzDrg+c5OBwWODqmsYaGzvK4SIGNaJtrq6qe9IvqZGWKDYRkBYV6C05UVRUlPbMomiBTf+eVKdlEfcmUgE/w9RxYgbIeVquIqqrs73kykHMNzTOEZA9qMk66y7xPb1HYvv04tmw5ik8+OYaTJ9vh80UOsvSnoiIfl146HZdeOh0TJgwPZZycPCmitDQPkiSivd0HwzAgCAJyciwoKMjp9duzaAWCTAo0zYDVaumSscLBbu+/uXV/+i91Syww0TVw011/gZtoBTKSLBEzkpIhsH8upv1vO7kNaw+v7bG9yFaEyydeHvo3LwrQTQO8xGHM+HwMr7Bja+0pNJ7y9rrvd9/djo8+2ofvfvcSXHPNmX1mPzEWWI1PUQR4vQYYM8FxPKxWoWNFwMRXZww0L+/8+RqGCU0L/HwTLXUkA2PNmjVYt25d6EIAAMaNG4ff/OY3mD9/ftKP9+Mf/xiVlZV49tlnQ9uqqqqSfpxMkOk9bSLpL+ujr5KpaDJgUhUkGujm3vFIZrmZooi47LIZKC/Pw5EjzRgxogCzZ4/GoUMNPTKLamsPobw8H4yxlLwOozmvTMkm6kumBTC3bTuW0aWJsRrIuWYozTOEZDtqMk66y8pZ7tNP67B69T40NfV+Id+boqIcXHzx53DppdMxc2ZVxAv8oiIHxo8fhv37TyM/3x7KeJJlEWecUYyiosQyngBAUWQUFHBob1dhGIEMlpwcBZKU+FLRgRXaxIhZT4oiguOSFRiKLXATq0CAK3UZNrHuv7feTjfNuCmsVjnXLiM/zwb1VBt0mLDaBMxeMBzHj7Rj5ycN0PyRA0Mulw/33fcSli/fjJ/+9GpUVRVFvB/HBZ779nYNhsE6Xp8MXq8Jp1NMuLm7aZoRg4oAoKoaBIGjzKcsMGLECGgRUhN1XUd5eXnSj/f6669j6dKluPLKK7Fq1SqUl5fjf/7nf/DVr3414v1VVYWqdmaotrW1JX1MqZANPW0i6S/rIxklU9kQJEqFZJabqaqOV1+tDdtfXV1zjwBPfr4NjAE///k7KCoK9AlM9uswmvPKlGyi/mTKazMbShNjNZBzTazzDJC9cw0h2S6RlfHI4JS5n5IjYIzh1Vc34+c/fwceTx+1Xt3k5lpw/vlTcOml0zF//phQz6PeiKKIqqpSWK0KmprcocBQQYEdpaV5EMXEnzae5+HzGR1NrQPBIp/PgKIoSemR5PMFmnN37wHE83y/q7bFcg6pDAylmslMaIbWGTgTJPC9BOUOtxzG23ve7rE9R87BNVOvCdvmzHOgdFgB/H490MPLZBB4DqUzHFh47jhsWF2Hj9Yd6HVca9fuw7nn/gT3338Bbr11QY9V/GSZg6rqHT2ejFBglOcFqKoOWU7s9RNsTu/3a3C5/NA0HZIkwuGQIctSws3pg6iUL7V+8pOf4M4778STTz6JGTNmgOM4bNq0CXfddRd+9rOfJf14Bw4cwFNPPYV77rkH3/72t7FhwwZ84xvfgKIouP7663vc//HHH8cjjzyS9HGkWrZeOPaX9ZENJVOZKpnPXW+vrxtuCM8cKSpy4OWXN+KMM0rC7pfM12E05xVtNlE2ZgmmwmD8PRvIuSbWeQbI3rmGkGyXyMp4ZHDKmln/4MHTeOCBZVizZk9U97dYJHzhCzW49NIZWLRoPCyW2DKJbDYZlZUFKCzMDZUy2WxiUoJOQOCivmfJG4Pfr4GxxH4huzbnluXkN+ceDDRDg2p06QfGAM3UoAgKJKHna+UPG/8Ak/XMVPry1C8j1xJeUlCQlwOn04L2QhvcLh2mAfACYHeIKC/Lw3VPnoVN6+vw0EP/RGNj5NUWfT4NjzzyGl5/fQt+/vP/wvjxw0O3mSYHQeDg9Wrw+3XougFRFGAYInJyFJhmYoEn0zTR1ubD6dOt0HUWCmy1tHAoLnaioMCe0P4BKuUbCDfeeCM8Hg/mzJkTet8K9pO7+eabcfPNN4fu29QU30IMXZmmiZkzZ+KHP/whAGDatGnYsWMHnnrqqYgXBA8++CDuueee0L/b2tpS0vA82bL5wrGvrI9sKJnKVMl87np7ffE8H5ZZ5PGoKCpywG7v/Lxgmgw7dx5PWnAn2vPqL5somVmC8QawUh34inb/g/H3bCDnmljnGSB75xpCsomqq9jbuBen20+jOKczwBTvynjZpLdzJz1lfATC79fx1FMf4Fe/erfXhtldORwKFi8ejx//+L+Qk5PY6nC9rdqWqEApU2DFtu4ZHqqqwzQT69HTtTl394ynZDTnznYmM6EaKnRTh8/vg8EMCJwAixx4vQi8EJb51OBuwLLty3rsRxZk3DTjph7beU6Aw2KHzLvAbEJnqSYvwmGxg+dEXHLJNJx1VjW+973lWLZsU499BG3ZcgRLl/4cd965GN/4xmLIsghdN+DzaWhp8aK93QNV1aEoInJybHA4FBiGkdDzo+s66utb4Pd37sc0AV0H6utb4HDIMQdyu6JSvoHxq1/9akCPN3z4cNTU1IRtmzBhAl555ZWI91cUBYqSfRNztBeO2Zbh0VfJVLady0BLZrlZb6+voqIczJs3JpRZxHHA0aPN4PnAFw2myXD8eAv27DmJvXvrQ2NIpPQuWeeVjCxBVdWxe/cJvPvudqxduw92uwKe56I6x1SXx8ay/2wpTYzFQM41sc4zQPbONYRkC1VXsWz7sh4ldVdMumLQB2CG8rnHI6M/Oa5ffwDf+tZLoQ9RveE4oLDQhtxcGcOGFWLJkikJB51SKVCq1HsPJl1PfFWyVDfnzmaaocHr96JVbYVhGqHnp11rh1NxQuKlsDeLP2/5M/yGv8d+Lq25FKU5pT33r2ngORGlJYXwuL2hjCeb3QqeE6HrgZ97QYEdv/71tbj00un41rdewvHjLZHHqxn4xS/ewZtvfoqf//y/UFLiwMmTLTh6tAGq2tlcXFE8kCSgvLwgoefH7faHBZ268vsNuN1+OBzxL4sYLOXr6/ZsLuHMFDfccMOAHm/+/PnYvXt32LY9e/Zg5MiRAzqOVIvmwjEb+0D1VjIFIOvOZaAls3l1X6+vrplFqqqjrq45dD+3W8WkSeVoaHCFHpdo6V2yzivRLMHg79Phw414+eWNAAKlhmVleVGdYzyBr1iCrbHsP9ManSfDQM41Q2WeISSb7G3cGxZ4AYCVB1Zi6vCpgz7baSifezwycqZrbnbjscf+hRde+Ljf++bmWlBWlguLRYLVKmH69GGYNGl4v49Lp0D2UaAHk2F0BoYEIRAYSrDFU0i292BKFc3Q0OJriVhqxxiDTbZBQSDw1O5vx1+3/LXHPjhwuHXmrRH37/VqUGQRXq8Iu80CA4AAQODFju3h2T7nnjsBH3xwP374w3/hz3/uuWpe0O7dJ3HxxU/g8ss/hzPOKIbbrYYFFnWdx7FjzRgzpj2h4JPfb3T0CdN6ZMxZLBJ0PbGMqmBGn2ma0HUzlBEW7EHW/6qMJBp//OMfccstt/TYrus6/t//+394/PHHk3q8u+++G/PmzcMPf/hDXHXVVdiwYQOefvppPP105Kb82SqaC8ds6wPV/SL77LOrQ+fTfQUuILPPJV2S1bw62sBE9/t5PD78+9+fobnZE3a/REtAk3FeiZaXBX+fysvzQtsaGlxwOq1wOCz9nmOsga9YA8ex7j9TGp0ny0DONUNlniEkWplQ5nW6/XTk7e7I2weToXzu8ciowFOwefjDDy/vtfdNUEGBDfPmVaGoKAd+P6AoHJxOC846qxplZYUDNOL4yLIArxcRS+0AUI+bFPNqXqiGCsZYWMaTwAtQDRUevwd2OdDH6MWtL6JN7bkCyuIxi3FG4RkR9y9wIryqCk4ywXMATIDnAU404VVViELPn6/DYcHjj1+BSy+dhm9+80UcOBD5DYsxhlde2YLcXAvmzKnCsGFOMBbI+vP5AI7j0dqqRnxstIKlG7oe6MMU7CEFSACkhFdFDDTW13pk/AUDXpKUUW9LWeub3/wmVqxYgWeeeQYFBYFA5K5du3DNNdegtbU16YGnWbNm4dVXX8WDDz6I73//+xg1ahR+9atf4dprr03qcTJBfxeOqegDlapyt/4ushM5FyrRi06k5ymawETX1+G2bcfQ3Fzb4z6Z0Dso0fKy4GvQZgu/mFJVHQ5H/+cYa+Ar1sBxaakT+fk2FBU54PGosNkUNDS4MuK5HwgDOdcMpXmGkP5kSplXcU5x5O32yNsHk6F87vHImE+A0TYP5zgON944H7ffvhBtbW4cOdIUamI4YkQBysoKM/6Dbdfm373dRlLHYAYMw4DP8MFknRk9PMfDIljAOpq++w0//rTpTxH3cdvs23rdv6yIEEUBbo831D7eNADOMGHLVfpsUD9nzhl477178ctfvounnvoAhhG5LK2tzYf33tuFMWOKMW3aiNBrRtcNaFrPssBYOBwyDhzwwuXydjQXB3g+0FfKNBlGjUossCsI6LVfW+BCgmqik2HLli247rrrMHnyZDz33HPYs2cP7rvvPlxxxRV48sknU3LMiy66CBdddFFK9p1Nkt1AOJWle/1dZMd7LtlYbpgOyXqeMrl3UKLlZcHXYEODCwsXjsfKlbtC+43mHGN9bmINtlZVFaG42IGXXtoY2nbVVbNQVVXU57gGi4Gea2ieIYNFotlKmVLmNbZwLBaOXtgjADa2cOyAjSFdhvK5xyPtn/5iaR4+cWI5fvKTKzFt2khomgGn04phwwrCehhlQ/Psrs2/u/dgyobxZzsOHAxmwKt5e2Q8SbwUWmzw9c9ex8n2kz0eP7tiNqaVTet1/7KFgyxJsNvs0DUdDAAHQJREyJIExdp3LaXVKuPb374IF100Fd/85ovYsaOu1/vu23cadXUtmDy5HNXVpeB5gOcTy5jjeR6iyENVO0vqDCPwJz8/8T5hhoE+A6+GASRp8cghbdSoUVi9ejXuvvtunHfeeRAEAX/5y1/wX//1X+ke2qCX7CBAskv3umbYeDwq8vNtvZZoxXsu2VZumGq9ZX8l63nK9N5BiZSXdX0N5ufbcNNNZyEvzwarVQpb8bWvY8fy3EQKtpomA8cB77+/o0f23qFDDdi16yTOOKMktNjHrl0ncehQw5B4rdNcQ0jskpGtlCllXoqo4IpJV2Dq8Kk47T6NYvvQWdltKJ97PNL6iWT9+gO4//6XsGdP383DrVYZ3/rW+bjllrM7Sn6yv3l2to8/m0m8BL/hB9fxX+B/gf/8hh8iL8JkJp7Z8EzEx39t9tf63D/PCygsygHf6IZudq7+JvI88gsDq9pFY8qUSqxYcTeeeuo/+MUv3um14bfXq2HDhkPYsuUopkwpx/TpVVHtvzdutwarVUZxsQNerx+GYUAQBFitMqxWGR6PBocj/v2bptnx+ucivv6px1Py/Otf/8Lf//53zJs3D7t378YzzzyDBQsWoKysLN1DG9SSHQRIZule9wwbl8uHysqCHsGnYEZTvOeSinLDbNVXVlMyn6fB1jsoqOtrsKHBje3bj+Hf//4MPM9hxYptUWWIxfLcdA+2mibD+PHD8PrrW0K/I93LUXmeg8NhCZsbh9JrneYaQmKTjGylTCrzUkRlyDbTHsrnHqu0RDmam924994Xcdllv+k36LR4cQ1Wrboft922MBR0Cgo0zxZhsUhQFDHrgjbZPv6sxQEiLwIcIAgCREGEIAih7RzH4T/7/4N9Tft6PHRc0TgsHLWwz92LIg9HrgWlwx1wOhTYbAqcDgWlwx1w5FogitF3j5ckAd/4xhfw3nv3YebMqj7vq2kGamuP4NZb/4Ivf/n/8O6723st1etLsHk4zwOiKIT+BF+eiTYXD77Oe3v9c8nqrj/E3XbbbbjqqqvwrW99C6tXr8bWrVuhKAomT56Ml156Kd3DG/SCF7qLF9dg8uSKhDJPklm61z3Dxm5XsH17HYqKAlfMpskwa9YoNDS4sG3bsVAGR6znkuxyw0yiqnpH0/UdoeeoL71lNe3ZczKrn6dYn4dEBF+DRUU52LjxYKgXIdD5XCbzWFdfPQf33Xcerr9+Pm666SycPu0KC8y+//5O7N59Atu2HUNjoxtjx5YiPz98tdds+BkmA801hMQuGdlKwTKvrqjMi2SylGc8maYJTTNhmiY4jsO//vUJHnnk9X6bhw8b5sSjj16O88+fTBeiJKkM04BTcYLjOLh9bpjMBM/xsFvsyJVzYZgGnt4YeYWUr83+Wr+vR4tsAc+7YRiA1W6BNXhcI9C4W5EtMY957NhSLF9+J557bi0ee+yNHivjdbdq1W6sWrUblZUFuOGG+fiv/5qDggJ7VMfieQ4ej4qWFk+XHk8avF4udHsiJImHpvW+qp0kUQA2GdauXYv169dj6tSpAIBhw4ZhxYoVePLJJ3HzzTfjqquuSvMISbSSWbrXPcOG5zmUleWhunoY5swZjdOnXdiw4QA2bjwYOk48fZkyuedQIuLpydRXVtPZZ1cP2POUzGbv6erhNVCZdF0zpN5/f0ePUtT8fBvefXcHtm49CtNkOH68BZMmlYcyBwfDaz1aNNcQErtkZCuloswrE1bJI4NXSgNPmmbg9OkWnDzZioMHG/HHP67BJ58c7fMxHMfhppvOwv33XwCHI/YLdEL6I/ESdFOHbgaa0gebMAW37Ti1A7V1PVcHKnOU4cJxF/a/f5GHxEmwWE0YuhFadU4QBUicBEmML7DC8zxuvvlslJc78cMfvom9e0/1+5ijR5vw6KNv4Gc/exuXXTYdN954Vr89J0SRQ3u7ClXVoetmaPyGwaO9XYUgJBZ4Cq7m2NbmhWF0Bp4EgUdenp0y/5KktrYWitLzw8LXv/51LF68OA0jIvFKZulepAwbnudQUxMoifnrXz8Ku62vfkN9BTIyvedQvOLpydRXVtNAPU/JDhSlq4dXOjLESkpyMXZsadiKdUVFDqxduxcOhyUUvD16tAk33XQWRo0qHhSv9WjRXENI7JLVlDqZZV6ZskoeGbxSNiuapokDB07g3//egXff/QybNh2Bafbdu6Vr83BCUkUQBKiGCsYYdF2HaZqBYIjEQzVU/HnLnyM+7pZZt0ASpIi3dWUYgN1qBQcOXvjAmAmO42GVLLBZLQk3z87Ls+Lyy6dhw4YD2LbtBBoa+s4eBACfT8Pf/74ef//7esyaNQo33XQWLrhgCmS550BMk0FRRLS0eOHxqKEeTzabAkUR+/097o9pmlDVQMZWsJ1T8G9V1WCxUNlpMiiKgv379+PZZ5/F/v378cQTT6CkpARvv/02Kisr0z08EqNk9e/pKxNpzZrdER8TKZskmkDGYOw5FE/GTX/ZXwPxPEUKFL377g6Ul+eDMRZzBlRvz8OJEy2h2xPNqopkoDPpVFVHbe1hfPDBLjQ0uAAACxeOR3l5Hnbv7izvC/Z4stmUIdFQvCuaawiJXSY2pc6UVfLI4JWywFNjYyv+8Id/4+WXt/XaFDkoUvNwQlLFNE2InAhVV6GbOhgYOJMD0xma3E1YdXBVj8fkWfJw1aTo0sVN0wR4E5zMoPBiKGOIExnAmwk3z2aMobnZhZISJxYtcqC52YN9+07jyJGmqIJCGzcexMaNB1FS4sBXvjIPX/nKXAwb1vktsqoaUFUNLpcXuq5D1xlE0YBhmMjLs0DTEuvxpKo6VFUHYyb8fh2GYUIQeAiCGLrNapUTOgYBVq1ahfPPPx/z58/H6tWr8dhjj6GkpARbt27FH/7wByxbtizdQxwUklm+NBD6yrCJJZtkqK5aF0/GTSZkf3UPFAXLw95/fwfq6loARM6A6u313dvKb6dPu8Ky5pJdfhfLc5mM3809e07igw8+Q1lZHpxOK1RVx9GjTbj44qkRy86HSl+nrmiuISQ+mdaUOlNWySODV0o+9bhcPtx++wv46KOezZm7W7y4Bj/84ZdQUVGQiqEQ0oNmajCYAYtkgSmagcATOPAcj+e3PB/xMddNuw422Rbxth44QDX84HkOfLeMItXww4LEvs1gzATPc1BVPzSNQZYlTJxYhgkTStHU5MaBA004fdrV735OnXLhF794B7/+9Xu48MKpuOmmszBr1ijouopTp9rh92vwevUuq9oxnDrVjrFj/QmNX1V1eL3+jsBW56p2Hg8Ph8PasXpeQocI6y0X7Bs11LKoHnjgATz66KO455574Oiy1NKiRYvwxBNPpHFkg0e6+twkqrcMm/6ySbpeyDc2ukNlsl0N9pW84s24SXf2V/dAkdutoqHBBZutcz7qHjjs6/Ud6XmYM2c0Nmw4EHac/oKR0QSHut6npCQXpmmioaG9z2BSsn43gwG77qvWMYZB2cMsHjTXEDI4ZNIqeWRwSsknY0HgsXPnsT7vk5Oj4Je//DIuuGAKNQ8nA0ozNAiCAIlJcKtuGKYBgRfg1b1YfWh1j/tbRAuun3Z99Afg+8kI6u/2fgSDTZIkgeM6e0iJooyxY0tw991fgMdj4tlnP8SaNXv63Z+um3jttS147bUtmDixHEuW1MBm49HcrKIzOcuA1+uHJAFeb+wr5XVlGAZaWz3weAJ9pIIZT8EyPqczsd5ummaESvkCxzOhaYCiSJCkoZNRuW3bNrzwwgs9thcXF6OxsTENIxp8du8+gcOHG1Fenhfq/ZLNWT99ZZN0v5AfO7YUx4+3oKwsLyz4NNgzPjIheyke3QNFqqph4cLxofKxoK6Bw/6y2ro/Dw0NrlBT+t722VU0waGu98nPt4ExYPv2utDrrrdgUrIy8nrLcBs+PA/nnDM+614HqUBzDSGZI5Hm4MnqO0VIb1IyQ9psMhYtGo3ly3dGvF0QgK9+dSYuvHBqKg5PSJ8kQYLH70GTpwmGYYQynpbvWg6D9QwKXTn5ShTYos/I4zgudKHWnaIk3r9IEHhwHI+iIjtUVesSuJHAcTwkScJ551XjvPMmY+/eejz33Id46aWNcLvVfve9Y0cdduyog8UiYeLE4Rg/vhR2uwJRDASGDIOHqva/n75omoHW1kD/qK7bfD4Nmmb2W5rbl2D/KF3X4fEYME0DPC/AZgsEnASBGzKZT3l5eThx4gRGjRoVtn3Lli0oLy9P06gGD1XV8e672/HyyxtD2xYuHI/8fFtWZ/30lpnT/UK+ocGFSZPKcfRoU2ghkKGS8ZHu7KV4dA+YcRzw+utbeqzW1jVw2F8/q+7Pw7Ztkb9w7C0YGU1wqOt9ioocod83p9MKh8PSazApWavf9ZXhlo2vg1SguYaQzJBoc/BM7DtFBpeUfTVTUzMCb765C5rWmR3BcYAsAxaLDKvV0cejCUkdDhy8mhcevwc+wxcIVhgqPjz8YY/7CpyAW2feGtv+OQ6SJEAQOOhaZymZmKRyL4fDgpwcC+rqGqHrgb4agdXhVBQU5IStBjl2bCkee+xLeOCBC/Hyyxvx7LMfYv/+/lfD8/k01NYeQW3tEZSX56GmZhiqqvKRm2uHYSSWoRjoG6WDsUCgKPj88DwPXddhGD0DdtHSNBMej4bWVk8gqNixb7dbgNNpgySJUJShEXi65pprcP/99+Pll18Gx3EwTRNr167Fvffei+uvjyGDj0S0Z89JrF0bXk6+cuUuXHnlrEGZ9dP1Qt40GY4caUJOjoKrr56NggI7hg/PG7IZH9mia6BEVXXU1TX3WSrWXz+r7mVyVVVFMZWfRRMc6nqfrl9WqKoeKnuLFExK1up36c5wy4YecjTXEJIZktEcPNP6TpHBJWWz1/jxlaipGYZPPz0OAJAkDqIIyLKE6upSjB9PK12Q9PAbfqi6Cp/hg27o0E0dHx35CH6jZ++iC8dfiApnbCUzkiBBMzXwPA85QpAjmpXx+pKbq0BRAqvM+f1GqI+RLAtQFAEOR8/G3A6HBTfffDZuuuksrFmzB88++yHee29HVM3I6+paUFfXgpwcBVOmlGPChNKExq9pDIoiwefTYZqso1SQgeMC2zWt/330vm8NLS3t8Hr9MAwW6kEjCBwYM2GziRn3oT1VHnvsMdx4440oLy8HYww1NTUwDAPXXHMNvvOd76R7eFmvvr4VdruCoiJHWLlSYaF9UGb9BC/kg02pg+dcXT0MI0cW4pxzxg+Z3614ZFoAIZqASnX1MCxaNAGvv74FqqpBUSRccsk0VFcP67VM7rLLZkQdpIkmONT1Pl37UXXdZ6RgUjJXv0tXZlO29JCjuYaQzEDNwUmmS9nMtXBhNU6daoLH8x+4XFpHeRAwbJgNV189GwsXVqfq0IT0yaN54NN80AwNhmHAb/ix/tj6iPf92qyvxbx/nuOhCApUo2dJmiIo4LnEMm44TkBengOqqsPn06HrBkRRgMUiIi/PAaD3PkYcx2HBgnFYsGAcjh5twl/+shYvvPBxj3KLSNrbVaxbdwDr1x/CBRdMwfXXz8O8eWNi7tEmihx03YTNJkPXzS7beei6CUmKP6PK49Hgdqvw+TToemfgSRQ5mCaDx6MhJyfBzuVZQpIk/O1vf8MPfvADbN68GaZpYtq0aRg7lmr1k6G01Ame58JWu1IUEfPmjcmoi8JkCV7Iv/rqZni9flgsEhYvroEs8zh8uBHLl29GTU1Z2gMqvYkn8JOsYFGmBhCiCajYbBKmTx8Jj0eFzabAZgt8cdJ3mVx0QZpogkNd79PQ4MLCheOxfXsd7HYl4v27nls29uLqKltWjqS5hpDMkMrm4In0jiIkKGUzsCiKuPTS2cjJceDQoZPQdS9E0YqqqmFYvHgCRDF7Jn8yuPj8PuimDpEXYRomtp7YCo/WM/CyoGoBJpRMiOsYkiBB4AVohha2LdGgEwAYBkNurgWm6YTbrYYynux2Bbm5li4NwftWWVmAhx66GPfcsxSvvbYFf/rTGmzfXhfF8U288cYneOONT3DGGSW47rq5uPLKWcjPt0d1XEWRkZOjwOXygee5UDkcEFh0QJLizwjTNA0ejx8+nw6/XwsF5WRZ6ihJjL+Mr6tsWjVv9OjRGD16dLqHMeh0vSAOrna1eHENJkwYnD1NFEXEZZfNgNerYdOmg1AUEQcPnkZZWT5WrtyF0lIniopyMiKg0l1fgR8AEYNLyQwWZUsAobs9e07izTe3hm3bu7ceNTXlSemhFE1wqPt9SkocMAwTjY3ufoNJ2d6DKVl9qgYKzTWEDJxIgaBYmoPHEkhKtHcUIUEp/WRos8m44IKJ8HjGgTETHMfDZhMp6ETSiud5qLoKj98Dv+7Hx3UfR7zfbbNvS+w4HJ+SN2SO42CxSLDZZEiSEFrVTpIEWCyxB22sVhn/9V9zcPXVs1Fbexg///lb+PDDvTCM/iNY+/efwve+9xp+9KMVuPjiqbj++vmYPn1kn1lQNltg7F6vDlVVoesMoshBFBXYbHLoG/V4mCbg9+s4daoFmmZ2eW54DBuWF9U59UfTDLjd3h7Ny+1265BaNW+oGwwZFbE6dKgBb7+9LdQnbsGCaixbtgllZXlQFBH5+baMzH7qLfAzZUoFtm49FjG4lMxgUbYFEIL6Gncyeyh17TsVKQgYKYAUvO+aNbszonQxFZL1HBNCMkcyMof6CgRF0xw81kBSMnpHEQKkOPAEBDKfcnMH14cBkt1EXoTIiVB1FZ+d/gytas8P11OGTcGcyjlpGF3/bDYRpslgtysdPZKCzbkD5WQ2W3y/bxzHYebMKjz00EV4552tWL/+MD755Aja2/tfxc7n0/Dyy5vw8subUFNThuuvn4fLL5+BnBxLj/sKggBJksBYIGtI0wwwJkCWTUiSBEGIP3hjmiYaG9s7VsnrLEMERDQ2tuOMMxJLNzZNE83NHrS2ujsaugdK+dxuHn4/Q1GRPWMzn0jyZXtGRay697UKrtwpywIqKvLBGPDyyxtRUVGQUdlPvQVQdu+u7zW4lMxgUboCCImWCvY17mT2UAqONdoMs0wtXUy2ZD/HhJD0SlbmUH+BoP6CQbEGkqh3FEmWwTNDExIlkRfBOAZRELH5xOaI97lt9m0x9y4aKIqiIC/PjpYWNwQhfIx5eXbIcmJZVlariMrKQni9fowdW4zDhxuxZ88pHDvWHNXjd+48jgceWIYf/OANXH75dFx33XxMmtRZfmSaJjweFV6v1tFg3IRhMPA8D48nUDoYL00LBJuamjxgLNi4XIfH44csi9C0xErtvF4/mpvb0drajtZWLzTNgCQJcDqtHcFACXZ7z2AbIdksGMBobHRj3LhhyM+34dixZhQXO2C3K8jPt6OkJDe01H3w4j9Tysl6C6BYrZE/AiUzowdITwAhGcGZvsad7Iy/WDLMsrV0MVZDMauSkMEsWZlDiQaCYn18KntHkaGFZi+SsUxmQjO0UEZPsnokAYBFsuDoiaM45T7V47by3HJ8YcwXknKcVJAkHkVFObBYeLS366Ey1pwcETk5NshyYuVegsDD59MgyyIAHuPGlWL8+OHwelUcPNiITz89FlUzcrdbxfPPf4Tnn/8I06ePxHXXzcPFF38OHo+KlpZ2uN0+mCYLZW253T60tAjwep1xB280TYOmmRAEDh5PoHm8IAiw2eRQdlUiXC4VJ0+24PDhJvh8Pvj9JmSZh8ViwciROvLybBR4IoNK1wBGcEW7SZPKMWJEAdxuFddcMwe7dp0MLXVfVOQINX4GMqOcrLcASlVV5A/Nyc7oiSWAkKyG5skIzvQ37mRm/MWSYZatpYvxGGpZlYQMZsnKHEo0EBTr42PpHUVIXyjwRDKSZmjhq8IxQDM1KIICSYi/BxAAMMYg8RJqj9dGvP2qSVdB4DO3V48oirDbFfA8B7vdDAXmBIGH1SonVKoGAH6/EQj0SSIMQ4dhBIJRRUUOjB9fhoceugj79zfg+efXYe3afVHtc/Pmw9i8+TC+973lWLJkIiZPLofNJsPj8cHn02GxiLDZLGhvV9Ha6kVRUeRsg/5oGoPf74fHo8LvN0LZVACDzSYlIfDUjgMHTqOu7hR8PhOaxiBJHCwWHoZhoKLC2WumRDqsWbMGv//977F//34sW7YM5eXleP755zFq1CicddZZ6R4eyQJdAxjBVfyOHm3CTTedhVGjilFVVYRDh07j4MEG7Np1MvTeFJQJ/Wh6C6AAGLCMnmgCCMksIUtWcGagAh+xZJhR76PMQ3MNSZdsWm0tWZlDiQaCYn28IipR9Y4ipD8UeCIZx2RmeNCpC9VQIfBCQplPgiCgrq0Oe5v29rgtV8nFeWPPi3vfA0GSeEiSAEFQoOudgSdR5EMrrCVCVTUwFgg2WSxSl8AWF1ox75JLpuGSS6Zh7956/PWv6/DSSxvR2urtd9+trd5QL6jycifGjx+GsjInrNZAo/SKiiKoqtbvfnrDcYDHo6KtzQddN7o0Lhdgs0lhF8TxaGpqx7Fjp3DiRBt8Pg26bkIUeSiKCMaAhoZhOOOMhA6RNK+88gquu+46XHvttdiyZQtUNfA75XK58MMf/hArVqxI8whJNugewOB5Dg6HBTabEsqcmTy5EtXVw9HS4snYfjS9BVAGKqMnGsksIRvI4ExvWVqxZG/FkmHW332TlTVGokNzDUmXbFttLVmZQ4kGguJ5vCIqg76ReDYFMbMVzcQk42hG34EHzdASeiOQBAmv73o94m1LxixBjiUn7n0PBJ7noSgSVFWDLIcHmRRFSri5tWEwcBwgigI4zoRpAjwfCERxHGAYnT2Yxo4txSOPXIYHHrgQb7zxCZ5/fh1qaw9HdZy6ulbU1QUuagUhELxxOBRUVhagqqoYhYU5KCiwo6Ag8HdhYeD/Fxba4XRaI56nYZhgjAcQyHQyjEAZoigGtut63/2jNM1Aa6uno4G4Fy0tbrS2etHc7EFLiweffnoE+/bVo6XFA7/fgGEEjsMYwxVXfA4tLf03Yh8ojz76KP7v//4P119/Pf7xj3+Ets+bNw/f//730zgykqiBvLCONoCRrf1oMqmUKZklZAPVV6q3LK3LLpuBV1+tjTp7K5bXT1/3HSqNxzMJzTUkXbJttbVkZg4lGggaCoGkWGRbEDNb0SxMMg7rSKsxmQnd0DszegQxKT2eXv/sdew4taPHdotowfwR8yFwmVtmFxTIeOKgaZ0ZT5LEJ2VFNUkKnL8si9B1A4bBIAhcx+pwgCj2PIbVKuOqq2bjqqtmY/v2Ovz1r+vwyiu1cLujC8QYhgmPxw+Px4/6ehc2beo7eCUIPPLzbR0BqZxQkEpV/Th92gVN08HzPESR7whGMRw71orjx1vwxhvb0NLiQWtrIJjUGWTyRD3eSI4caUFrqzvuxyfb7t27sWDBgh7bc3Nz0dLSMvADIkkx0BfWsQQwMimIk42SmaUUbSAn0SBmb1la5eX5MWdvxfL66e2+Q6XxeCahuYakSzautkYBn8yUbUHMbEWBJ5JxOI6DpmvwG/7OjR09nmRBhsTH3+PpcMth/Oaj30S8bW7lXDgtTpgs/lXVBlIg8yk5zda7cjgssNuVjhXmAv2RTBMwDAN2ux0OR9/NsydNKsePfnQlvvOdi/HPf9biL39Zh507jyd1jIZhoqGhHQ0N7QDqk7rveDU3u+D3x18mmGzDhw/Hvn37UFVVFbb9ww8/xOjRo9MzKJKwgb6wztZMpmyU7Cyl/gI5yQhi9paldeRIUy/3T04D8N4CZkOp8XimoLmGpAuttkaSJRuDmNmIPjmSjCPwAvyGH7qpw6f5YDADAifAIgUCHg7FEdd+DdPAt976Fny6r8dtTsWJi8dfDEVSoOmZEzxIB1mW4HTa4fX64fNpoVI7iyWwXZKiC/zl5Fhw/fXzcd1187B582E8//w6vP76J/D5Bufzq+sAkFgPqWS67bbbcNddd+FPf/oTOI7D8ePH8dFHH+Hee+/Fd7/73XQPj8QpHRfWlMk0MAY6yJeMIGZvWVojRhT0cv/Ee0z1FTCjxuMDj+Yaki602hpJFgpiDgwKPJGMY5gGTMNEo7cRhmmESsncmhvF1mIYpgGRj/2l+6faP2FT3aaIt105+UqYMOHz+8Al2IA62wkCIMs8cnOtkGUJAAPAwWIRIcs8RDG254fjOMyYUYUZM6rw8MNfxC9/+TaWLatFS0v/zcizSaDpe+a8pX7rW99Ca2srFi1aBJ/PhwULFkBRFNx777244447Unrsxx9/HN/+9rdx11134Ve/+lVKjzXU0IX14DaQQb5kBDF7y9KaPXs06uqak5K91T27iTHWa8BsoHpbpUo2NkanuYakC622RpKFgpgDI7NnMzIkaYaGdq0dXMd/gf8F/mvX2pFr5kJBbJPKnoY9+MWHv4h425yKOaguqAYHDl7dG1dQazAxTQ6KIsNq1cHzHAzD7Gj+LUFRZJgJVCLm59txzTVz4HBI2Lz5KE6fdkNVNfj9BjTNABAIVLlcPrS3Z06j7q5EMdBknee5jj88ZJlDXp4NdntmvXYee+wxPPTQQ9i5cydM00RNTQ1yclLbPH/jxo14+umnMWXKlJQep6tsvFiLV7ZfWJPMkYwgZl9ZWsnI3oqU3TRlSgXy821obvaE3TcQMKvI2tLQbG6MPlTmGpJ5BmPPJFpdbeBREHNgZPZMRoYkr+aFaqihhuJdqYYKj98Du2yPen+aoeG+t+4L7xnVocBagMVnLIbOdHCMQ46SA93UEz6HbMfzCAVXgECAJfDvxPet6wzDhhVg/HgNlZV++P0GZFmA3S5j9OhSzJxZhQkTKqCqOpqa2tHU5EZjY/jfgT/taGx0h23vuuJeXxRFRF6eDXl5NjidVuTl2ZGXZ+2yLfB3fn7w9sC2TZv2Y8WKT3H4cGPHanYGOC7Q6H3EiALYbH33v0oHm82GmTNnDsix2tvbce211+KZZ57Bo48+OiDHzOaLtXhQzyWSLPEEMXsL8kbK0kpG9lakcsC1a/dh+vSRPQJPwYBZtpaGZntj9ME+1xDSm74CRbEGkWh1tfQZjEHMTEOfVEnGMZjR5+0MLKb9PbX+KWyv395jOwcOV0y6AjlK+LdyyVg5L5vxfODiguM4CIIIjmMdgScOqqonHHwyDAOMccjNtUFRpLCMKsY46Hrg56soIoYPz8Pw4XlR7dc0TXz88V68995OHDvWBJdLharqUBQROTkyysrysWjReJx55lhYrXJcY7dYRJSWOsDzgMejwTQDz43NJqG42AFZzpy3VLfbjR/96Ef497//jVOnTsHslqp24MCBpB/z61//Oi688EIsXry434sBVVWhqp1ZbW1tbXEdM9sv1uKRrRfWJLPEGsRMR5A3Ujmg3a6gsNCOvXs7tw2GrL9sbYw+VOYaQiLpK1AEIOYgUvfV1Uxm4o3P3kCeJQ8j80dSFg7JaplzlURIB4ETIPMy/GbPDCWZl8Ej+sjHtpPb8OTHT0a87dzR56KmuAYmTPDgIfIidKYP+YwnxjhIkgRV1UJZTlxHWydJkmCaifXAkiQeHo8PsizCMACOMyAIAmRZhMfjgyDEFlgM4nke+fk5qK4eBlkGvF4Dum5CFHlYrQKqqoahvLwg7qATADidVuTnOyCKAnw+DZrGIEkcLBYJDocNubmZk/F06623YtWqVbjuuuswfPhwcFxqe5f94x//wObNm7Fx48ao7v/444/jkUceSfi42XqxRkgmiCWImY4gb6RyQJ7nMG/eGJx9dvWgyfoLftlTXp4Hm01BQ4MrlNGV6f3bhspcQ4aeaLKVugeKAGDlgZWYOnxq6P9Huq23zJquq6uZzMRx13E0ehqx/th6LN+5nLKfSFbL3lmaDFp22Q6X3wWBF6AbnUEgURDBcRxssi2q/ai6ivveui9iIGm4YzguGndRxMd1L+8bajgOkCQOVqvcUU7GOrKfOEgSB0FIbP+iKMFul3HkSFOXflEGVNWPESMKol41L/K+OZgmgyCIEEUA4CCKPARB7MhOii+oFVRS4kRpaR44DrDZTDAWeL5EkUdJSR6KizPnAuGtt97Cm2++ifnz56f8WEePHsVdd92Fd999FxZLdMG3Bx98EPfcc0/o321tbaisrIz52NRsm5CBkY4gb2/lgBMmlHcEzVJz3IEUzCR7990dOH68BQ0NLixcOB75+TbMmFGV8ZlcQ2WuIUNLtCVvXQNFXZ12n0ZvBRqn3ZEfA4Svrub2u9HoaQQA2KVAi5Gugatk94Ki3lIk1Yb2FTbJSBbJAofsgMvvgiSGByEcsgMWKboPG79c+0vsbdzbY7vACfjvWf8NgzPATAYGBg4cdOjI4/JiyqgajDiOh82mdDQWDw88WSwyGEvs+eE4BotFRn6+DT6fDsNgHfsWYbHICZXymSYHl8sNURQhywDPGxBFAaIowuVywzAS+ya2sDAHo0YVwWoV4Hb7oOsMosjBbrdg2LB8FBamtplqLPLz81FQEHlJ82Srra3FqVOnMGPGjNA2wzCwevVq/Pa3v4WqqhC6RSwVRYGiJP6BhpptZ5ah1Oh9qElHkDedPc0G6rUczCTjeQ5lZXlwOq3Yt+8UvvnNpTjrrOqM//0ZKnMNGVr6ymTqmq3UNVDUVbE98vbgbe2+dmys24i6tjqU55ZjVvks5FhywlZXU41AieiCqgVocDeEHn/afTrpvaCotxQZCJk9m5Ehied45FnzIAoiXD4XDNOAwAtwWBzIkXOi6sG0qW4T/rDxDxFvu2H6DTij+Ay4VTd8fl+o1M4iW8CJqU0RzwaKwkMUBdhsCgzD7BJ44sHzPCyWxFKeAqV8IhwOK2S5M/CkKCIkKVB+Fy9d12CzWVBffwp6R6KbqgayqYqKSmCaCewcgCzLGDWqBBaLALdbC/WnstslDB9eCFmOv4wv2X7wgx/gu9/9Lv785z/DZosuSzBen//857Ft27awbTfddBPGjx+P+++/v8eFQDJRs+3MMdQavQ816QrypqOn2UC+lrtmkvE8B4fDAocDYAxZ8XszVOYaMrT0mcnURddAUdDC0QsxtnBs6P93v63CUYHfrv8t/rnjn6Htl0+8HHfMuQM5lpzQ6mq7T+/GvsZ9aHA3oMXXErpvsb046sBYtJK9P0IiyfwZjQxZAi8gR8kJZSQJfHQfKNx+N+57676ITcgnlkzEzTNuxqHmQxAFETnW8AwVZrKojzNYybIMh8MCl8sHvlv6kcNhSagULrB/HhwHCAIPi0UKBbaCK+dZrfG/Lfl8Bvx+HcXFufD5dDBmguN4WCwi/H4dqppY4AkI9HmyWkvQ1uaHaRrgeQG5uXJGBZ0A4Oc//zn279+P0tJSVFVV9fi5bd68OWnHcjgcmDQp/IOJ3W5HYWFhj+2pQM22M8NQbPQ+lAylIO9AvpazvVx4KM01ZPALlps1ehsxpnBMxKBPV4qohAJFp92nUWwPL1GLdNu6w+vCgk4A8M8d/8ScijlYdMai0OpqYwvHYtn2ZdjXuC90v2BQa93hdRHH31cZX1+iDbQRkojB92mBZD2TmaH0UgCBGumORCTVUCHwQp9ZTz9Z/RMcaTnSY7ssyPjp+T+FxEvIs+ShVW2FqqmhwIciKXAqTnAY2llPksTDZpMhyzx8Ph2maXZkOokQRRGSlFipnShKcDqt0DQDfr/REbzhIcsCnE4rOC7+wJ8och3BMg6yLIZ6MAGB7RyXWI+nIFmWUVSUWYGm7i699NJ0D4EMMdToffBLNMibCaWY0YxhIF/L2V4uTHMNGSy6lpsFG3tPLJmIPEseWnwtYZlMXQUDRZFEuq2urS7ifbtv7x7UKrAWQOCFUNDJZGaP66G+Svz66uEUT8kgIbGiwBPJOJqhQTM0+I0uq9oxQDM1yIIMjdd6rTf+8NCH+Osnf414293z78a44nFo9jZD4AVIggS/7ocBAyInQhIkCLww5DOeeJ6HogS+sczJCX+LUBSpRxZUrBRFgCDwkOXAvhnjwXGBQFEgCyr+599iUWC1KvD79bCVdQKZVArsdmtCY88mDz/8cFqPv3LlyrQenwy8bM/cIKmVCaWY0Y5hIF/L2Z5JRnMNGSy6lpvxHI8yRxmOtR7DddOuw8j8kUlrtl2eW97r9kjBoWAj8a49mPIsefjc8M+h3d8Or+aFXbbjjIIzIgbGgP57OPVXMkhIMmTHrEaGFMM0woNOXfgNPyxi5Obibb423P/O/RFvm142HbfMvAUAoAgKVF1Fu78d4ALNxhkY2v3tkHkZspDZmSwDQZIECAIHTevs8SRJfMJBJwChIJMkCZCk8CCTLIvgoujh1Ru7XcHw4bngOAa3W4WuB5qL2+0Khg3LhcWSWJlgtmlpacGyZcuwf/9+3HfffSgoKMDmzZtRWlqK8vLIH3wIiVe2Z26Q1EpnKWYwy+ngwVN49dXNsNsDC2j0NoaBfi1ne7kwzTVkMOhebsZzPByKAzbZltQ+R7PKZ+HyiZf36PE0bfi0XoNDkXowHXcdx67TuwAErm3KHGW9HrO/Hk79lQwSkgwUeCIZx+inAbRu6ECE+MEPPvgBTrpO9thuFa346fk/DWUyGcyACRNW0QrDNMJ6SJkwYbDE+wANBoHMp+Sv8GeagM2mQJYFeL2dpXxWa6CUzzDiL4fLzVUgyxLsdgskSQwFzWRZhCxLyM0dOhPo1q1bsXjxYjidThw6dAhf/epXUVBQgFdffRWHDx/GX/7yl3QPkQwyA5G5kQmlWiQ+6SrF7JrlVF6eh/37T6GoyIGysrxQ8Kn7GLI9C2kg0VxDBouBKjfLseTgjjl3YE7FnLBV7Q61Huo1ONQ1KGYyE7lKLl7Z+QoqcitQmlMKnuOx+tBqTCufFjFIFk0Pp75KBglJBppBScYJBohMZkI39FDwQBRE8BwPUej5sn1v33s9GvUFPXDOA6jKrwr9W9VU2KTAyit+zR9a1U4WZdgkG/y6Hxg68YkBFyyz0/VAbyfGENaXKZEeUoIgwOFQ4PNpHY3LEWpk7nAoQ2rFm3vuuQc33ngjfvKTn8DhcIS2n3/++bjmmmvSODIymKUycyMTSrX6Q4Gx3qWrFLNrppXNFpjcGxpccDqtcDgsvY4h27OQBgrNNWSwGMhysxxLDhadsShs2+m63oNDwaBYsPeURbTAp/ng9rtx3HUcZY4y8BzfazNw6uFEMgF9GiJxM5kJzdA6S7EEqc+m39ESeAE8eLT726EbeigwJAoicpXcHsdo9DTioXcfiriv+SPn45rPhX/wMWCAMQae4yGJUijjied4MMZgMjPhcyC9k6RAOZ3Pp8EwGBgL/jFht1shRAgsRkvTTNhsFgwfLsLnU2GaDDzPwWJRIMsiNG3o/Gw3btyI3//+9z22l5eX4+TJnpmBhGS6dJVqRRtMyobAWDqlqxSza6ZVQ4MLCxeOx8qVu6CqOhwOKgdNFM01JBX6aoSdKukuNyvOKcaYwjFw+92wy/bQinrBcSwcvRBvfPYGGj2NGF88HoW2QvgNP9yaG07FCYfi6DWQRD2cSCagT0IkLpqhwat7ewSGrKIVkpBYHx2BF6AaKnRTh8fvgQEDAgTYFFtoVbsgxhi++9530ehp7LGfHDkHP1r6ox6BKoVX4Df9gcCZroVKvTiJg9/0Q+KHVh+ggSbLgKr6YbPJocATx3EQBA6q6oecQIstxljoAi/YvDxIUURwQ2jBQovFgra2th7bd+/ejeJi+oaLZJ90lGrFEkxKZw+jbJCM8rV4Msq6Zlo1N3uQn2/DlVfOQnX1MNTUlFFWWoJoriHJ1l8j7FRKV7mZqqvYUrcFKw+uDF3TLKhagKVjl4aCX1dMugJ5ljysP7YeJbYSWEQL/r3/34HHGyouHn1xr4GkdAfVCAEo8ETiYDIT7f52+HRfj8CTYRpwWpwJZT5phgZVU9Hia4HJOptb+31+iJwIzdAg8oGX7hu73sDbe9+OuJ/vnvtdlOX2bLQniRKYydDibQn0eAoGPnQBBdYCSCIFnlLJ7wdycy1oa/Ohe6/y3FwL/H7AErl/fL8EQYAoCuB5DoZhdglqBRqjJ9K4PNt88YtfxPe//3289NJLAAJN3Y8cOYIHHngAX/rSl9I8OkJil45SrViCSenqYZRNEilfizejrHumVXOzBzNmVOHSS6dTwCkJaK4ZulKVldRfI+zBaG/jXqw+tBrDcoYFyuh0H/Y37ce1U68NPaeKqGBk/kgs37kcJ9pOIM+Sh8trLodH92DRqEWYN3Jen88/9XAi6UYzLomZqqtw+91hK88ZMKCZGgzTgEW0wCrFv2y9V/OiXWsHMxl8mg8GMyBwAqySFe1aO3yaD1bJivr2ejz8fuRlfBefsRiXT7w84m2MBUrrGGPQDA0mM8FzPHiOB4chlBKTJoZhQFFk5OcL8Pu1UDmcLEsQRQEsgVJHm02Ex6OC5yOvwGezDZ23vJ/97Ge44IILUFJSAq/Xi3POOQcnT57E3Llz8dhjj6V7eITELB2lWrEEk9LVw2ioiDejjBqFpxbNNUNTKrOSommEPdicbj8Nk5k42X4yrIpjQ90G1JTWhJ7TriVzLb4WAMCM8hkwTAN7G/dSFhPJaDTrkpipmhoWdOrKb/jh1/0JBZ7cfjdcPhfatfbQNp3pUNVA+Z3b6kaeNQ8PvvMg2tSe6d351nw8tuQxcL3UVXk1LwwYsEgWCLwQ1qPKgAGP3wO7bI97/KRvgQbfOkQxkJ3UXSJZSaIoIjfXirY2b4/bcnOtEMWh85aXm5uLDz/8EP/5z3+wefNmmKaJ6dOnY/HixekeGiFxSUcAIZZgUrp6GA0ViWSUUaPw1KG5ZmhKZVbSYGqEHW1WWHFOMdx+d4/WIS3eFuxt3Bt6TruWzDV5mrDz1E68t++9UKXJQJUkEhKPoXMVRpJGM7W+bzf6vr0/uqmHBZ26atfaYTADL257EasOrop4nx8s/gGK7EV9jk811EAJVpfAE8dxUA0VhmkkNH7St2BWUl+3J7Z/GbLMw+PRwZgJjuNhs4lDKujU1bnnnotzzz033cMgJCkGOoAQSzCJMmtSizLKMhvNNUNLKrOSBksj7FiywsYWjsXcEXNxoPlAaNuCqgVocDf0eE6DJXPb67ejtq42rL3JYC9JJNmNPg2RmAWbbzPGoBt6KHAjCmIocyghDFAEBT7dF1jtrGPVOY7jYBEtqGutww8/+GHEh148/mKcP+78vnfPMYgQ4dJdYcfUTA0OyQETQ2fls3QYiKykwDGG5tub1+vFv//9b1x00UUAgAcffBCq2hnoEwQBP/jBD2CJt5EWIUNIrMGkbMmsiadJd7r1FwTMxnPqLpvOgeaaoS2VWUmDpRF2LFlhiqjg82M+D6/mhUf3wC6Fr2oXyVAsSSTZLTNnM5IUJjOhGVpYKVkiTb+DFEkB7+Ph0lzhzblNAQ7ZAVlMYFmyjv3Lggyv3wtVU0Or2imSApEX8Yu1v4Bbc/d4XIm9BN/7/Pf63b8syNChwypaw8fPC9ChQxYSGz/pH2Ulpc5f/vIX/Otf/wpdDPz2t7/FxIkTYbUGyl937dqFsrIy3H333ekcJiFZI1uCSb3pHsyoqirCq6/WxtykO936CgLG23g8k2TbOdBcM7SlOitpMDTCjjUwNLZwLCrzKqN+TgdTSSIZGjJvJiNJESwnC+nI6FEEJeGMJEmQwHEcTGbCb/hDgRuFU5KS8WQVreAZD9VQoSHQ/NvkTMAA1u9djy0ntkR83A+X/hB51rx+9y/zMhySAy7N1dFvqJNDcoQyukhqDeWspFT629/+1uOD/gsvvIDRo0cDAP7617/iySefpIsBQoaASMGMWbNGYd+++rD7RdOkOxP0FgSMt/F4Jsm2c6C5ZmgbLFlJqRRrYCjW53SwlCSSoYOu+gYhk5nhQacuVEOFwAsJZT5phhYofQMHnuNhwgytCMeBg2ZoEPn4X1oCL8DkTFhEC3idD5XaNfma8OL2FyM+5qrJV2HR6EVR7V8SJDgsDvA8D5/qC2VUWRQL7LI94YwtQtJpz549qK6uDv3bYrGErfA3e/ZsfP3rX0/H0AjJWtlUAtVVpGDGW29txdy5Y9Dc7AnbHk2T7kyVSOPxTJFt50BzDUlWVlK0DbizTTyBoVieUwr+kWyT+Z+aSMz6a+6tGVpCb0qqpkLVA024fX4fdKZD5EQIigBVVxNe1c6reWEVrfDyXmjQoJs6ePD46yd/jdjYvCK3Ag8teijq/Qu8AEVQoPIqLHJn7wGRF6EISlLKEQlJl9bW1rCSxdOnw1O6TdMM68NBCOlbtpVAdRUpmKEoUsQFHrK5SfdgaDyebedAcw1JhlgacKdTPMGxgQgMDYaSRDJ0ZPYnJhIXxlhK96+aKpp8TWjyNME0zVCpnUtzocBWAKc18oenaGmGBs3Q4PK7QqV8Kw+uxKGWQxHv/+PzfowcOSfq/Qu8ABMmbLIt0By9I6NKFESYMCHwQv87ISRDVVRUYPv27Rg3blzE27du3YqKiswr2yAkU2VbCVRXkYIZdruCSZMqUFfXEtrW20p92SLa1QczOXMtlhUUMwHNNSQZYmnAHYtkZlHFGhyLdGwKDhFCgadBieM4gAG6qcPn98FgBgROgEW2JFQCF6QZGprcTfAZPpjMDAVueJNHk7sJFbmJfdAwYOCE+wQ4noPCK6hrq8N/Dvwn4n1vnH4jzhxxZmz7Nw3Iggy/4e9RVicLMgzTSMrzREg6XHDBBfjud7+LCy+8sMdqQl6vF4888gguvPDCNI2OkOyTbSVQXUUKZixZMhGXXDINU6ZURLVSXzaIZvXBTM9ci3UFxXSjuYYkQypWZusvUBRrUCqW4FjXY5vMhNvvxtwRc/H5MZ+nMjgy5GXmbEYSIgkS2nxtaFPboBt6oAcTeLg1N3KVXNjstoT2r2pqIIVaV8Mynnieh8zJULXEUqsNwwiUwnWU7b3wyQswmNHjfqMLRuO+s++Lef+MMUiCFFjFztBD20VBpDI7kvW+/e1v46WXXsK4ceNwxx13oLq6GhzHYdeuXfjtb38LXdfx7W9/O93DJCRrZFsJVFd9BTOyeaW+SPo7p0Qz1wYiWyqbfi4015B4dA/6pGJltr4CRWMLx8Zc2hdLcCx4bJOZOO46jkZPIw40H4BP96HMUYZp5dMGXS8rQqJFgadByGQmPJoHHs0TFngSTREiLyKf5ScUYDGYAQYGwzSg6Vooo0oSJbCO/xLBczyskhU+w4f39r+Hk+0nI97np+f/FBbJEmEPfQtmhPEcT43EyaBTWlqKdevW4b//+7/xwAMPhEpvOY7DF77wBfzud79DaWlpmkdJSPbIthKo7mINZmRyOVoiEslcy/RsqXSguYbEKlIm0oKqBVg6dine2ftOaFuiK7P1FyiKtbQvluBY8NhuvxuNnsbQdrffjRe2voDjruPY17gPQGb2siIklYbmbDnIuVU3VF0FYwwGMwLlcByDwALNv92qO6E+TAInwGQmNF0LZD0xMxTIMmUTHLiExi8JEhRBQUN7Az448EHE+9w+53Z8bvjn4t5/pCblXW8nJJuNGjUKb7/9NpqamrBvX+ADzpgxY1BQUJDmkRGSfbKtBCoRgznAkkjmWjb3+UolmmtILCJlIq0+tBp3zL0D40vGJ60Bd1+BonhK+2JZnS547O6ri0u8hEZPI9yaO7QtGb2sSHQG68qJ2Sa7P0WQiLyaF17NC9XseNPjABMmfIYPjDGoemKlcJIoQTVUaCyQ7WQwAwIEaEyDaqiQxPgDN62+Vmyq24S397yN9UfXR8yeGls4FnfOvTPuY/AcHyjlM3o+D7SqHRlMCgoKMHv27HQPg5Csl00lUIkYzAGWRDLXsrnP10CguYZEo7egT5O3CYtGL0racWIJFAX1VdoXy+p0wWO/8dkboW1nV52NA80HYBEtqMitgAgRiqTA7XfjcPNhCoKkWLasnDgUUOBpENJNHaqpgpkMft0fykiSRRkqVPgNf0L7N00TIidC0wIZT4ZpBFaCY4BoFWGaZtT7ave3Y+Oxjfj4yMf46OhH2Fm/s89SPZEX8aOlP4IsJFYiF+zxpBla2DYKOhFCCBmqBnOAJZHMtWzu80VIpkhFP6dI+goUxROUCu4zmsyk4LEnlU7CxmMbYTADJ9pOQDd1nFl5JtYeXouy3DK8s+8daKaGPQ170OJroSBICqVq5UQSOwo8DUIiL0LTNbSpbeGrzmk8cpXchEvJVEOFZmgwTTNUxmcyE6ZpQjO0iJlEQV7Ni83HN+OjIx/h4yMfY+vJrREbh/fm2qnXYlT+qITGH8RzPL3JE0IIIR0Ge4Al3sy1bO/zRUgmiDfoE4/eAkWxZC8lcuzxxeOx9cRW/HPnP9HoaQTP8ZhWNg2VeZVYtm0ZhjuGY1TBKEwsmYjdp3dj3eF1mDdyHl2XpEAqVk4k8aHA0yAkCAIM04BP80EztVDGk8RLsEv2QHZSAjRNg1fzwkRgRTuTmeA4DiZMeDUvNL0zi0jVVXxy4pNQRtMnxz/ps79SX0bnj8a5o8+N+/GEEEII6R0FWCIbSn2+CEmVgQj6RDuOZGW69NY7aG/jXqw+tBoA4NN9AIC1h9biyklXwiJZMCJvBIrsRXh156sAgG3123DcdbzXzCfqURS/gcq0I/2jGXMQMk0TYIG//ZofpmmC53kIUqAczjCjzzCKxDANaKYGVVPhM33QTR0iL4IxBp/ow45TO7Dy8Ep8fORj1B6vTbinFAA4FSeu/9z14DguKfsjhBBCSDgKsPRuqPT5IiRa8QRDkhn0Sbe+egcFs2wUocvzwQE2xQZZkDGmYAze2N3ZB0oRlF7Lv6hHUWIGMtOO9I0+SQxCmqnBo3nQprbB5w9kPUl8YCU3u2yPqbQtEo7j4PF7cNpzGn7Tj1ZfKxq8DWj0NKLJ25S0jCS7ZEdVfhWq8qowpXQKREGEy+eCYSQ2fkJI8j3++OP45z//iV27dsFqtWLevHn48Y9/jHHjxqV7aISQGGRSgEVVdezZcxL19a0oLXVSEGyIo3kmc1AwpO/eQcEsG7tsR6GtEI2eRgAAMxkun3g5PH5P6DGFtkLYZTuAyOVf1KMoMZmSaUco8DQoef1eNHub0eJtgW7qME0TKq/CZ/hgES1wq+7+d9JNu78dh5oP4UDTAaw9uBZbTmxBk7cJbWpbwoGsIKfFidkVs1FdVA2ZkyELcig7SzM0tKvtkDgJJhd983JCyMBYtWoVvv71r2PWrFnQdR0PPfQQlixZgp07d8Jut6d7eISQLKOqOl58cX2Psr+rr54TU/AplcErCowNLJpnMgcFQ/ruHTRvxLxQlk2ZowxOxYm5I+Zi4RkLUeWswsa6jdhWvw2KoMAu20OLG0Uq/6IeRYkbTJl22YxmxzQyWaAZN2MMHMclbVU1j9+DRk9jqNk3AwNncODBo9HTCK/mjfg43dRxrPUYDjYdxIHmAzjYfBAHmg7gUPMh1LfXJzyu7nLkHMyqmIUzK8/EmSPOxITiCRB4AbtP7camY5vg0TwAh1CPKiDQ2FzVqNSOkEzz9ttvh/372WefRUlJCWpra7FgwYI0jYoQkq327DkZFnQCgPff34lp00Zi8uSKqPaRrODVQO+bREbzTOagYEjfvYMUUcEXx38RZY4y1LXVoTy3HLPKZyHHkgMAmDdyHo67jkdV/kU9ishgQTNjmmiGhla1FW3eNvgNP2RBRq41F07Fmfiqc5oKZjK4dBdgAiZM8ODhZ35IvIRTrlPYeGwjDjQFgksHmw7iYPNBHGk5ktLG3RbRgpnlMzF3xFycOeJMTCqdBJHv+RI0TAMWyRIIkDGABw8wgAMHi2QBYyxlYySEJEdra2BZ9oKCgoi3q6oKVe0MIre1tQ3IuAghsUlXVk99fWsv29uiLgNMRvAqHfsm0elvngForkkVCob03TtI1VW8tuu1sNu6Ng+PpfyLehSRwYICT2lgMhP1bfU42nYUPt0HwzQg8AIs7RZU5laiLK8socwnxjEwMPj9fhxpO4JWXyva/e2Bvk/+QKBrIMiCjGll0zC3MhBomjJsStT1tDx42GQbNL3LqnyiFApCEUIyF2MM99xzD8466yxMmhQ5tfnxxx/HI488MsAjI4TEIpjVU1t7CEVFDng8KiZNqsAll0yDw2FJ6bFLS529bM+Neh/JCF6lY9+kf9HMMwDNNalCwZC+ewdtr9/ebyli9/IvVVexvX57j2bt1KOIDBYUeEoDl+rCwZaDoXK4YGCF53nopg6H1QGnJfIHrr6caj+FDcc2YMVnK7CxbiOafE0pGH3veI5HZW4lFo5eiMVjF2P68OmwSLF/MLUrdkiCBJ/pgyiKgUATB3B8oBzRptiSP3hCSNLccccd2Lp1Kz788MNe7/Pggw/innvuCf27ra0NlZWVAzE8QkiU9uw5idraQ2AMePnljQCAN9/ciqYmN772tYUpzXyqrh6GxYtrepSyVVcPi3ofyQhepWPfpH/RzDMAzTWpQsGQgN56B/VXith9RcAqZ1WPDKmuzdqpRxEZDCjwlAanXKfQ4G6AV/fCMIxQxpMgCGBuhtPtp6MKPNW11mHDsQ2hP4eaD6V+8B0kQUKBtQAFlgIUWAswMm8kzig8AzWlNTij4AwU2gvj3rdNtiFXyYVmatA5PdCjChxEXkSukgubTIEnQjLVnXfeiddffx2rV69GRUXv5SaKokBRhtYHVEKyTX19K4qKHKGgU9Bbb23DwoXjU1pSpigirr56DqZNG4H6ehdKS3NjLvNLRvAqHfsmfYt2ngForkklCob0rq9SxEgrAs4on4H9jfsBBCpj3H43lm1fhjJHGeaNnDfkAnpkcKLAUxq0+drgUT1o9DbCr/uhMx0iJ0IWZRRaC+HyuXo8hjGGQy2HsPHYRqw/uh4bj21EXVtdSsfJgUNZbhlG5Y/CqIJRGJU/CqMLRiPfmh9ojK5paPW1wmAGBE6A0+KEIivgOC6h48q8jEJ7ITSmwa/5Q/uXpcB2iU+sBxYhJPkYY7jzzjvx6quvYuXKlRg1alS6h0QISVBpqRMeT88FPRRFHJCSMkURMXlyZdzHSUbwKh37JpHRPEOyRV+liJFWBHxn7zs4s/JMNHmbcNx1HI2eRgDABwc/COsNRUg2o9kxDQzDQKO7EcfbjsOtuUPNv+2SPdAM3DTBGMPexr1hgaZT7lMpGU+ukovRBaNDAabR+aMxumA0RuaNjFgq1+ptxfG24/BxPuRYczrPCwYECBA4IaHxCLwQyKiyFUDV1FBGmCIpkAQJokAvW0Iyzde//nW88MILeO211+BwOHDy5EkAgNPphNVqTfPoCCHxqK4ehkmTKvDmm1tD24qKHLDblawpKUs0eJWufZOeaJ4h2aKvUsRIZXiKoMCtueH2u0NBJwCwS/YevaEIyVZ0BZ8GHMehrrkOTWoT/KYfMAMNwU+7T2NPwx58VPcR9jXtQ5M3eT2aOHBwKA4UWAswpXQK5lXNCwWaCqwFMWUpCbwQCA7xEryqN5SxZVWsEHgh4cAQYwyKqMCn+yDwAjiOA8/xEDgBiqjANM2E9k8ISb6nnnoKALBw4cKw7c8++yxuvPHGgR8QISRhiiLikkumoanJjbfe2gZFEWG3K1iyZCKVlJEBR/MMySa9lSJGKsOzy3bUFNdg28ltoW0Lqhagwd0AoLM3FCHZjAJPaaDqKsAFmoE3+BrQrrXDo3tgsuQFVOySHYWWQjgVJ/KUPOTKubCLdhQ7i7F4zGJMqZgS974ZYxAgwKf54DN8ME0TOq+D0zjkSrkJB4Y4joPf8IN1LF/HIRAUY2DwG37wfPwr/hFCUoMxWm6SkMHI4bDga19biIULx0UsKVNVHXv2nER9fStKS50DVm6WruOS9Mn0eaZ7w+ih2Gyb9C9SGd65Z5yLi8ZdhNKcUnxw8APYJTsa3A1o8bUACPSGIiTb0QydBrtO78L6E+tR56qDieQEmyqdlZhVMQtzKubAJtqws34ndtfvho/5YDADzVozvLoXRXlFCR+TcQwtagv8hh9AYLU5APAbfrSoLSjJLUlo/4ZpQDM0+HQfDNMAYwwcx8HUTUi8BN3QE9o/IYQQQqLXW0mZqup48cX1PRpsX331nJQGgdJ1XEJ6E6lhdNdVyQgJ6qsMb97IeTjuOh6xNxQh2Y5m5wHCGMOaQ2vwp9o/Yc2hNQnvb3TB6FCgaVbFLJTlloVuW7t3LRraGmCTbZCZDDAAHCByIk63nobH60no2F6/F5qpwTAN+DRfqNTOIlmgmRo8fg8ciiPu/RssEHjiwAEMYX9rhpbUzDBCCCGExGfPnpNhwR8AeP/9nZg2bWRKV7xL13GDKNuKdBepYTT15iH96pbE11dQipDeZEu2Jc2SKebTfFj+2XI8V/sc9jbujXs/1UXVoSDT7MrZfaZcqqYKURLBMQ48OsvSOHAQJRGq2XOFmliougqf7kOr2grGGBgYdOhQTRU8z0MztIT2rxkawAWCdQYzQs3FBSYAHBLePyGEEEISV1/f2sv21K54l67jApRtRSKL1DAaSKw3T7ZcTGaLTHk++8uO6603FCGRZFO2Jc2QfTCZCc3QQqVekiCB56LrL3TafRp/3fJXvPDpCzE3CefAoaa0JhRomlUxC/nW/Fh2gDxrHsADpmFCN3SIgghe4JGn5IEXEuuRZDADXs0LjuN6NCX3al7oZmKlcBw4GKYBt+aGpnc+/5qoQRKkmBqhE0IIISQ1SkudvWyPf8W7aLKJUnHcaKU724pkpkgNo4H4e/Nk08VkJugvqJRJzydlx5FkyqbXEwWeeqEZGry6F7qhw4QJHjxEQYRVtEISpF4f99mpz/Cn2j/hX7v+FeqBFA27ZEeulItiezFu+NwNuHTqpXGPvcBeAFmQYZftYT2SBF6ALMiBoFQiWKABuMzL0E0dDCyQTcWL4Dgu4eaPHM/B4/cEnj8OgeynjsbiHr+HAk+EEEJIBqiuHobFi2t6ZP/Eu+JdtNlEsRw32WVx6cy2IpkrUsPoRHrzZNPFZLpFE1QaiOcz2oyqVGTHkaErm15PFHiKwGQm2v3tPZpbC4YAwzTgtDjDMp9MZuKDAx/gT5v+hI+Pfhz1cXiOR76Sj2JrcSCgJUkYmTsSVsma0PjL88pRZCuC1q5BgxYKnEm8hCJbEcryyvrfSR8kUYJNtKHJ2wTTNDubfzMTBXIBLJIlof2bpglRFGFl1rBG4qIgQhRFGKaR0P4JIYQQkjhFEXH11XMwbdqIiCvexSrabKJoj5uKsrh0ZluRzJXs3jzZdDGZbtEElQaiFHJL3RasPrQ6dHtvGVXJzo4jQ1s2vZ4o8BSBqqvw+D045TmFxtZG+AwfLIIFhc5ClNhKYBEtsEpWePwevLLjFTy3+Tkcaj4U9f6dihOVuZUY5RgFxrFQ828LbwHHc7DKiQWecuQc1JTWQDM0tKqd38w5FSdqSmuQI+cktH9FUGCRLFBUBT7TB8YFMp4UPrBd5BN7WZkwYREs4BgHjdNgMAMCJ0ASJCgCpRcTQgghmaK3Fe/iEUs2UTTHTUVZXLKzvEh26SurJZm9ebLpYjLdogkqpboUckzhGKw8uBJljrJQckJvGVXJzo4jQ1s2vZ4o8BSBqqk41HgIO0/uRKvWGgp8OJucqBlWg3a1Hcs/W45/bP0H2tS2qPc7sWQibp55M8rt5fj4yMc40nIEOuvM6DE5ExWOCjitkb9Ni5ZhGrApNtQMr4HL6wqN32F1wKbYEl4VThEVmMyEIivgeb6zB5YoBbYnWCstcYEAk9/wg+f40Bs4x3FQBCXhwBYhhBBCMk+ys4lSURaX7Cwvkj0Gsk9QNl1Mpls0QaVUl0K6/W40ehrhVJxhK3tHyqiiletIMmXT6ynls2QiDbrTpdHbiC11W3Ck6Qh8zBcK3HhUD17d/Sr2Nu2NOnjDgcPnx3weN8+4GbMrZoPjOBxvO47i5mIwMLj97tBzY5ftKHYUoyS3JKHxq7oKURBRYCtArtL5YU0UAj2YfJoPdtke9/4NZsAu2aEaKgRZCLvNLtkTD2xJCgwYoV+Y4POjiIHtsigntH9CCCH9oyXjyUBLdjZRqsrikpnlRbLHQPZdyqaLyXSLJqiU6lLI4HWVaqhwoDPw1FtGVaLZcZmyQh/JDNmyEmJKP0FqhgbVUDs3MEAzNSiC0meD7nQ73nQcR5qPoNXXCh06TnlOoc5Vh1Z/5G/OIrFJNlwx6QrcMP0GVOVXhd1ml+wY6RwJr98beB5MADxgES0Y6RwJm2hLaPzB5tvBLKTuBF7osS0WhmHArtgBHmhxt0AzAqvN5dnzYJfsCfdgEnkRVsGKRq0R4DrPRzVUFEqFlPFECCFxijaYREvGk3RIdjYRlcWRZBrovkvZcjGZbtEGlVJZCtngbsCCqgU41nostC1VGWqZtEIfIbFI2adHk5nhQacuVEOFwAsZm/lU767HMdcxHGw5iGa1GZqpRf3YMkcZrp9+Pa6efDVyLZG/UeM4DjaLDWOKx8Dlc8FkJniOh8PigM1iA88n9rxIvARZkCOuqicLMgQuscCTIAhwq240+ZoADhBEASZMNHmaAAtQYC1IaP+MMSiSgnzkw6/7QxlPsihDkegNlRBC4hFLMImWjCfpksxsIiqLI8lEfZcy10AH6bpnWbX4WrB07FKMnTYWTd6mlGao0YqHJFulbObVjECwxmQmdEMPrawmCiJ4jodmaAn/MuqmDq/fGyqFs8rWuLNhdFPHhmMb8M6ed/Daztfg8rtievy04dNw04ybsLR6ab9j0AwNVtEK3srDoTjAEGjOLfBCoLeR7gcSeGqsshUe3QOBF3qsCsdzfMLNywVOgFt3R7zNrbsTDigapgFJkMBxHHh09pCyyIHG5bqhA5mbMEcIIRkplmASLRlPBgsqiyPJksw+QVQqld3SWQpJKx6SbJWywBNjDJqhhWXdGDCgmRpkQYbEJxY58Pg9aPQ2wuPzQGc6RE6EzWJDobUQNjm6UjVVV7Hu8Dq8s/cdvL//fTR7m2Mag8AJWFq9FDfPuBnTyqZF/TiO4yAKYigwFAw8BXswJVoKJ/IiHLIDLr+rRz8kh+xIuFRN1dXAqnPgYDIzNH6e46EIClQ9cqZbtAReCL12OJ4DBy50XCYwiPRNJSGExCyWYBItGU8IIeGSFWygUqnBIV2lkJR5R7JV6gJPYPAb/ogZT37DD4toiXvfuqnjROsJnHSfhMvngmZqkHgJDosDfr8fIwtH9hpc8fg9WH1oNd7e8zY+OPAB2v3tMR/fIljwlWlfwQ3Tb0BZblnMj+9aCte9B1MySuEAwCbbIIsyvH5vKDCUSEZYV8Hm4jzHQ9XVUKmgIiqwiollUwGBwBwHDoyxQGCuI+NJFMRQEIoQQkhsYgkmUW8cQgjpKRnBBiqVIomgFQ9Jtkp9c3FdhWEaoeCBYAgJR/PbfG041HwIx1qPod3XDp/pg4W3IMeSA5/fh3x7PgpsBWH3/8+B/+Cdve9g9cHV8Om+uI4rciLKHeW4ecrN+MqZX4l7/N1L4bpmPCWjFC40Xl6Ew+Lo/44xkngp1PRbEqTQz5bjOIBDUnp3sY7/wAVWBgTXZRshhJCYxRJMot44hBCSGlQqRRJBKx6SbJXSUjvd0NHmbYNL7ZKVpDiQb8tPaN+n20/jQNMBHGg4AJ/hC2VTWQQL/IYfI/JGgIHh/X3v4+09b+OjIx/F1CC8Kx487LIduXIunKITDpsDVmtigaFUl8Klml2x40T7iVAWUleqoQZWvEsAY52le7wQHsSijCdCCIlPrMEk6o1DCCHJR6VSJFG04iHJRiltLt7kacJn9Z/hdPtpaIYGSZBQnFOMCaUTkG/Nh1WKL4Bzqu0U9p7eC4/mgW7oMGBAgIA21obde3Zj+a7l2NO4ByYzuwMjuAAAGhBJREFU49q/XbKj0FIIWZBhEwP9oniOhyAIGGYdBoeUeBZRKkvhUo0DhwJLQWBVu24KLAUJB4cM0wgEtDjAr/thmiZ4nocsytRcnBBCEkDBJJJuqqpjz56TqK9vRWmpkzLpyJBDpVKDFzWNJ6R3KZvp3X43Pjn8CWqP18JjekIrz9l4G1SfiuG5w+G0Ru43AQSyXvyGv+cf3Y8dp3bghOsETrsDAS2v4UWbvw1ewxv3eEvsJVgydgmWVi8FZ3BYfXA1jjcfhw49FPgQIaLUWYoiR1Hcx+kqVaVwqcYYQ641F1bZCpfPFerx5LA4IAmJR4SCJYiqEWhSzvEcGFigabkAai5OCCGEZCFV1fHii+t7lHteffUcCj6RjJbMgEKqS6UGS/Aj286DmsYT0reUzfIfHvgQz297Hm7NDRNmKPuIMYYNJzfgxd0vQhEV+A0/VEMNBZW6BplSrSK3Akurl2Lp2KWYVjYt1Jtof8N+jCkaAwtvQbvRHirlyxFyUFFQgTxrXsrHlsk4jgMYIAkSCuwF/T8gjv331suJejwRQggh2WnPnpNhQScAeP/9nZg2bSQmT65I06gI6VsqAgqpKpUaLMGPbDwPahpPSN9SFnjafWo3jrQf6fX2JrVnmdZAGFMwBkuql+C8seehpqQmEETppjinGMMcwyCLMjw+DzSmQeIk2Cw2FFgLUJSTnIynbCUJUp89s5KR9SQJUq/N1wkhhBCSferrW3vZ3kblnyRjZVNAIZvG2pdsPA9qGk9I31IWeDLN+PorpcLEkomhzKYxhWP6vX+OkoOReSMhCAL8Fn9o1TZZlFHhqECOkjMAo85cPMdDEZRQKVxXiqAkHBziwEEWZPgNf4/m67IgU/CJEEIIyUKlpZFbLJSW5g7wSAiJXjYFFLJprH0ZyPNIVkkfNY0npG8pCzx1X+1soE0vm46lYwPBpsq8ypgey3M8SnNLYVEsaPO0hXo85dpy4VScFPhAZ0aSZmhh25Lx3HAcF5bxFEQZT4QQQkj2qq4ehsWLa3r0eKquHpbGURHSt2wKKGTTWPsyUOeRzJI+ahpPSN9SF3hKw+psDtGB0pxSXDv5Wlw/5/qE9iUJEgqsBXDIjrBtFPjoxHN8Suqsg6V8PMf3yHgK3k4IIYSQ7KIoIq6+eg6mTRuB+noXSktzaVU7kvGyKaCQTWPty0CdRzJL+lLdNJ6QbJeymd4qWsEh0D+J4ziE/uM4KIKCfEs+hjmHQRbknn9EGYqghP276+27Tu7C9pPb0eJrgcEMcBwHi2CBVbSi3FGO0pzSpJxDqgIrpG+pLuUjhBBCSHooiojJkyuppxPJGtkUUMimsfZloM4j2SV9qWoaT8hgkLLA0/iS8VgyakmgVE0IrGrHczx4I1CydlnNZfjChC/Ete81tjXwaT64fW74mT/UfFrmZNgUG+yyPclnQwZaKkv5CCGp87vf/Q4//elPceLECUycOBG/+tWvcPbZZ6d7WIQQQgaJdMwz2RRQSOWKecnohRStgXjOB0tpIiHZIGVX8cNyh6HSUQmbYoPIiZB5GSInwqbYUOmoxLDc+Ov5nVYnRuWNgl2ywyJaQn/skh2j8kYh356fxDMh6RLMOAv+oaATIZntxRdfxP/+7//ioYcewpYtW3D22Wfj/PPPx5Ejva9wSgghhESL5pn0CPZCemLtE3jh0xfwxNonsGz7Mqh6z+qEbBIs6esqG0sTCckGHGOMpWLHR5qPYGvdVuys34k2fxuYycDxHHLlXNSU1mBq+VRU5sfW9Du075Yj2HR0E463HIdbc4eyqeySHWV5ZZhZORMj8kYk+YwIIYT0Zc6cOZg+fTqeeuqp0LYJEybg0ksvxeOPP97nY9va2uB0OtHa2orcXFrhihAyMFRVx549J1Ff34rSUueg6jk1GN9XE5lngMH5nAyE7fXb8cTaJ3psv2v+XVmTCdabUCZXFpcmEpJO0b6vpmxmzVFyMDxvOCRBQru/HbquQxRF5Mg5KHIUIUfJiXvfDsWBXCUXzMng03xgjAX6PEkW5Cq5cCiO/ndCCCEkafx+P2pra/HAAw+EbV+yZAnWrVvX4/6qqkJVO78pbWtrS/kYCSGkK1XV8eKL63ussnf11XMGTfBpMIl1ngForkmWZPdCyiTZVEZJSDZLXXNxyYpCWyFEQYRf69KHSZLhVJywSJa49y1AwAjnCNQJdfAb/lA2lSzIKM8ph8jRhwVCCBlIDQ0NMAwDpaXhizuUlpbi5MmTPe7/+OOP45FHHhmo4RFChqi+Mpr27DkZFnQCgPff34lp00Zi8uSKdAyX9CHWeQaguSZZqBcSISRRKYvQKKICp9UJSZTg8/tC2y2yBTbJllAKI+MYnHYnFEVBu7cdBjMgcAJyrDmwiBYwpKR6kBBCSD84jgv7dzAjtbsHH3wQ99xzT+jfbW1tqKyMr/yaEEIi6S+jqb6+NeLj6uvbaNW9DBbtPAPQXJMswV5IKw+sDG2jXkiEkFikLPDEczxy5BwIvACraO08oCDCKloTahQtCzJ8ui/QVNzRM3NKEqS4900IISR2RUVFEAShx7fOp06d6vHtNAAoigJFoR4KhJDU6S+jqbTUGfFxpaXU+ycTxTrPADTXJIsiKrhi0hWYOnwq9UIihMQlpcuESYKEHDkHdtke+pMj5yQcGFJEBbIgR7xNFmR6EySEkAEmyzJmzJiB9957L2z7e++9h3nz5qVpVISQoayvjCYAqK4ehsWLa8JuW7y4BtXV8a+8TFKH5pn0CvZCWjR6ESaVTqLrLUJITFLeDInn+KS/MQWzqby6F7qhh7YnI5uKEEJIfO655x5cd911mDlzJubOnYunn34aR44cwe23357uoRFChqD+MpoURcTVV8/BtGkjUF/vQmlp7qBa1W4wonmGEEKyU9bOrJIgQeAFaIYWto2CToQQkh5XX301Ghsb8f3vfx8nTpzApEmTsGLFCowcOTLdQyOEDEHBjKbuPZ66ZjQpiojJkyupp1OWoHmGEEKyE8cYo07chBBC0qqtrQ1OpxOtra3IzaX+KoSQ5AisandiSGY00ftqT/ScEEJIckX7vjo0Zl5CCCGEEDLkUEYTIYQQkn5Ul0YIIYQQQgghhBBCUoICT4QQQgghhBBCCCEkJSjwRAghhBBCCCGEEEJSgno8EUIISbvgOhdtbW1pHgkhhAwOwfdTWkeoE801hBCSXNHONRR4IoQQknYulwsAUFlZmeaREELI4OJyueB0OtM9jIxAcw0hhKRGf3MNx+hrEEIIIWlmmiaOHz8Oh8MBjuNifnxbWxsqKytx9OjRIbVENp03nfdQMFTPG0js3BljcLlcKCsrA89Tdw0gsblmqL4O6byH1nkDQ/fc6bzjO+9o5xrKeCKEEJJ2PM+joqIi4f3k5uYOqQ8LQXTeQwud99AT77lTplO4ZMw1Q/V1SOc99AzVc6fzjl00cw19/UEIIYQQQgghhBBCUoICT4QQQgghhBBCCCEkJSjwRAghJOspioKHH34YiqKkeygDis6bznsoGKrnDQztc880Q/VnQec9tM4bGLrnTued2vOm5uKEEEIIIYQQQgghJCUo44kQQgghhBBCCCGEpAQFngghhBBCCCGEEEJISlDgiRBCCCGEEEIIIYSkBAWeCCGEEEIIIYQQQkhKUOCJEEJIVvjd736HUaNGwWKxYMaMGVizZk2f91+1ahVmzJgBi8WC0aNH4//+7/8GaKTJFct5//Of/8QXvvAFFBcXIzc3F3PnzsU777wzgKNNnlh/3kFr166FKIr43Oc+l9oBpkis562qKh566CGMHDkSiqLgjDPOwJ/+9KcBGm3yxHref/vb3zB16lTYbDYMHz4cN910ExobGwdotMmxevVqXHzxxSgrKwPHcVi+fHm/jxks72uZiuYZmmeiQfNMds4zwNCbazJqnmGEEEJIhvvHP/7BJElizzzzDNu5cye76667mN1uZ4cPH454/wMHDjCbzcbuuusutnPnTvbMM88wSZLYsmXLBnjkiYn1vO+66y724x//mG3YsIHt2bOHPfjgg0ySJLZ58+YBHnliYj3voJaWFjZ69Gi2ZMkSNnXq1IEZbBLFc96XXHIJmzNnDnvvvffYwYMH2fr169natWsHcNSJi/W816xZw3ieZ0888QQ7cOAAW7NmDZs4cSK79NJLB3jkiVmxYgV76KGH2CuvvMIAsFdffbXP+w+W97VMRfMMzTM0z0Q2GOYZxobmXJNJ8wwFngghhGS82bNns9tvvz1s2/jx49kDDzwQ8f7f+ta32Pjx48O23XbbbezMM89M2RhTIdbzjqSmpoY98sgjyR5aSsV73ldffTX7zne+wx5++OGsvCCI9bzfeust5nQ6WWNj40AML2ViPe+f/vSnbPTo0WHbfv3rX7OKioqUjTHVorkgGCzva5mK5plONM/0juaZ7DXU55p0zzNUakcIISSj+f1+1NbWYsmSJWHblyxZgnXr1kV8zEcffdTj/kuXLsWmTZugaVrKxppM8Zx3d6ZpwuVyoaCgIBVDTIl4z/vZZ5/F/v378fDDD6d6iCkRz3m//vrrmDlzJn7yk5+gvLwc1dXVuPfee+H1egdiyEkRz3nPmzcPx44dw4oVK8AYQ319PZYtW4YLL7xwIIacNoPhfS1T0TxD8wxA80wkg2GeAWiuiVYq39fEhB5NCCGEpFhDQwMMw0BpaWnY9tLSUpw8eTLiY06ePBnx/rquo6GhAcOHD0/ZeJMlnvPu7uc//zncbjeuuuqqVAwxJeI577179+KBBx7AmjVrIIrZ+dEmnvM+cOAAPvzwQ1gsFrz66qtoaGjA//zP/6CpqSlr+m/Ec97z5s3D3/72N1x99dXw+XzQdR2XXHIJfvOb3wzEkNNmMLyvZSqaZ2ieAWieiWQwzDMAzTXRSuX7GmU8EUIIyQocx4X9mzHWY1t/94+0PdPFet5Bf//73/G9730PL774IkpKSlI1vJSJ9rwNw8A111yDRx55BNXV1QM1vJSJ5edtmiY4jsPf/vY3zJ49GxdccAF+8Ytf4Lnnnsu6b6NjOe+dO3fiG9/4Br773e+itrYWb7/9Ng4ePIjbb799IIaaVoPlfS1T0TwTQPNMOJpnBsc8A9BcE41Uva9lZ7iWEELIkFFUVARBEHp8I3Xq1Kke38oEDRs2LOL9RVFEYWFhysaaTPGcd9CLL76IW265BS+//DIWL16cymEmXazn7XK5sGnTJmzZsgV33HEHgMAHZcYYRFHEu+++i3PPPXdAxp6IeH7ew4cPR3l5OZxOZ2jbhAkTwBjDsWPHMHbs2JSOORniOe/HH38c8+fPx3333QcAmDJlCux2O84++2w8+uijWZFpEo/B8L6WqWieoXkGoHkmksEwzwA010Qrle9rlPFECCEko8myjBkzZuC9994L2/7ee+9h3rx5ER8zd+7cHvd/9913MXPmTEiSlLKxJlM85w0EvoG+8cYb8cILL2RlH4JYzzs3Nxfbtm3DJ598Evpz++23Y9y4cfjkk08wZ86cgRp6QuL5ec+fPx/Hjx9He3t7aNuePXvA8zwqKipSOt5kiee8PR4PeD78I6wgCAA6v5kdjAbD+1qmonmG5hmA5plIBsM8A9BcE62Uvq8l3J6cEEIISbHgErh//OMf2c6dO9n//u//Mrvdzg4dOsQYY+yBBx5g1113Xej+weVg7777brZz5072xz/+MauXuY72vF944QUmiiJ78skn2YkTJ0J/Wlpa0nUKcYn1vLvL1tWGYj1vl8vFKioq2BVXXMF27NjBVq1axcaOHctuvfXWdJ1CXGI972effZaJosh+97vfsf3797MPP/yQzZw5k82ePTtdpxAXl8vFtmzZwrZs2cIAsF/84hdsy5YtoaW9B+v7WqaieYbmGZpnBu88w9jQnGsyaZ6hwBMhhJCs8OSTT7KRI0cyWZbZ9OnT2apVq0K33XDDDeycc84Ju//KlSvZtGnTmCzLrKqqij311FMDPOLkiOW8zznnHAagx58bbrhh4AeeoFh/3l1l6wUBY7Gf92effcYWL17MrFYrq6ioYPfccw/zeDwDPOrExXrev/71r1lNTQ2zWq1s+PDh7Nprr2XHjh0b4FEn5oMPPujz93Uwv69lKppnaJ4Jonmm02CZZxgbenNNJs0zHGODNE+MEEIIIYQQQgghhKQV9XgihBBCCCGEEEIIISlBgSdCCCGEEEIIIYQQkhIUeCKEEEIIIYQQQgghKUGBJ0IIIYQQQgghhBCSEhR4IoQQQgghhBBCCCEpQYEnQgghhBBCCCGEEJISFHgihBBCCCGEEEIIISlBgSdCCCGEEEIIIYQQkhIUeCKEEEII6cehQ4fAcRw++eSThPazcuVKcByHlpaWpIyLEELI4EDzDBnMKPBECCGEkIxz4403guM4cBwHSZIwevRo3HvvvXC73ekeWtQWLlyI//3f/w3bNm/ePJw4cQJOpzM9gyKEEAKA5hlCBpKY7gEQQgghhERy3nnn4dlnn4WmaVizZg1uvfVWuN1uPPXUU+keWtxkWcawYcPSPQxCCCGgeYaQgUIZT4QQQgjJSIqiYNiwYaisrMQ111yDa6+9FsuXL8enn36KRYsWweFwIDc3FzNmzMCmTZtCj1u3bh0WLFgAq9WKyspKfOMb3wj7BpvjOCxfvjzsWHl5eXjuuedC/96wYQOmTZsGi8WCmTNnYsuWLT3Gt2rVKsyePRuKomD48OF44IEHoOs6gMA36atWrcITTzwR+kb90KFDPUognnvuOeTl5eFf//oXxo0bB5vNhiuuuAJutxt//vOfUVVVhfz8fNx5550wDCN0bL/fj29961soLy+H3W7HnDlzsHLlysSfdEIIGUJonqF5hgwMCjwRQgghJCtYrVZomoZrr70WFRUV2LhxI2pra/HAAw9AkiQAwLZt27B06VJcfvnl2Lp1K1588UV8+OGHuOOOO6I+jtvtxkUXXYRx48ahtrYW3/ve93DvvfeG3aeurg4XXHABZs2ahU8//RRPPfUU/vjHP+LRRx8FADzxxBOYO3cuvvrVr+LEiRM4ceIEKisrIx7P4/Hg17/+Nf7xj3/g7bffxsqVK3H55ZdjxYoVWLFiBZ5//nk8/fTTWLZsWegxN910E9auXYt//OMf2Lp1K6688kqcd9552Lt3b6xPKyGEkA40z9A8Q1KEEUIIIYRkmBtuuIF98YtfDP17/fr1rLCwkF111VXM4XCw5557LuLjrrvuOva1r30tbNuaNWsYz/PM6/UyxhgDwF599dWw+zidTvbss88yxhj7/e9/zwoKCpjb7Q7d/tRTTzEAbMuWLYwxxr797W+zcePGMdM0Q/d58sknWU5ODjMMgzHG2DnnnMPuuuuusON88MEHDABrbm5mjDH27LPPMgBs3759ofvcdtttzGazMZfLFdq2dOlSdttttzHGGNu3bx/jOI7V1dWF7fvzn/88e/DBByM+L4QQQsLRPEPzDBk41OOJEEIIIRnpX//6F3JycqDrOjRNwxe/+EX85je/we9+9zvceuuteP7557F48WJceeWVOOOMMwAAtbW12LdvH/72t7+F9sMYg2maOHjwICZMmNDvcT/77DNMnToVNpsttG3u3Lk97jN37lxwHBfaNn/+fLS3t+PYsWMYMWJE1Odps9lC4weA0tJSVFVVIScnJ2zbqVOnAACbN28GYwzV1dVh+1FVFYWFhVEflxBChjqaZ2ieIQODAk+EEEIIyUiLFi3CU089BUmSUFZWFipz+N73vodrrrkGb775Jt566y08/PDD+Mc//oHLLrsMpmnitttuwze+8Y0e+wt+SOc4DoyxsNs0TQv9/+63RcIYC7sY6Pq47tv7EzyvoOAKS923maYJADBNE4IgoLa2FoIghN2v60UEIYSQvtE8E76N5hmSKhR4IoQQQkhGstvtGDNmTMTbqqurUV1djbvvvhtf/vKX8eyzz+Kyyy7D9OnTsWPHjl4fBwDFxcU4ceJE6N979+6Fx+MJ/bumpgbPP/88vF4vrFYrAODjjz8O20dNTQ1eeeWVsAuDdevWweFwoLy8HEBgZaGujVqTZdq0aTAMA6dOncLZZ5+d9P0TQshQQfNMZDTPkGSj5uKEEEIIyRperxd33HEHVq5cicOHD2Pt2rXYuHFjqLTh/vvvx0cffYSvf/3r+OSTT7B37168/vrruPPOO0P7OPfcc/Hb3/4WmzdvxqZNm3D77beHffN7zTXXgOd53HLLLdi5cydWrFiBn/3sZ2Hj+J//+R8cPXoUd955J3bt2oXXXnsNDz/8MO655x7wfODjVVVVFdavX49Dhw6hoaEh9E1yoqqrq3Httdfi+uuvxz//+U8cPHgQGzduxI9//GOsWLEiKccghJChiuYZmmdI8lHgiRBCCCFZQxAENDY24vrrr0d1dTWuuuoqnH/++XjkkUcAAFOmTMGqVauwd+9enH322Zg2bRr+3//7fxg+fHhoHz//+c9RWVmJBQsW4JprrsG9994b1mcjJycHb7zxBnbu3Ilp06bhoYcewo9//OOwcZSXl2PFihXYsGEDpk6dittvvx233HILvvOd74Tuc++990IQBNTU1KC4uBhHjhxJ2vPw7LPP4vrrr8c3v/lNjBs3DpdccgnWr1/f64pGhBBCokPzTADNMySZOBZNgSkhhBBCCCGEEEIIITGijCdCCCGEEEIIIYQQkhIUeCKEEEIIIYQQQgghKUGBJ0IIIYQQQgghhBCSEhR4IoQQQgghhBBCCCEpQYEnQgghhBBCCCGEEJISFHgihBBCCCGEEEIIISlBgSdCCCGEEEIIIYQQkhIUeCKEEEIIIYQQQgghKUGBJ0IIIYQQQgghhBCSEhR4IoQQQgghhBBCCCEpQYEnQgghhBBCCCGEEJIS/x9LDEMj2bqbLAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 1500x300 with 3 Axes>"
       ]
@@ -487,75 +437,41 @@
     }
    ],
    "source": [
-    "VisualUtils.plotTimeSeries('SERTAD2', aligner, plot_cells=True)"
+    "VisualUtils.show_gene_alignment('TNF', aligner, adata_ref, adata_query, annotation_colname, joint_cmap)\n",
+    "\n",
+    "# Visualise gene-level alignment in terms of only the cell-type composition \n",
+    "# VisualUtils.visualize_gene_alignment(aligner.results_map['TNF'], adata_ref, adata_query, annotation_colname, cmap=joint_cmap)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d0deeac2-d3da-48d8-aeda-6938512eb064",
-   "metadata": {},
-   "source": [
-    "The below attributes and functions can be used to examine any gene-alignment object"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "49e1485d-cf2b-4029-a729-0b45cfa5b995",
+   "id": "entitled-brass",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DDDIDIDIDDDMMMMMIIIIIID\n",
-      "\u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n"
-     ]
-    }
-   ],
    "source": [
-    "GENE = 'TNF'\n",
-    "gene_obj = aligner.results_map[GENE]\n",
+    "### Aggregate (average) cell-level alignment across all aligned genes\n",
     "\n",
-    "al = gene_obj.alignment_str\n",
-    "print(al)\n",
-    "print(VisualUtils.color_al_str(al)) "
+    "This is an average alignment which is sampled based on the frequency distribution of alignment states between each pair of reference and query timepoints. The heatmap value gives the number of genes where the corresponding timepoints have been matched. Note: There can still be different patterns of alignment across these genes (100% mismatching, 100% matching, early mismatching, late mismatching gene groups) which we will find by clustering in the next section. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
-   "id": "a796f7c8-64de-415b-91ae-ba4080ea3509",
+   "execution_count": 13,
+   "id": "spanish-bowling",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "01234567890123456789012 Alignment index \n",
-      "012 3 4 56789012      3 Reference index\n",
-      "\u001b[91m***\u001b[0m-\u001b[91m*\u001b[0m-\u001b[91m*\u001b[0m-\u001b[91m***\u001b[0m\u001b[92m*****\u001b[0m------\u001b[91m*\u001b[0m\n",
-      "---\u001b[91m*\u001b[0m-\u001b[91m*\u001b[0m-\u001b[91m*\u001b[0m---\u001b[92m*****\u001b[0m\u001b[91m******\u001b[0m-\n",
-      "   0 1 2   34567890123  Query index\n",
-      "DDDIDIDIDDDMMMMMIIIIIID 5-state string \n"
+      "Average Alignment:  \u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m (cell-level)\n",
+      "% similarity: 47.37\n"
      ]
-    }
-   ],
-   "source": [
-    "print(gene_obj.al_visual)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "7da06bc7-5478-49b2-bf3a-96e3765d26e6",
-   "metadata": {},
-   "outputs": [
+    },
     {
      "data": {
-      "image/png": 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\n",
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yl2EYGjFihLp27aqWLVuaWmvjxo3q1KmTfvrpJ1WtWlXz5s3TxRdfbGrNt956S+vWrQvqnI6idOzYUa+//rqaNm2q/fv364knnlDnzp21adMm1axZ05SaO3bs0IwZMzRixAg99NBDWrVqle677z653W7ddNNNptT8pffff19HjhzRzTffbGqdMWPGKD8/X82bN1d4eLi8Xq8mTJiggQMHmlYzJiZGnTp10uOPP64WLVooPj5eb775plauXKkmTZqYVvess987hX0n7dq1y/T6dvnpp5/04IMPatCgQYqNjTW11kcffaQBAwboxIkTSkxMVFZWlmrVqmVKrcmTJysiIkL33XefKdfHhXN8EDrr13+rNQzD1L/p2umee+7Rhg0bLPnbbbNmzbR+/XodOXJE7777roYMGaIlS5aYFob27Nmj+++/X5999tk5f6M3S9++ff3/3KpVK3Xq1EmNGjXS7NmzNWLECFNq+nw+tW/f3v8m9ssuu0ybNm3SjBkzLAlCr776qvr27Wv6fIe5c+fqjTfe0Jw5c3TJJZdo/fr1Gj58uJKSkkr9ZvvS+Pvf/66hQ4eqTp06Cg8PV9u2bTVo0CCtW7fOtJq/5qTvpNOnT2vAgAHy+XyaPn266fV69uyp9evX68CBA3r55Zd1/fXXa+XKlapdu3ZQ66xdu1bPPfec1q1bF9L/34XqSI1VHH9rrFatWgoPDz9n9CcvL6/Yt3aXR/fee6/mz5+vRYsWqW7duqbXi4yMVOPGjdW+fXtlZmaqdevWeu6550yrt3btWuXl5aldu3aKiIhQRESElixZoueff14RERHyer2m1T4rOjparVq10rZt20yrkZiYeE6YbNGihakT/M/atWuXFi5caMmb3keNGqUHH3xQAwYMUKtWrTR48GA98MADyszMNLVuo0aNtGTJEh07dkx79uzRqlWrdPr0aaWkpJhaV5L/iUOnfCedPn1a119/vXJycpSVlWX6aJD085/Rxo0b6ze/+Y1effVVRURE6NVXXw16nS+//FJ5eXmqX7++//to165d+r//+z81aNAg6PVQNo4PQpGRkWrXrp3/CZizsrKy1LlzZ5u6Cj7DMHTPPffovffe0xdffGHJF/r5+vB4PKZdv3fv3tq4caPWr1/v39q3b68bb7xR69evV3h4uGm1z/J4PNq8ebMSE817o3OXLl3OWf4gOztbycnJptU8a+bMmapdu7auuuoq02udOHFCYWGBX1Ph4eGmPz5/VnR0tBITE3X48GF9+umn6t+/v+k1U1JSlJCQEPCddOrUKS1ZsqRCfSdJ/wtB27Zt08KFC027lVwcs76XBg8erA0bNgR8HyUlJWnUqFH69NNPg16vrHyGy7ItFHFrTNKIESM0ePBgtW/fXp06ddJLL72k3bt368477zSt5rFjx7R9+3b/zzk5OVq/fr1q1Kih+vXrB73esGHDNGfOHH3wwQeKiYnx/20zLi5OUVFRQa8nSQ899JD69u2revXq6ejRo3rrrbe0ePFiLViwwJR60s/zO3497yk6Olo1a9Y0bT7UyJEj1a9fP9WvX195eXl64oknVFBQYOqtmwceeECdO3fWxIkTdf3112vVqlV66aWX9NJLL5lWU/r5ltzMmTM1ZMgQRUSY//XRr18/TZgwQfXr19cll1yir7/+WlOmTNHQoUNNrfvpp5/KMAw1a9ZM27dv16hRo9SsWTPdcsstQbl+cX/+hw8frokTJ6pJkyZq0qSJJk6cqCpVqmjQoEGm1Tx06JB2797tX8fnbNBOSEgo87pYRdVMSkrSH//4R61bt04fffSRvF6v/3upRo0aioyMDHrNmjVrasKECbr66quVmJiogwcPavr06dq7d2+Zl4Eo7n/XX4e7SpUqKSEhQc2aNStTPZjAxifWQsoLL7xgJCcnG5GRkUbbtm1Nf6x80aJFhqRztiFDhphSr7BakoyZM2eaUs8wDGPo0KH+/00vuugio3fv3sZnn31mWr3zMfvx+RtuuMFITEw0KlWqZCQlJRnXXnutsWnTJtPqnfXhhx8aLVu2NNxut9G8eXPjpZdeMr3mp59+akgytm7danotwzCMgoIC4/777zfq169vVK5c2WjYsKExduxYw+PxmFp37ty5RsOGDY3IyEgjISHBGDZsmHHkyJGgXb+4P/8+n88YN26ckZCQYLjdbqN79+7Gxo0bTa05c+bMQo+PGzfOlJpnH9MvbFu0aJEpNU+ePGlcc801RlJSkhEZGWkkJiYaV199tbFq1SpT6hUmFB+fX7+rrmVbKHIZhhGqSxwBAACTrdsd/LsQ59O2vvnzGEvL8XOEAACAczFHCAAAB/M6fEzE2Z8eAAA4GiNCAAA4WKg+1m4VRoQAAIBjMSIEAICD8YoN+Hk8Ho0fP97UlY+paU1NJ3xGalKTmtTEhWMdoV8oKChQXFyc8vPzLXnfDTUrTj1qUpOa1AyFmmXx5c7GltXq1mB78SdZjBEhAADgWMwRAgDAwXwOHxNx9qcHAACOViHnCPUJK9tbhH2GVznaohQ1V5grPMhdUdPKmk74jNSkJjUrbs0s39smdXWuL3Y2s6xWrwZbLatVUgQhAABCDEHIOswRAgDAwbyGs2fJOPvTAwAAR2NECAAAB/OxsjQAAIAzMSIEAICDeR0+JuLsTw8AAByNIAQAAByLW2MAADiY0x+fd3QQ6nhVW111e5/zHj9+5IQmD/mrhR0BAAArOToIJTSorU792p/3+OH9R6xrBgAAGzj9pau2BqG9e/dqxowZWr58uXJzc+VyuRQfH6/OnTvrzjvvVL169exsDwAAVHC2BaFly5apb9++qlevntLS0pSWlibDMJSXl6f3339ff/3rX/XJJ5+oS5cuRV7H4/HI4/EE7PMZXsteqgcAQHnmNZy9oKJtQeiBBx7Qn//8Zz377LPnPT58+HCtXr26yOtkZmbq0UcfDdiXohZqpEuC1isAAKiYbLsx+O233+rOO+887/E77rhD3377bbHXycjIUH5+fsCWoubBbBUAgArLqzDLtlBk24hQYmKili9frmbNmhV6/KuvvlJiYmKx13G73XK73QH7uC0GAABKwrYgNHLkSN15551au3at+vTpo/j4eLlcLuXm5iorK0uvvPKKpk6dald7AAA4go91hOxx9913q2bNmnr22Wf14osvyuv1SpLCw8PVrl07vf7667r++uvtag8AADiArY/P33DDDbrhhht0+vRpHThwQJJUq1YtVapUyc62AABwjFCdu2OVkFhQsVKlSiWaDwQAABBMIRGEAACAPZy+jpCzx8MAAICjMSIEAICDOf1dY87+9AAAwNEYEQIAwMG8Dl9HyNmfHgAAOBpBCAAAOBa3xgAAcDCfnP34PEEoSMJbOeON99tuqm55zch86/+QJk/dYHlN77FjltcEAKcjCAEA4GBMlgYAAHAoRoQAAHAwp7901dmfHgAAOBojQgAAOJiPl64CAAA4EyNCAAA4GHOEAAAAHIoRIQAAHMzn8HWECEJFiKlRVc8ufbxE57qio0zuJjScrF22f2XOeH3KPnhQH2zerPX79gW5KwAAyoYgVISIShFq2dUZr86wwm/q19ONrS/VtBUr9fxXX9ndDgBAktfh7xpz9ngYLBceFqb7O3fSmO7d7G4FAIDQDkJ79uzR0KFDizzH4/GooKAgYPMZXos6RFnd3qEDYQgAQoDPCLNsC0Wh2dV/HTp0SLNnzy7ynMzMTMXFxQVsOdpSout7Tp4KRpsoI8IQAMButs4Rmj9/fpHHd+zYUew1MjIyNGLEiIB918TdXKL6OzbsKtF5MM/tHTpIkiYv/dLmTgDAmZw+R8jWIJSeni6XyyXDMM57jstV9P9Bbrdbbrc7YF+YK7xE9bevy9G3y7YwIdpmhCEAgF1svTWWmJiod999Vz6fr9Bt3bp1ptb3+XyaNPh5Za8tfuQJ5uI2GQDYw+lzhGwdEWrXrp3WrVun9PT0Qo8XN1oUDPt3/aj7Oj2k3/y+nRq3TVF0bJUyXcdVs3qQOwtNR1q4iz/pFyLCwnRdy5ZyRxT/rxojQwAAq9kahEaNGqXjx4+f93jjxo21aNEi0/vwnvHq3++v0r/fX1Xma4S3csbttW03lT7wff6fHfpb/6sJQwCAEsvMzNR7772nLVu2KCoqSp07d9bkyZPVrFkz/zk333zzOQ9VdezYUStWrChxHVvHqbp166bf/va35z0eHR2t1NRUCzuCGZbu3Kk7P5gvz5kzJTqf22QAYB2vEWbZVhpLlizRsGHDtGLFCmVlZenMmTNKS0s7ZwDlt7/9rfbt2+ff/vWvf5WqDitLwxJnwxAjQwCAkliwYEHAzzNnzlTt2rW1du1ade/e3b/f7XYrISGhzHVCc+YSKiRGhgAg9PjksmwrbBFkj8dToj7z8/MlSTVq1AjYv3jxYtWuXVtNmzbVbbfdpry8vFJ9foIQLFWWMDQyjTAEABVBYYsgZ2ZmFvt7hmFoxIgR6tq1q1q2bOnf37dvX/3jH//QF198oWeeeUarV69Wr169ShyuJG6NwQalvU12a9f2kqSnP+M2GQAEW2nn7lyIhwtZBPnXawEW5p577tGGDRu0bNmygP033HCD/59btmyp9u3bKzk5WR9//LGuvfbaEvXEiBBsUdqRoVu7tmdkCADKObfbrdjY2ICtuCB07733av78+Vq0aJHq1q1b5LmJiYlKTk7Wtm3bStwTQQi2IQwBgP18hsuyrTQMw9A999yj9957T1988YVSUlKK/Z2DBw9qz549SkxMLHEdghBsRRgCABRm2LBheuONNzRnzhzFxMQoNzdXubm5OnnypCTp2LFjGjlypL766ivt3LlTixcvVr9+/VSrVi1dc801Ja5DEILtCEMAYB+vwizbSmPGjBnKz89Xjx49lJiY6N/mzp0rSQoPD9fGjRvVv39/NW3aVEOGDFHTpk311VdfKSYmpsR1mCwdLDl7LS/pPXbM8poJK35jynWzV+QoY8sHyhzVX+7Ikk2gTmv1tXYcfsqUft5J72DKdYvy3ci2ltestCbb8pq+SxpaXtNYucHymgAuTHGv2IqKitKnn356wXUYEULIWLl+pzKe+kCeUyUbGaoXd5saVh9lclcAULGF6hwhqxCEEFIIQwAAKxGEEHIIQwBgHZ/CLNtCUWh2BccjDAEArEAQQsg6G4Z8RsmWSicMAUDpeQ2XZVsoIgghpK1cv1Pf5t1NGAIAmIIghJB3+OSXhCEAMAlPjQHlAGEIAGAGghDKDcIQACDYCEIoVwhDABBcPiPMsi0UhWZXQBEIQwCAYCEIoVwqSxhKqT7S5K4AoPzxymXZFopsD0InT57UsmXL9N13351z7KefftLrr79e5O97PB4VFBQEbD7Da1a7CCGlDUP1424nDAEAAtgahLKzs9WiRQt1795drVq1Uo8ePbRv3z7/8fz8fN1yyy1FXiMzM1NxcXEBW462mN06QgRhCAAuDI/P22jMmDFq1aqV8vLytHXrVsXGxqpLly7avXt3ia+RkZGh/Pz8gC1FzU3sGqGGMAQAKKsIO4svX75cCxcuVK1atVSrVi3Nnz9fw4YNU7du3bRo0SJFR0cXew232y232x2wL8wVblbLCFFnw1DL2tMV5nIXe379uNslSTmHnza7NQAIaaH6NJdVbP30J0+eVEREYBZ74YUXdPXVVys1NVXZ2dk2dYbyiJEhAEBp2Toi1Lx5c61Zs0YtWrQI2P/Xv/5VhmHo6quvtqkzlFeMDAFA6fhC9Gkuq9g6InTNNdfozTffLPTYtGnTNHDgQBmGYXFXKO8YGQIAlJStQSgjI0P/+te/znt8+vTp8vl8FnaEioIwBAAl4zVclm2hyNkzpFChEYYAAMWxdY4QYLayzhnSoSUmdwYAoYGnxoAKriwjQ1cnDTC5KwBAKCAIwRFKG4b6JPQjDAFwBFaWBhyCMAQA+DWCEByFMAQAgXxyWbaFIoIQHIcwBAA4iyAERyIMAcDPnD5HiMfng8R77JjlNcOrVrW8Zsyn31pe8yHdYdq1O7b5SJmj+ssdWfwfhT4J/dQsap9pr+NY9Lz1/w4tGtPF8ppVtv5oeU1vx0str2ms3GB5TQClx4gQHG3l+p3KeOoDeU6dKdH5LLoIABULQQiORxgC4GQ+I8yyLRSFZleAxQhDAOBMBCHgvwhDAJzI6ZOlCULAL5wNQ7yoFQCcgSAE/MrK9Tt5az0Ax2BBRQDnKMuLWglDAFD+EISA8yAMAXAC5ggBOC/CEABUbAQhoBiEIQAVGSNCAIpFGAKAiokgBJQQYQhARcSIkM02b96smTNnasuWLZKkLVu26K677tLQoUP1xRdfFPv7Ho9HBQUFAZvP8JrdNhyKMAQAFYutQWjBggVq06aNRo4cqcsuu0wLFixQ9+7dtX37du3evVtXXnllsWEoMzNTcXFxAVuOtlj0CeBEhCEAFQkjQjZ67LHHNGrUKB08eFAzZ87UoEGDdNtttykrK0sLFy7U6NGjNWnSpCKvkZGRofz8/IAtRc0t+gRwKsIQAFQMtgahTZs26eabb5YkXX/99Tp69Kj+8Ic/+I8PHDhQGzZsKPIabrdbsbGxAVuYK9zMtgFJhCEAFQMrS4eIsLAwVa5cWdWqVfPvi4mJUX5+vn1NAcUgDAFA+WZrEGrQoIG2b9/u//mrr75S/fr1/T/v2bNHiYmJdrQGlBhhCEB5xhwhG911113yev/3hFfLli0VERHh//mTTz5Rr1697GgNKBXCEACUTxHFn2KeO++8s8jjEyZMsKgT4MKdDUMta09XmMtd7Pn1426XJOUcftrs1gAA5xEyc4SAioCRIQDlDbfGAAQVYQgAyg+CEGCCsoShK+JvMrkrADgXI0IATFHaMNT1oj8QhgDAYgQhwESEIQChjhEhAKYiDAFA6CIIARYgDAEIVYbhsmwLRQQhwCKEIQAIPbYuqIjyx3vsmOU1Yz791vKao6OKXuzzQvym1cd68v6r5Y4s/o9f14v+oPqRB01bdLHWM0dNuW5R/j7x95bXrLq3ZOEzmMI7Xmp5TWNl0S+pBgoTqi9DtQojQoDFVmzcqdHPzZfn1JkSnc86QwBgHoIQYAPCEIBQwVNjAGxBGAIA+xGEABsRhgDYjafGANjqbBji3WQAYD2CEBACVmzcyYtaAdiCOUIAQgJvrQcA6xGEgBBCGAJgNeYIAQgphCEAsA5BCAhBhCEAVmGOUIgxDMPuFoCQQBgCAPOFXBByu93avHmz3W0AIYEwBADmsu2lqyNGjCh0v9fr1aRJk1SzZk1J0pQpU4q8jsfjkccT+B8Jn+FVmCs8OI0CNjsbhlrWnq4wl7vY8+vH3S5Jpr2oFUDF4vQbMbYFoalTp6p169aqVq1awH7DMLR582ZFR0fL5Sr+fmJmZqYeffTRgH0paqFGuiSY7QK2IgwBgDlsC0ITJkzQyy+/rGeeeUa9evXy769UqZJmzZqliy++uETXycjIOGd06Zq4m4PZKhASCEMAzOBTaE5itoptc4QyMjI0d+5c3XXXXRo5cqROnz5dpuu43W7FxsYGbNwWQ0XFnCEACC5bJ0t36NBBa9eu1Y8//qj27dtr48aNJbodBjgZYQhAMLGgos2qVq2q2bNnKyMjQ3369JHX67W7JSDkEYYAIDhsD0JnDRgwQGvWrNF7772n5ORku9sBQh5hCEAwhOqCipmZmerQoYNiYmJUu3Ztpaena+vWrQHnGIah8ePHKykpSVFRUerRo4c2bdpUqjohE4QkqW7duurfv7+io6PtbgUoFwhDACqqJUuWaNiwYVqxYoWysrJ05swZpaWl6fjx4/5znnzySU2ZMkXTpk3T6tWrlZCQoD59+ujo0aMlrmPbU2MAgoOnyQBciFBdR2jBggUBP8+cOVO1a9fW2rVr1b17dxmGoalTp2rs2LG69tprJUmzZ89WfHy85syZozvuuKNEdUJqRAhA2TAyBKA88Hg8KigoCNh+vSjy+eTn50uSatSoIUnKyclRbm6u0tLS/Oe43W6lpqZq+fLlJe6JIARUEGUJQ50uus3krgCEOiufGsvMzFRcXFzAlpmZWYIeDY0YMUJdu3ZVy5YtJUm5ubmSpPj4+IBz4+Pj/cdKgltjQAVS2ttk7WoOlCR99ePLZrcGAIUugux2F/9ddc8992jDhg1atmzZOcd+veyOYRilWoqHIARUMIQhAKVh5fo+bre7RMHnl+69917Nnz9fS5cuVd26df37ExISJP08MpSYmOjfn5eXd84oUVG4NQZUQKW9Tdau5kBukwEIKYZh6J577tF7772nL774QikpKQHHU1JSlJCQoKysLP++U6dOacmSJercuXOJ6zAiBFRQjAwBKInSru9jlWHDhmnOnDn64IMPFBMT45/3ExcXp6ioKLlcLg0fPlwTJ05UkyZN1KRJE02cOFFVqlTRoEGDSlyHIARUYIQhAOXVjBkzJEk9evQI2D9z5kzdfPPNkqTRo0fr5MmTuvvuu3X48GF17NhRn332mWJiYkpcx2UYobqCQNn1CbvO7hYqrPCqVS2v6T12zPKaEfG1La9Z0LWhadfu2KaBMkf1lzuyZH/3OVDwgvLyJ5rSyxfHm5ly3aLMnNLP8prVtv9kec1KB09YWs+7cYul9Zwky/e2ZbUufn+8ZbW+S7euVkkxRwhwgJXrdyrjqQ/kOXWmROfXih2m2nEPmdwVANiPIAQ4BGEIAM5FEAIchDAE4NesXFAxFBGEAIchDAHA/xCEAAc6G4Z8Rskm9BKGgIqLESEAjrRy/U7tOXArYQiAoxGEAAc7/tNiwhDgcIaFWygiCAEORxgC4GQEIQCEIcDBmCMEACIMAXAmghAAP8IQ4EAOnyREEAIQgDAEwElC6u3zhw8f1uzZs7Vt2zYlJiZqyJAhqlevXpG/4/F45PF4Avb5DK/CXOFmtgpUaGfDUL1aryrMVbnY82vFDpMk017UCsA8oTp3xyq2jgglJSXp4MGDkqScnBxdfPHFmjx5srZt26YXX3xRrVq10pYtRb/dODMzU3FxcQFbjngjMnChGBkC4AS2BqHc3Fx5vV5J0kMPPaTmzZvrP//5jz777DNt375d3bp10yOPPFLkNTIyMpSfnx+wpai5Fe0DFR5hCKj4DMO6LRSFzByhlStX6pFHHlGVKlUkSW63Ww8//LBWrFhR5O+53W7FxsYGbNwWA4KHMASgIrM9CLlcP9+b9Hg8io+PDzgWHx+vH3/80Y62APwCYQiouFhHyGa9e/dW27ZtVVBQoOzs7IBju3fvVq1atWzqDMAvEYYAVES2PjU2bty4gJ/P3hY768MPP1S3bt2sbAlAEXiaDKiAQnSkxiohFYR+7amnnrKoEwAlRRgCUJHYfmsMQPnDbTKg4uCpMQAoA8IQgIqAIASgzMoShjpddJvJXQFAyRGEAFyQ0oahdjUHEoaAUMJLVwHgwhCGAJRXBCEAQUEYAsonFlQEgCAhDAEobwhCAIKKMASUMw6fI2Trgooof7zHjlleM7xqVctrntmfZ3nN2K+rFH9SkN115/2mXfvydh/qiYevkTuy+K+ZdjUHqkXkIR0/as6iixeNPmrKdYsy5bGBlteMO3nG0nrhrZpbWk+SvBu3WF4TFRsjQgBMsWptjh5+Yp48p0r2H+cqMcMUHcM6Q4DVmCMEACYhDAEIdQQhAKYiDAEhzuFzhAhCAExHGAIQqghCACxxNgwZJXyajDAEWMVl4RZ6CEIALLNqbY7yD91KGAIQMghCACx12rOYMASEEuYIAYC1CEMAQgVBCIAtCENAiGBECADsQRgCYDeCEABbEYYAmxku67YQRBACYDvCEAC72BqEvv76a+Xk5Ph/fuONN9SlSxfVq1dPXbt21VtvvVXsNTwejwoKCgI2n+E1s20AJiAMAbCDrUHo1ltv1c6dOyVJr7zyim6//Xa1b99eY8eOVYcOHXTbbbfptddeK/IamZmZiouLC9hyxNuJgfKIMARYzzCs20JRhJ3Ft27dqkaNGkmSpk+frqlTp+r222/3H+/QoYMmTJigoUOHnvcaGRkZGjFiRMC+a+JuNqVfAOY7G4biarwql6tysedXiRkmSTp+dKLZrQGogGwdEYqKitKPP/4oSfr+++/VsWPHgOMdO3YMuHVWGLfbrdjY2IAtzBVuWs8AzMfIEGAhHp+3T9++fTVjxgxJUmpqqt55552A4//85z/VuHFjO1oDYDPCEAAr2HprbPLkyerSpYtSU1PVvn17PfPMM1q8eLFatGihrVu3asWKFZo3b56dLQKwEbfJAAuE6GPtVrF1RCgpKUlff/21OnXqpAULFsgwDK1atUqfffaZ6tatq3//+9/63e9+Z2eLAGzGyBAAM9k6IiRJ1apV06RJkzRp0iS7WwEQohgZAszjCtG5O1ZhQUUA5QIjQwDMUKYgdPHFF+vJJ5/U999/H+x+AOC8CEOACXhqrPS2bNmijIwMNWjQQFdeeaXmzJmjkydPBrs3ADhHWcLQpTXvMbkrAOVVmYLQ8OHDlZycLK/Xq6ysLA0ePFjx8fG69dZbtXjx4iC3CACBShuGWlQfQhgCzoeXrpbelClTtGPHDq1Zs0YZGRlq2rSpjh07plmzZql3795q1KiRpk+fHuxeAcCPMAQgGC7oqbG2bduqSpUqOn36tPbu3asTJ07IMAzl5OTo3nvv1bZt2/Tss88Gq1cACFDap8laVB8iSdpwcJrZrQHlR4jO3bFKmUaEjh07pldeeUWdO3fWJZdcomeeeUbHjx9XQkKCHnnkEb355puqXr26Xn/99WD3CwABGBkCcCHKNCKUmJjoH/2RpJ49e+quu+5Senq6IiJ+vuS8efP09ttvB69TADgPRoaAC+DwEaEyBaHjx4+rWrVquummm3TXXXepWbNm55xzzz33qG/fvhfcIOA9dszymuFVq1pe08g7YHnNKpZXlH47fqRp1+50yb/0zN1Xy12p+K+2FtWHqHklj4xjT5vSy7Lha025blE2PNLa0npuS6v9LLxVc8trejdusbwmrFPqIOTz+fTyyy+rS5cuatasmVyuwmeBd+3aVV27dr3gBgGgpL7atEv/N31+icOQq+rtkmRaGALKBYePCJVpjtAdd9yhK6+88rwhCADscjYMeU6fKdH5rqq3y1XVvFEqAKGt1EEoLCxMycnJqlLFjkF1ACgeYQgoBdYRKr3x48dr27Zt+tvf/hbsfgAgKAhDAEqiTEHoL3/5iyIiIjRs2DBVrVpVKSkpatiwoRo2bKhGjRoFu0cAKJOzYcgwPCU6nzAEOE+ZnhrbtWuX/59PnDgR8DPzhgCEkq827ZJx+G6p+nS5XMU/58QEajiNy+GTpcsUhMaNGxfsPgDAPKe+JAwBKBRBCIAzEIaAwjEiVDYej0dz5szRihUrlJCQoFtvvVU7d+5Uy5YtVaNGjWD2CADBQRgC8Ctlmix98OBBtW/fXn/+85/1yiuvKCsrS5s3b1bPnj31/PPPB7tHAAie/4YhJlADkMoYhEaPHq1NmzapcuXK/veNXXHFFapSpYo++eSToDYIAEFHGALwX2UKQh999JHi4uL0n//8x78vPDxcycnJ2rFjR9CaAwDTEIYAST8/NWbVForKFITy8/PVoEEDJSQkBOz3er06evRoia9z77336ssvvyxLC34ej0cFBQUBm8/wXtA1ATgEYQhwvDIFofr162vTpk1atmyZf9+HH36orVu3qkGDBiW+zgsvvKAePXqoadOmmjx5snJzc0vdS2ZmpuLi4gK2HPGmYAAlRBiC0/GKjdIbOHCgzpw5o9TUVLlcLq1cuVLp6elyuVwaOHBgqa712Wef6Xe/+52efvpp1a9fX/3799dHH30kn89Xot/PyMhQfn5+wJai5mX5WACcijAEOFaZgtDYsWPVt29fGYYRsKWlpSkjI6NU12rVqpWmTp2qH374QW+88YY8Ho/S09NVr149jR07Vtu3by/y991ut2JjYwO2MFd4WT4WACcjDMGpDAu3EFSmdYQiIyP18ccfa+nSpVq1apUMw9Dll1+u1NTUMjdSqVIlXX/99br++uu1e/duvfbaa5o1a5YmTZokr5c5PwAswDpDgOOUeUFFSerevbu6d+8erF786tevr/Hjx2vcuHFauHBh0K8PAOdFGILThOhIjVXKFIR69ep13mMul0uff/55ia6TnJys8PDz38ZyuVzq06dPqfsDgAtCGAJCwtKlS/XUU09p7dq12rdvn+bNm6f09HT/8ZtvvlmzZ88O+J2OHTtqxYoVJa5RpiC0ePFiuVwu/2KKkvw/l+bt8zk5OWUpDwDmIwzBIUJ1fR9JOn78uFq3bq1bbrlFf/jDHwo957e//a1mzpzp/zkyMrJUNcoUhG666aaAwJOfn6/Fixfr6NGjGjBgQFkuCQChhzAE2Kpv377q27dvkee43e5z1jUsjTIFoVmzZp2z78CBA7r00ktVt27dMjcDACGnjGFIWmJuX0CwWDgi5PF45PEEPpnpdrvldhf/Z+t8Fi9erNq1a6tatWpKTU3VhAkTVLt27RL/fpkeny9MrVq11Lhx40JDEgCUa2V4tP7qJEbHgV8rbBHkzMzMMl+vb9+++sc//qEvvvhCzzzzjFavXq1evXqdE7aKUqYRocceeyzgZ6/Xq+zsbC1btkzVq1cvyyUBILSVcmSoT0I/SdL8H94yuzOg3MjIyNCIESMC9l3IaNANN9zg/+eWLVuqffv2Sk5O1scff6xrr722RNcoUxAaP378eSdF//73vy/LJQEg9BGGUBFZeGvsQm+DFScxMVHJycnatm1biX+nTEGofv36AUHI5XKpdu3a6t27d6lXlgaAcoUwBISsgwcPas+ePUpMTCzx75QpCO3cuVNHjhzRf/7zH1WqVEmNGjVSdHR0WS4FAOUPYQgVSCg/Pn/s2LGAV23l5ORo/fr1qlGjhmrUqKHx48frD3/4gxITE7Vz50499NBDqlWrlq655poS1yh1ENq3b5/uvvvugBejVqpUSQMHDtTUqVMVFxcnSdq7dy9PkKHc8h47ZnnNiPiSP+UQLGd27LS8ZrX6NSyv2fzVu0y5breGH2v6dVfLHVH8V6kVYajKmO9Nu3ZhTkyuY2k9STLvpsr52fFnEz9bs2aNevbs6f/57PyiIUOGaMaMGdq4caNef/11HTlyRImJierZs6fmzp2rmJiYEtcoVRA6cuSIunbtqp07dwYspnjq1Cm9/vrrWrt2rVavXq3Nmzfr97//vfbu3VuaywNAufLljl26++35IRWGgFIzSr4QstV69OgRkDd+7dNPP73gGqUKQk899ZR/Nehu3bqpbdu2crlcWrdunZYuXapNmzZp4MCB+vzzz3XMhr9RA4DVCENA+VaqIPTBBx/I5XLp9ddf14033hhw7O9//7uGDBmiDz74QNK5j9gDQEVFGEK5FsJzhKxQqgUVc3JyVK9evXNCkCQNHjxY9evXlyS99957Gjt2bHA6BIBy4GwY8pw5U6Lz+yT0Y9FFIAQEbWVpSQoLC1NUVJT69+8fzMsCQLlAGEJ55DKs20JRqYJQw4YNtWfPHs2ZM+ecY++++6527typhg0bBq05AChvzoah075TJTqfMATYq1RzhNLT07Vp0yYNHjxYL7/8sn+y9IYNG/T555/L5XLp6quvNqtXACgXvtyxSy/veFa3NXxAlcIiiz2fOUOwVYiO1FilVEFo5MiRevPNN7Vjxw4tXbpUS5cu9R8zDEPJyckaOXJk0JsEgPJmc8EGwhBQDpTq1lhcXJyWLVum/v37y+VyyTAMGYYhl8ul3//+9/ryyy9VrVo1k1oFgPLlbBjiNhlCmdPnCJV6ZemEhATNmzdP+fn5ys7OliQ1btyYt84DQCEYGQJCW5mfGouLi1OHDh3UoUMHQhAAFIGRIYQ0w8ItBAX18XkAQOEIQ0BoIggBgEUIQwhJjAjZ669//auGDBmif/7zn5J+flXHxRdfrObNm+uhhx7SmWIWJvN4PCooKAjYfIbXitYBoNQIQ0BosTUIPf744xo7dqyOHz+u+++/X5MnT9YDDzygG2+8UUOGDNErr7yixx9/vMhrZGZmKi4uLmDL0RaLPgEAlB5hCKHE6U+N2RqEZs2apVmzZumdd97RggULNHbsWD333HMaO3asMjIy9OKLLxa6ivUvZWRkKD8/P2BLUXOLPgEAlA1hCAgNtgahffv2qX379pKk1q1bKywsTG3atPEfb9u2rX744Ycir+F2uxUbGxuwhbnCzWwbAIKCMATYz9YglJCQoO+++06StG3bNnm9Xv/PkrRp0ybVrl3brvYAwHSEIcBepV5QMZgGDRqkm266Sf3799fnn3+uMWPGaOTIkTp48KBcLpcmTJigP/7xj3a2CACmY9FFwD62BqFHH31UUVFRWrFihe644w6NGTNGl156qUaPHq0TJ06oX79+xU6WBoCKgDAE24ToJGar2BqEwsPDNXbs2IB9AwYM0IABDPsCcB7CEGA929cRAgD8D3OGYDUenwcAhBTCEGAdghAAhKCyhKGhKf1M7goVEq/YAACEotKGoevqXUEYAkqJIAQAIYwwBNMxIgQACGWEIcA8BCEAKAcIQzCL058as3UdIQD/Yxw/YXnN8KpVLa8ZsfuQ5TWj9idZXnPDI62Df01JOe0+0BMPXyN3ZPFf39fVu0KH98Tp6UXLgt6LJOmWY+ZctwgNnrG8pC1/NmEdRoQAoBxZtTZHDz8xT55TZ0p0/u2dO2hkz64md4VyjTlCAIDyhDAEBA9BCADKIcIQgsXpc4QIQgBQThGGgAtHEAKAcowwhAvGHCEAQHnmD0NnCENAaRGEAKACWLU2R3e/PZ8wBJQSQQgAKogvd+wiDKH0uDUGAKgoCENA6RCEAKCCIQyhNHh8HgBQ4RCGgJIhCAFABUUYQokwR8g++/bt01/+8hf16tVLLVq0UMuWLdWvXz+9+uqr8nq9drYGABUCYQgomm1BaM2aNWrRooU+/PBD/fTTT8rOzlbbtm0VHR2tkSNHqlu3bjp69Gix1/F4PCooKAjYfAYhCgDOIgyhSIwI2WP48OF64IEH9PXXX2v58uWaPXu2srOz9dZbb2nHjh06efKkHn744WKvk5mZqbi4uIAtR1ss+AQAUH4QhoDC2RaE1q1bp8GDB/t/HjRokNatW6f9+/erevXqevLJJ/XOO+8Ue52MjAzl5+cHbClqbmbrAFAuEYZQGJ4as0nt2rW1b98+/8/79+/XmTNnFBsbK0lq0qSJDh06VOx13G63YmNjA7YwV7hpfQNAeUYYAgLZFoTS09N15513asGCBVq0aJFuvPFGpaamKioqSpK0detW1alTx672AKDCIgwhAHOE7PHEE0/o4osvVr9+/dS7d295PB699tpr/uMul0uZmZl2tQcAFRphCPhZhF2Fq1atqrlz5+qnn37SmTNnVLVq1YDjaWlpNnUGAM5wNgxNv+5quSOK/8/B7Z07SJKeXrTM7NZgoVCdu2MV2xdUrFy58jkhCABgDUaG4HS2ByEAgL0IQw7HHCEAgNOVKQy16m1yV4D5CEIAAEllCEPNOhOGKgJGhAAA+BlhCE5DEAIABCAMOYvLwi0UEYQAAOcgDMEpCEIAgEIRhuAEti2oCMB+3mPHLK8ZUbuW5TWr7vNZXjPipNfymg3/eTTo1/xeh/TY0qMaN3WQIt2Vij3/9madFf5DJT37iTmLLu7pY8pli5R8oq71Ra0UopOYrcKIEACgSGuXb9ejw+folOd0ic6/tWcHPdCXdYZQPhCEAADFIgxVXC7Dui0UEYQAACVCGEJFRBACAJQYYagCYkFFAABK7mwY8pwu2dNkhCGEMoIQAKDU1i7frvtmzycMVQSMCAEAUHr/zt5FGEK5RxACAJQZYaj846kxAAAuAGEI5ZntK0sfP35cc+bM0fLly5WbmyuXy6X4+Hh16dJFAwcOVHR0tN0tAgCKcTYMPT/karkrFf+fllt7dpAk01agRimE6EiNVWwdEfruu+/UtGlTjR49WocPH1b9+vVVt25dHT58WKNGjVKzZs303Xff2dkiAKCEGBlCeWTriNCwYcPUvXt3zZ49W5GRkQHHTp06pZtvvlnDhg3TokWLbOoQAFAaZRkZ2rgnVwu/3W5BdyhMqM7dsYqtI0IrV67UI488ck4IkqTIyEg99NBDWrlypQ2dAQDKqrQjQ/f9tovJHQHnZ2sQql69urZt23be49u3b1f16tWLvIbH41FBQUHA5jOsf+szAOB/ShOGGtauoWj3uX8hhkVYR8g+t912m4YMGaKnn35a33zzjXJzc7V//3598803evrppzV06FDdcccdRV4jMzNTcXFxAVuOtlj0CQAA51OaMFS1MkEI9rB1jtD48eMVFRWlKVOmaPTo0XK5XJIkwzCUkJCgBx98UKNHjy7yGhkZGRoxYkTAvmvibjarZQBAKfw7e5fGv7tQmQN+a3crOA+nzxGy/fH5MWPGaMyYMcrJyVFubq4kKSEhQSkpKSX6fbfbLbfbHbAvzBUe9D4BAGWz+8ARu1sAzitkFlRMSUlRp06d1KlTJ38I2rNnj4YOHWpzZwAAoKIKmSBUmEOHDmn27Nl2twEAQMXl8MnStt4amz9/fpHHd+zYYVEnAAAg1CxdulRPPfWU1q5dq3379mnevHlKT0/3HzcMQ48++qheeuklHT58WB07dtQLL7ygSy65pMQ1bA1C6enpcrlcMozzx8SzE6gBAIAJQnSkRvr5NVytW7fWLbfcoj/84Q/nHH/yySc1ZcoUzZo1S02bNtUTTzyhPn36aOvWrYqJiSlRDVtvjSUmJurdd9+Vz+crdFu3bp2d7QEAABv17dtXTzzxhK699tpzjhmGoalTp2rs2LG69tpr1bJlS82ePVsnTpzQnDlzSlzD1iDUrl27IsNOcaNFAADgwrgM67bCFkH2eDxl6vvs0+ZpaWn+fW63W6mpqVq+fHmJr2NrEBo1apQ6d+583uONGzfmPWMAAFQQhS2CnJmZWaZrnV1yJz4+PmB/fHy8/1hJ2DpHqFu3bkUej46OVmpqqkXdAADgQBbeeClsEeRfrwVYWr+eS2wYRqnmF9u+oCIAAHCGwhZBLquEhARJP48MJSYm+vfn5eWdM0pUlJBeRwgAAJjLZRiWbcGUkpKihIQEZWVl+fedOnVKS5YsKXLaza8xIgQAAELSsWPHtH37dv/POTk5Wr9+vWrUqKH69etr+PDhmjhxopo0aaImTZpo4sSJqlKligYNGlTiGgQhABVexAmv5TUrrcm2vOaPAy61tF7Sl8dKdF6tlieKPSd+5XGF/1j89X7oVrVENYPpaNNqlte0VAg/nL1mzRr17NnT//PZ+UVDhgzRrFmzNHr0aJ08eVJ33323f0HFzz77rMRrCEkEIQAAEKJ69OhR7KLL48eP1/jx48tcgyAEAICDuUJ4RMgKTJYGAACOxYgQAABOxogQAACAMzEiBACAgzFHCAAAwKEYEQIAwMkYEQIAAHCmkA5C+/fv12OPPWZ3GwAAoIIK6SCUm5urRx991O42AACosFyGdVsosnWO0IYNG4o8vnXrVos6AQAATmRrEGrTpo1cLleh7xE5u9/lctnQGQAADhGiIzVWsTUI1axZU5MnT1bv3r0LPb5p0yb169evyGt4PB55PJ6AfT7DqzBXeND6BAAAFZOtQahdu3b64YcflJycXOjxI0eOFPnWWUnKzMw8Zx5RilqokS4JWp8AAFRUoTp3xyq2Tpa+44471KBBg/Mer1+/vmbOnFnkNTIyMpSfnx+wpah5kDsFAAAVka0jQtdcc02Rx6tXr64hQ4YUeY7b7Zbb7Q7Yx20xAABKqJg7LxVdSD8+v2fPHg0dOtTuNgAAQAUV0kHo0KFDmj17tt1tAABQYbGOkI3mz59f5PEdO3ZY1AkAAHAiW4NQenr6edcROot1hAAAMFGIjtRYxdZbY4mJiXr33Xfl8/kK3datW2dnewAAoIKzNQi1a9euyLBT3GgRAAC4MC6fdVsosvXW2KhRo3T8+PHzHm/cuLEWLVpkYUcAAMBJbA1C3bp1K/J4dHS0UlNTLeoGAAAHcviNl5B+fB4AAMBMto4IAQAAe4Xq+j5WYUQIAAA4FiNCAFBB1HhluaX1Dv65c4nOy29YpdhzDjeP1sGE4ocm6n56qEQ1g+lo02qW14R1CEIAADiZw5ep4dYYAABwLEaEAABwMCZLAwAAOBQjQgAAOBkjQgAAAM7EiBAAAA7GHCEAAACHYkQIAAAnYx0h++3du1fHjh07Z//p06e1dOlSGzoCAABOYGsQ2rdvny6//HIlJyerWrVqGjJkSEAgOnTokHr27GljhwAAVGwuw7otFNkahB588EGFh4dr5cqVWrBggb777jv16NFDhw8f9p9jOHzIDgAAmMfWILRw4UI999xzat++va644gotW7ZMdevWVa9evXTo0M8v1nO5XHa2CABAxWZYuIUgW4NQfn6+qlev7v/Z7XbrnXfeUYMGDdSzZ0/l5eUVew2Px6OCgoKAzWd4zWwbAABUELYGoYYNG2rDhg0B+yIiIvT222+rYcOG+v3vf1/sNTIzMxUXFxew5WiLWS0DAFChMEfIRn379tVLL710zv6zYahNmzbFzhHKyMhQfn5+wJai5ma1DAAAKhBb1xGaMGGCTpw4UeixiIgIvffee9q7d2+R13C73XK73QH7wlzhQesRAIAKzReiQzUWsXVEKCIiQrGxsec9/sMPP+jRRx+1sCMAAOAkIbGg4vkcOnRIs2fPtrsNAAAqLoc/NWbrrbH58+cXeXzHjh0WdQIAAJzI1iCUnp4ul8tV5IRo1hECAABmsfXWWGJiot599135fL5Ct3Xr1tnZHgAAFR6Pz9uoXbt2RYad4kaLAAAALoStt8ZGjRql48ePn/d448aNtWjRIgs7AgDAYRw+4GBrEOrWrVuRx6Ojo5WammpRNwAAwGlsDUIAAMBeoTp3xyohvY4QAACAmRgRAgDAyRw+IkQQAmCt44W/X9BMESe9ltd0ghqvLC/RebG/aSo9OKDIc6q9uUZnvj9U7LV2Pdy5RDWDqV7WMctrwjoEIQAAHMzl8KfGmCMEAAAcixEhAACczGd3A/ZiRAgAADgWI0IAADgYc4QAAAAcihEhAACczNkDQowIAQAA52JECAAAJ3P4HCHbg9DBgwe1YcMGtW7dWjVq1NCBAwf06quvyuPx6LrrrlOLFi3sbhEAYLIhj92gk0d/Kva8ox0Sy1zjh/wCrdqxR5tzfyzzNVDx2BqEVq1apbS0NBUUFKhatWrKysrSddddp4iICBmGoUmTJmnZsmVq27atnW0CAEz221t6WVLH5zP0+op1mrxgqSX1EPpsnSM0duxYXXfddcrPz9dDDz2k9PR09e7dW9nZ2dq2bZsGDRqkxx9/3M4WAQAVSFiYSzd3bqc/tm1pdyshw2VYt4UiW4PQ2rVrNWLECMXExOj+++/XDz/8oNtuu81/fNiwYVq9erWNHQIAKqLBnS6zuwWECFtvjZ06dUpRUVGSpEqVKqlKlSqqVauW/3jNmjV18ODBIq/h8Xjk8XgC9vkMr8Jc4cFvGABQaj5v6L3DodFFNVQpPEynQ7A3yzl8srStI0L16tXTjh07/D+/9dZbSkz830S4ffv2BQSjwmRmZiouLi5gy9EW03oGAJTOni3f293COcLDwhQRxgoysDkIDRgwQHl5ef6fr7rqKv8IkSTNnz9fl19+eZHXyMjIUH5+fsCWouam9QwAKJ0TR0/qq/lr7G4D5+HyWbeFIltvjY0bN67I42PHjlV4eNG3uNxut9xud8A+bosBQGh57q6XVLdZkuo1S7K7FSCA7esIFeXgwYMaN26cXnvtNbtbAQBcgIP7DuvOy0bp6rvTdHGnZqoeX61M1/HUiynReXFRldW4ds0y1XAch88RCukgdOjQIc2ePZsgBAAVwKmfTumdKR9J+qjM19j7cOcSnde9SQO9OPiaMteBc9gahObPn1/k8V9OpAYAACYI0QGh8ePH69FHHw3YFx8fr9zc3KDWsTUIpaeny+VyyShiWM7lclnYEQAACBWXXHKJFi5c6P+5uHnDZWHrU2OJiYl699135fP5Ct3WrVtnZ3sAAFR4LsOwbCutiIgIJSQk+LeLLroo6J/f1iDUrl27IsNOcaNFAACg/PB4PCooKAjYfr0o8i9t27ZNSUlJSklJ0YABA0yZMmNrEBo1apQ6dz7/xLfGjRtr0aJFFnYEAIDDGIZlW2GLIGdmZhbaVseOHfX666/r008/1csvv6zc3Fx17ty52DdOlJatc4S6detW5PHo6GilpqZa1A0AADBTRkaGRowYEbDv12sBntW3b1//P7dq1UqdOnVSo0aNNHv27HOucSFC+vF5AABgMgtXfC5sEeSSio6OVqtWrbRt27ag9sSLVgAAQMjzeDzavHlzwDtJg4ERIQAAHKwsT3NZYeTIkerXr5/q16+vvLw8PfHEEyooKNCQIUOCWocgBIQI77FjdrdgiTP784o/KcjCbKjptbyiM9R9YnmJzqvV96RUzMrSdSav1E8nzv/E0llbX+5QopoIrr1792rgwIE6cOCALrroIv3mN7/RihUrlJycHNQ6BCEAAJwsREeE3nrrLUvqMEcIAAA4FkEIAAA4FrfGAABwshC9NWYVRoQAAIBjMSIEAICTWbigYihiRAgAADgWI0IAADhYqC6oaBVGhAAAgGOF5IhQw4YN9emnn6pJkyZ2twIAqKBGvHynzpwufg3wgk41g1LPaxga9eUnQblWUDl8RMjWIPT8888Xun/37t2aOXOmEhISJEn33XeflW0BAByg58CultY75fWGZhByOFuD0PDhw1WnTh1FRAS24fP59Prrr6tSpUpyuVwEIQAAzMKIkH1uu+02rVq1SnPmzFGLFi38+ytVqqTPPvtMF198sY3dAQCAis7WydIvvviixo0bpyuvvFLTpk0r0zU8Ho8KCgoCNp/Be58BwMlKMvcH/2UY1m0hyPanxtLT0/XVV19p3rx56tu3r3Jzc0v1+5mZmYqLiwvYcrTFpG4BAOXBrk177G4B5YTtQUiS6tSpo4ULF6p79+667LLLZJQiNWZkZCg/Pz9gS1FzE7sFAIS6g/sOa82n6+1uo3zwWbiFoJB5fN7lcikjI0NpaWlatmyZEhMTS/R7brdbbrc7YF+YK9yMFgEA5ciU219U5idjlXxxXbtbQQgLmSB0Vrt27dSuXTtJ0p49ezRu3Di99tprNncFAChvftxzQHe1G60rb+6hFh2bKqZm1TJd53jrakHp54wvNIdEnL6ydMgFoV86dOiQZs+eTRACAJTJac9pffRilj56MavM18h+uUMQO0KosTUIzZ8/v8jjO3bssKgTAAAcihEh+6Snp8vlchU5OdrlclnYEQAAcBJbnxpLTEzUu+++K5/PV+i2bt06O9sDAAAVnK1BqF27dkWGneJGiwAAwAXyGdZtIcjWW2OjRo3S8ePHz3u8cePGWrRokYUdAQAAJ7E1CHXr1q3I49HR0UpNTbWoGwAAHMjhd15CYmVpAAAAO4T0OkIAAMBkjAgBAAA4U4UcEcryvW13CwAAlA+MCAEAADhThRwRAgAAJRSi6/tYhREhAADgWIwIAQDgZIbP7g5sxYgQAABwLEaEAABwMp4aAwAAcCZGhAAAcDKeGgMAAHAmRoQAAHAy5ggBAAA4EyNCAAA4GSNCAAAAzkQQAgAAjsWtMQAAnIxbYwAAAM7EiBAAAE7m46WrAAAAjsSIEAAATsYcIQAAAGdiRAgAACdjRAgAAMCZGBECAMDJfIwIAQAAOBIjQgAAOJhhsI4QAACAIzEiBACAkzFHCAAAwJkYEQIAwMlYRwgAAMCZGBECAMDJePs8AACAMxGEAACAY3FrDAAAJ2OyNAAAgDMxIgQAgIMZTJYGAABwJkaEAABwMuYIAQAAOBMjQgAAOBkvXQUAAHAmRoQAAHAyg6fGAAAAHIkRIQAAHMxgjhAAAIAzMSIEAICTMUcIAADAmQhCAAA4mOEzLNvKYvr06UpJSVHlypXVrl07ffnll0H9/AQhAAAQkubOnavhw4dr7Nix+vrrr9WtWzf17dtXu3fvDloNl2E4/CUjAAA4WJ+w6yyrleV7u1Tnd+zYUW3bttWMGTP8+1q0aKH09HRlZmYGpSdGhAAAQMg5deqU1q5dq7S0tID9aWlpWr58edDq8NQYAACwhMfjkcfjCdjndrvldrvPOffAgQPyer2Kj48P2B8fH6/c3Nyg9UQQAgDAwUp7u+pCjB8/Xo8++mjAvnHjxmn8+PHn/R2XyxXws2EY5+y7EAQhAABgiYyMDI0YMSJgX2GjQZJUq1YthYeHnzP6k5eXd84o0YVgjhAAALCE2+1WbGxswHa+IBQZGal27dopKysrYH9WVpY6d+4ctJ4YEQIAACFpxIgRGjx4sNq3b69OnTrppZde0u7du3XnnXcGrQZBCAAAhKQbbrhBBw8e1GOPPaZ9+/apZcuW+te//qXk5OSg1WAdIQAA4FjMEQIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI5FEAIAAI71/5LqjovQemHbAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<Figure size 500x500 with 2 Axes>"
+       "<Figure size 700x700 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -563,75 +479,35 @@
     }
    ],
    "source": [
-    "gene_obj.landscape_obj.plot_alignment_landscape()"
+    "aligner.get_aggregate_alignment() \n",
+    "# Note: White path represents the average alignment path where diagonals represent matches; \n",
+    "# vertical and horizontal paths could represent either warp matches or indels (mismatches)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d1dcce83-80bf-4bab-a66f-d1d06e8aefee",
+   "id": "powered-wyoming",
    "metadata": {},
    "source": [
-    "### The alignment distribution across all genes \n",
+    "## 3. Analysing gene-level alignments\n",
     "\n",
-    "We can use the alignment similarity percentage statistic of genes to rank genes from highly distant to highly similar"
+    "Ranking genes based on their alignment similarities"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "c9f9c3b8-86fc-4de3-ad0a-a5c1be745f57",
+   "execution_count": 14,
+   "id": "chubby-stomach",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean matched percentage: \n",
+      "Mean alignment similarity percentage (matched %): \n",
       "50.39 %\n"
      ]
     },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1000x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = aligner.get_stat_df() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "41fe40c3-1319-46df-b14b-8f93e3c43424",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "VisualUtils.plot_alignmentSim_vs_l2fc(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "f5204bb1-299b-4ac1-babf-8d3a1affc2fc",
-   "metadata": {},
-   "outputs": [
     {
      "data": {
       "text/html": [
@@ -666,7 +542,7 @@
        "      <th>63</th>\n",
        "      <td>CCRL2</td>\n",
        "      <td>0.2174</td>\n",
-       "      <td>55.943685</td>\n",
+       "      <td>55.943686</td>\n",
        "      <td>-0.487688</td>\n",
        "      <td>red</td>\n",
        "      <td>0.487688</td>\n",
@@ -720,7 +596,7 @@
        "      <th>34</th>\n",
        "      <td>NUP54</td>\n",
        "      <td>0.75</td>\n",
-       "      <td>30.744063</td>\n",
+       "      <td>30.744062</td>\n",
        "      <td>0.012993</td>\n",
        "      <td>green</td>\n",
        "      <td>0.012993</td>\n",
@@ -738,7 +614,7 @@
        "      <th>19</th>\n",
        "      <td>PLAGL2</td>\n",
        "      <td>0.8235</td>\n",
-       "      <td>31.807956</td>\n",
+       "      <td>31.807955</td>\n",
        "      <td>-0.051268</td>\n",
        "      <td>green</td>\n",
        "      <td>0.051268</td>\n",
@@ -747,7 +623,7 @@
        "      <th>51</th>\n",
        "      <td>ZSWIM4</td>\n",
        "      <td>0.8235</td>\n",
-       "      <td>30.214575</td>\n",
+       "      <td>30.214576</td>\n",
        "      <td>0.030379</td>\n",
        "      <td>green</td>\n",
        "      <td>0.030379</td>\n",
@@ -768,16 +644,16 @@
       ],
       "text/plain": [
        "      Gene alignment_similarity_percentage opt_alignment_cost      l2fc  \\\n",
-       "63   CCRL2                          0.2174          55.943685 -0.487688   \n",
+       "63   CCRL2                          0.2174          55.943686 -0.487688   \n",
        "77  NFKBIA                          0.2174          54.673471 -0.091748   \n",
        "68   NLRP3                          0.2174          57.177548  0.069058   \n",
        "3      TNF                          0.2174          57.990078 -0.006439   \n",
        "45   C5AR1                          0.2727          57.858236    0.8711   \n",
        "..     ...                             ...                ...       ...   \n",
-       "34   NUP54                            0.75          30.744063  0.012993   \n",
+       "34   NUP54                            0.75          30.744062  0.012993   \n",
        "15    CD44                          0.7647          28.366715 -0.021366   \n",
-       "19  PLAGL2                          0.8235          31.807956 -0.051268   \n",
-       "51  ZSWIM4                          0.8235          30.214575  0.030379   \n",
+       "19  PLAGL2                          0.8235          31.807955 -0.051268   \n",
+       "51  ZSWIM4                          0.8235          30.214576  0.030379   \n",
        "26   SGMS2                          0.8667          37.626682 -0.020399   \n",
        "\n",
        "    color  abs_l2fc  \n",
@@ -796,85 +672,202 @@
        "[89 rows x 6 columns]"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
+    "df = aligner.get_stat_df() # ordered genes according to alignment similarity statistics \n",
     "df"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "6fc51fc0-daa4-458a-9bd9-a741e495d4c2",
-   "metadata": {},
-   "source": [
-    "### Run gene set overrepresentation analysis over the top k mismatching genes\n",
-    "\n",
-    "Let us use 30% alignment similarity (=0.3) as a threshold in this case"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 66,
-   "id": "1f5f6254-ffb4-4671-a7c3-2faf44aa4a5b",
+   "execution_count": 16,
+   "id": "controversial-calgary",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "63      CCRL2\n",
-       "77     NFKBIA\n",
-       "68      NLRP3\n",
-       "3         TNF\n",
-       "45      C5AR1\n",
-       "84    SPATA13\n",
-       "33      CXCL2\n",
-       "12     RALGDS\n",
-       "31     INSIG1\n",
-       "6       MALT1\n",
-       "Name: Gene, dtype: object"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 66,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "topDEgenes = df[list(df['alignment_similarity_percentage'] <=0.3)]['Gene']\n",
-    "topDEgenes"
+    "VisualUtils.plot_alignmentSim_vs_l2fc(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "under-sponsorship",
+   "metadata": {},
+   "source": [
+    "A ranked list of genes based on their first match occurrence "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
-   "id": "1312623d-b71d-471b-af3c-06677271efbb",
+   "execution_count": 17,
+   "id": "cleared-jimmy",
    "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>Gene_set</th>\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In the order of the first match occurrence along pseudotime\n",
+      "Gene      Alignment\n",
+      "--------  ------------------------\n",
+      "PTAFR     \u001b[92mM\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mII\u001b[0m\n",
+      "OSBPL3    \u001b[92mM\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "RFFL      \u001b[92mM\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
+      "TNFAIP2   \u001b[92mM\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "SGMS2     \u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[92mM\u001b[0m\n",
+      "SLC16A10  \u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "FPR1      \u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "FAM20C    \u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMM\u001b[0m\u001b[91mIIII\u001b[0m\n",
+      "CLEC4D    \u001b[91mI\u001b[0m\u001b[92mMM\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "TSHZ1     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "IL1F9     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "PSTPIP2   \u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[92mVVVVV\u001b[0m\u001b[91mII\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "RELA      \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\n",
+      "NUP54     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\n",
+      "DDHD1     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\n",
+      "NRP2      \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "TREM1     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "GRAMD1B   \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMM\u001b[0m\n",
+      "TOP1      \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "ICOSL     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMMM\u001b[0m\n",
+      "DUSP16    \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMMM\u001b[0m\n",
+      "PTPRE     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "LDLR      \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "TNIP1     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "PLAGL2    \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[92mVVV\u001b[0m\n",
+      "ZSWIM4    \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[92mVVV\u001b[0m\n",
+      "ZC3H12C   \u001b[91mDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[92mVVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "AK150559  \u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[92mVVVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDDD\u001b[0m\n",
+      "F10       \u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMM\u001b[0m\u001b[92mVVVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDDDDD\u001b[0m\n",
+      "FAM108C   \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
+      "RBM7      \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
+      "RASA2     \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
+      "SLC25A37  \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
+      "IRAK-2    \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "PLEKHO2   \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "LCP2      \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "TRIM13    \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMM\u001b[0m\n",
+      "PTX3      \u001b[91mDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMMMMM\u001b[0m\n",
+      "SPATA13   \u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "BCL2L11   \u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMM\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\n",
+      "CD44      \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[92mVV\u001b[0m\n",
+      "AK163103  \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "LZTFL1    \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "IRAK3     \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mII\u001b[0m\u001b[91mDDDDDD\u001b[0m\n",
+      "ARG2      \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\n",
+      "ZEB2      \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "TLR2      \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "MCOLN2    \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "CPD       \u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "RCAN1     \u001b[91mDDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMMM\u001b[0m\u001b[91mI\u001b[0m\n",
+      "PILRA     \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "ARHGEF3   \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMMM\u001b[0m\n",
+      "C5AR1     \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDDDDDDD\u001b[0m\n",
+      "SLC39A14  \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMM\u001b[0m\u001b[91mIIII\u001b[0m\n",
+      "CLCN7     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "BC031781  \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "NUPR1     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "CDC42EP4  \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "NFKBIE    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMM\u001b[0m\n",
+      "PLSCR1    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "NCK1      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMM\u001b[0m\n",
+      "ADORA2B   \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "ORAI2     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "KLF7      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "NIACR1    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMM\u001b[0m\u001b[92mVVVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
+      "PDE4B     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\n",
+      "SERTAD2   \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "CXCL1     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "MPP5      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "TGM2      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
+      "PIP5K1A   \u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "FLRT3     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\n",
+      "SOCS3     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMM\u001b[0m\u001b[92mVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[92mMMMM\u001b[0m\n",
+      "TNFAIP3   \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\n",
+      "RASGEF1B  \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\n",
+      "SLC25A25  \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[92mM\u001b[0m\n",
+      "INSIG1    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "CXCL2     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "MALT1     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "RALGDS    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "H1F0      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMMM\u001b[0m\n",
+      "IL1A      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\n",
+      "NLRP3     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "TNF       \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "NFKBIA    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
+      "PLK2      \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\n",
+      "NFKBIZ    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[92mVVVVVVV\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMM\u001b[0m\n",
+      "NFKBID    \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\n",
+      "CCRL2     \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "earliest_match_sorted_genes_list = aligner.show_ordered_alignments() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "retired-legislation",
+   "metadata": {},
+   "source": [
+    "## Gene-set overrepresentation analysis on the top dissimilar genes \n",
+    "\n",
+    "Checking top dissimilar genes, i.e, only <=30% similarity along pseudotime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "bound-sheep",
+   "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>Gene_set</th>\n",
        "      <th>Term</th>\n",
        "      <th>Overlap</th>\n",
        "      <th>P-value</th>\n",
@@ -964,156 +957,6 @@
        "      <td>4.392729</td>\n",
        "      <td>4.392729</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>KEGG_2021_Human</td>\n",
-       "      <td>Lipid and atherosclerosis</td>\n",
-       "      <td>4/215</td>\n",
-       "      <td>2.592326e-06</td>\n",
-       "      <td>0.000048</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>62.492891</td>\n",
-       "      <td>803.843248</td>\n",
-       "      <td>NFKBIA;NLRP3;TNF;CXCL2</td>\n",
-       "      <td>4.316101</td>\n",
-       "      <td>4.316101</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>KEGG_2021_Human</td>\n",
-       "      <td>Legionellosis</td>\n",
-       "      <td>3/57</td>\n",
-       "      <td>2.596486e-06</td>\n",
-       "      <td>0.000048</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>158.222222</td>\n",
-       "      <td>2034.951617</td>\n",
-       "      <td>NFKBIA;TNF;CXCL2</td>\n",
-       "      <td>4.316101</td>\n",
-       "      <td>4.316101</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>KEGG_2021_Human</td>\n",
-       "      <td>Coronavirus disease</td>\n",
-       "      <td>4/232</td>\n",
-       "      <td>3.507534e-06</td>\n",
-       "      <td>0.000054</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>57.783626</td>\n",
-       "      <td>725.796849</td>\n",
-       "      <td>NFKBIA;C5AR1;NLRP3;TNF</td>\n",
-       "      <td>4.264666</td>\n",
-       "      <td>4.264666</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>KEGG_2021_Human</td>\n",
-       "      <td>Shigellosis</td>\n",
-       "      <td>4/246</td>\n",
-       "      <td>4.425543e-06</td>\n",
-       "      <td>0.000059</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>54.402204</td>\n",
-       "      <td>670.676767</td>\n",
-       "      <td>NFKBIA;NLRP3;TNF;MALT1</td>\n",
-       "      <td>4.230649</td>\n",
-       "      <td>4.230649</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>KEGG_2021_Human</td>\n",
-       "      <td>IL-17 signaling pathway</td>\n",
-       "      <td>3/94</td>\n",
-       "      <td>1.177987e-05</td>\n",
-       "      <td>0.000137</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>93.715856</td>\n",
-       "      <td>1063.592345</td>\n",
-       "      <td>NFKBIA;TNF;CXCL2</td>\n",
-       "      <td>3.863467</td>\n",
-       "      <td>3.863467</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>KEGG_2021_Human</td>\n",
-       "      <td>T cell receptor signaling pathway</td>\n",
-       "      <td>3/104</td>\n",
-       "      <td>1.596141e-05</td>\n",
-       "      <td>0.000165</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>84.394625</td>\n",
-       "      <td>932.167056</td>\n",
-       "      <td>NFKBIA;TNF;MALT1</td>\n",
-       "      <td>3.782688</td>\n",
-       "      <td>3.782688</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>KEGG_2021_Human</td>\n",
-       "      <td>TNF signaling pathway</td>\n",
-       "      <td>3/112</td>\n",
-       "      <td>1.993521e-05</td>\n",
-       "      <td>0.000185</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>78.169069</td>\n",
-       "      <td>846.025656</td>\n",
-       "      <td>NFKBIA;TNF;CXCL2</td>\n",
-       "      <td>3.731896</td>\n",
-       "      <td>3.731896</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>KEGG_2021_Human</td>\n",
-       "      <td>Yersinia infection</td>\n",
-       "      <td>3/137</td>\n",
-       "      <td>3.642707e-05</td>\n",
-       "      <td>0.000308</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>63.505330</td>\n",
-       "      <td>649.037070</td>\n",
-       "      <td>NFKBIA;NLRP3;TNF</td>\n",
-       "      <td>3.511485</td>\n",
-       "      <td>3.511485</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>KEGG_2021_Human</td>\n",
-       "      <td>Influenza A</td>\n",
-       "      <td>3/172</td>\n",
-       "      <td>7.174680e-05</td>\n",
-       "      <td>0.000556</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>50.264582</td>\n",
-       "      <td>479.643099</td>\n",
-       "      <td>NFKBIA;NLRP3;TNF</td>\n",
-       "      <td>3.254896</td>\n",
-       "      <td>3.254896</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>KEGG_2021_Human</td>\n",
-       "      <td>Pathogenic Escherichia coli infection</td>\n",
-       "      <td>3/197</td>\n",
-       "      <td>1.073310e-04</td>\n",
-       "      <td>0.000768</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>43.731959</td>\n",
-       "      <td>399.692315</td>\n",
-       "      <td>NFKBIA;NLRP3;TNF</td>\n",
-       "      <td>3.114735</td>\n",
-       "      <td>3.114735</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
@@ -1125,16 +968,6 @@
        "0   MSigDB_Hallmark_2020             TNF-alpha Signaling via NF-kB   4/200   \n",
        "1   MSigDB_Hallmark_2020                     Inflammatory Response   4/200   \n",
        "15       KEGG_2021_Human       NOD-like receptor signaling pathway   4/181   \n",
-       "16       KEGG_2021_Human                 Lipid and atherosclerosis   4/215   \n",
-       "17       KEGG_2021_Human                             Legionellosis    3/57   \n",
-       "18       KEGG_2021_Human                       Coronavirus disease   4/232   \n",
-       "19       KEGG_2021_Human                               Shigellosis   4/246   \n",
-       "20       KEGG_2021_Human                   IL-17 signaling pathway    3/94   \n",
-       "21       KEGG_2021_Human         T cell receptor signaling pathway   3/104   \n",
-       "22       KEGG_2021_Human                     TNF signaling pathway   3/112   \n",
-       "23       KEGG_2021_Human                        Yersinia infection   3/137   \n",
-       "24       KEGG_2021_Human                               Influenza A   3/172   \n",
-       "25       KEGG_2021_Human     Pathogenic Escherichia coli infection   3/197   \n",
        "\n",
        "         P-value  Adjusted P-value  Old P-value  Old Adjusted P-value  \\\n",
        "13  1.414253e-07          0.000007            0                     0   \n",
@@ -1142,16 +975,6 @@
        "0   1.944057e-06          0.000013            0                     0   \n",
        "1   1.944057e-06          0.000013            0                     0   \n",
        "15  1.305897e-06          0.000040            0                     0   \n",
-       "16  2.592326e-06          0.000048            0                     0   \n",
-       "17  2.596486e-06          0.000048            0                     0   \n",
-       "18  3.507534e-06          0.000054            0                     0   \n",
-       "19  4.425543e-06          0.000059            0                     0   \n",
-       "20  1.177987e-05          0.000137            0                     0   \n",
-       "21  1.596141e-05          0.000165            0                     0   \n",
-       "22  1.993521e-05          0.000185            0                     0   \n",
-       "23  3.642707e-05          0.000308            0                     0   \n",
-       "24  7.174680e-05          0.000556            0                     0   \n",
-       "25  1.073310e-04          0.000768            0                     0   \n",
        "\n",
        "    Odds Ratio  Combined Score                     Genes  \\\n",
        "13  132.600000     2091.300110    NFKBIA;NLRP3;TNF;MALT1   \n",
@@ -1159,79 +982,71 @@
        "0    67.326531      885.393278    NFKBIA;CCRL2;TNF;CXCL2   \n",
        "1    67.326531      885.393278  NFKBIA;C5AR1;CCRL2;NLRP3   \n",
        "15   74.625235     1011.068962    NFKBIA;NLRP3;TNF;CXCL2   \n",
-       "16   62.492891      803.843248    NFKBIA;NLRP3;TNF;CXCL2   \n",
-       "17  158.222222     2034.951617          NFKBIA;TNF;CXCL2   \n",
-       "18   57.783626      725.796849    NFKBIA;C5AR1;NLRP3;TNF   \n",
-       "19   54.402204      670.676767    NFKBIA;NLRP3;TNF;MALT1   \n",
-       "20   93.715856     1063.592345          NFKBIA;TNF;CXCL2   \n",
-       "21   84.394625      932.167056          NFKBIA;TNF;MALT1   \n",
-       "22   78.169069      846.025656          NFKBIA;TNF;CXCL2   \n",
-       "23   63.505330      649.037070          NFKBIA;NLRP3;TNF   \n",
-       "24   50.264582      479.643099          NFKBIA;NLRP3;TNF   \n",
-       "25   43.731959      399.692315          NFKBIA;NLRP3;TNF   \n",
        "\n",
        "    -log10 Adjusted P-value  -log10 FDR q-val  \n",
        "13                 5.182020          5.182020  \n",
        "14                 5.182020          5.182020  \n",
        "0                  4.898378          4.898378  \n",
        "1                  4.898378          4.898378  \n",
-       "15                 4.392729          4.392729  \n",
-       "16                 4.316101          4.316101  \n",
-       "17                 4.316101          4.316101  \n",
-       "18                 4.264666          4.264666  \n",
-       "19                 4.230649          4.230649  \n",
-       "20                 3.863467          3.863467  \n",
-       "21                 3.782688          3.782688  \n",
-       "22                 3.731896          3.731896  \n",
-       "23                 3.511485          3.511485  \n",
-       "24                 3.254896          3.254896  \n",
-       "25                 3.114735          3.114735  "
+       "15                 4.392729          4.392729  "
       ]
      },
-     "execution_count": 69,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pathway_df = PathwayAnalyserV2.run_overrepresentation_analysis(topDEgenes) # this is a wrapper function call for GSEAPy enrichr inferface\n",
-    "pathway_df[pathway_df['Adjusted P-value']<0.001] "
+    "threshold_similarity = 0.3 \n",
+    "topDEgenes = df[list(df['alignment_similarity_percentage'] <=threshold_similarity)]['Gene']\n",
+    "# Calling wrapper function for GSEAPy enrichr inferface\n",
+    "pathway_df = PathwayAnalyser.run_overrepresentation_analysis(topDEgenes) \n",
+    "pathway_df.head(5)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b1e44da0-cec7-4c85-ba70-66f6ac707d70",
+   "id": "helpful-remove",
    "metadata": {},
    "source": [
-    "### Clustering genes using their alignments\n",
+    "## Clustering alignments \n",
     "\n",
-    "We first run cluster diagnostics to decide on a distance threshold with a good tradeoff between the number of clusters and the quality of structure. We use levenshtein distance metric"
+    "Running experiment to determine the distance threshold for alignment clusters from hierarchical clustering. We aim to select a locally optimal threshold that gives a good trade-off between high mean Silhouette score and low number of clusters which can be biologically meaningful. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
-   "id": "0e1870b8-4de0-4da9-87a0-f23f5ebf9416",
+   "execution_count": 19,
+   "id": "grand-pakistan",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "compute distance matrix\n",
-      "using levenshtein distance metric\n"
+      "Compute distance matrix\n",
+      "- using levenshtein distance metric\n",
+      "Experimental mode: exploring different thresholds\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 68%|██████▊   | 67/99 [00:00<00:00, 248.94it/s]\n"
+      " 68%|██████▊   | 67/99 [00:00<00:00, 243.98it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-- Cluster diagnostic plots\n",
+      "Potential candidates for distance threshold: a locally optimal thresholds that gives a good trade-off between high mean Silhouette score and low number of clusters \n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1000x500 with 3 Axes>"
       ]
@@ -1241,356 +1056,31 @@
     }
    ],
    "source": [
-    "# Running experiment to determine the distance threshold with a good trade-off\n",
     "df = ClusterUtils.run_clustering(aligner, metric='levenshtein', experiment_mode=True) "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1808b9df-a49a-4adc-8194-283bb4adcd7b",
-   "metadata": {},
-   "source": [
-    "We can inspect structures of distance thresholds that give local optimals of the mean silhouette score."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "2fa1ce00-f812-49d3-b821-b0d7f4fd6f96",
-   "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>Distance threshold</th>\n",
-       "      <th>Mean Silhouette Score</th>\n",
-       "      <th>Number of clusters</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>66</th>\n",
-       "      <td>0.67</td>\n",
-       "      <td>0.371279</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>65</th>\n",
-       "      <td>0.66</td>\n",
-       "      <td>0.371279</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>64</th>\n",
-       "      <td>0.65</td>\n",
-       "      <td>0.371279</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63</th>\n",
-       "      <td>0.64</td>\n",
-       "      <td>0.371279</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62</th>\n",
-       "      <td>0.63</td>\n",
-       "      <td>0.371279</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>61</th>\n",
-       "      <td>0.62</td>\n",
-       "      <td>0.371279</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>0.61</td>\n",
-       "      <td>0.371279</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>59</th>\n",
-       "      <td>0.60</td>\n",
-       "      <td>0.395886</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58</th>\n",
-       "      <td>0.59</td>\n",
-       "      <td>0.395886</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>0.58</td>\n",
-       "      <td>0.395886</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>0.57</td>\n",
-       "      <td>0.395886</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>0.56</td>\n",
-       "      <td>0.391956</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>0.55</td>\n",
-       "      <td>0.391956</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>0.54</td>\n",
-       "      <td>0.391956</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>0.53</td>\n",
-       "      <td>0.391956</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.391956</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>0.51</td>\n",
-       "      <td>0.391956</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.391956</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>0.49</td>\n",
-       "      <td>0.391956</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>0.48</td>\n",
-       "      <td>0.391956</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>0.47</td>\n",
-       "      <td>0.391956</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>0.46</td>\n",
-       "      <td>0.391956</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>0.45</td>\n",
-       "      <td>0.391956</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>0.44</td>\n",
-       "      <td>0.391956</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.374176</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>0.39</td>\n",
-       "      <td>0.374176</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>0.38</td>\n",
-       "      <td>0.374176</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>0.37</td>\n",
-       "      <td>0.374944</td>\n",
-       "      <td>7.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0.10</td>\n",
-       "      <td>0.384710</td>\n",
-       "      <td>57.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0.09</td>\n",
-       "      <td>0.398382</td>\n",
-       "      <td>65.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0.08</td>\n",
-       "      <td>0.415730</td>\n",
-       "      <td>68.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0.07</td>\n",
-       "      <td>0.415730</td>\n",
-       "      <td>68.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.06</td>\n",
-       "      <td>0.415730</td>\n",
-       "      <td>68.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.415730</td>\n",
-       "      <td>68.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.415730</td>\n",
-       "      <td>68.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.03</td>\n",
-       "      <td>0.415730</td>\n",
-       "      <td>68.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.02</td>\n",
-       "      <td>0.415730</td>\n",
-       "      <td>68.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.01</td>\n",
-       "      <td>0.415730</td>\n",
-       "      <td>68.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Distance threshold  Mean Silhouette Score  Number of clusters\n",
-       "66                0.67               0.371279                 2.0\n",
-       "65                0.66               0.371279                 2.0\n",
-       "64                0.65               0.371279                 2.0\n",
-       "63                0.64               0.371279                 2.0\n",
-       "62                0.63               0.371279                 2.0\n",
-       "61                0.62               0.371279                 2.0\n",
-       "60                0.61               0.371279                 2.0\n",
-       "59                0.60               0.395886                 3.0\n",
-       "58                0.59               0.395886                 3.0\n",
-       "57                0.58               0.395886                 3.0\n",
-       "56                0.57               0.395886                 3.0\n",
-       "55                0.56               0.391956                 4.0\n",
-       "54                0.55               0.391956                 4.0\n",
-       "53                0.54               0.391956                 4.0\n",
-       "52                0.53               0.391956                 4.0\n",
-       "51                0.52               0.391956                 4.0\n",
-       "50                0.51               0.391956                 4.0\n",
-       "49                0.50               0.391956                 4.0\n",
-       "48                0.49               0.391956                 4.0\n",
-       "47                0.48               0.391956                 4.0\n",
-       "46                0.47               0.391956                 4.0\n",
-       "45                0.46               0.391956                 4.0\n",
-       "44                0.45               0.391956                 4.0\n",
-       "43                0.44               0.391956                 4.0\n",
-       "39                0.40               0.374176                 6.0\n",
-       "38                0.39               0.374176                 6.0\n",
-       "37                0.38               0.374176                 6.0\n",
-       "36                0.37               0.374944                 7.0\n",
-       "9                 0.10               0.384710                57.0\n",
-       "8                 0.09               0.398382                65.0\n",
-       "7                 0.08               0.415730                68.0\n",
-       "6                 0.07               0.415730                68.0\n",
-       "5                 0.06               0.415730                68.0\n",
-       "4                 0.05               0.415730                68.0\n",
-       "3                 0.04               0.415730                68.0\n",
-       "2                 0.03               0.415730                68.0\n",
-       "1                 0.02               0.415730                68.0\n",
-       "0                 0.01               0.415730                68.0"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[df['Mean Silhouette Score'] > 0.37].sort_values('Distance threshold', ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "459d4c8c-c638-4f4d-b516-08a0956e0b92",
+   "id": "metallic-duncan",
    "metadata": {},
    "source": [
-    "If we select distance threshold 0.37 which gives a local optimal of 0.3749 silhouette_score with 7 clusters"
+    "Run clustering with the chosen distance threshold. In this case we select 0.37"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
-   "id": "0106240e-aaf5-424d-bfaf-554a60086a31",
+   "execution_count": 20,
+   "id": "equal-above",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "compute distance matrix\n",
-      "using levenshtein distance metric\n",
-      "run agglomerative clustering |  0.37\n",
-      "silhouette_score:  0.37494418907841714\n"
+      "Compute distance matrix\n",
+      "- using levenshtein distance metric\n",
+      "run agglomerative clustering | distance threshold = 0.37\n",
+      "silhouette_score:  0.3757459494624326\n"
      ]
     }
    ],
@@ -1600,21 +1090,29 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b2fdca1-ad4c-4437-a011-66a01a71a9cc",
+   "id": "forty-psychology",
    "metadata": {},
    "source": [
-    "Below visualizes all alignment paths in each cluster along with its number of genes "
+    "Visualise gene alignment grouped together in each cluster <br>\n",
+    "Note: diagonals represent matches; vertical and horizontal paths could represent either warp matches or indels (mismatches)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
-   "id": "738b8bff-1585-4ce3-9362-9764304c5710",
+   "execution_count": 21,
+   "id": "alert-story",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cluster ID | Number of genes in the cluster\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1200x600 with 8 Axes>"
       ]
@@ -1629,21 +1127,21 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ca5454b-954b-47f5-a31c-82053d8770b8",
+   "id": "fancy-meter",
    "metadata": {},
    "source": [
-    "Below is the Levenshtein distance heat map of all genes ordered based on the above clustering structure"
+    "Visualise the distance matrix used in the clustering "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
-   "id": "0b606b04-4d2b-48ea-a3e2-da52f35974ca",
+   "execution_count": 22,
+   "id": "suspected-ordering",
    "metadata": {},
    "outputs": [
     {
      "data": {
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gvv71r6tLly7mZz0eT4fN98CBAxo3bpzq6+t12WWX6Qtf+ILq6uq0detW/f73v9evfvUrSdL8+fP16KOPavfu3Zo/f/7R6UeOHHn0fz/77LP6/Oc/r/j4eH3qU59Sjx49tHHjRi1atEiLFy/W22+/rayslo9C27Ztm8aPH69hw4bpy1/+sioqKpSYaD8y8lgSEg4/hs7tZlcFAAAAAHQu/Ev1OFqxYoWkwyMxTqSnn35aVVVV+p//+R9997vfbZGVlZUd/d9FRUVasmSJdu/eHXEUTHl5ua699lrl5eVpxYoV6tmz59Hsf//3f3X11VfrJz/5iX7/+9+3mG7FihW6/fbbdccdd8S0HLW1tXrqqaeUlJSkyZMnx9RWZ1V5Vq6ZZ6+xLwvzFaabeWKVfU+gjN0uM0+sdL6nUE3fZDNP39lo5sFk+zTkrvWbeeq+eHt6X2z3bXJah3HBBDP3HgyYubu8wcwDU+x9xF1Zb+Z66327/QvHmLnT9nMtX2fm7kH9zdypf5VfHG9P78C7rSqm6R2PsVr7Jt9Ox7CThhnjzNzz3DtmHu+wfQ+Lruh9ovhT7TyQYm+DbnlVZl4p+xx2qqsrPLUHJTtt/4S6E9OPaDV0Pdk9wPFW28v+HeCkulds/xyr7mX/TnCcvsT+npPDPuw7ZD/QI79PhZl391TaMwBOAaf2N20nd/Dg4eeGFxYWnpT5Jye3/qGYm9v2E+9f/vIX1dTUaOHChS0KKpL0xS9+UWeddZb+/ve/t5qua9euuu2229rf4U/4xje+oUOHDmnevHnKycmJuT0AAAAAADoSI1VOQ5dffrl+/OMf69vf/rZeeeUVXXzxxZo0aZIGDhzYrnbeeuuto/9/27ZtrfKmpiaVlZWprKysRbHmzDPPbHW5T6SRMDfddJMyMzMjznvevHl67LHHdPHFF2vevHnt6jcAAAAAACcCRZXjqGvXrtq0aZP279+vQYNO3DPE+/Tpo1WrVmnBggV68cUX9eSTT0o6fOPXO++8U1dddVWb2qmoODxc79577zU/V19f36KoEun+MZFuxDtr1qyIRZUFCxZo4cKFOv/88/XMM88oPt6+vAMAAAAAgJOBy3+Oo4kTJ0qSXnvttZjaOfJUnECg9b0ZqqurI04zYsQIPf3006qoqNCqVav0k5/8RIcOHdIXvvCFo/d6cZKefvgayw8++EDhcPiYr169erWYzuVqfZ+OSNP17t271ecWLFigoqIinXfeeXruueciXsIEAAAAAEBnQFHlOJo1a5bi4+P1pz/9SaWlpeZnfT7fMbMjT9fZv39/q+y9994z201ISND48eO1YMEC/e53v1M4HNbzzz9/ND8yCiQYDLaa9uyzz5YkrVq1ypxHRykqKlJRUZHOPfdcvfDCC/J67RtfAQAAAABwMlFUOY769++vH/7whyorK/v/7N15fFTV3T/wz+wzmcm+hxASEgjKKiIgoGIsCETR2iJCFYhL7VPU8rNqtYIkGMWduqGPjxZKq9AKrQUVFFCIRkQ2BUE0CSRAAiEL2WYyk9l+f1BSxkm+N8kkJODn/XrxMs7n3nPOnLvM5OTeczF58mQcPnzYbxm73Y4XXnihxTlHzkhPT4fFYsHatWubb8kBgPLycuTm5votv2PHDpw8edLv9fLycgC+E9hGREQAAI4dO+a3fFZWFoKDg/Hoo49i//79frnNZmuedyVQCxcuRE5ODq644goOqBAREREREdF5gXOqdLHc3FzY7XYsWbIE6enpyMjIwKBBg6DT6XD48GFs2rQJVVVVLQ6OnKHX63HPPffgqaeewvDhw3HDDTegvr4e69atw1VXXYWioiKf5d9++20sXboU48ePR1paGkJCQnDgwAF8+OGHiIqKwu233968bEZGBlavXo1p06ZhypQpMBqNGDx4MDIzMxEdHY2VK1di2rRpGDp0KCZNmoQBAwbAbrejpKQEW7duxZgxY7Bhw4aA+mj58uVYtGgRtFotRo4ciWeffdZvmfHjx2P8+PEB1UNERERERETUmbp1UCXKrIdRq4bd5enOZrTKqFUjyqxXXlCgVqvxwgsvYObMmXjttdeQl5eHvLw8eDwexMfHY+LEicjKysKECRPEcnJzc6HX67Fs2TK8/vrrSE5OxoIFC3D99ddjzZo1PsvOmDEDdrsd+fn52LFjBxwOBxITEzF37lw88MADPo94vuuuu1BcXIxVq1bhiSeegMvlwuzZs5GZmQkAyMzMxJ49e/Dss89i06ZN2LhxI8xmMxITE5GVlYVbb701oP4BgOLiYgCn54x5/vnnW12OgypERERERETUk6i8Xq+3qwq32+04fPgwUlJSYDQaW1zmyCkbKq1NXdWEgESZ9UgK520o55u27HetUT+wrota1bLhNQUBrR981CnmutrW5+oBgOpBFjFvCvGfdPjHjNXyKcQeIZehr5PXj/i2QcxPjAkWc6f8FqGTi0fcF/Vy+aEGMXeEymPXSn2stI27WtVAeWA5cn/Xnr/re+vEXO0/f7cPrS2wjzhDrVyB0yI/nUzX4D9f1dmU3p/S8aHUPu3GnWIOAAWvjhJz03H5PTqi5D+MmI907Z3G9alyH5sT5IO8sUQ+hyiJ2i0fwx6FP19VD5G3sVL5jVFybqiRy1eq3x0kb19Vk8I5viawJ/ipFU4xSudwJY6IwNY3VMu5NUnuP4828K/hapfCPhgmf44YLIGdx9Uq+T30jawS8yijVcw/K0oVc88p+XNKqX+UhKTWiLlje4A7UYACPgbGyd9zEsNrxPzYqbCA6r8hdZ+YPzP03YDKJzoXuv32n6TwIA5cEBEREREREdF5hxPVEhERERERERF1AAdViIiIiIiIiIg6gIMqREREREREREQdwEEVIiIiIiIiIqIO6PaJaomIiIiIiIgC5awtxrFl/cVlUuadfuLV4T/96MlRKg00pmgY4i5DyPB5MCVe0RzV71+Byo13/mhxIzTBiQjqMxGhIx+G1hznV1fjsc9gO/Q+mk7uhuPkHnib6mC56DZEX/tWB98h9UQcVCEiIiIiIqILhjY0FZYBMxSXUxsjETL0fwAAXlcjmir3wXZoHWyH3kdM5jsw9/uFz/LG3hkwJowBALjtVbAf/RR13yyFtWgtes3cDk1QtM/yDfuXo+G7v0KlDYI2uDecTXWd9A6pJ+GgCtFZihJ/obxQJ7o69UkxNx3XiLk1QS/mgJyrhteKue24RaF8oMGpEnNvhEPMTRY5tyaEirnSe8Buef3I/U1ifnRisJg3xrvFPKRAvsvSqdDFStvYXOZVWF/ePkrs/e1i7rQYxTzQ9in1r/mIfIyYKsQYNelyrquX+98R5RFzj06hfcfl96+8/eT2Nd44SmF9oN/c7WKuGjlYzt3yNq4YESLmljKXmCtReeSvMq5j8jmg38fyF1x1jVXMq8b6/2XybMFHnWIe+Y18jJ0aKJ+Dwgvk/rNFy/tg8jq5fZWDDGJeN0w+h7pcgZ2DPDp5/woZXS3mJw9HiLn+lNw/Wnnzoz5VPkf1u6hUzA/t7C3m7mh5+wAAauVjIHivvA2dFjlXNFQ+hoo+SRHzA/3kY0BVpfBdRyvvI4GqqZQ/qLXBgdWvqw/sGFH6HqH0Obpl5Gti/mLlVWI+K/FLMc+v7Sfmoy2FYt5RurBUhF/+mOJyGlOk33L13/4ZlZt+g+rPHvEbVDElZSDssoea/9/r9aD83z9HY/F61H2zFOGXL/RZPmTYbxE64n7owgfAUb4Tx/9+BejCwzlViIiIiIiIiABYBs6BSmeGq64Y7sZKcVmVSo3gi28DADhO7vHLDbGXQh85ECq1PIBL5zcOqhARERERERGd4W37FUhenF5WpeLAyU8Vb/8hIiIiIiKiC4azpginti3ye92UfC2M8fKtsQ37l8PrskEbkgyNKUpc1utxo2H/XwAAxl5jO95gOq9xUIWIiIiIiIh6HIfDAYfDd/49g8EAg0GeC8hVW4Sa7bl+r6sNYT6DKu7GqubBF6/bjqaKvWgs+RhQqRFxxVN+6zce+QRe1+l5gDz2ajQe2QTnqR9giBuF4CF3t/v90YWBgyo9THZ2NnJycvDpp59i/Pjx3d0cIiIiIiKibrF48WLk5OT4vLZw4UJkZ2eL65n6TETcz99XLN9jr/rv4ItKA40pCkGpUxE6fB6Mvcb5LW8/+gnsRz/xec0QPxpxv/gYaq08eT9duLp9UMVVdwRue1V3N6NFGmMktCFJnVLWrl27sHTpUuTl5aGsrAwejwcJCQkYM2YMZs2ahQkTJnRKPe01fvx4bN26Fd523DfYVQoKCvDoo4/i008/RUNDA/r164df//rX+O1vfwu1mtP/EBERERH9lDzyyCO4//77fV5TukqlPXTh/ZE4+9s2Lx8+Nhdhlz0Er9cDV10xar58HA3fvY3KTb9BzKTlndYuOr9066CKq+4Ijv1lELxu+VFq3UWlMSJx9rcBDax4PB488MADWLJkCbRaLTIyMjB16lTodDocOnQIH3zwAf72t79h0aJFWLBgQSe2/vxy4MABjBkzBjabDTfffDN69eqF9evX495778XevXvxxhtvdHcTiYiIiIjoHGrLrT7dQaVSQxfaF1ET/wxX3RFYD74Da9rPYU67obubRt2gWwdV3PaqHjugApy+r85trwpoUGX+/PlYsmQJhg0bhtWrVyM1NdUnb2xsxCuvvIKqqp55tc658j//8z+ora3FBx98gClTpgAAcnNzMXnyZPzf//0fZsyYgauvvrqbW0lERERERHSaSqVCxFXPo+ydUTiVPx9Bfa/j45N/grr99p8LWWFhIZ555hlERkZiw4YNiI2N9VvGZDLhwQcf9JuA6WxbtmzB1Vdf3eL9g8XFxUhJScHs2bOxfPny5tcLCgrw5JNPYsuWLTh+/DgsFguSkpJw9dVX4/nnnwdw+iRwxtk//7isvXv34sknn8TWrVtRVVWF+Ph4TJ06FdnZ2YiMjGyxLQ8//DD++Mc/Ii8vD1VVVTh8+DCSk5NbfH8//PAD8vLycPXVVzcPqACATqfDE088gc2bN+P//u//LshBFdNx+aRrLuva27Iq4y1irtQ+AFA75dzhkP+6YLPoxDxKoQ8U34OYKlPeBnIf6evk9fV1culNISoxDz6qsAEg968SZ7C8/ZT6J/D2KRwjx+X6zcfl+ptC9GKutP3cBvnWRE3rp3YAXX+MK/UfAKhGDhZz71f7xNx1zaVibilzKbYhEE75FABHlEdeP0zexw01VjG3lDbJ65ecEnNHn/CAyncFBXYOCpSmQj6GDafkc5gSj8Ip4qQ5RMyN5Qr9UyuXr2uQc49OLv9QhPzkEGOF3D/2NpzDvfruv4Vb4jL37PYFKui4vA1t8fL7V9rHlCgf4/I+urUxRcx7GeRzWH5tPzEvtYWK+VFLpJifDwwxwxCUOhW2on+j4fuVCL7o1u5uEp1jHFTpQsuXL4fb7cbdd9/d4oDK2TrzsraysjKMHDkSVqsVmZmZmD59OhoaGlBQUICXX365eVBl4cKFWL58OUpKSrBw4cLm9YcNG9b889q1a3HzzTdDo9Fg6tSp6N27Nw4cOIBXXnkFH330EbZv347wcN8vhIWFhRg9ejQGDhyI2bNno7q6Gnp967+4bNmyBQAwceJEv2zkyJEICwvD1q1bA+gRIiIiIiKirhE+egFsRWtRs/1JWNJvgUp9+tdse2k+6r/9MwDA3Vh5+rWyL1Dx0R0AAF1EOsIue6h7Gk2dhoMqXSg/Px8AkJGRcU7rXbNmDWpqavDiiy/ivvvu88kqKyubf87OzsaWLVtQUlLS4gzaVVVVuO222xAdHY38/HwkJf33NqiVK1di5syZeOyxx/Dyyy/7rJefn48FCxZg0SL/Z8O3pKCgAADQr5//SLdKpUJaWhp27twJm82GoKCgNpVJRERERER0LuijhyAo7UbYCv+Fhu/+huCBcwAAzpoiNHz3V59lXbVFaKgtAgAYe13JQZULAAdVutCJEycAAImJid1Sv8nkf+NDVJR8GerZVqxYgbq6Orz66qs+AyoAMGPGDDz33HNYtWqV36BKXFwc5s+f3+Z6amtPX3sbGtry5YEhISHNy3FQhYiIiIiIWqILTUbKPPm2yTPauhwABA+cheCBs8RlYq/7e4fWo/MfB1UuQNdddx0efvhhzJ07Fxs3bsSkSZMwbtw49O/fv13lfPnll83/LSws9MvtdjsqKytRWVnpM1gzdOhQv9t9WroSZt68eQgLC2tXm4iIiIiIiIh6Cg6qdKG4uDgcPHgQpaWlSE9PP2f1pqSkYNu2bcjJycH69evx7rvvAgDS09Px+OOPY9q0aW0qp7q6GgDw6quvistZrVafQZWW5o/Jycnxe23OnDkICwtrvkLlzBUrP1ZXd3omzzNXrBARERERERH1BPJjCyggY8eOBQBs3rw5oHLU6tObyeXyf4JCawMRQ4YMwZo1a1BdXY1t27bhscceQ3l5OaZPn94814uSM4MY+/btg9frbfVfnz59fNY7+0lCZ7S03pmnAZ2ZS+XM3Co/Xq+wsBAJCQkwm81tajcRERERERHRucBBlS40Z84caDQavPHGG6ioqBCXlR6pfObpOqWlpX7Znj17xHJ1Oh1Gjx6NnJwcvPTSS/B6vXj//febc43m9GPW3G6337qjRo0CAGzbtk2sI1Djx48HAHz88cd+2VdffYWamhpcddVVXdoGIiIiIiIiovbi7T9dKC0tDQ899BAWL16MyZMn491330VKiu+z4O12O5YuXYqKigosXry4xXLS09NhsViwdu1aVFdXIyIiAgBQXl6O3Nxcv+V37NiBPn36ICYmxuf18vJyAL4T2J4p69ixY35XnGRlZSE3NxePPvooxowZg4EDB/rkNpsNe/fuxejRo9vSHa3q378/rrzySnz66af48MMPMWXKFACA0+lsnvD2rrvuCqiOnsoR5RFztbNrxz01Ea0P5gGAw2kMuA632X/A7mxai1PMG6PlyYkDfQ/WeJ1C/f5XXvmU38XbsDFeLt9YHVj7lTgjlLaPXL/a1d39K9dvj5TXdhvk9rmC5fa5guXyu/oYV+o/AFC5vWLuuuZSMdds3iXm1l9dLuaWY/IxrD9aLeYnRiaIefg+uY+V2o/UFDFu6KUXcyA8oPprZ8n9F1wi95+9j0HMcUqOwwv9r5I9W10/hWOkSSNXoMCjk/fPmJg6Ma8ulyfoVzsDO0e6zHL7+sZWivkRc5KYu4OUj+GgUrmPg8rlNjqtgfVBdaX8OW1UKN9lk38dMZXLx7BbYRfXyIeIIkdTYJ8j6ib5/Su1X0mgn6NXmQ6L+SPHrhfzSZHfivk/bCPEnOhCwEGVLpabmwu73Y4lS5YgPT0dGRkZGDRoEHQ6HQ4fPoxNmzahqqqqxcGRM/R6Pe655x489dRTGD58OG644QbU19dj3bp1uOqqq1BUVOSz/Ntvv42lS5di/PjxSEtLQ0hICA4cOIAPP/wQUVFRuP3225uXzcjIwOrVqzFt2jRMmTIFRqMRgwcPRmZmJqKjo7Fy5UpMmzYNQ4cOxaRJkzBgwADY7XaUlJRg69atGDNmDDZs2BBwP7322msYM2YMfv7zn+Pmm29GQkICNmzYgL179+LOO+/E1VdfHXAdRERERERERJ2JgypdTK1W44UXXsDMmTPx2muvIS8vD3l5efB4PIiPj8fEiRORlZWFCRMmiOXk5uZCr9dj2bJleP3115GcnIwFCxbg+uuvx5o1a3yWnTFjBux2O/Lz87Fjxw44HA4kJiZi7ty5eOCBB3we8XzXXXehuLgYq1atwhNPPAGXy4XZs2cjMzMTAJCZmYk9e/bg2WefxaZNm7Bx40aYzWYkJiYiKysLt956a6f008UXX4yvvvoKjz76KNavX4+GhgakpaXhpZdewty5czulDiIiIiIiIqLO1K2DKhpjJFQaI7xue3c2o1UqjREao8I1fW00YsQIvPXWW4rLZWdnt/j4YY1Gg5ycnBafouP1+l7WOWrUqOb5UJRotVo8/fTTePrpp1tdJj09HW+++aZiWcnJyX5taY/+/fs3P6mIiIiIiIiIqKfr1kEVbUgSEmd/C7e9qjub0SqNMRLaEPleVyIiIiIiIiL6aer223+0IUkcuCAiIiIiIiKi8w4fqUxERERERERE1AEcVCEiIiIiIiIi6gAOqhARERERERERdUC3z6lC9FMWvUMl5lVD5Kcp9fnQIeYNiQYxd0SYxFxuXdvaUHSzTsyj3pfbAMh9YKgJEvPqy+X2NUbJfWQ5Jtcf92WTmJdeZRRzl1ku3xPmFHOVWy/manl1xOySn75WlCRvP6XyVW45V1pf6RiJ2FYm5sduTBBzwym5fqX+UTrGbLFy+xtj5e1vKm/LUdg68xHlv51UjAgRc0uZS8ytv7pczEPe3ibm2r7JYt7UO0LMLcfEGPUpcq5VaL+SyM+Pi7lS++sU6lcqX4kqMV7MrXHyOaQxSt4He33qEXPTyUYxV+JVy/WfLI8W8z4Kx7DulNw+VZ1NzJv6yE+JPFEuzxuY9KVcvi1e/gwBgFP95bx8gvw5ZbDIuRLd98FinppxWMw9Ct82vrfEyuufUvgcdAV2Hg1JrZHLf18+xm1y8+GI6PiTM9tC6XPg5m+z5LzPLjFfuG2q3ACr/OtmlNEqr090HuCVKkREREREREREHcBBFSIiIiIiIiKiDuCgChERERERERFRB3BQhYiIiIiIiIioAzioQkRERERERETUAXz6DxEREREREV0wGo9uQf3eN2A//iXcjSeh1pqhi7wI5rSfI3jI3VBrfZ+sdfzdn8FemnfWKyqo9SHQRV4My0W3InjwHVCpWr8e4dS2bNRsfxLQ6JF0Zwk0ppafDHamnt53HYHWHAcAcDWUwvrDGtiKN8B56nu4rSegMUbAkHA5Qi99AMb4kYF2B3UxDqoQERERERHRec/rcaHqk/tQ/+2bUOnMMCVfC11oKjxNdWgs2YTqvAdRv+8NxN7wb+jC0vzWDxn+/6DWmQGvG676I7AWvoeqT+aiqeJrRF3zast1ej2oP/A3ACrA3YSGg+8g9JJ729zmuq9fRe3O56ANTYUp6RpoTNFw1hTCVrQWtqK1iJ78V1j6T+tol9A50O2DKkcaTqHS3jOfTx5lNCPJEn5O68zOzkZOTg4+/fRTjB8//pzWTfRjLotHcZmGRIPCEnIZtliVmAeVe8XcaVaovospv3/qSk29I7q1fqdF3n/PB5YyV2DrH3OIubZvspi7DhWLuV6hfl1ivJgbK+RtpNT+rhZo/Ur9ZxgYI+a6Onn7u4xGMbfGyXeSe7SBnSM9Wnn7NcbKnxFK52ijRf4qrKuT379S+Urts8UH1r8AYDop501h8lHksAb264BOIS+sjBLz0CC7XECF3MeGGrmPNAEe4jWhFjHv3k+hwA2LKhXzUof8u1BMTJ2YnzwZ0u42BeJU/nzUf/sm9LEjEHv9u9BaejVnXo8bNdtzUbP9CZx4byp6zfgSaoNv+0Iv/X/NV5AAQNioR1H69mWo3/cmQkf8HrrQvn51NpZsgrv+CIKH3I2G7/6G+v3L2jWoYoi7DPHTPoGx1zif1+2ln+P4mmtR9cm9MPedClWA51PqOt06qHKk4RQu+ufTsLsD+0LXVYwaLb676Q+dMrCya9cuLF26FHl5eSgrK4PH40FCQgLGjBmDWbNmYcKECZ3Q4vYbP348tm7dCq9X/tA/1959913cfPPNAICVK1filltu6eYWERERERFRT+U8VYDa3X+C2hiBuKn/gsYc65Or1BqEX74QzpoiWL9fhdrdLyD88myxTF1YGoy9rkRj8Xo0ndzT4qBKw/5lAICQIXfD01QP68F34DixE4a4EW1qtznt5y2+buw1DqbE8Wg8shFNVd/CEHtpm8qjc69bJ6qttFt77IAKANjdroCvovF4PLj//vsxYsQIrFixAn379sVvfvMb/O53v8Oll16KDz74ABMnTsTjjz/eSa0+/508eRK//e1vYTZ38yUIRERERER0Xqg/sALwehA86A6/AZWzhY364+nl9/+ljSX/54/PKv/rEdyNVbAeWgd99DDoowYh+KJb/1P2sna1vVWa/1wLptJ0TnnUJbr99p8L3fz587FkyRIMGzYMq1evRmpqqk/e2NiIV155BVVVVd3Uwp7nf/7nf2A2mzF79mw8//zz3d0cIiIiIiLqBg6HAw6H7z1cBoMBBoP/rTCO418CAExJGWKZ+ogB0JgT4G4ohav+KLTBvVtdtqn6e9iP5QFqHQxxl/nlDd+9DbibYLnoVwAAY1IGNJZENHz/d0Rc+SzUuiDF99gaV90R2I9shiYoDvqowR0uh7oeH6nchQoLC/HMM88gMjISGzZs8BtQAQCTyYQHH3wQOTk5rZazZcsWqFQqZGdn+2XFxcVQqVSYM2eOz+sFBQXIyspCSkoKjEYjoqKiMHz4cPz+979vXkalUmHr1q3NP5/59+Oy9u7di1tuuQXx8fHQ6/Xo06cP7r33Xr+BoLPbcvDgQdx0002IioqCSqVCcXGx3Fn/8c477+Cf//wn3njjDVgs8j2sRERERER04Vq8eDFCQ0N9/i1evLjFZd3WEwAAjSVRsVxtcKLPOmfU7lqCU9sW4dQXC1HxURbK3hkFr8uGiLGPQ2tJ8Cun/sByQKWBOX06AEClUsMy4BZ4m+pgLVjTnrfqw+t2ouKjLHjdDoRf8SRUal6p0pPxSpUutHz5crjdbtx9992IjW39EjQALY62dlRZWRlGjhwJq9WKzMxMTJ8+HQ0NDSgoKMDLL7/cfPXHwoULsXz5cpSUlGDhwoXN6w8bNqz557Vr1+Lmm2+GRqPB1KlT0bt3bxw4cACvvPIKPvroI2zfvh3h4b5zzhQWFmL06NEYOHAgZs+ejerqauj1SlMNAidOnMC9996L22+/HRMnTsQXX3zROR1CRERERETnnUceeQT333+/z2ud8ntT83ySvpNh1+1e4rdoxFXPtzjxrOPEDjgrv4Wpz7U+k9taLr4NtTufQ8P+vyD44ts60DQPKjbeBXvpZwgedEfzLUXUc3FQpQvl5+cDADIy5EvQOtuaNWtQU1ODF198Effdd59PVllZ2fxzdnY2tmzZgpKSkhavgqmqqsJtt92G6Oho5OfnIykpqTlbuXIlZs6cicceewwvv/yyz3r5+flYsGABFi1a1K5233333TAajbzlh4iIiIiIWr3VpyUacxycp76Hu+EYEJEuLutqKP3POr5/+O591xFozXHwuBrhOP4VKjfdjerP/gBdeDqCkif6LFu/fzkAwHLRTJ/X9REXQR97KeyleXDWFLb46ObWeL1eVG76DawH34F5wExEtvIYZ+pZePtPFzpx4vTlZImJypegdQWTyeT3WlSU/Fi7s61YsQJ1dXVYvHixz4AKAMyYMQPDhw/HqlWr/NaLi4vD/Pnz29XWFStWYO3atXjttdcQFhbWrnWJiIiIiOinzRA/GgDQeOQTcbmm6oNwW8ugsfRqdT4VtdYEU++rEHvDewBUqNz4a3ictubc47Sh4fu/AwAqNszG4T/pff41le8C8N+Bl7bwej2o3PhrNOxfDnP6dERPfAsqFX9dPx/wSpUL0HXXXYeHH34Yc+fOxcaNGzFp0iSMGzcO/fv3b1c5X375ZfN/CwsL/XK73Y7KykpUVlb6DNYMHTrU73aflq6EmTdvHsLCwlBWVoZ58+bhlltuwdSpU9vVRiIiIiIiouCLb0PtzmdR/+2fETp8HjRB0S0uV/PVU6eXHzhbsUx9xACEDP0f1O15CXV7XkLYyIcBANaCNfA21UEfPRT6mOEtrtvw3d/QcOBvCL88R3FOlNMDKnej4cBfYO4/DdHXLuc8KucRDqp0obi4OBw8eBClpaVIT5cvQetMKSkp2LZtG3JycrB+/Xq8++67AID09HQ8/vjjmDZtWpvKqa6uBgC8+qp82ZnVavUZVGlp/piWJuKdM2cOwsLC8Nvf/hYajcbvNqKfAlusSmEJr5g2JHbeXDwdpfweZI2x8nsMKg9s/a6m9P5d5q5tn9Mi1+9UmO9ZeR/yBFS+0xpY+2xQ2r8COwYC7R+bPF0WdA3tbFA7KbW/q+sHAP3RajFv6h0hr69QvutQsZg7J/hPHHg28wl5Hw60/Uq5M0T+qmXefzKg8pX6z6GQK7GccIn5icvk9+fRBvZXVq/C7xSuWPkd2mLlY9hllNuvt8jtV/wMUGifNU5uX2PLvxP6sByVc1O5wnnYGtgvbm6F07CjXH76SUWITsxN5fI20NfJ9Wvscq7EES63z2kOrPzuNja0QMxXHBst5sOiSsX8a4X6Yw0KG7AddOH9ETLsXtTteRHla3+OmOvfhdYc35x7vR7UfLUY1oPvQBuaitDh9wul/VfYiAdRv+//ULtrCUKG/hZqQwga/nMFSsSVz8HU+6oW1/PYq2Er+jcaizcgqG9mq+U3X6FyYAXM/X6B6El/4YDKeYaDKl1o7Nix2LJlCzZv3hzQvCpq9ekPE5fL/4tNbW1ti+sMGTIEa9asgdPpxK5du7B+/Xq89NJLmD59OhISEjB27FjFekNCQgAA+/btw6BBg9rcXpXK/8Pb6239F8uvv/4alZWViI5u+ZvDjBkzMGPGDCxZsgTz5s1rczuIiIiIiOinI+KKxfA01aJh/3IcW34xgpInQxvWF56mejSWbISrphDasDTE3bgWakNIm8rUmGMRPORu1O3+E2r3vAjLgBmwl34GbWhfGBOvbHW94IGzYSv6N+r3LxMHVWq+zEXDgRVQ6SzQhvdDzfYn/ZYJSp0KQ8ywNrWXzj0OqnShOXPm4KmnnsIbb7yBefPmtTpoAJx+BntrkzCdebpOaan/SPCePXvENuh0OowePRqjR49GWloaZs2ahffff795UEWjOT0K6na7m38+Y9SoUfjnP/+Jbdu2tWtQpb1uueUWnwl0z9i9ezf27NmDq6++Gn379u3SNhARERER0flNpdYiesIbsKRPR/2+N2Ev+wLuon9DrTNDFzEAIUN+jeAhd0Ot9Z97UhJ26e9Rv/cN1O1+CW7r6cuoLRfPavGPyWeYkidBExQL2+EP4baW+02Ke4arrgQA4HU2oPY/tyb9mDYkmYMqPRgHVbpQWloaHnroISxevBiTJ0/Gu+++i5SUFJ9l7HY7li5dioqKilafuZ6eng6LxYK1a9eiuroaERGnLwUuLy9Hbm6u3/I7duxAnz59EBMT4/N6efnpE8DZE9ieKevYsWPo06ePz/JZWVnIzc3Fo48+ijFjxmDgwIE+uc1mw969ezF6tHxZoJKnnmr55JGdnY09e/bg17/+NW655ZaA6iAiIiIiop8GU9I1MCVd0+bl46dtEnONORbJ99Q0/3/UNa8olqlSa5H0a9/781qqJ/ratxB97Vttayj1SBxU6WK5ubmw2+1YsmQJ0tPTkZGRgUGDBkGn0+Hw4cPYtGkTqqqqWhwcOUOv1+Oee+7BU089heHDh+OGG25AfX091q1bh6uuugpFRUU+y7/99ttYunQpxo8fj7S0NISEhODAgQP48MMPERUVhdtvv7152YyMDKxevRrTpk3DlClTYDQaMXjwYGRmZiI6OhorV67EtGnTMHToUEyaNAkDBgyA3W5HSUkJtm7dijFjxmDDhg1d1n9EREREREREPRUHVbqYWq3GCy+8gJkzZ+K1115DXl4e8vLy4PF4EB8fj4kTJyIrKwsTJkwQy8nNzYVer8eyZcvw+uuvIzk5GQsWLMD111+PNWvW+Cw7Y8YM2O125OfnY8eOHXA4HEhMTMTcuXPxwAMP+Dzi+a677kJxcTFWrVqFJ554Ai6XC7Nnz0Zm5un7/jIzM7Fnzx48++yz2LRpEzZu3Aiz2YzExERkZWXh1ltv7fxOIyIiIiIiIjoPdOugSpTRDKNGC7tbnlm+uxg1WkQZO2dK7xEjRuCtt5Qv68rOzm7x8cMajQY5OTktPkXnx5PAjho1CqNGjWpTu7RaLZ5++mk8/fTTrS6Tnp6ON998U7Gs5ORkcULa9mqtL4iIiIiIiIh6gm4dVEmyhOO7m/6ASru1O5vRqiijGUmW8O5uBhERERERERH1QN1++0+SJZwDF/STpfIEtn5jdOszjgOA06JUgnxlkem4RswBwKFw+KqCnfICDS0/9eqMuhT5PQYdl4u3QS/mwUfkjXBqgFrMncFyH+rqFbaRwvqwyqfpxtYfKgYAcJnl8pX2IaXt52qQ+0epfKX26evk9R1hCu1XOMYC7R+PTi6/MTawq/dcQQq5QvvtsconGZVH3seUziMnRiaIueWYvL4uMV6uf4JcfuT/fiHmpY+MEfOqQXL5SsIKFLZBhLwPKfWfUvkqhf4LOikfw1WDjGJuk4uHSuFiY6V9WElTmPz+jYfkzxCl/dcRIefGCoXPAIXy9UcV2hdg/wBAfYqcN4W75QXMgV0xrtLI28hgkvfB0CC7mFcOksu3nZI/59Uu+RhUEpJaI+aOOnknssfK/a9V+BwNlNLn4KtF48U82CBvn48PXCTmA5PLxLyX4ZSYE50PuvYoJiIiIiIiIiK6QHFQhYiIiIiIiIioAzioQkRERERERETUARxUISIiIiIiIiLqAA6qEBERERERERF1AAdViIiIiIiIiIg6gIMqREREREREREQdoO3uBhD9lNVe7BLz3skVYn40PDqg+i3RVjEPN9sUyzhlDRLzGIUyTgXL6zdUmBXbIFHqwzJDrJi7g+RtpNSHjTa9mCdE14i5Uv82hgVWvtI+1DvulJgjTo7LKsLEPND2KdGYnWJuCmoS89pwef8LtHylYyzQ/lPafwDAdSxUzB1RHjEP3yf/faY+Ra7fWKESc/MJuf7SR8aIea/FX4h59R3y+o0xYoya/nL7XZbA+k+pfKN8ikNjlHyOcCnsIvHb5HNgyS/l9+d1Bvb3O6VjTKNwjClpVPyMkb8q6wbWinmUwjF+/Gv5JOqOlt8/AKhr5TaqnfI+hBqdYh0Sj0lhHygxivkpVbBcgVku31wq72PeAH/bqdHK50hvknyMKPEk2gNaX0ltuLx9+4XUiPmur/uK+aXDDon5iLBiMSe6EPBKFSIiIiIiIiKiDuCgChERERERERFRB3BQhYiIiIiIiIioA7p9ThVn1RG46iu7uxkt0gZHQReZdE7rzM7ORk5ODj799FOMHz/+nNZNRERERERERG3XrYMqzqojKPxDOrzOrp2gqaNUOiPSnv6+UwZWdu3ahaVLlyIvLw9lZWXweDxISEjAmDFjMGvWLEyYMKETWtx+48ePx9atW+H1erulfgDwer146KGHsGPHDvzwww+orq5GaGgoUlNTcccdd2DWrFnQ6QKbRI2IiIiIiIios3Xr7T+u+soeO6ACAF6nPeCraDweD+6//36MGDECK1asQN++ffGb3/wGv/vd73DppZfigw8+wMSJE/H44493UqvPP263Gy+//DJcLhcyMzNx//3346abbsLx48dx55134vrrr4fHI8/8TkRERERERHSudfvtPxe6+fPnY8mSJRg2bBhWr16N1NRUn7yxsRGvvPIKqqqquqmF3U+r1aKmpgZGo+8j91wuFyZOnIiPPvoI69evR2ZmZje1kIiIiIiIejpnbTGOLevv+6JaB01QLIy9xiJsxINwnPwalRvvbHOZlotuQ/S1b+HUtkWo2Z7rk6m0QdCG9YU59UaEjvg91Lr/Pqb96Fv94KovOWthNdSGMOijhyJk8F0w9/+lX131+1coti1k2L2IHP98m9tPXY+DKl2osLAQzzzzDCIjI7FhwwbExsb6LWMymfDggw/C4XC0Ws6WLVtw9dVXY+HChcjOzvbJiouLkZKSgtmzZ2P58uXNrxcUFODJJ5/Eli1bcPz4cVgsFiQlJeHqq6/G88+fPghVKlXz8mf//OOy9u7diyeffBJbt25FVVUV4uPjMXXqVGRnZyMyMrLFtjz88MP44x//iLy8PFRVVeHw4cNITk5u9T3+eEAFOD3YcuONN+LTTz9FYWFhq+sSERERERGdoQ1NhWXADACAx2mF48R2WL//O2yF7yHuFx8jbNR8n+WbKr6B7dA6GHtdCWPilT6ZPnqoz/8Hpf0c+siBAAC39QRsh95HzfZc2A5/iITpeVBp9P9dWKVB2MhHAABejxOumkJYi9bCfvRThNceQthlD7XYfmPvDBgTxrSYGeJHtb0j6JzgoEoXWr58OdxuN+6+++4WB1TOZjAYOq3esrIyjBw5ElarFZmZmZg+fToaGhpQUFCAl19+uXlQZeHChVi+fDlKSkqwcOHC5vWHDRvW/PPatWtx8803Q6PRYOrUqejduzcOHDiAV155BR999BG2b9+O8PBwn/oLCwsxevRoDBw4ELNnz0Z1dTX0ej3ay+PxYMOGDQCAQYMGdaAnej6NTb4D79TmeDG3BFi/PVieq6asRN5v26LBHCTmWotTzC1F8mnKPswm5mUH5PcQtVsl5tZ4uf5GW7CYm44rbGOY5PLj5VvflNp/Kj6wfeioKkpev1Deh6KOy/M1KbVPo/D+lfrXfFwj5jX95P63NIgxGuPl+huhcG4/HirGcusC338AoN/HdWLuDJPfg2bzLjHX/upyMbcca/2PCgCgP1ot5lWDEsS8+o6Wv5SeEfHWF2KuTU2R6x8bJ+aWUvkcp9R/mlly/wWXyP1X30fefpZvm8TcbZSPIVjl3FCjsL4Cj05e3zxYPkir98nnMHONfA7Vyh8xsP0QIuamy2oVypfrdyv0LwAYy+XzUPAR+TzstMhtUFI9XD7PqOVdDPZeLjEPKpY/h11mMYZGPkQUKX1X05wKrP90DV3765jS58DzSf8S80fU14v5pMhvxfwfx0eI+cSYA2LeUbqwVIRf/pjPa9VfPIbar57CqS8eQ/wvN/pk9ftXnB5USbzSb70fM/e7CZb06c3/72l6GmUrx6Dp5G40fL8KwRfPas5Uaq1fefayL3D83QzUbH8CIcPugVrn/13ZlJTR6oAL9TwcVOlC+fn5AICMjIxzWu+aNWtQU1ODF198Effdd59PVln53zlisrOzsWXLFpSUlPhdAQMAVVVVuO222xAdHY38/HwkJf13wt6VK1di5syZeOyxx/Dyyy/7rJefn48FCxZg0aJF7W77mXZUVlZi8+bNOHjwIObMmYNrrrmm3WUREREREREBQOjQuaj96ik4ynd2arlqfTAsF8/CqfxH4Sjf5TOo0hJjwhjowtPhrP4OzurvYIi9tFPbQ+ceB1W60IkTJwAAiYmJ3VK/yeT/F8yoKPkvNmdbsWIF6urq8Oqrr/oMqADAjBkz8Nxzz2HVqlV+gypxcXGYP9/3krq2ysnJaf5ZpVLhgQcewOLFiztUFhEREREREQBAFdhVRZ3rP1eQqQK7mo96Bg6qXICuu+46PPzww5g7dy42btyISZMmYdy4cejfv7/yymf58ssvm//b0pwmdrsdlZWVqKys9BmsGTp0qN/tPi1dCTNv3jyEhYX5vOb1euHxeFBWVob3338fjzzyCLZt24YPP/wQISHyJbZERERERHThcDgcfnNPGgyGDk2dUPv1K6fXj5VvSWovT1M9Gg6s+E/ZyledNB77DM5TP0BtjIQuYkDLyxz5BF5Xy0/JNaffDH0r61H34KBKF4qLi8PBgwdRWlqK9PT0c1ZvSkoKtm3bhpycHKxfvx7vvvsuACA9PR2PP/44pk2b1qZyqqtP38f+6quvistZrVafQZWW5o85+wqUM+bMmeM3qAIAarUaiYmJ+M1vfoPIyEjcfPPNeOKJJ/D000+3qd1ERERERHT+W7x4sd/vES09vOPHnDVFOLXt9FQEZyaqdZR9AZXGiPAxjwfUJmvBP+Gs/h4A4LaVw1a0Dm7bCehjL4Ul/RafZb0eV3M7zp6oFlAh8uoXodb6P6wDAOxHP4H96CctZvrooRxU6WE4qNKFxo4diy1btmDz5s0BzauiVp+eIMvl8p/Iq7a25QnQhgwZgjVr1sDpdGLXrl1Yv349XnrpJUyfPh0JCQkYO3asYr1nrgzZt29fuyaKVbVwaZ3XK0+S1pqJEycCOP0EJCIiIiIi+ul45JFHcP/99/u81parVFy1Rf99/PF/HqlsTr8FYZc9CH3U4IDaZCv8F2yFpyf4VWmDoAtLRfCQuxB66f2+T/4BAK/b7zHMUGkQM+VtmPvd1God4WNzOVHteYSDKl1ozpw5eOqpp/DGG29g3rx5iI6ObnVZh8PR6gnizNN1SktL/bI9e/aIbdDpdBg9ejRGjx6NtLQ0zJo1C++//37zoIpGc/o+Prfb3fzzGaNGjcI///lPbNu2rduevlNWVgbg9OOViYiIiIjop6Ojt/qY+kxE3M/f74IWAdGT/+rz9B+JSmNA8r31AABPUwMaj36Kyo2/RsXHd0IblgrDjx7XTOcn+RlhFJC0tDQ89NBDqKysxOTJk3H48GG/Zex2O1544QXxErb09HRYLBasXbu2+ZYcACgvL0dubq7f8jt27MDJkyf9Xi8vLwfgO4FtREQEAODYsWN+y2dlZSE4OBiPPvoo9u/f75fbbLbmeVcCcfDgwRbba7PZmkemJ0+eHHA9RERERERE3UGtt8Ccej1iprwNr7MBlR/f2eGr+aln4Z//u1hubi7sdjuWLFmC9PR0ZGRkYNCgQdDpdDh8+DA2bdqEqqqqFgdHztDr9bjnnnvw1FNPYfjw4bjhhhtQX1+PdevW4aqrrkJRUZHP8m+//TaWLl2K8ePHIy0tDSEhIThw4AA+/PBDREVF4fbbb29eNiMjA6tXr8a0adMwZcoUGI1GDB48GJmZmYiOjsbKlSsxbdo0DB06FJMmTcKAAQNgt9tRUlKCrVu3YsyYMdiwYUNAfbRhwwb84Q9/wPjx49G3b1+EhoaitLQU69evR1VVFcaOHet32R/RT4XpuNLYNz+Mu5O2Xt4+rmDPOWpJx6lrrGJuUMiRmtKJrfHX1DsioPUbY+Rcq9B+V5H/H0R8jI1rZ4vaV39XMxRXi3nNCP950nzxHNTdXGY5d1rkJ544FdZXogryvz39bC5zYE83UXp/br28D3q1gT3xxR0kn8c1jgv76S1xxrrubsIFx5SUgaDUqbAVrYX1+1WwDJjR3U2iAHFQpYup1Wq88MILmDlzJl577TXk5eUhLy8PHo8H8fHxmDhxIrKysjBhwgSxnNzcXOj1eixbtgyvv/46kpOTsWDBAlx//fVYs2aNz7IzZsyA3W5Hfn4+duzYAYfDgcTERMydOxcPPPCAzyOe77rrLhQXF2PVqlV44okn4HK5MHv2bGRmZgIAMjMzsWfPHjz77LPYtGkTNm7cCLPZjMTERGRlZeHWW28NuI9+9rOf4Y477sDnn3+OHTt2oL6+HqGhoRg0aBBuueUW3Hnnnbz9h4iIiIiILghhoxfAVrQONdtzYe5/M1TqC3tw7kLXrb+paoOjoNIZ4XW2/Lio7qbSGaENjlJesA1GjBiBt956S3G57OzsFm8F0mg0yMnJafEpOj++bGzUqFEYNWpUm9ql1Wrx9NNPi0/WSU9Px5tvvqlYVnJycocuYRs0aBCWLl3a7vWIiIiIiIjON4booQhKuwG2wvfQcPBtBF88yyeXHqmsDUlG8MBZLWbUPbp1UEUXmYS0p7+Hq76yO5vRKm1wFHSRSd3dDCIiIiIiIrqAhI+aD1vhv1Gz/UlYBsyESv3fX82lRyobe13JQZUeptvvqdBFJnHggoiIiIiIiAKiC01Gyrymdq8XPHCW4kBF+OWPIfzyx9pcZu87CsRcHz0EKfMc7W4H9Tx8+g8RERERERERUQdwUIWIiIiIiIiIqAM4qEJERERERERE1AEcVCEiIiIiIiIi6oBun6iW6KcseZ1TzMtHGMQ8afVxMXckR8jlI0jMnRYxBgAkbnGI+bHx8nsIK5BPQ5ZSufyG4yYxrxze/sd8ny12p1y/kuoB8vtX6uPGeI+Yx+6U9yFAL6aR+SfEvGhOvJibj8v9aylVmixObp/S+zMUV4t51dg4MQ8rkNuv1D9K5QMqMbXK3avYv0rMx+X6AeX3oLQNG3op7GOfy+cpJU295fOY0jas6S/3geI2VMhDV2wTc21qSkD1K5bfN1nMLdrIgOpv6CX3X/QOuf+DKl1irsSr8Oe/U+XRYp5QINevkk+x0NXJ56CmQzoxL6uWH8YQVSg3oMmi/PdPR5icVw9xywuYA9tG6ir5HBAxWH7KZ2SQVcwPqBLFXFujEfNA6aJbfqztGWFfyd+lavopn4e7UkiBvA/d/G2WnPfZJeYLt01td5vOFmWUtz/R+YBXqhARERERERERdQAHVYiIiIiIiIiIOoCDKkREREREREREHcBBFSIiIiIiIiKiDuCgChERERERERFRB3BQpYfJzs6GSqXCli1burspRERERERERCTo9kcqN9XY4bIqPXKze2jNeujDjJ1S1q5du7B06VLk5eWhrKwMHo8HCQkJGDNmDGbNmoUJEyZ0Sj3tNX78eGzduhVeb2CP7ewsW7ZswZIlS7Bt2zbU1tYiJiYGI0aMQHZ2NoYOHdrdzSMiIiIiIiJq1q2DKk01dnz7/DZ4XZ7ubEarVFo1Bv3+8oAGVjweDx544AEsWbIEWq0WGRkZmDp1KnQ6HQ4dOoQPPvgAf/vb37Bo0SIsWLCgE1t//nniiScwf/58JCQk4MYbb0RUVBTKy8uRn5+Pffv2XZCDKnV99GJuqpAHu+x9IwOq36OTc2OVchkNveT3oFRGfZJKzM3H5VxpfUOlUvnyoK5XrVB/b7kTg8rl81tTo1y+RydfUGgoOSXmXnWEmCvtQ0rbz3zcKeaBtk/pGAmBvL5S+xyh8segUv9Y4+Xtp3QMK+VK7VeitH8CQPDRwLYhEC6mTb3lbaTEGaKwjSLkbeCyyMegpTSwPtampsj1Fx2W60+W+0ep/KZEuf+VzmH6erl/TCc1Ym6LUTiHaQP7qulVWL0xVj6GdFa5AJVLLl9vkc/BjjA5b4yX+1fXIK/vDBLj08uEyrmxXN6GLnNgF667ExxifvJkiJjXBxvE3HBS3oZufdf+YdBZIf8e4FHYRzVy9yjmSvR18vu3JsjH6MioUjHvrasW85iYOjFXMjykJKD1iXqCbh1UcVmbeuyACgB4XR64rE0BDarMnz8fS5YswbBhw7B69Wqkpqb65I2NjXjllVdQVdWG314vYGvXrsX8+fNx44034p133oHJZPLJXS6Fbz1ERERERERE5xjnVOlChYWFeOaZZxAZGYkNGzb4DagAgMlkwoMPPoicnJxWy9myZQtUKhWys7P9suLiYqhUKsyZM8fn9YKCAmRlZSElJQVGoxFRUVEYPnw4fv/73zcvo1KpsHXr1uafz/z7cVl79+7FLbfcgvj4eOj1evTp0wf33nuv30DQ2W05ePAgbrrpJkRFRUGlUqG4uFjsq4cffhjBwcFYvny534AKAGgD/EsXERERERERUWfjb6pdaPny5XC73bj77rsRGxsrLmswyJc+tkdZWRlGjhwJq9WKzMxMTJ8+HQ0NDSgoKMDLL7+M559/HgCwcOFCLF++HCUlJVi4cGHz+sOGDWv+ee3atbj55puh0WgwdepU9O7dGwcOHMArr7yCjz76CNu3b0d4uO+lx4WFhRg9ejQGDhyI2bNno7q6Gnp965fw7927F9999x1uuukmWCwWrF+/Hnv37kVQUBCuvPLKC/K2HyIiIiIiIjr/cVClC+Xn5wMAMjIyzmm9a9asQU1NDV588UXcd999PlllZWXzz9nZ2diyZQtKSkpavAqmqqoKt912G6Kjo5Gfn4+kpKTmbOXKlZg5cyYee+wxvPzyyz7r5efnY8GCBVi0aFGb2rtz504AQGRkJMaNG4cvv/zSJ//Vr36FP//5z+LADBEREREREdG5xtt/utCJEycAAImJid1Sf0u30URFRbV5/RUrVqCurg6LFy/2GVABgBkzZmD48OFYtWqV33pxcXGYP39+m+s5efIkAODPf/4zKisr8cknn6C+vh67d+/G5ZdfjrfffvsnP4kvERERERER9Ty8UuUCdN111+Hhhx/G3LlzsXHjRkyaNAnjxo1D//7921XOmStGvvzySxQWFvrldrsdlZWVqKys9BmsGTp0qN9VJS1dCTNv3jyEhYXB4zk9WbHH48E//vEPXHLJJQCASy65BO+99x7S0tLwyiuvYNGiRZ16mxQRERERERFRIDio0oXi4uJw8OBBlJaWIj09/ZzVm5KSgm3btiEnJwfr16/Hu+++CwBIT0/H448/jmnTprWpnOrq049Qe/XVV8XlrFarz6BKS/PHtDQR75w5cxAWFobQ0NPPAkxMTGweUDkjJiYGo0aNwqZNm/Ddd9/5zPdCRERERER0+E/tmyYgZV4TnLXFOLbs9B+dTSlTEHfDe37LNR7dihNrJiB48F2Iuua/vxNVfHQHGr77K+KnfwZj/KjW26HSQGOKhiHuMoQMnwdT4hXNkcdpQ/3e/4Xj5G40ndwD56kCAF4kZv0AXWhyu94PdS8OqnShsWPHYsuWLdi8eXNA86qo1afv0mrpscK1tbUtrjNkyBCsWbMGTqcTu3btwvr16/HSSy9h+vTpSEhIwNixYxXrDQkJAQDs27cPgwYNanN7VSqV32ter7fV5c8MOIWFhbWYn3m9sbGxzW0gIiIiIqKfhrBR/lMP1GzPhVofipBL7lVcv/Hwh2g89pnPoEdHqY2RCBn6PwAAr6sRTZX7YDu0DrZD7yMm8x2Y+/0CAOC2nUT1Z38AAGiD+0BtDIfHXh1w/XTucVClC82ZMwdPPfUU3njjDcybNw/R0dGtLutwOFq9teXM03VKS0v9sj179oht0Ol0GD16NEaPHo20tDTMmjUL77//fvOgikajAQC43e7mn88YNWoU/vnPf2Lbtm3tGlRpr9GjR8NkMuHQoUOw2+0wGo0++XfffQcASE5O7rI2dJfqIa0PNgGA6bg87VFjdGCT9zYNtIm5o9wo5gDQkCznbrP/YODZtBanmJ90Bom50nvwKrwHa7xOIfcfJDxbY7xHzJW2oRJHlFx+1Rj5yWJK7VfSkCZvH7dB3gfN8YG1T6l/G6PlWwLNx+VjrKafXL+uQX5/iu2LF+MuP8aV2gcAkd/YxdzRJ1zMNZt3iXndry4Xc8sxh5ib958U8xMjE8Q8fJ/cx0rt16amiHnV2DgxtyRHBFR/1Sy5/4JL5P5TOsdZSpvEXNMkH0NVl8jHkDNUI+ZKPDq5/ojBlWJeDXkuOX2NQvst8v5jS5DblzrkmJgfqUkSc0eUW8wBIKhU7uPgI3IbnZbAPieqw+RfJ4ylct7YS25/kFWuX+WS26+RDxFFbr28D9SlBNZ/avljVlFjtFy/0veIP8Z9LOaPHLtezOembhHzfxwfIeadJfzyx/xeq9meC7UhrMXsbNqQZLjqj+DU53+E6ZbPAm6LxhTpV2f9t39G5abfoPqzR5oHVTSmKMT9/EPoY4dDY4zAiX9dh8YSeXtQz8SJartQWloaHnroIVRWVmLy5Mk4fPiw3zJ2ux0vvPBCi3OOnJGeng6LxYK1a9c235IDAOXl5cjNzfVbfseOHc2Tv56tvLwcgO8EthERp7/sHTvm/6GflZWF4OBgPProo9i/f79fbrPZ/J7U0xEWiwW33XYbrFar3/v561//iv3792PcuHGIj1f47YSIiIiIiKgddOH9YRnwKzhObIe18F9dUodl4ByodGa46orhbjw9GKzWW2Dq8zNojPLgO/V8vFKli+Xm5sJut2PJkiVIT09HRkYGBg0aBJ1Oh8OHD2PTpk2oqqpqcXDkDL1ej3vuuQdPPfUUhg8fjhtuuAH19fVYt24drrrqKhQVFfks//bbb2Pp0qUYP3480tLSEBISggMHDuDDDz9EVFQUbr/99uZlMzIysHr1akybNg1TpkyB0WjE4MGDkZmZiejoaKxcuRLTpk3D0KFDMWnSJAwYMAB2ux0lJSXYunUrxowZgw0bNgTcT08++SS2bNmCJ554Ap9//jlGjBiBgoICrFu3DuHh4fjf//3fgOsgIiIiIiL6sfDLF8L6wz9wKn8BgvpOhUod2FV2LRKmQ6DzGwdVupharcYLL7yAmTNn4rXXXkNeXh7y8vLg8XgQHx+PiRMnIisrCxMmTBDLyc3NhV6vx7Jly/D6668jOTkZCxYswPXXX481a9b4LDtjxgzY7Xbk5+djx44dcDgcSExMxNy5c/HAAw/4POL5rrvuQnFxMVatWoUnnngCLpcLs2fPRmZmJgAgMzMTe/bswbPPPotNmzZh48aNMJvNSExMRFZWFm699dZO6afIyMjmyXX/9a9/4YsvvkBERARuvfVWZGdno2/fvp1SDxERERERnR8cDgccDt97uAwGQ6c/EVQbkoTgof+Dut1/Qv3+ZQgZfGenlt+wfzm8Lhu0IcnQmOTbEun8062DKlqzHiqtGl6X8j3f3UGlVUNrDux+9jNGjBiBt956S3G57OzsFm8F0mg0yMnJafEpOj+eBHbUqFEYNWqU33It0Wq1ePrpp/H000+3ukx6ejrefPNNxbKSk5PFCWmVRERE4MUXX8SLL77Y4TKIiIiIiOjCsHjxYr/ffxYuXChOndBRYSMfRsO3y1DzZS4sA2ZCrZPn9WuNu7EKp7YtAgB43XY0Vew9PVeKSo2IK57qzCZTD9Gtgyr6MCMG/f5yuKzyJGndRWvWQx+mPFEnERERERERda5HHnkE999/v89rnX2VyhkaYwRCRzyAU18sQN2elxA28uEOleOxV6Fm+3+mdlBpoDFFISh1KkKHz4Ox17hObDH1FN1++48+zMiBCyIiIiIiIvLRFbf6SEKG34e6b15D7c7nETz4rg6VoQvvj8TZ33Zyy6gn49N/iIiIiIiI6CdPrTUhbPQCeJpqUbOj9ekRiM7W7VeqEP2UJa9zinn5CHlkPmn1cTF3JMuPaCuHfK+o0yLGAIDELQ4xPzZefg9hBfJpyFIql99w3CTmlcMDm2k9dqdcv5LqAfL7b0sfSyylSrdPyvNCReafEPOiOfKjzM3H5f4NtH2xO+VjxFBcLeZVY+PEPKxAbr9S/yiVr8Sq8KR4pf5VYj6uUlzm1MBgMVfahrWzLhfzyM/l85SSpt7yeUxpG9b0l/tAo9B+JaErtom5NjVFzKsU6lcsv2+ymFu0kWLe0Es+Bht6yf0XvUPu/6BKl5gr8Sr8+e9UebSYJxTI9asUpvXT1cnnoKZDOjEvq04S86hCuQFNFuW/fzrC5Lx8jMKbNAe2jdQ1ch+EjK4Q85Qgq5gfUCWKubZGfkqLV6t8HpSo+9jEPOwD+btUTb/A6g9USIG8D938bZac99kl5gu3TW13m84WZZS3f3cIHjjn9IS137wGfdTg7m4OnQd4pQoRERERERERAJVag/Cxj8PrdqBm+5Pd3Rw6D/BKFSIiIiIiIqL/MKfdCEP8aDiOf9ml9VTl/QGexkoAQFPl6XlYqj/7A9S605cyh172IPQRA7q0DRQ4DqoQERERERERnSVi3JM4/m5Gl9ZhK/gnXPUlvq8V/qv5Z8vFswAOqvR4HFQhIiIiIiKiC0rKPHlOMF1osriMsde4VvPoa99C9LVvtbvOH+t9R0G7lqeeiXOqEBERERERERF1AAdViIiIiIiIiIg6gIMqREREREREREQdwDlViLqRNU4v5o4Ir5g3JYZ3afkui0fM21JHU4RbzBujNGKucsvlN0apxNwTJt/b2hhlCKh+JfZIOXeZ5W3gCXOKuVL/K/WP0j7U3dtPaX2VS26/UvlKlPrHZZLLd5rl8pWOQbUzsPa3RXiBS8xdQfI2Di5xBFS/61CxmCsdgarEeDE3VsjrB9p+bd9kMVfah5TqVypfqf9cA2MCqt+tM4q506JwjrEH9vc7j1YuvzFWPoaCyuX911gjn+OUOELk8pWOcSVK/QsAugY51zbI28AV4K8DGqtcfq1N3ocUy7fJ5Wttch9pAjvEYbPpxFzpc0Atf4wHvI8oUfocGRZVKualDvkcFhNTJ+YnT4aIOdGFgFeqEBERERERERF1QLdfqeKor4KrUWGIvZtoTRYYghX+zNzJsrOzkZOTg08//RTjx48/p3UTERERERERUdt166CKo74K+1b8EV63wnVx3USl0WHwrCc7ZWBl165dWLp0KfLy8lBWVgaPx4OEhASMGTMGs2bNwoQJEzqhxe03fvx4bN26FV5v1156qGTdunX4+OOPsXv3bnz99dew2WxYuHAhsrOzu7VdRERERERERK3p1kEVV2NDjx1QAQCv2wlXY0NAgyoejwcPPPAAlixZAq1Wi4yMDEydOhU6nQ6HDh3CBx98gL/97W9YtGgRFixY0ImtP788//zz2Lp1K0JCQpCQkIDCwsLubhIRERERERGRqNtv/7nQzZ8/H0uWLMGwYcOwevVqpKam+uSNjY145ZVXUFVV1U0t7Bkef/xxxMXFIS0tDX//+98xY8aM7m4SERERERERkYgT1XahwsJCPPPMM4iMjMSGDRv8BlQAwGQy4cEHH0ROTk6r5WzZsgUqlarFW2GKi4uhUqkwZ84cn9cLCgqQlZWFlJQUGI1GREVFYfjw4fj973/fvIxKpcLWrVubfz7z78dl7d27F7fccgvi4+Oh1+vRp08f3HvvvX4DQWe35eDBg7jpppsQFRUFlUqF4uJisa+uuOIK9OvXDypV1z/pgoiIiIiIiKgz8EqVLrR8+XK43W7cfffdiI2NFZc1GOTHurZHWVkZRo4cCavViszMTEyfPh0NDQ0oKCjAyy+/jOeffx4AsHDhQixfvhwlJSVYuHBh8/rDhg1r/nnt2rW4+eabodFoMHXqVPTu3RsHDhzAK6+8go8++gjbt29HeLjvo9YKCwsxevRoDBw4ELNnz0Z1dTX0+sAeS0tERERERETU03BQpQvl5+cDADIyMs5pvWvWrEFNTQ1efPFF3HfffT5ZZWVl88/Z2dnYsmULSkpKWrwKpqqqCrfddhuio6ORn5+PpKSk5mzlypWYOXMmHnvsMbz88ss+6+Xn52PBggVYtGhR574xIiIiIiIioh6Egypd6MSJEwCAxMTEbqnfZDL5vRYVFdXm9VesWIG6ujq8+uqrPgMqADBjxgw899xzWLVqld+gSlxcHObPn9+xRv/EuBUuUAo6Lt8O5dHLd/AFnZQngm48rnQFkUYhBxxhcm46LpdhqJGfPKX0HtwGnZi7CuVOjtrXJOZK6nvL9VuOye+vKUTexjbI20jjkMtXeQLbh5S2n8ojxgG3T+kYcUTI/R98RG6gUv8r9Y9H4VNUaf9Wev+WY3L7GxLl9unrlJ/sZouWt7FSGfY+8kZSJcaLuWFgjJg7xLQN57ko+RiqV2i/EotWnszeq5a3sTVe4RyiUL5Lof8M674S89pZl4u50yLGcITLuUcb2J3mTaFyrrXKuU2+UBj1SfJBbDop50rtUyt8xNT0lfcPl1leHwA0CgeJ1qp0a3Vg28gZ5ZIXaJCPwYpG+Rjo6TeGN0bLuT3WLeYhBcrftSRK5+imEHn9ryt7iXlqWKWYnzwpVxATUyfmsQY5JzofcFDlAnTdddfh4Ycfxty5c7Fx40ZMmjQJ48aNQ//+/dtVzpdfftn835aexmO321FZWYnKykqfwZqhQ4f63e7T0pUw8+bNQ1hYWLvaRERERERERNRTcFClC8XFxeHgwYMoLS1Fenr6Oas3JSUF27ZtQ05ODtavX493330XAJCeno7HH38c06ZNa1M51dXVAIBXX31VXM5qtfoMqrQ0f0xLE/HOmTOHgypERERERER03uLTf7rQ2LFjAQCbN28OqBy1+vRmcrn8L6+sra1tcZ0hQ4ZgzZo1qK6uxrZt2/DYY4+hvLwc06dPb57rRUlIyOnL+fbt2wev19vqvz59+vis19ITfFpaLzk5uU3tICIiIiIiIuqJOKjShebMmQONRoM33ngDFRUV4rIOR+s3xJ55uk5paalftmfPHrFcnU6H0aNHIycnBy+99BK8Xi/ef//95lyjOX0fp9vtf7/nqFGjAADbtm0T6yAiIiIiIiL6KeKgShdKS0vDQw89hMrKSkyePBmHDx/2W8Zut+OFF15occ6RM9LT02GxWLB27drmW3IAoLy8HLm5uX7L79ixAydPnvR7vby8HIDvBLYREREAgGPHjvktn5WVheDgYDz66KPYv3+/X26z2ZrnXSEiIiIiIiL6qeGcKl0sNzcXdrsdS5YsQXp6OjIyMjBo0CDodDocPnwYmzZtQlVVVYuDI2fo9Xrcc889eOqppzB8+HDccMMNqK+vx7p163DVVVehqKjIZ/m3334bS5cuxfjx45GWloaQkBAcOHAAH374IaKionD77bc3L5uRkYHVq1dj2rRpmDJlCoxGIwYPHozMzExER0dj5cqVmDZtGoYOHYpJkyZhwIABsNvtKCkpwdatWzFmzBhs2LAh4H5677338N577wFA8+DTe++9h+LiYgDAuHHjcOeddwZcDxERERERXVgO/0npiZa+UuY1wVlbjGPLTj/Iw5QyBXE3vOe3XOPRrTixZgKCB9+FqGv+O89kxUd3oOG7v7Zaftio+Qi//DGf18rezYCj9HPoY4aj18zW/zB9+E966ML7I3H2t82v1e9fgcqNvr8LqTRGaEOSYEqejLCRf4DGdHqOS6/bCduhdbAd+gCOE1/BVX8UUKmhj7gIlotvQ/Dgu6BSB/bUKfLFQZUuplar8cILL2DmzJl47bXXkJeXh7y8PHg8HsTHx2PixInIysrChAkTxHJyc3Oh1+uxbNkyvP7660hOTsaCBQtw/fXXY82aNT7LzpgxA3a7Hfn5+dixYwccDgcSExMxd+5cPPDAAz6PeL7rrrtQXFyMVatW4YknnoDL5cLs2bORmZkJAMjMzMSePXvw7LPPYtOmTdi4cSPMZjMSExORlZWFW2+9tVP66euvv8Zf/vIXn9e++eYbfPPNN83/z0EVIiIiIiL6sbBR8/1eq9meC7U+FCGX3Ku4fuPhD9F47DOYEq9oV72WgVnQWvwfS21MvMrn/52nCuAo/RyACk0nd8NR8Q0M0UPbVRcAGHtnwJgwBgDgbqxEY8lG1O15EbaifyNhxjZoTJFw1hbh5Ae3QKUPhilxPIL6XgdPUx1shz5A1af3obHkI8Rc/88W58GkjlF5vV754eYBsNvtOHz4MFJSUmA0Gv1yR30V9q34I7xuZ1c1ISAqjQ6DZz0JQ3BkdzeF2kFpv5O0d5Q7UNeoF4u5tl6+Q0/XEFj99mE2MfeWt6//WuI2+8/XczatRT7+jV8HibnSe3A16MQ8/Gs5d1rEGI3xHjEPKZC3oS1OLl9JWIF8CrfGB/aB2ZAmbx9Lodx/5uOBtU+pf03HAztGlLavEqX1XcFy+7v6GFfqPwBIXte1n8HWOPm8ajnW+pxibVE1SD5PNYXI68d9JddvKK4W86qx8kGsr5e3gb7OfxL6szX0kvsvuERuf30fg5iHrpDnTWvKHCnmJZPlY1hfE9hfQz06+RwSMbhSzKv3RYm5vkZuv1b+iIEtQW5fymVHxfxIXpKYO6Lkz1BAuY8N8i4c8HnQ0c8u5poyeR90hcnvUV8h/w3YrZe3gaYpsM/BJqX2nerZf/FX+hz65KbnxPzFyqvEfJj5iJj/4/gIMZ8Yc0DMfzdgk5hLDv9JD21wH/S+o6DF/MyVKtqQZLjqj8AQexkSbvnMZxmlK1Xip38GY/woxbZUf/4oanc+i9BL70ftrhcQMmwuIscvabXdrV2pEj42F2GXPdT8utftxIl/ZcJ+bAvCRi9A+OgFcDWUwlb0PiwX3wa17r/foz1OK46v/hmaynchZso7MPf/pWK7qW269UoVQ3AkBs96Eq7GAL81dhGtycIBFSIiIiIioguULrw/jL2uQMN3f4W18F8wp/28U8v3etxo+O5v0ATFInzM42j4YTUaDq5ExLinoNLKg45KVBodggffCfuxLXCU7wIAaC29EDL0br9l1TozQi/5HSo2zEJj6WccVOlE3X77jyE4kgMXRERERERE1C3CL18I6w//wKn8BQjqO7VT5xxpLF4Pt/U4Qi75HVQaHSwDZqB2x9OwFr0HS/r0TqunLVSa01cYq1TdPgxwQWFvEhERERER0U+WNiQJwUP/B3W7/4T6/csQMrhtcznWf/tnNBZ/5POaSmv0uUWn/ttlAADLRTMBAMEX34baHU+jfv/ygAdVvG4n6vf9HwDAEHupcnv3n57D0tTnZwHVS744qEJEREREREQ9jsPhgMPhO3eUwWCAwRDYbTMtCRv5MBq+XYaaL3NhGTDTZz6S1jTsX+b3mlof2jyo4raWw1a8HrrIi2GIuQTA6duNDHEjYT/yCZx1JdCF9GlzGxuPfAKv6/Q8Rm57FRqLP4artgjakBSEDJsrrlu37000Fm+AsffVCEqZ3OY6SZk8Qx4RERERERFRN1i8eDFCQ0N9/i1eLD/ooaM0xgiEjngAbmsZ6va81KZ14qd/hpR5TT7/+vy2ojmv/+6vgMcFy4Bf+axnuehWAF407P8L2sN+9BPUbM9FzfZcNHz7Z6jUGoQMn4eEGfnQGCNaXc92+ENUffo7aIP7IHrS8nbVScp4pQoRERERERH1OI888gjuv/9+n9e64iqVM0KG34e6b15D7c7nETz4roDLa9j/F0ClhmXADJ/Xzek3oyrvAdQfWIGw0fOhUrXtWocfP/2nLWzFH+Pk+9OhCYpF3C8/gtYc3671SRkHVYiIiIiIiKjH6apbfVqj1poQNnoBqjb/D2p2PI2glMwOl2Uv+wLOU98DAI6+1bfFZdz1R9B4ZDOC+kzocD0SW/FHOLluGtSmKMT/8mPoQltuBwWGgypEREREREREAIIHzjk9Ye03r0EfNbjD5ZyZoNaUPAmaFq4O8dirYCtai4Zvl3XJoMrpAZVfQm2MQPwvPoYuLK3T66DTOKhC1J2iHWLsCNKJuSs4sGmREiJrxfxovT6g8gHAEmULaP3GeI+Ym4xOuQCF3BYfKuYui1y/O0jO6/qJseL6Shps8mncHhtY+Urbr9EaLOZetbyPKrZP4RhxNpjkXG4eHDEuMdfYApx6rJuP8bbsX5WDAvsLYHih3IeNUSoxdxmNYm45IZdvU7iKOX6bvL7bKD82s2ZErJg39JLfn+mkXL6myRtQ+W6d3H9OixjDlDlSzPUffCXmullDxdyh79q/MPcOqRHz8vBwMVc75XOoyyzX7wx3i3mIXuEcECWvr4u2yw0A4HLJ58Hgw/J5RCU3QVFTlfxdwWOQ93FdpbwNjBViDLdePka8Af62o3LJx7A9IbDPkUC/ByjRmOXvQSvrLhHzT0vlLzIxKXViPi6qUMx7IpVag/Cxj+Pk+zejZvuTHSrD09QAa8FqqHRmxEx5B2q9/8nY63HhyJvJsB5aC3djFTSmyECb3qx5QMUQjrhffAxduMIXUgoIB1WIiIiIiIiI/sOcdiMM8aPhOP5lh9a3/vAPeJ1WWC6e3eKACgCo1FpYBsxE3e4/oeHgOwi95N5AmtysqfogTq77JbxuB4yJV8L6/d9h/dEy2pBkBA+c1Sn1EQdViIiIiIiIiHxEjHsSx9/N6NC6Z279CR44R1wu+OJZp2812r+s0wZV3NZyeN2nr5Kz/vCPFpcx9rqSgyqdiIMqREREREREdEFJmdck5rrQZHEZY69xrebR176F6GvfanXdhFs+a1Mb9VGD/Opoqc7ggbPaPAhi6n2V4nunzhXgzeJERERERERERD9N3X6litfmApq6doKmDtOroQo6t12UnZ2NnJwcfPrppxg/fvw5rZuIiIiIiIiI2q5bB1W8Nhfcm8qBHjqmAjWg+Vlspwys7Nq1C0uXLkVeXh7Kysrg8XiQkJCAMWPGYNasWZgwoWueTa5k/Pjx2Lp1K7xeeWb2c9WOllx77bXYsGHDOW4RERERERERkax7r1Rp8vTcARXgdNuaPEBQAEV4PHjggQewZMkSaLVaZGRkYOrUqdDpdDh06BA++OAD/O1vf8OiRYuwYMGCTmv6+WrhwoV+r6Wl8ZnqRERERERE1PN0++0/F7r58+djyZIlGDZsGFavXo3U1FSfvLGxEa+88gqqqqq6qYU9S3Z2drfWnxZ637mtr8Ak5pqLfvwANF/mb8xi3hgt119+KkTMVU0quQAAvT6Vl7HOccsFbIgQY5fCe0BpqBjHTToq5kW95G1gKNGLeUiBPDVVzQh5ojB9kFPMlbgN8mnco5WvQgv7TiNXkCpvP6tC+W6DXLxS+wwKx0jUPnlkvvRquXylfVxp+yqp0cr7jz7aLubOJmNA9bflGK4bJu+jmgqdvH4/uY5en8rbyBon9/GJy+R9XOUSY5T8UuGvN1aFYwDyPhS9Q85tMXL/VF0i50rlOy3y+o5wMUbJZHl93ayhYp48/Rsx1/bpLTdAgatXpJjXoZeYpwTL/aert7W7TWdzBsvHx4mPUsX8op2lYu4qkT/DAKBi3QAxv/LKAjHvZTilWIdkc4Vc/5yEL8Rcp3AQf1afLuYn7PJ3mUDFGevEfO2mkWLujpY/5wP9HqDEWSF/jixfLV8tb7q0Wsz//pq8fs1F8vcIVaj8/n8n715EPQInqu1ChYWFeOaZZxAZGYkNGzb4DagAgMlkwoMPPoicnJxWy9myZQtUKlWLAw7FxcVQqVSYM2eOz+sFBQXIyspCSkoKjEYjoqKiMHz4cPz+979vXkalUjXfcqNSqZr//bisvXv34pZbbkF8fDz0ej369OmDe++9128g6Oy2HDx4EDfddBOioqKgUqlQXFwsdxYRERERERHReYZXqnSh5cuXw+124+6770ZsbKy4rMGg8OfcdigrK8PIkSNhtVqRmZmJ6dOno6GhAQUFBXj55Zfx/PPPAzh9q83y5ctRUlLic9vNsGHDmn9eu3Ytbr75Zmg0GkydOhW9e/fGgQMH8Morr+Cjjz7C9u3bER7u+2ewwsJCjB49GgMHDsTs2bNRXV0NvV7+a+0Zq1atwuHDh2E2m3HZZZfh8ssvD7xDiIiIiIiIiLoAB1W6UH5+PgAgIyPjnNa7Zs0a1NTU4MUXX8R99/nezlJZWdn8c3Z2NrZs2YKSkpIWr4KpqqrCbbfdhujoaOTn5yMpKak5W7lyJWbOnInHHnsML7/8ss96+fn5WLBgARYtWtTuts+YMcPn/y+77DL8/e9/R0pKSrvLIiIiIiIiIupKvP2nC504cQIAkJiY2C31m0z+cxFERUW1ef0VK1agrq4Oixcv9hlQAU4PfgwfPhyrVq3yWy8uLg7z589vV1tvvPFGrF+/HsePH4fVasXXX3+NWbNmYceOHfjZz34Gmy2we56JiIiIiIiIOhuvVLkAXXfddXj44Ycxd+5cbNy4EZMmTcK4cePQv3//dpXz5ZdfNv+3sLDQL7fb7aisrERlZaXPYM3QoUP9bvdp6UqYefPmISwsrPnnsw0dOhR/+ctf4HK58M4772DZsmWYO3duu9pPRERERERE1JU4qNKF4uLicPDgQZSWliI9XZ65vDOlpKRg27ZtyMnJwfr16/Huu+8CANLT0/H4449j2rRpbSqnuvr0bN+vvvqquJzVavUZVGlp/piWJuKdM2dO86BKa+644w688847yM/P56AKERERERER9Si8/acLjR07FgCwefPmgMpRq09vJpfL/5FztbW1La4zZMgQrFmzBtXV1di2bRsee+wxlJeXY/r06c1zvSgJCTn9iLp9+/bB6/W2+q9Pnz4+66lU/o9nbGm95ORkxTacGazh7T9ERERERETU03BQpQvNmTMHGo0Gb7zxBioqKsRlHQ5Hq9mZp+uUlpb6ZXv27BHL1el0GD16NHJycvDSSy/B6/Xi/fffb841Gg0AwO32f4b8qFGjAADbtm0T6+hK27dvB4A2DcAQERERERERnUu8/acLpaWl4aGHHsLixYsxefJkvPvuu35PsbHb7Vi6dCkqKiqwePHiFstJT0+HxWLB2rVrUV1djYiICABAeXk5cnNz/ZbfsWMH+vTpg5iYGJ/Xy8vLAfhOYHumrGPHjvldcZKVlYXc3Fw8+uijGDNmDAYOHOiT22w27N27F6NHj25Ld7Tq0KFDMJlMiI+P93n9u+++w6OPPgoAuOWWWwKqo6cynJLz2lr5UdtRlR4x92jlcVOl8jUu/6uOfsx0svUBQQA4Xuc/YfLZYgN8D7oGMcaJuhAxV9fKp0GlbWRSaH+dQvlNTvn9WaKtYq5RaJ9XqxFzpfaXK2w/fY1cvlL/KbVPsf8V9j99jdx+JUr902SRt59S/zj08jFoUFi/M7gUjnPDKTl3NSnsYycbxdyjlftA6RzgChJjeBWOsUD7OKjS/yrSs3m08jnAGSrXr1R+k11+f0r9p3QMKu2j2j69xdxVclTMlWhVcvudvSPE3Hhc/pBQ1QV2JawmRN4BHReFirk3xCzmSv0LAHUK5+kj4XIfOTyB/TpQXCWX706Qt+EJZ6SYK7XvZKNFXt8d2PuLM9aJudI50q4P7HtAoJTOcc4w+XMuMUj+HnI8RN7+Sp+Dmni5fKLzAQdVulhubi7sdjuWLFmC9PR0ZGRkYNCgQdDpdDh8+DA2bdqEqqqqFgdHztDr9bjnnnvw1FNPYfjw4bjhhhtQX1+PdevW4aqrrkJRUZHP8m+//TaWLl2K8ePHIy0tDSEhIThw4AA+/PBDREVF4fbbb29eNiMjA6tXr8a0adMwZcoUGI1GDB48GJmZmYiOjsbKlSsxbdo0DB06FJMmTcKAAQNgt9tRUlKCrVu3YsyYMdiwYUNAfZSXl4e77roLV199NVJTUxEcHIyCggJ88MEHcDqdeOyxxwIeuCEiIiIiIiLqbBxU6WJqtRovvPACZs6ciddeew15eXnIy8uDx+NBfHw8Jk6ciKysLEyYMEEsJzc3F3q9HsuWLcPrr7+O5ORkLFiwANdffz3WrFnjs+yMGTNgt9uRn5+PHTt2wOFwIDExEXPnzsUDDzzg84jnu+66C8XFxVi1ahWeeOIJuFwuzJ49G5mZmQCAzMxM7NmzB88++yw2bdqEjRs3wmw2IzExEVlZWbj11lsD7qPhw4djxowZ2LlzJ7766itYrVZERkZi8uTJmDt3LiZOnBhwHURERERERESdrXsHVfTq07O6yFeddR81TrexE4wYMQJvvfWW4nLZ2dktPn5Yo9EgJyenxafoeL1en/8fNWpU83woSrRaLZ5++mk8/fTTrS6Tnp6ON998U7Gs5ORkv7a0xZAhQ7BixYp2r0dERERERETUnbp1UEUVpIXmZ7FAUw8dVdGroQrixTxERERERERE5K/bRwxUQVpAYZI5IiIiIiIiIqKeptsHVYiIiIiIiIgCVVxfjdTVT/q8plWpEWOyYExMMh4cfDVGRLX8VC2v14u01U+iuOEUbuozGO9mzBbrmNgrHesn3tWmdnm9Xqw7egBvF+3CVxVHcNJ++slk8aYQDI9KxI1JgzAtZSh06v8+LWnL8UJcs+F1n3LMWj3C9SYMDI/DVXGpuC3tUiQEtfyUsSMNp/D0vk+wqbQAR201p/vBaMGAsBhcGdsXcy8aC7NOfsIbtQ0HVYiIiIiIiOiCkRociV+lDgcAWF1N2F15DKuL9+LfR/bj42t/jSvjUv3W2Xy8AMUNp6CCCuuOHkCFvQHRRvmR3W1R7bDhli1/xeayAoTojMiIT0NqSCTUUOGorRZbjxdhTfFevPpdPvKvu9dv/UsjE5HZ+yIAgM3lxInGemw7WYyPSr/Hoq8/xtMjrsM9F4/zWeeb6jJkrH8NNU2NGBuTjEmJA6BXa3C4oRq7Ko9i/bGDuCl5CNI4qNIpOKhCREREREREF4zUkCgsvORan9ee3vsJ/rjrQyzc/RE+nfJbv3X+/MNXAID7B12J57/dir8V7sL/G3RVQO1wedy4afMyfFZ+GLPTRmDJqBsQqjf5LOPxevCvkm/xv99va7GMS6MS/d4LAPy75Fvclf8P/G77ezBpdbij/38fVPLAV2tR09SI5VfMwG1pl/qtu+1kMaIM5oDeG/1X5zzahoiIiIiIiKiHur3/SADArqpjftkphw3vHfkWl0YmYsGwCQjS6vDngq8CrvMvhTvxWflhXBPfD2+Nm+43oAIAapUav0gegg8n3Nmusm/oMwir/3OL0h93fgir09GcbTtZgjC9qcUBFQC4PCYZYQb/tlDHcFCFiIiIiIiIfhK0av9fgd8u2g2H24Xb0i5FsM6IG5IG4UBNOb48WRJQXcsLdgAAHh6SAZVKpdAujZi35Mq4VFwZ2xeVDis+OV7Y/HqEIQgNTgdO2OraXSa13zkZVPF6veeiGiIA3N+IiIiIiC4EDocDdXV1Pv8cDofyii144+CXAICxMSl+2bKCr6BVqTE9ZRgAYFbaCAAI6GoVl8eNHZVHoVNrMDbWv87OcmVcXwDAzsqjza/9MnkIXF4PrvzwVSz5dit2VByB3eXssjb81HXpnCoazenRNqfTCZOJlxfRueF0nj5hnNn/iIiIiIjo/LN48WLk5OT4vLZw4UJkZ2eL6xXVVSJnz0cATk9Uu7PyKLaeOIQYowXPXHadz7K7Ko/h6+oyTEm8CDGmYADAzxL6ISEoBP84/DWWjJzaoafkVDtscHrciDMFw6Dx/7X7zz98haPWUz6v3dF/FBLNYe2qJz4oBABQ6bA2v/bEpVNQ7bDh7UO78cCOdQAAjUqNoRHxuDFpMOZeNJa3/3SiLh1U0el0MBgMqK2tRXBwsOIlT0SB8nq9qK2thcFggE6n6+7mEBERERFRBz3yyCO4//77fV4zGJQHOIrqq7Do640+r8UYLdg6ZS76h0b7vP7ngu0AgFtT/zv/iFqlxsy+w/Hct1vwbvFezOl3WbvbrnTt/PKCr5B/stjntWt7DWj3oEpL9Zi0Oiy/cgZyL52MD48dxI6KI9hReRS7q0qxu6oUb3y/DZ9O+S36Bke2qy5qWZc//ScqKgqlpaU4duwYQkNDodPpOLhCnc7r9cLpdKK2thYNDQ3o1atXdzeJiIiIiIgCYDAY2jSI8mMTe6Vj/cS7AAAV9gasKNiJh3d9gJs2L8OX1/8Olv9ceWJ3ObHq0NcI0RkxNWmgTxmz0kbguW+3YFnBVx0aVIk0BEGrUqPSboXD7fK7WiUv857mn7M+W4UVhTvbXQeA5nlTWnr8c6I5DL9OH41fp48GcPoKnjs//wfyyg/h/u1r8d7PsjpUJ/nq8kGVkJD/XI5UWYnS0tKuro5+4gwGA3r16tW83xERERER0U9XtNGC3w8ej1qnHU98swkLdm/AklE3AADWlOxDTVMjAMDy10daXP/z8sP4vvYk0kNj2lWvVq3BZVG9sa2iBJ+XH8Y1Cf0CeyOt2HqiCAAwIqq34rKpIVH48xXTkbZ6MT49a2JbCkyXD6oApwdWQkJC4HQ64Xa7z0WV9BOk0Wh4yw8REREREfl5ZMg1WFbwFV47+AV+d/EVSA6OwLIfTt/688vkIQjRGf3WKbGewuayAiz74Ss89aO5WNpidr/LsK2iBE/v/QQZ8WmdfsfG1hNF+Kz8MGKMFmTEp7VpHbNW36ltoHM0qHKGTqfjL71EZzFVyndb1jnlE69HK+dehbl6VQrlqx3KJ35NgzwDu8cpT4IV6HtoCpVza63/B6QPnbwNlOrX2TxirnbIBXh08vt3ueWHtCk9fU/dJOdK/e9xKtSvUL5S/ymtr3SMeNUK7VfYvlprgMeYwqeo0vtTOgYN1fL6Tv8rfdtNqY88Ch/bSusrbqNAzwFhcv0as/y0A48usEnNvQrPUVTaR5T7T2F9hf5TOkcq1a/E1Uu+H1+rCuxBk65i+XGmTYNjxVxXekrMlXhDgsRc1dAo5h5tmJjbE4LFXBes8BnWBpF6q5jH6bv2kau9tPI2MKrkE+X3iOvM5rRbL4Pcfo3Cg2j01fIx4GrDd61AKH2ONIUHdg6wJcp/MA8uks+xroBqbx+TVoeHBl+Nedv/jdxvNuGPQ6/BlhOHkGKJwKrxt7U44FFpt6L33xdhRdEu5F46ud2PPZ7T7zKsKNyJzccLcMfnf8efRt2IEL3vce31elHXZG/3+1l7ZD/u/PzvAIDFIzIRdNZgyeNff4w5aSPR2xLmV9dTez8BAIyNTW53ndSyczqoQkRERERERNQd7uo/Gs/s+xR/LdwJm6sJXngxu9+IVq8giTKakdn7YvyrZB8+OPodbugzqDn79tRxZH22qsX1hkf2wr0XXwGdWoN/XZOFWz5dgb8U7sS/Sr5FRnwa0kKioAJworEeeeWHUNJwCn2DI5EQ5D+Fwa7KY81PMrK7XThuq8MXJ4tRVF8Fk0aHV0b/3G/OlyX785CzZyNGRCVieGQiIgxBqHJY8enxQhTUVSLSEITnRl7fwV6kH+OgChEREREREV3wjFodHh6cgfu2v4f88sNQq1SYnSZPQjun32X4V8k+/LngK59BlTJbXauTy9Y0NeLei68AcHpgZuOk3+C9I9/i7aLd2Fl5FOtLD0IFIM4UjOGRicgdPhm/TB4CfQuPXt5VdQy7qo4BAIK0OkTog3BxeBzu7D8Kt6WNaH6k8tn+fc3tWH/sIPJOFGHd0f2osFthUGvRNzgCvx90Ff7fwKtaXI86hoMqREREREREdN5LDo6AO+s5cZm5F4/D3IvHtbnM63pf7FNmW+r4MZVKhZ/3GYyf9xnc5nXGx6e1u54zrojriyvi+nZoXWq/wG50JSIiIiIiIiL6ieKgChERERERERFRB3BQhYiIiIiIiIioAzioQkRERERERETUAZyolqgbOS0tP77tDG1DYOOeOqucK5WvtcrtAwBXiFFewCqfZizHHGLutMjl6xq8Yl4vpsqU+lBX5xJzrcL7Vxrbdpj1Yi6ngK5BYQElCu0PtHyl9ZWOkYZEg5gr7cMB948CpfJdZnn7Oy2Bld8WIaOrxfykWX46QExMnbx+ebSYN8bKx7ArVj5HGA/J+4AmqEnMzYMD68RTAb6/iMGVXVq+VuEcplR/75AaMa9DLzF39o6QG6CgaXCsmBvWfSWvf9VwMbfFKZ1FZUEnTAGtr6t3BrQ+ACRE14j52NACMU/WyfuAkm2RKWLeRyt/EhtVch+MC5Hbb1DLn8MOT2C/7vTWyedIpfO0d4j8/l0Nge2DSp/TjQrfM4KLNGJeFCQfgxF75fWrh7jFPLAjiKhn4JUqREREREREREQdwEEVIiIiIiIiIqIO4KAKEREREREREVEHcFCFiIiIiIiIiKgDOKhCRERERERERNQBHFQhIiIiIiIiIuoADqoQEREREREREXVAYA9uJ6KA6Bq8Aa1vqHOLudPS9Ye4/mi1whKxYuoMCayNTosqoPW1Def32LLOKudOs5wr7UNAYP0baPuUjpGwA7ViXpsaJlegoLuPMV1DlxYPADh5OELMjeUaMa8ujxLzPrvsYt6QaBBzW6ycOy1irKh6n9x+JQkFLjHXWeV9pBpy/UrlBylsH5t8ClZ8/+Xh4WKeEiwfo8bjge3EutJTYt501XAxV2/dLeZhyX3E3NlbPj5Un+2R1797jJjb4o1irmtQOkcDZRVhYp4f1k/MS4yBHQPFVXIflSQFi3mVW/4g+LxObv/3tTFi7nAHdp7ua6oQ86Byef3q8iAx7+rvIaZy+XO8MVY+hlOT5DdYcjJRzJU+Q4JTHGJOdD44v3+bICIiIiIiIiLqJhxUISIiIiIiIiLqAA6qEBERERERERF1AAdViIiIiIiIiIg6gIMqREREREREREQdwEEVIiIiIiIiIqIO4COViYiIiIiI6LxXXF+N1NVPYmKvdKyfeBcAIGfPR1j09UYAwN+vvg2/TB7qt17WZ6uwonAn8jPvxeiY/z5q3eVx443vv8TbRbtxoKYcjW4nIg1BSDKH4fKYZNyWNgKXRPZSLOeMorpKLD34BT49XojihmpYnU0I05twcVgsJvTqj1lpI5BoDvNZJ+9EEdYdOYDdVcewu6oUdU47ZqWNwLIrbumMLqNOwEEVIiIiIiIiuuAt2LUBNyYNglatUVzW7fFgysY3sbmsAAlBIfhl8hBEGy0os9Xi+9qTePm7z2HW6n0GVSRLvt2Kh3d+ALfXi9HRSbg19VKE6Iyodtiwo/IIHtv9ER7/eiO2X/87DIlIaF5vWcEOrCjciSCtDknmcNTV2jv8/qlrcFCFqBs1RqnE3BnukvMg+Q4+l1Gu3xnuFnOPQfkOQWevcDFXhzWJuTXGJOZK70GJOVT+4Gms0SnUL28jW5xezJsiPGLuDpJzjU7OlfrHESHnSvuQOsyhUL68/VTyLqbYPpVb7v9gi0HMPfLmCbh/DDXy9mnoLa+vdAyqnV3/Ma0/JX+x1NfK66ud8jbSnWoUc6NFfo8uo5wrbcPGCrOYm2vk9itRybsAVPJpHHqF+pXKN9bI+1B9ktx/SvUr7YO6epuYq+rkPFBK5+CwZP+/FJ/NVVwi5vInBACl8hXO0UHHA//lyG2V++BATayYVwXJx4gSu02u/wtbmpgHqeXvCeX2EDGvssntd7qUf3mWlDrk7zlK21jpHOuIUThJBEjpGFb6HBoSXibmhw3ygIJH4TPiXEoNjsQPdRV484ft+M2AMYrLv3NoDzaXFWBir3Ss/dnt0P1oIOaErQ5ltro21f2/B7fhgR3rkBociVXjb8PwqES/Zb6rKcdjuzegzun73WvuRWPxwKDxGBAagx2VRzH2g5fbVCedO5xThYiIiIiIiC5o/2/QVQjXm/D41xthdcp/NAKALyuKAQB3p1/uN6ACAHFBIS0OjvzYKYcND+/8AEaNFh9MuLPVdS4Ki8W7GbMxOjrJ5/URUb0xMDwOGjV/de+puGWIiIiIiIjoghauN+EPQzJworEef9r/meLyEfogAKfnQQnE6uK9qHPa8cvkoegXGq24fFtuTaKehYMqRERERERE1OM4HA7U1dX5/HM4lK8yac29F41DYlAonvt2CyrtVnHZG/sMhkalxoI9G3Dvtn9i/bHvUN5Y3+46v6w4fZvh+PjUDrWZej4OqhAREREREVGPs3jxYoSGhvr8W7x4cYfLM2p1eOySiahz2vHEN5vEZS+NSsRb46bDotVj6cEvcN3Gt5CwKgd9/v44bv9sFXZVHmtTnSdspwdiEoJC/bLdlceQs+cjn3/vHz3Q/jdG3YoT1RIREREREVGP88gjj+D+++/3ec1gkCepVzIn7TL8aX8eXj/4BX538RVIDm59xvPb0i7FtOQh2Fj2A/LLD2N3VSm+OFmMvxTuxF+LduHl0T9XnPTWC2+r2e7q0ubHPZ/x6/TRuK73xe17U9SteKUKERERERER9TgGgwEhISE+/wIdVNGo1cgdPhlNHjcW7N6guLxRq8P1SQPx1GXX4eNJd6Ny5iIsumQSPF4v/t/2f+OEwhOAYk3BAIBSq//j9O7sPwrurOfgznoOmyf9pmNviLodB1WIiIiIiIjoJ+OGPoMwNiYZKw/twTfV8mOjf8yo1eHRYT/DlbF90eRxI/9ksbj85THJAIAtJwo72Frq6Xj7DxEFxBmiU1pCTPUNHnltizz2q2uQa3fLMbQNPXts2RTUpLTEOWkHtaxJYf88H2jlefoUjzElqjqbXH6dUcz1Cn1srFDaBvJXHa3cPEW6OoVznEL7Fc9xCuUrMZ2U37/TIq/vMgdUfcC8IUEBre/s3fpl/QCg9AnmKi4Rc+8Vl8jlKxw/2tIquf5ekXIBADQ2eR86ZQ2sD5V4bfI+dtQubwOLRp50tNwm76RWm3zVgtsV2Hn6hD1EzF1d271dTmMO7ByjxGWRv+d1p8UjMnHlh6/ikZ0fNF9N0h5BWn2blvtl8hA8tGMdVhfvxYKhE9r0BCA6v5z/3waJiIiIiIiI2mFsbAqu7z0QH5V+j/zyw375qkN78ElZAbxe/zlRvigvxtYTRdCq1Bgd3UesJ8IQhCcvnQKH24XMjW9iT1Vpi8vVNNk79kao2/FKFSIiIiIiIvrJeXLEZHx47DsU1ftfNba94gheOvAZegWF4oq4vkgyh6HJ48aBmnJsKvsBHq8Xiy+dgl5m/6f6/NhvLxoLq6sJj+5aj8vW/gmjo5NwaVRvBOsMqHJYcbDmJD4rPwyDRosRUb191v28/DDe+mE7AKDCfvryt/zyw8j6bBUAYEBoDP4wJCPQrqAAcFCFiIiIiIiIfnIuDovDrLQRWFbwlV92/8CrkBociY/LvsfOiqNYd2Q/nB434kzBuKnPYNydfjkyEvq1ua4HB1+NG5IGYel3+dhyoggrCnfC5mpCmN6Ei8JikH3JRMxOuwy9LWE+6xXWVWJF4U6f14rqq5oHgq6K68tBlW7GQRUiIiIiIiI67yUHR8Cd9ZzPawsvuRYLL7m21XXeHHcz3hx3s9/rvS1huOficbjn4nFtrn/ZFbdg2RW3tJr3D43Gn0bf2ObyAGBOv8swp99l7VqHzi3OqUJERERERERE1AEcVCEiIiIiIiIi6gAOqhARERERERERdQAHVYiIiIiIiIiIOoAT1RJ1I1Ol/3Pvz2Y7pRFznc0t5lq7PG6qUyhfa1WJhYTCoQAAQwdJREFUOQAEfXdCzD01sXIbbB65DQrvwatwFrPWGuXydfI20NrlPgg60STm+mq5fpdDLt+ql9ePsIsxDNVyrtT/nhp9QOVrA2yf0jGiaXCIubrJJOa6Brl+pf5xhMn7p9L78+jkY1Bp/c5QnyqfR5Ta6DLL26ipT6SYNyQaxNwWKx8jTosYQzewVi7/hxC5AAVNh3RirrSP2BIU+k+p/BB5+zQpPOmzMVau3xku7x/OYLl9mpAguQEKVA2NYh50Qj7GVZ/tkStI7iPG3isuCah878VjxLxuRC8x1zXI/Q8AiJbPg2N7HRbzROMp5ToEK+vNYj49wv+pKmercsvrN7jlc8T3GpeYO9yB/bpzaWiJXH/1RWJenShvQ6XvYoFS/hySv2fsjUsQc7XC95i2fJckOt/xShUiIiIiIiIiog7goAoRERERERERUQdwUIWIiIiIiIiIqAM4qEJERERERERE1AEcVCEiIiIiIiIi6gAOqhARERERERERdQAHVYiIiIiIiIiIOiCwB7cTUUB0No+YOyPkvC5JJ+a2OLl+j8Er5k0KOQAcn5wg5poIm5jXJQWJudJ70DXIucbgFnNnhEvMG5rkPnaEGcW8sbdTzJX0TyoX85KjSQGVX5ckfwwobT9Htbz9HOHtbpKP0MPyMVA+JkzMmxSOoaYIuX6NQ+4fp0VevzFert9tlvdPy5HAjnFXsFw/APS7qFTMD0VEiXnf2EoxP1Eu76ONsfJ5xhXrEHP9UYOYR5nlfdh0Wa2YKymrVnh/CvtA6pBjAZXviJD7T90kxki57KiYh+jl/j/xUaqYOy4KlRugwKMNC2h9591jxNwln8IVP2O8F8vlR/7vF2Ju+8VoMXdaNHIDAJiC5H080XhKzPsY5GNYSbBJ3kcC1dtYLeanmuTPoSZP1/66o22Uj0HzEeVt2JVMlXL7GmPl9YuOyAso9a7SMXTyaIBfFIh6AF6pQkRERERERETUARxUISIiIiIiIiLqAA6qEBERERERERF1AAdViIiIiIiIiIg6gIMqREREREREREQdwEEVIiIiIiIiIqIO4COViYiIiIiI6LxXXF+N1NVPAgCmJF6EdRPu8Ftmy/FCXLPhdfw6fTReG/NLAEDWZ6uwonAn8jPvxeiYPmIdfd99AiUN/31UuVqlQpjehGERCfh1+uWYljLUb53lBTtwx+d/93nNqNGijyUckxMvwiNDrkGU0dyc/d/3X2Ld0f3Yf+oETtoboFVpkGwJx9SkgfjdwCsRYZAfJU7nFgdViIiIiIiI6ILy4bHvkHeiCFfGpXZ62RqVGo8OvQYA4PR4UFBXgX8f2Y9PjhfiUH0V/jAko8X1ronvh7GxyQCACrsVH5d+jz/tz8N7Jfvw1fXzEPmfgZW3i3bhlKMR42L7It4UDIfHje0VJcj9ZhNWFO7EtuvuQ1xQSKe/L+oYDqoQdaOKoRox14fYFUrQianKLa+tiW8Uc6dNLh8AdA3yMsEWpfcgj7QrvQd45Dg5pkrMi6oSAqpf1yDnKq1XXj/IKeYVVotcvkL7HFHyAkEn5H1QafvVhBvE3FApl6/UPqVjJGqfvAPUXCT3f6CUtr9VYfvrQ5rEvOairr9L99DO3mJurFCJ+RFzkpgnfWkTc1u8UcytcfI+5lT4Y93xr+PEXGuT35+SqEJ5H9Q1yNvwSI3cf0rlK6npq7D98uT6lY7Ri3aWirk3xCzmSuwJwWKuq5fPoUr7V9Bx+RynLZU/Q+pG9JLr/8Vouf41X8r195GPTwAovS5WzLdWpIl5jEk+RpTUN8rH6I7GFDE/2ST/Yni0MVxev1H+nKyyBbYP6tUuufxB8vruIPkY0kUrfU8KjLVCPgaUXJ5eJOa7Sy4Sc1uc/DloipI/Izoq2RKOI9YaPLLzQ+Rfd2+nl69Vq7Hwkmt9XssvP4zx65fi8a834t6LxyFIq/db75qEfj4DLk6PG5M/egOfnijCq9/l47FLJgIANkz8NYxa/+/Yj+3egCe+2YQX9m/FM5dd38nvijqKc6oQERERERHRBaN/aAxuTb0UX1aU4J/F+85JnWNjUzAgNAaNbicO1JS3aR2dWoO70i8HAOyoPNr8eksDKgDwy+QhAIDCOnnAl84tDqoQERERERHRBSXnkmth0Ggxf/eHcHsCu+qvrbze01fmaFVd82v2h8e+AwAMCg/sCjPqXLz9h4iIiIiIiHoch8MBh8Ph85rBYIDBIN92BgBJlnD8dsAYLNmfh7cKvsKv0+Xb8QKVd6II39dVINIQhAGhMW1ax+lx443vtwEALovyv91vecEOlDRUo97pwJ6qUmw5UYRLInrh/w28slPbToHhoAoRERERERH1OIsXL0ZOTo7PawsXLkR2dnab1v/j0J/hzwVf4fGvP8atqcNbnOekI1weD3L2fATAd6JaFVR4efRNrd6+s7m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\n",
       "text/plain": [
        "<Figure size 1000x1000 with 5 Axes>"
       ]
@@ -1658,29 +1156,29 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8121961c-ed5e-4611-9e4f-ed8232c85340",
+   "id": "welcome-darwin",
    "metadata": {},
    "source": [
-    "Print the cluster-specific aggregate (average) alignments for each cluster along with its number of genes"
+    "Print the aggregate (average) cell-level alignments for each cluster"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
-   "id": "ec92f666-fea2-427e-b2ae-f4b0ada437be",
+   "execution_count": 23,
+   "id": "swiss-marketplace",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "cluster:  0 \u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDDD\u001b[0m 18\n",
-      "cluster:  1 \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m 3\n",
-      "cluster:  2 \u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m 36\n",
-      "cluster:  3 \u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[92mM\u001b[0m\u001b[92mVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m 12\n",
-      "cluster:  4 \u001b[91mIII\u001b[0m\u001b[91mDDDDDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mD\u001b[0m 12\n",
-      "cluster:  5 \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDDD\u001b[0m\u001b[92mMMMM\u001b[0m\u001b[92mV\u001b[0m\u001b[92mM\u001b[0m\u001b[92mVV\u001b[0m\u001b[92mM\u001b[0m 2\n",
-      "cluster:  6 \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMMM\u001b[0m 6\n"
+      "cluster:  0 \u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDDD\u001b[0m ( 18 genes)\n",
+      "cluster:  1 \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m ( 3 genes)\n",
+      "cluster:  2 \u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m ( 36 genes)\n",
+      "cluster:  3 \u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[92mM\u001b[0m\u001b[92mVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m ( 12 genes)\n",
+      "cluster:  4 \u001b[91mIII\u001b[0m\u001b[91mDDDDDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mD\u001b[0m ( 12 genes)\n",
+      "cluster:  5 \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDDD\u001b[0m\u001b[92mMMMM\u001b[0m\u001b[92mV\u001b[0m\u001b[92mM\u001b[0m\u001b[92mVV\u001b[0m\u001b[92mM\u001b[0m ( 2 genes)\n",
+      "cluster:  6 \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMMM\u001b[0m ( 6 genes)\n"
      ]
     }
    ],
@@ -1689,37 +1187,93 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "86ab8028-9f3e-423d-bb6a-985ddf3d7a69",
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "continent-ancient",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['FPR1', 'TREM1', 'TOP1', 'CXCL1', 'ORAI2', 'PILRA', 'CLEC4D', 'C5AR1', 'PTPRE', 'LDLR', 'PLSCR1', 'LCP2', 'TNIP1', 'ADORA2B', 'KLF7', 'CPD', 'TNFAIP2', 'SPATA13']\n",
+      "\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "\u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "\u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "\u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "\u001b[91mI\u001b[0m\u001b[92mMM\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDDDDDDD\u001b[0m\n",
+      "\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "\u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
+      "\u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
+      "\u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "\u001b[92mM\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
+      "\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDDDDD\u001b[0m\n"
+     ]
+    }
+   ],
    "source": [
-    "# Gene-set specific alignments"
+    "# To access the genes in a particular cluster\n",
+    "cluster_id = 0\n",
+    "print(aligner.gene_clusters[cluster_id]) \n",
+    "\n",
+    "# To print all gene alignments in the cluster\n",
+    "aligner.show_cluster_alignment_strings(cluster_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "neural-wales",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To get the cluster id of an alignment object, e.g. TNF, \n",
+    "aligner.results_map['TNF'].cluster_id"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "77e312ec-1cff-4684-af2d-f66bb2963f74",
+   "id": "confirmed-nepal",
    "metadata": {},
    "source": [
-    "We can obtain the aggregate (average) alignment for any given gene subset in the aligner \n"
+    "### Average alignment of any given subset of genes \n",
+    "e.g. `gene_list[40:60]` or a specific gene set in a specific pathway, e.g. EMT"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "248fa1f3-fdb6-47ae-98fb-19cb5f514e8d",
+   "execution_count": 26,
+   "id": "operational-wayne",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average Alignment:  IDDDMMMMMMMIIDIIIIDDD\n"
+      "Average Alignment:  \u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDDD\u001b[0m (cell-level)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 700x700 with 2 Axes>"
       ]
@@ -1735,43 +1289,63 @@
   },
   {
    "cell_type": "markdown",
-   "id": "76e303e4-c87c-4ffc-904e-a349e8cce8b8",
+   "id": "alone-maryland",
+   "metadata": {},
+   "source": [
+    "###  Exploring alignment and trends of a given gene set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "welcome-wilderness",
    "metadata": {},
    "source": [
-    "We can check aggregate alignment and statistics of a pathway gene set with below provided wrapper function calls to retrieve pathway gene sets. Make sure to give the path to the msigdb folder (downloaded from https://www.gsea-msigdb.org/gsea/downloads.jsp)."
+    "Following call extracts a specified pathway gene set from msigdb database. It requires downloading the database from https://www.gsea-msigdb.org/gsea/downloads.jsp and specifying path to the msigdb folder and its version. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "e63d4027-0acd-4f97-81ad-64e65e946e5f",
+   "execution_count": 27,
+   "id": "surprised-bryan",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['CD44', 'CXCL1', 'PTX3', 'TGM2', 'TNFAIP3'], dtype=object)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "IGS = PathwayAnalyserV2.InterestingGeneSets(MSIGDB_PATH='../MSIGDB/msigdb7.5.1/')\n",
-    "IGS.add_new_set_from_msigdb('hallmark', 'HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION', aligner.gene_list, 'EMT')   "
+    "IGS = PathwayAnalyser.InterestingGeneSets(MSIGDB_PATH='../../OrgAlign/msigdb', version='7.5.1') # need to create db folder and pass args\n",
+    "IGS.add_new_set_from_msigdb('hallmark', 'HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION', aligner.gene_list, 'EMT')   \n",
+    "IGS.SETS['EMT']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "356f60e4-5b78-47d9-9856-508b8ceeaefb",
+   "execution_count": 28,
+   "id": "aboriginal-dakota",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Gene set: =======  EMT\n",
       "mean matched percentage:  51.04 %\n",
-      "mean matched percentage wrt ref:  64.29 %\n",
-      "mean matched percentage wrt query:  67.14 %\n",
-      "Average Alignment:  IDDDMMMMMMMMMMIMI\n",
-      "Z-normalised Interpolated mean trends\n"
+      "Average Alignment:  \u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[92mM\u001b[0m\u001b[91mI\u001b[0m (cell-level)\n",
+      "- Plotting average alignment path\n",
+      "- Plotting z-normalised interpolated mean trends\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x700 with 2 Axes>"
       ]
@@ -1781,9 +1355,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 600x600 with 4 Axes>"
+       "<Figure size 640x480 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -1791,174 +1365,65 @@
     }
    ],
    "source": [
-    "PathwayAnalyserV2.get_pathway_alignment_stat(aligner, IGS.SETS['EMT'], 'EMT', cluster=True, FIGSIZE=(6,6))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6116b402-1a29-4b8d-bdfe-3634fcfffb18",
-   "metadata": {},
-   "source": [
-    "# Complementary functions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cef9b738-9ba5-4443-979a-b646899cca79",
-   "metadata": {},
-   "source": [
-    "Show all alignments"
+    "PathwayAnalyser.get_pathway_alignment_stat(aligner, IGS.SETS['EMT'], 'EMT', cluster=True, FIGSIZE=(3,6))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
-   "id": "66246675-4a08-46d5-9e71-f99f47b9cc4b",
+   "execution_count": 29,
+   "id": "single-medium",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Gene      Alignment\n",
-      "--------  ------------------------\n",
-      "PTAFR     \u001b[92mM\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\n",
-      "OSBPL3    \u001b[92mM\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
-      "RFFL      \u001b[92mM\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
-      "TNFAIP2   \u001b[92mM\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
-      "SGMS2     \u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[92mM\u001b[0m\n",
-      "SLC16A10  \u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDD\u001b[0m\n",
-      "FPR1      \u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
-      "FAM20C    \u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMM\u001b[0m\u001b[91mIIII\u001b[0m\n",
-      "CLEC4D    \u001b[91mI\u001b[0m\u001b[92mMM\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
-      "TSHZ1     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
-      "IL1F9     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
-      "PSTPIP2   \u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[92mVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\n",
-      "RELA      \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\n",
-      "NUP54     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\n",
-      "DDHD1     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\n",
-      "NRP2      \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "TREM1     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDD\u001b[0m\n",
-      "GRAMD1B   \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMM\u001b[0m\n",
-      "TOP1      \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
-      "ICOSL     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMMM\u001b[0m\n",
-      "DUSP16    \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMMM\u001b[0m\n",
-      "PTPRE     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
-      "LDLR      \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
-      "TNIP1     \u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDD\u001b[0m\n",
-      "PLAGL2    \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[92mVVV\u001b[0m\n",
-      "ZSWIM4    \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[92mVVV\u001b[0m\n",
-      "ZC3H12C   \u001b[91mDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[92mVVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
-      "AK150559  \u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[92mVVVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDDD\u001b[0m\n",
-      "F10       \u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMM\u001b[0m\u001b[92mVVVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDDDDD\u001b[0m\n",
-      "FAM108C   \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
-      "RBM7      \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
-      "RASA2     \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
-      "SLC25A37  \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
-      "IRAK-2    \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "PLEKHO2   \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "LCP2      \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
-      "TRIM13    \u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMM\u001b[0m\n",
-      "PTX3      \u001b[91mDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMMMMM\u001b[0m\n",
-      "SPATA13   \u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
-      "BCL2L11   \u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMM\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\n",
-      "CD44      \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[92mVV\u001b[0m\n",
-      "AK163103  \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
-      "LZTFL1    \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
-      "IRAK3     \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mII\u001b[0m\u001b[91mDDDDDD\u001b[0m\n",
-      "ARG2      \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\n",
-      "ZEB2      \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "TLR2      \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "MCOLN2    \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "CPD       \u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
-      "RCAN1     \u001b[91mDDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMMM\u001b[0m\u001b[91mI\u001b[0m\n",
-      "PILRA     \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
-      "ARHGEF3   \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMMM\u001b[0m\n",
-      "C5AR1     \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDDDDDDD\u001b[0m\n",
-      "SLC39A14  \u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMM\u001b[0m\u001b[91mIIII\u001b[0m\n",
-      "CLCN7     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[92mVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\n",
-      "BC031781  \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "NUPR1     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "CDC42EP4  \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "NFKBIE    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMM\u001b[0m\n",
-      "PLSCR1    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
-      "NCK1      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMM\u001b[0m\n",
-      "ADORA2B   \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
-      "ORAI2     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
-      "KLF7      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDDD\u001b[0m\n",
-      "NIACR1    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMM\u001b[0m\u001b[92mVVVVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\n",
-      "PDE4B     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\n",
-      "SERTAD2   \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "CXCL1     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
-      "MPP5      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[92mMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "TGM2      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\n",
-      "PIP5K1A   \u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "FLRT3     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMMM\u001b[0m\u001b[91mIIIII\u001b[0m\n",
-      "SOCS3     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMM\u001b[0m\u001b[92mVVVVV\u001b[0m\u001b[91mI\u001b[0m\u001b[92mMMMM\u001b[0m\n",
-      "TNFAIP3   \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\n",
-      "RASGEF1B  \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\n",
-      "SLC25A25  \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[92mM\u001b[0m\n",
-      "INSIG1    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "CXCL2     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
-      "MALT1     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "RALGDS    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "H1F0      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[92mMMM\u001b[0m\n",
-      "IL1A      \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mIIII\u001b[0m\n",
-      "NLRP3     \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mDD\u001b[0m\n",
-      "TNF       \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "NFKBIA    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\u001b[91mIIIIII\u001b[0m\u001b[91mD\u001b[0m\n",
-      "PLK2      \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\n",
-      "NFKBIZ    \u001b[91mDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDDD\u001b[0m\u001b[91mI\u001b[0m\u001b[92mVVVVVVV\u001b[0m\u001b[91mD\u001b[0m\u001b[92mMMMM\u001b[0m\n",
-      "NFKBID    \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMMM\u001b[0m\u001b[91mI\u001b[0m\n",
-      "CCRL2     \u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mI\u001b[0m\u001b[91mD\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mDDDDD\u001b[0m\u001b[92mMMMMM\u001b[0m\n"
+      "Gene set: =======  EMT\n",
+      "mean matched percentage:  50.04 %\n",
+      "Average Alignment:  \u001b[91mII\u001b[0m\u001b[91mDDDD\u001b[0m\u001b[92mMMMMMMMMM\u001b[0m\u001b[91mIII\u001b[0m\u001b[91mD\u001b[0m (cell-level)\n",
+      "- Plotting average alignment path\n",
+      "- Plotting z-normalised interpolated mean trends\n"
      ]
     },
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "30      PTAFR\n",
-       "40     OSBPL3\n",
-       "74       RFFL\n",
-       "80    TNFAIP2\n",
-       "26      SGMS2\n",
-       "       ...   \n",
-       "77     NFKBIA\n",
-       "81       PLK2\n",
-       "88     NFKBIZ\n",
-       "46     NFKBID\n",
-       "63      CCRL2\n",
-       "Name: genes, Length: 89, dtype: object"
+       "<Figure size 700x700 with 2 Axes>"
       ]
      },
-     "execution_count": 94,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "aligner.show_ordered_alignments() "
+    "PathwayAnalyser.get_pathway_alignment_stat(aligner, gene_list[0:40], 'EMT', cluster=True, FIGSIZE=(3,6))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "092c896f-9082-4da8-849f-01f9440c376f",
+   "id": "secret-terminology",
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
-  "environment": {
-   "kernel": "genes2genes",
-   "name": "pytorch-gpu.1-9.m82",
-   "type": "gcloud",
-   "uri": "gcr.io/deeplearning-platform-release/pytorch-gpu.1-9:m82"
-  },
   "kernelspec": {
-   "display_name": "genes2genes",
+   "display_name": "g2g_installed_env",
    "language": "python",
-   "name": "genes2genes"
+   "name": "g2g_installed_env"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1970,7 +1435,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/pyproject.toml b/pyproject.toml
index c6bf0ae..ae354a5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,24 +7,21 @@ name = "genes2genes"
 authors = [{name = "Dinithi Sumanaweera", email = "ds40@sanger.ac.uk"}]
 readme = "README.md"
 license = {file = "LICENSE"}
-classifiers = ["License :: OSI Approved :: MIT License"]
+classifiers = [
+    "License :: OSI Approved :: MIT License",
+    "Intended Audience :: Science/Research",
+    "Topic :: Scientific/Engineering :: Bio-Informatics",
+    "Development Status :: 5 - Production/Stable",
+]
 dynamic = ["version", "description"]
+keywords = ["single cell", "trajectory alignment", "dynamic programming"]
 dependencies = [
     "anndata",
     "regex",
-    "textdistance",
     "tabulate",
     "gsea-api",
     "gseapy",
-    "gprofiler-official",
-    "levenshtein",
     "torch",
-    "gpytorch",
-    "scanpy",
-    "adjusttext",
-    "ipympl",
-    "colorcet",
-    "optbinning",
     "scipy",
     "tqdm",
     "matplotlib",
@@ -36,4 +33,5 @@ dependencies = [
 requires-python = ">=3.8"
 
 [project.urls]
-Home = "https://github.com/Teichlab/Genes2Genes"
+Home = "https://teichlab.github.io/Genes2Genes"
+Repository = "https://github.com/Teichlab/Genes2Genes"
\ No newline at end of file