From 55b3e925396dd9af1820bb8720ef039be44662c8 Mon Sep 17 00:00:00 2001
From: Fize Jacques <jacques.fize@cirad.fr>
Date: Tue, 10 Sep 2019 23:20:08 +0200
Subject: [PATCH] Clean Files

---
 auto_fill_annotation.py                       |  255 --
 eval_disambiguation.py                        |   56 -
 extract_pdf_from_results.py                   |   78 -
 generate_selected_document.py                 |   58 -
 generate_str.py                               |  172 -
 notebooks_old/Baseline_computation.ipynb      |  181 -
 notebooks_old/Clustering in STR.ipynb         | 1236 -------
 notebooks_old/Eval_mada_light_30_mars.ipynb   |  113 -
 notebooks_old/MADA_growth_criteria.pdf        |  Bin 17209 -> 0 bytes
 .../Result AnaysisV2PADI.ipynb                | 1715 ---------
 .../Result_AnaysisV2_MADA.ipynb               | 3143 -----------------
 .../Des_Mordecai.ipynb                        | 2100 -----------
 notebooks_old/NER Evaluation.ipynb            | 1208 -------
 notebooks_old/PADI_growth_criteria.pdf        |  Bin 17180 -> 0 bytes
 notebooks_old/SimiGraph.ipynb                 |  475 ---
 notebooks_old/StanfordMadaAgro.ipynb          |  940 -----
 notebooks_old/Untitled.ipynb                  |  953 -----
 notebooks_old/Untitled1.ipynb                 |  102 -
 notebooks_old/corpusmadahard.ipynb            | 2620 --------------
 notebooks_old/intersection.pdf                |  Bin 162104 -> 0 bytes
 run_automatic_annotation.py                   |   49 -
 run_test.py                                   |  134 -
 run_test_comparedto.py                        |  166 -
 23 files changed, 15754 deletions(-)
 delete mode 100644 auto_fill_annotation.py
 delete mode 100644 eval_disambiguation.py
 delete mode 100644 extract_pdf_from_results.py
 delete mode 100644 generate_selected_document.py
 delete mode 100644 generate_str.py
 delete mode 100644 notebooks_old/Baseline_computation.ipynb
 delete mode 100644 notebooks_old/Clustering in STR.ipynb
 delete mode 100644 notebooks_old/Eval_mada_light_30_mars.ipynb
 delete mode 100644 notebooks_old/MADA_growth_criteria.pdf
 delete mode 100644 notebooks_old/MatchingAnalysis/Result AnaysisV2PADI.ipynb
 delete mode 100644 notebooks_old/MatchingAnalysis/Result_AnaysisV2_MADA.ipynb
 delete mode 100644 notebooks_old/Mordecai_disambiguation/Des_Mordecai.ipynb
 delete mode 100644 notebooks_old/NER Evaluation.ipynb
 delete mode 100644 notebooks_old/PADI_growth_criteria.pdf
 delete mode 100644 notebooks_old/SimiGraph.ipynb
 delete mode 100644 notebooks_old/StanfordMadaAgro.ipynb
 delete mode 100644 notebooks_old/Untitled.ipynb
 delete mode 100644 notebooks_old/Untitled1.ipynb
 delete mode 100644 notebooks_old/corpusmadahard.ipynb
 delete mode 100644 notebooks_old/intersection.pdf
 delete mode 100644 run_automatic_annotation.py
 delete mode 100644 run_test.py
 delete mode 100644 run_test_comparedto.py

diff --git a/auto_fill_annotation.py b/auto_fill_annotation.py
deleted file mode 100644
index 02cb1da..0000000
--- a/auto_fill_annotation.py
+++ /dev/null
@@ -1,255 +0,0 @@
-# coding = utf-8
-
-
-import argparse, os
-import warnings
-
-import os, re, glob,json
-import networkx as nx
-import numpy as np
-
-import pandas as pd
-from tqdm import tqdm
-
-tqdm.pandas()
-
-from strpython.eval.automatic_annotation import AnnotationAutomatic
-from strpython.models.str import STR
-from strpython.helpers.sim_matrix import matrix_to_pandas_dataframe, read_bz2_matrix
-
-
-def main(dataset, matrix_sim_dir, raw_graph_dir, selected_graphs,
-         threshold, inclusion_fn, adjacency_fn,
-         min_carac_fn, min_size_G1,min_size_G2,n_car_min_doc1,n_car_min_doc2,
-         formatG1,format_fn):
-    annotater = AnnotationAutomatic(dataset, threshold, inclusion_fn, adjacency_fn)
-    first_step_output = "output_first_step_{0}_{1}".format(dataset, threshold)
-    last_step_output = "output_final_{0}_{1}".format(dataset, threshold)
-    generate_annotation_dataframe(matrix_sim_dir, selected_graphs, first_step_output)
-    size_str = extract_criteria_4_all(annotater, first_step_output, raw_graph_dir, dataset, threshold)
-
-    if not os.path.exists(last_step_output):
-        os.makedirs(last_step_output)
-
-
-    for fn in tqdm(glob.glob(os.path.join(first_step_output,"*.csv")),desc="Annotate sample"):
-         annotate_eval_sample(annotater, fn, os.path.join(last_step_output, os.path.basename(fn)),size_str)
-
-    min_carac_dict=None
-    if min_carac_fn != "" and os.path.exists(min_carac_fn):
-        min_carac_dict=json.load(open(min_carac_fn))
-
-    format_data = None
-    if format_fn and formatG1:
-        format_data = json.load(open(format_fn))
-        for form in formatG1.split(","):
-            synthesize(last_step_output,"{0}_{1}.csv".format(dataset,threshold),min_size_G1,min_size_G2,min_carac_dict,n_car_min_doc1,n_car_min_doc2,form,format_data)
-    else:
-        synthesize(last_step_output, "{0}_{1}.csv".format(dataset, threshold), min_size_G1, min_size_G2, min_carac_dict,
-                   n_car_min_doc1, n_car_min_doc2)
-
-def generate_annotation_dataframe(matrix_sim_dir, selected_graphs, output_dir):
-    """
-    First Step
-    Parameters
-    ----------
-    matrix_sim_dir
-    selected_graphs
-    output_dir
-
-    Returns
-    -------
-
-    """
-
-    if not os.path.exists(matrix_sim_dir):
-        raise FileNotFoundError("Similarity matrix directory not found : {0}".format(matrix_sim_dir))
-
-    for fn in glob.glob(os.path.join(matrix_sim_dir,"*.bz2")):
-        measure = os.path.basename(fn).split("_")[0]
-
-        type_ = "_".join(os.path.basename(fn).split("_")[1:]).replace(".npy.bz2", "")
-        print("Proceeding...", measure, type_)
-        if os.path.exists(os.path.join(output_dir, "{0}_{1}.csv".format(measure, type_))):
-            continue
-        try:
-            df = matrix_to_pandas_dataframe(np.nan_to_num(read_bz2_matrix(fn)),
-                                            selected_graphs,
-                                            measure, type_,1)
-            if not os.path.exists(output_dir):
-                os.makedirs(output_dir)
-            df.to_csv(os.path.join(output_dir, "{0}_{1}.csv".format(measure, type_)))
-        except:
-            print("Could'not read {0}".format(fn))
-
-
-def extract_criteria_4_all(annotater, csv_input_dir, raw_graph_dir, dataset, threshold, output_file="temp_out.csv"):
-    """
-    Second STEP
-    Parameters
-    ----------
-    annotater
-    csv_input_dir
-    raw_graph_dir
-    dataset
-    threshold
-    output_file
-
-    Returns
-    -------
-
-    """
-    if not os.path.exists(csv_input_dir) or not os.path.exists(raw_graph_dir):
-        raise FileNotFoundError("Error in Input")
-
-    # Extract all match found using every combination of measure and type of STR
-    all_str_matchin_available = []
-    for filename in glob.glob("{0}/*".format(csv_input_dir)):
-        couples = pd.read_csv(filename)["G1 G2".split()].apply(lambda x: "_".join(x.values.astype(str)),
-                                                               axis=1).values.tolist()
-        all_str_matchin_available.extend(couples)
-    all_str_matchin_available = set(all_str_matchin_available)
-
-    # Store in a dataframe
-    matching_dataframe = pd.DataFrame([cp.split("_") for cp in all_str_matchin_available], columns="G1 G2".split())
-    matching_dataframe = matching_dataframe.sort_values(by="G1 G2".split())
-
-    # Load STRs
-    strs = {}
-    size_STR={}
-
-    def load(fn):
-        id_ = int(re.findall("\d+", fn)[-1])
-        strs[id_] = STR.from_networkx_graph(nx.read_gexf(fn))
-        size_STR[id_] = len(strs[id_])
-
-    for file in tqdm(glob.glob(os.path.join(raw_graph_dir, "*.gexf")), desc="Load Graphs"):
-        id_ = int(re.findall("\d+", file)[-1])
-        strs[id_] = STR.from_networkx_graph(nx.read_gexf(file))
-        size_STR[id_]= len(strs[id_])
-    #Do the annotation for a match between two STR
-    def annotate(x):
-        try:
-            return annotater.all(strs[int(x.G1)], strs[int(x.G2)], int(x.G1), int(x.G2))
-        except KeyError as e:
-            annotater.matching_cache.add(int(x.G1), int(x.G2), *(0, 0, 0, 0,300000,0))
-            return [0, 0, 0, 0,300000,0,0]
-
-    # Annotation Time
-    print("Computing Criteria for each match")
-    matching_dataframe["res"] = matching_dataframe.progress_apply(lambda x: annotate(x), axis=1)
-    matching_dataframe.res = matching_dataframe.res.apply(lambda x: [int(x[0]),int(x[1]),int(x[2]),int(x[3]),float(x[4]),float(x[5])] if x else [])
-    for ix, col in enumerate("c1 c2 c3 c4 c5 c6".split()):
-        matching_dataframe[col] = matching_dataframe.res.apply(lambda x: x[ix] if len(x) > 0 else 0)
-
-    del matching_dataframe["res"]
-    # Writiting output
-    return size_STR
-
-
-def annotate_eval_sample(annotater, csv_file, output_file, size_str):
-    """
-    Third Step
-    Parameters
-    ----------
-    annotater
-    csv_file
-    output_file
-
-    Returns
-    -------
-
-    """
-    if os.path.exists(output_file):
-        return
-    if not os.path.exists(csv_file):  # or not os.path.exists(args.graph_dir):
-        raise FileNotFoundError("Error in Input : {0}".format(csv_file))
-
-    df = pd.read_csv(csv_file, index_col=0)
-
-    def foo(x):
-        try:
-            return annotater.all(None, None, x.G1, x.G2)
-        except Exception as e:
-
-            return [0, 0, 0, 0,300000,0]
-
-    df["res"] = df.apply(lambda x: foo(x), axis=1)
-    df.res = df.res.apply(lambda x: list(map(float, x)) if x else [])  # if bool
-    df[["c1"]] = df.res.apply(lambda x: x[0] if len(x) > 0 else 0)
-    df[["c2"]] = df.res.apply(lambda x: x[1] if len(x) > 0 else 0)
-    df[["c3"]] = df.res.apply(lambda x: x[2] if len(x) > 0 else 0)
-    df[["c4"]] = df.res.apply(lambda x: x[3] if len(x) > 0 else 0)
-    df[["c5"]] = df.res.apply(lambda x: x[4] if len(x) > 0 else 300000)
-    df[["c6"]] = df.res.apply(lambda x: x[5] if len(x) > 0 else 0)
-    df["size_G1"] =df.apply(lambda x: size_str[x.G1] if x.G1 in size_str else 0, axis=1)
-    df["size_G2"] = df.apply(lambda x: size_str[x.G2] if x.G2 in size_str else 0, axis=1)
-    del df["res"]
-
-    df.to_csv(output_file)
-
-
-def synthesize(last_step_output,output_filename,min_size_G1=None,min_size_G2=None,
-               min_carac_dict=None,ncar_min_doc1=0,ncar_min_doc2=0,
-               formatG1=None,format_data=None):
-    """
-    Fourth Step
-    Parameters
-    ----------
-    last_step_output
-    output_filename
-
-    Returns
-    -------
-
-    """
-    fns = glob.glob(os.path.join(last_step_output, "*.csv"))
-    if min_size_G1:
-        output_filename= output_filename+"_ming1_{0}".format(min_size_G1)
-    if min_size_G2:
-        output_filename= output_filename+"_ming2_{0}".format(min_size_G2)
-    if min_carac_dict and ncar_min_doc1 > 0:
-        output_filename= output_filename+"_mindoc1len_{0}".format(ncar_min_doc1)
-    if min_carac_dict and ncar_min_doc2 > 0:
-        output_filename= output_filename+"_mindoc2len_{0}".format(ncar_min_doc2)
-    if formatG1 and format_data:
-        output_filename = output_filename + "_format_{0}".format(formatG1)
-    data = []
-
-
-
-    for fn in tqdm(fns,desc="Synthetise Results"):
-        df = pd.read_csv(fn)
-        if formatG1:
-            df["formatG1"] = df.G1.apply(lambda x: format_data[str(x)])
-        if min_size_G1:
-            df= df[df.size_G1 >= min_size_G1]
-
-        if min_size_G2:
-            df = df[df.size_G2 >= min_size_G2]
-
-        if formatG1 and format_data:
-            df = df[df.formatG1 == formatG1]
-
-        if min_carac_dict and ncar_min_doc1>0:
-            df["len_doc1"]=df.apply(lambda x:min_carac_dict[str(x.G1)],axis=1)
-            df =df[df.len_doc1 >= ncar_min_doc1]
-
-        if min_carac_dict and ncar_min_doc2>0:
-            df["len_doc2"]=df.apply(lambda x:min_carac_dict[str(x.G2)] if str(x.G2) in min_carac_dict else 0,axis=1)
-            df =df[df.len_doc2 >= ncar_min_doc2]
-
-        df = df.replace([np.inf, -np.inf], 300000)
-        df["c5"] = 1 - (df.c5 - df.c5.min()) / (df.c5.max() - df.c5.min())
-        #df["c6"] = (df.c6 - df.c6.min()) / (df.c6.max() -df.c6.min())
-        if len(df) <1:
-            continue
-        mes = np.unique(df.sim_measure)[0]
-        type_ = np.unique(df.type_str)[0]
-        val = df.groupby("G1").mean().mean()["c1 c2 c3 c4 c5 c6".split()].values.tolist()
-        val.insert(0, type_)
-        val.insert(0, mes)
-        data.append(val)
-
-    res = pd.DataFrame(data, columns="mesure type c1 c2 c3 c4 c5 c6".split())
-    res.to_csv(output_filename)
\ No newline at end of file
diff --git a/eval_disambiguation.py b/eval_disambiguation.py
deleted file mode 100644
index 7109dae..0000000
--- a/eval_disambiguation.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# coding = utf-8
-
-import argparse
-import sys
-import numpy as np
-from numpy import inf
-import glob,re,sys,os,json
-import pandas as pd
-from strpython.eval.disambiguation import *
-import logging
-for _ in ("boto", "elasticsearch", "urllib3"):
-    logging.getLogger(_).setLevel(logging.CRITICAL)
-
-
-parser= argparse.ArgumentParser()
-
-parser.add_argument("corpus_name",default="padiweb",help="Corpus you want to evaluate",choices=["padiweb","agromada"])
-parser.add_argument("measure",default="accuracy",help="Performance measure you want to compute",choices=["accuracy","accuracy_k","mean_distance_error"])
-parser.add_argument("-k",type=float,default=1,help="K value for the accuracy@k computation")
-
-args= parser.parse_args()
-
-if args.corpus_name == "padiweb":
-    corpus_dir="data/disambiguation_data/padiweb_disambiguation"
-    data_lang = json.load(open("data/disambiguation_data/padiweb_disambiguation/data_lang.json"))
-
-else:
-    corpus_dir = "data/disambiguation_data/mada_disambiguisation"
-    data_lang = json.load(open("/Users/jacquesfize/DATA_THESIS/BVLAC/raw_bvlac/associated_lang.json"))
-
-data_lang = {int(k): (v if v in ["fr",'en'] else "en") for k, v in data_lang.items()}
-corpus_files=glob.glob("{0}/*.csv".format(corpus_dir))
-acc_MC,acc_GEO,acc_wiki=[],[],[]
-i=0
-for fn in corpus_files:
-    i+=1
-    id_=int(re.findall(r"\d+",fn)[-1])
-    df=pd.read_csv(fn)
-    lang=data_lang[id_]
-    #acc_MC.append(efficiencyMostCommon(df,lang,args.measure,args.k))
-    #acc_GEO.append(efficiencyGeodict(df,lang,args.measure,args.k))
-    acc_wiki.append(efficiencyWiki(df,lang,args.measure,args.k))
-    #acc_GEO=np.array(acc_GEO)
-    #acc_GEO[acc_GEO == inf] = 0
-    #acc_GEO=acc_GEO.tolist()
-    # sys.stdout.write("\r{0}/{1} -- {5}Wiki : {2} | {5}MC : {3} | {5}GEO : {4}".format(
-    #     i,
-    #     len(corpus_files),
-    #     np.mean(np.nan_to_num(acc_wiki)),
-    #     np.mean(np.nan_to_num(acc_MC)),
-    #     np.mean(np.nan_to_num(acc_GEO)),
-    #     args.measure
-    #     )
-    # )
-
-print(args,"\naccGEO",np.mean(np.nan_to_num(acc_GEO)),"acc_MC",np.mean(np.nan_to_num(acc_MC)),"accWiki",np.mean(np.nan_to_num(acc_wiki)))
\ No newline at end of file
diff --git a/extract_pdf_from_results.py b/extract_pdf_from_results.py
deleted file mode 100644
index 9a200a9..0000000
--- a/extract_pdf_from_results.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# %%
-import matplotlib.pyplot as plt
-#%matplotlib inline
-import numpy as np
-import pandas as pd
-import seaborn as sns
-plt.style.use(['seaborn'])
-from tqdm import tqdm
-
-#%%
-from glob import glob
-import os
-
-
-def break_line(title):
-    return "\n".join(title.split("&"))
-    final_=""
-    for i,ch in enumerate(title):
-        final_+=ch
-        if i> 0 and i%40 == 0:
-            final_+="\n"
-    return final_
-def get_name(fn):
-    import re
-    new_fn= "PadiWeb Corpus" if "padi"in fn else "AgroMada Corpus"
-    if "ming1" in fn:
-        min_=re.findall("ming1_(\d+)",fn)[0]
-        new_fn += " - (STR minsize = {0})".format(min_)
-    if "format" in fn:
-        form = re.findall("format_(\w+).xlsx", fn)[0]
-        new_fn += " - (File Format = {0})".format(form)
-    return new_fn
-
-def get_name_combination_wsm(x):
-    dict_={
-        "c1_c2_c3_c4_c5_c6": r"$\bf{All Criteria}$",
-        "c2_c5_c6": r"$\bf{Spatial\ relatedeness\ privileged}$"
-    }
-    if x not in dict_:
-        return x
-    return dict_[x]
-
-#%%
-def generate_increase_graph(fn,dir_out,vs="ClassicBOW"):
-    df = pd.read_excel(fn)
-    df = df[df.name.isin(["c1_c2_c3_c4_c5_c6","c2_c5_c6"])]
-    df = df[df.type_score.isin(["wsm"])]
-    df["name_all"]=df.apply(lambda x:"_".join([str(x["name"]),str(x.type_score)]),axis=1)
-    g = sns.catplot(col="name_all",
-                    data=df, kind="bar",
-                    height=4, aspect=1.,col_wrap=3,sharex=False)
-    for ix,ax in enumerate(g.axes.flatten()):
-
-        ax.set_title(break_line("{name} & {mesure}-{type_}".format(
-            name=get_name_combination_wsm(df.iloc[ix]["name"]),
-            mesure=df.iloc[ix].mesure,
-            type_=df.iloc[ix].type)),fontsize=11)
-        ax.set_ylim((-1,1))
-    fn2 = os.path.basename(fn) 
-    plt.subplots_adjust(hspace=0.4, wspace=0.4)
-    g.fig.suptitle("{file}".format(file=get_name(fn2)),fontsize=15,y=1.1,x=0.35)
-    g.savefig(os.path.join(dir_out,".".join(fn2.split(".")[:-1])+".pdf"))
-
-
-
-
-for dirpath in [
-    #["csv_results_24_05_19_VS_CLASSICBOW","CLASSICBOW"],
-    #["csv_results_24_5_19_top1_vs_BOWSE_str_object","BOWSE"],
-    ["csv_results_24_5_19_top1_vs_PolyIntersect_str_object", "POLY"]
-]:#[["./csv_results_8_4_19_top1_ClassicBow/","CLASSICBOW"],["./csv_results_8_4_19_top1_BOW_STR_object/","BOWSE"]]:
-    fns = glob(os.path.join(dirpath[0],"*.xlsx")) 
-    for fn in tqdm(fns):
-        generate_increase_graph(fn,dirpath[0],vs=dirpath[1])
-
-
-
-
diff --git a/generate_selected_document.py b/generate_selected_document.py
deleted file mode 100644
index e7aba2a..0000000
--- a/generate_selected_document.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# coding = utf-8
-import argparse,glob,random,re
-import networkx as nx
-import numpy as np
-
-parser = argparse.ArgumentParser()
-parser.add_argument("graph_input_dir")
-args=parser.parse_args()
-
-graphs={}
-for file in glob.glob(args.graph_input_dir+"/*.gexf"):
-    id=int(re.findall("\d+",file)[-1])
-    graphs[id]=nx.read_gexf(file)
-
-median=np.median([len(g) for g in graphs.values()])
-if median <=2:
-    median=int(np.mean([len(g) for g in graphs.values()]))
-
-cat_interval=[
-    [1,median],
-    [median,median*2],
-    [median*2,1000000]
-]
-print("Interval",cat_interval)
-size_selection=100
-cat_size=[
-    size_selection/5,
-    (size_selection/5)*2,
-    (size_selection/5)*2
-]
-
-per_size={0:[],1:[],2:[]}
-for i,g in graphs.items():
-    size_ = len(g)
-    for c in range(len(cat_interval)):
-        cat=cat_interval[c]
-        if size_ >= cat[0] and size_ < cat[1]:
-            per_size[c].append(i)
-            break
-
-for k,p in per_size.items():
-    random.shuffle(p)
-
-selected=[]
-for k,p in per_size.items():
-    selected.extend(p[:int(cat_size[k])])
-print(sorted(selected))
-
-count={0:0,1:0,2:0}
-for i in selected:
-    size_ = len(graphs[i])
-    for c in range(len(cat_interval)):
-        cat=cat_interval[c]
-        if size_ >= cat[0] and size_ < cat[1]:
-            count[c]+=1
-            break
-
-print("Check if good proportions {0}".format(count))
\ No newline at end of file
diff --git a/generate_str.py b/generate_str.py
deleted file mode 100644
index 4d7fa77..0000000
--- a/generate_str.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import sys, os, re, argparse, warnings, json
-
-import logging
-
-logger = logging.getLogger("elasticsearch")
-logger.setLevel(logging.ERROR)
-
-import numpy as np
-import pandas as pd
-import networkx as nx
-from tqdm import tqdm
-
-tqdm.pandas()  # for progressbar when apply with dataframes
-tqdm.monitor_interval = 0
-
-from strpython.pipeline import Pipeline
-from strpython.nlp.pos_tagger.tagger import Tagger
-from strpython.models.str import STR
-
-from strpython.nlp.ner.spacy import Spacy as spacy_ner
-# from strpython.nlp.ner.polyglot import Polyglot as poly_ner
-from strpython.nlp.ner.stanford_ner import StanfordNER as stanford_ner
-
-from strpython.nlp.disambiguator.wikipedia_cooc import WikipediaDisambiguator as wiki_d
-from strpython.nlp.disambiguator.share_prop import ShareProp as shared_geo_d
-from strpython.nlp.disambiguator.most_common import MostCommonDisambiguator as most_common_d
-
-from mytoolbox.text.clean import *
-from mytoolbox.exception.inline import safe_execute
-
-from thematic_str.helpers.terminology.matcher import matcher_agrovoc
-from stop_words import get_stop_words
-
-import logging
-import gc
-
-logger = logging.getLogger("elasticsearch")
-logger.setLevel(logging.ERROR)
-logger = logging.getLogger("Fiona")
-logger.setLevel(logging.ERROR)
-
-disambiguator_dict = {
-    "occwiki": wiki_d,
-    "most_common": most_common_d,
-    "shareprop": shared_geo_d
-}
-
-ner_dict = {
-    "spacy": spacy_ner,
-    # "polyglot":poly_ner,
-    "stanford": stanford_ner
-}
-
-help_input = """Filename of your input. Must be in Pickle format with the following columns :
-  - filename : original filename that contains the text in `content`
-  - id_doc : id of your document
-  - content : text data associated to the document
-  - lang : language of your document 
-"""
-
-parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter)
-
-# REQUIRED
-parser.add_argument("input_pkl", help=help_input)
-# OPTIONAL
-parser.add_argument("-n", "--ner",
-                    help="The Named Entity Recognizer you wish to use",
-                    choices=list(ner_dict.keys()),
-                    default="spacy")
-parser.add_argument("-d", "--disambiguator",
-                    help="The Named Entity disambiguator you wish to use",
-                    choices=list(disambiguator_dict.keys()),
-                    default="most_common")
-parser.add_argument("-t", "--transform",
-                    help="Transformation to apply",
-                    action="append",
-                    choices=["gen", "ext"])
-
-parser.add_argument("-o", "--output",
-                    help="Output Filename",
-                    default="output.pkl"
-                    )
-
-args = parser.parse_args()
-
-if not os.path.exists(args.input_pkl):
-    raise FileNotFoundError("Input file does not found !")
-if os.path.exists(args.output.replace(".bz2","")):
-    exit()
-df = pd.read_pickle(args.input_pkl)
-
-dataset_name = args.input_pkl.replace(".pkl", "")
-
-cols = set(df.columns)
-if not "filename" in cols or not "id_doc" in cols or not "content" in cols or not "lang" in cols:
-    raise ValueError("Missing data column in input given")
-
-languages = np.unique(df.lang.values)
-print("Languages available in the corpus", languages)
-
-pipelines = {
-    lang: Pipeline(lang=lang, ner=ner_dict[args.ner](lang=lang), tagger=Tagger(),
-                   disambiguator=disambiguator_dict[args.disambiguator](), corpus_name=dataset_name)
-    for lang in tqdm(languages, desc="Load Pipelines model")
-}
-
-stopwords = {
-    lang: matcher_agrovoc(lang).terminology_data
-    for lang in tqdm(languages, desc="Load stopwords")
-}
-for lang in stopwords:
-    stopwords[lang].extend(get_stop_words(lang))
-
-print("Clean input content ...")
-if not "entities" in df:
-    df["content"] = df.content.progress_apply(lambda x: clean_text(x))
-
-count_error = 0
-
-
-def build(pipelines, x):
-    global count_error
-    try:
-        if "entities" in x:
-            return pipelines[x.lang].build(x.content, stop_words=stopwords[x.lang])
-    except Exception as e:
-        print(e)
-
-    try:
-        return pipelines[x.lang].build(x.content)
-    except Exception as e:
-        print(e)
-        try:
-            return pipelines[x.lang].build(str(x.content).encode("utf-8").decode("utf-8"))
-        except Exception:
-            warnings.warn("Could not build STR for doc with id = {0}".format(x.id_doc))
-            count_error += 1
-            return STR.from_networkx_graph(nx.Graph())
-
-
-print("Transforming text to STR ...")
-df["str_object"] = STR.from_networkx_graph(nx.Graph())
-df_fin = df.iloc[[]]
-for lang in tqdm(languages, desc="Computing STR"):
-    corpus_ = df[df.lang == lang].content
-    df2 = df[df.lang == lang]
-    df2["str_object"] = pipelines[lang].pipe_build(
-        corpus_)  # df.progress_apply(lambda x: build(pipelines,x) if len(x.content) >0 else STR.from_networkx_graph(nx.Graph()) , axis = 1)
-    df_fin = pd.concat((df_fin, df2))
-
-df = df_fin
-
-gc.collect()
-
-if "ext" in args.transform:
-    print("Extending STR ...")
-    df["ext_1"] = df.progress_apply(
-        lambda x: pipelines[x.lang].transform(x.str_object, type_trans="ext", adjacent_count=1, distance="100"), axis=1)
-    df["ext_2"] = df.progress_apply(
-        lambda x: pipelines[x.lang].transform(x.str_object, type_trans="ext", adjacent_count=2, distance="100"), axis=1)
-
-if "gen" in args.transform:
-    print("Generalising STR ...")
-    df["gen_region"] = df.progress_apply(
-        lambda x: pipelines[x.lang].transform(x.str_object, type_trans="gen", type_gen="bounded", bound="region"),
-        axis=1)
-    df["gen_country"] = df.progress_apply(
-        lambda x: pipelines[x.lang].transform(x.str_object, type_trans="gen", type_gen="bounded", bound="country"),
-        axis=1)
-
-print("Done with {0} error(s)... Now saving !".format(count_error))
-df.to_pickle(args.output,compression="bz2")
\ No newline at end of file
diff --git a/notebooks_old/Baseline_computation.ipynb b/notebooks_old/Baseline_computation.ipynb
deleted file mode 100644
index 4b3a67b..0000000
--- a/notebooks_old/Baseline_computation.ipynb
+++ /dev/null
@@ -1,181 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import networkx as nx\n",
-    "import numpy as np\n",
-    "import geopandas as gpd\n",
-    "import pandas as pd\n",
-    "from glob import glob\n",
-    "from mpl_toolkits.basemap import Basemap"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dir_=\"../data/graph_data/graph_exp_july_19/normal/\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "graphs=[nx.read_gexf(fn) for fn in glob(dir_+\"*.gexf\")]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from strpython.models.str import *"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "graphs=[STR.from_networkx_graph(g) for g in graphs]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from strpython.helpers.collision import getGEO\n",
-    "def getGeoDataFrame(str_,label):\n",
-    "    spatial_entities=str_.spatial_entities\n",
-    "    data=[]\n",
-    "    for k,v in spatial_entities.items():\n",
-    "        geo_d=getGEO(k)\n",
-    "        data.append([k,v,geo_d.values[0]])\n",
-    "    df=gpd.GeoDataFrame(data,columns=\"id label geometry\".split())\n",
-    "    df[\"geometry\"]=df[\"geometry\"].apply(lambda x : x[0] if type(x) == type(np.array([])) else x)\n",
-    "    return gpd.GeoDataFrame([[1,df.unary_union,label]],columns=\"id geometry label\".split())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x13001cf28>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t=getGeoDataFrame(graphs[7],\"lab1\")\n",
-    "t.plot(cmap=\"tab10\",figsize=(20,20))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "96485.9740837231\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e95d1d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df1=getGeoDataFrame(graphs[6],\"1st set of spatial entities\")\n",
-    "df2=getGeoDataFrame(graphs[7],\"2nd set of spatial entities\")\n",
-    "res_intersection = gpd.overlay(df1, df2, how='intersection')\n",
-    "import matplotlib.pyplot as plt\n",
-    "x0,y0=5,5\n",
-    "fig, ax = plt.subplots(figsize  = (12, 8))\n",
-    "world.plot(ax=ax,facecolor='none',edgecolor='gray')\n",
-    "df2.plot(ax=ax, facecolor='none',edgecolor='green')\n",
-    "df1.plot(ax=ax, facecolor='none', edgecolor='red')\n",
-    "res_intersection.plot(ax=ax,color=\"gray\",alpha=0.5,figsize=(15,15))\n",
-    "ax.text(23, -5,u'\\u25B2 \\nN ', ha='center', fontsize=30, family='Arial', rotation = 0)\n",
-    "ax.set_xlim(20,60)\n",
-    "ax.set_ylim(-30,0)\n",
-    "\n",
-    "\n",
-    "from matplotlib.patches import Patch\n",
-    "from matplotlib.lines import Line2D\n",
-    "\n",
-    "legend_elements = [Line2D([0], [0], color='r', lw=2, label='1st set of spatial entities'),\n",
-    "                   Line2D([0], [0],  color='g',lw=2, label='2nd set of spatial entities',\n",
-    "                          markerfacecolor='g', markersize=15),\n",
-    "                   Patch(facecolor='gray',\n",
-    "                         label='Intersection')]\n",
-    "\n",
-    "from geopy.distance import distance\n",
-    "print(distance((-30,50),(-30,51)).m)\n",
-    "from matplotlib_scalebar.scalebar import ScaleBar\n",
-    "scalebar = ScaleBar(96485,units=\"m\",location=\"lower right\") # 1 pixel = 0.2 meter\n",
-    "ax.add_artist(scalebar)\n",
-    "#ax.annotate(\"Source: London Datastore, 2014\",xy=(0.1, .08),  xycoords=\"figure fraction\", horizontalalignment=\"left\", verticalalignment=\"top\", fontsize=12, color=\"#555555\")\n",
-    "ax.legend(title=\"Legend\",handles=legend_elements)\n",
-    "plt.savefig(\"intersection.pdf\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks_old/Clustering in STR.ipynb b/notebooks_old/Clustering in STR.ipynb
deleted file mode 100644
index 1e65dfb..0000000
--- a/notebooks_old/Clustering in STR.ipynb	
+++ /dev/null
@@ -1,1236 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-08-14T15:15:12.808343Z",
-     "start_time": "2018-08-14T15:15:12.802190Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/Users/jacquesfize/nas_cloud/Code/str-python\n"
-     ]
-    }
-   ],
-   "source": [
-    "cd .."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-08-14T15:15:15.354309Z",
-     "start_time": "2018-08-14T15:15:13.414210Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from strpython.models.str import STR\n",
-    "import networkx as nx\n",
-    "import numpy as np\n",
-    "import geopandas as gpd\n",
-    "%matplotlib inline\n",
-    "from shapely.geometry import MultiPoint,Polygon,Point"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-08-14T15:15:40.601156Z",
-     "start_time": "2018-08-14T15:15:37.553352Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<networkx.classes.multidigraph.MultiDiGraph at 0x11d0b0ac8>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "str_=STR.from_networkx_graph(nx.read_gexf(\"data/graph_exp_july_19/normal/1003.gexf\"))\n",
-    "str_.build()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-08-14T15:16:16.089106Z",
-     "start_time": "2018-08-14T15:16:16.028814Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "data=str_.get_geo_data_of_se()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-08-14T15:16:17.447615Z",
-     "start_time": "2018-08-14T15:16:17.434217Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\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>classe</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>label</th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>A-ADM1</td>\n",
-       "      <td>POINT (55.5325 -21.114444444444)</td>\n",
-       "      <td>Réunion</td>\n",
-       "      <td>55.532500</td>\n",
-       "      <td>-21.114444</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>A-PCLI</td>\n",
-       "      <td>POINT (47 -20)</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>47.000000</td>\n",
-       "      <td>-20.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (47.8333 -22.35)</td>\n",
-       "      <td>Vohipeno</td>\n",
-       "      <td>47.833300</td>\n",
-       "      <td>-22.350000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (48.216666666667 -17.583333333333)</td>\n",
-       "      <td>Amparafaravola</td>\n",
-       "      <td>48.216667</td>\n",
-       "      <td>-17.583333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (48.416666666667 -17.833333333333)</td>\n",
-       "      <td>Ambatondrazaka</td>\n",
-       "      <td>48.416667</td>\n",
-       "      <td>-17.833333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (151.75 -32.916666666667)</td>\n",
-       "      <td>Newcastle</td>\n",
-       "      <td>151.750000</td>\n",
-       "      <td>-32.916667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (46.6 -18.7833)</td>\n",
-       "      <td>Maritampona</td>\n",
-       "      <td>46.600000</td>\n",
-       "      <td>-18.783300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (47.25 -17.6)</td>\n",
-       "      <td>Morafeno, Maevatanana</td>\n",
-       "      <td>47.250000</td>\n",
-       "      <td>-17.600000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (46.995 -21.2667)</td>\n",
-       "      <td>Ambondrona</td>\n",
-       "      <td>46.995000</td>\n",
-       "      <td>-21.266700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>P</td>\n",
-       "      <td>POINT (48.25 -17.5)</td>\n",
-       "      <td>Ambondroala</td>\n",
-       "      <td>48.250000</td>\n",
-       "      <td>-17.500000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   classe                                  geometry                  label  \\\n",
-       "0  A-ADM1          POINT (55.5325 -21.114444444444)                Réunion   \n",
-       "1  A-PCLI                            POINT (47 -20)             Madagascar   \n",
-       "2   P-PPL                    POINT (47.8333 -22.35)               Vohipeno   \n",
-       "3   P-PPL  POINT (48.216666666667 -17.583333333333)         Amparafaravola   \n",
-       "4   P-PPL  POINT (48.416666666667 -17.833333333333)         Ambatondrazaka   \n",
-       "5   P-PPL           POINT (151.75 -32.916666666667)              Newcastle   \n",
-       "6   P-PPL                     POINT (46.6 -18.7833)            Maritampona   \n",
-       "7   P-PPL                       POINT (47.25 -17.6)  Morafeno, Maevatanana   \n",
-       "8   P-PPL                   POINT (46.995 -21.2667)             Ambondrona   \n",
-       "9       P                       POINT (48.25 -17.5)            Ambondroala   \n",
-       "\n",
-       "            x          y  \n",
-       "0   55.532500 -21.114444  \n",
-       "1   47.000000 -20.000000  \n",
-       "2   47.833300 -22.350000  \n",
-       "3   48.216667 -17.583333  \n",
-       "4   48.416667 -17.833333  \n",
-       "5  151.750000 -32.916667  \n",
-       "6   46.600000 -18.783300  \n",
-       "7   47.250000 -17.600000  \n",
-       "8   46.995000 -21.266700  \n",
-       "9   48.250000 -17.500000  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-08-14T15:16:22.716682Z",
-     "start_time": "2018-08-14T15:16:22.714034Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.cluster import DBSCAN\n",
-    "from sklearn.cluster import MeanShift, estimate_bandwidth\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-08-14T15:16:23.384459Z",
-     "start_time": "2018-08-14T15:16:23.362535Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bandwidth = estimate_bandwidth(data[[\"x\",\"y\"]].values, quantile=0.1)\n",
-    "ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)\n",
-    "ms.fit(data[[\"x\",\"y\"]].values)\n",
-    "data[\"cluster\"]=ms.labels_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-08-14T15:16:24.387715Z",
-     "start_time": "2018-08-14T15:16:24.369680Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>classe</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>label</th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "      <th>cluster</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>A-ADM1</td>\n",
-       "      <td>POINT (55.5325 -21.114444444444)</td>\n",
-       "      <td>Réunion</td>\n",
-       "      <td>55.532500</td>\n",
-       "      <td>-21.114444</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>A-PCLI</td>\n",
-       "      <td>POINT (47 -20)</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>47.000000</td>\n",
-       "      <td>-20.000000</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (47.8333 -22.35)</td>\n",
-       "      <td>Vohipeno</td>\n",
-       "      <td>47.833300</td>\n",
-       "      <td>-22.350000</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (48.216666666667 -17.583333333333)</td>\n",
-       "      <td>Amparafaravola</td>\n",
-       "      <td>48.216667</td>\n",
-       "      <td>-17.583333</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (48.416666666667 -17.833333333333)</td>\n",
-       "      <td>Ambatondrazaka</td>\n",
-       "      <td>48.416667</td>\n",
-       "      <td>-17.833333</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (151.75 -32.916666666667)</td>\n",
-       "      <td>Newcastle</td>\n",
-       "      <td>151.750000</td>\n",
-       "      <td>-32.916667</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (46.6 -18.7833)</td>\n",
-       "      <td>Maritampona</td>\n",
-       "      <td>46.600000</td>\n",
-       "      <td>-18.783300</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (47.25 -17.6)</td>\n",
-       "      <td>Morafeno, Maevatanana</td>\n",
-       "      <td>47.250000</td>\n",
-       "      <td>-17.600000</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (46.995 -21.2667)</td>\n",
-       "      <td>Ambondrona</td>\n",
-       "      <td>46.995000</td>\n",
-       "      <td>-21.266700</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>P</td>\n",
-       "      <td>POINT (48.25 -17.5)</td>\n",
-       "      <td>Ambondroala</td>\n",
-       "      <td>48.250000</td>\n",
-       "      <td>-17.500000</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>P</td>\n",
-       "      <td>POINT (-86.75808000000001 41.71782)</td>\n",
-       "      <td>Andry</td>\n",
-       "      <td>-86.758080</td>\n",
-       "      <td>41.717820</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>P</td>\n",
-       "      <td>POINT (4.59263 50.56481)</td>\n",
-       "      <td>Communes</td>\n",
-       "      <td>4.592630</td>\n",
-       "      <td>50.564810</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>A-ADM2</td>\n",
-       "      <td>POINT (1.5763888888889 44.558888888889)</td>\n",
-       "      <td>Lot</td>\n",
-       "      <td>1.576389</td>\n",
-       "      <td>44.558889</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (48.45 -17.6833)</td>\n",
-       "      <td>Ambohitsilaozana</td>\n",
-       "      <td>48.450000</td>\n",
-       "      <td>-17.683300</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>P-PPL</td>\n",
-       "      <td>POINT (48.4167 -17.8833)</td>\n",
-       "      <td>Ilafy</td>\n",
-       "      <td>48.416700</td>\n",
-       "      <td>-17.883300</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>P</td>\n",
-       "      <td>POINT (44.65 -21.21667)</td>\n",
-       "      <td>Bevava</td>\n",
-       "      <td>44.650000</td>\n",
-       "      <td>-21.216670</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>P</td>\n",
-       "      <td>POINT (48.53333 -20.23333)</td>\n",
-       "      <td>Sahatelo</td>\n",
-       "      <td>48.533330</td>\n",
-       "      <td>-20.233330</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>P</td>\n",
-       "      <td>POINT (49.08333 -12.78333)</td>\n",
-       "      <td>Mahatsara</td>\n",
-       "      <td>49.083330</td>\n",
-       "      <td>-12.783330</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>P</td>\n",
-       "      <td>POINT (-81.90486 38.91341)</td>\n",
-       "      <td>Plants</td>\n",
-       "      <td>-81.904860</td>\n",
-       "      <td>38.913410</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>L</td>\n",
-       "      <td>POINT (48.21667 -17.51667)</td>\n",
-       "      <td>Ampanobe</td>\n",
-       "      <td>48.216670</td>\n",
-       "      <td>-17.516670</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>P</td>\n",
-       "      <td>POINT (49.91667 -14.78333)</td>\n",
-       "      <td>Ankalampona</td>\n",
-       "      <td>49.916670</td>\n",
-       "      <td>-14.783330</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    classe                                  geometry                  label  \\\n",
-       "0   A-ADM1          POINT (55.5325 -21.114444444444)                Réunion   \n",
-       "1   A-PCLI                            POINT (47 -20)             Madagascar   \n",
-       "2    P-PPL                    POINT (47.8333 -22.35)               Vohipeno   \n",
-       "3    P-PPL  POINT (48.216666666667 -17.583333333333)         Amparafaravola   \n",
-       "4    P-PPL  POINT (48.416666666667 -17.833333333333)         Ambatondrazaka   \n",
-       "5    P-PPL           POINT (151.75 -32.916666666667)              Newcastle   \n",
-       "6    P-PPL                     POINT (46.6 -18.7833)            Maritampona   \n",
-       "7    P-PPL                       POINT (47.25 -17.6)  Morafeno, Maevatanana   \n",
-       "8    P-PPL                   POINT (46.995 -21.2667)             Ambondrona   \n",
-       "9        P                       POINT (48.25 -17.5)            Ambondroala   \n",
-       "10       P       POINT (-86.75808000000001 41.71782)                  Andry   \n",
-       "11       P                  POINT (4.59263 50.56481)               Communes   \n",
-       "12  A-ADM2   POINT (1.5763888888889 44.558888888889)                    Lot   \n",
-       "13   P-PPL                    POINT (48.45 -17.6833)       Ambohitsilaozana   \n",
-       "14   P-PPL                  POINT (48.4167 -17.8833)                  Ilafy   \n",
-       "15       P                   POINT (44.65 -21.21667)                 Bevava   \n",
-       "16       P                POINT (48.53333 -20.23333)               Sahatelo   \n",
-       "17       P                POINT (49.08333 -12.78333)              Mahatsara   \n",
-       "18       P                POINT (-81.90486 38.91341)                 Plants   \n",
-       "19       L                POINT (48.21667 -17.51667)               Ampanobe   \n",
-       "20       P                POINT (49.91667 -14.78333)            Ankalampona   \n",
-       "\n",
-       "             x          y  cluster  \n",
-       "0    55.532500 -21.114444        0  \n",
-       "1    47.000000 -20.000000        0  \n",
-       "2    47.833300 -22.350000        0  \n",
-       "3    48.216667 -17.583333        0  \n",
-       "4    48.416667 -17.833333        0  \n",
-       "5   151.750000 -32.916667        3  \n",
-       "6    46.600000 -18.783300        0  \n",
-       "7    47.250000 -17.600000        0  \n",
-       "8    46.995000 -21.266700        0  \n",
-       "9    48.250000 -17.500000        0  \n",
-       "10  -86.758080  41.717820        1  \n",
-       "11    4.592630  50.564810        2  \n",
-       "12    1.576389  44.558889        2  \n",
-       "13   48.450000 -17.683300        0  \n",
-       "14   48.416700 -17.883300        0  \n",
-       "15   44.650000 -21.216670        0  \n",
-       "16   48.533330 -20.233330        0  \n",
-       "17   49.083330 -12.783330        0  \n",
-       "18  -81.904860  38.913410        1  \n",
-       "19   48.216670 -17.516670        0  \n",
-       "20   49.916670 -14.783330        0  "
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-08-14T15:21:21.065486Z",
-     "start_time": "2018-08-14T15:21:21.061619Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "points=list(map(lambda x: np.array(x).tolist(),data.geometry.values))\n",
-    "labels=data.cluster.values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-08-14T15:24:43.660274Z",
-     "start_time": "2018-08-14T15:24:43.653868Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.9012116432225495"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import silhouette_score\n",
-    "silhouette_score(np.array(points),labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-07-26T13:52:54.295498Z",
-     "start_time": "2018-07-26T13:52:54.291181Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "c=data['cluster'].value_counts().idxmax()\n",
-    "X=data[data[\"cluster\"] == c]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-07-26T13:54:55.035406Z",
-     "start_time": "2018-07-26T13:54:54.447388Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "X=X[[\"x\",\"y\"]]\n",
-    "bandwidth = estimate_bandwidth(X.values)\n",
-    "ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)\n",
-    "ms.fit(X.values)\n",
-    "X[\"cluster\"]=ms.labels_+(data['cluster'].max()+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 193,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-07-26T13:52:11.647055Z",
-     "start_time": "2018-07-26T13:52:11.637528Z"
-    }
-   },
-   "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>x</th>\n",
-       "      <th>y</th>\n",
-       "      <th>cluster</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>55.532500</td>\n",
-       "      <td>-21.114444</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>47.000000</td>\n",
-       "      <td>-20.000000</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>47.833300</td>\n",
-       "      <td>-22.350000</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>48.216667</td>\n",
-       "      <td>-17.583333</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>48.416667</td>\n",
-       "      <td>-17.833333</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>46.600000</td>\n",
-       "      <td>-18.783300</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>47.250000</td>\n",
-       "      <td>-17.600000</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>46.995000</td>\n",
-       "      <td>-21.266700</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>48.250000</td>\n",
-       "      <td>-17.500000</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>48.450000</td>\n",
-       "      <td>-17.683300</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>48.416700</td>\n",
-       "      <td>-17.883300</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>44.650000</td>\n",
-       "      <td>-21.216670</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>48.533330</td>\n",
-       "      <td>-20.233330</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>49.083330</td>\n",
-       "      <td>-12.783330</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>48.216670</td>\n",
-       "      <td>-17.516670</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>49.916670</td>\n",
-       "      <td>-14.783330</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            x          y  cluster\n",
-       "0   55.532500 -21.114444        6\n",
-       "1   47.000000 -20.000000        4\n",
-       "2   47.833300 -22.350000        4\n",
-       "3   48.216667 -17.583333        3\n",
-       "4   48.416667 -17.833333        3\n",
-       "6   46.600000 -18.783300        4\n",
-       "7   47.250000 -17.600000        3\n",
-       "8   46.995000 -21.266700        4\n",
-       "9   48.250000 -17.500000        3\n",
-       "13  48.450000 -17.683300        3\n",
-       "14  48.416700 -17.883300        3\n",
-       "15  44.650000 -21.216670        7\n",
-       "16  48.533330 -20.233330        4\n",
-       "17  49.083330 -12.783330        5\n",
-       "19  48.216670 -17.516670        3\n",
-       "20  49.916670 -14.783330        5"
-      ]
-     },
-     "execution_count": 193,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f, ax = plt.subplots(1, figsize=(16, 12))\n",
-    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
-    "world.plot(ax=ax,color='white', edgecolor='black')\n",
-    "X.plot(ax=ax)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-07-26T13:55:04.944433Z",
-     "start_time": "2018-07-26T13:55:04.362679Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  after removing the cwd from sys.path.\n"
-     ]
-    }
-   ],
-   "source": [
-    "lab=ms.labels_\n",
-    "lab+=data['cluster'].max()+1\n",
-    "\n",
-    "data[\"cluster\"][data[\"cluster\"] == c]=X[\"cluster\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-07-26T13:55:14.667840Z",
-     "start_time": "2018-07-26T13:55:14.654087Z"
-    }
-   },
-   "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>geometry</th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "      <th>cluster</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>POINT (55.5325 -21.114444444444)</td>\n",
-       "      <td>55.532500</td>\n",
-       "      <td>-21.114444</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>POINT (47 -20)</td>\n",
-       "      <td>47.000000</td>\n",
-       "      <td>-20.000000</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>POINT (47.8333 -22.35)</td>\n",
-       "      <td>47.833300</td>\n",
-       "      <td>-22.350000</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>POINT (48.216666666667 -17.583333333333)</td>\n",
-       "      <td>48.216667</td>\n",
-       "      <td>-17.583333</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>POINT (48.416666666667 -17.833333333333)</td>\n",
-       "      <td>48.416667</td>\n",
-       "      <td>-17.833333</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>POINT (151.75 -32.916666666667)</td>\n",
-       "      <td>151.750000</td>\n",
-       "      <td>-32.916667</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>POINT (46.6 -18.7833)</td>\n",
-       "      <td>46.600000</td>\n",
-       "      <td>-18.783300</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>POINT (47.25 -17.6)</td>\n",
-       "      <td>47.250000</td>\n",
-       "      <td>-17.600000</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>POINT (46.995 -21.2667)</td>\n",
-       "      <td>46.995000</td>\n",
-       "      <td>-21.266700</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>POINT (48.25 -17.5)</td>\n",
-       "      <td>48.250000</td>\n",
-       "      <td>-17.500000</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>POINT (-86.75808000000001 41.71782)</td>\n",
-       "      <td>-86.758080</td>\n",
-       "      <td>41.717820</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>POINT (4.59263 50.56481)</td>\n",
-       "      <td>4.592630</td>\n",
-       "      <td>50.564810</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>POINT (1.5763888888889 44.558888888889)</td>\n",
-       "      <td>1.576389</td>\n",
-       "      <td>44.558889</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>POINT (48.45 -17.6833)</td>\n",
-       "      <td>48.450000</td>\n",
-       "      <td>-17.683300</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>POINT (48.4167 -17.8833)</td>\n",
-       "      <td>48.416700</td>\n",
-       "      <td>-17.883300</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>POINT (44.65 -21.21667)</td>\n",
-       "      <td>44.650000</td>\n",
-       "      <td>-21.216670</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>POINT (48.53333 -20.23333)</td>\n",
-       "      <td>48.533330</td>\n",
-       "      <td>-20.233330</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>POINT (49.08333 -12.78333)</td>\n",
-       "      <td>49.083330</td>\n",
-       "      <td>-12.783330</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>POINT (-81.90486 38.91341)</td>\n",
-       "      <td>-81.904860</td>\n",
-       "      <td>38.913410</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>POINT (48.21667 -17.51667)</td>\n",
-       "      <td>48.216670</td>\n",
-       "      <td>-17.516670</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>POINT (49.91667 -14.78333)</td>\n",
-       "      <td>49.916670</td>\n",
-       "      <td>-14.783330</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                    geometry           x          y  cluster\n",
-       "0           POINT (55.5325 -21.114444444444)   55.532500 -21.114444        7\n",
-       "1                             POINT (47 -20)   47.000000 -20.000000        5\n",
-       "2                     POINT (47.8333 -22.35)   47.833300 -22.350000        5\n",
-       "3   POINT (48.216666666667 -17.583333333333)   48.216667 -17.583333        4\n",
-       "4   POINT (48.416666666667 -17.833333333333)   48.416667 -17.833333        4\n",
-       "5            POINT (151.75 -32.916666666667)  151.750000 -32.916667        3\n",
-       "6                      POINT (46.6 -18.7833)   46.600000 -18.783300        5\n",
-       "7                        POINT (47.25 -17.6)   47.250000 -17.600000        4\n",
-       "8                    POINT (46.995 -21.2667)   46.995000 -21.266700        5\n",
-       "9                        POINT (48.25 -17.5)   48.250000 -17.500000        4\n",
-       "10       POINT (-86.75808000000001 41.71782)  -86.758080  41.717820        1\n",
-       "11                  POINT (4.59263 50.56481)    4.592630  50.564810        2\n",
-       "12   POINT (1.5763888888889 44.558888888889)    1.576389  44.558889        2\n",
-       "13                    POINT (48.45 -17.6833)   48.450000 -17.683300        4\n",
-       "14                  POINT (48.4167 -17.8833)   48.416700 -17.883300        4\n",
-       "15                   POINT (44.65 -21.21667)   44.650000 -21.216670        8\n",
-       "16                POINT (48.53333 -20.23333)   48.533330 -20.233330        5\n",
-       "17                POINT (49.08333 -12.78333)   49.083330 -12.783330        6\n",
-       "18                POINT (-81.90486 38.91341)  -81.904860  38.913410        1\n",
-       "19                POINT (48.21667 -17.51667)   48.216670 -17.516670        4\n",
-       "20                POINT (49.91667 -14.78333)   49.916670 -14.783330        6"
-      ]
-     },
-     "execution_count": 200,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-08-14T15:25:27.279976Z",
-     "start_time": "2018-08-14T15:25:25.767557Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x11d416128>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11d407898>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f, ax = plt.subplots(1, figsize=(16, 12))\n",
-    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
-    "world.plot(ax=ax,color='white', edgecolor='black')\n",
-    "data.plot(column=\"cluster\",ax=ax,markersize=5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-07-26T13:56:11.964385Z",
-     "start_time": "2018-07-26T13:56:11.959881Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def to_Multipoints(x):\n",
-    "    #print(x[[\"x\",\"y\"]].values)\n",
-    "    return MultiPoint([Point(z) for z in x[[\"x\",\"y\"]].values]).buffer(1)\n",
-    "\n",
-    "def to_Polygon(x):\n",
-    "    points = [Point(z) for z in x[[\"x\",\"y\"]].values]\n",
-    "    if len(points) > 2:\n",
-    "        coords = [p.coords[:][0] for p in points]\n",
-    "        poly = Polygon(coords)\n",
-    "        return poly\n",
-    "    elif len(points)==1:\n",
-    "        return points[0]\n",
-    "    else:\n",
-    "        coords = [p.coords[:][0] for p in points]\n",
-    "        return LineString(coords)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-07-26T13:56:14.018754Z",
-     "start_time": "2018-07-26T13:56:12.584409Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x12e705588>"
-      ]
-     },
-     "execution_count": 202,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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0A11HtbCwkPj4eJYvX8769etZvXo13t7e2ueLi4sZOXIkBw8eRF9fnw4dOuDh4UGHDh3o3LkzNjY2Dyw2cXemTJnC1q1bMTU1pU6dOkRERKCvr09CQsJ/+v+rtLSUqKgoTpw4oW2ZmZls3ryZ1q1bA39WmO7WrRunTp3CyMiI2rVro6enR8uWLTl9+jQvv/wydnZ26OvrY2lpybBhw+64JzopKYmVK1fy7rvv0qJFC86ePftIrH8bHh7O6tWr2bx5M/n5+Xh4eODl5cXbb78tNz2qSVlZGWfPnuXIkSN88sknLFu2jP79+9/RMbKzs3n77bfZtm0bixYtYujQoQ8oWiHE30miKoS4JxqNhjFjxnD27Fneeecdhg4des/D9gICAhg6dCi2tra0bNmS//3vf/j6+j6UtT3Dw8NZtGgR58+fJz4+nuzsbBo0aICnpycLFiy45VBepRTXrl0jPDyc8PBwAgMD0dPT48yZMw88ZnH3Fi5cyPz589m7d+9/dg5xcnIygwcP5vTp07i4uNChQwfat29Phw4diI+PZ9q0aTRp0oSysjLS09Pp3r07q1at0iaPMTExLFmyhIULFwLg7e1NgwYNCA0N5ZlnnqFDhw7Y2Nhom5mZmXZfpRR5eXlkZWWRnZ1NVlYWv/32Gz/88AMvvfQSb731FmZmZtX23twNjUYDIEPFa5iQkBCeffZZTp48ibW19R3vHxYWRs+ePcnNzZUbD0I8JJKoCvEfExoaysGDB3F0dNS2hg0b3lNRD41Gw/bt2/nyyy+5du0a9vb2aDQabv6eJiQk4ODgcEfH+/LLLwkMDCQiIgJ7e3sAtm/fru3ZuVv5+fk4ODiQk5MDgJmZGY6OjuTk5JCbm0udOnV4/fXX6dKlC05OTtjZ2d3WBWdGRgaRkZFEREQQERHBvn37+Omnn/51zq6oPosXL+abb74hKCgIJyen6g6n2pSUlODh4cFrr71W5dqwFy5cIC0tDT09PfT19WndunWVN6OSk5PZsWMHO3bsYP/+/bRp0wZ9fX2MjY3JyMggJSWFlJQUiouLsbKyoqioiOzsbIyMjLCwsMDCwgJzc3Nat27Nu+++i6Oj48M4ffEfMnPmTDZu3Mhzzz3H008/jaur623vGxcXh5eXFwkJCSQnJ7NlyxaOHz+Orq4uTZo0YcCAAbRp0+aR6P0X4lEhiaoQ/zHTp08nNDQUV1dXEhISSEhI4Pr16zRs2JCOHTvi4eGBh4cHbdu2rXIob0FBAYaGhrfsNT137hy5ubksWrSIdevW8e677/Lhhx/e9oe3UootW7Ywb948YmNj6dWrF23atGHBggWcOXOG+vXr39P5l5aWMn78eNauXav93tmzZzEzM9POg/23xDQ5OVmbkN5s6enptGvXjvbt29O+fXs6der0n+2hexQEBQXx3HPPcfjw4Ro7B/JhCg8PZ9CgQZw+ffq+DH/Ozs5m27ZtbNiwgYMHD9K/f39efvllevToQWFhIenp6dSuXRszMzNZskU8NDcLJa1fv56NGzdibW1Nz549adGihbbZ2tpW+rxSSrFu3TqWLFlCYGAg3bp1w8PDgx49eqCrq8u5c+cICAjQrj89cuRI3NzcqukshXh8SKIqxCNGKcWhQ4dYsGABu3fvpn79+nTu3JmuXbvi6OiIr6/vPyaFe/fuZfTo0Tg6OuLn58c777yDgYEBUVFR2mGr4eHhnDt3jnbt2rF+/XptkaSUlBTs7e3R0dHRzkFzd3dnzJgx+Pv7V7jgnDJlCkopFi5ceNtDdi9fvsyUKVOIiorC3Nyc+Ph4LC0t8fDwYNSoUfd1blBoaChPPfUUq1ev5sknn6xyG6UUCQkJnDhxokJSWlhYqE1Ibw6RbNy4sQz1e0Skpqbi7u7OL7/8Qo8ePao7nBrj3XffJTAwkJUrV9KxY8e7Pk5RURHHjx/nwIEDhIWFERYWhr29PZMmTWL69On3MWIh7l55eTkHDx7k+PHjREdHa9dVLS8vp0WLFlhYWJCamlphJICuri5t27Zl4MCBfPTRRxWOp5Ti2LFjbNy4kV9++QULCwvefPNNnn/++Wo6QyEefZKoCvGI+fHHH5k4cSI2NjaMGjWKhIQEtmzZon2+sLDwX6vOrl27lpdffpmCggKWLFmCo6MjxsbGeHh4UKdOHeDPIkHffPMNX331FatXr6Zv374UFxdjZmZGamoqhoaGpKSkcOzYMZYsWUJsbCwff/wxY8eOBf6ct3ZzXdU5c+bQvHlz7OzsMDc3r5BI35yjtnjxYubPn4+7uzuRkZEsXbqUvn37Ymlpef/fxP973c8++4zZs2dz+fJlGjZsqH3ul19+YcWKFURERKCjo6Ods3ezOTk5yfCuR1hkZCTjx48nMjKyukOpUZRSrF+/nmnTpjFu3Djef//9O56LV1BQQMuWLbG2tqZbt250794dLy8vlFJcuHBBW+HX3Nyct99+W36PRI2TlpZGdHQ02dnZ2nnV1tbW6OrqMnDgQJo1a8Z33333jz+7hYWF9OnTh5YtW/LDDz88xOiFeLxIoirEI0Qpxdq1a9m0aRO5ubnk5eVhaGhIu3btGDx4ML169bqtQkZz587l3LlzlJSUUFxcTElJCTk5OZw8eZIuXbrw5JNP4uPjg5OTEyNGjGDnzp2UlJRgYGDA5MmTycjIYMWKFdqkNjs7m7Zt2+Lt7c3y5csrxLtp0ya+++47EhMTSUpKori4GFtbWwwMDLQFVAwMDPDx8WHBggV89NFHHD16lLi4OJo1a8awYcN4++2378vwQKUUsbGx7Nmzhy1btpCUlMRnn33GkCFDKlx0NG7cmJkzZ+Lr66vtQRaPj7NnzzJq1CjOnj1b3aHUSCkpKUyZMoXTp0/z/fff88QTT9z278CCBQsICwurcPNs6NChBAYG4unpSbNmzWjatCkbNmzgmWeeYcKECbLWsKjxysrK8Pf3R0dHh4ULF5KUlERiYiIJCQnar399nJubS2lpKQcOHKBbt27VHb4QjyxJVIV4RFy+fJlXX32V6OhounTpgqOjIw4ODmRmZhIVFcXmzZtZtGgRU6ZMuevXyM3NZd++fezYsYPQ0FASEhIwMjIiOzubVatW8fzzz1NYWMj48ePZu3cvPXv25OzZs1y5coXS0lLGjBlDo0aN0NfXp2vXrvTs2bPSBW5BQQHJycmUlZVpi6cYGhoC8OGHH/LDDz9QVlaGh4cHlpaW/P7779jZ2bFu3TqaNGly1+e2fft2pk6dSnl5Of369aN///4MHjy4UgKslKJnz55ERkbSvXt3+vXrR9++fWnevLkkrI+JmJgY/Pz8iImJqe5QarStW7fy5ptvUlZWxsCBA/H19b3l1IKbwyhHjhzJ7t27adOmjfa5/fv34+Pjw9q1axk9ejQA58+fZ9q0aezZs0e7XVlZ2UOp7C3EnVq0aBFTp05FT08PS0tLHBwctJ/Bf/9qZ2eHpaUl+vr68pkhxD2SRFWIahAdHU1wcDC6urro6emho6PDlStXOHfuHNu2bWP+/Pm8/vrrFfZ57733+OSTT/D39+eZZ56hYcOGHDx4kLCwMNavXw9AXl7ePVXv/TulFJmZmSQkJNCoUaMKx46Pj+ePP/6gadOmDBkyhKSkJHR0dDAxMaFWrVqkp6drj3G7Pv30U06cOMH8+fM5cOAAoaGh/PHHH1y4cAFTU1PWr1/PoEGD7ugcysrK2Lt3L88//zybN2++7d6h9PR0goOD2bt3L3v27EEphbOzM3Z2dtja2mJnZ1fhsa2tLba2tv867FpUv+vXr+Pu7s7x48dp2rRpdYdToymliIyMZPjw4SQnJ3PmzBkaNWpUYZsbN27QqVMnrKysmDRpEi+99FKF/Rs1akRcXBxjxoyhe/fuNGzYkAMHDrB7927Onz9P3bp1sbW15ciRI/e8tJUQD0JhYaG2RsPNG6s3KaWIjo4mKCiI4OBgQkND0dPTo2/fvgwYMIB+/fr9p9dnFuJeSKIqxEMUERHBZ599RmhoKIMHD0ZPT4/y8nI0Gg1OTk64urri7+/P2bNnq6wYmJCQwNatW9m0aROpqal4eXnRrVs3vLy8aNiwIbVq1Xro53TlyhWOHj1KREQEpaWlFBcXk5+fT1RUFCYmJoSGht72seLj42nZsiUXLlyosJxNYmIiBw4coEGDBnTt2vW2jnXmzBm+/vprtm/fjrOzM7Nnz75l0aR/o5TiypUrxMTEEBQUxL59+265Pqqfnx/btm27q9cRD8/333/P7NmzWbduHT4+PtUdTo320ksv8f3337Ns2TLGjx9f6UbP3LlzuXTpEsuWLaty//z8fE6ePMmJEycIDw/XLvPRr18/unbtWunCX4hHQXp6Ou+++y7bt29HX18fHx8fevfujbe3NxkZGbz++uvs2bMHHR0dDh8+TOfOnas7ZCEeOZKoCvEQHDx4kI8//pgzZ87wxhtvMHHixCp7PjUaDcbGxmRmZj4SC4pfuHCBTp064eTkRF5eHnl5eSQnJwN/VhB9+eWXtWug3sqaNWsIDAzk1KlT3LhxAzc3N1auXHnPpf2/+OILZsyYQefOnbG2tqawsJCxY8fy7LPP3vGxFi5cyOLFi7l+/Tqurq60bt2aNm3a4O7ujpWVFSYmJhgbG1O7dm3q1Kkjy23UUBqNhoiICEJCQsjIyGD//v2cOHGCrVu34ufnV93h1VgFBQX8/PPPLFy4kLKyMl599VWee+45TE1NUUrRokULVq1aRZcuXao7VHEHlFKkpaWRmJjIjRs3SExMRE9Pj+eee04qmP+LHTt2MHnyZEaMGMGUKVNo3LhxhRs4s2bN4quvvqKoqAgTExM6d+7M/v37mTt3LjNmzKjGyIV4tEiiKsQDlJOTw/Tp09mzZw/vvfceY8aMqbLXUylFfHw8L774IsXFxYSEhNzR6yil+Pnnn5k1axa1a9fGzc2N119//bZ7H//puEeOHOHw4cPUqVMHMzMzRo0axbZt2/Dz82PcuHGcO3eOlStX0rJlS+1+MTExLFy4kHXr1ml7S27+DXFxceGLL76gZ8+eAPTu3ZtOnToxZswYmjZtet+G/hUUFBAQEEBWVha7du0iKCiIiRMnsmjRojs+1okTJ5gyZQqFhYXMmzdPeuAeMSdPnmThwoXs2rULCwsL+vTpg52dHcbGxhgbG+Pj41NpOKuoTClFaGgoixYtYt++fejq6pKXl0fHjh05dOiQzMd7BGg0Gg4dOsTmzZvZsmULBQUFODg4aNu5c+do2LAhq1evfiRullaHK1eu4OHhQUBAAE888cQtt1NKkZWVRWJiIomJicyYMYOysjLeeecdNmzYQEFBAbq6utqmo6ODrq4uDg4ODBkyhF69eslNT/GfJ4mqEA/I3r17mThxIv369WPevHmYmZlpn1NKceLECX7++WcCAwO5fv06tWvXZvr06UyaNInly5ezbt06HB0dtb13rVu3xtXVtcpE92YF06VLl2Jubs6uXbtYuXIl586du6OLx40bN1JcXIyxsTHHjx9n48aN1K5dGx8fH4qKisjKymLnzp0YGRkRGBhIo0aNWLp0KcuXL8fBwQFnZ2ftXE1bW1s6duxI/fr1AbRxhISE8MYbb/DKK68wY8YMpk+fzs8//4ytrS0mJiaYmJhgYWGBk5MTTk5OODo6kp+fT2pqKmlpaaSlpWkfazQamjdvjqurq7Y5OTmRk5NDYGAgGzdu5PDhw/Tv35+RI0cycODAu55DqpRiy5YtzJw5kyZNmjBjxowqi0WJmicgIICXX36ZevXqMXfuXHx9faXH6B5lZWWhlMLU1FQupmuQlJQUrl+/rl378+/t5MmTWFtbM3z4cIYNG1bhBiP8+fe5T58+MsqAP280BwQE8Msvv5CRkYGenh76+vokJyfj6+vLvHnz7uh4KSkpODk54eXlxbhx47C2tkaj0VRoNyvTb9myhYsXL+Ln58fw4cPx8fGRIfLiP0kSVSEegP/973988803vPPOO7z77rsAXLp0idjYWCIiIti4cSMATz/9NP4AjxHxAAAgAElEQVT+/jRp0gQjIyP++OMPhgwZgp+fH5MmTSIrK4tTp05x+vRpTp06xeXLl2nSpAkjRozgtddew9zcHIDly5czZ84cfv/9dxo3boxSCl1dXaZOncrcuXM5cuQIABYWFri5uVX5gaeUQl9fn1GjRlFYWEjz5s15+umnadWqVaVkbPv27UyfPp0LFy4AUFpaytGjR0lMTCQ5OZnk5GSSkpLYtm0bU6dOZebMmRUuZgcOHIirqyuffvop8Ofc1Pz8fPLz8ykoKCAjI4P4+Hji4uJISEjA1NQUa2trrKyssLa21j4GtIu032xpaWno6+vTu3dvRo0axaBBg+5rgamSkhJWrFjBokWLUEoxbNgwysrKyMvLIz8/XzsE+uayP39vtWrVwszMjDp16lCnTh3q1q2Lj48Pvr6+mJqa3rc4RUUlJSX88ssvfP3112RlZbFy5Uq6d+9e3WEJcU/Ky8s5duwYv/32Gzt27CA+Ph5nZ2ft+p9/bdbW1jRv3lxbQKygoIATJ05w7Ngxjh07xtGjRykuLmbp0qUMHjy4ms/swTh58iT79u0jJyeH3NxccnJyyMnJ0Y4MGD9+PFFRUaxdu5Zdu3bRs2dPRo8eTcOGDSkrK6O8vJzy8nK8vLzu6qZnUVHRbe939epVtm7dyubNm7l27Ro//fSTdiTS4yIvL4+jR49y9OhRwsPDKSoqolatWhgaGmJoaEitWrUwMjLCzc2NLl264O7uLgXX/mMeWqKqo6PTHNj4l281AmYDFsBEIPX/vv+OUmrnPx1LElVR0508eZKlS5dy8uRJTp8+jUajwcXFhaZNm+Lm5sbQoUPx8PCokABGRkbSv39/1q9fj7e3d5XHLSoq4syZM3z77bf89ttvvPbaa0ydOhUzMzO+//57Pv30U0JCQmjUqBH79+9n6NChlJeX06pVKwwNDbly5QqjRo3i888/r/L45ubmxMfHY2FhgVJKm/D+XWpqKi1btiQ1NbXScxqNBh0dHXR0dLh27RqTJk2iVq1aBAYGardZsWIFy5cv5+zZswwePJgPP/wQZ2fnf3xP8/PzOXv2LGlpaWRmZpKVlUVmZqZ2mZu6detSr149OnTogLm5+QNP+pRS/PHHHwQFBWFsbIyJiQmmpqaYmppiYmJC7dq1MTAw0H7gGhoaYmBgQHFxsfYiKTc3l+TkZH777TcOHTpE7969GTFiBIMGDdKuUSvuL6UUX3zxBefPn2fVqlXVHY4QdywrK4u9e/eyY8cOdu3ahZ2dHb6+vgwaNAhPT88qL+SVUsTExHDw4EGOHj3KsWPHiI2Nxd3dnc6dO9OpUyc6d+5MkyZNHovRBjk5OaSlpZGenq4diZOWlsbcuXMZNWoUlpaW2uksZmZmGBsbs3v3blavXo27uzvPPvssI0aMqDFr/O7atYsXXniBhQsXMnz48OoO574oKyujTZs2WFhY0KVLFzp27EidOnW0N3lvfs3Pz+fUqVMcPnyYhIQEPDw88PT0pEuXLnTp0qXG/B+JB6NaelR1dHT0gASgMzAOyFNK3fYYCklUxaOksLAQAwODf7wLeOHCBXr06MHixYsZOnTobR03NjaWjz76iD179uDl5UXdunX5/fffKS8v5/jx49jY2HDp0iUsLS2xsLAA4MCBA0ybNo1b/f44OztjYGBAbm4uGRkZdO7cmQMHDlTaLiQkhNdee43Tp08Df1YjvnnRFBwcTNu2bVmyZAmtWrUiIyMDFxcXsrKyKvXMpqWl8e2337JgwQJMTU21vYxWVlbaXoD09HSOHz/OxYsXadGiBfb29lhYWGiTUz09PbKzs8nMzCQpKYnIyEjeeustXnnllUdqjlVGRgbbt29n06ZNhIWF0bdvX9asWSPL3TwAx44dY/LkyURGRlZ3KELclvz8fAIDA1m7di0HDx6kW7duDBo0CF9fX5ycnCptX1paSmRkJGFhYRw4cICwsDBMTEzw8vLC09OTzp0706ZNm2qpFP+gZGdns379epYvX865c+ewsbHBysoKS0tL7VcfH5+7rv5e3fr378/48ePx9/ev7lDui1WrVrFq1SpCQkJuewpNRkYGR48e5fDhwxw+fJhjx46Rk5PDnDlzeP/99/n555+5ceMGTk5OODs74+zsjKWlpUzReYRVV6LaF3hfKeWlo6MzB0lUxX9YYWGhdn7lxx9/jJubG0op7TDYv7eb3y8vL2fUqFFcvXqV06dPk5mZSWZmJjk5ObzyyivY2tpWeB2NRsPo0aOpXbs2K1eurDKWS5cuUVxcTL169bh8+TKvvvoqJ06cqLTdqFGj8PLy4tVXX+X8+fM88cQT9O/fn/79++Pj40NAQACzZ89mwYIFPPPMM7i4uLB161batWtX5esWFBSQnp5OXl4eOTk5pKena4cQm5ub07FjR1q1anVbF1Xnzp1j5syZFBYWsm/fvtv4H6h5srKycHV15dChQ7i4uFR3OI+NrKwsVq9erS0AVlhY+Fj0HonHk0ajYe/evaxdu5YdO3bQtWtXnn32Wfz8/KocMaLRaNi5cyeLFy/m4MGDuLi40L17d7p160a3bt1o0KBBNZzFg1FaWsqhQ4e0Uy1+++03tm/fTp8+fXjhhRfo06cPenp61R3mfREeHs7SpUsJCAggLi7usRlt4+rqysiRI5k9e/Zd/x3etGkT/v7+xMXFUVZWhru7O6NHjyYzM5O4uDji4uLQaDS0aNECV1dXWrRoQYsWLTAxMcHb21v+/j8CqitRXQFEKKUW/1+iOhbIAcKB6UqpzH/aXxJV8TgpKyvj+++/59ixY0RGRnL+/Hn09fW1FUlvtry8PC5evAhA7dq1GThwIKtWrdJesFy9epUjR46QlpZGRkYGmZmZZGRkaFtycjKWlpaEhITcVi9ddHQ0HTp0oFWrVnTu3BmNRkNSUhJJSUlERUVx+fJlzM3NcXNzw8vLiyVLllSYh3rs2DE6d+7M7t27OX78OFFRUSxYsAA7O7sH80b+RU5ODra2tsTGxj6yF2d+fn5cuXKFt956i1GjRknBmvvg/PnzeHh40L9/fyZNmkS/fv2qOyQhbik4OJjBgwfz2WefMXLkSGxsbKrcrrS0lPXr1/PFF19gYGDAG2+8wcCBA6lbt+5DjvjhyMzMZNiwYaSlpVG/fn1q165Nt27dePbZZ7G2tq7u8O6ZUopTp04REBDAli1byM/PZ9KkSYwbN+6hfH4+LLt372bWrFmUlpYye/Zshg4dqk0c09PTOXHiBHl5eejp6VVqurq6lJWV8dxzzxEYGIinpyfx8fFMmTKF0NBQunfvztChQxkwYAAGBgbExMQQHR3Ntm3b2LFjB/DnNdOjen3wX/LQE1UdHR1DIBFwU0ol6+jo2AJpgAI+AuyVUuOr2G8SMAmgYcOGHeLj4+9LPELUNEqpSsNUVq9ezauvvoqvr6/2j29BQQEhISEEBwcTHBxMTk4OXl5e2NnZaedr3mw3/920adM7Gkqan59PeHg4x48fx9DQEDs7O+zs7GjatCn29vYopfj222/5+eefiYmJoVu3btrhVkeOHCErK4vw8HAyMjJ49tlnCQ8PR19fnzZt2tC2bVvtWqROTk6Ym5v/4/AcjUZDVlYWqamp2qavr0+TJk1o1KiRtijDkSNHWL9+Pe3atWPFihWPbIEipRR79+7liy++IC4ujrCwsH9dk1b8u5CQEPz9/dm8eTM9evSo7nCEuKX09HQcHR2Jjo6ucg5/Xl4eP/74I1999RVNmzZlxowZ9OnT57Ee5nj58mV8fX0ZMGAAX3755WPTa6rRaDhy5Ahbt25l69atAAwdOpShQ4fi6en52Pb8KaX47bff+OCDDygqKqJNmzYcPXqUlJQU2rdvj4WFhbaA1V+bRqOhvLyckSNHMmXKlArHzMnJYdeuXWzdupXg4GCys7OpV68eVlZWpKSkMG3aNKZOnfrIXhv811RHojoYeEUp1beK55yBHUop9386hvSoipokKSmJ1atXV+i9/GvLzs6mcePGeHp64unpiYuLC0ZGRtSuXVv79eaSLLeyc+dOnnvuOZycnCgpKdFWxn3iiSfw9vamd+/euLm5VeuH2fXr1zl27Ji2aIW+vj6vv/56hbm5SikSEhI4deoUp06d4uTJk5w7d474+Hh0dHRo0KABZmZmFaorlpaWkpGRQXp6OiYmJhWq/paVlXHhwgWuXr2KkZERHTt2xNPTEx8fn8eqOuKcOXMIDQ1l3759UvHwLhQWFnLy5EntMMFff/2VjRs3EhISQqdOnao7PCEqKC8vZ8WKFbz//vt4e3uzZMmSCsublZeX8+mnn7Jw4UJ69erFW2+9hYfHv17HPRa8vb2xsbFhw4YN1R3KfXP06FFeeeUVCgoKGD58OEOHDqVNmzaP9Q2Hv1NKsWfPHhISEvD09KRFixb37SZEaWkp6enp2uWBbq6WIB4N1ZGobgD2KKVW/t+/7ZVSN/7v8TSgs1Jq1D8dQxJVUZOcO3cOT09PPDw8GDVqVKVeTDMzM2JiYrST/xMTEykqKqKwsFD7NScnBw8PD+2Qrb8nnBqNhoiICHR1dbUl211cXB5K0lJeXn7bHxgZGRmsWLGCn376iby8PG2shoaGmJub06pVK9q3b0+7du1wdXXVxq+UIjs7m6tXr5Kfn69dr+7m13r16mFpaXnLdeRKS0vR1dV9bO6u/115eTn9+vWjS5cufPTRR9UdziNn165d+Pn5UVZWho+PD/b29piamvL888/TuXPn6g5PCOD/X6y/+eab1KtXj/nz51dKQMvLyxk/fjzx8fH8+OOPNGnSpJqirR4RERH4+PjwxRdfMGHChOoO556VlJTg6OhIZmYmvr6+tG/fnl69etG5c2dq1apFRkYGERER9O7d+z+VuApx00NNVHV0dIyBa0AjpVT2/31vDdCWP4f+xgGTbyautyKJqqhpjh8/zpNPPsmiRYsYMWLEHe9fWlrKpk2b+PLLLzE0NOTbb7+973fIs7Ozyc7OJj8/H319fe1aerei0WhYtGgR//vf/xg+fDiDBg2iadOmJCYmkpCQwPXr12nXrh2jR48G4MiRI/j6+uLr68ukSZNwdHSsUGY+MzOTU6dOERkZye+//05eXh4JCQmVCkMUFBQQGxtLdHS0tt0s3PRX5eXlnDp1itDQUGxsbHjmmWcA+OWXX/jjjz8wMjLSJrg3h/106dLlka2im5ycTPv27Vm5ciV9+1YakCL+xaVLl5g1axa///47MTExcldd1CilpaX4+fmxb98+vvrqK1599dVKiUl5eTljx44lMTGRX3/9FWNj42qK9v4oKysjKCiI06dPc+bMGc6cOcONGze0NzhvrqGpr6+vLbBXWlqKra0tnp6e2vXIH3VlZWVcvHiRqKgojh07xv79+4mOjqZZs2ZcvHgRPT09fv75Z3r27ElsbCyxsbHExMSQkpKi/Wyrqt1O8cEVK1ZQWFiorZDr5ORUY4bE7tq1i5UrV3L16lWuXbtGgwYNmDx5MiNHjnzkf/bF7auWYkr3ShJVUdNoNBqeeuopHBwcWLp06T0d5+OPP2bOnDnMmTOHmTNn3rIX8U68+uqrLF68mPr162Nqakp2djalpaX4+/vz7bffVto+OjqaCRMmUFZWxowZM0hLS2Pfvn3Ex8fj6OiIo6Mj1tbWzJ07l+joaIyNjWnbti1Lly5l0KBB/xjLsWPHGDx4MJ988gnjx4/Xnvfy5cuZO3cuiYmJNG7cmBYtWmBpacmyZcv47bffGDBgABEREQQFBREaGsrBgwdxcHCgR48e7N+/H39/f958801sbGyYO3eudsjwzWHDCQkJXL16lTfeeINJkyZhYmJyz+/rwxYSEsLo0aM5ceIEDg4O1R1OjXVzpIJSCo1Gw5UrV4iKiiIoKIiEhAT2798vvROiRikvL2fOnDl88skn9OjRg5CQkErPP//88yQnJ7Nt27bH4kI9LCyMwYMHM2bMGFq1akXr1q2pX78+JSUlFBUVUVxcTHFxMaWlpVhaWmJra4uZmdl/4nc3MzOTs2fP0r59e3bt2sWsWbPIycmhXr16NGvWjGbNmmFra0tWVlaFdWJvtpKSEmJjY6lXr94/vo6NjQ0DBgwgJSWFuLg44uPjMTAwQFdXF41Gg1KKdu3a8d577z20Xt2MjAz8/f25cOECH3zwAc2aNaN+/fqcOXOG7777jiNHjvDBBx/wyiuvPPBYRPW73UQVpVSNaR06dFBC1CRvvfWW8vLyUoWFhXe1f15envruu+9Uu3btlLm5uWrWrJkyNzdXZmZmysfHRz3xxBNKo9Hc9vHKysrU6tWr1ZgxY5SXl5fq1auX0tfXVwsXLlTl5eUqMTFRAapHjx5V7j9r1ixlb2+v0tLSqnw+MTFRde/eXQ0YMEDl5uaqvXv3qp49e/5rXDt37lRWVlZq+/bt2u9duHBBderUSXXt2lUdOXJElZaWKqWUKioqUoCaOnWqUkqpDRs2KDs7OzVlyhS1adMmlZycrD1GSkqK8vDwUOPGjVNWVlbq888/V+Xl5ZVePyIiQg0fPlzZ2NioVq1aqYyMjH+Nuab58MMPVffu3bXvk6jM29tbAcrCwkLVq1dPtW3bVj3zzDPqk08+UQkJCdUdnhAVaDQa9fPPPytbW1s1ffp0lZ+fX+H5oqIiNWzYMNW3b19VUFDwUOO6du3aA/tbExkZqVq3bv1Ajv040Wg06umnn1a//vrrbe8zfPhw9eKLL6p169apX3/9VYWGhqrIyEh16dIllZqaqoqKipRSShkZGVX4edNoNCojI0NlZGSozMxMlZmZqdasWaOaN2+uunbtqvbs2XPfz+/vwsLCFH+OslR169ZVzZo1U15eXqpfv36qT58+ytraWgGqrKzsgcciqh8Qrm4jN5QeVSFuYdu2bbz++uscPXoUKyurO9o3Pj6exYsXs2zZMiwtLcnKyqJhw4Y8/fTT+Pv7Y2pqSpMmTcjOzmbr1q3aCrwajYYPPvgALy8vABITE3nttdcwMTHhww8/xNzcHBsbG2bPnk3nzp05evQov/zyCxcuXKBZs2bUqlWLPn368PHHH1eIZ/fu3aSmpmJjY8P8+fO5ceMGu3bton79+hW28/Pz49ChQ2zdupXu3buzb98+PvroIw4cOFDpHBcvXkxOTg6hoaGcPHmSgIAAunbtCvx5A6xXr1707t2bWbNmVZibW1ZWhoGBAYMHD2bu3Ll0796dPXv20L59+yrfy9zcXIYMGYKBgQFZWVnUqVOHDz/8kOvXr3Py5EkyMjIAqFu3LvHx8axbt47g4GC8vb3v6P+supWXlzNgwABcXFz47rvvHttqkPfi+vXrPPXUUzRv3pwff/yR2rVrV3dIQlQpOTmZiRMncvnyZVasWFGpuFdeXh5DhgzB3NycdevW3dZwzvth48aNfPTRR9y4cYP8/HycnZ1p3rw5zZo1o3nz5nh5eeHq6qrd/sqVK6xfv5727dvTv3//23qNuLg4evTogazicP+dPXuWhQsXkpubS05OjvbrX5uOjg4GBgbk5ub+a09peXk5mzZtYvr06axZs+aBf25euXKFTZs2UatWLUxMTDA0NMTY2Jg6deqgp6fHwYMHeffddx/buhTi/5Ohv0Lcg9zcXNzc3Pjpp5/o1avXbe93+vRpPvzwQ4KDgzEzM0NXV5exY8cycuRIWrRood0uOjoaV1dXrK2t6dixIx07dsTDw4P09HRmz55Nhw4d6NOnD3PmzOHFF19Eo9GwZMkSvv32W4KCgmjQoAHvv/8+8GdSGBgYyOTJk2nYsCHHjx+v9OHUokULGjRogFKKpKQkkpOTGTp0KN9//32l8/7pp59YuHAhpqamzJ07l2effZY//vijQvxbtmxh+PDh+Pv78/TTT9OjR48Ka/sFBAQwe/ZsIiMjqywMVVxcTO/evTlz5gyff/45L7744j++r8XFxQwYMIBBgwZRUFDApk2bcHFxwcHBgT179nD16lUcHBxo0aIFzs7OvPfee5WS8EdBTk4OTz75JK6urvc01Pxxs2bNGi5duqRNUidMmEBSUhLBwcHVHZoQVZo9ezYxMTGsWbOm0jSPjIwMBg4ciLu7O0uXLn1oFb+Tk5NxdXVl8+bN9OrVi+LiYi5evKidGxkTE8O2bdu0S4TBn0uoffDBB2RnZ3P8+HFcXFz+9XWysrKoX78+WVlZUs28GhQXF1NWVnZH02B++ukn1qxZQ1BQ0AOJ6fLly3z66acEBATg7+9PcXEx169f5/r161y7dg2lFPXr16+yOTo6Ur9+faysrP4Tw8P/KyRRFeIevPbaa+Tk5LBy5crb3icgIIBJkyYxbdo01q5dS+/evfn666+rvDOolCI5ORlbW9tKf3iLiopYuHAhe/bs4fPPP9cWXwoPD8fPz4/t27fj6+vLlStXKsxnunHjBi1btuTKlSuVlsR56623WLFiBZMnT+a111675QLzN2k0GiZPnoy9vT36+vrEx8ezfPly4M/e2TFjxrBnzx7atWtXad+SkhJtotWnT59bvkZKSgqbN2/mpZdeuq0Pn1GjRuHj44Orq6u20nJoaChz5sxhwoQJBAcHs2HDBn799Vc6duzI888/z6hRox6pC6WysjL69OlDt27dpArw/5k3bx5vvvkmbm5uZGRkUKtWLfr27csvv/xCZmZmdYcnRCXl5eX873//w8LCosrfY19fX5o0acLXX3/9UC+83377bXJzc1m8ePEtt7l5g/bm5050dDQDBw5k4sSJhIeHs2XLFgBiY2MJCAggNTWVgoICbTMzM+OVV15hxowZdOvWjdmzZz+UcxP3Jj4+nkaNGpGWllbhpvP9UF5eTt26dRk3bhzvv/9+lfNrc3JytInrzXazuOPNlp+fr01a/5rANmjQgAEDBjyyBRX/qyRRFeIuhYWF4e/vz5kzZ7C0tPzX7ZVSzJ8/n6+//prAwEA++eQTGjRowDfffHPfL0KeffZZGjduzK5du3jzzTcrVSIePXo0HTt2ZNq0aZX2vXz5MvPmzWPDhg2MHj2aN954o8rF5m8aM2YM3t7e+Pn50aRJE+zt7bGxsSEqKorAwEDtMN+/y8jIoGHDhuTk5NzX4ateXl4cP36cNm3a4OnpSZcuXejduze2trYVtisoKGDnzp0sWrSIlJQUPvroI4YOHfpIDKWdPn06UVFR/PbbbzL06f/ExcWxbt06du/ezcmTJ/Hy8qJ9+/YkJiayatWq6g5PiArmzZvHl19+ib29PV988UWVlbyffPJJxo8fz5AhQx5aXKmpqbRo0YKIiAicnJyq3ObKlSt4enoSHByMu7s7hYWFzJs3j3nz5rF06VK++eYbnn76adauXcu1a9cYPnw4zs7OGBsba1tcXBzffPMN9erV4/z584SFheHp6fnQzlPcucLCQjw8PHjuueeYOXNmldvczBXu9ppm0qRJWFlZ8emnn951nAUFBZWS14SEBE6cOIGFhQXbtm27L0UqxcMhxZQeAI1Go5KTk1VSUpJ2wrp4/MyZM0dZWFioSZMmqZCQkH+d2L9jxw7VpEkTdfXqVbVw4ULVoUOHB/bzERcXp+rUqaMaN25cZcGgQ4cOqcaNG/9jgaYbN26omTNnqnr16qnJkydXWSgqNTVVtW7dWoWGhqrY2Fjl6OiomjdvroyNjZW1tbWaOnWqys3NveVrODs7q/Pnz9/dSd5CcnJypWIk/0Sj0ajdu3er9u3bqwEDBlR4rqio6I6KWD0MmzZtUvXr11fp6elKqT8LZ9W0GKtbcnKyeuqpp1SbNm3U2bNnqzscISrx9/dXixYt+sdt3nrrLfXJJ588pIiUOnz4sHJyclJz5sy55TaFhYWqffv26uuvv1YajUZt2LBBOTk5qWHDhqlLly6pCRMmKB0dHdW2bVu1Z8+efyzEVFRUpH788UfVrFkz5e7u/iBOSdxHRUVFysvLS40dO7bK6x2NRqN8fX2Vm5ub+u6771ReXp72ucLCQvXHH3+oVatWqaioqAqfWQUFBWrnzp0qIiJCnT59WtWrV69CscT7pbS0VD311FNq2LBhD7Uombg33GYxpWpPTv/aanqi+sEHHyhAWVtbqzp16ig/Pz+1bNmyB/KL9zCUlZWpbdu2qZCQEJWTk1Pd4dQocXFx6vPPP1dt27ZVDg4Oatq0aery5cuVtktLS1ODBg1StWvXVkop1aFDB2Vra6tGjhypvvnmGxUWFqaOHj2qDh48qDZu3FjhD/zd2rBhwy2TQI1Go5o2baoiIiL+9TiZmZlqxIgRytPTU23dulVFRkaqDRs2qEGDBilzc3P1zDPPqEOHDil7e3u1bNky7fEvXbqkxo0bp5ydnVVwcHCVxx4xYoRas2bN3Z/kfVRaWqqMjY3Vhx9+qEaMGKGaNWum9PT01MiRI1VxcXF1h6e1efNmZW9vr3r16qWeeOIJZWJiolq3bq2CgoKqO7RqERsbq7788ktVUlJS4fsajUYtW7ZMWVlZqUOHDlVTdEJUbe7cucrNzU29//77as+ePSorK6vSNlu2bFHOzs4qNDT0gcZSXl6uvvjiC2VjY6O2bdv2j9u+9tprys/PT2k0GjVw4EDl7u6ufv/9d6XUn79zTk5Oav78+crZ2fm2q7KWlZWp2NjYez4P8eDl5eUpHx8fNWLEiEqfi5s3b1YtW7ZUe/bsUYMHD1b16tVTY8eOVd27d1cmJiaqQ4cOyt/fX7m4uChLS0v11FNPqTlz5ihnZ2fVpUsX1apVK2VqaqoA9eWXXz6Q+IuKitTIkSOVjY2Neu+999SNGzceyOvUdJmZmeqDDz5Qbm5uNer6piqSqD4AkydPVkuWLFFK/ZmgfPnllwp45MqwazQatXXr/2PvzONq3L4/vk7qaB5Op0FzkuYbaSSklBtChiIhQoS4ZkLuNd2bIWNC3Gueu8hMuVJKlJQ5pLg0S6Om8zwX+EYAACAASURBVPn9cV/O69uvQfPA8369nhfnefZee+3Tefaz197rWSsE+vr6/PQhYmJi/LQPzC5OVZ49e4ZFixaha9euyMzMRGRkJFauXAlzc3NISEhAVVUVrq6uAP77bpOTk3Hw4EEMHTqUH4r969HSExPgvwnHmjVr6lW2srISAQEBcHJygqGhIRwcHHDw4EHk5+cjPz8fXC4Xp0+frrHu5cuXIScnh5iYmGrXtm3bBjabDUlJSUhJSeGXX35pUp+ayurVq7Fo0SIcPXoUjx8/RkFBAUaMGAEHB4dmWTxoLr58+YJTp07h2rVryM3NxZkzZ9C1a1c4OTnhxYsXba1eq5KYmAgigqmpaY199/f3h5eXVxtoxlBfsrOzcfXqVdy+fRv379/H48ePcf/+ffz1119YtGgRJCUlQUTYs2cP35Ogo/PlyxecP38eS5cu5S841TT+nTt3DsrKypg5cyY+f/7c7Hp8+vQJgwcPhpWVFd6+ffvN8keOHAGXy8WVK1ewZcsWaGho4NWrVwD+e665uLhASkoKRITw8PBm15eh7SkpKcGwYcNgZmaGQ4cOoaioCAUFBVBVVeUvWgBASkoKtm7dimvXrlXb5Hj37h2OHTuGefPmVVnI5vF4VdLntBTPnj3DjBkzIC0tDQ8PDzx69KjGcpWVlcjNzcWrV68QGxuLa9eu4cGDB8jOzu6wc+AdO3ZAVlYWEydOhJCQULua29QEY6i2AF5eXujZsyfs7e35hsfEiRPrtXvVVlRUVCAsLAxTpkyBhYUFRo4ciV69eqFHjx64dOkS/4YsKyvDtWvXQESYNm0aRowYgd69e2PQoEEYM2YMfHx8asxf+SMxf/58CAoKokePHli8eDHCwsJqHXRnzJgBMTExjBw5Ert27cLz589bbfC7d+8epKWlMWHCBDx8+LDRcl69eoWuXbvWWebcuXNQUVFBenp6lfM8Hg+RkZFQV1eHs7Nzu1zdLC8vx+TJk6GmpgZPT08cPnwYaWlpba0WAODNmzeYNWsWPD09MXXqVOjo6EBQUBBz5879bib034LH40FHRweTJk0Cl8tFcHBwlXvo5cuX6NKlyw8/LrVX4uPjoaamhn79+qFPnz4wMTGBrq4ujI2NMW7cOKxduxYnTpzAli1bMGbMGEhKSmLYsGE4efLkd+W+l5WVBSkpKRw/fhzXrl3DvXv38Pz5c6SnpyM9PR3Tpk0Dh8PBr7/+2qztHjhwALa2ttU8Euri3LlzsLCwAAAEBQVBWVkZT5484V8vLy9HXFxcu58AMzSe8vJyhISEwNHRERwOBxYWFpgwYUJbq9VgsrOzsX79eigpKcHa2hqOjo6wsLBAt27dwOFw0KlTJ0hLS6Nr164wNTWFnZ0devToASkpKYiLi8PQ0BCzZ8/uUL/1UaNGYciQIVi0aBGICJ8+fWprleqkvoYqE0ypATx79owePXpEHA6HPn/+TNbW1tSlS5e2Vqsanz59osTERLpw4QKdOHGCFBQUaPz48WRubk7p6ekkIiJCgwcPrjG4TF5eHnl4eFDv3r2pT58+lJ+fTzk5OTRhwgQaO3YsGRoakr6+PpWVldHLly+prKyMH9impkhu7Z3Y2FjKzc2l3r17k6SkZJ1lAVB+fj5JSUl9U+79+/dpyJAh5OTkRMLCwiQkJESVlZX8PGefP3+m/Px8kpWVJWNjY/6ho6NDQkJCTe7Xp0+faO/evbRjxw7S0dEhHx8fGjJkSIMi4D569IgmTpxIjx49qrPcqlWr6J9//qGwsLAqup87d44mTpxIq1atIg8PD5KVlW13oeUBUFJSEt2+fZt/SEpK0oABA8jX17deqRhagq+5dq2srKiwsJASEhIoLi6Onj17RlJSUnT79m0yMjJqE91ag4qKCvr9999p+/btdPHiRRIXFycXFxf66aefSENDg54+fUpPnz6ld+/eUUpKCikqKra1ygz/w+nTp8nb25sCAwOrBXyrjfz8fAoJCaFjx47R/fv3adiwYTR37txa8yt3JIKCgujmzZv0+fNnysvL4/+bl5dHLBaLysrKiOi/tCLNFQzmxo0btHTpUnrw4EG9x92ysjJSUFCgZ8+ekaKiIu3YsYO2b99OycnJzaITQ8fi3bt3dOrUKZo4cSLJycm1tTqNoqysjG7cuEEsFos4HA7/kJaWrnU+lJeXR2/fvqWAgAB68OABnTt3jrS1tVtZ84bz/Plz+uuvv4jD4VBoaChdvXq1QSmKWhsm6u8PyIMHD8jT05PevHlDBgYGNHDgQBo/fnyV5N2NJTExkR4+fEhPnz6lJ0+eUOfOnal79+7UqVMniomJodjYWFJWVqbevXuTjo4OcTgckpWVJVlZWVJTUyN1dXVisVhUXFxMT548IXFxcdLV1W1VwyUrK4vu3r1LSkpKZGZmRkRERkZGJCwsTCkpKbRv375qURgLCgooJyenzui4tZGUlEQxMTFUVlZGX758oZSUFH6kutLSUlJSUqKioiJ6/Pgxff78mYiIPDw8GpQS51uUlZXRqVOnaPfu3fT27VuaMmUKeXp61tifiooKevPmDWlraxOLxaLIyEhasmQJRUVF1dkGj8cjJycn6t69OwUEBFS59urVK/rll1/ozp07RETUrVs36t69O/Xq1YvMzc3JxMSk0QNpYmIiaWpqkoSERKPq1wSPx6OnT59SSEgI7dixgzZt2kSTJk1qNvn1ITU1lUxNTent27fVvpvi4mJ6/PgxGRgYtOsHUFMoKSkhGxsbun//Pnl7e1P37t1JRESEAND27dtJUlKSevXqRYMHDyY7OzsmymMbUVFRQdHR0XTx4kXKysoiCQkJEhcXp5ycHLpy5QqdO3euxvRVX4mOjqakpCSaOnUqf9G0srKSAgICaO/evZSVlUUrV66k+fPnt1aXWh0A9OXLF+rUqVOz/455PB7p6enR3r17qX///vWuN2jQIJo1axYNGzaMZs6cSRISEuTv79+sujEwdBRmzpxJMjIyTYpWXBcAqLi4+Lt9ntdFfQ3VjpNgkKFWANCGDRto69attH37dnJxcWn2VBw//fQT/fTTT7Ver6iooMePH1NUVBSlpKTQ8+fPKScnh3Jycuj169fEZrNJVFSUUlNTSUdHh3JyckhISIicnJxo8ODBZGZm1uy5uwBQdHQ0HThwgO7cuUPp6elkaWlJDx8+JH9/f3r//j3l5ubSu3fvKC4ujkaNGkUJCQnk5+dHAgIClJSURKNGjSJVVVUKCwsjIqLCwkKKi4sjGRkZ4nA4pKSkVOt3bWRkRFJSUjR16lSKjo4mLpdLBgYGZGdnR4qKilRUVEQFBQVkbW1NxsbGZGpqSlpaWs36HbDZbHJ3dyd3d3d6/Pgx7du3j3r16kVr166lmTNn8st9+fKFXF1dKTw8nLp160YzZsygnj170pMnT2j9+vXk4+ND4uLiNbbx8eNHysjIqDHlQbdu3Sg0NJQAUG5uLr169YqeP39ODx48oFOnTlFSUhLJycmRjo4OqaiokJaWFmloaJCioiL/4HA4/F2Hjx8/0vv37+nKlSu0a9cukpKSou3bt5OlpSVxudwm/+4FBATI0NCQDA0NacSIEeTs7EyioqL13hVqDgoLC4nL5db44BIVFSUTExP6+++/SVtbm3r06NFqerUm48aNI0dHRyouLqbk5GQqKSmh4uJi0tTUpOvXr1N0dDQVFBSQo6NjW6v6Q1FUVESXL1+m0NBQunz5MqmqqpKTkxP16dOHCgoKqLCwkBQUFCg2NrZa2igiovj4eL4HRnFxMQkKCtIvv/xCM2bMoA0bNhAR0ZEjR0hOTo4ePXpEIiIilJSURKmpqTRkyJB255HRVFgsFomIiNRZpqysjJKSkkhTU7NBXksCAgI0f/58CggIaJCh+unTJ5KXl6fXr1/T6dOn6cWLF/Wuy8DwvSEgINCinpMTJ06k48ePk6ysLHXt2rXaweVyqbi4mIqKiviHmpoa9erVq1m87zoCzI7qd0BUVBRNmDCBbt++Taqqqm2tTjUA0MuXL6m0tJR0dXWJzWYTAEpMTKTQ0FC6fv06JSQkUEFBAfXv358cHBzIxMSETExMSF5e/pvy09PT6cyZMyQkJEQaGhqkoaFBCQkJFBAQQFlZWeTt7U0DBw4kQ0ND6tSpEyUmJpKLiwuZm5uTr68v6ejoEBFRRkYGjR49mmRkZMjJyYmWL19Os2bNoiNHjlBycjKlpKSQp6cn5eTkEADKzMwkKSkp8vb2pkmTJtVoaOfl5ZGNjQ1ZWlrS7t2722SiBYA+fPhA2dnZlJOTQwEBAeTg4EBz5swhov924n18fEhNTY0OHjxIERERFBgYSJGRkTRnzhx6+vQp3b59m4KCgmj48OFVZJeXl1PXrl2psrKSVq5cSQYGBqSmpkZqamr1Mho9PT0pIiKCiIjvCicoKEidO3cmAQEB4vF4VFxcTBISElRQUEAKCgqkoqJC2tra9Mcff1BiYiL5+vpSSkoKAaBTp07RwIEDm+27i4mJoREjRlBiYmK9fovNwYcPH8jIyIieP39exd2qoKCA9u/fT1u3bqX09HRat24dLViwoFV0amvKy8vp8OHD/BzFfn5+ZGNj890ZLu2Z27dvk4eHB3Xv3p2GDx9OQ4cOJTU1tXrXLysrIzExMdLU1KTJkyfT3Llzic1m06pVq2jz5s3Us2dPiomJofz8fHJxcaGioiKSl5enu3fvkry8PImIiNCWLVtqzd/8PVJcXEzOzs6UnJxMOTk51LlzZ9LV1SUdHR3S1dUlXV3dOl+72b59O925c4dOnz5d7zaVlJQoNjaWfH19SUtLi1atWtVc3WFg6HCMGTOGiouLaeHChdSvXz/Kzc2lqKgoun//PhUUFFBZWRmVl5dTZWUlaWho8O9LHR0dYrFYlJmZSRkZGZSRkUGamppkYGDAl33z5k2aPn06JSUl0efPn+nNmzf05s0bSklJ4f8/OzubREVFSVxcnMTExEhUVJQ/H+3Tpw/Z2NiQjY0N9erVq0GvdrUHmDyqPwj5+fkYPXo0/Pz82lqVJlFZWQkFBQX069cPCxcuhK2tLaSlpWFsbIwjR47UmLPt7t27GDFiBKSlpTFx4kR4enrCzs4OWlpa6N+/P/7+++96h9H/SmlpKby9vaGrq4vExEQUFxdDUFAQMjIyUFFRwaxZs/gyvwYMcnNzg7S0NNzd3XHgwAEkJydXCfqSk5ODXr16wcfHB58/f0ZkZCSSkpJaLbhSZGQkiAiqqqqwsbHBqFGj+FFUV65cCUFBQezcubPad/X06VNoaWlhw4YNiI6OhpKSErZu3VpN/q1bt7B582Z4enrCysoKRMRPn/P69es6++nu7o6DBw/yP/N4PLx79w5hYWHw9fUFh8PBwoUL8erVq2/+LW/fvg15eflmT4uzaNEijB49ulll1kVFRQWmT58OKSkpjBs3Dtu2bcPYsWMhKysLFxcX3Lt3D/3798f169dbTae2oqSkBHv27IGGhgbs7OyqRJ5kaB1KSkqwYMECKCkp4eLFi1WuvXz5EgMGDMDly5e/KWfRokUQFxev8ZqYmBhWr16N2NhYDB48GMLCwujUqRPmz5+PwsJCVFZW4uDBg1BRUcHo0aNx7tw5JCYm1pnL+Xvg4sWL6Ny5Mz8/5YcPHxAeHo7du3dj7ty5sLe3h6SkJAYPHoxDhw5Vi8BqZWUFDQ0NODs7fzO3K/BfUEUhISGUl5fj559/rjXiOwPDj8KHDx/g7++PHj16QFJSEpKSkhg0aBD8/PywdetWBAYGIjg4GPv378eKFSswevRoGBgYoHPnzmCz2VBRUYGJiQl+/vlnyMnJITAwkD8nSkpKApfLxYMHDxqsV3Z2NkJCQjBnzhwYGRlBUlISw4cPx61btzpM1GJiov42P7m5uTXmRGtNeDwevnz5gry8POzevRuKiooYP348srOz21Sv5kBTU7NKugkej4fLly/DxsYG6urqCAgIQGxsLDIyMrBq1SooKipiz549LZID9n9v9Pj4+GpRbf8/GRkZ2LVrF8aOHQtlZWUoKipi9OjR2Lp1KyIjI5GWlgYrKyuIiIjAzMwM6urq0NDQgI+PD54/f94g3fLy8vD3339j5syZMDMzg6amJiQkJGBgYIAZM2bg6NGjSE1NrdKHdevWQV1dvVqE1Pj4eDg4OEBdXR27d+/GiRMnsGLFCoSGhqK0tBT//vsviAhcLhcpKSnQ09PD+vXr69SPw+EgKysLmzdvhoCAANzd3WuN4jl69GicOnWqVlnv37+Hh4cHNDQ06jX4hoSEgIgQHR39zbL14fHjx/D29gYRISUlpVlk1pfc3Fxs27YNU6dOxYEDB/gRiT9//gxpaelv/iY7Munp6fDz84OCggIcHR0RGRnZ1ir9kDx8+BCGhoYYNWoUsrKy+OdXrFgBVVVVfkozFosFDw+PWiMwP3/+HCIiIpg3b161a0uWLAERQVhYGCwWCzo6OggICKhxcbKoqAi///47hgwZAj09PYiIiEBOTg4WFhYYN24cAgMDO1SUzvoQHBwMeXl53Lhxo8br+fn5OHr0KIYOHQpZWVmsXLmSPx/Iy8vjpwOSkpLC48eP62wrNTUVCgoK4PF42LZtG8zNzeHv74+zZ882eNGXgeF74+PHjw3KIfz/5ywvX76EkZER3N3d+ePUyZMnoaysjIyMjCbplpWVhb1790JbWxuWlpa4dOlSk+S1Boyh2gLMnj0bEhISWLx4caun21i8eDHYbDaICEJCQhAXF4e9vT3i4uJaVY+W5P8bqv9LTEwMJk6cCBMTE0hLS8PR0REfPnxoZQ3rB4/HQ0pKCg4dOoTp06fD3NwcoqKiUFdXh6KiIjp16oTevXvj+PHjWL58OdTV1assNBQXFyM1NRWxsbEIDQ1FcHAw1q1bBx8fH/Tp0wfi4uKwsLDA1KlT8euvv8Lb2xsTJkxAcHAwNm/eDGdnZ8jLy0NcXBw//fQTRowYgUGDBmHUqFG16nz37l0MHz4cI0aMwLJly2BtbQ0OhwN7e3tIS0vzk8Vv27YNHh4edfb/azJwAwMDPH/+HK6urjAzM8P79++rlR00aBAuXLjwze9UVFT0m7snFRUVUFRUhLCwMPz8/BAZGdnohNdnzpyBubk5lJSUsHz5ciQnJzdKTnPz6dMnTJo0CVOmTGlrVVqEhIQEeHh4QFpaGl5eXlVSYzC0HhUVFVi/fj3k5ORw6NChahMuWVlZWFpawszMDCwWC0pKShAUFISgoCAEBARARBAUFISlpSW8vLwgICAAMzOzGu9HW1tbCAoK4uDBgw3OJ/p1lzEqKgqHDh2Cs7Mz5OTksGLFinaxkJObm4vdu3ejT58+6NmzJyZMmFDrghuPx8OdO3fg6uoKExMTJCUl8c+PGzcOlpaW32zv5cuXmDp1KmRkZDB//nz8+++//GsHDx4El8vFyZMna61fWFgIQ0NDzJgxA58+feKP92w2G7m5uQ3sPQMDw/+nqKgIEyZMgIGBARYtWgRFRUX06dMHb968aRb5FRUVOH36NNhsdrtP88UYqi2Al5cXli1bhtmzZ0NGRgYzZszA69evW7zdK1euQE1NDRkZGd91zsC6DNWOTnl5OV6+fIn379/jy5cvCA4ORpcuXTBp0iR4e3tDRUUF6urqEBMTA5vNhrKyMkxMTODo6AgPDw8sWbIEW7ZswcaNG6GgoABTU1MMHDgQo0ePxsqVK+Hv7w8tLS3Y2dkhMzMTwH9GTXx8PM6cOYOtW7d+c8XuwoULmDBhArp16wYPDw8cPHgQe/bsqbKT4uDggLNnz9Yph8PhoF+/fvyJDY/Hw7x58zB8+PAq5Z4/fw5ZWdkq8mvi0qVLkJOTq9dOSV5eHkJDQzF//nz07NkTEhIScHNz40/66svKlSshKioKFxcXREVFtakrDY/Hw9WrV+Hq6gpJSUmMGTPmu8mlmpmZibCwMAQEBGDAgAFQVlbG+vXrvwsPkY7Ky5cv0bt3bwwYMACpqak4f/48OnfuDA6HA11dXQwaNAgCAgJ81/NHjx5BT08PLBYLDg4OuHfvHv79918cO3YMFhYWkJKSwubNm2ttz8zMDA4ODs2m/4sXLzBjxgxIS0tj2rRp/NcQvsXs2bNhbW2NmTNnIjAwEBEREY0yzsrLy3Hp0iWMGTMGUlJScHFxQVBQEDgcDlauXAkej4ezZ8/C398fa9euxapVq7BkyRL06NED2tra2LZtG4KDg8HlcnH8+HFMnjwZZmZmDdpxeffuHebNmwcZGRl4eXnx5ykXL14Ei8Wq04j//PkzBg8eDDs7OxQWFmL16tWYOnVqg78HBgaGmuHxeNi/fz8WL16Mp0+ftkgbIiIiKCoqahHZzQVjqLYAXl5e2L17N4D/XD2XL18OWVlZuLu74+3bty3W7i+//AIOh4OZM2ciIiKiw/ifNxQNDY3v1lD9/7x//x7jxo2DtLQ0IiMjER8fj9evXyM/P7/Wv29ZWRkkJCQwadKkGhcsKioq4OvrCw0NDTx48ABpaWm4e/duvX+bUlJS2LhxIx4+fIiAgAD07t0bMjIyOH/+PBISEhAdHQ1xcfFvulpfv34dJSUlVc75+vpi2bJlVc4NGzYMf/zxR52yYmJiICcnV29X3n379sHZ2RlaWlrw9fXFmzdvsGHDBigoKGD48OENehckNzcXW7ZsgZaWFnr27Ing4OA2SaC9f/9+aGlpYdeuXR3SQE1JScGRI0dw6NAh7Nu3DwsXLoSDgwMUFRUhJSWFvn37YtasWThx4gTKysraWt0flsrKSuzYsQOysrLYunUrf4yxtraGjo4O1qxZg7Fjx8LU1BS6urrV7vHg4GCw2WwoKSk1aGFo0KBBYLPZCA4Obtb+ZGZmYvXq1ZCXl4eTkxNu375d57Pz5s2bEBQUxMKFCzF16lRYWFhAXFwcysrKCAoK+mZ7SUlJWLhwIRQVFWFpaYndu3cjNzcX+fn5UFNTg76+Po4cOYKRI0fCyMgICxcuxLJly+Dn54e1a9fi6tWrVcb1uLg4qKmpYciQIY12Z87MzISvry9/nmJiYoINGzZ8s15paSmkpaWRlJQEKysrHD16tFHtMzAwtA2MocoYqnw+f/6M1atXg8PhYMWKFS0W3OH169dYt24ddHV1YWNj02xuAu0JDQ0NzJgxo63VaHH27t0LYWFhGBsbIz4+nj954vF4yMvLw9u3b/Hw4UOEhYVhzZo16N27N37//XfweDz+xKFPnz61GownTpyAtLQ0lJWVYW5uDi6XCzs7Oxw7dow/uayoqMCbN28QFhaGW7duISoqChoaGkhISKgia9WqVbCysgKHw0HPnj0bvZBgaWlZJfjPxYsXoaGhUW2yW1NfNDQ0+LvEdREREQE1NTUcO3YMDx48wPjx42FqagrgP3ebbdu2gcvlIiYmpkG6V1ZW4sqVK3BycoK4uDi6d+8Od3d37N27t8U9HAoKCqCkpITY2NgWbaclyMvLw+LFi/lBoNzd3eHh4YH169fj4sWLSEtL+24X3Toa7969g52dHSwsLPD8+XPcv3+f78JLRPUOUPbp0ydYWVmBxWLVO9hXZWUlfHx8ICAggF69ejX7YkxxcTF2794NbW1tWFhY1Pns9PPzq+L5UVlZiYcPH4LL5dbohv727Vv88ccf6NGjB5SVlbFs2bJqMQfKysqwf/9+LFmyBK6urli6dOk3x72vlJSUNMsYk5eXh/Xr12PZsmX1uucuXLgAMzMzAMC1a9egqqra5vE5GBgY6o+wsDBjqLbE0REN1a+kpaXBzc0NysrKOHToUItNYCsqKuDv7w8ul1tnEJqOyI9iqN67dw9LliyBo6MjlJWVISEhAQ6Hg06dOkFcXBwqKiowMjJCv3794OPjg9DQUJiYmMDDwwOPHj2CiYkJhg4d2qDJzokTJ2Bvbw9ZWVkYGRlBREQEqqqq6N+/P/r37w8rKyuYmZlVc5P79ddfQUQ4duxYk/o8d+5c2NjYoLi4GHfu3AGXy8WdO3fqVffrO7PfmmA5ODhg//79/M9paWlQUlKqUubMmTPQ1dVttIFUXl6OxMREBAcHw9LSEhMnTmzRICOrVq2Cm5tbi8lvKTIyMqClpYXJkydXeU+Oof3x+fNn6OnpYeXKlfwARioqKujTpw+KiorqPc78L/Ly8li+fHmD6jx9+hSSkpLV7tnmoqKiAgEBAVBRUanV3c7d3Z0fQb+yshKbNm3CpEmToKCgABcXFwD/jQE7duxA7969ISsri+nTpyM8PPy7CTb06dMnGBgY8Mf8Fy9egMPh1CuWAAMDQ/tAWFiYeUe1JY6ObKh+5e7duzAzM4OFhQXu37/fYrokJCRAQUHhu3p4/CiG6v8nJycHmZmZdbo9FhYWYvjw4RAVFUVQUFCjDa3U1FTExcXV25Vs9erVMDY2bvLCS0VFBcaNG4f+/ftDTk6uQalV0tPTIS0t/c0+9+3bl38/vH37FoMGDcKYMWOqlOHxeDA0NER4eHjDO/H/KCoqwsCBAzF27NgWcVl9//49OBxOi75W0BIUFRXBwsICK1eubGtVGL5BRUUFhgwZUmXcXbt2LQQFBZu0s6moqIjFixc3qM7NmzfBYrHqlUalKQQGBsLKyqra+bS0NAgLC/Pd+3Nzc9G5c2cEBwcjIiKCv6N44sQJGBoa4uLFi40O1tZeKS4uhrW1NebOnQsej4e7d+9CXl6+2d2yGRgYWpbvyVDtWNlhOwBWVlYUExNDW7ZsoT59+pC9vT3JysqSnJwcycvLk5ycHMnJyZG+vj517dq10e0YGxtTaGgoDR48mNzd3Wn+/PmkqqrajD1pff773f541Jas/X8RExOjkJAQ+vTpE8nKyja6LTU1NVJTU6t3eVdXVxo5ciQJCAg0uk0iok6dOtHBgwdp9uzZtGDBArK3t69XvczMTFq2bBn16tWLWCxWnWXHdJFK4gAAIABJREFUjh1L48aNIx6PRyUlJTRixAg6cuRIlTL37t2j9PR0UlBQaHRfviIqKkqhoaE0evRocnFxoQULFpCMjAxJS0uTnJwcsdnsJsmPi4sjQUFBys3NJXV19Sbr2xpUVlaSu7s7aWtr06+//trW6jB8gxUrVlBRURFt376diIjy8/Pp119/pWXLltVrXKoNAQEBqqioqHf5zMxMGjJkCLm6utLs2bMb3W59cHBwIH9//2rnFRQUqHfv3rRkyRIKCgoiAQEBEhAQoJEjR5KMjAy/XHh4OE2ZMoWGDBnSonq2NhUVFeTq6kpqamq0ZcsWYrFY5OfnR3/88Qd5eHi0tXoMDAw/KIyh2gIICAhQt27dqKysjOLj42n9+vWUmZlJWVlZ9PTpU8rKyqLY2FhasmQJzZ8/v1FGQFlZGWlqatKtW7for7/+ImNjYxo7dixt376dBAU77p/1W8bIj4yAgECTjNTGoKur22yyhISEaM+ePfUqW1hYSFu2bKHt27eTu7s7HT9+/Jt1vLy8yMnJiWRkZEhQUJCEhYWrXH/9+jU5OzvTn3/+Sfr6+o3qw/9HWFiYQkJCaMGCBbRkyRLKy8ujDx8+kI2NDf39999Nkj1s2DDKz88nW1tbys3Nbff3RnFxMS1cuJA+ffpEx48fb/f6/ugcO3aMTp48SbGxsSQkJERERC4uLiQjI0O//fZbk2Q31FBNS0uj0tJS8vHxqbPc3bt36e+//6bMzEzq168f2dnZkYaGRoN0e/bsGZWUlFQ7z2az6dy5c2RjY0NBQUE0Y8YM8vLyIiMjI5oyZQo9efKEYmNjqaysjKKiohrUZnsHAHl5eVFZWRn9+eef9PTpUzp27Bg9ePCAzp8/39bqMTAwNJDvaeOn41o07ZxLly4R0X8rxS4uLiQqKlrl+tu3b8nNzY1u3LhBBw8eJEVFxW/K/PLlC129epVOnz5Nly5dIkFBQfr06RM9ePCAli9fTmPHjiUfHx/atWsXM0lkaPc8evSIBg4cSO7u7jR79mxisVj05MkTevjwIQUFBdGAAQMoNja23p4HnTp1qtWrIDs7mxwdHWn16tU0dOjQeutYXFxMr169ouTkZOrcuXONddlsNu3YsYP/ef78+VV2YJqCrKws6evrt+v7uaioiIKCgmjTpk1kaWlJISEh1Llz5xZpq6SkhF69ekUvX76kN2/ekIODAxkbG1crx+Pxqi0ApqWl0e+//04XL14kCQkJkpaWprt379Lw4cPp3LlzLaJve+X+/fs0b948CgsLIy6XS0T/eRtcv36dbt682WT5AgICVF5eXu/ypqamZG9vT0OHDqWsrKxaF29Xr15NERERtHjxYgoPD6cVK1aQiIgI2drakq2tLQ0YMIC6dOlSY92ysjJasWIFHT16lA4fPkwvX74kPz8/AkBKSkrk4eFB+vr6VFpaSkpKSsRisSggIICcnZ0pNDSURo0aRf7+/tS1a9d2fT82Bl9fX0pKSqLw8HDy9PSkf/75h8aNG0dRUVEkIiLS1uoxMDA0gu9lnGqaP98Pyvv37+nAgQP08ePHWsvs27ePHj16RBMmTKhxZVlDQ4MiIiLIwsKCevbsSVeuXKlRDo/HoytXrpCbmxspKirStm3bqE+fPvT8+XPKysoicXFxunr1Kh0+fJjMzc1pz549FBQU1Gx9bW2+lxuL4dvs3buXXF1dic1mk7m5OQ0YMIACAwMpPz+fLl68SEePHm2Se/z/snjxYho8eDB5eXnVu87Hjx+pS5cuNG7cODp8+DDNmzePVq1aVedKZVFREZ07d67Z3AJDQkLI2dm5WWQ1NwUFBeTv709aWloUExNDV69epb///rvZjPT/T3R0NCkoKJCrqysdPnyYUlNTaeDAgbRq1SoqKysjIqLS0lIKCAggeXl5MjAwoN9++40iIyPJy8uLevbsSZKSknTz5k06efIk/f777yQsLEza2totom975cOHDzRy5Ejat28fGRkZEdF/zxlnZ2eytrYmW1vbJrfRo0cPCgkJaVCdCxcuUGlpKbm7u9da5tKlS+Tr60tBQUHk6OhIHz58oIsXL1KPHj3ozJkzZGBgQPr6+jR79mwKCQmhuLg42r59O40cOZKUlJToxYsXdP/+fYqNjaXevXtTz549afjw4SQtLU329vZkZ2dHXC6Xhg0bxm+zX79+tHHjRnJzcyMtLa3v7hnF4/EoPDycHj9+TKamppSYmEjJycnk7+9Penp6ba0eAwPDj059XmRtraO9B1OytbUFEUFGRgZE1CwpI3g8Hnx8fCAoKFglR2NJSQn27dsHPT099OjRA4GBgdWSdH+tO23aNMyZMwcLFy6Er68vbty40WS92gJ1dXV4e3u3tRoMrcD9+/fB5XJx+/ZtlJWVtWialw8fPkBGRqbBwWGuXbuGAQMG8D9nZGSgR48emDVrVo36FhYWwsbGBpMnT262tCv+/v746aefkJWV1SzyGktFRQViY2OxYcMGDBw4EBwOB0JCQnB1dW1QzszGUlhYiG7duuHs2bNVzr9//x5Dhw6FoaEhtm7dCg0NDQwdOhSJiYmIjIyEj48P9PT0sGzZshq/QyUlJbx8+bLF9W8vFBcXw9zcHGvXrq1yfsKECVUCCTWGsLAwHDhwAB4eHlBTU4OQkBD69euHyMjIessIDQ0Fi8XC3bt36ywXHx8PQ0PDagGbKioqEBcXh40bN2Lw4MEwMDDAtGnTcOTIEbx79w4xMTEwNDTE4MGDqwUpy83NxYoVK/D48eP6d/o7ori4GPfu3WvzsYaBgaHpsNnsRkVsb02Iifrb/IwePRpEhNu3b4PL5eLQoUOIj4/HwYMHsWfPHjx58uSbE9QPHz7gxo0bOHToEPz9/TFkyBAYGBhUM3rd3d0hKiqKy5cv/zC5BhlD9cfg06dP0NTUhJubGyQkJEBE2L59e4u0VVpaitjYWIiJiSE5OblBdc+fPw8tLa0qE9q8vDz07dsXbm5uVSJ+fjVSPTw8mtXo5vF4WLp0KYyNjZGdnd1scutDeno6du7cCWdnZ8jIyMDAwAA+Pj44f/48MjIyWjyH7P+ydu1aSEhIYPny5Th69CgSEhL4D2Eej4cjR45g2LBhuH37doPkjhgxAkOHDm33+eaag8rKSowcORLjx4+v8kw5f/48WCwWLl261GjZjx8/BhGhW7duCAwMxNOnT/n5Q9XV1fHzzz/Xa6EoIyMDAgICOHfu3DfLfo2wXx8qKirg4+MDRUVFHDt27Id5pjIwMPyYsNlsfPnypa3VqBPGUG0BvLy8wGaz4e3tjcTERHTt2hWGhoYYO3YsJk6cCE1NTWhra9cZsn7AgAEwNTWFm5sbFixYgMDAwBp/TJmZmXB2doaRkREePXrUkt1qNzCG6vcPj8eDs7Mz5syZAwcHB5w7dw6zZs2ChoYGDAwMEBYW1mCZ165dQ9++fWFlZQVzc3OYmJhAX18fsrKyEBISgrKyMnr16oWbN282WNeAgAB06dKlyg5PcXExRo4cCW1tbRw/fhwFBQWwsbHBpEmTWiSXIo/Hw6JFi9CzZ88m7Xg1hI8fP0JLSwvjx4/HkSNH8OHDh1ZptzaysrJw+vRprF69GmPGjIG+vj5ERUWRkZHRJLllZWVwd3eHtbU1cnNzm0nb9smCBQvQr1+/Ks+bnJwcCAsLY9KkSY2W+/btW6ioqODIkSM1Xv/y5Qvs7Oxw/Pjxb8r6+eefoaKiUq92k5OToampWe18ZmZmNUO0uLgYcnJyuHbtWr1kMzAwMHRkGEP1BzVUZ8yYAT09vTpXhq2trbFjx45a8ypaWloiKiqqXu3xeDz89ddf4HK5iIuLa5TOHQnGUP3+iYqKQteuXVFQUAAJCQlkZ2cjOTkZO3bsQEhICFRUVDBkyJB63yOFhYVQVVXFX3/9haioKMTExODBgwdISkpCVlZWs+z6Xbx4EVwuF9OnT0daWhr//M2bN2Fubg4JCYkWM1K/wuPxMGzYMOzdu7fF2vhKXl4ejI2N8dtvv7V4Ww0hLi4Of/zxByZOnAhTU1Ow2exmcdvNyMgAh8NBUFBQM2jZPtm5cyd0dHSqPbsMDQ2hqqrapPvExMQEAQEBNV4rLy/Hzp070aVLFxw4cKBOOVlZWWCxWAgNDa1Xux8/foS8vHyVc9u3bwebzUb37t3h7+9f5XWZP//8E6ampi16nzIwMDC0Nc+ePUOnTp0YQ7UljvZuqKakpFR7T/T/888//8DKygocDgeTJ09GQkJCleumpqYNfrf1yJEj0NbWxt69e/HixQv+anFpaSlev37NT0R+7949LFq0CNra2iAijB8/vkHttDXq6uqYNWtWW6vB0ELk5OTA3NwcO3bsQEJCAnR1dauVKSkpwe7du6GpqYnevXvjyJEjdQ62vr6+GDduXK3XY2NjsXfv3iYP2NnZ2Vi6dCk4HA58fHyquJ0+fPiwVSa/K1euxOrVq1u8nc2bN2P06NHtzj1y/fr1kJCQgICAACZNmtRgV+6aeP36NbS1tbF8+fJ219/mIjQ0FF26dMHr16+rnE9KSgIR4eHDh02Sb2BggPj4+Grn3759iz59+mDAgAG4d+/eN+UUFBSAzWbXe3c3Pz8fYmJi/M83btxA586dkZycjKioKEyePBlSUlIYOXIkLl26hJMnT4LNZuPq1av17hsDAwNDeyY+Ph4XL15EbGws4uPjMWnSJHC53FoXD9sTjKHaxqSlpWHjxo2Qk5PDmjVrEBsbi5iYGCgoKDQ4WAOPx8PRo0fh7u4OFRUVKCoqQklJCUJCQlBTU4O0tDScnJygpKSEFStWQFNTE0pKSt80qtsbjKH6/ZKSkgJdXV0sWLAAlZWVCA0NhaOjY63ly8vLERISgoEDB0JRUbHW37KzszPMzc1x7949vHjxAnfu3EFISAj27NmDNWvWoE+fPpCRkYGamhqCgoLqdMuvD+np6XB2dsbgwYNb3bAJDAzE9OnTW7ydNWvWYPny5S3eTmMoKSlBSEgIXF1dISkpiSFDhjRqEaK4uBjHjx9Hly5dEBgY2AKatg/OnDkDLpeLmJiYGq8rKCjg559/brT8iooK9O7dGydOnKhy/sSJE5CTk8PGjRsbtFt7+fJlsFgsBAcH16ttQUFB/PXXX8jPz8eXL19gb2+PGTNm8Mt8/vwZe/bsgbm5OaytrXH69GlmR5WBgeG7YenSpSAiaGpqQktLCytXrmy1V4SaCmOothPS0tLg4uKCXr16QU9PD1u3bm3SBJfH4yElJQWpqakoLy8H8N/keevWrXj16hWA/yYn6urqOHnyZLP0obVQU1PD7Nmz21oNhmbg+fPn2LZtG/z8/DBnzhwoKytj69at/Ou7du3C1KlTa6z74sULrFmzhj/BtbGxwapVqxAYGIjCwsIqZSsrK7F3716oqalBS0sLVlZWGDZsGKZOnYply5YhICAAL1++RHR0NAYNGgR1dXX8+eef/HunMZSXl0NfX79R79M2hYiICHTv3r1JuteHJUuWwM/Pr0XbaCp///03FBUVsWTJknqPpxUVFQgLC8PkyZMhLS0Ne3v773Z3raKiAkuXLoW6ujoePHhQ5dqtW7cwYMAADBo0CAICAtizZ0+j2khPT4etrS1sbW35LsX5+fnw8PCAtrZ2tXbry8SJEyEpKVmvshcvXsSwYcMgKSkJNzc3nDlzBkpKSo1ql4GBgaGjwePxsGzZMnTv3r3DbU4xhmo7hsfjgcViIT4+Hnl5eXjx4kWzt7F582ZMnDix2eW2JIyh+n1w9uxZcLlceHl5YdWqVdi6dSsiIiL412/evAk5OTlcunSp2m7LlStXICcnB11dXSxbtgw8Hg9btmyBk5MThg0bBm1t7SYFF7tz5w769u0LHR0dnD59utZyt27dwsePH2u9HhQUhGHDhjVaj8bA4/FgbW2NgwcPtlgbRUVFUFRUbLI7aEtSXl4OAwMDsNlsmJqawsPDA5s2bcLVq1fx/v37KoYrj8dDQkICFi1aBGVlZfTo0QObNm3Cv//+24Y9aFmys7Nhb28PW1tbZGZmVruuoKAAVVVVWFhYNPoZERERAWVlZaxYsYK/Q5mcnIxu3brB09MTBQUFjdbf0NAQ/fv3b1CdrKws+Pv7Q05Orkk7xAwMDAwdEW9vbyxYsKCt1WgQjKHajikqKgIRQVJSkp+X1d7eHqGhoTVOLOoDj8fDhg0bMHnyZMydOxeysrK1RmFsrzCGascnNjYWCgoKuH//fo3XY2JiICgoCDabDTabjdGjRwP4L4fhggUL0KVLF9y5cwcZGRnQ19dHly5dMGbMGGzbtg2LFi2CpKQkjh071iQdeTweNm3aBBEREXz+/LnGMnPmzIGwsDBmz55dJYDSV4qKisDlcvleDK3F7du3oaysjNTU1BaR7+fnh1GjRrWI7OYmLy8Pd+/exd69e+Hj4wNbW1vIy8tDWloaffr0wcSJE9GtWzeoqalh6dKlrZLvta2JiIiAhoYGFi5cWOPO+9c8pQ3NKfyV0tJS+Pn5QV5eHpcvX+afT01Nhbq6epMDUpWWloKIsGnTpkbVLywsbHIkaAYGBoaOxrt37yAjI9PqaeyaQn0NVUFiaHUEBQVJT0+Pnj17RpKSkiQnJ0fS0tK0efNm+ueff8jc3Jzu3btXp4zo6GgKDw+njIwMsrOzo3PnztGzZ89o8uTJ9O7dO4qPjyc1NbVW6hEDw3+8efOG+vbtS6ampjVeNzIyorCwMFJSUiJJSUnS0dGhNWvW0I4dO2jEiBEUHx9PioqKRET0+PFjevv2LUVGRlJkZCSx2Wx69uwZKSkpNUlHFotFgoKCJCkpSWFhYTRixAhisVhVymzdupUEBQUpICCA9uzZQ5MmTaKlS5eSlpYWERGJioqSubk5xcbG8s+1Bv369aOFCxeSg4MD3blzh+Tk5JpFbmFhIc2ePZvu3btHly9fbhaZLY2UlBRZWVmRlZVVlfNZWVn05MkTSk5Oprlz51LPnj2r/X2/JwBQeHg4rV27llJTU2nTpk00atSoGssuWLCAbGxsiMPhNLidxMREmjRpEikpKdHDhw/592FOTg7Z2dnRvHnzyMvLq0l9YbPZNHHiRFq0aBHdu3ePTpw4QQICAvWuLyYmRmJiYk3SgYGBgaGjoaKiQiNHjiRfX1/avXv39/XMq48121rHj7KjCvz3bl1FRQV4PB6OHTsGIyMjSEtLQ0dH55v55j59+gQOh4PFixfjjz/+QN++fTF69Ohq7+91NFRVVZkd1Q4Ij8fDpUuXsHbtWvTt2xdeXl71rjt//nw4OTnhyZMn/HPh4eHw8fHBvHnzMH/+fFy/fr0l1EZ4eDj09PTg6OiIO3fuVHvXkcfjYd26dZCSkoKbmxtkZWXh4uKCkJAQ5OTkQFJSstEeEE1l+fLlMDU1RX5+fpNlJSYmQldXFx4eHh1+DPmR4PF4uHDhAiwsLKCrq4tDhw598/1lQUFBXLp0qUHtlJeXY+3ateByudi/f3+1+8THx6dKAKPm4OrVqxATEwOXy20x7wEGBgaG74msrCxYWlq2SnaA5oAY19+OBY/Hw6tXr+oVGGTVqlVwd3dvBa1aF1VVVcyZM6et1WBoADweD/Pnz4euri4WL16Mv/76q0nv/506dQpycnL4448/sHbtWhAR/ltPq5vMzEyEhYU12HArLS3F9u3b0b17dxgaGmLXrl3V3IH37NkDRUVFXL9+Hbt27YKtrS2EhYUb/B5dc8Lj8TB9+nTY2dk1KfXOixcvwOFwWvS9V4bmJyoqCsbGxjA2Nm5QJFs2m13vPKVfWbhwIfr371+jwZiamgpZWdkWcbd99eoViAi3bt1qdtkMDAwMzUF5eTnevn2LqKioVh+reDweHj9+XGXBPCIiApaWlq2qR2NhDNXvlODgYKiqquLt27dtrUqzwxiqHYuvRqqJiQlyc3ObLC8vLw+dOnXCP//8g4SEBOjr62Po0KFV8mVmZWVh+PDhGDt2LH/3b8uWLeBwOLCwsICYmBhMTU0xf/58HDt2DPfv38e9e/cwZswY6Onp4ejRozWmy+DxeAgLC8Po0aMhLS0NT09PHDlyBK9fvwaPx0NoaCi4XC5/NyozMxMfPnxocp+bQkVFBUaNGoUpU6Y0WkZ6ejqkpaVRXFzcjJoxtCSxsbGQk5PDqVOnGhxBXkREpEHveFdUVEBRUbHWgH/JyclQUFBokUjUFhYWUFdXb3a5DAwMDM0Bj8dDv379oKSkBG1tbXC53FZJW5eXl4edO3fC0NAQqqqqkJaWhoKCAgYOHIghQ4bAycmpxXVoDhhD9Tvk0qVLUFJSapEowe0BxlDtWCxbtgwmJiaNDsxSE1OmTIGIiAikpKRw8ODBaoO+vb09vL29QUQQExMDAJiYmPDTjJSUlCAiIgJr167F6NGjYWxsDC0tLWzatAnXrl2DqakpLCws6ryH/v33X2zZsgWjRo2CkpISFBQUMGLECAwbNgz6+vrN1tfmID8/H7Kysnjz5k2jZdja2iIkJKQZtWJoKZ4+fQoFBQVcuHChUfXFxcUblI4mPDwcPXv2rLOMmZlZs6b5qayshKWlJdhsdqNT3DAwMDC0NDdv3oSOjg4qKirg5+fX4q+uxcXFYdq0aZCWlsaYMWMQHh4OHo8HHo+HtLQ0XLp0CVu2bOkwUe3ra6gywZQ6CADIz8+PAgMDqXv37m2tDsMPTkREBB06dIgePXrUqMAstbF//37av38/Aag1GICAgACJiopSUlISERF169aNcnJyiIhIWFiY+vbtS3379q2x7sCBA2n37t3Up08f2rBhA3l6elZrR0lJiX755Rf65ZdfCAClpaVRdHQ0RUdHExHVqVtrIyEhQZ6enrRt2zbaunVro2SYm5vT/fv3ydnZuZm1Y2huzpw5Qy4uLuTk5NSo+kJCQvTkyZN6lz958iS5urrWWWb8+PF09OhRGjRoUKN0+l/KysrI2NiY0tLSKCkpiXnWMTAwtColJSX06NEjiouLo4SEBJo6dSpZWFhUKweA1q1bR8uWLSMWi0UHDx6ks2fPNqsuZWVldPnyZXrw4AHduHGD0tPTafr06fT06VPq0qVLlbKqqqqkqqpKgwcPblYd2gP1D6fH0KbEx8fTixcvyN7evq1VaVHaiwHAUDtfvnyh6dOn044dO0hWVrZF2qjtd7B7927Kzc2lpUuXkqamJhH9Z6i+evWqXnIFBARo1qxZdPv2bdq5cyctWrTom3qoq6vT2LFjadu2bXT+/Pl29xudM2cOHTp0iDIzMxtcFwBduHCBhgwZ0gKaMTQ3cnJyVFJS0uj6ixcvpp07d9I///zzzbLl5eV09uxZcnFxqbOcjo4OffjwodE6feXTp0+koaFBGRkZ9PLlS8ZIZWBgaBUePHhAU6ZMoZ9++olkZWVp5syZlJCQQAICArRq1apq5d+9e0dDhgyhgoICcnNzo9u3b5OEhAT17Nmz2XR6+vQpWVpa0qZNm0hAQIB+/fVXevPmDfn6+lYzUr93mB3VDoKBgQE5OjrSwIED6cKFC8TlcttEj/T0dPL29qaCggK6evUqderUqU30YGg71q9fT/r6+m2yA6elpUVHjx6tcq5bt25069atBsnR19en8PBw6t69O3E4HOJyuSQjI0MyMjLUt29f6ty5c3Oq3aKoqKjQ3Llzydrami5dukTa2tr1rpuUlERFRUXVUrwwtE8UFRUpIyOj0fWXLl1KV69eJU9PT3r9+nWdZfPy8qi0tJQEBeueJiQnJzfZqExNTSVjY2OSlJSk1NRUkpCQaJI8BgYGhvpw7949cnJyoqVLl9LMmTPJyMiIhIWFiYiotLSUNDQ06MmTJ2RgYEAAaN++feTr60s+Pj60ZMkSYrFYtH37dpo8eXKzLGLzeDzauXMn/fbbb7R+/XqaNm1au1scb20YQ7WDICwsTMePH6dRo0bR8ePHac6cOa3aPgA6evQoTZgwgYj+y1eXmppKXbt25ZeprKykBw8e0OXLl0lSUpK8vb1JRESkVfVkaFlevXpFu3fvpoSEhLZWhU+3bt1o3759Da7H4XDo2LFjdOPGDXrz5g3l5eXRnTt3aM+ePTRixIgW0LTl8PPzoy5dulDfvn3Jzc2NBAQEiMViEYvFIjk5OZo7dy6x2exq9Y4ePUrjxo1rUK5KhrZDQUGhSYbqV77mKq4LOTk5WrJkCU2bNo2uXLlS62SpqbufCQkJZGVlRdra2hQXF0dCQkKNlsXAwMBQX+7fv09OTk70559/1uhV1LlzZ5o5cyZt27aNli5dStOmTaOCggK6desWGRoa0rNnz8jDw4MkJCRo8uTJTdbn9evXNH36dCouLqaYmBjq1q1bk2V+DzCzkw6EgIAAqaurN8n1q7Hs2bOHb6Ru2rSJ3r9/X8VIJSLq0aMHTZ48mYqLiyk6Opq6d+9OBw4coLKyslbXl6FlOHLkCLm7u5OysnJbq8JHX1+fnj59SqWlpQ2u6+DgQBs3bqTg4GA6c+YMmZmZdVgvgenTp9PZs2dJSUmJFBQUSE5OjmRlZemff/6hgQMHUnZ2dpXyPB6Pjh8/Tm5ubm2kMUNDaQ5D9cWLFzW+c1UTixcvpuzsbFq3bt1/0RdrgMVi0eHDh+nGjRu1lvlKeXk5vX//npKSkigiIoICAwPJzMyMrK2tKSEhgTFSGRgYWoX4+HgaOnQoBQcH1/nqy4wZM+jUqVNkbm5Ojo6OdPfuXdLT06PNmzdTv379aPLkyXTjxg2SlpauV7tPnjyh9evXU1ZWFv9cRUUFbdy4kSwsLGjw4MF0584dxkj9X+oTcam1Dibqb1WKi4uxefNmODo6QkVFBf3798eCBQsgKysLb29vFBUVtYoer1+/hqysLMaOHYtffvml1nLm5ua4c+cO/3N0dDRsbW0hKysLLy8vRERE1Jga5CuqqqqQkpKCvr4+XF1dO0zZAsXzAAAgAElEQVTksh8JQ0NDREZGtrUa1bC2tuanjmkK9vb29Y5gevLkSWzevLnJbbY0lZWVWLZsGbp27YonT57wz0dERMDQ0LANNWNoKIWFhRAWFm5SCgRBQcEGRel99+4djIyMMGvWrBrztVZWVuL48ePo3r07Fi5cWO16QUEBTp06BTc3N8jIyKBLly4wMDCAtbU1LC0tISoqih07dtT5bGBgYGBoLh4+fAh5efl6R7s/c+YMP1NAdnY2+vXrh/79++P169e11vnfbAg8Hg/h4eFwdHSEoqIiXF1doaysjPDwcJSVlcHKygoDBw6sU973CDHpaTo+b9++5T/U7969i23btoGIkJ2dDTc3N5iamiItLQ3x8fHYtWsXNm7ciLKysmbVIS0tDSYmJti4cSN69eqF8PDwamWKi4tx/fp1mJubY9u2bdWup6SkYP369TAwMICamhrGjBkDGxsb6OrqQldXF/PmzUN4eDjOnTuHadOm8W9iFouFfv36tUiOPoaG8+HDB8jKyrbLCeWWLVvg6enZZDnOzs44ceLEN8udO3cOCgoK4HK5ePbsWZPbbQ0OHToELpeLXr16oWvXrhATE0NAQEBbq8XQQMTExPD58+dG1Q0KCgKLxUJpaWmD6uXl5aFnz574/fffay2Tk5MDZWVlHD58GMeOHcPChQsxYMAASEhIwMHBAbt3764x9/CLFy+gp6eHCRMmNLg/DAwMDA3h0aNHUFBQwOnTpxtcNy0tDXp6eli4cGGVeVBaWhq+fPkCAPjy5Qt8fX3RqVMnBAYG4sSJE+jVqxd0dHSwd+9elJSUAACuX7+OLl26wM7ODnZ2dq2Sf7W9wRiq3wk5OTmYMWMGpKWlISQkBCJCZWUleDwe1q1bh06dOkFPTw+enp4YOHDg/7F33mFRXd3bXmfovQ9NijSRDmJBERRFsSJiL1iDiYoFNZbYxQ4RNSrYUWNJyE+NorG3gAYEoqAoKlZEREGkCcPM8/3h63wvL22AcQb13Nc1l87Z7TnDlL32XnstdOvWTWw7rSdOnACXy8WaNWuQm5sLdXV1oSEsEAhw9OhR+Pr6QlVVFZ06dcLMmTOr7NjUxK1bt/Drr7/i/PnzSE9PR3JyMpYvXw53d3doa2tj5MiROHLkCAoLC5GQkAA1NTVYWVkJP9yfSExMhJOTE3R1dSW2s/ytk5GRARsbG2nLqJEdO3agc+fOTe5n0aJFWLRoUZ11srOzoaOjg6SkJISHh6NXr15NHldSPHz4EImJiXjw4AHy8vK+yR/HLx1LS8tG5dJOTk4Gh8PB0qVLG9y2sLAQXC4Xt2/frrPe+fPnYWdnh4EDByIsLAynT59GQUEBSkpKkJiYiOTk5CoLj3w+H2vXrgWXy210blgWFpZvm/z8fOzYsQNxcXF48OBBrZsbFRUV0NPTw/bt2xs8Rnp6OkxMTKp5Ud2/fx/y8vJQUFCApaUlLCws0K9fP1y5cgWamprw9PTE8ePHa1zgf/XqFUaNGoV///23wXq+BlhD9Svj7du3yM3NrXb9vz+QPB4PWlpauHfvXpPHmz9/PkxMTIRunkVFRVBSUkJRURHi4uLQrVs32NnZ4bfffsO7d++aPB7w0cVs27Zt6NWrF3R1dREbG4vc3FxoaWnBxsYGfD4ffD4f3bp1A8MwaNOmDbS1teHt7S2W8VnqJjU1FU5OTtKWUY3MzEzo6uoiNTW1yX0dOXIEAQEBddYJCgrCvHnzAADl5eWwtrbGiRMnmjw2C4so9OzZE8ePH6+3nkAgQHh4OMLCwsDj8aCmpobu3bs3asxFixYhKChI5Po8Hg8XLlzApEmTYGVlBSUlJbi4uMDe3h5qamro1KkTPDw8oK+vDysrKzx58qRRulhYWL5OCgsLkZKSgj/++AMnT55EYmIinjx5gtLSUmEdPp+P3bt3Q19fHwEBAejRowfMzc2hoKBQq1tvaGgoPD09q21+1MXff/8NLpeLAwcOVCtbuXIlpkyZgvLycmRkZCAhIUG4APzfWlmqI6qhykb9/ULQ1tau8fp/pw7Izc0lGRkZYQTGhw8fUk5ODnXu3LlBYwGga9euUY8ePahjx45ERKSqqkr6+vqkr69P7u7uNHz4cJo4cWK9qQsaQosWLej777+n77//npKSkmjIkCHk7+9P//77L7Vu3Zq8vb0pJyeHcnJy6ObNm+Tm5kY3b96k9u3b0/jx42n37t1i08JSnQ8fPgjDtjcXPnz4QEOGDKFly5aRi4tLk/tzdHSkn376qdbypKQkOnfuHN2/f5+IiOTl5SkqKorGjBlDnTp1Ii0trSZrYGGpiw4dOtCNGzeof//+tdbh8/kUEhJC8fHxlJ+fT1euXCE+n09nzpxp8HivXr2iLVu2UEpKikj18/LyyMnJiYyNjWnw4MF07NgxatWqlfC3Ij8/n27fvk0cDof69u1Ljo6OZGZm1mBdLCwsXz4fPnyg33//nTIzM+nRo0fCR1lZGVlaWlLLli2poqKCXr9+LXzIy8uTnp4ecTgc0tHRoZMnT5K7u7uwz1GjRlFxcXGVcYqLi+nOnTtkY2NDO3fupFmzZtGWLVvq1ZeYmEgBAQG0b98+8vPzq1L2/v172r17N+3atYvk5eXJ1ta2Sjmb9UJMiGLNSurB7qg2jdLSUigoKCAnJweLFy+Gjo4O9PT00LZtW8TFxeHdu3f49ddf8eTJEyxevBjOzs54//59jX0VFhbCwcEB69evF17LzMxEfn6+pG4H+fn56NevH9q3b4+//voLsrKy0NfXr7azfOrUKcjIyKBr164NPnvFIjq3bt2CgoICHBwcMGzYMGzYsEHq51VDQ0MREBAgNhdWHo8HU1NTODo6Yvbs2VVWRIuLi+Hh4YFdu3ZVazdlyhT2jB1LrXz48EFs79HTp0+ja9eutZaXlJTA398f3bp1Q2FhIS5evAgiwsKFCxs0TlpaGqZPnw5DQ0PMnz9f5HZ5eXnQ1NSst563tzfU1NRQVFTUIF0sLCxfB4WFhejSpQu6du2KJUuWICYmBn///TdycnJq/b4UCAQoLCzEgwcPcPPmzRrnIL1790b//v0xatQodOvWDRYWFlBSUoKbmxvGjh2LiIgIpKWl1asvLS0N+vr6NXpM8fl89OvXD8HBwewRmkZCrOvvt4mrqyt0dHQwePBgPH/+HMOHD4ejoyN0dXXh6uoKQ0NDaGhowMTEBH5+frX2c+PGDejp6SE5OVmC6qsjEAiwbt066OvrIzIystYgIsnJydDQ0IC2trZIX0AsjaO0tBQpKSnYt28fLC0tq0R5lgabNm1Cy5Yt8fDhQ7H1yePxcOPGDfj7+2PkyJHg8/k4ePAgWrRogdGjR9f4w1hcXAxLS0scO3ZMbDpYvkwyMjJgaWmJdu3aoVevXnBxcQERoVWrVmLpPz8/H2pqajh69Gi1CVJeXh46dOiAkSNHVlm08/Hxwd69exs0TsuWLTF37lzcvXu3Qe0EAgFUVVVx6dIl8Hi8WidxMjIyjQpowsLC8uXz6tUruLq61hpNvCnExsZi1apV2Lt3L86ePYt79+41OCjno0ePYGxsjIMHD9ZYvmDBAnTu3JndHGkCrKHKAuDj2dL8/Hz83//9H3766Sfw+XyUlZVBR0en1sBHmZmZMDQ0xMmTJyWstnbi4+PRp08faGpqIigoCEuXLsWqVasQHh6OrVu34tixY0hLS0ObNm2grq4u9Z2+b4GlS5fWma5IUkRFRcHY2FjsCxQlJSVwdXWFlZUVXF1dcfXq1TrrX7t2DYaGhsjLyxOrDpYvBz6fj86dO2PNmjVISEjAn3/+iVWrVkFRUbHRkXpr4uzZs3BwcICXlxeuXr2Ko0ePYvLkyTA2Nsa8efOqff8dPnwYPXr0ELn/T945jZ1Anj9/HlwuF0RUazorLpeLOXPmNKp/FhaWL5dHjx7B0tISy5Yta5a7kdnZ2bCwsMC2bdtqLD958iTMzc3x+vVrCSv7uhDVUGU+1m0euLu74+bNm9KW8dXz5MkT6ty5Mz1//rzK9Q8fPtD69espMjKSVq5cSRwOh65du0bW1tb0ww8/kJ6enpQU/39evXpFsbGxlJeXRxUVFVRRUUHFxcWUk5NDV69epYyMDLK0tKSJEyfSpk2bpC33q+b27dvk7+9PWVlZxDCMVLUcOnSIQkNDKSMjQ+TE26KQnZ1NV69epSFDhpCMjEy99WfMmEFFRUW0a9cusWlg+XLYuXMn7dixgxISEkhGRoaKiorI0dGRtm/fTj169BDrWHw+n/bs2UPr1q2jli1bUvfu3alHjx7k7OxcrW5paSkZGxtTRkYGGRgY1Nv37du3adiwYXT37t1G63v+/DmZmpqSt7c3vXz5koKCgmjBggXE4XCIiMjHx4fKysro+vXrjR6DhYXly+LWrVvUu3dvWrRoEX3//ffSlkNEHzfszp8/T/Hx8fTPP//QjRs3aN68eTR37twa669Zs4ZevXpFkZGRElb6dcEwTDIA93orimLNSurB7qhKhtOnT8PHx0f4vKSkBFu2bIG5uTkCAgKwbds2mJmZoU+fPti+fTtGjx4Nd3f3Zh/BbOTIkdi0aRM2b94MGRmZGqMks4gPgUCAVq1a1bpjImnGjRuHBQsWSFVDYWEhjI2NER8fL1UdLNLB3NwciYmJwudTpkyBvr4+Bg0aBF9fX/Ts2RPLli2TiJbKykrs27cP3t7eKCsrQ1BQECIjI0Vq+/vvv2PAgAEij1VaWoo1a9agVatWwqT1ZWVlICKYmJigf//+kJeXh6GhoTA6t5OTE6ytrRt+YywsLF8kly9fhp6eXrNz+b927Rr09fUxb948HDt2DDk5OfXWNzAwgK2tLX788UdhDlWWhkEi7qhyPrvJzNKsOH/+PE2YMIEGDx5MRERZWVlkYWFBZ8+epV9//ZUCAgJo3bp1tGfPHjp58iSNHz+eDA0NKT8/n0pLS6Wsvm7Gjh1Le/bsoalTp1KLFi0oMDBQ2pK+ahiGoeDgYIqOjiYiotevX9Pp06cpKytLKnqWLl1KUVFR9OrVK6mMT0Skrq5O4eHh9MMPP1BlZaXUdLBInhcvXlBJSUmV6JMcDodGjhxJAQEBNHv2bGrdujWlpaV9di1lZWXk4+ND27Zto2vXrpFAIKBRo0bR3r17P575qYf79+8Lo8fXx8uXL8nW1pYSExPJ39+fRo8eTZWVlfTs2TNq2bIlPXv2jI4fP065ublkYWFBrq6uxDAM3b59m0aNGtXUW2VhYfkCOHbsGA0ePJgOHTpEgwYNkracKmRmZpKnpyetXr2a/P396/U68fT0pOzsbNq/fz9t375dqnOObwJRrFlJPdgd1c9LXFwcuFwuzp07J7w2Z86cKucMT5w4IYwomZmZCSKCsrIy3rx5I3G9DYXP58PCwgK7du1CYmIiGIbBpUuXpC3rq+bNmzdQV1eHubk5NDQ00KVLF+jp6cHDwwObN2+W+K72d999h7Vr10p0zP9FIBCgW7du+Pnnn6Wqg0WyHD58GP7+/nXWmTZtGtatW/fZNOzduxcuLi7Q0tLCiBEjkJaWBi0tLQgEAvD5fDg4OODUqVP19jN69Ogao1vXxA8//IBZs2YBgDDP9fjx4zFp0iT07du3xjZ8Ph/ff/895OTkRL85FhaWL5KdO3fC0NAQN2/elLaUaqSmpkJPTw/nz59vVHs1NTW8e/dOzKq+DYgNpsTyiaKiIkydOhVGRka4cuUKgI+uC926dUPLli3x4MEDYd3CwkLY2tqia9eu0NHRwejRo7+o9AH37t2DiYkJNm/eDF9fXxgaGkpb0lfPP//8g4yMDGEAl4qKCpw6dQojR46EhoYGRowYgWfPnklES0REBKZPny6Rsepi+/bt6N+/v7RlsEiQ6dOnIywsrNby8vJy2NjYfLZI2dnZ2dDR0cGFCxfw6tUrCAQCfP/991iyZImwzuHDh+Hh4VFnAJMPHz6Ay+UiMzOz3jFfvHgBHR2dKgHEXrx4galTp2L69Ol1Tv7evn0LImKjZrKwfMWsX78eZmZmuH//vrSlVOPFixcwNDRstCtyZWUlZGRk2OCdjURUQ5V1/f3KuXr1Kjk6OlJxcTGlp6eTjY0N+fr6UlBQEI0YMYLu379PVlZWwvrq6uqUmJhIgYGBlJ6eTvv27SNVVVUp3kHDaNWqFV29epUiIyOpffv2lJeXRx4eHqxrxmekXbt2ZGtrKwySIicnR7169aIDBw7QixcvyNLSklxcXCgiIkIkt8OmoKSkRAUFBZ91DFHYs2cPjRs3TtoyWCSIl5cX7d+/nz58+FBj+erVq8na2po6der0Wcbfv38/DRo0iHx8fEhfX58YhqHOnTvTmjVrhAHGBg0aRPn5+XTp0qVa+zl27Bg5ODiQtbV1vWM+ePCA7O3tSVdXl16+fEkPHjwgHR0d2rx5M0VGRpK9vT3FxsbW+Jpoa2uTkpIS/fLLL42/aRYWlmYJAPrpp59o165ddO3aNZGPEkiSEydOUPfu3Rvtivz+/XtSVVUVzn1YPg/sq/sVc/fuXQoMDKRffvmF9uzZQ1paWhQeHk5mZmZ0//59Gj9+PMnJyVVrp6amRlOmTBEpOmRzxNzcnK5evUrbtm2j2NhYys3NJWNjYxo/fjx7blDCqKqq0vLlyyk5OZn27t1LS5Ys+WzGqkAgoOjoaAoICPgs/YtKWVkZ3b17l27evEnl5eVS1cIiOQYOHEi2tra0ffv2amW3bt2iLVu2UHR09GeLkF1YWEgmJiZVrtnY2JCxsTEVFhYSEVF5eTn169ePNm7cWKXenj17aPr06fTzzz9TREQEBQcHizRmWVkZJSUlkYWFBRkbG1OrVq1ISUmJGIYhDodDhoaGNGTIEHJ2dqZ9+/ZVa29vb0/Lly+nv//+u5F3zcLC0twQCAQ0ZcoUOnPmDF29erXa91JzIT4+njp37tzgduXl5bR37176+eefxZplgKVmWEP1KwIAxcTEUEBAADk6OlK7du0oPDyc+vTpQ+Xl5bRhwwbavXs3LVq0qEYD9WvCyMiIOnXqROXl5ZSVlUU7d+6k2NhY0tDQoPz8fGnL++YwNzenixcv0rFjx8jT05M2btxI2dnZYh3j0KFDpKCgQP7+/mLtt6EoKSnRnTt3KD09nVxdXSkhIUGqelgkR4cOHejFixeUk5NT5fqyZcsIAE2aNIlOnTr1WcaWk5MjHo8nfF5aWkrDhg0jW1tb2rhxI40YMYKMjIwoOjqaLl++LFwwysnJoVmzZpGJiQk9ffqUHBwcaMCAASKNuWvXLiorKyNHR0fKzMwkgUBAfD6fcnJyKC0tjXg8Hl2+fJmUlZVp3Lhx5O3tTRUVFXThwgUyMzOj5ORkUlFRIS8vL9LS0qKLFy9SfHz8Z3l9WFhYPj88Ho9Gjx5Nd+7coYsXLzaLtIa1ER8f3ygPl09GallZGa1cufIzKGOpgij+wZJ6sGdUG09JSQnGjBkDe3t7HDlyBPHx8VizZg369euHkJAQWFhYoE+fPkhPT5e2VImxcuXKKoGi+Hw+ZGRkcOHCBSmq+rYpLy9HXFwcxo4dC21tbXz33XdVzrc1lpMnT0JPTw83btwQg0rxIBAIEBsbC319fRw5ckTaclgkwMaNG6GnpwdFRUWkpKQIr+fn5yMxMRGbN2+GiYnJZ0lnsGLFiirpmWbMmIGRI0ciKysLcnJy2LJlC3JzcxEeHg4iEp4ZmzNnDkJCQho1ZmZmJkxMTESqe+vWLWhqakJGRgYMw8DHx0cYbK2oqAiGhoYgIjAMAzU1NcyfP79RmlhYWKRDSUkJ+vTpg759+zb7dIYvX76ElpZWo86Xdu3aFf/3f//3GVR9W5CIZ1RlpWwnszQRAHTy5ElasGABOTo60vnz52n37t00depU6tixIw0fPpxevHhBO3bsIB8fH2nLlSj9+vWjbt26UXFxMS1btowMDQ2Jw+FQUVGRtKV9s8jLy1Pv3r2pd+/e9O7dO1qyZAnZ29vT0KFDSUtLi9TU1EhdXZ2MjY3J0tKSLCwsSF5evs4+Dx8+TDNmzKATJ05Q+/btJXQn9cMwDAUGBpK1tTX5+fnRu3fvaMyYMaSgoCBtaSxiAgAVFhZSRUUFcblcUlFRIR6PR/b29nT58mVydXUlIiItLS1q27YttW3bluLi4mjPnj1iT3ZvYWFBu3btEj7fu3cvJSQkkJmZGampqZGdnR1xuVx68eIFycvLU48ePYT6b9261agxLS0tKS8vj7Kzs8nY2LjOuk5OTvTy5UtSVlamO3fukJ2dnbBMVVWV/v33XxIIBKSurk5z586ltWvX0qpVqxqli6Vm5s6dS7/88gtpaGhQq1atqEOHDhQYGFglpRILS2MoLCykfv36kYmJCe3du7fZe+0lJydTmzZtGny+NC8vj5KTk8nPz+8zKWOphijWrKQe7I5qw7h06RKcnJzg7OyMQ4cOYcOGDeByuRg+fDju3r0rbXnNgvz8fMyaNQstWrTA0KFDISsrK5YdPBbxcevWLYSHh2PJkiWYOXMmJkyYgJ49e8LY2BiDBg2qsY1AIMC1a9cQEBCAFi1aIC0tTcKqG0ZGRgbat28PFRUVdOnSBYsXL26WURBZRCctLQ3KyspQUlKCqqoq1q5di/z8fPz1119QU1NDYWFhje2uX78OU1NTsUe7raiogLGxMUJDQ/Hw4UOYm5vD09MTAHD27FlwuVysX78e2traSE1NRWpqKh4/flxvVPeaIgRnZ2cjPDwcLi4uUFRUBBFBQ0MDw4YNQ0lJSY395OXloXPnzmAYpt6Iwrm5uSAilJWViXj3LLWRm5uLDRs2wNLSErKysli4cCGmTZsGDw8PcLlcEJHE04ixfF28fv0abm5umDx58hcTAfeff/6Bq6trg9sVFhbC0NAQCQkJn0HVtwWx6Wm+Xt69e4fg4GC0aNECmzZtwr59++Dk5IRu3bo1+wm7tNi0aROICIcPH5a2FBYRiY6OxoQJE6pdP3v2LNq2bQsrKyv88ssvX1T6pMLCQpw6dQqzZ8+Grq4u9u/fL21JLI3g+vXr4HK50NLSwqBBg/D8+XM4OTkhKCgIRUVFsLa2Rt++fbF69eoaJzR+fn6IjIysM01MYxgyZAjk5ORARCAiqKurC8syMzNha2uL4ODgevvh8/k4ffo0/P39IS8vj3bt2mHBggUoLS1FcXExdHR0MH78eFy8eBGVlZXIzs7G0qVLoaioiN69e9fYp7GxMQwMDERyzw8NDYWKioroN84iJDk5Gd9//z0cHBygpKQEIoKqqio6deqE7OzsavWVlZURFRUlBaUsXwMVFRVo1aoVzM3NERMTg8uXL38RKafev38PJSUlVFZWityGx+PhyJEjsLa2Ful7lKVuWEP1K2bOnDkgInC5XOjo6GDgwIH47bffxD7p+ZooLCxkJz5fGPPnz8eKFSuqXV+0aBEsLS2b/RmY+rh9+zY0NDTw+vVraUthaQBHjx6Frq4u4uLi0KNHD+GqfGFhIYgIrVq1Ql5eHo4cOYKZM2eCy+UiNja2Sh+pqakwNzeHnZ1dlTzWTSUsLAzz5s0Dj8dDQkICNDQ00LNnT5w4cQIAUFxcjMWLF2PJkiU1JqkvKirCxo0bYWFhATc3N+zYsQNv3rzB5cuX4eLigl69eqF79+5QVVXFmDFjsGHDBuzduxc7duzA1q1bISMjI9zF/W8+vTai5GYFgKdPn4LD4WDLli1Ne0G+EXJzcxEbG4vhw4eDYRgYGxujf//+iI6OrnVn/xOtW7fGwIEDkZaWhpCQEKSmptZZ/8mTJ1BQUEBQUJA4b4HlC4XP5yMmJgbz58/HiBEjYGdnh8mTJzeojzdv3iA+Pv4zKawdExMTPHr0qN56lZWVCA8Ph6mpKTw9PfHHH380yMBlqRnWUP2Kyc3NRXR0NG7duvXFuFlIik8TopiYGGzfvh3Lly9HcXExeDweuFwuu4P1BTFp0iRs3ry52vXKykoEBgZi3LhxUlAlXgIDA7F7925py2ARkY0bN8LIyAhJSUkAAC6XCxsbG0RFRSEjIwMaGhrVjhbcuHEDXC4Xz58/r3JdIBBgxowZmDNnjtj0nThxAu3atRM+Ly4uxvbt26GpqYnly5fD0dERfn5+GDNmDPT09PDzzz9DIBAgNzcXP/74I3R0dDBo0CBcv369Wt9paWnYuXMnVq5cCR8fH1haWkJdXR3KyspQUVGBmpqacCfX2dlZ2G7Hjh3Q1NSEtrZ2g+5l9uzZkJOTw5UrVxr/gnxF8Pl8HD58GDNnzkSPHj1gbW0NDQ0NcDgcEBEUFBRgaGiIgwcPNqjfoKAgMAwDIoKuri4YhoGRkRE2bNhQpV5ZWRmGDRsm/Btv2rRJnLfH8pWQl5cHbW1tZGVlAfh4TMDS0hJaWlqwtbXF3Llzq2yq5OfnQ1VVFcrKyqioqJCoVj8/P/z555/11ouLi4OtrS0SExMloOrbgTVUWb5JMjMzQUQICAjAuHHjICMjI3R3unPnDkxMTBARESFllSyisHnzZnz33Xc1lhUUFEBFRUXiP2ziZt++fejatWuNLnkszYuoqCjY2tri8ePHwmu+vr4wNzcXutsOGTKkxrYhISE1RrFNT0+HsbFxk1bnS0pKcOfOHSQlJQmjTP/3RPD9+/fw9PREaGgozp49K1zcTE9PR9u2beHn5wcul4vJkyeLtLtQH0+fPhUaM/Ly8pCRkUFQUFCtZ1drg8/nw9vbGwzDfPNxBfh8PpydnSEjIwMjIyO0a9cOo0ePRkREBG7cuAEej9fovp88eYIff/xR+Bo/f/4cffv2BcMwwjqrV6+GgoICNDU1ERMTgxYtWkBLS4v93mKpkdWrV4PL5WLq1Klo06YNwsLCkJeXh9u3b8PV1RWrV68W1hUIBPD19QUR1bhA9jkJDQ3FmjVr6q23bNkyzJ07VwKKvi1YQ5Xlm6SoqAiqqpayLe0AACAASURBVKooKirCq1evoK2tXWXS9uzZM9ja2mLt2rVSVMkiCjdu3Kgz2IG9vT1u3rwpQUXip7CwEEOHDoW2tjZGjx7Nuu83Y6KiojBs2LAay/h8PjQ1NYW7UHw+HwcOHMCxY8dw48YN9OnTB1u3bq2xrbOzMy5evNggLQ8ePICVlRU2bdqEFi1awMbGBlwuFxoaGsLdXlF49+4dZGRkEB4e3qDx60NGRgZjxozB4cOHUVBQ0Oh+7t27Bx0dHTAM801/Zx84cABEhHv37klkvNjYWDAMA29vb+jq6kJWVhbz5s0TLnKUl5fDzs4OysrK31TKu+ZOXl4eDh8+jKioKERERCA6OrrBC0TiIjMzEytWrMDy5cur/K5lZ2fD1NQUhw4dAvDxCEyrVq1ARFi1apVENe7atQujR4+ut17//v3x22+/SUDRtwVrqLJ8s/j7++O7777DmTNn4OXlVa08ISEB7Hut+VNWVgYlJSUUFxfXWD5hwoSv5gxbWVkZHB0d2WBfzZjXr19DQ0Oj1onf4sWLsXDhQgBASkoKdHV10bdvX7i7u0NLS6vWs3/r168X+bzfnTt3MHbsWMjKyoKIMGzYMDAMg8LCQty9e7dRrmn29vbw8/NrcLu6sLa2hq+vb5P7OXbsGDZv3oy2bdtCQUFBDMq+TPh8PnR0dNClSxeJjHf37l04OjrCy8sLEydOrPGcK5/Ph4eHB9TV1dkjSFIkLy8P7u7uUFZWFrqAq6qqQlNTE0pKSmAYBmZmZggJCWk2gQdv374NPT097Ny5E7q6uoiJicEff/yBXr16SVTH9evX65wL8vl8pKWlwcDAQCzeJixVYQ1Vlm+W9+/fo3PnzjA0NKzxHOObN2+grq7O7l59AfTr1w/bt2+vsWzHjh0irYZ+Kaxbtw6zZs2StgyWOvD19a11ZX3SpElYtGgRgI8r9SNHjhSpzzdv3kBLSwtHjhypM01IUlIStLW1sWLFCrx9+1Z4XVZWFsePH2/AXVRFTk6uRrfkpnDgwAHIyso2qY+kpCQQEdTU1KCiooLo6Ggxqfsy2bBhA4ioSX9rcVNeXg4lJSWEhIRIW8o3ycOHD6GqqgozMzNERUVV+V74RHJyMkaOHAlNTU0oKChgx44dUlBanRMnTkBfXx9jxowB8DH2ioaGhkSDFBUWFkJZWbnamFlZWRg4cCB0dHRgaWmJmTNnsvPFz4CohmrDMt2ysHwBqKmp0a5duygnJ4f27NlDmZmZVcp1dHRIWVmZVqxYQe/fv5eSShZRCAkJoU2bNn1cVfsfOnToQDdu3JCCqs+DiooKlZSUSFsGSx3o6OjQixcvql3PyMigP/74g2bMmEFERCUlJVRQUFDj+7amPnfs2EF79+4lGxsbatOmTY3toqOjac6cObRw4ULS1tYmIqJ3795RZWUldenSpdH3pKmpKdbPkUAgoJSUFJHuvS7c3NxISUmJ5s6dS8XFxRQcHCwmhV8e9vb2FBoaSu3btyc3NzdpyxEiLy9P69ato61bt9KbN2+kLeebw9PTkwBQZmYmTZo0Sfi98N+4ubnRgQMH6O3btzRp0iQKDg6mmJgYKaitSvv27am4uJhUVFSIiIjL5ZKenh7du3dPYhrU1dXJyMiI7t+/X+X6r7/+SgoKCnTr1i16+PAh/fzzz8QwjMR0sVSFNVRZvkqsra1p4cKFREQ0cOBAqqioqFJ+5coVyszMJEtLS9q8ebM0JLKIQPfu3amyspKuXLlSrUxTU5Py8vKkoOrzwBqqzZuMjAy6cOECTZgwocp1ADRnzhyaP3++cKIYHBxMT58+pYMHD4rUd2BgIJ06dYru3r1Lz58/rzYpKioqotjYWBo7dmyV66dPnyYFBQVSV1dv9H399ttvdPny5WrfkY1lypQptGHDBlqwYEGT+uFwOOTp6Ul//PEHXb9+nY4ePUrr16+nsWPHUnp6uli0fglUVFRQTk4OdenShW7cuEEtWrSQtqQqTJ06lYyNjSkwMFDaUr45du/eTZWVlTR9+vR663I4HNq4cSP5+flRWFiYBNTVjYKCAvF4PKGhSvRxk+HDhw8S1eHm5kYpKSlVrqWkpFD//v3J2NhYolpYaoY1VFm+WpYtW0bDhw+nO3fu0NGjR6uU2djY0IEDB+jo0aO0YsUKKSlkqQ+GYSgkJIQWLVpEpaWlVcp+//13CggIkJIy8QKA7t+/TzweT9pSWGph6dKlNGvWLKFRCID++usv8vDwoNzcXJoyZQoREWVlZdGUKVMoOzubCgoKGjRGSkoKmZubV7t++PBh6tq1KxkYGFS5fuTIETIxMWncDf2HLl26kIKCAu3atatJ/XwiKCiIiKhJO6ClpaU0ZMgQSktLo9TUVOrUqRMNHTqUVq1aRSdPnqROnTpJfEIrDTIyMsjQ0JAA0KZNm6Qtp1Z+++03unbtGsXHx0tbSoOYM2fOF73o0atXL7K2tm7QbnZERAQ9fPiQHj9+/BmV1c+zZ89ITk6uiqFaWVlJcnJyEtVRk6GamprarDwXvnVYQ5Xlq4XD4dCvv/5Kr1+/piFDhtRYp7KykqytrSWsjKUhBAcHk5mZGfXp04eKi4uF1w8dOkTDhw+XojLxUFxcTCNHjqS4uDhavny5tOWw1MCjR4/ot99+o4KCAoqNjaXjx49Tp06dKDQ0lGbOnEn//PMPKSgoENFHF9379+/TgwcPaOrUqSKPcfPmTRo3bhwtWbKkWtnOnTtp4sSJwudnzpwhVVVVOn78OM2ZM6fJ9ycnJye2HVUPDw9SVlam/fv3U+vWrYlhGGIYhjgcDikrK5O1tTUtWrSI3r17V2sfnTp1orNnz5KjoyNZWVnR4sWLqaKiggoKCujVq1ckJydHPXv2FItecZGfn08///wzhYWF0YIFC+jHH39ssjEQFhZG+fn5NH/+fHJwcBCTUvHTvn176tKlCw0dOlTaUkRm6dKlFBERQU5OTuTr6/tFui6fPHmSMjIyyNbWVuQ2rVu3JlNTUwoNDf2Myupn2bJl1LFjxyrHryorK0lWVlaiOlxdXSk1NVX4PCYmhiorK8nKykqiOljqQJSDrJJ6sMGUWCRNfHw8bG1t2YPyzZzKykpMmDABHTt2xLt373D06FFwudwm5Q+UNgKBAPHx8WjdujW8vLyE6UVu374tbWks/8OHDx+wf/9+zJs3D/7+/vD09MTBgwdrDPzx+PFjaGhowMvLC5MmTcK0adPw7NmzesfIyMiAnp5elQirpaWlWLhwISwsLHD//n1MnDgRsbGxwmBFcXFxYrk/LpeLsWPHiqUvAFBUVAQRwdzcHGlpaSgsLER6ejr27t2LgQMHQkNDA0QEGRkZqKqqwsTEBBYWFti9ezfCwsIgLy+PmJiYWvs3MjJC165dxaa3qVy5cgWKiopQVlaGjo4ODAwMoKenB4Zh0K5dO9y6davRfS9YsAAyMjJo1aoVnjx5IkbV4uXt27eQkZHBhg0bwOfzG5QmSdI8efIEHA4HERERuHDhAoyNjUFEUFZWhrOzMzZt2oTy8nJpy6yT3NxcyMjIYOLEiQ1ue+DAAXA4HDx9+vQzKKuflJQUGBoa4ubNmzAwMBDmQ/fx8cGJEyckqiU/Px/q6up4//49du7cCWNjY2RkZEhUw7cKsVF/WVjqRyAQwM7OrsF5DFkkD5/Px+TJk6Gvrw9LS0tcuXJF2pIajEAgQEJCAkJDQ2FmZgZra2u4ubnB3d0dUVFRMDAwQGlpqbRlsjSR3NxczJo1C0QEIsKZM2fqbcPj8eDo6Ijt27cjKysLkyZNErbncGSgq24NR0s/6GnYYM3qNWAYRixa09LSEBwcDCISW8TNXr16wczMrM46T548wZUrVzB79mx06dIFXC4XysrK0NTUBMMwdebnNDExgYqKClq3bi2x3KK1sXr1anA4HAwcOLBampa///4bjo6OYBgGDg4OuHbtWrX25eXlyM7ORlZWFtLT05GTk1OtztOnT9G6dWtwOJxmk2KkJkJDQ6GoqIgxY8aAiGBvb1/NGHr+/LnU09lYW1vDzs6uyrXc3FxERUWhW7duUFRUBIfDwcGDB6WksH68vb3r/YzVhaWlJbp37y4+QQ0gMDAQGzduBAB07NgRx44dA/DxsySNCNJ9+vRBv379YGJigszMTImP/60icUOViJ4QURoR/ftpcCLSJqJzRPTgP/9q1dUHa6iySJqysjL88ssv6Nu3Lz58+CBtOSz1IBAI8Oeff0otiXlT4PP5CA4OhqWlJRYvXozbt29jw4YNaN++PSoqKtClSxds27ZN2jJZxMCFCxegp6eHU6dO1TopLykpwfPnz5GdnY1nz56hV69e8PX1hZeXF7S1tUFEWL58Od69K8SEQeHo13E5erZZjJ7ui9DGahSImBrzW4pKeXk5Fi5cKMzJSkRwcnJqUp+fSE9PBxEhOzu7yX3VRGpqKhYuXIgWLVrAycnps4xRH3w+H7179waHw0FkZGSdddPS0uDh4QGGYWBjY4OEhATweDxMmDABHA4HRASGYYSP3r17o6SkBKdOncKPP/6I1atXY+zYsc0+lyyfzxfulkdGRsLW1hYcDgfDhg1D27ZtoaSkJNy5PHLkiFjGTExMbNDu599//y3Se9PFxQU9evRoqrzPQlJSEhiGQUJCQqP72L17N+Tk5KSyaGBvby/0HNq9ezcGDBgAALh58yZMTU0lric2NhZmZmZsrlQJIy1DVfd/rq0jonn/+f88IlpbVx+socoiSVJSUsDhcODh4QFra2toampi/PjxOHv27BftUsrS/ODz+Zg4cSI8PT3x/v17AB93SXR0dPDw4UNMnjwZRMS+774CkpKSoKenh8uXLwuvrVu3DiNHjsTx48cxd+5cdOjQASoqKjA2Nha6iU6aNAmnT59G27ZtUV5eDmdnZzAMg66dBqJ/pzD0cFssfPRuvwS66taN3k3MzMyEkpISVFRUEBISgrdv3yIjIwPq6uogIpiZmaGwsLBJk1gdHR3MnDmz0e1FITk5GUQksV2Q/349LCwsoKys3CBj4eHDh+jYsSMYhoGCggJUVFSquTifP38eOjo6UFBQAIfDgb6+PrS1taGiooKpU6eK7V4+F+fPn0doaKjw+ebNm2FoaAhvb29ERUWhqKgIAQEBUFJSavT3HZ/PR2RkJHR1dUFEUFBQwLx583D27Fn8/fffKCsrq7Utj8eDnJxcvYsL48aNa9KO5efE2toaHh4eTeqDz+dDXl4ee/fuFZMq0TEzM0NWVhaAj27znp6eAD7+bYgI//zzj8Q1STJ/K8tHmouhep+IDP/zf0Miul9XH6yhyiJJrl69ipYtW+LEiRMYP348NDQ0YGpqCl1dXejo6DTrMzYskqegoAB//vknoqKiEBYWhrCwMKxatQpr167FgQMHkJiYiHfv3lVpU1paitu3b2PMmDHw8vKq4rY3d+5cYSJxR0dHrF+/XtK3xCJmMjIyYGBggOjoaMyYMQPr1q3D4sWLYWlpiTlz5qBbt25YsmQJLl68WKNXwKJFizB//nzh89TUVFgYdEYPt0VVDNWebRajpUHnBhuSRUVF6Ny5MxiGQYcOHWpsv2XLFnA4HMjLy4NhGHTu3LlBY/B4PHh5eYFhGMTGxjaobUPh8XiwtbUFEcHa2hqTJk2ChYUFBg4c+FnGs7GxgbGxMe7evQsiwtu3bxvVT3p6OpYsWVKrocbn8zFz5kwEBgY2RW6zhc/nQ0VFBT4+Pg1+DXfu3AlVVVXIyckhKCgIBQUFWLhwIdTU1CArKwsOhwNNTc06+3V0dIS9vX2d41y6dAkcDgd6enoIDw8Hn89HYmIixowZA1dXV+jq6kJRUREmJibo06eP0PBqDHw+H9HR0XW6u/83cnJyOH78eKPH+4STkxN69erV5H4aio6ODl6/fg0AOHfuHHx8fIRlP/zwA5YsWSJxTSySRxqG6mMiSiGiZCIK/s+1d/9Tp6CuPlhDlUWSCAQCuLq6Yt26dXj9+jVKSkpw5coV/PLLL1BTU4O2trZIZ8tYvn7Ky8vRvn17eHl54bvvvsO8efMwf/58fPfddxg1ahSGDBkCV1dXqKioQF9fH+3atUOLFi2gqKiI1q1bY+zYsSguLhb2V1JSAl1dXTx8+BC///47iAh2dnbYvXs3G9jrCyYwMBAODg4wNTVFSEgIpk+fjoCAgHonsXw+H0eOHIGxsTHOnz9fpWza94vh4zy3iqHa3XU+dNWtG3xeMTMzE0SEAwcO1KklJCQEpqamiIuLAxGhY8eO2Lx5s0hj9OnTByoqKsJzZ5Lg7t276NmzJwwMDBAYGAhZWVkEBgYiJiYGeXl5Yhnj0qVLYBgG5ubmkJWVhZycnFj6/VY5ceIEuFwuGIYRyegqKioS7kZ///33tbr7lpeXw9TUFJqamujVqxdGjBiB3bt3C+tPmzYNHA4Hly5dqnfMt2/fIigoCPLy8uBwOGAYBqampujduzcWLlyI/fv3CxdHZGRksGPHjga9BsDHs9otWrSAjIwMiAguLi7g8Xjg8/l4+vRptcWkVatWgYia5Pb7iTlz5kBPT6/J/TQEgUAABQUF4UJdXFwc/Pz8hOUpKSkwNzeX+jlmls+PNAxVo//8yyWiW0TkJYqhSkTBRHSTiG5Kwzed5dvmxo0b6NevH9TV1eHv7y80EjZu3AgHBwcYGBhg5cqV7JfmN05ISAj69+9fxYjMzs4Wnu3T09PD6NGjcenSJbx48QLx8fHIysqq1Z1ox44d6Nu3LwCAiKCoqIgtW7ZASUkJBQUFErknFvHz/v17hIWF4ejRozWWl5aWYtq0acLzcXw+H3/88QccHBzQrl07nD59ulqbyko+hvRYAV/XhfB1XYhuLvPgbj0aRAzWrVvXYI2amppYsWKFyPWXLl0Kb29vMAyD5OTkOuvu378fDMPUGDRIkpw4cQKqqqrC6MMmJiaYPXt2kz5btra28PT0BJ/PR/fu3WFpaSlGxd8uw4cPh6KiIpycnKCtrQ01NbVqCzuxsbFQVFSEvr6+SBGUS0pK4O/vj7Zt28LGxgYKCgpgGEZoGP/+++8N0sjj8RAXF1enS/G8efPAMAwmT54scr9RUVGQlZWFo6MjCgoK8PTpU6iqqgqN1k+/DbNnz8bz589hZ2cHGRkZrF27tkH6a+PevXsgIgwbNkxin9nnz59DX19f+PzYsWPo16+f8LlAIICTkxMb4PIbQKpRf4loKRHNZl1/Wb4UKioqYG1tjUWLFqG0tBR8Ph9du3bF2LFj0aFDB/Tp06fRbl5fA8+fP0f37t1F3lX5Wnj79i1mzZoFCwuLGie5cXFx4HK5CA4ORlhYGExNTREUFCR0a6qJTz/En3br8/PzIRAIsHTpUhgZGWHPnj3w8/ODr68vzp07x+6wfkWUlZVBRkYGOjo6WLlyJVxcXODm5oaTJ0/W+XfetPoE1iyMxf7oi/D1HgwiBkRUxU1YVNzd3cHlchuU6uTt27dVJs4XLlzAiBEjMGfOHIwYMQJOTk64dOkSFBQUmt05yrt372LkyJFQUlJCp06dGtXHp+A10o4w/DXC4/HQu3dv+Pv7Y9myZbC0tISLiwuSk5OxYMECODg4gGEYjBs3rkkLxrdu3cLkyZMbbKQ2hP/7v/8DwzDCnfz//c1IS0tDUFAQ7O3toaqqCoZhsGDBgip1CgoKcOrUKSxbtgyysrKYO3cuVFRUhO7t4kwpU1lZiQ4dOsDU1BQMw0BOTg7t2rX7rIulJ06cqBKkas+ePRgxYgQACCPe//zzzwgKCvpsGliaB6IaqszHuk2DYRgVIuIAKPrP/88R0XIi6kZEbwGsYRhmHhFpA/ixtn7c3d1x8+bNJuthYWkMz549o9DQUEpJSaEzZ86QhoYG/fDDD3T79m1ydXWlpKQk2rdvH5mbm4t9bIZhmnWfa9eupc2bN5OOjs4XmRi9oVRUVFB4eDht2LCBAgICaN68eXTq1ClKT0+nly9f0suXL6lt27YUGRlJOTk59OOPP9KZM2eoY8eOdPnyZfrw4QPV9t164cIFmjp1Kt29e7fK3wgAnT59mjZu3Ejt27cnCwsLWrlyJQUGBtKaNWskdessnxl7e3uaOXMmnT9/noYPH079+/ev87PK5wtoVK8IWr9jHLUw0yUioh07dlB0dDQlJydTq1atyMXFhXx8fGjUqFGkrKxc5/hv3rwhLy8vevLkCT18+JCMjIyIiOjVq1dUXl5OZmZmVeoLBAJSU1MjJSUlcnJyIhkZGTp//jxxuVyqrKwkNTU1MjAwoMTERAJAs2bNovDw8Ca+SuLH3d2d5OTk6Pr16w1ue+jQIRo5ciTdvn2bHBwcPoM6lk+kpqaSm5sbEREpKytT586dacmSJeTh4SFlZaIxc+ZMioyMFD6Xl5cnQ0ND4vP5lJ2dTaampuTq6kpdunShwYMHCz9//4uRkRF5eXnR4cOHSSAQUEpKCrm7u3823ZWVlXTkyBGaO3cuFRcX061bt6p9F4iDsLAwKioqorVr1xIR0bRp08jU1JSCg4PJwMCA7OzsqEuXLrRt2zZ69eoVqampiV0DS/OAYZhkAPW/qUWxZut7EJEFfXT3vUVEd4jop/9c1yGiC/QxPc0F+miosjuqLM2aVatWoXv37sJdsePHj0NLSwu7du0Shtf/Fh/y8vJQVlZu9onQxcGMGTPQtWtX3L9/H6dPn4aVlRX69++PrVu34ujRo7h+/ToGDhwIT09P4fskPz8f0dHR6N+/Pz5+tVYnMzMThoaGIgfCGDduHDZt2iS2+2KRHEVFRfjrr78wd+5cDBkyBOvXr8f169cRGBiIPXv2iNzP7ZQnmDR0S41lJ06cwNChQ2FjYwMiwrhx40Tqk8/nw8rKCioqKrh79y7mz58vTJHSv39/RERE4MKFCxg7diwsLCzA4XCq7LLcuHGjWp/fffcdiAhjxowR+d4kRXp6OhiGqVG3KKSmpoJhGKSlpYlZGcv/0rZtW2hra0NPT08qOTXFSUlJCWJjYzFx4kSMGDFCZC+Gc+fOgWEYvHr16jMrrA6Px4OTkxMUFRXrdfVvDIMGDcKvv/4qfN6xY0dcvHgRR48ehY+PD86fP4/g4GBwuVxcvXpV7OOzNB9Imq6/jX2whipLc+DDhw+YOHEiNDU1MXr0aPzzzz/w8/MTBl5qyBmUr4mCggJoamqCw+FAS0sLHTp0wJIlS8QWrKS5cOrUKZiYmCA1NRUBAQGwtLREXFxctXrFxcUwMDAQOffp69ev0bJlS2zfvl2k+gKBAMbGxjAyMsLKlSsbdA8s0uXNmzdQVlaGl5cXlixZgpiYGEydOhUuLi7gcDhYuHChyH1tWRuHX3dcrrPOp0A/DXEL9PT0rBIkJiYmBqdOnYKBgQHU1NSEC1Rt27ZFampqtfbz588Hl8vFgAED8PTpU+jq6or83VheXo7jx4+LJWerKERGRkJJSanR7Z2dneHm5iZGRSz/TUFBAVatWgVvb2/IyMggMzMTAwYMgLGx8TcZH8LOzk6YskUa8Pl8eHh4wMjIqN66CQkJNX4/1IaVlRXu3LkjfP7JzXvSpEmIiIgQXmePvHz9sIYqC0sTefPmDTp16iQMyEFE+Pfff2FlZSVtaVKDx+Ph0qVLWLFiBby9vaGpqQmGYdCjRw9s2bIFvXr1wrBhw4Q/XHw+H+vWrYOjo6NYwul/bl69egUDAwOMGzcO2traWL58eY0BNCoqKtC7d2+MGjVK5InU48ePYWBgIHKie4FAgJUrV2LhwoXo06dPg+6DRboUFxdDSUmpxslWYWGh8CxWffD5fAzvuR7PHte9GOTk5NSg85cjRowAwzC4dOkSYmJisHv37mp11NTU0L179yrXysvLERoaCnd3dzAMg4kTJwrPtxERvLy86syNuXv3bhgbG4OIICMjA3l5eaxbt+6zBxArKCgAETX6fB+Hw6kyiWYRLyNGjBAGFfp0hvTJkydQUVGBnZ3dN5Vf+tPuv7R37z+lYPrfyOI8Hg+LFi2Co6Mj5OTkQERo3769SH1eu3YN+vr6Vf6ePj4+OHv2LMzMzKoYsCxfP6yhysIiBtq1a4eQkBAEBARAXV0dq1atgoaGxle3i9gUjh07BnNzcygrK8PZ2RlmZmZgGAaqqqpQUlKCvLw82rdvD4Zh0KZNG7x48ULakmuEz+fDz88Pnp6e4HK5yMjIqLXumjVr4Ovri4qKCpH7v3LlCrhcboODeWRlZUFfX59NSP6FoaKigps3bzapj7SUJ5g0pGa33/9GQ0MD4eHhwrQW9WFmZlZvsJJPi3NXrlwBj8fD6NGjQURQUlKCg4MD9u/fL6ybmpqKX3/9FRwOB0QEBQUFtGrVCs7Ozjh+/Di2bt2Knj17Cg1aIsLly5cxe/ZsYUTWqKio+l+QBsLn85GWloa9e/eCiPDw4cNG9fPDDz9AVlYWd+/eFbNCFgDo379/jTvWz58/h4aGBtTV1TFjxowGp2P6EgkNDYWGhgZyc3OF84zMzEyYm5vDwcGh0e/hxqCiolJlgebGjRvQ0tKCkpISevXqhf3790NGRgYHDx6st6/379/DwsKiWtqqQYMGYdmyZTAxMWF3Ub8xWEOVhUUMbNu2DYMGDQIAPHr0CNra2mjdujUSExOlrKx5U1hYiHXr1iEiIkJ4pjUtLQ1WVlbgcDiYPn16s3PpWrduHQwNDaGrq4tz587VWXfz5s0IDg5uUP9BQUEYO3YsLl26hPj4eCQlJSE5ORlnz57FwYMHkZKSIqz78OFD+Pv7C41lJycnXLlypeE3xSI1IiMj0aJFC7Rp0wZbtmxplJvr1nVx2B99qdZyHo8HHo8HDoeDgIAA6OnpQVZWFgEBATh37hyys7ORlJSE9PR0pKWlISAgAPb29lBQUEDnzp1r7dfZ2RlEBH19fTAMA4ZhICsriy1bajaaP7kK0n9SNe3cuRPDhg2Dw7g3cQAAIABJREFUt7c3iAiysrJwdXUVnrf28/MDl8sVtg8JCYGiomKDX5+6yM3NRYsWLcAwTJMi/n6iQ4cOkJeXh5aWFoYOHSomlSwA4OvrCw8PjxrLioqKMG3aNKH3zoABAySsTrJkZ2dXWdBRUVGBjIwM3Nzc4OjoCA6Hg507d0pES7du3YRn1z9p8vX1FXoZrV27VmSX+uDgYIwdO7ba9QkTJmDo0KGwtbX9pnbOWVhDlYVFLOTm5kJDQ0O4m9W9e3cQkcjumyzV2bJlCxQVFaGtrY2//vpL2nIAfDyX+mnlnohw7NgxLFy4EH///XeN9ePj4+Hu7t6gMW7cuAFfX194e3vDw8MDbdq0gYuLC3x8fDBkyBCYmZnBxcUFf/zxB6ytrWFhYYGlS5cCACZPnow1a9Y0+T5ZJEtlZSX++usvoYHYEE+MT26/T7NqT3Xk5uYGIgKHw4GRkRFsbW0RGRkJDQ0NoXvtp4kmEcHc3ByBgYEgInTp0qXWfpWUlKCpqYnnz5/j6dOnSE9Pr1Pr5s2bISsrW2OOy+Tk5GqpvQoKCqpMuA8cOACGYRAbG1vnOA3B29sb+vr6ePnypVi8EXg8HpYtW4YFCxaAYZhqO0Ms9VNQUICwsLBqeVLt7OzQrVu3etufOnUKHA4Hhw8f/lwSmwXHjh1DVlYW3r59iw0bNlTxNli4cCE4HI5E8p7yeDzcu3dP+PiU/xn4mGpLVVVVpDQyp06dgpmZGd69e1etbMiQITh48CC6d++O8PBwsepnad6whioLi5ho3bq1MPrdvn37QERISkqSsqovm9LSUvj7+4NhGHTu3Bn5+flS05KRkQFtbW2YmpoKd3/+eyW7JmO1uLgYysrKKCsrqzMyIp/PR25urkg6Ll++LBx7wYIFGDBggHBBxM7ODtevX2/cDbJIHYFAgHnz5sHV1VXkndX0f5/iu8G/1Fqek5MDRUVF9OjRAzk5OVXKIiIiICMjg2nTpmHAgAEoLy/H/v370bNnT4SGhiI7O7vGs9fAx/esvLw8duzYIfL9cblcjBw5UuT6wMcowUpKSigpKQHwcRHQzMysQX3URF5eHmJiYmBgYCBceKptJ7ixDBs2DOrq6tWuP378GBcvXmx23iLNhcmTJwsXTZSUlODm5obevXuDw+GI/Js6bNgwaGpqftOv8cCBA6GgoFDtcy9Jxo4dC01NzXp3Qd+8eQMjIyNcvHixxvJu3brh7NmzePDgAXR0dPD48ePPoJalOcIaqiwsYmLSpEnYuHEjgI8Rgc+cOSNlRV8PSUlJMDIygqqqqtQ0tGrVSuiySERISEgQGpdmZma1pttwcHDAmDFjwOFw8OjRoxrrhIeHixxowtzcHESE0aNHQyAQwMrKCrGxsbh//z4MDQ2/6YnZ14BAIEBQUBB++OEHkepvCz+F/dE1T+7Ky8vB5XJhY2NT4/siISFBaKTJyclBTU0NcnJywvd6dHR0tTRTZWVlSE9Px7Bhwxp0njMqKqpa+hpR4PP50NbWRv/+/QEAFy5cAMMwVVzcs7OzRdqFLi8vx4sXL/Dnn3/C2NgY/v7+QldkfX39RqelqY2srCwQkfC1f//+PebNmwdtbW3Y2dnB1tYWwcHBGDx4MObPny/Wsb9krK2tMXjwYBQVFWHr1q3o3r07DAwMGuTKWlZWBllZ2c9yprm5kpycjIiICIwePRodO3bE8OHDweFwGhzvQJxYWVlhxIgR9dZbunQpJkyYUGu5s7MzkpOTcfPmTYSFhSEwMFCcMlmaMayhysIiJtavX4/Q0FBpy/hq4fF4YBgGJiYm2Lt3r8THP336NF6+fAlTU1P4+/tXKbO2tq7VUJ01axb09fWFZ4kOHTqE8+fP488//8S7d+/w8uVLqKmpwcLCQiQdRUVFePTokdBNMSoqCmZmZpCXl8dPP/3UpHtkaR4UFBTA2NgYly9frrMen8/HiF7hePKo5t3448ePg8Ph1Ogqy+fzsXXrVjx69Ah9+vSBmpoa3N3dhRFvP7kEMwwDRUVF2NraYsyYMZCVlQXDMEJ3YWdn53oXR8rLy6GkpCSy8f2/zJgxA8b/j73zjmv6+v7/eSchAcIOe08FXICAINYqU5S6Jy5qtYrbj+Kqe2+qte6tdaEVZ61F60YsiqPuQVERFygoMkLy+v3Bj/e3aRgBgjjez8cjf+S+7z33JCThfe5ZVlbYvn07IiMj4eTkxBYcMzc3h6amZrn5oFKpFPPnz4eOjg7Mzc3h5eWF33//HW3atEGdOnVw+PDhGjng2b9/PxiGweLFizFs2DCYmZmhT58+SE9Ph1wux8mTJ7F8+XJs3rwZOjo63CETij+XfD4fR44cqbYsdcn52Ll79y7q1KkDhmGgr68PFxcXtGjRAi4uLjA0NGSjEWoDkUiEbdu2VTivYcOG5fZDbdq0KRYsWAAiQqdOneDh4aFONTk+YjhDlYNDTWzevLnSYW0clWPMmDEgIujq6tZKWX6ZTAaGYZTCfMszVBMSEthWGyWtORo2bMh6Zfv27Yv+/ftDLBar3Oj9vxQVFancyoTj0yA+Ph7Ozs7l/l1vXH2E/p1/KvP6nj172M9dibdQKpVi2bJlWLduHXuNx+Nh/vz5pcr466+/QETQ09ODlZUVpk6dips3b0JbWxtCoRAikQh2dnZKocorVqzAiBEj0LNnT0gkEujp6VXZEBszZgzMzc1hYWHB6mxoaAgXFxccOXIE165dg66uLjp37ozly5fj+vXrCpVBg4KCEBQUpJTzePXqVZiYmNRYuLxMJkNAQAC0tbUxffp03Llzp8y5dnZ2uHv3bo3o8Slx9OhR8Pn8ahvtV69eBcMwn33hndevX0NTUxP16tXD48ePa1sdBTIyMkBEFRrKFy5cqLBifVRUFDQ0NDB79mxYWVnB19dX3epyfKRwhioHh5o4evSoUj9BDvWTlpYGQ0NDENEH3zsuLg4CgUBpvDxDtaioCOPGjYOhoSG8vb1hZGQECwsL9OvXDz///DMsLS2Rk5ODpUuXwsLCArdv367hV8HxqdCxY8dy+3KuWvwbtqwqPewXKDaUjh49ivr160MsFiMhIQF2dnZs4aTw8HC8ffu2wnBcDQ0NhfDB4cOHQyKRQCqVIjMzE1ZWVtDR0cH48eMxatQoaGhoQCAQwNzcHEQELy+vauXJhYWFwdXVFQzD4PDhwwCKCyt5e3uzBmlaWho2b96Mfv36wcrKCpMmTWLXlxRL+i8PHjyARCLBvXv3qqxbechkMgQFBamUx+vg4MAZqgC6dOmilh7kEyZMgLGxsRo0+rhp0KABbGxsPkpv/MiRI2FgYFDunKysLNjb21dYIG3u3Llo2LAhioqKcPnyZTbNiuPzR1VDlUccHBzlYmZmRi9evKhtNT57bG1t6cCBA0RE1L59eyoqKvpge0+cOJH8/PyUxvl8Pj179qzUNXw+n+bNm0dJSUlkbm5OOTk5lJubS1evXqWTJ0/S1q1bSUtLi1JTU0lbW5tEIlFNvwyOT4S6detSfn5+qdfkcjmdSbhJXwW5l7mex+NRWFgY1a1bl3Jzcyk0NJT4fD7dvHmTEhMT6dChQ6Sjo0MGBgZKa2UyGS1ZsoTc3Yvlr1ixgrp27UpLly6lZcuWUceOHUkgEJCRkRH9888/1LlzZ1q1ahXFxsaSVCql9PR0ysjIoAULFlBKSgrxeFW/jbCwsKDbt2+TpaUltW7dmtLT02nUqFG0YsUKYhiGiIp/F3r37k2hoaGkra1NN2/eZNcbGBjQnTt3FGQCoP79+9OECRPI2dm5yrqVxsOHD6lTp05kYmJCeXl51Ldv33Lny2QyevLkCdnY2KgkPzU1lSZMmPBZ/r85e/YshYSEVFtOUlISWVpaqkGjj4+ioiJasmQJ2dvb061bt+js2bPV+n7VBHK5nNauXUuDBg0qcw4A6tu3L7Vr1446depUrrxOnTrRwYMHic/nk6enJw0fPlzdKnN86qhizX6oB+dR5fgYuXfvHhwcHGpbjS+G48ePQ09PDwYGBlXqPVkZjh07Bm9vbzAMoxQ+CBR7WhmGwf79+/H69WucP38eBw4cQFxcHLZu3aqQJ/Xu3TuFtY8ePULLli0RFhZWq1WNOT4+oqOjy6xEe/PaI3zXcZlCiGtZXLhwAQzDqFz4CCiurvv111/j1KlTCv0a6f+H3d6/fx+5ubk4cuSIUlsaiUSCkJAQ9jkRwc7OrtKFlEo4e/YsiAja2trg8/mwtLRkWzL9m40bN4KIlNpZ7dixAxKJBI6OjhCLxejfvz+6du0KX19ftbSk+TcymQzNmzfHuHHj8OTJkzLnnTx5EsOHD0dISAhGjhwJCwuLcuU+f/4cw4YNY73Umpqa0NDQwIYNG9Sqf20gk8mwdetWuLq6gs/nqyWqJD4+HgzDYMWKFVX+3H1syGQydO7cGQKBAJqamujQocNH5YVftWoVIiMjsXPnTixatAgaGhrlhl4vWLAATZo0USrYVhq+vr5lRi1xfN4QF/rLwaEeEhMTubyJD0xBQQFEIhE2b95cLTkl+cWRkZHo0aMHunfvjm7duqFLly7o0qUL+Hw+6tevX25rhH79+oFhGIjFYnh7e6NNmzbw9PQEEaFHjx4Kc2UyGX777Te0a9cOhoaGmDJlitpvmDk+fXr06IFffvlFYayoSIbEU7cx8tu1mD1+N4qKKg75mz59Ovh8fqXCbw0MDNhKulpaWmjYsCHEYjEmT56MnJwc8Pl88Hg8tqiStrY27O3t4ejoiODgYGhra7OytmzZwuaUVpWEhASEhIRg4cKFYBim1Jvb06dPw9PTE9bW1goGfkFBATZs2IDOnTvD0NAQhoaGaN26daX61arKunXrKjSAX716BW1tbcyePRv79+/H+PHjMWXKlFLn7ty5kw3Xlkgk6N+/P1vw6n//+x8YhkHLli3LbCP0sfL27VtMnz4d7u7u4PF40NDQgL+/P/va1EHHjh2hpaXFfj4dHR0RFhaGWbNmfZShsv8mKioKzs7OGDBgAG7evInnz5/D2toaYrEYmzZt+qj0z8jIQN26dcHj8eDo6AihUMhWpi+LM2fOwMzMTKW/d3Z2NsRiMdzc3GqlkCJH7cIZqhwcaiI+Ph4RERG1rcYXh42NDQYNGlSltTKZDP7+/uDxeKhTpw5cXV3h6uoKNzc3uLu7w93dHfXq1YO/v3+FRTmeP38OLS0t1ru0ZcsWGBsbY9my//N6ZWVlYd68eXBwcICnpyfWrFmDt2/fVkl3js+f8PBwNicTKDZSxw7ciG+azkSo1xRE+M/A2IEbKzRWW7VqhYYNG6q8r1wuh4mJCVucZfjw4XB2dsb58+fZdkgMwyA0NBTZ2dnIzc1FbGwsBg4ciNDQUNbz+m/Wrl0LoVCItLQ0LFq0qEo32k5OTiAiDB06tNx5P/74I0JDQwEUe5Pr1auHJk2aYPLkyfjzzz9x7do1mJmZqb1tR0ZGBkxMTHD16tUy50ilUqSmpsLW1lYlmZaWlmjUqJGS17qES5cuQSKRQEdHBwkJCVXSuzaIjIwEj8dD69ata7wy78uXL7Fq1Sr07dsX3t7ebAEwVXtXf2hu374NhmHQpk0bWFpasjnlTk5OH6V3ePLkyRCJRArFnB4/flzmd7zE6P73b1t5HD58GC1btsT169dhbGyMGzduqEVvjk8DzlDl4FATq1evLrcPGEfN0Lx5czRr1qzS62QyGfz8/KClpVXtYirx8fEwNzdHTEwMCgsLMXDgQLi5ubE3rHK5HFu3boW5uTl69+6NpKQklUI2Ob5s/Pz8cO7cOfZ54qnbaBswC6FeU9hH24BZSDxVfqjkjz/+CLFYrDD26NGjMkPusrKyIBKJ2Ov5+fl4/Pgx8vLy4OLigg0bNkBTUxPffvttqetdXV1Rp04dhbHc3FzWM0hEmDp1akUvXwG5XA49PT1cunSpwrkjRoxAixYtMGLECJibm2PHjh1K37dLly7BxsYGrVu3RlJSUqV0KYuff/6ZNZBL48CBA+DxeLC0tISbm5tKMq2srDBmzJhy50ilUnTs2BEMw3wy/4MSEhLA4/FqxRP88uVLODk5QSQSIT4+/oPvXxE+Pj6oX78++zw7Oxu//vrrR+VF/TdnzpwBj8dTaW5RURGCg4Mr1Td4/vz5GD16NIDiiAVXV1fcunVLQWZl0ho4Pi1UNVQ/rixtDo6PkBcvXpCZmVltq/HF0bBhQ/rnn38qva5ly5aUkpJCV65cqXIxlQsXLlBYWBiNHj2a4uLiaMGCBaShoUFxcXGUkJBADRs2pIKCAurUqRMtWbKE9u/fT1u2bCFfX1+2CAwHR1m8efOGDA0N2ecP7mRQQX6hwpyC/EJ6eLf0Ql4l9OjRg3Jzc8nPz4/S09OpZ8+eVK9ePWrUqBGdOHFCab6hoSEFBATQnj17iIhIJBKRtbU1aWpqUkhICA0YMIA2b95MmzZtKrWIWL169ejJkyeUlZXFjmlra9PDhw9p8uTJRER05cqVcnVOSkqiyMhI6tevH8nlcnrx4gUJBALy8vIqdx0R0YwZMyggIIDy8/Pp+vXr1L17d6Xvm5eXF927d48iIiKoU6dONGrUqOJT+WrQs2dPunHjBp05c6bU63p6emRmZkZLliyhffv2qSSTYRiSyWTlzhEIBLR3716Kj4+njRs30pIlSyqt+4cmKCiINDU16aeffvrgexsbG9Pdu3epU6dO1KFDB9LU1KR79+59cD3KQltbm96/f88+19PTow4dOnx0BZNKaNq0KQGgy5cvVzh31qxZJJVKacaMGSrLv3//Pjk5ORERUb9+/Wj48OHk5uZGlpaWlJGRQZ07d6avv/66yvpzfCaoYs1+qAfnUeX4GBk6dCh+/PHH2lbji2Pbtm3Q1NSs1JqRI0dCQ0OjSr1Yi4qKEB8fj6CgIFhbW2PVqlVKnilLS0s8efIE79+/R6tWrdC5c2cUFhZWei+OT5/c3Nwq9XJ89eoV9PX18erVK3asqh5VANi/fz8YhoG/vz+Cg4ORl5eH+Ph4ODg4oFGjRkhPT1eY/+eff8LExETJ43Tv3j0YGhoiOTkZtra2aNSokdJeMpkMenp6GDZsmNK1Xbt2gYggFArh6emJadOm4fbt22yvxZ07d0JbWxsMw8DR0RHa2tqwsLDAzJkz4e/vr9J7V1lev34NHx8fDB8+vNqRDnv37oWrqyvy8/OVrpVEVhgaGiInJ0cleTY2Nhg+fLjK+8+cORN8Pr/Uom9A8efxY/HMtW7dGq6urrWqg1QqhYWFxUdVqOfx48dgGKbcEPKPDU1NTWzfvr3cOceOHYOFhUWpraLKIzAwEMeOHVMYS0hIgEQigVAoRLdu3SASibhe4p8pxIX+cnCoh7Zt22L37t21rcYXR0lT8dKMAalUigEDBsDc3BympqYwMTGBiYkJGIbBokWLKr1XQUEBPD090aRJE2zdurXUm1GgOJeuf//++PrrrxEZGfnZN53nKJ0DBw5AIpFAV1cXgYGBiImJweTJk/HDDz9gypQpOHDgALKyslBUVISMjAw8ffoUWVlZeP/+PYYMGYIhQ4YoyCvJUW0bMAthjYuNVFVyVIHi8Dwigp+fHzIzM9lxmUyGDh06lFo9NikpCdbW1koHcLt374aDgwMuXrwIDQ0NtG7dWsH4OXToEBiGwbZt25RkXr16FUSE+vXrIygoCHp6ekpVhQcNGsQe/mRnZ8PPzw9EVGOGKlBsrHp7e6NVq1YYNWoU/vnnnyrJkcvlaNOmDZYvX17mnPDw8Apv6kuwtbWtMCf3vzRo0ABmZmYICQmBtbU1tLS02KJXRARdXd0yDdkPydmzZ8EwTK3n6Q8fPhwmJia1qsO/iY2NBcMwn1RFZx6PhwsXLpR5/cmTJzA3N8eJE2X3fS4LW1tbPHjwoNRrOTk5kMvlqFOnDm7evFlp2RwfP5yhysGhBqRSKQwMDCp9UshRfQ4fPgwej1eql2D//v3sje+kSZMwefJkTJ48GStWrKjSXrGxsQgPD69w3rlz57Bw4UIsXLiQq+b7BSKVSjFhwgTY2Njg/PnzyMzMxOHDhzF79mzMmDEDs2bNwqRJkxASEgJdXV0IBAKYmJjA3NwcBgYG0NTUBMMwSm1WgP+r+vvL2pNIPHWbNVJLihT5+vrC3d0dffv2xfLly5GUlIRDhw7BxMSkzKI1sbGxiI6OLvXagwcPYGhoqGRMTJs2DUZGRmjfvj34fD67/uDBg6xBFBcXh9zcXPTu3Vuh4rCLi4tSKy+ZTIaXL1+WWok3Li4O9vb2ePHiRTnvevXJysqCkZERq7+2tjacnJwQHh6On376SWVP5IgRI7B48eIyr2/YsAEdOnRQSZadnV2Zf5uyePnyJerWrQtvb28MHjwYmzdvxpkzZ5CWloaCggJ4eXlBJBIhJSWlUnJLQyqVYujQoejevXulqkqXoKOjU2qroQ9Jeno6iKjMglUfkujoaPB4PMycObO2VVEZmUwGIkJmZiZ+++03per4V65cQf369TFr1iyVZZ48eRKBgYFo2LAhtLW1yz3sTU9Ph4eHBw4dOlTl18Dx8cIZqhwcauD8+fOVqqrJoT6CgoLQoEGDMq9raGhg79691d4nMzMTJiYmXMVBjgr55ptvEBQUpJJhJZVKSw0LDwwMhFgsxqxZsyrsMzhw4EC2dcmxY8eQkpKCNWvWoH///mjUqBHMzc1x5swZpXXbt2/HzZs3cfbsWZT3f7Vt27ZYu3at0nhaWhqioqIUDFNbW1u0adMGAwYMAMMwMDY2Zr14JUZodHQ0+Hy+ypEGTZo0qfHKsCWkp6fD2toaHh4eiI6ORu/eveHp6QkNDQ0YGBhg3bp1Fcro2rVrqd7kErKysiCRSPDdd99VaBw5ODhUuap5WchkMoSEhEAgEFS6Z+l/jfWS3rSWlpZgGAZNmjTByJEj0axZMwQGBiI8PBzt27fHoEGDsHjxYhw7dow99Bg6dCh4PF6ZRbk+JI0bN4adnV2t6rBgwQK2H/enwJw5c3DmzBnMmTMHRIQZM2bAwsICZmZmuHfvHgoLCzFt2jSYmJhg48aNlQqr9/Pzw+LFi5GSklLq76hMJsPUqVPRoEEDGBoaokuXLh9FlACH+uEMVQ4ONTBnzpxK5RFxqIe0tDTweDzs2rVL6drbt2/Rpk0bEBEOHDhQ7b1WrFiByMjIasvh+Pzp1asXYmJiqiUjLy8P3bt3h1gsBo/HQ5MmTRAZGQmJRKIUEWBpaVmpUFW5XI4ffvgBRITZs2cjLy8PdnZ2ZR7oHD58GPXr12fzSP8ra/bs2ayxyjAMduzYgZs3b+Krr74CEWH48OGwsbGBlpYWrl69Ch6Pp3JYY2pqKoyNjT9ojndeXh527NgBY2NjtuVLXl4eevfuDR6PBwcHBwwYMABhYWFKuXMAMG/ePERERJR7Y/7y5UvMnDkT5ubmmDFjRpnzHB0dMWDAAKXxiIgItGvXrtS/iapoaWmVegBRGikpKbCysgIRQUtLCy1btsSePXvA4/Fw/vx5AMV5zY0aNYKBgQFatGiBgIAA+Pj4oGHDhrC1tYWenh4EAgFEIhHu3bsHIsKyZcuqrL86efnyJQQCASwsLGBpaQkLCwv4+vqif//+2LRpE9LT09nqu//73/8watSocvtqV4bt27fDzs4ODMN8NO9HRTx+/BhEpBBS7uvri6dPn2L16tXQ0dGBvr4+wsPD8eTJk0rJ/uuvv2Bra8tGI924cQOJiYkKczZt2gQvLy8kJiZyUUufOZyhysGhBn755Re0a9euttX4bCgqKkLiwWRsnRGHxIPJZf4j8vPzg4uLi8LYhQsXMGDAAGhoaMDU1LTU8MmqsGjRIowaNUotsjg+bx49egRdXV0UFBSoJf8uPj4ePj4+0NXVRbt27cDj8eDi4oJ27dqhTp064PP5lbpZK2nNMmvWLPTv3x8AcPHiRZiYmJTqYZPJZOjVqxciIiLK9IImJiZCX18fRITLly9DQ0MDRAR9fX0AxbmmRISQkBClHqvlsW/fPrRo0ULl+erk119/RZMmTRTGMjMzYWhoCBMTE9SvXx88Hk+pJ2pBQQFcXV2xb9++Cve4e/cumx+Zl5eHuLg4hZYtTk5OSi1nQkNDIRQKYWRkBJFIVG6YcXkIhUKV27M0atQItra2+Pvvv/Hjjz/Cw8MDfD4fDMNUujiTt7c3hEIhiAinT5+uiuo1wvHjxxEdHY0xY8Zg1KhRCAkJgb29PbS0tFhjTCgUwtzcHGZmZmAYBiKRSOV6B2fOnEFERAQMDAwgEokgEAjA4/HA5/PRpk0bpKWl1fArVA83b95E3bp1YWtry/4dO3furPC5ffbsGZ4/f16l4mSTJk3C+PHj2eft2rWDlpYWhg4dirdv3yInJwcWFhbl5sRyfD5whioHhxp4/vw59PX1ucquaqCoqAgxQdMQodsLIbwuiNDthZigaUo34tnZ2WAYBidPngQATJ06FQKBAAzDwNzcXO05Ptu2bUN4eDhGjBhR68U/OD5unj17xt7U2tvbq13+48eP4e/vjwYNGqB79+5gGKZShuqCBQswePBg/PzzzwpG44YNG+Dg4FCqB6SwsBDh4eGIioqCTCYr1TiRSqWYPHky61ldvHgxa7BqamqCiCASiUBEFYbQ3r17F8HBwWjSpAl4PF6l8v/z8/ORlJSE06dP4/jx4zh69GilvToA8P79e2hra5f7fX/79i34fL5SX9gTJ07A1ta2wt+Ko0ePgohgYmKikBtbEiXi4uKCqKgoAMVGsp+fHwQCAS5dugSZTIaxY8eCz+eXWmG5Ivh8PjZv3lzunMzMTDRt2pT92/0bmUxWpaJTMpkM7du3Zw81PgUKCgqUvNcymQwTJ04En8+Hg4NDqWHcMpkMgwYNgpaWFhiGQd26dTF16lTs2bMHx48fR0pKSq30kq0KW7duhaOjIxiGgYuLC1v4UP/kAAAgAElEQVQDgojKLHZUFRYsWID//e9/AIqLJenq6uLBgwfo27cv7O3t0bFjx4+qSjNHzcIZqhwcaqJu3bq4du1abavxyZN4MBkRur0QzHRmHxG6PZF4MFlh3tGjR8EwDBISEuDm5gY+n49FixbVWOuF9+/fQyKRgIjQsWPHGtmD4/Ng6dKlICJ0794dRIRWrVpBU1MTBgYGavcCvHr1CkKhUOV2J0BxwaPg4GBMnTpVyZCeN28e3N3dFdrilPDu3Ts0a9aMNVpKC3sFivP9iAgFBQVsuOjIkSPZasPDhg2DlpZWmfrVq1ePvRn29/eHpqYmpk+fXuHrSk5OxqBBg2BkZIRGjRohICAALVq0QHBwMCQSCebPn1/pw8R69eohOTm53Dlz584FEUEsFmPVqlXseMeOHdGpU6dy1w4YMIC92dfT08OIESPQu3dv8Pl8PH/+HHXq1EGfPn1w9OhRCAQCWFlZ4dKlSwoygoODq1QRuaRwVFxcXKnXz58/D21tbdaIjoiIqPQe/2X//v2wsrICj8djvfmfOhkZGfD29mY9rCKRCEZGRjhy5AisrKwgEokwd+7cT8Yg/Tdjx47F+fPn2YiIsLAw3L17F0VFRfD29sbixYsr9dujCps3b0bPnj0BADt27FAoYHj48GF89dVXXOHKLwjOUOXgUBPe3t5ISkqqbTU+ebbOiEMIr4uCoRrC64JtM/cozf36669BRHBycqrRsKl79+5hzZo1CA4OBhGxp70cHKXx4MEDEBHkcjn69OkDDw8PTJgwAY6OjmoNY83Ozoa3tzfGjh1bqXWZmZnQ1dVF48aNsWeP8vdq/PjxqF+/Ph49eqR0TS6XQy6XIzQ0FCNHjixVfmFhISwsLJCcnAxra2uIxWKF6xcvXgSPxwMAXL9+Hb6+vujSpQt7yFTSrqZEt59//hmNGzcus++xXC7HwoULYW5ujtmzZ5f6W3D//n20atUK7u7uOHXqVDnvzv+RnJwMIoKNjQ1MTU0xZswYpTmPHz+GTCaDra0ttLS0wOPx8Ndff+H69esQCoUQCoXl5jKuXLkSRISZM2fi77//hpWVFZszHBAQgLp166J3794YP348DAwMSpXRp08fpRQIVbG1tcXAgQOVxmNjY8Hj8RAREQGJRMIWrKvOQWCzZs3AMAzCw8NLre78qXPp0iXEx8cjPj4e3t7eICI0btwYr1+/rm3VqsT27dtBRBAIBOjWrRuIiC1qtnv3bvj7+1e773Bp/P777zA1NUXr1q1hZ2enUgEzjs8XzlDl4FATAQEBH1W+zadKaR7VcK3uSh7VmuTJkyeYPn06wsLCUL9+fZiamqJ3797o378/Dhw4wPVF5SgXuVwOR0dHBQNFJpOhcePGMDU1rbb87OxsHDlyBM2aNcOgQYOqdLO4evVqCIXCUj2ncrkcixYtgrW1NSZPnlxqruWSJUsgkUgwe/ZsparE7969g5aWFgoLC9G4cWMEBwcDKH4Ptm3bhm+++Yb1yjIMgwYNGkBbWxuGhoYwNzdnPYwlVTwLCwvx448/wtjYGAMHDsTq1atx8OBBXL58GU+ePEGvXr3g6elZ4WGVXC7Hnj17WPlHjhxRMLzev3+PoqIiyOVyrFu3DhKJBMbGxhgwYAAGDx4MgUAAT09P7NmzB+PHj4e/vz+ICIaGhhg1ahSICA0bNmRzc0v6LTds2LDM0Oz379+DiLBw4UIAQLdu3RAVFYW//voLDMOAz+eDz+dDQ0MDfD6/VBmbN28GwzDw8fFB/fr10aBBA5V7SgYGBsLOzg6LFy/G2rVr8fr1a7a/6fz58yGTyeDs7AxNTU32YNDCwqLMQ4P/8vr1axw8eBD9+/cHj8fD3bt3VVr3qSOTyUqttP2pkJeXBy0tLXz77bcYPHgw3NzcYGdnxxbOWrFiRakHHOogPz8f69atQ6dOneDi4gJTU9MKK59zfL5whioHh5po3LixUmU6jsrzfzmqPRHC64LW2j3QStQdB1f9/kH2L6lU+u8Hl3vMUVkGDhyI5cuXs8+3bdsGHo+nFLZZGeRyOdq0aQOxWIwWLVpgwYIFlfZwPXjwAFOmTEFoaCiICDt27Chz7okTJzBlyhTY29tj9+7dStcfPnyI1q1bw93dnb2BLRkvKTA0bdo0CAQCDBkyBE5OTuDz+RCLxZg7dy727NmDlJQU/PPPP5g9ezZsbW3RoUMHeHl5wdzcHACwZs0aNG3aFK6urhCLxRCLxfjuu+/QqlUrNGjQAEZGRoiMjKxU9duMjAwsXLgQXl5ecHBwQI8ePeDu7g5NTU1oamrC1tYWXl5eSqkc9+/fZ38TTE1NUa9ePRARGjRoAIFAACJivVBmZmZsMZkmTZqUGV4LFBuLGzduBFBs5Nva2mL9+vXYunUrJkyYgLVr12LXrl24ePFimTJWr16NwMBAdO7cGfXq1WOLWFXEtm3bYGJiAgMDA2hoaKBu3boICAiAh4cHCgoKsGnTJoSFhcHd3R0uLi749ddf4efnBx6PV6Enf+DAgSAiaGhowMTEpFJ9NDnUj1wux+7duyuM/Lp79y5MTU1hbGxc5u/L+vXr0adPn5pQE0Bx2HyfPn1w6tQpNGrUCGfPnq2xvTg+bjhDlYNDDchkMojFYmRnZ9e2Kp8FJVV/t83cg8SDyUi79Rh9nIdg/cRfaiTUqISSAh8lN53W1tYYMWJEje7J8XkyZMgQxMbGss9/+ukn6OrqVktmamoqjIyMqpXr9ueff0JfXx/Tpk3DgQMH4OLiUuFBzMWLF2FoaIjo6GilnoYlN78mJiasMZacnAwPDw92Tt26dcHn89G0aVMFr2dqaip8fHxgbGyMb7/9lg2TZhgGmzdvRuvWrUFESEhIwM2bNzFnzhw2d01dXLx4ERs2bMDly5fZgjl///13me/Jzz//rJBfGxwcDKFQCABlRlrEx8fD09OzzD6PY8eOhUAgYHNqXV1dq3Vj/vbtWzAMg5SUlEqtS0lJYQthtWrVis25ZBgGvXv3ZudlZ2fD0NAQRkZGSjLOnz+PU6dOQSaTQUtLC3Pnzq3y6+BQHw8ePEBQUBDq1KkDW1vbMu9VVqxYAT6fj8aNG5dZCKwkNHfNmjU1ouvDhw8hkUjY/UePHl1uCyeOzxvOUOXgUAP37t1TalHAoV5ev3iDoX4TMLfXUhQWqN/D+fLlS9ZTMnPmTKSkpHAGKkeVadq0KY4fP84+X7x4MfT09KosTyaTITg4GFOmTKmWXvn5+dDR0WHz5vz9/XHo0KEK16WnpyMyMlIp3G/w4MHo1asXxo0bB1NTU2zdulXBo3r9+nXweDwMHjyYXbN+/Xo0a9YMb968gYuLi0Ivz6KiIlhaWiImJgZEBHd3d3h6eqJu3brQ0dFReE9rg9jYWIW/Y0xMDAQCQbl5qDKZDH369IGenh62bdumdD08PBx79+7Fvn37sGnTJrWkFtjZ2VWp7/PMmTMxceJE6OnpsR5TgUCAwYMH4/bt27h9+zb09PRgZWWlkGd67NgxtmULj8djD/y4NInaJyUlBRYWFliwYAGkUim6dOmiVKUaKPauMwxT6rUSHjx4AFNTU5w4caLG9N20aRO6devGPj9y5EittajiqH04Q5WDQw08ffoUEonki8m/qS3ycvMxteMCjAmcirev36ldftOmTcsN0ePgUIXCwkJoaWkpeCQ8PDzg7e1dZZnLly9Hs2bN1HLj7+zsjDt37gAAgoKCyqze+1+eP38OIyMjTJ8+HXK5HHfu3IGpqSlb7Khx48Zo0KABCgsLYWNjg59//hmTJk1SKgLUqlUrEBHmzZuHFStWQCgUKrQ5mTdvHiwsLGBmZoZ169YhOTkZN2/eRHp6erVfe3UpuZn39PRkdbazs4Obm1uZa/Lz8+Hs7Ax9fX0MGTJE6bqNjQ1WrFih1qI7o0aNgoGBQZWKH128eBEMw6BRo0Zo1aoVDAwMFFIh/P39FT6Hw4cPB8MwiIiIYI3X69evlxuqzPFhOHPmDExMTNjCZA8ePICJiQnOnTunMO/SpUvg8/kYPXp0qXJevXqFmJgYmJqaKqQ01AS//fYbQkND2edv376Fjo5OpcL7OT4fOEOVg0NNLFmyBIGBgbWtxmdPUVERlg9fj+/qjcTztBcVL6gEVlZWZYbncXCoysOHD2Ftbc0+z87OhpGREUaNGlVlmT4+PmrzJrq7u7PFcDw9PStsv/Jv7t+/Dzs7O1y8eBGjRo1CdHQ0gOJKwg4ODujXrx+A/wvfIyJ2TgkzZsxA69at4evrC29vb8TGxioUGyosLMTXX3+NiRMnVvel1ggpKSlwdHSEUChEREQENDQ0yvVe3rx5Ey4uLmVGaEydOhW2trZYtGiR2nTMzMyEjo4OnJycKh0qXqdOHTRq1AhEBA8PD2hra6NZs2Z49+6dUnuln376CQzDlOop5qhd7ty5A2NjY/z+e3F9B6lUCg8PDyxbtgxA8e/S9u3b0bVrV4hEIpiamuLNmzdKcrKysuDu7o7+/furXKSrOly9ehX169dXGKtbt26pfWo5Pn84Q5WDQ01IpVIIhcJPslfap8je2EPobv09bv91D4kHk7F1RhwSDyaXWV2zNORyOSZMmICCggLEx8ejbt26XLgvR7WJjIxU8JyVFNupag/Vx48fQyKRqC2M0sPDA5cvXwZQ7A2s7OHMnDlz0K9fPyQkJLAeVaC4Wva/w0E7deoEPT09Ja9ecnIyrKys2L6q/2X69Olo2bLlRxs2mp6ezvaHLSmcRETw9vaGTCbD9u3bYWhoyPY2jY+Pr7AHqZeXl9qrxD5//hwmJiYwNTWtsB3MqVOnoKmpCbFYDB6Px1ZHLslRlUgkiImJQW5uLqRSKVJSUrB27Vo0bdoUGhoaatWbo/rI5XK0bNkSS5YsYcfy8vKgq6uL58+fs62Q2rRpg7Vr1+Lx48cYNGgQdHR0lNpO/f777wgICPhgur948UIp/9nZ2ZmLWPtC4QxVDg41Ym9vj/v379e2Gl8MJ3efQythN4Rr9UAIrwsidHshJmiaSsbq8ePHIZPJQESwsrKCoaEh/vzzz5pXmuOz5tmzZ9DX11cIU5s/fz5EIlGVe1Du2rULISEh6lIRPj4+OHDgAPr27QuhUFiqF6U8Xrx4AQMDA7x48QIXLlwoMz//5s2bsLCwwLt3ymH6Q4YMQa9evUpdN3PmTDRp0uSjDfVbtGgRhEIhW3yNiBAYGAgiwoIFC8AwDCIjI0FEOH78OMaPH1+ud7ik8FBpPW2rS25uLuzs7BSK05SGv78/nJ2dsW7dOpw6dQpSqRSxsbHYs2cPrl+/jkGDBkFPT499vQzDQFtbGwKBAEFBQWrXm6NqbN++Hd27d8dXX32Fxo0bKxz2vH37FjY2Nhg7dizMzMywfft2pfXnzp2Dq6urwtjSpUvh5OT0wQ5xr1y5AgcHB4UxKysrhfQAji8HVQ1VHnFwcFSIra0t3b59u7bV+GIQaYmIJ+CTNF9KACj/XT7duniP/vrtSrnrMjMzKSgoiB4+fEjffPMNpaenk729PbVo0eLDKM7x2fLixQuytrYmbW1tdmzMmDEklUrp6NGjVZIZHBxMycnJ9PTpU7Xo2LFjR+rYsSMRES1dupT09PQqtd7ExIRCQkJo/fr1FBcXR87OzqXOc3NzoxYtWtDcuXOVrp06dYqsra1LXffDDz+QlZUVzZo1q1J6fShCQkJIKpXStm3bSEdHh4yMjOjMmTNERDRu3DiaOXMmtWzZkgQCAenp6dGGDRuoa9euZcrj8Xh05MgRGjt2rNpfs7a2Nt2+fZs0NDTI3d2dsrKySp1nZ2dHDx48oDVr1lDz5s1JIBDQyJEjqVOnTlS/fn1auXIlZWdn0/Xr1+nly5ckl8spNzeXvvnmG3r27JladeaoGklJSTRy5EgKDw+niRMn0u+//04CgYC9PmrUKHr8+DGdOXOGNm7cSD169FCSkZCQQOHh4QpjgYGBJBKJaNy4cTX+GoiIdu3aRV26dGGfHz58mLS0tMjS0vKD7M/xacIZqhwcKtC/f3+aMmUKyWSy2lbli+B+SipJ86UKYwW5hfTgyj/lrrtw4QIREb18+ZIWLFhARERnz56tER05vizy8/OLw5D+xc2bN0kul1f5IERfX5+cnJwoISFBDRoWG1P79++ndevW0aBBg4hhmEqtLywspD/++INu3bpFiYmJNG/evDLnLly4kFauXEmpqakK4926daNTp05RQUGB0hqGYejNmzfUoEGDSun1oahfvz4REd25c4eOHTtGBgYGJJVKiWEYWrx4Mf3www905swZEolE9NNPP9HQoUOpUaNG5cps0aIFTZkyhe7cuaN2fTU1NenGjRvEMAxZWlrSrl27lObs2LGDbty4QRcvXqQnT54oXX/06BERFb92Y2Njdrx58+Z048YN6tOnD71//17tunOozty5cykmJob69OlDrVq1IolEonC9ffv2dOPGDTp//rySMVpCcnIy2djYKIzVr1+fTp8+TRs2bCBPT88a05+oOHpz165d7MFOXl4eDRs2jJYvX04aGho1ujfHpw1nqHJwqECvXr1IR0eHVq1aVduqfBE4ezqQSCxSGBOJheTkYV/mmnfv3tGLFy+IiCg1NZVMTU1JIBCQlpZWTarK8YXg5eVF79+/p3HjxrE37lu2bCF9fX0FL2tlmDRpEuno6FD37t3VoiPDMNS6dWsFb4uqZGVl0ciRI8nS0pJOnz5NsbGx5OPjU+Z8KysrGjlyJP3www8K4xMnTiQbGxuKiIigt2/fKly7desW3bhxgzp16lRp/T4EPB6PDAwM6NChQ+Tv708PHjwgACSXy2nUqFFERLR27VrS1NSkLVu2qGzAPXv2jMzNzWtEZyMjI0pNTaWoqCjq0aMHeXl50eXLlxXmuLm5kVgspn379imM9+zZk+zs7Eo9aOncuTNJJBI6fPgwGRsbk1wurxH9OSqmR48eFBcXp3RQVkKbNm3I3d29zPXXrl2jS5cuKfzOPHv2jKKioqhjx46UmZlJV65cofz8fLXrXsLly5eJYRjy8vIiIqJ58+ZR48aNKSwsrMb25Pg84AxVDg4VYBiGVqxYQdOmTauRk3EORXzCPcjN15k0dUTEMAxp6ojIzdeFfMI9ylyTlpZG/fr1IyKiAQMGUHR0NEVFRVXaq8TBURp8Pp9+++03SkhIoIMHDxJR8efs3bt3tGbNmkrLk8lktGLFCtq+fTsJhUJ1q1sm27dvJwcHB/r6668pMjKSAgMDycLCguzt7WnlypX07Nkzkkgk5RqpJURHR9ORI0cUbqBzcnJo7dq1REQUFxenMD81NZXEYvFH66GTy+X07t27co1KoVBIiYmJZGFhQffv31dJ7vPnz8nMzIx9fujQIRo9ejS1adOGRCIRWVhYUExMTJX15vF4tGrVKkpOTiYiIm9vb2rQoAG9efOGnaOvr083btxgn1+7do127NhBM2fOpFOnTrGe1RKsra3p1atXdOfOHcrLyyMej7tdrC26dOlCeXl5NGTIEDp//rzCtZKDlPKIj48nHx8f0tLSorS0NCIiatWqFe3bt48A0JgxYygsLIw2btxYI/pnZ2fToEGDFKI8lixZQgsXLqyR/Tg+M1RJZP1QD66YEsfHzvr16z9o8YEvmaKiIiQeTMa2mXtUrvr7zz//gIhgZGQEIsKjR48+gKYcXxLDhg1DbGwsACAjIwM8Hg+HDx+utJyUlBQ4OzurW70K+e677zBt2jScOHECW7ZswbFjx/D48WMkJyfD0tISYWFhlapSa25ujrS0NPZ5SZ9PBwcHtmrwvxk+fDhatmyJgoICtbwedVJQUACGYbB///4K5967dw8WFhYq/S8YNGgQ2zrk2LFjYBgG5ubmbJ/VYcOGQUNDA/b29tUuLPPXX38hLi4OGhoa7J7Pnz8HwzC4evUqALBFlAwNDZGWllZu4aTc3FwQkVp7wXJUnocPH2Ly5MmQSCRsH1upVIqoqCg4Ozvj9OnTZa59+vQp/Pz8FHrmMgyj0JLm/PnzsLW1RWFhoVr1fvv2LZo2bYohQ4YofFdEIhHev3+v1r04Pi2IK6bEwaF+IiMj6dGjR6XmX3GoFz6fT34RjannpE7kF9GY+Hx+hWvs7OyoadOmlJWVRQEBAUo5ORwc1UUkErEhct988w3Z29tT69atVV5fWFhI/fv3pxYtWtD3339fIzrm5OTQ1atXS82p19bWpj179pBUKqXevXtTSEgIWVtb05EjR6hr16509OhRatasmcp7eXt7s14emUxGtra2JBAIqEuXLtS4cWOl+UuWLCGZTFZqPmVtcO7cORo9ejTt27ePCgsLqUWLFip5N52dncna2pq8vLxo7ty5lJOTU+ZcV1dXunnzJu3evZsiIiKoR48elJGRQY8ePaLo6GhatmwZPX36lHR1dcnR0ZFsbW1pxIgRVXo9HTp0oG7dupFUKqUDBw7QxYsXaerUqWRsbEx8Pp9WrFhBDRo0oDVr1lBUVBTZ2tqSu7s76erqElGxVzkwMJCsra3Jz8+PwsLCiGGYj+bv9aXi4OBAM2bMoCZNmtC5c+dIKpVSz549KT09nebMmUPdu3enVq1a0ffff68UwmthYUGbNm2iiRMnsjUb/vjjD3Jzc2Pn+Pv7k4uLC/3yyy/V0lMqlbIe3vfv39M333xD7u7utGzZMoXoJj6fz9X84FANVazZD/XgPKocnwIhISH45ZdfalsNjjJo0KABiOiDNDDn+PLo3LkzduzYAaDYmxgZGVlue5D/Mn/+fAQFBVXY/7I85HI5Dh06hPnz52POnDmIi4sDUOxhWblyJczMzODo6AgjIyPcunULf/zxB+vNkMvl2Lt3L5ydnREaGoo//vgDUqkUP/74I/r161dpXVavXo3u3bsDAH755RdIJBJMnTq13DV79+5F8+bNK71XdUlLS8PgwYMRHR2NgIAAti2LqakphEIh2zOViFTqjVtUVIQ///wTnTt3RvPmzcvstT1jxgwQEfh8Ptq0aVOuzKNHj2LAgAHg8/kYNmyYyq9t7Nix0NXVBY/Hw3fffQcigra2NhiGAcMwsLGxAcMwICKEhoYqrLW3t8fAgQORl5eH5s2bQ1NTE0OHDkVISAh8fHzA4/Ggq6ursi4cNcfZs2fh6uoKS0tLREREsJ+5rKwsxMfHo06dOjh//ny5MiZNmoRvv/1WaXznzp345ptvqqXfoEGDEBwcjOzsbISGhqJ3796lRkMRER4/flytvTg+bYjro8rBUTPs3r0bLVq0qG01OMqgZ8+e8PX1rW01OD5TfH19IRaL4e/vD319fXh6ekIsFuOnn36qcG1aWhokEkmFPZllMhlSU1Px22+/Yf369Zg/fz7Gjh2LIUOGYP/+/Wjbti3c3NwwZswYjB07FnXr1oWvry90dHTg7++Py5cvAwC8vb1hb28PIsL169cV9igoKMDPP/+Mxo0bo2HDhhg5cmSpN68V8fTpUxgYGODly5e4f/8+zMzMKuwrW1hYCDMzM9y+fbvS+5Vw//59XLp0SWFs+vTpePnyJWbOnAlHR0d069YNKSkpOHv2LFq0aAGGYWBqagobGxsEBASAYRiMGDGCXX/mzBloampCJBKhXbt2Kusik8nQvXt3NG/eHLGxsThx4gTS0tIQHx+P4cOHw9raGg0bNqxUv924uDgwDINVq1apNJ/P52Pw4MFKhybp6enw8/ODkZERtm3bhsWLF+P58+cKc/z8/GBgYAAejwexWIx58+Zh5MiR8PPzA8MwEIvFICK0bdtWZf05ao7CwkIcO3ZMIXw+NTUVy5Ytg52dHdauXVvu2k2bNoHH4ymE7APFvaINDAxUSrMpjZycHBgYGKBVq1aQSCTo2rWrQr/XEu7duwci4kJ/v3A4Q5WDo4YoKCiAqakp7ty5U9uqcJRCXl4ejIyM8PTp09pWheMzJDExEXfv3sX+/ftZQ+n8+fNwc3Nj5zx79gxfffUVxGIxjIyMYGxsDH19fYhEIsyZM6dUuc+ePcOECRPg4eEBbW1tWFtbIzg4GFFRURg9ejTmzJmDefPmwcfHB9OmTUN+fj679vXr19i8ebNSLuH3338Pf39/uLq6luntk8vlWLlyJUQiEXbu3Fml92TSpElwcHBAUlIS7OzsWEO5PCZPngyxWAw9PT2YmJjgwIED7LX27dtDX18fVlZW6NKli9IN7atXr2Bvbw8rKyv4+PggKCgIEyZMYPPvxGIx2rVrB2NjY/B4PPB4PDg6OuLo0aMAin/DAwICoKurW6rxOHv2bIjF4kq9BwUFBVi3bh0GDx6MgIAAmJqaIiQkBHPnzkVSUlKpN+wVMW3aNPB4PHTp0qVcL/Xjx49BRFXaAwCys7PB5/PB4/HAMAyEQiEsLCzg7+/PvmcXL16EhoYGJk+eXKU9OGoGuVyO9u3bw8TEBFFRUdi7d2+Z33UA2LJlCywtLbFq1apSPy9mZmZIT0+vki7r1q1D27ZtkZ+fj1WrVpWZ77po0SIMGDCgSntwfD5whioHRw0yZswYxMTE1LYaHGXQpEkTnD17trbV4PhCkMlksLGxwalTp/Dy5Us4ODhgypQpyMnJwcuXL/H8+XNkZWUhJydHaa1cLsfMmTNhaGiI6OhoXLhwodR5FbF27VpoaWkpjdetWxenTp2qcH16enqlPH7/ZceOHTAzM4OJiYlKxaXkcjlevHiBN2/e4OzZs7C0tMTs2bOxatUqmJiY4MWLF3j06BF69OiBsLAwPHnyBJcuXcL+/fsRGBiIMWPGoKioCAcPHgSfz8fGjRtVDtn18vKCrq5umSGSeXl54PF4rJFWm8TExMDd3R1EVKYBMXz4cGhra1d5jzFjxoDH46Fv375ISUkpc167du0UDmQ4ap89e/agfv36Kh9SVFQEzMLCAk+ePKmSLv7+/goHTmURGhqKX3/9tUp7cHw+cCwtXBUAACAASURBVIYqB0cNcu/ePRgbG1f55JGjZmnXrh327t1b22pwfEEcOHAAFhYW8PPzw8iRI1Vak5mZiVGjRsHDw6PKN4clnDp1Cnw+X2nc2toaDx8+rJbsD8GjR48QFhaGbt264ffff2fHpVIp+vTpAyMjIzRq1AgRERGYMGECe2Muk8ng6uoKIyMjODk5qbSXi4sL+vbtW+4cS0tLCIXCKnsp1Q2fz1c4fJNKpZg1axbq1asHHo+HFStWVEludnY2iAhbt26tcO727duhoaGBzMzMKu3FoV7+/PNPmJmZ4fjx45Va5+TkxFaA/i+WlpZVyh29ceMGLCwsVPq+9OvXD8uXL6/0HhyfF6oaqlzVXw6OKuDs7EyDBg2iYcOG1bYqHKVgbm5Oz549q201OL4gvLy8yMDAgC5cuMD2ES2LdevWUcOGDcne3p5SU1Pp+PHjZGVlVa39PTw8SCaTUWFhocJ4eHg4jR8/vlqyK8uSJUvIyMio+DRcRWxsbOjo0aO0c+dOCg0NZccFAgFt3ryZMjMz6cqVK3Tw4EEaM2YMxcTE0Pv374nH41GHDh2ooKCALl68qNJeEomEMjIyyp0zb948KiwsJDMzM3r37p3Kr6OmkMlkChVZBw8eTNOmTSNLS0u6cOECRUdHV0munp4eaWpq0vPnzyuc26VLFzI2NiZjY2Nyc3Oj1atXU25ubpX25agesbGx1L17d9qyZQsFBgZWam1YWBgdO3as1Gs8Hq/c7y0Aun//Pm3ZsoUGDRpE3bp1o/Xr19OSJUsoKiqKBAJBhfsbGhrSkydPKqUzx5cLZ6hycFSRH374gW7cuEHx8fG1rQrHf3j58mWlbpI5OKrL9u3bydPTk06ePEmampo0dOhQmjZtGo0YMYL27dtH79+/J5lMRqNHj6ZFixbRypUrKTMzk/bt20dGRkbV3l9PT48YhqEHDx4ojA8ZMoRu3LhRbfmV4cqVK/T69Wvq1KkTRUVFUc+ePWnq1KmUnJxMb968qZLMS5cu0ejRoyk6OpokEgn9+OOPbMuqK1eu0OrVq1V+H/Py8khDQ6PcOb1796a8vDzKycmh06dPV0lndbNy5Upq0qQJjRs3jrZt20bfffcdHTt2jHx8fKol18vLiw4ePFjhPIFAQE+fPqWkpCSys7Oj6Oho0tHRYduRcHwYkpOTadGiRZSUlKRwqKMqWlpaJJVKlcZTU1MpLy+PJBKJ0jUANHz4cDI3N6cWLVrQ4cOHyc3NjVq1akV//PEHHTt2jL777rsK937z5g1t2rSJBgwYUGm9Ob5MOEOVg6OKaGpq0po1a2jYsGHcP+qPiMTEREpKSqKoqKjaVoXjC2LHjh3Ur18/+vrrr+natWtEVNyT0srKipYvX04WFhZkaWlJx44do8TERAoICKjQWKoMDx48IADk4uKiMJ6RkUGWlpZq20cVzM3NiYho3759dOLECfL19aUZM2aQj48Pbdu2rVKyANCwYcPI29ublixZQqtWrSKi4l6xIpGIiIhev35NDg4OKst88OABBQcHVzhPU1OThEIhPXz4sFI6q5vk5GRiGIYuXbpEOTk5tH79egoMDKQFCxaoRb6JiQk9fvxY5fk+Pj4KHtwhQ4aoRQ8O1Zg9ezaNGzeO7OzsqrT+1q1bVK9ePYWxe/fuUUxMDPXv35+0tbWV1qxatYrOnTtHf/31Fz158oR27dpFI0aMoG+//ZZ27txJjx49Iicnp3L3ff36NQ0bNozatGlDjo6OVdKd4wtElfjgD/XgclQ5PkWsrKzwzz//1LYaHACuXbsGDw8PbN68ubZV4fiCuHDhAhwcHMpt65CZmYmOHTvC3Ny8RnRYvHgx9PT0lMZjY2MrzMesCfLy8nD8+HEQEfT09GBhYYGvvvoKjx49qpQcuVwOJycneHl5ISQkhK3s+28iIiKwb98+lfWicgoT/VeuUCis9RzfmJgYmJiY1IjsM2fOgGGYUtsr/f3335g2bZrS68/IyIBQKMS3336LdevWgcfjcf8DPxCvX78GwzA4efJklWXUq1cPV65cAQD8888/CAgIgLm5OaKjo/Hq1Sul+devX4dEIsGtW7cqvVd+fj5evHiBpUuXwtTUFAMGDCh1D44vD+KKKXFwfBhCQ0MxceJEXLt2TaFlBMeH4+HDhwgMDISlpSXmzJlTreqlHByVpWfPnli0aFGF89LT08EwDP7++2+16+Dq6gofHx+FseTkZBgbG+PixYtq309V1PFd3LhxI/T19SGRSLBo0SKlthcuLi4gIkyfPr1CWbt27YJQKKxwnlQqBRGpVDG5pvH19UXLli1rRHa3bt3g6OioNP727VuIxWJoaWmBiKCpqQlnZ2fo6emBiODu7s7OdXV1RcOGDWtEPw5lfv31V5iYmGDFihVlVu8tj+DgYMybNw8//fQTzMzMsGjRojK/p/v27YOJiQm2b9/Ojt29exdbt25FTEwMgoODMXjwYIU1ixcvhrGxMYRCIQQCAYyMjNC6dWulXs4cXzacocrB8YG4f/8+OnfuDFdXVxgbG6vUQ5BDfeTn56Nx48aYPn26QgN0Do4PQUZGBgwMDJCVlaXSfLFYjNWrV6tVhzFjxkAgECAtLU1h3MvLC1u2bFHrXrVFVlZWqZ6YO3fuwNjYGG3btoWdnV25MjZv3gwNDQ2VjL6EhIRSqyjXBnp6emX2360u4eHhcHV1VRqPjIyEoaEhZDIZcnNzsW7dOvTo0QMLFixQ8rDevXsXRITbt2/XiI4cyty9exfOzs44ePBgpdcuWrQIX331Fb799ttyD2Lmz58PGxsbtuVTamoq2rVrBzMzM7bXbsuWLfHve/f4+HhYWVnhzp07yMvLq5IhzfFlwBmqHBy1wK5du6CnpwdHR0fk5ubWtjqfPUePHkWHDh3Qvn177h8ixwcnIyMDvr6+GDt2rMprJBIJpk2bplY9dHR0MGnSJKXxdu3aYePGjeWGJH/qfP/994iKioJQKETHjh1LnVNQUICwsDAwDKNy66CaDLetDC9fvgQRISMjQ20yX79+DQ0NDQiFQhBRqakSs2bNgr6+vsoyiYgL//3ALFiwAEOGDKkR2SWHD6NHj8acOXMwYsQI6OvrQ0NDA9bW1oiJicG4ceNAROxBx7Vr12BsbIykpKQa0Ynj80JVQ5UrpsTBoUa6du1Kb968ITs7O+rUqVOtF+H4nNmwYQN9//335OXlRRs3biSGYWpbJY4viNOnT1OTJk0oIiKC5s2bp/I6S0tLunz5slp1EYlEbGGhf9O2bVsaOXIktWvXTq37fSwUFRVRXFwcJSYmkouLC+3du1dpTlJSEpmamtKFCxfo3LlzFBsbq5LsDh060KtXr5SqKH9odu7cSTwej1JTU9Um08DAgAQCAbVt25YSEhKoT58+SnMGDBhA2dnZKrX5ysnJIaLiFkMcH47g4GBKSEhQu9ycnBwaMWIEdejQgZYtW0aJiYlkYGBA1tbWtGzZMlq9ejUNHTqUoqKiSEdHh/Lz82nixInUrl07Wrp0Kfn6+qpdJ44vGFWs2Q/14DyqHJ8Lb9++xZQpU+Dh4YG8vLzaVuez48WLF9DX1+dCzTg+OAUFBRgzZgws/h975x0V1dW18efOUKVIl46IqCA2FMEuNqzYewnGWKKxYK8xiSVqVIi9YPclGrtYY3yxYI/GXrDEgqISbIiIw8zz/eHLfJnQhikMJve31qzlnLLPc2Vg7r5nn71dXDQKuxs4cCDd3d11qsnLy4u9e/fOtS8jI4O2tra8ffu2TtcsDhw/fpy2trYEwI0bN+bob9GiBQEwLCxMo2MBvr6+bNCggQ6Uak5qaipLlizJatWq6dRucHBwgaHSNjY2ue7U/53k5GQCEM8gFjFyuZz29vY63cn+/vvv6eDgwF69evHdu3f86aef6O7uzpiYGNasWZMvX75ky5YtaWpqyipVqjA6OprJyckMCQlR67MiIpINxNBfERHDolAo2LFjR0ZGRhpayj8KuVzOoUOHsmPHjoaWIvIvZOzYsWzSpAlTUlI0mn/gwAGdn32cPXs2BUHgrl27cu1fuHAhTUxMOGzYsEJn3S3OtGrVSpkF+K8JluRyOcPCwgiAEolEY/u//PILBUHg+fPndSFXIxITEykIAo8fP64zm7Vr16ZUKuX06dPzHdexY8dcEy3lRuXKlRkYGKgLeSKF4KuvvuLEiRN1Zs/T05M7d+5UaZs5cyYrV67MTp06MTo6mm3atOGOHTvYqlUrymQyna0t8u9CdFRFRIoB9+/fp5OTExUKBZOTkxkXF8fp06ezR48e3LJli6HlfXJkZmayZ8+erFWrlpjiXqTIuXjxIh0dHfn06VONbcjlcgqCwCVLluhQGTlo0CBKpVImJibm2i8IAnv27ElbW1v6+/srd03279+v8fnuffv2sXPnzqxUqZKyVMnfEzrpC4VCQTs7O3bo0IEAlJErqampLF26NEuUKMGTJ08SQJ7/J+rQvHlzCoLAYcOG6Up6oVi7dq1aWYrV5fz58xQEQa1olN9//12tLNUvX77ksmXLaGFhoSuZImqSmJhIBwcHneXEmDp1Kr/66iuVtujoaHbu3Jmurq60t7fn9u3bdbKWyL8b0VEVESkGKBQKlihRgoIgEACNjY1pZ2dHALS1tTXok/pPDZlMxpYtWzI8PFxMVCViEFq1aqUTBzM0NDTXTKvaYm1tnadDBYCpqal89eoVT506xXv37nHx4sWsWLEiQ0JCeOjQoUI5rMuXL1eGBJ45c4aLFy9mz5496eDgwJcvX+rqkvLk/v37yt1U/K98yogRI2hhYUFPT0+mpqaSJG1tbdUqW5Mfq1evpomJCb29vYu89FVGRgYFQeClS5d0Yq9u3bqFKiXTqFEjGhsbMyEhIc8xZmZmFASBNjY2upAoUkhat27NpUuX6sTWgwcPaG9vz4ULFyr/HoSHh3PTpk188eIFp0+fLn7/iugE0VEVESkmdOnShZMnT1a5wfn9999ZtWpVCoJAPz8/xsfHG07gJ8K4cePYpEkTMdRIxGA4OTkxKSlJo7m7du1iixYt6OHhQUEQOH78eB2r+/8Q4L8603K5nDVr1qSpqWmuTlZWVhZ/+uknlitXjuPGjVNrnV27dtHT0zPXc6+fffYZ69aty61bt2p+IWoQFxendFI9PT05ZswYWlhYsGnTpirXWblyZTo7O/PcuXNarZeamkoTExPOmzdPW+mFxsXFhV9++aVObAUEBLBTp06FmuPv709LS8s8+x0dHdmyZUu9/8xFcuf06dN0dXXlmzdvdGIvMTGR7u7uPHr0KGUyGUuWLKlVFImISG6IjqqISDFh69atDAsLU2lLTk5mcnIyr1+/zjp16lAQBJYuXVqtkJqbN29y7969ytfJkyf1Jb3YkP1FrOm5QBERbXn48CHt7e01CpPNPutYpUoVDhw4MM+zpLqgb9++dHJyUr7v378/zczMCgx//eOPP+jg4KBy1jMvatWqleffqpcvX3L16tUsW7YsHR0d2bhxY546dapwF6EGI0eOVO6kDhw4UKVPJpMxOjqaPj4+BEAnJycKgkBra2tGRUVpvGbv3r1V/m+Lirp167Jhw4Y6sVWpUiU2adJE7fELFiygIAhcsGBBnmNq1KjBZs2a6UKeiIZ89tlnhSqTVRCenp68c+cOz5w5w4CAAJ3ZFRHJRnRURUSKCa9fv6aVlRVfv36tbOvRowft7Oz43Xff8dixY3zw4AHDwsIokUjo7OzMmJiYPO1ZWFhQKpXSxMREWQdPH7szxYlGjRpx5cqVhpYh8i9m+fLl7NGjR6HmyOVyZah/Xll5dc3evXtpZGRE8mMtUEEQcs2Imxu1atXivn378h2Tnp5OIyOjAh1ahULBpKQkzpkzh15eXhw1ahSHDx/OMWPG8MKFC3nOu3XrFjdv3sz58+czPj4+z2y91tbWyh3V7Lq0jx8/ZmhoKI2MjGhqasrw8HDl+crXr19z2LBhlEgkDA4OZlpaWr76cyM1NZUA+OjRo0LP1YYOHTrQxcWFjx8/1mh+aGgoTU1NKQgCJRJJgT/jbDIzMykIAufMmZPvuH79+hWYQVhEvzx58oSWlpZ8+/at1rZevnxJS0tLyuVyfv/99wY7ny3yz0Z0VEVEihHt27dXcbTKli3L5cuXc8iQISxVqhQXLVpE8mNx906dOlEqlbJEiRK0s7Ojra0tbW1taWNjQxsbGwJQCbFavHgxBUHgpk2bivy6ioIbN27Qzc1NrZ0eERF9ER4errbDl83Lly8JgMnJyXpSlZP09HQCYHR0NCUSSaE0L168mOXKlePcuXPzzQ5cunRp3rp1Sy2bAwcOpIWFBX/44QdGRUVxypQp9PDwYEhICDdv3qwSyr9161Y6ODiwQ4cOHDx4MKtUqcLGjRtz7dq1KmGNO3fuVHFU7969S5LcuHEjAXD27Nl5niW9evUqnZycaG5uzgMHDqh1Ddk8ePCAAIr8nOrRo0fp5OREAPk+xPwrcrmcjRo1YpkyZSgIAtetW8eEhASePXtW7XUzMzP5sYph/iQkJFAikYgZ7g2MlZUVX716pbWdmzdv0sHBgefOnaOHhwcPHz6sA3UiIqqIjqqISDEiLi6OtWvXVr5v2bKlMnTu7t27tLKyUrmZTU9P57x58zhz5kx+//33nD17NufOnct58+ZxyZIlOc5pjhw5koIgcPPmzUVzQUXIjh072Lp1a0PLEPmX4+3tXei6vXfu3KEgCHpSlDcmJiYcOHAgzczMCjVPLpfz4MGD/OKLL2hnZ8euXbvm6ti0b9+esbGxatm0tLTMUV9RJpNx+/btrFu3LkuXLs1FixZxwIABdHV1VUkwl+10m5mZ0d7enr169eLIkSNpampKf39/njlzhgBU6khWrFiRvr6+BV5n586dKZFICuWsrlu3jgAMlvW0QoUKau/qDxw4kKampoyIiND4XK1MJiMAtfICbN68mRKJRGdnaUUKx4cPH2hmZqaTHVWSHDVqFAVB0FmSJhGRvyM6qiIixQiZTEYXFxfeuHGDJDlx4kSVTJRubm78448/tFpjxIgRlEql/7gzqz/88AOHDx9uaBki/3IaNGig9s6CXC7n2LFjaWZmxlKlSulZWU5cXFzYrFkzrcqavH79mvPnz6enpycbN26s3LV89OgRHR0defHiRbXsuLu784svvsiz/9SpU2zbti2HDBmSa7bgtWvX8u7du7x37x5jYmL43Xffcfz48TQ1NVXu5P11h/Px48eUSqWcOXNmgdoiIiIolUp5+vRpta7l/v37tLGxYYsWLdQar2sCAwPVOl+anJxMQRC4YcMGrdbr1q0brays1N5B3r59O6VSKWvXrq0sFySif548ecKqVauyTZs2Otvtz8rKyjdEX0REW0RHVUSkmDF27FhlsoMFCxZwyJAhyr6OHTtyzZo1Wq9Ru3ZtlitXTms7uuLy5ctct24d58+fzwMHDvDGjRvcvXs3Z82axV9++UUtG8uXL2dERISelYqI5E/fvn3VPift4uJCc3NzTpkypcjDREmyatWqBEAPDw+tbclkMs6bN48ODg5cuHAhAwMDOW3aNLXne3l5sU+fPlrr+CsZGRkEwF69euWa3Khu3bpq1/Rs1aoVTUxM1N4tr1WrFmvXrs03b97w2bNnhdKtDefOnVM7aubw4cPKc8qacvbsWQqCUOjEX1evXlUeVTlw4AAbN25c6BBrkcLx3//+l35+fhrXQxYRMQTqOqoSiIiIFAl9+/bF+vXrIZPJ4OTkhIcPHyr7wsPDsXv3bq3XWLFiBW7fvo0//vhDa1vaMnHiRLRo0QIHDx7EvXv3MGfOHLRu3RpLlizB06dPMWjQIPTp0wcKhQIAcOXKFezfv//jE7S/ULFiRVy7ds0QlyAioqRMmTK4d+9egeMWLVqElJQU/Pnnn/juu+8gkRT912xGRgYA4OzZs1rbMjIywsiRI3H48GFs3LgRnTt3xqRJk9Sam5WVhYcPH2LAgAFa6/grZmZmcHJywo4dO1CxYkWVvqdPnyIhIQHLly9Xy9aePXtQrVo1BAYG4smTJ3mO+/DhAyZMmICaNWvi5MmTaNq0Kfz9/bFu3ToMHz4837naolAo0KpVK9SrVw9dunQpcPzbt2+1+twpFAqEh4ejfv36CA8PL9TcihUr4unTp/Dx8UHz5s1x+fJltGjRAnXq1EFWVpbGmkTypk6dOnjy5AkuX75saCkiIrpHHW+2qF7ijqrIP53atWtz9+7dfPnyJR0dHZUZKf/880/a2NjopFaZs7NzjnNCq1evZt++fTlo0CCtQ7LevXvHw4cPc+3atUxNTSX5MdTx7ztHkydP5ogRI/K0k5GRwZCQEEZFRXH69Ol0cHBghQoVctRyfP36NS0tLfn+/XutdIuIaMOMGTPyDGGVy+U8fPgwQ0JCKAhCvp/7oiAhIYGCIBhkN/evZGfJzf47oSnLly/PsePZpk0bAlAeoZDJZPTy8iKAQkeVyOVyVqhQgba2trmGH5MfM9tKpVJlAidbW1seOHCAwcHBDAoKKtQuc2Hp06cPBUFQK+vvs2fPCrWjnBtRUVE0NjZWyVRfGJ49e0ZLS0tWrlyZcrmcly5dorW1NX19ffPM4iyiHbGxsXRwcGBCQoKhpYiIqAXE0F8RkeJHTEwM69evz7S0NC5evJjBwcHKbLaDBw/mhAkTtF6jT58+tLW1ZXp6Osn/Dxnz8fGhlZUV7e3tNSqv8Pr1a44ePZr29vasXbs227dvTzs7OzZq1Ig2NjasUqWKyg1pTExMgSG7t27dYokSJdi4cWM+ePCAz58/p5OTU47zb0FBQTxy5EihNYuI6IIHDx7QwcFB+WDpr+3+/v6USCSUSCSsWLFisTkjLpVKi0XIpYWFhVa1S0kSAIOCglTashMbXb58meTHoxXm5ua8c+eORmtkZmbSzc2NLi4uOR7mZZfkWbBgAXft2sWGDRvy3bt3yv7Tp0/T19dXb6GXY8aMIQBKJBI2b96c9+/f57NnzxgVFcWYmBh2796dNWrUYN26dSkIAj08PLSq1evu7s5OnTppPL9Tp04sVaqUyoOS5ORk2tjY0NvbWyX5lb6RyWR6rVtcnOjfvz8XL15saBkiImohOqoiIsWQ9+/fMyIigr6+vnzy5AnDwsI4ZcoUkuS9e/dob2+vdDA1JTU1lU5OTrS1teW+ffvo5+envMnLyMhg+fLl6eLiUiibWVlZbNGiBbt166ZMqkJ+vFHfs2cPk5OTOXLkSAYHB/PUqVOMjY2lu7s7ARRYViYrK0vlBm/lypWsVauWyk3OuHHjlP9PIiJFiUKhYFhYGKdPn67SHhUVRalUSj8/Px4/ftzgu5d/x83NjV999ZWhZbBmzZps2LChVjYaNWqUoyxMbGwsAdDS0pJVqlRRq95nQbx+/Zp2dnb08vLi5MmTuXDhQm7fvp1t27aljY0NSXLfvn1s0KCByjyFQsFy5cqpnZRJE7y8vOjl5UVvb2/lrq6lpSVNTEwoCAIbN27MatWqce/evVqtc+nSJQqCwAcPHmhsw8nJKdfamykpKfTx8SEATpw4URuZeSKXy9m2bVs2atSI586dY+nSpQmgWDy00TfNmzfX+ucvIlJUiI6qiEgxZtKkSQwLC+Pjx4/p5OTEK1eukPxYfkDdbJr5kZGRwZYtW1IQBAqCwMTERGVfamoqJRJJoUrZjB8/nqGhofk6nQqFguPGjWPNmjUZHBzMGTNmcMaMGYXWLpfLGRQUxJ07dyrbjh8/zvLly4vJIkSKlPfv3/OLL75gzZo1lZ/9tLQ0Vq9enRKJhN98842BFeZNQEAAO3bsaGgZ7NatG/39/bWysX37dgJQ2a1u2LAhnZycOH/+/FwfJGhKcnIyAwIC6OTkRCsrK5qamlIQBAJgiRIluGbNGrq6uub4WzR9+nQOHjxYJxpyIz4+noIgMDk5mTdv3lQJUdY0RDc3QkNDWaFCBY3np6WlEUC+yam+/fZbmpmZ6TwzcGpqKj08PGhhYcHy5csTAJ2dnRkSEsIyZcrodK3iSLly5Xjt2jVDyxARUQvRURURKcZ8+PCBwcHBjIqKYlRUlHLHc/LkyTq9uUxMTMy13mGrVq3o6elZ4PzMzExOmTKFpUuXZkpKis50FcTSpUvZvXt35XuFQsGAgACx8LhIkRIREcFWrVrxzZs3yrbx48fTzMys0DVVi5rSpUvnWxamqNCFo5p91vWvZ++zjx0UFY8ePaKfnx8rVapEOzs7njlzRqX//v37tLe3V/ms6BoXFxdWq1ZNb6Vffv31VwqCwF9//VVjG0ePHiUAdu3aNc/zqHK5nE5OTpRIJGzYsKHWUUTkx5rFFhYW9PLyUh5BSUlJoUwm45IlSyiVSrVeozjz+vVrWlhYFBjBJCJSXFDXURWz/oqIGABjY2PMnTsXa9aswdChQ3Hu3DmsWLECkyZNwpUrV7BlyxadrOPr64vu3bvnaF+yZAkePnyIGzdu5Dn38uXLqF69Oi5evIiEhAQ4ODjoRJM61KxZE5cuXVK+FwQBX3zxBWJjY4tMg8i/m9WrV+PMmTPYtGkTrKyslO0fPnyAnZ0dypcvb0B1BZOSkoLAwEBDy9AJdnZ2cHNzw9KlS5GRkYGUlBS8e/cO5cqVKzIN7u7umDFjBm7duoU3b95g3bp1Kv1eXl5o3LgxYmJi9KZhz549uH//PhwdHXHs2DGd2s7KykLHjh0RHh6Oxo0ba2ynfv362Lp1Kw4cOAAbGxscOnQoxxiJRIJnz55h+/btuHbtGtzc3HDr1q0CbcfExODBgwc52t+/f4+aNWuibNmyuHfvHuzs7AAADg4OMDIywuzZs2FiYoLPPvsMw4cPR0REhMbXV1z57bffULVqVRgbGxtaioiIThEdVRERA5Geng5LS0tIpVLMnTsX27Ztg5mZGTZu3IghQ4aoVQpDecMkRQAAIABJREFUUzw9PVGmTBkMHz481/6UlBS0atUKkZGR2LVrF9zc3PSmJTcuX76MKlWqqLQ5OzsjLS2tSHWI/Lt48eIFvv76a4SHh2Ps2LHYunUrLC0tVcZIJJKP4UjFnHfv3iE4ONjQMnTGokWLAABff/019u/fD0dHR9y4cQPXr18vMg3t27dHZmYm9uzZo/IgLZuxY8di/vz5+PDhg17WDwwMxPPnz1GuXDmdOFsXL15EdHQ0Dh48iE6dOkEul+Pnn3/W2m7Hjh3x4sULtGvXDmFhYZgyZUqu49q2bYukpCRUqFABFSpUgIeHBwYPHow9e/bgzz//xLVr19CwYUOcPHkSZcuWxcCBA+Ht7Y1KlSqhc+fOeP/+PQCgbt26AIDTp0/nWpZn6dKlaN68OXbv3o1FixZh/fr12Lx5s9bXWVwYP348wsPD0a5dO0NLERHRPepsuxbVSwz9Ffk3kJmZycjISPr5+XH16tUkycePH9POzk5ZgmXhwoW0tLQsMGuuNmSHee3bty9HX8eOHTl27Fi9rV0Q48aNy3H+78SJE6xataqBFIn808nIyGDdunXZs2dPbtu2jUlJSbmO++KLL+jo6FjE6gqPkZFRsUggo4vQX/Jj9lYTExMCoCAInDJlCgcOHJgjsVFR8P79e1pbW/P58+c5+po2bcr27dvzt99+09v6kZGRdHNz08pGSkoKjY2NaWFhQSMjI0okEr0k4lm5cqUy6Vh+2eavXLnCzz77jC4uLjQ2NlYmjBIEgW5ubrS2tuazZ88YHx/PVq1a0dLSkgBoZmZGY2NjtbI9h4WFsWTJkqxXr55W53CLEw8fPqSdnR2fPXtmaCkiIoUC4hlVEZHiydu3bwmAERERKgk5OnbsyMjISOX7V69e0cLCQq/1Qzt37qzMZpnN8+fPWbJkSZ2cG9KUjRs3sl27diptHz58YMmSJcUvZBG9MG3aNIaHhxeYvdfc3PyTOO9mYmLCLVu2GFqGzhxVkpwwYQL9/PzYtm1bfvjwgTKZjD4+PgYpXTVq1Cj6+/vz3r17Ku2vX7/m3Llz6eHhwS+++EIvCeAmTJjAUqVKaWWjUqVKLF26tI4U5c/9+/dZvnx5SqVSTpgwQa18B8uWLaOJiQnLlClDANy6datK/6tXrwiArq6uan9XCYLALVu28Pfff6eJiQnDwsI0up7ixJgxYzhy5EhDyxARKTSioyoiUoyJjY2lv7+/St3R7Dpzf20LDAzk0aNH9abj9evXBKBy4xAXF8dmzZrpbU11ePHiBW1sbHLc0ISHh+eaHErMBiyiDTKZjB4eHvz999/zHbdlyxblTk92NERxJDMzM8fvtaHQpaN6/vx5li1bVuX3ffXq1QwNDdWJ/cIycuRIjhgxIte+t2/fMigoKM/M0HK5nMePH+eJEycKve7evXspCILGZX+mT59OqVRapPVMSXLOnDk0NzdX1oS1s7PLs+7w8uXLlcmR8nLKt2zZQhMTk3zXlMvlbN68OZ2dnVXK1EybNo12dnbaXZCBefPmDe3t7Yv85ygiogvUdVTFM6oiIgagW7duaNWqFfz8/LB9+3bI5XI4Ozujb9++6NWrF+RyOQBgwIAB+Oqrr5Cenq4XHdbW1ihRooRK8iaZTAZzc3O9rKcutra2CA8Pz5GwpGnTpsrkHHfu3MHixYsxffp0SCQSxMfHG0KqyD+Affv2wc3NDVWrVlW27dmzB76+vjA3N4eXlxdq1KiBrl27Kvv79euH5cuXG0Jugbx79w4AlEll/ilUq1YNMpkMV69eVbb16tUL9+/fx/Hjx4tcz5AhQxAbG5tr8jsLCwvExcVh7dq1WLFixcedgf8RFxcHHx8fDBo0CL169cLkyZMLtW7Lli2xc+dOJCQkFFpzVlYWvv76a4waNQpeXl6Fnq8NY8aMwbt375CZmYljx47B398fzZo1U35e/0rt2rXx7t079OzZE8+fP0ePHj2wY8cOtG7dGp9//jm++eYbrFixAqampnmu9/TpU3h6euL48eMICwuDs7Mz7O3tAQCWlpbK79lPlXXr1iE0NLTIf44iIkWKOt5sUb3EHVWRfxvHjh0jANaoUYPv3r3jhw8f2KhRI86aNYvkx53CiIgINm3alE+fPtWLhqCgIAYHByvfb9u2LUfYrSE4efIkfX19VXZP9uzZw+bNm5Mkq1evzpCQEOUOFwC2bdvWUHJFPlHev3/PevXqcd26dSrtPj4+9PPzY0xMDFu3bs3q1avz8OHDHDRoEEuXLs3p06dTEAQuWLDAQMrzB0Cx2GnR5Y4q+fF85t93KVetWsXGjRvrbI3CcPHiRTo7O/Ps2bO59l+7do3+/v708/Pjjz/+yKFDh9LLy0sZrnzt2jV6e3sXas2EhASWLl26wN3EvAgMDKSFhUW+Z0aLAplMRmdnZ0okEjo5ObFt27bKHc9Hjx4RALOysrh9+3ZaWVlRIpGwYsWKLFOmDO3t7enm5sY+ffrkavv69es0MzOjr6+vSs3ZbBYvXkwrKyu9Xp8+USgUrFixIuPj4w0tRUREIyDuqIqIFH/q1auHS5cuoWzZsmjTpg1kMhkmT56MXbt2AfhYlmX58uUoV64cevXqpRcN3377Lc6ePYuFCxcC+LjLWhyy64aEhMDMzExlp/Tt27fKUiE+Pj64ffs2qlSpAl9fXwDAlStXDKJV5NMkNTUVoaGhcHJyQrdu3ZTtCoUCf/zxBxYuXIh+/fohLi4Ov/32Gxo1aqQcM2nSJMyePRvDhw/HDz/8YAj5ebJ//34IggBBEAwtRed06NABO3bsUGnr3bs3rly5gjt37hS5nipVqiAsLCzPvz3+/v64evUqli5dilOnTuHNmze4ePEiGjRogIiICNy4cQOvXr1CcnJygWu9evUKNWrUQL169eDu7o67d+9qpPncuXOwsrLCsGHDNJqvK4yMjPD48WPEx8ejV69euHnzJlq2bAmpVApPT08YGRnBxMQE165dw5s3b5S76Xfv3sWff/6JpKSkHFE32cTGxsLU1BQ3b96EjY1Njv6MjAxIpVJ9X6LeOHHiBORyORo0aGBoKSIiekV0VEVEDEzlypWxceNGuLm5oXXr1ggKCsLly5eRkZEBADAxMUHt2rX1FsbXokULjBkzBpGRkfD09MTVq1dx9epVZep/QyEIAgYOHIhly5Yp2/7qqMbGxiIlJQUXL17E9u3bAQATJkwwiFaRT5Po6GiULVsWP//8M0xMTJTt3t7eUCgUuHHjBtasWYPNmzdj9+7dOHz4MJ4+faocN2bMGERHR2PcuHGYMWOGIS4hVwYPHozQ0FB4enoaWorOqVWrFp4/f45r164p24yNjdG5c2ds2rTJIJrS09NhZmaGFStWoFGjRjn+dgqCgAYNGuCnn37C2rVrYWNjgxUrVmDdunU4ffo0atWqhZMnTxa4zoQJE3Dz5k1cuHABx48fh7u7u0Z6JRIJqlSpgkePHmk0X5dIJBLUr18f8+bNw82bNyGTyZCQkIDffvsNISEhsLGxwdSpU1G1alWV74KCsLKygiAIuZarAYC0tLRPuubosmXLMGjQoH/kwygRkb+itaMqCIKHIAjxgiDcEAThmiAIw//X/o0gCI8FQbj4v1dL7eWKiPwzkUqlWL16Ne7cuYMnT54gICAAZ86cUfY/ePAAjo6Oelt/9uzZSEpKQsWKFTFq1Cg8e/YMUVFReltPXXr16oWjR48qz4C9ePEC1tbWAD7+n2V/Sfv4+MDLywu1atUymFaRTwu5XI61a9dizJgxKjezt2/fxsOHD2FkZIRJkyZh6NChiIiIQOfOndG8eXPExcWpOIDDhg3D0qVLMWXKFHz99deGuJQc1K9fHxcuXIBCoTC0FJ0jlUoxaNAgREdHq7T37NkTMTExePHiRZFrynZ6xo0bB5JYunRpvuNfv36Nb775BgCQlJSEOnXqYMGCBVi1ahXOnTuHrKysXOedOHECDRs2VDlLrSmenp549uyZ1nZ0jUQiQa1atRAYGIgLFy5g6NChSEhIQFZWFsaOHau2nbi4OHh4eOTZHxsbW+T1wXXF8+fPsXfvXvTp08fQUkRE9I868cH5vQC4AAj837+tACQC8AfwDYDRhbElnlEV+bcTHh7OLVu28Pvvv2ffvn2V7YmJibS3t+eKFSv0nuH29u3btLOzY1ZWll7XUZfz58/T1taWcrmcbdq04U8//WRoSSL/AG7dupXr2cDx48cTAKOjowtlb/Xq1RQEwaD1h7PJyMigiYkJp02bZmgpOj+jSv5/Ca0XL16otA8ePJhDhw7V6VrqEBUVxerVq7NevXr84YcfCiwXEhERwQEDBtDR0ZGJiYl8+fIlf/jhB/bp04eVKlWig4MD+/bty23btqlkbrawsODChQtztalQKJiUlMRff/2VixYt4ooVK/LVcP78eQqCwGXLlhX+gouIDh060MTEhImJiYyPj6eRkZFa816+fJlnjXCSHDhwIE1MTJicnKxLuUXGqFGjOGTIEJW2zMxMA6kREdEMFNUZVZLJJC/8799pAG4A+DQfU4mIGBhLS0u8f/8e/fr1w44dO5CamgoA8PX1xbFjx7Bo0SL069dPrxp+/fVX5Tmh4kBgYCCcnJwwbNgwHD16FI0bN1b2paeno379+vjpp58MqFDkUyQjI0MZRv5XDhw4AACFzgjat29fbNiwAXPnzsXw4cN1olFTzMzM0KpVq3/s74WjoyOaNWuGzZs3q7RHRkZi8+bNee5I6ovBgwfj8ePHMDIygiAI+X521q5di1OnTmHu3LkoV64cnjx5AhsbG4wePRrr1q3D5cuX8dtvv6Fy5cpYuXIlfHx8EBAQgAEDBiA9PR3Xrl1D+/btUblyZfj6+sLb2xvu7u6wtrZG9erVMW3aNFy+fBlz5szJNRtxNoGBgRg/fjyGDh2qj/8SnbBt2zZYWlqiTZs2CAgIgFwuh4WFBfz9/VG1alVUrlwZ06dPzxE5MHbsWDg4OKBFixY5bO7fvx8rVqzAf/7zHzg7OxfVpeiM5ORkrFmzBhMnTlS2JSUlwdXVFXPnzjWgMhER/aDTM6qCIJQGUA1AdsziV4IgXBYEYbUgCLZ5zBkgCMJvgiD8lpKSoks5IiKfHJmZmTAzM4OjoyPCw8OxZs0aZZ+/vz/OnDmDI0eO4NixY3rTcPjwYYSFhenNvibExcWhZMmS+M9//qMSAn3mzBkcP34cM2fONKA6kU+Rq1evwtvbO0f7rVu3IJFICuXsLFmyBIIgoFevXlAoFFiwYAEGDBigS7mFxt3d3SBhsEVF//798eOPP6r8nMqWLYu3b98WeTK47DwCp06dwoULF1CpUqVcx125cgVjxozBtm3bUKdOHZw6dQq//vprjnFeXl4YMWIE9u/fj9TUVKxduxY+Pj4IDg6Gh4cHevbsiXXr1mHv3r3473//i1OnTiEpKQlPnz7FkSNHsHz5cmzYsAFDhw7F8+fP89Q9ffp0ZGVl4datWzr7v9AlLVu2RFpaGmJiYuDg4IA///wT8+fPR8WKFVGmTBl4enpi6tSpiIiIUJl38eJFBAYG5mozKioKVatWRadOnYrgCnTPzJkzERERAVdXVwAfE79FRESgZ8+emDp1qlpJuUREPinU2XZV5wXAEsB5AB3+974UACk+OsMzAKwuyIYY+ivyb2fMmDEMDg7mw4cPGRsbyy5duuQYs3btWtapU0dvobl+fn68dOmSXmzrmu+//54DBgygnZ0dk5KSDC1H5BNBoVAwPDycK1euVGnv27cvjYyMaGNjw5kzZ6ptb86cObS2tmZqaiozMjK4c+dOSiQS9uvXT9fS1ebevXsUBIHdunXj1KlTDaZDH6G/5MefYf369blq1SqV9sqVK/O///2vztcriOjoaAKgVCrl+fPnc/RnZmaycuXKXLNmDUnS2NiYnTp1UikNpmtGjx7NL7/8Mt8xFhYWXLJkid40aEpKSgoFQeDx48fzHJOZmUmJRMK4uDiVdg8PD0ZEROQ6p3r16mzRooVOtRYViYmJtLOz47Nnz5RtCxYsYK1atZiSkkJjY+Nic2RHRKQgoGbor66cVGMABwGMzKO/NICrBdkRHVWRfzsKhYLff/89vb29GRMTQ1dXV0ZFRamMkclkbNiwIUeNGqXz9WUyGU1NTZmRkaFz2/rg/PnzdHBwYEBAADds2GBoOSKfCCNHjmT16tVVzjjOmzePgiBw7969tLe3L9T5zk2bNtHMzEylbd++fZRKpXnWeSwKKlWqRAsLC0okEoPVRtaXo0qSp06dooeHB9+9e6dsi4qKYpMmTZiamqqXNfNj586dtLCwoLe3N58/f85Dhw7xxIkTvHr1KseNG8c2bdoocwwIgsAzZ86wdOnSPHbsmF703Lx5kx4eHso1a9SoQYlEovICwBEjRuhlfW148OABzczM6OTkxNevX+c6ZubMmSxRooRKW0pKCgHk+bDV19eXvXv31rleffPHH3/Qy8uLy5cvV7bJZDK6urry4sWLnDt3Lj/uPYmIfBoUmaMKQACwHkD039pd/vLvSACbCrIlOqoiIh+Jioqiu7s7lyxZQk9Pzxw3MqmpqfT19VX50tIFKSkptLW11alNfTN69GgCKJa7AiLFj8uXL9PR0ZF//vmnsu3AgQMUBIFz584lSTo6OvKbb75R2+bp06cpkUhytP/666+USqXs1q2b9sI1QC6XkyQTEhJobGzMyMjIItegT0eVJNu1a8d58+Yp36enp3Pw4MF0dXXlH3/8obd188Lf358AWLJkSTZo0IAhISGsUKECAwIC+OTJE5IfE28B4NSpU7ljxw6WKlWKR48eVf68dIVCoaCHhwePHDnCWbNmUSqVMj4+nufPn+e5c+eUL12vqyvS0tIoCAITEhJy9DVq1IgAOHDgQJL/77SWL1+e9vb2edp0cXGho6MjS5YsyZCQEL1p1yUPHjygt7c3FyxYoNK+a9cu5TV8++23BSbxEhEpThSlo1oXAAFcBnDxf6+WADYAuPK/9t1/dVzzeomOqojI/zN27FiGhYVx/fr1DAoKynEzkZiYSCcnJ549e1Znaz58+JCurq46s1cUxMfHEwDFvx8i6tCiRQuVG76XL1/S2NiYn332mbLNycmJU6ZMUdtm9i5ObsTHx1MqlbJDhw4aa9YFU6ZMobm5eZE7Jfp2VLt3785Zs2blaJ8/fz6rVatW5Ne7cOFCSiQSvn//Ps8xFStWZGhoqPL9tm3bWLZsWTo5OXHMmDF8+vSpzvRs2rSJzs7OFATBoCHgmnD9+nUKgpDjZ5iRkUGJRMIRI0Zw9OjRJMnevXvT2tqazs7O+f7u9uzZk7Vr12bHjh1zREEURx49ekQfHx/Onz8/R1/r1q25evVqkh8d97179xa1PBERjSnS0F9dvcQbTRGR/0cmk7FevXqMiopijRo1ci3LsmbNGgYHB+vsZuzWrVssW7asTmzpAnXO28jlcm7btu2TLTUgUnQ8e/aM1tbWKqGiGzZsUN6wHj9+nF9++SVNTU05YcKEQtkGoFJK5K8kJCTQyMiIrVu31ly8lly5coUAaGdnl2eY/Pnz51mtWjWePn1aZ+vq21Ft0qQJDx48mKNdoVCwcuXKPHr0qN7Wzo3Xr18TAB88eJDnmFKlSil3Av/K7du32b9/f9aoUUOnZcjevHnDevXqsVOnTp/UGcbr168TANPS0lTad+3aRUEQaG1tTQBMTExk586dGRAQoLbt7IdL6enphdZ19uxZzpkzp9DzCsvNmzfp5eWljPT4O+bm5kxNTeX79+9paWmZZ4i0iEhxRF1HVadZf0VERHSHkZERvv32W8TGxqJKlSq5Zmbs06cPSGLDhg06WTMjIwPm5uY6saUpr169wq5du1C9enUYGRmhQYMG6Nq1K/r06YOVK1fmyMYqkUjQoUOHT7LUgEjRsnv3bjRv3lzlM37y5Ek4OjoiKysLDRo0wO7duxEUFITevXsXyrZEIsHdu3dz7atTpw5OnDiBgwcPonnz5lpdgybs2rUL1apVg5+fH2rUqIGIiAhYWlpizJgxyjFr1qxBUFAQnj9/jlq1amHcuHFFrlMTzM3Nc81uLAgCunXrhk2bNhWpHmtra9ja2mL27Nk5yqYAQEJCAp4/f45vvvkmR1/ZsmWxbNkyyGQy7Ny5U2earKyscOjQIaSmpmLo0KHZ0XDFHj8/P1haWmLRokUq7a1bt4azszPevHkDY2NjxMbGIjMzE8bGxmrbdnBwgLGxMTp06JBvZuS/c+jQIeXvx/Tp09WeV1h+++03NGzYEFOnTsWoUaNyHePo6IjXr1/j3LlzKF++PKytrfWmR0TEYKjjzRbVS9xRFRFRJTMzkwBYqlQplTN1f+X06dN0cXHhmzdvtF7v2LFjrFWrltZ2NEGhUHDlypV0cnJiaGgow8PDuW3bNv7yyy+MjY3lmjVrGBoaSj8/v3wzQYqI5MXo0aNzZPOtU6cOGzRowNWrV9PExERj22ZmZty0aVO+Y86fP08TExM2atRI43UKy7Rp0ygIgsoOXmZmJr/++mtKJBIuW7aMQUFBBMCJEyeSJFeuXEkjIyNWrFhR66RE+t5R3b59O93d3fno0aMcfXfv3qWTkxNlMpne1s+NyMhIZbIib29vRkREKHd2v/nmG9rY2OQ7f8+ePfT399f57ufr169ZrVo1lTO9xZ2mTZuyWrVqyvfr16/nyJEjOWDAALq7u7N27dqsV68eXVxc2LNnz0LZ3rx5My0tLRkYGJhrf1paGqOiojhv3jxGRkaybt26lEgk7NOnD6OjoymRSGhiYqKzsP4HDx5wz549nDVrFh0dHblr1658x4eEhPDo0aOcNm2aXpIriojoE4ihvyIi/wzOnj3Lu3fv5jumc+fOObIDa8KGDRvYtWtXre1owujRo1mlShWeO3eOjRs3poeHB6VSqUqJHoVCwW3bttHJyYmxsbGcOnUqt2zZYhC9Ip8ew4YN43fffafS5ubmxmHDhnHs2LEsVaqUxrZtbGzUCge8dOkSTU1NWa9ePb2fn+zUqRMlEgkXL16ca//YsWMJgIIg0NTUVKXvwYMH9PDwoKmpKXfu3KmxBn07qiQ5e/ZsVq1aNUeIKElWq1ZNb1l180Mul/OXX35hz5496enpSYlEQqlUSmNjYwLI13lWKBQMCwtjw4YNde5k3759m/b29nz16pVO7eqLyZMn09HRUfm+U6dO7NSpE1u3bs0BAwZwypQptLOzoyAIGoWsly5dWuV8OvnxZ/ftt9/S1NSU5ubmtLGxoYuLC+vXr8+FCxcqx8lkMnbt2pUlS5bU+Ppu3rzJcePGcdOmTbS1tWVYWBiHDRvGU6dOFTh34sSJHDZsGKtXr85ff/1VYw0iIoZAdFRFlMTExOS4ORP5Z3Hy5En6+vpqbWfdunUGcVSTkpJYsmRJbtiwgeHh4ezQoQNlMhk9PT3Zp08f7tu3j0lJScpzW0uWLGHLli05fvx4enh4cO3atUWuWeTT4u3bt3RwcOC9e/eUbWlpaZRKpdy+fTuXLFlCS0tLje27ubmpXebj6tWrNDMzY0hIiF6c1YyMDAYEBNDU1JTx8fH5jh0xYgR37NiRa9ZiuVzOvn37UhAE9ujRQyOtReGoKhQKfvHFFyrlX7IJDQ0tFjfxcrmccXFx7N+/P42NjTl9+vR8x797946mpqa5Ot/asG/fvnwjdIobW7duVYl06Ny5M1etWqXMUJ+dTE8ikWj0PSCVSnMkIQoLC1PuiGdmZuY7Pzw8XOPPd3p6OgMCAujs7ExbW9tC1y+/f/8+rays6Ozs/EmdPRYRIUVHVeR/ZGVlER+zMqskEBH5ZyGXy2lhYaH1U/Lr16/T29tbR6rU49GjR/T09GRgYCDt7Ow4YcIEZR3Xhw8fcuTIkWzSpAkdHR0ZHBycI2HNjRs3WKpUKe7Zs6dIdYt8OigUCrZp04b9+vVTaW/evDmdnJxIfiwlY2RkpPEaFSpUKFQZmps3b9Lc3JzVq1fXqbN6//592tvb097ePt+EPn8lPT2dAPKsn7xv3z6am5vT1dWVd+7cKZSe0NBQAqCJiYleX9k7lX/fPQ4ODubJkycLpVnfdO3alV5eXvmOiY2NZePGjXW+dpMmTQpVesnQ3Lp1i4IgsHz58jQ3N2fJkiWVTnafPn24ZMkSAmC5cuUKdCr/jlwup6mpKf38/Pjy5UuSH7MkBwUFMTQ0lBYWFgWWfFm7di0FQeC+ffsKtfbhw4cZGBjI3r178/Hjx7x69Wqh5mcTHh7OYcOGaTRXRMSQqOuoGhngWKxIESKVSnHlyhXExMTAzMwMR44cwbhx47Bnzx44OjoaWp6IjpBIJKhfvz5+/vln9O/fX2M75cuXB0mcPXsWNWvW1Ik2mUyG1NRUlCxZEq9evUJKSgoqV66s7BcEASEhIXj9+jWmT5+OFi1aKPs8PDwwb948AB8fqk2aNAm+vr7o1q0bqlWrBg8PD7Ro0QI7d+5EmzZtEBcXh5CQEJ3oFvnn8OLFCxw9ehR//vknFAoFXr16hSdPnuDXX3/F+vXrAXz87P89UVdhsLW1RUpKitrjy5cvjytXrqBy5coIDAzEhQsXIJFol9/w2LFjaNq0KcqXL4+zZ8/CzMxMrXklSpSARCLB1atXUaNGjRz9LVq0wNOnT9GgQQOUL18e0dHR+Oqrr9Sy7ejoCA8PDyxbtqxQ16IJP//8M+7fv6/SZmtrm2uyJUPy6tUrODg45DtmxYoVGDx4sM7Xnj9/PkJDQ9GzZ0+ULVtW5/a15c2bNzh8+DDi4+Nx6tQpvHr1Cs2bN0dYWBgePnwIJycn2NvbAwAaNWqE/fv3QyqVYsmSJTAxMSnUWhKJBImJiahduzZcXFzg4OCApKQkuLu7w9bWFpGRkZg3b57yOyg3PvvsM8THx6Nly5ZwdnaGt7c3ZDIZWrZsiZ49e6J9YfYzAAAgAElEQVRcuXLKse/evcOZM2cwZ84c3L59GzNnzkSnTp0gkUjg6uqq0f/X+vXrC5VESkTkk0Mdb7aoXuKOqv6ZOnWqcof11q1bYjrzfxBHjx7VSYjd7Nmz2b9/f63tHDlyhJ9//jnt7e3p4OBAU1NT2tnZ0c7OjgcOHNDYbnJyMr/55hv27NmTrq6uylC/vXv3slSpUoyPj+fbt2+11i/yz+HChQvKv3t/f1WtWpWBgYEMDAzMt8RMQbRu3Vol6Yu63L9/nxYWFqxYsaJW5xETEhIolUrZvn17jeaXKFGCS5YsKXDc1KlTKZFIGBoaqtYOVlGE/mZz8OBB1q9fX6Wtf//+XLp0aZGsry7Ozs6MjIzMs//YsWN0cnLKtxarNvz444+sWbMmP3z4oNJ+48YNfv3113pZU126d+/OoKAgzpo1iwkJCTx//jyfP3+e69ijR48qf48TEhI0XlMul/PLL79kaGgoJRIJbWxs6OHhQZlMRolEolZ90rt37yq17Nu3jyNGjGCpUqXYrl07ymQyhoaGskSJEgwKCuLChQsLvfsrIvJPA2Lor0hupKSk8PPPP2eZMmWUf1SvX79uaFkiOiAxMZHu7u5a2zlz5oxGN9zZpKSkMDw8nD4+Ppw3b55K+KFCoeD48eM5Y8YMrXUqFAp6e3vz999/V7Zt3LiR5cuXZ6lSpQrMwCry72Hbtm0EwAYNGrBVq1Zs164d27Zty7Zt2zI8PJytW7dm69at2a1bN43DcPv168cyZcpoNPfRo0e0srJi+fLlNXJWz549SyMjI62yj7Zr146mpqZqPbw8f/48bW1tWbJkyQIT2BSlo/rixQtaWFio/B+uWrWKLVq0KJL11UEul+eb+CcxMZEAtHqYVxAKhYLNmzfn5MmTSX480zxlyhTa29vTxMSEL1680NvaBdG7d2+uWbNGrbHZzqG9vb3WSafkcjl9fX2V90XZR2AqVarEJk2a5Dv33bt3bN68OTt27KjigEZHR7NHjx78/fffWa5cuTxD60VE/o2IjqpIvrx584a7du1S/lH++eefDS1JREsmTJjAL7/8Ums7aWlpNDc31zg5w6RJk9ijR488dwO6dOmi1s6NOvz444+sVKkSly1bplKeYsWKFSxTpgyXLl36ySQNEdEft27dorW1NQ8fPqy3Nf6enbSwJCcns2TJkixbtmyhdlt+//13Ghsbs1WrVhqvnY1UKlU78VBmZibDwsIoCALHjh2b57iidFS3bNnCqlWrqiRUysjIoLOzMy9fvlwkGgri6NGjuSauymb58uWsXLmy3nUkJyfTzc2N1atXp4+PDzt27MikpCRl5nVDMXDgQC5YsEDt8QAYEBCg9bodOnRgiRIl+OzZM5X27DI0Pj4+9PX15ejRo1X6MzIy2KhRI/bo0SOHszxx4kROnjyZy5cvz5FZWETk3466jqp2B2JEPlmsrKwQHh6Ob7/9FgBw9uxZAysS0Yb3798jISEBVlZWWtuytLSEr68v9u3bp9H806dPo0+fPjA1Nc3Rd+bMGRw/fhw9evTQViYAYOjQofjqq69w4sQJhISE4M6dOwA+nqnr2bMn4uPjERQUhKtXr+pkPZFPk3LlymHHjh3o0aMHMjMz9bKGu7s73r17p/F8Z2dnJCYm4unTpxgwYIBac65du4bg4GA0aNAAe/bs0XjtbPz9/dG7d2+1xpqYmODAgQNYsWIF5s+fj4CAAIOfBZXJZChfvjwEQVC2mZmZYejQofjhhx8MqOz/2bx5M1xcXHK0y+VyjB8/HnPnzsW0adP0rsPZ2Rn37t1DdHQ0YmJisHXrVri5ucHHxwd3797V+/q58erVK+zatQu1a9dWe07Pnj1hbGz8cedFQx49eoSdO3dixYoVcHJyUukbOnQoxo4di4YNG6JcuXKIioqCQqFQ9s+ZMwcWFhZYv349jIxU077UrVsXR48exbFjxxAcHKyxPhGRfzXqeLNF9RJ3VA3D9evX6e7uzri4OENLEdGQtLQ0mpqaqpTe0Ib9+/fTx8dHozNS1atX59mzZ3Pta9iwoV5Kyfzxxx+0t7fPNZxu8eLFLF26tM7XFPn0aNCgAbdv364X2/v27aOxsbHWdkaPHk0rK6sCy5IkJibSzMyM9erV03rNbM6fP59v9t+8+GvN1R07dpD8WGMyPT2dHTt2LLId1cOHD7NBgwY52l+8eEEvL69cd72KmsqVK+e6+71p0yZWr16dc+bMyXF2tCgZOHCgziJeCkNWVhY///zzQudH+PDhAytUqMDdu3drtO6AAQOU5W1q166d71i5XK4sZ0V+/Nzb29vz/v37uY5/9eoVLS0t6ezsrLe/OyIinyoQd1RF1CUxMRFJSUkYMmQI1qxZY2g5IhpgaWmJZs2a4b///a9O7DVv3hwVK1bEwoULCz335cuXsLGxydGelpaGc+fOoUuXLrqQqELHjh1hbGyMCRMmICAgAC1atMBnn32G0NBQjBs3Du3btwfwMcNwYGAgZsyYgYMHD+Kzzz5Dv379sHTpUpWn5CL/TIKDg3H9+nW92C5TpoxWWYOzmTRpEszNzeHk5IR169blOubBgweoUqUKqlSpgiNHjmi9ZjZLly6Fm5ub2tmCs/H09MT9+/fRo0cPdOjQAd27d4exsTEsLCywbdu2AjPc6gpnZ2c8ffo0R7utrS2uX7+OZ8+eYe7cuUWi5e88f/4crVq1wpUrV9C9e/cc/UeOHEGfPn0wZswYg2dx/euOdFHw4MEDhIaG4t69e5g1a1ah5hobGyMqKgqRkZE4duyY2n/Hnz59iqVLl2LFihXo1asXTp8+jZMnTxY4z8HBAatWrQIAjB07FkOGDIGXl1eOcQcOHECHDh1gamqK2rVr4/PPP8ft27cLdW0iIiIQd1RFPj5BnTFjBn///Xc6OzuL9VY/UY4cOUJvb2+dZRM8cuQIa9SoUag5qamptLKyylVDfHw8AwICVM6P6QKFQsGlS5dy7dq1PHToEC9evMjdu3dz1apVPHjwoLI+3qVLlwiArVq1YmRkJI2MjAiAK1euZL169ejt7c1Vq1bpVJtI8aJbt27cuHGjXmxnZmYSgE5+/+RyOfv160dBELhw4UKVvpiYGJYoUYLVqlXTaf1VkgwJCSkwcUxB7Nu3j2ZmZvx4e1G0ZJ+vz2tH8v79+3RwcOC1a9eKTFNKSgrbtGlDiURCd3d37ty5M9dxjRs35v79+4tMV14MGDCgSLMkx8bG0tHRkXPmzNHq8xwdHU0HBwdu27atwLELFiwgABoZGXHo0KEkP0YAAGB0dDQfP36cY45MJqOjoyNLlCjBuLg4JiUl0c7Ojunp6TnGnj9/ng4ODty0aZMyKql169Y5avyKiPybgZhMSURdVq1axS5dunDDhg20sLDgkydPDC1JREOaNWvGr7/+WifOYGZmJq2trZmcnKz2nLVr1+aZYTMtLY1SqdRgD0JkMhnXr19PS0tLJiUlcfDgwXRzcyP50TGYN28eXVxc2Lt3b7Fs0z+Ufv366fUmXBAEJiYm6syem5sbIyMj+fLlS06bNo22traUSqXs3r27zp1UkvT09NRJQrZHjx4ZxFElST8/P5VM4H9n5syZHDhwoN51pKamsl27dpRIJHR1deWWLVvyHd+rVy+1s93qkwEDBnDZsmVFstaRI0fo6urKCxcu6MRexYoVeerUqQLHVa1alU2bNs3RHhAQQKlUyjp16uToi4yMpJmZmfJB1Pbt29myZctc7QcHB3P9+vXK90ePHqWbm5vyoamIiIgY+itSCBwcHPDzzz+jd+/eOHnyZK6JHkQ+DVauXIldu3YhIiICGRkZWtkyMTFBnz59MGfOHLXGHzx4EGPGjMHEiRNz7b979y58fX1hbm6ulS5NMTIyQu/eveHi4oJ79+7Bzs4OHz58APCx8PuIESMwd+5cxMXFYdKkSXj79q1BdIrojwoVKuDWrVt6sy+VSnHv3j2d2csOa7S1tcWUKVPQoUMHvHnzBrGxsZBIdP/1/eLFC1SqVElrO7a2tjpQoxk1atTA+fPn8+xv2rQpTpw4ofN1FQoFTp06hXHjxqFixYpwcHDAmTNnEBsbi8ePH6NTp055zj106BBu376N5ORknesqLBYWFnjz5o3e18nMzMTAgQOxePFiVKtWTSc2mzVrhq1btxY47vr16+jTp0+O9itXrsDb2xs+Pj45+ipUqIAPHz6gTZs2yMrKwqVLl+Dv75+r/dTUVJXkSTExMZg4cWKuR2JERETyR3RURRAWFoYvv/wSFy9eROXKlQ0tR0QLPD09cfLkSchkMoSGhiodMU2ZNGkSNm7ciI4dO2LPnj2Qy+Uq/WlpaVi1ahVq166Nvn37Yvv27ahbt26utk6fPo2QkBCt9GjCvXv3sGHDBty5cwck8d1336F///6Ii4uDs7OzcpxEIkGPHj2wZcsWPH36FF26dFG53uyneyKfLqVKlcLz58/1Zt/U1BQPHjzQmb0DBw4gISEBu3btgrGxMWJiYlCiRAmd2f877969Q1BQkNZ2/p79tCipUaMGzp07l2d/lSpVcP/+fbx69UqrdRQKBb799lvUq1cPjo6OMDIyQt26dbF27Vq8fPkSdnZ2ePLkCbp27ZqvnR9++AFffPEFgoKCdJYNXRt8fX2V2dP1yaxZs+Dn54d27drpzObw4cOxZs2afB3tI0eOQCaToVu3brn2P336NNffgQEDBuDkyZM4deoUXF1dUaNGDWzbti3Xc+nGxsaQyWQAPv5ObdiwAY0bN9bwqtTj79/NIiL/FERHVQSmpqZYsmQJqlSpYmgpIjqgRIkS+M9//gMHBwfMmzdPK1vOzs64ffs2wsLCMHnyZIwcORLPnj3DpEmTEBISAldXV8TFxWHChAl4+PBhrk7qmjVrIAgCBg0apPMU/S9evEC9evUgCAIEQcC0adNy7CQvW7YM33//PRo2bAh7e3tMnz4dlpaWuHHjBsLCwnDlyhVMmzYNhw8fRmRkJHbu3Im2bdsiPj4eb968we3bt+Hs7AyJRAKJRIIvv/xSvCn4RLG3t9drCZUSJUogKSlJZ/bKly+POnXqFElynRcvXkChUKBq1apa2zKkoxoWFoZt27bh0KFDufYbGxujadOm+PnnnzVe48aNG3B2dlYm/unfvz/OnDkDmUyGZ8+e4fDhw0hNTS3ws3bz5k3MmzcPJ06cwMKFC3NNylPU+Pr6IjExUa9r3Lp1C4sWLcKCBQt0atfLywtNmzZFTExMrv2//fYbGjdujCZNmuT5GU1PT0doaKjyvUKhwObNm3H58mUEBQUpk3WtWbMGHh4e2LRpUw4bdnZ2WLp0KRITE1GrVi0AQOnSpbW8utyZOHEiBEGAkZER1q5dWywedoiI6BR14oOL6iWeURUR0R13796lm5sbW7ZsySNHjjArK0sre6mpqXRxcaGtrS2HDBnCo0eP8s2bN/nOefz4MZ2cnAiA5ubmvHnzplYa/s6ePXvo7u7OEydOcPPmzezSpQv9/Pz+j73zDIvq6t7+fWboA1JkqIJIEwvYQEWxBey9RY0NFY0xGMWO5YktGktiwcSCscReYg+xG3sXY0El1lhQEVEEqTP3+8GX83dCkTKDGs/vuubD7LL22uM4nLXX2msxOTlZHLNkyRK6urryiy++oJmZGfX19WljY0MbGxtWqFCBNjY2VCqVLFWqFMePH8/vv/+ednZ2HD9+PMk3ZSNatGjBmJgY7tq1izY2Njxy5IhW9yFRMpw6dYp+fn46k1+uXDmGhIRoXa62St+U5BoAdHKPtiAcPXqUSqUyz5Jr+/bto7e3d5Hu8p8/f54ymYy1atXKNZFONubm5pwwYUK+srZu3ZprqZr3yd27d8W7+7rg9evXrF69OhcsWKAT+SdPnqSnp2eufdl3p/O7Rw6Ar1694tixYxkREcGIiAgCoCAI4ueybNkyyuVybt++nRUrVszxPX/06BGDg4Mpk8k4ZcoUrScQfBtPT08ePHiQs2bNolwuJ4Bi/62XkCgJICVTkpCQSE1N5ZIlS1ixYkUaGxtz3rx5xZJ348aNfJMrtWzZklWrVmW1atVYo0YNAiAAdurUibNnzy7W2rmRlZXFPn360NfXl5GRkWzcuDENDQ1zJC5ZvXo1J06cyCtXrnD37t28dOkSW7duTYVCwYYNG/LJkycaD52pqaniw8e5c+dobGxMe3t70fB/37UYJYrGsWPH6O/vrzP5VatWZdu2bbUutyQM1RkzZtDCwkJr8qClDMhFpWPHjjQ0NMzVWFar1SxfvjyjoqIKLbdWrVr08fF557i2bdvmaTBt376d/v7+tLe3Z2RkZKF10CUqlYrGxsYah33aQq1WMzg4mF27dtWZ8ZaZmUlDQ8M8k/Y5OzuzS5cuec4HwLp161Iul1MQBAJgr169+PLlSxoYGDA8PJwkaWFhwYEDB9LX1zfPGqm6Thz4+PFjWlhYiIbpkSNHWKVKFZ2uKSGhLSRDVUJCQoO///6b1tbW3L59u87WAMAmTZqwTZs2bNmyJQGwW7duDAgI0FnpBZVKxeXLlzMoKIjLly/niRMnxJIA+RETE0MAOitXIvHhsWXLFp0Yktl89tlnuWYMLS4lYaiGh4fT1tZWa/IA5Otx1CWxsbGUyWS0tLTMM/ph3759VCqVjI6OZmpqKsuXL8/t27dzw4YNXLp0KRcvXpzD0H748CEFQeDhw4ffqcOhQ4coCEIOGQ8ePKCFhQV37NjBc+fO6dTbVlQqVarEixcval3uxIkT6e3trRMj+G3Kly/Py5cv56mDiYlJnt7+7Oif8+fPaxxYkuQPP/xAuVzOuLg4TpkyhUZGRty0aRP9/Pzey7/jtm3b2KxZM/H9xYsX6erq+kF+pyQk/k1BDVXpjqqExCeCu7s7oqKiMHDgQMyZM+fNSZUWyU7EtXz5cmzfvh27du1CqVKlUKdOHZiYmIjJJbSNTCZDcHAw9u3bh+DgYPj7+8PQ0PCd8ypUqICEhATpTs8nxNOnT2Fra6sz+UqlEomJiTqTr0tSU1O1frc0t0QzuuTFixdwdnaGp6cnatasibCwMGzatCnXsUFBQRg6dCgiIyMxZ84cxMbGom3btujVqxeGDBmC0NBQKBQK2Nvbw9XVFZ06dUJoaCgcHR1Rv379d+rSsGFDGBoaYsmSJRrtcXFxsLOzQ+vWrVGjRg0IgqCVvWsTT09Prd9TnThxIjZu3Ih9+/ZBoVBoVfa/cXd3zzMhVHh4OEhi+PDhefbfvXsX1atXh5GRkUZ27WHDhsHc3Bxz5swRs9tfunQJSUlJOHjwoPY38g7i4+Ph4OAgvvfx8YFarcaVK1dKXBcJCV0hGaoSEp8Qfn5+OHHiBNatW4eGDRvi2rVrWpN9//59AICNjY3YplarIZfLER8f/0Gm5reysvogHxQldMOpU6fg5eWlM/m2trYlUtpDF6Smpmo9aVNJG6pt2rRBZmYm7t69i5MnT6Jz58747bffoFarcx3v5+eHw4cPY/v27QgICEBCQgLS0tKQnJyM5ORk/Pbbb+jZsydq1aqF3377Ddu2bStwua5s+StWrEBSUhJ69OiBatWqoX379hgwYIC2tqwTPDw88Pfff2tN3rp167Bx40YcPHhQpwdF2Tx9+hRKpTJH+9atW/HixQtMmzYNERERuSa7mjZtmsbfsH9jbm6Oly9fQiaToX///pg7dy5Gjx6N6dOna3UPBSElJUXD6BcEAXXr1sXp06dLXBcJCV0hGaoSEp8YLi4u4kNc/fr10bNnT6xfv77YD5U3btyAqakp6tatK7apVCpYWFggKSkp1wcHCYmSIi0tDVu3bkW3bt3E0iJ9+vTBxYsXtbaGvb09UlJStCYvm61bt8LU1FTrct8mKSnpozdU//rrL4wcOVLMnuvl5QVTU1Ncvnw51/FBQUGwsbHBP//8g/j4eFhZWYl9BgYGaNOmDWbOnIl169Zh7ty5uHz5Mrp161ZgfVq0aIHz58/Dx8cHCoUCCxYswJo1axAWFla8jeoYbWf+vXv3Ltq2bVsiRqpKpcLVq1dzlNrbuHEjOnTogEWLFmHo0KFQKpX4+uuvCy3fyMhIPIyaOXMmUlNTkZiYiNjYWOzZs0creygoycnJObzT165dQ6VKlUpUDwkJXSIZqhISnyByuRyhoaG4cuUK6tatiwULFuCzzz7Lt7QGyTw9E8AbT+rx48dx5swZbN++HcCbB9WnT5/i+fPncHV11fo+JCQKw8uXL1GuXDmYmZnh+++/x759++Dn5yd+X4uLk5MTXr9+rRVZ2bx48QLLli3D5MmTtSr3bY4fP44NGzagY8eOWpVb0oZqampqDo95zZo1cf78+VzHC4IAe3t7xMXFoVevXvnKHjJkSKEMgJ9//hmzZs2CtbU1fv31VyxevBh169YtUNjw+8bT01NrHtX9+/fjp59+KrHyd9nlxMzMzMS2ffv2ITQ0FIsXL8a6detAEsOGDcO2bdvy/ZuWG2ZmZrhz5w6AN0brV199hfDwcMybNw89e/bEsWPHtLqfvDh//jz27duncYCVlpaG69eva6XElITEB0NBLrKW1EtKpiQh8X5QqVScPn06bW1tOX36dD58+FDse/36NYcNG0ZbW1uWLl2aU6ZM4YsXLzTmT5o0iZUqVWLFihUpk8k4ceJEkhSzJuZWiuDq1as8deoUHz16xAsXLuh2gxKfPGq1mgDYsmVLjh07Vkz0079/fwqCwPr16xd7jfj4eAqCoNWEZYMGDWLp0qW1Ju/fxMbG0sDAgO3atdOqXAC8d++eVmXmR0pKCgEwPj5eo/3HH3/koEGD8pw3efJk2tvba1UXtVpNJycnnSQkKgkePXpEa2vrIs9XqVS8du0au3btyrJly3LPnj1a1C5/NmzYwM8++0xMKLRt2zYqlUoeO3aMarWaLi4uvHTpElUqFQVB4IEDBwolf//+/RQEgSdOnBDbXFxcWKFCBe7du5dKpTLPRE7aZMCAAQTAOXPmkCSTk5PZvn17nSaLk5DQJpCSKUlISBQUmUyGMWPG4I8//sDt27fh7e2NESNGYOPGjahZsybu37+PU6dO4dixY7hx4wb8/PzEO6kZGRmYPHky7O3t4e3tja5du6J///4AICZsWrBgAcqWLQtra2v07NkT/fv3h5+fH7p06YJy5cqhSZMm8PPz03mheYlPl2vXrsHJyQk7d+7Ed999BxMTEwDAkiVLcPHiRZw8eRLff/99sdYwMjICSdSsWVMbKgN44yFyd3fXmry3ef78OWrUqAEfHx9s3bpVKzJ37NiBHTt2AIDOEqjlxq5du2BgYABra2uN9nr16mHHjh253kcEgIoVK8Lb21urujx69Ajp6ek5wk8/Fuzs7GBiYpKnJ/pd9OzZE4GBgXBzc0NMTAyaNGmiZQ3zpkaNGnj69Cnq1auH4cOHY+DAgfjjjz9Qt25dCIKA6tWr48aNG4iJiQEA1K5du1DyAwMD0aBBA43og6NHj+LGjRtISkrCuHHjMG7cOK3uKTc8PDzg6+uLNm3aAADmzp2LzMxMbNiwQedrS0iUJJKhKiEhIVKtWjUsWbIEV69eRXJyMlauXInx48djw4YNcHFxgZeXF1atWoUBAwagSZMmeP36NWbNmgVDQ0Ps27cP69evx5o1a8RMhKmpqejSpQuuX7+Onj17YtWqVahXrx6qVq2Ky5cv486dO3j9+jXi4uIQHByMoKAg3L179/1+CBL/SdatW4f27dvnmjzLx8cHoaGhmDx5shiuunjxYmzcuBHAmxDWgwcPolKlSihfvjw8PT3h4eEBd3d3uLm5wdXVFS4uLnB3d4ehoSHs7OyKpGNGRgbOnj2LlStXYty4cQgICMChQ4fg5uZW9I3nw8SJEyGXy3Hy5EmtyPvnn3/Qtm1bdOrUCYIgaIRf6prdu3drZEDNxtfXFx07dkTfvn1zDfO8e/cuPD09taZHamoqZs2aBRcXl482UZsgCAgNDcW8efMKPTczMxO7du3CxYsXMXXqVPFAqKRwc3NDdHQ0wsLCIJPJcODAAdSoUUPst7Ozw+PHj7FgwQLRIC8sW7duRXx8PFauXAkAKFOmDDw8PLBu3Tp8+eWXOHfuHC5cuKC1PeWGoaEhateuLV6puXHjBjp06FCgjPcSEh8T2s1FLyEh8Z/Azs4OixYtyrN/xIgRuHDhAsaMGYPdu3ejUaNGuY777bffsGnTJoSFhWHq1Km5jhEEATKZTExs4evri3379qFatWrF34iEBN5kx1y8eDGOHz+e55jZs2djyZIlGDBgAHr16oWBAwdCJpNh1KhRePjwIbKysuDm5iZ6ZmQyWY6XIAioU6dOgXR68eIFNmzYgP79+0MmkyEtLQ22trZITk6GoaEhTE1N4e7ujhkzZiA0NFRbH4UGR48ehb+/v9bK0gwbNgxOTk74559/tCKvMJw7dw6VK1cW35MUDcUZM2agcePGCA0NxU8//aRhQF6/fj3PUiaFQaVSYdu2bRgzZgx8fX2xc+fOYst8n4SEhMDNzQ1xcXGwt7cv8Lxz587BxcXlvSbP09PTQ8eOHXO9c21nZ4czZ85g06ZN6N69e5HkW1hYQKFQaJSiqlevHvbs2QMjIyOMHDkSP/zwA9asWVPkPbwLpVKJQ4cOie/v3LmDvn376mw9CYn3hWSoSkhIFInsBBmZmZmIj4/PdUy/fv0wYMAA/Pjjj3nKycrKwrJly/DFF18gMzMTCQkJaNWqFR4+fKgr1SU+IVQqFb799lsEBATAw8Mjz3EymQyRkZHo3r07MjIyYGtrizNnziAkJASDBw9Gq1atULZsWRgZGRVLH7VajZ49e2LdunUAgFGjRiErKwsqlQomJiZIT0/Xej3TvPj7778RHBysNXm7du0qdvh0Ubl79664l5CQEPzyyy/IyMiAIAjQ19fHrl27EBgYiFGjRmHmzAv1Lz4AACAASURBVJmiserm5oZ79+4Ved2kpCT88ssvmD9/Puzt7TFv3jy0aNFCG1t6r1haWqJbt25YuHBhoRJ5PX78WMy6/CFiZ2eHuXPnAkCOGreFISsrSyNi4O3fhaCgoHwPerWBv78/Bg8ejKSkJJQqVQpZWVkaNV8lJP4zFOQia0m9pGRKEhIfF/v27SMADhw4MEefSqWiXC7n5s2b85URFBREAARAfX19Ojs7c9SoUbpSWeITIzIykqamprxz506Bxtvb2xMA+/Tpo3Vdbt68SSsrK5qYmHDLli28e/cuw8LCCICLFi1iQkKC1tfMi5cvXxIAnzx5ohV5hw8fpkwmY2ZmplbkFRZDQ0OuX7+eJOnj40MAdHR0pFwup5eXF8eMGcNDhw7R29tbTPZGks+fP6exsXGR9L58+TKVSiU///xznjx5Umt7+VC4fv06bWxsmJ6eXuA5p0+f5of8LLdnzx76+voSQJGTHqWmplImk3H37t2cP38+t2/fzqZNm9LX15ckmZaWRkNDw0J9bkWhY8eOBMC2bdvyyy+/ZEREhE7Xk5DQJihgMqX3bpy+/fqQf9wkJCRyp0WLFqxTp06OP8qVK1cmAI4bNy7Puenp6ZTL5WzcuDEjIyNJkgsXLtSJkSDxaZKWlsagoKB8v4dvM3/+fL45w9U+AQEBdHZ2ZmpqqtgWGxtLAFSpVDpZMy82bdpEuVyuNcOyZ8+eLFu2rFZkFQUzMzPq6enRz8+Penp6nDZtGl1cXDhy5Ei6ublRqVRSEAQGBwfTxcWFv//+uzjX1dWV165dK9R6T548oYuLC1etWqXtrXxQeHh4MCYmpsDjHzx4QDs7Ox1qVDzi4uJYunRpent708fH553jz5w5w3bt2rFMmTKUy+U0NjamqakpZTIZX716RZlMRkEQaGBgwA4dOojzPD09eeXKFV1uhbdu3SIA2tjYcOHChfT39+fPP//MoUOH8vHjxzpdW0KiuBTUUJXiBCQkJIrFjh07YGNjgxYtWmDv3r24cuUK5s2bhytXrqBLly6YNm0aPv/8c/zwww/iHLVajc8++wxly5ZFqVKlsGPHDoSEhAAATp8+nW+IpoREYTA0NMTChQuxdOlSMVGSWq0WayH+m0qVKukk9LZbt244ceIEli1bphEm6ObmBkEQCpxhNS0tTSv1SVu0aAFjY2N8/vnnxZYFvKm9+ejRI63IKirt2rWDiYkJHB0dMXz4cNy5cwczZ87EzZs38fTpUyxfvhxr1qxBqVKlMHDgQLx69QoA4O3tjejo6AKtsWnTJkRERKB169b44osv0KNHD11u6b3j6elZqGzstra2SEhIKNGMz4XB1tYWMpkM8+bNw+XLl7Fnz548x6rVajRs2BDR0dFo2bIlfv/9d8yfPx/m5uZQq9Xw9PSEubk5zp07B0EQkJGRIc6tWLGimFlYV7i6uiIgIADLli1Dz5490aJFCxw5cgRZWVnw8fGRMgBL/DcoiDVbUi/Joyoh8XGSmZnJGTNmsGHDhixTpgwBsF+/fiTJwYMHEwAFQeCoUaO4fv16GhkZieG+b9daVKvVtLa25v3799/XViT+o7i5uTE8PJyrVq2iq6srAdDb25t169Zlx44dxXEJCQm51uMsDsHBwdTX18+zZqOXl5cYNpgbL1++ZIUKFWhoaEgANDAw4LfffltkL6xKpeLdu3c5cuRIrXm/XFxcGBAQoBVZRUEmk/HYsWPvHLdp0ybKZDI2bNiQQ4YMIUkuWrSIXbt2fefcJUuW0MXFhSEhIZw3b16Je8HfB2FhYZw5c2ah5tjb2/PBgwc60qj4BAYGMioqikFBQSxfvnye48aOHUsjI6Ncow6OHj1Ka2trzp49myTZpUsXenh4aMx9O8RcVzRt2pQ9e/bk9OnT2bp1a0ZFRZF8E4Lt5eXFKVOm6FwHCYmigAJ6VKVkShISEsVGT08Po0aNwqhRo5CZmYmKFSsiKioK3bp1w/Tp0zF//nzo6+uLZW8A4MqVK6hQoYJGAojExERkZGTA0dHxfW1F4j/Kq1evMH36dBgbG0OlUkFfXx+XL1+GXC7XKFtiZWUFhUKByMhIhIeHF3m9rKws+Pn5wcbGBgcOHMCMGTPw2Wef5Tp2w4YNqFq1Kg4cOIDAwMAc/V26dMGzZ8+wfPlyNG3aFPPnz8f06dOxfPly3LlzB2lpaTh+/DgaN26sMS85ORlqtRqjRo1C2bJlMWTIEBgZGaFy5cq4du0afH19kZycjKVLl6Jv377FSsYik8l0VkbnXTx69AhqtVqjDEledOrUCQ0bNsTRo0fx119/oWfPnihTpgxevHiRY+zDhw9x4sQJPH78GDt27MDff/+Nffv2fVIRH56enoWup2pmZiZ6qz9EGjZsiI0bN6J9+/YYPXo0srKy8MMPPyA5ORmurq7o06cP1Go15syZg7CwsFwjLAICAjSSCDZv3hzbt28X31esWFGsJ6wrDh06BENDQ0RHRyMrKwtNmjRBnz59UKtWLbRt2xZff/01rl69qlMdJCR0jRT6KyEhoVX09fURGxuLAwcOwNvbG9WrV0eHDh2QlZWFvXv3YuXKlVAqlahUqVKOB+OXL1/CwsLio60/KPHh8vTpUxgYGMDe3h4ZGRmoUqWKGII7cuRIjbH9+vXDhAkTipUNNjQ0FLGxsbhz5w6GDBmC4cOH5znWx8cH3t7emDlzZq79ly9fRteuXdGtWzdYWVlh4sSJSEhIQHx8PLy9vWFvb48mTZrA09NTDGlesGABzMzMYG5ujvXr1+O7776DpaUlPD09ce/ePXTo0AFXrlyBiYkJ+vfvjxs3bhR5r8Cbg4DiZkQuKgsXLoSlpWWB19+3bx8UCgXs7OwwaNAgpKen5/jNiYuLQ9WqVbF27VrcuHEDvXr1Qmxs7CdlpAKAh4dHoUJ/AUChUCAlJUVHGhWf0NBQ7Nq1CxYWFkhNTcWDBw/w/fff48mTJxpXVFJTUzF06NACyczMzIRcLhffm5qa4vXr11rX/W309PRw4MABdOzYEevWrUO7du0wcOBA7N+/H5MnT8azZ8/w8uXLNwlpJCQ+UiRDVUJCQusIgoAKFSpg7NixuHr1Krp06YIRI0YgMzMTKSkpOHbsWK7zTE1NP+iTeImPFw8PD1SsWBHVqlXD2bNncfbsWaSmpiIrKwszZszQGDtv3jyYmpqKJWSKwpkzZ9CoUSPExsZqPPzmhVKpRHR0NNatW6fh4c3IyMDjx49zeFpNTU2xc+dOmJubo2nTprh37x6MjIzg7u6OatWq4ZtvvsH06dORkpKCFy9eICkpCeHh4cjIyMC5c+ewePFipKeni17Y8uXLF3mvV69exbNnz/Kslaxrtm/fjtq1axd4vEwmw4gRI3Dr1i3I5XKsWrUKJ0+eRFZWFtLS0nD16lVUr14dQ4cOxdatW7FgwQL07NkTBgYGOtzFh0lh76gCwOvXrzWMtg8NCwsL+Pn54datWwDeRB44OjqiZcuWKFeuHIA33xF9fX2cOnWqQDIfPnwIY2Nj8f2rV680ytfogoCAAJQvXx6+vr4AgOjoaEyYMAGxsbGoXLkyLly4gKioKNy+fVunekhI6BLJUJWQkNAp9vb26NKlC0aPHg2FQoGoqCg4OzvnOrZ06dJ48eJFiZwAp6en4/nz5zpfR+LDoGPHjrCyskJcXBwaN24MY2NjmJmZwdLSEmZmZrCxsUGnTp3wzz//4JdffoGBgUGxkgM9ePAAPj4+BR4/c+ZMlClTBr169YKBgYH4ED1u3DiYmJigdevWOeYEBgbixIkT2LhxI5ydnXHp0iUsXLgQjo6O+OmnnzBmzBiYmJgAePPgPXHiRPzzzz+oUKECrK2tUaVKFaxZswYAsHr16iLvdc+ePSCJb775psgyikpSUhKuXbuGnj17FmpeeHg4ZDIZTp48iW3btiE9PR1+fn7w9PREs2bN8Pnnn2PcuHE60vrjwdHREVlZWQU2du7evYvnz5/D29tbx5oVj1evXsHLywsqlQp3796Fqakpnj59ij///BNOTk7o168fTE1NceHChQLJe/LkCRQKhYZ8XRuqgiBgxIgRGDt2LEqVKoWWLVtCLpfD0dERGzduRHp6Ol6+fIkZM2ZgxIgRBTa6JSQ+JCRDVUJCokSwtrZGVFQUgoOD87w3c+HCBZQqVUrDo6QLVCoVAgMD4ejoiN9++w3Am1P13O6pSfx3OHz4MIA3GXiXLVuG2bNnY9y4cVCpVHB2dsaxY8dQtmxZhISEID4+HseOHRO/HxcuXICtrS2aNGnyznWWL1+OhIQE9OvXr8C6Va9eHRcuXEB6ejrUajU+//xzfPfddzh16hR8fX0LfH90wIAB2LVrF7766qt3jp0zZ47o+SqONzQ4OBi9e/fGli1biizjXXTq1AmjRo3K8dtQq1YtKJVKdOnSpVDyZDIZLCwsIJPJ4O7uDgcHByxcuBBbtmzB/fv3MW/ePG2q/9Eik8nQpUuXAh9k/P7772jRosUH7VEF3vzeu7m5wcLCAkuWLIGpqSl69+6N8+fP4/Dhw8jIyEBiYqL4m5Efz58/x65du2BlZSW2lYShCry5v66vr4/q1atr/EaYmJhgx44dGDJkCDZu3Ag9PT34+/tLEUsSHx8FybhUUi8p66+ExH+fyMhI1q5dO9e+OXPm8Ouvv9a5DtOmTWO9evW4evVq1qtXj5mZmaxXrx67d++u87Ul3g+3b9+mUqnk6dOnc/TJZDJu2bKF5Ju6iSkpKfT29hazVffp04eCILBGjRqUyWQaNTj/TUpKCo2MjPjVV18VSU+1Wk0ANDMzo0wmo6WlJdu2bVskWQXh2LFjYibh4hAXF6eT+rOrV6+mo6OjmCX84cOHYt+kSZNoYGCg0VZQtm/fTkEQ+PDhQ0ZFRbFChQrF0nPt2rWsU6dOvt+Nj5XTp0/T3d2darX6nWPbt2/PtWvXloBWxaN8+fL866+/2KxZMwJgWFiYRr9araanpycdHBzylbN69WoaGBjQycmJN2/eFNvHjx9fIll/SfLSpUv8/fffOXDgwBz/RllZWaxUqRJ37NhBAP/5ur8SHw+Q6qhKSEh8iPTp0wfx8fHYtm1bjr7U1FSdJmQhiYkTJ+KXX35BqVKl0KNHDzx69AiXLl3C9evXsWvXLqSmpupsfYn3R7ly5dC3b1+sXbtWbEtKSsKgQYOgVquhr68PAPDz84OJiQkuXLiA1NRUhIaGYuvWrRg9ejTOnTuHypUrY8KECXmu07dvX5iYmGDBggWF1vH58+cIDg4G8CYr9ty5c5GYmKjT5GJ37tyBTCYrdm3W7PubSUlJ2lBLJCsrCw8fPsTSpUsBAHZ2dmLf4sWL0bVrVzg4OBRablhYGIKCgnDx4kV06NABU6ZMKZJ+W7ZswdChQxEWFgZbW1ucO3euSHI+ZPz8/KCnp4cjR468c2xycjJKly5dAloVjwYNGuD3338Xw3Xr16+v0S8IAg4dOoTU1FSN+qjZZGfZ7dmzJ0JCQnD37l2NrNcJCQkl4lEF3tQBbtiwIRYtWgR/f3/s27cP169fBwDI5XJMmDAB8+fPR+/evZGWllYiOklIaI2CWLMl9ZI8qhISnwYnT56kUqnkrVu3NNq7detW6Jp970KtVnPv3r28dOkSR40aRR8fH0ZERIgemqNHj/LKlSs0MjJiUFAQ3d3d2aJFCyYlJWlVD4n3z/3792ljY8NatWrx6NGjFASBBgYGHDVqVIFlxMTEUBAE7ty5M9d+f39/BgYGFkk/Hx8fKhQKsTYjSZYuXZoDBw4skryCULVqVTo5OWnFG+rg4EBvb28taKWJnp4eJ06cSH19fbEtNjY2Rx3mwmBqaso5c+bw2bNntLGxYYsWLZiVlfXOeWq1mgsXLmSjRo1YuXJlAmDlypV5+fJlDho0iPPnzy+SPh86S5cuZVBQ0DvHNW7cmHv27CkBjYrHn3/+ySpVqog1vZ89e5bruDp16oi1Sd8mOxJh7969ufYplUpev35d63qT5L179/jHH3/kaG/UqBG9vLzo5+fHmjVrkiQXLFhAa2tr6unpcdKkSTk8xxIS7wtIdVQlJCQ+VGrXro3BgwfDzc0NVlZWaNasGZydnXH37l307dtXa+ts3LgRUVFR2LJlCxwdHWFjY4ODBw+iUaNGAIDhw4dj5cqV2LJlC0aMGIHRo0fjzp07mDNnDvr164cNGzZIpXL+Q5QpUwaPHj3CmDFjUK9ePVStWhXR0dGFkpGdnMjW1jbX/suXL2Pw4MEabWlpadizZw+eP3+OxMREvHjxAuXKlcOGDRuQmZkJHx8fpKWl4fLly9ixYwdatWolzi1VqpTOSn1MmjQJly9fxrZt29CmTZtiybpy5QosLS1FT442MTQ0RFJSElQqldi2bNkyKJXKPBOzvQtLS0v8/PPPWLFiBZ4+fYqrV69i27Zt6NixY67jHz9+jOnTp+P333+HtbU1Hjx4AODNXURTU1MAb0raZP+2/Nfo2bMnRowYgSdPnuT53Qfw0ZRCefr0KR4+fIi0tDRUrFgxTy9wdumX5s2ba7T7+/ujdOnSiIyM1KhffOfOHXTq1AkrV64sVibt/Jg7dy7Onz+PZs2aabSbmJhg5syZSE9PR0REBK5du4Zvv/1WrNE8ZcqUPGs5S0h8sBTEmi2pl+RRlZD4dPjxxx+pr6/Pzz77jFZWVpTL5eJdH22we/du2tnZccGCBTx//rzYHhYWRj09Pc6aNYsAOGDAgByn6ampqfTx8eHWrVvFNrVazbS0NI1x6enp/OeffxgdHc2DBw9y9+7d3LlzJytVqkQ3Nzc2b95cK3uR0C6ZmZkEwIYNGxZ67ubNmwmAI0aMyNF3/fp1AmB6erpGe+/evSkIAk1MTGhubk6lUklDQ0Pa29tTEASam5sTABs1apRDZq9eveji4lJoPd/F7du3KZPJOHfuXJ49e5aCIDAzM7NIsmJiYqinp0cXFxet31NdtmwZZTIZr1y5QgB8+fIlExMTaWNjw4CAgCLLnTJlCr29vamnp0cjIyP+/PPPbN26tXjHLzMzkwcPHuTcuXPZt29fWllZcdiwYbx8+TKzsrLYtm1bVqtWTUNmq1atuH379mLt90OmefPm4l3uvAgKCsrVy/ihMWDAAAKgvr4+Bw0alOe458+fU6FQ8MaNGyTJ8+fP08bGRozIqVq1qjj23LlzdHR05E8//aQzvbOysujg4EB7e/scfbVq1eJvv/1Gc3Nz6uvrEwCVSqX4+7J48WKuX79eZ7pJSBQGFNCj+t6N07dfkqEqIfHpEBcXRyMjI/GhftWqVaxYsSIB5DAIC4taraZSqeThw4c12l+9ekW5XM6ffvqJe/fuJQDu2LEjVxmjRo2iu7s7AwMDWbVqVZqamtLCwoIXL14UxzRq1Ig2Njb08fFhQEAAAVAulxMAhwwZwlKlShVrHxK6IyMjg0qlstCho6mpqTQ3N2f9+vXFthUrVrB27do8cuQIAVClUmnMadeuncYD7duYmZnxm2++IQDevXs3R3///v1zfSgtLl999RXt7OxIkiqVikZGRpwxY0aRZJUuXZp16tShSqWigYEBJ0yYoNGvUqm4bds2PnnypFBy09PTaWhoyNDQUJKklZUVa9euzTp16tDGxoaJiYlF0vdtLC0tOXToUL5+/ZoVK1ZkeHg4//e//9HR0ZG+vr786quvGBERkeN7kpuh6u/v/1EYaUXlu+++47Bhw/IdExgY+FF8BrVr1yYAOjg4cMOGDfmOdXBwIADWr1+fMpmMDRs2ZFxcnMaYrVu30tramr/99psu1eahQ4dYrlw5ymSyHAdiDRo0oJGREStUqCAa0pUqVeK4ceMIgD/88INOdZOQKAySoSohIfFBc/bsWVatWpVZWVkahmnjxo0ZERFRLNmPHj2itbV1jvbVq1fT0NCQJDl69Oh8syAmJCTw0KFD3Lt3L8+cOcPExESuX7+ezs7OXLFiBZcsWUJra2sx42hcXBzlcjn37t1La2trbt68WSeeMAnt0aRJE27evDlHu0ql4qtXr5iamprrvJEjR4p3E7/66ivxoTD75evrq2GsdujQIc+7m9OmTaO+vj6NjY3Zq1evHP1eXl5s165dEXeYN66uruzWrZv4vnPnzkWKZli1ahXlcrn4WU2fPp16enqMj48n+cYDnX144+/vn6+s8PBwli5dWrzbN2zYMJqamoqf5fXr1ymXyykIAqdOnVpoXXMjICCAlSpVIvnGM9y3b1+GhYXxr7/+ynfevw3Ve/fu0draOofx8F/izz//ZK1atXLty8zMZLVq1ahQKBgdHV3CmhWebK+ohYUFHz16lO/Y9PR03rt3j6NHj6aVlRVjY2M1+k+ePEkbGxueOXNGlyqTfJNVGQD79euXo2/NmjU8dOgQPT09NX6P5s6dSwCFuosvIaFrJENVQkLig+b+/ftiOB0A8cE2JiaG1tbWvH37dpFlHz58ONeH4vDwcNrY2DA+Pp6WlpacPHkya9WqVaCyC9ls3LiRn3/+Of39/Xnp0iWNvh49erBKlSpieZEBAwYUeQ8SumfHjh20t7fn2bNnxbZNmzbRzMxMfMhbtGhRjnnBwcHU09Ojp6cny5YtyzZt2nDq1Kn08PDg0aNHqaenx8WLF3Pu3LkMCgpiw4YNRe/lv1GpVATAyMhICoLAU6dOafR7e3uzQYMGWt03Serr63Pbtm20srKilZWVGCr46tWrQslxdXXNEeJuYWHBMWPGkHxTBqRRo0bcv38/BUHg5cuXc5Vz9+5dAqCHhweNjY0ZExNDhULBkSNHaoyrUaMGAeQpp7BER0dTEAReuXKlUPP+bahu2bKFrVu31opOHyopKSk0MTFhSkpKjr4dO3aIv6VFTXBVkmQfDrm5uRVq3pIlS+jh4SEeSKSlpbFixYolElJ76NAh8YAsr0O0NWvW0NfXl7dv36a5uTkjIiJ4/vx5zpw5s8ih/RISukAyVCUkJD54NmzYIBoE8+bNE9vHjx/P8uXL5+qdSEhI4LRp03L1hGWzatUqDW9RNi1btqS3tzevXbtGR0dHJiUl0dDQUGt/wJ8/f85ly5axX79+dHZ25t9//60VuRK6Y/369XR1dWVycjJv375NQRDYoEEDqlQqBgcHs3Tp0jnmtGnThl5eXnnK7NWrF2UyGWUymWhYAcjzQAT/vz6ol5cXW7VqJbZfvnw5V+O1uNy+fZsAGBUVRZlMxrCwMK5YsSLX7Kb5kW3kvV0/kiTt7e05bNgwqlQqDf1r1KhBV1dXtmrVipUrV2b79u2pUql46NAhmpiYsGzZslSpVPTx8SGAXD2UlSpV0vo92MqVK9PX17dQc1q2bKkRzj158mTROP8vU6tWLf7555852lu3bs1ffvmFt27dokwm465du96DdgUnICCAY8aMKXSW6oSEBJqZmYlRQN9++y3btGlTqMPOopCamkoPDw8CyNdzGxAQwLVr13LLli0ad96zsrLo6Oj4zjvGEhIlhWSoSkhIfBSsWLGCixYtoqurK/v378+srCza2dlRT0+PkydP1hj7+PFjNmnShK1atSIAli5dmmPHjtUIHY6JiWG1atU4fPjwHGuZm5tz/PjxVKvVdHd359y5c8U0/tpm8eLFdHJyytX7IPFh0bVrV06cOJG9e/fW8HwmJiZSEIQcoYyHDx+mIAj5hnlGRESID4UHDhzI17gCwNu3b7NBgwYakQBeXl6sUqVKUbeVJwEBAfT09OTOnTuL5E0kyTNnzrBUqVKsXr26RrtKpaJcLmdUVBRPnDhBmUwm9t27d4+WlpZUKpXs3Lkz9fX1aWhoSJlMRg8PDzHEV6VSiSH1/0YXCZuyk0kVxitmaGioocfnn3/O1atXa1WvD5Hhw4fnGnZdp04d/vrrr9y8eTPLly9PpVLJmJiY96Dhu1Gr1bSysuLJkydpZ2fH6OjoHPfK82Ljxo1s0aIFSXLnzp20s7PjgwcPdKkuSTIkJIQA8ozMyGb+/PkMDAxkly5d+PPPP4vtarWaenp6VCqVjIyM1LW6EhLvRDJUJSQkPiqSkpLYoEEDfvfdd7S0tGTnzp1pbm7OGTNmMCMjg0uXLqWlpSWHDBnC/fv3c9OmTYyNjWXjxo05ePBgkm9OnStXrszZs2czIyNDQ352iKW5uTnv37/PkSNHUqFQ5GrQaotKlSq9866bxPsnNjaWFhYWFARB4+GOfJPsaO7cuTnmZIfOFoTs715eIZF6eno8dOgQV6xYIRp2W7ZsoUwm00kYpYWFBadNm0aSrFmzJu3t7fMMJcwmJiaGFSpUYLly5Vi9enXKZDI2btw4RzRCdkblxMREjh07lkqlMk+ZN2/e5Nq1a7l27do8DdN/s3r1ahoZGRVobGEYNGgQBUFg+/bt3xn+/ODBAwqCwN27d4ttrq6uWgtH/pDZsmWLRqh3ZmYmf/rpJzo5OfGbb77hhAkTOG7cOM6ZM4ft27d/j5rmzZ07d+jg4MDU1FQ2b96cDg4O/PHHHws0d8GCBezevTtPnz5Na2trrUc75MaaNWvEqIx169blOzYzM5OVK1emg4MDnz9/TvJNpM+6detobGzMqKgoenh46FxnCYl3IRmqEhISHx2bN29ms2bN6OTkRFdXV3799dcEIL6/ceMGDxw4QHt7e1aoUIEDBgzg8+fP6erqymHDhjEkJISdO3fOMwzr3r17VCgUnDlzJp8/f85Tp07lMGi1SatWrbh69WqdriGhHXx8fHK9Y+jg4JBrkqOyZcsyJCSkwPINDQ25adOmXPtMTEy4dOlSJiQkiF66L774QicPlCkpKRpe1MTERFpZWdHExCRfD/GECRNoYGDAkJAQNm3aNNe7u9lYWlrS09OTRkZGWjdWNmzYoBNDlXzz+6NUKqmnp8ewsLA8vWyNGzdm2bJlxfexsbF0cHDQefjnh0D2/f7sHAKRkZGszs1P/wAAIABJREFUVq0ajx8/TvJNWPzGjRuZnJzM0qVL89atW+9T3VzZtWsXmzZtKr7//fffNbJ458fjx49pbm5Oe3v7PDPGa5OsrCzRSD19+nSB5sTExHDv3r1iDoWvv/6atWrVYmhoKBMSEmhiYvJJfFclPmwkQ1VCQuKjY9WqVfT19aWLiwvbtm1LtVrN5ORknj9/nunp6Xz48CGrV6/ORYsW8dWrV3RycuKRI0fYvXt36unpsWXLlkxISMh3jbp169Ld3b1E9vO///2PCoWCzs7O+d6plXj/hIeHc8qUKTnaW7RoQUEQaGxszJUrV5IkT506RQMDAwYGBhZYvlKp5Pjx43Pt8/DwYJMmTfjw4UPRUK1YsSI7dOhQhJ3kjUqlopeXF21sbHK06+vr5/kdValU9PX1zfW+bm6cP3+egiDQwMCAWVlZxdY7m7NnzzIoKKjAehSVmTNn0tjYmAqFIkdNzJiYGAqCwP3794tt8+bNY9++fXWq04fEjBkz2KhRI6pUKjZp0oQbN24U+8qWLSvWHB0xYgRHjx79vtTMk4ULFzIwMJDJyckkydevX9PMzEz0QL6LL7/8kkuWLNGliiKTJk0iAO7bt6/Ac+Li4ujh4cGyZcvywIEDdHBw0EgYB0DKnyDx3pEMVQkJiY+OrKwshoWFiSfItWvX5q+//kryTfiSsbExx40bJ54GDxkyhObm5pwzZw4VCkWB1shOJFPQO0nFJSYmRtxPSZQvkCgaP/30E/v375+jvUmTJmIWZwcHB5Jkx44daWNjU6hSJJ6enuzSpUuufTNnzqRCoRAz35JvjNfevXsXfiP5EBERQX19/VzDbL28vNi2bdtc53Xs2JGGhoZi2ZiCcOXKFSoUCnp5eWnl/1p2jVYrK6sSqQeZmZnJgQMHUi6X087Ojjt37mRCQgIrVaqU495ws2bN8vSW/xfJyspi7dq1GRoaSnNzczFU+uXLlzQxMREPJ86ePUtPT8/3qWqu/PjjjwTAHj16iG3NmjUrcCh/SbJq1SrOnj27wOP37t1LJycnTp06lS1btqSzs3OOu9MA+Mcff2hbVQmJQiEZqhISEh8tffv2ZVRUFNetW0elUsk9e/bw3LlztLS01HjIHjNmDEeNGsUnT54QAF++fPlO2dl36P5dsF0XxMfHU6FQ0N/fn9u2bSt22R0J3ZH9UP12SFxKSgoNDAw4YsQIxsXFiYmV+vfvT2dn5wLLzjayli5dmmv/V199RScnJ8bGxlIQBJJvPKp5GbZFpUWLFhqZat9m7NixtLCwIEkePXqUKpWKL1++ZHBwcA4PYkGJi4ujQqFgw4YNi6U3+SZJjFwuL/BdVm2RmJjI5s2bUxAE8cDpbYM9KSmJZmZmfPHiRYnq9b65du0azc3NNbLIvnr1isbGxuJ7tVpNBweHQh1wlARHjhwhAJYtW5bbt28nSYaFhXHmzJnvWbPi8ejRI1pYWHDv3r3MyMjg6tWrNRINZtO8efMPPiuzxH+fghqqMkhISEh8YPzyyy9o3rw5unbtin79+qFp06YYNWoU+vXrh3Hjxonj6tSpg507d8LW1hampqYoVarUO2UfO3YMcrkcdnZ2utwCAGDKlCnQ19fHsWPH0LZtWwwfPhyDBg16c0oo8UFRo0YNGBoaYsOGDWJb3759YWpqilmzZonfl379+iEyMhIqlarAsrdv3w6VSoU+ffrk2v/XX3/Bzc0N6enpYhtJyGTa/RN98uRJNG7cONe+du3a4cWLFxgyZAjq1asHW1tbWFpaYufOnVi0aBECAwMLvZ6dnR1WrlyJ48ePF1d1ZGZmwtDQEGXKlMHp06eLLa+gWFhYICoqChkZGTAzM0PXrl1Rvnx5sf+PP/5A3bp1YW5uXmI6fQh4eXnh2bNnaN++vdimUChAEikpKQAAQRBQvnx5hIWFvS81c8XPzw9DhgzBihUrEBoailevXsHNzQ23bt1636oVi507d6JZs2bw8fHBokWL0KNHD8yaNSvHODs7O8TFxb0HDSUkCo9kqEpISHzQTJs2DStXrsTDhw+xdetWeHh4iH2NGjXCtWvXAAAXLlwokDxHR0eo1WoMHz5cJ/q+TVxcHBQKhWhwDB8+HPfv38e+fft0vrZE4RAEAZGRkQgLC8ODBw8AAJs3b8acOXPEMSYmJuL3bOXKlQWWvXDhQvj4+ORpeD569Aju7u7IyMiAIAgAgKysLBgaGhZ1Ozm4ceMGEhMTMX78+Fz7e/ToAQBYsWIFevbsCWdnZ6xduxbPnj3DgAEDiryuv78/VCoVwsPDiywDAIYNG4aUlBQEBgaiRYsWUKvVxZJXWPr27QuSWLVqlUb7li1bNIy1Twk9PT2N94IgQKlUIj4+XmyztbXF4cOHS1q1fDEyMsLcuXPRsGFDfPbZZ5g1axZ8fHxw8OBBjcOij40dO3bA1tYWXl5emDFjBrZu3YqIiAicP38eAPDq1StERETAysoKiYmJ71lbCYmCIRmqEhISHzSCIKBDhw5o0qQJxo8fj7Fjx4p9pqam+Oabb6Cvrw8XF5cCyfPw8MDs2bMxd+5c2NjYwMjICPr6+qhSpQoePXqkVd1nzZqFhw8f4uDBgwAAfX191KlTB3fu3NHqOhLaoVatWhg1ahT8/f2xfPlyqFQqfPHFF2L/sWPHMGPGDCgUCsTGxhZYbmxsLGrWrJlnv7m5OeLj45Geni4aqpmZmTAyMiryXp4+fQoHBwfY2trizp07WL16db5RBy9evMCgQYPw8uVL/Prrrzh//jy6dOlS5PWzcXBwwPLlyzFjxgwNb3VR2blzJ5KSkrBly5ZiyyooV69exerVq7Fs2TIN4ywtLQ27d+9G27ZtS0yXD51/G6qurq7FPqTQJf369cPu3bsREBCAihUrYurUqe9bJZGbN29i9erVSEpKKtD4hw8fIjIyEmvWrMGDBw9Qu3ZtZGRkwNXVFQAwadIkfPPNN7h//77WozUkJHRGQeKDS+ol3VGVkJAoCkFBQVy2bFmBx6tUKo4ZM4bjx4/n+vXruXnzZtra2tLf319rOoWHh1NPT4/W1tYad2e/++47GhgYiEmiJD48Nm/eTAA0NDTMtd/IyChHvdX8MDY2zvf72aVLF7q7uzM6OpqCIFClUrFMmTIcOnRooXXPxsfHh2XKlKG3tzcNDQ0pCALHjh2b69h79+4RAO/fv1/k9d5FSEgIjYyMmJKSUmxZfn5+tLS0ZGxsrBY0ezflypWjr69vjvZt27axXr16JaLDx0Lz5s017q327duXkZGR71Gj/Hn9+jVLly7NMWPG8MiRI3RxcaGHhwcPHz78XvVSq9WsUqUKq1Spwm7duhVozqZNm7hmzRrx/R9//CF+PxMTE2lsbMyaNWtSJpOVSP1XCYn8gJRMSUJC4lNh6dKlDA4OLpaMU6dOURAEjTT+KSkpYkbLwlK2bNk8s6hevnyZ1tbWH1ySEYn/Izt5119//aXRfuXKlQIn7iLfHIoA4L179/Ics3fvXspkMmZlZVEmk/HQoUO0trbml19+ycePH/PBgwd89uxZgdbLzMykm5sbZTIZb9++zczMTLZo0YK7d+/Oc06vXr1oa2tbIPlFRaVS0dzcnIMGDSq2rNTUVHp4eLBSpUpa0Cx/Zs6cSblczsePH+foa9OmTZ4Jsj5VIiIi+Pnnn5N8YwR6e3t/8Blmf/75ZwKgTCbjpk2buHHjRrq4uDApKem96XT48GF6enoyOTmZHh4e/P333wst4/nz5zQ1NWV6ejrVajXt7OzYp08fKhQKqba3xHtHMlQlJCQ+Gfbv36+VzKI1a9aknp4e9fT0aGpqSkEQKJfLeeDAgULJUalULFWqFMPCwvIcs3DhQpYvX55Pnjwh+cbA+Pvvv3no0KH3+oAk8X8sX76clStXZmJiotimUqlobW1Nc3PzAnkl4uLiCCDfA4/sOqbh4eEEQCMjIwKgiYkJlUolHRwcaGZmpnGIkheLFy+mvr5+gT0m6enpBFCoiISi8sUXX9DFxUUrso4ePUpBEIp8kFQQEhMTqa+vz3HjxuXoy86wKv1f1SQxMZHm5uZcsGABq1Spwi+++EKrtXR1QVZWFh0cHNimTRsC4J9//kkXFxe6uLjw9evXJa6PWq1m69atOX/+fJLkt99+y//9739FkuXr68tFixaRJIODgxkSEsLQ0FCt6SohUVQkQ1VCQuKT4erVq3R1dS22nPv37/PLL7/k7t27GRERwejoaJYqVYoTJkwolJwTJ068swSOWq0Wy100bdqUDg4OdHJyYu3atWlubs7OnTt/UrUZP0TUajWHDh3K6tWra3g0ExIS6OfnRysrqwLVCDUzM+PEiRPz7I+NjRW/C4GBgVy0aFGOGq3r168XPSz5UaNGjUKFo968eVMsiaNrTpw4QUEQOGnSJK3IK1WqVJ7hzNqgQYMGtLe3z7Xv+++/Z9++fXW29sfMiBEj2Lp1a+7atavE6lUXl/bt23PDhg3U09Nj/fr1KQgCq1evztGjR5e4LsuWLWOlSpVEI3nbtm20s7Pjhg0bNMpnFYSLFy/S3d2dAwYMYPfu3T/6EjwS/x0kQ1VCQuKTYdq0aezdu7fW5b569SpH3cSCYm9vz8aNG+c7xtzcnBMnTuSWLVv47bffijXvHj9+TGtra+rp6TEwMJBHjhwpkv4SxUetVnP06NGsWrWqhpGYmppKuVzOnTt3vlNG+/bt6e7unqP9+fPnHDp0KEuXLs2JEye+M5y4R48eDA8Pz7NfpVJRT0+vUAccBw4coJ6eXoHHF5eff/6ZMpmMISEhxZbVsWNHmpmZ5RtWXVSioqIoCAJPnz6do0+tVtPT05PHjx/X+roS2mPr1q1cv349z5w5807PaJ06dXjkyBH6+fnR0dGREyZM4OPHj2ljY8Po6OgS0pg8fvw4S5cuzStXruRor1y5MuvXr1+gyIq3efnyJbt06UJ9ff0SqR8uIVEQJENVQkLik2HGjBn5htkWlUGDBtHS0rJIcw8fPkxBEFixYkUxhOvf6Onp5RpWfO/ePQqCwOjoaP7yyy+0t7fntm3biqSHRPFRq9UMDg5m165dNTwa+vr6XLVqlcbY9PR0nj59mitWrODSpUt57949bt++PYcxeO7cOdrb2/PLL78Uw7/fxYULF+ju7p6nV2X37t2Uy+WF8mIdPXqUcrm8wOMLQ3x8PDMzMzlnzhyWL1+ejRs35vjx49m8efNC65kbTk5ONDAw0LqhrVKpaGlpyXbt2uXav3//flaoUKHQ3i2JkqVMmTJs0qQJq1atSmNjY1asWJEDBgzIEa1AkuXLl+eFCxdytM+ePZs9evQoCXUZGxtLMzMzBgQE5NqflZXFOXPmFCl6SK1W859//imuihISWkMyVCUkJD4Zdu7cyaZNm2pdbo0aNdiiRYsiz3d2dqapqWkOQyA1NZVNmzalIAi5etG6dOnCMmXKiO/PnDlDpVKZI7GPRMnx+vVrVq9enT/++CPJ//O2p6amknxjeAYGBlKhUNDHx4fdu3fnF198QSsrK+rp6READx8+zOTkZM6ePZtKpVIjO2pBUKvVLFOmDGNiYnLt7969O52dnQslM/uOakGy8e7evZsKhYLt2rXL9WE/G5VKxa5duxIAra2tKQgC27Rpwxo1alCpVLJUqVKUyWScOnVqoXR9m7CwMBoYGIgZi4vLihUr6OTkRGtrayoUChobG4v/tm+jVqtZr149rly5sthrSugWf39/7tu3jySZlpbG6OhoBgUF8fvvv88xdtiwYRw5cmSO9oSEBFpYWPDp06c61TU2NpaOjo40Nzdn+/btcx0zb948+vr6ajU7vYTE+0IyVCUkJD4Z/v7770I/oBcENze3ImcTTklJobGxMevXr099fX2NvqlTp1JPT49Hjx7Nda6Pj08Ob866detYtmzZAnvfJLTP3bt36ezszMGDB/PChQsUBIHx8fEMDQ2ljY0Nf/nllxzJdV69esXt27ezX79+dHNzo0KhYMeOHYt86BAeHs4+ffpotKlUKq5du5YA6OHhUSh5169fL1AW47/++osymYxNmzalhYUFFQoFz5w5ozHm22+/pa2trdi/adMmtmzZkoMHD84hLzQ0lGZmZqJXNTMzk+PHj2d8fPw7db506RJlMhkjIyOZmppKANy7d28hdq3JsWPHKJPJ2K5dO06cOJERERG8fft2rmMPHDhADw8PZmZmFnk9iZJh8eLFrF27tobn/ubNmyxdunSOUkw3b96kUqnMVU7Lli0LfahUGJKTk8UEVK6uruzXr1+u40JCQti7d2+dG80SEiWBZKhKSEh8Mhw/fpzVqlXTulxnZ2d++eWXhZqTmprKkJAQ6unp0cnJicePH6dMJmP//v3FMSNGjKCNjU2eMho1apRrQpz+/ftz4sSJH02Ckv8iz58/Z9euXUUvqbW1NUNDQwtkYGVmZvLhw4fFWv/ly5e0s7Pj+vXruXfvXnp7e9Pe3p41a9Zkp06d8gxXzQ8bGxs2adIk3zHVqlVjlSpVSL4xjLPDd/v168eoqCiSpEKhoK+vL7t165arN/Jt0tPTqa+vz/Lly1NfX5/6+voEwK5du+Y7Lysri0qlknXq1BHbOnXqJJYX6dy5c77z9+/fL/7/iYmJ4dKlS2lkZJRnKam3UavVDAgIyBHuLfFholKpWKdOHS5cuFCjfcKECWIJnWzUajVNTExyzeLcq1cvLl++XGd6Nm3alAAYHBxMAwMDtmzZkp07d2bnzp3ZvXt37ty5k8+ePRMTrklI/BeQDFUJCYlPhgULFuR48NAGQUFB9PX1zbWvcuXK1NfXZ7169Xj48GFGR0ezRo0alMvlNDc355w5c8SxkyZNopmZmfi+Ro0aDAoKynPdfv365ZptdNKkSZTL5Rw2bFgxdiWhDX799VfWqlUrzzBcXbJlyxY2aNCANWvW5NKlSxkTE0O1Ws20tDTa2try1q1bhZK3f/9+AsjzACS7Fuy/k8qMHTuWTk5O/H/s3XdUVMf7P/D3Zem9d6QIKkVQQbFgFGyIfFSwRI29YmLDiFGxizF2AzFiN7FFRbFGY0cssYANERAVVIoC4gpI3+f3hz/364a2C4sU53UO52Tnzjz3ucRddu7MneHxeKSoqEgcx1XZQf1cREQEderUiZYsWUKhoaG0bNkykpOTo6SkpArbdOvWjQCUWVAmJiaGDh06RDwer9zObkZGBjk6OhIA8vDwoAsXLpCMjAwpKytThw4dxLr5c+rUKWrevDkbTW1APu1ZnZqaKizLy8sja2tr2rdvn0jdZs2a0ePHj8vEmDBhAv3++++1kl9OTg7p6+uThoYGAaDOnTuTrq4u6erqkoODAzVt2pR4PB5xHEc//PCDWFP0GaYhYB1VhmG+Gs+ePSMtLa1yF8OoiZ9//pmUlJTK/RLboUMHAkAODg7EcRzJyMiQra0tHThwoEz9bdu2iTyn2rdv30oXxEhJSSGO48qsKmpiYkKdOnUiKysrtpALU65p06ZR27ZtKSEhQaJ2MjIywpFRoo8dOz8/P0pISKDnz59XOpJTXFxMP//8M82ZM6faeRN97BA7OTmRpqYmbdq0iXr37k2JiYk0d+5csrCwIENDQ+LxeGRqakru7u7lxrh06RLxeDz67rvvKD8/n0pLSykvL48UFRXJ3Nycjhw5QgoKCgSADAwMxM6tqKiIWrRoQSdPnqzRNTJf3ty5c8nT01PkBsPdu3dJV1dXZEV3Hx+fche+6969u8h7Q5oWL15MQ4YMoatXr9L3339PRB8Xaft8qnFoaCh5enrWyvkZpq6wjirDMF+N+Ph40tXVLXfaVk0UFhaSkpISzZ49u9zjHMdRQkICJSUl0Zw5cyoclbG2tiZvb2/h60/PBV6/fr3Cc5uZmdGIESOErz/tQZmamkrGxsYSj5oxX4fS0lKaO3cuDRs2TKJ2HTp0IHt7e+Hr5s2bk7y8PAEgBQUFkpGRkXaq5SosLCRFRUUCQCYmJgSAVFRUaNiwYTR16lT6999/6dixYyQjI1Ph++38+fPE4/EIAKmpqZGVlRVpaGgIj8fGxpZ7I6gywcHB1KNHD3aDqAEqKioiT09PGj9+vMj/v9DQULKzs6Ps7Gwi+vgctr6+Pr19+1akvYeHR610VJ89e0Y6Ojoi2yt9Whzs08wEgUBAjo6ONXoGm2HqI9ZRZRjmqzF+/HhauHBhrcQ2NDSs8Lk3AJSVlVVlDDU1NdqwYYNImbOzMwGghw8flttGTk6O9u/fL3zt5uYmfEbQ19dX5BjDfC4rK4s0NDTE+rf5SUxMDHEcRytXrqSAgACSk5OjlJQUSklJoVGjRpGSklItZixq9uzZwo7xy5cvy+2Qftq+qSKlpaXE5/Np6tSp5OrqKvJcadu2bcnBwUHsfLKyskhPT6/C9ypT/+Xk5JCTk5PIdF+BQEBTp04lDw8PKikpIaKPf0sWL14s0vaHH34o8/ldU1lZWeTl5UXLly8XKV+xYoXIYyL37t0jU1NTti4B0+iI21GVAcMwTAOWkZGBsLAwTJkyReqxf/rpJ2RlZeH333+v9PxVycvLg4uLi0jZnTt3oK6ujrNnz5ap//79exQXF8PHx0dYFhUVhUmTJgEA2rVrh5s3b4p7GcxXRltbG46OjoiKihK7jb29PYKDg/HTTz8hNDQUs2bNgrGxMYyNjWFhYQFlZeVazFgUEcHAwAAAYGpqChmZsl9V1NXVcerUqQpjyMjIQF1dHcHBwfj3338xfPhwAMCDBw9w584d7Ny5U+x8Fi1ahIEDB8LBwUHCK2HqC1VVVSxcuFDks5zjOKxfvx4FBQXCfw+enp6Ijo4Waauvry/W53xViouLsXbtWri6usLCwgIqKir48ccfhcffv3+PJUuWwNfXV1jWtGlTFBUVITY2tsbnZ5iGiHVUGYZpsHbv3o2OHTti6NCh0NPTk3r8p0+fAgD4fH65x5WVlfHnn3+ioKCg0jhEBCUlpTLlVlZWCA8PBwCEhYXhwYMHWLduHf766y/Iy8tDQUEBwMdObX5+PsaMGQPgY0f11q1b1b4upvF7/fo1TExMJGrz6WZPTk4O5s+fLywPDw//op203NxcyMvLV1rH0dERJ0+elDh2cHAwTE1Ny9w4qsjhw4dx7NgxLF26VOJzMfXL//73Pzx9+hSPHj0SlvF4PAQHB2P+/Pl49+4dnJ2dce3aNZEbgS4uLti/fz+ysrJqdH4/Pz+cOXMGv/zyCzIyMnDw4EHhZzwArFy5EvLy8tixY4ewTFVVFQEBAVi0aFGNzs0wDZY4w65f6odN/WUYRlyFhYVkYGBAZ8+erdVVOG1sbKhTp07lHvP19RVuU1LZPnvm5ub07bfflik/e/YscRxHUVFRwq0HPi30YmZmRtra2vTzzz9TkyZNRBaz4fP5pKysTEVFRTW/QKbRKS0tJVlZWXJzc6OePXtSjx49qGXLltSyZUuaPXs2NW/enPT19WnEiBE0ZcoUOnXqlLAtAJH9JIuLi4njOLp06dIXy3/cuHGkqqpa6YJQa9euJRUVFYljjx49Wuy9Zu/du0e6urp0584dic/D1E/Lli0jd3f3MqtTjx8/nvz9/YmI6MSJE6Svry+ysvSYMWMoKCioRudu0aJFpdPHnZ2dqVu3bmXK8/LyyMjIiO7du1ej85fn02rhDPOlgU39ZRimMcvKygIRoUePHpCVla2Vc6Snp6OgoKDCabZ79+6Fm5sbOI5DUVFRhXHc3d1x+/ZtkTKBQIA7d+5AWVkZY8eOBQA8e/YMHz58wIgRI/Dy5Uvk5eVh3rx5ePXqFR4/fixsq66uDgsLCzx48EAKV8k0Ns+fP4eCggI4jgOfz8e9e/fw5MkTGBkZYevWrUhKSsKbN29w69YtHDt2DN7e3jAzM8OBAwegrq4unCYLAGfPngWPx0PXrl2/WP5Lly7Fhw8fsGLFigrrjB07Fnl5eUhNTZUoNsdxHxfoqEJKSgr69++P4OBgODs7S3QOpv6aO3cu9PT0MHjwYBQXFwvLly9fjj179qBp06YICAjA4MGD0adPH9y7dw8AMGTIkEqnmpdn+/bt2LRpE4CP036fP38Oa2vrCuvHxcWhX79+ZcqVlZVhZGSEt2/fSnR+cQwYMABOTk5Sj8swUiNOb/ZL/bARVYZhxLVp06ZqjahUJCUlhVxcXMjZ2ZlcXV2pU6dOpK+vLxzp/Hxlxk/mzZtHioqKla7eS0S0atUqUlRUpJycHCL6OBrcoUMH4Wgsx3EEgFJSUoRt+Hw+FRYW0rZt2ygjI6NMzKCgINLX16+1bROYhuuvv/6i/v37C19PmTKFmjRpInw9cOBAkVHFtLQ06tOnD8nIyBAA+vPPP4XH/Pz8yMzM7Msk/v/169ePFBQUKt0zsrS0lGRkZOj8+fMSxR4zZgw1bdq00jqvXr0iGxsbWrlypUSxmYahsLCQevfuTYsWLRIpf/fuHSUmJtK1a9fIwMCAFi9eTKampvTq1SsqKCggdXX1cj+Ly/P+/XtSU1MjADRs2DDq3LkztW3btsL6KSkpBEC4AvHnXr9+TQDo/v37El3n5x48eFBm5lFmZqZwdW22PyvzpYGNqDIM05ht374d48ePl1q8GzduIDo6GoaGhtDU1ISioiIcHR2F5wgLCxOpf/v2bWzZsgXOzs7o0KFDpbGnT58OdXV1tG7dGpMnT4aKigpiY2Nx+/ZtEBEEAgH4fD6MjY2FbdTV1SEvL49x48ZBV1e3TMzAwEDMnDkTp0+flsLVM41JVFSUyDOY/x1FHD9+PJ4+fYqpU6di+vTpMDQ0xMmTJ8Hn87FkyRJ8++23wrpPnjyBmZnZF8t9z549OHHiBC5dulTpAk47duyArKws3N3dJYpf3sJMn0tNTYWHhwfGjh2L2bNnSxSbaRjk5eWxZMkSHDhwQKRcQ0MDTZs2RceOHbFnzx6EhoZixIgR6Nt4ozbxAAAgAElEQVS3L4qLi2Frayv2okbLli2Dqqoq1NXVYWNjA39/f1y9ehXAx+eezczMYGBggKCgIKSnp2PkyJHQ0NCApqZmmVj6+vpYvXo1hg4dipycHJFjRIQ3b95UmsupU6fg6OhY5r3y8OFD2NrawtLSEkZGRnj58qVY18YwX5Q4vdkv9cNGVBmGEcdff/1F6urqwu00pOHYsWPE4/HowoULIuUxMTHCZ0aJ/m+bA47jyNXVlVJTU8WKn5aWRoaGhsRxHM2aNUsq2w1s2rSJJk6cWOM4TOPSsWNH6t27N02ePJmmTp1KrVu3JlNTU5E6NjY2ZGJiQjwej37++ecKY7Vs2ZIGDBhQ2ykLGRkZka+vb5X1WrVqRW5ubhLHHzduHFlZWZV7LDU1lZo3b17p74NpHEpLS8nExIQeP35cYZ2ffvqJJk+eTGPGjCEej0dGRkaUmJgoVnwjIyM6c+YMcRxHv//+OxF9HMlNTEwkWVlZ8vLyonHjxgn3DDYwMKA//vij0pgTJkwgHx8fkb8dixYtIjk5OfL396cXL16U2ef333//JV1dXQJA48aNEzn222+/Cf9+AKAFCxaIdW0MIw0Qc0S1dh7s+gzHcZ4AfgXAA7CNiH6p7XMyDNM45ebmYurUqbh48SKuXLmCy5cvw9/fH/v378elS5fKvRstrvbt28Pc3BzdunVDZGQk3NzcAHzctmPq1KnYuHEjxo8fj8ePH+Pp06d48OCBRCuhGhoaIi0trdr5lScyMhItWrSQakym4Xv//j2uX78Oc3NzCAQCCAQC9OzZU6ROQkICgI/Pv1X2vnn79u0XG1F9/Pgx0tLSsHnz5krrxcfH4/79+7h27ZrE5+A4rtzygoIC9OvXD0OGDMHcuXMljss0LDIyMvDy8sI///xT4WfojBkzYGdnh/j4eISEhEBFRUWs2ESEzMxMuLu7Y9GiRZgyZQoWL16MN2/egOM4tGrVSvi865YtW5CamgpTU9MK45WUlGDHjh3gOA7h4eHYv38/vvvuO5w7dw5btmzBnTt3sHHjRjg7O6OoqAh2dnZQUlLC+/fvkZiYCF9fX0RHR2PLli0icdPT02FoaAjg/2bwMEx9U6sdVY7jeAA2AugB4BWA2xzHHScitiEUwzASu3z5MqKjo/Hw4UOoq6vDyckJLVq0QJ8+fbBgwQKEhIQgPT0d+vr6VU7x+6/Q0FAkJycD+PhH+3PBwcEwNjbGihUroKmpiU2bNtX5noqXL19GZGRkmS8fDOPm5oYJEyZg2rRpldZLTk5Gfn4+RowY8YUyq1xISAiMjIzKner+uXHjxqF58+ZVTrkvT3kdVSKCn58fLC0t2TYgX5FOnTrh9OnTmD59ernHDQ0NMWDAAKxYsQLr1q0TO+67d++grKwMeXl5LFq0CElJSSgsLMS6desQFxcnvAkKfOwwV9ZJBYATJ05g/fr1mDhxIk6dOoUuXbrg7du3GDVqFPbs2QN7e3usXr1aeK64uDiYmZlBQ0MDenp68PLywvr168v8TbSyssLFixcBfPzMaN26tdjXyDBfjDjDrtX9AdABwD+fvZ4LYG5F9dnUX4ZhKnP+/Hnq0KFDmfIFCxYQANLQ0CAA5OzsLPHUWktLS/L29hbZkqC+EggEZGdnR2FhYXWdClMPdezYUaztZBYsWEBaWlqV1rG2tqYRI0ZIKbPKeXl5UVXfA16+fEkcx5WZoi+uCRMmkKWlpUjZpk2byMnJiXJzc6sVk2mYEhISyMzMrMx02c+lpqaSnZ0d/fjjj2L/TXnx4gUZGxtLK0367rvvaOPGjSJlmzdvJgDk7u5OSkpKpKqqSoaGhmRtbU0mJiZkYWFBI0aMIGNjYxo8eHC513j9+nVq27Ytubq6kpqaGl28eFFqOTNMVVBPFlMyAfD509mv/n8ZwzCMxMLCwuDl5VWmfOnSpUhJScHMmTOxdu1axMbGwsTEBEOGDEFJSYlYsYkIJiYmIovQ1Hf37t3D+fPn8ezZM7Gvk2nciouLcf/+fbRp06bKuhEREbC0tKy0zsuXL/HNN99IK71K5ebmQlVVtdI6a9euha6uLjw8PKp1jv+OqKanp2PBggXYv3+/2FM7mcbB2toampqa2Lt3b4V1jIyMEBkZif379+P69etixdXX10dGRgYEAoFU8rx8+TI8PT1Fyv7++28AwIQJE5Ceno6cnBykpaXhyZMnePnyJU6cOAFXV1ecO3cOBw4cKHcmQfPmzXH79m3Ex8dj165dcHV1lUq+DCNNtd1RLe9hEJENzDiOm8hx3B2O4+5kZGTUcjoMwzRURUVFOHDgAEaPHl3ucWNjYyxcuBAzZ85EQkIC0tPTceDAAbi6uor1hUFDQwORkZFSzrp2cByHQ4cOIS0tDcuXL4e7uztUVFSwbNmyuk6NqWMPHjyApaVlmenr5Zk4cSLu3btX6aqhhYWFGDx4sDRTrFBaWlqV034LCwsrXQ24Kv9dAXnOnDkYM2YMbG1tqx2TaZg4jsOff/4Jf39/4WMfFdXLyclBq1atxIqroKAADQ0NSOs7rYyMjMhe4USEK1euAAAGDRpU5r3OcRwcHBzwww8/wM7OrsK42traAD7e3PL19a30fZWSkoLDhw/jw4cPNbkUhpFYbXdUXwH4fBUGUwAiu3MT0RYiciEiFz09vVpOh2GYhiomJgampqZVPs8DAKampnB0dETXrl3x6NEjTJo0qco24eHhSEhIwC+/NIz13uzs7LBt2zZcunQJycnJCA0NRWJiIn799VcsWLAAqampVQdhGp2TJ0+iS5cuYtX97rvvYGRkhO+//77c4zExMQDKPrNdGz4t/DJnzpxK6/F4PJGOpqQ+H1m6fv06zp8/jwULFlQ7HtOwtWrVCj/++CNGjx5d4b+rEydOwMPDo8rR/s9ZW1vj/v37UslRUVERBQUFwtdJSUnIycmBqqoq8vPzRerm5+fj3LlzCAwMFGu7mXHjxiEvL6/Cm7SlpaW4du0a3NzcsHbtWpiZmWHy5MmIioqq2UUxjJhqu6N6G4ANx3GWHMfJAxgC4Hgtn5NhmEaGiHD06FGJFnswMzPDu3fvsHz5cvz5558oKiqqtL6lpSU6deqEEydO1DTdOqGgoICIiAisWbMGWVlZsLe3x7Bhw8Te949pHPbu3YuxY8eKXb99+/bCFYA/JxAIMGjQIFhYWEgxu4q9fv0aAODi4oJnz54hJCQEW7ZswZw5c7By5Ur89NNPGD9+PMLDw2s0UvXo0SMkJSWBiDBlyhSsXr0aampq0roMpgEKCAjAy5cvK+x8cRyHR48eISEhQeybJN9++y327NlT49xSUlKQlZUFHo8nLPv333/Rt29fDBo0SHhjZ/v27fD09IS+vj569uyJFStW4MWLF1XG9/f3BwAcPXpUpPzu3bsYOHAg9PT0MHnyZCxatAjXr1/H/fv3YWJiAm9v7wb7t5JpYMR5kLUmPwC8ACQAeAogsLK6bDElhmHK8+DBA9LR0aFHjx6J3aZZs2bUr18/Ki0tJR0dHTI0NKScnJxK28yYMYOMjIxqmm6dOHToEPF4PLp69SoREWVnZ9PKlStJV1eX/Pz8KD09vY4zZL4EfX19ev78udj1Z8yYQSYmJmXKu3fvTkpKSpSSkiLF7CoWGxtLHMeRl5cXASBVVVVSVVUlAKSmpkaGhoZkZWVFhoaGBID69+9frb2Ir169SgBo3759ZGlpWelCOszXY9GiRTR9+vQKj4eEhJCamhotXrxYrHivX78mDQ0Nev/+fbVzyszMJDs7O1qxYoVI+dSpU2nVqlWUnZ1NpqamNGHCBGrevDmFhYXRxYsXSUdHh6ytren169dinefKlStUVFQkUnbq1CnS0tKiFy9elNumb9++9LELwTDVAzEXU6r1jqokP6yjyjBMeSRdRTE/P584jqOIiAgiIsrJySE1NTWaNm1ape2+//57MjMzq1GudSUzM5NOnz5dpjwrK4v8/f1JR0eHVq1axb6YN3KjRo2i1atXi11/8eLFpKOjI1J27Ngx4jiO7t69K+30KjRv3jwCQLKysgSANmzYUGHdyMhIUlBQIHt7e8rPz5f4XE5OTgSApkyZUpOUmUYkISGBDAwMqLi4uMI6R44cIW9vb7Fjjhkzhrp160bv3r2rVk6dOnWigICAMp/ZnTp1Eq7q/alDGRcXR/n5+eTp6Unr1q2r1vk+JxAIqFu3buTp6Um7d++mDx8+iBw/cOAAmZqa1vg8zNeLdVQZhmk0SkpKSF5engoKCsSq7+npSdra2iJlfn5+pKenV2m7/v37k4ODQ7XzrM8SExPJ2tq63M4s03hERkZSixYtKrwhUVpaKjISOWzYMGrevLlIHX19ffLx8anVPP9LW1ubRo8eTa9fv6aBAweShYVFpfWTk5NJR0eHdHR0KDk5WaJzrV69mgBQYmJiTVJmGhlXV9dKPx/j4uLIwsJC7Jt9JSUl1Lt3b9q8ebPEuQgEAgJQ7qwBU1NTkX/zhYWFdOvWLbK1tSVfX98yncrq4vP5tGPHDurUqRN9//33IscSEhLIysqq3HZ37twhR0dHMjY2JgsLCwoODqaSkhKp5MQ0HuJ2VGv7GVWGYZgaKykpgaKiIvh8PgoLCyutGxYWhn/++QfHj4s+Dr9kyRJkZmbi3r17AICbN2/iyZMnInVSUlJgbGws3eTriaZNm2L16tUICAiQ2rYJTP3TqVMnEBGuXbtW7vFx48bB0NBQuJ1RTEwMzM3NhcdfvHiBN2/eIDQ09Ivk+0lhYSHc3Nygr69f5cq/ANCkSRO8evUKRkZGaNasGa5evSrWefbt24e1a9fi+vXraNq0aU3TZhqR4cOH488//6zwuI2NDWRkZHD79m2x4vF4PLRq1apaz1SXlpaCx+NBRkb0a3pxcTFev34t8ndKXl4egYGB6NOnD8LCwqCkpCTx+cqjrq6OMWPG4NChQ9i3bx+io6MRFxeH+Ph4aGhoICUlpczfkj/++AOenp6YN28ebt26hbCwMBw+fBiurq6Ii4uTSl7MV0ac3uyX+mEjqgzDlOf27duko6ND4eHhZGBgQIWFheXW4/P5pKioSOPGjSv3eLNmzUhVVZXatGlDHMcRx3EkIyNDWlpalJycTBYWFjR+/PjavJQ6lZ+fT/Ly8pSUlESrV6+m/fv3V/i7ZBqu4OBg6tOnT5nyI0eOkJ6eHgEgOzs7mjFjBnEcRxcuXBDW8ff3J11d3S+ZLsXGxhIAWr9+PVlaWpKamhqJ+32gtLSUfH19SUZGhrZu3Vpp3aKiIrK0tKQbN25II22mkcnKyiJNTU1KTU2tsM6qVavIycmJlixZItYzoGvWrCF/f3+Jc/nw4QMpKCiUKT9z5gy1adOmTPnx48fJycmp1h7tCAwMpObNmxM+bjFJPj4+pKurK7L2wa+//ko2NjZl1pIQCAS0efNm0tXVpZUrV1JcXFyt5Zmfn0+5ubm1EpuRLrARVYZhGgtnZ2eMGDECPj4+yM3NRURERJk6f//9N5o3bw5NTU1s2bKl3DiRkZHw8/ODnJwcTp48ifDwcJw7dw6mpqZo0aIFMjMzYW1tXduXU2cePXqEkpIStGrVCo8fP8aWLVvQpEkTBAYGirVCJNMwTJw4EQ8fPhQZVR06dCgGDhwIAwMD/PHHH8K9eLt27QoPDw9hvePHj8Pd3f2L5nv58mWoqqpi9+7dyM/PR05OjtgrisrIyODw4cMIDAzExIkThauY/ldJSQlsbGzQsmVLuLq6SjN9ppHQ1tbG0KFDsWnTpgrrTJ8+HXPnzsW9e/cwe/bsKmPq6OggMzNT4lzevXtX7mrUGzduxOTJk4Wv79y5g+nTp8Pb2xscx1VrJV6BQICcnBykpqYKV9/+r549e8LQ0BBWVlbo2bMnrKysYGZmJvy78e7dOyxbtgwnT54ss3crx3GYOHEiIiIiEBcXh+7du0NdXR0hISFSnd1DROjbty9atmwpnDnFNALi9Ga/1A8bUWUYpiKlpaW0a9cu0tHRoYSEBGF5YmIimZubE8dx1KNHD0pLS5M4dl5eHnEcRwDo+PHj0ky7Xnn79i3t3r2b+Hy+sCw2NpamTZtG2tra1KZNG2rZsiU1bdqUrKys6MSJE3WYLVMTO3bsIDc3N+HIhYqKCs2ePbvKdhoaGrR27draTk/EpEmThCM1p06dIh6PR7///rvEcf766y/i8XjUvXv3Ms/2PXv2jJo0aSKtlJlGKi4ujvT09KocLc3OziYdHR2Kj4+vtN6pU6fI0tKSNmzYQLt376bQ0FDhgk2VjSpeuHCBOnfuXKZcXV1duBLv4cOHSUNDQ7geQ1hYGLVt21as0UqBQEACgYBKS0upRYsWpKKiQoaGhqSpqUnu7u70+++/C38Hb968IR6PRwEBASKLTf3444/UqVMnyszMpMDAQBo7dmyV5/10bh6PR9bW1uTq6kpRUVFitatKYWEhtWvXTvhZ8mkxRaZ+AltMiWGYxiYwMJB8fX1FylxdXcnMzKxG22j4+/uThoYGAZB4YZbGIjc3l27cuEH37t2jhIQEunDhAhkZGdH69evZSsENUElJCbVt25ZCQ0OJiMjLy4tsbGyqbKetrU1BQUG1nZ6ItLQ0AkBr1qwhIiIPDw9q27ZttWJFRUURj8ejHTt2iJSfPXuWunbtWuNcmcZv5syZNGrUqCrrjRo1ijZt2lRpnZKSEtq1axf5+fnRkCFDyNbWlrZs2UJbtmyhZs2aUceOHSk7O7tMu+DgYPLz8ytT3rdvX9qzZw8RES1YsIBGjRpF5ubmRPTxZq6lpSXdvHmzwnxmz55Nurq6xOPxaNKkSRQVFUWWlpb04cMHio2NpYcPH9KePXsIALm4uAjbHTx4kPT19en+/fvCstLSUpo9ezY1a9aMtLW1KSkpqdLfxed4PB5duXKFtm/fTgYGBjR58mR69eqV2O0rUlJSQuvWrRN2Vv/+++8ax2Rqh7gdVdm6GcdlGIaRzPPnz7F8+XIsXLhQWPbixQvcunULly9frtEiSDt27ICvry927twJMzMzaaTb4KioqKB9+/bC1zY2Nrh+/Tr+97//ISEhAcHBwZCVZX8yGgoej4ddu3ahS5cu6NmzJ+zs7HDu3Lkq23Ech4KCgi+Q4f8xNDSEuro6ZGVlUVJSgqioKPTo0aNasdq0aQMdHZ0yC6Vt27YNvXr1kka6TCO3ePFi2NvbIyIiAl26dKmwXlpamshCZOXh8XgYNWoURo0aBQC4dOkSPDw84OXlhc2bN+PEiRNo0qQJmjZtiu3bt6NNmzYoKirCxYsX0a1btzLx+vfvj1mzZiEjIwOqqqpIT0+HqqoqAKCoqAivX7+u9PEVZWVldOvWDYsWLYKdnR3s7e2Rm5sLDQ0NmJubo7S0FKmpqWjXrh3k5OSE7QYNGoS7d+/ir7/+QmFhIeLj4zF8+HCsXLkSVlZW4PP5Vf4uyjN27Fj0798fQUFBaNmyJXx9fREQEIDmzZtLHAv4+Pv29/dHSUkJZs+ejYEDByI1NRUaGhrVisfUA+L0Zr/UDxtRZRimPHw+nxwcHEhWVpbGjBkjLPfy8hLeTa6uZ8+eCReXkZOTq2GmjQ+fz6eePXs26kWmGrNVq1aRiooKcRxHwcHBldaNiIggjuPo4cOHXyi7/6Ojo0PLli2jgIAAUlJSqtEiX+3btycDAwPKy8sjoo9TFzU0NNgiK4zY9u3bR25ubpXWsba2pri4OIljZ2RkiLzOzMykoKAgGjBgAJ0/f55atGhBAEQWOftEIBDQpk2bqEePHrRx40b69ttvycjIiE6fPk2lpaXk4OBAJ0+erPDcycnJ1KlTJ5KXl6fBgwdXmJ+3tzdxHCeS65o1a2jy5MnUq1cvUlVVpaCgoGrPtuHxeHT58uUyv4fFixeTnp4eDRgwQKzFqioiEAhoxowZBIAGDx7MZgXVQ2BTfxmGaSz8/PyoS5cuxHGcyHMnmpqatGzZshrFzs/Pp0WLFhEAUlVVrWmqjdL79++pWbNmtHv37rpOhZFQSUkJAajyS3d2djYpKytT3759v1BmogwNDWnmzJmkqKhIc+fOrVGsnJwc0tPTI3NzcyosLKQzZ86wab+MRIqLi8nKyoquXr1a7vFPe3vn5+dL5Xw5OTmkqalJ5ubmdPjwYQJQYUft/v37ZGdnR7t27aLhw4dTZGQk6enpUWJiIh0+fJhat25dZccsMTGRioqKKjwuEAjo6NGjInUSEhJIWVmZjIyM6NmzZ+Tg4EA//vhjtTqBsrKydOnSpXKP5eTk0IwZM6ht27bVurlUVFREK1eupLlz55K2tjYBoGvXrkkch6ld4nZU2aq/DMPUawKBAOHh4Rg6dCiICF5eXpg9ezYEAgH4fD769+9frbglJSX49ddfYWZmhmXLlgH4uG8cU5aamhoOHToEf39/PH78uK7TYSTA4/Fw7tw5pKWlobi4GAcPHkRAQABKSkqwcOFCLFmyBAKBAN988w10dHQQHh5eJ3ny+XysX78eysrKCAoKqlEsVVVVxMXFgc/nw8nJCcePH4eLi4uUMmW+BrKysli+fDmGDh2KhISEMsfT09OhpaUFRUVFqZxPVVUVV69eRWxsLHx9fUFE0NfXL7euqakpUlJSoKCgINx/2MXFBVFRUXBzc8Pdu3erfB83bdpUZGrvf3Ech379+onUsbGxwblz5xAbGwtLS0tERETg2rVrGDZsGI4cOYLk5GSJrrmiFX9VVVWxbt062Nvb47vvvvs4qiaBnJwczJs3DwkJCTAwMACAMvuqMw0He+CIYZh6LTo6GlpaWhgzZgwSExOhpKSEoKAg3Lp1C/Ly8nBwcJA4ZnJyMpycnFBQUAB3d3fs3LkThoaGtZB94+Ho6Iiff/4ZgwYNwo0bN8rdOoGpn7p3744mTZpg1qxZCAkJARFh27ZtyM3NRWlpKZ49e4ZHjx4hMTERMjJ1c/9aIBBAXl4e586dk0oO2traePToEZo1a4Z9+/aV29lgmMoMGTIE+fn5cHd3x9mzZ2Fvby88VlhYCGVlZame7/P4lXn//j2UlJQgJyeHkpISAEBmZibOnTuHSZMmAQCCgoLg6+sr1fwAoGPHjsL/1tbWxrlz5/DLL79g165dmDRpEr7//nsEBgZCXl6+yliVdUA5jsPmzZvRuXNnBAcHY/r06WLnqKWlBXl5eXTv3h2vXr0CADx79kzs9kz9wkZUGYap15SUlJCXlwc5OTmsXr0aS5cuhbOzMyIjI3H27FmJ40VHR6NFixawsLBAbm4uTp8+zTqpYho/fjzc3d1hZ2eH7du3IyoqqsJ995j6ZdmyZQgODoaNjQ2Aj/se7tmzB+7u7vjzzz8xadIkWFpa1ll+Hh4ecHBwQJs2baQW09jYGEOGDIG/vz/09PSkFpf5eowZMwZr1qxB9+7dERMTIywvKSmps8Xl7ty5g7Zt20JOTg7FxcUAgKVLlyI1NRV6enpYtWoVUlJSvkguqqqqCAoKwvHjx3H//n1ER0cLR3erUtUeqvLy8ti/fz/mz5+P/Pz8SutevHgRu3btAhFh6tSpsLe3B5/Ph52dHf744w8cPHgQAJCbm1tns0aYahJnfvCX+mHPqDIMUx5bW1u6ceOG8HVeXh7FxsZKHOf169ckKytLvXr1KrPPIiO+a9eukbe3N7Vq1Yo0NDRE/t8w9ZeFhQVpa2tT9+7dRcrPnDlT5+8Hf39/MjIyknrcFi1aUHR0tNTjMl+X7du3k6Ojo3CRr5iYGLK1ta2TXGbPnk1Lly6lv//+m3r16iUsz8jIICUlJXr06BEZGhrWeh5FRUVlPjcEAgHt3r2b9PT0aN68eVRSUlJuWzk5OTpz5oxY53FwcCjzHhYIBJSVlSV8raOjQwBo//795OjoSO/evaOWLVvS2bNnRdr98ccfBKDcLYGYLwvsGVWGYRqLwYMHC++IAh+X2Le1tZU4zoULFyArK4szZ87U2RTHxqBjx444ceIE7t69i9DQUMyYMaOuU2LE4O3tjbdv38LZ2VmkvFevXnX+fjA0NKxy1ERSr1+/Rnp6OhwdHaUal/n6jBkzBk2aNMHy5csBfBxRrewZz9pkZGSEI0eOYOvWrSI5aGhowN7eHu7u7hg2bFit5+Hu7o7//e9/ImUcx2H48OG4f/8+/v77bxw5cqTC9lWNqH7i5uaGKVOmiIzS3rp1Czo6OmjatCk8PT2RlZUFAJg4cSL8/PxgY2ODhw8fwsPDQyRWixYtAHzcFodpGNgzqgzD1Hva2tqIj4+vcZxTp05BR0dHChkxn3h7e2Ps2LF1OhWOEc+nPYJv3boFV1dXvHz5EhzHlfl58eIF+Hz+F11cLC4uDtra2lKNeeXKFXTu3Bk8Hk+qcZmvz6dnJlu1aoWHDx/ixo0bSE9Px5w5c6CjowNjY2MMGjRIrGcza2r69OkgIpSWlmL48OHCcjk5OYSFheH69esYMmRIrebw/v17XLt2rcLjRkZGmDlzJnbu3IlBgwaVW4fEXCQpJCQEO3fuhLe3N3r27Inly5cjNzcXRkZGWLBgAbS0tLB9+3YQEeTl5TFs2DBMnDgRP/30U5n3voODA3g8Hv79919cvXoVbm5u4l80UyfYkALDMPXexo0b4ePjU+M4R48exeTJk6WQEfOJqqoqjIyMkJiYWNepMFX47rvvwHEcoqOjUVxcjOHDh2PYsGEYPHgwfH190a9fP3h7ewMA/v777y+am7y8PAoLC6UaMzs7W7jqJ8PUlLGxMS5evIhhw4ZhyZIlUFZWhpKSEl6/fo2tW7eic+fOX2TRHo7j4O/vj1mzZpVZX8Hc3BxDhw4Fx3G1msM///yDrl27AgCKiorKrTNgwADcuHEDqampZY5xHCd2R1VWVhYTJkxAQkICTE1N4eTkBFtbW4SEhGD27Nk4fvw4srKycOfOHbRu3Rr29vaYP39+uQv+KSsrw8PDA6olELUAACAASURBVDY2NggMDERpaan4F83UCXb7m2GYei8wMBBTp06FgYEBunTpUu04srKyX+SO99fG0dERDx48EE6rYuonIoK6ujpevnxZ6arNZ8+exYkTJ2p9VOZzgwcPxtatW6U6Mq+trY3nz5+DiGr9izvzdXBwcICDgwOWL1+OgQMHYtGiRQA+vrd+/fVXtG7dGkOHDkVoaKiwTWFhIRYsWIDevXujS5cudT7NXhqKiooQHR0NABVOgf7UKbxy5Uq5nyXidlQ/UVNTw/Lly5GcnIzjx4/Dz88P3bp1w6pVqzBw4ECoqqpi9+7dZab7/tesWbMwffp06Ovrw9/fH0FBQVBTU2OfEfVUw3+3MAzT6I0cORJ79+7FoEGDqj1yt23bNuTk5Eg5MwYAWrZsiYcPH9Z1GkwVTE1N4e3tjQ0bNlRar0OHDpVO6wM+PqMn7jNm4ujatSuUlJSk+mxd9+7dkZOTg5EjR0o1V+br9uHDB2zYsAHe3t748OEDgI8jhDNmzEBycjI2b96MWbNm4bfffsNff/2FdevW4dixY5g2bRqaNWuGFStWIC0trY6vomb09fXx/v17nD59utIOnp2dXYV7b0v6nrxy5QpatGiBw4cPIzMzEwCgqamJn3/+GQkJCYiOjq6ykwoAPXr0gL6+PgQCAUJCQqChoSHR9jfMl8U6qgzDNAjdu3fH4sWL4evri7y8PInbT5gwATNnzkRAQEAtZPd1+zSiytR/ixcvxq+//lrpaMbMmTPx4sUL5Obmljm2c+dOmJqaQk5ODk5OTlLLS0ZGBgcPHkRYWBguX74slZiampq4dOkSrly5gtjYWKnEZJi8vDy0a9cOCxYsQNu2bUXeS5qamujcuTOKiorw+PFjhIeH49y5c1i9ejUePHiAvXv34tmzZ7Czs2vQe/u2aNECw4cPR69evaqsV1FHVdIR1cLCQqiqqoLP52P+/PkStf0cx3HYu3cvrl69ChcXF3h4eCAkJAQbN26sdkym9nCS/kOpTS4uLnTnzp26ToNhmHqKiDBo0CC4urpK1OF89eoVzMzMUFhYyKb+1oL4+Hh4eXnh6dOndZ0KIwZzc3NcuHAB1tbWFdZRV1fH5MmTsXLlSmFZdHQ0nJ2dYWVlBR6Ph8TERCgqKoLjOJw/fx4dOnSocW6tWrWCnp4ezp07V+NYAFBcXAwDAwM8evQIRkZGUonJMMDHv0cmJiZYvHgxhgwZItHiY7NmzYKCgoJwFeHGavfu3fj111/x3+/2ioqK2L9/v0RrT7x9+xY6OjrIycmBqqqq1HJ88+aN8Fn2BQsWID8/Hz169EDPnj2ldg6mLI7joojIpap6bESVYZgGg+M4jBo1CqdPn5ao3ahRo2BsbMw6qbWkoKAASkpKdZ0GIyYXFxfcvn270joTJkzAxo0bRabnhYSEwMzMDE+fPsXFixexfft2bNmyBQKBQGoj6oMGDcLNmzelEgsArl69iqZNm7JOKiN1HMdh9+7dOHnyJMzMzODr64t9+/Zh4cKFWL58OS5fvlzhqOGoUaMQGhoKPz8/bNq0CTdu3EBJSckXvoLaFRoairlz51bYGa9o6m9BQQFCQkLwyy+/4P3798JyTU1NAICvr69UF0HS19fHmzdv0KdPH3Ts2BFr1qxBr169sHbtWolHfRnpYx1VhmEaFHd3d9y+fRvZ2dlit+nUqVO1pgsz4nn8+DFbSKkBcXFxKTPC8V8rVqxAcXExvv32W3Tt2hWKiorYu3evcFVgU1NTjBkzBsOHD4eCgkK504SrY/LkycjJycHz58+lEu/Vq1ewsbGRSiyG+a9u3brh+PHjSEpKgre3N/bt24fs7Gzw+XxMnToVLi4uOHHiRJkOT8uWLXHmzBnY29sjKioKEyZMQNeuXfHixYtyz/PkyROxc0pOTq5wJd4vhYiwbt06HDlyBL169RK7E378+HFYW1vj/PnziI2NhbW1NVasWIG8vDzIyMigoKAAJSUlwi16pEVPTw8nT56Ep6en8EbZrFmzMGnSJLYycF0jonrz4+zsTAzDMFWZMGECTZ8+Xez6WVlZBIASEhJqMauv18KFC2n+/Pl1nQYjpvPnz1Pnzp2rrDd79mxSVVUle3t7WrlyJXXv3p2ys7PL1HN2diY3N7ca5RQTE0NBQUHk6elJAGjcuHE1ivfJ4cOHqV+/flKJxTCSKC0tpfDwcHJyciIrKyuaOnUqHTx4kG7cuEHPnj2juLg4io2NJYFAQKWlpbRy5UrS19en8PBwkTjPnz8nALRhwwbatWsX8fl8IiJKS0ujNWvW0KVLl4iISCAQ0L59+wgA/fDDDxXmJRAIKCIigq5cuVJr1x4VFUVWVlaUk5NDFy5cIHl5eercuTOtWbOG8vPzSUFBgQ4dOiTSZtOmTWRiYkKRkZHCsri4OOrduzdNmjRJWPbu3TuysLCg69ev11r+Fy9eJEVFRQJAgYGB9PjxY7px40atne9rBOAOidE3rPPO6ec/rKPKMIw4MjIySEtLi16/fi12GyMjIxo4cGAtZvX1Gjx4MO3du7eu02DE9O7dO1JRUaHi4mKpxJswYQKZmZlVq21paSmZmJgQANLU1KQ2bdqQlpYWycjI0IkTJ2qc2+bNm2n06NE1jsMw1SUQCOj+/fu0YsUK8vHxoXbt2lGTJk2oWbNmZGJiQh4eHnT37l1KSEigjh07kpGRkfC9OXr0aDIyMqL27dtThw4dqHnz5rR27Vo6e/YsaWho0MiRI8nY2Jh8fHyoVatW5ODgQAAoMTFRJIfU1FRaunQp2djYkJycHGlraxMAunfvXq1cc2hoKAEgJSUl0tXVpd27d9OpU6fI3d2dpk2bRoqKinTgwAGRNgMGDKA9e/aUiZWSkkKampqUk5MjLFu9ejWNHDmyVnL/JDo6mgAQAPL29i7398pUn7gdVTb1l2GYBkdXVxc9e/bEiRMnxG4THByMw4cPIz4+vhYz+zo9fvwYtra2dZ0GI6YHDx5AX19favs5Pnr0CFZWVtVqe+jQIbx+/Rp8Ph/Z2dmIiorC27dvMWbMGPTt2xeOjo44efJktXNr27YtLl++zLanYeoMx3FwdHTEnDlzcOTIEdy8eRPJycmIj49HUlISBg0ahN69e6Njx47o3bs34uLiICsri5iYGJw9exZnzpzB0qVLhXuIX7p0Cc+ePYOFhQXatGmDoKAg5OXlgc/nIzU1Fe3atYOlpaXw/Lm5uTA2NkZSUhL27duH8PBwaGlpISAgAI6OjrVyzRMnToRAIMCHDx+QkZGB4cOHw8vLC4cPH8bRo0dRWFhYZuqumZkZ0tPTy8QyNjZG586dcejQIWHZ6NGjcfz4cbx9+7ZW8geA1q1bIz09HdevX8c333wDADh16lStnY8pH+uoMgzTIPXv3x/h4eFi1x84cCCaNWuGCRMm1GJWX5+MjAw8f/4czZo1q+tUGDGFhoZi1qxZUuuovn37ttqLFW3duhUtW7Yss2Lqtm3bcOvWLWhpaaFv37745ptvUFBQIHH8Vq1aQVVVFVevXq1WfgxTm2RlZeHn54enT5/i+fPnmD9/vvC9cPToUfD5fHzzzTeYP38+ZGVlsXnzZoSFhcHHxwejR49GYmIiDhw4AAUFBcyaNQt37tzBzZs3he/tq1evol27dgAALy8v/Prrr/jhhx8QHByMVatWVboHak1wHFdubC0tLfz5558gIuzfvx8rV67EhQsX8OTJE0RGRlZ4Q+mHH37Azz//LFxrQldXF15eXujTpw9++umnWrkGAFBWVkbbtm3h6ekJANi7d2+tnYspH+uoMgzTIPXu3RtXrlxBTk6O2G2ys7PLXajl2rVrIqsLMuJbtWoVRo4cCRUVlbpOhRGTQCCAoqKi1OJpaWkhMzOzWm0LCgogJydX7jEXFxdERETg5MmTiIyMhJKSEpo0aVLlisWf4zgOAwYMkGj2BcN8acrKymW2XJk1axaio6Px5MkT3Lx5E8uWLUPHjh0RERGBhQsXoqioCMHBwThz5gyOHz+O77//XmQk9fTp0xg0aBCWLFkCFRUVjBw5Eubm5oiJiYGXl9eXvkShLl26YPXq1XBxcUFWVhamTZuG1q1bY8SIEZg5c2a5bXr16gVXV1fMmTNHWLZhwwZ4eXnh7NmztZbr4MGDYWpqivDwcPj6+uLWrVvspteXJs784C/1w55RZRhGEr169SqzIENl+vTpQ+rq6qSmpkalpaVERLRnzx4CQAsWLKiyfXZ2Nv3xxx+0fv36aufcmCQnJ5OWlha9evWqrlNhJLB161YaMmSI1OKNHz+eNDU1he8pSXNRVFQUqx4A4bN9krh+/To5OjpKnBvD1Defnv2cNWsWtWvXjiZNmkQCgaDcuh07dqQDBw7QzZs3SVdXl54+ffqFsxVPaWmpyPOnFXn79i2Zm5vTwYMHhWWJiYlkampKHMfR1atXpZ6bo6MjBQUFUfv27SkgIED4zCpTc2CLKTEM09ht2rSJhg0bJlGbwsJCkpOToz59+tDUqVNJRkaGOI6jZcuWVdgmIiKCVFRUCADJysoSj8eraeoNnkAgoO7du9Py5cvrOhVGQsnJyaSjo0MlJSVSiZefn09ycnL0+++/S9x28+bNpKKiIlZdLS0tmjJlCnEcJ9EKnMXFxaSlpUWpqakS58cw9UlJSQkFBgZSkyZNKCIiguzt7cssSkREdPfuXbKwsKDi4mKytramnTt3fvlka0F0dDTp6uqKLAKVmZlJAGjJkiXVullWmSNHjlCzZs2EHdStW7fSsWPHpHqOr5W4HVU29ZdhmAarb9++OH36tER7xsnLy2P58uWIiYnBoUOH4OfnB3V1dSgrK5db//Lly+jWrRt69OiB7OxsbNq0SarTJhuqLVu2gM/nY/bs2XWdCiOhJk2awMjISKIptJVRVFSEqqoq+Hy+xG2fPHlS5vnUilhbW+Px48dwcnISmQJYFVlZWQwdOhS9evXChw8fJM6RYeoLHo+HoKAgrF+/HgMGDMCjR4+wa9euMvXu3buHzMxMyMnJITExESNHjvzyydaC1q1b47fffoObmxvs7Owwbtw4HDx4ELt27cKJEyfwzTffSPU97uPjg/j4eLx//x58Ph8//vgjiIjtrfoFsY4qwzANlrGxMVq0aIHz589L1C4gIABJSUlIS0vDxo0bUVpaWub5IIFAgLFjx8LDwwM+Pj4IDw+HpqYmXrx4ASUlJWleRoOTlJSE+fPnY9euXZCVla3rdJhqGDx4MDZu3Ci1eOW9h8SRlJQEbW1tseo6OTkhPj4e7du3L/OceVUeP36Mhw8f4vr16xLnyDD1ja+vL27evIn169dj2bJlZY737NkTI0eOxMWLF1FUVCS1hdNqA5/PR1ZWltj1v/32W2RnZ2Pv3r1wcXFBVFQUZs6cCR8fH0RFRUm0boW41NTUoK6ujvfv36N///6QlZUts2oxUzvq779chmEYMQwbNgz79u2rUYzS0lKoqKjgwIEDWLt2LZ4+fYoff/wRu3fvxqFDh3Dw4EFhXRcXF7x7966maTdYAoEA48aNw6xZs2BnZ1fX6TDVNGPGDBw7dkxq2zsUFxcjPj4emzZtgqurK7p06YKQkJAq26WmpsLAwECsc0RERKBVq1Zo3bq12F9sCwoK4OXlhcjISPTt2xf37t0Tqx3D1HdWVlaYMWMGnJ2dyxwzNjbGxo0b4e7uXuFiZfWBQCBA//79MXHiRInaycrKonXr1pg8eTK2bduGnTt34uXLl9i+fbvYnyfVsWfPHujp6QEAbty4UWvnYf4PuxXOMEyDNmjQIMyfPx95eXnVXnlWS0sLo0aNEk7nmTVrFjiOw7JlyzBgwACRulZWVl/1tJ/w8HDhFCim4VJTU4OBgQEyMzPFHtGsTH5+Pn777TcoKCigbdu2iIyMxJUrV5Ceno6uXbuiW7du5Y7qZGVllftFuzyvXr3C/Pnz4ezsXOn0vtTUVPzzzz94+PAhNm7cCGVlZfzzzz84ffo020+VYSSUlZWF33//HTk5Ofjpp5+go6MjtdgXLlzAmzdvcPfuXWRlZVU7dt++fdG3b1+p5VWRYcOGoWnTpigoKECbNm1q/XwMG1FlGKaBMzAwQPv27Wu0/cTLly+xe/du/PPPP+A4Dnw+HwKBAIGBgWXqPn36tF7foa5tO3bswIwZM9iU30ZAQUGhWnuTlictLQ2lpaUoKChAZGQkDAwMwHEctm7dCk9PT5EvdQKBACUlJQA+TvszMTGpMn5RURHy8/PRs2dP2Nragohgb28PR0dHrFq1CkVFRXj69CmOHTuGZs2aYfLkydixYwcWLFiArKwseHh4oLCwEAoKClK5Xob5Wpw8eRILFy7EyZMnYWtrizt37kgt9vPnz9GuXTs0a9YMT58+lVrc2sJxHNq3b4+uXbuytSq+ENZRZRimwfvuu+9qtBG3jIwMhg4dip49e0JDQwPLly+vsK6Ojg6Ki4tx7ty5ap+voUpLS8P169fh4+NT16kwUpCRkQF9fX2pxDI0NBQZMc3OzsZff/2FN2/eICYmBg8ePMDcuXNx+PBhqKqqCvdxzMvLE6uj+uTJE3AcB0NDQ+HiaTweDyYmJliyZAmUlJRgY2ODwYMHw8LCAh8+fMC7d+8wf/58YV6so8owkhs1ahROnTqFFy9eYPXq1ejXr1+FC7Glp6dLFPvmzZto27YttLW18erVK2mkyzQyrKPKMEyD179/f1y5ckWiBRkq8r///a/STq+bmxuGDh2K//3vf0hNTa3x+RqSvXv3wsfHp9pTrJn6o7CwEG/fvoWurm6txFdRUcHDhw8BALa2tpg+fTpCQ0MxcOBAmJiY4OrVq3jz5g3s7e0xb948PHjwALm5uRXGa968OYgIr169wv79+6GoqIgHDx7g9OnTyMvLw8mTJ3Ht2jUUFhYiJiam3GnGSkpKUlkRtJQEuJUVg5lhy3Dp5S2UEptOzDROn24Kde3aFUpKSujevTtWrlyJAQMGwNXVFW3atEFERAQCAgLQoUMHGBkZif0cOBEhIiICbm5uGDZsmEQreTNfD64+rVrl4uJC0pxSwDDM12PIkCFwc3PDlClTahTn+fPnsLKyQnJyMpo0aVJhPVtbW7x//x7x8fHVWu20oSksLIStrS327NmDjh071nU6TA3t3LkT+/btq7WZAU5OTjAxMcHff/9d5lhubi6srKyQkZEBJycnaGlp4cqVKxAIBCguLq5wWrmSkhI2b96Mo0ePIi4uDrGxsRLl9Msvv+Dt27dYtWpVta4J+NhJXfDwd8S+e4YiQRG4Ug5Oes2xrOX34HHs3j/TeBw6dAgjR46EpaUlUlNT4ebmhrCwMCgqKqK4uBinT58Gn8/H9OnT0blzZ4wePRrR0dG4dOkSrl69WmX8x48fo1evXkhOTkZeXh709PSQm5sLHo/3Ba6OqWscx0URkUtV9dinKsMwjYKfnx82btxY4yXjLS0toa2tjfXr11da7+bNmygpKYGamhrU1NQwZMiQGp23vgsODkbLli1ZJ7URKCwsRFBQEObNm1dr53j+/Dnc3d3LPaaqqoo3b94gKSkJMTExGDRoEBYuXAig4qmDmZmZKC4uhoyMDNTV1SXaO/kTQ0NDiacm/lfU21jEv09CMUrAycgAchzi+EmIeitZp5lh6rMzZ87A398f169fx44dO5CQkICTJ08Kn8uUk5ND3759MWLECGRkZODo0aPw8fGBt7e32DObMjIyoKenB47joKqqilatWiE8PLw2L4tpgFhHlWGYRqFLly6Qk5OTeE/V/8rNzUV2dnaVz2Gqq6vj9evXSElJwcKFCxEWFlbu4kuNQVJSElauXFmjkSim/tiwYQMcHBwq7EhK6vTp0zA3N4e5uTksLCzQtGlT5ObmVrmNk7m5OcaPH4+5c+diwYIFkJGRqbAjOW3aNOjr62P48OFo3bo13rx5I3Ge6urqlU4vFsfT3FcoFIh2kgsFRXiWm1KjuAxTXxARAgMDERISgtatW6N9+/aVPsvO4/HAcRyAj4+H/Hel/IooKioiLS1N+DogIAC//fZbzZJnGh22bCPDMI0Cx3GYOnUqQkJC0KNHj2rHSUhIAMdx+Oabb8Sqb2xsjICAACgqKsLf3x8bN27EqVOn0KlTp2rnUJ98+PABTZs2xbJly9C8efO6ToepoezsbKxatQo3b96UWsxz584hKysLo0aNgkAgQGlpKQQCAUaPHl1l2w0bNmDHjh3o0KEDiKjCDui///4rfF937twZeXl5Eucp+//Yu++wqK6tD8C/M0MHqYJDEwQBBREQEBsKiDUqVlAiaizEEg3mejXXmmgSWywpduxGvbagIhqxS1GECCg2LKBIU3ovM+f7w+t8EkEYmALMep/Hx8wpe69JwjDr7L3XVlAQVhtuLEsNEyhzlFD+YbJaJYCFRv0FoQhpCRISEpCZmQkfH58G3yMQCBAWFoYjR44gISGhQfeEh4cjICBA+Lpbt2548eKFyPGS1o1GVAkhrcbnn3+OqKgopKSkNLqNN2/eNGqNzNy5c1FYWIgePXqgX79+MDExwYQJE1r8vo1RUVHo0aOHRKeJEuk5fPgwBgwYgI4dO4qtzerqaujq6mLLli3Ytm0bdu7cieDgYFhZWdV7r4qKCrZu3QqBQAAXFxc4OjrWel1aWhpGjhwJAHB0dIRAIMCyZctEilMciWo3nc4w4GtBUP6uHS44yH+QBUcteohDWofHjx+LXHTs3r17GDduHLZu3QojI6MG3fPq1SuYmZkJX+vq6oqlICJpXShRJYS0GsrKyk3+Mpqfn9/oYg5qamq4cOECfvrpJ4waNQqnT5+u84t3S3H9+nV4eHjIOgwiJqGhofD19RVrm3w+v9Yquw01ffp03LlzBzExMbV+yS0tLUVVVZVwWjCHw0G/fv2wevVqfPPNNw3uRxyJqhHPENu9VsCjsDM89V1gqs5D6vpoZLyWrwrgpOViWRZXr16tdT3omzdvMHPmTFy5ckWkn+n3hQfHjh3b4HsyMzPRrl074esXL140aqYEad1o6i8hpFXh8/lQVFRs9P0VFRXC9TaNtXDhQgDAoEGDMHr06Ca1JWs5OTmws7OTdRhEDPh8PqKjo3HgwAGxt9vUn5lPuXDhAjQ1NTF79mwUFhZi0aJFuHbtGrZt24a5c+di3bp1dVYK/pCCggKqqqqaFIu7uztUVFTw79FzUCqoQMCtpbCY5IKbGX/DpL0pVf4lzRafz8fjx48REBCAkpISVFVV4f79+1i6dKnw5/fAgQPw8fGBk5OTSG1fuHAB7u7uIn0O8Hg8ZGVlCV9bWloCAJ4/f46ioiJ06dKFKgATGlElhLQeXC4XPj4+OHHiRKPbeF9ZVByMjIxQXV3doqf/KisrN6rCKml+EhMTYWhoCH19fbG229QR1doUFhZizpw50NLSwrhx4+Dk5IS5c+di8eLFcHZ2RlJSEmbNmgUlJaV6K3S/p62tjTdv3jQ6JpZlkZycjKlTp4JlgJ8e7Ea1oBoaA0xxouIGlt3bSnuqkmZr//79sLOzg6mpKR48eIDIyEgcO3YMW7duFV4THByM6dOni9z2li1bMGPGDJHuMTQ0rLEXuYaGBqZOnYqAgAA4Ojri0KFDIsdBWh9KVAkhrcq4ceNw8ODBRk/xO378+CcrHIrC0dERSkpK+O2338TSniwoKSmhoqJC1mEQMUhKSpLIVPTq6mqxJaqxsbHo2bMntLW1cfToUXz55ZcoKirCtWvX8Ouvv2LGjBnIzc2Fm5sbAGDw4MHYtWtXg9ru2rUrXr58iby8vEbFdvr0aZSWlsLT01O4TY0ALBgOgyqGj8eFtE0NaZ7OnTuHpUuXYuXKldi+fTs4HA54PB6OHTuG7777Tpgw5uTkwNTUVKS2IyIikJWVJfLsoTdv3qBt27Y1jk2YMAF8Ph9Hjx7FsmXLWvSa1bKyMuHnFWk8SlQJIa2Kt7c3eDyeSGvX3issLMTFixfFWiJfWVkZ9+/fF1t70qakpEQjqq1ERkYGeDye2NsVCARim/rr4+OD9PR0nD9/Hjk5OVi3bh3U1NSE57dv346kpCSUlJSgsrISjo6ODf4iWFFRAZZlG7U04M6dO5gxYwb++OMPcDgc2qaGtBgsy2L+/PkIDg7GsmXLanwGdO7cGXPmzIG1tTVMTExQVlaGmzdvitT+06dP4eTkJPI0XUtLSyQnJ9c45u3tjVu3bsHPzw9+fn6YOHGiSG02J4WFhQgODoaenh7KyspkHU6LRYkqIaRV4XK5OHr0aKMSzvebmWtoaIglloMHD6KwsFDk9T7NCU39bT2ioqLQvXt3sbdbXV0ttrVkKioqGDhwIAYNGlTnNffu3RMmxjo6Og1ed3r9+nU4OzuL/POdkZEBHx8f7N69WziS+36bmg8pc5RomxrS7Gzfvh06OjoYMmRIredXrFiB1NRUREZG4sCBAyIXzxs8eDBu376NPXv2iHSfmpraJ/c1njt3LuLj40VqszlRUFCAjo4OAIhcRZn8PyqmRAhpdbS1tXHu3Dl4e3ujpKQEixYtatB9lZWVYBgGhYWFYonj/ZftSZMmiaU9WVBSUkJBQYGswyBNJBAIcOPGDWzZskUibTd1RLW4uBgLFizA69ev6/1yumvXLpiYmEBJSUmkRPXs2bN1flmvC8uymDVrFqZOnYoRI0YIjzvr2sJG0xz33j4Bn8NCUFENG0NrOOvaitQ+IZL06NEjLF++HJGRkXX+jDIMAz09Pejp6dXYLqaheDwewsPD4eHhgfLycsycObNBSwGioqI+ud94eno6jI1b7oMfdXV14TKD58+fQ09PT8YRtUw0okoIaZUsLS0RERGBgwcPYuHChWBZtt57YmNjwbIsBg8eLJYYeDweVFRUcPLkSbG0JwuSKJRDpO/BgwfQ1dVt8B6HomjqiGp5eTk6deqEI0eOQE9PD48eghP8sQAAIABJREFUPULPnj2xfPnyWq9nGEY4fVdVVbVB69ErKipw/PhxTJgwoUExsSyLrVu3wtnZGU+fPv1oz1Yuw8Eq+9koOPgET/bcQvr2u1hlP5uq/pJm5fLlyxg9ejSsra0l2o+1tTUuX76Mffv2wdPTE2lpafXec/PmTfTp06fO85aWlnj27FmDfnc3RyoqKsIdAF6/piUBjUWfqCJYvnz5Rwu/CSHNl7GxMW7cuIEbN25g+vTp9X6h9fDwgJKSEnbv3i22GMzNzREcHIzw8HDcvn0bDx8+FNuIrTSkpaXBxMRE1mGQJnr+/Dk6deokkbYZhmnSGqxRo0ahtLQUr169wpEjR+Dk5AR1dXWsXr0aAwYM+Oh6Ho+HoqIipKenY8KECXBwcKi3j+PHj8Pe3l6432NdXrx4gR07dqBXr17Yt28fNm3ahMjISCgrK390LZfhoCNjhGcHYzHd05+SVNKs7N+/H0ePHhW5OFJjde7cGdHR0ejfvz+8vLxqVPT9p9evX6OgoOCTn0n6+vpQVlZuUNLbXK1duxbAu+nV0kq44+PjMWLEiCZvxdVssCzbbP44OzuzzRkA9t2/MkJIS1JUVMQOGDCAHTp0KJubm/vJaz09PVkHBwex9b1q1SpWWVmZVVRUZDkcDguAVVJSElv7kjZs2DA2JCRE1mGQJtq6dSsbGBgokbZjYmJYhmHYW7duiXzvb7/9xiooKLDff//9R+fi4uJYhmFYTU1N9vTp0x8d79OnD6unp1dvH9nZ2SyPx2MjIiLqvXbKlCls//792UOHDrF8Pr/e63/++Wd27ty59V5HiLRcvXqVPXz4MKunp8d27NiRPX78uNRj+P7771lXV1e2rKys1vP//e9/2eHDh9fbzoABA9hz586JOzypGjlyJAuA9fHxkWg/r169YmfNmsXq6+uzHA6HLS4ulmh/TQUglm1AbkiP/0QwcuRIqT2ZIoSIj4aGBkJDQ2FlZQVnZ2f8/fffdV6ro6ODoqIisfW9dOlSlJeXo7KyEnw+H6tXr4aqqqrY2pe0V69e0edeK5CZmQlDQ0OJtO3q6ooePXogICBApPtiY2Mxb948TJw4EYsXL/7ofLdu3ZCZmYlhw4Zh1KhRiIyMFB6fPn06IiIi4Onp+ck+8vPzMWnSJAQEBHxyPdx7T58+xdKlS/H55583aMo7l8tt0fskk9alsrIS06ZNg7+/PwwMDJCcnIwxY8ZIPY5ly5bB3NwcU6ZMQVJS0kfnIyIi4O7uXm879vb2iIuLk0SIUvPjjz8CgESLEpaWlqJr16549OgR8vPzYW5uDiUlpfpvbAEoURXBn3/+iZcvX8o6DEJIIygpKWHz5s1Ys2YNBg0ahJ07d340FUcgECAsLAwzZ86UWBxPnjwRVgJsCV6+fFnvdEnS/KmrqyMzMxO+vr7Q0NCAuro6hg4dKrb2jx49imfPnjV42nx1dTUGDRoEDw8P7N27FwoKtdd2NDAwwB9//AEdHR1cvXpVeHz79u0YPHgwVqxYUet9LMvi+PHjsLW1hZmZGVauXFlvTCUlJUhKSoKtbc2CSHyBAFcfP8evV6Pw9bFQeG0KRv9Nu/HzxRs4cepUi67qTVqXHTt2oGPHjtDV1cXDhw9x8+ZNsW0dJQqGYbB3716oq6vD09Pzowr8ERERn1yf+p6fnx82bdqE1NRUSYUqcba2tmBZFmFhYRLr4/2OBVevXoWamhoSExMbtQ1Xc0RVfwkhcsXX1xcODg4YM2YMoqKisGfPHuHIyZEjR1BdXY358+dLrH83NzccOnQIycnJsLKyklg/4lBcXIzy8nKqVtgKKCoqYseOHeDxeFixYgW0tLTw1VdfwdzcHJ07d0Z1dXWtf/h8PqqrqxEUFITAwMA622/fvj2CgoIwa9YsGBkZYfXq1cjPz0ePHj2wffv2j0Yn/f39UVlZ2egvbxwOB+fPn6/1XHp6OmbPno0nT57g+PHjDRpJBYDDhw+jb9++MDAwEB7jCwSYdvAUEl5loOwfa9yDo+LA9h4C/xa81yNpPQQCAX744QeEh4fj999/x8mTJ2WarKirq2P37t148eIFHBwcMHbsWPB4POTm5uLJkyfo1q1bvW10794dXl5eiIyMbFRF4tauvLwckZGR2LFjB+bPn4+ZM2dCX19f1mGJFY2oEkLkjo2NDW7fvo1r167VmJY0ceJEGBoa1jm6Iw7Tpk2Dg4MDbG1t8fbtW4n1Iw7vp/3K4ok8ES8TExO4uLggIyMD//73vxEYGIhz586hoqICOTk5KC0thUAggJKSEjQ1NWFgYAAzMzNYW1vD3NwcM2fO/ORoaWpqKgoLC1FdXY2hQ4ciPz8flpaW2L9/PywtLZGbmyu8Njk5GSdOnMAff/whHAkQl0uXLsHR0RH29va4e/dug5NUALhx40aNLWgA4EZyChJfZ6KoIB/ppw7h6c8r8Pj7b1DxNgsAwCgpY9vNO2J9D4Q0RlFREcrKytC1a1esW7cOjx49Qo8ePWQdFjp06IAFCxagS5cuWLZsGdzd3REYGFhrgbLauLq6IiYmRsJRtkxBQUHw9/fH8ePHsXz5crRp00bWIYkdjagSQuSSuro6evbsid9++w3btm1DRUUFAHxyA3JxUFBQwJ07d6CpqYk9e/YIy9c3R8+ePaP1qa0Ej8f76IvhgAEDkJGR0aD7v/vuO0yfPh1ffvklVFRUoKGhAS0tLejp6eHZs2fIzs6GsrIylJSU4O/vj4kTJ2LWrFmorKxESkoK9PT0wOVyYWJigqqqKtjZ2X2UFDZVdnY2AgICcPToUXh5eYl8v4aGBsrLy2sce5iZjbLKKnCUlKCkp4+KrAzwS4qQfmwfOsx+tz/zufuP8Y13/dMYCZGkoqIiYaKira0t42hqWr58Ofz9/bFmzRrMnz8f06dPb/C9tra2uHbtmuSCa6HKy8uxa9cu5ObmQl1dHVpaWigvLxf7wz9ZoxFVQojc2r59O54/f46RI0fCy8sL6urqKCgowLFjxyTet729Pc6ePSvxfppi06ZN8PPzk3UYRAwMDAzw/Plz4QMZUX333XfIysrCpUuXsHHjRnzxxRfQ0tJCXFwcCgsLweVywefz0blzZ+zbtw8DBgxAu3btEBISAm9vb3Tp0gWXLl2Cvb09eDweQkJCROq/vinoLMtixowZmDx5cqOSVADQ0tJCQUFBjWM27dpCSYELjqIS2vYbhA6z/g3TL+bCdPLsDzpvVHeEiFVubi40NDRkHUadOnbsiODgYJGSVODdA6h27dpJKKqWS0VFBXp6eti9ezeWLFkCU1PTZveAQhxoRJUQIre0tLQQFhaGgQMH4sGDB4iKisK0adOwc+dO+Pr6SrRvDocDLpcr0T6aoqioCNHR0bhw4YKsQyFiYGNjAycnJ2zbtg1BQUGNasPAwAAGBgbw8PAAAFhYWEBDQwNdu3bFlClTEBAQAA6Hg/Hjx8PY2BgbNmwA8C6JHDVqFHr37i28VxSVlZUoKSnB8OHD67xmz549SE1NbdJDprZt29bYs5EvECA4NQYVitVgP1ieqta+w/+/YBgM6yKZ/WkJaSg+n49//etfEv+9JSuNfcDW2p08eRK7du2Cjo4OTpw4IetwJIISVUKIXFNSUsLs2bNx7NgxdO3aFVZWVp/cvkZcUlNTP/nFW9aysrLA4/FaTeVAAqxbtw6enp6YMmWKWJ68CwQC+Pr6Ytu2bTWOHz16tMbrzz77DAAwevToRs0iiIiIEE4brs3hw4exaNEiXLt2rcHr3mrTtWtXhIaGCl9fS3+OhJwM5GYkQIVnBkVlTXCLOWD4AMCAZVloKClijqdbo/skRBy2bNmCqqqqOqtgt2RDhw7FggULUFlZ2Wq2XBEXd3f3Bm3z05LR1F9CiNwzNDQUrtXr2rUrUlJSUFhYKLH+BAIBMjMzG1SeX1ZoulXrY2dnBx8fH6xfv15sbTak0JaioiJ+/fVXhIeHN6qPrKysOr+gxsXFYf78+bhy5Qq6dOnSqPbfc3Z2xt27d4X7HSblZqGsugol0fEoTkoAX4+PqvZVcLYzQjfjdiiJuYnIf38JJQkWXyOkIQ4dOoSlS5dKtBCgrBgaGqJr167CZJXIF0pUCSFy78NEdcGCBdDV1UXPnj2b1GZlZSUOHDiAzz77DB06dIClpSVcXV2RlpaGLVu2gGGYZr3+Mzs7u8Y2HaR1mDFjhtjWRrMs2+CK0LNmzUJ1dfVHo68NkZ+fX+s0+aqqKkybNg0///wzunbtKnK7/6Snpwc7OztcvnwZAGCn2w6qCorg6miiIvndPo5KClxMceuGMbrKcOBUQoVGeIiMXb9+HampqejXr5+sQ5GYU6dOITU1Fb169cLTp09lHQ6RIkpUCSFyj8fjISMjAyzLQkFBAT/++COePXvWqLbevn0LExMTKCsrIzAwEK9evUL//v3h5eWFgoICdOzYESEhITAxMflob8nmJCUlhUZUWyEnJyc8f/4c+fn5TW6LZdkGr7NWUFDAvHnzEBQUVGOrmoawsbFBaWlpjYq8VVVVmDlzJgwNDTFRjPuYOjs74/HjxwAADyMLOLY1hKKOljBRreTzsSH+JnYl30V7b3fwBQKx9U2IKFiWxfXr1zF+/HgcOnSoVS/T0NPTQ0hICCZPngx3d3fcu3dP1iERKWl9cwQIIURE7ysllpaWQl1dHeHh4Y3eluX7779HcXEx7t+/Dzs7uxrnBAIB/Pz8cOLECWhqajY5bkmpqqrCb7/9hj179sg6FCJmioqK6NWrF65evYpRo0Y1qS1RRlQBYOPGjTh58iS8vb1FWgfu4eEBgUCAjIwMdOjwrpDRyZMnkZiYiCtXroh1n983b96gV69eAAAuh4MvOrviqm4o+LkFKLp8CxruzniU/wYwVMcLDgcBl/6Lg95+4Dbjh06k9cjOzsbvv/+OyspKnD9/HuXl5di8eTMGDBgg69AkjmEYzJ07FwYGBhg4cCAuXrwIe3t7WYcF4N3vTC6X26wfPrdU9G+UEELwrtT7+8qCd+7cgaura6Pa+e9//wt/f/+PklTgXaXf48ePA0Cz+QVbm3379sHCwqJVTyWTZ0OHDkVYWFiT2xFlRPW9S5cuISEhAZs3b27wPQ8fPgQAmJmZCY9lZGSgd+/eYt3gnmVZ3L17F5aWlv/fd142oPWuj9wDp/FqzipkrNyK0r8foIIVIP5tOq6lPxdbDITUhmVZxMTEwNfXF8+ePYOGhgbWr1+Phw8fNuslJJLg5+eHzZs3Y+DAgc1mZNXa2lq4iwARL0pUCSEE7xLV91MLc3NzYW5uLnIbycnJePPmDZYvX/7J64yMjBrVvjRUVFTghx9+wKpVq2QdCpGQ94kqyzZtA1BRR1QBwMrKCsuXL8f8+fMbXPX6q6++gqmpaY3RitzcXLHPSrh69TL69+HCpXMM2PKrYFk+7HTbQa2tjvAa/fmTUZWWBUZVBQBQVl2FB7lZDe5DwPLxqjgSCTl78ao4EgKWL9b3QFoPPp+P+Ph4PH36FCNHjoS/vz+6d++O4OBgLF26FAMHDpTbETw/Pz/Mnz8fixYtknUoAN49eB43bhy++OKLZr8/eksjn/+HE0LIP3w4ourm5oaQkBCR20hISICKigp4PN4nr/Pz88PFixcbFaekBQcHo0uXLujRo4esQyESYmVlBVVVVSQkJDSpncaMqALAihUrsGLFCkRGRjbo+rt372LWrFk1jvXs2RPnz58Xue+6sCwfetyF2Pg9A5T8BjZ/Pti8qXBvZ1ojUX279Qj050yAqu27UVdVBUXY6jZsLbeA5SP8dRBuZC5HfG4wbmQuR/jrIEpWyUcSExNhb2+PMWPGoFOnTrCxscGDBw+wbt06qKqqyjq8ZsHY2BiFhYVNfuAmDsuXL0dYWBj279+PqVOnIjExUdYhtRqUqBJCCABlZWXhiOr8+fPx+PFjVFdXi9RGbm5ug7646+rqQtBMi7CcPHkSc+bMkXUYRIIYhsGYMWPwxx9/NKmdxoyovufh4YGCgoJ6f8ays7NRUFCAyZMn1zg+aNAg5OTk4M6dO43q/5/4ZVdhYVoMJYUqACyAUqAyAQ+yT4CvoAAovPu51vEbAtWuNgAANQVFOLY1goeRRYP6eF1yC2/LH6CaLQPAopotw9vyB3hdckss74G0DmfPnkX//v3xn//8B8+ePUNRURHWrVtHe4j+g5+fH0pKSj7at1kWXFxc4Ovri2PHjmHVqlVwcHBAenq6rMNqFShRJYQQvEtU34+oDhgwAAKBQOTpr9euXYORkVG915WUlDTbCo1paWmwsGjYF2/ScgUGBmLfvn0oKytrUjuNGVEF3iWqioqKWLFixScf2owYMQLm5uYf/VxxuVxMnToVhw4dalT//3Ro72L8bzbvB8pQWpaIsuoqqLm+26O1OjcfLMvCWYeH3/r6iFRIKbfiCarZ8hrHqtly5FY+EcM7IK3BkydPMG3aNISGhiIgIAAAaAS1DgoKCti0aVOzWabyww8/ICIiAioqKnBxcYG/v7/wOwVpPEpUCSEEQHFxsbD6L/CuOurevXuFrwUCgbCoS10aWoSpuSaqLMsiLS0NxsbGsg6FSJilpSVcXFxw5MgRke99+fIl/vrrL5SVlTWp4m5gYCDWrVsHZWVlHDhw4KPzp0+fRkxMDM6cOVPr/R4eHoiKimp0/+9VVlbidFgyGI7aP86oQk21K1QVFKE/czwAoDDsBiBg0dvUAh5GFiJV+9VVtoYCUzMbVmBUoKtk3dS3QFqw169fIykpCZs3b0avXr2wZs0auLm5yTqsFsHd3R3p6el48+aNrEOBpqYmQkJCsHDhQvz888/Q09PDtGnTmsXU5JaMElVCiNxjWRaZmZk19g398ssva4z0zJw5E7a2tlBWVkaXLl0wa9YslJeXw8XFBS4uLrCxscHTp0/h4+NTb3/FxcUQCAQQCASorKzEmTNnmsVU4Js3b0JVVVWslVRJ87Vs2TIsW7YMBQUFIt1nYWGBYcOGoaqqCt26dWt0/7/++isqKioQFBSEKVOmICgoCI8fP0ZlZSWqq6sxefJkjBs3rs4K2U5OToiNjUVxcXGjYwCAp0+f4tFzfTBKDgDUADDv/lZygD3PF45tjaCmoAj1no5gyyvBcDnYlRSDgEv/FWkfVWP1HmirYgsFRhUAAwVGFW1VbGGsTuvB5dny5cvRq1cvHDhwAJGRkZg6daqsQ2oxuFwuevbsiatXr6K0tFTW4cDW1haHDx/GuHHjMHnyZNy7dw87duyQdVgtGiWqhBC59/6L7ocjqoMGDUJW1rtqni9evMDu3buxZ88e7Nq1C1wuF9u3b8fy5ctx7949tGvXDpmZmQAAT0/Pevtzc3NDdnY2FBUVoaWlBR8fH/B4PJH2lhS3vXv3YuzYsWKbSkmav169eqF3794ir/ESCARITExESUkJxowZ06QYOBwOHB0dAQA7duxA586doaqqCkVFRRQUFODgwYN13qum9m4EtKlr1PLz86GlpQtGZw8Y7U1gNL5+97fOHnC5ijjo7Yfptq7Q/ezddk0sy6KMXy3y1jQchosBxpvRl7cSjnrT0Ze3EgOMN4PDNG76NGn5SkpKcOzYMRQWFmLt2rWwsbGRdUgtjru7OyZOnIhBgwY1i5FVb29vnDp1CtOnT0f79u2xatUqGlVtAkpUCSFyLysrCzwer8Y0xoEDB6K6uhovX77EsGHDYGtriy+++AKTJk1CQkICOnXqhPXr18PDwwPnzp1Deno6FBUVMXDgwHr7+/LLL1FZWYnLly9jwYIFyMnJgb6+PsaPHy/Jt1mn0tJSzJ07Fzdu3MCQIUNkEgORje7du+PRo0cy63/GjBkICAhAUFAQysrKcOPGDZw4cQLjxo1DmzZtPllAhmEYTJ48udHrZN/T19fH27dvwTBcMCqeYDRmv/v7fwkkl8OBAocLrikPbQb2Ft4n6tY0wLtk1VSjNxx0v4CpRm9KUuXclStXwDAMOnbsiP79+8s6nBbp888/x++//46srCwYGBggLy9P1iGhT58+iImJgYqKCk6dOtWkJRLyTkHWARBCiKxlZmZ+tKWMkpIS1NXVYWlpierqasTHx9c4f/DgQbi6uiIoKAgAoK6ujsePH8PKygr/+c9/sHr16nr79fDwgIeHBwBg8+bNGDp0qHjeUAPw+XzExMSgqKgISUlJcHR0RKdOnaTWP2keTExMEBMTI5O+33+ZCwkJwYgRI4THAMDa2hrHjx9HdXU1FBTq/qpSVVXV5Gqo7dq1Q0ZGxif7stNtB1UFReh+Pkx4TJStaQipjZaWFuzs7HDu3Dm53RO1qczMzBAYGIjhw4fDzs4OaWlp0NHRqf9GCTM3N8fx48dlHUaLRz8VhBC5l5WVVWN96ntbt27F1q1bweVyhVN733NxcUFOTk6NEcgOHTpg+/btWLt2bYP3iHyvY8eOIm+H0xgPHz7Et99+CzMzM3z55ZdYv349wsLCMHPmTIn3TZofBQUF8PnS38dzzpw5uH37NpKSkoRJ6ofs7OygqKiIc+fOfbKdkpISqKh8VK5XJJqamrCxsUF0dHSd13gYWcBGXQdsReW7Fawibk1DSG0MDAzw5s0b6OrqyjqUFs/Q0BCurq5IS0uTdShEjChRJYTIvdpGVAFg0qRJ6Nu3L/h8fq3Tsmr7cjF9+nQMHToUgwcPFqm4g6mpKYB3FUglgc/nIzAwEF5eXuDz+bhw4QISExMRHh6O8PBwTJw4USL9kuaNy+VKPVE9duwYtm3bhiNHjsDKyqrO60xNTXHy5Mk6z7Msi9jYWOEa16YYOnToJ5NiLoeDiXxtmN1OxjeO7iJvTUNIbZ49ewZLS0tZh9FqGBsb49mzZ7IOg4gRfcISQuTePyv+fujIkSPQ1dX95PTDfwoJCYGqqir69u3b4Gq+CgoK4HA4iIuLw8mTJ8VaBTgtLQ3+/v549uwZkpOTsX79enTp0kVs7ZOWSxaJ6qxZszBx4kSMHTv2k9e5ubl9cpQTAHR0dBAbG9vkmPr164dbt2598pr79+6hj4Ep5nbtjf4mHSlJJU12/PhxuLu7yzqMVmPSpEn44Ycf8OrVK1mHQsSkSZ+yDMOsZxjmEcMwiQzD/MkwjPb/jpszDFPGMEz8//5sF0+4hBAifhkZGbXuHVpeXo6DBw+ic+fOIrWnoKCAmzdvIikpCYqKirC3t0dubm699/F4PPTp0wdjx44VyyhRTEwMhg0bhq5du4LH4yE0NLRGZWNCNDQ0UFhYKLX+Hj9+jNzcXGzcuLHea/38/JCSklLneYZhYGEhnqm3Ojo6KCoq+uQ1UVFRDdonmZCGCgkJQWBgoKzDaDU8PDwQFBSEcePGoaKiQtbhEDFo6uPAcABdWJbtCuAJgP98cO4Zy7KO//tDi58IIc3W69evYWRkVOPYX3/9BX19feTl5WH7dtGftdnY2KCsrAzh4eEoKipChw4dkJqa+sl7EhISsH//fhw8eBDJycki9/mhyMhIDBs2DD4+Pnj16hV++eUXqKqqNqlN0vpYWlpKdarckiVLYGpqirZt29Z77WeffQY+n1/niOmNGzdw+/ZtsVRLbdOmzScT1crKSkRFRQmLnxHSVPn5+eDz+dDX15d1KK3KokWLwOPx8M0338g6FCIGTUpUWZa9yLLs++oftwCYND0kQgiRrvT09BqJanl5OYYMGYLBgwcjOzu7SdNkvby88PTpU1hYWKBTp04fVQ/+UNu2bTFx4sQml7K/ffs2Ro0ahUOHDmHGjBlQV1dvUnuk9TI1NUVOTo5I66kbsydgbm4uhg8fjj///BM//fRTg+5RUFCAoqIi0tPTaxyvrq7Gxo0bMXr0aOzfv79BSW99DAwMkJWVhbKyslrP3759GzY2Ns2imihpHZSVlaGmpoYHDx7IOpRWhWEY7N+/HxcuXKi3GBtp/sS5wGIqgPMfvO7AMMxdhmGuMwxDE/AJIc3WPxNVgUAAlmVx/Phxkdam1kVBQQFxcXHo27cvunXrhgkTJqC8vLzO6+/fvw9NTc1G9fX06VOMGDEC+/bta9CerkS+cTgcWFhY4OnTpyLdN2fOHJGuDwoKwpUrV7BkyRKRCndVVlbWmAaflJQENzc3hIWFITo6GoMGDRIpjrro6OjA1dUVISEhH50rLS3F/v374enpKZa+CAEAVVVVTJo06ZMFw0jjaGlpYceOHZg9e3a9U/pJ81ZvosowzCWGYe7X8sfng2uWAKgG8Mf/DmUAaM+yrBOAbwAcZhim1m9dDMMEMgwTyzBM7Js3b5r+jgghRAQVFRUoKCioMSrzfl9GcRY04nA4+Ouvv3D06FFcuHABbdu2rbP9u3fvCqsAi2rBggX45ptvpLonK2nZrKysRJ5qfvXq1Y+2bALerUFdvHjxR+teIyIiMGrUKKxcubLBfbzfZsLExAR8Ph/r16+Hh4cHZs2ahfDw8E9WDG6M//znP5g3bx7Wrl0LPp8PlmXx22+/wdzcHG/evBE5OSekPv3798eZM2dkHUar5O3tDU9PTyxbtkzWoZAmqDdRZVnWm2XZLrX8OQ0ADMNMBjAMwOfs/+YDsSxbwbJszv/+OQ7AMwDWdbS/k2VZF5ZlXWiePiFE2t5vTfPhZuvvR1Elsa+pr68vcnJyUF5ejsuXL9d6TWJiIoqLi2tNBD7l6tWrSEhIwNdffy2OUImcsLGxwcOHDxt8PcMw0NbWxsKFC2scP3/+PGxtbfHLL7+gbdu2wv+/Q0NDkZqainHjxokUV0xMDBQVFcHhcDB//nz8+eefiImJwfTp05s8Pb42/fv3R2xsLP766y/06dMHI0aMwIEDB3D9+nWcPn0a5ubmYu+TyLdu3bohNzcXd+7ckXUordLq1auxd+9emewVTcSjqVV/BwNYBGAEy7KlHxzXZxiG+79/tgBgBeB5U/oihBBJSE9Ph6GhYa3nJLUdxqlgAAAgAElEQVSn6fsEOC8vr9bzGRkZePz4MUaNGtXgNgUCAb755husXbsWKioqYomTyAdXV1fcvn1bpHsmTpyIEydO1Di2ZMkS9OjRAyUlJRg7diy8vb3BMAxGjBgBHx8fDB8+vMHtW1paYsyYMaiqqkJkZCROnz6NsLAwdOjQQaQ4RWVmZoZLly5h0qRJcHFxQWRkpMhVvwlpCJZl0a5dO6SkpDSqYB+pn6GhIQwNDXH//n1Zh0IaqalrVH8H0AZA+D+2oekLIJFhmAQAJwDMZFm2/r0ZCCFEyvLy8qCrq1vrOUklqt9//z1UVFTg6+v70bm3b98CeDeadPv2bURERDSozcePHyM/P1/kUStCevbsiejoaJGKJE2ePBllZWXChy4nT55EfHw8lixZAgA4fPgw+Hw+nj9/jszMTJw6darGrIVPSU9Px/Pnz3H69GkAQHx8PDQ1NaGsrCziO2scDoeDWbNmYcWKFcJlAISIW0FBAYB3CeuePXtkHE3r1bt3b0RFRck6DNJITa3625FlWdN/bkPDsuxJlmXtWJZ1YFm2G8uyZ8UTLiGEiFd+fj60tbVrHLt27RoYhoGamppE+ty+fTsCAgJqPfd+uqOrqyv09fURFhbWoDZfvHgBa2triUyJJK2bsbExuFwuMjIyGnxPcnIyOBwOFBQUcOfOHfj6+uKrr76qsTaaw+GgQ4cOMDAwECmeWbNmoX379hgxYgQMDAzw5MkT5Obm4tChQyK1Q0hz9n4NNpEsExMTZGdnyzoM0kjirPpLCCEtTkFBwUeJalhYGDQ0NCQyhfbMmTPIz8/H2rVraz1/9uxZYcVflmUbPIr08uXLRhdgIsTa2hpPnjxp8PWqqqpgWRZr1qxB//790b9/f/z6669iieXx48fo06cPAIDL5WL//v1QUFCAlpaWWNonpDmws7MDAKipqaGiokLG0bReysrKn6yyT5o3SlQJIXKtthHVb7/9FkVFRbhy5YrY+1u8eDH69OlT6/YzN27cwI4dO7By5UoUFRWhsLCwwaO6JiYmePnypbjDJXJC1ES1c+fOsLKywo8//ghHR8cGj/w3hLOzM06fPo3S0lLk5eWhrKwMaWlpda4lJ6QlYhgGq1atwrhx42iKuQQVFxfTXuItGCWqhBC5lp+f/9FIjY+PD3R1dYWjOuL04sUL3L17F/Pnz68xHSk9PR2DBw/GqFGj4O/vD0tLS+jq6mLWrFkNatfe3p4KRpBGc3BwwK1bt0S65/HjxygqKsKNGzfEst/wewcPHoSamhp4PB7Ky8tx9OhRaGpqonfv3mLrg5DmYM6cOTh79ixSU1NlHUqrlZubCx0dHVmHQRqJElVCiFz759Tf33//HVFRUbh+/bpEnnJnZWXhiy++wL59+9CuXTuoqKhAT08PxsbGsLCwwJYtW2BpaQllZWU8ffoUGhoaDX4fdRWFIqQ+Y8aMQUhISLOYIsfhcBAdHQ1fX19cvnwZioqKcHNza3AxJkJaCh0dHQwbNgwXL16UdSitlq2tLaKjo2UdBmkk+tQnhMi1pKSkGvsjrlixApMnT0aXLl0k0p+GhgZ++eUX5OXloaCgAMHBwQgMDMTz589x6dIlWFtbQ1dXF8nJySIVc0pJSYGZmZlEYiatn7GxMZycnHDq1ClZhwLg3fY0wcHB8PLywqtXr2gPU9JqtW/fHq9fv5Z1GK3WhAkTEBoaiqKiIlmHQhqBElVCiNzKysrCvXv34OHhAQD4448/kJ+fL7aiMPXR1NTExIkTsXr1apiZmcHNzQ0GBgZ49OiRyIWcUlNTKVElTbJs2TIsXLgQWVlZ6NChA3bv3i3rkAAAmZmZ4PF4sg6DEIkwMTERaX04EY2+vj769u0r3O6KtCyUqBJC5NaZM2cwaNAgYVK4ZMkSDBkypMHTbcVp/PjxyM7ORkxMDLhcrsj3R0VFwdHRUQKREXnh4eGBzz77DDweDxkZGZgxYwbS09NrXFNaWirSfqvi8PDhQ7Rv316qfRIiLaNHj8b169dx6dIlWYfSapmamiI/P1/WYZBGoESVECK3/vzzT4waNQrAu8qAqamp2LBhg9Tj2L17N06cOIHQ0NBGrTPNzMzEhQsXMHz4cAlER+TJ6tWrAQCbNm2ClpYWZs+ejbdv3wL4/y2QDAwMYGlp2eS+0tLSYGdnB1tbW1RXV9d6zZs3b3Dx4kX4+Pg0uT9CmiN9fX189913zWYGQ2tUUlJClX9bKEpUCSFy69atW8JpvwcOHICqqipsbGykGsPbt28xc+ZMLFiwAP379xf5/vLycowcORJBQUG0fQdpMl1dXQQFBeH169dYtGgRrl27BgMDA1hbW8PS0hKGhoZITU1tcpXfzMxMdOrUCRUVFXj48CEUFRU/uqasrAw+Pj6YM2cO9PT0mtQfIc2Zo6MjYmNjIRAIZB1Kq1RcXCxSzQfSfFCiSgiRWxoaGsKN1k+cOCHcgF2ahgwZAmNjY6xbt65R9y9evBhmZmZYvny5mCMj8mry5Mk4dOgQFi5ciPz8fJw7dw729vbYuHEjEhMTRV4/XRsXFxcYGRnhyZMnuHnzJoB3D4ve4/P5UFNTQ2ZmJlatWtXk/ghpzlxcXKCqqoqrV6/KOpRW6e7duxIrkEgkS3wbnxFCSAujra2NvLw8mJmZISMjA05OTlLtf//+/fj7778bvf9pUVER9u3bh8TERDAMI+boiLxycHCApqYmbt68iX79+mHIkCEYMmSI2NrfvHkzsrKy8ObNG3A4HLRr1w4Mw8DV1VV4zd69e6Grq4t79+7RtjSk1WMYBuPHj0doaGijZtaQumVkZCA/Px+2trayDoU0An36E0Lklra2trDAgqamJnJzc6XWd0lJCWbOnInAwEB07ty5UW0cOnQInp6eMDExEXN0RJ4xDIOAgIAaI5zi9N1332HKlCnC/Yu//fZbtG/fXvhzUFZWhhUrVuD8+fO0rozIDQ6HA2VlZVmH0erExsbCxcWFHua2UJSoEkLkloqKCkpKSgDUTFqlQUdHB+Xl5Th16pSwWI0oWJbFli1bMGfOHAlER+TdxIkTcerUKYnsPWhhYYErV65AIBDA09MTf/75J+bOnSs8f/r0adjZ2aF79+5i75uQ5oplWVqjKgFxcXFwdnaWdRikkShRJYTIJZZlcffuXTg4OACAMGGVlqqqKjg7O0NNTQ0ODg51Vj2ty82bN8Hn8+Hp6SmhCIk8MzQ0RL9+/XDs2DGxtx0WFoaXL1+ib9++iIiIwF9//YV//etfwvN79+5FQECA2PslpDnr0qUL7t27J+swWp1Hjx7JpP4EEQ9KVAkhcunRo0fQ0NCAiYkJKisrcevWLcyfP18qfb8fQb1y5QoSEhJQUlICR0dHbN26tcFtbNmyBbNnz6bpTERiZsyYgR07doh931Qej4dVq1YhJiYG33zzDQYMGCA8FxUVhUePHsHX11esfRLS3Lm6uuL27dvCAn9EPBiGoXXuLRj9lyOEyKUHDx6ga9euAIA1a9ZAWVkZfn5+Uun75MmTUFVVhaamJjQ1NREXF4f8/HzMmzevQUVrHj58iPDwcEyaNEkK0RJ5NXjwYOTl5SEiIkLsbX/77beorKzE2rVrhcdYlsWSJUuwYsUKWqtH5A6Px0OXLl1w8eJFWYfSqlRVVdED3RaMElVCiFxq27Yt8vLyAAA7d+7EqFGjpNb3jRs3ahRAsrS0RFpaGm7duoUrV66gV69eda5VKisrg5+fH9atWwctLS1phUzkEJfLxYIFC2okk5IUHByMvLw8egBD5NaECRNw5MgRWYcBlmVRVFSE1NRU8Pl8WYfTJElJSVTxtwWjRJUQIpeMjIyQnp4OAKisrISpqanU+r53716tvzhdXFwQHx+P+Ph4uLu713rvv/71L3Tu3BnTpk2TdJiEYPLkyYiLi8PDhw8l2s+DBw+wePFiHDlyBAoKtHMekU9jx47FuXPnpD79t7q6GidOnIC3tzdMTEygqqoKQ0NDuLi4YODAgcjOzpZqPOJSWlqKlJQUdOrUSdahkEaiRJUQIpcMDQ2Rnp4OlmVhYWGB27dvS63vV69eoUePHrWe69y5M+7evYvY2Fh89tlnNc798ssvCA8Px86dO2kqE5EKFRUV9OjRQ6KJanl5OSZMmICffvqp0Vs1EdIa6OvrQ19fH6mpqVLpLycnB2vXroWFhQV++eUXBAYGIjo6Gnl5eSguLkZmZiZ69eoFZ2dnrF27FgcOHGhRa2jDw8PRq1cvKCkpyToU0kj02JIQIpc0NDSgqKiIgoICdOvWDWfOnJFa3yUlJXBycqrzvI2NDSIiItCjRw8cOXIEEyZMwOHDh7Fp0yZcv36dpvwSqVJSUkJlZaXE2l+wYAGsrKwwffp0ifVBSEvRvn17pKamwtraWuxtHz16FGfOnIGhoSHy8/Nx6tQp+Pj4ICQkBN26dfvoei6Xi1WrVqFfv37466+/EB4ejk2bNuHw4cPN9qESXyBAdEIKHqdmI+xUOEZKcVkPET9KVAkhcktbWxu5ubnw8vLC7t27ZR1ODa6urvDz88Ps2bPh5+eHn3/+GXv27IGZmZmsQyNyJC8vD/fu3cOECRMk0v4ff/yBCxcuIDY2lmYJEIJ3DzI1NDTE3ubcuXMRGRmJRYsWIScnB0ZGRli9ejUMDAzqvd/b2xve3t5gWRa7du1C3759sXbtWkydOlWscTYVXyDAvLUnkfQsE+UVVeBXGyPhrTb4AgG4VPm3RaJElRAil0pKSvDmzRuYmprCwMAAlZWVyM3Nha6urlT6b0iBip07d6JNmzYIDg5GWloa+vXrJ4XICPl/8fHx4PP58PHxEXvbERERCAoKwqVLl6CtrS329glpiV6+fIn27duLpa3s7Gzs2rULO3bsgKenJ+Li4pqUBDMMg8DAQCgrK2PDhg11JqoCgQBJSUmwt7dvdF+NEZ2QgqRnmSirqAIAcBSU8TQtD9EJKejjZCHVWIh40OMFQohcSkxMhK2tLRQVFaGhoQFlZWUEBwdLvN/i4mLw+fwGbb8RGBiINm3aoKioCMOHDweXy5V4fIR8qHfv3sjOzkZmZqZY2z19+jRGjx6Nw4cPw8HBQaxtE9KSFRcXi21E1dfXFw8fPkRISAj2798vtna7deuGjIwM3Lhxo9bzu3fvRu/evcXSlygep2ajvLKqxrHyyio8SW2ZxaAIJaqEEDkVHx8PR0dH4eupU6di5cqVEu935cqVaNOmDTw9Peu99vbt2+jRowc2bNhAVX6JTCgpKcHLywvXrl0TS3vPnj3D2LFjMXfuXJw7dw4DBgwQS7uEtAYpKSlimwJfVlaGO3fuYMeOHbWuP20Ke3t7HDp0CP7+/igpKalxLjMzE0uWLEFJSYlE17bXxsbMAMpKNSeLqigpwtqs/unNpHmiRJUQIpf+mah6eXlJZb+4lJQUmJiYgNOA9TIjRoxAeHg4Zs+ejV69ekk8NkJqo6amhurq6nqvq66uBsuyiI+Px4MHD8CyLAQCAWJiYhAZGQlnZ2d0794dTk5OePToEVxdXaUQPSEtw6VLl+Dk5ITly5eLVDAvNzcXKSkpHx3ftm0bPD09oa6uLsYo/9+gQYPQt29frFu3TnhMIBBg7ty5CAgIgJqaGoqKiiTSd116OphDX4MDsNVgGEBVWRF2ljz0dDCXahxEfGiNKiFELsXFxWHSpElS7zcvL6/BU3idnJxgaWmJxYsXSzgqQupWXFyMjRs3IiAgoNbzZWVlWL9+PdauXQvg3R7FlZWV4PP50NLSQnFxMdLS0rB48WIsXLgQbdq0kWb4hLQIjx8/hr+/PxYsWFDvtWfPnsWvv/6K1NRUZGZmQkVFBQYGBrC2tkZCQgJyc3MhEAhw584dica8Zs0aODg4YP78+VBSUoK/vz9yc3Ph7u6O7t27Q09PT6L9/xOXw0Hx43MY2n80TDvaw9rMAD0dzKmQUgtGiSohRO68efMGycnJNUZ0MjMzGzTK2VTt27dHbGwszp49CxcXF+jr60NB4eOPYpZl8fvvv2PDhg1SiYuQuowePRqTJk3CpUuX0L9/fzAMA5ZlcevWLYSFheHgwYPo3r07Hjx4gDZt2kBXVxcsy+Lp06d4+fIlvLy8kJ6eDn19fdrPkJA6qKio4Nq1a4iJiUH37t3rvC45ORlTp07F1q1bYWtrCxsbG3A4HMTExODFixf46aefoK+vjzZt2kj85619+/ZQVlZGWVmZcI/VS5cuoVevXlizZg1evXoFU1NTicbwoYcPHyL+7l2E/PknVFRUpNavuFVXV9f6vUAe0bcfQojc+euvv+Dl5VXjl/iOHTvg5uYm8b51dHRgb2+PyZMno0uXLujRowcePXoE4F1yCrwbdR09ejQUFRUxfPhwicdEyKcEBAQgNDQUY8aMwZQpUyAQCLBgwQL4+/ujuroax44dw7Fjx2BmZiasms0wDKysrISJrbGxMSWphHzClClTsGDBAgwfPhxHjx796HxRURF8fHzQuXNnfP/99xg3bhzs7OygoKAADoeDHj16YMKECejUqRP09PSk9vOmpKSEixcv4tSpU5g9eza4XC4ePnyIJ0+eSH3JSlhYGMaOHduik9SDBw9CU1MTO3bskHUozQKl64QQuXP+/HkMGTJE+Do3NxdJSUmIjIyUeN8//PADRowYgWHDhmHPnj0IDg6Gu7s79PT0kJOTg88//xynT5+Gj48Pjh49SqOppFn47LPPkJmZiV69esHMzAympqb4+++/oaOjI+vQCGkVuFwuvvjiC7i4uGDIkCHIysqCr6+vsEL8oEGD4OTkhKysLKlPqf2U0NBQjB49GtnZ2RgwYADOnDmDjh07YuvWrcjIyEBaWhpMTEykEktkZCTGjRsnlb4kRU9PD1wuF0uXLsWQIUPEtlVRS8W8f4LfHLi4uLCxsbGyDoMQ0orx+XzweDz8/fffwilJ8+bNw+HDh/H27VupxFBaWophw4bB3NwcwcHBePnyJd6+fQtNTU0cOHAA3bt3x4gRI6QSCyGieL/e1NLSEoqKirIOh5BWKSUlBePGjUNaWhrKyspQWFiIoKAgbNiwQWxVgcUpNzcXf//9N9zc3NC5c2fw+XyYmJhAS0sL8+bNk8rvMz6fD2NjY9y+fRtmZmYS709S+Hw+zM3N4e3tjXPnzuGbb77BggULWt1UYIZh4liWdanvutb1rgkhpB6xsbFo165djXUzT58+RYcOHaQWg5qaGs6ePQsLCws8efIEnTp1grm5OYB3I66ENFcaGhro1KmTrMMgpFUzNzevUQiJz+c36320dXV14e3tjbS0NAwdOhQDBw6Em5sbHBwc4OJSby4iFseOHUOHDh1a/Agkl8vFV199hdu3b+P69esYP348rKysMGbMGFmHJhM0p4wQIlcuX76MgQMH1jimpKTUoO03xElNTQ3FxcUwMjKSar+EEEJaluacpH7IxMQEO3fuxNixY/HixQuUlZXBy8sL//73vyHpGZxr167F999/3yxHnEX19ddfIyEhAWlpaRg7dixiYmJkHZLMUKJKCJErV69ehaenZ41jRUVFEAgEUo2juLgYVVVVKCsrk2q/hBBCiKT17dsXhYWFOHbsGG7evInVq1dLrK/c3Fw8f/4c/fv3l1gf0qSiooJNmzZh3rx5sLe3x/bt22UdksxQokoIkRuVlZW4desW3N3dhcdOnz6Nq1evYtmyZVKNpU2bNli6dCkGDBiAgoICqfZNCCGESJqioiK6du2KkydPYs2aNSguLpZIP7du3YKrq2uLGXluiOHDh8Pa2ho7d+5EYWEhsrOzZR2STFCiSgiRG3fu3IG1tTW0tbUBAIWFhfD19cXUqVMxduxYqcezbNkyqKur49atW1LvmxBCCJEGY2NjuLm5ITw8XCLtJyYmolu3bhJpW1YYhsHevXsRFxeHvn37YvDgwRg+fDiKiopkHZpUUaJKCJEb165dg4eHh/D1vXv3UF1djeDgYJnEwzAMysvLhYkzIYQQ0hq5u7vXKBAlTikpKVItiCgturq66NatG7y8vBAfH4+LFy9iyZIlsg5LqihRJYTIhZUrV2Lt2rU19k8FINPCCzExMcjJyYGjo6PMYiCEEEIkrXPnznj48KFE2s7IyACPx5NI27I2depUXL9+HSNGjICtrS0ePHgg65CkihJVQohcePToEdatWwdvb2/hMQ5Hth+BK1euxLfffivc0J0QQghpjRwcHBAdHS32mgylpaV4/fo1Kisrxdpuc+Hj44O4uDisWrUKGzZswJMnT1BeXi48n56ejoiICPD5fBlGKTmUqBJC5EKbNm0+Gj01NTUFn8+v8aEvLXFxcYiPj8fUqVOl3jchhBAiTdbW1hg1ahTmzZvXoOuTkpLqTL5CQ0Mxbdo0+Pr6wtXVFZ06dWq1+4wqKSmhbdu2UFNTQ79+/dCzZ0/Y29vjyJEjiIqKQs+ePTFp0iTMnDlT1qFKBCWqhBC5oKGh8VERAhMTEygpKeHcuXNSjYXP52PFihVYtGgRVFRUpNo3IYQQIgs///wzoqOjMW3atDqnARcVFWHw4MHo0qULUlNTa5zLz8/H5MmT8fXXX8PFxQVjxozBL7/8ggMHDkBRUVEab0EmVFRUUFpaCi6Xi//+97/45ZdfcOjQIQQEBGDx4sWYOnUq9PT0ZB2mRCjIOgBCCJEGbW1t5ObmfnTcyMgIoaGhUnsae/HiRSxcuBBt2rTB9OnTpdInIYQQImvq6uqIiorC1q1b4eHhAVNTU/Tp0wdz5syBlZUVBAIBJk2aBHV1dXC5XKiqqgrvraysxLBhw2BjY4OEhARoaGjI8J1ID8uyeP36NQwNDYXHhg4diqFDhwpfjxs3DqNHj5ZFeBJHI6qEELlgZ2eHe/fu1TiWm5uL/Px8ZGRkiKUPlmVRUFAAlmWFx/Ly8hAdHY29e/di0KBB+Oqrr7B8+XLcuHGjxi9hQgghpLVr27Ytli9fjtTUVPz222/Q0NBAr169MHr0aMybNw+ZmZnYu3cvZsyYAVtbW2zevBksy+Krr76CtrY2du3aJTdJKgC8ffsWDMPUOWLKsizu3LkDBwcHKUcmHcyHX6hkzcXFhY2NjZV1GISQViglJQW9e/fG69evhcfat28PDoeD+/fvi+UX382bN9G3b19oaWnBzMwMWVlZKC0tRadOndCpUye4u7tjypQprXqKEiGEECKKkpIS7Nu3DyEhIdi/fz+MjIwAvPu9PXLkSACAoqIirly5gjZt2sgyVKnKyMhAQEAA2rZti6NHj350XiAQ4O+//8aECRPw5MkTme5iICqGYeJYlnWp9zpKVAkh8oBlWWhrayM5ORkGBgaorq6GoqIinjx5AisrK7H08fbtW1haWiI5ORmvXr0Cj8eDkZFRi/rlQQghhDQXpaWl2LhxI2bOnIm2bdvKOhyp6t27N/r06YMff/wRCgo1V2seP34c48ePB8uy+Omnn/Dtt9/KKMrGaWiiSmtUCSFyITExERoaGtDW1gYAXL58GQoKCmJLUoF3U5pUVVVRVlYGZ2dnsbVLCCGEyCM1NTUsXbpU1mHIRE5OjnDm1z+tXbsWoaGh8PLygpKSkgyikw5ao0oIkQubNm3CV199JfxAP3fuHAwMDMTaR1FREYqKimBiYiLWdgkhhBAiX/bv348DBw7Azc0NiYmJNc7p6uqCZVkoKyu36llblKgSQuTC2bNnMWnSJOHr+Ph4WFpairWP+/fvw9bWFlwuV6ztEkIIIUS+uLm5ITo6Gn5+fvj666+Fx/+PvTuPqznv/z/+OC0qlShCm6gooajs+5DBEMbSMIaxzZiLsQwuzGLGPi7LjOWaMVwYM/btso4tY4RC0aINLRSplLRI6+f3x8x1ftNX9nJSr/vtdm70+XzO+/M8R+q8Pu/35/3Oysoqk88w5ZEUqkKICi8zM5OcnBz1BA3w5yQNTZo0KdXz6OnpkZ2dXaptCiGEEKJyyMjIwNfXV/21lpYWn376KWFhYURHRwOwfv16OnfuTKNGjTQV87WRQlUIUeHdvHmTevXqFRsec+/ePVq3bv3SbQYEBJCUlFRsm7m5OVFRUTx8+PCl2xVCCCEqM19fX0aNGkWbNm2KrX+el5fH8ePH8ff3Jzc3V4MJy0ZGRgZvv/02b7/9NllZWertWVlZFBQUoK+vT15eHsuXL2fGjBkaTPr6yGRKQogK73+F6t/l5eW99LCZkydPMmTIEFQqFTNmzKBVq1YEBASwevVq5s+fT9WqVUsjthBCCFHpnD17lmvXruHv70/jxo2ZNWsWcXFxbNmyBTs7O5KSkhg8eDCLFy/WdNRSNXLkSJo1a4afnx+jRo0iNzeX3r17k5KSgpeXF5aWlmzYsAEnJyfc3Z85YW6FID2qQogKr6RCtUqVKuphNC/q6NGjTJkyhfPnz+Pv78/MmTO5efMmP/zwA7NmzSqNyEIIIUSlZGBggIODA4qicPjwYc6dO4eRkRHnz5/Hz8+P6dOnk56erumYpUpRFH7//Xfmzp3LqlWr2LVrFyqVigULFrB69Wrat29PQkIC8+fPZ/bs2ZqO+9pIj6oQosIrqVA1MjIiJibmpdqrXbs2d+/epWHDhuzdu7c0IgohhBACGDp0KN988w3/+te/cHNzY+fOncX2V61atcLdYnPkyBEMDQ0xNzdnzJgx1K9fn169emFmZsayZcuYOnUqX375JZ9++ikdO3bUdNzXRgpVIUSFFxcXR9++fYttMzExIS4u7qXaq1OnDkFBQQDk5+cDoKur+0oZhRBCCPHnfA/9+/fnp59+KrH3sKIVqvfv32f48OEcPnwYAH19ffWQ3/z8fAYNGkRhYSG2trZ069ZNw2lfLxn6K4So8K5fv05hYSFdu3ZVDx26efMmhoaGL9Ve48aNOX78OOvXr8fd3Z1//vOfpZxYCCGEqLzGjh3L9u3bS9wXGxuLnp7ea41NTXcAACAASURBVE5Udn799Vd69OhBmzZtim1ftGgRI0aMwMjIiDFjxlS6IhVApSiKpjOoubu7KwEBAZqOIYSoQBRFoVq1asyZM4ctW7YQFxeHh4cHISEh3LlzBy2tl7te5+fnx/jx4+nSpQu//PILt2/frlC/OIUQQghNyczMpE6dOmRlZRWbsT87OxsrKysuXLhAw4YNNZiwdBQWFuLq6sr3339P165d1dujoqJo164dV69epU6dOhpMWDZUKlWgoijPnBFKelSFEBVaUlISenp63Llzh/feew9DQ0NcXV159OjRSxepAG3atGH+/Pn4+PhgZ2dHXl5eKaYWQgghKi9jY2NMTEy4du1ase3x8fGYmppWiCIVYN68eZiZmdG5c2f1NkVRmDx5MrNmzaqQReqLkEJVCFGhXb9+HQcHB8LDw7GwsCA9PZ2+ffsWW6PsZezcuZPx48ezcOFC/P39MTY2LqXEQgghhBg1ahTfffed+mtfX1+cnJxITEzUYKrSs3v3btatW8f27duLXTjfvHkzcXFxTJw4UYPpygeZTEkIUaEVFBQQFhZGfn4+w4cPx8nJiZYtW1JYWMi9e/eoWbPmC7fp7+/PhAkTOHHiBC4uLmWQWgghhKjcJk2aRKNGjfjqq68wMTFh1KhRDBky5KV+b5c3//nPf/jyyy85fPhwsV7TsLAwpk2bxunTp6lSpYoGE5YPUqgKISq0Ll26EBsbi7+/P4mJiTg7O1OlShWsra3p1KkTYWFhL9zm559/ztKlS6VIFUIIIcpIrVq1GDduHPb29jx69IjBgwezbds2Tcd6JUVFRcyZM4dff/2VP/74AwcHh2L7tbS0UKlU2NjYaChh+SJDf4UQFVpRURHVqlWjd+/ehIWF4ezsDPx5NTM8PPylprhPSEjAw8OjtKMKIYQQ4m8WLVpEYmIiFy9eZO3atZqO80r8/f3p2bMnf/zxB/7+/sWK1JycHGbMmEHnzp25d+8ex48f12DS8kMKVSFEhbZhwwYsLCxYsGABAQEBNG7cGICvv/4aU1PTlxpak5iYSN26dUs7qhBCCCH+RqVSUa1aNdzc3KhWrZqm47y0b775hiFDhtCzZ098fHyoXbu2et/du3dp3rw5cXFx+Pv7k5qayoABAzSYtvyQQlUIUaGFhYUxcOBAoqKiuHjxIs2aNQNg7ty5PHz4kJEjR75Qe5mZmeTn52NiYlIGaYUQQghRkWzbto2NGzdy8eJFJk+ejK6ubrH9e/bsoUWLFuzcuZP69etTo0aNYkvyVGZSqAohKrTo6Gg8PT3ZvHkzDx48wNraGoAOHTpgbW1NSEjIC7X3xx9/0Lp1a/klIoQQQoin8vf3Z9KkSRw8eLBYL+rfnThxgt69e7/mZG8GmUxJCFGhRUdHY2dnB4C+vr56+/Lly4mNjcXHx+eZbTx48IDBgwcTEhJCVlYWixcvLrO8QgghhHjzJScnM2DAADZs2EDTpk1LPCYjI4PTp0+zfv3615zuzSCFqhCiwioqKiI2Npb69es/tq9u3bro6urSsWPHp7aRnp5Ojx49aNmyJZs2bUKlUj3xqqgQQgghBPx5Qbxfv3688847Tzxmz549dO7cuUIsuVMWpFAVQlRYiYmJGBsbY2xs/Ng+CwsL8vPzn9nGwoULady4MStXrpThvkIIIYR4pvv377Nu3TouX7781ON+/vlnJk2a9JpSvXnkHlUhRIU1f/78J9738fvvv1OjRo1ntuHj48OYMWOkSBVCCCHEc/H19aVJkybUq1fvicfs2rWLiIgIuT/1KaRQFUJUSAcOHODo0aOsWLGixP0XLlygQYMGT20jIyODsLAw3NzcyiKiEEIIISqgdu3aERQURNu2bXn33XfZs2ePep+iKHz77bdMnTqVY8eOvdQyeZWFDP0VQlQ4ycnJjB49mk2bNj1xGZmoqCj69+//1HYMDQ3x8PDgiy++YOnSpWURVQghhBAVjJmZGZGRkURHR3Pr1i1mzJjBgQMHaNy4MaGhoVy9ehU/Pz+srKw0HbVckx5VIUSFk5ubS8uWLXnvvffo3Lkz3333HUVFRcWOSUpKolu3bk9tR1tbm/3793PkyBG+++67sowshBBCiAqkbt26tG/fnqFDhxIQEECjRo1ITU3F1tYWX19fKVKfg0pRFE1nUHN3d1cCAgI0HUMIUUHk5OQwYsQIrl69SmhoKNra2gBs3ryZ0aNHk52d/VxDbm7evEmXLl0wNjZmwIABTJ8+napVq5Z1fCGEEEKICkelUgUqiuL+rONk6K8QosLat28ffn5+nDt3Tl2kwp+TLL311lvPfV9IvXr1uH79OufPn8fT05Nhw4Zhb29fVrGFEEIIISo9KVSFEBVScHAwU6ZMwcfHBxsbG/X25ORkrl+/zr59+16oPW1tbSwsLDA1NcXOzq604wohhBBCiL+Re1SFEBXS9u3bGTNmDE2aNCm2XV9fH4D69eu/cJs+Pj506dJFlqoRQgghhChjUqgKISqkI0eOlLg2mZGRESqVivDw8OduS1EU1q1bx+eff86HH35YmjGFEEIIIUQJpFAVQlQ4CQkJ3L59m1atWj22b8eOHWhra9OiRYvnaisnJ4fBgwezZs0azpw5w1tvvVXacYUQoswtWrSIGjVqUFBQoOkoQgjxXKRQFUJUOLdv36ZBgwbFJlACyMvLY9asWbi5uaGl9Xw//rZt20ZaWhr+/v44OTmVRVwhhChzMTExpKen06lTJ3744QcuX76s6UhCCPFUr1SoqlSqr1Uq1W2VShX016PX3/bNUqlUN1QqVZRKperx6lGFEOL5GBsbk5WVVWxbWloa9erVIyMjg507dz53W5s3b2bixInqe1uFEOJNdOPGDY4cOcK4ceMICAigW7du+Pv7azqWEEI8UWnM+rtCUZSlf9+gUqkaA96AM2ABnFSpVA0VRSkshfMJIcRTGRkZkZmZWWzbTz/9RHp6OikpKRgZGT1XO3FxcYSFhdGrV69nHyyEEOVYSkoKVlZW9OzZE29vbw4dOoSZmZmmYwkhxBOV1fI0XsB2RVFygViVSnUDaAn4ldH5hBBCzdjY+LFCtUaNGujo6Dx3kQqwfv16Bg8e/NzrrQohRHlVtWpV3nvvPQoKCkhNTaVVq1Y4ODhoOpYQQjxRaRSqE1Qq1QdAAPCZoij3AUvg7+NJEv7aJoQQZe5/Q38VRVEvJWNubk5+fv5zPT8tLY3Jkyfj6+vL0aNHyzKqEEK8Fr/88gtpaWlUr16d6tWrU6tWLU1HEkKIp3pmoapSqU4CdUrY9TnwAzAPUP76cxkwCihpkUHlCe2PA8YB2NjYPFdoIYR4Gh0dHXR1dcnMzKRatWoAWFhYkJeXR0FBATo6T/7RV1BQQJcuXejQoQOhoaEv1AMrhBDlVaNGjTQdQQghXsgzJ1NSFKWboihNSnjsVxQlSVGUQkVRioB1/Dm8F/7sQbX+WzNWwJ0ntP+ToijuiqK4y9U9IURp6dWrF//5z3/UX3t4eGBsbEzDhg1xdnbm+vXrJT5v/fr1mJmZsWrVKilShRBCCCE05FVn/a37ty/7A1f/+vsBwFulUumpVKr6gANw8VXOJYQQL+Krr75iyZIl5OTkAKClpcXBgwexsLAgMjLyiUszRERE8Pbbb6uHDJe2pKQkioqKyqRtIYQQQoiK4lXXUV2iUqlCVSpVCNAFmAKgKEoYsBMIB44C/5AZf4UQr5OjoyMPHz4stkxNx44dOXv2LLq6uk9cbsbNza1U1xe8du0a48eP58SJE5w9exY7OzveffddsrOzS+0cQgghhBAVzSsVqoqiDFcUpamiKM0URemrKEri3/YtUBTFTlGURoqi/PbqUYUQ4vmdOnWKpk2bljhhiI6ODhERESU+z8PDg4CAgFLJsGPHDtq1a4eRkRFTp07F09OTHTt2YGJiQteuXVGUEm/dF0IIIYSo9F61R1UIIcqljIwMTExMStz37rvvsnTpUjIyMh7b17BhQ9LS0ggNDX2l86tUKry9vTly5AhmZmb8+uuvJCYm0rZtW2rXro2JiUmZDS8WQgghhHjTqcrTFX13d3eltHoyhBCVW0ZGBlZWVsTHxz9WsGZkZFC3bl0ePnwIgK6uLnp6ehgaGlKjRg2qVavGrVu3uHbtGsbGxi91/gEDBhAUFESDBg1IS0sjJSUFFxcXfH196datG0uWLMHOzu6VX6cQQgghxJtEpVIFKori/qzjpEdVCFEhVatWjffee4+33nrrsWG+1apVIzs7m8LCQmJiYjh48CDTpk0jKSmJyMhILl68yN27d/nxxx9f+vx79uyha9euuLu7c/HiRZYvX86wYcNISEhgz549UqQKIYQQQjyF9KgKISosRVGYM2cO//73v5kwYQJz5sx5bLhtfn4+q1atYu7cuVSpUgVra2suX76Mnp4eOTk5MjxXCCGEEKIUSY+qEKLSy8vL44cffkBfX5958+Yxfvx4CguLT0C+bNkyfv75Z1QqFStXriQgIICmTZuSm5tLQkKChpILIYQQQlRuUqgKISosPT09du/eTVJSEhYWFmzfvp1+/fqRl5enPiYyMhJra2s++ugjvL29UalU6ntaly9frqnoQgghhBCVmhSqQogKrVOnTqxcuRJjY2M++ugjjh8/TqNGjdiyZQv5+fncunWL4OBg+vXrV+x5bm5ubN68ucSZgYUQQgghRNmSQlUIUeF9/PHHdOjQgcjISE6ePImWlhZjxozB1NQUX19frK2tadmyZbHnmJiY4OnpyZQpU3j06JGGkgshhBBCVE5SqAohKjyVSsWqVasAmDVrFufOneP06dN0794da2tr9u7di5bW4z8O16xZQ0ZGBh4eHgQHB7/u2EIIIYQQlZYUqkKISqFKlSrs27eP7t274+HhwaNHj9i7dy8xMTHUqVOnxOeYmpqyc+dOpk+fTrdu3Rg5ciTbtm0jJSXlNacXQgghhKhcpFAVQlQaWlpazJkzh7Vr1zJ06FAmTZrEw4cPSzz2+vXrTJ8+nZkzZ5Kens6lS5fw8PBgx44d2Nra8sMPP7zm9EIIIYQQlYeOpgMIIcTr1qtXL0JDQ/n0009p1qwZgwYNwsPDg65du1K9enV69erF6tWr2bp1K/n5+aSkpHD06FE2bdqEo6Mj+/fvp3v37pp+GUIIIYQQFZZKURRNZ1Bzd3dXAgICNB1DCFGJnDlzhtOnT+Pv78+VK1dYsmQJgwYNQl9fn7i4OCZPnoyOjg6PHj3Cz8+PtLQ0TExMSE9P13R0IYQQQog3jkqlClQUxf2Zx0mhKoQQf7p48SKfffYZgYGBWFlZkZqaysSJE5k+fTqGhoaajieEEEII8cZ73kJVhv4KIcRfWrZsia+vLzk5Ody6dQszMzNq1qyp6VhCCCGEEJWOFKpCCPF/GBgY0KhRI03HEEIIIYSotGTWXyGEEEIIIYQQ5YoUqkIIIYQQQgghyhUpVIUQQgghhBBClCtSqAohhBBCCCGEKFekUBVCCCGEEEIIUa5IoSqEEEIIIYQQolyRQlUIIYQQQgghRLkihaoQQgghhBBCiHJFClUhhBBCCCGEEOWKFKpCCCGEEEIIIcoVKVSFEEIIIYQQQpQrUqgKIYQQQgghhChXpFAVQgghhBBCCFGuSKEqhBBCCCGEEKJckUJVCCGEEEIIIUS5IoWqEEIIIYQQQohyRQpVIYQQQgghhBDlihSqQgghhBBCCCHKFSlUhRBCCCGEEEKUK1KoCiGEEEIIIYQoV6RQFUIIIYQQQghRrkihKoQQQgghhBCiXJFCVQghhBBCCCFEuaKj6QBCCFGZBAcHc/LkSZKSkpg+fTq1atXSdCQhhBBCiHJHelSFEOI1UBSFZcuW8fbbbxMbG0t2djZOTk4kJSVpOpoQQgghRLkjPapCCFGGMjIyOHPmDPPnz6eoqIiLFy+ira3NN998g7OzM2ZmZpqOKIQQQghR7kiPqhBClLKCggI2b95My5YtMTExoU+fPjg7O/Pf//4XGxsbrK2t0dHRYdeuXejoyPVCIYQQQoj/Sz4hCSFEKcnJySE2NpaPPvqIs2fPAmBra4uHhwdhYWFYW1tjZWVFYGAg5ubmGk4rhBBCCFF+SaEqhBCvQFEU9u/fz2effUZCQgJ5eXkAuLq6snjxYjw9PVGpVAA8fPiQqlWrajKuEEIIIcQbQQpVIYR4SdnZ2XzyySf4+/uzbt06Hjx4wEcffcTq1asZNGiQukD9HylShRBCCCGejxSqQgjxkj7++GNyc3O5fPkyhoaGNG3alK1bt9KtWzdNRxNCCCHK1J07d1i1ahUzZ87ExMRE03FEBSSTKQkhxAsqLCzkxx9/5PTp02zYsAFDQ0MURSEiIoIOHTpoOp4QQohXFBcXR9++fZk5cyaRkZGajlMuXb58mTVr1tC0aVOSk5M1HUdUQFKoCiHECwgODqZFixZs2bKFQ4cOYWRkBIBKpaJWrVqEhIRoOKEQQtMURSEkJITVq1czdOhQPDw8qFOnDg4ODnTq1IkxY8awdOlSbt68qemoogTBwcG0b9+eli1boqWlRceOHRk7dixpaWmldo4rV64wduxYZs2axbVr10qt3dcpODiYDz74gJYtW3L06FFNxxEVkBSqQgjxnLZs2UK3bt2YNm0aZ86cwcXFpdj+qVOn0qdPHzIyMjSUUAihSffu3eObb77BwcGBAQMGEBwcjKenJ6tWreLy5cscPnyYOXPm4OHhQXR0NC1atGDKlCkUFhZqOnq5EhwczJ49ezRy7lOnTtG9e3eWL1/OF198wcKFC4mKikJPTw8PD4+Xvhjp5+dH69atMTc3x8DAgL59+9KgQQMKCgpo3749K1euLOVXUvpyc3OJiIjgwIEDLF26lLVr1zJs2DD69OnD5s2bNR1PVEAqRVE0nUHN3d1dCQgI0HQMIYQoJj8/n2nTpnH48GH27t1Ls2bN1PsOHTrEL7/8QlxcHNHR0UyePJnZs2ejpSXXAYWoTObNm8d3333HwIEDGTt2LG5ubo9NqPZ/JScn4+7uztGjR2ncuPFrSlq+KYpC+/btCQoKYteuXfTq1eu1nPfBgwdMnz6dgwcPsnXrVrp06fLYMVu3bmXSpEnMmzePjz766Jn/vgCxsbHMnz+fI0eOsGLFCrp06UK1atXQ19dXP//mzZt0796dTp06sWzZMoyNjcnJycHAwOC5zgF//p7atm0b4eHh3L17lyZNmtC2bVucnJyoUaMGWVlZnDt3jvj4eACqV69O48aNsbe3p0qVKiiKQlRUFD4+Pvj7+xMYGEhKSgoPHjwgPz8fAB0dHWxtbWnYsCEODg60aNGC4cOHU1BQgLOzM0uWLKFfv37P+5aLSkylUgUqiuL+rONkMiUhhHiK5ORkBg4ciLGxMZcuXaJGjRrqff/5z3/48ssvWbRoEQ0bNsTZ2Zlq1appMK0QQhNCQ0NZvXo1wcHBWFlZPffzzM3Nad++PTNmzGDlypU0aNCgDFO+GY4cOcL9+/c5fPgww4YN4+rVq8V+7paF9PR0PD09cXZ2JiIigurVq5d43NChQ3Fzc8Pb25tjx46xZMkSDA0N0dHRQUdHh7y8PMLCwggJCSE0NJSQkBBiY2P5xz/+QXh4+BNfR7169QgICGDatGmYm5uTn5+PtrY2bdq0YcmSJbRs2fKpBeuNGzd4//33MTAwoFu3btjZ2REcHMz27duJioqiSpUq5Obm0qJFCxwcHFCpVKSkpBAREUF8fDwNGjQgNzeX3NxcPD096dSpE9OnT6dOnTqYmJhQpUoV9blKyqGrq8vPP/+Ml5cXsbGx9OnTBx0dHe7du8eDBw/Iy8sjLy+PjIwMEhMTSU9PR0tLS/3Q1tYu9rWBgQGWlpZYW1vj5uaGtrb2C/6LiopCelSFEOIJioqK6N69O82aNWPZsmXFekn/9a9/sWbNGo4fP07Dhg01mFII8br5+Pjw3//+Fz09PW7dusXJkydZuHAhH3/88Qu3lZOTw/Lly1m+fDlTp07ln//8Jzo6FasfoaCggNjYWKytrdHX1+fRo0dERERw9epVwsLCUBSFWrVqERMTw65du9iyZQuenp5MmzaNrVu34u3tTYcOHbC1tSU/P5/g4GCSkpKwsrLC0tISIyMjtLS0CAoKIjU1FUtLSzp06KAu/HNzc9VrXCcmJnL+/HmSk5NxdXWlatWqjBo1ij59+rB06dLn6sHMzc3l888/Z+fOnRQUFKgf2traNG7cmKZNm9KsWTOaNWtG06ZNMTQ0fO73KisrS93bumHDBhYsWECVKlUYMGAAXbp0oV69etjY2JCXl0dCQgK7d+9m9erVfPnll0ycOPGx0TyKopCSkoKRkVGJS6Q9evRIfY9s06ZNn7sHtyTnzp1j48aNHD16FG1tbWrWrImJiQl6enro6upiZGSEhYUFNWrUQFEUioqKKCwspKioqNgjOzub27dvExkZiampKZs2bcLBweGlc4ny53l7VKVQFUKIJ/j5559Zu3Ytvr6+6iu6iqIwe/Zs9u/fz/Hjx1+o90QI8Wa7e/cuU6dO5fz58/zjH/9ApVJhbGyMt7f3Ky/PER8fz6hRowgPD8fZ2ZlGjRrx/vvv06pVqyc+58qVK1y6dIl+/fphbm7+SucvC9evX2f58uXs3r0bAwMDkpOTMTU15f79+9jZ2dG0aVOcnZ3R0dEhJSUFfX19pkyZQs2aNdVtREZGsn37di5fvkxcXBza2tq4uLhQt25dbt++TUJCAg8fPiQ/P59mzZphbm5OQkICx48fp3nz5qSnpxMaGqou/s3MzGjTpg21a9cmKCiImJgYFi1axLBhwzT1Nj2VoihcvnyZPXv2cOHCBeLj47l16xY6OjrY2Njg7u7OggULsLa21nTUUldUVMSMGTMICAjg9OnTmo4jSpEM/RVCiFekra2NlZVVsWFH//73vzlx4gRnzpwp9mFKCFGxpKens2nTJrZv346FhQX16tXj119/ZcyYMaxbt+6Fesmeh7W1NcePHycmJoZr164RGhpKv3798PLy4quvvsLCwkJ9bFFREcuXL2fJkiV06tSJGTNm4OLiQosWLXB1dcXFxYXGjRsXG7L5um3atIlp06bxySefcOnSJWxtbSkoKODOnTvUqVPnubM5Ojry9ddfv/D5Hz58yKFDh6hbty7u7u4YGBi8cBvlgUqlws3NDTc3N/W2/3UyvUrv55tAS0sLHR0dqlatSm5uLnp6epqOJF4z6VEVQogniIuLo1WrViQmJqqHU3l6evLxxx8zYMAADacTQpSF0NBQ1qxZw44dO+jZsycjR47k/v37REREMGjQIJydnV9blvv37zN//nw2btxIz549sbGxoaCggB07dlC/fn1+/vlnbG1tefjwIb6+vgQHBxMUFERwcDDR0dHUrVuXevXqqYvqESNGMHjw4DLLm5+fz5EjR9i4cSNXrlzh2LFjODo6ltn5RMWXkpLCuHHjCAsLU3//yjDgN58M/RVCiFJgbW2Nj4+P+j7UmTNnkpSUxMaNGzWcTAhRGgoLCwkJCeHs2bPs2bOH69ev89FHHzF27Fjq1q2r6XjAn/dV/vbbb9y9e5e8vDwGDBhQbPbxkuTm5qqHiebk5HD//n2mTp3KjRs3Sn3St/j4eJYvX87WrVtp1KgRI0eOZNCgQRgbG5fqeUTlpCgKZ8+eZceOHezevRsLCwsGDx5M//79adiwYYXvWa6IpFAVQohXlJiYiKOjI/fu3UNXVxf4cwmDFi1aMHLkSD7//HNZhkaI1+inn37i6NGjtG7dmu7du9O8efOXaufWrVts3rwZX19f/P39sbS0pH379nh6euLl5aX+/17RfPDBBzRo0OClhtI+ia+vL4MHD2b48OGMGzcOe3v7UmtbiP+rsLCQM2fOsHPnTg4fPkxmZiZubm64u7urH/Xq1XuseM3KyuLHH3+kW7duuLq6aii9+B8pVIUQ4jlkZWXh6+tLZmYmgwYNKvbL7f3336dOnTosXbq02HPu3r3Lu+++S2pqKpMmTWLMmDEV9oOtEOXFggUL2Lx5M7NnzyYwMJBt27axd+9eOnTo8FzPVxSF0NBQVq1axZ49exg2bBjdu3enbdu2leZ+85iYGFq2bElkZGSpvOaEhARcXFzYunUrPXr0KIWEQryYpKQkAgMDCQgIUP/56NEj3N3dcXV1Vc86vHHjRurVq0dQUBCurq4sW7ZMhqVrkBSqQgjxDDt37mT06NG4u7tz584dRowYwezZs4E/JwJZsGABQUFBJU6a8r+hSPPnz+fOnTusX7/+qbNzCiFenp+fHwMGDCA4OFg9u+2hQ4f44IMPGDp0KL179yY5OZm0tDRGjhz52HqV27ZtY+bMmWhra/P+++8zadIkzMzMNPFSNK5v3754e3szdOjQV2pHURSGDRuGlZUVS5YsKaV0Qry6O3fuEBgYSHBwMNnZ2eo1ZIcNG0Zubi5r165l/vz5fPjhh0yYMAEbGxtNR650pFAVQoinCA0NpWvXrpw4cQJXV1fu3LlD8+bNOX36NLVq1cLBwYHz58/j5OT01HYURWHnzp1MnDiRgIAA+YUnRCk7ffo0o0eP5ttvv2XgwIHF9iUkJLBu3TrOnj2LhYUFBQUFXLhwga1bt9K6dWsAkpOTcXZ2Zt++fbRr167S38/WunVrli9fTtu2bV/q+fn5+QQFBbFlyxb++OMPzp49W+ozIAtR1m7fvs3ixYvZunUrTZs2xdHRETs7O+zt7bGzs8POzk6+r8uQFKpCCPEUX375Jfn5+SxevFi97auvvuLWrVuMGDGCr776Cl9f3+dub+HChQQGBrJnz56yiCuERuTl5ZGVlYVKpUJPTw99ff0yvS+7qKiIwMBATp48SUREBOHh4aSmprJo0SK8vb2fq409e/YwceJE2rRpg0ql4sSJE0yaNIm5c+eWWe43wcOHD9UX1WJjY585C2XpbAAAIABJREFU9DcqKorz58+TnJxMRkYGd+/eJTQ0lIiICOrXr0+7du34/PPPZS1p8Ub734zZN27cIDo6Wv3nrVu32LlzJz179tR0xApJ1lEVQoin0NbWLrY+KsC0adNwdnbGxcXlsX3PMnHiRGxsbLhz506x9Q6FKEsFBQVMnjyZ3377jRkzZmBtbU1gYCCmpqZ88sknJfYeBgcHs3btWmJiYjA1NaVGjRqMHj0aZ2dnvv/+e86dO0d4eDi3bt2iqKgIIyMjFEUhNzcXlUpFo0aNcHR0xNLSklq1atG9e3datGjxSq8jNTWVVatW8cMPP1CzZk08PT3p3LkzY8eOpWXLli+0fuK7775Lz549Wb9+PVWrVmXdunWPDQWuTBRFYfPmzXz22We0bt2aXbt2PbNIXbNmDXPnzqVbt25YWlpibGyMh4cHo0ePpkmTJqU+a7AQmlK1alV69Ojx2D3W586do3///kyfPp1+/frJkjgaIoWqEKJS0tHRIScnp9i2atWqsXTpUhYtWkR8fDzJycnq++GexdjYmA8++IB//etfrFixoiwiC6H28OFD/vvf/7JmzRp1MbZs2TJyc3Nxc3Nj7969REdHs2zZMnWxmpeXR//+/QkJCWHcuHG888473L9/nxs3btC/f39MTExo0KABI0aMoHHjxtja2qKnp1es2M3MzCQyMpLIyEju3r3L3bt3efvttzlw4IB6qO3fKYpCQUEBBQUFFBYWqv8eHR1NUFAQQUFBXLlyRb1G6ZkzZ2jUqNErvz9Vq1bl008/feV2KoJJkybh4+PDqVOnnrmkDUB4eDhff/0158+flw/notJq164d+/fvZ/PmzXTq1Alra2s2btxI48aNNR2tUpGhv0KISmn69OnUqlWLGTNmFNteVFRE8+bNsbW1xdbWlu+///6520xMTMTd3Z0VK1YwePDg0o4sKrGCggJOnTrFgQMH8Pf3Jzw8nI4dO/Lhhx8yYMCAx2advn//Pj179qR58+asXr2a0NBQVqxYQVpaGvv27UNHp/h16sWLF2NhYcHw4cNf+B7Ow4cPM3LkSCZMmKBeOiIqKoqsrCyys7PVoxe0tbXR0dFBW1sbW1tbXF1d1Q8XFxdMTExe+X0Sj+vQoQPvvPMODRs2xNTUlE6dOj31eC8vL7p06cLkyZNfU0IhyreioiI2bNjArFmzGDhwIG3atMHV1RVtbW1yc3PR1dWlVq1amJqaUlRURF5eHlpaWhgZGWk6erkl96gKIcRTtGvXjtmzZ9O7d+/H9n333XdMmTIFU1NTAgICqF+//nO3GxISQpcuXQgLC6NOnTqlGVmUQ8nJyezatQtra2scHR1p2LBhqbR79+5dgoODCQ4OJigoCB8fH2xtbRk4cCDt2rWjefPmGBgYPLWNjIwMevfuzZUrV7CwsKB79+7MmzcPU1PTUsn4d7GxscyZMwcrKys6duyoHh5qaGj4wsPoRenavHkz27dvJzMzk8TERG7cuFHicVlZWUycOBE/Pz+uXLnyzO8vISqb2NhY9u3bx8WLFwkJCQGgSpUq5Ofnc+/ePVJTU9HW1kZPT4+CggK0tLSwsLDA3t4eV1dX3Nzc6NGjhxSwSKEqhBBqt27dYvfu3Zw7dw5FUTAwMODUqVPEx8c/1rMEfxYJdevW5YsvvuDmzZts3rz5hc43ceJEzMzM+Prrr0vpFQhNKSws5MSJE+jq6mJmZkaDBg3U9+c9fPiQzp07U6dOHQoLC/H392fbtm14enqqn5+bm6uegOb69evExsair69PzZo16dChA7169QLg2rVrHD16lJMnT3LhwgUKCgpwcXFRPzp06ICdnd0L58/LyyM1NZW6deuWzhsi3liffvopWVlZbNiw4bF9CQkJ9O7dW90DLx+khXg1iqKQmZnJnTt3iIqKIigoCD8/P/z8/Hj77bfx8vKiR48elXaZLJlMSQhR6d2/f5/Fixezbt06+vXrx8CBA9HV1SUzM5NJkyaVWKQC1KlTh9q1a+Pt7U3Xrl0JCQl5rnu7/sfd3Z2TJ0+W1ssQGnLhwgXGjh1LlSpVMDY2JjU1lbS0NE6fPo29vT2//PILNWvWZP/+/ahUKrZs2cLQoUNxcHDAzMyMpKQkwsLCcHBwoEmTJtjb2/PWW2+Rl5dHSkoKI0eOZNGiRezdu5fAwED69u3LsGHDWLNmDVZWVqWyjEqVKlWkSBWsXLkSHx+fEmcyLywspE+fPgwePJjZs2dX+uV7hCgNKpWKatWqUa1aNRwdHfHy8gLg3r177Nu3jx07djB+/Hg6dOjAypUradCggYYTl09SqApRCWRnZ7Nu3ToOHjyIjY0NjRs3xsHBAXNzc2rVqoW+vn6pfTAuTzw9PXF2dubq1asvNBPvzZs3KSgowNHRkalTp7Jy5UrWr1//3M+vW7cuiYmJLxNZlBPJycn069eP5cuX4+3trf6/8dNPP+Hs7IyBgQFFRUVMmzZNvW/YsGF4eHiQkpJCamoqpqamtGjRgqpVq5Z4Djs7OxYsWMCnn37Knj170NfXf22vT1QeFy5cYMGCBVy4cKHEYd+LFi3CxMREilQhXoOaNWsyduxYxo4dS25uLt9//z1ubm6Eh4fLRcUSSKEqRCVw+PBhpkyZwvbt28nIyCA8PJwzZ86QkpJCSkoKMTExGBkZ0aVLF/T09IiIiCA9PZ1Tp06V2j13mlBYWIiXl5e6SC0qKiI6OprQ0FBu377N+PHjH+tVzcjIYMmSJQwdOhRtbW06derEjh07Xui8lpaWREdHoyiKfPB7A6WmpjJkyBBGjhzJe++9V2zfuHHj+PDDD8nKyiIzM5PatWsX29+wYcPn/j/j7e393GuDCvGybt68SWFhIfv27aN3797Y2NigUqkIDAzk4MGDbN++nbNnz8rPKiFeMz09PSZMmMDcuXPlnvAnkHtUhagEFEXhgw8+ICoqiuHDh9O1a1cKCwu5f/8+mZmZVKlShZSUFMLDw0lPT1evxZiQkICvr+8Th8iWdwcOHGD8+PG4urqSlpZGaGgoZmZmpKenY2Jiwu+//05+fj6+vr6cPHmSixcvkpycTLNmzdi1axdWVlasXbuWkydPsmvXruc+b1FREe7u7nzxxRcMGDCgDF+hKE2KonD8+HHGjRvHoEGDWLRo0WOz6QrxJoqMjOTzzz8nODiY+Ph4tLS0cHJyol27dkyZMkWGHQqhATk5OfTp0wcbG5sS7x2vyGQyJSEquWPHjrF//37eeust6tevj5GREefPn+e3337Dz88PHR0dzM3NMTMzIzc3l8zMTHJzczEyMsLIyIjs7Gyio6M5fPgwzZs3f+HzJyYm4uXlxddff62eMEYTMjMz2bdvH7a2tjRr1ozq1avTt29fIiMjycvLQ6VS0bZtW7p160bbtm2xt7dXz1J68uRJhg0bxvbt2+nSpcsLnXflypVERUWxZs2asnhZohQ9ePCAgwcPsnz5ch49esS3335Lnz59NB1LiDJRWFhIbm7uE4ekCyFej0mTJhEfH8+uXbsq3ezoMpmSEJVcRkYGe/bs4ebNm9y5c4e0tDQePHiAoaEhNWrUwMDAgPDwcGrWrEnnzp2ZMGECLVq0KJVzFxYWMmzYMBwdHRk9ejSffvopQ4YMoX79+qhUKhRFIT09nerVq5f5cDNjY2M++OCDYtsOHDhQYub4+Hh+//13Ll26xK5du0hKSnqpIhWgefPm/Pvf/6agoOCN7ZGuyG7evMmBAwc4cOAAFy5coEOHDnz99de88847aGlpaTqeEGVGW1tbilQhNOzEiRPs3buX4ODgSlekvgjpURXiLxcuXODYsWP07NkTd3f3JxZQERERaGtr4+DgUK7v6cnMzMTS0pKEhAT1chr/V1FREdeuXWP//v2sWbMGe3t79uzZQ40aNUo8Picnh8uXLxMQEEBUVBTXr18nOztbPaOulpaWurgLDg7mxIkT3Lp1i5kzZ+Lr64uiKJiYmHDr1i0AOnXqxKZNmx67z68kGRkZdOrUiXbt2jFv3rwnZnye9yU8PJyrV68SHh7OjRs31MuG1KxZUz1Da//+/enYseNL/wJRFIW2bdsyc+ZM9Wx/QvNu377N6NGjCQwMpHfv3nh5edG9e3dZjkMIIcRrcf/+fVxcXPjPf/5D9+7dNR1HI2TorxAvYOrUqaxYsYI6depQVFREcnIyH3/8MV999VWxWdjy8vKoV68eOjo6pKenk5WVxezZs3F1dcXCwoLAwEBmzJjBL7/8wqBBgzT4iv7Up08fBg0a9FiPYkkKCgqYPHkyISEh9O/fnxo1alC1alViYmKIiIjg6tWrREZG4uTkRMuWLXFycsLBwQEtLS12797NgQMH0NXVpXbt2tjY2LBmzZpi752iKMTGxpKTk4ONjQ36+vp88803rFu3jqZNm2JoaIixsTG2trY0bNgQZ2dnXF1d0dbW5v79+wwbNoxatWphaGjI3r17cXd3JyEhgdu3b2NmZkb//v157733SlxGJjc3l99++42NGzeqJ4hq0qQJTk5ONGzYEAcHB+zs7Eq9l6Fjx458/fXXdO3atVTbFS/n5s2btG7dmk8++YRZs2ZJT7cQQojX6tGjR4waNQozMzNWrVql6TgaI4WqEC8gMTGRXbt2ceTIEc6dO0dWVlax/efOnaNt27bEx8djY2PDxIkTef/999m4cSPm5uYEBwcTEBDA7du3Abh27RoODg5PPF9BQQFbt25l2bJltGnThuXLl5fJUKx9+/bx5Zdf4uvr+1w9kPn5+Xz//ffEx8dz//59srOzqV+/Pk5OTjRu3BhXV9dSn5kuKiqKW7dukZ2dzYMHD4iNjeX69esEBQVx9+5dunbtysWLF/Hy8mLJkiXo6+sTGhpKbGwslpaW6l7jffv2sWHDBj766CMGDhxIfHy8eijv0aNHadq0KSNHjmTQoEEYGxuX6msoSWhoKB07duT69evUrFmzzM8nnm3YsGHUqFGD1atXazqKEEKISiIxMZEZM2YQGBhIbGwsbdq04dChQ5V6CP5rKVRVKtUOoNFfX1YH0hVFcVWpVLZABBD11z5/RVE+flZ7UqiK8iArK4vo6Gji4+OJiIhg9erVbNq0SX2fYnJyMgsWLGDDhg2Ymppibm5O1apVCQkJYciQIXz44YecP3+ey5cvc+XKFczMzOjatSsdO3bkzp07+Pn5ceTIEaytrfnnP//Jr7/+SkxMDL6+vqU+w6iiKEybNk09pLl27do4OjryzjvvlOp5ysqdO3c4ceIEVlZWvPXWW888PjExkbFjxxITE4O1tTWWlpa0bt2avn37UqdOndeQ+E83btzgrbfeYvHixY8tbyI0Z+HChaxZswYzMzN++OEH2rVrp+lIQgghKqgHDx5w5swZJk2axJAhQ/D29sbJyYkqVapoOprGvfYeVZVKtQx4oCjK3L8K1UOKojR5kTakUBXPIz09HR8fH44dO8aJEycoKirC3t4eBwcHjIyMiImJ4caNG9y+fRsHBwfc3NxwcXFBR0eH/Px8Hj16RHR0NJGRkURHR2NiYqLumbO0tMTCwgJLS0sMDAzIzs4mOzub/Px8dHV1iz2KiorIzMwkOTmZe/fuYWFhwaFDhzh37hwDBw6kZcuWuLq6kpyczKlTp/D19cXCwoI2bdrQqVMnPDw8gD+LyZ49e+Lo6MiyZcvIysri119/xcLCAi8vr1ee2EVRFPbt28f169dJSkri4MGDjBs3junTp5fGP4fgz/f4zp07REVFcfXqVRYuXMi8efMYO3aspqOJ/yMrKwt7e3t2795N+/btNR1HCCFEBXPp0iUmTJhAWFgYLVu2ZMyYMQwdOlTTscqV11qoqv6cUeYW0FVRlOtSqIrSVFhYSEBAAMeOHePYsWOEhITQrl07evTogaenJwYGBty4cYMbN26QmZmJnZ0d9vb21K1bl6ioKAIDAwkNDUVRFKpUqYKenh62trY4OjpiZ2dHRkYGd+7c4fbt29y+fVv990ePHmFoaIihoSG6urrk5+c/9sjLy0NPT099XJcuXRg+fDiGhoYv9BqTk5MZNGgQmZmZxMXF0b17d2JjY8nIyGDatGm0bt2a+vXrv3C7JYmPj6dz587069ePxYsXyzqRr0hRFIYPH86xY8dwdnamUaNGeHt7v9RMwaLsZGRksHLlSlatWoWXlxc//fSTpiMJIYSoYJKTk/Hw8GDu3LkMGTIEfX19TUcql153odoRWP6/E/5VqIYB14AM4AtFUXyf1Y4UqpVTZmYmfn5+5OTkkJ+fT25uLnfv3iUhIYHY2Fh1T2SPHj3o0aMH7du3L/X7JMuD/Px89u7dS4cOHbCwsEBRFE6dOsWaNWuIiIggLi6OGjVq8M477/D+++/Tvn178vLySE9PJzExkcDAQC5dusSNGzcwMDCgatWqGBoaUq9ePRwdHWnUqBFOTk7o6+uTmpqKk5MTCxYskF6/V1BYWMjUqVO5cOECv//+e4X8vnzTFRQUsGnTJr788ku6devG7NmzcXJy0nQsIYQQFUxoaCheXl6MHj2azz//XNNxyrVSK1RVKtVJoKSbuz5XFGX/X8f8ANxQFGXZX1/rAUaKoqSqVCo34L+As6IoGSW0Pw4YB2BjY+N28+bNZ2UWb4jMzEz27NnDpUuXCAgIoHnz5qxcuRItLS0iIiIICAjgwIED+Pj40Lx5c0xMTNDV1UVPT486depgZWWFtbU1bdu2xdLSUtMvR+OKioqIi4tj9+7d/PLLL+plcqpXr06tWrVwc3PDw8ODRo0akZuby8OHD8nMzCQ2NpaoqCgiIiKIiYmhYcOG1K5dm7S0NM6cOSPF1UsoKCjg2LFjLFu2DC0tLXbu3ImpqammY4m/FBYWcubMGXbs2MHevXtxdnZm6dKluLm5aTqaEEKICqawsJCVK1eyYMECvv/+e4YNG6bpSOXea+tRValUOsBtwE1RlIQnHHMamKYoylO7S6VHtWL58MMPiYuLo0+fPri5ufHdd98REBBAWloa1tbWNG/enB49euDl5fXSa2JWZo8ePUJPT++F1nJ99OgRoaGhhIaG0qtXr9c6wdCbTFEUbt++TUhICKdOnWLLli3Y2toycuRIRo0aJcOny4GHDx9y4sQJ9u/fz6FDh7C0tMTb25vBgwdTv359TccTQghRQY0bN47w8HA2btz41BUfxP/3OgvVt4FZiqJ0+tu2WkCaoiiFKpWqAeALNFUUJe1pbUmhWrEYGxtz7tw59bqWhYWFhISEYG9v/1qWBxHiZRUUFHDlyhVOnz7N6dOn8fPzo0qVKjRt2pTWrVvz/vvv06hRo2c3JMqMoihERUXxxx9/cOTIEX7//Xfc3d3x8vKib9++UpwKIYQocz4+PowaNYqrV6/KZ9sX8DoL1U38ufzMj3/b9i4wFygACoE5iqIcfFZbUqhWLPPnzycyMpJff/1V01GEeKbs7GzWrl3LqVOnOHv2LNbW1nTu3JnOnTvTrl076X1+jRRFISUlhZs3b5KdnU1OTg7Z2dnEx8dz48YN9Tq7hoaGdOjQAU9PT3r16iXDr4UQQrxWnTp14uOPP5al6F7Qa1+epjRIofrmS0lJISQkhMTERC5evIifnx+XLl3SdCwhnuratWsMGDAAR0dHvL296dixI+bm5pqOVSlkZGQQFBTE5cuXCQsLIyIigoiICBRFoUGDBhgZGVG1alUMDAywsrLC3t4ee3t7mjRpgrW1tabjCyGEqKQePHiApaUl6enp6OjoaDrOG+V5C1V5VyuZhw8foq+v/8prc/5fV69eZcWKFezduxcXFxcsLCyoW7cuM2fOLNXzCPEkkyZNIiIiQv11fn4+aWlppKamcu/ePQoLC9HX18fAwAADAwOKiorIycnh4cOHAKxYsYJx48a90D2/4uX98ccfTJo0iRs3btC0aVOaN29O8+bNGTZsGE5OTpibm8u/hRBCiHIrJiYGOzs7KVLLkLyzlUyvXr24du0an3zyCZMnT8bIyAj4c6jdgwcPiIuLIyYmRj07bN++fUtsR1EUbty4we7du9m1axd3797lH//4B9evX6dmzZqv8yUJAUBOTg6pqakMGTIER0dHDA0NMTU1pWbNmpiZmaGrq0tOTg6PHj0iJycHlUql7qkzMDAo9Ys3omTh4eGsXLmSgwcPsmrVKvr27Su/5IUQQrxxioqKKCwsRFEUubBaRuTTQSVja2uLh4cH4eHh2NvbU69ePZKSkkhKSkJXVxdbW1vq169P/fr1+fHHHzl9+jRTpkwhOTmZu3fvcuvWLc6dO8eZM2coKCigX79+LFu2jI4dO6Ktra3plycqsblz5/LZZ5+xefNmoqOjsbW1pU+fPowYMUI9RFRXV5dq1appOGnlk5yczPbt29m8eTOJiYkMHz6cq1evymzfQggh3lhOTk7o6ury1VdfMW/ePE3HqZDkHtVKZsWKFURHR7N69WoiIyNJT0+ndu3a1K5dm6pVqxY7Ni0tjQ8//JCLFy9St25d6tSpg4WFBa1ataJTp044ODjIFSRRLuXl5XH16lV27tzJL7/8gpOTE9u2baNWrVqajlYp5Ofnc/XqVS5evMjhw4c5c+YMffv2Zfjw4XTt2lUuagkhhKgQUlJSaNGiBXv37sXDw0PTcd4YMpmSKNGpU6eYM2cOvr6+mo4ixGtRWFiIt7c3ly9fJiQkBENDQ01HKhN5eXmEh4eTmprKhQsX8Pf35+rVqzg6OtKqVSssLS3p3bs3devWLfVzZ2Zmcu7cOU6fPs3Zs2cJCgqiXr16tGzZki5dujBgwAD1bQZCCCFERbJw4UL8/PzYv3+/3Eb0nKRQrUSOHz+Or69vsWG71tbWJd73lZGRgYuLCz169GDp0qXy4VG88RRFITg4mBMnTnDixAkiIyMxMzPD3Nwcc3NzoqOjuX37NhMmTOCzzz6rkL9E7t27R6NGjSgoKKBZs2a0atWK1q1b4+zsTGRkJBcuXODbb78F4Mcff2TUqFHo6uq+8nmzs7OZPHky27Ztw93dnc6dO9OhQwc8PDxkiLUQQohKITc3F09PT7S0tHBxccHKygpra2uaN29Ow4YNNR2vXJJCtZL4+eef+eyzz/j444+Jj48nLi6O2NhY8vPzmTx5MuPHj3/sA+ODBw+YPHkyBw8eZMiQIYwYMQIPDw8ZxiveSGvXrmXu3Ll4eXnRvXt3XFxcuH//PsnJyaSkpFCjRg169uxZYSfsiY+Pp2fPnnh5eTF//vyn/j++dOkSs2fPJi4uDh8fH2xsbF7p3MuWLePYsWPs3r1bClMhhBCVVmZmJr/99hsJCQnEx8eTkJDAqVOnCAoKkqXUSiDL01QSp0+fpqCggCFDhtC0aVP19tDQUL799lscHBxYtWoVg/9fe/ceVVWd/3/89RESOIwXGJKLoHjBCyhe8q45lFqZlWJpWrMytdLG7s5M3+bXLMea1Zq+9Vs1X1eTjtlUjlra5E+LagRLnWl0xJAQUhQQRDiASnjhonHYvz/A88VRCvFy9sHnY63P2ufsc9nvw9vtPu/z+ezPnj7d/ViHDh30l7/8Rfn5+frrX/+q+++/X9HR0Vq3bp06duzoiY8BtNj+/fv19NNP65e//KWnQ7nqsrOzNX78eD311FNauHDhjz5/6NChSk5O1q9+9Su9/PLLeuONNy5p+7m5ubrzzjspUgEA17R27dpp9OjRysjIkJ+fn3x8fBQQEKCXXnpJb775pqfD81r0qHo5y7L05z//WYsXL1ZSUpIGDRp0zuOpqan6+c9/runTpzc5I5nL5dLTTz+tzz77TKNGjVJISIi7hYWFqUuXLoqKilJQUBC9rrCdWbNm6cYbb9RDDz3k6VCuquPHj2v48OFauHChHn744Yt6bWlpqfr06XPJl5O64447NGfOHE2dOrXZr7EsSy6Xq9X2cAMAWieXy6X169ervLxcNTU1qqmpUWVlpfbs2aN///vfOn36tAYNGqQePXqoe/fu6t69u0aMGKHIyEhPh247DP29xnz44YeaN2+epk+frvnz52vAgAGS6q/xlJmZqQEDBqigoKDJoX6WZWnLli0qKCjQ0aNHdezYMR05ckROp1OHDh1SYWGhamtrFRISosDAQHfr0KGDEhISNHnyZHXr1u1qfmRAUv05l88995wSExP11FNPKT4+3tMhXXGVlZWaOHGiBg0apD/+8Y8teo9bb71Vjz/+uO64444Wx7F48WKlpqZqxowZ7nWWZamsrEyFhYXu/zuKi4vd17CtqamRZVnaunWrxo4d2+JtAwBwtdTV1emhhx5Senq6hgwZIn9/f3fr27evhg8frm7dutGh00wUqtegoqIirVixQsuXL1f79u1VVVUlp9Opdu3a6ejRo3rttdf01FNPtfj9jx8/rvLyclVWVurUqVOqrKzUsWPHtGnTJm3cuFHh4eGaMmWKpkyZooEDB7Kz4qo5evSo3nrrLb388svKzs5Wp06dPB2SJGnFihVKS0vT4sWLL6nnsrE9e/bokUceUZ8+fbRixYoWTw715JNPKjo6Wk8//XSLY6moqNCzzz6rysrKc9aHhIS4R2J06dJFERERCgwMlJ+fn/z8/DR16lRNnTpVDz74YIu3DQDA1fKLX/xCmZmZ+uyzz1rt1QOuJgrVa1htba12796t4OBgRUREKCAg4Ipv0+VyuafmXr9+vb7//nutXr1ao0ePvuLbBs6aM2eOCgoK9PzzzyshIcGjP5b86U9/0ssvv6xJkyZp3bp16tevn5KTky96yOuJEyeUnp6utLQ07dixQ1988YV+97vfad68eS2+Hunp06e1YMECRUVFadGiRS16j0vx5ZdfaubMmUpJSVG/fv2u+vYBAGius6fLHDp0SO3atfN0OK0ChSo8xrIsrVq1Sq+88orS0tJa/GUauFjV1dV677339Prrr8vf31/jxo1TVFSUe6r4rl27KjQ0tEVSQ5fiAAATk0lEQVTvfbb30LIsd89g27ZtFRAQoKCgIAUHB7vb6tWr9fnnn+vTTz9V9+7d9dprr+mZZ56Ry+Vqdg/oxx9/rIULF6qoqEjx8fEaPHiwBg0apMTERP30pz+96PgLCwu1detWbdy4UZs2bVJsbKxeeuklJSQkXPR7XQ6rV6/WggULtHTpUt17770eiQEAgB+zdOlSffrpp9q4caOnQ2k1mPUXHrNv3z6988478vX11ffff0+hiqsmICBA8+bN08MPP6yUlBTt3r1bBw4c0Jdffum+fJPD4dDo0aM1evRojRkzRgMGDGhW8ehwOOR0OvXxxx/riSeeUGhoqM6cOaOqqirt3btX5eXl7ta1a1dt375dQUFBkqSvv/5ar7/++kUN012yZImefPJJzZs376J7YU+dOqU9e/Zo9+7d2r59u/7xj3+oqqpKY8aM0aRJk7RkyZIWF+wXq7a2Vk6nU4cPH3ZP25+amqotW7aoXbt22r9//1WJAwCAi7Vnzx799re/1ebNmz0dyjWJHlU0S1VVlQICApocSllYWKi1a9fq/fff16FDh/Tss8/qiSeeYGZP2IplWcrJydFXX32lr776Stu2bVNVVZXuueceTZs2TSNGjPjBYrKurk6LFi3SsmXLNGDAAN19992S6vePs83lcqlTp06KiIhQeHi4HA6Hbr31VuXm5io4OLjZsT755JN69913dfvtt+uBBx5QVFSUHA6HAgIC3Mvy8nLl5OTowIEDysnJ0f79+/XNN9+osLBQsbGxGjhwoEaMGKExY8aod+/eV2QodF1dnQoKCpSZmakDBw64rx93tigtKytTSEiIu2c7MjJSAwcOVEJCgqKjozmXHQBgS3l5eRo7dqxeffXVcyYNxKVj6C9+VHp6uvtyNhMnTlRYWJgCAgLUtm1bHT16VMXFxSouLlZRUZFqamoUFRWlGTNmaObMmercubO2bdumLVu2aOvWrSooKFBiYqJmzJihhIQEClR4jW+//Vbr1q3TunXrVFJSoptuuknjxo3TzTffrJiYmAsWUjU1NVq/fr3+/ve/y8/PTw6HQ4GBgXI4HGrTpo1KS0vldDpVXFwsp9OpmTNn6oUXXrjo2MrLy7V69Wp98MEHOnbsmKqrq1VVVeVeduzYUTExMerZs6e7xcfHq0+fPrruuusux5/HzbIsHT58WFlZWcrMzHQv9+7dq6CgIMXFxal3796Kioo6pyiNiIi47LEAAHClffLJJ5o1a5YGDRqk2bNn6+6775a/v7+nw2oVKFTxo6qrq/Xwww9r1apV7nVz585V3759FRISooiICHfr2LGj0tLStGbNGr3//vuqqKjQqFGjlJCQoISEBA0ZMkRt27b14KcBLt3hw4f1xRdf6IsvvtDmzZtVXl6usLAwhYeHu5fh4eHq06eP4uLi1KNHj1bzo8y+ffv00UcfacOGDSopKZHL5Tqn1dTUqH379oqLi1O/fv0UFxenuLg4xcbGqmPHjp4OHwCAy66mpkYbNmzQm2++KT8/PyUlJbWa474nUajiopw6dUpr1qzRb37zGz366KNKTExUfHz8Bc8vraurU11dHTsqWr2TJ0+qpKRETqfTvSwqKtK+ffuUmZkpp9Op3r17Ky4uTkOGDNH48ePVr1+/Kz6c1bIs5eXl6Z///Kd27drlHnLcuLVt21adOnXS9ddf716Ghoaqd+/e6tChg/u9jhw5orvuukuFhYVKTExUYmKiunfvLh8fn3Oan58fsx0CAK5JtbW1mjhxosLDw/XGG29wPLxEFKpokYKCAv3hD3/Qli1bVFpaqhtvvFELFy7U2LFjPR0aYDunTp3S3r17lZWVpR07diglJUWnTp3SuHHjNH78eI0fP15RUVGXtA3LslRUVKT09HR98803SktL07/+9S/5+PhozJgxGjZsmNq3b39eYXn69GkdOXJER44cUVlZmcrKylRSUqLs7GwFBwcrPj5e/fv3V1JSku688069+OKLLb4mKwAArd2JEyf0zDPPKDk5Wa+88oqmTp1Kp00LUajikpWUlGjlypX629/+ph07dng6HMArHDx4UJs3b1ZKSoo2b96s4OBgjR8/Xvfdd1+zritsWZYyMzO1adMmpaSkaOfOnfL19dXAgQPdbdSoUerSpUuLem7r6up08OBB7dmzRxkZGQoJCdGjjz7KpEYAADRDcnKyXnjhBR08eFCPPPKIHnjgAUVHR3s6LK9CoYrLIiMjQz/72c9UXl7OF1ngItXV1SkjI0PJycl6/fXXNWvWLC1evPi8yYWcTqdSUlKUnJys5ORkORwO3XLLLZowYYJGjRqlsLAwD30CAABwIRkZGVq6dKnWrVun6Oho3XPPPbr77rvVs2dPT4dmexSquGR1dXVKSEjQ9OnT9dhjj3k6HMCrlZWV6cEHH9TJkyf1+eef68yZM3rrrbe0cuVKFRYW6uabb9aECRM0YcIE9ejRw9PhAgCAZqitrdW2bdv04Ycf6qOPPlJYWJjuueceTZkyRbGxsZxWcwEUqrhkb7/9tpYtW+Y+Hw7Apamrq9Ps2bO1e/duHT58WJMmTdL8+fM1fPhwznMBAMDLuVwuffXVV/rwww+VlJSko0ePavDgwRo6dKiGDh2qkSNHKjIy0tNhehyFKi7ZyJEjtXjxYt1yyy2eDgVoNc6cOaM1a9ZowoQJioiI8HQ4AADgCjl27Jh27dql1NRUpaamatu2bdq0aZOGDh3q6dA8ikLVC1iWpZMnT6q0tFTHjx/XgAEDzjt3zVMsy1JgYKD69u2rHj16qGvXroqOjlbXrl3Vq1cv9erVy9MhAgAAAF7jgw8+0KJFi7Ro0SJFRUWpS5cuioiIuOZGVTW3UL22/iqXSWFhoXJycjRy5Ej5+/u71584cUKrVq2Sw+HQtGnT5HA43I9VVFRo6dKl2r59u0pLS1VaWqqSkhL5+PgoNDRUAQEBKisr07Rp03T//fdr5MiRHp28yBij/Px85ebmKj8/XwUFBcrMzFRSUpLS0tI0depUvfrqq+d8RgAAAAAXdu+996q4uFjr169XYWGhCgsLVVZWpk6dOikqKkqjR4/W888/r44dO57zuurqauXl5cnpdCo2NvaaGZFFj+pFys7O1rhx4xQWFqbs7GwNHz5c48aNU0VFhVasWKGbb75ZNTU12r59u+bOnauZM2dq7dq1WrZsmSZNmqTJkycrLCxMoaGhCg0N1U9+8hP3e+fl5Wn16tVatWqVampqdN999+n+++9XbGysBz/x+SoqKrRgwQJt3bpV/fv3V2RkpCIjIxUVFeW+3blzZzkcDvn6+jJbMAAAAHAB33//vZxOpwoLC7Vy5Upt2LBBc+bMUWlpqXJycpSbm6sjR44oOjpaoaGhysrKkr+/v4YOHaphw4Zp6NChGjJkyHnFrZ0x9PcK+Prrr3XXXXfp97//vWbPnq0TJ05o69atSklJka+vrx5//HH3dZRyc3O1ZMkSrV27VpMnT9avf/1rdevWrVnbsSxL6enpWrVqldasWSM/Pz/FxMSoR48e57Tu3bsrMDDwCn7iH5aVlaX8/HwdPnz4nFZYWKiioiJVV1fL5XKpTZs28vX1dTcfHx/5+voqKChI3377LRM1AQAAAJJSU1P10UcfKTo6Wj179lTPnj0VGRnp/r5sWZby8/O1c+dOpaamaufOndq9e7ciIiI0bNgwjR07Vg899JCtO4ooVK+AuXPnyuVy6Z133rlq23S5XDpw4IByc3OVl5en3Nxcd8vPz1fXrl01bNgwd4uPj5efn1+LtlVZWSlJcjgcl+0ft2VZcrlccrlcqq2tVW1trY4dO6akpCQtX75cGRkZl2U7AAAAwLWotrZWe/fu1c6dOzV//nxVVFR4tDPrx3CO6hVw3XXXXfVZunx8fNSnTx/16dPnvMdqa2uVlZXl/jVl+fLlys7OVqdOnRQaGqqwsDB3a3zf4XAoLy9PBw4cOKdVVFTIGCOXy6WgoCAFBwef14KCghQaGqoBAwYoPj5eAQEBF4zb5XLp4MGDysrKUnZ2tvbv36/9+/crOztblZWV6tWrl5577rkr/ecDAAAAWjVfX1/1799f/fv31xNPPOHpcC4belQvwvz58zVw4EDNnz/f06E0qaamRk6n0z1ZU0lJifv22WVlZaW6deummJgYxcTEqGfPnoqJiVHnzp3Vpk0bVVdX67vvvtN3332n8vLyc9p3332n4uJipaena9++ferZs6cGDx6sQYMGqbq6WllZWe7iNCQkRHFxcerbt697puDevXsrPDzc1sMRAAAAAG8UGBiosrIyelRhP/7+/urWrVuzz4e9kICAAAUEBPzojGKnT59WZmam0tLSlJaWJofDoZtuukmPPfaYYmNj1a5duxbHAAAAAODaRaGKFvPz89MNN9ygG264wdOhAAAAAGhF2ng6AAAAAAAAGqNQBQAAAADYCoUqAAAAAMBWKFQBAAAAALZCoQoAAAAAsBUKVQAAAACArVCoAgAAAABshUIVAAAAAGArFKoAAAAAAFuhUAUAAAAA2AqFKgAAAADAVihUAQAAAAC24uvpALzNzp071b59e0+HAQAAAADnqK2t9XQIlw2F6kW47bbbtHbtWn3yySeeDgUAAAAAzvHggw/K39/f02FcFsayLE/H4DZkyBBr165dng4DAAAAAHAFGGO+tixryI89j3NUAQAAAAC2QqEKAAAAALAVClUAAAAAgK1QqAIAAAAAbIVCFQAAAABgKxSqAAAAAABboVAFAAAAANgKhSoAAAAAwFYoVAEAAAAAtkKhCgAAAACwFQpVAAAAAICtUKgCAAAAAGyFQhUAAAAAYCsUqgAAAAAAW6FQBQAAAADYCoUqAAAAAMBWKFQBAAAAALZCoQoAAAAAsBUKVQAAAACArVCoAgAAAABshUIVAAAAAGArFKoAAAAAAFuhUAUAAAAA2AqFKgAAAADAVihUAQAAAAC2QqEKAAAAALAVClUAAAAAgK1QqAIAAAAAbMVYluXpGNyMMUckFXg6DlyUEElHPR0EWoz8eT9y6P3IoXcjf96PHHo/cuhdulqWdf2PPclWhSq8jzFml2VZQzwdB1qG/Hk/cuj9yKF3I3/ejxx6P3LYOjH0FwAAAABgKxSqAAAAAABboVDFpfqzpwPAJSF/3o8cej9y6N3In/cjh96PHLZCnKMKAAAAALAVelQBAAAAALZCoYpmMcZMM8ZkGWPqjDFDGq2PNsZUG2PSG9rSRo/dYIzZY4zJMcb8jzHGeCZ6SE3nsOGx5xrylG2MubXR+tsa1uUYY/7r6keNphhjfmeMKWq0793e6LEL5hP2wv7lnYwx+Q3HtnRjzK6GdcHGmGRjzIGGZZCn48T/Msa8bYwpM8ZkNlp3wZyZev/TsF9mGGMGey5ynNVEDjkOtnIUqmiuTElTJW27wGO5lmUNbGjzG61/U9IjkmIa2m1XPkz8gAvm0BgTK2mGpDjV5+hPxhgfY4yPpDckTZQUK2lmw3NhH6812vc+lZrOpyeDxPnYv7zeTQ373dkf/f5L0mbLsmIkbW64D/t4R+d/B2kqZxP1v99bHlH9dxl43ju68PdIjoOtGIUqmsWyrL2WZWU39/nGmHBJ7S3L2m7Vnwj9nqQpVyxA/KgfyOFkSe9blnXasqyDknIkDWtoOZZl5VmWdUbS+w3Phb01lU/YC/tX6zJZ0rsNt98VxztbsSxrm6Ty/1jdVM4mS3rPqrdDUseG7zTwoCZy2BSOg60EhSouh27GmN3GmK3GmBsb1nWWdLjRcw43rIP9dJZU2Oj+2Vw1tR728VjD0LS3Gw01JG/egTx5L0vSJmPM18aYRxrWhVqW5ZSkhmUnj0WH5moqZ+yb3oXjYCvm6+kAYB/GmBRJYRd46P9YlrWhiZc5JXWxLOuYMeYGSf/PGBMn6ULnozLF9BXWwhw2lasL/ZBFDq+iH8qn6oejvaj6nLwo6f9KmiP2PW9BnrzXaMuyio0xnSQlG2P2eTogXFbsm96D42ArR6EKN8uyxrfgNaclnW64/bUxJldSL9X/ehXZ6KmRkoovR5xoWktyqPpcRTW63zhXTa3HVdDcfBpjlkv6pOHuD+UT9kGevJRlWcUNyzJjzHrVDyksNcaEW5blbBgmWubRINEcTeWMfdNLWJZVevY2x8HWiaG/uCTGmOvPnqBujOmu+skH8hqG0Zw0xoxomO33AUlN9ejBszZKmmGM8TPGdFN9DndKSpUUY4zpZoxpq/qJCTZ6ME408h/nTCWqfrIsqel8wl7Yv7yQMSbQGNPu7G1Jt6h+39soaVbD02aJ4503aCpnGyU90DD77whJx88OEYa9cBxs/ehRRbMYYxIlLZF0vaQkY0y6ZVm3Shor6QVjTK0kl6T5lmWdPdn9UdXP0hYg6bOGBg9pKoeWZWUZY9ZK+lZSraQFlmW5Gl7zmKS/S/KR9LZlWVkeCh/n+29jzEDVD2fKlzRPkn4on7APy7Jq2b+8Uqik9fW/v8pX0mrLsj43xqRKWmuMmSvpkKRpHowR/8EYs0ZSgqQQY8xhSYsk/UEXztmnkm5X/QQ8VZJmX/WAcZ4mcpjAcbB1M/UTsgIAAAAAYA8M/QUAAAAA2AqFKgAAAADAVihUAQAAAAC2QqEKAAAAALAVClUAAAAAgK1QqAIAAAAAbIVCFQAAAABgKxSqAAAAAABb+f8V51NWK9O5cwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x12e6fcf98>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f, ax = plt.subplots(1, figsize=(16, 12))\n",
-    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
-    "world.plot(ax=ax,color='white', edgecolor='black')\n",
-    "data.plot(column=\"cluster\",ax=ax,markersize=5)\n",
-    "geo=data.groupby(\"cluster\").apply(to_Polygon)\n",
-    "cluster_polybuff=gpd.GeoDataFrame(geometry=geo)\n",
-    "cluster_polybuff.plot(ax=ax)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  },
-  "toc": {
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": "block",
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16.0,
-    "lenType": 16.0,
-    "lenVar": 40.0
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks_old/Eval_mada_light_30_mars.ipynb b/notebooks_old/Eval_mada_light_30_mars.ipynb
deleted file mode 100644
index 2362e7f..0000000
--- a/notebooks_old/Eval_mada_light_30_mars.ipynb
+++ /dev/null
@@ -1,113 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-04-06T12:39:03.577820Z",
-     "start_time": "2018-04-06T12:39:02.474507Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/requests/__init__.py:80: RequestsDependencyWarning: urllib3 (1.22) or chardet (2.3.0) doesn't match a supported version!\n",
-      "  RequestsDependencyWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import networkx as nx\n",
-    "import json,glob,re\n",
-    "from scipy.spatial.distance import jaccard\n",
-    "import numpy as np\n",
-    "from math import*\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "#%matplotlib inline\n",
-    "#from IPython.display import set_matplotlib_formats\n",
-    "#set_matplotlib_formats('pdf', 'svg')\n",
-    "#sns.set_palette(sns.color_palette(\"hls\", 8))\n",
-    "\n",
-    "\n",
-    "import plotly.graph_objs as go\n",
-    "import plotly.offline as py\n",
-    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
-    "init_notebook_mode(connected=True)\n",
-    "\n",
-    "\n",
-    "def jaccard_similarity(x,y): \n",
-    "    intersection_cardinality = len(set.intersection(*[set(x), set(y)]))\n",
-    "    union_cardinality = len(set.union(*[set(x), set(y)]))\n",
-    "    return intersection_cardinality/float(union_cardinality)\n",
-    "%cd .."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  },
-  "toc": {
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": "block",
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks_old/MADA_growth_criteria.pdf b/notebooks_old/MADA_growth_criteria.pdf
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diff --git a/notebooks_old/MatchingAnalysis/Result AnaysisV2PADI.ipynb b/notebooks_old/MatchingAnalysis/Result AnaysisV2PADI.ipynb
deleted file mode 100644
index f99329b..0000000
--- a/notebooks_old/MatchingAnalysis/Result AnaysisV2PADI.ipynb	
+++ /dev/null
@@ -1,1715 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-28T06:54:50.985305Z",
-     "start_time": "2018-09-28T06:54:50.262332Z"
-    }
-   },
-   "source": [
-    "### import pandas as pd\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Preparing\n",
-    "## Load the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-28T06:54:51.367491Z",
-     "start_time": "2018-09-28T06:54:51.354971Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data=pd.read_csv(\"../../padi_resultWA.csv\",index_col=0)\n",
-    "data=data[data.mesure != \"BP\"]\n",
-    "data[\"sum\"]=np.mean(data[\"c1 c2 c3 c4\".split()].values,axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Helpers \n",
-    "\n",
-    " * Pareto on Multiple dimension : `pareto_frontier_multi()`\n",
-    " * Highlighter  `highlight_min()` and `highlight_max()`(yellow for max value and red for min value)\n",
-    " * Colorizer using Highlighter methods `colorize()`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-28T06:54:53.009076Z",
-     "start_time": "2018-09-28T06:54:52.480363Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from weasyprint import HTML\n",
-    "\n",
-    "def pareto_frontier_multi(myArray):\n",
-    "    # Sort on first dimension\n",
-    "    myArray = myArray[myArray[:,0].argsort()]\n",
-    "    # Add first row to pareto_frontier\n",
-    "    pareto_frontier = myArray[0:1,:]\n",
-    "    indices,i=[],1\n",
-    "    # Test next row against the last row in pareto_frontier\n",
-    "    for row in myArray[1:,:]:\n",
-    "        \n",
-    "        if sum([row[x] >= pareto_frontier[-1][x]\n",
-    "                for x in range(len(row))]) == len(row):\n",
-    "            # If it is better on all features add the row to pareto_frontier\n",
-    "            pareto_frontier = np.concatenate((pareto_frontier, [row]))\n",
-    "            indices.append(i)\n",
-    "        i+=1\n",
-    "    return indices,pareto_frontier\n",
-    "\n",
-    "def highlight_max(s):\n",
-    "    '''\n",
-    "    highlight the maximum in a Series yellow.\n",
-    "    '''\n",
-    "    is_max = s == s.max()\n",
-    "    return ['background-color: yellow' if v else '' for v in is_max]\n",
-    "def highlight_min(s):\n",
-    "    '''\n",
-    "    highlight the maximum in a Series yellow.\n",
-    "    '''\n",
-    "    is_max = s == s.min()\n",
-    "    return ['background-color: #d64541;color:white;' if v else '' for v in is_max]\n",
-    "\n",
-    "def colorize(df,fields):\n",
-    "    return df.style.apply(highlight_max,subset=fields).apply(highlight_min,subset=fields)\n",
-    "\n",
-    "def my_print_pdf(style_dataframe, filename):\n",
-    "    html = HTML(string=style_dataframe.render())\n",
-    "    html.write_pdf(filename)\n",
-    "    \n",
-    "to_colorize=\"c1 c2 c3 c4 sum\".split()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Analysis \n",
-    "\n",
-    "In this first section, we'll try to find which measure 'may' be optimal for our needs. Bluntly, we compute the average performance of each measure on every type of STR. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-28T06:55:02.084899Z",
-     "start_time": "2018-09-28T06:55:02.048969Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style  type=\"text/css\" >\n",
-       "    #T_f7b077fe_fec0_11e8_bfd8_38c98640874arow4_col0 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
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-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_f7b077fe_fec0_11e8_bfd8_38c98640874arow4_col3 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
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-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
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-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
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-       "            background-color:  yellow;\n",
-       "            : ;\n",
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-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_f7b077fe_fec0_11e8_bfd8_38c98640874arow7_col4 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
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-       "            background-color:  yellow;\n",
-       "            : ;\n",
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-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }</style>  \n",
-       "<table id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874a\" > \n",
-       "<thead>    <tr> \n",
-       "        <th class=\"blank level0\" ></th> \n",
-       "        <th class=\"col_heading level0 col0\" >c1</th> \n",
-       "        <th class=\"col_heading level0 col1\" >c2</th> \n",
-       "        <th class=\"col_heading level0 col2\" >c3</th> \n",
-       "        <th class=\"col_heading level0 col3\" >c4</th> \n",
-       "        <th class=\"col_heading level0 col4\" >sum</th> \n",
-       "    </tr>    <tr> \n",
-       "        <th class=\"index_name level0\" >mesure</th> \n",
-       "        <th class=\"blank\" ></th> \n",
-       "        <th class=\"blank\" ></th> \n",
-       "        <th class=\"blank\" ></th> \n",
-       "        <th class=\"blank\" ></th> \n",
-       "        <th class=\"blank\" ></th> \n",
-       "    </tr></thead> \n",
-       "<tbody>    <tr> \n",
-       "        <th id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874alevel0_row0\" class=\"row_heading level0 row0\" >BOW</th> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow0_col0\" class=\"data row0 col0\" >0.912333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow0_col1\" class=\"data row0 col1\" >0.272333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow0_col2\" class=\"data row0 col2\" >0.904</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow0_col3\" class=\"data row0 col3\" >0.415</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow0_col4\" class=\"data row0 col4\" >0.625917</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874alevel0_row1\" class=\"row_heading level0 row1\" >BagOfCliques</th> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow1_col0\" class=\"data row1 col0\" >0.761333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow1_col1\" class=\"data row1 col1\" >0.248</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow1_col2\" class=\"data row1 col2\" >0.751667</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow1_col3\" class=\"data row1 col3\" >0.278667</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow1_col4\" class=\"data row1 col4\" >0.509917</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874alevel0_row2\" class=\"row_heading level0 row2\" >GraphEditDistance</th> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow2_col0\" class=\"data row2 col0\" >0.689333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow2_col1\" class=\"data row2 col1\" >0.211333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow2_col2\" class=\"data row2 col2\" >0.690333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow2_col3\" class=\"data row2 col3\" >0.313333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow2_col4\" class=\"data row2 col4\" >0.476083</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874alevel0_row3\" class=\"row_heading level0 row3\" >GraphEditDistanceW</th> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow3_col0\" class=\"data row3 col0\" >0.6912</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow3_col1\" class=\"data row3 col1\" >0.2168</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow3_col2\" class=\"data row3 col2\" >0.6948</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow3_col3\" class=\"data row3 col3\" >0.3164</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow3_col4\" class=\"data row3 col4\" >0.4798</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874alevel0_row4\" class=\"row_heading level0 row4\" >GreedyEditDistance</th> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow4_col0\" class=\"data row4 col0\" >0.082</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow4_col1\" class=\"data row4 col1\" >0.185667</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow4_col2\" class=\"data row4 col2\" >0.138667</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow4_col3\" class=\"data row4 col3\" >0.056</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow4_col4\" class=\"data row4 col4\" >0.115583</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874alevel0_row5\" class=\"row_heading level0 row5\" >HED</th> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow5_col0\" class=\"data row5 col0\" >0.754667</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow5_col1\" class=\"data row5 col1\" >0.181333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow5_col2\" class=\"data row5 col2\" >0.738</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow5_col3\" class=\"data row5 col3\" >0.333667</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow5_col4\" class=\"data row5 col4\" >0.501917</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874alevel0_row6\" class=\"row_heading level0 row6\" >Jaccard</th> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow6_col0\" class=\"data row6 col0\" >0.889333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow6_col1\" class=\"data row6 col1\" >0.317667</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow6_col2\" class=\"data row6 col2\" >0.894667</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow6_col3\" class=\"data row6 col3\" >0.394333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow6_col4\" class=\"data row6 col4\" >0.624</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874alevel0_row7\" class=\"row_heading level0 row7\" >MCS</th> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow7_col0\" class=\"data row7 col0\" >0.911</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow7_col1\" class=\"data row7 col1\" >0.337667</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow7_col2\" class=\"data row7 col2\" >0.909333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow7_col3\" class=\"data row7 col3\" >0.428333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow7_col4\" class=\"data row7 col4\" >0.646583</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874alevel0_row8\" class=\"row_heading level0 row8\" >PolyIntersect</th> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow8_col0\" class=\"data row8 col0\" >0.8372</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow8_col1\" class=\"data row8 col1\" >0.4028</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow8_col2\" class=\"data row8 col2\" >0.8916</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow8_col3\" class=\"data row8 col3\" >0.2548</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow8_col4\" class=\"data row8 col4\" >0.5966</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874alevel0_row9\" class=\"row_heading level0 row9\" >VertexEdgeOverlap</th> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow9_col0\" class=\"data row9 col0\" >0.914333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow9_col1\" class=\"data row9 col1\" >0.279333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow9_col2\" class=\"data row9 col2\" >0.908</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow9_col3\" class=\"data row9 col3\" >0.399667</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow9_col4\" class=\"data row9 col4\" >0.625333</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874alevel0_row10\" class=\"row_heading level0 row10\" >WeisfeleirLehmanKernel</th> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow10_col0\" class=\"data row10 col0\" >0.515333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow10_col1\" class=\"data row10 col1\" >0.382667</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow10_col2\" class=\"data row10 col2\" >0.580333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow10_col3\" class=\"data row10 col3\" >0.118333</td> \n",
-       "        <td id=\"T_f7b077fe_fec0_11e8_bfd8_38c98640874arow10_col4\" class=\"data row10 col4\" >0.399167</td> \n",
-       "    </tr></tbody> \n",
-       "</table> "
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x1193904e0>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "colorize(data.groupby(\"mesure\").mean(),to_colorize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-28T06:55:02.660999Z",
-     "start_time": "2018-09-28T06:55:02.654918Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mesure,c1,c2,c3,c4\n",
-      "BOW,0.9123333333333333,0.2723333333333332,0.9039999999999999,0.4150000000000002\n",
-      "BagOfCliques,0.7613333333333333,0.24799999999999986,0.7516666666666665,0.2786666666666667\n",
-      "GraphEditDistance,0.6893333333333332,0.21133333333333326,0.6903333333333332,0.3133333333333333\n",
-      "GraphEditDistanceW,0.6911999999999999,0.21679999999999994,0.6947999999999999,0.31639999999999996\n",
-      "GreedyEditDistance,0.08200000000000002,0.18566666666666656,0.13866666666666663,0.056000000000000015\n",
-      "HED,0.7546666666666667,0.18133333333333326,0.7379999999999999,0.3336666666666667\n",
-      "Jaccard,0.8893333333333334,0.3176666666666665,0.8946666666666667,0.39433333333333337\n",
-      "MCS,0.9110000000000001,0.3376666666666665,0.9093333333333334,0.42833333333333345\n",
-      "PolyIntersect,0.8372000000000002,0.40280000000000016,0.8915999999999998,0.2547999999999999\n",
-      "VertexEdgeOverlap,0.9143333333333333,0.2793333333333332,0.908,0.39966666666666667\n",
-      "WeisfeleirLehmanKernel,0.5153333333333333,0.38266666666666654,0.5803333333333333,0.11833333333333329\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(data.groupby(\"mesure\").mean().to_csv())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T08:23:14.583733Z",
-     "start_time": "2018-09-26T08:23:13.874794Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "my_print_pdf(colorize(data.groupby(\"mesure\").mean(),to_colorize),\"test.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Based on above results, we observe that strict criteria like c1(shared common entity) and c4(share exact dispertion of spatial entities) are respected by **structure-based** measure(BOW, MCS,HED, GEDs). While more permissive criteria like c2(one entity in one STR close an other entity in an other STR) and c3(share a group of significant entity), are respected by **pattern-based** methods such as WeisfeilerLehmanKernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-28T06:55:06.986830Z",
-     "start_time": "2018-09-28T06:55:06.963521Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\begin{tabular}{lllrrrr}\n",
-      "\\toprule\n",
-      "{} &             mesure &         type &     c1 &     c2 &     c3 &     c4 \\\\\n",
-      "\\midrule\n",
-      "2  &  VertexEdgeOverlap &  gen\\_country &  0.838 &  0.334 &  0.888 &  0.408 \\\\\n",
-      "6  &       BagOfCliques &          all &  0.786 &  0.238 &  0.758 &  0.264 \\\\\n",
-      "8  &      PolyIntersect &       normal &  0.840 &  0.386 &  0.888 &  0.258 \\\\\n",
-      "10 &  GraphEditDistance &  extension\\_1 &  0.674 &  0.188 &  0.674 &  0.306 \\\\\n",
-      "\\bottomrule\n",
-      "\\end{tabular}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "index,data_pa=pareto_frontier_multi(data[\"c1 c2 c3 c4\".split()].values)\n",
-    "colorize(data.iloc[index],to_colorize)\n",
-    "print(data.iloc[index].to_latex())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-28T06:55:34.480323Z",
-     "start_time": "2018-09-28T06:55:34.434319Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style  type=\"text/css\" >\n",
-       "    #T_7f5d4bda_c2eb_11e8_9658_4c327598678brow2_col2 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_7f5d4bda_c2eb_11e8_9658_4c327598678brow2_col4 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_7f5d4bda_c2eb_11e8_9658_4c327598678brow2_col5 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_7f5d4bda_c2eb_11e8_9658_4c327598678brow4_col2 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_7f5d4bda_c2eb_11e8_9658_4c327598678brow7_col3 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_7f5d4bda_c2eb_11e8_9658_4c327598678brow10_col4 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_7f5d4bda_c2eb_11e8_9658_4c327598678brow13_col3 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_7f5d4bda_c2eb_11e8_9658_4c327598678brow15_col5 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }</style>  \n",
-       "<table id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678b\" > \n",
-       "<thead>    <tr> \n",
-       "        <th class=\"blank level0\" ></th> \n",
-       "        <th class=\"col_heading level0 col0\" >mesure</th> \n",
-       "        <th class=\"col_heading level0 col1\" >type</th> \n",
-       "        <th class=\"col_heading level0 col2\" >c1</th> \n",
-       "        <th class=\"col_heading level0 col3\" >c2</th> \n",
-       "        <th class=\"col_heading level0 col4\" >c3</th> \n",
-       "        <th class=\"col_heading level0 col5\" >c4</th> \n",
-       "    </tr></thead> \n",
-       "<tbody>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row0\" class=\"row_heading level0 row0\" >1</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow0_col0\" class=\"data row0 col0\" >BOW</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow0_col1\" class=\"data row0 col1\" >gen_region</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow0_col2\" class=\"data row0 col2\" >0.926</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow0_col3\" class=\"data row0 col3\" >0.278</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow0_col4\" class=\"data row0 col4\" >0.918</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow0_col5\" class=\"data row0 col5\" >0.424</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row1\" class=\"row_heading level0 row1\" >2</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow1_col0\" class=\"data row1 col0\" >VertexEdgeOverlap</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow1_col1\" class=\"data row1 col1\" >gen_country</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow1_col2\" class=\"data row1 col2\" >0.838</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow1_col3\" class=\"data row1 col3\" >0.334</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow1_col4\" class=\"data row1 col4\" >0.888</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow1_col5\" class=\"data row1 col5\" >0.408</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row2\" class=\"row_heading level0 row2\" >4</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow2_col0\" class=\"data row2 col0\" >GreedyEditDistance</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow2_col1\" class=\"data row2 col1\" >gen_region</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow2_col2\" class=\"data row2 col2\" >0.054</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow2_col3\" class=\"data row2 col3\" >0.178</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow2_col4\" class=\"data row2 col4\" >0.1</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow2_col5\" class=\"data row2 col5\" >0.04</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row3\" class=\"row_heading level0 row3\" >6</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow3_col0\" class=\"data row3 col0\" >BagOfCliques</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow3_col1\" class=\"data row3 col1\" >all</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow3_col2\" class=\"data row3 col2\" >0.786</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow3_col3\" class=\"data row3 col3\" >0.238</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow3_col4\" class=\"data row3 col4\" >0.758</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow3_col5\" class=\"data row3 col5\" >0.264</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row4\" class=\"row_heading level0 row4\" >7</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow4_col0\" class=\"data row4 col0\" >VertexEdgeOverlap</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow4_col1\" class=\"data row4 col1\" >normal</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow4_col2\" class=\"data row4 col2\" >0.936</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow4_col3\" class=\"data row4 col3\" >0.236</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow4_col4\" class=\"data row4 col4\" >0.908</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow4_col5\" class=\"data row4 col5\" >0.384</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row5\" class=\"row_heading level0 row5\" >9</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow5_col0\" class=\"data row5 col0\" >GraphEditDistance</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow5_col1\" class=\"data row5 col1\" >extension_1</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow5_col2\" class=\"data row5 col2\" >0.674</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow5_col3\" class=\"data row5 col3\" >0.188</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow5_col4\" class=\"data row5 col4\" >0.674</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow5_col5\" class=\"data row5 col5\" >0.306</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row6\" class=\"row_heading level0 row6\" >10</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow6_col0\" class=\"data row6 col0\" >GreedyEditDistance</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow6_col1\" class=\"data row6 col1\" >gen_country</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow6_col2\" class=\"data row6 col2\" >0.09</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow6_col3\" class=\"data row6 col3\" >0.192</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow6_col4\" class=\"data row6 col4\" >0.154</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow6_col5\" class=\"data row6 col5\" >0.058</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row7\" class=\"row_heading level0 row7\" >12</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow7_col0\" class=\"data row7 col0\" >WeisfeleirLehmanKernel</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow7_col1\" class=\"data row7 col1\" >gen_region</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow7_col2\" class=\"data row7 col2\" >0.648</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow7_col3\" class=\"data row7 col3\" >0.458</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow7_col4\" class=\"data row7 col4\" >0.672</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow7_col5\" class=\"data row7 col5\" >0.096</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row8\" class=\"row_heading level0 row8\" >13</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow8_col0\" class=\"data row8 col0\" >GraphEditDistanceW</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow8_col1\" class=\"data row8 col1\" >gen_region</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow8_col2\" class=\"data row8 col2\" >0.702</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow8_col3\" class=\"data row8 col3\" >0.214</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow8_col4\" class=\"data row8 col4\" >0.692</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow8_col5\" class=\"data row8 col5\" >0.312</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row9\" class=\"row_heading level0 row9\" >14</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow9_col0\" class=\"data row9 col0\" >Jaccard</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow9_col1\" class=\"data row9 col1\" >extension_1</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow9_col2\" class=\"data row9 col2\" >0.91</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow9_col3\" class=\"data row9 col3\" >0.296</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow9_col4\" class=\"data row9 col4\" >0.89</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow9_col5\" class=\"data row9 col5\" >0.396</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row10\" class=\"row_heading level0 row10\" >15</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow10_col0\" class=\"data row10 col0\" >VertexEdgeOverlap</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow10_col1\" class=\"data row10 col1\" >gen_region</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow10_col2\" class=\"data row10 col2\" >0.928</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow10_col3\" class=\"data row10 col3\" >0.276</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow10_col4\" class=\"data row10 col4\" >0.92</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow10_col5\" class=\"data row10 col5\" >0.412</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row11\" class=\"row_heading level0 row11\" >19</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow11_col0\" class=\"data row11 col0\" >BOW</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow11_col1\" class=\"data row11 col1\" >gen_country</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow11_col2\" class=\"data row11 col2\" >0.826</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow11_col3\" class=\"data row11 col3\" >0.328</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow11_col4\" class=\"data row11 col4\" >0.884</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow11_col5\" class=\"data row11 col5\" >0.416</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row12\" class=\"row_heading level0 row12\" >21</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow12_col0\" class=\"data row12 col0\" >GraphEditDistance</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow12_col1\" class=\"data row12 col1\" >normal</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow12_col2\" class=\"data row12 col2\" >0.684</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow12_col3\" class=\"data row12 col3\" >0.186</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow12_col4\" class=\"data row12 col4\" >0.67</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow12_col5\" class=\"data row12 col5\" >0.29</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row13\" class=\"row_heading level0 row13\" >22</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow13_col0\" class=\"data row13 col0\" >HED</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow13_col1\" class=\"data row13 col1\" >extension_1</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow13_col2\" class=\"data row13 col2\" >0.752</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow13_col3\" class=\"data row13 col3\" >0.156</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow13_col4\" class=\"data row13 col4\" >0.718</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow13_col5\" class=\"data row13 col5\" >0.328</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row14\" class=\"row_heading level0 row14\" >39</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow14_col0\" class=\"data row14 col0\" >GraphEditDistanceW</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow14_col1\" class=\"data row14 col1\" >extension_2</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow14_col2\" class=\"data row14 col2\" >0.67</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow14_col3\" class=\"data row14 col3\" >0.19</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow14_col4\" class=\"data row14 col4\" >0.664</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow14_col5\" class=\"data row14 col5\" >0.304</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row15\" class=\"row_heading level0 row15\" >40</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow15_col0\" class=\"data row15 col0\" >MCS</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow15_col1\" class=\"data row15 col1\" >all</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow15_col2\" class=\"data row15 col2\" >0.924</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow15_col3\" class=\"data row15 col3\" >0.36</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow15_col4\" class=\"data row15 col4\" >0.92</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow15_col5\" class=\"data row15 col5\" >0.428</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row16\" class=\"row_heading level0 row16\" >42</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow16_col0\" class=\"data row16 col0\" >VertexEdgeOverlap</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow16_col1\" class=\"data row16 col1\" >extension_1</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow16_col2\" class=\"data row16 col2\" >0.928</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow16_col3\" class=\"data row16 col3\" >0.276</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow16_col4\" class=\"data row16 col4\" >0.904</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow16_col5\" class=\"data row16 col5\" >0.392</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678blevel0_row17\" class=\"row_heading level0 row17\" >47</th> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow17_col0\" class=\"data row17 col0\" >BagOfCliques</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow17_col1\" class=\"data row17 col1\" >normal</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow17_col2\" class=\"data row17 col2\" >0.774</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow17_col3\" class=\"data row17 col3\" >0.212</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow17_col4\" class=\"data row17 col4\" >0.744</td> \n",
-       "        <td id=\"T_7f5d4bda_c2eb_11e8_9658_4c327598678brow17_col5\" class=\"data row17 col5\" >0.266</td> \n",
-       "    </tr></tbody> \n",
-       "</table> "
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x115424be0>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "index,data_pa=pareto_frontier_multi(data[\"c1 c4\".split()].values)\n",
-    "colorize(data.iloc[index],to_colorize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-28T06:55:45.055686Z",
-     "start_time": "2018-09-28T06:55:45.024671Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style  type=\"text/css\" >\n",
-       "    #T_85ab47f8_c2eb_11e8_923d_4c327598678brow1_col2 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_85ab47f8_c2eb_11e8_923d_4c327598678brow3_col3 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_85ab47f8_c2eb_11e8_923d_4c327598678brow4_col2 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_85ab47f8_c2eb_11e8_923d_4c327598678brow4_col3 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_85ab47f8_c2eb_11e8_923d_4c327598678brow4_col4 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_85ab47f8_c2eb_11e8_923d_4c327598678brow4_col5 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_85ab47f8_c2eb_11e8_923d_4c327598678brow9_col4 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_85ab47f8_c2eb_11e8_923d_4c327598678brow9_col5 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }</style>  \n",
-       "<table id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678b\" > \n",
-       "<thead>    <tr> \n",
-       "        <th class=\"blank level0\" ></th> \n",
-       "        <th class=\"col_heading level0 col0\" >mesure</th> \n",
-       "        <th class=\"col_heading level0 col1\" >type</th> \n",
-       "        <th class=\"col_heading level0 col2\" >c1</th> \n",
-       "        <th class=\"col_heading level0 col3\" >c2</th> \n",
-       "        <th class=\"col_heading level0 col4\" >c3</th> \n",
-       "        <th class=\"col_heading level0 col5\" >c4</th> \n",
-       "    </tr></thead> \n",
-       "<tbody>    <tr> \n",
-       "        <th id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678blevel0_row0\" class=\"row_heading level0 row0\" >5</th> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow0_col0\" class=\"data row0 col0\" >GraphEditDistanceW</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow0_col1\" class=\"data row0 col1\" >gen_country</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow0_col2\" class=\"data row0 col2\" >0.726</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow0_col3\" class=\"data row0 col3\" >0.306</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow0_col4\" class=\"data row0 col4\" >0.774</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow0_col5\" class=\"data row0 col5\" >0.37</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678blevel0_row1\" class=\"row_heading level0 row1\" >7</th> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow1_col0\" class=\"data row1 col0\" >VertexEdgeOverlap</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow1_col1\" class=\"data row1 col1\" >normal</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow1_col2\" class=\"data row1 col2\" >0.936</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow1_col3\" class=\"data row1 col3\" >0.236</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow1_col4\" class=\"data row1 col4\" >0.908</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow1_col5\" class=\"data row1 col5\" >0.384</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678blevel0_row2\" class=\"row_heading level0 row2\" >18</th> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow2_col0\" class=\"data row2 col0\" >HED</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow2_col1\" class=\"data row2 col1\" >gen_region</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow2_col2\" class=\"data row2 col2\" >0.758</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow2_col3\" class=\"data row2 col3\" >0.182</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow2_col4\" class=\"data row2 col4\" >0.738</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow2_col5\" class=\"data row2 col5\" >0.332</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678blevel0_row3\" class=\"row_heading level0 row3\" >22</th> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow3_col0\" class=\"data row3 col0\" >HED</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow3_col1\" class=\"data row3 col1\" >extension_1</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow3_col2\" class=\"data row3 col2\" >0.752</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow3_col3\" class=\"data row3 col3\" >0.156</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow3_col4\" class=\"data row3 col4\" >0.718</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow3_col5\" class=\"data row3 col5\" >0.328</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678blevel0_row4\" class=\"row_heading level0 row4\" >24</th> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow4_col0\" class=\"data row4 col0\" >WeisfeleirLehmanKernel</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow4_col1\" class=\"data row4 col1\" >all</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow4_col2\" class=\"data row4 col2\" >0.454</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow4_col3\" class=\"data row4 col3\" >0.37</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow4_col4\" class=\"data row4 col4\" >0.522</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow4_col5\" class=\"data row4 col5\" >0.104</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678blevel0_row5\" class=\"row_heading level0 row5\" >28</th> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow5_col0\" class=\"data row5 col0\" >HED</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow5_col1\" class=\"data row5 col1\" >all</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow5_col2\" class=\"data row5 col2\" >0.772</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow5_col3\" class=\"data row5 col3\" >0.178</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow5_col4\" class=\"data row5 col4\" >0.732</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow5_col5\" class=\"data row5 col5\" >0.328</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678blevel0_row6\" class=\"row_heading level0 row6\" >33</th> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow6_col0\" class=\"data row6 col0\" >GraphEditDistanceW</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow6_col1\" class=\"data row6 col1\" >extension_1</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow6_col2\" class=\"data row6 col2\" >0.674</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow6_col3\" class=\"data row6 col3\" >0.188</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow6_col4\" class=\"data row6 col4\" >0.674</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow6_col5\" class=\"data row6 col5\" >0.306</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678blevel0_row7\" class=\"row_heading level0 row7\" >34</th> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow7_col0\" class=\"data row7 col0\" >Jaccard</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow7_col1\" class=\"data row7 col1\" >gen_region</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow7_col2\" class=\"data row7 col2\" >0.908</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow7_col3\" class=\"data row7 col3\" >0.306</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow7_col4\" class=\"data row7 col4\" >0.904</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow7_col5\" class=\"data row7 col5\" >0.42</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678blevel0_row8\" class=\"row_heading level0 row8\" >41</th> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow8_col0\" class=\"data row8 col0\" >BagOfCliques</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow8_col1\" class=\"data row8 col1\" >extension_1</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow8_col2\" class=\"data row8 col2\" >0.748</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow8_col3\" class=\"data row8 col3\" >0.248</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow8_col4\" class=\"data row8 col4\" >0.718</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow8_col5\" class=\"data row8 col5\" >0.246</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678blevel0_row9\" class=\"row_heading level0 row9\" >54</th> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow9_col0\" class=\"data row9 col0\" >MCS</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow9_col1\" class=\"data row9 col1\" >gen_region</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow9_col2\" class=\"data row9 col2\" >0.924</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow9_col3\" class=\"data row9 col3\" >0.336</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow9_col4\" class=\"data row9 col4\" >0.924</td> \n",
-       "        <td id=\"T_85ab47f8_c2eb_11e8_923d_4c327598678brow9_col5\" class=\"data row9 col5\" >0.446</td> \n",
-       "    </tr></tbody> \n",
-       "</table> "
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x115447898>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "index,data_pa=pareto_frontier_multi(data[\"c2 c3\".split()].values)\n",
-    "colorize(data.iloc[index],to_colorize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-28T06:55:58.440861Z",
-     "start_time": "2018-09-28T06:55:58.415228Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style  type=\"text/css\" >\n",
-       "    #T_8da5dee6_c2eb_11e8_b092_4c327598678brow3_col2 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_8da5dee6_c2eb_11e8_b092_4c327598678brow3_col3 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_8da5dee6_c2eb_11e8_b092_4c327598678brow3_col4 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_8da5dee6_c2eb_11e8_b092_4c327598678brow3_col5 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_8da5dee6_c2eb_11e8_b092_4c327598678brow4_col2 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_8da5dee6_c2eb_11e8_b092_4c327598678brow4_col4 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_8da5dee6_c2eb_11e8_b092_4c327598678brow4_col5 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_8da5dee6_c2eb_11e8_b092_4c327598678brow5_col3 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }</style>  \n",
-       "<table id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678b\" > \n",
-       "<thead>    <tr> \n",
-       "        <th class=\"blank level0\" ></th> \n",
-       "        <th class=\"col_heading level0 col0\" >mesure</th> \n",
-       "        <th class=\"col_heading level0 col1\" >type</th> \n",
-       "        <th class=\"col_heading level0 col2\" >c1</th> \n",
-       "        <th class=\"col_heading level0 col3\" >c2</th> \n",
-       "        <th class=\"col_heading level0 col4\" >c3</th> \n",
-       "        <th class=\"col_heading level0 col5\" >c4</th> \n",
-       "    </tr></thead> \n",
-       "<tbody>    <tr> \n",
-       "        <th id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678blevel0_row0\" class=\"row_heading level0 row0\" >2</th> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow0_col0\" class=\"data row0 col0\" >VertexEdgeOverlap</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow0_col1\" class=\"data row0 col1\" >gen_country</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow0_col2\" class=\"data row0 col2\" >0.838</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow0_col3\" class=\"data row0 col3\" >0.334</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow0_col4\" class=\"data row0 col4\" >0.888</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow0_col5\" class=\"data row0 col5\" >0.408</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678blevel0_row1\" class=\"row_heading level0 row1\" >5</th> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow1_col0\" class=\"data row1 col0\" >GraphEditDistanceW</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow1_col1\" class=\"data row1 col1\" >gen_country</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow1_col2\" class=\"data row1 col2\" >0.726</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow1_col3\" class=\"data row1 col3\" >0.306</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow1_col4\" class=\"data row1 col4\" >0.774</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow1_col5\" class=\"data row1 col5\" >0.37</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678blevel0_row2\" class=\"row_heading level0 row2\" >6</th> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow2_col0\" class=\"data row2 col0\" >BagOfCliques</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow2_col1\" class=\"data row2 col1\" >all</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow2_col2\" class=\"data row2 col2\" >0.786</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow2_col3\" class=\"data row2 col3\" >0.238</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow2_col4\" class=\"data row2 col4\" >0.758</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow2_col5\" class=\"data row2 col5\" >0.264</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678blevel0_row3\" class=\"row_heading level0 row3\" >8</th> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow3_col0\" class=\"data row3 col0\" >MCS</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow3_col1\" class=\"data row3 col1\" >extension_1</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow3_col2\" class=\"data row3 col2\" >0.928</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow3_col3\" class=\"data row3 col3\" >0.336</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow3_col4\" class=\"data row3 col4\" >0.9</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow3_col5\" class=\"data row3 col5\" >0.422</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678blevel0_row4\" class=\"row_heading level0 row4\" >10</th> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow4_col0\" class=\"data row4 col0\" >GreedyEditDistance</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow4_col1\" class=\"data row4 col1\" >gen_country</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow4_col2\" class=\"data row4 col2\" >0.09</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow4_col3\" class=\"data row4 col3\" >0.192</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow4_col4\" class=\"data row4 col4\" >0.154</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow4_col5\" class=\"data row4 col5\" >0.058</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678blevel0_row5\" class=\"row_heading level0 row5\" >11</th> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow5_col0\" class=\"data row5 col0\" >GraphEditDistance</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow5_col1\" class=\"data row5 col1\" >extension_2</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow5_col2\" class=\"data row5 col2\" >0.67</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow5_col3\" class=\"data row5 col3\" >0.19</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow5_col4\" class=\"data row5 col4\" >0.664</td> \n",
-       "        <td id=\"T_8da5dee6_c2eb_11e8_b092_4c327598678brow5_col5\" class=\"data row5 col5\" >0.304</td> \n",
-       "    </tr></tbody> \n",
-       "</table> "
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x115460898>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "index,data_pa=pareto_frontier_multi(data[\"c1 c2 c3\".split()].values)\n",
-    "colorize(data.iloc[index],to_colorize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T08:23:14.740634Z",
-     "start_time": "2018-09-26T08:23:14.717081Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style  type=\"text/css\" >\n",
-       "    #T_69e65b7a_c165_11e8_903a_4c327598678brow0_col2 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_69e65b7a_c165_11e8_903a_4c327598678brow1_col3 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_69e65b7a_c165_11e8_903a_4c327598678brow1_col4 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_69e65b7a_c165_11e8_903a_4c327598678brow2_col5 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_69e65b7a_c165_11e8_903a_4c327598678brow3_col2 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_69e65b7a_c165_11e8_903a_4c327598678brow3_col3 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_69e65b7a_c165_11e8_903a_4c327598678brow3_col4 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_69e65b7a_c165_11e8_903a_4c327598678brow3_col5 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_69e65b7a_c165_11e8_903a_4c327598678brow4_col2 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }</style>  \n",
-       "<table id=\"T_69e65b7a_c165_11e8_903a_4c327598678b\" > \n",
-       "<thead>    <tr> \n",
-       "        <th class=\"blank level0\" ></th> \n",
-       "        <th class=\"col_heading level0 col0\" >mesure</th> \n",
-       "        <th class=\"col_heading level0 col1\" >type</th> \n",
-       "        <th class=\"col_heading level0 col2\" >c1</th> \n",
-       "        <th class=\"col_heading level0 col3\" >c2</th> \n",
-       "        <th class=\"col_heading level0 col4\" >c3</th> \n",
-       "        <th class=\"col_heading level0 col5\" >c4</th> \n",
-       "    </tr></thead> \n",
-       "<tbody>    <tr> \n",
-       "        <th id=\"T_69e65b7a_c165_11e8_903a_4c327598678blevel0_row0\" class=\"row_heading level0 row0\" >5</th> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow0_col0\" class=\"data row0 col0\" >GraphEditDistanceW</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow0_col1\" class=\"data row0 col1\" >gen_country</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow0_col2\" class=\"data row0 col2\" >0.726</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow0_col3\" class=\"data row0 col3\" >0.306</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow0_col4\" class=\"data row0 col4\" >0.774</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow0_col5\" class=\"data row0 col5\" >0.37</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_69e65b7a_c165_11e8_903a_4c327598678blevel0_row1\" class=\"row_heading level0 row1\" >21</th> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow1_col0\" class=\"data row1 col0\" >HED</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow1_col1\" class=\"data row1 col1\" >extension_1</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow1_col2\" class=\"data row1 col2\" >0.752</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow1_col3\" class=\"data row1 col3\" >0.156</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow1_col4\" class=\"data row1 col4\" >0.718</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow1_col5\" class=\"data row1 col5\" >0.328</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_69e65b7a_c165_11e8_903a_4c327598678blevel0_row2\" class=\"row_heading level0 row2\" >23</th> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow2_col0\" class=\"data row2 col0\" >BagOfCliques</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow2_col1\" class=\"data row2 col1\" >gen_region</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow2_col2\" class=\"data row2 col2\" >0.774</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow2_col3\" class=\"data row2 col3\" >0.226</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow2_col4\" class=\"data row2 col4\" >0.754</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow2_col5\" class=\"data row2 col5\" >0.262</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_69e65b7a_c165_11e8_903a_4c327598678blevel0_row3\" class=\"row_heading level0 row3\" >31</th> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow3_col0\" class=\"data row3 col0\" >MCS</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow3_col1\" class=\"data row3 col1\" >gen_country</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow3_col2\" class=\"data row3 col2\" >0.826</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow3_col3\" class=\"data row3 col3\" >0.378</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow3_col4\" class=\"data row3 col4\" >0.896</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow3_col5\" class=\"data row3 col5\" >0.42</td> \n",
-       "    </tr>    <tr> \n",
-       "        <th id=\"T_69e65b7a_c165_11e8_903a_4c327598678blevel0_row4\" class=\"row_heading level0 row4\" >45</th> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow4_col0\" class=\"data row4 col0\" >GraphEditDistance</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow4_col1\" class=\"data row4 col1\" >gen_country</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow4_col2\" class=\"data row4 col2\" >0.726</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow4_col3\" class=\"data row4 col3\" >0.306</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow4_col4\" class=\"data row4 col4\" >0.774</td> \n",
-       "        <td id=\"T_69e65b7a_c165_11e8_903a_4c327598678brow4_col5\" class=\"data row4 col5\" >0.37</td> \n",
-       "    </tr></tbody> \n",
-       "</table> "
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x11600e2e8>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "index,data_pa=pareto_frontier_multi(data[\"c2 c3 c4\".split()].values)\n",
-    "colorize(data.iloc[index],to_colorize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-14T16:49:40.713792Z",
-     "start_time": "2018-09-14T16:49:40.711286Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T08:23:14.796833Z",
-     "start_time": "2018-09-26T08:23:14.742799Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  \n"
-     ]
-    }
-   ],
-   "source": [
-    "data2=data[data[\"type\"]==\"normal\"]\n",
-    "data2[\"sum_c\"]=data2[[\"c1\",\"c2\",\"c3\",\"c4\"]].apply(lambda x : x.sum(),axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T08:23:14.810444Z",
-     "start_time": "2018-09-26T08:23:14.799671Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "to_plot=[]\n",
-    "for t in np.sort(np.unique(data.type.values))[::-1]:\n",
-    "    data_t=data[(data[\"type\"]==t)]\n",
-    "    to_plot.append([t,data_t.c1.max(),data_t.c2.max(),data_t.c3.max(),data_t.c4.max()])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T08:23:14.817774Z",
-     "start_time": "2018-09-26T08:23:14.812363Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df_plot=pd.DataFrame(to_plot,columns=\"type c1 c2 c3 c4\".split())\n",
-    "df_plot[\"type\"]=df_plot[\"type\"].astype(\"category\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T08:23:14.823105Z",
-     "start_time": "2018-09-26T08:23:14.819822Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df_plot.rename(columns={\"c1\":\"SSE\",\"c2\":\"CSE\",\"c3\":\"SSC\",\"c4\":\"SCSC\"},inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T12:54:41.155835Z",
-     "start_time": "2018-09-26T12:54:40.829546Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ace5b38>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_plot.plot.line(x=\"type\",legend=True,marker='o',grid=\"on\",linestyle='--').get_legend().set_bbox_to_anchor((0.7, 0.3))#,linestyle='--')#,grid=\"on\")\n",
-    "plt.ylabel(\"A@P\")\n",
-    "plt.xticks(np.arange(len(df_plot[\"type\"])), df_plot[\"type\"])\n",
-    "plt.yticks(np.linspace(0.3,1,15))\n",
-    "#plt.title(\"STR transformation effects on each criterion maximum value\")\n",
-    "plt.savefig(\"PADI_growth_criteria.pdf\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T08:23:15.535735Z",
-     "start_time": "2018-09-26T08:23:12.625Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data.groupby(\"type\").max()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T08:23:27.735435Z",
-     "start_time": "2018-09-26T08:23:27.711179Z"
-    }
-   },
-   "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>mesure</th>\n",
-       "      <th>type</th>\n",
-       "      <th>c1</th>\n",
-       "      <th>c2</th>\n",
-       "      <th>c3</th>\n",
-       "      <th>c4</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>BagOfCliques</td>\n",
-       "      <td>gen_country</td>\n",
-       "      <td>0.724</td>\n",
-       "      <td>0.330</td>\n",
-       "      <td>0.802</td>\n",
-       "      <td>0.370</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>BOW</td>\n",
-       "      <td>gen_region</td>\n",
-       "      <td>0.926</td>\n",
-       "      <td>0.278</td>\n",
-       "      <td>0.918</td>\n",
-       "      <td>0.424</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>VertexEdgeOverlap</td>\n",
-       "      <td>gen_country</td>\n",
-       "      <td>0.838</td>\n",
-       "      <td>0.334</td>\n",
-       "      <td>0.888</td>\n",
-       "      <td>0.408</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>MCS</td>\n",
-       "      <td>extension_2</td>\n",
-       "      <td>0.928</td>\n",
-       "      <td>0.322</td>\n",
-       "      <td>0.906</td>\n",
-       "      <td>0.424</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>GreedyEditDistance</td>\n",
-       "      <td>gen_region</td>\n",
-       "      <td>0.054</td>\n",
-       "      <td>0.178</td>\n",
-       "      <td>0.100</td>\n",
-       "      <td>0.040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>GraphEditDistanceW</td>\n",
-       "      <td>gen_country</td>\n",
-       "      <td>0.726</td>\n",
-       "      <td>0.306</td>\n",
-       "      <td>0.774</td>\n",
-       "      <td>0.370</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>VertexEdgeOverlap</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>0.936</td>\n",
-       "      <td>0.236</td>\n",
-       "      <td>0.908</td>\n",
-       "      <td>0.384</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>MCS</td>\n",
-       "      <td>extension_1</td>\n",
-       "      <td>0.928</td>\n",
-       "      <td>0.336</td>\n",
-       "      <td>0.900</td>\n",
-       "      <td>0.422</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>GraphEditDistance</td>\n",
-       "      <td>extension_1</td>\n",
-       "      <td>0.674</td>\n",
-       "      <td>0.188</td>\n",
-       "      <td>0.674</td>\n",
-       "      <td>0.306</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>GreedyEditDistance</td>\n",
-       "      <td>gen_country</td>\n",
-       "      <td>0.090</td>\n",
-       "      <td>0.192</td>\n",
-       "      <td>0.154</td>\n",
-       "      <td>0.058</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>GraphEditDistance</td>\n",
-       "      <td>extension_2</td>\n",
-       "      <td>0.670</td>\n",
-       "      <td>0.190</td>\n",
-       "      <td>0.664</td>\n",
-       "      <td>0.304</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>WeisfeleirLehmanKernel</td>\n",
-       "      <td>gen_region</td>\n",
-       "      <td>0.648</td>\n",
-       "      <td>0.458</td>\n",
-       "      <td>0.672</td>\n",
-       "      <td>0.096</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>GraphEditDistanceW</td>\n",
-       "      <td>gen_region</td>\n",
-       "      <td>0.702</td>\n",
-       "      <td>0.214</td>\n",
-       "      <td>0.692</td>\n",
-       "      <td>0.312</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Jaccard</td>\n",
-       "      <td>extension_1</td>\n",
-       "      <td>0.910</td>\n",
-       "      <td>0.296</td>\n",
-       "      <td>0.890</td>\n",
-       "      <td>0.396</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>VertexEdgeOverlap</td>\n",
-       "      <td>gen_region</td>\n",
-       "      <td>0.928</td>\n",
-       "      <td>0.276</td>\n",
-       "      <td>0.920</td>\n",
-       "      <td>0.412</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>Jaccard</td>\n",
-       "      <td>extension_2</td>\n",
-       "      <td>0.910</td>\n",
-       "      <td>0.302</td>\n",
-       "      <td>0.890</td>\n",
-       "      <td>0.396</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>GreedyEditDistance</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>0.072</td>\n",
-       "      <td>0.164</td>\n",
-       "      <td>0.124</td>\n",
-       "      <td>0.046</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>HED</td>\n",
-       "      <td>gen_region</td>\n",
-       "      <td>0.758</td>\n",
-       "      <td>0.182</td>\n",
-       "      <td>0.738</td>\n",
-       "      <td>0.332</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>BOW</td>\n",
-       "      <td>gen_country</td>\n",
-       "      <td>0.826</td>\n",
-       "      <td>0.328</td>\n",
-       "      <td>0.884</td>\n",
-       "      <td>0.416</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>GraphEditDistance</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>0.684</td>\n",
-       "      <td>0.186</td>\n",
-       "      <td>0.670</td>\n",
-       "      <td>0.290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>HED</td>\n",
-       "      <td>extension_1</td>\n",
-       "      <td>0.752</td>\n",
-       "      <td>0.156</td>\n",
-       "      <td>0.718</td>\n",
-       "      <td>0.328</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>WeisfeleirLehmanKernel</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>0.496</td>\n",
-       "      <td>0.322</td>\n",
-       "      <td>0.552</td>\n",
-       "      <td>0.132</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>BagOfCliques</td>\n",
-       "      <td>gen_region</td>\n",
-       "      <td>0.774</td>\n",
-       "      <td>0.226</td>\n",
-       "      <td>0.754</td>\n",
-       "      <td>0.262</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>WeisfeleirLehmanKernel</td>\n",
-       "      <td>extension_2</td>\n",
-       "      <td>0.482</td>\n",
-       "      <td>0.386</td>\n",
-       "      <td>0.558</td>\n",
-       "      <td>0.106</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>HED</td>\n",
-       "      <td>extension_2</td>\n",
-       "      <td>0.752</td>\n",
-       "      <td>0.148</td>\n",
-       "      <td>0.722</td>\n",
-       "      <td>0.328</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>WeisfeleirLehmanKernel</td>\n",
-       "      <td>extension_1</td>\n",
-       "      <td>0.460</td>\n",
-       "      <td>0.322</td>\n",
-       "      <td>0.522</td>\n",
-       "      <td>0.130</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>GraphEditDistanceW</td>\n",
-       "      <td>extension_1</td>\n",
-       "      <td>0.674</td>\n",
-       "      <td>0.188</td>\n",
-       "      <td>0.674</td>\n",
-       "      <td>0.306</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>Jaccard</td>\n",
-       "      <td>gen_region</td>\n",
-       "      <td>0.908</td>\n",
-       "      <td>0.306</td>\n",
-       "      <td>0.904</td>\n",
-       "      <td>0.420</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>MCS</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>0.936</td>\n",
-       "      <td>0.294</td>\n",
-       "      <td>0.910</td>\n",
-       "      <td>0.430</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>MCS</td>\n",
-       "      <td>gen_country</td>\n",
-       "      <td>0.826</td>\n",
-       "      <td>0.378</td>\n",
-       "      <td>0.896</td>\n",
-       "      <td>0.420</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>BagOfCliques</td>\n",
-       "      <td>extension_2</td>\n",
-       "      <td>0.762</td>\n",
-       "      <td>0.234</td>\n",
-       "      <td>0.734</td>\n",
-       "      <td>0.264</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>VertexEdgeOverlap</td>\n",
-       "      <td>extension_2</td>\n",
-       "      <td>0.928</td>\n",
-       "      <td>0.258</td>\n",
-       "      <td>0.904</td>\n",
-       "      <td>0.392</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>GraphEditDistanceW</td>\n",
-       "      <td>extension_2</td>\n",
-       "      <td>0.670</td>\n",
-       "      <td>0.190</td>\n",
-       "      <td>0.664</td>\n",
-       "      <td>0.304</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>BagOfCliques</td>\n",
-       "      <td>extension_1</td>\n",
-       "      <td>0.748</td>\n",
-       "      <td>0.248</td>\n",
-       "      <td>0.718</td>\n",
-       "      <td>0.246</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>VertexEdgeOverlap</td>\n",
-       "      <td>extension_1</td>\n",
-       "      <td>0.928</td>\n",
-       "      <td>0.276</td>\n",
-       "      <td>0.904</td>\n",
-       "      <td>0.392</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>GraphEditDistanceW</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>0.684</td>\n",
-       "      <td>0.186</td>\n",
-       "      <td>0.670</td>\n",
-       "      <td>0.290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>GraphEditDistance</td>\n",
-       "      <td>gen_region</td>\n",
-       "      <td>0.702</td>\n",
-       "      <td>0.214</td>\n",
-       "      <td>0.692</td>\n",
-       "      <td>0.312</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>HED</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>0.760</td>\n",
-       "      <td>0.142</td>\n",
-       "      <td>0.730</td>\n",
-       "      <td>0.320</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>GreedyEditDistance</td>\n",
-       "      <td>extension_2</td>\n",
-       "      <td>0.102</td>\n",
-       "      <td>0.212</td>\n",
-       "      <td>0.152</td>\n",
-       "      <td>0.058</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>BagOfCliques</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>0.774</td>\n",
-       "      <td>0.212</td>\n",
-       "      <td>0.744</td>\n",
-       "      <td>0.266</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>Jaccard</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>0.916</td>\n",
-       "      <td>0.278</td>\n",
-       "      <td>0.896</td>\n",
-       "      <td>0.396</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>BOW</td>\n",
-       "      <td>normal</td>\n",
-       "      <td>0.936</td>\n",
-       "      <td>0.248</td>\n",
-       "      <td>0.908</td>\n",
-       "      <td>0.412</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>GreedyEditDistance</td>\n",
-       "      <td>extension_1</td>\n",
-       "      <td>0.084</td>\n",
-       "      <td>0.200</td>\n",
-       "      <td>0.148</td>\n",
-       "      <td>0.060</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>GraphEditDistance</td>\n",
-       "      <td>gen_country</td>\n",
-       "      <td>0.726</td>\n",
-       "      <td>0.306</td>\n",
-       "      <td>0.774</td>\n",
-       "      <td>0.370</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>MCS</td>\n",
-       "      <td>gen_region</td>\n",
-       "      <td>0.924</td>\n",
-       "      <td>0.336</td>\n",
-       "      <td>0.924</td>\n",
-       "      <td>0.446</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>Jaccard</td>\n",
-       "      <td>gen_country</td>\n",
-       "      <td>0.774</td>\n",
-       "      <td>0.390</td>\n",
-       "      <td>0.872</td>\n",
-       "      <td>0.344</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>BOW</td>\n",
-       "      <td>extension_2</td>\n",
-       "      <td>0.928</td>\n",
-       "      <td>0.252</td>\n",
-       "      <td>0.900</td>\n",
-       "      <td>0.412</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>WeisfeleirLehmanKernel</td>\n",
-       "      <td>gen_country</td>\n",
-       "      <td>0.552</td>\n",
-       "      <td>0.438</td>\n",
-       "      <td>0.656</td>\n",
-       "      <td>0.142</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>BOW</td>\n",
-       "      <td>extension_1</td>\n",
-       "      <td>0.928</td>\n",
-       "      <td>0.254</td>\n",
-       "      <td>0.898</td>\n",
-       "      <td>0.412</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>HED</td>\n",
-       "      <td>gen_country</td>\n",
-       "      <td>0.734</td>\n",
-       "      <td>0.282</td>\n",
-       "      <td>0.788</td>\n",
-       "      <td>0.366</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    mesure         type     c1     c2     c3     c4\n",
-       "0             BagOfCliques  gen_country  0.724  0.330  0.802  0.370\n",
-       "1                      BOW   gen_region  0.926  0.278  0.918  0.424\n",
-       "2        VertexEdgeOverlap  gen_country  0.838  0.334  0.888  0.408\n",
-       "3                      MCS  extension_2  0.928  0.322  0.906  0.424\n",
-       "4       GreedyEditDistance   gen_region  0.054  0.178  0.100  0.040\n",
-       "5       GraphEditDistanceW  gen_country  0.726  0.306  0.774  0.370\n",
-       "6        VertexEdgeOverlap       normal  0.936  0.236  0.908  0.384\n",
-       "7                      MCS  extension_1  0.928  0.336  0.900  0.422\n",
-       "8        GraphEditDistance  extension_1  0.674  0.188  0.674  0.306\n",
-       "9       GreedyEditDistance  gen_country  0.090  0.192  0.154  0.058\n",
-       "10       GraphEditDistance  extension_2  0.670  0.190  0.664  0.304\n",
-       "11  WeisfeleirLehmanKernel   gen_region  0.648  0.458  0.672  0.096\n",
-       "12      GraphEditDistanceW   gen_region  0.702  0.214  0.692  0.312\n",
-       "13                 Jaccard  extension_1  0.910  0.296  0.890  0.396\n",
-       "14       VertexEdgeOverlap   gen_region  0.928  0.276  0.920  0.412\n",
-       "15                 Jaccard  extension_2  0.910  0.302  0.890  0.396\n",
-       "16      GreedyEditDistance       normal  0.072  0.164  0.124  0.046\n",
-       "17                     HED   gen_region  0.758  0.182  0.738  0.332\n",
-       "18                     BOW  gen_country  0.826  0.328  0.884  0.416\n",
-       "20       GraphEditDistance       normal  0.684  0.186  0.670  0.290\n",
-       "21                     HED  extension_1  0.752  0.156  0.718  0.328\n",
-       "22  WeisfeleirLehmanKernel       normal  0.496  0.322  0.552  0.132\n",
-       "23            BagOfCliques   gen_region  0.774  0.226  0.754  0.262\n",
-       "24  WeisfeleirLehmanKernel  extension_2  0.482  0.386  0.558  0.106\n",
-       "25                     HED  extension_2  0.752  0.148  0.722  0.328\n",
-       "27  WeisfeleirLehmanKernel  extension_1  0.460  0.322  0.522  0.130\n",
-       "28      GraphEditDistanceW  extension_1  0.674  0.188  0.674  0.306\n",
-       "29                 Jaccard   gen_region  0.908  0.306  0.904  0.420\n",
-       "30                     MCS       normal  0.936  0.294  0.910  0.430\n",
-       "31                     MCS  gen_country  0.826  0.378  0.896  0.420\n",
-       "32            BagOfCliques  extension_2  0.762  0.234  0.734  0.264\n",
-       "33       VertexEdgeOverlap  extension_2  0.928  0.258  0.904  0.392\n",
-       "34      GraphEditDistanceW  extension_2  0.670  0.190  0.664  0.304\n",
-       "35            BagOfCliques  extension_1  0.748  0.248  0.718  0.246\n",
-       "36       VertexEdgeOverlap  extension_1  0.928  0.276  0.904  0.392\n",
-       "37      GraphEditDistanceW       normal  0.684  0.186  0.670  0.290\n",
-       "38       GraphEditDistance   gen_region  0.702  0.214  0.692  0.312\n",
-       "39                     HED       normal  0.760  0.142  0.730  0.320\n",
-       "40      GreedyEditDistance  extension_2  0.102  0.212  0.152  0.058\n",
-       "41            BagOfCliques       normal  0.774  0.212  0.744  0.266\n",
-       "42                 Jaccard       normal  0.916  0.278  0.896  0.396\n",
-       "43                     BOW       normal  0.936  0.248  0.908  0.412\n",
-       "44      GreedyEditDistance  extension_1  0.084  0.200  0.148  0.060\n",
-       "45       GraphEditDistance  gen_country  0.726  0.306  0.774  0.370\n",
-       "47                     MCS   gen_region  0.924  0.336  0.924  0.446\n",
-       "48                 Jaccard  gen_country  0.774  0.390  0.872  0.344\n",
-       "49                     BOW  extension_2  0.928  0.252  0.900  0.412\n",
-       "51  WeisfeleirLehmanKernel  gen_country  0.552  0.438  0.656  0.142\n",
-       "52                     BOW  extension_1  0.928  0.254  0.898  0.412\n",
-       "54                     HED  gen_country  0.734  0.282  0.788  0.366"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
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diff --git a/notebooks_old/MatchingAnalysis/Result_AnaysisV2_MADA.ipynb b/notebooks_old/MatchingAnalysis/Result_AnaysisV2_MADA.ipynb
deleted file mode 100644
index a43373c..0000000
--- a/notebooks_old/MatchingAnalysis/Result_AnaysisV2_MADA.ipynb
+++ /dev/null
@@ -1,3143 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-28T05:03:07.327486Z",
-     "start_time": "2018-09-28T05:03:06.275342Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Preparing\n",
-    "## Load the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-28T05:03:09.093753Z",
-     "start_time": "2018-09-28T05:03:09.079051Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data=pd.read_csv(\"../../bvlac_2_0.5.csvming1_10\",index_col=0)\n",
-    "data=data[data.mesure != \"BP\"]\n",
-    "data[\"mean\"]=np.mean(data[\"c1 c2 c3 c4\".split()].values,axis=1)\n",
-    "data[\"sum\"]=np.sum(data[\"c1 c2 c3 c4\".split()].values,axis=1)\n",
-    "#data[\"x\"]=np.arange(len(data))\n",
-    "#data[\"color\"]=data.type.apply(lambda x: \"blue\" if not \"window\" in x else \"red\")\n",
-    "data[\"mesure\"]=data.mesure.apply(lambda x:\"BOW\" if x == \"BagOfNodes\" else x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data=data[data.type != \"all\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data2 = data.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'colorize' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-4-d2caa3de5718>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"c5_w\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"sum_w\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"c1_w c2_w c3_w c4_w c5_w\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mcolorize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"sum\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"sum_w\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"c1_w c2_w c3_w c4_w c5_w sum_w\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'colorize' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "data[\"c1_w\"]=data.c1.apply(lambda x: 0.1*x)\n",
-    "data[\"c2_w\"]=data.c2.apply(lambda x: 0.4*x)\n",
-    "data[\"c3_w\"]=data.c3.apply(lambda x: 0.4*x)\n",
-    "data[\"c4_w\"]=data.c4.apply(lambda x: 0.1*x)\n",
-    "data[\"c5_w\"]=data.c5.apply(lambda x: 0.1*x)\n",
-    "data[\"sum_w\"]=np.sum(data[\"c1_w c2_w c3_w c4_w c5_w\".split()].values,axis=1)\n",
-    "colorize(data.sort_values(\"sum\",ascending=False).head(10).sort_values(\"sum_w\",ascending=False),\"c1_w c2_w c3_w c4_w c5_w sum_w\".split())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style  type=\"text/css\" >\n",
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-       "            background-color:  #d64541;\n",
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-       "            : ;\n",
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-       "            : ;\n",
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-       "            background-color:  yellow;\n",
-       "            : ;\n",
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-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_697ca87e_4af9_11e9_8dca_6a0002e84820row9_col9 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_697ca87e_4af9_11e9_8dca_6a0002e84820row9_col10 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
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-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }</style><table id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >mesure</th>        <th class=\"col_heading level0 col1\" >type</th>        <th class=\"col_heading level0 col2\" >c1</th>        <th class=\"col_heading level0 col3\" >c2</th>        <th class=\"col_heading level0 col4\" >c3</th>        <th class=\"col_heading level0 col5\" >c4</th>        <th class=\"col_heading level0 col6\" >mean</th>        <th class=\"col_heading level0 col7\" >sum</th>        <th class=\"col_heading level0 col8\" >c1_w</th>        <th class=\"col_heading level0 col9\" >c2_w</th>        <th class=\"col_heading level0 col10\" >c3_w</th>        <th class=\"col_heading level0 col11\" >c4_w</th>        <th class=\"col_heading level0 col12\" >sum_w</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820level0_row0\" class=\"row_heading level0 row0\" >3</th>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row0_col0\" class=\"data row0 col0\" >BOW</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row0_col1\" class=\"data row0 col1\" >inra</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row0_col2\" class=\"data row0 col2\" >0.732</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row0_col3\" class=\"data row0 col3\" >0.23</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row0_col4\" class=\"data row0 col4\" >0.336</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row0_col5\" class=\"data row0 col5\" >0.19</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row0_col6\" class=\"data row0 col6\" >0.372</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row0_col7\" class=\"data row0 col7\" >1.488</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row0_col8\" class=\"data row0 col8\" >0.2928</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row0_col9\" class=\"data row0 col9\" >0.023</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row0_col10\" class=\"data row0 col10\" >0.1344</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row0_col11\" class=\"data row0 col11\" >0.019</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row0_col12\" class=\"data row0 col12\" >0.4692</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820level0_row1\" class=\"row_heading level0 row1\" >106</th>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row1_col0\" class=\"data row1 col0\" >BOW</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row1_col1\" class=\"data row1 col1\" >ext_2</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row1_col2\" class=\"data row1 col2\" >0.744</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row1_col3\" class=\"data row1 col3\" >0.22</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row1_col4\" class=\"data row1 col4\" >0.326</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row1_col5\" class=\"data row1 col5\" >0.198</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row1_col6\" class=\"data row1 col6\" >0.372</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row1_col7\" class=\"data row1 col7\" >1.488</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row1_col8\" class=\"data row1 col8\" >0.2976</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row1_col9\" class=\"data row1 col9\" >0.022</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row1_col10\" class=\"data row1 col10\" >0.1304</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row1_col11\" class=\"data row1 col11\" >0.0198</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row1_col12\" class=\"data row1 col12\" >0.4698</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820level0_row2\" class=\"row_heading level0 row2\" >92</th>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row2_col0\" class=\"data row2 col0\" >BOW</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row2_col1\" class=\"data row2 col1\" >ext_1</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row2_col2\" class=\"data row2 col2\" >0.736</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row2_col3\" class=\"data row2 col3\" >0.216</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row2_col4\" class=\"data row2 col4\" >0.328</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row2_col5\" class=\"data row2 col5\" >0.198</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row2_col6\" class=\"data row2 col6\" >0.3695</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row2_col7\" class=\"data row2 col7\" >1.478</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row2_col8\" class=\"data row2 col8\" >0.2944</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row2_col9\" class=\"data row2 col9\" >0.0216</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row2_col10\" class=\"data row2 col10\" >0.1312</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row2_col11\" class=\"data row2 col11\" >0.0198</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row2_col12\" class=\"data row2 col12\" >0.467</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820level0_row3\" class=\"row_heading level0 row3\" >101</th>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row3_col0\" class=\"data row3 col0\" >BOW</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row3_col1\" class=\"data row3 col1\" >inra_ext_2</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row3_col2\" class=\"data row3 col2\" >0.724</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row3_col3\" class=\"data row3 col3\" >0.226</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row3_col4\" class=\"data row3 col4\" >0.336</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row3_col5\" class=\"data row3 col5\" >0.19</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row3_col6\" class=\"data row3 col6\" >0.369</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row3_col7\" class=\"data row3 col7\" >1.476</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row3_col8\" class=\"data row3 col8\" >0.2896</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row3_col9\" class=\"data row3 col9\" >0.0226</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row3_col10\" class=\"data row3 col10\" >0.1344</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row3_col11\" class=\"data row3 col11\" >0.019</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row3_col12\" class=\"data row3 col12\" >0.4656</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820level0_row4\" class=\"row_heading level0 row4\" >67</th>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row4_col0\" class=\"data row4 col0\" >BOW</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row4_col1\" class=\"data row4 col1\" >str_object</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row4_col2\" class=\"data row4 col2\" >0.732</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row4_col3\" class=\"data row4 col3\" >0.208</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row4_col4\" class=\"data row4 col4\" >0.332</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row4_col5\" class=\"data row4 col5\" >0.198</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row4_col6\" class=\"data row4 col6\" >0.3675</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row4_col7\" class=\"data row4 col7\" >1.47</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row4_col8\" class=\"data row4 col8\" >0.2928</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row4_col9\" class=\"data row4 col9\" >0.0208</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row4_col10\" class=\"data row4 col10\" >0.1328</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row4_col11\" class=\"data row4 col11\" >0.0198</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row4_col12\" class=\"data row4 col12\" >0.4662</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820level0_row5\" class=\"row_heading level0 row5\" >73</th>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row5_col0\" class=\"data row5 col0\" >Jaccard</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row5_col1\" class=\"data row5 col1\" >biotex_bvlac</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row5_col2\" class=\"data row5 col2\" >0.726</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row5_col3\" class=\"data row5 col3\" >0.214</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row5_col4\" class=\"data row5 col4\" >0.334</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row5_col5\" class=\"data row5 col5\" >0.192</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row5_col6\" class=\"data row5 col6\" >0.3665</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row5_col7\" class=\"data row5 col7\" >1.466</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row5_col8\" class=\"data row5 col8\" >0.2904</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row5_col9\" class=\"data row5 col9\" >0.0214</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row5_col10\" class=\"data row5 col10\" >0.1336</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row5_col11\" class=\"data row5 col11\" >0.0192</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row5_col12\" class=\"data row5 col12\" >0.4646</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820level0_row6\" class=\"row_heading level0 row6\" >137</th>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row6_col0\" class=\"data row6 col0\" >MCS</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row6_col1\" class=\"data row6 col1\" >gen_region</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row6_col2\" class=\"data row6 col2\" >0.69</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row6_col3\" class=\"data row6 col3\" >0.228</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row6_col4\" class=\"data row6 col4\" >0.334</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row6_col5\" class=\"data row6 col5\" >0.198</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row6_col6\" class=\"data row6 col6\" >0.3625</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row6_col7\" class=\"data row6 col7\" >1.45</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row6_col8\" class=\"data row6 col8\" >0.276</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row6_col9\" class=\"data row6 col9\" >0.0228</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row6_col10\" class=\"data row6 col10\" >0.1336</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row6_col11\" class=\"data row6 col11\" >0.0198</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row6_col12\" class=\"data row6 col12\" >0.4522</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820level0_row7\" class=\"row_heading level0 row7\" >134</th>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row7_col0\" class=\"data row7 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row7_col1\" class=\"data row7 col1\" >inra_ext_2</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row7_col2\" class=\"data row7 col2\" >0.71</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row7_col3\" class=\"data row7 col3\" >0.23</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row7_col4\" class=\"data row7 col4\" >0.316</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row7_col5\" class=\"data row7 col5\" >0.194</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row7_col6\" class=\"data row7 col6\" >0.3625</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row7_col7\" class=\"data row7 col7\" >1.45</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row7_col8\" class=\"data row7 col8\" >0.284</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row7_col9\" class=\"data row7 col9\" >0.023</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row7_col10\" class=\"data row7 col10\" >0.1264</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row7_col11\" class=\"data row7 col11\" >0.0194</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row7_col12\" class=\"data row7 col12\" >0.4528</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820level0_row8\" class=\"row_heading level0 row8\" >51</th>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row8_col0\" class=\"data row8 col0\" >MCS</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row8_col1\" class=\"data row8 col1\" >ext_2</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row8_col2\" class=\"data row8 col2\" >0.7</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row8_col3\" class=\"data row8 col3\" >0.216</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row8_col4\" class=\"data row8 col4\" >0.332</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row8_col5\" class=\"data row8 col5\" >0.198</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row8_col6\" class=\"data row8 col6\" >0.3615</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row8_col7\" class=\"data row8 col7\" >1.446</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row8_col8\" class=\"data row8 col8\" >0.28</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row8_col9\" class=\"data row8 col9\" >0.0216</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row8_col10\" class=\"data row8 col10\" >0.1328</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row8_col11\" class=\"data row8 col11\" >0.0198</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row8_col12\" class=\"data row8 col12\" >0.4542</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820level0_row9\" class=\"row_heading level0 row9\" >6</th>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row9_col0\" class=\"data row9 col0\" >BOW</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row9_col1\" class=\"data row9 col1\" >inra_gen_country</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row9_col2\" class=\"data row9 col2\" >0.664</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row9_col3\" class=\"data row9 col3\" >0.246</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row9_col4\" class=\"data row9 col4\" >0.342</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row9_col5\" class=\"data row9 col5\" >0.194</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row9_col6\" class=\"data row9 col6\" >0.3615</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row9_col7\" class=\"data row9 col7\" >1.446</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row9_col8\" class=\"data row9 col8\" >0.2656</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row9_col9\" class=\"data row9 col9\" >0.0246</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row9_col10\" class=\"data row9 col10\" >0.1368</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row9_col11\" class=\"data row9 col11\" >0.0194</td>\n",
-       "                        <td id=\"T_697ca87e_4af9_11e9_8dca_6a0002e84820row9_col12\" class=\"data row9 col12\" >0.4464</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x13c511c88>"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[\"c1_w\"]=data.c1.apply(lambda x: 0.4*x)\n",
-    "data[\"c2_w\"]=data.c2.apply(lambda x: 0.1*x)\n",
-    "data[\"c3_w\"]=data.c3.apply(lambda x: 0.4*x)\n",
-    "data[\"c4_w\"]=data.c4.apply(lambda x: 0.1*x)\n",
-    "data[\"sum_w\"]=np.sum(data[\"c1_w c2_w c3_w c4_w\".split()].values,axis=1)\n",
-    "colorize(data.sort_values(\"sum\",ascending=False).head(10),\"c1_w c2_w c3_w c4_w sum_w\".split())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style  type=\"text/css\" >\n",
-       "    #T_69a12140_4af9_11e9_9621_6a0002e84820row0_col11 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
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-       "            background-color:  yellow;\n",
-       "            : ;\n",
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-       "            background-color:  yellow;\n",
-       "            : ;\n",
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-       "            background-color:  yellow;\n",
-       "            : ;\n",
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-       "            background-color:  yellow;\n",
-       "            : ;\n",
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-       "            background-color:  yellow;\n",
-       "            : ;\n",
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-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
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-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
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-       "            background-color:  yellow;\n",
-       "            : ;\n",
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-       "            : ;\n",
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-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
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-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_69a12140_4af9_11e9_9621_6a0002e84820row9_col8 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_69a12140_4af9_11e9_9621_6a0002e84820row9_col9 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_69a12140_4af9_11e9_9621_6a0002e84820row9_col10 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_69a12140_4af9_11e9_9621_6a0002e84820row9_col12 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }</style><table id=\"T_69a12140_4af9_11e9_9621_6a0002e84820\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >mesure</th>        <th class=\"col_heading level0 col1\" >type</th>        <th class=\"col_heading level0 col2\" >c1</th>        <th class=\"col_heading level0 col3\" >c2</th>        <th class=\"col_heading level0 col4\" >c3</th>        <th class=\"col_heading level0 col5\" >c4</th>        <th class=\"col_heading level0 col6\" >mean</th>        <th class=\"col_heading level0 col7\" >sum</th>        <th class=\"col_heading level0 col8\" >c1_w</th>        <th class=\"col_heading level0 col9\" >c2_w</th>        <th class=\"col_heading level0 col10\" >c3_w</th>        <th class=\"col_heading level0 col11\" >c4_w</th>        <th class=\"col_heading level0 col12\" >sum_w</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_69a12140_4af9_11e9_9621_6a0002e84820level0_row0\" class=\"row_heading level0 row0\" >3</th>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row0_col0\" class=\"data row0 col0\" >BOW</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row0_col1\" class=\"data row0 col1\" >inra</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row0_col2\" class=\"data row0 col2\" >0.732</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row0_col3\" class=\"data row0 col3\" >0.23</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row0_col4\" class=\"data row0 col4\" >0.336</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row0_col5\" class=\"data row0 col5\" >0.19</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row0_col6\" class=\"data row0 col6\" >0.372</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row0_col7\" class=\"data row0 col7\" >1.488</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row0_col8\" class=\"data row0 col8\" >0.732</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row0_col9\" class=\"data row0 col9\" >0.23</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row0_col10\" class=\"data row0 col10\" >0.336</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row0_col11\" class=\"data row0 col11\" >0.19</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row0_col12\" class=\"data row0 col12\" >1.488</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_69a12140_4af9_11e9_9621_6a0002e84820level0_row1\" class=\"row_heading level0 row1\" >106</th>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row1_col0\" class=\"data row1 col0\" >BOW</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row1_col1\" class=\"data row1 col1\" >ext_2</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row1_col2\" class=\"data row1 col2\" >0.744</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row1_col3\" class=\"data row1 col3\" >0.22</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row1_col4\" class=\"data row1 col4\" >0.326</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row1_col5\" class=\"data row1 col5\" >0.198</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row1_col6\" class=\"data row1 col6\" >0.372</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row1_col7\" class=\"data row1 col7\" >1.488</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row1_col8\" class=\"data row1 col8\" >0.744</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row1_col9\" class=\"data row1 col9\" >0.22</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row1_col10\" class=\"data row1 col10\" >0.326</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row1_col11\" class=\"data row1 col11\" >0.198</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row1_col12\" class=\"data row1 col12\" >1.488</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_69a12140_4af9_11e9_9621_6a0002e84820level0_row2\" class=\"row_heading level0 row2\" >92</th>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row2_col0\" class=\"data row2 col0\" >BOW</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row2_col1\" class=\"data row2 col1\" >ext_1</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row2_col2\" class=\"data row2 col2\" >0.736</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row2_col3\" class=\"data row2 col3\" >0.216</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row2_col4\" class=\"data row2 col4\" >0.328</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row2_col5\" class=\"data row2 col5\" >0.198</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row2_col6\" class=\"data row2 col6\" >0.3695</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row2_col7\" class=\"data row2 col7\" >1.478</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row2_col8\" class=\"data row2 col8\" >0.736</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row2_col9\" class=\"data row2 col9\" >0.216</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row2_col10\" class=\"data row2 col10\" >0.328</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row2_col11\" class=\"data row2 col11\" >0.198</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row2_col12\" class=\"data row2 col12\" >1.478</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_69a12140_4af9_11e9_9621_6a0002e84820level0_row3\" class=\"row_heading level0 row3\" >101</th>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row3_col0\" class=\"data row3 col0\" >BOW</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row3_col1\" class=\"data row3 col1\" >inra_ext_2</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row3_col2\" class=\"data row3 col2\" >0.724</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row3_col3\" class=\"data row3 col3\" >0.226</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row3_col4\" class=\"data row3 col4\" >0.336</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row3_col5\" class=\"data row3 col5\" >0.19</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row3_col6\" class=\"data row3 col6\" >0.369</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row3_col7\" class=\"data row3 col7\" >1.476</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row3_col8\" class=\"data row3 col8\" >0.724</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row3_col9\" class=\"data row3 col9\" >0.226</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row3_col10\" class=\"data row3 col10\" >0.336</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row3_col11\" class=\"data row3 col11\" >0.19</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row3_col12\" class=\"data row3 col12\" >1.476</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_69a12140_4af9_11e9_9621_6a0002e84820level0_row4\" class=\"row_heading level0 row4\" >67</th>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row4_col0\" class=\"data row4 col0\" >BOW</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row4_col1\" class=\"data row4 col1\" >str_object</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row4_col2\" class=\"data row4 col2\" >0.732</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row4_col3\" class=\"data row4 col3\" >0.208</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row4_col4\" class=\"data row4 col4\" >0.332</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row4_col5\" class=\"data row4 col5\" >0.198</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row4_col6\" class=\"data row4 col6\" >0.3675</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row4_col7\" class=\"data row4 col7\" >1.47</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row4_col8\" class=\"data row4 col8\" >0.732</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row4_col9\" class=\"data row4 col9\" >0.208</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row4_col10\" class=\"data row4 col10\" >0.332</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row4_col11\" class=\"data row4 col11\" >0.198</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row4_col12\" class=\"data row4 col12\" >1.47</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_69a12140_4af9_11e9_9621_6a0002e84820level0_row5\" class=\"row_heading level0 row5\" >73</th>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row5_col0\" class=\"data row5 col0\" >Jaccard</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row5_col1\" class=\"data row5 col1\" >biotex_bvlac</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row5_col2\" class=\"data row5 col2\" >0.726</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row5_col3\" class=\"data row5 col3\" >0.214</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row5_col4\" class=\"data row5 col4\" >0.334</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row5_col5\" class=\"data row5 col5\" >0.192</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row5_col6\" class=\"data row5 col6\" >0.3665</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row5_col7\" class=\"data row5 col7\" >1.466</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row5_col8\" class=\"data row5 col8\" >0.726</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row5_col9\" class=\"data row5 col9\" >0.214</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row5_col10\" class=\"data row5 col10\" >0.334</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row5_col11\" class=\"data row5 col11\" >0.192</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row5_col12\" class=\"data row5 col12\" >1.466</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_69a12140_4af9_11e9_9621_6a0002e84820level0_row6\" class=\"row_heading level0 row6\" >137</th>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row6_col0\" class=\"data row6 col0\" >MCS</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row6_col1\" class=\"data row6 col1\" >gen_region</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row6_col2\" class=\"data row6 col2\" >0.69</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row6_col3\" class=\"data row6 col3\" >0.228</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row6_col4\" class=\"data row6 col4\" >0.334</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row6_col5\" class=\"data row6 col5\" >0.198</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row6_col6\" class=\"data row6 col6\" >0.3625</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row6_col7\" class=\"data row6 col7\" >1.45</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row6_col8\" class=\"data row6 col8\" >0.69</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row6_col9\" class=\"data row6 col9\" >0.228</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row6_col10\" class=\"data row6 col10\" >0.334</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row6_col11\" class=\"data row6 col11\" >0.198</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row6_col12\" class=\"data row6 col12\" >1.45</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_69a12140_4af9_11e9_9621_6a0002e84820level0_row7\" class=\"row_heading level0 row7\" >134</th>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row7_col0\" class=\"data row7 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row7_col1\" class=\"data row7 col1\" >inra_ext_2</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row7_col2\" class=\"data row7 col2\" >0.71</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row7_col3\" class=\"data row7 col3\" >0.23</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row7_col4\" class=\"data row7 col4\" >0.316</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row7_col5\" class=\"data row7 col5\" >0.194</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row7_col6\" class=\"data row7 col6\" >0.3625</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row7_col7\" class=\"data row7 col7\" >1.45</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row7_col8\" class=\"data row7 col8\" >0.71</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row7_col9\" class=\"data row7 col9\" >0.23</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row7_col10\" class=\"data row7 col10\" >0.316</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row7_col11\" class=\"data row7 col11\" >0.194</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row7_col12\" class=\"data row7 col12\" >1.45</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_69a12140_4af9_11e9_9621_6a0002e84820level0_row8\" class=\"row_heading level0 row8\" >51</th>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row8_col0\" class=\"data row8 col0\" >MCS</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row8_col1\" class=\"data row8 col1\" >ext_2</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row8_col2\" class=\"data row8 col2\" >0.7</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row8_col3\" class=\"data row8 col3\" >0.216</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row8_col4\" class=\"data row8 col4\" >0.332</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row8_col5\" class=\"data row8 col5\" >0.198</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row8_col6\" class=\"data row8 col6\" >0.3615</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row8_col7\" class=\"data row8 col7\" >1.446</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row8_col8\" class=\"data row8 col8\" >0.7</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row8_col9\" class=\"data row8 col9\" >0.216</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row8_col10\" class=\"data row8 col10\" >0.332</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row8_col11\" class=\"data row8 col11\" >0.198</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row8_col12\" class=\"data row8 col12\" >1.446</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_69a12140_4af9_11e9_9621_6a0002e84820level0_row9\" class=\"row_heading level0 row9\" >6</th>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row9_col0\" class=\"data row9 col0\" >BOW</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row9_col1\" class=\"data row9 col1\" >inra_gen_country</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row9_col2\" class=\"data row9 col2\" >0.664</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row9_col3\" class=\"data row9 col3\" >0.246</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row9_col4\" class=\"data row9 col4\" >0.342</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row9_col5\" class=\"data row9 col5\" >0.194</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row9_col6\" class=\"data row9 col6\" >0.3615</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row9_col7\" class=\"data row9 col7\" >1.446</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row9_col8\" class=\"data row9 col8\" >0.664</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row9_col9\" class=\"data row9 col9\" >0.246</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row9_col10\" class=\"data row9 col10\" >0.342</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row9_col11\" class=\"data row9 col11\" >0.194</td>\n",
-       "                        <td id=\"T_69a12140_4af9_11e9_9621_6a0002e84820row9_col12\" class=\"data row9 col12\" >1.446</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x13d4dfc50>"
-      ]
-     },
-     "execution_count": 118,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[\"c1_w\"]=data.c1.apply(lambda x: 1*x)\n",
-    "data[\"c2_w\"]=data.c2.apply(lambda x: 1*x)\n",
-    "data[\"c3_w\"]=data.c3.apply(lambda x: 1*x)\n",
-    "data[\"c4_w\"]=data.c4.apply(lambda x: 1*x)\n",
-    "data[\"sum_w\"]=np.sum(data[\"c1_w c2_w c3_w c4_w\".split()].values,axis=1)\n",
-    "colorize(data.sort_values(\"sum\",ascending=False).head(10),\"c1_w c2_w c3_w c4_w sum_w\".split())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Helpers \n",
-    "\n",
-    " * Pareto on Multiple dimension : `pareto_frontier_multi()`\n",
-    " * Highlighter  `highlight_min()` and `highlight_max()`(yellow for max value and red for min value)\n",
-    " * Colorizer using Highlighter methods `colorize()`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T12:55:10.491478Z",
-     "start_time": "2018-09-26T12:55:10.485081Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def pareto_frontier_multi(myArray):\n",
-    "    # Sort on first dimension\n",
-    "    myArray = myArray[myArray[:,0].argsort()]\n",
-    "    # Add first row to pareto_frontier\n",
-    "    pareto_frontier = myArray[0:1,:]\n",
-    "    indices,i=[],1\n",
-    "    # Test next row against the last row in pareto_frontier\n",
-    "    for row in myArray[1:,:]:\n",
-    "        if sum([row[x] >= pareto_frontier[-1][x]\n",
-    "            for x in range(len(row))]) == len(row):\n",
-    "                # If it is better on all features add the row to pareto_frontier\n",
-    "                pareto_frontier = np.concatenate((pareto_frontier, [row]))\n",
-    "                indices.append(i)\n",
-    "        i+=1\n",
-    "    return indices,pareto_frontier\n",
-    "\n",
-    "to_colorize=\"c1 c2 c3 c4 c5 mean sum\".split()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Analysis \n",
-    "\n",
-    "In this first section, we'll try to find which measure 'may' be optimal for our needs. Bluntly, we compute the average performance of each measure on every type of STR. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T12:55:10.899176Z",
-     "start_time": "2018-09-26T12:55:10.493327Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "        }</style><table id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >c1</th>        <th class=\"col_heading level0 col1\" >c2</th>        <th class=\"col_heading level0 col2\" >c3</th>        <th class=\"col_heading level0 col3\" >c4</th>        <th class=\"col_heading level0 col4\" >c5</th>        <th class=\"col_heading level0 col5\" >mean</th>        <th class=\"col_heading level0 col6\" >sum</th>    </tr>    <tr>        <th class=\"index_name level0\" >mesure</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820level0_row0\" class=\"row_heading level0 row0\" >BOW</th>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row0_col0\" class=\"data row0 col0\" >0.67284</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row0_col1\" class=\"data row0 col1\" >0.245059</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row0_col2\" class=\"data row0 col2\" >0.395483</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row0_col3\" class=\"data row0 col3\" >0.223377</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row0_col4\" class=\"data row0 col4\" >0.683918</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row0_col5\" class=\"data row0 col5\" >0.38419</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row0_col6\" class=\"data row0 col6\" >1.53676</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820level0_row1\" class=\"row_heading level0 row1\" >VertexEdgeOverlap</th>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row1_col0\" class=\"data row1 col0\" >0.672501</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row1_col1\" class=\"data row1 col1\" >0.245398</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row1_col2\" class=\"data row1 col2\" >0.38611</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row1_col3\" class=\"data row1 col3\" >0.223377</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row1_col4\" class=\"data row1 col4\" >0.693066</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row1_col5\" class=\"data row1 col5\" >0.381846</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row1_col6\" class=\"data row1 col6\" >1.52739</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820level0_row2\" class=\"row_heading level0 row2\" >MCS</th>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row2_col0\" class=\"data row2 col0\" >0.657256</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row2_col1\" class=\"data row2 col1\" >0.247883</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row2_col2\" class=\"data row2 col2\" >0.388707</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row2_col3\" class=\"data row2 col3\" >0.223377</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row2_col4\" class=\"data row2 col4\" >0.684032</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row2_col5\" class=\"data row2 col5\" >0.379305</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row2_col6\" class=\"data row2 col6\" >1.51722</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820level0_row3\" class=\"row_heading level0 row3\" >Jaccard</th>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row3_col0\" class=\"data row3 col0\" >0.640994</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row3_col1\" class=\"data row3 col1\" >0.240542</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row3_col2\" class=\"data row3 col2\" >0.372671</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row3_col3\" class=\"data row3 col3\" >0.210615</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row3_col4\" class=\"data row3 col4\" >0.660207</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row3_col5\" class=\"data row3 col5\" >0.366206</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row3_col6\" class=\"data row3 col6\" >1.46482</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820level0_row4\" class=\"row_heading level0 row4\" >DeepWalk</th>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row4_col0\" class=\"data row4 col0\" >0.49712</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row4_col1\" class=\"data row4 col1\" >0.389723</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row4_col2\" class=\"data row4 col2\" >0.152005</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row4_col3\" class=\"data row4 col3\" >0.0658385</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row4_col4\" class=\"data row4 col4\" >0.610745</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row4_col5\" class=\"data row4 col5\" >0.276172</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row4_col6\" class=\"data row4 col6\" >1.10469</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820level0_row5\" class=\"row_heading level0 row5\" >WeisfeleirLehmanKernel</th>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row5_col0\" class=\"data row5 col0\" >0.394128</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row5_col1\" class=\"data row5 col1\" >0.493619</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row5_col2\" class=\"data row5 col2\" >0.0725014</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row5_col3\" class=\"data row5 col3\" >0.00496894</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row5_col4\" class=\"data row5 col4\" >0.528249</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row5_col5\" class=\"data row5 col5\" >0.241304</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row5_col6\" class=\"data row5 col6\" >0.965217</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820level0_row6\" class=\"row_heading level0 row6\" >GraphEditDistance</th>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row6_col0\" class=\"data row6 col0\" >0.379693</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row6_col1\" class=\"data row6 col1\" >0.170248</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row6_col2\" class=\"data row6 col2\" >0.186068</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row6_col3\" class=\"data row6 col3\" >0.121842</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row6_col4\" class=\"data row6 col4\" >0.430875</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row6_col5\" class=\"data row6 col5\" >0.214463</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row6_col6\" class=\"data row6 col6\" >0.857851</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820level0_row7\" class=\"row_heading level0 row7\" >PolyIntersect</th>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row7_col0\" class=\"data row7 col0\" >0.189091</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row7_col1\" class=\"data row7 col1\" >0.122597</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row7_col2\" class=\"data row7 col2\" >0.0774026</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row7_col3\" class=\"data row7 col3\" >0.052987</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row7_col4\" class=\"data row7 col4\" >0.227495</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row7_col5\" class=\"data row7 col5\" >0.110519</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row7_col6\" class=\"data row7 col6\" >0.442078</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820level0_row8\" class=\"row_heading level0 row8\" >Graph2Vec</th>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row8_col0\" class=\"data row8 col0\" >0.0246189</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row8_col1\" class=\"data row8 col1\" >0.0280068</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row8_col2\" class=\"data row8 col2\" >0.00316206</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row8_col3\" class=\"data row8 col3\" >0</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row8_col4\" class=\"data row8 col4\" >0.0395227</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row8_col5\" class=\"data row8 col5\" >0.0139469</td>\n",
-       "                        <td id=\"T_1102a8f4_4c06_11e9_af47_6a0002e84820row8_col6\" class=\"data row8 col6\" >0.0557877</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x10ede50f0>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "colorize(data.groupby(\"mesure\").mean().sort_values(\"sum\",ascending=False),to_colorize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T12:55:10.910426Z",
-     "start_time": "2018-09-26T12:55:10.901497Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mesure,c1,c2,c3,c4,mean,sum,c1_w,c2_w,c3_w,c4_w,sum_w\n",
-      "BOW,0.6673913043478259,0.2156521739130435,0.32417391304347826,0.19373913043478247,0.35023913043478255,1.4009565217391302,0.6673913043478259,0.2156521739130435,0.32417391304347826,0.19373913043478247,1.4009565217391302\n",
-      "DeepWalk,0.4632173913043479,0.3486086956521738,0.12704347826086956,0.06008695652173916,0.24973913043478255,0.9989565217391302,0.4632173913043479,0.3486086956521738,0.12704347826086956,0.06008695652173916,0.9989565217391302\n",
-      "Graph2Vec,0.020608695652173922,0.026434782608695667,0.0024347826086956524,0.0,0.01236956521739131,0.04947826086956524,0.020608695652173922,0.026434782608695667,0.0024347826086956524,0.0,0.04947826086956524\n",
-      "GraphEditDistance,0.3727272727272727,0.14418181818181813,0.1505454545454545,0.10709090909090904,0.19363636363636358,0.7745454545454543,0.3727272727272727,0.14418181818181813,0.1505454545454545,0.10709090909090904,0.7745454545454543\n",
-      "Jaccard,0.6090434782608696,0.21721739130434783,0.30373913043478257,0.1829565217391303,0.3282391304347826,1.3129565217391304,0.6090434782608696,0.21721739130434783,0.30373913043478257,0.1829565217391303,1.3129565217391304\n",
-      "MCS,0.6499130434782606,0.21530434782608693,0.31817391304347814,0.19469565217391296,0.34452173913043466,1.3780869565217386,0.6499130434782606,0.21530434782608693,0.31817391304347814,0.19469565217391296,1.3780869565217386\n",
-      "PolyIntersect,0.1695999999999998,0.11239999999999999,0.05960000000000001,0.04080000000000001,0.09559999999999996,0.38239999999999985,0.1695999999999998,0.11239999999999999,0.05960000000000001,0.04080000000000001,0.38239999999999985\n",
-      "VertexEdgeOverlap,0.6646086956521736,0.21217391304347827,0.31617391304347825,0.1944347826086955,0.34684782608695647,1.3873913043478259,0.6646086956521736,0.21217391304347827,0.31617391304347825,0.1944347826086955,1.3873913043478259\n",
-      "WeisfeleirLehmanKernel,0.35243478260869565,0.5419130434782609,0.05617391304347829,0.004173913043478262,0.2386739130434783,0.9546956521739132,0.35243478260869565,0.5419130434782609,0.05617391304347829,0.004173913043478262,0.9546956521739132\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(data.groupby(\"mesure\").mean().to_csv())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Based on above results, we observe that strict criteria like c1(shared common entity) and c4(share exact dispertion of spatial entities) are respected by **structure-based** measure(BOW, MCS,HED, GEDs). While more permissive criteria like c2(one entity in one STR close an other entity in an other STR) and c3(share a group of significant entity), are respected by **pattern-based** methods such as WeisfeilerLehmanKernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T12:55:10.937714Z",
-     "start_time": "2018-09-26T12:55:10.912437Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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-       "            : ;\n",
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-       "            color: white;\n",
-       "        }</style><table id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >mesure</th>        <th class=\"col_heading level0 col1\" >type</th>        <th class=\"col_heading level0 col2\" >c1</th>        <th class=\"col_heading level0 col3\" >c2</th>        <th class=\"col_heading level0 col4\" >c3</th>        <th class=\"col_heading level0 col5\" >c4</th>        <th class=\"col_heading level0 col6\" >c5</th>        <th class=\"col_heading level0 col7\" >mean</th>        <th class=\"col_heading level0 col8\" >sum</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820level0_row0\" class=\"row_heading level0 row0\" >6</th>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row0_col0\" class=\"data row0 col0\" >BOW</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row0_col1\" class=\"data row0 col1\" >inra_gen_country</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row0_col2\" class=\"data row0 col2\" >0.685714</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row0_col3\" class=\"data row0 col3\" >0.290909</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row0_col4\" class=\"data row0 col4\" >0.418182</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row0_col5\" class=\"data row0 col5\" >0.223377</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row0_col6\" class=\"data row0 col6\" >0.708985</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row0_col7\" class=\"data row0 col7\" >0.404545</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row0_col8\" class=\"data row0 col8\" >1.61818</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820level0_row1\" class=\"row_heading level0 row1\" >5</th>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row1_col0\" class=\"data row1 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row1_col1\" class=\"data row1 col1\" >gen_country</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row1_col2\" class=\"data row1 col2\" >0.654545</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row1_col3\" class=\"data row1 col3\" >0.280519</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row1_col4\" class=\"data row1 col4\" >0.397403</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row1_col5\" class=\"data row1 col5\" >0.223377</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row1_col6\" class=\"data row1 col6\" >0.706389</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row1_col7\" class=\"data row1 col7\" >0.388961</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row1_col8\" class=\"data row1 col8\" >1.55584</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820level0_row2\" class=\"row_heading level0 row2\" >21</th>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row2_col0\" class=\"data row2 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row2_col1\" class=\"data row2 col1\" >biotex_lda_bvlac_gen_country</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row2_col2\" class=\"data row2 col2\" >0.662338</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row2_col3\" class=\"data row2 col3\" >0.272727</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row2_col4\" class=\"data row2 col4\" >0.38961</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row2_col5\" class=\"data row2 col5\" >0.223377</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row2_col6\" class=\"data row2 col6\" >0.703791</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row2_col7\" class=\"data row2 col7\" >0.387013</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row2_col8\" class=\"data row2 col8\" >1.54805</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820level0_row3\" class=\"row_heading level0 row3\" >29</th>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row3_col0\" class=\"data row3 col0\" >MCS</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row3_col1\" class=\"data row3 col1\" >biotex_lda_bvlac_ext_1</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row3_col2\" class=\"data row3 col2\" >0.67013</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row3_col3\" class=\"data row3 col3\" >0.267532</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row3_col4\" class=\"data row3 col4\" >0.374026</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row3_col5\" class=\"data row3 col5\" >0.223377</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row3_col6\" class=\"data row3 col6\" >0.708985</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row3_col7\" class=\"data row3 col7\" >0.383766</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row3_col8\" class=\"data row3 col8\" >1.53506</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820level0_row4\" class=\"row_heading level0 row4\" >19</th>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row4_col0\" class=\"data row4 col0\" >MCS</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row4_col1\" class=\"data row4 col1\" >biotex_lda_bvlac_ext_2</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row4_col2\" class=\"data row4 col2\" >0.664935</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row4_col3\" class=\"data row4 col3\" >0.267532</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row4_col4\" class=\"data row4 col4\" >0.371429</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row4_col5\" class=\"data row4 col5\" >0.223377</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row4_col6\" class=\"data row4 col6\" >0.708985</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row4_col7\" class=\"data row4 col7\" >0.381818</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row4_col8\" class=\"data row4 col8\" >1.52727</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820level0_row5\" class=\"row_heading level0 row5\" >69</th>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row5_col0\" class=\"data row5 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row5_col1\" class=\"data row5 col1\" >dev_du_gen_region</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row5_col2\" class=\"data row5 col2\" >0.667532</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row5_col3\" class=\"data row5 col3\" >0.249351</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row5_col4\" class=\"data row5 col4\" >0.381818</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row5_col5\" class=\"data row5 col5\" >0.223377</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row5_col6\" class=\"data row5 col6\" >0.690807</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row5_col7\" class=\"data row5 col7\" >0.380519</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row5_col8\" class=\"data row5 col8\" >1.52208</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820level0_row6\" class=\"row_heading level0 row6\" >20</th>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row6_col0\" class=\"data row6 col0\" >MCS</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row6_col1\" class=\"data row6 col1\" >inra_gen_country</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row6_col2\" class=\"data row6 col2\" >0.646753</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row6_col3\" class=\"data row6 col3\" >0.262338</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row6_col4\" class=\"data row6 col4\" >0.374026</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row6_col5\" class=\"data row6 col5\" >0.223377</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row6_col6\" class=\"data row6 col6\" >0.66224</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row6_col7\" class=\"data row6 col7\" >0.376623</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row6_col8\" class=\"data row6 col8\" >1.50649</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820level0_row7\" class=\"row_heading level0 row7\" >54</th>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row7_col0\" class=\"data row7 col0\" >Jaccard</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row7_col1\" class=\"data row7 col1\" >inra</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row7_col2\" class=\"data row7 col2\" >0.654545</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row7_col3\" class=\"data row7 col3\" >0.212987</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row7_col4\" class=\"data row7 col4\" >0.394805</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row7_col5\" class=\"data row7 col5\" >0.223377</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row7_col6\" class=\"data row7 col6\" >0.649255</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row7_col7\" class=\"data row7 col7\" >0.371429</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row7_col8\" class=\"data row7 col8\" >1.48571</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820level0_row8\" class=\"row_heading level0 row8\" >28</th>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row8_col0\" class=\"data row8 col0\" >DeepWalk</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row8_col1\" class=\"data row8 col1\" >biotex_lda_bvlac_ext_2</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row8_col2\" class=\"data row8 col2\" >0.462338</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row8_col3\" class=\"data row8 col3\" >0.433766</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row8_col4\" class=\"data row8 col4\" >0.119481</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row8_col5\" class=\"data row8 col5\" >0.0623377</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row8_col6\" class=\"data row8 col6\" >0.659632</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row8_col7\" class=\"data row8 col7\" >0.269481</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row8_col8\" class=\"data row8 col8\" >1.07792</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820level0_row9\" class=\"row_heading level0 row9\" >30</th>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row9_col0\" class=\"data row9 col0\" >WeisfeleirLehmanKernel</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row9_col1\" class=\"data row9 col1\" >biotex_bvlac</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row9_col2\" class=\"data row9 col2\" >0.47013</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row9_col3\" class=\"data row9 col3\" >0.527273</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row9_col4\" class=\"data row9 col4\" >0.0415584</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row9_col5\" class=\"data row9 col5\" >0.00519481</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row9_col6\" class=\"data row9 col6\" >0.568792</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row9_col7\" class=\"data row9 col7\" >0.261039</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row9_col8\" class=\"data row9 col8\" >1.04416</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820level0_row10\" class=\"row_heading level0 row10\" >52</th>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row10_col0\" class=\"data row10 col0\" >Graph2Vec</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row10_col1\" class=\"data row10 col1\" >biotex_lda_bvlac</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row10_col2\" class=\"data row10 col2\" >0.0285714</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row10_col3\" class=\"data row10 col3\" >0.0415584</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row10_col4\" class=\"data row10 col4\" >0.0025974</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row10_col5\" class=\"data row10 col5\" >0</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row10_col6\" class=\"data row10 col6\" >0.0545411</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row10_col7\" class=\"data row10 col7\" >0.0181818</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row10_col8\" class=\"data row10 col8\" >0.0727273</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820level0_row11\" class=\"row_heading level0 row11\" >1</th>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row11_col0\" class=\"data row11 col0\" >Graph2Vec</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row11_col1\" class=\"data row11 col1\" >biotex_bvlac_ext_1</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row11_col2\" class=\"data row11 col2\" >0.0155844</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row11_col3\" class=\"data row11 col3\" >0.0207792</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row11_col4\" class=\"data row11 col4\" >0</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row11_col5\" class=\"data row11 col5\" >0</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row11_col6\" class=\"data row11 col6\" >0.0363613</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row11_col7\" class=\"data row11 col7\" >0.00909091</td>\n",
-       "                        <td id=\"T_239dc4d8_4c06_11e9_895b_6a0002e84820row11_col8\" class=\"data row11 col8\" >0.0363636</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x135903da0>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "index,data_pa=pareto_frontier_multi(data[\"c1 c2 c3 c4 c5\".split()].values)\n",
-    "colorize(data.iloc[index].sort_values(\"sum\",ascending=False),to_colorize)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style  type=\"text/css\" >\n",
-       "    #T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col2 {\n",
-       "            background-color:  yellow;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col3 {\n",
-       "            background-color:  yellow;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col4 {\n",
-       "            background-color:  yellow;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col5 {\n",
-       "            background-color:  yellow;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col6 {\n",
-       "            background-color:  yellow;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col7 {\n",
-       "            background-color:  yellow;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col8 {\n",
-       "            background-color:  yellow;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }</style><table id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >mesure</th>        <th class=\"col_heading level0 col1\" >type</th>        <th class=\"col_heading level0 col2\" >c1</th>        <th class=\"col_heading level0 col3\" >c2</th>        <th class=\"col_heading level0 col4\" >c3</th>        <th class=\"col_heading level0 col5\" >c4</th>        <th class=\"col_heading level0 col6\" >c5</th>        <th class=\"col_heading level0 col7\" >mean</th>        <th class=\"col_heading level0 col8\" >sum</th>        <th class=\"col_heading level0 col9\" >c1_w</th>        <th class=\"col_heading level0 col10\" >c2_w</th>        <th class=\"col_heading level0 col11\" >c3_w</th>        <th class=\"col_heading level0 col12\" >c4_w</th>        <th class=\"col_heading level0 col13\" >sum_w</th>        <th class=\"col_heading level0 col14\" >c5_w</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
-       "                        <td id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col0\" class=\"data row0 col0\" >BOW</td>\n",
-       "                        <td id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col1\" class=\"data row0 col1\" >dev_du</td>\n",
-       "                        <td id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col2\" class=\"data row0 col2\" >0.698</td>\n",
-       "                        <td id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col3\" class=\"data row0 col3\" >0.194</td>\n",
-       "                        <td id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col4\" class=\"data row0 col4\" >0.342</td>\n",
-       "                        <td id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col5\" class=\"data row0 col5\" >0.202</td>\n",
-       "                        <td id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col6\" class=\"data row0 col6\" >0.589836</td>\n",
-       "                        <td id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col7\" class=\"data row0 col7\" >0.359</td>\n",
-       "                        <td id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col8\" class=\"data row0 col8\" >1.436</td>\n",
-       "                        <td id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col9\" class=\"data row0 col9\" >0.0698</td>\n",
-       "                        <td id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col10\" class=\"data row0 col10\" >0.0776</td>\n",
-       "                        <td id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col11\" class=\"data row0 col11\" >0.1368</td>\n",
-       "                        <td id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col12\" class=\"data row0 col12\" >0.0202</td>\n",
-       "                        <td id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col13\" class=\"data row0 col13\" >0.363384</td>\n",
-       "                        <td id=\"T_f8f26ac6_4bfe_11e9_94f0_6a0002e84820row0_col14\" class=\"data row0 col14\" >0.0589836</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x12b1a1940>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "index,data_pa=pareto_frontier_multi(data[data.mesure==\"BOW\"][\"c1 c2 c5\".split()].values)\n",
-    "colorize(data[data.mesure==\"BOW\"].iloc[index].sort_values(\"sum\",ascending=False).head(15),to_colorize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mesure,type,c1,c2,c3,c4,mean,sum,c1_w,c2_w,c3_w,c4_w,sum_w\n",
-      "BOW,dev_du,0.6979999999999998,0.196,0.33599999999999985,0.19999999999999996,0.35749999999999993,1.4299999999999997,0.6979999999999998,0.196,0.33599999999999985,0.19999999999999996,1.4299999999999997\n",
-      "BOW,biotex_bvlac_gen_region,0.622,0.20799999999999996,0.31399999999999995,0.1919999999999999,0.33399999999999996,1.3359999999999999,0.622,0.20799999999999996,0.31399999999999995,0.1919999999999999,1.3359999999999999\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(data[data.mesure==\"BOW\"].iloc[index].sort_values(\"sum\",ascending=False).head(15).to_csv(index=False))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T12:55:10.967755Z",
-     "start_time": "2018-09-26T12:55:10.939214Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style  type=\"text/css\" >\n",
-       "    #T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col5 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col6 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col7 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_71a5f438_4af4_11e9_bef5_6a0002e84820row1_col2 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_71a5f438_4af4_11e9_bef5_6a0002e84820row3_col3 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_71a5f438_4af4_11e9_bef5_6a0002e84820row3_col4 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_71a5f438_4af4_11e9_bef5_6a0002e84820row6_col3 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_71a5f438_4af4_11e9_bef5_6a0002e84820row6_col5 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_71a5f438_4af4_11e9_bef5_6a0002e84820row10_col2 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_71a5f438_4af4_11e9_bef5_6a0002e84820row13_col4 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_71a5f438_4af4_11e9_bef5_6a0002e84820row14_col6 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_71a5f438_4af4_11e9_bef5_6a0002e84820row14_col7 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }</style><table id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >mesure</th>        <th class=\"col_heading level0 col1\" >type</th>        <th class=\"col_heading level0 col2\" >c1</th>        <th class=\"col_heading level0 col3\" >c2</th>        <th class=\"col_heading level0 col4\" >c3</th>        <th class=\"col_heading level0 col5\" >c4</th>        <th class=\"col_heading level0 col6\" >mean</th>        <th class=\"col_heading level0 col7\" >sum</th>        <th class=\"col_heading level0 col8\" >c1_w</th>        <th class=\"col_heading level0 col9\" >c2_w</th>        <th class=\"col_heading level0 col10\" >c3_w</th>        <th class=\"col_heading level0 col11\" >c4_w</th>        <th class=\"col_heading level0 col12\" >sum_w</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820level0_row0\" class=\"row_heading level0 row0\" >3</th>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col0\" class=\"data row0 col0\" >BOW</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col1\" class=\"data row0 col1\" >inra</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col2\" class=\"data row0 col2\" >0.736</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col3\" class=\"data row0 col3\" >0.228</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col4\" class=\"data row0 col4\" >0.348</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col5\" class=\"data row0 col5\" >0.192</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col6\" class=\"data row0 col6\" >0.376</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col7\" class=\"data row0 col7\" >1.504</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col8\" class=\"data row0 col8\" >0.0736</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col9\" class=\"data row0 col9\" >0.0912</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col10\" class=\"data row0 col10\" >0.1392</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col11\" class=\"data row0 col11\" >0.0192</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row0_col12\" class=\"data row0 col12\" >0.3232</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820level0_row1\" class=\"row_heading level0 row1\" >92</th>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row1_col0\" class=\"data row1 col0\" >BOW</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row1_col1\" class=\"data row1 col1\" >ext_1</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row1_col2\" class=\"data row1 col2\" >0.744</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row1_col3\" class=\"data row1 col3\" >0.214</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row1_col4\" class=\"data row1 col4\" >0.344</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row1_col5\" class=\"data row1 col5\" >0.2</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row1_col6\" class=\"data row1 col6\" >0.3755</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row1_col7\" class=\"data row1 col7\" >1.502</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row1_col8\" class=\"data row1 col8\" >0.0744</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row1_col9\" class=\"data row1 col9\" >0.0856</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row1_col10\" class=\"data row1 col10\" >0.1376</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row1_col11\" class=\"data row1 col11\" >0.02</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row1_col12\" class=\"data row1 col12\" >0.3176</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820level0_row2\" class=\"row_heading level0 row2\" >67</th>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row2_col0\" class=\"data row2 col0\" >BOW</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row2_col1\" class=\"data row2 col1\" >str_object</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row2_col2\" class=\"data row2 col2\" >0.738</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row2_col3\" class=\"data row2 col3\" >0.206</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row2_col4\" class=\"data row2 col4\" >0.348</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row2_col5\" class=\"data row2 col5\" >0.2</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row2_col6\" class=\"data row2 col6\" >0.373</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row2_col7\" class=\"data row2 col7\" >1.492</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row2_col8\" class=\"data row2 col8\" >0.0738</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row2_col9\" class=\"data row2 col9\" >0.0824</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row2_col10\" class=\"data row2 col10\" >0.1392</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row2_col11\" class=\"data row2 col11\" >0.02</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row2_col12\" class=\"data row2 col12\" >0.3154</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820level0_row3\" class=\"row_heading level0 row3\" >6</th>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row3_col0\" class=\"data row3 col0\" >BOW</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row3_col1\" class=\"data row3 col1\" >inra_gen_country</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row3_col2\" class=\"data row3 col2\" >0.67</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row3_col3\" class=\"data row3 col3\" >0.244</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row3_col4\" class=\"data row3 col4\" >0.358</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row3_col5\" class=\"data row3 col5\" >0.196</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row3_col6\" class=\"data row3 col6\" >0.367</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row3_col7\" class=\"data row3 col7\" >1.468</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row3_col8\" class=\"data row3 col8\" >0.067</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row3_col9\" class=\"data row3 col9\" >0.0976</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row3_col10\" class=\"data row3 col10\" >0.1432</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row3_col11\" class=\"data row3 col11\" >0.0196</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row3_col12\" class=\"data row3 col12\" >0.3274</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820level0_row4\" class=\"row_heading level0 row4\" >51</th>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row4_col0\" class=\"data row4 col0\" >MCS</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row4_col1\" class=\"data row4 col1\" >ext_2</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row4_col2\" class=\"data row4 col2\" >0.704</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row4_col3\" class=\"data row4 col3\" >0.212</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row4_col4\" class=\"data row4 col4\" >0.344</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row4_col5\" class=\"data row4 col5\" >0.2</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row4_col6\" class=\"data row4 col6\" >0.365</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row4_col7\" class=\"data row4 col7\" >1.46</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row4_col8\" class=\"data row4 col8\" >0.0704</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row4_col9\" class=\"data row4 col9\" >0.0848</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row4_col10\" class=\"data row4 col10\" >0.1376</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row4_col11\" class=\"data row4 col11\" >0.02</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row4_col12\" class=\"data row4 col12\" >0.3128</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820level0_row5\" class=\"row_heading level0 row5\" >15</th>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row5_col0\" class=\"data row5 col0\" >BOW</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row5_col1\" class=\"data row5 col1\" >gen_region</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row5_col2\" class=\"data row5 col2\" >0.696</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row5_col4\" class=\"data row5 col4\" >0.35</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row5_col5\" class=\"data row5 col5\" >0.2</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row5_col6\" class=\"data row5 col6\" >0.364</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row5_col7\" class=\"data row5 col7\" >1.456</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row5_col8\" class=\"data row5 col8\" >0.0696</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row5_col9\" class=\"data row5 col9\" >0.084</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row5_col10\" class=\"data row5 col10\" >0.14</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row5_col11\" class=\"data row5 col11\" >0.02</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row5_col12\" class=\"data row5 col12\" >0.3136</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row6_col0\" class=\"data row6 col0\" >BOW</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row6_col1\" class=\"data row6 col1\" >dev_du</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row6_col2\" class=\"data row6 col2\" >0.706</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row6_col3\" class=\"data row6 col3\" >0.194</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row6_col4\" class=\"data row6 col4\" >0.35</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row6_col5\" class=\"data row6 col5\" >0.202</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row6_col6\" class=\"data row6 col6\" >0.363</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row6_col7\" class=\"data row6 col7\" >1.452</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row6_col8\" class=\"data row6 col8\" >0.0706</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row6_col9\" class=\"data row6 col9\" >0.0776</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row6_col10\" class=\"data row6 col10\" >0.14</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row6_col11\" class=\"data row6 col11\" >0.0202</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row6_col12\" class=\"data row6 col12\" >0.3084</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820level0_row7\" class=\"row_heading level0 row7\" >142</th>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row7_col0\" class=\"data row7 col0\" >Jaccard</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row7_col1\" class=\"data row7 col1\" >biotex_bvlac_gen_region</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row7_col2\" class=\"data row7 col2\" >0.7</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row7_col3\" class=\"data row7 col3\" >0.206</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row7_col4\" class=\"data row7 col4\" >0.342</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row7_col5\" class=\"data row7 col5\" >0.194</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row7_col6\" class=\"data row7 col6\" >0.3605</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row7_col7\" class=\"data row7 col7\" >1.442</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row7_col8\" class=\"data row7 col8\" >0.07</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row7_col9\" class=\"data row7 col9\" >0.0824</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row7_col10\" class=\"data row7 col10\" >0.1368</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row7_col11\" class=\"data row7 col11\" >0.0194</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row7_col12\" class=\"data row7 col12\" >0.3086</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820level0_row8\" class=\"row_heading level0 row8\" >4</th>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row8_col0\" class=\"data row8 col0\" >MCS</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row8_col1\" class=\"data row8 col1\" >inra</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row8_col2\" class=\"data row8 col2\" >0.688</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row8_col3\" class=\"data row8 col3\" >0.214</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row8_col4\" class=\"data row8 col4\" >0.336</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row8_col5\" class=\"data row8 col5\" >0.196</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row8_col6\" class=\"data row8 col6\" >0.3585</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row8_col7\" class=\"data row8 col7\" >1.434</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row8_col8\" class=\"data row8 col8\" >0.0688</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row8_col9\" class=\"data row8 col9\" >0.0856</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row8_col10\" class=\"data row8 col10\" >0.1344</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row8_col11\" class=\"data row8 col11\" >0.0196</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row8_col12\" class=\"data row8 col12\" >0.3084</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820level0_row9\" class=\"row_heading level0 row9\" >108</th>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row9_col0\" class=\"data row9 col0\" >Jaccard</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row9_col1\" class=\"data row9 col1\" >biotex_lda_bvlac_gen_region</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row9_col2\" class=\"data row9 col2\" >0.646</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row9_col3\" class=\"data row9 col3\" >0.242</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row9_col4\" class=\"data row9 col4\" >0.338</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row9_col5\" class=\"data row9 col5\" >0.196</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row9_col6\" class=\"data row9 col6\" >0.3555</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row9_col7\" class=\"data row9 col7\" >1.422</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row9_col8\" class=\"data row9 col8\" >0.0646</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row9_col9\" class=\"data row9 col9\" >0.0968</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row9_col10\" class=\"data row9 col10\" >0.1352</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row9_col11\" class=\"data row9 col11\" >0.0196</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row9_col12\" class=\"data row9 col12\" >0.3162</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820level0_row10\" class=\"row_heading level0 row10\" >5</th>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row10_col0\" class=\"data row10 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row10_col1\" class=\"data row10 col1\" >gen_country</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row10_col2\" class=\"data row10 col2\" >0.634</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row10_col3\" class=\"data row10 col3\" >0.24</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row10_col4\" class=\"data row10 col4\" >0.34</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row10_col5\" class=\"data row10 col5\" >0.198</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row10_col6\" class=\"data row10 col6\" >0.353</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row10_col7\" class=\"data row10 col7\" >1.412</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row10_col8\" class=\"data row10 col8\" >0.0634</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row10_col9\" class=\"data row10 col9\" >0.096</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row10_col10\" class=\"data row10 col10\" >0.136</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row10_col11\" class=\"data row10 col11\" >0.0198</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row10_col12\" class=\"data row10 col12\" >0.3152</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820level0_row11\" class=\"row_heading level0 row11\" >2</th>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row11_col0\" class=\"data row11 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row11_col1\" class=\"data row11 col1\" >biotex_lda_bvlac_ext_1</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row11_col2\" class=\"data row11 col2\" >0.672</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row11_col3\" class=\"data row11 col3\" >0.21</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row11_col4\" class=\"data row11 col4\" >0.334</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row11_col5\" class=\"data row11 col5\" >0.196</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row11_col6\" class=\"data row11 col6\" >0.353</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row11_col7\" class=\"data row11 col7\" >1.412</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row11_col8\" class=\"data row11 col8\" >0.0672</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row11_col9\" class=\"data row11 col9\" >0.084</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row11_col10\" class=\"data row11 col10\" >0.1336</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row11_col11\" class=\"data row11 col11\" >0.0196</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row11_col12\" class=\"data row11 col12\" >0.3044</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820level0_row12\" class=\"row_heading level0 row12\" >11</th>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row12_col0\" class=\"data row12 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row12_col1\" class=\"data row12 col1\" >biotex_lda_bvlac_ext_2</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row12_col2\" class=\"data row12 col2\" >0.67</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row12_col3\" class=\"data row12 col3\" >0.208</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row12_col4\" class=\"data row12 col4\" >0.336</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row12_col5\" class=\"data row12 col5\" >0.196</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row12_col6\" class=\"data row12 col6\" >0.3525</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row12_col7\" class=\"data row12 col7\" >1.41</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row12_col8\" class=\"data row12 col8\" >0.067</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row12_col9\" class=\"data row12 col9\" >0.0832</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row12_col10\" class=\"data row12 col10\" >0.1344</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row12_col11\" class=\"data row12 col11\" >0.0196</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row12_col12\" class=\"data row12 col12\" >0.3042</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820level0_row13\" class=\"row_heading level0 row13\" >29</th>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row13_col0\" class=\"data row13 col0\" >MCS</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row13_col1\" class=\"data row13 col1\" >biotex_lda_bvlac_ext_1</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row13_col2\" class=\"data row13 col2\" >0.66</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row13_col3\" class=\"data row13 col3\" >0.226</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row13_col4\" class=\"data row13 col4\" >0.318</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row13_col5\" class=\"data row13 col5\" >0.196</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row13_col6\" class=\"data row13 col6\" >0.35</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row13_col7\" class=\"data row13 col7\" >1.4</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row13_col8\" class=\"data row13 col8\" >0.066</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row13_col9\" class=\"data row13 col9\" >0.0904</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row13_col10\" class=\"data row13 col10\" >0.1272</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row13_col11\" class=\"data row13 col11\" >0.0196</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row13_col12\" class=\"data row13 col12\" >0.3032</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820level0_row14\" class=\"row_heading level0 row14\" >21</th>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row14_col0\" class=\"data row14 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row14_col1\" class=\"data row14 col1\" >biotex_lda_bvlac_gen_country</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row14_col2\" class=\"data row14 col2\" >0.638</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row14_col3\" class=\"data row14 col3\" >0.226</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row14_col4\" class=\"data row14 col4\" >0.336</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row14_col5\" class=\"data row14 col5\" >0.194</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row14_col6\" class=\"data row14 col6\" >0.3485</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row14_col7\" class=\"data row14 col7\" >1.394</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row14_col8\" class=\"data row14 col8\" >0.0638</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row14_col9\" class=\"data row14 col9\" >0.0904</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row14_col10\" class=\"data row14 col10\" >0.1344</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row14_col11\" class=\"data row14 col11\" >0.0194</td>\n",
-       "                        <td id=\"T_71a5f438_4af4_11e9_bef5_6a0002e84820row14_col12\" class=\"data row14 col12\" >0.308</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x13a110f98>"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "index,data_pa=pareto_frontier_multi(data[\"c1 c4\".split()].values)\n",
-    "colorize(data.iloc[index].sort_values(\"sum\",ascending=False).head(15),to_colorize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T12:55:10.998668Z",
-     "start_time": "2018-09-26T12:55:10.970120Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style  type=\"text/css\" >\n",
-       "    #T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col2 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col6 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col7 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row2_col4 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row2_col5 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row12_col3 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row14_col5 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row16_col5 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
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-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row17_col5 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row18_col3 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row18_col5 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row19_col3 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row19_col5 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col2 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col4 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col5 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col6 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col7 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }</style><table id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >mesure</th>        <th class=\"col_heading level0 col1\" >type</th>        <th class=\"col_heading level0 col2\" >c1</th>        <th class=\"col_heading level0 col3\" >c2</th>        <th class=\"col_heading level0 col4\" >c3</th>        <th class=\"col_heading level0 col5\" >c4</th>        <th class=\"col_heading level0 col6\" >mean</th>        <th class=\"col_heading level0 col7\" >sum</th>        <th class=\"col_heading level0 col8\" >c1_w</th>        <th class=\"col_heading level0 col9\" >c2_w</th>        <th class=\"col_heading level0 col10\" >c3_w</th>        <th class=\"col_heading level0 col11\" >c4_w</th>        <th class=\"col_heading level0 col12\" >sum_w</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row0\" class=\"row_heading level0 row0\" >92</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col0\" class=\"data row0 col0\" >BOW</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col1\" class=\"data row0 col1\" >ext_1</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col2\" class=\"data row0 col2\" >0.744</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col3\" class=\"data row0 col3\" >0.214</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col4\" class=\"data row0 col4\" >0.344</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col5\" class=\"data row0 col5\" >0.2</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col6\" class=\"data row0 col6\" >0.3755</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col7\" class=\"data row0 col7\" >1.502</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col8\" class=\"data row0 col8\" >0.744</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col9\" class=\"data row0 col9\" >0.214</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col10\" class=\"data row0 col10\" >0.344</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col11\" class=\"data row0 col11\" >0.2</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row0_col12\" class=\"data row0 col12\" >1.502</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row1_col0\" class=\"data row1 col0\" >BOW</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row1_col1\" class=\"data row1 col1\" >inra_gen_country</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row1_col2\" class=\"data row1 col2\" >0.67</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row1_col3\" class=\"data row1 col3\" >0.244</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row1_col4\" class=\"data row1 col4\" >0.358</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row1_col5\" class=\"data row1 col5\" >0.196</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row1_col6\" class=\"data row1 col6\" >0.367</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row1_col7\" class=\"data row1 col7\" >1.468</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row1_col8\" class=\"data row1 col8\" >0.67</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row1_col9\" class=\"data row1 col9\" >0.244</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row1_col10\" class=\"data row1 col10\" >0.358</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row1_col11\" class=\"data row1 col11\" >0.196</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row1_col12\" class=\"data row1 col12\" >1.468</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row2\" class=\"row_heading level0 row2\" >50</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row2_col0\" class=\"data row2 col0\" >BOW</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row2_col1\" class=\"data row2 col1\" >dev_du_gen_country</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row2_col2\" class=\"data row2 col2\" >0.636</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row2_col3\" class=\"data row2 col3\" >0.232</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row2_col4\" class=\"data row2 col4\" >0.362</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row2_col5\" class=\"data row2 col5\" >0.202</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row2_col6\" class=\"data row2 col6\" >0.358</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row2_col7\" class=\"data row2 col7\" >1.432</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row2_col8\" class=\"data row2 col8\" >0.636</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row2_col9\" class=\"data row2 col9\" >0.232</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row2_col10\" class=\"data row2 col10\" >0.362</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row2_col11\" class=\"data row2 col11\" >0.202</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row2_col12\" class=\"data row2 col12\" >1.432</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row3\" class=\"row_heading level0 row3\" >45</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row3_col0\" class=\"data row3 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row3_col1\" class=\"data row3 col1\" >gen_region</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row3_col2\" class=\"data row3 col2\" >0.682</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row3_col3\" class=\"data row3 col3\" >0.22</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row3_col4\" class=\"data row3 col4\" >0.322</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row3_col5\" class=\"data row3 col5\" >0.198</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row3_col6\" class=\"data row3 col6\" >0.3555</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row3_col7\" class=\"data row3 col7\" >1.422</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row3_col8\" class=\"data row3 col8\" >0.682</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row3_col9\" class=\"data row3 col9\" >0.22</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row3_col10\" class=\"data row3 col10\" >0.322</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row3_col11\" class=\"data row3 col11\" >0.198</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row3_col12\" class=\"data row3 col12\" >1.422</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row4_col0\" class=\"data row4 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row4_col1\" class=\"data row4 col1\" >gen_country</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row4_col2\" class=\"data row4 col2\" >0.634</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row4_col3\" class=\"data row4 col3\" >0.24</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row4_col4\" class=\"data row4 col4\" >0.34</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row4_col5\" class=\"data row4 col5\" >0.198</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row4_col6\" class=\"data row4 col6\" >0.353</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row4_col7\" class=\"data row4 col7\" >1.412</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row4_col8\" class=\"data row4 col8\" >0.634</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row4_col9\" class=\"data row4 col9\" >0.24</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row4_col10\" class=\"data row4 col10\" >0.34</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row4_col11\" class=\"data row4 col11\" >0.198</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row4_col12\" class=\"data row4 col12\" >1.412</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row5\" class=\"row_heading level0 row5\" >37</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row5_col0\" class=\"data row5 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row5_col1\" class=\"data row5 col1\" >str_object</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row5_col2\" class=\"data row5 col2\" >0.7</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row5_col3\" class=\"data row5 col3\" >0.19</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row5_col4\" class=\"data row5 col4\" >0.318</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row5_col5\" class=\"data row5 col5\" >0.198</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row5_col6\" class=\"data row5 col6\" >0.3515</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row5_col7\" class=\"data row5 col7\" >1.406</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row5_col8\" class=\"data row5 col8\" >0.7</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row5_col9\" class=\"data row5 col9\" >0.19</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row5_col10\" class=\"data row5 col10\" >0.318</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row5_col11\" class=\"data row5 col11\" >0.198</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row5_col12\" class=\"data row5 col12\" >1.406</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row6\" class=\"row_heading level0 row6\" >29</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row6_col0\" class=\"data row6 col0\" >MCS</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row6_col1\" class=\"data row6 col1\" >biotex_lda_bvlac_ext_1</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row6_col2\" class=\"data row6 col2\" >0.66</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row6_col3\" class=\"data row6 col3\" >0.226</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row6_col4\" class=\"data row6 col4\" >0.318</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row6_col5\" class=\"data row6 col5\" >0.196</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row6_col6\" class=\"data row6 col6\" >0.35</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row6_col7\" class=\"data row6 col7\" >1.4</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row6_col8\" class=\"data row6 col8\" >0.66</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row6_col9\" class=\"data row6 col9\" >0.226</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row6_col10\" class=\"data row6 col10\" >0.318</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row6_col11\" class=\"data row6 col11\" >0.196</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row6_col12\" class=\"data row6 col12\" >1.4</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row7\" class=\"row_heading level0 row7\" >21</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row7_col0\" class=\"data row7 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row7_col1\" class=\"data row7 col1\" >biotex_lda_bvlac_gen_country</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row7_col2\" class=\"data row7 col2\" >0.638</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row7_col3\" class=\"data row7 col3\" >0.226</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row7_col4\" class=\"data row7 col4\" >0.336</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row7_col5\" class=\"data row7 col5\" >0.194</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row7_col6\" class=\"data row7 col6\" >0.3485</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row7_col7\" class=\"data row7 col7\" >1.394</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row7_col8\" class=\"data row7 col8\" >0.638</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row7_col9\" class=\"data row7 col9\" >0.226</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row7_col10\" class=\"data row7 col10\" >0.336</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row7_col11\" class=\"data row7 col11\" >0.194</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row7_col12\" class=\"data row7 col12\" >1.394</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row8\" class=\"row_heading level0 row8\" >19</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row8_col0\" class=\"data row8 col0\" >MCS</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row8_col1\" class=\"data row8 col1\" >biotex_lda_bvlac_ext_2</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row8_col2\" class=\"data row8 col2\" >0.65</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row8_col3\" class=\"data row8 col3\" >0.224</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row8_col4\" class=\"data row8 col4\" >0.316</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row8_col5\" class=\"data row8 col5\" >0.196</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row8_col6\" class=\"data row8 col6\" >0.3465</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row8_col7\" class=\"data row8 col7\" >1.386</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row8_col8\" class=\"data row8 col8\" >0.65</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row8_col9\" class=\"data row8 col9\" >0.224</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row8_col10\" class=\"data row8 col10\" >0.316</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row8_col11\" class=\"data row8 col11\" >0.196</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row8_col12\" class=\"data row8 col12\" >1.386</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row9\" class=\"row_heading level0 row9\" >47</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row9_col0\" class=\"data row9 col0\" >Jaccard</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row9_col1\" class=\"data row9 col1\" >dev_du</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row9_col2\" class=\"data row9 col2\" >0.554</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row9_col3\" class=\"data row9 col3\" >0.212</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row9_col4\" class=\"data row9 col4\" >0.288</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row9_col5\" class=\"data row9 col5\" >0.16</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row9_col6\" class=\"data row9 col6\" >0.3035</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row9_col7\" class=\"data row9 col7\" >1.214</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row9_col8\" class=\"data row9 col8\" >0.554</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row9_col9\" class=\"data row9 col9\" >0.212</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row9_col10\" class=\"data row9 col10\" >0.288</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row9_col11\" class=\"data row9 col11\" >0.16</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row9_col12\" class=\"data row9 col12\" >1.214</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row10\" class=\"row_heading level0 row10\" >38</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row10_col0\" class=\"data row10 col0\" >DeepWalk</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row10_col1\" class=\"data row10 col1\" >biotex_lda_bvlac_ext_1</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row10_col2\" class=\"data row10 col2\" >0.49</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row10_col3\" class=\"data row10 col3\" >0.358</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row10_col4\" class=\"data row10 col4\" >0.164</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row10_col5\" class=\"data row10 col5\" >0.082</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row10_col6\" class=\"data row10 col6\" >0.2735</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row10_col7\" class=\"data row10 col7\" >1.094</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row10_col8\" class=\"data row10 col8\" >0.49</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row10_col9\" class=\"data row10 col9\" >0.358</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row10_col10\" class=\"data row10 col10\" >0.164</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row10_col11\" class=\"data row10 col11\" >0.082</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row10_col12\" class=\"data row10 col12\" >1.094</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row11\" class=\"row_heading level0 row11\" >111</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row11_col0\" class=\"data row11 col0\" >DeepWalk</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row11_col1\" class=\"data row11 col1\" >biotex_bvlac_gen_country</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row11_col2\" class=\"data row11 col2\" >0.508</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row11_col3\" class=\"data row11 col3\" >0.342</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row11_col4\" class=\"data row11 col4\" >0.144</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row11_col5\" class=\"data row11 col5\" >0.058</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row11_col6\" class=\"data row11 col6\" >0.263</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row11_col7\" class=\"data row11 col7\" >1.052</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row11_col8\" class=\"data row11 col8\" >0.508</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row11_col9\" class=\"data row11 col9\" >0.342</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row11_col10\" class=\"data row11 col10\" >0.144</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row11_col11\" class=\"data row11 col11\" >0.058</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row11_col12\" class=\"data row11 col12\" >1.052</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row12\" class=\"row_heading level0 row12\" >30</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row12_col0\" class=\"data row12 col0\" >WeisfeleirLehmanKernel</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row12_col1\" class=\"data row12 col1\" >biotex_bvlac</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row12_col2\" class=\"data row12 col2\" >0.412</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row12_col3\" class=\"data row12 col3\" >0.576</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row12_col4\" class=\"data row12 col4\" >0.032</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row12_col5\" class=\"data row12 col5\" >0.004</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row12_col6\" class=\"data row12 col6\" >0.256</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row12_col7\" class=\"data row12 col7\" >1.024</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row12_col8\" class=\"data row12 col8\" >0.412</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row12_col9\" class=\"data row12 col9\" >0.576</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row12_col10\" class=\"data row12 col10\" >0.032</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row12_col11\" class=\"data row12 col11\" >0.004</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row12_col12\" class=\"data row12 col12\" >1.024</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row13\" class=\"row_heading level0 row13\" >28</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row13_col0\" class=\"data row13 col0\" >DeepWalk</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row13_col1\" class=\"data row13 col1\" >biotex_lda_bvlac_ext_2</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row13_col2\" class=\"data row13 col2\" >0.43</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row13_col3\" class=\"data row13 col3\" >0.402</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row13_col5\" class=\"data row13 col5\" >0.05</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row13_col6\" class=\"data row13 col6\" >0.248</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row13_col7\" class=\"data row13 col7\" >0.992</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row13_col8\" class=\"data row13 col8\" >0.43</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row13_col9\" class=\"data row13 col9\" >0.402</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row13_col10\" class=\"data row13 col10\" >0.11</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row13_col11\" class=\"data row13 col11\" >0.05</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row13_col12\" class=\"data row13 col12\" >0.992</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row14\" class=\"row_heading level0 row14\" >36</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row14_col0\" class=\"data row14 col0\" >WeisfeleirLehmanKernel</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row14_col1\" class=\"data row14 col1\" >gen_region</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row14_col2\" class=\"data row14 col2\" >0.294</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row14_col3\" class=\"data row14 col3\" >0.55</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row14_col4\" class=\"data row14 col4\" >0.122</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row14_col5\" class=\"data row14 col5\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row14_col6\" class=\"data row14 col6\" >0.2415</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row14_col7\" class=\"data row14 col7\" >0.966</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row14_col8\" class=\"data row14 col8\" >0.294</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row14_col9\" class=\"data row14 col9\" >0.55</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row14_col10\" class=\"data row14 col10\" >0.122</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row14_col11\" class=\"data row14 col11\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row14_col12\" class=\"data row14 col12\" >0.966</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row15\" class=\"row_heading level0 row15\" >12</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row15_col0\" class=\"data row15 col0\" >WeisfeleirLehmanKernel</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row15_col1\" class=\"data row15 col1\" >biotex_lda_bvlac_gen_region</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row15_col2\" class=\"data row15 col2\" >0.364</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row15_col3\" class=\"data row15 col3\" >0.56</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row15_col4\" class=\"data row15 col4\" >0.028</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row15_col5\" class=\"data row15 col5\" >0.004</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row15_col6\" class=\"data row15 col6\" >0.239</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row15_col7\" class=\"data row15 col7\" >0.956</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row15_col8\" class=\"data row15 col8\" >0.364</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row15_col9\" class=\"data row15 col9\" >0.56</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row15_col10\" class=\"data row15 col10\" >0.028</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row15_col11\" class=\"data row15 col11\" >0.004</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row15_col12\" class=\"data row15 col12\" >0.956</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row16\" class=\"row_heading level0 row16\" >112</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row16_col0\" class=\"data row16 col0\" >Graph2Vec</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row16_col1\" class=\"data row16 col1\" >biotex_lda_bvlac_ext_2</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row16_col2\" class=\"data row16 col2\" >0.032</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row16_col3\" class=\"data row16 col3\" >0.038</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row16_col4\" class=\"data row16 col4\" >0.014</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row16_col5\" class=\"data row16 col5\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row16_col6\" class=\"data row16 col6\" >0.021</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row16_col7\" class=\"data row16 col7\" >0.084</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row16_col8\" class=\"data row16 col8\" >0.032</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row16_col9\" class=\"data row16 col9\" >0.038</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row16_col10\" class=\"data row16 col10\" >0.014</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row16_col11\" class=\"data row16 col11\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row16_col12\" class=\"data row16 col12\" >0.084</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row17\" class=\"row_heading level0 row17\" >57</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row17_col0\" class=\"data row17 col0\" >Graph2Vec</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row17_col1\" class=\"data row17 col1\" >inra_gen_country</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row17_col2\" class=\"data row17 col2\" >0.022</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row17_col3\" class=\"data row17 col3\" >0.028</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row17_col4\" class=\"data row17 col4\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row17_col5\" class=\"data row17 col5\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row17_col6\" class=\"data row17 col6\" >0.0125</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row17_col7\" class=\"data row17 col7\" >0.05</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row17_col8\" class=\"data row17 col8\" >0.022</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row17_col9\" class=\"data row17 col9\" >0.028</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row17_col10\" class=\"data row17 col10\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row17_col11\" class=\"data row17 col11\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row17_col12\" class=\"data row17 col12\" >0.05</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row18\" class=\"row_heading level0 row18\" >117</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row18_col0\" class=\"data row18 col0\" >Graph2Vec</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row18_col1\" class=\"data row18 col1\" >inra_ext_2</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row18_col2\" class=\"data row18 col2\" >0.022</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row18_col3\" class=\"data row18 col3\" >0.024</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row18_col4\" class=\"data row18 col4\" >0.002</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row18_col5\" class=\"data row18 col5\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row18_col6\" class=\"data row18 col6\" >0.012</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row18_col7\" class=\"data row18 col7\" >0.048</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row18_col8\" class=\"data row18 col8\" >0.022</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row18_col9\" class=\"data row18 col9\" >0.024</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row18_col10\" class=\"data row18 col10\" >0.002</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row18_col11\" class=\"data row18 col11\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row18_col12\" class=\"data row18 col12\" >0.048</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row19\" class=\"row_heading level0 row19\" >33</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row19_col0\" class=\"data row19 col0\" >Graph2Vec</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row19_col1\" class=\"data row19 col1\" >dev_du_gen_country</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row19_col2\" class=\"data row19 col2\" >0.016</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row19_col3\" class=\"data row19 col3\" >0.024</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row19_col4\" class=\"data row19 col4\" >0.004</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row19_col5\" class=\"data row19 col5\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row19_col6\" class=\"data row19 col6\" >0.011</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row19_col7\" class=\"data row19 col7\" >0.044</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row19_col8\" class=\"data row19 col8\" >0.016</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row19_col9\" class=\"data row19 col9\" >0.024</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row19_col10\" class=\"data row19 col10\" >0.004</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row19_col11\" class=\"data row19 col11\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row19_col12\" class=\"data row19 col12\" >0.044</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820level0_row20\" class=\"row_heading level0 row20\" >1</th>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col0\" class=\"data row20 col0\" >Graph2Vec</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col1\" class=\"data row20 col1\" >biotex_bvlac_ext_1</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col2\" class=\"data row20 col2\" >0.012</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col3\" class=\"data row20 col3\" >0.026</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col4\" class=\"data row20 col4\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col5\" class=\"data row20 col5\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col6\" class=\"data row20 col6\" >0.0095</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col7\" class=\"data row20 col7\" >0.038</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col8\" class=\"data row20 col8\" >0.012</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col9\" class=\"data row20 col9\" >0.026</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col10\" class=\"data row20 col10\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col11\" class=\"data row20 col11\" >0</td>\n",
-       "                        <td id=\"T_a366063a_4af3_11e9_bd73_6a0002e84820row20_col12\" class=\"data row20 col12\" >0.038</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x13a240a90>"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "index,data_pa=pareto_frontier_multi(data[\"c2 c3\".split()].values)\n",
-    "colorize(data.iloc[index].sort_values(\"sum\",ascending=False),to_colorize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T12:55:11.028009Z",
-     "start_time": "2018-09-26T12:55:11.000606Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style  type=\"text/css\" >\n",
-       "    #T_747963b6_4af4_11e9_8794_6a0002e84820row0_col2 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_747963b6_4af4_11e9_8794_6a0002e84820row0_col4 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_747963b6_4af4_11e9_8794_6a0002e84820row0_col5 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_747963b6_4af4_11e9_8794_6a0002e84820row0_col6 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_747963b6_4af4_11e9_8794_6a0002e84820row0_col7 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_747963b6_4af4_11e9_8794_6a0002e84820row5_col3 {\n",
-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_747963b6_4af4_11e9_8794_6a0002e84820row8_col5 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_747963b6_4af4_11e9_8794_6a0002e84820row9_col3 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_747963b6_4af4_11e9_8794_6a0002e84820row9_col5 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_747963b6_4af4_11e9_8794_6a0002e84820row10_col2 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_747963b6_4af4_11e9_8794_6a0002e84820row10_col4 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_747963b6_4af4_11e9_8794_6a0002e84820row10_col5 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_747963b6_4af4_11e9_8794_6a0002e84820row10_col6 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }    #T_747963b6_4af4_11e9_8794_6a0002e84820row10_col7 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }</style><table id=\"T_747963b6_4af4_11e9_8794_6a0002e84820\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >mesure</th>        <th class=\"col_heading level0 col1\" >type</th>        <th class=\"col_heading level0 col2\" >c1</th>        <th class=\"col_heading level0 col3\" >c2</th>        <th class=\"col_heading level0 col4\" >c3</th>        <th class=\"col_heading level0 col5\" >c4</th>        <th class=\"col_heading level0 col6\" >mean</th>        <th class=\"col_heading level0 col7\" >sum</th>        <th class=\"col_heading level0 col8\" >c1_w</th>        <th class=\"col_heading level0 col9\" >c2_w</th>        <th class=\"col_heading level0 col10\" >c3_w</th>        <th class=\"col_heading level0 col11\" >c4_w</th>        <th class=\"col_heading level0 col12\" >sum_w</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_747963b6_4af4_11e9_8794_6a0002e84820level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row0_col0\" class=\"data row0 col0\" >BOW</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row0_col1\" class=\"data row0 col1\" >dev_du</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row0_col2\" class=\"data row0 col2\" >0.706</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row0_col3\" class=\"data row0 col3\" >0.194</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row0_col4\" class=\"data row0 col4\" >0.35</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row0_col5\" class=\"data row0 col5\" >0.202</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row0_col6\" class=\"data row0 col6\" >0.363</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row0_col7\" class=\"data row0 col7\" >1.452</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row0_col8\" class=\"data row0 col8\" >0.0706</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row0_col9\" class=\"data row0 col9\" >0.0776</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row0_col10\" class=\"data row0 col10\" >0.14</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row0_col11\" class=\"data row0 col11\" >0.0202</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row0_col12\" class=\"data row0 col12\" >0.3084</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_747963b6_4af4_11e9_8794_6a0002e84820level0_row1\" class=\"row_heading level0 row1\" >34</th>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row1_col0\" class=\"data row1 col0\" >BOW</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row1_col1\" class=\"data row1 col1\" >biotex_bvlac_gen_country</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row1_col2\" class=\"data row1 col2\" >0.65</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row1_col3\" class=\"data row1 col3\" >0.234</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row1_col4\" class=\"data row1 col4\" >0.34</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row1_col5\" class=\"data row1 col5\" >0.196</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row1_col6\" class=\"data row1 col6\" >0.355</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row1_col7\" class=\"data row1 col7\" >1.42</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row1_col8\" class=\"data row1 col8\" >0.065</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row1_col9\" class=\"data row1 col9\" >0.0936</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row1_col10\" class=\"data row1 col10\" >0.136</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row1_col11\" class=\"data row1 col11\" >0.0196</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row1_col12\" class=\"data row1 col12\" >0.3142</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_747963b6_4af4_11e9_8794_6a0002e84820level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row2_col0\" class=\"data row2 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row2_col1\" class=\"data row2 col1\" >gen_country</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row2_col2\" class=\"data row2 col2\" >0.634</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row2_col3\" class=\"data row2 col3\" >0.24</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row2_col4\" class=\"data row2 col4\" >0.34</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row2_col5\" class=\"data row2 col5\" >0.198</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row2_col6\" class=\"data row2 col6\" >0.353</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row2_col7\" class=\"data row2 col7\" >1.412</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row2_col8\" class=\"data row2 col8\" >0.0634</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row2_col9\" class=\"data row2 col9\" >0.096</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row2_col10\" class=\"data row2 col10\" >0.136</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row2_col11\" class=\"data row2 col11\" >0.0198</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row2_col12\" class=\"data row2 col12\" >0.3152</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_747963b6_4af4_11e9_8794_6a0002e84820level0_row3\" class=\"row_heading level0 row3\" >29</th>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row3_col0\" class=\"data row3 col0\" >MCS</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row3_col1\" class=\"data row3 col1\" >biotex_lda_bvlac_ext_1</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row3_col2\" class=\"data row3 col2\" >0.66</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row3_col3\" class=\"data row3 col3\" >0.226</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row3_col4\" class=\"data row3 col4\" >0.318</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row3_col5\" class=\"data row3 col5\" >0.196</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row3_col6\" class=\"data row3 col6\" >0.35</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row3_col7\" class=\"data row3 col7\" >1.4</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row3_col8\" class=\"data row3 col8\" >0.066</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row3_col9\" class=\"data row3 col9\" >0.0904</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row3_col10\" class=\"data row3 col10\" >0.1272</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row3_col11\" class=\"data row3 col11\" >0.0196</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row3_col12\" class=\"data row3 col12\" >0.3032</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_747963b6_4af4_11e9_8794_6a0002e84820level0_row4\" class=\"row_heading level0 row4\" >21</th>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row4_col0\" class=\"data row4 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row4_col1\" class=\"data row4 col1\" >biotex_lda_bvlac_gen_country</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row4_col2\" class=\"data row4 col2\" >0.638</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row4_col3\" class=\"data row4 col3\" >0.226</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row4_col4\" class=\"data row4 col4\" >0.336</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row4_col5\" class=\"data row4 col5\" >0.194</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row4_col6\" class=\"data row4 col6\" >0.3485</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row4_col7\" class=\"data row4 col7\" >1.394</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row4_col8\" class=\"data row4 col8\" >0.0638</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row4_col9\" class=\"data row4 col9\" >0.0904</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row4_col10\" class=\"data row4 col10\" >0.1344</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row4_col11\" class=\"data row4 col11\" >0.0194</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row4_col12\" class=\"data row4 col12\" >0.308</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_747963b6_4af4_11e9_8794_6a0002e84820level0_row5\" class=\"row_heading level0 row5\" >30</th>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row5_col0\" class=\"data row5 col0\" >WeisfeleirLehmanKernel</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row5_col1\" class=\"data row5 col1\" >biotex_bvlac</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row5_col2\" class=\"data row5 col2\" >0.412</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row5_col3\" class=\"data row5 col3\" >0.576</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row5_col4\" class=\"data row5 col4\" >0.032</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row5_col5\" class=\"data row5 col5\" >0.004</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row5_col6\" class=\"data row5 col6\" >0.256</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row5_col7\" class=\"data row5 col7\" >1.024</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row5_col8\" class=\"data row5 col8\" >0.0412</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row5_col9\" class=\"data row5 col9\" >0.2304</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row5_col10\" class=\"data row5 col10\" >0.0128</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row5_col11\" class=\"data row5 col11\" >0.0004</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row5_col12\" class=\"data row5 col12\" >0.2848</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_747963b6_4af4_11e9_8794_6a0002e84820level0_row6\" class=\"row_heading level0 row6\" >28</th>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row6_col0\" class=\"data row6 col0\" >DeepWalk</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row6_col1\" class=\"data row6 col1\" >biotex_lda_bvlac_ext_2</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row6_col2\" class=\"data row6 col2\" >0.43</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row6_col3\" class=\"data row6 col3\" >0.402</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row6_col4\" class=\"data row6 col4\" >0.11</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row6_col5\" class=\"data row6 col5\" >0.05</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row6_col6\" class=\"data row6 col6\" >0.248</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row6_col7\" class=\"data row6 col7\" >0.992</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row6_col8\" class=\"data row6 col8\" >0.043</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row6_col9\" class=\"data row6 col9\" >0.1608</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row6_col10\" class=\"data row6 col10\" >0.044</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row6_col11\" class=\"data row6 col11\" >0.005</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row6_col12\" class=\"data row6 col12\" >0.2528</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_747963b6_4af4_11e9_8794_6a0002e84820level0_row7\" class=\"row_heading level0 row7\" >14</th>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row7_col0\" class=\"data row7 col0\" >DeepWalk</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row7_col1\" class=\"data row7 col1\" >gen_region</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row7_col2\" class=\"data row7 col2\" >0.4</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row7_col3\" class=\"data row7 col3\" >0.314</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row7_col4\" class=\"data row7 col4\" >0.11</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row7_col5\" class=\"data row7 col5\" >0.052</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row7_col6\" class=\"data row7 col6\" >0.219</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row7_col7\" class=\"data row7 col7\" >0.876</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row7_col8\" class=\"data row7 col8\" >0.04</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row7_col9\" class=\"data row7 col9\" >0.1256</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row7_col10\" class=\"data row7 col10\" >0.044</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row7_col11\" class=\"data row7 col11\" >0.0052</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row7_col12\" class=\"data row7 col12\" >0.2148</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_747963b6_4af4_11e9_8794_6a0002e84820level0_row8\" class=\"row_heading level0 row8\" >31</th>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row8_col0\" class=\"data row8 col0\" >GraphEditDistance</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row8_col1\" class=\"data row8 col1\" >inra_gen_country</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row8_col2\" class=\"data row8 col2\" >0.104</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row8_col3\" class=\"data row8 col3\" >0.096</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row8_col4\" class=\"data row8 col4\" >0.002</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row8_col5\" class=\"data row8 col5\" >0</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row8_col6\" class=\"data row8 col6\" >0.0505</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row8_col7\" class=\"data row8 col7\" >0.202</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row8_col8\" class=\"data row8 col8\" >0.0104</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row8_col9\" class=\"data row8 col9\" >0.0384</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row8_col10\" class=\"data row8 col10\" >0.0008</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row8_col11\" class=\"data row8 col11\" >0</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row8_col12\" class=\"data row8 col12\" >0.0496</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_747963b6_4af4_11e9_8794_6a0002e84820level0_row9\" class=\"row_heading level0 row9\" >33</th>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row9_col0\" class=\"data row9 col0\" >Graph2Vec</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row9_col1\" class=\"data row9 col1\" >dev_du_gen_country</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row9_col2\" class=\"data row9 col2\" >0.016</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row9_col3\" class=\"data row9 col3\" >0.024</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row9_col4\" class=\"data row9 col4\" >0.004</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row9_col5\" class=\"data row9 col5\" >0</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row9_col6\" class=\"data row9 col6\" >0.011</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row9_col7\" class=\"data row9 col7\" >0.044</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row9_col8\" class=\"data row9 col8\" >0.0016</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row9_col9\" class=\"data row9 col9\" >0.0096</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row9_col10\" class=\"data row9 col10\" >0.0016</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row9_col11\" class=\"data row9 col11\" >0</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row9_col12\" class=\"data row9 col12\" >0.0128</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_747963b6_4af4_11e9_8794_6a0002e84820level0_row10\" class=\"row_heading level0 row10\" >1</th>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row10_col0\" class=\"data row10 col0\" >Graph2Vec</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row10_col1\" class=\"data row10 col1\" >biotex_bvlac_ext_1</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row10_col2\" class=\"data row10 col2\" >0.012</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row10_col3\" class=\"data row10 col3\" >0.026</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row10_col4\" class=\"data row10 col4\" >0</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row10_col5\" class=\"data row10 col5\" >0</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row10_col6\" class=\"data row10 col6\" >0.0095</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row10_col7\" class=\"data row10 col7\" >0.038</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row10_col8\" class=\"data row10 col8\" >0.0012</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row10_col9\" class=\"data row10 col9\" >0.0104</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row10_col10\" class=\"data row10 col10\" >0</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row10_col11\" class=\"data row10 col11\" >0</td>\n",
-       "                        <td id=\"T_747963b6_4af4_11e9_8794_6a0002e84820row10_col12\" class=\"data row10 col12\" >0.0116</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x13a302128>"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "index,data_pa=pareto_frontier_multi(data[\"c1 c2 c3\".split()].values)\n",
-    "colorize(data.iloc[index].sort_values(\"sum\",ascending=False),to_colorize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T12:55:11.060611Z",
-     "start_time": "2018-09-26T12:55:11.030272Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "            background-color:  yellow;\n",
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-       "            background-color:  yellow;\n",
-       "            : ;\n",
-       "        }    #T_76d815b4_4af4_11e9_906a_6a0002e84820row13_col3 {\n",
-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
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-       "            : ;\n",
-       "            background-color:  #d64541;\n",
-       "            color: white;\n",
-       "        }</style><table id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >mesure</th>        <th class=\"col_heading level0 col1\" >type</th>        <th class=\"col_heading level0 col2\" >c1</th>        <th class=\"col_heading level0 col3\" >c2</th>        <th class=\"col_heading level0 col4\" >c3</th>        <th class=\"col_heading level0 col5\" >c4</th>        <th class=\"col_heading level0 col6\" >mean</th>        <th class=\"col_heading level0 col7\" >sum</th>        <th class=\"col_heading level0 col8\" >c1_w</th>        <th class=\"col_heading level0 col9\" >c2_w</th>        <th class=\"col_heading level0 col10\" >c3_w</th>        <th class=\"col_heading level0 col11\" >c4_w</th>        <th class=\"col_heading level0 col12\" >sum_w</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row0_col0\" class=\"data row0 col0\" >Graph2Vec</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row0_col1\" class=\"data row0 col1\" >biotex_bvlac_ext_1</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row0_col2\" class=\"data row0 col2\" >0.012</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row0_col3\" class=\"data row0 col3\" >0.026</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row0_col4\" class=\"data row0 col4\" >0</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row0_col5\" class=\"data row0 col5\" >0</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row0_col6\" class=\"data row0 col6\" >0.0095</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row0_col7\" class=\"data row0 col7\" >0.038</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row0_col8\" class=\"data row0 col8\" >0.0012</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row0_col9\" class=\"data row0 col9\" >0.0104</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row0_col10\" class=\"data row0 col10\" >0</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row0_col11\" class=\"data row0 col11\" >0</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row0_col12\" class=\"data row0 col12\" >0.0116</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820level0_row1\" class=\"row_heading level0 row1\" >5</th>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row1_col0\" class=\"data row1 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row1_col1\" class=\"data row1 col1\" >gen_country</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row1_col2\" class=\"data row1 col2\" >0.634</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row1_col3\" class=\"data row1 col3\" >0.24</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row1_col4\" class=\"data row1 col4\" >0.34</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row1_col5\" class=\"data row1 col5\" >0.198</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row1_col6\" class=\"data row1 col6\" >0.353</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row1_col7\" class=\"data row1 col7\" >1.412</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row1_col8\" class=\"data row1 col8\" >0.0634</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row1_col9\" class=\"data row1 col9\" >0.096</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row1_col10\" class=\"data row1 col10\" >0.136</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row1_col11\" class=\"data row1 col11\" >0.0198</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row1_col12\" class=\"data row1 col12\" >0.3152</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820level0_row2\" class=\"row_heading level0 row2\" >6</th>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row2_col0\" class=\"data row2 col0\" >BOW</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row2_col1\" class=\"data row2 col1\" >inra_gen_country</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row2_col2\" class=\"data row2 col2\" >0.67</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row2_col3\" class=\"data row2 col3\" >0.244</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row2_col4\" class=\"data row2 col4\" >0.358</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row2_col5\" class=\"data row2 col5\" >0.196</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row2_col6\" class=\"data row2 col6\" >0.367</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row2_col7\" class=\"data row2 col7\" >1.468</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row2_col8\" class=\"data row2 col8\" >0.067</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row2_col9\" class=\"data row2 col9\" >0.0976</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row2_col10\" class=\"data row2 col10\" >0.1432</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row2_col11\" class=\"data row2 col11\" >0.0196</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row2_col12\" class=\"data row2 col12\" >0.3274</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820level0_row3\" class=\"row_heading level0 row3\" >12</th>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row3_col0\" class=\"data row3 col0\" >WeisfeleirLehmanKernel</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row3_col1\" class=\"data row3 col1\" >biotex_lda_bvlac_gen_region</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row3_col2\" class=\"data row3 col2\" >0.364</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row3_col3\" class=\"data row3 col3\" >0.56</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row3_col4\" class=\"data row3 col4\" >0.028</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row3_col5\" class=\"data row3 col5\" >0.004</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row3_col6\" class=\"data row3 col6\" >0.239</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row3_col7\" class=\"data row3 col7\" >0.956</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row3_col8\" class=\"data row3 col8\" >0.0364</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row3_col9\" class=\"data row3 col9\" >0.224</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row3_col10\" class=\"data row3 col10\" >0.0112</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row3_col11\" class=\"data row3 col11\" >0.0004</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row3_col12\" class=\"data row3 col12\" >0.272</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820level0_row4\" class=\"row_heading level0 row4\" >19</th>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row4_col0\" class=\"data row4 col0\" >MCS</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row4_col1\" class=\"data row4 col1\" >biotex_lda_bvlac_ext_2</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row4_col2\" class=\"data row4 col2\" >0.65</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row4_col3\" class=\"data row4 col3\" >0.224</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row4_col4\" class=\"data row4 col4\" >0.316</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row4_col5\" class=\"data row4 col5\" >0.196</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row4_col6\" class=\"data row4 col6\" >0.3465</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row4_col7\" class=\"data row4 col7\" >1.386</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row4_col8\" class=\"data row4 col8\" >0.065</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row4_col9\" class=\"data row4 col9\" >0.0896</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row4_col10\" class=\"data row4 col10\" >0.1264</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row4_col11\" class=\"data row4 col11\" >0.0196</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row4_col12\" class=\"data row4 col12\" >0.3006</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820level0_row5\" class=\"row_heading level0 row5\" >21</th>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row5_col0\" class=\"data row5 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row5_col1\" class=\"data row5 col1\" >biotex_lda_bvlac_gen_country</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row5_col2\" class=\"data row5 col2\" >0.638</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row5_col3\" class=\"data row5 col3\" >0.226</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row5_col4\" class=\"data row5 col4\" >0.336</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row5_col5\" class=\"data row5 col5\" >0.194</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row5_col6\" class=\"data row5 col6\" >0.3485</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row5_col7\" class=\"data row5 col7\" >1.394</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row5_col8\" class=\"data row5 col8\" >0.0638</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row5_col9\" class=\"data row5 col9\" >0.0904</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row5_col10\" class=\"data row5 col10\" >0.1344</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row5_col11\" class=\"data row5 col11\" >0.0194</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row5_col12\" class=\"data row5 col12\" >0.308</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820level0_row6\" class=\"row_heading level0 row6\" >28</th>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row6_col0\" class=\"data row6 col0\" >DeepWalk</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row6_col1\" class=\"data row6 col1\" >biotex_lda_bvlac_ext_2</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row6_col2\" class=\"data row6 col2\" >0.43</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row6_col3\" class=\"data row6 col3\" >0.402</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row6_col4\" class=\"data row6 col4\" >0.11</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row6_col5\" class=\"data row6 col5\" >0.05</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row6_col6\" class=\"data row6 col6\" >0.248</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row6_col7\" class=\"data row6 col7\" >0.992</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row6_col8\" class=\"data row6 col8\" >0.043</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row6_col9\" class=\"data row6 col9\" >0.1608</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row6_col10\" class=\"data row6 col10\" >0.044</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row6_col11\" class=\"data row6 col11\" >0.005</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row6_col12\" class=\"data row6 col12\" >0.2528</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820level0_row7\" class=\"row_heading level0 row7\" >29</th>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row7_col0\" class=\"data row7 col0\" >MCS</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row7_col1\" class=\"data row7 col1\" >biotex_lda_bvlac_ext_1</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row7_col2\" class=\"data row7 col2\" >0.66</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row7_col3\" class=\"data row7 col3\" >0.226</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row7_col4\" class=\"data row7 col4\" >0.318</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row7_col5\" class=\"data row7 col5\" >0.196</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row7_col6\" class=\"data row7 col6\" >0.35</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row7_col7\" class=\"data row7 col7\" >1.4</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row7_col8\" class=\"data row7 col8\" >0.066</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row7_col9\" class=\"data row7 col9\" >0.0904</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row7_col10\" class=\"data row7 col10\" >0.1272</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row7_col11\" class=\"data row7 col11\" >0.0196</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row7_col12\" class=\"data row7 col12\" >0.3032</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820level0_row8\" class=\"row_heading level0 row8\" >30</th>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row8_col0\" class=\"data row8 col0\" >WeisfeleirLehmanKernel</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row8_col1\" class=\"data row8 col1\" >biotex_bvlac</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row8_col2\" class=\"data row8 col2\" >0.412</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row8_col3\" class=\"data row8 col3\" >0.576</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row8_col4\" class=\"data row8 col4\" >0.032</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row8_col5\" class=\"data row8 col5\" >0.004</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row8_col6\" class=\"data row8 col6\" >0.256</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row8_col7\" class=\"data row8 col7\" >1.024</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row8_col8\" class=\"data row8 col8\" >0.0412</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row8_col9\" class=\"data row8 col9\" >0.2304</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row8_col10\" class=\"data row8 col10\" >0.0128</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row8_col11\" class=\"data row8 col11\" >0.0004</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row8_col12\" class=\"data row8 col12\" >0.2848</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820level0_row9\" class=\"row_heading level0 row9\" >33</th>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row9_col0\" class=\"data row9 col0\" >Graph2Vec</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row9_col1\" class=\"data row9 col1\" >dev_du_gen_country</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row9_col2\" class=\"data row9 col2\" >0.016</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row9_col3\" class=\"data row9 col3\" >0.024</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row9_col4\" class=\"data row9 col4\" >0.004</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row9_col5\" class=\"data row9 col5\" >0</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row9_col6\" class=\"data row9 col6\" >0.011</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row9_col7\" class=\"data row9 col7\" >0.044</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row9_col8\" class=\"data row9 col8\" >0.0016</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row9_col9\" class=\"data row9 col9\" >0.0096</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row9_col10\" class=\"data row9 col10\" >0.0016</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row9_col11\" class=\"data row9 col11\" >0</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row9_col12\" class=\"data row9 col12\" >0.0128</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820level0_row10\" class=\"row_heading level0 row10\" >37</th>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row10_col0\" class=\"data row10 col0\" >VertexEdgeOverlap</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row10_col1\" class=\"data row10 col1\" >str_object</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row10_col2\" class=\"data row10 col2\" >0.7</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row10_col3\" class=\"data row10 col3\" >0.19</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row10_col4\" class=\"data row10 col4\" >0.318</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row10_col5\" class=\"data row10 col5\" >0.198</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row10_col6\" class=\"data row10 col6\" >0.3515</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row10_col7\" class=\"data row10 col7\" >1.406</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row10_col8\" class=\"data row10 col8\" >0.07</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row10_col9\" class=\"data row10 col9\" >0.076</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row10_col10\" class=\"data row10 col10\" >0.1272</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row10_col11\" class=\"data row10 col11\" >0.0198</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row10_col12\" class=\"data row10 col12\" >0.293</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820level0_row11\" class=\"row_heading level0 row11\" >47</th>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row11_col0\" class=\"data row11 col0\" >Jaccard</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row11_col1\" class=\"data row11 col1\" >dev_du</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row11_col2\" class=\"data row11 col2\" >0.554</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row11_col3\" class=\"data row11 col3\" >0.212</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row11_col4\" class=\"data row11 col4\" >0.288</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row11_col5\" class=\"data row11 col5\" >0.16</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row11_col6\" class=\"data row11 col6\" >0.3035</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row11_col7\" class=\"data row11 col7\" >1.214</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row11_col8\" class=\"data row11 col8\" >0.0554</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row11_col9\" class=\"data row11 col9\" >0.0848</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row11_col10\" class=\"data row11 col10\" >0.1152</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row11_col11\" class=\"data row11 col11\" >0.016</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row11_col12\" class=\"data row11 col12\" >0.2714</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820level0_row12\" class=\"row_heading level0 row12\" >111</th>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row12_col0\" class=\"data row12 col0\" >DeepWalk</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row12_col1\" class=\"data row12 col1\" >biotex_bvlac_gen_country</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row12_col2\" class=\"data row12 col2\" >0.508</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row12_col3\" class=\"data row12 col3\" >0.342</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row12_col4\" class=\"data row12 col4\" >0.144</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row12_col5\" class=\"data row12 col5\" >0.058</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row12_col6\" class=\"data row12 col6\" >0.263</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row12_col7\" class=\"data row12 col7\" >1.052</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row12_col8\" class=\"data row12 col8\" >0.0508</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row12_col9\" class=\"data row12 col9\" >0.1368</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row12_col10\" class=\"data row12 col10\" >0.0576</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row12_col11\" class=\"data row12 col11\" >0.0058</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row12_col12\" class=\"data row12 col12\" >0.251</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820level0_row13\" class=\"row_heading level0 row13\" >117</th>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row13_col0\" class=\"data row13 col0\" >Graph2Vec</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row13_col1\" class=\"data row13 col1\" >inra_ext_2</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row13_col2\" class=\"data row13 col2\" >0.022</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row13_col3\" class=\"data row13 col3\" >0.024</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row13_col4\" class=\"data row13 col4\" >0.002</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row13_col5\" class=\"data row13 col5\" >0</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row13_col6\" class=\"data row13 col6\" >0.012</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row13_col7\" class=\"data row13 col7\" >0.048</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row13_col8\" class=\"data row13 col8\" >0.0022</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row13_col9\" class=\"data row13 col9\" >0.0096</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row13_col10\" class=\"data row13 col10\" >0.0008</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row13_col11\" class=\"data row13 col11\" >0</td>\n",
-       "                        <td id=\"T_76d815b4_4af4_11e9_906a_6a0002e84820row13_col12\" class=\"data row13 col12\" >0.0126</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x13a302978>"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "index,data_pa=pareto_frontier_multi(data[\"c2 c3 c4\".split()].values)\n",
-    "colorize(data.iloc[index],to_colorize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T12:55:11.075207Z",
-     "start_time": "2018-09-26T12:55:11.062618Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "to_plot=[]\n",
-    "for t in np.sort(np.unique(data.type.values))[::-1]:\n",
-    "    data_t=data[(data[\"type\"]==t)]\n",
-    "    to_plot.append([t,data_t.c1.max(),data_t.c2.max(),data_t.c3.max(),data_t.c4.max()])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T12:55:11.081736Z",
-     "start_time": "2018-09-26T12:55:11.076971Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df_plot=pd.DataFrame(to_plot,columns=\"type c1 c2 c3 c4\".split())\n",
-    "df_plot[\"type\"]=df_plot[\"type\"].astype(\"category\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T12:55:11.087597Z",
-     "start_time": "2018-09-26T12:55:11.084299Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df_plot.rename(columns={\"c1\":\"SSE\",\"c2\":\"CSE\",\"c3\":\"SSC\",\"c4\":\"SCSC\"},inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T12:55:11.516059Z",
-     "start_time": "2018-09-26T12:55:11.090058Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1440 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_plot.plot.line(x=\"type\",legend=True,marker='o',grid=\"on\",linestyle='--',rot=45,figsize=(20,20))#.get_legend().set_bbox_to_anchor((0.7, 0.1))#,linestyle='--')#,grid=\"on\")\n",
-    "plt.xticks(np.arange(len(df_plot[\"type\"])), df_plot[\"type\"])\n",
-    "plt.yticks(np.linspace(0.5,1,15))\n",
-    "plt.ylabel(\"A@P\")\n",
-    "#plt.title(\"STR transformation effects on each criterion maximum value\")\n",
-    "plt.savefig(\"MADA_growth_criteria.pdf\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-09-26T12:55:11.545585Z",
-     "start_time": "2018-09-26T12:55:11.518128Z"
-    }
-   },
-   "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>c1</th>\n",
-       "      <th>c2</th>\n",
-       "      <th>c3</th>\n",
-       "      <th>c4</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>c1_w</th>\n",
-       "      <th>c2_w</th>\n",
-       "      <th>c3_w</th>\n",
-       "      <th>c4_w</th>\n",
-       "      <th>sum_w</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>type</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>normal</th>\n",
-       "      <td>0.166</td>\n",
-       "      <td>0.110</td>\n",
-       "      <td>0.060</td>\n",
-       "      <td>0.040</td>\n",
-       "      <td>0.0940</td>\n",
-       "      <td>0.376</td>\n",
-       "      <td>0.0166</td>\n",
-       "      <td>0.0440</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>0.0040</td>\n",
-       "      <td>0.0886</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>extension_2</th>\n",
-       "      <td>0.168</td>\n",
-       "      <td>0.112</td>\n",
-       "      <td>0.060</td>\n",
-       "      <td>0.040</td>\n",
-       "      <td>0.0950</td>\n",
-       "      <td>0.380</td>\n",
-       "      <td>0.0168</td>\n",
-       "      <td>0.0448</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>0.0040</td>\n",
-       "      <td>0.0896</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>extension_1</th>\n",
-       "      <td>0.170</td>\n",
-       "      <td>0.114</td>\n",
-       "      <td>0.060</td>\n",
-       "      <td>0.040</td>\n",
-       "      <td>0.0960</td>\n",
-       "      <td>0.384</td>\n",
-       "      <td>0.0170</td>\n",
-       "      <td>0.0456</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>0.0040</td>\n",
-       "      <td>0.0906</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>str_object</th>\n",
-       "      <td>3.426</td>\n",
-       "      <td>1.716</td>\n",
-       "      <td>1.480</td>\n",
-       "      <td>0.780</td>\n",
-       "      <td>1.8505</td>\n",
-       "      <td>7.402</td>\n",
-       "      <td>0.3426</td>\n",
-       "      <td>0.6864</td>\n",
-       "      <td>0.5920</td>\n",
-       "      <td>0.0780</td>\n",
-       "      <td>1.6990</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>biotex_lda_bvlac</th>\n",
-       "      <td>3.382</td>\n",
-       "      <td>1.780</td>\n",
-       "      <td>1.444</td>\n",
-       "      <td>0.838</td>\n",
-       "      <td>1.8610</td>\n",
-       "      <td>7.444</td>\n",
-       "      <td>0.3382</td>\n",
-       "      <td>0.7120</td>\n",
-       "      <td>0.5776</td>\n",
-       "      <td>0.0838</td>\n",
-       "      <td>1.7116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>biotex_lda_bvlac_gen_country</th>\n",
-       "      <td>3.346</td>\n",
-       "      <td>1.830</td>\n",
-       "      <td>1.458</td>\n",
-       "      <td>0.830</td>\n",
-       "      <td>1.8660</td>\n",
-       "      <td>7.464</td>\n",
-       "      <td>0.3346</td>\n",
-       "      <td>0.7320</td>\n",
-       "      <td>0.5832</td>\n",
-       "      <td>0.0830</td>\n",
-       "      <td>1.7328</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>biotex_lda_bvlac_ext_2</th>\n",
-       "      <td>3.470</td>\n",
-       "      <td>1.842</td>\n",
-       "      <td>1.458</td>\n",
-       "      <td>0.834</td>\n",
-       "      <td>1.9010</td>\n",
-       "      <td>7.604</td>\n",
-       "      <td>0.3470</td>\n",
-       "      <td>0.7368</td>\n",
-       "      <td>0.5832</td>\n",
-       "      <td>0.0834</td>\n",
-       "      <td>1.7504</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>biotex_lda_bvlac_gen_region</th>\n",
-       "      <td>3.478</td>\n",
-       "      <td>1.870</td>\n",
-       "      <td>1.438</td>\n",
-       "      <td>0.824</td>\n",
-       "      <td>1.9025</td>\n",
-       "      <td>7.610</td>\n",
-       "      <td>0.3478</td>\n",
-       "      <td>0.7480</td>\n",
-       "      <td>0.5752</td>\n",
-       "      <td>0.0824</td>\n",
-       "      <td>1.7534</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>biotex_bvlac_gen_country</th>\n",
-       "      <td>3.492</td>\n",
-       "      <td>1.796</td>\n",
-       "      <td>1.530</td>\n",
-       "      <td>0.838</td>\n",
-       "      <td>1.9140</td>\n",
-       "      <td>7.656</td>\n",
-       "      <td>0.3492</td>\n",
-       "      <td>0.7184</td>\n",
-       "      <td>0.6120</td>\n",
-       "      <td>0.0838</td>\n",
-       "      <td>1.7634</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>biotex_bvlac_gen_region</th>\n",
-       "      <td>3.564</td>\n",
-       "      <td>1.762</td>\n",
-       "      <td>1.508</td>\n",
-       "      <td>0.836</td>\n",
-       "      <td>1.9175</td>\n",
-       "      <td>7.670</td>\n",
-       "      <td>0.3564</td>\n",
-       "      <td>0.7048</td>\n",
-       "      <td>0.6032</td>\n",
-       "      <td>0.0836</td>\n",
-       "      <td>1.7480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dev_du_gen_country</th>\n",
-       "      <td>3.356</td>\n",
-       "      <td>1.908</td>\n",
-       "      <td>1.576</td>\n",
-       "      <td>0.858</td>\n",
-       "      <td>1.9245</td>\n",
-       "      <td>7.698</td>\n",
-       "      <td>0.3356</td>\n",
-       "      <td>0.7632</td>\n",
-       "      <td>0.6304</td>\n",
-       "      <td>0.0858</td>\n",
-       "      <td>1.8150</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dev_du_ext_2</th>\n",
-       "      <td>3.488</td>\n",
-       "      <td>1.810</td>\n",
-       "      <td>1.540</td>\n",
-       "      <td>0.872</td>\n",
-       "      <td>1.9275</td>\n",
-       "      <td>7.710</td>\n",
-       "      <td>0.3488</td>\n",
-       "      <td>0.7240</td>\n",
-       "      <td>0.6160</td>\n",
-       "      <td>0.0872</td>\n",
-       "      <td>1.7760</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>biotex_bvlac_ext_1</th>\n",
-       "      <td>3.612</td>\n",
-       "      <td>1.778</td>\n",
-       "      <td>1.504</td>\n",
-       "      <td>0.834</td>\n",
-       "      <td>1.9320</td>\n",
-       "      <td>7.728</td>\n",
-       "      <td>0.3612</td>\n",
-       "      <td>0.7112</td>\n",
-       "      <td>0.6016</td>\n",
-       "      <td>0.0834</td>\n",
-       "      <td>1.7574</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>biotex_lda_bvlac_ext_1</th>\n",
-       "      <td>3.550</td>\n",
-       "      <td>1.810</td>\n",
-       "      <td>1.504</td>\n",
-       "      <td>0.866</td>\n",
-       "      <td>1.9325</td>\n",
-       "      <td>7.730</td>\n",
-       "      <td>0.3550</td>\n",
-       "      <td>0.7240</td>\n",
-       "      <td>0.6016</td>\n",
-       "      <td>0.0866</td>\n",
-       "      <td>1.7672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>biotex_bvlac_ext_2</th>\n",
-       "      <td>3.634</td>\n",
-       "      <td>1.750</td>\n",
-       "      <td>1.542</td>\n",
-       "      <td>0.864</td>\n",
-       "      <td>1.9475</td>\n",
-       "      <td>7.790</td>\n",
-       "      <td>0.3634</td>\n",
-       "      <td>0.7000</td>\n",
-       "      <td>0.6168</td>\n",
-       "      <td>0.0864</td>\n",
-       "      <td>1.7666</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dev_du_gen_region</th>\n",
-       "      <td>3.522</td>\n",
-       "      <td>1.922</td>\n",
-       "      <td>1.544</td>\n",
-       "      <td>0.836</td>\n",
-       "      <td>1.9560</td>\n",
-       "      <td>7.824</td>\n",
-       "      <td>0.3522</td>\n",
-       "      <td>0.7688</td>\n",
-       "      <td>0.6176</td>\n",
-       "      <td>0.0836</td>\n",
-       "      <td>1.8222</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inra_ext_2</th>\n",
-       "      <td>3.592</td>\n",
-       "      <td>1.772</td>\n",
-       "      <td>1.650</td>\n",
-       "      <td>0.842</td>\n",
-       "      <td>1.9640</td>\n",
-       "      <td>7.856</td>\n",
-       "      <td>0.3592</td>\n",
-       "      <td>0.7088</td>\n",
-       "      <td>0.6600</td>\n",
-       "      <td>0.0842</td>\n",
-       "      <td>1.8122</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inra_gen_region</th>\n",
-       "      <td>3.552</td>\n",
-       "      <td>1.862</td>\n",
-       "      <td>1.620</td>\n",
-       "      <td>0.844</td>\n",
-       "      <td>1.9695</td>\n",
-       "      <td>7.878</td>\n",
-       "      <td>0.3552</td>\n",
-       "      <td>0.7448</td>\n",
-       "      <td>0.6480</td>\n",
-       "      <td>0.0844</td>\n",
-       "      <td>1.8324</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inra_gen_country</th>\n",
-       "      <td>3.528</td>\n",
-       "      <td>2.000</td>\n",
-       "      <td>1.544</td>\n",
-       "      <td>0.856</td>\n",
-       "      <td>1.9820</td>\n",
-       "      <td>7.928</td>\n",
-       "      <td>0.3528</td>\n",
-       "      <td>0.8000</td>\n",
-       "      <td>0.6176</td>\n",
-       "      <td>0.0856</td>\n",
-       "      <td>1.8560</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>biotex_bvlac</th>\n",
-       "      <td>3.732</td>\n",
-       "      <td>1.846</td>\n",
-       "      <td>1.566</td>\n",
-       "      <td>0.840</td>\n",
-       "      <td>1.9960</td>\n",
-       "      <td>7.984</td>\n",
-       "      <td>0.3732</td>\n",
-       "      <td>0.7384</td>\n",
-       "      <td>0.6264</td>\n",
-       "      <td>0.0840</td>\n",
-       "      <td>1.8220</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dev_du</th>\n",
-       "      <td>4.094</td>\n",
-       "      <td>1.870</td>\n",
-       "      <td>1.752</td>\n",
-       "      <td>1.016</td>\n",
-       "      <td>2.1830</td>\n",
-       "      <td>8.732</td>\n",
-       "      <td>0.4094</td>\n",
-       "      <td>0.7480</td>\n",
-       "      <td>0.7008</td>\n",
-       "      <td>0.1016</td>\n",
-       "      <td>1.9598</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ext_1</th>\n",
-       "      <td>4.072</td>\n",
-       "      <td>1.866</td>\n",
-       "      <td>1.848</td>\n",
-       "      <td>1.044</td>\n",
-       "      <td>2.2075</td>\n",
-       "      <td>8.830</td>\n",
-       "      <td>0.4072</td>\n",
-       "      <td>0.7464</td>\n",
-       "      <td>0.7392</td>\n",
-       "      <td>0.1044</td>\n",
-       "      <td>1.9972</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ext_2</th>\n",
-       "      <td>4.058</td>\n",
-       "      <td>1.914</td>\n",
-       "      <td>1.850</td>\n",
-       "      <td>1.050</td>\n",
-       "      <td>2.2180</td>\n",
-       "      <td>8.872</td>\n",
-       "      <td>0.4058</td>\n",
-       "      <td>0.7656</td>\n",
-       "      <td>0.7400</td>\n",
-       "      <td>0.1050</td>\n",
-       "      <td>2.0164</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inra</th>\n",
-       "      <td>4.110</td>\n",
-       "      <td>1.826</td>\n",
-       "      <td>1.944</td>\n",
-       "      <td>1.038</td>\n",
-       "      <td>2.2295</td>\n",
-       "      <td>8.918</td>\n",
-       "      <td>0.4110</td>\n",
-       "      <td>0.7304</td>\n",
-       "      <td>0.7776</td>\n",
-       "      <td>0.1038</td>\n",
-       "      <td>2.0228</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gen_region</th>\n",
-       "      <td>4.062</td>\n",
-       "      <td>2.064</td>\n",
-       "      <td>1.846</td>\n",
-       "      <td>1.022</td>\n",
-       "      <td>2.2485</td>\n",
-       "      <td>8.994</td>\n",
-       "      <td>0.4062</td>\n",
-       "      <td>0.8256</td>\n",
-       "      <td>0.7384</td>\n",
-       "      <td>0.1022</td>\n",
-       "      <td>2.0724</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gen_country</th>\n",
-       "      <td>3.956</td>\n",
-       "      <td>2.152</td>\n",
-       "      <td>1.940</td>\n",
-       "      <td>1.098</td>\n",
-       "      <td>2.2865</td>\n",
-       "      <td>9.146</td>\n",
-       "      <td>0.3956</td>\n",
-       "      <td>0.8608</td>\n",
-       "      <td>0.7760</td>\n",
-       "      <td>0.1098</td>\n",
-       "      <td>2.1422</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                 c1     c2     c3     c4    mean    sum  \\\n",
-       "type                                                                      \n",
-       "normal                        0.166  0.110  0.060  0.040  0.0940  0.376   \n",
-       "extension_2                   0.168  0.112  0.060  0.040  0.0950  0.380   \n",
-       "extension_1                   0.170  0.114  0.060  0.040  0.0960  0.384   \n",
-       "str_object                    3.426  1.716  1.480  0.780  1.8505  7.402   \n",
-       "biotex_lda_bvlac              3.382  1.780  1.444  0.838  1.8610  7.444   \n",
-       "biotex_lda_bvlac_gen_country  3.346  1.830  1.458  0.830  1.8660  7.464   \n",
-       "biotex_lda_bvlac_ext_2        3.470  1.842  1.458  0.834  1.9010  7.604   \n",
-       "biotex_lda_bvlac_gen_region   3.478  1.870  1.438  0.824  1.9025  7.610   \n",
-       "biotex_bvlac_gen_country      3.492  1.796  1.530  0.838  1.9140  7.656   \n",
-       "biotex_bvlac_gen_region       3.564  1.762  1.508  0.836  1.9175  7.670   \n",
-       "dev_du_gen_country            3.356  1.908  1.576  0.858  1.9245  7.698   \n",
-       "dev_du_ext_2                  3.488  1.810  1.540  0.872  1.9275  7.710   \n",
-       "biotex_bvlac_ext_1            3.612  1.778  1.504  0.834  1.9320  7.728   \n",
-       "biotex_lda_bvlac_ext_1        3.550  1.810  1.504  0.866  1.9325  7.730   \n",
-       "biotex_bvlac_ext_2            3.634  1.750  1.542  0.864  1.9475  7.790   \n",
-       "dev_du_gen_region             3.522  1.922  1.544  0.836  1.9560  7.824   \n",
-       "inra_ext_2                    3.592  1.772  1.650  0.842  1.9640  7.856   \n",
-       "inra_gen_region               3.552  1.862  1.620  0.844  1.9695  7.878   \n",
-       "inra_gen_country              3.528  2.000  1.544  0.856  1.9820  7.928   \n",
-       "biotex_bvlac                  3.732  1.846  1.566  0.840  1.9960  7.984   \n",
-       "dev_du                        4.094  1.870  1.752  1.016  2.1830  8.732   \n",
-       "ext_1                         4.072  1.866  1.848  1.044  2.2075  8.830   \n",
-       "ext_2                         4.058  1.914  1.850  1.050  2.2180  8.872   \n",
-       "inra                          4.110  1.826  1.944  1.038  2.2295  8.918   \n",
-       "gen_region                    4.062  2.064  1.846  1.022  2.2485  8.994   \n",
-       "gen_country                   3.956  2.152  1.940  1.098  2.2865  9.146   \n",
-       "\n",
-       "                                c1_w    c2_w    c3_w    c4_w   sum_w  \n",
-       "type                                                                  \n",
-       "normal                        0.0166  0.0440  0.0240  0.0040  0.0886  \n",
-       "extension_2                   0.0168  0.0448  0.0240  0.0040  0.0896  \n",
-       "extension_1                   0.0170  0.0456  0.0240  0.0040  0.0906  \n",
-       "str_object                    0.3426  0.6864  0.5920  0.0780  1.6990  \n",
-       "biotex_lda_bvlac              0.3382  0.7120  0.5776  0.0838  1.7116  \n",
-       "biotex_lda_bvlac_gen_country  0.3346  0.7320  0.5832  0.0830  1.7328  \n",
-       "biotex_lda_bvlac_ext_2        0.3470  0.7368  0.5832  0.0834  1.7504  \n",
-       "biotex_lda_bvlac_gen_region   0.3478  0.7480  0.5752  0.0824  1.7534  \n",
-       "biotex_bvlac_gen_country      0.3492  0.7184  0.6120  0.0838  1.7634  \n",
-       "biotex_bvlac_gen_region       0.3564  0.7048  0.6032  0.0836  1.7480  \n",
-       "dev_du_gen_country            0.3356  0.7632  0.6304  0.0858  1.8150  \n",
-       "dev_du_ext_2                  0.3488  0.7240  0.6160  0.0872  1.7760  \n",
-       "biotex_bvlac_ext_1            0.3612  0.7112  0.6016  0.0834  1.7574  \n",
-       "biotex_lda_bvlac_ext_1        0.3550  0.7240  0.6016  0.0866  1.7672  \n",
-       "biotex_bvlac_ext_2            0.3634  0.7000  0.6168  0.0864  1.7666  \n",
-       "dev_du_gen_region             0.3522  0.7688  0.6176  0.0836  1.8222  \n",
-       "inra_ext_2                    0.3592  0.7088  0.6600  0.0842  1.8122  \n",
-       "inra_gen_region               0.3552  0.7448  0.6480  0.0844  1.8324  \n",
-       "inra_gen_country              0.3528  0.8000  0.6176  0.0856  1.8560  \n",
-       "biotex_bvlac                  0.3732  0.7384  0.6264  0.0840  1.8220  \n",
-       "dev_du                        0.4094  0.7480  0.7008  0.1016  1.9598  \n",
-       "ext_1                         0.4072  0.7464  0.7392  0.1044  1.9972  \n",
-       "ext_2                         0.4058  0.7656  0.7400  0.1050  2.0164  \n",
-       "inra                          0.4110  0.7304  0.7776  0.1038  2.0228  \n",
-       "gen_region                    0.4062  0.8256  0.7384  0.1022  2.0724  \n",
-       "gen_country                   0.3956  0.8608  0.7760  0.1098  2.1422  "
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.groupby(\"type\",as_index=True).sum().sort_values(\"sum\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
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-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  },
-  "toc": {
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": "block",
-   "toc_window_display": false
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-   "cols": {
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-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
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- "nbformat": 4,
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-}
diff --git a/notebooks_old/Mordecai_disambiguation/Des_Mordecai.ipynb b/notebooks_old/Mordecai_disambiguation/Des_Mordecai.ipynb
deleted file mode 100644
index 717f0a3..0000000
--- a/notebooks_old/Mordecai_disambiguation/Des_Mordecai.ipynb
+++ /dev/null
@@ -1,2100 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import geopandas as gpd\n",
-    "import sys,json,re,os\n",
-    "import gazpy as ga\n",
-    "from elasticsearch import Elasticsearch\n",
-    "from shapely.geometry import Point\n",
-    "gd=ga.Geodict(Elasticsearch())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fns=glob.glob(\"../data/disambiguation_data/padiweb_disambiguation/*.csv\")\n",
-    "truth_data={int(re.findall(\"\\d+\",fn)[-1]):pd.read_csv(fn) for fn in fns}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fns_mordecai=glob.glob(\"padi_result_mordecai/*.csv\")\n",
-    "output_data={int(re.findall(\"\\d+\",fn)[-1]):pd.read_csv(fn) for fn in fns_mordecai}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def foo(x,index):\n",
-    "    if not \"word\" in output_data[index]:\n",
-    "        return None\n",
-    "    res=output_data[index][output_data[index].word == x]\n",
-    "    if len(res)==0:\n",
-    "        return None\n",
-    "    else:\n",
-    "        return res.iloc[0].id\n",
-    "\n",
-    "for k,v in truth_data.items():\n",
-    "    if k in output_data:\n",
-    "        truth_data[k][\"mord_GID\"]=truth_data[k].text.apply(lambda x: foo(x,k))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def distance(id_truth,id_sys):\n",
-    "    try:\n",
-    "        p1=gd.get_by_id(id_truth)[0]\n",
-    "        p2=gd.get_by_id(id_sys)[0]\n",
-    "    \n",
-    "        return Point(p1.coord.lon,p1.coord.lat).distance(Point(p2.coord.lon,p2.coord.lat))\n",
-    "    except:\n",
-    "        return np.nan\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in truth_data:\n",
-    "    if len(truth_data[i]) >0 and i in output_data:\n",
-    "        truth_data[i][\"distance\"]=truth_data[i].apply(lambda x : distance(x.GID,x.mord_GID) if x.GID and x.mord_GID else np.nan,axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "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>Unnamed: 0</th>\n",
-       "      <th>text</th>\n",
-       "      <th>GID</th>\n",
-       "      <th>geonamesId</th>\n",
-       "      <th>mord_GID</th>\n",
-       "      <th>distance</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>Rivers State</td>\n",
-       "      <td>GD4106855</td>\n",
-       "      <td>2324433</td>\n",
-       "      <td>GD4106855</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Kano</td>\n",
-       "      <td>GD4103071</td>\n",
-       "      <td>2335204</td>\n",
-       "      <td>GD4103071</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Kano</td>\n",
-       "      <td>GD4103071</td>\n",
-       "      <td>2335204</td>\n",
-       "      <td>GD4103071</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Lagos</td>\n",
-       "      <td>GD4468122</td>\n",
-       "      <td>2332459</td>\n",
-       "      <td>GD1683276</td>\n",
-       "      <td>0.374537</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Lagos</td>\n",
-       "      <td>GD4468122</td>\n",
-       "      <td>2332459</td>\n",
-       "      <td>GD1683276</td>\n",
-       "      <td>0.374537</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Port Harcourt</td>\n",
-       "      <td>GD791183</td>\n",
-       "      <td>2324774</td>\n",
-       "      <td>GD791183</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0           text        GID  geonamesId   mord_GID  distance\n",
-       "0           0   Rivers State  GD4106855     2324433  GD4106855  0.000000\n",
-       "1           1           Kano  GD4103071     2335204  GD4103071  0.000000\n",
-       "2           2           Kano  GD4103071     2335204  GD4103071  0.000000\n",
-       "3           3          Lagos  GD4468122     2332459  GD1683276  0.374537\n",
-       "4           4          Lagos  GD4468122     2332459  GD1683276  0.374537\n",
-       "5           5  Port Harcourt   GD791183     2324774   GD791183  0.000000"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "truth_data[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c=0\n",
-    "for k in truth_data:\n",
-    "    if not k in output_data:\n",
-    "        c+=1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "39\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(c)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average Accuracy 0.7491928437440436\n"
-     ]
-    }
-   ],
-   "source": [
-    "res_mean=[]\n",
-    "for k in truth_data:\n",
-    "    if k in output_data:\n",
-    "        res_mean.append((truth_data[k].GID== truth_data[k].mord_GID).mean())\n",
-    "        avg_acc=np.nanmean(res_mean)\n",
-    "res_mean.extend([0]*c)\n",
-    "print(\"Average Accuracy\",avg_acc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average Accuracy@0.5 0.764269003852523\n"
-     ]
-    }
-   ],
-   "source": [
-    "res_mean=[]\n",
-    "for k in truth_data:\n",
-    "    if k in output_data:\n",
-    "        if 'distance' in truth_data[k]:\n",
-    "            res_mean.append((truth_data[k].distance <0.5).mean())\n",
-    "res_mean.extend([0]*c)\n",
-    "avg_acc=np.nanmean(res_mean)\n",
-    "print(\"Average Accuracy@0.5\",avg_acc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average Accuracy@1 0.7757269101959446\n"
-     ]
-    }
-   ],
-   "source": [
-    "res_mean=[]\n",
-    "for k in truth_data:\n",
-    "    if k in output_data:\n",
-    "        if 'distance' in truth_data[k]:\n",
-    "            res_mean.append((truth_data[k].distance <1).mean())\n",
-    "res_mean.extend([0]*c)\n",
-    "avg_acc=np.nanmean(res_mean)\n",
-    "print(\"Average Accuracy@1\",avg_acc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average Error Dist 2.7385322763179394\n"
-     ]
-    }
-   ],
-   "source": [
-    "res_mean=[]\n",
-    "for k in truth_data:\n",
-    "    if k in output_data:\n",
-    "        if len(truth_data[k]) > 0:\n",
-    "            res_mean.append(np.nanmean(truth_data[k][\"distance\"]))\n",
-    "        else:\n",
-    "            pass\n",
-    "print(\"Average Error Dist\",np.nanmean(res_mean))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# AgroMada\n",
-    "\n",
-    "Disambiguation Test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fns=glob.glob(\"../data/disambiguation_data/mada_disambiguisation/*.csv\")\n",
-    "truth_data={int(re.findall(\"\\d+\",fn)[-1]):pd.read_csv(fn) for fn in fns}\n",
-    "truth_data={k:td[td[\"GID\"] !=\"O\"] for k,td in truth_data.items()}\n",
-    "\n",
-    "fns_mordecai=glob.glob(\"csv_agromada_mordecai/*.csv\")\n",
-    "output_data={int(re.findall(\"\\d+\",fn)[-1]):pd.read_csv(fn) for fn in fns_mordecai}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def foo(x,index):\n",
-    "    if not \"word\" in output_data[index]:\n",
-    "        return None\n",
-    "    res=output_data[index][output_data[index].word == x]\n",
-    "    if len(res)==0:\n",
-    "        return None\n",
-    "    else:\n",
-    "        return res.iloc[0].id\n",
-    "\n",
-    "for k,v in truth_data.items():\n",
-    "    truth_data[k][\"mord_GID\"]=truth_data[k].text.apply(lambda x: foo(x,k))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def distance(id_truth,id_sys):\n",
-    "    p1=gd.get_by_id(id_truth)\n",
-    "    p1=p1[0] if p1 else None\n",
-    "    p2=gd.get_by_id(id_sys)[0]\n",
-    "    try:\n",
-    "        return Point(p1.coord.lon,p1.coord.lat).distance(Point(p2.coord.lon,p2.coord.lat))\n",
-    "    except Exception as e:\n",
-    "        return np.nan"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in truth_data:\n",
-    "    if len(truth_data[i]) >0:\n",
-    "        truth_data[i][\"distance\"]=truth_data[i].apply(lambda x : distance(x.GID,x.mord_GID) if x.GID and x.mord_GID else np.nan,axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average Accuracy 0.5583854961091139\n"
-     ]
-    }
-   ],
-   "source": [
-    "res_mean=[]\n",
-    "for k in truth_data:\n",
-    "    res_mean.append((truth_data[k].GID== truth_data[k].mord_GID).mean())\n",
-    "    avg_acc=np.nanmean(res_mean)\n",
-    "print(\"Average Accuracy\",avg_acc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average Accuracy@0.5 0.612513898161536\n"
-     ]
-    }
-   ],
-   "source": [
-    "res_mean=[]\n",
-    "for k in truth_data:\n",
-    "    if 'distance' in truth_data[k]:\n",
-    "        res_mean.append((truth_data[k].distance <0.5).mean())\n",
-    "avg_acc=np.nanmean(res_mean)\n",
-    "print(\"Average Accuracy@0.5\",avg_acc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average Accuracy@1 0.6416014256652058\n"
-     ]
-    }
-   ],
-   "source": [
-    "res_mean=[]\n",
-    "for k in truth_data:\n",
-    "    if 'distance' in truth_data[k]:\n",
-    "        res_mean.append((truth_data[k].distance <1).mean())\n",
-    "avg_acc=np.nanmean(res_mean)\n",
-    "print(\"Average Accuracy@1\",avg_acc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average Error Dist 13.030912180070953\n"
-     ]
-    }
-   ],
-   "source": [
-    "res_mean=[]\n",
-    "for k in truth_data:\n",
-    "    if len(truth_data[k]) > 0:\n",
-    "        res_mean.append(np.nanmean(truth_data[k][\"distance\"]))\n",
-    "    else:\n",
-    "        pass\n",
-    "print(\"Average Error Dist\",np.nanmean(res_mean))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'lat': -17.5, 'lon': 48.5}"
-      ]
-     },
-     "execution_count": 118,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(distance(\"GD861398\",\"GD861398\"))\n",
-    "gd.get_by_id(\"GD861398\")[0].coord"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "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>Unnamed: 0</th>\n",
-       "      <th>Unnamed: 0.1</th>\n",
-       "      <th>Unnamed: 0.1.1</th>\n",
-       "      <th>Unnamed: 0.1.1.1</th>\n",
-       "      <th>diff2</th>\n",
-       "      <th>text</th>\n",
-       "      <th>pos_</th>\n",
-       "      <th>ent_type_</th>\n",
-       "      <th>GID</th>\n",
-       "      <th>mord_GID</th>\n",
-       "      <th>distance</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Paris XII</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD1375537</td>\n",
-       "      <td>GD14200769</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Lac Alaotra</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>LAC ALAOTRA</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>7</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7</td>\n",
-       "      <td>Ambatondrazaka</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3353681</td>\n",
-       "      <td>GD3353681</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>9</td>\n",
-       "      <td>9</td>\n",
-       "      <td>9</td>\n",
-       "      <td>9</td>\n",
-       "      <td>9</td>\n",
-       "      <td>vallée Marianina</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD10785054</td>\n",
-       "      <td>GD2891542</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>10</td>\n",
-       "      <td>10</td>\n",
-       "      <td>10</td>\n",
-       "      <td>10</td>\n",
-       "      <td>10</td>\n",
-       "      <td>Ambatondrazaka</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3353681</td>\n",
-       "      <td>GD3353681</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>13</td>\n",
-       "      <td>13</td>\n",
-       "      <td>13</td>\n",
-       "      <td>13</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Rhone</td>\n",
-       "      <td>NOUN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD5199827</td>\n",
-       "      <td>GD3938233</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>14</td>\n",
-       "      <td>14</td>\n",
-       "      <td>14</td>\n",
-       "      <td>14</td>\n",
-       "      <td>14</td>\n",
-       "      <td>Languedoc</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD2098343</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>16</td>\n",
-       "      <td>16</td>\n",
-       "      <td>16</td>\n",
-       "      <td>16</td>\n",
-       "      <td>16</td>\n",
-       "      <td>lac Alaotra</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>17</td>\n",
-       "      <td>17</td>\n",
-       "      <td>17</td>\n",
-       "      <td>17</td>\n",
-       "      <td>17</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>20</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20</td>\n",
-       "      <td>Mer</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3433615</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>25</td>\n",
-       "      <td>25</td>\n",
-       "      <td>25</td>\n",
-       "      <td>25</td>\n",
-       "      <td>25</td>\n",
-       "      <td>Lavaka</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD8260896</td>\n",
-       "      <td>GD789632</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>30</td>\n",
-       "      <td>30</td>\n",
-       "      <td>30</td>\n",
-       "      <td>30</td>\n",
-       "      <td>30</td>\n",
-       "      <td>lac Alaotra</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>31</td>\n",
-       "      <td>31</td>\n",
-       "      <td>31</td>\n",
-       "      <td>31</td>\n",
-       "      <td>31</td>\n",
-       "      <td>Ilafy</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD1602018</td>\n",
-       "      <td>GD9268944</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>32</td>\n",
-       "      <td>32</td>\n",
-       "      <td>32</td>\n",
-       "      <td>32</td>\n",
-       "      <td>32</td>\n",
-       "      <td>Ambatondrazaka</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3353681</td>\n",
-       "      <td>GD3353681</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>34</td>\n",
-       "      <td>34</td>\n",
-       "      <td>34</td>\n",
-       "      <td>34</td>\n",
-       "      <td>34</td>\n",
-       "      <td>Didy</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD4111765</td>\n",
-       "      <td>GD4111765</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>35</td>\n",
-       "      <td>35</td>\n",
-       "      <td>35</td>\n",
-       "      <td>35</td>\n",
-       "      <td>35</td>\n",
-       "      <td>Ambatondrazaka</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3353681</td>\n",
-       "      <td>GD3353681</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>38</td>\n",
-       "      <td>38</td>\n",
-       "      <td>38</td>\n",
-       "      <td>38</td>\n",
-       "      <td>38</td>\n",
-       "      <td>Ambatondrazaka</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3353681</td>\n",
-       "      <td>GD3353681</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>46</td>\n",
-       "      <td>46</td>\n",
-       "      <td>46</td>\n",
-       "      <td>46</td>\n",
-       "      <td>46</td>\n",
-       "      <td>lac Alaotra</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>47</td>\n",
-       "      <td>47</td>\n",
-       "      <td>47</td>\n",
-       "      <td>47</td>\n",
-       "      <td>47</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>54</td>\n",
-       "      <td>54</td>\n",
-       "      <td>54</td>\n",
-       "      <td>54</td>\n",
-       "      <td>54</td>\n",
-       "      <td>lac Alaotra</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63</th>\n",
-       "      <td>63</td>\n",
-       "      <td>63</td>\n",
-       "      <td>63</td>\n",
-       "      <td>63</td>\n",
-       "      <td>63</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>65</th>\n",
-       "      <td>65</td>\n",
-       "      <td>65</td>\n",
-       "      <td>65</td>\n",
-       "      <td>65</td>\n",
-       "      <td>65</td>\n",
-       "      <td>lac Alaotra</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>66</th>\n",
-       "      <td>66</td>\n",
-       "      <td>66</td>\n",
-       "      <td>66</td>\n",
-       "      <td>66</td>\n",
-       "      <td>66</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67</th>\n",
-       "      <td>67</td>\n",
-       "      <td>67</td>\n",
-       "      <td>67</td>\n",
-       "      <td>67</td>\n",
-       "      <td>67</td>\n",
-       "      <td>lac Alaotra</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>69</th>\n",
-       "      <td>69</td>\n",
-       "      <td>69</td>\n",
-       "      <td>69</td>\n",
-       "      <td>69</td>\n",
-       "      <td>69</td>\n",
-       "      <td>lac Alaotra</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>76</th>\n",
-       "      <td>76</td>\n",
-       "      <td>76</td>\n",
-       "      <td>76</td>\n",
-       "      <td>76</td>\n",
-       "      <td>76</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>77</th>\n",
-       "      <td>77</td>\n",
-       "      <td>77</td>\n",
-       "      <td>77</td>\n",
-       "      <td>77</td>\n",
-       "      <td>77</td>\n",
-       "      <td>République démocratique de Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>80</th>\n",
-       "      <td>80</td>\n",
-       "      <td>80</td>\n",
-       "      <td>80</td>\n",
-       "      <td>80</td>\n",
-       "      <td>80</td>\n",
-       "      <td>Afrique</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD5460722</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>81</th>\n",
-       "      <td>81</td>\n",
-       "      <td>81</td>\n",
-       "      <td>81</td>\n",
-       "      <td>81</td>\n",
-       "      <td>81</td>\n",
-       "      <td>canal du Mozambique</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3104481</td>\n",
-       "      <td>GD2891542</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>335</th>\n",
-       "      <td>335</td>\n",
-       "      <td>335</td>\n",
-       "      <td>335</td>\n",
-       "      <td>335</td>\n",
-       "      <td>335</td>\n",
-       "      <td>Antananarivo</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3682867</td>\n",
-       "      <td>GD14756294</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>338</th>\n",
-       "      <td>338</td>\n",
-       "      <td>338</td>\n",
-       "      <td>338</td>\n",
-       "      <td>338</td>\n",
-       "      <td>338</td>\n",
-       "      <td>lac Alaotra</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>339</th>\n",
-       "      <td>339</td>\n",
-       "      <td>339</td>\n",
-       "      <td>339</td>\n",
-       "      <td>339</td>\n",
-       "      <td>339</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>341</th>\n",
-       "      <td>341</td>\n",
-       "      <td>341</td>\n",
-       "      <td>341</td>\n",
-       "      <td>341</td>\n",
-       "      <td>341</td>\n",
-       "      <td>Montpellier</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD128756</td>\n",
-       "      <td>GD11325234</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>344</th>\n",
-       "      <td>344</td>\n",
-       "      <td>344</td>\n",
-       "      <td>344</td>\n",
-       "      <td>344</td>\n",
-       "      <td>344</td>\n",
-       "      <td>Paris</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD5400765</td>\n",
-       "      <td>GD9519388</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>345</th>\n",
-       "      <td>345</td>\n",
-       "      <td>345</td>\n",
-       "      <td>345</td>\n",
-       "      <td>345</td>\n",
-       "      <td>345</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>346</th>\n",
-       "      <td>346</td>\n",
-       "      <td>346</td>\n",
-       "      <td>346</td>\n",
-       "      <td>346</td>\n",
-       "      <td>346</td>\n",
-       "      <td>Lac Alaotra</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>349</th>\n",
-       "      <td>349</td>\n",
-       "      <td>349</td>\n",
-       "      <td>349</td>\n",
-       "      <td>349</td>\n",
-       "      <td>349</td>\n",
-       "      <td>Antananarivo</td>\n",
-       "      <td>NOUN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3682867</td>\n",
-       "      <td>GD14756294</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>350</th>\n",
-       "      <td>350</td>\n",
-       "      <td>350</td>\n",
-       "      <td>350</td>\n",
-       "      <td>350</td>\n",
-       "      <td>350</td>\n",
-       "      <td>lac Alaotra</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>353</th>\n",
-       "      <td>353</td>\n",
-       "      <td>353</td>\n",
-       "      <td>353</td>\n",
-       "      <td>353</td>\n",
-       "      <td>353</td>\n",
-       "      <td>Antananarivo</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3682867</td>\n",
-       "      <td>GD14756294</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>354</th>\n",
-       "      <td>354</td>\n",
-       "      <td>354</td>\n",
-       "      <td>354</td>\n",
-       "      <td>354</td>\n",
-       "      <td>354</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>356</th>\n",
-       "      <td>356</td>\n",
-       "      <td>356</td>\n",
-       "      <td>356</td>\n",
-       "      <td>356</td>\n",
-       "      <td>356</td>\n",
-       "      <td>Montpellier</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD128756</td>\n",
-       "      <td>GD11325234</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>357</th>\n",
-       "      <td>357</td>\n",
-       "      <td>357</td>\n",
-       "      <td>357</td>\n",
-       "      <td>357</td>\n",
-       "      <td>357</td>\n",
-       "      <td>France</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3117352</td>\n",
-       "      <td>GD3117352</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>361</th>\n",
-       "      <td>361</td>\n",
-       "      <td>361</td>\n",
-       "      <td>361</td>\n",
-       "      <td>361</td>\n",
-       "      <td>361</td>\n",
-       "      <td>Italy</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD1333473</td>\n",
-       "      <td>GD1333473</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>366</th>\n",
-       "      <td>366</td>\n",
-       "      <td>366</td>\n",
-       "      <td>366</td>\n",
-       "      <td>366</td>\n",
-       "      <td>366</td>\n",
-       "      <td>lac Alaotra</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>367</th>\n",
-       "      <td>367</td>\n",
-       "      <td>367</td>\n",
-       "      <td>367</td>\n",
-       "      <td>367</td>\n",
-       "      <td>367</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>369</th>\n",
-       "      <td>369</td>\n",
-       "      <td>369</td>\n",
-       "      <td>369</td>\n",
-       "      <td>369</td>\n",
-       "      <td>369</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>373</th>\n",
-       "      <td>373</td>\n",
-       "      <td>373</td>\n",
-       "      <td>373</td>\n",
-       "      <td>373</td>\n",
-       "      <td>373</td>\n",
-       "      <td>Lac Alaotra</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>377</th>\n",
-       "      <td>377</td>\n",
-       "      <td>377</td>\n",
-       "      <td>377</td>\n",
-       "      <td>377</td>\n",
-       "      <td>377</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>379</th>\n",
-       "      <td>379</td>\n",
-       "      <td>379</td>\n",
-       "      <td>379</td>\n",
-       "      <td>379</td>\n",
-       "      <td>379</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>384</th>\n",
-       "      <td>384</td>\n",
-       "      <td>384</td>\n",
-       "      <td>384</td>\n",
-       "      <td>384</td>\n",
-       "      <td>384</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>385</th>\n",
-       "      <td>385</td>\n",
-       "      <td>385</td>\n",
-       "      <td>385</td>\n",
-       "      <td>385</td>\n",
-       "      <td>385</td>\n",
-       "      <td>Lac Alaotra</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>386</th>\n",
-       "      <td>386</td>\n",
-       "      <td>386</td>\n",
-       "      <td>386</td>\n",
-       "      <td>386</td>\n",
-       "      <td>386</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>397</th>\n",
-       "      <td>397</td>\n",
-       "      <td>397</td>\n",
-       "      <td>397</td>\n",
-       "      <td>397</td>\n",
-       "      <td>397</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>399</th>\n",
-       "      <td>399</td>\n",
-       "      <td>399</td>\n",
-       "      <td>399</td>\n",
-       "      <td>399</td>\n",
-       "      <td>399</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>400</th>\n",
-       "      <td>400</td>\n",
-       "      <td>400</td>\n",
-       "      <td>400</td>\n",
-       "      <td>400</td>\n",
-       "      <td>400</td>\n",
-       "      <td>lac Alaotra</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>GD861398</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>403</th>\n",
-       "      <td>403</td>\n",
-       "      <td>403</td>\n",
-       "      <td>403</td>\n",
-       "      <td>403</td>\n",
-       "      <td>403</td>\n",
-       "      <td>Paris I</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD5400765</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>404</th>\n",
-       "      <td>404</td>\n",
-       "      <td>404</td>\n",
-       "      <td>404</td>\n",
-       "      <td>404</td>\n",
-       "      <td>404</td>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>GD3404996</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>406</th>\n",
-       "      <td>406</td>\n",
-       "      <td>406</td>\n",
-       "      <td>406</td>\n",
-       "      <td>406</td>\n",
-       "      <td>406</td>\n",
-       "      <td>Antananarivo</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3682867</td>\n",
-       "      <td>GD14756294</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>407</th>\n",
-       "      <td>407</td>\n",
-       "      <td>407</td>\n",
-       "      <td>407</td>\n",
-       "      <td>407</td>\n",
-       "      <td>407</td>\n",
-       "      <td>Antananarivo</td>\n",
-       "      <td>NOUN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3682867</td>\n",
-       "      <td>GD14756294</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>178 rows × 11 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Unnamed: 0  Unnamed: 0.1  Unnamed: 0.1.1  Unnamed: 0.1.1.1  diff2  \\\n",
-       "0             0             0               0                 0      0   \n",
-       "1             1             1               1                 1      1   \n",
-       "5             5             5               5                 5      5   \n",
-       "7             7             7               7                 7      7   \n",
-       "9             9             9               9                 9      9   \n",
-       "10           10            10              10                10     10   \n",
-       "13           13            13              13                13     13   \n",
-       "14           14            14              14                14     14   \n",
-       "16           16            16              16                16     16   \n",
-       "17           17            17              17                17     17   \n",
-       "20           20            20              20                20     20   \n",
-       "25           25            25              25                25     25   \n",
-       "30           30            30              30                30     30   \n",
-       "31           31            31              31                31     31   \n",
-       "32           32            32              32                32     32   \n",
-       "34           34            34              34                34     34   \n",
-       "35           35            35              35                35     35   \n",
-       "38           38            38              38                38     38   \n",
-       "46           46            46              46                46     46   \n",
-       "47           47            47              47                47     47   \n",
-       "54           54            54              54                54     54   \n",
-       "63           63            63              63                63     63   \n",
-       "65           65            65              65                65     65   \n",
-       "66           66            66              66                66     66   \n",
-       "67           67            67              67                67     67   \n",
-       "69           69            69              69                69     69   \n",
-       "76           76            76              76                76     76   \n",
-       "77           77            77              77                77     77   \n",
-       "80           80            80              80                80     80   \n",
-       "81           81            81              81                81     81   \n",
-       "..          ...           ...             ...               ...    ...   \n",
-       "335         335           335             335               335    335   \n",
-       "338         338           338             338               338    338   \n",
-       "339         339           339             339               339    339   \n",
-       "341         341           341             341               341    341   \n",
-       "344         344           344             344               344    344   \n",
-       "345         345           345             345               345    345   \n",
-       "346         346           346             346               346    346   \n",
-       "349         349           349             349               349    349   \n",
-       "350         350           350             350               350    350   \n",
-       "353         353           353             353               353    353   \n",
-       "354         354           354             354               354    354   \n",
-       "356         356           356             356               356    356   \n",
-       "357         357           357             357               357    357   \n",
-       "361         361           361             361               361    361   \n",
-       "366         366           366             366               366    366   \n",
-       "367         367           367             367               367    367   \n",
-       "369         369           369             369               369    369   \n",
-       "373         373           373             373               373    373   \n",
-       "377         377           377             377               377    377   \n",
-       "379         379           379             379               379    379   \n",
-       "384         384           384             384               384    384   \n",
-       "385         385           385             385               385    385   \n",
-       "386         386           386             386               386    386   \n",
-       "397         397           397             397               397    397   \n",
-       "399         399           399             399               399    399   \n",
-       "400         400           400             400               400    400   \n",
-       "403         403           403             403               403    403   \n",
-       "404         404           404             404               404    404   \n",
-       "406         406           406             406               406    406   \n",
-       "407         407           407             407               407    407   \n",
-       "\n",
-       "                                      text   pos_ ent_type_         GID  \\\n",
-       "0                                Paris XII  PROPN       LOC   GD1375537   \n",
-       "1                              Lac Alaotra  PROPN       LOC    GD861398   \n",
-       "5                              LAC ALAOTRA  PROPN       LOC    GD861398   \n",
-       "7                           Ambatondrazaka  PROPN       LOC   GD3353681   \n",
-       "9                         vallée Marianina  PROPN       LOC  GD10785054   \n",
-       "10                          Ambatondrazaka  PROPN       LOC   GD3353681   \n",
-       "13                                   Rhone   NOUN       LOC   GD5199827   \n",
-       "14                               Languedoc  PROPN       LOC   GD2098343   \n",
-       "16                             lac Alaotra  PROPN       LOC    GD861398   \n",
-       "17                              Madagascar  PROPN       LOC   GD3404996   \n",
-       "20                                     Mer  PROPN       LOC   GD3433615   \n",
-       "25                                  Lavaka  PROPN       LOC   GD8260896   \n",
-       "30                             lac Alaotra  PROPN       LOC    GD861398   \n",
-       "31                                   Ilafy  PROPN       LOC   GD1602018   \n",
-       "32                          Ambatondrazaka  PROPN       LOC   GD3353681   \n",
-       "34                                    Didy  PROPN       LOC   GD4111765   \n",
-       "35                          Ambatondrazaka  PROPN       LOC   GD3353681   \n",
-       "38                          Ambatondrazaka  PROPN       LOC   GD3353681   \n",
-       "46                             lac Alaotra  PROPN       LOC    GD861398   \n",
-       "47                              Madagascar  PROPN       LOC   GD3404996   \n",
-       "54                             lac Alaotra  PROPN       LOC    GD861398   \n",
-       "63                              Madagascar  PROPN       LOC   GD3404996   \n",
-       "65                             lac Alaotra  PROPN       LOC    GD861398   \n",
-       "66                              Madagascar  PROPN       LOC   GD3404996   \n",
-       "67                             lac Alaotra  PROPN       LOC    GD861398   \n",
-       "69                             lac Alaotra  PROPN       LOC    GD861398   \n",
-       "76                              Madagascar  PROPN       LOC   GD3404996   \n",
-       "77   République démocratique de Madagascar  PROPN       LOC   GD3404996   \n",
-       "80                                 Afrique  PROPN       LOC   GD5460722   \n",
-       "81                     canal du Mozambique  PROPN       LOC   GD3104481   \n",
-       "..                                     ...    ...       ...         ...   \n",
-       "335                           Antananarivo  PROPN       LOC   GD3682867   \n",
-       "338                            lac Alaotra  PROPN       LOC    GD861398   \n",
-       "339                             Madagascar  PROPN       LOC   GD3404996   \n",
-       "341                            Montpellier  PROPN       LOC    GD128756   \n",
-       "344                                  Paris  PROPN       LOC   GD5400765   \n",
-       "345                             Madagascar  PROPN       LOC   GD3404996   \n",
-       "346                            Lac Alaotra  PROPN       LOC    GD861398   \n",
-       "349                           Antananarivo   NOUN       LOC   GD3682867   \n",
-       "350                            lac Alaotra  PROPN       LOC    GD861398   \n",
-       "353                           Antananarivo  PROPN       LOC   GD3682867   \n",
-       "354                             Madagascar  PROPN       LOC   GD3404996   \n",
-       "356                            Montpellier  PROPN       LOC    GD128756   \n",
-       "357                                 France  PROPN       LOC   GD3117352   \n",
-       "361                                  Italy  PROPN       LOC   GD1333473   \n",
-       "366                            lac Alaotra  PROPN       LOC    GD861398   \n",
-       "367                             Madagascar  PROPN       LOC   GD3404996   \n",
-       "369                             Madagascar  PROPN       LOC   GD3404996   \n",
-       "373                            Lac Alaotra  PROPN       LOC    GD861398   \n",
-       "377                             Madagascar  PROPN       LOC   GD3404996   \n",
-       "379                             Madagascar  PROPN       LOC   GD3404996   \n",
-       "384                             Madagascar  PROPN       LOC   GD3404996   \n",
-       "385                            Lac Alaotra  PROPN       LOC    GD861398   \n",
-       "386                             Madagascar  PROPN       LOC   GD3404996   \n",
-       "397                             Madagascar  PROPN       LOC   GD3404996   \n",
-       "399                             Madagascar  PROPN       LOC   GD3404996   \n",
-       "400                            lac Alaotra  PROPN       LOC    GD861398   \n",
-       "403                                Paris I  PROPN       LOC   GD5400765   \n",
-       "404                             Madagascar  PROPN       LOC   GD3404996   \n",
-       "406                           Antananarivo  PROPN       LOC   GD3682867   \n",
-       "407                           Antananarivo   NOUN       LOC   GD3682867   \n",
-       "\n",
-       "       mord_GID  distance  \n",
-       "0    GD14200769       NaN  \n",
-       "1      GD861398       NaN  \n",
-       "5      GD861398       NaN  \n",
-       "7     GD3353681       NaN  \n",
-       "9     GD2891542       NaN  \n",
-       "10    GD3353681       NaN  \n",
-       "13    GD3938233       NaN  \n",
-       "14         None       NaN  \n",
-       "16     GD861398       NaN  \n",
-       "17    GD3404996       NaN  \n",
-       "20         None       NaN  \n",
-       "25     GD789632       NaN  \n",
-       "30     GD861398       NaN  \n",
-       "31    GD9268944       NaN  \n",
-       "32    GD3353681       NaN  \n",
-       "34    GD4111765       NaN  \n",
-       "35    GD3353681       NaN  \n",
-       "38    GD3353681       NaN  \n",
-       "46     GD861398       NaN  \n",
-       "47    GD3404996       NaN  \n",
-       "54     GD861398       NaN  \n",
-       "63    GD3404996       NaN  \n",
-       "65     GD861398       NaN  \n",
-       "66    GD3404996       NaN  \n",
-       "67     GD861398       NaN  \n",
-       "69     GD861398       NaN  \n",
-       "76    GD3404996       NaN  \n",
-       "77    GD3404996       NaN  \n",
-       "80         None       NaN  \n",
-       "81    GD2891542       NaN  \n",
-       "..          ...       ...  \n",
-       "335  GD14756294       NaN  \n",
-       "338    GD861398       NaN  \n",
-       "339   GD3404996       NaN  \n",
-       "341  GD11325234       NaN  \n",
-       "344   GD9519388       NaN  \n",
-       "345   GD3404996       NaN  \n",
-       "346    GD861398       NaN  \n",
-       "349  GD14756294       NaN  \n",
-       "350    GD861398       NaN  \n",
-       "353  GD14756294       NaN  \n",
-       "354   GD3404996       NaN  \n",
-       "356  GD11325234       NaN  \n",
-       "357   GD3117352       NaN  \n",
-       "361   GD1333473       NaN  \n",
-       "366    GD861398       NaN  \n",
-       "367   GD3404996       NaN  \n",
-       "369   GD3404996       NaN  \n",
-       "373    GD861398       NaN  \n",
-       "377   GD3404996       NaN  \n",
-       "379   GD3404996       NaN  \n",
-       "384   GD3404996       NaN  \n",
-       "385    GD861398       NaN  \n",
-       "386   GD3404996       NaN  \n",
-       "397   GD3404996       NaN  \n",
-       "399   GD3404996       NaN  \n",
-       "400    GD861398       NaN  \n",
-       "403        None       NaN  \n",
-       "404   GD3404996       NaN  \n",
-       "406  GD14756294       NaN  \n",
-       "407  GD14756294       NaN  \n",
-       "\n",
-       "[178 rows x 11 columns]"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
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-       "      <td>EDBM</td>\n",
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-       "      <td>LOC</td>\n",
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-       "    </tr>\n",
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-       "      <th>4</th>\n",
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-       "      <td>4</td>\n",
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-       "      <td>Stade</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
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-       "    </tr>\n",
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-       "      <th>5</th>\n",
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-       "      <td>5</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>Coopérative</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>Région</td>\n",
-       "      <td>NOUN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>7</td>\n",
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-       "      <td>7</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>Andasibe</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>GD3545507</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>8</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>Vetiver prévue</td>\n",
-       "      <td>VERB</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>9</td>\n",
-       "      <td>9</td>\n",
-       "      <td>9</td>\n",
-       "      <td>9</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>Atelier</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>10</td>\n",
-       "      <td>10</td>\n",
-       "      <td>10</td>\n",
-       "      <td>10</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>BV Lac</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>11</td>\n",
-       "      <td>11</td>\n",
-       "      <td>11</td>\n",
-       "      <td>11</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>Rappel</td>\n",
-       "      <td>NOUN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>12</td>\n",
-       "      <td>12</td>\n",
-       "      <td>12</td>\n",
-       "      <td>12</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>Cellule</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>13</td>\n",
-       "      <td>13</td>\n",
-       "      <td>13</td>\n",
-       "      <td>13</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>Commande</td>\n",
-       "      <td>PROPN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>14</td>\n",
-       "      <td>14</td>\n",
-       "      <td>14</td>\n",
-       "      <td>14</td>\n",
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-       "      <td>Bilan</td>\n",
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-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
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-       "      <td>15</td>\n",
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-       "      <td>DET</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>16</td>\n",
-       "      <td>16</td>\n",
-       "      <td>16</td>\n",
-       "      <td>16</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>l'Est</td>\n",
-       "      <td>DET</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>17</td>\n",
-       "      <td>17</td>\n",
-       "      <td>17</td>\n",
-       "      <td>17</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>Fokontany</td>\n",
-       "      <td>NOUN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>NR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>Directeur</td>\n",
-       "      <td>NOUN</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>19</td>\n",
-       "      <td>19</td>\n",
-       "      <td>19</td>\n",
-       "      <td>19</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>AUX</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>20</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>AUX</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>21</td>\n",
-       "      <td>21</td>\n",
-       "      <td>21</td>\n",
-       "      <td>21</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>FAUR</td>\n",
-       "      <td>SPACE</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>22</td>\n",
-       "      <td>22</td>\n",
-       "      <td>22</td>\n",
-       "      <td>22</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>Lot</td>\n",
-       "      <td>X</td>\n",
-       "      <td>LOC</td>\n",
-       "      <td>O</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Unnamed: 0  Unnamed: 0.1  Unnamed: 0.1.1  Unnamed: 0.1.1.1  diff2  \\\n",
-       "0            0             0               0                 0    0.0   \n",
-       "1            1             1               1                 1    1.0   \n",
-       "2            2             2               2                 2    2.0   \n",
-       "3            3             3               3                 3    3.0   \n",
-       "4            4             4               4                 4    4.0   \n",
-       "5            5             5               5                 5    5.0   \n",
-       "6            6             6               6                 6    6.0   \n",
-       "7            7             7               7                 7    7.0   \n",
-       "8            8             8               8                 8    8.0   \n",
-       "9            9             9               9                 9    9.0   \n",
-       "10          10            10              10                10   10.0   \n",
-       "11          11            11              11                11   11.0   \n",
-       "12          12            12              12                12   12.0   \n",
-       "13          13            13              13                13   13.0   \n",
-       "14          14            14              14                14   14.0   \n",
-       "15          15            15              15                15   15.0   \n",
-       "16          16            16              16                16   16.0   \n",
-       "17          17            17              17                17   17.0   \n",
-       "18          18            18              18                18   18.0   \n",
-       "19          19            19              19                19   19.0   \n",
-       "20          20            20              20                20   20.0   \n",
-       "21          21            21              21                21   21.0   \n",
-       "22          22            22              22                22   22.0   \n",
-       "\n",
-       "              text   pos_ ent_type_        GID  \n",
-       "0          Souhait    AUX       LOC          O  \n",
-       "1       Madagascar  PROPN       LOC  GD3404996  \n",
-       "2           Suisse  PROPN       LOC          O  \n",
-       "3             EDBM   NOUN       LOC          O  \n",
-       "4            Stade  PROPN       LOC          O  \n",
-       "5      Coopérative  PROPN       LOC          O  \n",
-       "6           Région   NOUN       LOC          O  \n",
-       "7         Andasibe  PROPN       LOC  GD3545507  \n",
-       "8   Vetiver prévue   VERB       LOC          O  \n",
-       "9          Atelier  PROPN       LOC          O  \n",
-       "10          BV Lac  PROPN       LOC          O  \n",
-       "11          Rappel   NOUN       LOC          O  \n",
-       "12         Cellule  PROPN       LOC          O  \n",
-       "13        Commande  PROPN       LOC          O  \n",
-       "14           Bilan   NOUN       LOC          O  \n",
-       "15           l'Est    DET       LOC          O  \n",
-       "16           l'Est    DET       LOC          O  \n",
-       "17       Fokontany   NOUN       LOC         NR  \n",
-       "18       Directeur   NOUN       LOC          O  \n",
-       "19               A    AUX       LOC          O  \n",
-       "20               A    AUX       LOC          O  \n",
-       "21          FAUR    SPACE       LOC          O  \n",
-       "22             Lot      X       LOC          O  "
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "truth_data[941]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks_old/NER Evaluation.ipynb b/notebooks_old/NER Evaluation.ipynb
deleted file mode 100644
index d01c1dc..0000000
--- a/notebooks_old/NER Evaluation.ipynb	
+++ /dev/null
@@ -1,1208 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:19:37.341397Z",
-     "start_time": "2018-05-08T15:19:37.337211Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/Users/jacquesfize/nas_cloud/Code/str-python\n"
-     ]
-    }
-   ],
-   "source": [
-    "%cd .."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:19:37.412267Z",
-     "start_time": "2018-05-08T15:19:37.343429Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import os,sys,re,glob\n",
-    "from elasticsearch import Elasticsearch\n",
-    "es_client=Elasticsearch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:19:37.738728Z",
-     "start_time": "2018-05-08T15:19:37.414740Z"
-    },
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "docs=set([])\n",
-    "def load_data(annotation_file):\n",
-    "    data=json.load(open(annotation_file))\n",
-    "    return data\n",
-    "\n",
-    "def get_spatial_entities(annotation_data):\n",
-    "    data={}\n",
-    "    for id_,ann in annotation_data.items():\n",
-    "\n",
-    "        if \"type\" in ann:\n",
-    "            if ann[\"type\"] == \"location\" and ann[\"annotation\"] == \"correct\":\n",
-    "                data[id_]=ann\n",
-    "            \n",
-    "                \n",
-    "    return data\n",
-    "\n",
-    "def get_all_annotation_data(directory):\n",
-    "    files = glob.glob(os.path.join(directory,\"*.json\"))\n",
-    "    annotations={}\n",
-    "    for filepath in files:\n",
-    "        id_doc=int(re.findall(\"\\d+\",filepath)[-1])\n",
-    "        annotations[id_doc]=get_spatial_entities(load_data(filepath))\n",
-    "    return annotations\n",
-    "\n",
-    "def get_text_data(directory):\n",
-    "    files = glob.glob(os.path.join(directory,\"*.json.processed.json\"))\n",
-    "    texts={}\n",
-    "    for filepath in files:\n",
-    "        id_doc=int(re.findall(\"\\d+\",filepath)[-1])\n",
-    "        data=json.load(open(filepath))[\"content\"]\n",
-    "        texts[id_doc]=data\n",
-    "    return texts\n",
-    "        \n",
-    "    \n",
-    "ann_data=get_all_annotation_data(\"data/EPI_ELENA/fully_annoted_es_data/\")\n",
-    "texts=get_text_data(\"data/EPI_ELENA/data/\") # Raw text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:19:37.750366Z",
-     "start_time": "2018-05-08T15:19:37.740586Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'0': {'annotation': 'correct',\n",
-       "  'content': ' Ogun   State  ',\n",
-       "  'index': 3,\n",
-       "  'info': {'coordinates': [7.0, 3.58333],\n",
-       "   'countryCode': 'NG',\n",
-       "   'featureClass': 'A',\n",
-       "   'featureCode': 'ADM1',\n",
-       "   'id': 2327546},\n",
-       "  'length': '2',\n",
-       "  'type': 'location'},\n",
-       " '16': {'annotation': 'correct',\n",
-       "  'content': ' Nigeria  ',\n",
-       "  'index': 227,\n",
-       "  'info': {'coordinates': [10.0, 8.0],\n",
-       "   'countryCode': 'NG',\n",
-       "   'featureClass': 'A',\n",
-       "   'featureCode': 'PCLI',\n",
-       "   'id': 2328926},\n",
-       "  'length': '1',\n",
-       "  'type': 'location'},\n",
-       " '6': {'annotation': 'correct',\n",
-       "  'content': ' Abeokuta  ',\n",
-       "  'index': 45,\n",
-       "  'info': {'coordinates': [7.15571, 3.34509],\n",
-       "   'countryCode': 'NG',\n",
-       "   'featureClass': 'P',\n",
-       "   'featureCode': 'PPLA',\n",
-       "   'id': 2352947},\n",
-       "  'length': '1',\n",
-       "  'type': 'location'}}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ann_data[1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Chargement des Données"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Transformation des données\n",
-    "\n",
-    "Sachant que les entités spatiales sont liés à Geonames, on doit faire le lien avec notre gazetier (Geodict). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:19:37.754389Z",
-     "start_time": "2018-05-08T15:19:37.752283Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "#missed_again"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:19:37.768916Z",
-     "start_time": "2018-05-08T15:19:37.756469Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def get_geodict_id(geonameID):\n",
-    "    global es_client\n",
-    "    data=es_client.search(\"gazetteer\",\"place\",{\"query\":{\"bool\":{\"must\":[{\"term\":{\"geonameID\":geonameID}}]}}})\n",
-    "    #print(data)\n",
-    "    if data[\"hits\"][\"total\"] < 1:\n",
-    "        return False\n",
-    "    else:\n",
-    "        return data[\"hits\"][\"hits\"][0][\"_source\"][\"id\"]\n",
-    "def transform_annotation_data(ann_data):\n",
-    "    for id_,doc in ann_data.items():\n",
-    "        to_del=[]\n",
-    "        for ind_ in doc.keys():\n",
-    "            \n",
-    "            if str(doc[ind_][\"info\"][\"id\"]) in ['149753', '7874184']: # Bad annotation and no Geonames Entry Associated\n",
-    "                to_del.append(ind_) \n",
-    "            else:\n",
-    "                ann_data[id_][ind_][\"geodict_id\"]=get_geodict_id(str(doc[ind_][\"info\"][\"id\"]))\n",
-    "        i=0\n",
-    "        for de in to_del:\n",
-    "            del ann_data[id_][de]\n",
-    "            i+=1\n",
-    "    return ann_data\n",
-    "    \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:19:41.186582Z",
-     "start_time": "2018-05-08T15:19:37.771576Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ann_data=transform_annotation_data(ann_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:19:42.471682Z",
-     "start_time": "2018-05-08T15:19:41.188081Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from pipeline import *\n",
-    "from nlp.pos_tagger.tagger import Tagger\n",
-    "from nlp.disambiguator.pagerank import *\n",
-    "from nlp.disambiguator.geodict_gaurav import *\n",
-    "from nlp.pos_tagger.treetagger import TreeTagger\n",
-    "from nlp.ner.stanford_ner import StanfordNER\n",
-    "from nlp.ner.polyglot import Polyglot\n",
-    "from nlp.ner.nltk import NLTK\n",
-    "from nlp.ner.gate_annie import GateAnnie\n",
-    "from nlp.ner.spacy import Spacy\n",
-    "from nlp.ner.ner import NER\n",
-    "from progressbar import ProgressBar\n",
-    "from polyglot.text import Text\n",
-    "\n",
-    "\n",
-    "from nlp.disambiguator.disambiguator import Disambiguator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:19:43.131232Z",
-     "start_time": "2018-05-08T15:19:42.473854Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:5: DeprecationWarning: Call to deprecated class Spacy (Not finished yet !).\n",
-      "  \"\"\"\n",
-      "/usr/local/lib/python3.6/site-packages/msgpack_numpy.py:84: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead\n",
-      "  dtype=np.dtype(descr)).reshape(obj[b'shape'])\n",
-      "/usr/local/lib/python3.6/site-packages/msgpack_numpy.py:88: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead\n",
-      "  dtype=np.dtype(descr))[0]\n"
-     ]
-    }
-   ],
-   "source": [
-    "pipStanford=Pipeline(lang=\"english\",tagger=Tagger(),ner=StanfordNER(lang=\"en\"))\n",
-    "pipNLTK=Pipeline(lang=\"english\",tagger=Tagger(),ner=NLTK(lang=\"en\"))\n",
-    "pipPolyglot=Pipeline(lang=\"english\",tagger=Tagger(),ner=Polyglot())\n",
-    "pipGate=Pipeline(lang=\"english\",tagger=Tagger(),ner=GateAnnie(lang=\"en\"))\n",
-    "pipSpacy=Pipeline(lang=\"english\",tagger=Tagger(),ner=Spacy(lang=\"en\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Évaluation des différents NER\n",
-    "\n",
-    "Dans cette partie, on évalue les différents NER sur le corpus EPI de 500 documents.\n",
-    "\n",
-    "### Identification des EN\n",
-    "\n",
-    "Dans cette première expérimentation, on veut évaluer si quelles NER identifie le mieux **au moins une occurrence de chaque EN dans un texte**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:23:08.238218Z",
-     "start_time": "2018-05-08T15:23:08.179062Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<input>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "<ipython-input-13-b29052c515c1>:2: DeprecationWarning: invalid escape sequence \\s\n",
-      "  return re.sub(\"[!]+\",\" \",re.sub(\"\\s\",\"!!\",label.strip()))\n"
-     ]
-    }
-   ],
-   "source": [
-    "def parse_epi_labels(label):\n",
-    "    return re.sub(\"[!]+\",\" \",re.sub(\"\\s\",\"!!\",label.strip()))\n",
-    "def extract_label_only(ann_data):\n",
-    "    res=set([])\n",
-    "    for a in ann_data:\n",
-    "        res.add(parse_epi_labels(ann_data[a][\"content\"]))\n",
-    "    return res\n",
-    "def ner_recall_epi(texts,ann_data,pipeline):\n",
-    "    sum,total,nb=0,0,0\n",
-    "    with ProgressBar(max_value=len(texts)) as bar:\n",
-    "        for i in texts:\n",
-    "            if not i in ann_data or not texts[i]:continue\n",
-    "            try:\n",
-    "                _,output,spat_entities=pipeline.parse(texts[i])\n",
-    "            except:\n",
-    "                #print(texts[i])\n",
-    "                continue\n",
-    "            out = Disambiguator.parse_corpus(output)\n",
-    "\n",
-    "            all_en_ = out[out[:, 1] == NER._unified_tag[\"place\"]][:, 0]\n",
-    "            spat_entities=set(all_en_)\n",
-    "\n",
-    "            epi_ann=extract_label_only(ann_data[i])\n",
-    "            #print(spat_entities,epi_ann)\n",
-    "            if len(epi_ann) <1:continue\n",
-    "            sum+=(len(spat_entities.intersection(epi_ann))/len(epi_ann))\n",
-    "            total+=1\n",
-    "            \n",
-    "            nb+=1\n",
-    "            bar.update(nb)\n",
-    "    print(total,nb)\n",
-    "    return sum/total\n",
-    "\n",
-    "def ner_precision_epi(texts,ann_data,pipeline):\n",
-    "    sum,total,nb=0,0,0\n",
-    "    with ProgressBar(max_value=len(texts)) as bar:\n",
-    "        for i in texts:\n",
-    "            if not i in ann_data or not texts[i]:continue\n",
-    "            try:\n",
-    "                _,output,spat_entities=pipeline.parse(texts[i])\n",
-    "            except:\n",
-    "                #print(texts[i])\n",
-    "                continue\n",
-    "            out = Disambiguator.parse_corpus(output)\n",
-    "            all_en_ = out[out[:, 1] == NER._unified_tag[\"place\"]][:, 0]\n",
-    "            spat_entities=set(all_en_)\n",
-    "            epi_ann=extract_label_only(ann_data[i])\n",
-    "            if len(epi_ann) <1:\n",
-    "                nb+=1\n",
-    "                continue\n",
-    "            if len(spat_entities)<1:continue\n",
-    "            sum+=(len(spat_entities.intersection(epi_ann))/len(spat_entities))\n",
-    "            total+=1\n",
-    "            nb+=1\n",
-    "            bar.update(nb)\n",
-    "    print(total,nb)\n",
-    "    return sum/total"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Identifications des différentes occurences des EN\n",
-    "\n",
-    "Contrairement à la partie précédente, on va chercher ici à savoir si le modèles est capable d'identifier ** les EN mais surtout toutes leurs apparitions dans le texte**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:19:43.278565Z",
-     "start_time": "2018-05-08T15:19:43.190533Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def extract_label_only_all(ann_data):\n",
-    "    res = []\n",
-    "    sorted_ = sorted(ann_data.items(), key=lambda x: int(x[0]))\n",
-    "    for a in sorted_:\n",
-    "        res.append(parse_epi_labels(a[1][\"content\"]))\n",
-    "    return res\n",
-    "\n",
-    "\n",
-    "def ner_recall_epi_all(texts, ann_data, pipeline):\n",
-    "    sum, total, nb = 0, 0, 0\n",
-    "    with ProgressBar(max_value=len(texts)) as bar:\n",
-    "        for i in texts:\n",
-    "            if not i in ann_data or not texts[i]:\n",
-    "                continue\n",
-    "            try:\n",
-    "                _,output, spat_entities = pipeline.parse(texts[i])\n",
-    "            except:\n",
-    "                # print(texts[i])\n",
-    "                continue\n",
-    "            out = Disambiguator.parse_corpus(output)\n",
-    "\n",
-    "            all_en_ = out[out[:, 1] == NER._unified_tag[\"place\"]][:, 0]\n",
-    "            epi_ann = extract_label_only_all(ann_data[i])\n",
-    "\n",
-    "            count_en_ann = count_per_tp(epi_ann)\n",
-    "            count_en_ner = count_per_tp(all_en_)\n",
-    "            # print(\"epi_ann\",epi_ann,\"all_en\",all_en_)\n",
-    "            # print(\"ann\",count_en_ann,\"ner\",count_en_ner)\n",
-    "            sc, tt = 0, 0\n",
-    "            for en_ in count_en_ann:\n",
-    "                tt += count_en_ann[en_]\n",
-    "                if en_ in count_en_ner:\n",
-    "                    if not count_en_ner[en_] > count_en_ann[en_]:\n",
-    "                        sc += count_en_ner[en_]\n",
-    "                    else:\n",
-    "                        sc += count_en_ann[en_]\n",
-    "            if tt < 1:\n",
-    "                continue\n",
-    "            sum += sc / tt\n",
-    "            total += 1\n",
-    "\n",
-    "            nb += 1\n",
-    "            bar.update(nb)\n",
-    "    print(total, nb)\n",
-    "    return sum / total\n",
-    "\n",
-    "\n",
-    "def count_per_tp(input_):\n",
-    "    count = {}\n",
-    "    for i in input_:\n",
-    "        if not i in count:\n",
-    "            count[i] = 0\n",
-    "        count[i] += 1\n",
-    "    return count\n",
-    "\n",
-    "\n",
-    "def ner_precision_epi_all(texts, ann_data, pipeline):\n",
-    "    sum, total, nb = 0, 0, 0\n",
-    "    with ProgressBar(max_value=len(texts)) as bar:\n",
-    "        for i in texts:\n",
-    "            if not i in ann_data or not texts[i]:\n",
-    "                continue\n",
-    "            try:\n",
-    "                _,output, spat_entities = pipeline.parse(texts[i])\n",
-    "            except:\n",
-    "                #print(texts[i])\n",
-    "                continue\n",
-    "            out = Disambiguator.parse_corpus(output)\n",
-    "\n",
-    "            all_en_ = out[out[:, 1] == NER._unified_tag[\"place\"]][:, 0]\n",
-    "            epi_ann = extract_label_only_all(ann_data[i])\n",
-    "\n",
-    "            count_en_ann = count_per_tp(epi_ann)\n",
-    "            count_en_ner = count_per_tp(all_en_)\n",
-    "            sc = 0\n",
-    "            for en_r in all_en_:\n",
-    "                if en_r in set(epi_ann):\n",
-    "                    sc += 1\n",
-    "            if len(epi_ann) < 1:\n",
-    "                nb += 1\n",
-    "                continue\n",
-    "            if len(all_en_) < 1:\n",
-    "                continue\n",
-    "            sum += sc / len(all_en_)\n",
-    "            total += 1\n",
-    "            nb += 1\n",
-    "            bar.update(nb)\n",
-    "    print(total, nb)\n",
-    "    return sum / total"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:25:06.163620Z",
-     "start_time": "2018-05-08T15:23:14.468336Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
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-    },
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-      "464 464\n"
-     ]
-    },
-    {
-     "name": "stderr",
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-    {
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-     "output_type": "stream",
-     "text": [
-      "464 501\n"
-     ]
-    }
-   ],
-   "source": [
-    "rec_spacy = ner_recall_epi(texts, ann_data, pipSpacy)\n",
-    "prec_spacy = ner_precision_epi(texts, ann_data, pipSpacy)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:37:02.477840Z",
-     "start_time": "2018-05-08T15:25:25.959366Z"
-    }
-   },
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-    },
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-     ]
-    },
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-     "name": "stderr",
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "443 478\n"
-     ]
-    }
-   ],
-   "source": [
-    "rec_SFNER = ner_recall_epi(texts, ann_data, pipStanford)\n",
-    "prec_SFNER = ner_precision_epi(texts, ann_data, pipStanford)\n",
-    "\n",
-    "rec_poly = ner_recall_epi(texts, ann_data, pipPolyglot)\n",
-    "prec_poly = ner_precision_epi(texts, ann_data, pipPolyglot)\n",
-    "\n",
-    "rec_nltk = ner_recall_epi(texts, ann_data, pipNLTK)\n",
-    "prec_nltk = ner_precision_epi(texts, ann_data, pipNLTK)\n",
-    "\n",
-    "rec_gate = ner_recall_epi(texts, ann_data, pipGate)\n",
-    "prec_gate = ner_precision_epi(texts, ann_data, pipGate)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:48:32.390113Z",
-     "start_time": "2018-05-08T15:37:09.480442Z"
-    }
-   },
-   "outputs": [
-    {
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-     ]
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-      "465 465\n"
-     ]
-    },
-    {
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-    },
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-      "463 500\n"
-     ]
-    },
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-     "output_type": "stream",
-     "text": [
-      "443 478\n"
-     ]
-    }
-   ],
-   "source": [
-    "rec_SFNER_all = ner_recall_epi_all(texts, ann_data, pipStanford)\n",
-    "prec_SFNER_all = ner_precision_epi_all(texts, ann_data, pipStanford)\n",
-    "\n",
-    "rec_poly_all = ner_recall_epi_all(texts, ann_data, pipPolyglot)\n",
-    "prec_poly_all = ner_precision_epi_all(texts, ann_data, pipPolyglot)\n",
-    "\n",
-    "rec_nltk_all = ner_recall_epi_all(texts, ann_data, pipNLTK)\n",
-    "prec_nltk_all = ner_precision_epi_all(texts, ann_data, pipNLTK)\n",
-    "\n",
-    "rec_gate_all = ner_recall_epi_all(texts, ann_data, pipGate)\n",
-    "prec_gate_all = ner_precision_epi_all(texts, ann_data, pipGate)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:50:37.503408Z",
-     "start_time": "2018-05-08T15:48:45.036943Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100% (532 of 532) |######################| Elapsed Time: 0:00:56 Time:  0:00:56\n",
-      "N/A% (0 of 532) |                        | Elapsed Time: 0:00:00 ETA:  --:--:--"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "464 464\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "464 501\n"
-     ]
-    }
-   ],
-   "source": [
-    "rec_spacy_all = ner_recall_epi_all(texts, ann_data, pipSpacy)\n",
-    "prec_spacy_all = ner_precision_epi_all(texts, ann_data, pipSpacy)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:51:07.306398Z",
-     "start_time": "2018-05-08T15:51:07.295232Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "cols=[\"NER\",\"Precision(ID)\",\"Recall(ID)\",\"Precision(ALL)\",\"Recall(ALL)\"]\n",
-    "df=pd.DataFrame(columns=cols)\n",
-    "df=pd.DataFrame([[\"StanfordNER\",prec_SFNER,rec_SFNER,prec_SFNER_all,rec_SFNER_all],\n",
-    "                        [\"Polyglot\",prec_poly,rec_poly,prec_poly_all,rec_poly_all],[\"NLTK\",prec_nltk,rec_nltk,prec_nltk_all,rec_nltk_all],\n",
-    "                       [\"GATE\",prec_gate,rec_gate,prec_gate_all,rec_gate_all],\n",
-    "                [\"Spacy\",prec_spacy,rec_spacy,prec_spacy_all,rec_spacy_all]],columns=cols)\n",
-    "df[\"F-Measure(D)\"]= df.apply(lambda x: 2*((x[\"Precision(ID)\"]*x[\"Recall(ID)\"])/(x[\"Precision(ID)\"]+x[\"Recall(ID)\"])), axis=1)\n",
-    "df[\"F-Measure(ALL)\"]= df.apply(lambda x: 2*((x[\"Precision(ALL)\"]*x[\"Recall(ALL)\"])/(x[\"Precision(ALL)\"]+x[\"Recall(ALL)\"])), axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:51:08.070983Z",
-     "start_time": "2018-05-08T15:51:08.058901Z"
-    }
-   },
-   "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>NER</th>\n",
-       "      <th>Precision(ID)</th>\n",
-       "      <th>Recall(ID)</th>\n",
-       "      <th>Precision(ALL)</th>\n",
-       "      <th>Recall(ALL)</th>\n",
-       "      <th>F-Measure(D)</th>\n",
-       "      <th>F-Measure(ALL)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>StanfordNER</td>\n",
-       "      <td>0.594245</td>\n",
-       "      <td>0.771514</td>\n",
-       "      <td>0.666652</td>\n",
-       "      <td>0.718504</td>\n",
-       "      <td>0.671375</td>\n",
-       "      <td>0.691608</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Polyglot</td>\n",
-       "      <td>0.532444</td>\n",
-       "      <td>0.724127</td>\n",
-       "      <td>0.608216</td>\n",
-       "      <td>0.666334</td>\n",
-       "      <td>0.613666</td>\n",
-       "      <td>0.635950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>NLTK</td>\n",
-       "      <td>0.429511</td>\n",
-       "      <td>0.665637</td>\n",
-       "      <td>0.497519</td>\n",
-       "      <td>0.617828</td>\n",
-       "      <td>0.522119</td>\n",
-       "      <td>0.551185</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>GATE</td>\n",
-       "      <td>0.578102</td>\n",
-       "      <td>0.626061</td>\n",
-       "      <td>0.633567</td>\n",
-       "      <td>0.585320</td>\n",
-       "      <td>0.601126</td>\n",
-       "      <td>0.608488</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Spacy</td>\n",
-       "      <td>0.406803</td>\n",
-       "      <td>0.652530</td>\n",
-       "      <td>0.404245</td>\n",
-       "      <td>0.616491</td>\n",
-       "      <td>0.501167</td>\n",
-       "      <td>0.488301</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           NER  Precision(ID)  Recall(ID)  Precision(ALL)  Recall(ALL)  \\\n",
-       "0  StanfordNER       0.594245    0.771514        0.666652     0.718504   \n",
-       "1     Polyglot       0.532444    0.724127        0.608216     0.666334   \n",
-       "2         NLTK       0.429511    0.665637        0.497519     0.617828   \n",
-       "3         GATE       0.578102    0.626061        0.633567     0.585320   \n",
-       "4        Spacy       0.406803    0.652530        0.404245     0.616491   \n",
-       "\n",
-       "   F-Measure(D)  F-Measure(ALL)  \n",
-       "0      0.671375        0.691608  \n",
-       "1      0.613666        0.635950  \n",
-       "2      0.522119        0.551185  \n",
-       "3      0.601126        0.608488  \n",
-       "4      0.501167        0.488301  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T15:20:22.410670Z",
-     "start_time": "2018-05-08T15:19:38.062Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from tabulate import tabulate\n",
-    "print(tabulate(df, headers='keys', tablefmt='pipe'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  },
-  "toc": {
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": "block",
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
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diff --git a/notebooks_old/SimiGraph.ipynb b/notebooks_old/SimiGraph.ipynb
deleted file mode 100644
index 5f3d779..0000000
--- a/notebooks_old/SimiGraph.ipynb
+++ /dev/null
@@ -1,475 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-29T16:29:17.829765Z",
-     "start_time": "2018-10-29T16:29:16.807906Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import networkx as nx\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import glob,re, os, sys\n",
-    "from strpython.helpers.sim_matrix import read_bz2_matrix\n",
-    "import json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-16T06:12:04.324113Z",
-     "start_time": "2018-10-16T06:12:04.320270Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def megaGraph(sim_matrix):\n",
-    "    gr_=nx.DiGraph()\n",
-    "    gr_.add_nodes_from([i for i in range(sim_matrix.shape[0])])\n",
-    "    for i in range(sim_matrix.shape[0]):\n",
-    "        index_=np.argsort(sim_matrix[i])[::-1][1:6]\n",
-    "        for vi in index_:\n",
-    "            gr_.add_edge(i,vi,weight=sim_matrix[i,vi])\n",
-    "    return gr_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-29T16:29:33.519354Z",
-     "start_time": "2018-10-29T16:29:33.515635Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "raw_text_path=\"../data/corpora/EPI_ELENA/raw_text/\"\n",
-    "\n",
-    "path_matrixPADI=\"../data/sim_matrix/padiWeb/VertexEdgeOverlap_data_graph_graph_exp_fev_18_extension_1.npy\"\n",
-    "path_matrixBOWPADI=\"../mat_padi_bow_tf.npy\"\n",
-    "\n",
-    "path_matrixMADA=\"../data/sim_matrix/Agro_mada_all/MCS_data_graph_graph_exp_july_19_gen_region.npy.bz2\"\n",
-    "path_matrixBOWMADA=\"../data/corpora/mat_bvlac_bow_5552.npy\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-29T16:29:34.352742Z",
-     "start_time": "2018-10-29T16:29:34.333386Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "sim_matrixPADI=np.load(path_matrixPADI)\n",
-    "d=[0, 1, 10, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 11, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 12, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 13, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 14, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 15, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 16, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 17, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 18, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 19, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 2, 20, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 21, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 22, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 23, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 24, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 25, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 26, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 27, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 28, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 29, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 3, 30, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 31, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 32, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 33, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 34, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 35, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 36, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 37, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 38, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 39, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 4, 40, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 41, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 42, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 43, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 44, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 45, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 46, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 47, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 48, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 49, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 5, 50, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 51, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 52, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 53, 530, 531, 54, 55, 56, 57, 58, 59, 6, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 7, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 8, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 9, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]\n",
-    "sim_matrixPADI=sim_matrixPADI[np.argsort(d)]\n",
-    "sim_matrixPADI=sim_matrixPADI[:,np.argsort(d)]\n",
-    "sim_matrixBOWPADI=np.load(path_matrixBOWPADI)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-29T16:36:19.323489Z",
-     "start_time": "2018-10-29T16:36:05.771244Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "_5552=json.load(open(\"../data/corpora/5552_doc.json\"))\n",
-    "sim_matrixMADA=read_bz2_matrix(path_matrixMADA)\n",
-    "sim_matrixMADA=sim_matrixMADA[_5552]\n",
-    "sim_matrixMADA=sim_matrixMADA[:,_5552]\n",
-    "sim_matrixBOWMADA=np.load(path_matrixBOWMADA)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-29T16:38:03.736203Z",
-     "start_time": "2018-10-29T16:38:03.731322Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([4011, 4899, 1381, 5014,  526])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argsort(sim_matrixMADA[5])[::-1][1:6]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-16T06:13:54.618428Z",
-     "start_time": "2018-10-16T06:13:54.573302Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "sim_matrixMADA[np.isnan(sim_matrixMADA)]=0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-05T07:39:58.441510Z",
-     "start_time": "2018-10-05T07:39:55.252723Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "nx.write_gexf(megaGraph(sim_matrixBOWMADA),\"mada5552THEGraph.gexf\")\n",
-    "nx.write_gexf(megaGraph(sim_matrixMADA),\"mada5552SPAGraph.gexf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-02T16:11:06.281945Z",
-     "start_time": "2018-10-02T16:11:06.139397Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "nx.write_gexf(megaGraph(sim_matrixBOWPADI),\"padiBOWGraph.gexf\")\n",
-    "nx.write_gexf(megaGraph(sim_matrix),\"padiGraph.gexf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-02T16:14:03.315262Z",
-     "start_time": "2018-10-02T16:14:03.311801Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.7331793324454037"
-      ]
-     },
-     "execution_count": 110,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sim_matrixBOWPADI[431,115]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-02T16:14:01.094531Z",
-     "start_time": "2018-10-02T16:14:01.090516Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.2926829268292683"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sim_matrix[431,115]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-05T08:06:54.663572Z",
-     "start_time": "2018-10-05T08:06:53.717156Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from strpython.models.str import STR"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-16T06:14:11.532449Z",
-     "start_time": "2018-10-16T06:14:10.429070Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "gr=megaGraph(sim_matrixMADA)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-16T06:14:20.729206Z",
-     "start_time": "2018-10-16T06:14:17.514951Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "graph_vocab=set([])\n",
-    "belong_to={}\n",
-    "for n in range(sim_matrixMADA.shape[0]):\n",
-    "    try:\n",
-    "        g=nx.read_gexf(\"../data/graph_data/graph_exp_july_19/normal/{0}.gexf\".format(n))\n",
-    "        [graph_vocab.add(node) for node in list(g.nodes())]\n",
-    "        for node in list(g.nodes()):\n",
-    "            if not node in belong_to:belong_to[node]=[]\n",
-    "            belong_to[node].append(n)\n",
-    "    except:\n",
-    "        continue\n",
-    "graph_vocab=list(graph_vocab)\n",
-    "for node in belong_to:\n",
-    "    belong_to[node]=list(set(belong_to[node]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-16T06:14:32.692875Z",
-     "start_time": "2018-10-16T06:14:32.685504Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "new_gr=nx.Graph()\n",
-    "new_gr.add_nodes_from(graph_vocab)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2018-10-16T06:14:37.050Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "81/2434"
-     ]
-    }
-   ],
-   "source": [
-    "nodes=list(new_gr.nodes)\n",
-    "i=0\n",
-    "for n1 in nodes:\n",
-    "    i+=1\n",
-    "    sys.stdout.write(\"\\r{0}/{1}\".format(i,len(nodes)))\n",
-    "    for n2 in nodes:\n",
-    "        if n1 == n2:continue\n",
-    "        if (n1,n2) in new_gr.edges:continue\n",
-    "        b1,b2=belong_to[n1],belong_to[n2]\n",
-    "        values_sim=[]\n",
-    "        for d1 in b1:\n",
-    "            for d2 in b2:\n",
-    "                if (d1,d2) in gr.edges():\n",
-    "                    values_sim.append(sim_matrixMADA[d1,d2])\n",
-    "                elif (d2,d1) in gr.edges():\n",
-    "                    values_sim.append(sim_matrixMADA[d1,d2])\n",
-    "        if values_sim:\n",
-    "            new_gr.add_edge(n1,n2,weight=np.mean(values_sim))\n",
-    "        \n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-15T22:24:19.454427Z",
-     "start_time": "2018-10-15T22:24:19.451862Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import gazpy as ga\n",
-    "from elasticsearch import Elasticsearch\n",
-    "gd=ga.Geodict(Elasticsearch())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-15T22:28:01.378647Z",
-     "start_time": "2018-10-15T22:27:51.798944Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "for n1 in nodes:\n",
-    "    data=gd.get_by_id(n1)[0].coord\n",
-    "    new_gr.node[n1][\"lat\"]=data[\"lat\"]\n",
-    "    new_gr.node[n1][\"lon\"]=data[\"lon\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-15T22:26:42.218605Z",
-     "start_time": "2018-10-15T22:26:42.214414Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'weight': 0}"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_gr.node[\"GD304619\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-10-15T22:29:04.653162Z",
-     "start_time": "2018-10-15T22:28:28.011774Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "nx.write_gexf(new_gr,\"test_es.gexf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  },
-  "toc": {
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": "block",
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks_old/StanfordMadaAgro.ipynb b/notebooks_old/StanfordMadaAgro.ipynb
deleted file mode 100644
index fb4d66d..0000000
--- a/notebooks_old/StanfordMadaAgro.ipynb
+++ /dev/null
@@ -1,940 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-15T05:07:11.698091Z",
-     "start_time": "2018-05-15T05:07:11.253243Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-15T05:07:12.270692Z",
-     "start_time": "2018-05-15T05:07:12.257655Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "selected=pd.read_csv(\"/Users/jacquesfize/LOD_DATASETS/selected_mada.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-15T05:07:12.613016Z",
-     "start_time": "2018-05-15T05:07:12.610457Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "base_dir='/Users/jacquesfize/LOD_DATASETS/raw_bvlac/'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-15T05:07:12.920064Z",
-     "start_time": "2018-05-15T05:07:12.914272Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/Users/jacquesfize/nas_cloud/Code/str-python\n"
-     ]
-    }
-   ],
-   "source": [
-    "cd .."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-15T05:07:14.617383Z",
-     "start_time": "2018-05-15T05:07:13.522309Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from pipeline import *\n",
-    "from nlp.ner.stanford_ner import StanfordNER"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-15T05:07:14.850467Z",
-     "start_time": "2018-05-15T05:07:14.760004Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data_lang=pd.DataFrame(data=list(\n",
-    "    json.load(open(\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac/associated_lang.json\")).items()),\n",
-    "    columns=[\"id_doc\",\"lang\"]\n",
-    ")\n",
-    "data_lang[\"id_doc\"]=data_lang[\"id_doc\"].astype(int)\n",
-    "selected[\"id_doc\"]=selected[\"id_doc\"].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T17:11:00.213069Z",
-     "start_time": "2018-05-08T17:11:00.208408Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-15T05:07:15.813223Z",
-     "start_time": "2018-05-15T05:07:15.808854Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data_lang=data_lang[data_lang[\"id_doc\"].isin(selected[\"id_doc\"])]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-15T05:07:16.101978Z",
-     "start_time": "2018-05-15T05:07:16.096855Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data_lang=data_lang.set_index(\"id_doc\")\n",
-    "selected=selected.set_index(\"id_doc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-15T05:07:16.371936Z",
-     "start_time": "2018-05-15T05:07:16.368656Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "selected[\"lang\"]=data_lang[\"lang\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-15T05:07:16.794152Z",
-     "start_time": "2018-05-15T05:07:16.775373Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['fr', 'en'], dtype=object)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "selected[\"lang\"].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-15T05:07:17.265106Z",
-     "start_time": "2018-05-15T05:07:17.261840Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "pipeline= {\n",
-    "    \"en\":Pipeline(lang=\"english\",tagger=Tagger(),ner=StanfordNER(lang=\"en\")),\n",
-    "    \"fr\":Pipeline(lang=\"french\",tagger=Tagger(),ner=StanfordNER(lang=\"fr\"))\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-15T05:07:17.802814Z",
-     "start_time": "2018-05-15T05:07:17.795266Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "dfEn=selected[selected.lang == \"en\"]\n",
-    "dfFr=selected[selected.lang == \"fr\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-15T05:07:18.417499Z",
-     "start_time": "2018-05-15T05:07:18.395886Z"
-    }
-   },
-   "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>Unnamed: 0</th>\n",
-       "      <th>count</th>\n",
-       "      <th>format</th>\n",
-       "      <th>lang</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id_doc</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>12</td>\n",
-       "      <td>txt</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000</th>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>txt</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1001</th>\n",
-       "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>pdf</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1002</th>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>docx</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1003</th>\n",
-       "      <td>26</td>\n",
-       "      <td>11</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1004</th>\n",
-       "      <td>37</td>\n",
-       "      <td>11</td>\n",
-       "      <td>xls</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10044</th>\n",
-       "      <td>41</td>\n",
-       "      <td>5</td>\n",
-       "      <td>pdf</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1005</th>\n",
-       "      <td>47</td>\n",
-       "      <td>6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10052</th>\n",
-       "      <td>50</td>\n",
-       "      <td>4</td>\n",
-       "      <td>docx</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1006</th>\n",
-       "      <td>58</td>\n",
-       "      <td>6</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10060</th>\n",
-       "      <td>59</td>\n",
-       "      <td>5</td>\n",
-       "      <td>docx</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10062</th>\n",
-       "      <td>61</td>\n",
-       "      <td>4</td>\n",
-       "      <td>xls</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10065</th>\n",
-       "      <td>64</td>\n",
-       "      <td>5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1007</th>\n",
-       "      <td>69</td>\n",
-       "      <td>7</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10070</th>\n",
-       "      <td>70</td>\n",
-       "      <td>5</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10073</th>\n",
-       "      <td>73</td>\n",
-       "      <td>4</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10074</th>\n",
-       "      <td>74</td>\n",
-       "      <td>4</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10078</th>\n",
-       "      <td>78</td>\n",
-       "      <td>6</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1008</th>\n",
-       "      <td>80</td>\n",
-       "      <td>7</td>\n",
-       "      <td>xls</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10084</th>\n",
-       "      <td>85</td>\n",
-       "      <td>5</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10086</th>\n",
-       "      <td>87</td>\n",
-       "      <td>5</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10087</th>\n",
-       "      <td>88</td>\n",
-       "      <td>4</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1009</th>\n",
-       "      <td>91</td>\n",
-       "      <td>7</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10092</th>\n",
-       "      <td>94</td>\n",
-       "      <td>5</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10094</th>\n",
-       "      <td>96</td>\n",
-       "      <td>4</td>\n",
-       "      <td>xls</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10099</th>\n",
-       "      <td>101</td>\n",
-       "      <td>4</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1010</th>\n",
-       "      <td>103</td>\n",
-       "      <td>7</td>\n",
-       "      <td>docx</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1011</th>\n",
-       "      <td>114</td>\n",
-       "      <td>4</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1012</th>\n",
-       "      <td>125</td>\n",
-       "      <td>4</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10129</th>\n",
-       "      <td>135</td>\n",
-       "      <td>4</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9709</th>\n",
-       "      <td>13323</td>\n",
-       "      <td>4</td>\n",
-       "      <td>pdf</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9710</th>\n",
-       "      <td>13325</td>\n",
-       "      <td>4</td>\n",
-       "      <td>pdf</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9711</th>\n",
-       "      <td>13326</td>\n",
-       "      <td>4</td>\n",
-       "      <td>pdf</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>973</th>\n",
-       "      <td>13340</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>974</th>\n",
-       "      <td>13351</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>975</th>\n",
-       "      <td>13360</td>\n",
-       "      <td>7</td>\n",
-       "      <td>pdf</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>976</th>\n",
-       "      <td>13371</td>\n",
-       "      <td>7</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>977</th>\n",
-       "      <td>13380</td>\n",
-       "      <td>9</td>\n",
-       "      <td>xls</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>978</th>\n",
-       "      <td>13389</td>\n",
-       "      <td>9</td>\n",
-       "      <td>pdf</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>979</th>\n",
-       "      <td>13400</td>\n",
-       "      <td>4</td>\n",
-       "      <td>pdf</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>980</th>\n",
-       "      <td>13411</td>\n",
-       "      <td>4</td>\n",
-       "      <td>docx</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>981</th>\n",
-       "      <td>13418</td>\n",
-       "      <td>6</td>\n",
-       "      <td>pdf</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>982</th>\n",
-       "      <td>13428</td>\n",
-       "      <td>6</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>983</th>\n",
-       "      <td>13438</td>\n",
-       "      <td>6</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>984</th>\n",
-       "      <td>13448</td>\n",
-       "      <td>6</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>985</th>\n",
-       "      <td>13458</td>\n",
-       "      <td>5</td>\n",
-       "      <td>pdf</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>986</th>\n",
-       "      <td>13469</td>\n",
-       "      <td>5</td>\n",
-       "      <td>pdf</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>987</th>\n",
-       "      <td>13480</td>\n",
-       "      <td>4</td>\n",
-       "      <td>pdf</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>988</th>\n",
-       "      <td>13489</td>\n",
-       "      <td>4</td>\n",
-       "      <td>pdf</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>989</th>\n",
-       "      <td>13499</td>\n",
-       "      <td>6</td>\n",
-       "      <td>pdf</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>990</th>\n",
-       "      <td>13511</td>\n",
-       "      <td>6</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>991</th>\n",
-       "      <td>13522</td>\n",
-       "      <td>10</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>992</th>\n",
-       "      <td>13531</td>\n",
-       "      <td>10</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>993</th>\n",
-       "      <td>13542</td>\n",
-       "      <td>7</td>\n",
-       "      <td>pdf</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>994</th>\n",
-       "      <td>13549</td>\n",
-       "      <td>7</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>995</th>\n",
-       "      <td>13560</td>\n",
-       "      <td>11</td>\n",
-       "      <td>docx</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>996</th>\n",
-       "      <td>13569</td>\n",
-       "      <td>11</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>997</th>\n",
-       "      <td>13578</td>\n",
-       "      <td>6</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>998</th>\n",
-       "      <td>13586</td>\n",
-       "      <td>6</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999</th>\n",
-       "      <td>13597</td>\n",
-       "      <td>6</td>\n",
-       "      <td>doc</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5273 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Unnamed: 0  count format lang\n",
-       "id_doc                               \n",
-       "1                1     12    txt   fr\n",
-       "1000             3      6    txt   fr\n",
-       "1001             9      5    pdf   fr\n",
-       "1002            15      5   docx   fr\n",
-       "1003            26     11    doc   fr\n",
-       "1004            37     11    xls   fr\n",
-       "10044           41      5    pdf   fr\n",
-       "1005            47      6    NaN   fr\n",
-       "10052           50      4   docx   fr\n",
-       "1006            58      6    doc   fr\n",
-       "10060           59      5   docx   fr\n",
-       "10062           61      4    xls   fr\n",
-       "10065           64      5    NaN   fr\n",
-       "1007            69      7    doc   fr\n",
-       "10070           70      5    doc   fr\n",
-       "10073           73      4    doc   fr\n",
-       "10074           74      4    doc   fr\n",
-       "10078           78      6    doc   fr\n",
-       "1008            80      7    xls   fr\n",
-       "10084           85      5    doc   fr\n",
-       "10086           87      5    doc   fr\n",
-       "10087           88      4    doc   fr\n",
-       "1009            91      7    doc   fr\n",
-       "10092           94      5    doc   fr\n",
-       "10094           96      4    xls   fr\n",
-       "10099          101      4    doc   fr\n",
-       "1010           103      7   docx   fr\n",
-       "1011           114      4    doc   fr\n",
-       "1012           125      4    doc   fr\n",
-       "10129          135      4    doc   fr\n",
-       "...            ...    ...    ...  ...\n",
-       "9709         13323      4    pdf   fr\n",
-       "9710         13325      4    pdf   fr\n",
-       "9711         13326      4    pdf   fr\n",
-       "973          13340      4    NaN   fr\n",
-       "974          13351      4    NaN   fr\n",
-       "975          13360      7    pdf   fr\n",
-       "976          13371      7    doc   fr\n",
-       "977          13380      9    xls   fr\n",
-       "978          13389      9    pdf   fr\n",
-       "979          13400      4    pdf   fr\n",
-       "980          13411      4   docx   fr\n",
-       "981          13418      6    pdf   fr\n",
-       "982          13428      6    doc   fr\n",
-       "983          13438      6    doc   fr\n",
-       "984          13448      6    doc   fr\n",
-       "985          13458      5    pdf   fr\n",
-       "986          13469      5    pdf   fr\n",
-       "987          13480      4    pdf   fr\n",
-       "988          13489      4    pdf   fr\n",
-       "989          13499      6    pdf   fr\n",
-       "990          13511      6    doc   fr\n",
-       "991          13522     10    doc   fr\n",
-       "992          13531     10    doc   fr\n",
-       "993          13542      7    pdf   fr\n",
-       "994          13549      7    doc   fr\n",
-       "995          13560     11   docx   fr\n",
-       "996          13569     11    doc   fr\n",
-       "997          13578      6    doc   fr\n",
-       "998          13586      6    doc   fr\n",
-       "999          13597      6    doc   fr\n",
-       "\n",
-       "[5273 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dfFr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2018-05-15T05:07:19.146Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "IntProgress(value=0, description='Processing', max=5273)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import json,os\n",
-    "from ipywidgets import IntProgress\n",
-    "from IPython.display import display\n",
-    "p=IntProgress(description=\"Processing\",max=len(dfFr))\n",
-    "display(p)\n",
-    "\n",
-    "for row in dfFr.itertuples():\n",
-    "    p.value+=1\n",
-    "    id_doc=row[0]\n",
-    "    if not os.path.exists(\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac_ner/{0}.csv\".format(id_doc)):\n",
-    "        try:\n",
-    "        #print(len(open(base_dir+str(id_doc)+\".txt\").read()))\n",
-    "            test=pipeline[\"fr\"].ner.identify(open(base_dir+str(id_doc)+\".txt\").read())\n",
-    "            pd.DataFrame(test,columns=[\"text\",\"pos\"]).to_csv(\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac_ner/{0}.csv\".format(id_doc))\n",
-    "\n",
-    "        except Exception as e:\n",
-    "            print(e)\n",
-    "            print(id_doc,row[-2],row[-1])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-08T19:18:48.737157Z",
-     "start_time": "2018-05-08T18:43:55.338361Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "IntProgress(value=0, description='Processing', max=279)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import json\n",
-    "from ipywidgets import IntProgress\n",
-    "from IPython.display import display\n",
-    "p=IntProgress(description=\"Processing\",max=len(dfEn))\n",
-    "display(p)\n",
-    "\n",
-    "for row in dfEn.itertuples():\n",
-    "    p.value+=1\n",
-    "    id_doc=row[0]\n",
-    "    try:\n",
-    "        test=pipeline[\"en\"].ner.identify(open(base_dir+str(id_doc)+\".txt\").read())\n",
-    "        pd.DataFrame(test,columns=[\"text\",\"pos\"]).to_csv(\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac_ner/{0}.csv\".format(id_doc))\n",
-    "        \n",
-    "    except:\n",
-    "        print(id_doc,row[-2],row[-1])\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-09T07:52:26.414920Z",
-     "start_time": "2018-05-09T07:52:26.410473Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10014"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(open(base_dir+str(id_doc)+\".txt\").read())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  },
-  "toc": {
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": "block",
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16.0,
-    "lenType": 16.0,
-    "lenVar": 40.0
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks_old/Untitled.ipynb b/notebooks_old/Untitled.ipynb
deleted file mode 100644
index 26f42fc..0000000
--- a/notebooks_old/Untitled.ipynb
+++ /dev/null
@@ -1,953 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from tqdm import tqdm\n",
-    "tqdm.pandas()\n",
-    "tqdm.monitor_interval=0\n",
-    "get_ipython().config.get('IPKernelApp', {})['parent_appname'] = \"\"\n",
-    "from glob import glob\n",
-    "from langdetect import detect\n",
-    "\n",
-    "import re\n",
-    "def TextCleaner(RawText, remove_punc = False, lower = False):\n",
-    "\n",
-    "    #remove html tags\n",
-    "    CleanedText = re.sub('<br />', \" \", RawText)\n",
-    "    CleanedText = re.sub('&#13;', \"\", CleanedText)\n",
-    "    CleanedText = re.sub('\\n', \"\", CleanedText)\n",
-    "    CleanedText = re.sub('\\t', \"\", CleanedText)\n",
-    "\n",
-    "    #add whitespace after a dot \n",
-    "    rx = r\"\\.(?=\\S)\"\n",
-    "    CleanedText = re.sub(rx, \". \", CleanedText)\n",
-    "\n",
-    "    if remove_punc:\n",
-    "        CleanedText = re.sub('[^A-Za-z0-9]+', ' ', CleanedText)\n",
-    "\n",
-    "    if lower:\n",
-    "        CleanedText = CleanedText.lower()\n",
-    "\n",
-    "    return(CleanedText.strip())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fns=glob(\"../data/corpora/EPI_ELENA/raw_text/*.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame([[fn.split(\"/\")[-1],[TextCleaner(open(fn).read())]] for fn in fns],columns=\"filename content\".split())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from polyglot.detect import Detector\n",
-    "df['lang']=df.content.apply(lambda x :\"en\") #Detector(x[0]).language.code if x[0] else \"en\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import spacy\n",
-    "nlp = spacy.load(\"en\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mkdir: padi_nlp: File exists\n"
-     ]
-    }
-   ],
-   "source": [
-    "%mkdir padi_nlp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 532/532 [00:48<00:00, 11.07it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "for ix,row in tqdm(df.iterrows(),total=len(df)):\n",
-    "    doc = nlp(row.content[0])\n",
-    "    doc.to_disk(\"padi_nlp/{0}.pkl\".format(ix))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nlp.vocab.to_disk(\"padi_nlp/vocab.pkl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"idx_files\"]=df.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_pickle(\"data_padi.pkl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df=pd.read_pickle(\"data_padi.pkl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"idx_files\"]=df[\"idx_files\"].apply(lambda x : [x])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"id_doc\"]=df.filename.apply(lambda x: int(x.replace(\".txt\",\"\")))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import networkx as nx\n",
-    "df[\"nx_object\"]=df.id_doc.apply(lambda x: nx.read_gexf(\"../data/graph_data/graph_exp_fev_18/normal/{0}.gexf\".format(x)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_pickle(\"data_padi_final.pkl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from tqdm import tqdm\n",
-    "tqdm.pandas()\n",
-    "from strpython.pipeline import Pipeline\n",
-    "from strpython.nlp.ner.spacy import Spacy\n",
-    "from strpython.nlp.pos_tagger.tagger import Tagger\n",
-    "from strpython.nlp.disambiguator.wikipedia_cooc import WikipediaDisambiguator\n",
-    "from strpython.models.str import STR\n",
-    "df= pd.read_pickle(\"data_padi_final.pkl\")\n",
-    "import networkx as nx\n",
-    "get_ipython().config.get('IPKernelApp', {})['parent_appname'] = \"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip=Pipeline(lang=\"english\",ner=Spacy(lang=\"en\"),tagger=Tagger())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"content\"]=df.content.apply(lambda x: x[0] if x else \"\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
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-      "\n",
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-      "WARNING:elasticsearch:GET http://localhost:9200/gazetteer/place/_search [status:400 request:0.058s]\n",
-      "WARNING:Fiona:Skipping field labels: invalid type 5\n",
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-      "\n",
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-      "\n",
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-      "\n",
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-      "WARNING:Fiona:Skipping field labels: invalid type 5\n",
-      "WARNING:Fiona:Skipping field labels: invalid type 5\n",
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-      "\n",
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-      "WARNING:Fiona:Skipping field labels: invalid type 5\n",
-      "WARNING:Fiona:Skipping field labels: invalid type 5\n",
-      "WARNING:Fiona:Skipping field labels: invalid type 5\n",
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-      "\n",
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-      "\n",
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-      "\n",
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-      "\n",
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-      " 99%|█████████▉| 529/532 [12:58<00:10,  3.34s/it]\u001b[A\n",
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-      "100%|█████████▉| 531/532 [13:09<00:03,  3.93s/it]\u001b[AWARNING:Fiona:Skipping field labels: invalid type 5\n",
-      "WARNING:Fiona:Skipping field labels: invalid type 5\n",
-      "\n",
-      "100%|██████████| 532/532 [13:12<00:00,  3.83s/it]\u001b[A\n",
-      "\u001b[A"
-     ]
-    }
-   ],
-   "source": [
-    "df[\"str_object\"] = df.content.progress_apply(lambda x: pip.build(x) if len(x)>0 else STR.from_networkx_graph(nx.Graph()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"str_object\"]=df[\"str_object\"].apply(lambda x: x[0] if isinstance(x,tuple) else x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df=pd.read_pickle(\"data_padi_final_2.pkl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#transform(str_,type_trans=\"gen\",type_gen=\"bounded\",bound=\"region\")\n",
-    "#transform(str_,type_trans=\"ext\",adjacent_count=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  1%|          | 3/532 [00:00<02:37,  3.36it/s]/Users/jacquesfize/nas_cloud/Code/str-python/strpython/models/str.py:118: UserWarning: Labels list is empty. @en labels from Geo-Database will be used by default\n",
-      "  warnings.warn(\"Labels list is empty. @en labels from Geo-Database will be used by default\")\n",
-      "  3%|â–Ž         | 14/532 [00:09<06:04,  1.42it/s]/usr/local/lib/python3.6/site-packages/tqdm/_monitor.py:89: TqdmSynchronisationWarning: Set changed size during iteration (see https://github.com/tqdm/tqdm/issues/481)\n",
-      "  TqdmSynchronisationWarning)\n",
-      "  3%|â–Ž         | 18/532 [00:12<06:36,  1.30it/s]/usr/local/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
-      "  out=out, **kwargs)\n",
-      "100%|██████████| 532/532 [03:47<00:00,  1.11it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "df[\"ext_1\"]=df.str_object.progress_apply(lambda x: pip.transform(x,type_trans=\"ext\",adjacent_count=1,distance=\"100\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 532/532 [07:06<00:00,  2.27s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "df[\"ext_2\"]=df.str_object.progress_apply(lambda x: pip.transform(x,type_trans=\"ext\",adjacent_count=2,distance=\"100\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  2%|▏         | 11/532 [00:05<03:33,  2.44it/s]/usr/local/lib/python3.6/site-packages/tqdm/_monitor.py:89: TqdmSynchronisationWarning: Set changed size during iteration (see https://github.com/tqdm/tqdm/issues/481)\n",
-      "  TqdmSynchronisationWarning)\n",
-      "100%|██████████| 532/532 [09:45<00:00,  2.76s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "df[\"gen_region\"]=df.str_object.progress_apply(lambda x: pip.transform(x,type_trans=\"gen\",type_gen=\"bounded\",bound=\"region\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 532/532 [05:23<00:00,  1.23s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "df[\"gen_country\"]=df.str_object.progress_apply(lambda x: pip.transform(x,type_trans=\"gen\",type_gen=\"bounded\",bound=\"country\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_pickle(\"data_padi_final_2.pkl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/var/folders/b8/mtx4cy2s27vc7h_7tklgcml40000gn/T/nx_8nsufno9.png'"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from nxpd import draw\n",
-    "i=1\n",
-    "draw(df.iloc[i].str_object.graph)\n",
-    "draw(df.iloc[i].ext_1.graph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks_old/Untitled1.ipynb b/notebooks_old/Untitled1.ipynb
deleted file mode 100644
index 9242245..0000000
--- a/notebooks_old/Untitled1.ipynb
+++ /dev/null
@@ -1,102 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_pickle(\"../bvlac_strs.pkl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nxpd\n",
-    "nxpd.nxpdParams[\"show\"] = \"ipynb\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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28B9U3cN8Coy8A/JZ8acN2i8JR+hkZb93ayxXfQ/7KIFAlxEgH1x//fVZ97Slh9JBSv+hb7IJkwtUuqeqjzTHsqb+qU/qn8ZYOif9xrYEQZBVFL8RDE3HUfEbVNeMEgiMGgHze5Gji7zQ3JZ97ZoMQG4o2+Z+kQ/0gfkUc7y+VGy4tuWz/62oNuWC5r6+VT4XGcDWHF8+D9ofdFyZ2835C9l3bPM3RXcp+ktTFmjO/fAbVNm3jTPF1l0++/18CnzN98axMoeX/bIt35vPzfllbi/7Zctu651FCQTaRIA+f/XVV2ffEP8QezH7oSIJ7JqJe8gnpLI5ar9lTDGGNPuLMYwd2Pxvy15hq/jd4x//+Gxv3GKLLarNN988t//8z/izZATC37tkCOMEDQTIJfovv4Ha3MeZMzeWYm4nvxsfmvbHMrfZmheL/FBkiLI1lzfn7TJf2xpTmvKT+1KNW8YWMlIpZA8cFGMWWyQ7ZDMJoO/da5TJQ8A8Q4fEOTLW9W7Zy7UJbalZyF3apnZRZC/bXnnMnKZ99qvsOdqWNlrsSU350r77a8rtxf5XvuN/ZbfnI+ot+oz5lY/PnNu7pSuTC6OMFgHvikzEF4s3Qzbyno0hxhLvpfBmcM+NcXwt2pJjmv7Ywn8iG2m/fDr0EG0LfxJXRuWX9f6jrBgBtp9iYyo8B3OUvqjoL96R91LsS7b6WtGN6Wv29WnvwrtVzW2F2+A6qjnPmONYhX4mSWKxM9niOprToiwNAf0GrxVvpVRcZ++gjO/6mLHS+zW2q2x8tvphr31Df+zV+13HuGze8L5tvWucNnJHKWyU+Cved6mPetSjchsox8R2/gjAmM6Jh6jqx+xkCj82mc22VPJj0Tlt9fGmjYYtmCyokgNw44p8qo088YlPrIrOqZ+a46MsjwAZ3zynmvPMT+xr8MbjMycVO4B+1nwfxkR460/eo/dgnvMufpYSE+lPCplLjGGZ59gAfBdlMALegXdB9ij1u9/9bsa3/Eo/0c7Nb8Wf4h3BtjnXafd0KnNcme/IqWWOK1tytn2F3OvdFy5fme/MrVHaR8BYR64ptXCBbMmRZdx0J2TJEgPZ1MOb+2Seonfbluo9F73bNcu+dtLUwYsublvGXe2pFOfD6ad7N3lA+ECquTnK6BHwvkrMWIkbI2MZo40xirahr7Pj4JIVuco+ebopU5V2o530ylXsM0WuIlPZNyeQ3xyvaKuuJdl4M27M55gTMkSd/+Pd0om811K1p7JPBiBXF52sPJB3r02Zo8h23vegSp9u2mT81ueytV/sh8U2U7aua1/7NE4WfyqbTG/1LGrTnlju170ZV0u8rC29v3y2b/7VP6KMD4Gw14wP+3Jlc8l3vvOdZboEPc/Yr+jzdIliM6NP6P/FZqb/FPsLXcKcZTwxjhhTxDEWvVzuEnFm7GUq/TzK/BAg27OZ0SNKFVdWZMkyN3s/xuhmNRYWnaLIA8bfolMUvUJfNPaX6pr0iqb92/nZUzbeeOMcLyRmyPxPtogSCAQCw0HAmKxvq+QeW+Nrb2XD8b9iy2n+33elljHa5xXtGwsWW4p+YYwp+2Xb/K4pG8533/hWKrtw2R+0dQz92LZ3v/dz8zjP7nPZNvfLd73boov12/b7zu+Nv6U6Zj775OJSHV/2+23L/4tdwHa+tdgRylabKPvNredYaIFnmYvYI+mvxS7Z3O/9n89kjmb1HdtEv/85zrzn/6rP5V0u9J7j+EAgEJg/AsaznyUfQsl3QAco+UzYHppzDF4H3aL4eUvug7LVt8sc0twa35pjkX3nNVeSV1W6SNmX/4JOY6wsxTX4reg3pYp9lPOA/TXK0hGA/wzwMY6JhLJLbyszcwaCmKQRl1xySXXppZfmpBEGMINgSe5KsS7OGEaYYRYKQ3FOcSxw5ktmS9knJCFKbLPNNtXTnva0bKgxSEcZDwKMZ95VWYnU1nvioOdM5KgphTLEEcRIY8sAXqq2pR2Z9GxLJSRPSjF5lwndczdrM3mufZM9QyTHVinw4QBg+GeMXDM5xAvZGPnYd46JMh0IEAj1nSKI2iLN6EP6jzG3FAbT4iBqbpuG1EJY67JDnDOsEOOahtzi5Gtu9aVSKN/6g36gTxCECcW2vmOoiTJ6BDhSJJW69tpr81bCeUY+4xT5gCOeAZ7SYvwytnHSM7K0WYy9ZQ7Sn0pSU9tCRHI/iHzqZpttlom32lmUhSNgTiMzlvqNb3wjK7jktULK4FBDTEdy1w5Uzrq2Cyc8WUQlm0hsW6r2qxhHN91002X1CU94QpZB2r63WTg/ueiHP/zhMrJhIR2W8Z0xA5HLWK4a3wshwlw3bP1iLszNUcVJbFuMReZnlbFGYfyhBxUCo63Pvo8yNwLmCTrmpz71qeqzn/1sJv+RYZB4OeIRRM0dxomlFo4lbdD4b75CIkAqYKgTRPyc5zyn2mmnnfL1lnqtWfm9vlECZkqf1lcU8zu5rPRp/ds8oD+r9L9R6jJk6aJzuW9zAcJr6dPmBXYP85V7Zedo9msk6CiLQ4BDtyQhlYhUgIa+z+nFloOsocJdm1kzyfltyIeuqX3Ssbx3/V9lY6KLkUWMO5KYqhYxCr2iyk6LL37xi9UFF1xQXXTRRdlmYUxGkoKXOY+cv1SnRBmjBWhpIwJHjNfewVOf+tTqec97Xh6j2YyizI1AjM9zYxRHzI2A8VufROwrY6Z9epViHCezlwCNIr+z86qCicahV/MjmPPpeLacu8WvUGzWxdlLHjGOlbnIlr4a4//c7SOOGD4CdGY2LH2s+BSahAHflc+2JYlJc1vadr+7YyNuJq8o+4WQ1CQn+a75WX9vkpr67evvapCa+qHf3nd0mEKgJO+SqZrb3n1jZCFwDtpnD+mtTd9E82m8b22FLN1bC2nGtlQyY9ln56H/hz+3iWjsDwMB8//ll19eXXbZZbnS/xT6JjsTXZ+tieyiLlWX6XfPxnJyB/2TzllsJgJR9Bs61FOe8pRc6Z/6QpRAYD4ImPe1r1KL7OBz2e8nR5AdBo3lZF9juEBd1X6RE4zxqs/NrX3yAhmhyBJlv2zJC2rIBvN5s/M7hh3bXK+ax83zts398l0JVDWn2+/d4vSo2lTZbxJTm3dExitzeHNb5vKyNZY19/nbtJUogUA/BNjmP/3pT+fKFqh9m58l3eGvN1fSz41LSy3GR/5gNg48Br4h8gFCt/l4xx13zP4h/osocyMQ/t65MYoj5o8A+YTv9sYbb8xVP7XPZ6eQI/AFyPJFfscxYXdk17Plvxulv7H5dOZY+kcJ8mCXL3zZwvczzypkJOMav1izsqNGGT8CxrafJR9iCRhqbvHNzFMK/6U2ac5Q+cts+a5wnVUykC1ZuEuFDQk3E0+uVImZ6Kls6Cr/ua22XYq+1gxUKvxMHIBx2P/LfU3bFu44M//8z/+cfaTaJNlIEjw8RgkQ8OzoX0sp2gE5iA+W/16gFH6n8ZjN5LnPfW6uxqkoVV44CL+BjYmtiUypGAeKjYncao4yV7XBKfFuzIvml8JxkJjDfEn/o3ORobfeeutccZ/CxzV369Xn6CGlwhTWxm59TZ/zbnFYyB64xcb6Nm0c7DnetXmn2BTZFVV2HdfGu8anU40NZIs272luJLt7BJ82HqL69a9/Pd+ocRV2uMHer3fNX7HUQk4gR5BrtSV6p2redf5tt9222mGHHfJ2GNdb6v2O4/cCtc035jkVVuRj3GxzncSvZTyVNGMphfxtnjNO6uOSs7NBkFue/vSnZ94RO0AbY/ZS7ntcvzXGmONUcx750DxivDHXqfbLXLdUWaTfc3pnxj219CN9yXvTv8y7ZZ6zNTZHWRoCxqfCAbI1Zuo3fM4KfcY7L1wgcg6+Lx2cDqv/LLWvLuYJ+EEKFwgfyLzZ5ALRyY037k07wf9p8oHM51GGh4D+qd2Y8/DxVfKyd8CnRE4x96raEvxHIVO5L+2DfkuuMpYY69yrhHZkaHYkOk8zdoyuG2X0CGgvJZEjfYc9pnD76EDFrubOzEHmBONRc2tMYovho7JV+bK6KifjdRmHjWnNrXYLC2NZ2bI5atOKsc2zG5PVEptR7DTBu8kwDf1P2GuGDumCT8iWRVa98MILs+1MX+GPFr9AlyCv0uGHYWc3b5gz5CyhR9An+Nj1OTYzcQx08XHIQQsGbkQ/MGbDio3R1jiusCmba0uMCQzXTPFB7Olt2a3YTujmqjmFTkjOJZ+Qc+mg5n7tRvvxLmPsHFFDicuMFQGyB5mq1MILLjyiJpeod38Qv7hwlOZ6MOMle6v+16/6H3uNbanNz737Pqt8dWXb3C/fla3/qXghbY09c2EQ/x8fAuz9pZoH9AW17A/almMKP8/WseXzoH26Zr/qd/SeFRW6iz5SOJiFl+lz2Tdnleq7Fe0XDqit9h8lEJhFBPjayYEl74EtmzOurMKGQK8uOjV5tdga6N7D4CvOF3d6P/2fPUClh7JnFc4Ku7nxTGGzlfOg5D2wjTjr+SE9g/p9JJSdX9OY3aMMLJLHfuITn8gGF8ZKBnyJWyVzkDxi3E4VxlPGma985Sv5XhFLCfjPetazql133TU74AW9RBkuAoTXnyXjCqdKcayUSYmTrhQTUDH4aCuMPqWaTBmHwohW0LpjS5kuDgCGSI4stZDC4E6BULR1Ez9hRUVuQajgfGsjAPKOO4y/S0WAYEeQazrgGZwZURm6FX2DIMp5qg+VfqT/6DuMGrNWSt/QHzgHS4UbQ6/5QNEv9AfOZ/2hEAEYnrvqFJzUd2ksQiC6+OKLczUfGNNhj3TH0I7whUzU1TYr+am+iMRQEqAizjO2COQmTzzzmc/MzzCp76nt+9Y3Jfz60pe+VH35y1/OwXj6mn7o/avIyvpjl50tSLnkSATSUo0z2rQkA894xjMyoRAJ2zgTZW4EGFARQbUPVYAmgwv8jBNwLaRDssukkTQ5U5CLjCGMSRJD2OdA8YzafVmVVLsZpRFp7rcz/iMY4c4+++zqwx/+cHZMmTckdH32s5+dx4tRzdne1+WJZCBwGYmc8U3b3Hvvvas99tgjZOqepkLmKn2aDEBHUchZpU/bGvMFVYwrgLPntuf1kf3DuI88UPq0LWKWYl4rfdrW80UZjIB5VfJRgW+SkeprbAKwI2MZF8mIXbAHePfk2LKymDbOroFwtP322+dgAjIhp9ssFXPamWeemcdp8h4CDUKUQBf66SgKHU/7QQSzJVuYJ17xildk2WxUc8UonnWp14jx+Y6V4GN8XlpLoh+zs5PbVcRI/Q5Bgfxe7Cu2xnAkzEnsh+xynIGCkRAGjXe2Pnte9gCyvLlKRRw0J0QJBBaCAN23JGYwn5V9W5/52tSS+M1+ISg0r6OP8W+VBJwlgZfPdEz/G1T9v0kY6oLc1Xy22J8sBIyPZHq2EFvt1b7toNovoWHxbTWfHmGtmXyuuV+SsTQTspT9sM81UYx9Pjd6g8q2iwQsCH3rFFwsUZy5vQvB+uYBvoiSiITdwVhP3xJ8QueKZHaz057xLcgA5HCVXbLsN2WHsk+GMB73FnN+GTttSyU3FNmhd7/ID36LlB8lEIAA+08JIhFwVao5vXefHFtqkWubiccKotpXSarWnNft4xLhRNiWqv1GmV4EtK+Pfexj2TfEL69tbLfddjmhKx7iKN+/8ZZv+zOf+cwy+zWZ4R//8R/znBxj453t0NwT/t47bI/h772zXSx2T7B3SbBFLta26Inkd344gd+CjG3xB/gfu8o1mi8GZDn6Ci5Ks5L9FPI/+yN+la2Aj0l/5vliM67jyDW4HfgK7MJ8w/yExWZhfipJOcpWMiJ+TpyWSbSJLxRr8h9+ZuFk8kGVitdMl8EDgI8+q+IIaL94rVHmhwAfNVnkPe95T16EmX4m6aBFkHHURrX4jABXCR/49d0PfoTkW/vuu+9Mcma09ZLwkMyqrWvbOA4qbhN9ZtxFICTOmneHu6Pqn+xf/OlsTDivs8ZxGPRejPF4IF/4whdyhR35g8/T/KuyH+Lrd5HnxK/Jb0t+Ikt94xvfyDq8xH64LKpxY5Q61SCsx/k9WfODH/xgde6552a/L182zo8+wU7MPjbKwveszX3uc5/LfZUMgW/z0pe+NL+zLra1YeMjOen73//+6qyzzsqyhJgn/FBzHfl3VH4eHDDvwlyH424O9i7MdfrOLPlQzR/mDnMdPMSq6RtlAXacG3JAF+wibI1k9jLP4bKwX9IdzXOq+IRZkM+X0jfJ7sajwgOyJe8o9BtzYankerrPqMfLpTxf87fmewkW6N50vVILx9m8WXhAtnSYSKzSRHDufXMtm6rYMeMpmwd5k0+2xI6RnenPXeyb+GLaSJGryPvGGQmB6LLmBDK0BORsA1GGiwCbPI6eWpL8ssfgnyjakjGIraEZQ4urL6nLpI5NS0HRvE2OoQ+IJRfjYFvsNWw32rX+JtYYv1M1npvP7YedceFvIOw1C8esjV9o6/SI973vfbkfiDsk/1kcQvsexTyjD/JhkJvJz2wJfBb77LNP9bKXvSwn22/j2bt8Tv2DPlEWcDEe0R3YVOgUKrmA778rRa4G7449RayQ6r7po2x9dEN2g1HFxnQFl7iP7iNAJ+bPws0tfJ3ez4W3W3g+hc/b/NyPZ1aeXj9ocsx798lnK0pw2Ux2ab83aWzIIQXp2M46AuwVvclm2bhUfX0++/Qmx9qWWj6bnwcV/RBPlA+yX/U/upba3O/9rH9HCQS6jIB+ULht7MlkPvOhPiDPATtgqfKxjcoPPwzM6CX0IzZPPJdS2QQU9nJybYnZZx+IcgcCM67fR0LZ6Aj9EWCQlTTiQx/6UA4C4GjbZZddsvOWYbbLhYGZ4/2Tn/xkJllx8DIU7bfffjlgrMv33tV7o1QKaCuGe1tthICqMPCYOE022octIz4jSpCRhv9WOXU5BIoDoJA1GbYk9eXMUjhMOHcZSVWGU+9mlkgPw0d/4WcsTngELoKoqg/pP4zXCGiFCM9ZgxCvH1G8oiwMAUFr+oAVI4xRtkjfhQQAU/2AkZpxuhAAF3aVOJqxQdI9wV6IAIwZlCkOdHUagmgEc0ioj/AgYT2iPKfPzjvvXO222265/cx6SxAcyiFm0QGJGM092gECxdOe9rTcDroQoL/U94TQyNmlrV9yySU50IeBW1t//vOfnx1HXU6Su9TnX8zvGSbI4hyEsGPsRMKSFKGs5Gjem1YSlnnfGIK86PmR0clqCMCPfexjs6ORs5HxaRYLfIwdJ5xwQpaJyD2CcyVu1U7GXdwfYyHigfENyWbPPfesDjvssDwPjPv+xnF9hlPzoX4tmSM9hBMA+a/0azLVNIz5g/A1F0hGo0+r5HlOVSQ1AV1IBBbc6QKRetAzjOp7uAgy04fMnfQdQRmCAQRpkKcmpSBSa/eex5hu3vIMCEBIo9Mc2KHPH3PMMZlIztYjgOLFL37x2APjyBRIWYIqOXfo0q973evyOD2LK8bG+FxVMT4vfURlm6LPCZ7S99lUjN3sU5tvvnmuArnYqaZ53CtIchayJQnGNParZHmY0GGM/+Z+pMdRBbeVe4vt+BFALJXogzyslmRv/bbGp15yDjJCM5EWEoLAXtvefZ/J1yp7pjYYJRCYFgTYEBFb2VubCZX77ZdkzLb+T2dvFv2jJJ8btBUwzt4wzTprE5NZ29cucBnOOeec7HeTzIOOLoiEnj4JPmp9gRzGXvb5z38+E9jIXxY6esELXjARzzBr7W4+z8s+IomLKrhWJT+UfVtyBZmhLDRazmu8KmNaSb7ZlCHKvi1ZQp1FnbjgFdtuIaDtG5vVMo8LKC/7Zeu7IkfjIjULG5w+YP42j5e5vOzbCuy2jbbfRK7b+wjMb3vb27LdGMlZEnUJdCRw7YK9Qdvl/2DXtkiaIAocw1e/+tUTtxjmsFpC+HvD3zustmTML9wbW/wtvEU+YnIvf6PKFzFr9jayID4uW6QqiYm5Eg58r4WDZb8LY+Ww2sSoz8NGJ6AGvgVnyWPYGMjUeIRsv6VaSG3Wk/HN9Y74FdjNJX8pSZK15RKwRI7TbvVxVexB8KqWRxX38rTTTqve+c53Zr1QIkiJKCTaH/dYqG9IrmBx6I9//OO5r/CP48xM8wI45quShBLfmD6O2yXhIRsT+XASCj8fvh5ur2Rx/BJsZexM+Jyz5mvAaybff+QjH8nyvvELv7UkYOULxQOdxMKeZH6TzE01x+m/uNsvfOELq1133XWmkqDhdL31rW/NbZ/fAAYveclLOpVokv3DYmiSq+KTmi8POOCAav/995/K5GhkheOOO6766Ec/mvsZbiiOaBc4s8YC9njJqXCPxLsdeuihmQ9m3JzWInaJL8U7Me8ZD81z4izxmidB5yDbk1PMc/hj5Hrxcjht5JWux7mOsm1ZIICtSxUHI+bDnEdOL1wgsVTGolkofNPmTe0HD8iW/o0HDQ88IBVXKspdEdDXxI3RD+h+xkq8KXYLcUNiVidhDLnrk93xDduwpLL8tSr7gdgB9iq+WnJVLJwyCL3B3/ODiqEtcbQw5itVJLOW8LTEz9qKdQ6cB+M56D/4N2LL8R1LHC17Df67ts2Pxg5rzC8xtGxh0xrHNQin+X4f9pr5ItXuceJ03vzmN+cYDrIKOU/tQrJP/YtMfd5552Xum3niDW94Q/ZvtIvKeM/O3oBn7rnptfRb+oS4GpUNeNw2zYUihLNj3mc3YlfxTOamF73oRbnGnLRQROP4fgjgReAF0sfoH4VL47vCnR20T4fx+97Cvsvf0kz+yBZWkkQ298t3za3/41OWxLHBuelFOD4HApOJANm/JJcl0zaTSpd9c13Zb259b8xR7ft9v2K8KLEtfOlz7fPDlxgZY1bktuqHany3VAQsvlJiv9n62f7pEHIeSK7KfkVunVYdmJ2ff4zPSc4DNhg2dLoTOZ2PWFz/pMnqS20Xfh/6fUYxEsoOozFNyzko9hT6U089NTtHrQAqaQSnutXhJrFQpjwThy+yBPIpxztyTGTC7/9GCY3FWVaSXyIZaR8EtubqkyZQhmVCXZRuIMBIwGEnmWZzZU3EDP+j6FvxkSMAWdOq2pPav7uB+F3vgkOGE5HwxeFs7GHsIWwhH8PeOyiE5EkIaL3rU07WNxRZRE8ObKuJqvqIPiEgTT9ApKMcIMWEIeyu7xdWjOSIs7YUCiRTyeYRLBFyprV4dgqVZGKIEMZYyaskluWUIi/NSpHECwYIZYJ9kD8k1BH0hxQieHPai+A5zqILLriguuyyy/J4gUS0++6752R502pYmOu9Ihuef/752fhC/uBsMEYIdmBwsNDALBekGHIBcp7EEAgxxk3BB8ZRwQezYBRFKJOgEGFFYo/XvOY1mYzX1bbBOG7eO+mkk3LwE2LzUUcdVa05QQkxF4utAEbjPfItY6pABLoDQ+I222yT5dlZHe9gaj4kG0iYSi4iYyLcGvckTpWAnu48S0Ui7Xe/+93LFiUiFwjO0GY4nSe9MLDrDx/4wAeygd0YLtn0K1/5yqkiMSL7HHHEEZk0TcY//PDD8wp1XXx/CI9vf/vb8zsRvPjGN74xv5NpD4KL8XnFrTHG5xXjU/4rqJu9nFxKRhVMiQhHtzPPI/dF0r2C1h2Ji9n36MDkeTZW9lUyvHlOkBXbUpTJRYCsS0cTVNWszcRv/k8e4CMphfxXkr312wo45zcpieC0myiBQCCweATYaJFr9cWSjI5spLK7SEpnvySnc5zflGIBECShUksyOrJ9qcjgkTSmINbtLdLTu971rry4LBu1IA0Bw0hgkx6wSOagewqG5ldkdz7wwAMjiLVDTZJ/DBHx5z//+bLqMzmibI1HTbkBObaMP7bGoPK5KUcIoJxlm1uHXnPcyggRwK0o83eZy4ssTg5Xy2c+/1L4VFZbbbU8j7MNmc9tcV9K9d0kzwvlWSd5a6w8+uijs6/FuMcvxK7aZbsD2RLHkKwh4EJyt9e//vUzkVg2/L0r7m3h710xPuW/ZCA8OcnsBOPioLLbS1ojyQheFp/jNPiOyjMPc1sWrqUXWPDYAgXGTLZIfhvJnsx/UQYjQF7H25Q0if1boiCBQ3y32p6qPUpmFpzZwTgu5j/sNfo8zjlfuvegDeNhwlv/l0x+q622yjb2xVxj0n/Dj/WOd7wjV21VLMOrXvWqziZqFTjKRmFhAPOgBQFwZsh101KME3gOuAASY7ExSRwhwHDSdQn6FfvSueeem8dCXCdcVwsn8FtMc8Fz8twSRgoYxF30bvEYpzUxMn0Zh8ni6eQwvq/C28HtnVZOPP4uLiKui/mdzsl32/WkoLfeemt15pln5sWUJax7xStekfk5bIiTXvDrjzzyyJx4EGdYQnJc+67G7YmRO/nkk/MCM8ZGi1q//OUvn5o+w+6G/2rhbjKad2I+f/7znz8VnG6xQXjr5BX2eXKm/iT546TP4wsdC4oubu7Ha/nud79b8Y3ym+ECwUbs2rTOB4vBS/+XaIHtgv4t3o+OSPeWbME8OovJFgqW/PL6lpghflljJN4/uUJ76uq4Xu5/KVt6EJuCWCHxY+RJfYnPVg3uT3908XnJZBa2YIuR5FSRxESMpjha45CY9FmIOeuP0ui+ZXPwTtj8JfMVQ8tmIzbG/OB9SKitbauzFvPQ+ybCXtOLyHg+a6/8cWJz6Hdkc/bwLsp15GyxVieccEJ18803Z/n62GOPzbHG40Gvnaviv9Bd6RR8vvIh4GbRu6cpps6YSS4U78iuwsYiLsyiKOLCutgG23njcdYVIWCuwEcdVMnPvVVbanLHnJ9ONlcSRvxVc3OpfKplX7z2LMQfr+hdxP8CgUCgXQTw7unFqnFMLfuDkmA3v3d8k7vvbo1bxr6SYLa5LXE2/bZdt/O3+ybi7IMQIH+zV7HZ0HNLTL/YPv5AC9bMaiGv4Guxd0q0W3K9sA+z65Hj4TXNJfT75d5uJJRdDo4Z/UAh+eQnP5kd6lbJkQAFYQSJc5oSMTAqcYZa2ZUC9drXvjY/5zQ7MubTpBmDGewZ7q+66qpsJJZIgGCGQMlwrzL4dGElp/k8UxxzVwRMfhzDJZkmUoD+TigXSMMRgLTJuSfp6TT1/buiMdxvkHkInBIMciZzfulDgo8RCyUrlYxDH5plp/JwUV/62ZDF9QOCsXdm/GPQ4+D13syBiAD6wywXAXxnn312nj8lnUHslmie4jCrycS1G46fD3/4wzmIEXEEwVtS0Wk1yEr+SIZCDBE4R7FGKJMMkyF6VotgCIRyTiPjv8BvxGsLMjz0oQ+delgQ8D7ykY9k5yBinmc2NmgfZIqY8/o3AboXYgZHN/IeGYLMIFkp56pkXtNWJNuU+MJc6zklK7QoxaQUJHtGRivdmgvpkZIsTpseSX5lSOXwRypldNefjfXmOIb5KP0RMB5KysfQCjvyAAKLPk1OmGYirvHsrW99a7YpmQcE10skS8ec1iKYl0xERub0954PPfTQav3115/YR5aY45BDDsnyLSeBgBv2oEkot912W3X88cfnFb/d8xlnnFFtvPHGk3Dr877HGJ/nDdVdDpzl8bkXDLq9QBryu0Buepz53TzF9hGrqfciNvizAD82QHM+R6vF2RCrybkCk2adYD0YufH8h93WPIfQSpYvid/KvnFC/3CcwiYukRtZpiR6K9ve5G+xSNh43mlcNRCYLwL6NdtdSUDXTEZX9iU1MQ7wFZSCKGNebCake/CDH5wDCMt2lu2hBadxbSWDOO6443JyBD43fAbJIKbxnWi/H/rQh7KOI6iV3VXwjOeO0i4CbIFkBnKfKgmCrcXmfG8MKbKD5K/kBkHGtqoxpOyXz2Erb/edxdlnBwGclzJ/l625vFR91PeFmC64Sz/UR9kuLRKK86TaN99Pq2933K3Cu2I3PvHEE/O4+IY3vCHrzZOUNJuMeM455+RgUM8jeZoFxqbN3q//hL934T1mFv29K0IJHhZk4k+1kBU9zFgraZugWzyjsJmtCMHB/7NYOi4KO6RkJgLV8RDpQTg7kVz2DuzI6vgXX/jCF/JiqZK9mOcFDOEAqhtuuGHwYQc3tdb+ww4rGB9XhE6vTZtLN9100+yjME5INjsLBb/s1a9+deanSnh40EEHTczCRpIq8I/jzEi0RS7CAZpUuci8ZcyQ9MP8hTuP68fHM61BdBIJve9978vyLd8zXgd+wDQlVyWz4/OeeuqpOVZCgiRcDvPlNCVBns94aR7E/yLnmxs9vwSLkmTit05DsQAono6xFdcfF9G8P2lFIP573/venLjbWGshZXrnJOnOBXN98C1veUu2A0gsZK4wrk6K3YU9SQL1008/PfO/JBvHgZ7UIj4Q518ie74GSX0lQ5rkZ1rRu2ALpDPh8hkX2AFxfPfaa6/OJ5he0XPN53/45/i+El9J+LXOOutkvi8u0LQn/ZwPPvM9RhuSNBUPCA9YrBDfo9hqiw1YnHtWEol59tNOOy1zzMxHZCnVQjeTKv/Ptx30O44dRrsQK2SxAv4+/GixY5MUB9Hv2Zb6nTHH2KuKR7cICX2K3apUuv+sxhwuFd82fs+/LS6wxNDKJUBXJK+xzejn+OPsaLOUuCnsNW20toWdU9IxupD5Rw4LMQza4iQU/UoC8je96U2ZU0IGlQhX4uZJLmTMt7/97Tl22jhOrmZjYWOf9mLuFxPGFsp+iCvIjrv33ntPJTdt2t/nip6PjZT8gj9etvbLIshlWxLIsns0C9lY/xBnaas2EyQO2o/FCZooxn4gEAhMKwLs3r1Jtvt9ZrdUjbX+X/h+BRdjZkk0K75H5eMoW1wJn8s2OLoFuenbaidsM+edd15e1FZ8F069vAf4SJMuf7f1xsRck2n5DC1qLR+GBRPYtvjUpi2fXOj3d2lJkVD2LpDM2BfIWla4ll2akR+J+xGPeMRUo0C5O+mkk3IAFicPEjuDxrQNeINeIsJBySxuNWIOHwKW5CeC37fYYouc/NLnWcFkEFbT/j3SEMen9mAs4AwgpDNecHqWhJoCbKIsjwCiheQRBCiEK8oK0hXcOFE44Dnko0wOAoiinGNXXHFFFooR8SmggswkU7PSrPc7bcnjBr0hSoLAbAZwSrcVqREKH/awhw36ycx9L6jYGGC1PQEcAg8FdJMppoUwYW5ABicvGNM4QLSFCEq5a3PXZ5DRkK8FlFup5cgjj8wr6d716Mn9hsPTvIcwiQBiAQLET8mTkAdCdlz4u0XEQGhH4pacACED0Qimkz7nSK5lXERMlVz/lFNOySsaLxylbvyCHvWud70rkxQ49xgg6U+TXrRBBAzEUknD6QDan3EskmQt/O0KoLJgj35NruS4QMgWnIGsPC1FuxFgxKgsmY1ADfPBrBBnvUeEEf2GfUmwCvK94AjEkUkqArwlYbKSrXFaMuRJLBaREljDvoGMJThl0ttjjM/DbYmzMj43USuyuyA0AQ9kS05TyQYkcgiHeROtxe2TnQQOSNaLRMi29LznPS8HHk9i0OLiUBj/r+jgJclbSfwmiYR9CaXI8QoCPPIAW3dJCtlM9lYSvk1ikOb430LcQSAw2QhYHb0korMVOFwST5ct31kpZGcB0SUhXXPfd5NuzynP2aUteR9BX/IZ5C8yP/vFLBQynUWpJDnh0xe8KnAjfJBLe/vkA7ZY+ryFY2zLPvmhkFP5x0oft2XbacoSfMOTkhRhaYjFrwOByUFA/zWX68uquZwPr+gK9kuwD9lf31533XWzD5wfvOz7ftJtS+N6a+brPffcMycbw0E0h0+yDQKvyjzM16V9SPg+6QtaFZtR+HuH10v6+XvZq3ffffep1g/YX84999zMKzK+CiZme2SDnMZFVIfXYhZ3JomucTT4IXGVfGbn5YekJ0zyWLsYRCxqK4GxetNNN+Vgbrw+yQZwN9dbb73FnDZ+0zICbLmXXXZZbsts6z6zy+ImsK3j3E6bDCbATT8VwCQZlYT77NSTWJrJAsnO/MyTFm8i0dE//dM/VWxNggwPO+ywHDMxie9jMffMr3XWWWdl2Vb/w2PhV8eBmtTimfAc2MvMjWQRctjjH//4SX2kod43eQ2/2WIZbNy4vzitxt5JLDjbxx57bF50WJyT927x1EkvYlLone985zuznQJvf1IWgYb9tddem+c4sTQSKR1wwAETmRTXs7ARSwCPX4Evf/LJJ08Uf5Ldm66P96+/iPsw783SAsM/+clPcnJg/cj8ZsyQAGuauO34TziL5nRxwPxF7A+77LJLtdFGG2nKUZaIAJsyXZO8e91112X53RxqXOALnsbC3kAuNKazfZIT6S+R8OrOt023e//7358TdpsvyCAwkzx1FgqbMm4um9RnP/vZvGCMhC3ih9in6PMSQQbnarJag8R5Yh3oymIGLXQrMTDbmhhadVJtGHO9ibDXzIXQaP4vNp+sxr8rBsN8O4lyG9lbnJl5QVIzsYGTpNOVt83mfvjhh+ecCZtssknWjV74whfOnO+h4IFTxObED4YXJHbq4IMPntoFqcpzT/IWh18iWAtfNis7aKnyC9lvckE9M7m3JChsbksiw96thTQncbya5Pcb9x4IBALTjYAxnM2rJPLu3ZZE32VLlyn8v4KMXBf4vKocKLb0mVJ9ZieVRyvKZCAg95mFJMsiP7hIs7b407DelD4j7hHvUX45XHj2P/ZOcs4kl9DvB769YwS5RplBBJLSU6fM0XVqGnUyYtdp5e+ZQyEJDHUa5OqkzNfJgF1/97vfnVoMktBUp4RHdUrwUt/73vfO7z05Duu0qnb98Y9/vNYeogQCyfhaX3/99XVKiFOnVUnrlEAqt5VE+qsTca6+6qqrasfMakkrnuV+lBwidVptr04JI+u0Um+dklJP9fgxq+9bW08CcX300UfXyYhfJwNf7hMp+XqdVvGtE/lwKqFJgXV1IhDVyZFbp0C5OiWeqZNSPZXPOsyHSo7T+mUve1keF8yviSxQJ4fQMC8x0nOlhMp1Sn6T5wDjXEogWicSwEjvYVIv5r0bI1JS0IxfSspWp0D/SX2cZfedVt6r04r1dQoIyONhSthQJ7JUnRKGLjsmdpaOAFkrJaXOckYyTNZppdWazjKJhX6ZnMh1cqrVibg4iY8w8J6T87AmD9IjjzjiiDoRcQce2+V/JPJPvd122+WxKgVB129729vqZBjs8i1P3L2RqxIxpE6BGHUKfKvJkd/61rcm7jmaN8y2kBYlyjJPIiDXKdCv+e+Z3CcjpVXuajJgShxVpyQJdUoy0HksUmBVll/ZBb3TadBvvIvTTz+9TkTVbOfTByexxPjc/lubxvG5iRr7VQqeyzo9W0Yi+NaJoDgV/bz5nF3bhzu5d9NNN83yVUqgETaVIb6klOCxvuyyyzLG7NRpZdQ6BbFkfcNcprLVpsV+cptPAfp1Cg6rk8O7TsT3OgXMTqzcPkQY41SBQCCwBASM8zfeeGOdFtXIMmdaVCP7XB/3uMfVbDhlLDL30gFTwFRtLEpB4NlWmEj3NRk8ysIQYBfjr2CDMccmctjCTjBlR6cg9pqMkRJF1SmIo06JBqbsCYf/OCkAoE7BjdlnQ4ZIibbqlFgqyw2l3ybCaJ0WjapTcFKdFlrMOv7Xv/71sJMN/3XEGQOBTiDAfpSCh+prrrmmTouCZS4Av0wK+q5TYNCyOd1YiydD92Dvo1uw7U6DDa2tF8FnaKw1b/OP8qVMU0kJSfJ8oW3wp0yi7zz8vaNpkdPk7+2HmLZPLt12220zb4AsRT+iL0UZHQJ4XDi/aYH07IM0h6WFN+pJ9YvMF7mUsCX7Xsn05Pm02EOdEpbVl156aXBX5gtih44znuAqp8Rz2dbrnWrL/JYpiVGH7nTxt6JtpsDEOi1wlu3biz9Tt35JLiI/88mmxYu7dXMD7iYlgKhT0uI8duDGT3O8yAAIlvua7J6SjNYpyUmdku3VKRhz4riu5sK06EMeN+51r3vVacHzieXYLfdyWvpAF/DOzZ36bkqYOXGxQ7iIKUlZ5uWkxKsT12bn82r5M1NCtizfsUV0nZsrziIlLs22VvoBe8u0lLSIQx4fU+LiLK9MwnOlBXCz7Vsf5z9ICRgm4bZbu0d2KbGiOJvGjiuvvLK1a43qxMZBPjP8RHNfCvafGrl5VBgu5jr0UHIG/y+7J+64OJtpKSmBZL355psvk5Onoa+0/W7osmKrmrilxMNtX3Ys5zfXX5a4Wvvtt1+dkvHkdiKWiCyp7USc4VheS6sXZVsUQ8qnLqYc/0VbJ3+nZNutXnuUJ59me433NQn2GrpOSs6Z29huu+02NbJrWvQ052oRky3fwKT4EumiKTFXfh9pgZ46JZofZZfs/LXSQvXZL0EG5xMzTk5yzHjnAR9wg+QOsjnZJC2wUack1NmXkBLLZU4hG7y+V7hgtilJeo7x2mKLLTLvIy1Cle0Y3mFaAC7zyW699dbgdA7APL4OBAKBQKDrCOBP//jHP84cwJRwNPtAxHGzCZof+DLlySEfN+cHOaJ8T3ZOCzTVaaH0zAMTcyfuiE/xz3/+c9cff2rvjwx9wQUX1ORS702OJzl7IgZjeK88LZpep4Xo6pVXXjnbmvfff//c7od3hdGdKfT7FWJ9dCSUXSE+0/lPpE7Eq5Q1OieCms6nnP9TIaURCCiLgu0nxVAz1xNK9CLxl4Rfgqk5DiUMSquOTuyAPtczx/+HiwBh1yT6mte8pt5ggw2y0JVWX6gJBQwvs5Bc1jMaMxmn9SFKg+AXgudvfvOb4QIeZ+s0AoglCHVlTJWcWwLRr371q52+7/nenOA3iQu1cck30or0kWhjvuA1jmNE3meffbJMIcmaZFCTVH7wgx8sSy74zGc+Mxs/Jun+u3avEkyQMRkt0qqMExm8w1ErMRsilPHBYgSMBVHaRUBST8ZLOhsyhuCvSZI7yE4SyT7xiU/MRtl20Rrf2ZlzOBoAAEAASURBVOlaHIzbbLNNzUk8KYVhW2IbY5Pk4Wml8KnRgbv6DiQd/uhHP5rJyXBHKr3hhhu6ersD74vTXECRoLf3ve99M6EPDgSjzz/ozwKWGNPXWmutTstRFhUqia8+85nP9Hmayf5K4LqEH0isk5TEOcbn0be7aRmfC3JpVcX6yCOPrNOq13lBMY5t5Kkoo0dA0kBJBOlQxqITTzxxIpKNjx6p5a+oT7JLIABIBrvHHntk+QkRkwyl0o/YWwR+v/a1r83kTME6SK1B0lwez/gUCAQCo0VA0kqJKCzAZNxnQ5PUXYC+4EJjmGAb5GUB4RLesLnxtcXiLv3fleBnOii7JCL4tPjv+z/t/L81X/L1S/ZB95z1JLsFOXLATTfdlBdSsAAU2wvSp35XZAiLwLHTW0jSwjBktklYEKY8Y2wDgUBgNAiw9VsoUgJZOrZFux/1qEdl/c54Yl4XME0neVNKfmaRSQuPzvo8xSbB78A3dPbZZ4/mZY3hKvhDAkDxC7WBSUkwHP7eMTSWdMlJ9/f2okYOxZWzwAEZ6xnPeEYtKCfsMb1Ijf6zhZjIuHQnHGGJ0r/3ve+N/kZauqIgYTqhxbDNxZ7z4IMPri0CEWW6EJCk1CIfZC/v+uEPf3h90kknTWyCSIlWy6Kz05jQzfhPHvaM/CHmia4WNjjcHv7jWDB3+bfEpinpoOBZAZl0m0kol1xySW3hbD4j9y9eJsr8EMBrMT6ZT+973/tmTvwk6LPkTu8bF/Hmm2+e38NO6FHeh3HL89Kzu9q+6cMSjRk/TjnllAlFe8W3/Ytf/CL7efj8+a+7WsgZ/OrkJ/FO/OZR7kSA30AyeXrsgQceOJHJD8W7skN5BkmO+YhiwcE73/Go9si/xgJjs/5GdsK9ntRiPpUM3LNYrGZaE6K2/X7IpRathOPuu+9emzumoUhgbfG4NdZYIz/bYx7zmKyv43RFmR0ELEphnNtrr70yN4F/TMzMBz7wgYlO5BP2mvG3YQt6a0t8inwO01bodGyafIkveMELan2pq4Xf8+STT852M4u48XtHGYwAvx9evsVHJTibJj/M4Kce3X/EN5FJLaaoD+FWsjvgeomtJW+Vqn/hZEoEiM8h2T2+Jo4HPrm4Z2NNlEAgEAgEAoFAoCAgNwM7ocVR6DQnnHBCnmvY3DbddNPsM+HzLHMNO5ycWuZ8x0i+aaENtiG8wq7a7cvzTuqWLwhXgP6588475+Tvk/osk3DfkvSyNcsrhWskP9IkLSYT+v2crSwSys4J0RQdgLAjMaQJzIoxiCBR7kCA8eP444/PA52kq5M6iSOBE0QoypRiwdacElZJjYCoaO1LRYDT0Mo9JaO/wFdkrGlMLCd5qMQFDEvGTIG/kiZNUrKwpb7v+P1gBBiACcjIaZRDBGaK4KSSfwWtr7POOtkBgJgdK6cMfvfz/Q8nunFD+0Ca7vrY4Z1LKMypwdCuTUQZHgKMGIJLOTyNFeTOSSgSB7pvhEhOL6tlRhktAgwyHGGSZ0hQyFnb5SAM6Jxxxhk5YAR5ZBbmE4kKJdcUeHLbbbeNtoEs8GoSyu200055bpK85qqrrlrgGeLwYSBgZXpkUjqGfjIJYysCsoUUyDV77rln5+WaYbynpZzj9ttvz4lrOC8OPfTQzo2FVl8k+6vTnGiSzdNiIOSvrq+UHePzUnrc8H47ieNzeXrBWuzaEskiTdnvug5a7n3atwhuFoewKJHEshyWXZfnR/VOJLqwCAsyBv8Fxz+bBHmDnMQmKzGJoC64WfTMb6IEAoFAIDCJCAgOsOgBXy2/Ex+9BR6aCbPN4U95ylPyuHfWWWflxDiTkqBs2O9EsiALW5oTJEIKuaY/wnRPnAYkRrb9WUrkRZ4SQK2v7LffftlXZ1FQbYYte5NNNsn9jE9bYmIB9JOQGKP/m45vA4FAoCsI8O2xq/H5vfnNb84BgPwCeFnGH/O6wHH+NMGPAsgmxR+4VIzhIsiQHved73xnqaebiN9bdHiVVVbJ3Cncoi6X8PeO/+1Mor+3iRo5SjJPPgVjHn+RQJso3UMAb9i7shgTH5XFFCYlMWA/NPlQ3/CGN2SbN7uh5FgXX3zxzMyv/TCZpe8szso2bDEVeh7unYV8JqVYDI2dm9w47cXipZIeStTWtRgFcpokWWwnFp8xTkbpjwD9RZIo7/LMM8/sf1AHvhXbw15IB3vuc59b//znP+/AXU3mLeivhxxySI6b2mKLLTot3/ElGlNf/vKXz1Q/thjWQx/60CyHdy15269//escZE8vvvLKKyezE8zzrtnd2aDJ111MnIvrL55r9dVXz7bweT7WTB523nnnZU7NBhtsUJM1J6GwuUlCZgwUv8cuGb6Obrw5C5yQRbybzTbbrLaI/KQUPjbxYhKCi4GTSCXK0hGQgM94jB8mjmISC33pIx/5yLIFfdgC35QWEZlk29Ikvoeu3rP2Qf83L7HT4Km+6lWv6rQe0Q/LsNf0Q2W03+F/sl+TX3E+prngu+orktF3kfckdmHLLbfMfh+co1mI/RtWe2NHk1OA34JcNUt8raVgyF/Kxk+vefvb357nEbw3OlrhfLH5kbEl8BNzJ1msnEhiaeXNIYfjy80KD2MpeMdvA4FAIBAIBBaOgDndQjHXXnttTnBuvpK0XELZxz72sXdJcC5e9ZGPfGS94447Zr4g+7FFOeSX6fKiAgtHpv1f0A0sMEwOoHfCMMroEND2zz333JxYllxGPyC7dbmEfj+vtxMJZecF0xQcpMMixlhl2SobUfojYAUTznfJs2699db+B3XwW8mLEJ4EpiPvSihrJZZZDTbs4CuaulvioD7mmGNyYAhDzdZbb12ff/75E58UAelm7733zg5ShqfXv/719U9/+tOpe3/xQMNDQKAEhVCiP8qf/a4nlCtPz6knyRUFS4K5Sbnvcv+TsDUuGkvIFl1N3MeRsfHGG+f2+653vSscGS01LAago446KjuMGDa63N+sIMOIZX63cn2X77Wl19W500qKJ1gKAYMDvasrgZ9++um53cxCUEyzkTAUS4BlJaYuJrky33snpf10PbFiE9tp3kcg1GYQCCVJ6yrZVwCOcUdyI06NKPNHQOII+gEi0H/8x3/M/4ctHmmMIpdyZFkkY9qL8U+yLuNfF4nbMT53swVOyvhc0JMUZM0118yJa8z3koBH6R4CAlzZXwRkIA1IPDNLRRI3fcvCZJLENleLZzORANyqveecc07WdaIdz1LriGcNBAIBdjcJtt/61rfmZAgW+yK/sstJdrHhhhtmmdYiVWTariXmGPYbFIi++eab56AKc0eUuRGQ5EOb2WGHHaa2fdBl8R4ECQhoKYEEEgtZ2M/3EmfxdUSgxtxtJo4IBAKB4SIguE4SCosTH3DAATmpbEkab2ucstDIpz71qdo8N20Fr26NNdbIyfK7nlh12NgLtFx33XXzgoNdXLQ+/L3DfuNLP9+k+HubT4rjwp9w97vfvd53332DN9AEp8P7fI4XXnhh9l/jER988MGdDFgfBCE9WdIuttQHPehBOSB7Fnxag/CY9e/x38lZFg7Bb8RlEjTe5cIXYNycpTiNb37zm9nuv80223QmQNLiThY8kATpa1/7WpebTGfuje/6yCOPzH1N4s6uLZB4+eWX53lBoi4LCEUZDgISeUiCwt72nve8ZzgnHeJZ8CUl8jz11FOHeNbJORUZiL181VVX7UwA9e9///uaH2OttdaaqQRzJ554YvbbvPvd7+5MA+IbIHPsvPPOneGldQacATfCVmJxR2MenamrhQwsho1OZEErvKAo3URAsgsLK/DrWtTFwihdLuIiJcDVByzKSP6LMjwE+PEPO+yw7OMXv28R8kkobJbmOXZ+ifG05a985Sud5bRPAqbTfo+/+c1vanGH5ii2Gu1dm+l6CXvNn8b+isRSSBwpofmsxCiyTz3wgQ/MPJcu5fn4/Oc/n3lZFhdyj1EWjoCEprh8ZPZnPvOZ9W9/+9uFn2QKf0G+lABO0lhcSAvFiaHWD8jMpd7vfvfLCWMljMOleO9731uLc7z55ps7Y1+ewtcTjxQIBAKBQCAwBATk7DPXkafkUqBn7LLLLnnRxvve977L5jp+Df4svEE+N8lpxWXjmgXH+c4XAU98FvEST3rSk+pvfOMbd/4z9kaOAB7sO97xjqwr8AF1NQ9F6Pfz1u8joezIe9EYLoi4vOmmm2bCTleTD40BloGX5MTh6LbS0Xe+852Bx3XhHxxQu+++e3YGIz5RsmeNpN+F9zDr9yAJgiQtnEeEW0LtpAX+MzbpSwR0BFSr0MbKWrPeshf2/BzAlD8JyfWFV7ziFTXiSVcLBzXlaqWVVqolu4rSHgKCAyV6R9zqGrHxwx/+cF5sYIsttojk2e01geXOTLaUQGiVVVapL7744uX+14UPVhZmuOKonbUkR13Af657+MlPfpKdjQxkSPxdIu8LhkEKOfbYY+d6jKn8PwI3coNEL10KHP7hD3+YAz3N94x5YXDuVvP74x//mPuyYE7BVZITd6kIxpPkTcLkSVpwp0sYmvclcKUj3HLLLWO9NUQV79Ic36Vxqm1QjHtWgyTfdIn0FONz229+aefv+vjs6fRjRCrkqr322ivswUt75SP7teAM9gFys6QI05gUkFP/sssuy4kenv3sZ+cAR+3UM6+33nr185///PqEE06ov/jFL05M8MTIGkhcKBAIBAKB/0KAvYfsyk+FKCW4tRDN2ITI9fvss09OqMK3NS3FQpZ0JySkWF18YW/1mmuuyTyQxz3ucVMhF7JBSBj00pe+NCd+IUtImit5gkUd+TUQK6MEAoFAINBVBASQmcv44Pfff/+cbJUNmF7Eh4Aobizr4gJ1C8EUtw5PDb9GAPEsFguyWehHwk1JdbpSwt/blTfR/z667O8td6w9SyBr3Np+++1ri7NHmTwEzEcWcFpttdVysK4A3i4XCQUsOiUAmx3x3HPPjeQ2XX5hY7g3HKutt946+0X4/rpoE8HfN3Za9GTWiliG+9znPjmpnfFnnEVwm4V9Lfw6S37xYWFuvsAxkgxDcqkuFJzbkjQxEnQM/43gVLzxjW/M8RN77713Z+Zfi6kaU8kzs1zE4+B3i2sbt00Uj8LYKhEMvWbWyvHHH5/bJJ17nMUCEgKm9Y+jjz56nLcykdfmg8PVEDMm5q1r5eqrr876EL+gBMbB9+3aG+p/P5KIWHjKe/vABz7Q/6Axf0tGtlDjxhtvHH7Ylt+FBS3WXnvtnKC1y4lQcNaOO+643C60DXEpt99+e8voxOmnDYEvf/nL9XbbbZdtNeJTL0ucwS6WsNeM316DUyq/iZiKrsXLtN1mLYxsnMWl7YJsJ4k4WRjvnI4XZWkIiLeSlB3fTWzIrJQ//elPedFdcaRHHHFE5sXz67DflaSxfFMWDcf9whm3kDgbcpd86rPyvuI5A4FAIBAIBEaHAM4D+eCjH/1ozcfx4he/OC/qRx4sc6QcRPiDEtHyzZx//vk1mbFLuSJGgZgFS9dff/0sK0eeo1EgPv9ryFe422675Tb7qle9qlN6Q+j3C9LvI6Hs/Jv9ZB5p5RorxzK2jNuJPEkIIsEI0LOiq0DvrhUrAu+00055EOaYt+rkuElYXcMo7mf0CBAOjjrqqNoKQSoDY5dWz+qHiMCWAw88MBurBFpa3SFKILAUBBj3kYUklbN6K2WO46NLhTzASY24cNNNN3Xp1qb6Xhi/EbgEPCN0jbsYrxkgDjnkkJkzNIwbe3PjS17ykrxqjlXiu1AkUUeS00YlqgjHYBfeyuB7sPriPe5xj0zQ7cIK1t/61rdyYJfxZJaL4O+HPOQhOaiiC+P8Jz7xifqe97xnXlFeUpYo3UXghhtuyI6A+9///p1ZpVsymnvd61555fBJWyyka29awmkkKMl5xxl0LbBSgEmXF71o690hrSArIut0oT3H+NzWmx7+ebs4PnvKK6+8sn7Qgx6UE5Zceumlw3/wOGPrCHzyk5/MSd/Yjtj5J7kIMPjCF75QH3bYYXm+kySJrcG8x3/BFqKd/u53v5vkx4x7DwQCgUBg7AiwM7Dr8z0cdNBBecxFKjPm8mXvvPPOeTEzpLJJLAJG2FT46ehQURaOAD4Dn89GG200cfOuYIELL7ww26c9g3aNQLnjjjtmXzMbRSwCuvA2Eb8IBAKBbiHANyiQFllcYia2V+PdIx7xiJw8T9KBrnNrmogisUtoIzDM4qqzXMzBAuLYX8ddwt877jewsOt3zd9b7l7yGomS+RPIaFEmHwGJ9yQINu+87GUv69wiV/Rd/cGizA9+8INzItkuBNZP/puf3idgb3784x9fs0UffvjhneFlSniLc3XyySdPL/hzPBn/FXvVOJPbkbklphag+Ze//GWOO45/D0JAcgcyriTO4+YQvva1r819i78pSrsIfO5zn8u6Kp113ItiSjJCdukKt7Zd5Oc+u7g2tnO+5XFyXl7xilfkhN2T6oeYG+m5j8CttviZBcbHVSzCIEmPpARRFo8Amc0405WksvSiY489NscVWKTYQk5RJgsBMhO5RYK2F77whWMdr3uRs4ijcYOMPEk26N7nmKTPbDHbbrttjvUgY3WpGG/OTYv4sP2VuN9xyhddwibuZfEI0CGf+9zn5rl1hx12qG+55ZbFn2zIvwx7zR1843HbayQvxQHpUtsYclNb4emuu+66rEe84Q1vWOFxbf/zNa95TZZVLNwTZXgIiOnkNzevijuYtuL5cMTZ5l7wghfUG2ywwbLEsXe7291yfA4dhq3e4gqS68eCUNPWCuJ5AoFAIBAIBIaBAH7dVVddlRfRk4fB4hxrrbVW9oGxU5LZ8b/Zb9gsL7nkkqld4P3ss8/O/lz+qLCDDqN1tXMO8c8rr7xy/djHPra+7bbb2rnIAs4a+v2C9ftIKLuA9jVxh0owyhiHbDfOpBUTB9x/3TDn+6Mf/eic2Vw2+C4UQXT77LNPNtxss802OYFAF+4r7iEQaCKASHTSSSdlIxiib1cJC1ZsEGiLCG+fYyxKIDAsBJDb3/Oe9+Q2RqGzqmsXCueL1cEltZr14K5xvA/KE6X+5S9/+VjHHIQyRvtzzjlnHDDENf8LAav6MvSYM8dZBOw/9alPzWTPz3zmM+O8lbj2AhBAzBWoa465+eabF/DL4R5KZ5LowWIcEdhVZ+enwKVxBzK85S1vycZkScxnbXWy4bbw0Z3NAgR77LFHdq6Pe1W173//+9nY+/SnP72WiDPK0hGQRO8xj3lMDoSVfHrURSAusvIsJ56EO6IOUto4S4zP40R/cdfu0vjsCc4666wcJL3rrrtOXKKwxb2B6f3V7bffXrPvr7TSSvWnPvWpiXrQb3/721netdAdGwe9VtLuvffeOxMCf/KTn0zU88TNBgKBQCAwqQgITJSo4/jjj6+f9axnLUtMJ/jLYlZ8cxZV7Hpxj5LpqV3xx3cds0H397Of/axeY4016ic/+cljT/Yx6B7L9xY7Oe200+pnPOMZWZ6weBZyoMVKBZyFz7YgFdtAIBCYVgTY7SXMlmBWkia6lbFQsBX/bdcTrEuO4H5vvPHGaX1FC3qur3zlK9n3/q53vWtBvxvmweHvHSaaoztXV/y95YlPP/30bHuUIDlk84LK9GwF/OJJCkK69dZbO/Fggk5wVSR/7OKC7Z0AKW6iLwJ0Rj51bRpfRTLscZZf/epXOfmmgPpZL6ecckqWi9isRl0shm3hBu9B/EyUpSEgWaIgQYsHjosPJtGJ5GcW2YoyGgTY5SyGbX4eVzLhH/7wh7kv77///qN56Am5CnuqdzOuueaCCy7IPmHJfme5GA8taC2BDi7JqAt/EP5XV2OzRo3HUq/3jne8I3Nsx83V1Jbo4fSiM844Y6mPFb8fMwIWOLAA86Me9ahOJFoQz2bhCzp3lNEiYM4Q/y2moCu8MHLWlltumceb173udTWbcpRAYJgIXHvttfXmm2+e2xj/17gXein2mt12222YjzmR5xqnvYZOj1/66U9/eiKxG9ZN87+Q5S+//PJhnXJB55HMVhyxHApRho8AmV7slViVccZ3LvXJxIdKXkf3ZBPEQdN/VTIuXtehhx5af/CDH8zJc8dlO1rqc8bvA4FAIBAIBAKBLiEgL9fXv/712oJAcgHwz8gPWObgtddeuxY/KA/JFVdcMfGLBbFHsFUdddRRwRPvUkMccC8//elP64033jgvTCSOcFwl9Ps7kV+Afh8JZe+Ebfr2jjnmmEw6R0KPsjgEDCwPechD8spwizvD8H71oQ99KK/EtOGGG+YVXYZ35jhTINAOApxLVhYS/CIgsAuZ5z0pw5ZVPxlB3d84CB3tIB5n7SICgqH33XffrNwceOCB9Z///Oex3aYVwSRQfuITn5j7wdhuZMYvbJVZxIDXv/71Y0GCkm38k9w2yvgREFjIsGNFnXEUc/UTnvCE7OSJoM9xvIGlXVMgH2LPaqutNrYFRA444IBMVqY3RbkDAcZZ4/y4HMFHHnlkJhuceeaZ8UomEIE3velNWW4c1/szLwi6Q+gKPWm4DchiDg9/+MPzwg6j1AnoAALn6L6zXpDRyF3jSqwb4/Nkt8Bxj8/QQ9DiOLUwRZTpQEDwAFsRoui5557b2YcSeI18QPbnKzGWCtSXDN+K8gIYowQCgUAgEAiMH4FmYjr2IskWVIsAsUGOY3GLuVCRAAbZ3PzSxfub6/67+P+ySMzLXvayzt0evViQjPZJrvXeJYb4/Oc/P7YEFZ0DKW4oEAgEZhaBP/zhD/WFF15YG7/5fPhycWwktOhaUDcSu3EckT3KnQjgaP7d3/3dWJI0hr/3zvcwiXtd8PfC7bDDDss2Kgn+o0wvAr/4xS8yP8RcM85gDwhfdNFFOVHh4x//+Pqmm26aXtDjyVpF4N///d9rSUHITuMcv3CRLQaNlxylrnfYYYe8CN0oE8dYkOGBD3xgXnRplNed9vf9ta99rf77v//7nChi1M/69re/PcsmH/vYx0Z96Zm/3ne/+90cryRAedSLPrkefjv5YJS8mkl56bgu9PFRJwCic0pmKylelDr7xu973/uOnIfFbsRPPy5O4bS+e4m1+NLGFXNLfuQzwb+QhC/KdCBA937sYx9bP+hBDxobjxySkoEHz2z8bYovVMLocSy60Xx68wfZXmKaW265pfmv2A8EhooAmV5sHHmJXD/OhenDXrP8qx2Hvea3v/1t1i9f+cpXLn8zM/pJgs511lln5MmWzzvvvKxLdJmjPA1NQuzVZpttlmOxtP1JKOJA2d70UYnCcNnpneRY7fXYY4/NnK7bb799Eh4n7jEQCAQCgUAgEJgqBH7+85/nRRksErTddttl+6F5Wt4COUH+6Z/+KS9gI4/RpJSDDjoo338sJDkpb+yO+8RrFethIdIbbrhhLDcf+v3ysM9Tv4+EssvDNj2fEP44tgSaR1kaAl/5yleyE+Wss85a2okW+WtZ5ZHeJOXswupci3yM+NkMI/CDH/yg3mKLLep73/veOQhmnFBwREjig8A5Lsf/OJ8/rj0+BATEInsgu0nqNOoiScmTn/zkTNZFpo4yXgQE/1HcR53U9ZOf/GS+7riSl44X9e5eXZJfcvtVV1010ptEoN96663rBz/4wWN11I/0oafwYnSFrbbaKicF/uUvfznSJ7RStbZrBfModyJgzn3kIx+Zg2Tu/HY0e1YX4sS14meUyUXgrW99a7ZBjFpOgNiLXvSivHpsJIlup/0YN+95z3uONKgAwWP11VevzRdR6hxEiLA96sCnGJ+no/WNc3x+xzvekef4c845ZzrAjKdYDgE2/3EE/y13E30+SEhnNXnkQDYMMq7k2JIHjXoc7XN78VUgEAgEAoHAHAggpwss33333ev73Oc+WZaQXFaAQFcWEJHoFrntuuuum+Np4t8LQeAzn/lMnrsFmI+7kBkuvvji+nnPe15+15LIHnLIIVmeGPe9xfUDgUAgEOgqAhb1+OpXv5qTbkv4J8j7xS9+cX311Vd34pYtRibRRZTlEZDsaL311qslXRpl4e8l44W/d5SoD/9a4/T3ehryGbl8HH6p4aMZZ5wLAfqggCPBHt/73vfmOryV/9MF+bQFDEXix1YgnrmT4sFJ0LPnnnvWFtwZZSnJ9nHyotyBwE9/+tP8PvhHR1V23HHHzAmflGQNo8JlGNdhS+TDEkszqnL55ZfneUJS2SjjQYBOSj7kHx9lsZAlGeH6668f5WUn6lp77bVX1v9GaeO3gDXZ0WIYUe5AAH/DojK33XbbSCCRvMc72HfffUdyvVm6CB/G9ttvX6+55poj59b96U9/qp/0pCflPh3JHaev1UkWLHbLogejGiuaKFq45R73uEdeOLr5feyPHgH2/p133jknZx91nIenZXchP4hJN38F32v0bWBWryj5kRgniWUvueSSkcMQ9pq7Qj4Oew3fw/3ud7/6d7/73V1vaAa/+dnPfpb1iJNPPnlkTy92ZVyLBY3sITt0ITFY+Nbm/i4WOggOlwR0+OAlKd2mm25av+Y1r6nZuGNB+C6+ubinQCAQCAQCgUDgDgTI9JKxHnDAAfWjH/3ovOgqn4o4WXZ8vp1R+8rn+26OO+64nIvin//5n+f7kziuQwiQI7fddtucK4teM8oS+v1d0Z6nfh8JZe8K3XR8w7FEiZPMJsrSETCprrLKKrUVVkdZKN8UcysPffOb3xzlpeNagcBQETAWvelNb8qC6THHHDPUc8/3ZEjQEnoKpoiEmvNFLY4bJgLIAJtsskke0//lX/5lmKee81wnnHBCdjpYPT5KNxCw2izn6KhWaSNTSBwQKzt24/037wIxw8p9Av0QiEZVDjzwwJzsfVxBQqN6zlm4Dgf7Ix7xiJy0fJQGv+c///n1RhttVCM7RVkegS996UvZuXrttdcu/48WPwmouNvd7lZHQEWLII/w1Jz0yBuInaMqX/7yl3O7/exnPzuqS87kdc4444zcV2+88cbWn/8Xv/hFdraMa4Gk1h9wEReAu4C3UTqgYnxexIvq8E/GMT5/7nOfy+321FNP7TAycWtLReCwww7LAdacneMsZHvJQ/iXkAUf9rCH1UcffXR98803j/O24tqBQCAQCAQCS0RAgjNJRq3QTNe0ACTf949+9KMlnnnxP0eit+DGG97whsWfJH45EAGBgYIU/vM//3PgMW3+Q3CixRAs8impj8TGEo9EkGKbqMe5A4FAYBoRwLNhr33Oc56T7XyPe9zjss42rvGUPMG2Fsl9+rc274ou/a1vfav/AS18G/7eFkAd0ynH5e9997vfnZN2RSLEMb34MV1WsMfTn/70vGjtqBdFl5iOT/vMM88c09PHZacVgSuvvDInWhOsP8q4iWc+85mRbL9Po7JY3f3vf//aeNN24fclg/HJRmkHAbxG/qJRJAGXaN+CRBYnijJeBN72trflpLKj4pbiPLJn7rfffuN98I5fnezGvi8mYBRFvI3Eqe985ztHcbmJuYbxUHzfPvvsM5J7Zl9fa621Rp7wdCQP14GL8Ff9wz/8Q15sd5S385KXvCTHqEryFWU6ERAT8pjHPKZ+1KMeNVJ/nTlVzNoWW2zR2SQi0/nGBz/VH/7wh3r99devn/WsZw0+qIX/0MforHQzOnOUQGDUCBiPXvWqV41lMbOw1/R/26997WtHZq/5t3/7t8wVsbhYlDsRGOU7cFXyAP/yKOMM73za2dy79NJLsz+9K343csiHP/zhbGtbaaWVsk/w8Y9/fH3EEUfU4sdGuWDNbLaIeOpAIBAIBAKBQKA9BOS+E28oMbw8D/yl8tbsscce9YUXXlj/8Y9/bO/iCzjzF7/4xSwfBUdkAaB18FA+XLZOyYzFpYyqhH7fH+l56JZH/5WfpoEhyhQhkBTO6mlPe1p1zTXXVJttttmSniwlOasSyWbOcyTyRpWMC8sdlxzI1be//e18L8v9478+pKChKjm1l/tXCiqq0gCSv0sTWPX5z39+uf8n40WVEqsu950PD3jAA6pkXK/S6n13+d8wvkik3WrttdeuUtKA6sgjjxzGKec8R0r8UaXA7SqRI6pPf/rTVUpoO+dv2jrg4x//eLXLLrtUKUP9UC+RJo1Ke9VOUrLRoZ677ZP94Ac/qC666KJq4403rhLBtu3LrfD8P/nJT3JfWWONNarnPve5Kzx23P9Mwmf1ohe9qEoOgerEE08c2e2kRJ5VSrJdPeEJT6hSUoQqBS+O7NpduJAx7Atf+MJdbiWtOlqtttpqVUpCV93rXvda7v8pEVqVVpNe7rt+H3bccccqGROX/Ut/Nmbp32lFi2qbbbap0spV+b07KCVWq9LKA8uOH7TjHSVS4KB/T+z35ra0AkNljvza175WJQJK689iPllvvfXyOJsSlLRyvUSOqlKy2uqGG26o0qoGVUqMWZnTn/jEJ+b2kJIOVikgoJVrz3XSLo3XzXtNgdxVShqf5/oPfOADzX+1sr/bbrtVKbiv+s53vlOlpAGtXCMlSs7X0BbIDIlIXCUnQ5UCCyvtcMstt6wS8alKBooqKY3VU5/61FbuY66TptV1q0RyrFJS37kOHdn/jQkbbLBBlQhiVUoG2fp1yeH0hfPPP7/SP6e9NPUO7XHXXXdd4ZiUSDO5zRZcUqBuZc7sYtsp96hv0ZVe97rXVa9//evL161tk4O/InumJIXVnnvuuejr/Pa3v62SQfAuv08JsLOMYhxJJOi7/H8SvkiEvKzbnnvuua3frjnFGFJk3TYvOJ+xvs3rL/Tcvf02rXid9bgU0F2dffbZCz3dyI5PydyqrbfeuvJuv/GNbwxdF+/3IGR3NpUU9N7v3wv+blxjL8zYGegdxx9//ILvu/ygrbbCDGu8ZutJCRjK5VrZen59IC0sMHQdmL532WWXVVdddVWVgn8Xff9t4byiG3rGM55R/e3f/m2WSVd03DD+1+b43NTZm/bUct/97ACJlFytvPLK5ZChbOfTFpr3uqKL9toXVnTsuP436vGZzLXhhhtm+8h73/veVh87OfSyjZbOeOutt1bs/eQL9jT2vORUbvX6s35y88Ozn/3sKiVurVLy69b09kE4p2REVVqttkoLgWW7HT3xFa94RdbjB/0mvg8EAoFAAAKD/Mht+20L+r06X/l+0Jbt+thjj83jHbvGLJYUvFh96EMfqk4//fRsqzXmp+Th2ZY7Sjxe/epXV4k8X91yyy3Z7jbsa0+a/WLYz4+Dse6662a/0CGHHDLs06/wfB/72MeqFHiQ/YQHHXRQ9fKXv3ysPIcV3mz8MxAIBAKBCUKAjS8lfsxz+EMf+tAqJfepEmF2pE+w/fbbV3gBKbhsSdftAmckBU5WV1xxRbYR4nxtt912S3qm8mN2HD7xtu04rteWv7ef7y4lHViO/8n3z59aSkqMkbkP5XPvti28e68z6Z9H7e/F78HpefOb31wlovnQ4etnF05BLJmjNvSLzeOEYf9cHiS6Id+yOQWvbxQlBS1nTsz73ve+JXEMRnGvw77Gr3/96+qSSy5ZdlrzBd7xoNLr48FdZ6uPsmIEcABwVfGTyU1tFxxY7+aCCy5YEm+7a+PlMHAjuxpf9PsXvOAFwzhl33PwreA+608f/ehH+x6zkC/7yUF4ZniXSj85li8NP2g+JS0onLn+m2+++V1ibubz+3Edo63zR59yyinZztPmfbzxjW+s0iKb2V+26qqrtnmpKmJjVgwv3yFZEW+vOYet+FeL/y9bsfFCTMyaa665+BP1/HIaZUDxbCmZdubqDzu2qwe+6uSTT670y1/+8pdVWiCu998L/sym8MEPfjD7w8UZ0J9xWMSrzDfusR/PqN93C765Bf7AmJgWzMt+smFgM+jy+BviK/WRYcTVdF0uHVfMR0p0VomxMQa1FZPafMe4GS9+8Yszd3rcsYjN+2prf1ztbj6ctraeuZyXv1LMjDluFDqK65b2LJ4HB3/Wylyx7PAYB2dAjL95b1jj+Vzvla4kvjgtbJ7tyOT5WSvs/3TTZhG/Joa2FHG2bDClpAV6y27mUQ6KjR3Ek1n24//a6ZdvofeYWfh81FFHZT6/OEL87bbLsOw15T6HIcM6Vxf8JKOy13hefC5cJP1FLN5iSz9bTL9zTUocenkH5MG2YznZLF1DLBJ5ZKml12Zdzrei/ATlmFnbirHkR8WJu/vd7z7yxycHiPMRL2cuE9PPz7/DDjtUYklGkc9g5A8dFwwEAoFAIBBYMALjstcs+EbjB/NGgOwtP5/YcBwQMrJcGnvvvffYuAYpuX32MdLH+QamvfTz+/d75kmIn+1332xq/BViA8Qetl2Grd+X+11IjMlcudPKOXu3bfmhy3WKbrkCPsYxVVIMokwZAinhUf3kJz95KE+VguTrZHTJKx+mhlWnIOI6DdS5vv/9769Twqs6EQtrq3r3lpS4ok4knd6vl31Ojt86JRGV0DhXq0SnRAvL/p+ICHVKTFGnZGs5I7rV0KwcfvXVV+eVVf3uH//xH/Nq9QcffHCdgqLq5Lyr0+S27BzD3Dn88MPr1VdffSSrlyfHVV6FMDnGx76SqBVmkqO+9n6GXVJypfp+97tfXmlv2Odu83w//vGP6zTJ5XabCK5tXmrOc7sXbVN/SAb2OY/vwgHJ4ZJXl0ukr5HcTjLc52z3idjUmZUURvLgPRdJBIt6q622ym3FHKHtajNWck/JJeuXvvSltRUoS7ES3IEHHpiPN7bqr2X8f8973lMbd5MiUSfDZvlJnZJi1ikxbW31uOR8qxNpKfdx35WSFI88VqcA1jo5B+oUSJqvkRJm1Ylck+cFq1xZAWNay29+85s6JXitU2LZkTyiFR0TQbe11R5Sovc8B1u9xOogydmdVy5JRJe8YojxyQq74yhdGq/7PX8iMueV3Zr9qN9xS/0uEcryu/jUpz611FP1/b2VRPRpK9EfeuiheUWbFMBWn3DCCVmGSMTqLDOmwLJ6v/32y30+OSX6nmsUXyYieZ0CLUZxqQVdIxGFMoaJfLmg3y30YDI2+XqnnXZa6E8n9ngrKZ1zzjm57RmTUlKDgc9CDif7Os5KOd/73veWHdvVtlNuMCX0q5Mztm67DbleClTOODX1t3IfC9lqj8mgUq+zzjoZ85REK8sTKblLbR5JiWXzitx00kkrZ5xxRn4fo5gDyXRkJ/JbW2W+Y31b11/seZv91uqqiXyRZdGUzHOxpxzZ71LgcJ2SXtajmDPN28Y9ct2wyrjGXivokX1TcqhFP0rbbYVMRj7yjtssiRBcpyRNrVwiLdRSpyCeOhEeF33+tnEedGPkgESMqX/1q18NOmRo37c5PpNZUuBO7rvmy5SEcrn7NsemwNlsX0yByXUiBtW+G3aZT1tYjH1h2Pc5zPONcnx+2ctelsc077vNct1112U7BTt/0etTork6kcfyeNW067R5H7N+buMSmU7fHmUxVqTE8lkfZctLjsVRXj6uFQgEAhOOAD/yuPy2oGvqfPOBkuxC90mEqfkcPvXHwIHv7G/+5m9qKxWPajVytpIUYJ1txsMGeVLtF8PGwfnM6ykov9ZPR1EScatOpL/M4+AbwDGIEggEAoFAIDB8BFKSq8wRwtVIC4PXieA//Iv0OWMi0ma/dgo+7PPfhX3VBc4IXXjffffNsmFaPHFhD7CCo3FeyDltz4Nt+3vxNdkoyM74cWSsZnF9PE52btyhJteoeVzZbwvvcv5p2o7K3+sdpsXv6qc85Smt2I29k6ZdmE2eL6q3LY3q3YX9sz/SKZA8+0vYgtsueFFpwfr6yCOPbPtSnT0/zlRKOpnHVvaMFflsjjvuuHyccRgPcxScg84Ct8Abu+iii7LMkpL0LPCXCz9c/IOYgpQQZOE/bvyiS+Nl47aWvJsC4eqUKGbJ51nRCbxv8khK1Lqiw+b9P/3y+uuvr1Oi4NwH8XtTYNyy3//+97/P9ix9Uz3vvPPmLffx5abFG/PvcEYnreyzzz6tx3qkxCTZriT+qO0SsTHzQ/jyyy/PbZYPoO2SEipm3/QwrzOtMiC5ytg3Cj9HWjwkx3kM473od2IL0+I4dUpSnONS6CPGUzGJ8yn9eEb9vpvPuZZ6jFhInPm2Y8n22muvzPde6v02f99VuXScMR/sGinBYJ2SBDehamWfbO9aBxxwQCvn7+pJx9Hu5sNpGwVeOMspAXiWM9u+nracFn6tX/Oa17R9qc6ef65Ydjc+Ls5ASiycuZyjAC8l9ch2mLSg1Cgu18lriNukx5A1xNc0dbtywziZdNdVVlml/v73v1++njM2din5FpZdZMZ2xBGK0brttttaf/Jh2Wvc6DBk2PLAXfGTjMJe45nFLMpFstTSBZ/iUp+h9/di5lJSz96vh/6ZDwh+wywLzU8wzGtP0rluvfXWOiWSrVNyp5HeNjs1HVXcCC6gvBHywszlxx3pTcbFAoFAIBAIBDqFwDjsNZ0CYIpvht2cLELuZJeSu4n/fEUchTbgOOKII7LOzZ4+C6Xp959vfq5JwyUtRJh9I7ikbZdh6vfudaExJvPJndYPgzb90M3rzaHfHx0JZZtoTcE+soqEI5K9DrMwUjCgInb2FmT13oSyEr8KUBr0m3IOg4RjVpTM5c1vfnNOSFZ+Y5tWEc+/SyulLfsakYSiy0HehsEbmWeu51l2M0vceeUrX5mJZiaMcReJIz33sJIU9z4PIWT99dfv/XrBn2ElUemoCkcBXBDCxl0IdO5lUhLKwgvRVYDLsIh8K3oHghYlvkirD6/osJn4nwSu2kovObt8v//++y+Hg4RSK+r/EkiWRHsMi3DmcGsWfUVyG+1UIZg0HW1phcF8jWaiI+cynk9zMU8xDEvS22YhcHov3nEbBcGB3PGSl7ykr3FZMj1t6F//9V/buPy8ztml8br3hjmSH5oSnulLbRaJmzfYYINWLiHJQFqpMPdzQWO9BcGLTEieU9Lq3blNjCI5nuv1kw84/peaBNO5h12MfRLtF6yGff5yvrS6UJaXm2Nx+d80b42Hxl1jEsfooHLaaafVq666aj7uda973XKHdbXtlJvUhhDBGNnaLkjcu++++9AuY6EQ76Y32a9kVnRNye8liZykIiGYZ2qbvG08SyuE1shfbZWFjvVt3cdiztuv36ZV11dog1jMddr6jYB2CxG0baxHPucYGHYZ19grIfVSEsoWHNpqK2RAdrA2SehId8agy1IS0bZKWrU6B9Mt9fxt4TzovowLZILzzz9/0CFD+X4U47MbpY951+T9fgHFkrSnVd6H8kyDTjKftrAQ+8Kg65Tv+8n45X+j2o5ifJaQS1ttO5ifk9h1yHbkyd5CtpTwIcpoEHjLW96S9Wt+nlEU9iLvn8wdttNRIB7XCASmF4Fx+G2h2U/nmwvlUSVdm+s+uvT/tApyXnyFX6Z3oYI27pN8g7g+bILYuO0XiENdKgI52tYLy/N++ctfzknnLEgwisWuynVjGwgEAoHALCPwox/9qN5yyy2znXGYC4UNwtQCxDg+/WwHg34z6Ps2OCOLsRdZlNVcOcyEsoItnNPc2GYZhb+XjcKz8OH1K+b8hyauw3wXJVgs3ot5t/3ud1K+G5W/99Of/nQOGrnxxhtbhabYhS1mNK4yTvvnJLTfvffeu7ZAYdsLUWy//fZ5Uau2rzOudjbf6x522GG57xlfcSb7lb/85S+ZT40jIbirLX7TJLTPfvjM5zscWL5ivuo2yxZbbHEXruxir9eF8XKx9z7od+RHifbaTCb+whe+sH7Sk5406BYW/X3hMPWTE/lv9GGc3IWOaV/72tfyb9tMKNtW3y5ttBm3s2iAB/xQvIqxTzxQ2yViY+aPMDluzz33nP8PFnGkuQ7vYZgxOeOUARcBwYJ/8uhHP7o+8MADF/y7hfygcB+HFZ8lwR/ZpjfIWNzJQhMf9uMZ9ftuIc+7mGMlTh8mh7b3HujbFq059dRTe/+15M9dkkubDzPOmA+YWFy97XLSSSdlOUKiw1kr42h38+G0jeI9WOC7N+66jeviQ+IAzepC0vONZYf9ODgDkknSJSzm1WbhI+aLf+9739vmZSbi3Pgk4rPkGhiUyBQP9PTTT1/2POyz84mN9YOF5ltYdpEZ3GHv2mijjXJMcduPP0x7zTBlWM+9WD/JMDEbhb2m6BJf/OIXl3zrbfgUl3xTSzyBeFr2D/2irfLd7343zzlXXHHF0C9R8hDMNz/B0G9gQk6400471U972tNGdrd0d3Fn97///XPcyDhj+Uf20HGhQCAQCAQCgaEgMA57zYpuvC0edlv+sxU9S1f+Jzbx0EMPzYs6ygdzzTXXjOTW6OQWM8c/m6VSfKqD8vM183NNIi70GFwMbartMkz9fqExJguxD/Xi0LYfulxvDv3+6L9OhsgoU4RAUvCrtIpIlQh4Q32qZAgdeL40iFdJ+V/u/4l0Wm211VZVMrhWKcP0cv9rfrj3ve+dP5Zt839l3/nVZul3PylhYZWUbEmSq0S2ax4+lP2kTFdqWhF1KOcbdJKUeK0688wzq1NOOaVKyvugw0b2fUp2WCXDW6VtpQSIQ79uSkiQ28lSTpxIWVVyxldJmFnKaRb020RoyMeX7YJ+POSDk8NvyGds/3RpRdUqkYyqlJSi1Yulib1629veVh1++OFVSmjY6rUm4eTGyX4lJQPNX1966aXL/bvfWNs8ICWBrZLAl79KqzVWKWlslch8zUOqlNSmSg62KgWT5O9TUFP+brmDej6kQKTqpS99ac+30/Vxk002qRIJqkqJI/O81dbTeadJ4cpj5LCvkZzoVSJg5zk6kZUq7623pNUEq80337xKhL/ef43scxmny3ZkF57HhdyT+Sut9jaPoxd3CLnI+Us/X9xZBv8qJaiqrr/++iopfpX+3VvWWWedLCcmkn7+l3l/VGWQfLDSSitVyfk2qtuY93X0obTycHXBBRfM+zeLOTAlbKie8pSnzDkWL+bcXf5NWlm4evjDH16lxBhVMshUKcHeXW5Xf0mKepWCpvL/eufBrrad8iDaUApqqLzjNou+lYIcqmTQGtplBumDaRWmKhG8q7QARbXLLrtUKahiaNds+0SrrbZa1h8T8avVS6WEtVUKWKlS8vDWrrPQsb61G1nEifv1W/oTW8UklLTYTHXLLbdUKSin1dtNhJ0qrUA79GuMa+wdlrzRVlshA7Jfwb2tkpw7lftPZOS2LlF5jmHI2G3hPOjBjQv0MRi1WUYxPrv/ddddt0pBKlUiAmeZnzzTLKusskq18sorN78a+v582kKvXNV7E037Qu//mp8HyfjNY0axP4rxmU3W+9ttt91ae6TkFK9e9apXVWSxtCBMX70+LWCVbT8p4Le1+4gT34kAO0pyPFZpMYU7v2xpj102LaJRJYJqlVabDdtpSzjHaQOBWUGg31zftt8Wtv10vrkwTwFDcx0yc//fddddq7R4YJUSglWJfFOlgN1WMbj44ourlOyjSgvkDPU647RfsDW27fNcKFhsobgNX/rSlxb60wUdjzuRguWqgw8+uPrMZz5Trb766gv6fRwcCAQCgUAgsDgE2IQuv/zy6jnPeU711Kc+tfr2t7+9uBPN81d8HZtuumlf28E8T7HssGFzRhZrL2KXVIbpL0gLiVVrr712lRZiXfa8beyMwt+LC8JmlBYZrtJCAHd5jLRIZLXffvtVKVHcXf7X74vF4L3Yd9vv+pPy3aj8vSlBXvWsZz2reuQjH9kqNEVXpLuNo4zT/jkp7RfXJyUpz3NKW++IvnnRRRdVJ5544lD8Sm3d5yjOy1Zi7lbg0a984hOfqLbddts8vho72+A3TUr77IfPfL5Li/HmuatN7gyfCe5RP67cfO6x95hxj5e99zOMz7Dh70hJk4Zxurucg0+UzSMlELzL/5b6ReEwlW3zfOW7lOBvwWNa4TEMU/5s3lubfRvfHwe/TY7Dpz71qWq77barBvHcm8+61P2IjZk/gvjNaUGLKi2EPf8fLfBIPMQUgFptvfXWC/xl/8PHKQP2v6PhfwsrsV1tlquvvjqPc2IAhlHYLbSjtFDycqdLweR9dc7lDur50I9n1O+7np8N/SM/R5vcUO9ADAiO2bBLV+TS3ucqPLSy7f1/m5/h/LMUj5gWXmzzMlUK8q5Sou7WuVytPsQiTz6OdjcfTtsiH2dBPzvooIMqcdYpkfCCfrfQg3GNxIjg3s9imW8sO2zGwRlISVtyHEvbnLCUADPb/Eo8zCy2hfLMbJLil+mPp512Wvl62VYOBn6WZvzqfGNjnaTo8stO2Njpl2+h8e+Z201JjnOOCXyCNvITFECLvYacNowyTBnW/SzGTzKM52ieo217jWsVXWKzzTZrXnpR+8P2KS7qJob8I88k50GbfYE9/gEPeMDQbJdNCAbZbUrccm9+guZvZ2k/LWyQ5xg6XZslJdTPcQD0GTHJ5rE3vvGN1aqrrtrmZePcgUAgEAgEAlOEwDjsNYPga4uH3ab/bNCzdOl7sQG4CeQEMQLyAPKnt13k/CMLzZp9YkW2ApjPN3627fez2PPT7+UxwSfsjWFe7Dn7/W7Y+v1CY0wWYh/qvf+2/dDlenPp93cwY8vRsZ14BBgREJNHZdjmzJDYBMG0WSRETavEVmkVk5zcVZJUZPbFlIU4cEoiQ0pwGwU5XtK0NosA7rXWWisnTGrzOvM591e/+tWcZCOt4p0DwCQHTln9+/5U4oq0elQWINJqNtmRKgARgYjznyHw2muvzYmnnvjEJ/Y9h2QeggjTimNVWn1xuWMk0UEYSasjZUGlkMBMBHvssUdO9MvIgWglAW4JVvO+EPUlM+R8kWSjScZKq9zmAH0OAgGSHEgPechD8jl7HdGIFxwFyNvO9f/ZOw+4O4qq/6+F+AqvL0EQUSkRDC1AiFSDNOlCKIK0CNIRESkSaiCG0HsVAoReAoKUiJTQlShNpClSpEhV0JdXffXj/8X9n++Rueyz2bs7e+/M3t1759zP89x79+7OzJ6ZnTlzyu9AybJoPw4tAGjCJ5w1DHhLu/ZTBgsCgmiall12WQVd5ThKQurmfnAq22GHHSICEbIIQzbOCTwHtG+99dZrGQTz2pFVls9jgLziIPvGG2+0+st1fSg/AdgCjCFQew4YsE+cHG0Jx0DGt1ECA/TGhgLgyrPPPlsVkaas/fffP0I4hHBEtyHJrmBzWqPPAbQFwJYHH3wwajcvdnuDzAUEgABq55rYLLLuAi5jHGWz6jAgOOa3P//5zwoISAAS6wXzumQXV8NY3hzFnIYygLkZoxJOijjtAK5DUHSSiuZrzgXkmHH86quv6rqyzjrrJIuIcCpkDuEdUFTmfWQs10SA33HHHacyk49+gqfcg2S1c910XfdRIgAWJ9nu25aP4xOG3zQhR9KP9AEyQ7of88ZD0fqdJx/ADxQRu+yyy5Am5Y2JvHE7pJAuv9BPkuFWAbqLlAadVkWA/+GHH97p5Y2+jvmD+QanDwDncZpKEnPSSiut1HbOzBo7HMubK/LGVbJuV5/HjRsXHXPMMRr0NXLkSFfFDimHvR0g8gQLVEHI3pIlW+WOiy66KGJvYqibecKUUdSH5rxO3ldccUXvQdw808hkvgy+7PE6nevz9mK26zpKRfalOOSwDwIMhf2NoaJnLOu5Ndc24Z29MXtq+hl9hA9iHaafMQj4IB9zL+0kQA+ZjyA0gooACE0T4wedFXoG5DkCXZJ7+KLxky7P5Xf4DYg5Dolmr+Sy/KefflqfF+Q0V4TsBuAgOgfmN/ib5Keph/HKHgcQUxxTAMQsS2V0IGXL5vwxY8YoYFYn19pe43t+Nu3AwXD69OkqwxBkh4EjmfiLZ5C/NOXN0Zybp+OyHQvpOtt9T+sXOC9rfUYPlqcDbFeGCvbIAABAAElEQVS+j+NVzM/MXch2JrjUx30wXtjrTJw4se2+3jjzmiDBoueT39Gvop+lHwFXJlDB3Av2AvaHjEv2gUafgGyAIyGO3MixlIEBDh1weg3MkwHhE0ZndBGvvPKKloWshL7W8NJWDqGsqtcK9GzoCej/nXbaiSZ4IeaLE044QfvCdXJCLw0OhQYOBA40lgN5dtt2cyw6YJNUA1nSOBJhI0POZP9rAnqy9nxZcoTR67KescfEFoIOyBB6DtYsbJ3swbAl8m6ozNphrmnaO/I7azR2OxKOoLcfNmyYl9tgP+c6qUmn+gsXfc+YhW/sj9jnGdmnSKfsYu9U1EHYUuC3L2JvgK4O+0ByH+KrvlBu4EDgQOBA4MBQDrDPJQgamYs9NjpBX+s3uqS0XW1oa+y/deIz0k6XlWcTpkVFOgT7Vtufie7UZxAoLUGO8G3vxVZNkuRTTjklwg80XR8gfdhIDbXrI/N7u/d2+4Kivm1XXj8c923vxS4xc+ZM9VeqG79snlkb+5C5r070n1xbtE/oxl8lb5/Q7nkw9+PjHT8dfFTZE6OX9EHYMNiX+yrfR5t9lolf9OOPP652TvQfxr/Y1ElSdxLttvPTLhqfRTqMovm16Dks0r+b+6hiz2vqSr+jT0LvAPj5Hnvskf7ZyXeAmOFlVX4z6UYX2Wo4n77CFoB/Jc/5Bhts0PL1TZfn4zsxI9iAAJV2ASCSbuOLL76oAISugA7T5Zf5jv/jCy+8oDpHdJk8p8TQsOYSy4DNPE3oVACQBlgRWX7EiBFDTslbE7LWEnyR8dHk2cuKpRhSeIdffOuZiDFh7fZNgxAb45KH7APxt2Quwd7qg9hL4Dud1Ml3U48vGZA22azDtjbvbu4R+8YZZ5zhzeeItjF/m7m8m7aaa7G7XHvttTpXEWuy4IIL6k8kniNZWZKK5JHkuUWf8+bTomuLfieuAJ8ExoUPf++HH35YnwvDq6L2lP29W7k0r5+K9kzJttrEfOTVlSyrm8/oc9CrwXfionzQa6+9pvq7M88800fxjSjT57iDATY+bTaytGtm4kOFr+Xdd9/tNVaYuF1iYweVbGPZs3wGsmRsE+Pmkp/oRugnX8Q+lX0GMZ1Zvr2+6q1zucQtEq9HLAr27GTcLDFl7JWTSdNsY2Pz7pm5KAtvIe+aQfiNvcWoUaPUrxMfbh9k9DUm7r/bOsrIsNTVqZ2kaJ/R7X0krzcyvi99DXW53EuUtSnmrfNFetMkn3zKn8wz+EnDp7RuONmGbj7jXwe4UJVrQR4+Qbt9GfpLEiCCyYFPOYnWiINHX4Z+nDLR8+JTnte3ZXwNffZtss8AtmbMMS+sscYayZ+cfsbPHJ9KsFey4qmcVhYKCxwIHAgcCBzoWw50q68pkmdt5LB2ftgwPW/9Liq7yDbet52acWPY0NFLEqOHjPW3v/1NY8oyTnVyCFsJ+EmdxBU7aUANC0nGz4KxRtIh7MngJbBf5jnAlwSij8C9g/L0VkXPnxbg+N8mm2wSTZ48WW2H4D34IJf7+05iTDrVD5WxQ3fLt8L9vSikA/URBwQYKxZFlfM7kkCgWAZjLMaLIWWL03B86qmnDjn25JNPxuL8osck8FuvE4PEkHPMFwF61N/FwcAcmu1dnN9jCb4aclyypeh1olxtHZfAtlicCWJRpsTPPPNM67jLD5MmTYrz2uqiLlECxRJ05aKorsvYaqutYgFZ1XJEURuLgTSWhWlIuaKcicVIov0hi1Isivb40EMPjUXhEUvwQizO4zHjh3PEmB0L0EUsyolWGRKwHguAbiyLRsxn+MtY+8Y3vtE657TTTovXWmutWIw2sTiBxeI4FQvYmv4ugRGxKI30GlEQxrJIxrIg6m8CYhlLJp9YHLViUbzEAvag5bz99tv6uwRGxp/61Kf0WuqQAFRtB/VLRr5W/Xw47LDDYnH0ikVJFQt4SiwKNb1OkNP1uwDY6Hf6ToIGYwFtiUWY0jLy2s8J8FYcI2JZ3GMBSlIefPzjH4/F+Uavl0U8FtAMvTcRauMpU6bEAtwZi0JMfxdlmNYtoI76HT7RBgF+imVx02P8K2pH68SKPgjwTyygFLE4DXurkTEgxn1v5Tet4GnTpulYESNYq+nvvPNOzJzK880zkSTGIM+DKC2Th2MRTPUZFyeYIcfFoVnP5xrmAwGnGvJ71hcxwOk1kk0h6+e+PybZ3mLWUl9EPzAP+iDmLvpanNiti7/kkkt0fmQtYLyMHj1ay2D+y5ujxKAaC3CsnisAQrGAgcWSqVfXfXG6jRnHhvLma3MO8owEP+naIA5qsRiFdf0yv7OOiMN3zPzLvMs6JoZ187PTd3EO0vsS8HOn5ZrCeK7pJ9nYm0PO3iWgRMsWY411mayhtAf+C3hqLODRMc8BMpxZnyksbzzYrN9Z8gHlS2bvWBz3tL5ko/PGRN64TZbh4rMotpQ/yC0+SMCbtPy0TO+jrjqWiSwoCkCVqxiH7B+SJEqXWIJtY3G2VT4ZeZB5IGvsFM0VeeMqWa/Lz0YuRLb0RdwX/Hv55ZedVYHMRpkS0JNZJrIHsgp7PT5D3c4TlFHUh5zTDSHfsNb5JOS0vfbay1sVncz1NCZvL1Z2XWe/B4mzbsx+2VDeM9buueVaCcbRvakpp+7vEjisso+vdopTtj5/4qjhpQrXcy+NFODDmP0ve2L20MiWyf2EOEbEyIh77713LBnPdH/MHCNBI617zBs/5iSfYwV9Fm0SsERTndN3SaoSi/OdszLRs0lASiwgozoPo6cTwO9YAo1bdbDGojdhf0C/oFOShFe6trZOyviQ5nORDiSjiNKHBPxAdUSlLyxxge/52TSFZwxCH8u+Rpy/YglaND+rTlUchFvf+ZA3RyOv5+m4bMbCkMre/1JGv9Bufc6S8Tm3V+R7fh4+fHgsSWi83h7rKnORgEVb1VP0fIpTWiyOe1omug4J1I7R16InFScD1eGyp2d/z1iFh5AEE6keibZIEIXqiNExM5czxyfblycDUhZyBnMT6xt6XPSzlMscxlpRRg6xWSuo0zUxR5XZZ3dSPzoPdOGBAgcCBwIHXHGgrN22aI5lPWD+FhCBVhOxfWFPxO7Qbs/XTo6gEHQ+yKiUm1xjkV0FVCO+/vrrYwGjjU8++WSVq8QxXesus3a0GtvgD9gVkfUFIMzbXWADRdfmkjrRX7jqewGMiyVpiNqcsVPzPU+n3OneqRN+YVNGPvNFkrQrFpCSGJtvoMCBwIHAgcCB3nHgj3/8o67fZez2ZVuLLff4448ve5nV+UU+I3m6rDx9UZEOAfkQ2VASv1u10/YkAfOKJVjZ9vTS51Vp78UnCN0M9vzkeo8t2/jEcQN5fWRuMIvfefuCvL41Zfbru297rwQp69hHp+yb2umFs+otema5psg+lC63rP6T64v2CZ36q7Bfzdsn5D0P6fty/R09Ib4Svoiygy7y39zFBwVfWXziWYPQmydJEse3/Pwk8Yv6SCR/LxqfNjqMvPm16Dks0r/T1ir3vEnepD8LUIzqd9LHXX1n/08foktyQWXmyyJbDe2RpElq78MnEx8bfB8lWE996V2017YMSfo6my++7bVF5wlwqvZB0t+w6Brb35Ft6N92Pkz8hg0rSRLkN8QPhdgKAdSNBYS1dRo+L1y7ww47xALwoH2EHxTPezIeJm9NaLeWMH+0i6VoNaDLD9iv0KP6IPY08IZ+9U39Hhvjmn/45dA36fgCl/VgwzV+Dy7K9SED0q6idbiMzbvb+5RAbO2XdBxHt+Umryf+j3gpV4QPuwQ/a7uJ3xLg7cyii+QRLkr7GbU7ljefZlZe8qCZ1335fu25555Ofb+St9etXJrXTzZ7JtMWm5iPvLpMOa7esadgV/FFAhigz0AvfZ183ZtNuT7HHfXb+LTZyNI299LJOQKuEB955JGdXGp1jVmzieMeRLKNZc/yGWgnY7Ofck3oAwRM3XWxrfLw70V2I/Y60AccwIcRvhAjlCT8UrL62TY2tgzeQrLeQf5M3L0ACnljgWt9ja0Myw11aicp2mf4YJZPfQ3tpZ/xofFBeTbFvHXeRm9q2luF/Ak+xgknnGCqdP6O/gZZ2weVxSco2pehEwOHRAC+hzRXgOI1FoiDeX1rLiryNeS8KvrWtId3fOdd+8cly8dXToB4VV+RPB4+Bw4EDgQOBA4EDpThQLf6miJ51lYOy/LD5j7y1m+bsvNs42X41G/n3nrrrRrTl7UndnWv2BeI7R40amf3z8LnAuMIfUXSd1GAWvXY7bffrqzL01sVPX++eI9PCDGhxN74Ipf7+05iTLgvW/1Qkgc2dujk+d1+ztnfTybDWaA+4gCgcWknMxe3ZxScgMdJNjT9M8CfaUBZlKyCmqzVvvfeewp8CaABi22augWUJSCdCRGjMEGEBx98cPz888+nq3H2nUB4yX7rrLysgiifYMVeEw4GSUfRLEVPso1zzz23BuYbkFOcoQALXWWVVVrApyhRAYJKgrgAIssxAwJswFBZ+JiYIUHGHiIsEJgKuIUhFjrOp42GCPTEGSs57szimwSrPeSQQ/TapBMSjv0E1RuiHSilGK+GKJ86AZSFBOFcv3Mtgaw4CuLgBBW13wSlci6KLspNjgGCNlHsGHBYc7/Ggcw4kgOogwM/wDGAJqSpqB3p86v4DlBueg5xWS+gk4DrBvo3B8xzjGCAYn4RASZmvC266KItAOMkr8wzA4iJmfvXXHNNDQ7luixHJBx8mA/4HdBGAwCWLDf5OU+RnzyvXz8z1xx00EHebm+dddZRABfXFbC+SxZO7eckSLipB9AW5iQApfgD6G7mzJn6M+AxjA/JmqHfcWCGiuYoM9cBjGXABHE6oiwDmGQzX2NEYMzTRkMAjVGOAShAwGesG8LZy8z35pird9ZG6uZZ8EGXX375bAEGruoBuJy2GxAgm3INoGwyWIT+S/Yj5RSNB5v126yXSfmAsgFaJujRkM2YaDduTRmu3pEf4AUbXR+EvEX5PpU8PtrtqkzjeAzQAXzYcccdW0XjSCTZLPV7GlDWnJQeO3lzhc24MuW6fvdtdDRrN/foioqCMaiH/qPfJFuqVutinsjrQxf3dtRRR3kF7TB8IXjSF3Uy19vsxWzWdfaGgFEm50Szl7R9xtLPLXzKcir3xT8X5QKOmtz/uigzWQZJYHi2kEl8kOu5F0Uz+8gk0c/JRCLwDAAk9jOGkLvNPt92/PgcK2av5WtNJtETfHFF6HbQwRni+USmTgLKoseYNGmSOUUBIhlbaQef1gnvf0jzuUgHkr6+k++01bd+j7Hvc342922eMb7zfGCQYU9uxj/gv0lAWZs5Ok/HZTMWTNuS72bM2+gX8tbndjJ+sq6qPvucn3nG6EuMg76IOtDZ85w++uijVtXYPJ/o+SgzmZTF7N+SxkKCTpmr0S9A6PS5jjnBELpQgslIUmb0AEUyIInO0HcZ4t4oF0cCQzZyiO1aYcp0+U5b02udy/IpC123Lz2H67aG8gIHAgeawQEDKGtjt7WZY0kUiV2M9cIQekWSVCUpvefLkyO4juRCrAsGUBaHlqzgPWxL2C4JKINs1g49sU/+AcROolAfhA2VPkjKBS7qKau/cN33PAPpPUY7nXKne6dO+ISeEwA4X4TNnaSxgQIHAgcCBwIHes8BAKnYf/sidAgAs/kgY3dKJg0z9djostrpi4p0CFkAp6bebt7RkSJj+iKjZ/OlW0632/iMXnTRRa2fABYyPn02fcSFaX7b7Ava9W2rIX38wae91yQafOONN7xz0IzXdCLzrIqLnlkb+1Cy3E70n7b7BKPvzPM3bTd+s/YJNs9D8t5cf8ZvzdjQXJdNeaxRvoLXfbTXZ5kmIA8fa+zg6D6wyRgiuMT4Q6QBZW3Hp40Oo934LHoObfTvVe55Dd+y3o0vmvFnzzqnm2P4PaLfMLaLbsri2jLzZZGtBt0Lz11SdsNWgq7L+Fh2217b69ExkWzeB914443aB//85z+dF298mPBBwP88/UffpwFleX6xZyWJeIIsQNlvfvObrdPwvSXWwiRXtlkTstYSCmz3bLcq6/IDQCfE6vgg9MHw9ZFHHvFRfKvMQYmNad2wow/MH8w9vojYM1cJk33JgLbrsK3Nu1temufd+N93W17W9eh+AeRxSSRI2XDDDfV555knVg0g3iQVySOcm/YzyjpmM58m6+3ks5m7bP0tytbhMw6qG7mU+yjqJ5s9k03Mh01dZfmadz5rv8+4HpJB4asxqOR73Nn4tBXJ0j77BhANdGq+CB8r5lYTz+2rnrqWWyaWPe0zwD21k7Fd3y++WiTw8kVmn4TsEugDDhBDhS8m+xn2zBBySR7gpU1srNGd2+AtfNCawf5E4kJf+0o461pfQ5k2MmyndhLbfQbtcEk+9TW0k71EmTjTMveWZ1MsWudt9Ka0pUjWLdPeducyb/i06/oErC2DT2C7L0NXhl0siQVCPDq+glBR33KOja9hFX1LWwzhr5UGMze/uXhn78JYChQ4EDgQOBA4EDjQDQe60dfYyrO2cliWH3bR+m1TttGnp7FGuuFbP1w7//zzx2eeeaa3WyEZp8/kSt4a3mXBxu5vEz9rEoInAWUNdpIBlKU5WXor2+evy9tpezk4Yj6fKZf7+7IxJsmbttEPmfPL2qHNdd285+zvJ39YlMWB+ogDEoQdyYPv7Y4EUDWSjKb69+tf/zqS7ItD6pINeyQLaiROoHpcHN4iAZGLBLQtkklsyLkuvkhgdfTqq69GEqAeCdhhJErFSIASXRSdWQa8FaeIzN9cHRQQ1Ag+9poE2DQSg0qrGaLkjiQLXsRxAS1tHTcfaDe8//jHP66HZAGIPvvZz0aSpbN1TJQ6kQTTRS+++KK5TN9FGIiWWGIJ/SwKeh0zfBFgGz127733RgIcpJ8Zd+I4EIkjpX5P/uNaQ6effnokDvqRgGiYQ5EAnUSidI5EgRQJ4K0eN+3lXENLL710JJO1+Rodd9xxkTjNRtyjIXHe0o+mTu4VEoDcSIy8kYAdROL0qceK2i9gZnoe9yXBttHYsWMjcUjTY/yD9yIMRAJ+p7wXA5/+luaBZCqNJHg2kixe0eqrr9663nwoaoc5r8p3AekdwlfXdTMOTV+7LrvJ5YmDSSQZ6XUuv+aaa/TZEGEhOuaYYzJvSwBqWnM/4+jll1+OxOCeee4OO+wQibEtEmedSJS+kQR461wigkzm+YN+kPHJOPVFAvqaOWd3Wx9zn1kPxQFvtuLmmmuuSAKlInGa1vWf7+uuu66eZ+ZLcbTV72b+LZqjuBfqZa0RI7pey3wNmTnbZr4WZ5xIFASRKM0jyWyif+JAoeWKM5+WR5uYa8WIFQk4uK4dzK8+yKyp3J8Polz6QUB6nBdv+qGTskePHt1qzzLLLKOfxXjTOlY0HmzWb1OYWavNd+TlJNmMiXbjNlmOi8++x4PhmwRMuGhuY8tAxkVGpO/ZS0BipIsEKCP3ntJjJ2+usBlXuZV1+CPrLePI9HWHxeReJoEM+ruAT+ae5/pH9pQQawrkYp7I60OtpMt/8Mjwq8ui2l5OX/t8pjuZ6232YjbrOvM3+0QBxoxuuukm5YEEG+m77TOWfm7bMrLGP9C/Pp9pwyOzBvlihau5lz0L++4kXXfddZEkBkgeUp6hAzCEvGFkDdvxY6718W74bfjvug7mSldzg2SGjiR4NZIAnlYzeT7ZQyblLAGRjCQjZEvGRjbnGZaMj63rbD7Y6kBsymp3DvOzWU/andPtcd/zc1b72LMI6FqErkUMqbonT59nM0cb2Tet47IdC+k6k99t9As263Ny7CXLr/Kzz/mZ+2N+cPUcZ/GFOtBhQrZ7Opvn0+hiJcN9q1qj903uA+lndO2vv/66nmeeSQlybl2HLhS9EjK70ScXyYDM9ezjJYBYy6FOykbvashGDunlWsEc5XPthw/w1qyLhi/hPXAgcCBwwAUHbOy2NnOsJC+IJKg4EtCqSAJ6tGl8TtotOZiWp4vkiPT5t912m9ozVl111SG3L0kRdC0RRxc9brN2DCmgwV/gNzYgAcXwchfIH/y59mcoq7/w0fdpGdXI1WlbiKu9k00Hwef0uLe5zvYcCX7Q8WJ7fjgvcCBwIHAgcMAPB9jXv/baaxHzsi/CN6BquxD3YqPLMvecXouLdAjmOtfv2LOML4XrsikP2RTyqTfSCt7/Z+ynyDAQOhf8jZCZoTJ9pBe8/89mX2DOT/etOd6v777tvfhvQlWNIdt+Knpmbe1Dpr5O9J+2+wSju2MPaijtb2qOp8dv1j6hzPNgynX5zvxuxoXLck1Z6GfxIQz0AQcYQwIkFOFzZ+ZX9uLoto1f8gdn//uT7fgso8NIj8+i59BG/17lnjfNo+R3xhz6B7NuJn9z8dnYNHohHxXZagScTeM4kjZ1ATbV51CSwri4fesy4I/hlfVFlicaecu1jilZvYBZRJIEcLa/5DmdfE76oeKHTXzCQw89FL399tvqy1bk25q1liTbkX62k7918xkfB196JvOsGj+KbtqZd+2gxMbk8aDsb+w32QP46nvaw/Ns/ALLti99vi8Z0HYdNnNekc073e6y3w2/zFxY9nqb87kX1+ucBKhHt956q851xFrNnDkzEvAbXbdMm4rkEXNe0XsVMrbhj+n3ojaV/Z3nzuc6R3s6kUu5rqifbPZMNjEfNnVxjivyudbRRvjCvOq7X13xw1c5PsadrU9bkSzt654p16efGeVXJU9RV92obCx7llxTJGO7umdkfdNXrspMliOJ4fRr0MMkuRKpHzN7ZPwPb7jhBv2R2PKddtpp6ImJb2ViY4vwFhLFDvxHYmjNOPXBDCOXGTnNRR02MmyndhLbfYaL+0iW4VNfQz0+9hLJ9rf7XLTOM/+yZ8yLS6bsIlm3Xf1ljvvuA9Y63zoWG3wC230Z8d3ISszNEOsYf4sssoh+L+pbTrLxNayib7XB7/+jD3yu+yNGjNC1Laz7Sa6Hz4EDgQOBA4EDnXKgE32NrTxrK4fR9rSNq2j97qbsTnnVD9cRX0e8L/KEL2JM1c0vyde9ZpVrEz+bdV3WsSy9le3zl1Wei2O+dVwu9/dlY0yS/CmjHyprh07W0+nnvL1lAJTtlKs1vY7gLpzEq6I999xTAUNNfQTzvfHGG9Gaa67Z+gPsFTrrrLNmC04HXIdFPS9oHSeEdsZugDfPOeecSDKCRQT1nX322aYpXt4JXvcVQGcajGMQjte9JAyU8BNHKNOXBG7iYIfT4pVXXmnVvCwjC31epJQlcBMwYgMoQAAqzlIApUomXVXaZQEYGgERx2rOy1IwG6BVAC/bEfeZBL98/PHHIwN2Z64xdZnvtBfi2jTZtp/niUDNiy++WO/flEPZBPkLAr86kC611FL6U5oHLPo4A6aBdEw5tu0w5/t+B3wahxbGvC9C0PrlL3/ZCjj2VU9Ty0UBvvXWW0czZszQMTZx4sRIMrUV3g6bq8MOO6ytMzmBStdee200ffp0VXpecMEF0axZswrLHbQT2GjhaM049UVZIN4u6mIOxIkLSoNbJ8tnjEFJZ3czX5p3c34nc5SZc82cbTNfP/3007qWIz+YP8Y9YLIAyEKA1ANYJxlgdc1hXs5a00zbu3k3oDj0lQ9aeOGFdU176aWXnBcPIDyUNwZsKs3aiHU6HsxYSNabXrOTv/HZZkyY8Wre02W4+v673/1Oi6LffBBKA8bys88+66P4xpRJkgDkLsCWMd7jkA94/zrrrFPqHvLmCptxVaoyy5OZy5ARSaLgi3DmhdgXVEU4e/F8AMJuZGEX80ReH7q4N3hk+OWivKwy6Gufz3TZub6bvVh6Xed+2ePzzAKMCDi9Sb7Sq2csqw98H6N/fT7TRqGcBNrzcU8u5l50R/R92ljBWm/kiXZt53eje6rD+DH8Nvxv1+5Oj88777y6vnV6ffI6ZGwoTy/Cs4kOR7JBt2RsZG10L+hzyhDylo0OpEyZ6XNZ++GRT/I9P7dr+1FHHRVJZnfVi5HAKCkf287RRuY187Kpy2YsmHNt37P0Czbrc5GMb1t/N+f5np/ZT+fpL7tpu7nWJGmx3dN1+nxm7acN6H6RntiAgxvZs0gGBPwao7PRr5OAC/vCeuutZ247892Md/PM9HKtoN+NPiWzsQ4O7rrrrpFkk1VbjoPiQhGBA4EDgQMtDtjYbW3nWJzFsTlLpmXVdSCLrLjiiq26sj7YyBHJ60hiCaXticaWiK2xHaXXjnbnNe04tmFsd9tuu623prMPMnsiV5WU1V/46Pu0jGrkavPOvbrcO9nwDj772ndSPzYVnmnsjIECBwIHAgcCB3rHAfwxWL9J+uuLsHWYvbmvOtLl2uqyzHXptbhIh2Cuc/2O7tGnbYi1vUp7Lz5d+A5iS7399tvVp2jTTTdVv7ayfZTkte2+gGvSfZsspx8/+7b3oneCp0YmrgsP857ZTu1DZfWfhied7hGNbjHJ0/T4NfsD8865ZZ6HZNmuPnPfX/jCF1wVN1s52JceffTR2Y4P+gH0HgD5XnLJJbrGYhffb7/92rKl2/FJwekxmh6fec8h1xfp36ve89KmdoTPMHNA+h7bnV/2uLEzsu5XTcwfebZUdGgEWKXlkXZxGD7bD3/mm28+L1UYfYdrHVOysYDGYPNN/yXPcfEZnSqEvd1mTTBriHlPt8HXuIfXvmJo4DVj1IePq+HPoMfGGD6UfQfwHPLl30zZrvebPmTAbtZh7jFt8+ZYN2T25+m5vpsy09e69DtKl439AZ7iC8haMWHChNYpRfJI68SCDzbzaUERhT8bOcDIBYUXlDzBhy0lqwll5VLK6KSfsKsl5VFkljxfNNPWTuoy15Z9921TMX4gvn2Byt53L853Pe5sfdqKZGlfvMCvHz8zn3tvEoDjo2tik3zdSx3LLRvLnnUPRrY271nnuDhG//iUq0gAz36grM+ui3urexkmcRrgr9B1111X6BPRaWxsGm+h7rypsn0PP/yw1xhao4MwcprLe2snw3ZjJ+l2n9Hp/fnU19Am+sFHHxTdbyfrfJbvVxXyJ/zxtY+ATwsuuGD0yiuvFLHMye/IuO3wCWz3ZSuttFLE39SpU7VNYBKMHz++1T7bvi3yNayib02jAdBGX06dvog4EZ43fLcGPWmFLx6HcgMHAgcCBwaNA2X1Nd3Is1lyGPxO27g6Wb9tyx60/jX3i5zC/oaYjK9+9avmsPN37Ks+cQ+cN9hzgVnxs7ZVGn2Veee6bp4/23rbnQfWB7h4JHXwRS7392VjTNL3ZKMfcmWHTtdd9D1vfx8AZYu417DfMYAT3EaQdBWEEdpkrMbAcdlll+nEc99990Xm75FHHom23HLLCIcCk8XLtA1U8ZEjR+pvTBhZBOBenrGb4HOAC3Em2X///aN77rknqxgnxwDANJOFkwIzCmHxvf/++1sTeMYp3g+hcNlrr72iBx54oNWP9CdZGyGAt2woLbCZa9odN79jxMIx1ywgRxxxRHT00UdHJ5xwgo4lI8SZ8827KZf3eeaZJ0LJbABjzDmMN4jfbYhxCQjBgw8+mHm6qTPzx/cP2rSfZ4eMu9yncRoxZWIwArgRQEZAPE1mJfO7eSfTEn8I64zVNNm0I32Nz+/nnntutOSSS0bJLMyu62N+InCGgONA7TkAuJIB/LR14CZABMU1ik2ekyzQ8G222SYCdR9Kz//tWzM4v5A1DeddnK19EcE99KkPpbAJqs9bd83GxNVcmccn2/maNeS3v/2tAkm2K492n3TSSRoQhRPsLrvsomtQu/O7OQ7YMg4cxkmom7KyrsUpgIAyA6aTdU6nxxhfrNds/ABHdkku16yi8WczJlzeW15Z9BNjzji9553byW9mvr/rrrs6ubyvrtl3333VEf3888/X5/vb3/526fvLmyt6Na6Q11lbRo8eXfp+bC9Anjb3Z3tNt+exP4I22GCDVuIFF/NEXh9222auZ71B3vVJBJb89Kc/9aYDKDvXu9yLwTf2CgR78YySUe6LX/yiZj8zYxBg6H4mAgCefPLJyAQQ+bhXnFTRzdjuA7ppQ7dzL05X6J66Baqpw/iB3+ztAcr2QSMkQyBB50lH/E7rwVAEZelFjJxl5H7Ga7dkqwPpph4MUvDIJ/men9u1nT5hr8n6c+ONN0ZnnHFG69Ru52ibsdCqrMSHtH7BZn02Y69ENU5PrWJ+Zs99xx13OG13urC11lpLD82cOTP9U+b3Tp/PvP7K+41GmCBFWz0xwNY4gKPbJtEdCbqOO+64iGRpZahXawXr3J133hkZnUuZNpc5Fx0z8yBAuz6Drcu0KZwbOBA40D8cKLLb2s6xG220kdoJcRYnoSLfi8hGjkiW8clPflK/phM1IqtzH7a2xGSZTf4MGN2hhx6q9h6fDu0ECLPXd0ll9Rc++r5IruF+Xe6dbPj32GOPzRaQbXOd7TnoIA8++OCIIIUq9Aq27QrnBQ4EDgQODBIH0B0APnfyySd7sy/CT3RN2DyqJNbWMr5f6bXYhR2pk/sFFMSnbagX9t5ksD3+bfhPQGX7KMlP232BqSd5bb9/9m3vHT58uAbtk7S+DvSHP/xBfZryntlO7UNl9Z+92ieUeR5c99m7774bsScGONoXEYSEjcknSKCvtvssF79L5tO///3v0fHHH6+2cOwl7aiK8Zn3HNKuIv171XvedrzCZxz7wMYbb9zulK6Po1+HqpSPzHxZZKvBzkAyvzyfTm28538k/MOeZnjlurolllhC/b76QR+BnyByFYGVLtaEtFzsivfo8/BF9UHIuJSd1tO6rCvExnTGzV/84heqK0+DPnZWWvZVJJd/9dVXNd4j+4xyR33IgN2uw2mbd7k7mv1s4gUBCPLlc0SNzN/4hadjsWZvTfER1q6bbrppyIkEAwMAyLyHTyDxKFCRPDKkkJwvLubTnOL1J+QA+sCMj6Lzy/7OvEgdRUl6y5abPr+sXMr13faTbcyHi7rS99vuO7EQ7I98rXXUy3wHv337ArW7xzoddz3ubH3aimRpXzwiXoi4xi9/+cu+qtBy8a+mrkGiTmLZe8kf+ge7ti8itv5LX/qSxvf7qqOp5SIj8owg3xL/Cj5AOqkU9+YiNjaJt9BUfvloNz6K6Co233xzH8VrmSbm3gVoka0M242dxMiR6b2wT58l3/oaOoK9BLKVi71EmcHiap3vVtYtajOAP2CmoAfyRfjyVA0ubuyX3JPR15XZl4GJQRwKzwM4G0lwM9u+LfI19N23yf4E2wTyGdtJ7CjJQAETI27FJFlItiN8DhwIHAgcCBwIHCjDgbL6Gh/ybNrG5XL9Tpddhjf9ci44BADJgjuEzh55zRehn6A+dMGB/s2BdPxsN3zx8fzZtoe9PXgEPmVdl/v7sjEm8KGsfsiVHdq2DzivaH//4TKFhXPrz4E11lgj+sc//qETa9WtZcEAkDBLmfrd735Xm5MFRLrKKqvogtNOUYczIdllkpQGxMCZhgeM42Sz8eF8iHGQTTw89kkoLeDjPvvs4wT4o5O2AjZhnL+T1+MMAvDhU089pYHtyd9cfia4DYMevEDZA8gqWXJYVCAMPkkywltSyci4+stf/hJRVpJwaMJAYkAIkr9lfUaRheGWbEhvvfVW1im5x2za/+abb2qAB0obAj0MYaSAvv/97yvwYRK82ZyTfgcUDaXrFltsMSSTl0070mX5/A7QNE4g3JtPWnjhhaOvf/3r0aRJk3LBI322oSllk90eKmuc5Nlk7sXZadq0abPdLsAUEFkTsig9n2ed04/HWFMAySbzpE+HLgBTcDwn4N41AT7CukDAULfB3y7mKNv5ms0JzlXnnXfeEJbgjPaDH/xAjzGWWWsYv6wj66yzjgr+Qy5w9OX6669XgERfm27WTtZuHOVdE8qhyZMnq5HvoIMOyi0+C+i83QUuxgNlZ8kHWXXajIms63wc8x3QQJu32morBRczzlM+7qOOZbLekCTAEPsH1jDkxauvvroww7C5LvmeN1f0alxdcsklmvADIGlfxH6P+zMgr77qMeU+99xzGpiEwdzM3a7mibw+NPV3+s6+nP2jbwdA5H4cDQEN9EGdzPWu9mIopi+//HKVlc4555zolltuid54443oRz/6kY7BovXcBz+qLpP7n3POOaP111/fW9WAJDFOfQQOu557jbzHXhlHoyRdeeWVKvcmj7X73Ks5Otke+P2Vr3wlecjpZ4wuyLfob7ol4yhvkgxllUdiIOZpEsew/0gS4KZlslyX0YEk67H9jJzPGPIJ1ExbfM/P1JF+xjgG0R+sCySuIJAoSd3M0TZjIVlX2c9Gv5C3PtvK+GXrLnt+FfMzSaPYaxvHurJttDkfwDjkYvb1eY5k6NoxNPl+PrPazNyDroqMjjYyIGsFSTrQOy633HLRaaedpgCzWWXnHevVWvHjH/9YddAkafJJ6OlwJMSITCKza665xmd1oezAgcCBAeBAWs+fZ7e1nWNZ9wEIB/j8lFNOibbffvtCTubJEVkXIxtBaR0HcjQJTJCrB4EIVPjmN7+pyVzYf2fZiF3ygWAtEl65dBIrq79w3feM16Sduh2/XO6d2tVhjiO/4WdhABTMcdfvxxxzjOpZAaBCTg4UOBA4EDgQOFANB5C/2POOGzdO/WE6SZxYpqXokNMyU5nr885Ny5LJc210WVn6IhsdQrIeV5+xW6DL8W0bqtrei78aydGRzZF58MUyZNNH5tzku82+IKtvk2X06+cq7L3oYrGR47vUa9p9993VhpDnH9qpfais/tPlPqHM+LV5Hnz108UXXxzBX59ADoB6EuzB3mHQCV1DMin8AQccoEFap556arTzzju3kpBk8cn3+LRZO4v071XuebN4ZI5deumlEb6w+GX6IvQQAJr6SPDers3Ml/gXFtlqjD3vqquuGlLUO++8E91www1Djvn88sADD2jxvvRr2DlWXXVVL36xefJpu994PvAT6oTuu+++aLXVVlPfmG7WhDJrT9l2EueA/yd+qL4IX+ebb755thgRV/WF2JjOOMm8QSJMQEp8EbFZ6Ipdzak+ZMBu1+GkzdsFHwFg9Z2glHkJn8R0LFYn7Qc8dv/99x8iB1HOQgstpOspn/F3tZFHONeGuplPbcrnHILhWecMqL7tdbbnoXNnD05CWtfUjVzqop/MHq8oRs9FXba8A7QK32gAFXwRshyxbcae6aueupbrc9wZGTjPvxG+FMnSvnhHUk/mJWJTfRLAa/hZJ/edPuurQ9mdxrL3ou0k3GDtsEmk2037SA5GTFK3MX7dtKGu16KHgXgnaU4WdRobm1VWODaUA0ceeaTqCH3qIY2+hmetW7KVYamnUztJt/uMTu7Rt76GNuGfz16iTByp7b2008twvYt1vgr5kz5Ah4Neyxetu+66GkNikpv4qiddbhqfoMy+DJ9pnmH2jvh+J+OsbfsWvrbzNayib5P8wBecBKQkg/FJ4KRgm15++eV1L4P/HwCzgQIHAgcCBwIHAgdsOdCNvsa1PMtanvTDdrV++7Sf2fK51+eBc0CcK3g5m222WYRMCuaaT6I+fJJmzJjhs5pGlm3iZ9GTQ53YmF0/f2UYiT8GekiD/1fmWttzXe7vKassHlJZ/VAv7NBF+/sAKGs72hpyHuANbPzSjkDdNt9k/GwH1Ioi5thjj9VFJKsuDP1sfBmQZlCa83bddVedKMggkwadAGyP+0kvRlntwUkFp0My9LCIAQ7lklCmY+xNZrZxWX6yLALeAbcAMb9qYkEGUAYDfRaZgE36xhD9j1N82uiE4o8gxCRxXnpB47wkSCy8RgGEQMJvEIDBAJ+h0CVIgSA4fqOfAQmAyD5EW5544ono+OOPV8eCZOAadXAOvxmFkgFT++c//6ll8I8xxL0YBePBBx+svwHyy3HKMUH2OK/gaMd9mWv1w/v/bNpPYAc8wUHXOBTQHgBxIMoGOAnAGdpmAA9RsPEsGHA0BHaM2Nddd11ENnqELPObTTveb7L3NxzLAH4GQd83OAI3c9JJJ6nj+eGHH+793upegQFdMuOV9jL2CEbAKZFsi0mHP6M0NnNu8v6Yr1HQsolizvjCF74QATCanuN5dgHBGj9+fPLy1mdTdh2CFFqNquADzz288z3Ps/YCikoWBNeEkM98/elPf1rXXRzU0sRcBJk5l89m/DF3GrKZoziHeTk9X1OGkR9s5mvmHda4Aw88UOcHAJauvfbaaI899oh22GEHbRLghQRDQYxfDJYYA10T6xXPHkEBPgljN45O7cD7u6mbpAHM6YD74bhu+sKUyTwCb41cZvrdvHOekRXMu814sFm/s+QD6mMtZ84xgAU2YyJr3FKWS2IsAOLUzjnBVV077bSTzj9ZiR5c1VHHcpClXnvttSFyKPMAczEyHmuZIeRMyKyD5nh67OTNFTbjypTr6p15iz3Evvvu66rItuWwv7vjjjva/l72B7PPTM4hPKPMLQBp4jCM0xeKG8jVPJHXh2XvIX0+6yJrlu+EJKz1X/va13Q/nlSYp9vTzfeyc73NXsxmXWfdB0TY7MsYC6zH/Nk+Y+nnFj6wBjCvm3K74Y3Pa2kjwYOsCz4DM7gH+InTAg6LLsnH3EuyEPqOfQsAiMg4rG0cM0po5AzGGP1vCDmD/TH7H9vx42usIH8C6kM7fBGONARtuggqYO+OQwv6FQMagT4C2eXVV19VHQxz9oQJE/Q7QLnMgQSV0F/wMQkwkL7nNJ8Z+3k6kPT1Zb9jBKNO36AOVczPWc+Y4QdBrOiVjJ7JHLeZo43siw4qSbZjIXmN+WzkKqMDMMd5T+sX8tbndjJ+sjzfn6uan5EhSLI2ceJEb7dEghuebUBF0XmbZ9xUyDzKvv/kk0/Wtcjm+TR7v+QcbGQ3s+ejfDPO0npiMswbQn7nmTV6aFNOnp4YYGt0o+hJkcUAtDZtMuXayCG2a4Up08U7shzzJjoI9Gy+aZ555lEwP3RU6ENYW9GdBwocCBwIHOiEA2aNN/t7ymhnty0zxwJsCjgE82JWYrb0ni9PjqBNZn0ycg6O6zhSswYmkyBg+xs5cqTqNbnOZu3gvKYRchj7Tu6VPQo2WJ9gK4Y/AKCxb2M/55LK6C9c9z1yKvZP7HAvvPCCyjpG3knqwrnfTvdOZXmFzIRtxiQsLXu97fnoV7FB4QyFXAFY1G9/+1vby8N5gQOBA4EDgQMdcMAAluIjh50bsBzfBIgJ+mMfQehGlkRnlyYbXVaWvshGh2DqM+em6+7kO6D57O9924aqtvey3uMfBKV9G2z6iOvS/LbZF2T1LWX1M1Vl78WHA90x/r4+yeiFk34+pj5sNsjwSQC+PL2fjX3IlG3ey+o/bfcJ3firZO0TbJ4Hc08u3/GLOO644zTAmgR1voh9CTpmQJOYJweZsKsldSf4+wMohY6eud0Qawl6bZ4d9pqQ7fi00WFkza9mPcx7Dm3071XteQ2v0u/wGP/B/fbbLxoxYkT6Z6ffWe+Nf2G3BdvOl8yZRbYa7HkkkiOQ61vf+pY+dyQiQM9WRfyD4QU+RUsvvXTLz8ccd/mOzyJJNo2c4apsI5+afkmWa+pCNjZzOr/jz4LOkTgAjvOOTghdkfFDM+WYMvhOOfh6nX322fqzzZpg6k3rnLKebVNnt+/YE4lPAPTVF+24447qb+AjsHTQYmNc9RG2Wvz10KH7JHzQAdhzpS/2IQParsOGT3k2b3NOp+/ICNgwkvEenZaVdx19go+ei7WOPkH+x/5g7DTUDZ8AusFWjc+XjTzCdcyjzIVJ37/0MZv5lLI6JeQ1gCt9AvuSdBd5gzXdNXUjl9r0k82eySbmw6YuV7yBz8Sh4qvsk9CvPP/88wOZoM/nuGNOtPFvZO7w6ZeYNXaY54jxJmbBNwFGwfPnOqbcd7s7LZ91oJNYdrMWGZ8B6m8nY3fatqzr8IfHF5xYX59ETAFAgshwZh71WV+TykY+wK8XOccAr6TbXyY21uwdk7qedHnh+785QKIK1lr2vgYwxxdvXOlrbGVY7qNTO0nZfYYLnlWhr+EZI+7LxV4ifc/muUvqVsw5Res8cyJrR9JeYdYCE8dm5s08Hampr9N3+LLMMstEw4cP77SIwuuIJf/Upz4VXXjhhYXnlj2hDD5BmX0Z/oFgzTzyyCOzxdUW9W3yHtr5GlbRt6YdjKcrrrgiMlgs5rivd2KGsL/g/42ukWQLjAEwUMzY9lV3KDdwIHAgcCBwoPkc6EZfYyvP2shhcBI7V9IP22DU5MlmNmX7tJ/VeQSgIwK3jqRoJDNA7wmuDdhbvnWg8AVb/bhx41R3k9Tl15lnLtpm7Mtm75IsE9ksic9Fcnf4xBjnumeeeUZjOLmGuGyDwZelt7J9/pL1u/hMPDnxJvh7+SZX+3vaWSbGhPPL6Ic6sUNTR7dUuL+XBy9Qn3FAwCZj2TzHslh2fWfiCBBL9qdYDLSxDMZYlDmxgE8NKfett96KJTua/i4BSbEAYQ35XRTteo0ovfUcyXgSiwPPkHMkUC8W400sRs9YFoVYnHxiyWoaT5kyZch5tEfA9+Jhw4ZpWQKQEXO/hmRCjCXguvWbODGYn7p6p1xR0sRiXOmqnDIX03ZxpowlAKDMZV2dKw5MsSjrtL9FQTtbWbNmzYrF2Uz5y3gQBU0sAZbaT3wXJVMsi1Usjgo6bjgmytNYgshicQKIRTmq14qyKxYlsJYvk1QszmuxGCtiyRQUi6NALEAJsQT9t+oXJU4syuJYJt1YDCixgALoGBCQklgcovQ8AZ/VssUQGMtiqcdkIYplAY3FOTCGn+JsFJ9zzjmtcgXgJGY80k4B6YnFMBhfffXVsShQ9BjtMe0QwSgWUEF9tiTraCxOrsorAUKORamtZVOOgB/reBfFYquevPYLkKfWJUAf8Xe+8x39oy0CpBxLNiQtA74vssgisQhmsRiOlOcrrLBCLEH/sTj1xuLkrWUIQIvepwgXrWdSHPL0nigorx2Gj61Ge/ogoDOxKKRiAS7VceKpmtmKpY8kwCGeOnXqbL8NwgFxSIzFsT4Ww5eOFXHsi3lmRPiPGTc8S8ytAuLRYocAz8Qrr7yyns/YZsxx3lprrRWLgKnjkeNmTeBZFqCTWBxjYnF01udYDGyxCLKxOIO3yjUfeEaYGxijlCOO6fHRRx8dS6CtOaVv3806xrNdBd1zzz3KYzE4eKmOOV8Am3UNkCyCOo8x74rzqI4XnjsBmNK6xRARf+5zn9P2sNZLkHqrTXlzFGuNCOp6HbKCCNYxMgFzIuOHMSkGAy0rb76WADc9RxwzdGxyLX+s8eY3TkD2QS5hjIpDhdad/F0LcfBPnLR1DXRQVG4R4rgWjxo1SmWk3BO7+FGchnU+EedOlRXoT55/+lk2sFqyKJZ0foDn8Je5gb7lHI6xForxQs/NGw8333yz9fqdlA8kiD0+88wzdf2mvoMOOihGjoXyxkTeuNWLHfyjj5gzkYeqoBNPPFHlPQHZqKK6ntchIFixKA50nIlRLBZHzlabxEAXs05ColSJBUSjtV6KQ6yuj2Ikzhw7RXNF3rhqNcDRB2RtnivmxSpIgpWVn8jb3RAy6CGHHKJ7Bp5L5hAJstA/AZ+IJWiqtZdI1+Ninijqw3SdZb7TF4y7KkgAw2MBRY4lCMhrdTZzvWlA3l5MFPVW67ooSGNR2MfbbrttzHPMGk+fGcp7xngm0nM++0R4ZPQblGXWAVNmnd4lS3zM3lmM+t6bxfyHLkASRDiry9fcSwMvuOAC5Q3zBvt39ASG2NNL0KXOUfBQjB8xexvuj/PFMTdGT5U3fhh7PscKugnkJHRNPgndAvtvFyRZHXW/Bw/Ro7B+or8TUNZYgkdjeMb9CHiF6m84Dz0OczxyTha143OeDgS9VbeEDgXZuAryOT/nPWPJe0OvmNbx5c3RyMyMUfowS8dlMxaS9fO5rH6haH1OyvhGB5iu0+f3KudnSdqj+izWYJ+EThTZBX0Rek9JFqJ7E2wBjB8zXxU9n/zO/pzxI07psTgOxveITgI9IMeQ755++umY89BJcYz9IM8KbeC7ANSozpn5BF3U9ddfP+TW82RAZEt0gMYOQXnmj70WddjKIVSat1YMaZSjL+h50f/2Yp/GvRq9P7o/7DdGl+Po9kIxgQOBA33KgU7ttmXmWOZ+9BBJytrzsb/LkyMEmCEWIFNdG9AHS1INLRK5FFsfMiI2O3SBrFnoLaEya4de0IB/2OqwGWHXZf+ErpB9UpWE/QB7kg+y1V+47HtkHvZA7OPRR+TplMvunTrhEfZtAQpWuaqT6zu9RoLlYglSVF4I2FzLNtBpeeG6wIHAgcCBwIGhHEAm2nLLLdWXDBlJHOeHnuD5G3YofLpcka3PSJ4uy7QlS1+Up0PAv2WDDTZQ2RCfNUnwbYrq6h05krZUQVXbe5HBkZmT/nDmPov6CL+QLH7b7Auy+tbU22/vVdt7sbUgw6Kr80FJvTA+e/hD0J/4FTGWsG2iuzv//PO1+rxn1vg15tmH8u7BVv9JGUX7hDL+punxm7dPsHke8u6xk9+222479eGS4PtOLi99DX3MPrQX+s/SjXV8Af5S2GHxP8Zmif6bsQaxvuN3ZwhfegFVa+m28S0zvpdF47OMDiM9Pqm/6Dks0r9TRhV7XurJInwY8V/F9zrpB5t1rotjzAfMY7/61a+6Kq7sfFlkq6ExjDn8o5h/+UPvz7GqiLGKzknAnbxWyfxFjIKrOAvWG8pCv0PfCuiz+uibm0D/Y/SL/I4OxKyjjD9j+8K+Jgm09XdkINYviHHJc7bYYotpXAPzggBrzDaG8taEvLWEOrKebY53Q/Qnvuf4BPsmYoCwNbq0UQ1abIzLPsJmvNBCC1WiOybGiGcP/yVX5FIGpE1F6zDn2Nq8ObdTwn6PTGF8Xjstx+a6ffbZR+O3jL+AzTXtzmF+QqdBjCDlMr6QiyRJ2ZB+z5NHsEml/bny/AHz5tN27bQ9jh87a6zvuBdJJBRLMpBYkpfbNi33PFdyaV4/lfHxt4n5yKvL7BVzb9riR3zKWdvx0amCiInBP505YxCoinHHWLDxabORpV32CfY6Ae1QnYiLudSmbfiMEkeLnqmfqdNY9iyfgSIZ2wUf0eUj6xCnXAXBH2R4dApV+wFUcX/d1IFeHXt+O7KJjUUmwTfFxCNk4S20K38QjxMjiD8n8XtVkCt9DW21lWE5t1M7ic0+g/JdUFX6GtoKTgKxEq7IxqaYt84Ta2Ibl+xT/qQPwIRgD+ybJk+erGvPn/70JydVdYJPQMVl9mXIc+iG0pTXt1mxJfRh2teQMn32bbLNp5xyiq4RvYpVk+QtsSQa0zgrdKfjx49XX3z0l4ECBwIHAgcCBwIHDAdc6WuK5Nky9uu0HzZtzVu/y2DP+LCfGV7W6R3ZT4DtNT4QzEF0kMRA+tZjt+PBU089pX6OxB4PAiXt/uibi/C54Al6KXRGxHPiwyOArS27LDG+eXqroufPNc+xdROrjr6pCnK5vzfttY0xsdEPUWYndmgX9j2L/f1kMqoE6jMOoPQHdIUNX9OIDSlOxASy1Ykuu+wyNcSyYFVJAKcSRI7BvAoHtyrvLV0X49YEZ6Z/4zsgMElK8wPjWpbDG8cBsnv44Ye75iHGvN///vfaDJSQZYwqRe1P3lvWZwBYEJgNcV9l6jfXddsOU04n74AxAl6Kg7gr432ZdgAmjOOGb6CtMm3qp3Nx4gLMGOJ5pr8JHK3K8N0EXsILDGAEZKQBUXy3H2AYQLmT84iPOjF0Y+wzc2XZOlzNUbbzNWM0C4CI6yEU92Zcl72XovMBO2MzyMauCiK4gPoku5zX6lA2ENzAOOh2rncxHnjusuSDdkxoNybane/qMrD9QAAAQABJREFUOMYiAqaqkjUZ4wRRAOzU7w5DrvooqxzbuaKKcYVilGAMHFSqIsYPgQ29pG7nCds+LHuPrC2s977B35LtIhCHvaMBWE/+5vqz7VzPHNztXow+Yt+TtV6b+6riGTN1VfVO8Dp7F3QRVREO2ji3vSiOGE0g9siMi3Zgpbb3UPX4QTeBoz+yoG/Ceca1szvyvtlTtHMqQbZApukm0MeVDiTNYxNUiOxVFVU5P7e7pyyHpG7naJux0K49RceL1ueyMn5RfWV+78X8PGHCBHWoTSdxK9Nu23MJzmTtJiENhsesYE1fz6cJriNQl/kDMFr6OovyZECMdhjkACXASff222/XoF2AsAFs7YSqWCvYsyM/nnHGGZ000dk1yJKA/NIWAgsIZnYVnOaskaGgwIHAgb7igM0cW0auLJIj8piHHphA0U5123ll1+E37KoAWeB4b+b5E044YTb7a1VtNfuVdBJal/Xb6i9c9T3lpOWUvPtxsXdqVz6gAuibe+UEiH0A0GD2vyQmBnTIPJ/t2hyOBw4EDgQOBA5kcwDnTxy9SWDHvEpy36rsiekWkZQR52XW2KqpSJfVTl+UXpvTvmYu7+P555/XPgLspQpiba3a3puXAK+oj/J4krcvaNe3eeU19bde2HsBwvvsZz+b67NZJT9tntlu7EM2+k9zvy72CZ2M37znwbTNxTt2wY985CMxQVlVEXPw2muvrcB0JBkL1DkHfI7PvOewjP7d5543i3O0m4R1AB9WqdshmQvJLKsmW1sNwUjd+u91cm8kbUL/VEUsCIBMJKGtCyAbNlRDyPLtiHFapPfsZE3oZO1p10ZzHNsd4JVV9Cf2yWHDhsUnn3yyqb7R78yFdY+Nacfgn/3sZ+q3RPBrFcSzg98bsR6uybUMmLcOl7F5d3Kf3AvJXtg7VEH4KOB3hM29W0rOITwXxJm08zvKk0c6aUcn82lRPei/v/KVrxSd1vXvrPkEaJPApm7kqp/Qbxj5rV2Mnqu6snjIc0Vg//rrr5/1s5dj+N7xLJPI3aeuykvje1yozVgo8mmzlaVd3Oq+++6rPl/4YlVFrEVzzz23goVUVWeop5gDxFuwXzW+t8VXdH8GSd9J7MOa1W7N7b6W5pXAPixvL8ZvyHsQ8nyIje2uj/GJBMxwxx137Nrf37Yl7Itd6WvKyLC0j7o7jZ3J22fY3nvReVXqax577DHdS9x5551FzXL6u6t13kbm6KThVfYB9/DpT3+6NjgztvuydnN0mb5tVwZ95qtvzXgwshi+170m1rHp06erryBxYujcSEKGfYgkacxZgQIHAgcCBwIHAgdcccCVPEs56fU6/b0TfZoP+5kr3nVTDrpNkudMmTIlXn311dUHhD0YwKQ33XRTZtLwburr5NojjjhC7YyA3gfK5gA2ZTPO0ZEj+5YhV89fUZ0kDsEuj2xfBbnc36fbWxRjUnf9kMXeMgDKpju9X77T+YCPVOnw1y+8S98HG3jAmXrh9EVbcMjAcZeMy2GRTPdO+N4EDiCwEFiCwgnwhjyFnO/7mTZtmgZQ7rrrrgFAzzezQ/lDOIBz7mabbaYOATNmzBjyWxVfMGJhDGYDGKj3HMDxlQ0TWdarJDZqKALI7heoPhwA5Ijg/ioyTCbvGoAm5gVAJEIwf5IzzfuM4z7BXrfeemuljSf5BXtOEoIEGsoB1tvFFlusowQQQ0uy/4bMjePwAgssoABs9leGM+vGAbLhsl6TWKZKArh31KhRGshZVulcZTubXBfK/WWWWUZ5XJUDBo7ngE0E+jcHDj74YNXxYSipisL8XBWn/dfTq/kZWX2DDTbQBHKApPYrJYPrOr1HwFDRo2MUTxN6malTp6YP1+L7Qw89pDqCb37zm7VoD40gYGvy5Mnx5z//eXXsXW655eKjjjoq2Cdq00OhIYEDgQOBA3YcwB4HkCfBOQQOoj8iKzQJ7+qgDxw/frwCmLvItGzHkcE4i+Qr2B/Y//SaABzYbbfd1DaGzuqAAw4IusRed0qoP3AgcKARHECfdO+998Z77bVXPM8888TDhw+PAR8AsLSXhOMygYdV27h7ec9l6ibJ71JLLVWpnBXsvWV6qN7n9srey3MNcA4B9mUS9dabm6F1RRw499xz1c7Pe9UEgMk666wTzzvvvJUl4K76Hvu1vjrr3wlYQoeNPrtqO8oFF1ygwKlVAkTVfYxhE19kkUUqAztE/0Xf45sbyD0HAKH6j//4j0r9GknYSrwByXoD9YYDb7/9drzwwgvH48aNq7QB7HvxZ22yvtiFzTuP6WeddZY+H1WC85MwbMyYMaUDmPPuo+m/3X333WpDB2y/CkJHhJ/u5ZdfXkV1A1cHvvusO1XHViA/EqeKPgmQgED9xwFkGhKEkfCzarr00kt13rj55purrjrUl8EBo4chwXvVRBJx9kv4hhPDFihwoEoOnH766aozIY6x6riEoK+Zvaer1tfQAgD7SYxYVczE7HddryMAn40YMSLeeeedK2sYYKIkCbnlllsqq3OQK2Ks4+//hS98oacYGll9AKg9cim4GgsuuKCOC/YjxBSfcsop6ssV9iVZnAvHAgcCBwIHAgcCB+rFAQDj77///hi9E/EAxKAj74FlsPfeeyvGRCeAuz7vkngFkrNhdwp+ST457bfsM888U/WNVes6w/5+9n613N8HQNnZWdcfR9h4onDBKafJRvVe9wZ8ZCElC1wv+fjmm2+qsxNgY4ceemilGel63Qeh/mZzAEdSFL8ELrJY14FwqgBcgkAOABsCBQ745gCGX9YRAjDI3tUrIqvfRz/60fjwww/vVRNCvcIBHAYXXXTReOzYsZVnlQYojgwzbLpNNu/QKb3lAMkCMMBstdVWPTGS/vznP1eF0ZZbblkp8GVvud5ftaP4w1n0/PPP78mNMaesuuqqlTt59ORmLSsFJBolbNWKMZpH0Ocqq6yickeVjuOWrAmnWXCA/QkAAYAD98IgD1jixz72sbgOGXEt2NW4UwAKpH9ffPHFytrOWt+rOamym7SsCL4T7EbCm6opzM9Vc9x9fb2enwmyByAaEK7HHnvM/Q3WoMQXXnhB56sJEyZ03JqLLrpIZePzzjtPQXYwPBM8fuWVVyqgWpVg0rY3QbAZRnyc8nqx9he1E/sI8i2O3ehTWVOwOX3729/WjLXMb4ECBwIHAgcCB+rFAQJQcdohyBv5l0BF9Dccw95cJwKY4HOf+1y86aabBt2So47BERDdEMlMcBysC7377rtqJ1577bV1TBIk873vfU/BEusAblwXPoV2BA4EDgw2B5jDSRyIc/dnPvMZtevjr8aetk5zunHSfeKJJwa7w1J3D4gMe+aqkz/SjF/84hfB3pvqj6Z97bW9949//KMCROFH8tRTTzWNfaG9JTlAEin8C04++eSSV7o7Hd+lXXbZRZOeHH300ZkJwtzVFkpyxYG66t9JmoP9FT88fPKqJgBRll12WY0zqLruutY3ZcqUeM4554xfe+21ypp433336ZzSy7mtsputsCL0OfjZ83xVqb/huUK3yb4IwOhA1XKAIETs0gCPISdWScRqAShLcpWmkgubd7t7R79PwpmDDjqo3SlejrNHIFndhRde6KX8phXKfDh69Oh4ww03rLTpAC7/53/+ZwzQdyB3HECGILbmpJNOcldoiZLQ6ZAQEl9RAJ4C9QcH8LPB/xYbbS9jKPfYYw9N+BhiJns7rgDwY54hiXev6A9/+EO83nrr6T7tnHPO6UnMUq/uPdTbGw4AEGT8VfDj7AUFfc3sXO+FvoYYatbDyy67bPYGDeARbEHozKoG0SJ+hb1kABb3P+gOOeQQjcV68MEH/VfWZQ0k1Dj77LM1nnn++edXOzd+hugB999/f/VRYMwg2wYKHAgcCBwIHAgcCBzoDQf+7//+T314Lr74YvUnXHHFFWPw5vBPAyB+/PjxqnsiGXnd6U9/+pMmiAV4/5VXXql7c0P7UhxAn4SfUS9issP+PtUZ8tVyfx8AZWdnXf8cwTkKR88111yzcsCyfuEiG1/AVH72s5/V4pauvvpqDawDPACFah2DzGvBqNCInnMAhxiEUAQDsqbWTbDDIAZ4HkppAnLeeeednvMsNKD/OIBT7Pbbb6/Pwe677x4DANNrMtlmjzzyyF43ZSDrZ0wsueSS6mjLPNQLYr7DmQ2HT+bqQL3jwC9/+ct4vvnmiwmixxG3V/TAAw9ocMUaa6xRuRNwr+65H+plH4DzNM66vQKThY+PP/647pcmTZrUD2zt+h5QbAKwRZB3rwigtNVWWy2ed955YxxeAzWHA4AQ4ySy2Wab9VSHM23aNDUsnHbaac1hXgNaeuCBB+qc/ZOf/KTy1uIIhKNHL4IoK7/ZNhViRGNuBFSJQOVeUJife8F1N3XWZX4meARH27nmmiu+9tpr3dxcTUoB8Pkb3/iGrj8kYCEwvZNnFcc1MqWvtdZaKqPCK5IfTJ06taPyfLMHZzwM+jvuuGOlgbid3hf8JaCJREVjxoxRfRdBD1/60pc0GOb222+vhe6r0/sL1wUOBA4EDjSVA88//3zMPo71BAcxHMVI8vi1r31Nj7/11lu1vjV0kx//+Mfjb33rW7VuZxMax74HnQIJzAAWrisRHINTGT4s6DYJXNlmm210vAaQkLr2WmhX4EDggC8OEISFwy3zN3vYYcOGKSDJueeeW1ubHU666NmWXnrpAHTx/sBA3iIJCwlEe0XB3tsrzndXb13svdwF+muAJZiL8E8N1H8coI/x4WStueSSS2pxg+xlAcVaaaWVepqgvRbMaEAj6qZ/B1SPvST+vyQs6aUv/b333qvtQIYbdAKsinnmxBNPrJwV6BrwV7/88ssrr7sfKySpBUmq0Df+/ve/r/wWAbPFFoXd8OWXX668/kGtkH5ff/311eesV6CVV111leq4r7vuusZ1gyubd9aNA2KKDZxg617EIuy3335qdyCZ66ATAEH49/3mN7+plBX4T6BTJ0lfEwACKmVOh5Xhww+Y69Zbb91TkKRHH31Uk0sT2xH6tsPOrNFlzNHoCIlDJlFYL4m1Y5NNNlE7HL4+garnAGCyALPttttu1VeeqpE9PeDZ2OXxpWPuCRQ44JoD6Gbw3fzEJz4Rf/GLX4x7nZiQOBr0RkFfE8e91NcQt49PyKD7giB7Ih8AKls1Ea+Kjy9YM0HH4o/7xF6hmyR2v4n07LPPxoDV7bnnnvHyyy+vgPj4IDKnoyPcZ599NNHMww8/XKtkuE3kdWhz4EDgQOBA4EDgQBYH/ud//ieeNWuW7l/wp2fvzh6e9Rjb68orr6zrMfqmpsrW4Opg+8PHLuglskZB/Y7hJ4pNBDm3l4ldw/7+g7FRYn8fAGU/YFt/fiIjKEYuDBJVZgfuB26ecMIJOrFNnz69VrcDeACATQQDosQh8Pxvf/tbrdoYGjO4HMCBiKBVwARw9r3rrrtqzYwbbrghHjFihCqmjz/++BDwUuveak7jAJM79NBDdaMGYNE999xTq8YDzEKQLllne+lMXSumVNAYjKELLbSQgli9/vrrFdTYvoq3335b5+hPfepTtQHNb9/a/vzlpptu0sAYstTXQY5j/V5sscV0jNYlkUJ/9rybu8KIi0GX/cDNN9/sptAuSiHYGWcLAKQGmVCOjRs3Tp12//jHP/aUFRj9CZ5CJmdfiyNYoPpyAHlswoQJqn/47ne/GwMA02uaPHmyGhyCE5Wbnpg4caL274UXXuimwJKlYFQi0Grdddetxfgq2XwnpwN+iHPuk08+6aS8TgsJ83OnnOvNdXWcn1kjDjjgAJ1T2NOjJ+4HIvjpz3/+85C/buWXOus72JMD8oduBn1kU4mAfRwSdtllF01ag7MC94RDOM6D/BYS2TS1d0O7AwcCB+rKAQL60d3hlEPSRBKQGmcxkkVhP/7pT3/aOJ8AdKWsIQcffHBdWV/7dqGXwj5LIDtOhU0hEuBddtll8XbbbRdjr2A8o6dG1gUABhCGQIEDgQOBA/3EAQBkAc8jAZQBggcIZKeddop/+MMfxujxmkAASpHUjuS6g074Yn7lK19R/Stgjb2kYO/tJffL1103ey93gO7R2KtI/oTvU6D+4MDMmTPVFwRf47rtFwBMAsyYPSGBUXVPitIfI6L7u+il/h1bGz4QBLKT1P3+++/v/oYclHDEEUeoLRJwiEEl1g0S3PNMd2vj6ZSHBLThJ3P99dd3WkS4TjjAc4ZPIzL/r3/9657xBN8rACzYM5HwPJBfDmC/HDt2bDzffPPFvZ7L9tprL53newVq2ymnfdi8TVv2339/jUno1bPwj3/8QwO9sQMzRwwq3XrrreqrccEFF/SEBUmw7V6Afffkpj1VyvyCTQK5pZNkx66b9corr2hcBz7hV1xxheviQ3kVcQDQVmxcgGLUJRaD+XvTTTdV++GNN95YESdCNXDg/PPP173J7rvvHmPHrQuRrJaE9gCBoN/ne6DAgW45wBgnMQRzIGsZ/ix1iIXgvoK+JlY9fy/1Nfg5jRo1SuPf6iB3dTveO7keOX7kyJGaJKRXawK+OSQwwEZQ5wTVnfC3DtcAFIy/Ewmv+oV4dn/+858rfg0+4ugDAEXmPokfJeHN5ptvHhOjwx4GXVId4qP7hf/hPgIHAgcCBwIH+pcD+AY++OCDmgT4oIMOijfeeON4kUUW0X066yw+4KussoqCvE+dOjV+5JFHaqG/dNUj3D96CWJ80Z0Eqi8HAADeYIMNtK/qoLMO+/vS+/sAKFvfx8tdy8h+TSb3r371q30TZO6OO9klAQSIcvyMM87IPqEGR1HiHHbYYQqEOc8888QIDCFAuwYdM4BNQMkPmBkGdQRVhNQ6gJvZdgVGUpxMP/nJT6oj1NFHHx3zfAUKHCjLAUBCCXjGYRknxvPOO682RrD0vfCM0s7VVlstfvXVV9M/h++OOUDgM5v4tddeWwFyHBffUXEo6bfYYot4jjnmUINtrxy5O2p8gy8iqMJkYyFrX50SPgDgxJjAsINcifEnUL04wHMKGCGOFsstt1ytDLnjx4/XdWWQs5jj9IUisy6OgIxespxitAVU5rnnnqvXgA6tUQ489thj6pyBXFYHxWqyW6ZMmaJ6EdaEXjmvJNvTxM84PgEOQRBsr4IJDN/IPkZmwt12280cGph31k50FXUyNIX5uf7Dr87zM9wDdG3++edXw/Ett9xSf4aGFrY4wHpPYBIJruoS6N5qXJcfCHICRJYgTwJ8Wf+Yfwk4xpCM4+B1112nNoygg+iS2eHywIHAgYHgAPpbHLN/8IMfxOgcmFsBxGBuxS6MQxX7NvwA+kGPd+mll+r97bzzziERXskR/pe//CXeZJNN1AZBQHtTCfmAAANstoxvdKCMd+xtJE5iH/XAAw/0xXhvah+FdgcOBA6U4wDz8z2SeJZ5DfubAc5mj7TZZpupP1ovwZnK3c3sZ992221qZwbUZlAJvfm2226rPpmPPvpoLdgQ7L216IbcRtTZ3msafscdd2hQMfPWJZdc0jNQQNOe8N45B/BjAxwYuRqQEOaIuhLA6iRHnGuuudSnptcJXOvKp0FuF+B1JDtmjzj33HOrjNVLYNt0X+BDvc4662jyn0EE5CEBIomxAaUgGV0viYR3+L71E4BDlfyk/4gBYN+Cj0GviaQNJHBgfZg+fXqvm9O39bOfIWCZtfg3v/lNz+8TX5svf/nL8Wc+85n42Wef7Xl7et2Ao446Sv23sMP2kugLYm3QRdfJ77kqnhDkT0wmid16SSRgWHbZZRWwEjCBQOU5wJ53+PDh6lOL/q4uxNz3ve99T+UYnrOXXnqpLk0L7SjgAEAY6EiRQek7wBbqROxVSOBCjPTEiRNrG99WJ5510xb2riTOhN/f//73uynK67Xo98eMGaO+XcSAPPHEE17rC4X3JwdYuy6++OJ4iSWWiIcNGxZ/+9vfrl2yqKCvqYe+5qmnnlL/j69//esDF5PCurDmmmuq/N7r+G2SQRFHjm51xowZ/TkxVXxX+OuxjuIrjX9fvxO6AJ7nq6++WrFtSFwAsCxyMHYo5B/0w+CMfOc731F/CPzY0JcPoh6h38dDuL/AgcCBwIHAgfYcYK8EiD8y1ymnnKJ6GfBlSELEmskf8QBLLbVUvOWWW8aTJk3SOCsS1Q9CDDf3eOSRR6oMhTyBX0ugenGA5K3ErJI4pi4+kWF/X3p/HwBl6/VY+WvNww8/rM7pK664Yvzaa6/5q6jhJaOgwYmSBfiyyy5rxN1gxDz99NPjxRdfXDfcOKXhMICDVqDAAZ8cQJAF1BjhlWcGgfWnP/2pzyq9lo0h95hjjlHhBuBHgP56lcnZ642Gwp1zYNasWar8BZgTp7ozzzwzBqi47kR2ZTabBJ/32tGs7rzqtH0EOGy33Xa6PuPkU0flN0GMjF2UEb/73e86vdVwnQUHWFPIyIeD80UXXWRxRW9OmTZtmjrLkYUUoKpA9eAAoGY4SiNzAV6OUrFORKAO2bBYU+qiIKqKPwR+fve739W+qaNhnbkHp6+P/cfH4kOOOSRk/axqYBTUg0MG4wbngbXWWqu2azCgd4AS4+hKmwPZcwDd29ixY9UBCufLOhBzFOvIhAkT6tCcStpAADLPGYamupGZnwEjx/gXsjLXo4eaMj/DLfabOKJhTEYOazIITT16328rCDDDGZM5iWDmOgUm+bpz7hGQwxNPPDHeeuut1ZhsHCBwSiXpAfIQgN/o1t59911fTQnlBg4EDgQO1JoD6BVefPFFdRw77rjjVJ+M3t44W2OvApCD9QPbNbY5rulHwomcgGz2yXULtqwrvxk7o0ePVtsm8kY/EU6DAMziB0FgEUEHxpESXRfBmOeee25Mgqt+AFXup74L9xI4MIgcwNcEXxn8BABHHzVqVGstJ4iK/TtzFgFW/bSOA+iEzEIyzUEjnJRJJoZu7+67767d7Qd7b+26RBtUd3tvkmvodfbbbz+1KeDjMHPmzOTP4XPNOYCeDbsDvikEePzkJz+peYv/3Tx8Ds466ywFDCVJ4t577x1A5BrRc34biS0En95Pf/rTOqaxc77zzjt+K+2wdGRCYjRIKPfyyy93WErzLmNPDkgAQOTorepAJ598ssqpJGkiLiSQHQfw90L/MnLkyFrNv/jc7rvvvqoX2mWXXWJAZgO54QD7Gux4gD/xHL/99ttuCnZQCnMq4MYAiddlbnFwW6WLMGCyU6dOLX2tjwvQxSJjklyljv7wPu6ZMn/1q18pmO5Xv/rVWiTEQ94noSv2owC2Xa7X0d3hO0fMaN18sM2dkFgP3SJ7oiOOOCJmPgxUTw6wjqKDW2CBBVQWJnlmnemCCy7QcbXqqqsO9Nrqs49IyLD00ktrLMWNN97osyonZWOruOGGG+KVVlpJZW0SOfB9kNZ4J4wcwEIAxQQwGV0N6xXJ5+usBwn6mnroa/Ajxaa22267DQRIFlMD8ua4ceN0XXjyySdrMVvQpl133VXjnLEhhDm/82557rnnNDk8iVfuvPPOzgvqgyvRvxIXc80118STJ09W30f05CaRuPH1wl7FXhYA8pNOOikGqAy/sDonQ+yD7gm3EDgQOBA4EDjgiQPYsMHwY/07/vjj1Z8ZnDdwhoz/P2sgsgK6GBKVYfNm/QPfp06JUz2xqLDYn/3sZ2qTRGY444wzgmxayDH/J7zwwgu6hyFRAHb2usU9hv19qf19AJT1/8jUpwayghKANt988wVwqIxuQSmD4Q0QJALWmkZ//ddf42MfOzZe/BYBln3yQ/Gcn5kz3n777VWZH4Komtab9W0vQgDKGpzFEWIJfpkyZUpfIf+jwMJYutxyy+k9YhwjuKeujqj1HS393TKyXZAVhHWDZ4GAbrJrNU2RzvqAAz6CPQbgAEDjZtwS6My8wUZ/wQUXjOsCItbu7nAGJmM5xlzm9OBI3Y5TnR3HiRlAYZzQAATFaFR3evPNN9VhjrkBkB8c1AL1hgPIXigLUSICQIVzal2JuQPjHk7DN998c12b6bRd3DPgWDjWo/ytIz0YPxjv995+8dz/M3c87KJhmgwCJ/OgdO5NbwEYyX4KvQOOVBdLZu66E8p55BkSifz4xz+ue3Nr0T5AnoYPHx4vueSSChJRi0a934jLL79cwQxxBmra3qUsH88555wWcGPZa6s6H8fyU089VbN984yF+bkqzs9eTxPnZ3MX9913n+rpkBeRG8lKGqg+HMDZbfPNN1fdEeB4AIcMMuH4d88998SnnXZavNNOO+nYxVEY3Rp/BCoTiHfggQeqfhZAJpwtAgUOBA4EDvQDB5C/WadJ4ESQPuAHBMQDoGrmwc985jPx+uuvr0kgSAKH0xgy4yARa+Wiiy6qAKmDol/qtH/RKeBIB6DsoCSLe+ONN2ICMA899FAFucDvhecH0H78YEjwd+yxx6puEp70E2hjp+MkXBc4EDjglgOsy6znOHcTDLXVVlupzwz2NOYj1nL2NATgAd5XJzAet5z4oLRLLrmkBcTRhKS7H7S8808At2y44YYK3FJnkMZg7+28j11f2SR7b/ref/Ob32iwAHMcvg51931Jt3/QvuPbCOgYdkj8ldh71hUkKa9vaDP2kiWWWEL92dgns/YG+3Ye1/rvN3TD2DxIfgpQKckjmyBboc9mn45cOAjJmN9666145ZVX1riUut0vQEjY7NEXENAZqD0H0FviMzrHHHPUDlQ02Wr0qgC28XfVVVclfwqfO+AAwKDLL7+8AvqccMIJtdSjYVfEN59n+fbbb+/gLpt7Cftrkpig98QXvU4EUA7+ofiJDgLQ5R133KF6eGIs6hSTx9xN8lb2aiQyCgA8+U8JieE32mgjfaZY8+pO9C/+ZOzrsIOwt6tb8H7deeizfeiIkUXwDUV+4lkkgXkTCNvzCiusoD7vBx98cPzXv/61Cc2ufRvRx+y5554a6wF4zCuvvFL7NqcbiP/hFltsobp+5G2SyDFeAgUOGA4gn1533XXxJptsouspiR+wkzXFry/oa0xP9vYdHyB8Rbfccsu+j1tFNlhzzTU1PmDWrFm9ZXxG7cRYEEMMRsSg+zRnsCf3ELIg8jn8Q656/vnnc88f9B+xVxOPhk8BCTPAvAFUb/7552/5S7KvnXvuuVWvvtlmm8X77LNPTMKwa6+9VpOMg+NArH6gwIHAgcCBwIHAgao4wHr/+9//XnE1wA7ChgIg+sYbb6y4Qkm/f3wGiQcF+2GHHXbQfdIVV1yha1gTbNtV8bRdPeAzTJw4UfcJ+Iew7wx+3+245e/4H/7wh3j//fdXneEyyywToyeqK4X9vXXPBEBZa1b1yYkExxOgxgYLZXUwHMYKoMHmEmUUC3VTFPfvxe/Fj8jrWHmtLa9h8pKw63hZee39j73jU684NV533XVVmY/RHkXbxRJYxgY8UOCALQdQtOA0hEMmjo7MHQAfIfQOArgcCtudd945/sQnPqHG3k033TQGgCdkOLcdQf11HsLw+eefr3MrQC3zzjuvgrGSQavphBM2Aj5ODQj8OPsG6owDM2fOVIMAjnzf+c53YgLKmkA4H5EFh/luoYUWii+88MIQjNFlxyF349CFMxd/BLw0TZHx4IMPxmuvvbau/8iVg541scshUepynHAAWAKIGEUYhrAmEHMJWWNZJ5lT+tlo99JLLynwCwF5gGLViR6LH4sPkdfn5cUecaS8JsrrgT89oMZVgq7IdoYjAHNVIP8cQPfCM4HhG6MBiu4mOdXT/m984xu6HhCwAIBLoNk5QFA4e0aMQfvuu2+tAgmSrQUYeM4559Tgjn40TrEWAeaP/uLoo49O3nptP2PMwPklzM/Vd1HT52fDMfYZ06dP16BY5DAAbXDACtQbDtAfgGsQRMdcRLKqOgPM9IZLH9SKw8UzzzyjoBAAXmyzzTaa6CsJNIsObuzYsbpHYm5nvD/yyCNBR/sBG8OnwIHAgZpwAD0I+gJ0aOedd54CZOPoDHAFunfWBf4IwgJoHFsbe/N77723MYE2VbCa/TJ6MXiFjQ7bTKAPOMAYY1yx92Tv00SAqA/upvtP+HYAKDJJwBsJdCQZKjIx4wcfCYBt0GUcd9xx8Y9+9CNNrBiAqLrneyghcKDfOYCz8hNPPKG2GfYpBDaNGTNG/cqYX7DdsL6TcO6YY47RPd8g+2IZcBX2bU3xuet0DJOgl6TDyHNNASYL9t5Oe7v765pq7826c3wHAcxmDsS3iETtwcaYxaneHGOskUwc+Rc9GmAOTfFVyuMYetZbb71VQY3xwcLvBqCen//853mXhd8azIFnn31WQZFHjhyp8w2JePB5bxpoPXqN9dZbT5/Jpvj6dDJsSKj3+c9/Pl5sscVi+q6ORLAnAIDI74cddlgAzMroJGwt2LGwyZx++um1923Etkv8E3oxwLrqBmScweLaHeK52H333Vs8fO6552rXxmSDWAPwV0IW6HdfRHPfL774ooKrkEwMWaiOxNxBPNFyyy0X130MdcO/s88+W9cQAFvrqodnjABkD6gbSRgCDeUAdrtp06Zp0g1sB03bS7DuIcMwH+CnfPjhh8eA4wbqDQcA9WVeQP5lXQIkBH/RphE+OsRQEzO04IILKrgX/paBynMAO8KZZ56puhgSoTDfNJ2YY7CJMM7RA5KAABmsrnu+pvO77u1H/sHnESwKgP7YWwOgRAIX5pKmUdDX1KPH7r//fpVrSB7C/rQfiUR92JFZZ5988sna3iJ7udVXX12f7QkTJgSsGYuewi8fEFniPUh2HWQoC6blnEJyA56RGTNmxGeddZb6wBH/gJ8Xtnj0f8gj/OF3SQwkyS+33XZb9ck87bTT4h/+8IeKc4Iupa779hwWhJ8CBwIHAgcCB3rAAZKGAQiPXEpMEjoScHW+/vWva4I7ZDj0PmYNwhcZ3SvyK2sQCXpIAodeFrkP3UCg7jnAWo5/JvwG04y+aeK+s3tOVFvCq6++qnIVvkbYGRjbTeB72N9bjZMAKGvFpj486ZprrtENFQprAMOaBm7lqkvuuuuueOmll1ZHGBTedZ/cXo9fj6fJaxt5zSsvwIEWkNc4ee0hr/PldZO8bpfXffL6hbzu+dM98VHXHhWPnTA2/ujhH42ji6N4zNgx8cGHHqwBjUFIcTWa+qecl19+Ob7oootU6GKOQOBddNFF1SGXoNZ+BgZr14s4wV955ZXqoIzCb9iwYQoKgaLqd7/7XbvLwvE+4AABQSeddJJmhWMDiEBMYBgGsH5TMrIGErxOJhSypLEBDmBh9oOYzT8KAeZMjKQEGTaR6PO99tpLjRsAyzLPBVmhXE8SmEPwKGsooIEoiHDqajLdfffd6oTN+EYZQ4BYyErtvkeRsci6uuGGG6rhi33KpZdeWvs9ShYnAFNGXgIcpR+Dhy+77DJ10lx22WUV/CqLB1Ufezp+Oj5SXovLi33iCHkdJK9H5ZUm+oSgQtZ7ggqZp4JMm+aSm+8EcAIwz3qAMxVZ25uSjTuLAwCvYIjnfghGDcHC/+YSWaQPOOAAnfeYu1k3606ACCy88MJq1KobKHY3vMNJm+BS9m0kg2kahfm5uh7rt/nZcA55Eucosncju5M9HjC7fgAPMPdY53fA7k455RRNCAH/Scpx++2317nJtW4b4xkZFX0LQcyALq6//vqqp046aQAkgQMhQLTItYx5+E4wQ9OABmrdIaFxgQOBAy0OkJQB8DAAQU488UTVpaLLISEQuhDWAP4I7mQtxp5AsCc6HkCYmq4nbDGigg8EP6OjZj+NnabfbDJlWUigKsHCAHww3pqw9yx7j67OR1/Bc4q9G9BdwM+wdRugWYLdACjiOIBUBHsSDEfATACbddULoZzAgfpzgP0Cjt3YZbBpmD0HoFjJ+WLxxRdXIG/2G+ibfvWrX4W9Rkb3PvXUU+p/x7rdRL1cxi0NOYRfJXtT1mF8Appo+wr23iFd6u1LP9l7s5iEHwzJH3gWeN6ZOx966KGsU8MxzxzAX+OSSy6J11hjDd2DIt+eccYZfevHAaAJAdqAerPnHjFihMr6JFDnuQvUXA5gr8HHCh0KfUugEj6LTfW7Mz3B3hKfDO4J4JV+8rFCLkJPgx4MsNa6JwKivfg/Dh8+XMcXIE9h3ohjAgR33HFH9U9jLeFZbBI98MADLV9dEi+xTwuUzwHWUvyXiIFgHb3iiivyL6jZrwR103YSmfQzgOnUqVPVJwugVuIX6kwEeAOkgw9ZPwDoJXmNb9+4ceMUOIAYw7rHWmK3IqkaYDvM6QD+BorVhgIQIvYAEsM3WR7DtjhlyhSNAeZ+ALggwVPdx2a/jEPkDNZQ9CBzzjmn2oabCCSb7o/XX3893mOPPRScC/BQ5vIAipbmUvZ3wGcAMGP/ypg48MAD+9IHANA84soAkWdvS8wEfg+zZs0Ke6rsodEXR5Er2Ctst912Ou8hXyCDYx956623Gn+PQV9Tjy5krwPg6ic/+cm+S4pw/vnn69qAD3cTAHORJ4klJ/6VBAYnnHBCzDoXaCgHAD1lj8h6iF940/RoQ++mOd/w5wDw75577omJ4cSOgGyy6aabqj0BGSUJOkv/4E+OzIKvOftk4vewnYGpBDbKM888E//3f/93c5gQWho4EDgQOBA4YM2Bd955R3Xq+EZdddVV6gsIaD4J60hQSLwv8g7rhfnDPxBMHRIvbrHFFqr/QR4CUwrAWXTgwZ/YugucnIjcRVwY8WL4cGKTpm8DueUAidd4NgDtBzAZ39mm7QPC/r5wTEz+EKfIhBdoADkggeTRkUceGcmGP5INkn7efPPNI9lA9T03xJE1EoNa9OMf/zgS0LdIgoQiCSKq3X3/K/pX9KC8bpHXT+T1mLw+Lq/V5bW+vNaT13Lyelxey8urNP2fXPGPKBr+7PBop5k7RRuN2SgSx/dIMg2WLipc0FwOiFIlEuda/RPhNhLjZiTgUpFkmIrWW2+9SILpIhGSm3uDjlvO3MnccdNNN0UCThAJgn205JJLRqIMVH6tueaakRiLHdcaiquKA2L8imSzGN15553RzJkzo5deeikSoDVdK0TZGG200UaRGH2rak5P6hFlayROaZFkEo1EQRpJxpToW9/6ViTATD1pT50rlaDkSBQLkWTciB577LFIgAMiAVeLBMikzs22aps4kOoYkMQDkYAfRDvvvHO02267RZKh2+r6QTxJFBWRGP8iCZKM2GKJ81Ak4HI6h/QLP375y19GYkiJxJASiYNuJJn/ovHjx0cCGhqJ8qxfbrPy+5BAmEiUjDqf8Owhf+2zzz669jR5b8a8KFmhIgGqjiZNmqTPhCiYKuevywqRmyV4KbrlllsiAZuIREGsz4LLOsqU9VL0UnSVvK6W11Py+py8vi6vbeS1qrySBMrs3+X1V3n9TV68v/bfr0XTfzw9mnH3jOjP/+/P0eJfXDwavdro6JClDonGfGJM8vLw2ZID7OGn/mVq9NJtL0X3nXlf9ODPHtS1UwJqo1133VXXVMuiansasqI4Q0bHHXdcJKCdkThcq6woAVi1bbOvholzXCTBZ6pXE2dtlQP33HPPSIwWvqp0Wq44m+u4ZG/LGEVP1tR+lOQY0dlnn626TTEYqazCPr2pxL6UPQb7MtbRtddeW2WuLbfcsi/mkV71C3wV4LdIHF4jMX713fyc5qsAR+s44p6RKSWYUseROElFTZfJ0vfay++SgEX1hMjzAnwaCZhGJIbVSJzWomWWWaaXTevruiWIRXV2yOf8ibNG6w9dHvpbiLEvToORgKhHAkio78nPYvzW35uydvd1p4abCxyoCQfQ96KbEUCFSEDC9E8c/Id85hwIXRjzCPInfyNGjIgk2E5lDHSo4mxfk7tqdjPEQUn11OIUFklQXiQOfqqrZs0dFBIgWZXrxGErYk+Ofwe6wyDTlR8B8E9A5yMBkNR35AgBoNA/9gsQcsGCCy445Nk2z7kk2YnEgbQx+/7yHApXBA70FwfYN7CmS0Jl3T+k9w0SMB9JkJze9AILLBAJEJ/+sY4LcLf6gPBZgLL6izEe74Z59qCDDlI9Hb54rF3wtemEfRQ9uCQHiI444ohIAN4bvRYEe6+fEdmv9t523JKAjejiiy+OBMBfZSv08fgWSUKNSALB210WjnfJAUmyEd12221qAxFA9Ii1Dl82fHnQ+zbZt6AMax5//HHV9UsizEiCf3X/vcEGG6hPH3yQgOEyxYVzK+YA+3wJ3FZ9Ojp1fJXR4RK7IMBYahPrJ/8j7LDGT0DAV/SZrZjlTqvj+cMfDhuUAOyp7NeU/mLtQqeC/ZV1SxJGRAKQE2HnHyRif4ScLsnk9dlD54b/X1MJ/y1kdAF6U99dAXLW9aAp47IKvj/66KPq03L11VerHyv7GWSHJuoW8c8VIGTV66En5g+/pX4g9mn4GhPPw30xxzZBH4E8KsBykSRd1Xgj1rom+5jj9y0gNbpG4B+ND8CXv/zlxgwxSbIW7bfffuoP87WvfU3vQ8AQGtN+Vw0lDogYGOKAiPth3WuyD1mSLxIkr3sh5BkBelT/B3y0+SMmOJA7DmAvnj59us4D+MJL0rFo9913jyRZRCTgd+4qqkFJ+NgcffTRkSRHVRuYgMzqHgadeaChHIBXxAoJ+G6E/ZYYO/YV7Gn7mbCjEGt84403RjNmzIgkObjKlZJgRGNeBJinljH5/dwnLu8NfSO2DxNDK8D06gtDbDR6RwFVUtu5yzrrUFbQ1/S+F9ARYnsjZhVZBv0EvhhNJXxPiC8DawBbKTEZTdI5gY8gSdU1RghcBPR/xMQQUz/IJAmNlCc33HBDJIlfNH4QG0Sg+nAAvcSbb74Z4fuBDM87sTf8cdx8lsQxLd8QWs+eHxlu/vnnb73zGX/P5Dv2Jv76HU+iPj0aWhI4EDgQOPABByQ5VIRPL3O4+ZMklxF/fCeml8/mnTXBEHIY8zm6DfzOk3/InOaP30MckeFavd7xIQADUJIsR+hEiYckTg+/kCbat+rAXWQlMJHAwMHeJgl/NRaB/VgT7FHteBj29+04Ex0VAGXb8mZwfpBsKAp6cd1110WSwT069NBD1TmkyQ99u96TTCS6aUcxI1l+ou9///u6aLQ7vxfH34neiW6XFwCyt8mL7yPk9VV5bSyvteUFqGyaFooWil6VV1la4o0lovlnzB89+9iz0VuvvhV96P8+FC3y2UWiUUuMipZdctlo1OKjouUXWz5aZo4QAF+Wt3U8H8H50hcuja75yDXROy+/E735/JvR//7xf6OP/uOj0WILLBYtt+hy0YpLrhitPGrlaN6PzRvNJa//lBfvc8rrQ/IK9AEH2FzgvMOcIlleIxwmcc4ePXp0JBl+FZSXuYag4kD15ACBYwDJ4FRx3333aaADzoQrrriirg8oeenDQdwQAooC0M4PfvADdbgcM2aMGgNwIB10gwBGUjahbJrYiEq2EzWWMG76jVCe40DMH0GXGP5xdJfMepFklu+32y19P3/6058iDEMYMTGk4zCE0QwAXslWVLq8plyAPGEUBzwPKNBwsgfkDPDp4JRd3JOAFqCowOn0qaeeUtARFFqSAbHRTrXpO2ctAXQSAz/AKhiZmT+aRgSWAB4LoDKB5Cgj1xIg5V4Q+8Nr5XWlvGbJS8yTLRBZEo/8Rl67yOtdeQEa+7/vv/5BJpEiknRDHzn4I9EcZ82h/cT6hiwUEo4UMS6KGCMEvOFEequ8/nXzv6IPv/fhaPH/XTza+L82jr70oS8pyC+gv/1CGF0AlgX0EodBHGYBz2tyYIBt3xAYTnImnGhxmsGhicCkpiYWIRiCgBSIOZu1qEn7n7vuukvBvgEFwkEXvWa/gEsBlPuTn/xEA1ZIbIO+gXU0zM+2T+vQ+RndDWMDJ1cCzXBmHoQAexLFkBCC/SvObQBHM44wqJIcqalzl/0ocH8mDgjoAXGUZ/1H5mU87bDDDronCo5j7nletkRA09H5EcwBIGQaDBJdh8m1yZqHMwj6W/7Y35p3jhtHEpwCw163bE+E8wMH6sMB5mqefeMozLtxIubd/BlAalpO4kXAqNOA1ABKAi4JQHVwSqquj5nP0c0AWIX8QlA0eup+Bu5l/TrvvPPURsO+m0QggAr08z1XN6Jmr4nnH3BZgiCRI8w7nwFbMQ6nyA7IC0mQeuYJI0vwHhxNZ+dvOBI44JoDPJME/5g1nPcsud8AxqIPYA0n0bgBieYzf+gzg83TbQ8BEofOFFsYIOiHHHJII9cvxhR+hfgFkJyd5GL4TPQLBXtv9z05KPbeIk4BKojvAImt2GthS0X3SAJ7np1+9EEu4onL31nvAEICQBYdN0lOVlttNfXPwFYw6P5bPIfYTwAURP/93nvvaWA3vk3Y8+HVoPPI5XjspCzAIR566CH1ywTcC98qfO0IwAeAnj/min7WvfIcH3jggTpXkqwe/5mmJaQjGBNwQ8DLVl55ZbWVN1UuYt4AZI61i/09SaWxj/eznx/PLiCD+Drh04LugjEJqGg/2NWx97AOcH+AILHnA9xr/Pjx2sedzF1NvwaAM4DX8edBVgNkkL0Ztsym9zn6APz3AL7DJsvchO2/qTInOsjJkyerLZ/1EEDWJgKAAjIH4BB+O+iwAVBqWkLrWbNmRd/73vd0vsT/jTHWVF8K/BiOPfZYvRcSWNMnAKs2yRes7DyMfIkv/8knnxzh085+ALBj/Dj6lZBp8PnDBx0dFvEL+PBvsskm6sffz/3tq09//etfq58eayj7lv/6r//SpBesn8QG9jvhX0PcGLZYQOVI+oEtlueoSYB4rvuJJGr4hhErRJIfQGgA3SVeqN+BZNvxkmcFHRV/yAD4YpCsEwDS1VdfPRo7dqziE/TzPr8db5pwHF9S9DTE0NJ/7BcY58T5EDMCOA8+pcyB/U5BX1OPHka3CxArNquJEyfq3hV/raYQfibEyJFkA3mMtbRJSSnSfMbGg34FXyXmBuQgdCxN1QOm78/mO/ps5EH4gD6NvTp7RZJ2DELMgQ2PmngOtiPmGcY4fwZ8MPlObIABKQRwPUnMSwZcNv2ODYqkE/wlP6NTCGMmycXwOXAgcGBQOYDvHvsQsDj4IwbbfGZu5jtzMO98N39Zc7EB/gYslr8kODif8dvlnbk6zMH9MeIAFsYXCT0oe1jWWvxkSFSLL8gg66xsehhZhz0XdnH8ObHbwD9s42DB9AuF/X1mTwZA2Uy2DOhBgIwwfF5//fXqGAMQFkruxRZbrNEcQaDAuZsscL/97W9VOU12XRScdaFnomeiG+U1Q16/kNdH5PVleRkQ2aWipTKbGkdx9Ki8uG6qvN6SVxky4KCU05ZejKI5tpsjGv3e6Gj55ZdXRz6c+fjrdweqtjxpwA8I1yDvA3TDH0CnZC/GWAzNPWXu6N3D343Ah/3ovyTDury/96H3ZCTkjAW57j/k9Sl5/VpeAM0GGsoBNiwIowhUvDOv0hcEEiJUATa5wgorKGJ/v2UmHcqJen5D6CXbOn84i2D44hibBeY3DJg4k2PEbKoTkC/O4yiFUQNnG5xucAzYeuutFYhnUNYC5lA2nfwRQIyhB6daMg0PQvABinMcb5GnMP7z3CBLATBM5tFBMBib5wuFFM8CyRgIdoAXADJhJMP5btAUTc8884w6tAJShezB84DzIUEfjJFBmSPM+Gj3jgIT+YDniD+cgVFMAmiGkRWjcT+PHeZNwAoxrOKkjrMmSru6OwuRnRLjPkExGAAnTZqkgJlVO1v+Pfp7dLO8AJEl6cgc8tpMXuPltYG8PiovQ8jzJBsROBxzqPD9w9GHdQ96VXRVtO5/r6trHY6mBN3RRzhDmmAu1r9A/+YA+yzzTONAyrhgHQAc+sWtX4yOHHaknjgsGhb9U16QmCt0rz82GqsAsytEK+geS39s6D+cR3EaOfvss3W/yXyGPglZsZ+AGDBMXX311dHFF1+se2ueBQCMuNemB9ww9DDO4YiFrAcoDvM0QTd1NqwQQE1wEI6NBKYTNNTPgMb0EXuRMD8XT5Z58zOy11xzzVVcSJ+eARAb+xgT/ID8ib6KAGZkd3RWdZfPetE1gDuTjMg4w6NXAkAQ/RF7YsbVoAYJ9KI/XNSJbisJPJX+bLLUE/RgCFkPxxOCQ3A2MY4oWe84qzQ1cNXcb3gPHGgCB3DKx4k36w9bjXEA5j0JFMv6h33GAEgnQSDNZwKe0NsEqh8H6M9TTz1VE6ABpARQFcGMBDf1gxwDIAJAUdOmTVPZgzWFBCYAPjQtAL9+o6fzFmHrRT5AngZclvfkH7IEegNDyA3Ih8wpzDVGfkCG4M9855yPfexj5rLwHjgQOCAcSILAM+fj7GnWdD7zLPLMsf6bJBGs7cyXPHPotcwfALLmM89eP9tg6jh4sC1jWwEYB+du1mtAq+iTuhO2T4Ds0cMxjwPCsv3229e92V21L9h77dg36PbeIi4hM2FbxJdixowZ0fPPP6+6WOyM6623ngIRAKAY5uN8TjJnkhAdGwigeE8//bTKjPARMBf2QKx5gWbnADZLfDLgG748AJxASy65pIKZoAvnb9SoUX0NpjU7Z6o9gi8MPpmAk6BXx9+OvS7+s9jSASXB75A1dtAIf2JA9/Hl5nkmjgG7TJ0Jf3MAcElEjz7tmGOO0QCzfpjL2d9zbySRZYxi68EPlPHZDzomxhU+jldccYXqmfBlx18LAAyAVuvsB9DNM4Fci+8KsjzrAra8bbfdVu15/a7vZT+NDxOgwbwjm+HXC8gnvvH9RtgA8ONDj0rf4r8DsFtT/JixNwM6RNzeCAHvYn4liLfJhE2d5Nz48fAZvTZApnV/9pAfp0yZovIjzwq+R6NHj25yV7TaToJu9BvIx+ik8QNjrRs5cmTrnKZ/QK4C/JJ5H78q5j0SGwGAPyiEjhS5G18gwITx1caehNyNHxB/yOKBZucAY4bnA18ggEJfffX/s3ceYJKUVRe+5JyjZAFlQUFBQZCcg+QcBEEygqiAhF9QUIIKIpKDAiI5riBJkCQoOQssIis5CAgLuATZ/7wFd63t7Z7p6TDT03PueWqqu6fjW199uc73XHENBHOlmQvE2ONQHMPBPI5rQujbJW1xXQgGaswHpk3X3/Pnxz9y7X+EPkDSBXM1GbdlTJrfjuE4/TLdWpduhCys6AOgPKU/i9vwmmaaaQqTfIzyafeyscCfo38J0EZ44IEHxl5HS18NhuSUHSzEQXspr6MdysfH/TX9my6rfRplz5FHHllct8W1J/RdsOByJ8/1xrOEBUEwHaUf6ZBDDina391SRpCXc80MfSx4stCnznWP9Ckxp67bgj6Um266qbg2lnY65RtlPgtRYZTuGHoE6Fek74mt0uAwjQ5zz//feOONoi+kTIq8gWuq6VfPjfu013Ir3y/fxtuiW/KTMhPfNgETGLwEGEejH6W8kffl/crbaRjLnufkHL8kwLVY5I30OdB/3NvGvMBuukY5OXjfNwKM7Z5zzjnF9d3MBaE85VpI+vLwNBkKXj+9EeNcox8AE1k2Fkjgunf6OanP44XTzde5uX0/Tgqxoew4OHynIMAEfCb/sHGbwTQGQ9gYsB4MQeVi+PDhxaAYAzwM4mDqQuc9E2IGOj6Kjwrj2OExvDCSHREjCpPOdWPd0Jrrsbo0rVQt3o134wYJE1nZUcWLEoZBS0i8Xytj7TFrx0GPHhQj7hoR999/fzGRDVOGvPCSiy4WWWSRWHhhWd5qw1CFAWYuAOiWCVWt5NmO92IS8ZNPPllsDz/1cDz48oPxzM3PFB11dKYycY9OfiY2YJjJIAymplygdrGECdV/JdJkPYEJ8RbS+ZKjdwJ0HDEgxkAqFS5MTLngiWBgPs2ZmTDPubTQQgsVZmW9v7Of0RMBzgsGudgefpIVqEUAAEAASURBVPjhscbKZfacB5RvrBLGICUrlTt6J8CAAIPiTBJg4gQXgsGRhgQbt7tlkgCdukymuv7664uNSSJceMikPbYllliid2Bd+gzqWVwEdPHFFxeMKGu46CIvAiIddFOnNQNBmCrnBTtM5qTBTEcDk2NoQLsz6uPETr5LJwMXiMGMzgfKOiYb5Eb9cSjEqFGjCga33XZbsDEBg7oZZT5phlXgl1566SFXZ6ZcZgI0+QcXumHEyDZfB7UzKduYCMaEc9IyHc5c5Myk8/6sL1BHv1HCRPZyiXYg7UTq7xtKPS3ucFQcFQdLH0q9BWa0U0pXS8tK5SC/v/rqq4vzmnKfdiATAPJ8Zs/kgG64WKj8u6vdJl3QmZrnNAaWmBaQPjCy5LymLsQkuIzNY/Pi2FUehzTw/SA+KIx8Px+fjxWkpT/R/DE4J80xkYJ6E5O06Y+hT4CJyUymgM9gNNpjoIG6LxuTHSn/McNmcj3pvxuDSeXk06zcR/rG6IFFFDrF6IF8iIveML6lf4pJ71wkRLtuKIXz5/8d7Uby5/+9emjfYtCefJsyno0+EyYg0bZjAhwb9dVy2TZUiHGO0ZdHm4aNujyrztMnQDuQwWYuKHY7sPtTBOUO50aliRWPlQ0smTRI30E5OHfKk1vKE164nRME2eekQS5yHQp16zIn3zYBCDDRjLyXsol9eStPvM3b7JmIy+vKwcSgsskzbRD6odjSvDHvMxnNMbgJcPET/UvMZ6CNzvGm/Yn5xyqrrDKoJjxRz6Avij53+lYpf+hr33nnnYuLn51eB0dape8Xk0vGkspm9VmPYM/GGGY5qINn3kU7PG+zpy6RdYjcd/JFU+Xf5dsmkAQYK8myO/d54U25Ts1tHuf55eAcyXKcPRtjC7nRX40ZWTdPdC3zGIy3KbPPOOOMwqyLfJA2Nf2rjJN10ngy+TgL8DA2xAWDzH/DgIS5hp30PfsjDXi893+UPd77PxaN3GK8IefbYO7JhUL0g6SpJ32P3KYtN1SD+RRcjM3ClcwvZM94Ov3ejMHmXCzMZPtznLpbjgd9B7QX2WDLXB/KJRaSZR7L4osvXsynZT45vKl3OOonQNmJcSNpFiMv5pez0b/D3EG4co4vu+yyxQK29K87ophHxdgzxnWkSfjQ/mcMulPO8xz3p8+FOTP0p+2///7FmHE3GmrRTmcuLPVAxob4vZjRYfpLH9Ng+80sgMN8DTbmOJDnYajKuD/n5FAJFha85ppr4rzzzivSMfeZ08o4Hxvzx7vhOpcRI0aMHevleJM3r7TSSsWCGJtsssmQqGfRD3fssccWc0jIv7jebfvtty+MsTptzI2xDwx/zzrrrOKaEuoi5K98526Zd08eQzsKY1kWhuM2v4+yjjptp0TOPaKsox6DSSAGUJw/3RgY/jOfj7THOUPaYzyFsm6wmefShqLtRDmHeSr10QUWWKAo5zj36Tsc6kEbkzKQeUBZNjA/O+cBsac91E35Tr3HnIVvch4QexZwIagX0GfJxnVl3VBHqJdJb88bOXJkUVem/GL+NH04zJfK/opOmvvf22/p7f/kJ9mPhTkq47fMSaUuTXuNdoKjdwKYyjOvN+fbcQ0t+RL1NPpd6CsgD2LPNbRsjIc6miMA96eeeqq4hpa8jWNAHYd8j35G5qNx/SN5HH2ylAVO0+Myp47h/ppxmQzEPcaKjz766DjppJOKsTnG6GhLkG90StDPzDUM9CVhzMQiGiwQPVgWN2mEI7+Z60qoDzDOQ90J0y7GeslbOq3tXe9vxPeBuVosysM1c6Q/fhsLcWy11VYun+oF6ecVBChHSFPlea/VbpcNFzmfuE/fCa+vDOaHkbdQh2Ir3y7f53Hmq+dWed9zaSrJ+r4JDC0CXN9C/kReU7nxeG70l7Jxv9ptxtgrg/4T8hz6Csqm2HldTPlamcrbQ/EatUp+vt8cAdrAjGPTjuN6f/od6Gvgem/GItgG47XsfaVCm5/2P0aqbLBgji5tfursjHlzfX+nzEPo6+9r5Plu34+lZkPZsSh8YzwCZB4YZ7GiGoNtNEwwpCTDYJIMmWinXLBCxycXdjP5FPM3JgEywIWxBB33DHYO9AXeo2N0YQh0hSxkMYN9WVpQ2kDCEEiX6QfGMtXipXipeA2GsZgKvSctKa33iSaLyeIC6UjpfamZ4DuMkY6Q9pcwEK0MBoVYrRrn9tzo4KeSSDCBChNTVidjnxsGmpiQcAHUYO0oqmTR7vtPjH4ibnzjxljw8QXjhX++UKwYyqA+k67Zc8EFAc8Z9p0hXj/q9Vjw4QVjs39uFuvNsl5hWtZTpfr2uD009Bn/kSqNjmr9tlvilsLwqNb//XjPBLiQMA2aqaAxqTYHaWg8MXk2zZkxaGaSA+cQg61DqbLWM8WPJxyRF3EuwC+NlZkgB2Mqe1xkC0smuzB4wJ6OavIgR/ME6BRg0JzOcza40/BncJHJpww0MglzMFz8QXoh7TDIkSbQDKJyTvJbqE/kRFKXX+OmHTq3c/IE9UbSAZ1AefxJA2yDqeHNbyAdULdk4zYdXuTHTIBJ49yeytdxKQ3Ne6QNJqPRCcGeMo9OGTiSF+fFOewH+4QE6sBMlGLLi2SoK9Oe4veyci9tp5U04dQXyXx8PtBhx4Tcs88+O15++eWio442Gx1UMOvvwISGCWBMNMW8g4VVOG5MQGBSc39eIPJIPBJnS7+TaAcqFy1MZLeMLWNWqZ6gralL6ouFI3p6Pmayc0h/lD4r9RS0ubl4JjsXmezFhTV06LNoRfmcpv41mCZVavijMOjN9ji/lXYu5/Rdj9wVj979aHExFwMnDGost9xyxTlNGuG31/qttLE4fo9LvbW1JolJiudMoPb3gxIms4M5uECTDnn6kqgfcNEKrGDGBkNYdlpgXJFpnDyBthr1GiYLk0fRkT5Uyn8uNDv11FOLCwrgQj/gxhtvXFxQgElHfwbnHhN1MHTAXIlgog6LRpGuhnp0c/5ceWzL+TN1LvJpLrbta/5c+b6+//EFzLSB6VO//Xb1FmojH6CMY+J29qvQt8LW3/lAO48R5uH89txIV/Qx0U9AvxwXc7MxsMxCUA4TqEWAfrI0wWLPQHwaZuXt8n2eTxu5HIxpUUdioz+tvDEBsPI+9ZTyRj2lP9su5e/u20OXAPklbUP6RiiT2bPlhDImvubGY3k7J8Qy9lxprghN0jLmiWmgmAbNlffLxos23Ry66ZC+/UsuuaRoM1A/JG/kAnD64tiov9Rquw8ENeq1fE/anWz0nTJxk7EV2l30RTGm7uhOAizcyPh+2aCe/lHqD+WNegN97JX1BS5KyvyRfrmsO+Tt3Ge9IS8s4D4mMg4TaIQA6TbL8SzjKcPZSKeVt3mM/kE2THvKQZ03y/NyOc44Yt7nNuNGbE63ZXqD+zbjMIy/YC7LvD7qd5iXMC6ECcFAHGvqr5hr0JfNJHPSOhc/ssAWfcKdVH8YqKPv8V6P97Yq7TFuft999xVmEow3MjeHfjnm39AHR52dhVlzW3DBBbvO3IYyFEMH5hEw/sSevkjax1zMyRwK5tlgVsI4Wjf1wbYqHTX7PrTFYE97jPTIxnEg/ycwqqcPmDRZ3pgDPVQvuKU98uyzz46dl1men8mcD85t+mOGDRtWzBcgHefWKdcWNJtu2vl6rndgPJYxdepCaWLKPMX+5sex5AIzvgtjshx3xvV32WWXop9iqJwDmEjx+6m3YnxEn3vOcaCPiXkxnWa+RruLY0cfExvz8/jeLIzMwkvUb/s7PbXzvGnkvel/Zn5zmuthpki7lPE/yl02jJU6fX4+5RjjmdSl2BjXpT5F3w/XdtGG4bgPVTNF6lQYCJ955pnF+cscTfJVzgHO34HKx5iLjDkNc12YP8WCJZj9stAK+Us3B0Z8HBPMlu6+++6gjs9vZ/4V8/L7ey4+aYRjQVnHns/HJHDXXXct5rh387HI30Z5zyI6lHNwYI4s5wpzwkiPnCudOL+ZMSDKOMZSqD/Rt04dnbTEmApjK/2dnpJpp++59iHnALGnLU5/GNfWcm1wzgFij+HCQF9z2yqe9ElTr8t5QOxp/2ESlu3vnAvE3H7qBY7eCdAepD5FvYpzknye+f7la8dIS4NhzJ75zMw9zGvHOD8os6lX0VdNm4y6lcdre08X9TyDfIc+MNoquTFHP/tjGCso98dQZ8jr0G02+z/CjLHQVucaWvpkso+GPdfU5ngc6ZY8nfmebOT3XNfhsvJ/LHu75f6a3gi1///0ddC2oy1BGudagWxLLLzwwu3/AhWfQJlBHfrSSy8trjHivKLPbNttt+2a+lPFT656l3lNN9xwQzGmShuX/hXG82lLMKbBRl2gU8dXmdPANXD0p7BxrTD93/QLrbfeesW4teeIVz30frDNBEiHjCGSRum7yI3HyvNwe7pNvYp5vNWCNhD9xbnR7qO/mH1u5fvl2/SXcp995e2BmN9R7ff5MRPoBgKcv/Th0M7mfGafG/dzY6yDjfs93aYNlhvtiGpBeU0ewFxr2sK57+k2/ytf48J9tzOq0fVj/U2AspO+qvQyoQ+C8pX+BdoSjO/mNssss/T312vZ59G/T59nepiwZ+4LdQTOzTTTpZ+fORs+P6MYTxjC8zFsKNuys6/L34jKAhO66fxnz+RGOvnJOMlE2chUWKm93QPuZN50fObEPvYMMlOxYVJfdt5j9EHlZSBD1bX4g3SxdLXE/S9LGMhiJPs5qVbIqlX2scPj99Kd0uTS6tL60tckVfniQukiSZe8hy6nCF1aGSOkD6RyYBT0kYRRLKoVPE9rlcSlkmxeaj2t5uNc7MSALR11HKOy8SmDbxmkEY4VE1/ZmLzCvnyRCBeLMDDX6RN08jfVs38xXozZJB2KolEPk/KFYQxuMzDPxoVkDEgV3G7Uu2shrQnPmTDmum6uGDZmWDE4wkA4FRk69tlPMvkkxbE7Oo6OuyXZHcS+EmkmzZGqfU/SzGoS3683o6P5Y/54SnK0lgAdqpw7DIphzpznEecSHUEZnCOcO3POOWdxzlTuOY+o8HVqx2v+jp72VGa5KIVOZc6FansGwRgcyKDynhO5OR/ovKU84rxg8pWjfwhQN6COkBd/jJThL40Njg0XfeQKouw5TgPVaUhe+9hjjxXnG9+Z846JIpxrGBVQn+EiDQYyqFPQqeGonwBsGUDNCRbkYwSG8qQDBu9y44ICLm7ur6Aepu7rsR9HGUzjOdMDey4eIY2Qj/I9mbjI4BBpgYkJjsYJMJDBhBtMeumoYHIWdR2C8o30kHl57qnrdEpdkEkWmGqRpssbZTb5HR23pGcGh8lHWJmSDhjKZkdtAkx2Z4Iuph/syYtJC0zSIx9m0h7poNXBZDAu/sjBYC6uoOOM48cFzNtss02RHlv9ubXeT1ZTcb50lqRL12I+aTtpW4lFSBqJNWKNuEmqVb+n7SdLuLhWmkXqa3DsOJcZVOd85jb5KI9z3pKH5rnMnnoZW6d0unLO0n7lfL79ldvj3E+fGwsdtVD8fcTfi7KB+vlEe04UE+83cax19lqxxkxrFGmSSVZ96Ux9Jp6JL0pap69ok/fEmWOyl/QLqZuCQSzM56kn0jlP/QuG1Ak553LCGhNI6Afor6CtweTEnKhIGiZP5wIwvhed57mgUrv7ufrrNzfyOfQHYqjAYlOYujLwSB8g+XTm1a2eVE2aYZJOmvtSf6CdyIQjjJUwk3UdvfbR7Kb8ubx4DPk1dXfyZ85J2hb0S1Pvos7Q1/y5NkH/JwlQV89ynvySdjN5J8F5T98LFwZUbpT1fSkr8/PatWcsgTYe/dT0V7ORtrhPmqKdQnBhFGUR5RL5HBc89Ge51K7f7/ftXAI5CZB+UDbaQuXbOSkw96TVvM2+1gQf8kjGxdiYAJgT/sq38zH2TParNeGv/Dh9Zo7BTYA0x4Qz6nPliWZ5m31OOmPPmGtOOKu2pw3Nxv9472pBGktDQ/bU4cr3uU1fSm6MbeTtgeo7rvY7/NjgIkD/HW0Y+qi5uJi8lbRFX11OCGNPH0V/GICQX1PnoO1Ju5ONPinOHS6soO1J3zMXJ9BH6jCBMgHyV+oIZXP6NKivVn/IugTpq9qFAszvybw46wq5p65Qvs39rCdknaFyj7mto/MIcOzp/yYdZNmet8t7blOW5wRy9uX71D9zo6+hMmj3kWay7C6X43mbtmOawuee8r+T2oyVv8v3+4cApkfnnntuscgf8/2o+zEelONCLFZKPtTqIE9lLgP9jWzcpt+Rz8WEhH5Hl8c9Uydf8Hhvz4z83/oJMDeSeSXUkZmnwUafHWUZ9QzGFxknzo35GtymH69T5hCUfy3fmzkomDqw8VvYcqF05roR1KmY00afNht5Hm0U972UafbfbY4bxwljWeaPMRaR8zY5ngRtR+Y8YcjBvM3cczs3+sX7o43ZSjL8dspG5maSPhkTKG9pUsK4DEHdL8f/mffHvA7GatgPBsOgVrJr9XsxR5y6EcYUzGGhj5f6CXOO2DN3jfZZK4M6Pn0VzJVhyz4UjikmpMyZGeoGCpwDLDqA8R58OE7UUeljYj5BbpwD/TWfgD6BnOOQ/UzM06GNxRgX/UxrrLFG0dfUX9+plemyv96LOgfGnoyD0iZg/JN55pTPbJwHeXu++eYbkPydehJzayif2LNx7OnHp2+HfIGxTBbVYu958uOmHrhdcMEFxTnMGDf1L9qcsGKj/kW51uqgbGUsmrTFRtuJ70IevvrqqxdzEjFBakd7t9W/pdXvR5mTZR2Msm88yzrOu1Zf+0L7lXM8yzpuk19inspx2GKLLdqSDlrNrl3vR3plLjfXh96s+Xy0zaj3Ue9lvkSWc+yp//ZHfx794bSfSC85psK1osz34DziPKas4zpRyj1H3wnAmHyR4w1jblMu0lfMMZ5P5R51/sp5QLTHyUs7KUivjAnmPKDc51wzxudopzCPOU0Vyf8xkffYRvNHEv60Xagnk57IT8h36eelryOvG6NOxW3yllbn8/X8Cto95Cu099moU7GnfCaNMIbBdWNs1KuoY7leVQ/Z5p9DOcQ5zPGgTVPuj6Fvgv8T5D2kH66drbx+Nu/TJzPY2z+Mz9EGqHUNLX03bIxnEPSNklfTP0O+Tfudc408byjWNQsobfjj/po2QO3jW5IXUFelfTd8+PDi2h/SPHVC2hJs1F9aGXwmeRLXMNCWoF+IPiLyIsYS6TOjTuGIYmwHo3k40QZmTittbcY7qA9QZ2cjf+rPvIljyDGjnkt9NzfKG/5HXx5ph+tS1lprLS8u4MTcFQRI27Trcv5Pee5PPpb7ynlDzCviscrHc2yoFiDakNRJcqPdTnug3o12WXljnDTvl2/zWN5nP9jrfbV4DtTjeE5NIDk+JsC5hIcA6Z+5d7nv6Tb/y405+vVunHs5f5/X8Nk9BecW5xltJLZat/P/lL2Uy+yrbby+P/r7evpN/p8JtItAzm3j2mR8D9jyWkj8Pip9D2hb066gnOmEYDw4+zjL/SXUZ8kv6POkz43racvX1A5E31sn8KrnOwzR9r0NZetJHH7O+AQYlEuzMAbpGEyi0kImQ+cIF2HRMcnAERde0znLxkUCNA7ITHPjNVSocqPShFEgE/I4MdlyoIeMj858npuTNxiwZFCHzvtOmMAkn/9xTGT1ywpz1k1j08JEVhaq4wPVI1S6MY69/BPJqqow9lkvtLqNhJnsK9IFEiayGA3NKm0ibS7JyiOul9aWyjFRTBS6HDMmlWRVWnxO+f95G8NRrbMcl0iY07Y6KJzoiOFYsqVJJIUvG2mKY54rouXnTzL/JDHtnNPGLK/OMnbVAi5AyY1KbDY4q+3HTDUmHp3u0Vjlw1WKST1M3GQj/eTt3FPZpnOdjYGjvF2+z6BRVtDLe35f3qcS/+rbrxadYG+/+nYx8ZLJly/N81K8user8eHmuuDmY6+C/JnFBB8uTKASUt4YgP/gsx/E1xf9enw0wccX5WJKvJu0haRq/9j3KN+4NW6VrezRcZX0Gem70jckNcfLTxt7m7S1lqTp2TVNp0hLe0q/lBz9R4DOVPI9tpEyrMuBoPJAEflmBnlq+SItBjm5kCv32VDMxiL78kbenOdInieV+zwn8jwp77lNHp0dSewrNzqfuKiRvD73eZvfW274cl7nQF8OArKnYk4Zw0Y+4Og8AuTrefFHDr7TcCGNENQLqDOwkddxXCsvOuSiQtIkjRs6+3IjDZIfU2bkRh6cF8uS53KbOgRlT5Y/7MmvCc4TBiVyEmhepOFOxQJPy/5wHJgkQoObxuoNK94Qr9/9erx/2vvFZ5Ancfxz42IL6oukhSzr2ZNPcWzOn/r82GnMTsVt8ibSU6YB9qQL8hjSQHkjLdwx4x3xyuuvxHsXvlekiUwL5CF0AJAe2LJu6bylZcmg5hvRscEEdwbo8qIc8gnKuiwLmARdrhtxm/wjL3xnn7cpM8gvKjc6NzOtkEZyo/ykc4iN8if3lEk5KSPrqnzX/E6UqTkBgwFpJrQy6MnFa47GCXCMGHhn0j6DyphrUKeg8zrPUdp7pAEWpGAjf8gBI/aUD5zbHFvKBdoXHEs2LlAmH2LyEReF8VzeiwtwMEVcd911+/UYsggIi45gIssiJGohq8W4aVFnZ3GPRgZkMI9lQZKTpBulWkHbj0VKWKCkVvug1mt7ehzuTLS77qnr4rQlTothPx4Wz9z9TMGbPJqgXM8FRfLcZpJ4mgpxPk8w0wTxzOzPxNoTrD22DlA+r6mrkl7yXM49n8HAZ57LeX5THuQ5Xd7zOmLy6SeP0a+Pjrlumyu2vmbrWHihhYtzevpFpo8dJ9lRLatb4whpH6mR46JLsGJlSS3N4vOq/eF9ad//XarVxqv2usH4GHks5zr1g5wATr2R4HzPOj4XBVM/TLMJ9uS/5PXUCUhL7EkbHEvyC9JAtkX4HPLu3KgH0qbKi3fJIwjqIDkJPhcV4Hs4xifAOU4ezcZkYCbaUDZynLJOzXHLc5xzm+PFwCV5NG1V3oN8mo36WZaz9BPRZiAP4RgxgZ02H5N1uJiDPJr6oaMxApk/U+8qL2ID677kz1nnop7MuZfnYObRrc6fSTv0L1Pfou7FJC7qXNTZ+UxH/xMgT+ViRc5V6u15QcZI9VdlucqxoY8z62vkBVlvIw2Rx5b35To8bbzK9FStzKefKcv83FPu0/6kTKG8Z+M2BvLU+wjKDvKpvPiFtJUTF6mLOExgMBGgLCX950ZfSN7OPY/lRL/Kff6P84l6EedaT0G7mvI8J/tVu8051ttG2ZHlR+457/N27skPciv3Vedj7HmcjTpGbuX75dv8Nn5DbtXu8xh1m2z75+3K+9RT2MhberoN0/LG88v38zZlMXlo7su38zHquL1tWc/KfbaNs+6V+TS/s1ZwXHPcICeVVdtTF2AjT6/cyo9zDBwmMJAEOH+pu9AGzUlh9A1x/pH30JdH3YCN9kfOZaD9yW3Oh8zXMn/ivGTLc5L8lbZNtj3pi6JuRD2J+jZtUc5/3if7EOl/pq1Df5fDBNpBgDRHfYA+73K/d/aZ8b9y3SFv5z7rDdQRshys9j0pV6kbZNu/cp/9AaR/yhi2ytucW71t5fKfc7d8P+sE1AX4PlknqOd2+TU8n9+aZXtfb/M6XlMu7yl7s7xnn/ezrM/8pNqe8pyNvCZvl+9n+c4x4nZ5z/N6Om78bo4VZXy5TM8yvLyn7ZZ9AbSZ8na26Xgvhwk0S4D2OwsO0t+I2Q59uJyTzCGgD4jykj7j7F9gT/rNfIV8gfMozxXysuwTYE95zLgQG32RBGNNjA2tpAtO1157bV8o2ORBpB7k8d4mIfrlYwnQT5FjBfQ/cg7nRt9fBuUV+QH9kOzZmKNMeVXZTuV+rT5I6hKU41lWV5bXWT/KvpbcMwaVfY/s2WgL8D4E7WHysWxvsCc/oy+SPI18ztH5BKg/5zwWyifad5ia5L6cJjmmpL/yuGaObTK+Va1/JftgqDdXq+PmY7x3uV7J7XK9kzpj1uHZcx6V79MmyDZrefyU27xPBt8xDXIZP+X2fGor5xiNTdeTVHv39DFcddVVcbPMMujPoI+BejfHIs2YuJ1jMBwX8risG/HcrBfRVuDYUyeiHkT6pV+EfJa0TR2K8XcMFJgzw2I3XPvhGJ8AbSzqk9nHRN2HsTLOP5hzvmSez/GhTCrnB7ShaA/T/s095U/2L3EsOF7kKxyz7Gti/lzOcaA8JF8iKPeY48C4KaZXHD/mTzkaI8B8MoxPmL/CcWVj/gJBXsx8CPoRc64z513Oc859zm0t93GQf1e2+Tk/Oc7ljeNN2cL3yGtseB5BH2XOxcCEZZlllinaSW6PF3jq+gNTFk/G9I7jzDlFcN5SP2MOAudv1inZ5zmbeSvlZWXemnVAzlPmYLCoGBvlMOc5fcCYXzLfhWvceMzxMQHmGtEPQJ7KMSFvgzXHgvTOceG8y7lHnGf8P/saqQvl8YA38wEo5yjvOL55vUKex/QBUNZxLNZZZ53i+PpYjE8AplzzQd8M5Rx5ImUPZSDsKd+yrCM/LJdz3KY+QjrPco7+GtpX5bKOOmrWS7O8I+/Lsm6k6j3km+RxzBGinGPjXOJ6Ud7T0XoCHGPGtBhPo67INbs5F4jzKoP2Aucl+WTWRcvtcPLO7DemHc55SznKcStvlI+kjfLGec2xpw1OnsBG+ztvU1byXch7c19uj1EPynlA1Gc57zGRJT9xuskj2N49dVvSD0YdXBNC3Zn8OPtjs06V9SnanFmn4vhlnYr+HupTHLesV/HaTC/luhVphHSQ141xmzRCvSrrVKQXynHSHZ9NfzNlDemDejTtXUfnEaDsyPKBMoK2bOV1tPR1lIO0QzrK62bZ523yJurrtbZyvwx9eqQ5trzNnryysj8m+2bYkzapl9TaKANJq/TFkFYr96TxDD6bvJb6ENdZsGcjDef8ev7v6F8C7q/pX97VPo2yhrrqtddeW7QlWLCS/II+WfJ2NuqQeX0S9ZWsk3CeU7ZwrtL/Qt2XfIRygrKKjfZctiU4Z6nf0ganz4V2BAsMOWoT4PjQ1sv+FeoD9K/AkqDNQJsiN45T5tW5L/et0K7gmPG+HDeOde6pI5KP5sb5SdlPe4KNMoTnE+SdaWxLm4K2BX0tDhMwgd4JUPdhLhIbdZy8XXm//L/y/KXMb9nX2siPOV/LdaHev1mMbStkH0S53z0fy322L8hTcuvtsawP1runvlhro4+j1v9op/B/tnpv8zyivC/frvxf8WT9oT5LlPf4Wh0v7fnRnuP/T88nD2bjNdVuV/6P+9SNa221/k9aq3fLtmF5T/lQvp+383H2uZXLEx6rvM9r+xKkqexDpr5Rbct+zfwfdQxus2ej/OvtNv8nHTlMwAQaJ8B8Hq4jSC+TnAdCX1IG9dJsh2e7gscq581yP8uZLFtyT15XmQ9xn3pxtbncfH62SXJP+UhQXtEnTzuHLa+p5VoE/udojMAQat/bULaxJOJXVRKgckenSQ4gMYjERkOczJUGQSNBRkoDnY5PBnZyI8OjIU8lqhNCa28Uxp2YsWIKpKplrCRtJm0syU636tfEROgmCRPZ4dKL0gLShtJGEgavskOLi6VzpdslzGF5T8xE+QxMPjP4XFmthppo+VCx5zEMg86TVH0f538TyLiGSv9+0pFS+f3GeWI/3aEgJM2wvfDmC7HvMvvGxKMnju1O226sMRzPSZM4Ck8aoNnQpJAdG4vr1oXaDv5kP/Yfdd7Ae3UebU/0/nwK3ayws3/thNciZotY8egVY5YpPzbDnWbGaeLQfQ6NGUbPEEc8eEQsMcUSxcQxBlJ7K7S/Hd+OkyVMojhmSM3X2FLaWVpWqhaanhK/kM6RdIlv7C19S5peqgzSDemKNFyZTnguaQMTpC9Je0l8tob9K9/G9weAAB2gVBKpNGZnaA4yle9z3nDO5CAVjez+CirCOQDHhIJqg3VUqvPxnAhEpdrRPQToZKGRxUSTHKjPPemXdMvgfrNBemNyAWmKiX85+YA9kztpNJHGHP1LALN8DdPFCWNOiNVHrl4YEHH8mbiRkzcY7CcdvD736/HhbMqjri59xzV1+yJt05Ue6+Um5WvmK+/t9168uuar8Z3TvxPzzT1fYSDHpCHSiKOzCFC3oy3BQF52gpT3lG0MAjJ5LDuyW/ELqMNR7pB/ZDmUHT/smcDE5CH+72g/ATrlWbyEgeQcnCddMHDP//oS5AOc6znxmElhrL5Eh1p/x/1xvyxkzyraZhrGLoxGWfiBhUI0Xamhr0M78jTpdInbLDKy+ydSDjvee7JIxHGShp3G+1+zD7Cgylcl4g6J9gL9BeT3TDImny+fz9ynEy47RD/8tPL+P+jFB2i7jHepEpQDq2i7vMr/Sg9RH8gOWyYf5vlcnmTFRFLSxkwTzFS0vbeKrYq2U7aLaSv/TDpYov19tiQ749Kn1HfzlDilOCI9PfvSuLRo6/f0nG79H5P7OdfzomD2TATkfGcCeXaEN/L7yduZhMLx55xnQh17jj0X7pI/OBojQPuSScAcu8yrKbs5Zn0pn5mwyXlI3sygBhsTr6i7O9pLoE/5cwv7D/qSP+cEh/aS8Ls3SyDTUubd5N/ljXw+y3rq+q0O+pnISyj3yxevUKfnfpYB1AGYdOMwARMYnwB9dpyfbOUJftwuT+ajXpb3K29zn7ZaTxufUzlJgsfIR4Z6UOZRRuZGnxa32bNNNplaVz1slZPOyvfLt3Mcq3ISGhPUnEcO9VQ4NH4/eRXtl7I5Pm1Q+qfpn8iLGRqhwcUozGtIo1ranbQ/6YOmvcM57TCBwUSAtj11gxxbZp+3c25G1h/K98t1Bc65rCPk7bxPnYF6QOXWlz6FwcSz2nel7M2yvnJP+c1G+Z+3y/vyRHPK9/Jk83J5T5lPmyn3/M9hAp1MgPEDFhxME1j2lNP0OY4zH6yXH8HFFMzFolzGPCLNAuh39IWCvcBr0b8pIzze2yKYfpuCAPMDqLtjIsH8Ivoc2edGfZ7nMOeI+kirg3I7jdeZL8DYI32PlRsXRDO+wUWGju4mQD6XpiaUU7U2+sazLk3a7I/6LvXGrAPSd878uUrTr7xP/znjdJ6j2ZnplTSGQQZzZnLeDI8xt64vQXpgjIQ+CsZiMdnAkJR6ksfi+kLyf89l7jXXzGAkmfMcck5szoFp9Hynfcg5ShlD31Ju9DVx/Hjc0V4CzEmkLcLYJ3Mg2HKeM8eXc5D+jGaCNkvOcWZOYs5tpg1DfYLzkzaNo7UEGMNmPiLnb24cW65b6us1FeStjEczn5TjxcY5Sv5Kf46jdwK087mAGyPLLOu4xoC+Aepa9QZlGfkm51G2/ynrllpqKZ9H9UKs8jzaVhyfNBfN8o75ntR9+3KMKt+e84f+GeqhWc6x53xiPp/7MCuJDcx9jnHWb8g/aX+X5wKV2+HNlouVv5ByknE3tqwXkeeWN+q31I88/7OSXufcpz1MeTtS5m5Zl2Kf47LUqRgzajbIM0gH5CuUBeWN+cKU0eQ7ju4hQP6U1yJQJpGW8tpZ9nmfPXX7vIa2FemtXor0JeY1tOzpdyGd0keTW/k+dX/6aOhz9NydeikP3PPcXzNw7POTOZ8ZT8TENK9hoN5CfaUvbTv6YKhf4FuSfWYYj7Owgef4JO3G9vSL0YbAd4b+FeoDuXGcyK+byZdpB9L3TV2RfhX6UtjyWiHahO7zbuzY+VUm0N8E6B8iP2BjLlmt2zk3nfZnzjnLfa3HeJw567nP+eu5r/Y45UhPW3/zqfl5eAliX/WitpK1Us3nV/7jS3oA7wYu2+vbZdqV79TS+70Z+ea8evaVG+V6tcfoq815eXm7cs//y4+V5+jxeN7P2+xdb2/pofebmcCAEKDPgOsJ6K9K34Pc0+/AtdP0bzXTF175w7Lfk7oqfVnpdUCfALfZ6CtnvJDnOtpLoIvb9zaUbW/S8bsnASbCMViUA4dZaWdPxwCVptyoUGVHPoM/nRoY5WC8ea7EHnPYlSVMZDGDnVmqFrzuegmzmN9LGMZ+UeI1aFFJ620U/+O9r5UwDt1A2lpaU+J+ZWhKouxsL4lDpFclYnnpQGk1idesLv1JSrNQrZkWugyl+A3rx/qVbzmg9/mO8PiLdJek7py6vg9pigL5xAlOjB9N96PCAPW6+6+LqUdPPXZVCxpyXKCcW96nE4mGBgUr2wmLnhCPTv9onHXvWTHxRB8/TkOi2sU4lYXxw/FwrCoNk/4gYeZKLC3dKWmdiThf4rjWE/+OfxcMtN7mOE/nuJL2Pi3tJmFGNRtOthUhi97CNOqkOKlgsmvsGt+V5pDKAXfMa0+UysHnkLYxoz1BIq1hSouZLWZVc0mOwUeAzo4cGMs9j+U5kZ0enCv5GLdJ73mulPd5m/y8PPDFbc4dhwnUQ4C0lqbhXOyRnXrZcUcapaMlO3ByT95M/YFJllzE4eg8Ahjij5AelNKor9a3xLjvsv9eFte+cG0xoE+5utPnd4qJxkwUF19/cdGRS1oh38k0QD7DbfKcTAvlSWWYNV4m/UbaQXIMfgIMGDBpMc2paHNkh37mGexpb5A+ciOdcJvyigHE3HjMMTgIMMGHiYmUEzlQxJ46Cu3JvJieDjUG+DnWAxmyyS4MZH8dv44HpM9I230iTV1r+KuxOAn1+ysk6uY7SrQJsu2EEer/SSxKkXFMHBPfk9oVmNWymMW9kpaE6dPH3BK3xHpj1otRE4yK65+8PqZ7Y7qqg3b3z3B/HLbiYXH6zafHvB/MO865TR2Ac5oOVdJBvbFYLKaS5uHCZHfT2LQ4XuWy6u64u2iP0x7DVBbT3r7GLrFLUQaxSEc5aJfPLWE6u4bkGJ8AbRX6k5g4Ql6f9UP21AcyTyd/53yn/M8LI/uSDsb/ZD/SCAHKXvJoLubmeOVGeZx5NHsm8DDYUa6vNfJ5fk3/EOA8zDoXFz7XmjyR9a3ynnOz0fy5f36dP6U/CZBvU4cvmzuU6/D8n/vZ3iunJdp/3CffyDo8aauyX7Y/f48/ywRMoDUEqCeUyxbygFpbZV81fQNsPF7tNo/x/gT73CrvMz5EsM+t8j6P0y+ZG/lPrdv8j3yr1sb/M4+jrOR5DhMwgYEnQDuz2pyGzKM4b2l3snHucvEhbRs27jtMwASaJ0AdgHMx2wnlOkG2FXiM25T/uZXrA+XHsi6QdYDc803zNvss/9kT5ft5m3I/y//yvny7XPaTZ+T9vO3yv8DrPybQZwKc9/Q3stFPlWNDlNGUy/Q3slE2Y7DFxdecs47OJ0A+7fHezj9Og+0bkmdk/yPpi3GKrD9kfSL3lPNZPlNu52325Cn0QzKHmXkoWU8YbDz8fTuHAPVOyrE0mGWf452k0Wob3560SZ0z65blPW1R0ifpNTee6+huAqQbxmMxQCSPo27EnjSW9aKcS8l4LGnD0b8EOJ+Z38A8By5G5JixUX9lz3ma/UtZn2W+I31MnXztTP9S7OxPIz9n/hob161wbMsb5yP1CfLp3Dg/00SW4+y6ReccY9olnK+Y2nDOZpszz9fMW9lzntLupPx1tI8A9Xku2GaeSpZ1OT+UMo5jwbyBXOiB883RvwQ4LoynlOfzZXlHOcgxoYzL8o5zJsdTeMzRXQQ4Pzlv2bLuk+1u9rll+Uj6KLfBKSvLbXDXX7srffT0a8hLuHaMOlXOSaRORZphT35CWsn6VO6ZL0a9ijq085SeCPt/ZQKMH1J3z76ZHGvg8eyTqXY7xwezPyb7aHJPnka9hLIuN89dL5MfOrepC7m/ZmCPN2076qjlth1lDWUK5UW2JWiTc50Z15u4bT5wx4w8mfYEW7lvhXOJY0Y+m2V/tisYs+G4UQfg/w4TMAET6G8C5fpiuQ7J45RD7Gtt5f9zmzYy+3pv81t5DfHEdE/E/ivtH6dcd0rM+u6sY+fA8b98Dvss58p7bl+y0CVx3iLnxd737h0rP7cyLyuey//YqAPnvvJ2tftZZyZvzq3aY/m/3Gcdmz2POUzABEygEwnQT0W/Z15XS301+zvLe/I9+gjKG/VZ+grS84B+T/JXR2cT6IL2vQ1lOzuJ+dt1GgHMNjG5OVfCEPYtaUUJo1fMT7VGWNWvjEEsprOYcGIu+raEsSjGZpirfVrSZaxxs3SWhNmZ1tIojGC3iW1iQ0nT/cZ771ExKi6XMCa9QdKUk8Kc9rl4Lq6SMKctBwZGmNjwOzDHWUTCfEjrV5ef1hG3vx/fj19JGOB+Vao3MF7FVPVKCaafkx6RGgmt6V4whHG9xq/lz/lb/K0wleX4Ygys9TFjf+lYCVMnvt8PP5GaV+WXVr19WpxWGETxumqBERHHdh2J44zJEY+VgzSDURHfQV1thY0VrDG1KgdGU/tK5bgtbovlJOJF6dRPhJkxaXQvaQXJYQImYAImYAKVBP4cfy6M7qkHUU71FhjiXyfdJ1HmLCG9JM0q6VLB3l5e9f+YNj4rzS5pXcPQNLWqz/ODJmACJtAKAtTZb5Rog9GeoF7OAg07SlmnbuRzqM//VmIBiMekZaQ9JN67Ml9jUQkWkKCNwOefJ2GW2q6gjcz7XyhtLvUlMGmFDWartH35nbWC55CX016hfdWKoD1P25iQPVTRTr8gLhjHAJ12PItp0B+wj3SExMIb9QYLgdBeukcqm/zSjmfxF8pI2nFHS7kgSb3v7eeZgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAl0JwF8pNaT3pWmkPoaXI/8V+mL0v2SwwRMwARMwARMoKsIHDZhV/0c/xgTaBMBKsKY1WBEtoqEudkPpGckDE93kirNZKmAYyC7hTSLhKEORmiYzmBmdoeECQ1mOAdLmI7y3piQ8pznpWukr0tlM1mtX1wY3fB+s0k7SxjQnCNhGISx7ONSpZksaDD9zNg+to+7pE40kz0zzoyfS2dIfTGTpeGCgSzmvRg4Ye5Tj2ldMqnc817rSxyPRgLD3pulf0oYBGm9zcLwCSOhNIU9LA4rGmyYE/cWpDO+E2bA1QJTIsyiSDd877kkDPPKgTHRftLT0gkSBsnDJAyoyg0+0uZFUhokLRgLjmN89an4VPxI4hzA/AkTY8yVMfz7ncRvdJiACZiACZhAEqDsWVWqt1ymTMP88B1pLQnzcqKRzk1ehwkg9S+C+tJJksMETMAE2kEAE2zaDwtItAGoj1Pv5nHaOY2ayT4RT8S3pTkl8tRlJervtCtpM1aayfLbMOGmDcjCFrRL2mkmy+/EEHY3qS9msrSLaFvTPqVtTCwu9RS0h/hdtLdbFfPFfEW7mvejTcUiL7Tly8avtMtpd/9GOlniWNKuqjdoWw2XZpayTYd57aESA3m0v/hc2vKYETtMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARM4OV4ufCfasRvAU8jPKaIB6S8baomYAImYAImYALdQ8CGst1zLP1LWkwAQ9ejpIUlTDIvl3aQHpMwlMVwEzOfcoyO0WONZzCRxYAG0zLMUV+QbpL2lDD1xCwVA5rPSBjS8NxHpbulvSTMfzIw2blV2lWaXdpEelU6TsKkFlOaLaWpJCr+tSr/mN6uK2EAyudPLnVa3B63FyZEB8VBhTFSPd8PPj+TMFWiAZSmP5iariY1E3wPGkKNGvosFAsV5k2kpzUkDGHLwXe/TiKNjZB6CsyGTpHSaKnWc/n/BNIeEsZI1QLDKcyIMR++QHpK4juwGsmdEoHJLGl2eok0WS0wRdpa+osEJ34v5wmfe6T0uuQwARMwARMY2gSop1C2/FSqNygfCepHmO1n2V6rjtPb+z4YD459CkaBmPfVY+Y+9kW+YQImYAI9ECBfYVELTE5ZhOQYiQUeHpGoJ7MwBG3AvgZ54R+kNSXapVdKLEbCYg6nS6wC2FscEAcUbcy+LNTR23tW/p88mjYBv/1Yqd6g/Uybo7yAB4ulfEXqLTDHvUfCyLYVwXfPsof345heIWGOm2VQfg7mt/dK70mY3/bF2JZ2PuaxaSiL8TB9CwQs6BP4koQZMe05DNEdJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACQ5cA/lVlL6q+kMAzietmiYkl/KocJmACJmACJmAC3UXAhrLddTz9a5okgCHMRdLaEoYyGMGuKmEChOHmj6VhUjkwLcXkZ1uJijcmMJiaHi2liezusXvMJt0mfUPCFBaDzrmka6RnJEzWFpHKgXkthqaYc64o/VU6UOL5GH1iCDqD1Je4NC6N7aROjGfj2dhYgv9PpHoCY12MWjFKovFSNlulEbO81EwsFUsVaaBsctTX98M0mOOFqezXpTmkcmBQ9E8JQ1fSUk+BaS7mevy2WoE50VbSIVJvgUktaRaTZIyN/iUtLcEUE2M+DxNA0ndvsWQsGedL/5C2kTD5nVvCCKk3s9ze3tv/NwETMAETGJwEKJv/T8L4D4O8vgT1HepJZSO/KWPKvrzF2OdiKFsuOzHoo67mMAETMIFmCLC4x+HS/NLXpDclFu+gHfhLqXIxiXo/C8NrBmM+K7EgCG0cFjihTbqfNKNUb3w5vlwsYlLv8xt53o/iR8WKhCxUUe+iJQxc0dbAdLxs5Er7mnZRb0E7HQ59MXPt6T1p//PZ5YD77yXaS5X/YyEN2kkY6fJ/2vfvS/UE5eGZEsFCLyzSkUGfAm32cyX6JhaTbpYcJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACQ5PA6/F6zCQ1EtfGtWOvZcW74UKJ63wdJmACJmACJmAC3UPAhrLdcyz9S5ogcE/cE9+SPiVhCIPJJpXfF6UTJAw2y4E5GqYuu0gYxa4npeEspqj8L01kqUBjTIsR7QrSI9IvJMyHMNxZS8IANAOD1F9JmHNiMPs7CXPQhyXM0DAQmlNqNMpmao2+Rzte9268GxtIGOjwmyeQegs4Y9LEvmxCxOt4PcdtCqnZwNT3TxKGQY1G2VQW4+LyMec9aXDBgLSEKVXl7yl/7jFxzHivz/9zfEk7v5H6GphgYZ78RwkzJEyMEWa3fTEuxkQWM1nOhaOk6yXS//rS7ZLDBEzABExg6BA4J86JJ6R6jeKTzP1xf2HQnytd5eNTxVR5s0/7B2R1WA5MAqmfuaOzTMW3TcAE6iXASnwYZVPvpW3HohiPSywiQXtyMqmRoE35HYmFRzDjXk16VLpBoq1EO7XTgrbYkdKxUuUCKbW+K78J09iHpLJpOM+nHbS41FvQ7oHJxVIrgmNZLSgvrpQ43pWmshznkyTa9RgJf1VicY16gnRCu4s+gmpBHwB9BxjKriLtLY2WHCZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAkOLAIayM0qNBNfJlq+RxZPpNMlhAiZgAiZgAibQPQQ6z42ke9j6l3Q4gX/Fvwrzn0Vj0cKAE8PQAyRMMP8gYRgzqVQOzM32leaRVpYwGN1felq6Q8LkZQ4J8zNWZ+A9MAM6QlpVuk+6V9pNmk7KwLzzMglDHF6PeRBGqRgVjZQw5fy81M2xQ+wQz0jDpamlngJTnx9JGOu8JlWaEPFaDIbWlloRfM5XJI5jM5GmsjSy+A2VgXkSOljaSHpbqhYYCv9QqjSU4jeTZpeXGjWx4vMwrrpZulWaXFpDWkbCGLYvwXHcSxohkb4555aTlpV+L/FbHSZgAiZgAt1LgPoN5RVl/GeleoN6GPWyatFbHaHaa3jsLqmyvsD9H0sOEzABE6iHwJvxZhwnsVAC9WUWH2ERh+clTGUXkhoN6t60Bckrr5AOkZ6TTpbqNWlt9LObeR2DT1+XNpR2leoJ2hRLSS9Llfkyr6cdU2+ZsVlsVuTvtCObDdr4tYK2G2UTxsHlAbN8/haxRdHO53kY5V4u1ROko55i9pi9SA+/jd8WhrUsHILJrMMETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETGDoEHgj3ogZpL7GE/FEcU10+XVc33u8VO063/LzfNsETMAETMAETGDwELCh7OA5Vv6mLSKASeY2Ekavh0qYW/5Fekz6vvQpqRyY02DoisEr5jAYY24vYeTyoISh7LwSgWHmz6QFJcxMX5V+Lb0gnSgtLpUDg1kMNzGRxQznXeksCXMd9hiZVpqGll/fLbcPj8MLrhfHxTG/1FPAckUJAzgMSTHvrRYY/awutSoOjAO13saV8ajUTGAqi/FUT8HvwrDoS9LfpWrxvfiebGXnHJs+SCeYv5Iej5H4f7OBMe11EsbJNCrXlDhfbpD6Enw3TKYwXb5NYsUT7nNOnSlhOOgwARMwARPoPgKnx+nxkoQxYr3xQDxQGPLXev5UMVWtf9V8HIM/6nmVQQcnZo2Y9ztMwARMoBaBh+Ph2EXKhT9WiBWUUz0Qt0vbStTBGwnaK+dItDFZrISFMi6SnpJYxGR6qdNj59hZaxBOEGdI9cQpcUrRTv5P/KfmINNisdjYNk5v74khK5wukZqN2WK2YlGSWu9DWXKNxMIf1dovtPPoV8BcFuPZ/aRWDaRh2kuam1bCVPYkyWECJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJjA0CDRqKHttXFv1+tlX4pW4XHKYgAmYgAmYgAl0BwEbynbHcfSv6IUAleJfSotImJE+KWHwijkppjZLS+UYFaMK00+MfeaTfi5hHIRp0D+kn0iYYWZglInJCya1R0rrSxiX3SJhMjSFlIHp7HHSFyQMQ6+XMP/8p/RHCbPbKaWhElfH1YXR3LFxbGGk1NvvxiyV44Dpak8xdUxd8O3pOX35H8d0YemnUrOBmeqkUm+BITEGVtVispgsTpDSUBcjp+ESpn3nSvwPg6f8f7X3qPexpWIpHaWrC4OkaWKawqgXs9k/SX2N5WK5wpgXQ2bed1cJE+GjpbclhwmYgAmYQHcQGB2j4wiJfJ76UT3xfDxfmJdjslgtMChvpI40IkZUNf/jMyg/fyA5TMAETKBMAPPQS6WVJAxOadfRziOfOk2iLddo/Dv+XSxYQjvzm9JC0l3Sn6VNpImkwRAsDMFA0W+l3lY0pE3yXWl3idu12nKTxCTjtc17YsHzN5BaYShLeYCpbG/Bwh+0taoF5sKnSmdLmL6uKmGs3oogvbA4DovgfFvid9O34DABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABE+huAm/GmzGd1Nfgulium64MvBt+ITlMwARMwARMwAS6g4ANZbvjOPpX1CCA0es3pDmkgyVMMO+TMOzZUZpKysDY5gbCOiGIAABAAElEQVQJY9jZJcxuZpIuk16UTpa+KmW8E+8UZrSYCS0rPS5hUovJEOa1w6QM3vs6aTNpTgnjsiUlTIOekA6S6jVby/fshj3GvhjoYrq7p1RPYCh7hTS9NLFULWi0rCaxb1VgMLS/dL6E+W8zgUkUZqrVIg2kSLdPSRtJtQKTW0yKCIyLVpGILSXMnX4nbS3VMuYrntyHPxgvs/IIhr6YJfHZK0kYbPU1MHc+S8Kgme97qDSvdJiEAbTDBEzABExgcBOg3oRp4oFSPYGp+JrS61K1Dkneo1FD2QfiAZXiE1T9Gh/Gh3GeVMvAveqL/KAJmEDXEiAPYgGJT0ubSyxSQf2Xth4mno0MtCSskTEy9pbmljCnpZ5OXZj2BW3DwRR8b3jsI7EIS2/BIBUrFRLZ3qn2GvL/xaW+xKaxafxVek5qNjBtrRbZ7lwr1oq7JRaE6Sm2i+2K70Q/Ar8HI9hWBOxoN90kUbbRF3Gj5DABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABE+heAqNiVEwj9SXei/eKa1zHxJjxXoYXFtfnPig5TMAETMAETMAEBj+BCQf/T/AvMIFxCYyO0XGm9EUJo1cMwjB4xcwF081KgxpMOzF4xcxydQmT059LPP8SaUNpUikDI6D9JAxgMZJZQrpTukfCpHZKKeOZeCZ+JGFIhPnMSxLfgf0ZEt9vqAbGcZilLiidIvUlNogNYoS0nlQtMI1bQ2p1YPqEOTGmts3GCrHCOOmK98Moj/fHaJj0MYPUW5wQJ8T/SaS9cnwtvhbXSKwUAmfOi1YFxsp/lG6TMDXCVBYTQM6Bvgbn0dESJr2YCnOuYuSEAeGrksMETMAETGDwEXg33o2jpD0kTPrrCQzmH5UweK0VlJNTSH0NTPcmkWoFZRmm8Q4TMIGhS+CxeKxYUASz1yMkTEppb1wlUc+tZUpdDzEWNNlKot3DwhgYgmJ+SpuTzxtsgekri7AsIB0u1RO0a86VWJjic1Itngw+0b7uS9Dum1a6VGo2+E3lRUnSSJb2DgNipIcvS/XEorFo0T6i7cRCHLRzWhUslMP3oT+BPgzKsFYtItKq7+j3MQETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETaA0BfJr6aih7q+xkMZWtFXgwHC85TMAETMAETMAEBj8BG8oO/mPoX/AJAUx5MNbElGc3CQMXjF4x8NlVmlrKwOjsHGll6TPSryXMQv8m8RoM0GaUynFL3BIbSxgBXSB9X3pWwrx2KSkDg53fSxh6YiR7srS59LiEAef20lTSUA5WrviGhGHoZdLkUl9j5pg5DpIwgeP1afbD+3AMVpNaHXwGJsKkl9ekZgJjofclgt9AzCa9I31eqjeGxbD4iVQtMD66UbpDwhi5p0Zetdf39thysVzx/nzGW9KSEuZbGHL1NTjfMNfCWBYz2d9IGMt+V3pecpiACZiACQweAifGiappvdsnk1Zec5iEsTpRNvQr//JGDGUxPM8yt/xeeRsTW0zYMTp0mIAJDC0C1GPXljA5ZcGEIyXalb+QMBdtJng/zD6/JFE/PltiMRPaE30dsGnme7T6tZjI3i9hEFteeKWez6ENxGuPlcjPy204Xs/9RaS+BN+BhUYulpqNeWKewuyWATBMb7eQ6FeYSVpM6muk0e2P48exj4R5OuVjK2L6mD4ukliIhDIUk1n6JxwmYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYALdRWBUjBrHO6ueX3ddXKcrZiep+dQP4oPCf+uNeKPmc/wPEzABEzABEzCBwUHAhrKD4zj5W/ZAAPMvjF4wb8WEck8JU0oMY8tGr7zFAxJmsZ+SdpQwYRkuYbzyU2lhqRyYj2EY+0UJc86XpfOkpyVMLzGWycB46IfSvBLmnbz2QonHfy4tJDk+JgBrTHcx/cEAuJHAeBVDHo4LxkyrSBkcX4yC2xE7xU6Fge0JcUJTb790LD329Rgbny69ImEwBJ9WBecAZlYYJWOI3JOhXqOfCfu/SJxLIyQMcbeXRkp9Dcy1DpB47RESaWR+ifOWc8lhAiZgAibQ2QQox46WviVh/l5vzBVzxcESef31EvU6Ik3XuY2535RSX4PFBcqBYWG545P7LBiA8b/DBEyg+wnk4AZtPBahIN+6QqIe+22pGbPXj+KjuET6srSGxP1rJdqhtF0qDVQHG+17415Zo/44jpIw4W0kMAx/RMJsdWeJ+8mF9ng5f673/TeLzYpFNF6IF+p9SdXnYShLbCs9Kf1O2ldiEZQXpUaD9g3G5aQFTHXpT2hVfDO+Kdv0ezQMOCqWkG6QHCZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAt1DgOuhp5L6Eng7cV11rcC/AR+iX0sOEzABEzABEzCBwU1gwsH99f3thyqBNAH6UnwplpMwnzxLwkgWU9fZpYy34+3CrHPJWDIWl26UfiBhWHa5tJ6UBjb5GlZOOFLCHHY3aVHpbgnz2s2lfP6YGFOYnm0UG8V80inS1yXMZzDx3FRqxBAnv0c37pM/JrsrSI3Gd+I78Zr0W2kOiVUxzpCmkNaW2hU0rjDJw1D2P1KjgRnxitIFEmkFo1rMi0dLsHlealVwnmDOR/rdRGqHqSzfdX0JsyzMnPksTJT3kjDK7WtwHPeW/iH9SvqDhNkfhtGtZNPX7+Xnm4AJmIAJ9EyAuhB1r32kRoJOx9UlyirqWz+TFpCI9yTKh77ES/FS/FsiZpCWkZaVMKq9SqLORtnLHsM/hwmYQPcSwHDzGAnDakw4MS+ljXeLRD0WY9NGg/YpdXnek8VOaEfeJdH2WVPqhiCv3E5aXsJ4t9GgncDA0nHSSRLmsrwnUV50oy/vD+OppUulZgKDYcxe+X5Z9nA8p5NOk5oJDIYx5KWcw3D4T1KrYlgMK9Ib3x8Wh0v0VThMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwAQGNwGu8eW60b54LeCrhYdCBv4K00gzSl+UVpW4hhZfrb4a1eZ7em8CJmACJmACJtA5BCYYo+icr+NvYgI9E8AE6HTpWAmDsM0kzGyqGc88HA8XBq+YW2JKhpHmztJKUq3AkJb3xkCGivCuEqaWGJaWA2MyDIMwwPm7hAHO7hKfMankqE7g2Xg2lpBoVGCk2mhcFpcVrNlj5lsOjuHL0lJSu+LVeDXmkX4hcdwbjY/io/GMqzC52lfCKPk2qZVxZ9wpG6M1YmXpYqmdZscfxofxG+lQifN2P+l7UqONSM5h3u8ICYPaXaQDpU9JDhMwARMwgc4ggNH6/NI20tFSo/Hn+HNRt6KcShN0DAgxjqcMw9Cw3mCRgBESZnsYAhLXSpjPU5/Lx+p9Pz/PBExg8BGg3cgCBSdLGL/SJvyuRH2+2WAAhryJBSFelMj/MKdmYYVuC4zC+a20sxtlRxuBRV7mlMiLy3FFXFGYrW4QG5Qfrvs27Glv3iq1Omh3nC3R1my2DcUKkBgaY35L268Zc95qv/P4OL4wdafdR18IZuoOEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzCBwUkAXwSuF/2jtJpUT7wZb8pS9rmYXuK1U0p4ZXFdKz5ADhMwARMwARMwga4icJgNZbvqeHbvj8GcBxOgUyRMaHaSqpkAYTp2iYTRK8ZjC0qYwu4gzSTVivvj/viZhMkm5rHfkTAaYmWFcjwSj8ie5fj4nTShtK30LelzkqNnAhwbjHffkTA2bdRYlMbKFyTMe0+TBipIVzdJj0ukhVbGhrFhDJdI73xOK+OOuCPWklaXLpQmltoZGCZh0sz5ReMSg9kdJQybGwnSEabSR0qvSfDBtGt2yWECJmACJjCwBI6L4wqz76fj6ZhNajQ2j81VM7u/MO3HfH0CqZXxt/hbUXd7KB6KRSWHCZhAdxIYGSOLOiiDG9NKe0l7SKyc12zQpqGujnk2gzDUb78vNWq02uz3affrb4lbCkNvFnjYXmo0aBP8UKJdvYDUyrg8Lo9NpeelVrcNMJLFMJ3+go2lVgTtmR9I8MTsuJUL0/wl/qKSdPPC/Jb+ERZ1cZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACQw+Avhu4Yd1m7Sc1Gjgl8U10e9JDhMwARMwARMwga4icFhrXRC7io1/TCcQwKwT89j5JIyA9pOekTCoLJv1vBAvFMY0PLadNKt0vTRC2leqZSaL6ew6EgYrfNbZ0j+k70lpJouR2e+lVSVMx26WMH/BqAbjWpvJ1pdSWKECxpdJjZrJ/jf+G9tIHN9fSgMZGBr/XbpSanXAaDoJs+K7pVbGV+OrcbV0nUQjb4zUzsBE9v8kWG0m7Sl9XrpCaiQwWoLLU1KaQGNGdaD0huQwARMwARMYGAIfxAeFseIusUtTZrLUrzAF3EAi/iO1OuaOuYu3fDaebfVb+/1MwAQ6gMBj8Vh8Q/qMRL0X01fMZTHvbNZM9u14O34qfVo6RKJtgon2CVK5fdoBGFr2FfjNmJ6uLzVjJgsnFpegbdBqM1l+LItmTCFRhrQ65o15Yw3pDKlVQfuFRUQwfF1FelVqVSwTy8R9Eia4y0q/lhwmYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAKDj8DoGF186clj8qa+/CQxiVwhPmjqPfxiEzABEzABEzCBziRgQ9nOPC5D/lvdG/fGhtIi0p8lDHr+KR0kzSBl/DX+GltKGM6eLH1TwqgGQ87VpQmkaoHZ7EoSqy68JWE0dL+EIdDEEoFxzvHSZyW+C5VinocpKuao00qO+giwQsWp0lkSPBuNn8RP4k7pAgmj0oGMYTEsviYdI7U6JowJi/SMge6aEuaprQzS/aXS+dL3pf6IWWKW+JWEwdcXpI2l5aV7pEZispgs9pJgc5h0uoRhEmbP70oOEzABEzCB/iVwbpwbL0v7SM3EiXFizCxh3Ee0I09n0QDqcZjXOkzABLqHwCPxSGwhsXjBXRLmnyxqwGIEGI02E+/EO2ONZGmTsDADJrWY1c4udXPsH/vHKIn2XDOxW+xWGPHyfu0IjjGLxdDOaUdwzFmU4zmpVbFurBt/kVgg5yvSo1KrgvYX35fFcnaW9pZoXzpMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwAQGD4GP4qPiy04UEzX1pSeNSWOM5OtNm8LoF5uACZiACZhARxKwoWxHHpah+6UwiMWk88sSJl8Yw2JAiQEKBpLEh9KF0jKf6Ml4sjCTfDaejSOkuaVqQYX2CmlJCZNO3u9mCcPataUMPheTG96HPca0f5OulXheLZPafL334xLg+GEe9F1pI6nRuC1uix9LmDZhSNoJgTkP3+s+qdWBUfLnJAJjpNelVgbnAAa/GOL+XOqvWCAWKAyBMQYmlpK2kxo19WP1FMwL/yFh9IyhLJ+BCfX7ksMETMAETKD9BKhj/UzaWqpVD6vnW2Aei2HhHtJ0EvEfqR0xR8xRGPi14739niZgAv1L4MF4MDaRFpNot7FoAsac35BysZBGvxF5EPXlT0sYye4kjZSoc2LY2e1xS9xSLHRxXBwXs0mNBguM/FFiEQgWamlXkA74zq9JrY71Y/2YUaIN1cpgER0MkOeUvipdI7UqGBg8XGJBFtjT1/Km5DABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABExgcBNIAtllD2bzG94P4YHD8cH9LEzABEzABEzCBugnYULZuVH5iOwlg6rqGhEnsG9LV0t3ShlIauI6KUXGshFkkhmWfkjCLuVfCLCgNZ6t9T4xkF5c2luaSeO/rpBWlDEyHtpcwC/qttK+ESe3J0jDJ0XcCGMNtJi0q/VRqNDBT5ZivK+0pdUqsHCsXxlW/jF+2/CuR7g+VOB/ekjDjbbVBKkw5pzBOPlvqz8DYGTNezKE5/z8r/UgizTQS08a0Ba+n4ilZ8W5ZnL8LxUJxjpQrrTTyvn6NCZiACZhA7wSGx/B4XKI8aSYw6cO8cXdpSol4R2pH2FC2HVT9nibQvwRov9HWoJ3H4gKXSA9Jm0sTSs0E9e4TJdqeh0i0E5+WMJKdSRoKQb18R4k22DZSo4G5K4uLkLfT3m9nsBAHg2FXSq0OVl1kIYzfSBiptzJmjpnjRok233oSaa+VwTlxq/SwtLREm8lhAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAibQ+QTSULbZ66dtKNv5x9rf0ARMwARMwAQaJdCcy0qjn+rXmcAnBG6Km2TJuXIsL70n/VG6Q1pbyngxXowDpXmkg6UNpL9Ll0krSD0FRjJLSBjJYgb0oHS59GUpg8/DJAfT0zslDGRHSv8nDRWzoGTR6v0esYeO3ouFaWg2Kvr6GZj17CARmPd0Wuwdexe/76V4qeVfjXT7eelL0gPSN6VWB9+f82sn6SqpvwMTsMekH0qY22Isi6FzoyZNs8Qsxfs8GU/GqhJpB343SA4TMAETMIH2EDgqjor1pYWlRoN8H4P2bSWM9aaSiEaNxnv7HjaU7Y2Q/28CnUtgRIyIraTFpCckjGTvk6g752IkjX57FiKgLsrCBPtI1FUx3/yZRN40lOKgOEhWsK/FqVIzgZksi79gxtvumCamidUk2vztiDQWZkGMVgeGtRirs6jIXhIL3DTaJqr23egDYWEdytelpJslhwmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYQGcT4PpnYiKpmUjvpw/ig2bexq81ARMwARMwARPoQAI2lO3AgzIUvtLtcXusKK0iUVnFzOQWCfOXDIx7dpU+LZ0pYajyjPQricd6iqvj6lhSwtxsXul+6VIJ09iM6+K64jssG8vG69IV0t+kHSUMbxzNEcD8FTMmhBlwo3FMHKOjeXWcL80odVpsHVvHdNIpUqsDQ6z9JdLq8dIF0hFSq+PwODy2l7aQMOPq7+B8+76ECSzmzhjnLi3dJTUac8fccYb0kDSntLqEUfXDksMETMAETKB1BKjDYch/gNRMsAgACwZgPEikoew78U4zb1vztZ+KTxWm9zWf4H+YgAl0HIHn4rnYWfqcxEIhtA/Yt8JIlh9LPoRJLXVR2qkY1x4nzS4NtaC9TvuDBR/ILxuNa+KaOEc6UZpW6o/YKDaK66V2lB/0J5BGzpXaFSxs8zsJ/hgaj5ZaFZip3yax8MYa0umSwwRMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMoHMJ/Df+W3y5VhnKvh/vd+6P9TczARMwARMwARNoiIANZRvC5hc1SuCBeCC+Ji0nYZb5Z+kGCXPZjEfikdhGWkjif7+U/ilhrNKboeitcWt8VeIzML7BHPNy6QtSBkZBS0lrSZNLN0l3SJjP8p0czRPAtHNPCZNQjkWjwXE5UMLwlDTTiUEa2kk6TWrHChxbxpaFIerdcXdxLvwgflCYH7eaxclxsqyVly3OgxfihVa/fV3vN2vMWhjzYgA9hYSpLAbPr0iNxiKxSFwl/Ul6VfqixHs+LzlMwARMwASaJ3BUHFXU48izm4mj4+hYRxomEWko+3a83czb1nztbDFbvCw5TMAEOp8Ai3+wuMhnpD9KmGDS3thcakX7DVPsFSTag5+VaI/+WmpmUYzOp1r7G2JgiqnumhKLTjQab8VbsYu0lbSB1F/BcWQgi0U52hH0VVwstXOwjEVLSOu0YTA35hxoVdDOulCinc0CPhi558qUrfoMv48JmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmEBrCKSh7ITRnFVcGtL6utLWHBe/iwmYgAmYgAl0EoHmagmd9Ev8XTqawIgYIVvMLWMJCXNIzF1uljCwzHgoHorNpMUkbp8t8brdpMmknuLBeLAwIcOYFgMyzDd/Ly0uZQyP4cXnYzAzu3SXxPdYSXK0jgDGbxzHL0s/kRqNf8W/YgsJ49/9pE4O0ijpGvPiVsfEMXF8T/qNxDmEKdPXJc6RVgafc5E0jYTh03+kgYpFY9Eifzg/zo/rJYy9jpM+lBqNlWPlIl/4bfw2bpR4z4OldySHCZiACZhAYwRYKIC61AFSM3F73B63SftLGVPGlMXNduXTGMo2Y1ie39N7EzCB9hF4L96T1fTRsaBEHe6nEu1DTE5zwKKZT386ni7q1xhij5FYzOIyKY2tm3nvwfxa6sgvSSyY0UzsE/sUpqvHx/HNvE2fXztLzFIsMkP7vx2B2esb0jVSOwOTY9Iki33Qb/KM1KrAiPlQifbWKdKm0kC2/1r1u/w+JmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJtBtBNIAttnrq1tlTNttfP17TMAETMAETKAbCNhQthuOYgf/hmfj2dhJWkR6WLpEwux1DSnjkXgkNpG+KD0pXSxhlrmN1FtF9h/xj+J5GMe+Kv3xE2FmmnF1XB1LShtK80r3S5jN8pij9QQwPH1dwpwGk9JGAkOnbSWOP8bCGN50cswT88S60olSO4JzaAqJ98eQifS9kYSRUStj+pg+rpIw19pO4jgMZGAo/Lj0LQmTQfKIP0mNBumIfOUJCQMlWC4knSMN9G9t9Df5dSZgAiYwkASOiqPiCxLm783EkXFkYf63fCw/9m1YHQtTWYzq2xEYyr4v/VtymIAJdB4B2hLU034o7SH9Xfq2NKnUbIyKUUXdcuFYOO6TMJHF1HoZaajHnXFnHCth5DuX1GjQLj9Dov0yk9TfwQIZtGtyYKuVnw8XFrI5T2p3YG6MqSzp/qsSfSqtDNpbHKtbpNWk1ySHCZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZhA5xDI62XxYGgmWmVM28x38GtNwARMwARMwATaQ6C5WkJ7vpPftQsIvBVvxQHSZ6Qbpd9ImJ9sLGVgHou5I0ZkT0kY+WD2irlsbwaiGJ3sJWGwco90kXSXhAlKBqZAy0lfk2aX7pUulzCldLSHwKlxalwo/U6aU2o0MJYj3fBeM0qDITC6ulX6m9TqwFBvV+kUCeNT0jsmeFtJ2Vhr1WcuEAvEpdJw6WBpoGOqmCoOlx6VPi2tKm0tvSQ1GpPFZLGvRB5E/rC9hIEYBloOEzABEzCB+ghgPs5CARh+NxPUDzH/P1CqjKlj6rYZys4asxYf90q8Uvmxvm8CJjCABGjTYZz5dWkVifraT6RppWaDejTtUtqop0s/lahjslCDI+I96ZvSytLOUqOBETiv3/QTNfo+zbxu/Vi/WOTk9ri9mbep+drNY/P4g/Qfqd0xR8xRtDMXjAVlu758/FlqZdBnAqfnpWWlkZLDBEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEygMwikDxfXSjcTrTKmbeY7+LUmYAImYAImYALtIWBD2fZwHbLv+mF8GCdLmJ1g0vMzaYS0nZSrHLwYL8Yu0iLSfdL5EkayG0pZga0FEBPNYyTeHxOzEyRMgDCryddiTLaOtII0qfRX6UppCcnRPgIPxAOxt/R/0hpSo3FL3BKHSD+XviINllg9Vo/5pdOkdsTusXv8SyLdY4KHAfPNUjtMX1eMFQvzWoxc+bxOCIxuOY9/L90hYSZ9otSMoe4sMYsskE8t8qEpYorCVHbb2LYwU+qE3+zvYAImYAKdTOC4OK4wj98sNmvqax4VR8XnJAy+K6OdhrIzx8zFx1G2OkzABAaewMvxcnxDWlqaRLpbwvwVM81WBO9H24J2KAuYYFRL24XPcnxMAOPeZ6QzpGbi+/H9eEeirj5Q8dn4WLQf2hGYEGMme63UHzFdTBfXSZgsryldI7UyaFvRb0KbCFPZRySHCZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZjAwBPAP4v4QGom0qNnopiombfxa03ABEzABEzABDqQgA1lO/CgDNavdHVcHYtJ35EwZfy79G0pTXpGxaj4gYQZLMYrGM5iVLK5lGawPf32S+PSWFjCQPNbEiZAGAJNLBHPS9+UvihhSHS99CdpMJmS9vT7O/l/GAZtKS0j/UhqNF6JV2IraQMJg6fBFKThnaXfSqOlVsdcMVdhunx8HF+89ZKxZGHSdGQcGX+QWh2cS3tI7DGF7pRYL9aLv0kY7H5X4vy+V2omvhBfiJuki6XbpYWkIyQMrB0mYAImYALjE3gr3iqMHqnnZT1s/Gf1/sjT8XRcKB0gVasL9oeh7Kvxau9f1M8wARNoGwEWJPmlhP0niyVcJLHARKsWA3k9Xi/ajNQZMctkIROMTmeSHP8jwCItP5VYUGJeqdGgTn2K9CuJRTAGMmg3tKOdxG+aXVpO6s/FNyaLyYr2Cv0ntJc5V1oZ/CbOQfprWJznL5LDBEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABExgYAmkd1ezhrL/jf8WP2TCsOXcwB5Rf7oJmIAJmIAJtJ6AS/fWMx1y7/hQPBSrS1+TMHzF7PEYaQaJYHWC06QFpJMkDEcxqNxeqmfFgnvinlhe2kzCsPQJ6ScSJmPEf6TDJEyIMJDF0JPX8J0c/UNgz9gz/iX9Tmq00UA62VrC6Ok30mCMHWKHeFvC/LgdgXHfX6W7JWJHabtP9Ew80/KPPDaOjWHSJtK7UqfElDGlbHSPjAckbi8l7SW9KTUT/M7HpIMkzLQ+L2FM7TABEzABExiXAIsCEDtJzcTP4+cxj7SFVC3aaSiLMR/vT/3FYQImMDAEMKzEOBZTaeq51MM2lVoVZ8fZRV32yriyaKdgVLuo5BiXwJgYU5juLh6Lq1W357j/7MM9FhmhXFhfYpGQgY51Yp0iTY2MkW35KqTVq6T3pP4K+k9oK7PADozPkFoZ08V0cZ1E/8tqErcdJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACA0egVYayeDsR9fh9Ddyv9SebgAmYgAmYgAk0QsCGso1Q82sKAq/H67GbhPEMRo63SRhpYhybcVPcVJgEYUyDWehT0n7S5FJvgcHXLtJXJOJOCcPSuaWMi+KiWEg6WvqBhNnsNtIEkqN/CJwX58VZ0pnSnFKjgSnwn6WLJYxsBmPMFrMVxsrtMsTF2Gcx6RQpA5PmOaTNpWZXEsn3zP2kMWlcIr0gca53WiwSi8gW7JbCVOnCuFD3FokrpGYCg0EMZTE0w3BsTQmzqGclhwmYgAmYQGjNqf/G8dI3pWbK65fj5aLusG/sGxNL1WKamKYwaq/2v1Y8NnPMHK9JDhMwgf4l8O/4d+wuLSvNLj0i/VhioYBWBG3OVSXyKerIj0u0RR3VCZwcJ8ddEmbhjS4OwjsfKL0hldsq1T+xfx5dLpYrjMOvjqvb8oEbxUbxlsSiNv0Z9HWw8Af9H/SX0B5sZdBXQ78ObSDMgYdLDhMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwgYEh0CpDWbwiiGauJx4YAv5UEzABEzABEzCB3gjYULY3Qv7/eATGxJg4Q8LIFXMRzEQxe8WwJQMDyq2kVaS5pIelX0ozSL0FqxlgivJZ6Q/SORJmtUtKGZgCrSZtKWEWNELCwAYzSEf/EfhH/KMwg/p2fDvWkxqNa+PawkQKY5wlpMEcO8QOslG+KUZK7YidYifZKF801mAP4y1MeDHiOlhqdcwT8xRGzpg5nyp1YnwjvlEYha0eq8vWaaPC/OileKmpr8rvxkiJtPmQtLB0lPS+5DABEzCBoUwA425Mtin7mwnqhRjSYvhYK6aWFeAoqV0xY8yo5RFeb9fb+31NwASqEKD9yCIAl0vnStdLC0qtCNqRx0gsCvCqdId0gtSM+XUrvlcnv8fz8XzRjsbcm4UrGo1b49aC9XFxXGES3Oj7tPJ1LI5Bn8E1UjuCfg4W17lKGog4NA6Nw6VvSbSjWxkYvdPPQxmNsSyLdzhMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwAT6n0AayjbrecO12MREksMETMAETMAETKC7CNhQtruOZ9t/zb1xbywj7S59XXpC2laaQCJYiQAzk2HSXdKVEgYrmM/WE5j+fEn6joRxJu+/tZTxXrxXmGZidoMB2O3SmdLskqN/CXwYHxaGvvPH/PEzqdHAeHWbT0S6GuyxTqwTs0pnS+0IWH0glU19ON8wb+I43Ci1OtaOteMH0t7Sg1InBqaAmB5hSnafhAEsxtcYYDcTa8aahSH2AXFAHCZ9QbpZcpiACZjAUCXwq/hVrCtR/jcab8abxeIB1Pcml2oFhrJvS+0KFjp4Q3KYgAm0n8C/4l/FgiMbxoaxhvSYxAIkrQoWGGGBk4MkFhqh3foVydEzAczBZ5EOkRoNjL9Z4IEFRugb6KSgbfYniX6EdgTl4f+zdydwNtb9G8e/9iXSTiIhS1lCISpL0k5ET0S79qJVpb2n5Wmj0vZo1aZNSZuKkjUiQtZCsiUlsmb7f6/fvzPPmGaf+x4z4/O9XseMmXN+5573zJy57zOvuW6dBGdnjb7WVaJ8recBT5Sj53ie9lzl0XMycR3fRrnNrIUAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCBQ0gUShrPqGcjKJv7dNrJeTtbgtAggggAACCOQtAQpl89bnI89ujcq2Lvc08RT3qLBRxbG7exKjoskjPSpevMbzvUcFK5mZ3+w3O9+jEiAV2kzzqBxTRWKJGWWjrJ7nUc9Dnm88Krdl4hVIq5DzDrsjfI7fsDeshCc7s9E22umeyp7/egrCFLWioXDnVXs1lg9HxakdPc97ks+FdqF19qjESd9PUc+ddmco5VKRdOIAMer7iGK9ttbWZngu8FzqOdbzgycno69vFerO9NTwtPZofZVaMwgggEBBFfjRfgwnCkj+8Wn/TPtjKpfLyfS3/uFkBBkVyZe1sl5T+GdO7ird2+pnKo/l6RLxTgQiEVDhpo7jdDKQYR6dBECFzlHNk/akNfBoH3WS5zYPv8j4n25a++4f28f2rucpTylPdkdlpir/ftaT10Ynh1jv0ddeHKPnO37yTPfsrJG/fq7qeRg9TxL19LW+YW0d/+h7l0EAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIPYHE300nCmX1t7NLPer2SuvviFPbOvU8lfQU8jAIIIAAAgggULAEKJQtWJ/PWD4alYbU9KhsRq8nil0Td6adTZX2HOHRTuNUz10evZ6ZecVesdqezzxve/RS/0/MOlsXistaWSur5Znl6eUp4mHiFfjD/gglwiroTD76GviPR6XC+pxkd1Qkt8Cjr62clBhl9/7jup1KV1ViOsETx6g8drxH3wvJZ4ANCN8XGRX0Jb9NZl8vbIVtoGeR52ZPXp7SVtoe8XztURl2fY++Vrd5cjIH2UE21POO51OPHqde8zAIIIBAQRS4xW6xip7bPQs9GhXWHeI5zpPdUUGsHpO1L1fOk97EXSirQkv9nGAQQCAeAf0y4hKPSjeP96h0UwWfUc0KW2Ene/R4cr1H+94qrmV2FHjanraTPHM9idlgG+xKz5kefW6yOyoLfs6jk4Ps58lrc6AdGE4I8bl9HsumNbbGVt7zoWdnjj6Xj3l6e/QzNuq51+4Nx4A6DtUxIYMAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQj4D+rvNSj/4OWN0OLT3FPad7inp28xzg0d+5ZqUcVn9fnNk+sHg+MlZFAAEEEEAAgbgEKJSNS7YArKuiT5XLqDSkq2eO52xP8lEpUBOPSktUYKKiURWNZWZUuNnWc55HO7Aqx+zkST4qhDzM87rnZc8HnkoeJncEvrQvbZKngecBz1aPSmb1ddDOc7Enu6PSIZXRvOqp5ilI08gahbJRfWxxTBtrE74PUpaZ7mF72IseFZ7qeybqUaHq455HPV948vqo5Fpfvzd5bvToAFmPOzkdPU7N9HT26HtBxWjzPQwCCCBQkARU6K+yxvs9VT0q9h/s0ROPORn9HNH+xDWejKaMlbG1nrhGPze1X8MggED0AjpO1L6Y9kv12KH9/oxKpLOyFcNteDhOnG2zbbTnbo9+AcL8U2CyTbZhnkM9N3jWeP7t+c2Tk/JR3b6HR/vD+gVUXh0956CvlzhGv2TTcyY6Kc7Onp7W0/p6rvWoAD7qucfuCcdVF9gF4bmZqNdnPQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAGzJR71Mr3lGeHR323/5fnTo64Gjf7GVR0QKprN7Gy0jVbKwyCAAAIIIIBAwRMoXPA+JD6inApss22hMLKe1bOlnrEelX+lLABSqaRKglT29Z3nck9mzlqw2TbbfR6tr6KycZ4nPLt7EqNtuMtztKeGZ4anu4fJXQEV7xTz6GCij6epp5tHn8PnPNmd8Tbe62562m2eUzwFcVTCrPKs7Z6oR99nKmF+w5NydGaRKzxXevT9G/Wca+daR4+KoPNDCZ+KxW73fONRKaEKqvV4ltPPix4Pn/Lo8VHOdT0PehIH3lG7sx4CCCCQ2wKJItcttiXctQob9bivn90qq/vek9XRE5Qqu9M+gMpcM5qyVjY8qZnR9bL7fj2Wr/YwCCAQrYCOE3TSkX09Ok6MsmxU+3Aqj1Whv04WMMXTzMOkLTDBJoR3aj9Vx/BVPNpvVUHo/p7sjk4uomNF7Vvn5dHx0bee3z1xjApr9ZzGes/OHpW160QwvTw60UjUozNa9vaoVPZtD4MAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQrYA6gzKawlY4y51N62ydlfYwCCCAAAIIIFDwBCiULXif0xx9RCpuVSHPjZ7rPSpeOdKTfFbaSmvnucFzh+crT3VPZkaFQioXUnnNnZ7JHpWUJp9ltszaeO73qPDmE09Oim6Sr83rWRP42D4O5bG6lUp+9fkb5ung2duTndHnt5NHJVD6Giioo49RH6vKheIYFdb+6FFRaspRidBeHpU8xzEDbED4urjKropj+VjWVJHsRI8et/TYprOsyC+no8dLPU7e4lFxrf6fnZLFnG4Ht0cAAQSiFkhZjKf9ABU5rvHoRAAq0taJBd7zZHb6W3+vp93idXfXZOomOmlBotg2UzfI4pUolM0iGFdHIAOBTbbJenhUNKr9rS88lTxRjQqgT/Wo1FIlpjq5QsqTnkR1XwVlHZ0p8AdPYvQYrHJwlcu+4Pnak51RWake/wd6MlMQnp37iOo2ra11WGqkjYxqyR3W0XMXOqujitfzwqjwVccmF3niKH3V8zQ6gYlONKPnCxgEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB6AQOtUOthaeIJ63R3wqf6MnKqLtBHQ4MAggggAACCBQ8AQplC97nNFsfkQpQVA7byKMzEKggUWWfxT3JZ5JNssM9Kp4d5enj0fUzms1eP3m3p7FHO5YqJlVpbVFP8lEJS0PPUs8ET2bOmJD89rwencDP9rMt9CQfFRCpUO4Zjw48Ur4/+XVTe11fZypa3d3zqqeQp6BOHatjNT3veuIYfR/W8KhIK+XobCDPet73xFEipDJhra/PoQqG88sUs2LhcU2PLSrSUsnsfz05Ha2r0qYpHj0e6nFURWf6fmEQQACB/CqQslA2+ceh/TqN9gcrezIzKoJ82NPLs6cnM1PWypq2Q/secQyFsnGosuauKrDclofjg8E22D7w/NuT3i8psuo01+aGE5FMtanhOFSFlkzGAtNsWigDT+2a0216OBnC2XZ2OBFGatdJ7W3zbX54LL/OrvOq1v8va03tennlbSq8re+Jq/C1olX0X8sdasM9eWX0/ZcofdUJeqIenfhHXzc6tv/SwyCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghEJ3C1Xe2VsVvTXFA9D9U9WZk/7U9vcCiblZtwXQQQQAABBBDIJwIZN4Hmkw+Ezcy+gIpkmnge8TzkGetRGWbKeclesqM9h3gme5p5MjMqj9X6//E86PnKoyLMlPOUPWXHepp7VFyrskdm5wmoECetsuDtXkv0tUfFOSoWzexcZVeF8rkhNiSUymb2dvn1eirYiatQViZdPG95UhuVO/XwqJR5lSfqOdVOtTM8Kira4MlPo9JqPcb09FzuaedZ4cnp6LFRj58qk73H09SjEi8GAQQQyI8CGT22qxRepeZHeDIzfa1vuJoKCDM7OgmB9jnWeeIYFcqq7H6jh0EAgewLqORVJw5RcfREzymeKEdFoDr21Pes9uG0j8VkTkAnikmr2DfxSySdJKKuRyd1yWh0m+6eah7t8+aX0clQdEKcuKaNtbEvPHlpVPrazdPZoxNqRDnaB9DzAKf9HX2dMQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALRCLS39lbBk9oUs2L+3vapvSvdt621tRTKpivEOxFAAAEEEMi/AhTK5t/PXY63fJtts4c9Kv/ZzaPiw16elCWiKvLq4znfoxKwjz17eTIalc3826P1VQimYlmd/SDl+rqe7lfFl3d4Bns4m0FGuvG//zP77B+fq+T3usW2hCLRx+yxTJUPPWPPhNKZV+wVq+3ZFeZ0O90WeqZ44piO1tEWe9Iq8FFBtMp+9P0bx+hzv9Jztye/jQ6O7/Oo4HqGp57nA09OR4Vd13v0eFfSo6LFuzz6fmEQQACB/CSQUaGs9iE7eDIzv9lv1s+jx0cVQmZ2tP+o0ROTcUxi/bgKa+PYZtZEIK8JqERTZZ3av9cJJ1I7cUhOtvk9e8/aelp5Rnr29zCZF9BxiI4H0pqiVjSc6EMn/KjoyWhUIqs1X/MU9+SXOcaOCfvnOnNiHKPvAe3/x7V+drY5UfqqE43oZCBzPFGOntfRsf2RnpM9P3oYBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgZwLqL9GfVz6W+CUs9k224merM4aW0OnV1bRuD4CCCCAAAL5RIBC2XzyiYp6M3+yn+xYj4omVeI6ylPNk3K0A3mW5xHPQI8KZFIWwqa8jf4/36PSFhU2PuBRaWNq5UKbbJP9y/Os503PrZ70Cm9Suy/eFo/Ap/ZphiWYF9qF9o0no/Ihff57evS1dppnVxmViR7oUUlyHNPQGlolz1BParOH7RFKowfYgPB5Su06OXmbCr30Pa7HB5Wy5sc52o4OZdon2Unh7CuX2CUWRbFgTatpoz0PevQY2Mwz28MggAAC+UVgo21MdVO1H3ix51pPZkePgyrZ1hOWWZlE4WtchbI6oYImrvWz8rFyXQTyo4D2cbUP1c6jk45o3zPK0fHnGR4dc7ztKeVhsiYwwSakeUynXyDpWGKyR8fuGc1YGxtOJKH92zqe/DT6+HQiG30McYyOKbT+eE9eGn2O3/Ic7NEvBpd5ohydpONdj76OTvD84mEQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEci7Qw3qk2sOlvx9t7cnqrLJVtqeHQQABBBBAAIGCJ0ChbMH7nGb4EamYp75npWei52aPzkqQcjbYBuvg+cijctFzPJkZrd/Ao1LGSZ5rPKmV0K639XaK5wvPZx6VBTF5Q2C6TfdDgFWpbowOKkp73vA858mo2GmhLbTOnvae2z272nS0jva+J65RgVdahbK6z+6eFp7LPds9Uc9ldpnX2jb0GtZLYlk/6u1Nbb2yfv6Ulzzv/B19PCrWyunoce9qzxSPirIbefp74vg85HRbuT0CCCCQUiC1QlntA+iEBE95Mjsqr3vSo/3NRIFrZm+bKJSNoug7tftMrE+hbGo6vA2B9AVU8NrFo33AVz0qloxyXrQX7QLPTR49hqR2PBnl/RXEtbZ4lexMT2qj4/+mHu3zqmw0o1ltq62bR6WkV3ny25S38n66h/8/4UMc217BKlh1zxhPXhsdu3/oKeE51RP1z1T9LP3Eo+MdHZvqeR4GAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIGcC+9g+1tWT/O+49feczTxZ7W7QllAom7PPB7dGAAEEEEAgLwtQKJuXPzsRb5t26jp5zvdc6FHZq4pfU5tNtikUgE6wCaHwtZW1Su1qO7xN65/p0foXeVRWW8eT2qhkRGUm33lGeo72MHlHYLgN98q4ov/YIBUPHeqZ5tHnOqNRWc1pngM8KhrWQcmuNifbyTbDs9gTx6i0R4Wl6a2vElNdR5+DqEflXv/16LHiNU9+Hj0+qky5iqe5p58nivLXWlbLxnl6e671nOBZ4mEQQACBvCygfcHko/0ClQ6+60ntRATJr5v89bvsLtvbowLyrE7iScy4Cl8T60ddrpfVj5PrI5DfBHSyhLM8KhZ93BP1Pv6b9qafL6+H3eK5x8NkT2CWzbLNnpSjz5d+eaQTu+zlycxcbBfbXx4V/ebXOcqOsvGeuEbPaYz15MXRz+GPPT979L27zRPl7Gv7hlLZ+TY/FA9HvX6U28paCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAfhHoaT13+HthdT2osys7Q6FsdtS4DQIIIIAAAvlDgELZ/PF5yvFWquyxoUcvR3j6ekp6UpsttsXO8Ez2qFj0CE9GM8bGWH2PXn7mecRTwpPaaP3OHhU3qsTmMA+TtwQ+tU9tqycxiZKoK+wK+8ZT3ZPRqIjzbM8yj0qnEqVtGd2uoL2/hbWwUh6ZxjHH2rEZrl/X6noF1MXWxxNHKZ+KqVUkrdKvjZ78PBWtYvhc3Wl3hgJYFfau9OR0VMSoNVUy9ZOnnkdlaQwCCCCQFwW0D5B8P0BPKu7h0T5eWU9mZ67Ntec9KpVNa78zvbUS+w5xFb6WsTLh7uP42Zjex8X7EMjPAjre6+JR4auOKaOeL+3LcAzRy3rZ3R4m+wI6oYRO/pBy5PqKp7gnM/OCvWBve172qDg0v46e1/jWE8UJI1Iz0AkpdFKdvFqmWs2q2RCPfpZf54l6VDqv9T/x3OBhEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBHImcLgdHrq/En8zrN6uEz3Zmd/sN9vbwyCAAAIIIIBAwRP4Z7tIwfsYd/mPSEU/x3gO8Uz1tPakN5fb5aHoVUUgKopMb1TG8oBHazbyqCT2OE96o2LL0Z6PPSpVZPKWwGY/L8VXnkTRjoowVbimcpjHPJktHrrVbrWPPIM9VTy76qhAr5UnrkJZFTcf7VE5c3qj4qgNHn2/xjFaXweOj3vy++gg+maPvg9meFR6PdITxTSxJl7tNcW6elTGdo6HIsMoZFkDAQSiFNDPi8SoVF77AsM8lT1ZGRWN1/Kc68nO5FahbFyFtdn5mLkNAnlZYKEttA6ekz1PeqKeH+wH6+Q53aMTlDA5E1B5qgrBNdq/LeZ5w6PjtMzOLJtlV3lUEJrRcX5m19xZ11Oh7GrPPE8co1/Iab9eZep5dVR6+5JHx/XPeqIeHZe+6Onnec7DIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCORM4Bq7JqkHSoWw9T1ZnY220f707OthZ60AyQAAQABJREFUEEAAAQQQQKDgCVAoW/A+p0kf0e/2u7X33OhR2aMKXPfxpDeP2qP2vOd1T1NPeqPyyFM9t3n+43nfs5cnvVGxyEDPm57GHibvCXxtX/shwMawYSoeauhRqeZpnszOK/aK3ed5xqMy4119jrVjQyFpoqQ3ag+tn1GhrL73VZKq78FfPFHP/ra/XefR512PDQVhVLikEu4jPW08t3u2eXI6pa10KGFT4bIKGlXGrcIvBgEEEMgrAskLZbVNKiBUUV5WZqJNtHc893sSZ7vKyu11XRUhqsh+vSeOUSm77oNi7zh0WbOgCej4oKOnkkf7+tn9vk7LRY87KpOt7lHhpcqsmZwJ6HFYJwtRKfjuHp0s4UxPZkefk3959Eulez35fXSSCJXqTvLEMTpZjn5mTfbk5dHXgJ7DudIz1hP16MQZKi2+whPH+lFvL+shgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJ5WaCzdbY9PRp1fWVnVtiKcLP9bL/s3JzbIIAAAggggEAeF6BQNo9/grK7eeNtfCgCnWJTQpHlTXZThqU8Kpzp7VFZjIpo05txNi6paFTFNCqSzGgm2ISwvgonT/YweVNguA1P2jB9PehzfaAns6PSmIs8uu35HsastedXz/eeOEaFsss9Mz3pzVV2le3h+bcnjrnBbrCSnrjWj2ObM1pTXoM9T3ge8hzvSRwkZ3TbjN6vx8HvPJU9zTwq+42rdDijbeH9CCCAQHKB5IWyD9vD1sGT1bnerrejPe08ORmVcMdVKKvtKuNZ52EQQCB9AZ2YYL5niEffl1GPjlcXed72aH+SyZmA9im1n6k5yKOTF2h/MyujY4clHp0MRqW0+X1UIl7XE1ehrMpkVSob1/pR+t9pd9pJHpU4/+yJeu6yu5LWX2yLo16e9RBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgV1GQH/DeqVHo78Pzc6o90hDoWx29LgNAggggAACeV+g0HafvL+ZbGFWBB61R73W8QY7wTPQs7cno1FRV33PwZ5PPIU8aU1i/RPtxLD+XrZXWldNeruKyVSuUsPzsSe99ZNutAu8snXrVlu9erX98ccftmrVqh1e6m26rF+/3jZs2BBe6vXU/r9582bbsmWLab3kl5RvK1SokBUpUsSKFi0aXur1xCXxthVDVtiWg7dY1ZurWoVpFax0aa+M8kupUqWSXtf/d9ttN9tzzz1tjz32SHq5fr/11rV6V2u2vZkNLTrUCnsYs20efZ+orPkKT9Tjn/XwfZ6Z9Z+1Z8M2zLE5VtUT9Txjz1hPT1zrR729WVlPRVw6a8tfnrc8zT1RjL4+/uO5w6PC2pc8+3oYBBBAICqB5cuX2/ffe625XxYsWGBLly61ZcuWhcu6devCfsbGjRvtr7/+shIlSlixusVszcQ1Vm5QOWvw3wZWsWLFcKlevbrVqVMnXPbeO+39y3ft3fB4qZMJNPbkZA6wA8J+7dV2dU6WSfO2ca+f5h3zDgTykcAYG2MtPNpHOccT9STW17Hr2Z6CNtu2bQvHer/++qutXLkyXBKv61hw7dq1aV70uKxjurQuhQsXDsd2OpZLHM/ppR/W24qxK6zUxFJW68Za4aQSZcp4hXaKix7L99lnH9t3333Dy8TrQ0oPsW4eFQif5ikoc7FdHI5TdFKcOOYSu8Rme+JaP8pt/tP+DCXDpayUf4eP8brdElEu76v/b32ddEa/rGQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGCILBp0yZbs2aN/fmn/0WlX/S6uht0UTdU4vVEn4O6o9TpoEvy1/W3xKld9LfF+htl9Ujp5eZ9N9vvk3633Wvvbva7dxkle1/y19Ujpb8/1iX564WKedtXpUJWdLl3TinJrlesWDErXtz/EjSVS8mSJU0X9U4lf13/V/dU8ov6qPS3zGXLlg2X3XffPbzUugwCCCCAAAIIxCpwN4Wysfrm7uK+S2kXet7x3OO50ZPZ4lYV0D7nmenZ35Pa5GR9bct/PbM8aa2f2n3mx7dph19FbcnL2lTa9ssvv5iKg1asWBEuv/32WzggSK3TOXlZq15Pq9Q1UfKq8jftqOuSKBJK/D/52xIHAGmVzq4rvM5ePOVFO+2906zIiiJJJbapFdrqQCZReqtC2zD60rnfL96ZWuyvYqFoVuVE++23Xygp0svy5cuHUrr99/evBL+opE7XUdltQR6d4UOlsq954hgVPO/jedWT3ngdlR3iOcqjUrCoJ+71o97erK7nNc+hSG2YDbMHPVEWHI638XaWZ5PndU8rD4MAAghkVUBPDE6ePNlGjRplo0ePtnHjxpn2OTQqCqxRo0b4+Zv4Oawn4xJP3umJOO3HzNttnr1z+DvW5dUu9svSX5L2a+bNmxd+9mst3b558+bWokULO+aYY+ywww4LTyqqdPtQT1NPFD/zdDKCCzw3e+IYnUzhQk9c68exzayJQG4KaN+ukcdrpX3vZ1jkdx33+pFvcIoF9Zj7888/p3lZsmRJeAzWcVjy0S9D9Ji81157/aPkNXnpq47zdHyX1kXrplY2O6nqJJtVeZad+P6JtuHPDakW1up4Tj8fVHKrNZJP8bbFrfSJpa3ekHpWuXLlf1wOPPBAS69YPPlaeen1J+1Ju83zu35TFsPEvX7UmzzP5tkRHpUHP+WJerT+4R4VRcuGQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE8oqAyl71d7b6e9vff/89vEy8rv8nepX0cvXq1Un/V4FsUtdSKh+MCloTRavqjNLryctYkxe06m+JUyt01dvUGZW8HPbLBl/a8d8fv8Pbkr9f3U3qsUqU0CZeJvqm9DL564n362NJrdRW3RMpC3ATZbjJC3NVoJvyb5WTs6jHQuWy5cqVsz322OMfF/29tS7622VdEq/rb7HVhcEggAACCCCAQIYCFMpmSJRPrjDX5trpnl88b3jaeDI7U21qKBF52p62izypTU7W/86+C+v3t/52qSe/z9q1a23BggU7XBYtWhRKhPRSpbHJS2K1k6rStQoVKiSVqqpAVZc999wzXBI7u/q/Xldh0M6Y9bbe/LwQ5ueZyNLdaydfBz+rVq3a4aUOkhIluomXKtddvnx5OGBI3IkObipVqhSKilROdNBBB1nVqlWTLnqfDmDy89xld9nLnh89ccyddmcok/3BfshweRX8neuZ4antiXriXj/q7c3qetttu/3HozKqjp4XPH5ulKwuk+r1V9vqUGw4xIZkuRg81QV5IwII7BICeoLts88+s/fee88++OCD8PNYBe5HH310uKjstU6dOqHgPTMgXosfHtdSe2xTYf73339vU6ZMsTFjxtjYsWPDk6Paz2nfvr1tv3q7vVz7ZZtbaK4d6Mnp1Lf61sFztyeOqWN1rLNHP6cZBBD4p0Bf62t9PNpvVAFz1BP3+lFtr45h5s71o+K/L3PmzAmvz58/P+kXPjqG08kyVMCaOLbRSz0e69hPv7RIXHT8E+d4xa15DWym70LHcvqFV+KiYzc93qssd/HixUmFuTreS4yOc2vWrBkutWrVSnpdxeV59Zczo2yUtfQs9hzgiXriXj/q7dV6gz36OahjOJ3cIup52962f3n0PNWZHgYBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhLQMWviW4j9Rslv6xYsSL0ICU6kFSOmnJUfKq/odUl0QOlLqjkZai6ji5ly3ojg18Sr5cpUyaUx6oMNo5R300hT14cFdKqf0q9XGvWrDGV7uqSeF2fl+SlvImyXv3tssp7ddFtU45MEz1d++23X3hd3Ra6JPq8Eq/rugwCCCCAAAK7qACFsgXhEz/UhtrZHhVDvuPJSnGMPv5Wnk2ecZ7UdhqjWP8v+8vGelJbPy9+DnQ2BZUDzZo1K1xUHDRv3rxw0UFDYrTzr/JTXVSEmigOUomQyoS0wxl3WVBiW/LbS+3QyzJRUJR4+dNPP9nChQtN5bw6WNDIsFq1aqZyIl1q165thxxyiB166KHh4Cs/fOyf2qd2okelz/t5op5hNsxO8qzweF1xusv7+ULsMI8eM1TwE/XEvX7U25vd9b60L62rx2uh7X2P12lld6l/3K6f9bPeHn1OVUTs51f5x3V4AwIIIKBS1wEDBthrr70Wnlxr3ry5dejQwU499VRTuV9ujEr0p0+fbkOHDrUhQ4bY5MKTrUzzMnZJ0UvsoosuyvF2NLWmdoznYU8cc7gdbm09KgpnEEBgR4EltsQO8VzjiaN0WeXV2h+Na/0dP5rM/U9nv5s5c6ZNnjw5FGfr8W3atGnhlxBaQWcAPPjgg3coT1Wpqo4F9UuH/H4SjIyU9IscHbf9+OOP4dg4UbCrY2WVz+o4WgYyql/fK8H90qhRIzv88MPDsXFG68f9fv91kvm5Ce0Tj47Nop641496exPr6XvwWc83Hn3PRz09rae95JnkifKYKertZD0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyLsCKidVL5E6iRKXJUv8L6L/vuhvYPW3sIkpVKhQ6CVKFJCWL18+qZxUJaWJgtK99/a/PvWLCmTjKoNNbBMv0xZQz5SKZX/77TdbuXJlUvmvCoATFxUE//LLL6EoWKW0yUfFvgcccEDo/9JLXfQ34LpUqVIlvNTfijMIIIAAAggUQAEKZfPzJ1WljXd67vH08PT3lPBkZd6z96yTZ7xHhV3JJ4r1P7APrL1H6x/pyYujs0dMnTo1FAWpLEiXOXPm2MaNG8Pm6mBABaYqMlUxjl5Wr17dqlatGs4QkRc/poKwTSojWrp0aSj2VUHRDz/8kFRapP8n//zUq1cvlBXp5WGHHWZ16tSx4sWL5ymGP+wP8/OP2BCPvieinlW2KpQjqQD6VE9Go+043aNSn0aeqCfu9aPe3uyupyI0Oc72vO452RPVqIT7TE9xj8rC4/g8RbWtrIMAArkr8OGHH9q9995rX3/9dShY79Gjh3Xv3j08YZm7W/LPe9MTrwMHDrTnn38+PBl73HHH2a233motW7b855Uz8ZaW1tLqeZ7wxDFH2VHW2POoh0EAgR0FzrKzbILne09JT9QT9/qZ2V79YmjcuHHh8XT8+PHhuFDHGTqhRd26dZOOMXScoRNa6BcHTOoCctMx24wZM0LRuMp4dZytolmNTrjStGlTa9asmR155JHWuHFjK1ky+q+r1Lfuf2/1X/+EEuPr7fr/vTHC1+JeP8JNTVpqs222Fp41HpXKlvZEOTrB0NGejR49ppTyMAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQHKBzZs3h56hH3/80RYsWBBenz9/fnipItnVq1cnXV3FoJUrVw4X/Q1rokBUL/X//fffP/Qv5LX+oaQPgFdyLKC/bU6UyyZKhfVSfVV6qb9x1iXRUaU73Guvveyggw6yatWqhYv6w/S6usT09iJFiuR4u1gAAQQQQACBnSBAoexOQI/kLlVO2d3zuUdFshd7sjrbbbvV9xzqedOTfKJYX4W0df/OW/ZW8uV32us6s8DEiRNtwoQJNnny5HBRiZBGZ41QUZAuKg9SYZAuOnsEk7cEVDarA79Zs2bZzJkzkwqL9H+dbUIHc/o8NmrUKBQVqbBIJbOFCxfeqR+IvtdO89zviWNqW23r7FHJdGZGJdIquf3EE8fEvX4c25ydNTfZJrvU87JH9jd7opoVtsKr3M6yMR491l/kYRBAYNcV+Oijj+y2226zKVOmWLt27ax379529NFH50kQ/az+7LPP7IEHHrCRI0daixYtQgluVrf3RDvRKnme88Qxx9qxVsvztIdBAIH/CUy0ieGEIINtsHX0RD0qldQJR3SCkw6e3Br9AuDzzz8Pj0ujRo0Kv0DSE/s6VtAxQ5MmTezwww8P/+cMgtF8VnQGwG+//da++eabcByuY3Gd1EWlvfJW4XibNm2sefPmuXJSkBPsBKvgGeiJY+JeP45t1pr+KzFr4NEJhwZ4op6FtjCcIEPHq3GsH/X2sh4CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIxCOwbNmypM6guXPn2rx588Jl4cKFtnXr1nCnu+++u6nsM/mlSpUqduCBB4YS2b333juejWPVAiegv2tetGhRKJfV15g6qxIX/X/dunXhY9bflqtctkaNGuFSq1atpP6xfffdt8C58AEhgAACCBQoAQpl8+Onc5bNCqWU6229veNREU92RiWvXT3TPSq6TExU62vbzvTM9Kioa2eMdt5UFDR69GgbN26czZ4927Zv3x4ODBo3bhwKRxs2bBheVqhQYWdsIvcZocCWLVtCwawKi1S2p9Jgvb5hwwYrU6ZMKCxSkZ1K7Zo1a2alS5eO8N4zXuo8O8+Wej7zxDHn2rlhfRVNZ2ZG2Ag7zvOVp4Un6ol7/ai3N6frPW6P23We0z0vekp7ohiVc9/uuc/Tw/OEp7iHQQCBXUdAZ9Dq1auXqVD29NNPt9tvv90OO+ywfAMwduxYu+OOO2zEiBHWrVs3e+ihh8IZvTLzAahocjfPa5445mQ72cp79LjNIIDA/wSOsWOskGeUJ47R+oU92g+Nc3R8oGPBDz/80IYNGxaOFXTyCZXH6pjgmGOOCUWmOlZgck/ghx9+CJ8XfW6+/PJL0y9bdGymctlTTjklXHQWvzjmervevvT46WXiWN5Xj3f9WDb670XftXdDoaxexlEknVh/iA0Jz2nF+bGwNgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjsXIHffvvNpk2bZtOnTw8vv//++1Aku3r16rBhKo1VaWeiwPPggw8Or+slhbE793O3K937L7/8YvrbZxUb66UuKjqeM2eOrV+/PlDo6/HQQw+1OnXqWP369a1evXrhUq5cuV2Jio8VAQQQQCDvClAom3c/N6lv2VAbat09vkthgz0VPNmdI+wIq+ZRsWxiolz/cDvc/DwPofQ2sX7cL5cvXx7KyoYPHx5e/vzzz1aiRAk74ogjTEWiKg5q2rRppkvM4t5e1o9fQCVSOrCcMGFCKBUeM2ZMOEuEzgqhUuHjjvNKVb/oa0Nvi3Mes8fsHs+vnjhGhab/9mRlfRXKbvCM9cQxca8fxzbnZE2VU/3Lc4DnfU8VT1Sj9c721Pfo8V8FiAwCCBRsAZXg9+3b12655ZbwxGf//v2tVatW+faDHjJkiF177bWmJ34ff/xxO/fcczP8WLpYF9vs0eNeHKMS8BKeQR4GAQT+X0Clj509EzyNPVFP3OvrZBKffvqpvfPOO6FIVr9Uql27tp188snWtm3bUCSb2yeWiNqwoK03f/580zG8Sn/18s8//wy/ROnUqZN17tw5/HIlqo/5BXvBrvKs9ag0OeqJe/2otzflehfZRfaeZ5qnoifqucAusA89OrERxzNR67IeAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACO0dg0aJF9u233+5wWbZsWdiYvfbay+rWrRsuKuTU3/3qUrFi9H/HuHM+eu61IAqo70Nf17Nnzw4XFSLPmDHD9HLNmjXhQ65SpYo1bNjQGjVqlHTZf//9CyIHHxMCCCCAQN4WoFA2b39+dtw6FUXe4bnQ86SnuCe7M8pGWUvP156mHk2U66tU8VjPJI+KZeMa7XipKPSjjz4KlylTpoQCWZWDHnvssda6dWtr0qRJeFtc28C6+U9gyZIlNmrUKPviiy/CRQVGZcuWDQVTp5xyiulSvnz0ZZ1f2VfWyvOzp5In6kmsv9gWh0LTzKw/0SaGx4AP7AM71RP1xL1+1NsbxXoLbaGd5vnFoxLYxGNsFGvPtJlh7Y22MRQ9qRicQQCBgimgsxipcHXEiBF211132Y033mhFihTJ9x/sxo0brU+fPvboo49aly5d7JlnnjGdOSytOc/Os5Uelc/FMV2tq23yqOCSQQABsy2eQz0qkn3NE/Uk1tc+zOueqGbbtm1hv/7VV1+1d99919atWxdOKNKxY0c77bTTrGrVqlHdFevELLB58+ZwrPbee++Zish17KZfEnbv3t26detmlSrl7DhqnI2zozzaZ4/y5A8JlrjXT9xPXC/X2TrzX1lZZc/nnqhLd/+0P62B5xBPXD/b47JhXQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAw+/PPP0PfkzqfEpcVK1ZY4cKFrVatWtaggf8l4d+XevXqGQWbfNUUNAEVzU6fPt2mTp0aLuo8U3+VutD0t9BNm3rb0N+Xxo0bW6lSpQoaAR8PAggggEDeEqBQNm99PlLfmg22wau0zgtFV4/ZY3a5J6dzpp1pizzjPXGs38W62E8erR/1qCzoq6++ssGDB4eyIJ2N4uCDD7aTTjopXFq1asVOVNToBXy9BQsW2LBhw+yTTz4JRVQbNmyw5s2bW6dOnaxz5845Li1K8K221banRyWj7TxRzx/2R1jf65XtZE9m53Q73X70TPEU9kQ9ca8f9fZGsZ6KkvQ4qHLtgZ4zPFHNKlvl9YddvZ74KxvgOdvDIIBAwRLQWYm0X1OiRAkbNGiQ6QmigjafffZZKMzV2cT08/fAAw9M9UO81C61HzzDPXHM+XZ+KAD/2D6OY3nWRCDfCbxgL/h33aXm50mzap6o5zl7LhzPzrW5dpAnp6Njweeff96ee+45++mnn6xZs2bWtWtX+9e//hXLCSJyur3cPmsC+qXJuHHjws/Ct956y3777Tc78cQT7eKLL7ZTTz01W0Xr2pfeyzPMc4In6ol7/ai3N7X1Jttka+a533OdJ+pR6W4LT3/PZR4GAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBPKuwMqVK0PX0+jRo02X7777zrZu3Wo1atQIXQjqQ9ClYcOGVrp06bz7gbBlCMQosGbNGvv2229t4sSJ9s0334SL/v69WLFidsQRR9jRRx9txxxzjLVo0cLKlSsX45awNAIIIIDALihAoWxe/6QvtsXWwbPQ87antSen87v9bhU9T3mO90S9/q/2q1Xy/NejItyoZtq0afbyyy+HMpmlS5dakyZNrEMH33q/HHLIIVHdDevs4gIbN2604cOH25AhQ+z9998PpUUtW7a07t27h2KqsmXL5kioulW3cz23e+KYKlbFLvH08WR2ZtpMq+9R8Wk3T9QT9/pRb29U6221rXatR0VJ93iy8jnJaBu09k2ehz3Xex7wxFEGnNF28H4EEIhe4Msvv7SOHTvaYYcdFn4W7bnnntHfSR5ZcfHixXbyySebnkD++OOPw1nGUm5aL+tl33r8qeWU74rk/yqym+P5wsMgsKsLbLbNVtPT1qPS+qgnsb6OQXWsmJPRWdr69u1rb775pqmY+rzzzrMLLrjAatasmZNluW0eFti8ebN9+OGHoUA4UUTes2dP69Gjh2X1GK28lbebPVd74pi4149jm1OueZ/dZ//26KQjtT1Rz612q/XzaH097jAIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkDcE1L0zcuTI0L8zYsSIUCBbvHjx0PXUvHlzO+qoo0wv995777yxwWwFAnlUYNmyZTZu3DgbO3ZsuKhwdvv27aFgtk2bNta2bdvw/aTSWQYBBBBAAIEcCFAomwO82G860SbaaZ69PEM9KqKMYp60J+1Gz4eerp6o13/cHrfbPMs9pTw5mQ0bNtjrr79uAwYMCO37devWtbPOOsu6dOliVatWzcnS3BaBDAW2bNliOrAdNGiQDR48OOyQ62vv0ksvDTvmGS6QyhU6WScr5HnHE8foMaOk501PVuZ8O99GeWZ7inminrjXj3p7o1xPj7kqROzuUTlbcU9U85q9Zhd6TvLo9dIeBgEE8q+AngTSEz7t27cPJfp6UrWgj86ydPrpp9vUqVPD2chSniSgt/W2Lz1+/qVYKK6xa3yPe6L502+xrM+iCOQnAe2nXOWZ5znQE/VEsb7OyHbXXXeFEmqdqbBXr152xhln2K7weBn15yM/r7dw4UJ78skn7dlnn7UiRYrY1VdfHb4Wdt9990x9WC2tpR3iecYTx8S9fhzbnHLNLbbFmnmKesZ4iniiHBVMH+nR8YuOQ3WMzCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIDAzhFYsmSJDR061D766CP74osvbPPmzda0aVM79thjrVWrVqFAtmTJkjtn47hXBAqIwNq1a0Ovhgqb1WmlgtmyZcuGnpFTTjnF2rVrZ/vss08B+Wj5MBBAAAEEclHg7sK5eGfcVRYE3ra3rZWnoWe8J6oyWW2C1j7Mc7InrvXbWbsclckuX77cbr31VqtcuXIoh2nUqFEolJ0+fbrdfPPNlMlm4WuJq2ZfoGjRonbCCSfYSy+9ZPqafOKJJ2zWrFmm8qqjjz7a3n333VAym5V7qGN1bKYnrtH39neerM6ddqct8TzriWPiXj+ObY5qzSvsilDg/Z69Zyd61niimm7WzUZ4VPLUwrPMwyCAQP4UmDZtmp166qnh585rr722y5QjqvxPTyzXrl3bjj/+eFu0aNEOn8ASVsI2eeIarb/RwyCwqwuo3PEeTw9PHGWyf9lfdq9HRfjZWf+HH36wzp07h1886ayGeoJc5bLdunXbZR4vd/Wv0eQf/0EHHWQPPfRQ+JnRu3dve/zxx6169erhpX5BmdGoTFYn0ohr4l4/ru1Ovq6KZF/yTPE84ol6dBKTFzxfe3QCDgYBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEclfg559/tkceeSSUxarj6d///rdVrFjRXnnlFVu5cqWNGTPG7r777lAqS5ls7n5uuLeCKVCmTBk76aST7IEHHrBJkybZL7/8Yk8//bSVLl3abrjhBqtQoYK1adMmvG3FihUFE4GPCgEEEEAgFgEKZWNhzdmi99l9dqZHZT4feHb3RDW/2C82yjPOE8f6KqQc6/mXJzuj0s5rrrnGqlWrZoMGDbLbb7/ddAYL7fioxJNBYGcJ7LbbbnbeeefZ2LFjw9kdatSoYV26dLF69erZW2+9leliWZULzfOotCuOOdQOtR89WV2/ilWxSz3/9qz3RD1xrx/19ka9nopkR3vmeFT8utwT1RxlR4UiprW21pp6pnkYBBDIXwJr1qyxjh07WoMGDeyNN96wIkWK5K8PIIdbqye3dKayPfbYwzp16hTOVpZYMu5C2ZJWMtbC2sTHwUsE8rrA6/Z6KKa/0W6MZVNftVfD/k8f65Ol9VUee8cdd1jdunVt4cKFNnz48FAmq7MZMgiolPzGG2+0+fPn21VXXWV9+vSxhg0bhjPzpadTy2qF/fL0rpOT99W0mjbXk99HJ0O5w3O7Z5Yn6tHJUPSYc7PnJw+DAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAvEKrFu3zgYOHBhKYqtUqWLPPPOMtWrVysaPHx86ngYMGBD+5r9cuXLxbgirI4CA7bvvvnbWWWfZyy+/HMpl9bf09evXt/vuu88OOOAAa9eunb399tu2adMmtBBAAAEEEEhXgELZdHly951bbItd4FFZR3/P454inqhG63f2bPc84ol6fW3nx55SnhM8WRkVBd17772mks5hw4bZs88+a3PnzrWePXuaSmIYBPKSgEqKXnzxRfvxxx/DAfI555xjTZo0CWdWyWg7Vfiq70WVysYxKkfS+iqVzercYrd4Jelae8wTx8S9fhzbHOWa9a1+KPPeaButuSfKr4HqVt3Ge/TyaM8nHgYBBPKPwCWXXGJ64lVlsiVKlMg/Gx7hluoJ5XfeecdmzZplN998c9LKKpT9yxPXaH09LjMI7MoCOj58yNPVc6An6tH6D3vO8lTyZHamTJliRxxxhD3xxBPhMnHixHBGtczenuvtOgJly5YNJ6OZPXu21alTx1q2bGnXXnttmr8cqWE1QsGxjn3iGK2/zLPOk9+nt/X2o5j6dr5nmyfquc1us8qeSzwMAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCMQjoL/B7NWrl1WsWNF69+5tDRo0sAkTJti8efNCeWXTpk2tUKFC8dw5qyKAQIYCRYoUCeXO/fr1s0WLFtmIESNCqexFF11klStXtj59+oS3Z7gQV0AAAQQQ2CUFKJTNI5/2NbbGTva87RnqucIT5STW/9q+DmUg19q1US6ftNbn9rm18KicK7MzcuRIq1u3rvXv39/69u1rM2bMsG7dupl2chgE8rKAdrYff/xxmzNnjtWqVctatGhhPXr0sNWrV6e52Sp8LeyZ5YljalpNK+SZ48nq7Gv7+iPDtfagZ5Un6ol7/ai3N471qlgVG+ORxVGeSZ6oZk/b0z7zdPK087zgYRBAIO8LDB06NBTJvvLKK1a+fPm8v8ExbqF+lqo4UvuDkydPDvdU3IrbJk9co/XjLKyNa7tZF4EoBT6yj+x7zw2eOEbrz/Zc78nsPPfcc9asWTOrWrWqff/992Efu3Bhnr7IrN+uer1KlSrZm2++aYMHDzb9XG3evHmqvxg52A4ORD/YD7FQxb1+LBudxqI6yZGOK771POWJevTc0XMePZc00MMggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQHQC33zzjXXo0MHq1KljM2fOtOeff94WL14c/qa/cePG0d0RKyGAQGQCKndWj9Uzzzxjy5Yts4ceesi+/PJLq169up1zzjk2a1Y8vVWRfQAshAACCCCQ6wI0suQ6+T/vcIktsWM8MzyjPCqWjXKSr1/OylkXTxyzzbbZCE9bT2Zmy5YtdsMNN1ibNm2sbdu2pjNZqBGfItnM6HGdvCRQpUoVe/XVV8OO95gxY0JB8rhx41LdxJJW0qp6ZnrimN1sNzvAk51CWW2Pir6Kev7jiWPiXj+ObY56zX1sH/vCc4SntUclsFFNMStmL3r6eC70xPV5jGp7WQeBXV1A+0I33nijde7cOewL7eoe+vjPO+88O/LII+26664LHCqai7PwVY+bmz0MAruywEP2UDgGrWf1YmHQyQpO8tTxZGZ0VsNLL73U7rnnHvvggw+sQoUKmbkZ10EgSaBjx442ZcoUK168uOmXmVOnTk16n16p5tFJPuIqlI17/R0+mFz4T12rGwqndYyh55einubW3K706OQmv3oYBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMiZwJw5c6x9+/bWvHlz22OPPcLfWn7++eeh26BYsWI5W5xbI4BArgmUKlXKzj33XBs/fryNGjXK1q5da/Xq1QvFsiqHZhBAAAEEEJAAhbI7+etgls3y6ozmtsXztaehJ8pJvv5b9pb95jnaE8fMttn2u6eVJ6NZvXp1KE4bOHCgffjhh/b000+Hg4+Mbsf7EcjLAi1btrRvv/3WTjrpJGvVqlU4K0tq21vTasZWXqT7q+XJbqFsWSsbykj7W39b6ol64l4/6u2Naz0V/w71nO451fOaJ8q52+62Jzy3eFTMtN3DIIBA3hNQGfn8+fPtwQcfzHsbtxO3qF+/fvbVV1/Z8OHDrbhnkyeuUaGs9sMZBHZVge/su3BSE+0vxDGTbJKN9tzgycz06tXL+vfvb++++65df/31mbkJ10EgVYFKlSqFE34cddRR4dgseamsfrYc6JnniWNUhl7ZE9f6cWxzRmveZrdZec9VnjjmXrvXSntu9DAIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkD2BTZs22Y033hgKJ3fbbTebOXOmvfTSS+H/2VuRWyGAQF4RaNasWfg7/G+++cZ+/fVXq1mzpj3wwAO2devWvLKJbAcCCCCAwE4SKLqT7pe7dYFxnnaeQzwfePb0RDkp1//EPrGinsM9ccxEm2glPfU86c2qVausdevWtmXLFtPOSZUqVdK7eqzve+utt8KZMwoXzn/dyiqgu+eee+zuu+82leWsX7/eRowYEc4mcN9996Xr1rdvXytZsqRdfvnl6V4vp+/88ccf7eOPPw7b17Fjx5wuly9uX7p0aRswYIA1bNjQLr30UlN58rXX7ljQVd2q2xRPXKNCWZWDZXcut8vtUc9dnv96op641496e+NaT4/HL3lUzHS2R4XcURY0XWFX2D6eczy/el7wqDiRQQCBvCOgnxcdOnSwqlWrZnujli1bZiNHjszw9rqPI4880jZv3hzOOqRC/7Zt29rJJ5+c6tsyXDDGKzRt2jSc6Uw+7Y9rb3954ho9Lm72MAjsqgJP2pNW29PGE8c8bU9bXU9mTjqiYxSdaGTIkCHhsSmO7cmra37xxRf2yy+/hM0rVKiQnXHGGVakSJE0N3f06NGW/Ixxp512muk4JC9MyuPUnblNOubVMX+nTp3CST908o/9998/bNLBdnCsJ/mIe/3cdtVzPfp+but533OaJ8opY2Wsr+dMTw+PTrzEIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDmBVQe27lzZ9u+fbt9/vnn1rJly8zfmGsikI8Epk6dau+//76tXbvWDj/8cGvTpo19+umn1r1793z0UWR/U9Vr9cknnwSDK6+80t577z17++23rXLlytlflFsigAACCORrgfzXopmvuf+38Sp3Pc5zjOdzT9RlsqmtP9Wm2qGe0p44RoWyDT3pFRaqxKx9+/ahTFblZzuzTFYFrCr8HDp0aBwcsa+pMpwXX3zRpk+fHu5LO7VXXXWVvfLKKxne9wsvvGAvv/xyhtfLyRVUJvvcc89Zz549bdq0aTlZKl/e9rLLLgufn969e9sbb7yxw8dQzarZj564RuVF8z3ZnRJWwu70qIB0nifqiXv9qLc3zvUKWSF70HO/p6fnEU+Uo0KmDz1DPCp9Wu9hEEAgbwjMnj07lMBffPHFOdqg8uXLh0Ja7QOcddZZoSxWZw/S5a+//jIVzj766KPWv3//cD/ab1C5nt62dOnSNN+Wo42K4MZy0RN4f639K9bCVwplI/hksUS+FVhtq+11j8r+45g/7A8b5LnMk9FMnjzZbrrpJlOprIqud7Vp3ry5bdiwITyOd+3a1QYPHpwmwbp160wFsnrMf+ihh6x+/fp5pkxWG53yODXNDySX3lG0aFEbNGiQ7bPPPnb22Wcn3Wvcha9aP85jvqQPJBdf0XNYOhHGlZ61nqjnDDsjPE+mxyTfk4l6edZDAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgwAp89tln1qxZMzvyyCPD33pSJltgP9W7/AemzqwWLVrYXnvtFXrMJk6caIceeqhdfnk8fzOfl8H1N+fq9dpvv/2scePGNmnSpLy8uWwbAggggECMAhTKxoib1tJv2puh2K+reVGMp5Qnyklr/Rk2w+p64prpNt0aeNKbu+++22bMmGEfffRRKHRJ77pxv+/VV1+1VatWWb9+/eK+q1jW1xlBfv31VzvppJPC+h07dgw7u5m5swkTJtiXX375j6tGWTJbvXp1u+WWW/5xH9l9Q5Tblt1tyOrtdNaK2267zVSK99NPPyXdvLpVt+WeuMo9D7KDwvqbbFPSfWb1lXPsHKvhuc0Tx8S9fhzbHOeaN9qN9qjnes99niinrbW1LzzfeNp4fvcwCCCw8wWGDx9u5cqVs9atW+doYwoXLhye1NWTu5p//etf4axJ+hl07rnn2rXXXmvDhg1Luo9GjRrZFVdckfR/vZLa23a4wk74j5642rJli837fp5t92z2xDEUysahypr5ReAle8mr7QvZuZ44RusX8aiAMqNRKbZ+MaUzoO2KU7JkSevSpYup/FSjoti0ZuDAgVasWLHw7hNPPNHq1KmT1lV3yttTHqfulI1IcaelS5cOJ17RSW10dj2NTvKxwBPXVLWqsa4f13ZntK5OgLHO829PHPOEPWGzPHrJIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDGAqNHj7YOHTpY79697YUXXrBSpaLtssp4C7gGArkjsGnTpvD36Pq7bP19+jHHHGN9+/a1UaNGmbo31q5dmzsbkofuZc8997T333/fTj/9dDvhhBPs+++/z0Nbx6YggAACCOSWAIWyuSX99/0MsAF2lucqz3MeFexEOemtP9NmWh1PXDPbZlttT1qzdOnSUIyjcpwqVaqkdbVce/srr7wSzjKgHcIpU6bk2v1GeUf77LPPDssVKZK5r6fddtvtHwd/Kpjt06fPDuvl9D+JQqScrhPHtuV0mzJ7exXKHnTQQaFYNnEblRdp5nvimCpWJZTvLbJF2V5ej033et7yfOuJeuJeP+rtzY31elkve8pzq+cOT5TT2BrbGM8yTyvPrx4GAQR2rsDYsWOtefPm4UmpKLakbNmyaS6jJ4CS/4xP/HwuVKhQ0m1Se1vSO3fCK3vssUcoSZw7Y26497/sr1i2gkLZWFhZNJ8I6HhUx6a7e+KYZ+wZ6+4p60lvxowZY+PHj7cHHnggvasV+Pep9LR27drhDHg6+1tqJwDZvn27/fe//7UePXoEj/Qe+3cmWMrj1J25LYn7btCggXXt2tUefPDB8CYVvi7xxPXzRSf5WOzZ4ilIs6/tG8pk+1k/m+OJempaTbvBc7tHxy4MAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCKQtsGrVKjvzzDPtoosusltuuSXtK/IeBAqAwPz58+3PP/+0P/74Y4eP5pBDDrGLL77Y1G+2K466Q5566ilr3bp1eDxQ8S6DAAIIILBrCRTdtT7cnfvRPmaP2dWeuz23eaKe9NZXiYnKTBJFllHf9+/2u6301PKkNTqDxd57723nn39+WlfJtbd/9dVXliiUGTp0qD366KM2cODAVO9/1qxZtnz5cmvZsqV98sknNmfOHDvjjDOscuXKtm3bNlMhnAqQWrRoYUceeWTSGosXLzatfdlll5nu79NPP7UDDjjALrzwwh3KXDds2GAjR460b7/91lQIe/bZZ4frJRbSgdugQYPs8ssvD/c/bdo0u+6660IBndYtU6aMNW7cOHH18FJFQxMnTgz3Wb16dTvrrLMseWncihUr7MMPP7QLLrggXF9lRaeddlq4jgqKKlasaO3atTOto/uYOnVq2DYVHLVt2zbpvrSD/fHHH5uM5HH88ceHl0lXSPHKwoULTYVRW7ZsCfeltXRfmrlz59rXX39t+viOOuoo69ixY3h7Wtumd44bN87++usv0069Pn+tWrWyJk2aWEamYeFc+kdnr7j55pvtnHPOsccff9xUkJf4PlxgC6yuJ+pRoazmJ08NT3ano3W0Jp4+nmGeqCfu9aPe3txY7zK7zIp7Lvao3Op+T1Sjx+fRntZ/Z4SNsPIeBgEEdo7A9OnTk37WxbkF+ln+xhtvhBK97N7P8OHDbcKECaZiWj2ZrP25xGif4qOPPjK91D5Ho0aNrFq1aol35+jl4YcfbjN+mBHW2Gybc7RWWjdOnNxhq22N/EQPad0nb0cgLwhMtsk2w6MTksQx4218KJt83V7PcPm33nrLGjZsGB4/MrxyAb+Cjh10rKdjZp2IRU/aJx8dj+rYr3z51PfhMnMcpDPr6eQqixYtsho1aoTjJx1PJT85SUbrpPfYr2PklMepP//8s7377rvhTH8zZ84MZ7k78MADrVu3bpEVqyd3Sut1FfHqmHHBggVWtWpV2+bRSTgO9kQ9KqzVz5afPXq9IM2ldqk96+np+dQT9dxit9hrnps8Az0MAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCKQuoL9HLV68uD344IOpX4G3IlCABGrVqmVVqlSx9957z5544gm78sorkz66a665xooVK5b0f72SXjfWsmXLwt8/b968OfRp1alTx9Rz9d1334U1Tj/9dNPfQ2sy6uAKV8oD/zz77LPB5/nnnw9dZXlgk9gEBBBAAIFcEiicS/ezy9/Ng/ZgKJN92B6OpUw2o/VVYqIyk4M8ccxCWxiWrW7V01z+iy++sJNOOmmHopo0rxzzO7RDqILW5s2bh/IiFa2pNDb5aIfw+uuvt0MPPTTsQF511VWhPHbIkCGhfEZFqt27dw9lOP3797djjjkmlK1pjddee83q168fbq/7UWGPilK1hgpstCOpUZmPSnxKlSplN910UyhaVZmqCnw0KkmtVKmS9erVK2yDikl1vQ8++CAUuh177LE2efLkcN3EP1u3bg33ox08ldRqG++7777wbr3vpZdesoMPPtj69OmTuEkoiNP2lihRwrTjrHJYza233mo//PCDXX311dasWbPw/8SNtPOrbdWO9BVXXBHO3CCrl19+OXGVf7zUDvk777xjv/76q5144olJZbIq9L3kkktCma521K+99lp7+umnw+1VXpdy23766Sc75ZRTwv1rB1+3veuuu+w///lPhqb/2KhceIO2VSW6o0ePDvdW2krbXh59X8Yxe9veVsajQtmcjgpNVRI00hPHxL1+HNsc95oX2oX2kuchz7WeKKeyVbavPCqrVbHscg+DAAI7R+C3336zfffdN/Y7//zzz02FsNkZlbbrTGQrV660U089NTz5pXJ5lQFqdNamk08+ORTta59JZYHa94hq9ttvP1uzck1YTo9bcUzyQtk41mdNBPKqgPY1anqaeeKYl+1lq+Np5MlodCIMnZyD+X8BnQxEJyJReazKx5OPjpt0rJTaZHRsqdvoZCUq665bt244ttNJRurVqxeO9fRLEk1G66T32K+fDyoeT36cqmNX3aeOKXWCjb59+4YTieiEGw888EC4z9z6R8e0RYsWDSdfSTw3knguI+ptSJTI6iQiBW30s/MJz2ee9zxRTykr5c+gPWiveCZ5GAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBIXUCdSRdccEHoDEr9GrwVgYIjULhw4dDnpR4n9Xh16tTJlixZEj7A/fff3/bZZ5+kDzajbixdX50W+hvrr7/+OtyudevWtmbNmvC22bNnJ62VXgdX0pXywCvq6erSpYvpcYFBAAEEENi1BCiUzYXP9312n93k6e+5zhP1ZGb9pbY03O0BdkDUdx/WS6xf0Sqmuf7SpUtDg32aV8ild/z888+2evVqO+SQQ8I9qsBUhWlPPfXUDltQtmxZe/jhh61cuXKm2+h1FbN+9NFHpp3Lu+++29TGr7fPmTMnvC1R2NatW7dQeLpx48ZwJgNdT7e77bbbQnnNCy+8EO7r/fffN52tQNtSpEgRa9eunaksdcaMGeH95557rnXs2DGUkapUaOrUqTZr1qzwtttvv32H7U385/fff7eePXvac889Zyq/VXmPSlc1uo/zzjsvnBUhcX29bNCgQSi1K1myZCi81f91ZoQBAwaE8lld54gjjrD27dvr1eClnUdtm86moEK86667LrxfxXOJorlw5b//2bRpUyjx1fVuuOEGK1++fNK7n3zySdNZGgoVKmQHHXRQ2B6VG2lS2zYV0z722GPh/WPGjLHBgwfbwoULw/ZmZBpulMv/7L777qG0V5/rxFSySrbYE9dUsSpe87wwx8urdPR4jx7D4pi4149jm3NjzbPtbHvNo58bV3miHP0cUEHwNk9LT+LxO8r7YC0EEMhYQD+v99prr4yvmMVr6OdsmzZtwqVRo0ah8DWLSyRdXYX52v/Qz/zDDjvM+vXrF8plE2WGr776qpUp4xXmftE+xr333ptUmp+0SA5e0ZNVa1etDStQKJsDSG6KQAoBfT8N8pzniWO0/pueczyZGZ1sokKFCpm56i5xHZ0FUuWrGh1rJkbHiCpD1Uk8UpvMHAfpLJM6LtPJUHbbbbekE4aoxFaP8ZqM1knvsV/blvI4Vce4F154YVhb5bU6FlbJrH5G6TguN0e2+tmrrzmdhKOsJ67C131tX9vNE8UxWW4aZfa+jrajrbtHJ8DY4Il6zrAzrLkn6hNsRL2drIcAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMDOEti6dWvo20nrb0931nZxvwjEKaCusJdffjl0gr377ruht0s9W8lHXWKZ6cZK7XunYcOGyZdKt4Nrhyvmkf/oY/rxxx/zyNawGQgggAACuSVAoWzM0g/ag3ar50nPlZ6oJ7Pr/2q/hrtWqUkcs8SWWDlPaU9ao1LNP/74I61359rbn3nmGbv44ouT7q9r166hVEZvVwFsytF2V69e3UqVKhXepaLZihUrWo0aNZLeVrp0aatcubItWLAg6eYq6FHhj4pSE3PTTTeFt40aNSq8SfetYiCVq+q+v/rqq/D2efPmJW4S7kv/Oe2008LbateuHV6WKFEivEz5j7azZs2aSW+uW7fuP3by0rqtCl0To9dr1aplZ555ZigV0tuvv/768O5hw4aZzqJw5JFHJq4eXp5wwgmhbFYFusln1apVoXhWRbcqL0o5I0eOtHvuuSe8WTlktpgAAEAASURBVGW0KvBNbqB3JN82/V+fA80pp5wSSuxUaquzRGTGNNwwF//RAfiff/5p+tpJTNyFsgfagfazJ4q53+63iZ73PHFM3OvHsc25seaZdqa94XnGE3UZucq/VSpbxKNS2TjLjXPDivtAID8KaD9h3bp1kW/6I488YiNGjAiXb7/91t55551s30ffvn1typQpdsUVV4TL/fffH/YNVIar0T6J9l26d+8eyvmqVq0aft5n+w5T3FA+pYv+/77lZtuc4r3R/LeoFQ0LbbWt0SzIKgjkA4FhNsx+96jAPo75wD6w1Z5unsyMHg/Xrv3/8ujMXH9XuI6OV3Vik0GDBtnixf9/EgqdUEOl4WlNZo6D9OS/ylT1SxCNysLlr+OvxGS0TkaP/akdayaOpRPHsrov/TJi0aJFibvNtZf6WtPHrKnqiatQVusf5Ilzfd3Hzhw9H/Wb52FPHNPP+tkYz2APgwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAI7ChQpUsT22GMPW7FixY7v4H8IFHCBs88+O3RfnXHGGaHT6aKLLgp9Ytu3bw8feVa7sdLjSq+DK73b7az36fFg77333ll3z/0igAACCOwkAQplY4Tva33tRs/jnss8UU9W1lfJx26ekp44ZqWttP086c0RRxxhiSLV9K4X5/s2bdpkKjvt16+ftWzpNYp+OfHEE0MhqYp1XnvttUzdfWolOcWKFcuwGE7Fs5UqVQolPrqjwoULhzLZ22+/3VTadsghh4T737ZtW9J26DqaxMukd2TyFZXaqtA0M5OytPWJJ54wFep26NDBjjvuuKRCYJW+asqUKbPDsomy2FmzZu3wdu1kf/zxxzZ+/Pgd3p74zwEHHGATJ040Fc7qtirwTW6g66XctoSHDm6TT2ZMk18/N17Xx71lyxbT90Bi4i6UrWAVbLknimlkjewMj8qx4yjci3v9KAx21hqdrJO94nnUc4snytHXiEpl9XNBpbKLPAwCCOSegErQV65cGfsdtm/fPtUy94zuWCcBWLp0qfXo0cOefNJPjfD3RYXy+pmtOfbYY0PZ/Ouvvx5+dr/44ouW2j5SRveV1vvls2eZPcO7/7L/Lz9M67rZfbuKtTVx/HzL7jZxOwTiFnjb3rbmHu2PxjGDbJC19hzgyczoBBwqwGb+J6BjsEsuucQ2b95sjz76aPh5oRORtGnT5n9XSvFaZo6DWrdubevXr7cxY8aEW+vEHyqXbdu2bdJqGa0T1WO/juMSv5RJuvOYX5kzZ074+BMnfVHh60+euCbuY764tjuz6+5v+9tNHhXLLvNEPY2tcSim7m29fS8gnv2AqLeZ9RBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMhNgeOPP97efvvt3LxL7guBPCFQoUIFe+utt+yNN96wkiVL2rPPPmvjxo0L25bVbqyMPqC0Orgyul1uv19/uz148GDT4wKDAAIIILBrCVAoG9Pne4ANsOs8/TxXeqKerK6/1tZaGU9cs8bW2O6e9Oass86ySZMmJZWQpXfduN6nHcDLLrvMxo4da1999VXS5Ysvvgh3qbKezEzKctPEbdJ6e+L9KrRdvny5VatWLbxpwYIF1rBhQ2vSpIn16dPHqlSpkrjqTnmZcvsbNGgQyp0uv/xyGzly5P+xdx/gUZVpG8dvelNAQBSwoGBXFBUURbC79r5id9UVV8Xe1rViW11de1l7L2tXsPcuFixYsaCi4ipI78L33K/fYIgJTJJzIAn/576GhCTzzplfkplzkiv30ZprrqlRo0apVatWaftKF8R6+12su8giv5W/Fe7E7rvvLl8OPfRQvfvuu4U3z3p5yimn6KyzztJ5552nnXfeORX8znrn/79SettKv7/w/+pm6u1yCV+3bt203HLLFTYzFXgN1/BZ/8/6lSwLZb1tZ0U+i9wSyWPyXj+PbZ5Xa/ZRH90QOTdipyzHReDPRfz8sHEkjyKoLLeXtRCoTQIdO3aUi+3yHhcD7rfffulmXJ5f7Ph6ng8++KDcq/hj/vWvf+mJJ55Qu3bttP/++6fn8nKvUMF32GeJtr8VXk7TtApeu7gPp1C2OCc+qvYITNEUPRzxyQLymImaqMciFVl/q622ko/H5kXJdh73Oa81jzjiCDVs2FDXXHNNemz1MdmcppjjIJeEH3PMMemY2L8c9IlNzj333HSSlcLac1sn78f+wnbk8fLuu+9OJ3Txca1nyUieJ1Vwoex3kdo8R+totYr45CN5jI+BfIxySYRBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgdkFjj/+eL388su6//77Z38H/0OgFgpcdtll+vXXX2e7Z7vttpv23nvv9LYHHnggvaxoN9ZsC5bxn/I6uMr40Pn6Jvt8//33Ovzww+frdnDjCCCAAALzXoBC2RzM79Sd+lukf+TISNZTmfVd7NM0kteM0zgtHJnT9OzZU1tssYUOO+wwTZuWTynYnG7f77vkkktS2Vnpj1t11VW10UYbaciQIXr66adLvzuz/7uAdfLkydpmm23SmqeffnqyKPx/xowZmd1WRRdyYWvJHWaX3956661aeOGFUyHqwIED9cMPP6QDyHXWWSct/+KLL852M/bz57ZHjx6zvd3/cRGSy/N23HHH2cqiXFjkMtm99tpLTZo0Sdcr7VB62/6weIk3VCdTb9YzzzwjFxf179+/xFZK7SN5lne6UPbHSFaznJbT/pHTIy4iy3ryXj/r7Z3X6+2rfXVV5JTI5ZEsp43a6OlI/cjmkVERBgEE8hdYb7310g9m87+l327BZY3XX3990TfXvHlzLbPMMrrqqqs0adKk2a5322236Ztvvknr+Tl7s8020+DBg7XJJpvIP2DKYrw/8cYbb2jt1ddOy1Eom4UqayAgPRHxsdsukTzmcT2uyZEdIsXOLrvsohYtWqSC6mKvUxs/zmd8mzhx4qy71r59+3SMNG7cON15553q06fPrPeV9Uoxx0H169dPBeA33HCDunTpoosuuigVzJZcb27r+Lkkr8f+ktuR9eujR4+Wz/7Xt29f2cHjQtlvI3lNB3Wo9YWyjdU4nfjiJt2k9yJZj0t5j42cHeE4JWtd1kMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqOkCa665pk499dTUp/Tmm2/W9LvD9iMwR4Gvv/66zN4Md154GjdunF4W241V+Jtj94GVN3Pq4CrvOvPj7Y899phcMH3ppZdqqaWWmh+bwG0igAACCMxHAQplM8Z3gc4+kaMjLv7Leiq7vku4GkbymgmaoGaRuc21116rzz//PJXKzu1js37/I488ogYNGmjJJZcsc+k99tgjvf28886b9X6X+kyYMEHesSs548eP16hRs5cu+uNK7xxOnz5dH3/88ayr3nffferdu/esQllfxyWtjz76aCpZvfLKK9PHuunfhTcef4xn5MiR6WXhn8I2/fzzz4U3pY/xthXe53d4O11MVHLb/P4xY8bI21eYdu3aacSIEfryyy/1xRdfyOtcffXVsoFn8803V5s2UT0Zl9VXX1377ruvXCjrQrnC+Iwlyy23nA466KD0pkIhkkvhFlpoId1777363//+l0plC+/z7XjuuusujR07Vi+99FJa95dffknb4AKl0ttmk4JLyfvvdYox9cfNi7HjnnvuqQMPPFB/+tOfZrvJtmqr8ZFJkTxmMS2mnyIzIlnNaTotrXmlfvs6zWrdwjp5r1+4nZr6sq/66p+RwyO3RbKcRbVoKpV1wdyWEX9tMgggkK9Ar1695FJ17xdlMf7Bl6ew/1ByzUGDBqXn7cIPwbwP4Ck8B/v1st523HHHafjw4dp44431/PPPp9LY0047LX2sf4A0dOhQPfXUU766mjZtqh122CHtJ6Q3VPGfV199Ne2/rNdtvbTSVE2t4oplX72O6qR3zNRv+ztlfxRvRaD2CNyn+9Qj4qLLPMbrrx/xvmix45NKnH322anc9J133in2arXu43xc+N1338123HbsscfKJ9fo169fOpYt3GkfK3kKj/1+vZjjIJeE+5jMx2dTp05Nx3I+3io5c1tnbo/9hWPRksdpPs7z+DYL4/f7YwvHm4W35/XymGOOSUWyNi2MC2W/j2R5zFRY2y/9fTY8Uttnd+2utSPHRPKY43ScGkTOizAIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggMLvAKaecon322UfuE3CHEYNAbRXo3LmzTjrpJL3yyiuz3UX3Vrnzwj1PnmK7sZZffnl17Ngx9V7577Y/+eQT3XPPPWmNwYMHa8aMGelvocvr4EofWA3+ueWWW1Kf1xlnnKG//OUv1WCL2AQEEEAAgXktUO/0mHl9o7X19t7Um9o64jKNKyJZT1XWf0Ev6IPIYZE85n7dHzVfU9N9n9P6LVq00Prrry+XuLhYdIsttkgFOXO6Thbvu+mmm3TooYemgp6WLVuqe/fusy372muv6YYbbtCnn36aClVdnta1a1dddtll6UDJZa4uTvMOoMuOBgwYkMpXF154Ya222mr697//rfvvvz+9rX379mmn0h/jHUOPS9i8/k8//ZQKfBo1apTe7nLbp59+Op35wDuUZ555ZipTdfnt0ksvrbffflsu/XHJz7Bhw9LbOnTooDfeeEPnnnuuPvroo7SmP9blS975dAHQpEmT1KNHD7nAtvA2l/X47Aku9b355pvTmn6bd4CbNYs64Lh459BWSyyxhNZdd115J9Hb4PFZCLp06aK//e1v6f8uSP3xxx+Th6/rj/N2//e//1WrVq1SyZzvj9/uHeZOnTql67/11lt69tln9eCDD6p169baZJNN9O233866rgtpd9llF915551ymdzOO++cyulKblvbtm118skn67333ksuDRs21FprraV69eqlwuA5ma6xxhpp+/P+57PPPksH2iuuuKJuv/32tG0lb3O0Ruu6yEGRFpGsZ6RG6sbIIZGFIlnMwlpYYyOXRv4WaRTJcvJeP8ttnV9r9VRPTYycGOkaWSGS1TRX8/QcdpEuSuWyu2k31Y8wCCCQj4D3K66//nr9+uuv2nTTTSt9Iy4f9O78Qw89lNbyc6z3Qfzc432I/v3768ILL0xFhN5f8VnF/PEuPffzuLfDxYSl3+bn47XXXjuV/7l88MYbb0zb632JE044Ie2/uQTea3u83vvvv5/WcRF8Vcc/tPbz++EnHK4LI/tGlopkPd/om/R86RK8rJ4vs95G1kMgKwGXZrqgfv+IS1+zHh8P/jVyaGTdSEXGZz90+fXll1+uPn36pJNRVOT6Nf1j/TjrM775sdQOPh5bZplltOiii8rHFT4zpM+I55Ny2OiKK65IJ+PwcZbLwf3YvOyyy87x2NLHQX7O8PGhjwn9HOHjXR9X+pcmPoGITwIyt2NUH5uW99hf1nGqi3J90hY/1/hY1cfifp7yffBaLszt2bOn6tbN73xHvs/nnHNOOj5eaaWVZn25jNGYdEzm7wsfi2Q9P+rH9BxzrI6N0/vkd4KfrLe7ouu5nH3liE+q5GLZ5SNZjo87XSh7buQvkTw+V1luL2shgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAwrwW23HJLuc/ogAMOSH+/6b/dbNCgwbzeDG4PgVwF/PfK7vN64YUXUheWOy5cMPvNN9+kjo2SnWJz68byhvrvnN0fdtttt6W/4R4xYoQOOuig9Dfb7uJwX5b70ubUwZXrHZ7L4vZwp5r/Xtz9JYVesLlcjXcjgAACCNQ+gRfqzIypffdr3t+jL/SFekRcnvFwJOsSvqqu31/9dXvk00ges5f20rjIQ5Fi5sUXX9R2222nXr16pRJTl7zWtjn44INTiezUqVNTYap3Dps3b/6Hu+kzEbgA1qWsHn9LTps2LZWo/eGDc37DmDFjUpGPd3Q906dPT2dK8M6ud3LLGl/nww8/TO938VFlx2VChdv1Gi67LRTv+v+lt81vK2/mt+mTTz6p3XffPZX6umDXZ7AoPV/qS3WKuCjajxtZj7/XV4y8G1k9ktX8ol+0bOTwyBmRrCfv9bPe3vm1nouIb4s8E/FzT5bzvt7XhpENIvdFsn4+y3JbWQuBmi7g0lSX+rk03kWB1XW8n/Lll1+mcsOSz2neT6hfv346SYCfs72vk8W4gN/7HRdffLF26buL2kSejWwUyXpe1svp8e57fa92EQaB2izwul5P+w0+0ciqkaznOT2njSPez10mUtEZO3ZsOj500bZPELHYYotVdIkF/uPndhz01FNPpVJZ/xLQx3guqHXJqwttfaKUE088MRnOaZ28Hvvz+uT5xCQ+k95FF12kww8/fLab+Vpfq2PE3xvrRLKe9/Se1oh8EsnyRBBZb2dW6+2iXdJ99fFE3UiWM0VTtFzEJ3G6KsIggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwB8F3njjjfR3le4IuPDCC7XTTjv98YN4CwI1VMB/G+1eLndb+Gv8k08+UatWrVI/hcthy5piurEmT56c1nX3ldevV69e6uAqrFdMB1fhY+fFS/89vv+G2n8b3rFjR910001aaaWV5sVNcxsIIIAAAtVToH+2DQ/V807mvlUuQHSpxdKReyJZl+9lsX5jNdbkSJ5TR2XvVJV1my6Sfe211/TFF1+oS5cueuaZZ8r6sFrztiWXXLLMMlnfwbp1684qk/X/vXPasGFDvzrPxzvLJUtdXRLnbSmvTNYb6Oust956qkqZrNcpebv+f8kyWf+/9Lb5beXN/DL1Qccxxxwjn7XFZ2x58MEHyyyT9Xa3jXj+F8ljWqt1WnaURmW6/CJaRMdH/h35KZL15L1+1ts7v9ZzidKmkW0jWReFd1EXPRpxWe1+kZkRBgEE8hHwmX5c5OcfxFbnadKkiVZZZZU/PKd5P8HTtm3bzMpkvd5pp52Wfmi39957q0HEMy2SxxQK73isy0OXNaubwEAN1FKRPMpkfV8fiywfqUyZrK/vk2/4xAx+bOnRo4c++ugjv5mpgMCcjoPefvtt7bfffvJja+fOneVS2c0331w77rijrrjiivS4W7ipOa2T12N/4bazfHnOOeek++yz6pUuk/XttI/4eeCbSB7TQR3SssM1PI/lq92aZ+vsVCh7q27NfNsaqZFOj1wX+TzCIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMAfBdZZZx0NHjxYffv2TcWy3bp108CBAzVzJt0hf9TiLTVNoGnTprO6LdyD0bVrVy299NKpr6u8+1JMN1bjxo1ndV81aNBgtjJZr1tMB1d5t5/l210ke9ddd2nVVVdNZbJnnHGGXn31Vcpks0RmLQQQQKCGClAoW8VPnMutdo5MjDwcaRbJcrJav6mapm3McttKrlWZwlq32g8aNEjbbbddKrJxsc33339fctka/brLRX12gfHjx9fo+8HGFyfw8MMPp7K9//73vxowYIDOP//8dDBQ3rUX0kJqEsmjlNW32TLicSF11nOEjtDCkbMieUze6+exzfN6zXqqp7sjy0X+FBkRyXLW1bp6MHJv5JgIgwAC+QgsvvjiOv744/XPf/6zVu0DVUXrgw8+0DXXXKOzzjorFdjmXShbOCEChbJV+axx3Zoi4MJ4nwglr3lcj6f9kqqs74Lq559/XiuuuKK6d++ezoxWlfW47u8C77//vn744Qddd9116cQuPlb9/PPPdccdd6Tnod122+33D67hr40cOVLbb7+9zjzzTN18883pubasu+TnmMUi30XymDZqEzWojXJbP49trsqaK2gF7R85LTIlkvXsq33VKXJqhEEAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBsgUaNWqUyib9d6QbbbSR+vTpkzpxrr32Wk2YMKHsK/FWBBCotgK//PKLLrroInXq1EmHHXaY9thjDw0dOlQHH3yw6tWrV223mw1DAAEEEJh3AhTKVtG6n/rpzcgjkXaRrCer9V1eOS6S11S2sLZZs2a6/PLL9cILL+jDDz/Ucsstp5NOOkmjRo3Ka1Pnybq33367nnzyyXR2jhNOOEHvvvvuPLldbmTeC7z00kvaYIMNtMsuu6TSIn8db7nllkVtSCu10qhIHlNf9eO7fiGNjmQ9/n4/JXJ15OtI1pP3+llv7/xaz4XEAyINI9tEXGye5WyqTXVj5OLIlREGAQTyETjmmGPUvn177bXXXpoxY0Y+N1JDVp00aVL6wZXPerbvvvumrS4Uyk7V1FzuRaFQdoYWbPtccFm0WgmM1EgNjmwRyWNcyPlBZMtIVcdnefMJGo477jgdcMAB2mGHHfTdd/kUflZ1W2vS9ffbbz9dcMEF6axzq6yyilq2bCmf1MUnQOnfv/+ss/HVpPtU1rbee++96Zean3zyiV5++eV0H8v6uMLb2qu9vo/kNR3UYYEplLWhy2T/F7kqkvX4pBo+qcldET/eMAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBQvsCiiy6q888/X19//XX6O37/PWm7du3Ut29fvf766+VfkfcggMB8F3D/yLPPPpu6SNxJctVVV+moo45K38+nnHKKmjdvPt+3kQ1AAAEEEKg+AhTKVuFzcY1+yy26RatHsp4s12+jNpoSmRDJY1xsWJW1e/bsqbfeeiuVy955551aeumldeyxx+rbb7/NY3NzX3ObbbaRC2zc7n/22WdrhRVWyP02uYF5JzBz5kw9+uij6tWrl3r37q3FF19cH3zwgS6++OIK7Wy3VMtcCl8LEotoEf0SyWMO1IFaMnJqJI/Je/08tnl+rNlarfVoxMW+e0ayLkTcXbvrzMjhEd8OgwAC2Qs0bdo0lfu98sorqdAv+1uoOSv269dPw4cP1x133KG6dX87THFBuktfp0UYBBCovMALeiF9L/VSr8ovModrPq2n1SjSO5LF+DHgtNNOkx8bv/zyy3Q8de6552ry5MlZLL9ArlGnTh0dffTReu655zRu3LhUJPvaa6/poIMOUsOGDWu8yZAhQ7TZZpuls2T6TJmDBw/WWmutNdf7NS8KZYdr+Fy3o7Z8gAt0fWKksyNjI1nPztpZq0V8jMIggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwNwFWrVqpZNPPlnDhg3TTTfdlP6mf4MNNtCyyy6rk046Kf1N5txX4SMQQCBvAZfIvvrqqzryyCO1xBJLaKutttKUKVP00EMP6dNPP9URRxyhZs2a5b0ZrI8AAgggUAMFKJSt5Cftdb2eSjJO0SnaMZL1ZL2+Swc9P0fyGBdXjo5UZVwa9Je//EWfffaZLrroIg0YMEDLLLOMdtlll9SW7xLPmjItWrRQy5ZRF/r/lyZNmtSUTWc75yAwatSoVBrrguDtt99eHTp00Hvvvad77rmnUqXBLdRCYyJ5jQtr8yqUbaAGqcTnNt2mDyNZT97rZ72983O9TuqkByOPRY6PZD3/0D+0T2S3yLsRBgEEshfo2rWrLrnkklQoe/3112d/AzVgRZ/N7MYbb0w/gPaJBUqOS2XzLpR1aS2DQG0WeF7Pa42Ij9vymBf1orpHfKKRLKd79+5655130uOjz4DYuXNnXX311ekH31nezoK2VoMGDWrNXR46dKj22Wcfrb766qlweNCgQemYzYXtxUzehbJLaAl9F1mQ5kSdqOmRCyNZj5+v/XO4+yIfRRgEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKE6gXr162mmnnTRw4MBUKtuvX7/U6bTWWmvJf+N/+OGH66mnnuLveIvj5KMQyERgwoQJeuSRR3TQQQepffv26tWrV+qzOu200zRixIjUa7X55purTh06MTIBZxEEEECglgpQKFuJT6yLU/tENo2cHsl68li/ndqlzfxBP2S9uWm9NmqjnyJZjMttDjzwQH388cepHX/8+PHadNNN1alTp1Qk9MUXX2RxM6yBQFEC06ZN06OPPqrddtst7XSfffbZ2mGHHfTll1/qzjvv1GqrrVbUOmV9kAtfq1rEXNa6hbdlUfRcWKusl34c7BI5KZLH5L1+Hts8v9ZcX+vrhohLm66PZD3/0X9SSdw22maBK8TK2pL1EChP4OCDD9Ypp5ySfshz1113lfdhtfLtPpGAf5jlkkgXtpeehmqYW6HsTP12wgIKZUur8//aJvCcntOGkbzGhbK9InlM/fr1dfTRR8vHgbvvvruOOeaYdMbDf/3rXxo9umonNclje1lz3gi8+eab6RhtxRVXTL8Uuf/++/XSSy9pzTXXrNAG5F0o20EdFrj9Zx+H+kQXF0VGRrKenbWzVoqcFWEQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoOICiy22mI466ii9/vrr+vrrr9Pf8g4ZMkRbb721WrVqpa222kqXXHKJPvjgA82c+dvf5Ff8VrgGAgiUFvj111/11ltv6fzzz9cmm2ySvt/+/Oc/p5Ln/v376/vvv9dzzz2nvn37qmXLlqWvzv8RQAABBBAoU4BC2TJZ5vzG/bV/KrW6WTcrj/KpPNZ3iUndyDeRPGZRLapxkSmRrMat+D7IePzxx1N55x577KEbbrhBnTt3Vrdu3XTBBRekUqGsbo91ECgITJ06VU888UQq9Vt88cW13XbbpbKqm2++Wd99913aIV9yySULH17pl3kXyrZQC42J5DV+/Dsn8nDktUjWk/f6WW/v/F5vD+2hf0QOibwayXIaqIHui/hryqWy4yMMAghkL3DGGWfoyCOP1J577ql///vf2d9ANVvRPzg+/vjjUznkhRdeqL/+9a9lbqEfg6ZF8phCoWwea7MmAtVFYJRG6cNIXoWyPmnJ55G8CmULjv7lk0tkhw0bpn333VfnnnuuOnToIBdyv/POO4UP42UtFpg4caJuueUWrbvuuurevbu++eYb3XfffXr33XfLLCQvhsIn3/k+ktfkXVib13ZXdd1+6qdGkQsiWY+PE0+J3B35NMIggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQOUF3KFzxBFH6Nlnn9XPP/8s9+u4b8edB126dFHbtm21yy676LLLLkt/0+tCTAYBBIoTcI/Va6+9lrrStt12W7Vu3Tp1p1133XVaYYUV0t9Kjxw5Uo8++mjquvL3G4MAAggggEBFBepEkROnAKiA2hW6QkdEnon0jmQ9ea7vIpOjI8dGsp7X9bp6RL6OLBXJa/zl+vLLL+uOO+7Qgw8+qBEjRmjVVVdNhZ9bbrllKpapX79+XjfPurVY4KeffkolsgMHDkw72OPGjVOPHj206667qk+fPulAN+u77+LPjyPPRfKYPuqTCvhcBJrn+LHQpT7PR/KYvNfPY5vn15ouRtwh8kbkrcgSkSxnmIZp3Uj3yEMRf94ZBBDIXsA/WD322GPTD3suvvhiNW7cOPsbmc8rjhkzRvvvv78GDBiQThjgEt3ypq3a6vSInzezHhdwrx/5LuJ9ZQaB2ijwuB7XlpH/RXwikKzHpY57RkZHForMq3G5qH8hdfXVV+v999/X6quvnopmd9ttN7Vvz/fzvPo85H07/hnAiy++qNtvv1133323pkyZkn7p2K9fP62zzjpVvvmBGjjrhAnN1KzK65Ve4Hbdrr9EpkYWtHGZrJ+/v4pk/dgzQzO0amStyK0RBgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSyFxg6dKieeeYZPffcc6nz6fvvv1ezZs3S33iuu270j3Tvnsox+dve7O1ZsWYKDBs2TG+++aYGDRqk119/XW+99ZYmT56sjh07aoMNNtBGG22kTTbZREstlV9HW82UY6sRQAABBKog0J9C2Qrovat3U5He3/V3nRbJevJev5d6afnIdZGs53t9rw6RFyMbRObFzJgxQ6+88koqlnUJ6KeffqqWLVtq4403TjtNfrniiivOi03hNmqggAuo/PXjg1Zf3n77bTVq1EgbbrihfDaHHXbYIfciquN1fCphHaRBuQjur/31Q+SxSJ5TKOPz7fwpkvXkvX7W2zu/1xuncem5qoma6JVIo0iW48/HhpETImdGGAQQyEfggQceSIWrSyyxRCrSX2211fK5ofmwqk8OsNdee8lnUrrzzjvVu/ecT9LgfUw/Z/qkDlmPHyd7Rrwv2y7CIFAbBc7QGbo58mUkj/FJS3xigXci82veeOONVE59zz33yIXVvXr10s4775z26f04ytQsAR/r+7ni/vvvT2fVGz58uNZcc03ts88+2nvvvdWqVavM7pC/bl1KOjTSOZL1PKkntUXkl0jLyII0EzVRy0b2irhcNuu5Q3don8gnkTw+d1lvL+shgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBQ0wW++OKL9Degr776qvz3vUOGDNGvv/6aOnrWXnttrbHGGuratWt66QJNBoHaKjBz5kx9/vnnGjx4sN5999300uWxP//8sxo0aJC+B9ZZZx2tt956qUiWv3mvrV8J3C8EEECgWghQKFvsp8FFGF0j7SNRP6m6kSwn7/W9rQdGPoy8Fsl6ZmqmGkeuj7gsZH6MDzgee+wxPfXUU3r++ec1duxYLb744urZs2faqfLLLl26qH79+vNj87jN+SwwcuRI+WDUxUS+eAfcRXYuHd5ss83SxWdvaNq06Tzb0tN1uu6NDInkMYfpsLS2C77ynu20nYZH3o7UiWQ9ea+f9fbO7/U+1+daO7Jr5NpI1nOFrlC/iL9+d4owCCCQj8DXX3+tPffcMz1nnXDCCTrxxBPVpEmTfG5sHqw6evRonXrqqbryyiu15ZZb6sYbb1SbNm3messd1VGHRo6LZD2xV5BOhkChbNayrFedBLbSVmoRuTOSx/jEJStGronM75kyZYoeffRRuVh2wIABGj9+fCoi3WqrUIhLt27dVK9evfm9mdx+GQKjRo3Sk08+mT5/Pq73L0tWWWWVVAzcp08frbTSSmVcq+pv+k7faYmIC8bXi2Q9gzVYa0byKqzNenuzXu9iXayTIi60XjyS5fyqX7VCZNPI1REGAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOatwIQJE1Ifwptvvql33nknXYYOHaoZM2aoZcuWWnXVVWe7rLzyylp00UXn7UZyawhUUeCHH37Qhx9+mAqUXaLsi//vv2V3l5n/DnrNNeMviuPiv2f3y0aNGlXxVrk6AggggAACRQtQKFss1VE6SjdFXMjqUtmsJ+/1vb0XRM6MjInkMZ3VWX+J/CMyv2f69OnpYOPFF1+UL6+88opcYuYSNu9wub2/sPO13HLLqU6d7Asw57fBgnz73tn2mRvefvttDRo0KJ3RxIXDHhfI9uoVtVdx2XDDDdWhQ4f5RnWeztN/Ii7XyWOO1/FRJfu8BkXyHpfirh65PdInkvXkvX7W21sd1ntYD2uHiAtlD4hkPftoHz0Q8dfXShEGAQTyEfDZuC699FKdfvrp6Qem559/vnbddVfVrZvtyQ3y2frfVp02bVoqjz355JPTPtd5552n/fbbr+ibXF7La7+IC+mynthTVO/Ij5G2EQaB2ijQRm10cuTISNYzQzNSWe2FulAHRarTuFz2mWee0SOPPKKBAwfq22+/TY+jPgbwiSR69+6dfgHFseC8/6z5hDQfjvtQn772qQY9M0hPP/10On7z52L99dfXNttso2233TYdu+W9dVM1VY0i90d2jGQ93+pbLRV5NdIjsqDNZE1Wp4hPdOFy2aznKl2loyPDIotFGAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCYvwLu/Xnvvff0/vvv64MPPkgvXcA5ZsxvnVetWrVKf0PqDiBfll9+eXXu3FmdOnVS48aN5+/Gc+sLrIDLkd1P5ULkzz77TJ988smsy9ixY5NL69attdpqq6lLly6zXvp1vm4X2C8b7jgCCCBQXQQolC3mM/G6Xtf6kesiLkzNevJev7C9T+pJbRH5JrJkJOvZUlumIq6bdXPWS1d5PZ+14qOPPtLrr4d2XN544w19/PHHcknbwgsvrNVXjyrMuHiHzRef3aJ58+ZVvl0WyFdg5syZGjZsWDp49AGkLy6SLZypZJFFFtFaa62lddddd9bFO+bVZS7TZTo7MiKSx5ym03RfxGWs82JcMPpa5ONI/UjWk/f6WW9vdVjP5YsXRVxg1TWS5UzSJK0XmRJ5M9IswiCAQH4CI0aM0AknnKDbbrtNK6ywgk466STtvvvuqlevXn43WsWVXeZ4/fXXywWy3v6DDz5Y/fv3V4sWLSq08ipaRX+O+Hkt63lOz2njyM+R1hEGgdomMFzD07HfC3pBcUqFzO/eR/oovkNX0duROGda5utnuaCPB5966ql0eemll+Qf3Pt4Yb311lOPHj1mnXSkoo9RWW5jbV7Lx2iFY/FXX31V73Z/VzOvnKnollWjyY3UcmZLLdF4CbWv3z4qkNukx+RWalXuy6ZqmhmXb+ecyMGRrMeFqk0iD0W2iyyIc7ku13GRryKLR7IcH5MsHXGh9VkRBgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSqp8Dw4cNT35P/5te9T764uNNdBJ46depoiSWW0HLLLadll11WyyyzzKxLx44dtdhii6WPqZ73jq2q7gLuH/vhhx/01VfxF68lLl9++WXqqvL7PP467NChQyo6XnnllbXSSivNuiy+eLZ/J1vdzdg+BBBAAIEaI0Ch7Nw+VS7KcwmfC1ifiGQ9ea9fcnt/1I+pvOMxPaY/RbKeo3SUXokMitSEmTRpUjqbxdtvv51KSH1WC5/NYuLEiWnzvWPnHbrC2Sx8sOGzWSy99NLVurytJthXdBvHjRuXdrw///zzdCDoA8LCWRwKny8fEPqMDb6sueaaqUjWB4fVea7X9fFdc5TGRvKY83Se/hP5MjIvZpiGaYXIpZG+kawn7/Wz3t7qsN4MzdBmEReJvxNZOJLluBRqrYifU+6IMAggkL/Ap59+qrPPPlt33HGH2rVrp/333z9dvH9SXeaab6/RZ5d8pptvvll+Dj/wwANTGe6SS1buhAZraA1tHXEJe9bztJ5Oj5OjNEqLRBgEapvAo3o0ff/k9TV+q27VgZFxkYaRmjI+sYhPRPHiiy/K5aavvfaavvvuu/QDfh/z+XjCl8LZ4XxsyBQn4DJxH6sVTvbxzjvvaPDgwRo9erQaNGiQTuTiAt+ePXtqzGZj9LdF/qZfIyWnnn6L3+b92emR0uMCUR/rZDHxaxz1ieRRXO7t8z74JZF4xs5ic2vcGi7VXTZi439Hsp4zdWY6iYaPeRaKMAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBQcwQKvUJDhw5NvUIu+Cxcvv/+e7kI1NOoUSO5s2CppZaadXHfkP8OuHBp3bo1pbM151Of2Zb6a+Snn35Kfy/uvxn35dtvv02Xb76Jv0CNiwuNp02blm6zXr166WvJ3VSFy/LLL5/KjP235k2bNs1s21gIAQQQQACBeSBAoezckE/Wyan4Y4iGaOlI1pP3+qW3N3Z/dVjk75Gs5xpdo2MjeZVjZr29Za3nncMvvvhCH3744awzWRTOZjF27G+lny7BKZzBwmev8Ot+6QMOX1zuVr9+/bKW523lCPjAruROuM/iMGzYsHQ2Bx/g/e9//0vX9BkcbOyS38LZG3wmh1VXXVWLLFLzSuDu1J3aJzItksdcFLU6F0aGR+bV9FM/3R/5PNIkkvXkvX7W21sd1huhEVo9skkkj9JXl5RvE7ksckiEQQCBeSPg58prr71WN954Y3qe7N27t3bccUdtv/326Yef82Yrfr8Vn/nrwQcf1AMPPKDX//m6Wg5uqWMnHKsDDjhAVT3LUnd114aR8yNZj08Y4VLsMZHmEQaB2ibgEwxcHokfd+dy107UiVFZ+6jej9T08S8B3nzzTfmEI4WTjhTOaOhjDR+D+BcBhYtPOOLLgvoLAf8CrvCLOT8H+HWXnvskINP7TlfdVnW14tMrqtvy3dLJPtZaay117dpVTZrMfozgYu9tI1MjLo8tdgZrcNSNr1Hsh8/x4/wcs0rkikgeE+e/1MGREyIL6lysi/WPiE9I0TaS5bgwO371G7WyZ6YTtmS5NmshgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwPwTmDp1auogcg9RoRj066+/Tq+7q8jFoZMnT561gQ0bNlT79u1Tx4F7Dkpe2raNv3CMy6KLLpouLVu2pHx2llz1e8U9YL/88kvq03BZrC8//vij/PffpS8//PCDpk+fPutONGvWLJUMu6dq6aWXnlVA7NfdE+ZSYvrBZnHxCgIIIIBAzRegUHZOn8N39a66RVx8cWgk68l7/bK2dzftpvGRgZGs52W9rA0iX0dc5lHbxjuULslxQY5LTl3kVrh4p3LmzJnpLvsMBD6Y8MGFLy6Y9WWxxRabdVBROLho0aKF6tatW2OpXPpTN1I/Unp8sDVq1KhZO+UuhfXFO+QuH7KZLz4wGzNmzKyru2DIO9+F0l6/9JkbXNbUqVMnNW7ceNbH1vRX7tW92jXi4qQ6kaznUl2qcyMhnfXS5a73o35Up8ipkeMjWU/e62e9vdVlvSf1ZCpMdPH3gZGs5zSdlooe39Ab6hJhEEBg3gn4DEgDBgzQPffco4EDB8oF+F26dFGvXr20wQYbqGfPnml/JOst8g9cX3rppXR54YUX0pm+2rRpo+22206jTx6t+5e5X7tEbolUtWB8fa0/a5886/vhUuytIt4/bhZhEKhtAntqT/0ScelrHuMiUH/v3BWpjeNfLHzwwQcaMmRIepxzYarLU/0LJh//+YQXPubz8Yt/oeCzGPplydf9S6Xq+guFoRqqqMX9w6duwoQJ6TjNJbuFE3+UfN3HwePHj0/Xc6Guj9UKRbs+8cc7G72j69pdJx/i+GcpR0XiV2t/uJ3CGwZpkDaLTIxMj8xp6qle1Ix316uRrObP+nM6JvPxWR7j7e0VuSCyoM4kTYpa3WXSCVXyKIg/UkemE5vEOUfL/PnEgurO/UYAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKC2C4wcOTL9Xaw7jHxxn5H7jQov/bp7oyZOnDgbhf/+1x0JvrRq1UqtW7dOLwuvL7LIInI3lItnS16aN28u9yMxxQn475bdgzF69Oh0cc9U4XUXxvrz544qXwqv//zzz+n1X3/9dbYbWWihhWYrCS4UBvvvvTt06DDr4s8XgwACCCCAwAIkQKFseZ/smZqp9SIuK4mqrMzLHvNev7z7dZkuS0WPIzUyFYGW93GVefs4jVOLyAOR7SML0vhsFj6gKFm244OKksWpLlMdN27c7Cy9pRaft9AiDRZJBw4+kCgcQBRe9xkPfBDhop7CpfD/Hxf5UU0aN9FKdVeSi2x98cFK4fXSL1165B3lwsVnVii8XnjpYrpJk6LuJS4+ECpcSv/f92XkuJEasPcArXLOKho74vcdd++slzx7h++0t8UHUC7WdcFuoWzXLwulS37pj1lQ5iE9pB0iLuZtEMl6rtSVOj0SVb5ZLz3H9U7RKboi4jKfOCSe48dW5p15r1+ZbaoJ1/m7/h4Vw5fqncgKkSwnHlW0UeTnyFuReLTKcnnWQgCBIgW8P/Lss8/qqaeeSkWvgwcPTmdR8nPrKqusopVXXjmV/hWeg/18vPDCC6eydu9bNGjQQF7Dz/l+HvcP4rw/U9incYnihx9+qI8++ij9gM5n5+rWrVsqr91iiy1Sea2f710suXvEpfMumXaRZdTrF3kv/vhhfnxZOeLnlqxngAZEHea2UXM3SY0jDAK1TWANraHNI3kUONrKJxLYL+L9swVp/DjpE434cfGLL76YdRxYOB50EW1hXDrrYzw/FhcuLpn16z7m8+Owf3lR1qVRo0bp+M7HeCUvfqz1/6fMnKK7Z9ytXafumh7vfXxXuPi4zsdyLn4t6+LjuRt2vkFLD1xajZ5ulM7K51+u+FLyWM6P9S7KLVmW6wLdQoGsf7ni+1h6xmpsety+SBdpQsQnNTguEiuV/tD0/4/0UdqfjF/3zLVU9j7dp50iWc1hOkwfRF6I5DFba+tUqHuTbspj+Rqzpgt1z4gMi7SOZDk+sZEfj26L9IkwCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBASQEXm/pvgN0B5ZeFS6HItFBm6pfuTnLpqf9Gt6zx3/n6b4QLF5fM+m+F3RPlfii/LFz8f/c5NG4cjQZxKfm6/5bYf8tb+uLuh7p166a+Jr8s73X/zXHh/X45Y8aMdHGPlF8vvCzvdb/ff5PsnonCpfD/KVOmzOqeKHRQ+G+QCx1U9ix9sZf/htkFsn7pi2+79Phvk+3lv8EuFPgWXhaKff332L60bds2vfTrtmMQQAABBBBA4A8CFMr+geT/33C7btc+kbcjLuHJevJev7ztHazBWjPyXsQFX1nP8lo+FYi5JIT5o4B3igsHFc9Nf04nrX2Sthm0jbq/0H3WgYQPJgoHFS5yKxS6+uWsYh/39Rwel9PjsmRcyj72iHeUMfXjbdPLePtc3uQDiMIBil8WdsoH3zVYjaY20k7/2UntG7WfrRjXBUmFHXPvtPvAg/ldYKAGapuIC5byKOD8j/6jkyIukJ6XM0ZjtGzk4MjZkawn7/Wz3t7qst40TUtF6d6eVyNZlxgP13CtHnG517URBgEE5r+Af9j25ptvasiQIakI1mWwX331VTp7lgsH5zreZ4j+6QafNUhnaerUqVMqpnU57Wqrraa11147/cCy9Dqf63MtF/FEBaJaRR6LeB+0MuMyzKUjeTy2PKgHtWMkr3L3ytxfroNAlgLxa4io87wolXlmua7XchFz1KDqv5GdI8zvAj5uGz58eDrpSOGXSYWyVv+/8Ho69lvuF409bKxm7BG/DKnIcZ1v7qC4bB2XkudzWTz+PyIuZYyP6fzLJx/L+RdUY84aoxE7jdD6t66vnh/3nK3w1icC8Qk//EuWsgpjy1i+zDf568SP3/+K+EQXe0dOiBSeJ0peyaWgLhH/NhLVuCXfNet1F5G+Finr+rM+qIKv+CQc90Q+jOQxLl2OXyvG0d/APJavMWv6uNfP54dG8viZkR+HogY/HevUGBQ2FAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSqrYB7Gdz/5L8J9sWvlyxLLfl66XJV/7/QG1UoYvVLX2bOnFlt73PpDXNfVOky3EJpbuFloTzXL/03zC7YLVm269ddHutLixYt0sV/98wggAACCCCAQCYCFMqWxThRE6M7awVtGbkmkvXkvf6ctvdX/apFIv+MHBLJevqoTyrHfESPZL10rVrPRTU9IxtHXFwT54Eo6v75YODTyZ9qv/r76Y0Gb2i9Cevp8s8uT2d48BkffBDilyUvJd/22hKv6bYut+ma565Rw7oN01kovHPts174ZeF178R7h90Xn5nBL30mi7LG5ZEPRDpFno0sFWGKE3hST2qLiAtSm0eynut0nY6NxCFp1kvPdb0LdIFOi3wRWTyS9eS9ftbbW13W+1SfpkLHI3VkLmW/fizwY4ILGrefrdmsugiwHQggYAGfwckF9yNGjEhnxCr88NFnjPIZrApntbp72bt1x2J3aHid4Wpcp3GF8Fwy6eI4T+xhpGJZn1ChMqWTLl9vE7kpkvV4P+zPkZkRBoHaJjAiWkXbRZ6LbBjJenySEp98xcc2K0eYygkco2M0IPLxjI/TY7LLwAsXPy77eK6sy5Rfp+iQrQ7RlHpT9N+n/5uO6QYuPVA3L3OzHn77YTWu99sZCl0eW7iUPuuef97RN+I5NZJHyWdBxcXdt0bOi/gYYZfI3yOlT+Dj0tlNIx9HSpfK+vmkY+SryHYR79P2jlR1LtflOjPyYySP8THZi5FBkQV9+qu/Lo64PNiF11nOC3ohPda9qTe1doRBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSqo8CUKVNUuPjviadNm5b6o/x64f/uhXCHlF8WLiX/X/p191K5/LVwcY9U4XW/LPn/wuuFrin3SpV1cfdEeZ1T1dGVbUIAAQQQQGABFehffwG943O82y5bHRs5K5LH5L3+nLbZBSzrRl6K5FEo213ddW6EKV9guIbrT5HVIi5WK7ZM1iveWOdG9WvST3FIkG5gn2b7qGvXruXfWKn3rKW1dFGk3tb1UmFyqXdX6r+ralU9HHEhjEtbnon4vjFzF6gf9Xqe0kVJc79mcR/h7/e81p7bFhymw1JRkB9HXdCU9eS9ftbbW13Wc1n6hZFDIy5o7BHJcnbUjvpL5K8Rr902wiCAQPUT8A/7Fl886r7jMqdZVsvq6sjdkX0jFZlu6qbnIx6f0MBxeaAL+06OVGTiR49RQzi1Ilcp+mOnaVoquy36CnwgAjVIYKiGpq3trM65bLVLPT3LRJjKCbjM+t7I3hE/Nvtse74UMzfqRo2KeFbcasV4xF5WK0a87z2qxyjtEJnb+Fgbw+8+AABAAElEQVSuMH589jGdT0pROE4pvC+Ll34sPyDifUWXefvnBl0jW0VcLOsTrni8//hyxG9/I1LyeMbb5be5JNmlpC5KdiGti2V3j/g2KjOt1VojI/581IlkPYtqUf0cYaR+EZ8c5KrI8ZEsx+XCq0cujdwSYRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgeoo0KhRI/nCIIAAAggggAACWQjUzWKR2rTGN/omlVucolNyKcLLe/1iPheba3M9FXGxV9bjEhgXpXwaYf4oMFqjU5lsC7VIJayN1fiPH1TGW37Uj6lQxwU8EyOFz9222raMjy7/TS57csmKy4eympW1suI8Fqnox4VG60VejDBzFygUNbnMLo9xoaw/N/Nj/LV9auSaSKFsLMvtyHv9LLe1uq11sA7WphGXeU2OZD2X6BI1ixwYYRBAoGYLtFO7VAJ7mS6r8B3xCQzKKvbzc0OfSEUef/IulG2gBhW+f1wBgZog8Lk+jz2yxuoQyWO8jxfV1GoSYSonMEiD4icQ36TH2oqs4OPB0yMuP3Vej3iWimwcKfZ4bxWtMutmXaZ6W2TLyPhIXuMTquwWeTcyMDImskGkV+SxiCdqdfV0ZItI4QQsfqz2/qvLX11Q7tLZNyM+HvXJDHzfz4j8L1LRaaM26Rjbx+t5jNf/KcJIi0T+Fvl3pCL7AsXaHa7DUxG+f4bCIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAArVdgELZUp/hE3SCloi4hCKPyXv9YrZ5G22jkZHXIllPV3VV04jLXZjZBaZqqnaIuDDn8UjLSDFzr+7VChGXAJec1bW62kcqOvtrfz0SqUzRTlm35RIilw95XGzkwttNIvdHmPkr4M9HoYBpfmyJv9Y6RlwemMfkvX4e21xd1rxO1+mHyMmRrGdhLaybIy4IuyXCIIBAzRbop356O/JqpCLTTd1iz2fqH67ifQbv26wfGREpZlwiWNZaxVx3bh8zPSrxKZSdmxLvr6kCLipdOuLC0TzGhbLe12MqL+DHw86RNSIVmTt0h76N+DHVJ6koFMp6DZeuPhop5jHWJzpZLFIYHz88H/Fj9Lwo5NwqTpvinx34hCQLRfx/W9wV8WPzg5E9Ix6fhOOISMlZW2vr9siwiE++4gJ0F8v69Q8ixY4LXz0+OU4e4xJVl/T6OYeRjoq4vPemSNazh/ZIhcRX6+qsl2Y9BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoNoJUChb4lMyWIN1d+S8SMNI1pP3+sVu74paUZ0iLhXNelz60j1Coezssi762S/irwGX+7i0eG7zi35Rn8iukbGRkuUzdt4lUpnx9Vz6m1XR4/JafraSqhmakbbVt3NVhClfoFDEm1fJlwuh6kXm17jc6syIC6+GRLKevNfPenur03pLakn9O3JRpGQBWVbb2Eu9dFjkyEgxZWZZ3S7rIIBA9gI91COqYbvp4khFxiV/5Y2fn96P+EQE70bmNo3UKLdCWRcUUig7t88A76+pAt/pO3WI5DUulF0mwlRewIWpO0UqMj7eOi1SGD+OuZC1MF7P5azFHu+VLrP1cedHET+OfxaZF7OBNkjHyT5e9s8r9or4pCo3RK6JuIB0s4jfV9b4RCtnR1yye2nEJ8/pEvF1fPxdOO4q67p+W96Fsi7u9fi4npEWj/jnIxdEvE+Q5TRWY/018p9IyZ+hZHkbrIUAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghUFwEKZUt8Jk7Wyak0ZUftWOKt2b2a9/oV2dJtta0GRPKYnuqpVyLM7wIn6STdG7k/slpkbvOEnkhlOffpvvShpQtwXBq0faQy00RNtEfk+kgW45K3pSKlx9t8SOTUCDNngdpaKOt7/eeIy5z+Eclj8l4/j22uLmseoAO0ceSgiB9Tsp5zdI5aRv4WYRBAoGYLHK2j0z6MyyOLHe8b+DGgvHHJ20+RdSMPROY0PtHD1Ege43UplM1DljWrg8BwDS/qRBaV3VavX9ZxQGXXW9Cu96E+1OeRih7X/Vf/jUfjr2YrSfXJGyZHPC7U3DPiMtZiZnWt/ocT6vgx2icF8Mli8jj5QHnb5XLbuyKfRDaJHB5ZNtIucnVkbuNjXe/b2vaxSN3I1pGVIr7+xEhZ01qt05tHamRZ767y25qreVqDQtnfKY/VsenruPAzj9/fU/XX/DXwYySPkyhVfetYAQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMhOgELZ/7d0AeqjkbMjeUze61d0m7fRNvoo8mUk69lAG2ho5LsII/0n8s/ItRGX4sxpJmiC+kb+FPk54iKfsqa92hdVTFvWdf22v0Zc0vNiJItx8U95pahn6ayorTxAv0aY2QUKRcHl2c3+0RX/n83rRebn+L65WPThSB5lVHmvPz/t5sVtu1jLRWYXRLKeZmqm6yIPRvIoisp6e1kPAQTKF9hFu6hD5JJIRaabupW7f+B1/BjuMtcfInOaPAtlp2hKVOM3mtPN8z4EaqyAj8f8vZvXuHB08QhTOYGH9JAWi7hYu9jx8YNP2FH6+MH7/W9HCuPjr08jL0XmNj7hSVknF/Cx6PhI74i3dV5OZ3VOx9EuznU57pmRtSI+ScX/InMb+/iY2idqcdmuf0ZxVGTJiNf4PlJymqppKtUdrdEl35zZ6y3UIq01RmMyW7OmL+TP8c6R8yJZT0d11OYR/yyGQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBGqzAIWy///ZPUNnqFdks0gek/f6Fd1m31eXmrjoL+txWUvjyJORBX0e02M6NOLP/76ROc1beksrR66PeGZEyhoXr7nYrSrTVV2j4q1bZgUrq2rVVAhX1ja59OimyPaRSRHmd4EFoVDW93bLiB8XTorkMXmvn8c2V5c1O6lTKiXrr/6pWDbr7dpYG2u/yBGRcREGAQRqpkB91U/fx95HqUjZXg/1KHP/oFB27pJAn9zgkMicxoWyLn7NYyiUzUOVNauLgEszfSKKPMbHKj9FKJStvK6PxbeN1I0UOy7p98lbCscRhev5GLHkyRt8vOcCVp/UZG7jY7nS6xWu46Jal83uGLkqMq+nndrpX5FvIsdGfLKCpSM+xnbZbDGzilZJDl7D+6R+LnPh6D6RwZHCLKJF9Eskj2mu5mnZsRqbx/I1ds3jdbzeiTwTyXoO0kF6KjIswiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAK1VaD45pLaKhD3y8UrLpo4NZLH5L1+ZbbZhTM7R26PZD1N1CSV8z6hJ7Jeukat977e126RvSPFfG0tp+VSyatLe+Y0LvRxOWtVp6/6Rh3Rffo5UtVxEa63q7xx4dSjkQ0joyLMbwKF4qY6qpMLib+WCqV9udxABRY9R+fouYgfa/OYvNfPY5ury5ou5+ocOTySx7gEbHLk5AiDAAI1V+BAHZhKD/8TdfTFztpaW1MjJcfPeW0ir0VcOL9oZG7TSI3+sM7crlPs+10o6xMhMAjUNgHvZ3q/u5jvscrcd5fJel+TQtnK6EkjIj6hSEWO6/w59XFlWQW0/ly8Eik5f9VfdW9kbkXgK2mleGQu/3jEt+u4/LsizwElt6Wqr7dUS/0j4nLQCyI+tvTxs4vJ34sUM/5esN/XEd+PdyNrRjaKPBLxbeRVKOuT+XjGRJjfBbyf4J8R+HOa9bisebFIMaXKWd826yGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALzSqDuvLqh6nw7Z+pMrRfZJJLH5L1+Zbd5L+2ldyIfR7KeLbRFKo50keiCOD/oB20TWStyTaSYccmMC39cbtMwUj9S1jRTs1TYW9b7KvK2PuqTCtRu1I0VuVqZH+tCWZcMlTe+Ly4p6hqZHmF+EyhYlPe5rqqTS35dHl0dpqd6aqvISZE8Ju/189jm6rKmv/4ujzwWeTiS9bg40qWyV0QGRxgEEKiZAs3VXAdHLo64hLWYcVFcYfxYs3DEa/wYmVuBfuF6fun9omJvs+T1inndhdcurGUQqG0CLhH1sVirSB7jQlQPhbKV0/V+lx97No4UOw/poXTsXtYxtt/2cqTk7KE9Uvnsbbqt5Jv/8Lq3o2OkvPHjt8tWr4wcEJmf45PXHBoZGrk5MiSyRmTLyPORYsb39y8RnwDGJ7vwmi72ddGs15gYyXp8G3YcG2FmFzhOx+nxiD+XWY6994/45x2F4+4s12ctBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoDoILPCFsi4ReTRyciSPyXv9qmxzb/XWEpG5FcxU5jZcKDsq8lZkQRsX0GwbaRq5P1LRQs+DdJDejricqV6k5LgUxWv7ZVXHxbT7RK6OzKkMtpjbWVErpsLY0h9b2P4/6U/6MOLbahthfhOYqqnpFRfl5TEuyXN5UXWZs3V2+tr290Uek/f6eWxzdVnTzwe7R46M+Osm69lP+2mdSL8IgwACNVfAjxG/RFziV8y0U7v0vO9S+X0jX0VcSOiTOBwdKXb/w+V/eRXKel2vzyBQ2wRGamS6S63VOpe75mM9j4vjmYoLDNRAbRjxMWOxc6pOTQWx5X38T/pJwyOFcYm3TyLiE5bMbXziDz9Wlxwfc/pY9tiIy1b/FsniOLTkbVT2dW/HnpH3Irb0MfhGkXUjD0TKKt0t67Y21abp50E+VvXX8ouRpSL++VChNLms61XmbT6BDIWyf5RzGbBPUHNhJOs5UAemEvtH9EjWS7MeAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggUC0EFvhC2fN1vlaLuMQij8l7/apsc92oo9kjcnuk2EKvYm9vFa2Symof02PFXqVWfJyLa1xs48IdF9ssEqnMTNCEVGCzntab7eq/6lftEMlqDtEh+jLyeKQq01iN0+e7sEahjMhlprdGXODi0llmdoG8C2VdrFSdCmXX0Br6c8QFTcWWPM0uNuf/5b3+nG+95r/3Al0QNWQ/xb8XZH5n/JhwaeS1iJ9zGAQQqJkCLoh1Gf2/IsU+jntf443IdZFCsaVL43zSgbsixUzehbLej2EQqG0ChULZVmqVy10bozFpXZdkMhUTmKZpeiqydaTY8bHlB5E5PfZ6f8v7WiWnr/pqSOTVyJxmda0+60QohdLYNbWmdo2cG2keqa6zlbbSCxHfdz9P7RxxQen1kcLx1ty2fSWtpF6RzSI+AcK1kaUjPimCTxKUxdiw8H2TxXq1ZQ1/3R4VuSOSdYlvR3VMJfY36sbawsX9QAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBGYTWKALZV36eXfk+Egek/f6WWzzXtorlZ++pJeyWG62NbbTdnogsiDNiToxKnQf04ORTpHKzM/6ORX3uMzGxTguW2sacdGKS4CzLD92yevGkSsiVZ0u6pKW8DYuFfH3lgtcXCbLlC3ggqN6EZvlMZM0qVoVyvo+9o98FrktksfkvX4e21xd1myv9johcl4k6zIn38e1IvtHfBsuzWYQQKBmChyn41IZ/X26r6g7cJpOU7dIyXFxoUv6/h6ZHJnb5FkoW93K1+dmwfsRKFagUFxZ2RNczO12Rmt02s9sEDWkTMUEXtbLGhupSKHsGToj3UhDNSz32MFFsK9HSo4ff7tG/hOZ06yqVWeVr3ZXd70Z8T7bnZF3IjVh1tW66ecPH+mjOC3LelFnfoiWibgE3d5zG3+v+DnBz1vfRK6MuPzcz1k+Nn8iUpVxoWwx21GV26ip191be6tlJIufS5Q22Ff7pp/R+MQZDAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFDbBPJpMawhSpfqUrWL9InkMXmvn8U2r6bVtEbk+kjWs5N20nuRLyMLwtykm1JZzbW6VutHKjMzNEN7Rlww6sJNl8juFvkg4iKbXhEX0WQ5h+rQVLBS1c+TC2VdwnNJZGjkz5HTI/dEvP3MHwVcKOtSqLymOhbKLq/lU4mgi5qmRbKevNfPenur23rH6JhU5nSqTs1l087RORoXuTDCIIBAzRRYTstp54i/n6syZ+rMqND/OfbGL53rMnkXyrq4n0Ggtgm4GNOT19e3C2tbRJiKC7iY1PusLjstZqZrus6KXBNxqbePF10Su1Ck5IkpvG/9SqT09FVf/TfyS6S8caGsH999QhivsXbEx/PrRFwsW5PGJ025IeLj290jfr7xCU988pcfIuWNC00LRn7eOSAyJOITxnj+FPHPT26M+DiuouPvl0LRc0WvW9s/3t5/i1wV8TFslrOjdkzl13fojiyXZS0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgWggssIWy4zU+lagepsNUP5L15L1+ltt7iA5JBTMu9cpyequ3WkXuj9T2eUkvRU1PX/09snekstNf/fV8xCWstivMslpWgyIua816ttf26hC5MlKV2V/76+uIv6caRDwuIXLRrItlmT8KLIiFslZwmazLnFy+nMfkvX4e21xd1mwSdUsuiXQJ14eRrGdRLZpKyf4V9ds/RhgEEKiZAv/QP/RuZECkstNe7XV05NxIocCvvLUaq7EmR/IYF9flVbiZx/ayJgLFCkzQhHScW9gvL/Z6xX4chbLFSv3x457Uk9oiUuz45xWbR/4acbHsLZFTIv4cj434GOzFyK2RXSOlxwW0/jq4KVLeuEzW+347RErOeTpPT0eeitS08THuBZFvIy6TvTniEl87fhopPT45yuhI6XGRrO+/T5azZsTH/UtHvM88KlLs+MQw/nwxZQv451L+OZq/jrMcP8f7+8KffwYBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCobQILbKGsy/KmR1wmksfkvX6W2+yCGRd1XR/Jclx8s12kthfKfqWvUnHq1tpaZ0cqO4/rcZ0ZuTjSLVJ6XAK0WiTrqad6+lvEX7MTI5WdzuqshSMlp47q6IzIAxEX8DCzC7jEzt97eU11LclbUkvq4IgLsbyNWU/e62e9vdVtvb20l1aNuDAyjzlKR6lFhKLpPHRZE4F5I7C6Vte2ET+OV2WO03Gp5NClfHMaP1f++v+Z08dV5n3e96FQtjJyXKe6C+T9te19OBfRMxUT+J/+lwq5XRBblfEx6OKRZpGlIhtEvA93TKT0LKSF0vuu1tWaGSlrfNxWVvlwL/XSVhGfOKW865a1XnV6m/c7XSg7LHJZxOW7K0V2jLwWKYwLZedUcO4TpbiU1OvsG/EJEnzc4ROqfBGZ23g7XMTMlC3QVm3jJ1N7ppPoZP215s/X4MiQCIMAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjUJoEFslDW5RSXR/aJuDQk68l7/ay31yVa+0VcMDMjkuXsFFWrr0e+j9TGGaux2ibiIplbIy7iqcy4EMjlKXtEXO46r8fFyi6dui2S9Wyv7bVGpH+EmV3A5i6BymvGa3yu61dlu0/SSfHdMzY9FldlnfKum/f65d1ubXi7H8dcjv1QZFAk63H5nB8Prot8HmEQQKBmCpysk/VG5OlIZcdF9KdEXPD3TaS8KZSvT9bk8j6k0m/3czGlmJXm44rVWCDvQtmpmqpGEaZiAk/pqTjtSn1tGKnKuNR0mUix42PMzyLPRio65+rcVMZ5r+6t6FWr1cf769XHvR9HfF9+iKwXcRnvIxEXvvr4yQXmc5r2aq9/Rr6N2ObRyPKRnSOvRsobP+eNizDlCxyhI/RR5MlIluPPsb9fXAjMIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAArVJoG5tujPF3hcXXw2NHBrJY/JeP49tdsHM15EBkSxnM22m5pG7I7VtXBy8V2RU5OFIZYtBJ2lSKp9xKe01kfkxbdRGu0dc6JbHuCzugcgHEeZ3gQmaEHXOTX9/Q8avjdGYVIyU8bKZLNdWbXV45LxIHsVKea+fCUI1XmRrbZ0KtlzMm8fsq321bOSMCIMAAjVToLu6x17eZjozUpU5WAerQ+TUSHlTKJT1PlPWk3fpZtbby3oIFCswTdPUMJLXTNGUXNfPa7vn97ouynSJ6UKRqoxPSFKRQtnVtJrWj1wZqeh0UZd0rOgi8emRmj51VTdOe/PbiW9e0AvpeMknQTkq4hkZKWb8OfTxjH+2dFfku4iNe0RcWFu6mNbHfXk8jxWzrTXlY/y1tnHkkkiW4xNm+Gc3d0b8cxwGAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqC0CC2Sh7BW6Qr0jq0TymLzXz2Obl9fyqRTM257luABs58htkdo2Lkl9InJ/ZIlIZcdFasMiXqdJZH5NP/XTkMhzkaxnB+0QFUarVbl0Luvtmt/ruVC2skXExWx7dS6U9fYfF3Ep1UWRPCbv9fPY5uq05tk6W89EXopkPfVUT6dH7oh8HGEQQKBmCrgE9sVIVfYdGqhBPNqcrVsj3g8pawqFspM1uax3V+ltLjVfOMIgUNsEZmhG1Gbmd7jvQtlGEaZiAs/qWW0aqer4+LFjpCLjE+r4RCguPq3o9Ff/qLD9SjdEatP0Uq90Uh2f+MTHqx6Xmp4f8bFUMeP92l0jr0dejrSL7Bbxz1h8whYf83n8XEah7NxFj9ARejzyaSTL6aM+6WvfnyMGAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqC0C+TXMVFOhb/VtKgw5RIfksoV5r5/LRv//oi7ueDLyXiTL2Vt7653IR5HaMvfonlR+dqWuVI9IZcfXd9nu7ZFlI/Nz1tSaWj9yaSTrqaM6Ub97iu6LUB75u+6CXii7iBbRMZF/R36JZD15r5/19la39TbUhtogcmYkj3HR1sqRMyIMAgjUTIGe6qlNIi6Irsr48cBFft5XKGuaqml6cx5FfOM1XgtFGARqm0DehbJTNVUNI0zxAkM1VMMjG0eqOi53XSZSkfGJXlpF/hOp6PhY9aCIi2XzKPeu6PZk/fE+2dC/Ip4tIy46XyriE1T4c1bs+HjaJ4pxGeqfIidGloz8I+ITadRGu2Jtiv24bbRN+tlI1j+X8HHHqpG7IgwCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBQWwQWuELZG3SDWkd2jOQxea+fxzYX1txKW6lL5NxIltNbvVOJiotTa8O8r/f1l8hhkQMilZ2X9bKOjJwWcWlNdZjDdbgeiQyLZD0uMFoxck6E+U1goiaqWSSvGaMxahGpzuPvgfqRCyJ5TN7r57HN1WlNlzs+FXkjkvXUVd1UruWCbhesMQggUDMFXAr9YuTZSGXHxfMu73swMihSepqoSXpTXoWyC2vh0jfJ/xGo8QJ5F8oayN+7TPECfpx0gXW3SFXmZ/0sn5iiY6Qi4wLgv0aujUyLVHRO1snpJBBX6IqKXrVGfHzhuaCv+uqbiEtg74i4THffyAeRYqezOofSFWmdo3SUrotcGHE5LSdYmbOijxH6RW6O+Hg2y3GBvU9y82uEQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBGqDwAJVKOtSHRe+ugykQSTryXv9rLe3rPX+rr/r3sjnkazGRUN7RG6PzIzU5HF5z/YRlwBdFKnsfKtvtUvEJb4ubKwus5N2UrtIHiVB/jrw19ddka8ijDQ+klehrEtyvH51L5R1cdNxkUsj/v7KevJeP+vtrW7rbabNtE7ERY95zJ/1Z3WK/DPCIIBAzRRYX+tr08jpkarM1tpa60Vc4Fd6CoWyLmLPcqbqt7jgkUGgtgn42DTPwleXPvo2mOIFXCi7QaSqP4soHEsto2WKv/H//0iXpf4UcalmRWdxLR6nHzk8nYBmrMZW9OrV/uMLhbLjNC4dQx2v49Nx6zW6Rm9HfPIdnwimIgXqPpmRj/e/juwamRxZJbJtxGXsTNkCPoGPH2Ouj2Q5LpT9MfJ8hEEAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRqg8ACVSj7hJ7QN5EDI3lM3uvnsc2l13TJiYtpzotkOXtr72Rfk4s7pmt6VB/+ObHco3tUP1KZmaRJ2jHSJnJrJM+ip4pun+/TIZHrIhMiWc/u2l1LRbL++sp6O+fVemM0JrfC10LJU3UvlLX1YZGmkX9F8pi8189jm6vTmifqRA2IfBLJelwU5fX9WOiibQYBBGqmwP+xd9/xUVZpG8cvmiJFFJEioEhTRJQiCoINxYKiqMuKva/Yu66+Yl97wV7XunaxYANUxIKIoFQRERBEQQSk9/be93GHBSQQJs9JZia/+/qEkGTmzJlvyjwzf1yPl8l+bvnIUpC5RbeENT7RJ2ss448RPkkXynrxuk+qRDB8wD8I5IiAl5b685dY489hsv1kIbFs8lrX/7a1sxR0vFC2lKW2ZWPHr3O45UFLOuMlq37iinssuTapxwIvlE3NJtpEp1hGWN6z+HP5/S0tLH6ilPz+jpVV2VC+7gXpb1pmWfax7G7x1xbclPmfwObaPJwIyk90k2RxdQM1UDPLKxYGAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyAWBYlUo+5SeCqUdDdUwyvcu9vpRNr3Wol5Mc6XlOcuvlqSmsRqHshQvKs3WuVgXa6DlLYuXwaY7Xmg83vK2JVVak+5aMa73D/1Diy3+M5D0pH6+ntEzmmwp7uNFQltYYswf+iMsu6W2jLF8omuWV3mrprrC6oIe0jRL0hN7/aT3m2nreelYfUus4jAvHN/a8oCFQQCB7BRoozY62HKNpSDj5XoHWtZeJ1ahbKp8PROPxwriyHURcAEvwlxiiTVeCp9k0WOsfWbKut/r+3Cc63/nCjoTNEE1LV4anM74yRb6W4ZaNnb8ucXlFj8unGHJpfHnql74unqhbOr+eYFyB4ufIOdrix8bn2Dx990tqYLy1OXX9d7X9ufZR1i8hH2AxU+20sXiRade8pt0cfq69pEtnztf51t18k/hxBZJ7vkYHaM3LJT4JqnKWggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggUFQCxaZQ1stOelpOtcSY2OvH2HNea56kk0K53526M6+LpPV5LyrtYcnG4hkvC/aCFy9C3dWS7rjpK/9NPdVLd5mo19tKW+l4y/2WlZak5xSdYrewVbRyyqT3G3O92ZqtSpYYkypm9aLObJizdbbVypaP9nMRe/1sME53j14Yd4nlecvvlqTHy9C82OxxS37KuJK+fdZDAIFkBG7WzaF4/x29U6AFb9SN+tLS25IaL+Hzv0VJF+15sbtPrHL31P55j0BRCMQulPWCTQpl8/+d/UyfqYKlmaWg4yWb21vSnXZqp50s/vw2nblQF4bi1dt0WzpXz+jrbK7N11kou/qmW6pleEb/o3600y4cHkrQa6u2/mlZ30lTyqqsllpSRaat1EqvW8ZYDrH4CTZ8nW6WqZbiPn4iqIMsSZ904igdFV6T+kJfFHdi7j8CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCOSAQLEplH1JL8lLdf5miTGx14+x57zWdCcvQ3nMMsmS1HRRl/A9eE7PJbVkoawzQAOs6vJsXW3pbEl3eqlXcL1Dd6i9JZPnAl2g0ZY+lqTHf74utvjP10xLcR4vsotVYpdthbLlVE6XWh6y/GFJemKvn/R+M229k3WyfYfKhdLXGHs7S2eFgi0v72YQQCA7BVqohY60eBFeQQrp99Ae6mC5zrL6+N+g+ZYkx4vdfWKVuye5V9ZCYGMFYhfKejnmYguTPwEvlN3TUtpS0JmgCapjKch4mf+LlnSOu/0kEFdZ/Lh9iiWXpqIqao4lP+Olvn4SFn/N5HLLsxb/nJ/EaKRl7fFydJ9FltXHTzTjlj9bvKzXnydvZ/ET8vxgKc7jr0t8ZBllSWoaqIEaWd6yMAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEC2CxSbQtmn9XQoA/XykxgTe/0Ye17fml5eUt1ygyWpcfvjLE9YsmUma7KOshxoucmS7vyoH3Ws5XjLJZZMnyZqonaW+ywxxssjvUzpYUtxHi+yi1ViN13T7TeuvNUW/VlclA3O5+icUDp9r+6Nst3Y60fZdIYs6j9Hp1m84Gq5JemprMo6yfKgpSBFlEnvi/UQQGDjBPxYaYTlNUtBxo8/B1ret6SmgiponiXJ8WJ3n1jl7knulbUQ2FiBTbVp1MJXL3leYGHyJ+CFsntbkpif9FMoLi3IWn7c5aXD6T439xOubGW52ZJL44Wycy0bM1tqy3DyGS/69ee3X1v8+fQhlo8tqfESZp+FlnVNFVXRtRYvlvXn4f0sXnzaydLfUhznYB1s9a8NwnOEJO//ETpCb1sYBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAINsFikWh7CiN0rcWL02JMbHXj7HnDa3p5TI3Wp6xjLYkNV5U+73lC0umz1ItDSXEXvj5gqWkJZ2ZozlWV3JEKEJ5XI+ns0SRXOdCXaheljGWpMeLerzc04ty8irUSfo2M229+ZqvZZZYhbLTNE1bW7JpvCzwYssDllTJX5L7j71+knvNxLW8OMxLtmOVL/nfBC/fXr14KxMd2BMCCOQt0FiNQ4F+N3ULj3F5X3L9X9lNu6mj5TpLavxv+MaW/KWum9d7L3b3gj8/7mUQyDUBP95eZPHnNDHGy+YplM2frBfA/mJJolDWi/cnWupYCjJ+4gk/WYAXoKZzsgAvLPa/9U9afD+5Mv5Y48/T0hk3Od0y0vKuxX//DrA0t7xoKWPx8c+vb/xxyU/A4q/D9LBMtbS1tLG8ZSlOJ18ooRI61/KcxR+zkxov6fXfSy/BZxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFsFkivITPL7vF/9B/VtuxjiTGx14+x5/ysebyOVyPL/1mSGi9TaWF5xJLp48WWwy1vWDa3pDNe9nKCZablTYuXw2TLHKbDtL3lfkuM8cJaL4Z72lIc5w/9Ee72Vtoqyt3PxkJZhzjf4sVBXiobY2KvH2PPmbJmXdXVwZZHLTGmiZqEoqxseHyIcf9ZE4FcEfATEnhJ21OWgswNukGDLV7K5+Mlf/MsSY6Xl29hYRDIRYHU85eki5hTVuVUjkLZFMYG3vdX/1Bcvbt238AlN/xlLxf1QtKCFsr6LXlRpxfd9rSkM6fqVNW03GzJlfGi3XQLZVMG/lzmUMsnFn8c28HiJzf6m8XHn6flZ/yENkdaBlg+t/jzxqMs/hrNE5bFluIw/nPmpkm+buC/izUssU6UURy+L9xHBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIDIGcL5T1Qs8XLF6O6iUUSU/s9ZPe78as5wUmt1i8UHWQJak5T+fpNctvlkyd5/W8HrJ4GdpOlnSnm7qpt6WHxct2smn8++/lm89YvHAt6amqqjrFco9lhaW4zXRND3e5iqpEuevZWijr5Wf+c3efpaBlTuuCjb3+um4zlz53hs7Qx5aJlhhzts4OpWaZ/PgQ436zJgK5JODl02dZvBB2oSXdaaZm6mhJFRXGKJT1cvfKFgaBXBRIFcrO0Zwod49C2fyzeqGsn1RlU0tBZ4ImhCXqWKVsQaee6qmDxY+705kyKqNrLf58cZwlF8Z/rpN8DuLf95cs7nOQxWdvyxWWSZb8Tlu1DcfI3+k7+19be0XlPG1nudUS47l6fvdVGJfzv2VeyOuvz/jrb0mMvzboxxgUyiahyRoIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFCUAjlfKPuZPtPPlhMsMSb2+jH2vDFresnGnpYrLUlNF3VRJcujlkycoRoaitAu02XqbEl3vDjmX5aHLW6YjXOaTrNa2ZL6tyXGXKJL9JPFS4uL26QKZbfSVlHuuhdyemlvNs6FulCLLI9ZYkzs9WPsOVPWPEyHWQVylVAcFmNPR+toeWnkfywMAghkr4AX6nuJZbolhal7fo2u0UBLH0tFy1xLkjNDM+xROM7jcJL7ZC0E0hHw3xmfWIWy/nxutoXZsMCX+jKx54NeKFvaUsuSxPhx8acWfw6czpyoE61GvG4oEU/n+pl2nfIqrwWWpMfLX71o3edki5/0yN38xEffWvI7jdRIT1r858BPznK7ZVvLpZZfLLk6XqA71tLLktT485pvLH4iFgYBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIVoGcL5T1Us9dLY0tMSb2+jH2vLFr3qW71M+SVOlnWZXVPyxeFrnEkkkzS7OszvBotbLcZkl3vPzMy1gvtpxuydbZXJvrVMuDluWWpKeBGugIi/+MFbfxQlkvg9rCEmMma7JqWrJxvNyvq+Vuy2JL0hN7/aT3m0nrlVEZqw07MRTKrtTKxLfmjw9eOv60hUEAgewV8EJzL433ors/LOnO7tpdB1lussQor6RQNt3vDNfLBoHUMaY/v4kxlVU5PJeLUb4ZY79FtaYX+o60JHWCES8S9TLZUpYk5gAdEF4rSbcA3PdxneVFy2hLtk85ldN8S4xJfc/8WNq/j37Slu8sLSz7Wd615Pf4uoZqhNcrJmmSrrW8YvGCWi+r9Z+3XBsv0t3f8oAlqXFzfz7upfUMAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkK0COV0ou0zL9LrFy+liTOz1Y+w5nTVbq7VOsFxqWWRJYs7W2VanOV2vWTJlvLzlJIsXWL5sSRW+bOz+vNSlk6WdJReKUs/X+frZ8rYlxlyuy61+d6A+txSn8RI7L+IqYYkxXii7jSVbx//euNEzlhgTe/0Ye86UNb2oykuwvrDEmFN0ikZZBlkYBBDIXoHLdJlVUJfRvywFmW7qFv7ezNM8q2WcU5Cl/nJdCmX/QsInckigiqqE48xpmhblXm2pLcO6BSmNjrKxDFv0K32lFZYkC2XrqE6i9/JCXWin4XlJv1vSGX+9ZQfL9ZZsHy+UjVWSXFJ/vvzmJ2rxx0d/7WGo5UOLn1ThcIsXpz5uye/rLhVV0R5tL9N4i19vsGUXy2GWXHt+7a9L9LKMtSQxFVRBbSy9LQwCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQrQI5XSjbR31CIWGsQtnY62fSD9Xtuj0UwN6hO/6yrb7qaxWqk/7y+fV9opZq6UjL/ZbCHr/NVyxrj9/HDyz+taqWdGa+5ociGC9w8lLaVGlMOmtlynXqq74OtdxniTFeWOwFS3dbitN4obL/nMQYL0X2kryalmydGqphtaKn6E6Lly4lPbHXT3q/mbReEzXRzhYvHosxe2gPNbC8aGEQQCB7BbzkzssFH7T8ZFl78lsa7ccJXvj2reVXy1uWJy23Wbwc/N+WdIdC2XTluF42CJRWaW1hiV0oO1Mzs4GjyPbohbLbW6pbkhgv9U+6UNZPHuPlmo9a0hl/znuD5VXLCEs2jxfKLrTEmNQJa9Z+bnOADgivQ4zUSLW1XGDZ1uKPofkt+d1Em4TnTr5GT8tsy94Wfwz1x00/eU62j5fkustDltT46y/+c/sPSzpzkA6yVw375IRPOvef6yCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQPYL5HShrBd6eoFG0oUrqW977PVTt5MJ7718sZvFy7t+tvh4ece5lv0tvSwbO16U8rXFS3YKc7yEzEuGT7ekymL6qZ+usXhhrpeWpTMrtELHW7zs7B2LF6nlylyoC/WZZYglxnghnJuNtRSX8XKgrS0xZrImh2W30TYxli+0NS/TZaGEsId6RLnN2OtH2XSGLHqsjtVrlmWWGON/o339XCi/iuHDmghki4AXvHmR4lWW1IzSKO1n8ePHvH7H+6t/KJaupEpWiVnaPupvj2yTNdriJyToarnWco9lqSXd8aLNWI/F6e6J6yGQpICfJCO/hZQbe7uVVTlc5Q/9sbFXLVaXH6iB2t2S1EzURG1nSXI202ahkPMRPaIllnTmaB2tXS3XWbJ5yqrsqtcIkr4feRXKpm5nJ+0UCtP9e+yPc16c6t/rMy3fW/IzJVTCalcP0+eWLy3VLEdZfO2nLOl+f/Nz27Ev435nW562eHGuv4bg988/5yc5Sme8UHaqZaiFQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBLJRIGcLZRdrsd62HGOJMbHXj7Hngq55kS5SLYuXMHq5aCPL4xYvLfnQsrHTVm3V0nK3pbDGy15HWHyes3jpzScWLy/sZLnYku5coStCse6bejNaiXG6eyvo9bz0bWfLfZYYc4SOCGU53dU9xvIZueZv+k3VLTHGf859sr1Qtr7qWzXV0brdEmNirx9jz5mypv/NnG5Jt7xpQ/fDH7v959jLsBgEEMheAS+D9b/hr1j874UfSzax+O/2XMs4y7pmD+0Rvj5Hc9ZZOrtcy1cVyfoxSrrjJXJeRscgkKsCXpjsxckxpoqqqKQlVmFtjD0XxZp+AhX/m5bUeNlojBPm+Ili/NjO/16nM/6awA2WtyzZXM7p5bqpk86k47C+66QKZf1ENOsbf1y60eIn8rnX4q+9NLYcatmYY28/uZJ/P76z+P+9eNVL3u+0+ONrto2X0PvzNy+S92OJByx+kiOfWZZ0pqmahuOA3uqdztW5DgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFDkAjlbKOuFEF5U9TdLjIm9fow9F3TNTbSJbrN4Aes+lsmWZRYv9vBCWX+/sXOpLrX61Tf1k6Uw5h29o1SRi+/db/cgy5aWpyzpzmN6LBTj+hptLLk4F+pCvWyJURrl3xNf/xnLTEtxmJiFsv676aaxCmsL8/tzpa7Ut5aPLDEm9vox9pwJa9ZVXatfahr+fsfYj5dmeV63MAggkN0Ch+vwcBKCjuqohyxepOeFsF4+6H/f1zVeRHuJJXXMtq7L+Of8ca6BJZ2Zrdl2+onFqmphEMhVAf8d8WPOGOO/n15Y68edzLoFxmt8KGlNqlDWn4d52el2lqSnpmqG104KcgIR/3vf3HK9JVvHC2UXWWJM6jHNHwPzM76XrpbRFi+G9fJUL1H3Y3A/Oc4SS37GTwTkr1P4z+Oxlpss21qutnixeqbPPM3TwxYvk/XX91L324tlU5NuQa4fi7Sz+GtcDAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJCNAjlbKPuqXg3Fnl6MEmNirx9jzwVds7/6W/3rpasKP1cvQpmlWRpp2dg5Wkfbd6imulsKY97QG2sU33qprBeReEnLqRYvF9vY6aM+Os9yg+U4S67O8TpeFSyPWmLMaTrN6uNK63FLcZiYhbKTNCmU7KVKi7LZs4VahJKfO3RHlLsRe/0om86QRY/UkXrbkk6ZeH7uQid1Cuvn57JcBgEEMlPga31tf8VbhOOsBVoQTkSQ2mkZlcmzUNYvc6bFjwvyGv/awZZ0J1WiV03V0l2C6yGQ8QK1VMuOCidF22cN1dAUC7NugYEaaH/pyoSS1XVfYuM+O1ETwxViFMr6wn6Cj28sX1jSneutTNaPD4dYsnHKqmwo7Y2x99Rzs9VfR8nP7XjpqZf19rMMtvhJF86w1LHcYplhyc/46y53WX62XGH5t8XXONsyzpKJ86k+tb8yNXS+xQtxffw1nLXHX9NJtwjYT5b0pWVd6659O3yMAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIZJpAThbKLtZi9bT83RJjYq8fY88FWdOLObxIdi+LFxKtqwDFS70+tmzs+PUutniZyR+WmDNP8/SJZYVlXeM/M17O8pUlv+Mlup0tx1quteTybKbNQrnbI3oklPAmfV8rqmIoxnlIDxWLMhcvsqtuiTETNCGUA8VYuyjWvFyX60PLcEuMib1+jD1nwppeKOslcl6WFmOO0BGh8GqohsZYnjURQCCigD/GnWLZwzLCsq7i6SVast6/H1tqS51u8TLGdY0fj7a3pDu/6/dwVQpl0xXketkgUFu1KZQtwm+Ul2o3sXhJaRLjhbIlLf59jTGt1EqtLfdY0p3DdJh2s3ixbDaOP+dNt5h0Q/c33ULZ1df1kvYXLF6uepLlTsu2lnMsYyz5mS20ha62+M+Tf6/9BDk7WLpYMq0IeG/tbc84jrRK3RIbvGvpvp7khbL+WtG3FgYBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDINoGcLJT1QgwvhDjaEmNirx9jz+mu6WUqe1q8aMTLwNZVJutre0mrFz6mM2dYjegmFi8SjTm91Xu9RaXL7KuTLW0tb1g2NF6keKilqeVJS3GYc3WupltetcQYX/9Xy1uWXJ45mqMFlliFsl4OtJ0lV+YgHRTKngtSbrU+i9jrr++2s/lrXpDmP2fvW2KMl5HVtHjZN4MAAtkl8KAe1LMWHz++ymu+0Td5fSl8/iJdlGeJvR+XtrOkO37M5+V+VS0MArkq4MWjv1nW93tYkPteQzVCuXxB1sjl6/rfOD+eSWp+tqp9f/6QV9F2ErfjJ3t52zLOku54mawfv2VjQeem2jT8vuR1Epp0Tfx6SRTKpm6/lmrpNssvltst/hrVjpaOlr6W/IwXHZ9t8SLa/1h+sDS3+HOjfpZMGC+SfcpyoMVPRrS+mamZ6/tynl9zNz8W+NTCIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAtkmkJOFsj3UQ60t21hiTOz1Y+w53TW9ZOQ5S33L+go8vHDFS0fyKpxd3+1XUAWdY3nAstASa7ykdH33wQteSlqusxxuWd94YfFhls0sb1q8ELc4jBfXHGm53xJjttf2oQQn1vox9pzOml7s5ROrUHaCJqiOJVfGi4Qusbxk8QLApCf2+knvN5PWO1gHq5clxvj35RBLrPVj7Jk1EUDgT4GbdJMetvhxlf8u5zVzNdcesSbk9WU1sPjfgXUdv9VTvQI9jnoJXzVLquAvz03wBQSyWMCP3f15mp+wIcb4+pMszF8F3H2IpYUlqfFC2W0tMecoHRVu4z7dl/bN+ElXWlqut2TbeKGsz2JL0uOPiT7pvGaS117Kq7zOs3gprJ8UZ7Zlf4uf9MaL3ZdYNjT+ONjF4j+vftzt930/SyuLv4biBe5FOX4M4K/Bedntuo4HUntLt1DWr7+35TMLgwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg9IYcgAAAQABJREFUgAACCCCAAAIIIIAAAggggAACCGSbQM4Vyi7VUvW0HG2JMbHXj7Hngq65s3bWUIuXjKxvFmiBBlnSmQt0gVWKzdVTlhjjpS3+c7HMsq7xEhUvJvP9d7Osr6jE1/q7xYvI3rdUthSnuVAX6mvLQEuM8Z+Fzy3+M5erkyr1ilV6nWuFsv5zcLzFf9cetMSY2OvH2HMmrOmFst9YpltizIE6MPy9maVZMZZnTQQQiChwts7WB5ZylvWVtn6rb9e7i8t02TqP32qr9nqvt6Ev+mNxTQuDQC4LbKftwt2bqIlR7qafDCLW2lE2XIiL/qAf7BQk87SbJakpjEJZ/3vtz8eethTk+MvLZN+xDLZk06QKZRdpUeLbTj0WJlkom9qkl9V2sngpqpv7azhnWvxvgJe85/dY/SAdFE4UNEADQmm7Fww3tng5rb8WVlTjJ/LpbfHXbPJ6raYghbL7aB99YfEiaAYBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIJoGcK5Ttq77yIgkvvogxsdePseck1iyv8nre4oWvXrKyrhKPMipj+n3TurmqqqrTLHdZ8ip9TWvh/16pv/prjmXt8eKVEpaLLMMtzSwbmnN0jj61vGupaylu00Zt1NxyvyXGtFO7UFoTa/0Ye97YNb2MeBPL1pak5w/9EcqZ66hO0ksX6Xr+d8fLCR+3LLQkPbHXT3q/mbLe/trf/oqW1EeWGHOADtBKy8cWBgEEsk8gVQpdQzXWeezoj4UbKpT144KdLH68tvqM1ugCFb95oWwtC4NALgv4yQu81HmsJcb48aYfl/1mYdYU8MJ9/xvnxZ5JjRfKpkqCk1pzXeucrtPD8d0TemJdX87X5zqog3a33GDJpkkVyi7W4sS37Y9jnhiFsqtvtoVa6D8WP8nIqZbuFi9h94LZ7yz5mVZqpbcsIy0tLX5dL3P15+h+IqGimC20RXi9qZqq/eWYwp+PFKRQdk/tGQqU/diCQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBLJJIOcKZd/QG1af0cKqbepE+T7EXj/KphNc1AtJvPhre8vapbJeBNvLku5cpss0yfKyJenpqZ5Wd1tmjWV9/zUtXg7rRbap8pg1LrTWB7foFj1pecnixSrFdc7X+XrNEqs8ytd34+mWXBwvlPWfvbXL8ZK4r14c5BPrb2BYvIj+6aqummfxgqQYE3v9GHsu6jUrqqJ2s/SzxJgttWV4TKdQNoYuayJQOAJeBuvHjl7aX8qy+izREg20bGgu1+VrXMQfP/0Y5D1LupN6LE73+lwPgWwR8BLImIWy7pA6/swWk8LYpxfKNrF4qWxS44Wy21piz+ba3CplT9cDloKc7OV6XR9OwjJYg2NvObH1U68JxCiU9U364+AKS2GMF0r76xf+Gss9ls8tXnDsZe8fWPykDRsafwx/1vKlpZPlKouXGt9smWUp7PH79ImlgmX1Ywr/v59YJd3x39WylkEWBgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMgmgZwqlPVijrctR1piTOz1Y+w5xppeKjLUcpxl9fFCkq8siyzpjJfUdrHcZslPucnG3Mbrel1LLT4lLT6nWEZZ9rLkZ7xI5f8s91sOtxTn8e9TJctjlhhzok7UZpbHLbk4XmJXyxJjxmt8+BkvjLKpGPtf35pVVVXHWvx3MMbEXj/GnjNhzX20TyjmjrUXX/8zC4MAAtkrsLW2DkV2ndX5L2Xq+Ska9GNOL5hOjZ884mBLQR4PvAAzF8vXU0a8RyAlUF/1oxXK1lbtcJKRn/RT6uZ4/18BL9JubklqFmqhplkK6xj/Ql2oyRY/iUi6c4gO0e6WGyzZMqkCYC88jzVJv9axoX2WUzmdbfnekipi76AO9qrOTnrUssCyoblW14bXU/y55lmWuy3+s3ilJdZJZvLaUwM10EcW/16lXtvxovmZlnTHTz7U1PK1hUEAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSySSCnCmUHaICmWmIVysZeP5t+cLyUxAtWn7Fsailt8fHS1i8t6c5VuiqUvHoxcFIzWqM10eLj+6xsed/yhKWCJT/TS710hsX3d66luE9ZldWZFi+UTRX1JmniP1+nWx6xLLPk2sQslP1RP4ZyH/+9zMXxcquRlo8tMSb2+jH2XNRreuGr/531x98Ys7f2Do8LMzQjxvKsiQAChSTgj0svWa6zrD6zNEu/WtY3Xhrnf5/9OM5L37yk8HyLF8p5Qd7GjhcFelEihbIbK8fls1HAC2X9+DDGlFKpcNzpJZPMmgLDNCwUVK752fQ/+lk/hysXVqHsdtpOR1nusRRkvIj0XcsQSzZMqlA2xnNcv/9+kiL/vSmK8dJVL5LtY/HnU20tF1u8GNpf5/DnqOuasVZJ/YHFy2f9tRD/nvrP4zWW5yz+WOqFtYVZLO3F8u9Y/D75eElvQQplfQ0vP6ZQ1iUYBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIJoGcKpR9U2+qoWUnS4yJvX6MPcde82SdrG8tdS2pUtmCFD02VmMdYfmXJalZvZzWy4Z/sHgRWX5nkAbpb5YTLLdYmD8Fuqqrfre8YYkxXlYzxRJr/Rh7zu+asQtlG6hBfreSdZdratVcXn70kCXGxF4/xp6Les02aqOSloKUia/vPvj32+cLC4MAAtkv4IWyXizrxbD+t8PnG8uGxsvqvDjOS/72t/ixnD/ePWDZ2PETDXj53PYWBoFcF/Dnx14o6z/zMWYH7RCK5WOsna1rTtAEzbb4cWVSkyr79PLPwppLdIkGWz6zpDuH6lCr/myhGy3ZMP7Y5OPF4zHGC2VTj30x1s/vmv66i59gZ5LFS2X9ZEH+mNjF8pVl9XlQD4bHbP/cWxZ//PX7cIXFS2S7W3pb/G/NiZZRlsIYPxZ42ZI6NihooWxLtdRwy2ILgwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCGSLQImVNtmy2Q3ts77qh+LP23Tbhi6a1tfrqZ46W2Ktn9amMuRKs5bMUtcVXfVK2Ve0w/wd9MCXD2jWrFmr3ubMmaMFCxaEt4ULF8rf/OO13y9evFgLGy/U7LNnq+L5FbVizgotX778L28rVlgRS8mSKlWqlEqXLh3e+//X9Tb1zala0mCJ6t9RX9sO2FabbbaZypUrF96n/u8fV6pUSVtsscUab7OqzNIxtY/RbpZ3S767qjQ3Q9iLfBtH6ShNs3xuiTFeADzdEmv9GHvOz5pVVVXdLOdbkp69tJd2scQqXE16v+ms58VBXvDsZV21LElP7PWT3m8mrOelVIdbbrXEmEZqZH9tjkq0bDzGPlkTAQTyLzBAA3ToykM1s8RMnTv9XB3z/TGaNs2OKuzt999/lx87zp07N7zNmzcvvB9x0QjNOGiGajWupeULl2v+vvO1Yu4KqZ+0bNmyVW9+jOjHh6u/lSlTJnzs78s0LaMp109RyxtbaktLxYoVw1uFChW01VZbaeuttw5vVatWXfV/P2ZkEMhGAf9d29My3hKjRNlLR/1Y3U/Cwfwp4KWbftwyx1LBksQ8o2d0jsWeUSexXL7X8OcW/neypyXd8et2sgy1+POUTB4vHK9j+driBaNJjpfJ2isW6mHxn49MGi9rf8Vyn8VLhHe3XGQ5yOIlxqv/3PmJhPz7+KGlssXHHoFDWby/Vva9xU8SdLUlacO1zfw1pbvn361rt75WTac01WmvnxaOIWbPtkpne/NjidXfL1q0SEuXLg3HC/4+9eavOZVqUEor71qpzS7ZTJvO3FTheMGOGfzxf/PNNw+vF63+3l87qlatmmrUqKHq1auvel+2bNm1t8nHCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQCyBG3OmUHaMxmgHyxeWNpakZ7RGW51do2jrJ73fgq7nPcNe5jVlyhT99ttvq95PnTp1VdlXqvRr5syZoRw23OZJ9u/99ratvc2RvJgrVdJavnz5NUpcU2Wuq7/38o21C2LX/thLY70ozEtlvfjDC8TWVTrrn5tVZpZePvBlHfj6gSo5teRfCmxT5bZeVOZFI16CO3/+/HBXwj/eVXmLvZ0tlbdUrlx5VbFYqmxs7RIRLxSpUqXK/9bI4f99rI91gMXLgXa1JD2p9YdruJpYcmGsTlnlLF405UU7SU91Vdc/LV4AlKvjhUdebHSG5WZL0hN7/aT3mwnrnabTQsFvX/WNsp2TdbImW7y4ikEAgewR8GOrn376ST///LMmTpy4xnv/3O/lftfy95ZLP9p96vjn/fKCfz/G2nLLP4te/VgyVfi6sN5C9e/UX2e9dVYoevNjxLXfvADOjxH9+DBVGrd62eySJUv+UlSbKqz1Atvp06eHY12/3Orj+9hmm2207bbbarvttlvne98Lg0CmCXipaSXLO5bDLEnPY3pMl1v8dpg/Ba7X9XrR4q9RJDU36SY9b0lyzfzs7W29baf4OFKjLDta0p1maman56mn1y2ZPH68WdMS43Ulf46xieVNixfsZur0V391t/g+/W+HnSbIqnCtvH218VJZL6j+xOJeqVmpleF5rp9kwkum21v+z7KPJZ3x13T8OGLcuHF/efPjCn/cDnOt/XuwVOXwKqHc1V+D8uMJL4BNlcD6x/7akz9Wp8pi/X3qOCJ13JAqmU197KW1q5fSekmtv/nrYP5a2R9//LHGXfPbrlOnjurVs5/4td78+KFEiRJrXJ4PEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgQII5E6h7H26z2pbrtd0SylL0nOP7tG/LL9bYqyf9H43tJ4XqXoBx4QJE1a9+ce//PJLeJs8ebJWL9LaZJNNQjFH9erV/1KoutVWW60qjfXyjOlbT9fmFTdXy4ot5eWvRTle3rmZZWPGC0RS5bJeMLv624wZM9Yo1PXSXS8R8aJdLxxJjRfj1qxZU7Vq1VLt2rVD8ZiXh3ixiL/5/900F8aLlve2eJlU0uOFNF5ctL/lYUsuzA/6IdynIRqippYkZ67myipz9K7lUEsuTzd10xOWSZYylqQn9vpJ77eo13tUj+oKy2xLCUvS86AetO94N/1hibF+0vtlPQSKk4CfhMDLYUePttMvrPXmx0ip8eNFL2NdvZDVS/jLVS+nZ3Z9Rg8vfjgU8m/o+GiwBms3S+zxY8HUyRP8vR/zTZo0KdxXv7/+5h8vXrw4bMVL6erXr68dd9xRjRo1Cu/9//7mhbgMAkUpYL95OtdypSXp+VSfal/Lr5ZtLIxCAasXbr5mSWrO0lkaa/ETbhTmeJGoPx/z7/HjlnTnDb2hv1n8RCE7WzJ1/PWkrS1elOr3OclZrMUqa+lp6ZhqUU/yBhJea6ImhsdbN1nX+M+4W/WzNLSsPX4iiFss/nU/6ZMXyx5iyWv8tZbhw+0n5L9vw4YN03fffadFixaFq/hrTV7QWrdu3fB+++23D0Xv/hqVH0/0qN5DF5S6IK/lo33eXzfz4x1/8xMy+Wtp48ePD29ehOv/TxXfejn9zjvvrF133VW77LLLqjcvvWUQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBNARyp1D2YB0cihRf1atpOGz4KgfoAFW1vGjJlvGCUy+wSJV7jR1rFTT/ffv111/lBWA+5cuXDwWnXnTq5adegpoqQ/X3XtBRuXLlbLnbRbJPt/QCFC8QcVsv5k2998IxL+v1t1QZSsmSJUOhmhePeSlKgwYNVhWPeTGKfz1b5gE9oKssXiJVyZL0dFd3XWvx9Stasn16q7f9tTpYMy1bWJKcb/WtWli8tHZdpT5J3lZRr+VFsttbXrJ0tiQ9sddPer9Fvd5ADVQrixed1bMkPV/pK7W2jLPUtTAIIFA0An6848eVgwYN0jfffKOhQ4fKC9+8fNXHS2O9QHWHHXZYdVzjxzheIluuXLk8N71cy7PyhA3u4UWzP/30k374wR597S113O3H4F4yV6JEiVB+17Sp1cjbW8uWLcMbx9Z5/jjwhQgCXuLoxY/PWZKeqZqq6paPLH4SCEZqYDnR4s9hkpoO6hC+h8/q2aSWzPc6fuKAiy1eMOqviaQzfqKQXS07WV62ZOrY6WS0paWPpb0lyVmgBSpvyZaTf7yv9zd4khIvla1g8d9/fx66rvlSX+pmyweWZhYvlj1y5ZEa/f1o9e/fX19++WV4/+OPP4ar+7GEl602adJk1Xt/3SSbHze9mH7MmDEaMWJEKMz19/7mx09+nNC4cWPtueeeatOmTXjv95dBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE8iGQG4WyC7VQlS0PW061JD1e/OGlIk9avBgm08bLrLy0ygspRo4cueq9l8cuXbo0bLdatWqh3CtVYOolpv5Wx0pkq1Spkml3KSf3kyodmzBhQvh++ffHv2/+3gvIvJDWZ9NNN1XDhg1DgcrOO++8qkjFy9gyceZojmpavCTmQkvS48Wrvv7dlrMt2T6P63FdbpltSXq8XPUki//NKmPJ9emojlpk+dASY2KvH2PPRbXmfM0Phc891ENHWpKeeZoXSuPf1Js6wsIggEDhCMyfP18DBgwIRW9ffPGFvv76a82ZM0dlypQJJW9ekLrrrruGotSddtopFMoWzs4y/1aWL1+u8ePHh+NyL9318t1vv/02nHTAd+/H5F4e17Zt2/DWqFGjzL9T7DBrBa7QFaEgc6iGRrkPXjLqJ5jw0tHiPn5s6qWhfqKboy1JTRM1CcdA/pyrsMdfb9nW4s/FbrSkO6/pNXWxjLQ0smTi+DGnn8TEy1S9iDnJSa3txap+gpFMHz+pUj+Ll76vb0pZJfwmlvcs+1nymvcmv6dui7ppSJ0hKn9Mec1/fX4om/ei9dat7dQR9ub/r1GjRl5L5Nzn/aRDXtLvx1r+5scJixcvVtWqVdWuXTsdfPDBOuigg8JJnnLuznOHEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgCYHcKJT1MsEDLb9atrEkPR/rY6vSOECTLLUsRTleSjpmzJhQ6PXNN99oyJAhoaDKy71KlCghLx31EtLGjRuHtx133DEUyVaqVKkot81t50PAC2W9WHb06NH67rvvQjmwFwRPnjw5XLty5cpq1qxZeNttt920++67a/vtt8/HyvEv0lVdQ9nMaI2OcmOn6TQNtgy3ZPtcrav1riXGfemmblbn2UOjLMVh3tE7oVjrR/0oq8hO/C7HXj/xDRfxgg3UQCdYrrPEGP8ee2n8NRYGAQTiCHgJ6ldffaU+ffqob9++GjhwYDg5wXbbbRfKT70AdY899ghlsl6Az2y8wG+//RbK49y5f//+4f8LFiwIZXH77bef9t9//1AgV7NmzY1fnGsgkIdA6qQDXmi5qSXpaa/24Xny03o66aWzbr0hGqLmlu8tO1qSGj/Bza0Wf95VFHO9rteDlp8t5SzpzAqtsFrcJrJntPqPJRNnsRarrKWnxU8ukeT4CUW2sPSx+O9MJo8/r9+Y0t+S+jOv6BUdZfHxY4rPPvtMPXv2VK9evcLrHOXKldNux+2mTo07ae+99g6l9KVLl85kikLdm5fJeqnsp59+Go7F/DjBTxLl5f1eLnvEEUeE4zB/7Y9BAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEETCA3CmW93OR5yzhLjLlW1+pFy1hLYc+8efNCsZcXSaRKp2bNmqUyZcqE4tjmzZuradOm4c1LJipWrFjYW+T2IgvMnDkzlAYPHTo0vPeCke+//z4UtGy99dahWLZNmzZq27atWrZsqbJly0be0V+X93LUXS0fWfa3JD1eJtvS8rmlrSWb51gdq4WWtyxJz9E6WiUsr1uKwyzXcm1nOdHiBVtJT+z1k95vUa93pI5UGcurlhjTSZ1CCZ4XVTEIIJCcwOzZs/X+++/rrbfeCuVlfpzpJyg44IADtO+++4a32rVrJ3eDrLSGgBfFDR48WP369Qslvl988YUWLVqkJk2a6LDDDlOnTp3C8R3lcWuw8cFGCvygH0K56SAN0m6WpOdSXapPLN9aivt4UerplvmW0pYkZoEWqLzFT3ZwmKUoZpqmaVvL3ZZzLOmOv65yksULS+tbMm38+N+/b29Y/Ng2yZmpmapsifWcOcm9XqJLdK9l7SmlP+Of94LgZZbVp8TKErrsx8s05545euONNzRt2jTttNNOOvTQQ3XggQdqr732EoX0q4ut///z588Pxwe9e/fWe++9p/Hjx6tWrVrq3Lmzjj/+eLVo0WL9C/BVBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIdYHcKJRtr/aqYXnOEmP21t5qYPm3JfYsWLBAXiTVt2/f8OblocuXL1edOnW05557qnXr1qFUystji6I4NPb9Z/38CXixiP9sDBo0SF9++WV4mzJlijbZZJNQMNuuXTv5W6tWrQqtsMWLXqtbYpWZeqFsQ8sLlmyeVmql1pZ1FfQU9H41UiN1ttxoKS7TTd3C3+ZJmhTqjZK+37HXT3q/RbnelbpSvS1WfR1lG75+H8sQC4MAAgUTmDNnTiiQffnll/XRRx9p5cqV2nvvvdWxY0cddNBBatSoUcFugGunLbBw4UJ9+umn+uCDD9SzZ09NmDBBNWvWDOVxXbp00R577JH22lyx+Ap48WMli5eB/sOS9PjzcF93niWpEtWk91hY612tq0Px6wiNSOwmf9SP4XmQF/Y2sxTVdFXXUIY6RmNU0pLOeGGrP2fZy1IYr6+ks0c/QYefIMGfVyU50zVdW1u8fHlfS6bPYi3WH//NDM1Y9f/U5/y9f/53y5SlUzRl0RTNLz1fK8uuVPX7quuChRfo6KOPVsOGDTP9rmbN/oYNG6YePXropZde0tixY0NZ7+mnn66TTz5ZW221VdbcDzaKAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAKJCWR/oawXkmxhuctyliXpWaqlqmh5zHKyJcaMGjUqlEZ5cdTnn3+uJUuWhGKIffbZR/vuu6/atm2rbbbZJsZNs2YOCXjhmP/8eAlZv379NG7cOJUrV0777befDjnkkPBWt27daPf4Rb0YfkcmaqK2sSQ9T+tpqzDqql8sXsSTrVNVVXWN5QJLkrNES1Te8h/LMZbiMuM0LhR+v2PVXYdakp7Y6ye936Jcz0vBLrR4mVyMeVJP6mLLXAuDAAIbL+ClsR9//LGeeuopvfnmm6FE1stjO3furMMOO0xbbLHFxi/KNaILDB8+XK+//rpee+01jR49WvXr19cpp5wSyuNq1aoV/fa5gdwR8ALPxpZHLUmPl8l70elIi99GcZ4jdIQ2s7xsSWr6qZ/2s0y1+HOJohovtt3R4vetIGWr/rzOX7sZa9nWkmlTRmXCyYqO1bGJbs2/f34Clk8tftKiXJj+/fure/fu4biicuXKOvHEE3XCqSeo5s41VcWSbvFwLtjEvg9ff/11OKbzkwMsWrRIxx13nC688EL5yacYBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoNgI3lsz2uzpMw0J5XRu1iXJXRmiEFltaWpIaL/T68ssvdfnll6tBgwZq3Lix7rzzzlAa++9//1u//fabvvvuOz388MP6+9//TplsUvA5vk6dOnVCgcuTTz6psWPHauLEibrvvvtCqWy3bt1Ur149NWnSRNdee62GDBmSuMbf9DdtaXnCEmO6qEsoTPXSymwdL9qcZqlrSXrGaIyWWXayFKepp3qhkMmLqWJM7PVj7Lmo1myohppv+dUSYxpYdbD/Dk2xMAggkH+BWbNm6e677w7HnO3bt9ekSZN0//33a+rUqXr77bd1wgknUCabf85Cv+Quu+yiG2+8Ud9//304fuvYsWP4/vlx31FHHRVKggt9U9xgVgq0UAsNssQYL5EtaxlsKe7znb5LvFTXj6285LSoT6rhx2JHWu60FGRO0AnhBCS36/aCLBPtuqVUyk5btDzx9VdoRVjT18/26dWrVzjxkZ/86Ndff9Xzzz8f3vvxRrOdm4XiY8pk436Xd999dz366KOaPHmyHnjgAQ0ePFhNmzZVhw4d5EW/DAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIFA+BrC+U9dKaihYvsYkxXrrj6+9oKegMHTpUV1xxhbbbbju1adNGffr0UZcuXTRw4EBNmTJFzz33XCj1qlatWkFviusjoG233VZnnHGGXn31VU2bNk2ffvqpDj74YL388stq3ry5dthhB1133XUaM2ZMIlqbaBOdZvFC2RgFPJtpM51iecySKuNJZOOFuMg4jQu35iWlSc8ojbJqolJW6dkw6aUzfr1TdarescywxJjY68fYc1Gsub22Dzc7QROi3Hzq92a8xkdZn0URyDUBL3i79NJLVbt2bd18883yIlIvJf3888915plnqlKlSrl2l3P+/nhR3D333KNffvlFL730krws+IADDlCzZs3C8d2KFX+WFeY8BHcwLYHWaq3hlgWWpMfLTne1xCqsTXq/sdZbqIX6yZL0axNeKLuNpYSlqOdyXR6+z5/ok7S34j8vV1r8RCGZeKKAWIWyqefI2Vy06kWlXiJ7yCGHqHLlyqG41E+WdOyxx6pMmTJp/0xwxfQFypUrF47rhg8fLi/6XbBgQfgeHXroofLPMQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkNsCWV8oO0zD1MQSq1zFS3GaW9It/Zg9e7YeeuihUODpZU8ffPCBzjrrLP3www8aNmyYbrrpJu2+++4qUaLoy2Fy+0e9eN+7UqVKae+999add94ZCmS93Lhz58564YUXQrHsXnvtpWeeeUYLFy4sENRZOiuUAvVUzwKtk9eVu6qrJlo+sGTj/Kgfw9+Suqqb+Pa/03eqb9nUUtzmaB2t0pZXLTEm9vox9lwUa9ZQjVBqPEmTotx87PWjbJpFESgCAS+Rv/jii1W/fn316NFD//rXvzRp0iTde++92nHHgp8goQjuEje5loCX9vlxXN++fcPziV122UUnnniidt55Z7322mtrXZoPEfhToJVaaZnFT8gSY3bX7vraUpzne30fTnwRo1C2pmpmBO0e2kP7WO6wFGT8RCRbWe6yZNr4az+p8tck9+a/fz5eqJttM3nyZB133HGhqLR8+fIaNGiQevbsqT333DPb7kpO7/eggw5Sv3799PHHH2vGjBnhdchzzz1XM2fOzOn7zZ1DAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoDgLZH2h7HAN1y6WWDNEQ9TCsrEzZswYnXfeeapVq5auvfbaUOb5zTffaMSIEfq///s/NWzYcGOX5PIIJCaw66676uabb9bYsWP1+eefh4K5888/P/y8/vOf/wzFc+nc2PbaXgdZHrXEmIZqqP0tD1uycbxQdltLjNLXoRqqXS3FcSqogjpZ/mOJMbHXj7HnolizlNXJbmOJVSibWv8X/VIUd4/bRCDjBZYuXaq77rorFMm+8cYbeuCBB/Tjjz/qggsuUIUKFTJ+/2wwPQEvk3322WfDySpat26tY489Vm3atNHgwXFKQ9PbJdfKBIHttJ1Vv9fQAEuMaamWdqqXYVpiKa4zSqO0iaWeJcmZrMnhGCvJNQuy1hW6Qr0sIyzpjj8fuszizxunWzJp/JhzhSXpWaRFYckYzwWT3uvq6z399NNq3LhxeFx577331Lt3b+22226rX4T/Z5hAu3btNGDAAD333HN66623wvfv7bffzrBdsh0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEhCICcKZWOVKHqJyPeWnS35neHDh+uYY45Ro0aNQoHDgw8+qF9//VXdu3dX8+b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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nxpd.draw(df.iloc[7].str_object.graph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nxpd.draw(df.iloc[7].gen_country.graph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks_old/corpusmadahard.ipynb b/notebooks_old/corpusmadahard.ipynb
deleted file mode 100644
index a6fdaad..0000000
--- a/notebooks_old/corpusmadahard.ipynb
+++ /dev/null
@@ -1,2620 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:00:15.686303Z",
-     "start_time": "2018-05-16T10:00:15.680340Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/Users/jacquesfize/nas_cloud/Code/str-python\n"
-     ]
-    }
-   ],
-   "source": [
-    "%cd .."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:00:15.895344Z",
-     "start_time": "2018-05-16T10:00:15.892752Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from ipywidgets import IntProgress\n",
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:00:16.511473Z",
-     "start_time": "2018-05-16T10:00:16.102310Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%load_ext Cython"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:00:17.122139Z",
-     "start_time": "2018-05-16T10:00:16.556517Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os,re\n",
-    "import numpy as np\n",
-    "output_dir=\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac_ner_spacy/\"\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:00:17.284662Z",
-     "start_time": "2018-05-16T10:00:17.167430Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from helpers.gazeteer_helpers import get_most_common_id,get_data,get_most_common_id_v2,get_most_common_id_alias_v2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T20:55:10.902547Z",
-     "start_time": "2018-05-16T20:55:10.890232Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('GD13263662', -1)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def get_most_common_id_v3(label,lang='fr'):\n",
-    "    id_,score=get_most_common_id_v2(label,lang)\n",
-    "    if id_:\n",
-    "        return id_,score\n",
-    "    if not id_ and lang !='en':\n",
-    "        id_,score=get_most_common_id_v2(label,'en')\n",
-    "        if id_:\n",
-    "            return id_,score\n",
-    "    id_,score=get_most_common_id_alias_v2(label,lang)\n",
-    "    if not id_ and lang !='en':\n",
-    "        id_,score=get_most_common_id_v2(label,'en')\n",
-    "        if id_:\n",
-    "            return id_,score\n",
-    "    return None,-1\n",
-    "get_most_common_id_v3(\"Tibet\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:00:17.624018Z",
-     "start_time": "2018-05-16T10:00:17.395144Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "stop_words={\n",
-    "    \"fr\":set(open(\"/Users/jacquesfize/LOD_DATASETS/language_resources/stop_words_fr.txt\").read().split(\"\\n\")),\n",
-    "    \"en\":set(open(\"/Users/jacquesfize/LOD_DATASETS/language_resources/stop_words_en.txt\").read().split(\"\\n\")),\n",
-    "}\n",
-    "common_words={\n",
-    "    #\"fr\":set(open(\"/Users/jacquesfize/LOD_DATASETS/language_resources/french_common_words.txt\").read().split(\"\\n\")),\n",
-    "    \"fr\":set(json.load(open(\"/Users/jacquesfize/LOD_DATASETS/language_resources/dic_fr.json\"))),\n",
-    "    \"en\":set(open(\"/Users/jacquesfize/LOD_DATASETS/language_resources/english_common_words_filtered.txt\").read().split(\"\\n\")),\n",
-    "}\n",
-    "def disambiguate(label,lang='fr'):\n",
-    "    if re.match(\"^\\d+$\",label):\n",
-    "        return 'O',-1\n",
-    "    if label.lower() in stop_words[lang] or label.lower() in common_words[lang]:\n",
-    "        return 'O',-1\n",
-    "    \n",
-    "    plural=label.rstrip(\"s\")+\"s\"\n",
-    "    if plural.lower() in stop_words[lang] or plural.lower() in common_words[lang]:\n",
-    "        return 'O',-1\n",
-    "    \n",
-    "    id_,score=get_most_common_id_v3(label,lang)\n",
-    "    if id_:\n",
-    "        id_en,score_en=get_most_common_id_v3(label,\"en\")\n",
-    "        if id_en and score_en:\n",
-    "            if score_en> score:\n",
-    "                id_,score=id_en,score_en\n",
-    "        id_alias,score_alias=get_most_common_id_alias_v2(label,lang)\n",
-    "        if id_alias and score_alias:\n",
-    "            if score_alias> score:\n",
-    "                id_,score=id_alias,score_alias\n",
-    "    return id_,score   "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Improving string merge  with C/C++ translation!!!!!**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:00:19.411617Z",
-     "start_time": "2018-05-16T10:00:19.403105Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%cython\n",
-    "\n",
-    "#cdef list ch=[\"Le\",\"pont\",\"d'\",\"avignon\",\"est\",\"-\",\"sympa\"]\n",
-    "def foo2(list ch):\n",
-    "    return [c+(\"\" if c[-1] in [\"\\'\",\"’\",\"-\"] else \" \") for c in ch]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:00:21.268761Z",
-     "start_time": "2018-05-16T10:00:21.226880Z"
-    },
-    "format": "row"
-   },
-   "outputs": [],
-   "source": [
-    "def reformat_data(data):\n",
-    "    index=pd.DataFrame(data[data[\"ent_type_\"] == \"LOC\"].index)\n",
-    "    index[\"diff\"]=index[0].diff()\n",
-    "    index=index.set_index(0)\n",
-    "    data[\"diff\"]=index\n",
-    "    data[\"diff2\"]=(data[(data[\"ent_type_\"]==\"LOC\")][\"diff\"]>1).cumsum()\n",
-    "    mx_=data[\"diff2\"].notnull().max()\n",
-    "    def foo(x):\n",
-    "        if pd.isnull(x).any():\n",
-    "            mx_+=1\n",
-    "            return mx_\n",
-    "        return x\n",
-    "    f={\n",
-    "        'text':lambda x: \"\".join(foo2(list(map(str,x)))).rstrip(),\n",
-    "        'pos_':'max',\n",
-    "        'ent_type_':'max'\n",
-    "\n",
-    "    }\n",
-    "    \n",
-    "    return data.groupby(\"diff2\",as_index=False).agg(f)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:00:23.252597Z",
-     "start_time": "2018-05-16T10:00:23.056531Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/Users/jacquesfize/LOD_DATASETS/raw_bvlac/associated_lang.json\r\n",
-      "/Users/jacquesfize/LOD_DATASETS/raw_bvlac/association.json\r\n",
-      "/Users/jacquesfize/LOD_DATASETS/raw_bvlac/count_agro_eco_mada.json\r\n",
-      "/Users/jacquesfize/LOD_DATASETS/raw_bvlac/tokens.json\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls /Users/jacquesfize/LOD_DATASETS/raw_bvlac/*.json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:00:25.134319Z",
-     "start_time": "2018-05-16T10:00:25.080059Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data_lang=pd.DataFrame(data=list(\n",
-    "    json.load(open(\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac/associated_lang.json\")).items()),\n",
-    "    columns=[\"id_doc\",\"lang\"]\n",
-    ")\n",
-    "data_count_agro=pd.DataFrame(data=list(\n",
-    "    json.load(open(\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac/count_agro_eco_mada.json\")).items()),\n",
-    "    columns=[\"id_doc\",\"count\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:00:27.099706Z",
-     "start_time": "2018-05-16T10:00:27.062820Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data_ext=pd.DataFrame(data=list(\n",
-    "    json.load(open(\"/Users/jacquesfize/LOD_DATASETS/raw_bvlac/association.json\")).items()),\n",
-    "    columns=[\"id_doc\",\"format\"]\n",
-    ")\n",
-    "data_ext[\"format\"]=data_ext[\"format\"].apply(lambda x : x.split(\".\")[-1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:00:28.928246Z",
-     "start_time": "2018-05-16T10:00:28.908037Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "good_lang=data_lang[(data_lang[\"lang\"] == \"fr\") | (data_lang[\"lang\"] == \"en\")]\n",
-    "selected=data_count_agro[data_count_agro[\"id_doc\"].isin(good_lang[\"id_doc\"])]\n",
-    "selected[\"format\"]=data_ext[data_ext[\"id_doc\"].isin(good_lang[\"id_doc\"])][\"format\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:00:30.954784Z",
-     "start_time": "2018-05-16T10:00:30.949524Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "selected=selected[selected[\"count\"]>3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:10:31.246298Z",
-     "start_time": "2018-05-16T09:10:31.220975Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "90848"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "open(\"selected_mada.json\",'w').write(selected.to_csv())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:00:33.484417Z",
-     "start_time": "2018-05-16T10:00:33.477182Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "format,id_doc,count\n",
-      "doc,2163,2163\n",
-      "docx,194,194\n",
-      "html,791,791\n",
-      "pdf,598,598\n",
-      "txt,43,43\n",
-      "xls,826,826\n",
-      "xlsx,35,35\n",
-      "xml,7,7\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(selected.groupby('format').count().to_csv())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:00:35.621805Z",
-     "start_time": "2018-05-16T10:00:35.615322Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "format,id_doc\n",
-      "doc,6651\n",
-      "docx,838\n",
-      "html,3465\n",
-      "pdf,1931\n",
-      "ppt,228\n",
-      "pptx,40\n",
-      "sql,1\n",
-      "txt,157\n",
-      "xls,2544\n",
-      "xlsx,126\n",
-      "xml,29\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(data_ext.groupby('format').count().to_csv())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:10:31.667687Z",
-     "start_time": "2018-05-16T09:10:31.266005Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x114914d68>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x114914550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "selected.plot.hist(bins=100,cumulative=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:01:04.739745Z",
-     "start_time": "2018-05-16T10:01:04.644633Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "categories=pd.qcut(selected[\"count\"],4,labels=range(4)).sort_index()\n",
-    "selected[\"class\"]=categories\n",
-    "selected=selected[selected[\"count\"]>0]\n",
-    "selected=selected.reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:01:22.697656Z",
-     "start_time": "2018-05-16T10:01:22.693785Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def read_csv_ner(path):\n",
-    "    header=[\"text\", \"lemma_\", \"pos_\", \"tag_\", \"dep_\",\"shape_\", \"is_alpha\", \"is_stop\",\"ent_type_\"]\n",
-    "    data=pd.read_csv(path,sep=\"\\t\",names=header,error_bad_lines=False,warn_bad_lines=False,header=None).dropna(axis=0, how='all')\n",
-    "    return data\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T10:01:24.995281Z",
-     "start_time": "2018-05-16T10:01:24.869933Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mkdir: /Users/jacquesfize/LOD_DATASETS/bv_lac_pos_ner_disambiguate_5: File exists\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!mkdir /Users/jacquesfize/LOD_DATASETS/bv_lac_pos_ner_disambiguate_5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:10:31.904875Z",
-     "start_time": "2018-05-16T09:10:31.901715Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "skip=0\n",
-    "skipPercentage=0.10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:10:32.031609Z",
-     "start_time": "2018-05-16T09:10:31.907100Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mkdir: /Users/jacquesfize/LOD_DATASETS/disambiguate_1: File exists\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "%mkdir /Users/jacquesfize/LOD_DATASETS/disambiguate_1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T11:46:45.348959Z",
-     "start_time": "2018-05-16T10:02:19.180944Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "IntProgress(value=0, description='Processing', max=5552)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
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-   "source": [
-    "import sys\n",
-    "i=0\n",
-    "n=len(selected)\n",
-    "import time\n",
-    "p=IntProgress(description=\"Processing\",max=n)\n",
-    "display(p)\n",
-    "\n",
-    "for id_,row in selected.iterrows():\n",
-    "    p.value+=1\n",
-    "    i+=1\n",
-    "    try:\n",
-    "        df=reformat_data(read_csv_ner(output_dir+\"{0}.csv\".format(row[\"id_doc\"])))\n",
-    "    except Exception as e:\n",
-    "        print(\"\\n\",row[\"id_doc\"])\n",
-    "    #df=read_csv_ner(output_dir+\"{0}.csv\".format(row[\"id_doc\"]))\n",
-    "    #df=df[skip:]\n",
-    "    #df=reformat_data(df)\n",
-    "    #skip=int(skipPercentage*len(df))\n",
-    "    #df=df[skip:]\n",
-    "    sys.stdout.write(\"\\r{0}/{1}\".format(i,n))\n",
-    "    if df.empty:\n",
-    "        print(\"\\nEmpty\",row[\"id_doc\"])\n",
-    "        df.to_csv(\"/Users/jacquesfize/LOD_DATASETS/disambiguate_1/{0}.csv\".format(row[\"id_doc\"]))\n",
-    "        continue\n",
-    "    df[\"GID\"]=df[df[\"ent_type_\"] == \"LOC\"][\"text\"].apply(\n",
-    "        lambda x: disambiguate(x,lang=data_lang[data_lang[\"id_doc\"] == row[\"id_doc\"]][\"lang\"].values[0])[0]\n",
-    "    )\n",
-    "    df.to_csv(\"/Users/jacquesfize/LOD_DATASETS/disambiguate_1/{0}.csv\".format(row[\"id_doc\"]))\n",
-    "\n",
-    "    \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:13:16.096156Z",
-     "start_time": "2018-05-16T09:13:15.843266Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df=reformat_data(read_csv_ner(output_dir+\"{0}.csv\".format(2)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.133829Z",
-     "start_time": "2018-05-16T09:10:29.700Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from glob import glob\n",
-    "import numpy as np\n",
-    "import sys\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.135250Z",
-     "start_time": "2018-05-16T09:10:29.702Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "files=glob(\"/Users/jacquesfize/LOD_DATASETS/bv_lac_pos_ner_disambiguate_5/*.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.136229Z",
-     "start_time": "2018-05-16T09:10:29.704Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "i=0\n",
-    "n=len(files)\n",
-    "points={}\n",
-    "p=IntProgress(description=\"Processing\",max=n)\n",
-    "display(p)\n",
-    "for fn in files:\n",
-    "    i+=1\n",
-    "    p.value+=1\n",
-    "    sys.stdout.write(\"\\r{0}/{1}\".format(i,n))\n",
-    "    df=pd.read_csv(fn)\n",
-    "    df=df.fillna(\"O\")\n",
-    "    for id,row in df.iterrows():\n",
-    "        if not row[\"GID\"] or row[\"GID\"] == \"O\":\n",
-    "            continue\n",
-    "        if not row[\"GID\"] in points:\n",
-    "            data=pd.Series(get_data(row[\"GID\"]))\n",
-    "            if \"coord\" in data:\n",
-    "                points[row[\"GID\"]]=[data[\"coord\"][\"lat\"], data[\"coord\"][\"lon\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.137502Z",
-     "start_time": "2018-05-16T09:10:29.706Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "i=0\n",
-    "n=len(files)\n",
-    "count={}\n",
-    "p=IntProgress(description=\"Processing\",max=n)\n",
-    "display(p)\n",
-    "for fn in files:\n",
-    "    i+=1\n",
-    "    p.value+=1\n",
-    "    sys.stdout.write(\"\\r{0}/{1}\".format(i,n))\n",
-    "    df=pd.read_csv(fn)\n",
-    "    df=df.fillna(\"O\")\n",
-    "    for id,row in df.iterrows():\n",
-    "        if not row[\"GID\"] or row[\"GID\"] == \"O\":\n",
-    "            continue\n",
-    "        if not row[\"GID\"] in count:\n",
-    "            count[row[\"GID\"]]=0\n",
-    "        count[row[\"GID\"]]+=1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.139281Z",
-     "start_time": "2018-05-16T09:10:29.708Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "i=0\n",
-    "n=len(files)\n",
-    "count_idf={}\n",
-    "p=IntProgress(description=\"Processing\",max=n)\n",
-    "display(p)\n",
-    "for fn in files:\n",
-    "    i+=1\n",
-    "    p.value+=1\n",
-    "    sys.stdout.write(\"\\r{0}/{1}\".format(i,n))\n",
-    "    df=pd.read_csv(fn)\n",
-    "    df=df.fillna(\"O\")\n",
-    "    for gid in df[\"GID\"].unique():\n",
-    "        if not gid or gid == \"O\":\n",
-    "            continue\n",
-    "        if not gid in count_idf:\n",
-    "            count_idf[gid]=0\n",
-    "        count_idf[gid]+=1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.140723Z",
-     "start_time": "2018-05-16T09:10:29.708Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df=pd.DataFrame.from_dict(points, orient='index')\n",
-    "df=df.rename(columns={0:\"lat\",1:\"lon\"})\n",
-    "df[\"count\"]=pd.DataFrame.from_dict(count, orient='index')\n",
-    "df[\"idf\"]=len(fn)/pd.DataFrame.from_dict(count_idf, orient='index')\n",
-    "df[\"idf\"]=np.log(df.idf)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.142191Z",
-     "start_time": "2018-05-16T09:10:29.710Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.143577Z",
-     "start_time": "2018-05-16T09:10:29.712Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Libraries\n",
-    "from mpl_toolkits.basemap import Basemap\n",
-    "import matplotlib.pyplot as plt\n",
-    " \n",
-    "# Set the dimension of the figure\n",
-    "my_dpi=96\n",
-    "plt.figure(figsize=(2600/my_dpi, 1800/my_dpi), dpi=my_dpi)\n",
-    " \n",
-    "# Make the background map\n",
-    "m=Basemap(llcrnrlon=-180, llcrnrlat=-65,urcrnrlon=180,urcrnrlat=80)\n",
-    "#m.drawmapboundary(fill_color='#A6CAE0', linewidth=0)\n",
-    "m.fillcontinents(color='grey', alpha=0.3)\n",
-    "m.drawcoastlines(linewidth=0.1, color=\"#666666\")\n",
-    "#m.arcgisimage(service='ESRI_Imagery_World_2D', xpixels = 1500)\n",
-    "# Add a point per position\n",
-    "m.scatter(df['lon'], df['lat'], s=df['count'].apply(lambda x:np.log(x))*2,c=df['count'].apply(lambda x:np.log(x)),  cmap=\"YlOrRd\")\n",
-    " \n",
-    "# copyright and source data info\n",
-    "#plt.text( -170, -58,\"Répartition des entités spatiales dans le corpus BVLAC (5500 documents)\", ha='left', va='bottom', size=9, color='#555555' )\n",
-    "m.colorbar()\n",
-    "plt.title(\"Spatial Entities Occurrence in BVLAC Corpus (World Scale)\",fontdict={\"fontsize\":24})\n",
-    "# Save as png\n",
-    "plt.savefig('SE_Dispersion_World_{0}Per.pdf'.format(skipPercentage), bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.145171Z",
-     "start_time": "2018-05-16T09:10:29.714Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Libraries\n",
-    "from mpl_toolkits.basemap import Basemap\n",
-    "import matplotlib.pyplot as plt\n",
-    " \n",
-    "# Set the dimension of the figure\n",
-    "my_dpi=96\n",
-    "plt.figure(figsize=(2600/my_dpi, 1800/my_dpi), dpi=my_dpi)\n",
-    " \n",
-    "# Make the background map 43.2541870461, -25.6014344215, 50.4765368996, -12.0405567359)\n",
-    "m=Basemap(llcrnrlon=43.2541870461, llcrnrlat=-25.6014344215,urcrnrlon=50.4765368996,urcrnrlat=-12.0405567359,resolution='h')\n",
-    "#m.drawmapboundary(fill_color='#A6CAE0', linewidth=0)\n",
-    "m.fillcontinents(color='grey', alpha=0.3)\n",
-    "m.drawcoastlines(linewidth=0.1, color=\"#666666\")\n",
-    "#m.arcgisimage(service='ESRI_Imagery_World_2D', xpixels = 1500)\n",
-    "\n",
-    "df2=df[(df['lon'] > 43.5) & (df['lon'] < 50.47) & (df['lat'] > -25.6) & (df['lat'] < -12.04) ]\n",
-    "\n",
-    "# Add a point per position\n",
-    "#m.scatter(df2['lon'], df2['lat'], s=df2['count']/6, alpha=0.4,  cmap=\"autumn\")\n",
-    "m.scatter(df2['lon'], df2['lat'],s=df2['count'].apply(lambda x:np.log(x))*3, c=df2['count'].apply(lambda x:np.log(x)),  cmap=\"YlOrRd\")\n",
-    "# copyright and source data info\n",
-    "#plt.text( -170, -58,\"Répartition des entités spatiales dans le corpus BVLAC (5500 documents)\", ha='left', va='bottom', size=9, color='#555555' )\n",
-    " \n",
-    "# Save as png\n",
-    "m.colorbar(location='bottom')\n",
-    "plt.title(\"Spatial Entities Occurrence in BVLAC Corpus (Madagascar Scale)\",fontdict={\"fontsize\":15})\n",
-    "plt.savefig('SE_Dispersion_MADA_{0}Per.pdf'.format(skipPercentage), bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.146513Z",
-     "start_time": "2018-05-16T09:10:29.716Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Libraries\n",
-    "from mpl_toolkits.basemap import Basemap\n",
-    "import matplotlib.pyplot as plt\n",
-    " \n",
-    "# Set the dimension of the figure\n",
-    "my_dpi=96\n",
-    "plt.figure(figsize=(2600/my_dpi, 1800/my_dpi), dpi=my_dpi)\n",
-    " \n",
-    "# Make the background map\n",
-    "m=Basemap(llcrnrlon=-180, llcrnrlat=-65,urcrnrlon=180,urcrnrlat=80)\n",
-    "#m.drawmapboundary(fill_color='#A6CAE0', linewidth=0)\n",
-    "m.fillcontinents(color='grey', alpha=0.3)\n",
-    "m.drawcoastlines(linewidth=0.1, color=\"#666666\")\n",
-    "#m.arcgisimage(service='ESRI_Imagery_World_2D', xpixels = 1500)\n",
-    "# Add a point per position\n",
-    "m.scatter(df['lon'], df['lat'], s=df['idf'].apply(lambda x:np.log(x)),c=df['idf'],  cmap=\"autumn\")\n",
-    " \n",
-    "# copyright and source data info\n",
-    "#plt.text( -170, -58,\"Répartition des entités spatiales dans le corpus BVLAC (5500 documents)\", ha='left', va='bottom', size=9, color='#555555' )\n",
-    "m.colorbar()\n",
-    "plt.title(\"Spatial Entities IDF in BVLAC Corpus (World Scale)\",fontdict={\"fontsize\":24})\n",
-    "# Save as png\n",
-    "plt.savefig('SE_Dispersion_IDF_World_{0}Per.pdf'.format(skipPercentage), bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.147911Z",
-     "start_time": "2018-05-16T09:10:29.718Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Libraries\n",
-    "from mpl_toolkits.basemap import Basemap\n",
-    "import matplotlib.pyplot as plt\n",
-    " \n",
-    "# Set the dimension of the figure\n",
-    "my_dpi=96\n",
-    "plt.figure(figsize=(2600/my_dpi, 1800/my_dpi), dpi=my_dpi)\n",
-    " \n",
-    "# Make the background map 43.2541870461, -25.6014344215, 50.4765368996, -12.0405567359)\n",
-    "m=Basemap(llcrnrlon=43.2541870461, llcrnrlat=-25.6,urcrnrlon=50.4765368996,urcrnrlat=-11.5,resolution=\"h\")\n",
-    "#m.drawmapboundary(fill_color='#A6CAE0', linewidth=0)\n",
-    "m.fillcontinents(color='grey', alpha=0.3)\n",
-    "m.drawcoastlines(linewidth=0.1, color=\"#666666\")\n",
-    "#m.arcgisimage(service='ESRI_Imagery_World_2D', xpixels = 1500)\n",
-    "\n",
-    "df2=df[(df['lon'] > 43.5) & (df['lon'] < 50.47) & (df['lat'] > -25.6) & (df['lat'] < -12.04) ]\n",
-    "\n",
-    "# Add a point per position\n",
-    "#m.scatter(df2['lon'], df2['lat'], s=df2['count']/6, alpha=0.4,  cmap=\"autumn\")\n",
-    "m.scatter(df2['lon'], df2['lat'],s=df2['idf'].apply(lambda x:np.log(x)),c=df2['idf'],  cmap=\"YlOrRd\")\n",
-    "# copyright and source data info\n",
-    "#plt.text( -170, -58,\"Répartition des entités spatiales dans le corpus BVLAC (5500 documents)\", ha='left', va='bottom', size=9, color='#555555' )\n",
-    " \n",
-    "# Save as png\n",
-    "m.colorbar(location='bottom')\n",
-    "plt.title(\"Spatial Entities IDF in BVLAC Corpus (Madagascar Scale)\",fontdict={\"fontsize\":15})\n",
-    "plt.savefig('SE_Dispersion_IDF_MADA_{0}Per.pdf'.format(skipPercentage), bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.149138Z",
-     "start_time": "2018-05-16T09:10:29.720Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ch=[\"Le\",\"pont\",\"d'\",\"avignon\",\"est\",\"-\",\"sympa\"]\n",
-    "[c+(\"\" if c[-1] in [\"\\'\",\"-\"] else \" \") for c in ch]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.150112Z",
-     "start_time": "2018-05-16T09:10:29.722Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "files=glob(\"/Users/jacquesfize/LOD_DATASETS/bv_lac_pos_ner_disambiguate_3/*.csv\")\n",
-    "i=0\n",
-    "n=len(files)\n",
-    "old_points={}\n",
-    "for fn in files:\n",
-    "    i+=1\n",
-    "    sys.stdout.write(\"\\r{0}/{1}\".format(i,n))\n",
-    "    df=pd.read_csv(fn)\n",
-    "    df=df.fillna(\"O\")\n",
-    "    for id,row in df.iterrows():\n",
-    "        if not row[\"GID\"] or row[\"GID\"] == \"O\":\n",
-    "            continue\n",
-    "        if not row[\"GID\"] in old_points:\n",
-    "            data=pd.Series(get_data(row[\"GID\"]))\n",
-    "            if \"coord\" in data:\n",
-    "                old_points[row[\"GID\"]]=[data[\"coord\"][\"lat\"], data[\"coord\"][\"lon\"]]\n",
-    "\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.151756Z",
-     "start_time": "2018-05-16T09:10:29.722Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "len(points),len(old_points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.153353Z",
-     "start_time": "2018-05-16T09:10:29.724Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "diff={}\n",
-    "new_keys=list(old_points.keys())\n",
-    "for k in new_keys:\n",
-    "    if not k in points:\n",
-    "        diff[k]=old_points[k]\n",
-    "len(diff)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.155215Z",
-     "start_time": "2018-05-16T09:10:29.726Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Libraries\n",
-    "# Set the dimension of the figure\n",
-    "\n",
-    "df=pd.DataFrame.from_dict(diff, orient='index')\n",
-    "df=df.rename(columns={0:\"lat\",1:\"lon\"})\n",
-    "\n",
-    "my_dpi=96\n",
-    "plt.figure(figsize=(2600/my_dpi, 1800/my_dpi), dpi=my_dpi)\n",
-    " \n",
-    "# Make the background map\n",
-    "m=Basemap(llcrnrlon=-180, llcrnrlat=-65,urcrnrlon=180,urcrnrlat=80)\n",
-    "#m.drawmapboundary(fill_color='#A6CAE0', linewidth=0)\n",
-    "m.fillcontinents(color='grey', alpha=0.3)\n",
-    "m.drawcoastlines(linewidth=0.1, color=\"#666666\")\n",
-    "#m.arcgisimage(service='ESRI_Imagery_World_2D', xpixels = 1500)\n",
-    "# Add a point per position\n",
-    "m.scatter(df['lon'], df['lat'], s=1 ,cmap=\"autumn\")\n",
-    " \n",
-    "# copyright and source data info\n",
-    "#plt.text( -170, -58,\"Répartition des entités spatiales dans le corpus BVLAC (5500 documents)\", ha='left', va='bottom', size=9, color='#555555' )\n",
-    "#m.colorbar()\n",
-    "plt.title(\"Spatial Entities IDF in BVLAC Corpus (World Scale)\",fontdict={\"fontsize\":24})\n",
-    "# Save as png\n",
-    "plt.savefig('SE_Dispersion_Diff_World_{0}Per.pdf'.format(skipPercentage), bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-05-16T09:11:35.156455Z",
-     "start_time": "2018-05-16T09:10:29.728Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Libraries\n",
-    "from mpl_toolkits.basemap import Basemap\n",
-    "import matplotlib.pyplot as plt\n",
-    " \n",
-    "# Set the dimension of the figure\n",
-    "my_dpi=96\n",
-    "plt.figure(figsize=(2600/my_dpi, 1800/my_dpi), dpi=my_dpi)\n",
-    " \n",
-    "# Make the background map 43.2541870461, -25.6014344215, 50.4765368996, -12.0405567359)\n",
-    "m=Basemap(llcrnrlon=43.2541870461, llcrnrlat=-25.6,urcrnrlon=50.4765368996,urcrnrlat=-11.5,resolution=\"h\")\n",
-    "#m.drawmapboundary(fill_color='#A6CAE0', linewidth=0)\n",
-    "m.fillcontinents(color='grey', alpha=0.3)\n",
-    "m.drawcoastlines(linewidth=0.1, color=\"#666666\")\n",
-    "#m.arcgisimage(service='ESRI_Imagery_World_2D', xpixels = 1500)\n",
-    "\n",
-    "df2=df[(df['lon'] > 43.5) & (df['lon'] < 50.47) & (df['lat'] > -25.6) & (df['lat'] < -12.04) ]\n",
-    "\n",
-    "# Add a point per position\n",
-    "#m.scatter(df2['lon'], df2['lat'], s=df2['count']/6, alpha=0.4,  cmap=\"autumn\")\n",
-    "m.scatter(df2['lon'], df2['lat'],s=1,  cmap=\"YlOrRd\")\n",
-    "# copyright and source data info\n",
-    "#plt.text( -170, -58,\"Répartition des entités spatiales dans le corpus BVLAC (5500 documents)\", ha='left', va='bottom', size=9, color='#555555' )\n",
-    " \n",
-    "# Save as png\n",
-    "#m.colorbar(location='bottom')\n",
-    "plt.title(\"Spatial Entities IDF in BVLAC Corpus (Madagascar Scale)\",fontdict={\"fontsize\":15})\n",
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diff --git a/notebooks_old/intersection.pdf b/notebooks_old/intersection.pdf
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diff --git a/run_automatic_annotation.py b/run_automatic_annotation.py
deleted file mode 100644
index 57a92c4..0000000
--- a/run_automatic_annotation.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# coding = utf-8
-
-import argparse, shutil, os
-import logging, json
-from mytoolbox.env import yes_or_no
-from auto_fill_annotation import main
-
-for _ in ("boto", "elasticsearch", "urllib3", "sklearn"):
-    logging.getLogger(_).setLevel(logging.CRITICAL)
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument("dataset", help="Name of the dataset")
-parser.add_argument("sim_matrix_dir", help="Similarity Matrix Directory")
-parser.add_argument("graph_data_dir", help="STR without transformation graph directory")
-parser.add_argument("adjacency_fn", help="Adjacency Information json filename")
-parser.add_argument("inclusion_fn", help="Inclusion Information json filename")
-parser.add_argument("selected_json_file", help="Filename containing the STR graph you want to make your evaluation on")
-parser.add_argument("length_json_file", help="Filename containing the STR text length")
-parser.add_argument("-t", "--threshold", default=0.5, help="Threshold for the third criteria")
-parser.add_argument("-g", "--ming1",type=int, default=0, help="Return evaluation results based on min size for G1")
-parser.add_argument("-j", "--ming2",type=int, default=0, help="Return evaluation results based on min size for G2")
-parser.add_argument("-m", "--nb_car_doc1",type=int, default=0, help="Return evaluation results based on min size of associated text for G1")
-parser.add_argument("-n", "--nb_car_doc2",type=int, default=0, help="Return evaluation results based on min size of associated text for G2")
-
-parser.add_argument("--formatg1",type=str, default="", help="Return evaluation results based on min size of associated text for G1")
-parser.add_argument("--formatfile",type=str, default=None, help="Return evaluation results based on min size of associated text for G2")
-
-args = parser.parse_args()
-# if os.path.exists("temp_cluster") and yes_or_no("Do you want to compute STR's clusters all over again ?"):
-#     shutil.rmtree('temp_cluster', ignore_errors=True)
-#     os.makedirs("temp_cluster")
-
-
-
-main(args.dataset,
-     args.sim_matrix_dir,
-     args.graph_data_dir,
-     json.load(open(args.selected_json_file)),
-     args.threshold,
-     args.inclusion_fn,
-     args.adjacency_fn,
-     args.length_json_file,
-     args.ming1,
-     args.ming2,
-     args.nb_car_doc1,
-     args.nb_car_doc2,
-     args.formatg1,
-     args.formatfile)
diff --git a/run_test.py b/run_test.py
deleted file mode 100644
index d200d18..0000000
--- a/run_test.py
+++ /dev/null
@@ -1,134 +0,0 @@
-# coding = utf-8
-import argparse
-import os
-
-import pandas as pd
-import numpy as np
-
-from tqdm import tqdm
-from skcriteria.madm import closeness, simple
-from skcriteria import Data, MIN, MAX
-
-
-def pareto_frontier_multi(myArray):
-    # Sort on first dimension
-    myArray = myArray[myArray[:, 0].argsort()]
-    # Add first row to pareto_frontier
-    pareto_frontier = myArray[0:1, :]
-    indices, i = [], 1
-    # Test next row against the last row in pareto_frontier
-    for row in myArray[1:, :]:
-        if sum([row[x] >= pareto_frontier[-1][x]
-                for x in range(len(row))]) == len(row):
-            # If it is better on all features add the row to pareto_frontier
-            pareto_frontier = np.concatenate((pareto_frontier, [row]))
-            indices.append(i)
-        i += 1
-    return indices, pareto_frontier
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument("input")
-parser.add_argument("output_fn")
-parser.add_argument("-t","--topn",type=int,default=5)
-args = parser.parse_args()
-
-writer = pd.ExcelWriter(args.output_fn, engine='xlsxwriter')
-
-if not os.path.exists(args.input):
-    raise FileNotFoundError("{0} does not exists !".format(args.input))
-
-data = pd.read_csv(args.input, index_col=0)
-if len(data) == 0:
-    write_excel(writer, result, args.input.split("/")[-1])
-    exit()
-data["mesure"] = data.mesure.apply(lambda x: "BOW" if x == "BagOfNodes" else x)
-data["sum"] = data["c1 c2 c3 c4 c5".split()].sum(axis=1)
-
-combination_pareto_criteria = [
-    ("c1_c2_c3_c4_c5_c6", "c1 c2 c3 c4 c5 c6".split()),
-    ("c1_c2_c3", "c1 c2 c3".split()),
-    ("c3_c4_c5", "c3 c4 c5".split()),
-    ("c2_c5_c6", "c2 c5 c6".split()),
-    ("c5_c6", "c5 c6".split()),
-    ("c2_c5", "c2 c5".split()),
-    ("c6", "c6".split())
-]
-
-weight_criteria = [
-    ("c1_c2_c3_c4_c5_c6", [0.16, 0.16, 0.16, 0.16, 0.16, 0.16]),
-    ("c1_c2_c3", [0.33, 0.33, 0.33, 0., 0., 0.]),
-    ("c3_c4_c5", [0., 0., 0.33, 0.33, 0.33, 0]),
-    ("c2_c5_c6", [0., 0., 0.33, 0., 0.33,0.33]),
-    ("c5_c6", [0., 0., 0., 0., 0.5, 0.5]),
-    ("c2_c5", [0., 0.5, 0., 0., 0.5, 0.]),
-    ("c6", [0., 0., 0., 0., 0.,1.])
-]
-
-
-def get_top_combination_wsm(dataframe, weights,topn):
-    data = dataframe["c1 c2 c3 c4 c5".split()].values
-    dd = Data(data, criteria=[MAX, MAX, MAX, MAX, MAX], weights=weights[1])
-    index_max = np.argsort(simple.WeightedSum().decide(dd)._rank)[:topn]
-    df = dataframe.iloc[index_max]
-    df["name"]=weights[0]
-    df["type_score"] = "wsm"
-    return df
-
-
-def get_top_combination_pareto(dataframe, columns,topn):
-    index, data_pa = pareto_frontier_multi(dataframe[columns[1]].values)
-    df = data.iloc[index]
-    df = df.sort_values(by = "sum",ascending=False).head(topn)
-    df["name"]=columns[0]
-    df["type_score"] = "pareto"
-    return df
-
-
-def write_excel(writer, dataframe, title):
-    dataframe.to_excel(writer, "result", index=False)
-    number_of_rows=len(dataframe)
-    worksheet = writer.sheets["result"]
-    worksheet.set_header(title)
-    workbook = writer.book
-    C_letter = 67
-    I_letter= 73
-
-    format1 = workbook.add_format({'bg_color': '#FFC7CE',
-                                   'font_color': '#9C0006'})
-
-    # Add a format. Green fill with dark green text.
-    format2 = workbook.add_format({'bg_color': '#C6EFCE',
-                                   'font_color': '#006100'})
-    for i in range(C_letter,I_letter):
-        begin=2
-        for end in range(6,number_of_rows + 1,5):
-            ch_=chr(i)
-            color_range = "{0}{1}:{0}{2}".format(ch_,begin,end)
-            worksheet.conditional_format(color_range, {'type': 'bottom',
-                                                       'value': '1',
-                                                       'format': format1})
-
-            worksheet.conditional_format(color_range, {'type': 'top',
-                                                       'value': '1',
-                                                       'format': format2})
-            begin=end+1
-    writer.save()
-
-result = None
-for comb_ in tqdm(combination_pareto_criteria, desc="Pareto computation"):
-    dd = get_top_combination_pareto(data, comb_,args.topn)
-    if not isinstance(result,pd.DataFrame):
-        result = dd
-    else:
-        result = pd.concat((result,dd),axis=0)
-
-for weight in tqdm(weight_criteria, desc="WSM computation"):
-    dd= get_top_combination_wsm(data, weight,args.topn)
-    if not isinstance(result,pd.DataFrame):
-        result = dd
-    else:
-        result = pd.concat((result,dd),axis=0)
-
-
-write_excel(writer,result,args.input.split("/")[-1])
\ No newline at end of file
diff --git a/run_test_comparedto.py b/run_test_comparedto.py
deleted file mode 100644
index 2b15ad6..0000000
--- a/run_test_comparedto.py
+++ /dev/null
@@ -1,166 +0,0 @@
-# coding = utf-8
-import argparse
-import os
-
-import pandas as pd
-import numpy as np
-
-from tqdm import tqdm
-from skcriteria.madm import closeness, simple
-from skcriteria import Data, MIN, MAX
-
-def identify_pareto(scores):
-    # Count number of items
-    population_size = scores.shape[0]
-    # Create a NumPy index for scores on the pareto front (zero indexed)
-    population_ids = np.arange(population_size)
-    # Create a starting list of items on the Pareto front
-    # All items start off as being labelled as on the Parteo front
-    pareto_front = np.ones(population_size, dtype=bool)
-    # Loop through each item. This will then be compared with all other items
-    for i in range(population_size):
-        # Loop through all other items
-        for j in range(population_size):
-            # Check if our 'i' pint is dominated by out 'j' point
-            if all(scores[j] >= scores[i]) and any(scores[j] > scores[i]):
-                # j dominates i. Label 'i' point as not on Pareto front
-                pareto_front[i] = 0
-                # Stop further comparisons with 'i' (no more comparisons needed)
-                break
-    # Return ids of scenarios on pareto front
-    return pareto_front,population_ids[pareto_front]
-
-def pareto_frontier_multi(myArray):
-    # Sort on first dimension
-    myArray = myArray[myArray[:, 0].argsort()]
-    # Add first row to pareto_frontier
-    pareto_frontier = myArray[0:1, :]
-    indices, i = [], 1
-    # Test next row against the last row in pareto_frontier
-    for row in myArray[1:, :]:
-        if sum([row[x] >= pareto_frontier[-1][x]
-                for x in range(len(row))]) == len(row):
-            # If it is better on all features add the row to pareto_frontier
-            pareto_frontier = np.concatenate((pareto_frontier, [row]))
-            indices.append(i)
-        i += 1
-    return indices, pareto_frontier
-
-def evolution(dataframe,mesure,type_,col="c1 c2 c3 c4 c5 c6 sum mean".split()):
-    dataframe2=dataframe.copy()
-    dataframe2.iloc[:,2:2+len(col)] = dataframe2[col].values - dataframe2[(dataframe2.mesure == mesure) & (dataframe2.type == type_)][col].values
-    return dataframe2
-
-parser = argparse.ArgumentParser()
-parser.add_argument("input")
-parser.add_argument("output_fn")
-parser.add_argument("-n","--topn",type=int,default=5)
-parser.add_argument("-m","--mesure",type=str,default="BOW")
-parser.add_argument("-t","--type",type=str,default="str_object")
-args = parser.parse_args()
-
-writer = pd.ExcelWriter(args.output_fn, engine='xlsxwriter')
-
-if not os.path.exists(args.input):
-    raise FileNotFoundError("{0} does not exists !".format(args.input))
-
-data = pd.read_csv(args.input, index_col=0)
-data = data.fillna(0)
-
-if len(data) == 0:
-    write_excel(writer, result, args.input.split("/")[-1])
-    exit()
-data["mesure"] = data.mesure.apply(lambda x: "BOW" if x == "BagOfNodes" else x)
-data["sum"] = data["c1 c2 c3 c4 c5 c6".split()].sum(axis=1)
-data["mean"] = data["c1 c2 c3 c4 c5 c6".split()].mean(axis=1)
-data = evolution(data, args.mesure, args.type)
-
-combination_pareto_criteria = [
-    ("c1_c2_c3_c4_c5_c6", "c1 c2 c3 c4 c5 c6".split()),
-    # ("c1_c2_c3", "c1 c2 c3".split()),
-    # ("c3_c4_c5", "c3 c4 c5".split()),
-    ("c2_c5_c6", "c2 c5 c6".split()),
-    ("c5_c6", "c5 c6".split()),
-    ("c2_c5", "c2 c5".split()),
-    ("c6", "c6".split()),
-    ("sum", "sum".split())
-]
-
-weight_criteria = [
-    ("c1_c2_c3_c4_c5_c6", [.16,.16, .16, .16, .16, .16]),
-    # ("c1_c2_c3", [0.33, 0.33, 0.33, 0., 0., 0.]),
-    # ("c3_c4_c5", [0., 0., 0.33, 0.33, 0.33, 0]),
-    ("c2_c5_c6", [0., .33, 0., 0.,.33,.33]),
-    ("c5_c6", [0., 0., 0., 0., .5,.5]),
-    ("c2_c5", [0., .5, 0., 0., .5, 0.]),
-    # ("c6", [0., 0., 0., 0., 0.,1.])
-]
-
-
-
-def get_top_combination_wsm(dataframe, weights,topn):
-    datas = (dataframe["c1 c2 c3 c4 c5 c6".split()].values)
-    #dd = Data(datas, criteria=[MAX, MAX, MAX, MAX, MAX, MAX], weights=weights[1])
-    index_max = np.argsort(np.dot(datas, weights[1]))[::-1][:topn]
-    df = dataframe.iloc[index_max]
-
-    df["name"]=weights[0]
-    df["type_score"] = "wsm"
-    return df
-
-
-def get_top_combination_pareto(dataframe, columns,topn):
-    index, data_pa = identify_pareto(dataframe[columns[1]].values)
-    df = dataframe.iloc[index]
-    df = df.sort_values(by = "sum",ascending=False).head(topn)
-    df["name"]=columns[0]
-    df["type_score"] = "pareto"
-    return df
-
-
-def write_excel(writer, dataframe, title):
-    dataframe.to_excel(writer, "result", index=False)
-    number_of_rows=len(dataframe)
-    worksheet = writer.sheets["result"]
-    worksheet.set_header(title)
-    workbook = writer.book
-    C_letter = 67
-    J_letter= 74
-
-    format1 = workbook.add_format({'bg_color': '#FFC7CE',
-                                   'font_color': '#9C0006'})
-
-    # Add a format. Green fill with dark green text.
-    format2 = workbook.add_format({'bg_color': '#C6EFCE',
-                                   'font_color': '#006100'})
-    for i in range(C_letter,J_letter):
-        begin=2
-
-        ch_=chr(i)
-        color_range = "{0}{1}:{0}{2}".format(ch_,begin,number_of_rows)
-        worksheet.conditional_format(color_range, {'type': 'bottom',
-                                                   'value': '1',
-                                                   'format': format1})
-
-        worksheet.conditional_format(color_range, {'type': 'top',
-                                                   'value': '1',
-                                                   'format': format2})
-    writer.save()
-
-result = None
-for comb_ in tqdm(combination_pareto_criteria, desc="Pareto computation"):
-    dd = get_top_combination_pareto(data, comb_,args.topn)
-    if not isinstance(result,pd.DataFrame):
-        result = dd
-    else:
-        result = pd.concat((result,dd),axis=0)
-
-for weight in tqdm(weight_criteria, desc="WSM computation"):
-    dd= get_top_combination_wsm(data, weight,args.topn)
-    if not isinstance(result,pd.DataFrame):
-        result = dd
-    else:
-        result = pd.concat((result,dd),axis=0)
-
-
-write_excel(writer,result,args.input.split("/")[-1])
\ No newline at end of file
-- 
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