Repository: datactive/bigbang (language: python)

Issue:
refactor old bigbang code into ingress/analysis/visualization



Discussion / hints:

Fix patch:
diff --git a/bigbang/entity_resolution.py b/bigbang/analysis/entity_resolution.py
similarity index 100%
rename from bigbang/entity_resolution.py
rename to bigbang/analysis/entity_resolution.py
diff --git a/bigbang/graph.py b/bigbang/analysis/graph.py
similarity index 100%
rename from bigbang/graph.py
rename to bigbang/analysis/graph.py
diff --git a/bigbang/process.py b/bigbang/analysis/process.py
similarity index 100%
rename from bigbang/process.py
rename to bigbang/analysis/process.py
diff --git a/bigbang/repo_loader.py b/bigbang/analysis/repo_loader.py
similarity index 98%
rename from bigbang/repo_loader.py
rename to bigbang/analysis/repo_loader.py
index c9104356..a0a1c624 100644
--- a/bigbang/repo_loader.py
+++ b/bigbang/analysis/repo_loader.py
@@ -14,7 +14,7 @@
 
 from config.config import CONFIG
 
-from .git_repo import GitRepo, MultiGitRepo
+from bigbang.ingress.git_repo import GitRepo, MultiGitRepo
 
 repoLocation = CONFIG.repo_path
 examplesLocation = CONFIG.urls_path
@@ -37,7 +37,7 @@ def url_to_name(url):
     Converts a github url (e.g. https://github.com/sbenthall/bigbang.git) to
     a human-readable name (bigbang) by looking at the word between the last "/" and ".git".
     """
-    url = url.replace("\n","")
+    url = url.replace("\n", "")
     name = nameRegex.search(url).group(1)
     return name
 
diff --git a/bigbang/thread.py b/bigbang/analysis/thread.py
similarity index 100%
rename from bigbang/thread.py
rename to bigbang/analysis/thread.py
diff --git a/bigbang/twopeople.py b/bigbang/analysis/twopeople.py
similarity index 100%
rename from bigbang/twopeople.py
rename to bigbang/analysis/twopeople.py
diff --git a/bigbang/archive.py b/bigbang/archive.py
index e6e01e72..eac2fd07 100644
--- a/bigbang/archive.py
+++ b/bigbang/archive.py
@@ -15,10 +15,10 @@
 import pytz
 
 import bigbang.analysis.utils as analysis_utils
-import bigbang.mailman as mailman
+import bigbang.ingress.mailman as mailman
 from bigbang.parse import get_date, get_text
-import bigbang.process as process
-from bigbang.thread import Node, Thread
+import bigbang.analysis.process as process
+from bigbang.analysis.thread import Node, Thread
 from config.config import CONFIG
 
 from . import utils
diff --git a/bigbang/git_repo.py b/bigbang/ingress/git_repo.py
similarity index 97%
rename from bigbang/git_repo.py
rename to bigbang/ingress/git_repo.py
index f2e1979f..fe8d8ef9 100644
--- a/bigbang/git_repo.py
+++ b/bigbang/ingress/git_repo.py
@@ -10,8 +10,8 @@
 
 from config.config import CONFIG
 
-from . import utils
-from .entity_resolution import entity_resolve
+from bigbang import utils
+from bigbang.analysis.entity_resolution import entity_resolve
 
 ALL_ATTRIBUTES = (
     CONFIG.all_attributes
@@ -145,7 +145,7 @@ def populate_data(self, attribs=ALL_ATTRIBUTES):
 
         time_index = pd.DatetimeIndex(raw["Time"]).to_period("H")
         time_index = utils.add_freq(time_index, freq=None)
-        self._commit_data = pd.DataFrame(raw, index = time_index);
+        self._commit_data = pd.DataFrame(raw, index=time_index)
 
     def by_committer(self):
         """Return commit data grouped by commiter."""
diff --git a/bigbang/mailman.py b/bigbang/ingress/mailman.py
similarity index 99%
rename from bigbang/mailman.py
rename to bigbang/ingress/mailman.py
index 9cd73f30..e9d1de4e 100644
--- a/bigbang/mailman.py
+++ b/bigbang/ingress/mailman.py
@@ -24,7 +24,7 @@
 
 from config.config import CONFIG
 
-from . import listserv, parse, w3crawl
+from .. import listserv, parse, w3crawl
 
 ml_exp = re.compile(r"/([\w-]+)/?$")
 txt_exp = re.compile(r'href="(\d\d\d\d-\w*\.txt)"')
diff --git a/bigbang/plot.py b/bigbang/visualisation/plot.py
similarity index 100%
rename from bigbang/plot.py
rename to bigbang/visualisation/plot.py
diff --git a/bigbang/w3crawl.py b/bigbang/w3crawl.py
index 9aba6e11..9b49380d 100644
--- a/bigbang/w3crawl.py
+++ b/bigbang/w3crawl.py
@@ -16,7 +16,7 @@
 from bs4 import BeautifulSoup
 
 import bigbang.archive as archive
-import bigbang.mailman as mailman
+import bigbang.ingress.mailman as mailman
 
 
 class W3cMailingListArchivesParser(email.parser.Parser):
diff --git a/bin/collect_git.py b/bin/collect_git.py
index d9fb356f..9a75d48f 100644
--- a/bin/collect_git.py
+++ b/bin/collect_git.py
@@ -1,7 +1,7 @@
-import sys;
+import sys
 import getopt
 
-import bigbang.repo_loader as loader
+import bigbang.analysis.repo_loader as loader
 
 
 def main(argv):
@@ -12,31 +12,34 @@ def main(argv):
     try:
         opts, args = getopt.getopt(argv, "u:f:g:", ["update"])
     except getopt.GetoptError as e:
-        print('GetoptError: %s' % (e))
+        print("GetoptError: %s" % (e))
         sys.exit(2)
 
-    
     for opt, arg in opts:
         print((opt, arg))
-        if opt == '-f':
-            load_by_file(arg, update);
+        if opt == "-f":
+            load_by_file(arg, update)
             sys.exit()
-        elif opt == '-u':
-            load_by_URL(arg, update);
-        elif opt == '-g':
-            load_by_org(arg, update);
+        elif opt == "-u":
+            load_by_URL(arg, update)
+        elif opt == "-g":
+            load_by_org(arg, update)
 
-def load_by_file(filePath, update = False):
-    urls = open(filePath, "r");
+
+def load_by_file(filePath, update=False):
+    urls = open(filePath, "r")
 
     for url in urls:
-        loader.get_repo(url, "remote", update);
+        loader.get_repo(url, "remote", update)
+
+
+def load_by_URL(url, update=False):
+    loader.get_repo(url, "remote", update)
+
 
-def load_by_URL(url, update = False):
-    loader.get_repo(url, "remote", update);
+def load_by_org(org_name, update=False):
+    loader.get_org_repos(org_name)
 
-def load_by_org(org_name, update = False):
-    loader.get_org_repos(org_name);
 
 if __name__ == "__main__":
     main(sys.argv[1:])
diff --git a/bin/collect_mail.py b/bin/collect_mail.py
index 831523c5..7bb70377 100644
--- a/bin/collect_mail.py
+++ b/bin/collect_mail.py
@@ -4,7 +4,7 @@
 import sys
 from argparse import RawTextHelpFormatter
 
-import bigbang.mailman as mailman
+import bigbang.ingress.mailman as mailman
 
 parser = argparse.ArgumentParser(
     formatter_class=RawTextHelpFormatter,
diff --git a/bin/mail_to_activity.py b/bin/mail_to_activity.py
index b7e8de24..a0dd7f80 100644
--- a/bin/mail_to_activity.py
+++ b/bin/mail_to_activity.py
@@ -2,13 +2,15 @@
 import os
 import os.path
 import getopt
-import bigbang.mailman as mailman
+import bigbang.ingress.mailman as mailman
 import bigbang.archive
 import pandas as pd
 import logging
 import argparse
 
-parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter, description=r"""
+parser = argparse.ArgumentParser(
+    formatter_class=argparse.RawTextHelpFormatter,
+    description=r"""
 Creates activity frames from mailing list archives.
 
 Provide the path to a directory of archives, containing subdirectories for each mailing list.
@@ -17,38 +19,61 @@
 
 python bin/mail_to_activity.py --archives ../archives
 
-""")
+""",
+)
 
-parser.add_argument('--archives', type=str, help='Path to a specified directory of downloaded mail archives', required=True)
+parser.add_argument(
+    "--archives",
+    type=str,
+    help="Path to a specified directory of downloaded mail archives",
+    required=True,
+)
 
-parser.add_argument('-f', '--force', action='store_true', help='Overwrite existing -activity.csv files; by default this is false and directories with an existing file are skipped.')
+parser.add_argument(
+    "-f",
+    "--force",
+    action="store_true",
+    help="Overwrite existing -activity.csv files; by default this is false and directories with an existing file are skipped.",
+)
 
 args = parser.parse_args()
 
 logging.basicConfig(level=logging.INFO)
 
+
 def main(args):
     subdirectories = next(os.walk(args.archives))[1]
 
     for subdirectory in subdirectories:
-        logging.info('Processing archives in %s' % subdirectory)
+        logging.info("Processing archives in %s" % subdirectory)
 
         if not args.force:
-            out_path = os.path.join(args.archives, subdirectory, ('%s-activity.csv' % subdirectory))
-            if os.path.isfile(out_path): # if file already exists, skip
+            out_path = os.path.join(
+                args.archives, subdirectory, ("%s-activity.csv" % subdirectory)
+            )
+            if os.path.isfile(out_path):  # if file already exists, skip
                 continue
         try:
             archives = mailman.open_list_archives(subdirectory, args.archives)
             activity = bigbang.archive.Archive(archives).get_activity()
             # sum the message count, rather than by date, to prevent enormous, sparse files
-            activity = pd.DataFrame(activity.sum(0), columns=['Message Count'])
+            activity = pd.DataFrame(activity.sum(0), columns=["Message Count"])
 
-            out_path = os.path.join(args.archives, subdirectory, ('%s-activity.csv' % subdirectory))
+            out_path = os.path.join(
+                args.archives, subdirectory, ("%s-activity.csv" % subdirectory)
+            )
 
-            with open(out_path, 'w') as f:
-                activity.to_csv(f, encoding='utf-8')
+            with open(out_path, "w") as f:
+                activity.to_csv(f, encoding="utf-8")
         except Exception:
-            logging.warning(('Failed to produce activity frame export for %s.' % subdirectory), exc_info=True)
+            logging.warning(
+                (
+                    "Failed to produce activity frame export for %s."
+                    % subdirectory
+                ),
+                exc_info=True,
+            )
+
 
 if __name__ == "__main__":
     main(args)
diff --git a/bin/tenure.py b/bin/tenure.py
index f4d1a061..4b15a293 100644
--- a/bin/tenure.py
+++ b/bin/tenure.py
@@ -2,7 +2,7 @@
 import os
 import os.path
 import getopt
-import bigbang.mailman as mailman
+import bigbang.ingress.mailman as mailman
 import bigbang.archive
 import pandas as pd
 import numpy as np
@@ -11,7 +11,9 @@
 import enlighten
 import gc
 
-parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter, description=r"""
+parser = argparse.ArgumentParser(
+    formatter_class=argparse.RawTextHelpFormatter,
+    description=r"""
 Calculates the tenure and total messages sent by each person.
 
 Provide the path to a directory of archives, containing subdirectories for each mailing list.
@@ -21,31 +23,52 @@
 python bin/tenure.py --archives ../archives
 
 With the `--combine` parameter, gathers and merges together the values in all subdirectory CSV files into a single combined tenure CSV file in the archives directory.
-""")
+""",
+)
+
+parser.add_argument(
+    "--archives",
+    type=str,
+    help="Path to a specified directory of downloaded mail archives",
+    required=True,
+)
+parser.add_argument(
+    "-f",
+    "--force",
+    action="store_true",
+    help="Overwrite existing -tenure.csv files; by default this is false and directories with an existing file are skipped.",
+)
+parser.add_argument(
+    "-c",
+    "--combine",
+    action="store_true",
+    help="Aggregate values from all subdirectories with existing tenure files into a single CSV.",
+)
 
-parser.add_argument('--archives', type=str, help='Path to a specified directory of downloaded mail archives', required=True)
-parser.add_argument('-f', '--force', action='store_true', help='Overwrite existing -tenure.csv files; by default this is false and directories with an existing file are skipped.')
-parser.add_argument('-c', '--combine', action='store_true', help='Aggregate values from all subdirectories with existing tenure files into a single CSV.')
 
 logging.basicConfig(level=logging.DEBUG)
 logging.getLogger("chardet.charsetprober").setLevel(logging.WARNING)
 logging.getLogger("chardet.universaldetector").setLevel(logging.WARNING)
 
-# these standalone earliest, latest and total_messages methods aren't currently in use, but they'll be useful again soon.
+
 def earliest(s):
     """
     Finds the earliest date from the columns of an activity dataframe that show messages sent per date.
 
     Takes a pandas.Series where the columns are the dates (and a couple of calculated columns which will be ignored for the purposes of the calculation) and the cells are the number of messages sent on each date.
     """
-    just_dates = s.drop(['Earliest Date', 'Latest Date', 'Total Messages', 'Tenure'], errors='ignore')
-    
+    # these standalone earliest, latest and total_messages methods aren't currently in use, but they'll be useful again soon.
+    just_dates = s.drop(
+        ["Earliest Date", "Latest Date", "Total Messages", "Tenure"],
+        errors="ignore",
+    )
+
     earliest = None
 
-    if 'Earliest Date' in s.index:
-        if pd.notna(s['Earliest Date']):
-            earliest = s['Earliest Date']
-    
+    if "Earliest Date" in s.index:
+        if pd.notna(s["Earliest Date"]):
+            earliest = s["Earliest Date"]
+
     # limited to all the dates where the value is greater than 0
     # then take the array of the dates and calculate the min value
     earliest_date = just_dates.loc[just_dates > 0].index.min()
@@ -55,20 +78,24 @@ def earliest(s):
             return earliest
     return earliest_date
 
+
 def latest(s):
     """
     Finds the latest date from the columns of an activity dataframe that show messages sent per date.
 
     Takes a pandas.Series where the columns are the dates (and a couple of calculated columns which will be ignored for the purposes of the calculation) and the cells are the number of messages sent on each date.
     """
-    just_dates = s.drop(['Earliest Date', 'Latest Date', 'Total Messages', 'Tenure'], errors='ignore')
-    
+    just_dates = s.drop(
+        ["Earliest Date", "Latest Date", "Total Messages", "Tenure"],
+        errors="ignore",
+    )
+
     latest = None
 
-    if 'Latest Date' in s.index:
-        if pd.notna(s['Latest Date']):
-            latest = s['Latest Date']
-    
+    if "Latest Date" in s.index:
+        if pd.notna(s["Latest Date"]):
+            latest = s["Latest Date"]
+
     # limited to all the dates where the value is greater than 0
     # then take the array of the dates and calculate the max value
     latest_date = just_dates.loc[just_dates > 0].index.max()
@@ -78,75 +105,99 @@ def latest(s):
             return latest
     return latest_date
 
+
 def total_messages(s):
     """
     Finds the total number of messages sent from an activity dataframe that show messages sent per date.
 
     Takes a pandas.Series where the columns are the dates (and a couple of calculated columns which will be ignored for the purposes of the calculation) and the cells are the number of messages sent on each date. This does not ignore but instead adds up an existing 'Total Messages' column.
     """
-    just_dates = s.drop(['Earliest Date', 'Latest Date', 'Tenure'], errors='ignore')
+    just_dates = s.drop(
+        ["Earliest Date", "Latest Date", "Tenure"], errors="ignore"
+    )
     return just_dates.sum()
 
+
 def main(args):
     subdirectories = next(os.walk(args.archives))[1]
 
     if args.combine:
-        combined_out_path = os.path.join(args.archives, 'combined-tenure.csv')
+        combined_out_path = os.path.join(args.archives, "combined-tenure.csv")
 
         combined_df = None
-        combined_lists = [] # list of all the mailing lists included in the combined dataframe
+        combined_lists = (
+            []
+        )  # list of all the mailing lists included in the combined dataframe
         for subdirectory in subdirectories:
-            in_path = os.path.join(args.archives, subdirectory, ('%s-tenure.csv' % subdirectory))
+            in_path = os.path.join(
+                args.archives, subdirectory, ("%s-tenure.csv" % subdirectory)
+            )
             if os.path.isfile(in_path):
-                in_df = pd.read_csv(in_path, encoding='utf-8', index_col=0)
+                in_df = pd.read_csv(in_path, encoding="utf-8", index_col=0)
                 if combined_df is not None:
                     both = pd.concat([in_df, combined_df])
-                    combined_df = both.groupby(both.index).agg({'Earliest Date': np.min, 'Latest Date': np.max, 'Total Messages': np.sum})                    
-                    combined_lists.append(subdirectory) # add subdirectory name to combined_lists
-                    logging.info('Merged tenure from %s' % subdirectory)
+                    combined_df = both.groupby(both.index).agg(
+                        {
+                            "Earliest Date": np.min,
+                            "Latest Date": np.max,
+                            "Total Messages": np.sum,
+                        }
+                    )
+                    combined_lists.append(
+                        subdirectory
+                    )  # add subdirectory name to combined_lists
+                    logging.info("Merged tenure from %s" % subdirectory)
                 else:
                     combined_df = in_df
                     combined_lists = [subdirectory]
-                    logging.info('Started with tenure from %s' % subdirectory)
+                    logging.info("Started with tenure from %s" % subdirectory)
             else:
-                logging.warning('No tenure file in %s' % subdirectory)
-        
-        with open(combined_out_path, 'w') as f:
-            combined_df.to_csv(f, encoding='utf-8')
-            logging.info('Completed combined tenure frame output.')
-            logging.info('Subdirectories included: %s' % ','.join(combined_lists))
+                logging.warning("No tenure file in %s" % subdirectory)
+
+        with open(combined_out_path, "w") as f:
+            combined_df.to_csv(f, encoding="utf-8")
+            logging.info("Completed combined tenure frame output.")
+            logging.info(
+                "Subdirectories included: %s" % ",".join(combined_lists)
+            )
     else:
         manager = enlighten.get_manager()
-        progress = manager.counter(total=len(subdirectories), desc='Lists', unit='lists')
+        progress = manager.counter(
+            total=len(subdirectories), desc="Lists", unit="lists"
+        )
         for subdirectory in subdirectories:
-            logging.info('Processing archives in %s' % subdirectory)
-            
-            out_path = os.path.join(args.archives, subdirectory, ('%s-tenure.csv' % subdirectory))
+            logging.info("Processing archives in %s" % subdirectory)
+
+            out_path = os.path.join(
+                args.archives, subdirectory, ("%s-tenure.csv" % subdirectory)
+            )
 
             if not args.force:
-                if os.path.isfile(out_path): # if file already exists, skip
+                if os.path.isfile(out_path):  # if file already exists, skip
                     progress.update()
                     continue
             try:
-                logging.debug('Opening list archives in %s', subdirectory)
-                archives = mailman.open_list_archives(subdirectory, args.archives)
-                
-                logging.debug('Creating archive')
+                logging.debug("Opening list archives in %s", subdirectory)
+                archives = mailman.open_list_archives(
+                    subdirectory, args.archives
+                )
+
+                logging.debug("Creating archive")
                 archive = bigbang.archive.Archive(archives)
-                
-                logging.debug('Creating activity frame from archive')
+
+                logging.debug("Creating activity frame from archive")
                 activity = archive.get_activity()
                 del archive
                 gc.collect()
 
-                logging.debug('Transposing activity frame')
+                logging.debug("Transposing activity frame")
                 person_activity = activity.T
 
-                logging.debug('Iterating for tenure calculations')
-                earliest_column, latest_column, total_column = [],[],[]
+                logging.debug("Iterating for tenure calculations")
+                earliest_column, latest_column, total_column = [], [], []
                 i = 0
                 for _, s in person_activity.iterrows():
-                    i = i+1
+                    i = i + 1
                     if i % 100 == 0:
                         logging.debug("checkpoint: %d", i)
                         gc.collect()
@@ -160,17 +211,33 @@ def main(args):
                 del person_activity
 
                 # delete the other columns
-                logging.debug('Create new frame from columns')
-                tenure_frame = pd.DataFrame(index=index_column, data={'Earliest Date':earliest_column,'Latest Date':latest_column,'Total Messages':total_column})
-
-                logging.debug('Writing tenure frame to file')
-                with open(out_path, 'w') as f:
-                    tenure_frame.to_csv(f, encoding='utf-8')
-                    logging.info('Completed tenure frame export for %s' % subdirectory)
+                logging.debug("Create new frame from columns")
+                tenure_frame = pd.DataFrame(
+                    index=index_column,
+                    data={
+                        "Earliest Date": earliest_column,
+                        "Latest Date": latest_column,
+                        "Total Messages": total_column,
+                    },
+                )
+
+                logging.debug("Writing tenure frame to file")
+                with open(out_path, "w") as f:
+                    tenure_frame.to_csv(f, encoding="utf-8")
+                    logging.info(
+                        "Completed tenure frame export for %s" % subdirectory
+                    )
             except Exception:
-                logging.warning(('Failed to produce tenure frame export for %s.' % subdirectory), exc_info=True)
+                logging.warning(
+                    (
+                        "Failed to produce tenure frame export for %s."
+                        % subdirectory
+                    ),
+                    exc_info=True,
+                )
             progress.update()
 
+
 if __name__ == "__main__":
     args = parser.parse_args()
-    main(args)
\ No newline at end of file
+    main(args)
diff --git a/bin/tograph.py b/bin/tograph.py
index 96bb5d32..9898375b 100644
--- a/bin/tograph.py
+++ b/bin/tograph.py
@@ -1,14 +1,16 @@
 import bigbang.parse as parse
-import bigbang.graph as graph
+import bigbang.analysis.graph as graph
 import networkx as nx
 import matplotlib.pyplot as plt
 from pprint import pprint as pp
 
-fns = ["archives/numpy-discussion/2011-November.txt",
-       "archives/ipython-dev/2011-November.txt",
-       "archives/ipython-user/2011-November.txt",
-       "archives/scipy-dev/2011-November.txt",
-       "archives/scipy-user/2011-November.txt"]
+fns = [
+    "archives/numpy-discussion/2011-November.txt",
+    "archives/ipython-dev/2011-November.txt",
+    "archives/ipython-user/2011-November.txt",
+    "archives/scipy-dev/2011-November.txt",
+    "archives/scipy-user/2011-November.txt",
+]
 
 mls = [parse.open_mail_archive(fn) for fn in fns]
 
@@ -22,32 +24,33 @@
 
 pdig = nx.to_pydot(IG)
 
-pdig.set_overlap('False')
+pdig.set_overlap("False")
 
-pdig.write_png('interactions.png', prog='neato')
+pdig.write_png("interactions.png", prog="neato")
 
 nx.write_gml(IG, "interactions.gml")
 
 # matplotlib visualization
-pos = nx.graphviz_layout(IG, prog='neato')
-node_size = [data['sent'] * 40 for name, data in IG.nodes(data=True)]
+pos = nx.graphviz_layout(IG, prog="neato")
+node_size = [data["sent"] * 40 for name, data in IG.nodes(data=True)]
 
-nx.draw(IG,
-        pos,
-        node_size=node_size,
-        node_color='w',
-        alpha=0.4,
-        font_size=18,
-        font_weight='bold'
-        )
+nx.draw(
+    IG,
+    pos,
+    node_size=node_size,
+    node_color="w",
+    alpha=0.4,
+    font_size=18,
+    font_weight="bold",
+)
 
 
 # edge width is proportional to replies sent
-edgewidth = [d['weight'] for (u, v, d) in IG.edges(data=True)]
+edgewidth = [d["weight"] for (u, v, d) in IG.edges(data=True)]
 
 # overlay edges with width based on weight
-nx.draw_networkx_edges(IG, pos, alpha=0.5, width=edgewidth, edge_color='r')
+nx.draw_networkx_edges(IG, pos, alpha=0.5, width=edgewidth, edge_color="r")
 
 plt.show()
 
-nx.write_gexf(IG, 'nov-11.gexf')
+nx.write_gexf(IG, "nov-11.gexf")
diff --git a/examples/attendance/Entity Resolution on Organizations.ipynb b/examples/attendance/Entity Resolution on Organizations.ipynb
index 9654286c..914b4043 100644
--- a/examples/attendance/Entity Resolution on Organizations.ipynb
+++ b/examples/attendance/Entity Resolution on Organizations.ipynb
@@ -7,7 +7,7 @@
    "outputs": [],
    "source": [
     "import bigbang.ietfattendance as ia\n",
-    "import bigbang.process as process\n",
+    "import bigbang.analysis.process as process\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -270,9 +270,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "bigbang3",
+   "display_name": "bigbang-env",
    "language": "python",
-   "name": "myenv"
+   "name": "bigbang-env"
   },
   "language_info": {
    "codemirror_mode": {
@@ -284,7 +284,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.9.5"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/examples/attendance/IETF Attendance.ipynb b/examples/attendance/IETF Attendance.ipynb
index 3c3ff3ab..c0cb598e 100644
--- a/examples/attendance/IETF Attendance.ipynb
+++ b/examples/attendance/IETF Attendance.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,9 +29,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:ietfdata:glasgow-ietfdata/0.5.1 (cache disabled)\n"
+     ]
+    }
+   ],
    "source": [
     "datatracker = DataTracker()\n",
     "\n",
@@ -41,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,16 +79,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:ietfdata:glasgow-ietfdata/0.5.1 (cache disabled)\n"
+     ]
+    }
+   ],
    "source": [
     "dt = DataTrackerExt() # initialize, for all meeting registration downloads"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,12 +117,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -137,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,22 +162,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe01772eb90>"
+       "<AxesSubplot:xlabel='date', ylabel='attendees'>"
       ]
      },
-     "execution_count": 151,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -196,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -218,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -260,83 +276,83 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>{'uri': '/api/v1/stats/meetingregistration/448...</td>\n",
-       "      <td>Episteme Technology Consulting LLC</td>\n",
+       "      <td>{'uri': '/api/v1/stats/meetingregistration/388...</td>\n",
+       "      <td>Apple</td>\n",
        "      <td>True</td>\n",
        "      <td>US</td>\n",
-       "      <td>resnick@episteme.net</td>\n",
-       "      <td>Pete</td>\n",
-       "      <td>44859</td>\n",
-       "      <td>Resnick</td>\n",
-       "      <td>{'uri': '/api/v1/meeting/meeting/1002/', 'para...</td>\n",
-       "      <td>{'uri': '/api/v1/person/person/18321/', 'param...</td>\n",
-       "      <td>remote</td>\n",
-       "      <td>full_week_pass</td>\n",
-       "      <td>109</td>\n",
+       "      <td>tpauly@apple.com</td>\n",
+       "      <td>Thomas</td>\n",
+       "      <td>38824</td>\n",
+       "      <td>Pauly</td>\n",
+       "      <td>{'uri': '/api/v1/meeting/meeting/866/', 'root'...</td>\n",
+       "      <td>{'uri': '/api/v1/person/person/119325/', 'root...</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>{'uri': '/api/v1/stats/meetingregistration/448...</td>\n",
-       "      <td>Keio University / WIDE Project</td>\n",
+       "      <td>{'uri': '/api/v1/stats/meetingregistration/388...</td>\n",
+       "      <td>Apple</td>\n",
        "      <td>True</td>\n",
-       "      <td>JP</td>\n",
-       "      <td>kato@wide.ad.jp</td>\n",
-       "      <td>Akira</td>\n",
-       "      <td>44860</td>\n",
-       "      <td>Kato</td>\n",
-       "      <td>{'uri': '/api/v1/meeting/meeting/1002/', 'para...</td>\n",
-       "      <td>{'uri': '/api/v1/person/person/108587/', 'para...</td>\n",
-       "      <td>remote</td>\n",
-       "      <td>full_week_pass</td>\n",
-       "      <td>109</td>\n",
+       "      <td>US</td>\n",
+       "      <td>ekinnear@apple.com</td>\n",
+       "      <td>Eric</td>\n",
+       "      <td>38825</td>\n",
+       "      <td>Kinnear</td>\n",
+       "      <td>{'uri': '/api/v1/meeting/meeting/866/', 'root'...</td>\n",
+       "      <td>{'uri': '/api/v1/person/person/124846/', 'root...</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>{'uri': '/api/v1/stats/meetingregistration/448...</td>\n",
-       "      <td>Facebook</td>\n",
+       "      <td>{'uri': '/api/v1/stats/meetingregistration/388...</td>\n",
+       "      <td>Moremar</td>\n",
        "      <td>True</td>\n",
-       "      <td>US</td>\n",
-       "      <td>woo@fb.com</td>\n",
-       "      <td>Guowu</td>\n",
-       "      <td>44861</td>\n",
-       "      <td>Xie</td>\n",
-       "      <td>{'uri': '/api/v1/meeting/meeting/1002/', 'para...</td>\n",
-       "      <td>{'uri': '/api/v1/person/person/107868/', 'para...</td>\n",
-       "      <td>remote</td>\n",
-       "      <td>full_week_pass</td>\n",
-       "      <td>109</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>jordi.palet@consulintel.es</td>\n",
+       "      <td>Jordi</td>\n",
+       "      <td>38826</td>\n",
+       "      <td>Palet Martinez</td>\n",
+       "      <td>{'uri': '/api/v1/meeting/meeting/866/', 'root'...</td>\n",
+       "      <td>{'uri': '/api/v1/person/person/106303/', 'root...</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>{'uri': '/api/v1/stats/meetingregistration/448...</td>\n",
-       "      <td>AMS</td>\n",
+       "      <td>{'uri': '/api/v1/stats/meetingregistration/388...</td>\n",
+       "      <td>RFC Editor</td>\n",
        "      <td>True</td>\n",
        "      <td>US</td>\n",
-       "      <td>jbui@amsl.com</td>\n",
-       "      <td>Jenny</td>\n",
-       "      <td>44862</td>\n",
-       "      <td>Bui</td>\n",
-       "      <td>{'uri': '/api/v1/meeting/meeting/1002/', 'para...</td>\n",
-       "      <td>{'uri': '/api/v1/person/person/124319/', 'para...</td>\n",
-       "      <td>remote</td>\n",
-       "      <td>full_week_pass</td>\n",
-       "      <td>109</td>\n",
+       "      <td>rse@rfc-editor.org</td>\n",
+       "      <td>Heather</td>\n",
+       "      <td>38827</td>\n",
+       "      <td>Flanagan</td>\n",
+       "      <td>{'uri': '/api/v1/meeting/meeting/866/', 'root'...</td>\n",
+       "      <td>{'uri': '/api/v1/person/person/113431/', 'root...</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>{'uri': '/api/v1/stats/meetingregistration/448...</td>\n",
-       "      <td>Port 9 Labs</td>\n",
+       "      <td>{'uri': '/api/v1/stats/meetingregistration/388...</td>\n",
+       "      <td>Akamai Technologies</td>\n",
        "      <td>True</td>\n",
        "      <td>US</td>\n",
-       "      <td>mc@port9labs.com</td>\n",
-       "      <td>Mike</td>\n",
-       "      <td>44863</td>\n",
-       "      <td>Coleman</td>\n",
-       "      <td>{'uri': '/api/v1/meeting/meeting/1002/', 'para...</td>\n",
-       "      <td>{'uri': '/api/v1/person/person/127095/', 'para...</td>\n",
-       "      <td>remote</td>\n",
-       "      <td>full_week_pass</td>\n",
-       "      <td>109</td>\n",
+       "      <td>krose@krose.org</td>\n",
+       "      <td>Kyle</td>\n",
+       "      <td>38828</td>\n",
+       "      <td>Rose</td>\n",
+       "      <td>{'uri': '/api/v1/meeting/meeting/866/', 'root'...</td>\n",
+       "      <td>{'uri': '/api/v1/person/person/119463/', 'root...</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -355,173 +371,173 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1228</th>\n",
-       "      <td>{'uri': '/api/v1/stats/meetingregistration/400...</td>\n",
-       "      <td>Meetecho/University of Napoli</td>\n",
+       "      <th>1295</th>\n",
+       "      <td>{'uri': '/api/v1/stats/meetingregistration/448...</td>\n",
+       "      <td></td>\n",
        "      <td>True</td>\n",
-       "      <td>IT</td>\n",
-       "      <td>spromano@unina.it</td>\n",
-       "      <td>Simon Pietro</td>\n",
-       "      <td>40054</td>\n",
-       "      <td>Romano</td>\n",
-       "      <td>{'uri': '/api/v1/meeting/meeting/866/', 'param...</td>\n",
-       "      <td>{'uri': '/api/v1/person/person/108722/', 'para...</td>\n",
+       "      <td>KE</td>\n",
+       "      <td>rotichdonald51@gmail.com</td>\n",
+       "      <td>Donald</td>\n",
+       "      <td>44848</td>\n",
+       "      <td>Kipsoi</td>\n",
+       "      <td>{'uri': '/api/v1/meeting/meeting/1004/', 'root...</td>\n",
+       "      <td>{'uri': '/api/v1/person/person/127394/', 'root...</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>104</td>\n",
+       "      <td>107</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1229</th>\n",
-       "      <td>{'uri': '/api/v1/stats/meetingregistration/400...</td>\n",
-       "      <td>Internet Society</td>\n",
+       "      <th>1296</th>\n",
+       "      <td>{'uri': '/api/v1/stats/meetingregistration/448...</td>\n",
+       "      <td>&lt;a href='https://ghanarecruitment.com'&gt;Recruit...</td>\n",
        "      <td>True</td>\n",
-       "      <td>US</td>\n",
-       "      <td>polk@isoc.org</td>\n",
-       "      <td>Ryan</td>\n",
-       "      <td>40055</td>\n",
-       "      <td>Polk</td>\n",
-       "      <td>{'uri': '/api/v1/meeting/meeting/866/', 'param...</td>\n",
-       "      <td>{'uri': '/api/v1/person/person/125944/', 'para...</td>\n",
+       "      <td>NI</td>\n",
+       "      <td>calebduncanville@gmail.com</td>\n",
+       "      <td>Caleb</td>\n",
+       "      <td>44849</td>\n",
+       "      <td>Asanaenyi</td>\n",
+       "      <td>{'uri': '/api/v1/meeting/meeting/1004/', 'root...</td>\n",
+       "      <td>{'uri': '/api/v1/person/person/127395/', 'root...</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>104</td>\n",
+       "      <td>107</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1230</th>\n",
-       "      <td>{'uri': '/api/v1/stats/meetingregistration/400...</td>\n",
-       "      <td>eLance journalist</td>\n",
+       "      <th>1297</th>\n",
+       "      <td>{'uri': '/api/v1/stats/meetingregistration/448...</td>\n",
+       "      <td></td>\n",
        "      <td>True</td>\n",
-       "      <td>DE</td>\n",
-       "      <td>monika@ermert.de</td>\n",
-       "      <td>Monika</td>\n",
-       "      <td>40056</td>\n",
-       "      <td>Ermert</td>\n",
-       "      <td>{'uri': '/api/v1/meeting/meeting/866/', 'param...</td>\n",
-       "      <td>{'uri': '/api/v1/person/person/123854/', 'para...</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>habibulbashar531@gmail.com</td>\n",
+       "      <td>habibul</td>\n",
+       "      <td>44850</td>\n",
+       "      <td>bashar</td>\n",
+       "      <td>{'uri': '/api/v1/meeting/meeting/1004/', 'root...</td>\n",
+       "      <td>{'uri': '/api/v1/person/person/127396/', 'root...</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>104</td>\n",
+       "      <td>107</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1231</th>\n",
-       "      <td>{'uri': '/api/v1/stats/meetingregistration/400...</td>\n",
+       "      <th>1298</th>\n",
+       "      <td>{'uri': '/api/v1/stats/meetingregistration/448...</td>\n",
        "      <td></td>\n",
        "      <td>True</td>\n",
-       "      <td>SE</td>\n",
-       "      <td>henrik@levkowetz.com</td>\n",
-       "      <td>Henrik</td>\n",
-       "      <td>40057</td>\n",
-       "      <td>Levkowetz</td>\n",
-       "      <td>{'uri': '/api/v1/meeting/meeting/866/', 'param...</td>\n",
-       "      <td>{'uri': '/api/v1/person/person/105730/', 'para...</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>renjith.rkr@gmail.com</td>\n",
+       "      <td>Renjith</td>\n",
+       "      <td>44851</td>\n",
+       "      <td>Raj</td>\n",
+       "      <td>{'uri': '/api/v1/meeting/meeting/1004/', 'root...</td>\n",
+       "      <td>{'uri': '/api/v1/person/person/127397/', 'root...</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>104</td>\n",
+       "      <td>107</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1232</th>\n",
-       "      <td>{'uri': '/api/v1/stats/meetingregistration/400...</td>\n",
-       "      <td>Network Time Foundation</td>\n",
+       "      <th>1299</th>\n",
+       "      <td>{'uri': '/api/v1/stats/meetingregistration/448...</td>\n",
+       "      <td>Cisco Systems</td>\n",
        "      <td>True</td>\n",
        "      <td>US</td>\n",
-       "      <td>rwelty@nwtime.org</td>\n",
-       "      <td>Richard</td>\n",
-       "      <td>40058</td>\n",
-       "      <td>Welty</td>\n",
-       "      <td>{'uri': '/api/v1/meeting/meeting/866/', 'param...</td>\n",
-       "      <td>{'uri': '/api/v1/person/person/125945/', 'para...</td>\n",
+       "      <td>collsull@cisco.com</td>\n",
+       "      <td>Collin</td>\n",
+       "      <td>44852</td>\n",
+       "      <td>Sullivan</td>\n",
+       "      <td>{'uri': '/api/v1/meeting/meeting/1004/', 'root...</td>\n",
+       "      <td>{'uri': '/api/v1/person/person/126770/', 'root...</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>104</td>\n",
+       "      <td>107</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>7157 rows × 13 columns</p>\n",
+       "<p>7158 rows × 13 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "                                           resource_uri  \\\n",
-       "0     {'uri': '/api/v1/stats/meetingregistration/448...   \n",
-       "1     {'uri': '/api/v1/stats/meetingregistration/448...   \n",
-       "2     {'uri': '/api/v1/stats/meetingregistration/448...   \n",
-       "3     {'uri': '/api/v1/stats/meetingregistration/448...   \n",
-       "4     {'uri': '/api/v1/stats/meetingregistration/448...   \n",
+       "0     {'uri': '/api/v1/stats/meetingregistration/388...   \n",
+       "1     {'uri': '/api/v1/stats/meetingregistration/388...   \n",
+       "2     {'uri': '/api/v1/stats/meetingregistration/388...   \n",
+       "3     {'uri': '/api/v1/stats/meetingregistration/388...   \n",
+       "4     {'uri': '/api/v1/stats/meetingregistration/388...   \n",
        "...                                                 ...   \n",
-       "1228  {'uri': '/api/v1/stats/meetingregistration/400...   \n",
-       "1229  {'uri': '/api/v1/stats/meetingregistration/400...   \n",
-       "1230  {'uri': '/api/v1/stats/meetingregistration/400...   \n",
-       "1231  {'uri': '/api/v1/stats/meetingregistration/400...   \n",
-       "1232  {'uri': '/api/v1/stats/meetingregistration/400...   \n",
+       "1295  {'uri': '/api/v1/stats/meetingregistration/448...   \n",
+       "1296  {'uri': '/api/v1/stats/meetingregistration/448...   \n",
+       "1297  {'uri': '/api/v1/stats/meetingregistration/448...   \n",
+       "1298  {'uri': '/api/v1/stats/meetingregistration/448...   \n",
+       "1299  {'uri': '/api/v1/stats/meetingregistration/448...   \n",
        "\n",
-       "                             affiliation  attended country_code  \\\n",
-       "0     Episteme Technology Consulting LLC      True           US   \n",
-       "1         Keio University / WIDE Project      True           JP   \n",
-       "2                               Facebook      True           US   \n",
-       "3                                    AMS      True           US   \n",
-       "4                            Port 9 Labs      True           US   \n",
-       "...                                  ...       ...          ...   \n",
-       "1228       Meetecho/University of Napoli      True           IT   \n",
-       "1229                    Internet Society      True           US   \n",
-       "1230                   eLance journalist      True           DE   \n",
-       "1231                                          True           SE   \n",
-       "1232             Network Time Foundation      True           US   \n",
+       "                                            affiliation  attended  \\\n",
+       "0                                                 Apple      True   \n",
+       "1                                                 Apple      True   \n",
+       "2                                               Moremar      True   \n",
+       "3                                            RFC Editor      True   \n",
+       "4                                   Akamai Technologies      True   \n",
+       "...                                                 ...       ...   \n",
+       "1295                                                         True   \n",
+       "1296  <a href='https://ghanarecruitment.com'>Recruit...      True   \n",
+       "1297                                                         True   \n",
+       "1298                                                         True   \n",
+       "1299                                      Cisco Systems      True   \n",
        "\n",
-       "                     email    first_name     id  last_name  \\\n",
-       "0     resnick@episteme.net          Pete  44859    Resnick   \n",
-       "1          kato@wide.ad.jp         Akira  44860       Kato   \n",
-       "2               woo@fb.com         Guowu  44861        Xie   \n",
-       "3            jbui@amsl.com         Jenny  44862        Bui   \n",
-       "4         mc@port9labs.com          Mike  44863    Coleman   \n",
-       "...                    ...           ...    ...        ...   \n",
-       "1228     spromano@unina.it  Simon Pietro  40054     Romano   \n",
-       "1229         polk@isoc.org          Ryan  40055       Polk   \n",
-       "1230      monika@ermert.de        Monika  40056     Ermert   \n",
-       "1231  henrik@levkowetz.com        Henrik  40057  Levkowetz   \n",
-       "1232     rwelty@nwtime.org       Richard  40058      Welty   \n",
+       "     country_code                       email first_name     id  \\\n",
+       "0              US            tpauly@apple.com     Thomas  38824   \n",
+       "1              US          ekinnear@apple.com       Eric  38825   \n",
+       "2              ES  jordi.palet@consulintel.es      Jordi  38826   \n",
+       "3              US          rse@rfc-editor.org    Heather  38827   \n",
+       "4              US             krose@krose.org       Kyle  38828   \n",
+       "...           ...                         ...        ...    ...   \n",
+       "1295           KE    rotichdonald51@gmail.com     Donald  44848   \n",
+       "1296           NI  calebduncanville@gmail.com      Caleb  44849   \n",
+       "1297           IN  habibulbashar531@gmail.com    habibul  44850   \n",
+       "1298           IN       renjith.rkr@gmail.com    Renjith  44851   \n",
+       "1299           US          collsull@cisco.com     Collin  44852   \n",
        "\n",
-       "                                                meeting  \\\n",
-       "0     {'uri': '/api/v1/meeting/meeting/1002/', 'para...   \n",
-       "1     {'uri': '/api/v1/meeting/meeting/1002/', 'para...   \n",
-       "2     {'uri': '/api/v1/meeting/meeting/1002/', 'para...   \n",
-       "3     {'uri': '/api/v1/meeting/meeting/1002/', 'para...   \n",
-       "4     {'uri': '/api/v1/meeting/meeting/1002/', 'para...   \n",
-       "...                                                 ...   \n",
-       "1228  {'uri': '/api/v1/meeting/meeting/866/', 'param...   \n",
-       "1229  {'uri': '/api/v1/meeting/meeting/866/', 'param...   \n",
-       "1230  {'uri': '/api/v1/meeting/meeting/866/', 'param...   \n",
-       "1231  {'uri': '/api/v1/meeting/meeting/866/', 'param...   \n",
-       "1232  {'uri': '/api/v1/meeting/meeting/866/', 'param...   \n",
+       "           last_name                                            meeting  \\\n",
+       "0              Pauly  {'uri': '/api/v1/meeting/meeting/866/', 'root'...   \n",
+       "1            Kinnear  {'uri': '/api/v1/meeting/meeting/866/', 'root'...   \n",
+       "2     Palet Martinez  {'uri': '/api/v1/meeting/meeting/866/', 'root'...   \n",
+       "3           Flanagan  {'uri': '/api/v1/meeting/meeting/866/', 'root'...   \n",
+       "4               Rose  {'uri': '/api/v1/meeting/meeting/866/', 'root'...   \n",
+       "...              ...                                                ...   \n",
+       "1295          Kipsoi  {'uri': '/api/v1/meeting/meeting/1004/', 'root...   \n",
+       "1296       Asanaenyi  {'uri': '/api/v1/meeting/meeting/1004/', 'root...   \n",
+       "1297          bashar  {'uri': '/api/v1/meeting/meeting/1004/', 'root...   \n",
+       "1298             Raj  {'uri': '/api/v1/meeting/meeting/1004/', 'root...   \n",
+       "1299        Sullivan  {'uri': '/api/v1/meeting/meeting/1004/', 'root...   \n",
        "\n",
-       "                                                 person reg_type  \\\n",
-       "0     {'uri': '/api/v1/person/person/18321/', 'param...   remote   \n",
-       "1     {'uri': '/api/v1/person/person/108587/', 'para...   remote   \n",
-       "2     {'uri': '/api/v1/person/person/107868/', 'para...   remote   \n",
-       "3     {'uri': '/api/v1/person/person/124319/', 'para...   remote   \n",
-       "4     {'uri': '/api/v1/person/person/127095/', 'para...   remote   \n",
-       "...                                                 ...      ...   \n",
-       "1228  {'uri': '/api/v1/person/person/108722/', 'para...            \n",
-       "1229  {'uri': '/api/v1/person/person/125944/', 'para...            \n",
-       "1230  {'uri': '/api/v1/person/person/123854/', 'para...            \n",
-       "1231  {'uri': '/api/v1/person/person/105730/', 'para...            \n",
-       "1232  {'uri': '/api/v1/person/person/125945/', 'para...            \n",
+       "                                                 person reg_type ticket_type  \\\n",
+       "0     {'uri': '/api/v1/person/person/119325/', 'root...                        \n",
+       "1     {'uri': '/api/v1/person/person/124846/', 'root...                        \n",
+       "2     {'uri': '/api/v1/person/person/106303/', 'root...                        \n",
+       "3     {'uri': '/api/v1/person/person/113431/', 'root...                        \n",
+       "4     {'uri': '/api/v1/person/person/119463/', 'root...                        \n",
+       "...                                                 ...      ...         ...   \n",
+       "1295  {'uri': '/api/v1/person/person/127394/', 'root...                        \n",
+       "1296  {'uri': '/api/v1/person/person/127395/', 'root...                        \n",
+       "1297  {'uri': '/api/v1/person/person/127396/', 'root...                        \n",
+       "1298  {'uri': '/api/v1/person/person/127397/', 'root...                        \n",
+       "1299  {'uri': '/api/v1/person/person/126770/', 'root...                        \n",
        "\n",
-       "         ticket_type  num  \n",
-       "0     full_week_pass  109  \n",
-       "1     full_week_pass  109  \n",
-       "2     full_week_pass  109  \n",
-       "3     full_week_pass  109  \n",
-       "4     full_week_pass  109  \n",
-       "...              ...  ...  \n",
-       "1228                  104  \n",
-       "1229                  104  \n",
-       "1230                  104  \n",
-       "1231                  104  \n",
-       "1232                  104  \n",
+       "      num  \n",
+       "0     104  \n",
+       "1     104  \n",
+       "2     104  \n",
+       "3     104  \n",
+       "4     104  \n",
+       "...   ...  \n",
+       "1295  107  \n",
+       "1296  107  \n",
+       "1297  107  \n",
+       "1298  107  \n",
+       "1299  107  \n",
        "\n",
-       "[7157 rows x 13 columns]"
+       "[7158 rows x 13 columns]"
       ]
      },
-     "execution_count": 153,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -532,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -541,7 +557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -550,7 +566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -559,7 +575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -568,12 +584,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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eQZRw0szhkdQTDfUPS+7pXPLduEPISvsOaxF3CDstiDuAnZpCC1d+fv74vffeu1fnzp3XNmvWLOn3WbYrLy+30tLS3iUlJeOBM6tub/DEwsyGEAzqPNndK07qcqBbwm5dgYrO2erKd+HujwCPABQXF0fyYZ340FNRVBMJzQoRaTxe7f9g3CFU6hV3AE1Pn1xOKgCaNWvmnTt3Xl9SUtIn2fYGTSzCGR6jgBPcPXGRiCnAk2Z2L8HgzR7APwlaMnqY2f7ACoIBnoMbKl7NwRepH7X6JVe69xtxh5AgewaGbzm1sPadcl+zXE4qKoS/Q9KunExON30K6A90MrPlwC0Es0BaAq+YGcDb7v4Td59vZs8QDMosA4a7+46wnp8CLxFMN33U3ednKuaqZo/Lnlxeq8CJNB7tPpwVdwjShLVp0+aITZs2vQcwadKk9iNHjuz2t7/97eOHHnqo05/+9KdOBQUFZdu3b7eRI0eu+vGPf5z2uguZnBVyUZLiP9Sw/+3A7UnKpwPTIwwtZQ/s/19xHDYpzQqRXKRR/slpxllyTbGVeO/XZh8VZX0lJx7+bqr7Tp48ud3Pfvazbi+++OLCHj16bAP4yU9+svrWW29dPW/evJb9+vXrfemll65t2bJlWi0s2fOvPgs1kWY5EWlg5yzOpi9QaYpefPHFtsOHDy+aOnXqwkMOOWRr1e19+/bd2qpVq/LPP/88r7CwsCydupVY1GDWSxviDmEnrbwp0mjonhjJ6SZkDWPbtm12wQUXHPjyyy9/dMQRR2xJts/f//73Nvvtt9+WdJMKUGJRI61bL1I/Gz5ssBX4c8rkddvjDqFSNg2vVcLVMJo3b+5HHnnkxocffrjTMccck7hWFA8//HCXiRMndl6+fHmL5557bmFd6ldiUQOtWy9SP+pOTG743v8RdwgJ1scdgDQwM2PKlCmffve73z3ohhtu2Hvs2LGVKxpUjLGYMGFChyuvvHL/U045ZV6bNm00xkJEskNTHIyXiqawEFRdqJW44bRr1678xRdfXPid73ynZ5cuXcquvfbazxO3DxkyZN3EiRP3fPDBB/e8/vrrP6+unmSUWNRAI9pF6kd95smpJUeyQZcuXXa8+OKLH59wwgk9O3fuvNtYijFjxqy6+OKLv3ndddd9npeXl3K92fPNmYWawrr1Ipmk9RqSU0uOVEhnemhUKtawADjwwAO3r1ixYh7Aj370o3WJ+x1//PGblixZ8n669SuxEJGMadXxurhDyEpqyUlOfy+NgxKLGqgfVEQyQbMfpDFTYiEi0sA2/GVo3CHsNDZ77qEijYMSixpogJWIiEh6lFjUQAOsROrn4X7XxB1CpeFZNLdKM86S091wG4fs+esWEWkiNONMGjMlFjXQyO3kPuyZPbeT77VAVxXZTF+gyWlguMSp4rbpH330UYuePXv2ve2225bddNNNawAuueSS7sXFxV9fffXVX9S1fiUWIpIx+gJN7g/fvzruEBKcEXcATdqMVw+I9LbpJ5/0SVrrYhQUFJSNGzdurxEjRpS2atUqraW7q6PEQtL2av8H4w6hUva0nUgyGgAtkt0KCgrKvvWtb2188MEH9xwxYkRaS3dXR4mFpK107zfiDiFBFvURyW40ADo5dbNKNhk9evSqAQMG9LjmmmuUWEg8tEyzSP1ogazkNFsmHj179tx2+OGHfz1u3LiCKOrLnk8xC+kff3Ijer0ZdwiSI3RlLpIbRo8eXXL++ecfcMwxx2yob11KLCRtM77bKe4QKin1ExGpvyOOOGJLjx49Ns+YMaP90Ucf/XV96lJiIWk7+OXH4g5hJ12FZjW1+iU3afGdcYdQaQTHxx1CJU1PjtfNN9+86jvf+U7v+tajxKIG+sefnM6LiEg00p0eGoWK26YffPDB2xYuXDi/orxfv36by8vL6x2PEgtJ22OnfxZ3CJVGxB2A1EhJaHK6Pbg0ZkosaqB//MmpuVJERKqjxELSptUUJVVKzpPTzbaS0/8tjUOzuAMQERGRxkMtFjXQVUVyWqZZUqV/QyJNjxILSZuWaRYRkeoosZC0aTVFSZWWaE5O50XiZGZHXXHFFat///vfLwcYPXp0l40bN+bde++9K6+77rp927Ztu+PWW29dXdf6s+evOwvpH39yWvRIRCQaY8aMifS26WPGjKl1HYoWLVr49OnTO65atapkn332KYvy+KDEQkREsoTGbzWMvLw8v+SSS0p/9atfdXnggQdWRF1/xhILM3sUGAiscfc+YVkBMAkoIpjNc767rzUzA34DnA5sAi5193+H7xkC/Dys9jZ3n5CpmCU1WvRIUqU1T5LTeZG4XX/99Wv69u17yJgxY0qirjuTLRaPAb8DJiaU3QDMcPexZnZD+HoUMADoET6OAR4CjgkTkVuAYsCBd81siruvzWDclfSPX1LVd0LfuEOoNG9INnWciUg2KigoKD/vvPO+GDt27F6tW7cuj7LujCUW7v6GmRVVKT4L6B8+nwDMJEgszgImursDb5tZBzPbJ9z3FXf/EsDMXgFOA57KVNwidVHa/fG4Q5AcooWgJBvceOONq4888sjeF1544edR1tvQYyy6uPsqAHdfZWZ7heWFwLKE/ZaHZdWVS4y0muLuSl4/Ie4QdjpxfdwRiEgO6NKly45BgwatffLJJztddNFFX0RVb7YM3rQkZV5D+e4VmA0FhgJ07949ushkN1r0aHdjuDbuECqNiTsAEckZN910U8mECRM6J5bdd999+4wbN65LxevVq1fPTafOhk4sVpvZPmFrxT7AmrB8OdAtYb+uwMqwvH+V8pnJKnb3R4BHAIqLi5MmHyKZ8tz+z8UdQqUxWZRaqMlfpGapTA+NWsVt0wG6detWtnnz5srX995778p77713ZX3qb+jEYgowBBgb/pycUP5TM3uaYPDm+jD5eAn4lZl1DPf7PnBjA8csUqtLp+8Xdwg7DYk7AJG60aq+jUMmp5s+RdDa0MnMlhPM7hgLPGNmlwNLgfPC3acTTDVdRDDd9DIAd//SzH4J/Cvc79aKgZwSHy0ctrvHTv8s7hAqjYg7AKmV1muQxiyTs0IuqmbTbss2hrNBknbcu/ujwKMRhiYSOU1NFhEJZM+lp0gO01gCEZGAEgtJm67Od/d39og7hKykJn+RpkeJRQ10FZqczsvuJq/bHncIlbJpMrAkp0GK0pgpsRCJwPC9/yPuEBJogSwRqV5eXt5RPXr02FzxevLkyYsWLlzY8qKLLjqga9eu27Zs2WKnnHLK+kceeWR5XepXYiESgb1PeD3uECpFfkehetCVuUjNlt/wZqS3Te869vha18Vo2bJl+YIFCz5ILFu4cGHL4uLija+99tqijRs3Wt++fXu//PLLa7///e9/nW4MSixEIjDrpQ1xh7DTiXEHICK5rG3btn7IIYdsXrp0aQtAiYVkngbk7U63khepvzffuDjuECqdfFLcEWTO1q1bm/Xs2bM3QLdu3ba+8soruzTnlZaW5i1evLjl97///TpdMSmxEImAbswmIrkiWVcIwKxZs9oedNBBvZcsWdJq+PDhJd27dy+rS/1KLEQi8HC/a+IOodLwrFmPVERyScUYi7lz57bs379/z/POO2/tscceu7n2d+5KiUUN1OQvIiJNzaGHHrr1mmuuWXXHHXfsPXXq1MXpvl+JhUgEtGiYiDQmI0aMKP3mN7+594IFC1r07NlzWzrvVWIhEgEtGpacBuMlp/MiFVKZHhq1xNumVxg4cOCGgQMHVg7WbNu2ra9Zs2ZuXepvVp/gRERERBKpxULSpkWPRCQTrtiy282vJQcpsaiBvkAlVRroKyISUGIhaVP/sIiIVEdjLERERCQyarEQiYC6zZJTn7lI06PEogZq8pdU6W9F0qGES+K0dOnS/GHDhnWfM2dOmxYtWnjXrl23PvDAA8sOPfTQrb/4xS/2+tWvftV15cqVc/bcc88ddalfiYWIiEhM7rlgYKS3TR8xaVqN62KUl5dz5plnHjh48OAvpk2b9inAW2+91XrlypXNDz300K3PPvvsnn369Pn6iSee6HD11Vd/UZcYlFhI2nS1tTudExHJBdOmTWuXn5/vI0eOLK0oq7gfyPz581tu2rSp2dixY5fdcccd+yixEImRbpsuIrlg7ty5rQ877LBNybZNmDCh4Ac/+MGXp5122sahQ4e2WrFiRX5hYWHadzhVYiEiIllBCXq8XnjhhYLnn39+UV5eHgMGDFg7ceLEjjfeeGNp7e/clRILERGRJqJv376b//KXv3SsWv7OO++0/uyzz1qedtppBwFs377dunXrtlWJhYhkFV2BimSXQYMGbbj55pvtnnvu6TRixIjPAV5//fU2I0eO7DpixIiVd9xxR0nFvoWFhX0//vjjFgcddJDubirS0Fp1vC7uEEREatWsWTOmTJnyybBhw7rdf//9e7ds2dK7du269Z///Ge7hx9+eGnivgMGDFg7YcKEgttvv72kuvqSUWJRA430T05XoSIi0ahtemgmFBUVbZ8+ffqnte03fvz45XWpX4mFSAROmjk87hASfBh3AFILJefSmOleISIiIhIZtViIROD8G7Pnn9K8uAMQkSZNLRYiIiISmZQTCzP7RlQHNbNrzWy+mb1vZk+ZWSsz29/M3jGzhWY2ycxahPu2DF8vCrcXRRWHiIiIRKvW9lszOxYYD7QFupvZYcCP3X1YXQ5oZoXA1UBvd99sZs8AFwKnA/e5+9Nm9jBwOfBQ+HOtux9oZhcCdwIX1OXYIpkyb/HS2ncSEWkCUukYvg84FZgC4O5zzOy7ERy3tZltB9oAq4CTgMHh9gnAGILE4qzwOcCzwO/MzNzd6xmDiIhkEa0H0zDatGlzxHvvvTf/sMMO61NUVLRl+/btdswxx2x4/PHHl+bl5dW7/pRGnLn7MjNLLKrTPdrDulaY2d3AUmAz8DLwLrDO3StudrIcKAyfFwLLwveWmdl6YE/g87rGICIikg0e/Mmrkd42ffjDJ6W8Lka3bt22Lliw4IPt27fTr1+/g//0pz91GDJkyLr6xpBKYrEs7A7xcNzD1dRjoryZdSRohdgfWAf8GRiQZNeKFgmrYVtivUOBoQDdu3eva3gidVK05cm4Q6i0JO4ARCSnNG/enKOPPnrjwoULW0ZRXyqJxU+A3xC0HCwnaGGoz2pA3wMWu3spgJk9DxwLdDCz/LDVoiuwMtx/OdANWG5m+UB74Muqlbr7I8AjAMXFxZF0k2gRGxHJBDX5SzbZsGFDszfeeGOP0aNHr4iivloTC3f/HPhhFAcLLQW+bWZtCLpCTgZmAa8B5wJPA0OAyeH+U8LX/wi3v6rxFSK5QV+gItlr2bJlLXv27NnbzBgwYMC6888//6so6k1lVsgE4Bp3Xxe+7gjc4+7/WZcDuvs7ZvYs8G+gDHiPoKXhr8DTZnZbWPaH8C1/AB43s0UELRUX1uW4Ipm05dTC2ncSEckiFWMsoq43la6QQyuSCgB3X2tmR9TnoO5+C3BLleJPgaOT7LsFOK8+xxMREZGGkcoCWc3CVgoAzKwALQUuIiKSc7Zv306LFi0yOpwglQThHuCtsPsCgtaD2zMXkoiISNOQzvTQKMyaNat1t27dth588MHbFi5cOD8Tx0hl8OZEM3sXOJFg6ucP3D3yPhkRERHJnF//+tedx40bt9ddd921LJPHSbVLYwGwtmJ/M+vu7lrDWEREJEeMHDmydOTIkaWZPk4qs0KuIhhouZpgxU0jWKDq0MyGFj9NlUtO50VERKqTSovFNcDB7v5FpoMREZGm66SZ9Vl7MWp1XmC6yUtpSW9gfaYDEcllT/g5cYeQ4JO4AxCRJiyVxOJTYKaZ/RXYWlHo7vdmLCoRkUZMV+bSmKWSWCwNHy3Ch4hISvQFKpJ9li1blj9s2LBu7733Xtv27duXNW/e3K+77rqSgoKCHRdddNEBhYWF28rLy+nUqVPZn//8508LCwvLaq91p1Smm/6i7uGLiIhIdT7s2SvS26b3WvBhjetilJeXM2jQoAMHDx78xdSpUxcDfPzxxy3+/Oc/dygoKNhcXFy88bXXXlsEMHz48MK77757r/vuu29lTXVWVevKm2bW2czuMrPpZoVSflsAABbTSURBVPZqxSOdg4iIiEj8pk6d2q558+aeOO30oIMO2nbTTTetSdyvvLycDRs25HXs2DGt1gpIrSvkCWASMJDgFupDgIzPgxXJJW++cXHcIVQ6+aS4IxCRbDVv3rzWhx566Kbqts+aNattz549e69bty6/devWO+6///7l6R4jlXuF7OnufwC2u/vr4V1Nv53ugURERCS7XHzxxd0PPvjg3n369OkFUFxcvHHBggUflJSUzB08ePAXP/3pT7umW2cqicX28OcqMzsjvLNp2gcSERGRePXt23fz3Llz21S8fvzxx5fOnDnz47Vr1+7Wg3HOOeese+edd9qle4xUukJuM7P2wAjgAWAP4Np0D5SLNKJdREQak0GDBm24+eab7c477+w8atSoUoCNGzcmbWR47bXX2u63335bk22rSSqzQqaFT9cT3IhMmjglXCIiualZs2ZMnTr1k+HDh3f77W9/u3dBQUFZmzZtdowZM2Y57Bxj4e60a9dux6OPProk3WNUm1iY2QME9wRJyt2vTvdgIo3VFVtOjjsEEclBtU0PzYT99ttv+7Rp0z5Ntm3Dhg2z61t/TWMsZgHvAq2AI4GF4eNwgpuRiYiIiOyi2hYLd58AYGaXAie6+/bw9cPAyw0SnYiIiOSUVGaF7AskjgptG5aJiIiI7CKVWSFjgffM7LXw9QnAmIxFJCLSyJ1/Yyr/9TaMeXEHkEDnpXFIZVbIH83sf4FjwqIb3L0ks2GJiIhILko1PcwjWMY7HzjIzA5y9zcyF5ZIbpm0+M64Q6g0guPjDkFEmrBaEwszuxO4AJgPlIfFDiixaKLUXCmp0t+KSPbJy8s7qkePHpvLysosLy/PL7rooi9uvvnm1Xl5eUybNq1dxa3TK/YfO3bssrPPPntDqvWn8q/+bOBgd0979S0RERGpXt8JfSO9bfq8IfNqXRejZcuW5QsWLPgAYMWKFfnnnXfeN9evX59XcXv0xFun10Uqs0I+BZrX9QAiIiKSnQoLC8vGjx+/5I9//ONe5eXltb8hBam0WGwCZpvZDKCy1UIrb4qIiOS+3r17bysvL2fFihX5sHNZ74rtzz333CeHHHJIyr0WqSQWU8JHk6P+YRERaQrcd97Bo75dIalMN51gZq2B7u7+UV0PJCIiItnngw8+aJGXl0dhYWHZnDlz6l1fKrNCBgF3Ay2A/c3scOBWdz+z3kcXaSRadbwu7hBERNK2cuXK/CuvvHK/yy67bE2zZqkMu6xdKm39Y4CjgZkA7j7bzPaP5OgiIiLSoLZu3dqsZ8+evSumm15wwQVf3HLLLasrtlcdYzFq1KhVl1122dpU608lsShz9/VmllhW7e3UU2FmHYDxQJ+wrv8EPgImAUXAEuB8d19rwYF/A5xOMJD0Unf/d32OLyIikg1SmR4atR07dlR7zIEDB26o763TU2n3eN/MBgN5ZtbDzB4A3qrPQQkShRfdvSdwGPAhcAMww917ADPC1wADgB7hYyjwUD2PLSIiIhmSSovFVcBNBFNNnwReAn5Z1wOa2R7Ad4FLAdx9G7DNzM4C+oe7TSDoehkFnAVM9GDI6ttm1sHM9nH3VXWNQepn3uKlcYcgIiJZKpUWizPc/SZ3/1b4+DlQn4Gb3yS478gfzew9MxtvZt8AulQkC+HPvcL9C4FlCe9fHpbtwsyGmtksM5tVWlpaj/BERESkrlJpsbgR+HMKZekc80jgKnd/x8x+w85uj2QsSdluYzzc/RHgEYDi4uJ6jQEREckktfpJY1ZtYmFmAwgGTBaa2W8TNu0BlNXjmMuB5e7+Tvj6WYLEYnVFF4eZ7QOsSdi/W8L7uwIr63F8ERERyZCaukJWArOALcC7CY8pwKl1PaC7lwDLzOzgsOhk4IOw3iFh2RBgcvh8CnCJBb4NrNf4ChERkexUbYuFu88B5phZF3efkLjNzK4hmNlRV1cBT5hZC4KbnF1GkOQ8Y2aXA0uB88J9pxO0nCwimG56WT2OKyINSE3+kg79vTSMpUuX5g8bNqz7nDlz2rRo0cK7du269YEHHlh22GGH9bntttuW3XTTTWsALrnkku7FxcVfX3311V+kU38qYywuBH5dpexS6pFYuPtsoDjJppOT7OvA8LoeS0REJGuNaR/pbdMZs77GdTHKy8s588wzDxw8ePAX06ZN+xTgrbfear1y5crmBQUFZePGjdtrxIgRpa1atarzWMVqu0LM7CIzm0qwjPeUhMdM4PO6HlBERETiMW3atHb5+fk+cuTIyumTxx577Ob9999/W0FBQdlxxx234cEHH9yzPseoqcXiLWAV0Am4J6HcgQvqc1ARERFpeHPnzm192GGHbapu++jRo1cNGDCgxzXXXFPnBoSaxlh8BnwG9AtvPDYYOB9YDDxX1wOKNEYnzcym3roP4w5ARHJUz549tx1++OFfjxs3rqCuddTUFXKQmY02sw+B3xEsUmXufqK7/66uBxQREZF49O3bd/OcOXPa1LTP6NGjS+6///59ysvL63SMmqabLiAYTDnI3Y9z9weAHXU6ioiIiMRu0KBBG7Zt22b33HNPp4qy119/vc2iRYtaVLw+4ogjtvTo0WPzjBkz2tflGDUlFucAJcBrZvZ7MzuZ5KtgioiISA5o1qwZU6ZM+WTGjBl7dOvWrc+BBx54yC233LJv9+7dtyfud/PNN69avXp1i+rqqUlNYyxeAF4I7+NxNnAt0MXMHgJecPeX63JAERERCdUyPTQTioqKtk+fPv3TquULFy6cX/G8X79+m8vLy+sUW603IXP3r939CXcfSLCc9mxqvreHiIiINFGp3N20krt/6e7j3P2kTAUkIiIiuSutxEJERESkJqks6S0itTj/xuz5pzQv7gBEpElTi4WIiIhEJnsusyRnFG15Mu4QKi2JOwAREdmFEosa6Ba+IiLS2Hz00UctBg4c2CNxeul11123b9u2bXeccMIJG6+99tpu27Zta7Zt2zY7++yz1957770r06lfiYWIiEhMim74a6S3TV8y9ox6rYtx+eWX7//UU0990q9fv81lZWXMmTOnVbp1KLEQkYxRt5lIbvnyyy/zK1bhzM/P56ijjtqSbh0avCkiIiIADB06dHWvXr36nHLKKQfcddddnTZt2pT2rTyUWIiIiDQhZslzBTPj7rvvXvWPf/zjw+9973tfPfPMM3v279//oHTrV2IhIiLShHTp0qVs/fr1eYllX375ZV6nTp3KAA455JCto0aNKn3rrbc+WrBgQeuSkpK85DUlpzEWIiINTGNPJE7t27cv32uvvbZPnjy53VlnnbVh9erVeTNnzmx//fXXr3n66afbn3/++eubNWvGvHnzWuXl5XmnTp12pFO/EgsREckKSrgazoQJExYPGzas+6hRo7oBjBo1amXYUlF4ww03dGvVqlV5fn6+jx8/fnF+fnqpghILkQhozRMRqYv6Tg+tq6OOOmrLO++883HV8mnTpu12O/V0aYyFiIiIREaJhYiIiERGiYWIiIhERomFiIhIwykvLy9Pe9GpbBP+DuXJtimxEBERaTjvl5aWts/l5KK8vNxKS0vbA+8n265ZISIiIg2krKzsipKSkvElJSV9yN2L+3Lg/bKysiuSbVRiISIi0kCOOuqoNcCZcceRSbmaLYmIiEgWii2xMLM8M3vPzKaFr/c3s3fMbKGZTTKzFmF5y/D1onB7UVwxi4iISM3ibLG4Bvgw4fWdwH3u3gNYC1well8OrHX3A4H7wv1EREQkC8UyxsLMugJnALcD11lwD9eTgMHhLhOAMcBDwFnhc4Bngd+Zmbm7ZzpOrVsvIiKSnrhaLO4HRrJzDuyewDp3LwtfLwcKw+eFwDKAcPv6cH8RERHJMg2eWJjZQGCNuyfeeCXZfF5PYVtivUPNbJaZzSotLY0gUhEREUlXHC0W3wHONLMlwNMEXSD3Ax3MrKJrpiuwMny+HOgGEG5vD3xZtVJ3f8Tdi929uHPnzpn9DURERCSpBk8s3P1Gd+/q7kXAhcCr7v5D4DXg3HC3IcDk8PmU8DXh9lcbYnyFiIiIpC+b1rEYRTCQcxHBGIo/hOV/APYMy68DbogpPhEREalFrCtvuvtMYGb4/FPg6CT7bAHOa9DAREREpE6yqcVCREREcpwSCxEREYmMEgsRERGJjBILERERiYwSCxEREYmMEgsRERGJTKzTTUUaC92wTkQkoBYLERERiYwSCxEREYmMEgsRERGJjBILERERiYwSCxEREYmMEgsRERGJjBILERERiYwSCxEREYmMEgsRERGJjBILERERiYwSCxEREYmMEgsRERGJjBILERERiYwSCxEREYmMEgsRERGJjBILERERiYwSCxEREYmMEgsRERGJjBILERERiYwSCxEREYmMEgsRERGJjBILERERiYwSCxEREYmMEgsRERGJTIMnFmbWzcxeM7MPzWy+mV0TlheY2StmtjD82TEsNzP7rZktMrO5ZnZkQ8csIiIiqYmjxaIMGOHuvYBvA8PNrDdwAzDD3XsAM8LXAAOAHuFjKPBQw4csIiIiqWjwxMLdV7n7v8PnG4APgULgLGBCuNsE4Ozw+VnARA+8DXQws30aOGwRERFJQaxjLMysCDgCeAfo4u6rIEg+gL3C3QqBZQlvWx6WiYiISJaJLbEws7bAc8B/u/tXNe2apMyT1DfUzGaZ2azS0tKowhQREZE0xJJYmFlzgqTiCXd/PixeXdHFEf5cE5YvB7olvL0rsLJqne7+iLsXu3tx586dMxe8iIiIVCuOWSEG/AH40N3vTdg0BRgSPh8CTE4ovyScHfJtYH1Fl4mIiIhkl/wYjvkd4GJgnpnNDsv+HzAWeMbMLgeWAueF26YDpwOLgE3AZQ0broiIiKSqwRMLd/87ycdNAJycZH8Hhmc0KBEREYmEVt4UERGRyCixEBERkcgosRAREZHIKLEQERGRyCixEBERkcgosRAREZHIKLEQERGRyCixEBERkcgosRAREZHIKLEQERGRyCixEBERkcgosRAREZHIKLEQERGRyCixEBERkcgosRAREZHIKLEQERGRyCixEBERkcgosRAREZHIKLEQERGRyCixEBERkcgosRAREZHIKLEQERGRyCixEBERkcgosRAREZHIKLEQERGRyCixEBERkcgosRAREZHIKLEQERGRyCixEBERkcgosRAREZHIKLEQERGRyORMYmFmp5nZR2a2yMxuiDseERER2V1OJBZmlgc8CAwAegMXmVnveKMSERGRqnIisQCOBha5+6fuvg14Gjgr5phERESkilxJLAqBZQmvl4dlIiIikkXM3eOOoVZmdh5wqrtfEb6+GDja3a9K2GcoMDR8eTDwUYMHmlwn4PO4g8hCOi/J6bwkp/OyO52T5LLpvOzn7p3jDqKh5ccdQIqWA90SXncFVibu4O6PAI80ZFCpMLNZ7l4cdxzZRuclOZ2X5HRedqdzkpzOS/xypSvkX0APM9vfzFoAFwJTYo5JREREqsiJFgt3LzOznwIvAXnAo+4+P+awREREpIqcSCwA3H06MD3uOOog67pnsoTOS3I6L8npvOxO5yQ5nZeY5cTgTREREckNuTLGQkRERHKAEot6MrNHzWyNmb2fUFZgZq+Y2cLwZ8cq7/mWme0ws3MbPuKGkc55MbP+ZrbezGaHj9HxRZ456f6thOdltpnNN7PX44m6AZg9itkaEs4LZgWYvYLZwvBnx7D8esxmh4/3MduBWUFcoWdUeuelPWZTMZuD2XzMLosr7IxL77x0xOwFzOZi9k/M+sQVdlOixKL+HgNOq1J2AzDD3XsAM8LXQOXy5HcSDERtzB4jjfMCvOnuh4ePWxsoxob2GCmeEzPrAPwPcKa7HwKc14BxNrTHqOa8UPVvxf0u3A/H/XDgRuB13L9swFgb0mOkel5gOPAB7ocB/YF7CGbQNUaPkfp5+X/AbNwPBS4BftNQQTZlSizqyd3fAKr+x3YWMCF8PgE4O2HbVcBzwJrMRxefOpyXRi/NczIYeN7dl4bvbbx/L3X/W7kIeCqDkcUrvfPiQDvMDGgbvq+sIcJscOmdl94EiQa4LwCKMOvSAFE2aUosMqOLu68CCH/uBWBmhcB/AA/HGFuckp6XUD8zm2Nm/2tmh8QTXiyqOycHAR3NbKaZvWtml8QWYTy6EJ4Xdv9bAbM2BFetzzV4ZPGq7rz8DuhFsHDgPOAa3MtjiTAe1Z2XOcAPADA7GtiPYIFFyaCcmW7aSNwPjHL3HcGFhYT+TbD07UYzOx34C9Aj5pjilg8cBZwMtAb+YWZvu/vH8YaVNQYB/9eIu0HSdSowGzgJOAB4BbM3cf8q3rBiNxb4DWazCRKu92isLTlZRC0WmbHazPYBCH9WNGMXA0+b2RLgXOB/zKwpdQckPS/u/pW7bwyfTweam1mn+MJsUNX9rSwHXnT3r939c+AN4LCYYozDasLzwq7npcKFNOZukOpVd14uA57H3XFfBCwGesYTYiySnxf3r3C/LByTcwnQmeDcSAYpsciMKcCQ8PkQYDKAu+/v7kXuXgQ8Cwxz97/EE2Iskp4XM9vbwiYcC5ormwFfxBJhw0t6TsKfx5tZvgXN/scAH8YQX1yqOy/BDAg4YZeypqO687KUoHWLcAzBwcCnDR1cjJKfF7MOCYNYrwDeUCtOAwgSXD3q+iC4aloFbCe4yrwc2JNgwNDC8GdBkvc9Bpwbd/zZcF6AnwLzCfpD3waOjTv+uM9JuP/1wAfA+8B/xx1/xh7wlMMqh+0Oyx0ud9jTYYbDwvBnQcL+lzo8HXvc2XReYF+Hlx3mObzv8KPY48+O89IvLFvg8LxDx9jjbwIPrbwpIiIikVFXiIiIiERGiYWIiIhERomFiIiIREaJhYiIiERGiYWIiIhERomFSBYzs43hzyIz25xwB9jZZnaJmb0TPl9qZqUJ24rMbImZzUsoOzZJ/W5mjye8zg/rmVbHeIvMbHDC62Iz+21d6hKR3KQlvUVyxycerCCYaCKAmV0KFLv7Tys2hGuOnejByp3V+RroY2at3X0zcAqwoh4xFhHcQO1JAHefBcyqR30ikmPUYiEi/wucET7f5Y6hZvYNM3vUzP5lZu+Z2VlheZ6Z3RWWzzWzH4dvGUuwYuhsM7vWzPpXtH6Y2Ziwrplm9qmZXZ1wnJvNbIGZvWJmT5nZzxrkNxeRyCmxEMkdB1TpCjk+hfe8Fu77Tg37PA1caGatgEOBxH1vAl51928BJwJ3mdk3CFYNXR+Wfwu40sz2B24A3nT3w939viTH6klww6yjgVvMrLmZFQPnAEcQ3ImyOIXfS0SylLpCRHJHsq6Q2tTWFYK7zzWzIoLWiulVNn8fODOhBaEV0D0sP9TMzg3L2xPckXZbLfH81d23AlvNbA3QBTgOmBx2xWBmU1P5xUQkOymxEBEIbuJ0N9Cf4P4lFQw4x90/Stw5vGncVe7+UpXy/rUcZ2vC8x0E/wdZ3UIWkWykrhARAXgUuNXd51Upfwm4KuHus0cklP+XmTUPyw8Ku0g2AO3SPPbfgUFm1srM2rJzvIeI5CC1WIjkjgPMbHbC60fdPZKpnO6+HPhNkk2/BO4H5obJxRJgIDCeYAbIv8PyUuBsYC5QZmZzCO7g+14Kx/6XmU0huLvtZwSzSNbX7zcSkbjo7qYiEjsza+vuG82sDfAGMNTd/x13XCKSPrVYiEg2eMTMehMMDp2gpEIkd6nFQkRERCKjwZsiIiISGSUWIiIiEhklFiIiIhIZJRYiIiISGSUWIiIiEhklFiIiIhKZ/w8L4Ei/WPDZEwAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 540x360 with 1 Axes>"
       ]
@@ -604,7 +620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -634,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -643,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -969,7 +985,7 @@
        "[249 rows x 16 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -980,7 +996,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -989,7 +1005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1005,7 +1021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1022,7 +1038,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1031,7 +1047,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1040,7 +1056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1049,7 +1065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1058,7 +1074,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1147,7 +1163,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>109</th>\n",
-       "      <td>530</td>\n",
+       "      <td>531</td>\n",
        "      <td>413</td>\n",
        "      <td>296</td>\n",
        "      <td>20</td>\n",
@@ -1166,10 +1182,10 @@
        "106               381     274   300      13             12       24\n",
        "107               581     347   264      48             23       15\n",
        "108               529     363   254      25             19       11\n",
-       "109               530     413   296      20             20       18"
+       "109               531     413   296      20             20       18"
       ]
      },
-     "execution_count": 171,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1180,7 +1196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1189,7 +1205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1278,12 +1294,12 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>109</th>\n",
-       "      <td>40.863531</td>\n",
-       "      <td>31.842714</td>\n",
-       "      <td>22.821897</td>\n",
-       "      <td>1.542020</td>\n",
-       "      <td>1.542020</td>\n",
-       "      <td>1.387818</td>\n",
+       "      <td>40.909091</td>\n",
+       "      <td>31.818182</td>\n",
+       "      <td>22.804314</td>\n",
+       "      <td>1.540832</td>\n",
+       "      <td>1.540832</td>\n",
+       "      <td>1.386749</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1297,10 +1313,10 @@
        "106         37.948207  27.290837  29.880478  1.294821       1.195219  2.390438\n",
        "107         45.461659  27.151800  20.657277  3.755869       1.799687  1.173709\n",
        "108         44.046628  30.224813  21.149042  2.081599       1.582015  0.915903\n",
-       "109         40.863531  31.842714  22.821897  1.542020       1.542020  1.387818"
+       "109         40.909091  31.818182  22.804314  1.540832       1.540832  1.386749"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1311,7 +1327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1320,7 +1336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1334,7 +1350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1343,7 +1359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1352,12 +1368,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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OOmtzfHw8/fr127Vhw4ZGpY+1e/duu+yyyzotWbKkWVxcHF9//XWTWjrNaqUkSkREpIHIyMjY+eqrr7aOLNu4cWPcmjVrGqemphaWru/u1qVLl52ffvppbul1o0aNOuzFF1/8/Nhjj9350EMPHTx9+vSk8LqmTZt6xD5+FMdf//rXtocccsiel1566auSkhKaNWtWrdeG1RYN54mIiDQQZ5111tZdu3bFPfzwwwcDFBUVcdVVV3U499xzvzv11FO3PPbYY2327NkDwLp16+J79+69a+PGjQnvv/9+C4DCwkKbO3duU4AdO3bEdezYcU9hYaFNnjw5uSpxFBQUxLdr125PfHw8jzzyyMHFxcXVfKa1Qz1RIiK1TM+gk1iJi4vj1Vdf/XzUqFGd7rnnnnYlJSUMGjSo4KGHHlqVkJDgy5Yta9K9e/cjEhISfOTIkfl//OMf8ydPnvzF1Vdf3XHr1q3xxcXFNnr06HX9+/ffddNNN60eMGBAj+Tk5KK+fftu27ZtW3xl47j22mvXZ2VldZ48efLBgwYNKmjWrFlJTZ53TbGyutnqu/79+/vcuXP3ez+9JvSqhmiqx8KR+lUn9Y9+hsqmdinbgdYuZjbP3ftHluXk5KzIzMz8br93LrUmJyenTWZmZlpZ6zScJyIiIhIFJVEiIiIiUVASJSIiIhIFJVEiIiIiUVASJSIiIhIFJVEiIiIiUag7DyuReuPe88+IdQh7/f75qbEOQUSkXrnxxht/8tJLLx0cFxfncXFxPPLII18PGjSoypP/Tp06NalJkyYl4YmDs7Ky0s4444yCSy+9dNO+ts3Ozm41YsSIzvPnz1/cp0+fXdGcx77MmDGj+VNPPXXw+PHjV9bE/kFJlIiISMz888oPq3W6k9+OGzSvovXvv/9+i3feeafVwoULlzRr1szXrFmTUFhYaNEc68MPP0xKTEwsDidRVTF58uTkvn37bps4cWJynz59Vkdz/Irs2bOHE088cceJJ564Y9+1o6fhPBERkQZi1apVjZKTk4uaNWvmAO3atStKS0vbA/Daa68l9ejRo2d6enrPc889N23nzp0G0L59+15r1qxJgFDvzoABA7rl5eU1njhxYsq4cePadu/evefbb7+dCDB9+vTEPn36dE9NTe319NNPty4rhoKCgrg5c+YkPv300yteeeWVvdPFTJ06Nemoo47qdvLJJ3dOTU3tddVVV7V/9NFHk3v16tUjPT295+LFi5sArF69OmHw4MGdMzIyemRkZPR49913WwCMHTv20LPPPvuwvn37dh8+fPhhU6dOTfrZz37WJXzMc845Jy09Pb1nenp6z/Hjx7cCuPjiiztmZGT06NKlyxHXXXfdoVVtT/VESZU1bT021iGIiEgUzj777C133XXXoWlpaRnHH3/8lgsvvHDj6aefvm3Hjh12xRVXHPbuu+/m9e7du3DYsGFp99xzT8qtt966vqz9dOvWbfeIESPyExMTi++88851AE888USbdevWNZo7d27up59+2nTYsGFdyhramzRpUquBAwcW9O7du7B169ZFM2fObH7CCSfsAMjNzW22aNGixYccckhRp06dejVp0uS7hQsXLv3LX/5yyL333nvIU089tfKKK67oMHbs2HWDBw/etnz58saDBw/u+uWXXy4GWL58edPZs2fnJiYm+tSpU/dOiHzTTTe1O+igg4qXLVu2BCA/Pz8e4L777lvVtm3b4qKiIo477rhus2fPbnb00UfvrGx7KokSERFpIFq2bFmyaNGiJW+//XbSBx98kDRy5MjOt95667dHHXXUjtTU1MLevXsXAlxyySUb/vnPfx4ClJlEleess87aHB8fT79+/XZt2LChUVl1pkyZknz11VevB8jKytqYnZ2dHE6ievXqtb1Tp057ADp27Fh42mmnFQBkZmbunD59ehLAxx9/fNDy5cubhfe3bdu2+IKCgjiAIUOGbE5MTPzRfHYzZsw4aPLkyV+Gl1NSUooBJkyYkDx+/Pg2RUVFlp+f3ygnJ6epkigREREpU0JCAmecccbWM844Y2vv3r13ZmdnH3zUUUeVe+1QfHy8l5SE5gfeuXNnhZcBNW3adG8CU9bcvOvWrYufNWtWUl5eXrMxY8ZQXFxsZuYlJSXfAjRp0mTvRnFxcXv3FxcXR3FxsYX3O3/+/KXNmzf/0QFatGhR6YmMc3NzGz/88MNt582btzQlJaU4KysrbdeuXVW6zEnXRImIiDQQOTk5TRYuXNgkvLxgwYJmqampuzMzM3etWrWq8aJFi5oATJw48eATTjhhK0Bqaurujz/+uDnAlClT9l7nlJSUVLx169b4qhw/Ozu79bBhwzauXr164apVqxauXbv2s9TU1N3vvPNOYmX3cfzxx2+56667Dgkv/+c//2lWUX2Ak046acv999+/d5v8/Pz4TZs2xTdr1qwkOTm5eOXKlQnTpk1rWZVzAfVESRQGTfttrEOIsDTWAYiI1BtbtmyJv/rqqztu2bIlPj4+3tPS0gonTJjwdfPmzX3cuHErzj333M7FxcVkZmbuuP766/MBbr311tVXXnll2p133ll83HHHbQ3vKysra/M555zT+a233mr1wAMPfFOZ47/wwgvJN9xww9rIsqFDh2565plnki+88MJ9PhoB4PHHH195+eWXd0xPT+9ZXFxsRx999NbjjjuuwuPfdddday699NKOXbt2PSIuLs7/+Mc/rh45cuTmjIyMHZ07d85o167d7n79+m2rzPEjWVndbfVd//79fe7cufu9n14TelVDNNVj4ciFsQ5hL7WLVJY+K2VTu5TtQGsXM5vn7v0jy3JyclZkZmZ+t987l1qTk5PTJjMzM62sdRrOExEREYmCkigRERGRKCiJEhEREYmCkigRERGRKCiJEhEREYmCkigRERGRKCiJEhERaWCys7NbmVm/BQsWNIXQpL69e/fu3qNHj72TCUc6//zzO82bN69p7Udat+lhmyIiIjGytHuPftW5vx65S+dVpt7kyZOT+/btu23ixInJffr0WT116tSkHj167Hz++ee/Ll23qKiIsspFPVEiIiINSkFBQdycOXMSn3766RWvvPJK8n/+859mt912W+q7777bqnv37j23bdtmzZs37/Ob3/wmtVu3bj0/+OCDxAEDBnSbMWNGc4AXX3zxoJ49e/bo1q1bz2OPPTYd4KOPPmp+5JFHdu/Ro0fPPn36dM/JyWlScRQHhhpLoszsKTNbb2aLIsqSzew9M1sefG0dlJuZPWRmn5vZZ2bWN2KbkUH95WY2sqbiFRERaQgmTZrUauDAgQW9e/cubN26dVFxcbHdfPPNq88888xNubm5SxITE33nzp1xRx999Pa8vLwlgwcP3jsdyurVqxPGjBmT9vLLL3+Rl5e35NVXX/0CIDMzc9ecOXNyly5duuS2225b9Yc//CE1dmdYe2qyJ2o8MKRU2U3AB+7eFfggWAY4DegavEYBj0Io6QJuA44GBgC3hRMvERERqbopU6bsnacuKytrY3Z2dnLpOvHx8VxyySU/mstu2rRpLQYMGLC1e/fuuwHatm1bDLBx48b4X/ziF527du16xB/+8IcOy5YtaxDXT9XYNVHuPsPM0koVDwUGBu8nANOAG4PyiR6ayG+WmbUys3ZB3ffcfSOAmb1HKDF7rqbiFonGveefEesQ9vr981NjHYKI1FHr1q2LnzVrVlJeXl6zMWPGUFxcbGbmRxxxxM7Ieo0bNy5JSKh8inDjjTe2P+mkk7a+9957X+Tl5TUeNGhQt2oPvg6q7Wui2rr7muD9WqBt8L49sDKi3rdBWXnlP2Jmo8xsrpnNzc/Pr96oRUREDgDZ2dmthw0btnH16tULV61atXDt2rWfpaam7l65cmXjymw/cODA7Z988klSbm5uYwglZQBbtmyJT01N3Q3w2GOPtam5M6hbYnZhedDr5NW4v8fdvb+7909JSamu3YqIiBwwXnjhheThw4f/YJhu6NChmx5++OF2ldn+0EMPLXrooYdWDBs2rEu3bt16Dhs27HCAG2+8ce3tt9+e2qNHj55FRUU1EXqdVNuPOFhnZu3cfU0wXLc+KF8FdIiolxqUreL74b9w+bRaiFNERKTGVfaRBNVl9uzZy0qX3XLLLetvueWW9ZFlO3bsWBC5/Mknn+SF35933nlbzjvvvCWR60855ZTtK1as2Hsj2UMPPbS6+qKuu2q7J+p1IHyH3UjgtYjyEcFdescABcGw3zvAz82sdXBB+c+DMhEREZGYqrGeKDN7jlAvUhsz+5bQXXZ3A1PM7DLga+C8oPqbwC+Az4EdwKUA7r7RzP4CzAnq3Rm+yFykLmnaemysQxARkVpWk3fnXVjOqpPLqOvAb8vZz1PAU9UYWqUt/OqbWBxWRERE6gFN+yJSDQZNK/N/gBhZGusAREQaBE37IiIiIhIFJVEiIiIiUdBwnkg1OO/muvOjtDDWAYhInZednd1qxIgRnefPn7+4T58+u8qrd9JJJ3V56aWXvmrTpk1xbcZXX9Sd3/wiIiINTK8JvfpV5/4WjlxYqedOTZ48Oblv377bJk6cmNynT59yn+k0ffr0z6svugOPkigREakTdEd07SgoKIibM2dO4vvvv5931llndb3//vtXf/31142ysrIO37ZtW3xxcbH9v//3/74eMmTItvbt2/eaO3fu0nbt2hWdcsopndesWdO4sLAw7sorr1x3/fXXfxfrc4k1JVEiIiINyKRJk1oNHDiwoHfv3oWtW7cumjlzZvP3338/6eSTTy74+9//vraoqIitW7f+6JrpZ599dkXbtm2Lt23bZn369On5y1/+ctNPfvKTBj3MpwvLRUREGpApU6YkX3jhhZsAsrKyNmZnZycfc8wx25977rk2Y8eOPfSTTz5p1rp165LS2/39739v261bt579+vXrsXbt2kaLFy9uWvvR1y3qiRKRGqPhGZG6Zd26dfGzZs1KysvLazZmzBiKi4vNzHzcuHHfzpgxI++ll15q+etf//qwMWPGrBszZsyG8HZTp05Nmj59etLcuXNzk5KSSgYMGNBt586dDb4jRkmUiIhIA5Gdnd162LBhGydNmvR1uOyoo47q9tZbbyUOHjx42+9///vvCgsLbf78+c2BvUnU5s2b41u2bFmclJRUsmDBgqY5OTktYnICdYySKBERkQbihRdeSL7hhhvWRpYNHTp006hRow5r3rx5SUJCgjdv3rz42Wef/SqyTlZWVsHjjz+ecvjhhx9x+OGH78rMzNxeu5HXTUqiREREYqSyjySoLrNnz15WuuyWW25Zf8stt6wvq/6qVav2PnpuxowZy2sytvqowY9nioiIiERDSZSIiIhIFJREiYiIiERB10SJiNQyPfpB5MCgnigRERGRKCiJEhEREYmChvNEREQakPj4+H5du3bdGV4ePnz4xr/97W9rK9pGyqYkSkREJFZub9mvevdXsM/nTjVp0qQkNzd3STS737NnD40aNYpm0wOShvNERESE9u3b91qzZk0CwIwZM5oPGDCgG8DYsWMPPfvssw/r27dv9+HDhx+Wl5fX+JhjjklPT0/veeyxx6YvX768MUBWVlbaRRdd1DEjI6NHWlpaxnPPPdcSoKioiCuuuCI1IyOjR3p6es977rmnTezOsnopiRIREWlACgsL47p3794z/HriiSda72ub5cuXN50xY0beG2+88dXo0aM7XnzxxRuWLVu25Pzzz98wevToDuF6K1eubJKTk7P0jTfeWH7ttdd22rFjhz3wwANtWrZsWbxo0aKlOTk5SydMmJCSm5vbuGbPsnZoOE+qTLdni4jUX9EM5w0ZMmRzYmKiAyxYsKDFW2+99QXA6NGjN95xxx2p4XpZWVkb4+Pj6dWrV2GHDh0KP/3006bvv//+Qbm5uc1ff/311gBbt26NX7JkSdPu3bvvrs7zigUlUSIiIkJ8fLyXlJQAsHPnzh+MVLVo0aKkMvswsx8tu7vde++932RlZW2prljrCg3niYiICKmpqbs//vjj5gBTpkwpd4ivT58+25988snWAI899lhy//79t4XXvfzyy62Li4tZvHhxk5UrVzbJzMzcdeqppxY8+uijKYWFhQbw2WefNdmyZcsBkX+oJ0pERKQBCV8TFV4eNGhQwSOPPLLq1ltvXX3llVem3XnnncXHHXfc1vK2Hzdu3DcjRoxIe/DBB39y8MEHF02cOHFFeF379u13Z2Zm9ti2bVv8Aw888HXz5s39uuuu+27FihVNevXq1cPdLTk5ec+bb775RQ2fZq1QEiUiIhIrlXgkQXUrLi4u85hDhgzZtmLFikWly++7777Vkcvp6em7Z82ataysfZx66qlbJ02a9IMLZ+Pj43n44YdXAav2I+w66YDoThMRERGpbeqJEhERkf320ksvrYh1DLVNPVEiIiIiUYhJEmVm15nZYjNbZGbPmVlTMzvMzGab2edm9ryZNQ7qNgmWPw/Wp8UiZhEREZFItZ5EmVl74Gqgv7tnAPHABcDfgfvdvQuwCbgs2OQyYFNQfn9QT0RERCSm9plEmVm6mX1gZouC5d5mdst+HjcBaGZmCUBzYA0wCHgxWD8BODt4PzRYJlh/spV+mpeIiIhILatMT9QTwM3AHgB3/4xQz1FU3H0V8L/AN4SSpwJgHrDZ3YuCat8C7YP37YGVwbZFQf2Doz2+iIhIQ2Zm/X7zm9/snarl1ltvbTt27NhDq7KPqVOnJr333nstwstZWVlpTz/99D7n4APIzs5uZWb9FixY0LQqx6yKGTNmNL/kkks67Lvm/qnM3XnN3f2TUp0/ReVV3hcza02od+kwYDPwAjAk2v1F7HcUMAqgY8eO+7s7ERGRGpd207/7Vef+Vtx9+j6fO9W4cWN/8803W69Zs2Ztu3btqvz3fM+ePXz44YdJiYmJxaeeeur2qm4/efLk5L59+26bOHFicp8+fVbve4uqx3fiiSfuOPHEE3dU975Lq0xP1Hdm1hlwADM7h1APUrROAb5y93x33wO8DPwUaBUM7wGk8v1DuVYBHYJjJwAtgQ2ld+ruj7t7f3fvn5KSsh/hiYiIHLji4+N9xIgR+X/729/all6Xl5fX+JhjjklPT0/veeyxx6YvX768MYR6mi666KKOvXv37n766ad3njhxYsq4cePadu/evefbb7+dCDB9+vTEPn36dE9NTe1VXq9UQUFB3Jw5cxKffvrpFa+88kpyuHzq1KlJRx11VLeTTz65c2pqaq+rrrqq/aOPPprcq1evHunp6T0XL17cBGD16tUJgwcP7pyRkdEjIyOjx7vvvtsCYOzYsYeeffbZh/Xt27f78OHDD5s6dWrSz372sy7hY55zzjlp6enpPdPT03uOHz++FcDFF1/cMSMjo0eXLl2OuO6666rUExdWmSTqt8BjQHczWwVcC4yO5mCBb4BjzKx5cG3TycAS4CPgnKDOSOC14P3rwTLB+g/d3ffj+CIiIg3aDTfcsP7ll19O3rBhQ3xk+ejRoztefPHFG5YtW7bk/PPP3zB69Oi9Q2Jr1qxpPH/+/Nx33333ixEjRuRfeeWV63Jzc5cMGTJkG8C6desazZ07N/e1115bftttt7UvfUyASZMmtRo4cGBB7969C1u3bl00c+bM5uF1ubm5zZ566qlvli9fvujFF188eNmyZU0XLly49Fe/+tV399577yEAV1xxRYexY8euW7Ro0dJXXnnliyuvvDItvP3y5cubzpgxI++NN974KvKYN910U7uDDjqoeNmyZUuWLVu25PTTT98KcN99961atGjR0tzc3MUff/xx0uzZs5tVtR33OZzn7l8Cp5hZCyDO3cudT6cy3H22mb0IzCc0LLgAeBz4NzDZzP4nKPtXsMm/gGwz+xzYyH5cjyUiIiKQnJxccu655264++67D2nWrFlJuHzBggUt3nrrrS8ARo8evfGOO+7Ye+3U8OHDNyUklJ82nHXWWZvj4+Pp16/frg0bNjQqq86UKVOSr7766vUAWVlZG7Ozs5NPOOGEHQC9evXa3qlTpz0AHTt2LDzttNMKADIzM3dOnz49CeDjjz8+aPny5XuTnW3btsUXFBTEAQwZMmRzYmLijzpZZsyYcdDkyZO/DC+npKQUA0yYMCF5/PjxbYqKiiw/P79RTk5O06OPPnpnJZpvr30mUWb2N+Af7r45WG4N/N7do75Dz91vA24rVfwlMKCMuruAc6M9loiIiPzYzTffvK5v3749L7jggu8qUz8xMbGkovVNmzbdm8CUNWC0bt26+FmzZiXl5eU1GzNmDMXFxWZmXlJS8i1AkyZN9m4UFxe3d39xcXEUFxdbeL/z589f2rx58x8doEWLFhXGFyk3N7fxww8/3HbevHlLU1JSirOystJ27dpV5cc+VWaD08IJFIC7bwJ+UdUDiYiISN3Rtm3b4jPPPHPTpEmT2oTL+vTps/3JJ59sDfDYY48l9+/ff1tZ2yYlJRVv3bo1vqx15cnOzm49bNiwjatXr164atWqhWvXrv0sNTV19zvvvJNY2X0cf/zxW+66665Dwsv/+c9/9jkEd9JJJ225//77926Tn58fv2nTpvhmzZqVJCcnF69cuTJh2rRpLatyLmGVSaLizaxJeMHMmgFNKqgvIiIi9cCf/vSntZs3b947KjVu3LhvsrOz26Snp/d87rnnDn7kkUdWlrVdVlbW5n//+9+tIi8s35cXXnghefjw4Zsiy4YOHbrpmWeeSS5vm9Ief/zxlfPnz2+Rnp7es3Pnzkc8/PDD+7yT7K677lqzefPm+K5dux7RrVu3nm+++WbSscceuzMjI2NH586dM84777zD+/XrV2ayuC+2r2u0zexG4Ezg6aDoUuB1d/9HNAesDf379/e5c+fu/45ujyoxrRm3F8Q6gu+pXX6k14ResQ5hr4UjF8Y6hO/ps1I2tUvZDrB2MbN57t4/siwnJ2dFZmZmpYbPpG7Iyclpk5mZmVbWuspcWP53M/uM0F10AH9x93eqMT4RERGReqcyD9vE3d8C3qrhWERERETqjcrMnTfczJabWYGZbTGzrWa2pTaCExEREamrKtMT9Q/gTHdfWtPBiIiIHOBKSkpKLC4uTg+NrgdKSkoMKPfRCZW5O2+dEigREZFqsSg/P79l8MdZ6rCSkhLLz89vCSwqr05leqLmmtnzwKtAYbjQ3V/e7whFREQakKKiosvXrl375Nq1azOoXEeGxE4JsKioqOjy8ipUJok6CNgB/DyizAlNHCwiIiKV1K9fv/XAWbGOQ6pHZR5xcGltBCIiIiJSn1Rm7rymwGXAEUDTcLm7/7oG4xIRERGp0yozHpsN/AQYDEwHUoGtNRmUiIiISF1XmSSqi7v/Gdju7hOA04GjazYsERERkbqtMknUnuDrZjPLAFoCh1RQX0REROSAV5m78x43s9bAn4HXgUTg1hqNSkRERKSOq8zdeU8Gb6cDh9dsOCIiIiL1Q7lJlJmNrWhDd7+v+sMRERERqR8q6olKCr52A44iNJQHcCbwSU0GJSIiIlLXlZtEufsdAGY2A+jr7luD5duBf9dKdCIiIiJ1VGXuzmsL7I5Y3h2UiYiIiDRYlbk7byLwiZm9EiyfDUyosYhERERE6oHK3J33VzN7Gzg+KLrU3RfUbFgiIiIidVtleqIAPgXWhOubWUd3/6amghIRERGp6yozAfHvgNuAdUAxYIADvWs2NJH6Y+FX+p9CRKShqUxP1DVAN3ffUNPBiIiIiNQXlbk7byVQUNOBiIiIiNQnlemJ+hKYZmb/BgrDhXpiuYiIiDRklUmivglejYOXiIiISINXmUcchJ9c3tzdd9R8SCIiIiJ13z6viTKzY81sCZAbLGea2SM1HpmIiIhIHVaZC8sfAAYDGwDcPQc4cX8OamatzOxFM8s1s6VBopZsZu+Z2fLga+ugrpnZQ2b2uZl9ZmZ99+fYIiIiItWhMkkU7r6yVFHxfh73QeBtd+8OZAJLgZuAD9y9K/BBsAxwGtA1eI0CHt3PY4uIiIjst8pcWL7SzI4D3MwaEXpu1NJoD2hmLQn1ZF0C4O67gd1mNhQYGFSbAEwDbgSGAhPd3YFZQS9WO3dfE20Msn/Sdk2KdQh7rYh1ACIi0mBVpifqSuC3QHtgFXAkcNV+HPMwIB942swWmNmTZtYCaBuRGK0F2gbv2xN6VlXYt0GZiIiISMxUJonq5u4Xu3tbdz/E3X8J9NiPYyYAfYFH3b0PsJ3vh+4ACHqdvCo7NbNRZjbXzObm5+fvR3giIiIi+1aZJOr/VbKssr4FvnX32cHyi4SSqnVm1g4g+Lo+WL8K6BCxfWpQ9gPu/ri793f3/ikpKfsRnoiIiMi+lXtNlJkdCxwHpJjZ2IhVBwHx0R7Q3dea2Uoz6+buecDJwJLgNRK4O/j6WrDJ68AYM5sMHA0U6HooERERibWKLixvDCQGdZIiyrcA5+zncX8HPGtmjQlNK3MpoV6xKWZ2GfA1cF5Q903gF8DnwI6grojUA7oJQUQOZOUmUe4+HZhuZjvd/R+R68zsXGB5tAd190+B/mWsOrmMuk7ownYRERGROqMy10RdUEbZzdUdiIiIiEh9UtE1UacRGkZrb2YPRaxKAvbUdGAiIiIidVlF10StBuYBZwVfwzoRujZJREREpMEqdzjP3XPcfTzQBfgMyADuAH7GfjyxXERERORAUNFwXjpwYfD6DngeMHf/WS3FJiIiIlJnVTSclwvMBM5w988BzOy6WolKREREpI6r6O684cAa4CMze8LMTgasdsISERERqdsquibqVXe/AOgOfARcCxxiZo+a2c9rKT4RERGROqmi4TwA3H07MAmYZGatgXOBG4F3azg2ERFpQPSEe6lv9plERXL3TcDjwUtERKKgZEHkwFCZJ5aLiIiISClKokRERESioCRKREREJApKokRERESioCRKREREJApKokRERESioCRKREREJApKokRERESioCRKREREJApKokRERESioCRKREREJApKokRERESioCRKREREJAoJsQ6gLtNM6yIiIlIe9USJiIiIREFJlIiIiEgUlESJiIiIREFJlIiIiEgUlESJiIiIREFJlIiIiEgUlESJiIiIRCFmSZSZxZvZAjObGiwfZmazzexzM3vezBoH5U2C5c+D9WmxillEREQkLJY9UdcASyOW/w7c7+5dgE3AZUH5ZcCmoPz+oJ6IiIhITMUkiTKzVOB04Mlg2YBBwItBlQnA2cH7ocEywfqTg/oiIiIiMROrnqgHgD8AJcHywcBmdy8Klr8F2gfv2wMrAYL1BUH9HzCzUWY218zm5ufn12DoIiIiIjFIoszsDGC9u8+rzv26++Pu3t/d+6ekpFTnrkVERER+JBYTEP8UOMvMfgE0BQ4CHgRamVlC0NuUCqwK6q8COgDfmlkC0BLYUPthi4iIiHyv1nui3P1md0919zTgAuBDd78Y+Ag4J6g2EngteP96sEyw/kN391oMWURERORHYtETVZ4bgclm9j/AAuBfQfm/gGwz+xzYSCjxEqlT0nZNinUIe62IdQAiIg1ETJMod58GTAvefwkMKKPOLuDcWg1MREREZB/0xHIRERGRKCiJEhEREYmCkigRERGRKCiJEhEREYmCkigRERGRKCiJEhEREYmCkigRERGRKCiJEhEREYmCkigRERGRKCiJEhEREYmCkigRERGRKCiJEhEREYmCkigRERGRKCiJEhEREYmCkigRERGRKCiJEhEREYmCkigRERGRKCiJEhEREYmCkigRERGRKCiJEhEREYmCkigRERGRKCiJEhEREYmCkigRERGRKCiJEhEREYmCkigRERGRKCiJEhEREYmCkigRERGRKCiJEhEREYmCkigRERGRKCiJEhEREYmCkigRERGRKNR6EmVmHczsIzNbYmaLzeyaoDzZzN4zs+XB19ZBuZnZQ2b2uZl9ZmZ9aztmERERkdJi0RNVBPze3XsCxwC/NbOewE3AB+7eFfggWAY4DegavEYBj9Z+yCIiIiI/VOtJlLuvcff5wfutwFKgPTAUmBBUmwCcHbwfCkz0kFlAKzNrV7tRi4iIiPxQTK+JMrM0oA8wG2jr7muCVWuBtsH79sDKiM2+DcpK72uUmc01s7n5+fk1F7SIiIgIMUyizCwReAm41t23RK5zdwe8Kvtz98fdvb+7909JSanGSEVERER+LCZJlJk1IpRAPevuLwfF68LDdMHX9UH5KqBDxOapQZmIiIhIzMTi7jwD/gUsdff7Ila9DowM3o8EXosoHxHcpXcMUBAx7CciIiISEwkxOOZPgV8BC83s06Dsj8DdwBQzuwz4GjgvWPcm8Avgc2AHcGmtRisiIiJShlpPotz9/wArZ/XJZdR34Lc1GpSIiIhIFemJ5SIiIiJRUBIlIiIiEgUlUSIiIiJRUBIlIiIiEgUlUSIiIiJRUBIlIiIiEgUlUSIiIiJRUBIlIiIiEgUlUSIiIiJRUBIlIiIiEgUlUSIiIiJRUBIlIiIiEgUlUSIiIiJRUBIlIiIiEgUlUSIiIiJRUBIlIiIiEgUlUSIiIiJRUBIlIiIiEgUlUSIiIiJRUBIlIiIiEgUlUSIiIiJRUBIlIiIiEgUlUSIiIiJRUBIlIiIiEgUlUSIiIiJRUBIlIiIiEgUlUSIiIiJRUBIlIiIiEgUlUSIiIiJRUBIlIiIiEgUlUSIiIiJRqDdJlJkNMbM8M/vczG6KdTwiIiLSsNWLJMrM4oF/AqcBPYELzaxnbKMSERGRhqxeJFHAAOBzd//S3XcDk4GhMY5JREREGjBz91jHsE9mdg4wxN0vD5Z/BRzt7mMi6owCRgWL3YC8Wg+0bG2A72IdRB2kdimb2qVsapcfU5uUrS61Syd3T4l1EFJzEmIdQHVx98eBx2MdR2lmNtfd+8c6jrpG7VI2tUvZ1C4/pjYpm9pFalN9Gc5bBXSIWE4NykRERERior4kUXOArmZ2mJk1Bi4AXo9xTCIiItKA1YvhPHcvMrMxwDtAPPCUuy+OcViVVeeGGOsItUvZ1C5lU7v8mNqkbGoXqTX14sJyERERkbqmvgzniYiIiNQpSqJEREREoqAkaj+Z2VNmtt7MFkWUJZvZe2a2PPjautQ2R5lZUfD8qwNSVdrFzAaaWYGZfRq8bo1d5DWnqp+VoF0+NbPFZjY9NlHXvCp+Vm6I+JwsMrNiM0uOXfQ1p4rt0tLM3jCznODzcmnsIq9ZVWyX1mb2ipl9ZmafmFlG7CKXA5GSqP03HhhSquwm4AN37wp8ECwDe6ew+Tvwbm0FGCPjqUK7ADPd/cjgdWctxVjbxlPJNjGzVsAjwFnufgRwbu2FWevGU8l2cfd7wp8T4GZgurtvrMVYa9N4Kv8z9FtgibtnAgOBe4M7mQ9E46l8u/wR+NTdewMjgAdrK0hpGJRE7Sd3nwGU/iU+FJgQvJ8AnB2x7nfAS8D6Gg8uhqJolwNeFdvkIuBld/8m2PaA/bzsx2flQuC5mosstqrYLg4kmZkBicF2RbUQZq2rYrv0BD4MtssF0sysbS2EKQ2Ekqia0dbd1wTv1wJtAcysPTAMeDRWgcVYme0SODYYinjLzI6IQWyxUl6bpAOtzWyamc0zsxGxCS9mKvqsYGbNCfVGvFTbgcVYee3yMNADWA0sBK5x95IYxBcr5bVLDjAcwMwGAJ0IPaxZpFrUi+dE1Wfu7mYWfo7EA8CN7l4S+oex4SrVLvMJzTG1zcx+AbwKdI1ZcDFSqk0SgH7AyUAz4L9mNsvdl8UswBgp1S5hZwIfH8BDeftUql0GA58Cg4DOwHtmNtPdt8Qqvlgp1S53Aw+a2aeEkssFQHGsYpMDj3qiasY6M2sHEHwND8X0Byab2QrgHOARMzs7JhHGRpnt4u5b3H1b8P5NoJGZtYldmLWqvM/Kt8A77r7d3b8DZgCZMYoxFsprl7ALOICH8ipQXrtcSmj41939c+AroHuMYoyFin63XBpcQzcCSAG+jFmUcsBRElUzXgdGBu9HAq8BuPth7p7m7mnAi8BV7v5qTCKMjTLbxcx+ElzLEe5yjwM2xCTC2ldmmwRfjzezhGDo6mhgaQzii5Xy2gUzawmcFFnWgJTXLt8Q6rUkuOanGw0rWSjvd0uriAvsLwdmNMTeOalB7q7XfrwI/Te8BthDqPfgMuBgQneILAfeB5LL2G48cE6s468L7QKMARYTun5hFnBcrOOPdZsE9W8AlgCLgGtjHX8dapdLgMmxjrsutQtwKKE7fhcGn5dfxjr+OtIuxwLLgDzgZaB1rOPX68B6adoXERERkShoOE9EREQkCkqiRERERKKgJErqNDO7yMw6xjoOERGR0pRE7UMwN1d4nq4XgjulGhQzczN7JmI5wczyzWxqlPtrZWZXVaLeZcAhHjy1ex91t0UTS1WZ2Z+Cuck+Cz4XR5vZk2bWszaOXx+Z2UdmNrhU2bVm9pWZ3VTONgPN7Lj9PG6tfCaipXYpm9pF6hMlUfu200NzdWUAu4ErI1eaWUN4YOl2IMPMmgXLpwKr9mN/rYAyk6jI9nT3f7n7A/txnGplZscCZwB9PTQX1ynASne/3N2XxDa6HwrmaKwrniP0XKdIFwAj3f3u0pWDz8BAYL/+KNYDapeyqV2k3lASVTUzgS7Bfz0zzex1QregY2avBtNzLDazUeENzOwyM1sWzCD+hJk9HJSPN7NzIupti3h/g5nNCXo77qi906vQm8DpwfsfzFlmoRnUXw3inWVmvYPy2y004/o0M/vSzK4ONrkb6Bz05NxTuj3NLD4oD7fBFcH+Es3sAzObb2YLzWxo7Z0+AO2A79y9EMDdv3P31cH59Q9i3GZmf7XQFDazgmf2YGadg+WFZvY/4e93eedkZmlmlmtmz5rZUjN7MdwLamYnm9mCoP5TZtYkKF9hZn83s/nAuWb2czP7b7DvF8wssZbbK+xF4HQLntdjZmmEbsnvXOrnYZyZzQamEPpn5brgM3JCeT8vdeAzsT/ULmVTu0i9oSSqkiz0385phJ7DAtCX0PxU6cHyr929H6Gnkl9tZgeb2aHAn4FjgJ9SiScIm9nPCU15MgA4EuhnZidW57lEaTJwgZk1BXoDsyPW3QEsCHpn/ghMjFjXndCUFAOA28ysEaEZ1r8IevhuCOpFtudlQIG7HwUcBfzGzA4DdgHD3L0v8DNCM9XX5vw57wIdLJQUP2JmJ5VRpwUwy90zCT1l/DdB+YPAg+7ei9CzbcIqOqduwCPu3gPYAlwVtP944PxgXwnA6Ij9bQj29T5wC3BKsDwXGLuf5x8VD03N8gmhnx8I9SpMITRpbqRUQs8IGw6MA+4PPiMzK9h9rD8TUVO7lE3tIvWJkqh9a2aheZfmEnoq8L+C8k/c/auIelebWfhhkR34PhGa7u4b3X0P8EIljvfz4LWA0Jxy3akD88i5+2dAGqFeqDdLrT4eyA7qfQgcbGYHBev+7e6FHpq6ZD2lJpKNENmePwdGBO0+m9CD9LoCBvzNzD4jlCS0r2B/1c5DU9P0A0YB+cDzZnZJqWq7gfC1YvMItRmEHvoX/v5Piqhf0TmtdPePg/fPEGrnbsBX/v0cehOAyCT7+eDrMYRmsP84aMeRhCZfjZXIIZrypmx5wd2rOq9ZTD8T1UDtUja1i9QLDeF6nv2100PzLu0V/OOyPWJ5IKHrY4519x1mNg1ouo/9FhEksWYWB4SnJjDgLnd/bP9Dr3avA/9L6PqDgyu5TWHE+2LK/8xtj3hvwO/c/Z3ICkHCkgL0c/c9FpqDcF/tXK2CX9rTgGlmtpDvp5oI2+PfP8G2ovMNu5jyz6n0f96VeTJuuB0NeM/dL6zENrXhNeB+M+sLNHf3eWbWq1Sd7WVsF1bez0tF7VcfqF3KpnaRekE9UdWjJbApSKC6E+oFAJgDnGRmrYPhwKyIbVYQ6tUAOAtoFLx/B/h1+PoVM2tvZofU9AlU0lPAHe6+sFT5TEK/nMIJ5Xde8fxUW4GkCta/A4wOhv4ws3Qza0GondcHv/x+Ri33rJhZNzOL7BU8Evi6kpvP4vvvf+RFsxWdU0cLXcwOcBHwf4Smr0gzsy5B+a+A6eUc76fhembWwszSy6hXK4JevI8IfYYqM3Fw6c/ICsr+eYnpZ2J/qV3KpnaR+kJJVPV4G0gws6WELpqeBeDuq4C/ERrf/5jQD3ZBsM0ThBKsHEJDPduDbd4lNNzz36Cn40UqTjhqjbt/6+4PlbHqdkLXbn1G6PxL986U3s8GQsNMi8zsnjKqPEnogv35ZrYIeIxQj86zQP+gXUYAuVGfTHQSgQlmtiQ4156Ezr0yrgXGBtt14fvPQUXnlAf8NvhctQYedfddwKXAC8E2JYSuB/kBd88nNMfcc8Ex/0slrsmrYc8BmVTuj+IbwLDwhcKU8/NC7D8T1UHtUja1i9R5mjuvhplZortvC3qiXgGecvdXYh2X1C4L3Vm3093dzC4ALnT3cu8MCu5Imho8WkNEROogXRNV8243s1MIjbu/C7wa23AkRvoBDwd3Am0Gfh3bcEREZH+pJ0pEREQkCromSkRERCQKSqJEREREoqAkSkRERCQKSqJE6rCIOb/SzGxncAt3+DXCzGYH778xs/yIdWkWmktvYUTZjyZoNTM3s2cilhOC/UwtXbeS8aaZ2UURy/3NrKzHYoiI1Hu6O0+k/vii9NPzCeYpDJ7m3t/dx4RXBE/W/1kw5U55tgMZZtbM3XcCpwKr9iPGNEIPBp0E4O5zCU2ZJCJywFFPlIi8CZwevL+QiIcbBk86f8rMPjGzBRbMem9m8WZ2j5nNMbPPzOyKYJO7gROCnq/rzGxguFfLzG4P9jXNzL40s6sjjvNnM8szs/8zs+fM7PpaOXMRkf2gJEqk/uhcajjvhEps81FQd3YFdSYDF5hZU6A3oUmfw/4EfOjuAwjNen9PMAXPZUCBux8FHAX8xswOA24CZrr7ke5+fxnH6g4MJjQ5921m1sjMjiI0JU4mcBrQvxLnJSIScxrOE6k/yhrO25d9Defh7p8FT0i/kFCvVKSfA2dF9Aw1BToG5b3N7JygvCXQFdi9j3j+7e6FQKGZrQfaAj8FXgumtNllZm9U7tRERGJLSZSIALwO/C8wEDg4otyALHfPi6wcPHn9d+7+Tqnygfs4TmHE+2L0O0hE6jEN54kIwFPAHe6+sFT5O8DvgqQJM+sTUT7azBoF5enBMN9Wqj5h9sfAmWbW1MwSgTOiPQkRkdqk/wJF6o/OZvZpxPJT7l4tjw9w92+Bsvb1F+AB4DMziwO+IpTkPEnoTrz5QYKVD5wNfAYUm1kOMB5YUIljzzGz14Nt1wELgYL9OiERkVqgufNEJObMLNHdt5lZc2AGMMrd58c6LhGRiqgnSkTqgsfNrCehC9cnKIESkfpAPVEiIiIiUdCF5SIiIiJRUBIlIiIiEgUlUSIiIiJRUBIlIiIiEgUlUSIiIiJR+P/U26h00cS2yAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 540x360 with 1 Axes>"
       ]
@@ -1388,7 +1404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1397,12 +1413,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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7d0RxcXHjjh07HqmcSJkZu3btemjlypWFlY915513dp49e/aX/fr1OzR+/PjWCxcujPOti46ONr9jnBDHE0880fass8469uqrr35dUVGBmJiYWh27VV/UDSciItLADB48eN/hw4cjJkyY0BoAysrKcPfdd58zdOjQnZdddtl3kyZNSjh27BgAYPv27ZG9e/c+vHv37qgPPvigGQAcOXKEeXl50QBw8ODBiI4dOx47cuQIZ8yYEV+TOEpLSyPbtWt3LDIyEs8991zr8vLyWr7S+qFkSUREpIGJiIjA66+//uVrr73WqlOnTmmdO3dOa9KkScX48eO33H///SWJiYlHu3fv3jMlJSX1H//4R3x0dLTNmDFjw9ixYxNTUlJSe/bsmbpw4cJYABg7duzW8847r8eFF16Y3K1bt8M1ieOXv/zljpdffrl1enp696KiouiYmJiKurniusVAzWani6ysLMvLyzvl4/Sa2qsWojl1BSMKQh3CceFSJ4DqpSqql8BULydSnQRWW/VCcpmZZfmX5efnb0xPT99ZKyeQepGfn98mPT09KdA6tSyJiIiIeFCyJCIiIuJByZKIiIiIByVLIiIiIh6ULImIiIh4ULIkIiIi4iEkyRLJ+0muIbma5Msko0l2JvkpyfUkZ5JsHIrYREREGoJf//rXZ3ft2rVncnJyavfu3VM/+uijk5rEdt68eXH+E+Dm5OQk+SbNrc60adNaksxcsWJF9MmcOxiLFi1qOnLkyHPq6vhACKY7IdkBwH0AUs3sEMlZAG4EcBWAP5vZDJKTANwGYGJ9xyciIlLb/jbqo1qd5uMXky5d5rX+gw8+aPbee++1LCgoWBsTE2Pbtm2LOnLkCE/mXB999FFcbGxs+eWXX36gpvvOmDEjvk+fPvtzc3PjMzIytp7M+b0cO3YMF1100cGLLrroYPVbn7xQdcNFAYghGQWgKYBtAC4FMNtdPxXAdSGKTURE5LS2ZcuWRvHx8WUxMTEGAO3atStLSko6BgBz586N69GjR2pycnLq0KFDkw4dOkQA6NChQ69t27ZFAU5rzXnnnZfyxRdfNJ42bVrCpEmT2nbv3j313XffjQWAhQsXxmZkZHRPTEzsVVUrU2lpaUReXl7slClTNs6ZM+f4NvPmzYvr27dvylVXXfWjpKSktLvvvrvDxIkT43v16tUjOTk5dc2aNU0AYOvWrVFXXHFFl7S0tB5paWk95s+f3wwAxowZ037YsGGdLrjggm7Z2dmd582bF3fJJZd09Z3z+uuvT0pOTk5NTk5OffHFF1sCwM0339wxLS2tR9euXXvef//97Wtan/WeLJnZFgBPAfgGTpJUCmAZgL1mVuZu9i2ADoH2J3knyTySeSUlJfURsoiIyGnluuuu+27r1q2Nk5KS0m655ZaOb731ViwAHDx4kHfddVfnmTNnbigqKlpbVlaGP/3pTwlVHSclJeXo8OHDS0aNGrW9sLBw7aBBg/YDwPbt2xvl5eUVzp07d/1jjz0W8Pf19OnTWw4YMKC0d+/eR1q2bFm+ePHipr51hYWFMRMnTty8bt26NbNnz25dVFQUXVBQsO7WW2/d+fTTT58FAHfdddc5Y8aM2b569ep1c+bM2TBq1Kgk3/6rVq1q+t5773355ptvfu1/zrFjx7Zr3rx5eVFR0dqioqK1V1999T4AeOaZZ7asXr16XWFh4ZolS5bEffrppzE1qc96T5ZItgIwBEBnAO0BNANwZYBNA87DYmaTzSzLzLISEqr8fEVERM5YLVq0qFi9evXaCRMmbEpISCgbMWJEl/Hjx7fOz8+PTkxMPNK7d+8jADBy5Mhdixcvjqvp8QcPHrw3MjISmZmZh3ft2tUo0DazZs2KHzZs2B4AyMnJ2Z2bm3t8Et5evXod6NSp07GYmBjr2LHjkSuvvLIUANLT0w998803jQFgyZIlzUePHt2xe/fuqddee23X/fv3R+7ZsycCAAYNGrQ3Njb2hDxh0aJFze+///4dvuWEhIRyAJg6dWp8ampqj9TU1NT169dH5+fn12gMVb2PWQJwGYCvzawEAEi+BuAnAFqSjHJblxIB1HrfZlUKvv6mvk4lIiJSL6KionDNNdfsu+aaa/b17t37UG5ubuusrKwqx/ZERkZaRYUzz+2hQ4c8G1Oio6OPJyqB5pgtLi6OXLp0afOioqKYe+65B+Xl5SRpEydO/BYAmjRpcnyniIiI48eLiIhAeXk5fcfNy8tbFygpatasWcAJec0M5A+HZhUWFjaeMGFC22XLlq1LSEgoz8nJSTp8+HCNGotCMWbpGwA/JtmUzhUNBLAWwL8BXO9uMwLA3BDEJiIictrLz89vUlBQ0MS3vGLFipjExMSj55577uEtW7Y0Xr16dRMAmDZtWusLL7xwHwAkJiYeXbJkSVMAmDVr1vExRnFxceX79u2LrMn5c3NzW2VnZ+/aunVrwZYtWwqKi4tXJSYmHp0/f35ssMfo37//d+PGjTvLt/zJJ59U23U2YMCA75555pnj+5SUlETu2bMnMiYmpiI+Pr588+bNUQsWLGhRk2sBQjNm6VM4A7mXAyhwY5gM4NcAxpD8EkBrAP+o79hEREQagu+++y5y+PDhnbt06dIzOTk5tbCwMGbcuHFbmzZtapMmTdo4dOjQLsnJyakRERF48MEHSwDg0Ucf3frQQw91zMzMTImMjDzempOTk7P3rbfeauk/wLs6r7zySuvs7Ow9/mVDhgzZ498VV53JkydvXr58ebPk5OTULl269JwwYUK1Y2/++Mc/btu7d29kt27deqakpKS+/fbbcf369TuUlpZ2sFu3bj1vvfXWpMzMzP3BxuDDQM1np4usrCzLy8s79QM9XuMks248XhrqCI7rNbVXqEM4rmBEQahDOE71EpjqJbBwqRfVSWC1VS8kl5lZln9Zfn7+xvT09J21cgKpF/n5+W3S09OTAq3TE7xFREREPIRigLecBjToXURExKGWJREREREPSpZEREREPChZEhEREfGgZElERETEg5IlERGRBmratGktSWauWLHi+PQed911V2LXrl173nXXXYmVt58+fXqLhx9++Oz6jTL86W44ERGROraue4/M2jxej8J1y4LZbsaMGfF9+vTZn5ubG5+RkbEVAKZPn55QUlKyMiYm5gcPWjx27BhuvvnmUjgT3IsftSyJiIg0QKWlpRF5eXmxU6ZM2ThnzpxWAHDppZd2PXToUERGRkaPv//9761ycnKSbr/99sTzzz8/+e67704cP3586+HDh3cEgM2bN0ddfvnlXVJSUlJTUlJS33///WYAcNlll3Xp2bNnj65du/Z86qmn2oTyGuuLWpZEROqInlcmoTR9+vSWAwYMKO3du/eRli1bli9evLjpRx999GXTpk0zCgsL1wLAu+++22LDhg3RS5YsKYqKisL48eNb+/YfNWpUxwsvvHDfo48+uqGsrAylpaWR7nE3tm3btnz//v3MyMhIveWWW/acffbZ5aG6zvqgliUREZEGaNasWfHDhg3bAwA5OTm7q5qXLTs7e09U1IltJ5988kncr371qxIAiIqKQuvWrcsBYNy4cW1TUlJSMzMzexQXFzdas2ZN9Ak7NzBqWRIREWlgiouLI5cuXdq8qKgo5p577kF5eTlJ2sSJE7+tvG1sbGxFsMedN29e3MKFC+Py8vIK4+LiKs4777yUQ4cONfiGlwZ/gSIiImea3NzcVtnZ2bu2bt1asGXLloLi4uJViYmJR+fPnx8b7DEuuOCCfX/6058SAKCsrAy7d++O2Lt3b2SLFi3K4+LiKlasWBGdn5/frO6uInwoWRIREWlgXnnlldbZ2dl7/MuGDBmyp6quuEAmTpz4zcKFC+OSk5NT09LSUpcvXx6Tk5NTWlZWxuTk5NSHH364fXp6+oHajz78qBtORE6ZBjJLsM7U70qwt/rXls8+++yLymWPPPLIDvft8Q/h1Vdf3ei/zX333bcLwC4AOOecc8o+/PDDDZWPs2jRovW1GuxpQC1LIiIiIh6ULImIiIh4ULIkIiIi4kHJkoiIiIgHJUsiIiIiHpQsiYiIiHhQsiQiItJATZs2rSXJzBUrVnhOSXLxxRd33blzZ2R9xXW60XOWRERE6livqb0ya/N4BSMKgnpu04wZM+L79OmzPzc3Nz4jI2NrVdstXLjwy9qLruFRy5KIiEgDVFpaGpGXlxc7ZcqUjXPmzGkFAJs2bWqUlZWV0r1799Ru3br1fPfdd2MBoEOHDr22bdsWBQCXXXZZl549e/bo2rVrz6eeeqpNKK8hXKhlSUREpAGaPn16ywEDBpT27t37SMuWLcsXL17c9P33348bOHBg6bhx44rLysqwb9++ExpNpk+fvrFt27bl+/fvZ0ZGRuott9yy5+yzzy4PxTWEC7UsiYiINECzZs2KHzZs2B4AyMnJ2Z2bmxv/4x//+MDLL7/cZsyYMe0/++yzmFatWlVU3m/cuHFtU1JSUjMzM3sUFxc3WrNmjed4pzOBWpZEREQamOLi4silS5c2LyoqirnnnntQXl5OkjZx4sRvFy1a9MWrr77aYuTIkZ3vu+++7ffcc88u337z5s2LW7hwYVxeXl5hXFxcxXnnnZdy6NChM75hRcmSiIhIA5Obm9sqOzt710svvbTJV9a3b9+Ud955J/Y//uM/9j/wwAM7Dxw4ELF8+fKmcCfOBYC9e/dGtmjRojwuLq5ixYoV0fn5+c1CcgFhRsmSiIhIA/PKK6+0fuihh7b5lw0ZMmTPnXfe2blp06YVUVFR1rRp0/Lp06d/7b9NTk5O6eTJkxOSk5NTu3Tpcjg9Pf1A/UYenpQsiYiI1LFgb/WvLZ999tkXlcseeeSRHY888siOQNtv2bKlwPd+0aJF6+syttPRGd8PKSIiIuJFyZKIiIiIByVLIiIiIh6ULImIiIh40ABvkRoo+PqbUIcgIiL1TC1LIiIiIh7UsiQiItIARUZGZnbr1u2Qbzk7O3v3H/7wh+JQxnS6UrIkIiJS1x5vkVm7xyut9rlNTZo0qSgsLFx7Moc/duwYGjVqdDK7Nkg16oYj2YxkZF0FIyIiInWrQ4cOvbZt2xYFAIsWLWp63nnnpQDAmDFj2g8bNqzTBRdc0C07O7vzwYMHef311yclJyen9ujRI/XNN9+MA4Dx48e3HjhwYJcLL7ywW1JSUtoDDzzQznfs5557Lr5Xr149unfvnnrTTTd1KisrC81F1jLPliWSEQBuBHAzgL4AjgBoQrIEwNsAJpuZnvQpIiISZo4cORLRvXv3VN/yAw88sO2OO+7Y47XPqlWrmn766aeFsbGx9thjj7UFgKKiorUrVqyIvuqqq7pt2LBhtbtds4KCgjWxsbEVGRkZqUOGDCmNjY2tmD17dnxeXl5hkyZN7JZbbuk4adKk1v4T9Z6uquuG+zeADwD8BsBqM6sAAJLxAC4B8CTJOWb2r7oNU0RERGriZLrhBg0atDc2NtYA4JNPPom99957dwBARkbG4fbt2x8tKCiIBoD+/ft/d/bZZ5cDwNVXX71nwYIFsVFRUbZ69eqm6enpPQDg8OHDEWeddVaDaFqqLlm6zMyOVS40s90AXgXwKkl1aoqIiJwmIiMjraKiAgBw6NChHwzHadasWYXvvZlVeQySJyybGYcOHbrrb3/725ZaDTgMeI5ZqpwokYwmeTvJe0m2DrSNiIiIhK/ExMSjS5YsaQoAs2bNalXVdv3799//r3/9Kx4AVq1a1WTbtm2Ne/fufRgAFi9e3Hz79u2R+/fv59tvv93y4osv3j9o0KDv5s2b12rLli1RALB9+/bIoqKixvVxTXWtps9ZehZAJIDDAF6v/XBERESkNvjGLPled999dwcAePTRR7c+9NBDHTMzM1MiIyOrbD566KGHdpSXlzM5OTn1hhtu6PL8889vjImJMQDIysraf8MNN3ROS0vree211+656KKLDmZmZh5+5JFHtgwcODA5OTk59dJLL03evHlzg+h9qm6A90sA/tvMNrhF8QCmu+9H12VgIiIiDUYQt/rXtvLy8oDnHDRo0P6NGzeurlz+zDPPbPVfbtq0qb366qsbAx2jTZs2ZdOmTTthSoM77rhjT3WDyODqEYwAAB8hSURBVE9H1bUsPQLgf0g+RbIFgKcAvAFgPoDHT/akJFuSnE2ykOQ6kv1IxpN8n+R692eVTYMiIiIi9cWzZcnMvgJwE8n+AGYCeAvA5WZWfornfRbAu2Z2PcnGAJoCeBjAh2b2JMmxAMYC+PUpnkdERERq0X333bcLwGn/OICa8GxZItmK5C8ApAL4KYBSAO+RvOZkT0iyOYCLAPwDAMzsqJntBTAEwFR3s6kArjvZc4iIiIjUluq64V6H8yDKaAC5ZjYNwLUAMkm+cZLn/BGAEgBTSK4g+QLJZgDamtk2AHB/nhVoZ5J3kswjmVdSUnKSIYiIiIgEp7pkqTWAl+AM6u4AAGZ2yMx+B+CukzxnFIA+ACaaWQaAA3C63IJiZpPNLMvMshISEk4yBBEREZHgVJcsPQbgfQBzUCmh8bUCnYRvAXxrZp+6y7PhJE/bSbYDAPfnjpM8voiIiEitqe6hlK+a2QVmdpGZfVAbJzSzYgCbSaa4RQMBrIVzl90It2wEgLm1cT4REZEzEcnMO+64I9G3/Oijj7YdM2ZM+5ocY968eXHvv/9+M99yTk5O0pQpU4K6W33atGktSWauWLEiuibnrIlFixY1HTly5Dl1dXyf6p6zNBnAeDM74XkM7jijGwAcMbPpJ+zs7V4A09074b4C8DM4idsskrcB+AbA0BoeU0REJCwljX0rszaPt/HJq6t9blPjxo3t7bffbrVt27bidu3a1XiOtmPHjuGjjz6Ki42NLb/88ssP1HT/GTNmxPfp02d/bm5ufEZGxtbq96h5fBdddNHBiy666GBtH7uy6rrhngPwqPsspFdIPkfynyQ/BvAJgDg43Wg1YmYr3XFHvc3sOjPbY2a7zGygmXVzf+4+iesREREROHPADR8+vOQPf/hD28rrioqKGvfr1y85OTk5tV+/fsnr169vDDgtR7fffnvi+eefn3zNNdd0mTZtWsKkSZPadu/ePfXdd9+NBYCFCxfGZmRkdE9MTOxVVStTaWlpRF5eXuyUKVM2zpkz5/g28+bNi+vbt2/KVVdd9aOkpKS0u+++u8PEiRPje/Xq1SM5OTl1zZo1TQBg69atUVdccUWXtLS0HmlpaT3mz5/fDADGjBnTftiwYZ0uuOCCbtnZ2Z3nzZsXd8kll3T1nfP6669PSk5OTk1OTk598cUXWwLAzTff3DEtLa1H165de95///01alnzqa4bbqWZ/RRAXwB/A/AxnO6y280s3cyeNbMjJ3NiERERqVu/+tWvdrz22mvxu3btivQvHzVqVMebbrppV1FR0dobbrhh189//vPjXVkbNmyIXrJkSdF77723Yfjw4SWjRo3aXlhYuHbQoEH7AWD79u2N8vLyCufOnbv+scce6xDovNOnT285YMCA0t69ex9p2bJl+eLFi5v61hUWFsZMnDhx87p169bMnj27dVFRUXRBQcG6W2+9defTTz99FgDcdddd54wZM2b76tWr182ZM2fDqFGjknz7r1q1qul777335Ztvvvm1/znHjh3brnnz5uVFRUVri4qK1l599dX7AOCZZ57Zsnr16nWFhYVrlixZEvfpp5/G1LQePbvhfMxsP4AFNT24iIiIhE58fHzF0KFDdz355JNnxcTEVPjKV6xY0eydd97ZAAA///nPd//ud787PrYpOzt7T1RU1enB4MGD90ZGRiIzM/Pwrl27As79NmvWrPjRo0fvAICcnJzdubm58f379z8IAL169TrQqVOnYwDQsWPHI1deeWUpAKSnpx9auHBhHAAsWbKk+fr1648nNfv374/cs2dPBAAMGjRob2xs7Alz2i1atKj5jBkzvvItJyQklAPA1KlT41988cU2ZWVlLCkpaZSfnx99/vnnHwqi+o4LKlkSERGR09NvfvOb7X369Em98cYbdwazfWxsbIXX+ujo6OOJitmJ8/AWFxdHLl26tHlRUVHMPffcg/LycpK0iRMnfgsATZo0Ob5TRETE8eNFRESgvLycvuPm5eWtC5QUNWvWLGB8ZgaSPygrLCxsPGHChLbLli1bl5CQUJ6Tk5N0+PDh6oYgnaDGO4iIiMjpo23btuXXXnvtnpdeeqmNrywjI+PACy+80AoAnn/++fisrKz9gfaNi4sr37dvX2SgdVXJzc1tlZ2dvWvr1q0FW7ZsKSguLl6VmJh4dP78+bHBHqN///7fjRs37vjDqT/55JNqu84GDBjw3TPPPHN8n5KSksg9e/ZExsTEVMTHx5dv3rw5asGCBS1qci0+NUqW3DvgRERE5DTy29/+tnjv3r3He5MmTpz4TW5ubpvk5OTUl19+ufVzzz23OdB+OTk5e996662W/gO8q/PKK6+0zs7O3uNfNmTIkD25ubnxwcY7efLkzcuXL2+WnJyc2qVLl54TJkyo9inUf/zjH7ft3bs3slu3bj1TUlJS33777bh+/fodSktLO9itW7eet956a1JmZmbApLA6DNSEdsJG5E8AvAAg1sw6kkwHcJeZ3X0yJ60tWVlZlpeXd+oHevykEs3a93hpqCP4XrjUCaB6qYrqJTDVy4lUJ4HVUr2QXGZmWf5l+fn5G9PT04Pq9pLwkJ+f3yY9PT0p0LpgW5b+DOAKuLMMm1k+nMlwRURERBq0oAd4m9nmSgOnyms/HAkXSYdfCnUIx20MdQAiInJGCzZZ2ux2xZn71O37AKyru7BEREREwkOw3XCjAPwCQAc4E+Ge6y6LiIjIiSoqKipY/WYSDtzPqspHJgT7UMqdAG6uraBEREQauNUlJSWpCQkJpREREdXfSSUhU1FRwZKSkhYATpgH1yeoZInkVACjzWyvu9wKwNNm9l+1EqmIiEgDUlZWdntxcfELxcXFadAzDcNdBYDVZWVlt1e1QbBjlnr7EiUAMLM9JDNONToREZGGKDMzcweAwaGOQ2pHsNluhNuaBAAgGQ9NlSIiIiJngGATnqcBfEJytrs8FMATdROSiIiISPgIdoD3NJLLAFwCgACyzWxtnUYmIiIiEgZq0pVWCGCPbx+SHc3smzqJSkRERCRMBHs33L0AHgOwHc6TuwnAAPSuu9BEREREQi/YlqXRAFLMbFddBiMiIiISboK9G24zgDCatlpERESkfgTbsvQVgAUk3wJwxFdoZs/USVQiIiIiYSLYZOkb99XYfYmIiIicEYJ9dMDv6joQERERkXAU7N1wCQAeAtATQLSv3MwuraO4RERERMJCsAO8p8N5zlJnAL8DsBHA53UUk4iIiEjYCDZZam1m/wBwzMwWmtl/AfhxHcYlIiIiEhaCHeB9zP25jeTVALYCSKybkERERETCR7DJ0v+SbAHgAQB/BdAcwP11FpWIiIhImAj2brh57ttSOJPpioiIiJwRPJMlkn+FMwdcQGZ2X61HJCIiIhJGqhvgnQdgGZzHBfQBsN59nQtnQl0RERGRBs2zZcnMpgIAyZEALjGzY+7yJADz6zw6ERERkRAL9tEB7QHE+S3HumUiIiIiDVqwd8M9CWAFyX+7yxcDeLxOIhIREREJI8HeDTeF5DsAzneLxppZcd2FJSKnk6TDL4U6hOM2hjoAEWlwgu2GA4BIACUA9gBIJnlR3YQkIiIiEj6CnUh3HIAbAKwBUOEWG4BFdRSXiIiISFgIdszSdQBSzOxIXQYjItKQhEv35MZQByBymgu2G+4rAI3qMhARERGRcBRsy9JBACtJfgjgeOuSnuAtIiIiDV2wydIb7ktERETkjBLsowOmkowB0NHMvqjjmERERETCRlBjlkheC2AlgHfd5XNJqqVJREREGrxgB3g/DuA8AHsBwMxWAuhcRzGJiIiIhI1gk6UyMyutVGa1HYyIiIhIuAk2WVpN8iYAkSS7kfwrgE9O5cQkI0muIDnPXe5M8lOS60nOJNn4VI4vIiIiUhuCvRvuXgC/hfPYgJcAvAfgf07x3KMBrAPQ3F0eB+DPZjaD5CQAtwGYeIrnEBGRMBIuD+oE9LBOCV6wLUtXm9lvzayv+3oEwOCTPSnJRABXA3jBXSaASwHMdjeZCuep4SIiIiIhFWyy9Jsgy4L1FwAP4ft55loD2GtmZe7ytwA6BNqR5J0k80jmlZSUnEIIIiIiItXz7IYjeSWAqwB0IDneb1VzAGWB9/JG8hoAO8xsGckBvuIAmwYcQG5mkwFMBoCsrCwNMhcREZE6Vd2Ypa0A8uB0uS3zK98H4P6TPOcFAAaTvApANJzE6y8AWpKMcluXEt1zi4iIiISUZ7JkZvkA8km2NbOp/utIjgbwbE1PaGa/gduF57YsPWhmN5N8BcD1AGYAGAFgbk2PLSIiIlLbgh2zdGOAspG1GAcA/BrAGJJfwhnD9I9aPr6IiIhIjVU3ZmkYgJsAdK40vUlzALtO9eRmtgDAAvf9V3CeEi4iIiISNqobs/QJgG0A2gB42q98H4D8ugpKJFzpGTEiImee6sYsbQKwCUA//3KSFwAYD+AXdReaiIiISOgF+wRvkDwXTpfcTwF8DeC1ugpKREREJFxUN2YpGc7g7mFwxijNBEAzu6QeYqs34dK1sjHUAYiIiMgJqmtZKgTwMYBrzexLACB5ss9XEhERETntVPfogBwAxQD+TfLvJAci8NO2RURERBokz2TJzOaY2Q0AusO5xf9+AG1JTiT5H/UQn4iIiEhIBfVQSjM7YGbTzewaOFORrAQwtk4jExEREQkDwT7B+zgz221mz5vZpXURkIiIiEg4qXGyJCIiInImUbIkIiIi4kHJkoiIiIgHJUsiIiIiHpQsiYiIiHhQsiQiIiLiQcmSiIiIiAclSyIiIiIelCyJiIiIeFCyJCIiIuJByZKIiIiIByVLIiIiIh6ULImIiIh4ULIkIiIi4kHJkoiIiIgHJUsiIiIiHpQsiYiIiHhQsiQiIiLiQcmSiIiIiAclSyIiIiIelCyJiIiIeFCyJCIiIuJByZKIiIiIByVLIiIiIh6ULImIiIh4ULIkIiIi4kHJkoiIiIgHJUsiIiIiHpQsiYiIiHhQsiQiIiLiQcmSiIiIiAclSyIiIiIelCyJiIiIeFCyJCIiIuJByZKIiIiIByVLIiIiIh6ULImIiIh4qPdkieQ5JP9Nch3JNSRHu+XxJN8nud792aq+YxMRERGpLBQtS2UAHjCzHgB+DOAXJFMBjAXwoZl1A/ChuywiIiISUvWeLJnZNjNb7r7fB2AdgA4AhgCY6m42FcB19R2biIiISGUhHbNEMglABoBPAbQ1s22Ak1ABOKuKfe4kmUcyr6SkpL5CFRERkTNUyJIlkrEAXgXwSzP7Ltj9zGyymWWZWVZCQkLdBSgiIiKCECVLJBvBSZSmm9lrbvF2ku3c9e0A7AhFbCIiIiL+QnE3HAH8A8A6M3vGb9UbAEa470cAmFvfsYmIiIhUFhWCc14A4FYABSRXumUPA3gSwCyStwH4BsDQEMQmIiIi8gP1niyZ2WIArGL1wPqMRURERKQ6eoK3iIiIiAclSyIiIiIelCyJiIiIeFCyJCIiIuJByZKIiIiIByVLIiIiIh6ULImIiIh4ULIkIiIi4kHJkoiIiIgHJUsiIiIiHpQsiYiIiHhQsiQiIiLiQcmSiIiIiAclSyIiIiIelCyJiIiIeFCyJCIiIuJByZKIiIiIByVLIiIiIh6ULImIiIh4ULIkIiIi4kHJkoiIiIgHJUsiIiIiHpQsiYiIiHhQsiQiIiLiQcmSiIiIiAclSyIiIiIelCyJiIiIeFCyJCIiIuJByZKIiIiIByVLIiIiIh6ULImIiIh4ULIkIiIi4kHJkoiIiIgHJUsiIiIiHpQsiYiIiHhQsiQiIiLiQcmSiIiIiAclSyIiIiIelCyJiIiIeFCyJCIiIuJByZKIiIiIByVLIiIiIh6ULImIiIh4ULIkIiIi4kHJkoiIiIiHsEqWSA4i+QXJL0mODXU8IiIiImGTLJGMBPA3AFcCSAUwjGRqaKMSERGRM13YJEsAzgPwpZl9ZWZHAcwAMCTEMYmIiMgZjmYW6hgAACSvBzDIzG53l28FcL6Z3VNpuzsB3OkupgD4ol4DrVobADtDHUQYUr2cSHUSmOolMNVLYOFUL53MLCHUQUjdiQp1AH4YoOyETM7MJgOYXPfh1AzJPDPLCnUc4Ub1ciLVSWCql8BUL4GpXqQ+hVM33LcAzvFbTgSwNUSxiIiIiAAIr2TpcwDdSHYm2RjAjQDeCHFMIiIicoYLm244MysjeQ+A9wBEAvinma0JcVg1EXZdg2FC9XIi1UlgqpfAVC+BqV6k3oTNAG8RERGRcBRO3XAiIiIiYUfJkoiIiIgHJUtBIvlPkjtIrvYriyf5Psn17s9WlfbpS7LcfYZUg1OTOiE5gGQpyZXu69HQRV63avpdcetmJck1JBeGJuq6V8Pvy6/8viur3X9H8aGLvu7UsF5akHyTZL77fflZ6CKvOzWsk1Yk55BcRfIzkmmhi1waKiVLwXsRwKBKZWMBfGhm3QB86C4DOD59yzg4A9YbqhdRgzoB8LGZneu+fl9PMYbCiwiyXki2BPAcgMFm1hPA0HqMs769iCDrxcz+5PuuAPgNgIVmtrs+g61HLyL4f0e/ALDWzNIBDADwtHv3cEPzIoKvk4cBrDSz3gCGA3i2voKUM4eSpSCZ2SIAlf+zHgJgqvt+KoDr/NbdC+BVADvqPrrQOIk6OSPUsF5uAvCamX3j7qvvy4mGAXi5DkMLqRrWiwGII0kAse5+ZfURZ32qYZ2kwkmeYGaFAJJItq2POOXMoWTp1LQ1s20A4P48CwBIdgDwnwAmhTC2UAlYJ65+bvfBOyR7hia8kKmqXpIBtCK5gOQyksNDFmFoeH1fQLIpnBaGV0MQWyhVVS8TAPSA88DeAgCjzawiNCHWu6rqJB9ANgCQPA9AJzgPNRapNWHznKUG5i8Afm1m5c4fgAJgOZz5k/aTvArA6wC6hTimcBAFIBPAQAAxAP4fyaVmVhTasMLGtQCWNOAuuJq6AsBKAJcC6ALgfZIfm9l3oQ0rpJ4E8CzJlXASyBVogK1tElpqWTo120m2AwD3p68LJQvADJIbAVwP4DmSZ0p3VMA6MbPvzGy/+/5tAI1ItgldmPWuqu/KtwDeNbMDZrYTwCIA6SGKMRSqqhefG9GAu+A8VFUvP4PTbWtm9iWArwF0D1GM9c3r/5afuePbhgNIgFMvIrVGydKpeQPACPf9CABzAcDMOptZkpklAZgN4G4zez00Ida7gHVC8mx3nIWvqTwCwK6QRBgaAevF/XkhySi3y+l8AOtCEF+oVFUvINkCwMX+ZWeQqurlGzitkHDH5aQA+KreowuNqv5vaek3yP12AIvO8JY2qQtmplcQLzh/3W4DcAxOa8BtAFrDGVi43v0ZH2C/FwFcH+r4Q10nAO4BsAbO+IKlAH4S6vjDoV7c7X8FYC2A1QB+Ger4w6heRgKYEeq4w6leALQHMB9Od9NqALeEOv4wqJN+blkhgNcAtAp1/Ho1vJemOxERERHxoG44EREREQ9KlkREREQ8KFmSsEDyApIXhjoOERGRypQsAXDnnfLNQfWKe1fSGYekkcz1W44iWUJy3kkeryXJu4PYLgPOLdFLg9h2Acmsk4mnJkj+1p17a5X73Tif5AskU+v63Kcr97O5olLZL915vmZXsU8SyZtq4bx1/p04GaqTwFQvcrpRsuQ4ZM48VGkAjgIY5b+SjjOhrg4ASCMZ4y5fDmDLKRyvJYCAyZI7dx4AwMxWmNntZnbsFM5Va0j2A3ANgD7mzDd1GYDNboxrQxvdD/nXYxh4Gc5zkfzdCGCKmZ0wmTTJKABJcKZ8aahUJ4GpXuS0ciYkADX1MYCu7l8x60g+B+fp0+eQnEgyz21x+J1vB5JXkSwkuZjkeF9LDMnHST7ot91qkknu+1vcGbJXknw+jH7pvQPgavf9D+bkojPr9+tua8tSkr3d8sfdvwgXkPyK5H3uLk8C6OJe459IDiD5b5Ivwbn1ucp6qKqu60k7ADvN7AgAmNlOM9vq/1cpyf0kn6AzfctS95k3INnFXf6c5O9J7nfLY0l+SHI5yQKSQ9zyJPe7M9Wt19m+lk2SA0mucLf/J8kmbvlGko+SXAxgqHvOd+lMl/IxyVA9pHA2gGv84kyCc6v7t3Rnjyc5kk7r7ZtwboF/Es5zplaSvN9dP8F3QJLzSA5w34fyO3GyVCeBqV7ktKJkyQ+dv16uhPuLHM4D36aZWYaZbQLwWzPLAtAbwMUke5OMBvA8gCvNrD+cp8dWd54eAG4AcIE5T50tB3Bz7V/RSZkB4Eb3unoD+NRv3e8ArHBbWx4GMM1vXXc4UzGcB+Axko3gzAq+wW21+5W73Xlw6jG1mno4oa7r4mKrMB9OclxE8jmSFwfYphmApebM/r4IwB1u+bMAnjWzvnDm7/I5DOA/zawPgEvgzBbvmwsnBcBkt16/A3C3W/8vArjBzHrBmRbl5/7HM7P+ZjYDwGQA95pZJoAHATx3qhVwMsxsF4DP8P1s8TcCmAln8ld//QCMMLNL4XxHPna/I3+u5hSh/E6cFNVJYKoXOd0oWXLE0JlXKA/OE3L/4ZZvMjP/cTQ/JbkcztxDPeHMdt0dwFdm5nu8fjBTMwyEMx/Y5+55BwL40alfxqkzs1VwmruHAXi70ur+AHLd7T4C0JrOU5YB4C0zO2LOlB07AFQ16/dnfnXlVQ+B6rpemDMtSyaAOwGUAJhJcmSlzY4C8I3lWganzgDnP/dX3Pcv+W1PAH8guQrABwA64Ps62mxmS9z3/4JTzykAvrbv54ibCuAiv+PNBJwWKwA/AfCKW4fPw2kZCxX/7pWqpip5305urreQfSdOkeokMNWLnDY0ka7jkNuycZz7R/8Bv+XOcP5q72tme0i+CCAazi/BqpThhwlptO9wAKaa2W9OPfQ68QaApwAMgPPUXJ9A1+r7S/CIX1k5qv5uHfB7H7AePOq63phZOYAFABaQLMD30yz4HLPvn+jqdb0+N8Npdcw0s2N05g30XVPlv6YN3t8r4Pt6jACwt/L3N4ReB/AMyT4AYsxsudvF4u/ACXt9L+C/mXD4TpwC1Ulgqhc5bahlKXjN4fzDLXXHp1zplhcC+JHfP/Ib/PbZCKAPALj/IXR2yz8EcD3Js9x18SQ71WXwNfRPAL83s4JK5YvgdpO5YwN2mvccTPsAxHmsr6oeqqrrekEyhWQ3v6JzAWwKcvelAHLc9/4DWFsA2OEmSpcA8P+8O9IZVA44LXqL4Xyvkkh2dctvBbCw8snc+v+a5FA3dpIM2US8bqvcAjjfoWBaWSt/RzYCOJdkBMlz4HTbAiH+TpwK1Ulgqhc5nahlKUhmlk9yBZz5zb4CsMQtP0Tn9vh3Se6E0w/v8yqA4W73yOcAitx91pJ8BMB8OnfZHQPwCwT/C7lOmdm3cMbeVPY4gCluV9JBnNjaUvk4u0gucQdsvgPgrUrrA9aDmS0NVNf1KBbAX0m2hPPX65dwuuQC3tJcyS8B/IvkA3Cut9Qtnw7gTZJ5AFbCSYZ81gEYQfJ5OHNcTTSzwyR/Bqd7LQrO92dSFee8GcBEty4bwRl3lh/01da+l+HM0VX5bqdAVgEoI5kPZ4zWX+DMGO+b+2w5UPW/v9OI6iQw1YucFjQ3XC0gGWtm+90Bu38DsD6IAYjSANG5k+2QmRnJGwEMM7MhHtsnAZjnPrZCRETCkFqWascdJEcAaAxnQOHzIY5HQicTwAQ3cd4L4L9CHI+IiJwitSyJiIiIeNAAbxEREREPSpZEREREPChZEhEREfGgZEkkxPj9/HFJJA+5c1/5XsNJfuq+/4Zkid+6JDrzxBX4lf0kwPGNZK7fcpR7nHmVtw0y3h/M/k4yi+T4kzmWiMjpQHfDiYSXDQGexj0NcCYWBZBlZvf4VrhPmr/EnWamKgcApJGMMbNDAC4HsOUUYkyCM/v7SwBgZnlwpgoSEWmQ1LIkcmZ4B8DV7vth8HtiMslmJP9J8nOSK0gOccsjSf7JLV9F8i53l8qzvw/wtVKRfNw91gKSX5G8z+88/02ykOT7JF8m+WC9XLmIyClSsiQSXrpU6oa7MIh9/u1u+6nHNjMA3EgyGs5M7P7b/hbAR2bWF8AlAP5EshmA2wCUuuV94TxPrDOqn/29O4Ar4Ew/8RjJRiSz4EwDkwEgG0BWENclIhIW1A0nEl4CdcNVp7puOJjZKvdp4cMAvF1p9X8AGOzX0hMNoKNb3pvk9W55CwDdABytJp63zOwIgCMkdwBoC6A/gLluNyBIvhnMhYmIhAMlSyJnjjcAPAVgAIDWfuUEkGNmX/hv7D6F/F4ze69S+YBqznPE7305nP9neHIhi4iEnrrhRM4c/wTwezMrqFT+HoB73eQIJDP8yn9OspFbnux2z1We/T0YiwFcSzKaZCy+Hz8lIhL21LIkEl66kFzpt/xPM6uV2/LN7FsAzwZY9T9wZnBf5SZMGwFcA+AFOHe+LXfLSwBchxNnf18RxLk/J/kGgHwAm+DcPVd6alckIlI/NDeciNQLkrFmtp9kUwCLANxpZstDHZeISHXUsiQi9WUyyVQ4A8inKlESkdOFWpZEREREPGiAt4iIiIgHJUsiIiIiHpQsiYiIiHhQsiQiIiLiQcmSiIiIiIf/D69kptfEk8UmAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 540x360 with 1 Axes>"
       ]
@@ -1434,7 +1450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1457,7 +1473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -1478,7 +1494,7 @@
        "Length: 3600, dtype: int64"
       ]
      },
-     "execution_count": 182,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1490,7 +1506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1499,7 +1515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1550,13 +1566,13 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>104</td>\n",
-       "      <td>Google</td>\n",
+       "      <td>Cisco Systems</td>\n",
        "      <td>26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>104</td>\n",
-       "      <td>Cisco Systems</td>\n",
+       "      <td>Google</td>\n",
        "      <td>26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1683,7 +1699,7 @@
        "      <th>2897</th>\n",
        "      <td>109</td>\n",
        "      <td></td>\n",
-       "      <td>199</td>\n",
+       "      <td>200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2898</th>\n",
@@ -1718,8 +1734,8 @@
        "0     104                    185\n",
        "1     104            Huawei   31\n",
        "2     104             Cisco   30\n",
-       "3     104            Google   26\n",
-       "4     104     Cisco Systems   26\n",
+       "3     104     Cisco Systems   26\n",
+       "4     104            Google   26\n",
        "579   105                    142\n",
        "580   105            Google   33\n",
        "581   105  Juniper Networks   28\n",
@@ -1740,14 +1756,14 @@
        "2237  108             Cisco   30\n",
        "2238  108          Ericsson   26\n",
        "2239  108            Google   24\n",
-       "2897  109                    199\n",
+       "2897  109                    200\n",
        "2898  109             Cisco   38\n",
        "2899  109            Huawei   34\n",
        "2900  109          Ericsson   25\n",
        "2901  109     Cisco Systems   21"
       ]
      },
-     "execution_count": 184,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1758,7 +1774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1767,7 +1783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1776,7 +1792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1784,17 +1800,18 @@
       "text/plain": [
        "array(['Cisco', 'Google', 'Huawei', 'Cisco Systems', 'Ericsson',\n",
        "       'Juniper Networks', 'Nokia', 'Internet Society', 'RIPE NCC',\n",
-       "       'Microsoft', 'Akamai Technologies', 'Huawei Technologies',\n",
+       "       'Akamai Technologies', 'Microsoft', 'Huawei Technologies',\n",
        "       'Verisign', 'ETRI', 'NIST', 'Apple', 'Facebook', 'AMS', 'Ciena',\n",
        "       'Futurewei', 'ICANN', 'Cloudflare', 'Deutsche Telekom', 'Fastly',\n",
        "       'Mozilla', 'Salesforce', 'Orange', 'ISC', 'NTT', 'BT',\n",
-       "       'China Mobile', 'Huawei Technologies Co., Ltd.', 'China Telecom',\n",
-       "       'NLnet Labs', 'CAICT', 'APNIC', 'Tsinghua University', 'Inria',\n",
-       "       'AT&T', 'Oracle', 'Akamai', 'Intel', 'HUAWEI', 'JPRS', 'LACNIC',\n",
-       "       'None', 'Verizon', 'InterDigital', 'Meetecho'], dtype=object)"
+       "       'China Mobile', 'Huawei Technologies Co., Ltd.', 'CAICT',\n",
+       "       'China Telecom', 'NLnet Labs', 'Tsinghua University', 'APNIC',\n",
+       "       'Inria', 'AT&T', 'Akamai', 'LACNIC', 'Oracle', 'Intel', 'JPRS',\n",
+       "       'HUAWEI', 'None', 'InterDigital', 'Verizon', 'Meetecho'],\n",
+       "      dtype=object)"
       ]
      },
-     "execution_count": 187,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1805,7 +1822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1814,12 +1831,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 1 Axes>"
       ]
@@ -1857,7 +1874,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1867,9 +1884,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:bigbang37] *",
+   "display_name": "bigbang-env",
    "language": "python",
-   "name": "conda-env-bigbang37-py"
+   "name": "bigbang-env"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1881,7 +1898,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.9.5"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/examples/datatracker/Working Group Affiliations.ipynb b/examples/datatracker/Working Group Affiliations.ipynb
index 13ad5366..7562357c 100644
--- a/examples/datatracker/Working Group Affiliations.ipynb
+++ b/examples/datatracker/Working Group Affiliations.ipynb
@@ -11,7 +11,7 @@
     "import networkx as nx\n",
     "import pandas as pd\n",
     "\n",
-    "import bigbang.process as process"
+    "import bigbang.analysis.process as process"
    ]
   },
   {
@@ -25,23 +25,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "window = {\n",
     "    'since' : \"2018-01-01T00:00:00\",\n",
-    "    'until' : \"2020-02-01T00:00:00\"\n",
+    "    'until' : \"2018-01-10T00:00:00\"\n",
     "}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {
     "code_folding": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:ietfdata:glasgow-ietfdata/0.5.1 (cache disabled)\n"
+     ]
+    }
+   ],
    "source": [
     "dt = DataTracker()\n",
     "\n",
@@ -75,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -627,9 +635,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "bigbang3",
+   "display_name": "bigbang-env",
    "language": "python",
-   "name": "myenv"
+   "name": "bigbang-env"
   },
   "language_info": {
    "codemirror_mode": {
@@ -641,7 +649,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.9.5"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/examples/experimental_notebooks/Analyze Senders.ipynb b/examples/experimental_notebooks/Analyze Senders.ipynb
index 1abf6f3f..c65d7d5c 100644
--- a/examples/experimental_notebooks/Analyze Senders.ipynb
+++ b/examples/experimental_notebooks/Analyze Senders.ipynb
@@ -11,7 +11,6 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -37,9 +36,9 @@
     }
    ],
    "source": [
-    "import bigbang.mailman as mailman\n",
-    "import bigbang.graph as graph\n",
-    "import bigbang.process as process\n",
+    "import bigbang.ingress.mailman as mailman\n",
+    "import bigbang.analysis.graph as graph\n",
+    "import bigbang.analysis.process as process\n",
     "from bigbang.parse import get_date\n",
     "from bigbang.archive import Archive\n",
     "import imp\n",
@@ -57,7 +56,6 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -85,7 +83,6 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -117,7 +114,6 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -169,7 +165,6 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -185,7 +180,6 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -224,7 +218,6 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -254,7 +247,6 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -284,7 +276,6 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -327,7 +318,6 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -381,7 +371,6 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -427,7 +416,6 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -472,7 +460,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -483,7 +470,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -497,7 +484,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/experimental_notebooks/Assortativity Study.ipynb b/examples/experimental_notebooks/Assortativity Study.ipynb
index dc758831..d8ceccd1 100644
--- a/examples/experimental_notebooks/Assortativity Study.ipynb
+++ b/examples/experimental_notebooks/Assortativity Study.ipynb
@@ -27,9 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from bigbang.archive import Archive\n",
@@ -99,9 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import bigbang.graph as graph\n",
@@ -112,9 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -161,9 +155,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import networkx as nx\n",
@@ -193,9 +185,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline"
@@ -204,9 +194,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt"
@@ -215,9 +203,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -348,9 +334,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -438,16 +422,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -460,9 +442,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/experimental_notebooks/Auditing Fernando.ipynb b/examples/experimental_notebooks/Auditing Fernando.ipynb
index a8d4e58d..4cb42bab 100644
--- a/examples/experimental_notebooks/Auditing Fernando.ipynb
+++ b/examples/experimental_notebooks/Auditing Fernando.ipynb
@@ -455,7 +455,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -468,7 +468,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/experimental_notebooks/Basic List Statistics | Corinne questions.ipynb b/examples/experimental_notebooks/Basic List Statistics | Corinne questions.ipynb
index bd3a44bc..5835d607 100644
--- a/examples/experimental_notebooks/Basic List Statistics | Corinne questions.ipynb
+++ b/examples/experimental_notebooks/Basic List Statistics | Corinne questions.ipynb
@@ -11,8 +11,8 @@
     "import pandas as pd\n",
     "import datetime\n",
     "import pytz\n",
-    "from bigbang.thread import Thread\n",
-    "from bigbang.thread import Node\n",
+    "from bigbang.analysis.thread import Thread\n",
+    "from bigbang.analysis.thread import Node\n",
     "from collections import defaultdict"
    ]
   },
@@ -335,7 +335,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -348,7 +348,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/experimental_notebooks/Cohort Visualization.ipynb b/examples/experimental_notebooks/Cohort Visualization.ipynb
index 77972281..f015fa54 100644
--- a/examples/experimental_notebooks/Cohort Visualization.ipynb
+++ b/examples/experimental_notebooks/Cohort Visualization.ipynb
@@ -310,7 +310,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -323,7 +323,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/experimental_notebooks/Collaboration Robustness.ipynb b/examples/experimental_notebooks/Collaboration Robustness.ipynb
index 96ca9148..75eb6a74 100644
--- a/examples/experimental_notebooks/Collaboration Robustness.ipynb
+++ b/examples/experimental_notebooks/Collaboration Robustness.ipynb
@@ -33,10 +33,10 @@
    "source": [
     "from bigbang.archive import Archive\n",
     "import bigbang.parse as parse\n",
-    "import bigbang.graph as graph\n",
-    "import bigbang.mailman as mailman\n",
-    "import bigbang.process as process\n",
-    "import bigbang.twopeople as twoppl\n",
+    "import bigbang.analysis.graph as graph\n",
+    "import bigbang.ingress.mailman as mailman\n",
+    "import bigbang.analysis.process as process\n",
+    "import bigbang.analysis.twopeople as twoppl\n",
     "import matplotlib.pyplot as plt\n",
     "import networkx as nx\n",
     "import numpy as np\n",
@@ -6965,7 +6965,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -6978,7 +6978,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/experimental_notebooks/Committer Dominance.ipynb b/examples/experimental_notebooks/Committer Dominance.ipynb
index aa49b054..49540157 100644
--- a/examples/experimental_notebooks/Committer Dominance.ipynb
+++ b/examples/experimental_notebooks/Committer Dominance.ipynb
@@ -3,14 +3,12 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "from bigbang.archive import Archive\n",
-    "from bigbang import repo_loader;\n",
+    "from bigbang.analysis import repo_loader;\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n"
@@ -19,9 +17,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -41,9 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "convolveArray = [1/(n+1) for n in range(9)]"
@@ -52,9 +46,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -75,9 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -108,9 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -143,34 +131,28 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -183,9 +165,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/experimental_notebooks/Corr between centrality and community 0.1.ipynb b/examples/experimental_notebooks/Corr between centrality and community 0.1.ipynb
index 46077c04..6c0f74f5 100644
--- a/examples/experimental_notebooks/Corr between centrality and community 0.1.ipynb
+++ b/examples/experimental_notebooks/Corr between centrality and community 0.1.ipynb
@@ -10,9 +10,7 @@
   {
    "cell_type": "code",
    "execution_count": 142,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline"
@@ -21,16 +19,14 @@
   {
    "cell_type": "code",
    "execution_count": 143,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from bigbang.archive import Archive\n",
     "import bigbang.parse as parse\n",
-    "import bigbang.graph as graph\n",
-    "import bigbang.mailman as mailman\n",
-    "import bigbang.process as process\n",
+    "import bigbang.analysis.graph as graph\n",
+    "import bigbang.ingress.mailman as mailman\n",
+    "import bigbang.analysis.process as process\n",
     "import networkx as nx\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
@@ -44,9 +40,7 @@
   {
    "cell_type": "code",
    "execution_count": 172,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "urls = [\"https://mm.icann.org/pipermail/cc-humanrights/\",\n",
@@ -66,9 +60,7 @@
   {
    "cell_type": "code",
    "execution_count": 199,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -103,9 +95,7 @@
   {
    "cell_type": "code",
    "execution_count": 200,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "date_from = []\n",
@@ -125,9 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": 201,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def filter_by_date(df,d_from,d_to):\n",
@@ -137,9 +125,7 @@
   {
    "cell_type": "code",
    "execution_count": 202,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "IG = []\n",
@@ -161,9 +147,7 @@
   {
    "cell_type": "code",
    "execution_count": 203,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -183,9 +167,7 @@
   {
    "cell_type": "code",
    "execution_count": 204,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -213,9 +195,7 @@
   {
    "cell_type": "code",
    "execution_count": 205,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2859,9 +2839,7 @@
   {
    "cell_type": "code",
    "execution_count": 206,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "for i in range(len(new_dict1)):\n",
@@ -2872,9 +2850,7 @@
   {
    "cell_type": "code",
    "execution_count": 207,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "#check if there's name difference, return nothing if perfect.\n",
@@ -2888,9 +2864,7 @@
   {
    "cell_type": "code",
    "execution_count": 208,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "#A list of corresponding betweenness centrality and community membership for all users, monthly\n",
@@ -2904,9 +2878,7 @@
   {
    "cell_type": "code",
    "execution_count": 209,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "corr = []\n",
@@ -2919,9 +2891,7 @@
   {
    "cell_type": "code",
    "execution_count": 210,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2952,9 +2922,7 @@
   {
    "cell_type": "code",
    "execution_count": 211,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2989,9 +2957,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
@@ -3016,7 +2982,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -3029,9 +2995,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/experimental_notebooks/EME Diversity Analysis.ipynb b/examples/experimental_notebooks/EME Diversity Analysis.ipynb
index 2fa401d0..fea3e49a 100644
--- a/examples/experimental_notebooks/EME Diversity Analysis.ipynb
+++ b/examples/experimental_notebooks/EME Diversity Analysis.ipynb
@@ -15,8 +15,8 @@
    },
    "outputs": [],
    "source": [
-    "import bigbang.mailman as mailman\n",
-    "import bigbang.process as process\n",
+    "import bigbang.ingress.mailman as mailman\n",
+    "import bigbang.analysis.process as process\n",
     "from bigbang.archive import Archive\n",
     "\n",
     "\n",
@@ -86,9 +86,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -120,9 +118,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -154,9 +150,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Remove Dupicate senders\n",
@@ -181,9 +175,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "sender_categories = pd.read_csv('people_tag.csv',delimiter=',', encoding=\"utf-8-sig\")\n",
@@ -239,9 +231,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -267,9 +257,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -300,9 +288,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -329,9 +315,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -357,7 +341,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -370,9 +354,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/experimental_notebooks/File Dependency Network.ipynb b/examples/experimental_notebooks/File Dependency Network.ipynb
index c83f8354..305057b2 100644
--- a/examples/experimental_notebooks/File Dependency Network.ipynb
+++ b/examples/experimental_notebooks/File Dependency Network.ipynb
@@ -3,9 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -19,9 +17,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def create_dic(filename):\n",
@@ -52,9 +48,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -78,9 +72,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def create_graph(dic):\n",
@@ -97,9 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "G = create_graph(dic)"
@@ -108,9 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -143,9 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "nx.write_gexf(G,'depen)"
@@ -154,7 +140,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -167,9 +153,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/experimental_notebooks/Git Collection.ipynb b/examples/experimental_notebooks/Git Collection.ipynb
index 7fe874d1..40090332 100644
--- a/examples/experimental_notebooks/Git Collection.ipynb
+++ b/examples/experimental_notebooks/Git Collection.ipynb
@@ -8,7 +8,7 @@
    "source": [
     "%matplotlib inline\n",
     "from bigbang.archive import Archive\n",
-    "from bigbang import repo_loader;\n",
+    "from bigbang.analysis import repo_loader;\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n"
@@ -360,7 +360,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -373,7 +373,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/experimental_notebooks/Git Commit Network.ipynb b/examples/experimental_notebooks/Git Commit Network.ipynb
index 5b3471ca..48652439 100644
--- a/examples/experimental_notebooks/Git Commit Network.ipynb
+++ b/examples/experimental_notebooks/Git Commit Network.ipynb
@@ -10,15 +10,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "from bigbang.archive import Archive\n",
-    "from bigbang import repo_loader;\n",
-    "from bigbang.git_repo import GitRepo;\n",
+    "from bigbang.analysis import repo_loader;\n",
+    "from bigbang.ingress.git_repo import GitRepo;\n",
     "import matplotlib.pyplot as plt\n",
     "import networkx as nx\n",
     "import pandas as pd"
@@ -27,9 +25,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -55,9 +51,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class Commit:\n",
@@ -81,9 +75,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def get_commits():\n",
@@ -105,9 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def make_graph(nodes):\n",
@@ -124,9 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -152,16 +140,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -174,9 +160,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/experimental_notebooks/Git Diffs.ipynb b/examples/experimental_notebooks/Git Diffs.ipynb
index fccb15e8..827fadae 100644
--- a/examples/experimental_notebooks/Git Diffs.ipynb
+++ b/examples/experimental_notebooks/Git Diffs.ipynb
@@ -3,14 +3,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "from bigbang.archive import Archive\n",
-    "from bigbang import repo_loader;\n",
+    "from bigbang.analysis import repo_loader;\n",
     "from bigbang.git_repo import GitRepo,ALL_ATTRIBUTES\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -20,18 +18,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -51,9 +45,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -70,9 +62,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import networkx as nx;\n"
@@ -81,9 +71,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "G = nx.Graph()\n",
@@ -106,9 +94,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -135,9 +121,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from networkx.readwrite import json_graph\n",
@@ -147,9 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import json\n",
@@ -169,7 +151,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -182,9 +164,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/experimental_notebooks/Git Interaction Graph.ipynb b/examples/experimental_notebooks/Git Interaction Graph.ipynb
index abb4b70a..15a0e25c 100644
--- a/examples/experimental_notebooks/Git Interaction Graph.ipynb
+++ b/examples/experimental_notebooks/Git Interaction Graph.ipynb
@@ -10,14 +10,12 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
-    "from bigbang.git_repo import GitRepo;\n",
-    "from bigbang import repo_loader;\n",
+    "from bigbang.ingress.git_repo import GitRepo;\n",
+    "from bigbang.analysis import repo_loader;\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import networkx as nx\n",
@@ -27,9 +25,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -114,9 +110,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class Commit:\n",
@@ -153,9 +147,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def get_authors():\n",
@@ -190,9 +182,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def make_graph(nodes):\n",
@@ -214,9 +204,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "nodes = get_authors()\n",
@@ -230,7 +218,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -243,9 +231,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/experimental_notebooks/Git Loading Tutorials.ipynb b/examples/experimental_notebooks/Git Loading Tutorials.ipynb
index d1fce327..40a2ca36 100644
--- a/examples/experimental_notebooks/Git Loading Tutorials.ipynb
+++ b/examples/experimental_notebooks/Git Loading Tutorials.ipynb
@@ -46,9 +46,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1164,9 +1162,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2329,7 +2325,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2342,9 +2338,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/experimental_notebooks/IETF Participants.ipynb b/examples/experimental_notebooks/IETF Participants.ipynb
index 4faa5e78..e7c9e481 100644
--- a/examples/experimental_notebooks/IETF Participants.ipynb
+++ b/examples/experimental_notebooks/IETF Participants.ipynb
@@ -34,9 +34,9 @@
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
-    "import bigbang.mailman as mailman\n",
-    "import bigbang.graph as graph\n",
-    "import bigbang.process as process\n",
+    "import bigbang.ingress.mailman as mailman\n",
+    "import bigbang.analysis.graph as graph\n",
+    "import bigbang.analysis.process as process\n",
     "from bigbang.parse import get_date\n",
     "from bigbang.archive import Archive\n",
     "import bigbang.utils as utils\n",
@@ -1656,7 +1656,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1669,7 +1669,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   },
   "notify_time": "10"
  },
diff --git a/examples/experimental_notebooks/List Correlation.ipynb b/examples/experimental_notebooks/List Correlation.ipynb
index 9dbeadcb..2c98776d 100644
--- a/examples/experimental_notebooks/List Correlation.ipynb
+++ b/examples/experimental_notebooks/List Correlation.ipynb
@@ -8,7 +8,7 @@
    },
    "outputs": [],
    "source": [
-    "from bigbang import plot\n",
+    "from bigbang.analysis import plot\n",
     "import numpy as np\n",
     "import networkx as nx\n",
     "import matplotlib.pyplot as plt"
@@ -57,9 +57,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from bigbang.archive import Archive\n",
@@ -77,9 +75,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "act = arx.get_activity(resolved=True)"
@@ -88,9 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "#act.shape"
@@ -106,9 +100,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -144,9 +136,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -198,9 +188,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "G = nx.Graph(pc)\n",
@@ -211,9 +199,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -235,9 +221,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -280,9 +264,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from bigbang.process import modularity"
@@ -320,9 +302,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "partition = ksc(pc,k=3)"
@@ -331,9 +311,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -362,9 +340,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -394,9 +370,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from collections import defaultdict\n",
@@ -426,9 +400,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -466,9 +438,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "k = np.argmax(mods) + 2\n",
@@ -478,9 +448,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -509,7 +477,6 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -534,9 +501,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -556,9 +521,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "node_parts = {}\n",
@@ -571,9 +534,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -606,7 +567,6 @@
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -630,9 +590,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -652,9 +610,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -674,9 +630,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -705,7 +659,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -718,9 +672,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/experimental_notebooks/MultiGitRepos.ipynb b/examples/experimental_notebooks/MultiGitRepos.ipynb
index 702d7c39..8a370426 100644
--- a/examples/experimental_notebooks/MultiGitRepos.ipynb
+++ b/examples/experimental_notebooks/MultiGitRepos.ipynb
@@ -3,9 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -19,8 +17,8 @@
    "source": [
     "%matplotlib inline\n",
     "from bigbang.archive import Archive\n",
-    "from bigbang import repo_loader;\n",
-    "from bigbang.git_repo import GitRepo;\n",
+    "from bigbang.analysis import repo_loader;\n",
+    "from bigbang.ingress.git_repo import GitRepo;\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -44,9 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "org_name = \"glass-bead-labs\"\n",
@@ -70,9 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "G = nx.Graph()\n",
@@ -109,9 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -152,9 +144,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "G = nx.Graph()\n",
@@ -188,9 +178,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -232,9 +220,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "repos = [node[0] for node in G.nodes(data=True) if node[1][\"type\"] == 'repo']\n",
@@ -271,9 +257,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "json.dump(json_graph.node_link_data(NG), open(\"./viz/git/\" + str(org_name) + \"_repo2repo.json\", \"w\"))"
@@ -282,9 +266,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -320,7 +302,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -333,9 +315,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/experimental_notebooks/Person-Group Bipartite Graph.ipynb b/examples/experimental_notebooks/Person-Group Bipartite Graph.ipynb
index 8645a24d..51de2ed6 100644
--- a/examples/experimental_notebooks/Person-Group Bipartite Graph.ipynb
+++ b/examples/experimental_notebooks/Person-Group Bipartite Graph.ipynb
@@ -121,7 +121,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -134,7 +134,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/experimental_notebooks/Plot Activity.ipynb b/examples/experimental_notebooks/Plot Activity.ipynb
index 918099c6..87314d4b 100644
--- a/examples/experimental_notebooks/Plot Activity.ipynb
+++ b/examples/experimental_notebooks/Plot Activity.ipynb
@@ -20,7 +20,6 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -36,9 +35,9 @@
     }
    ],
    "source": [
-    "import bigbang.mailman as mailman\n",
-    "import bigbang.graph as graph\n",
-    "import bigbang.process as process\n",
+    "import bigbang.ingress.mailman as mailman\n",
+    "import bigbang.analysis.graph as graph\n",
+    "import bigbang.analysis.process as process\n",
     "from bigbang.parse import get_date\n",
     "#from bigbang.functions import *\n",
     "from bigbang.archive import Archive"
@@ -55,7 +54,6 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -83,7 +81,6 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -110,7 +107,6 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     },
@@ -818,7 +814,6 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -839,7 +834,6 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     },
@@ -907,7 +901,6 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -961,7 +954,6 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -977,7 +969,6 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     },
@@ -1036,7 +1027,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1050,7 +1041,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/experimental_notebooks/Show Interaction Graph.ipynb b/examples/experimental_notebooks/Show Interaction Graph.ipynb
index 2f9a17af..92db7e4d 100644
--- a/examples/experimental_notebooks/Show Interaction Graph.ipynb
+++ b/examples/experimental_notebooks/Show Interaction Graph.ipynb
@@ -12,9 +12,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline"
@@ -30,16 +28,14 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from bigbang.archive import Archive\n",
     "import bigbang.parse as parse\n",
-    "import bigbang.graph as graph\n",
-    "import bigbang.mailman as mailman\n",
-    "import bigbang.process as process\n",
+    "import bigbang.analysis.graph as graph\n",
+    "import bigbang.ingress.mailman as mailman\n",
+    "import bigbang.analysis.process as process\n",
     "import networkx as nx\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
@@ -59,9 +55,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "urls = [\"ipython-dev\",\"hot\",\n",
@@ -82,9 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "date_from = pd.datetime(2011,11,1,tzinfo=pytz.utc)\n",
@@ -101,9 +93,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def filter_by_date(df,d_from,d_to):\n",
@@ -122,9 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def draw_interaction_graph(ig):\n",
@@ -161,9 +149,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -211,9 +197,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "dfs = [filter_by_date(arx.data,\n",
@@ -241,9 +225,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -267,9 +249,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "nx.write_edgelist(IG, \"ig-edges.txt\",delimiter=\"\\t\")"
@@ -278,9 +258,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "nx.write_gexf(IG,\"all.gexf\")"
@@ -289,9 +267,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "arx = archives[0]"
@@ -300,9 +276,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1284,16 +1258,14 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1306,9 +1278,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/experimental_notebooks/Single Word Trend.ipynb b/examples/experimental_notebooks/Single Word Trend.ipynb
index 7a05bf7f..c8bfef64 100644
--- a/examples/experimental_notebooks/Single Word Trend.ipynb
+++ b/examples/experimental_notebooks/Single Word Trend.ipynb
@@ -26,9 +26,9 @@
    "source": [
     "from bigbang.archive import Archive\n",
     "import bigbang.parse as parse\n",
-    "import bigbang.graph as graph\n",
-    "import bigbang.mailman as mailman\n",
-    "import bigbang.process as process\n",
+    "import bigbang.analysis.graph as graph\n",
+    "import bigbang.ingress.mailman as mailman\n",
+    "import bigbang.analysis.process as process\n",
     "import networkx as nx\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
@@ -2836,7 +2836,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2849,7 +2849,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/experimental_notebooks/Special words analysis.ipynb b/examples/experimental_notebooks/Special words analysis.ipynb
index 56c5d3c1..976d0f31 100644
--- a/examples/experimental_notebooks/Special words analysis.ipynb
+++ b/examples/experimental_notebooks/Special words analysis.ipynb
@@ -8,9 +8,9 @@
    "source": [
     "from bigbang.archive import Archive\n",
     "import bigbang.parse as parse\n",
-    "import bigbang.graph as graph\n",
-    "import bigbang.mailman as mailman\n",
-    "import bigbang.process as process\n",
+    "import bigbang.analysis.graph as graph\n",
+    "import bigbang.ingress.mailman as mailman\n",
+    "import bigbang.analysis.process as process\n",
     "import networkx as nx\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
@@ -1415,7 +1415,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1428,7 +1428,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/experimental_notebooks/Testing Power Law Response Time Hypothesis.ipynb b/examples/experimental_notebooks/Testing Power Law Response Time Hypothesis.ipynb
index 15417eae..acf23ec5 100644
--- a/examples/experimental_notebooks/Testing Power Law Response Time Hypothesis.ipynb
+++ b/examples/experimental_notebooks/Testing Power Law Response Time Hypothesis.ipynb
@@ -12,9 +12,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -35,9 +33,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "arx.data.drop_duplicates(subset=('From','Date'),inplace=True)"
@@ -65,7 +61,6 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -97,9 +92,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -131,9 +124,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -163,9 +154,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -215,7 +204,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -228,9 +217,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/experimental_notebooks/Threads-research-in-progress.ipynb b/examples/experimental_notebooks/Threads-research-in-progress.ipynb
index 719d22eb..904b87e5 100644
--- a/examples/experimental_notebooks/Threads-research-in-progress.ipynb
+++ b/examples/experimental_notebooks/Threads-research-in-progress.ipynb
@@ -3,15 +3,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "from bigbang.archive import Archive\n",
-    "from bigbang.thread import Thread\n",
-    "from bigbang.thread import Node\n",
+    "from bigbang.analysis.thread import Thread\n",
+    "from bigbang.analysis.thread import Node\n",
     "import matplotlib.pyplot as plt\n",
     "import datetime"
    ]
@@ -26,9 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "url = \"https://lists.wikimedia.org/pipermail/analytics/\"\n",
@@ -45,9 +41,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -75,9 +69,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -108,9 +100,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -141,9 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -167,9 +155,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -200,9 +186,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -219,9 +203,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "threads = arx.get_threads"
@@ -230,9 +212,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -253,9 +233,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -282,9 +260,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "short_threads = []\n",
@@ -297,9 +273,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -318,9 +292,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -340,9 +312,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "dist_short = []\n",
@@ -358,9 +328,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -381,9 +349,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -404,9 +370,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -425,9 +389,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "ename": "TypeError",
@@ -454,9 +416,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "ename": "IndexError",
@@ -489,9 +449,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -512,9 +470,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -533,9 +489,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import re\n",
@@ -545,9 +499,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -703,9 +655,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "mess.split('\\n')\n",
@@ -722,9 +672,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -850,9 +798,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "ename": "NameError",
@@ -872,9 +818,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "print(mess)"
@@ -883,9 +827,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "threads[85].get_leaves()[0].data"
@@ -894,7 +836,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -907,9 +849,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/experimental_notebooks/Threads.ipynb b/examples/experimental_notebooks/Threads.ipynb
index c187758f..2883755f 100644
--- a/examples/experimental_notebooks/Threads.ipynb
+++ b/examples/experimental_notebooks/Threads.ipynb
@@ -3,15 +3,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "from bigbang.archive import Archive\n",
-    "from bigbang.thread import Thread\n",
-    "from bigbang.thread import Node\n",
+    "from bigbang.analysis.thread import Thread\n",
+    "from bigbang.analysis.thread import Node\n",
     "import matplotlib.pyplot as plt\n",
     "import datetime"
    ]
@@ -26,9 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "url = \"https://lists.wikimedia.org/pipermail/analytics/\"\n",
@@ -45,9 +41,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -75,9 +69,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -108,9 +100,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -141,9 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -167,9 +155,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -200,9 +186,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -219,9 +203,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -242,9 +224,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -271,9 +251,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "short_threads = []\n",
@@ -286,9 +264,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -314,9 +290,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -355,16 +329,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -377,9 +349,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/experimental_notebooks/Walkers and Talkers.ipynb b/examples/experimental_notebooks/Walkers and Talkers.ipynb
index d286ca6a..c05fcb1e 100644
--- a/examples/experimental_notebooks/Walkers and Talkers.ipynb
+++ b/examples/experimental_notebooks/Walkers and Talkers.ipynb
@@ -8,8 +8,8 @@
    "source": [
     "%matplotlib inline\n",
     "from bigbang.archive import Archive\n",
-    "import bigbang.entity_resolution\n",
-    "from bigbang import repo_loader;\n",
+    "import bigbang.analysis.entity_resolution\n",
+    "from bigbang.analysis import repo_loader;\n",
     "import matplotlib.pyplot as plt\n",
     "from matplotlib import animation, colors\n",
     "import pylab\n",
@@ -383,7 +383,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -396,7 +396,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/experimental_notebooks/WordTypeClassifier ICANN.ipynb b/examples/experimental_notebooks/WordTypeClassifier ICANN.ipynb
index 2c71a02b..935bcf84 100644
--- a/examples/experimental_notebooks/WordTypeClassifier ICANN.ipynb
+++ b/examples/experimental_notebooks/WordTypeClassifier ICANN.ipynb
@@ -17,9 +17,9 @@
    "source": [
     "from bigbang.archive import Archive\n",
     "import bigbang.parse as parse\n",
-    "import bigbang.graph as graph\n",
-    "import bigbang.mailman as mailman\n",
-    "import bigbang.process as process\n",
+    "import bigbang.analysis.graph as graph\n",
+    "import bigbang.ingress.mailman as mailman\n",
+    "import bigbang.analysis.process as process\n",
     "import networkx as nx\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
@@ -1229,7 +1229,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1242,7 +1242,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/experimental_notebooks/WordTypeClassifier.ipynb b/examples/experimental_notebooks/WordTypeClassifier.ipynb
index 17e42ca7..fee91620 100644
--- a/examples/experimental_notebooks/WordTypeClassifier.ipynb
+++ b/examples/experimental_notebooks/WordTypeClassifier.ipynb
@@ -17,9 +17,9 @@
    "source": [
     "from bigbang.archive import Archive\n",
     "import bigbang.parse as parse\n",
-    "import bigbang.graph as graph\n",
-    "import bigbang.mailman as mailman\n",
-    "import bigbang.process as process\n",
+    "import bigbang.analysis.graph as graph\n",
+    "import bigbang.ingress.mailman as mailman\n",
+    "import bigbang.analysis.process as process\n",
     "import networkx as nx\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
@@ -1217,7 +1217,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1230,7 +1230,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/experimental_notebooks/Working Group Emails and Drafts-hrpc.ipynb b/examples/experimental_notebooks/Working Group Emails and Drafts-hrpc.ipynb
index 2c5c830b..f8bf7e1e 100644
--- a/examples/experimental_notebooks/Working Group Emails and Drafts-hrpc.ipynb
+++ b/examples/experimental_notebooks/Working Group Emails and Drafts-hrpc.ipynb
@@ -33,7 +33,7 @@
     }
    ],
    "source": [
-    "import bigbang.mailman as mailman\n",
+    "import bigbang.ingress.mailman as mailman\n",
     "from bigbang.parse import get_date\n",
     "#from bigbang.functions import *\n",
     "from bigbang.archive import Archive\n",
@@ -778,9 +778,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "bigbang3",
+   "display_name": "bigbang-env",
    "language": "python",
-   "name": "myenv"
+   "name": "bigbang-env"
   },
   "language_info": {
    "codemirror_mode": {
@@ -792,7 +792,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.9.5"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/examples/experimental_notebooks/Working Group Emails and Drafts-httpbis.ipynb b/examples/experimental_notebooks/Working Group Emails and Drafts-httpbis.ipynb
index d08264a5..8e3ce4f4 100644
--- a/examples/experimental_notebooks/Working Group Emails and Drafts-httpbis.ipynb
+++ b/examples/experimental_notebooks/Working Group Emails and Drafts-httpbis.ipynb
@@ -33,7 +33,7 @@
     }
    ],
    "source": [
-    "import bigbang.mailman as mailman\n",
+    "import bigbang.ingress.mailman as mailman\n",
     "from bigbang.parse import get_date\n",
     "#from bigbang.functions import *\n",
     "from bigbang.archive import Archive\n",
@@ -732,9 +732,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "bigbang3",
+   "display_name": "bigbang-env",
    "language": "python",
-   "name": "myenv"
+   "name": "bigbang-env"
   },
   "language_info": {
    "codemirror_mode": {
@@ -746,7 +746,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.9.5"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/examples/name-and-gender/Analyze Senders - Name and Gender.ipynb b/examples/name-and-gender/Analyze Senders - Name and Gender.ipynb
index 8799c9ee..6ef4a206 100644
--- a/examples/name-and-gender/Analyze Senders - Name and Gender.ipynb
+++ b/examples/name-and-gender/Analyze Senders - Name and Gender.ipynb
@@ -38,9 +38,9 @@
     }
    ],
    "source": [
-    "import bigbang.mailman as mailman\n",
-    "import bigbang.graph as graph\n",
-    "import bigbang.process as process\n",
+    "import bigbang.ingress.mailman as mailman\n",
+    "import bigbang.analysis.graph as graph\n",
+    "import bigbang.analysis.process as process\n",
     "from bigbang.parse import get_date\n",
     "from bigbang.archive import Archive\n",
     "import imp\n",
@@ -376,7 +376,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -390,7 +390,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/network_analysis/InteractionNetwork.ipynb b/examples/network_analysis/InteractionNetwork.ipynb
index bf383f38..64c9571b 100644
--- a/examples/network_analysis/InteractionNetwork.ipynb
+++ b/examples/network_analysis/InteractionNetwork.ipynb
@@ -28,23 +28,14 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/sb/projects/bigbang-multi/bigbang/config/config.py:8: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
-      "  dictionary = yaml.load(stream)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from bigbang.archive import Archive\n",
     "from bigbang.archive import load as load_archive\n",
     "import bigbang.parse as parse\n",
-    "import bigbang.graph as graph\n",
-    "import bigbang.mailman as mailman\n",
-    "import bigbang.process as process\n",
+    "import bigbang.analysis.graph as graph\n",
+    "import bigbang.ingress.mailman as mailman\n",
+    "import bigbang.analysis.process as process\n",
     "import networkx as nx\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
@@ -57,7 +48,29 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: '../../archives/6lo.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_66786/849687399.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0marchives_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mload_archive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marchive_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marchive_path\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marchives_paths\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0marchives\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mArchive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marchives_list\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[0;32m/tmp/ipykernel_66786/849687399.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0marchives_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mload_archive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marchive_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marchive_path\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marchives_paths\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0marchives\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mArchive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marchives_list\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[0;32m~/projects/bigbang/bigbang/archive.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\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[0;32m---> 27\u001b[0;31m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mArchive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/bigbang/bigbang-env/lib/python3.9/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/bigbang/bigbang-env/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/bigbang/bigbang-env/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/bigbang/bigbang-env/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    809\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 811\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    813\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0;32m~/projects/bigbang/bigbang-env/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1038\u001b[0m             )\n\u001b[1;32m   1039\u001b[0m         \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0;32m~/projects/bigbang/bigbang-env/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/bigbang/bigbang-env/lib/python3.9/site-packages/pandas/io/parsers/base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m    220\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \"\"\"\n\u001b[0;32m--> 222\u001b[0;31m         self.handles = get_handle(\n\u001b[0m\u001b[1;32m    223\u001b[0m             \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/bigbang/bigbang-env/lib/python3.9/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    700\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    703\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../../archives/6lo.csv'"
+     ]
+    }
+   ],
    "source": [
     "#Insert a list of archive names\n",
     "archives_names = [\"6lo\"]\n",
@@ -200,9 +213,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:bigbang] *",
+   "display_name": "bigbang-env",
    "language": "python",
-   "name": "conda-env-bigbang-py"
+   "name": "bigbang-env"
   },
   "language_info": {
    "codemirror_mode": {
@@ -214,7 +227,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.9.5"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/examples/threads_analysis/ThreadsAnalysis.ipynb b/examples/threads_analysis/ThreadsAnalysis.ipynb
index dff04a3e..e37f326e 100644
--- a/examples/threads_analysis/ThreadsAnalysis.ipynb
+++ b/examples/threads_analysis/ThreadsAnalysis.ipynb
@@ -19,15 +19,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "from bigbang.archive import Archive\n",
     "from bigbang.archive import load as load_archive\n",
-    "from bigbang.thread import Thread\n",
-    "from bigbang.thread import Node\n",
+    "from bigbang.analysis.thread import Thread\n",
+    "from bigbang.analysis.thread import Node\n",
     "from bigbang.utils import remove_quoted\n",
     "import matplotlib.pyplot as plt\n",
     "import datetime\n",
@@ -46,29 +46,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "IOError",
-     "evalue": "File ../../archives/ietf.csv does not exist",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-31-52364c1279d0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0marchives_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mload_archive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marchive_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marchive_path\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marchives_paths\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0marchives\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mArchive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marchives_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/berra/bigbang/bigbang/archive.pyc\u001b[0m in \u001b[0;36mload\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mArchive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/berra/.conda/envs/bigbang/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    653\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    654\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 655\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    657\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/berra/.conda/envs/bigbang/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    403\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 405\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    406\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/berra/.conda/envs/bigbang/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    762\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    763\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 764\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    765\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/berra/.conda/envs/bigbang/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m    983\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 985\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    986\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    987\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/berra/.conda/envs/bigbang/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1603\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'allow_leading_cols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1605\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1607\u001b[0m         \u001b[0;31m# XXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__ (pandas/_libs/parsers.c:4209)\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source (pandas/_libs/parsers.c:8873)\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mIOError\u001b[0m: File ../../archives/ietf.csv does not exist"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#Insert a list of archive names\n",
     "archives_names = [\"ietf\"]\n",
@@ -90,9 +70,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
@@ -105,14 +83,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "885\n"
+      "24782\n"
      ]
     }
    ],
@@ -129,19 +107,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fbc8e446650>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -164,17 +144,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fbc8bbd6410>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -195,17 +177,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[6lo] draft-chairs-6lo-dispatch-iana-registry-00.txt  (51 emails)\n",
-      "[6lo] Comments needed for Security Bootstrapping of IEEE 802.15.4 based Internet of Things  (43 emails)\n",
-      "[6lo] WG Review: IPv6 over Networks of Resource-constrained Nodes (6lo)  (40 emails)\n",
-      "[6lo] Request for Ethertype for 6lowpan-encoded IPv6  (33 emails)\n"
+     "ename": "TypeError",
+     "evalue": "'<' not supported between instances of 'Thread' and 'Thread'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_25813/2999664989.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mthreads_emailcount\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mthread\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_num_messages\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[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mthread\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcount\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthreads_emailcount\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreverse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mk_v\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk_v\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk_v\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mn_top\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[1;32m      9\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_root\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Subject'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m'  ('\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m' emails)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'Thread' and 'Thread'"
      ]
     }
    ],
@@ -233,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,9 +241,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "bigbang-env",
    "language": "python",
-   "name": "python3"
+   "name": "bigbang-env"
   },
   "language_info": {
    "codemirror_mode": {
@@ -271,7 +254,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,


Test patch:
diff --git a/tests/unit/test_archive.py b/tests/unit/test_archive.py
index 43287e9c..b11a75be 100644
--- a/tests/unit/test_archive.py
+++ b/tests/unit/test_archive.py
@@ -13,12 +13,6 @@
 from testfixtures import LogCapture
 
 import bigbang.archive as archive
-import bigbang.mailman as mailman
-import bigbang.parse as parse
-import bigbang.process as process
-import bigbang.utils as utils
-import bigbang.w3crawl as w3crawl
-from bigbang import repo_loader
 from config.config import CONFIG
 
 test_txt = ""
diff --git a/tests/unit/test_bigbang.py b/tests/unit/test_bigbang.py
index 83ce37a4..80b9b435 100644
--- a/tests/unit/test_bigbang.py
+++ b/tests/unit/test_bigbang.py
@@ -12,12 +12,12 @@
 from testfixtures import LogCapture
 
 import bigbang.archive as archive
-import bigbang.mailman as mailman
+import bigbang.ingress.mailman as mailman
 import bigbang.parse as parse
-import bigbang.process as process
+import bigbang.analysis.process as process
 import bigbang.utils as utils
 import bigbang.w3crawl as w3crawl
-from bigbang import repo_loader
+from bigbang.analysis import repo_loader
 from config.config import CONFIG
 
 test_txt = ""
diff --git a/tests/unit/test_mailman.py b/tests/unit/test_mailman.py
index dd335fc0..e3ea5d2d 100644
--- a/tests/unit/test_mailman.py
+++ b/tests/unit/test_mailman.py
@@ -13,12 +13,7 @@
 from testfixtures import LogCapture
 
 import bigbang.archive as archive
-import bigbang.mailman as mailman
-import bigbang.parse as parse
-import bigbang.process as process
-import bigbang.utils as utils
-import bigbang.w3crawl as w3crawl
-from bigbang import repo_loader
+import bigbang.ingress.mailman as mailman
 from config.config import CONFIG
 
 test_txt = ""