the two series of the tokens owned now run in paralell

3. Using WikiWho to analyze an editor in the context of a page.ipynb

@@ -304,8 +304,8 @@
    "outputs": [],
    "source": [
     "from visualization.owned_listener import OwnedListener\n",
-    "owned = calculator.all_actions\n",
-    "listener = OwnedListener(owned, editor_inputname)\n",
+    "all_actions = calculator.all_actions\n",
+    "listener = OwnedListener(all_actions, editor_inputname)\n",
     "\n",
     "traces = ['Tokens Owned', 'Tokens Owned (%)']\n",
     "\n",
@@ -315,9 +315,24 @@
     "from ipywidgets.widgets import Dropdown\n",
     "\n",
     "interact(listener.listen,\n",
-    "         _range = get_date_slider_from_datetime(owned['rev_time']),\n",
+    "         _range = get_date_slider_from_datetime(all_actions['rev_time']),\n",
     "         granularity=Dropdown(options=['Yearly', 'Monthly', 'Daily'], value='Monthly'),\n",
-    "         trace=Dropdown(options=traces, value='Tokens Owned (%)'))\n"
+    "         trace=Dropdown(options=traces, value='Tokens Owned (%)'))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = all_actions\n",
+    "df = df.sort_values(['token_id', 'rev_time'], ascending=True).set_index('token_id')\n",
+    "last_action = df.groupby('token_id').last()\n",
+    "surv = last_action[last_action['action'] != 'out']\n",
+    "#sum(surv['o_editor'] == editor_inputname)\n",
+    "surv\n"
    ]
   },
   {
@@ -354,7 +369,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,

develop.py

@@ -0,0 +1,51 @@
+from external.wikipedia import WikipediaDV, WikipediaAPI
+wikipedia_dv = WikipediaDV(WikipediaAPI(domain='en.wikipedia.org'))
+the_page = wikipedia_dv.get_page('The Camp of the Saints')
+
+from wikiwho_wrapper import WikiWho
+
+
+
+wikiwho = WikiWho(lng='en')
+agg_actions = wikiwho.dv.edit_persistence(the_page.page_id)
+
+
+editors = wikipedia_dv.get_editors(agg_actions['editor_id'].unique()).rename(columns = {
+    'userid': 'editor_id'})
+
+# Merge the namesof the editors to the aggregate actions dataframe
+agg_actions = agg_actions.merge(editors[['editor_id', 'name']], on='editor_id')
+agg_actions.insert(3, 'editor', agg_actions['name'])
+agg_actions = agg_actions.drop(columns=['name'])
+agg_actions['editor'] = agg_actions['editor'].fillna("Unregistered")
+
+all_content = wikiwho.dv.all_content(the_page['page_id'])
+
+
+
+revisions = wikiwho.dv.rev_ids_of_article(the_page['page_id'])
+
+from metrics.conflict import ConflictManager
+calculator = ConflictManager(all_content, revisions)
+calculator.calculate()
+
+editors_conflicts = calculator.get_conflict_score_per_editor()
+
+editors['editor_id'] = editors['editor_id'].astype(str)
+if len(editors_conflicts) > 0:
+    editors_conflicts = editors[['editor_id', 'name']].merge(editors_conflicts, 
+                                                right_index=True, left_on='editor_id').set_index('editor_id')
+
+
+
+from visualization.owned_listener import OwnedListener
+owned = calculator.all_actions
+listener = OwnedListener(owned, '28921814')
+
+
+listener.listen(
+	_range = (owned['rev_time'].dt.date.min(), owned['rev_time'].dt.date.max()),
+    granularity='Monthly',
+     trace='Tokens Owned (%)')
+
+import ipdb; ipdb.set_trace()  # breakpoint b86e2bcc //

visualization/owned_listener.py

@@ -1,27 +1,67 @@
 import pandas as pd
 import plotly
 from plotly import graph_objs
-
+import datetime
 
 class OwnedListener():
 
     def __init__(self, df, editor):
         self.df = df.sort_values(['token_id', 'rev_time'], ascending=True).set_index('token_id')
         self.editor = editor
+        self.days = days = pd.Series(df.loc[df['o_editor'] == editor, 'rev_time'].dt.to_period('D').unique()).sort_values(ascending=False)
+
+        days = self.days.dt.to_timestamp('D') + pd.DateOffset(1)
+
+        _all = []
+        _abs = []
+        df = self.df
+        for rev_time in days:
+            df = df[df['rev_time'] <= rev_time]
+            last_action = df.groupby('token_id').last()
+            surv = last_action[last_action['action'] != 'out']
+            _abs.append(sum(surv['o_editor'] == self.editor))
+            _all.append(len(surv))
+
+        self.summ = pd.DataFrame({
+            'day': days,
+            'abs': _abs,
+            'all': _all
+            })
+        self.summ['res'] = 100 * self.summ['abs'] / self.summ['all']
+
         self.df_plotted = None
 
     def listen(self, _range, granularity, trace):
         df = self.df
 
         df = df[(df.rev_time.dt.date >= _range[0]) &
-                (df.rev_time.dt.date <= _range[1])]
-
-        self.doi = df.loc[df['editor'] == self.editor, 'rev_time'].dt.to_period(
-            granularity[0]).dt.to_timestamp(granularity[0]).sort_values(ascending=False).unique()
+                (df.rev_time.dt.date <= _range[1] + datetime.timedelta(days=1))]
 
+        self.doi = pd.Series(self.days.dt.to_timestamp(granularity[0]).unique()) + pd.DateOffset(1)
         self.traces = []
         self.is_norm_scale = True
-        df = self.__add_trace(df, trace, 'rgba(0, 0, 0, 1)')
+
+        if trace == 'Tokens Owned':
+            self.is_norm_scale = False
+            _df = self.summ
+            _df['time'] = _df['day'].dt.to_period(granularity[0]).dt.to_timestamp(granularity[0])
+            _df = _df[~_df.duplicated(subset='time', keep='first')]
+            _y = _df['abs']
+
+        elif trace == 'Tokens Owned (%)':
+            _df = self.summ
+            _df['time'] = _df['day'].dt.to_period(granularity[0]).dt.to_timestamp(granularity[0])
+            _df = _df[~_df.duplicated(subset='time', keep='first')]
+            _y = _df['res']
+
+        self.traces.append(
+            graph_objs.Scatter(
+                x=_df['time'], y=_y,
+                name=trace,
+                marker=dict(color='rgba(255, 0, 0, .5)'))
+        )
+
+        self.__add_trace(df, trace, 'rgba(0,0,255, .5)')
 
         _range = None
         if self.is_norm_scale:
@@ -61,7 +101,7 @@ def __add_trace(self, df, trace, color):
 
         self.traces.append(
             graph_objs.Scatter(
-                x=pd.Series(self.doi), y=_y,
+                x=self.doi, y=_y,
                 name=trace,
                 marker=dict(color=color))
         )