display and report
This commit is contained in:
gauthiier
parent
cb7e7825c0
commit
9a2badf32a
101
nettime/format.py
Normal file
101
nettime/format.py
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@ -0,0 +1,101 @@
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import query
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import logging, html
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from tabulate import tabulate
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class Html:
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query = None
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def __init__(self, q=None):
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if not isinstance(q, query.Query):
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logging.error("HtmlFormat constructor Error: query must be of type nettime.query.Query")
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raise Exception()
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self.query = q
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def threads_ranking(self, rank=5):
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data = self.query.threads_ranking(rank=rank)
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h = html.HTML()
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t = h.table()
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r = t.tr
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r.td('date', klass='td_date_t')
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r.td('from', klass='td_from_t')
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r.td('replies', klass='td_rep_t')
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r.td('subject', klass='td_subject_t')
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for i, row in data.iterrows():
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r = t.tr
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r.td(str(row['date']), klass='td_date')
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r.td(row['from'], klass='td_from')
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r.td(str(row['nbr-references']), klass='td_rep')
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r.td('', klass='td_subject').text(str(h.a(row['subject'], href=row['url'])), escape=False)
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return str(t)
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def from_dataframe(self, data_frame, table_name=None, name_map={}):
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header = []
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header.append(data_frame.index.name)
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for h in data_frame.columns:
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if h in name_map:
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h = name_map[h]
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header.append(h)
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css_header = []
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css_element = []
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for i in header:
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css_header.append('td_' + i + '_t')
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css_element.append('td_' + i)
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h = html.HTML()
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if table_name:
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t = h.table(id=table_name, klass=table_name + '_t')
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else:
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t = h.table()
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#header
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r = t.tr
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n = 0
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for j in header:
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r.td(str(j), klass=css_header[n])
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n += 1
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#elements
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for k, row in data_frame.iterrows():
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r = t.tr
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r.td(str(k), klass=css_element[0])
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n = 1
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for l in row:
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r.td(str(l), klass=css_element[n])
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n += 1
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return str(t)
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class Tab:
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query = None
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def __init__(self, q=None):
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if not isinstance(q, query.Query):
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logging.error("HtmlFormat constructor Error: query must be of type nettime.query.Query")
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raise Exception()
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self.query = q
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def from_dataframe(self, data_frame, name_map={}):
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header = []
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header.append(data_frame.index.name)
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for h in data_frame.columns:
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if h in name_map:
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h = name_map[h]
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header.append(h)
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return tabulate(data_frame, headers=header)
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70
nettime/plot.py
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70
nettime/plot.py
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@ -0,0 +1,70 @@
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import numpy as np
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import pandas as pd
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import query
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# for colormaps see:
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# http://scipy.github.io/old-wiki/pages/Cookbook/Matplotlib/Show_colormaps
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# http://pandas.pydata.org/pandas-docs/stable/visualization.html#colormaps
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# http://matplotlib.org/examples/color/colormaps_reference.html
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# for colors see:
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# http://matplotlib.org/examples/color/named_colors.html
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def bar_plot_series(series, title, color='blueviolet'):
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return series.plot(kind = 'bar', title=title, color=color, alpha=0.8, stacked=True)
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class Plot:
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query = None
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def __init__(self, q=None):
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if not isinstance(q, query.Query):
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logging.error("HtmlFormat constructor Error: query must be of type nettime.query.Query")
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raise Exception()
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self.query = q
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'''
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activity
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'''
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def activity_from_ranking(self, resolution='y', rank=5, colormap='spectral', figsize=(8, 7)):
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activity_rank = self.query.activity_from_ranking(rank=rank, series=True).keys()
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series = []
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for k in activity_rank:
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series.append(self.query.activity_from(k, resolution, series=True))
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df = pd.concat(series, axis=1)
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return df.plot.area(colormap='spectral', figsize=figsize, stacked=False)
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'''
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content lenght
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'''
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def content_length_from_ranking(self, resolution='y', rank=5, colormap='spectral', figsize=(8, 7)):
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content_rank = self.query.content_length_from_ranking(rank=rank, series=True).keys()
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series = []
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for k in content_rank:
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series.append(self.query.content_length_from(k, resolution, series=True))
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df = pd.concat(series, axis=1)
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return df.plot.area(colormap=colormap, figsize=figsize, stacked=False)
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'''
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threads
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'''
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def threads_from_ranking(self, resolution='y', rank=5, colormap='spectral', figsize=(8, 7)):
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threads_rank = self.query.threads_from_ranking(rank=rank, series=True).keys()
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series = []
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for k in threads_rank:
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series.append(self.query.threads_from(k, resolution, series=True))
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df = pd.concat(series, axis=1)
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return df.plot.area(colormap=colormap, figsize=figsize, stacked=False)
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289
nettime/query.py
289
nettime/query.py
@ -1,7 +1,7 @@
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import numpy as np
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import pandas as pd
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import archive
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import logging, html
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import logging
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class Query:
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@ -30,65 +30,78 @@ class Query:
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return self.activity
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def activity_from(self, email_address, resolution='y'):
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def activity_from(self, email_address, resolution='y', series=False):
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eaddr = email_address.replace('@', '{at}').lower()
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freq = 'M'
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if resolution.lower() == 'y':
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freq = 'AS'
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elif resolution.lower() == 'm':
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freq = 'M'
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else:
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return None
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self._activity()
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try:
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if resolution.lower() == 'm':
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return self.activity[eaddr]
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elif resolution.lower() == 'y':
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y = self.activity[eaddr].resample('AS').sum()
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y.index = y.index.year
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return y
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else:
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return None
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af = self.activity[eaddr]
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except KeyError:
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return None
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def activity_overall(self, resolution='y'):
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activity_from = af.groupby([pd.TimeGrouper(freq=freq)]).sum()
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self._activity()
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try:
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sum_activity_month = self.activity.sum(axis=1)
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if resolution.lower() == 'm':
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sum_activity_month.rename
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return sum_activity_month
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elif resolution.lower() == 'y':
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y = sum_activity_month.resample('AS').sum()
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y.index = y.index.year
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return y
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else:
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return None
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except:
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return None
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if freq == 'AS':
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activity_from.index = activity_from.index.format(formatter=lambda x: x.strftime('%Y'))
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activity_from.index.name = 'year'
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else:
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activity_from.index = activity_from.index.format(formatter=lambda x: x.strftime('%Y-%m'))
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activity_from.index.name = 'year-month'
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def activity_from_ranking(self, resolution='y', rank=5, filter_nettime=True):
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# finish this -- re resolution AND filtering
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if series:
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return activity_from
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return activity_from.to_frame('nbr-messages').astype(int)
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def activity_from_ranking(self, rank=5, filter_nettime=True, series=False):
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self._activity()
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afr = self.activity.sum(axis=0).order(ascending=False)
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if filter_nettime:
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p = r'^((?!nettime*).)*$'
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afr = afr[afr.index.str.contains(p)]
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return afr[:rank]
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def plot_activity_from_ranking(self, resolution='y', rank=5, figsize=(8, 7)):
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if series:
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return afr[:rank]
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activity_rank = self.activity_from_ranking(rank=rank).keys()
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series = []
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for k in activity_rank:
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series.append(self.activity_from(k, resolution))
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df = pd.concat(series, axis=1)
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colors = np.random.rand(len(df),3)
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return afr[:rank].to_frame('nbr-messages').astype(int)
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if figsize:
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df.plot(colors=colors, figsize=figsize)
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def activity_overall(self, resolution='y', series=False):
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freq = 'M'
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if resolution.lower() == 'y':
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freq = 'AS'
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elif resolution.lower() == 'm':
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freq = 'M'
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else:
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df.plot(colors=colors)
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return None
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self._activity()
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y = self.activity.sum(axis=1)
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y = y.groupby([pd.TimeGrouper(freq=freq)]).sum()
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if freq == 'AS':
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y.index = y.index.format(formatter=lambda x: x.strftime('%Y'))
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y.index.name = 'year'
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else:
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y.index = y.index.format(formatter=lambda x: x.strftime('%Y-%m'))
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y.index.name = 'year-month'
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if series:
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return y
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return y.to_frame('nbr-messages').astype(int)
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'''
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content lenght
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@ -103,63 +116,78 @@ class Query:
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return self.content_length
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def content_length_from(self, email_address, resolution='y'):
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def content_length_from(self, email_address, resolution='y', series=False):
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eaddr = email_address.replace('@', '{at}').lower()
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freq = 'M'
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if resolution.lower() == 'y':
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freq = 'AS'
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elif resolution.lower() == 'm':
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freq = 'M'
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else:
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return None
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self._content_length()
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try:
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if resolution.lower() == 'm':
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return self.content_length[eaddr]
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elif resolution.lower() == 'y':
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y = self.content_length[eaddr].resample('AS').sum()
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y.index = y.index.year
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return y
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else:
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return None
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af = self.content_length[eaddr]
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except KeyError:
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return None
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def content_length_overall(self):
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content_length_from = af.groupby([pd.TimeGrouper(freq=freq)]).sum()
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self._content_length()
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try:
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sum_content_length_month = self.content_length.sum(axis=1)
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if resolution.lower() == 'm':
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return sum_content_length_month
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elif resolution.lower() == 'y':
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y = sum_content_length_month.resample('AS').sum()
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y.index = y.index.year
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return y
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else:
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return None
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except:
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return None
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if freq == 'AS':
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content_length_from.index = content_length_from.index.format(formatter=lambda x: x.strftime('%Y'))
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content_length_from.index.name = 'year'
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else:
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content_length_from.index = content_length_from.index.format(formatter=lambda x: x.strftime('%Y-%m'))
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content_length_from.index.name = 'year-month'
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def content_length_from_ranking(self, resolution='y', rank=5, filter_nettime=True):
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# finish this -- re resolution
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if series:
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return content_length_from
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return content_length_from.to_frame('nbr-bytes').astype(int)
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def content_length_from_ranking(self, resolution='y', rank=5, filter_nettime=True, series=False):
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self._content_length()
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cfr = self.content_length.sum(axis=0).order(ascending=False)
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if filter_nettime:
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p = r'^((?!nettime*).)*$'
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cfr = cfr[cfr.index.str.contains(p)]
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return cfr[:rank]
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def plot_content_length_from_ranking(self, resolution='y', rank=5, figsize=(8, 7)):
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if series:
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return cfr[:rank]
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content_rank = self.content_length_from_ranking(rank=rank).keys()
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series = []
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for k in content_rank:
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series.append(self.content_length_from(k, resolution))
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df = pd.concat(series, axis=1)
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colors = np.random.rand(len(df),3)
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return cfr[:rank].to_frame('nbr-bytes').astype(int)
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if figsize:
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df.plot(colors=colors, figsize=figsize)
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def content_length_overall(self, resolution='y', series=False):
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freq = 'M'
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if resolution.lower() == 'y':
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freq = 'AS'
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elif resolution.lower() == 'm':
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freq = 'M'
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else:
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df.plot(colors=colors)
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return None
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self._content_length()
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y = self.content_length.sum(axis=1)
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y = y.groupby([pd.TimeGrouper(freq=freq)]).sum()
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if freq == 'AS':
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y.index = y.index.format(formatter=lambda x: x.strftime('%Y'))
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y.index.name = 'year'
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else:
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y.index = y.index.format(formatter=lambda x: x.strftime('%Y-%m'))
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y.index.name = 'year-month'
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if series:
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return y
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return y.to_frame('nbr-bytes').astype(int)
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'''
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threads
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@ -171,37 +199,39 @@ class Query:
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self.threads = self.netarchive.dataframe[self.netarchive.dataframe['nbr-references'] > thresh].reindex(columns=['from','nbr-references','subject', 'url', 'message-id']).sort_values('nbr-references', ascending=False)
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return self.threads;
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def threads_ranking(self, rank=5, output=None):
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def threads_ranking(self, rank=5, resolution=None):
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self._threads()
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data = self.threads.drop('message-id', axis=1)[:rank]
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data['date'] = data.index
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if output is None:
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return data
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elif output == 'string':
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return data.to_string()
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elif output == 'html':
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h = html.HTML()
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t = h.table()
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r = t.tr
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r.td('date', klass='td_date_t')
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r.td('from', klass='td_from_t')
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r.td('replies', klass='td_rep_t')
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r.td('subject', klass='td_subject_t')
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if resolution == None:
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data = self.threads.drop('message-id', axis=1)[:rank]
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return data.reindex_axis(['subject', 'from', 'nbr-references', 'url'], axis=1)
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for i, row in data.iterrows():
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r = t.tr
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r.td(str(row['date']), klass='td_date')
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r.td(row['from'], klass='td_from')
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r.td(str(row['nbr-references']), klass='td_rep')
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r.td('', klass='td_subject').text(str(h.a(row['subject'], href=row['url'])), escape=False)
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return str(t)
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freq = 'M'
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if resolution.lower() == 'y':
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freq = 'AS'
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elif resolution.lower() == 'm':
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freq = 'M'
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else:
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return None
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def threads_from(self, email_address, resolution='y'):
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# get the threads ranking per time resolution
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#
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data = self.threads.drop('message-id', axis=1)
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data = data.groupby([pd.TimeGrouper(freq=freq)])
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r = {}
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for k, v in data:
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if freq == 'AS':
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time_key = k.strftime('%Y')
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else:
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time_key = k.strftime('%Y-%m')
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frame = v[:rank]
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frame = frame.reindex_axis(['subject', 'from', 'nbr-references', 'url'], axis=1)
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r[time_key] = frame
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return r
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def threads_from(self, email_address, resolution='y', series=False):
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freq = 'M'
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if resolution.lower() == 'y':
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@ -219,9 +249,22 @@ class Query:
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threads_from = self.threads.reindex(columns=['from', 'nbr-references'])
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threads_from_ranking = threads_from.groupby([pd.TimeGrouper(freq=freq), 'from']).sum()
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threads_from_ranking = threads_from_ranking.reset_index().pivot(columns='from', index='date', values='nbr-references').fillna(0)
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return threads_from_ranking[eaddr]
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|
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def threads_from_ranking(self, rank=5, filter_nettime=True):
|
||||
if series:
|
||||
return threads_from_ranking[eaddr]
|
||||
|
||||
threads_from_ranking = threads_from_ranking[eaddr].to_frame('nbr-threads').astype(int)
|
||||
|
||||
if freq == 'AS':
|
||||
threads_from_ranking.index = threads_from_ranking.index.format(formatter=lambda x: x.strftime('%Y'))
|
||||
threads_from_ranking.index.name = 'year'
|
||||
else:
|
||||
threads_from_ranking.index = threads_from_ranking.index.format(formatter=lambda x: x.strftime('%Y-%m'))
|
||||
threads_from_ranking.index.name = 'year-month'
|
||||
|
||||
return threads_from_ranking
|
||||
|
||||
def threads_from_ranking(self, rank=5, filter_nettime=True, series=False):
|
||||
|
||||
self._threads()
|
||||
threads_from = self.threads.reindex(columns=['from', 'nbr-references'])
|
||||
@ -233,24 +276,11 @@ class Query:
|
||||
p = r'^((?!nettime*).)*$'
|
||||
tfr = tfr[tfr.index.str.contains(p)]
|
||||
|
||||
return tfr[:rank]
|
||||
|
||||
def plot_threads_from_ranking(self, resolution='y', rank=5, figsize=(8, 7)):
|
||||
|
||||
threads_rank = self.threads_from_ranking(rank=rank).keys()
|
||||
series = []
|
||||
for k in threads_rank:
|
||||
series.append(self.threads_from(k, resolution))
|
||||
|
||||
df = pd.concat(series, axis=1)
|
||||
|
||||
colors = np.random.rand(len(df),3)
|
||||
|
||||
if figsize:
|
||||
df.plot(colors=colors, figsize=figsize)
|
||||
else:
|
||||
df.plot(colors=colors)
|
||||
if series:
|
||||
return tfr[:rank]
|
||||
|
||||
tfr = tfr[:rank].to_frame('nbr-threads').astype(int)
|
||||
return tfr
|
||||
|
||||
def threads_overall(self, resolution='y', aggregate='sum', tresh=0):
|
||||
|
||||
@ -263,7 +293,7 @@ class Query:
|
||||
return None
|
||||
|
||||
agg = aggregate.lower()
|
||||
if not agg in ['sum', 'mean']:
|
||||
if not agg in ['sum', 'mean', 'count']:
|
||||
return None
|
||||
|
||||
if not self.threads is None:
|
||||
@ -273,11 +303,20 @@ class Query:
|
||||
self._threads(tresh)
|
||||
|
||||
if agg == 'sum':
|
||||
# number of replies total (re: sum all the replies)
|
||||
y = self.threads.groupby([pd.TimeGrouper(freq=freq)]).sum()
|
||||
else:
|
||||
elif agg == 'mean':
|
||||
y = self.threads.groupby([pd.TimeGrouper(freq=freq)]).mean()
|
||||
else:
|
||||
# number of threads (re: msgs with at least one reply)
|
||||
y = self.threads['nbr-references'].groupby([pd.TimeGrouper(freq=freq)]).count()
|
||||
y = y.to_frame('nbr-threads')
|
||||
|
||||
if freq == 'AS':
|
||||
y.index = y.index.year
|
||||
y.index = y.index.format(formatter=lambda x: x.strftime('%Y'))
|
||||
y.index.name = 'year'
|
||||
else:
|
||||
y.index = y.index.format(formatter=lambda x: x.strftime('%Y-%m'))
|
||||
y.index.name = 'year-month'
|
||||
|
||||
return y
|
||||
return y
|
||||
|
||||
90
nettime_report.py
Normal file
90
nettime_report.py
Normal file
@ -0,0 +1,90 @@
|
||||
import nettime.query
|
||||
import nettime.format
|
||||
import nettime.plot
|
||||
|
||||
class Report:
|
||||
|
||||
query = None
|
||||
matrix = None
|
||||
|
||||
def __init__(self, q=None):
|
||||
|
||||
if not isinstance(q, nettime.query.Query):
|
||||
logging.error("HtmlFormat constructor Error: query must be of type nettime.query.Query")
|
||||
raise Exception()
|
||||
|
||||
self.query = q
|
||||
|
||||
'''
|
||||
(basic) stats
|
||||
'''
|
||||
|
||||
def matrix_msgs_threads(self):
|
||||
|
||||
if self.matrix is None:
|
||||
|
||||
# nbr messages
|
||||
mat = self.query.activity_overall()
|
||||
|
||||
# nbr threads
|
||||
mat['nbr-threads'] = self.query.threads_overall(aggregate='count')['nbr-threads']
|
||||
|
||||
# nbr replies
|
||||
mat['nbr-replies'] = self.query.threads_overall(aggregate='sum')['nbr-references']
|
||||
|
||||
# nbr non-replies (aka. non-threads)
|
||||
mat['nbr-single-messages'] = mat['nbr-messages'] - mat['nbr-replies'] - mat['nbr-threads']
|
||||
|
||||
# avg. rep per message
|
||||
mat['avg-rep-per-msg'] = mat['nbr-replies'] / mat['nbr-messages']
|
||||
|
||||
# avg. rep per thread
|
||||
mat['avg-rep-per-thrd'] = mat['nbr-replies'] / mat['nbr-threads']
|
||||
# same as:
|
||||
# mat['avg-rep-per-thrd'] = q.threads_overall(aggregate='mean')['nbr-references']
|
||||
|
||||
self.matrix = mat
|
||||
|
||||
return self.matrix
|
||||
|
||||
'''
|
||||
plots
|
||||
'''
|
||||
|
||||
def plot_nbr_msgs(self, title='Nbr. Messages', label='messages', color='mediumblue'):
|
||||
|
||||
self.matrix_msgs_threads()
|
||||
|
||||
nettime.plot.bar_plot_series(self.matrix['nbr-messages'].to_frame(label), title=title, color=color)
|
||||
|
||||
def plot_nbr_threads(self, title='Nbr. Threads', label='threads', color='crimson'):
|
||||
|
||||
self.matrix_msgs_threads()
|
||||
|
||||
nettime.plot.bar_plot_series(self.matrix['nbr-threads'].to_frame(label), title=title, color=color)
|
||||
|
||||
def plot_nbr_replies(self, title='Nbr. Replies in Threads', label='replies', color='dimgray'):
|
||||
|
||||
self.matrix_msgs_threads()
|
||||
|
||||
nettime.plot.bar_plot_series(self.matrix['nbr-replies'].to_frame(label), title=title, color=color)
|
||||
|
||||
def plot_avg_rep_p_msg(self, title='Avg. Replies per Messages', label='replies-per-messasges', color='limegreen'):
|
||||
|
||||
self.matrix_msgs_threads()
|
||||
|
||||
nettime.plot.bar_plot_series(self.matrix['avg-rep-per-msg'].to_frame(label), title=title, color=color)
|
||||
|
||||
def plot_avg_rep_p_thrd(self, title='Avg. Replies per Thread', label='replies-per-thread', color='blueviolet'):
|
||||
|
||||
self.matrix_msgs_threads()
|
||||
|
||||
nettime.plot.bar_plot_series(self.matrix['avg-rep-per-thrd'].to_frame(label), title=title, color=color)
|
||||
|
||||
def plot_msgs_replies(self, title='Nbr. Messages segments (individual messages vs thread replies)'):
|
||||
|
||||
self.matrix_msgs_threads()
|
||||
|
||||
nettime.plot.bar_plot_series(self.matrix[['nbr-single-messages', 'nbr-threads', 'nbr-replies']], color=['mediumblue', 'red', 'dimgray'], title=title)
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user