In [2]:

    

import numpy as np

import pandas as pd
pd.options.display.float_format = '{:20,.4f}'.format

# from  matplotlib import pyplot
# import seaborn

import sqlite3

# pylab.rcParams['figure.figsize'] = (12.0, 8.0)


    

In [3]:

    

tweetdetails = pd.read_csv('data/BMCevolBioTweetDetails.txt', encoding='utf8', sep="\t", index_col='tweet_id')
tweetdetails.sort_index(inplace=True)

dois = list(tweetdetails.doi.unique())

tweetdetails['created_at'] = pd.to_datetime(tweetdetails.created_at)
tweetdetails['user_id_str'] = tweetdetails.user_id_str.astype(int)


    

    




---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-3-b5b404d349f9> in <module>()
----> 1 tweetdetails = pd.read_csv('data/BMCevolBioTweetDetails.txt', encoding='utf8', sep="\t", index_col='tweet_id')
      2 tweetdetails.sort_index(inplace=True)
      3 
      4 dois = list(tweetdetails.doi.unique())
      5 

/usr/local/lib/python3.6/site-packages/pandas/io/parsers.py in parser_f(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)
    644                     skip_blank_lines=skip_blank_lines)
    645 
--> 646         return _read(filepath_or_buffer, kwds)
    647 
    648     parser_f.__name__ = name

/usr/local/lib/python3.6/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
    387 
    388     # Create the parser.
--> 389     parser = TextFileReader(filepath_or_buffer, **kwds)
    390 
    391     if (nrows is not None) and (chunksize is not None):

/usr/local/lib/python3.6/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
    728             self.options['has_index_names'] = kwds['has_index_names']
    729 
--> 730         self._make_engine(self.engine)
    731 
    732     def close(self):

/usr/local/lib/python3.6/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
    921     def _make_engine(self, engine='c'):
    922         if engine == 'c':
--> 923             self._engine = CParserWrapper(self.f, **self.options)
    924         else:
    925             if engine == 'python':

/usr/local/lib/python3.6/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
   1388         kwds['allow_leading_cols'] = self.index_col is not False
   1389 
-> 1390         self._reader = _parser.TextReader(src, **kwds)
   1391 
   1392         # XXX

pandas/parser.pyx in pandas.parser.TextReader.__cinit__ (pandas/parser.c:4184)()

pandas/parser.pyx in pandas.parser.TextReader._setup_parser_source (pandas/parser.c:8449)()

FileNotFoundError: File b'data/BMCevolBioTweetDetails.txt' does not exist

In [42]:

    

tweetdetails = pd.read_csv('data/BMCBioTweetDetails.txt', encoding='utf8', sep="\t", index_col='tweet_id')
tweetdetails.sort_index(inplace=True)

dois = list(tweetdetails.doi.unique())

tweetdetails['created_at'] = pd.to_datetime(tweetdetails.created_at)
tweetdetails['user_id_str'] = tweetdetails.user_id_str.astype(int)


    

In [3]:

    

df = pd.read_csv('data/community_counts1930.txt', sep='\t', index_col='community')
df = df.stack()
df.div(df.sum(axis=0, level=0), level=0).multiply(100).unstack()


    

In [3]:

    

edgelist = pd.read_csv('data/evolBioAllFollowers.csv', dtype={'in': str, 'out': str, 'doi': str}).drop_duplicates()


    

In [43]:

    

con = sqlite3.connect("data/BMCbioSample.db")
friends = pd.read_sql_query("SELECT * FROM friends", con, index_col="user_id")
friends.index = friends.index.astype(int)
followers = pd.read_sql_query("SELECT * FROM followers", con, index_col="user_id")
followers.index = followers.index.astype(int)

df = tweetdetails[['doi', 'user_id_str']].drop_duplicates().set_index('user_id_str').join(friends)[['friend_id', 'doi']]
# df.index.rename('user_id', inplace=True)

edgelist = df.reset_index()
edgelist.columns = ['in', 'out', 'doi']


    

In [454]:

    

allcommunities = pd.read_csv('data/follower_communities.txt', sep='\t')
allcommunities = allcommunities[allcommunities['user_id_str'] != -1]
allcommunities['user_id_str'] = allcommunities.user_id_str.astype(int)


    

In [7]:

    




    

    
Out[7]:





[0, 1]

In [ ]:

    

def check_activation(user_id_str):
        global activated
        activated.add(user_id_str)
        activated = activated.union(edgelist[edgelist['in'] == user_id_str]['out'])
        return len(activated)
    
    
    def group_color(name):
        if name[1]: 
            return 'b'
        return 'r'
    
    def group_marker(name):
        if name[0]:
            return 'o'
        return '*'
    
    def mode(angle):
        if angle > 75:
            return 1
        elif angle > 15:
            return 2
        else:
            return 3

    fig, axes = plt.subplots(nrows=2, ncols=2)
    plot_map = {0: [0,0], 1: [0,1], 2: [1,0], 3: [1,1]}
    for i, doi in enumerate(dois): 
        tweets = tweetdetails[tweetdetails.doi == doi]
    #     communities = allcommunities[(allcommunities.doi == doi)].set_index('user_id_str')
        
        
        activated = set()
    #     activated_communities = set()
        
        num_activated_users = []
    #     num_activated_communities = []
        
        for row in tweets.iterrows():
            user_id = row[1]['user_id_str']
            num_activated_users.append(check_activation(user_id))
        
    #         try: 
    #             activated_communities.add(communities.ix[user_id]['community'])
    #         except KeyError:
    #             print "%s not found" % user_id
    #             print 
    #         num_activated_communities.append(len(activated_communities))
        
        tweets['activated_users'] = num_activated_users
        tweets['activated_users_p'] = tweets['activated_users'].multiply(100.0).div(tweets['activated_users'].max())
    #     tweets['activated_communities'] = num_activated_communities
    #     tweets['activated_communities_p'] = tweets['activated_communities'].multiply(100.0).div(tweets['activated_communities'].max())
        # Create matplotlib figure
        ax = axes[plot_map[i][0], plot_map[i][1]]
        ax2 = ax.twinx() 
        
        tweets['event_number'] = tweets.index.map(lambda x: tweets.index.get_loc(x))
          
    #     for name, group in [('odd', 1), ('even', 0)]:
    #         tweets[tweets.user_id_str % 2 == group].set_index('created_at').activated_users_p.plot(style='o', ms=12, label=name,ax=ax, alpha=0.5)
    #     tweets.set_index('created_at').activated_users.plot(style='o', ms=6,ax=ax, alpha=0.5)    
        
        
        tweets['new_user'] = ~tweets.duplicated('user_id_str', keep='first')
        print "%.2f%% new users (out of %s)" % (len(tweets[tweets['new_user']])*100./len(tweets), len(tweets))
        print "%.2f%% retweets (out of %s)" % (len(tweets[tweets['is_retweet']])*100./len(tweets), len(tweets))
        groups = ['new_user', 'is_retweet']
        for name, group in tweets.set_index('created_at').groupby(groups).activated_users:
            print "new user: %s, is_retweet: %s: %s%s" % (name[0], name[1], group_marker(name), group_color(name))
            group.plot(marker=group_marker(name), color=group_color(name), linestyle="", ms=6, alpha=0.5, ax=ax)
#         max is because there are cases with tweets at exact same second
    #     df = tweets.activated_users_p.diff(-1).fillna(0).divide(tweets.created_at.diff(-1).fillna(0).map(lambda x: max(x.value/1000000000.0/60/60, 1))).fillna(0)
    #     df.index = tweets.created_at
        
    #     ax.vlines(x=df[df.diff().abs() > df.abs().describe()['mean'] + df.abs().describe()['std']].index, ymin=ymin, ymax=ymax-1, color='r')
    
        
#         df=tweets.set_index('created_at').activated_users_p
#         df = df.resample('48H').max().dropna().astype(int) 
#         df.plot(style='ob', ms=6, ax=ax)
        
#         activations = df.diff(-1).fillna(0).astype(int)
#         days = df.reset_index().created_at.diff(-1).fillna(0).map(lambda x: x.days)
#         days.index = df.index
        
#         angles = np.degrees(np.arctan2(activations, days)) #.map(lambda x: (x+180) % 360)        
#         changes = angles.map(mode) != angles.shift(1).map(mode)
#         changes = changes[3:]
#         changes = changes[changes].shift(-1)
#         ymin, ymax = ax.get_ylim()
# #         angles.plot(style="oy", ax=ax2)
#         ax.vlines(x=changes.index, ymin=ymin, ymax=ymax-1, color='r')
        fig.suptitle(doi)
#         fig.savefig('figures/%s-activation.png' % doi[doi.find('/')+1:].replace('-', ''))
    #     break
    
    
    #     tweets.set_index('event_number').activated_users_p.plot(style='o', ms=12, alpha=0.5)
    #     tweets.set_index('event_number').activated_communities.plot(style='s', ms=12, secondary_y=True, alpha=.2)
    
    
    
    #     print "%s total users reached" % tweets.activated_users.max()
    #     print "%s total communities" % tweets.activated_communities.max()
        fig.savefig('figures/%s-activation-eventnumber.png' % doi.replace('-', '')[-4:])


    

    




93.44% new users (out of 61)
54.10% retweets (out of 61)
new user: False, is_retweet: False: *r
new user: False, is_retweet: True: *b
new user: True, is_retweet: False: or
new user: True, is_retweet: True: ob

In [262]:

    

tweets.iloc[0:50]


    

    
Out[262]:






  

    

      

      
doi
      
created_at
      
created_at_dayofweek
      
screen_name
      
user_id_str
      
user_utc_offset
      
user_followers_count
      
user_friends_count
      
user_description
      
is_retweet
      
retweet_of_status_id_str
      
retweet_of_screen_name
      
is_reply
      
in_reply_to_status_id_str
      
in_reply_to_screen_name
      
text
      
activated_users
      
activated_users_p
      
event_number
      
new_user
    
    

      
tweet_id
      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

    
  
  

    

      
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The latest live science news: medicine, health...
      
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An inside-out origin for the eukaryotic cell h...
      
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In [244]:

    

fig = plt.figure() 

ax = fig.add_subplot(111) 
ax2 = ax.twinx() 
df=tweets.set_index('created_at').activated_users_p.head(30)
df = df.resample('168H').max().dropna().astype(int) 
df.plot(style='ob', ms=6, ax=ax)

activations = df.diff(1).fillna(0).astype(int)
days = df.reset_index().created_at.diff(1).fillna(0).map(lambda x: x.days)
days.index = df.index

angles = np.degrees(np.arctan2(activations, days)) #.map(lambda x: (x+180) % 360)

changes = angles.map(mode) != angles.shift(1).map(mode)
changes = changes[3:]
changes = changes[changes].shift(-1)
ymin, ymax = ax.get_ylim()
angles.plot(style="oy", ax=ax2)
ax.vlines(x=changes.index, ymin=ymin, ymax=ymax-1, color='r')


    

    
Out[244]:





<matplotlib.collections.LineCollection at 0x10def0b10>






    

In [243]:

    

24*7


    

    
Out[243]:





168

In [233]:

    




    

    
Out[233]:





created_at
2014-01-31     True
2014-02-01     True
2014-02-02     True
2014-02-03     True
2014-02-04     True
2014-02-05    False
2014-02-06    False
2014-02-07      NaN
Freq: 24H, Name: activated_users_p, dtype: object

In [231]:

    

angles.shift(1)


    

    
Out[231]:





created_at
2014-01-31                    nan
2014-02-01                 0.0000
2014-02-02                45.0000
2014-02-03                 0.0000
2014-02-04                45.0000
2014-02-05                71.5651
2014-02-06                 0.0000
2014-02-07                 0.0000
Freq: 24H, Name: activated_users_p, dtype: float64

In [221]:

    




    

    
Out[221]:





created_at
2014-01-31     True
2014-02-01     True
2014-02-02     True
2014-02-03     True
2014-02-04     True
2014-02-05    False
2014-02-06    False
2014-02-07    False
Freq: 24H, Name: activated_users_p, dtype: bool

In [166]:

    




    

In [167]:

    




    

    
Out[167]:





created_at
2014-02-25    True
2014-03-06    True
2014-03-23    True
2014-03-25    True
2014-05-28    True
2014-06-10    True
2014-11-21    True
2014-11-24    True
2015-01-11    True
2015-02-21    True
dtype: bool