def conversation_stats(number, convo_gap=timedelta(hours=14)):
info_dict = {}
messages = data_manager.messages(number)
all_msgs = data_manager.messages()
# assumes messages are sorted by time
prior = messages[:-1].reset_index()
messages = messages[1:].reset_index()
delta = messages.date - prior.date
# conversations started /ended as a percentage of total
new_convos = messages[delta > val_from_delta(convo_gap)]
end_convos = prior[delta > val_from_delta(convo_gap)]
my_new_convos = new_convos[new_convos.is_from_me == 1]
my_end_convos = end_convos[end_convos.is_from_me == 1]
info_dict['started'] = round(
(my_new_convos.shape[0] / max(1, new_convos.shape[0])) * 100, 1)
info_dict['ended'] = round(
(my_end_convos.shape[0] / max(1, end_convos.shape[0])) * 100, 1)
# response_time in seconds
rt_sent = delta[(messages.is_from_me == 1) & (prior.is_from_me == 0) &
(delta < val_from_delta(convo_gap))]
rt_received = delta[(messages.is_from_me == 0) & (prior.is_from_me == 1) &
(delta < val_from_delta(convo_gap))]
info_dict['sent_response_time'] = delta_from_value(max(
0, rt_sent.mean())).total_seconds()
info_dict['received_response_time'] = delta_from_value(
max(0, rt_received.mean())).total_seconds()
# # gap within conversations
# if not prev['is_from_me'] and curr['is_from_me']:
# your_rt = your_rt + (curr.timestamp - prev.timestamp)
# # check if there was a message sent in that time
# is_sent = all_msgs['is_from_me'] == 1
# is_time = all_msgs['timestamp'] > prev['timestamp']
# is_before = all_msgs['timestamp'] < curr['timestamp']
# is_ignored = len(all_msgs.loc[is_time & is_before & is_sent]) > 0
# ignored_tot += 1
# ignored += int(is_ignored)
#
# total = max(total, 1)
# ignored_tot = max(ignored_tot, 1)
# them_tot = max(them_tot, 1)
# your_rt = timedelta(seconds=int((your_rt / ignored_tot).total_seconds()))
# their_rt = timedelta(seconds=int((their_rt/ them_tot).total_seconds()))
# info_dict['ignored'] = round((ignored * 100.0) / ignored_tot, 1)
return info_dict
def simple_stats(number):
info_dict = {}
messages = data_manager.messages(number)
sent, received = split_sender(messages)
info_dict['sent'] = sent.shape[0]
info_dict['received'] = received.shape[0]
return info_dict
def diction_summary(number):
start = request.args.get('start', None)
end = request.args.get('end', None)
msg = data_manager.messages(number=number, start=start, end=end)
result = language_stats.contact_summary(msg)
return make_response(result, OK, headers)
def summary():
start = request.args.get('start', None)
end = request.args.get('end', None)
msg = data_manager.messages(start=start, end=end)
result = general_stats.summary(msg)
return make_response(result, OK, headers)
def sentiment_summary(number=None):
n = int(request.args.get('n', 5))
start = request.args.get('start', None)
end = request.args.get('end', None)
msg = data_manager.messages(number=number, start=start, end=end)
result = sentiment_stats.sentiment(msg)
return make_response(result, OK, headers)
def frequency(number=None):
start = request.args.get('start', None)
end = request.args.get('end', None)
period = request.args.get('period', 'M')
msg = data_manager.messages(number=number, start=start, end=end)
result = general_stats.frequency(msg, period=period)
return make_response(result, OK, headers)
def contact_summary():
n = int(request.args.get('n', 100))
if n == -1: n = None
start = request.args.get('start', None)
end = request.args.get('end', None)
msg = data_manager.messages(start=start, end=end)
result = general_stats.contacts_summary(msg, n)
content = jsonify(result)
return make_response(content, OK, headers)
def emoji(self, number, start=None, end=None):
n = 5
msg = data_manager.messages(number=number, start=start, end=end)
result = emoji_stats.contact_summary(msg, n)
return result
def sentiment(self, number, start=None, end=None):
msg = data_manager.messages(number=number, start=start, end=end)
result = sentiment_stats.contact_summary(msg)
return result
def summary(self, start=None, end=None):
msg = data_manager.messages(start=start, end=end)
result = general_stats.summary(msg)
return result
def frequency(self, number=None, start=None, end=None):
msg = data_manager.messages(number=number, start=start, end=end)
result = general_stats.frequency(msg, period='M')
return result
result['label'] = text
if len(text.split()) > 5:
vals.append(result)
x.append(time)
y.append(result['neg'])
pos = sorted(vals, key=lambda x: x['pos'])
com = sorted(vals, key=lambda x: x['compound'])
neg = sorted(vals, key=lambda x: x['neg'])
pdb.set_trace()
f = interpolate.interp1d(x, y)
num = 70
xx = np.linspace(x[0], x[-1], num)
yy = f(xx)
# plt.plot(x,y, 'bo-')
plt.plot(xx, yy, 'g.-')
plt.show()
# plt.plot(x, y, linewidth=2.0)
# plt.show()
msg = dm.messages(handle=1066, start=None, end=None)
process(msg)
def _all_emojis():
all_text = data_manager.messages().text.str.cat(sep=" ")
return _extract_emoji(all_text)
def unique_words(messages):
(words, text) = extract_words(messages)
(all_words, all_text) = extract_words(data_manager.messages())
uni = unique(words, all_words)
df = pd.DataFrame({'name': uni.index, 'value': uni.values})[:100]
return df.to_dict(orient='records')
