def individual_reciprocity_analysis(labelled_data, pid_dict, location_to_store):
reciprocity_info = {}
ff = filterfields()
ff.setdata(labelled_data)
polarity_data = {}
for pid in pid_dict:
print 'Working with PID: ', pid, '(', pid_dict[pid], ')'
messages_by_participant = ff.filterbyequality(pr.m_source, pid)
messages_to_participant = ff.filterbyequality(pr.m_target, pid)
polarity_data[pid] = __get_polarity_composition(messages_by_participant+messages_to_participant, pid)
reciprocity_info[pid] = {}
n = len(messages_by_participant)
idx = 0
for message in messages_by_participant:
print 'idx=' + str(idx) + '/' + str(n)
idx += 1
closest_message = find_closest_message(message, messages_to_participant, ff)
target_type = 'P' if message[pr.m_target_type] == 'participant' else 'NP'
target = message[pr.m_target]
if target_type not in reciprocity_info[pid]:
reciprocity_info[pid][target_type] = {}
if target not in reciprocity_info[pid][target_type]:
reciprocity_info[pid][target_type][target] = __basic_reciprocity_dict()
sent_message_type = message[-1]
reply_message_type = 'X' if closest_message is None else closest_message[-1]
reciprocity_info[pid][target_type][target][sent_message_type][reply_message_type] += 1
print 'saving checkpoint...'
hlp.dumpvariable([reciprocity_info, pid, pid_dict], 'checkpoint.chp', location_to_store)
print 'saved!'
return reciprocity_info, polarity_data
def find_reciprocity(labelled_data, location_to_store):
ff = filterfields()
ff.setdata(labelled_data)
messages_sent_by_participants = ff.filterbyequality(pr.m_source_type, 'participant')
reciprocity_dict = {'P': {'P': 0, 'U': 0, 'N': 0, 'X': 0},
'N': {'P': 0, 'U': 0, 'N': 0, 'X': 0},
'U': {'P': 0, 'U': 0, 'N': 0, 'X': 0}}
n = len(messages_sent_by_participants)
idx = 1
message_pairs = []
for message in messages_sent_by_participants:
print 'at message ', idx, ' of ', n
idx += 1
reply_message = find_closest_message(message, ff)
sent_message_type = message[-1]
if reply_message is None:
reply_message_type = 'X'
else:
reply_message_type = reply_message[-1]
reciprocity_dict[sent_message_type][reply_message_type] += 1
message_pairs.append((message, reply_message))
if 0 == idx%500:
print 'saving...'
hlp.dumpvariable([idx, reciprocity_dict, message_pairs, messages_sent_by_participants],
'checkpoint.chp', location_to_store)
print 'done... out of the loop'
to_use = {'P': '+', 'N': '-', 'U': 'u', 'X': 'null'}
for sent_type in reciprocity_dict:
recvd_types = reciprocity_dict[sent_type]
for recvd_type in recvd_types:
print 'N('+to_use[recvd_type]+'|'+to_use[sent_type]+')=', recvd_types[recvd_type]
return reciprocity_dict, message_pairs
def getstats(filepath, participant_dict, p_type, message_type='sms'):
ff = filterfields(filepath)
ff.setdata(ff.filterbyequality(pr.m_type, message_type))
participant_stats = {}
for participant_id in participant_dict:
survey_no_list = participant_dict[participant_id]
p_data = ff.filterbyequality(pr.m_source, participant_id) + \
ff.filterbyequality(pr.m_target, participant_id)
if [] == p_data:
print 'no data exists for pid: ' + participant_id
continue
pid_dict = hlp.getuniqueparticipants(p_data)
for survey_no in survey_no_list:
print 'Participant no.', participant_id, ' S.no.: ', survey_no
idx = survey_no
survey_no = survey_no_list[survey_no][0]
end_date = ff.converttodate(survey_no[sr.s_time])
start_date = end_date - dt.timedelta(days=7)
data_between_dates = ff.filterbetweendates(start_date, end_date, data_to_work=p_data)
original_start_date = ff.converttodate(pr.start_datetime)
data_start_to_date = ff.filterbetweendates(original_start_date, start_date, data_to_work=p_data)
between_stats, before_stats = graphstats(data_start_to_date, data_between_dates, participant_id, p_type,
original_start_date, start_date, ff, pid_dict)
temp_dict = {'between': between_stats, 'before': before_stats, 'pid_dict': pid_dict}
participant_stats[participant_id] = {idx: temp_dict}
return participant_stats
def main():
parser = __define_process_parser()
old_dataset_file, new_dataset_mapped, missing_data, \
survey_file, location_to_store = __define_process_parser(True, parser)
old_dataset = hlp.readcsv(old_dataset_file, delimiter_sym=',', remove_first=True)
new_dataset = hlp.readcsv(new_dataset_mapped, delimiter_sym=',', remove_first=True)
old_data_missing = hlp.readcsv(missing_data, delimiter_sym=',', remove_first=True)
old_missing = __dictify(0, old_data_missing)
wi = weeklyinfo()
week_info = wi.getweeklyfo(survey_file)
week_list = week_info.keys()
bullying_positives = __find_positive_survey(survey_file, week_info)
if bullying_positives is None:
print 'Exiting...'
exit()
ff = filterfields()
old_data_weekly = hlp.divideintoweekly(old_dataset, week_info, ff, date_field=pr.m_time_sent)
new_data_weekly = hlp.divideintoweekly(new_dataset, week_info, ff, date_field=nd.m_timecreated)
bullying_res = [['pid_hash', 'survey_id', 'time_of_survey', 'n_old', 'n_new', 'raw', 'semi', 'ordered', 'other']]
for datum in bullying_positives:
bullying_week = datum[-1]
prev_week = bullying_week - 1 if bullying_week > min(week_list) else min(week_list)
next_week = bullying_week + 1 if bullying_week < max(week_list) else max(week_list)
old_data_pos = old_data_weekly[prev_week] + old_data_weekly[bullying_week] + old_data_weekly[next_week]
new_data_pos = new_data_weekly[prev_week] + new_data_weekly[bullying_week] + new_data_weekly[next_week]
pid_hash = datum[s_i.s_participant]
n_old, n_new, nfr_dict = compare_old_new(old_data_pos, new_data_pos, old_missing, pid_hash, ff)
temp = [pid_hash, datum[s_i.s_id], datum[s_i.s_time], n_old, n_new, nfr_dict['raw'], nfr_dict['semi'],
nfr_dict['ordered'], nfr_dict['other']]
bullying_res.append(temp)
hlp.writecsv(bullying_res, location_to_store+'bullying_res.csv', delimiter_sym=',')
def __find_positive_survey(survey_file, week_info):
week_no = week_info.keys()
week_no.sort()
ff = filterfields()
s_obj = surveys()
survey_data = hlp.readcsv(survey_file, delimiter_sym=',')
n_data = s_obj.interpretanswers(survey_data, True)
bullying_positives = ff.filterbyequality(s_i.s_qno, '4', data=n_data[1:])
new_bullying_positives = []
for datum in bullying_positives:
datetime_of_survey = ff.converttodate(datum[s_i.s_time])
found_match = False
for week in week_no:
(start_date, end_date) = week_info[week]
if start_date <= datetime_of_survey <= end_date:
datum.append(week)
new_bullying_positives.append(datum)
found_match = True
break
if not found_match:
print 'Something funky happened...', datum
return None
return new_bullying_positives
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-f', '--messageFile', type=str, required=True)
parser.add_argument('-mt', '--messageTypes', type=str, nargs='+')
parser.add_argument('-o', '--outputFolder', type=str, required=True)
parser.add_argument('-of', '--outputFile', type=str, required=True)
parser.add_argument('-pd', '--participantDictionary', type=str)
parser.add_argument('-i', '--ignoreParticipants', type=str)
parser.add_argument('-mc', '--messageTypeConvert', type=str, nargs='*')
args = parser.parse_args()
message_file = args.messageFile
message_types = args.messageTypes
output_folder = args.outputFolder
output_file = args.outputFile
pid_dict = args.participantDictionary
ignore_pids = args.ignoreParticipants
message_type_conversions = args.messageTypeConvert
ff = filterfields(message_file)
ff.setdata(ff.getdata()[1:])
to_set_data = []
# extract the relevant data
for message_type in message_types:
to_set_data.extend(ff.filterbyequality(pr.m_type, message_type))
ff.setdata(to_set_data)
if ignore_pids is not None:
ignore_pids = hlp.recovervariable(ignore_pids)
for pid in ignore_pids:
ff.removebyequality(pr.m_source, pid)
ff.removebyequality(pr.m_target, pid)
# set the pid to normal id dictionary
if pid_dict is None:
pid_dict = hlp.getuniqueparticipants(ff.getdata(), mtype='all', separate_pid_npid=True)
# replace the message type names with the ones provided
if message_type_conversions is not None:
for idx in range(0, len(message_type_conversions), 2):
message_to_convert = message_type_conversions[idx]
to_convert_to = message_type_conversions[idx+1]
ff.replacebyequality(pr.m_type, message_to_convert, to_convert_to)
message_types = ff.getuniqueelements(pr.m_type)
coded_participant_list = pid_dict[pr.participant['all']].values()
storage_dict = initiatestorage(coded_participant_list, message_types)
storage_dict = getperparticipantinout(ff.getdata(), storage_dict, pid_dict)
plotperparticipantbar(storage_dict, 'Participant ID', '# of Messages', message_types, 'Per Participant Messages',
output_folder+output_file)
hlp.dumpvariable(pid_dict, 'pid_dict.dict', output_folder)
hlp.dumpvariable(ff.getdata(), 'messageData.list', output_folder)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-o', '-O', required=True, help='Old dataset csv')
parser.add_argument('-n', '-N', required=True, help='New dataset csv')
parser.add_argument('-s', '-S', required=True, help='Survey file')
parser.add_argument('-p', '-P', required=True, help='folder to store figures in, should end with /')
parser.add_argument('-m', '-M', required=True, help='Master hash mapping csv')
parser.add_argument('-mt', '-MT', required=True, nargs='+', help='Types of messages to look for')
parser.add_argument('-d', '-D', action='store_true', help='Flag to debug')
args = parser.parse_args()
old_dataset_file = args.o
new_dataset_file = args.n
survey_file = args.s
location_to_store = args.p
master_hash_csv = args.m
message_types = args.mt
do_debug = args.d
print 'Reading data...'
master_csv = hlp.readcsv(master_hash_csv, delimiter_sym=',', remove_first=True)
master_dict = {datum[1]: datum[0] for datum in master_csv}
ff = filterfields()
filtered_old = []
filtered_new = []
old_dataset = hlp.readcsv(old_dataset_file, delimiter_sym=',', remove_first=True)
new_dataset = hlp.readcsv(new_dataset_file, delimiter_sym=',', remove_first=True)
print 'Filtering message types'
for message_type in message_types:
filtered_old.extend(ff.filterbyequality(pr.m_type, message_type, data=old_dataset))
filtered_new.extend(ff.filterbyequality(pr.m_type, message_type, data=new_dataset))
wi = weeklyinfo()
weekly_info = wi.getweeklyfo(survey_file)
week_list = weekly_info.keys()
week_list.sort()
print 'Creating in out dictionary'
in_out_message_dict = get_message_counts(filtered_old, filtered_new, week_list, weekly_info, master_dict, ff,
location_to_store, do_debug)
print 'Plotting...'
for pid in in_out_message_dict:
print pid
plot_distribution(in_out_message_dict[pid][0][0], in_out_message_dict[pid][0][1],
in_out_message_dict[pid][1][0], in_out_message_dict[pid][1][1], week_list, pid,
location_to_store)
print 'TADAA!!'
def filterweeklydata(self, pid_dict, message_list, week_info, message_type='sms'):
participant_dict = pid_dict[pr.participant[message_type]]
ff_obj = filterfields('')
ff_obj.setdata(message_list)
min_week = min(week_info.keys())
max_week = max(week_info.keys())
min_date = week_info[min_week][0]
max_date = week_info[max_week][1]
weekly_dist = {}
for pid in participant_dict.keys():
weekly_dist[pid] = self.__perparticipantprocessing(pid, ff_obj, curr_min=min_date, curr_max=max_date,
send_week_info=False, week_info=week_info)
return weekly_dist
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-f', '-F', type=str, required=True,
help='filepath for message file')
parser.add_argument('-o', '-O', type=str, required=True,
help='path to store figure in')
parser.add_argument('-mt', '-MT', type=str, nargs='*')
parser.add_argument('-c', '-C', type=str, nargs='*')
args = parser.parse_args()
message_filename = args.f
output_location = args.o
message_types = args.mt
combine_change = args.c
to_combine = None if combine_change is None else []
to_change_to = None if combine_change is None else []
if combine_change is not None:
for idx in range(0, len(combine_change), 2):
to_combine.append(combine_change[idx].lower())
to_change_to.append(combine_change[idx+1])
ff = filterfields(message_filename)
ff.setdata(ff.getdata()[1:])
message_types = ff.getuniqueelements(pr.m_type) if message_types is None else message_types
numbers = []
for message_type in message_types:
numbers.append(len(ff.filterbyequality(pr.m_type, message_type)))
if to_combine is not None:
numbers, message_types = combineandconvert(numbers, message_types, to_combine, to_change_to)
for idx in range(len(numbers)):
print message_types[idx], numbers[idx]
fig = plt.figure(figsize=[12, 12])
ax = fig.add_subplot(111)
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
for i in range(len(tableau20)):
r, g, b = tableau20[i]
tableau20[i] = (r / 255., g / 255., b / 255.)
patches, texts, autotexts = ax.pie(numbers, labeldistance=1.05,
colors=tableau20, autopct='%1.1f%%', startangle=90)
for idx in range(len(texts)):
texts[idx].set_fontsize(20)
for idx in range(len(autotexts)):
autotexts[idx].set_fontsize(20)
plt.legend(labels=message_types, loc='upper right', fontsize=20, bbox_to_anchor=(1.05, 1))
plt.savefig(output_location, bbox_inches='tight')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-d', '-D', required=True,
help='labelled data from validate_balance_theory.py')
parser.add_argument('-f', '-F', required=True,
help='folder to save the data in')
parser.add_argument('-w', '-W', required=False,
help='survey file for weekly data processing')
args = parser.parse_args()
data_file = args.d
location_to_store = args.f
weekly_surveys = args.w
all_data = hlp.recovervariable(data_file)
labelled_data = all_data[2]
pid_dict = all_data[3]
if weekly_surveys is None:
reciprocity_info, polarity_info = individual_reciprocity_analysis(labelled_data, pid_dict['participants'],
location_to_store)
analyze_info(reciprocity_info, pid_dict, location_to_store, 'pr_overall.csv')
analyze_polarity(polarity_info, pid_dict, location_to_store, 'polarity_overall.csv')
hlp.dumpvariable([reciprocity_info, labelled_data, pid_dict, polarity_info],
'reciprocity_info_overall.dict', location_to_store)
else:
# working with bimonthly data
months2 = [[1, 2, 3, 4, 5, 6, 7, 8],
[9, 10, 11, 12, 13, 14, 15, 16],
[17, 18, 19, 20, 21, 22, 23, 24, 25]]
wi = weeklyinfo()
weekly_info = wi.getweeklyfo(weekly_surveys)
ff = filterfields()
weekly_data = hlp.divideintoweekly(labelled_data, weekly_info, ff)
idx = 1
for bi_month in months2:
print 'For weeks: ', bi_month
bi_month_data = []
for weekno in bi_month:
bi_month_data.extend(weekly_data[weekno])
reciprocity_info, polarity_info = individual_reciprocity_analysis(bi_month_data, pid_dict['participants'],
location_to_store)
analyze_info(reciprocity_info, pid_dict, location_to_store, 'pr_bimonthly_'+str(idx)+'.csv')
analyze_polarity(polarity_info, pid_dict, location_to_store, 'polarity_bimonthly_'+str(idx)+'.csv')
hlp.dumpvariable([reciprocity_info, labelled_data, pid_dict, polarity_info],
'reciprocity_info_bimonthly_'+str(idx)+'.data', location_to_store)
idx += 1
print 'tadaa!'
def main():
parser = argparse.ArgumentParser('Script to generate distribution '
'of edge weights/degrees for all '
'participants')
parser.add_argument('-m', '-M', type=str, required=True,
help='location of the message file')
parser.add_argument('-mt', '-MT', type=str, default='all',
help='types of messages to plot, currently supports '
'one of the following: sms, fb, twitter, or all')
parser.add_argument('-r', '-R', type=str, required=True,
help='survey file')
parser.add_argument('-s', '-S', type=str, required=True,
help='folder to store data in, leading / required')
parser.add_argument('-p', '-P', action='store_true',
help='flag to generate plots')
args = parser.parse_args()
survey_file = args.r
message_file = args.m
m_type = args.mt
folder_to_store = args.s
generate_plots = args.p
wi = weeklyinfo()
week_info = wi.getweeklyfo(survey_file)
ff = filterfields(message_file)
filtered_data = []
if m_type == 'all':
for message_type in ['sms', 'fb_message']:
filtered_data.extend(ff.filterbyequality(pr.m_type, message_type))
else:
filtered_data = ff.filterbyequality(pr.m_type, m_type)
_, links_tuple, _, pid_dict = hlp.creategraph(filtered_data, filterType=args.mt)
gh = ghelper()
plt = plots()
weekly_deg_dist, _ = gh.getweeklydistributions(pid_dict, filtered_data, message_type=args.mt,
is_degree=True, week_info=week_info)
hlp.dumpvariable(weekly_deg_dist, 'weekly_deg_dist.dict', folder_to_store)
weekly_ew_dist, _ = gh.getweeklydistributions(pid_dict, filtered_data, message_type=args.mt,
is_degree=False, week_info=week_info)
hlp.dumpvariable(weekly_ew_dist, 'weekly_ew_dist.dict', folder_to_store)
if generate_plots:
plt.plotweeklyprogression(weekly_deg_dist, folder_to_store + 'deg_', 'No. of friends',
'Week No.', 'Friends')
plt.plotweeklyprogression(weekly_ew_dist, folder_to_store + 'ew_', 'No. of messages exhanged',
'Week No.', 'Messages')
print 'done...'
def get_degree_message_count(dataset, pid_dict):
ff = filterfields()
ff.setdata(dataset)
in_d = {}
out_d = {}
in_m = {}
out_m = {}
for pid in pid_dict:
incoming_messages = ff.filterbyequality(nd.m_target, pid)
outgoing_messages = ff.filterbyequality(nd.m_source, pid)
people_sending_me_messages = ff.getuniqueelements(nd.m_source, data=incoming_messages)
people_i_am_sending_messages = ff.getuniqueelements(nd.m_target, data=outgoing_messages)
in_m[pid] = len(incoming_messages)
out_m[pid] = len(outgoing_messages)
in_d[pid] = len(people_sending_me_messages)
out_d[pid] = len(people_i_am_sending_messages)
return in_m, out_m, in_d, out_d
def generate_new_dataset_dictionary(new_dataset, use_m_id=False):
ff = filterfields()
new_dataset_dictionary = {}
for datum in new_dataset:
if not use_m_id:
src = datum[nd.m_source]
trg = datum[nd.m_target]
if (src, trg) not in new_dataset_dictionary:
new_dataset_dictionary[(src, trg)] = {}
message_type = datum[nd.m_type]
if message_type not in new_dataset_dictionary[(src, trg)]:
new_dataset_dictionary[(src, trg)][message_type] = {}
create_time = datum[nd.m_timecreated]
create_time_dt = ff.converttodate(create_time)
if create_time_dt not in new_dataset_dictionary[(src, trg)][message_type]:
new_dataset_dictionary[(src, trg)][message_type][create_time_dt] = []
new_dataset_dictionary[(src, trg)][message_type][create_time_dt].append(datum)
else:
new_dataset_dictionary[datum[nd.msg_id]] = datum
return new_dataset_dictionary
def getweeklydistributions(self, pid_dict, message_list, message_type='sms',
is_degree=True, week_info=None):
# is_degree = F --> edge weight distribution
week_info = {} if week_info is None else week_info
participant_dict = pid_dict[pr.participant[message_type]]
ff_obj = filterfields('')
ff_obj.setdata(message_list)
if week_info is not None:
min_week = min(week_info.keys())
max_week = max(week_info.keys())
min_date = week_info[min_week][0]
max_date = week_info[max_week][1]
else:
min_date, max_date = self.getminmaxdates(message_list, ff_obj)
weekly_dist = {}
for pid in participant_dict.keys():
weekly_dist[pid] = {}
if week_info is None:
weekly_dict, temp_week_info = self.__perparticipantprocessing(pid, ff_obj,
curr_min=min_date, curr_max=max_date,
send_week_info=True)
week_info[pid] = temp_week_info
else:
weekly_dict = self.__perparticipantprocessing(pid, ff_obj, curr_min=min_date, curr_max=max_date,
send_week_info=False, week_info=week_info)
weekly_graphs = self.__weeklygraphs(weekly_dict, pid_dict, message_type=message_type)
for weekno in weekly_graphs.keys():
go = weekly_graphs[weekno]
if is_degree:
g_info = go.getdegrees(participant_dict[pid])
else:
g_info_ew = go.getedgeweights(participant_dict[pid])
in_w = 0
out_w = 0
for e_tuple in g_info_ew[0]:
in_w += e_tuple[2]['weight']
for e_tuple in g_info_ew[1]:
out_w += e_tuple[2]['weight']
g_info = [in_w, out_w]
weekly_dist[pid][weekno] = g_info
return weekly_dist, week_info
def get_count_degrees_messages_directed(labelled_data, pid_dict):
fobj = filterfields()
fobj.setdata(labelled_data)
in_m = []
out_m = []
in_d = []
out_d = []
pid_order = []
for pid in pid_dict:
in_data = fobj.filterbyequality(pr.m_target, pid)
out_data = fobj.filterbyequality(pr.m_source, pid)
in_m.append(len(in_data))
out_m.append(len(out_data))
people_sending_me_messages = fobj.getuniqueelements(pr.m_source, data=in_data)
people_i_am_sending_messages_to = fobj.getuniqueelements(pr.m_target, data=out_data)
in_d.append(len(people_sending_me_messages))
out_d.append(len(people_i_am_sending_messages_to))
pid_order.append(pid)
return in_m, out_m, in_d, out_d
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-d', '-D', required=True,
help='labelled data from validate_balance_theory.py')
parser.add_argument('-f', '-F', required=True,
help='folder to save the data in')
parser.add_argument('-w', '-W', required=False,
help='survey file for weekly data processing')
args = parser.parse_args()
data_file = args.d
location_to_store = args.f
weekly_surveys = args.w
all_data = hlp.recovervariable(data_file)
labelled_data = all_data[2]
pid_dict = all_data[3]
if weekly_surveys is None:
reciprocity_dict, message_pairs = find_reciprocity(labelled_data, location_to_store)
hlp.dumpvariable([reciprocity_dict, message_pairs], 'reciprocity_counts_msgPairs_overall', location_to_store)
else:
months2 = [[1, 2, 3, 4, 5, 6, 7, 8],
[9, 10, 11, 12, 13, 14, 15, 16],
[17, 18, 19, 20, 21, 22, 23, 24, 25]]
wi = weeklyinfo()
weekly_info = wi.getweeklyfo(weekly_surveys)
ff = filterfields()
weekly_data = hlp.divideintoweekly(labelled_data, weekly_info, ff)
idx = 1
for bi_month in months2:
print 'For weeks: ', bi_month
bi_month_data = []
for weekno in bi_month:
bi_month_data.extend(weekly_data[weekno])
reciprocity_dict, message_pairs = find_reciprocity(bi_month_data, location_to_store)
hlp.dumpvariable([reciprocity_dict, message_pairs],
'reciprocity_counts_msgPairs_bimonthly_'+str(idx)+'.data', location_to_store)
def main():
ff = filterfields(sys.argv[1])
print 'filtering...'
filtered_data = ff.filterbyequality(pr.m_type, sys.argv[6])
hlp.dumpvariable(filtered_data, 'filtered_'+sys.argv[6], sys.argv[5])
print 'done'
if '-' is not sys.argv[2]:
writecsv(sys.argv[2], filtered_data)
if '-' is not sys.argv[3]:
links, link_tuple, graph_obj, pid_dict = hlp.creategraph(filtered_data)
hlp.dumpvariable(links, 'static_links', sys.argv[5])
hlp.dumpvariable(link_tuple, 'static_links_tuple', sys.argv[5])
hlp.dumpvariable(graph_obj, 'static_graph_obj', sys.argv[5])
hlp.dumpvariable(pid_dict, 'pid_dict', sys.argv[5])
graph_obj.writegraph(sys.argv[3])
if '-' is not sys.argv[4]:
to_write_edge, to_write_nodes, week_dict, pid_dict, week_content = hlp.creategraph(filtered_data, False)
writetofile(sys.argv[4]+'_el.csv', to_write_edge)
writetofile(sys.argv[4]+'_nl.csv', to_write_nodes)
hlp.dumpvariable(week_dict, 'dynamic_week_dict', sys.argv[5])
hlp.dumpvariable(pid_dict, 'pid_dict', sys.argv[5])
hlp.dumpvariable(week_content, 'week_content', sys.argv[5])
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m', '-M', required=True,
help='Sentiment Message file')
parser.add_argument('-t', '-T', action='store_true',
help='Sentiment type flag, if used then vader, else afinn')
parser.add_argument('-f', '-F', required=True,
help='Folder to store checkpoints, and final result')
parser.add_argument('-w', '-W', required=False,
help='Per week/month analysis')
args = parser.parse_args()
message_file = args.m
sentiment_type = args.t
location_to_store = args.f
survey_file = args.w
# get message data, only sms and fb_message
ff = filterfields(message_file)
ff.setdata(ff.getdata()[1:])
sms_data = ff.filterbyequality(pr.m_type, 'sms')
pid_dict_sms = hlp.getuniqueparticipants2(sms_data)
fb_message_data = ff.filterbyequality(pr.m_type, 'fb_message')
pid_dict_fb = hlp.getuniqueparticipants2(fb_message_data)
message_data = sms_data + fb_message_data
# put the labels on
labelled_data = hlp.processvadersentiment(message_data, label_only=False) if sentiment_type else \
hlp.processafinnsentiment(message_data, label_only=False)
if survey_file is not None:
wi = weeklyinfo()
weekly_info = wi.getweeklyfo(survey_file)
weekly_data = hlp.divideintoweekly(labelled_data, weekly_info, ff)
#__temp_testing_for_discrepancy(labelled_data, weekly_data)
# get the pid_dict for easier handling
pid_dict = hlp.getuniqueparticipants2(labelled_data)
if survey_file is not None:
over_sent, in_sent, out_sent, xtick, ytick = per_participant_sentiment(weekly_data, pid_dict['participants'])
__plot_imshow(over_sent, 'Participant', 'Week #', xtick, ytick, location_to_store+'sent_imshow_over.pdf')
__plot_imshow(in_sent, 'Participant', 'Week #', xtick, ytick, location_to_store+'sent_imshow_in.pdf')
__plot_imshow(out_sent, 'Participant', 'Week #', xtick, ytick, location_to_store+'sent_imshow_out.pdf')
print '***SMS***'
print 'P: ', len(pid_dict_sms['participants'].values()), ' NP: ', len(pid_dict_sms['nonparticipants'].values())
print '***FB***'
print 'P: ', len(pid_dict_fb['participants'].values()), 'NP: ', len(pid_dict_fb['nonparticipants'].values())
print '***OVERALL***'
print 'P: ', len(pid_dict['participants'].values()), 'NP: ', len(pid_dict['nonparticipants'].values())
summary_src_trg = summarize_message_by_src_trg(labelled_data)
print '***Message Distribution***'
for m_type_1 in summary_src_trg:
print m_type_1, summary_src_trg[m_type_1]
if survey_file is not None:
week_list = weekly_data.keys()
week_list.sort()
# this is not good, as there aren't enough triads
months = [[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
[17, 18, 19, 20],
[21, 22, 23, 24, 25]]
# this has at least 8 triads, always, use this
months2 = [[1, 2, 3, 4, 5, 6, 7, 8],
[9, 10, 11, 12, 13, 14, 15, 16],
[17, 18, 19, 20, 21, 22, 23, 24, 25]]
month_idx = 1
for month in months2:
labelled_data = []
for week in month:
labelled_data.extend(weekly_data[week])
general_graph, random_graph = conduct_triad_analysis(labelled_data, pid_dict)
frac_triad = general_graph[3]
summary_triad = general_graph[2]
frac_triad_rand = random_graph[3]
summary_triad_rand = random_graph[2]
print '** Months ', 2*month_idx-1, 2*month_idx, ': ', month,' ***'
print 'len(LD): ', len(labelled_data)
for summary in frac_triad:
print summary, 'Study: ', frac_triad[summary], '(', len(summary_triad[summary]), ')', ' Random: ', \
frac_triad_rand[summary], '(', len(summary_triad_rand[summary]), ')'
words_list, short_list = word_count(labelled_data)
toWrite_wl_csv = create_word_count_csv(words_list)
hlp.writecsv(toWrite_wl_csv, location_to_store+'word_list_'+str(2*month_idx-1)+'-'+str(2*month_idx)+'.csv',
delimiter_sym=',')
for mtype in words_list:
counted_words = Counter(words_list[mtype])
counted_short = Counter(short_list[mtype])
print '***For '+mtype+' ***'
print 'Top 20 words: ', __get_top_word_sentiment(counted_words.most_common(20))
print 'Top 20 short: ', counted_short.most_common(20)
print '\n\n'
hlp.dumpvariable([general_graph, random_graph, labelled_data, pid_dict], 'month_'+str(month_idx)+'.list', location_to_store)
month_idx += 1
else:
print 'len(LD): ', len(labelled_data)
words_list, short_list = word_count(labelled_data)
toWrite_wl_csv = create_word_count_csv(words_list)
hlp.writecsv(toWrite_wl_csv, location_to_store+'word_list.csv', delimiter_sym=',')
for mtype in words_list:
counted_words = Counter(words_list[mtype])
counted_short = Counter(short_list[mtype])
print '***For '+mtype+' ***'
print 'Top 20 words: ', __get_top_word_sentiment(counted_words.most_common(20))
print 'Top 20 short: ', counted_short.most_common(20)
print '\n\n'
general_graph, random_graph = conduct_triad_analysis(labelled_data, pid_dict)
frac_triad = general_graph[3]
summary_triad = general_graph[2]
frac_triad_rand = random_graph[3]
summary_triad_rand = random_graph[2]
for summary in frac_triad:
print summary, 'Study: ', frac_triad[summary], '(', len(summary_triad[summary]), ')', ' Random: ', \
frac_triad_rand[summary], '(', len(summary_triad_rand[summary]), ')'
hlp.dumpvariable([general_graph, random_graph, labelled_data, pid_dict], 'Overall.list', location_to_store)
# plot_degree_dist(general_graph[4], 'Degree(d)', '# of Participants with Degree d')
pos, neg, neu = get_polarity_directionality(labelled_data)
print '***Polarity Distribution***'
print 'Positive: \n', pos
print 'Negative: \n', neg
print 'Neutral: \n', neu
in_m, out_m, in_d, out_d = get_count_degrees_messages_directed(labelled_data, pid_dict['participants'])
print '***Incoming Messages***'
print 'Total: ', sum(in_m), 'Mean: ', np.mean(in_m), 'Std. dev.: ', np.std(in_m)
print '***Outgoing Messages***'
print 'Total: ', sum(out_m), 'Mean: ', np.mean(out_m), 'Std. dev.: ', np.std(out_m)
print '***In Degree***'
print 'Total: ', sum(in_d), 'Mean: ', np.mean(in_d), 'Std. dev.: ', np.std(in_d)
print '***Out Degree***'
print 'Total: ', sum(out_d), 'Mean: ', np.mean(out_d), 'Std. dev.: ', np.std(out_d)
print '***COUNTS***'
plot_messages_degree([in_m, out_m], '# of Messages', 'Cumulative Participant Prob.',
location_to_store+'in_out_messages.pdf')
# plot_messages_degree(out_m, '# of Outgoing Messages', 'Cumulative Participant Prob.',
# location_to_store+'out_messages.pdf')
plot_messages_degree([in_d, out_d], 'Degree', 'Cumulative Participant Prob.',
location_to_store+'in_out_degree.pdf', True)
# plot_messages_degree(out_d, 'Out Degree', 'Cumulative Participant Prob.',
# location_to_store+'out_degree.pdf', True)
print 'TADAA!!'
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m', '-M', type=str, required=True,
help='Message list file')
parser.add_argument('-r', '-R', type=str, required=True,
help='survey file')
parser.add_argument('-p', '-P', type=str, required=True,
help='PID dict inverted')
parser.add_argument('-b', '-B', type=str, required=True,
help='bullying dictionary')
parser.add_argument('-o', '-O', type=str, required=True,
help='Output folder')
parser.add_argument('-l', '-L', type=str, nargs='+',
help='Filters chosen')
parser.add_argument('-f', '-f', type=str, nargs='+',
help='Filter files')
args = parser.parse_args()
output_folder = args.o
message_data = hlp.recovervariable(args.m)
pid_dict = hlp.recovervariable(args.p)
filters_chosen = args.l
filter_files = args.f
catch_all_data = hlp.getfilterdata(filters_chosen, filter_files, catch_all=True)
wi = weeklyinfo()
weekly_info = wi.getweeklyfo(args.r)
ff = filterfields()
gh = ghelper()
bullying_overlay = gh.createbullyingoverlay(catch_all_data, weekly_info, ff)
bullying_overlay = flip_bullying_overlay(bullying_overlay, weekly_info.keys())
pid_list = pid_dict.keys()
pid_list.sort()
for pid in pid_list:
training_set_final = []
testing_set_final = []
pid_list_training = deepcopy(pid_list)
pid_list_training.remove(pid)
ff.setdata(message_data)
testing_raw_data = ff.filterbyequality(pr.m_source, pid_dict[pid]) + \
ff.filterbyequality(pr.m_target, pid_dict[pid])
ff.removebyequality(pr.m_source, pid_dict[pid])
ff.removebyequality(pr.m_target, pid_dict[pid])
training_raw_data = ff.getdata()
fe = raw_features(data=None)
_, _ = fe.get_scoring_factors(training_raw_data)
training_weekly_data = {}
for training_pid in pid_list_training:
training_weekly_data[training_pid] = {}
data_to_use = ff.filterbyequality(pr.m_source, pid_dict[training_pid]) + \
ff.filterbyequality(pr.m_target, pid_dict[training_pid])
if 0 == len(data_to_use):
print 'no data found, probably filtered into the testing set, Training PID: '+\
training_pid+', Testing PID: '+pid
continue
pid_weekly_w_bullying, global_in_degree, global_out_degree, global_in_ew, global_out_ew, incoming_ss, \
outgoing_ss = get_pid_level_features(data_to_use, weekly_info, ff,
bullying_overlay, pid_dict,
training_pid, fe)
for week_no in pid_weekly_w_bullying:
fr_in_degree, fr_out_degree, fr_in_ew, \
fr_out_ew, fr_in_senti, fr_out_senti, \
current_in_ss, current_out_ss = get_week_features(pid_weekly_w_bullying, week_no, fe,
global_in_degree, global_out_degree,
global_in_ew, global_out_ew,
incoming_ss, outgoing_ss,
pid_dict[training_pid])
training_set_final.append(
[training_pid, week_no,
fr_in_senti[0], fr_in_senti[1], fr_in_senti[2],
fr_out_senti[0], fr_out_senti[1], fr_out_senti[2],
fr_in_degree, fr_out_degree,
fr_in_ew, fr_out_ew,
current_in_ss, current_out_ss,
pid_weekly_w_bullying[week_no]['label']])
# testing pid
pid_weekly_w_bullying, global_in_degree, global_out_degree, \
global_in_ew, global_out_ew, incoming_ss, outgoing_ss = get_pid_level_features(testing_raw_data, weekly_info,
ff, bullying_overlay, pid_dict,
pid, fe)
for week_no in pid_weekly_w_bullying:
fr_in_degree, fr_out_degree, fr_in_ew, \
fr_out_ew, fr_in_senti, fr_out_senti, \
current_in_ss, current_out_ss = get_week_features(pid_weekly_w_bullying, week_no, fe,
global_in_degree, global_out_degree,
global_in_ew, global_out_ew,
incoming_ss, outgoing_ss,
pid_dict[pid])
testing_set_final.append(
[pid, week_no,
fr_in_senti[0], fr_in_senti[1], fr_in_senti[2],
fr_out_senti[0], fr_out_senti[1], fr_out_senti[2],
fr_in_degree, fr_out_degree,
fr_in_ew, fr_out_ew,
current_in_ss, current_out_ss,
pid_weekly_w_bullying[week_no]['label']])
header = ['pid', 'wkno',
'frWInSenPos', 'frWInSenNeu', 'frWInSenNeg',
'frWOutSenPos', 'frWOutSenNeu', 'frWOutSenNeg',
'frInDegO', 'frOutDegO',
'frInEdgeO', 'frOutEdgeO',
'inSenSc', 'outSenSc',
'label']
training_set_final = [header] + training_set_final
testing_set_final = [header] + testing_set_final
hlp.writecsv(training_set_final, output_folder+pid+'_tr.csv')
hlp.writecsv(testing_set_final, output_folder+pid+'_ts.csv')
from sentimentanalysis import sentiment
from filterByField import filterfields
from basicInfo import twitterdataset as td
from basicInfo import privateInfo as pr
import helper as hlp
import random
data = hlp.readcsv('../ignore_data/Sentiment_Twitter.csv')
data = data[1:]
ff = filterfields('../ignore_data/messages.csv')
smsdata = ff.filterbyequality(pr.m_type, 'sms')
k = len(data)
l = len(smsdata)
seed = 254
random.seed(seed)
tr_n = 1000000
ts_n = 30
idx = 0
tr_before = []
ts_before = []
while idx < tr_n:
i = random.randint(0, k)
datum = data[i]
tweet_type = td.sentiment_dict[datum[td.sentiment]]
tweet_content = datum[td.sentiment_text]
tr_before.append((tweet_content, tweet_type))
idx += 1
random.seed(seed)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-o', '-O', help='Old Dataset', required=True)
parser.add_argument('-n', '-N', help='New Dataset', required=True)
parser.add_argument('-f', '-F', help='Folder to store results in, ending with /', required=True)
parser.add_argument('-p', '-P', help='text file with list of people who were ordered to be removed', required=True)
parser.add_argument('-s', '-S', help='text file with list of people who were semi-consented', required=True)
args = parser.parse_args()
old_dataset_file = args.o
new_dataset_file = args.n
location_to_store = args.f
ordered_removed_file = args.p
semi_consented_file = args.s
print '***Reading data from arguments...'
old_dataset = hlp.readcsv(old_dataset_file, delimiter_sym=',', remove_first=True)
new_dataset = hlp.readcsv(new_dataset_file, delimiter_sym=',')
new_dataset_dictionary = generate_new_dataset_dictionary(new_dataset[1:])
new_dataset_msg_id_dictionary = generate_new_dataset_dictionary(new_dataset[1:], use_m_id=True)
with open(ordered_removed_file, 'r') as f:
ordered_removed = eval(f.read())
with open(semi_consented_file, 'r') as f:
semi_consented = eval(f.read())
print '***Filtering old data within dates of study...'
ff = filterfields()
old_dataset_within_dates = ff.filterbetweendates(ff.converttodate(pr.start_datetime),
ff.converttodate(pr.end_datetime), data_to_work=old_dataset,
right_equality=True, date_field=pr.m_time_sent)
old_dataset = old_dataset_within_dates
old_dataset_counts = {}
for datum in old_dataset:
m_type = datum[pr.m_type]
if m_type not in old_dataset_counts:
old_dataset_counts[m_type] = 0
old_dataset_counts[m_type] += 1
print '*** OLD DATASET COUNTS***', old_dataset_counts
print '***Finding mapping...'
mapping_dict = {}
inverted_mapping_dict = {}
missed_dict = {}
no_reason = []
counts_no_match = {'ord': {'sms': 0, 'fb_message': 0, 'twitter_status': 0, 'twitter_message': 0,
'fb_activity': 0, 'fb_like': 0, 'fb_comment': 0},
'semi': {'sms': 0, 'fb_message': 0, 'twitter_status': 0, 'twitter_message': 0, 'fb_activity': 0,
'fb_like': 0, 'fb_comment': 0},
'no': {'sms': 0, 'fb_message': 0, 'twitter_status': 0, 'twitter_message': 0, 'fb_activity': 0,
'fb_like': 0, 'fb_comment': 0}}
counts_match = {'sms': 0, 'fb_message': 0, 'twitter_status': 0, 'twitter_message': 0, 'fb_activity': 0,
'fb_like': 0, 'fb_comment': 0}
no_reason_counts = {}
for datum in old_dataset:
m_result, msg_val = message_exists(datum, new_dataset_dictionary, ff)
if m_result:
mapping_dict[datum[pr.msg_id]] = msg_val
if msg_val[1] not in inverted_mapping_dict:
inverted_mapping_dict[msg_val[1]] = []
inverted_mapping_dict[msg_val[1]].append(datum[pr.msg_id])
m_type = datum[pr.m_type]
if m_type in counts_match:
counts_match[m_type] += 1
else:
src = datum[pr.m_source]
trg = datum[pr.m_target]
m_type = datum[pr.m_type]
if src in ordered_removed or trg in ordered_removed:
reason = 'ordered removed'
if m_type in counts_no_match['ord']:
counts_no_match['ord'][m_type] += 1
elif src in semi_consented or trg in semi_consented:
reason = 'semi consented'
if m_type in counts_no_match['semi']:
counts_no_match['semi'][m_type] += 1
else:
reason = ''
temp = datum
temp.append(msg_val)
no_reason.append(temp)
if m_type in counts_no_match['no']:
counts_no_match['no'][m_type] += 1
if m_type not in no_reason_counts.keys():
no_reason_counts[m_type] = {}
if msg_val not in no_reason_counts[m_type].keys():
no_reason_counts[m_type][msg_val] = 0
no_reason_counts[m_type][msg_val] += 1
missed_dict[datum[pr.msg_id]] = [msg_val, datum[pr.m_type], reason]
print '\n\n**NOT FOUND**'
for key_v in counts_no_match.keys():
print key_v
print counts_no_match[key_v]
print '\n\n**NO REASON**'
for key_v in no_reason_counts.keys():
print key_v
print no_reason_counts[key_v]
print '\n\n**FOUND**', counts_match
print '***Creating new dataset with mappings...'
new_dataset_header = new_dataset[0]
new_dataset_header.extend(['Old Message IDs'])
final_dataset = [new_dataset_header]
for new_msg_id in new_dataset_msg_id_dictionary.keys():
datum = new_dataset_msg_id_dictionary[new_msg_id]
old_msg_id = [''] if new_msg_id not in inverted_mapping_dict else inverted_mapping_dict[new_msg_id]
datum.extend(old_msg_id)
final_dataset.append(datum)
print '***Writing data...'
hlp.writecsv(final_dataset, location_to_store + 'new_old_mapped_hashed_dataset.csv', delimiter_sym=',')
mapping_dict_list = [[x, mapping_dict[x][0], mapping_dict[x][1]] for x in mapping_dict]
mapping_header = [['old_id', 'cosine_val', 'new_id']]
mapping_header.extend(mapping_dict_list)
hlp.writecsv(mapping_header, location_to_store + 'old_to_new_mapping.csv', delimiter_sym=',')
missed_dict_list = [[x, missed_dict[x][0], missed_dict[x][1], missed_dict[x][2]] for x in missed_dict]
missed_header = [['old_id', 'Reason', 'm_type', 'Explanation']]
missed_header.extend(missed_dict_list)
hlp.writecsv(missed_header, location_to_store + 'old_not_found.csv', delimiter_sym=',')
hlp.writecsv(no_reason, location_to_store + 'old_not_found_no_reason.csv', delimiter_sym=',')
print 'TADAA!!!'
def main():
parser = argparse.ArgumentParser('Script to perform sentiment analysis using VADER')
parser.add_argument('-m', '-M', type=str, required=True,
help='Location of the message file')
parser.add_argument('-mt', '-MT', type=str, required=True, nargs='+',
help='types of messages to filter')
parser.add_argument('-f', '-F', type=str, required=True,
help='filename where data is stored, no extension needed')
parser.add_argument('-s', '-S', type=str, required=True,
help='location of folder to store the file, ends with a /')
parser.add_argument('-p', '-P', action='store_true',
help='flag to store polarities separately')
parser.add_argument('-w', '-W', type=str, required=False,
help='conduct weekly analysis, path to the survey data for '
'creating week information')
parser.add_argument('-l', '-L', type=str, nargs='+', required=True,
help='the filters to use, make one or more choices: seenB, wasB, didB')
parser.add_argument('-lf', '-LF', type=str, nargs='+', required=True,
help='location of filtered data, from runSurveyStats.py, in same order as -l/L flag')
args = parser.parse_args()
message_file = args.m
message_types = args.mt
filename_to_store = args.f
location_to_store = args.s
separate_polarity_score = args.p
survey_file = args.w
filters_chosen = args.l
filter_files = args.lf
catch_all_data = hlp.getfilterdata(filters_chosen, filter_files, catch_all=True)
if separate_polarity_score and survey_file is not None:
print 'Cannot have separate polarity scores and weekly analysis together, ' \
'please remove the -p/-P flag'
return
if survey_file is not None:
wi = weeklyinfo()
week_dates = wi.getweeklyfo(survey_file)
gh = ghelper()
ff = filterfields(message_file)
data = []
for message_type in message_types:
data.extend(ff.filterbyequality(pr.m_type, message_type))
pid_dict = hlp.getuniqueparticipants(data, 'all' if len(message_types) > 1 else message_types[0])
sentiment_analyzer = vadersenti(data[1:])
returned_data = sentiment_analyzer.compilesentiment(pr.m_content, separate_sentiment_list=separate_polarity_score)
if separate_polarity_score:
hlp.dumpvariable(returned_data, filename_to_store + '.data', location_to_store)
else:
header = pr.message_header + ['pos', 'neg', 'neu', 'compound']
final_data = [header] + returned_data
hlp.writecsv(final_data, location_to_store + filename_to_store + '.csv')
weekly_data = gh.filterweeklydata(pid_dict, returned_data, week_dates,
'all' if len(message_types) > 1 else message_types[0])
hlp.dumpvariable(weekly_data, 'weekly_data.dict', location_to_store)
summarized_sentiment = {}
for pid in weekly_data:
summarized_sentiment[pid] = {}
participant_data = weekly_data[pid]
for week_no in participant_data:
summarized_sentiment[pid][week_no] = sentiment_analyzer.summarizesentiment(participant_data[week_no],
separate_in_out=True,
message_type=message_type)
hlp.dumpvariable(summarized_sentiment, 'weekly_summarized_sentiment.dict', location_to_store)
plt = plots()
overlay_data = gh.createbullyingoverlay(catch_all_data, week_dates, ff)
plt.plotweeklyprogression(summarized_sentiment, location_to_store, 'Sentiment Progress', 'Week',
'Sentiment Value', sentiment_legend=['Positive', 'Negative', 'Neutral'],
overlay_data=overlay_data)
print 'done'
def main():
parser = argparse.ArgumentParser('Script to generate a CDF comparing the degrees of our participants')
parser.add_argument('-l', '-L', type=str, nargs='+', required=True,
help='the filters to use, make one or more choices: seenB, wasB, didB')
parser.add_argument('-f', '-F', type=str, nargs='+', required=True,
help='location of filtered data, from runSurveyStats.py, in the same order as -l/L flag')
parser.add_argument('-m', '-M', type=str, required=True,
help='location of the message file')
parser.add_argument('-mt', '-MT', type=str, default='sms',
help='type of message we are filtering, default: sms')
parser.add_argument('-n', '-N', action='store_true',
help='flag indicates that processing should include participants which did not witness '
'anything mentioned in the values passed for flags -l/L')
parser.add_argument('-a', '-A', action='store_true',
help='flag indicates that processing should include a plot of all participants')
parser.add_argument('-s', '-S', type=str, required=True,
help='folder to store in, leading /')
parser.add_argument('-r', '-R', type=str, required=True,
help='survey file')
args = parser.parse_args()
filters_chosen = args.l
for filter_v in filters_chosen:
if filter_v not in ['seenB', 'didB', 'wasB']:
raise Exception('filter value was not from the ones specified')
filter_files = args.f
assert len(filter_files) == len(filters_chosen), e.len_filter_file_ne_len_filters_chosen
include_other_participants = args.n
include_all_participants = args.a
location_to_store = args.s
if not os.path.exists(location_to_store):
os.mkdir(location_to_store)
message_file = args.m
message_type = args.mt
survey_file = args.r
wi = weeklyinfo()
week_info = wi.getweeklyfo(survey_file)
gh = ghelper()
plt = plots()
# get the filtered messages
ff = filterfields(message_file)
filtered_data = []
if message_type == 'all':
for message_type in ['sms', 'fb', 'twitter']:
filtered_data.extend(ff.filterbyequality(pr.m_type, message_type))
else:
filtered_data = ff.filterbyequality(pr.m_type, message_type)
# generate the links and the graph for the filtered data
links, links_tuple, graph_obj, pid_dict = hlp.creategraph(filtered_data, filterType=message_type)
# get the pids from the chosen filters
bullying_pid_dict = hlp.getfilterdata(filters_chosen, filter_files)
cumulative_bully_pid = hlp.getfilterdata(filters_chosen, filter_files, cumulative_list=True)
# get all the information from the filters
catch_all_data = hlp.getfilterdata(filters_chosen, filter_files, catch_all=True)
# generate the distributions for in degree and plot them
in_distributions = gh.generatedistributions(graph_obj, bullying_pid_dict, include_all_participants,
include_other_participants, pid_dict, message_type,
cumulative_bully_pid, in_dist=True)
in_distributions_ew = gh.generatedistributions(graph_obj, bullying_pid_dict, include_all_participants,
include_other_participants, pid_dict, message_type,
cumulative_bully_pid, in_dist=True, is_degree=False)
plt.generatetablehist(in_distributions, location_to_store + 'in_degree_table.csv', generate_totals=True)
plt.generatetablehist(in_distributions_ew, location_to_store + 'in_edge_weight.csv', generate_totals=True)
# generate the distributions for out degree and plot them
out_distributions = gh.generatedistributions(graph_obj, bullying_pid_dict, include_all_participants,
include_other_participants, pid_dict, message_type,
cumulative_bully_pid, in_dist=False)
out_distributions_ew = gh.generatedistributions(graph_obj, bullying_pid_dict, include_all_participants,
include_other_participants, pid_dict, message_type,
cumulative_bully_pid, in_dist=False)
plt.generatetablehist(out_distributions, location_to_store + 'out_degree_table.csv', generate_totals=True)
plt.generatetablehist(out_distributions_ew, location_to_store + 'out_edge_weight.csv', generate_totals=True)
# line plot of degrees
weekly_dist_degrees, _ = gh.getweeklydistributions(pid_dict, filtered_data,
message_type=message_type,
is_degree=True, week_info=week_info)
overlay_info = gh.createbullyingoverlay(catch_all_data, week_info, ff)
plt.plotweeklyprogression(weekly_dist_degrees, location_to_store +'deg_', 'No of friends',
'Week No', 'Friends', overlay_data=overlay_info)
# line plot of weights
weekly_dist_ew, _ = gh.getweeklydistributions(pid_dict, filtered_data,
message_type=message_type,
is_degree=False, week_info=week_info)
overlay_info = gh.createbullyingoverlay(catch_all_data, week_info, ff)
plt.plotweeklyprogression(weekly_dist_ew, location_to_store +'ew_', 'No. of messages exchanged',
'Week No', 'Messages', overlay_data=overlay_info)
print 'TADAAA!'
