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 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 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 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():
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()
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')
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('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!'
