def __weeklygraphs(self, weekly_dict, pid_dict, message_type='sms'):
weekly_graphs = {}
for weekno in weekly_dict.keys():
links, link_tuple, graph_obj, p_d = hlp.creategraph(weekly_dict[weekno], pid_dict=pid_dict,
filterType=message_type)
weekly_graphs[weekno] = graph_obj
return weekly_graphs
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('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('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!'
