def test_box_plot(self, mock_savefig, mock_boxplot):
data = [0, 1, 2, 3, 4, 5, 6, 7, 8]
vis.box_plot(data, self.test_data_dir, 'box_plot_test')
plt.boxplot.assert_called_once_with(data)
plt.savefig.assert_called_once_with(self.test_data_dir + "/" +
"box_plot_test" + ".png")
def correlational_activity(log_directory, output_directory, channel_name):
pearson = []
for month in xrange(1, 12):
log_data_m1 = reader.linux_input(log_directory, channel_name,
"2013-" + str(month) + "-1",
"2013-" + str(month) + "-31")
nicks_m1, nick_same_list_m1 = nickTracker.nick_tracker(log_data_m1)
bin_matrix_m1, total_messages_m1 = network.message_number_bins_csv(
log_data_m1, nicks_m1, nick_same_list_m1)
monthly_sum_bins_m1 = [sum(i) for i in zip(*bin_matrix_m1)]
log_data_m2 = reader.linux_input(log_directory, channel_name,
"2013-" + str(month + 1) + "-1",
"2013-" + str(month + 1) + "-31")
nicks_m2, nick_same_list_m2 = nickTracker.nick_tracker(log_data_m2)
bin_matrix_m2, total_messages_m2 = network.message_number_bins_csv(
log_data_m2, nicks_m2, nick_same_list_m2)
monthly_sum_bins_m2 = [sum(i) for i in zip(*bin_matrix_m2)]
corr = np.corrcoef(monthly_sum_bins_m1, monthly_sum_bins_m2)[0, 1]
print "\n----------------------------------"
print "For months", month, "and", month + 1
print "Bins for M1:", monthly_sum_bins_m1
print "Bins for M2:", monthly_sum_bins_m2
print "Pearson correlation:", corr
pearson.append(corr)
vis.box_plot(pearson, output_directory, "pearson2013")
saver.save_csv([pearson], output_directory, "pearson2013")
def correlational_activity(log_directory, output_directory, channel_name,
start_date, end_date):
"""
The function selects a month in the given date range and creates heatmap bins for the current month and the next
month. It then calculates the correlational calculates the correlational vectors between the two heatmaps and
then produces a box plot for all the correlational coefficients of the months in the given date range.
Args:
log_directory(str): path to the location of Logs
output_directory(str): path to the location where the results are to be stored
channel_name(list): channels for which the analysis is to be done
start_date(datetime): starting date for the logs to be analysed. This has to be the beginning of the month.
end_date(datetime): ending date for which the logs are to be analysed. This has to be the end of the month.
Returns:
null
"""
start_date = start_date.strptime('%Y-%m-%d')
end_date = end_date.strptime('%Y-%m-%d')
pearson = []
for dt in rrule(MONTHLY, dtstart=start_date, until=end_date):
last_day_of_the_month1 = dt + relativedelta(
months=1) - datetime.timedelta(days=1)
log_data_m1 = reader.linux_input(
log_directory, channel_name, dt.strftime("%Y-%m-%d"),
last_day_of_the_month1.strftime("%Y-%m-%d"))
nicks_m1, nick_same_list_m1 = nickTracker.nick_tracker(log_data_m1)
bin_matrix_m1, total_messages_m1 = network.message_number_bins_csv(
log_data_m1, nicks_m1, nick_same_list_m1)
monthly_sum_bins_m1 = [sum(i) for i in zip(*bin_matrix_m1)]
next_month_dt = dt + relativedelta(months=1)
last_day_of_the_month2 = next_month_dt + relativedelta(
months=1) - datetime.timedelta(days=1)
log_data_m2 = reader.linux_input(
log_directory, channel_name, next_month_dt.strftime("%Y-%m-%d"),
last_day_of_the_month2.strftime("%Y-%m-%d"))
nicks_m2, nick_same_list_m2 = nickTracker.nick_tracker(log_data_m2)
bin_matrix_m2, total_messages_m2 = network.message_number_bins_csv(
log_data_m2, nicks_m2, nick_same_list_m2)
monthly_sum_bins_m2 = [sum(i) for i in zip(*bin_matrix_m2)]
corr = np.corrcoef(monthly_sum_bins_m1, monthly_sum_bins_m2)[0, 1]
print "\n----------------------------------"
print "For months", dt.month, "and", dt.month + 1
print "Bins for M1:", monthly_sum_bins_m1
print "Bins for M2:", monthly_sum_bins_m2
print "Pearson correlation:", corr
pearson.append(corr)
vis.box_plot(pearson, output_directory, "pearson2013")
saver.save_csv([pearson], output_directory, "pearson2013")
def codelengths(log_directory, output_directory, channel_name):
codelengths = []
for month in xrange(1, 13):
log_data_m1 = reader.linux_input(log_directory, channel_name,
"2013-" + str(month) + "-1",
"2013-" + str(month) + "-31")
nicks_m1, nick_same_list_m1 = nickTracker.nick_tracker(log_data_m1)
message_number_graph_m1 = network.message_number_graph(
log_data_m1, nicks_m1, nick_same_list_m1, False)
try:
#FOS is a reserved word in igraph and if 'fos' is a username in the nx graph, it generates an error
saver.save_net_nx_graph(message_number_graph_m1, output_directory,
"message-exchange-" + str(month))
msg_igraph, msg_community = community.infomap_igraph(
ig_graph=None,
net_file_location=output_directory + "message-exchange-" +
str(month) + '.net')
codelengths.append(msg_community.codelength)
except:
node_labels = message_number_graph_m1.nodes()
labels = {}
for label in node_labels:
if label == "fos":
labels[label] = "fos_"
else:
labels[label] = label
message_number_graph_m1 = nx.relabel_nodes(message_number_graph_m1,
labels)
saver.save_net_nx_graph(message_number_graph_m1, output_directory,
"message-exchange-" + str(month))
print "error in", month
msg_igraph, msg_community = community.infomap_igraph(
ig_graph=None,
net_file_location=output_directory + "message-exchange-" +
str(month) + '.net')
codelengths.append(msg_community.codelength)
vis.box_plot(codelengths, output_directory, "codelengths2013")
saver.save_csv([codelengths], output_directory, "codelengths2013")
def box_plot_for_degree(log_directory, output_directory, channel_name):
cutoff = 0
for channel_name_iter in channel_name:
out_degree_fit_parameters = np.zeros((12, 4))
in_degree_fit_parameters = np.zeros((12, 4))
total_degree_fit_parameters = np.zeros((12, 4))
for month in range(1, 13):
log_data = reader.linux_input(log_directory, channel_name_iter,
"2013-" + str(month) + "-1",
"2013-" + str(month) + "-31")
nicks, nick_same_list = nickTracker.nick_tracker(log_data)
message_number_graph = network.message_number_graph(
log_data, nicks, nick_same_list, False)
degree_anal_message_number = network.degree_analysis_on_graph(
message_number_graph)
out_degree_fit_parameters[month - 1] = vis.generate_log_plots(
degree_anal_message_number["out_degree"]["raw_for_vis"],
output_directory, channel_name_iter[0])
in_degree_fit_parameters[month - 1] = vis.generate_log_plots(
degree_anal_message_number["in_degree"]["raw_for_vis"],
output_directory, channel_name_iter[0])
total_degree_fit_parameters[month - 1] = vis.generate_log_plots(
degree_anal_message_number["total_degree"]["raw_for_vis"],
output_directory, channel_name_iter[0])
parameters = ['slope', 'intercept', 'r_square']
for para_ind in range(len(parameters)):
vis.box_plot(
out_degree_fit_parameters[:, para_ind], output_directory,
"out_degree_" + str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
vis.box_plot(
in_degree_fit_parameters[:, para_ind], output_directory,
"in_degree_" + str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
vis.box_plot(
total_degree_fit_parameters[:, para_ind], output_directory,
"total_degree_" + str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
saver.save_csv([out_degree_fit_parameters[:, para_ind].tolist()],
output_directory, "out_degree_" +
str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
saver.save_csv([in_degree_fit_parameters[:, para_ind].tolist()],
output_directory, "in_degree_" +
str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
saver.save_csv([total_degree_fit_parameters[:, para_ind].tolist()],
output_directory, "total_degree_" +
str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
# Correlational : statistical distribution as illustrated by box plot for RT, CL, CRT parameters
percentiles = [0, 1, 5, 10, 20]
for channel_name_iter in [["#kubuntu-devel"], ["#ubuntu-devel"], ["#kubuntu"]]:
for cutoff in percentiles:
conv_len_curve_fit_parameters = np.zeros((12, 4))
resp_time_curve_fit_parameters = np.zeros((12, 4))
conv_ref_time_curve_fit_parameters = np.zeros((12, 5))
for month in range(1, 13):
log_data = reader.linux_input(log_directory, channel_name_iter, "2013-"+str(month)+"-1", "2013-"+str(month)+"-31")
nicks, nick_same_list = nickTracker.nick_tracker(log_data)
default_cutoff = config.CUTOFF_PERCENTILE
config.CUTOFF_PERCENTILE = cutoff
truncated_rt, rt_cutoff_time = channel.response_time(log_data, nicks, nick_same_list, config.CUTOFF_PERCENTILE)
conv_len, conv_ref_time = channel.conv_len_conv_refr_time(log_data, nicks, nick_same_list, rt_cutoff_time, config.CUTOFF_PERCENTILE)
conv_len_curve_fit_parameters[month-1] = vis.exponential_curve_fit_and_plot(conv_len, output_directory, "conv_len_cutoff" + str(cutoff))
resp_time_curve_fit_parameters[month-1] = vis.exponential_curve_fit_and_plot(truncated_rt, output_directory, "resp_time_cutoff" + str(cutoff))
conv_ref_time_curve_fit_parameters[month-1] = vis.exponential_curve_fit_and_plot_x_shifted(conv_ref_time, output_directory, "conv_ref_time_cutoff" + str(cutoff))
parameters = ['a', 'b', 'c']
for para_ind in range(len(parameters)):
vis.box_plot(conv_len_curve_fit_parameters[:, para_ind], output_directory, "conv_len_"+str(parameters[para_ind])+"_2013_"+channel_name_iter[0]+"_cut_"+str(cutoff))
vis.box_plot(resp_time_curve_fit_parameters[:, para_ind], output_directory, "resp_time_"+str(parameters[para_ind])+"_2013_"+channel_name_iter[0]+"_cut_"+str(cutoff))
vis.box_plot(conv_ref_time_curve_fit_parameters[:, para_ind], output_directory, "conv_refr_"+str(parameters[para_ind])+"_2013_"+channel_name_iter[0]+"_cut_"+str(cutoff))
saver.save_csv([conv_len_curve_fit_parameters[:, para_ind].tolist()], output_directory, "conv_len_"+str(parameters[para_ind])+"_2013_"+channel_name_iter[0]+"_cut_"+str(cutoff))
saver.save_csv([resp_time_curve_fit_parameters[:, para_ind].tolist()], output_directory, "resp_time_"+str(parameters[para_ind])+"_2013_"+channel_name_iter[0]+"_cut_"+str(cutoff))
saver.save_csv([conv_ref_time_curve_fit_parameters[:, para_ind].tolist()], output_directory, "conv_refr_"+str(parameters[para_ind])+"_2013_"+channel_name_iter[0]+"_cut_"+str(cutoff))
config.CUTOFF_PERCENTILE = default_cutoff
def box_plot_for_degree(log_directory, output_directory, channel_name,
start_date, end_date):
"""
Correlational : statistical distribution of curve fit parameters generated for degree distribution. The function
takes the given time duration and selects one month at a time for generation of a degree distribution sample. Each
degree distribution sample shall have 3 curve fit parameters namely slope, intercept & r_square. The function collects these parameters
for all the months of the given time duration. The function produces box plot separately for each parameter.
Args:
log_directory(str): path to the location of Logs
output_directory(str): path to the location where the results are to be stored
channel_name(list): channels for which the analysis is to be done.
start_date(datetime): starting date for the logs to be analysed. This has to be the beginning of the month.
end_date(datetime): ending date for which the logs are to be analysed. This has to be the end of the month.
Returns:
null
"""
start_date = start_date.strptime('%Y-%m-%d')
end_date = end_date.strptime('%Y-%m-%d')
cutoff = 0
for channel_name_iter in channel_name:
out_degree_fit_parameters = np.zeros((12, 4))
in_degree_fit_parameters = np.zeros((12, 4))
total_degree_fit_parameters = np.zeros((12, 4))
for dt in rrule(MONTHLY, dtstart=start_date, until=end_date):
last_day_of_the_month = dt + relativedelta(
months=1) - datetime.timedelta(days=1)
# for month in range(1, 13):
log_data = reader.linux_input(
log_directory, channel_name_iter, dt.strftime("%Y-%m-%d"),
last_day_of_the_month.strftime("%Y-%m-%d"))
nicks, nick_same_list = nickTracker.nick_tracker(log_data)
message_number_graph = network.message_number_graph(
log_data, nicks, nick_same_list, False)
degree_anal_message_number = network.degree_analysis_on_graph(
message_number_graph)
out_degree_fit_parameters[dt.month - 1] = vis.generate_log_plots(
degree_anal_message_number["out_degree"]["raw_for_vis"],
output_directory, channel_name_iter[0])
in_degree_fit_parameters[dt.month - 1] = vis.generate_log_plots(
degree_anal_message_number["in_degree"]["raw_for_vis"],
output_directory, channel_name_iter[0])
total_degree_fit_parameters[dt.month - 1] = vis.generate_log_plots(
degree_anal_message_number["total_degree"]["raw_for_vis"],
output_directory, channel_name_iter[0])
parameters = ['slope', 'intercept', 'r_square']
for para_ind in range(len(parameters)):
vis.box_plot(
out_degree_fit_parameters[:, para_ind], output_directory,
"out_degree_" + str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
vis.box_plot(
in_degree_fit_parameters[:, para_ind], output_directory,
"in_degree_" + str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
vis.box_plot(
total_degree_fit_parameters[:, para_ind], output_directory,
"total_degree_" + str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
saver.save_csv([out_degree_fit_parameters[:, para_ind].tolist()],
output_directory, "out_degree_" +
str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
saver.save_csv([in_degree_fit_parameters[:, para_ind].tolist()],
output_directory, "in_degree_" +
str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
saver.save_csv([total_degree_fit_parameters[:, para_ind].tolist()],
output_directory, "total_degree_" +
str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
def correlational_CL_RT_CRT(log_directory, output_directory, start_date,
end_date):
"""
Correlational : statistical distribution as illustrated by box plot for RT, CL, CRT parameters. The function
takes the given time duration and selects one month at a time for generation of a degree distribution sample. Each
degree distribution sample shall have 3 curve fit parameters namely a,b & c. The function collects these parameters
for all the months of the given time duration. The function produces box plot separately for each parameter.
Args:
log_directory(str): path to the location of Logs
output_directory(str): path to the location where the results are to be stored
channel_name(list): channels for which the analysis is to be done
start_date(datetime): starting date for the logs to be analysed. This has to be the beginning of the month.
end_date(datetime): ending date for which the logs are to be analysed. This has to be the end of the month.
Returns:
null
"""
start_date = start_date.strptime('%Y-%m-%d')
end_date = end_date.strptime('%Y-%m-%d')
percentiles = [0, 1, 5, 10, 20]
for channel_name_iter in [["#kubuntu-devel"], ["#ubuntu-devel"],
["#kubuntu"]]:
for cutoff in percentiles:
conv_len_curve_fit_parameters = np.zeros((12, 4))
resp_time_curve_fit_parameters = np.zeros((12, 4))
conv_ref_time_curve_fit_parameters = np.zeros((12, 5))
for dt in rrule(MONTHLY, dtstart=start_date, until=end_date):
last_day_of_the_month = dt + relativedelta(
months=1) - datetime.timedelta(days=1)
log_data = reader.linux_input(
log_directory, channel_name_iter, dt.strftime("%Y-%m-%d"),
last_day_of_the_month.strftime("%Y-%m-%d"))
nicks, nick_same_list = nickTracker.nick_tracker(log_data)
default_cutoff = config.CUTOFF_PERCENTILE
config.CUTOFF_PERCENTILE = cutoff
truncated_rt, rt_cutoff_time = channel.response_time(
log_data, nicks, nick_same_list, config.CUTOFF_PERCENTILE)
conv_len, conv_ref_time = channel.conv_len_conv_refr_time(
log_data, nicks, nick_same_list, rt_cutoff_time,
config.CUTOFF_PERCENTILE)
conv_len_curve_fit_parameters[
dt.month - 1] = vis.exponential_curve_fit_and_plot(
conv_len, output_directory,
"conv_len_cutoff" + str(cutoff))
resp_time_curve_fit_parameters[
dt.month - 1] = vis.exponential_curve_fit_and_plot(
truncated_rt, output_directory,
"resp_time_cutoff" + str(cutoff))
conv_ref_time_curve_fit_parameters[
dt.month -
1] = vis.exponential_curve_fit_and_plot_x_shifted(
conv_ref_time, output_directory,
"conv_ref_time_cutoff" + str(cutoff))
parameters = ['a', 'b', 'c']
for para_ind in range(len(parameters)):
vis.box_plot(
conv_len_curve_fit_parameters[:, para_ind],
output_directory, "conv_len_" + str(parameters[para_ind]) +
"_2013_" + channel_name_iter[0] + "_cut_" + str(cutoff))
vis.box_plot(
resp_time_curve_fit_parameters[:,
para_ind], output_directory,
"resp_time_" + str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
vis.box_plot(
conv_ref_time_curve_fit_parameters[:, para_ind],
output_directory,
"conv_refr_" + str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
saver.save_csv(
[conv_len_curve_fit_parameters[:, para_ind].tolist()],
output_directory, "conv_len_" + str(parameters[para_ind]) +
"_2013_" + channel_name_iter[0] + "_cut_" + str(cutoff))
saver.save_csv(
[resp_time_curve_fit_parameters[:, para_ind].tolist()],
output_directory,
"resp_time_" + str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
saver.save_csv(
[conv_ref_time_curve_fit_parameters[:, para_ind].tolist()],
output_directory,
"conv_refr_" + str(parameters[para_ind]) + "_2013_" +
channel_name_iter[0] + "_cut_" + str(cutoff))
config.CUTOFF_PERCENTILE = default_cutoff
def codelengths(log_directory, output_directory, channel_name, start_date,
end_date):
"""
The function iterate through the months in the given date range and computes the infomap number. It then plots a
box plot for the infomap numbers of all the whole months in the given time period.
Args:
log_directory(str): path to the location of Logs
output_directory(str): path to the location where the results are to be stored
channel_name(list): channels for which the analysis is to be done
start_date(datetime): starting date for the logs to be analysed. This has to be the beginning of the month.
end_date(datetime): ending date for which the logs are to be analysed. This has to be the end of the month.
Returns:
null
"""
start_date = start_date.strptime('%Y-%m-%d')
end_date = end_date.strptime('%Y-%m-%d')
codelengths = []
for dt in rrule(MONTHLY, dtstart=start_date, until=end_date):
last_day_of_the_month1 = dt + relativedelta(
months=1) - datetime.timedelta(days=1)
log_data_m1 = reader.linux_input(
log_directory, channel_name, dt.strftime("%Y-%m-%d"),
last_day_of_the_month1.strftime("%Y-%m-%d"))
nicks_m1, nick_same_list_m1 = nickTracker.nick_tracker(log_data_m1)
message_number_graph_m1 = network.message_number_graph(
log_data_m1, nicks_m1, nick_same_list_m1, False)
try:
#FOS is a reserved word in igraph and if 'fos' is a username in the nx graph, it generates an error
saver.save_net_nx_graph(message_number_graph_m1, output_directory,
"message-exchange-" + str(dt.month))
msg_igraph, msg_community = community.infomap_igraph(
ig_graph=None,
net_file_location=output_directory + "message-exchange-" +
str(dt.month) + '.net')
codelengths.append(msg_community.codelength)
except:
node_labels = message_number_graph_m1.nodes()
labels = {}
for label in node_labels:
if label == "fos":
labels[label] = "fos_"
else:
labels[label] = label
message_number_graph_m1 = nx.relabel_nodes(message_number_graph_m1,
labels)
saver.save_net_nx_graph(message_number_graph_m1, output_directory,
"message-exchange-" + str(dt.month))
print "error in", dt.month
msg_igraph, msg_community = community.infomap_igraph(
ig_graph=None,
net_file_location=output_directory + "message-exchange-" +
str(dt.month) + '.net')
codelengths.append(msg_community.codelength)
vis.box_plot(codelengths, output_directory, "codelengths2013")
saver.save_csv([codelengths], output_directory, "codelengths2013")
