Source code for lib.analysis.channel
import re
import numpy
from networkx.algorithms.components.connected import connected_components
import util
import config
[docs]def conv_len_conv_refr_time(log_dict, nicks, nick_same_list):
""" Calculates the conversation length (CL) that is the length of time for which two users communicate
i.e. if a message is not replied to within Response Time(RT),
then it is considered as a part of another conversation.
This function also calculates the conversation refresh time(CRT)
For a pair of users, this is the time when one conversation ends and another one starts.
Args:
log_dict (str): Dictionary of logs data created using reader.py
nicks(List) : list of nickname created using nickTracker.py
nick_same_list :List of same_nick names created using nickTracker.py
Returns:
row_cl(zip List): Conversation Length
row_crt(zip List) :Conversation Refresh time
"""
conv = []
conv_diff = []
G = util.to_graph(nick_same_list)
conn_comp_list = list(connected_components(G))
util.create_connected_nick_list(conn_comp_list)
# We use connected components algorithm to group all those nick clusters that have atleast one nick common in their clusters. So e.g.
#Cluster 1- nick1,nick2,nick3,nick4(some nicks of a user) #Cluster 2 -nick5,nick6,nick2,nick7. Then we would get - nick1,nick2,nick3,nick4,nick5,nick6,nick7 and we can safely assume they belong to the same user.
conversations=[[] for i in range(config.MAX_CONVERSATIONS)] #This might need to be incremented from 10000 if we have more users. Same logic as the above 7000 one. Applies to all the other codes too.
## I would advice on using a different data structure which does not have an upper bound like we do in arrays.
graphx1 =[]
graphy1 =[]
graphx2 =[]
graphy2 =[]
dateadd = -1 #Variable used for response time calculation. Varies from 0-365.
for day_content_all_channels in log_dict.values():
for day_content in day_content_all_channels:
day_log = day_content["log_data"]
dateadd = dateadd + 1
send_time = [] #list of all the times a user sends a message to another user
#code for making relation map between clients
for line in day_log:
flag_comma = 0
if(util.check_if_msg_line (line)):
nick_sender = ""
nick_receiver = ""
m = re.search(r"\<(.*?)\>", line)
nick_to_search = util.correctLastCharCR(m.group(0)[1:-1])
for d in range(len(nicks)): #E.g. if names are rohan1,rohan2,rohan3...,then var will store rohan1.
if((d < len(conn_comp_list)) and (nick_to_search in conn_comp_list[d])):
nick_sender = conn_comp_list[d][0]
break
for nick in nicks:
rec_list = [e.strip() for e in line.split(':')]
util.rec_list_splice(rec_list)
if not rec_list[1]:
break
rec_list = util.correct_last_char_list(rec_list)
for names in rec_list:
if(names == nick):
send_time.append(line[1:6])
if(nick_to_search != nick):
for d in range(len(nicks)):
if((d < len(conn_comp_list)) and (nick in conn_comp_list[d])):
nick_receiver = conn_comp_list[d][0]
break
for i in range(config.MAX_CONVERSATIONS):
if (nick_sender in conversations[i] and nick_receiver in conversations[i]):
conversations[i].append(config.HOURS_PER_DAY*config.MINS_PER_HOUR*dateadd + int(line[1:6][0:2])*config.MINS_PER_HOUR+int(line[1:6][3:5])) # We add response times in conversations for every conversation
break #between userA and userB. If they havent already conversed
if(len(conversations[i]) == 0): #before than add time at a new array index and later append to it.
conversations[i].append(nick_sender)
conversations[i].append(nick_receiver)
conversations[i].append(config.HOURS_PER_DAY*config.MINS_PER_HOUR*dateadd + int(line[1:6][0:2])*config.MINS_PER_HOUR+int(line[1:6][3:5]))
break
if "," in rec_list[1]:
flag_comma = 1
rec_list_2 = [e.strip() for e in rec_list[1].split(',')]
rec_list_2 = util.correct_last_char_list(rec_list_2)
for names in rec_list_2:
if(names == nick):
send_time.append(line[1:6])
if(nick_to_search != nick):
for d in range(len(nicks)):
if((d < len(conn_comp_list)) and (nick in conn_comp_list[d])): #Lines 212-255 consider all cases in which messages are addressed such as - nick1:nick2 or nick1,nick2,
nick_receiver = conn_comp_list[d][0] #or nick1,nick2:
break
for i in range(config.MAX_CONVERSATIONS):
if (nick_sender in conversations[i] and nick_receiver in conversations[i]):
conversations[i].append(config.HOURS_PER_DAY*config.MINS_PER_HOUR*dateadd + int(line[1:6][0:2])*config.MINS_PER_HOUR+int(line[1:6][3:5]))
break
if(len(conversations[i]) == 0):
conversations[i].append(nick_sender)
conversations[i].append(nick_receiver)
conversations[i].append(config.HOURS_PER_DAY*config.MINS_PER_HOUR*dateadd + int(line[1:6][0:2])*config.MINS_PER_HOUR+int(line[1:6][3:5]))
break
if(flag_comma == 0):
rec = util.correctLastCharCR(line[line.find(">") + 1:line.find(", ")][1:])
if(rec == nick):
send_time.append(line[1:6])
if(nick_to_search != nick):
for d in range(len(nicks)):
if ((d < len(conn_comp_list)) and (nick in conn_comp_list[d])):
nick_receiver = conn_comp_list[d][0]
break
for i in range(config.MAX_CONVERSATIONS):
if (nick_sender in conversations[i] and nick_receiver in conversations[i]):
conversations[i].append(config.HOURS_PER_DAY*config.MINS_PER_HOUR*dateadd + int(line[1:6][0:2])*config.MINS_PER_HOUR + int(line[1:6][3:5]))
break
if(len(conversations[i]) == 0):
conversations[i].append(nick_sender)
conversations[i].append(nick_receiver)
conversations[i].append(config.HOURS_PER_DAY*config.MINS_PER_HOUR*dateadd + int(line[1:6][0:2])*config.MINS_PER_HOUR + int(line[1:6][3:5]))
break
#Lines 212-290 consider all cases in which messages are addressed as - (nick1:nick2 or nick1,nick2 or nick1,nick2:) and stores their response times in conversations. conversations[i] contains all the response times between userA and userB throughout an entire year.
for i in range(len(conversations)): #Lines 295-297 remove the first two elements from every conversations[i] as they are the UIDS of sender and receiver respectively(and not RTs)
if(len(conversations[i]) != 0): # response times are calculated starting from index 2. So now we have all the response times in conversations.
del conversations[i][0:2]
for i in range(len(conversations)):
if(len(conversations[i]) != 0):
first = conversations[i][0]
for j in range(1, len(conversations[i])):
if(conversations[i][j]-conversations[i][j-1] > 9):
conv.append(conversations[i][j-1] - first) #We are recording the conversation length in conv and CRT in conv_diff. Here 9 is the average response
#time we have already found before(see parser-RT.py). For every channel this value differs and would have to be changed in the code.
conv_diff.append(conversations[i][j] - conversations[i][j-1])
first = conversations[i][j]
if(j == (len(conversations[i]) - 1)):
conv.append(conversations[i][j] - first)
break
for i in range(max(conv)):
graphx1.append(i)
graphy1.append(conv.count(i))
for i in range(max(conv_diff)):
graphx2.append(i)
graphy2.append(conv_diff.count(i))
#To plot CDF we store the CL and CRT values and their number of occurences as shown above.
row_cl = zip(graphx1, graphy1)
row_crt = zip(graphx2, graphy2)
return row_cl, row_crt
[docs]def response_time(log_dict, nicks, nick_same_list):
""" finds the response time of a message
i.e. the best guess for the time at which one can expect a reply for his/her message.
Args:
log_dict (str): Dictionary of logs data created using reader.py
nicks(List) : List of nickname created using nickTracker.py
nick_same_list :List of same_nick names created using nickTracker.py
output_directory (str): Location of output directory
Returns:
rows_RT(zip List): Response Time (This refers to the response
time of a message i.e. the best guess for the time at
which one can expect a reply for his/her message)
"""
G = util.to_graph(nick_same_list)
conn_comp_list = list(connected_components(G))
util.create_connected_nick_list(conn_comp_list)
graph_cumulative = []
graph_x_axis = []
graph_y_axis = []
for day_content_all_channels in log_dict.values():
for day_content in day_content_all_channels:
day_log = day_content["log_data"]
send_time = [] #list of all the times a user sends a message to another user
meanstd_list = []
totalmeanstd_list = []
x_axis = []
y_axis = []
real_y_axis = []
conversations = [[] for i in range(config.MAX_RESPONSE_CONVERSATIONS)]
#code for making relation map between clients
for line in day_log:
flag_comma = 0
if(util.check_if_msg_line (line)):
nick_sender = ""
nick_receiver = ""
m = re.search(r"\<(.*?)\>", line)
nick_to_search = util.correctLastCharCR(m.group(0)[1:-1])
for d in range(len(nicks)):
if((d < len(conn_comp_list)) and (nick_to_search in conn_comp_list[d])):
nick_sender = conn_comp_list[d][0]
break
for nick in nicks:
rec_list = [e.strip() for e in line.split(':')]
util.rec_list_splice(rec_list)
if not rec_list[1]:
break
rec_list = util.correct_last_char_list(rec_list)
for name in rec_list:
if(name == nick):
send_time.append(line[1:6])
if(nick_to_search != nick):
for d in range(len(nicks)):
if((d < len(conn_comp_list)) and (nick in conn_comp_list[d])):
nick_receiver = conn_comp_list[d][0]
break
for i in range(config.MAX_RESPONSE_CONVERSATIONS):
if (nick_sender in conversations[i] and nick_receiver in conversations[i]):
conversations[i].append(line[1:6])
break
if(len(conversations[i]) == 0):
conversations[i].append(nick_sender)
conversations[i].append(nick_receiver)
conversations[i].append(line[1:6])
break
if "," in rec_list[1]:
flag_comma = 1
rec_list_2 = [e.strip() for e in rec_list[1].split(',')]
rec_list_2 = util.correct_last_char_list(rec_list_2)
for name in rec_list_2:
if(name == nick):
send_time.append(line[1:6])
if(nick_to_search != nick):
for d in range(len(nicks)):
if((d < len(conn_comp_list)) and (nick in conn_comp_list[d])):
nick_receiver = conn_comp_list[d][0]
break
for i in range(config.MAX_RESPONSE_CONVERSATIONS):
if (nick_sender in conversations[i] and nick_receiver in conversations[i]):
conversations[i].append(line[1:6])
break
if(len(conversations[i]) == 0):
conversations[i].append(nick_sender)
conversations[i].append(nick_receiver)
conversations[i].append(line[1:6])
break
if(flag_comma == 0):
rec = util.correctLastCharCR(line[line.find(">") + 1:line.find(", ")][1:])
if(rec == nick):
send_time.append(line[1:6])
if(nick_to_search != nick):
for d in range(len(nicks)):
if ((d < len(conn_comp_list)) and (nick in conn_comp_list[d])):
nick_receiver = conn_comp_list[d][0]
break
for i in range(config.MAX_RESPONSE_CONVERSATIONS):
if (nick_sender in conversations[i] and nick_receiver in conversations[i]):
conversations[i].append(line[1:6])
break
if(len(conversations[i]) == 0):
conversations[i].append(nick_sender)
conversations[i].append(nick_receiver)
conversations[i].append(line[1:6])
break
for i in range(config.MAX_RESPONSE_CONVERSATIONS):
if(len(conversations[i]) != 0):
for j in range(2, len(conversations[i]) - 1):
conversations[i][j]=(int(conversations[i][j+1][0:2])*config.MINS_PER_HOUR+int(conversations[i][j+1][3:5])) - (int(conversations[i][j][0:2])*config.MINS_PER_HOUR+int(conversations[i][j][3:5]))
for i in range(config.MAX_RESPONSE_CONVERSATIONS):
if(len(conversations[i]) != 0):
if(len(conversations[i]) == 3):
conversations[i][2] = int(conversations[i][2][0:2])*config.MINS_PER_HOUR+int(conversations[i][2][3:5])
else:
del conversations[i][-1]
#Explanation provided in parser-CL+CRT.py
for i in range(config.MAX_RESPONSE_CONVERSATIONS):
if(len(conversations[i]) != 0):
for j in range(2, len(conversations[i])):
totalmeanstd_list.append(conversations[i][j])
if(len(totalmeanstd_list) != 0):
for i in range(max(totalmeanstd_list) + 1):
x_axis.append(i)
for i in x_axis:
y_axis.append(float(totalmeanstd_list.count(i)) / float(len(totalmeanstd_list)))
#finding the probability of each RT to occur=No. of occurence/total occurences.
real_y_axis.append(y_axis[0])
for i in range(len(y_axis)):
real_y_axis.append(float(real_y_axis[i-1]) + float(y_axis[i]))
#to find cumulative just go on adding the current value to previously cumulated value till sum becomes 1 for last entry.
for i in range(len(totalmeanstd_list)):
graph_cumulative.append(totalmeanstd_list[i])
if len(totalmeanstd_list) > 0:
totalmeanstd_list.append(numpy.mean(totalmeanstd_list))
totalmeanstd_list.append(numpy.mean(totalmeanstd_list)+2*numpy.std(totalmeanstd_list))
for i in range(config.MAX_RESPONSE_CONVERSATIONS):
if(len(conversations[i]) != 0):
for j in range(2, len(conversations[i])):
meanstd_list.append(conversations[i][j])
conversations[i].append(numpy.mean(meanstd_list))
conversations[i].append(numpy.mean(meanstd_list)+(2*numpy.std(meanstd_list)))
meanstd_list[:] = []
graph_cumulative.sort()
for i in range(graph_cumulative[len(graph_cumulative)-1] + 1):
graph_y_axis.append(graph_cumulative.count(i)) # problem when ti=0 count is unexpectedly large
graph_x_axis.append(i)
#Finally storing the RT values along with their frequencies in a csv file.
rows_rt = zip(graph_x_axis, graph_y_axis)
return rows_rt