def graphDB(self,graphName):
print self.dbFile
csv_reader = csv.reader(open(self.dbFile,'r'))
bigx = float(-sys.maxint -1)
bigy = float(-sys.maxint -1)
smallx = float(sys.maxint)
smally = float(sys.maxint)
verts = []
for row in csv_reader:
print row
verts.append(row)
x_arr = []
y_arr = []
for vert in verts:
date = vert[0].split(':')
print float(date[0])+(float(date[1])/60)
x_arr.append(float(date[0])+(float(date[1])/60))
y_arr.append(vert[1])
# dates.date2num(x_arr)
fig = plt.figure()
ax = fig.add_axes([0.1,0.1,0.8,0.8])
ax.set_xlabel('Time (hh:mm:ss)')
ax.set_ylabel('Decibels (db)')
#ax.set_xlim()
ax.set_ylim(30,80)
ax.plot(x_arr,y_arr)
plt.show()
plt.savefig(graphName)
def from_file_home_outside_calls():
file1 = "/home/sscepano/D4D res/ORGANIZED/SET3/Clustering/usr res/usr_home_calls.tsv"
file2 = "/home/sscepano/D4D res/ORGANIZED/SET3/Clustering/usr res/usr_outside_calls.tsv"
usr_home_c = n.zeros(500001)
usr_outside_c = n.zeros(500001)
i = 0
f1 = open(file1, 'r')
f2 = open(file2, 'r')
# read the file1
for line in f1:
i = i + 1
u, home_c = line.split('\t')
home_c = float(home_c)
u = int(u)
usr_home_c[u] = home_c
# read the file
for line in f2:
i = i + 1
u, outside_c = line.split('\t')
outside_c = float(outside_c)
u = int(u)
usr_outside_c[u] = outside_c
############################################################################################################################
# THIS is to plot pdf of home calls
############################################################################################################################
fig1 = plt.figure(1)
ax = fig1.add_subplot(211)
nn, bins, rectangles = ax.hist(usr_home_c, 100, normed=True)
#plt.plot(nc_distr_pct, 'ro', linewidth=0.5, label= 'pdf Num of calls')
plt.xlabel('NcH, number of calls from the home subprefecture')
plt.ylabel('P(NcH)')
plt.legend()
# # this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale('log')
plt.xscale('log')
# figure_name = "/home/sscepano/D4D res/allstuff/rg/pdf rg loglog.png"
#this is a regular plot file, then comment the previous loglog block
#figure_name = "/home/sscepano/D4D res/allstuff/rg/pdf home calls.png"
#print(figure_name)
#plt.savefig(figure_name, format = "png")
############################################################################################################################
# THIS is to plot pdf of outside calls
############################################################################################################################
#fig1 = plt.figure(1)
ax = fig1.add_subplot(212)
nn, bins, rectangles = ax.hist(usr_outside_c, 100, normed=True)
#plt.plot(nc_distr_pct, 'ro', linewidth=0.5, label= 'pdf Num of calls')
plt.xlabel('NcO, number of calls from outside the home subprefecture')
plt.ylabel('P(NcO)')
plt.legend()
# # this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale('log')
plt.xscale('log')
figure_name = "/home/sscepano/D4D res/allstuff/rg/pdf home outside calls.png"
# #this is a regular plot file, then comment the previous loglog block
# figure_name = "/home/sscepano/D4D res/allstuff/rg/pdf home outside calls.png"
print(figure_name)
plt.savefig(figure_name, format = "png")
return
# invoke the function for plotting number of calls and frequency probability distribution (percents of users)
#from_file_home_outside_calls()
def from_fq_files_hist_pdf():
file_name= "/home/sscepano/D4D res/ORGANIZED/SET3/Distr of Num and Fq of Calls/new results -- check the same/Users_and_their_total_calls_number.tsv"
file_name2 = "/home/sscepano/D4D res/ORGANIZED/SET3/Distr of Num and Fq of Calls/new results -- check the same/Users_and_their_calling_fq.tsv"
# here we store the num of calls made by a user
usr_and_his_num_calls = n.zeros(500001, dtype=n.int)
nc_distr = n.zeros(max_num_calls, dtype=n.int)
# a loop where we populate those two arrays from the file
i = 0
f = open(file_name, 'r')
# read the file
for line in f:
i = i + 1
u, nc = line.split('\t')
nc = int(nc)
u = int(u)
usr_and_his_num_calls[u] = nc
nc_distr[nc] += 1
mi = min(usr_and_his_num_calls)
mx = max(usr_and_his_num_calls)
print("Minimum number of calls ", mi)
print("Maximum number of calls ", mx)
total_u = float(sum(nc_distr))
print("Total users found ", total_u)
############################################################################################################################
# THIS is to plot number of users pdf
############################################################################################################################
# fig1 = plt.figure(1)
# ax = fig1.add_subplot(111)
# nn, bins, rectangles = ax.hist(usr_and_his_num_calls, 100, normed=True)
#
# plt.xlabel('N, num of calls')
# plt.ylabel('P(N)')
# plt.legend()
#
# # this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
# plt.yscale('log')
# plt.xscale('log')
# figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/hist of num of calls loglog.png"
#
## #this is a regular plot file, then comment the previous loglog block
## figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/hist of num of calls.png"
#
# print(figure_name)
# plt.savefig(figure_name, format = "png")
#
# plt.clf()
###############################################################################################################################
# THIS is to plot fq pdf
###############################################################################################################################
# here we store the num of calls made by a user
usr_and_his_fq = n.zeros(500001)
# a loop where we populate those two arrays from the file
i = 0
f2 = open(file_name2, 'r')
# read the file
for line in f2:
i = i + 1
u, fq = line.split('\t')
fq = float(fq)
u = int(u)
usr_and_his_fq[u] = fq
mi = min(usr_and_his_fq)
mx = max(usr_and_his_fq)
print("Minimum fq of calls ", mi)
print("Maximum fq of calls ", mx)
fig2 = plt.figure(2)
ax = fig2.add_subplot(111)
nn, bins, rectangles = ax.hist(usr_and_his_fq, 100, normed=True)
plt.xlabel('fq of calls')
plt.ylabel('P(fq)')
plt.legend()
figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/hist of fq of calls.png"
# # this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
# plt.yscale('log')
# plt.xscale('log')
# figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/hist of fq of calls loglog.png"
print(figure_name)
plt.savefig(figure_name, format = "png")
return
def from_file_num_calls(file_name):
# here we store the num of calls made by a user
usr_and_his_num_calls = n.zeros(500001, dtype=n.int)
# here we store the fq of calls made by a user, but we calculate it from the num_calls
fq_distr = n.zeros(max_num_calls)
# from the previous array obtained by counting users who made the same total number of calls
nc_distr = n.zeros(max_num_calls, dtype=n.int)
# here we just save percents of users
nc_distr_pct = n.zeros(max_num_calls)
# a loop where we populate those two arrays from the file
i = 0
f = open(file_name, 'r')
# read the file
for line in f:
i = i + 1
u, nc = line.split('\t')
nc = int(nc)
u = int(u)
usr_and_his_num_calls[u] = nc
nc_distr[nc] += 1
mi = min(usr_and_his_num_calls)
mx = max(usr_and_his_num_calls)
print("Minimum number of calls ", mi)
print("Maximum number of calls ", mx)
total_u = float(sum(nc_distr))
print("Total users found ", total_u)
# test_file_out = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/Obtained_num_calls_and_its_pct.tsv"
# fto = open(test_file_out,"w")
for j in range(0, max_num_calls):
nc_distr_pct[j] = (nc_distr[j] / total_u)
# fto.write(str(j) + '\t' + str(nc_distr_pct[j]) + '\n')
# # I was just checking that the total percents sums up to 100 and they do but it looked funny as we have so small values
# total = 0
# for i in range (max_num_calls):
# total += percent_users[i]
# print ("Check ", total)
############################################################################################################################
# THIS is to plot number of users pdf
############################################################################################################################
plt.figure(1)
plt.plot(nc_distr_pct, 'r', linewidth=0.5, label= 'distribution of N')
plt.xlabel('N, num of calls')
plt.ylabel('% Users')
plt.legend()
# # this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale('log')
plt.xscale('log')
# figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/SET3 distr of num of calls loglog.png"
#this is a regular plot file, then comment the previous loglog block
figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/distr of num of calls2.png"
print(figure_name)
plt.savefig(figure_name, format = "png", pdi=300)
#plt.show()
###############################################################################################################################
# THIS is to plot fq pdf
###############################################################################################################################
plt.figure(2)
# fq = []
#
# for j in range(max_num_calls):
# fq.append( float(j / 3360.0))
#
# ffq = []
#
# for j in range(max_num_calls):
# ffq.append(nc_distr_pct[j])
# for j in range(0, max_num_calls):
# nc_distr_pct[j] = (nc_distr[j] / total_u)
fq = []
for j in range(max_num_calls):
fq.append( float(j / 3360.0))
ffq = []
for j in range(max_num_calls):
ffq.append(nc_distr_pct[j])
# test_file_out2 = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/Calculated_fq_calls_and_its_pct2.tsv"
# fto2 = open(test_file_out2,"w")
# for j in range(0, max_num_calls):
# fto2.write(str(fq[j]) + '\t' + str(ffq[j]) + '\n')
# Finally understood here -- when I give two arrays: x, y (at least append values IN ORDER like here) -- pyplot will plot y versus x
plt.plot(fq, ffq, 'g', linewidth=0.3, label= 'distribution of Fq')
plt.xlabel('Fq of calls')
plt.ylabel('% Users')
plt.legend()
# this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale('log')
plt.xscale('log')
figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/distr of fq of calls2.png"
# # this is a regular plot file, then comment the previous loglog block
# figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/SET3 distr of fq of calls.png"
print(figure_name)
plt.savefig(figure_name, format = "png")
return
def save_to_plot(avg_usr_traj):
nits = []
its = []
# a loop where we populate those two arrays from the file
i = 0
# f = open(file_name, 'r')
# # read the file
for usr in range(500001):
i = i + 1
nits.append(int(avg_usr_traj[usr]))
its.append(usr)
mi = min(nits)
mx = max(nits)
print("Minimum radius of gyr ", mi)
print("Maximum radius of gyr ", mx)
total_nit = float(sum(nits))
print("Total radius of gyr ", total_nit)
pdf_nits = defaultdict(int)
for j in range(0, len(nits)):
pdf_nits[nits[j]] += 1
ordered = OrderedDict(sorted(pdf_nits.items(), key=lambda t: t[0]))
nits7s = []
its7s = []
test = 0
for j in ordered.iterkeys():
nits7s.append(ordered[j]/500000.0)
test += ordered[j]/500000.0
its7s.append(j)
print test
############################################################################################################################
# THIS is to plot number of users pdf
############################################################################################################################
plt.figure(7)
plt.plot(its7s, nits7s, 'o', linewidth=0.5, label= 'distribution of Rg')
plt.xlabel('rg [km]')
plt.ylabel('P(rg)')
plt.legend()
# this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale('log')
plt.xscale('log')
figure_name = "/home/sscepano/D4D res/allstuff/traj/avg daily/avg_daily_traj_total.png"
print(figure_name)
plt.savefig(figure_name, format = "png", dpi=300)
return
def from_file_radius_gyr3(file_name, subpref):
users_list = rd.read_in_subpref_users(subpref)
total = float(rd.read_in_subpref_num_users()[subpref])
if total > 0:
nits = []
its = []
# a loop where we populate those two arrays from the file
i = 0
f = open(file_name, 'r')
# read the file
for line in f:
i = i + 1
it, nit = line.split('\t')
nit = float(nit)
it = int(it)
if users_list[it] == 1:
nit = int(nit)
nits.append(nit)
its.append(it)
mi = min(nits)
mx = max(nits)
print("Minimum radius of gyr ", mi)
print("Maximum radius of gyr ", mx)
total_nit = float(sum(nits))
print("Total radius of gyr ", total_nit)
pdf_nits = defaultdict(int)
for j in range(0, len(nits)):
pdf_nits[nits[j]] += 1
ordered = OrderedDict(sorted(pdf_nits.items(), key=lambda t: t[0]))
nits7s = []
its7s = []
test = 0
#total = 500000.0
for j in ordered.iterkeys():
nits7s.append(ordered[j]/total)
test += ordered[j]/total
its7s.append(j)
print test
############################################################################################################################
# THIS is to plot number of users pdf
############################################################################################################################
plt.figure(7)
plt.plot(its7s, nits7s, 'o', linewidth=0.5, label= 'distribution of Rg')
plt.xlabel('rg [km]')
plt.ylabel('P(rg)')
plt.legend()
# this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale('log')
plt.xscale('log')
figure_name = "/home/sscepano/D4D res/allstuff/rg/1/rg_" + str(subpref) + ".png"
print(figure_name)
plt.savefig(figure_name, format = "png", dpi=300)
plt.clf()
return
def from_file_radius_gyr(file_name):
usr_rg = nn.zeros(500001)
# a loop where we populate those two arrays from the file
i = 0
f = open(file_name, 'r')
# read the file
for line in f:
i = i + 1
u, rg = line.split('\t')
rg = float(rg)
u = int(u)
usr_rg[u] = rg
mi = min(usr_rg)
mx = max(usr_rg)
print("Minimum number of calls ", mi)
print("Maximum number of calls ", mx)
# total_u = float(sum(nc_distr))
# print("Total users found ", total_u)
# test_file_out = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/Obtained_num_calls_and_its_pct.tsv"
# fto = open(test_file_out,"w")
# for j in range(0, max_num_calls):
# nc_distr_pct[j] = (nc_distr[j] / total_u) * 100
# fto.write(str(j) + '\t' + str(nc_distr_pct[j]) + '\n')
# # I was just checking that the total percents sums up to 100 and they do but it looked funny as we have so small values
# total = 0
# for i in range (max_num_calls):
# total += percent_users[i]
# print ("Check ", total)
############################################################################################################################
# THIS is to plot number of users pdf
############################################################################################################################
fig1 = plt.figure(1)
ax = fig1.add_subplot(111)
n, bins, rectangles = ax.hist(usr_rg, 100, normed=True)
#plt.plot(nc_distr_pct, 'ro', linewidth=0.5, label= 'pdf Num of calls')
plt.xlabel('rg [km]')
plt.ylabel('P(rg)')
plt.legend()
# # this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale('log')
plt.xscale('log')
figure_name = "/home/sscepano/D4D res/allstuff/rg/pdf rg loglog.png"
# #this is a regular plot file, then comment the previous loglog block
# figure_name = "/home/sscepano/D4D res/allstuff/rg/pdf rg.png"
print(figure_name)
plt.savefig(figure_name, format = "png")
################################################################################################################################
## THIS is to plot fq pdf
################################################################################################################################
#
# plt.figure(2)
#
# fq = []
#
# for j in range(max_num_calls):
# fq.append( float(j / 3360.0))
#
# ffq = []
#
# for j in range(max_num_calls):
# ffq.append(nc_distr_pct[j])
#
#
## test_file_out2 = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/Calculated_fq_calls_and_its_pct2.tsv"
## fto2 = open(test_file_out2,"w")
## for j in range(0, max_num_calls):
## fto2.write(str(fq[j]) + '\t' + str(ffq[j]) + '\n')
#
#
# # Finally understood here -- when I give two arrays: x, y (at least append values IN ORDER like here) -- pyplot will plot y versus x
# plt.plot(fq, ffq, 'g.', linewidth=0.3, label= 'pdf fq of calls')
#
# plt.xlabel('fq of calls')
# plt.ylabel('% Users')
# plt.legend()
#
# # this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
# plt.yscale('log')
# plt.xscale('log')
# figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/SET3 distr of fq of calls loglog.png"
#
## # this is a regular plot file, then comment the previous loglog block
## figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/SET3 distr of fq of calls.png"
#
# print(figure_name)
# plt.savefig(figure_name, format = "png")
return
play_game()
#Exercise 11
start_time = time.time()
games = 1000 #number of iterations
result = []
for i in range(games):
result.append(play_game())
end_time = time.time()
print(end_time - start_time)
plt.hist(result) #histogram of 1000 games
plt.savefig('tictactoe_hist.pdf')
plt.show()
#Result:
"""
Player 1 wins more than Player 2, games sometimes end in draw
total amount of time is a few seconds
"""
#Exercise 12
def play_strategic_game():
"""
Player 1 always start middle
Otherwise, both players place random markers
"""
def from_files_usr_movements():
file_name1 = "/home/sscepano/D4D res/allstuff/USER GRAPHS stats/user_number_of_edges_v1.tsv"
file_name2 = "/home/sscepano/D4D res/allstuff/USER GRAPHS stats/user_number_of_nodes_v1.tsv"
file_name3 = "/home/sscepano/D4D res/allstuff/USER GRAPHS stats/user_number_of_displacements_v1.tsv"
nits = []
its = []
# a loop where we populate those two arrays from the file
i = 0
f = open(file_name3, "r")
# read the file
for line in f:
i = i + 1
it, nit = line.split("\t")
nit = int(nit)
it = int(it)
nit = int(nit)
nits.append(nit)
its.append(it)
mi = min(nits)
mx = max(nits)
print ("Minimum # edges ", mi)
print ("Maximum # edges ", mx)
total_nit = float(sum(nits))
print ("Total # edges ", total_nit)
pdf_nits = defaultdict(int)
for j in range(0, len(nits)):
pdf_nits[nits[j]] += 1
ordered = OrderedDict(sorted(pdf_nits.items(), key=lambda t: t[0]))
nits7s = []
its7s = []
test = 0
for j in ordered.iterkeys():
nits7s.append(ordered[j] / 500000.0)
test += ordered[j] / 500000.0
its7s.append(j)
print test
############################################################################################################################
# THIS is to plot number of users pdf
############################################################################################################################
plt.figure(7)
plt.plot(its7s, nits7s, "o", linewidth=0.5, label="distr. of # displacements ")
plt.xlabel("# displacements")
plt.ylabel("P(# displacements)")
plt.legend()
# this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale("log")
plt.xscale("log")
# figure_name1 = "/home/sscepano/D4D res/allstuff/USER GRAPHS stats/usr_num_edges.png"
# figure_name2 = "/home/sscepano/D4D res/allstuff/USER GRAPHS stats/usr_num_nodes.png"
figure_name3 = "/home/sscepano/D4D res/allstuff/USER GRAPHS stats/usr_num_displacements.png"
print (figure_name3)
plt.savefig(figure_name3, format="png", dpi=300)
return