Ejemplo n.º 1
0
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()
Ejemplo n.º 2
0
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   
Ejemplo n.º 3
0
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
Ejemplo n.º 4
0
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
Ejemplo n.º 5
0
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   
Ejemplo n.º 6
0
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