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for_bivariate_analysis.py
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for_bivariate_analysis.py
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#!/usr/bin/env python
'''
Created by Julia Poncela, on Feb. 2016
'''
import datetime as dt
import csv
import pickle
import histogram_bins_increasing
import histograma_gral
import numpy
import networkx as nx
import random
import sys
import datetime as dt
import Herfindahl_index
def main():
path="../Data/95_05NYCgamentdata/"
initial_year=1985
final_year=2005
first_day_history=dt.datetime(1985,1,1)
last_day_history= dt.datetime(2005,12,31)
flag_hist="NO"
flag_network_metrics="NO" # YES or NO
slicing="yearly" #"monthly" # or "yearly"
threshold_neg_tran=0. # fraction of negative dollars out of the total amount of dollars transacted, to consider a link infected between two companies
flag_remove_selfloops="YES"
string_filename_loops=""
if flag_remove_selfloops=="NO":
string_filename_loops="_with_self_loops"
string_filename=""
if flag_network_metrics=="NO":
string_filename="_no_network_metrics"
dict_firm_tuple_zip_code_state = pickle.load(open("../Results/dict_firm_id_zip_code.pickle", 'rb')) # example: 05188210: ('94108', 'CA')
dict_tuple_zips_dist_miles= pickle.load(open("../Results/dict_zip_tuples_dist_miles_from_34Gb.pickle", 'rb')) ### example: (zip1,zip2): distance_miles
list_jobbers_tot = pickle.load(open("../Results/List_jobbers_tot.dat"))
name1="../Results/Time_evol_num_transactions_"+slicing+"_slicing.dat"
file1= open(name1, 'wt')
file1.close()
name2="../Results/Time_evol_num_active_firms_"+slicing+"_slicing.dat"
file2= open(name2, 'wt')
file2.close()
name3="../Results/Time_evol_num_neg_transactions_"+slicing+"_slicing.dat"
file3= open(name3, 'wt')
file3.close()
name4="../Results/Time_evol_num_self_transactions_"+slicing+"_slicing.dat"
file4= open(name4, 'wt')
file4.close()
name5="../Results/Time_evol_num_self_neg_transactions_"+slicing+"_slicing.dat"
file5= open(name5, 'wt')
file5.close()
name7="../Results/Time_evol_tot_num_infected_links_nodes_GC_"+slicing+"_slicing.dat"
file7= open(name7, 'wt')
file7.close()
name8="../Results/Time_evol_tot_num_infected_links_nodes_GC_with_neg_transact_larger_than"+str(threshold_neg_tran)+"_"+slicing+"_slicing.dat"
file8= open(name8, 'wt')
file8.close()
name6="../Results/Scatter_plot_Pinf_vs_mulitiple_variables_"+slicing+"_slicing"+string_filename+"_for_doublechecking.dat"
file6= open(name6, 'wt')
# header for the file:
print >> file6, "P_inf", "Mi", "Cj", "Mi_Cj", "adj_gross_ij", "cumulat_adj_gross_ij", "pos_adj_gross_ij", "neg_adj_gross_ij", "cumulat_pos_adj_gross_ij", "cumulat_neg_adj_gross_ij", "pos_adj_gross_i", "neg_adj_gross_i", "pos_adj_gross_j", "neg_adj_gross_j", "cumulat_pos_adj_gross_i", "cumulat_neg_adj_gross_i", "cumulat_pos_adj_gross_j", "cumulat_neg_adj_gross_j", "P_inf_previous_month_link", "Cumulat_num_inf_months_link", "frac_previous_inf_months_link", "P_inf_previous_month_i", "Cumulat_num_inf_months_i", "frac_previous_inf_months_i", "P_inf_previous_month_j", "Cumulat_num_inf_months_j", "frac_previous_inf_months_j", "ki", "kj", "K_nn_i", "K_nn_j", "HHIi_as_manuf", "HHIi_as_contr", "HHIj_as_manuf", "HHIj_as_contr", "betweenness_i", "betweenness_j", "link_betweenness_ij", "max_clique_i", "max_clique_j", "cumulat_max_clique_i", "cumulat_max_clique_j","kshell_i", "kshell_j","cumulat_kshell_i", "cumulat_kshell_j", "num_manuf_i", "num_contr_i", "num_manuf_j", "num_contr_j", "num_transact_ij", "num_transact_i", "num_transact_j", "amount_pos_self_trans_i", "amount_pos_self_trans_j", "amount_neg_self_trans_i", "amount_neg_self_trans_j", "num_pos_self_trans_i", "num_pos_self_trans_j", "num_neg_self_trans_i", "num_neg_self_trans_j", "fract_pos_bussiness_of_M_with_C", "fract_pos_bussiness_of_C_with_M", "degree_asymmetry_ij", "business_asymmetry_ij", "error_size_ij", "dist", "month", "season", "fract_inf_links_global", "fract_inf_links_local_i", "fract_inf_links_local_j", "fract_inf_links_local_i_previous", "fract_inf_links_local_j_previous", "history_ij", "month_count","year","N", "L"
# print G_period.nodes(data=True) #example: (2318295, {'degree': 3, 'num_contractors': 0, 'HHI_as_contr': 0.3724172065553589, 'CC': 0.0, 'num_transact': 4.0, 'vol_transct': 13121.0, 'fract_neg_transct': 0.25, 'HHI_as_manuf': 'NA', 'max_clique_size': 2, 'num_manuf': 3, 'vol_pos_transct': 13122.0, 'vol_neg_transct': -1.0, 'kshell': 2, 'betweeness': 0.0008438954375540839})
# print G_period.edges(data=True) # example: (514603, 2302275, {'pos_weight': 23989, 'num_neg_trans': 0.0, 'fract_neg_trans': 0.0, 'num_pos_trans': 1.0, 'link_betweeness': 0.0016097736255839023, 'neg_weight': 0.0})
G=nx.Graph() ## this is the cumulative network including all transactions up to time period t
print
print
list_periods=[]
list_firm_ids=[]
cont_transactions=0.
cont_neg_transactions=0.
cont_neg_transactions_threshold=0.
cont_self_transactions=0.
cont_self_neg_transactions=0.
dict_firm_id_active_periods={}
dict_tuple_ids_active_periods={}
dict_firm_total_trans_volum={}
dict_firm_num_pos_trans={}
dict_firm_num_neg_trans={}
dict_tuple_link_cumulat_previous_Pinf={}
dict_tuple_link_fract_previous_inf_periods={}
dict_tuple_link_Pinf_previous_period={}
dict_firm_cumulat_previous_Pinf={}
dict_firm_fract_previous_inf_periods={}
dict_firm_Pinf_previous_period={}
dict_tuple_link_previous_periods_together={} # number of periods the two companies have been working together (history)
dict_manuf_dict_contr_amounts={} # for each manufact., dict of its contractors and total amounts
dict_contr_dict_manuf_amounts={} # for each contract., dict of its manuf. and total amounts
dict_link_num_pos_trans={}
dict_link_num_neg_trans={}
dict_link_num_trans={}
list_neg_adj_gross=[]
list_pos_adj_gross=[]
list_tuplas=[]
#### to do further slicing of the data into months (instead or years)
list_starting_date_marks=[]
y= initial_year
if slicing=="monthly":
while y <= final_year:
aux_day=1
aux_month=1
aux_year=y
while aux_month <= 12:
aux_date=dt.datetime(aux_year, aux_month, aux_day)
list_starting_date_marks.append(aux_date)
aux_month += 1
y +=1
elif slicing=="yearly":
aux_day=1
aux_month=1
aux_year=y
while aux_year <= final_year:
aux_date=dt.datetime(aux_year, aux_month, aux_day)
list_starting_date_marks.append(aux_date)
aux_year +=1
else:
print "wrong slicing"
exit()
list_manuf_tot=[] # list all manuf, and contr (excluding any selftransaction)
list_contr_tot=[]
list_sizes_inf_components_tot=[]
list_sizes_inf_components_tot_threshold=[]
cont_periods=0
############# loop over periods
for mark_date_initial in list_starting_date_marks:
current_year=mark_date_initial.year
if slicing=="monthly":
mark_date_final = mark_date_initial + dt.timedelta(days = 30)
elif slicing=="yearly":
mark_date_final = mark_date_initial + dt.timedelta(days = 365)
cont_periods +=1
print "\n\nperiod:",cont_periods,"with",slicing, "slicing, mark_initial_date:", mark_date_initial
### period dicts:
list_neg_adj_gross_period=[]
list_pos_adj_gross_period=[]
list_firm_ids_period=[]
list_tuplas_period=[]
dict_firm_total_trans_volum_period={}
dict_firm_tot_pos_trans_period={}
dict_firm_tot_neg_trans_period={}
dict_firm_num_pos_trans_period={}
dict_firm_num_neg_trans_period={}
dict_link_num_pos_trans_period={}
dict_link_num_neg_trans_period={}
dict_link_num_trans_period={}
dict_firm_amount_pos_self_trans_period={}
dict_firm_amount_neg_self_trans_period={}
dict_firm_num_pos_self_trans_period={}
dict_firm_num_neg_self_trans_period={}
dict_manuf_dict_contr_amounts_period={} # for each manufact., dict of its contractors and total amounts
dict_contr_dict_manuf_amounts_period={} # for each contract., dict of its manuf. and total amounts
cont_transactions_period=0.
cont_neg_transactions_period=0.
cont_neg_transactions_period_threshold=0.
cont_self_transactions_period=0.
cont_self_neg_transactions_period=0.
## examples of firms that act boths as manuf. and contractor (AFTER EXCLUDING ALREADY SELF-TRANSACTIONS!!) "JOBBERS"
#overlap between manuf. and contr: set([409601, 518153, 506199, 512023, 2324507, 901155, 9900068, 2310181, 518207, 600131, 116804, 9900101, 9900111, 200785, 3201107, 2324571, 2300011, 2392173, 9900147, 522363, 106321, 202885, 2001032, 2306190, 260241, 510099, 66404501, 2300055, 510105, 700575, 2304181, 700599, 1300672, 106689, 403659, 2324684, 2390221, 2304211, 405723, 1300702, 2366873, 510181, 401638, 501992, 2320617, 102635, 800466, 6203632, 401651, 116981, 2320630, 508287, 6203644, 600325, 700681, 9900299, 2324866, 205072, 106771, 411929, 102684, 151841, 9101603, 411942, 9900329, 9900330, 203051, 2314541, 411954, 114996, 9900342, 2314552, 2304314, 2324799, 522562, 2318659, 2320708, 1300805, 9101640, 522569, 514381, 9101652, 500055, 512345, 2302299, 2322781, 2324830, 203105, 2003314, 801173, 6601081, 2306426, 2322811, 12200316, 901866, 412031, 403842, 2308484, 520585, 9101706, 2392461, 2304399, 504208, 520597, 2396567, 700824, 20591002, 2316699, 500124, 2320794, 412065, 9101731, 500136, 405929, 9101743, 700851, 2324921, 2366197, 2322882, 408311, 2003405, 115150, 2300369, 2365908, 6203863, 512473, 2320861, 12200419, 2001381, 6203887, 408049, 510452, 109046, 512503, 107001, 6203899, 2365952, 403970, 800171, 102921, 2320927, 2316833, 10500187, 100900, 2365990, 400804, 2325044, 401985, 2306827, 1301060, 102982, 2319457, 2003533, 506448, 600660, 6203991, 150125, 205424, 510580, 2366073, 205436, 600702, 109186, 203397, 9106054, 2394765, 111247, 522909, 402084, 412326, 901799, 504488, 2319020, 109231, 2306736, 2366134, 400055, 10500795, 2314940, 2364098, 2323141, 901830, 408268, 2366157, 2366159, 512722, 506591, 2300643, 703205, 2321130, 207597, 105201, 113396, 412405, 516855, 203521, 15502083, 2319111, 6204171, 2366223, 2312980, 2366233, 2321181, 402217, 406314, 6204211, 3200139, 2323281, 2304855, 404317, 901982, 504671, 516961, 2319202, 207721, 500586, 2311021, 207731, 2319226, 207743, 516995, 902020, 101254, 521097, 2321294, 2325391, 402321, 101278, 20595620, 2366373, 2323370, 904107, 6204338, 2393013, 406454, 406466, 510749, 500677, 2366413, 2001871, 408530, 402394, 2700256, 900783, 2319330, 703461, 2325329, 2323436, 204626, 601071, 703473, 2366452, 517291, 408581, 15502344, 10501131, 2324994, 2366478, 902159, 402448, 2319378, 113694, 404512, 2323503, 2366516, 410691, 2366535, 502856, 2302135, 2321488, 410706, 2318179, 2323540, 201820, 513118, 513121, 513131, 2366572, 101485, 2002036, 402281, 2325627, 2324672, 900229, 12203147, 801151, 2323606, 410780, 2321579, 402291, 20591798, 506059, 2301131, 408785, 410834, 509139, 2323679, 408803, 9905388, 6601972, 2366709, 2321658, 15502587, 105727, 521474, 410883, 509191, 2319627, 10503437, 109848, 1500442, 2324015, 150812, 517409, 111909, 2002218, 408876, 402734, 504713, 513337, 2366779, 2307388, 3200322, 2319688, 402761, 404810, 101710, 2323795, 408918, 2301271, 1502554, 406879, 2325861, 505201, 3200371, 603508, 411000, 515450, 402813, 501128, 517513, 2366858, 521620, 505237, 2307480, 521796, 9100702, 900515, 2321828, 2301350, 522563, 9900615, 2323886, 2366573, 13200816, 2323898, 2309567, 6604225, 66405826, 6600131, 800201, 19000783, 406995, 153047, 2366714, 800223, 20590055, 2309609, 501232, 2323953, 20596211, 2301428, 260010, 20500055, 511501, 509462, 2303577, 9100829, 2323998, 509474, 800296, 1500715, 513581, 511535, 6602290, 9910537, 108091, 507453, 6207042, 105739, 1500740, 1201741, 1201744, 2324052, 517719, 151129, 151130, 2700891, 6602332, 106082, 2303590, 2313832, 106094, 104048, 2319986, 2700918, 2324091, 104061, 405127, 2324106, 411280, 405140, 800405, 6207126, 2305689, 6203036, 1900079, 2324131, 411310, 2324143, 507568, 116403, 2305720, 521929, 153291, 513741, 6207182, 1500880, 411346, 100055, 6207194, 2305756, 1300192, 507623, 2301672, 412284, 2322158, 2324222, 2307595, 411401, 151312, 503769, 407321, 501530, 2324261, 501542, 2307881, 2322055, 509747, 151348, 517941, 114492, 505675, 6604625, 6604626, 6604630, 501591, 110425, 113979, 2324325, 1503078, 409455, 411504, 104309, 505493, 15507329, 20596612, 2314125, 2000787, 2324374, 2318121, 2700271, 522565, 1300386, 501669, 202666, 522568, 202678, 2305975, 202681, 20593994, 200645, 6203334, 401353, 2324428, 405462, 12202660, 501724, 411619, 401389, 2701297, 102386, 9900020, 600055, 516090, 260095]) (excluding first all self transactions)
G_period=nx.Graph() ## this is the cumulative network including all transactions during time period t
list_manuf_period=[]
list_contr_period=[]
list_non_self_contractors=[]
cont_jobber_involved=0
##################
####### input datafile: (I NEED TO READ IT EVERY TIME, BECAUSE IT GETS EMPTY EVERY TIME AFTER ITERATING OVER IT)
name0="fhistory_ALL.csv"
print "reading: ", path+name0, "......."
#### paidbyfi,paidforf,periodfr,periodto,adjgr,gross,net,caf,liqdmg,cafper,rateper,ratecode
cont=1
csvfile=open(path+name0, 'rb')
reader = csv.reader(csvfile, delimiter=',', quotechar='"')
next(reader, None) # to skip the header
for list_row in reader:
cont +=1
flag_ignore_row=0
########### initial date for transaction period
from_date=list_row[2] # format examples 040185 #010185
if len(from_date) <6:
from_date="0"+from_date # when the date is 40185 instead of 040185
month=int(from_date[:2])
day=int(from_date[-4:-2])
year=int(from_date[-2:])
if year > 80:
year += 1900
else:
year += 2000
try:
initial_date=dt.datetime(year, month, day)
except ValueError: # there are a bunch of incorrect dates!!! 043185 063185 113185 023185
day =1
month +=1
initial_date=dt.datetime(year, month, day)
########### final date for transaction period
to_date=list_row[3]
if len(to_date) <6: # when the date is 40185 instead of 040185
to_date="0"+to_date
month=int(to_date[:2])
day=int(to_date[-4:-2])
year=int(to_date[-2:])
if year > 80:
year += 1900
else:
year += 2000
if year == 2030:
year = 2003 # there are a bunch of entries with impossible year: 13201742,13200981,40103,63030,-0.18,-18,
try:
final_date=dt.datetime(year, month, day)
except ValueError: # there are a bunch of incorrect dates!!! 043185 063185 113185 023185
day =1
month +=1
try:
final_date=dt.datetime(year, month, day)
except ValueError:
flag_ignore_row= 1 # there is at least one month = 21 213101
###### i calculate the time inverval for the current transaction
period=(final_date-initial_date).days
if period < 0:
old_initial=initial_date
old_final=final_date ##### some time periods have interved TO and FROM dates!
initial_date =final_date
final_date = old_initial
period=(final_date-initial_date).days
list_periods.append(period)
middle_point=initial_date + dt.timedelta( days=int(float(period)/2.) )
##### i only look at the rows of the datafile for dates corresponding to the current observation period
if middle_point >= mark_date_initial and middle_point <= mark_date_final :
current_data_month= middle_point.month
# print middle_point, current_data_month
if current_data_month <=3:
current_data_season=1
elif current_data_month >3 and current_data_month <=6 :
current_data_season=2
elif current_data_month >6 and current_data_month <=9 :
current_data_season=3
elif current_data_month >9 and current_data_month <=12 :
current_data_season=4
try: # some lines are missing the contractor or manufacturer: skip
flag_manuf_jobber=0
flag_contr_jobber=0
manufacturer=int(list_row[0]) ##paidbyfirm
contractor=int(list_row[1]) ## paidforfirm
if manufacturer in list_jobbers_tot:
flag_manuf_jobber=1
if contractor in list_jobbers_tot:
flag_contr_jobber=1
if manufacturer != contractor :
list_manuf_tot.append(manufacturer)
list_contr_tot.append(contractor)
list_manuf_period.append(manufacturer)
list_contr_period.append(contractor)
cont_transactions +=1
cont_transactions_period +=1
########### list of active periods for pairs
tupla_link=(manufacturer, contractor)
try:
dict_tuple_ids_active_periods[tupla_link].append(cont_periods)
except KeyError:
dict_tuple_ids_active_periods[tupla_link]=[]
dict_tuple_ids_active_periods[tupla_link].append(cont_periods)
############## for now i deal with integers Dollars !! (easier for histograms)
adj_gross=int(round(float((list_row[4]))))
if manufacturer == contractor:
cont_self_transactions +=1.
cont_self_transactions_period +=1.
if flag_remove_selfloops=="YES":
flag_ignore_row=1
########## if it is a self-transaction, i only record these variables (no network variables nor HHI etc)
if adj_gross <0.:
cont_self_neg_transactions +=1.
cont_self_neg_transactions_period +=1.
###### count number and amount of self neg transactions
try:
dict_firm_amount_neg_self_trans_period[manufacturer] += adj_gross
except KeyError:
dict_firm_amount_neg_self_trans_period[manufacturer] = adj_gross
try:
dict_firm_num_neg_self_trans_period[manufacturer] +=1.
except KeyError:
dict_firm_num_neg_self_trans_period[manufacturer] =1.
else:
###### count number and amount of self pos transactions
try:
dict_firm_amount_pos_self_trans_period[manufacturer] += adj_gross
except KeyError:
dict_firm_amount_pos_self_trans_period[manufacturer] = adj_gross
try:
dict_firm_num_pos_self_trans_period[manufacturer] +=1.
except KeyError:
dict_firm_num_pos_self_trans_period[manufacturer] =1.
else:
list_non_self_contractors.append(contractor)
if flag_ignore_row == 0 : # in general i do not count the self-transactions (for network metrics nor HHI etc)
list_tuplas_period.append(tupla_link)
list_tuplas.append(tupla_link)
list_firm_ids.append(manufacturer)
list_firm_ids.append(contractor)
list_firm_ids_period.append(manufacturer)
list_firm_ids_period.append(contractor)
########### list of active periods for firms
try:
dict_firm_id_active_periods[manufacturer].append(cont_periods)
except KeyError:
dict_firm_id_active_periods[manufacturer]=[]
dict_firm_id_active_periods[manufacturer].append(cont_periods)
if manufacturer != contractor:
try:
dict_firm_id_active_periods[contractor].append(cont_periods)
except KeyError:
dict_firm_id_active_periods[contractor]=[]
dict_firm_id_active_periods[contractor].append(cont_periods)
########### i need to initialize dicts
try:
dict_firm_num_neg_trans_period[manufacturer]
except KeyError:
dict_firm_num_neg_trans_period[manufacturer] =0.
try:
dict_firm_num_neg_trans_period[contractor]
except KeyError:
dict_firm_num_neg_trans_period[contractor] =0.
try:
dict_firm_num_neg_trans[manufacturer]
except KeyError:
dict_firm_num_neg_trans[manufacturer] =0.
try:
dict_firm_num_neg_trans[contractor]
except KeyError:
dict_firm_num_neg_trans[contractor] =0.
try:
dict_firm_num_pos_trans_period[manufacturer]
except KeyError:
dict_firm_num_pos_trans_period[manufacturer] =0.
try:
dict_firm_num_pos_trans_period[contractor]
except KeyError:
dict_firm_num_pos_trans_period[contractor] =0.
try:
dict_firm_num_pos_trans[manufacturer]
except KeyError:
dict_firm_num_pos_trans[manufacturer] =0.
try:
dict_firm_num_pos_trans[contractor]
except KeyError:
dict_firm_num_pos_trans[contractor] =0.
#########
########### initialize the same for links
try:
dict_link_num_pos_trans[tupla_link]
except KeyError:
dict_link_num_pos_trans[tupla_link] = 0.
try:
dict_link_num_neg_trans[tupla_link]
except KeyError:
dict_link_num_neg_trans[tupla_link] = 0.
try:
dict_link_num_trans[tupla_link]
except KeyError:
dict_link_num_trans[tupla_link] = 0.
try:
dict_link_num_pos_trans_period[tupla_link]
except KeyError:
dict_link_num_pos_trans_period[tupla_link] = 0.
try:
dict_link_num_neg_trans_period[tupla_link]
except KeyError:
dict_link_num_neg_trans_period[tupla_link] = 0.
try:
dict_link_num_trans_period[tupla_link]
except KeyError:
dict_link_num_trans_period[tupla_link] = 0.
############
######## for the HHI index of manuf. and contr.
# HHI is a measure of the size of firms in an industry, and an indicator of the amount of competition among them. Bounded by: 1/N (equally distributed industry), 1 (total monopoly).
try:
dict_manuf_dict_contr_amounts[manufacturer]
except KeyError:
dict_manuf_dict_contr_amounts[manufacturer]={}
try:
dict_manuf_dict_contr_amounts[manufacturer][contractor]
except KeyError:
dict_manuf_dict_contr_amounts[manufacturer][contractor] = 0.
dict_manuf_dict_contr_amounts[manufacturer][contractor] += adj_gross
try:
dict_manuf_dict_contr_amounts_period[manufacturer]
except KeyError:
dict_manuf_dict_contr_amounts_period[manufacturer]={}
try:
dict_manuf_dict_contr_amounts_period[manufacturer][contractor]
except KeyError:
dict_manuf_dict_contr_amounts_period[manufacturer][contractor] = 0.
dict_manuf_dict_contr_amounts_period[manufacturer][contractor] += adj_gross
try:
dict_contr_dict_manuf_amounts[contractor]
except KeyError:
dict_contr_dict_manuf_amounts[contractor]={}
try:
dict_contr_dict_manuf_amounts[contractor][manufacturer]
except KeyError:
dict_contr_dict_manuf_amounts[contractor][manufacturer]=0.
dict_contr_dict_manuf_amounts[contractor][manufacturer] += adj_gross
try:
dict_contr_dict_manuf_amounts_period[contractor]
except KeyError:
dict_contr_dict_manuf_amounts_period[contractor]={}
try:
dict_contr_dict_manuf_amounts_period[contractor][manufacturer]
except KeyError:
dict_contr_dict_manuf_amounts_period[contractor][manufacturer]=0.
dict_contr_dict_manuf_amounts_period[contractor][manufacturer] += adj_gross
########## total volume of (net) transactions
try:
dict_firm_total_trans_volum[manufacturer] += adj_gross
except KeyError:
dict_firm_total_trans_volum[manufacturer] =0.
dict_firm_total_trans_volum[manufacturer] += adj_gross
try:
dict_firm_total_trans_volum[contractor] += adj_gross
except KeyError:
dict_firm_total_trans_volum[contractor] =0.
dict_firm_total_trans_volum[contractor] += adj_gross
########## volume of (net) transactions PERIOD
try:
dict_firm_total_trans_volum_period[manufacturer] += adj_gross
except KeyError:
dict_firm_total_trans_volum_period[manufacturer] =0.
dict_firm_total_trans_volum_period[manufacturer] += adj_gross
try:
dict_firm_total_trans_volum_period[contractor] += adj_gross
except KeyError:
dict_firm_total_trans_volum_period[contractor] =0.
dict_firm_total_trans_volum_period[contractor] += adj_gross
dict_link_num_trans[tupla_link] += 1.
dict_link_num_trans_period[tupla_link] += 1.
G_period.add_node(manufacturer)
G_period.add_node(contractor)
G.add_node(manufacturer)
G.add_node(contractor)
if flag_manuf_jobber ==0:
G.node[manufacturer]["type"] = "manuf"
G_period.node[manufacturer]["type"] = "manuf"
else:
G.node[manufacturer]["type"] = "jobber"
G_period.node[manufacturer]["type"] = "jobber"
if flag_contr_jobber ==0:
G.node[contractor]["type"] = "contr"
G_period.node[contractor]["type"] = "contr"
else:
G.node[contractor]["type"] = "jobber"
G_period.node[contractor]["type"] = "jobber"
##########
if adj_gross <0:
list_neg_adj_gross.append(-1*adj_gross)
list_neg_adj_gross_period.append(-1*adj_gross)
cont_neg_transactions +=1
cont_neg_transactions_period +=1
##### num of neg. transactions PERIOD
dict_firm_num_neg_trans_period[manufacturer] +=1.
dict_firm_num_neg_trans_period[contractor] +=1.
##### tot num of neg. transactions
dict_firm_num_neg_trans[manufacturer] +=1.
dict_firm_num_neg_trans[contractor] +=1.
#### same for the link
dict_link_num_neg_trans_period[tupla_link] += 1.
dict_link_num_neg_trans[tupla_link] += 1.
try:
G.edge[manufacturer][contractor]["neg_weight"] += adj_gross
except KeyError:
G.add_edge(manufacturer,contractor, neg_weight= adj_gross)
try:
G_period.edge[manufacturer][contractor]["neg_weight"] += adj_gross
except KeyError:
G_period.add_edge(manufacturer,contractor, neg_weight= adj_gross)
#### add up to the total neg. PERIOD amount for each firm
try:
dict_firm_tot_neg_trans_period[manufacturer] += adj_gross
except KeyError:
dict_firm_tot_neg_trans_period[manufacturer] =0.
dict_firm_tot_neg_trans_period[manufacturer] += adj_gross
try:
dict_firm_tot_neg_trans_period[contractor] += adj_gross
except KeyError:
dict_firm_tot_neg_trans_period[contractor] =0.
dict_firm_tot_neg_trans_period[contractor] += adj_gross
else:
list_pos_adj_gross.append(adj_gross)
list_pos_adj_gross_period.append(adj_gross)
##### num of posit. transactions PERIOD
dict_firm_num_pos_trans_period[manufacturer] +=1.
dict_firm_num_pos_trans_period[contractor] +=1.
##### tot. num of posit. transactions
dict_firm_num_pos_trans[manufacturer] +=1.
dict_firm_num_pos_trans[contractor] +=1.
#### same for the link
dict_link_num_pos_trans_period[tupla_link] += 1.
dict_link_num_pos_trans[tupla_link] += 1.
try:
G.edge[manufacturer][contractor]["pos_weight"] += adj_gross
except KeyError:
G.add_edge(manufacturer,contractor, pos_weight= adj_gross)
try:
G_period.edge[manufacturer][contractor]["pos_weight"] += adj_gross
except KeyError:
G_period.add_edge(manufacturer,contractor, pos_weight= adj_gross)
#### add up to the total pos. PERIOD amount for each firm
try:
dict_firm_tot_pos_trans_period[manufacturer] += adj_gross
except KeyError:
dict_firm_tot_pos_trans_period[manufacturer] =0.
dict_firm_tot_pos_trans_period[manufacturer] += adj_gross
try:
dict_firm_tot_pos_trans_period[contractor] += adj_gross
except KeyError:
dict_firm_tot_pos_trans_period[contractor] =0.
dict_firm_tot_pos_trans_period[contractor] += adj_gross
except ValueError: pass # some lines (very rare, one single instance) are missing the contractor or manufacturer
############################# end of loop to read ALL-years file
###########################
if flag_hist == "YES":
try:
name_h="../Results/histogram_pos_adj_gross_values_"+slicing+"_slicing_"+str(cont_periods)+".dat"
histograma_gral.histogram(list_pos_adj_gross_period, name_h)
except: pass
try:
name_h="../Results/histogram_neg_adj_gross_values_"+slicing+"_slicing_"+str(cont_periods)+".dat"
histograma_gral.histogram(list_neg_adj_gross_period, name_h)
except: pass
for link in dict_link_num_pos_trans_period:
G_period[link[0]][link[1]]['num_pos_trans']= dict_link_num_pos_trans_period[link]
for link in dict_link_num_neg_trans_period:
G_period[link[0]][link[1]]['num_neg_trans']= dict_link_num_neg_trans_period[link]
G_period[link[0]][link[1]]['fract_neg_trans']= dict_link_num_neg_trans_period[link] / (dict_link_num_neg_trans_period[link] + dict_link_num_pos_trans_period[link] )
if G_period[link[0]][link[1]]['num_neg_trans'] == 0.:
G_period[link[0]][link[1]]['neg_weight']=0.
if G_period[link[0]][link[1]]['num_pos_trans'] == 0.:
G_period[link[0]][link[1]]['pos_weight']=0.
try:
G[link[0]][link[1]]['num_neg_trans'] #+= G_period[link[0]][link[1]]['num_neg_trans']
G[link[0]][link[1]]['neg_weight'] #+= G_period[link[0]][link[1]]['neg_weight']
except KeyError:
G[link[0]][link[1]]['num_neg_trans'] = 0.
G[link[0]][link[1]]['neg_weight'] = 0.
try:
G[link[0]][link[1]]['pos_weight'] #+= G_period[link[0]][link[1]]['pos_weight']
except KeyError:
G[link[0]][link[1]]['pos_weight'] = 0.
for firm in G_period.nodes():
G_period.node[firm]['vol_transct']=dict_firm_total_trans_volum_period[firm]
G_period.node[firm]['fract_neg_transct']=dict_firm_num_neg_trans_period[firm] /(dict_firm_num_neg_trans_period[firm] + dict_firm_num_pos_trans_period[firm])
G_period.node[firm]['num_transact']= dict_firm_num_neg_trans_period[firm] + dict_firm_num_pos_trans_period[firm]
for firm in dict_firm_tot_pos_trans_period:
G_period.node[firm]['vol_pos_transct']=dict_firm_tot_pos_trans_period[firm]
try:
G.node[firm]['vol_pos_transct'] +=G_period.node[firm]['vol_pos_transct']
except KeyError:
G.node[firm]['vol_pos_transct']=0.
for firm in dict_firm_tot_neg_trans_period:
G_period.node[firm]['vol_neg_transct']=dict_firm_tot_neg_trans_period[firm]
try:
G.node[firm]['vol_neg_transct'] +=G_period.node[firm]['vol_neg_transct']
except KeyError:
G.node[firm]['vol_neg_transct']=0.
###### fill in the gaps for those firms without positive or neg. transactions
for node in G_period.nodes():
try:
G_period.node[node]['vol_pos_transct']
except KeyError:
G_period.node[node]['vol_pos_transct']=0.
try:
G_period.node[node]['vol_neg_transct']
except KeyError:
G_period.node[node]['vol_neg_transct']=0.
try:
G.node[node]['vol_pos_transct']
except KeyError:
G.node[node]['vol_pos_transct']=0.
try:
G.node[node]['vol_neg_transct']
except KeyError:
G.node[node]['vol_neg_transct']=0.
list_jobbers_period= list(set(list_manuf_period) & set(list_contr_period))
jobbers_period=len(list_jobbers_period)
jobbers_total=len(set(list_manuf_tot) & set(list_contr_tot))
list_manuf_period_no_jobbers= list(set(list_manuf_period) - set(list_jobbers_period))
num_manuf_period_no_jobbers=len(set(list_manuf_period_no_jobbers))
list_contr_period_no_jobbers= list(set(list_contr_period) - set(list_jobbers_period))
num_contr_period_no_jobbers=len(set(list_contr_period_no_jobbers))
try:
print " period",cont_periods
print " # firms:", len(set(list_firm_ids_period))
print " # manufacturers:", len(set(list_manuf_period)), " excluding jobbers:", num_manuf_period_no_jobbers, float(jobbers_period) / num_manuf_period_no_jobbers
print " # contractors", len(set(list_contr_period)), " excluding jobbers:", num_contr_period_no_jobbers, float(jobbers_period) / num_contr_period_no_jobbers
# print " # non-self contractors", len(set(list_non_self_contractors))
print " # jobbers (overlap between manuf. & contr period):", jobbers_period , "(excluding self-trans) and total:", jobbers_total
print " manuf + contr+ jobbers:", num_manuf_period_no_jobbers + num_contr_period_no_jobbers + jobbers_period
print " # transactions:", int(cont_transactions_period)
print " # negative transactions:", int(cont_neg_transactions_period), " ", cont_neg_transactions_period/cont_transactions_period*100., "%"
print " # self-transactions:", int(cont_self_transactions_period), " ", cont_self_transactions_period/cont_transactions_period*100., "%"
print " # self-neg-transactions:", int(cont_self_neg_transactions_period), " ", cont_self_neg_transactions_period/cont_transactions_period*100., "%"
# print "num instances with jobber involved:", cont_jobber_involved, float(cont_jobber_involved)/(2.*cont_transactions_period)
except ZeroDivisionError: pass
print " # unique tuples:",len(set(list_tuplas_period))
print "row count:", cont-1
file1= open(name1, 'at')
print >> file1, cont_periods, int(cont_transactions_period)
file1.close()
file2= open(name2, 'at')
print >> file2, cont_periods, len(set(list_firm_ids_period))
file2.close()
try:
file3= open(name3, 'at')
print >> file3, cont_periods, int(cont_neg_transactions_period), cont_neg_transactions_period/cont_transactions_period*100.
file3.close()