def print_densities_for_thresholds(data, definition, T1_thresholds, T2_thresholds, log_file_name):
f1 = open(OUT_DIR.DEFINITION_C + log_file_name + '-edges.txt', 'w')
f2 = open(OUT_DIR.DEFINITION_C + log_file_name + '-traids.txt', 'w')
f3 = open(OUT_DIR.DEFINITION_C + log_file_name + '-nodes.txt', 'w')
countries_list = ['USA', 'Iran', 'Iraq', 'Argentina', 'UK', 'China', 'Kuwait', 'France,Monac']
for pruning_T in T1_thresholds:
for classifying_T in T2_thresholds:
args1 = args_for_definition_C(pruning_T, classifying_T, f1)
args2 = args_for_definition_C(pruning_T, classifying_T, f1)
for year in range(1969, 2001):
(positive_edges, negative_edges) = (0, 0)
for (A, B) in countries.country_pairs(countries_list):
link_sign = definition(data, year, A, B, args1)
if link_sign == POSITIVE_LINK: positive_edges += 1
if link_sign == NEGATIVE_LINK: negative_edges += 1
N = 203
density = 2.0 * (positive_edges + negative_edges) / (N * (N - 1)) * 100
print "%d,%f,%d,%f,%d,%d,%d" % (
pruning_T, classifying_T, year, density, positive_edges, negative_edges, N)
for (A, B, C) in combinations(countries_list, 3):
linkAtoB = definition(data, year, A, B, args2)
linkBtoC = definition(data, year, B, C, args2)
linkCtoA = definition(data, year, C, A, args2)
triangle = [linkAtoB, linkBtoC, linkCtoA]
pcount = triangle.count(POSITIVE_LINK)
ncount = triangle.count(NEGATIVE_LINK)
mcount = triangle.count(NO_LINK)
f2.write("%d,%.2f,%d,%s,%s,%s,%s,%s,%s,T%d%d%d\n" % (
year, pruning_T, classifying_T, A, B, C, linkAtoB, linkBtoC, linkCtoA, pcount, ncount, mcount))
for A in countries_list:
f3.write("%d,%s,%d\n" % (year, A, degree_sum(data, year, A, definition, args1)))
f1.close()
f2.close()
f3.close()
from project.config import WORLD_TRADE_FLOW_DATA_FILE_ORIGINAL
from project.export_data.exportdata import ExportData
from project.signed_networks.definitions import definition_C3, args_for_definition_C
from project.signed_networks.structural_balance.metrics.vertex import degree_sum, degree_count, positive_edge_count, negative_edge_count
from project.util import file_safe
data = ExportData()
data.load_file('../../' + WORLD_TRADE_FLOW_DATA_FILE_ORIGINAL, should_read_world_datapoints=True)
definition = definition_C3
def_args1 = args_for_definition_C(10, 5000)
def_args2 = args_for_definition_C(10, 1000)
def print_degree_sum_over_time(args, other_args, this, other):
print "x=%s;" % str([year for year in data.all_years]).replace(",", " ")
for A in data.countries():
print "degreecount=%s;" % str([degree_count(data, year, A, definition, args) for year in data.all_years]).replace(",", " ")
print "degreesum1=%s;" % str([degree_sum(data, year, A, definition, args) for year in data.all_years]).replace(",", " ")
print "positivecount1=%s;" % str([positive_edge_count(data, year, A, definition, args) for year in data.all_years]).replace(",", " ")
print "negativecount1=%s;" % str([negative_edge_count(data, year, A, definition, args) for year in data.all_years]).replace(",", " ")
print "degreesum2=%s;" % str([degree_sum(data, year, A, definition, other_args) for year in data.all_years]).replace(",", " ")
print "plot(x,degreesum1,'b-o',x,degreesum2,'b-*',x,degreecount,'m-o',x,positivecount1,'g-o',x,negativecount1,'r-o');"
print "hline = refline([0 0]);"
print "set(hline,'Color','b');"
print "legend('degree-sum1(T2=%d)','degree-sum2(T2=%d)','degree-count','positivecount1','negativecount1','Location','Best')" %(this,other)
print "saveas(gcf,'%s-%d','png');" % (file_safe(A),this)
print_degree_sum_over_time(def_args1, def_args2, 5000, 1000)
print_degree_sum_over_time(def_args2, def_args1, 1000, 5000)
from project.config import WORLD_TRADE_FLOW_DATA_FILE_ORIGINAL
from project.export_data.exportdata import ExportData
from project.signed_networks.definitions import definition_C3, args_for_definition_C, POSITIVE_LINK, NEGATIVE_LINK
from project.signed_networks.structural_balance.metrics.network import print_table, table1, link_type_ratio, traid_type_ratio
data = ExportData()
data.load_file('../../' + WORLD_TRADE_FLOW_DATA_FILE_ORIGINAL, should_read_world_datapoints=True)
year = 2000
definition = definition_C3
def_args1000 = args_for_definition_C(10, 1000)
def_args5000 = args_for_definition_C(10, 5000)
# print_table(table1(data, year, definition, def_args1000))
def print_code_for_sign_distribution_over_time():
print "x=%s;" % str([year for year in data.all_years]).replace(",", " ")
# print "p1000=%s;" % str([link_type_ratio(data, year, definition, def_args1000, POSITIVE_LINK)
# for year in data.all_years]).replace(",", " ")
print "p5000=%s;" % str([link_type_ratio(data, year, definition, def_args5000, POSITIVE_LINK)
for year in data.all_years]).replace(",", " ")
# print "n1000=%s;" % str([link_type_ratio(data, year, definition, def_args1000, NEGATIVE_LINK)
# for year in data.all_years]).replace(",", " ")
print "n5000=%s;" % str([link_type_ratio(data, year, definition, def_args5000, NEGATIVE_LINK)
for year in data.all_years]).replace(",", " ")
print "plot(x,p5000,x,n5000);"
print "legend('positive edges','negative edges')"
print "saveas(gcf,'sign-distribution-over-time(Not-density)','png');"
if n == 0: return 1
if n < 1:
n = 1 / n
return n - 1
def generate_network_graph_data(data, year, subset_of_countries, out_file, definition, args):
f = open(out_file, 'w')
f.write(html_header())
for (c1, c2) in country_pairs(subset_of_countries):
link_type = definition(data, year, c1, c2, args)
if link_type != NO_LINK:
ratio = data.export_import_ratio(c1, c2, year)
f.write('{source:"%s", target:"%s", type:"%s",repulsionpercentage:"%f"},\n' % (
c1, c2, link_type, normalize(ratio)))
f.write(html_footer(year))
f.close()
data = ExportData()
data.load_file('../' + WORLD_TRADE_FLOW_DATA_FILE_ORIGINAL, should_read_world_datapoints=True)
for year in range(1968, 2001):
top_countries_and_exports = data.top_countries_by_export(year, 50)
# top_countries_and_exports = data.countries()
generate_network_graph_data(data, year, top_countries_and_exports, output_file_html(year),
definition_C3,
args_for_definition_C(10, 5000))
print "saveas(gcf,'individual-traid-embeddedness-%s-all','png');" % year
print "plot(x,t1p,'m-*',x,t1n,'r-o',x,t2p,'g-*',x,t2n,'b-o');"
print "xlabel('Number of common neighbours');"
print "ylabel('Percentage of triangles');"
print "legend('T1+','T1-','T2+','T2-');"
print "saveas(gcf,'individual-traid-embeddedness-%s-t1-and-t2','png');" % year
def embeddedness_vs_positive_edges_over_time(def_args):
print "x=%s" % (str(common_neighbours_count_range).replace(",", " "))
for year in data.all_years:
fractions = fraction_of_embedded_positive_signs(data, year, definition_C3, def_args)
(_, positive_edges, negative_edges) = count_edge_types(data, year, definition_C3, def_args)
randomNetwork = RandomNetwork(positive_edges, negative_edges, definition_C3, def_args)
random_fractions = fraction_of_embedded_positive_signs(data, year, randomNetwork.link_sign, def_args)
print "actual=%s" % (str([fractions[i] for i in common_neighbours_count_range]).replace(",", " "))
print "random=%s" % (str([random_fractions[i] for i in common_neighbours_count_range]).replace(",", " "))
print "plot(x,actual,'b-*',x,random,'r-*');"
print "saveas(gcf,'link-embeddedness-%s','png');" % year
#traid_counts = traids_per_common_edge_count(data, year, definition_C3, args_for_definition_C(10, 5000))
#
#for i in range(0, max_common_neighbours_possible + 1):
# (t0, t1, t2, t3) = traid_counts[i]
# print "%d\t%d\t%d\t%d\t%d" % (i, t0, t1, t2, t3)
# embeddedness_vs_positive_edges_over_time(args_for_definition_C(10, 1000))
# embeddedness_vs_balanced_traids_over_time(args_for_definition_C(10, 1000))
embeddedness_vs_individual_traids_over_time(args_for_definition_C(10, 5000))
self.export_quantity = []
def add_data(self, year, p, n, ex):
self.positive_links_count.append([year, p])
self.negative_links_count.append([year, n])
self.export_quantity.append([year, ex])
all_nations_dict = []
json_file = open(out_dir + 'nations_gen.json', 'w')
for A in countries_list:
nation = NationData(A)
for year in data.all_years:
positive = 0
negative = 0
for B in countries_list:
if A == B: continue
link_type = definition(data, year, A, B, def_args)
if link_type == NEGATIVE_LINK: negative += 1
elif link_type == POSITIVE_LINK: positive += 1
nation.add_data(year, positive, negative, data.total_exports(A, year))
all_nations_dict.append(nation.__dict__)
json_file.write(json.dumps(all_nations_dict))
json_file.close()
data = ExportData()
data.load_file('../../' + WORLD_TRADE_FLOW_DATA_FILE_ORIGINAL, should_read_world_datapoints=True)
positives_and_negatives_data_d3(data, definition_C3, args_for_definition_C(10,1000), OUT_DIR.POSITIVES_AND_NEGATIVES)
