def write_relationship_types(data, definition, def_args, out_dir):
for year in data.all_years:
with open(out_dir + '%s.csv' % year, 'wb') as csvfile:
out_file = csv.writer(csvfile, delimiter=',', quotechar='"', quoting=csv.QUOTE_ALL)
out_file.writerow(['Country1', 'Country2', 'Link type', 'Export/Import ratio'])
for (A, B) in countries.country_pairs(data.countries()):
link_type = definition(data, year, A, B, def_args)
if link_type != NO_LINK:
out_file.writerow([A, B, link_type, data.export_import_ratio(A, B, year)])
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()
def sign_distributions(data, year, definition, def_args):
positive_edges_count, negative_edges_count, no_edge_count = 0, 0, 0
for (A, B) in countries.country_pairs(data.countries()):
link_type = definition(data, year, A, B, def_args)
if link_type == NEGATIVE_LINK:
negative_edges_count += 1
elif link_type == POSITIVE_LINK:
positive_edges_count += 1
elif link_type == NO_LINK:
no_edge_count += 1
edges_count = positive_edges_count + negative_edges_count
return edges_count, positive_edges_count, negative_edges_count, no_edge_count
def print_graph_densities_for_different_thresholds(data, thresholds, years):
f = open(OUT_DIR.DEFINITION_D + 'combinations.txt', 'w')
for T in thresholds:
for year in years:
positive_edges = 0
negative_edges = 0
for (A, B) in countries.country_pairs(data.countries()):
link_sign = definition_D(data, year, A, B, args_for_definition_D(T, log_file=f))
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,%d,%f,%d,%d,%d" % (T, year, density, positive_edges, negative_edges, N)
f.close()
def print_histogram_matlab_code(data, low, high):
i = 0
str = ""
list = []
for (A, B) in countries.country_pairs(data.countries()):
total = data.total_exports_from_C1_to_C2(A, B)
if total != 0 and low < total < high:
str = "%s %d" % (str, total)
list.append(total)
i += 1
print "y=[%s];" % str
print "hist(y,100000);"
print "xlabel('Export quantity');"
print "ylabel('Count of country pairs');"
print "set(gca, 'Xscale', 'log');"
print "saveas(gcf,'definition_C_histogram','png');"
def print_missing_links_db(data, year, T, log_file_name):
two_way_args = args_for_definition_D(T)
one_way_args = args_for_definition_D(T, mode='one-way')
f = open(OUT_DIR.DEFINITION_D + log_file_name + ".%d.txt" % T, 'w')
for (A, B) in countries.country_pairs(data.countries()):
if definition_D(data, year, A, B, two_way_args) == NO_LINK:
one_way = definition_D(data, year, A, B, one_way_args)
other_way = definition_D(data, year, B, A, one_way_args)
f.write("Y%d,%s,%s,%s,%s\n" % (year, file_safe(A), file_safe(B), one_way, other_way))
if one_way != other_way:
for Y in range(1963, 2001):
f.write("%d,%s,%s,%.4g,%.4g\n" % (
Y, file_safe(A), file_safe(B),
data.export_data_as_percentile(Y, A, B),
data.export_data_as_percentile(Y, B, A)))
f.close()
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()
