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global_trade_network.py
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global_trade_network.py
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# IMPORTING PACKAGES ************************************************************
import pandas as pd
import numpy as np
import math
import comtradeapicall
import time
import pygraphviz as pgv
import networkx as nx
import dash_cytoscape as cyto
cyto.load_extra_layouts()
from plotly.colors import hex_to_rgb
from dash import Dash, html, Input, Output, callback, ctx
import dash_bootstrap_components as dbc
from datetime import datetime
import warnings
warnings.filterwarnings('ignore')
# COMTRADE SUBSCRIPTION KEY *****************************************************
subscription_key = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
# CODES *************************************************************************
commodities = pd.read_csv('comtrade_codes/harmonized-system.csv')
reporters = pd.read_csv('comtrade_codes/reporterAreas.csv')
partners = pd.read_csv('comtrade_codes/partnerAreas.csv')
# EXTRACTING THE DATA ***********************************************************
comtrade_exp = comtradeapicall.getFinalData(subscription_key,
typeCode='C',
freqCode='A',
clCode='HS',
period='2022',
reporterCode=None,
cmdCode='1001',
flowCode='X',
partnerCode=None,
partner2Code='0',
customsCode='C00',
motCode='0',
maxRecords=250000)
time.sleep(10)
comtrade_imp = comtradeapicall.getFinalData(subscription_key,
typeCode='C',
freqCode='A',
clCode='HS',
period='2022',
reporterCode=None,
cmdCode='1001',
flowCode='M',
partnerCode=None,
partner2Code='0',
customsCode='C00',
motCode='0',
maxRecords=250000)
comtrade_imp = comtrade_imp[
comtrade_imp['reporterCode'] != comtrade_imp['partnerCode']]
# GRAPH PARAMETERS **************************************************************
n_links = 2
chosen_color_parameter = 'region' # 'export_share' 'pagerank'
pic_width = 700
output_pic_name = 'wheat_and_meslin'
# DATA PROCESSING ***************************************************************
export_totals = comtrade_exp[comtrade_exp['partnerCode'] == 0][[
'reporterCode', 'partnerCode', 'fobvalue'
]].groupby(['reporterCode', 'partnerCode']).agg('sum').reset_index()
import_totals = comtrade_imp[comtrade_imp['partnerCode'] == 0][[
'reporterCode', 'partnerCode', 'cifvalue'
]].groupby(['reporterCode', 'partnerCode']).agg('sum').reset_index()
export_by_country = comtrade_exp[comtrade_exp['partnerCode'] != 0][[
'reporterCode', 'partnerCode', 'fobvalue'
]].groupby(['reporterCode', 'partnerCode']).agg('sum').reset_index()
import_by_country = comtrade_imp[comtrade_imp['partnerCode'] != 0][[
'reporterCode', 'partnerCode', 'cifvalue'
]].groupby(['reporterCode', 'partnerCode']).agg('sum').reset_index()
for dataset in [
export_totals, import_totals, export_by_country, import_by_country
]:
for col in ['reporterCode', 'partnerCode']:
dataset[col] = [
'00' + str(x) if len(str(x)) == 1 else '0' +
str(x) if len(str(x)) == 2 else str(x)
for x in dataset[col].tolist()
]
export_by_country.columns = ['exporter', 'importer', 'value']
import_by_country.columns = ['importer', 'exporter', 'value']
# NODES *************************************************************************
total_trade = export_totals[[
'reporterCode', 'fobvalue'
]].set_index('reporterCode').join(
import_totals[['reporterCode', 'cifvalue']].set_index('reporterCode'),
how='outer').reset_index().rename(columns={
'reporterCode': 'country_code',
'fobvalue': 'export',
'cifvalue': 'import'
}).set_index('country_code')
for col in ['export', 'import']:
total_trade[col + '_note'] = [
1 if math.isnan(x) else 0 for x in total_trade[col].tolist()
]
imports_to_add = export_by_country[['exporter', 'importer',
'value']].groupby('importer').agg('sum')
exports_to_add = import_by_country[['importer', 'exporter',
'value']].groupby('exporter').agg('sum')
trade_to_add = exports_to_add.rename(columns={
'value': 'export_by_partners'
}).join(imports_to_add.rename(columns={'value': 'import_by_partners'}),
how='outer')
df_nodes = total_trade.join(trade_to_add, how='outer')[[
'export', 'import', 'export_by_partners', 'import_by_partners'
]]
df_nodes[['export', 'import', 'export_by_partners',
'import_by_partners']] = df_nodes[[
'export', 'import', 'export_by_partners', 'import_by_partners'
]].fillna(0)
df_nodes['trade'] = [
ex + im if ex != 0 and im != 0 else exp +
im if ex == 0 and im != 0 else ex +
imp if ex != 0 and im == 0 else exp + imp for ex, im, exp, imp in zip(
df_nodes['export'], df_nodes['import'], df_nodes['export_by_partners'],
df_nodes['import_by_partners'])
]
df_nodes = df_nodes[df_nodes['trade'] > 0]
df_nodes['trade_rescaled'] = df_nodes['trade'] / df_nodes['trade'].max() * 60
df_nodes['trade_rescaled'] = [
0.15 if x <= 0.15 else x for x in df_nodes['trade_rescaled'].tolist()
]
df_nodes['diameter'] = [
np.sqrt(x / np.pi) * 2 for x in df_nodes['trade_rescaled'].tolist()
]
diameter_dict = df_nodes['diameter'].to_dict()
# LINKS *************************************************************************
export_by_country = export_by_country[
(export_by_country['exporter'].isin(df_nodes.index.tolist()))
& (export_by_country['importer'].isin(df_nodes.index.tolist()))]
import_by_country = import_by_country[
(import_by_country['exporter'].isin(df_nodes.index.tolist()))
& (import_by_country['importer'].isin(df_nodes.index.tolist()))]
df_list = []
for dataset in [export_by_country, import_by_country]:
dataset['source_target'] = [
'_'.join(sorted([exporter, importer]))
for exporter, importer in zip(dataset['exporter'], dataset['importer'])
]
dataset['order'] = dataset.groupby('source_target').cumcount()
df = dataset[dataset['order'] == 0].set_index('source_target')[[
'exporter', 'importer', 'value'
]].rename(columns={
'value': 'to'
}).join(dataset[dataset['order'] == 1].set_index('source_target')[[
'value'
]].rename(columns={'value': 'back'}))
df_list.append(df)
source_target_df = df_list[0].combine_first(df_list[1])
source_target_df = source_target_df.fillna(0)
source_target_df[[
'source', 'target'
]] = [[exporter, importer] if to - back >= 0 else [importer, exporter]
for exporter, importer, to, back in
zip(source_target_df['exporter'], source_target_df['importer'],
source_target_df['to'], source_target_df['back'])]
df_links = source_target_df.reset_index()[['source', 'target']]
rank_df = export_by_country[['importer', 'exporter', 'value']].set_index([
'importer', 'exporter'
]).combine_first(import_by_country[['importer', 'exporter', 'value'
]].set_index(['importer',
'exporter'])).reset_index()
rank_df['supplier_rank'] = rank_df.sort_values(
by='value', ascending=False).groupby('importer').cumcount() + 1
supplier_rank_dict = rank_df.set_index(['importer', 'exporter'
])['supplier_rank'].to_dict()
df_links['supplier_rank_source'] = df_links.set_index(
['target', 'source']).index.map(supplier_rank_dict)
df_links['supplier_rank_target'] = df_links.set_index(
['source', 'target']).index.map(supplier_rank_dict)
link_dict = dict()
for e in export_by_country.exporter:
link_dict[e] = []
for i in export_by_country.importer:
if i not in link_dict.keys():
link_dict[i] = []
for i in import_by_country.importer:
if i not in link_dict.keys():
link_dict[i] = []
for e in import_by_country.exporter:
if e not in link_dict.keys():
link_dict[e] = []
for e in export_by_country.exporter:
data = export_by_country[export_by_country.exporter == e]
importers_list = data['importer'].unique().tolist()
for i in importers_list:
link_dict[e].append(i)
for i in export_by_country.importer:
data = export_by_country[export_by_country.importer == i]
exporters_list = data['exporter'].unique().tolist()
for e in exporters_list:
link_dict[i].append(e)
for e in import_by_country.exporter:
data = import_by_country[import_by_country.exporter == e]
importers_list = data['importer'].unique().tolist()
for i in importers_list:
link_dict[e].append(i)
for i in import_by_country.importer:
data = import_by_country[import_by_country.importer == i]
exporters_list = data['exporter'].unique().tolist()
for e in exporters_list:
link_dict[i].append(e)
for key in link_dict.keys():
link_dict[key] = len(list(dict.fromkeys(link_dict[key])))
# PYGRAPHVIZ LAYOUT *************************************************************
df = df_links[
(df_links['supplier_rank_source'].isin(list(np.arange(n_links + 1))[1:])) |
(df_links['supplier_rank_target'].isin(list(np.arange(n_links +
1))[1:]))].copy()
layout_dict = dict()
for s in df['source'].unique().tolist():
s_list = df[df['source'] == s]['target'].unique().tolist()
layout_dict[s] = s_list
G = pgv.AGraph(layout_dict)
for i, node in enumerate(G.iternodes()):
node.attr['shape'] = 'circle'
node.attr['width'] = diameter_dict[node]
node.attr['height'] = diameter_dict[node]
node.attr['fixedsize'] = True
node.attr['fontsize'] = 1
G.layout(prog='fdp')
graph_width = int(G.graph_attr['bb'].split(',')[2])
graph_height = int(G.graph_attr['bb'].split(',')[3])
# CYTOSCAPE LAYOUT **************************************************************
# Nodes
node_dict = dict()
for node in G.nodes():
node_dict[node] = dict()
node_dict[node]['x'] = float(node.attr['pos'].split(',')[0])
node_dict[node]['y'] = float(node.attr['pos'].split(',')[1])
node_dict[node]['diameter'] = float(node.attr['width']) * 63.5
node_data = pd.DataFrame.from_dict(node_dict, orient='index')
for dataset in [reporters, partners]:
dataset['id'] = [
'00' + x if len(x) == 1 else '0' + x if len(x) == 2 else x
for x in dataset['id'].tolist()
]
country_codes = reporters.set_index('id')['text'].to_dict()
country_codes.update(partners.set_index('id')['text'].to_dict())
country_codes['380'] = 'Italy'
node_data['country'] = node_data.index.map(country_codes)
# Coloring parameters: UN Region
ccode_to_region_dict = dict()
regions = [
'Africa', 'Oceania', 'Antarctica', 'Americas', 'Asia', 'Europe',
'Special categories and unspecified areas'
]
ccode_lists = [
[
'012', '024', '072', '086', '108', '120', '132', '140', '148', '174',
'175', '178', '180', '204', '226', '231', '232', '260', '262', '266',
'270', '288', '324', '384', '404', '426', '430', '434', '450', '454',
'466', '478', '480', '504', '508', '516', '562', '566', '577', '624',
'638', '646', '654', '678', '686', '690', '694', '706', '710', '716',
'728', '729', '732', '736', '748', '768', '788', '800', '818', '834',
'854', '894'
],
[
'016', '036', '090', '162', '166', '184', '242', '258', '296', '316',
'334', '520', '527', '540', '548', '554', '570', '574', '580', '581',
'583', '584', '585', '598', '612', '772', '776', '798', '876', '882'
], ['010'],
[
'028', '032', '044', '052', '060', '068', '074', '076', '084', '092',
'124', '136', '152', '170', '188', '192', '212', '214', '218', '222',
'238', '239', '254', '304', '308', '312', '320', '328', '332', '340',
'388', '473', '474', '484', '500', '531', '533', '534', '535', '558',
'591', '600', '604', '630', '636', '637', '652', '659', '660', '662',
'663', '666', '670', '740', '780', '796', '840', '842', '850', '858',
'862'
],
[
'004', '031', '048', '050', '051', '064', '096', '104', '116', '144',
'156', '196', '268', '275', '344', '356', '360', '364', '368', '376',
'392', '398', '400', '408', '410', '414', '417', '418', '422', '446',
'458', '462', '490', '496', '512', '524', '586', '608', '626', '634',
'682', '699', '702', '704', '760', '762', '764', '784', '792', '795',
'860', '887'
],
[
'008', '020', '040', '056', '070', '100', '112', '191', '203', '208',
'233', '234', '246', '248', '250', '251', '276', '292', '300', '336',
'348', '352', '372', '380', '428', '438', '440', '442', '470', '492',
'498', '499', '528', '568', '578', '579', '616', '620', '642', '643',
'674', '680', '688', '703', '705', '724', '744', '752', '756', '757',
'804', '807', '826', '831', '832', '833'
], ['837', '838', '839', '899']
]
for r, c in zip(regions, ccode_lists):
ccode_to_region_dict.update(dict.fromkeys(c, r))
node_data = node_data[node_data.index.isin(
[item for sublist in ccode_lists for item in sublist])]
node_data['region'] = node_data.index.map(ccode_to_region_dict)
# Coloring parameters: Export/Import Balance
node_data = node_data.join(
df_nodes[['export', 'import', 'export_by_partners', 'import_by_partners']])
node_data['export_for_val'] = [
ex if ex != 0 else exp
for ex, exp in zip(node_data['export'], node_data['export_by_partners'])
]
node_data['import_for_val'] = [
im if im != 0 else imp
for im, imp in zip(node_data['import'], node_data['import_by_partners'])
]
node_data['export_share_val'] = node_data['export_for_val'] / node_data[[
'import_for_val', 'export_for_val'
]].sum(axis=1)
node_data['export_share'] = pd.cut(node_data['export_share_val'],
list(np.linspace(0, 1, 11)),
labels=[
'0' +
str(n) if len(str(n)) == 1 else str(n)
for n in np.arange(1, 11, 1)
],
include_lowest=True)
# Coloring parameters: Pagerank
node_data['pagerank_val'] = node_data.index.map(
nx.pagerank(nx.from_pandas_edgelist(df_links, 'source', 'target')))
node_data['pagerank'] = pd.cut(node_data['pagerank_val'],
list(
np.linspace(node_data['pagerank_val'].min(),
node_data['pagerank_val'].max(),
11)),
labels=[
'0' + str(n) if len(str(n)) == 1 else str(n)
for n in np.arange(1, 11, 1)
],
include_lowest=True)
node_data['trade_links'] = node_data.index.map(link_dict)
node_data = node_data.drop(
['export_for_val', 'import_for_val', 'export_share_val', 'pagerank_val'],
axis=1)
# Edges
sources = []
targets = []
for edge in G.edges():
sources.append(edge[0])
targets.append(edge[1])
edge_data = pd.DataFrame([sources, targets]).T
edge_data.columns = ['source', 'target']
for side in ['source', 'target']:
edge_data['country_' + side] = edge_data[side].map(
node_data['country'].drop_duplicates().to_dict())
for parameter in ['region', 'export_share', 'pagerank']:
for side in ['source', 'target']:
edge_data[parameter + '_' + side] = edge_data[side].map(
node_data[parameter].to_dict())
# Labels
label_dict = dict()
label_dict_short = {
'Bolivia (Plurinational State of)': 'Bolivia',
'Brunei Darussalam': 'Brunei',
'Central African Rep.': 'CAR',
'China, Hong Kong SAR': 'Hong Kong',
'China, Macao SAR': 'Macao',
"Dem. People's Rep. of Korea": 'North Korea',
'Dem. Rep. of the Congo': 'Congo-Kinshasa',
'Falkland Isds (Malvinas)': 'Falkland Isds',
'Holy See (Vatican City State)': 'Holy See',
"Lao People's Dem. Rep.": 'Laos',
'Neth. Antilles and Aruba': 'Neth. Antilles',
'Rep. of Korea': 'Korea',
'Rep. of Moldova': 'Moldova',
'Russian Federation': 'Russia',
'Saint Barthelemy': 'St Barthelemy',
'Saint Helena': 'St Helena',
'Saint Lucia': 'St Lucia',
'Saint Maarten': 'St Maarten',
'Saint Kitts and Nevis': 'St Kitts and Nevis',
'Saint Kitts, Nevis and Anguilla': 'St Kitts and Nevis',
'Saint Pierre and Miquelon': 'St Pierre and Miquelon',
'Saint Vincent and the Grenadines': 'St Vincent',
'So. African Customs Union': 'SACU',
'United Arab Emirates': 'UAE',
'United Rep. of Tanzania': 'Tanzania',
'USA (before 1981)': 'USA',
'Africa CAMEU region, nes': 'Africa CAMEU, nes',
'Br. Antarctic Terr.': 'Br. Antarctic',
'Br. Indian Ocean Terr.': 'Br. Indian Ocean',
'Fr. South Antarctic Terr.': 'Fr. South Antarctic',
'Heard Island and McDonald Islands': 'Heard and McDonald Isds',
'North America and Central America, nes': 'North and Centr America, nes',
'South Georgia and the South Sandwich Islands': 'SGSSI',
'United States Minor Outlying Islands': 'US Minor Outlying Isds'
}
for n in node_data['country'].sort_values().unique().tolist():
if len(n) > 10 and n in label_dict_short.keys():
label_dict[n] = label_dict_short[n]
else:
label_dict[n] = n
nlinks_filter = []
for index in node_data.index.unique().tolist():
if node_data.loc[index]['trade_links'] >= 50:
nlinks_filter.append(index)
max_diameter = node_data['diameter'].max()
node_data['label'] = [
label_dict[country] if diameter >= max_diameter * 0.25
or index in nlinks_filter else np.nan for country, diameter, index in zip(
node_data['country'], node_data['diameter'], node_data.index)
]
node_data['label_selected'] = [
label_dict[country] for country in node_data['country']
]
# COLORING **********************************************************************
region_color_dict = {
'Antarctica': '#7E6EBD',
'Africa': '#FB9038',
'Asia': '#D1085C',
'Europe': '#08B0D1',
'Americas': '#d4e2e8',
'Oceania': '#134DD1',
'Special categories and unspecified areas': '#F9F871'
}
export_share_color_dict = {
'01': '#08b4d1',
'02': '#39c3da',
'03': '#6bd2e3',
'04': '#9ce1ed',
'05': '#cef0f6',
'06': '#ffe5cd',
'07': '#ffcc9b',
'08': '#ffb268',
'09': '#ff9936',
'10': '#ff7f04'
}
pagerank_color_dict = {
'01': '#3e26a8',
'02': '#4746eb',
'03': '#3e70ff',
'04': '#2797eb',
'05': '#08b4d1',
'06': '#32c69f',
'07': '#81cc59',
'08': '#dbbd28',
'09': '#fcd030',
'10': '#f9fb15'
}
background_color = '#010103'
def rgb_to_hex(rgb):
r = rgb[0]
g = rgb[1]
b = rgb[2]
return '#{:02x}{:02x}{:02x}'.format(r, g, b)
def hex_gradient_list(hex_color1, hex_color2, n_colors):
assert n_colors > 1
color1_rgb = np.array(hex_to_rgb(hex_color1)) / 255
color2_rgb = np.array(hex_to_rgb(hex_color2)) / 255
ordered = np.linspace(0, 1, n_colors)
gradient = [
list(((1 - order) * color1_rgb + (order * color2_rgb)))
for order in ordered
]
gradient_transformed = [[int(round(val * 255)) for val in color]
for color in gradient]
return [rgb_to_hex(color) for color in gradient_transformed]
def hex_gradient_str(hex_color1, hex_color2, n_colors):
assert n_colors > 1
color1_rgb = np.array(hex_to_rgb(hex_color1)) / 255
color2_rgb = np.array(hex_to_rgb(hex_color2)) / 255
ordered = np.linspace(0, 1, n_colors)
gradient = [
list(((1 - order) * color1_rgb + (order * color2_rgb)))
for order in ordered
]
gradient_transformed = [[int(round(val * 255)) for val in color]
for color in gradient]
return " ".join([rgb_to_hex(color) for color in gradient_transformed])
# Node Edges
node_data['region_edge_color'] = node_data['region'].map(region_color_dict)
node_data['export_share_edge_color'] = node_data['export_share'].map(
export_share_color_dict)
node_data['pagerank_edge_color'] = node_data['pagerank'].map(
pagerank_color_dict)
# Nodes
node_data['region_color'] = [
hex_gradient_list(color, background_color, 3)[1]
for color in node_data['region_edge_color']
]
node_data['export_share_color'] = [
hex_gradient_list(color, background_color, 3)[1]
for color in node_data['export_share_edge_color']
]
node_data['pagerank_color'] = [
hex_gradient_list(color, background_color, 3)[1]
for color in node_data['pagerank_edge_color']
]
# Chosen Nodes
node_data['chosen_region_color'] = [
hex_gradient_list(color, '#ffffff', 7)[3]
for color in node_data['region_color']
]
node_data['chosen_export_share_color'] = [
hex_gradient_list(color, '#ffffff', 7)[3]
for color in node_data['export_share_color']
]
node_data['chosen_pagerank_color'] = [
hex_gradient_list(color, '#ffffff', 7)[3]
for color in node_data['pagerank_color']
]
# Node Labels
node_data['region_label_color'] = [
hex_gradient_list(color, '#ffffff', 5)[3]
for color in node_data['region_edge_color']
]
node_data['export_share_label_color'] = [
hex_gradient_list(color, '#ffffff', 5)[3]
for color in node_data['export_share_edge_color']
]
node_data['pagerank_label_color'] = [
hex_gradient_list(color, '#ffffff', 5)[3]
for color in node_data['pagerank_edge_color']
]
# Chosen Node Labels
node_data['chosen_region_label_color'] = [
hex_gradient_list(color, '#ffffff', 6)[5]
for color in node_data['region_edge_color']
]
node_data['chosen_export_share_label_color'] = [
hex_gradient_list(color, '#ffffff', 6)[5]
for color in node_data['export_share_edge_color']
]
node_data['chosen_pagerank_label_color'] = [
hex_gradient_list(color, '#ffffff', 6)[5]
for color in node_data['pagerank_edge_color']
]
# Edges
for parameter, d in zip(
['region', 'export_share', 'pagerank'],
[region_color_dict, export_share_color_dict, pagerank_color_dict]):
for side in ['source', 'target']:
edge_data[parameter + '_color_' + side] = edge_data[parameter + '_' +
side].map(d)
for parameter in ['region', 'export_share', 'pagerank']:
edge_data[parameter + '_colors_source'] = [
hex_gradient_list(background_color, color, 11)[2]
for color in edge_data[parameter + '_color_source']
]
edge_data[parameter + '_colors_target'] = [
hex_gradient_list(background_color, color, 11)[7]
for color in edge_data[parameter + '_color_target']
]
for parameter in ['region', 'export_share', 'pagerank']:
edge_data[parameter + '_colors'] = [
hex_gradient_str(color_source, color_target, 10)
for color_source, color_target in zip(
edge_data[parameter +
'_colors_source'], edge_data[parameter +
'_colors_target'])
]
# BUILDING THE GRAPH ************************************************************
color_dict = {
'region': {
'color': 'region_color',
'chosen_color': 'chosen_region_color',
'label_color': 'region_label_color',
'chosen_label_color': 'chosen_region_label_color',
'edge_color': 'region_edge_color',
'colors': 'region_colors'
},
'export_share': {
'color': 'export_share_color',
'chosen_color': 'chosen_export_share_color',
'label_color': 'export_share_label_color',
'chosen_label_color': 'chosen_export_share_label_color',
'edge_color': 'export_share_edge_color',
'colors': 'export_share_colors'
},
'pagerank': {
'color': 'pagerank_color',
'chosen_color': 'chosen_pagerank_color',
'label_color': 'pagerank_label_color',
'chosen_label_color': 'chosen_pagerank_label_color',
'edge_color': 'pagerank_edge_color',
'colors': 'pagerank_colors'
}
}
elements = []
# Node parameters and positions
for i in node_data.index:
el_dict = dict()
el_dict['data'] = dict()
el_dict['position'] = dict()
el_dict['data']['id'] = i
el_dict['data']['label'] = node_data.loc[i]['label']
el_dict['data']['label_selected'] = node_data.loc[i]['label_selected']
el_dict['data']['color'] = node_data.loc[i][color_dict[chosen_color_parameter]['label_color']]
el_dict['data']['color_chosen'] = node_data.loc[i][color_dict[chosen_color_parameter][
'chosen_label_color']]
el_dict['data']['background_color'] = hex_gradient_list(
node_data.loc[i][color_dict[chosen_color_parameter]['color']], background_color,
4)[2] + ' ' + node_data.loc[i][color_dict[chosen_color_parameter]['color']] + ' ' + node_data.loc[
i][color_dict[chosen_color_parameter]['edge_color']]
el_dict['data']['background_color_chosen'] = hex_gradient_list(
node_data.loc[i][color_dict[chosen_color_parameter]['chosen_color']], background_color, 4
)[2] + ' ' + node_data.loc[i][color_dict[chosen_color_parameter]['edge_color']] + ' ' + node_data.loc[
i][color_dict[chosen_color_parameter]['edge_color']]
el_dict['data']['border_color'] = node_data.loc[i][color_dict[chosen_color_parameter]['edge_color']]
el_dict['data']['size'] = node_data.loc[i]['diameter']
el_dict['data']['opacity'] = 0.75
el_dict['data']['font_size'] = 40
el_dict['position']['x'] = node_data.loc[i]['x']
el_dict['position']['y'] = node_data.loc[i]['y']
elements.append(el_dict)
# Edge parameters
for i in edge_data[['source', 'target']].drop_duplicates().index:
el_dict_e = dict()
el_dict_e['data'] = dict()
el_dict_e['data'][
'id'] = edge_data.loc[i]['source'] + '_' + edge_data.loc[i]['target']
el_dict_e['data']['source'] = edge_data.loc[i]['source']
el_dict_e['data']['target'] = edge_data.loc[i]['target']
el_dict_e['data']['colors'] = edge_data.loc[i][color_dict[chosen_color_parameter]['colors']]
elements.append(el_dict_e)
# Final layout
app = Dash(__name__)
default_stylesheet = [{
'selector': 'node',
'style': {
'label': 'data(label)',
"background-fill": "radial-gradient",
"background-gradient-stop-colors": 'data(background_color)',
"background-gradient-stop-positions": '0, 80, 90, 100',
'color': 'data(color)',
'text-valign': 'center',
'text-halign': 'center',
'font-size': 'data(font_size)',
'border-color': 'data(border_color)',
'border-width': 1.5,
"border-opacity": 1,
'width': 'data(size)',
'height': 'data(size)',
'opacity': 0.98
}
}, {
'selector': 'edge',
'style': {
"line-fill": "linear-gradient",
"line-gradient-stop-colors": 'data(colors)',
"line-gradient-stop-positions": "10, 20, 30, 40, 50, 60, 70, 80, 90",
'width': 2.5,
'curve-style': 'bezier',
'source-endpoint': 'outside-to-node',
'target-endpoint': 'outside-to-node'
}
}]
styles = {
'Lab': {
'height': '100%',
'color': '#ffffff',
'font-family': 'Courier New',
'font-size': '80%',
'padding-top': '10%',
'padding-bottom': '10%',
'padding-left': '10%'
},
'Output': {
'height': '100%',
'color': '#08B0D1',
'font-family': 'Courier New',
'font-size': '80%',
'padding-top': '16%',
'padding-bottom': '10%',
'padding-left': '10%'
},
'Button': {
'height': '100%',
'width': '100%',
'color': '#ffffff',
'background-color': '#010103',
'border-color': '#010103',
'border-width': 0,
'padding-left': '20%',
'font-family': 'Courier New',
'font-size': '80%',
'text-align': 'left',
'cursor': 'pointer'
}
}
app.layout = dbc.Container([
html.Div([
html.Div([
html.Div(html.P('Country Hovered:', style=styles['Lab']),
style={
'width': '15%',
'height': '100%',
'margin-left': '2%'
}),
html.Div(html.Div(id='mouseoverNodeData', style=styles['Output']),
style={'width': '25%'}),
html.Div(html.P('Country Highlighted:', style=styles['Lab']),
style={
'width': '15%',
'height': '100%',
'margin-left': '2%'
}),
html.Div(html.Div(id='output-country', style=styles['Output']),
style={'width': '25%'}),
html.Div(html.Button(
"Save PNG", id="btn-get-png", style=styles['Button']),
style={
'width': '20%',
'background-color': '#010103',
'margin-left': '2%'
})
],
style={
'width': '100%',
'display': 'flex'
}),
html.Div([
cyto.Cytoscape(id='cytoscape',
layout={'name': 'cola'},
style={
'width': str(pic_width) + 'px',
'height':
str(graph_height * pic_width / graph_width) + 'px',
'background-color': '#010103'
},
elements=elements,
stylesheet=default_stylesheet)
],
style={
'width': '100%',
'background-color': '#010103'
})
],
style={
'display': 'inline-block',
'width': str(pic_width) + 'px'
})
],
style={
'display': 'flex',
'justify-content': 'center',
'margin-bottom': '50px'
})
@app.callback(Output('mouseoverNodeData', 'children'),
Input('cytoscape', 'mouseoverNodeData'))
def display_hover_data(data):
if data:
return data['label_selected']
@app.callback(
[Output('cytoscape', 'stylesheet'),
Output('output-country', 'children')],
[Input('cytoscape', 'tapNode'),
Input('cytoscape', 'selectedNodeData')])
def generate_stylesheet(node, data_list):
if not data_list:
return default_stylesheet, 'No country selected'
elif node:
node_id = node['data']['id']
stylesheet = [{
"selector": 'node',
'style': {
'label': 'data(label)',
"background-fill": "radial-gradient",
"background-gradient-stop-colors": 'data(background_color)',
"background-gradient-stop-positions": "0, 80, 90, 100",
'color': 'data(color)',
'text-valign': 'center',
'text-halign': 'center',
'font-size': 'data(font_size)',
'border-color': 'data(border_color)',
'border-width': 1.5,
"border-opacity": 1,
'width': 'data(size)',
'height': 'data(size)',
'opacity': 0.3
}
}, {
'selector': 'edge',
'style': {
"line-fill": "linear-gradient",
"line-gradient-stop-colors": 'data(colors)',
"line-gradient-stop-positions":
"10, 20, 30, 40, 50, 60, 70, 80, 90",
'width': 2,
'curve-style': 'bezier',
'source-endpoint': 'outside-to-node',
'target-endpoint': 'outside-to-node',
'opacity': 0.2
}
}, {
"selector": 'node[id = "{}"]'.format(node_id),
"style": {
'label': 'data(label_selected)',
"background-fill": "radial-gradient",
"background-gradient-stop-colors":
'data(background_color_chosen)',
"background-gradient-stop-positions": "0, 98, 99, 100",
'color': 'data(color_chosen)',
'text-valign': 'center',
'text-halign': 'center',
'font-size': 'data(font_size)',
'border-color': 'data(border_color)',
'border-width': 1.5,
"border-opacity": 1,
'width': 'data(size)',
'height': 'data(size)',
'opacity': 0.98,
'z-index': 9999
}
}]
for edge in node["edgesData"]:
if edge['source'] == node_id:
stylesheet.append({
"selector":
'node[id = "{}"]'.format(edge['target']),
"style": {
'label': 'data(label)',
"background-fill": "radial-gradient",
"background-gradient-stop-colors":
'data(background_color)',
"background-gradient-stop-positions": "0, 80, 90, 100",
'color': 'data(color)',
'text-valign': 'center',
'text-halign': 'center',
'font-size': 'data(font_size)',
'border-color': 'data(border_color)',
'border-width': 1.5,
"border-opacity": 1,
'width': 'data(size)',
'height': 'data(size)',
'opacity': 0.98
}
})
stylesheet.append({
"selector":
'edge[id= "{}"]'.format(edge['id']),
"style": {
"line-fill": "linear-gradient",
"line-gradient-stop-colors": 'data(colors)',
"line-gradient-stop-positions":
"10, 20, 30, 40, 50, 60, 70, 80, 90",
'width': 7,
'curve-style': 'bezier',