IIabm/analysis_prod_network.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
import math

plt.rcParams['font.sans-serif'] = 'SimHei'

count_prod = pd.read_csv("analysis\\count_prod.csv")
print(count_prod)

# category
print(count_prod.describe())

# pie
count_prod_trim = count_prod[count_prod['count'] > 50]
plt.pie(count_prod_trim['count'], labels=count_prod_trim['Name'])
plt.savefig("analysis\\count_prod_pie")
plt.close()

# prod_networkx
BomNodes = pd.read_csv('BomNodes.csv', index_col=0)
BomNodes.set_index('Code', inplace=True)
BomCateNet = pd.read_csv('BomCateNet.csv', index_col=0)
BomCateNet.fillna(0, inplace=True)

G = nx.from_pandas_adjacency(BomCateNet.T, create_using=nx.MultiDiGraph())

labels_dict = {}
for code in G.nodes:
    node_attr = BomNodes.loc[code].to_dict()
    index_list = count_prod[count_prod['id_product'] == code].index.tolist()
    index = index_list[0] if len(index_list) == 1 else -1
    node_attr['count'] = count_prod['count'].get(index, 0)
    node_attr['node_size'] = 5 * count_prod['count'].get(index, 0)
    node_attr['node_color'] = count_prod['count'].get(index, 0)
    labels_dict[code] = node_attr
nx.set_node_attributes(G, labels_dict)
# print(labels_dict)

pos = nx.nx_agraph.graphviz_layout(G, prog="twopi", args="")
dict_node_name = nx.get_node_attributes(G, 'Name')
node_labels = {}
for node in nx.nodes(G):
    node_labels[node] = f"{node} {str(dict_node_name[node])}"
    # node_labels[node] = f"{str(dict_node_name[node])}"
colors = list(nx.get_node_attributes(G, 'node_color').values())
vmin = min(colors)
vmax = max(colors)
cmap = plt.cm.Blues
fig = plt.figure(figsize=(10, 10), dpi=300)
nx.draw(G,
        pos,
        node_size=list(nx.get_node_attributes(G, 'node_size').values()),
        labels=node_labels,
        font_size=6,
        node_color=colors,
        cmap=cmap,
        vmin=vmin,
        vmax=vmax,
        edge_color='grey')
sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm._A = []
position = fig.add_axes([0.01, 0.05, 0.01, 0.3])
plt.colorbar(sm, fraction=0.01, cax=position)
# plt.savefig("analysis\\count_prod_network")
plt.close()

# dcp_prod
count_dcp = pd.read_csv("analysis\\count_dcp.csv",
                        dtype={
                            'up_id_firm': str,
                            'down_id_firm': str
                        })
count_dcp_prod = count_dcp.groupby(['up_id_product','up_name_product', 'down_id_product', 'down_name_product'])['count'].sum()
count_dcp_prod = count_dcp_prod.reset_index()
count_dcp_prod.sort_values('count', inplace=True, ascending=False)
count_dcp_prod.to_csv('analysis\\count_dcp_prod.csv',
                       index=False,
                       encoding='utf-8-sig')
count_dcp_prod = count_dcp_prod[count_dcp_prod['count'] > 2]
# print(count_dcp_prod)

list_prod = count_dcp_prod['up_id_product'].tolist(
) + count_dcp['down_id_product'].tolist()
list_prod = list(set(list_prod))

# init graph bom

BomNodes = pd.read_csv('BomNodes.csv', index_col=0)
BomNodes.set_index('Code', inplace=True)

g_bom = nx.MultiDiGraph()
g_bom.add_nodes_from(list_prod)

bom_labels_dict = {}
for code in list_prod:
    dct_attr = BomNodes.loc[code].to_dict()
    bom_labels_dict[code] = dct_attr
nx.set_node_attributes(g_bom, bom_labels_dict)


count_max = count_dcp_prod['count'].max()
count_min = count_dcp_prod['count'].min()
k = 5 / (count_max - count_min)
for _, row in count_dcp_prod.iterrows():
    # print(row)
    lst_add_edge = [(
        row['up_id_product'],
        row['down_id_product'],
        {
            'count': row['count']
        })]
    g_bom.add_edges_from(lst_add_edge)

# dcp_networkx
pos = nx.nx_agraph.graphviz_layout(g_bom, prog="dot", args="")
node_labels = nx.get_node_attributes(g_bom, 'Name')
temp = {}
for key, value in node_labels.items():
    temp[key] = key + " " + value
node_labels = temp
colors = nx.get_edge_attributes(g_bom, "count")
colors = [w for (n1, n2, _), w in colors.items()]
vmin = min(colors)
vmax = max(colors)
cmap = plt.cm.Blues

# dct_row = {}
# for node, p in pos.items():
#     if p[1] not in dct_row.keys():
#         dct_row[p[1]] = {node: p}
#     else:
#         dct_row[p[1]][node] = p
# dct_row = dict(sorted(dct_row.items(), key=lambda d: d[0], reverse=True))
# dct_up = dct_row[max(dct_row.keys())]
# dct_up = dict(sorted(dct_up.items(), key=lambda d: d[1][0], reverse=True))
# h = list(dct_row.keys())[0] - list(dct_row.keys())[1]
# n = len(dct_up.items())
# arr_h = np.linspace(list(dct_row.keys())[0]-h/2, list(dct_row.keys())[0]+2*h, num=n)
# dct_up_new = {}
# for index, (node, p) in enumerate(dct_up.items()):
#     dct_up_new[node] = (p[0], arr_h[index])
# pos_new = {}
# for row, dct in dct_row.items():
#     if row == list(dct_row.keys())[0]:
#         pos_new.update(dct_up_new)
#     else:
#         pos_new.update(dct)
pos_new ={}
for node, p in pos.items():
    pos_new[node] = (p[1], p[0])

fig = plt.figure(figsize=(6, 10), dpi=300)
# plt.subplots_adjust(right=0.7)
nx.draw(g_bom,
        pos_new,
        node_size=50,
        labels=node_labels,
        font_size=6,
        width = 1.5,
        edge_color=colors,
        edge_cmap=cmap,
        edge_vmin=vmin,
        edge_vmax=vmax)
plt.axis('off')
axis = plt.gca()
axis.set_xlim([1.2*x for x in axis.get_xlim()])
axis.set_ylim([1.2*y for y in axis.get_ylim()])

sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm._A = []
position=fig.add_axes([0.1, 0.4, 0.01, 0.2])
plt.colorbar(sm, fraction=0.01, cax=position)
# plt.savefig("analysis\\count_dcp_prod_network")
plt.close()