176 lines
5.5 KiB
Python
176 lines
5.5 KiB
Python
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() |