analysis

analysis_prod_network.py

@@ -1,8 +1,6 @@
 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'
 
@@ -32,7 +30,7 @@ for code in G.nodes:
     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_size'] = 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)
@@ -62,8 +60,10 @@ nx.draw(G,
 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")
+cb = plt.colorbar(sm, fraction=0.01, cax=position)
+cb.ax.tick_params(labelsize=8)
+cb.outline.set_visible(False)
+plt.savefig("analysis\\count_prod_network")
 plt.close()
 
 # dcp_prod
@@ -72,13 +72,17 @@ count_dcp = pd.read_csv("analysis\\count_dcp.csv",
                             '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.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]
+                      index=False,
+                      encoding='utf-8-sig')
+count_dcp_prod = count_dcp_prod[count_dcp_prod['count'] > 10]
 # print(count_dcp_prod)
 
 list_prod = count_dcp_prod['up_id_product'].tolist(
@@ -126,28 +130,7 @@ 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 ={}
+pos_new = {}
 for node, p in pos.items():
     pos_new[node] = (p[1], p[0])
 
@@ -157,8 +140,8 @@ nx.draw(g_bom,
         pos_new,
         node_size=50,
         labels=node_labels,
-        font_size=6,
-        width = 1.5,
+        font_size=5,
+        width=1.5,
         edge_color=colors,
         edge_cmap=cmap,
         edge_vmin=vmin,
@@ -170,7 +153,9 @@ 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()
+position = fig.add_axes([0.75, 0.1, 0.01, 0.3])
+cb = plt.colorbar(sm, fraction=0.01, cax=position)
+cb.ax.tick_params(labelsize=8)
+cb.outline.set_visible(False)
+plt.savefig("analysis\\count_dcp_prod_network")
+plt.close()