import pandas as pd
import matplotlib.pyplot as plt
import networkx as nx

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

# count firm category
count_firm = pd.read_csv("output_result\\risk\\count_firm.csv")
print(count_firm.describe())

count_dcp = pd.read_csv("output_result\\risk\\count_dcp.csv",
                        dtype={
                            'up_id_firm': str,
                            'down_id_firm': str
                        })
# print(count_dcp)
count_dcp = count_dcp[count_dcp['count'] > 35]

list_firm = count_dcp['up_id_firm'].tolist(
) + count_dcp['down_id_firm'].tolist()
list_firm = list(set(list_firm))

# init graph firm
Firm = pd.read_csv("input_data\\Firm_amended.csv")
Firm['Code'] = Firm['Code'].astype('string')
Firm.fillna(0, inplace=True)
Firm_attr = Firm.loc[:, ["Code", "Type_Region", "Revenue_Log"]]
firm_product = []
for _, row in Firm.loc[:, '1':].iterrows():
    firm_product.append(row[row == 1].index.to_list())
Firm_attr.loc[:, 'Product_Code'] = firm_product
Firm_attr.set_index('Code', inplace=True)

G_firm = nx.MultiDiGraph()
G_firm.add_nodes_from(list_firm)

firm_labels_dict = {}
for code in G_firm.nodes:
    firm_labels_dict[code] = Firm_attr.loc[code].to_dict()
nx.set_node_attributes(G_firm, firm_labels_dict)

count_max = count_dcp['count'].max()
count_min = count_dcp['count'].min()
k = 5 / (count_max - count_min)
for _, row in count_dcp.iterrows():
    # print(row)
    lst_add_edge = [(
        row['up_id_firm'],
        row['down_id_firm'],
        {
            'up_id_product': row['up_id_product'],
            'down_id_product': row['down_id_product'],
            'edge_label': f"{row['up_id_product']} - {row['down_id_product']}",
            'edge_width': k * (row['count'] - count_min),
            'count': row['count']
        })]
    G_firm.add_edges_from(lst_add_edge)

# dcp_networkx
pos = nx.nx_agraph.graphviz_layout(G_firm, prog="dot", args="")
node_label = nx.get_node_attributes(G_firm, 'Revenue_Log')
# desensitize
node_label = {
    key: key
    for key in node_label.keys()
}
node_size = list(nx.get_node_attributes(G_firm, 'Revenue_Log').values())
node_size = list(map(lambda x: x**2, node_size))
edge_label = nx.get_edge_attributes(G_firm, "edge_label")
edge_label = {(n1, n2): label for (n1, n2, _), label in edge_label.items()}
edge_width = nx.get_edge_attributes(G_firm, "edge_width")
edge_width = [w for (n1, n2, _), w in edge_width.items()]
colors = nx.get_edge_attributes(G_firm, "count")
colors = [w for (n1, n2, _), w in colors.items()]
vmin = min(colors)
vmax = max(colors)
cmap = plt.cm.Blues
fig = plt.figure(figsize=(10, 8), dpi=300)
nx.draw(G_firm,
        pos,
        node_size=node_size,
        labels=node_label,
        font_size=8,
        width=3,
        edge_color=colors,
        edge_cmap=cmap,
        edge_vmin=vmin,
        edge_vmax=vmax)
nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=6)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm._A = []
position = fig.add_axes([0.95, 0.05, 0.01, 0.3])
cb = plt.colorbar(sm, fraction=0.01, cax=position)
cb.ax.tick_params(labelsize=10)
cb.outline.set_visible(False)
plt.savefig("output_result\\risk\\count_dcp_network")
plt.close()