import pickle

from sqlalchemy import text
from orm import engine, connection
import pandas as pd
import networkx as nx
import json
import matplotlib.pyplot as plt

# Prepare data
Firm = pd.read_csv("input_data/input_firm_data/Firm_amended.csv")
Firm['Code'] = Firm['Code'].astype('string')
Firm.fillna(0, inplace=True)
BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0)

# SQL query
with open('SQL_analysis_risk.sql', 'r') as f:
    str_sql = text(f.read())

result = pd.read_sql(sql=str_sql, con=connection)
result.to_csv('output_result/risk/count.csv', index=False, encoding='utf-8-sig')
print(result)

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

exp_id = 1
G_bom_df = pd.read_sql(
    sql=text(f'select g_bom from iiabmdb.without_exp_experiment where id = {exp_id};'),
    con=connection
)

if G_bom_df.empty:
    raise ValueError(f"No g_bom found for exp_id = {exp_id}")

G_bom_str = G_bom_df['g_bom'].tolist()[0]
if G_bom_str is None:
    raise ValueError(f"g_bom data is None for exp_id = {exp_id}")

G_bom = nx.adjacency_graph(json.loads(G_bom_str))
pos = nx.nx_agraph.graphviz_layout(G_bom, prog="twopi", args="")
node_labels = nx.get_node_attributes(G_bom, 'Name')

plt.figure(figsize=(12, 12), dpi=300)
nx.draw_networkx_nodes(G_bom, pos)
nx.draw_networkx_edges(G_bom, pos)
nx.draw_networkx_labels(G_bom, pos, labels=node_labels, font_size=3)
plt.savefig(f"output_result/risk/g_bom_exp_id_{exp_id}.png")
plt.close()

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

sample_id = 1
# G_firm_df = pd.read_sql(
#     sql=text(f'select g_firm from iiabmdb.without_exp_sample where id = {sample_id};'),
#     con=connection
# )
#
# if G_firm_df.empty:
#     raise ValueError(f"No g_firm found for sample_id = {sample_id}")
#
# G_firm_str = G_firm_df['g_firm'].tolist()[0]
# if G_firm_str is None:
#     raise ValueError(f"g_firm data is None for sample_id = {sample_id}")
#
# G_firm = nx.adjacency_graph(json.loads(G_firm_str))

with open("firm_network.pkl", 'rb') as f:
    G_firm = pickle.load(f)
print(f"Successfully loaded cached data from firm_network.pkl")

# 1. 移除孤立节点
isolated_nodes = list(nx.isolates(G_firm))  # 找出所有没有连接的孤立节点
G_firm.remove_nodes_from(isolated_nodes)   # 从图中移除这些节点

# 2. 重新布局和绘图
pos = nx.nx_agraph.graphviz_layout(G_firm, prog="twopi", args="")
node_label = {key: key for key in nx.get_node_attributes(G_firm, 'Revenue_Log').keys()}
node_size = [value * 10 for value in nx.get_node_attributes(G_firm, 'Revenue_Log').values()]  # 节点大小扩大10倍
edge_label = {(n1, n2): label for (n1, n2, _), label in nx.get_edge_attributes(G_firm, "Product").items()}

plt.figure(figsize=(12, 12), dpi=500)
nx.draw(G_firm, pos, node_size=node_size, labels=node_label, font_size=5, width=0.5)
nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=2)
plt.axis('equal')  # 锁定坐标轴比例，确保图形内容是正方形
plt.savefig(f"output_result/risk/g_firm_sample_id_{sample_id}_de.png", bbox_inches='tight', pad_inches=0.1)
plt.close()


# Count firm product
count_firm_prod = result.value_counts(subset=['id_firm', 'id_product'])
count_firm_prod.name = 'count'
count_firm_prod = count_firm_prod.to_frame().reset_index()
count_firm_prod.to_csv('output_result/risk/count_firm_prod.csv', index=False, encoding='utf-8-sig')
print(count_firm_prod)

# Count firm
count_firm = count_firm_prod.groupby('id_firm')['count'].sum()
count_firm = count_firm.to_frame().reset_index()
count_firm.sort_values('count', inplace=True, ascending=False)
count_firm.to_csv('output_result/risk/count_firm.csv', index=False, encoding='utf-8-sig')
print(count_firm)

# Count product
count_prod = count_firm_prod.groupby('id_product')['count'].sum()
count_prod = count_prod.to_frame().reset_index()
count_prod.sort_values('count', inplace=True, ascending=False)
count_prod.to_csv('output_result/risk/count_prod.csv', index=False, encoding='utf-8-sig')
print(count_prod)

# DCP disruption causing probability
result_disrupt_ts_above_0 = result[result['ts'] > 0]
print(result_disrupt_ts_above_0)
result_dcp = pd.DataFrame(columns=[
    's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'
])

result_dcp_list = []  # 用列表收集数据，避免DataFrame逐行增长的问题
for sid, group in result.groupby('s_id'):
    ts_start = max(group['ts'])
    while ts_start >= 1:
        ts_end = ts_start - 1
        while ts_end >= 0:
            up = group.loc[group['ts'] == ts_end, ['id_firm', 'id_product']]
            down = group.loc[group['ts'] == ts_start, ['id_firm', 'id_product']]
            for _, up_row in up.iterrows():
                for _, down_row in down.iterrows():
                    result_dcp_list.append([sid] + up_row.tolist() + down_row.tolist())
            ts_end -= 1
        ts_start -= 1

# 转换为DataFrame
result_dcp = pd.DataFrame(result_dcp_list, columns=[
    's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'
])

# 统计
count_dcp = result_dcp.value_counts(
    subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product']
).reset_index(name='count')

# 保存文件
count_dcp.to_csv('output_result/risk/count_dcp.csv', index=False, encoding='utf-8-sig')

# 输出结果
print(count_dcp)