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

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

count_prod = pd.read_csv("output_result/risk/count_prod.csv")
print(count_prod)

# category
print(count_prod.describe())

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

bom_nodes = pd.read_csv('input_data/input_product_data/BomNodes.csv')
bom_nodes['Code'] = bom_nodes['Code'].astype(str)
bom_nodes.set_index('Index', inplace=True)

bom_cate_net = pd.read_csv('input_data/input_product_data/合成结点.csv')
g_bom = nx.from_pandas_edgelist(bom_cate_net, source='UPID', target='ID', create_using=nx.MultiDiGraph())

labels_dict = {}
for code in g_bom.nodes:
    node_attr = bom_nodes.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'] = (count_prod['count'].get(index, 0))/10
    node_attr['node_color'] = count_prod['count'].get(index, 0)
    labels_dict[code] = node_attr
nx.set_node_attributes(g_bom, labels_dict)
# print(labels_dict)

pos = nx.nx_agraph.graphviz_layout(g_bom, prog="twopi", args="")
dict_node_name = nx.get_node_attributes(g_bom, 'Name')
node_labels = {}
for node in nx.nodes(g_bom):
    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_bom, 'node_color').values())
vmin = min(colors)
vmax = max(colors)
cmap = plt.cm.Blues
# 创建绘图对象
fig = plt.figure(figsize=(10, 10), dpi=300)
ax = fig.add_subplot(111)

# 绘制网络图（优化样式参数）
nx.draw(g_bom, pos,
        node_size=list(nx.get_node_attributes(g_bom, 'node_size').values()),
        labels=node_labels,
        font_size=3,
        node_color=colors,
        cmap=cmap,
        vmin=vmin,
        vmax=vmax,
        edge_color='#808080',  # 中性灰
        width=0.3,
        edgecolors='#404040',
        linewidths=0.2)

# 创建颜色条（修正实现方式）
sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm.set_array([])

# 设置颜色条位置和样式
cax = fig.add_axes([0.88, 0.3, 0.015, 0.4])  # 右侧垂直对齐
cb = plt.colorbar(sm, cax=cax)
cb.ax.tick_params(labelsize=4, width=0.5, colors='#333333')
cb.outline.set_linewidth(0.5)
cb.set_label('Risk Level', fontsize=5, labelpad=2)

# 添加图元信息
ax.set_title("Production Risk Network", fontsize=6, pad=8, color='#2F2F2F')
plt.text(0.5, 0.02, 'Data: USTB Production System | Viz: DeepSeek-R1',
         ha='center', fontsize=3, color='#666666',
         transform=fig.transFigure)

# 调整边界和保存
plt.subplots_adjust(left=0.05, right=0.85, top=0.95, bottom=0.1)  # 适应颜色条
plt.savefig(r"output_result/risk/count_prod_network.png",  # 规范路径格式
           dpi=600,
           bbox_inches='tight',
           pad_inches=0.05,
           transparent=False)
plt.close()

# dcp_prod
count_dcp = pd.read_csv("output_result/risk/count_dcp.csv",
                        dtype={
                            'up_id_firm': str,
                            'down_id_firm': str
                        })
count_dcp_prod = count_dcp.groupby(
    ['up_id_product',
     'down_id_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('output_result\\risk\\count_dcp_prod.csv',
                      index=False,
                      encoding='utf-8-sig')
count_dcp_prod = count_dcp_prod[count_dcp_prod['count'] > 1000]
# 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('input_data/input_product_data/BomNodes.csv')
BomNodes.set_index('Index', 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="twopi", args="")
node_labels = nx.get_node_attributes(g_bom, 'Name')

temp = {}
for key, value in node_labels.items():
    temp[key] = str(key) + " " + value
node_labels = temp
node_labels ={
    38: 'SiC Substrate',
    39: 'GaN Substrate',
    40: 'Si Substrate',
    41: 'AlN Substrate',
    42: 'DUV LED Substrate',
    43: 'InP Substrate',
    44: 'Mono-Si Wafer',
    45: 'Poly-Si Wafer',
    46: 'InP Cryst./Wafer',
    47: 'SiC Cryst./Wafer',
    48: 'GaAs Wafer',
    49: 'GaN Cryst./Wafer',
    50: 'Si Epi Wafer',
    51: 'SiC Epi Wafer',
    52: 'AlN Epi',
    53: 'GaN Epi',
    54: 'InP Epi',
    55: 'LED Epi Wafer',
    90: 'Power Devices',
    91: 'Diode',
    92: 'Transistor',
    93: 'Thyristor',
    94: 'Rectifier',
    95: 'IC Fab',
    99: 'Wafer Test'
}
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

pos_new = {node: (p[1], p[0]) for node, p in pos.items()}  # 字典推导式优化

fig = plt.figure(figsize=(8, 8), dpi=300)
plt.subplots_adjust(right=0.85)  # 关键调整：右侧保留15%空白

# 使用Axes对象精准控制
main_ax = fig.add_axes([0.1, 0.1, 0.75, 0.8])  # 主图占左75%宽，上下各留10%边距
nx.draw(g_bom, pos_new,
        ax=main_ax,
        node_size=50,
        labels=node_labels,
        font_size=5,
        width=1.5,
        edge_color=colors,
        edge_cmap=cmap,
        edge_vmin=vmin,
        edge_vmax=vmax,
        )
main_ax.axis('off')

# 颜色条定位系统
cbar_ax = fig.add_axes([0.86, 0.15, 0.015, 0.3])  # 右边缘86%位置，底部15%起，占30%高度
sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm._A = []  # 必需的空数组

# 微调颜色条样式
cbar = fig.colorbar(sm, cax=cbar_ax, orientation='vertical')
cbar.ax.tick_params(labelsize=4,
                    width=0.3,    # 刻度线粗细
                    length=1.5,   # 刻度线长度
                    pad=0.8)      # 标签与条间距
cbar.outline.set_linewidth(0.5)   # 边框线宽

# 输出前验证边界
print(f"Colorbar position: {cbar_ax.get_position().bounds}")  # 应输出(0.86,0.15,0.015,0.3)

# 专业级保存参数
plt.savefig("output_result/risk/count_dcp_prod_network.png",
            dpi=900,
            bbox_inches='tight',  # 自动裁剪白边
            pad_inches=0.05,      # 保留0.05英寸边距
            metadata={'CreationDate': None})  # 避免时间戳污染元数据
plt.close()