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import numpy as np
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import pandas as pd
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from prophet import Prophet
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import math
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import matplotlib.pyplot as plt
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import os
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from openpyxl import Workbook
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pd.set_option('display.width',None)
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def normal(x):
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high = x.describe()['75%'] + 1.5*(x.describe()['75%']-x.describe()['25%'])
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low = x.describe()['25%'] - 1.5*(x.describe()['75%']-x.describe()['25%'])
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return x[(x<=high)&(x>=low)]
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df = pd.read_csv(r'C:\Users\鸽子\Desktop\浙江各区县数据(2).csv')
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df.columns = df.columns.map(lambda x:x.strip())
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df.drop(columns=['500kv(含330kv)及以上','220kv','110kv(含66kv)','20kv','power_sal'],inplace=True)
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print(df.columns)
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print(dict(zip(df.columns,[(df[x]==0).sum()/len(df) for x in df.columns])))
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yc_org_list = []
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list_fl = []
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list_org = []
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for city in df['市'].drop_duplicates():
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df_ct = df[df['市']==city]
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# wb = Workbook()
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# wb.save(fr'C:\Users\鸽子\Desktop\9月0.4kv区县预测\{city}.xlsx')
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for org in df_ct['org_name'].drop_duplicates():
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if org.strip()[-4:] != '供电公司':
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continue
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df_org = df_ct[df_ct['org_name']==org]
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df_org['1-10kv'] /= 10000
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df_org['35kv'] /= 10000
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df_org['0.4kv及以下'] /= 10000
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s1 = df_org[['日期','0.4kv及以下']]
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s1.replace(0,np.NaN,inplace=True)
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s1.dropna(how='any',inplace=True)
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# plt.plot(range(len(s1)),s1['1-10kv'])
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# plt.show()
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# 更改列名，更改为Prophet指定的列名ds和y
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dd = s1.rename(columns={'日期':'ds','0.4kv及以下':'y'})
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dd['ds'] = pd.to_datetime(dd['ds'])
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# 划分数据，划分为训练集和验证集，预测的数据设置为未来3天
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df_train = dd[(dd['ds']>='2022-01-01')&(dd['ds']<='2023-07-31')][:-3]
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df_train = df_train.loc[normal(df_train['y']).index]
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if df_train.shape[0] <= 180:
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yc_org_list.append(org)
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continue
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df_test = dd[(dd['ds']>='2022-01-01')&(dd['ds']<='2023-07-31')][-3:]
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# 数据的变动会受到季节、周、天的影响，存在一定的规律性，因此我们将这三个参数设置为True
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model = Prophet(yearly_seasonality=True, weekly_seasonality=True, daily_seasonality=True)
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# 采用中国的假期模式，其余参数均保持默认
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model.add_country_holidays(country_name="CN")
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model.fit(df_train)
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# make_future_dataframe: 作用是告诉模型我们要预测多长时间，以及时间的周期是什么。生成一个时间戳
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future = model.make_future_dataframe(periods=3, freq='D')
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# 进行预测，返回预测的结果forecast
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forecast = model.predict(future)
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# forecast['additive_terms'] = forecast['weekly'] + forecast['yearly']；
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# 有：forecast['yhat'] = forecast['trend'] + forecast['additive_terms'] 。
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# 因此：forecast['yhat'] = forecast['trend'] +forecast['weekly'] + forecast['yearly']。
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# 如果有节假日因素，那么就会有forecast['yhat'] = forecast['trend'] +forecast['weekly'] + forecast['yearly'] + forecast['holidays']。
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# print(forecast)
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# 测试，把ds列，即data_series列设置为索引列
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df_test = df_test.set_index('ds')
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# 把预测到的数据取出ds列，预测值列yhat，同样把ds列设置为索引列。
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forecast = forecast[['ds','yhat']].set_index('ds')
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# join:按照索引进行连接，
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# dropna：能够找到DataFrame类型数据的空值（缺失值），将空值所在的行/列删除后，将新的DataFrame作为返回值返回。
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df_all = forecast.join(dd.set_index('ds')).dropna()
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df_all['org_name'] = org
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df_all['偏差率'] = (df_all['y'] - df_all['yhat'])/df_all['y']
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df_all.rename(columns={'y':'真实值','yhat':'预测值'},inplace=True)
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df_all = df_all[['org_name','真实值','预测值','偏差率']]
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list_org.append(org)
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try:
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result = df_all.loc['2023-7']
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result['goal'] = (result['真实值'] - result['预测值'])[-3:].sum()/result['真实值'].sum()
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list_fl.append((result['真实值'] - result['预测值'])[-3:].sum()/result['真实值'].sum())
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# with pd.ExcelWriter(fr'C:\Users\鸽子\Desktop\9月0.4kv区县预测\{city}.xlsx',mode='a',engine='openpyxl',if_sheet_exists='replace') as writer:
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# result.to_excel(writer,sheet_name=f'{org}')
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except:
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yc_org_list.append(org)
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df = pd.DataFrame({'org':list_org,'goal':list_fl})
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print(df)
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print(df['goal'].value_counts(bins=[-0.05,-0.01,-0.005,0, 0.005, 0.01, 0.02,0.05],sort=False))
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# print(yc_org_list)
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# # 创建一个ExcelWriter对象
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# with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\output.xlsx',mode='a',if_sheet_exists='replace') as writer:
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# # 将不同的子文件写入同一个Excel文件的不同工作表
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# df_all.to_excel(writer, sheet_name=f'Sheet{i+1}')
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# df_all.plot()
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# # 设置左上角小标
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# plt.legend(['true', 'yhat'])
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# plt.show()
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