添加代码

calculate.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# time: 2023/6/16 10:15
+# file: calculate.py
+# author: David
+# company: shenyang JY
+import requests
+import json, time, datetime
+
+url = 'http://49.4.78.194:17160/apiCalculate/calculate'
+'''
+准确率接口使用手顺：
+①入口方法为 calculate_acc
+② 按照参数传传值
+data含有C_TIME时间、realValue实际功率、ableValue可用功率（没有数值用实际功率替代）、forecastAbleValue预测功率
+opt为包含场站必要信息的字典，字段为：cap装机容量 province省份 formulaType公式类型 electricType电站类型 stationCode场站编码
+具体介绍参考接口文档
+③公式计算分为按天和按点两种，指定好opt.formulaType，即可设置公式类型，再在求取的每天或每个点的结果上进行平均，结果返回
+'''
+
+
+def wrap_json(df, opt):
+    """
+    包装json
+    :param df: 列名为 C_TIME realValue ableValue forecastAbleValue的DataFrame
+    :param opt: 参数字典
+    :return: json列表
+    """
+    d = opt.formulaType.split('_')[0]
+    jata, dfs = [], []
+    if d == 'POINT':
+        df['time'] = df['C_TIME'].apply(datetime_to_timestamp)
+        for i, row in df.iterrows():
+            dfs.append(row.to_frame().T)
+    elif d == 'DAY':
+        df['time'] = df['C_TIME'].apply(datetime_to_timestamp)
+        df['C_TIME'] = df['C_TIME'].dt.strftime('%y%m%d')   # 转换成年月日
+        for i, group in df.groupby('C_TIME'):
+            dfs.append(group)
+    outter_dict = {"electricCapacity": str(opt.installedCapacityActual), "province": opt.provinceDispatch, "formulaType": opt.formulaType, "electricType":opt.stationType, "stationCode": opt.stationCodeExternal}
+    timestamp = int(time.mktime(datetime.datetime.now().timetuple()) * 1000 + datetime.datetime.now().microsecond / 1000.0)
+    inner_dict = {"genTime": str(timestamp)+"L", "capacity": str(opt.installedCapacityActual), "openCapacity": str(opt.installedCapacityActual)}
+    for df in dfs:
+        calculationInfoList = df.iloc[:, 1:].to_json(orient='records')
+        # print("++++", calculationInfoList)
+        outter_dict['calculationInfoList'] = [dict(calculation, **inner_dict) for calculation in eval(calculationInfoList)]
+        jata.append(json.dumps(outter_dict))
+    return jata
+
+
+def send_reqest(url, jata):
+    """
+    发送请求
+    :param url: 请求地址
+    :param jata: Json数据
+    :return: 准确率
+    """
+    headers = {
+        'content-type': 'application/json;charset=UTF-8',
+        "Authorization": "dXNlcjoxMjM0NTY="
+    }
+    acc, number = 0, 0
+    for i in range(len(jata)):
+        res = requests.post(url, headers=headers, data=jata[i])
+        if res.json()['code'] == '500':
+            print("状态码是:"+ res.json()['code'], "报错信息："+res.json()['msg'])
+            continue
+        number += 1
+        acc += float(res.json()['data'][:-1])
+    acc /= number
+    return acc
+
+
+def calculate_acc(data, opt):
+    """
+    准确率调用接口计算
+    :param data: 列名为 C_TIME realValue ableValue forecastAbleValue的DataFrame
+    :param opt: 参数字段
+    :return: 计算结果
+    """
+    jata = wrap_json(data, opt)
+    acc = send_reqest(url=url, jata=jata)
+
+    return acc
+
+
+def datetime_to_timestamp(dt):
+    return int(round(time.mktime(dt.timetuple()))*1000)

formula.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# time: 2023/9/27 9:54
+# file: formula.py
+# author: David
+# company: shenyang JY
+
+import math
+
+
+def get_mse(records_real, records_predict):
+    """
+    均方误差
+    """
+    if len(records_real) == len(records_predict):
+        return sum([(x - y) ** 2 for x, y in zip(records_real, records_predict)]) / len(records_real)
+    else:
+        return None
+
+
+def get_rmse(records_real, records_predict):
+    """
+    均方根误差
+    """
+    mse = get_mse(records_real, records_predict)
+    if mse:
+        return math.sqrt(mse)
+    else:
+        return None
+
+
+def get_mae(records_real, records_predict):
+    """
+    平均绝对误差
+    """
+    if len(records_real) == len(records_predict):
+        return sum([abs(x - y) for x, y in zip(records_real, records_predict)]) / len(records_real)
+    else:
+        return None