platform/cache/data/plt.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# time: 2024/4/26 11:53
# file: plt.py
# author: David
# company: shenyang JY


import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import math


tower = pd.read_csv('tower-1-process.csv')


tower = tower.apply(pd.to_numeric, errors='ignore')
powers = pd.read_csv('power.csv')
tower['C_TIME'] = pd.to_datetime(tower['C_TIME'])
powers['C_TIME'] = pd.to_datetime(powers['C_TIME'])
union = pd.merge(powers, tower, on='C_TIME')

predict_dt = tower.head(1)['C_TIME'][0]
history_dt = tower.iloc[-1, :]["C_TIME"]


if history_dt - predict_dt != pd.Timedelta(hours=1):
    print("环境数据-鉴权失败")




union.set_index('C_TIME', inplace=True)
# union = union.loc['2023-06-01': '2023-06-10']
union = union.loc['2023-06']
union.reset_index(inplace=True)
union = union[['C_TIME', 'C_REAL_VALUE', 'C_WS_INST_HUB_HEIGHT']]
union['C_TIME'] = pd.to_datetime(union['C_TIME'])
# union.to_csv("./趋势对比1分钟.csv", index=False)


union5, union_index = [], [0]  # 功率表，索引表
ps, wss = 0, 0
for i, power in union.iterrows():
    real_value = power['C_REAL_VALUE']
    ws = power["C_WS_INST_HUB_HEIGHT"]
    ps += real_value
    wss += ws
    if str(power['C_TIME'].minute) in ('0', '15', '30', '45'):
        union_index.append(i)
        num = union_index[-1] - union_index[-2]
        num = num if num != 0 else 1
        psa = round(ps / num, 2)
        wsa = round(wss / num, 2)
        union5.append([power['C_TIME'], psa, wsa])
        ps = 0
        wss = 0


union5 = pd.DataFrame(union5, columns=['C_TIME', 'C_REAL_VALUE', 'C_WS_INST_HUB_HEIGHT'])
union5.rename(columns={'C_REAL_VALUE':"C_REAL_VALUE_AVE", 'C_WS_INST_HUB_HEIGHT': 'C_WS_INST_HUB_HEIGHT_AVE'}, inplace=True)
union5['C_TIME'] = pd.to_datetime(union5['C_TIME'])
UNION = pd.merge(union, union5, on='C_TIME', how='left')

UNION.to_csv('./趋势对比union1.csv', index=False)

fig, ax = plt.subplots()
wss, rps, indexs = [], [], []
indexs1, wss1, rps1 = [],[],[]
UNION = UNION.iloc[:575, :]
for index, row in UNION.iterrows():
    ws = round(row['C_WS_INST_HUB_HEIGHT']*1.5, 3)
    ws_ave = round(row['C_WS_INST_HUB_HEIGHT_AVE']*1.5, 3)
    rp = row['C_REAL_VALUE']
    rp_ave = row['C_REAL_VALUE_AVE']
    wss.append(ws)
    rps.append(rp)
    indexs.append(index)
    if not math.isnan(rp_ave):
        indexs1.append(index)
        wss1.append(ws_ave)
        rps1.append(rp_ave)
ax.plot(indexs, rps, color='red', label='1minRP')
ax.plot(indexs1, wss1, color='blue', label='15minWS')
plt.legend()
plt.savefig('./趋势对比4.png')