python提取时间序列白天和黑夜的时间段

玩转Python datetime已针对时间序列进行了相关处理,得到了满足AIWIND计算要求的测风塔时间序列

Atmospheric Boundary Layer Stability一文我们知道地表边界层在昼夜存在不同;白天由于地表被加热,边界层较薄,反之亦然;为此需要对雷达的测量结果提取昼夜的分布信息

Astral库计算日出日落时间一文解决获取当天日出日落时间,进一步可参考Astral手册

原始数据格式

原始数据格式:第一列为时间序列,2,3列分别为雷达40m处的风向风速,依次类推

  import pandas as pd
  import numpy as np
  import datetime
  import astral

  ##https://www.jianshu.com/p/3d1d648406f3
  longititude=21.45 ## 城市经度信息
  latitude=112.15 ## 城市纬度度信息
  location_yangjiang = astral.Location(('Yangjiang', 'China',21.45 ,112.15 , 'Asia/Shanghai', 0))
  #location_yangjiang = astral.Location(('YanDun', 'China',42.3369 ,94.382 , 'Asia/Shanghai', 0))
  #location_yangjiang = astral.Location(('huailai', 'China',42.242 ,115.35 , 'Asia/Shanghai', 0))
  #time_sunrise=location_yangjiang.sunrise(date=datetime.date.today(),local=True)
  time_sunrise=location_yangjiang.sunrise(date=datetime.date(2019,6,15),local=True)
  time_sunrise_update=time_sunrise + datetime.timedelta(hours=0) #提前/延后日出时间量
  time_sunrise_new=str(time_sunrise_update)
  print(f"日出时间:{time_sunrise_new}")

  #time_sunset=location_yangjiang.sunset(date=datetime.date.today(),local=True)
  time_sunset=location_yangjiang.sunset(date=datetime.date(2019,6,15),local=True)

  time_sunset_update=time_sunset + datetime.timedelta(hours=0) #提前/延后日落时间量
  time_sunset_new=str(time_sunset_update)
  print(f"日落时间:{time_sunset_new}")


  #dongpingLidar=pd.read_excel(r"C:\Users\yezhaoliang\Desktop\dongpingLidar67.xlsx")
  dongpingLidar=pd.read_excel(r"C:\Users\yezhaoliang\Desktop\work\AIWind\功率曲线\手算白天黑夜\dongpingLidar14WithTime.xlsx")
  positions=[40,65,90,115,140,150,160,170,180,190,200,210]
  windDirection=180
  minusPlus=30
  velMean=np.zeros(positions.__len__())

  def getSunriseFromDay(today):
      ## 日出
      year=today[0:4]
      month=today[4:6]
      day=today[6:8]
      time_sunrise = location_yangjiang.sunrise(date=datetime.date(int(year), int(month), int(day)), local=True)
      time_sunrise_update = time_sunrise + datetime.timedelta(hours=0)  # 提前/延后日出时间量
      time_sunrise_new = str(time_sunrise_update)
      return time_sunrise_new[11:19]

  def getSunsetFromDay(today):
      year=today[0:4]
      month=today[4:6]
      day=today[6:8]
      ## 日落
      time_sunset = location_yangjiang.sunset(date=datetime.date(int(year), int(month), int(day)), local=True)
      time_sunset_update = time_sunset + datetime.timedelta(hours=0)  # 提前/延后日落时间量
      time_sunset_new = str(time_sunset_update)
      return time_sunset_new[11:19]


  def getDayArrayCursorByDay():
      timeseries=dongpingLidar.iloc[:,0]

      ## timeSeries 9-19 为小时:分钟:秒      time_sunset_new 11:19为小时:分钟:秒
      # for (idx, val) in enumerate(timeseries):
      #     time_sunrise_new,time_sunset_new=getSunriseSunsetFromDay(val)
      #     if(val[9:19] >= time_sunrise_new[11:19] and val[9:19] <= time_sunset_new[11:19]]):
      #         dayIndex

      # dayIndex = [idx for (idx, val) in enumerate(timeseries) if val[9:19] >= time_sunrise_new[11:19] and val[9:19] <=time_sunset_new[11:19]]
      # nightIndex = [idx for (idx, val) in enumerate(timeseries) if val[9:19] < time_sunrise_new[11:19] or val[9:19] > time_sunset_new[11:19]]
      dayIndex = [idx for (idx, val) in enumerate(timeseries) if val[9:19] >= getSunriseFromDay(val) and val[9:19] <=getSunsetFromDay(val)]
      nightIndex = [idx for (idx, val) in enumerate(timeseries) if val[9:19] < getSunriseFromDay(val) or val[9:19] > getSunsetFromDay(val)]
      return dayIndex,nightIndex

  def getDayArrayCursor():
      timeseries=dongpingLidar.iloc[:,0]

      ## timeSeries 9-19 为小时:分钟:秒      time_sunset_new 11:19为小时:分钟:秒
      dayIndex = [idx for (idx, val) in enumerate(timeseries) if val[9:19] >= time_sunrise_new[11:19] and val[9:19] <=time_sunset_new[11:19]]
      nightIndex = [idx for (idx, val) in enumerate(timeseries) if val[9:19] < time_sunrise_new[11:19] or val[9:19] > time_sunset_new[11:19]]
      return dayIndex,nightIndex

##1. 逐日跟踪日落日出
  dayIndex,nightIndex=getDayArrayCursorByDay()

##2. 测试期中间日期跟踪日落日出
  # dayIndex,nightIndex=getDayArrayCursor()
  for i in range(len(positions)):
      print(f"{i}:")
      dir1 = dongpingLidar.iloc[:, 2*i+1]
      vel = dongpingLidar.iloc[:, 2*i+2]

      dayDir1=dir1[dayIndex]
      dayVel=vel[dayIndex]

      # dayDir1=dir1[nightIndex]
      # dayVel=vel[nightIndex]
      idx = [idx for (idx, val) in enumerate(dayDir1) if val >= (windDirection-minusPlus) and val <=(windDirection+minusPlus)]
      ## dir1[idx]  vel[idx]
      countVel=0
      for cursor1 in range(len(idx)):
          cursor=idx[cursor1]
          countVel=countVel+dayVel.iloc[cursor]

      velMean[i]=countVel/len(idx)

  profiles=pd.DataFrame([velMean,positions]).T
  profiles.to_csv("Lidar387Profiles14Day30.csv")

结论

  1. 1-2个月的雷达用中间日期即可,差别不大;如果全年的话,最好是跟踪每日。
  2. 白天的计算速度边界层较薄,夜晚较厚一些,原因是白天的地表收到加热效应。

顺便提及,我喜欢用TotalCommander对文件夹和文件进行颜色控制

  1. 凡是有备注的都显示粉色
  2. 凡是一周之内有变动的文件和文件夹都显示蓝色
  3. 凡是一天之内有变动的文件和文件夹都显示红色
  4. 凡是office文件都显示灰色
令狐冲
令狐冲
Engineer of offshore wind turbine technique research

My research interests include distributed energy, wind turbine power generation technique , Computational fluid dynamic and programmable matter.

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