update code
This commit is contained in:
zhaojinghao
parent
8fd634888a
commit
ffdebde177
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# 模块导入
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import numpy as np
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import netCDF4 as nc
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from osgeo import gdal, osr, ogr
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import os
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import glob
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def NC_to_tiffs(data, Output_folder):
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# 读取一下基本信息
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nc_data_obj = nc.Dataset(data)
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Lon = nc_data_obj.variables["longitude"][:]
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Lat = nc_data_obj.variables["latitude"][:]
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# 读取变量的时候，会自动根据scale factor对数值进行还原，但是Nodata的栅格会存储为-32768
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# 无论是日数据还是小时数居，变量名都是"SWR"
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AOD_arr = np.asarray(nc_data_obj.variables["SWR"]) # 将SWR数据读取为数组
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# 这个循环将所有Nodata的值（即-32768）全部改为0
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for i in range(len(AOD_arr)):
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for j in range(len(AOD_arr[0])):
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if AOD_arr[i][j] == -32768:
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AOD_arr[i][j] = 0.0
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# 影像的四秩
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LonMin, LatMax, LonMax, LatMin = [Lon.min(), Lat.max(), Lon.max(), Lat.min()]
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# 分辨率计算，其实可以写死，都是2401*2401
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N_Lat = len(Lat)
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N_Lon = len(Lon)
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Lon_Res = (LonMax - LonMin) / (float(N_Lon) - 1)
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Lat_Res = (LatMax - LatMin) / (float(N_Lat) - 1)
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# 此句代码必须有
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AOD_arr = np.array([AOD_arr])
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for i in range(len(AOD_arr[:])):
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# 创建.tif文件
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driver = gdal.GetDriverByName("GTiff")
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TIFF_name = os.path.basename(data)
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out_tif_name = Output_folder + "\\" + TIFF_name.split("_")[1] + "_" + TIFF_name.split("_")[2] + ".tif"
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out_tif = driver.Create(out_tif_name, N_Lon, N_Lat, 1, gdal.GDT_Float32)
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# 设置影像的显示范围
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# -Lat_Res一定要是负的
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geotransform = (LonMin, Lon_Res, 0, LatMax, 0, -Lat_Res)
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out_tif.SetGeoTransform(geotransform)
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# 获取地理坐标系统信息，用于选取需要的地理坐标系统
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srs = osr.SpatialReference()
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srs.ImportFromEPSG(4326) # 定义输出的坐标系为"WGS 84"，AUTHORITY["EPSG","4326"]
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out_tif.SetProjection(srs.ExportToWkt()) # 给新建图层赋予投影信息
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# 数据写出
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out_tif.GetRasterBand(1).WriteArray(AOD_arr[i]) # 将数据写入内存，此时没有写入硬盘
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out_tif.FlushCache() # 将数据写入硬盘
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out_tif = None # 注意必须关闭tif文件
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import os
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import ftplib
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import time
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# 这个函数用于将日期从整型转为FTP路径所需的字符串
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def getDateStr(yearNum, monNum, dayNum):
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# 四位数年份
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yearStr = str(yearNum)
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# 两位数月份
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if monNum < 10:
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monStr = "0" + str(monNum)
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else:
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monStr = str(monNum)
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# 两位数天
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if dayNum < 10:
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dayStr = "0" + str(dayNum)
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else:
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dayStr = str(dayNum)
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return yearStr, monStr, dayStr
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# 这个函数用于获取前一天的日期号
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def getYesterday(year, month, day):
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if day == 1:
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if month == 1:
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year -= 1
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month = 12
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day = 31
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elif month == 2 or month == 4 or month == 6 or month == 8 or month == 9 or month == 11:
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month -= 1
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day = 31
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elif month == 5 or month == 7 or month == 10 or month == 12:
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month -= 1
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day = 30
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elif month == 3:
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# 闰年
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if year % 4 == 0 and year % 400 == 0:
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day = 29
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month -= 1
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# 闰年
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elif year % 4 == 0 and year % 100 != 0:
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day = 29
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month -= 1
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else:
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day = 28
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month -= 1
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else:
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day -= 1
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return year, month, day
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# 获取文件后缀名
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def suffix(file, *suffixName):
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array = map(file.endswith, suffixName)
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if True in array:
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return True
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else:
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return False
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# 删除目录下扩展名为.temp的文件
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def deleteFile(fileDir):
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targetDir = fileDir
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for file in os.listdir(targetDir):
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targetFile = os.path.join(targetDir, file)
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if suffix(file, '.temp'):
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os.remove(targetFile)
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class myFTP:
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ftp = ftplib.FTP()
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# 连接FTP，host是IP地址，port是端口，默认21
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def __init__(self, host, port=21, YesdayNum=1):
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self.ftp.connect(host, port)
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self._dayNum = YesdayNum
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# 登录FTP连接，user是用户名，password是密码
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def Login(self, user, password):
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self.ftp.login(user, password)
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print(self.ftp.welcome) # 显示登录信息
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# 下载单个文件，LocalFile表示本地存储路径和文件名，RemoteFile是FTP路径和文件名
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def DownLoadFile(self, LocalFile, RemoteFile):
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bufSize = 102400
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file_handler = open(LocalFile, 'wb')
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print(file_handler)
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# 接收服务器上文件并写入本地文件
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self.ftp.retrbinary('RETR ' + RemoteFile, file_handler.write, bufSize)
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self.ftp.set_debuglevel(0)
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file_handler.close()
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return True
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# 下载整个目录下的文件，LocalDir表示本地存储路径， emoteDir表示FTP路径
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def DownLoadFileTree(self, LocalDir, RemoteDir, choice):
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# print("remoteDir:", RemoteDir)
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# 如果本地不存在该路径，则创建
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if not os.path.exists(LocalDir):
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os.makedirs(LocalDir)
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# 获取FTP路径下的全部文件名，以列表存储
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# 好像是乱序
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self.ftp.cwd(RemoteDir)
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RemoteNames = self.ftp.nlst()
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RemoteNames.reverse()
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# print("RemoteNames：", RemoteNames)
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for file in RemoteNames:
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# 先下载为临时文件Local,下载完成后再改名为nc4格式的文件
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# 这是为了防止上一次下载中断后，最后一个下载的文件未下载完整，而再开始下载时，程序会识别为已经下载完成
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Local = os.path.join(LocalDir, file[0:-3] + ".temp")
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LocalNew = os.path.join(LocalDir, file)
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'''
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下载小时文件，只下载UTC时间1时至10时（北京时间9时至18时）的文件
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下载的文件必须是nc格式
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若已经存在，则跳过下载
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'''
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# 小时数据命名格式示例：H08_20200819_0700_1HARP030_FLDK.02401_02401.nc
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if choice == 1:
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if (int(file[13:15]) >= 0 and int(file[13:15]) <= 12) or (int(file[13:15]) >= 21):
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if not os.path.exists(LocalNew):
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print("Downloading the file of %s" % file)
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self.DownLoadFile(Local, file)
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os.rename(Local, LocalNew)
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print("The download of the file of %s has finished\n" % file)
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elif os.path.exists(LocalNew):
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print("The file of %s has already existed!\n" % file)
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else:
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pass
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# 天数据命名格式示例：H08_20200802_0000_1DARP030_FLDK.02401_02401.nc
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elif choice == 2:
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if int(file[10:12]) == self._dayNum and not os.path.exists(LocalNew):
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print("Downloading the file of %s" % file)
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self.DownLoadFile(Local, file)
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os.rename(Local, LocalNew)
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print("The download of the file of %s has finished\n" % file)
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elif int(file[10:12]) == self._dayNum and os.path.exists(LocalNew):
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print("The file of %s has already existed!" % file)
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elif choice == 3:
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# 分钟级别的数据已经明确了小时关系，所以不需要根据小时筛选
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if not os.path.exists(LocalNew) and '02401_02401' in file:
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print("Downloading the file of %s" % file)
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self.DownLoadFile(Local, file)
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os.rename(Local, LocalNew)
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print("The download of the file of %s has finished\n" % file)
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elif os.path.exists(LocalNew):
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print("The file of %s has already existed!\n" % file)
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else:
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pass
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self.ftp.cwd("..")
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return
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def close(self):
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self.ftp.quit()
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File diff suppressed because one or more lines are too long
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import os
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import Download_AOD as Daod
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import datetime as dt
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today = dt.date(2022, 8, 1)
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print(today)
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_yearNum = today.year
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_monNum = today.month
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_dayNum = today.day
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_yearStr = ""
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_monStr = ""
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_dayStr = ""
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_hourStr = [f"0{x}"if x < 10 else str(x) for x in range(23)]
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if __name__ == "__main__":
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# 传入IP地址
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# 传入的YesdayNum在下载日数据时（昨天的）会需要
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ftp = Daod.myFTP(host='ftp.ptree.jaxa.jp', YesdayNum=_dayNum - 1)
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# 传入用户名和密码，可以自行注册并修改
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ftp.Login('jh_zhao_asagi.waseda.jp', 'SP+wari8')
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# 从目标路径ftp_filePath将文件下载至本地路径dst_filePath
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dst_filePath = './data'
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dst_filePath = dst_filePath + "/" + dt.datetime.strftime(today, '%Y-%m-%d')
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if not os.path.exists(dst_filePath):
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os.makedirs(dst_filePath)
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'''
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下载小时数据和日数据时，前置路径都是：/pub/himawari/L3/ARP/031(20-22年)
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下载每10分钟数据时，前置路径是：/pub/himawari/L2/ARP/030(20-22年)
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下载日数据时，示例路径：/pub/himawari/L3/ARP/031/202008/daily/
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下载小时数据时，示例路径：/pub/himawari/L3/ARP/031/202008/19/
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下载10分钟数据时，示例路径：/pub/himawari/L2/ARP/030/202210/10/
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'''
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print("请选择要下载的数据：")
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_choice = int(input("1.AOD小时数据（当天所有） 2.AOD日均数据（昨天） 3.PAR分钟数据（当天所有）\n"))
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# Download_Path用于存储下载的原始数据
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Download_Path = ""
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# Analysis_Path用于存储处理后的数据（即转为TIFF后的数据）的文件夹
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Analysis_Path = ""
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# 如果选择为AOD小时数据
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if _choice == 1:
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_yearStr, _monStr, _dayStr = Daod.getDateStr(_yearNum, _monNum, _dayNum)
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ftp_filePath = "/pub/himawari/L3/ARP/031" + "/" + _yearStr + _monStr + "/" + _dayStr + "/"
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Download_Path = dst_filePath + "/AOD_Hourly_Download"
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if not os.path.exists(Download_Path):
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os.makedirs(Download_Path)
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Daod.deleteFile(Download_Path) # 删除存储路径中的临时文件（也就是上次未下载完整的文件）
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Analysis_Path = dst_filePath + "/AOD_Hourly_Analysis"
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if not os.path.exists(Analysis_Path):
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os.makedirs(Analysis_Path)
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ftp.DownLoadFileTree(Download_Path, ftp_filePath, _choice)
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# 如果选择为AOD日数据（昨天的）
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elif _choice == 2:
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_yearNum, _monNum, _dayNum = Daod.getYesterday(_yearNum, _monNum, _dayNum)
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_yearStr, _monStr, _dayStr = Daod.getDateStr(_yearNum, _monNum, _dayNum)
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ftp_filePath = "/pub/himawari/L3/ARP/030" + "/" + _yearStr + _monStr + "/" + "daily" + "/"
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Download_Path = dst_filePath + "/AOD_Daily_Download"
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if not os.path.exists(Download_Path):
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os.makedirs(Download_Path)
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Daod.deleteFile(Download_Path) # 删除存储路径中的临时文件（也就是上次未下载完整的文件）
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Analysis_Path = dst_filePath + "/AOD_Daily_Analysis"
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if not os.path.exists(Analysis_Path):
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os.makedirs(Analysis_Path)
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ftp.DownLoadFileTree(Download_Path, ftp_filePath, _choice)
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elif _choice == 3:
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while today <= dt.date.today():
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_yearNum = today.year
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_monNum = today.month
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_dayNum = today.day
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_yearStr = ""
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_monStr = ""
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_dayStr = ""
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_hourStr = [f"0{x}" if x < 10 else str(x) for x in range(23)]
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_yearStr, _monStr, _dayStr = Daod.getDateStr(_yearNum, _monNum, _dayNum)
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ftp_filePath = "/pub/himawari/L2/PAR/020" + "/" + _yearStr + _monStr + "/" + _dayStr + "/"
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Download_Path = dst_filePath + "/PAR_Minutes_Download"
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if not os.path.exists(Download_Path):
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os.makedirs(Download_Path)
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Daod.deleteFile(Download_Path) # 删除存储路径中的临时文件（也就是上次未下载完整的文件）
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for hour in _hourStr:
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print(f"{ftp_filePath}{hour}/")
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ftp.DownLoadFileTree(Download_Path, f"{ftp_filePath}{hour}/", _choice)
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# Analysis_Path = dst_filePath + "/PAR_Minutes_Analysis"
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# if not os.path.exists(Analysis_Path):
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# os.makedirs(Analysis_Path)
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today = today + dt.timedelta(days=1)
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else:
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print("选择错误！")
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# 下载结束
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ftp.close()
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print("下载完成！")
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# 下面开始数据处理
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# 读取所有nc数据
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# data_list = glob.glob(Download_Path + "\\*.nc")
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# # for循环完成解析
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# for i in range(len(data_list)):
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# data = data_list[i]
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# trans.NC_to_tiffs(data, Analysis_Path)
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# print(data + "-----转tif成功")
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print("----转换结束----")
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import netCDF4 as nc"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"outputs": [],
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"source": [
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"from osgeo import gdal, osr, ogr"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"outputs": [
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{
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"data": {
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"text/plain": "<class 'netCDF4._netCDF4.Dataset'>\nroot group (NETCDF4 data model, file format HDF5):\n title: Himawari-08 AHI equal latitude-longitude map data\n id: H08_20221107_1800_RFL020_FLDK.02401_02401.nc\n date_created: 2022-11-07T18:25:18Z\n pixel_number: 2401\n line_number: 2401\n upper_left_latitude: 60.0\n upper_left_longitude: 80.0\n grid_interval: 0.05\n band_number: 6\n algorithm_version: 0201\n Ancillary meteorological data: JMA forcast\n Ancillary ozone data: JMA objective analysis\n BRDF correction: on (Morel and Maritorena 2001)\n dimensions(sizes): latitude(2401), longitude(2401), band(6), time(1), geometry(17)\n variables(dimensions): float32 latitude(latitude), float32 longitude(longitude), int32 band_id(band), float64 start_time(time), float64 end_time(time), float64 geometry_parameters(geometry), int16 TAOT_02(latitude, longitude), int16 TAAE(latitude, longitude), int16 PAR(latitude, longitude), int16 SWR(latitude, longitude), int16 UVA(latitude, longitude), int16 UVB(latitude, longitude), uint8 QA_flag(latitude, longitude)\n groups: "
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data = r\"D:\\Datasets\\Himawari\\pub\\L2_PAR\\20221107\\18\\H08_20221107_1800_RFL020_FLDK.02401_02401.nc\"\n",
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"nc_data = nc.Dataset(data)\n",
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"nc_data"
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],
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"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "['latitude',\n 'longitude',\n 'band_id',\n 'start_time',\n 'end_time',\n 'geometry_parameters',\n 'TAOT_02',\n 'TAAE',\n 'PAR',\n 'SWR',\n 'UVA',\n 'UVB',\n 'QA_flag']"
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"list(nc_data.variables.keys())"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "<class 'netCDF4._netCDF4.Variable'>\nint16 PAR(latitude, longitude)\n long_name: Photosynthetically active radiation\n units: umol/m^2/s\n scale_factor: 0.1\n add_offset: 0.0\n valid_min: 0\n valid_max: 25000\n missing_value: -32768\nunlimited dimensions: \ncurrent shape = (2401, 2401)\nfilling on, default _FillValue of -32767 used"
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"nc_data['PAR']"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "<class 'netCDF4._netCDF4.Variable'>\nfloat32 latitude(latitude)\n long_name: latitude\n units: degrees_north\nunlimited dimensions: \ncurrent shape = (2401,)\nfilling on, default _FillValue of 9.969209968386869e+36 used"
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"nc_data['latitude']"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "array([[ 0. , 0. , 0. , ..., 0. , 0. ,\n 0. ],\n [ 0. , 0. , 0. , ..., 0. , 0. ,\n 0. ],\n [ 0. , 0. , 0. , ..., 0. , 0. ,\n 0. ],\n ...,\n [ 0. , 0. , 0. , ..., 181.6 , 139.7 ,\n 144.40001],\n [ 0. , 0. , 0. , ..., 201.6 , 318.6 ,\n 169.7 ],\n [ 0. , 0. , 0. , ..., 240.8 , 338.9 ,\n 340.1 ]], dtype=float32)"
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"par = np.asarray(nc_data['PAR'][:])\n",
|
||||
"par"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2401 2401\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"lat = list(map(lambda x: round(x, 2), np.asarray(nc_data['latitude'][:])))\n",
|
||||
"lon = list(map(lambda x: round(x, 2), np.asarray(nc_data['longitude'][:])))\n",
|
||||
"print(len(lat), len(lon))\n",
|
||||
"latMin, latMax, lonMin, lonMax = min(lat), max(lat), min(lon), max(lon)"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 分辨率\n",
|
||||
"lat_Res = (latMax - latMin) / (lat.shape[0]-1)\n",
|
||||
"lon_Res = (lonMax - lonMin) / (lon.shape[0]-1)"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cols = [str(x) for x in lat]\n",
|
||||
"rows = [str(x) for x in lon]"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"par_df = pd.DataFrame.from_records(par)\n",
|
||||
"par_df.columns = cols\n",
|
||||
"par_df.index = rows"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 58,
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": " 60.0 59.95 59.9 59.85 59.8 59.75 59.7 59.65 59.6 59.55 ... \\\n199.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n199.85 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n199.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n199.95 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n\n -59.55 -59.6 -59.65 -59.7 -59.75 \\\n199.8 160.699997 160.900009 155.400009 155.500000 142.100006 \n199.85 153.800003 174.500000 147.300003 139.400009 139.600006 \n199.9 164.199997 166.800003 151.000000 153.800003 153.900009 \n199.95 152.900009 159.800003 184.300003 164.000000 164.199997 \n200.0 149.199997 148.400009 148.600006 152.300003 152.800003 \n\n -59.8 -59.85 -59.9 -59.95 -60.0 \n199.8 143.199997 143.699997 138.100006 138.300003 139.600006 \n199.85 144.199997 144.199997 160.199997 142.199997 143.699997 \n199.9 169.000000 169.300003 181.600006 139.699997 144.400009 \n199.95 167.100006 167.600006 201.600006 318.600006 169.699997 \n200.0 159.699997 240.699997 240.800003 338.899994 340.100006 \n\n[5 rows x 2401 columns]",
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>60.0</th>\n <th>59.95</th>\n <th>59.9</th>\n <th>59.85</th>\n <th>59.8</th>\n <th>59.75</th>\n <th>59.7</th>\n <th>59.65</th>\n <th>59.6</th>\n <th>59.55</th>\n <th>...</th>\n <th>-59.55</th>\n <th>-59.6</th>\n <th>-59.65</th>\n <th>-59.7</th>\n <th>-59.75</th>\n <th>-59.8</th>\n <th>-59.85</th>\n <th>-59.9</th>\n <th>-59.95</th>\n <th>-60.0</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>199.8</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>160.699997</td>\n <td>160.900009</td>\n <td>155.400009</td>\n <td>155.500000</td>\n <td>142.100006</td>\n <td>143.199997</td>\n <td>143.699997</td>\n <td>138.100006</td>\n <td>138.300003</td>\n <td>139.600006</td>\n </tr>\n <tr>\n <th>199.85</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>153.800003</td>\n <td>174.500000</td>\n <td>147.300003</td>\n <td>139.400009</td>\n <td>139.600006</td>\n <td>144.199997</td>\n <td>144.199997</td>\n <td>160.199997</td>\n <td>142.199997</td>\n <td>143.699997</td>\n </tr>\n <tr>\n <th>199.9</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>164.199997</td>\n <td>166.800003</td>\n <td>151.000000</td>\n <td>153.800003</td>\n <td>153.900009</td>\n <td>169.000000</td>\n <td>169.300003</td>\n <td>181.600006</td>\n <td>139.699997</td>\n <td>144.400009</td>\n </tr>\n <tr>\n <th>199.95</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>152.900009</td>\n <td>159.800003</td>\n <td>184.300003</td>\n <td>164.000000</td>\n <td>164.199997</td>\n <td>167.100006</td>\n <td>167.600006</td>\n <td>201.600006</td>\n <td>318.600006</td>\n <td>169.699997</td>\n </tr>\n <tr>\n <th>200.0</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>149.199997</td>\n <td>148.400009</td>\n <td>148.600006</td>\n <td>152.300003</td>\n <td>152.800003</td>\n <td>159.699997</td>\n <td>240.699997</td>\n <td>240.800003</td>\n <td>338.899994</td>\n <td>340.100006</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 2401 columns</p>\n</div>"
|
||||
},
|
||||
"execution_count": 58,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"par_df.tail()"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 68,
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": " 60.0 59.95 59.9 59.85 59.8 59.75 59.7 59.65 59.6 59.55 ... \\\n120.85 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n\n -59.55 -59.6 -59.65 -59.7 -59.75 -59.8 -59.85 -59.9 \\\n120.85 12.5 27.4 53.100002 57.200001 11.1 11.900001 15.6 90.0 \n\n -59.95 -60.0 \n120.85 91.599998 94.900002 \n\n[1 rows x 2401 columns]",
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>60.0</th>\n <th>59.95</th>\n <th>59.9</th>\n <th>59.85</th>\n <th>59.8</th>\n <th>59.75</th>\n <th>59.7</th>\n <th>59.65</th>\n <th>59.6</th>\n <th>59.55</th>\n <th>...</th>\n <th>-59.55</th>\n <th>-59.6</th>\n <th>-59.65</th>\n <th>-59.7</th>\n <th>-59.75</th>\n <th>-59.8</th>\n <th>-59.85</th>\n <th>-59.9</th>\n <th>-59.95</th>\n <th>-60.0</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>120.85</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>12.5</td>\n <td>27.4</td>\n <td>53.100002</td>\n <td>57.200001</td>\n <td>11.1</td>\n <td>11.900001</td>\n <td>15.6</td>\n <td>90.0</td>\n <td>91.599998</td>\n <td>94.900002</td>\n </tr>\n </tbody>\n</table>\n<p>1 rows × 2401 columns</p>\n</div>"
|
||||
},
|
||||
"execution_count": 68,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"par_df[par_df.index=='120.85']"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
Loading…
Reference in New Issue