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我有 hdf 格式的时间序列数据。我使用下面的代码从 hdf 文件中读取数据。现在，我尝试根据经纬度加入具有相同 jdn（儒略日数）的数据。具有相同儒略日数的数据代表连续的空间数据

import glob
import numpy as np
import os
from pyhdf.SD import SD,SDC


files = glob.glob('MOD04*')
files.sort()
for f in files:
    product = f[0:5]+ '-Atmospheric Product'
    year = f[10:14]
    jdn = f[14:17] # julian day number

    # Read dataset.
    hdf = SD(f, SDC.READ)
    data3D = hdf.select('Deep_Blue_Aerosol_Optical_Depth_550_Land')
    data = data3D[:,:].astype(np.double)

    # Read geolocation dataset 
    lat = hdf.select('Latitude')
    latitude = lat[:,:]
    lon = hdf.select('Longitude')
    longitude = lon[:,:]

我的数据附在此链接中：https ://drive.google.com/folderview?id=0B2rkXkOkG7ExX2lTTWEySU1fOWc&usp=sharing

4

2 回答 2

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Numpy 的hstack、vstack或dstack（取决于您想要加入数组的轴）将加入多维数组。

请注意，特别是对于 MODIS 气溶胶数据，使用 hstack 连接数组有时会引发错误，因为有时数组是 203 x 135 有时是 204 x 135，因此水平尺寸不会总是匹配

以您的代码为基础（不漂亮，但功能强大）：

import glob
import numpy as np
import os
from pyhdf.SD import SD,SDC


files = glob.glob('MOD04*')
files.sort()
for n, f in enumerate(files):
    product = f[0:5]+ '-Atmospheric Product'
    year = f[10:14]
    jdn = f[14:17] # julian day number

    # Read dataset.
    hdf = SD(f, SDC.READ)
    data3D = hdf.select('Deep_Blue_Aerosol_Optical_Depth_550_Land')
    data = data3D[:,:].astype(np.double)

   # Read geolocation dataset 
    lat = hdf.select('Latitude')
    latitude = lat[:,:]
    lon = hdf.select('Longitude')
    longitude = lon[:,:]

    if n != 0 and jdn != old_jdn:
        #do analysis; write to file for later analysis; etc.
        pass

    if n == 0 or jdn != old_jdn:
        data_timeseries = data
        latitude_timeseries = latitude
        longitude_timeseries = longitude
    else:
        data_timeseries = np.vstack((data_timeseries, data))
        latitude_timeseries = np.vstack((latitude_timeseries, latitude))
        longitude_timeseries = np.vstack((longitude_timeseries, longitude))

    print data_timeseries.shape
    print latitude_timeseries.shape
    print longitude_timeseries.shape

    old_jdn = jdn
于 2016-07-13T15:22:14.267 回答
1

只是为了跟进 Heather QC 的回答，这里是 np.stack 函数的说明以及涉及哪些维度：

arr1 = np.array([[[1,2],[2,3]],
                 [[1,2],[2,3]],
                 [[1,2],[2,3]]])

arr2 = np.array([[[5,6],[8,7]],
                 [[7,6],[7,8]],
                 [[6,7],[7,8]]])

print("arr1 shape  ", arr1.shape)    
print("arr2 shape  ", arr2.shape)    
print("vstack shape", np.vstack((arr1, arr2)).shape)
print("hstack shape", np.hstack((arr1, arr2)).shape)
print("dstack shape", np.dstack((arr1, arr2)).shape)

>>> arr1 shape   (3, 2, 2)
>>> arr2 shape   (3, 2, 2)
>>> vstack shape (6, 2, 2)
>>> hstack shape (3, 4, 2)
>>> dstack shape (3, 2, 4)
于 2020-05-28T10:21:38.140 回答