[英]Writing interpolated grib2 data with pygrib leads to unusable grib file
[英]Shifting data from a GRIB2 file
我已经成功地从NCEP打开了一个grib2文件,并且无法使用matplotlib Basemap使用本帖子Plot GDAL栅格中的自定义convertXY
函数来转换坐标以使用matplotlib
进行绘制 。
我得到了我期望的结果,但是仅对于世界的一半,我可以通过从xmin
和xmax
减去180.0来解决它,但是然后我失去了坐标转换,我想问题是我没有移动数据,可能是因为shiftgrid
从mpl_toolkits
,但我不能让功能擦出火花,有什么建议?
这是不带减法的地图图像:
这是我从xmin
和xmax
变量中减去180.0时得到的结果:
您可以从以下位置下载我正在使用的grib2文件: https ://drive.google.com/open?id=1RsuiznRMbJNpNsrQeXEunvVsWZJ0tL2d
from mpl_toolkits.basemap import Basemap
import osr, gdal
import matplotlib.pyplot as plt
import numpy as np
def convertXY(xy_source, inproj, outproj):
# function to convert coordinates
shape = xy_source[0,:,:].shape
size = xy_source[0,:,:].size
# the ct object takes and returns pairs of x,y, not 2d grids
# so the the grid needs to be reshaped (flattened) and back.
ct = osr.CoordinateTransformation(inproj, outproj)
xy_target = np.array(ct.TransformPoints(xy_source.reshape(2, size).T))
xx = xy_target[:,0].reshape(shape)
yy = xy_target[:,1].reshape(shape)
return xx, yy
# Read the data and metadata
ds = gdal.Open(r'D:\Downloads\flxf2018101912.01.2018101912.grb2')
data = ds.ReadAsArray()
gt = ds.GetGeoTransform()
proj = ds.GetProjection()
xres = gt[1]
yres = gt[5]
# get the edge coordinates and add half the resolution
# to go to center coordinates
xmin = gt[0] + xres * 0.5
xmin -= 180.0
xmax = gt[0] + (xres * ds.RasterXSize) - xres * 0.5
xmax -= 180.0
ymin = gt[3] + (yres * ds.RasterYSize) + yres * 0.5
ymax = gt[3] - yres * 0.5
ds = None
# create a grid of xy coordinates in the original projection
xy_source = np.mgrid[xmin:xmax+xres:xres, ymax+yres:ymin:yres]
# Create the figure and basemap object
fig = plt.figure(figsize=(12, 6))
m = Basemap(projection='robin', lon_0=0, resolution='c')
# Create the projection objects for the convertion
# original (Albers)
inproj = osr.SpatialReference()
inproj.ImportFromWkt(proj)
# Get the target projection from the basemap object
outproj = osr.SpatialReference()
outproj.ImportFromProj4(m.proj4string)
# Convert from source projection to basemap projection
xx, yy = convertXY(xy_source, inproj, outproj)
# plot the data (first layer)
im1 = m.pcolormesh(xx, yy, data[0,:,:].T, cmap=plt.cm.jet)
# annotate
m.drawcountries()
m.drawcoastlines(linewidth=.5)
plt.show()
这是我随所有投影一起使用的结果:
from mpl_toolkits.basemap import Basemap
from mpl_toolkits.basemap import shiftgrid
import osr, gdal
import matplotlib.pyplot as plt
import numpy as np
# Read the data and metadata
# Pluviocidad.
#ds = gdal.Open( 'C:\Users\Paula\Downloads\enspost.t00z.prcp_24hbc (1).grib2', gdal.GA_ReadOnly )
# Sea Ice
ds = gdal.Open( 'D:\Downloads\seaice.t00z.grb.grib2', gdal.GA_ReadOnly )
data = ds.ReadAsArray()
gt = ds.GetGeoTransform()
proj = ds.GetProjection()
xres = gt[1]
yres = gt[5]
xsize = ds.RasterXSize
ysize = ds.RasterYSize
# get the edge coordinates and add half the resolution
# to go to center coordinates
xmin = gt[0] + xres * 0.5
xmax = gt[0] + (xres * xsize) - xres * 0.5
ymin = gt[3] + (yres * ysize) + yres * 0.5
ymax = gt[3] - yres * 0.5
ds = None
xx = np.arange( xmin, xmax + xres, xres )
yy = np.arange( ymax + yres, ymin, yres )
data, xx = shiftgrid( 180.0, data, xx, start = False )
# Mercator
m = Basemap(projection='merc',llcrnrlat=-85,urcrnrlat=85,\
llcrnrlon=-180,urcrnrlon=180,lat_ts=0,resolution='c')
x, y = m(*np.meshgrid(xx,yy))
# plot the data (first layer) data[0,:,:].T
im1 = m.pcolormesh( x, y, data, shading = "flat", cmap=plt.cm.jet )
# annotate
m.drawcountries()
m.drawcoastlines(linewidth=.5)
plt.show()
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