Shifting data from a GRIB2 file

Question

I have successfully opened a grib2 file from NCEP and I am having trouble being able to convert the coordinates to plot them using matplotlib, using the custom convertXY function from this post Plot GDAL raster using matplotlib Basemap.

I got what I expect, but only for half of the world, I can solve it by subtracting 180.0 from my xmin and xmax, but then I lose the coordinate conversion, I guess the problem is that I am not shifting the data, possibly using shiftgrid from mpl_toolkits, but I can not get the function to work either, any suggestions?

Here is an image of the map without the subtraction:

Here is what I get when I subtract 180.0 from the xmin and xmax variables:

You can download the grib2 file I am using from: 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()

Answer 1

Here is what I came with that works with all projections:

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()