Indeed, good catch, I exported both of them to a new file and it's obvious now:
Laurentiu
On Thu, Feb 1, 2024, at 15:22, Even Rouault wrote:
> Hi,
>
> I don't think there's any problem regarding GDAL vs xarray. It is just a
> difference of convention on how GDAL and xarray choose the origin of rasters.
> GDAL selects the northern-west corner as the (0,0) image coordinate.
>
> The line in GDAL that contains values 13.75 14.25 14.75 which are at line 80
> for XArray is line 124 . And 80 + 124 + 1 = 205 , the dataset height
>
> Displaying the GRIB dataset with QGIS (/GDAL) on top of OSM shows a plausible
> georeferencing.
>
> Even
>
> Le 01/02/2024 à 13:57, Laurențiu Nicola via gdal-dev a écrit :
>> It's actually pretty easy to test:
>>
>> import numpy as np
>> import xarray as xr
>> from osgeo import gdal
>>
>> xarray_ds = xr.load_dataset("st4_pr.2017092016.01h", engine="cfgrib")
>> # GDAL uses 9999 as NODATA
>> xarray_data = np.nan_to_num(xarray_ds.tp.data, nan=9999.0)
>> gdal_ds = gdal.Open("st4_pr.2017092016.01h")
>> gdal_data = gdal_ds.GetRasterBand(1).ReadAsArray()
>>
>> >>> gdal_data[80]
>> array([9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 40.5 , 41.125, 41.75 , 42.5 ,
>> 43. , 26.5 , 26.875, 27. , 29.875, 49.875,
>> 50.375, 41.5 , 41.375, 0. , 0. , 0. ,
>> 0. , 0. , 0. , 0. , 0. , 0. ,
>> 0. , 0. , 0. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 0. , 0. , 0. , 0. , 0. , 0. ,
>> 0. , 0. , 0. , 0. , 0. , 0. ,
>> 0. , 0. , 0. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. ])
>> >>> xarray_data[80]
>> array([9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 13.75 , 14.25 , 14.75 ,
>> 19.125, 32.5 , 30.125, 27.125, 25.5 , 22.25 ,
>> 22.375, 20.25 , 18.375, 16.625, 15.25 , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. , 9999. , 9999. , 9999. , 9999. , 9999. ,
>> 9999. ], dtype=float32)
>>
>> The values are obviously different.
>>
>> I also tried to convert to NetCDF and open them in QGIS, but they look
>> almost the same (not identical) to me after fixing up the coordinates, so
>> there's probably more at play.
>>
>> Again, comparing the gdal_translate output with the GRIB data as read by
>> GDAL can't show any issues.
>>
>> Laurentiu
>>
>> On Thu, Feb 1, 2024, at 13:24, Laurențiu Nicola via gdal-dev wrote:
>>> Hi Jukka,
>>>
>>> If GDAL shuffled around the pixels in that file, you wouldn't be able to
>>> notice it by comparing the GRIB and the TIFF, because both would be
>>> shuffled.
>>>
>>> My suggestion was to convert the GRIB to a NC or even CSV using xarray,
>>> then comparing the GDAL output against that.
>>>
>>> Laurentiu
>>>
>>
>>
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>>
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> http://www.spatialys.com
> My software is free, but my time generally not.
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