rioxarray
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Published
20 hours ago •
corteva
corteva
merge_arrays - mirrowed y value
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Code Sample, a copy-pastable example if possible
A set of arrays are merged and than saved (the later for finding the errors). The array are from an DGM in south Germany. The example contains 4 raster files. Two files north of other files. I cannot share the files, for legal reasons.
rasters = [open_rasterio(_x) for _x in kwargs.values() if _x.exists()]
full = merge_arrays(rasters, method='last')
Problem description
Two things happen. After saving the megered raster I can see, that the southern 2 raster are turned upside down (north is south). The Northern two raster are correctly north of the first two raster, but also upside down.
The second problem. When trying the bounds parameter I get the error:
File "...\rioxarray\merge.py", line 169, in merge_arrays
merged_data, merged_transform = _rio_merge(
File "...\rasterio\merge.py", line 262, in merge
dest = np.zeros((output_count, output_height, output_width), dtype=dt)
ValueError: negative dimensions are not allowed
python-BaseException
Expected Output
Environment Information
rioxarray (0.11.1) deps:
rasterio: 1.2.1
xarray: 2022.3.0
GDAL: 3.1.4
GEOS: None
PROJ: None
PROJ DATA: None
GDAL DATA: None
Other python deps:
scipy: 1.8.1
pyproj: 3.0.1
System:
python: 3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:50:36) [MSC v.1929 64 bit (AMD64)]
executable: C:\Users\remote\anaconda3\envs\OBAL\python.exe
machine: Windows-10-10.0.19043-SP0
Installation method
- conda/mamba (conda-forge)
Conda environment information (if you installed with conda):
Environment (conda list):
Name Version Build Channel
affine 2.3.1 pyhd8ed1ab_0 conda-forge
alabaster 0.7.12 py_0 conda-forge
appdirs 1.4.4 pyh9f0ad1d_0 conda-forge
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babel 2.10.3 pyhd8ed1ab_0 conda-forge
boost-cpp 1.74.0 h9f4b32c_8 conda-forge
branca 0.5.0 pyhd8ed1ab_0 conda-forge
brotli 1.0.9 h8ffe710_7 conda-forge
brotli-bin 1.0.9 h8ffe710_7 conda-forge
brotlipy 0.7.0 py39hb82d6ee_1004 conda-forge
bzip2 1.0.8 h8ffe710_4 conda-forge
ca-certificates 2022.6.15 h5b45459_0 conda-forge
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cfitsio 3.470 h0af3d06_7 conda-forge
charset-normalizer 2.1.0 pyhd8ed1ab_0 conda-forge
click 7.1.2 pyh9f0ad1d_0 conda-forge
click-plugins 1.1.1 py_0 conda-forge
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conda 4.13.0 py39hcbf5309_1 conda-forge
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hdf5 1.10.6 nompi_h5268f04_1114 conda-forge
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idna 3.3 pyhd8ed1ab_0 conda-forge
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libpng 1.6.37 h1d00b33_3 conda-forge
libpq 13.5 hfcc5ef8_1 conda-forge
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libwebp-base 1.2.2 h8ffe710_1 conda-forge
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libzip 1.9.2 hfed4ece_0 conda-forge
libzlib 1.2.12 h8ffe710_1 conda-forge
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lz4-c 1.9.3 h8ffe710_1 conda-forge
lzo 2.10 he774522_1000 conda-forge
m2w64-gcc-libgfortran 5.3.0 6 conda-forge
m2w64-gcc-libs 5.3.0 7 conda-forge
m2w64-gcc-libs-core 5.3.0 7 conda-forge
m2w64-gmp 6.1.0 2 conda-forge
m2w64-libwinpthread-git 5.0.0.4634.697f757 2 conda-forge
mamba 0.24.0 py39hb3d9227_1 conda-forge
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mkl 2022.1.0 h6a75c08_874 conda-forge
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proj 8.0.0 h1cfcee9_0 conda-forge
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pycosat 0.6.3 py39hb82d6ee_1010 conda-forge
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pygments 2.12.0 pyhd8ed1ab_0 conda-forge
pyopenssl 22.0.0 pyhd8ed1ab_0 conda-forge
pyparsing 3.0.9 pyhd8ed1ab_0 conda-forge
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pysocks 1.7.1 py39hcbf5309_5 conda-forge
python 3.9.13 h9a09f29_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python_abi 3.9 2_cp39 conda-forge
pytz 2022.1 pyhd8ed1ab_0 conda-forge
pywin32 303 py39hb82d6ee_0 conda-forge
rasterio 1.2.1 py39hfec4536_0 conda-forge
rasterstats 0.16.0 pyhd8ed1ab_0 conda-forge
reproc 14.2.3 h8ffe710_0 conda-forge
reproc-cpp 14.2.3 h0e60522_0 conda-forge
requests 2.28.1 pyhd8ed1ab_0 conda-forge
rioxarray 0.11.1 pyhd8ed1ab_0 conda-forge
wheel 0.37.1 pyhd8ed1ab_0 conda-forge
win_inet_pton 1.1.0 py39hcbf5309_4 conda-forge
xarray 2022.3.0 pyhd8ed1ab_0 conda-forge
xerces-c 3.2.3 h0e60522_5 conda-forge
xorg-libxau 1.0.9 hcd874cb_0 conda-forge
xorg-libxdmcp 1.1.3 hcd874cb_0 conda-forge
xyzservices 2022.6.0 pyhd8ed1ab_0 conda-forge
xz 5.2.5 h62dcd97_1 conda-forge
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zlib 1.2.12 h8ffe710_1 conda-forge
zstd 1.5.2 h6255e5f_2 conda-forge
This sounds like something worth looking into. However, we need a reproducible example. Are you able to provide a simple code snipped with example rasters that re-produce this issue?
I tired the version 0.12.0 error is the same.
I fixed the first problem of turning the rows upsidedown with: raster.rio.reproject(raster.rio.crs).
original transform: Affine(5.0, 0.0, 588802.5, 0.0, 5.0, 5235857.5),
changed: Affine(5.0, 0.0, 588802.5, 0.0, -5.0, 5235997.5)
I cannot share the data, because of legal reasons.
The latest rasterio is 1.3.2. I would recommend upgrading that as well and seeing if it helps.
I cannot share the data, because of legal reasons.
@Meresmata check out https://github.com/cogeotiff/rio-faux
Unfortunately there isn't much we can do here with the current information.