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id node_id number title user state locked assignee milestone comments created_at updated_at ▲ closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
263403430 MDU6SXNzdWUyNjM0MDM0MzA= 1613 Should sel with slice objects care about underlying coordinate order? leeviannala 25015426 open 0     22 2017-10-06T10:35:29Z 2023-10-23T18:50:54Z   CONTRIBUTOR      

If I have a xarray DataArray, that has x-coordinates in descending order, and I try to crop the DataArray using da.sel(x=slice(10,50)), it gives me empty DataArray. If I use da.sel(x=slice(50,10)), it finds everything it is supposed to find.

I think it would be useful to be able to use this feature without knowing the exact order of the DataArray. Is this intentional? 

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     xarray 13221727 issue
278453914 MDU6SXNzdWUyNzg0NTM5MTQ= 1755 Is there a reason for why DataArray.swap_dims() cannot be done in place like Dataset.swap_dims? leeviannala 25015426 closed 0     8 2017-12-01T12:37:39Z 2017-12-02T20:09:17Z 2017-12-02T10:13:39Z CONTRIBUTOR      

Problem description

This is a problem if I want to swap_dims in DataArray Accessor.

Code Sample

This is what I'm forced to do: ```python import xarray as xr import numpy as np @xr.register_dataarray_accessor('testing') class TestAccessor(object): def init(self, xarray_obj): self._obj = xarray_obj def the_problem(self): self._obj = self._obj.swap_dims({'x':'x2'}) print(self._obj)

arr = np.random.rand(4,3,2) cube = xr.DataArray(arr, dims=['ya', 'x', 'y'], coords={'y':[1,3], 'ya':[1,2,3,6], 'x':[1,2,5]}) cube.coords['x2'] = ('x', [1,2,3]) cube.testing.the_problem() print(cube) this prints: <xarray.DataArray (ya: 4, x2: 3, y: 2)> array([[[ 0.659583, 0.167555], [ 0.357974, 0.46081 ], [ 0.85115 , 0.845257]],

   [[ 0.280308,  0.777399],
    [ 0.512527,  0.542036],
    [ 0.838603,  0.799414]],

   [[ 0.572031,  0.350464],
    [ 0.205219,  0.812232],
    [ 0.687778,  0.984928]],

   [[ 0.803385,  0.63981 ],
    [ 0.089909,  0.499857],
    [ 0.25266 ,  0.967909]]])

Coordinates: * y (y) int32 1 3 * ya (ya) int32 1 2 3 6 x (x2) int32 1 2 5 * x2 (x2) int32 1 2 3 <xarray.DataArray (ya: 4, x: 3, y: 2)> array([[[ 0.659583, 0.167555], [ 0.357974, 0.46081 ], [ 0.85115 , 0.845257]],

   [[ 0.280308,  0.777399],
    [ 0.512527,  0.542036],
    [ 0.838603,  0.799414]],

   [[ 0.572031,  0.350464],
    [ 0.205219,  0.812232],
    [ 0.687778,  0.984928]],

   [[ 0.803385,  0.63981 ],
    [ 0.089909,  0.499857],
    [ 0.25266 ,  0.967909]]])

Coordinates: * y (y) int32 1 3 * ya (ya) int32 1 2 3 6 * x (x) int32 1 2 5 x2 (x) int32 1 2 3 ``` where the two xarrays are clearly different.

I would want to do: ```python import xarray as xr import numpy as np @xr.register_dataarray_accessor('testing') class TestAccessor(object): def init(self, xarray_obj): self._obj = xarray_obj def the_problem(self): self._obj.swap_dims({'x':'x2'}, inplace = True) print(self._obj)

arr = np.random.rand(4,3,2) cube = xr.DataArray(arr, dims=['ya', 'x', 'y'], coords={'y':[1,3], 'ya':[1,2,3,6], 'x':[1,2,5]}) cube.coords['x2'] = ('x', [1,2,3]) cube.testing.the_problem() print(cube) this would keep the two xarrays the same, as they should be: <xarray.DataArray (ya: 4, x2: 3, y: 2)> array([[[ 0.659583, 0.167555], [ 0.357974, 0.46081 ], [ 0.85115 , 0.845257]],

   [[ 0.280308,  0.777399],
    [ 0.512527,  0.542036],
    [ 0.838603,  0.799414]],

   [[ 0.572031,  0.350464],
    [ 0.205219,  0.812232],
    [ 0.687778,  0.984928]],

   [[ 0.803385,  0.63981 ],
    [ 0.089909,  0.499857],
    [ 0.25266 ,  0.967909]]])

Coordinates: * y (y) int32 1 3 * ya (ya) int32 1 2 3 6 x (x2) int32 1 2 5 * x2 (x2) int32 1 2 3 <xarray.DataArray (ya: 4, x: 3, y: 2)> array([[[ 0.659583, 0.167555], [ 0.357974, 0.46081 ], [ 0.85115 , 0.845257]],

   [[ 0.280308,  0.777399],
    [ 0.512527,  0.542036],
    [ 0.838603,  0.799414]],

   [[ 0.572031,  0.350464],
    [ 0.205219,  0.812232],
    [ 0.687778,  0.984928]],

   [[ 0.803385,  0.63981 ],
    [ 0.089909,  0.499857],
    [ 0.25266 ,  0.967909]]])

Coordinates: * y (y) int32 1 3 * ya (ya) int32 1 2 3 6 x (x2) int32 1 2 5 * x2 (x2) int32 1 2 3 ```

I have version 0.10.0, the newest on conda-forge.

 
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   completed xarray 13221727 issue
252541192 MDU6SXNzdWUyNTI1NDExOTI= 1520 Rasterio CRS-attribute can be None leeviannala 25015426 closed 0     1 2017-08-24T09:29:04Z 2017-09-01T17:58:42Z 2017-09-01T17:58:42Z CONTRIBUTOR      

open_rasterio does not work when rasterio crs is None. It is also clearly visible from the code that this is the case: Here's the code from line 144 onward python attrs = {}` if hasattr(riods, 'crs'): # CRS is a dict-like object specific to rasterio # We convert it back to a PROJ4 string using rasterio itself attrs['crs'] = riods.crs.to_string() if hasattr(riods, 'res'): # (width, height) tuple of pixels in units of CRS attrs['res'] = riods.res if hasattr(riods, 'is_tiled'): # Is the TIF tiled? (bool) # We cast it to an int for netCDF compatibility attrs['is_tiled'] = np.uint8(riods.is_tiled) if hasattr(riods, 'transform'): # Affine transformation matrix (tuple of floats) # Describes coefficients mapping pixel coordinates to CRS attrs['transform'] = tuple(riods.transform)`

I have version 1.0a9 for rasterio and 0.9.6 for xarray. For what I looked, the issue #1466 is related, but not same issue.

CRS is None when I read ENVI-dataset that has no georeferencing with rasterio.

 
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   completed xarray 13221727 issue

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