issue_comments

How about deprecating these? Would that be too much of a change you think? (I never use them)

Yes, this is a good idea! Opened #1756 to keep track.

If I did it again, I would remove the inplace keyword argument from even Dataset methods.

How about deprecating these? Would that be too much of a change you think? (I never use them)

Thank you for your explination. I will to look into my problem from another angle.

If I did it again, I would remove the inplace keyword argument from even Dataset methods. Almost every method in xarray creates new underlying objects, so it's misleading to call them "inplace". (Most of our inplace methods actually create a new object and then assign its properties to an existing object.)

Why not make your accessor method return a new DataArray object? That would be more inline with how most xarray methods work. If you have any special attributes you've set on the accessor you can copy those over to the new result.

Seems like I forgot a confusing line in my example. For example in dataarray_accessor, we have a need for in-place swap. python cube = xr.DataArray(arr, dims=['ya', 'x', 'y'], coords={'y':[1,3], 'ya':[1,2,3,6], 'x':[1,2,5]}) #First dataarray object. cube now referes to this. cube.coords['x2'] = ('x', [1,2,3]) cube = cube.swap_dims({'x':'x2'}) #Second dataarray object. cube now refers to this and first dataarray object is now forgotten vs: python cube = xr.DataArray(arr, dims=['ya', 'x', 'y'], coords={'y':[1,3], 'ya':[1,2,3,6], 'x':[1,2,5]}) #First dataarray object. cube now referes to this. cube.coords['x2'] = ('x', [1,2,3]) cube.swap_dims({'x':'x2'}, inline=True) #This just changes the first dataarray object. cube still refers to the first dataarray object

So the question again was, is there a reason, why the inline keyword is implemented on dataset, but not on dataarray.

Ah right. So you would do python cube = cube.swap_dims({'x':'x2'})

@rabernat, please note that i'm working with dataarrays. There is no inplace keyword.

swap_dims is not an in-place operation http://xarray.pydata.org/en/stable/generated/xarray.Dataset.swap_dims.html

It returns a new dataset with swapped dims. If you want to do it in place, there is a keyword argument for that.

Sent from my iPhone

On Dec 1, 2017, at 7:37 AM, Leevi Annala notifications@github.com wrote:

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:

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.swap_dims({'x':'x2'}) 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:

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.swap_dims({'x':'x2'}) 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 (x) int32 1 2 5 * x2 (x) int32 1 2 3 I have version 0.10.0, the newest on conda-forge.

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