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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 |
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1371769156 | I_kwDOAMm_X85Rw4lE | 7030 | Assigning with .loc indexing: implicit effect of dimension order? | darikg 6875882 | closed | 0 | 5 | 2022-09-13T17:19:07Z | 2023-09-12T18:17:11Z | 2023-09-12T18:17:11Z | CONTRIBUTOR | What happened?Assigning a DataArray to a subset of another DataArray seems to depend on the order of the dimensions What did you expect to happen?I would expect xarray to automatically align the dimensions appropriately. Minimal Complete Verifiable ExampleExample 1: ```python from xarray import DataArray from numpy import zeros, ones, arange Two arrays abc and acb, identical except for dimensional orderingabc = DataArray(zeros((2, 3, 4)), dims=('a', 'b', 'c')) acb = DataArray(zeros((2, 4, 3)), dims=('a', 'c', 'b')) assert (abc == acb).all() Assign a subsetbc = DataArray(ones((3, 4)), dims=('b', 'c')) abc.loc[dict(a=0)] = bc acb.loc[dict(a=0)] = bc # ValueError: could not broadcast input array from shape (3,4) into shape (4,3) ``` Example 2: Same as example 1, but instead of raising a ValueError, runs without error, but gives incorrect results because it ignores dimension order ```python This time, make b and c dimensions identicalabc = DataArray(zeros((2, 3, 3)), dims=('a', 'b', 'c')) acb = DataArray(zeros((2, 3, 3)), dims=('a', 'c', 'b')) assert (abc == acb).all() Assign a subsetbc = DataArray(arange(9).reshape(3, 3), dims=('b', 'c')) abc.loc[dict(a=0)] = bc acb.loc[dict(a=0)] = bc assert (abc == acb).all() # Assertion error ``` MVCE confirmation
Relevant log outputNo response Anything else we need to know?No response Environment
INSTALLED VERSIONS
------------------
commit: None
python: 3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:50:36) [MSC v.1929 64 bit (AMD64)]
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('English_United States', '1252')
libhdf5: 1.12.1
libnetcdf: 4.8.1
xarray: 2022.6.0
pandas: 1.4.3
numpy: 1.23.1
scipy: 1.9.0
netCDF4: 1.6.0
pydap: None
h5netcdf: None
h5py: 3.7.0
Nio: None
zarr: None
cftime: 1.6.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: None
distributed: None
matplotlib: 3.5.2
cartopy: None
seaborn: None
numbagg: None
fsspec: 2022.7.1
cupy: None
pint: None
sparse: None
flox: None
numpy_groupies: None
setuptools: 63.4.2
pip: 22.2.2
conda: 4.14.0
pytest: 7.1.2
IPython: 8.4.0
sphinx: None
|
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completed | xarray 13221727 | issue | ||||||
873713013 | MDU6SXNzdWU4NzM3MTMwMTM= | 5240 | Coord name not set when `concat`ing along a DataArray | darikg 6875882 | closed | 0 | 4 | 2021-05-01T16:18:32Z | 2021-08-23T17:00:39Z | 2021-08-23T17:00:39Z | CONTRIBUTOR |
I would've expected the name of the new coordinate to be the name of the DataArray variable but instead it's Environment: Output of <tt>xr.show_versions()</tt>INSTALLED VERSIONS ------------------ commit: None python: 3.9.2 | packaged by conda-forge | (default, Feb 21 2021, 04:59:43) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 142 Stepping 12, GenuineIntel byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: English_United States.1252 libhdf5: None libnetcdf: None xarray: 0.17.0 pandas: 1.2.4 numpy: 1.20.2 scipy: None netCDF4: None pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: None distributed: None matplotlib: None cartopy: None seaborn: None numbagg: None pint: None setuptools: 49.6.0.post20210108 pip: 21.1.1 conda: None pytest: None IPython: 7.23.0 sphinx: None |
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completed | xarray 13221727 | issue | ||||||
828805728 | MDU6SXNzdWU4Mjg4MDU3Mjg= | 5022 | Extracting `formatting_html` as a standalone library? | darikg 6875882 | open | 0 | 3 | 2021-03-11T06:23:04Z | 2021-07-16T17:31:20Z | CONTRIBUTOR | Hi, xarray is a superb library but my favorite icing on the cake is the out-of-the-box rich HTML display in Jupyter. I've been finding myself wanting the same thing for every class I use in python that is fundamentally a hierachically structured numerical dataset. Has there been any discussion of generalizing the whole thing? I'd be happy to give it a shot myself, but I was hoping to pick your collective brains first, especially @benbovy and @jsignell, (who based on my very preliminary github sleuthing were early heavy-lifters?) |
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xarray 13221727 | issue | ||||||||
686495257 | MDExOlB1bGxSZXF1ZXN0NDc0MDUyNjc1 | 4379 | Use deepcopy recursively on numpy arrays | darikg 6875882 | closed | 0 | 12 | 2020-08-26T17:31:56Z | 2020-08-27T15:58:49Z | 2020-08-27T14:56:49Z | CONTRIBUTOR | 0 | pydata/xarray/pulls/4379 |
(Not sure this is worth noting in whats-new) |
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xarray 13221727 | pull | |||||
683649612 | MDU6SXNzdWU2ODM2NDk2MTI= | 4362 | Surprising deepcopy semantics with dtype='object' | darikg 6875882 | closed | 0 | 4 | 2020-08-21T15:40:59Z | 2020-08-27T14:56:48Z | 2020-08-27T14:56:48Z | CONTRIBUTOR | ```python from copy import deepcopy import numpy as np import xarray as xr class Dummy: pass a0 = np.array([Dummy()]) a1 = deepcopy(a0) print(a0[0] is a1[0]) # False, as expected x0 = xr.DataArray(a0, dims='dummy') x1 = deepcopy(x0) print(x0.values[0] is x1.values[0]) # unexpectedly True ``` I think this is a bug, and would be fixed with an extra deepcopy around here |
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completed | xarray 13221727 | issue |
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