issues

Builds on the refactoring in #7206

• [x] xref #6610
• [x] Use TimeResampleGrouper

• [x] Tests added (for all bug fixes or enhancements)
• [x] Tests passed (for all non-documentation changes)
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later)

Added support for errorbar plotting. This works by providing the kwargs xerr and/or yerr to plot() or plot.line(). It will only work for 1D data.

Errorbars for plots that usehue argument require that we loop and plot each line individually. I'm happy to add this is you think it's a good idea.

Example from docs: air.isel(time=10, lon=10).plot.line(y='lat', xerr=3, yerr=1.5, ecolor='r')

• [x] Tests added
• [x] Passes pre-commit run --all-files
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst

From 7s to 25 ms and 3.5GB to 850MB memory usage =) by passing the coordinate locations directly to the sparse constructor. ``` asv run -e --bench unstacking.UnstackingSparse.time_unstack_to_sparse --cpu-affinity=3 HEAD [ 0.00%] · For xarray commit c9251e1c <sparse-unstack>: [ 0.00%] ·· Building for conda-py3.8-bottleneck-dask-distributed-netcdf4-numpy-pandas-scipy-sparse [ 0.00%] ·· Benchmarking conda-py3.8-bottleneck-dask-distributed-netcdf4-numpy-pandas-scipy-sparse [ 0.01%] ··· Running (unstacking.UnstackingSparse.time_unstack_to_sparse_2d--).. [ 0.02%] ··· unstacking.UnstackingSparse.time_unstack_to_sparse_2d 623±30μs [ 0.02%] ··· unstacking.UnstackingSparse.time_unstack_to_sparse_3d 22.8±2ms [ 0.06%] ··· unstacking.UnstackingSparse.peakmem_unstack_to_sparse_2d 793M [ 0.06%] ··· unstacking.UnstackingSparse.peakmem_unstack_to_sparse_3d 794M

[ 0.04%] · For xarray commit 80905135 <main>: [ 0.04%] ·· Building for conda-py3.8-bottleneck-dask-distributed-netcdf4-numpy-pandas-scipy-sparse.. [ 0.04%] ·· Benchmarking conda-py3.8-bottleneck-dask-distributed-netcdf4-numpy-pandas-scipy-sparse [ 0.05%] ··· Running (unstacking.UnstackingSparse.time_unstack_to_sparse_2d--).. [ 0.06%] ··· unstacking.UnstackingSparse.time_unstack_to_sparse_2d 596±30ms [ 0.06%] ··· unstacking.UnstackingSparse.time_unstack_to_sparse_3d 7.72±0.1s [ 0.02%] ··· unstacking.UnstackingSparse.peakmem_unstack_to_sparse_2d 867M [ 0.02%] ··· unstacking.UnstackingSparse.peakmem_unstack_to_sparse_3d 3.56G ```

cc @bonnland

xref #5409

This is a simple copy-paste.

• [x] Closes #5454
• [x] Tests added
• [x] Passes pre-commit run --all-files
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [x] New functions/methods are listed in api.rst

Builds on #5474

Here's an example ``` python import numpy as np import xarray as xr

ds = xr.Dataset( { "vart": ("time", np.arange(48)), "varx": ("x", np.arange(10)), "vartx": (("x", "time"), np.arange(480).reshape(10, 48)), "vary": ("y", np.arange(12)), }, coords={"time": np.arange(48), "y": np.arange(12)}, ) ds.coarsen(time=12, x=5, boundary="trim").construct( {"time": ("year", "month"), "x": ("x", "x_reshaped")} ) ```

What do people think of this syntax: {"time": ("year", "month"), "x": ("x", "x_reshaped")? Should we instead do {"time": "month", "x": "x_reshaped"} and have the user later rename the x or time dimension if they want?

• [x] Closes #3746
• [x] Tests added
• [x] Passes isort -rc . && black . && mypy . && flake8
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

@shoyer do you think this is the right thing to do?

We fixed the docs for 0.15.0 by pinning versions for RTD on the stable branch. This brings that change over to master.

Adds a simple groupby repr. Feedback appreciated

da = xr.DataArray( np.random.randn(10, 20, 6), dims=["x", "y", "z"], coords={"z": ["a", "b", "c", "a", "b", "c"], "x": [1, 1, 1, 2, 2, 3, 4, 5, 3, 4]}, )

da.groupby("x") DataArrayGroupBy, grouped over 'x' 5 groups with labels 1, 2, 3, 4, 5 da.groupby("y") DataArrayGroupBy, grouped over 'y' 20 groups with labels 0, 1, 2, 3, 4, 5, ..., 15, 16, 17, 18, 19 da.groupby("z") DataArrayGroupBy, grouped over 'z' 6 groups with labels 'a', 'b', 'c', 'd', 'e', 'f'

• [x] Tests added
• [x] Passes black . && mypy . && flake8
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

This PR adds two changes:

1. First, it refactors concat so that non-concatenated variables are passed to merge. concat gains a compat kwarg which tells merge how to merge things. concat's previous behaviour was effectively compat='equals' which is now the default.

2. Also adds compat="override" to skip equality checking in merge. concat's data_vars='minimal' and coords='minimal' options are now more useful because you can specify compat='override' to skip equality checking and just pick the first one. Previously this would raise an error if any of the variables differed by floating point noise.

3. [x] xref #2064, closes #1823

4. [x] Tests added
5. [x] Passes black . && mypy . && flake8
6. [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

• [x] Closes #3300
• [x] Passes black . && mypy . && flake8

Just increased expected number of warnings by 1 till h5netcdf is fixed to not throw a warning (xref https://github.com/shoyer/h5netcdf/issues/62)

• [x] Closes #1997

Inserting the string "The out keyword argument is not supported" is hacky. Can we do without that and just remove out=None from the signature?

I thought it'd be a good idea to make the "quick overview" example more prominent.

This PR moves that section one level up; adds some more text; talks about plotting and adding metadata.

• [x] Closes #2407
• [x] Tests added
• [x] Tests passed
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API