What happened?

With xarray 2022.11.0, plotting a DataArray with a cftime time axis fails.

It fails with a matplotlib error : TypeError: float() argument must be a string or a real number, not 'cftime._cftime.DatetimeNoLeap'

What did you expect to happen?

With previous versions of xarray, the nc_time_axis package was imported by xarray and these errors were avoided.

Minimal Complete Verifiable Example

Python import xarray as xr da = xr.DataArray( list(range(10)), dims=('time',), coords={'time': xr.cftime_range('1900-01-01', periods=10, calendar='noleap', freq='D')} ) da.plot()

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

```Python

TypeError Traceback (most recent call last) Cell In [1], line 7 1 import xarray as xr 2 da = xr.DataArray( 3 list(range(10)), 4 dims=('time',), 5 coords={'time': xr.cftime_range('1900-01-01', periods=10, calendar='noleap', freq='D')} 6 ) ----> 7 da.plot()

File ~/mambaforge/envs/xclim/lib/python3.10/site-packages/xarray/plot/accessor.py:46, in DataArrayPlotAccessor.call(self, kwargs) 44 @functools.wraps(dataarray_plot.plot, assigned=("doc", "annotations")) 45 def call(self, kwargs) -> Any: ---> 46 return dataarray_plot.plot(self._da, **kwargs)

File ~/mambaforge/envs/xclim/lib/python3.10/site-packages/xarray/plot/dataarray_plot.py:312, in plot(darray, row, col, col_wrap, ax, hue, subplot_kws, kwargs) 308 plotfunc = hist 310 kwargs["ax"] = ax --> 312 return plotfunc(darray, kwargs)

File ~/mambaforge/envs/xclim/lib/python3.10/site-packages/xarray/plot/dataarray_plot.py:517, in line(darray, row, col, figsize, aspect, size, ax, hue, x, y, xincrease, yincrease, xscale, yscale, xticks, yticks, xlim, ylim, add_legend, _labels, args, kwargs) 513 ylabel = label_from_attrs(yplt, extra=y_suffix) 515 _ensure_plottable(xplt_val, yplt_val) --> 517 primitive = ax.plot(xplt_val, yplt_val, args, **kwargs) 519 if _labels: 520 if xlabel is not None:

File ~/mambaforge/envs/xclim/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1664, in Axes.plot(self, scalex, scaley, data, args, kwargs) 1662 lines = [self._get_lines(args, data=data, *kwargs)] 1663 for line in lines: -> 1664 self.add_line(line) 1665 if scalex: 1666 self._request_autoscale_view("x")

File ~/mambaforge/envs/xclim/lib/python3.10/site-packages/matplotlib/axes/_base.py:2340, in _AxesBase.add_line(self, line) 2337 if line.get_clip_path() is None: 2338 line.set_clip_path(self.patch) -> 2340 self._update_line_limits(line) 2341 if not line.get_label(): 2342 line.set_label(f'_child{len(self._children)}')

File ~/mambaforge/envs/xclim/lib/python3.10/site-packages/matplotlib/axes/_base.py:2363, in _AxesBase._update_line_limits(self, line) 2359 def _update_line_limits(self, line): 2360 """ 2361 Figures out the data limit of the given line, updating self.dataLim. 2362 """ -> 2363 path = line.get_path() 2364 if path.vertices.size == 0: 2365 return

File ~/mambaforge/envs/xclim/lib/python3.10/site-packages/matplotlib/lines.py:1031, in Line2D.get_path(self) 1029 """Return the ~matplotlib.path.Path associated with this line.""" 1030 if self._invalidy or self._invalidx: -> 1031 self.recache() 1032 return self._path

File ~/mambaforge/envs/xclim/lib/python3.10/site-packages/matplotlib/lines.py:659, in Line2D.recache(self, always) 657 if always or self._invalidx: 658 xconv = self.convert_xunits(self._xorig) --> 659 x = _to_unmasked_float_array(xconv).ravel() 660 else: 661 x = self._x

File ~/mambaforge/envs/xclim/lib/python3.10/site-packages/matplotlib/cbook/init.py:1369, in _to_unmasked_float_array(x) 1367 return np.ma.asarray(x, float).filled(np.nan) 1368 else: -> 1369 return np.asarray(x, float)

TypeError: float() argument must be a string or a real number, not 'cftime._cftime.DatetimeNoLeap' ```

Anything else we need to know?

I suspect #7179.

This line: https://github.com/pydata/xarray/blob/cc7e09a3507fa342b3790b5c109e700fa12f0b17/xarray/plot/utils.py#L27 does not import nc_time_axis. Further down, the variable gets checked and if False an error is raised, but if the package still is not imported if True.

Previously we had: https://github.com/pydata/xarray/blob/fc9026b59d38146a21769cc2d3026a12d58af059/xarray/plot/utils.py#L27-L32 where the package is always imported.

Maybe there's a way to import nc_time_axis only when needed?

Environment

What happened?

I was trying to use polyval with a cftime coordinate and it failed with TypeError: unsupported operand type(s) for *: 'float' and 'cftime._cftime.DatetimeNoLeap'. The error seems to originate from #6548, where the process transforming coordinates to numerical values was modified. The new _ensure_numeric method seems to ignore the possibility of cftime arrays.

What did you expect to happen?

A polynomial to be evaluated along my coordinate.

Minimal Complete Verifiable Example

```Python import xarray as xr import numpy as np

use_cftime=False will work

t = xr.date_range('2001-01-01', periods=100, use_cftime=True, freq='YS') da = xr.DataArray(np.arange(100) ** 3, dims=('time',), coords={'time': t}) coeffs = da.polyfit('time', 4) da2 = xr.polyval(da.time, coeffs).polyfit_coefficients ```

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

```Python

TypeError Traceback (most recent call last) Input In [5], in <cell line: 4>() 2 da = xr.DataArray(np.arange(100) ** 3, dims=('time',), coords={'time': t}) 3 coeffs = da.polyfit('time', 4) ----> 4 da2 = xr.polyval(da.time, coeffs).polyfit_coefficients

File ~/Python/xarray/xarray/core/computation.py:1931, in polyval(coord, coeffs, degree_dim) 1929 res = zeros_like(coord) + coeffs.isel({degree_dim: max_deg}, drop=True) 1930 for deg in range(max_deg - 1, -1, -1): -> 1931 res *= coord 1932 res += coeffs.isel({degree_dim: deg}, drop=True) 1934 return res

File ~/Python/xarray/xarray/core/_typed_ops.py:103, in DatasetOpsMixin.imul(self, other) 102 def imul(self, other): --> 103 return self._inplace_binary_op(other, operator.imul)

File ~/Python/xarray/xarray/core/dataset.py:6107, in Dataset._inplace_binary_op(self, other, f) 6105 other = other.reindex_like(self, copy=False) 6106 g = ops.inplace_to_noninplace_op(f) -> 6107 ds = self._calculate_binary_op(g, other, inplace=True) 6108 self._replace_with_new_dims( 6109 ds._variables, 6110 ds._coord_names, (...) 6113 inplace=True, 6114 ) 6115 return self

File ~/Python/xarray/xarray/core/dataset.py:6154, in Dataset._calculate_binary_op(self, f, other, join, inplace) 6152 else: 6153 other_variable = getattr(other, "variable", other) -> 6154 new_vars = {k: f(self.variables[k], other_variable) for k in self.data_vars} 6155 ds._variables.update(new_vars) 6156 ds._dims = calculate_dimensions(ds._variables)

File ~/Python/xarray/xarray/core/dataset.py:6154, in <dictcomp>(.0) 6152 else: 6153 other_variable = getattr(other, "variable", other) -> 6154 new_vars = {k: f(self.variables[k], other_variable) for k in self.data_vars} 6155 ds._variables.update(new_vars) 6156 ds._dims = calculate_dimensions(ds._variables)

File ~/Python/xarray/xarray/core/_typed_ops.py:402, in VariableOpsMixin.mul(self, other) 401 def mul(self, other): --> 402 return self._binary_op(other, operator.mul)

File ~/Python/xarray/xarray/core/variable.py:2494, in Variable._binary_op(self, other, f, reflexive) 2491 attrs = self._attrs if keep_attrs else None 2492 with np.errstate(all="ignore"): 2493 new_data = ( -> 2494 f(self_data, other_data) if not reflexive else f(other_data, self_data) 2495 ) 2496 result = Variable(dims, new_data, attrs=attrs) 2497 return result

TypeError: unsupported operand type(s) for *: 'float' and 'cftime._cftime.DatetimeGregorian' ```

Anything else we need to know?

I also noticed that since the Horner PR, polyfit and polyval do not use the same function to convert coordinates into numerical values. Isn't this dangerous?

Environment

What happened?

This is a the reverse problem of #6379, the DataArrayGroupBy._combine method seems broken when the mapped function returns a Dataset (which worked before #5692).

What did you expect to happen?

No response

Minimal Complete Verifiable Example

```Python import xarray as xr

ds = xr.tutorial.open_dataset("air_temperature")

ds.air.resample(time="YS").map(lambda grp: grp.mean("time").to_dataset()) ```

Relevant log output

```Python

TypeError Traceback (most recent call last) Input In [3], in <module> ----> 1 ds.air.resample(time="YS").map(lambda grp: grp.mean("time").to_dataset())

File ~/Python/myxarray/xarray/core/resample.py:223, in DataArrayResample.map(self, func, shortcut, args, kwargs) 180 """Apply a function to each array in the group and concatenate them 181 together into a new array. 182 (...) 219 The result of splitting, applying and combining this array. 220 """ 221 # TODO: the argument order for Resample doesn't match that for its parent, 222 # GroupBy --> 223 combined = super().map(func, shortcut=shortcut, args=args, kwargs) 225 # If the aggregation function didn't drop the original resampling 226 # dimension, then we need to do so before we can rename the proxy 227 # dimension we used. 228 if self._dim in combined.coords:

File ~/Python/myxarray/xarray/core/groupby.py:835, in DataArrayGroupByBase.map(self, func, shortcut, args, kwargs) 833 grouped = self._iter_grouped_shortcut() if shortcut else self._iter_grouped() 834 applied = (maybe_wrap_array(arr, func(arr, *args, kwargs)) for arr in grouped) --> 835 return self._combine(applied, shortcut=shortcut)

File ~/Python/myxarray/xarray/core/groupby.py:869, in DataArrayGroupByBase._combine(self, applied, shortcut) 867 index, index_vars = create_default_index_implicit(coord) 868 indexes = {k: index for k in index_vars} --> 869 combined = combined._overwrite_indexes(indexes, coords=index_vars) 870 combined = self._maybe_restore_empty_groups(combined) 871 combined = self._maybe_unstack(combined)

TypeError: _overwrite_indexes() got an unexpected keyword argument 'coords' ```

Anything else we need to know?

I guess the same solution as #6386 could be used!

Environment

What happened?

Got a TypeError when resampling a dataset along a dimension, mapping a function to each group. The function returns a DataArray.

Failed with : TypeError: _overwrite_indexes() got an unexpected keyword argument 'variables'

What did you expect to happen?

This worked before the merging of #5692. A DataArray was returned as expected.

Minimal Complete Verifiable Example

```Python import xarray as xr

ds = xr.tutorial.open_dataset("air_temperature")

ds.resample(time="YS").map(lambda grp: grp.air.mean("time")) ```

Relevant log output

```Python

TypeError Traceback (most recent call last) Input In [37], in <module> ----> 1 ds.resample(time="YS").map(lambda grp: grp.air.mean("time"))

File /opt/miniconda3/envs/xclim-pip/lib/python3.9/site-packages/xarray/core/resample.py:300, in DatasetResample.map(self, func, args, shortcut, kwargs) 298 # ignore shortcut if set (for now) 299 applied = (func(ds, *args, kwargs) for ds in self._iter_grouped()) --> 300 combined = self._combine(applied) 302 return combined.rename({self._resample_dim: self._dim})

File /opt/miniconda3/envs/xclim-pip/lib/python3.9/site-packages/xarray/core/groupby.py:999, in DatasetGroupByBase._combine(self, applied) 997 index, index_vars = create_default_index_implicit(coord) 998 indexes = {k: index for k in index_vars} --> 999 combined = combined._overwrite_indexes(indexes, variables=index_vars) 1000 combined = self._maybe_restore_empty_groups(combined) 1001 combined = self._maybe_unstack(combined)

TypeError: _overwrite_indexes() got an unexpected keyword argument 'variables' ```

Anything else we need to know?

In the docstring of DatasetGroupBy.map it is not made clear that the passed function should return a dataset, but the opposite is also not said. This worked before and I think the issues comes from #5692, which introduced different signatures for DataArray._overwrite_indexes (which is called in my case) and Dataset._overwrite_indexes (which is expected by the new _combine).

If the function passed to Dataset.resample(...).map should only return Datasets then I believe a more explicit error is needed, as well as some notice in the docs and a breaking change entry in the changelog. If DataArrays should be accepted, then we have a regression here.

I may have time to help on this.

Environment

What happened?

When concatenating two objects with xr.concat along a new dimension given through a DataArray, the attributes of this given coordinate are lost in the concatenation.

What did you expect to happen?

I expected the concatenation coordinate to be identical to the 1D DataArray I gave to concat.

Minimal Complete Verifiable Example

```Python import xarray as xr

ds = xr.tutorial.open_dataset("air_temperature")

concat_dim = xr.DataArray([1, 2], dims=("condim",), attrs={"an_attr": "yep"}, name="condim")

out = xr.concat([ds, ds], concat_dim) out.condim.attrs ```

Before #5692, I get: {'an_attr': 'yep'} with the current master, I get: {}

Anything else we need to know?

I'm not 100% sure, but I think the change is due to xr.core.concat._calc_concat_dim_coord being replaced by xr.core.concat.__calc_concat_dim_index. The former didn't touch the concatenation coordinate, while the latter casts it as an index, thus dropping the attributes in the process.

If the solution is to add a check in xr.concat, I may have time to implement something simple.

Environment

• [x] Closes #4123
• [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

Based on comments on dask/dask#3096, I fixed the dask indexing error that occurred when idxmax/idxmin were called on ND data (where N > 2). Added tests are very simplistic, I believe the 1D and 2D tests already cover most cases, I just wanted to test that is was indeed working on ND data, assuming that non-dask data was already treated properly.

I believe this doesn't conflict with #3936.