weighted polyfit with NaNs appears broken

[bbuzz31]

What happened?

polyfit can't seem to handle weights still (despite #5933). nans are involved.

What did you expect to happen?

No response

Minimal Complete Verifiable Example

np.random.seed(10)
y = np.random.rand(10)
x = np.arange(len(y))
y[3] = np.nan
y[5] = np.nan
w = y/10

msk = ~np.isnan(y)
x = x[msk]
y = y[msk]
w = w[msk]

da_test = xr.DataArray(y, dims='time', coords={'time': x})
da_test.polyfit('time', 1, w=w)

# np.polyfit(x, y, 1)#, w=w) # <-- should match this

MVCE confirmation

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

Relevant log output

No response

Anything else we need to know?

not inserting the nans and/or converting the weights to a data array didn't matter

Environment

INSTALLED VERSIONS

commit: None
python: 3.12.3 | packaged by conda-forge | (main, Apr 15 2024, 18:38:13) [GCC 12.3.0]
python-bits: 64
OS: Linux
OS-release: 5.14.0-284.30.1.el9_2.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.14.3
libnetcdf: 4.9.2

xarray: 2024.3.0
pandas: 2.2.2
numpy: 1.26.4
scipy: 1.13.0
netCDF4: 1.6.5
pydap: None
h5netcdf: 1.3.0
h5py: 3.11.0
Nio: None
zarr: None
cftime: 1.6.3
nc_time_axis: None
iris: None
bottleneck: None
dask: 2024.5.0
distributed: 2024.5.0
matplotlib: 3.8.4
cartopy: 0.23.0
seaborn: 0.13.2
numbagg: None
fsspec: 2024.3.1
cupy: None
pint: None
sparse: None
flox: None
numpy_groupies: None
setuptools: 69.5.1
pip: 24.0
conda: None
pytest: None
mypy: None
IPython: 8.22.2
sphinx: None

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[gcaria]

This seems to be alright now

   ...: da_test.polyfit('time', 1, w=w)
Out[4]:
<xarray.Dataset> Size: 32B
Dimensions:               (degree: 2)
Coordinates:
  * degree                (degree) int64 16B 1 0
Data variables:
    polyfit_coefficients  (degree) float64 16B -0.009755 0.7121

In [5]: np.polyfit(x, y, 1, w=w)
Out[5]: array([-0.00975477,  0.71212604])