issues: 374279704

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374279704 MDU6SXNzdWUzNzQyNzk3MDQ= 2514 interpolate_na with limit argument changes size of chunks 102827 closed 0     8 2018-10-26T08:31:35Z 2021-03-26T19:50:50Z 2021-03-26T19:50:50Z CONTRIBUTOR      

Code Sample, a copy-pastable example if possible

```python import pandas as pd import xarray as xr import numpy as np

t = pd.date_range(start='2018-01-01', end='2018-02-01', freq='H') foo = np.sin(np.arange(len(t))) bar = np.cos(np.arange(len(t)))

foo[1] = np.NaN bar[2] = np.NaN

ds_test = xr.Dataset(data_vars={'foo': ('time', foo), 'bar': ('time', bar)}, coords={'time': t}).chunk()

print(ds_test) print("\n\n### After .interpolate_na(dim='time')\n") print(ds_test.interpolate_na(dim='time')) print("\n\n### After .interpolate_na(dim='time', limit=5)\n") print(ds_test.interpolate_na(dim='time', limit=5)) print("\n\n### After .interpolate_na(dim='time', limit=20)\n") print(ds_test.interpolate_na(dim='time', limit=20)) ```

Output of the above code. Note the different chunk sizes, depending on the value of limit: ``` <xarray.Dataset> Dimensions: (time: 745) Coordinates: * time (time) datetime64[ns] 2018-01-01 2018-01-01T01:00:00 ... 2018-02-01 Data variables: foo (time) float64 dask.array<shape=(745,), chunksize=(745,)> bar (time) float64 dask.array<shape=(745,), chunksize=(745,)>

After .interpolate_na(dim='time')

<xarray.Dataset> Dimensions: (time: 745) Coordinates: * time (time) datetime64[ns] 2018-01-01 2018-01-01T01:00:00 ... 2018-02-01 Data variables: foo (time) float64 dask.array<shape=(745,), chunksize=(745,)> bar (time) float64 dask.array<shape=(745,), chunksize=(745,)>

After .interpolate_na(dim='time', limit=5)

<xarray.Dataset> Dimensions: (time: 745) Coordinates: * time (time) datetime64[ns] 2018-01-01 2018-01-01T01:00:00 ... 2018-02-01 Data variables: foo (time) float64 dask.array<shape=(745,), chunksize=(3,)> bar (time) float64 dask.array<shape=(745,), chunksize=(3,)>

After .interpolate_na(dim='time', limit=20)

<xarray.Dataset> Dimensions: (time: 745) Coordinates: * time (time) datetime64[ns] 2018-01-01 2018-01-01T01:00:00 ... 2018-02-01 Data variables: foo (time) float64 dask.array<shape=(745,), chunksize=(10,)> bar (time) float64 dask.array<shape=(745,), chunksize=(10,)> ```

Problem description

When using xarray.DataArray.interpolate_na() with the limit kwarg this changes the chunksize of the resulting dask.arrays.

Expected Output

The chunksize should not change. Very small chunks which results from typical small values of limit are not optimal for the performance of dask. Also, things like .rolling() will fail if the chunksize is smaller than the window length of the rolling window.

Output of xr.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 2.7.15.final.0 python-bits: 64 OS: Darwin OS-release: 16.7.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: de_DE.UTF-8 LOCALE: None.None xarray: 0.10.9 pandas: 0.23.3 numpy: 1.13.3 scipy: 1.0.0 netCDF4: 1.4.1 h5netcdf: 0.5.0 h5py: 2.8.0 Nio: None zarr: None cftime: 1.0.1 PseudonetCDF: None rasterio: None iris: None bottleneck: 1.2.1 cyordereddict: 1.0.0 dask: 0.19.4 distributed: 1.23.3 matplotlib: 2.2.2 cartopy: 0.16.0 seaborn: 0.8.1 setuptools: 38.5.2 pip: 9.0.1 conda: 4.5.11 pytest: 3.4.2 IPython: 5.5.0 sphinx: None 
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