DataArrayResample.interpolate coordinates out of bound.

[aurghs]

Code Sample, a copy-pastable example if possible

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

time = np.arange('2007-02-01', '2007-03-02', dtype='datetime64').astype('datetime64[ns]')
arr = xr.DataArray(
    np.arange(time.size), coords=[time,], dims=('time',), name='data'
)
arr.resample(time='M').interpolate('linear')

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-20-ff65c4d138e7> in <module>()
      7     np.arange(time.size), coords=[time,], dims=('time',), name='data'
      8 )
----> 9 arr.resample(time='M').interpolate('linear')

~/devel/c3s-cns/venv_op/lib/python3.6/site-packages/xarray/core/resample.py in interpolate(self, kind)
    108 
    109         """
--> 110         return self._interpolate(kind=kind)
    111 
    112     def _interpolate(self, kind='linear'):

~/devel/c3s-cns/venv_op/lib/python3.6/site-packages/xarray/core/resample.py in _interpolate(self, kind)
    218             elif self._dim not in v.dims:
    219                 coords[k] = v
--> 220         return DataArray(f(new_x), coords, dims, name=dummy.name,
    221                          attrs=dummy.attrs)
    222 

~/devel/c3s-cns/venv_op/lib/python3.6/site-packages/scipy/interpolate/polyint.py in __call__(self, x)
     77         """
     78         x, x_shape = self._prepare_x(x)
---> 79         y = self._evaluate(x)
     80         return self._finish_y(y, x_shape)
     81 

~/devel/c3s-cns/venv_op/lib/python3.6/site-packages/scipy/interpolate/interpolate.py in _evaluate(self, x_new)
    632         y_new = self._call(self, x_new)
    633         if not self._extrapolate:
--> 634             below_bounds, above_bounds = self._check_bounds(x_new)
    635             if len(y_new) > 0:
    636                 # Note fill_value must be broadcast up to the proper size

~/devel/c3s-cns/venv_op/lib/python3.6/site-packages/scipy/interpolate/interpolate.py in _check_bounds(self, x_new)
    664                              "range.")
    665         if self.bounds_error and above_bounds.any():
--> 666             raise ValueError("A value in x_new is above the interpolation "
    667                              "range.")
    668 

ValueError: A value in x_new is above the interpolation range.

Problem description

It raise an error if I try to interpolate. If time range is exactly a month, then it works:

time = np.arange('2007-02-01', '2007-03-01', dtype='datetime64').astype('datetime64[ns]')
arr = xr.DataArray(
    np.arange(time.size), coords=[time,], dims=('time',), name='data'
)
arr.resample(time='M').interpolate('linear')

<xarray.DataArray 'data' (time: 1)>
array([27.])
Coordinates:
  * time     (time) datetime64[ns] 2007-02-28

The problem for the interpolation seems to be that the resampler contains indices out bound ('2007-03-31'). It is ok for the aggregations, but it doesn't work with the interpolation.

resampler = arr.resample(time='M') 

resampler._full_index
DatetimeIndex(['2007-02-28', '2007-03-31'], dtype='datetime64[ns]', name='time', freq='M')

Expected Output

<xarray.DataArray 'data' (time: 1)>
array([27.])
Coordinates:
  * time     (time) datetime64[ns] 2007-02-28

Output of xr.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.6.0.final.0 python-bits: 64 OS: Linux OS-release: 4.13.0-43-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8

xarray: 0.10.3
pandas: 0.22.0
numpy: 1.14.3
scipy: 1.1.0
netCDF4: 1.3.1
h5netcdf: None
h5py: None
Nio: None
zarr: None
bottleneck: None
cyordereddict: None
dask: 0.17.4
distributed: None
matplotlib: 2.2.2
cartopy: 0.16.0
seaborn: None
setuptools: 39.2.0
pip: 10.0.1
conda: None
pytest: 3.5.1
IPython: 6.4.0
sphinx: 1.7.4

[shoyer]

This is a good point -- the current code only really makes sense when interpolating to a higher frequency, e.g., daily -> hourly.

I do think going to higher frequencies is the main use-case for interpolation, but I don't think it needs to raise errors in this case. At the least, it would be OK to fill in the invalid values with NaN.

I think the fix might be as simple as setting bounds_error=False in these two places in xarray/core/resmample.py:

xarray/xarray/core/resample.py

Line 202 in cf19528

f = interp1d(x, y, kind=kind, axis=axis, bounds_error=True,

xarray/xarray/core/resample.py

Line 341 in cf19528

axis=axis, bounds_error=True,

Any interest in putting together a pull request?

[aurghs]

Also with oversampling we have the same problem (2007-02-02 02:00:00 is out of bound):

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

time = np.arange('2007-01-01 00:00:00', '2007-02-02 00:00:00', dtype='datetime64[ns]')
arr = xr.DataArray(
    np.arange(time.size), coords=[time,], dims=('time',), name='data'
)
resampler = arr.resample(time='3h', base=2, label='right')

resampler
DatetimeIndex(['2007-01-01 02:00:00', '2007-01-01 05:00:00',
               '2007-01-01 08:00:00', '2007-01-01 11:00:00',
               '2007-01-01 14:00:00', '2007-01-01 17:00:00',
               '2007-01-01 20:00:00', '2007-01-01 23:00:00',
               '2007-01-02 02:00:00', '2007-01-02 05:00:00',
               ...
               '2007-01-31 23:00:00', '2007-02-01 02:00:00',
               '2007-02-01 05:00:00', '2007-02-01 08:00:00',
               '2007-02-01 11:00:00', '2007-02-01 14:00:00',
               '2007-02-01 17:00:00', '2007-02-01 20:00:00',
               '2007-02-01 23:00:00', '2007-02-02 02:00:00'],
              dtype='datetime64[ns]', name='time', length=257, freq='3H')

The fix is really very easy, I can try to make pull request.