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Jan 3, 2019
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Use built-in interp for interpolation with resample #2640

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9c84d38
Use built-in interp for interpolation with resample
spencerkclark Jan 1, 2019
811b11a
Add test for 2197
spencerkclark Jan 1, 2019
6593f87
lint
spencerkclark Jan 1, 2019
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flake8
spencerkclark Jan 2, 2019
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10 changes: 10 additions & 0 deletions doc/whats-new.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -24,9 +24,19 @@ Breaking changes
Enhancements
~~~~~~~~~~~~

- Upsampling an array via interpolation with resample is now dask-compatible,
as long as the array is not chunked along the resampling dimension.
By `Spencer Clark <https://github.com/spencerkclark>`_.

Bug fixes
~~~~~~~~~

- Interpolating via resample now internally specifies ``bounds_error=False``
as an argument to ``scipy.interpolate.interp1d``, allowing for interpolation
from higher frequencies to lower frequencies. Datapoints outside the bounds
of the original time coordinate are now filled with NaN (:issue:`2197`). By
`Spencer Clark <https://github.com/spencerkclark>`_.

.. _whats-new.0.11.1:

v0.11.1 (29 December 2018)
Expand Down
96 changes: 10 additions & 86 deletions xarray/core/resample.py
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Expand Up @@ -2,7 +2,6 @@

from . import ops
from .groupby import DEFAULT_DIMS, DataArrayGroupBy, DatasetGroupBy
from .pycompat import OrderedDict, dask_array_type

RESAMPLE_DIM = '__resample_dim__'

Expand Down Expand Up @@ -110,7 +109,16 @@ def interpolate(self, kind='linear'):
return self._interpolate(kind=kind)

def _interpolate(self, kind='linear'):
raise NotImplementedError
"""Apply scipy.interpolate.interp1d along resampling dimension."""
# drop any existing non-dimension coordinates along the resampling
# dimension
dummy = self._obj.copy()
for k, v in self._obj.coords.items():
if k != self._dim and self._dim in v.dims:
dummy = dummy.drop(k)
return dummy.interp(assume_sorted=True, method=kind,
kwargs={'bounds_error': False},
**{self._dim: self._full_index})


class DataArrayResample(DataArrayGroupBy, Resample):
Expand Down Expand Up @@ -182,46 +190,6 @@ def apply(self, func, shortcut=False, args=(), **kwargs):

return combined

def _interpolate(self, kind='linear'):
"""Apply scipy.interpolate.interp1d along resampling dimension."""
from .dataarray import DataArray
from scipy.interpolate import interp1d

if isinstance(self._obj.data, dask_array_type):
raise TypeError(
"Up-sampling via interpolation was attempted on the the "
"variable '{}', but it is a dask array; dask arrays are not "
"yet supported in resample.interpolate(). Load into "
"memory with Dataset.load() before resampling."
.format(self._obj.data.name)
)

x = self._obj[self._dim].astype('float')
y = self._obj.data

axis = self._obj.get_axis_num(self._dim)

f = interp1d(x, y, kind=kind, axis=axis, bounds_error=True,
assume_sorted=True)
new_x = self._full_index.values.astype('float')

# construct new up-sampled DataArray
dummy = self._obj.copy()
dims = dummy.dims

# drop any existing non-dimension coordinates along the resampling
# dimension
coords = OrderedDict()
for k, v in dummy.coords.items():
# is the resampling dimension
if k == self._dim:
coords[self._dim] = self._full_index
# else, check if resampling dim is in coordinate dimensions
elif self._dim not in v.dims:
coords[k] = v
return DataArray(f(new_x), coords, dims, name=dummy.name,
attrs=dummy.attrs)


ops.inject_reduce_methods(DataArrayResample)
ops.inject_binary_ops(DataArrayResample)
Expand Down Expand Up @@ -308,50 +276,6 @@ def reduce(self, func, dim=None, keep_attrs=None, **kwargs):
return super(DatasetResample, self).reduce(
func, dim, keep_attrs, **kwargs)

def _interpolate(self, kind='linear'):
"""Apply scipy.interpolate.interp1d along resampling dimension."""
from .dataset import Dataset
from .variable import Variable
from scipy.interpolate import interp1d

old_times = self._obj[self._dim].astype(float)
new_times = self._full_index.values.astype(float)

data_vars = OrderedDict()
coords = OrderedDict()

# Apply the interpolation to each DataArray in our original Dataset
for name, variable in self._obj.variables.items():
if name in self._obj.coords:
if name == self._dim:
coords[self._dim] = self._full_index
elif self._dim not in variable.dims:
coords[name] = variable
else:
if isinstance(variable.data, dask_array_type):
raise TypeError(
"Up-sampling via interpolation was attempted on the "
"variable '{}', but it is a dask array; dask arrays "
"are not yet supprted in resample.interpolate(). Load "
"into memory with Dataset.load() before resampling."
.format(name)
)

axis = variable.get_axis_num(self._dim)

# We've previously checked for monotonicity along the
# re-sampling dimension (in __init__ via the GroupBy
# constructor), so we can avoid sorting the data again by
# passing 'assume_sorted=True'
f = interp1d(old_times, variable.data, kind=kind,
axis=axis, bounds_error=True,
assume_sorted=True)
interpolated = Variable(variable.dims, f(new_times))

data_vars[name] = interpolated

return Dataset(data_vars, coords)


ops.inject_reduce_methods(DatasetResample)
ops.inject_binary_ops(DatasetResample)
45 changes: 38 additions & 7 deletions xarray/tests/test_dataarray.py
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Expand Up @@ -2524,6 +2524,16 @@ def test_upsample_interpolate(self):
# done here due to floating point arithmetic
assert_allclose(expected, actual, rtol=1e-16)

@requires_scipy
def test_upsample_interpolate_bug_2197(self):
dates = pd.date_range('2007-02-01', '2007-03-01', freq='D')
da = xr.DataArray(np.arange(len(dates)), [('time', dates)])
result = da.resample(time='M').interpolate('linear')
expected_times = np.array([np.datetime64('2007-02-28'),
np.datetime64('2007-03-31')])
expected = xr.DataArray([27., np.nan], [('time', expected_times)])
assert_equal(result, expected)

@requires_scipy
def test_upsample_interpolate_regression_1605(self):
dates = pd.date_range('2016-01-01', '2016-03-31', freq='1D')
Expand All @@ -2536,21 +2546,42 @@ def test_upsample_interpolate_regression_1605(self):
@requires_dask
@requires_scipy
def test_upsample_interpolate_dask(self):
import dask.array as da

times = pd.date_range('2000-01-01', freq='6H', periods=5)
from scipy.interpolate import interp1d
xs = np.arange(6)
ys = np.arange(3)
times = pd.date_range('2000-01-01', freq='6H', periods=5)

z = np.arange(5)**2
data = da.from_array(np.tile(z, (6, 3, 1)), (1, 3, 1))
data = np.tile(z, (6, 3, 1))
array = DataArray(data,
{'time': times, 'x': xs, 'y': ys},
('x', 'y', 'time'))
chunks = {'x': 2, 'y': 1}

expected_times = times.to_series().resample('1H').asfreq().index
# Split the times into equal sub-intervals to simulate the 6 hour
# to 1 hour up-sampling
new_times_idx = np.linspace(0, len(times) - 1, len(times) * 5)
for kind in ['linear', 'nearest', 'zero', 'slinear', 'quadratic',
'cubic']:
actual = array.chunk(chunks).resample(time='1H').interpolate(kind)
actual = actual.compute()
f = interp1d(np.arange(len(times)), data, kind=kind, axis=-1,
bounds_error=True, assume_sorted=True)
expected_data = f(new_times_idx)
expected = DataArray(expected_data,
{'time': expected_times, 'x': xs, 'y': ys},
('x', 'y', 'time'))
# Use AllClose because there are some small differences in how
# we upsample timeseries versus the integer indexing as I've
# done here due to floating point arithmetic
assert_allclose(expected, actual, rtol=1e-16)

with raises_regex(TypeError,
"dask arrays are not yet supported"):
array.resample(time='1H').interpolate('linear')
# Check that an error is raised if an attempt is made to interpolate
# over a chunked dimension
with raises_regex(NotImplementedError,
'Chunking along the dimension to be interpolated'):
array.chunk({'time': 1}).resample(time='1H').interpolate('linear')

def test_align(self):
array = DataArray(np.random.random((6, 8)),
Expand Down