def test_applyFillvalue_withFillAndMissingValue(self):
result = apply_fillvalue(data=np.array([1, 2, 3]),
attributes={
'missing_value': 1,
'_FillValue': 2
})
self.assertArraysEqual(result,
ma.array([1, 2, 3], mask=[False, True, False]))
def _get_data_from_slices(self, dim_slices):
"""Get this variable's data as a numpy array.
dim_slices: list of slice objects or integer indices; length of
dim_slices should match the dimensionality of this variable
"""
if len(dim_slices) > 0:
vardata = self._var[dim_slices].copy()
else:
# Scalar data
vardata = np.array(self._var.getValue())
# NOTE(wjs, 2015-12-23) We do our own application of the mask, rather
# than relying on the maskandscale argument to netcdf_file, for two
# reasons: (1) maskandscale was added very recently, and is not yet
# supported in any scipy release, (2) the application of the mask using
# the maskandscale option is not ideal - for example, it allows for
# small differences from the given _FillValue, rather than requiring an
# exact match.
vardata_filled = apply_fillvalue(vardata, self.get_attributes())
return vardata_filled
def _get_data_from_slices(self, dim_slices):
"""Get this variable's data as a numpy array.
dim_slices: list of slice objects or integer indices; length of
dim_slices should match the dimensionality of this variable
"""
if len(dim_slices) > 0:
vardata = self._var[dim_slices].copy()
else:
# Scalar data
vardata = np.array(self._var.getValue())
# NOTE(wjs, 2015-12-23) We do our own application of the mask, rather
# than relying on the maskandscale argument to netcdf_file, for two
# reasons: (1) maskandscale was added very recently, and is not yet
# supported in any scipy release, (2) the application of the mask using
# the maskandscale option is not ideal - for example, it allows for
# small differences from the given _FillValue, rather than requiring an
# exact match.
vardata_filled = apply_fillvalue(vardata, self.get_attributes())
return vardata_filled
def test_applyFillvalue_withNoFill(self):
result = apply_fillvalue(data=np.array([1, 2, 3]), attributes={})
self.assertArraysEqual(result, ma.array([1, 2, 3]))
def test_applyFillvalue_withFillAndMissingValue(self):
result = apply_fillvalue(data = np.array([1,2,3]),
attributes = {'missing_value':1,
'_FillValue':2})
self.assertArraysEqual(result, ma.array([1,2,3], mask=[False,True,False]))
def test_applyFillvalue_withNoFill(self):
result = apply_fillvalue(data = np.array([1,2,3]),
attributes = {})
self.assertArraysEqual(result, ma.array([1,2,3]))