def test_masked_rankdata_2d(self, seed_value, method, use_mask, set_missing, ascending): eyemask = ~eye(5, dtype=bool) nomask = ones((5, 5), dtype=bool) seed(seed_value) asfloat = (randn(5, 5) * seed_value) asdatetime = (asfloat).copy().view('datetime64[ns]') mask = eyemask if use_mask else nomask if set_missing: asfloat[:, 2] = nan asdatetime[:, 2] = NaTns float_result = masked_rankdata_2d( data=asfloat, mask=mask, missing_value=nan, method=method, ascending=True, ) datetime_result = masked_rankdata_2d( data=asdatetime, mask=mask, missing_value=NaTns, method=method, ascending=True, ) check_arrays(float_result, datetime_result)
def test_masked_rankdata_2d(self, seed_value, method, use_mask, set_missing, ascending): eyemask = ~eye(5, dtype=bool) nomask = ones((5, 5), dtype=bool) seed(seed_value) asfloat = (randn(5, 5) * seed_value) asdatetime = (asfloat).copy().view('datetime64[ns]') mask = eyemask if use_mask else nomask if set_missing: asfloat[:, 2] = nan asdatetime[:, 2] = np_NaT float_result = masked_rankdata_2d( data=asfloat, mask=mask, missing_value=nan, method=method, ascending=True, ) datetime_result = masked_rankdata_2d( data=asdatetime, mask=mask, missing_value=np_NaT, method=method, ascending=True, ) check_arrays(float_result, datetime_result)
def _compute(self, arrays, dates, assets, mask): """ For each row in the input, compute a like-shaped array of per-row ranks. """ return masked_rankdata_2d( arrays[0], mask, self.inputs[0].missing_value, self._method, self._ascending, )