def _compute(self, arrays, dates, assets, mask):
"""
For each row in the input, compute a like-shaped array of per-row
ranks.
"""
inv_mask = ~mask
data = arrays[0].copy()
data[inv_mask] = nan
# OPTIMIZATION: Fast path the default case with our own specialized
# Cython implementation.
if self._method == 'ordinal':
result = rankdata_2d_ordinal(data)
else:
# FUTURE OPTIMIZATION:
# Write a less general "apply to rows" method that doesn't do all
# the extra work that apply_along_axis does.
result = apply_along_axis(rankdata, 1, data, method=self._method)
# rankdata will sort nan values into last place, but we want our
# nans to propagate, so explicitly re-apply.
result[inv_mask] = nan
return result
def _compute(self, arrays, dates, assets, mask):
"""
For each row in the input, compute a like-shaped array of per-row
ranks.
"""
inv_mask = ~mask
data = arrays[0].copy()
data[inv_mask] = nan
# OPTIMIZATION: Fast path the default case with our own specialized
# Cython implementation.
if self._method == 'ordinal':
result = rankdata_2d_ordinal(data)
else:
# FUTURE OPTIMIZATION:
# Write a less general "apply to rows" method that doesn't do all
# the extra work that apply_along_axis does.
result = apply_along_axis(rankdata, 1, data, method=self._method)
# rankdata will sort nan values into last place, but we want our
# nans to propagate, so explicitly re-apply.
result[inv_mask] = nan
return result
def compute_from_arrays(self, arrays, mask):
"""
For each row in the input, compute a like-shaped array of per-row
ranks.
"""
# OPTIMIZATION: Fast path the default value with our own specialized
# implementation.
if self._method == 'ordinal':
result = rankdata_2d_ordinal(arrays[0])
else:
# FUTURE OPTIMIZATION:
# Write a less general "apply to rows" method that doesn't do all
# the extra work that apply_along_axis does.
result = apply_along_axis(
rankdata,
1,
arrays[0],
method=self._method,
)
# rankdata will sort nan values into last place, but we want our
# nans to propagate, so explicitly re-apply
result[~mask.values] = nan
return result
