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import numpy as np
from .aggregate_numpy import _aggregate_base
from .utils import (
aggregate_common_doc,
aliasing,
check_boolean,
get_func,
maxval,
minimum_dtype,
minimum_dtype_scalar,
minval,
)
def _anynan(group_idx, a, size, fill_value, dtype=None):
return _any(group_idx, np.isnan(a), size, fill_value=fill_value, dtype=dtype)
def _allnan(group_idx, a, size, fill_value, dtype=None):
return _all(group_idx, np.isnan(a), size, fill_value=fill_value, dtype=dtype)
def _any(group_idx, a, size, fill_value, dtype=None):
check_boolean(fill_value)
ret = np.full(size, fill_value, dtype=bool)
if fill_value:
ret[group_idx] = False # any-test should start from False
np.logical_or.at(ret, group_idx, a)
return ret
def _all(group_idx, a, size, fill_value, dtype=None):
check_boolean(fill_value)
ret = np.full(size, fill_value, dtype=bool)
if not fill_value:
ret[group_idx] = True # all-test should start from True
np.logical_and.at(ret, group_idx, a)
return ret
def _sum(group_idx, a, size, fill_value, dtype=None):
dtype = minimum_dtype_scalar(fill_value, dtype, a)
ret = np.full(size, fill_value, dtype=dtype)
if fill_value != 0:
ret[group_idx] = 0 # sums should start at 0
np.add.at(ret, group_idx, a)
return ret
def _len(group_idx, a, size, fill_value, dtype=None):
return _sum(group_idx, 1, size, fill_value, dtype=int)
def _prod(group_idx, a, size, fill_value, dtype=None):
"""Same as aggregate_numpy.py"""
dtype = minimum_dtype_scalar(fill_value, dtype, a)
ret = np.full(size, fill_value, dtype=dtype)
if fill_value != 1:
ret[group_idx] = 1 # product should start from 1
np.multiply.at(ret, group_idx, a)
return ret
def _min(group_idx, a, size, fill_value, dtype=None):
"""Same as aggregate_numpy.py"""
dtype = minimum_dtype(fill_value, dtype or a.dtype)
dmax = maxval(fill_value, dtype)
ret = np.full(size, fill_value, dtype=dtype)
if fill_value != dmax:
ret[group_idx] = dmax # min starts from maximum
np.minimum.at(ret, group_idx, a)
return ret
def _max(group_idx, a, size, fill_value, dtype=None):
"""Same as aggregate_numpy.py"""
dtype = minimum_dtype(fill_value, dtype or a.dtype)
dmin = minval(fill_value, dtype)
ret = np.full(size, fill_value, dtype=dtype)
if fill_value != dmin:
ret[group_idx] = dmin # max starts from minimum
np.maximum.at(ret, group_idx, a)
return ret
_impl_dict = dict(
min=_min,
max=_max,
sum=_sum,
prod=_prod,
all=_all,
any=_any,
allnan=_allnan,
anynan=_anynan,
len=_len,
)
def aggregate(group_idx, a, func="sum", size=None, fill_value=0, order="C", dtype=None, axis=None, **kwargs):
func = get_func(func, aliasing, _impl_dict)
if not isinstance(func, str):
raise NotImplementedError("No such ufunc available")
return _aggregate_base(
group_idx,
a,
size=size,
fill_value=fill_value,
order=order,
dtype=dtype,
func=func,
axis=axis,
_impl_dict=_impl_dict,
**kwargs,
)
aggregate.__doc__ = (
"""
Unlike ``aggregate_numpy``, which in most cases does some custom
optimisations, this version simply uses ``numpy``'s ``ufunc.at``.
As of version 1.14 this gives fairly poor performance. There should
normally be no need to use this version, it is intended to be used in
testing and benchmarking only.
"""
+ aggregate_common_doc
)
|
