def asfarray(a, dtype=_nx.float_):
"""
Return an array converted to a float type.
Parameters
----------
a : array_like
The input array.
dtype : str or dtype object, optional
Float type code to coerce input array `a`. If `dtype` is one of the
'int' dtypes, it is replaced with float64.
Returns
-------
out : ndarray
The input `a` as a float ndarray.
Examples
--------
>>> np.asfarray([2, 3])
array([ 2., 3.])
>>> np.asfarray([2, 3], dtype='float')
array([ 2., 3.])
>>> np.asfarray([2, 3], dtype='int8')
array([ 2., 3.])
"""
if not _nx.issubdtype(dtype, _nx.inexact):
dtype = _nx.float_
return asarray(a, dtype=dtype)
def asfarray(a, dtype=_nx.float_):
"""
Return an array converted to a float type.
Parameters
----------
a : array_like
The input array.
dtype : str or dtype object, optional
Float type code to coerce input array `a`. If `dtype` is one of the
'int' dtypes, it is replaced with float64.
Returns
-------
out : ndarray
The input `a` as a float ndarray.
Examples
--------
>>> np.asfarray([2, 3])
array([2., 3.])
>>> np.asfarray([2, 3], dtype='float')
array([2., 3.])
>>> np.asfarray([2, 3], dtype='int8')
array([2., 3.])
"""
if not _nx.issubdtype(dtype, _nx.inexact):
dtype = _nx.float_
return asarray(a, dtype=dtype)
def _nanvar(a, axis=None, dtype=None, out=None, ddof=0,
keepdims=False):
# Using array() instead of asanyarray() because the former always
# makes a copy, which is important due to the copyto() action later
arr = array(a, subok=True)
mask = isnan(arr)
# First compute the mean, saving 'rcount' for reuse later
if dtype is None and (issubdtype(arr.dtype, nt.integer) or
issubdtype(arr.dtype, nt.bool_)):
arrmean = um.add.reduce(arr, axis=axis, dtype='f8', keepdims=True)
else:
mu.copyto(arr, 0.0, where=mask)
arrmean = um.add.reduce(arr, axis=axis, dtype=dtype,
keepdims=True)
rcount = (~mask).sum(axis=axis, keepdims=True)
if isinstance(arrmean, mu.ndarray):
arrmean = um.true_divide(arrmean, rcount,
out=arrmean, casting='unsafe', subok=False)
else:
arrmean = arrmean / float(rcount)
# arr - arrmean
x = arr - arrmean
mu.copyto(x, 0.0, where=mask)
# (arr - arrmean) ** 2
if issubdtype(arr.dtype, nt.complex_):
x = um.multiply(x, um.conjugate(x), out=x).real
else:
x = um.multiply(x, x, out=x)
# add.reduce((arr - arrmean) ** 2, axis)
ret = um.add.reduce(x, axis=axis, dtype=dtype, out=out,
keepdims=keepdims)
# add.reduce((arr - arrmean) ** 2, axis) / (n - ddof)
if not keepdims and isinstance(rcount, mu.ndarray):
rcount = rcount.squeeze(axis=axis)
rcount -= ddof
if isinstance(ret, mu.ndarray):
ret = um.true_divide(ret, rcount,
out=ret, casting='unsafe', subok=False)
else:
ret = ret / float(rcount)
return ret
def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
arr = asanyarray(a)
# Cast bool, unsigned int, and int to float64
if dtype is None and (issubdtype(arr.dtype, nt.integer) or
issubdtype(arr.dtype, nt.bool_)):
ret = um.add.reduce(arr, axis=axis, dtype='f8',
out=out, keepdims=keepdims)
else:
ret = um.add.reduce(arr, axis=axis, dtype=dtype,
out=out, keepdims=keepdims)
rcount = _count_reduce_items(arr, axis)
if isinstance(ret, mu.ndarray):
ret = um.true_divide(ret, rcount,
out=ret, casting='unsafe', subok=False)
else:
ret = ret / float(rcount)
return ret
def _var(a, axis=None, dtype=None, out=None, ddof=0,
keepdims=False):
arr = asanyarray(a)
# First compute the mean, saving 'rcount' for reuse later
if dtype is None and (issubdtype(arr.dtype, nt.integer) or
issubdtype(arr.dtype, nt.bool_)):
arrmean = um.add.reduce(arr, axis=axis, dtype='f8', keepdims=True)
else:
arrmean = um.add.reduce(arr, axis=axis, dtype=dtype, keepdims=True)
rcount = _count_reduce_items(arr, axis)
if isinstance(arrmean, mu.ndarray):
arrmean = um.true_divide(arrmean, rcount,
out=arrmean, casting='unsafe', subok=False)
else:
arrmean = arrmean / float(rcount)
# arr - arrmean
x = arr - arrmean
# (arr - arrmean) ** 2
if issubdtype(arr.dtype, nt.complex_):
x = um.multiply(x, um.conjugate(x), out=x).real
else:
x = um.multiply(x, x, out=x)
# add.reduce((arr - arrmean) ** 2, axis)
ret = um.add.reduce(x, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
# add.reduce((arr - arrmean) ** 2, axis) / (n - ddof)
if not keepdims and isinstance(rcount, mu.ndarray):
rcount = rcount.squeeze(axis=axis)
rcount -= ddof
if isinstance(ret, mu.ndarray):
ret = um.true_divide(ret, rcount,
out=ret, casting='unsafe', subok=False)
else:
ret = ret / float(rcount)
return ret
def _nanmean(a, axis=None, dtype=None, out=None, keepdims=False):
# Using array() instead of asanyarray() because the former always
# makes a copy, which is important due to the copyto() action later
arr = array(a, subok=True)
mask = isnan(arr)
# Cast bool, unsigned int, and int to float64
if dtype is None and (issubdtype(arr.dtype, nt.integer) or
issubdtype(arr.dtype, nt.bool_)):
ret = um.add.reduce(arr, axis=axis, dtype='f8',
out=out, keepdims=keepdims)
else:
mu.copyto(arr, 0.0, where=mask)
ret = um.add.reduce(arr, axis=axis, dtype=dtype,
out=out, keepdims=keepdims)
rcount = (~mask).sum(axis=axis)
if isinstance(ret, mu.ndarray):
ret = um.true_divide(ret, rcount,
out=ret, casting='unsafe', subok=False)
else:
ret = ret / float(rcount)
return ret
def _make_along_axis_idx(arr_shape, indices, axis):
# compute dimensions to iterate over
if not _nx.issubdtype(indices.dtype, _nx.integer):
raise IndexError("`indices` must be an integer array")
if len(arr_shape) != indices.ndim:
raise ValueError("`indices` and `arr` must have the same number of dimensions")
shape_ones = (1,) * indices.ndim
dest_dims = list(range(axis)) + [None] + list(range(axis + 1, indices.ndim))
# build a fancy index, consisting of orthogonal aranges, with the
# requested index inserted at the right location
fancy_index = []
for dim, n in zip(dest_dims, arr_shape):
if dim is None:
fancy_index.append(indices)
else:
ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim + 1 :]
fancy_index.append(_nx.arange(n).reshape(ind_shape))
return tuple(fancy_index)
def _make_along_axis_idx(arr_shape, indices, axis):
# compute dimensions to iterate over
if not _nx.issubdtype(indices.dtype, _nx.integer):
raise IndexError('`indices` must be an integer array')
if len(arr_shape) != indices.ndim:
raise ValueError(
"`indices` and `arr` must have the same number of dimensions")
shape_ones = (1,) * indices.ndim
dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))
# build a fancy index, consisting of orthogonal aranges, with the
# requested index inserted at the right location
fancy_index = []
for dim, n in zip(dest_dims, arr_shape):
if dim is None:
fancy_index.append(indices)
else:
ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
fancy_index.append(_nx.arange(n).reshape(ind_shape))
return tuple(fancy_index)
