def test_binops(self, space):
bool_dtype = get_dtype_cache(space).w_booldtype
int8_dtype = get_dtype_cache(space).w_int8dtype
int32_dtype = get_dtype_cache(space).w_int32dtype
float64_dtype = get_dtype_cache(space).w_float64dtype
# Basic pairing
assert find_binop_result_dtype(space, bool_dtype,
bool_dtype) is bool_dtype
assert find_binop_result_dtype(space, bool_dtype,
float64_dtype) is float64_dtype
assert find_binop_result_dtype(space, float64_dtype,
bool_dtype) is float64_dtype
assert find_binop_result_dtype(space, int32_dtype,
int8_dtype) is int32_dtype
assert find_binop_result_dtype(space, int32_dtype,
bool_dtype) is int32_dtype
# With promote bool (happens on div), the result is that the op should
# promote bools to int8
assert find_binop_result_dtype(
space, bool_dtype, bool_dtype, promote_bools=True) is int8_dtype
assert find_binop_result_dtype(
space, bool_dtype, float64_dtype,
promote_bools=True) is float64_dtype
# Coerce to floats
assert find_binop_result_dtype(
space, bool_dtype, float64_dtype,
promote_to_float=True) is float64_dtype
def concatenate(space, w_args, axis=0):
args_w = space.listview(w_args)
if len(args_w) == 0:
raise OperationError(
space.w_ValueError,
space.wrap("need at least one array to concatenate"))
args_w = [convert_to_array(space, w_arg) for w_arg in args_w]
dtype = args_w[0].get_dtype()
shape = args_w[0].get_shape()[:]
_axis = axis
if axis < 0:
_axis = len(shape) + axis
for arr in args_w[1:]:
for i, axis_size in enumerate(arr.get_shape()):
if len(arr.get_shape()) != len(shape) or (i != _axis and
axis_size != shape[i]):
raise OperationError(
space.w_ValueError,
space.wrap(
"all the input arrays must have same number of dimensions"
))
elif i == _axis:
shape[i] += axis_size
a_dt = arr.get_dtype()
if dtype.is_record_type() and a_dt.is_record_type():
# Record types must match
for f in dtype.fields:
if f not in a_dt.fields or \
dtype.fields[f] != a_dt.fields[f]:
raise OperationError(space.w_TypeError,
space.wrap("invalid type promotion"))
elif dtype.is_record_type() or a_dt.is_record_type():
raise OperationError(space.w_TypeError,
space.wrap("invalid type promotion"))
dtype = interp_ufuncs.find_binop_result_dtype(space, dtype,
arr.get_dtype())
if _axis < 0 or len(arr.get_shape()) <= _axis:
raise operationerrfmt(space.w_IndexError,
"axis %d out of bounds [0, %d)", axis,
len(shape))
# concatenate does not handle ndarray subtypes, it always returns a ndarray
res = W_NDimArray.from_shape(space, shape, dtype, 'C')
chunks = [Chunk(0, i, 1, i) for i in shape]
axis_start = 0
for arr in args_w:
if arr.get_shape()[_axis] == 0:
continue
chunks[_axis] = Chunk(axis_start, axis_start + arr.get_shape()[_axis],
1,
arr.get_shape()[_axis])
Chunks(chunks).apply(space, res).implementation.setslice(space, arr)
axis_start += arr.get_shape()[_axis]
return res
def dtype_agreement(space, w_arr_list, shape, out=None):
""" agree on dtype from a list of arrays. if out is allocated,
use it's dtype, otherwise allocate a new one with agreed dtype
"""
from pypy.module.micronumpy.interp_ufuncs import find_binop_result_dtype
if not space.is_none(out):
return out
dtype = w_arr_list[0].get_dtype()
for w_arr in w_arr_list[1:]:
dtype = find_binop_result_dtype(space, dtype, w_arr.get_dtype())
out = base.W_NDimArray.from_shape(space, shape, dtype)
return out
def dtype_agreement(space, w_arr_list, shape, out=None):
""" agree on dtype from a list of arrays. if out is allocated,
use it's dtype, otherwise allocate a new one with agreed dtype
"""
from pypy.module.micronumpy.interp_ufuncs import find_binop_result_dtype
if not space.is_none(out):
return out
dtype = w_arr_list[0].get_dtype()
for w_arr in w_arr_list[1:]:
dtype = find_binop_result_dtype(space, dtype, w_arr.get_dtype())
out = base.W_NDimArray.from_shape(space, shape, dtype)
return out
def concatenate(space, w_args, axis=0):
args_w = space.listview(w_args)
if len(args_w) == 0:
raise OperationError(space.w_ValueError, space.wrap("need at least one array to concatenate"))
args_w = [convert_to_array(space, w_arg) for w_arg in args_w]
dtype = args_w[0].get_dtype()
shape = args_w[0].get_shape()[:]
_axis = axis
if axis < 0:
_axis = len(shape) + axis
for arr in args_w[1:]:
for i, axis_size in enumerate(arr.get_shape()):
if len(arr.get_shape()) != len(shape) or (i != _axis and axis_size != shape[i]):
raise OperationError(space.w_ValueError, space.wrap(
"all the input arrays must have same number of dimensions"))
elif i == _axis:
shape[i] += axis_size
a_dt = arr.get_dtype()
if dtype.is_record_type() and a_dt.is_record_type():
#Record types must match
for f in dtype.fields:
if f not in a_dt.fields or \
dtype.fields[f] != a_dt.fields[f]:
raise OperationError(space.w_TypeError,
space.wrap("record type mismatch"))
elif dtype.is_record_type() or a_dt.is_record_type():
raise OperationError(space.w_TypeError,
space.wrap("invalid type promotion"))
dtype = interp_ufuncs.find_binop_result_dtype(space, dtype,
arr.get_dtype())
if _axis < 0 or len(arr.get_shape()) <= _axis:
raise operationerrfmt(space.w_IndexError, "axis %d out of bounds [0, %d)", axis, len(shape))
# concatenate does not handle ndarray subtypes, it always returns a ndarray
res = W_NDimArray.from_shape(space, shape, dtype, 'C')
chunks = [Chunk(0, i, 1, i) for i in shape]
axis_start = 0
for arr in args_w:
if arr.get_shape()[_axis] == 0:
continue
chunks[_axis] = Chunk(axis_start, axis_start + arr.get_shape()[_axis], 1,
arr.get_shape()[_axis])
Chunks(chunks).apply(space, res).implementation.setslice(space, arr)
axis_start += arr.get_shape()[_axis]
return res
def test_binops(self, space):
bool_dtype = get_dtype_cache(space).w_booldtype
int8_dtype = get_dtype_cache(space).w_int8dtype
int32_dtype = get_dtype_cache(space).w_int32dtype
float64_dtype = get_dtype_cache(space).w_float64dtype
# Basic pairing
assert find_binop_result_dtype(space, bool_dtype, bool_dtype) is bool_dtype
assert find_binop_result_dtype(space, bool_dtype, float64_dtype) is float64_dtype
assert find_binop_result_dtype(space, float64_dtype, bool_dtype) is float64_dtype
assert find_binop_result_dtype(space, int32_dtype, int8_dtype) is int32_dtype
assert find_binop_result_dtype(space, int32_dtype, bool_dtype) is int32_dtype
# With promote bool (happens on div), the result is that the op should
# promote bools to int8
assert find_binop_result_dtype(space, bool_dtype, bool_dtype, promote_bools=True) is int8_dtype
assert find_binop_result_dtype(space, bool_dtype, float64_dtype, promote_bools=True) is float64_dtype
# Coerce to floats
assert find_binop_result_dtype(space, bool_dtype, float64_dtype, promote_to_float=True) is float64_dtype
def test_binops(self, space):
bool_dtype = space.fromcache(interp_dtype.W_BoolDtype)
int8_dtype = space.fromcache(interp_dtype.W_Int8Dtype)
int32_dtype = space.fromcache(interp_dtype.W_Int32Dtype)
float64_dtype = space.fromcache(interp_dtype.W_Float64Dtype)
# Basic pairing
assert find_binop_result_dtype(space, bool_dtype, bool_dtype) is bool_dtype
assert find_binop_result_dtype(space, bool_dtype, float64_dtype) is float64_dtype
assert find_binop_result_dtype(space, float64_dtype, bool_dtype) is float64_dtype
assert find_binop_result_dtype(space, int32_dtype, int8_dtype) is int32_dtype
assert find_binop_result_dtype(space, int32_dtype, bool_dtype) is int32_dtype
# With promote bool (happens on div), the result is that the op should
# promote bools to int8
assert find_binop_result_dtype(space, bool_dtype, bool_dtype, promote_bools=True) is int8_dtype
assert find_binop_result_dtype(space, bool_dtype, float64_dtype, promote_bools=True) is float64_dtype
# Coerce to floats
assert find_binop_result_dtype(space, bool_dtype, float64_dtype, promote_to_float=True) is float64_dtype
def where(space, w_arr, w_x=None, w_y=None):
"""where(condition, [x, y])
Return elements, either from `x` or `y`, depending on `condition`.
If only `condition` is given, return ``condition.nonzero()``.
Parameters
----------
condition : array_like, bool
When True, yield `x`, otherwise yield `y`.
x, y : array_like, optional
Values from which to choose. `x` and `y` need to have the same
shape as `condition`.
Returns
-------
out : ndarray or tuple of ndarrays
If both `x` and `y` are specified, the output array contains
elements of `x` where `condition` is True, and elements from
`y` elsewhere.
If only `condition` is given, return the tuple
``condition.nonzero()``, the indices where `condition` is True.
See Also
--------
nonzero, choose
Notes
-----
If `x` and `y` are given and input arrays are 1-D, `where` is
equivalent to::
[xv if c else yv for (c,xv,yv) in zip(condition,x,y)]
Examples
--------
>>> np.where([[True, False], [True, True]],
... [[1, 2], [3, 4]],
... [[9, 8], [7, 6]])
array([[1, 8],
[3, 4]])
>>> np.where([[0, 1], [1, 0]])
(array([0, 1]), array([1, 0]))
>>> x = np.arange(9.).reshape(3, 3)
>>> np.where( x > 5 )
(array([2, 2, 2]), array([0, 1, 2]))
>>> x[np.where( x > 3.0 )] # Note: result is 1D.
array([ 4., 5., 6., 7., 8.])
>>> np.where(x < 5, x, -1) # Note: broadcasting.
array([[ 0., 1., 2.],
[ 3., 4., -1.],
[-1., -1., -1.]])
NOTE: support for not passing x and y is unsupported
"""
if space.is_none(w_y):
if space.is_none(w_x):
raise OperationError(space.w_NotImplementedError, space.wrap("1-arg where unsupported right now"))
raise OperationError(space.w_ValueError, space.wrap("Where should be called with either 1 or 3 arguments"))
if space.is_none(w_x):
raise OperationError(space.w_ValueError, space.wrap("Where should be called with either 1 or 3 arguments"))
arr = convert_to_array(space, w_arr)
x = convert_to_array(space, w_x)
y = convert_to_array(space, w_y)
if x.is_scalar() and y.is_scalar() and arr.is_scalar():
if arr.get_dtype().itemtype.bool(arr.get_scalar_value()):
return x
return y
dtype = interp_ufuncs.find_binop_result_dtype(space, x.get_dtype(), y.get_dtype())
shape = shape_agreement(space, arr.get_shape(), x)
shape = shape_agreement(space, shape, y)
out = W_NDimArray.from_shape(space, shape, dtype)
return loop.where(out, shape, arr, x, y, dtype)
def where(space, w_arr, w_x=None, w_y=None):
"""where(condition, [x, y])
Return elements, either from `x` or `y`, depending on `condition`.
If only `condition` is given, return ``condition.nonzero()``.
Parameters
----------
condition : array_like, bool
When True, yield `x`, otherwise yield `y`.
x, y : array_like, optional
Values from which to choose. `x` and `y` need to have the same
shape as `condition`.
Returns
-------
out : ndarray or tuple of ndarrays
If both `x` and `y` are specified, the output array contains
elements of `x` where `condition` is True, and elements from
`y` elsewhere.
If only `condition` is given, return the tuple
``condition.nonzero()``, the indices where `condition` is True.
See Also
--------
nonzero, choose
Notes
-----
If `x` and `y` are given and input arrays are 1-D, `where` is
equivalent to::
[xv if c else yv for (c,xv,yv) in zip(condition,x,y)]
Examples
--------
>>> np.where([[True, False], [True, True]],
... [[1, 2], [3, 4]],
... [[9, 8], [7, 6]])
array([[1, 8],
[3, 4]])
>>> np.where([[0, 1], [1, 0]])
(array([0, 1]), array([1, 0]))
>>> x = np.arange(9.).reshape(3, 3)
>>> np.where( x > 5 )
(array([2, 2, 2]), array([0, 1, 2]))
>>> x[np.where( x > 3.0 )] # Note: result is 1D.
array([ 4., 5., 6., 7., 8.])
>>> np.where(x < 5, x, -1) # Note: broadcasting.
array([[ 0., 1., 2.],
[ 3., 4., -1.],
[-1., -1., -1.]])
NOTE: support for not passing x and y is unsupported
"""
if space.is_none(w_y):
if space.is_none(w_x):
raise OperationError(
space.w_NotImplementedError,
space.wrap("1-arg where unsupported right now"))
raise OperationError(
space.w_ValueError,
space.wrap("Where should be called with either 1 or 3 arguments"))
if space.is_none(w_x):
raise OperationError(
space.w_ValueError,
space.wrap("Where should be called with either 1 or 3 arguments"))
arr = convert_to_array(space, w_arr)
x = convert_to_array(space, w_x)
y = convert_to_array(space, w_y)
if x.is_scalar() and y.is_scalar() and arr.is_scalar():
if arr.get_dtype().itemtype.bool(arr.get_scalar_value()):
return x
return y
dtype = interp_ufuncs.find_binop_result_dtype(space, x.get_dtype(),
y.get_dtype())
shape = shape_agreement(space, arr.get_shape(), x)
shape = shape_agreement(space, shape, y)
out = W_NDimArray.from_shape(space, shape, dtype)
return loop.where(out, shape, arr, x, y, dtype)
