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from pypy.interpreter.error import OperationError, oefmt
from pypy.interpreter.gateway import unwrap_spec
from pypy.module.micronumpy import loop, descriptor, support
from pypy.module.micronumpy import constants as NPY
from pypy.module.micronumpy.base import convert_to_array, W_NDimArray
from pypy.module.micronumpy.converters import clipmode_converter
from pypy.module.micronumpy.strides import (
Chunk, new_view, shape_agreement, shape_agreement_multiple)
from .casting import find_binop_result_dtype, find_result_type
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):
arr = convert_to_array(space, w_arr)
return arr.descr_nonzero(space)
raise oefmt(space.w_ValueError,
"Where should be called with either 1 or 3 arguments")
if space.is_none(w_x):
raise oefmt(space.w_ValueError,
"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 = find_result_type(space, [x, y], [])
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(space, out, shape, arr, x, y, dtype)
def dot(space, w_obj1, w_obj2, w_out=None):
w_arr = convert_to_array(space, w_obj1)
if w_arr.is_scalar():
return convert_to_array(space, w_obj2).descr_dot(space, w_arr, w_out)
return w_arr.descr_dot(space, w_obj2, w_out)
def concatenate(space, w_args, w_axis=None):
args_w = space.listview(w_args)
if len(args_w) == 0:
raise oefmt(space.w_ValueError, "need at least one array to concatenate")
args_w = [convert_to_array(space, w_arg) for w_arg in args_w]
if w_axis is None:
w_axis = space.newint(0)
if space.is_none(w_axis):
args_w = [w_arg.reshape(space,
space.newlist([w_arg.descr_get_size(space)]),
w_arg.get_order())
for w_arg in args_w]
w_axis = space.newint(0)
dtype = args_w[0].get_dtype()
shape = args_w[0].get_shape()[:]
ndim = len(shape)
if ndim == 0:
raise oefmt(space.w_ValueError,
"zero-dimensional arrays cannot be concatenated")
axis = space.int_w(w_axis)
orig_axis = axis
if axis < 0:
axis = ndim + axis
if ndim == 1 and axis != 0:
axis = 0
if axis < 0 or axis >= ndim:
raise oefmt(space.w_IndexError, "axis %d out of bounds [0, %d)",
orig_axis, ndim)
for arr in args_w[1:]:
if len(arr.get_shape()) != ndim:
raise oefmt(space.w_ValueError,
"all the input arrays must have same number of "
"dimensions")
for i, axis_size in enumerate(arr.get_shape()):
if i == axis:
shape[i] += axis_size
elif axis_size != shape[i]:
raise oefmt(space.w_ValueError,
"all the input array dimensions except for the "
"concatenation axis must match exactly")
dtype = find_result_type(space, args_w, [])
# concatenate does not handle ndarray subtypes, it always returns a ndarray
res = W_NDimArray.from_shape(space, shape, dtype, NPY.CORDER)
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])
view = new_view(space, res, chunks)
view.implementation.setslice(space, arr)
axis_start += arr.get_shape()[axis]
return res
@unwrap_spec(repeats=int)
def repeat(space, w_arr, repeats, w_axis):
arr = convert_to_array(space, w_arr)
if space.is_none(w_axis):
arr = arr.descr_flatten(space)
orig_size = arr.get_shape()[0]
shape = [arr.get_shape()[0] * repeats]
w_res = W_NDimArray.from_shape(space, shape, arr.get_dtype(), w_instance=arr)
for i in range(repeats):
chunks = [Chunk(i, shape[0] - repeats + i, repeats, orig_size)]
view = new_view(space, w_res, chunks)
view.implementation.setslice(space, arr)
else:
axis = space.int_w(w_axis)
shape = arr.get_shape()[:]
chunks = [Chunk(0, i, 1, i) for i in shape]
orig_size = shape[axis]
shape[axis] *= repeats
w_res = W_NDimArray.from_shape(space, shape, arr.get_dtype(), w_instance=arr)
for i in range(repeats):
chunks[axis] = Chunk(i, shape[axis] - repeats + i, repeats,
orig_size)
view = new_view(space, w_res, chunks)
view.implementation.setslice(space, arr)
return w_res
def count_nonzero(space, w_obj):
return space.newint(loop.count_all_true(convert_to_array(space, w_obj)))
def choose(space, w_arr, w_choices, w_out, w_mode):
arr = convert_to_array(space, w_arr)
choices = [convert_to_array(space, w_item) for w_item
in space.listview(w_choices)]
if not choices:
raise oefmt(space.w_ValueError, "choices list cannot be empty")
if space.is_none(w_out):
w_out = None
elif not isinstance(w_out, W_NDimArray):
raise oefmt(space.w_TypeError, "return arrays must be of ArrayType")
shape = shape_agreement_multiple(space, choices + [w_out])
out = descriptor.dtype_agreement(space, choices, shape, w_out)
dtype = out.get_dtype()
mode = clipmode_converter(space, w_mode)
loop.choose(space, arr, choices, shape, dtype, out, mode)
return out
def put(space, w_arr, w_indices, w_values, w_mode):
arr = convert_to_array(space, w_arr)
mode = clipmode_converter(space, w_mode)
if not w_indices:
raise oefmt(space.w_ValueError, "indices list cannot be empty")
if not w_values:
raise oefmt(space.w_ValueError, "value list cannot be empty")
dtype = arr.get_dtype()
if space.isinstance_w(w_indices, space.w_list):
indices = space.listview(w_indices)
else:
indices = [w_indices]
if space.isinstance_w(w_values, space.w_list):
values = space.listview(w_values)
else:
values = [w_values]
v_idx = 0
for idx in indices:
index = support.index_w(space, idx)
if index < 0 or index >= arr.get_size():
if mode == NPY.RAISE:
raise oefmt(space.w_IndexError,
"index %d is out of bounds for axis 0 with size %d",
index, arr.get_size())
elif mode == NPY.WRAP:
index = index % arr.get_size()
elif mode == NPY.CLIP:
if index < 0:
index = 0
else:
index = arr.get_size() - 1
else:
assert False
value = values[v_idx]
if v_idx + 1 < len(values):
v_idx += 1
arr.setitem(space, [index], dtype.coerce(space, value))
def diagonal(space, arr, offset, axis1, axis2):
shape = arr.get_shape()
shapelen = len(shape)
if offset < 0:
offset = -offset
axis1, axis2 = axis2, axis1
size = min(shape[axis1], shape[axis2] - offset)
dtype = arr.dtype
if axis1 < axis2:
shape = (shape[:axis1] + shape[axis1 + 1:axis2] +
shape[axis2 + 1:] + [size])
else:
shape = (shape[:axis2] + shape[axis2 + 1:axis1] +
shape[axis1 + 1:] + [size])
out = W_NDimArray.from_shape(space, shape, dtype)
if size == 0:
return out
if shapelen == 2:
# simple case
loop.diagonal_simple(space, arr, out, offset, axis1, axis2, size)
else:
loop.diagonal_array(space, arr, out, offset, axis1, axis2, shape)
return out
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