def test_asanyarray():
b = numpy.random.random((2, 3))
a = afnumpy.array(b)
iassert(afnumpy.asanyarray(a), numpy.asanyarray(b))
# zero dim arrays not supported
iassert(afnumpy.asanyarray(1), numpy.asanyarray(1))
iassert(afnumpy.asanyarray([1, 2]), numpy.asanyarray([1, 2]))
iassert(afnumpy.asanyarray(b), numpy.asanyarray(b))
def test_asanyarray():
b = numpy.random.random((2,3))
a = afnumpy.array(b)
iassert(afnumpy.asanyarray(a), numpy.asanyarray(b))
# zero dim arrays not supported
iassert(afnumpy.asanyarray(1), numpy.asanyarray(1))
iassert(afnumpy.asanyarray([1,2]), numpy.asanyarray([1,2]))
iassert(afnumpy.asanyarray(b), numpy.asanyarray(b))
def meshgrid(*xi, **kwargs):
ndim = len(xi)
copy_ = kwargs.pop('copy', True)
sparse = kwargs.pop('sparse', False)
indexing = kwargs.pop('indexing', 'xy')
if kwargs:
raise TypeError("meshgrid() got an unexpected keyword argument '%s'" %
(list(kwargs)[0], ))
if indexing not in ['xy', 'ij']:
raise ValueError("Valid values for `indexing` are 'xy' and 'ij'.")
s0 = (1, ) * ndim
output = [
afnumpy.asanyarray(x).reshape(s0[:i] + (-1, ) + s0[i + 1::])
for i, x in enumerate(xi)
]
shape = [x.size for x in output]
if indexing == 'xy' and ndim > 1:
# switch first and second axis
output[0].shape = (1, -1) + (1, ) * (ndim - 2)
output[1].shape = (-1, 1) + (1, ) * (ndim - 2)
shape[0], shape[1] = shape[1], shape[0]
if sparse:
if copy_:
return [x.copy() for x in output]
else:
return output
else:
# Return the full N-D matrix (not only the 1-D vector)
if copy_:
# Numpy uses dtype=int but Arrayfire does not support int64 in all functions
mult_fact = afnumpy.ones(shape, dtype=numpy.int32)
return [x * mult_fact for x in output]
else:
return afnumpy.broadcast_arrays(*output)
def meshgrid(*xi, **kwargs):
ndim = len(xi)
copy_ = kwargs.pop('copy', True)
sparse = kwargs.pop('sparse', False)
indexing = kwargs.pop('indexing', 'xy')
if kwargs:
raise TypeError("meshgrid() got an unexpected keyword argument '%s'"
% (list(kwargs)[0],))
if indexing not in ['xy', 'ij']:
raise ValueError(
"Valid values for `indexing` are 'xy' and 'ij'.")
s0 = (1,) * ndim
output = [afnumpy.asanyarray(x).reshape(s0[:i] + (-1,) + s0[i + 1::])
for i, x in enumerate(xi)]
shape = [x.size for x in output]
if indexing == 'xy' and ndim > 1:
# switch first and second axis
output[0].shape = (1, -1) + (1,)*(ndim - 2)
output[1].shape = (-1, 1) + (1,)*(ndim - 2)
shape[0], shape[1] = shape[1], shape[0]
if sparse:
if copy_:
return [x.copy() for x in output]
else:
return output
else:
# Return the full N-D matrix (not only the 1-D vector)
if copy_:
# Numpy uses dtype=int but Arrayfire does not support int64 in all functions
mult_fact = afnumpy.ones(shape, dtype=numpy.int32)
return [x * mult_fact for x in output]
else:
return afnumpy.broadcast_arrays(*output)
def imag(x):
return afnumpy.asanyarray(x).imag
def real(x):
return afnumpy.asanyarray(x).real
def imag(x):
return afnumpy.asanyarray(x).imag
def real(x):
return afnumpy.asanyarray(x).real
