def test_take():
a = [4, 3, 5, 7, 6, 8]
indices = [0, 1, 4]
iassert(afnumpy.take(a, indices), numpy.take(a, indices))
b = numpy.array(a)
a = afnumpy.array(a)
iassert(afnumpy.take(a, indices), numpy.take(b, indices))
def percentile(a, q, axis=None, out=None,
overwrite_input=False, interpolation='linear', keepdims=False):
if interpolation is not 'linear':
raise ValueError('Only linear interpolation is supported')
if out is not None:
raise ValueError('"out" parameter is not supported')
if isinstance(axis, collections.Sequence):
raise ValueError('axis sequences not supported')
if axis is None:
input = a.flatten()
axis = 0
else:
input = a
s = afnumpy.sort(input,axis=axis)
low_idx = numpy.floor(q*(s.shape[axis]-1)/100.0)
high_idx = numpy.ceil(q*(s.shape[axis]-1)/100.0)
u = (q*(s.shape[axis]-1)/100.0) - low_idx
low = afnumpy.take(s, low_idx, axis=axis)
high = afnumpy.take(s, high_idx, axis=axis)
ret = u*high+(1-u)*low
if keepdims is True:
ret.reshape(ret.shape[:axis]+(1,)+ret.shape[axis:])
return ret
def test_take():
a = [4, 3, 5, 7, 6, 8]
indices = [0, 1, 4]
iassert(afnumpy.take(a, indices), numpy.take(a, indices))
b = numpy.array(a)
a = afnumpy.array(a)
iassert(afnumpy.take(a, indices), numpy.take(b, indices))
def ifftshift(x, axes=None):
tmp = afnumpy.asarray(x)
ndim = len(tmp.shape)
if axes is None:
axes = list(range(ndim))
elif isinstance(axes, numbers.Integral):
axes = (axes,)
y = tmp
for k in axes:
n = tmp.shape[k]
p2 = n-(n+1)//2
mylist = afnumpy.concatenate((afnumpy.arange(p2, n), afnumpy.arange(p2)))
y = afnumpy.take(y, mylist, k)
return y
def percentile(a, q, axis=None, out=None,
overwrite_input=False, interpolation='linear', keepdims=False):
if interpolation is not 'linear':
raise ValueError('Only linear interpolation is supported')
if out is not None:
raise ValueError('"out" parameter is not supported')
if isinstance(axis, collections.Sequence):
raise ValueError('axis sequences not supported')
if axis is None:
input = a.flatten()
axis = 0
else:
input = a
s = afnumpy.sort(input,axis=axis)
low_idx = numpy.floor(q*(s.shape[axis]-1)/100.0)
high_idx = numpy.ceil(q*(s.shape[axis]-1)/100.0)
u = (q*(s.shape[axis]-1)/100.0) - low_idx
low = afnumpy.take(s, low_idx, axis=axis)
high = afnumpy.take(s, high_idx, axis=axis)
ret = u*high+(1-u)*low
if keepdims is True:
ret.reshape(ret.shape[:axis]+(1,)+ret.shape[axis:])
return ret