def radial_symetry_af2(background, inds=None, tots=None, is_fft_shifted=True):
if (inds is None) or (tots is None):
print('initialising...')
i = np.fft.fftfreq(background.shape[0]) * background.shape[0]
j = np.fft.fftfreq(background.shape[1]) * background.shape[1]
k = np.fft.fftfreq(background.shape[2]) * background.shape[2]
i, j, k = np.meshgrid(i, j, k, indexing='ij')
rs = np.rint(np.sqrt(i**2 + j**2 + k**2)).astype(np.int)
if is_fft_shifted is False:
rs = np.fft.fftshift(rs)
rs = afnumpy.array(rs.ravel())
# store a list of indexes and totals
inds = []
tots = []
for i in range(0, afnumpy.max(rs) + 1):
m = (rs == i)
j = afnumpy.where(m)
inds.append(j)
tots.append(afnumpy.float(afnumpy.sum(m)))
out = background.ravel()
for ind, tot in zip(inds[:5], tots[:5]):
out[ind] = 2.
#a = afnumpy.sum(background[ind])
#out[ind] = afnumpy.sum(background[ind]) #/ tot
return out, inds, tots
def radial_symetry_af2(background, inds = None, tots = None, is_fft_shifted = True):
if (inds is None) or (tots is None) :
print 'initialising...'
i = np.fft.fftfreq(background.shape[0]) * background.shape[0]
j = np.fft.fftfreq(background.shape[1]) * background.shape[1]
k = np.fft.fftfreq(background.shape[2]) * background.shape[2]
i, j, k = np.meshgrid(i, j, k, indexing='ij')
rs = np.rint(np.sqrt(i**2 + j**2 + k**2)).astype(np.int)
if is_fft_shifted is False :
rs = np.fft.fftshift(rs)
rs = afnumpy.array(rs.ravel())
# store a list of indexes and totals
inds = []
tots = []
for i in range(0, afnumpy.max(rs)+1):
m = (rs == i)
j = afnumpy.where(m)
inds.append(j)
tots.append(afnumpy.float(afnumpy.sum(m)))
out = background.ravel()
for ind, tot in zip(inds[:5], tots[:5]) :
out[ind] = 2.
#a = afnumpy.sum(background[ind])
#out[ind] = afnumpy.sum(background[ind]) #/ tot
return out, inds, tots
def test_where():
a1 = afnumpy.array([1,2,3])
b1 = numpy.array(a1)
a2 = afnumpy.array([0,2,1])
b2 = numpy.array(a2)
# Test where with input as indices
iassert(afnumpy.where(a2, a1, a2), numpy.where(b2, b1, b2))
# Test where with input as indices
iassert(afnumpy.where(a2), numpy.where(b2))
# Test where with input as booleans
iassert(afnumpy.where(a2 < 2, a1, a2), numpy.where(b2 < 2, b1, b2))
# Test where with input as booleans
iassert(afnumpy.where(a2 < 2), numpy.where(b2 < 2))
# And now multidimensional
a1 = afnumpy.array([[1,2,3],[4,5,6]])
b1 = numpy.array(a1)
a2 = afnumpy.array([[0,2,1],[1,0,1]])
b2 = numpy.array(a2)
# Test where with input as indices
iassert(afnumpy.where(a2, a1, a2), numpy.where(b2, b1, b2))
# Test where with input as indices
iassert(afnumpy.where(a2), numpy.where(b2))
# And now multidimensional
b1 = numpy.random.random((3,3,3)) > 0.5
a1 = afnumpy.array(b1)
# Test where with input as indices
iassert(afnumpy.where(a1), numpy.where(b1))
def test_where():
a1 = afnumpy.array([1, 2, 3])
b1 = numpy.array(a1)
a2 = afnumpy.array([0, 2, 1])
b2 = numpy.array(a2)
# Test where with input as indices
iassert(afnumpy.where(a2, a1, a2), numpy.where(b2, b1, b2))
# Test where with input as indices
iassert(afnumpy.where(a2), numpy.where(b2))
# Test where with input as booleans
iassert(afnumpy.where(a2 < 2, a1, a2), numpy.where(b2 < 2, b1, b2))
# Test where with input as booleans
iassert(afnumpy.where(a2 < 2), numpy.where(b2 < 2))
# And now multidimensional
a1 = afnumpy.array([[1, 2, 3], [4, 5, 6]])
b1 = numpy.array(a1)
a2 = afnumpy.array([[0, 2, 1], [1, 0, 1]])
b2 = numpy.array(a2)
# Test where with input as indices
iassert(afnumpy.where(a2, a1, a2), numpy.where(b2, b1, b2))
# Test where with input as indices
iassert(afnumpy.where(a2), numpy.where(b2))
# And now multidimensional
b1 = numpy.random.random((3, 3, 3)) > 0.5
a1 = afnumpy.array(b1)
# Test where with input as indices
iassert(afnumpy.where(a1), numpy.where(b1))
def __setitem__(self, idx, value):
try:
if idx.dtype == numpy.dtype('bool') or (idx[0].dtype == 'bool' and len(idx) == 1):
# Special case for boolean setitem
self_flat = self.flat
idx = afnumpy.where(idx.flat)
self_flat[idx] = value
return
except AttributeError:
pass
except RuntimeError:
# idx is all False
return
idx, idx_shape = indexing.__convert_dim__(self.shape, idx)
if any(x is None for x in idx):
# one of the indices is empty
return
idx = tuple(idx)
if len(idx) == 0:
idx = tuple([0])
if(isinstance(value, ndarray)):
if(value.dtype != self.dtype):
raise TypeError('left hand side must have same dtype as right hand side')
value = indexing.__expand_dim__(self.shape, value, idx).d_array
elif(isinstance(value, numbers.Number)):
pass
else:
raise NotImplementedError('values must be a afnumpy.ndarray')
self.d_array[idx] = value
# This is a hack to be able to make it look like arrays with stride[0] > 1 can still be views
# In practise right now it only applies to ndarray.real and ndarray.imag
try:
self._base[self._base_index] = self
except AttributeError:
pass
def __getitem__(self, args):
if not isinstance(args, tuple):
args = (args,)
if len(args) == 1 and isinstance(args[0], afnumpy.ndarray) and args[0].dtype == numpy.dtype('bool'):
# Special case for boolean getitem
return self.flat[afnumpy.where(args[0].flat)]
idx, new_shape = indexing.__convert_dim__(self.shape, args)
if any(x is None for x in idx):
# one of the indices is empty
return ndarray(indexing.__index_shape__(self.shape, idx), dtype=self.dtype)
idx = tuple(idx)
if len(idx) == 0:
idx = tuple([0])
s = self.d_array[idx]
shape = pu.af_shape(s)
array = ndarray(shape, dtype=self.dtype, af_array=s)
if(shape != new_shape):
array = array.reshape(new_shape)
if new_shape == () and Ellipsis not in args:
# Return the actual scalar
return numpy.array(array)[()]
return array
