def cmap_test():
a = cmap(np.array([5, 5]))
# print a[:, :, 0]
# print a[:, :, 1]
assert a.shape == (5, 5, 2)
assert_allclose(a[2, 2, 0], 0)
assert_allclose(a[2, 2, 1], 0)
def cmap_test():
a = cmap(np.array([5, 5]))
# print a[:, :, 0]
# print a[:, :, 1]
assert a.shape == (5, 5, 2)
assert_allclose(a[2, 2, 0], 0)
assert_allclose(a[2, 2, 1], 0)
def __init__(self, shape, neighborarea, centers=None, topology='plane'):
'''
:param shape:
:param neighborarea:
:param centers: if not None, area around centers
Suppose
shape = (H, W)
nighborarea = (X, Y)
A = X * Y
N = H * W
Then
self.neighbor_indices_flat
is a K x N array.
'''
neighborarea = np.array(neighborarea)
if not np.all(neighborarea <= shape):
neighborarea = np.minimum(neighborarea, shape)
assert neighborarea[0] <= shape[0]
assert neighborarea[1] <= shape[1]
# for each sensel, create an area
cmg = cmap(np.array(neighborarea), max_shape=shape)
# if the area size is even, it is bumbed to the next integer
assert cmg.shape[0] >= neighborarea[0]
assert cmg.shape[1] >= neighborarea[1]
assert cmg.shape[0] <= neighborarea[0] + 1
assert cmg.shape[1] <= neighborarea[1] + 1
# but we cannot be bigger than the area anyway
assert cmg.shape[0] <= shape[0]
assert cmg.shape[1] <= shape[1]
self.area_shape = cmg.shape[0], cmg.shape[1]
self.shape = shape
self.H, self.W = shape
self.X = self.area_shape[0]
self.Y = self.area_shape[1]
self.N = self.H * self.W
self.A = self.X * self.Y
self.topology = topology
check_is_in(topology, self.topology, ['torus', 'plane'])
# this is the important state
self.neighbor_indices_flat = np.zeros((self.N, self.A), 'int32')
# logger.info('Creating Flattening..')
self.flattening = Flattening.by_rows(tuple(shape))
# logger.info('..done')
if centers is None:
self.centers = diffeo_identity(shape)
else:
if centers.dtype == 'float':
# continuous diffeo
centers = np.round(centers).astype('int')
self.centers = centers
for coord in coords_iterate(shape):
k = self.flattening.cell2index[coord]
cm = cmg.copy()
center = self.centers[coord]
cm[:, :, 0] += center[0]
cm[:, :, 1] += center[1]
# torus topology here
if self.topology == 'torus':
cm[:, :, 0] = cm[:, :, 0] % shape[0]
cm[:, :, 1] = cm[:, :, 1] % shape[1]
elif self.topology == 'plane':
for i in range(2):
cmin = cm[:, :, i].min()
if cmin < 0:
cm[:, :, i] -= cmin
assert cm[:, :, i].min() >= 0
cmax = cm[:, :, i].max()
if cmax >= shape[i]:
delta = -(cmax - (shape[i] - 1))
cm[:, :, i] += delta
assert cm[:, :, i].max() < shape[i]
assert cm[:, :, i].min() >= 0
else:
assert False
for i in range(2):
cmin, cmax = cm[:, :, i].min(), cm[:, :, i].max()
assert cmin >= 0
assert cmax < shape[i]
indices = np.zeros(self.area_shape, 'int32')
for a, b in coords_iterate(self.area_shape):
c = tuple(cm[a, b, :])
indices[a, b] = self.flattening.cell2index[c]
# XXX using numpy's flattening
indices = np.array(indices.flat)
# warnings.warn('remove bias due to ordering')
# indices = np.random.permutation(indices)
self.neighbor_indices_flat[k, :] = indices
def __init__(self, shape, neighborarea, centers=None, topology="plane"):
"""
:param shape:
:param neighborarea:
:param centers: if not None, area around centers
Suppose
shape = (H, W)
nighborarea = (X, Y)
A = X * Y
N = H * W
Then
self.neighbor_indices_flat
is a K x N array.
"""
neighborarea = np.array(neighborarea)
if not np.all(neighborarea <= shape):
neighborarea = np.minimum(neighborarea, shape)
assert neighborarea[0] <= shape[0]
assert neighborarea[1] <= shape[1]
# for each sensel, create an area
cmg = cmap(np.array(neighborarea), max_shape=shape)
# if the area size is even, it is bumbed to the next integer
assert cmg.shape[0] >= neighborarea[0]
assert cmg.shape[1] >= neighborarea[1]
assert cmg.shape[0] <= neighborarea[0] + 1
assert cmg.shape[1] <= neighborarea[1] + 1
# but we cannot be bigger than the area anyway
assert cmg.shape[0] <= shape[0]
assert cmg.shape[1] <= shape[1]
self.area_shape = cmg.shape[0], cmg.shape[1]
self.shape = shape
self.H, self.W = shape
self.X = self.area_shape[0]
self.Y = self.area_shape[1]
self.N = self.H * self.W
self.A = self.X * self.Y
self.topology = topology
check_is_in(topology, self.topology, ["torus", "plane"])
# this is the important state
self.neighbor_indices_flat = np.zeros((self.N, self.A), "int32")
# logger.info('Creating Flattening..')
self.flattening = Flattening.by_rows(tuple(shape))
# logger.info('..done')
if centers is None:
self.centers = diffeo_identity(shape)
else:
if centers.dtype == "float":
# continuous diffeo
centers = np.round(centers).astype("int")
self.centers = centers
for coord in coords_iterate(shape):
k = self.flattening.cell2index[coord]
cm = cmg.copy()
center = self.centers[coord]
cm[:, :, 0] += center[0]
cm[:, :, 1] += center[1]
# torus topology here
if self.topology == "torus":
cm[:, :, 0] = cm[:, :, 0] % shape[0]
cm[:, :, 1] = cm[:, :, 1] % shape[1]
elif self.topology == "plane":
for i in range(2):
cmin = cm[:, :, i].min()
if cmin < 0:
cm[:, :, i] -= cmin
assert cm[:, :, i].min() >= 0
cmax = cm[:, :, i].max()
if cmax >= shape[i]:
delta = -(cmax - (shape[i] - 1))
cm[:, :, i] += delta
assert cm[:, :, i].max() < shape[i]
assert cm[:, :, i].min() >= 0
else:
assert False
for i in range(2):
cmin, cmax = cm[:, :, i].min(), cm[:, :, i].max()
assert cmin >= 0
assert cmax < shape[i]
indices = np.zeros(self.area_shape, "int32")
for a, b in coords_iterate(self.area_shape):
c = tuple(cm[a, b, :])
indices[a, b] = self.flattening.cell2index[c]
# XXX using numpy's flattening
indices = np.array(indices.flat)
# warnings.warn('remove bias due to ordering')
# indices = np.random.permutation(indices)
self.neighbor_indices_flat[k, :] = indices
def cmap_test2():
a = cmap(np.array([4, 4]))
assert a.shape == (5, 5, 2)
def cmap_test2():
a = cmap(np.array([4, 4]))
assert a.shape == (5, 5, 2)
