def check(j):
func = FILTER1D_FUNCTIONS[j]
im = np.tile(np.hstack((np.zeros(10), np.ones(10))), (10, 1))
filter_size = 3
res = ndimage.generic_filter1d(im, func(filter_size),
filter_size)
std = ndimage.generic_filter1d(im, filter1d, filter_size,
extra_arguments=(filter_size,))
assert_allclose(res, std, err_msg="#{} failed".format(j))
def step(self, states):
""" Apply the specified rule to the states.
Parameters
----------
states : array
An array holding the current states of the automata.
Returns
-------
states : array
The new states of the automata after the rule has been applied.
"""
states = super(NDimRule, self).step(states)
wrap_args = {'mode': self.boundary}
if self.boundary == 'empty':
wrap_args['mode'] = 'constant'
wrap_args['cval'] = 0
elif self.boundary == 'filled':
wrap_args['mode'] = 'constant'
wrap_args['cval'] = 1
filled = (states == self.filled_state)
filled = ndimage.generic_filter1d(
filled, self._rule_filter, filter_size=3, **wrap_args)
states = np.full(filled.shape, self.empty_state, dtype='uint8')
states[filled] = self.filled_state
return states
def test_generic_filter1d():
def filter1d(input_line, output_line, filter_size):
for i in range(output_line.size):
output_line[i] = 0
for j in range(filter_size):
output_line[i] += input_line[i+j]
output_line /= filter_size
im = np.tile(np.hstack((np.zeros(10), np.ones(10))), (10, 1))
filter_size = 3
for mod in MODULES:
res = ndimage.generic_filter1d(im, mod.filter1d(filter_size),
filter_size)
std = ndimage.generic_filter1d(im, filter1d, filter_size,
extra_arguments=(filter_size,))
assert_allclose(res, std, err_msg="{} failed".format(mod.__name__))
def test_generic_filter1d(arr_dim, fsize):
arr = np.random.random(arr_dim)
axis = np.random.randint(0, arr.ndim - 1)
fsize = arr_dim[axis] // fsize
# just make sure it runs
filtered = generic_filter1d(arr,
jit_filter1d_function(fnc_zero_inplace),
fsize,
axis=axis)
assert np.allclose(filtered, 0.0)
def max_except_center_filter1d(a, size, axis=-1):
'''Applies the following digital filter to a {-1,0,1}-valued array a: If a is not 1, return
0; otherwise return the maximum over the window [-(size-1)/2, (size-1)/2] with the central
element excluded.'''
return ndimage.generic_filter1d(a,
__max_except_center_1d,
size,
extra_arguments=(size, ),
axis=axis,
mode='constant',
cval=0)
def max_except_center_filter1d(a, size, axis= -1):
'''Applies the following digital filter to a {-1,0,1}-valued array a: If a is not 1, return
0; otherwise return the maximum over the window [-(size-1)/2, (size-1)/2] with the central
element excluded.'''
return ndimage.generic_filter1d(a, __max_except_center_1d, size, extra_arguments=(size,),
axis=axis, mode='constant', cval=0)