def apply(self, x, d=None, dt=None, rng=np.random, copy=True, **kwargs):
"""Run process on a given input.
Keyword arguments that do not appear in the parameter list below
will be passed to the ``make_step`` function of this process.
Parameters
----------
x : ndarray
The input signal given to the process.
d : int, optional
Output dimensionality. If None, ``default_size_out`` will be used.
dt : float, optional
Simulation timestep. If None, ``default_dt`` will be used.
rng : `numpy.random.mtrand.RandomState`
Random number generator used for stochstic processes.
copy : bool, optional
If True, a new output array will be created for output.
If False, the input signal ``x`` will be overwritten.
"""
shape_in = as_shape(np.asarray(x[0]).shape, min_dim=1)
shape_out = as_shape(self.default_size_out if d is None else d)
dt = self.default_dt if dt is None else dt
rng = self.get_rng(rng)
state = self.make_state(shape_in, shape_out, dt)
step = self.make_step(shape_in, shape_out, dt, rng, state, **kwargs)
output = np.zeros((len(x), ) + shape_out) if copy else x
for i, xi in enumerate(x):
output[i] = step((i + 1) * dt, xi)
return output
def run_steps(self, n_steps, d=None, dt=None, rng=np.random, **kwargs):
"""Run process without input for given number of steps.
Keyword arguments that do not appear in the parameter list below
will be passed to the ``make_step`` function of this process.
Parameters
----------
n_steps : int
The number of steps to run.
d : int, optional
Output dimensionality. If None, ``default_size_out`` will be used.
dt : float, optional
Simulation timestep. If None, ``default_dt`` will be used.
rng : `numpy.random.mtrand.RandomState`
Random number generator used for stochstic processes.
"""
shape_in = as_shape(0)
shape_out = as_shape(self.default_size_out if d is None else d)
dt = self.default_dt if dt is None else dt
rng = self.get_rng(rng)
state = self.make_state(shape_in, shape_out, dt)
step = self.make_step(shape_in, shape_out, dt, rng, state, **kwargs)
output = np.zeros((n_steps, ) + shape_out)
for i in range(n_steps):
output[i] = step((i + 1) * dt)
return output
def apply(self, x, d=None, dt=None, rng=np.random, copy=True, **kwargs):
"""Run process on a given input.
Keyword arguments that do not appear in the parameter list below
will be passed to the ``make_step`` function of this process.
Parameters
----------
x : ndarray
The input signal given to the process.
d : int, optional (Default: None)
Output dimensionality. If None, ``default_size_out`` will be used.
dt : float, optional (Default: None)
Simulation timestep. If None, ``default_dt`` will be used.
rng : `numpy.random.RandomState` (Default: ``numpy.random``)
Random number generator used for stochstic processes.
copy : bool, optional (Default: True)
If True, a new output array will be created for output.
If False, the input signal ``x`` will be overwritten.
"""
shape_in = as_shape(np.asarray(x[0]).shape, min_dim=1)
shape_out = as_shape(self.default_size_out if d is None else d)
dt = self.default_dt if dt is None else dt
rng = self.get_rng(rng)
step = self.make_step(shape_in, shape_out, dt, rng, **kwargs)
output = np.zeros((len(x),) + shape_out) if copy else x
for i, xi in enumerate(x):
output[i] = step((i+1) * dt, xi)
return output
def run_steps(self, n_steps, d=None, dt=None, rng=np.random, **kwargs):
"""Run process without input for given number of steps.
Keyword arguments that do not appear in the parameter list below
will be passed to the ``make_step`` function of this process.
Parameters
----------
n_steps : int
The number of steps to run.
d : int, optional (Default: None)
Output dimensionality. If None, ``default_size_out`` will be used.
dt : float, optional (Default: None)
Simulation timestep. If None, ``default_dt`` will be used.
rng : `numpy.random.RandomState` (Default: ``numpy.random``)
Random number generator used for stochstic processes.
"""
shape_in = as_shape(0)
shape_out = as_shape(self.default_size_out if d is None else d)
dt = self.default_dt if dt is None else dt
rng = self.get_rng(rng)
step = self.make_step(shape_in, shape_out, dt, rng, **kwargs)
output = np.zeros((n_steps,) + shape_out)
for i in range(n_steps):
output[i] = step((i+1) * dt)
return output
def apply(self, x, d=None, dt=None, rng=np.random, copy=True, **kwargs):
"""Run process on a given input."""
shape_in = as_shape(np.asarray(x[0]).shape, min_dim=1)
shape_out = as_shape(self.default_size_out if d is None else d)
dt = self.default_dt if dt is None else dt
rng = self.get_rng(rng)
step = self.make_step(shape_in, shape_out, dt, rng, **kwargs)
output = np.zeros((len(x),) + shape_out) if copy else x
for i, xi in enumerate(x):
output[i] = step(i * dt, xi)
return output
def run_steps(self, n_steps, d=None, dt=None, rng=np.random, **kwargs):
"""Run process without input for given number of steps."""
shape_in = as_shape(0)
shape_out = as_shape(self.default_size_out if d is None else d)
dt = self.default_dt if dt is None else dt
rng = self.get_rng(rng)
step = self.make_step(shape_in, shape_out, dt, rng, **kwargs)
output = np.zeros((n_steps,) + shape_out)
for i in range(n_steps):
output[i] = step(i * dt)
return output
def filt(self, x, dt=None, axis=0, y0=None, copy=True, filtfilt=False):
"""Filter ``x`` with this synapse model.
Parameters
----------
x : array_like
The signal to filter.
dt : float, optional (Default: None)
The timestep of the input signal.
If None, ``default_dt`` will be used.
axis : int, optional (Default: 0)
The axis along which to filter.
y0 : array_like, optional (Default: None)
The starting state of the filter output. If None, the initial
value of the input signal along the axis filtered will be used.
copy : bool, optional (Default: True)
Whether to copy the input data, or simply work in-place.
filtfilt : bool, optional (Default: False)
If True, runs the process forward then backward on the signal,
for zero-phase filtering (like Matlab's ``filtfilt``).
"""
# This function is very similar to `Process.apply`, but allows for
# a) filtering along any axis, and b) zero-phase filtering (filtfilt).
dt = self.default_dt if dt is None else dt
filtered = np.array(x, copy=copy)
filt_view = np.rollaxis(filtered, axis=axis) # rolled view on filtered
if y0 is None:
y0 = filt_view[0]
shape_in = shape_out = as_shape(filt_view[0].shape, min_dim=1)
step = self.make_step(shape_in,
shape_out,
dt,
None,
y0=y0,
dtype=x.dtype)
for i, signal_in in enumerate(filt_view):
filt_view[i] = step(i * dt, signal_in)
if filtfilt: # Flip the filt_view and filter again
n = len(filt_view) - 1
filt_view = filt_view[::-1]
for i, signal_in in enumerate(filt_view):
filt_view[i] = step((n - i) * dt, signal_in)
return filtered
def filt(self, x, dt=None, axis=0, y0=None, copy=True, filtfilt=False):
"""Filter ``x`` with this synapse model.
Parameters
----------
x : array_like
The signal to filter.
dt : float, optional (Default: None)
The timestep of the input signal.
If None, ``default_dt`` will be used.
axis : int, optional (Default: 0)
The axis along which to filter.
y0 : array_like, optional (Default: None)
The starting state of the filter output. If None, the initial
value of the input signal along the axis filtered will be used.
copy : bool, optional (Default: True)
Whether to copy the input data, or simply work in-place.
filtfilt : bool, optional (Default: False)
If True, runs the process forward then backward on the signal,
for zero-phase filtering (like Matlab's ``filtfilt``).
"""
# This function is very similar to `Process.apply`, but allows for
# a) filtering along any axis, and b) zero-phase filtering (filtfilt).
dt = self.default_dt if dt is None else dt
filtered = np.array(x, copy=copy)
filt_view = np.rollaxis(filtered, axis=axis) # rolled view on filtered
if y0 is None:
y0 = filt_view[0]
shape_in = shape_out = as_shape(filt_view[0].shape, min_dim=1)
step = self.make_step(
shape_in, shape_out, dt, None, y0=y0, dtype=filtered.dtype)
for i, signal_in in enumerate(filt_view):
filt_view[i] = step(i * dt, signal_in)
if filtfilt: # Flip the filt_view and filter again
n = len(filt_view) - 1
filt_view = filt_view[::-1]
for i, signal_in in enumerate(filt_view):
filt_view[i] = step((n - i) * dt, signal_in)
return filtered
def test_as_shape_errors():
"""Tests errors generated by the `as_shape` function"""
with pytest.raises(ValueError, match="cannot be safely converted to a shape"):
as_shape(1.0) # float is noniterable and noninteger