def __init__(self, **kwargs):
"""
:Keywords:
snr : float
Signal to Noise Ratio (SNR) for the noise process.
Default=1.0
noise : bool
If False, do not setup noise generator. For setup in subclass
Default=True
"""
# super
super(Recorder, self).__init__(**kwargs)
# members
self._origin = self.position.copy()
if self.points is None:
self.nchan = 1
else:
self.nchan = self.points.shape[0]
self._noise_gen = None
self._snr = None
traj = kwargs.get('orientation', N.asarray([0.0, 0.0, 1.0]))
if traj is True or traj is False:
traj = [0.0, 0.0, 1.0]
self._trajectory = unit_vector(traj)
self._trajectory_pos = None
# set from kwargs
self.snr = kwargs.get('snr', 1.0)
self.trajectory_pos = 0.0
noise = kwargs.get('noise', True)
if noise is True:
self._noise_gen = NoiseGen(mu=N.zeros(self.nchan))
class Recorder(SimObject):
"""multichanneled recorder object with an intrinsic noise process
If the Recorder has self._points, these correspond to the recorder channels.
Else there will be only one channel at self.position.
"""
## constructor
def __init__(self, **kwargs):
"""
:Keywords:
snr : float
Signal to Noise Ratio (SNR) for the noise process.
Default=1.0
noise : bool
If False, do not setup noise generator. For setup in subclass
Default=True
"""
# super
super(Recorder, self).__init__(**kwargs)
# members
self._origin = self.position.copy()
if self.points is None:
self.nchan = 1
else:
self.nchan = self.points.shape[0]
self._noise_gen = None
self._snr = None
traj = kwargs.get('orientation', N.asarray([0.0, 0.0, 1.0]))
if traj is True or traj is False:
traj = [0.0, 0.0, 1.0]
self._trajectory = unit_vector(traj)
self._trajectory_pos = None
# set from kwargs
self.snr = kwargs.get('snr', 1.0)
self.trajectory_pos = 0.0
noise = kwargs.get('noise', True)
if noise is True:
self._noise_gen = NoiseGen(mu=N.zeros(self.nchan))
## properties
def get_origin(self):
return self._origin
origin = property(get_origin)
def get_snr(self):
return self._snr
def set_snr(self, value):
if value <= 0.0:
raise ValueError('cannot set SNR <= 0.0')
self._snr = float(value)
snr = property(get_snr, set_snr)
def get_trajectory(self):
return self._trajectory
def set_trajectory(self, value):
self._trajectory = value
trajectory = property(get_trajectory, set_trajectory)
def get_trajectory_pos(self):
return self._trajectory_pos
def set_trajectory_pos(self, value):
self._trajectory_pos = float(value)
self.position = self.origin + self._trajectory_pos * self.trajectory
trajectory_pos = property(get_trajectory_pos, set_trajectory_pos)
## methods public
def simulate(self, nlist=[], frame_size=1):
"""record a multichanneled frame from neurons in range
:Parameters:
nlist : list
List of Neuron instances to record from.
Default=[]
frame_size : int
Size of the frame in samples.
Default=1
:Returns:
list : A list of items for this frame. The first item is the noise
for this frame. Subsequent items are tuples of waveform and interval
data for the neurons that have significant (not all zero) waveforms
in this frame for this recorder.
"""
# init
rval = None
if self._noise_gen is None:
rval = [N.zeros((frame_size, self.nchan))]
else:
rval = [self._noise_gen.query(size=frame_size) / self.snr]
# for each neuron query waveform and firing data
for nrn in nlist:
try:
rval.extend(nrn.query_for_recorder(self.points[:self.nchan]))
except BadNeuronQuery:
continue
# return
return tuple(rval)