def __init__(self, signal, Fs, tr_length, dtype):
r"""A child of the StimulusModel class for auditory stimuli.
Paramaters
----------
dtype : string
Sets the data type the stimulus array is cast into.
tr_length : float
The repetition time (TR) in seconds.
"""
# this is a weird notation
StimulusModel.__init__(self, signal, dtype, tr_length)
# absorb the vars
self.Fs = Fs
self.tr_length = tr_length
# # create spectrogram
specgram, freqs, times, = generate_spectrogram(self.stim_arr, Fs, tr_length)
# share them
self.spectrogram = utils.generate_shared_array(specgram, ctypes.c_double)
self.freqs = utils.generate_shared_array(freqs, ctypes.c_double)
self.times = utils.generate_shared_array(times, ctypes.c_int16)
def __init__(self, signal, Fs, tr_length, dtype):
r"""A child of the StimulusModel class for auditory stimuli.
Paramaters
----------
dtype : string
Sets the data type the stimulus array is cast into.
tr_length : float
The repetition time (TR) in seconds.
"""
# this is a weird notation
StimulusModel.__init__(self, signal, dtype, tr_length)
# absorb the vars
self.Fs = Fs
self.tr_length = tr_length
# # create spectrogram
specgram, freqs, times, = generate_spectrogram(self.stim_arr, Fs,
tr_length)
# share them
self.spectrogram = utils.generate_shared_array(specgram,
ctypes.c_double)
self.freqs = utils.generate_shared_array(freqs, ctypes.c_double)
self.times = utils.generate_shared_array(times, ctypes.c_int16)
def resurrect_cached_model(self):
dat = pickle.load(open(self.cached_model_path, 'rb'))
timeseries = utils.generate_shared_array(np.array([d[0] for d in dat]),
np.double)
parameters = utils.generate_shared_array(np.array([d[1] for d in dat]),
np.double)
return timeseries, parameters
def __init__(self, stim_arr, dtype=ctypes.c_int16, tr_length=1.0):
r"""A base class for all encoding stimuli.
This class houses the basic and common features of the encoding stimulus, which
along with a `PopulationModel` constitutes what is commonly referred to as the
pRF model.
Paramaters
----------
stim_arr : ndarray
An array containing the stimulus. The dimensionality of the data is arbitrary
but must be consistent with the pRF model, as specified in `PopulationModel` and
`PopluationFit` class instances.
dtype : string
Sets the data type the stimulus array is cast into.
dtype : string
Sets the data type the stimulus array is cast into.
tr_length : float
The repetition time (TR) in seconds.
"""
self.dtype = dtype
self.stim_arr = utils.generate_shared_array(stim_arr, self.dtype)
self.tr_length = tr_length
def __init__(self, stim_arr, dtype=ctypes.c_int16, tr_length=1.0):
r"""A base class for all encoding stimuli.
This class houses the basic and common features of the encoding stimulus, which
along with a `PopulationModel` constitutes what is commonly referred to as the
pRF model.
Paramaters
----------
stim_arr : ndarray
An array containing the stimulus. The dimensionality of the data is arbitrary
but must be consistent with the pRF model, as specified in `PopulationModel` and
`PopluationFit` class instances.
dtype : string
Sets the data type the stimulus array is cast into.
dtype : string
Sets the data type the stimulus array is cast into.
tr_length : float
The repetition time (TR) in seconds.
"""
self.dtype = dtype
self.stim_arr = utils.generate_shared_array(stim_arr, self.dtype)
self.tr_length = tr_length
def __init__(self, stim_arr, viewing_distance, screen_width,
scale_factor, tr_length, dtype, interp='nearest'):
"""
A child of the StimulusModel class for visual stimuli.
Paramaters
----------
stim_arr : ndarray
An array containing the visual stimulus at the native resolution. The
visual stimulus is assumed to be three-dimensional (x,y,time).
viewing_distance : float
The distance between the participant and the display (cm).
screen_width : float
The width of the display (cm). This is used to compute the visual angle
for determining the pixels per degree of visual angle.
scale_factor : float
The downsampling rate for ball=parking a solution. The `stim_arr` is
downsampled so as to speed up the fitting procedure. The final model
estimates will be derived using the non-downsampled stimulus.
"""
StimulusModel.__init__(self, stim_arr, dtype, tr_length)
# absorb the vars
self.viewing_distance = viewing_distance
self.screen_width = screen_width
self.scale_factor = scale_factor
self.interp = interp
# ascertain stimulus features
self.pixels_across = self.stim_arr.shape[1]
self.pixels_down = self.stim_arr.shape[0]
self.run_length = self.stim_arr.shape[2]
self.ppd = pixels_per_degree(self.pixels_across, self.screen_width, self.viewing_distance)
# generate coordinate matrices
deg_x, deg_y = generate_coordinate_matrices(self.pixels_across, self.pixels_down, self.ppd)
# share coordinate matrices
self.deg_x = utils.generate_shared_array(deg_x, ctypes.c_double)
self.deg_y = utils.generate_shared_array(deg_y, ctypes.c_double)
self.stim_arr = utils.generate_shared_array(stim_arr, dtype)
if self.scale_factor == 1.0:
self.stim_arr0 = self.stim_arr
self.deg_x0 = self.deg_x
self.deg_y0 = self.deg_y
else:
# create downsampled stimulus
stim_arr0 = resample_stimulus(self.stim_arr, self.scale_factor)
# generate the coordinate matrices
deg_x0, deg_y0 = generate_coordinate_matrices(self.pixels_across, self.pixels_down, self.ppd, self.scale_factor)
# share the arrays
self.deg_x0 = utils.generate_shared_array(deg_x0, ctypes.c_double)
self.deg_y0 = utils.generate_shared_array(deg_y0, ctypes.c_double)
self.stim_arr0 = utils.generate_shared_array(stim_arr0, dtype)
def resurrect_cached_model(self):
dat = pickle.load(open(self.cached_model_path, 'rb'))
timeseries = utils.generate_shared_array(np.array([d[0] for d in dat]), np.double)
parameters = utils.generate_shared_array(np.array([d[1] for d in dat]), np.double)
return timeseries, parameters
def __init__(self, stim_arr, viewing_distance, screen_width,
scale_factor, tr_length, dtype, interp='nearest'):
"""
A child of the StimulusModel class for visual stimuli.
Paramaters
----------
stim_arr : ndarray
An array containing the visual stimulus at the native resolution. The
visual stimulus is assumed to be three-dimensional (x,y,time).
viewing_distance : float
The distance between the participant and the display (cm).
screen_width : float
The width of the display (cm). This is used to compute the visual angle
for determining the pixels per degree of visual angle.
scale_factor : float
The downsampling rate for ball=parking a solution. The `stim_arr` is
downsampled so as to speed up the fitting procedure. The final model
estimates will be derived using the non-downsampled stimulus.
"""
StimulusModel.__init__(self, stim_arr, dtype, tr_length)
# absorb the vars
self.viewing_distance = viewing_distance
self.screen_width = screen_width
self.scale_factor = scale_factor
self.interp = interp
# ascertain stimulus features
self.pixels_across = self.stim_arr.shape[1]
self.pixels_down = self.stim_arr.shape[0]
self.run_length = self.stim_arr.shape[2]
self.ppd = pixels_per_degree(self.pixels_across, self.screen_width, self.viewing_distance)
# generate coordinate matrices
deg_x, deg_y = generate_coordinate_matrices(self.pixels_across, self.pixels_down, self.ppd)
# share coordinate matrices
self.deg_x = utils.generate_shared_array(deg_x, ctypes.c_double)
self.deg_y = utils.generate_shared_array(deg_y, ctypes.c_double)
self.stim_arr = utils.generate_shared_array(stim_arr, dtype)
if self.scale_factor == 1.0:
self.stim_arr0 = self.stim_arr
self.deg_x0 = self.deg_x
self.deg_y0 = self.deg_y
else:
# create downsampled stimulus
stim_arr0 = resample_stimulus(self.stim_arr, self.scale_factor)
# generate the coordinate matrices
deg_x0, deg_y0 = generate_coordinate_matrices(self.pixels_across, self.pixels_down, self.ppd, self.scale_factor)
# share the arrays
self.deg_x0 = utils.generate_shared_array(deg_x0, ctypes.c_double)
self.deg_y0 = utils.generate_shared_array(deg_y0, ctypes.c_double)
self.stim_arr0 = utils.generate_shared_array(stim_arr0, dtype)
# add ppd for the down-sampled stimulus
self.ppd0 = pixels_per_degree(self.pixels_across*self.scale_factor, self.screen_width, self.viewing_distance)
