def generate_prediction(self, x, y, sigma, theta, phi, cpd):
# make sure theta and phi are 0-2*pi
theta = np.mod(theta, 2*np.pi)
phi = np.mod(theta, 2*np.pi)
# create mask for speed
distance = (self.stimulus.deg_x - x)**2 + (self.stimulus.deg_y - y)**2
mask = np.zeros_like(distance, dtype='uint8')
mask[distance < (5*sigma)**2] = 1
# generate the RF
rf = generate_gabor_receptive_field(x, y, sigma, theta, phi, cpd,
self.stimulus.deg_x, self.stimulus.deg_y)
# extract the stimulus time-series
response = generate_rf_timeseries(self.stimulus.stim_arr, rf, mask)
# convolve with the HRF
hrf = self.hrf_model(0, self.stimulus.tr_length)
# convolve it with the stimulus
model = fftconvolve(response, hrf, 'same')
return model
def receptive_field(self):
return generate_gabor_receptive_field(*self.estimate)