def generate_prediction(self, x, y, sigma, n, beta, baseline):
# generate the RF
rf = generate_og_receptive_field(x, y, sigma, self.stimulus.deg_x,
self.stimulus.deg_y)
# normalize by the integral
rf /= ((2 * np.pi * sigma**2) * 1 /
np.diff(self.stimulus.deg_x[0, 0:2])**2)
# extract the stimulus time-series
response = generate_rf_timeseries_nomask(self.stimulus.stim_arr, rf)
# compression
response **= n
# convolve with the HRF
hrf = self.hrf_model(self.hrf_delay, self.stimulus.tr_length)
# convolve it with the stimulus
model = fftconvolve(response, hrf)[0:len(response)]
# units
model /= np.max(model)
# offset
model += baseline
# scale it by beta
model *= beta
return model
def generate_ballpark_prediction(self, x, y, sigma, n):
# generate the RF
rf = generate_og_receptive_field(x, y, sigma,self.stimulus.deg_x0, self.stimulus.deg_y0)
# normalize by the integral
rf /= ((2 * np.pi * sigma**2) * 1/np.diff(self.stimulus.deg_x0[0,0:2])**2)
# extract the stimulus time-series
response = generate_rf_timeseries_nomask(self.stimulus.stim_arr0, rf)
# compression
response **= n
# convolve with the HRF
hrf = self.hrf_model(self.hrf_delay, self.stimulus.tr_length)
# convolve it with the stimulus
model = fftconvolve(response, hrf)[0:len(response)]
# units
model = (model - np.mean(model)) / np.mean(model)
# regress it
p = linregress(model, self.data)
# offset
model += p[1]
# scale it
model *= np.abs(p[0])
return model
def generate_prediction(self, x, y, sigma, n, beta, baseline):
# generate the RF
rf = generate_og_receptive_field(x, y, sigma, self.stimulus.deg_x, self.stimulus.deg_y)
# normalize by the integral
rf /= ((2 * np.pi * sigma**2) * 1/np.diff(self.stimulus.deg_x[0,0:2])**2)
# extract the stimulus time-series
response = generate_rf_timeseries_nomask(self.stimulus.stim_arr, rf)
# compression
response **= n
# convolve with the HRF
hrf = self.hrf_model(self.hrf_delay, self.stimulus.tr_length)
# convolve it with the stimulus
model = fftconvolve(response, hrf)[0:len(response)]
# convert units
model = (model - np.mean(model)) / np.mean(model)
# offset
model += baseline
# scale it by beta
model *= beta
return model
def generate_prediction(self, x, y, sigma, beta, baseline, hrf, nr_TRs):
# generate the RF
rf = generate_og_receptive_field(x, y, sigma, self.stimulus.deg_x,
self.stimulus.deg_y)
# normalize by the integral
rf /= ((2 * np.pi * sigma**2) * 1 /
np.diff(self.stimulus.deg_x[0, 0:2])**2)
# extract the stimulus time-series
response = generate_rf_timeseries_nomask(self.stimulus.stim_arr, rf)
# convolve HRF with the stimulus
model = fftconvolve(response, hrf)[0:len(response)]
# resample to TR (because hrf and stim in sample frequency)
model = signal.resample(model, num=nr_TRs, axis=0)
# units
model /= np.max(model)
# offset
model += baseline
# scale it by beta
model *= beta
return model
def generate_prediction(self, x, y, sigma, sigma_ratio, volume_ratio):
# extract the center response
rf_center = generate_og_receptive_field(x, y, sigma,
self.stimulus.deg_x,
self.stimulus.deg_y)
# extract surround response
rf_surround = generate_og_receptive_field(
x, y, sigma * sigma_ratio, self.stimulus.deg_x,
self.stimulus.deg_y) * 1 / sigma_ratio**2
# difference
rf = rf_center - np.sqrt(volume_ratio) * rf_surround
# extract the response
response = generate_rf_timeseries_nomask(self.stimulus.stim_arr, rf)
# generate the hrf
hrf = self.hrf_model(self.hrf_delay, self.stimulus.tr_length)
# convolve it
model = fftconvolve(response, hrf)[0:len(response)]
return model
def generate_prediction(self,
x,
y,
sigma,
n,
beta,
baseline,
unscaled=False):
# generate the RF
rf = generate_og_receptive_field(x, y, sigma, self.stimulus.deg_x,
self.stimulus.deg_y)
# normalize by the integral
rf /= ((2 * np.pi * sigma**2) * 1 /
np.diff(self.stimulus.deg_x[0, 0:2])**2)
# extract the stimulus time-series
response = generate_rf_timeseries_nomask(self.stimulus.stim_arr, rf)
# compression
response **= n
# convolve with the HRF
hrf = self.hrf_model(self.hrf_delay, self.stimulus.tr_length)
# convolve it with the stimulus
model = fftconvolve(response, hrf)[0:len(response)]
# units
model /= np.max(model)
# at this point, add filtering with a savitzky-golay filter
model_drift = savgol_filter(model,
window_length=self.window,
polyorder=self.sg_filter_order,
deriv=0,
mode='nearest')
# demain model_drift, so baseline parameter is still interpretable
model_drift_demeaned = model_drift - np.mean(model_drift)
# and apply to data
model -= model_drift_demeaned
# offset
model += baseline
# scale it by beta
model *= beta
return model
def generate_prediction(self, x, y, sigma, sigma_ratio, volume_ratio):
# extract the center response
rf_center = generate_og_receptive_field(x, y, sigma, self.stimulus.deg_x, self.stimulus.deg_y)
# extract surround response
rf_surround = generate_og_receptive_field(x, y, sigma*sigma_ratio,
self.stimulus.deg_x, self.stimulus.deg_y) * 1/sigma_ratio**2
# difference
rf = rf_center - np.sqrt(volume_ratio)*rf_surround
# extract the response
response = generate_rf_timeseries_nomask(self.stimulus.stim_arr, rf)
# generate the hrf
hrf = self.hrf_model(self.hrf_delay, self.stimulus.tr_length)
# convolve it
model = fftconvolve(response, hrf)[0:len(response)]
return model
def generate_ballpark_prediction(self, x, y, sigma, n, beta, baseline):
# generate the RF
rf = generate_og_receptive_field(x, y, sigma, self.stimulus.deg_x0,
self.stimulus.deg_y0)
# normalize by the integral
rf /= ((2 * np.pi * sigma**2) * 1 /
np.diff(self.stimulus.deg_x0[0, 0:2])**2)
# extract the stimulus time-series
response = generate_rf_timeseries_nomask(self.stimulus.stim_arr0, rf)
# compression
response **= n
# convolve with the HRF
hrf = self.hrf_model(self.hrf_delay, self.stimulus.tr_length)
# convolve it with the stimulus
model = fftconvolve(response, hrf)[0:len(response)]
# at this point, add filtering with a savitzky-golay filter
model = model - savgol_filter(
model,
window_length=self.sg_filter_window_length,
polyorder=self.sg_filter_order,
deriv=0,
mode='nearest')
# scale it by beta
model *= beta
# offset
model += baseline
return model
