def setUp(self):
global fn
fn = PowerSensoryFunc()
fn.sigmamin = 0.0
fn.sigmamax = 0.0
fn.exponent = 3
fn.a = 1
fn.c = 0
stim = np.linspace(0, 1, N) ####### # 2. Then, we need to set a sensory function that maps the sensory dimension # with the perceived dimension. # This repository includes some rudimentary functions from sensoryfunctions import PowerSensoryFunc # sets a quadratic sensory function with exponent 2: x^2 + epsilon, where x # is the stimulus dimension. epsilon is the gaussian-distributed noise # added to the sensory representation fn = PowerSensoryFunc() fn.exponent = 3.0 # it sets a fixed noise value fn.sigmamax = 0.05 fn.sigmamin = 0.05 ###### # 3. Now we simulate the observer performing the method of triads. # The simulated results are stored in the file fname fname = mlds.simulateobserver(fn, stim, nblocks=15) ####### # 4. Finally, we want to estimate the scale from the simulated data. obs = mlds.MLDSObject(fname, boot=True, standardscale=False) obs.load() # this takes a while as bootstrap is done obs.printinfo