def make_lp_noise(n,f_c_noise,rate):
# load experiment parameters
a,b = filt.make_low_pass_butterworth(f_c_noise, 6,rate)
N = 0 # to cut begining of filter convolution
wn = np.random.randn(n+N) # generate white noise
fwn = filt.apply_filter(wn,a,b)
return normalize(fwn[-n:])
def ABABl(f,l,timbre,t,fs):
Np = int(fs/f/2.*0.8)
A = np.random.randn(Np)
B = np.random.randn(Np)
C = l*A+(1-l)*B
y = ABAB(A,C,f,t)
(af,bf) = filt.make_low_pass_butterworth(timbre, 6,fs)
y = filt.apply_filter(y,af,bf)
return y[0:len(t)]
def make_act_control(ratio,f,timbre,exp):
# loading parameters
duration_stim = exp.duration_stim
rate = exp.rate
# function
time_stim = make_time(duration_stim,rate)
#fir,N,nyq_rate = filt.make_low_pass_FIR(f_c,f_w,rate)
if (timbre == exp.DARK):
f_c = exp.fc_ACT_DARK
a,b = filt.make_low_pass_butterworth(f_c, 6,rate)
x= filt.apply_filter(act(0,1/float(ratio),f,time_stim),a,b)
y= filt.apply_filter(act(1,1/float(ratio),f,time_stim),a,b)
return np.divide(x,np.std(y)) # cross normalization
if (timbre == exp.BROAD):
x= act(0,1/float(ratio),f,time_stim)
y= act(1,1/float(ratio),f,time_stim)
return np.divide(x,np.std(y)) # cross normalization
def make_act_target(ratio,f,timbre, exp):
# loading parameters
duration_stim = exp.duration_stim
rate = exp.rate
# function
time_stim = make_time(duration_stim,rate)
#fir,N,nyq_rate = filt.make_low_pass_FIR(f_c,f_w,rate)
y = act(1,1/float(ratio),f,time_stim)
if (timbre == exp.DARK):
f_c = exp.fc_ACT_DARK
a,b = filt.make_low_pass_butterworth(f_c, 6,rate)
z= normalize(filt.apply_filter(y,a,b))
if (timbre == exp.BROAD):
z = normalize(y)
return z