def mutual_information_data(data,dt,snr = [.5,1.],n_samples=1000,use_grat = True):
#get the BSDS file location
F = open("./CONFIG","r")
for l in F:
BSDSloc = l.split("=")[1]
break
F.close()
img_names = glob.glob(BSDSloc + "*.jpg")
###########################
maxframe = 20
par = data["params"]
#I want to make a bunch of gratings in 3 orientations
#I want to simulate them many times, with noise, and record the full temporal response for a while(until stability?)
#lets go for 100 presentations of each of the 3 gratings.
#I need enough to do a numerical estimation of the mutual information
if par["segmentation"] != "gsm":
print("Mutual Information is only for GSM models!")
exit()
f_pos = model_tools.get_f_pos(par["filter_position"],par["filter_distance"]*np.max(par["wavelengths"]),par["n_surrounds"])
indices = np.concatenate([[[a,b,c] for a in range(par["n_angles"]) for b in range(len(par["wavelengths"])) for c in range(2)] for p in f_pos])
positions = np.concatenate([[p for a in range(par["n_angles"]) for b in range(len(par["wavelengths"])) for c in range(2)] for p in f_pos])
fullsize = int(5*max(par["wavelengths"]) + 2*np.max(f_pos))
minwav = np.min(par["wavelengths"])
out = []
outv = []
imnum = 0
imind = np.random.choice(np.arange(len(img_names)),3,replace = False)
for o in np.linspace(0,np.pi,4)[:-1]:
print("ORI: {}".format(o))
#make the gratings
if use_grat:
grats = stim.make_grating(1.,o,par["wavelengths"][0],fullsize/2,fullsize)
print("Getting Coefficients")
#get filters
else:
grats = proc.load_grayscale_image(img_names[imind[imnum]])#.make_grating(1.,o,par["wavelengths"][0],fullsize/2,fullsize)
imnum += 1
print("Getting Coefficients")
#get filters
coeffs = make_data.get_filter_maps(grats,data["kernels"])
print(np.array(coeffs).shape)
#extract the right ones
out.append([])
outv.append([])
for n in range(len(snr)):
out[-1].append([])
outv[-1].append([])
print("SNR: {}".format(snr[n]))
path = [coeffs.shape[-1]/2 for k in range(maxframe)]
print(path)
rundat = make_data.sample_path(coeffs,path,indices,positions)/np.array([data["fac"]])
print(rundat.shape)
#add a few zeroes for before stimulus onset
Z = np.zeros([int(2)] + list(rundat.shape[1:]))
rundat = np.concatenate([Z,rundat],axis = 0)
#get all filters
ind = [k for k in range(len(indices))]
feps = [[linalg.logm(m)/data["params"]["walk_dt"] for m in f] for f in data["F"]]
FF = [[np.float32(linalg.expm(dt * data["params"]["walk_dt"] * m)) for m in f] for f in feps]
#print(FF[0][0])
QQ = [[inference.Q_self_con(data["C"][k][m],FF[k][m]) for m in range(len(data["F"][k]))] for k in range(len(data["F"]))]
NC = [[m/((snr[n])**2) for m in f] for f in data["C"]]
#add noise
noise = np.random.multivariate_normal(np.zeros(rundat.shape[1]),NC[0][0],[n_samples,rundat.shape[0]])
runf = noise + np.expand_dims(rundat,0)
#run the response analysis
for k in [1,len(Z)] + range(len(Z)+1,runf.shape[1],2) + [runf.shape[1]]:
print("{}\t{}".format(k,runf[:,:k].shape))
responses = inference.general_MGSM_g_att(runf[:,:k],data["segs"],data["C"],NC,QQ,FF,data["P"],ind,stable = True,op = False)
vresponses = inference.general_MGSM_p_att(runf[:,:k],data["segs"],data["C"],NC,QQ,FF,data["P"],ind,stable = True,op = False)
out[-1][-1].append(responses)
outv[-1][-1].append(vresponses)
out[-1][-1] = np.array(out[-1][-1])
outv[-1][-1] = np.array(outv[-1][-1])
return out,outv
def on_off_response(data,SNR,dt,nframes):
par = data["params"]
#I want to make a bunch of gratings in 6 orientations
#I want to simulate them many times, with noise, and record the full temporal response for a while(until stability?)
#lets go for 100 presentations of each of the 6 gratings.
#I need enough to do a numerical estimation of the mutual information
if par["segmentation"] != "gsm":
print("On off is only for GSM models!")
exit()
f_pos = model_tools.get_f_pos(par["filter_position"],par["filter_distance"]*np.max(par["wavelengths"]),par["n_surrounds"])
indices = np.concatenate([[[a,b,c] for a in range(par["n_angles"]) for b in range(len(par["wavelengths"])) for c in range(2)] for p in f_pos])
positions = np.concatenate([[p for a in range(par["n_angles"]) for b in range(len(par["wavelengths"])) for c in range(2)] for p in f_pos])
fullsize = int(5*max(par["wavelengths"]) + 2*np.max(f_pos))
minwav = np.min(par["wavelengths"])
out = []
grats = stim.make_grating(.5,0,par["wavelengths"][0],fullsize/2,fullsize)
print("Getting Coefficients")
#get filters
coeffs = make_data.get_filter_maps(grats,data["kernels"])
print("DT: {}".format(dt))
path = [coeffs.shape[-1]/2 for k in range(nframes)]
print(path)
rundat = make_data.sample_path(coeffs,path,indices,positions)/np.array([data["fac"]])
print(rundat.shape)
#add a few zeroes for before and after stimulus onset
Z = np.zeros([2] + list(rundat.shape[1:]))
#2 zeros before, 6 after
rundat = np.concatenate([Z,rundat,Z,Z,Z,Z,Z],axis = 0)
#get all filters
ind = [k for k in range(len(indices))]
feps = [[linalg.logm(m)/data["params"]["walk_dt"] for m in f] for f in data["F"]]
FF = [[np.float32(linalg.expm(dt * data["params"]["walk_dt"] * m)) for m in f] for f in feps]
#print(FF[0][0])
QQ = [[inference.Q_self_con(data["C"][k][m],FF[k][m]) for m in range(len(data["F"][k]))] for k in range(len(data["F"]))]
NC = [[m/(SNR**2) for m in f] for f in data["C"]]
runf = rundat
#run the response analysis
out = []
for k in range(1,runf.shape[0]):
print("{}\t{}".format(k,runf.shape))
responses = inference.general_MGSM_g_att(np.array([runf[:k]]),data["segs"],data["C"],NC,QQ,FF,data["P"],ind,stable = True,op = False)
out.append(responses)
return np.array(out)