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
return out
elif t < 1.75 and TARGET:
out += np.array([1.,.5])
elif t > .125 and MASK:
out += np.array([.5,1.])
return out
def stimfunc(STIM,t,nt,MASK = True,TARGET = True):
if STIM == 1:
return np.array([S1(T,MASK,TARGET) for T in np.linspace(0,t,nt)])
elif STIM == 2:
return np.array([S2(T,MASK,TARGET) for T in np.linspace(0,t,nt)])
elif STIM == 3:
return np.array([S3(T,MASK,TARGET) for T in np.linspace(0,t,nt)])
nta = 80
tot = .4
ta = tot/(nta)
Fa = np.exp(-ta)*np.identity(2)
stima = np.array([10*stimfunc(s,.4,nta + 1,MASK = M[1],TARGET = M[0])[:k] for k in range(1,81,5) for s in [1,2,3] for M in [[True,False],[False,True],[True,True]]])
stimp = np.array([10*stimfunc(s,.4,nta + 1,MASK = M[1],TARGET = M[0]) for s in [1,2,3] for M in [[True,False],[False,True],[True,True]]])
cor = [.1]
resp = np.array([inference.att_gexp(0,np.array([s]),cov(c),cov(0),inference.Q_self_con(cov(c),Fa),Fa) for c in cor for s in stima])
np.savetxt("./att_2d_resp.csv",resp)
np.savetxt("./att_2d_stim.csv",np.reshape(stimp,[-1,2]))
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)
grat = stim.make_SS_filters(.5,
0,
0,
0,
16,
0,
5 * 16,
int(np.max(np.linalg.norm(sites, axis=1))),
get_grat=True)
inp = np.array([get_grating_data(g, sites, RF) / fac for g in grat])
nc = CNS
f = .5 * np.identity(len(CNS))
q = inf.Q_self_con(CNS, f)
inp += np.random.multivariate_normal(np.zeros_like(inp[0]), nc, len(inp))
aI = np.tile(np.expand_dims(inp, 1), [1, 3, 1])
resp = inf.gnn(inp, CNS)
iresp = inf.att_gexp(0, aI, CNS, nc, q, f)
f = .75 * np.identity(len(CNS))
q = inf.Q_self_con(CNS, f)
aresp = inf.att_gexp(0, aI, CNS, nc, q, f)
print(oris)