def run_KW(k, w):
resp1.append([])
resp2.append([])
resp3.append([])
nresp1.append([])
nresp2.append([])
nresp3.append([])
W = w
CC = cov(N, .6)
NC = cov(N, .6 * w)
NC = NC * np.linalg.norm(CC) / np.linalg.norm(NC)
NN = 0 * np.random.multivariate_normal(np.zeros(len(NC)), NC,
[3, NUM_CON])
stim1 = np.array([cc * I1 for cc in con])
stim2 = np.array([cc * I2 for cc in con])
stim3 = np.array([cc * I3 for cc in con])
resp1[-1].append(MGSM.gexp(0, stim1 + NN[0] / (k), CC, NC / (k * k)))
resp2[-1].append(MGSM.gexp(0, stim2 + NN[1] / (k), CC, NC / (k * k)))
resp3[-1].append(MGSM.gexp(0, stim3 + NN[2] / (k), CC, NC / (k * k)))
nresp1[-1].append(MGSM.gnn(stim1 + 0 * NN[0] / (k), CC)[:, 0])
nresp2[-1].append(MGSM.gnn(stim2 + 0 * NN[0] / (k), CC)[:, 0])
nresp3[-1].append(MGSM.gnn(stim3 + 0 * NN[0] / (k), CC)[:, 0])
def make_GSM_resp(filt, quenched, K, mgs, tag, COR, ncor, n_trial=1):
#make sure everything is the right type
assert quenched in (True, False)
assert type(ncor) == np.ndarray
GV = []
GVnn = []
#generate samples for each filter set
if quenched:
nftemp = np.tile(filt, [n_trial, 1])
print(nftemp.shape)
noise = np.reshape(
np.random.multivariate_normal(np.zeros(np.prod(nftemp.shape[1:])),
ncor, n_trial * filt.shape[0]),
tuple([n_trial * filt.shape[0]]) + filt.shape[1:])
nftemp += noise
else:
nftemp = filt
#run it for each value of k
for k in K:
GV.append(
np.array([
MGSM.gexp(i, nftemp, COR, k * k * ncor, precom=True)
for i in range(filt.shape[1])
]).transpose())
GVnn.append(MGSM.gnn(filt, COR))
print(GV[-1].shape)
GV = np.concatenate(GV)
GVnn = np.concatenate(GVnn)
print(tag)
print(GV.shape)
print(GVnn.shape)
np.savetxt(tag + "_noisy.csv", GV)
np.savetxt(tag + "_clean.csv", GVnn)
def run():
#what it is that we want to do here? We want to look at COS
resp1 = []
resp2 = []
resp3 = []
nresp1 = []
nresp2 = []
nresp3 = []
NUM_CURVE = 20
NUM_CON = 50
CMAX = 2.5
CMIN = -2
N = 8
I1 = inp(N, float(N) / 5, 1, 1, -1 + N / 2)
I2 = inp(N, float(N) / 5, 1, 0, -1 + N / 2)
I3 = inp(N, float(N) / 5, 0, 1, -1 + N / 2)
KK = np.logspace(-1, 1, NUM_CURVE)
for k in KK:
print(k)
W = float(N) / (5)
CC = cov(N, W)
NC = CC
np.savetxt("./covariance_test.csv", CC)
stim1 = np.array([cc * I1 for cc in np.logspace(CMIN, CMAX, NUM_CON)])
stim2 = np.array([cc * I2 for cc in np.logspace(CMIN, CMAX, NUM_CON)])
stim3 = np.array([cc * I3 for cc in np.logspace(CMIN, CMAX, NUM_CON)])
resp1.append(MGSM.gexp(0, stim1, CC, NC / (k * k), precom=False))
resp2.append(MGSM.gexp(0, stim2, CC, NC / (k * k), precom=False))
resp3.append(MGSM.gexp(0, stim3, CC, NC / (k * k), precom=False))
nresp1.append(MGSM.gnn(stim1, CC)[:, 0])
nresp2.append(MGSM.gnn(stim2, CC)[:, 0])
nresp3.append(MGSM.gnn(stim3, CC)[:, 0])
resp1 = np.array(resp1)
resp2 = np.array(resp2)
resp3 = np.array(resp3)
nresp1 = np.array(nresp1)
nresp2 = np.array(nresp2)
nresp3 = np.array(nresp3)
AI = resp1 / (resp2 + resp3)
nAI = nresp1 / (nresp2 + nresp3)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_prop_cycle("color",
[cm(1. * i / NUM_CURVE) for i in range(NUM_CURVE)])
K = np.linspace(.5, 5, NUM_CURVE)
cc = np.logspace(CMIN, CMAX, NUM_CON) / (10**CMAX)
for k in range(0, len(AI), 1):
ax.plot(cc, AI[k], label=str(K[k]))
ax.plot(cc, nAI[k], '--')
handles, labels = ax.get_legend_handles_labels()
# ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
# ncol=2, mode="expand", borderaxespad=0.)
ax.plot(cc, [1 for k in range(len(cc))], 'k--')
plt.xscale('log')
fig.savefig("./paramfig.pdf")
MAX = np.array([[KK[k], np.max(AI[k])] for k in range(len(AI))])
plt.figure()
plt.plot(MAX[:, 0], MAX[:, 1], "r")
plt.xscale('log')
plt.savefig("./max_AI_by_k.pdf")
pts = []
for k in range(len(AI)):
print(np.argmax(AI[k]))
for j in range(np.argmax(AI[k]), len(AI[k]) - 1):
if AI[k][j] >= 1 and AI[k][j + 1] <= 1:
pts.append([KK[k], cc[j]])
break
pts = np.array(pts)
plt.figure()
plt.plot(pts[:, 0], pts[:, 1])
plt.xscale('log')
plt.yscale('log')
plt.savefig("./trans_by_K.pdf")
def run():
def c2(c):
return np.array([[1., c], [c, 1.]])
def cov(n, w, dr=0):
distance = np.array([[((i - j + (n / 2.)) % n) - n / 2.
for i in range(n)] for j in range(n)])
gauss = np.exp(-(distance**2) / (2. * w * w)) - dr
out = np.dot(gauss, gauss)
return out / out[0, 0]
def inp(n, w, c1, c2, i):
stim1 = np.array([((j + (n / 2.)) % n) - (n / 2.) for j in range(n)])
stim2 = np.array([(((j - (i % n)) + (n / 2.)) % n) - (n / 2.)
for j in range(n)])
gauss1 = np.exp(-(stim1**2) / (2. * w * w))
gauss2 = np.exp(-(stim2**2) / (2. * w * w))
return c1 * gauss1 + c2 * gauss2
LO = []
HI = []
nnLO = []
nnHI = []
ip = np.array([[1, 0], [0, 1], [1, 1]])
for c in np.linspace(-.9, .9, 20):
cov = c2(c)
nc = np.identity(2) * (1 + c)
sHI = GSM.gexp(0, 20 * ip, cov, nc * (2 * 2), precom=True)
sLO = GSM.gexp(0, ip, cov, nc * (2 * 2), precom=False)
snnLO = GSM.gnn(ip, cov)[:, 0]
snnHI = GSM.gnn(20 * ip, cov)[:, 0]
HI.append([c, sHI[2] / (sHI[0])])
LO.append([c, sLO[2] / (sLO[0])])
nnHI.append([c, snnHI[2] / (snnHI[0])])
nnLO.append([c, snnLO[2] / (snnLO[0])])
HI = np.array(HI)
LO = np.array(LO)
nnHI = np.array(nnHI)
nnLO = np.array(nnLO)
plt.plot(HI[:, 0], [1 for x in HI], 'k--', linewidth=1)
plt.plot(HI[:, 0], HI[:, 1], 'k')
plt.plot(nnHI[:, 0], nnHI[:, 1], 'k--')
plt.plot(LO[:, 0], LO[:, 1], 'r')
plt.plot(nnLO[:, 0], nnLO[:, 1], 'r--')
plt.xlabel("Correlation")
plt.ylabel("Modulation Ratio")
plt.xlim(-1, 1)
plt.ylim(0, 2)
plt.xticks([-.5, 0, .5])
plt.tight_layout()
plt.savefig("./2DAIparam.pdf")
print("done with AI")
#what it is that we want to do here? We want to look at COS
con = 20 * np.logspace(-2, 0, 50)
NN = 2
WW = np.array([.15])
out1 = []
out2 = []
nnout1 = []
nnout2 = []
out12 = []
out22 = []
nnout12 = []
nnout22 = []
NCOV = np.identity(2)
k = .75
I1 = np.array([[c1, c1 / 10] for c1 in con])
I2 = np.array([[c1 + con[-1] / 10, con[-1] + c1 / 10] for c1 in con])
I12 = np.array([[c1, 0] for c1 in con])
I22 = np.array([[c1, con[-1]] for c1 in con])
print(I1.shape)
print(I2.shape)
CC = c2(.6)
NCOV = np.identity(2)
out1 = GSM.gexp(0, I1, CC, NCOV / (k * k), precom=False)
out2 = GSM.gexp(0, I2, CC, NCOV / (k * k), precom=False)
nnout1 = GSM.gnn(I1, CC).T
nnout2 = GSM.gnn(I2, CC).T
out12 = GSM.gexp(0, I12, CC, NCOV / (k * k), precom=False)
out22 = GSM.gexp(0, I22, CC, NCOV / (k * k), precom=False)
nnout12 = GSM.gnn(I12, CC).T
nnout22 = GSM.gnn(I22, CC).T
print(nnout2)
plt.figure()
plt.plot(con / con[-1], out1, 'r')
plt.plot(con / con[-1], out2, 'k')
plt.plot(con / con[-1], nnout1[0], 'r--')
plt.plot(con / con[-1], nnout2[0], 'k--')
plt.xlabel("contrast")
plt.ylabel("Respose")
# plt.yscale("log")
plt.xscale("log")
plt.tight_layout()
plt.savefig("./2Dssfig_0.pdf")
plt.figure()
plt.plot(con / con[-1], out12, 'r')
plt.plot(con / con[-1], out22, 'k')
plt.plot(con / con[-1], nnout12[0], 'r--')
plt.plot(con / con[-1], nnout22[0], 'k--')
plt.xlabel("contrast")
plt.ylabel("Respose")
# plt.yscale("log")
plt.xscale("log")
plt.tight_layout()
plt.savefig("./2Dssfig_1.pdf")
def run_k():
#what it is that we want to do here? We want to look at COS
resp1 = []
resp2 = []
resp3 = []
nresp1 = []
nresp2 = []
nresp3 = []
NUM_CURVE = 10
NUM_CON = 50
NUM_W = 20
CMAX = 1.
CMIN = -2.
N = 8
I1 = inp(N, .6, 1, 1, -1 + N / 2)
I2 = inp(N, .6, 1, 0, -1 + N / 2)
I3 = inp(N, .6, 0, 1, -1 + N / 2)
KK = np.logspace(0, 1, NUM_CURVE)
con = np.logspace(CMIN, CMAX, NUM_CON)
WW = np.linspace(.1, 2, NUM_W)
for w in range(len(WW)):
resp1.append([])
resp2.append([])
resp3.append([])
nresp1.append([])
nresp2.append([])
nresp3.append([])
for k in KK:
print(k)
W = WW[w]
CC = cov(N, W)
NC = CC
np.savetxt("./covariance_test.csv", CC)
stim1 = np.array(
[cc * I1 for cc in np.logspace(CMIN, CMAX, NUM_CON)])
stim2 = np.array(
[cc * I2 for cc in np.logspace(CMIN, CMAX, NUM_CON)])
stim3 = np.array(
[cc * I3 for cc in np.logspace(CMIN, CMAX, NUM_CON)])
resp1[w].append(MGSM.gexp(0, stim1, CC,
NC / (k * k))) #,precom = False))
resp2[w].append(MGSM.gexp(0, stim2, CC,
NC / (k * k))) #,precom = False))
resp3[w].append(MGSM.gexp(0, stim3, CC,
NC / (k * k))) #,precom = False))
nresp1[w].append(MGSM.gnn(stim1, CC)[:, 0])
nresp2[w].append(MGSM.gnn(stim2, CC)[:, 0])
nresp3[w].append(MGSM.gnn(stim3, CC)[:, 0])
resp1 = np.array(resp1)
resp2 = np.array(resp2)
resp3 = np.array(resp3)
nresp1 = np.array(nresp1)
nresp2 = np.array(nresp2)
nresp3 = np.array(nresp3)
AI = resp1 / (resp2 + resp3)
nAI = nresp1 / (nresp2 + nresp3)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_prop_cycle("color",
[cm(1. * i / NUM_CURVE) for i in range(NUM_CURVE)])
K = np.linspace(.5, 5, NUM_CURVE)
cc = 100 * np.logspace(CMIN, CMAX, NUM_CON) / (10**CMAX)
for w in [1]:
for k in range(0, len(AI[w]), len(AI[w]) / 5):
ax.plot(100 * con / np.max(con), AI[w, k], label=str(K[k]))
ax.plot(100 * con / np.max(con), nAI[w, k], '--')
handles, labels = ax.get_legend_handles_labels()
# ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
# ncol=2, mode="expand", borderaxespad=0.)
ax.plot(cc, [1 for k in range(len(cc))], 'k--')
plt.xlabel("Contrast")
plt.ylabel("A.I.")
plt.tight_layout()
fig.savefig("./paramfig_K.pdf")
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_prop_cycle("color",
[cm(1. * i / NUM_CURVE) for i in range(NUM_CURVE)])
WW = np.linspace(.1, 2, NUM_W)
cc = 100 * np.logspace(CMIN, CMAX, NUM_CON) / (10**CMAX)
for k in [5]:
for w in range(0, len(AI), len(AI) / 5):
ax.plot(100 * con / np.max(con), AI[w, k], label=str(WW[w]))
ax.plot(100 * con / np.max(con), nAI[w, k], '--')
handles, labels = ax.get_legend_handles_labels()
# ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
# ncol=2, mode="expand", borderaxespad=0.)
ax.plot(cc, [1 for k in range(len(cc))], 'k--')
plt.xlim([0, 20])
plt.xlabel("Contrast")
plt.ylabel("A.I.")
plt.tight_layout()
fig.savefig("./paramfig_W.pdf")
plt.figure()
for w in [5]:
MAX = np.array([[1. / KK[k], np.max(AI[w, k])]
for k in range(len(AI[w]))])
plt.plot(MAX[:, 0], MAX[:, 1], colortab[0])
# plt.xscale('log')
plt.xlabel("1/SNR")
plt.ylabel("A.I.")
plt.tight_layout()
plt.savefig("./max_AI_by_k.pdf")
plt.figure()
for k in [5]:
MAX = np.array([[WW[w] * 180. / np.pi,
np.max(AI[w, k])] for w in range(len(AI))])
plt.plot(MAX[:, 0], MAX[:, 1], colortab[0])
plt.xlabel("Width (deg.)")
plt.ylabel("A.I.")
plt.tight_layout()
plt.savefig("./max_AI_by_W.pdf")
plt.figure()
for w in [5]:
pts = []
for k in range(len(AI[w])):
pts.append(
[1. / KK[k],
get_trans(AI[w, k], 100 * con / np.max(con))])
pts = np.array(pts)
plt.plot(pts[:, 0], pts[:, 1], colortab[0])
plt.xlabel("1/SNR")
plt.ylabel("Contrast")
plt.xscale('log')
plt.yscale('linear')
plt.tight_layout()
plt.savefig("./trans_by_K.pdf")
plt.figure()
for k in [5]:
pts = []
for w in range(len(AI)):
pts.append([
WW[w] * 180. / np.pi,
get_trans(AI[w, k], 100 * con / np.max(con))
])
pts = np.array(pts)
plt.plot(pts[:, 0], pts[:, 1], colortab[0])
plt.xlabel("Width (deg.)")
plt.ylabel("Contrast")
plt.yscale('linear')
plt.tight_layout()
plt.savefig("./trans_by_W.pdf")
def run_cov():
#what it is that we want to do here? We want to look at COS
resp1 = []
resp2 = []
resp3 = []
nresp1 = []
nresp2 = []
nresp3 = []
NUM_CURVE = 10
NUM_CON = 50
NUM_W = 10
CMAX = 1.
CMIN = -1.
N = 8
I1 = inp(N, .6, 1, 1, -1 + N / 2)
I2 = inp(N, .6, 1, 0, -1 + N / 2)
I3 = inp(N, .6, 0, 1, -1 + N / 2)
KK = np.logspace(0, 1, NUM_CURVE)
con = np.logspace(CMIN, CMAX, NUM_CON)
WW = [.1, .5, 1.]
for k in WW:
print(k)
CC = cov(N, .5)
if k == WW[0]:
NC = np.identity(N)
else:
NC = cov(N, k)
NC = NC * np.linalg.norm(CC) / (np.linalg.norm(NC) * 4)
sn = 1
stim1 = np.array([cc * I1 for cc in np.logspace(CMIN, CMAX, NUM_CON)])
stim2 = np.array([cc * I2 for cc in np.logspace(CMIN, CMAX, NUM_CON)])
stim3 = np.array([cc * I3 for cc in np.logspace(CMIN, CMAX, NUM_CON)])
resp1.append(sn * MGSM.gexp(0, stim1 / sn, CC, NC, precom=False))
resp2.append(sn * MGSM.gexp(0, stim2 / sn, CC, NC, precom=False))
resp3.append(sn * MGSM.gexp(0, stim3 / sn, CC, NC, precom=False))
nresp1.append(MGSM.gnn(stim1, CC)[:, 0])
nresp2.append(MGSM.gnn(stim2, CC)[:, 0])
nresp3.append(MGSM.gnn(stim3, CC)[:, 0])
resp1 = np.array(resp1)
resp2 = np.array(resp2)
resp3 = np.array(resp3)
nresp1 = np.array(nresp1)
nresp2 = np.array(nresp2)
nresp3 = np.array(nresp3)
AI = resp1 / (resp2 + resp3)
nAI = nresp1 / (nresp2 + nresp3)
print(AI[-1])
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_prop_cycle("color",
[cm(1. * i / NUM_CURVE) for i in range(NUM_CURVE)])
K = np.linspace(.5, 5, NUM_CURVE)
cc = 100 * np.logspace(CMIN, CMAX, NUM_CON) / (10**CMAX)
for k in range(0, len(AI)):
ax.plot(100 * con / np.max(con), AI[k], colortab[k], label=str(K[k]))
ax.plot(100 * con / np.max(con), nAI[k], colortab[k] + '--')
handles, labels = ax.get_legend_handles_labels()
# ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
# ncol=2, mode="expand", borderaxespad=0.)
ax.plot(cc, [1 for k in range(len(cc))], 'k--')
plt.xlabel("Contrast")
plt.ylabel("A.I.")
plt.tight_layout()
fig.savefig("./paramfig_NC.pdf")
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)
np.savetxt("./random_gsm_resp.csv", resp)
np.savetxt("./a_random_gsm_resp.csv", aresp)
np.savetxt("./i_random_gsm_resp.csv", iresp)
def run_k():
#what it is that we want to do here? We want to look at COS
resp1 = []
resp2 = []
resp3 = []
nresp1 = []
nresp2 = []
nresp3 = []
NUM_CURVE = 10
NUM_CON = 50
CMAX = 1.
CMIN = -2.
N = 8
I1 = inp(N, .6, 1, 1, -1 + N / 2)
I2 = inp(N, .6, 1, 0, -1 + N / 2)
I3 = inp(N, .6, 0, 1, -1 + N / 2)
KK = np.logspace(0, 1, NUM_CURVE)
con = np.logspace(CMIN, CMAX, NUM_CON)
if 0:
for k in KK:
print(k)
W = .6
CC = cov(N, W)
NC = CC
np.savetxt("./covariance_test.csv", CC)
stim1 = np.array(
[cc * I1 for cc in np.logspace(CMIN, CMAX, NUM_CON)])
stim2 = np.array(
[cc * I2 for cc in np.logspace(CMIN, CMAX, NUM_CON)])
stim3 = np.array(
[cc * I3 for cc in np.logspace(CMIN, CMAX, NUM_CON)])
resp1.append(MGSM.gexp(0, stim1, CC, NC / (k * k), precom=False))
resp2.append(MGSM.gexp(0, stim2, CC, NC / (k * k), precom=False))
resp3.append(MGSM.gexp(0, stim3, CC, NC / (k * k), precom=False))
nresp1.append(MGSM.gnn(stim1, CC)[:, 0])
nresp2.append(MGSM.gnn(stim2, CC)[:, 0])
nresp3.append(MGSM.gnn(stim3, CC)[:, 0])
np.savetxt("./param_files/kresp1.csv", resp1)
np.savetxt("./param_files/kresp2.csv", resp2)
np.savetxt("./param_files/kresp3.csv", resp3)
np.savetxt("./param_files/knnresp1.csv", nresp1)
np.savetxt("./param_files/knnresp2.csv", nresp2)
np.savetxt("./param_files/knnresp3.csv", nresp3)
else:
resp1 = np.loadtxt("./param_files/kresp1.csv")
resp2 = np.loadtxt("./param_files/kresp2.csv")
resp3 = np.loadtxt("./param_files/kresp3.csv")
nresp1 = np.loadtxt("./param_files/knnresp1.csv")
nresp2 = np.loadtxt("./param_files/knnresp2.csv")
nresp3 = np.loadtxt("./param_files/knnresp3.csv")
resp1 = np.array(resp1)
resp2 = np.array(resp2)
resp3 = np.array(resp3)
nresp1 = np.array(nresp1)
nresp2 = np.array(nresp2)
nresp3 = np.array(nresp3)
AI = resp1 / (resp2 + resp3)
nAI = nresp1 / (nresp2 + nresp3)
fig = plt.figure()
ax = fig.add_subplot(111)
K = np.linspace(.5, 5, NUM_CURVE)
cc = np.logspace(CMIN, CMAX, NUM_CON) / (10**CMAX)
for k in range(0, len(AI), 2):
ax.plot(con / np.max(con), AI[k], label=str(K[k]))
ax.plot(con / np.max(con), nAI[k], '--')
ax.plot(cc, [1 for k in range(len(cc))], 'k--')
plt.tight_layout()
plt.xlabel("contrast")
plt.ylabel("AI")
plt.ylim(0, 1.5)
fig.savefig("./paramfig_K.pdf")
MAX = np.array([[1. / KK[k], np.max(AI[k])] for k in range(len(AI))])
plt.figure()
plt.plot(MAX[:, 0], MAX[:, 1], "r")
plt.xscale('log')
plt.tight_layout()
plt.xlabel("1/SNR")
plt.ylabel("Max AI")
plt.savefig("./max_AI_by_k.pdf")
plt.figure()
plt.plot(con / np.max(con), resp2[NUM_CURVE / 2, :], "r")
plt.plot(con / np.max(con), nresp2[NUM_CURVE / 2, :], "r--")
plt.xscale('log')
plt.tight_layout()
plt.xlabel("Contrast")
plt.ylabel("Response")
plt.savefig("./param_CRF.pdf")
pts = []
for k in range(len(AI)):
pts.append([KK[k], get_trans(AI[k], con)])
pts = np.array(pts)
plt.figure()
plt.plot(1. / pts[:, 0], pts[:, 1])
plt.xscale('log')
# plt.yscale('log')
plt.tight_layout()
plt.xlabel("1/SNR")
plt.ylabel("Transition (con.)")
plt.savefig("./trans_by_K.pdf")
def run_w():
#what it is that we want to do here? We want to look at COS
resp1 = []
resp2 = []
resp3 = []
nresp1 = []
nresp2 = []
nresp3 = []
NUM_CURVE = 20
NUM_CON = 50
CMAX = 1
CMIN = -2
N = 8
I1 = inp(N, .6, 1, 1, -1 + N / 2)
I2 = inp(N, .6, 1, 0, -1 + N / 2)
I3 = inp(N, .6, 0, 1, -1 + N / 2)
KK = np.linspace(.27, .75, NUM_CURVE)
con = np.logspace(CMIN, CMAX, NUM_CON)
if 0:
for k in KK:
print(k)
W = k
CC = cov(N, W)
NC = cov(N, W)
np.savetxt("./covariance_test.csv", CC)
stim1 = np.array(
[cc * I1 for cc in np.logspace(CMIN, CMAX, NUM_CON)])
stim2 = np.array(
[cc * I2 for cc in np.logspace(CMIN, CMAX, NUM_CON)])
stim3 = np.array(
[cc * I3 for cc in np.logspace(CMIN, CMAX, NUM_CON)])
resp1.append(MGSM.gexp(0, stim1, CC, NC / (2 * 2), precom=False))
resp2.append(MGSM.gexp(0, stim2, CC, NC / (2 * 2), precom=False))
resp3.append(MGSM.gexp(0, stim3, CC, NC / (2 * 2), precom=False))
nresp1.append(MGSM.gnn(stim1, CC)[:, 0])
nresp2.append(MGSM.gnn(stim2, CC)[:, 0])
nresp3.append(MGSM.gnn(stim3, CC)[:, 0])
np.savetxt("./param_files/wresp1.csv", resp1)
np.savetxt("./param_files/wresp2.csv", resp2)
np.savetxt("./param_files/wresp3.csv", resp3)
np.savetxt("./param_files/wnnresp1.csv", nresp1)
np.savetxt("./param_files/wnnresp2.csv", nresp2)
np.savetxt("./param_files/wnnresp3.csv", nresp3)
else:
resp1 = np.loadtxt("./param_files/wresp1.csv")
resp2 = np.loadtxt("./param_files/wresp2.csv")
resp3 = np.loadtxt("./param_files/wresp3.csv")
nresp1 = np.loadtxt("./param_files/wnnresp1.csv")
nresp2 = np.loadtxt("./param_files/wnnresp2.csv")
nresp3 = np.loadtxt("./param_files/wnnresp3.csv")
resp1 = np.array(resp1)
resp2 = np.array(resp2)
resp3 = np.array(resp3)
nresp1 = np.array(nresp1)
nresp2 = np.array(nresp2)
nresp3 = np.array(nresp3)
AI = resp1 / (resp2 + resp3)
nAI = nresp1 / (nresp2 + nresp3)
fig = plt.figure()
ax = fig.add_subplot(111)
cc = con / np.max(con)
for k in range(0, len(AI), 2):
ax.plot(cc, AI[k], label=str(KK[k]))
ax.plot(cc, nAI[k], '--')
ax.plot(con / np.max(con), [1 for k in range(len(cc))], 'k--')
plt.xlabel("contrast")
plt.ylabel("A.I.")
plt.ylim(.5, 1.25)
plt.tight_layout()
fig.savefig("./paramfig_W.pdf")
MAX = np.array([[KK[k] * 180 / np.pi, np.max(AI[k])]
for k in range(len(AI))])
plt.figure()
plt.plot(MAX[:, 0], MAX[:, 1], "r")
plt.xscale('log')
plt.tight_layout()
plt.xlabel("w")
plt.ylabel("Max AI")
plt.savefig("./max_AI_by_W.pdf")
pts = []
for k in range(len(AI)):
pts.append([KK[k], get_trans(AI[k], con)])
pts = np.array(pts)
plt.figure()
plt.plot(pts[:, 0], pts[:, 1])
plt.xscale('log')
plt.yscale('log')
plt.tight_layout()
plt.xlabel("w")
plt.ylabel("Transition (con)")
plt.savefig("./trans_by_W.pdf")
plt.figure()
plt.plot(KK, AI[:, -6])
plt.tight_layout()
plt.title("con. = {}".format(KK[-6]))
plt.xlabel("w")
plt.ylabel("High Contrast AI")
plt.savefig("./HighAI_by_W.pdf")
def run_WDIFF_plots():
#what it is that we want to do here? We want to look at COS
resp1 = []
resp2 = []
resp3 = []
nresp1 = []
nresp2 = []
nresp3 = []
NUM_CURVE = 10
NUM_CON = 50
NUM_W = 20
CMAX = 1.
CMIN = -2.
N = 8
I1 = inp(N,.6,1,1,-1 + N/2)
I2 = inp(N,.6,1,0,-1 + N/2)
I3 = inp(N,.6,0,1,-1 + N/2)
WW = np.logspace(.1,10,NUM_CURVE)
con = np.logspace(CMIN,CMAX,NUM_CON)
for w in [0,1,-1]:
resp1.append([])
resp2.append([])
resp3.append([])
nresp1.append([])
nresp2.append([])
nresp3.append([])
for k in WW:
W = w
CC = cov(N,.6)
if W == 0 or W == -1:
NC = cov_special(N,W)
else:
NC = CC.copy()
NC = NC*np.linalg.norm(CC)/np.linalg.norm(NC)
np.savetxt("./covariance_test.csv",CC)
stim1 = np.array([cc * I1 for cc in np.logspace(CMIN,CMAX,NUM_CON)])
stim2 = np.array([cc * I2 for cc in np.logspace(CMIN,CMAX,NUM_CON)])
stim3 = np.array([cc * I3 for cc in np.logspace(CMIN,CMAX,NUM_CON)])
resp1[-1].append(MGSM.gexp(0,stim1,CC,NC/(k*k)))
resp2[-1].append(MGSM.gexp(0,stim2,CC,NC/(k*k)))
resp3[-1].append(MGSM.gexp(0,stim3,CC,NC/(k*k)))
nresp1[-1].append(MGSM.gnn(stim1,CC)[:,0])
nresp2[-1].append(MGSM.gnn(stim2,CC)[:,0])
nresp3[-1].append(MGSM.gnn(stim3,CC)[:,0])
resp1 = np.array(resp1)
resp2 = np.array(resp2)
resp3 = np.array(resp3)
nresp1 = np.array(nresp1)
nresp2 = np.array(nresp2)
nresp3 = np.array(nresp3)
AI = resp1/(resp2+resp3)
nAI = nresp1/(nresp2+nresp3)
for w in range(len(AI)):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_prop_cycle("color",[cm(1.*i/NUM_CURVE) for i in range(NUM_CURVE)])
K = np.linspace(.5,5,NUM_CURVE)
cc = 100*np.logspace(CMIN,CMAX,NUM_CON)/(10**CMAX)
for k in range(0,len(AI[w]),len(AI[w])/5):
ax.plot(100*con/np.max(con),AI[w,k],label=str(K[k]))
ax.plot(100*con/np.max(con),nAI[w,k],'--')
handles, labels = ax.get_legend_handles_labels()
# ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
# ncol=2, mode="expand", borderaxespad=0.)
ax.plot(cc,[1 for k in range(len(cc))],'k--')
plt.xlim([0,20])
plt.xlabel("Contrast")
plt.ylabel("A.I.")
plt.tight_layout()
fig.savefig("./paramfig_K_{}_by_W.pdf".format(w))