def __init__(self,
a=1.0,
b=3.0,
c=1.0,
d=5.0,
aa=6.0,
r=0.00035,
kvf=0.0,
kf=0.0,
ks=1.0,
tau=10.0,
iext=3.1,
iext2=0.45,
slope=0.0,
x0=-2.1,
tt=1.0):
a, b, c, d, aa, r, kvf, kf, ks, tau, iext, iext2, slope, x0, tt = \
assert_arrays([a, b, c, d, aa, r, kvf, kf, ks, tau, iext, iext2, slope, x0, tt])
self.nvar = 6
self.a = a
self.b = b
self.c = c
self.d = d
self.aa = aa
self.r = r
self.Kvf = kvf
self.Kf = kf
self.Ks = ks
self.tau = tau
self.Iext = iext
self.Iext2 = iext2
self.slope = slope
self.x0 = x0
self.tt = tt
def eqtn_fx1z_2d_zpos_jac(x1, K, w, ix0, iE, a, b, d, tau1, tau0):
x1, K, a, b, tau1, tau0 = assert_arrays([x1, K, a, b, d, tau1, tau0],
(1, x1.size))
# Correspondence with EpileptorDP2D
b = b - d
no_x0 = len(ix0)
no_e = len(iE)
i_x0 = np.ones((no_x0, 1), dtype="float32")
i_e = np.ones((no_e, 1), dtype="float32")
tau = np.divide(tau1, tau0)
jac_e_x0e = np.diag(np.multiply(tau[:, iE], (-4 * i_e.T)).flatten())
jac_e_x1o = -np.dot(np.dot(i_e, np.multiply(tau[:, iE], K[:, iE])),
w[iE][:, ix0])
jac_x0_x0e = np.zeros((no_x0, no_e), dtype="float32")
jac_x0_x1o = (np.diag(
np.multiply(tau[:, ix0],
(4 + 3 * np.multiply(a[:, ix0], np.power(x1[:, ix0], 2)) -
2 * np.multiply(b[:, ix0], x1[:, ix0]) + np.multiply(
K[:, ix0], np.sum(w[ix0], axis=1)))).flatten()) -
np.multiply(
np.dot(i_x0, np.multiply(tau[:, ix0], K[:, ix0])).T,
w[ix0][:, ix0]))
jac = np.empty((x1.size, x1.size), dtype=type(jac_e_x0e))
jac[np.ix_(iE, iE)] = jac_e_x0e
jac[np.ix_(iE, ix0)] = jac_e_x1o
jac[np.ix_(ix0, iE)] = jac_x0_x0e
jac[np.ix_(ix0, ix0)] = jac_x0_x1o
return jac
def eqtn_coupling(x1, K, w, ix, jx):
# Only difference coupling for the moment.
# TODO: Extend for different coupling forms
shape = x1.shape
x1, K = assert_arrays([x1, K], (1, x1.size))
i_n = np.ones((len(ix), 1), dtype='float32')
j_n = np.ones((len(jx), 1), dtype='float32')
# Coupling from (jx) to (ix)
coupling = np.multiply(K[:, ix],
np.sum(np.multiply(w[ix][:, jx], np.dot(i_n, x1[:, jx]) - np.dot(j_n, x1[:, ix]).T), axis=1))
return np.reshape(coupling, shape)
def update_active_regions(self, statistical_model, methods=["e_values", "LSA"], reset=False, **kwargs):
if reset:
statistical_model.update_active_regions([])
for m, th in zip(*assert_arrays([ensure_list(methods),
ensure_list(kwargs.get("active_regions_th", None))])):
if isequal_string(m, "e_values"):
statistical_model = self.update_active_regions_e_values(statistical_model, th)
elif isequal_string(m, "x0_values"):
statistical_model = self.update_active_regions_x0_values(statistical_model, th)
elif isequal_string(m, "lsa"):
statistical_model = self.update_active_regions_lsa(statistical_model, th)
elif isequal_string(m, "seeg"):
statistical_model = self.update_active_regions_seeg(statistical_model, th,
seeg_inds=kwargs.get("seeg_inds"))
return statistical_model
def eqtn_fx1z_diff(x1, K, w, ix, jx, a, b, d, tau1, tau0, zmode=np.array("lin")): # , z_pos=True
# TODO: for the extreme z_pos = False case where we have terms like 0.1 * z ** 7. See below eqtn_fz()
# TODO: for the extreme x1_neg = False case where we have to solve for x2 as well
x1, K, ix, jx, a, b, d, tau1, tau0 = assert_arrays([x1, K, ix, jx, a, b, d, tau1, tau0], (x1.size,))
tau = np.divide(tau1, tau0)
dcoupl_dx = eqtn_coupling_diff(K, w, ix, jx)
if isequal_string(str(zmode), 'lin'):
dfx1_1_dx1 = 4.0 * np.ones(x1[ix].shape)
elif isequal_string(str(zmode), 'sig'):
dfx1_1_dx1 = np.divide(30 * np.power(np.exp(1), (-10.0 * (x1[ix] + 0.5))),
np.power(1 + np.power(np.exp(1), (-10.0 * (x1[ix] + 0.5))), 2))
else:
raise_value_error('zmode is neither "lin" nor "sig"')
dfx1_3_dx1 = 3 * np.multiply(np.power(x1[ix], 2.0), a[ix]) + 2 * np.multiply(x1[ix], d[ix] - b[ix])
fx1z_diff = np.empty_like(dcoupl_dx, dtype=dcoupl_dx.dtype)
for xi in ix:
for xj in jx:
if xj == xi:
fx1z_diff[xi, xj] = np.multiply(dfx1_3_dx1[xi] + dfx1_1_dx1[xi] - dcoupl_dx[xi, xj], tau[xi])
else:
fx1z_diff[xi, xj] = np.multiply(- dcoupl_dx[xi, xj], tau[xi])
return fx1z_diff
def test_computations(self):
logger = initialize_logger(__name__, self.config.out.FOLDER_LOGS)
# ------------------------------------------------------------------------------------------------------------------
x1 = numpy.array([-4.1 / 3, -4.9 / 3, -5.0 / 3], dtype="float32")
w = numpy.array([[0, 0.1, 0.9], [0.1, 0, 0.0], [0.9, 0.0, 0]])
n = x1.size
K = 0.0 * K_DEF * numpy.ones(x1.shape, dtype=x1.dtype)
yc = YC_DEF * numpy.ones(x1.shape, dtype=x1.dtype)
Iext1 = I_EXT1_DEF * numpy.ones(x1.shape, dtype=x1.dtype)
slope = SLOPE_DEF * numpy.ones(x1.shape, dtype=x1.dtype)
Iext2 = I_EXT2_DEF * numpy.ones(x1.shape, dtype=x1.dtype)
a = A_DEF
b = B_DEF
d = D_DEF
s = S_DEF
gamma = GAMMA_DEF
tau1 = TAU1_DEF
tau2 = TAU2_DEF
tau0 = TAU0_DEF
x1, K = assert_arrays([x1, K])
w = assert_arrays([w]) # , (x1.size, x1.size)
zmode = numpy.array("lin")
pmode = numpy.array("const")
model = "EpileptorDPrealistic"
x1eq = x1
z = calc_eq_z(x1,
yc,
Iext1,
"2d",
x2=0.0,
slope=slope,
a=a,
b=b,
d=d,
x1_neg=True)
zeq = z
x0cr, r = calc_x0cr_r(yc,
Iext1,
zmode=zmode,
x1_rest=X1_DEF,
x1_cr=X1EQ_CR_DEF,
x0def=X0_DEF,
x0cr_def=X0_CR_DEF)
x0 = calc_x0(x1, z, K, w, zmode=zmode, z_pos=True)
calc_model_x0_to_x0_val(x0,
yc,
Iext1,
a,
b,
d,
zmode=numpy.array("lin"))
if model == "EpileptorDP2D":
eq = numpy.c_[x1eq, zeq].T.astype('float32')
model_vars = 2
dfun = calc_dfun(eq[0].T,
eq[1].T,
yc,
Iext1,
x0,
K,
w,
model_vars,
zmode=zmode,
pmode=pmode,
x0_var=x0,
slope_var=slope,
Iext1_var=Iext1,
Iext2_var=Iext2,
K_var=K,
slope=slope,
a=a,
b=b,
d=d,
s=s,
Iext2=Iext2,
gamma=gamma,
tau1=tau1,
tau0=tau0,
tau2=tau2,
output_mode="array")
jac = calc_jac(eq[0].T,
eq[1].T,
yc,
Iext1,
x0,
K,
w,
model_vars,
zmode=zmode,
pmode=pmode,
x1_neg=True,
z_pos=True,
x2_neg=False,
x0_var=x0,
slope_var=slope,
Iext1_var=Iext1,
Iext2_var=Iext2,
K_var=K,
slope=slope,
a=a,
b=b,
d=d,
s=s,
Iext2=Iext2,
gamma=gamma,
tau1=tau1,
tau0=tau0,
tau2=tau2)
else:
if model == "EpileptorDPrealistic":
# the 11D "realistic" simulations model
eq, slope_eq, Iext2_eq = calc_eq_11d(
x0,
K,
w,
yc,
Iext1,
Iext2,
slope,
EpileptorDPrealistic.fun_slope_Iext2,
x1,
a=a,
b=b,
d=d,
zmode=zmode,
pmode=pmode)
model_vars = 11
dfun = calc_dfun(eq[0].T,
eq[2].T,
yc,
Iext1,
x0,
K,
w,
model_vars,
zmode,
pmode,
y1=eq[1].T,
x2=eq[3].T,
y2=eq[4].T,
g=eq[5].T,
x0_var=eq[6].T,
slope_var=eq[7].T,
Iext1_var=eq[8].T,
Iext2_var=eq[9].T,
K_var=eq[10].T,
slope=slope,
a=a,
b=b,
d=d,
s=s,
Iext2=Iext2,
gamma=gamma,
tau1=tau1,
tau0=tau0,
tau2=tau2,
output_mode="array")
jac = calc_jac(eq[0].T,
eq[2].T,
yc,
Iext1,
x0,
K,
w,
model_vars,
zmode,
pmode,
x1_neg=True,
z_pos=True,
x2_neg=False,
y1=eq[1].T,
x2=eq[3].T,
y2=eq[4].T,
g=eq[5].T,
x0_var=eq[6].T,
slope_var=eq[7].T,
Iext1_var=eq[8].T,
Iext2_var=eq[9].T,
K_var=eq[10].T,
slope=slope,
a=a,
b=b,
d=d,
s=s,
Iext2=Iext2,
gamma=gamma,
tau1=tau1,
tau0=tau0,
tau2=tau2)
else:
# all >=6D models
eq = calc_eq_6d(x0,
K,
w,
yc,
Iext1,
Iext2,
x1,
a=a,
b=b,
d=d,
zmode=zmode)
model_vars = 6
dfun = calc_dfun(eq[0].T,
eq[2].T,
yc,
Iext1,
x0,
K,
w,
model_vars,
zmode,
y1=eq[1].T,
x2=eq[3].T,
y2=eq[4].T,
g=eq[5].T,
slope=slope,
a=a,
b=b,
d=d,
s=s,
Iext2=Iext2,
gamma=gamma,
tau1=tau1,
tau0=tau0,
tau2=tau2,
output_mode="array")
jac = calc_jac(eq[0].T,
eq[2].T,
yc,
Iext1,
r,
K,
w,
model_vars,
zmode,
x1_neg=True,
z_pos=True,
x2_neg=False,
y1=eq[1].T,
x2=eq[3].T,
y2=eq[4].T,
g=eq[5].T,
slope=slope,
a=a,
b=b,
d=d,
s=s,
Iext2=Iext2,
gamma=gamma,
tau1=tau1,
tau0=tau0,
tau2=tau2)
model = str(model_vars) + "d"
sx1, sy1, sz, sx2, sy2, sg, sx0, sx0_val, sK, syc, sIext1, sIext2, sslope, sa, sb, sd, stau1, stau0, stau2, v = \
symbol_vars(n, ["x1", "y1", "z", "x2", "y2", "g", "x0", "x0_val", "K", "yc", "Iext1", "Iext2",
"slope", "a", "b", "d", "tau1", "tau0", "tau2"], shape=(3,))
sw, vw = symbol_vars(n, ["w"], dims=2, output_flag="numpy_array")
v.update(vw)
del vw
numpy.fill_diagonal(sw, 0.0)
sw = numpy.array(sw)
a = numpy.ones((n, ))
b = 3.0 * a
d = 5.0 * a
s = 6.0 * a
tau1 = a
tau0 = a
tau2 = a
x1sq = -4.0 / 3 * a
if model == "2d":
y1 = yc
else:
y1 = eq[1].T
x2 = eq[3].T
y2 = eq[4].T
g = eq[5].T
if model == "11d":
x0_var = eq[6].T
slope_var = eq[7].T
Iext1_var = eq[8].T
Iext2_var = eq[9].T
K_var = eq[10].T
# -------------------------------------------- Test symbolic x0cr, r calculation ----------------------------------
logger.info("\n\nTest symbolic x0cr, r calculation...")
x0cr2, r2 = calc_x0cr_r(syc,
sIext1,
zmode=zmode,
x1_rest=X1_DEF,
x1_cr=X1EQ_CR_DEF,
x0def=X0_DEF,
x0cr_def=X0_CR_DEF) # test=True
lx0cr_r, sx0cr_r, v = symbol_eqtn_x0cr_r(
n, zmode=zmode,
shape=(n, )) # symbol_calc_x0cr_r(n, zmode=zmode, shape=(3, ))
sx0cr_r = list(sx0cr_r)
for ii in range(2):
sx0cr_r[ii] = Matrix(sx0cr_r[ii])
for iv in range(n):
sx0cr_r[ii][iv] = sx0cr_r[ii][iv].subs([
(v["a"][iv], a[iv]), (v["b"][iv], b[iv]),
(v["d"][iv], d[iv]), (v["x1_rest"][iv], X1_DEF),
(v["x0_rest"][iv], X0_DEF), (v["x1_cr"][iv], X1EQ_CR_DEF),
(v["x0_cr"][iv], X0_CR_DEF)
])
assert list(x0cr2) == list(sx0cr_r[0])
assert list(r2) == list(sx0cr_r[1])
# -------------------------------------------- Test coupling ------------------------------------------------------
coupling = calc_coupling(sx1, sK, sw)
scoupling = symbol_eqtn_coupling(n, shape=(n, ))[:2]
assert list(coupling) == list(scoupling[1])
assert list(calc_coupling(x1, K, w)) == list(scoupling[0](x1, K, w))
assert coupling.shape == scoupling[1].shape
# ---------------------------------------- Test coupling derivative to x1 ------------------------------------------
coupling_diff = calc_coupling_diff(sK, sw)
scoupling_diff = symbol_calc_coupling_diff(n, ix=None, jx=None,
K="K")[:2]
assert coupling_diff.shape == scoupling_diff[1].shape
# ------------------------------------- Test the fz with substitution of z via fx1 ----------------------------------
fx1z = calc_fx1z(sx1,
sx0,
sK,
sw,
syc,
sIext1,
sa,
sb,
sd,
stau1,
stau0,
zmode=zmode)
sfx1z = symbol_eqtn_fx1z(n, model, zmode, shape=(n, ))[:2]
# if model == "2d":
# fx1z = calc_fx1z(x1, x0, K, w, yc, Iext1, a=a, b=b, d=d, tau1=tau1, tau0=tau0, model=model, zmode=zmode)
# s_fx1z = sfx1z[0](x1, x0, K, w, yc, Iext1, a, b, d, tau1, tau0)
# assert list(fx1z) == list(s_fx1z)
# else:
# fx1z = calc_fx1z(x1, x0, K, w, yc, Iext1, a=a, b=b, d=d, tau1=tau1, tau0=tau0, model=model, zmode=zmode)
# s_fx1z = sfx1z[0](x1, x0, K, w, yc, Iext1, a, b, d, tau1, tau0)
# assert list(fx1z) == list(s_fx1z)
# ------------------------------- Test the derivative to x1 of fz with substitution of z via fx1 ---------------------
fx1z_diff = calc_fx1z_diff(sx1,
sK,
sw,
sa,
sb,
sd,
stau1,
stau0,
model=model,
zmode=zmode)
sfx1z_diff = symbol_eqtn_fx1z_diff(n, model, zmode)[:2]
# for ii in range(n):
# assert list(fx1z_diff[ii]) == list(sfx1z_diff[1][ii, :])
# -------------------------------- Test symbolic fx2 with substitution of y2 via fy2 ----------------------------------
if model != "2d":
sfx2y2 = symbol_eqtn_fx2y2(n, x2_neg=False, shape=(n, ))[:2]
# ----------------------------------------------- Test calc_fx1_2d_taylor ---------------------------------------------
x_taylor = symbol_vars(n, ["x1lin"],
shape=(n, ))[0] # x_taylor = -4.5/3 (=x1lin)
fx1lin = calc_fx1_2d_taylor(sx1,
x_taylor,
sz,
syc,
sIext1,
sslope,
sa,
sb,
stau1,
x1_neg=True,
order=2,
shape=(n, ))
sfx1lin = symbol_calc_2d_taylor(n,
"x1lin",
order=2,
x1_neg=True,
slope="slope",
Iext1="Iext1",
shape=(n, ))[:2]
# for ii in range(3):
# assert numpy.array(fx1lin[ii].expand(sx1[ii]).collect(sx1[ii])) == numpy.array(
# sfx1lin[1][ii].expand(sx1[ii]).collect(sx1[ii]))
calc_fx1_2d_taylor(x1,
-1.5,
z,
yc,
Iext1,
slope,
a=a,
b=b,
d=d,
tau1=tau1,
x1_neg=True,
order=2,
shape=(n, ))
# ----------------------------------------- Test calc_fx1y1_6d_diff_x1 -------------------------------------------------
fx1y1_6d_diff_x1 = calc_fx1y1_6d_diff_x1(sx1, syc, sIext1, sa, sb, sd,
stau1, stau0)
sfx1y1_6d_diff_x1 = symbol_calc_fx1y1_6d_diff_x1(n, shape=(n, ))[:2]
# for ii in range(n):
# assert fx1y1_6d_diff_x1[ii].expand(sx1[ii]).collect(sx1[ii]) == sfx1y1_6d_diff_x1[1][ii].expand(sx1[ii]).collect(sx1[ii])
# ------------------------------- Test eq_x1_hypo_x0_optimize_fun & eq_x1_hypo_x0_optimize_jac --------------------------
ix0 = numpy.array([1, 2])
iE = numpy.array([0])
x = numpy.empty_like(sx1).flatten()
x[ix0] = sx1[ix0]
x[iE] = sx0[iE]
eq_x1_hypo_x0_optimize(ix0,
iE,
x1eq,
zeq,
x0[ix0],
K,
w,
yc,
Iext1,
a=A_DEF,
b=B_DEF,
d=D_DEF,
slope=SLOPE_DEF)
eq_x1_hypo_x0_optimize_fun(x, ix0, iE, sx1, numpy.array(sz), sx0[ix0],
sK, sw, syc, sIext1)
eq_x1_hypo_x0_optimize_jac(x, ix0, iE, sx1, numpy.array(sz), sx0[ix0],
sK, sw, sy1, sIext1)
eq_x1_hypo_x0_optimize(ix0, iE, x1eq, zeq, x0[ix0], K, w, yc, Iext1)
eq_x1_hypo_x0_linTaylor(ix0, iE, x1eq, zeq, x0[ix0], K, w, yc, Iext1)
# ------------------------------------------ Test calc_fz_jac_square_taylor ----------------------------------------------
calc_fz_jac_square_taylor(numpy.array(sz),
syc,
sIext1,
sK,
sw,
tau1=tau1,
tau0=tau0)
lfz_jac_square_taylor, sfz_jac_square_taylor, v = symbol_calc_fz_jac_square_taylor(
n)
sfz_jac_square_taylor = Matrix(sfz_jac_square_taylor).reshape(n, n)
for iv in range(n):
for jv in range(n):
sfz_jac_square_taylor[iv,
jv] = sfz_jac_square_taylor[iv, jv].subs(
[(v["x_taylor"][jv], x1sq[jv]),
(v["a"][jv], a[jv]),
(v["b"][jv], b[jv]),
(v["d"][jv], d[jv]),
(v["tau1"][iv], tau1[iv]),
(v["tau0"][iv], tau2[iv])])
assert list(
calc_fz_jac_square_taylor(
z, yc, Iext1, K, w, tau1=tau1, tau0=tau0)[0]) == list(
lfz_jac_square_taylor(zeq, yc, Iext1, K, w, a, b, d, tau1,
tau0, x1sq)[0])
