def lamom2_s_fig(q0,
q1,
filenum,
eps=default_eps_lamom,
f=default_f_lamom,
a=default_a,
alpha=default_alpha,
beta=default_beta,
partype='s'):
"""
two weakly coupled lambda-omega models, stochastic "slowly" varying parameter figure
the model is simulated in this function. calls functions from lambda_omega.py
filenum: seed
"""
# initialize
#filename = "trb2_psi_maxn_s"+str(filenum)+".dat"
#filename = "trb2_psi_maxn_s1.dat"
dt = .05
noisefile = np.loadtxt("ounormed" + str(filenum) + "_mu1k.tab")
total = noisefile[2]
t = np.linspace(0, total, total / dt)
initc = [2 / np.sqrt(2), 2 / np.sqrt(2), -2 / np.sqrt(2), 2 / np.sqrt(2)]
# generate data for plots
lcsolcoupled = euler.ESolve(lambda_omega.lamom_coupled,
initc,
t,
args=(a, alpha, beta, eps, q0, q1, f, dt,
partype, noisefile))
phi1init = np.arctan2(initc[1], initc[0])
phi2init = np.arctan2(initc[3], initc[2])
# compute Hodd
# get theory phase
phi_theory = euler.ESolve(lambda_omega.Hodd,
phi2init - phi1init,
t,
args=(a, alpha, beta, eps, q0, q1, f, dt,
partype, noisefile))
theta1 = np.arctan2(lcsolcoupled[:, 1], lcsolcoupled[:, 0])
theta2 = np.arctan2(lcsolcoupled[:, 3], lcsolcoupled[:, 2])
phi_exp = np.mod(theta2 - theta1 + np.pi, 2 * np.pi) - np.pi
phi_theory = np.mod(phi_theory + np.pi, 2 * np.pi) - np.pi
# create plot object
fig = plt.figure()
gs = gridspec.GridSpec(2, 3)
#ax1 = plt.subplot2grid((3,3),(0,0),colspan=3,rowspan=2)
#ax2 = plt.subplot2grid((3,3),(2,0),colspan=3)
ax1 = plt.subplot(gs[:1, :])
# bold tick labels
ax1.set_yticks(np.arange(0, 0.5 + .125, .125) * 2 * np.pi)
x_label = [r"$0$", r"$\pi/4$", r"$\pi/2$", r"$3\pi/4$", r"$\pi$"]
ax1.set_yticklabels(x_label, fontsize=lamomfsize)
#ytick_locs = np.arange(np.amin(phi_theory),np.amax(phi_theory),
# (np.amax(phi_theory)-np.amin(phi_theory))/8.)
#plt.yticks(ytick_locs, [r"$\mathbf{%1.1f}$" % x for x in ytick_locs])
ax2 = plt.subplot(gs[1, :])
#fig, axarr = plt.subplots(2, sharex=True)
#axarr[0] = plt.subplot2grid(
fig.set_size_inches(10, 7.5)
#axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])
# plot data+theory
ax1.plot(t, phi_exp, lw=5, color="black")
ax1.plot(t,
phi_theory,
lw=5,
color="#3399ff",
ls='dashdot',
dashes=(10, 5))
if q0 == .9:
ax1.set_ylabel(r'$\bm{\phi(t)}$', fontsize=lamomfsize)
#ax1.yaxis.set_major_locator(MultipleLocator(0.4))
# make plot fit window
#ax1.set_ylim(np.amin(full_model),0.3)#np.amax(full_model))
#ax1.set_xlim(dat[:,0][0],dat[:,0][-1])
ax1.set_xlim(0, total)
ax1.set_ylim(-0.1, np.pi + 0.1)
# plot s param
q = q0 + (q1) * noisefile[3:]
print 'mean =', np.mean(q), 'for seed=' + str(filenum)
#ax2 = plt.subplots(2,1,1)
#ax2 = ax1.twinx()
s_N = len(noisefile[3:])
s_N_half = s_N #int(s_N/2.)
ax2.plot(np.linspace(0, t[-1], s_N), q, lw=1, color="red")
ax2.plot([t[0], t[-1]], [1, 1],
lw=3,
color='red',
linestyle='--',
dashes=(10, 2))
#ax2.set_xlim(dat[:,0][0],dat[:,0][-1])
if q0 == .9:
ax2.set_ylabel(r'$\bm{q(t)}$', fontsize=lamomfsize, color='red')
for tl in ax2.get_yticklabels():
tl.set_color('r')
ax2.set_xlabel(r'$\bm{t}$', fontsize=lamomfsize)
for tl in ax2.get_yticklabels():
tl.set_color('r')
ax2.yaxis.set_major_locator(MultipleLocator(0.4))
ax2.xaxis.set_major_locator(MultipleLocator(4000))
ax2.set_xlim(0, total)
#xtick_locs = np.arange(t[0], t[-1], 2000,dtype='int')
#minval=np.amin(q);maxval=np.amax(q)
#ytick_locs = np.arange(minval,maxval,(maxval-minval)/8.)
#plt.xticks(xtick_locs, [r"$\mathbf{%s}$" % x for x in xtick_locs])
#plt.yticks(ytick_locs, [r"$\mathbf{%1.1f}$" % x for x in ytick_locs])
#axes.set_xticks([])
#axes.set_yticks([])
#axes.set_frame_on(False)
ax1.set_xticks([])
#ax1.set_yticks([])
#ax1.set_frame_on(False)
ax1.tick_params(labelsize=lamomfsize, top='off', right='off')
#ax2.set_xticks([])
#ax2.set_yticks([])
ax2.tick_params(labelsize=lamomfsize, top='off', right='off')
ax2.set_frame_on(False)
return fig
def trb2_p_fig(gm0=default_gm0,
gm1=default_gm1,
eps=default_eps,
f=default_f,
partype='p'):
"""
two weakly coupled trab models, periodic slowly varying parameter figure
data files created using trb2simple.ode and trb2simple_just1.ode
"""
# initialize
#filename = "trb2_psi_maxn_qp"+str(filenum)
#filename = "trb2_psi_maxn_p1_ref.dat"
filename = "trb2_psi_maxn_p1_ref2.dat" # with reviewer's fix
#filename = "trb2_psi_maxn_p1_refined_2tables.dat"
dat = np.loadtxt(filename)
psi0 = np.mean(dat[:, 1][:int(5 / .05)])
T = dat[:, 0][-1]
N = len(dat[:, 0])
t = np.linspace(0, T, N)
noisefile = None
# generate data for plots
sol = euler.ESolve(phase_model.happrox,
psi0,
t,
args=(gm0, gm1, f, eps, partype, noisefile))
full_model = np.abs(np.mod(dat[:, 1] + .5, 1) -
.5) # [0] to make regular row array
slow_phs_model = np.abs(np.mod(sol + .5, 1) - .5)[:, 0]
# create plot object
fig, ax1 = plt.subplots()
fig.set_size_inches(10, 5)
## plot data+theory
ax1.scatter(dat[:, 0] / 1000.,
full_model * 2 * np.pi,
s=.5,
facecolor="gray")
ax1.plot(np.linspace(0, dat[:, 0][-1] / 1000., N),
slow_phs_model * 2 * np.pi,
lw=5,
color="#3399ff")
ax1.set_ylabel(r'$\bm{|\phi(t)|}$', fontsize=20)
ax1.set_xlabel(r'$\bm{t (s)}$', fontsize=20)
# set tick intervals
myLocatorx = mticker.MultipleLocator(2000 / 1000.)
#myLocatory = mticker.MultipleLocator(.5)
ax1.xaxis.set_major_locator(myLocatorx)
#ax1.yaxis.set_major_locator(myLocatory)
# make plot fit window
ax1.set_yticks(np.arange(0, 0.5, .125) * 2 * np.pi)
x_label = [r"$0$", r"$\pi/4$", r"$\pi/2$", r"$3\pi/4$"]
#x_label = [r"$0$", r"$\frac{\pi}{4}$", r"$\frac{\pi}{2}$", r"$\frac{3\pi}{4}$", r"$\pi$"]
ax1.set_yticklabels(x_label, fontsize=lamomfsize)
ax1.set_ylim(
np.amin([full_model]) * 2 * np.pi,
np.amax([full_model]) * 2 * np.pi)
ax1.set_xlim(dat[:, 0][0] / 1000., dat[:, 0][-1] / 1000.)
## plot P param
ax2 = ax1.twinx()
ax2.set_ylabel(r'$\bm{q(t)}$', fontsize=20, color='red')
# slowly varying parameter
gm = gm0 + (gm1 - gm0) * np.cos(eps * f * t)
# set tick intervals
myLocatory2 = mticker.MultipleLocator(.1)
ax2.yaxis.set_major_locator(myLocatory2)
# make param plot fit window
ax2.set_xlim(dat[:, 0][0] / 1000., dat[:, 0][-1] / 1000.)
ax2.set_ylim(np.amin(gm), np.amax(gm))
# plot param + stability line
ax2.plot(t / 1000., gm, lw=4, color="red", linestyle='--', dashes=(10, 2))
ax2.plot([dat[:, 0][0] / 1000., dat[:, 0][-1] / 1000.], [0.3, 0.3],
lw=2,
color='red')
# set ticks to red
for tl in ax2.get_yticklabels():
tl.set_color('r')
# beautify
ax1.tick_params(labelsize=20, top='off')
ax1.tick_params(axis='x', pad=8)
ax2.tick_params(labelsize=20, top='off')
plt.gcf().subplots_adjust(bottom=0.15)
return fig
def trb2newpar_p_fig(gm0=default_gm0,
gm1=default_gm1,
eps=default_eps,
f=default_f,
partype='p'):
"""
two weakly coupled trab models, periodic slowly varying parameter figure, with parameters in interval [0.05,0.3]
data files created using trb2_new_params/trb2simple_newpar.ode
"""
# initialize
# no more switch from stable/unstable. There always exists a stable point
#filename = "trb2_new_params/trb2newpar_psi_p.dat" # no normalization by variance
filename = "trb2_new_params/trb2newpar_psi_p2.dat" # includes normalization by variance
dat = np.loadtxt(filename)
psi0 = np.mean(dat[:, 1][:int(5 / .05)])
T = dat[:, 0][-1]
N = len(dat[:, 0])
dt = T / (1. * N)
t = np.linspace(0, T, N)
noisefile = None
# generate data for plots
sol = euler.ESolve(phase_model.happrox_newpar,
psi0,
t,
args=(gm0, gm1, f, eps, partype, noisefile))
full_model = np.abs(np.mod(dat[:, 1] + .5, 1) -
.5) # [0] to make regular row array
slow_phs_model = np.abs(np.mod(sol + .5, 1) - .5)[:, 0]
# create plot object
fig, ax1 = plt.subplots()
fig.set_size_inches(10, 5)
#axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])
## plot data+theory
ax1.scatter(dat[:, 0] / 1000.,
full_model * 2 * np.pi,
s=.5,
facecolor="gray")
ax1.plot(np.linspace(0, dat[:, 0][-1] / 1000., N),
slow_phs_model * 2 * np.pi,
lw=5,
color="#3399ff")
myLocatorx = mticker.MultipleLocator(2000 / 1000.)
#myLocatory = mticker.MultipleLocator(.5)
ax1.xaxis.set_major_locator(myLocatorx)
#ax1.yaxis.set_major_locator(myLocatory)
ax1.set_yticks(np.arange(0, 0.5 + .125, .125) * 2 * np.pi)
x_label = [r"$0$", r"$\pi/4$", r"$\pi/2$", r"$3\pi/4$", r"$\pi"]
#x_label = [r"$0$", r"$\frac{\pi}{4}$", r"$\frac{\pi}{2}$", r"$\frac{3\pi}{4}$", r"$\pi$"]
ax1.set_yticklabels(x_label, fontsize=lamomfsize)
ax1.set_ylabel(r'$\bm{|\phi(t)|}$', fontsize=20)
ax1.set_xlabel(r'$\bm{t (s)}$', fontsize=20)
# make plot fit window
ax1.set_ylim(
np.amin([full_model]) * 2 * np.pi,
np.amax(full_model) * 2 * np.pi)
ax1.set_xlim(dat[:, 0][0] / 1000., dat[:, 0][-1] / 1000.)
## plot P param
ax2 = ax1.twinx()
gm = gm0 + (gm1 - gm0) * np.cos(eps * f * t)
ax2.set_xlim(dat[:, 0][0] / 1000., dat[:, 0][-1] / 1000.)
ax2.set_ylabel(r'$\bm{q(t)}$', fontsize=20, color='red')
ax2.plot(t / 1000., gm, lw=4, color="red", linestyle='--', dashes=(10, 2))
myLocatory2 = mticker.MultipleLocator(.05)
ax2.yaxis.set_major_locator(myLocatory2)
#ax2.plot([dat[:,0][0],dat[:,0][-1]],[0.3,0.3],lw=2,color='red')
for tl in ax2.get_yticklabels():
tl.set_color('r')
# beautify
ax1.tick_params(labelsize=20, top='off')
ax1.tick_params(axis='x', pad=8)
ax2.tick_params(labelsize=20, top='off')
plt.gcf().subplots_adjust(bottom=0.15)
return fig
def trb2_s_fig(filenum=4,
gm0=default_gm0,
gm1=default_gm1,
eps=default_eps,
f=default_f,
partype='s'):
"""
two weakly coupled trab models, stochastic "slowly" varying parameter figure
data files created using
trb2simple.ode
trb2simple_just1.ode
generateou.ode
"""
# initialize
#filename = "trb2_psi_maxn_s"+str(filenum)+".dat"
#filename = "trb2_psi_maxn_s1.dat"
#filename = "trb2_psi_maxn_s"+str(filenum)+"_mu1k.dat"
filename = "trb2_psi_maxn_s" + str(
filenum) + "_mu1k2.dat" # with reviewer edit
dat = np.loadtxt(filename)
psi0 = np.mean(dat[:, 1][:int(5 / .05)])
T = dat[:, 0][-1]
N = len(dat[:, 0])
dt = T / (1. * N)
t = np.linspace(0, T, N)
#noisefile = np.loadtxt("ounormed"+str(filenum)+".tab")
noisefile = np.loadtxt("ounormed" + str(filenum) + "_mu1k.tab")
# generate data for plots
sol = euler.ESolve(phase_model.happrox,
psi0,
t,
args=(gm0, gm1, f, eps, partype, noisefile))
full_model = np.abs(np.mod(dat[:, 1] + .5, 1) -
.5) # [0] to make regular row array
slow_phs_model = np.abs(np.mod(sol + .5, 1) - .5)[:, 0]
# create plot object
fig = plt.figure()
fig.set_size_inches(10, 7.5)
gs = gridspec.GridSpec(2, 3)
ax1 = plt.subplot(gs[:1, :])
# plot data+theory
ax1.scatter(dat[:, 0] / 1000.,
full_model * 2 * np.pi,
s=.5,
facecolor="gray")
ax1.plot(np.linspace(0, dat[:, 0][-1] / 1000., N),
slow_phs_model * 2 * np.pi,
lw=4,
color="#3399ff")
ax1.set_ylabel(r'$\bm{|\phi(t)|}$', fontsize=20)
ax1.set_yticks(np.arange(0, 0.5 + .125, .125) * 2 * np.pi)
x_label = [r"$0$", r"$\pi/4$", r"$\pi/2$", r"$3\pi/4$", r"$\pi$"]
#x_label = [r"$0$", r"$\frac{\pi}{4}$", r"$\frac{\pi}{2}$", r"$\frac{3\pi}{4}$", r"$\pi$"]
ax1.set_yticklabels(x_label, fontsize=lamomfsize)
# make plot fit window
ax1.set_ylim(
np.amin(full_model) * 2 * np.pi,
np.amax(full_model) * 2 * np.pi) #np.amax(full_model))
ax1.set_xlim(dat[:, 0][0] / 1000., dat[:, 0][-1] / 1000.)
#myLocatory = mticker.MultipleLocator(.5)
#ax1.yaxis.set_major_locator(myLocatory)
## plot s param
ax2 = plt.subplot(gs[1, :])
s_N = len(noisefile[3:])
ax2.plot(np.linspace(0, dat[:, 0][-1] / 1000., s_N),
(gm0 + (gm1 - gm0) * noisefile[3:]),
lw=1,
color="red")
ax2.plot([dat[:, 0][0] / 1000., dat[:, 0][-1] / 1000.], [0.3, 0.3],
lw=3,
color='red',
linestyle='--',
dashes=(10, 2))
myLocatorx = mticker.MultipleLocator(2000 / 1000.)
ax2.xaxis.set_major_locator(myLocatorx)
ax2.set_xlim(dat[:, 0][0] / 1000., dat[:, 0][-1] / 1000.)
ax2.set_ylabel(r'$\bm{q(t)}$', fontsize=20, color='red')
myLocatory2 = mticker.MultipleLocator(.1)
ax2.yaxis.set_major_locator(myLocatory2)
ax2.set_xlabel(r'$\bm{t (s)}$', fontsize=20)
for tl in ax2.get_yticklabels():
tl.set_color('r')
ax1.tick_params(labelsize=20, top='off', right='off')
ax1.xaxis.set_ticklabels([])
#ax2.set_xticks([])
#ax2.set_yticks([])
ax2.tick_params(labelsize=20, top='off', right='off')
ax2.tick_params(axis='x', pad=8)
ax2.set_frame_on(False)
return fig
theta1 = np.loadtxt("trb2_new_params/trb2newpar_p_theta1.dat")
theta2 = np.loadtxt("trb2_new_params/trb2newpar_p_theta2.dat")
# slow param
eps = .0025
gm0 = .175
gm1 = .3
f = .5
gm = gm0 + (gm1 - gm0) * np.cos(eps * f * t)
# theoretical phase
# generate data for plots
partype = 'p'
noisefile = None
sol = euler.ESolve(phase_model.happrox_newpar,
psi0,
t,
args=(gm0, gm1, f, eps, partype, noisefile))
slow_phs_model = np.abs(np.mod(sol + .5, 1) - .5)[:, 0]
fig = plt.figure(figsize=(7, 7))
plt.ion()
#plt.show()
ax11 = plt.subplot2grid((2, 2), (0, 0))
ax11.set_title(r"\textbf{Oscillator 1}")
ax11.set_xlabel(r"\textbf{Voltage (mV)}", fontsize=15)
ax11.set_ylabel(r"$\mathbf{n}$", fontsize=15)
#ax11.plot(vlo,nlo)
ax12 = plt.subplot2grid((2, 2), (0, 1))
