def plot_results(alpha_a_0s, Ks_alpha_a_0,
gamma_a_0s, Ks_gamma_a_0,
figdir="."):
# Plot the number of inferred states as a function of params
fig = create_figure((5,1.5))
ax = create_axis_at_location(fig, 0.6, 0.5, 1.7, .8, transparent=True)
plt.figtext(0.05/5, 1.25/1.5, "A")
ax.boxplot(Ks_alpha_a_0, positions=np.arange(1,1+len(alpha_a_0s)),
boxprops=dict(color=allcolors[1]),
whiskerprops=dict(color=allcolors[0]),
flierprops=dict(color=allcolors[1]))
ax.set_xticklabels(alpha_a_0s)
plt.xlim(0.5,4.5)
plt.ylim(40,90)
# plt.yticks(np.arange(0,101,20))
ax.set_xlabel("$a_{\\alpha_0}$")
ax.set_ylabel("Number of States")
ax = create_axis_at_location(fig, 3.1, 0.5, 1.7, .8, transparent=True)
plt.figtext(2.55/5, 1.25/1.5, "B")
ax.boxplot(Ks_gamma_a_0, positions=np.arange(1,1+len(gamma_a_0s)),
boxprops=dict(color=allcolors[1]),
whiskerprops=dict(color=allcolors[0]),
flierprops=dict(color=allcolors[1]))
ax.set_xticklabels(gamma_a_0s)
plt.xlim(0.5,4.5)
plt.ylim(40,90)
# plt.yticks(np.arange(0,101,20))
ax.set_xlabel("$a_{\\gamma}$")
ax.set_ylabel("Number of States")
plt.savefig(os.path.join(figdir, "figure7.pdf"))
plt.savefig(os.path.join(figdir, "figure7.png"))
def plot_pred_ll_vs_time(models,
results,
burnin=0,
homog_ll=np.nan,
std_ll=np.nan,
nlin_ll=np.nan,
true_ll=np.nan,
baseline=0,
normalizer=1,
output_dir=".",
xlim=None,
ylim=None,
title=None):
from hips.plotting.layout import create_figure, create_axis_at_location
from hips.plotting.colormaps import harvard_colors
# Make the ICML figure
fig = create_figure((3, 2))
ax = create_axis_at_location(fig, 0.7, 0.4, 2.25, 1.35)
col = harvard_colors()
ax.grid()
t_start = 0
t_stop = 0
def standardize(x):
return (x - baseline) / normalizer
for i, (model, result) in enumerate(zip(models, results)):
ax.plot(result.timestamps[burnin:],
standardize(result.test_lls[burnin:]),
lw=2,
color=col[i],
label=model)
# Update time limits
t_start = min(t_start, result.timestamps[burnin:].min())
t_stop = max(t_stop, result.timestamps[burnin:].max())
if xlim is not None:
t_start = xlim[0]
t_stop = xlim[1]
# plt.legend(loc="outside right")
# Plot baselines
ax.plot([t_start, t_stop],
standardize(homog_ll) * np.ones(2),
lw=2,
color='k',
label="Homog")
# Plot the standard Hawkes test ll
ax.plot([t_start, t_stop],
standardize(std_ll) * np.ones(2),
lw=2,
color=col[len(models)],
label="Std.")
# Plot the Nonlinear Hawkes test ll
ax.plot([t_start, t_stop],
standardize(nlin_ll) * np.ones(2),
lw=2,
ls='--',
color=col[len(models) + 1],
label="Nonlinear")
# Plot the true ll
ax.plot([t_start, t_stop],
standardize(true_ll) * np.ones(2),
'--k',
lw=2,
label="True")
ax.set_xlabel("time [sec]")
ax.set_ylabel("Pred. LL. [nats/event]")
ax.set_xscale("log")
if xlim is not None:
ax.set_xlim(xlim)
else:
ax.set_xlim(t_start, t_stop)
if ylim is not None:
ax.set_ylim(ylim)
if title is not None:
ax.set_title(title)
output_file = os.path.join(output_dir, "pred_ll_vs_time.pdf")
fig.savefig(output_file)
plt.show()
def plot_locations(result, offset=0):
### Plot the sampled locations for a few neurons
_, _, _, _, Ls = result
Ls_rot = []
for L in Ls:
R = compute_optimal_rotation(L, pfs, scale=False)
Ls_rot.append(L.dot(R))
Ls_rot = np.array(Ls_rot)
fig = create_figure(figsize=(1.4,2.9))
ax = create_axis_at_location(fig, .3, 1.7, 1, 1)
# toplot = np.random.choice(np.arange(K), size=4, replace=False)
toplot = np.linspace(offset,K+offset, 4, endpoint=False).astype(np.int)
print(toplot)
wheel_cmap = gradient_cmap([colors[0], colors[3], colors[2], colors[1], colors[0]])
plot_colors = [wheel_cmap((np.pi+pfs_th[node_perm[j]])/(2*np.pi)) for j in toplot]
for i,k in enumerate(node_perm):
# plt.text(pfs[k,0], pfs[k,1], "%d" % i)
if i not in toplot:
color = 0.8 * np.ones(3)
plt.plot(pfs[k,0], pfs[k, 1], 'o',
markerfacecolor=color, markeredgecolor=color,
markersize=4 + 4 * pf_size[k],
alpha=1.0)
for i,k in enumerate(node_perm):
# plt.text(pfs[k,0], pfs[k,1], "%d" % i)
if i in toplot:
j = np.where(toplot==i)[0][0]
color = plot_colors[j]
plt.plot(pfs[k,0], pfs[k, 1], 'o',
markerfacecolor=color, markeredgecolor=color,
markersize=4 + 4 * pf_size[k])
# plt.gca().add_patch(Circle((0,0), radius=rad, ec='k', fc="none"))
plt.title("True Place Fields")
plt.xlim(-45,45)
plt.xticks([-40, -20, 0, 20, 40])
# plt.xlabel("$x$")
plt.ylim(-45,45)
plt.yticks([-40, -20, 0, 20, 40])
# plt.ylabel("$y$")
# Now plot the inferred locations
# plt.subplot(212, aspect='equal')
ax = create_axis_at_location(fig, .3, .2, 1, 1)
for L in Ls_rot[::2]:
for j in np.random.permutation(len(toplot)):
k = node_perm[toplot][j]
color = plot_colors[j]
plt.plot(L[k,0], L[k,1], 'o',
markerfacecolor=color, markeredgecolor="none",
markersize=4, alpha=0.25)
plt.title("Locations Samples")
# plt.xlim(-30, 30)
# plt.xticks([])
# plt.ylim(-30, 30)
# plt.yticks([])
plt.xlim(-3, 3)
plt.xticks([-2, 0, 2])
plt.ylim(-3, 3)
plt.yticks([-2, 0, 2])
plt.savefig(os.path.join(results_dir, "locations_%d.pdf" % offset))
def plot_mean_and_pca_locations(result):
### Plot the sampled locations for a few neurons
_, _, _, _, Ls = result
Ls_rot = []
for L in Ls:
R = compute_optimal_rotation(L, pfs, scale=False)
Ls_rot.append(L.dot(R))
Ls_rot = np.array(Ls_rot)
Ls_mean = np.mean(Ls_rot, 0)
# Bin the data
from pyhawkes.utils.utils import convert_continuous_to_discrete
S_dt = convert_continuous_to_discrete(S, C, 0.25, 0, T)
# Smooth the data to get a firing rate
from scipy.ndimage.filters import gaussian_filter1d
S_smooth = np.array([gaussian_filter1d(s, 4) for s in S_dt.T]).T
# Run pca to gte an embedding
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
pca.fit(S_smooth)
Z = pca.components_.T
# Rotate
R = compute_optimal_rotation(Z, pfs, scale=False)
Z = Z.dot(R)
wheel_cmap = gradient_cmap([colors[0], colors[3], colors[2], colors[1], colors[0]])
fig = create_figure(figsize=(1.4,2.9))
# plt.subplot(211, aspect='equal')
ax = create_axis_at_location(fig, .3, 1.7, 1, 1)
for i,k in enumerate(node_perm):
color = wheel_cmap((np.pi+pfs_th[k])/(2*np.pi))
plt.plot(Ls_mean[k,0], Ls_mean[k, 1], 'o',
markerfacecolor=color, markeredgecolor=color,
markersize=4 + 4 * pf_size[k],
alpha=0.7)
# plt.gca().add_patch(Circle((0,0), radius=rad, ec='k', fc="none"))
plt.title("Mean Locations")
plt.xlim(-3, 3)
plt.xticks([-2, 0, 2])
plt.ylim(-3, 3)
plt.yticks([-2, 0, 2])
# plt.subplot(212, aspect='equal')
ax = create_axis_at_location(fig, .3, .2, 1, 1)
for i,k in enumerate(node_perm):
color = wheel_cmap((np.pi+pfs_th[k])/(2*np.pi))
plt.plot(Z[k,0], Z[k, 1], 'o',
markerfacecolor=color, markeredgecolor=color,
markersize=4 + 4 * pf_size[k],
alpha=0.7)
# plt.gca().add_patch(Circle((0,0), radius=rad, ec='k', fc="none"))
plt.title("PCA Locations")
plt.xlim(-.5, .5)
# plt.xlabel("$x$")
plt.xticks([-.4, 0, .4])
plt.ylim(-.5, .5)
# plt.ylabel("$y$")
plt.yticks([-.4, 0, .4])
# plt.tight_layout()
plt.savefig(os.path.join(results_dir, "hipp_mean_pca_locations.pdf"))
plt.show()
def plot_mean_and_pca_locations(result):
### Plot the sampled locations for a few neurons
_, _, _, _, Ls = result
Ls_rot = []
for L in Ls:
R = compute_optimal_rotation(L, pfs, scale=False)
Ls_rot.append(L.dot(R))
Ls_rot = np.array(Ls_rot)
Ls_mean = np.mean(Ls_rot, 0)
# Bin the data
from pyhawkes.utils.utils import convert_continuous_to_discrete
S_dt = convert_continuous_to_discrete(S, C, 0.25, 0, T)
# Smooth the data to get a firing rate
from scipy.ndimage.filters import gaussian_filter1d
S_smooth = np.array([gaussian_filter1d(s, 4) for s in S_dt.T]).T
# Run pca to gte an embedding
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
pca.fit(S_smooth)
Z = pca.components_.T
# Rotate
R = compute_optimal_rotation(Z, pfs, scale=False)
Z = Z.dot(R)
wheel_cmap = gradient_cmap([colors[0], colors[3], colors[2], colors[1], colors[0]])
fig = create_figure(figsize=(1.4,2.9))
# plt.subplot(211, aspect='equal')
ax = create_axis_at_location(fig, .3, 1.7, 1, 1)
for i,k in enumerate(node_perm):
color = wheel_cmap((np.pi+pfs_th[k])/(2*np.pi))
plt.plot(Ls_mean[k,0], Ls_mean[k, 1], 'o',
markerfacecolor=color, markeredgecolor=color,
markersize=4 + 4 * pf_size[k],
alpha=0.7)
# plt.gca().add_patch(Circle((0,0), radius=rad, ec='k', fc="none"))
plt.title("Mean Locations")
plt.xlim(-3, 3)
plt.xticks([-2, 0, 2])
plt.ylim(-3, 3)
plt.yticks([-2, 0, 2])
# plt.subplot(212, aspect='equal')
ax = create_axis_at_location(fig, .3, .2, 1, 1)
for i,k in enumerate(node_perm):
color = wheel_cmap((np.pi+pfs_th[k])/(2*np.pi))
plt.plot(Z[k,0], Z[k, 1], 'o',
markerfacecolor=color, markeredgecolor=color,
markersize=4 + 4 * pf_size[k],
alpha=0.7)
# plt.gca().add_patch(Circle((0,0), radius=rad, ec='k', fc="none"))
plt.title("PCA Locations")
plt.xlim(-.5, .5)
# plt.xlabel("$x$")
plt.xticks([-.4, 0, .4])
plt.ylim(-.5, .5)
# plt.ylabel("$y$")
plt.yticks([-.4, 0, .4])
# plt.tight_layout()
plt.savefig(os.path.join(results_dir, "hipp_mean_pca_locations.pdf"))
plt.show()
def plot_results(result):
lls, plls, Weffs, Ps, Ls = result
### Colored locations
wheel_cmap = gradient_cmap([colors[0], colors[3], colors[2], colors[1], colors[0]])
fig = create_figure(figsize=(1.8, 1.8))
# ax = create_axis_at_location(fig, .1, .1, 1.5, 1.5, box=False)
ax = create_axis_at_location(fig, .6, .4, 1.1, 1.1)
for i,k in enumerate(node_perm):
color = wheel_cmap((np.pi+pfs_th[k])/(2*np.pi))
# alpha = pfs_rad[k] / 47
alpha = 0.7
ax.add_patch(Circle((pfs[k,0], pfs[k,1]),
radius=3+4*pf_size[k],
color=color, ec="none",
alpha=alpha)
)
plt.title("True place fields")
# ax.text(0, 45, "True Place Fields",
# horizontalalignment="center",
# fontdict=dict(size=9))
plt.xlim(-45,45)
plt.xticks([-40, -20, 0, 20, 40])
plt.xlabel("$x$ [cm]")
plt.ylim(-45,45)
plt.yticks([-40, -20, 0, 20, 40])
plt.ylabel("$y$ [cm]")
plt.savefig(os.path.join(results_dir, "hipp_colored_locations.pdf"))
# Plot the inferred weighted adjacency matrix
fig = create_figure(figsize=(1.8, 1.8))
ax = create_axis_at_location(fig, .4, .4, 1.1, 1.1)
Weff = np.array(Weffs[N_samples//2:]).mean(0)
Weff = Weff[np.ix_(node_perm, node_perm)]
lim = Weff[(1-np.eye(K)).astype(np.bool)].max()
im = ax.imshow(np.kron(Weff, np.ones((20,20))),
interpolation="none", cmap="Greys", vmax=lim)
ax.set_xticks([])
ax.set_yticks([])
# node_colors = wheel_cmap()
node_values = ((np.pi+pfs_th[node_perm])/(2*np.pi))[:,None] *np.ones((K,2))
yax = create_axis_at_location(fig, .2, .4, .3, 1.1)
remove_plot_labels(yax)
yax.imshow(node_values, interpolation="none",
cmap=wheel_cmap)
yax.set_xticks([])
yax.set_yticks([])
yax.set_ylabel("pre")
xax = create_axis_at_location(fig, .4, .2, 1.1, .3)
remove_plot_labels(xax)
xax.imshow(node_values.T, interpolation="none",
cmap=wheel_cmap)
xax.set_xticks([])
xax.set_yticks([])
xax.set_xlabel("post")
cbax = create_axis_at_location(fig, 1.55, .4, .04, 1.1)
plt.colorbar(im, cax=cbax, ticks=[0, .1, .2, .3])
cbax.tick_params(labelsize=8, pad=1)
cbax.set_ticklabels=["0", ".1", ".2", ".3"]
ax.set_title("Inferred Weights")
plt.savefig(os.path.join(results_dir, "hipp_W.pdf"))
# # Plot the inferred connection probability
# plt.figure()
# plt.imshow(P, interpolation="none", cmap="Greys", vmin=0)
# plt.colorbar()
# Plot the inferred weighted adjacency matrix
fig = create_figure(figsize=(1.8, 1.8))
ax = create_axis_at_location(fig, .4, .4, 1.1, 1.1)
P = np.array(Ps[N_samples//2:]).mean(0)
P = P[np.ix_(node_perm, node_perm)]
im = ax.imshow(np.kron(P, np.ones((20,20))),
interpolation="none", cmap="Greys", vmin=0, vmax=1)
ax.set_xticks([])
ax.set_yticks([])
# node_colors = wheel_cmap()
node_values = ((np.pi+pfs_th[node_perm])/(2*np.pi))[:,None] *np.ones((K,2))
yax = create_axis_at_location(fig, .2, .4, .3, 1.1)
remove_plot_labels(yax)
yax.imshow(node_values, interpolation="none",
cmap=wheel_cmap)
yax.set_xticks([])
yax.set_yticks([])
yax.set_ylabel("pre")
xax = create_axis_at_location(fig, .4, .2, 1.1, .3)
remove_plot_labels(xax)
xax.imshow(node_values.T, interpolation="none",
cmap=wheel_cmap)
xax.set_xticks([])
xax.set_yticks([])
xax.set_xlabel("post")
cbax = create_axis_at_location(fig, 1.55, .4, .04, 1.1)
plt.colorbar(im, cax=cbax, ticks=[0, .5, 1])
cbax.tick_params(labelsize=8, pad=1)
cbax.set_ticklabels=["0.0", "0.5", "1.0"]
ax.set_title("Inferred Probability")
plt.savefig(os.path.join(results_dir, "hipp_P.pdf"))
plt.show()
def plot_results(result):
lls, plls, Weffs, Ps, Ls = result
### Colored locations
wheel_cmap = gradient_cmap([colors[0], colors[3], colors[2], colors[1], colors[0]])
fig = create_figure(figsize=(1.8, 1.8))
# ax = create_axis_at_location(fig, .1, .1, 1.5, 1.5, box=False)
ax = create_axis_at_location(fig, .6, .4, 1.1, 1.1)
for i,k in enumerate(node_perm):
color = wheel_cmap((np.pi+pfs_th[k])/(2*np.pi))
# alpha = pfs_rad[k] / 47
alpha = 0.7
ax.add_patch(Circle((pfs[k,0], pfs[k,1]),
radius=3+4*pf_size[k],
color=color, ec="none",
alpha=alpha)
)
plt.title("True place fields")
# ax.text(0, 45, "True Place Fields",
# horizontalalignment="center",
# fontdict=dict(size=9))
plt.xlim(-45,45)
plt.xticks([-40, -20, 0, 20, 40])
plt.xlabel("$x$ [cm]")
plt.ylim(-45,45)
plt.yticks([-40, -20, 0, 20, 40])
plt.ylabel("$y$ [cm]")
plt.savefig(os.path.join(results_dir, "hipp_colored_locations.pdf"))
# Plot the inferred weighted adjacency matrix
fig = create_figure(figsize=(1.8, 1.8))
ax = create_axis_at_location(fig, .4, .4, 1.1, 1.1)
Weff = np.array(Weffs[N_samples//2:]).mean(0)
Weff = Weff[np.ix_(node_perm, node_perm)]
lim = Weff[(1-np.eye(K)).astype(np.bool)].max()
im = ax.imshow(np.kron(Weff, np.ones((20,20))),
interpolation="none", cmap="Greys", vmax=lim)
ax.set_xticks([])
ax.set_yticks([])
# node_colors = wheel_cmap()
node_values = ((np.pi+pfs_th[node_perm])/(2*np.pi))[:,None] *np.ones((K,2))
yax = create_axis_at_location(fig, .2, .4, .3, 1.1)
remove_plot_labels(yax)
yax.imshow(node_values, interpolation="none",
cmap=wheel_cmap)
yax.set_xticks([])
yax.set_yticks([])
yax.set_ylabel("pre")
xax = create_axis_at_location(fig, .4, .2, 1.1, .3)
remove_plot_labels(xax)
xax.imshow(node_values.T, interpolation="none",
cmap=wheel_cmap)
xax.set_xticks([])
xax.set_yticks([])
xax.set_xlabel("post")
cbax = create_axis_at_location(fig, 1.55, .4, .04, 1.1)
plt.colorbar(im, cax=cbax, ticks=[0, .1, .2, .3])
cbax.tick_params(labelsize=8, pad=1)
cbax.set_ticklabels=["0", ".1", ".2", ".3"]
ax.set_title("Inferred Weights")
plt.savefig(os.path.join(results_dir, "hipp_W.pdf"))
# # Plot the inferred connection probability
# plt.figure()
# plt.imshow(P, interpolation="none", cmap="Greys", vmin=0)
# plt.colorbar()
# Plot the inferred weighted adjacency matrix
fig = create_figure(figsize=(1.8, 1.8))
ax = create_axis_at_location(fig, .4, .4, 1.1, 1.1)
P = np.array(Ps[N_samples//2:]).mean(0)
P = P[np.ix_(node_perm, node_perm)]
im = ax.imshow(np.kron(P, np.ones((20,20))),
interpolation="none", cmap="Greys", vmin=0, vmax=1)
ax.set_xticks([])
ax.set_yticks([])
# node_colors = wheel_cmap()
node_values = ((np.pi+pfs_th[node_perm])/(2*np.pi))[:,None] *np.ones((K,2))
yax = create_axis_at_location(fig, .2, .4, .3, 1.1)
remove_plot_labels(yax)
yax.imshow(node_values, interpolation="none",
cmap=wheel_cmap)
yax.set_xticks([])
yax.set_yticks([])
yax.set_ylabel("pre")
xax = create_axis_at_location(fig, .4, .2, 1.1, .3)
remove_plot_labels(xax)
xax.imshow(node_values.T, interpolation="none",
cmap=wheel_cmap)
xax.set_xticks([])
xax.set_yticks([])
xax.set_xlabel("post")
cbax = create_axis_at_location(fig, 1.55, .4, .04, 1.1)
plt.colorbar(im, cax=cbax, ticks=[0, .5, 1])
cbax.tick_params(labelsize=8, pad=1)
cbax.set_ticklabels=["0.0", "0.5", "1.0"]
ax.set_title("Inferred Probability")
plt.savefig(os.path.join(results_dir, "hipp_P.pdf"))
plt.show()
def plot_locations(result, offset=0):
### Plot the sampled locations for a few neurons
_, _, _, _, Ls = result
Ls_rot = []
for L in Ls:
R = compute_optimal_rotation(L, pfs, scale=False)
Ls_rot.append(L.dot(R))
Ls_rot = np.array(Ls_rot)
fig = create_figure(figsize=(1.4,2.9))
ax = create_axis_at_location(fig, .3, 1.7, 1, 1)
# toplot = np.random.choice(np.arange(K), size=4, replace=False)
toplot = np.linspace(offset,K+offset, 4, endpoint=False).astype(np.int)
print toplot
wheel_cmap = gradient_cmap([colors[0], colors[3], colors[2], colors[1], colors[0]])
plot_colors = [wheel_cmap((np.pi+pfs_th[node_perm[j]])/(2*np.pi)) for j in toplot]
for i,k in enumerate(node_perm):
# plt.text(pfs[k,0], pfs[k,1], "%d" % i)
if i not in toplot:
color = 0.8 * np.ones(3)
plt.plot(pfs[k,0], pfs[k, 1], 'o',
markerfacecolor=color, markeredgecolor=color,
markersize=4 + 4 * pf_size[k],
alpha=1.0)
for i,k in enumerate(node_perm):
# plt.text(pfs[k,0], pfs[k,1], "%d" % i)
if i in toplot:
j = np.where(toplot==i)[0][0]
color = plot_colors[j]
plt.plot(pfs[k,0], pfs[k, 1], 'o',
markerfacecolor=color, markeredgecolor=color,
markersize=4 + 4 * pf_size[k])
# plt.gca().add_patch(Circle((0,0), radius=rad, ec='k', fc="none"))
plt.title("True Place Fields")
plt.xlim(-45,45)
plt.xticks([-40, -20, 0, 20, 40])
# plt.xlabel("$x$")
plt.ylim(-45,45)
plt.yticks([-40, -20, 0, 20, 40])
# plt.ylabel("$y$")
# Now plot the inferred locations
# plt.subplot(212, aspect='equal')
ax = create_axis_at_location(fig, .3, .2, 1, 1)
for L in Ls_rot[::2]:
for j in np.random.permutation(len(toplot)):
k = node_perm[toplot][j]
color = plot_colors[j]
plt.plot(L[k,0], L[k,1], 'o',
markerfacecolor=color, markeredgecolor="none",
markersize=4, alpha=0.25)
plt.title("Locations Samples")
# plt.xlim(-30, 30)
# plt.xticks([])
# plt.ylim(-30, 30)
# plt.yticks([])
plt.xlim(-3, 3)
plt.xticks([-2, 0, 2])
plt.ylim(-3, 3)
plt.yticks([-2, 0, 2])
plt.savefig(os.path.join(results_dir, "locations_%d.pdf" % offset))
def plot_pred_ll_vs_time(models, results, burnin=0,
homog_ll=np.nan,
std_ll=np.nan,
nlin_ll=np.nan,
true_ll=np.nan,
baseline=0,
normalizer=1,
output_dir=".",
xlim=None, ylim=None,
title=None):
from hips.plotting.layout import create_figure, create_axis_at_location
from hips.plotting.colormaps import harvard_colors
# Make the ICML figure
fig = create_figure((3,2))
ax = create_axis_at_location(fig, 0.7, 0.4, 2.25, 1.35)
col = harvard_colors()
ax.grid()
t_start = 0
t_stop = 0
def standardize(x):
return (x-baseline)/normalizer
for i, (model, result) in enumerate(zip(models, results)):
ax.plot(result.timestamps[burnin:],
standardize(result.test_lls[burnin:]),
lw=2, color=col[i], label=model)
# Update time limits
t_start = min(t_start, result.timestamps[burnin:].min())
t_stop = max(t_stop, result.timestamps[burnin:].max())
if xlim is not None:
t_start = xlim[0]
t_stop = xlim[1]
# plt.legend(loc="outside right")
# Plot baselines
ax.plot([t_start, t_stop], standardize(homog_ll)*np.ones(2),
lw=2, color='k', label="Homog")
# Plot the standard Hawkes test ll
ax.plot([t_start, t_stop], standardize(std_ll)*np.ones(2),
lw=2, color=col[len(models)], label="Std.")
# Plot the Nonlinear Hawkes test ll
ax.plot([t_start, t_stop], standardize(nlin_ll)*np.ones(2),
lw=2, ls='--', color=col[len(models)+1], label="Nonlinear")
# Plot the true ll
ax.plot([t_start, t_stop], standardize(true_ll)*np.ones(2),
'--k', lw=2,label="True")
ax.set_xlabel("time [sec]")
ax.set_ylabel("Pred. LL. [nats/event]")
ax.set_xscale("log")
if xlim is not None:
ax.set_xlim(xlim)
else:
ax.set_xlim(t_start, t_stop)
if ylim is not None:
ax.set_ylim(ylim)
if title is not None:
ax.set_title(title)
output_file = os.path.join(output_dir, "pred_ll_vs_time.pdf")
fig.savefig(output_file)
plt.show()
def plot_census_results(train, samples, test, test_pis):
# Extract samp[les
train_mus = np.array([s[0] for s in samples])
train_psis = np.array([s[1][0][0] for s in samples])
# omegas = np.array([s[1][0][1] for s in samples])
# Adjust psis by the mean and compute the inferred pis
train_psis += train_mus[0][None,None,:]
train_pis = np.array([psi_to_pi(psi_sample) for psi_sample in train_psis])
train_pi_mean = np.mean(train_pis, axis=0)
train_pi_std = np.std(train_pis, axis=0)
# Compute test pi mean and std
test_pi_mean = np.mean(test_pis, axis=0)
test_pi_std = np.std(test_pis, axis=0)
# Compute empirical probabilities
train_pi_emp = train.data / train.data.sum(axis=1)[:,None]
test_pi_emp = test.data / test.data.sum(axis=1)[:,None]
# Plot the temporal trajectories for a few names
names = ["Scott", "Matthew", "Ethan"]
states = ["NY", "TX", "WA"]
linestyles = ["-", "--", ":"]
fig = create_figure(figsize=(3., 3))
ax1 = create_axis_at_location(fig, 0.6, 0.5, 2.25, 1.75)
for name, color in zip(names, colors):
for state, linestyle in zip(states, linestyles):
train_state_inds = (train.states == state)
train_name_ind = np.array(train.names) == name.lower()
train_years = train.years[train.states == state]
train_mean_name = train_pi_mean[train_state_inds, train_name_ind]
train_std_name = train_pi_std[train_state_inds, train_name_ind]
test_state_inds = (test.states == state)
test_name_ind = np.array(test.names) == name.lower()
test_years = test.years[test.states == state]
test_mean_name = test_pi_mean[test_state_inds, test_name_ind]
test_std_name = test_pi_std[test_state_inds, test_name_ind]
years = np.concatenate((train_years, test_years))
mean_name = np.concatenate((train_mean_name, test_mean_name))
std_name = np.concatenate((train_std_name, test_std_name))
# Sausage plot
sausage_plot(years, mean_name, std_name,
color=color, alpha=0.5)
# Plot inferred mean
plt.plot(years, mean_name,
color=color, label="%s, %s" % (name, state),
ls=linestyle, lw=2)
# Plot empirical probabilities
plt.plot(train.years[train_state_inds],
train_pi_emp[train_state_inds, train_name_ind],
color=color,
ls="", marker="x", markersize=4)
plt.plot(test.years[test_state_inds],
test_pi_emp[test_state_inds, test_name_ind],
color=color,
ls="", marker="x", markersize=4)
# Plot a vertical line to divide train and test
ylim = plt.gca().get_ylim()
plt.plot((test.years.min()-0.5) * np.ones(2), ylim, ':k', lw=0.5)
plt.ylim(ylim)
# plt.legend(loc="outside right")
plt.legend(bbox_to_anchor=(0., 1.05, 1., .105), loc=3,
ncol=len(names), mode="expand", borderaxespad=0.,
fontsize="x-small")
plt.xlabel("Year")
plt.xlim(train.years.min(), test.years.max()+0.1)
plt.ylabel("Probability")
# plt.tight_layout()
fig.savefig("census_gp_rates.pdf")
plt.show()
plt.pause(0.1)
def draw_mixture_figure(Ns, Ss, z, lmbda, filename="figure1.png", saveargs=dict(dpi=300)):
fig = create_figure((5.5, 2.7))
ax = create_axis_at_location(fig, .75, .5, 4., 1.375)
ymax = 105
# Plot the rates
for i in range(n):
ax.add_patch(Rectangle([i*D,0], D, lmbda[z[i]],
color=colors[z[i]], ec="none", alpha=0.5))
ax.plot([i*D, (i+1)*D], lmbda[z[i]] * np.ones(2), '-k', lw=2)
if i < n-1:
ax.plot([(i+1)*D, (i+1)*D], [lmbda[z[i]], lmbda[z[i+1]]], '-k', lw=2)
# Plot boundaries
ax.plot([(i+1)*D, (i+1)*D], [0, ymax], ':k', lw=1)
# Plot x axis
plt.plot([0,T], [0,0], '-k', lw=2)
# Plot spike times
for s in Ss:
plt.plot([s,s], [0,60], '-ko', markerfacecolor='k', markersize=5)
plt.xlabel("time [ms]")
plt.ylabel("firing rate [Hz]")
plt.xlim(0,T)
plt.ylim(-5,ymax)
## Now plot the spike count above
ax = create_axis_at_location(fig, .75, 2., 4., .25)
for i in xrange(n):
# Plot boundaries
ax.plot([(i+1)*D, (i+1)*D], [0, 10], '-k', lw=1)
ax.text(i*D + D/3.5, 3, "%d" % Ns[i], fontdict={"size":9})
ax.set_xlim(0,T)
ax.set_ylim(0,10)
ax.set_xticks([])
ax.set_yticks([])
ax.yaxis.labelpad = 30
ax.set_ylabel("${s_t}$", rotation=0, verticalalignment='center')
## Now plot the latent state above that above
ax = create_axis_at_location(fig, .75, 2.375, 4., .25)
for i in xrange(n):
# Plot boundaries
ax.add_patch(Rectangle([i*D,0], D, 10,
color=colors[z[i]], ec="none", alpha=0.5))
ax.plot([(i+1)*D, (i+1)*D], [0, 10], '-k', lw=1)
ax.text(i*D + D/3.5, 3, "u" if z[i]==0 else "d", fontdict={"size":9})
ax.set_xlim(0,T)
ax.set_ylim(0,10)
ax.set_xticks([])
ax.set_yticks([])
ax.yaxis.labelpad = 30
ax.set_ylabel("${z_t}$", rotation=0, verticalalignment='center')
#fig.savefig(filename + ".pdf")
fig.savefig(filename, **saveargs)
plt.close(fig)
def draw_mixture_figure(Ns,
Ss,
z,
lmbda,
filename="figure1.png",
saveargs=dict(dpi=300)):
fig = create_figure((5.5, 2.7))
ax = create_axis_at_location(fig, .75, .5, 4., 1.375)
ymax = 105
# Plot the rates
for i in range(n):
ax.add_patch(
Rectangle([i * D, 0],
D,
lmbda[z[i]],
color=colors[z[i]],
ec="none",
alpha=0.5))
ax.plot([i * D, (i + 1) * D], lmbda[z[i]] * np.ones(2), '-k', lw=2)
if i < n - 1:
ax.plot([(i + 1) * D, (i + 1) * D], [lmbda[z[i]], lmbda[z[i + 1]]],
'-k',
lw=2)
# Plot boundaries
ax.plot([(i + 1) * D, (i + 1) * D], [0, ymax], ':k', lw=1)
# Plot x axis
plt.plot([0, T], [0, 0], '-k', lw=2)
# Plot spike times
for s in Ss:
plt.plot([s, s], [0, 60], '-ko', markerfacecolor='k', markersize=5)
plt.xlabel("time [ms]")
plt.ylabel("firing rate [Hz]")
plt.xlim(0, T)
plt.ylim(-5, ymax)
## Now plot the spike count above
ax = create_axis_at_location(fig, .75, 2., 4., .25)
for i in xrange(n):
# Plot boundaries
ax.plot([(i + 1) * D, (i + 1) * D], [0, 10], '-k', lw=1)
ax.text(i * D + D / 3.5, 3, "%d" % Ns[i], fontdict={"size": 9})
ax.set_xlim(0, T)
ax.set_ylim(0, 10)
ax.set_xticks([])
ax.set_yticks([])
ax.yaxis.labelpad = 30
ax.set_ylabel("${s_t}$", rotation=0, verticalalignment='center')
## Now plot the latent state above that above
ax = create_axis_at_location(fig, .75, 2.375, 4., .25)
for i in xrange(n):
# Plot boundaries
ax.add_patch(
Rectangle([i * D, 0],
D,
10,
color=colors[z[i]],
ec="none",
alpha=0.5))
ax.plot([(i + 1) * D, (i + 1) * D], [0, 10], '-k', lw=1)
ax.text(i * D + D / 3.5,
3,
"u" if z[i] == 0 else "d",
fontdict={"size": 9})
ax.set_xlim(0, T)
ax.set_ylim(0, 10)
ax.set_xticks([])
ax.set_yticks([])
ax.yaxis.labelpad = 30
ax.set_ylabel("${z_t}$", rotation=0, verticalalignment='center')
#fig.savefig(filename + ".pdf")
fig.savefig(filename, **saveargs)
plt.close(fig)
def plot_census_results(train, samples, test, test_pis):
# Extract samp[les
train_mus = np.array([s[0] for s in samples])
train_psis = np.array([s[1][0][0] for s in samples])
# omegas = np.array([s[1][0][1] for s in samples])
# Adjust psis by the mean and compute the inferred pis
train_psis += train_mus[0][None,None,:]
train_pis = np.array([psi_to_pi(psi_sample) for psi_sample in train_psis])
train_pi_mean = np.mean(train_pis, axis=0)
train_pi_std = np.std(train_pis, axis=0)
# Compute test pi mean and std
test_pi_mean = np.mean(test_pis, axis=0)
test_pi_std = np.std(test_pis, axis=0)
# Compute empirical probabilities
train_pi_emp = train.data / train.data.sum(axis=1)[:,None]
test_pi_emp = test.data / test.data.sum(axis=1)[:,None]
# Plot the temporal trajectories for a few names
names = ["Scott", "Matthew", "Ethan"]
states = ["NY", "TX", "WA"]
linestyles = ["-", "--", ":"]
fig = create_figure(figsize=(3., 3))
ax1 = create_axis_at_location(fig, 0.6, 0.5, 2.25, 1.75)
for name, color in zip(names, colors):
for state, linestyle in zip(states, linestyles):
train_state_inds = (train.states == state)
train_name_ind = np.array(train.names) == name.lower()
train_years = train.years[train.states == state]
train_mean_name = train_pi_mean[train_state_inds, train_name_ind]
train_std_name = train_pi_std[train_state_inds, train_name_ind]
test_state_inds = (test.states == state)
test_name_ind = np.array(test.names) == name.lower()
test_years = test.years[test.states == state]
test_mean_name = test_pi_mean[test_state_inds, test_name_ind]
test_std_name = test_pi_std[test_state_inds, test_name_ind]
years = np.concatenate((train_years, test_years))
mean_name = np.concatenate((train_mean_name, test_mean_name))
std_name = np.concatenate((train_std_name, test_std_name))
# Sausage plot
sausage_plot(years, mean_name, std_name,
color=color, alpha=0.5)
# Plot inferred mean
plt.plot(years, mean_name,
color=color, label="%s, %s" % (name, state),
ls=linestyle, lw=2)
# Plot empirical probabilities
plt.plot(train.years[train_state_inds],
train_pi_emp[train_state_inds, train_name_ind],
color=color,
ls="", marker="x", markersize=4)
plt.plot(test.years[test_state_inds],
test_pi_emp[test_state_inds, test_name_ind],
color=color,
ls="", marker="x", markersize=4)
# Plot a vertical line to divide train and test
ylim = plt.gca().get_ylim()
plt.plot((test.years.min()-0.5) * np.ones(2), ylim, ':k', lw=0.5)
plt.ylim(ylim)
# plt.legend(loc="outside right")
plt.legend(bbox_to_anchor=(0., 1.05, 1., .105), loc=3,
ncol=len(names), mode="expand", borderaxespad=0.,
fontsize="x-small")
plt.xlabel("Year")
plt.xlim(train.years.min(), test.years.max()+0.1)
plt.ylabel("Probability")
# plt.tight_layout()
fig.savefig("census_gp_rates.pdf")
plt.show()
plt.pause(0.1)
