def generate_emissions(theta,data):
def _eigsorted(cov):
vals,vecs = np.linalg.eigh(cov)
order = vals.argsort()[::-1]
return vals[order],vecs[:,order]
xmin = np.inf; xmax = -np.inf
ymin = np.inf; ymax = -np.inf
fig = plt.figure()
ax = fig.add_subplot(111)
for i in range(len(data)):
ax.plot(data[i][:,0],data[i][:,1],'k.')
xmin = np.min([xmin,np.min(data[i][:,0])])
xmax = np.max([xmax,np.max(data[i][:,0])])
ymin = np.min([ymin,np.min(data[i][:,1])])
ymax = np.max([ymax,np.max(data[i][:,1])])
vol = [0.25,0.5,0.75,0.95, 0.99]
ell = []
for i in range(len(theta)):
pos = theta[i].mean
vals,vecs = _eigsorted(theta[i].var)
th = np.degrees(np.arctan2(*vecs[:,0][::-1]))
for v in vol:
width,height = 2.0*np.sqrt(chi2.ppf(v,2))*np.sqrt(vals)
ell.append(Ellipse(xy=pos,width=width,height=height,angle=th))
for i,e in enumerate(ell):
ax.add_artist(e)
e.set_facecolor(my_color_map(i))
e.set_alpha(0.5)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
plt.show()
def generate_timeseriesplot(data,truestates):
NumTS = len(data)
for i in range(NumTS):
plt.subplot(NumTS,1,i+1)
plt.plot(data[i])
y1 = data[i].min()
y2 = data[i].max()
count = 0
for j in range(len(truestates[i])):
s = truestates[i]
plt.fill_between(range(count,count+s[j].NumTimePoints), y1, y2, facecolor=my_color_map(s[j].StateNumber))
count += s[j].NumTimePoints + 1
plt.axis('off')
plt.show()
def generate_timeseriesplot(data, truestates):
NumTS = len(data)
for i in range(NumTS):
plt.subplot(NumTS, 1, i + 1)
plt.plot(data[i])
y1 = data[i].min()
y2 = data[i].max()
count = 0
for j in range(len(truestates[i])):
s = truestates[i]
plt.fill_between(range(count, count + s[j].NumTimePoints),
y1,
y2,
facecolor=my_color_map(s[j].StateNumber))
count += s[j].NumTimePoints + 1
plt.axis('off')
plt.show()
def generate_emissions(theta, data):
def _eigsorted(cov):
vals, vecs = np.linalg.eigh(cov)
order = vals.argsort()[::-1]
return vals[order], vecs[:, order]
xmin = np.inf
xmax = -np.inf
ymin = np.inf
ymax = -np.inf
fig = plt.figure()
ax = fig.add_subplot(111)
for i in range(len(data)):
ax.plot(data[i][:, 0], data[i][:, 1], 'k.')
xmin = np.min([xmin, np.min(data[i][:, 0])])
xmax = np.max([xmax, np.max(data[i][:, 0])])
ymin = np.min([ymin, np.min(data[i][:, 1])])
ymax = np.max([ymax, np.max(data[i][:, 1])])
vol = [0.25, 0.5, 0.75, 0.95, 0.99]
ell = []
for i in range(len(theta)):
pos = theta[i].mean
vals, vecs = _eigsorted(theta[i].var)
th = np.degrees(np.arctan2(*vecs[:, 0][::-1]))
for v in vol:
width, height = 2.0 * np.sqrt(chi2.ppf(v, 2)) * np.sqrt(vals)
ell.append(Ellipse(xy=pos, width=width, height=height, angle=th))
for i, e in enumerate(ell):
ax.add_artist(e)
e.set_facecolor(my_color_map(i))
e.set_alpha(0.5)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
plt.show()
