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util_plots.py
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import numpy as np
from pyhsmm.util.general import relabel_by_permutation, rle
from math import ceil
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from matplotlib.colors import ListedColormap
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib import rc
matplotlib.rcParams['text.usetex'] = True
matplotlib.rcParams['text.latex.unicode'] = True
def relabel_model_z(model, index=0, plot_en=False):
"""
Relabel state from model by permutation
"""
Nmax = model.num_states
perm = np.argsort(model.state_usages)[::-1]
z = relabel_by_permutation(model.states_list[index].stateseq,
np.argsort(perm))
if plot_en:
plt.bar(np.arange(Nmax), np.bincount(z, minlength=Nmax))
plt.show()
return z , perm
def plot_pcs_slice(self,data_in,large_slice,plot_slice=None,
num_pcs=4,indiv=0, color_array=None,fs=30,sz=4):
"""
Plot PC slices and states
"""
if color_array == None:
color_array = self._get_colors()
# Plot params
fig = plt.figure(figsize=(sz,6))
gs = GridSpec(sz+len(self.states_list),1)
feature_ax = plt.subplot(gs[:sz,:])
data_in = data_in[:num_pcs,large_slice][::-1,:]
max_ = ceil(data_in.max()-data_in.min()) + 1
ttime = np.arange(data_in.shape[1])
for ii in range(0,num_pcs):
feature_ax.plot(ttime,data_in[ii,:]+ii*max_,'k')
feature_ax.set_yticks(np.arange(num_pcs)*max_)
feature_ax.set_yticklabels('')
feature_ax.set_ylim((data_in.min()-1,num_pcs*max_-1))
xlabel_= np.linspace(0,data_in.shape[1],5,dtype='int')
feature_ax.set_xticks(xlabel_)
feature_ax.set_xlim((xlabel_[0],xlabel_[-1]))
feature_ax.set_xticklabels(list(map(str,xlabel_ // fs)))
if not (plot_slice is None):
feature_ax.axvline(plot_slice[0], color=color_array[0],linestyle=':',lw=2)
feature_ax.axvline(plot_slice[-1], color=color_array[0],linestyle=':',lw=2)
plot_pcs_slice_sub(self,data_in,large_slice,plot_slice,indiv,color_array)
return
def plot_pcs_slice_sub(self,data_in,large_slice,plot_slice,
indiv=0,color_array=None,sz=8):
"""
Plot short PC slice
"""
fig = plt.figure(figsize=(sz,6))
gs = GridSpec(sz+len(self.states_list),1)
feature_ax = plt.subplot(gs[:sz,:])
stateseq_ax = plt.subplot(gs[sz+1])
if color_array is None:
color_array = self._get_colors()
r_plot_slice = list(map(lambda x: large_slice[0] + x, plot_slice))
z, perm = relabel_model_z(self,index=indiv)
z = z[r_plot_slice]
stateseq_norep, durations = rle(z)
max_ = ceil(data_in.max()-data_in.min()) +1
data_in=data_in[:,plot_slice]
ttime = np.arange(data_in.shape[1])
for ii in range(0,data_in.shape[0]):
feature_ax.plot(ttime,data_in[ii,:] + ii*max_,'k')
feature_ax.set_xlim((0,len(plot_slice)))
feature_ax.set_ylim((data_in.min()-1,data_in.shape[0]*max_-1))
feature_ax.set_yticks([])
feature_ax.set_xticks([])
stateseq_ax.imshow(z[:,np.newaxis].T,aspect='auto',
cmap=ListedColormap(color_array),vmin=0,vmax=len(perm))
stateseq_ax.set_yticks([])
stateseq_ax.set_xticks([])
for ii, pos in enumerate(durations.cumsum()):
if durations[ii] >=1:
feature_ax.axvline(pos,
color=color_array[stateseq_norep[ii]],
linestyle=':')
return
def state_correlation(z1,z2):
"""
Calculate state correlation
"""
dim1, dim2= z1.max()+1, z2.max()+1
fig = plt.figure(figsize=(dim1,dim2))
C = np.zeros(shape=(dim1,dim2))
for i in np.arange(dim1):
for j in np.arange(dim2):
C[i,j]=np.logical_and(z1==i,z2==j).sum()
C_pr = C.copy()
for col in np.arange(dim2):
if C[:,col].sum()!=0:
C_pr[:,col]=C[:,col]/C[:,col].sum()
avg_ind = np.sum(C_pr * np.arange(dim1)[:,None], axis=0)
perm = np.argsort(avg_ind)
ends_ = (avg_ind==0).sum()
new_l = perm.copy()
new_l[0:dim2-ends_] = perm[ends_:]
new_l[dim2-ends_:] = perm[0:ends_]
ax = fig.add_subplot(111)
Cpr = C_pr[:,new_l]
im = ax.imshow(Cpr,cmap='Reds',
vmin=Cpr.min(),vmax=Cpr.max())
ax.set_xticks([])
ax.set_xticklabels([])
ax.set_yticks([])
ax.set_yticklabels([])
ax.set_ylabel(r'$K = %d$'%dim1)
ax.set_xlabel(r'$K = %d$'%dim2)
divider = make_axes_locatable(ax)
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom", size="5%", pad=0.5)
plt.colorbar(im, cax=cax, orientation="horizontal",
spacing='uniform', format='%.2f',
ticks=np.linspace(Cpr.min(),Cpr.max(),5))
plt.tight_layout()
plt.show()
return