def plot_dimension_histograms_VMM_GMM(self,pub_fig = 0, filename='plot_dim_hist.pdf',specific_dimensions = None, extra_txt_labels = '', label_loc = [-2,1.5], ylim = None):
'''For each dimension in the data set, plot the histogram of the real and imag part of the measurements
overlay the GMM's - used for the GMM-GMM clustering method
SRH: 18May2014
'''
suptitle = self.settings.__str__().replace("'",'').replace("{",'').replace("}",'')
cluster_GMM_mu = self.cluster_details['EM_GMM_means']
cluster_GMM_sigma = self.cluster_details['EM_GMM_std']
dimensions = cluster_GMM_mu.shape[1]
instance_array_amps = self.feature_obj.misc_data_dict['mirnov_data']
tmp = np.zeros((instance_array_amps.shape[0], instance_array_amps.shape[1]-1),dtype=complex)
for i in range(1,instance_array_amps.shape[1]): tmp[:,i-1] = instance_array_amps[:,i]/instance_array_amps[:,i-1]
if specific_dimensions == None: specific_dimensions = range(dimensions)
fig_ang, ax_ang = make_grid_subplots(len(specific_dimensions), sharex = True, sharey = True)
fig_abs, ax_abs = make_grid_subplots(len(specific_dimensions), sharex = True, sharey = True)
amp_vals = np.abs(tmp)
amp_vals[np.angle(tmp)<0]*= (-1)
for i,dim in enumerate(specific_dimensions):
ax_abs[i].hist(amp_vals[:,dim], bins=180,normed=True,histtype='stepfilled',range=[-np.pi,np.pi])
ax_ang[i].hist(np.angle(tmp[:,dim]), bins=180,normed=True,histtype='stepfilled',range=[-np.pi,np.pi])
if self.cluster_assignments!=None: cluster_list = list(set(self.cluster_assignments))
x = np.linspace(-np.pi, np.pi, 300)
cluster_prob_list = []
for cluster in cluster_list:
cluster_prob_list.append(float(np.sum(self.cluster_assignments==cluster))/float(len(self.cluster_assignments)))
for i, dimension in enumerate(specific_dimensions):
#for ax_cur, op in zip([ax_re,ax_im],[np.real, np.imag]):
#for ax_cur, op in zip([ax_ang,ax_abs],[np.angle, np.abs]):
for ax_cur, op in zip([ax_abs],[np.abs]):
cluster_sum = x*0
for cluster, cluster_prob in zip(cluster_list, cluster_prob_list):
Z_EM = cluster_prob * norm(loc=cluster_GMM_mu[cluster][dimension], scale=cluster_GMM_sigma[cluster][dimension]).pdf(x)
cluster_sum += Z_EM
tmp = ax_cur[i].plot(x,Z_EM,'-',linewidth=0.8)
tmp = ax_cur[i].plot(x,cluster_sum,'-',linewidth=2)
print '{area},'.format(area = np.sum(cluster_sum*(x[1]-x[0]))),
ax_cur[i].text(label_loc[0], label_loc[1],r'$\Delta \psi_%d$ '%(dimension+1,) + extra_txt_labels, fontsize = 8)#,bbox=dict(facecolor='white', alpha=0.5))
ax_cur[i].locator_params(nbins=7)
print ''
#for ax_cur, fig_cur in zip([ax_re, ax_im],[fig_re, fig_im]):
for ax_cur, fig_cur in zip([ax_ang, ax_abs],[fig_ang, fig_abs]):
ax_cur[-1].set_xlim([-np.pi,np.pi])
ax_cur[-1].set_ylim([0,1.3])
fig_cur.subplots_adjust(hspace=0, wspace=0,left=0.05, bottom=0.05,top=0.95, right=0.95)
fig_cur.suptitle(suptitle.replace('_','\char`_'),fontsize = 8)
fig_cur.canvas.draw(); fig_cur.show()
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