def update(ii):
for kk in range(D):
if transpose_flags[kk]==False:
im_array[kk*D].set_data(max_intensity(denoised_data[ii],spatial_dims_ind[kk]))
im_array[kk*D+1].set_data(data[ii].max(spatial_dims_ind[kk]))
im_array[kk*D+2].set_data(residual[ii].max(spatial_dims_ind[kk]))
else:
im_array[kk*D].set_data(np.transpose(max_intensity(denoised_data[ii],spatial_dims_ind[kk]),[1,0,2]))
im_array[kk*D+1].set_data(np.transpose(data[ii].max(spatial_dims_ind[kk])))
im_array[kk*D+2].set_data(np.transpose(residual[ii].max(spatial_dims_ind[kk])))
title.set_text('Data, time = %.1f' % ii)
def update(ii):
for kk in range(D):
if transpose_flags[kk] == False:
im_array[kk * D].set_data(
max_intensity(denoised_data[ii], spatial_dims_ind[kk]))
im_array[kk * D + 1].set_data(data[ii].max(
spatial_dims_ind[kk]))
im_array[kk * D + 2].set_data(residual[ii].max(
spatial_dims_ind[kk]))
else:
im_array[kk * D].set_data(
np.transpose(
max_intensity(denoised_data[ii],
spatial_dims_ind[kk]), [1, 0, 2]))
im_array[kk * D + 1].set_data(
np.transpose(data[ii].max(spatial_dims_ind[kk])))
im_array[kk * D + 2].set_data(
np.transpose(residual[ii].max(spatial_dims_ind[kk])))
title.set_text('Data, time = %.1f' % ii)
def PlotAll(SaveNames,params):
from numpy import min, max, percentile,asarray,ceil,sqrt
import numpy as np
import sys
from scipy.signal import welch
from pylab import load
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.backends.backend_pdf import PdfPages
# from scipy.ndimage.measurements import label
from AuxilaryFunctions import GetRandColors, max_intensity,SuperVoxelize,GetData,PruneComponents,SplitComponents,ThresholdShapes,MergeComponents,ThresholdData,make_sure_path_exists
# from BlockLocalNMF_AuxilaryFunctions import HALS4activity
from mpl_toolkits.axes_grid1 import make_axes_locatable
# makse sure relevant folders exist, and add to path
Results_folder='Results/'
make_sure_path_exists(Results_folder)
OASIS_path='OASIS/'
make_sure_path_exists(OASIS_path)
sys.path.append(OASIS_path)
from functions import deconvolve
## plotting params
# what to plot
plot_activities=True
plot_activities_PSD=False
plot_shapes_projections=True
plot_shapes_slices=False
plot_activityCorrs=False
plot_clustered_shape=False
plot_residual_slices=False
plot_residual_projections=False
# videos to generate
video_shapes=False
video_residual=True
video_slices=False
# what to save
save_video=True
save_plot=True
close_figs=True#close all figs right after saving (to avoid memory overload)
# PostProcessing
Split=False
Threshold=False #threshold shapes in the end and keep only connected components
Prune=False # Remove "Bad" components (where bad is defined within SplitComponent fucntion)
Merge=True # Merge highly correlated nearby components
FineTune=False # SHould we fine tune activity after post-processing? (mainly after merging)
IncludeBackground=False #should we include the background as an extracted component?
# how to plot
detrend=True #should we detrend the data (remove background component)?
scale=2 #scale colormap to enhance colors
satuartion_percentile=96 #saturate colormap ont this percentile, when ma=percentile is used
dpi=200 #for videos
restrict_support=True #in shape video, zero out data outside support of shapes
C=4 #number of components to show in shape videos (if larger then number of shapes L, then we automatically set C=L)
color_map='gray' #'gnuplot'
frame_rate=10.0 #Hz
# Fetch experimental 3D data
data=GetData(params.data_name)
if params.SuperVoxelize==True:
data=SuperVoxelize(data)
if params.ThresholdData==True:
data=ThresholdData(data)
dims=np.shape(data)
if len(dims)<4:
plot_Shapes2D=False
video_residual_2D=False
if plot_shapes_projections or plot_shapes_slices:
plot_Shapes2D=True
if video_residual==True:
video_residual_2D=True
plot_shapes_projections=False
plot_shapes_slices=False
plot_activityCorrs=False
plot_clustered_shape=False
plot_residual_slices=False
plot_residual_projections=False
video_shapes=False
video_residual=False
video_slices=False
print('2D data, ignoring 3D plots/video options')
else:
plot_Shapes2D=False
video_residual_2D=False
min_dim=np.argmin(dims[1:])
denoised_data=0
detrended_data=data
for rep in range(len(SaveNames)):
resultsName=SaveNames[rep]
try:
results=load('NMF_Results/'+SaveNames[rep])
except IOError:
if rep==0:
print('results file not found!!')
else:
break
SS=results['shapes']
AA=results['activity']
if rep>=params.Background_num:
adaptBias=False
else:
adaptBias=True
if IncludeBackground==True:
adaptBias=False
L=len(AA)-adaptBias
if L==0: #Stop if we encounter a file with zero components
break
S=SS[:-adaptBias]
b=SS[L:(L+adaptBias)]
A=AA[:-adaptBias]
f=AA[L:(L+adaptBias)]
if rep==0:
shapes=S
activity=A
background_shapes=b
background_activity=f
else:
shapes=np.append(shapes,S,axis=0)
activity=np.append(activity,A,axis=0)
background_shapes=np.append(background_shapes,b,axis=0)
background_activity=np.append(background_activity,f,axis=0)
L=len(shapes)
adaptBias=0
if Split==True:
shapes,activity,L,all_local_max=SplitComponents(shapes,activity,adaptBias)
if Merge==True:
shapes,activity,L=MergeComponents(shapes,activity,L,threshold=0.7,sig=10)
if Prune==True:
# deleted_indices=[5,9,11,14,15,17,24]+range(25,36)
shapes,activity,L=PruneComponents(shapes,activity,L,params.TargetAreaRatio)
activity_NonNegative=np.copy(activity)
activity_NonNegative[activity_NonNegative<0]=0
activity_noisy=np.copy(activity_NonNegative)
if FineTune==True:
for ll in range(L):
activity[ll], spikes, baseline, g, lam = deconvolve(activity_NonNegative[ll],optimize_g=10,penalty=0)
# activity,background_activity,S,bl,c1,sn,g,junk = update_temporal_components(data.reshape((len(data),-1)).transpose(), shapes.reshape((len(shapes),-1)).transpose(), background_shapes.reshape((len(background_shapes),-1)).transpose(), activity,background_activity,**options['temporal_params'])
activity_noisy=np.copy(activity_NonNegative)
activity_NonNegative=activity
print(str(L)+' shapes detected')
detrended_data= detrended_data - background_activity.T.dot(background_shapes.reshape((len(background_shapes), -1))).reshape(dims)
if Threshold==True:
shapes=ThresholdShapes(shapes,adaptBias,[],MaxRatio=[])
if plot_residual_projections or video_shapes or video_residual or video_slices or video_residual_2D:
colors=GetRandColors(L)
color_shapes=np.transpose(shapes.reshape(L, -1,1)*colors,[1,0,2]) #weird transpose for tensor dot product next line
denoised_data = denoised_data + (activity_NonNegative.T.dot(color_shapes)).reshape(tuple(dims)+(3,))
residual = detrended_data - activity_NonNegative.T.dot(shapes.reshape(L, -1)).reshape(dims)
if detrend==True:
data=detrended_data
#%% After loading loop - Normalize (colored) denoised data
if plot_residual_projections or video_shapes or video_residual or video_slices or video_residual_2D:
# denoised_data=denoised_data/np.max(denoised_data)
denoised_data=old_div(denoised_data,np.percentile(denoised_data[denoised_data>0],99.5)) #%% normalize denoised data range
denoised_data[denoised_data>1]=1
# plt.close('all')
#%% plotting params
ComponentsInFig=20
left = 0.05 # the left side of the subplots of the figure
right = 0.95 # the right side of the subplots of the figure
bottom = 0.05 # the bottom of the subplots of the figure
top = 0.95 # the top of the subplots of the figure
wspace = 0.1 # the amount of width reserved for blank space between subplots
hspace = 0.12 # the amount of height reserved for white space between subplots
#%% ###### Plot Individual neurons' activities
index=0 #component display index
sz=np.min([ComponentsInFig,L+adaptBias])
# a=ceil(sqrt(sz))
# b=ceil(sz/a)
a=sz
b=1
if plot_activities:
pp = PdfPages(Results_folder + 'Activities'+resultsName+'.pdf')
for ii in range(L+adaptBias):
if index==0:
# fig0=plt.figure(figsize=(dims[1] , dims[2]))
fig0=plt.figure(figsize=(11,18))
ax = plt.subplot(a,b,index+1)
# dt=1/30 # 30 Hz sample rate
time=list(range(len(activity[ii])))
plt.plot(time,activity_noisy[ii],linewidth=0.5,c='r')
plt.plot(time,activity[ii],linewidth=3,c='b')
ma=np.max([np.max(activity[ii]),np.max(activity_noisy[ii])])
plt.setp(ax, xticks=[],yticks=[0,ma])
# component number
ax.text(0.02, 0.8, str(ii),
verticalalignment='bottom', horizontalalignment='left',
transform=ax.transAxes,
color='black',weight='bold', fontsize=13)
index+=1
if ((ii%ComponentsInFig)==(ComponentsInFig-1)) or ii==(L+adaptBias-1):
index=0
if save_plot==True:
plt.subplots_adjust(left*2, bottom, right, top, wspace, hspace*2)
pp.savefig(fig0)
pp.close()
if close_figs:
plt.close('all')
#%% Plot activities` PSDs
index=0 #component display index
sz=np.min([ComponentsInFig,L+adaptBias])
a=ceil(sqrt(sz))
b=ceil(old_div(sz,a))
if plot_activities_PSD:
pp = PdfPages(Results_folder + 'ActivityPSDs'+resultsName+'.pdf')
for ii in range(L+adaptBias):
if index==0:
# fig0=plt.figure(figsize=(dims[1] , dims[2]))
fig0=plt.figure(figsize=(11,18))
ax = plt.subplot(a,b,index+1)
ff, psd_activity = welch(activity[ii], nperseg=round(old_div(len(activity[ii]), 64)))
plt.plot(ff,psd_activity,linewidth=3)
plt.setp(ax, xticks=[],yticks=[0])
# component number
ax.text(0.02, 0.8, str(ii),
verticalalignment='bottom', horizontalalignment='left',
transform=ax.transAxes,
color='black',weight='bold', fontsize=13)
index+=1
if ((ii%ComponentsInFig)==(ComponentsInFig-1)) or ii==(L+adaptBias-1):
index=0
if save_plot==True:
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
pp.savefig(fig0)
pp.close()
if close_figs:
plt.close('all')
#%% 2D shapes
index=0 #component display index
sz=np.min([ComponentsInFig,L+adaptBias])
a=ceil(0.5*sqrt(sz))
b=ceil(old_div(sz,a))
if plot_Shapes2D:
if save_plot==True:
pp = PdfPages(Results_folder + 'Shapes2D_'+resultsName+'.pdf')
for ll in range(L+adaptBias):
if index==0:
fig=plt.figure(figsize=(18 , 11))
ax = plt.subplot(a,b,index+1)
temp=shapes[ll]
mi=0
try:
ma=np.percentile(temp[temp>0],satuartion_percentile)
except IndexError:
ma=0
im=plt.imshow(temp,vmin=mi,vmax=ma,cmap=color_map)
plt.setp(ax,xticks=[],yticks=[])
mn=int(np.floor(mi)) # colorbar min value
mx=int(np.ceil(ma)) # colorbar max value
md=old_div((mx-mn),2)
# divider = make_axes_locatable(ax)
# cax = divider.append_axes("right", size="5%", pad=0.05)
# cb=plt.colorbar(im,cax=cax)
# cb.set_ticks([mn,md,mx])
# cb.set_ticklabels([mn,md,mx])
# component number
ax.text(0.02, 0.8, str(ll),
verticalalignment='bottom', horizontalalignment='left',
transform=ax.transAxes,
color='white',weight='bold', fontsize=13)
#sparsity
spar_str=str(np.round(np.mean(shapes[ll]>0)*100,2))+'%'
ax.text(0.02, 0.02, spar_str,
verticalalignment='bottom', horizontalalignment='left',
transform=ax.transAxes,
color='white',weight='bold', fontsize=13)
#L^p
for p in range(2,6,2):
Lp=old_div((np.sum(shapes[ll]**p))**(old_div(1,float(p))),np.sum(shapes[ll]))
Lp_str=str(np.round(Lp*100,2))+'%' #'L'+str(p)+'='+
ax.text(0.02+p*0.2, 0.02, Lp_str,
verticalalignment='bottom', horizontalalignment='left',
transform=ax.transAxes,
color='yellow',weight='bold', fontsize=13)
index+=1
if (ll%ComponentsInFig==(ComponentsInFig-1)) or ll==L+adaptBias-1:
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
index=0
if save_plot==True:
pp.savefig(fig)
pp.close()
if close_figs:
plt.close('all')
#%% Re-write plot code from here, so that each figure has only ComponentsInFig components
#%% ###### Plot Individual neurons' area which is correlated with their activities
a=ceil(sqrt(L+adaptBias))
b=ceil(old_div((L+adaptBias),a))
if plot_activityCorrs:
if save_plot==True:
pp = PdfPages(Results_folder + 'CorrelationWithActivity'+resultsName+'.pdf')
for dd in range(len(shapes[0].shape)):
fig0=plt.figure(figsize=(11,18))
for ii in range(L+adaptBias):
ax = plt.subplot(a,b,ii+1)
corr_imag=old_div(np.dot(activity[ii],np.transpose(data,[1,2,0,3])),np.sqrt(np.sum(data**2,axis=0)*np.sum(activity[ii]**2)))
plt.imshow(np.abs(corr_imag).max(dd),cmap=color_map)
plt.setp(ax,xticks=[],yticks=[])
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot==True:
pp.savefig(fig0)
pp.close()
if close_figs:
plt.close('all')
#%% All Shapes projections
a=ceil(sqrt(L+adaptBias))
b=ceil(old_div((L+adaptBias),a))
if plot_shapes_projections:
if save_plot==True:
pp = PdfPages(Results_folder + 'Shapes_projections'+resultsName+'.pdf')
for dd in range(len(shapes[0].shape)):
fig=plt.figure(figsize=(18 , 11))
for ll in range(L+adaptBias):
ax = plt.subplot(a,b,ll+1)
temp=shapes[ll].max(dd)
if dd==2:
temp=temp.T
mi=np.min(shapes[ll])
ma=np.max(shapes[ll])
im=plt.imshow(temp,vmin=mi,vmax=ma,cmap=color_map)
plt.setp(ax,xticks=[],yticks=[])
mn=int(np.floor(mi)) # colorbar min value
mx=int(np.ceil(ma)) # colorbar max value
md=old_div((mx-mn),2)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb=plt.colorbar(im,cax=cax)
# cb.set_ticks([mn,md,mx])
# cb.set_ticklabels([mn,md,mx])
# component number
ax.text(0.02, 0.8, str(ll),
verticalalignment='bottom', horizontalalignment='left',
transform=ax.transAxes,
color='white',weight='bold', fontsize=13)
# #sparsity
spar_str=str(np.round(np.mean(shapes[ll]>0)*100,2))+'%'
ax.text(0.02, 0.02, spar_str,
verticalalignment='bottom', horizontalalignment='left',
transform=ax.transAxes,
color='white',weight='bold', fontsize=13)
# #L^p
# for p in range(2,2,2):
# Lp=(np.sum(shapes[ll]**p))**(1/float(p))/np.sum(shapes[ll])
# Lp_str=str(np.round(Lp*100,2))+'%' #'L'+str(p)+'='+
# ax.text(0.02+p*0.2, 0.02, Lp_str,
# verticalalignment='bottom', horizontalalignment='left',
# transform=ax.transAxes,
# color='yellow',weight='bold', fontsize=13)
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot==True:
pp.savefig(fig)
pp.close()
if close_figs:
plt.close('all')
#for ll in range(L+adaptBias):
# print 'Sparsity=',np.mean(shapes[ll]>0)
#%% All Shapes slices
transpose_shape= True # should we transpose shape
ComponentsInFig=3 # number of components in Figure
index=0 #component display index
# z_slices=[0,1,2,3,4,5,6,7,8] #which z slices to look at slice plots/videos
z_slices=list(range(dims[min_dim+1])) #which z slices to look at slice plots/videos
if plot_shapes_slices:
if save_plot==True:
pp = PdfPages(Results_folder + 'Shapes_slices'+resultsName+'.pdf')
for ll in range(L+adaptBias):
if index==0:
fig=plt.figure(figsize=(18, 11))
for dd in range(len(z_slices)):
ax = plt.subplot(ComponentsInFig,len(z_slices),index*len(z_slices)+dd+1)
temp=shapes[ll].take(dd,axis=min_dim)
if transpose_shape:
temp=np.transpose(temp)
mi=np.min(shapes[ll])
ma=np.max(shapes[ll])
im=plt.imshow(temp,vmin=mi,vmax=ma,cmap=color_map)
plt.setp(ax,xticks=[],yticks=[])
if dd==0:
# component number
ax.text(0.02, 0.8, str(ll),
verticalalignment='bottom', horizontalalignment='left',
transform=ax.transAxes,
color='white',weight='bold', fontsize=13)
#sparsity
spar_str=str(np.round(np.mean(shapes[ll]>0)*100,2))+'%'
ax.text(0.02, 0.02, spar_str,
verticalalignment='bottom', horizontalalignment='left',
transform=ax.transAxes,
color='white',weight='bold', fontsize=13)
mn=int(np.floor(mi)) # colorbar min value
mx=int(np.ceil(ma)) # colorbar max value
md=old_div((mx-mn),2)
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom", size="5%", pad=0.05)
cb=plt.colorbar(im,cax=cax,orientation="horizontal")
cb.set_ticks([mn,md,mx])
cb.set_ticklabels([mn,md,mx])
#L^p
for p in range(2,2,2):
Lp=old_div((np.sum(shapes[ll]**p))**(old_div(1,float(p))),np.sum(shapes[ll]))
Lp_str=str(np.round(Lp*100,2))+'%' #'L'+str(p)+'='+
ax.text(0.02+p*0.15, 0.02, Lp_str,
verticalalignment='bottom', horizontalalignment='left',
transform=ax.transAxes,
color='yellow',weight='bold', fontsize=13)
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
index+=1
if (ll%ComponentsInFig==(ComponentsInFig-1)) or ll==L+adaptBias-1:
if save_plot==True:
pp.savefig(fig)
index=0
pp.close()
if close_figs:
plt.close('all')
#for ll in range(L+adaptBias):
# print 'Sparsity=',np.mean(shapes[ll]>0)
#%% ###### Plot Individual neurons' shape projection with clustering
a=ceil(sqrt(L+adaptBias))
b=ceil(old_div((L+adaptBias),a))
if plot_clustered_shape:
from sklearn.cluster import spectral_clustering
pp = PdfPages(Results_folder + 'ClusteredShapes'+resultsName+'.pdf')
figs=[]
for dd in range(len(shapes[0].shape)):
figs.append(plt.figure(figsize=(18 , 11)))
for ll in range(L):
ind=np.reshape(shapes[ll],(1,)+tuple(dims[1:]))>0
temp=data[np.repeat(ind,dims[0],axis=0)].reshape(dims[0],-1)
delta=1 #affinity trasnformation parameter
clust=3 #number of cluster
similarity=np.exp(old_div(-np.corrcoef(temp.T),delta))
labels = spectral_clustering(similarity, n_clusters=clust, eigen_solver='arpack')
ind2=np.array(np.nonzero(ind.reshape(-1))).reshape(-1)
temp_shape=np.repeat(np.zeros_like(shapes[ll]).reshape(-1,1),clust,axis=1)
for cc in range(clust):
temp_shape[ind2[labels==cc],cc]=1
temp_shape=temp_shape.reshape(tuple(dims[1:])+(clust,))
for dd in range(len(shapes[0].shape)):
current_fig=figs[dd]
ax = current_fig.add_subplot(a,b,ll+1)
if dd==2:
temp_shape=np.transpose(temp_shape,axes=[1,0,2,3])
ax.imshow(temp_shape.max(dd))
plt.setp(ax,xticks=[],yticks=[])
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot==True:
for dd in range(len(shapes[0].shape)):
current_fig=figs[dd]
pp.savefig(current_fig)
pp.close()
if close_figs:
plt.close('all')
#%% ##### Video Shapes
if video_shapes:
components=list(range(min(asarray([C,L]))))
C=len(components)
if restrict_support==True:
shape_support=shapes[components[0]]>0
for cc in range(C):
shape_support=np.logical_or(shape_support,shapes[components[cc]]>0)
detrended_data=shape_support.reshape((1,)+tuple(dims[1:]))*detrended_data
fig = plt.figure(figsize=(16,7))
mi = 0
ma = max(data)*scale
#mi2 = 0
#ma2 = max(shapes[ll])*max(activity[ll])
ii=0
#import colormaps as cmaps
#cmap=cmaps.viridis
cmap=color_map
a=3
b=1+C
ax1 = plt.subplot(a,b,1)
im1 = ax1.imshow(data[ii].max(0), vmin=mi, vmax=ma,cmap=cmap)
title=ax1.set_title('Data')
#plt.colorbar(im1)
ax2=[]
ax4=[]
ax6=[]
im2=[]
im4=[]
im6=[]
for cc in range(C):
ax2.append(plt.subplot(a,b,2+cc))
comp=shapes[components[cc]].max(0)*activity_NonNegative[components[cc],ii]
ma2=max(shapes[components[cc]].max(0))*max(activity_NonNegative[components[cc]])*scale
im2.append(ax2[cc].imshow(comp,vmin=0,vmax=ma2,cmap=cmap))
#ax2[0].set_title('Shape')
# plt.colorbar(im2)
ax3 = plt.subplot(a,b,1+b)
im3 = ax3.imshow(data[ii].max(1), vmin=mi, vmax=ma,cmap=cmap)
#plt.colorbar(im3)
for cc in range(C):
ax4.append(plt.subplot(a,b,2+b+cc))
comp=shapes[components[cc]].max(1)*activity_NonNegative[components[cc],ii]
ma2=max(shapes[components[cc]].max(1))*max(activity_NonNegative[components[cc]])*scale
im4.append(ax4[cc].imshow(comp,vmin=0,vmax=ma2,cmap=cmap))
#plt.colorbar(im4)
ax5 = plt.subplot(a,b,1+2*b)
im5 = ax5.imshow(np.transpose(detrended_data[ii].max(2)), vmin=mi, vmax=ma,cmap=cmap)
#plt.colorbar(im5)
for cc in range(C):
ax6.append(plt.subplot(a,b,2+2*b+cc))
comp=np.transpose(shapes[components[cc]].max(2))*activity_NonNegative[components[cc],ii]
ma2=max(shapes[components[cc]].max(2))*max(activity_NonNegative[components[cc]])*scale
im6.append(ax6[cc].imshow(comp,vmin=0,vmax=ma2,cmap=cmap))
#plt.colorbar(im6)
fig.tight_layout()
ComponentsActive=np.array([])
for cc in range(C):
ComponentsActive=np.append(ComponentsActive,np.nonzero(activity_NonNegative[components[cc]]))
ComponentsActive=np.unique(ComponentsActive)
def update(tt):
ii=ComponentsActive[tt]
im1.set_data(data[ii].max(0))
im3.set_data(data[ii].max(1))
im5.set_data(np.transpose(data[ii].max(2)))
for cc in range(C):
im2[cc].set_data(shapes[components[cc]].max(0)*activity_NonNegative[components[cc],ii])
im4[cc].set_data(shapes[components[cc]].max(1)*activity_NonNegative[components[cc],ii])
im6[cc].set_data(np.transpose(shapes[components[cc]].max(2))*activity_NonNegative[components[cc],ii])
title.set_text('Data, time = %.1f' % ii)
if save_video==True:
writer = animation.writers['ffmpeg'](fps=10)
ani = animation.FuncAnimation(fig, update, frames=len(ComponentsActive), blit=True, repeat=False)
if restrict_support==True:
ani.save(Results_folder + 'Shapes_Restricted'+resultsName+'.mp4',dpi=dpi,writer=writer)
else:
ani.save(Results_folder + 'Shapes_'+resultsName+'.mp4',dpi=dpi,writer=writer)
else:
ani = animation.FuncAnimation(fig, update, frames=len(ComponentsActive), blit=True, repeat=False)
plt.show()
#%% ##### Plot denoised projection - Results
if plot_residual_projections==True:
dims=data.shape
cmap=color_map
pic_residual=percentile(residual, 95, axis=0)
pic_denoised = max_intensity(denoised_data, axis=0)
pic_data=percentile(data, 95, axis=0)
left = 0.05 # the left side of the subplots of the figure
right = 0.95 # the right side of the subplots of the figure
bottom = 0.05 # the bottom of the subplots of the figure
top = 0.95 # the top of the subplots of the figure
wspace = 0.05 # the amount of width reserved for blank space between subplots
hspace = 0.05 # the amount of height reserved for white space between subplots
fig1=plt.figure(figsize=(11,18))
mi=min(pic_data)
ma=max(pic_data)
ax = plt.subplot(311)
im=ax.imshow(pic_data.max(0),vmin=mi,vmax=ma,cmap=cmap)
ax.set_title('Data')
plt.colorbar(im)
plt.setp(ax,xticks=[],yticks=[])
ax2 = plt.subplot(312)
im2=ax2.imshow(max_intensity(pic_denoised,0),interpolation='None')
ax2.set_title('Denoised')
plt.setp(ax,xticks=[],yticks=[])
plt.colorbar(im2)
ax3 = plt.subplot(313)
im3=ax3.imshow(pic_residual.max(0),cmap=cmap)
ax3.set_title('Residual')
plt.setp(ax,xticks=[],yticks=[])
plt.colorbar(im3)
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
fig2=plt.figure(figsize=(11,18))
mi=min(pic_data)
ma=max(pic_data)
ax = plt.subplot(311)
im=ax.imshow(pic_data.max(1),vmin=mi,vmax=ma,cmap=cmap)
ax.set_title('Data')
plt.colorbar(im)
plt.setp(ax,xticks=[],yticks=[])
ax2 = plt.subplot(312)
im2=ax2.imshow(max_intensity(pic_denoised,1),interpolation='None')
ax2.set_title('Denoised')
plt.colorbar(im2)
plt.setp(ax,xticks=[],yticks=[])
ax3 = plt.subplot(313)
im3=ax3.imshow(pic_residual.max(1),cmap=cmap)
ax3.set_title('Residual')
plt.colorbar(im3)
plt.setp(ax,xticks=[],yticks=[])
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
fig3=plt.figure(figsize=(11,18))
mi=min(pic_data)
ma=max(pic_data)
ax = plt.subplot(311)
im=ax.imshow(pic_data.max(2).T,vmin=mi,vmax=ma,cmap=cmap)
ax.set_title('Data')
plt.colorbar(im)
plt.setp(ax,xticks=[],yticks=[])
ax2 = plt.subplot(312)
im2=ax2.imshow(np.transpose(max_intensity(pic_denoised,2),[1,0,2]),interpolation='None')
ax2.set_title('denoised')
plt.setp(ax,xticks=[],yticks=[])
plt.colorbar(im2)
ax3 = plt.subplot(313)
im3=ax3.imshow(np.transpose(pic_residual.max(2)),cmap=cmap)
ax3.set_title('Residual')
plt.colorbar(im3)
plt.setp(ax,xticks=[],yticks=[])
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot==True:
pp = PdfPages(Results_folder + 'Data_Denoised_Residual_Projections'+resultsName+'.pdf')
pp.savefig(fig1)
pp.savefig(fig2)
pp.savefig(fig3)
pp.close()
#fig = plt.figure()
#plt.plot(MSE_array)
#plt.xlabel('Iteration')
#plt.ylabel('MSE')
#plt.show()
#%% ##### Plot denoised slices - Results
# z_slices=[0,2,4,6,8] #which z slices to look at slice plots/videos
z_slices=list(range(dims[min_dim+1])) #which z slices to look at slice plots/videos
D=len(z_slices)
if plot_residual_slices==True:
dims=data.shape
cmap=color_map
pic_residual=percentile(residual, 95, axis=0)
pic_denoised = max_intensity(denoised_data, axis=0)
pic_data=percentile(data, 95, axis=0)
a=3 #number of rows
fig1=plt.figure(figsize=(18,11))
mi=min(pic_data)
ma=max(pic_data)
for kk in range(D):
ax2 = plt.subplot(a,D,kk+1)
temp=np.squeeze(np.take(pic_denoised,(z_slices[kk],),axis=min_dim))
im2=ax2.imshow(temp,interpolation='None')
ax2.set_title('Denoised')
plt.setp(ax2,xticks=[],yticks=[])
plt.colorbar(im2)
ax = plt.subplot(a,D,kk+D+1)
temp=np.squeeze(np.take(pic_data,(z_slices[kk],),axis=min_dim))
im=ax.imshow(temp,vmin=mi,vmax=ma,cmap=cmap)
ax.set_title('Data')
plt.colorbar(im)
plt.setp(ax,xticks=[],yticks=[])
ax3 = plt.subplot(a,D,kk+2*D+1)
temp=np.squeeze(np.take(pic_residual,(z_slices[kk],),axis=min_dim))
im3=ax3.imshow(temp,cmap=cmap)
ax3.set_title('Residual')
plt.setp(ax3,xticks=[],yticks=[])
plt.colorbar(im3)
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot==True:
pp = PdfPages(Results_folder + 'Data_Denoised_Residual_Slice_'+resultsName+'.pdf')
pp.savefig(fig1)
pp.close()
#fig = plt.figure()
#plt.plot(MSE_array)
#plt.xlabel('Iteration')
#plt.ylabel('MSE')
#plt.show()
#%% ##### 2D Video Residual
if video_residual_2D:
fig = plt.figure(figsize=(16,7))
mi = 0
ma = np.percentile(data,satuartion_percentile)
mi3 = 0
ma3 = old_div(ma,np.max([np.floor(old_div(ma,np.percentile(residual[residual>0],satuartion_percentile))),1]))
ii=0
#import colormaps as cmaps
#cmap=cmaps.viridis
cmap=color_map
a=1
b=3
im_array=[]
temp=np.shape(data[ii])
ax1 = plt.subplot(a,b,1)
pic=denoised_data[ii]
im_array += [ax1.imshow(pic,interpolation='None')]
ax1.set_title('Denoised')
plt.setp(ax1,xticks=[],yticks=[])
ax2 = plt.subplot(a,b,2)
pic=data[ii]
im_array += [ax2.imshow(pic, vmin=mi, vmax=ma,cmap=cmap)]
title=ax2.set_title('Data')
plt.setp(ax2,xticks=[],yticks=[])
divider = make_axes_locatable(ax2)
cax2 = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(im_array[-1], cax=cax2)
ax3 = plt.subplot(a,b,3)
pic=residual[ii]
im_array += [ax3.imshow(pic, vmin=mi3, vmax=ma3,cmap=cmap)]
ax3.set_title('Residual x' + '%.1f' % (old_div(ma,ma3)))
plt.setp(ax3,xticks=[],yticks=[])
divider = make_axes_locatable(ax3)
cax3 = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(im_array[-1], cax=cax3)
# fig.tight_layout()
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
def update(ii):
im_array[0].set_data(denoised_data[ii])
im_array[1].set_data(data[ii])
im_array[2].set_data(residual[ii])
if frame_rate!=[]:
title.set_text('Data, time = %.2f sec' % (old_div(ii,frame_rate)))
else:
title.set_text('Data, time = %.1f' % ii)
if save_video==True:
writer = animation.writers['ffmpeg'](fps=10)
ani = animation.FuncAnimation(fig, update, frames=len(data), blit=False, repeat=False)
ani.save(Results_folder + 'Data_Denoised_Residual_2D_' +resultsName+'.avi',dpi=dpi,writer=writer)
else:
ani = animation.FuncAnimation(fig, update, frames=len(data), blit=False, repeat=False)
plt.show()
#%% ##### Video Projections Residual
if video_residual:
fig = plt.figure(figsize=(16,7))
mi = 0
ma = max(data)*scale
mi3 = 0
ma3 = max(residual)*scale
ii=0
#import colormaps as cmaps
#cmap=cmaps.viridis
cmap=color_map
spatial_dims_ind=list(range(len(dims)-1))
D=len(spatial_dims_ind)
a=D
b=3
im_array=[]
transpose_flags=[]
for kk in range(D):
transpose_flags+= [False]
temp=np.shape(data[ii].max(spatial_dims_ind[kk]))
if temp[0]>temp[1]:
transpose_flags[kk]=True
for kk in range(D):
ax1 = plt.subplot(a,b,D*kk+1)
if transpose_flags[kk]==False:
pic=max_intensity(denoised_data[ii],spatial_dims_ind[kk])
else:
pic=np.transpose(max_intensity(denoised_data[ii],spatial_dims_ind[kk]),[1,0,2])
im_array += [ax1.imshow(pic,interpolation='None')]
ax1.set_title('Denoised')
plt.colorbar(im_array[-1])
plt.setp(ax1,xticks=[],yticks=[])
ax2 = plt.subplot(a,b,D*kk+2)
if transpose_flags[kk]==False:
pic=data[ii].max(spatial_dims_ind[kk])
else:
pic=np.transpose(data[ii].max(spatial_dims_ind[kk]))
im_array += [ax2.imshow(pic, vmin=mi, vmax=ma,cmap=cmap)]
title=ax2.set_title('Data')
plt.colorbar(im_array[-1])
plt.setp(ax2,xticks=[],yticks=[])
ax3 = plt.subplot(a,b,D*kk+3)
if transpose_flags[kk]==False:
pic=residual[ii].max(spatial_dims_ind[kk])
else:
pic=np.transpose(residual[ii].max(spatial_dims_ind[kk]))
im_array += [ax3.imshow(pic, vmin=mi3, vmax=ma3,cmap=cmap)]
ax3.set_title('Residual')
plt.colorbar(im_array[-1])
plt.setp(ax3,xticks=[],yticks=[])
# fig.tight_layout()
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
def update(ii):
for kk in range(D):
if transpose_flags[kk]==False:
im_array[kk*D].set_data(max_intensity(denoised_data[ii],spatial_dims_ind[kk]))
im_array[kk*D+1].set_data(data[ii].max(spatial_dims_ind[kk]))
im_array[kk*D+2].set_data(residual[ii].max(spatial_dims_ind[kk]))
else:
im_array[kk*D].set_data(np.transpose(max_intensity(denoised_data[ii],spatial_dims_ind[kk]),[1,0,2]))
im_array[kk*D+1].set_data(np.transpose(data[ii].max(spatial_dims_ind[kk])))
im_array[kk*D+2].set_data(np.transpose(residual[ii].max(spatial_dims_ind[kk])))
title.set_text('Data, time = %.1f' % ii)
if save_video==True:
writer = animation.writers['ffmpeg'](fps=10)
ani = animation.FuncAnimation(fig, update, frames=len(data), blit=False, repeat=False)
ani.save(Results_folder + 'Data_Denoised_Residual_Projections'+resultsName+'.mp4',dpi=dpi,writer=writer)
else:
ani = animation.FuncAnimation(fig, update, frames=len(data), blit=False, repeat=False)
plt.show()
#%% ##### Video Slices Residual
# z_slices=[0,2,4,6,8] #which z slices to look at slice plots/videos
z_slices=list(range(dims[min_dim+1])) #which z slices to look at slice plots/videos
if video_slices:
fig = plt.figure(figsize=(16,7))
mi = 0
ma = np.percentil(data[data>0],satuartion_percentile)
mi3 = 0
ma3 = np.percentil(data[data>0],satuartion_percentile)
ii=0
#import colormaps as cmaps
#cmap=cmaps.viridis
cmap=color_map
a=3
D=len(z_slices) #number of spatial dimensions
im_array=[]
transpose_flag= True
for kk in range(D):
ax1 = plt.subplot(a,D,kk+1)
temp=np.squeeze(np.take(denoised_data[ii],(z_slices[kk],),axis=min_dim))
if transpose_flag==False:
pic=temp
else:
pic=np.transpose(temp,[1,0,2])
im_array += [ax1.imshow(pic,interpolation='None')]
ax1.set_title('Denoised, z='+ str(z_slices[kk]+1))
plt.colorbar(im_array[-1])
plt.setp(ax1,xticks=[],yticks=[])
ax2 = plt.subplot(a,D,kk+D+1)
temp=np.squeeze(np.take(data[ii],(z_slices[kk],),axis=min_dim))
if transpose_flag==False:
pic=temp
else:
pic=np.transpose(temp)
im_array += [ax2.imshow(pic, vmin=mi, vmax=ma,cmap=cmap)]
title=ax2.set_title('Data')
plt.colorbar(im_array[-1])
plt.setp(ax2,xticks=[],yticks=[])
ax3 = plt.subplot(a,D,kk+2*D+1)
temp=np.squeeze(np.take(residual[ii],(z_slices[kk],),axis=min_dim))
if transpose_flag==False:
pic=temp
else:
pic=np.transpose(temp)
im_array += [ax3.imshow(pic, vmin=mi3, vmax=ma3,cmap=cmap)]
ax3.set_title('Residual')
plt.colorbar(im_array[-1])
plt.setp(ax3,xticks=[],yticks=[])
# fig.tight_layout()
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
def update(ii):
for kk in range(D):
temp1=np.squeeze(np.take(denoised_data[ii],(z_slices[kk],),axis=min_dim))
temp2=np.squeeze(np.take(data[ii],(z_slices[kk],),axis=min_dim))
temp3=np.squeeze(np.take(residual[ii],(z_slices[kk],),axis=min_dim))
if transpose_flag==False:
im_array[a*kk].set_data(temp1)
im_array[a*kk+1].set_data(temp2)
im_array[a*kk+2].set_data(temp3)
else:
im_array[a*kk].set_data(np.transpose(temp1,[1,0,2]))
im_array[a*kk+1].set_data(np.transpose(temp2))
im_array[a*kk+2].set_data(np.transpose(temp3))
title.set_text('Data, time = %.1f' % ii)
if save_video==True:
writer = animation.writers['ffmpeg'](fps=10)
ani = animation.FuncAnimation(fig, update, frames=len(data), blit=False, repeat=False)
ani.save(Results_folder + 'Data_Denoised_Residual_Slices'+resultsName+'.mp4',dpi=dpi,writer=writer)
else:
ani = animation.FuncAnimation(fig, update, frames=len(data), blit=False, repeat=False)
plt.show()
def PlotAll(SaveNames,params):
from numpy import min, max, percentile,asarray,mean,ceil,sqrt
import numpy as np
from pylab import load
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.backends.backend_pdf import PdfPages
from scipy.ndimage.measurements import label
from AuxilaryFunctions import GetRandColors, max_intensity,SuperVoxelize,GetData,PruneComponents,SplitComponents,ThresholdShapes
from mpl_toolkits.axes_grid1 import make_axes_locatable
## plotting params
# what to plot
plot_activities=True
plot_shapes_projections=False
plot_shapes_slices=True
plot_activityCorrs=False
plot_clustered_shape=False
plot_residual_slices=False
plot_residual_projections=False
# videos to generate
video_shapes=False
video_residual=False
video_slices=False
# what to save
save_video=True
save_plot=True
close_figs=False#close all figs right after saving (to avoid memory overload)
# PostProcessing
Split=False
Threshold=False #threshold shapes in the end and keep only connected components
Prune=False
IncludeBackground=False #should we include the background as an extracted component?
# how to plot
scale=0.5 #scale colormap to enhance colors
dpi=500 #for videos
restrict_support=True #in shape video, zero out data outside support of shapes
C=4 #number of components to show in shape videos (if larger then number of shapes L, then we automatically set C=L)
color_map='gnuplot'
# Fetch experimental 3D data
data=GetData(params.data_name)
if params.SuperVoxelize==True:
data=SuperVoxelize(data)
dims=np.shape(data)
min_dim=np.argmin(dims[1:])
denoised_data=0
residual=data
detrended_data=data
Results_folder='Results/'
for rep in range(len(SaveNames)):
resultsName=SaveNames[rep]
try:
results=load('NMF_Results/'+SaveNames[rep])
except IOError:
if rep==0:
print 'results file not found!!'
else:
break
shapes=results['shapes']
activity=results['activity']
if rep>=params.Background_num:
adaptBias=False
else:
adaptBias=True
if IncludeBackground==True:
adaptBias=False
L=len(activity)-adaptBias
if Split==True:
shapes,activity,L,all_local_max=SplitComponents(shapes,activity,adaptBias)
if Prune==True:
# deleted_indices=[5,9,11,14,15,17,24]+range(25,36)
shapes,activity,L=PruneComponents(shapes,activity,params.TargetAreaRatio,L)
orig_shapes=np.copy(shapes) #shapes before thresholding
if Threshold==True:
shapes=ThresholdShapes(shapes,adaptBias,[],MaxRatio=0.3)
if L==0: #Stop if we encounter a file with zero components
break
activity_NonNegative=np.copy(activity)
activity_NonNegative[activity_NonNegative<0]=0
colors=GetRandColors(L)
color_shapes=np.transpose(shapes[:L].reshape(L, -1,1)*colors,[1,0,2]) #weird transpose for tensor dot product next line
denoised_data = denoised_data + (activity_NonNegative[:L].T.dot(color_shapes)).reshape(tuple(dims)+(3,))
residual = residual - activity_NonNegative.T.dot(orig_shapes.reshape(L+adaptBias, -1)).reshape(dims)
detrended_data= detrended_data - adaptBias*((activity_NonNegative[-1].reshape(-1, 1)).dot(shapes[-1].reshape(1, -1))).reshape(dims)
# denoised_data =np.asarray(denoised_data ,dtype='float')
# plt.close('all')
#%% plotting params
a=ceil(sqrt(L+adaptBias))
b=ceil((L+adaptBias)/a)
left = 0.05 # the left side of the subplots of the figure
right = 0.95 # the right side of the subplots of the figure
bottom = 0.05 # the bottom of the subplots of the figure
top = 0.95 # the top of the subplots of the figure
wspace = 0.1 # the amount of width reserved for blank space between subplots
hspace = 0.12 # the amount of height reserved for white space between subplots
#%% ###### Plot Individual neurons' activities
if plot_activities:
fig0=plt.figure(figsize=(dims[1] , dims[2]))
for ii in range(L+adaptBias):
ax = plt.subplot(a,b,ii+1)
plt.plot(activity[ii])
plt.setp(ax,xticks=[],yticks=[0])
# component number
ax.text(0.02, 0.8, str(ii),
verticalalignment='bottom', horizontalalignment='left',
transform=ax.transAxes,
color='black',weight='bold', fontsize=13)
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot==True:
pp = PdfPages(Results_folder + 'Activities'+resultsName+'.pdf')
pp.savefig(fig0)
pp.close()
if close_figs:
plt.close('all')
#%% ###### Plot Individual neurons' area which is correlated with their activities
if plot_activityCorrs:
if save_plot==True:
pp = PdfPages(Results_folder + 'CorrelationWithActivity'+resultsName+'.pdf')
for dd in range(len(shapes[0].shape)):
fig0=plt.figure(figsize=(11,18))
for ii in range(L+adaptBias):
ax = plt.subplot(a,b,ii+1)
corr_imag=np.dot(activity[ii],np.transpose(data,[1,2,0,3]))/np.sqrt(np.sum(data**2,axis=0)*np.sum(activity[ii]**2))
plt.imshow(np.abs(corr_imag).max(dd),cmap=color_map)
plt.setp(ax,xticks=[],yticks=[])
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot==True:
pp.savefig(fig0)
pp.close()
if close_figs:
plt.close('all')
#%% All Shapes projections
if plot_shapes_projections:
if save_plot==True:
pp = PdfPages(Results_folder + 'Shapes_projections'+resultsName+'.pdf')
for dd in range(len(shapes[0].shape)):
fig=plt.figure(figsize=(18 , 11))
for ll in range(L+adaptBias):
ax = plt.subplot(a,b,ll+1)
temp=shapes[ll].max(dd)
if dd==2:
temp=temp.T
mi=np.min(shapes[ll])
ma=np.max(shapes[ll])
im=plt.imshow(temp,vmin=mi,vmax=ma,cmap=color_map)
plt.setp(ax,xticks=[],yticks=[])
mn=int(np.floor(mi)) # colorbar min value
mx=int(np.ceil(ma)) # colorbar max value
md=(mx-mn)/2
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb=plt.colorbar(im,cax=cax)
cb.set_ticks([mn,md,mx])
cb.set_ticklabels([mn,md,mx])
# component number
ax.text(0.02, 0.8, str(ll),
verticalalignment='bottom', horizontalalignment='left',
transform=ax.transAxes,
color='white',weight='bold', fontsize=13)
# #sparsity
# spar_str=str(np.round(np.mean(shapes[ll]>0)*100,2))+'%'
# ax.text(0.02, 0.02, spar_str,
# verticalalignment='bottom', horizontalalignment='left',
# transform=ax.transAxes,
# color='white',weight='bold', fontsize=13)
# #L^p
# for p in range(2,2,2):
# Lp=(np.sum(shapes[ll]**p))**(1/float(p))/np.sum(shapes[ll])
# Lp_str=str(np.round(Lp*100,2))+'%' #'L'+str(p)+'='+
# ax.text(0.02+p*0.2, 0.02, Lp_str,
# verticalalignment='bottom', horizontalalignment='left',
# transform=ax.transAxes,
# color='yellow',weight='bold', fontsize=13)
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot==True:
pp.savefig(fig)
pp.close()
if close_figs:
plt.close('all')
#for ll in range(L+adaptBias):
# print 'Sparsity=',np.mean(shapes[ll]>0)
#%% All Shapes slices
transpose_shape= True # should we transpose shape
ComponentsInFig=3 # number of components in Figure
index=0 #component display index
# z_slices=[0,1,2,3,4,5,6,7,8] #which z slices to look at slice plots/videos
z_slices=range(dims[min_dim+1]) #which z slices to look at slice plots/videos
if plot_shapes_slices:
if save_plot==True:
pp = PdfPages(Results_folder + 'Shapes_slices'+resultsName+'.pdf')
for ll in range(L+adaptBias):
if index==0:
fig=plt.figure(figsize=(18, 11))
for dd in range(len(z_slices)):
ax = plt.subplot(ComponentsInFig,len(z_slices),index*len(z_slices)+dd+1)
temp=shapes[ll].take(dd,axis=min_dim)
if transpose_shape:
temp=np.transpose(temp)
mi=np.min(shapes[ll])
ma=np.max(shapes[ll])
im=plt.imshow(temp,vmin=mi,vmax=ma,cmap=color_map)
plt.setp(ax,xticks=[],yticks=[])
if dd==0:
# component number
ax.text(0.02, 0.8, str(ll),
verticalalignment='bottom', horizontalalignment='left',
transform=ax.transAxes,
color='white',weight='bold', fontsize=13)
#sparsity
spar_str=str(np.round(np.mean(shapes[ll]>0)*100,2))+'%'
ax.text(0.02, 0.02, spar_str,
verticalalignment='bottom', horizontalalignment='left',
transform=ax.transAxes,
color='white',weight='bold', fontsize=13)
mn=int(np.floor(mi)) # colorbar min value
mx=int(np.ceil(ma)) # colorbar max value
md=(mx-mn)/2
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom", size="5%", pad=0.05)
cb=plt.colorbar(im,cax=cax,orientation="horizontal")
cb.set_ticks([mn,md,mx])
cb.set_ticklabels([mn,md,mx])
#L^p
for p in range(2,2,2):
Lp=(np.sum(shapes[ll]**p))**(1/float(p))/np.sum(shapes[ll])
Lp_str=str(np.round(Lp*100,2))+'%' #'L'+str(p)+'='+
ax.text(0.02+p*0.15, 0.02, Lp_str,
verticalalignment='bottom', horizontalalignment='left',
transform=ax.transAxes,
color='yellow',weight='bold', fontsize=13)
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
index+=1
if (ll%ComponentsInFig==(ComponentsInFig-1)) or ll==L+adaptBias-1:
if save_plot==True:
pp.savefig(fig)
index=0
pp.close()
if close_figs:
plt.close('all')
#for ll in range(L+adaptBias):
# print 'Sparsity=',np.mean(shapes[ll]>0)
#%% ###### Plot Individual neurons' shape projection with clustering
if plot_clustered_shape:
from sklearn.cluster import spectral_clustering
pp = PdfPages(Results_folder + 'ClusteredShapes'+resultsName+'.pdf')
figs=[]
for dd in range(len(shapes[0].shape)):
figs.append(plt.figure(figsize=(18 , 11)))
for ll in range(L):
ind=np.reshape(shapes[ll],(1,)+tuple(dims[1:]))>0
temp=data[np.repeat(ind,dims[0],axis=0)].reshape(dims[0],-1)
delta=1 #affinity trasnformation parameter
clust=3 #number of cluster
similarity=np.exp(-np.corrcoef(temp.T)/delta)
labels = spectral_clustering(similarity, n_clusters=clust, eigen_solver='arpack')
ind2=np.array(np.nonzero(ind.reshape(-1))).reshape(-1)
temp_shape=np.repeat(np.zeros_like(shapes[ll]).reshape(-1,1),clust,axis=1)
for cc in range(clust):
temp_shape[ind2[labels==cc],cc]=1
temp_shape=temp_shape.reshape(tuple(dims[1:])+(clust,))
for dd in range(len(shapes[0].shape)):
current_fig=figs[dd]
ax = current_fig.add_subplot(a,b,ll+1)
if dd==2:
temp_shape=np.transpose(temp_shape,axes=[1,0,2,3])
ax.imshow(temp_shape.max(dd))
plt.setp(ax,xticks=[],yticks=[])
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot==True:
for dd in range(len(shapes[0].shape)):
current_fig=figs[dd]
pp.savefig(current_fig)
pp.close()
if close_figs:
plt.close('all')
#%% ##### Video Shapes
if video_shapes:
components=range(min(asarray([C,L])))
C=len(components)
if restrict_support==True:
shape_support=shapes[components[0]]>0
for cc in range(C):
shape_support=np.logical_or(shape_support,shapes[components[cc]]>0)
detrended_data=shape_support.reshape((1,)+tuple(dims[1:]))*detrended_data
fig = plt.figure(figsize=(16,7))
mi = 0
ma = max(data)*scale
#mi2 = 0
#ma2 = max(shapes[ll])*max(activity[ll])
ii=0
#import colormaps as cmaps
#cmap=cmaps.viridis
cmap=color_map
a=3
b=1+C
ax1 = plt.subplot(a,b,1)
im1 = ax1.imshow(data[ii].max(0), vmin=mi, vmax=ma,cmap=cmap)
title=ax1.set_title('Data')
#plt.colorbar(im1)
ax2=[]
ax4=[]
ax6=[]
im2=[]
im4=[]
im6=[]
for cc in range(C):
ax2.append(plt.subplot(a,b,2+cc))
comp=shapes[components[cc]].max(0)*activity_NonNegative[components[cc],ii]
ma2=max(shapes[components[cc]].max(0))*max(activity_NonNegative[components[cc]])*scale
im2.append(ax2[cc].imshow(comp,vmin=0,vmax=ma2,cmap=cmap))
#ax2[0].set_title('Shape')
# plt.colorbar(im2)
ax3 = plt.subplot(a,b,1+b)
im3 = ax3.imshow(data[ii].max(1), vmin=mi, vmax=ma,cmap=cmap)
#plt.colorbar(im3)
for cc in range(C):
ax4.append(plt.subplot(a,b,2+b+cc))
comp=shapes[components[cc]].max(1)*activity_NonNegative[components[cc],ii]
ma2=max(shapes[components[cc]].max(1))*max(activity_NonNegative[components[cc]])*scale
im4.append(ax4[cc].imshow(comp,vmin=0,vmax=ma2,cmap=cmap))
#plt.colorbar(im4)
ax5 = plt.subplot(a,b,1+2*b)
im5 = ax5.imshow(np.transpose(detrended_data[ii].max(2)), vmin=mi, vmax=ma,cmap=cmap)
#plt.colorbar(im5)
for cc in range(C):
ax6.append(plt.subplot(a,b,2+2*b+cc))
comp=np.transpose(shapes[components[cc]].max(2))*activity_NonNegative[components[cc],ii]
ma2=max(shapes[components[cc]].max(2))*max(activity_NonNegative[components[cc]])*scale
im6.append(ax6[cc].imshow(comp,vmin=0,vmax=ma2,cmap=cmap))
#plt.colorbar(im6)
fig.tight_layout()
ComponentsActive=np.array([])
for cc in range(C):
ComponentsActive=np.append(ComponentsActive,np.nonzero(activity_NonNegative[components[cc]]))
ComponentsActive=np.unique(ComponentsActive)
def update(tt):
ii=ComponentsActive[tt]
im1.set_data(data[ii].max(0))
im3.set_data(data[ii].max(1))
im5.set_data(np.transpose(data[ii].max(2)))
for cc in range(C):
im2[cc].set_data(shapes[components[cc]].max(0)*activity_NonNegative[components[cc],ii])
im4[cc].set_data(shapes[components[cc]].max(1)*activity_NonNegative[components[cc],ii])
im6[cc].set_data(np.transpose(shapes[components[cc]].max(2))*activity_NonNegative[components[cc],ii])
title.set_text('Data, time = %.1f' % ii)
if save_video==True:
writer = animation.writers['ffmpeg'](fps=10)
ani = animation.FuncAnimation(fig, update, frames=len(ComponentsActive), blit=True, repeat=False)
if restrict_support==True:
ani.save(Results_folder + 'Shapes_Restricted'+resultsName+'.mp4',dpi=dpi,writer=writer)
else:
ani.save(Results_folder + 'Shapes_'+resultsName+'.mp4',dpi=dpi,writer=writer)
else:
ani = animation.FuncAnimation(fig, update, frames=len(ComponentsActive), blit=True, repeat=False)
plt.show()
#%% normalize denoised data range
# denoised_data=10*denoised_data/np.max(denoised_data)/scale
denoised_data=denoised_data/np.percentile(denoised_data,99.5)
denoised_data[denoised_data>1]=1
#%% ##### Plot denoised projection - Results
if plot_residual_projections==True:
dims=data.shape
cmap=color_map
pic_residual=percentile(residual, 95, axis=0)
pic_denoised = max_intensity(denoised_data, axis=0)
pic_data=percentile(data, 95, axis=0)
left = 0.05 # the left side of the subplots of the figure
right = 0.95 # the right side of the subplots of the figure
bottom = 0.05 # the bottom of the subplots of the figure
top = 0.95 # the top of the subplots of the figure
wspace = 0.05 # the amount of width reserved for blank space between subplots
hspace = 0.05 # the amount of height reserved for white space between subplots
fig1=plt.figure(figsize=(11,18))
mi=min(pic_data)
ma=max(pic_data)
ax = plt.subplot(311)
im=ax.imshow(pic_data.max(0),vmin=mi,vmax=ma,cmap=cmap)
ax.set_title('Data')
plt.colorbar(im)
plt.setp(ax,xticks=[],yticks=[])
ax2 = plt.subplot(312)
im2=ax2.imshow(max_intensity(pic_denoised,0),interpolation='None')
ax2.set_title('Denoised')
plt.setp(ax,xticks=[],yticks=[])
plt.colorbar(im2)
ax3 = plt.subplot(313)
im3=ax3.imshow(pic_residual.max(0),cmap=cmap)
ax3.set_title('Residual')
plt.setp(ax,xticks=[],yticks=[])
plt.colorbar(im3)
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
fig2=plt.figure(figsize=(11,18))
mi=min(pic_data)
ma=max(pic_data)
ax = plt.subplot(311)
im=ax.imshow(pic_data.max(1),vmin=mi,vmax=ma,cmap=cmap)
ax.set_title('Data')
plt.colorbar(im)
plt.setp(ax,xticks=[],yticks=[])
ax2 = plt.subplot(312)
im2=ax2.imshow(max_intensity(pic_denoised,1),interpolation='None')
ax2.set_title('Denoised')
plt.colorbar(im2)
plt.setp(ax,xticks=[],yticks=[])
ax3 = plt.subplot(313)
im3=ax3.imshow(pic_residual.max(1),cmap=cmap)
ax3.set_title('Residual')
plt.colorbar(im3)
plt.setp(ax,xticks=[],yticks=[])
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
fig3=plt.figure(figsize=(11,18))
mi=min(pic_data)
ma=max(pic_data)
ax = plt.subplot(311)
im=ax.imshow(pic_data.max(2).T,vmin=mi,vmax=ma,cmap=cmap)
ax.set_title('Data')
plt.colorbar(im)
plt.setp(ax,xticks=[],yticks=[])
ax2 = plt.subplot(312)
im2=ax2.imshow(np.transpose(max_intensity(pic_denoised,2),[1,0,2]),interpolation='None')
ax2.set_title('denoised')
plt.setp(ax,xticks=[],yticks=[])
plt.colorbar(im2)
ax3 = plt.subplot(313)
im3=ax3.imshow(np.transpose(pic_residual.max(2)),cmap=cmap)
ax3.set_title('Residual')
plt.colorbar(im3)
plt.setp(ax,xticks=[],yticks=[])
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot==True:
pp = PdfPages(Results_folder + 'Data_Denoised_Residual_Projections'+resultsName+'.pdf')
pp.savefig(fig1)
pp.savefig(fig2)
pp.savefig(fig3)
pp.close()
#fig = plt.figure()
#plt.plot(MSE_array)
#plt.xlabel('Iteration')
#plt.ylabel('MSE')
#plt.show()
#%% ##### Plot denoised slices - Results
# z_slices=[0,2,4,6,8] #which z slices to look at slice plots/videos
z_slices=range(dims[min_dim+1]) #which z slices to look at slice plots/videos
D=len(z_slices)
if plot_residual_slices==True:
dims=data.shape
cmap=color_map
pic_residual=percentile(residual, 95, axis=0)
pic_denoised = max_intensity(denoised_data, axis=0)
pic_data=percentile(data, 95, axis=0)
a=3 #number of rows
fig1=plt.figure(figsize=(18,11))
mi=min(pic_data)
ma=max(pic_data)
for kk in range(D):
ax2 = plt.subplot(a,D,kk+1)
temp=np.squeeze(np.take(pic_denoised,(z_slices[kk],),axis=min_dim))
im2=ax2.imshow(temp,interpolation='None')
ax2.set_title('Denoised')
plt.setp(ax2,xticks=[],yticks=[])
plt.colorbar(im2)
ax = plt.subplot(a,D,kk+D+1)
temp=np.squeeze(np.take(pic_data,(z_slices[kk],),axis=min_dim))
im=ax.imshow(temp,vmin=mi,vmax=ma,cmap=cmap)
ax.set_title('Data')
plt.colorbar(im)
plt.setp(ax,xticks=[],yticks=[])
ax3 = plt.subplot(a,D,kk+2*D+1)
temp=np.squeeze(np.take(pic_residual,(z_slices[kk],),axis=min_dim))
im3=ax3.imshow(temp,cmap=cmap)
ax3.set_title('Residual')
plt.setp(ax3,xticks=[],yticks=[])
plt.colorbar(im3)
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot==True:
pp = PdfPages(Results_folder + 'Data_Denoised_Residual_Slice_'+resultsName+'.pdf')
pp.savefig(fig1)
pp.close()
#fig = plt.figure()
#plt.plot(MSE_array)
#plt.xlabel('Iteration')
#plt.ylabel('MSE')
#plt.show()
#%% ##### Video Projections Residual
if video_residual:
fig = plt.figure(figsize=(16,7))
mi = 0
ma = max(data)*scale
mi3 = 0
ma3 = max(residual)*scale
ii=0
#import colormaps as cmaps
#cmap=cmaps.viridis
cmap=color_map
spatial_dims_ind=range(len(dims)-1)
D=len(spatial_dims_ind)
a=D
b=3
im_array=[]
transpose_flags=[]
for kk in range(D):
transpose_flags+= [False]
temp=np.shape(data[ii].max(spatial_dims_ind[kk]))
if temp[0]>temp[1]:
transpose_flags[kk]=True
for kk in range(D):
ax1 = plt.subplot(a,b,D*kk+1)
if transpose_flags[kk]==False:
pic=max_intensity(denoised_data[ii],spatial_dims_ind[kk])
else:
pic=np.transpose(max_intensity(denoised_data[ii],spatial_dims_ind[kk]),[1,0,2])
im_array += [ax1.imshow(pic,interpolation='None')]
ax1.set_title('Denoised')
plt.colorbar(im_array[-1])
plt.setp(ax1,xticks=[],yticks=[])
ax2 = plt.subplot(a,b,D*kk+2)
if transpose_flags[kk]==False:
pic=data[ii].max(spatial_dims_ind[kk])
else:
pic=np.transpose(data[ii].max(spatial_dims_ind[kk]))
im_array += [ax2.imshow(pic, vmin=mi, vmax=ma,cmap=cmap)]
title=ax2.set_title('Data')
plt.colorbar(im_array[-1])
plt.setp(ax2,xticks=[],yticks=[])
ax3 = plt.subplot(a,b,D*kk+3)
if transpose_flags[kk]==False:
pic=residual[ii].max(spatial_dims_ind[kk])
else:
pic=np.transpose(residual[ii].max(spatial_dims_ind[kk]))
im_array += [ax3.imshow(pic, vmin=mi3, vmax=ma3,cmap=cmap)]
ax3.set_title('Residual')
plt.colorbar(im_array[-1])
plt.setp(ax3,xticks=[],yticks=[])
# fig.tight_layout()
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
def update(ii):
for kk in range(D):
if transpose_flags[kk]==False:
im_array[kk*D].set_data(max_intensity(denoised_data[ii],spatial_dims_ind[kk]))
im_array[kk*D+1].set_data(data[ii].max(spatial_dims_ind[kk]))
im_array[kk*D+2].set_data(residual[ii].max(spatial_dims_ind[kk]))
else:
im_array[kk*D].set_data(np.transpose(max_intensity(denoised_data[ii],spatial_dims_ind[kk]),[1,0,2]))
im_array[kk*D+1].set_data(np.transpose(data[ii].max(spatial_dims_ind[kk])))
im_array[kk*D+2].set_data(np.transpose(residual[ii].max(spatial_dims_ind[kk])))
title.set_text('Data, time = %.1f' % ii)
if save_video==True:
writer = animation.writers['ffmpeg'](fps=10)
ani = animation.FuncAnimation(fig, update, frames=len(data), blit=False, repeat=False)
ani.save(Results_folder + 'Data_Denoised_Residual_Projections'+resultsName+'.mp4',dpi=dpi,writer=writer)
else:
ani = animation.FuncAnimation(fig, update, frames=len(data), blit=False, repeat=False)
plt.show()
#%% ##### Video Slices Residual
# z_slices=[0,2,4,6,8] #which z slices to look at slice plots/videos
z_slices=range(dims[min_dim+1]) #which z slices to look at slice plots/videos
if video_slices:
fig = plt.figure(figsize=(16,7))
mi = 0
ma = max(data)*scale
mi3 = 0
ma3 = max(residual)*scale
ii=0
#import colormaps as cmaps
#cmap=cmaps.viridis
cmap=color_map
a=3
D=len(z_slices) #number of spatial dimensions
im_array=[]
transpose_flag= True
for kk in range(D):
ax1 = plt.subplot(a,D,kk+1)
temp=np.squeeze(np.take(denoised_data[ii],(z_slices[kk],),axis=min_dim))
if transpose_flag==False:
pic=temp
else:
pic=np.transpose(temp,[1,0,2])
im_array += [ax1.imshow(pic,interpolation='None')]
ax1.set_title('Denoised, z='+ str(z_slices[kk]+1))
plt.colorbar(im_array[-1])
plt.setp(ax1,xticks=[],yticks=[])
ax2 = plt.subplot(a,D,kk+D+1)
temp=np.squeeze(np.take(data[ii],(z_slices[kk],),axis=min_dim))
if transpose_flag==False:
pic=temp
else:
pic=np.transpose(temp)
im_array += [ax2.imshow(pic, vmin=mi, vmax=ma,cmap=cmap)]
title=ax2.set_title('Data')
plt.colorbar(im_array[-1])
plt.setp(ax2,xticks=[],yticks=[])
ax3 = plt.subplot(a,D,kk+2*D+1)
temp=np.squeeze(np.take(residual[ii],(z_slices[kk],),axis=min_dim))
if transpose_flag==False:
pic=temp
else:
pic=np.transpose(temp)
im_array += [ax3.imshow(pic, vmin=mi3, vmax=ma3,cmap=cmap)]
ax3.set_title('Residual')
plt.colorbar(im_array[-1])
plt.setp(ax3,xticks=[],yticks=[])
# fig.tight_layout()
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
def update(ii):
for kk in range(D):
temp1=np.squeeze(np.take(denoised_data[ii],(z_slices[kk],),axis=min_dim))
temp2=np.squeeze(np.take(data[ii],(z_slices[kk],),axis=min_dim))
temp3=np.squeeze(np.take(residual[ii],(z_slices[kk],),axis=min_dim))
if transpose_flag==False:
im_array[a*kk].set_data(temp1)
im_array[a*kk+1].set_data(temp2)
im_array[a*kk+2].set_data(temp3)
else:
im_array[a*kk].set_data(np.transpose(temp1,[1,0,2]))
im_array[a*kk+1].set_data(np.transpose(temp2))
im_array[a*kk+2].set_data(np.transpose(temp3))
title.set_text('Data, time = %.1f' % ii)
if save_video==True:
writer = animation.writers['ffmpeg'](fps=10)
ani = animation.FuncAnimation(fig, update, frames=len(data), blit=False, repeat=False)
ani.save(Results_folder + 'Data_Denoised_Residual_Slices'+resultsName+'.mp4',dpi=dpi,writer=writer)
else:
ani = animation.FuncAnimation(fig, update, frames=len(data), blit=False, repeat=False)
plt.show()
def PlotAll(SaveNames, params):
from numpy import min, max, percentile, asarray, mean, ceil, sqrt
import numpy as np
from pylab import load
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.backends.backend_pdf import PdfPages
from scipy.ndimage.measurements import label
from AuxilaryFunctions import GetRandColors, max_intensity, SuperVoxelize, GetData, PruneComponents, SplitComponents, ThresholdShapes
from mpl_toolkits.axes_grid1 import make_axes_locatable
## plotting params
# what to plot
plot_activities = True
plot_shapes_projections = False
plot_shapes_slices = True
plot_activityCorrs = False
plot_clustered_shape = False
plot_residual_slices = False
plot_residual_projections = False
# videos to generate
video_shapes = False
video_residual = False
video_slices = False
# what to save
save_video = True
save_plot = True
close_figs = False #close all figs right after saving (to avoid memory overload)
# PostProcessing
Split = False
Threshold = False #threshold shapes in the end and keep only connected components
Prune = False
IncludeBackground = False #should we include the background as an extracted component?
# how to plot
scale = 0.5 #scale colormap to enhance colors
dpi = 500 #for videos
restrict_support = True #in shape video, zero out data outside support of shapes
C = 4 #number of components to show in shape videos (if larger then number of shapes L, then we automatically set C=L)
color_map = 'gnuplot'
# Fetch experimental 3D data
data = GetData(params.data_name)
if params.SuperVoxelize == True:
data = SuperVoxelize(data)
dims = np.shape(data)
min_dim = np.argmin(dims[1:])
denoised_data = 0
residual = data
detrended_data = data
Results_folder = 'Results/'
for rep in range(len(SaveNames)):
resultsName = SaveNames[rep]
try:
results = load('NMF_Results/' + SaveNames[rep])
except IOError:
if rep == 0:
print 'results file not found!!'
else:
break
shapes = results['shapes']
activity = results['activity']
if rep >= params.Background_num:
adaptBias = False
else:
adaptBias = True
if IncludeBackground == True:
adaptBias = False
L = len(activity) - adaptBias
if Split == True:
shapes, activity, L, all_local_max = SplitComponents(
shapes, activity, adaptBias)
if Prune == True:
# deleted_indices=[5,9,11,14,15,17,24]+range(25,36)
shapes, activity, L = PruneComponents(shapes, activity,
params.TargetAreaRatio, L)
orig_shapes = np.copy(shapes) #shapes before thresholding
if Threshold == True:
shapes = ThresholdShapes(shapes, adaptBias, [], MaxRatio=0.3)
if L == 0: #Stop if we encounter a file with zero components
break
activity_NonNegative = np.copy(activity)
activity_NonNegative[activity_NonNegative < 0] = 0
colors = GetRandColors(L)
color_shapes = np.transpose(
shapes[:L].reshape(L, -1, 1) * colors,
[1, 0, 2]) #weird transpose for tensor dot product next line
denoised_data = denoised_data + (activity_NonNegative[:L].T.dot(
color_shapes)).reshape(tuple(dims) + (3, ))
residual = residual - activity_NonNegative.T.dot(
orig_shapes.reshape(L + adaptBias, -1)).reshape(dims)
detrended_data = detrended_data - adaptBias * (
(activity_NonNegative[-1].reshape(-1, 1)).dot(shapes[-1].reshape(
1, -1))).reshape(dims)
# denoised_data =np.asarray(denoised_data ,dtype='float')
# plt.close('all')
#%% plotting params
a = ceil(sqrt(L + adaptBias))
b = ceil((L + adaptBias) / a)
left = 0.05 # the left side of the subplots of the figure
right = 0.95 # the right side of the subplots of the figure
bottom = 0.05 # the bottom of the subplots of the figure
top = 0.95 # the top of the subplots of the figure
wspace = 0.1 # the amount of width reserved for blank space between subplots
hspace = 0.12 # the amount of height reserved for white space between subplots
#%% ###### Plot Individual neurons' activities
if plot_activities:
fig0 = plt.figure(figsize=(dims[1], dims[2]))
for ii in range(L + adaptBias):
ax = plt.subplot(a, b, ii + 1)
plt.plot(activity[ii])
plt.setp(ax, xticks=[], yticks=[0])
# component number
ax.text(0.02,
0.8,
str(ii),
verticalalignment='bottom',
horizontalalignment='left',
transform=ax.transAxes,
color='black',
weight='bold',
fontsize=13)
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot == True:
pp = PdfPages(Results_folder + 'Activities' + resultsName +
'.pdf')
pp.savefig(fig0)
pp.close()
if close_figs:
plt.close('all')
#%% ###### Plot Individual neurons' area which is correlated with their activities
if plot_activityCorrs:
if save_plot == True:
pp = PdfPages(Results_folder + 'CorrelationWithActivity' +
resultsName + '.pdf')
for dd in range(len(shapes[0].shape)):
fig0 = plt.figure(figsize=(11, 18))
for ii in range(L + adaptBias):
ax = plt.subplot(a, b, ii + 1)
corr_imag = np.dot(
activity[ii],
np.transpose(data, [1, 2, 0, 3])) / np.sqrt(
np.sum(data**2, axis=0) * np.sum(activity[ii]**2))
plt.imshow(np.abs(corr_imag).max(dd), cmap=color_map)
plt.setp(ax, xticks=[], yticks=[])
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot == True:
pp.savefig(fig0)
pp.close()
if close_figs:
plt.close('all')
#%% All Shapes projections
if plot_shapes_projections:
if save_plot == True:
pp = PdfPages(Results_folder + 'Shapes_projections' +
resultsName + '.pdf')
for dd in range(len(shapes[0].shape)):
fig = plt.figure(figsize=(18, 11))
for ll in range(L + adaptBias):
ax = plt.subplot(a, b, ll + 1)
temp = shapes[ll].max(dd)
if dd == 2:
temp = temp.T
mi = np.min(shapes[ll])
ma = np.max(shapes[ll])
im = plt.imshow(temp, vmin=mi, vmax=ma, cmap=color_map)
plt.setp(ax, xticks=[], yticks=[])
mn = int(np.floor(mi)) # colorbar min value
mx = int(np.ceil(ma)) # colorbar max value
md = (mx - mn) / 2
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax)
cb.set_ticks([mn, md, mx])
cb.set_ticklabels([mn, md, mx])
# component number
ax.text(0.02,
0.8,
str(ll),
verticalalignment='bottom',
horizontalalignment='left',
transform=ax.transAxes,
color='white',
weight='bold',
fontsize=13)
# #sparsity
# spar_str=str(np.round(np.mean(shapes[ll]>0)*100,2))+'%'
# ax.text(0.02, 0.02, spar_str,
# verticalalignment='bottom', horizontalalignment='left',
# transform=ax.transAxes,
# color='white',weight='bold', fontsize=13)
# #L^p
# for p in range(2,2,2):
# Lp=(np.sum(shapes[ll]**p))**(1/float(p))/np.sum(shapes[ll])
# Lp_str=str(np.round(Lp*100,2))+'%' #'L'+str(p)+'='+
# ax.text(0.02+p*0.2, 0.02, Lp_str,
# verticalalignment='bottom', horizontalalignment='left',
# transform=ax.transAxes,
# color='yellow',weight='bold', fontsize=13)
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot == True:
pp.savefig(fig)
pp.close()
if close_figs:
plt.close('all')
#for ll in range(L+adaptBias):
# print 'Sparsity=',np.mean(shapes[ll]>0)
#%% All Shapes slices
transpose_shape = True # should we transpose shape
ComponentsInFig = 3 # number of components in Figure
index = 0 #component display index
# z_slices=[0,1,2,3,4,5,6,7,8] #which z slices to look at slice plots/videos
z_slices = range(
dims[min_dim + 1]) #which z slices to look at slice plots/videos
if plot_shapes_slices:
if save_plot == True:
pp = PdfPages(Results_folder + 'Shapes_slices' + resultsName +
'.pdf')
for ll in range(L + adaptBias):
if index == 0:
fig = plt.figure(figsize=(18, 11))
for dd in range(len(z_slices)):
ax = plt.subplot(ComponentsInFig, len(z_slices),
index * len(z_slices) + dd + 1)
temp = shapes[ll].take(dd, axis=min_dim)
if transpose_shape:
temp = np.transpose(temp)
mi = np.min(shapes[ll])
ma = np.max(shapes[ll])
im = plt.imshow(temp, vmin=mi, vmax=ma, cmap=color_map)
plt.setp(ax, xticks=[], yticks=[])
if dd == 0:
# component number
ax.text(0.02,
0.8,
str(ll),
verticalalignment='bottom',
horizontalalignment='left',
transform=ax.transAxes,
color='white',
weight='bold',
fontsize=13)
#sparsity
spar_str = str(
np.round(np.mean(shapes[ll] > 0) * 100, 2)) + '%'
ax.text(0.02,
0.02,
spar_str,
verticalalignment='bottom',
horizontalalignment='left',
transform=ax.transAxes,
color='white',
weight='bold',
fontsize=13)
mn = int(np.floor(mi)) # colorbar min value
mx = int(np.ceil(ma)) # colorbar max value
md = (mx - mn) / 2
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom",
size="5%",
pad=0.05)
cb = plt.colorbar(im,
cax=cax,
orientation="horizontal")
cb.set_ticks([mn, md, mx])
cb.set_ticklabels([mn, md, mx])
#L^p
for p in range(2, 2, 2):
Lp = (np.sum(shapes[ll]**p))**(
1 / float(p)) / np.sum(shapes[ll])
Lp_str = str(np.round(Lp * 100,
2)) + '%' #'L'+str(p)+'='+
ax.text(0.02 + p * 0.15,
0.02,
Lp_str,
verticalalignment='bottom',
horizontalalignment='left',
transform=ax.transAxes,
color='yellow',
weight='bold',
fontsize=13)
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
index += 1
if (ll % ComponentsInFig
== (ComponentsInFig - 1)) or ll == L + adaptBias - 1:
if save_plot == True:
pp.savefig(fig)
index = 0
pp.close()
if close_figs:
plt.close('all')
#for ll in range(L+adaptBias):
# print 'Sparsity=',np.mean(shapes[ll]>0)
#%% ###### Plot Individual neurons' shape projection with clustering
if plot_clustered_shape:
from sklearn.cluster import spectral_clustering
pp = PdfPages(Results_folder + 'ClusteredShapes' + resultsName +
'.pdf')
figs = []
for dd in range(len(shapes[0].shape)):
figs.append(plt.figure(figsize=(18, 11)))
for ll in range(L):
ind = np.reshape(shapes[ll], (1, ) + tuple(dims[1:])) > 0
temp = data[np.repeat(ind, dims[0],
axis=0)].reshape(dims[0], -1)
delta = 1 #affinity trasnformation parameter
clust = 3 #number of cluster
similarity = np.exp(-np.corrcoef(temp.T) / delta)
labels = spectral_clustering(similarity,
n_clusters=clust,
eigen_solver='arpack')
ind2 = np.array(np.nonzero(ind.reshape(-1))).reshape(-1)
temp_shape = np.repeat(np.zeros_like(shapes[ll]).reshape(
-1, 1),
clust,
axis=1)
for cc in range(clust):
temp_shape[ind2[labels == cc], cc] = 1
temp_shape = temp_shape.reshape(tuple(dims[1:]) + (clust, ))
for dd in range(len(shapes[0].shape)):
current_fig = figs[dd]
ax = current_fig.add_subplot(a, b, ll + 1)
if dd == 2:
temp_shape = np.transpose(temp_shape,
axes=[1, 0, 2, 3])
ax.imshow(temp_shape.max(dd))
plt.setp(ax, xticks=[], yticks=[])
plt.subplots_adjust(left, bottom, right, top, wspace,
hspace)
if save_plot == True:
for dd in range(len(shapes[0].shape)):
current_fig = figs[dd]
pp.savefig(current_fig)
pp.close()
if close_figs:
plt.close('all')
#%% ##### Video Shapes
if video_shapes:
components = range(min(asarray([C, L])))
C = len(components)
if restrict_support == True:
shape_support = shapes[components[0]] > 0
for cc in range(C):
shape_support = np.logical_or(shape_support,
shapes[components[cc]] > 0)
detrended_data = shape_support.reshape(
(1, ) + tuple(dims[1:])) * detrended_data
fig = plt.figure(figsize=(16, 7))
mi = 0
ma = max(data) * scale
#mi2 = 0
#ma2 = max(shapes[ll])*max(activity[ll])
ii = 0
#import colormaps as cmaps
#cmap=cmaps.viridis
cmap = color_map
a = 3
b = 1 + C
ax1 = plt.subplot(a, b, 1)
im1 = ax1.imshow(data[ii].max(0), vmin=mi, vmax=ma, cmap=cmap)
title = ax1.set_title('Data')
#plt.colorbar(im1)
ax2 = []
ax4 = []
ax6 = []
im2 = []
im4 = []
im6 = []
for cc in range(C):
ax2.append(plt.subplot(a, b, 2 + cc))
comp = shapes[components[cc]].max(0) * activity_NonNegative[
components[cc], ii]
ma2 = max(shapes[components[cc]].max(0)) * max(
activity_NonNegative[components[cc]]) * scale
im2.append(ax2[cc].imshow(comp, vmin=0, vmax=ma2, cmap=cmap))
#ax2[0].set_title('Shape')
# plt.colorbar(im2)
ax3 = plt.subplot(a, b, 1 + b)
im3 = ax3.imshow(data[ii].max(1), vmin=mi, vmax=ma, cmap=cmap)
#plt.colorbar(im3)
for cc in range(C):
ax4.append(plt.subplot(a, b, 2 + b + cc))
comp = shapes[components[cc]].max(1) * activity_NonNegative[
components[cc], ii]
ma2 = max(shapes[components[cc]].max(1)) * max(
activity_NonNegative[components[cc]]) * scale
im4.append(ax4[cc].imshow(comp, vmin=0, vmax=ma2, cmap=cmap))
#plt.colorbar(im4)
ax5 = plt.subplot(a, b, 1 + 2 * b)
im5 = ax5.imshow(np.transpose(detrended_data[ii].max(2)),
vmin=mi,
vmax=ma,
cmap=cmap)
#plt.colorbar(im5)
for cc in range(C):
ax6.append(plt.subplot(a, b, 2 + 2 * b + cc))
comp = np.transpose(shapes[components[cc]].max(
2)) * activity_NonNegative[components[cc], ii]
ma2 = max(shapes[components[cc]].max(2)) * max(
activity_NonNegative[components[cc]]) * scale
im6.append(ax6[cc].imshow(comp, vmin=0, vmax=ma2, cmap=cmap))
#plt.colorbar(im6)
fig.tight_layout()
ComponentsActive = np.array([])
for cc in range(C):
ComponentsActive = np.append(
ComponentsActive,
np.nonzero(activity_NonNegative[components[cc]]))
ComponentsActive = np.unique(ComponentsActive)
def update(tt):
ii = ComponentsActive[tt]
im1.set_data(data[ii].max(0))
im3.set_data(data[ii].max(1))
im5.set_data(np.transpose(data[ii].max(2)))
for cc in range(C):
im2[cc].set_data(shapes[components[cc]].max(0) *
activity_NonNegative[components[cc], ii])
im4[cc].set_data(shapes[components[cc]].max(1) *
activity_NonNegative[components[cc], ii])
im6[cc].set_data(
np.transpose(shapes[components[cc]].max(2)) *
activity_NonNegative[components[cc], ii])
title.set_text('Data, time = %.1f' % ii)
if save_video == True:
writer = animation.writers['ffmpeg'](fps=10)
ani = animation.FuncAnimation(fig,
update,
frames=len(ComponentsActive),
blit=True,
repeat=False)
if restrict_support == True:
ani.save(Results_folder + 'Shapes_Restricted' +
resultsName + '.mp4',
dpi=dpi,
writer=writer)
else:
ani.save(Results_folder + 'Shapes_' + resultsName + '.mp4',
dpi=dpi,
writer=writer)
else:
ani = animation.FuncAnimation(fig,
update,
frames=len(ComponentsActive),
blit=True,
repeat=False)
plt.show()
#%% normalize denoised data range
# denoised_data=10*denoised_data/np.max(denoised_data)/scale
denoised_data = denoised_data / np.percentile(denoised_data, 99.5)
denoised_data[denoised_data > 1] = 1
#%% ##### Plot denoised projection - Results
if plot_residual_projections == True:
dims = data.shape
cmap = color_map
pic_residual = percentile(residual, 95, axis=0)
pic_denoised = max_intensity(denoised_data, axis=0)
pic_data = percentile(data, 95, axis=0)
left = 0.05 # the left side of the subplots of the figure
right = 0.95 # the right side of the subplots of the figure
bottom = 0.05 # the bottom of the subplots of the figure
top = 0.95 # the top of the subplots of the figure
wspace = 0.05 # the amount of width reserved for blank space between subplots
hspace = 0.05 # the amount of height reserved for white space between subplots
fig1 = plt.figure(figsize=(11, 18))
mi = min(pic_data)
ma = max(pic_data)
ax = plt.subplot(311)
im = ax.imshow(pic_data.max(0), vmin=mi, vmax=ma, cmap=cmap)
ax.set_title('Data')
plt.colorbar(im)
plt.setp(ax, xticks=[], yticks=[])
ax2 = plt.subplot(312)
im2 = ax2.imshow(max_intensity(pic_denoised, 0), interpolation='None')
ax2.set_title('Denoised')
plt.setp(ax, xticks=[], yticks=[])
plt.colorbar(im2)
ax3 = plt.subplot(313)
im3 = ax3.imshow(pic_residual.max(0), cmap=cmap)
ax3.set_title('Residual')
plt.setp(ax, xticks=[], yticks=[])
plt.colorbar(im3)
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
fig2 = plt.figure(figsize=(11, 18))
mi = min(pic_data)
ma = max(pic_data)
ax = plt.subplot(311)
im = ax.imshow(pic_data.max(1), vmin=mi, vmax=ma, cmap=cmap)
ax.set_title('Data')
plt.colorbar(im)
plt.setp(ax, xticks=[], yticks=[])
ax2 = plt.subplot(312)
im2 = ax2.imshow(max_intensity(pic_denoised, 1), interpolation='None')
ax2.set_title('Denoised')
plt.colorbar(im2)
plt.setp(ax, xticks=[], yticks=[])
ax3 = plt.subplot(313)
im3 = ax3.imshow(pic_residual.max(1), cmap=cmap)
ax3.set_title('Residual')
plt.colorbar(im3)
plt.setp(ax, xticks=[], yticks=[])
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
fig3 = plt.figure(figsize=(11, 18))
mi = min(pic_data)
ma = max(pic_data)
ax = plt.subplot(311)
im = ax.imshow(pic_data.max(2).T, vmin=mi, vmax=ma, cmap=cmap)
ax.set_title('Data')
plt.colorbar(im)
plt.setp(ax, xticks=[], yticks=[])
ax2 = plt.subplot(312)
im2 = ax2.imshow(np.transpose(max_intensity(pic_denoised, 2),
[1, 0, 2]),
interpolation='None')
ax2.set_title('denoised')
plt.setp(ax, xticks=[], yticks=[])
plt.colorbar(im2)
ax3 = plt.subplot(313)
im3 = ax3.imshow(np.transpose(pic_residual.max(2)), cmap=cmap)
ax3.set_title('Residual')
plt.colorbar(im3)
plt.setp(ax, xticks=[], yticks=[])
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot == True:
pp = PdfPages(Results_folder +
'Data_Denoised_Residual_Projections' + resultsName +
'.pdf')
pp.savefig(fig1)
pp.savefig(fig2)
pp.savefig(fig3)
pp.close()
#fig = plt.figure()
#plt.plot(MSE_array)
#plt.xlabel('Iteration')
#plt.ylabel('MSE')
#plt.show()
#%% ##### Plot denoised slices - Results
# z_slices=[0,2,4,6,8] #which z slices to look at slice plots/videos
z_slices = range(dims[min_dim +
1]) #which z slices to look at slice plots/videos
D = len(z_slices)
if plot_residual_slices == True:
dims = data.shape
cmap = color_map
pic_residual = percentile(residual, 95, axis=0)
pic_denoised = max_intensity(denoised_data, axis=0)
pic_data = percentile(data, 95, axis=0)
a = 3 #number of rows
fig1 = plt.figure(figsize=(18, 11))
mi = min(pic_data)
ma = max(pic_data)
for kk in range(D):
ax2 = plt.subplot(a, D, kk + 1)
temp = np.squeeze(
np.take(pic_denoised, (z_slices[kk], ), axis=min_dim))
im2 = ax2.imshow(temp, interpolation='None')
ax2.set_title('Denoised')
plt.setp(ax2, xticks=[], yticks=[])
plt.colorbar(im2)
ax = plt.subplot(a, D, kk + D + 1)
temp = np.squeeze(np.take(pic_data, (z_slices[kk], ),
axis=min_dim))
im = ax.imshow(temp, vmin=mi, vmax=ma, cmap=cmap)
ax.set_title('Data')
plt.colorbar(im)
plt.setp(ax, xticks=[], yticks=[])
ax3 = plt.subplot(a, D, kk + 2 * D + 1)
temp = np.squeeze(
np.take(pic_residual, (z_slices[kk], ), axis=min_dim))
im3 = ax3.imshow(temp, cmap=cmap)
ax3.set_title('Residual')
plt.setp(ax3, xticks=[], yticks=[])
plt.colorbar(im3)
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
if save_plot == True:
pp = PdfPages(Results_folder +
'Data_Denoised_Residual_Slice_' + resultsName +
'.pdf')
pp.savefig(fig1)
pp.close()
#fig = plt.figure()
#plt.plot(MSE_array)
#plt.xlabel('Iteration')
#plt.ylabel('MSE')
#plt.show()
#%% ##### Video Projections Residual
if video_residual:
fig = plt.figure(figsize=(16, 7))
mi = 0
ma = max(data) * scale
mi3 = 0
ma3 = max(residual) * scale
ii = 0
#import colormaps as cmaps
#cmap=cmaps.viridis
cmap = color_map
spatial_dims_ind = range(len(dims) - 1)
D = len(spatial_dims_ind)
a = D
b = 3
im_array = []
transpose_flags = []
for kk in range(D):
transpose_flags += [False]
temp = np.shape(data[ii].max(spatial_dims_ind[kk]))
if temp[0] > temp[1]:
transpose_flags[kk] = True
for kk in range(D):
ax1 = plt.subplot(a, b, D * kk + 1)
if transpose_flags[kk] == False:
pic = max_intensity(denoised_data[ii], spatial_dims_ind[kk])
else:
pic = np.transpose(
max_intensity(denoised_data[ii], spatial_dims_ind[kk]),
[1, 0, 2])
im_array += [ax1.imshow(pic, interpolation='None')]
ax1.set_title('Denoised')
plt.colorbar(im_array[-1])
plt.setp(ax1, xticks=[], yticks=[])
ax2 = plt.subplot(a, b, D * kk + 2)
if transpose_flags[kk] == False:
pic = data[ii].max(spatial_dims_ind[kk])
else:
pic = np.transpose(data[ii].max(spatial_dims_ind[kk]))
im_array += [ax2.imshow(pic, vmin=mi, vmax=ma, cmap=cmap)]
title = ax2.set_title('Data')
plt.colorbar(im_array[-1])
plt.setp(ax2, xticks=[], yticks=[])
ax3 = plt.subplot(a, b, D * kk + 3)
if transpose_flags[kk] == False:
pic = residual[ii].max(spatial_dims_ind[kk])
else:
pic = np.transpose(residual[ii].max(spatial_dims_ind[kk]))
im_array += [ax3.imshow(pic, vmin=mi3, vmax=ma3, cmap=cmap)]
ax3.set_title('Residual')
plt.colorbar(im_array[-1])
plt.setp(ax3, xticks=[], yticks=[])
# fig.tight_layout()
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
def update(ii):
for kk in range(D):
if transpose_flags[kk] == False:
im_array[kk * D].set_data(
max_intensity(denoised_data[ii], spatial_dims_ind[kk]))
im_array[kk * D + 1].set_data(data[ii].max(
spatial_dims_ind[kk]))
im_array[kk * D + 2].set_data(residual[ii].max(
spatial_dims_ind[kk]))
else:
im_array[kk * D].set_data(
np.transpose(
max_intensity(denoised_data[ii],
spatial_dims_ind[kk]), [1, 0, 2]))
im_array[kk * D + 1].set_data(
np.transpose(data[ii].max(spatial_dims_ind[kk])))
im_array[kk * D + 2].set_data(
np.transpose(residual[ii].max(spatial_dims_ind[kk])))
title.set_text('Data, time = %.1f' % ii)
if save_video == True:
writer = animation.writers['ffmpeg'](fps=10)
ani = animation.FuncAnimation(fig,
update,
frames=len(data),
blit=False,
repeat=False)
ani.save(Results_folder + 'Data_Denoised_Residual_Projections' +
resultsName + '.mp4',
dpi=dpi,
writer=writer)
else:
ani = animation.FuncAnimation(fig,
update,
frames=len(data),
blit=False,
repeat=False)
plt.show()
#%% ##### Video Slices Residual
# z_slices=[0,2,4,6,8] #which z slices to look at slice plots/videos
z_slices = range(dims[min_dim +
1]) #which z slices to look at slice plots/videos
if video_slices:
fig = plt.figure(figsize=(16, 7))
mi = 0
ma = max(data) * scale
mi3 = 0
ma3 = max(residual) * scale
ii = 0
#import colormaps as cmaps
#cmap=cmaps.viridis
cmap = color_map
a = 3
D = len(z_slices) #number of spatial dimensions
im_array = []
transpose_flag = True
for kk in range(D):
ax1 = plt.subplot(a, D, kk + 1)
temp = np.squeeze(
np.take(denoised_data[ii], (z_slices[kk], ), axis=min_dim))
if transpose_flag == False:
pic = temp
else:
pic = np.transpose(temp, [1, 0, 2])
im_array += [ax1.imshow(pic, interpolation='None')]
ax1.set_title('Denoised, z=' + str(z_slices[kk] + 1))
plt.colorbar(im_array[-1])
plt.setp(ax1, xticks=[], yticks=[])
ax2 = plt.subplot(a, D, kk + D + 1)
temp = np.squeeze(np.take(data[ii], (z_slices[kk], ),
axis=min_dim))
if transpose_flag == False:
pic = temp
else:
pic = np.transpose(temp)
im_array += [ax2.imshow(pic, vmin=mi, vmax=ma, cmap=cmap)]
title = ax2.set_title('Data')
plt.colorbar(im_array[-1])
plt.setp(ax2, xticks=[], yticks=[])
ax3 = plt.subplot(a, D, kk + 2 * D + 1)
temp = np.squeeze(
np.take(residual[ii], (z_slices[kk], ), axis=min_dim))
if transpose_flag == False:
pic = temp
else:
pic = np.transpose(temp)
im_array += [ax3.imshow(pic, vmin=mi3, vmax=ma3, cmap=cmap)]
ax3.set_title('Residual')
plt.colorbar(im_array[-1])
plt.setp(ax3, xticks=[], yticks=[])
# fig.tight_layout()
plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
def update(ii):
for kk in range(D):
temp1 = np.squeeze(
np.take(denoised_data[ii], (z_slices[kk], ), axis=min_dim))
temp2 = np.squeeze(
np.take(data[ii], (z_slices[kk], ), axis=min_dim))
temp3 = np.squeeze(
np.take(residual[ii], (z_slices[kk], ), axis=min_dim))
if transpose_flag == False:
im_array[a * kk].set_data(temp1)
im_array[a * kk + 1].set_data(temp2)
im_array[a * kk + 2].set_data(temp3)
else:
im_array[a * kk].set_data(np.transpose(temp1, [1, 0, 2]))
im_array[a * kk + 1].set_data(np.transpose(temp2))
im_array[a * kk + 2].set_data(np.transpose(temp3))
title.set_text('Data, time = %.1f' % ii)
if save_video == True:
writer = animation.writers['ffmpeg'](fps=10)
ani = animation.FuncAnimation(fig,
update,
frames=len(data),
blit=False,
repeat=False)
ani.save(Results_folder + 'Data_Denoised_Residual_Slices' +
resultsName + '.mp4',
dpi=dpi,
writer=writer)
else:
ani = animation.FuncAnimation(fig,
update,
frames=len(data),
blit=False,
repeat=False)
plt.show()