from numpy import min, max, percentile,asarray,ceil,sqrt

import numpy as np

import sys

from scipy.signal import welch

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,LoadResults

# 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)

# 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 #10.0 #Hz or 'color'

# 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

shapes, activity, background_shapes, background_activity,resultsName=LoadResults(SaveNames,params.Background_num)

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_activity=0.95,threshold_shape=0.99,sig=10)

if Prune==True:

# deleted_indices=[5,9,11,14,15,17,24]+range(25,36)

shapes,activity,L=PruneComponents(shapes,activity,L,[0,params.TargetAreaRatio[1]])

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 = %.1f' % ii)

elif frame_rate=='color':#%for Fisseq

title.set_text('Data, frame = {0:d}, color={1:d}'.format(ii,np.mod(ii,4)))

else:

title.set_text('Data, time = %.2f sec' % (old_div(ii,frame_rate)))

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.percentile(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()

#

#fig = plt.figure(figsize=(dims[1] , dims[2]))

#mi = min(data)

#ma = max(data)

#dpi=100

#

#

#ax = plt.subplot(131)

#im = ax.imshow(data[ii], vmin=mi, vmax=ma)

#ax2 = plt.subplot(132)

#im2 = ax2.imshow(residual[ii], vmin=mi, vmax=ma)

#ax3 = plt.subplot(133)

#im3 = ax3.imshow(denoised_data[ii], vmin=mi, vmax=ma)

#def update(ii):

# im.set_data(data[ii])

## ax.set_title('Data t='+str(ii))

# im2.set_data(residual[ii])

## ax2.set_title('Residual')

# im3.set_data(denoised_data[ii])

## ax3.set_title('Denoised')

#

#

#ani = animation.FuncAnimation(fig, update, frames=len(data), blit=False, interval=30, repeat=False)

#ani.save('MaxProjections'+str(dd)+'.mp4',writer=writer,dpi=dpi)

#%% Figure for grant

#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 = 0.1 # the bottom of the subplots of the figure

#top = 0.95 # the top of the subplots of the figure