######################## ## PLOT WEIGHTS ######################## #L1Dat = None #arborIdx = np.arange(0,4) arborIdx = None i_frame = -1 # index, not actual frame number, -1 for last margin = 2 #pixels plotColor = True showPlot = False savePlot = True saveName = output_dir+'analysis/'+weights_chk[:-4]+'.png' weight_list = pw.plotWeights(weightChkDat,L1Dat,arborIdx,i_frame,margin,plotColor,showPlot,savePlot,saveName) #plotColor = True #showPlot = True #savePlot = True #saveName = output_dir+'analysis/'+weights_chk[:-4]+'_sorted.png' # #sorted_weight_list = pw.plotSortedWeights(weight_list,L1Dat,plotColor,showPlot,savePlot,saveName) # ######################## ## CLOSE FILESTREAMS ######################## #input_activityFile.close() #l1_activityFile.close() #err_activityFile.close()
#(L1Struct,L1Hdr) = pv.get_pvp_data(l1_activityFile,progressPeriod,lastFrame,startFrame,skipFrames)
# Gar Method
# divide the L2 norm of the residual by the L2 of the input to the
# residiual (i.e the image) to get % error
#TODO: pass param for error method, there are 3 that I know of. Gar method, pSNR, SNR
#print('Err:')
#(errStruct,errHdr) = pv.get_pvp_data(err_activityFile,progressPeriod,lastFrame,startFrame,skipFrames)
#Recon error?
# ABS gives distance from 0.
# Averaging over the 512x512 array gives err per frame
#plt.plot(np.average(np.average(np.abs(err_outStruct["values"]),2),2))
#plt.show()
print('Weights:')
(weightStruct,weightsHdr) = pv.get_pvp_data(weightsFile,progressPeriod,lastFrame,startFrame,skipFrames)
#l1_activityFile.close()
#err_activityFile.close()
weightsFile.close()
i_arbor = 0
i_frame = 1 # index, not actual frame number
margin = 2 #pixels
showPlot = True
savePlot = True
saveName = output_dir+'analysis/'+weights[:-4]+'_'+str(i_frame).zfill(5)+'.png'
weight_mat = pw.plotWeights(weightStruct,i_arbor,i_frame,margin,showPlot,savePlot,saveName)
# divide the L2 norm of the residual by the L2 of the input to the
# residiual (i.e the image) to get % error
#TODO: pass param for error method, there are 3 that I know of. Gar method, pSNR, SNR
#print('Err:')
#(errStruct,errHdr) = pv.get_pvp_data(err_activityFile,progressPeriod,lastFrame,startFrame,skipFrames)
#Recon error?
# ABS gives distance from 0.
# Averaging over the 512x512 array gives err per frame
#plt.plot(np.average(np.average(np.abs(err_outStruct["values"]),2),2))
#plt.show()
print('Weights:')
(weightStruct, weightsHdr) = pv.get_pvp_data(weightsFile, progressPeriod,
lastFrame, startFrame, skipFrames)
#l1_activityFile.close()
#err_activityFile.close()
weightsFile.close()
i_arbor = 0
i_frame = 1 # index, not actual frame number
margin = 2 #pixels
showPlot = True
savePlot = True
saveName = output_dir + 'analysis/' + weights[:-4] + '_' + str(i_frame).zfill(
5) + '.png'
weight_mat = pw.plotWeights(weightStruct, i_arbor, i_frame, margin, showPlot,
savePlot, saveName)
#percErr = pe.plotPercErr(inputDat,reconDat,showPlot,savePlot,'./percTest.png')
########################
## PLOT WEIGHTS
########################
#L1Dat = None
#arborIdx = np.arange(0,4)
arborIdx = None
i_frame = -1 # index, not actual frame number, -1 for last
margin = 2 #pixels
plotColor = True
showPlot = False
savePlot = True
saveName = output_dir + 'analysis/' + weights_chk[:-4] + '.png'
weight_list = pw.plotWeights(weightChkDat, L1Dat, arborIdx, i_frame, margin,
plotColor, showPlot, savePlot, saveName)
#plotColor = True
#showPlot = True
#savePlot = True
#saveName = output_dir+'analysis/'+weights_chk[:-4]+'_sorted.png'
#
#sorted_weight_list = pw.plotSortedWeights(weight_list,L1Dat,plotColor,showPlot,savePlot,saveName)
#
########################
## CLOSE FILESTREAMS
########################
#input_activityFile.close()
#l1_activityFile.close()
#err_activityFile.close()
