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visualization.py
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import pylab
import contrib.modelfit
import numpy
import __main__
from contrib.modelfit import *
from contrib.JanA.ofestimation import *
def showRFS(rfs,cog=False,centers=None,joinnormalize=True,axis=False):
print numpy.shape(rfs)
pylab.figure()
m = numpy.max([numpy.abs(numpy.min(rfs)),numpy.abs(numpy.max(rfs))])
for i in xrange(0,len(rfs)):
pylab.subplot(15,15,i+1)
w = numpy.array(rfs[i])
pylab.show._needmain=False
if not joinnormalize:
m = numpy.max([numpy.abs(numpy.min(w)),numpy.abs(numpy.max(w))])
pylab.imshow(w,vmin=-m,vmax=m,interpolation='nearest',cmap=pylab.cm.RdBu_r)
if centers != None:
cir = Circle( (centers[i][0],centers[i][1]), radius=1,color='r')
pylab.gca().add_patch(cir)
if cog:
xx,yy = contrib.modelfit.centre_of_gravity(rfs[i])
cir = Circle( (xx,yy), radius=1,color='b')
pylab.gca().add_patch(cir)
if not axis:
pylab.axis('off')
i+=1
def showRFSinCorticalSpace(rfs,locations,joinnormalize=True,scatter_value=None,scatter_value_cmap=pylab.cm.gray, colorbar=False):
pylab.figure(figsize=(24,24),dpi=100)
a = pylab.axes([0.0,0.0,1.0,1.0])
pylab.xticks([])
pylab.yticks([])
m = numpy.max([numpy.abs(numpy.min(rfs)),numpy.abs(numpy.max(rfs))])
for i in xrange(0,len(rfs)):
x = locations[i][0]/260
y = locations[i][1]/260
if not joinnormalize:
m = numpy.max([numpy.abs(numpy.min(rfs[i])),numpy.abs(numpy.max(rfs[i]))])
if scatter_value != None:
pylab.axes([x-0.0225,y-0.0225,0.045,0.045])
rect = numpy.zeros((100,100))+ scatter_value[i]
pylab.imshow(rect,vmin=0,vmax=1,cmap=scatter_value_cmap,zorder=i)
pylab.axis('off')
if colorbar and i == 0:
ax = pylab.axes([0.85,0.05,0.1,0.3])
pylab.axis('off')
cbar = pylab.colorbar(ax = ax,ticks=[0,0.5,1.0])
for t in cbar.ax.get_yticklabels():
t.set_fontsize(20)
pylab.axes([x-0.02,y-0.02,0.04,0.04])
pylab.imshow(rfs[i],vmin=-m,vmax=m,interpolation='nearest',cmap=pylab.cm.RdBu_r,zorder = i+0.5)
pylab.axis('off')
if colorbar and numpy.argmax([xxx[0] for xxx in locations]) == i:
ax = pylab.axes([x-0.01,y-0.02,0.04,0.04])
pylab.axis('off')
cbar = pylab.colorbar(ax = ax,shrink=1.8,aspect=7,ticks=[-m,0,m])
cbar.ax.set_yticklabels(["min", "0", "max"])
for t in cbar.ax.get_yticklabels():
t.set_fontsize(20)
def compareModelPerformanceWithRPI(training_set,validation_set,training_inputs,validation_inputs,pred_act,pred_val_act,raw_validation_set,sizex,sizey,modelname='Model'):
from contrib.JanA.regression import laplaceBias
num_neurons = numpy.shape(pred_act)[1]
kernel_size= numpy.shape(validation_inputs)[1]
laplace = laplaceBias(sizex,sizey)
X = numpy.mat(training_inputs)
rpi = numpy.linalg.pinv(X.T*X + __main__.__dict__.get('RPILaplaceBias',0.0001)*laplace) * X.T * training_set
rpi_pred_act = training_inputs * rpi
rpi_pred_val_act = validation_inputs * rpi
showRFS(numpy.reshape(numpy.array(rpi.T),(-1,sizex,sizey)))
print numpy.shape(numpy.mat(training_set))
print numpy.shape(numpy.mat(pred_act))
ofs = contrib.JanA.ofestimation.run_nonlinearity_detection(numpy.mat(training_set),numpy.mat(pred_act),num_bins=10,display=True,name=(modelname+'_piece_wise_nonlinearity.png'))
pred_act_t = numpy.mat(contrib.JanA.ofestimation.apply_output_function(numpy.mat(pred_act),ofs))
pred_val_act_t = numpy.mat(contrib.JanA.ofestimation.apply_output_function(numpy.mat(pred_val_act),ofs))
ofs = contrib.JanA.ofestimation.run_nonlinearity_detection(numpy.mat(training_set),numpy.mat(rpi_pred_act),num_bins=10,display=True,name='RPI_piece_wise_nonlinearity.png')
rpi_pred_act_t = numpy.mat(contrib.JanA.ofestimation.apply_output_function(numpy.mat(rpi_pred_act),ofs))
rpi_pred_val_act_t = numpy.mat(contrib.JanA.ofestimation.apply_output_function(numpy.mat(rpi_pred_val_act),ofs))
pylab.figure()
pylab.title('RPI')
for i in xrange(0,num_neurons):
pylab.subplot(11,11,i+1)
pylab.plot(rpi_pred_val_act[:,i],validation_set[:,i],'o')
contrib.modelfit.release_fig('RPI_val_relationship.png')
pylab.figure()
pylab.title(modelname)
for i in xrange(0,num_neurons):
pylab.subplot(11,11,i+1)
pylab.plot(pred_val_act[:,i],validation_set[:,i],'o')
contrib.modelfit.release_fig('GLM_val_relationship.png')
pylab.figure()
pylab.title('RPI')
for i in xrange(0,num_neurons):
pylab.subplot(11,11,i+1)
pylab.plot(rpi_pred_val_act_t[:,i],validation_set[:,i],'o')
contrib.modelfit.release_fig('RPI_t_val_relationship.png')
pylab.figure()
pylab.title(modelname)
for i in xrange(0,num_neurons):
pylab.subplot(11,11,i+1)
pylab.plot(pred_val_act_t[:,i],validation_set[:,i],'o')
contrib.modelfit.release_fig('GLM_t_val_relationship.png')
pylab.figure()
print numpy.shape(numpy.mean(numpy.power(validation_set - rpi_pred_val_act_t,2)[:,:num_neurons],0))
print numpy.shape(numpy.mean(numpy.power(validation_set - pred_val_act,2)[:,:num_neurons],0))
pylab.plot(numpy.mean(numpy.power(validation_set - rpi_pred_val_act_t,2)[:,:num_neurons],0),numpy.mean(numpy.power(validation_set - pred_val_act,2)[:,:num_neurons],0),'o')
pylab.hold(True)
pylab.plot([0.0,1.0],[0.0,1.0])
pylab.xlabel('RPI')
pylab.ylabel(modelname)
contrib.modelfit.release_fig('GLM_vs_RPI_MSE.png')
print '\n \n RPI \n'
print 'Without TF'
contrib.modelfit.performance_analysis(training_set,validation_set,rpi_pred_act,rpi_pred_val_act,raw_validation_set,85)
print 'With TF'
(signal_power,noise_power,normalized_noise_power,training_prediction_power,rpi_validation_prediction_power,signal_power_variance) = contrib.modelfit.performance_analysis(training_set,validation_set,rpi_pred_act_t,rpi_pred_val_act_t,raw_validation_set,85)
print '\n \n', modelname, '\n'
print 'Without TF'
(signal_power,noise_power,normalized_noise_power,training_prediction_power,validation_prediction_power,signal_power_variance) = contrib.modelfit.performance_analysis(training_set,validation_set,pred_act,pred_val_act,raw_validation_set,85)
print 'With TF'
(signal_power_t,noise_power_t,normalized_noise_power_t,training_prediction_power_t,validation_prediction_power_t,signal_power_variance_t) = contrib.modelfit.performance_analysis(training_set,validation_set,pred_act_t,pred_val_act_t,raw_validation_set,85)
significant = numpy.array(numpy.nonzero((numpy.array(normalized_noise_power) < 85) * 1.0))[0]
print significant
pylab.figure()
pylab.plot(rpi_validation_prediction_power[significant],validation_prediction_power[significant],'o')
pylab.hold(True)
pylab.plot([0.0,1.0],[0.0,1.0])
pylab.xlabel('RPI')
pylab.ylabel(modelname)
contrib.modelfit.release_fig('GLM_vs_RPI_prediction_power.png')
pylab.figure()
pylab.plot(rpi_validation_prediction_power[significant],validation_prediction_power_t[significant ],'o')
pylab.hold(True)
pylab.plot([0.0,1.0],[0.0,1.0])
pylab.xlabel('RPI')
pylab.ylabel(modelname+'+TF')
contrib.modelfit.release_fig('GLM_vs_RPI_prediction_power.png')
def extractContours(RFs):
a = 1
tr = 140
size = numpy.shape(RFs)[1]
on_contours = []
off_contours = []
onoff_contours = []
on_filled = []
off_filled = []
showRFS(RFs)
for rf in RFs:
import PIL
import Image
#pylab.figure()
#pylab.imshow(rf,interpolation='nearest')
image = Image.new('F',(size,size))
image.putdata(rf.T.flatten()/400+0.5)
image = image.resize((int(size*6), int(size*6)), Image.ANTIALIAS)
rf = (numpy.array(image.getdata()).reshape(int(size*6), int(size*6))-0.5)*400
rf = rf.T
#pylab.figure()
#pylab.imshow(rf,interpolation='nearest')
rf_on = rf * (rf > tr )
rf_off = rf * (rf < -tr )
on = extractContour(rf_on)
off = extractContour(-rf_off)
on_contours.append(on)
off_contours.append(-off)
onoff_contours.append(on-off)
on_filled.append((rf > tr))
off_filled.append((rf < -tr))
#showRFS(onoff_contours)
#showRFS(on_contours)
#showRFS(off_contours)
return (on_contours,off_contours,onoff_contours,on_filled,off_filled)
def extractContour(rf):
size = numpy.shape(rf)[0]
rf1 = numpy.copy(rf)
for x in xrange(0,size):
for y in xrange(0,size):
flag = 0
if x+1 < size:
if rf[x+1,y] == 0:
flag=1
if x-1 >= 0:
if rf[x-1,y] == 0:
flag=1
if y-1 >= 0:
if rf[x,y-1] == 0:
flag=1
if y+1 < size:
if rf[x,y+1] == 0:
flag=1
if x+1 < size and y+1 < size:
if rf[x+1,y+1] == 0:
flag=1
if x-1 >= 0 and y-1 >= 0:
if rf[x-1,y-1] == 0:
flag=1
if x+1 < size and y-1 >= 0:
if rf[x+1,y-1] == 0:
flag=1
if x-1 >= 0 and y+1 < size:
if rf[x-1,y+1] == 0:
flag=1
rf1[x,y]*= flag
rf1[x,y]= rf1[x,y] > 0
return rf1
def OnOffCenterOfGravityPlot(rfs):
a = 1
tr = 100
on_center = []
off_center = []
for rf in rfs:
rf_on = rf * (rf > tr )
rf_off = -rf * (rf < -tr )
on_center.append(centre_of_gravity(rf_on))
off_center.append(centre_of_gravity(rf_off))
f = pylab.figure(dpi=100,facecolor='w',figsize=(3,3))
x, y = zip(*on_center)
pylab.plot(numpy.array(x),numpy.array(y),'bo')
x, y = zip(*off_center)
pylab.plot(numpy.array(x),numpy.array(y),'ro')
pylab.xlim(0.0,numpy.shape(rfs[0])[0])
pylab.ylim(0.0,numpy.shape(rfs[0])[1])
pylab.hold('on')
def visualize2DOF(pred_act1,pred_act2,act,num_bins=10):
bin_size1 = (numpy.max(pred_act1,axis=0) - numpy.min(pred_act1,axis=0))/num_bins
bin_size2 = (numpy.max(pred_act2,axis=0) - numpy.min(pred_act2,axis=0))/num_bins
of = numpy.zeros((numpy.shape(act)[1],num_bins,num_bins))
ofn = numpy.zeros((numpy.shape(act)[1],num_bins,num_bins))
for i in xrange(0,numpy.shape(act)[0]):
idx1 = numpy.round_((pred_act1[i,:]-numpy.min(pred_act1,axis=0)) / bin_size1)
idx2 = numpy.round_((pred_act2[i,:]-numpy.min(pred_act2,axis=0)) / bin_size2)
idx1 = idx1 -(idx1 >= num_bins)
idx2 = idx2 -(idx2 >= num_bins)
j=0
for (x,y) in zip(numpy.array(idx1).flatten().tolist(),numpy.array(idx2).flatten().tolist()):
of[j,x,y] = of[j,x,y] + act[i,j]
ofn[j,x,y] = ofn[j,x,y] + 1
j=j+1
of = of - (ofn <= 0)
ofn = ofn + (ofn <= 0)
of = of/ofn
showRFS(of,joinnormalize=False)
def printCorrelationAnalysis(act,val_act,pred_act,pred_val_act):
num_pres,num_neurons = numpy.shape(act)
import scipy.stats
train_c=[]
val_c=[]
for i in xrange(0,num_neurons):
train_c.append(scipy.stats.pearsonr(numpy.array(act)[:,i].flatten(),numpy.array(pred_act)[:,i].flatten())[0])
val_c.append(scipy.stats.pearsonr(numpy.array(val_act)[:,i].flatten(),numpy.array(pred_val_act)[:,i].flatten())[0])
print 'Correlation Coefficients (training/validation): ' + str(numpy.mean(train_c)) + '/' + str(numpy.mean(val_c))
return (train_c,val_c)