In [1]:
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from scipy import io
import scipy.io as sio
%matplotlib inline
import pylab
import csv
from Tkinter import Tk
from tkFileDialog import askopenfilename
from tkFileDialog import askdirectory
import nibabel as nb
from scipy import io
#from nifti import NiftiImage
import nibabel as nb
from scipy.interpolate import interp1d
from scipy import ndimage
In [2]:
# from http://stackoverflow.com/questions/3579568/choosing-a-file-in-python-with-simple-dialog
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
filename = askopenfilename() # show an "Open" dialog box and return the path to the selected file
print(filename)
In [3]:
Ua=sio.loadmat(filename)
DT=Ua['TSo']
DT.shape
Out[3]:
In [4]:
# from http://stackoverflow.com/questions/3579568/choosing-a-file-in-python-with-simple-dialog
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
filename2 = askopenfilename() # show an "Open" dialog box and return the path to the selected file
print(filename2)
In [5]:
img1 = nb.load(filename2)
data = img1.get_data()
S=data.shape
S
Out[5]:
In [6]:
S=data.shape
S
Out[6]:
Z-score
In [7]:
Demean=np.zeros(S)
Dmaps=np.zeros(S)
Dvar=np.zeros(S)
Var=np.zeros(S[3])
D2=np.zeros([S[0],S[1],5,S[3]])
Tvar=np.zeros(S[3])
In [8]:
for i in range(S[3]):
Demean[:,:,:,i]=data[:,:,:,i]-np.mean(np.mean(np.mean(data[:,:,:,i],0),0),0)
In [9]:
for i in range(S[3]):
Dsq=np.reshape(Demean[:,:,:,i],S[0]*S[1]*S[2])
Var[i]=np.sqrt(np.var(Dsq))
Dvar=Demean[:,:,:,i]/Var[i]
Dmaps[:,:,:,i]=Dvar-2.5
Tvar[i]=np.var(DT[i,:])
Dmaps[Dmaps<0]=0
In [10]:
# from http://stackoverflow.com/questions/3579568/choosing-a-file-in-python-with-simple-dialog
from Tkinter import Tk
from tkFileDialog import askopenfilename
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
filenameM = askopenfilename() # show an "Open" dialog box and return the path to the selected file
print(filenameM)
img1 = nb.load(filenameM)
Masks = img1.get_data()
Sm=Masks.shape
Masks=np.array(Masks)
In [11]:
filenameM='/home/sophie/LargeRegionList'
with open(filenameM) as f:
content = f.readlines()
Names=[Line.replace('\n','').split(' ') for Line in content]
RegionName=[Names[i][1] for i in range(12)]
Num=[int(Names[i][0]) for i in range(12)]
In [12]:
RegionName
Out[12]:
In [13]:
Dmaps.shape
Out[13]:
In [14]:
M=np.zeros((S[3],13))
Mapmean=np.zeros(S[3])
MMasks=np.zeros(13)
In [15]:
for i in range(S[3]):
Mapmean[i]=np.mean(np.mean(np.mean(Dmaps[:,:,:,i])))
for j in range(12):
MMasks[j]=np.mean(np.mean(np.mean(Masks[:,:,:,j])))
if MMasks[j]:
M[i,j]=np.mean(np.mean(np.mean(Masks[:,:,:,j]*Dmaps[:,:,:,i])))/(MMasks[j]*Mapmean[i])
In [16]:
CompMainName=S[3]*['']
CompNameAdd=np.zeros((S[3],12))
for i in range(S[3]):
Max=np.max(M[i,:])
I=np.argmax(M[i,:])+1
for j in range(12):
J=[l for l in range(12) if Num[l]==(j+1)]
if M[i,j]>0.2*Max:
CompNameAdd[i,J]=1
J=[l for l in range(12) if Num[l]==I]
if J!= []:
CompMainName[i]=Names[np.array(J)][0]
In [17]:
J
Out[17]:
In [18]:
pylab.rcParams['figure.figsize'] = (13, 2.5)
h=5
tot=0
GoodICAnat=np.zeros(S[3])
for l in range(12):
Final_maps=np.zeros((S[0],S[1],3))
Fmap=np.zeros((S[0],S[1],3))
C=np.zeros(3)
n=0
for i in range(len(CompMainName)):
Dmmv=np.mean(data[:,:,:,i],2)
Dmmv[Dmmv<0.2*np.max(np.max(np.max(Dmmv)))]=0
C=np.squeeze(np.random.rand(3,1))
labeled, nrobject=ndimage.label(Dmmv>0)
if CompMainName[i]==Names[l][0] and (sum(CompNameAdd[i,:])<5) and nrobject<200:
n=n+1
for k in range(3):
Fmap[:,:,k]=0.7*Dmmv*C[k]/np.max(C)
Final_maps=Final_maps+Fmap
#plt.plot(Time_fluoICA.T,(DT[:,i]/np.sqrt(np.var(DT[:,i]))-h*n+2),color=C/2)
plt.plot((DT[:,i]/np.sqrt(np.var(DT[:,i]))-h*n+2),color=C/2)
tot=tot+1
GoodICAnat[i]=1
if n!=0:
print(RegionName[l])
plt.show()
FM=Final_maps/np.max(np.max(Final_maps))
FM[FM<0.1]=0
plt.imshow(FM,interpolation='none')
plt.show()
frame1 = plt.gca()
frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
In [49]:
BadICs=[6,73,95,65,25,98,99,116,101,79,23,40,48,77,92,100,76,27,75,43,102,60,67,0,1,2,4,7,8,10,12,16,17,20,30,31,32,38,44,93,13,26,29,39,47,62,33,5,35,41,42,45,46,50,51,56,57,58,59,68,78,81,83,87,96,103,37,84,71,15,21,91,64,80]
In [50]:
for idx in BadICs:
GoodICAnat[idx] = 0.0
In [51]:
pylab.rcParams['figure.figsize'] = (13, 2.5)
h=5
tot=0
NumberInLargeRegion=np.zeros(13)
for l in range(12):
Final_maps=np.zeros((S[0],S[1],3))
Fmap=np.zeros((S[0],S[1],3))
C=np.zeros(3)
n=0
for i in range(len(CompMainName)):
Dmmv=np.mean(data[:,:,:,i],2)
Dmmv[Dmmv<0.2*np.max(np.max(np.max(Dmmv)))]=0
C=np.squeeze(np.random.rand(3,1))
labeled, nrobject=ndimage.label(Dmmv>0)
if CompMainName[i]==Names[l][0] and (sum(CompNameAdd[i,:])<5) and nrobject<200 and GoodICAnat[i]==1:
n=n+1
for k in range(3):
Fmap[:,:,k]=0.7*Dmmv*(C[k]+0.2)/np.max(C+0.2)
Final_maps=Final_maps+Fmap
#plt.plot(Time_fluoICA.T,(DT[:,i]/np.sqrt(np.var(DT[:,i]))-h*n+2),color=C/2)
plt.plot((DT[:,i]/np.sqrt(np.var(DT[:,i]))-h*n+2),color=C/2)
tot=tot+1
GoodICAnat[i]=1
print(i)
if n!=0:
print(RegionName[l])
plt.show()
FM=Final_maps/np.max(np.max(Final_maps))
FM[FM<0.1]=0
plt.imshow(FM,interpolation='none')
plt.show()
frame1 = plt.gca()
frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
NumberInLargeRegion[l]=n
In [52]:
# Output number of component per region
np.savetxt('/'.join(filename.split('/')[:-1])+'/NumberInLargeRegionsV2.txt',NumberInLargeRegion)
In [ ]:
In [ ]: