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clear all
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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
import nibabel as nb
from scipy.interpolate import interp1d
from scipy import ndimage
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from sklearn import linear_model
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# 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)
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Ua=sio.loadmat(filename)
DT=Ua['TSo']
DT.shape
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# 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)
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img1 = nb.load(filename2)
data = img1.get_data()
S=data.shape
S
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Z-score
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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])
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for i in range(S[3]):
Demean[:,:,:,i]=data[:,:,:,i]-np.mean(np.mean(np.mean(data[:,:,:,i],0),0),0)
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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
Tvar[i]=np.var(DT[i,:])
Dmaps[Dmaps<0]=0
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# 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)
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Ua=sio.loadmat(filename)
Xk=Ua['Xk']
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# 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
filenamet = askopenfilename() # show an "Open" dialog box and return the path to the selected file
print(filenamet)
nimt=nb.load(filenamet)
Dtemp=np.squeeze(nimt.get_data())
Dtemp.shape
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%%javascript
IPython.OutputArea.auto_scroll_threshold =4000;
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if S[2]>5:
Nstack=5
Int100=[(i+1)*100/Nstack for i in range(Nstack)]
Percs=np.percentile(range(S[2]),Int100)
Indices=np.split(range(S[2]),Percs)
D1=np.zeros([S[0],S[1],Nstack])
Dmean=np.squeeze(data[:,:,range(Nstack),2])
for i in range(Nstack):
Vmean=np.mean(Dtemp[:,:,Indices[i]],2)
Dmean[:,:,i]=Vmean
else:
Nstack=S[2]
D1=np.zeros([S[0],S[1],S[2]])
Dmean=data[:,:,range(S[2])]
Dmean=np.squeeze(Dtemp[:,:,:])
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for j in range(S[3]):
a=''
if S[2]>5:
for i in range(Nstack):
V=Dmaps[:,:,Indices[i],j]
D1[:,:,i]=np.max(V,2)
D2[:,:,:,j]=D1
D1[D1==0]=np.nan
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plt.imshow(Dmean[:,:,1],cmap=plt.cm.gray)
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my_cmap=plt.cm.jet
my_cmap.set_bad(alpha=0)
Good_ICs=np.zeros(S[3])
Label_ICs=[]
pylab.rcParams['figure.figsize'] = (15, 2.5)
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algorithm = linear_model.LinearRegression()
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Sxk=Xk.shape
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Sxk
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X=np.zeros((Sxk[0],2))
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X[:,0]=(Xk[:,0]-np.mean(Xk[:,0]))/np.std(Xk[:,0])
X[:,1]=(Xk[:,1]-np.mean(Xk[:,1]))/np.std(Xk[:,1])
#X[:,2]=(Xk[:,3]-np.mean(Xk[:,3]))/np.std(Xk[:,3])
#X[:,3]=(Xk[:,4]-np.mean(Xk[:,4]))/np.std(Xk[:,4])
#X[:,4]=(Xk[:,6]-np.mean(Xk[:,6]))/np.std(Xk[:,6])
#X[:,5]=(Xk[:,7]-np.mean(Xk[:,7]))/np.std(Xk[:,7])
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plt.plot(X[:,0])
plt.plot(X[:,1])
#plt.plot(X[:,2])
#plt.plot(X[:,3])
#plt.plot(X[:,4])
#plt.plot(X[:,5])
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Rsq=np.zeros((1,S[3]))
Betas=np.zeros((2,S[3]))
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X.shape
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DT.shape
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for j in range(S[3]):
model = algorithm.fit(X, DT[:,j])
Betas[:,j] = model.coef_
Rsq[:,j] = model.score(X,DT[:,j])
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RsqUni=np.zeros((6,S[3]))
BetaUni=np.zeros((6,S[3]))
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Sx=X.shape
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for k in range(2):
for j in range(S[3]):
model = algorithm.fit(np.reshape(X[:,k],(Sx[0],1)), DT[:,j])
BetaUni[k,j] = model.coef_
RsqUni[k,j] = model.score(np.reshape(X[:,k],(Sx[0],1)),DT[:,j])
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plt.plot(RsqUni[0,:])
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import random
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del Final_map
del Fmaps
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if S[2]>5:
Final_map=np.zeros([S[0],S[1],5,3])
Fmaps=np.zeros([S[0],S[1],5,3])
else:
Final_map=np.zeros([S[0],S[1],3])
Fmaps=np.zeros([S[0],S[1],3])
C=np.zeros([S[3],3])
C1=np.zeros([6,3])
C1[0][:]=(1,0,0)
C1[1][:]=(0,1,0)
C1[2][:]=(0,0,1)
C1[3][:]=(0.8,0.8,0)
C1[4][:]=(0,1,1)
C1[5][:]=(1,0,1)
S1=DT.shape
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C=np.zeros((S[3],3))
i=0
l=0
Betas2=Betas
for j in range(S[3]):
if Betas2[0,j]>0.7*np.max(Betas2[0,:]):
#if 1>0.1:
#C[j,:]=C1[i%6][:]
C[j,2]=1
C[j,1]=random.uniform(0,1)
#C[j,2]=1
for k in range(3):
M=np.max(np.squeeze(np.reshape(D2[:,:,:,j],S[0]*S[1]*5)))
print(M)
Fmaps[:,:,:,k]=0.8*D2[:,:,:,j]*C[j,k]/M
Final_map=Final_map+Fmaps
#Betas[0,j]=0
#print(Indexo[j])
i=i+1
l=l+1
#if l==2:
#break
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C=np.zeros((S[3],3))
i=0
l=0
Betas2=Betas
for j in range(S[3]):
if Betas2[1,j]>0.7*np.max(Betas2[1,:]):
#if 1>0.1:
#C[j,:]=C1[i%6][:]
C[j,0]=1
C[j,1]=random.uniform(0,1)
#C[j,2]=1
for k in range(3):
M=np.max(np.squeeze(np.reshape(D2[:,:,:,j],S[0]*S[1]*5)))
Fmaps[:,:,:,k]=0.8*D2[:,:,:,j]*C[j,k]/M
Final_map=Final_map+Fmaps
#Betas2[1,j]=0
#print(Indexo[j])
i=i+1
l=l+1
#if l==2:
# break
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pylab.rcParams['figure.figsize'] = (15, 6)
C2=np.zeros(3)
Df=np.zeros([S[0],S[1],5,3])
for i in range(3):
Df[:,:,:,i]=Final_map[:,:,:,i]+Dmean/30
#Df=Df/(np.max(np.max(np.max(Df),3)))
if S[2]>5:
N=Nstack
else:
N=S[2]
for i in range(N):
#if Good_ICs[j]:
plt.subplot(1,N,i+1)
plt.imshow(Df[:,:,i],cmap=plt.cm.gray)
plt.imshow(Df[:,:,i,:],cmap=my_cmap,interpolation='none')
frame1 = plt.gca()
frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
plt.tight_layout(pad=0,w_pad=0,h_pad=0)
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