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
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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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In [7]:
S=data.shape
S
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Z-score
In [8]:
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 [9]:
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-2.5
Tvar[i]=np.var(DT[i,:])
Dmaps[Dmaps<0]=0
In [11]:
# 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)
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filenameM='/home/sophie/RegionList'
with open(filenameM) as f:
content = f.readlines()
Names=[Line.split('\t') for Line in content]
RegionName=[Names[i][0] for i in range(75)]
Num=[int(Names[i][2]) for i in range(75)]
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Dmaps.shape
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M=np.zeros((S[3],86))
Mapmean=np.zeros(S[3])
MMasks=np.zeros(86)
In [15]:
for i in range(S[3]):
Mapmean[i]=np.mean(np.mean(np.mean(Dmaps[:,:,:,i])))
for j in range(86):
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])
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CompMainName=S[3]*['']
CompNameAdd=np.zeros((S[3],86))
for i in range(S[3]):
Max=np.max(M[i,:])
I=np.argmax(M[i,:])+1
for j in range(86):
J=[l for l in range(74) if Num[l]==(j+1)]
if M[i,j]>0.2*Max:
CompNameAdd[i,J]=1
J=[l for l in range(74) if Num[l]==I]
if J!= []:
CompMainName[i]=Names[np.array(J)][0]
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J
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In [18]:
pylab.rcParams['figure.figsize'] = (13, 2.5)
h=5
tot=0
GoodICAnat=np.zeros(S[3])
for l in range(74):
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
#print(i)
for j in range(86):
if CompNameAdd[i,j]==1:
print(Names[np.array(j)][0])
print(i)
if n!=0:
print(Names[l][1])
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)
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BadICs=[98,21,41,157,154,162,194,147,126,65,2,39,60,68,79,22,144,143,16,52,37,40,38,45,4,20,24,29,44,47,57,76,49,56,64,66,70,75,78,82,84,100,135,166,85,3,5,36,8,46,63,193,7,108,31,58,101]
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for idx in BadICs:
GoodICAnat[idx] = 0.0
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LargerRegionsDic={'':'','AME_R':'OL','LO_R':'OL','NO':'CX','BU_R':'CX','PB':'CX','LH_R':'LH','LAL_R':'LX','SAD':'PENP'
,'CAN_R':'PENP','AMMC_R':'PENP','ICL_R':'INP','VES_R':'VMNP','IB_R':'INP','ATL_R':'INP','CRE_R':'INP'
,'MB_PED_R':'MB','MB_VL_R':'MB','MB_ML_R':'MB','FLA_R':'PENP','LOP_R':'OL','EB':'CX','AL_R':'AL',
'ME_R':'OL','FB':'CX','SLP_R':'SNP','SIP_R':'SNP','SMP_R':'SNP','AVLP_R':'VLNP','PVLP_R':'VLNP',
'IVLP_R':'VLNP','PLP_R':'VLNP','AOTU_R':'VLNP','GOR_R':'VMNP','MB_CA_R':'MB','SPS_R':'VMNP',
'IPS_R':'VMNP','SCL_R':'INP','EPA_R':'VMNP','GNG':'GNG','PRW':'PENP','GA_R':'LX','AME_L':'OL'
,'LO_L':'OL','BU_L':'CX','LH_L':'LH','LAL_L':'LX','CAN_L':'PENP','AMMC_L':'PENP','ICL_L':'INP',
'VES_L':'VMNP','IB_L':'INP','ATL_L':'INP','CRE_L':'INP','MB_PED_L':'MB','MB_VL_L':'MB',
'MB_ML_L':'MB','FLA_L':'PENP','LOP_L':'OL','AL_L':'AL','ME_L':'OL','SLP_L':'SNP','SIP_L':'SNP',
'SMP_L':'SNP','AVLP_L':'VLNP','PVLP_L':'VLNP','IVLP_L':'VLNP','PLP_L':'VLNP','AOTU_L':'VLNP',
'GOR_L':'VMNP','MB_CA_L':'MB','SPS_L':'VMNP','IPS_L':'VMNP','SCL_L':'INP','EPA_L':'VMNP','GA_L':'LX'}
SmallRegionsSorted=['ME_L','ME_R','LO_R','LO_L','LOP_R','LOP_L','AME_R','AME_L',
'PLP_R','PLP_L','PVLP_R','PVLP_L','AVLP_R','AVLP_L','AOTU_R','AOTU_L','IVLP_R','IVLP_L',
'AL_R','AL_L',
'MB_CA_R','MB_CA_L','MB_PED_R','MB_PED_L','MB_VL_R','MB_VL_L','MB_ML_R','MB_ML_L',
'SMP_R','SMP_L','SIP_R','SLP_L','SLP_R','SIP_L',
'LH_R','LH_L',
'CRE_R','CRE_L','ICL_R','ICL_L','SCL_R','SCL_L','IB_R','IB_L','ATL_R','ATL_L',
'EB','PB','NO','FB',
'BU_R','BU_L','LAL_R','LAL_L','GA_R','GA_L',
'GOR_R','GOR_L','EPA_R','EPA_L','VES_R','VES_L','SPS_R','SPS_L','IPS_R','IPS_L',
'AMMC_R','AMMC_L','SAD','FLA_R','FLA_L','PRW','CAN_R','CAN_L',
'GNG','']
Tozip=range(len(SmallRegionsSorted))
SmallRegionsDic=dict(zip(SmallRegionsSorted,Tozip))
LargerRegion=[LargerRegionsDic[CompMainName[i]] for i in range(S[3])]
LargerRegionInd={ 'OL':1,'VLNP':2,'VMNP':3,'AL':4,'MB':5,'LH':6,'SNP':7,'CX':8,'LX':9,'INP':10,'PENP':11,'GNG':12,'':13}
LargerRegionI=np.array([LargerRegionInd[LargerRegion[i]] for i in range(S[3])])
SmallRegion=np.array([SmallRegionsDic[CompMainName[i]] for i in range(S[3])])
NewOrder=np.argsort(SmallRegion)
SmallRegion[NewOrder]
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In [29]:
LargerRegionIndToName = {v: k for k, v in LargerRegionInd.iteritems()}
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LargerRegionI
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GoodICAnat
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In [32]:
pylab.rcParams['figure.figsize'] = (13, 2.5)
h=5
tot=0
NumberInLargeRegion=np.zeros(13)
for l in range(1,13):
print(LargerRegionIndToName[l])
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 LargerRegionI[i]==l:
if GoodICAnat[i]==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
print(i)
n=n+1
if n!=0:
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
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# Output number of component per region
np.savetxt('/'.join(filename.split('/')[:-1])+'/NumberInLargeRegions.txt',NumberInLargeRegion)
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