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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
import nibabel as nb
from scipy.interpolate import interp1d
from scipy import ndimage

from sklearn import linear_model

Open data





In [3]:



    


# 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)



    


















    






/media/test5/FreeBehaviorPanNeuronalGCaMP6/100498series/100499_100500_100501ConcatenatedStacksM308Smith0_4_60TS.mat

















In [2]:



    


filename="/media/test5/FreeBehaviorPanNeuronalGCaMP6/100498series/100499_100500_100501ConcatenatedStacksM308Smith0_4_60TS.mat"



    











In [3]:



    


Ua=sio.loadmat(filename)
DT=Ua['TSo']
DT.shape



    


















    
Out[3]:








(21496, 308)

















In [5]:



    


# 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)



    


















    






/media/test5/FreeBehaviorPanNeuronalGCaMP6/100498series/100499_100500_100501ConcatenatedStacksM308Smith0_4_60IC.nii

















In [6]:



    


filename2="/media/test5/FreeBehaviorPanNeuronalGCaMP6/100498series/100499_100500_100501ConcatenatedStacksM308Smith0_4_60IC.nii"



    











In [7]:



    


img1 = nb.load(filename2)
data = img1.get_data()
S=data.shape
S



    


















    
Out[7]:








(166, 111, 10, 308)

Z-score





In [17]:



    


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 [18]:



    


for i in range(S[3]):
    Demean[:,:,:,i]=data[:,:,:,i]-np.mean(np.mean(np.mean(data[:,:,:,i],0),0),0)



    











In [19]:



    


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-1
    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
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)



    


















    






/media/test5/FreeBehaviorPanNeuronalGCaMP6/100498series/Xk499_500_501.mat

















In [11]:



    


filename="/media/test5/FreeBehaviorPanNeuronalGCaMP6/100498series/Xk499_500_501.mat"



    











In [12]:



    


Ua=sio.loadmat(filename)
Xk=Ua['Xk']



    











In [13]:



    


Xk.shape



    


















    
Out[13]:








(21496, 2)

















In [ ]:



    


# 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)



    











In [14]:



    


filenamet="/media/test5/FreeBehaviorPanNeuronalGCaMP6/100498series/100498Registration/AVG_100498regcpsf.nii"



    











In [15]:



    


nimt=nb.load(filenamet)
Dtemp=np.squeeze(nimt.get_data())
Dtemp.shape



    


















    
Out[15]:








(166, 111, 10)

Fit turns





In [20]:



    


%%javascript
IPython.OutputArea.auto_scroll_threshold =4000;



    


















    



























In [21]:



    


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[:,:,:])



    


















    






/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py:422: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  sub_arys.append(_nx.swapaxes(sary[st:end], axis, 0))

















In [22]:



    


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



    











In [23]:



    


plt.imshow(Dmean[:,:,1],cmap=plt.cm.gray)



    


















    
Out[23]:








<matplotlib.image.AxesImage at 0x7f7146d87650>










    
























In [24]:



    


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)



    











In [25]:



    


algorithm = linear_model.LinearRegression()



    











In [26]:



    


Sxk=Xk.shape



    











In [27]:



    


Sxk



    


















    
Out[27]:








(21496, 2)

















In [28]:



    


X=np.zeros((Sxk[0],2))



    











In [29]:



    


CCcb=np.correlate(DT[:,59],DT[:,68],'full')
CCry=np.correlate(DT[:,1],DT[:,65],'full')
print(np.argmax(CCcb)-21496)
print(np.argmax(CCry)-21496)
plt.plot(CCcb[range(21496-200,21496+500)])
plt.plot(CCry[range(21496-200,21496+500)])



    


















    






4
10










    
Out[29]:








[<matplotlib.lines.Line2D at 0x7f7156678e90>]










    
























In [30]:



    


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])



    











In [31]:



    


del Time



    


















    






-------------------------------------------------
NameError       Traceback (most recent call last)
<ipython-input-31-510813148c42> in <module>()
----> 1 del Time

NameError: name 'Time' is not defined
















In [32]:



    


pylab.rcParams['figure.figsize'] = (15, 6)

Time=np.array(range(21496))*0.01-140
plt.rcParams["font.size"]=20
plt.plot(Time,(X[:,0]-np.mean(X[:,0]))/np.std(X[:,0]-np.mean(X[:,0])))
plt.plot(Time,(X[:,1]-np.mean(X[:,1]))/np.std(X[:,1]-np.mean(X[:,1])))

plt.plot(Time,DT[:,59]*3-3,'c')
plt.plot(Time,DT[:,68]*3-4,'b')
plt.plot(Time,DT[:,1]*3-5,'y')
plt.plot(Time,DT[:,65]*3-6,'r')
frame1 = plt.gca()
#frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
frame1.axes.set_xlabel("Time (s)",fontsize=20)
frame1.axes.set_xlim([0,55])
plt.savefig("100499LeftRight.svg",format="svg")
#plt.plot(np.smooth(X[:,1]-X[:,0]))
#plt.plot(X[:,2])
#plt.plot(X[:,3])
#plt.plot(X[:,4])
#plt.plot(X[:,5])
zero_crossings = np.where(np.diff(np.sign(X[:,1]-X[:,0])))[0]
print(zero_crossings.shape)

plt.show()



    


















    






(409,)










    
























In [33]:



    


pylab.rcParams['figure.figsize'] = (11, 3)
Time=np.array(range(6000,12000))*0.01
plt.plot(Time,X[range(6000,12000),0])
plt.plot(Time,X[range(6000,12000),1])

plt.plot(Time,DT[range(6000,12000),59]*2-2,'c')
plt.plot(Time,DT[range(6000,12000),68]*2-3,'b')
plt.plot(Time,DT[range(6000,12000),1]*2-4,'y')
plt.plot(Time,DT[range(6000,12000),65]*2-5,'r')


T=X
T[X<0]=0

#plt.plot(T[:,1]-T[:,0])
#plt.plot(Xk[:,1])
#plt.plot(X[:,1]-X[:,0])
#plt.plot(X[:,2])
#plt.plot(X[:,3])
#plt.plot(X[:,4])
#plt.plot(X[:,5])
zero_crossings = np.where(np.diff(np.sign(T[:,1]-T[:,0])))[0]
print(zero_crossings.shape)



    


















    






(775,)

Plot all components for turning left, right, walking, and grooming





In [34]:



    


Rsq=np.zeros((1,S[3]))
Betas=np.zeros((2,S[3]))



    











In [35]:



    


X.shape



    


















    
Out[35]:








(21496, 2)

















In [36]:



    


DT.shape



    


















    
Out[36]:








(21496, 308)

















In [37]:



    


for j in range(S[3]):
    model = algorithm.fit(X, DT[:,j])
    Betas[:,j] = model.coef_
    Rsq[:,j] = model.score(X,DT[:,j])



    











In [38]:



    


RsqUni=np.zeros((6,S[3]))
BetaUni=np.zeros((6,S[3]))



    











In [39]:



    


Sx=X.shape



    











In [40]:



    


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])



    











In [41]:



    


plt.plot(RsqUni[0,:])



    


















    
Out[41]:








[<matplotlib.lines.Line2D at 0x7f714521b4d0>]










    
























In [42]:



    


import random



    











In [43]:



    


Betas[:,0]=0
Betas[:,6]=0



    











In [45]:



    


#del Final_map
#del Fmaps

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

C=np.zeros((S[3],3))
i=1
l=0
Betas2=Betas
for j in range(S[3]):  
    if Betas2[1,j]>0.499*np.max(Betas2[1,:]):
    #if 1>0.1:
        #C[j,:]=C1[i%6][:]
        C[j,2]=1
        C[j,1]=1-i/2
        #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
        print(j+1)
        #print(Rsq[:,j])
        print(C[j,:])
        print(RsqUni[:,j])
        #if l==2:
         #   break


C=np.zeros((S[3],3))
i=1
l=0
Betas2=Betas
for j in range(S[3]):  
    if Betas2[0,j]>0.499*np.max(Betas2[0,:]):
    #if 1>0.1:
        #C[j,:]=C1[i%6][:]
        C[j,0]=1
        C[j,1]=1-i/2
        #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
        #Betas[0,j]=0
        #print(Indexo[j])
        i=i+1
        l=l+1
        print(j+1)
        print(C[j,:])
        print(RsqUni[:,j])
        #if l==2:
            #break
 
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/500
    #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)



    


















    






60
[ 0.  1.  1.]
[ 0.05060688  0.17697754  0.          0.          0.          0.        ]
69
[ 0.  0.  1.]
[ 0.14966472  0.05396544  0.          0.          0.          0.        ]
2
[ 1.  1.  0.]
[ 0.44209252  0.0511118   0.          0.          0.          0.        ]
66
[ 1.  0.  0.]
[ 0.4185168   0.01748524  0.          0.          0.          0.        ]










    
























In [74]:



    


# 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)



    


















    






/media/test5/FreeBehaviorPanNeuronalGCaMP6/100498series/100498Registration/JFRC100498Transformedfullpsftrimmed.nii

















In [89]:



    


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)]

# Average in masks to sort components by brain region

M=np.zeros((S[3],86))
Mapmean=np.zeros(S[3])
MMasks=np.zeros(86)

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])


NoList=[]
M[7,3]=0
M[171,3]=0
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
    if M[i,5]>0.95*Max:
        NoList.append(i)
    J=[l for l in range(74) if Num[l]==I]
    if J!=[]:
        CompMainName[i]=Names[np.array(J)][0]

NoList

del Final_map
del Fmaps

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

C=np.zeros((S[3],3))
i=0
l=0
Betas2=Betas
for j in range(S[3]):  
    #if j in NoList:
    if j==53:#/media/test5/FreeBehaviorPanNeuronalGCaMP6/100133/old/100133Final/AVG_100133ss2on250cregcpsf.nii
    #if 1>0.1:
        #C[j,:]=C1[i%6][:]
        C[j,2]=1
        C[j,1]=1-i/8
        #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]=0.5*D2[:,:,:,j]*C[j,k]/M
        Final_map=Final_map+Fmaps
        #Betas[0,j]=0
        #print(Indexo[j])
        i=i+1
        l=l+1
        print(j+1)
        print(Rsq[:,j])
        print(Betas[:,j])
        print(RsqUni[:,j])
        print(BetaUni[:,j])
        #if l==2:

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/400
    #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)



    


















    






/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:34: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future










    






54
[ 0.12457052]
[ 0.06253572 -0.00100261]
[ 0.12454166  0.00801972  0.          0.          0.          0.        ]
[ 0.06277088 -0.01622922  0.          0.          0.          0.        ]










    
























In [ ]:

Content source: sophie63/FlyLFM

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