notebook.community

Edit and run 





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

Open data





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)



    


















    






/media/sophie/008C0665790F0763/ComboPanNeuronalGCaMP6/938/938ss2onc250regcdFF20sMpsfkf250Smith0_4_60TS.mat

















In [3]:



    


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



    


















    
Out[3]:








(6011, 250)

















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)



    


















    






/media/sophie/008C0665790F0763/ComboPanNeuronalGCaMP6/938/938ss2onc250regcdFF20sMpsfkf250Smith0_4_60IC.nii

















In [5]:



    


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



    


















    
Out[5]:








(165, 111, 10, 250)

















In [6]:



    


S=data.shape
S



    


















    
Out[6]:








(165, 111, 10, 250)

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

Open Masks





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)



    


















    






/media/sophie/008C0665790F0763/ComboPanNeuronalGCaMP6/938/938Registration/JFRCTransformedLargefullpsftrimmed.nii

















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]:








['OL',
 'VLNP',
 'VMNP',
 'AL',
 'MB',
 'LH',
 'SNP',
 'CX',
 'LX',
 'INP',
 'PENP',
 'GNG']

Average in masks to sort components by brain region





In [13]:



    


Dmaps.shape



    


















    
Out[13]:








(165, 111, 10, 250)

















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]



    


















    






/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:12: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future

















In [17]:



    


J



    


















    
Out[17]:








[4]

















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)



    


















    






OL










    

















    

















    






VLNP










    

















    

















    






VMNP










    

















    

















    






AL










    

















    

















    






MB










    

















    

















    






LH










    

















    

















    






SNP










    

















    

















    






CX










    

















    

















    






INP










    

















    

















    






PENP










    

















    

















    






GNG
Looked at the components maps and time series and remove all the components which are localized on the edge of the brain and with activity unlike GCaMP6 transients.




In [23]:



    


BadICs=[30,53,77,105,31,51,20,22,27,33,47,59,60,37,58,100,72,69]



    











In [24]:



    


for idx in BadICs:
    GoodICAnat[idx] = 0.0



    











In [21]:



    


pylab.rcParams['figure.figsize'] = (13, 3)

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



    


















    






15
17
23
28
29
38
41
44
45
47
52
54
57
59
60
63
65
66
67
70
72
73
78
85
87
88
89
90
91
93
96
100
101
102
107
111
112
113
114
121
122
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126
128
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132
133
134
137
138
140
142
145
149
152
153
154
157
158
159
161
164
166
168
169
171
182
187
204
208
OL










    

















    

















    






84
104
106
VLNP










    

















    

















    






16
36
40
58
115
VMNP










    

















    

















    






14
18
25
26
39
61
71
79
82
109
165
AL










    

















    

















    






1
2
7
9
10
19
21
24
55
56
68
75
97
120
139
141
144
148
193
MB










    

















    

















    






0
3
5
6
42
50
118
175
180
LH










    

















    

















    






22
76
95
116
150
186
207
SNP










    

















    

















    






11
27
43
46
49
69
81
103
119
123
136
156
160
162
172
CX










    

















    

















    






77
LX










    

















    

















    






13
31
74
PENP










    

















    

















    
























In [19]:



    


BadICs=[92,99,72,156,216]



    











In [20]:



    


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



    


















    






/media/sophie/008C0665790F0763/ComboPanNeuronalGCaMP6/938/938XkStimuli.mat

















In [24]:



    


# 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



    


















    






/media/sophie/008C0665790F0763/ComboPanNeuronalGCaMP6/938/AVG_938ss2onc250regcpsf.nii










    
Out[24]:








(165, 111, 10)

















In [25]:



    


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[:,:,:])

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

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



    


















    
Out[25]:








<matplotlib.image.AxesImage at 0x7f2a2fc1a8d0>










    
























In [26]:



    


from sklearn import linear_model



    











In [27]:



    


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)

algorithm = linear_model.LinearRegression()

Sxk=Xk.shape

Sxk

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

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])

plt.plot(X[:,0])
plt.plot(X[:,1])



    


















    
Out[27]:








[<matplotlib.lines.Line2D at 0x7f2a2e20d910>]










    
























In [28]:



    


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

X.shape

DT.shape

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

plt.plot(DT)
plt.plot(X)



    


















    
Out[28]:








[<matplotlib.lines.Line2D at 0x7f2a2e1fa250>,
 <matplotlib.lines.Line2D at 0x7f2a2dddc190>]










    
























In [29]:



    


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

Sx=X.shape

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])
    

plt.plot(Betas[0,:])



    


















    
Out[29]:








[<matplotlib.lines.Line2D at 0x7f2a26d2c3d0>]










    
























In [30]:



    


import random



    











In [46]:



    


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



    











In [47]:



    


C=np.zeros((S[3],3))
i=0
l=0
Betas2=Betas
LightNuminRegion=np.zeros(12)
for j in range(S[3]):  
    if Betas2[0,j]>0.1*np.max(Betas2[0,:]) and abs(Betas2[1,j])<0.1*np.max(Betas2[1,:]):
    #if 1>0.1:
        #C[j,:]=C1[i%6][:]
        C[j,2]=1
        C[j,1]=Betas2[0,j]/np.max(Betas2[0,:])
        #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]=0.25*D2[:,:,:,j]*C[j,k]/M
        Final_map=Final_map+Fmaps
        #Betas[0,j]=0
        #print(Indexo[j])
        print(j+1)
        print(RegionName[int(CompMainName[j])-1])     
        LightNuminRegion[int(CompMainName[j])-1]=LightNuminRegion[int(CompMainName[j])-1]+1
        i=i+1
        l=l+1

        #if l==2:
            #break



    


















    






9
OL
15
OL
20
OL
32
OL
33
OL
35
OL
41
OL
56
OL
59
OL
62
OL
67
OL
68
OL
77
VMNP
95
OL
96
OL
97
OL
99
OL
106
OL
109
OL
116
OL
118
VMNP
119
OL
120
OL
126
OL
129
OL
137
OL
138
OL
139
OL
140
OL
143
OL
148
OL
152
OL
158
VMNP
159
OL
161
OL
163
OL
169
OL
171
VMNP
178
OL
180
OL
181
SNP
184
AL
186
INP
193
SNP
206
OL
214
LH
220
MB
235
OL

















In [48]:



    


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/10
    #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)



    


















    
























In [49]:



    


C=np.zeros((S[3],3))
i=0
l=0
Betas2=Betas
OdorNuminRegion=np.zeros(12)

for j in range(S[3]):  
    if Betas2[1,j]>0.1*np.max(Betas2[1,:]) and abs(Betas2[0,j])<0.1*np.max(Betas2[0,:]):
    #if 1>0.1:
        #C[j,:]=C1[i%6][:]
        C[j,0]=1
        C[j,1]=Betas2[1,j]/np.max(Betas2[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]=0.25*D2[:,:,:,j]*C[j,k]/M
        Final_map=Final_map+Fmaps
        #Betas2[1,j]=0
        #print(Indexo[j])
        OdorNuminRegion[int(CompMainName[j])-1]=OdorNuminRegion[int(CompMainName[j])-1]+1
        print(RegionName[int(CompMainName[j])-1])
        i=i+1
        l=l+1
        print(j+1)
        #if l==2:
         #   break

NumOdor=i
print('Number of odor components')
print(i)



    


















    






AL
1
LH
2
SNP
3
LH
4
LH
5
MB
6
LH
7
LH
8
AL
10
LH
11
AL
13
VLNP
14
LH
16
LH
17
LH
18
MB
19
PENP
21
GNG
22
MB
23
MB
24
AL
25
AL
27
AL
28
SNP
29
MB
30
PENP
31
AL
34
SNP
36
PENP
37
MB
38
PENP
39
MB
40
MB
42
MB
44
MB
45
PENP
47
MB
48
MB
49
SNP
50
PENP
52
AL
53
MB
54
AL
55
AL
60
MB
63
MB
64
AL
65
VLNP
66
MB
69
SNP
72
LH
74
INP
76
INP
78
SNP
80
CX
81
AL
83
AL
84
SNP
85
VLNP
86
MB
87
CX
88
AL
91
SNP
92
INP
101
VMNP
102
VLNP
105
AL
108
INP
110
VLNP
111
PENP
113
CX
115
AL
117
VLNP
122
SNP
123
VLNP
124
GNG
125
CX
128
INP
132
MB
135
SNP
141
OL
142
VLNP
144
INP
146
CX
154
VLNP
155
VLNP
160
SNP
162
INP
164
SNP
170
SNP
174
CX
183
CX
187
Number of odor components
92

















In [50]:



    


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/10
    #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)



    


















    
























In [51]:



    


np.savetxt('/'.join(filename.split('/')[:-1])+'/OdorNumberInLargeRegions.txt',OdorNuminRegion)
np.savetxt('/'.join(filename.split('/')[:-1])+'/LightNumberInLargeRegions.txt',LightNuminRegion)



    











In [52]:



    


plt.plot(OdorNuminRegion)
plt.plot(LightNuminRegion)



    


















    
Out[52]:








[<matplotlib.lines.Line2D at 0x7f2a26a34f90>]










    
























In [ ]:

Content source: sophie63/FlyLFM

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