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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
#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 [16]:



    


# 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 [17]:



    


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 [18]:



    


RegionName



    


















    
Out[18]:








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

Average in masks to sort components by brain region





In [19]:



    


Dmaps.shape



    


















    
Out[19]:








(165, 111, 10, 250)

















In [20]:



    


M=np.zeros((S[3],13))
Mapmean=np.zeros(S[3])
MMasks=np.zeros(13)



    











In [21]:



    


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 [22]:



    


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 [23]:



    


J



    


















    
Out[23]:








[4]

















In [24]:



    


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 [25]:



    


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



    











In [26]:



    


for idx in BadICs:
    GoodICAnat[idx] = 0.0



    











In [27]:



    


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



    


















    






8
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93
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194
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OL










    

















    

















    






13
85
97
104
121
123
143
154
159
181
190
VLNP










    

















    

















    






45
74
76
78
101
117
157
197
232
VMNP










    

















    

















    






0
9
12
24
26
27
33
54
64
82
83
90
107
116
212
AL










    

















    

















    






5
18
22
23
29
37
39
41
43
44
47
48
53
62
63
68
86
228
MB










    

















    

















    






1
3
4
6
7
10
15
16
17
73
175
227
LH










    

















    

















    






2
28
35
71
79
84
122
140
146
169
193
208
215
221
236
SNP










    

















    

















    






80
81
114
127
133
135
148
153
182
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207
223
CX










    

















    

















    






75
77
89
102
109
204
214
INP










    

















    

















    






20
30
36
38
46
50
112
PENP










    

















    

















    






21
239
GNG










    

















    

















    
























In [28]:



    


# Output number of component per region
np.savetxt('/'.join(filename.split('/')[:-1])+'/NumberInLargeRegionsV2.txt',NumberInLargeRegion)



    











In [ ]:

Content source: sophie63/FlyLFM

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