

In [2]:

    
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/test2/972/972ss1on500cregcdFF30sMpsfkf176Smith0_4_60TS.mat



In [3]:

    
filename ="/media/test2/972/972ss1on500cregcdFF30sMpsfkf176Smith0_4_60TS.mat"



In [4]:

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









    Out[4]:





(12642, 176)



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
filename = askopenfilename() # show an "Open" dialog box and return the path to the selected file
print(filename)









    



/media/test15/ArclightCombo/972/972ss1TimeFluoOn.mat



In [6]:

    
Ua=sio.loadmat(filename)
Tfluo=Ua['TimeFluoOn']
Tfluo.shape









    Out[6]:





(1, 13143)



In [7]:

    
Tfluo=Tfluo[0,range(501,13143)]



In [8]:

    
Tfluo.shape









    Out[8]:





(12642,)



In [9]:

    
Tfluo[0]









    Out[9]:





2.5242768980639516



In [58]:

    
# 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/test2/972/972combo20xvinegartruetruetrue_2015-06-30-174721-0000TimeFluoOn.mat



In [12]:

    
filename ="/media/test2/972/972combo20xvinegartruetruetrue_2015-06-30-174721-0000TimeFluoOn.mat"



In [13]:

    
Ua=sio.loadmat(filename)
Ua









    Out[13]:





{'TimeFluoOnVid': array([[  2.00000000e-02],
        [  3.00000000e-02],
        [  4.00000000e-02],
        ..., 
        [  8.09700000e+01],
        [  8.09800000e+01],
        [  8.09900000e+01]]),
 '__globals__': [],
 '__header__': 'MATLAB 5.0 MAT-file Platform: posix, Created on: Mon Oct 17 14:20:16 2016',
 '__version__': '1.0'}



In [14]:

    
Tvid=Ua['TimeFluoOnVid']



In [15]:

    
Tvid.shape









    Out[15]:





(8050, 1)



In [16]:

    
Tvid=Tvid[251:8050]



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/test2/972/972ss1on500cregcdFF30sMpsfkf176Smith0_4_60IC.nii



In [17]:

    
filename2 ="/media/test2/972/972ss1on500cregcdFF30sMpsfkf176Smith0_4_60IC.nii"



In [18]:

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









    Out[18]:





(80, 45, 10, 176)



In [19]:

    
S=data.shape
S









    Out[19]:





(80, 45, 10, 176)

Z-score



In [20]:

    
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 [21]:

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



In [22]:

    
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 [ ]:

    
# 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)



In [23]:

    
filenameM ="/media/test2/972/973Registration/972TransformedseparateLargepsf.nii"



In [24]:

    
img1 = nb.load(filenameM)
Masks = img1.get_data()
Sm=Masks.shape
Masks=np.array(Masks)



In [25]:

    
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 [26]:

    
RegionName









    Out[26]:





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

Average in masks to sort components by brain region



In [27]:

    
Dmaps.shape









    Out[27]:





(80, 45, 10, 176)



In [28]:

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



In [29]:

    
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 [30]:

    
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 [31]:

    
J









    Out[31]:





[2]



In [33]:

    
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,:])<6) and nrobject<150:
            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)

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 [34]:

    
BadICs=[1,2,3,4,5,7,8,9,11,13,14,15,18,19,20,22,25,26,27,31,34,36,37,40,41,42,43,44,46,47,50,51,61,63,64,65,69,70,72,74,82,83,84,86,88,91,92,95,98,100,102,104,15,106,107,108,110,112,114,115,116,117,119,125,126,127,130,132,133,136,137,140,range(142,177)]



In [ ]:



In [35]:

    
for idx in BadICs:
    GoodICAnat[np.array(idx)-1] = 0.0



In [87]:

    
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(Tfluo,(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.plot(Tvid[4:7797],Walk*40)
        plt.plot(Tvid[4:7797],Left*100+5)
        plt.plot(Tvid[4:7797],Right*100+10)
        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



In [86]:

    
plt.plot(Tvid[3:7797],Walk.T)









    



---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-86-a2ec53155b82> in <module>()
----> 1 plt.plot(Tvid[3:7797],Walk.T)

/usr/local/lib/python2.7/dist-packages/matplotlib/pyplot.pyc in plot(*args, **kwargs)
   3152         ax.hold(hold)
   3153     try:
-> 3154         ret = ax.plot(*args, **kwargs)
   3155     finally:
   3156         ax.hold(washold)

/usr/local/lib/python2.7/dist-packages/matplotlib/__init__.pyc in inner(ax, *args, **kwargs)
   1810                     warnings.warn(msg % (label_namer, func.__name__),
   1811                                   RuntimeWarning, stacklevel=2)
-> 1812             return func(ax, *args, **kwargs)
   1813         pre_doc = inner.__doc__
   1814         if pre_doc is None:

/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_axes.pyc in plot(self, *args, **kwargs)
   1422             kwargs['color'] = c
   1423 
-> 1424         for line in self._get_lines(*args, **kwargs):
   1425             self.add_line(line)
   1426             lines.append(line)

/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_base.pyc in _grab_next_args(self, *args, **kwargs)
    384                 return
    385             if len(remaining) <= 3:
--> 386                 for seg in self._plot_args(remaining, kwargs):
    387                     yield seg
    388                 return

/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_base.pyc in _plot_args(self, tup, kwargs)
    362             x, y = index_of(tup[-1])
    363 
--> 364         x, y = self._xy_from_xy(x, y)
    365 
    366         if self.command == 'plot':

/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_base.pyc in _xy_from_xy(self, x, y)
    221         y = _check_1d(y)
    222         if x.shape[0] != y.shape[0]:
--> 223             raise ValueError("x and y must have same first dimension")
    224         if x.ndim > 2 or y.ndim > 2:
    225             raise ValueError("x and y can be no greater than 2-D")

ValueError: x and y must have same first dimension



In [75]:

    
Tvid[4:7797].shape









    Out[75]:





(7793, 1)



In [76]:

    
Walk.shape









    Out[76]:





(7793, 1)



In [81]:

    
# 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)









    



/media/test15/ArclightCombo/972/972Behavior.mat



In [84]:

    
X=Ua['X']
Straight=X[2,:]
Right=X[0,:]
Left=X[1,:]



In [69]:

    
Tvid[3:7796].shape









    Out[69]:





(7793, 1)



In [85]:

    
Walk=Left+Right+Straight



In [66]:

    
Tvid.shape









    Out[66]:





(7799, 1)



In [25]:

    
# 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/ComboPanNeuronalGCaMP6/945/945Registration/AVG_945ss2cregcpsf.nii






    Out[25]:





(189, 114, 10)



In [32]:

    
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[32]:





<matplotlib.image.AxesImage at 0x7efe828e3950>



In [33]:

    
from sklearn import linear_model



In [34]:

    
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[34]:





[<matplotlib.lines.Line2D at 0x7efe80e601d0>]



In [35]:

    
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[35]:





[<matplotlib.lines.Line2D at 0x7efe828a4f50>,
 <matplotlib.lines.Line2D at 0x7efe7977be10>]



In [36]:

    
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[36]:





[<matplotlib.lines.Line2D at 0x7efe796285d0>]



In [37]:

    
import random



In [38]:

    
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 [39]:

    
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









    



3
OL
10
OL
14
OL
15
OL
19
OL
24
OL
25
OL
26
OL
29
OL
31
OL
35
OL
42
OL
51
OL
60
VLNP
61
INP
62
OL
65
OL
69
VMNP
72
OL
76
OL
80
OL
81
OL
86
OL
98
VMNP
103
CX
106
MB
107
OL
110
OL
122
MB
131
OL
137
OL
168
LH
190
OL



In [40]:

    
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 [41]:

    
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)









    



SNP
1
MB
4
SNP
5
LH
6
MB
8
LH
9
MB
12
MB
16
AL
17
SNP
20
LH
22
GNG
23
MB
27
LH
28
AL
33
AL
37
SNP
38
AL
40
CX
45
CX
47
MB
48
MB
50
SNP
54
CX
56
CX
58
MB
63
CX
67
LH
70
MB
71
LH
77
MB
82
MB
84
LH
87
MB
88
MB
92
MB
95
OL
99
LH
100
MB
102
LH
109
OL
113
SNP
115
CX
117
MB
118
AL
121
LX
124
SNP
134
MB
136
MB
141
MB
143
SNP
147
VMNP
149
Number of odor components
52



In [42]:

    
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 [43]:

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



In [44]:

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









    Out[44]:





[<matplotlib.lines.Line2D at 0x7efe79680950>]



In [93]:

    
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 [110,127]:                    
        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(Tfluo,(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:
        n=n+1
        print(RegionName[l])
        #plt.plot(Tvid[4:7797],Walk*40)
        plt.plot(Tvid[4:7797],Left*100+5)
        plt.plot(Tvid[4:7797],Right*100+10)
        plt.plot(Tvid[4:7797],(Right-Left)*100)
        plt.plot(Tfluo,((DT[:,110]/np.sqrt(np.var(DT[:,100])))-(DT[:,127]/np.sqrt(np.var(DT[:,127])))-h*n+2),color=C/2)
        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



In [ ]: