This pipeline opens the result of fsl's melodic analysis, lets the user interactively label the components that look like neuronal activity (rather than movement artefacts or noise), sort them by label, plots a final summary for the chosen components, and save the reordered maps and time series.



In [5]:

    
clear all



In [6]:

    
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

Open time series



In [65]:

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









    



/media/sophie/100051/100051ss1_1000regcdFF30spsfkf138Smith0_4_60TS.mat



In [66]:

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









    Out[66]:





(11571, 138)

Open maps



In [67]:

    
# 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
filename2 = askopenfilename() # show an "Open" dialog box and return the path to the selected file
print(filename2)









    



/media/sophie/100051/100051ss1_1000regcdFF30spsfkf138Smith0_4_60IC.nii



In [68]:

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









    Out[68]:





(92, 44, 11, 138)

Open time



In [11]:

    
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/100051/100051/100051TimeOn.mat



In [12]:

    
Ua=sio.loadmat(filename)
Time_fluo=Ua['Time']
Time_fluo.shape









    Out[12]:





(11571, 1)

Stimulus time series



In [96]:

    
Tmax=np.max(Time_fluo)
Tstim=np.array(range(1,100*(int(Tmax)+1)))
Tstim=Tstim/100
Odor=np.zeros(len(Tstim*100))
UV=np.zeros(len(Tstim*100))



In [97]:

    
for i in range(0,7):
    UV[10*100+i*600:100*10+i*600+300]=1
    
for i in range(0,5):    
    Odor[(10+8*6+15)*100+i*600:(10+8*6+15)*100+i*600+200]=1
UV[UV==0]=np.nan
Odor[Odor==0]=np.nan



In [98]:

    
plt.plot(Tstim,UV)
plt.plot(Tstim,Odor)









    Out[98]:





[<matplotlib.lines.Line2D at 0x49637390>]

Behavior

Cut the behavior movies to keep only frames with excitation on
Analyze the behavior by hand (using Video_annotate) or measuring the ball movement with PIV (in FIJI)
Extract time embedded in the movies, using avi2time



In [17]:

    
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)
B=sio.loadmat(filename)









    



/media/Seagate Backup Plus Drive/964/964vid/matlab.mat



In [92]:

    
Tb=B['T'].T
Left=B['green'].T
Right=B['blue'].T
Leftn=Left/np.max(Left)
Rightn=Left/np.max(Right)
Leftb=Leftn
Leftb[Leftn>0.2]=1
Leftb[Leftn<0.2]=np.nan
Rightb=Rightn
Rightb[Rightn>0.2]=1
Rightb[Rightn<0.2]=np.nan



In [93]:

    
Left.shape









    Out[93]:





(12117, 1)



In [103]:

    
plt.plot(Tb,Rightb, marker='o')
plt.plot(Tb,Leftb)
plt.plot(Tstim,UV+1, marker='o')
plt.plot(Tstim,Odor+1,marker='o')









    Out[103]:





[<matplotlib.lines.Line2D at 0x388f9b50>]



In [170]:

    
pylab.rcParams['figure.figsize'] = (15, 3)
plt.plot(Tstim/0.02,UV+1, marker='o')
plt.plot(Tstim/0.02,Odor+1,marker='o')









    Out[170]:





[<matplotlib.lines.Line2D at 0x83dbce50>]



In [22]:

    
%store









    



Stored variables and their in-db values:

Zscore maps



In [69]:

    
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])

Transform the maps to have zero mean



In [70]:

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

Transform the maps to have unit variance and zscore



In [71]:

    
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
    Tvar[i]=np.var(DT[i,:])
Dmaps[Dmaps<0]=0



In [72]:

    
plt.plot(Tvar,'g')
#plt.plot(Var)









    Out[72]:





[<matplotlib.lines.Line2D at 0x7fb81dadd910>]

Order ICs by variance before normalization if ICA done in matlab



In [73]:

    
Order=np.argsort(Var)[::-1]
datao=data[:,:,:,Order[:]]
Dmapso=Dmaps[:,:,:,Order[:]]
Varor=Var[Order]

Open time series from ROI and unmixed in ROI



In [74]:

    
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)
B=sio.loadmat(filename)









    









    



---------------------------------------------------------------------------
IOError                                   Traceback (most recent call last)
<ipython-input-74-9e0378dc19bb> in <module>()
      2 filename = askopenfilename() # show an "Open" dialog box and return the path to the selected file
      3 print(filename)
----> 4 B=sio.loadmat(filename)

/usr/local/lib/python2.7/dist-packages/scipy/io/matlab/mio.pyc in loadmat(file_name, mdict, appendmat, **kwargs)
    133     """
    134     variable_names = kwargs.pop('variable_names', None)
--> 135     MR = mat_reader_factory(file_name, appendmat, **kwargs)
    136     matfile_dict = MR.get_variables(variable_names)
    137     if mdict is not None:

/usr/local/lib/python2.7/dist-packages/scipy/io/matlab/mio.pyc in mat_reader_factory(file_name, appendmat, **kwargs)
     56        type detected in `filename`.
     57     """
---> 58     byte_stream = _open_file(file_name, appendmat)
     59     mjv, mnv = get_matfile_version(byte_stream)
     60     if mjv == 0:

/usr/local/lib/python2.7/dist-packages/scipy/io/matlab/mio.pyc in _open_file(file_like, appendmat)
     26                 file_like += '.mat'
     27                 try:
---> 28                     return open(file_like, 'rb')
     29                 except IOError:
     30                     pass  # Rethrow the original exception.

IOError: [Errno 2] No such file or directory: '.mat'



In [26]:

    
TS_ROI=B['TSzmapo']



In [75]:

    
my_cmap=plt.cm.jet
my_cmap.set_bad(alpha=0)
Good_ICs=np.zeros(S[3])
Label_ICs=[]
pylab.rcParams['figure.figsize'] = (13, 2.5)



In [76]:

    
Dtemp=np.mean(data,3)



In [77]:

    
Dtemp.shape









    Out[77]:





(92, 44, 11)



In [78]:

    
%%javascript
IPython.OutputArea.auto_scroll_threshold =4000;



In [79]:

    
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[:,:,:])



In [ ]:



In [80]:

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









    Out[80]:





<matplotlib.image.AxesImage at 0x7fb81d8b6250>



In [81]:

    
Dmean.shape









    Out[81]:





(92, 44, 5)



In [82]:

    
Nstack









    Out[82]:





5



In [83]:

    
for j in range(S[3]):

    if S[2]>5:
        for i in range(Nstack):
            V=Dmaps[:,:,Indices[i],Order[j]]
            D1[:,:,i]=np.max(V,2)
        D2[:,:,:,j]=D1
        D1[D1==0]=np.nan
           
    else:
        for i in range(S[2]):
            V=Dmaps[:,:,i,Order[j]]
            D1[:,:,i]=V 
            

    print(j)
    for i in range(Nstack):
        plt.subplot(1,5,i+1)
        plt.imshow(Dmean[:,:,i],cmap=plt.cm.gray)
        plt.imshow(D1[:,:,i], cmap=my_cmap,interpolation='none')
        frame1 = plt.gca()
        frame1.axes.get_xaxis().set_visible(False)
        frame1.axes.get_yaxis().set_visible(False)
        
    plt.show()
    
    #plt.plot(TS_ROI[:,Order[j]]/np.sqrt(np.var(TS_ROI[:,Order[j]])))
    plt.plot(DT[:,Order[j]]/np.sqrt(np.var(DT[:,Order[j]]))+1)

    #plt.plot(Tb,Rightb+0.5, marker='o')
    #plt.plot(Tb,Leftb)
    #plt.plot(Tstim,UV+0.3, marker='o')
    #plt.plot(Tstim,Odor+0.3,marker='o')
    plt.show()
    
    Label_ICs.append(raw_input())
    if Label_ICs[j]=='':
        Good_ICs[j]=0
    else:
        Good_ICs[j]=1









    



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---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-83-93a864b259d9> in <module>()
     34     plt.show()
     35 
---> 36     Label_ICs.append(raw_input())
     37     if Label_ICs[j]=='':
     38         Good_ICs[j]=0

/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.pyc in raw_input(self, prompt)
    687             self._parent_ident,
    688             self._parent_header,
--> 689             password=False,
    690         )
    691 

/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.pyc in _input_request(self, prompt, ident, parent, password)
    717             except KeyboardInterrupt:
    718                 # re-raise KeyboardInterrupt, to truncate traceback
--> 719                 raise KeyboardInterrupt
    720             else:
    721                 break

KeyboardInterrupt:



In [84]:

    
Dmaps.shape









    Out[84]:





(92, 44, 11, 138)



In [85]:

    
set(Label_ICs)









    Out[85]:





{'', 'a'}



In [86]:

    
if len(Label_ICs)<S[3]:
    for j in range(S[3]-len(Label_ICs)):
      Label_ICs.append('')



In [87]:

    
Dict={'a':0,'AL':0,'Co':1,'CO':1,'O':2,'o':2,'OG':3,'AVLP':3,'A':0,'G':5,'PN':5,'CA':6,'Ca':6,'L':4,'LH':6,'l':6,'KC':7,'kc':7,'M':7,'MB':1,'mb':1,'FB':2,'EB':3,'eb':8,'C':4,'c':8,'PB':9,'pb':9,'AMMC':10,'ammc':10,'V':5,'PS':11,'S':11,'SOG':11,'s':11,'PI':12,'I':12,'i':12,'D':5,'':14,'0':14}



In [88]:

    
Translated=[Dict[X] for X in Label_ICs]



In [89]:

    
G=Good_ICs.tolist();



In [90]:

    
len(Good_ICs)









    Out[90]:





138



In [91]:

    
G.count(1)









    Out[91]:





2



In [92]:

    
where_are_NaNs = np.isnan(D2)

D2[where_are_NaNs] = 0



In [93]:

    
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])



In [94]:

    
D2.shape









    Out[94]:





(92, 44, 5, 138)



In [95]:

    
S









    Out[95]:





(92, 44, 11, 138)



In [96]:

    
i=0
for j in range(S[3]):    
    if Good_ICs[j]:
        if i==0:
            C[j,:]=(1,0,1)
            i=i+1
        else:
            C[j,:]=(0,1,0)
        
        for k in range(3):
            M=np.max(np.squeeze(np.reshape(D2[:,:,:,j],S[0]*S[1]*5)))
            #M[M==0]=1
  #          Fmaps[:,:,:,k]=0.7*Dmaps[:,:,:,j]*C[j,k]/np.max(np.squeeze(np.reshape(Dmaps[:,:,:,j]*np.max(C[j,:],S[0]*S[1]*5))
            Fmaps[:,:,:,k]=0.8*D2[:,:,:,j]*C[j,k]/(M*np.max(C[j,:]))
        Final_map=Final_map+Fmaps



In [101]:

    
Final_map.shape









    Out[101]:





(92, 44, 5, 3)



In [114]:

    
pylab.rcParams['figure.figsize'] = (14, 5)

#Final_map[Final_map<0.1]=np.NaN

N=2

for i in range(N):
        plt.subplot(1,N,i+1)
        if i==0:
            plt.imshow(np.max(Final_map[:,:,0:3,:],2),interpolation='none')
        else:
            plt.imshow(Final_map[:,:,4,:],interpolation='none')
        #plt.imshow(Final_map[:,:,i]) 
        frame1 = plt.gca()
        frame1.axes.get_xaxis().set_visible(False)
        frame1.axes.get_yaxis().set_visible(False)



In [119]:

    
pylab.rcParams['figure.figsize'] = (15, 5)
h=5
i=0

for j in range(S[3]):
    if Good_ICs[j]:
        plt.plot(Time_fluo,(DT[:,Order[j]]/np.sqrt(np.var(DT[:,Order[j]]))+h*i),color=C[j,:])
        i=i+1
plt.xlim([np.min(Time_fluo),np.max(Time_fluo)])
plt.ylim([-3,8.5])
frame1=plt.gca()
frame1.axes.get_yaxis().set_ticks([])
matplotlib.rcParams.update({'font.size': 24})
plt.xlabel('Time (s)')
plt.show()



In [ ]:



In [ ]: