In [158]:
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
In [159]:
from sklearn import linear_model
In [160]:
# 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)
In [161]:
Ua=sio.loadmat(filename)
DT=Ua['TSo']
DT.shape
Out[161]:
In [162]:
# 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)
In [163]:
img1 = nb.load(filename2)
data = img1.get_data()
S=data.shape
S
Out[163]:
In [164]:
Time_fluoICA=np.array(range(5527))*0.01
Z-score
In [165]:
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 [166]:
for i in range(S[3]):
Demean[:,:,:,i]=data[:,:,:,i]-np.mean(np.mean(np.mean(data[:,:,:,i],0),0),0)
In [167]:
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-1.5
Tvar[i]=np.var(DT[i,:])
Dmaps[Dmaps<0]=0
In [168]:
# 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)
In [169]:
Ua=sio.loadmat(filename)
Xk=Ua['Xk']
In [170]:
Xk.shape
Out[170]:
In [171]:
# 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)
In [172]:
Masks.shape
Out[172]:
In [173]:
filenameM='/home/sophie/Downloads/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)]
In [176]:
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])
In [178]:
S=M.shape
M2=np.zeros((S[0],75))
for j in range(86):
J=np.array([l for l in range(75) if Num[l]==j+1])
if J.shape[0] != 0:
J=J[0]
M2[:,J]=M[:,j]
Mnorm=M2
for i in range(75):
Mnorm[:,i]=M2[:,i]/np.max(M2[:,i])
In [185]:
robjects.r['load']('/home/sophie/JefferisData.Rdata')
JefferisData=robjects.r['x']
e=np.array(JefferisData)
enorm=e[10:85,:].astype(float)
for i in range(75):
enorm[i,:]=np.divide(enorm[i,:],np.max(enorm[i,:]))
Out[185]:
In [360]:
Scores=np.zeros((enorm.shape[1],M2.shape[0]))
for i in range(enorm.shape[1]):
for j in range(M2.shape[0]):
#Scores[i,j]=np.inner(enorm[:,i],Mnorm[j,:])
Scores[i,j]=-sum(abs(enorm[:,i]-Mnorm[j,:]))
In [349]:
import csv
i=0
NeuronName=[]
with open('/home/sophie/fc_name_mapping.csv') as f:
file_read = csv.reader(f)
for row in file_read:
NeuronName.append(row[3])
i=i+1
In [230]:
CompMainName=S[0]*['']
CompNameAdd=np.zeros((S[0],75))
for i in range(S[0]):
Max=np.max(M2[i,:])
J=np.argmax(M2[i,:])
for j in range(75):
if M2[i,j]>0.2*Max:
CompNameAdd[i,j]=1
CompMainName[i]=Names[np.array(J)][0]
In [233]:
S=data.shape
In [365]:
pylab.rcParams['figure.figsize'] = (13, 2.5)
h=5
tot=0
GoodICAnat=np.zeros(S[3])
for l in range(74):
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)
tot=tot+1
GoodICAnat[i]=1
#print(i)
for j in range(75):
if CompNameAdd[i,j]==1:
print(Names[np.array(j)][0])
print(i)
I=np.argmax(Scores[:,i])
#print(I)
print(e[0][I])
print(NeuronName[np.int(np.float(e[3][I]))])
plt.plot(Time_fluoICA.T,Xk[:,0]/np.std(Xk[:,0])+2,color=(1,0,0))
plt.plot(Time_fluoICA.T,Xk[:,1]/np.std(Xk[:,1])+2,color=(0.5,0.5,0))
plt.plot(Time_fluoICA.T,Xk[:,4]/np.std(Xk[:,4])+6,color=(1,0,1))
plt.plot(Time_fluoICA.T,Xk[:,6]/np.std(Xk[:,6])+12,color=(0,1,0))
plt.plot(Time_fluoICA.T,Xk[:,7]/np.std(Xk[:,7])+12,color=(0,0,1))
#plt.plot(Time_fluoICA.T,2*Xk[:,2]/np.max(Xk[:,2])+1.5,color=(1,0,1))
if n!=0:
print(Names[l][1])
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)
# Open template
In [32]:
# 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
Out[32]:
In [33]:
LargerRegionsDic={'':'','AME_R':'OL','LO_R':'OL','NO':'CX','BU_R':'CX','PB':'CX','LH_R':'LH','LAL_R':'LX','SAD':'PENP'
,'CAN_R':'PENP','AMMC_R':'PENP','ICL_R':'INP','VES_R':'VMNP','IB_R':'INP','ATL_R':'INP','CRE_R':'INP'
,'MB_PED_R':'MB','MB_VL_R':'MB','MB_ML_R':'MB','FLA_R':'PENP','LOP_R':'OL','EB':'CX','AL_R':'AL',
'ME_R':'OL','FB':'CX','SLP_R':'SNP','SIP_R':'SNP','SMP_R':'SNP','AVLP_R':'VLNP','PVLP_R':'VLNP',
'IVLP_R':'VLNP','PLP_R':'VLNP','AOTU_R':'VLNP','GOR_R':'VMNP','MB_CA_R':'MB','SPS_R':'VMNP',
'IPS_R':'VMNP','SCL_R':'INP','EPA_R':'VMNP','GNG':'GNG','PRW':'PENP','GA_R':'LX','AME_L':'OL'
,'LO_L':'OL','BU_L':'CX','LH_L':'LH','LAL_L':'LX','CAN_L':'PENP','AMMC_L':'PENP','ICL_L':'INP',
'VES_L':'VMNP','IB_L':'INP','ATL_L':'INP','CRE_L':'INP','MB_PED_L':'MB','MB_VL_L':'MB',
'MB_ML_L':'MB','FLA_L':'PENP','LOP_L':'OL','AL_L':'AL','ME_L':'OL','SLP_L':'SNP','SIP_L':'SNP',
'SMP_L':'SNP','AVLP_L':'VLNP','PVLP_L':'VLNP','IVLP_L':'VLNP','PLP_L':'VLNP','AOTU_L':'VLNP',
'GOR_L':'VMNP','MB_CA_L':'MB','SPS_L':'VMNP','IPS_L':'VMNP','SCL_L':'INP','EPA_L':'VMNP','GA_L':'LX'}
In [34]:
SmallRegionsSorted=['ME_L','ME_R','LO_R','LO_L','LOP_R','LOP_L','AME_R','AME_L',
'PLP_R','PLP_L','PVLP_R','PVLP_L','AVLP_R','AVLP_L','AOTU_R','AOTU_L','IVLP_R','IVLP_L',
'AL_R','AL_L',
'MB_CA_R','MB_CA_L','MB_PED_R','MB_PED_L','MB_VL_R','MB_VL_L','MB_ML_R','MB_ML_L',
'SMP_R','SMP_L','SIP_R','SLP_L','SLP_R','SIP_L',
'LH_R','LH_L',
'CRE_R','CRE_L','ICL_R','ICL_L','SCL_R','SCL_L','IB_R','IB_L','ATL_R','ATL_L',
'EB','PB','NO','FB',
'BU_R','BU_L','LAL_R','LAL_L','GA_R','GA_L',
'GOR_R','GOR_L','EPA_R','EPA_L','VES_R','VES_L','SPS_R','SPS_L','IPS_R','IPS_L',
'AMMC_R','AMMC_L','SAD','FLA_R','FLA_L','PRW','CAN_R','CAN_L',
'GNG','']
In [35]:
Tozip=range(len(SmallRegionsSorted))
SmallRegionsDic=dict(zip(SmallRegionsSorted,Tozip))
In [36]:
LargerRegionInd={ 'OL':1,'VLNP':2,'VMNP':3,'AL':4,'MB':5,'LH':6,'SNP':7,'CX':8,'LX':9,'INP':10,'PENP':11,'GNG':12,'':13}
In [37]:
LargerRegionInd={ 'OL':1,'VLNP':2,'VMNP':3,'AL':4,'MB':5,'LH':6,'SNP':7,'CX':8,'LX':9,'INP':10,'PENP':11,'GNG':12,'':13}
In [38]:
LargerRegionI=np.array([LargerRegionInd[LargerRegion[i]] for i in range(S[3])])
In [39]:
SmallRegion=np.array([SmallRegionsDic[CompMainName[i]] for i in range(S[3])])
In [40]:
NewOrder=np.argsort(SmallRegion)
In [41]:
SmallRegion[NewOrder]
Out[41]:
In [42]:
%%javascript
IPython.OutputArea.auto_scroll_threshold =4000;
In [43]:
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 [44]:
plt.imshow(Dmean[:,:,1],cmap=plt.cm.gray)
Out[44]:
In [45]:
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 [46]:
algorithm = linear_model.LinearRegression()
In [47]:
Sxk=Xk.shape
In [48]:
Sxk
Out[48]:
In [51]:
X=np.zeros((5527, 6))
In [52]:
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 [53]:
#plt.plot(X[:,0])
#plt.plot(X[:,1])
#plt.plot(X[:,2])
plt.plot(X[:,3])
Out[53]:
In [54]:
Rsq=np.zeros((4,S[3]))
Betas=np.zeros((4,S[3]))
In [55]:
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
else:
for i in range(S[2]):
V=Dmaps[:,:,i,Order[j]]
D1[:,:,i]=V
if (CompMainName[j] != '') and (LargerRegionI[j]!=1) and (LargerRegionI[j]==1 or LargerRegionI[j]==1
):
print(j)
print(CompMainName[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()
model = algorithm.fit(X, DT[:,j])
betas = model.coef_
rsq = model.score(X,DT[:,j])
print('left:',betas[0],'right:',betas[1],'walk:',betas[2],'groom:',betas[3])
print(rsq)
plt.plot(Time_fluoICA.T,2*DT[:,j]+1.5)
plt.plot(Time_fluoICA.T,X[:,0],color=(1,0,0))
plt.plot(Time_fluoICA.T,X[:,1],color=(1,0,0))
plt.show()
a=raw_input()
Label_ICs.append(a)
if Label_ICs[j]!='':
Good_ICs[j]=1
In [56]:
Dmaps.shape
Out[56]:
In [57]:
fn=open('/home/sophie/Desktop/100160GoodICs150.txt','w')
for i in range(S[3]):
if Good_ICs[i]:
print>>fn, i
print>>fn, CompMainName[i]
print>>fn, Good_ICs[i]
In [58]:
if len(Label_ICs)<S[3]:
for j in range(S[3]-len(Label_ICs)):
Label_ICs.append('')
In [59]:
G=Good_ICs.tolist();
In [60]:
len(Good_ICs)
Out[60]:
In [61]:
Rsq=np.zeros((1,S[3]))
Betas=np.zeros((6,S[3]))
for j in range(S[3]):
model = algorithm.fit(X, DT[:,j])
Betas[:,j] = model.coef_
Rsq[:,j] = model.score(X,DT[:,j])
In [62]:
RsqUni=np.zeros((6,S[3]))
BetaUni=np.zeros((6,S[3]))
Sx=X.shape
for k in range(6):
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 [63]:
RsqUni.shape
Out[63]:
In [64]:
plt.plot(RsqUni[0,:])
Out[64]:
In [65]:
GoodICo=Good_ICs[NewOrder]
D2o=D2[:,:,:,NewOrder]
LargerRegionIo=LargerRegionI[NewOrder]
Ind=np.array(range(S[3]))
Indexo=Ind[NewOrder]
DTo=DT[:,NewOrder]
In [66]:
import random
In [72]:
del Final_map
del Fmaps
In [83]:
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 [84]:
random.uniform(0,1)
Out[84]:
In [86]:
C=np.zeros((S[3],3))
i=0
for j in range(S[3]):
if Betas[0,j]>0.85*np.max(Betas[0,:]):
#if 1>0.1:
#C[j,:]=C1[i%6][:]
C[j,0]=1
C[j,1]=random.uniform(0,1)
#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]=0.8*D2[:,:,:,j]*C[j,k]/M
Final_map=Final_map+Fmaps
J=j
#print(Indexo[j])
i=i+1
In [87]:
pylab.rcParams['figure.figsize'] = (19, 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/16
Df=Df/(np.max(np.max(Df)))
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 [150]:
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 [151]:
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 [152]:
GoodICo=Good_ICs[NewOrder]
D2o=D2[:,:,:,NewOrder]
LargerRegionIo=LargerRegionI[NewOrder]
Ind=np.array(range(S[3]))
Indexo=Ind[NewOrder]
DTo=DT[:,NewOrder]
In [153]:
C=np.zeros((S[3],3))
i=0
for j in range(S[3]):
if LargerRegionIo[j]<12 and GoodICo[j]:
C[j,:]=C1[i%6][:]
for k in range(3):
M=np.max(np.squeeze(np.reshape(D2o[:,:,:,j],S[0]*S[1]*5)))
Fmaps[:,:,:,k]=0.6*D2o[:,:,:,j]*C[j,k]/M
Final_map=Final_map+Fmaps
#print(Indexo[j])
i=i+1
In [154]:
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/16
Df=Df/(np.max(np.max(Df)))
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(Dmean[:,:,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 [123]:
pylab.rcParams['figure.figsize'] = (10, 15)
h=0.5
i=0
for j in range(S[3]):
if GoodICo[j]:
plt.plot(Time_fluoICA,(DTo[:,j]+h*i),color=C[j,:])
i=i+1
plt.xlim([np.min(Time_fluoICA),np.max(Time_fluoICA)])
plt.ylim([-0.5,h*i])
frame1 = plt.gca()
frame1.axes.get_yaxis().set_visible(False)
plt.show()
In [124]:
k=0
J=np.zeros(len(GoodICo[GoodICo==1]))
for j in range(len(GoodICo)):
if GoodICo[j]:
print(k)
print([CompMainName[Indexo[j]]])
J[k]=j
k=k+1
In [125]:
Sets=[range(10),range(10,12),range(12,17),range(17,20),20,range(21,23),range(23,25),25]
In [126]:
pylab.rcParams['figure.figsize'] = (12, 6)
for i in range(len(Sets)):
Final_map2=np.zeros([S[0],S[1],3])
Fmaps2=np.zeros([S[0],S[1],3])
Final_map3=np.zeros([S[0],S[1],5,3])
Fmaps3=np.zeros([S[0],S[1],5,3])
if type(Sets[i])==list:
for j in np.array(Sets[i]):
C=np.zeros((S[3],3))
C[j,:]=C1[j%6][:]
for k in range(3):
M=np.max(np.squeeze(np.reshape(D2o[:,:,:,J[j]],S[0]*S[1]*5)))
Fmaps2[:,:,k]=0.9*np.mean(D2o[:,:,:,J[j]],2)*C[j,k]/M
M=np.max(np.squeeze(np.reshape(D2o[:,:,:,J[j]],S[0]*S[1],5)))
Fmaps3[:,:,:,k]=0.9*D2o[:,:,:,J[j]]*C[j,k]/M
Final_map2=Final_map2+Fmaps2
Final_map3=Final_map3+Fmaps3
else:
j=Sets[i]
C[j,:]=C1[j%6][:]
for k in range(3):
M=np.max(np.squeeze(np.reshape(D2o[:,:,:,J[j]],S[0]*S[1]*5)))
Fmaps2[:,:,k]=0.8*np.mean(D2o[:,:,:,J[j]],2)*C[j,k]/M
Final_map2=Final_map2+Fmaps2
Df=np.zeros([S[0],S[1],3])
for l in range(3):
Df[:,:,l]=Final_map2[:,:,l]+np.mean(Dmean,2)/16
MM=np.max(np.max(Df))
Rotated=ndimage.rotate(Df[:,:,:]/MM,-90)
a=plt.imshow(Rotated,cmap=my_cmap,interpolation='none')
frame1 = plt.gca()
frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
plt.show()
In [ ]: