def init_rois(self, n_components=100, show=False):
Ain, Cin, center = greedyROI2d(self.Y,
nr=n_components,
gSig=[2, 2],
gSiz=[7, 7],
use_median=False)
Cn = np.mean(self.Y, axis=-1)
if show:
pl1 = pl.imshow(Cn, interpolation='none')
pl.colorbar()
pl.scatter(x=center[:, 1], y=center[:, 0], c='m', s=40)
pl.axis((-0.5, self.Y.shape[1] - 0.5, -0.5, self.Y.shape[0] - 0.5))
pl.gca().invert_yaxis()
active_pixels = np.squeeze(np.nonzero(np.sum(Ain, axis=1)))
Yr = np.reshape(self.Y,
(self.Y.shape[0] * self.Y.shape[1], self.Y.shape[2]),
order='F')
P = arpfit(Yr, p=2, pixels=active_pixels)
Y_res = Yr - np.dot(Ain, Cin)
model = ProjectedGradientNMF(n_components=1,
init='random',
random_state=0)
model.fit(np.maximum(Y_res, 0))
fin = model.components_.squeeze()
self.Yr, self.Cin, self.fin, self.Ain, self.P, self.Cn = Yr, Cin, fin, Ain, P, Cn
def init_rois(self, n_components=100, show=False):
Ain,Cin,center = greedyROI2d(self.Y, nr=n_components, gSig=[2,2], gSiz=[7,7], use_median=False)
Cn = np.mean(self.Y, axis=-1)
if show:
pl1 = pl.imshow(Cn,interpolation='none')
pl.colorbar()
pl.scatter(x=center[:,1], y=center[:,0], c='m', s=40)
pl.axis((-0.5,self.Y.shape[1]-0.5,-0.5,self.Y.shape[0]-0.5))
pl.gca().invert_yaxis()
active_pixels = np.squeeze(np.nonzero(np.sum(Ain,axis=1)))
Yr = np.reshape(self.Y,(self.Y.shape[0]*self.Y.shape[1],self.Y.shape[2]),order='F')
P = arpfit(Yr, p=2, pixels=active_pixels)
Y_res = Yr - np.dot(Ain,Cin)
model = ProjectedGradientNMF(n_components=1, init='random', random_state=0)
model.fit(np.maximum(Y_res,0))
fin = model.components_.squeeze()
self.Yr,self.Cin,self.fin,self.Ain,self.P,self.Cn = Yr,Cin,fin,Ain,P,Cn
Y=[]
for idx,y in enumerate(Yp):
Y.append(np.rot90(y,k=-1))
Y=np.transpose(np.array(Y),(1,2,0))
#%%
#Ymat = sio.loadmat('Y.mat')
Y1 = Ymat['Y']*1.
#%%
d1,d2,T = np.shape(Y)
#%% greedy initialization
nr = 30
t1 = time()
Ain,Cin,center = greedyROI2d(Y, nr = nr, gSig = [4,4], gSiz = [9,9])
t_elGREEDY = time()-t1
#%% arpfit
active_pixels = np.squeeze(np.nonzero(np.sum(Ain,axis=1)))
Yr = np.reshape(Y,(d1*d2,T),order='F')
p = 2;
P = arpfit(Yr,p=2,pixels = active_pixels)
#%% nmf
Y_res = Yr - np.dot(Ain,Cin)
model = ProjectedGradientNMF(n_components=1, init='random', random_state=0)
model.fit(np.maximum(Y_res,0))
#%%
#Y=np.load('M_FLUO_mc.npz')['mov']
#Y=Y[:,5:-5,5:-5]
#%%
Y=np.asarray(m)
Y = np.transpose(Y,(1,2,0))
#Ymat = sio.loadmat('Y.mat')
#Y = Ymat['Y']*1.
d1,d2,T = np.shape(Y)
#%%
nr = 20 # neuron numbers
t1 = time()
Ain,Cin,center = greedyROI2d(Y, nr = nr, gSig = [4,4], gSiz = [9,9], use_median = False)
t_elGREEDY = time()-t1
#%% plot centers
Cn = local_correlations(Y)
plt1 = plt.imshow(Cn,interpolation='none')
plt.colorbar()
plt.scatter(x=center[:,1], y=center[:,0], c='m', s=40)
plt.axis((-0.5,d2-0.5,-0.5,d1-0.5))
plt.gca().invert_yaxis()
#%%
crd = plot_contours(coo_matrix(Ain[:,::-1]),Cn,thr=0.9)
#%%
active_pixels = np.squeeze(np.nonzero(np.sum(Ain,axis=1)))
Yr = np.reshape(Y,(d1*d2,T),order='F')
Y = []
for idx, y in enumerate(Yp):
Y.append(np.rot90(y, k=-1))
Y = np.transpose(np.array(Y), (1, 2, 0))
#%%
#Ymat = sio.loadmat('Y.mat')
Y1 = Ymat['Y'] * 1.
#%%
d1, d2, T = np.shape(Y)
#%% greedy initialization
nr = 30
t1 = time()
Ain, Cin, center = greedyROI2d(Y, nr=nr, gSig=[4, 4], gSiz=[9, 9])
t_elGREEDY = time() - t1
#%% arpfit
active_pixels = np.squeeze(np.nonzero(np.sum(Ain, axis=1)))
Yr = np.reshape(Y, (d1 * d2, T), order='F')
p = 2
P = arpfit(Yr, p=2, pixels=active_pixels)
#%% nmf
Y_res = Yr - np.dot(Ain, Cin)
model = ProjectedGradientNMF(n_components=1, init='random', random_state=0)
model.fit(np.maximum(Y_res, 0))
