def initialize_components(Y, K=30, gSig=[5,5], gSiz=None, ssub=1, tsub=1, nIter = 5, maxIter=5, kernel = None, use_hals=True,Cn=None,sn=None):
"""Initalize components
This method uses a greedy approach followed by hierarchical alternative least squares (HALS) NMF.
Optional use of spatio-temporal downsampling to boost speed.
Parameters
----------
Y: np.ndarray
d1 x d2 x T movie, raw data.
K: [optional] int
number of neurons to extract (default value: 30).
tau: [optional] list,tuple
standard deviation of neuron size along x and y (default value: (5,5).
gSiz: [optional] list,tuple
size of kernel (default 2*tau + 1).
nIter: [optional] int
number of iterations for shape tuning (default 5).
maxIter: [optional] int
number of iterations for HALS algorithm (default 5).
ssub: [optional] int
spatial downsampling factor recommended for large datasets (default 1, no downsampling).
tsub: [optional] int
temporal downsampling factor recommended for long datasets (default 1, no downsampling).
kernel: [optional] np.ndarray
User specified kernel for greedyROI (default None, greedy ROI searches for Gaussian shaped neurons)
use_hals: [bool]
Whether to refine components with the hals method
Returns
--------
Ain: np.ndarray
(d1*d2) x K , spatial filter of each neuron.
Cin: np.ndarray
T x K , calcium activity of each neuron.
center: np.ndarray
K x 2 , inferred center of each neuron.
bin: np.ndarray
(d1*d2) X nb, initialization of spatial background.
fin: np.ndarray
nb X T matrix, initalization of temporal background.
"""
if gSiz is None:
gSiz=(2*gSig[0] + 1,2*gSig[1] + 1)
d1,d2,T=np.shape(Y)
# rescale according to downsampling factor
gSig = np.round([gSig[0]/ssub,gSig[1]/ssub]).astype(np.int)
gSiz = np.round([gSiz[0]/ssub,gSiz[1]/ssub]).astype(np.int)
print 'Noise Normalization'
if sn is not None:
min_noise=np.percentile(sn,2)
noise=np.maximum(sn,min_noise)
Y=Y/np.reshape(noise,(d1,d2))[:,:,np.newaxis]
# spatial downsampling
mean_val=np.mean(Y)
if ssub!=1 or tsub!=1:
print "Spatial Downsampling ..."
Y_ds = downscale_local_mean(Y,(ssub,ssub,tsub),cval=mean_val)
if Cn is not None:
Cn = downscale_local_mean(Cn,(ssub,ssub),cval=mean_val)
else:
Y_ds = Y
print 'Roi Extraction...'
Ain, Cin, _, b_in, f_in = greedyROI2d(Y_ds, nr = K, gSig = gSig, gSiz = gSiz, nIter=nIter, kernel = kernel)
if use_hals:
print 'Refining Components...'
Ain, Cin, b_in, f_in = hals_2D(Y_ds, Ain, Cin, b_in, f_in,maxIter=maxIter);
#center = ssub*com(Ain,d1s,d2s)
d1s,d2s,Ts=np.shape(Y_ds)
Ain = np.reshape(Ain, (d1s, d2s,K),order='F')
Ain = resize(Ain, [d1, d2, K],order=1)
Ain = np.reshape(Ain, (d1*d2, K),order='F')
b_in=np.reshape(b_in,(d1s, d2s),order='F')
b_in = resize(b_in, [d1, d2]);
b_in = np.reshape(b_in, (d1*d2, 1),order='F')
Cin = resize(Cin, [K, T])
f_in = resize(np.atleast_2d(f_in), [1, T])
center = com(Ain,d1,d2)
if sn is not None:
Ain=Ain*np.reshape(noise,(d1*d2,))[:,np.newaxis]
b_in=b_in*np.reshape(noise,(d1*d2,))
Y=Y*np.reshape(noise,(d1,d2))[:,:,np.newaxis]
return Ain, Cin, b_in, f_in, center
def initialize_components(Y, K=30, gSig=[5,5], gSiz=None, ssub=1, tsub=1, nIter = 5, maxIter=5, use_median = False, kernel = None):
"""Initalize components
This method uses a greedy approach followed by hierarchical alternative least squares (HALS) NMF.
Optional use of spatio-temporal downsampling to boost speed.
Parameters
----------
Y: np.ndarray
d1 x d2 x T movie, raw data.
K: [optional] int
number of neurons to extract (default value: 30).
tau: [optional] list,tuple
standard deviation of neuron size along x and y (default value: (5,5).
gSiz: [optional] list,tuple
size of kernel (default 2*tau + 1).
nIter: [optional] int
number of iterations for shape tuning (default 5).
maxIter: [optional] int
number of iterations for HALS algorithm (default 5).
ssub: [optional] int
spatial downsampling factor recommended for large datasets (default 1, no downsampling).
tsub: [optional] int
temporal downsampling factor recommended for long datasets (default 1, no downsampling).
use_median: [optional] bool
add back fluorescence median values or not during refinement.
kernel: [optional] np.ndarray
User specified kernel for greedyROI (default None, greedy ROI searches for Gaussian shaped neurons)
Returns
--------
Ain: np.ndarray
(d1*d2) x K , spatial filter of each neuron.
Cin: np.ndarray
T x K , calcium activity of each neuron.
center: np.ndarray
K x 2 , inferred center of each neuron.
bin: np.ndarray
(d1*d2) X nb, initialization of spatial background.
fin: np.ndarray
nb X T matrix, initalization of temporal background.
"""
if gSiz is None:
gSiz=(2*gSig[0] + 1,2*gSig[1] + 1)
d1,d2,T=np.shape(Y)
# rescale according to downsampling factor
gSig = np.round([gSig[0]/ssub,gSig[1]/ssub]).astype(np.int)
gSiz = np.round([gSiz[0]/ssub,gSiz[1]/ssub]).astype(np.int)
# d1s = np.ceil(d1/ssub).astype(np.int) #size of downsampled image
# d2s = np.ceil(d2/ssub).astype(np.int)
# Ts = np.floor(T/tsub).astype(np.int) #reduced number of frames
# spatial downsampling
if ssub!=1 or tsub!=1:
print "Spatial Downsampling ..."
Y_ds = scipy.ndimage.zoom(Y, [1./ssub, 1./ssub, 1./tsub], order=0,mode='nearest')
else:
Y_ds = Y
print 'Roi Extraction...'
Ain, Cin, _, b_in, f_in = greedyROI2d(Y_ds, nr = K, gSig = gSig, gSiz = gSiz, use_median = use_median, nIter=nIter, kernel = kernel)
print 'Refining Components...'
Ain, Cin, b_in, f_in = hals_2D(Y_ds, Ain, Cin, b_in, f_in,maxIter=maxIter);
#center = ssub*com(Ain,d1s,d2s)
d1s,d2s,Ts=np.shape(Y_ds)
Ain = np.reshape(Ain, (d1s, d2s,K),order='F')
Ain = resize(Ain, [d1, d2],order=0)
Ain = np.reshape(Ain, (d1*d2, K),order='F')
b_in=np.reshape(b_in,(d1s, d2s),order='F')
b_in = resize(b_in, [d1, d2]);
b_in = np.reshape(b_in, (d1*d2, 1),order='F')
Cin = resize(Cin, [K, T])
f_in = resize(f_in, [1, T])
center = com(Ain,d1,d2)
return Ain, Cin, b_in, f_in, center
def initialize_components(Y,
K=30,
gSig=[5, 5],
gSiz=None,
ssub=1,
tsub=1,
nIter=5,
maxIter=5,
kernel=None,
use_hals=True,
Cn=None):
"""Initalize components
This method uses a greedy approach followed by hierarchical alternative least squares (HALS) NMF.
Optional use of spatio-temporal downsampling to boost speed.
Parameters
----------
Y: np.ndarray
d1 x d2 x T movie, raw data.
K: [optional] int
number of neurons to extract (default value: 30).
tau: [optional] list,tuple
standard deviation of neuron size along x and y (default value: (5,5).
gSiz: [optional] list,tuple
size of kernel (default 2*tau + 1).
nIter: [optional] int
number of iterations for shape tuning (default 5).
maxIter: [optional] int
number of iterations for HALS algorithm (default 5).
ssub: [optional] int
spatial downsampling factor recommended for large datasets (default 1, no downsampling).
tsub: [optional] int
temporal downsampling factor recommended for long datasets (default 1, no downsampling).
kernel: [optional] np.ndarray
User specified kernel for greedyROI (default None, greedy ROI searches for Gaussian shaped neurons)
use_hals: [bool]
Whether to refine components with the hals method
Returns
--------
Ain: np.ndarray
(d1*d2) x K , spatial filter of each neuron.
Cin: np.ndarray
T x K , calcium activity of each neuron.
center: np.ndarray
K x 2 , inferred center of each neuron.
bin: np.ndarray
(d1*d2) X nb, initialization of spatial background.
fin: np.ndarray
nb X T matrix, initalization of temporal background.
"""
if gSiz is None:
gSiz = (2 * gSig[0] + 1, 2 * gSig[1] + 1)
d1, d2, T = np.shape(Y)
# rescale according to downsampling factor
gSig = np.round([gSig[0] / ssub, gSig[1] / ssub]).astype(np.int)
gSiz = np.round([gSiz[0] / ssub, gSiz[1] / ssub]).astype(np.int)
# spatial downsampling
mean_val = np.mean(Y)
if ssub != 1 or tsub != 1:
print "Spatial Downsampling ..."
Y_ds = downscale_local_mean(Y, (ssub, ssub, tsub), cval=mean_val)
if Cn is not None:
Cn = downscale_local_mean(Cn, (ssub, ssub), cval=mean_val)
else:
Y_ds = Y
print 'Roi Extraction...'
Ain, Cin, _, b_in, f_in = greedyROI2d(Y_ds,
nr=K,
gSig=gSig,
gSiz=gSiz,
nIter=nIter,
kernel=kernel)
if use_hals:
print 'Refining Components...'
Ain, Cin, b_in, f_in = hals_2D(Y_ds,
Ain,
Cin,
b_in,
f_in,
maxIter=maxIter)
#center = ssub*com(Ain,d1s,d2s)
d1s, d2s, Ts = np.shape(Y_ds)
Ain = np.reshape(Ain, (d1s, d2s, K), order='F')
Ain = resize(Ain, [d1, d2, K], order=1)
Ain = np.reshape(Ain, (d1 * d2, K), order='F')
b_in = np.reshape(b_in, (d1s, d2s), order='F')
b_in = resize(b_in, [d1, d2])
b_in = np.reshape(b_in, (d1 * d2, 1), order='F')
Cin = resize(Cin, [K, T])
f_in = resize(np.atleast_2d(f_in), [1, T])
center = com(Ain, d1, d2)
return Ain, Cin, b_in, f_in, center
