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nb=0 #1172

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nb=0 #1172

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315ac76
enable nb=0 (#1068); update deprecated selem in skimage.dilation (#1130)
j-friedrich Sep 19, 2023
b8f0153
fix bug when using patches and nb=0
j-friedrich Sep 19, 2023
132fca4
fix bug when using tsub or ssub and nb=0
j-friedrich Sep 20, 2023
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4 changes: 2 additions & 2 deletions caiman/base/rois.py
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Since the selem keyword was deprecated at scikit learn, and we still have it, it might be nice to define what it means in this function: e.g., in the docs for it maybe just mention it stands for 'structuring element'

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you mean skimage? I remember playing with this years ago, it means something like the "where stuff is multiplier" =S

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Yes oops scikitimage

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this explains selem, the structuring element https://scikit-image.org/docs/stable/auto_examples/applications/plot_morphology.html

Original file line number Diff line number Diff line change
Expand Up @@ -131,9 +131,9 @@ def extract_binary_masks_from_structural_channel(Y,
for i in range(areas[1]):
temp = (areas[0] == i + 1)
if expand_method == 'dilation':
temp = dilation(temp, selem=selem)
temp = dilation(temp, footprint=selem)
elif expand_method == 'closing':
temp = closing(temp, selem=selem)
temp = closing(temp, footprint=selem)

A[:, i] = temp.flatten('F')

Expand Down
27 changes: 18 additions & 9 deletions caiman/source_extraction/cnmf/initialization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -444,6 +444,10 @@ def initialize_components(Y, K=30, gSig=[5, 5], gSiz=None, ssub=1, tsub=1, nIter

f_in = resize(np.atleast_2d(f_in), [b_in.shape[-1], T])

elif nb == 0:
b_in = np.empty((np.prod(d), 0), dtype=np.float32, order='F')
f_in = np.empty((0, T), dtype=np.float32)

if Ain.size > 0:
Cin = resize(Cin, [K, T])
center = np.asarray(
Expand Down Expand Up @@ -970,11 +974,13 @@ def onclick(event):

res = np.reshape(Y, (np.prod(d[0:-1]), d[-1]),
order='F') + med.flatten(order='F')[:, None]
# model = NMF(n_components=nb, init='random', random_state=0)
model = NMF(n_components=nb, init='nndsvdar')
b_in = model.fit_transform(np.maximum(res, 0)).astype(np.float32)
f_in = model.components_.astype(np.float32)

if nb > 0:
model = NMF(n_components=nb, init='nndsvdar')
b_in = model.fit_transform(np.maximum(res, 0)).astype(np.float32)
f_in = model.components_.astype(np.float32)
else:
b_in = np.empty((A.shape[0], 0), dtype=np.float32, order='F')
f_in = np.empty((0, C.shape[1]), dtype=np.float32)
return A, C, np.array(center, dtype='uint16'), b_in, f_in

#%%
Expand Down Expand Up @@ -1140,7 +1146,7 @@ def hals(Y, A, C, b, f, bSiz=3, maxIter=5):
else:
ind_A = A>1e-10

ind_A = spr.csc_matrix(ind_A) # indicator of nonnero pixels
ind_A = spr.csc_matrix(ind_A) # indicator of nonzero pixels

def HALS4activity(Yr, A, C, iters=2):
U = A.T.dot(Yr)
Expand All @@ -1166,13 +1172,16 @@ def HALS4shape(Yr, A, C, iters=2):
return A

Ab = np.c_[A, b]
Cf = np.r_[C, f.reshape(nb, -1)]
Cf = np.r_[C, f]
for _ in range(maxIter):
Cf = HALS4activity(np.reshape(
Y, (np.prod(dims), T), order='F'), Ab, Cf)
Ab = HALS4shape(np.reshape(Y, (np.prod(dims), T), order='F'), Ab, Cf)

return Ab[:, :-nb], Cf[:-nb], Ab[:, -nb:], Cf[-nb:].reshape(nb, -1)
if nb == 0:
return Ab, Cf, b, f
else:
return Ab[:, :-nb], Cf[:-nb], Ab[:, -nb:], Cf[-nb:].reshape(nb, -1)


@profile
Expand Down Expand Up @@ -1385,7 +1394,7 @@ def compute_B(b0, W, B): # actually computes -B to efficiently compute Y-B in p
b_in, s_in, f_in = spr.linalg.svds(B, k=nb)
f_in *= s_in[:, np.newaxis]
else:
b_in = np.empty((A.shape[0], 0))
b_in = np.empty((A.shape[0], 0), order='F')
f_in = np.empty((0, T))
if nb == 0:
logging.info('Returning background as b0 and W')
Expand Down
32 changes: 17 additions & 15 deletions caiman/source_extraction/cnmf/spatial.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1050,22 +1050,24 @@ def computing_indicator(Y, A_in, b, C, f, nb, method, dims, min_size, max_size,
px = (np.sum(dist_indicator, axis=1) > 0)
not_px = ~px

if nb>1:
f = NMF(nb, init='nndsvda').fit(np.maximum(Y[not_px, :], 0)).components_
else:
if Y.shape[-1] < 30000:
f = Y[not_px, :].mean(0)
if nb > 0:
if nb > 1:
f = NMF(nb, init='nndsvda').fit(np.maximum(Y[not_px, :], 0)).components_
else:
print('estimating f')
f = 0
for xxx in np.where(not_px)[0]:
f += Y[xxx]
f /= not_px.sum()

f = np.atleast_2d(f)

Y_resf = np.dot(Y, f.T)
b = np.maximum(Y_resf, 0) / (np.linalg.norm(f)**2)
if Y.shape[-1] < 30000:
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We may want to consider making this threshold overridable or at least less magical

f = Y[not_px, :].mean(0)
else:
print('estimating f')
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There are a few print statements in here -- in final draft convert to logger info or debug statements?

f = 0
for xxx in np.where(not_px)[0]:
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Can we give this variable a better name?

f += Y[xxx]
f /= not_px.sum()
f = np.atleast_2d(f)
Y_resf = np.dot(Y, f.T)
b = np.maximum(Y_resf, 0) / (np.linalg.norm(f)**2)
else:
f = np.empty((0, Y.shape[-1]), dtype='float32')
b = np.empty((Y.shape[0], 0), dtype='float32')
C = np.maximum(csr_matrix(dist_indicator_av.T).dot(
Y) - dist_indicator_av.T.dot(b).dot(f), 0)
A_in = scipy.sparse.coo_matrix(A_in.astype(np.float32))
Expand Down
3 changes: 2 additions & 1 deletion caiman/source_extraction/cnmf/temporal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -197,7 +197,8 @@ def update_temporal_components(Y, A, b, Cin, fin, bl=None, c1=None, g=None, sn=N

A = scipy.sparse.hstack((A, b)).tocsc()
S = np.zeros(np.shape(Cin))
Cin = np.vstack((Cin, fin))
if fin is not None:
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Could you add a comment explaining how this works?

Cin = np.vstack((Cin, fin))
C = Cin.copy()
nA = np.ravel(A.power(2).sum(axis=0)) + np.finfo(np.float32).eps

Expand Down