def _updateCoords(self, A, dims):
'''See if we need to recalculate the coords
Also see if we need to add components
'''
if self.coords is None: #initial calculation
self.A = A
self.coords = get_contours(A, dims)
elif np.shape(A)[1] > np.shape(
self.A)[1] and self.frame_number % 200 == 0:
#Only recalc if we have new components
# FIXME: Since this is only for viz, only do this every 100 frames
# TODO: maybe only recalc coords that are new?
self.A = A
self.coords = get_contours(A, dims)
self.counter += 1
def nb_view_patches(Yr,
A,
C,
S,
b,
f,
d1,
d2,
YrA=None,
image_neurons=None,
thr=0.99,
denoised_color=None,
cmap='jet'):
"""
Interactive plotting utility for ipython notebook
Args:
Yr: np.ndarray
movie
A,C,b,f: np.ndarrays
outputs of matrix factorization algorithm
d1,d2: floats
dimensions of movie (x and y)
YrA: np.ndarray
ROI filtered residual as it is given from update_temporal_components
If not given, then it is computed (K x T)
image_neurons: np.ndarray
image to be overlaid to neurons (for instance the average)
thr: double
threshold regulating the extent of the displayed patches
denoised_color: string or None
color name (e.g. 'red') or hex color code (e.g. '#F0027F')
cmap: string
name of colormap (e.g. 'viridis') used to plot image_neurons
"""
# PREPROCESSING
nr, T = C.shape
nA2 = np.ravel(np.power(A,
2).sum(0)) if type(A) == np.ndarray else np.ravel(
A.power(2).sum(0))
b = np.squeeze(b)
f = np.squeeze(f)
if YrA is None:
Y_r = np.array(
spdiags(old_div(1, nA2), 0, nr, nr) *
(A.T * np.matrix(Yr) -
(A.T * np.matrix(b[:, np.newaxis])) * np.matrix(f[np.newaxis]) -
A.T.dot(A) * np.matrix(C)) + C)
else:
Y_r = C + YrA
x = np.arange(T)
if image_neurons is None:
image_neurons = A.mean(1).reshape((d1, d2), order='F')
coors = get_contours(A, (d1, d2), thr)
cc1 = [cor['coordinates'][:, 0] for cor in coors]
cc2 = [cor['coordinates'][:, 1] for cor in coors]
c1 = cc1[0]
c2 = cc2[0]
# PLOTTING
fig, axes = plt.subplots(2)
axes[0].imshow(image_neurons, cmap='gray')
axes[0].set_title('Neural map')
axes[1].plot(C[0], label='C: raw traces', c='blue')
axes[1].plot(Y_r[0], label='Y_r: residuals', c='red')
axes[1].plot(S[0], label='S: deconvolved activity', c='green')
plt.legend()
axes[1].set_xlabel('t [frames]')
# WIDGETS
neuron_nr_slider = IntSlider(description='Neuron Number',
value=0,
min=0,
max=len(C) - 1)
def neuron_nr_handler(*args):
i = neuron_nr_slider.value
axes[1].clear()
axes[1].plot(C[i], label='C: raw traces', c='blue')
axes[1].plot(Y_r[i], label='Y_r: residuals', c='red')
axes[1].plot(S[i], label='S: deconvolved activity', c='green')
plt.legend()
axes[1].set_xlabel('t [frames]')
neuron_nr_slider.observe(neuron_nr_handler, 'value')
widgets = [neuron_nr_slider]
return fig, widgets
from caiman.utils.visualization import get_contours
from pathlib import Path
import pickle
from utils import start_server
pi.install_traceback()
print('ready')
fld = Path(r'D:\Dropbox (UCL - SWC)\Project_vgatPAG\analysis\doric\BF136p3_dPAG_ECIC\19MAR11')
cnm_name = fld/'19MAR11_BF136p3_t1_ds126_ffc_crop_cnm.hdf5'
if not cnm_name.exists():
raise FileExistsError(f'Could not find cnm: {str(cnm_name)}')
c, dview, n_processes = start_server()
cnm = load_CNMF(cnm_name, n_processes=n_processes, dview=dview)
# Spatial components: in a d1 x d2 x n_components matrix
A = np.reshape(cnm.estimates.A.toarray(), list(cnm.estimates.dims)+[-1], order='F') # set of spatial footprints
np.save(fld/'A.npy', A)
conts = get_contours(cnm.estimates.A.toarray(), cnm.estimates.dims)
with open(str(fld/'all_contour_data.pkl'), 'wb') as fp:
pickle.dump(conts, fp)
pi.ok('Extracted A from cnm', f'Folder \n{fld.parent.name}/{fld.name}')
