#%%
fname = [u'demo_behavior.h5']
if fname[0] in ['demo_behavior.h5']:
# TODO: todocument
fname = [download_demo(fname[0])]
# TODO: todocument
m = cm.load(fname[0], is_behavior=True)
#%% load, rotate and eliminate useless pixels
m = m.transpose([0, 2, 1])
m = m[:, 150:, :]
#%% visualize movie
m.play()
#%% select interesting portion of the FOV (draw a polygon on the figure that pops up, when done press enter)
mask = np.array(behavior.select_roi(np.median(m[::100], 0), 1)[0], np.float32)
#%%
n_components = 4 # number of movement looked for
resize_fact = 0.5 # for computational efficiency movies are downsampled
# number of standard deviations above mean for the magnitude that are considered enough to measure the angle in polar coordinates
num_std_mag_for_angle = .6
only_magnitude = False # if onlu interested in factorizing over the magnitude
method_factorization = 'dict_learn' # could also use nmf
# number of iterations for the dictionary learning algorithm (Marial et al, 2010)
max_iter_DL = -30
spatial_filter_, time_trace_, of_or = cm.behavior.behavior.extract_motor_components_OF(m, n_components, mask=mask,
resize_fact=resize_fact, only_magnitude=only_magnitude, verbose=True, method_factorization='dict_learn', max_iter_DL=max_iter_DL)
#%%
mags, dircts, dircts_thresh, spatial_masks_thrs = cm.behavior.behavior.extract_magnitude_and_angle_from_OF(
np.sqrt(
np.diff(np.array(pts[0]).T, axis=0)**2 +
np.diff(np.array(pts[8]).T, axis=0)**2), 3, 1)[2:]
mat_files = [e1]
m = cm.load_movie_chain(mat_files[:1], fr=100)[2:]
pl.figure()
pl.imshow(m[10], cmap='gray')
pl.plot(pts[0][10] / 3, pts[8][10] / 3, 'r*')
if mask_all:
mask = np.ones(m.shape[1:])
np.save(e[:-4] + '_mask_all_lat.npy', mask)
else:
mask = behavior.select_roi(np.median(m[::100], 0), 1)[0]
np.save(e[:-4] + '_mask_lat_hl.npy', mask)
#%%
r_values = []
only_magnitude = False
n_components = 6
resize_fact = .5
num_std_mag_for_angle = .6
if whole_field:
max_iter_DL = -30
else:
max_iter_DL = -30
for e, e1 in zip(gt_names, expt_names):
mat_files = [e1]
def main():
pass # For compatibility between running under Spyder and the CLI
#%%
pl.ion()
fname = [u'demo_behavior.h5']
if fname[0] in ['demo_behavior.h5']:
# TODO: todocument
fname = [download_demo(fname[0])]
# TODO: todocument
m = cm.load(fname[0], is_behavior=True)
#%% load, rotate and eliminate useless pixels
m = m.transpose([0, 2, 1])
m = m[:, 150:, :]
#%% visualize movie
m.play()
#%% select interesting portion of the FOV (draw a polygon on the figure that pops up, when done press enter)
print(
"Please draw a polygon delimiting the ROI on the image that will be displayed after the image; press enter when done"
)
mask = np.array(
behavior.select_roi(np.median(m[::100], 0), 1)[0], np.float32)
#%%
n_components = 4 # number of movement looked for
resize_fact = 0.5 # for computational efficiency movies are downsampled
# number of standard deviations above mean for the magnitude that are considered enough to measure the angle in polar coordinates
num_std_mag_for_angle = .6
only_magnitude = False # if onlu interested in factorizing over the magnitude
method_factorization = 'dict_learn' # could also use nmf
# number of iterations for the dictionary learning algorithm (Marial et al, 2010)
max_iter_DL = -30
spatial_filter_, time_trace_, of_or = cm.behavior.behavior.extract_motor_components_OF(
m,
n_components,
mask=mask,
resize_fact=resize_fact,
only_magnitude=only_magnitude,
verbose=True,
method_factorization='dict_learn',
max_iter_DL=max_iter_DL)
#%%
mags, dircts, dircts_thresh, spatial_masks_thrs = cm.behavior.behavior.extract_magnitude_and_angle_from_OF(
spatial_filter_,
time_trace_,
of_or,
num_std_mag_for_angle=num_std_mag_for_angle,
sav_filter_size=3,
only_magnitude=only_magnitude)
#%%
idd = 0
axlin = pl.subplot(n_components, 2, 2)
for mag, dirct, spatial_filter in zip(mags, dircts_thresh,
spatial_filter_):
pl.subplot(n_components, 2, 1 + idd * 2)
min_x, min_y = np.min(np.where(mask), 1)
spfl = spatial_filter
spfl = cm.movie(spfl[None, :, :]).resize(1 / resize_fact,
1 / resize_fact, 1).squeeze()
max_x, max_y = np.add((min_x, min_y), np.shape(spfl))
mask[min_x:max_x, min_y:max_y] = spfl
mask[mask < np.nanpercentile(spfl, 70)] = np.nan
pl.imshow(m[0], cmap='gray')
pl.imshow(mask, alpha=.5)
pl.axis('off')
axelin = pl.subplot(n_components, 2, 2 + idd * 2, sharex=axlin)
pl.plot(mag / 10, 'k')
dirct[mag < 0.5 * np.std(mag)] = np.nan
pl.plot(dirct, 'r-', linewidth=2)
idd += 1
#%%
fname = [u'demo_behavior.h5']
if fname[0] in ['demo_behavior.h5']:
# TODO: todocument
fname = [download_demo(fname[0])]
# TODO: todocument
m = cm.load(fname[0], is_behavior=True)
#%% load, rotate and eliminate useless pixels
m = m.transpose([0, 2, 1])
m = m[:, 150:, :]
#%% visualize movie
m.play()
#%% select interesting portion of the FOV (draw a polygon on the figure that pops up, when done press enter)
mask = np.array(behavior.select_roi(np.median(m[::100], 0), 1)[0], np.float32)
#%%
n_components = 4 # number of movement looked for
resize_fact = 0.5 # for computational efficiency movies are downsampled
# number of standard deviations above mean for the magnitude that are considered enough to measure the angle in polar coordinates
num_std_mag_for_angle = .6
only_magnitude = False # if onlu interested in factorizing over the magnitude
method_factorization = 'dict_learn' # could also use nmf
# number of iterations for the dictionary learning algorithm (Marial et al, 2010)
max_iter_DL = -30
spatial_filter_, time_trace_, of_or = cm.behavior.behavior.extract_motor_components_OF(
m,
n_components,
mask=mask,
resize_fact=resize_fact,
#%
import caiman as cm
from caiman.source_extraction.cnmf import cnmf as cnmf
from caiman.components_evaluation import evaluate_components
from caiman.utils.visualization import plot_contours, view_patches_bar
from caiman.base.rois import extract_binary_masks_blob
from caiman.behavior import behavior
#%%
from scipy.sparse import coo_matrix
n_components = 3
m = cm.load('./test.tif')
m.play(gain=3.)
#%% extract optical flow and ry NMF on it
# select the portion of FOV interesting (could be all of it as well)
mask = behavior.select_roi(np.median(m[::100], 0), 1)[0]
resize_fact = .5
num_std_mag_for_angle = .6
whole_field = True
only_magnitude = False
spatial_filter_, time_trace_, of_or = cm.behavior.behavior.extract_motor_components_OF(
m,
n_components,
mask=mask,
resize_fact=resize_fact,
only_magnitude=only_magnitude,
max_iter=1000,
verbose=True,
method_factorization='nmf')
mags, dircts, dircts_thresh, spatial_masks_thrs = cm.behavior.behavior.extract_magnitude_and_angle_from_OF(
spatial_filter_,
print(expt_names)
print(expt_fold)
#%%
for e in gt_names:
print(e)
pts = scipy.io.loadmat(e)['points'][0][0]
whisk_sess = scipy.signal.savgol_filter(
np.abs(np.diff(np.array(pts[8]).T, axis=0)), 3, 1)[2:]
num_tr = str(re.findall('\d+', e[-8:-4])[0])
mat_files = [os.path.join(os.path.split(e)[0], 'trial' + num_tr + '.mat')]
m = cm.load_movie_chain(mat_files[:1], fr=100)[2:]
pl.figure()
pl.imshow(m[0], cmap='gray')
pl.plot(pts[0][10] / 3, pts[8][10] / 3, 'r*')
mask = behavior.select_roi(np.median(m[::100], 0), 1)[0]
if mask_all:
np.save(e[:-4] + '_mask_all.npy', mask)
else:
np.save(e[:-4] + '_mask_whisk.npy', mask)
#%%
r_values = []
only_magnitude = False
n_components = 3
resize_fact = .5
#%
import caiman as cm
from caiman.source_extraction.cnmf import cnmf as cnmf
from caiman.components_evaluation import evaluate_components
from caiman.utils.visualization import plot_contours, view_patches_bar
from caiman.base.rois import extract_binary_masks_blob
from caiman.behavior import behavior
#%%
from scipy.sparse import coo_matrix
n_components = 3
m = cm.load('./test.tif')
m.play(gain=3.)
#%% extract optical flow and ry NMF on it
mask = behavior.select_roi(
np.median(m[::100], 0),
1)[0] # select the portion of FOV interesting (could be all of it as well)
resize_fact = .5
num_std_mag_for_angle = .6
whole_field = True
only_magnitude = False
spatial_filter_, time_trace_, of_or = cm.behavior.behavior.extract_motor_components_OF(
m,
n_components,
mask=mask,
resize_fact=resize_fact,
only_magnitude=only_magnitude,
max_iter=1000,
verbose=True,
method_factorization='nmf')
mags, dircts, dircts_thresh, spatial_masks_thrs = cm.behavior.behavior.extract_magnitude_and_angle_from_OF(
#%
import caiman as cm
from caiman.source_extraction.cnmf import cnmf as cnmf
from caiman.components_evaluation import evaluate_components
from caiman.utils.visualization import plot_contours, view_patches_bar
from caiman.base.rois import extract_binary_masks_blob
from caiman.behavior import behavior
#%%
from scipy.sparse import coo_matrix
n_components = 3
m = cm.load('./test.tif')
m.play(gain=3.)
#%% extract optical flow and ry NMF on it
# select the portion of FOV interesting (could be all of it as well)
mask = behavior.select_roi(np.median(m[::100], 0), 1)[0]
resize_fact = .5
num_std_mag_for_angle = .6
whole_field = True
only_magnitude = False
spatial_filter_, time_trace_, of_or = cm.behavior.behavior.extract_motor_components_OF(
m, n_components, mask=mask, resize_fact=resize_fact, only_magnitude=only_magnitude, max_iter=1000, verbose=True, method_factorization='nmf')
mags, dircts, dircts_thresh, spatial_masks_thrs = cm.behavior.behavior.extract_magnitude_and_angle_from_OF(
spatial_filter_, time_trace_, of_or, num_std_mag_for_angle=num_std_mag_for_angle, sav_filter_size=3, only_magnitude=only_magnitude)
#%% if you want to visualize optical flow
ms = [mask * fr for fr in m]
ms = np.dstack(ms)
ms = cm.movie(ms.transpose([2, 0, 1]))
_ = cm.behavior.behavior.compute_optical_flow(
ms, do_show=True, polar_coord=True)
