N=5,
remove_baseline=True,
dview=dview,
robust_std=False,
Athresh=0.1,
thresh_C=0.3,
num_traces_per_group=20)
#%%
from caiman.components_evaluation import evaluate_components_CNN
predictions, final_crops = evaluate_components_CNN(
A, dims, gSig, model_name='use_cases/CaImAnpaper/cnn_model')
#%%
threshold = .95
from caiman.utils.visualization import matrixMontage
pl.figure()
matrixMontage(
np.squeeze(final_crops[np.where(predictions[:, 1] >= threshold)[0]]))
pl.figure()
matrixMontage(
np.squeeze(final_crops[np.where(predictions[:, 0] >= threshold)[0]]))
#%%
thresh = .95
idx_components_cnn = np.where(predictions[:, 1] >= thresh)[0]
print(' ***** ')
print((len(final_crops)))
print((len(idx_components_cnn)))
#print((len(idx_blobs)))
#%
idx_components_r = np.where((r_values >= .99))[0]
idx_components_raw = np.where(fitness_raw < -60)[0]
idx_components_delta = np.where(fitness_delta < -60)[0]
Npeaks = 10
traces = C + YrA
idx_components, idx_components_bad, fitness_raw, fitness_delta, r_values = cm.components_evaluation.estimate_components_quality(traces, Y, A, C, b, f, final_frate=final_frate,
Npeaks=10, r_values_min=.85,
fitness_min=-30, fitness_delta_min=-30, return_all=True, N=5,
remove_baseline=True, dview=dview, robust_std=False, Athresh=0.1, thresh_C=0.3, num_traces_per_group=20)
#%%
from caiman.components_evaluation import evaluate_components_CNN
predictions, final_crops = evaluate_components_CNN(
A, dims, gSig, model_name='model/cnn_model')
#%%
threshold = .95
from caiman.utils.visualization import matrixMontage
pl.figure()
matrixMontage(np.squeeze(
final_crops[np.where(predictions[:, 1] >= threshold)[0]]))
pl.figure()
matrixMontage(np.squeeze(
final_crops[np.where(predictions[:, 0] >= threshold)[0]]))
#%%
thresh = .95
idx_components_cnn = np.where(predictions[:, 1] >= thresh)[0]
print(' ***** ')
print((len(final_crops)))
print((len(idx_components_cnn)))
# print((len(idx_blobs)))
#%
idx_components_r = np.where((r_values >= .99))[0]
idx_components_raw = np.where(fitness_raw < -60)[0]
idx_components_delta = np.where(fitness_delta < -60)[0]
