From d11c83b46fac43669fb8b3dc5a0c746b83fd6bfe Mon Sep 17 00:00:00 2001 From: Till Raab Date: Fri, 16 Jun 2023 11:14:59 +0200 Subject: [PATCH] savety commit --- ethogram.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/ethogram.py b/ethogram.py index b5035d2..2479739 100644 --- a/ethogram.py +++ b/ethogram.py @@ -130,14 +130,18 @@ def main(base_path): event_times = np.array(event_times)[time_sorter] event_labels = np.array(event_labels)[time_sorter] - marcov_matrix = np.zeros((len(loop_labels), len(loop_labels))) + marcov_matrix = np.zeros((len(loop_labels)+1, len(loop_labels)+1)) for enu_ori, label_ori in enumerate(loop_labels): for enu_tar, label_tar in enumerate(loop_labels): n = len(event_times[:-1][(event_labels[:-1] == label_ori) & (event_labels[1:] == label_tar) & (np.diff(event_times) <= 5)]) marcov_matrix[enu_ori, enu_tar] = n + for enu_tar, label_tar in enumerate(loop_labels): + n = len(event_times[:-1][(event_labels[1:] == label_tar) & (np.diff(event_times) > 5)]) + marcov_matrix[-1, enu_tar] = n - + embed() + quit() ### get those cases where ag_on does not point to event and no event points to corresponding ag_off ... add thise cases in marcov matrix chase_on_idx = np.where(event_labels == loop_labels[4])[0] chase_off_idx = np.where(event_labels == loop_labels[5])[0]