raab/efishSignalDetector
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detection of rise time based on surpass of tau freq or steepest slope

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Till Raab 2023-12-04 12:22:16 +01:00
parent cc4408d75a
commit 8cd422461a
1 changed files with 29 additions and 9 deletions
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38
extract_from_bbox.py
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@ -65,16 +65,12 @@ def assign_rises_to_ids(raw_path, time_frequency_bboxes, bbox_groups):
fund_v, ident_v, idx_v, times = load_wavetracker_data(raw_path) fund_v, ident_v, idx_v, times = load_wavetracker_data(raw_path)
# rise_id = np.full(len(time_frequency_bboxes), np.nan)
# embed()
# quit()
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.plot(times[idx_v[~np.isnan(ident_v)]], fund_v[~np.isnan(ident_v)], '.') ax.plot(times[idx_v[~np.isnan(ident_v)]], fund_v[~np.isnan(ident_v)], '.')
mask = time_frequency_bboxes['file_name'] == raw_path.parent.name mask = time_frequency_bboxes['file_name'] == raw_path.parent.name
for index, bbox in time_frequency_bboxes[mask].iterrows(): for index, bbox in time_frequency_bboxes[mask].iterrows():
name, t0, f0, t1, f1, score = (bbox[0], *bbox[1:-1].astype(float)) name, t0, f0, t1, f1, score = (bbox[0], *bbox[1:-2].astype(float))
if bbox_groups[index] == 0: if bbox_groups[index] == 0:
color = 'tab:green' color = 'tab:green'
elif bbox_groups[index] > 0: elif bbox_groups[index] > 0:
@ -99,14 +95,37 @@ def assign_rises_to_ids(raw_path, time_frequency_bboxes, bbox_groups):
(f1 >= fund_v)] (f1 >= fund_v)]
) )
if len(possible_ids) == 1: if len(possible_ids) == 1:
time_frequency_bboxes.at[index, 'id'] = possible_ids[0] assigned_id = possible_ids[0]
# rise_id[index] = possible_ids[0] time_frequency_bboxes.at[index, 'id'] = assigned_id
elif len(possible_ids) > 1: elif len(possible_ids) > 1:
time_frequency_bboxes.at[index, 'id']= identify_most_likely_rise_id(possible_ids, t0, t1, f0, f1, fund_v, ident_v, times, idx_v) assigned_id = identify_most_likely_rise_id(possible_ids, t0, t1, f0, f1, fund_v, ident_v, times, idx_v)
time_frequency_bboxes.at[index, 'id'] = assigned_id
# rise_id[index] = identify_most_likely_rise_id(possible_ids, t0, t1, f0, f1, fund_v, ident_v, times, idx_v) # rise_id[index] = identify_most_likely_rise_id(possible_ids, t0, t1, f0, f1, fund_v, ident_v, times, idx_v)
else:
continue
rise_start_freq_th = f0 + (f1 - f0) * 0.37
wavetracker_mask = np.arange(len(fund_v))[
(times[idx_v] >= t0) &
(times[idx_v] <= t1) &
(ident_v == assigned_id)
]
if np.sum(fund_v[wavetracker_mask] > rise_start_freq_th) > 0:
# rise start time = moment where rise freq exceeds 37% of bbox freq range ...
rise_start_idx = wavetracker_mask[fund_v[wavetracker_mask] > rise_start_freq_th][0]
rise_time = times[idx_v[rise_start_idx]]
else:
### if this is never the case use the largest slope
rise_start_idx = wavetracker_mask[np.argmax(np.diff(fund_v[wavetracker_mask]))]
rise_time = times[idx_v[rise_start_idx]]
time_frequency_bboxes.at[index, 'event_time'] = rise_time
ax.plot(rise_time, fund_v[rise_start_idx], 'ok')
# embed()
# quit()
# time_frequency_bboxes['id'] = rise_id # time_frequency_bboxes['id'] = rise_id
# embed() # embed()
# plt.show()
plt.close() plt.close()
return time_frequency_bboxes return time_frequency_bboxes
@ -169,6 +188,7 @@ def main(args):
time_frequency_bboxes = pd.DataFrame(data= np.array(df_collect), columns=['file_name', 't0', 'f0', 't1', 'f1', 'score']) time_frequency_bboxes = pd.DataFrame(data= np.array(df_collect), columns=['file_name', 't0', 'f0', 't1', 'f1', 'score'])
time_frequency_bboxes['id'] = np.full(len(time_frequency_bboxes), np.nan) time_frequency_bboxes['id'] = np.full(len(time_frequency_bboxes), np.nan)
time_frequency_bboxes['event_time'] = np.full(len(time_frequency_bboxes), np.nan)
########################################### ###########################################
# for file_name in time_frequency_bboxes['file_name'].unique(): # for file_name in time_frequency_bboxes['file_name'].unique():
@ -207,7 +227,7 @@ def main(args):
for raw_path in file_paths: for raw_path in file_paths:
# mask = (time_frequency_bboxes['file_name'] == raw_path.parent.name) # mask = (time_frequency_bboxes['file_name'] == raw_path.parent.name)
mask = ((time_frequency_bboxes['file_name'] == raw_path.parent.name) & (~np.isnan(time_frequency_bboxes['id']))) mask = ((time_frequency_bboxes['file_name'] == raw_path.parent.name) & (~np.isnan(time_frequency_bboxes['id'])))
save_df = pd.DataFrame(time_frequency_bboxes[mask][['t0', 't1', 'f0', 'f1', 'score', 'id']].values, columns=['t0', 't1', 'f0', 'f1', 'score', 'id']) save_df = pd.DataFrame(time_frequency_bboxes[mask][['t0', 't1', 'f0', 'f1', 'score', 'id', 'event_time']].values, columns=['t0', 't1', 'f0', 'f1', 'score', 'id', 'event_time'])
save_df['label'] = np.ones(len(save_df), dtype=int) save_df['label'] = np.ones(len(save_df), dtype=int)
save_df.to_csv(raw_path.parent / 'risedetector_bboxes.csv', sep = ',', index = False) save_df.to_csv(raw_path.parent / 'risedetector_bboxes.csv', sep = ',', index = False)
quit() quit()