raab/GP2023_chirp_detection
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

adding boolien vector to reduce duplicates

Browse Source
This commit is contained in:
wendtalexander 2023-01-17 20:19:26 +01:00
parent 8138e3107f
commit 363f2dd14c
1 changed files with 27 additions and 15 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

42
code/chirpdetection.py
View File

@ -551,13 +551,16 @@ def main(datapath: str) -> None:
current_chirps = [] current_chirps = []
bool_timestamps = np.ones_like(timestamps, dtype=bool)
for tt in timestamps: for bo, tt in enumerate(timestamps):
if bool_timestamps[bo] == False:
continue
cm = timestamps_idx[(timestamps >= tt) & ( cm = timestamps_idx[(timestamps >= tt) & (
timestamps <= tt + config.chirp_window_threshold)] timestamps <= tt + config.chirp_window_threshold)]
if set([0, 1, 2]).issubset(timestamps_features[cm]): if set([0, 1, 2]).issubset(timestamps_features[cm]):
current_chirps.append(np.mean(timestamps[cm])) current_chirps.append(np.mean(timestamps[cm]))
electrodes_of_chirps.append(el) electrodes_of_chirps.append(el)
bool_timestamps[cm] = False
# for checking if there are chirps on multiple electrodes # for checking if there are chirps on multiple electrodes
@ -593,22 +596,31 @@ def main(datapath: str) -> None:
sort_chirps_electrodes = chirps_electrodes[np.argsort(chirps_electrodes)] sort_chirps_electrodes = chirps_electrodes[np.argsort(chirps_electrodes)]
sort_electrodes = electrodes_of_chirps[np.argsort(chirps_electrodes)] sort_electrodes = electrodes_of_chirps[np.argsort(chirps_electrodes)]
bool_vector = np.ones(len(sort_chirps_electrodes), dtype=bool) bool_vector = np.ones(len(sort_chirps_electrodes), dtype=bool)
# make index vector
index_vector = np.arange(len(sort_chirps_electrodes))
the_real_chirps = [] the_real_chirps = []
for chirp_index, seoc in enumerate(sort_chirps_electrodes):
if bool_vector[chirp_index] == False:
continue
else:
cm = index_vector[(sort_chirps_electrodes >= seoc) & (
sort_chirps_electrodes <= seoc + config.chirp_window_threshold)]
if set([0,1]).issubset(sort_electrodes[cm]):
the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
elif set([1,0]).issubset(sort_electrodes[cm]):
the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
elif set([0,2]).issubset(sort_electrodes[cm]):
the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
elif set([1,2]).issubset(sort_electrodes[cm]):
the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
bool_vector[cm] = False
for ct in the_real_chirps:
axs[0, el].axvline(ct, color='b', lw=1)
embed() embed()
for seoc in sort_chirps_electrodes: plt.show()
cm = sort_electrodes[[(sort_chirps_electrodes >= seoc) & (
sort_chirps_electrodes <= seoc + config.chirp_window_threshold)][bool_vector]]
if set([0,1]).issubset(sort_electrodes[cm]):
the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
elif set([0,2]).issubset(sort_electrodes[cm]):
the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
elif set([1,2]).issubset(sort_electrodes[cm]):
the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
bool_vector[sort_electrodes[cm]] = False