raab/GP2023_chirp_detection
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Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/raab/GP2023_chirp_detection

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weygoldt 2023-01-25 10:19:23 +01:00
commit 9e5ec1d70b
2 changed files with 84 additions and 79 deletions
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11
code/modules/behaviour_handling.py
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@ -100,14 +100,19 @@ def correct_chasing_events(
category = np.delete(category, wrong_bh)
timestamps = np.delete(timestamps, wrong_bh)
new_onset_ids = np.arange(
len(category))[category == 0]
new_offset_ids = np.arange(
len(category))[category == 1]
# Check whether on- or offset is longer and calculate length difference
if len(onset_ids) > len(offset_ids):
if len(new_onset_ids) > len(new_offset_ids):
len_diff = len(onset_ids) - len(offset_ids)
logger.info(f'Onsets are greater than offsets by {len_diff}')
elif len(onset_ids) < len(offset_ids):
elif len(new_onset_ids) < len(new_offset_ids):
len_diff = len(offset_ids) - len(onset_ids)
logger.info(f'Offsets are greater than onsets by {len_diff}')
elif len(onset_ids) == len(offset_ids):
elif len(new_onset_ids) == len(new_offset_ids):
logger.info('Chasing events are equal')
return category, timestamps

152
code/plot_chirp_bodylegth.py
View File

@ -105,7 +105,7 @@ def get_chirp_freq(folder_name, Behavior, order_meta_df):
freq_diff = chirp_freq_fish2 - chirp_freq_fish1
winner_fish_id = folder_row['rec_id2'].values[0]
loser_fish_id = folder_row['rec_id1'].values[0]
chirp_diff = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) - len(
Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
@ -144,80 +144,80 @@ def main(datapath: str):
# Get rid of tracking faults (two onsets or two offsets after another)
category, timestamps = correct_chasing_events(category, timestamps)
winner_chirp, loser_chirp = get_chirp_winner_loser(
foldername, bh, order_meta_df)
chirps_winner.append(winner_chirp)
chirps_loser.append(loser_chirp)
size_diff, chirp_diff = get_chirp_size(
foldername, bh, order_meta_df, id_meta_df)
size_diffs.append(size_diff)
size_chirps_diffs.append(chirp_diff)
freq_diff, freq_chirps_diff = get_chirp_freq(
foldername, bh, order_meta_df)
freq_diffs.append(freq_diff)
freq_chirps_diffs.append(freq_chirps_diff)
# folder_name = foldername.split('/')[-2]
# winner_row = order_meta_df[order_meta_df['recording'] == folder_name]
# winner = winner_row['winner'].values[0].astype(int)
# winner_fish1 = winner_row['fish1'].values[0].astype(int)
# winner_fish2 = winner_row['fish2'].values[0].astype(int)
# groub = winner_row['group'].values[0].astype(int)
# size_rows = id_meta_df[id_meta_df['group'] == groub]
# if winner == winner_fish1:
# winner_fish_id = winner_row['rec_id1'].values[0]
# loser_fish_id = winner_row['rec_id2'].values[0]
# size_winners = []
# for l in ['l1', 'l2', 'l3']:
# size_winner = size_rows[size_rows['fish']== winner_fish1][l].values[0]
# size_winners.append(size_winner)
# mean_size_winner = np.nanmean(size_winners)
# size_losers = []
# for l in ['l1', 'l2', 'l3']:
# size_loser = size_rows[size_rows['fish']== winner_fish2][l].values[0]
# size_losers.append(size_loser)
# mean_size_loser = np.nanmean(size_losers)
# size_diffs.append(mean_size_winner - mean_size_loser)
# elif winner == winner_fish2:
# winner_fish_id = winner_row['rec_id2'].values[0]
# loser_fish_id = winner_row['rec_id1'].values[0]
# size_winners = []
# for l in ['l1', 'l2', 'l3']:
# size_winner = size_rows[size_rows['fish']== winner_fish2][l].values[0]
# size_winners.append(size_winner)
# mean_size_winner = np.nanmean(size_winners)
# size_losers = []
# for l in ['l1', 'l2', 'l3']:
# size_loser = size_rows[size_rows['fish']== winner_fish1][l].values[0]
# size_losers.append(size_loser)
# mean_size_loser = np.nanmean(size_losers)
# size_diffs.append(mean_size_winner - mean_size_loser)
# else:
# pass
# print(foldername)
# all_fish_ids = np.unique(bh.chirps_ids)
# chirp_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id])
# chirp_loser = len(bh.chirps[bh.chirps_ids == loser_fish_id])
# freq_winner = np.nanmedian(bh.freq[bh.ident==winner_fish_id])
# freq_loser = np.nanmedian(bh.freq[bh.ident==loser_fish_id])
# chirps_winner.append(chirp_winner)
# chirps_loser.append(chirp_loser)
# chirps_diffs.append(chirp_winner - chirp_loser)
# freq_diffs.append(freq_winner - freq_loser)
# winner_chirp, loser_chirp = get_chirp_winner_loser(
# foldername, bh, order_meta_df)
# chirps_winner.append(winner_chirp)
# chirps_loser.append(loser_chirp)
# size_diff, chirp_diff = get_chirp_size(
# foldername, bh, order_meta_df, id_meta_df)
# size_diffs.append(size_diff)
# size_chirps_diffs.append(chirp_diff)
# freq_diff, freq_chirps_diff = get_chirp_freq(
# foldername, bh, order_meta_df)
# freq_diffs.append(freq_diff)
# freq_chirps_diffs.append(freq_chirps_diff)
folder_name = foldername.split('/')[-2]
winner_row = order_meta_df[order_meta_df['recording'] == folder_name]
winner = winner_row['winner'].values[0].astype(int)
winner_fish1 = winner_row['fish1'].values[0].astype(int)
winner_fish2 = winner_row['fish2'].values[0].astype(int)
groub = winner_row['group'].values[0].astype(int)
size_rows = id_meta_df[id_meta_df['group'] == groub]
if winner == winner_fish1:
winner_fish_id = winner_row['rec_id1'].values[0]
loser_fish_id = winner_row['rec_id2'].values[0]
size_winners = []
for l in ['l1', 'l2', 'l3']:
size_winner = size_rows[size_rows['fish']== winner_fish1][l].values[0]
size_winners.append(size_winner)
mean_size_winner = np.nanmean(size_winners)
size_losers = []
for l in ['l1', 'l2', 'l3']:
size_loser = size_rows[size_rows['fish']== winner_fish2][l].values[0]
size_losers.append(size_loser)
mean_size_loser = np.nanmean(size_losers)
size_diffs.append(mean_size_winner - mean_size_loser)
elif winner == winner_fish2:
winner_fish_id = winner_row['rec_id2'].values[0]
loser_fish_id = winner_row['rec_id1'].values[0]
size_winners = []
for l in ['l1', 'l2', 'l3']:
size_winner = size_rows[size_rows['fish']== winner_fish2][l].values[0]
size_winners.append(size_winner)
mean_size_winner = np.nanmean(size_winners)
size_losers = []
for l in ['l1', 'l2', 'l3']:
size_loser = size_rows[size_rows['fish']== winner_fish1][l].values[0]
size_losers.append(size_loser)
mean_size_loser = np.nanmean(size_losers)
size_diffs.append(mean_size_winner - mean_size_loser)
else:
continue
print(foldername)
all_fish_ids = np.unique(bh.chirps_ids)
chirp_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id])
chirp_loser = len(bh.chirps[bh.chirps_ids == loser_fish_id])
freq_winner = np.nanmedian(bh.freq[bh.ident==winner_fish_id])
freq_loser = np.nanmedian(bh.freq[bh.ident==loser_fish_id])
chirps_winner.append(chirp_winner)
chirps_loser.append(chirp_loser)
size_chirps_diffs.append(chirp_winner - chirp_loser)
freq_diffs.append(freq_winner - freq_loser)
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 5))
scatterwinner = 1.15
@ -246,7 +246,7 @@ def main(datapath: str):
colors1 = ps.orange
ps.set_boxplot_color(bplot2, colors1)
ax1.set_ylabel('Chirpscounts [n]')
embed()
ax2.scatter(size_diffs, size_chirps_diffs, color='r')
ax2.set_xlabel('Size difference [mm]')
ax2.set_ylabel('Chirps difference [n]')