raab/GP2023_chirp_detection
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trying different stuff

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wendtalexander 2023-01-25 10:16:28 +01:00
parent 4b5aec2e65
commit af088f5726
1 changed files with 76 additions and 76 deletions
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code/plot_chirp_bodylegth.py
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@ -144,80 +144,80 @@ def main(datapath: str):
# Get rid of tracking faults (two onsets or two offsets after another) # Get rid of tracking faults (two onsets or two offsets after another)
category, timestamps = correct_chasing_events(category, timestamps) category, timestamps = correct_chasing_events(category, timestamps)
winner_chirp, loser_chirp = get_chirp_winner_loser( # winner_chirp, loser_chirp = get_chirp_winner_loser(
foldername, bh, order_meta_df) # foldername, bh, order_meta_df)
chirps_winner.append(winner_chirp) # chirps_winner.append(winner_chirp)
chirps_loser.append(loser_chirp) # chirps_loser.append(loser_chirp)
size_diff, chirp_diff = get_chirp_size( # size_diff, chirp_diff = get_chirp_size(
foldername, bh, order_meta_df, id_meta_df) # foldername, bh, order_meta_df, id_meta_df)
size_diffs.append(size_diff) # size_diffs.append(size_diff)
size_chirps_diffs.append(chirp_diff) # size_chirps_diffs.append(chirp_diff)
freq_diff, freq_chirps_diff = get_chirp_freq( # freq_diff, freq_chirps_diff = get_chirp_freq(
foldername, bh, order_meta_df) # foldername, bh, order_meta_df)
freq_diffs.append(freq_diff) # freq_diffs.append(freq_diff)
freq_chirps_diffs.append(freq_chirps_diff) # freq_chirps_diffs.append(freq_chirps_diff)
# folder_name = foldername.split('/')[-2] folder_name = foldername.split('/')[-2]
# winner_row = order_meta_df[order_meta_df['recording'] == folder_name] winner_row = order_meta_df[order_meta_df['recording'] == folder_name]
# winner = winner_row['winner'].values[0].astype(int) winner = winner_row['winner'].values[0].astype(int)
# winner_fish1 = winner_row['fish1'].values[0].astype(int) winner_fish1 = winner_row['fish1'].values[0].astype(int)
# winner_fish2 = winner_row['fish2'].values[0].astype(int) winner_fish2 = winner_row['fish2'].values[0].astype(int)
# groub = winner_row['group'].values[0].astype(int) groub = winner_row['group'].values[0].astype(int)
# size_rows = id_meta_df[id_meta_df['group'] == groub] size_rows = id_meta_df[id_meta_df['group'] == groub]
# if winner == winner_fish1: if winner == winner_fish1:
# winner_fish_id = winner_row['rec_id1'].values[0] winner_fish_id = winner_row['rec_id1'].values[0]
# loser_fish_id = winner_row['rec_id2'].values[0] loser_fish_id = winner_row['rec_id2'].values[0]
# size_winners = [] size_winners = []
# for l in ['l1', 'l2', 'l3']: for l in ['l1', 'l2', 'l3']:
# size_winner = size_rows[size_rows['fish']== winner_fish1][l].values[0] size_winner = size_rows[size_rows['fish']== winner_fish1][l].values[0]
# size_winners.append(size_winner) size_winners.append(size_winner)
# mean_size_winner = np.nanmean(size_winners) mean_size_winner = np.nanmean(size_winners)
# size_losers = [] size_losers = []
# for l in ['l1', 'l2', 'l3']: for l in ['l1', 'l2', 'l3']:
# size_loser = size_rows[size_rows['fish']== winner_fish2][l].values[0] size_loser = size_rows[size_rows['fish']== winner_fish2][l].values[0]
# size_losers.append(size_loser) size_losers.append(size_loser)
# mean_size_loser = np.nanmean(size_losers) mean_size_loser = np.nanmean(size_losers)
# size_diffs.append(mean_size_winner - mean_size_loser) size_diffs.append(mean_size_winner - mean_size_loser)
# elif winner == winner_fish2: elif winner == winner_fish2:
# winner_fish_id = winner_row['rec_id2'].values[0] winner_fish_id = winner_row['rec_id2'].values[0]
# loser_fish_id = winner_row['rec_id1'].values[0] loser_fish_id = winner_row['rec_id1'].values[0]
# size_winners = [] size_winners = []
# for l in ['l1', 'l2', 'l3']: for l in ['l1', 'l2', 'l3']:
# size_winner = size_rows[size_rows['fish']== winner_fish2][l].values[0] size_winner = size_rows[size_rows['fish']== winner_fish2][l].values[0]
# size_winners.append(size_winner) size_winners.append(size_winner)
# mean_size_winner = np.nanmean(size_winners) mean_size_winner = np.nanmean(size_winners)
# size_losers = [] size_losers = []
# for l in ['l1', 'l2', 'l3']: for l in ['l1', 'l2', 'l3']:
# size_loser = size_rows[size_rows['fish']== winner_fish1][l].values[0] size_loser = size_rows[size_rows['fish']== winner_fish1][l].values[0]
# size_losers.append(size_loser) size_losers.append(size_loser)
# mean_size_loser = np.nanmean(size_losers) mean_size_loser = np.nanmean(size_losers)
# size_diffs.append(mean_size_winner - mean_size_loser) size_diffs.append(mean_size_winner - mean_size_loser)
# else: else:
# pass continue
# print(foldername) print(foldername)
# all_fish_ids = np.unique(bh.chirps_ids) all_fish_ids = np.unique(bh.chirps_ids)
# chirp_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id]) chirp_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id])
# chirp_loser = len(bh.chirps[bh.chirps_ids == loser_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_winner = np.nanmedian(bh.freq[bh.ident==winner_fish_id])
# freq_loser = np.nanmedian(bh.freq[bh.ident==loser_fish_id]) freq_loser = np.nanmedian(bh.freq[bh.ident==loser_fish_id])
# chirps_winner.append(chirp_winner) chirps_winner.append(chirp_winner)
# chirps_loser.append(chirp_loser) chirps_loser.append(chirp_loser)
# chirps_diffs.append(chirp_winner - chirp_loser) size_chirps_diffs.append(chirp_winner - chirp_loser)
# freq_diffs.append(freq_winner - freq_loser) freq_diffs.append(freq_winner - freq_loser)
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 5)) fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 5))
scatterwinner = 1.15 scatterwinner = 1.15
@ -246,7 +246,7 @@ def main(datapath: str):
colors1 = ps.orange colors1 = ps.orange
ps.set_boxplot_color(bplot2, colors1) ps.set_boxplot_color(bplot2, colors1)
ax1.set_ylabel('Chirpscounts [n]') ax1.set_ylabel('Chirpscounts [n]')
embed()
ax2.scatter(size_diffs, size_chirps_diffs, color='r') ax2.scatter(size_diffs, size_chirps_diffs, color='r')
ax2.set_xlabel('Size difference [mm]') ax2.set_xlabel('Size difference [mm]')
ax2.set_ylabel('Chirps difference [n]') ax2.set_ylabel('Chirps difference [n]')