raab/GP2023_chirp_detection
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making things worse

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This commit is contained in:
wendtalexander 2023-01-24 23:16:36 +01:00
parent 89823fdc28
commit c070192996
2 changed files with 191 additions and 83 deletions
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code/plot_chirp_bodylegth.py
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@ -17,11 +17,106 @@ ps = PlotStyle()
logger = makeLogger(__name__) logger = makeLogger(__name__)
def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
foldername = folder_name.split('/')[-2]
winner_row = order_meta_df[order_meta_df['recording'] == foldername]
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)
if winner > 0:
if winner == winner_fish1:
winner_fish_id = winner_row['rec_id1'].values[0]
loser_fish_id = winner_row['rec_id2'].values[0]
elif winner == winner_fish2:
winner_fish_id = winner_row['rec_id2'].values[0]
loser_fish_id = winner_row['rec_id1'].values[0]
chirp_winner = len(
Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
chirp_loser = len(
Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
return chirp_winner, chirp_loser
else:
return np.nan, np.nan
def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
foldername = folder_name.split('/')[-2]
folder_row = order_meta_df[order_meta_df['recording'] == foldername]
fish1 = folder_row['fish1'].values[0].astype(int)
fish2 = folder_row['fish2'].values[0].astype(int)
groub = folder_row['group'].values[0].astype(int)
size_fish1_row = id_meta_df[(id_meta_df['group'] == groub) & (
id_meta_df['fish'] == fish1)]
size_fish2_row = id_meta_df[(id_meta_df['group'] == groub) & (
id_meta_df['fish'] == fish2)]
size_winners = [size_fish1_row[col].values[0]
for col in ['l1', 'l2', 'l3']]
mean_size_winner = np.nanmean(size_winners)
size_losers = [size_fish2_row[col].values[0] for col in ['l1', 'l2', 'l3']]
mean_size_loser = np.nanmean(size_losers)
if mean_size_winner > mean_size_loser:
size_diff = mean_size_winner - mean_size_loser
winner_fish_id = folder_row['rec_id1'].values[0]
loser_fish_id = folder_row['rec_id2'].values[0]
elif mean_size_winner < mean_size_loser:
size_diff = mean_size_loser - mean_size_winner
winner_fish_id = folder_row['rec_id2'].values[0]
loser_fish_id = folder_row['rec_id1'].values[0]
else:
size_diff = np.nan
winner_fish_id = np.nan
loser_fish_id = np.nan
chirp_diff = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) - len(
Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
return size_diff, chirp_diff
def get_chirp_freq(folder_name, Behavior, order_meta_df):
foldername = folder_name.split('/')[-2]
folder_row = order_meta_df[order_meta_df['recording'] == foldername]
fish1 = folder_row['rec_id1'].values[0].astype(int)
fish2 = folder_row['rec_id2'].values[0].astype(int)
chirp_freq_fish1 = np.nanmedian(
Behavior.freq[Behavior.ident == fish1])
chirp_freq_fish2 = np.nanmedian(
Behavior.freq[Behavior.ident == fish2])
if chirp_freq_fish1 > chirp_freq_fish2:
freq_diff = chirp_freq_fish1 - chirp_freq_fish2
winner_fish_id = folder_row['rec_id1'].values[0]
loser_fish_id = folder_row['rec_id2'].values[0]
elif chirp_freq_fish1 < chirp_freq_fish2:
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])
return freq_diff, chirp_diff
def main(datapath: str): def main(datapath: str):
foldernames = [ foldernames = [
datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)] datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
path_order_meta = ( path_order_meta = (
'/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv' '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
order_meta_df = read_csv(path_order_meta) order_meta_df = read_csv(path_order_meta)
order_meta_df['recording'] = order_meta_df['recording'].str[1:-1] order_meta_df['recording'] = order_meta_df['recording'].str[1:-1]
@ -30,11 +125,11 @@ def main(datapath: str):
id_meta_df = read_csv(path_id_meta) id_meta_df = read_csv(path_id_meta)
chirps_winner = [] chirps_winner = []
size_diff = [] size_diffs = []
chirps_diff = [] size_chirps_diffs = []
chirps_loser = [] chirps_loser = []
freq_diff = [] freq_diffs = []
freq_chirps_diffs = []
for foldername in foldernames: for foldername in foldernames:
# behabvior is pandas dataframe with all the data # behabvior is pandas dataframe with all the data
@ -49,89 +144,102 @@ 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)
folder_name = foldername.split('/')[-2] winner_chirp, loser_chirp = get_chirp_winner_loser(
winner_row = order_meta_df[order_meta_df['recording'] == folder_name] foldername, bh, order_meta_df)
winner = winner_row['winner'].values[0].astype(int) chirps_winner.append(winner_chirp)
winner_fish1 = winner_row['fish1'].values[0].astype(int) chirps_loser.append(loser_chirp)
winner_fish2 = winner_row['fish2'].values[0].astype(int) size_diff, chirp_diff = get_chirp_size(
foldername, bh, order_meta_df, id_meta_df)
groub = winner_row['group'].values[0].astype(int) size_diffs.append(size_diff)
size_rows = id_meta_df[id_meta_df['group'] == groub] size_chirps_diffs.append(chirp_diff)
freq_diff, freq_chirps_diff = get_chirp_freq(
if winner == winner_fish1: foldername, bh, order_meta_df)
winner_fish_id = winner_row['rec_id1'].values[0] freq_diffs.append(freq_diff)
loser_fish_id = winner_row['rec_id2'].values[0] freq_chirps_diffs.append(freq_chirps_diff)
size_winners = [] # folder_name = foldername.split('/')[-2]
for l in ['l1', 'l2', 'l3']: # winner_row = order_meta_df[order_meta_df['recording'] == folder_name]
size_winner = size_rows[size_rows['fish']== winner_fish1][l].values[0] # winner = winner_row['winner'].values[0].astype(int)
size_winners.append(size_winner) # winner_fish1 = winner_row['fish1'].values[0].astype(int)
mean_size_winner = np.nanmean(size_winners) # winner_fish2 = winner_row['fish2'].values[0].astype(int)
# groub = winner_row['group'].values[0].astype(int)
size_losers = [] # size_rows = id_meta_df[id_meta_df['group'] == groub]
for l in ['l1', 'l2', 'l3']:
size_loser = size_rows[size_rows['fish']== winner_fish2][l].values[0] # if winner == winner_fish1:
size_losers.append(size_loser) # winner_fish_id = winner_row['rec_id1'].values[0]
mean_size_loser = np.nanmean(size_losers) # loser_fish_id = winner_row['rec_id2'].values[0]
size_diff.append(mean_size_winner - mean_size_loser) # size_winners = []
# for l in ['l1', 'l2', 'l3']:
elif winner == winner_fish2: # size_winner = size_rows[size_rows['fish']== winner_fish1][l].values[0]
winner_fish_id = winner_row['rec_id2'].values[0] # size_winners.append(size_winner)
loser_fish_id = winner_row['rec_id1'].values[0] # mean_size_winner = np.nanmean(size_winners)
size_winners = [] # size_losers = []
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_loser = size_rows[size_rows['fish']== winner_fish2][l].values[0]
size_winners.append(size_winner) # size_losers.append(size_loser)
mean_size_winner = np.nanmean(size_winners) # mean_size_loser = np.nanmean(size_losers)
size_losers = [] # size_diffs.append(mean_size_winner - mean_size_loser)
for l in ['l1', 'l2', 'l3']:
size_loser = size_rows[size_rows['fish']== winner_fish1][l].values[0] # elif winner == winner_fish2:
size_losers.append(size_loser) # winner_fish_id = winner_row['rec_id2'].values[0]
mean_size_loser = np.nanmean(size_losers) # loser_fish_id = winner_row['rec_id1'].values[0]
size_diff.append(mean_size_winner - mean_size_loser) # size_winners = []
else: # for l in ['l1', 'l2', 'l3']:
continue # size_winner = size_rows[size_rows['fish']== winner_fish2][l].values[0]
# size_winners.append(size_winner)
print(foldername) # mean_size_winner = np.nanmean(size_winners)
all_fish_ids = np.unique(bh.chirps_ids)
chirp_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id]) # size_losers = []
chirp_loser = len(bh.chirps[bh.chirps_ids == loser_fish_id]) # for l in ['l1', 'l2', 'l3']:
# size_loser = size_rows[size_rows['fish']== winner_fish1][l].values[0]
freq_winner = np.nanmedian(bh.freq[bh.ident==winner_fish_id]) # size_losers.append(size_loser)
freq_loser = np.nanmedian(bh.freq[bh.ident==loser_fish_id]) # mean_size_loser = np.nanmean(size_losers)
# size_diffs.append(mean_size_winner - mean_size_loser)
chirps_winner.append(chirp_winner) # else:
chirps_loser.append(chirp_loser) # pass
chirps_diff.append(chirp_winner - chirp_loser) # print(foldername)
freq_diff.append(freq_winner - freq_loser) # all_fish_ids = np.unique(bh.chirps_ids)
# chirp_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id])
fish1_id = all_fish_ids[0] # chirp_loser = len(bh.chirps[bh.chirps_ids == loser_fish_id])
fish2_id = all_fish_ids[1]
print(winner_fish_id) # freq_winner = np.nanmedian(bh.freq[bh.ident==winner_fish_id])
print(all_fish_ids) # freq_loser = np.nanmedian(bh.freq[bh.ident==loser_fish_id])
fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(10,5)) # chirps_winner.append(chirp_winner)
# chirps_loser.append(chirp_loser)
# 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 scatterwinner = 1.15
scatterloser = 1.85 scatterloser = 1.85
chirps_winner = np.asarray(chirps_winner)[~np.isnan(chirps_winner)]
chirps_loser = np.asarray(chirps_loser)[~np.isnan(chirps_loser)]
bplot1 = ax1.boxplot(chirps_winner, positions=[ bplot1 = ax1.boxplot(chirps_winner, positions=[
1], showfliers=False, patch_artist=True) 1], showfliers=False, patch_artist=True)
bplot2 = ax1.boxplot(chirps_loser, positions=[ bplot2 = ax1.boxplot(chirps_loser, positions=[
2], showfliers=False, patch_artist=True) 2], showfliers=False, patch_artist=True)
ax1.scatter(np.ones(len(chirps_winner))*scatterwinner, chirps_winner, color='r') ax1.scatter(np.ones(len(chirps_winner)) *
ax1.scatter(np.ones(len(chirps_loser))*scatterloser, chirps_loser, color='r') scatterwinner, chirps_winner, color='r')
ax1.scatter(np.ones(len(chirps_loser)) *
scatterloser, chirps_loser, color='r')
ax1.set_xticklabels(['winner', 'loser']) ax1.set_xticklabels(['winner', 'loser'])
ax1.text(0.9, 0.9, f'n = {len(chirps_winner)}', transform=ax1.transAxes, color= ps.white) ax1.text(0.9, 0.9, f'n = {len(chirps_winner)}',
transform=ax1.transAxes, color=ps.white)
for w, l in zip(chirps_winner, chirps_loser): for w, l in zip(chirps_winner, chirps_loser):
ax1.plot([scatterwinner, scatterloser], [w, l], color='r', alpha=0.5, linewidth=0.5) ax1.plot([scatterwinner, scatterloser], [w, l],
color='r', alpha=0.5, linewidth=0.5)
colors1 = ps.red colors1 = ps.red
ps.set_boxplot_color(bplot1, colors1) ps.set_boxplot_color(bplot1, colors1)
@ -139,11 +247,11 @@ def main(datapath: str):
ps.set_boxplot_color(bplot2, colors1) ps.set_boxplot_color(bplot2, colors1)
ax1.set_ylabel('Chirpscounts [n]') ax1.set_ylabel('Chirpscounts [n]')
ax2.scatter(size_diff, chirps_diff, 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]')
ax3.scatter(freq_diff, chirps_diff, color='r') ax3.scatter(freq_diffs, freq_chirps_diffs, color='r')
ax3.set_xlabel('Frequency difference [Hz]') ax3.set_xlabel('Frequency difference [Hz]')
ax3.set_yticklabels([]) ax3.set_yticklabels([])
ax3.set ax3.set

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poster/figs/chirps_winner_loser.pdf
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