diff --git a/code/plot_chirp_bodylegth.py b/code/plot_chirp_bodylegth.py
index b522c93..f629a24 100644
--- a/code/plot_chirp_bodylegth.py
+++ b/code/plot_chirp_bodylegth.py
@@ -253,7 +253,7 @@ def main(datapath: str):
ps.set_boxplot_color(bplot2, colors1)
ax2.scatter(size_diffs, size_chirps_diffs, color='r')
ax2.set_xlabel('Size difference [mm]')
- ax2.set_ylabel('Chirps difference [n]')
+ ax2.set_ylabel('Chirps [n]')
ax3.scatter(freq_diffs, size_chirps_diffs, color='r')
# ax3.scatter(freq_diffs, freq_chirps_diffs, color='r')
diff --git a/code/plot_chirp_size.py b/code/plot_chirp_size.py
index 894a80b..36a1f94 100644
--- a/code/plot_chirp_size.py
+++ b/code/plot_chirp_size.py
@@ -4,6 +4,7 @@ import os
import numpy as np
import matplotlib.pyplot as plt
+from scipy.stats import pearsonr, spearmanr
from thunderfish.powerspectrum import decibel
from IPython import embed
@@ -12,6 +13,7 @@ from modules.logger import makeLogger
from modules.plotstyle import PlotStyle
from modules.behaviour_handling import Behavior, correct_chasing_events
+
ps = PlotStyle()
logger = makeLogger(__name__)
@@ -50,6 +52,7 @@ def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
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)
+ winner = folder_row['winner'].values[0].astype(int)
groub = folder_row['group'].values[0].astype(int)
size_fish1_row = id_meta_df[(id_meta_df['group'] == groub) & (
@@ -59,32 +62,57 @@ def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
size_winners = [size_fish1_row[col].values[0]
for col in ['l1', 'l2', 'l3']]
- mean_size_winner = np.nanmean(size_winners)
+ size_fish1 = 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)
+ size_fish2 = np.nanmean(size_losers)
+ if winner == fish1:
+ if size_fish1 > size_fish2:
+ size_diff_bigger = size_fish1 - size_fish2
+ size_diff_smaller = size_fish2 - size_fish1
+
+ elif size_fish1 < size_fish2:
+ size_diff_bigger = size_fish1 - size_fish2
+ size_diff_smaller = size_fish2 - size_fish1
+ else:
+ size_diff_bigger = np.nan
+ size_diff_smaller = np.nan
+ winner_fish_id = np.nan
+ loser_fish_id = np.nan
+ return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id
- if mean_size_winner > mean_size_loser:
- size_diff_bigger = mean_size_winner - mean_size_loser
- size_diff_smaller = mean_size_loser - mean_size_winner
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_bigger = mean_size_loser - mean_size_winner
- size_diff_smaller = mean_size_winner - mean_size_loser
+ elif winner == fish2:
+ if size_fish2 > size_fish1:
+ size_diff_bigger = size_fish2 - size_fish1
+ size_diff_smaller = size_fish1 - size_fish2
+
+ elif size_fish2 < size_fish1:
+ size_diff_bigger = size_fish2 - size_fish1
+ size_diff_smaller = size_fish1 - size_fish2
+ else:
+ size_diff_bigger = np.nan
+ size_diff_smaller = np.nan
+ winner_fish_id = np.nan
+ loser_fish_id = np.nan
+ return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id
+
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
+ size_diff_bigger = np.nan
+ size_diff_smaller = np.nan
+ winner_fish_id = np.nan
+ loser_fish_id = np.nan
+ return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id
chirp_winner = len(
Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
chirp_loser = len(
Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
-
+
return size_diff_bigger, chirp_winner, size_diff_smaller, chirp_loser
@@ -92,27 +120,62 @@ 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)
+ fish1 = folder_row['fish1'].values[0].astype(int)
+ fish2 = folder_row['fish2'].values[0].astype(int)
+
+ fish1_freq = folder_row['rec_id1'].values[0].astype(int)
+ fish2_freq = folder_row['rec_id2'].values[0].astype(int)
+ winner = folder_row['winner'].values[0].astype(int)
chirp_freq_fish1 = np.nanmedian(
- Behavior.freq[Behavior.ident == fish1])
+ Behavior.freq[Behavior.ident == fish1_freq])
chirp_freq_fish2 = np.nanmedian(
- Behavior.freq[Behavior.ident == fish2])
+ Behavior.freq[Behavior.ident == fish2_freq])
+
+ if winner == fish1:
+ if chirp_freq_fish1 > chirp_freq_fish2:
+ freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
+ freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
+
+ elif chirp_freq_fish1 < chirp_freq_fish2:
+ freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
+ freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
+ else:
+ freq_diff_higher = np.nan
+ freq_diff_lower = np.nan
+ winner_fish_id = np.nan
+ loser_fish_id = np.nan
- 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
+ elif winner == fish2:
+ if chirp_freq_fish2 > chirp_freq_fish1:
+ freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
+ freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
+
+ elif chirp_freq_fish2 < chirp_freq_fish1:
+ freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
+ freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
+ else:
+ freq_diff_higher = np.nan
+ freq_diff_lower = np.nan
+ winner_fish_id = np.nan
+ loser_fish_id = np.nan
+
winner_fish_id = folder_row['rec_id2'].values[0]
loser_fish_id = folder_row['rec_id1'].values[0]
+ else:
+ freq_diff_higher = np.nan
+ freq_diff_lower = np.nan
+ winner_fish_id = np.nan
+ loser_fish_id = np.nan
- chirp_diff = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) - len(
+ chirp_winner = len(
+ Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
+ chirp_loser = len(
Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
- return freq_diff, chirp_diff
+ return freq_diff_higher, chirp_winner, freq_diff_lower, chirp_loser
def main(datapath: str):
@@ -128,8 +191,17 @@ def main(datapath: str):
id_meta_df = read_csv(path_id_meta)
chirps_winner = []
- size_diffs = []
- size_chirps_diffs = []
+
+ size_diffs_winner = []
+ size_diffs_loser = []
+ size_chirps_winner = []
+ size_chirps_loser = []
+
+ freq_diffs_higher = []
+ freq_diffs_lower = []
+ freq_chirps_winner = []
+ freq_chirps_loser = []
+
chirps_loser = []
freq_diffs = []
freq_chirps_diffs = []
@@ -151,17 +223,32 @@ def main(datapath: str):
foldername, bh, order_meta_df)
chirps_winner.append(winner_chirp)
chirps_loser.append(loser_chirp)
- size_diff, chirp_diff = get_chirp_size(
+
+ size_diff_bigger, chirp_winner, size_diff_smaller, chirp_loser = 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(
+ freq_diff_higher, chirp_freq_winner, freq_diff_lower, chirp_freq_loser = get_chirp_freq(
foldername, bh, order_meta_df)
- freq_diffs.append(freq_diff)
- freq_chirps_diffs.append(freq_chirps_diff)
+
+ freq_diffs_higher.append(freq_diff_higher)
+ freq_diffs_lower.append(freq_diff_lower)
+ freq_chirps_winner.append(chirp_freq_winner)
+ freq_chirps_loser.append(chirp_freq_loser)
+
+ if np.isnan(size_diff_bigger):
+ continue
+ size_diffs_winner.append(size_diff_bigger)
+ size_diffs_loser.append(size_diff_smaller)
+ size_chirps_winner.append(chirp_winner)
+ size_chirps_loser.append(chirp_loser)
+
+
+ embed()
+ size_winner_pearsonr = pearsonr(size_diffs_winner, size_chirps_winner )
+ size_loser_pearsonr = pearsonr(size_diffs_loser, size_chirps_loser )
- fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(22*ps.cm, 12*ps.cm), width_ratios=[1.5, 1,1])
+ fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(
+ 22*ps.cm, 12*ps.cm), sharey=True)
plt.subplots_adjust(left=0.098, right=0.945, top=0.94, wspace=0.343)
scatterwinner = 1.15
scatterloser = 1.85
@@ -173,9 +260,9 @@ def main(datapath: str):
bplot2 = ax1.boxplot(chirps_loser, positions=[
2], showfliers=False, patch_artist=True)
ax1.scatter(np.ones(len(chirps_winner)) *
- scatterwinner, chirps_winner, color='r')
+ scatterwinner, chirps_winner, color=ps.red)
ax1.scatter(np.ones(len(chirps_loser)) *
- scatterloser, chirps_loser, color='r')
+ scatterloser, chirps_loser, color=ps.orange)
ax1.set_xticklabels(['winner', 'loser'])
ax1.text(0.1, 0.9, f'n = {len(chirps_winner)}',
transform=ax1.transAxes, color=ps.white)
@@ -189,17 +276,13 @@ def main(datapath: str):
ps.set_boxplot_color(bplot1, colors1)
colors1 = ps.orange
ps.set_boxplot_color(bplot2, colors1)
- ax2.scatter(size_diffs, size_chirps_diffs, color='r')
- ax2.set_xlabel('Size difference [mm]')
- ax2.set_ylabel('Chirps difference [n]')
+ ax2.scatter(size_diffs_winner, size_chirps_winner, color=ps.red)
+ ax2.scatter(size_diffs_loser, size_chirps_loser, color=ps.orange)
- #ax3.scatter(freq_diffs, size_chirps_diffs, color='r')
- # ax3.scatter(freq_diffs, freq_chirps_diffs, color='r')
- ax3.set_xlabel('Frequency difference [Hz]')
- ax3.set_yticklabels([])
- ax3.set
+ ax2.set_xlabel('Size difference [cm]')
- #plt.savefig('../poster/figs/chirps_winner_loser.pdf')
+ # pearson r
+ plt.savefig('../poster/figs/chirps_winner_loser.pdf')
plt.show()
diff --git a/code/plot_chirps_in_chasing.py b/code/plot_chirps_in_chasing.py
new file mode 100644
index 0000000..98894d8
--- /dev/null
+++ b/code/plot_chirps_in_chasing.py
@@ -0,0 +1,81 @@
+import numpy as np
+
+import os
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import pearsonr, spearmanr
+from thunderfish.powerspectrum import decibel
+
+from IPython import embed
+from pandas import read_csv
+from modules.logger import makeLogger
+from modules.plotstyle import PlotStyle
+from modules.behaviour_handling import Behavior, correct_chasing_events
+from modules.datahandling import flatten
+
+
+ps = PlotStyle()
+
+logger = makeLogger(__name__)
+
+
+def main(datapath: str):
+
+ foldernames = [
+ datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
+ time_precents = []
+ chirps_percents = []
+ for foldername in foldernames:
+ # behabvior is pandas dataframe with all the data
+ if foldername == '../data/mount_data/2020-05-12-10_00/':
+ continue
+ bh = Behavior(foldername)
+
+ category = bh.behavior
+ timestamps = bh.start_s
+ # Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
+ # Get rid of tracking faults (two onsets or two offsets after another)
+ category, timestamps = correct_chasing_events(category, timestamps)
+
+ chasing_onset = timestamps[category == 0]
+ chasing_offset = timestamps[category == 1]
+ if len(chasing_onset) != len(chasing_offset):
+ embed()
+
+ chirps_in_chasings = []
+ for onset, offset in zip(chasing_onset, chasing_offset):
+ chirps_in_chasing = [c for c in bh.chirps if (c > onset) & (c < offset)]
+ chirps_in_chasings.append(chirps_in_chasing)
+
+ try:
+ time_chasing = np.sum(chasing_offset[chasing_offset<3*60*60] - chasing_onset[chasing_onset<3*60*60])
+ except:
+ time_chasing = np.sum(chasing_offset[chasing_offset<3*60*60] - chasing_onset[chasing_onset<3*60*60][:-1])
+
+
+ time_chasing_percent = (time_chasing/(3*60*60))*100
+ chirps_chasing = np.asarray(flatten(chirps_in_chasings))
+ chirps_chasing_new = chirps_chasing[chirps_chasing<3*60*60]
+ chirps_percent = (len(chirps_chasing_new)/len(bh.chirps))*100
+
+ time_precents.append(time_chasing_percent)
+ chirps_percents.append(chirps_percent)
+
+ fig, ax = plt.subplots(1, 1, figsize=(14*ps.cm, 10*ps.cm))
+
+ ax.boxplot([time_precents, chirps_percents])
+ ax.set_xticklabels(['Time Chasing', 'Chirps in Chasing'])
+ ax.set_ylabel('Percent')
+ ax.scatter(np.ones(len(time_precents))*1.25, time_precents, color=ps.white)
+ ax.scatter(np.ones(len(chirps_percents))*1.75, chirps_percents, color=ps.white)
+ plt.savefig('../poster/figs/chirps_in_chasing.pdf')
+ plt.show()
+
+
+if __name__ == '__main__':
+ # Path to the data
+ datapath = '../data/mount_data/'
+ main(datapath)
+
+
diff --git a/poster/figs/chirps_in_chasing.pdf b/poster/figs/chirps_in_chasing.pdf
new file mode 100644
index 0000000..836ca8e
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diff --git a/poster/figs/chirps_winner_loser.pdf b/poster/figs/chirps_winner_loser.pdf
index 723e444..58b5045 100644
Binary files a/poster/figs/chirps_winner_loser.pdf and b/poster/figs/chirps_winner_loser.pdf differ
diff --git a/poster/main.pdf b/poster/main.pdf
deleted file mode 100644
index b494d53..0000000
Binary files a/poster/main.pdf and /dev/null differ