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
2023-01-26 12:45:18 +01:00
parent b0447b8ab3 eca7c6cd05
commit e0d319d825
11 changed files with 167 additions and 120 deletions
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39
code/chirp_sim.py
View File

@@ -1,24 +1,37 @@
from modules.filters import create_chirp, bandpass_filter
import matplotlib.pyplot as plt
from chirpdetection import instantaneos_frequency
import numpy as np
from IPython import embed
# create chirp
import matplotlib.pyplot as plt
from thunderfish import fakefish
time, signal, ampl, freq = create_chirp(chirptimes=[0.05, 0.2501, 0.38734, 0.48332, 0.73434, 0.823424], )
from modules.filters import bandpass_filter
from modules.datahandling import instantaneous_frequency
from modules.simulations import create_chirp
# filter signal with bandpass_filter
signal = bandpass_filter(signal, 1/0.00001, 495, 505)
# trying thunderfish fakefish chirp simulation ---------------------------------
samplerate = 44100
freq, ampl = fakefish.chirps(eodf=500, chirp_contrast=0.2)
data = fakefish.wavefish_eods(fish='Alepto', frequency=freq, phase0=3, samplerate=samplerate)
# filter signal with bandpass_filter
data_filterd = bandpass_filter(data*ampl+1, samplerate, 0.01, 1.99)
embed()
exit()
fig, axs = plt.subplots(2, 1, figsize=(10, 10))
axs[0].plot(time, signal)
data_freq_time, data_freq = instantaneous_frequency(data, samplerate, 5)
# plot instatneous frequency
baseline_freq_time, baseline_freq = instantaneos_frequency(signal, 1/0.00001)
axs[1].plot(baseline_freq_time[1:], baseline_freq[1:])
fig, ax = plt.subplots(4, 1, figsize=(20 / 2.54, 12 / 2.54), sharex=True)
ax[0].plot(np.arange(len(data))/samplerate, data*ampl)
#ax[0].scatter(true_zero, np.zeros_like(true_zero), color='red')
ax[1].plot(np.arange(len(data_filterd))/samplerate, data_filterd)
ax[2].plot(np.arange(len(freq))/samplerate, freq)
ax[3].plot(data_freq_time, data_freq)
plt.show()
embed()

12
code/modules/plotstyle.py
View File

@@ -110,7 +110,7 @@ def PlotStyle() -> None:
plt.setp(bp["boxes"], color=color)
plt.setp(bp["whiskers"], color=white)
plt.setp(bp["caps"], color=white)
plt.setp(bp["medians"], color=white)
plt.setp(bp["medians"], color=black)
@classmethod
@@ -255,10 +255,10 @@ def PlotStyle() -> None:
plt.rcParams["boxplot.boxprops.color"] = gray
plt.rcParams["boxplot.whiskerprops.color"] = gray
plt.rcParams["boxplot.capprops.color"] = gray
plt.rcParams["boxplot.medianprops.color"] = gray
plt.rcParams["boxplot.medianprops.color"] = black
plt.rcParams["text.color"] = white
plt.rcParams["axes.facecolor"] = black # axes background color
plt.rcParams["axes.edgecolor"] = gray # axes edge color
plt.rcParams["axes.edgecolor"] = white # axes edge color
# plt.rcParams["axes.grid"] = True # display grid or not
# plt.rcParams["axes.grid.axis"] = "y" # which axis the grid is applied to
plt.rcParams["axes.labelcolor"] = white
@@ -284,9 +284,9 @@ def PlotStyle() -> None:
"#f5c2e7",
],
)
plt.rcParams["xtick.color"] = gray # color of the ticks
plt.rcParams["ytick.color"] = gray # color of the ticks
plt.rcParams["grid.color"] = dark_gray # grid color
plt.rcParams["xtick.color"] = white # color of the ticks
plt.rcParams["ytick.color"] = white # color of the ticks
plt.rcParams["grid.color"] = white # grid color
plt.rcParams["figure.facecolor"] = black # figure face color
plt.rcParams["figure.edgecolor"] = black # figure edge color
plt.rcParams["savefig.facecolor"] = black # figure face color when saving

112
code/plot_chirp_size.py
View File

@@ -133,41 +133,45 @@ def get_chirp_freq(folder_name, Behavior, order_meta_df):
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
# 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
# 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
winner_fish_id = folder_row['rec_id1'].values[0]
winner_fish_freq = chirp_freq_fish1
loser_fish_id = folder_row['rec_id2'].values[0]
loser_fish_freq = chirp_freq_fish2
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
# 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
# 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]
winner_fish_freq = chirp_freq_fish2
loser_fish_id = folder_row['rec_id1'].values[0]
loser_fish_freq = chirp_freq_fish1
else:
freq_diff_higher = np.nan
freq_diff_lower = np.nan
winner_fish_freq = np.nan
loser_fish_freq = np.nan
winner_fish_id = np.nan
loser_fish_id = np.nan
@@ -176,7 +180,7 @@ def get_chirp_freq(folder_name, Behavior, order_meta_df):
chirp_loser = len(
Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
return freq_diff_higher, chirp_winner, freq_diff_lower, chirp_loser
return winner_fish_freq, chirp_winner, loser_fish_freq, chirp_loser
def main(datapath: str):
@@ -229,11 +233,11 @@ def main(datapath: str):
size_diff_bigger, chirp_winner, size_diff_smaller, chirp_loser = get_chirp_size(
foldername, bh, order_meta_df, id_meta_df)
freq_diff_higher, chirp_freq_winner, freq_diff_lower, chirp_freq_loser = get_chirp_freq(
freq_winner, chirp_freq_winner, freq_loser, chirp_freq_loser = get_chirp_freq(
foldername, bh, order_meta_df)
freq_diffs_higher.append(freq_diff_higher)
freq_diffs_lower.append(freq_diff_lower)
freq_diffs_higher.append(freq_winner)
freq_diffs_lower.append(freq_loser)
freq_chirps_winner.append(chirp_freq_winner)
freq_chirps_loser.append(chirp_freq_loser)
@@ -247,9 +251,10 @@ def main(datapath: str):
size_winner_pearsonr = pearsonr(size_diffs_winner, size_chirps_winner)
size_loser_pearsonr = pearsonr(size_diffs_loser, size_chirps_loser)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(
13*ps.cm, 10*ps.cm), sharey=True)
plt.subplots_adjust(left=0.098, right=0.945, top=0.94, wspace=0.343)
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(
21*ps.cm, 7*ps.cm), width_ratios=[1, 0.8, 0.8], sharey=True)
plt.subplots_adjust(left=0.11, right=0.948, top=0.86,
wspace=0.343, bottom=0.198)
scatterwinner = 1.15
scatterloser = 1.85
chirps_winner = np.asarray(chirps_winner)[~np.isnan(chirps_winner)]
@@ -257,43 +262,54 @@ def main(datapath: str):
stat = wilcoxon(chirps_winner, chirps_loser)
print(stat)
winner_color = ps.gblue3
loser_color = ps.gblue1
bplot1 = ax1.boxplot(chirps_winner, positions=[
0.9], showfliers=False, patch_artist=True)
bplot2 = ax1.boxplot(chirps_loser, positions=[
2.1], showfliers=False, patch_artist=True)
ax1.scatter(np.ones(len(chirps_winner)) *
scatterwinner, chirps_winner, color=ps.red)
scatterwinner, chirps_winner, color=winner_color)
ax1.scatter(np.ones(len(chirps_loser)) *
scatterloser, chirps_loser, color=ps.orange)
ax1.set_xticklabels(['winner', 'loser'])
ax1.text(0.1, 0.9, f'n = {len(chirps_winner)}',
scatterloser, chirps_loser, color=loser_color)
ax1.set_xticklabels(['Winner', 'Loser'])
ax1.text(0.1, 0.95, f'n={len(chirps_winner)}',
transform=ax1.transAxes, color=ps.white)
for w, l in zip(chirps_winner, chirps_loser):
ax1.plot([scatterwinner, scatterloser], [w, l],
color=ps.white, alpha=1, linewidth=0.5)
ax1.set_ylabel('chirps [n]', color=ps.white)
ax1.set_xlabel('outcome', color=ps.white)
color=ps.white, alpha=0.6, linewidth=1, zorder=-1)
ax1.set_ylabel('Chirp counts', color=ps.white)
ax1.set_xlabel('Competition outcome', color=ps.white)
colors1 = ps.red
ps.set_boxplot_color(bplot1, colors1)
colors1 = ps.orange
ps.set_boxplot_color(bplot2, colors1)
ps.set_boxplot_color(bplot1, winner_color)
ps.set_boxplot_color(bplot2, loser_color)
ax2.scatter(size_diffs_winner, size_chirps_winner,
color=ps.red, label='winner')
color=winner_color, label=f'Winner')
ax2.scatter(size_diffs_loser, size_chirps_loser,
color=ps.orange, label='loser')
color=loser_color, label='Loser')
ax2.set_xlabel('size difference [cm]')
ax2.text(0.05, 0.95, f'n={len(size_chirps_winner)}',
transform=ax2.transAxes, color=ps.white)
ax2.set_xlabel('Size difference [cm]')
# ax2.set_xticks(np.arange(-10, 10.1, 2))
handles, labels = ax2.get_legend_handles_labels()
fig.legend(handles, labels, loc='upper center', ncol=2)
plt.subplots_adjust(left=0.162, right=0.97, top=0.85, bottom=0.176)
ax3.scatter(freq_diffs_higher, freq_chirps_winner, color=winner_color)
ax3.scatter(freq_diffs_lower, freq_chirps_loser, color=loser_color)
ax3.text(0.1, 0.95, f'n={len(freq_chirps_loser)}',
transform=ax3.transAxes, color=ps.white)
ax3.set_xlabel('EODf [Hz]')
handles, labels = ax2.get_legend_handles_labels()
fig.legend(handles, labels, loc='upper center',
ncol=2, bbox_to_anchor=(0.5, 1.04))
# pearson r
plt.savefig('../poster/figs/chirps_winner_loser.pdf')
plt.show()

42
code/plot_chirps_in_chasing.py
View File

@@ -45,30 +45,46 @@ def main(datapath: str):
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_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])
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 = 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
chirps_chasing_new = chirps_chasing[chirps_chasing < 3*60*60]
chirps_percent = (len(chirps_chasing_new) /
len(bh.chirps[bh.chirps < 3*60*60]))*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'])
fig, ax = plt.subplots(1, 1, figsize=(7*ps.cm, 7*ps.cm))
scatter_time = 1.20
scatter_chirps = 1.80
size = 10
bplot1 = ax.boxplot([time_precents, chirps_percents],
showfliers=False, patch_artist=True)
ps.set_boxplot_color(bplot1, ps.gray)
ax.set_xticklabels(['Time \nChasing', 'Chirps \nin 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)
ax.scatter(np.ones(len(time_precents))*scatter_time, time_precents,
facecolor=ps.white, s=size)
ax.scatter(np.ones(len(chirps_percents))*scatter_chirps, chirps_percents,
facecolor=ps.white, s=size)
for i in range(len(time_precents)):
ax.plot([scatter_time, scatter_chirps], [time_precents[i],
chirps_percents[i]], alpha=0.6, linewidth=1, color=ps.white)
ax.text(0.1, 0.9, f'n={len(time_precents)}', transform=ax.transAxes)
plt.subplots_adjust(left=0.221, bottom=0.186, right=0.97, top=0.967)
plt.savefig('../poster/figs/chirps_in_chasing.pdf')
plt.show()
@@ -77,5 +93,3 @@ if __name__ == '__main__':
# Path to the data
datapath = '../data/mount_data/'
main(datapath)

25
code/plot_event_timeline.py
View File

@@ -47,17 +47,17 @@ def main(datapath: str):
# Associate chirps to inidividual fish
fish1 = (bh.chirps[bh.chirps_ids == fish1_id] / 60) / 60
fish2 = (bh.chirps[bh.chirps_ids == fish2_id] / 60) / 60
fish1_color = ps.purple
fish2_color = ps.lavender
fish1_color = ps.gblue1
fish2_color = ps.gblue3
fig, ax = plt.subplots(5, 1, figsize=(
21*ps.cm, 10*ps.cm), height_ratios=[0.5, 0.5, 0.5, 0.2, 6], sharex=True)
# marker size
s = 80
ax[0].scatter(physical_contact, np.ones(
len(physical_contact)), color=ps.maroon, marker='|', s=s)
len(physical_contact)), color=ps.red, marker='|', s=s)
ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)),
color=ps.orange, marker='|', s=s)
color=ps.purple, marker='|', s=s)
ax[2].scatter(fish1, np.ones(len(fish1))-0.25,
color=fish1_color, marker='|', s=s)
ax[2].scatter(fish2, np.zeros(len(fish2))+0.25,
@@ -79,7 +79,6 @@ def main(datapath: str):
ax[0].set_xticks([])
ax[0].set_yticks([])
ps.hide_ax(ax[0])
ax[0].yaxis.set_label_coords(-0.1, 0.5)
ax[1].grid(False)
ax[1].set_frame_on(False)
@@ -99,19 +98,23 @@ def main(datapath: str):
labelpad = 30
fsize = 12
ax[0].set_ylabel('contact', rotation=0,
ax[0].set_ylabel('Contact', rotation=0,
labelpad=labelpad, fontsize=fsize)
ax[1].set_ylabel('chasing', rotation=0,
ax[0].yaxis.set_label_coords(-0.062, -0.08)
ax[1].set_ylabel('Chasing', rotation=0,
labelpad=labelpad, fontsize=fsize)
ax[2].set_ylabel('chirps', rotation=0,
ax[1].yaxis.set_label_coords(-0.06, -0.08)
ax[2].set_ylabel('Chirps', rotation=0,
labelpad=labelpad, fontsize=fsize)
ax[2].yaxis.set_label_coords(-0.07, -0.08)
ax[4].set_ylabel('EODf')
ax[4].set_xlabel('time [h]')
ax[4].set_xlabel('Time [h]')
# ax[0].set_title(foldername.split('/')[-2])
# 2020-03-31-9_59
plt.subplots_adjust(left=0.158, right=0.987, top=0.918)
# plt.savefig('../poster/figs/timeline.pdf')
plt.subplots_adjust(left=0.158, right=0.987, top=0.918, bottom=0.136)
plt.savefig('../poster/figs/timeline.pdf')
plt.show()
# plot chirps