Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/raab/GP2023_chirp_detection
This commit is contained in:
weygoldt
commit
e0d319d825
2
.gitignore
vendored
2
.gitignore
vendored
@ -14,7 +14,7 @@ output
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__pycache__/
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*.py[cod]
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*$py.class
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poster/main.pdf
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/poster/main.pdf
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# C extensions
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*.so
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@ -1,24 +1,37 @@
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from modules.filters import create_chirp, bandpass_filter
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import matplotlib.pyplot as plt
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from chirpdetection import instantaneos_frequency
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import numpy as np
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from IPython import embed
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# create chirp
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import matplotlib.pyplot as plt
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from thunderfish import fakefish
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time, signal, ampl, freq = create_chirp(chirptimes=[0.05, 0.2501, 0.38734, 0.48332, 0.73434, 0.823424], )
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from modules.filters import bandpass_filter
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from modules.datahandling import instantaneous_frequency
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from modules.simulations import create_chirp
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# filter signal with bandpass_filter
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signal = bandpass_filter(signal, 1/0.00001, 495, 505)
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# trying thunderfish fakefish chirp simulation ---------------------------------
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samplerate = 44100
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freq, ampl = fakefish.chirps(eodf=500, chirp_contrast=0.2)
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data = fakefish.wavefish_eods(fish='Alepto', frequency=freq, phase0=3, samplerate=samplerate)
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# filter signal with bandpass_filter
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data_filterd = bandpass_filter(data*ampl+1, samplerate, 0.01, 1.99)
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embed()
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exit()
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fig, axs = plt.subplots(2, 1, figsize=(10, 10))
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axs[0].plot(time, signal)
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data_freq_time, data_freq = instantaneous_frequency(data, samplerate, 5)
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# plot instatneous frequency
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baseline_freq_time, baseline_freq = instantaneos_frequency(signal, 1/0.00001)
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axs[1].plot(baseline_freq_time[1:], baseline_freq[1:])
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fig, ax = plt.subplots(4, 1, figsize=(20 / 2.54, 12 / 2.54), sharex=True)
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ax[0].plot(np.arange(len(data))/samplerate, data*ampl)
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#ax[0].scatter(true_zero, np.zeros_like(true_zero), color='red')
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ax[1].plot(np.arange(len(data_filterd))/samplerate, data_filterd)
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ax[2].plot(np.arange(len(freq))/samplerate, freq)
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ax[3].plot(data_freq_time, data_freq)
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plt.show()
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embed()
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@ -110,7 +110,7 @@ def PlotStyle() -> None:
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plt.setp(bp["boxes"], color=color)
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plt.setp(bp["whiskers"], color=white)
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plt.setp(bp["caps"], color=white)
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plt.setp(bp["medians"], color=white)
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plt.setp(bp["medians"], color=black)
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@classmethod
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@ -255,10 +255,10 @@ def PlotStyle() -> None:
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plt.rcParams["boxplot.boxprops.color"] = gray
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plt.rcParams["boxplot.whiskerprops.color"] = gray
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plt.rcParams["boxplot.capprops.color"] = gray
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plt.rcParams["boxplot.medianprops.color"] = gray
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plt.rcParams["boxplot.medianprops.color"] = black
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plt.rcParams["text.color"] = white
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plt.rcParams["axes.facecolor"] = black # axes background color
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plt.rcParams["axes.edgecolor"] = gray # axes edge color
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plt.rcParams["axes.edgecolor"] = white # axes edge color
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# plt.rcParams["axes.grid"] = True # display grid or not
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# plt.rcParams["axes.grid.axis"] = "y" # which axis the grid is applied to
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plt.rcParams["axes.labelcolor"] = white
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@ -284,9 +284,9 @@ def PlotStyle() -> None:
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"#f5c2e7",
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],
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)
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plt.rcParams["xtick.color"] = gray # color of the ticks
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plt.rcParams["ytick.color"] = gray # color of the ticks
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plt.rcParams["grid.color"] = dark_gray # grid color
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plt.rcParams["xtick.color"] = white # color of the ticks
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plt.rcParams["ytick.color"] = white # color of the ticks
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plt.rcParams["grid.color"] = white # grid color
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plt.rcParams["figure.facecolor"] = black # figure face color
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plt.rcParams["figure.edgecolor"] = black # figure edge color
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plt.rcParams["savefig.facecolor"] = black # figure face color when saving
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@ -133,41 +133,45 @@ def get_chirp_freq(folder_name, Behavior, order_meta_df):
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Behavior.freq[Behavior.ident == fish2_freq])
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if winner == fish1:
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if chirp_freq_fish1 > chirp_freq_fish2:
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freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
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freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
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elif chirp_freq_fish1 < chirp_freq_fish2:
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freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
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freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
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else:
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freq_diff_higher = np.nan
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freq_diff_lower = np.nan
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winner_fish_id = np.nan
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loser_fish_id = np.nan
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# if chirp_freq_fish1 > chirp_freq_fish2:
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# freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
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# freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
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# elif chirp_freq_fish1 < chirp_freq_fish2:
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# freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
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# freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
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# else:
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# freq_diff_higher = np.nan
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# freq_diff_lower = np.nan
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# winner_fish_id = np.nan
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# loser_fish_id = np.nan
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winner_fish_id = folder_row['rec_id1'].values[0]
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winner_fish_freq = chirp_freq_fish1
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loser_fish_id = folder_row['rec_id2'].values[0]
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loser_fish_freq = chirp_freq_fish2
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elif winner == fish2:
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if chirp_freq_fish2 > chirp_freq_fish1:
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freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
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freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
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elif chirp_freq_fish2 < chirp_freq_fish1:
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freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
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freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
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else:
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freq_diff_higher = np.nan
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freq_diff_lower = np.nan
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winner_fish_id = np.nan
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loser_fish_id = np.nan
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# if chirp_freq_fish2 > chirp_freq_fish1:
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# freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
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# freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
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# elif chirp_freq_fish2 < chirp_freq_fish1:
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# freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
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# freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
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# else:
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# freq_diff_higher = np.nan
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# freq_diff_lower = np.nan
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# winner_fish_id = np.nan
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# loser_fish_id = np.nan
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winner_fish_id = folder_row['rec_id2'].values[0]
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winner_fish_freq = chirp_freq_fish2
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loser_fish_id = folder_row['rec_id1'].values[0]
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loser_fish_freq = chirp_freq_fish1
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else:
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freq_diff_higher = np.nan
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freq_diff_lower = np.nan
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winner_fish_freq = np.nan
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loser_fish_freq = np.nan
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winner_fish_id = np.nan
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loser_fish_id = np.nan
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@ -176,7 +180,7 @@ def get_chirp_freq(folder_name, Behavior, order_meta_df):
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chirp_loser = len(
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Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
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return freq_diff_higher, chirp_winner, freq_diff_lower, chirp_loser
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return winner_fish_freq, chirp_winner, loser_fish_freq, chirp_loser
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def main(datapath: str):
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@ -229,11 +233,11 @@ def main(datapath: str):
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size_diff_bigger, chirp_winner, size_diff_smaller, chirp_loser = get_chirp_size(
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foldername, bh, order_meta_df, id_meta_df)
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freq_diff_higher, chirp_freq_winner, freq_diff_lower, chirp_freq_loser = get_chirp_freq(
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freq_winner, chirp_freq_winner, freq_loser, chirp_freq_loser = get_chirp_freq(
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foldername, bh, order_meta_df)
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freq_diffs_higher.append(freq_diff_higher)
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freq_diffs_lower.append(freq_diff_lower)
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freq_diffs_higher.append(freq_winner)
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freq_diffs_lower.append(freq_loser)
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freq_chirps_winner.append(chirp_freq_winner)
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freq_chirps_loser.append(chirp_freq_loser)
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@ -247,9 +251,10 @@ def main(datapath: str):
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size_winner_pearsonr = pearsonr(size_diffs_winner, size_chirps_winner)
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size_loser_pearsonr = pearsonr(size_diffs_loser, size_chirps_loser)
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(
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13*ps.cm, 10*ps.cm), sharey=True)
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plt.subplots_adjust(left=0.098, right=0.945, top=0.94, wspace=0.343)
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fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(
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21*ps.cm, 7*ps.cm), width_ratios=[1, 0.8, 0.8], sharey=True)
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plt.subplots_adjust(left=0.11, right=0.948, top=0.86,
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wspace=0.343, bottom=0.198)
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scatterwinner = 1.15
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scatterloser = 1.85
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chirps_winner = np.asarray(chirps_winner)[~np.isnan(chirps_winner)]
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@ -257,43 +262,54 @@ def main(datapath: str):
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stat = wilcoxon(chirps_winner, chirps_loser)
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print(stat)
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winner_color = ps.gblue3
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loser_color = ps.gblue1
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bplot1 = ax1.boxplot(chirps_winner, positions=[
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0.9], showfliers=False, patch_artist=True)
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bplot2 = ax1.boxplot(chirps_loser, positions=[
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2.1], showfliers=False, patch_artist=True)
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ax1.scatter(np.ones(len(chirps_winner)) *
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scatterwinner, chirps_winner, color=ps.red)
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scatterwinner, chirps_winner, color=winner_color)
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ax1.scatter(np.ones(len(chirps_loser)) *
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scatterloser, chirps_loser, color=ps.orange)
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ax1.set_xticklabels(['winner', 'loser'])
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ax1.text(0.1, 0.9, f'n = {len(chirps_winner)}',
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scatterloser, chirps_loser, color=loser_color)
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ax1.set_xticklabels(['Winner', 'Loser'])
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ax1.text(0.1, 0.95, f'n={len(chirps_winner)}',
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transform=ax1.transAxes, color=ps.white)
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for w, l in zip(chirps_winner, chirps_loser):
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ax1.plot([scatterwinner, scatterloser], [w, l],
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color=ps.white, alpha=1, linewidth=0.5)
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ax1.set_ylabel('chirps [n]', color=ps.white)
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ax1.set_xlabel('outcome', color=ps.white)
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color=ps.white, alpha=0.6, linewidth=1, zorder=-1)
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ax1.set_ylabel('Chirp counts', color=ps.white)
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ax1.set_xlabel('Competition outcome', color=ps.white)
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colors1 = ps.red
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ps.set_boxplot_color(bplot1, colors1)
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colors1 = ps.orange
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ps.set_boxplot_color(bplot2, colors1)
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ps.set_boxplot_color(bplot1, winner_color)
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ps.set_boxplot_color(bplot2, loser_color)
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ax2.scatter(size_diffs_winner, size_chirps_winner,
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color=ps.red, label='winner')
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color=winner_color, label=f'Winner')
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ax2.scatter(size_diffs_loser, size_chirps_loser,
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color=ps.orange, label='loser')
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color=loser_color, label='Loser')
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ax2.text(0.05, 0.95, f'n={len(size_chirps_winner)}',
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transform=ax2.transAxes, color=ps.white)
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ax2.set_xlabel('size difference [cm]')
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ax2.set_xlabel('Size difference [cm]')
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# ax2.set_xticks(np.arange(-10, 10.1, 2))
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handles, labels = ax2.get_legend_handles_labels()
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fig.legend(handles, labels, loc='upper center', ncol=2)
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plt.subplots_adjust(left=0.162, right=0.97, top=0.85, bottom=0.176)
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ax3.scatter(freq_diffs_higher, freq_chirps_winner, color=winner_color)
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ax3.scatter(freq_diffs_lower, freq_chirps_loser, color=loser_color)
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ax3.text(0.1, 0.95, f'n={len(freq_chirps_loser)}',
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transform=ax3.transAxes, color=ps.white)
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ax3.set_xlabel('EODf [Hz]')
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handles, labels = ax2.get_legend_handles_labels()
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fig.legend(handles, labels, loc='upper center',
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ncol=2, bbox_to_anchor=(0.5, 1.04))
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# pearson r
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plt.savefig('../poster/figs/chirps_winner_loser.pdf')
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plt.show()
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@ -45,30 +45,46 @@ def main(datapath: str):
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chirps_in_chasings = []
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for onset, offset in zip(chasing_onset, chasing_offset):
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chirps_in_chasing = [c for c in bh.chirps if (c > onset) & (c < offset)]
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chirps_in_chasing = [
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c for c in bh.chirps if (c > onset) & (c < offset)]
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chirps_in_chasings.append(chirps_in_chasing)
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try:
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time_chasing = np.sum(chasing_offset[chasing_offset<3*60*60] - chasing_onset[chasing_onset<3*60*60])
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time_chasing = np.sum(
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chasing_offset[chasing_offset < 3*60*60] - chasing_onset[chasing_onset < 3*60*60])
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except:
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time_chasing = np.sum(chasing_offset[chasing_offset<3*60*60] - chasing_onset[chasing_onset<3*60*60][:-1])
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time_chasing = np.sum(
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chasing_offset[chasing_offset < 3*60*60] - chasing_onset[chasing_onset < 3*60*60][:-1])
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time_chasing_percent = (time_chasing/(3*60*60))*100
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chirps_chasing = np.asarray(flatten(chirps_in_chasings))
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chirps_chasing_new = chirps_chasing[chirps_chasing<3*60*60]
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chirps_percent = (len(chirps_chasing_new)/len(bh.chirps))*100
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chirps_chasing_new = chirps_chasing[chirps_chasing < 3*60*60]
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chirps_percent = (len(chirps_chasing_new) /
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len(bh.chirps[bh.chirps < 3*60*60]))*100
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time_precents.append(time_chasing_percent)
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chirps_percents.append(chirps_percent)
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fig, ax = plt.subplots(1, 1, figsize=(14*ps.cm, 10*ps.cm))
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ax.boxplot([time_precents, chirps_percents])
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ax.set_xticklabels(['Time Chasing', 'Chirps in Chasing'])
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fig, ax = plt.subplots(1, 1, figsize=(7*ps.cm, 7*ps.cm))
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scatter_time = 1.20
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scatter_chirps = 1.80
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size = 10
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bplot1 = ax.boxplot([time_precents, chirps_percents],
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showfliers=False, patch_artist=True)
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ps.set_boxplot_color(bplot1, ps.gray)
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ax.set_xticklabels(['Time \nChasing', 'Chirps \nin Chasing'])
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ax.set_ylabel('Percent')
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ax.scatter(np.ones(len(time_precents))*1.25, time_precents, color=ps.white)
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ax.scatter(np.ones(len(chirps_percents))*1.75, chirps_percents, color=ps.white)
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ax.scatter(np.ones(len(time_precents))*scatter_time, time_precents,
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facecolor=ps.white, s=size)
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ax.scatter(np.ones(len(chirps_percents))*scatter_chirps, chirps_percents,
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facecolor=ps.white, s=size)
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for i in range(len(time_precents)):
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ax.plot([scatter_time, scatter_chirps], [time_precents[i],
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chirps_percents[i]], alpha=0.6, linewidth=1, color=ps.white)
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ax.text(0.1, 0.9, f'n={len(time_precents)}', transform=ax.transAxes)
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plt.subplots_adjust(left=0.221, bottom=0.186, right=0.97, top=0.967)
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plt.savefig('../poster/figs/chirps_in_chasing.pdf')
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plt.show()
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@ -77,5 +93,3 @@ if __name__ == '__main__':
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# Path to the data
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datapath = '../data/mount_data/'
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main(datapath)
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@ -47,17 +47,17 @@ def main(datapath: str):
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# Associate chirps to inidividual fish
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fish1 = (bh.chirps[bh.chirps_ids == fish1_id] / 60) / 60
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fish2 = (bh.chirps[bh.chirps_ids == fish2_id] / 60) / 60
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fish1_color = ps.purple
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fish2_color = ps.lavender
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fish1_color = ps.gblue1
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fish2_color = ps.gblue3
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fig, ax = plt.subplots(5, 1, figsize=(
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21*ps.cm, 10*ps.cm), height_ratios=[0.5, 0.5, 0.5, 0.2, 6], sharex=True)
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# marker size
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s = 80
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ax[0].scatter(physical_contact, np.ones(
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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
|
||||
|
||||
Binary file not shown.
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BIN
poster/main.pdf
BIN
poster/main.pdf
Binary file not shown.
@ -22,20 +22,20 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
|
||||
weakly electric fish impossible. This profoundly limits our current
|
||||
understanding of chirps to experiments
|
||||
with single - or physically separated - individuals.
|
||||
% \begin{tikzfigure}[]
|
||||
% \label{griddrawing}
|
||||
% \includegraphics[width=0.8\linewidth]{figs/introplot}
|
||||
% \end{tikzfigure}
|
||||
\begin{tikzfigure}[]
|
||||
\label{griddrawing}
|
||||
\includegraphics[width=0.6\linewidth]{figs/introplot}
|
||||
\end{tikzfigure}
|
||||
}
|
||||
\myblock[TranspBlock]{Chirp detection}{
|
||||
\begin{tikzfigure}[]
|
||||
\label{fig:alg1}
|
||||
\includegraphics[width=0.9\linewidth]{figs/algorithm1}
|
||||
\includegraphics[width=0.6\linewidth]{figs/algorithm1}
|
||||
\end{tikzfigure}
|
||||
\vspace{2cm}
|
||||
\begin{tikzfigure}[]
|
||||
\label{fig:alg2}
|
||||
\includegraphics[width=1\linewidth]{figs/algorithm}
|
||||
\includegraphics[width=0.9\linewidth]{figs/algorithm}
|
||||
\end{tikzfigure}
|
||||
\vspace{0cm}
|
||||
}
|
||||
@ -49,34 +49,35 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
|
||||
\noindent
|
||||
\begin{itemize}
|
||||
\setlength\itemsep{0.5em}
|
||||
\item Two fish compete for one hidding place in one tank,
|
||||
\item Two fish compete for one hidding place in one tank.
|
||||
\item Experiment had a 3 hour long darkphase and a 3 hour long light phase.
|
||||
\end{itemize}
|
||||
|
||||
\noindent
|
||||
\begin{minipage}[c]{0.7\linewidth}
|
||||
\begin{tikzfigure}[]
|
||||
\label{fig:example_b}
|
||||
\includegraphics[width=\linewidth]{figs/chirps_winner_loser.pdf}
|
||||
\end{tikzfigure}
|
||||
\end{minipage} % no space if you would like to put them side by side
|
||||
\begin{minipage}[c]{0.2\linewidth}
|
||||
\begin{itemize}
|
||||
\setlength\itemsep{0.5em}
|
||||
\item Fish who won the competition chirped more often than the fish who lost.
|
||||
\item
|
||||
\end{itemize}
|
||||
\end{minipage}
|
||||
}
|
||||
|
||||
\myblock[TranspBlock]{Interactions at modulations}{
|
||||
\vspace{-1.2cm}
|
||||
\begin{tikzfigure}[]
|
||||
\label{fig:example_c}
|
||||
\includegraphics[width=0.5\linewidth]{example-image-c}
|
||||
\label{fig:example_b}
|
||||
\includegraphics[width=\linewidth]{figs/chirps_winner_loser.pdf}
|
||||
\end{tikzfigure}
|
||||
\noindent
|
||||
\begin{itemize}
|
||||
\item Fish who lost the competition chirped more often than the fish who lost.
|
||||
\item Size has an effect on the Competition outcome, and the chirp count.
|
||||
\item Frequency of the fish has no effect on the competition outcome.
|
||||
\end{itemize}
|
||||
}
|
||||
|
||||
|
||||
\myblock[TranspBlock]{Are Chirps coding for onset or offset of physical interaction?}{
|
||||
\vspace{-1.2cm}
|
||||
\begin{minipage}{0.6666\linewidth}
|
||||
\begin{tikzfigure}[]
|
||||
\includegraphics[width=0.3\linewidth]{figs/chirps_in_chasing.pdf}
|
||||
\end{tikzfigure}
|
||||
\end{minipage}
|
||||
\begin{minipage}{0.3333\linewidth}
|
||||
\begin{tikzfigure}[]
|
||||
\includegraphics[width=\linewidth]{figs/chirps_in_chasing.pdf}
|
||||
\end{tikzfigure}
|
||||
\end{minipage}
|
||||
}
|
||||
|
||||
\myblock[GrayBlock]{Conclusion}{
|
||||
|
||||
Loading…
Reference in New Issue
Block a user