raab/GP2023_chirp_detection
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finishing event_time

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This commit is contained in:
wendtalexander 2023-01-23 12:04:33 +01:00
parent b755fe8bf3
commit d754013aba
1 changed files with 39 additions and 22 deletions
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61
code/plot_event_timeline.py
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@ -8,6 +8,9 @@ import matplotlib.pyplot as plt
from IPython import embed
from pandas import read_csv
from modules.logger import makeLogger
from modules.plotstyle import PlotStyle
ps = PlotStyle()
logger = makeLogger(__name__)
@ -35,13 +38,19 @@ class Behavior:
LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True)
csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0]
logger.info(f'CSV file: {csv_filename}')
self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
self.chirps = np.load(os.path.join(folder_path, 'chirps.npy'), allow_pickle=True)
self.chirps_ids = np.load(os.path.join(folder_path, 'chirps_ids.npy'), allow_pickle=True)
self.ident = np.load(os.path.join(folder_path, 'ident_v.npy'), allow_pickle=True)
self.idx = np.load(os.path.join(folder_path, 'idx_v.npy'), allow_pickle=True)
self.freq = np.load(os.path.join(folder_path, 'fund_v.npy'), allow_pickle=True)
self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True)
for k, key in enumerate(self.dataframe.keys()):
key = key.lower()
if ' ' in key:
@ -120,37 +129,57 @@ def main(datapath: str):
physical_contact = (timestamps[category == 2] / 60) /60
all_fish_ids = np.unique(chirps_fish_ids)
fish1_id = all_fish_ids[0]
fish2_id = all_fish_ids[1]
# Associate chirps to inidividual fish
fish1 = (chirps[chirps_fish_ids == all_fish_ids[0]] / 60) /60
fish2 = (chirps[chirps_fish_ids == all_fish_ids[1]] / 60) /60
fish1 = (chirps[chirps_fish_ids == fish1_id] / 60) /60
fish2 = (chirps[chirps_fish_ids == fish2_id] / 60) /60
fish1_color = ps.red
fish2_color = ps.orange
fig, ax = plt.subplots(4, 1, figsize=(10, 5), height_ratios=[0.5, 0.5, 0.5, 6], sharex=True)
# marker size
s = 200
ax[0].scatter(physical_contact, np.ones(len(physical_contact)), color='red', marker='|', s=s)
ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), color='blue', marker='|', s=s )
ax[1].scatter(chasing_offset, np.ones(len(chasing_offset)), color='green', marker='|', s=s)
ax[2].scatter(fish1, np.ones(len(fish1))-0.25, color='blue', marker='|', s=s)
ax[2].scatter(fish2, np.zeros(len(fish2))+0.25, color='green', marker='|', s=s)
ax[3].scatter(fish2, np.zeros(len(fish2))+0.25, color='green', marker='|', s=s)
ax[0].scatter(physical_contact, np.ones(len(physical_contact)), color='firebrick', marker='|', s=s)
ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), color='green', 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, color=fish2_color, marker='|', s=s)
freq_temp = bh.freq[bh.ident==fish1_id]
time_temp = bh.time[bh.idx[bh.ident==fish1_id]]
ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish1_color)
freq_temp = bh.freq[bh.ident==fish2_id]
time_temp = bh.time[bh.idx[bh.ident==fish2_id]]
ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish2_color)
# Hide grid lines
ax[0].grid(False)
ax[0].set_frame_on(False)
ax[0].set_xticks([])
ax[0].set_yticks([])
ps.hide_ax(ax[0])
ax[1].grid(False)
ax[1].set_frame_on(False)
ax[1].set_xticks([])
ax[1].set_yticks([])
ps.hide_ax(ax[1])
ax[2].grid(False)
ax[2].set_frame_on(False)
ax[2].set_yticks([])
ax[2].set_xticks([])
ps.hide_ax(ax[2])
ax[3].axvspan(0, 3, 0, 5, facecolor='grey', alpha=0.5)
ax[3].set_xticks(np.arange(0, 6.1, 0.5))
labelpad = 40
ax[0].set_ylabel('Physical contact', rotation=0, labelpad=labelpad)
@ -162,21 +191,9 @@ def main(datapath: str):
plt.show()
# plot chirps
# plot chirps
"""
for track_id in np.unique(ident):
# window_index for time array in time window
window_index = np.arange(len(idx))[(ident == track_id) &
(time[idx] >= t0) &
(time[idx] <= (t0+dt))]
freq_temp = freq[window_index]
time_temp = time[idx[window_index]]
#mean_freq = np.mean(freq_temp)
#fdata = bandpass_filter(data_oi[:, track_id], data.samplerate, mean_freq-5, mean_freq+200)
ax.plot(time_temp - t0, freq_temp)
"""
if __name__ == '__main__':
# Path to the data