raab/GP2023_chirp_detection
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implemented recording loop

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sprause 2023-01-24 11:07:52 +01:00
parent f68630974f
commit fda6821211
1 changed files with 152 additions and 140 deletions
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268
code/eventchirpsplots.py
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@ -32,13 +32,12 @@ class Behavior:
def __init__(self, folder_path: str) -> None:
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] # check if there are more than one csv file
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.chirps_ids = np.load(os.path.join(folder_path, 'chirp_ids.npy'), allow_pickle=True)
for k, key in enumerate(self.dataframe.keys()):
key = key.lower()
@ -56,7 +55,6 @@ class Behavior:
self.start_s = (self.start_s - shift) / factor
self.stop_s = (self.stop_s - shift) / factor
"""
1 - chasing onset
2 - chasing offset
@ -95,34 +93,26 @@ def correct_chasing_events(
offset_ids = np.arange(
len(category))[category == 1]
wrong_bh = np.arange(len(category))[category!=2][:-1][np.diff(category[category!=2])==0]
if onset_ids[0] > offset_ids[0]:
offset_ids = np.delete(offset_ids, 0)
help_index = offset_ids[0]
wrong_bh = np.append(wrong_bh[help_index])
category = np.delete(category, wrong_bh)
timestamps = np.delete(timestamps, wrong_bh)
# Check whether on- or offset is longer and calculate length difference
if len(onset_ids) > len(offset_ids):
len_diff = len(onset_ids) - len(offset_ids)
longer_array = onset_ids
shorter_array = offset_ids
logger.info(f'Onsets are greater than offsets by {len_diff}')
elif len(onset_ids) < len(offset_ids):
len_diff = len(offset_ids) - len(onset_ids)
longer_array = offset_ids
shorter_array = onset_ids
logger.info(f'Offsets are greater than offsets by {len_diff}')
logger.info(f'Offsets are greater than onsets by {len_diff}')
elif len(onset_ids) == len(offset_ids):
logger.info('Chasing events are equal')
return category, timestamps
# Correct the wrong chasing events; delete double events
wrong_ids = []
for i in range(len(longer_array)-(len_diff+1)):
if (shorter_array[i] > longer_array[i]) & (shorter_array[i] < longer_array[i+1]):
pass
else:
wrong_ids.append(longer_array[i])
longer_array = np.delete(longer_array, i)
category = np.delete(
category, wrong_ids)
timestamps = np.delete(
timestamps, wrong_ids)
return category, timestamps
@ -158,44 +148,66 @@ def event_triggered_chirps(
def main(datapath: str):
# behavior is pandas dataframe with all the data
bh = Behavior(datapath)
foldernames = [datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath + x)]
all_chirps = []
all_chirps_fish_ids = []
all_chasing_onsets = []
all_chasing_offsets = []
all_physicals = []
# chirps are not sorted in time (presumably due to prior groupings)
# get and sort chirps and corresponding fish_ids of the chirps
chirps = bh.chirps[np.argsort(bh.chirps)]
chirps_fish_ids = bh.chirps_ids[np.argsort(bh.chirps)]
for folder in foldernames:
# exclude folder with empty LED_on_time.npy
if folder == '../data/mount_data/2020-05-12-10_00/':
continue
bh = Behavior(folder)
# Chirps are already sorted
category = bh.behavior
timestamps = bh.start_s
chirps = bh.chirps
all_chirps.append(chirps)
chirps_fish_ids = bh.chirps_ids
all_chirps_fish_ids.append(chirps_fish_ids)
fish_ids = np.unique(chirps_fish_ids)
# 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)
# split categories
# Split categories
chasing_onsets = timestamps[category == 0]
all_chasing_onsets.append(chasing_onsets)
chasing_offsets = timestamps[category == 1]
all_chasing_offsets.append(chasing_offsets)
physical_contacts = timestamps[category == 2]
all_physicals.append(physical_contacts)
chasing_durations = []
# Calculate chasing duration to evaluate a nice time window for kernel density estimation
for onset, offset in zip(chasing_onsets, chasing_offsets):
duration = offset - onset
chasing_durations.append(duration)
embed()
# chasing_durations = []
# # Calculate chasing duration to evaluate a nice time window for kernel density estimation
# for onset, offset in zip(chasing_onsets, chasing_offsets):
# duration = offset - onset
# chasing_durations.append(duration)
# fig, ax = plt.subplots()
# ax.boxplot(chasing_durations)
# plt.show()
# plt.close()
# Get fish ids
fish_ids = np.unique(chirps_fish_ids)
# # Associate chirps to individual fish
# fish1 = chirps[chirps_fish_ids == fish_ids[0]]
# fish2 = chirps[chirps_fish_ids == fish_ids[1]]
# fish = [len(fish1), len(fish2)]
# Concolution over all recordings
# Rasterplot for each recording
# Define time window for chirps around event analysis
time_before_event = 30
time_after_event = 60
@ -282,97 +294,97 @@ def main(datapath: str):
#### Chirps around events, winner VS loser, one recording ####
# Load file with fish ids and winner/loser info
meta = pd.read_csv('../data/mount_data/order_meta.csv')
current_recording = meta[meta.index == 43]
fish1 = current_recording['rec_id1'].values
fish2 = current_recording['rec_id2'].values
# Implement check if fish_ids from meta and chirp detection are the same???
winner = current_recording['winner'].values
if winner == fish1:
loser = fish2
elif winner == fish2:
loser = fish1
winner_chirps = chirps[chirps_fish_ids == winner]
loser_chirps = chirps[chirps_fish_ids == loser]
# Event triggered winner chirps
_, winner_centered_onset, winner_cc_onset = event_triggered_chirps(chasing_onsets, winner_chirps, time_before_event, time_after_event, dt, width)
_, winner_centered_offset, winner_cc_offset = event_triggered_chirps(chasing_offsets, winner_chirps, time_before_event, time_after_event, dt, width)
_, winner_centered_physical, winner_cc_physical = event_triggered_chirps(physical_contacts, winner_chirps, time_before_event, time_after_event, dt, width)
# Event triggered loser chirps
_, loser_centered_onset, loser_cc_onset = event_triggered_chirps(chasing_onsets, loser_chirps, time_before_event, time_after_event, dt, width)
_, loser_centered_offset, loser_cc_offset = event_triggered_chirps(chasing_offsets, loser_chirps, time_before_event, time_after_event, dt, width)
_, loser_centered_physical, loser_cc_physical = event_triggered_chirps(physical_contacts, loser_chirps, time_before_event, time_after_event, dt, width)
########## Winner VS Loser plot ##########
fig, ax = plt.subplots(2, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='row')
offset = [1.35]
ax[1][0].set_xlabel('Time[s]')
ax[1][1].set_xlabel('Time[s]')
ax[1][2].set_xlabel('Time[s]')
# Plot winner chasing onsets
ax[0][0].set_ylabel('Chirp rate [Hz]')
ax[0][0].plot(time, winner_cc_onset, color='tab:blue', zorder=100)
ax0 = ax[0][0].twinx()
ax0.eventplot(np.array([winner_centered_onset]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100)
ax0.set_ylabel('Event')
ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax[0][0].set_zorder(ax0.get_zorder()+1)
ax[0][0].patch.set_visible(False)
ax0.set_yticklabels([])
ax0.set_yticks([])
# Plot winner chasing offets
ax[0][1].plot(time, winner_cc_offset, color='tab:blue', zorder=100)
ax1 = ax[0][1].twinx()
ax1.eventplot(np.array([winner_centered_offset]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100)
ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax[0][1].set_zorder(ax1.get_zorder()+1)
ax[0][1].patch.set_visible(False)
ax1.set_yticklabels([])
ax1.set_yticks([])
# Plot winner physical contacts
ax[0][2].plot(time, winner_cc_physical, color='tab:blue', zorder=100)
ax2 = ax[0][2].twinx()
ax2.eventplot(np.array([winner_centered_physical]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100)
ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax[0][2].set_zorder(ax2.get_zorder()+1)
ax[0][2].patch.set_visible(False)
ax2.set_yticklabels([])
ax2.set_yticks([])
# Plot loser chasing onsets
ax[1][0].set_ylabel('Chirp rate [Hz]')
ax[1][0].plot(time, loser_cc_onset, color='tab:blue', zorder=100)
ax3 = ax[1][0].twinx()
ax3.eventplot(np.array([loser_centered_onset]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100)
ax3.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax[1][0].set_zorder(ax3.get_zorder()+1)
ax[1][0].patch.set_visible(False)
ax3.set_yticklabels([])
ax3.set_yticks([])
# Plot loser chasing offsets
ax[1][1].plot(time, loser_cc_offset, color='tab:blue', zorder=100)
ax4 = ax[1][1].twinx()
ax4.eventplot(np.array([loser_centered_offset]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100)
ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax[1][1].set_zorder(ax4.get_zorder()+1)
ax[1][1].patch.set_visible(False)
ax4.set_yticklabels([])
ax4.set_yticks([])
# Plot loser physical contacts
ax[1][2].plot(time, loser_cc_physical, color='tab:blue', zorder=100)
ax5 = ax[1][2].twinx()
ax5.eventplot(np.array([loser_centered_physical]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100)
ax5.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax[1][2].set_zorder(ax5.get_zorder()+1)
ax[1][2].patch.set_visible(False)
ax5.set_yticklabels([])
ax5.set_yticks([])
plt.show()
# #### Chirps around events, winner VS loser, one recording ####
# # Load file with fish ids and winner/loser info
# meta = pd.read_csv('../data/mount_data/order_meta.csv')
# current_recording = meta[meta.index == 43]
# fish1 = current_recording['rec_id1'].values
# fish2 = current_recording['rec_id2'].values
# # Implement check if fish_ids from meta and chirp detection are the same???
# winner = current_recording['winner'].values
# if winner == fish1:
# loser = fish2
# elif winner == fish2:
# loser = fish1
# winner_chirps = chirps[chirps_fish_ids == winner]
# loser_chirps = chirps[chirps_fish_ids == loser]
# # Event triggered winner chirps
# _, winner_centered_onset, winner_cc_onset = event_triggered_chirps(chasing_onsets, winner_chirps, time_before_event, time_after_event, dt, width)
# _, winner_centered_offset, winner_cc_offset = event_triggered_chirps(chasing_offsets, winner_chirps, time_before_event, time_after_event, dt, width)
# _, winner_centered_physical, winner_cc_physical = event_triggered_chirps(physical_contacts, winner_chirps, time_before_event, time_after_event, dt, width)
# # Event triggered loser chirps
# _, loser_centered_onset, loser_cc_onset = event_triggered_chirps(chasing_onsets, loser_chirps, time_before_event, time_after_event, dt, width)
# _, loser_centered_offset, loser_cc_offset = event_triggered_chirps(chasing_offsets, loser_chirps, time_before_event, time_after_event, dt, width)
# _, loser_centered_physical, loser_cc_physical = event_triggered_chirps(physical_contacts, loser_chirps, time_before_event, time_after_event, dt, width)
# ########## Winner VS Loser plot ##########
# fig, ax = plt.subplots(2, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='row')
# offset = [1.35]
# ax[1][0].set_xlabel('Time[s]')
# ax[1][1].set_xlabel('Time[s]')
# ax[1][2].set_xlabel('Time[s]')
# # Plot winner chasing onsets
# ax[0][0].set_ylabel('Chirp rate [Hz]')
# ax[0][0].plot(time, winner_cc_onset, color='tab:blue', zorder=100)
# ax0 = ax[0][0].twinx()
# ax0.eventplot(np.array([winner_centered_onset]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100)
# ax0.set_ylabel('Event')
# ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
# ax[0][0].set_zorder(ax0.get_zorder()+1)
# ax[0][0].patch.set_visible(False)
# ax0.set_yticklabels([])
# ax0.set_yticks([])
# # Plot winner chasing offets
# ax[0][1].plot(time, winner_cc_offset, color='tab:blue', zorder=100)
# ax1 = ax[0][1].twinx()
# ax1.eventplot(np.array([winner_centered_offset]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100)
# ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
# ax[0][1].set_zorder(ax1.get_zorder()+1)
# ax[0][1].patch.set_visible(False)
# ax1.set_yticklabels([])
# ax1.set_yticks([])
# # Plot winner physical contacts
# ax[0][2].plot(time, winner_cc_physical, color='tab:blue', zorder=100)
# ax2 = ax[0][2].twinx()
# ax2.eventplot(np.array([winner_centered_physical]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100)
# ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
# ax[0][2].set_zorder(ax2.get_zorder()+1)
# ax[0][2].patch.set_visible(False)
# ax2.set_yticklabels([])
# ax2.set_yticks([])
# # Plot loser chasing onsets
# ax[1][0].set_ylabel('Chirp rate [Hz]')
# ax[1][0].plot(time, loser_cc_onset, color='tab:blue', zorder=100)
# ax3 = ax[1][0].twinx()
# ax3.eventplot(np.array([loser_centered_onset]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100)
# ax3.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
# ax[1][0].set_zorder(ax3.get_zorder()+1)
# ax[1][0].patch.set_visible(False)
# ax3.set_yticklabels([])
# ax3.set_yticks([])
# # Plot loser chasing offsets
# ax[1][1].plot(time, loser_cc_offset, color='tab:blue', zorder=100)
# ax4 = ax[1][1].twinx()
# ax4.eventplot(np.array([loser_centered_offset]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100)
# ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
# ax[1][1].set_zorder(ax4.get_zorder()+1)
# ax[1][1].patch.set_visible(False)
# ax4.set_yticklabels([])
# ax4.set_yticks([])
# # Plot loser physical contacts
# ax[1][2].plot(time, loser_cc_physical, color='tab:blue', zorder=100)
# ax5 = ax[1][2].twinx()
# ax5.eventplot(np.array([loser_centered_physical]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100)
# ax5.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
# ax[1][2].set_zorder(ax5.get_zorder()+1)
# ax[1][2].patch.set_visible(False)
# ax5.set_yticklabels([])
# ax5.set_yticks([])
# plt.show()
# plt.close()
@ -392,5 +404,5 @@ def main(datapath: str):
if __name__ == '__main__':
# Path to the data
datapath = '../data/mount_data/2020-05-13-10_00/'
datapath = '../data/mount_data/'
main(datapath)