raab/GP2023_chirp_detection
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plot winner loser chirp counts

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This commit is contained in:
wendtalexander 2023-01-24 11:43:14 +01:00
parent f36f8606d8
commit 2a32a29d4e
1 changed files with 69 additions and 41 deletions
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110
code/plot_chirp_bodylegth.py
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@ -36,8 +36,6 @@ 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)
csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0]
@ -45,7 +43,7 @@ class Behavior:
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)
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)
@ -61,7 +59,7 @@ class Behavior:
key = key.replace('(', '')
key = key.replace(')', '')
setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]]))
last_LED_t_BORIS = LED_on_time_BORIS[-1]
real_time_range = self.time[-1] - self.time[0]
factor = 1.034141
@ -70,6 +68,8 @@ class Behavior:
self.stop_s = (self.stop_s - shift) / factor
def correct_chasing_events(
category: np.ndarray,
timestamps: np.ndarray
@ -80,59 +80,87 @@ def correct_chasing_events(
offset_ids = np.arange(
len(category))[category == 1]
woring_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]
woring_bh = np.append(woring_bh, help_index)
category = np.delete(category, woring_bh)
timestamps = np.delete(timestamps, woring_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
def main(datapath: str):
# behabvior is pandas dataframe with all the data
bh = Behavior(datapath)
# 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)]
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)
path_to_csv = ('/').join(datapath.split('/')[:-2]) + '/order_meta.csv'
folder_name = datapath.split('/')[-2]
foldernames = [datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
path_to_csv = ('/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
meta_id = read_csv(path_to_csv)
meta_id['recording'] = meta_id['recording'].str[1:-1]
winner_id = meta_id[meta_id['recording'] == folder_name]['winner'].values[0]
chirps_winner = []
chirps_loser = []
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)
# chirps are not sorted in time (presumably due to prior groupings)
# get and sort chirps and corresponding fish_ids of the chirps
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)
folder_name = foldername.split('/')[-2]
winner_row = meta_id[meta_id['recording'] == folder_name]
winner = winner_row['winner'].values[0].astype(int)
winner_fish1 = winner_row['fish1'].values[0].astype(int)
winner_fish2 = winner_row['fish2'].values[0].astype(int)
if winner == winner_fish1:
winner_fish_id = winner_row['rec_id1'].values[0]
loser_fish_id = winner_row['rec_id2'].values[0]
elif winner == winner_fish2:
winner_fish_id = winner_row['rec_id2'].values[0]
loser_fish_id = winner_row['rec_id1'].values[0]
else:
continue
print(foldername)
all_fish_ids = np.unique(bh.chirps_ids)
chirp_loser = len(bh.chirps[bh.chirps_ids == loser_fish_id])
chirp_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id])
chirps_winner.append(chirp_winner)
chirps_loser.append(chirp_loser)
fish1_id = all_fish_ids[0]
fish2_id = all_fish_ids[1]
print(winner_fish_id)
print(all_fish_ids)
fig, ax = plt.subplots()
ax.boxplot([chirps_winner, chirps_loser])
ax.set_xticklabels(['winner', 'loser'])
ax.set_ylabel('Chirpscount per trial')
plt.show()
@ -144,6 +172,6 @@ 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)