From c6facd6f0cfc5421c2b8a4c0e67c782342fd52f7 Mon Sep 17 00:00:00 2001 From: wendtalexander Date: Mon, 23 Jan 2023 14:51:36 +0100 Subject: [PATCH 01/29] adding mask for bodylength --- code/plot_chirp_bodylegth.py | 141 +++++++++++++++++++++++++++++++++++ 1 file changed, 141 insertions(+) create mode 100644 code/plot_chirp_bodylegth.py diff --git a/code/plot_chirp_bodylegth.py b/code/plot_chirp_bodylegth.py new file mode 100644 index 0000000..e088da8 --- /dev/null +++ b/code/plot_chirp_bodylegth.py @@ -0,0 +1,141 @@ +import numpy as np + +import os + +import numpy as np +import matplotlib.pyplot as plt +from thunderfish.powerspectrum import decibel + +from IPython import embed +from pandas import read_csv +from modules.logger import makeLogger +from modules.plotstyle import PlotStyle + +ps = PlotStyle() + +logger = makeLogger(__name__) + + +class Behavior: + """Load behavior data from csv file as class attributes + Attributes + ---------- + behavior: 0: chasing onset, 1: chasing offset, 2: physical contact + behavior_type: + behavioral_category: + comment_start: + comment_stop: + dataframe: pandas dataframe with all the data + duration_s: + media_file: + observation_date: + observation_id: + start_s: start time of the event in seconds + stop_s: stop time of the event in seconds + total_length: + """ + + 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] + 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) + self.spec = np.load(os.path.join(folder_path, "spec.npy"), allow_pickle=True) + + for k, key in enumerate(self.dataframe.keys()): + key = key.lower() + if ' ' in key: + key = key.replace(' ', '_') + if '(' in key: + 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 + shift = last_LED_t_BORIS - real_time_range * factor + self.start_s = (self.start_s - shift) / factor + self.stop_s = (self.stop_s - shift) / factor + + +def correct_chasing_events( + category: np.ndarray, + timestamps: np.ndarray + ) -> tuple[np.ndarray, np.ndarray]: + + onset_ids = np.arange( + len(category))[category == 0] + offset_ids = np.arange( + len(category))[category == 1] + + # 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}') + 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) + + + + + pass + + + + +if __name__ == '__main__': + + # Path to the data + datapath = '../data/mount_data/2020-05-13-10_00/' + main(datapath) \ No newline at end of file From c967a4e5a96f0ab9167a5bc321243827cb1efe0a Mon Sep 17 00:00:00 2001 From: wendtalexander Date: Tue, 24 Jan 2023 08:14:31 +0100 Subject: [PATCH 02/29] save plot --- code/plot_chirp_bodylegth.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/code/plot_chirp_bodylegth.py b/code/plot_chirp_bodylegth.py index e088da8..d6c148f 100644 --- a/code/plot_chirp_bodylegth.py +++ b/code/plot_chirp_bodylegth.py @@ -124,13 +124,20 @@ def main(datapath: str): 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] + 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] - pass + embed() + exit() @@ -138,4 +145,5 @@ if __name__ == '__main__': # Path to the data datapath = '../data/mount_data/2020-05-13-10_00/' + main(datapath) \ No newline at end of file From 5763e807d0c44e4d1e9ee6eee519da24a64d6cbb Mon Sep 17 00:00:00 2001 From: weygoldt <88969563+weygoldt@users.noreply.github.com> Date: Tue, 24 Jan 2023 11:43:10 +0100 Subject: [PATCH 03/29] better chirpdetector --- code/chirpdetection.py | 18 +++++------ code/chirpdetector_conf.yml | 64 +++++++++++++++++-------------------- code/extract_chirps.py | 4 +-- 3 files changed, 40 insertions(+), 46 deletions(-) diff --git a/code/chirpdetection.py b/code/chirpdetection.py index 415da4a..39db060 100755 --- a/code/chirpdetection.py +++ b/code/chirpdetection.py @@ -886,25 +886,25 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None: # normalize all three feature arrays to the same range to make # peak detection simpler - baseline_envelope = minmaxnorm([baseline_envelope])[0] - search_envelope = minmaxnorm([search_envelope])[0] - baseline_frequency_filtered = minmaxnorm( - [baseline_frequency_filtered] - )[0] + # baseline_envelope = minmaxnorm([baseline_envelope])[0] + # search_envelope = minmaxnorm([search_envelope])[0] + # baseline_frequency_filtered = minmaxnorm( + # [baseline_frequency_filtered] + # )[0] # PEAK DETECTION ---------------------------------------------- # detect peaks baseline_enelope baseline_peak_indices, _ = find_peaks( - baseline_envelope, prominence=config.prominence + baseline_envelope, prominence=config.baseline_prominence ) # detect peaks search_envelope search_peak_indices, _ = find_peaks( - search_envelope, prominence=config.prominence + search_envelope, prominence=config.search_prominence ) # detect peaks inst_freq_filtered frequency_peak_indices, _ = find_peaks( - baseline_frequency_filtered, prominence=config.prominence + baseline_frequency_filtered, prominence=config.frequency_prominence ) # DETECT CHIRPS IN SEARCH WINDOW ------------------------------ @@ -1097,4 +1097,4 @@ if __name__ == "__main__": datapath = "../data/2022-06-02-10_00/" # datapath = "/home/weygoldt/Data/uni/efishdata/2016-colombia/fishgrid/2016-04-09-22_25/" # datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-03-13-10_00/" - chirpdetection(datapath, plot="show", debug="fish") + chirpdetection(datapath, plot="show", debug="false") diff --git a/code/chirpdetector_conf.yml b/code/chirpdetector_conf.yml index 4bc7d63..058448d 100755 --- a/code/chirpdetector_conf.yml +++ b/code/chirpdetector_conf.yml @@ -1,47 +1,41 @@ -# directory setup -dataroot: "../data/" -outputdir: "../output/" +# Path setup ------------------------------------------------------------------ -# Duration and overlap of the analysis window in seconds -window: 5 -overlap: 1 -edge: 0.25 +dataroot: "../data/" # path to data +outputdir: "../output/" # path to save plots to -# Number of electrodes to go over -number_electrodes: 3 -minimum_electrodes: 2 +# Rolling window parameters --------------------------------------------------- -# Search window bandwidth and minimal baseline bandwidth -minimal_bandwidth: 20 +window: 5 # rolling window length in seconds +overlap: 1 # window overlap in seconds +edge: 0.25 # window edge cufoffs to mitigate filter edge effects -# Instantaneous frequency smoothing usint a gaussian kernel of this width -baseline_frequency_smoothing: 5 +# Electrode iteration parameters ---------------------------------------------- -# Baseline processing parameters -baseline_envelope_cutoff: 25 -baseline_envelope_bandpass_lowf: 2 -baseline_envelope_bandpass_highf: 100 -# baseline_envelope_envelope_cutoff: 4 +number_electrodes: 2 # number of electrodes to go over +minimum_electrodes: 1 # mimumun number of electrodes a chirp must be on -# search envelope processing parameters -search_envelope_cutoff: 10 +# Feature extraction parameters ----------------------------------------------- -# Instantaneous frequency bandpass filter cutoff frequencies -# baseline_frequency_highpass_cutoff: 0.000005 -# baseline_frequency_envelope_cutoff: 0.000005 +search_df_lower: 20 # start searching this far above the baseline +search_df_upper: 100 # stop searching this far above the baseline +search_res: 1 # search window resolution +default_search_freq: 60 # search here if no need for a search frequency +minimal_bandwidth: 10 # minimal bandpass filter width for baseline +search_bandwidth: 10 # minimal bandpass filter width for search frequency +baseline_frequency_smoothing: 10 # instantaneous frequency smoothing -# peak detecion parameters -prominence: 0.7 +# Feature processing parameters ----------------------------------------------- -# search freq parameter -search_df_lower: 20 -search_df_upper: 100 -search_res: 1 -search_bandwidth: 20 -default_search_freq: 60 - -# Classify events as chirps if they are less than this time apart -chirp_window_threshold: 0.015 +baseline_envelope_cutoff: 25 # envelope estimation cutoff +baseline_envelope_bandpass_lowf: 2 # envelope badpass lower cutoff +baseline_envelope_bandpass_highf: 100 # envelope bandbass higher cutoff +search_envelope_cutoff: 10 # search envelope estimation cufoff +# Peak detecion parameters ---------------------------------------------------- +baseline_prominence: 0.00005 # peak prominence threshold for baseline envelope +search_prominence: 0.000004 # peak prominence threshold for search envelope +frequency_prominence: 2 # peak prominence threshold for baseline freq +# Classify events as chirps if they are less than this time apart +chirp_window_threshold: 0.02 diff --git a/code/extract_chirps.py b/code/extract_chirps.py index 4e229fa..9b32928 100644 --- a/code/extract_chirps.py +++ b/code/extract_chirps.py @@ -8,7 +8,7 @@ from IPython import embed def main(datapaths): for path in datapaths: - chirpdetection(path, plot='show', debug='electrode') + chirpdetection(path, plot='show') if __name__ == '__main__': @@ -43,6 +43,6 @@ if __name__ == '__main__': recs = pd.DataFrame(columns=['recording'], data=valid_datasets) recs.to_csv('../recs.csv', index=False) - # main(datapaths) + main(datapaths) # window 1524 + 244 in dataset index 4 is nice example From 2a32a29d4edc22c1fac00aa45d1b85f1b842762f Mon Sep 17 00:00:00 2001 From: wendtalexander Date: Tue, 24 Jan 2023 11:43:14 +0100 Subject: [PATCH 04/29] plot winner loser chirp counts --- code/plot_chirp_bodylegth.py | 110 ++++++++++++++++++++++------------- 1 file changed, 69 insertions(+), 41 deletions(-) diff --git a/code/plot_chirp_bodylegth.py b/code/plot_chirp_bodylegth.py index d6c148f..8512b1e 100644 --- a/code/plot_chirp_bodylegth.py +++ b/code/plot_chirp_bodylegth.py @@ -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) \ No newline at end of file From ce560bf9397dc3b54060246eb93ff782e4eb702c Mon Sep 17 00:00:00 2001 From: wendtalexander Date: Tue, 24 Jan 2023 12:06:29 +0100 Subject: [PATCH 05/29] export functions in modules, plot chirp --- code/modules/behaviour_handling.py | 99 +++++++++++ code/plot_chirp_bodylegth.py | 91 +--------- code/plot_event_timeline.py | 260 ++++++++++------------------- 3 files changed, 185 insertions(+), 265 deletions(-) create mode 100644 code/modules/behaviour_handling.py diff --git a/code/modules/behaviour_handling.py b/code/modules/behaviour_handling.py new file mode 100644 index 0000000..90a18ab --- /dev/null +++ b/code/modules/behaviour_handling.py @@ -0,0 +1,99 @@ +import numpy as np + +import os + +import numpy as np +from IPython import embed + + +from pandas import read_csv +from modules.logger import makeLogger + + +logger = makeLogger(__name__) + + +class Behavior: + """Load behavior data from csv file as class attributes + Attributes + ---------- + behavior: 0: chasing onset, 1: chasing offset, 2: physical contact + behavior_type: + behavioral_category: + comment_start: + comment_stop: + dataframe: pandas dataframe with all the data + duration_s: + media_file: + observation_date: + observation_id: + start_s: start time of the event in seconds + stop_s: stop time of the event in seconds + total_length: + """ + + 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] + 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, '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) + 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) + self.spec = np.load(os.path.join(folder_path, "spec.npy"), allow_pickle=True) + + for k, key in enumerate(self.dataframe.keys()): + key = key.lower() + if ' ' in key: + key = key.replace(' ', '_') + if '(' in key: + 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 + shift = last_LED_t_BORIS - real_time_range * factor + self.start_s = (self.start_s - shift) / factor + self.stop_s = (self.stop_s - shift) / factor + + +def correct_chasing_events( + category: np.ndarray, + timestamps: np.ndarray + ) -> tuple[np.ndarray, np.ndarray]: + + onset_ids = np.arange( + len(category))[category == 0] + 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) + 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) + 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 \ No newline at end of file diff --git a/code/plot_chirp_bodylegth.py b/code/plot_chirp_bodylegth.py index 8512b1e..a5966f6 100644 --- a/code/plot_chirp_bodylegth.py +++ b/code/plot_chirp_bodylegth.py @@ -10,100 +10,13 @@ from IPython import embed from pandas import read_csv from modules.logger import makeLogger from modules.plotstyle import PlotStyle +from modules.behaviour_handling import Behavior, correct_chasing_events ps = PlotStyle() logger = makeLogger(__name__) -class Behavior: - """Load behavior data from csv file as class attributes - Attributes - ---------- - behavior: 0: chasing onset, 1: chasing offset, 2: physical contact - behavior_type: - behavioral_category: - comment_start: - comment_stop: - dataframe: pandas dataframe with all the data - duration_s: - media_file: - observation_date: - observation_id: - start_s: start time of the event in seconds - stop_s: stop time of the event in seconds - total_length: - """ - - 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] - 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, '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) - 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) - self.spec = np.load(os.path.join(folder_path, "spec.npy"), allow_pickle=True) - - for k, key in enumerate(self.dataframe.keys()): - key = key.lower() - if ' ' in key: - key = key.replace(' ', '_') - if '(' in key: - 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 - shift = last_LED_t_BORIS - real_time_range * factor - self.start_s = (self.start_s - shift) / factor - self.stop_s = (self.stop_s - shift) / factor - - - - -def correct_chasing_events( - category: np.ndarray, - timestamps: np.ndarray - ) -> tuple[np.ndarray, np.ndarray]: - - onset_ids = np.arange( - len(category))[category == 0] - 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) - 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) - 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 - - - def main(datapath: str): foldernames = [datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)] @@ -157,7 +70,7 @@ def main(datapath: str): fig, ax = plt.subplots() ax.boxplot([chirps_winner, chirps_loser]) - + ax.set_xticklabels(['winner', 'loser']) ax.set_ylabel('Chirpscount per trial') plt.show() diff --git a/code/plot_event_timeline.py b/code/plot_event_timeline.py index 6c984be..bb370b7 100644 --- a/code/plot_event_timeline.py +++ b/code/plot_event_timeline.py @@ -10,188 +10,96 @@ from IPython import embed from pandas import read_csv from modules.logger import makeLogger from modules.plotstyle import PlotStyle +from modules.behaviour_handling import Behavior, correct_chasing_events ps = PlotStyle() logger = makeLogger(__name__) -class Behavior: - """Load behavior data from csv file as class attributes - Attributes - ---------- - behavior: 0: chasing onset, 1: chasing offset, 2: physical contact - behavior_type: - behavioral_category: - comment_start: - comment_stop: - dataframe: pandas dataframe with all the data - duration_s: - media_file: - observation_date: - observation_id: - start_s: start time of the event in seconds - stop_s: stop time of the event in seconds - total_length: - """ - - 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] - 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) - self.spec = np.load(os.path.join(folder_path, "spec.npy"), allow_pickle=True) - - for k, key in enumerate(self.dataframe.keys()): - key = key.lower() - if ' ' in key: - key = key.replace(' ', '_') - if '(' in key: - 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 - shift = last_LED_t_BORIS - real_time_range * factor - self.start_s = (self.start_s - shift) / factor - self.stop_s = (self.stop_s - shift) / factor - -def correct_chasing_events( - category: np.ndarray, - timestamps: np.ndarray - ) -> tuple[np.ndarray, np.ndarray]: - - onset_ids = np.arange( - len(category))[category == 0] - offset_ids = np.arange( - len(category))[category == 1] - - # 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}') - 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) - - # split categories - chasing_onset = (timestamps[category == 0]/ 60) /60 - chasing_offset = (timestamps[category == 1]/ 60) /60 - 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 == 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='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) - - #ax[3].imshow(decibel(bh.spec), extent=[bh.time[0]/60/60, bh.time[-1]/60/60, 0, 2000], aspect='auto', origin='lower') - - # 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) - ax[1].set_ylabel('Chasing events', rotation=0, labelpad=labelpad) - ax[2].set_ylabel('Chirps', rotation=0, labelpad=labelpad) - ax[3].set_ylabel('EODf') - - ax[3].set_xlabel('Time [h]') - - plt.show() + + foldernames = [datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)] + for foldername in foldernames: + if foldername == '../data/mount_data/2020-05-12-10_00/': + continue + # behabvior is pandas dataframe with all the data + bh = Behavior(foldername) + + 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) + + # split categories + chasing_onset = (timestamps[category == 0]/ 60) /60 + chasing_offset = (timestamps[category == 1]/ 60) /60 + physical_contact = (timestamps[category == 2] / 60) /60 + + all_fish_ids = np.unique(bh.chirps_ids) + fish1_id = all_fish_ids[0] + fish2_id = all_fish_ids[1] + # 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.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='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) + + #ax[3].imshow(decibel(bh.spec), extent=[bh.time[0]/60/60, bh.time[-1]/60/60, 0, 2000], aspect='auto', origin='lower') + + # 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) + ax[1].set_ylabel('Chasing events', rotation=0, labelpad=labelpad) + ax[2].set_ylabel('Chirps', rotation=0, labelpad=labelpad) + ax[3].set_ylabel('EODf') + + ax[3].set_xlabel('Time [h]') + ax[0].set_title(foldername.split('/')[-2]) + + plt.show() embed() # plot chirps @@ -199,5 +107,5 @@ 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) From fd2207c8c57131eb7ce459a31c4c3dc0cc7c6388 Mon Sep 17 00:00:00 2001 From: wendtalexander Date: Tue, 24 Jan 2023 13:23:12 +0100 Subject: [PATCH 06/29] finishing plot chirp_body length --- code/plot_chirp_bodylegth.py | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/code/plot_chirp_bodylegth.py b/code/plot_chirp_bodylegth.py index a5966f6..32da016 100644 --- a/code/plot_chirp_bodylegth.py +++ b/code/plot_chirp_bodylegth.py @@ -69,18 +69,15 @@ def main(datapath: str): fig, ax = plt.subplots() - ax.boxplot([chirps_winner, chirps_loser]) - + ax.boxplot([chirps_winner, chirps_loser], showfliers=False) + ax.scatter(np.ones(len(chirps_winner)), chirps_winner, color='r') + ax.scatter(np.ones(len(chirps_loser))*2, chirps_loser, color='r') ax.set_xticklabels(['winner', 'loser']) - ax.set_ylabel('Chirpscount per trial') - plt.show() - - - - embed() - exit() - + for w, l in zip(chirps_winner, chirps_loser): + ax.plot([1,2], [w,l], color='r', alpha=0.5, linewidth=0.5) + ax.set_ylabel('Chirpscounts [n]') + plt.show() if __name__ == '__main__': From ea98a20a6868461da6bddebc96a52291ac49d90c Mon Sep 17 00:00:00 2001 From: wendtalexander Date: Tue, 24 Jan 2023 13:56:41 +0100 Subject: [PATCH 07/29] adding plotstyle boxplots --- code/modules/plotstyle.py | 11 ++++------- code/plot_chirp_bodylegth.py | 36 +++++++++++++++++++++++------------- 2 files changed, 27 insertions(+), 20 deletions(-) diff --git a/code/modules/plotstyle.py b/code/modules/plotstyle.py index 2325f62..2c05369 100644 --- a/code/modules/plotstyle.py +++ b/code/modules/plotstyle.py @@ -108,9 +108,6 @@ def PlotStyle() -> None: @classmethod def set_boxplot_color(cls, bp, color): plt.setp(bp["boxes"], color=color) - plt.setp(bp["whiskers"], color=color) - plt.setp(bp["caps"], color=color) - plt.setp(bp["medians"], color=color) @classmethod def label_subplots(cls, labels, axes, fig): @@ -250,11 +247,11 @@ def PlotStyle() -> None: # dark mode modifications plt.rcParams["boxplot.flierprops.color"] = white - plt.rcParams["boxplot.flierprops.markeredgecolor"] = gray + plt.rcParams["boxplot.flierprops.markeredgecolor"] = white 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.whiskerprops.color"] = white + plt.rcParams["boxplot.capprops.color"] = white + plt.rcParams["boxplot.medianprops.color"] = white plt.rcParams["text.color"] = white plt.rcParams["axes.facecolor"] = black # axes background color plt.rcParams["axes.edgecolor"] = gray # axes edge color diff --git a/code/plot_chirp_bodylegth.py b/code/plot_chirp_bodylegth.py index 32da016..0cf1783 100644 --- a/code/plot_chirp_bodylegth.py +++ b/code/plot_chirp_bodylegth.py @@ -1,9 +1,9 @@ import numpy as np -import os +import os import numpy as np -import matplotlib.pyplot as plt +import matplotlib.pyplot as plt from thunderfish.powerspectrum import decibel from IPython import embed @@ -19,11 +19,13 @@ logger = makeLogger(__name__) def main(datapath: str): - 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' + 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] - + chirps_winner = [] chirps_loser = [] @@ -61,27 +63,35 @@ def main(datapath: str): 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], showfliers=False) - ax.scatter(np.ones(len(chirps_winner)), chirps_winner, color='r') - ax.scatter(np.ones(len(chirps_loser))*2, chirps_loser, color='r') + bplot1 = ax.boxplot(chirps_winner, positions=[ + 1], showfliers=False, patch_artist=True) + bplot2 = ax.boxplot(chirps_loser, positions=[ + 2], showfliers=False, patch_artist=True) + ax.scatter(np.ones(len(chirps_winner))*1.15, chirps_winner, color='r') + ax.scatter(np.ones(len(chirps_loser))*1.85, chirps_loser, color='r') ax.set_xticklabels(['winner', 'loser']) + for w, l in zip(chirps_winner, chirps_loser): - ax.plot([1,2], [w,l], color='r', alpha=0.5, linewidth=0.5) + ax.plot([1.15, 1.85], [w, l], color='r', alpha=0.5, linewidth=0.5) + + colors1 = ps.red + ps.set_boxplot_color(bplot1, colors1) + colors1 = ps.orange + ps.set_boxplot_color(bplot2, colors1) ax.set_ylabel('Chirpscounts [n]') plt.show() + if __name__ == '__main__': # Path to the data datapath = '../data/mount_data/' - - main(datapath) \ No newline at end of file + + main(datapath) From 726301c593a643b322ce8fffbd12135af8add876 Mon Sep 17 00:00:00 2001 From: wendtalexander Date: Tue, 24 Jan 2023 14:04:44 +0100 Subject: [PATCH 08/29] adding plot to poster --- code/modules/filehandling.py | 1 + code/plot_chirp_bodylegth.py | 2 +- poster/figs/chirps_winner_loser.pdf | Bin 0 -> 11986 bytes 3 files changed, 2 insertions(+), 1 deletion(-) create mode 100644 poster/figs/chirps_winner_loser.pdf diff --git a/code/modules/filehandling.py b/code/modules/filehandling.py index 334aefa..c3c71f2 100644 --- a/code/modules/filehandling.py +++ b/code/modules/filehandling.py @@ -3,6 +3,7 @@ import os import yaml import numpy as np from thunderfish.dataloader import DataLoader +import matplotlib.pyplot as plt class ConfLoader: diff --git a/code/plot_chirp_bodylegth.py b/code/plot_chirp_bodylegth.py index 0cf1783..fd34ebf 100644 --- a/code/plot_chirp_bodylegth.py +++ b/code/plot_chirp_bodylegth.py @@ -86,7 +86,7 @@ def main(datapath: str): ps.set_boxplot_color(bplot2, colors1) ax.set_ylabel('Chirpscounts [n]') - plt.show() + plt.savefig('../poster/figs/chirps_winner_loser.pdf') if __name__ == '__main__': diff --git a/poster/figs/chirps_winner_loser.pdf b/poster/figs/chirps_winner_loser.pdf new file mode 100644 index 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zIfZ#I#a=qq4FPU|;t;@v&HX?GfCm=E4f+=bhvM)LqRqiT%!vXPWC09?14^pw8?KmafI0vP;Rc>xR%{`*`Eh?IXDhx&bAz_JD7&?r3qw|&*% z&|txOYB&(ZEQFz8(*ghf0un>kPEu#7@3Rwsv!JY~J{o)k} zpMJv+kNbTtUhQ`}2$+ZR=XCOE*f+HK*#|JhZEH`aJbMt{%#)IXC8BAw7ll8iSlOBeE LkdZOmY=rnfO0ajV literal 0 HcmV?d00001 From a371755b1c1abc5d7da1efd08c4e687811bc8978 Mon Sep 17 00:00:00 2001 From: wendtalexander Date: Tue, 24 Jan 2023 14:07:38 +0100 Subject: [PATCH 09/29] adding variable for scatter plot --- code/plot_chirp_bodylegth.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/code/plot_chirp_bodylegth.py b/code/plot_chirp_bodylegth.py index fd34ebf..f722fee 100644 --- a/code/plot_chirp_bodylegth.py +++ b/code/plot_chirp_bodylegth.py @@ -69,16 +69,18 @@ def main(datapath: str): print(all_fish_ids) fig, ax = plt.subplots() + scatterwinner = 1.15 + scatterloser = 1.85 bplot1 = ax.boxplot(chirps_winner, positions=[ 1], showfliers=False, patch_artist=True) bplot2 = ax.boxplot(chirps_loser, positions=[ 2], showfliers=False, patch_artist=True) - ax.scatter(np.ones(len(chirps_winner))*1.15, chirps_winner, color='r') - ax.scatter(np.ones(len(chirps_loser))*1.85, chirps_loser, color='r') + ax.scatter(np.ones(len(chirps_winner))*scatterwinner, chirps_winner, color='r') + ax.scatter(np.ones(len(chirps_loser))*scatterloser, chirps_loser, color='r') ax.set_xticklabels(['winner', 'loser']) for w, l in zip(chirps_winner, chirps_loser): - ax.plot([1.15, 1.85], [w, l], color='r', alpha=0.5, linewidth=0.5) + ax.plot([scatterwinner, scatterloser], [w, l], color='r', alpha=0.5, linewidth=0.5) colors1 = ps.red ps.set_boxplot_color(bplot1, colors1) @@ -87,6 +89,7 @@ def main(datapath: str): ax.set_ylabel('Chirpscounts [n]') plt.savefig('../poster/figs/chirps_winner_loser.pdf') + plt.show() if __name__ == '__main__': From 632e0d5fd86f21c793a048144c3b5b6aa863e4d8 Mon Sep 17 00:00:00 2001 From: weygoldt <88969563+weygoldt@users.noreply.github.com> Date: Tue, 24 Jan 2023 14:08:07 +0100 Subject: [PATCH 10/29] new poster template --- code/extract_chirps.py | 4 +- poster/figs/logo_all.pdf | 529 +++++++++++++++++++ poster/main.pdf | Bin 418509 -> 65605 bytes poster/main.tex | 135 ++--- poster/packages.tex | 5 +- poster/style.tex | 31 +- {poster => poster_old}/figs/Untitled.png | Bin {poster => poster_old}/figs/algorithm.pdf | Bin {poster => poster_old}/figs/introplot.pdf | Bin {poster => poster_old}/figs/logo.png | Bin {poster => poster_old}/figs/logo.svg | 0 {poster => poster_old}/figs/placeholder1.png | Bin poster_old/main.pdf | Bin 0 -> 418509 bytes poster_old/main.tex | 119 +++++ poster_old/packages.tex | 11 + poster_old/style.tex | 119 +++++ 16 files changed, 854 insertions(+), 99 deletions(-) create mode 100644 poster/figs/logo_all.pdf rename {poster => poster_old}/figs/Untitled.png (100%) rename {poster => poster_old}/figs/algorithm.pdf (100%) rename {poster => poster_old}/figs/introplot.pdf (100%) rename {poster => poster_old}/figs/logo.png (100%) rename {poster => poster_old}/figs/logo.svg (100%) rename {poster => poster_old}/figs/placeholder1.png (100%) create mode 100644 poster_old/main.pdf create mode 100644 poster_old/main.tex create mode 100644 poster_old/packages.tex create mode 100644 poster_old/style.tex diff --git a/code/extract_chirps.py b/code/extract_chirps.py index 9b32928..61c8564 100644 --- a/code/extract_chirps.py +++ b/code/extract_chirps.py @@ -4,11 +4,13 @@ import numpy as np from chirpdetection import chirpdetection from IPython import embed +# check rec ../data/mount_data/2020-03-25-10_00/ starting at 3175 + def main(datapaths): for path in datapaths: - chirpdetection(path, plot='show') + chirpdetection(path, plot='false') if __name__ == '__main__': diff --git a/poster/figs/logo_all.pdf b/poster/figs/logo_all.pdf new file mode 100644 index 0000000..f54eedc --- /dev/null +++ b/poster/figs/logo_all.pdf @@ -0,0 +1,529 @@ +%PDF-1.5 %âãÏÓ +1 0 obj <>/OCGs[15 0 R 18 0 R 21 0 R 25 0 R 35 0 R 36 0 R 37 0 R 38 0 R]>>/Pages 2 0 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+\documentclass[25pt, a0paper, portrait, margin=0mm, innermargin=20mm, blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default values for poster format options. \input{packages} @@ -7,113 +7,86 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val \begin{document} \renewcommand{\baselinestretch}{1} -\title{\parbox{1900pt}{Pushing the limits of time-frequency uncertainty in the -detection of transient communication signals in weakly electric fish}} -\author{Sina Prause, Alexander Wendt, Patrick Weygoldt} -\institute{Supervised by Till Raab \& Jan Benda, Neurothology Group, -University of Tübingen} +\title{\parbox{1500pt}{Poster}} +\author{Sina Prause, Alexander Wendt, and Patrick Weygoldt} +\institute{Supervised by Till Raab \& Jan Benda, Neuroethology Lab, University of Tuebingen} \usetitlestyle[]{sampletitle} \maketitle \renewcommand{\baselinestretch}{1.4} \begin{columns} -\column{0.5} +\column{0.4} \myblock[TranspBlock]{Introduction}{ - \begin{minipage}[t]{0.55\linewidth} - The time-frequency tradeoff makes reliable signal detecion and simultaneous - sender identification of freely interacting individuals impossible. - This profoundly limits our current understanding of chirps to experiments - with single - or physically separated - individuals. - \end{minipage} \hfill - \begin{minipage}[t]{0.40\linewidth} - \vspace{-1.5cm} + The time-frequency tradeoff makes reliable signal detecion and simultaneous + sender identification of freely interacting individuals impossible. + This profoundly limits our current understanding of chirps to experiments + with single - or physically separated - individuals. + \vspace{0.6cm} \begin{tikzfigure}[] - \label{tradeoff} - \includegraphics[width=\linewidth]{figs/introplot} + \label{griddrawing} + \includegraphics[width=0.5\linewidth]{example-image-a} \end{tikzfigure} - \end{minipage} } -\myblock[TranspBlock]{A chirp detection algorithm}{ - \begin{tikzfigure}[] - \label{modulations} - \includegraphics[width=\linewidth]{figs/algorithm} - \end{tikzfigure} -} - -\column{0.5} -\myblock[TranspBlock]{Chirps and diadic competitions}{ - \begin{minipage}[t]{0.7\linewidth} +\myblock[TranspBlock]{Chirp detection}{ \begin{tikzfigure}[] - \label{modulations} - \includegraphics[width=\linewidth]{figs/placeholder1} + \label{fig:example_a} + \includegraphics[width=0.5\linewidth]{example-image-a} \end{tikzfigure} - \end{minipage} \hfill - \begin{minipage}[t]{0.25\linewidth} - \lipsum[3][1-3] - \end{minipage} + \vspace{0cm} +} - \begin{minipage}[t]{0.7\linewidth} +\column{0.6} +\myblock[TranspBlock]{Chirps during competition}{ \begin{tikzfigure}[] - \label{modulations} - \includegraphics[width=\linewidth]{figs/placeholder1} + \label{fig:example_b} + \includegraphics[width=0.5\linewidth]{example-image-b} \end{tikzfigure} - \end{minipage} \hfill - \begin{minipage}[t]{0.25\linewidth} - \lipsum[3][1-3] - \end{minipage} + \noindent +} - \begin{minipage}[t]{0.7\linewidth} +\myblock[TranspBlock]{Interactions at modulations}{ + \vspace{-1.2cm} \begin{tikzfigure}[] - \label{modulations} - \includegraphics[width=\linewidth]{figs/placeholder1} + \label{fig:example_c} + \includegraphics[width=0.5\linewidth]{example-image-c} \end{tikzfigure} - \end{minipage} \hfill - \begin{minipage}[t]{0.25\linewidth} - \lipsum[3][1-3] - \end{minipage} - + \begin{multicols}{2} + \begin{itemize} + \setlength\itemsep{0.5em} + \item $\Delta$EOD$f$ does not appear to decrease during synchronous modulations (). + \item Individuals that rise their EOD$f$ first appear to rise their frequency higher compared to reactors (\textbf{B}). + \vfill + \null + \columnbreak + \item Synchronized fish keep distances below 1 m (\textbf{C}) but distances over 3 m also occur (see \textbf{movie}). + \item Spatial interactions increase \textbf{after} the start of a synchronous modulation (\textbf{D}). + \end{itemize} + \end{multicols} + \vspace{-1cm} } \myblock[TranspBlock]{Conclusion}{ - \lipsum[3][1-9] -} - -% \column{0.3} -% \myblock[TranspBlock]{More Results}{ -% \begin{tikzfigure}[] -% \label{results} -% \includegraphics[width=\linewidth]{example-image-a} -% \end{tikzfigure} - -% \begin{multicols}{2} -% \lipsum[5][1-8] -% \end{multicols} -% \vspace{-1cm} -% } - -% \myblock[TranspBlock]{Conclusion}{ -% \begin{itemize} -% \setlength\itemsep{0.5em} -% \item \lipsum[1][1] -% \item \lipsum[1][1] -% \item \lipsum[1][1] -% \end{itemize} -% \vspace{0.2cm} -% } -\end{columns} - -\node[ - above right, + \begin{itemize} + \setlength\itemsep{0.5em} + \item Our analysis is the first to indicate that \textit{A. leptorhynchus} uses long, diffuse and synchronized EOD$f$ signals to communicate in addition to chirps and rises. + \item The recorded fish do not exhibit jamming avoidance behavior while close during synchronous modulations. + \item Synchronous signals \textbf{initiate} spatio-temporal interactions. + \end{itemize} + \vspace{0.2cm} + } + \end{columns} + +\node [above right, text=white, outer sep=45pt, minimum width=\paperwidth, align=center, draw, fill=boxes, - color=boxes, -] at (-0.51\paperwidth,-43.5) { -\textcolor{text}{\normalsize Contact: \{name\}.\{surname\}@student.uni-tuebingen.de}}; + color=boxes] at (-43.6,-61) { + \textcolor{white}{ + \normalsize Contact: \{name\}.\{surname\}@student.uni-tuebingen.de}}; \end{document} diff --git a/poster/packages.tex b/poster/packages.tex index 82f951e..50d9f0e 100644 --- a/poster/packages.tex +++ b/poster/packages.tex @@ -1,11 +1,10 @@ \usepackage[utf8]{inputenc} \usepackage[scaled]{helvet} -\renewcommand\familydefault{\sfdefault} +\renewcommand\familydefault{\sfdefault} \usepackage[T1]{fontenc} \usepackage{wrapfig} \usepackage{setspace} \usepackage{multicol} \setlength{\columnsep}{1.5cm} \usepackage{xspace} -\usepackage{tikz} -\usepackage{lipsum} +\usepackage{tikz} \ No newline at end of file diff --git a/poster/style.tex b/poster/style.tex index ac800ce..d627e22 100644 --- a/poster/style.tex +++ b/poster/style.tex @@ -1,9 +1,9 @@ \tikzposterlatexaffectionproofoff \usetheme{Default} -\definecolor{text}{HTML}{e0e4f7} -\definecolor{background}{HTML}{111116} -\definecolor{boxes}{HTML}{2a2a32} +\definecolor{text}{HTML}{ffffff} +\definecolor{background}{HTML}{080808} +\definecolor{boxes}{HTML}{1E1E1E} \definecolor{unired}{HTML}{a51e37} \colorlet{blocktitlefgcolor}{text} @@ -16,10 +16,11 @@ \colorlet{notefgcolor}{background} \colorlet{notebgcolor}{background} + % Title setup \settitle{ % Rearrange the order of the minipages to e.g. center the title between the logos -\begin{minipage}[c]{0.6\paperwidth} +\begin{minipage}[c]{0.8\paperwidth} % \centering \vspace{2.5cm}\hspace{1.5cm} \color{text}{\Huge{\textbf{\@title}} \par} @@ -30,26 +31,28 @@ \vspace{2.5cm} \end{minipage} \begin{minipage}[c]{0.2\paperwidth} -% \centering - \vspace{1cm}\hspace{1cm} - \includegraphics[scale=1]{example-image-a} -\end{minipage} -\begin{minipage}[c]{0.2\paperwidth} -% \vspace{1cm}\hspace{1cm} \centering - \includegraphics[scale=1]{example-image-a} + % \vspace{1cm} + \hspace{-10cm} + \includegraphics[width=\linewidth]{example-image-a} \end{minipage}} +% \begin{minipage}[c]{0.2\paperwidth} +% \vspace{1cm}\hspace{1cm} +% \centering +% \includegraphics[width=\linewidth]{example-image-a} +% \end{minipage}} -% definie title style with background box +% define title style with background box (currently white) \definetitlestyle{sampletitle}{ - width=1189mm, + width=841mm, roundedcorners=0, linewidth=0pt, innersep=15pt, titletotopverticalspace=0mm, titletoblockverticalspace=5pt }{ - \begin{scope}[line width=\titlelinewidth, rounded corners=\titleroundedcorners] + \begin{scope}[line width=\titlelinewidth, + rounded corners=\titleroundedcorners] \draw[fill=text, color=boxes] (\titleposleft,\titleposbottom) rectangle diff --git a/poster/figs/Untitled.png b/poster_old/figs/Untitled.png similarity index 100% rename from poster/figs/Untitled.png rename to 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landscape, margin=0mm, innermargin=20mm, +blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default values for poster format options. + +\input{packages} +\input{style} + +\begin{document} + +\renewcommand{\baselinestretch}{1} +\title{\parbox{1900pt}{Pushing the limits of time-frequency uncertainty in the +detection of transient communication signals in weakly electric fish}} +\author{Sina Prause, Alexander Wendt, Patrick Weygoldt} +\institute{Supervised by Till Raab \& Jan Benda, Neurothology Group, +University of Tübingen} +\usetitlestyle[]{sampletitle} +\maketitle +\renewcommand{\baselinestretch}{1.4} + +\begin{columns} +\column{0.5} +\myblock[TranspBlock]{Introduction}{ + \begin{minipage}[t]{0.55\linewidth} + The time-frequency tradeoff makes reliable signal detecion and simultaneous + sender identification of freely interacting individuals impossible. + This profoundly limits our current understanding of chirps to experiments + with single - or physically separated - individuals. + \end{minipage} \hfill + \begin{minipage}[t]{0.40\linewidth} + \vspace{-1.5cm} + \begin{tikzfigure}[] + \label{tradeoff} + \includegraphics[width=\linewidth]{figs/introplot} + \end{tikzfigure} + \end{minipage} +} + +\myblock[TranspBlock]{A chirp detection algorithm}{ + \begin{tikzfigure}[] + \label{modulations} + \includegraphics[width=\linewidth]{figs/algorithm} + \end{tikzfigure} +} + +\column{0.5} +\myblock[TranspBlock]{Chirps and diadic competitions}{ + \begin{minipage}[t]{0.7\linewidth} + \begin{tikzfigure}[] + \label{modulations} + \includegraphics[width=\linewidth]{figs/placeholder1} + \end{tikzfigure} + \end{minipage} \hfill + \begin{minipage}[t]{0.25\linewidth} + \lipsum[3][1-3] + \end{minipage} + + \begin{minipage}[t]{0.7\linewidth} + \begin{tikzfigure}[] + \label{modulations} + \includegraphics[width=\linewidth]{figs/placeholder1} + \end{tikzfigure} + \end{minipage} \hfill + \begin{minipage}[t]{0.25\linewidth} + \lipsum[3][1-3] + \end{minipage} + + \begin{minipage}[t]{0.7\linewidth} + \begin{tikzfigure}[] + \label{modulations} + \includegraphics[width=\linewidth]{figs/placeholder1} + \end{tikzfigure} + \end{minipage} \hfill + \begin{minipage}[t]{0.25\linewidth} + \lipsum[3][1-3] + \end{minipage} + + +} + +\myblock[TranspBlock]{Conclusion}{ + \lipsum[3][1-9] +} + +% \column{0.3} +% \myblock[TranspBlock]{More Results}{ +% \begin{tikzfigure}[] +% \label{results} +% \includegraphics[width=\linewidth]{example-image-a} +% \end{tikzfigure} + +% \begin{multicols}{2} +% \lipsum[5][1-8] +% \end{multicols} +% \vspace{-1cm} +% } + +% \myblock[TranspBlock]{Conclusion}{ +% \begin{itemize} +% \setlength\itemsep{0.5em} +% \item \lipsum[1][1] +% \item \lipsum[1][1] +% \item \lipsum[1][1] +% \end{itemize} +% \vspace{0.2cm} +% } +\end{columns} + +\node[ + above right, + text=white, + outer sep=45pt, + minimum width=\paperwidth, + align=center, + draw, + fill=boxes, + color=boxes, +] at (-0.51\paperwidth,-43.5) { +\textcolor{text}{\normalsize Contact: \{name\}.\{surname\}@student.uni-tuebingen.de}}; + +\end{document} diff --git a/poster_old/packages.tex b/poster_old/packages.tex new file mode 100644 index 0000000..82f951e --- /dev/null +++ b/poster_old/packages.tex @@ -0,0 +1,11 @@ +\usepackage[utf8]{inputenc} +\usepackage[scaled]{helvet} +\renewcommand\familydefault{\sfdefault} +\usepackage[T1]{fontenc} +\usepackage{wrapfig} +\usepackage{setspace} +\usepackage{multicol} +\setlength{\columnsep}{1.5cm} +\usepackage{xspace} +\usepackage{tikz} +\usepackage{lipsum} diff --git a/poster_old/style.tex b/poster_old/style.tex new file mode 100644 index 0000000..ac800ce --- /dev/null +++ b/poster_old/style.tex @@ -0,0 +1,119 @@ +\tikzposterlatexaffectionproofoff +\usetheme{Default} + +\definecolor{text}{HTML}{e0e4f7} +\definecolor{background}{HTML}{111116} +\definecolor{boxes}{HTML}{2a2a32} +\definecolor{unired}{HTML}{a51e37} + +\colorlet{blocktitlefgcolor}{text} +\colorlet{backgroundcolor}{background} +\colorlet{blocktitlebgcolor}{background} +\colorlet{blockbodyfgcolor}{text} +\colorlet{innerblocktitlebgcolor}{background} +\colorlet{innerblocktitlefgcolor}{text} +\colorlet{notefrcolor}{text} +\colorlet{notefgcolor}{background} +\colorlet{notebgcolor}{background} + +% Title setup +\settitle{ +% Rearrange the order of the minipages to e.g. center the title between the logos +\begin{minipage}[c]{0.6\paperwidth} +% \centering + \vspace{2.5cm}\hspace{1.5cm} + \color{text}{\Huge{\textbf{\@title}} \par} + \vspace*{2em}\hspace{1.5cm} + \color{text}{\LARGE \@author \par} + \vspace*{2em}\hspace{1.5cm} + \color{text}{\Large \@institute} + \vspace{2.5cm} +\end{minipage} +\begin{minipage}[c]{0.2\paperwidth} +% \centering + \vspace{1cm}\hspace{1cm} + \includegraphics[scale=1]{example-image-a} +\end{minipage} +\begin{minipage}[c]{0.2\paperwidth} +% \vspace{1cm}\hspace{1cm} + \centering + \includegraphics[scale=1]{example-image-a} +\end{minipage}} + +% definie title style with background box +\definetitlestyle{sampletitle}{ + width=1189mm, + roundedcorners=0, + linewidth=0pt, + innersep=15pt, + titletotopverticalspace=0mm, + titletoblockverticalspace=5pt +}{ + \begin{scope}[line width=\titlelinewidth, rounded corners=\titleroundedcorners] + \draw[fill=text, color=boxes] + (\titleposleft,\titleposbottom) + rectangle + (\titleposright,\titlepostop); + \end{scope} +} + +% define coustom block style for visible blocks +\defineblockstyle{GrayBlock}{ + titlewidthscale=1, + bodywidthscale=1, + % titlecenter, + titleleft, + titleoffsetx=0pt, + titleoffsety=-30pt, + bodyoffsetx=0pt, + bodyoffsety=-40pt, + bodyverticalshift=0mm, + roundedcorners=25, + linewidth=1pt, + titleinnersep=20pt, + bodyinnersep=38pt +}{ + \draw[rounded corners=\blockroundedcorners, inner sep=\blockbodyinnersep, + line width=\blocklinewidth, color=background, + top color=boxes, bottom color=boxes, + ] + (blockbody.south west) rectangle (blockbody.north east); % + \ifBlockHasTitle% + \draw[rounded corners=\blockroundedcorners, inner sep=\blocktitleinnersep, + top color=background, bottom color=background, + line width=2, color=background, %fill=blocktitlebgcolor + ] + (blocktitle.south west) rectangle (blocktitle.north east); % + \fi% +} +\newcommand\myblock[3][GrayBlock]{\useblockstyle{#1}\block{#2}{#3}\useblockstyle{Default}} + +% Define blockstyle for tranparent block +\defineblockstyle{TranspBlock}{ + titlewidthscale=0.99, + bodywidthscale=0.99, + titleleft, + titleoffsetx=15pt, + titleoffsety=-40pt, + bodyoffsetx=0pt, + bodyoffsety=-40pt, + bodyverticalshift=0mm, + roundedcorners=25, + linewidth=1pt, + titleinnersep=20pt, + bodyinnersep=38pt +}{ + \draw[rounded corners=\blockroundedcorners, inner sep=\blockbodyinnersep, + line width=\blocklinewidth, color=background, + top color=background, bottom color=background, + ] + (blockbody.south west) rectangle (blockbody.north east); % + \ifBlockHasTitle% + \draw[rounded corners=\blockroundedcorners, inner sep=\blocktitleinnersep, + top color=background, bottom color=background, + line width=2, color=background, %fill=blocktitlebgcolor + ] + (blocktitle.south west) rectangle (blocktitle.north east); % + \fi% +} +\renewcommand\myblock[3][TranspBlock]{\useblockstyle{#1}\block{#2}{#3}\useblockstyle{Default}} From 33fa9f92bc99f32b2d71693033b8cadc07eb3080 Mon Sep 17 00:00:00 2001 From: wendtalexander Date: Tue, 24 Jan 2023 14:19:42 +0100 Subject: [PATCH 11/29] chirp body changes --- code/plot_chirp_bodylegth.py | 2 ++ poster/figs/chirps_winner_loser.pdf | Bin 11986 -> 12221 bytes 2 files changed, 2 insertions(+) diff --git a/code/plot_chirp_bodylegth.py b/code/plot_chirp_bodylegth.py index f722fee..c6d5e08 100644 --- a/code/plot_chirp_bodylegth.py +++ b/code/plot_chirp_bodylegth.py @@ -78,6 +78,7 @@ def main(datapath: str): ax.scatter(np.ones(len(chirps_winner))*scatterwinner, chirps_winner, color='r') ax.scatter(np.ones(len(chirps_loser))*scatterloser, chirps_loser, color='r') ax.set_xticklabels(['winner', 'loser']) + ax.text(0.9, 0.9, f'n = {len(chirps_winner)}', transform=ax.transAxes, color= ps.white) for w, l in zip(chirps_winner, chirps_loser): ax.plot([scatterwinner, scatterloser], [w, l], color='r', alpha=0.5, linewidth=0.5) @@ -87,6 +88,7 @@ def main(datapath: str): colors1 = ps.orange ps.set_boxplot_color(bplot2, colors1) + ax.set_ylabel('Chirpscounts [n]') plt.savefig('../poster/figs/chirps_winner_loser.pdf') plt.show() diff --git a/poster/figs/chirps_winner_loser.pdf b/poster/figs/chirps_winner_loser.pdf index 91718185eb8351d411370b1ffc558ed701c3888b..bafabda5193d792498cee0128acce971397933ec 100644 GIT binary patch delta 3041 zcmZuwdpuP68#au^*qCO>2-`tahUU!7nHyF=5lNF~)Nj?eB#jV5VGw=7A_kExDdA{%I^0?`B{R~R!52F+C zS=36g9*>OW?LA^Wepqs+qht1SSj^;fi|f=FJF-D+b#wHtNEWYYIeV)9V@8(6#qBrh zKm8UzlPd02FXBC!vhuuD6W%}C=h1Y}tFE+pzjgiz7aaUuL($k2g@@d8fN(gZP1C_D?3b=hH^3Ti0bzyDMrh%l6r+ zw38NF`>G{d5bk{K=uG|qXGg)fXW_u1iAy@AhcI~_k*)tp!Gl=Q6OY&(AM#YWkL 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zcmR2pi02&)kf(X9kq10~wi^QcNroo!0rD&v-;3*knNF1iGnw;81X4(|PJS^$fh4gM3R9vBNV5cz z1)0-7Zmtt5M3oRor7$G0REDGx8YKCSMyAhV1^=5Tjmkhi_*`NdQ&KgZOp@pfol27s zL#Iz~^_+SnAKAX$`xj!TlGH0}= K=GGmi=zjw{aWU%v From 01f74a471ebd36ac5c683f41378c725c0ba19011 Mon Sep 17 00:00:00 2001 From: wendtalexander Date: Tue, 24 Jan 2023 15:32:52 +0100 Subject: [PATCH 12/29] adding size differences --- code/plot_chirp_bodylegth.py | 70 ++++++++++++++++++++++------ poster/figs/chirps_winner_loser.pdf | Bin 12221 -> 14087 bytes 2 files changed, 57 insertions(+), 13 deletions(-) diff --git a/code/plot_chirp_bodylegth.py b/code/plot_chirp_bodylegth.py index c6d5e08..dacbd5d 100644 --- a/code/plot_chirp_bodylegth.py +++ b/code/plot_chirp_bodylegth.py @@ -21,14 +21,19 @@ def main(datapath: str): foldernames = [ datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)] - path_to_csv = ( + path_order_meta = ( '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv' - meta_id = read_csv(path_to_csv) - meta_id['recording'] = meta_id['recording'].str[1:-1] + order_meta_df = read_csv(path_order_meta) + order_meta_df['recording'] = order_meta_df['recording'].str[1:-1] + path_id_meta = ( + '/').join(foldernames[0].split('/')[:-2]) + '/id_meta.csv' + id_meta_df = read_csv(path_id_meta) chirps_winner = [] + size_diff = [] chirps_loser = [] + for foldername in foldernames: # behabvior is pandas dataframe with all the data if foldername == '../data/mount_data/2020-05-12-10_00/': @@ -43,16 +48,52 @@ def main(datapath: str): category, timestamps = correct_chasing_events(category, timestamps) folder_name = foldername.split('/')[-2] - winner_row = meta_id[meta_id['recording'] == folder_name] + winner_row = order_meta_df[order_meta_df['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) + + groub = winner_row['group'].values[0].astype(int) + size_rows = id_meta_df[id_meta_df['group'] == groub] + + if winner == winner_fish1: winner_fish_id = winner_row['rec_id1'].values[0] loser_fish_id = winner_row['rec_id2'].values[0] + + size_winners = [] + for l in ['l1', 'l2', 'l3']: + size_winner = size_rows[size_rows['fish']== winner_fish1][l].values[0] + size_winners.append(size_winner) + mean_size_winner = np.nanmean(size_winners) + + + size_losers = [] + for l in ['l1', 'l2', 'l3']: + size_loser = size_rows[size_rows['fish']== winner_fish2][l].values[0] + size_losers.append(size_loser) + mean_size_loser = np.nanmean(size_losers) + + size_diff.append(mean_size_winner - mean_size_loser) + elif winner == winner_fish2: winner_fish_id = winner_row['rec_id2'].values[0] loser_fish_id = winner_row['rec_id1'].values[0] + + size_winners = [] + for l in ['l1', 'l2', 'l3']: + size_winner = size_rows[size_rows['fish']== winner_fish2][l].values[0] + size_winners.append(size_winner) + mean_size_winner = np.nanmean(size_winners) + + size_losers = [] + for l in ['l1', 'l2', 'l3']: + size_loser = size_rows[size_rows['fish']== winner_fish1][l].values[0] + size_losers.append(size_loser) + mean_size_loser = np.nanmean(size_losers) + + size_diff.append(mean_size_winner - mean_size_loser) + else: continue @@ -68,28 +109,31 @@ def main(datapath: str): print(winner_fish_id) print(all_fish_ids) - fig, ax = plt.subplots() + fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10,5)) scatterwinner = 1.15 scatterloser = 1.85 - bplot1 = ax.boxplot(chirps_winner, positions=[ + bplot1 = ax1.boxplot(chirps_winner, positions=[ 1], showfliers=False, patch_artist=True) - bplot2 = ax.boxplot(chirps_loser, positions=[ + bplot2 = ax1.boxplot(chirps_loser, positions=[ 2], showfliers=False, patch_artist=True) - ax.scatter(np.ones(len(chirps_winner))*scatterwinner, chirps_winner, color='r') - ax.scatter(np.ones(len(chirps_loser))*scatterloser, chirps_loser, color='r') - ax.set_xticklabels(['winner', 'loser']) - ax.text(0.9, 0.9, f'n = {len(chirps_winner)}', transform=ax.transAxes, color= ps.white) + ax1.scatter(np.ones(len(chirps_winner))*scatterwinner, chirps_winner, color='r') + ax1.scatter(np.ones(len(chirps_loser))*scatterloser, chirps_loser, color='r') + ax1.set_xticklabels(['winner', 'loser']) + ax1.text(0.9, 0.9, f'n = {len(chirps_winner)}', transform=ax1.transAxes, color= ps.white) for w, l in zip(chirps_winner, chirps_loser): - ax.plot([scatterwinner, scatterloser], [w, l], color='r', alpha=0.5, linewidth=0.5) + ax1.plot([scatterwinner, scatterloser], [w, l], color='r', alpha=0.5, linewidth=0.5) colors1 = ps.red ps.set_boxplot_color(bplot1, colors1) colors1 = ps.orange ps.set_boxplot_color(bplot2, colors1) + ax1.set_ylabel('Chirpscounts [n]') + ax2.scatter(size_w, chirps_winner, color='r') + ax2.scatter(size_l, chirps_loser, color='green') - ax.set_ylabel('Chirpscounts [n]') + plt.savefig('../poster/figs/chirps_winner_loser.pdf') plt.show() diff --git a/poster/figs/chirps_winner_loser.pdf b/poster/figs/chirps_winner_loser.pdf 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poster/figs/chirps_winner_loser.pdf | Bin 14087 -> 16896 bytes 2 files changed, 21 insertions(+), 7 deletions(-) diff --git a/code/plot_chirp_bodylegth.py b/code/plot_chirp_bodylegth.py index dacbd5d..6d5c782 100644 --- a/code/plot_chirp_bodylegth.py +++ b/code/plot_chirp_bodylegth.py @@ -31,7 +31,9 @@ def main(datapath: str): chirps_winner = [] size_diff = [] + chirps_diff = [] chirps_loser = [] + freq_diff = [] for foldername in foldernames: @@ -91,25 +93,32 @@ def main(datapath: str): size_loser = size_rows[size_rows['fish']== winner_fish1][l].values[0] size_losers.append(size_loser) mean_size_loser = np.nanmean(size_losers) - - size_diff.append(mean_size_winner - mean_size_loser) + size_diff.append(mean_size_winner - mean_size_loser) 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]) + chirp_loser = len(bh.chirps[bh.chirps_ids == loser_fish_id]) + + freq_winner = np.nanmedian(bh.freq[bh.ident==winner_fish_id]) + freq_loser = np.nanmedian(bh.freq[bh.ident==loser_fish_id]) + + chirps_winner.append(chirp_winner) chirps_loser.append(chirp_loser) + chirps_diff.append(chirp_winner - chirp_loser) + freq_diff.append(freq_winner - freq_loser) + fish1_id = all_fish_ids[0] fish2_id = all_fish_ids[1] print(winner_fish_id) print(all_fish_ids) - fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10,5)) + fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(10,5)) scatterwinner = 1.15 scatterloser = 1.85 bplot1 = ax1.boxplot(chirps_winner, positions=[ @@ -130,10 +139,15 @@ def main(datapath: str): ps.set_boxplot_color(bplot2, colors1) ax1.set_ylabel('Chirpscounts [n]') - ax2.scatter(size_w, chirps_winner, color='r') - ax2.scatter(size_l, chirps_loser, color='green') + ax2.scatter(size_diff, chirps_diff, color='r') + ax2.set_xlabel('Size difference [mm]') + ax2.set_ylabel('Chirps difference [n]') + + ax3.scatter(freq_diff, chirps_diff, 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code/chirpdetection.py | 106 ++++++++++++++---------- code/modules/plotstyle.py | 137 +++++++++++++++++++------------- code/plot_introduction_specs.py | 4 +- poster/figs/algorithm.pdf | Bin 0 -> 194677 bytes poster/figs/introplot.pdf | Bin 0 -> 25267 bytes poster/main.pdf | Bin 65605 -> 257986 bytes poster/main.tex | 16 ++-- poster/style.tex | 6 +- 8 files changed, 159 insertions(+), 110 deletions(-) create mode 100644 poster/figs/algorithm.pdf create mode 100644 poster/figs/introplot.pdf diff --git a/code/chirpdetection.py b/code/chirpdetection.py index 39db060..1d67e39 100755 --- a/code/chirpdetection.py +++ b/code/chirpdetection.py @@ -133,8 +133,10 @@ class ChirpPlotBuffer: data_oi, self.data.raw_rate, self.t0 - 5, - [np.min(self.frequency) - 100, np.max(self.frequency) + 200] + [np.min(self.frequency) - 300, np.max(self.frequency) + 300] ) + ax0.set_ylim(np.min(self.frequency) - 100, + np.max(self.frequency) + 200) for track_id in self.data.ids: @@ -157,27 +159,35 @@ class ChirpPlotBuffer: zorder=10, color=ps.gblue1) else: ax0.plot(t-self.t0_old, f, lw=lw, - zorder=10, color=ps.gray, alpha=0.5) - - ax0.fill_between( - np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate), - q50 - self.config.minimal_bandwidth / 2, - q50 + self.config.minimal_bandwidth / 2, - color=ps.gblue1, - lw=1, - ls="dashed", - alpha=0.5, - ) + zorder=10, color=ps.black) + + # ax0.fill_between( + # np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate), + # q50 - self.config.minimal_bandwidth / 2, + # q50 + self.config.minimal_bandwidth / 2, + # color=ps.gblue1, + # lw=1, + # ls="dashed", + # alpha=0.5, + # ) + + # ax0.fill_between( + # np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate), + # search_lower, + # search_upper, + # color=ps.gblue2, + # lw=1, + # ls="dashed", + # alpha=0.5, + # ) + + ax0.axhline(q50 - self.config.minimal_bandwidth / 2, + color=ps.gblue1, lw=1, ls="dashed") + ax0.axhline(q50 + self.config.minimal_bandwidth / 2, + color=ps.gblue1, lw=1, ls="dashed") + ax0.axhline(search_lower, color=ps.gblue2, lw=1, ls="dashed") + ax0.axhline(search_upper, color=ps.gblue2, lw=1, ls="dashed") - ax0.fill_between( - np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate), - search_lower, - search_upper, - color=ps.gblue2, - lw=1, - ls="dashed", - alpha=0.5, - ) # ax0.axhline(q50, spec_times[0], spec_times[-1], # color=ps.gblue1, lw=2, ls="dashed") # ax0.axhline(q50 + self.search_frequency, @@ -187,7 +197,11 @@ class ChirpPlotBuffer: if len(chirps) > 0: for chirp in chirps: ax0.scatter( - chirp, np.median(self.frequency) + 150, c=ps.black, marker="v" + chirp, np.median(self.frequency), c=ps.red, marker=".", + edgecolors=ps.red, + facecolors=ps.red, + zorder=10, + s=70, ) # plot waveform of filtered signal @@ -207,25 +221,31 @@ class ChirpPlotBuffer: c=ps.gblue3, lw=lw, label="baseline inst. freq.") # plot filtered and rectified envelope - ax4.plot(self.time, self.baseline_envelope, c=ps.gblue1, lw=lw) + ax4.plot(self.time, self.baseline_envelope * + waveform_scaler, c=ps.gblue1, lw=lw) ax4.scatter( (self.time)[self.baseline_peaks], - self.baseline_envelope[self.baseline_peaks], + (self.baseline_envelope*waveform_scaler)[self.baseline_peaks], edgecolors=ps.red, + facecolors=ps.red, zorder=10, - marker="o", - facecolors="none", + marker=".", + s=70, + # facecolors="none", ) # plot envelope of search signal - ax5.plot(self.time, self.search_envelope, c=ps.gblue2, lw=lw) + ax5.plot(self.time, self.search_envelope * + waveform_scaler, c=ps.gblue2, lw=lw) ax5.scatter( (self.time)[self.search_peaks], - self.search_envelope[self.search_peaks], + (self.search_envelope*waveform_scaler)[self.search_peaks], edgecolors=ps.red, + facecolors=ps.red, zorder=10, - marker="o", - facecolors="none", + marker=".", + s=70, + # facecolors="none", ) # plot filtered instantaneous frequency @@ -235,16 +255,20 @@ class ChirpPlotBuffer: self.frequency_time[self.frequency_peaks], self.frequency_filtered[self.frequency_peaks], edgecolors=ps.red, + facecolors=ps.red, zorder=10, - marker="o", - facecolors="none", + marker=".", + s=70, + # facecolors="none", ) ax0.set_ylabel("frequency [Hz]") - ax1.set_ylabel("a.u.") - ax2.set_ylabel("a.u.") + ax1.set_ylabel(r"$\mu$V") + ax2.set_ylabel(r"$\mu$V") ax3.set_ylabel("Hz") - ax5.set_ylabel("a.u.") + ax4.set_ylabel(r"$\mu$V") + ax5.set_ylabel(r"$\mu$V") + ax6.set_ylabel("Hz") ax6.set_xlabel("time [s]") plt.setp(ax0.get_xticklabels(), visible=False) @@ -323,7 +347,7 @@ def plot_spectrogram( aspect="auto", origin="lower", interpolation="gaussian", - alpha=0.6, + # alpha=0.6, ) # axis.use_sticky_edges = False return spec_times @@ -628,16 +652,16 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None: raw_time = np.arange(data.raw.shape[0]) / data.raw_rate # good chirp times for data: 2022-06-02-10_00 - # window_start_index = (3 * 60 * 60 + 6 * 60 + 43.5 + 5) * data.raw_rate - # window_duration_index = 60 * data.raw_rate + window_start_index = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate + window_duration_index = 60 * data.raw_rate # t0 = 0 # dt = data.raw.shape[0] # window_start_seconds = (23495 + ((28336-23495)/3)) * data.raw_rate # window_duration_seconds = (28336 - 23495) * data.raw_rate - window_start_index = 0 - window_duration_index = data.raw.shape[0] + # window_start_index = 0 + # window_duration_index = data.raw.shape[0] # generate starting points of rolling window window_start_indices = np.arange( @@ -1097,4 +1121,4 @@ if __name__ == "__main__": datapath = "../data/2022-06-02-10_00/" # datapath = "/home/weygoldt/Data/uni/efishdata/2016-colombia/fishgrid/2016-04-09-22_25/" # datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-03-13-10_00/" - chirpdetection(datapath, plot="show", debug="false") + chirpdetection(datapath, plot="save", debug="false") diff --git a/code/modules/plotstyle.py b/code/modules/plotstyle.py index 2c05369..b4a8a41 100644 --- a/code/modules/plotstyle.py +++ b/code/modules/plotstyle.py @@ -23,16 +23,16 @@ def PlotStyle() -> None: sky = "#89dceb" teal = "#94e2d5" green = "#a6e3a1" - yellow = "#f9e2af" - orange = "#fab387" - maroon = "#eba0ac" - red = "#f38ba8" - purple = "#cba6f7" - pink = "#f5c2e7" + yellow = "#f9d67f" + orange = "#faa472" + maroon = "#eb8486" + red = "#f37588" + purple = "#d89bf7" + pink = "#f59edb" lavender = "#b4befe" - gblue1 = "#8cb8ff" - gblue2 = "#7cdcdc" - gblue3 = "#82e896" + gblue1 = "#89b4fa" + gblue2 = "#89dceb" + gblue3 = "#a6e3a1" @classmethod def lims(cls, track1, track2): @@ -108,6 +108,9 @@ def PlotStyle() -> None: @classmethod def set_boxplot_color(cls, bp, color): plt.setp(bp["boxes"], color=color) + plt.setp(bp["whiskers"], color=color) + plt.setp(bp["caps"], color=color) + plt.setp(bp["medians"], color=color) @classmethod def label_subplots(cls, labels, axes, fig): @@ -226,7 +229,7 @@ def PlotStyle() -> None: plt.rc("legend", fontsize=SMALL_SIZE) # legend fontsize plt.rc("figure", titlesize=BIGGER_SIZE) # fontsize of the figure title - plt.rcParams["image.cmap"] = 'cmo.haline' + plt.rcParams["image.cmap"] = "cmo.haline" plt.rcParams["axes.xmargin"] = 0.05 plt.rcParams["axes.ymargin"] = 0.1 plt.rcParams["axes.titlelocation"] = "left" @@ -247,42 +250,44 @@ def PlotStyle() -> None: # dark mode modifications plt.rcParams["boxplot.flierprops.color"] = white - plt.rcParams["boxplot.flierprops.markeredgecolor"] = white + plt.rcParams["boxplot.flierprops.markeredgecolor"] = gray plt.rcParams["boxplot.boxprops.color"] = gray - plt.rcParams["boxplot.whiskerprops.color"] = white - plt.rcParams["boxplot.capprops.color"] = white - plt.rcParams["boxplot.medianprops.color"] = white + plt.rcParams["boxplot.whiskerprops.color"] = gray + plt.rcParams["boxplot.capprops.color"] = gray + plt.rcParams["boxplot.medianprops.color"] = gray plt.rcParams["text.color"] = white plt.rcParams["axes.facecolor"] = black # axes background color plt.rcParams["axes.edgecolor"] = gray # 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 - plt.rcParams["axes.axisbelow"] = True # draw axis gridlines and ticks: + plt.rcParams["axes.axisbelow"] = True # draw axis gridlines and ticks: plt.rcParams["axes.spines.left"] = True # display axis spines plt.rcParams["axes.spines.bottom"] = True plt.rcParams["axes.spines.top"] = False plt.rcParams["axes.spines.right"] = False plt.rcParams["axes.prop_cycle"] = cycler( - 'color', [ - '#b4befe', - '#89b4fa', - '#74c7ec', - '#89dceb', - '#94e2d5', - '#a6e3a1', - '#f9e2af', - '#fab387', - '#eba0ac', - '#f38ba8', - '#cba6f7', - '#f5c2e7', - ]) + "color", + [ + "#b4befe", + "#89b4fa", + "#74c7ec", + "#89dceb", + "#94e2d5", + "#a6e3a1", + "#f9e2af", + "#fab387", + "#eba0ac", + "#f38ba8", + "#cba6f7", + "#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["figure.facecolor"] = black # figure face color - plt.rcParams["figure.edgecolor"] = black # figure edge 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 return style @@ -292,12 +297,11 @@ if __name__ == "__main__": s = PlotStyle() - import matplotlib.pyplot as plt + import matplotlib.cbook as cbook import matplotlib.cm as cm import matplotlib.pyplot as plt - import matplotlib.cbook as cbook - from matplotlib.path import Path from matplotlib.patches import PathPatch + from matplotlib.path import Path # Fixing random state for reproducibility np.random.seed(19680801) @@ -305,14 +309,20 @@ if __name__ == "__main__": delta = 0.025 x = y = np.arange(-3.0, 3.0, delta) X, Y = np.meshgrid(x, y) - Z1 = np.exp(-X**2 - Y**2) - Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2) + Z1 = np.exp(-(X**2) - Y**2) + Z2 = np.exp(-((X - 1) ** 2) - (Y - 1) ** 2) Z = (Z1 - Z2) * 2 fig1, ax = plt.subplots() - im = ax.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn, - origin='lower', extent=[-3, 3, -3, 3], - vmax=abs(Z).max(), vmin=-abs(Z).max()) + im = ax.imshow( + Z, + interpolation="bilinear", + cmap=cm.RdYlGn, + origin="lower", + extent=[-3, 3, -3, 3], + vmax=abs(Z).max(), + vmin=-abs(Z).max(), + ) plt.show() @@ -325,22 +335,21 @@ if __name__ == "__main__": all_data = [np.random.normal(0, std, 100) for std in range(6, 10)] # plot violin plot - axs[0].violinplot(all_data, - showmeans=False, - showmedians=True) - axs[0].set_title('Violin plot') + axs[0].violinplot(all_data, showmeans=False, showmedians=True) + axs[0].set_title("Violin plot") # plot box plot axs[1].boxplot(all_data) - axs[1].set_title('Box plot') + axs[1].set_title("Box plot") # adding horizontal grid lines for ax in axs: ax.yaxis.grid(True) - ax.set_xticks([y + 1 for y in range(len(all_data))], - labels=['x1', 'x2', 'x3', 'x4']) - ax.set_xlabel('Four separate samples') - ax.set_ylabel('Observed values') + ax.set_xticks( + [y + 1 for y in range(len(all_data))], labels=["x1", "x2", "x3", "x4"] + ) + ax.set_xlabel("Four separate samples") + ax.set_ylabel("Observed values") plt.show() @@ -352,24 +361,42 @@ if __name__ == "__main__": theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False) radii = 10 * np.random.rand(N) width = np.pi / 4 * np.random.rand(N) - colors = cmo.cm.haline(radii / 10.) + colors = cmo.cm.haline(radii / 10.0) - ax = plt.subplot(projection='polar') + ax = plt.subplot(projection="polar") ax.bar(theta, radii, width=width, bottom=0.0, color=colors, alpha=0.5) plt.show() - methods = [None, 'none', 'nearest', 'bilinear', 'bicubic', 'spline16', - 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', - 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos'] + methods = [ + None, + "none", + "nearest", + "bilinear", + "bicubic", + "spline16", + "spline36", + "hanning", + "hamming", + "hermite", + "kaiser", + "quadric", + "catrom", + "gaussian", + "bessel", + "mitchell", + "sinc", + "lanczos", + ] # Fixing random state for reproducibility np.random.seed(19680801) grid = np.random.rand(4, 4) - fig, axs = plt.subplots(nrows=3, ncols=6, figsize=(9, 6), - subplot_kw={'xticks': [], 'yticks': []}) + fig, axs = plt.subplots( + nrows=3, ncols=6, figsize=(9, 6), subplot_kw={"xticks": [], "yticks": []} + ) for ax, interp_method in zip(axs.flat, methods): ax.imshow(grid, interpolation=interp_method) diff --git a/code/plot_introduction_specs.py b/code/plot_introduction_specs.py index 3f8395e..20fb562 100644 --- a/code/plot_introduction_specs.py +++ b/code/plot_introduction_specs.py @@ -41,9 +41,9 @@ def main(): freqtime2, freq2 = instantaneous_frequency( filtered2, data.raw_rate, smoothing_window=3) - ax1.plot(freqtime1*timescaler, freq1, color=ps.gblue1, + ax1.plot(freqtime1*timescaler, freq1, color=ps.red, 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a/poster/main.tex b/poster/main.tex index e01ba65..1738024 100644 --- a/poster/main.tex +++ b/poster/main.tex @@ -7,7 +7,7 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val \begin{document} \renewcommand{\baselinestretch}{1} -\title{\parbox{1500pt}{Poster}} +\title{\parbox{1500pt}{Detection of transient communication signals in weakly electric fish}} \author{Sina Prause, Alexander Wendt, and Patrick Weygoldt} \institute{Supervised by Till Raab \& Jan Benda, Neuroethology Lab, University of Tuebingen} \usetitlestyle[]{sampletitle} @@ -21,17 +21,15 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val sender identification of freely interacting individuals impossible. This profoundly limits our current understanding of chirps to experiments with single - or physically separated - individuals. - \vspace{0.6cm} - \begin{tikzfigure}[] - \label{griddrawing} - \includegraphics[width=0.5\linewidth]{example-image-a} - \end{tikzfigure} + % \begin{tikzfigure}[] + % \label{griddrawing} + % \includegraphics[width=1\linewidth]{figs/introplot} + % \end{tikzfigure} } - \myblock[TranspBlock]{Chirp detection}{ \begin{tikzfigure}[] \label{fig:example_a} - \includegraphics[width=0.5\linewidth]{example-image-a} + \includegraphics[width=1\linewidth]{figs/algorithm} \end{tikzfigure} \vspace{0cm} } @@ -67,7 +65,7 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val \vspace{-1cm} } -\myblock[TranspBlock]{Conclusion}{ +\myblock[GrayBlock]{Conclusion}{ \begin{itemize} \setlength\itemsep{0.5em} \item Our analysis is the first to indicate that \textit{A. leptorhynchus} uses long, diffuse and synchronized EOD$f$ signals to communicate in addition to chirps and rises. diff --git a/poster/style.tex b/poster/style.tex index d627e22..0397831 100644 --- a/poster/style.tex +++ b/poster/style.tex @@ -1,9 +1,9 @@ \tikzposterlatexaffectionproofoff \usetheme{Default} -\definecolor{text}{HTML}{ffffff} -\definecolor{background}{HTML}{080808} -\definecolor{boxes}{HTML}{1E1E1E} +\definecolor{text}{HTML}{e0e4f7} +\definecolor{background}{HTML}{111116} +\definecolor{boxes}{HTML}{2a2a32} \definecolor{unired}{HTML}{a51e37} \colorlet{blocktitlefgcolor}{text} From 39b46221bdb3f1134dc7a55425685c78a0f8f7d2 Mon Sep 17 00:00:00 2001 From: wendtalexander Date: Tue, 24 Jan 2023 17:38:39 +0100 Subject: [PATCH 15/29] plot_event_timepline --- code/plot_event_timeline.py | 6 +++--- poster/figs/chirps_winner_loser.pdf | Bin 16896 -> 15522 bytes 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/code/plot_event_timeline.py b/code/plot_event_timeline.py index bb370b7..96f4e31 100644 --- a/code/plot_event_timeline.py +++ b/code/plot_event_timeline.py 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17 00:00:00 2001 From: weygoldt <88969563+weygoldt@users.noreply.github.com> Date: Tue, 24 Jan 2023 17:47:43 +0100 Subject: [PATCH 16/29] plot subset --- code/chirpdetection.py | 12 ++++++------ code/extract_chirps.py | 3 ++- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/code/chirpdetection.py b/code/chirpdetection.py index 1d67e39..541015d 100755 --- a/code/chirpdetection.py +++ b/code/chirpdetection.py @@ -102,7 +102,7 @@ class ChirpPlotBuffer: self.t0 = 0 fig = plt.figure( - figsize=(14 / 2.54, 20 / 2.54) + figsize=(14 * ps.cm, 18 * ps.cm) ) gs0 = gr.GridSpec( @@ -652,16 +652,16 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None: raw_time = np.arange(data.raw.shape[0]) / data.raw_rate # good chirp times for data: 2022-06-02-10_00 - window_start_index = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate - window_duration_index = 60 * data.raw_rate + # window_start_index = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate + # window_duration_index = 60 * data.raw_rate # t0 = 0 # dt = data.raw.shape[0] # window_start_seconds = (23495 + ((28336-23495)/3)) * data.raw_rate # window_duration_seconds = (28336 - 23495) * data.raw_rate - # window_start_index = 0 - # window_duration_index = data.raw.shape[0] + window_start_index = 0 + window_duration_index = data.raw.shape[0] # generate starting points of rolling window window_start_indices = np.arange( @@ -675,7 +675,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None: multiwindow_chirps = [] multiwindow_ids = [] - for st, window_start_index in enumerate(window_start_indices): + for st, window_start_index in enumerate(window_start_indices[3175:]): logger.info(f"Processing window {st+1} of {len(window_start_indices)}") diff --git a/code/extract_chirps.py b/code/extract_chirps.py index 61c8564..5d5580e 100644 --- a/code/extract_chirps.py +++ b/code/extract_chirps.py @@ -10,7 +10,7 @@ from IPython import embed def main(datapaths): for path in datapaths: - chirpdetection(path, plot='false') + chirpdetection(path, plot='show') if __name__ == '__main__': @@ -45,6 +45,7 @@ if __name__ == '__main__': recs = pd.DataFrame(columns=['recording'], data=valid_datasets) recs.to_csv('../recs.csv', index=False) + datapaths = ['../data/mount_data/2020-03-25-10_00/'] main(datapaths) # window 1524 + 244 in dataset index 4 is nice example From 7652663fb7fe392aa9cf8ddef0c89a9cbd588c41 Mon Sep 17 00:00:00 2001 From: sprause Date: Sat, 21 Jan 2023 17:36:05 +0100 Subject: [PATCH 17/29] setup --- code/behavior.py | 20 +++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git a/code/behavior.py b/code/behavior.py index 4b03d6e..73ddcc5 100644 --- a/code/behavior.py +++ b/code/behavior.py @@ -199,7 +199,25 @@ def main(datapath: str): plt.show() plt.close() - # comparison between chasing events with and without chirps + # comparison between chasing events with and without chirps + + ##### Chasing triggered chirps CTC ##### + # Evaluate how many chirps were emitted in specific time window around the chasing onset events + + # Goal: + # Plot with Chasing onsets centered at t = 0 on x-axis as a function of event type (0, 1, 2) (or later as a function of recordings) with chirps as rasterplot in background + + # Chasing onset is defined at the point event 'chasing' + # Iterate over chasing onsets (later over fish) + # Get chirps which in a time window of -5 to +5 seconds aroung the chasing onset and save them + # Set Chasing onset at timepoint 0: Chasing onset timestamp - chasing onset timestamp + # Calculate chirp timestamps relative to chasing onset: Chirp timestamp - Chasing onset timestamp + # For rasterplot look at plt.eventplot() function + # Do the plot + # Then same with physical onset events (PTC) + + + From a8171814dd05728567efad58afdf45a507caf1a5 Mon Sep 17 00:00:00 2001 From: sprause Date: Sun, 22 Jan 2023 21:19:14 +0100 Subject: [PATCH 18/29] Fish loop needs debugging --- code/behavior.py | 94 +++++++++++++++++++++++++++++++----------------- 1 file changed, 62 insertions(+), 32 deletions(-) diff --git a/code/behavior.py b/code/behavior.py index 73ddcc5..2e0eb3d 100644 --- a/code/behavior.py +++ b/code/behavior.py @@ -6,6 +6,7 @@ import matplotlib.pyplot as plt from IPython import embed from pandas import read_csv from modules.logger import makeLogger +from scipy.ndimage import gaussian_filter1d logger = makeLogger(__name__) @@ -106,7 +107,6 @@ def correct_chasing_events( 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)): @@ -123,6 +123,32 @@ def correct_chasing_events( return category, timestamps +def event_triggered_chirps( + event: np.ndarray, + chirps:np.ndarray, + time_before_event: int, + time_after_event: int + )-> tuple[np.ndarray, np.ndarray]: + + + + event_chirps = [] # chirps that are in specified window around event + centered_chirps = [] # timestamps of chirps around event centered on the event timepoint + + for event_timestamp in event: + start = event_timestamp - time_before_event # timepoint of window start + stop = event_timestamp + time_after_event # timepoint of window ending + chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)] # get chirps that are in a -5 to +5 sec window around event + event_chirps.append(chirps_around_event) + if len(chirps_around_event) == 0: + continue + else: + centered_chirps.append(chirps_around_event - event_timestamp) + centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting + + return event_chirps, centered_chirps + + def main(datapath: str): # behavior is pandas dataframe with all the data @@ -144,11 +170,6 @@ def main(datapath: str): chasing_offset = timestamps[category == 1] physical_contact = timestamps[category == 2] - ##### TODO Physical contact-triggered chirps (PTC) mit Rasterplot ##### - # Wahrscheinlichkeit von Phys auf Ch und vice versa - # Chasing-triggered chirps (CTC) mit Rasterplot - # Wahrscheinlichkeit von Chase auf Ch und vice versa - # First overview plot fig1, ax1 = plt.subplots() ax1.scatter(chirps, np.ones_like(chirps), marker='*', color='royalblue', label='Chirps') @@ -160,11 +181,41 @@ def main(datapath: str): plt.close() # Get fish ids - all_fish_ids = np.unique(chirps_fish_ids) + fish_ids = np.unique(chirps_fish_ids) + + ##### Chasing triggered chirps CTC ##### + # Evaluate how many chirps were emitted in specific time window around the chasing onset events + + # Iterate over chasing onsets (later over fish) + time_around_event = 5 # time window around the event in which chirps are counted, 5 = -5 to +5 sec around event + + #### Loop crashes at concatenate in function #### + for i in range(len(fish_ids)): + fish = fish_ids[i] + chirps = chirps[chirps_fish_ids == fish] + print(fish) + + chasing_chirps, centered_chasing_chirps = event_triggered_chirps(chasing_onset, chirps, time_around_event, time_around_event) + physical_chirps, centered_physical_chirps = event_triggered_chirps(physical_contact, chirps, time_around_event, time_around_event) + + # Kernel density estimation ??? + # centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5) + + # centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0 + offsets = [0.5, 1] + fig4, ax4 = plt.subplots(figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True) + ax4.eventplot(np.array([centered_chasing_chirps, centered_physical_chirps]), lineoffsets=offsets, linelengths=0.25, colors=['g', 'r']) + ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed', 'Timepoint of event') + # ax4.plot(centered_chasing_chirps_convolved) + ax4.set_yticks(offsets) + ax4.set_yticklabels(['Chasings', 'Physical \n contacts']) + ax4.set_xlabel('Time[s]') + ax4.set_ylabel('Type of event') + plt.show() # Associate chirps to inidividual fish - fish1 = chirps[chirps_fish_ids == all_fish_ids[0]] - fish2 = chirps[chirps_fish_ids == all_fish_ids[1]] + fish1 = chirps[chirps_fish_ids == fish_ids[0]] + fish2 = chirps[chirps_fish_ids == fish_ids[1]] fish = [len(fish1), len(fish2)] #### Chirp counts per fish general ##### @@ -196,36 +247,15 @@ def main(datapath: str): fig3 , ax3 = plt.subplots() ax3.bar(['Chirps in chasing events', 'Chasing events without Chirps'], [counts_chirps_chasings, chasings_without_chirps], width=width) plt.ylabel('Count') - plt.show() + # plt.show() plt.close() # comparison between chasing events with and without chirps - ##### Chasing triggered chirps CTC ##### - # Evaluate how many chirps were emitted in specific time window around the chasing onset events - - # Goal: - # Plot with Chasing onsets centered at t = 0 on x-axis as a function of event type (0, 1, 2) (or later as a function of recordings) with chirps as rasterplot in background - # Chasing onset is defined at the point event 'chasing' - # Iterate over chasing onsets (later over fish) - # Get chirps which in a time window of -5 to +5 seconds aroung the chasing onset and save them - # Set Chasing onset at timepoint 0: Chasing onset timestamp - chasing onset timestamp - # Calculate chirp timestamps relative to chasing onset: Chirp timestamp - Chasing onset timestamp - # For rasterplot look at plt.eventplot() function - # Do the plot - # Then same with physical onset events (PTC) - - - - - - - embed() - - + exit() From 5d8c41c8995e7bdacb4d89bd57784f9c436591c4 Mon Sep 17 00:00:00 2001 From: sprause Date: Mon, 23 Jan 2023 09:49:19 +0100 Subject: [PATCH 19/29] new plotfile for CTC and PTC --- code/behavior.py | 50 +++++---- code/eventchirpsplots.py | 225 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 253 insertions(+), 22 deletions(-) create mode 100644 code/eventchirpsplots.py diff --git a/code/behavior.py b/code/behavior.py index 2e0eb3d..bbfda4b 100644 --- a/code/behavior.py +++ b/code/behavior.py @@ -131,7 +131,6 @@ def event_triggered_chirps( )-> tuple[np.ndarray, np.ndarray]: - event_chirps = [] # chirps that are in specified window around event centered_chirps = [] # timestamps of chirps around event centered on the event timepoint @@ -188,36 +187,43 @@ def main(datapath: str): # Iterate over chasing onsets (later over fish) time_around_event = 5 # time window around the event in which chirps are counted, 5 = -5 to +5 sec around event - #### Loop crashes at concatenate in function #### - for i in range(len(fish_ids)): - fish = fish_ids[i] - chirps = chirps[chirps_fish_ids == fish] - print(fish) + # for i in range(len(fish_ids)): + # fish = fish_ids[i] + # chirps = chirps[chirps_fish_ids == fish] + # print(fish) - chasing_chirps, centered_chasing_chirps = event_triggered_chirps(chasing_onset, chirps, time_around_event, time_around_event) - physical_chirps, centered_physical_chirps = event_triggered_chirps(physical_contact, chirps, time_around_event, time_around_event) + chasing_chirps, centered_chasing_chirps = event_triggered_chirps(chasing_onset, chirps, time_around_event, time_around_event) + physical_chirps, centered_physical_chirps = event_triggered_chirps(physical_contact, chirps, time_around_event, time_around_event) - # Kernel density estimation ??? - # centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5) - - # centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0 - offsets = [0.5, 1] - fig4, ax4 = plt.subplots(figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True) - ax4.eventplot(np.array([centered_chasing_chirps, centered_physical_chirps]), lineoffsets=offsets, linelengths=0.25, colors=['g', 'r']) - ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed', 'Timepoint of event') - # ax4.plot(centered_chasing_chirps_convolved) - ax4.set_yticks(offsets) - ax4.set_yticklabels(['Chasings', 'Physical \n contacts']) - ax4.set_xlabel('Time[s]') - ax4.set_ylabel('Type of event') - plt.show() + # Kernel density estimation ??? + # centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5) + + # centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0 + offsets = [0.5, 1] + fig4, ax4 = plt.subplots(figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True) + ax4.eventplot(np.array([centered_chasing_chirps, centered_physical_chirps]), lineoffsets=offsets, linelengths=0.25, colors=['g', 'r']) + ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed', 'Timepoint of event') + # ax4.plot(centered_chasing_chirps_convolved) + ax4.set_yticks(offsets) + ax4.set_yticklabels(['Chasings', 'Physical \n contacts']) + ax4.set_xlabel('Time[s]') + ax4.set_ylabel('Type of event') + plt.show() # Associate chirps to inidividual fish fish1 = chirps[chirps_fish_ids == fish_ids[0]] fish2 = chirps[chirps_fish_ids == fish_ids[1]] fish = [len(fish1), len(fish2)] + ### Plots: + # 1. All recordings, all fish, all chirps + # One CTC, one PTC + # 2. All recordings, only winners + # One CTC, one PTC + # 3. All recordings, all losers + # One CTC, one PTC + #### Chirp counts per fish general ##### fig2, ax2 = plt.subplots() x = ['Fish1', 'Fish2'] diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py new file mode 100644 index 0000000..e0adb14 --- /dev/null +++ b/code/eventchirpsplots.py @@ -0,0 +1,225 @@ +import os + +import numpy as np +import matplotlib.pyplot as plt + +from IPython import embed +from pandas import read_csv +from modules.logger import makeLogger + +class Behavior: + """Load behavior data from csv file as class attributes + Attributes + ---------- + behavior: 0: chasing onset, 1: chasing offset, 2: physical contact + behavior_type: + behavioral_category: + comment_start: + comment_stop: + dataframe: pandas dataframe with all the data + duration_s: + media_file: + observation_date: + observation_id: + start_s: start time of the event in seconds + stop_s: stop time of the event in seconds + total_length: + """ + + 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) + + for k, key in enumerate(self.dataframe.keys()): + key = key.lower() + if ' ' in key: + key = key.replace(' ', '_') + if '(' in key: + 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 + shift = last_LED_t_BORIS - real_time_range * factor + self.start_s = (self.start_s - shift) / factor + self.stop_s = (self.stop_s - shift) / factor + + +""" +1 - chasing onset +2 - chasing offset +3 - physical contact event + +temporal encpding needs to be corrected ... not exactly 25FPS. + +### correspinding python code ### + + factor = 1.034141 + LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True) + last_LED_t_BORIS = LED_on_time_BORIS[-1] + real_time_range = times[-1] - times[0] + shift = last_LED_t_BORIS - real_time_range * factor + + data = pd.read_csv(os.path.join(folder_path, file[1:-7] + '.csv')) + boris_times = data['Start (s)'] + data_times = [] + + for Cevent_t in boris_times: + Cevent_boris_times = (Cevent_t - shift) / factor + data_times.append(Cevent_boris_times) + + data_times = np.array(data_times) + behavior = data['Behavior'] +""" + + +def correct_chasing_events( + category: np.ndarray, + timestamps: np.ndarray + ) -> tuple[np.ndarray, np.ndarray]: + + onset_ids = np.arange( + len(category))[category == 0] + offset_ids = np.arange( + len(category))[category == 1] + + # 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}') + 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 event_triggered_chirps( + event: np.ndarray, + chirps:np.ndarray, + time_before_event: int, + time_after_event: int + )-> tuple[np.ndarray, np.ndarray]: + + + event_chirps = [] # chirps that are in specified window around event + centered_chirps = [] # timestamps of chirps around event centered on the event timepoint + + for event_timestamp in event: + start = event_timestamp - time_before_event # timepoint of window start + stop = event_timestamp + time_after_event # timepoint of window ending + chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)] # get chirps that are in a -5 to +5 sec window around event + event_chirps.append(chirps_around_event) + if len(chirps_around_event) == 0: + continue + else: + centered_chirps.append(chirps_around_event - event_timestamp) + centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting + + return event_chirps, centered_chirps + + +def main(datapath: str): + + # behavior 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) + + # split categories + chasing_onset = timestamps[category == 0] + chasing_offset = timestamps[category == 1] + physical_contact = timestamps[category == 2] + + # Get fish ids + fish_ids = np.unique(chirps_fish_ids) + + ##### Chasing triggered chirps CTC ##### + # Evaluate how many chirps were emitted in specific time window around the chasing onset events + + # Iterate over chasing onsets (later over fish) + time_around_event = 5 # time window around the event in which chirps are counted, 5 = -5 to +5 sec around event + #### Loop crashes at concatenate in function #### + # for i in range(len(fish_ids)): + # fish = fish_ids[i] + # chirps = chirps[chirps_fish_ids == fish] + # print(fish) + + chasing_chirps, centered_chasing_chirps = event_triggered_chirps(chasing_onset, chirps, time_around_event, time_around_event) + physical_chirps, centered_physical_chirps = event_triggered_chirps(physical_contact, chirps, time_around_event, time_around_event) + + # Kernel density estimation ??? + # centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5) + + # centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0 + offsets = [0.5, 1] + fig4, ax4 = plt.subplots(figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True) + ax4.eventplot(np.array([centered_chasing_chirps, centered_physical_chirps]), lineoffsets=offsets, linelengths=0.25, colors=['g', 'r']) + ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed', 'Timepoint of event') + # ax4.plot(centered_chasing_chirps_convolved) + ax4.set_yticks(offsets) + ax4.set_yticklabels(['Chasings', 'Physical \n contacts']) + ax4.set_xlabel('Time[s]') + ax4.set_ylabel('Type of event') + plt.show() + + # Associate chirps to inidividual fish + fish1 = chirps[chirps_fish_ids == fish_ids[0]] + fish2 = chirps[chirps_fish_ids == fish_ids[1]] + fish = [len(fish1), len(fish2)] + + ### Plots: + # 1. All recordings, all fish, all chirps + # One CTC, one PTC + # 2. All recordings, only winners + # One CTC, one PTC + # 3. All recordings, all losers + # One CTC, one PTC + + + embed() + exit() + + + +if __name__ == '__main__': + # Path to the data + datapath = '../data/mount_data/2020-05-13-10_00/' + main(datapath) From 8b327fdcbf7b3d348ac8ff0c9392f82d9a684f88 Mon Sep 17 00:00:00 2001 From: sprause Date: Mon, 23 Jan 2023 10:01:24 +0100 Subject: [PATCH 20/29] start implementing gammakernel --- code/eventchirpsplots.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py index e0adb14..6fe90b1 100644 --- a/code/eventchirpsplots.py +++ b/code/eventchirpsplots.py @@ -6,6 +6,9 @@ import matplotlib.pyplot as plt from IPython import embed from pandas import read_csv from modules.logger import makeLogger +from modules.datahandling import causal_kde1d + +logger = makeLogger(__name__) class Behavior: """Load behavior data from csv file as class attributes From 5a0853a0232b07f6c93cd73d18211725938947a4 Mon Sep 17 00:00:00 2001 From: sprause Date: Mon, 23 Jan 2023 13:48:40 +0100 Subject: [PATCH 21/29] all event triggered chirps + chirprate with gaussian kernel --- code/eventchirpsplots.py | 122 +++++++++++++++++++++++++-------------- 1 file changed, 80 insertions(+), 42 deletions(-) diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py index 6fe90b1..7162387 100644 --- a/code/eventchirpsplots.py +++ b/code/eventchirpsplots.py @@ -6,7 +6,7 @@ import matplotlib.pyplot as plt from IPython import embed from pandas import read_csv from modules.logger import makeLogger -from modules.datahandling import causal_kde1d +from modules.datahandling import causal_kde1d, acausal_kde1d logger = makeLogger(__name__) @@ -129,17 +129,18 @@ def event_triggered_chirps( event: np.ndarray, chirps:np.ndarray, time_before_event: int, - time_after_event: int - )-> tuple[np.ndarray, np.ndarray]: - + time_after_event: int, + dt: float, + width: float, + )-> tuple[np.ndarray, np.ndarray, np.ndarray]: event_chirps = [] # chirps that are in specified window around event centered_chirps = [] # timestamps of chirps around event centered on the event timepoint for event_timestamp in event: - start = event_timestamp - time_before_event # timepoint of window start - stop = event_timestamp + time_after_event # timepoint of window ending - chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)] # get chirps that are in a -5 to +5 sec window around event + start = event_timestamp - time_before_event + stop = event_timestamp + time_after_event + chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)] event_chirps.append(chirps_around_event) if len(chirps_around_event) == 0: continue @@ -147,7 +148,11 @@ def event_triggered_chirps( centered_chirps.append(chirps_around_event - event_timestamp) centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting - return event_chirps, centered_chirps + # Kernel density estimation + time = np.arange(-time_before_event, time_after_event, dt) + centered_chirps_convolved = (acausal_kde1d(centered_chirps, time, width)) / len(event) + + return event_chirps, centered_chirps, centered_chirps_convolved def main(datapath: str): @@ -167,46 +172,79 @@ def main(datapath: str): category, timestamps = correct_chasing_events(category, timestamps) # split categories - chasing_onset = timestamps[category == 0] - chasing_offset = timestamps[category == 1] - physical_contact = timestamps[category == 2] + chasing_onsets = timestamps[category == 0] + chasing_offsets = timestamps[category == 1] + physical_contacts = timestamps[category == 2] + + 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) - ##### Chasing triggered chirps CTC ##### - # Evaluate how many chirps were emitted in specific time window around the chasing onset events - - # Iterate over chasing onsets (later over fish) - time_around_event = 5 # time window around the event in which chirps are counted, 5 = -5 to +5 sec around event - #### Loop crashes at concatenate in function #### - # for i in range(len(fish_ids)): - # fish = fish_ids[i] - # chirps = chirps[chirps_fish_ids == fish] - # print(fish) + # # 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)] - chasing_chirps, centered_chasing_chirps = event_triggered_chirps(chasing_onset, chirps, time_around_event, time_around_event) - physical_chirps, centered_physical_chirps = event_triggered_chirps(physical_contact, chirps, time_around_event, time_around_event) + # Define time window for chirp around event analysis + time_before_event = 30 + time_after_event = 60 + dt = 0.01 + width = 1 - # Kernel density estimation ??? - # centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5) - - # centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0 - offsets = [0.5, 1] - fig4, ax4 = plt.subplots(figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True) - ax4.eventplot(np.array([centered_chasing_chirps, centered_physical_chirps]), lineoffsets=offsets, linelengths=0.25, colors=['g', 'r']) - ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed', 'Timepoint of event') - # ax4.plot(centered_chasing_chirps_convolved) - ax4.set_yticks(offsets) - ax4.set_yticklabels(['Chasings', 'Physical \n contacts']) - ax4.set_xlabel('Time[s]') - ax4.set_ylabel('Type of event') - plt.show() - - # Associate chirps to inidividual fish - fish1 = chirps[chirps_fish_ids == fish_ids[0]] - fish2 = chirps[chirps_fish_ids == fish_ids[1]] - fish = [len(fish1), len(fish2)] + #### Loop crashes at concatenate in function #### + for i in range(len(fish_ids)): + fish = fish_ids[i] + chirps_temp = chirps[chirps_fish_ids == fish] + print(fish) + + ##### Chirps around events ##### + time = np.arange(-time_before_event, time_after_event, dt) + + # Chirps around chasing onsets + _, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps_temp, time_before_event, time_after_event, dt, width) + # Chirps around chasing offsets + _, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps_temp, time_before_event, time_after_event, dt, width) + # Chirps around physical contacts + _, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps_temp, time_before_event, time_after_event, dt, width) + + fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all') + offset = [0.25] + ax[0].set_xlabel('Time[s]') + # Plot chasing onsets + ax[0].set_ylabel('Chirp rate [Hz]') + ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue') + ax0 = ax[0].twinx() + ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green']) + ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + ax0.set_yticklabels([]) + ax0.set_yticks([]) + # Plot chasing offets + ax[1].set_xlabel('Time[s]') + ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue') + ax1 = ax[1].twinx() + ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple']) + ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + ax1.set_yticklabels([]) + ax1.set_yticks([]) + # Plot physical contacts + ax[2].set_xlabel('Time[s]') + ax[2].plot(time, cc_physical_chirps, color='tab:blue') + ax2 = ax[2].twinx() + ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red']) + ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + ax2.set_yticklabels([]) + ax2.set_yticks([]) + plt.show() ### Plots: # 1. All recordings, all fish, all chirps From b1b9d9d3f18a31c80c50df7f1f0b2fe851ae65ea Mon Sep 17 00:00:00 2001 From: sprause Date: Mon, 23 Jan 2023 15:58:22 +0100 Subject: [PATCH 22/29] implemented bootstrapping, q5 of physical still weird --- code/eventchirpsplots.py | 133 +++++++++++++++++++++++++-------------- 1 file changed, 85 insertions(+), 48 deletions(-) diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py index 7162387..0627f2d 100644 --- a/code/eventchirpsplots.py +++ b/code/eventchirpsplots.py @@ -195,64 +195,101 @@ def main(datapath: str): # fish2 = chirps[chirps_fish_ids == fish_ids[1]] # fish = [len(fish1), len(fish2)] - # Define time window for chirp around event analysis + # Define time window for chirps around event analysis time_before_event = 30 time_after_event = 60 dt = 0.01 width = 1 + time = np.arange(-time_before_event, time_after_event, dt) + + + ##### Chirps around events, all fish, one recording ##### + # Chirps around chasing onsets + _, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, width) + # Chirps around chasing offsets + _, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, width) + # Chirps around physical contacts + _, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, width) + + ## Shuffled chirps ## + nbootstrapping = 1000 + nshuffled_chirps_onset = [] + nshuffled_chirps_offset = [] + nshuffled_chirps_physical = [] + + for i in range(nbootstrapping): + # Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths + interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps)) + np.random.shuffle(interchirp_intervals) + shuffled_chirps = np.cumsum(interchirp_intervals) + # Shuffled chasing onset chirps + _, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, width) + nshuffled_chirps_onset.append(cc_shuffled_onset_chirps) + # Shuffled chasing offset chirps + _, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, width) + nshuffled_chirps_offset.append(cc_shuffled_offset_chirps) + # Shuffled physical contact chirps + _, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, width) + nshuffled_chirps_physical.append(cc_shuffled_physical_chirps) + + shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(nshuffled_chirps_onset, (5, 50, 95), axis=0) + shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(nshuffled_chirps_offset, (5, 50, 95), axis=0) + shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(nshuffled_chirps_physical, (5, 50, 95), axis=0) + + embed() + + # Plot all events with all shuffled + fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all') + offset = [1.35] + ax[0].set_xlabel('Time[s]') + # Plot chasing onsets + ax[0].set_ylabel('Chirp rate [Hz]') + ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue', zorder=100) + ax0 = ax[0].twinx() + ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100) + ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + ax0.set_yticklabels([]) + ax0.set_yticks([]) + ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color='tab:gray', alpha=0.5) + ax[0].plot(time, shuffled_median_onset, color='k') + # Plot chasing offets + ax[1].set_xlabel('Time[s]') + ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue', zorder=100) + ax1 = ax[1].twinx() + ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100) + ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + ax1.set_yticklabels([]) + ax1.set_yticks([]) + ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color='tab:gray', alpha=0.5) + ax[1].plot(time, shuffled_median_offset, color='k') + # Plot physical contacts + ax[2].set_xlabel('Time[s]') + ax[2].plot(time, cc_physical_chirps, color='tab:blue', zorder=100) + ax2 = ax[2].twinx() + ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100) + ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + ax2.set_yticklabels([]) + ax2.set_yticks([]) + ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color='tab:gray', alpha=0.5) + ax[2].plot(time, shuffled_median_physical, color='k') + plt.show() + + embed() + exit() + + + + + + - #### Loop crashes at concatenate in function #### + #### Chirps around events, only winners, one recording #### for i in range(len(fish_ids)): fish = fish_ids[i] chirps_temp = chirps[chirps_fish_ids == fish] print(fish) - ##### Chirps around events ##### - time = np.arange(-time_before_event, time_after_event, dt) - - # Chirps around chasing onsets - _, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps_temp, time_before_event, time_after_event, dt, width) - # Chirps around chasing offsets - _, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps_temp, time_before_event, time_after_event, dt, width) - # Chirps around physical contacts - _, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps_temp, time_before_event, time_after_event, dt, width) - - fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all') - offset = [0.25] - ax[0].set_xlabel('Time[s]') - # Plot chasing onsets - ax[0].set_ylabel('Chirp rate [Hz]') - ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue') - ax0 = ax[0].twinx() - ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green']) - ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed') - ax0.set_yticklabels([]) - ax0.set_yticks([]) - # Plot chasing offets - ax[1].set_xlabel('Time[s]') - ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue') - ax1 = ax[1].twinx() - ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple']) - ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed') - ax1.set_yticklabels([]) - ax1.set_yticks([]) - # Plot physical contacts - ax[2].set_xlabel('Time[s]') - ax[2].plot(time, cc_physical_chirps, color='tab:blue') - ax2 = ax[2].twinx() - ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red']) - ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed') - ax2.set_yticklabels([]) - ax2.set_yticks([]) - plt.show() - - ### Plots: - # 1. All recordings, all fish, all chirps - # One CTC, one PTC - # 2. All recordings, only winners - # One CTC, one PTC - # 3. All recordings, all losers - # One CTC, one PTC + #### Chirps around events, only losers, one recording #### embed() From a96a638c3198553da6111cb7af05223befa522df Mon Sep 17 00:00:00 2001 From: sprause Date: Mon, 23 Jan 2023 17:49:07 +0100 Subject: [PATCH 23/29] winner vs loser plot, winner physical strange --- code/eventchirpsplots.py | 186 +++++++++++++++++++++++++++------------ 1 file changed, 130 insertions(+), 56 deletions(-) diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py index 0627f2d..d638a31 100644 --- a/code/eventchirpsplots.py +++ b/code/eventchirpsplots.py @@ -1,6 +1,7 @@ import os import numpy as np +import pandas as pd import matplotlib.pyplot as plt from IPython import embed @@ -182,10 +183,10 @@ def main(datapath: str): duration = offset - onset chasing_durations.append(duration) - fig, ax = plt.subplots() - ax.boxplot(chasing_durations) - plt.show() - plt.close() + # fig, ax = plt.subplots() + # ax.boxplot(chasing_durations) + # plt.show() + # plt.close() # Get fish ids fish_ids = np.unique(chirps_fish_ids) @@ -217,73 +218,150 @@ def main(datapath: str): nshuffled_chirps_offset = [] nshuffled_chirps_physical = [] - for i in range(nbootstrapping): - # Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths - interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps)) - np.random.shuffle(interchirp_intervals) - shuffled_chirps = np.cumsum(interchirp_intervals) - # Shuffled chasing onset chirps - _, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, width) - nshuffled_chirps_onset.append(cc_shuffled_onset_chirps) - # Shuffled chasing offset chirps - _, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, width) - nshuffled_chirps_offset.append(cc_shuffled_offset_chirps) - # Shuffled physical contact chirps - _, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, width) - nshuffled_chirps_physical.append(cc_shuffled_physical_chirps) + # for i in range(nbootstrapping): + # # Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths + # interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps)) + # np.random.shuffle(interchirp_intervals) + # shuffled_chirps = np.cumsum(interchirp_intervals) + # # Shuffled chasing onset chirps + # _, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, width) + # nshuffled_chirps_onset.append(cc_shuffled_onset_chirps) + # # Shuffled chasing offset chirps + # _, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, width) + # nshuffled_chirps_offset.append(cc_shuffled_offset_chirps) + # # Shuffled physical contact chirps + # _, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, width) + # nshuffled_chirps_physical.append(cc_shuffled_physical_chirps) - shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(nshuffled_chirps_onset, (5, 50, 95), axis=0) - shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(nshuffled_chirps_offset, (5, 50, 95), axis=0) - shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(nshuffled_chirps_physical, (5, 50, 95), axis=0) - - embed() + # shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(nshuffled_chirps_onset, (5, 50, 95), axis=0) + # shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(nshuffled_chirps_offset, (5, 50, 95), axis=0) + # shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(nshuffled_chirps_physical, (5, 50, 95), axis=0) # Plot all events with all shuffled - fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all') + # fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all') + # offset = [1.35] + # ax[0].set_xlabel('Time[s]') + # # Plot chasing onsets + # ax[0].set_ylabel('Chirp rate [Hz]') + # ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue', zorder=100) + # ax0 = ax[0].twinx() + # ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100) + # ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + # ax0.set_yticklabels([]) + # ax0.set_yticks([]) + # ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color='tab:gray', alpha=0.5) + # ax[0].plot(time, shuffled_median_onset, color='k') + # # Plot chasing offets + # ax[1].set_xlabel('Time[s]') + # ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue', zorder=100) + # ax1 = ax[1].twinx() + # ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100) + # ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + # ax1.set_yticklabels([]) + # ax1.set_yticks([]) + # ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color='tab:gray', alpha=0.5) + # ax[1].plot(time, shuffled_median_offset, color='k') + # # Plot physical contacts + # ax[2].set_xlabel('Time[s]') + # ax[2].plot(time, cc_physical_chirps, color='tab:blue', zorder=100) + # ax2 = ax[2].twinx() + # ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100) + # ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + # ax2.set_yticklabels([]) + # ax2.set_yticks([]) + # ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color='tab:gray', alpha=0.5) + # ax[2].plot(time, shuffled_median_physical, color='k') + # plt.show() + # plt.close() + + + + #### 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) + + fig, ax = plt.subplots(2, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='row') offset = [1.35] - ax[0].set_xlabel('Time[s]') - # Plot chasing onsets - ax[0].set_ylabel('Chirp rate [Hz]') - ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue', zorder=100) - ax0 = ax[0].twinx() - ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100) + 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') ax0.set_yticklabels([]) ax0.set_yticks([]) - ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color='tab:gray', alpha=0.5) - ax[0].plot(time, shuffled_median_onset, color='k') - # Plot chasing offets - ax[1].set_xlabel('Time[s]') - ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue', zorder=100) - ax1 = ax[1].twinx() - ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100) + # 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') ax1.set_yticklabels([]) ax1.set_yticks([]) - ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color='tab:gray', alpha=0.5) - ax[1].plot(time, shuffled_median_offset, color='k') - # Plot physical contacts - ax[2].set_xlabel('Time[s]') - ax[2].plot(time, cc_physical_chirps, color='tab:blue', zorder=100) - ax2 = ax[2].twinx() - ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100) + # 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') ax2.set_yticklabels([]) ax2.set_yticks([]) - ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color='tab:gray', alpha=0.5) - ax[2].plot(time, shuffled_median_physical, color='k') + # 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') + 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') + 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') + ax5.set_yticklabels([]) + ax5.set_yticks([]) plt.show() + plt.close() + embed() exit() - - - - - + - #### Chirps around events, only winners, one recording #### for i in range(len(fish_ids)): fish = fish_ids[i] chirps_temp = chirps[chirps_fish_ids == fish] @@ -291,10 +369,6 @@ def main(datapath: str): #### Chirps around events, only losers, one recording #### - - embed() - exit() - if __name__ == '__main__': From ced7f6f1ad07b94f062864300e50c14501ca9ada Mon Sep 17 00:00:00 2001 From: sprause Date: Mon, 23 Jan 2023 18:03:01 +0100 Subject: [PATCH 24/29] something weird --- code/eventchirpsplots.py | 105 ++++++++++++++++++++------------------- 1 file changed, 53 insertions(+), 52 deletions(-) diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py index d638a31..1ab279e 100644 --- a/code/eventchirpsplots.py +++ b/code/eventchirpsplots.py @@ -218,61 +218,61 @@ def main(datapath: str): nshuffled_chirps_offset = [] nshuffled_chirps_physical = [] - # for i in range(nbootstrapping): - # # Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths - # interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps)) - # np.random.shuffle(interchirp_intervals) - # shuffled_chirps = np.cumsum(interchirp_intervals) - # # Shuffled chasing onset chirps - # _, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, width) - # nshuffled_chirps_onset.append(cc_shuffled_onset_chirps) - # # Shuffled chasing offset chirps - # _, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, width) - # nshuffled_chirps_offset.append(cc_shuffled_offset_chirps) - # # Shuffled physical contact chirps - # _, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, width) - # nshuffled_chirps_physical.append(cc_shuffled_physical_chirps) + for i in range(nbootstrapping): + # Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths + interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps)) + np.random.shuffle(interchirp_intervals) + shuffled_chirps = np.cumsum(interchirp_intervals) + # Shuffled chasing onset chirps + _, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, width) + nshuffled_chirps_onset.append(cc_shuffled_onset_chirps) + # Shuffled chasing offset chirps + _, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, width) + nshuffled_chirps_offset.append(cc_shuffled_offset_chirps) + # Shuffled physical contact chirps + _, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, width) + nshuffled_chirps_physical.append(cc_shuffled_physical_chirps) - # shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(nshuffled_chirps_onset, (5, 50, 95), axis=0) - # shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(nshuffled_chirps_offset, (5, 50, 95), axis=0) - # shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(nshuffled_chirps_physical, (5, 50, 95), axis=0) + shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(nshuffled_chirps_onset, (5, 50, 95), axis=0) + shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(nshuffled_chirps_offset, (5, 50, 95), axis=0) + shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(nshuffled_chirps_physical, (5, 50, 95), axis=0) # Plot all events with all shuffled - # fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all') - # offset = [1.35] - # ax[0].set_xlabel('Time[s]') - # # Plot chasing onsets - # ax[0].set_ylabel('Chirp rate [Hz]') - # ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue', zorder=100) - # ax0 = ax[0].twinx() - # ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100) - # ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed') - # ax0.set_yticklabels([]) - # ax0.set_yticks([]) - # ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color='tab:gray', alpha=0.5) - # ax[0].plot(time, shuffled_median_onset, color='k') - # # Plot chasing offets - # ax[1].set_xlabel('Time[s]') - # ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue', zorder=100) - # ax1 = ax[1].twinx() - # ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100) - # ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed') - # ax1.set_yticklabels([]) - # ax1.set_yticks([]) - # ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color='tab:gray', alpha=0.5) - # ax[1].plot(time, shuffled_median_offset, color='k') - # # Plot physical contacts - # ax[2].set_xlabel('Time[s]') - # ax[2].plot(time, cc_physical_chirps, color='tab:blue', zorder=100) - # ax2 = ax[2].twinx() - # ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100) - # ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed') - # ax2.set_yticklabels([]) - # ax2.set_yticks([]) - # ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color='tab:gray', alpha=0.5) - # ax[2].plot(time, shuffled_median_physical, color='k') - # plt.show() - # plt.close() + fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all') + offset = [1.35] + ax[0].set_xlabel('Time[s]') + # Plot chasing onsets + ax[0].set_ylabel('Chirp rate [Hz]') + ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue', zorder=100) + ax0 = ax[0].twinx() + ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100) + ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + ax0.set_yticklabels([]) + ax0.set_yticks([]) + ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color='tab:gray', alpha=0.5) + ax[0].plot(time, shuffled_median_onset, color='k') + # Plot chasing offets + ax[1].set_xlabel('Time[s]') + ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue', zorder=100) + ax1 = ax[1].twinx() + ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100) + ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + ax1.set_yticklabels([]) + ax1.set_yticks([]) + ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color='tab:gray', alpha=0.5) + ax[1].plot(time, shuffled_median_offset, color='k') + # Plot physical contacts + ax[2].set_xlabel('Time[s]') + ax[2].plot(time, cc_physical_chirps, color='tab:blue', zorder=100) + ax2 = ax[2].twinx() + ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100) + ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + ax2.set_yticklabels([]) + ax2.set_yticks([]) + ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color='tab:gray', alpha=0.5) + ax[2].plot(time, shuffled_median_physical, color='k') + plt.show() + plt.close() @@ -303,6 +303,7 @@ def main(datapath: str): _, 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 physical strange! ########## 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]') From f68630974fec8c9b09a661de36c585625a5fbb18 Mon Sep 17 00:00:00 2001 From: sprause Date: Tue, 24 Jan 2023 09:15:36 +0100 Subject: [PATCH 25/29] corrected zorder in plots, q5 and physical problems okay --- code/eventchirpsplots.py | 36 +++++++++++++++++++++++++++--------- 1 file changed, 27 insertions(+), 9 deletions(-) diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py index 1ab279e..3114d9e 100644 --- a/code/eventchirpsplots.py +++ b/code/eventchirpsplots.py @@ -243,36 +243,42 @@ def main(datapath: str): ax[0].set_xlabel('Time[s]') # Plot chasing onsets ax[0].set_ylabel('Chirp rate [Hz]') - ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue', zorder=100) + ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue', zorder=2) ax0 = ax[0].twinx() - ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100) + ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=1) ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + ax[0].set_zorder(ax0.get_zorder()+1) + ax[0].patch.set_visible(False) ax0.set_yticklabels([]) ax0.set_yticks([]) ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color='tab:gray', alpha=0.5) ax[0].plot(time, shuffled_median_onset, color='k') # Plot chasing offets ax[1].set_xlabel('Time[s]') - ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue', zorder=100) + ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue', zorder=2) ax1 = ax[1].twinx() - ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100) + ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=1) ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + ax[1].set_zorder(ax1.get_zorder()+1) + ax[1].patch.set_visible(False) ax1.set_yticklabels([]) ax1.set_yticks([]) ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color='tab:gray', alpha=0.5) ax[1].plot(time, shuffled_median_offset, color='k') # Plot physical contacts ax[2].set_xlabel('Time[s]') - ax[2].plot(time, cc_physical_chirps, color='tab:blue', zorder=100) + ax[2].plot(time, cc_physical_chirps, color='tab:blue', zorder=2) ax2 = ax[2].twinx() - ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100) + ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=1) ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + ax[2].set_zorder(ax2.get_zorder()+1) + ax[2].patch.set_visible(False) ax2.set_yticklabels([]) ax2.set_yticks([]) ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color='tab:gray', alpha=0.5) ax[2].plot(time, shuffled_median_physical, color='k') plt.show() - plt.close() + # plt.close() @@ -303,7 +309,7 @@ def main(datapath: str): _, 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 physical strange! ########## + ########## 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]') @@ -316,6 +322,8 @@ def main(datapath: str): 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 @@ -323,6 +331,8 @@ def main(datapath: str): 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 @@ -330,6 +340,8 @@ def main(datapath: str): 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 @@ -338,6 +350,8 @@ def main(datapath: str): 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 @@ -345,6 +359,8 @@ def main(datapath: str): 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 @@ -352,10 +368,12 @@ def main(datapath: str): 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() + # plt.close() embed() From fda6821211247c3d19d52217461eb8b9d8612002 Mon Sep 17 00:00:00 2001 From: sprause Date: Tue, 24 Jan 2023 11:07:52 +0100 Subject: [PATCH 26/29] implemented recording loop --- code/eventchirpsplots.py | 292 ++++++++++++++++++++------------------- 1 file changed, 152 insertions(+), 140 deletions(-) diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py index 3114d9e..c3b3d2f 100644 --- a/code/eventchirpsplots.py +++ b/code/eventchirpsplots.py @@ -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() @@ -48,14 +47,13 @@ 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 shift = last_LED_t_BORIS - real_time_range * factor self.start_s = (self.start_s - shift) / factor self.stop_s = (self.stop_s - shift) / factor - """ 1 - chasing onset @@ -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 = [] + + 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 + 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) - # 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) - - # split categories - chasing_onsets = timestamps[category == 0] - chasing_offsets = timestamps[category == 1] - physical_contacts = timestamps[category == 2] - - 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 + # #### 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() + # 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) From ad513f180ea0de757f8b294e2e4d80bbca52a206 Mon Sep 17 00:00:00 2001 From: sprause Date: Tue, 24 Jan 2023 15:22:40 +0100 Subject: [PATCH 27/29] implemented plots for all recordings incl bootstrapping, std is over 9000 --- code/eventchirpsplots.py | 250 ++++++++++++++++++++++++--------------- 1 file changed, 153 insertions(+), 97 deletions(-) diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py index c3b3d2f..986910a 100644 --- a/code/eventchirpsplots.py +++ b/code/eventchirpsplots.py @@ -4,12 +4,15 @@ import numpy as np import pandas as pd import matplotlib.pyplot as plt +from tqdm import tqdm from IPython import embed from pandas import read_csv from modules.logger import makeLogger +from modules.plotstyle import PlotStyle from modules.datahandling import causal_kde1d, acausal_kde1d logger = makeLogger(__name__) +ps = PlotStyle() class Behavior: """Load behavior data from csv file as class attributes @@ -31,7 +34,7 @@ class Behavior: """ def __init__(self, folder_path: str) -> None: - + print(f'{folder_path}') 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 @@ -137,11 +140,16 @@ def event_triggered_chirps( continue else: centered_chirps.append(chirps_around_event - event_timestamp) - centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting - - # Kernel density estimation + time = np.arange(-time_before_event, time_after_event, dt) - centered_chirps_convolved = (acausal_kde1d(centered_chirps, time, width)) / len(event) + + # Kernel density estimation with some if's + if len(centered_chirps) == 0: + centered_chirps = np.array([]) + centered_chirps_convolved = np.zeros(len(time)) + else: + centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting + centered_chirps_convolved = (acausal_kde1d(centered_chirps, time, width)) / len(event) return event_chirps, centered_chirps, centered_chirps_convolved @@ -150,12 +158,13 @@ def main(datapath: str): 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 = [] + nrecording_chirps = [] + nrecording_chirps_fish_ids = [] + nrecording_chasing_onsets = [] + nrecording_chasing_offsets = [] + nrecording_physicals = [] + # Iterate over all recordings and save chirp- and event-timestamps for folder in foldernames: # exclude folder with empty LED_on_time.npy if folder == '../data/mount_data/2020-05-12-10_00/': @@ -167,9 +176,9 @@ def main(datapath: str): category = bh.behavior timestamps = bh.start_s chirps = bh.chirps - all_chirps.append(chirps) + nrecording_chirps.append(chirps) chirps_fish_ids = bh.chirps_ids - all_chirps_fish_ids.append(chirps_fish_ids) + nrecording_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 @@ -178,120 +187,172 @@ def main(datapath: str): # Split categories chasing_onsets = timestamps[category == 0] - all_chasing_onsets.append(chasing_onsets) + nrecording_chasing_onsets.append(chasing_onsets) chasing_offsets = timestamps[category == 1] - all_chasing_offsets.append(chasing_offsets) + nrecording_chasing_offsets.append(chasing_offsets) physical_contacts = timestamps[category == 2] - all_physicals.append(physical_contacts) + nrecording_physicals.append(physical_contacts) - 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() - - - # # 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 dt = 0.01 - width = 1 + width = 1.5 # width of kernel, currently gaussian kernel time = np.arange(-time_before_event, time_after_event, dt) - - ##### Chirps around events, all fish, one recording ##### - # Chirps around chasing onsets - _, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, width) - # Chirps around chasing offsets - _, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, width) - # Chirps around physical contacts - _, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, width) - - ## Shuffled chirps ## - nbootstrapping = 1000 - nshuffled_chirps_onset = [] - nshuffled_chirps_offset = [] - nshuffled_chirps_physical = [] - - for i in range(nbootstrapping): - # Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths - interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps)) - np.random.shuffle(interchirp_intervals) - shuffled_chirps = np.cumsum(interchirp_intervals) - # Shuffled chasing onset chirps - _, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, width) - nshuffled_chirps_onset.append(cc_shuffled_onset_chirps) - # Shuffled chasing offset chirps - _, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, width) - nshuffled_chirps_offset.append(cc_shuffled_offset_chirps) - # Shuffled physical contact chirps - _, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, width) - nshuffled_chirps_physical.append(cc_shuffled_physical_chirps) + ##### Chirps around events, all fish, all recordings ##### + # Centered chirps per event type + nrecording_centered_onset_chirps = [] + nrecording_centered_offset_chirps = [] + nrecording_centered_physical_chirps = [] + # Bootstrapped chirps per recording and per event: 27[1000[n]] 27 recs, 1000 shuffles, n chirps + nrecording_shuffled_convolved_onset_chirps = [] + nrecording_shuffled_convolved_offset_chirps = [] + nrecording_shuffled_convolved_physical_chirps = [] + + nbootstrapping = 10 + + for i in range(len(nrecording_chirps)): + chirps = nrecording_chirps[i] + chasing_onsets = nrecording_chasing_onsets[i] + chasing_offsets = nrecording_chasing_offsets[i] + physical_contacts = nrecording_physicals[i] + + # Chirps around chasing onsets + _, centered_chasing_onset_chirps, _ = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, width) + # Chirps around chasing offsets + _, centered_chasing_offset_chirps, _ = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, width) + # Chirps around physical contacts + _, centered_physical_chirps, _ = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, width) + + nrecording_centered_onset_chirps.append(centered_chasing_onset_chirps) + nrecording_centered_offset_chirps.append(centered_chasing_offset_chirps) + nrecording_centered_physical_chirps.append(centered_physical_chirps) + + ## Shuffled chirps ## + nshuffled_onset_chirps = [] + nshuffled_offset_chirps = [] + nshuffled_physical_chirps = [] + + for i in tqdm(range(nbootstrapping)): + # Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths + interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps)) + np.random.shuffle(interchirp_intervals) + shuffled_chirps = np.cumsum(interchirp_intervals) + # Shuffled chasing onset chirps + _, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, width) + nshuffled_onset_chirps.append(cc_shuffled_onset_chirps) + # Shuffled chasing offset chirps + _, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, width) + nshuffled_offset_chirps.append(cc_shuffled_offset_chirps) + # Shuffled physical contact chirps + _, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, width) + nshuffled_physical_chirps.append(cc_shuffled_physical_chirps) + + nrecording_shuffled_convolved_onset_chirps.append(nshuffled_onset_chirps) + nrecording_shuffled_convolved_offset_chirps.append(nshuffled_offset_chirps) + nrecording_shuffled_convolved_physical_chirps.append(nshuffled_physical_chirps) + + # vstack um 1. Dim zu cutten + nrecording_shuffled_convolved_onset_chirps = np.vstack(nrecording_shuffled_convolved_onset_chirps) + nrecording_shuffled_convolved_offset_chirps = np.vstack(nrecording_shuffled_convolved_offset_chirps) + nrecording_shuffled_convolved_physical_chirps = np.vstack(nrecording_shuffled_convolved_physical_chirps) - shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(nshuffled_chirps_onset, (5, 50, 95), axis=0) - shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(nshuffled_chirps_offset, (5, 50, 95), axis=0) - shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(nshuffled_chirps_physical, (5, 50, 95), axis=0) + shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile( + nrecording_shuffled_convolved_onset_chirps, (5, 50, 95), axis=0) + shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile( + nrecording_shuffled_convolved_offset_chirps, (5, 50, 95), axis=0) + shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile( + nrecording_shuffled_convolved_physical_chirps, (5, 50, 95), axis=0) + + # Flatten all chirps + all_chirps = np.concatenate(nrecording_chirps).ravel() # not centered + + # Flatten event timestamps + all_onsets = np.concatenate(nrecording_chasing_onsets).ravel() # not centered + all_offsets = np.concatenate(nrecording_chasing_offsets).ravel() # not centered + all_physicals = np.concatenate(nrecording_physicals).ravel() # not centered + + # Flatten all chirps around events + all_onset_chirps = np.concatenate(nrecording_centered_onset_chirps).ravel() # centered + all_offset_chirps = np.concatenate(nrecording_centered_offset_chirps).ravel() # centered + all_physical_chirps = np.concatenate(nrecording_centered_physical_chirps).ravel() # centered + + # Convolute all chirps + # Divide by total number of each event over all recordings + all_onset_chirps_convolved = (acausal_kde1d(all_onset_chirps, time, width)) / len(all_onsets) + all_offset_chirps_convolved = (acausal_kde1d(all_offset_chirps, time, width)) / len(all_offsets) + all_physical_chirps_convolved = (acausal_kde1d(all_physical_chirps, time, width)) / len(all_physicals) + # Plot all events with all shuffled - fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all') - offset = [1.35] + fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all') + # offsets = np.arange(1,28,1) ax[0].set_xlabel('Time[s]') # Plot chasing onsets ax[0].set_ylabel('Chirp rate [Hz]') - ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue', zorder=2) + ax[0].plot(time, all_onset_chirps_convolved, color=ps.yellow, zorder=2) ax0 = ax[0].twinx() - ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=1) - ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + nrecording_centered_onset_chirps = np.asarray(nrecording_centered_onset_chirps, dtype=object) + ax0.eventplot(np.array(nrecording_centered_onset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1) + ax0.vlines(0, 0, 1.5, ps.black, 'dashed') ax[0].set_zorder(ax0.get_zorder()+1) ax[0].patch.set_visible(False) ax0.set_yticklabels([]) ax0.set_yticks([]) - ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color='tab:gray', alpha=0.5) - ax[0].plot(time, shuffled_median_onset, color='k') + ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color=ps.gray, alpha=0.5) + ax[0].plot(time, shuffled_median_onset, color=ps.black) # Plot chasing offets ax[1].set_xlabel('Time[s]') - ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue', zorder=2) + ax[1].plot(time, all_offset_chirps_convolved, color=ps.orange, zorder=2) ax1 = ax[1].twinx() - ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=1) - ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + nrecording_centered_offset_chirps = np.asarray(nrecording_centered_offset_chirps, dtype=object) + ax1.eventplot(np.array(nrecording_centered_offset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1) + ax1.vlines(0, 0, 1.5, ps.black, 'dashed') ax[1].set_zorder(ax1.get_zorder()+1) ax[1].patch.set_visible(False) ax1.set_yticklabels([]) ax1.set_yticks([]) - ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color='tab:gray', alpha=0.5) - ax[1].plot(time, shuffled_median_offset, color='k') + ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color=ps.gray, alpha=0.5) + ax[1].plot(time, shuffled_median_offset, color=ps.black) # Plot physical contacts ax[2].set_xlabel('Time[s]') - ax[2].plot(time, cc_physical_chirps, color='tab:blue', zorder=2) + ax[2].plot(time, all_physical_chirps_convolved, color=ps.maroon, zorder=2) ax2 = ax[2].twinx() - ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=1) - ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + nrecording_centered_physical_chirps = np.asarray(nrecording_centered_physical_chirps, dtype=object) + ax2.eventplot(np.array(nrecording_centered_physical_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1) + ax2.vlines(0, 0, 1.5, ps.black, 'dashed') ax[2].set_zorder(ax2.get_zorder()+1) ax[2].patch.set_visible(False) ax2.set_yticklabels([]) ax2.set_yticks([]) - ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color='tab:gray', alpha=0.5) - ax[2].plot(time, shuffled_median_physical, color='k') + ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color=ps.gray, alpha=0.5) + ax[2].plot(time, shuffled_median_physical, ps.black) plt.show() # plt.close() + + 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() + + + # # 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 # #### Chirps around events, winner VS loser, one recording #### @@ -386,17 +447,12 @@ def main(datapath: str): # ax5.set_yticks([]) # plt.show() # plt.close() - - - embed() - exit() - - for i in range(len(fish_ids)): - fish = fish_ids[i] - chirps_temp = chirps[chirps_fish_ids == fish] - print(fish) + # for i in range(len(fish_ids)): + # fish = fish_ids[i] + # chirps_temp = chirps[chirps_fish_ids == fish] + # print(fish) #### Chirps around events, only losers, one recording #### From 1a0770f86f0229fd18745bc9223751fa8fa617d6 Mon Sep 17 00:00:00 2001 From: sprause Date: Tue, 24 Jan 2023 15:49:17 +0100 Subject: [PATCH 28/29] implemented individual recording plots --- code/eventchirpsplots.py | 82 ++++++++++++++++++++++++++++++++++------ 1 file changed, 70 insertions(+), 12 deletions(-) diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py index 986910a..e1de80d 100644 --- a/code/eventchirpsplots.py +++ b/code/eventchirpsplots.py @@ -198,7 +198,8 @@ def main(datapath: str): time_before_event = 30 time_after_event = 60 dt = 0.01 - width = 1.5 # width of kernel, currently gaussian kernel + width = 1.5 # width of kernel for all recordings, currently gaussian kernel + recording_width = 1 # width of kernel for each recording time = np.arange(-time_before_event, time_after_event, dt) ##### Chirps around events, all fish, all recordings ##### @@ -220,11 +221,11 @@ def main(datapath: str): physical_contacts = nrecording_physicals[i] # Chirps around chasing onsets - _, centered_chasing_onset_chirps, _ = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, width) + _, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, recording_width) # Chirps around chasing offsets - _, centered_chasing_offset_chirps, _ = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, width) + _, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, recording_width) # Chirps around physical contacts - _, centered_physical_chirps, _ = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, width) + _, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, recording_width) nrecording_centered_onset_chirps.append(centered_chasing_onset_chirps) nrecording_centered_offset_chirps.append(centered_chasing_offset_chirps) @@ -235,20 +236,73 @@ def main(datapath: str): nshuffled_offset_chirps = [] nshuffled_physical_chirps = [] - for i in tqdm(range(nbootstrapping)): + for j in tqdm(range(nbootstrapping)): # Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps)) np.random.shuffle(interchirp_intervals) shuffled_chirps = np.cumsum(interchirp_intervals) # Shuffled chasing onset chirps - _, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, width) + _, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, recording_width) nshuffled_onset_chirps.append(cc_shuffled_onset_chirps) # Shuffled chasing offset chirps - _, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, width) + _, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, recording_width) nshuffled_offset_chirps.append(cc_shuffled_offset_chirps) # Shuffled physical contact chirps - _, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, width) + _, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, recording_width) nshuffled_physical_chirps.append(cc_shuffled_physical_chirps) + + rec_shuffled_q5_onset, rec_shuffled_median_onset, rec_shuffled_q95_onset = np.percentile( + nshuffled_onset_chirps, (5, 50, 95), axis=0) + rec_shuffled_q5_offset, rec_shuffled_median_offset, rec_shuffled_q95_offset = np.percentile( + nshuffled_offset_chirps, (5, 50, 95), axis=0) + rec_shuffled_q5_physical, rec_shuffled_median_physical, rec_shuffled_q95_physical = np.percentile( + nshuffled_physical_chirps, (5, 50, 95), axis=0) + + #### Recording plots #### + fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all') + ax[0].set_xlabel('Time[s]') + + # Plot chasing onsets + ax[0].set_ylabel('Chirp rate [Hz]') + ax[0].plot(time, cc_chasing_onset_chirps, color=ps.yellow, zorder=2) + ax0 = ax[0].twinx() + ax0.eventplot(centered_chasing_onset_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1) + ax0.vlines(0, 0, 1.5, ps.white, 'dashed') + ax[0].set_zorder(ax0.get_zorder()+1) + ax[0].patch.set_visible(False) + ax0.set_yticklabels([]) + ax0.set_yticks([]) + ax[0].fill_between(time, rec_shuffled_q5_onset, rec_shuffled_q95_onset, color=ps.gray, alpha=0.5) + ax[0].plot(time, rec_shuffled_median_onset, color=ps.black) + + # Plot chasing offets + ax[1].set_xlabel('Time[s]') + ax[1].plot(time, cc_chasing_offset_chirps, color=ps.orange, zorder=2) + ax1 = ax[1].twinx() + ax1.eventplot(centered_chasing_offset_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1) + ax1.vlines(0, 0, 1.5, ps.white, 'dashed') + ax[1].set_zorder(ax1.get_zorder()+1) + ax[1].patch.set_visible(False) + ax1.set_yticklabels([]) + ax1.set_yticks([]) + ax[1].fill_between(time, rec_shuffled_q5_offset, rec_shuffled_q95_offset, color=ps.gray, alpha=0.5) + ax[1].plot(time, rec_shuffled_median_offset, color=ps.black) + + # Plot physical contacts + ax[2].set_xlabel('Time[s]') + ax[2].plot(time, cc_physical_chirps, color=ps.maroon, zorder=2) + ax2 = ax[2].twinx() + ax2.eventplot(centered_physical_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1) + ax2.vlines(0, 0, 1.5, ps.white, 'dashed') + ax[2].set_zorder(ax2.get_zorder()+1) + ax[2].patch.set_visible(False) + ax2.set_yticklabels([]) + ax2.set_yticks([]) + ax[2].fill_between(time, rec_shuffled_q5_physical, rec_shuffled_q95_physical, color=ps.gray, alpha=0.5) + ax[2].plot(time, rec_shuffled_median_physical, ps.black) + fig.suptitle(f'Recording: {i}') + plt.show() + # plt.close() nrecording_shuffled_convolved_onset_chirps.append(nshuffled_onset_chirps) nrecording_shuffled_convolved_offset_chirps.append(nshuffled_offset_chirps) @@ -290,45 +344,49 @@ def main(datapath: str): fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all') # offsets = np.arange(1,28,1) ax[0].set_xlabel('Time[s]') + # Plot chasing onsets ax[0].set_ylabel('Chirp rate [Hz]') ax[0].plot(time, all_onset_chirps_convolved, color=ps.yellow, zorder=2) ax0 = ax[0].twinx() nrecording_centered_onset_chirps = np.asarray(nrecording_centered_onset_chirps, dtype=object) ax0.eventplot(np.array(nrecording_centered_onset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1) - ax0.vlines(0, 0, 1.5, ps.black, 'dashed') + ax0.vlines(0, 0, 1.5, ps.white, 'dashed') ax[0].set_zorder(ax0.get_zorder()+1) ax[0].patch.set_visible(False) ax0.set_yticklabels([]) ax0.set_yticks([]) ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color=ps.gray, alpha=0.5) ax[0].plot(time, shuffled_median_onset, color=ps.black) + # Plot chasing offets ax[1].set_xlabel('Time[s]') ax[1].plot(time, all_offset_chirps_convolved, color=ps.orange, zorder=2) ax1 = ax[1].twinx() nrecording_centered_offset_chirps = np.asarray(nrecording_centered_offset_chirps, dtype=object) ax1.eventplot(np.array(nrecording_centered_offset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1) - ax1.vlines(0, 0, 1.5, ps.black, 'dashed') + ax1.vlines(0, 0, 1.5, ps.white, 'dashed') ax[1].set_zorder(ax1.get_zorder()+1) ax[1].patch.set_visible(False) ax1.set_yticklabels([]) ax1.set_yticks([]) ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color=ps.gray, alpha=0.5) ax[1].plot(time, shuffled_median_offset, color=ps.black) + # Plot physical contacts ax[2].set_xlabel('Time[s]') ax[2].plot(time, all_physical_chirps_convolved, color=ps.maroon, zorder=2) ax2 = ax[2].twinx() nrecording_centered_physical_chirps = np.asarray(nrecording_centered_physical_chirps, dtype=object) ax2.eventplot(np.array(nrecording_centered_physical_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1) - ax2.vlines(0, 0, 1.5, ps.black, 'dashed') + ax2.vlines(0, 0, 1.5, ps.white, 'dashed') ax[2].set_zorder(ax2.get_zorder()+1) ax[2].patch.set_visible(False) ax2.set_yticklabels([]) ax2.set_yticks([]) ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color=ps.gray, alpha=0.5) ax[2].plot(time, shuffled_median_physical, ps.black) + fig.suptitle('All recordings') plt.show() # plt.close() @@ -351,7 +409,7 @@ def main(datapath: str): # fish2 = chirps[chirps_fish_ids == fish_ids[1]] # fish = [len(fish1), len(fish2)] - # Concolution over all recordings + # Convolution over all recordings # Rasterplot for each recording From d629948c5249ebc8f341ea78866df39338dc9b5a Mon Sep 17 00:00:00 2001 From: sprause Date: Tue, 24 Jan 2023 17:31:28 +0100 Subject: [PATCH 29/29] adjusted kernel width slightly --- code/eventchirpsplots.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py index e1de80d..969b131 100644 --- a/code/eventchirpsplots.py +++ b/code/eventchirpsplots.py @@ -199,7 +199,7 @@ def main(datapath: str): time_after_event = 60 dt = 0.01 width = 1.5 # width of kernel for all recordings, currently gaussian kernel - recording_width = 1 # width of kernel for each recording + recording_width = 2 # width of kernel for each recording time = np.arange(-time_before_event, time_after_event, dt) ##### Chirps around events, all fish, all recordings #####