From ce560bf9397dc3b54060246eb93ff782e4eb702c Mon Sep 17 00:00:00 2001 From: wendtalexander Date: Tue, 24 Jan 2023 12:06:29 +0100 Subject: [PATCH] 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)