diff --git a/.gitignore b/.gitignore index 4ec96d9..3c2ae5f 100644 --- a/.gitignore +++ b/.gitignore @@ -1,9 +1,10 @@ -# Created by https://www.toptal.com/developers/gitignore/api/python,visualstudiocode +# Created by https://www.toptal.com/developers/gitignore/api/python,visualstudiocode # Edit at https://www.toptal.com/developers/gitignore?templates=python,visualstudiocode # Own stuff data env +output # Mac Stuff *.DS_Store @@ -13,6 +14,7 @@ env __pycache__/ *.py[cod] *$py.class +poster/main.pdf # C extensions *.so diff --git a/README.md b/README.md index 24a0519..959d09a 100644 --- a/README.md +++ b/README.md @@ -1,64 +1,248 @@ -# Chirp detection - GP2023 -## Git-Repository and commands - -- Go to the [Bendalab Git-Server](https://whale.am28.uni-tuebingen.de/git/) (https://whale.am28.uni-tuebingen.de/git/) -- Create your own account (and tell me ;D) - * I'll invite you the repository -- Clone the repository -- -```sh -git clone https://whale.am28.uni-tuebingen.de/git/raab/GP2023_chirp_detection.git -``` - -## Basic git commands - -- pull changes in git -```shell -git pull origin -``` -- commit chances -```shell -git commit -m '' file # commit one file -git commit -a -m '' # commit all files -``` -- push commits -```shell -git push origin -``` - -## Branches -Use branches to work on specific topics (e.g. 'algorithm', 'analysis', 'writing', ore even more specific ones) and merge -them into Master-Branch when it works are up to your expectations. - -The "master" branch should always contain a working/correct version of your project. - -- Create/change into branches -```shell -# list all branches (highlight active branch) -git banch -a -# switch into existing -git checkout -# switch into new branch -git checkout master -git checkout -b -``` - - -- Re-merging with master branch -1) get current version of master and implement it into branch -```shell -git checkout master -git pull origin master -git checkout -git rebase master -``` -This resets you branch to the fork-point, executes all commits of the current master before adding the commits of you -branch. You may have to resolve potential conflicts. Afterwards commit the corrected version and push it to your branch. - -2) Update master branch master -- correct way: Create -```shell -git checkout master -git merge -git push origin master -``` + + + + + + + + + + + + + + + + + + +
+
+ + Logo + + +

chirpdetector

+ +

+ An algorithm to detect the chirps of weakly electric fish. +
+ Explore the docs » +
+
+ View Demo + · + Report Bug + · + Request Feature +

+
+ + + + +
+ Table of Contents +
    +
  1. + About The Project + +
    2. +
  2. + Getting Started + +
    3. +
  3. Usage
    4. +
  4. Roadmap
    5. +
  5. Contributing
    6. +
  6. License
    7. +
  7. Contact
    8. +
  8. Acknowledgments
    9. +
+
+ + + + +## About The Project + +[![Product Name Screen Shot][product-screenshot]](https://example.com) + +Here's a blank template to get started: To avoid retyping too much info. Do a search and replace with your text editor for the following: `github_username`, `repo_name`, `twitter_handle`, `linkedin_username`, `email_client`, `email`, `project_title`, `project_description` + +

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+ + + + + + + + + + + + + + +

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+ + + + +## Getting Started + +This is an example of how you may give instructions on setting up your project locally. +To get a local copy up and running follow these simple example steps. + + + + + + + + + +### Installation + +1. Get a free API Key at [https://example.com](https://example.com) +2. Clone the repo + ```sh + git clone https://github.com/github_username/repo_name.git + ``` +3. Install NPM packages + ```sh + npm install + ``` +4. Enter your API in `config.js` + ```js + const API_KEY = 'ENTER YOUR API'; + ``` + +

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+ + + + +## Usage + +Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources. + +_For more examples, please refer to the [Documentation](https://example.com)_ + +

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+ + + + +## Roadmap + +- [ ] Feature 1 +- [ ] Feature 2 +- [ ] Feature 3 + - [ ] Nested Feature + +See the [open issues](https://github.com/github_username/repo_name/issues) for a full list of proposed features (and known issues). + +

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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +## Contact + +Your Name - [@twitter_handle](https://twitter.com/twitter_handle) - email@email_client.com + +Project Link: [https://github.com/github_username/repo_name](https://github.com/github_username/repo_name) + +

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+ + + + +## Acknowledgments + +* []() +* []() +* []() + +

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+ + + + + +[contributors-shield]: https://img.shields.io/github/contributors/github_username/repo_name.svg?style=for-the-badge +[contributors-url]: https://github.com/github_username/repo_name/graphs/contributors +[forks-shield]: https://img.shields.io/github/forks/github_username/repo_name.svg?style=for-the-badge +[forks-url]: https://github.com/github_username/repo_name/network/members +[stars-shield]: https://img.shields.io/github/stars/github_username/repo_name.svg?style=for-the-badge +[stars-url]: https://github.com/github_username/repo_name/stargazers +[issues-shield]: https://img.shields.io/github/issues/github_username/repo_name.svg?style=for-the-badge +[issues-url]: https://github.com/github_username/repo_name/issues +[license-shield]: https://img.shields.io/github/license/github_username/repo_name.svg?style=for-the-badge +[license-url]: https://github.com/github_username/repo_name/blob/master/LICENSE.txt +[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555 +[linkedin-url]: https://linkedin.com/in/linkedin_username +[product-screenshot]: images/screenshot.png +[Next.js]: https://img.shields.io/badge/next.js-000000?style=for-the-badge&logo=nextdotjs&logoColor=white +[Next-url]: https://nextjs.org/ +[React.js]: https://img.shields.io/badge/React-20232A?style=for-the-badge&logo=react&logoColor=61DAFB +[React-url]: https://reactjs.org/ +[Vue.js]: https://img.shields.io/badge/Vue.js-35495E?style=for-the-badge&logo=vuedotjs&logoColor=4FC08D +[Vue-url]: https://vuejs.org/ +[Angular.io]: https://img.shields.io/badge/Angular-DD0031?style=for-the-badge&logo=angular&logoColor=white +[Angular-url]: https://angular.io/ +[Svelte.dev]: https://img.shields.io/badge/Svelte-4A4A55?style=for-the-badge&logo=svelte&logoColor=FF3E00 +[Svelte-url]: https://svelte.dev/ +[Laravel.com]: https://img.shields.io/badge/Laravel-FF2D20?style=for-the-badge&logo=laravel&logoColor=white +[Laravel-url]: https://laravel.com +[Bootstrap.com]: https://img.shields.io/badge/Bootstrap-563D7C?style=for-the-badge&logo=bootstrap&logoColor=white +[Bootstrap-url]: https://getbootstrap.com +[JQuery.com]: https://img.shields.io/badge/jQuery-0769AD?style=for-the-badge&logo=jquery&logoColor=white +[JQuery-url]: https://jquery.com + diff --git a/README1.md b/README1.md new file mode 100644 index 0000000..24a0519 --- /dev/null +++ b/README1.md @@ -0,0 +1,64 @@ +# Chirp detection - GP2023 +## Git-Repository and commands + +- Go to the [Bendalab Git-Server](https://whale.am28.uni-tuebingen.de/git/) (https://whale.am28.uni-tuebingen.de/git/) +- Create your own account (and tell me ;D) + * I'll invite you the repository +- Clone the repository +- +```sh +git clone https://whale.am28.uni-tuebingen.de/git/raab/GP2023_chirp_detection.git +``` + +## Basic git commands + +- pull changes in git +```shell +git pull origin +``` +- commit chances +```shell +git commit -m '' file # commit one file +git commit -a -m '' # commit all files +``` +- push commits +```shell +git push origin +``` + +## Branches +Use branches to work on specific topics (e.g. 'algorithm', 'analysis', 'writing', ore even more specific ones) and merge +them into Master-Branch when it works are up to your expectations. + +The "master" branch should always contain a working/correct version of your project. + +- Create/change into branches +```shell +# list all branches (highlight active branch) +git banch -a +# switch into existing +git checkout +# switch into new branch +git checkout master +git checkout -b +``` + + +- Re-merging with master branch +1) get current version of master and implement it into branch +```shell +git checkout master +git pull origin master +git checkout +git rebase master +``` +This resets you branch to the fork-point, executes all commits of the current master before adding the commits of you +branch. You may have to resolve potential conflicts. Afterwards commit the corrected version and push it to your branch. + +2) Update master branch master +- correct way: Create +```shell +git checkout master +git merge +git push origin master +``` diff --git a/assets/logo.png b/assets/logo.png new file mode 100644 index 0000000..234652f Binary files /dev/null and b/assets/logo.png differ diff --git a/assets/logo.svg b/assets/logo.svg new file mode 100644 index 0000000..b34ed6c --- /dev/null +++ b/assets/logo.svg @@ -0,0 +1,1184 @@ + + + + diff --git a/code/behavior.py b/code/behavior.py index da02838..71c0926 100644 --- a/code/behavior.py +++ b/code/behavior.py @@ -1,16 +1,21 @@ -from pathlib import Path +import os +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 +from scipy.ndimage import gaussian_filter1d - - +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: @@ -20,23 +25,37 @@ class Behavior: media_file: observation_date: observation_id: - start_s: - stop_s: + start_s: start time of the event in seconds + stop_s: stop time of the event in seconds total_length: """ - def __init__(self, datapath: str) -> None: - csv_file = str(sorted(Path(datapath).glob('**/*.csv'))[0]) - self.dataframe = read_csv(csv_file, delimiter=',') - for key in self.dataframe: - if ' ' in key: - new_key = key.replace(' ', '_') - if '(' in new_key: - new_key = new_key.replace('(', '') - new_key = new_key.replace(')', '') - new_key = new_key.lower() - setattr(self, new_key, np.array(self.dataframe[key])) + 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 @@ -64,12 +83,191 @@ temporal encpding needs to be corrected ... not exactly 25FPS. 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): - # behabvior is pandas dataframe with all the data - behavior = Behavior(datapath) + + # 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] + + # First overview plot + fig1, ax1 = plt.subplots() + ax1.scatter(chirps, np.ones_like(chirps), marker='*', color='royalblue', label='Chirps') + ax1.scatter(chasing_onset, np.ones_like(chasing_onset)*2, marker='.', color='forestgreen', label='Chasing onset') + ax1.scatter(chasing_offset, np.ones_like(chasing_offset)*2.5, marker='.', color='firebrick', label='Chasing offset') + ax1.scatter(physical_contact, np.ones_like(physical_contact)*3, marker='x', color='black', label='Physical contact') + plt.legend() + # 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) + + 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 + + #### Chirp counts per fish general ##### + fig2, ax2 = plt.subplots() + x = ['Fish1', 'Fish2'] + width = 0.35 + ax2.bar(x, fish, width=width) + ax2.set_ylabel('Chirp count') + # plt.show() + plt.close() + + + ##### Count chirps emitted during chasing events and chirps emitted out of chasing events ##### + chirps_in_chasings = [] + for onset, offset in zip(chasing_onset, chasing_offset): + chirps_in_chasing = [c for c in chirps if (c > onset) & (c < offset)] + chirps_in_chasings.append(chirps_in_chasing) + + # chirps out of chasing events + counts_chirps_chasings = 0 + chasings_without_chirps = 0 + for i in chirps_in_chasings: + if i: + chasings_without_chirps += 1 + else: + counts_chirps_chasings += 1 + + # chirps in chasing events + 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.close() + + # comparison between chasing events with and without chirps + + + + embed() + exit() + if __name__ == '__main__': # Path to the data - datapath = '../data/mount_data/2020-03-13-10_00/' + datapath = '../data/mount_data/2020-05-13-10_00/' + datapath = '../data/mount_data/2020-05-13-10_00/' main(datapath) diff --git a/code/chirpdetection.py b/code/chirpdetection.py old mode 100644 new mode 100755 index ad733da..541015d --- a/code/chirpdetection.py +++ b/code/chirpdetection.py @@ -1,67 +1,313 @@ -import os +from itertools import compress +from dataclasses import dataclass import numpy as np from IPython import embed import matplotlib.pyplot as plt +import matplotlib.gridspec as gr from scipy.signal import find_peaks -from scipy.ndimage import gaussian_filter1d -from thunderfish.dataloader import DataLoader from thunderfish.powerspectrum import spectrogram, decibel from sklearn.preprocessing import normalize from modules.filters import bandpass_filter, envelope, highpass_filter -from modules.filehandling import ConfLoader, LoadData +from modules.filehandling import ConfLoader, LoadData, make_outputdir from modules.plotstyle import PlotStyle +from modules.logger import makeLogger +from modules.datahandling import ( + flatten, + purge_duplicates, + group_timestamps, + instantaneous_frequency, + minmaxnorm +) + +logger = makeLogger(__name__) ps = PlotStyle() -def instantaneos_frequency( - signal: np.ndarray, samplerate: int -) -> tuple[np.ndarray, np.ndarray]: - """ - Compute the instantaneous frequency of a signal. - - Parameters - ---------- - signal : np.ndarray - Signal to compute the instantaneous frequency from. - samplerate : int - Samplerate of the signal. - - Returns - ------- - tuple[np.ndarray, np.ndarray] +@dataclass +class ChirpPlotBuffer: """ - # calculate instantaneos frequency with zero crossings - roll_signal = np.roll(signal, shift=1) - time_signal = np.arange(len(signal)) / samplerate - period_index = np.arange(len(signal))[( - roll_signal < 0) & (signal >= 0)][1:-1] - - upper_bound = np.abs(signal[period_index]) - lower_bound = np.abs(signal[period_index - 1]) - upper_time = np.abs(time_signal[period_index]) - lower_time = np.abs(time_signal[period_index - 1]) - - # create ratio - lower_ratio = lower_bound / (lower_bound + upper_bound) + Buffer to save data that is created in the main detection loop + and plot it outside the detecion loop. + """ - # appy to time delta - time_delta = upper_time - lower_time - true_zero = lower_time + lower_ratio * time_delta + config: ConfLoader + t0: float + dt: float + track_id: float + electrode: int + data: LoadData + + time: np.ndarray + baseline: np.ndarray + baseline_envelope_unfiltered: np.ndarray + baseline_envelope: np.ndarray + baseline_peaks: np.ndarray + search_frequency: float + search: np.ndarray + search_envelope_unfiltered: np.ndarray + search_envelope: np.ndarray + search_peaks: np.ndarray + + frequency_time: np.ndarray + frequency: np.ndarray + frequency_filtered: np.ndarray + frequency_peaks: np.ndarray + + def plot_buffer(self, chirps: np.ndarray, plot: str) -> None: + + logger.debug("Starting plotting") + + # make data for plotting + + # get index of track data in this time window + window_idx = np.arange(len(self.data.idx))[ + (self.data.ident == self.track_id) + & (self.data.time[self.data.idx] >= self.t0) + & (self.data.time[self.data.idx] <= (self.t0 + self.dt)) + ] + + # get tracked frequencies and their times + freq_temp = self.data.freq[window_idx] + # time_temp = self.data.time[ + # self.data.idx[self.data.ident == self.track_id]][ + # (self.data.time >= self.t0) + # & (self.data.time <= (self.t0 + self.dt)) + # ] + + # remake the band we filtered in + q25, q50, q75 = np.percentile(freq_temp, [25, 50, 75]) + search_upper, search_lower = ( + q50 + self.search_frequency + self.config.minimal_bandwidth / 2, + q50 + self.search_frequency - self.config.minimal_bandwidth / 2, + ) + print(search_upper, search_lower) + + # get indices on raw data + start_idx = (self.t0 - 5) * self.data.raw_rate + window_duration = (self.dt + 10) * self.data.raw_rate + stop_idx = start_idx + window_duration + + # get raw data + data_oi = self.data.raw[start_idx:stop_idx, self.electrode] + + self.time = self.time - self.t0 + self.frequency_time = self.frequency_time - self.t0 + if len(chirps) > 0: + chirps = np.asarray(chirps) - self.t0 + self.t0_old = self.t0 + self.t0 = 0 + + fig = plt.figure( + figsize=(14 * ps.cm, 18 * ps.cm) + ) + + gs0 = gr.GridSpec( + 3, 1, figure=fig, height_ratios=[1, 1, 1] + ) + gs1 = gs0[0].subgridspec(1, 1) + gs2 = gs0[1].subgridspec(3, 1, hspace=0.4) + gs3 = gs0[2].subgridspec(3, 1, hspace=0.4) + # gs4 = gs0[5].subgridspec(1, 1) + + ax6 = fig.add_subplot(gs3[2, 0]) + ax0 = fig.add_subplot(gs1[0, 0], sharex=ax6) + ax1 = fig.add_subplot(gs2[0, 0], sharex=ax6) + ax2 = fig.add_subplot(gs2[1, 0], sharex=ax6) + ax3 = fig.add_subplot(gs2[2, 0], sharex=ax6) + ax4 = fig.add_subplot(gs3[0, 0], sharex=ax6) + ax5 = fig.add_subplot(gs3[1, 0], sharex=ax6) + # ax7 = fig.add_subplot(gs4[0, 0], sharex=ax0) + + # ax_leg = fig.add_subplot(gs0[1, 0]) + + waveform_scaler = 1000 + lw = 1.5 + + # plot spectrogram + _ = plot_spectrogram( + ax0, + data_oi, + self.data.raw_rate, + self.t0 - 5, + [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: + + t0_track = self.t0_old - 5 + dt_track = self.dt + 10 + window_idx = np.arange(len(self.data.idx))[ + (self.data.ident == track_id) + & (self.data.time[self.data.idx] >= t0_track) + & (self.data.time[self.data.idx] <= (t0_track + dt_track)) + ] - # create new time array - inst_freq_time = true_zero[:-1] + 0.5 * np.diff(true_zero) + # get tracked frequencies and their times + f = self.data.freq[window_idx] + # t = self.data.time[ + # self.data.idx[self.data.ident == self.track_id]] + # tmask = (t >= t0_track) & (t <= (t0_track + dt_track)) + t = self.data.time[self.data.idx[window_idx]] + if track_id == self.track_id: + ax0.plot(t-self.t0_old, f, lw=lw, + zorder=10, color=ps.gblue1) + else: + ax0.plot(t-self.t0_old, f, lw=lw, + 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.axhline(q50, spec_times[0], spec_times[-1], + # color=ps.gblue1, lw=2, ls="dashed") + # ax0.axhline(q50 + self.search_frequency, + # spec_times[0], spec_times[-1], + # color=ps.gblue2, lw=2, ls="dashed") + + if len(chirps) > 0: + for chirp in chirps: + ax0.scatter( + chirp, np.median(self.frequency), c=ps.red, marker=".", + edgecolors=ps.red, + facecolors=ps.red, + zorder=10, + s=70, + ) - # compute frequency - inst_freq = gaussian_filter1d(1 / np.diff(true_zero), 5) + # plot waveform of filtered signal + ax1.plot(self.time, self.baseline * waveform_scaler, + c=ps.gray, lw=lw, alpha=0.5) + ax1.plot(self.time, self.baseline_envelope_unfiltered * + waveform_scaler, c=ps.gblue1, lw=lw, label="baseline envelope") + + # plot waveform of filtered search signal + ax2.plot(self.time, self.search * waveform_scaler, + c=ps.gray, lw=lw, alpha=0.5) + ax2.plot(self.time, self.search_envelope_unfiltered * + waveform_scaler, c=ps.gblue2, lw=lw, label="search envelope") + + # plot baseline instantaneous frequency + ax3.plot(self.frequency_time, self.frequency, + c=ps.gblue3, lw=lw, label="baseline inst. freq.") + + # plot filtered and rectified envelope + ax4.plot(self.time, self.baseline_envelope * + waveform_scaler, c=ps.gblue1, lw=lw) + ax4.scatter( + (self.time)[self.baseline_peaks], + (self.baseline_envelope*waveform_scaler)[self.baseline_peaks], + edgecolors=ps.red, + facecolors=ps.red, + zorder=10, + marker=".", + s=70, + # facecolors="none", + ) + + # plot envelope of search signal + ax5.plot(self.time, self.search_envelope * + waveform_scaler, c=ps.gblue2, lw=lw) + ax5.scatter( + (self.time)[self.search_peaks], + (self.search_envelope*waveform_scaler)[self.search_peaks], + edgecolors=ps.red, + facecolors=ps.red, + zorder=10, + marker=".", + s=70, + # facecolors="none", + ) + + # plot filtered instantaneous frequency + ax6.plot(self.frequency_time, + self.frequency_filtered, c=ps.gblue3, lw=lw) + ax6.scatter( + self.frequency_time[self.frequency_peaks], + self.frequency_filtered[self.frequency_peaks], + edgecolors=ps.red, + facecolors=ps.red, + zorder=10, + marker=".", + s=70, + # facecolors="none", + ) + + ax0.set_ylabel("frequency [Hz]") + ax1.set_ylabel(r"$\mu$V") + ax2.set_ylabel(r"$\mu$V") + ax3.set_ylabel("Hz") + 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) + plt.setp(ax1.get_xticklabels(), visible=False) + plt.setp(ax2.get_xticklabels(), visible=False) + plt.setp(ax3.get_xticklabels(), visible=False) + plt.setp(ax4.get_xticklabels(), visible=False) + plt.setp(ax5.get_xticklabels(), visible=False) + + # ps.letter_subplots([ax0, ax1, ax4], xoffset=-0.21) + + # ax7.set_xticks(np.arange(0, 5.5, 1)) + # ax7.spines.bottom.set_bounds((0, 5)) + + ax0.set_xlim(0, self.config.window) + plt.subplots_adjust(left=0.165, right=0.975, + top=0.98, bottom=0.074, hspace=0.2) + fig.align_labels() + + if plot == "show": + plt.show() + elif plot == "save": + make_outputdir(self.config.outputdir) + out = make_outputdir( + self.config.outputdir + self.data.datapath.split("/")[-2] + "/" + ) - return inst_freq_time, inst_freq, true_zero + plt.savefig(f"{out}{self.track_id}_{self.t0_old}.pdf") + plt.savefig(f"{out}{self.track_id}_{self.t0_old}.svg") + plt.close() -def plot_spectrogram(axis, signal: np.ndarray, samplerate: float, t0: float) -> None: +def plot_spectrogram( + axis, + signal: np.ndarray, + samplerate: float, + window_start_seconds: float, + ylims: list[float] +) -> np.ndarray: """ Plot a spectrogram of a signal. @@ -73,28 +319,46 @@ def plot_spectrogram(axis, signal: np.ndarray, samplerate: float, t0: float) -> Signal to plot the spectrogram from. samplerate : float Samplerate of the signal. - t0 : float + window_start_seconds : float Start time of the signal. """ + + logger.debug("Plotting spectrogram") + # compute spectrogram spec_power, spec_freqs, spec_times = spectrogram( signal, ratetime=samplerate, - freq_resolution=50, - overlap_frac=0.2, + freq_resolution=10, + overlap_frac=0.5, ) - axis.pcolormesh( - spec_times + t0, - spec_freqs, - decibel(spec_power), + fmask = np.zeros(spec_freqs.shape, dtype=bool) + fmask[(spec_freqs > ylims[0]) & (spec_freqs < ylims[1])] = True + + axis.imshow( + decibel(spec_power[fmask, :]), + extent=[ + spec_times[0] + window_start_seconds, + spec_times[-1] + window_start_seconds, + spec_freqs[fmask][0], + spec_freqs[fmask][-1], + ], + aspect="auto", + origin="lower", + interpolation="gaussian", + # alpha=0.6, ) + # axis.use_sticky_edges = False + return spec_times - axis.set_ylim(200, 1200) - -def double_bandpass( - data: DataLoader, samplerate: int, freqs: np.ndarray, search_freq: float +def extract_frequency_bands( + raw_data: np.ndarray, + samplerate: int, + baseline_track: np.ndarray, + searchband_center: float, + minimal_bandwidth: float, ) -> tuple[np.ndarray, np.ndarray]: """ Apply a bandpass filter to the baseline of a signal and a second bandpass @@ -102,14 +366,16 @@ def double_bandpass( Parameters ---------- - data : DataLoader + raw_data : np.ndarray Data to apply the filter to. samplerate : int Samplerate of the signal. - freqs : np.ndarray + baseline_track : np.ndarray Tracked fundamental frequencies of the signal. - search_freq : float + searchband_center: float Frequency to search for above or below the baseline. + minimal_bandwidth : float + Minimal bandwidth of the filter. Returns ------- @@ -117,420 +383,742 @@ def double_bandpass( """ # compute boundaries to filter baseline - q25, q75 = np.percentile(freqs, [25, 75]) + q25, q50, q75 = np.percentile(baseline_track, [25, 50, 75]) # check if percentile delta is too small - if q75 - q25 < 5: - median = np.median(freqs) - q25, q75 = median - 2.5, median + 2.5 + if q75 - q25 < 10: + q25, q75 = q50 - minimal_bandwidth / 2, q50 + minimal_bandwidth / 2 # filter baseline - filtered_baseline = bandpass_filter(data, samplerate, lowf=q25, highf=q75) + filtered_baseline = bandpass_filter( + raw_data, samplerate, lowf=q25, highf=q75 + ) # filter search area filtered_search_freq = bandpass_filter( - data, samplerate, lowf=q25 + search_freq, highf=q75 + search_freq + raw_data, + samplerate, + lowf=searchband_center + q50 - minimal_bandwidth / 2, + highf=searchband_center + q50 + minimal_bandwidth / 2, ) - return (filtered_baseline, filtered_search_freq) + return filtered_baseline, filtered_search_freq -def main(datapath: str) -> None: +def window_median_all_track_ids( + data: LoadData, window_start_seconds: float, window_duration_seconds: float +) -> tuple[list[tuple[float, float, float]], list[int]]: + """ + Calculate the median and quantiles of the frequency of all fish in a + given time window. - # load raw file - file = os.path.join(datapath, "traces-grid1.raw") - # data = DataLoader(file, 60.0, 0, channel=-1) + Iterate over all track ids and calculate the 25, 50 and 75 percentile + in a given time window to pass this data to 'find_searchband' function, + which then determines whether other fish in the current window fall + within the searchband of the current fish and then determine the + gaps that are outside of the percentile ranges. - data = LoadData(datapath) + Parameters + ---------- + data : LoadData + Data to calculate the median frequency from. + window_start_seconds : float + Start time of the window. + window_duration_seconds : float + Duration of the window. + + Returns + ------- + tuple[list[tuple[float, float, float]], list[int]] + + """ + + frequency_percentiles = [] + track_ids = [] + + for _, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])): + + # the window index combines the track id and the time window + window_idx = np.arange(len(data.idx))[ + (data.ident == track_id) + & (data.time[data.idx] >= window_start_seconds) + & ( + data.time[data.idx] + <= (window_start_seconds + window_duration_seconds) + ) + ] + + if len(data.freq[window_idx]) > 0: + frequency_percentiles.append( + np.percentile(data.freq[window_idx], [25, 50, 75])) + track_ids.append(track_id) + + # convert to numpy array + frequency_percentiles = np.asarray(frequency_percentiles) + track_ids = np.asarray(track_ids) + + return frequency_percentiles, track_ids + + +def array_center(array: np.ndarray) -> float: + """ + Return the center value of an array. + If the array length is even, returns + the mean of the two center values. + + Parameters + ---------- + array : np.ndarray + Array to calculate the center from. + + Returns + ------- + float + + """ + if len(array) % 2 == 0: + return np.mean(array[int(len(array) / 2) - 1:int(len(array) / 2) + 1]) + else: + return array[int(len(array) / 2)] + + +def find_searchband( + current_frequency: np.ndarray, + percentiles_ids: np.ndarray, + frequency_percentiles: np.ndarray, + config: ConfLoader, + data: LoadData, +) -> float: + """ + Find the search frequency band for each fish by checking which fish EODs + are above the current EOD and finding a gap in them. + + Parameters + ---------- + current_frequency : np.ndarray + Current EOD frequency array / the current fish of interest. + percentiles_ids : np.ndarray + Array of track IDs of the medians of all other fish in the current + window. + frequency_percentiles : np.ndarray + Array of percentiles frequencies of all other fish in the current window. + config : ConfLoader + Configuration file. + data : LoadData + Data to find the search frequency from. + + Returns + ------- + float + + """ + # frequency window where second filter filters is potentially allowed + # to filter. This is the search window, in which we want to find + # a gap in the other fish's EODs. + current_median = np.median(current_frequency) + search_window = np.arange( + current_median + config.search_df_lower, + current_median + config.search_df_upper, + config.search_res, + ) + + # search window in boolean + bool_lower = np.ones_like(search_window, dtype=bool) + bool_upper = np.ones_like(search_window, dtype=bool) + search_window_bool = np.ones_like(search_window, dtype=bool) + + # make seperate arrays from the qartiles + q25 = np.asarray([i[0] for i in frequency_percentiles]) + q75 = np.asarray([i[2] for i in frequency_percentiles]) + + # get tracks that fall into search window + check_track_ids = percentiles_ids[ + (q25 > current_median) & ( + q75 < search_window[-1]) + ] + + # iterate through theses tracks + if check_track_ids.size != 0: + + for j, check_track_id in enumerate(check_track_ids): + + q25_temp = q25[percentiles_ids == check_track_id] + q75_temp = q75[percentiles_ids == check_track_id] + + bool_lower[search_window > q25_temp - config.search_res] = False + bool_upper[search_window < q75_temp + config.search_res] = False + search_window_bool[(bool_lower == False) & + (bool_upper == False)] = False + + # find gaps in search window + search_window_indices = np.arange(len(search_window)) + + # get search window gaps + # taking the diff of a boolean array gives non zero values where the + # array changes from true to false or vice versa + + search_window_gaps = np.diff(search_window_bool, append=np.nan) + nonzeros = search_window_gaps[np.nonzero(search_window_gaps)[0]] + nonzeros = nonzeros[~np.isnan(nonzeros)] + + if len(nonzeros) == 0: + return config.default_search_freq + + # if the first value is -1, the array starst with true, so a gap + if nonzeros[0] == -1: + stops = search_window_indices[search_window_gaps == -1] + starts = np.append( + 0, search_window_indices[search_window_gaps == 1] + ) + + # if the last value is -1, the array ends with true, so a gap + if nonzeros[-1] == 1: + stops = np.append( + search_window_indices[search_window_gaps == -1], + len(search_window) - 1, + ) - # load wavetracker files - # time = np.load(datapath + "times.npy", allow_pickle=True) - # freq = np.load(datapath + "fund_v.npy", allow_pickle=True) - # powers = np.load(datapath + "sign_v.npy", allow_pickle=True) - # idx = np.load(datapath + "idx_v.npy", allow_pickle=True) - # ident = np.load(datapath + "ident_v.npy", allow_pickle=True) + # else it starts with false, so no gap + if nonzeros[0] == 1: + stops = search_window_indices[search_window_gaps == -1] + starts = search_window_indices[search_window_gaps == 1] + + # if the last value is -1, the array ends with true, so a gap + if nonzeros[-1] == 1: + stops = np.append( + search_window_indices[search_window_gaps == -1], + len(search_window), + ) + + # get the frequency ranges of the gaps + search_windows = [search_window[x:y] for x, y in zip(starts, stops)] + search_windows_lens = [len(x) for x in search_windows] + longest_search_window = search_windows[np.argmax(search_windows_lens)] + + # the center of the search frequency band is then the center of + # the longest gap + + search_freq = array_center(longest_search_window) - current_median + + return search_freq + + return config.default_search_freq + + +def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None: + + assert plot in [ + "save", + "show", + "false", + ], "plot must be 'save', 'show' or 'false'" + + assert debug in [ + "false", + "electrode", + "fish", + ], "debug must be 'false', 'electrode' or 'fish'" + + if debug != "false": + assert plot == "show", "debug mode only runs when plot is 'show'" + + # load raw file + print('datapath', datapath) + data = LoadData(datapath) # load config file config = ConfLoader("chirpdetector_conf.yml") - # set time window # <------------------------ Iterate through windows here + # set time window window_duration = config.window * data.raw_rate window_overlap = config.overlap * data.raw_rate window_edge = config.edge * data.raw_rate - # check if window duration is even + # check if window duration and window ovelap is even, otherwise the half + # of the duration or window overlap would return a float, thus an + # invalid index + if window_duration % 2 == 0: window_duration = int(window_duration) else: raise ValueError("Window duration must be even.") - # check if window ovelap is even if window_overlap % 2 == 0: window_overlap = int(window_overlap) else: raise ValueError("Window overlap must be even.") + # make time array for raw data raw_time = np.arange(data.raw.shape[0]) / data.raw_rate # good chirp times for data: 2022-06-02-10_00 - t0 = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate - dt = 60 * data.raw_rate + # window_start_index = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate + # window_duration_index = 60 * data.raw_rate - window_starts = np.arange( - t0, - t0 + dt, + # 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] + + # generate starting points of rolling window + window_start_indices = np.arange( + window_start_index, + window_start_index + window_duration_index, window_duration - (window_overlap + 2 * window_edge), - dtype=int + dtype=int, ) - # ask how many windows should be calulated - nwindows = int( - input("How many windows should be calculated (integer number)? ")) - for start_index in window_starts[:nwindows]: + # ititialize lists to store data + multiwindow_chirps = [] + multiwindow_ids = [] - # make t0 and dt - t0 = start_index / data.raw_rate - dt = window_duration / data.raw_rate + for st, window_start_index in enumerate(window_start_indices[3175:]): - # set index window - stop_index = start_index + window_duration - - # t0 = 3 * 60 * 60 + 6 * 60 + 43.5 - # dt = 60 - # start_index = t0 * data.raw_rate - # stop_index = (t0 + dt) * data.raw_rate - - # calucate frequencies in wndow - median_freq = [] - track_ids = [] - for i, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])): - window_index = np.arange(len(data.idx))[ - (data.ident == track_id) & (data.time[data.idx] >= t0) & ( - data.time[data.idx] <= (t0 + dt)) - ] - median_freq.append(np.median(data.freq[window_index])) - track_ids.append(track_id) - median_freq = np.asarray(median_freq) - track_ids = np.asarray(track_ids) + logger.info(f"Processing window {st+1} of {len(window_start_indices)}") - # iterate through all fish - for i, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])): + window_start_seconds = window_start_index / data.raw_rate + window_duration_seconds = window_duration / data.raw_rate + + # set index window + window_stop_index = window_start_index + window_duration - print(f"Track ID: {track_id}") + # calucate median of fish frequencies in window + median_freq, median_ids = window_median_all_track_ids( + data, window_start_seconds, window_duration_seconds + ) - window_index = np.arange(len(data.idx))[ - (data.ident == track_id) & (data.time[data.idx] >= t0) & ( - data.time[data.idx] <= (t0 + dt)) + # iterate through all fish + for tr, track_id in enumerate( + np.unique(data.ident[~np.isnan(data.ident)]) + ): + + logger.debug(f"Processing track {tr} of {len(data.ids)}") + + # get index of track data in this time window + track_window_index = np.arange(len(data.idx))[ + (data.ident == track_id) + & (data.time[data.idx] >= window_start_seconds) + & ( + data.time[data.idx] + <= (window_start_seconds + window_duration_seconds) + ) ] # get tracked frequencies and their times - freq_temp = data.freq[window_index] - powers_temp = data.powers[window_index, :] + current_frequencies = data.freq[track_window_index] + current_powers = data.powers[track_window_index, :] + + # approximate sampling rate to compute expected durations if there + # is data available for this time window for this fish id - # time_temp = time[idx[window_index]] - track_samplerate = np.mean(1 / np.diff(data.time)) - expected_duration = ((t0 + dt) - t0) * track_samplerate +# track_samplerate = np.mean(1 / np.diff(data.time)) +# expected_duration = ( +# (window_start_seconds + window_duration_seconds) +# - window_start_seconds +# ) * track_samplerate # check if tracked data available in this window - if len(freq_temp) < expected_duration * 0.9: + if len(current_frequencies) < 3: + logger.warning( + f"Track {track_id} has no data in window {st}, skipping." + ) continue - fig, axs = plt.subplots( - 7, - config.electrodes, - figsize=(20 / 2.54, 12 / 2.54), - constrained_layout=True, - sharex=True, - sharey='row', - ) - # get best electrode - best_electrodes = np.argsort(np.nanmean( - powers_temp, axis=0))[-config.electrodes:] - - # frequency where second filter filters - search_window = np.arange(np.median(freq_temp)+config.search_df_lower, np.median( - freq_temp)+config.search_df_upper, config.search_res) + # check if there are powers available in this window + nanchecker = np.unique(np.isnan(current_powers)) + if (len(nanchecker) == 1) and nanchecker[0] is True: + logger.warning( + f"No powers available for track {track_id} window {st}," + "skipping." + ) + continue - # search window in boolean - search_window_bool = np.ones(len(search_window), dtype=bool) + # find the strongest electrodes for the current fish in the current + # window - # get tracks that fall into search window - check_track_ids = track_ids[(median_freq > search_window[0]) & ( - median_freq < search_window[-1])] + best_electrode_index = np.argsort( + np.nanmean(current_powers, axis=0) + )[-config.number_electrodes:] - # iterate through theses tracks - if check_track_ids.size != 0: + # find a frequency above the baseline of the current fish in which + # no other fish is active to search for chirps there - for j, check_track_id in enumerate(check_track_ids): + search_frequency = find_searchband( + config=config, + current_frequency=current_frequencies, + percentiles_ids=median_ids, + data=data, + frequency_percentiles=median_freq, + ) - q1, q2 = np.percentile( - data.freq[data.ident == check_track_id], config.search_freq_percentiles) + # add all chirps that are detected on mulitple electrodes for one + # fish fish in one window to this list - search_window_bool[(search_window > q1) & ( - search_window < q2)] = False + multielectrode_chirps = [] - # find gaps in search window - search_window_indices = np.arange(len(search_window)) + # iterate through electrodes + for el, electrode_index in enumerate(best_electrode_index): - # get search window gaps - search_window_gaps = np.diff(search_window_bool, append=np.nan) - nonzeros = search_window_gaps[np.nonzero( - search_window_gaps)[0]] - nonzeros = nonzeros[~np.isnan(nonzeros)] + logger.debug( + f"Processing electrode {el+1} of " + f"{len(best_electrode_index)}" + ) - # if the first value is -1, the array starst with true, so a gap - if nonzeros[0] == -1: - stops = search_window_indices[search_window_gaps == -1] - starts = np.append( - 0, search_window_indices[search_window_gaps == 1]) + # LOAD DATA FOR CURRENT ELECTRODE AND CURRENT FISH ------------ - # if the last value is -1, the array ends with true, so a gap - if nonzeros[-1] == 1: - stops = np.append( - search_window_indices[search_window_gaps == -1], len(search_window) - 1) + # load region of interest of raw data file + current_raw_data = data.raw[ + window_start_index:window_stop_index, electrode_index + ] + current_raw_time = raw_time[ + window_start_index:window_stop_index + ] - # else it starts with false, so no gap - if nonzeros[0] == 1: - stops = search_window_indices[search_window_gaps == -1] - starts = search_window_indices[search_window_gaps == 1] + # EXTRACT FEATURES -------------------------------------------- - # if the last value is -1, the array ends with true, so a gap - if nonzeros[-1] == 1: - stops = np.append( - search_window_indices[search_window_gaps == -1], len(search_window)) + # filter baseline and above + baselineband, searchband = extract_frequency_bands( + raw_data=current_raw_data, + samplerate=data.raw_rate, + baseline_track=current_frequencies, + searchband_center=search_frequency, + minimal_bandwidth=config.minimal_bandwidth, + ) - # get the frequency ranges of the gaps - search_windows = [search_window[x:y] - for x, y in zip(starts, stops)] - search_windows_lens = [len(x) for x in search_windows] - longest_search_window = search_windows[np.argmax( - search_windows_lens)] + # compute envelope of baseline band to find dips + # in the baseline envelope - search_freq = ( - longest_search_window[1] - longest_search_window[0]) / 2 + baseline_envelope_unfiltered = envelope( + signal=baselineband, + samplerate=data.raw_rate, + cutoff_frequency=config.baseline_envelope_cutoff, + ) - else: - search_freq = config.default_search_freq + # highpass filter baseline envelope to remove slower + # fluctuations e.g. due to motion envelope - print(f"Search frequency: {search_freq}") + baseline_envelope = bandpass_filter( + signal=baseline_envelope_unfiltered, + samplerate=data.raw_rate, + lowf=config.baseline_envelope_bandpass_lowf, + highf=config.baseline_envelope_bandpass_highf, + ) - for i, electrode in enumerate(best_electrodes): + # highbass filter introduced filter effects, i.e. oscillations + # around peaks. Compute the envelope of the highpass filtered + # and inverted baseline envelope to remove these oscillations - # load region of interest of raw data file - data_oi = data.raw[start_index:stop_index, :] - time_oi = raw_time[start_index:stop_index] + baseline_envelope = -baseline_envelope - # plot wavetracker tracks to spectrogram - # for track_id in np.unique(ident): # <---------- Find freq gaps later - # here + # baseline_envelope = envelope( + # signal=baseline_envelope, + # samplerate=data.raw_rate, + # cutoff_frequency=config.baseline_envelope_envelope_cutoff, + # ) - # # get indices for time array in time window - # window_index = np.arange(len(idx))[ - # (ident == track_id) & - # (time[idx] >= t0) & - # (time[idx] <= (t0 + dt)) - # ] + # compute the envelope of the search band. Peaks in the search + # band envelope correspond to troughs in the baseline envelope + # during chirps - # freq_temp = freq[window_index] - # time_temp = time[idx[window_index]] + search_envelope_unfiltered = envelope( + signal=searchband, + samplerate=data.raw_rate, + cutoff_frequency=config.search_envelope_cutoff, + ) + search_envelope = search_envelope_unfiltered + + # compute instantaneous frequency of the baseline band to find + # anomalies during a chirp, i.e. a frequency jump upwards or + # sometimes downwards. We do not fully understand why the + # instantaneous frequency can also jump downwards during a + # chirp. This phenomenon is only observed on chirps on a narrow + # filtered baseline such as the one we are working with. + + ( + baseline_frequency_time, + baseline_frequency, + ) = instantaneous_frequency( + signal=baselineband, + samplerate=data.raw_rate, + smoothing_window=config.baseline_frequency_smoothing, + ) - # axs[0].plot(time_temp-t0, freq_temp, lw=2) - # axs[0].set_ylim(500, 1000) + # bandpass filter the instantaneous frequency to remove slow + # fluctuations. Just as with the baseline envelope, we then + # compute the envelope of the signal to remove the oscillations + # around the peaks - # track_id = ids + # baseline_frequency_samplerate = np.mean( + # np.diff(baseline_frequency_time) + # ) - # filter baseline and above - baseline, search = double_bandpass( - data_oi[:, electrode], data.raw_rate, freq_temp, search_freq + baseline_frequency_filtered = np.abs( + baseline_frequency - np.median(baseline_frequency) ) - # compute instantaneous frequency on broad signal - broad_baseline = bandpass_filter( - data_oi[:, electrode], - data.raw_rate, - lowf=np.mean(freq_temp)-5, - highf=np.mean(freq_temp)+100 - ) + # baseline_frequency_filtered = highpass_filter( + # signal=baseline_frequency_filtered, + # samplerate=baseline_frequency_samplerate, + # cutoff=config.baseline_frequency_highpass_cutoff, + # ) - # compute instantaneous frequency on narrow signal - baseline_freq_time, baseline_freq, true_zero = instantaneos_frequency( - baseline, data.raw_rate - ) + # baseline_frequency_filtered = envelope( + # signal=-baseline_frequency_filtered, + # samplerate=baseline_frequency_samplerate, + # cutoff_frequency=config.baseline_frequency_envelope_cutoff, + # ) - # compute envelopes - baseline_envelope_unfiltered = envelope( - baseline, data.raw_rate, config.envelope_cutoff) - search_envelope = envelope( - search, data.raw_rate, config.envelope_cutoff) - - # highpass filter envelopes - baseline_envelope = highpass_filter( - baseline_envelope_unfiltered, - data.raw_rate, - config.envelope_highpass_cutoff - ) + # CUT OFF OVERLAP --------------------------------------------- - # baseline_envelope = np.abs(baseline_envelope) - # search_envelope = highpass_filter( - # search_envelope, - # data.raw_rate, - # config.envelope_highpass_cutoff - # ) + # cut off snippet at start and end of each window to remove + # filter effects - # envelopes of filtered envelope of filtered baseline - baseline_envelope = envelope( - np.abs(baseline_envelope), - data.raw_rate, - config.envelope_envelope_cutoff + # get arrays with raw samplerate without edges + no_edges = np.arange( + int(window_edge), len(baseline_envelope) - int(window_edge) + ) + current_raw_time = current_raw_time[no_edges] + baselineband = baselineband[no_edges] + baseline_envelope_unfiltered = baseline_envelope_unfiltered[no_edges] + searchband = searchband[no_edges] + baseline_envelope = baseline_envelope[no_edges] + search_envelope_unfiltered = search_envelope_unfiltered[no_edges] + search_envelope = search_envelope[no_edges] + + # get instantaneous frequency withoup edges + no_edges_t0 = int(window_edge) / data.raw_rate + no_edges_t1 = baseline_frequency_time[-1] - ( + int(window_edge) / data.raw_rate + ) + no_edges = (baseline_frequency_time >= no_edges_t0) & ( + baseline_frequency_time <= no_edges_t1 ) -# search_envelope = bandpass_filter( -# search_envelope, data.raw_rate, lowf=lowf, highf=highf) - - # bandpass filter the instantaneous - inst_freq_filtered = bandpass_filter( - baseline_freq, - data.raw_rate, - lowf=config.instantaneous_lowf, - highf=config.instantaneous_highf + baseline_frequency_filtered = baseline_frequency_filtered[ + no_edges + ] + baseline_frequency = baseline_frequency[no_edges] + baseline_frequency_time = ( + baseline_frequency_time[no_edges] + window_start_seconds ) - # test taking the log of the envelopes - # baseline_envelope = np.log(baseline_envelope) - # search_envelope = np.log(search_envelope) + # NORMALIZE --------------------------------------------------- - # CUT OFF OVERLAP ------------------------------------------------- + # normalize all three feature arrays to the same range to make + # peak detection simpler - # cut off first and last 0.5 * overlap at start and end - valid = np.arange( - int(window_edge), len(baseline_envelope) - - int(window_edge) - ) - baseline_envelope_unfiltered = baseline_envelope_unfiltered[valid] - baseline_envelope = baseline_envelope[valid] - search_envelope = search_envelope[valid] + # baseline_envelope = minmaxnorm([baseline_envelope])[0] + # search_envelope = minmaxnorm([search_envelope])[0] + # baseline_frequency_filtered = minmaxnorm( + # [baseline_frequency_filtered] + # )[0] - # get inst freq valid snippet - valid_t0 = int(window_edge) / data.raw_rate - valid_t1 = baseline_freq_time[-1] - \ - (int(window_edge) / data.raw_rate) + # PEAK DETECTION ---------------------------------------------- - inst_freq_filtered = inst_freq_filtered[(baseline_freq_time >= valid_t0) & ( - baseline_freq_time <= valid_t1)] + # detect peaks baseline_enelope + baseline_peak_indices, _ = find_peaks( + baseline_envelope, prominence=config.baseline_prominence + ) + # detect peaks search_envelope + search_peak_indices, _ = find_peaks( + search_envelope, prominence=config.search_prominence + ) + # detect peaks inst_freq_filtered + frequency_peak_indices, _ = find_peaks( + baseline_frequency_filtered, prominence=config.frequency_prominence + ) - baseline_freq = baseline_freq[(baseline_freq_time >= valid_t0) & ( - baseline_freq_time <= valid_t1)] + # DETECT CHIRPS IN SEARCH WINDOW ------------------------------ - baseline_freq_time = baseline_freq_time[(baseline_freq_time >= valid_t0) & ( - baseline_freq_time <= valid_t1)] + t0 - true_zero = true_zero + t0 - # overwrite raw time to valid region - time_oi = time_oi[valid] - baseline = baseline[valid] - broad_baseline = broad_baseline[valid] - search = search[valid] + # get the peak timestamps from the peak indices + baseline_peak_timestamps = current_raw_time[ + baseline_peak_indices + ] + search_peak_timestamps = current_raw_time[ + search_peak_indices] - # NORMALIZE ---------------------------------------------------- + frequency_peak_timestamps = baseline_frequency_time[ + frequency_peak_indices + ] - baseline_envelope = normalize([baseline_envelope])[0] - search_envelope = normalize([search_envelope])[0] - inst_freq_filtered = normalize([inst_freq_filtered])[0] + # check if one list is empty and if so, skip to the next + # electrode because a chirp cannot be detected if one is empty - # PEAK DETECTION ----------------------------------------------- + one_feature_empty = ( + len(baseline_peak_timestamps) == 0 + or len(search_peak_timestamps) == 0 + or len(frequency_peak_timestamps) == 0 + ) - # detect peaks baseline_enelope - prominence = np.percentile( - baseline_envelope, config.baseline_prominence_percentile) - baseline_peaks, _ = find_peaks( - np.abs(baseline_envelope), prominence=prominence) + if one_feature_empty and (debug == 'false'): + continue - # detect peaks search_envelope - prominence = np.percentile( - search_envelope, config.search_prominence_percentile) - search_peaks, _ = find_peaks( - search_envelope, prominence=prominence) + # group peak across feature arrays but only if they + # occur in all 3 feature arrays - # detect peaks inst_freq_filtered - prominence = np.percentile( - inst_freq_filtered, config.instantaneous_prominence_percentile) - inst_freq_peaks, _ = find_peaks( - np.abs(inst_freq_filtered), prominence=prominence) - - # PLOT ------------------------------------------------------------ - - # plot spectrogram - plot_spectrogram( - axs[0, i], data_oi[:, electrode], data.raw_rate, t0) - - # plot baseline instantaneos frequency - axs[1, i].plot(baseline_freq_time, baseline_freq - - np.median(baseline_freq)) - - # plot waveform of filtered signal - axs[2, i].plot(time_oi, baseline, c=ps.green) - axs[2, i].scatter( - true_zero, np.zeros_like(true_zero), c=ps.red) - - # plot broad filtered baseline - axs[2, i].plot( - time_oi, - broad_baseline, - ) + sublists = [ + list(baseline_peak_timestamps), + list(search_peak_timestamps), + list(frequency_peak_timestamps), + ] - # plot narrow filtered baseline envelope - axs[2, i].plot( - time_oi, - baseline_envelope_unfiltered, - c=ps.red + singleelectrode_chirps = group_timestamps( + sublists=sublists, + at_least_in=3, + difference_threshold=config.chirp_window_threshold, ) - # plot waveform of filtered search signal - axs[3, i].plot(time_oi, search) + # check it there are chirps detected after grouping, continue + # with the loop if not + + if (len(singleelectrode_chirps) == 0) and (debug == 'false'): + continue + + # append chirps from this electrode to the multilectrode list + multielectrode_chirps.append(singleelectrode_chirps) + + # only initialize the plotting buffer if chirps are detected + chirp_detected = (el == (config.number_electrodes - 1) + & (plot in ["show", "save"]) + ) + + if chirp_detected or (debug != 'elecrode'): + + logger.debug("Detected chirp, ititialize buffer ...") + + # save data to Buffer + buffer = ChirpPlotBuffer( + config=config, + t0=window_start_seconds, + dt=window_duration_seconds, + electrode=electrode_index, + track_id=track_id, + data=data, + time=current_raw_time, + baseline_envelope_unfiltered=baseline_envelope_unfiltered, + baseline=baselineband, + baseline_envelope=baseline_envelope, + baseline_peaks=baseline_peak_indices, + search_frequency=search_frequency, + search=searchband, + search_envelope_unfiltered=search_envelope_unfiltered, + search_envelope=search_envelope, + search_peaks=search_peak_indices, + frequency_time=baseline_frequency_time, + frequency=baseline_frequency, + frequency_filtered=baseline_frequency_filtered, + frequency_peaks=frequency_peak_indices, + ) + + logger.debug("Buffer initialized!") + + if debug == "electrode": + logger.info(f'Plotting electrode {el} ...') + buffer.plot_buffer( + chirps=singleelectrode_chirps, plot=plot) + + logger.debug( + f"Processed all electrodes for fish {track_id} for this" + "window, sorting chirps ..." + ) - # plot envelope of search signal - axs[3, i].plot( - time_oi, - search_envelope, - c=ps.red - ) + # check if there are chirps detected in multiple electrodes and + # continue the loop if not - # plot filtered and rectified envelope - axs[4, i].plot(time_oi, baseline_envelope) - axs[4, i].scatter( - (time_oi)[baseline_peaks], - baseline_envelope[baseline_peaks], - c=ps.red, - ) + if (len(multielectrode_chirps) == 0) and (debug == 'false'): + continue - # plot envelope of search signal - axs[5, i].plot(time_oi, search_envelope) - axs[5, i].scatter( - (time_oi)[search_peaks], - search_envelope[search_peaks], - c=ps.red, - ) + # validate multielectrode chirps, i.e. check if they are + # detected in at least 'config.min_electrodes' electrodes - # plot filtered instantaneous frequency - axs[6, i].plot(baseline_freq_time, np.abs(inst_freq_filtered)) - axs[6, i].scatter( - baseline_freq_time[inst_freq_peaks], - np.abs(inst_freq_filtered)[inst_freq_peaks], - c=ps.red, - ) + multielectrode_chirps_validated = group_timestamps( + sublists=multielectrode_chirps, + at_least_in=config.minimum_electrodes, + difference_threshold=config.chirp_window_threshold, + ) - axs[6, i].set_xlabel("Time [s]") - axs[0, i].set_title("Spectrogram") - axs[1, i].set_title("Fitered baseline instanenous frequency") - axs[2, i].set_title("Fitered baseline") - axs[3, i].set_title("Fitered above") - axs[4, i].set_title("Filtered envelope of baseline envelope") - axs[5, i].set_title("Search envelope") - axs[6, i].set_title( - "Filtered absolute instantaneous frequency") + # add validated chirps to the list that tracks chirps across there + # rolling time windows - plt.show() + multiwindow_chirps.append(multielectrode_chirps_validated) + multiwindow_ids.append(track_id) + + logger.info( + f"Found {len(multielectrode_chirps_validated)}" + f" chirps for fish {track_id} in this window!" + ) + # if chirps are detected and the plot flag is set, plot the + # chirps, otheswise try to delete the buffer if it exists + + if debug == "fish": + logger.info(f'Plotting fish {track_id} ...') + buffer.plot_buffer(multielectrode_chirps_validated, plot) + + if ((len(multielectrode_chirps_validated) > 0) & + (plot in ["show", "save"]) & (debug == 'false')): + try: + buffer.plot_buffer(multielectrode_chirps_validated, plot) + del buffer + except NameError: + pass + else: + try: + del buffer + except NameError: + pass + + # flatten list of lists containing chirps and create + # an array of fish ids that correspond to the chirps + + multiwindow_chirps_flat = [] + multiwindow_ids_flat = [] + for track_id in np.unique(multiwindow_ids): + + # get chirps for this fish and flatten the list + current_track_bool = np.asarray(multiwindow_ids) == track_id + current_track_chirps = flatten( + list(compress(multiwindow_chirps, current_track_bool)) + ) + + # add flattened chirps to the list + multiwindow_chirps_flat.extend(current_track_chirps) + multiwindow_ids_flat.extend( + list(np.ones_like(current_track_chirps) * track_id) + ) + + # purge duplicates, i.e. chirps that are very close to each other + # duplites arise due to overlapping windows + + purged_chirps = [] + purged_ids = [] + for track_id in np.unique(multiwindow_ids_flat): + tr_chirps = np.asarray(multiwindow_chirps_flat)[ + np.asarray(multiwindow_ids_flat) == track_id + ] + if len(tr_chirps) > 0: + tr_chirps_purged = purge_duplicates( + tr_chirps, config.chirp_window_threshold + ) + purged_chirps.extend(list(tr_chirps_purged)) + purged_ids.extend(list(np.ones_like(tr_chirps_purged) * track_id)) + + # sort chirps by time + purged_chirps = np.asarray(purged_chirps) + purged_ids = np.asarray(purged_ids) + purged_ids = purged_ids[np.argsort(purged_chirps)] + purged_chirps = purged_chirps[np.argsort(purged_chirps)] + + # save them into the data directory + np.save(datapath + "chirps.npy", purged_chirps) + np.save(datapath + "chirp_ids.npy", purged_ids) if __name__ == "__main__": + # datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-05-13-10_00/" datapath = "../data/2022-06-02-10_00/" - main(datapath) + # 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="save", debug="false") diff --git a/code/chirpdetector_conf.yml b/code/chirpdetector_conf.yml old mode 100644 new mode 100755 index dd3d285..058448d --- a/code/chirpdetector_conf.yml +++ b/code/chirpdetector_conf.yml @@ -1,42 +1,41 @@ -# Duration and overlap of the analysis window in seconds -window: 5 -overlap: 1 -edge: 0.25 +# Path setup ------------------------------------------------------------------ -# Number of electrodes to go over -electrodes: 3 +dataroot: "../data/" # path to data +outputdir: "../output/" # path to save plots to -# Boundary for search frequency in Hz -search_boundary: 100 +# Rolling window parameters --------------------------------------------------- -# Cutoff frequency for envelope estimation by lowpass filter -envelope_cutoff: 25 +window: 5 # rolling window length in seconds +overlap: 1 # window overlap in seconds +edge: 0.25 # window edge cufoffs to mitigate filter edge effects -# Cutoff frequency for envelope highpass filter -envelope_highpass_cutoff: 3 +# Electrode iteration parameters ---------------------------------------------- -# Cutoff frequency for envelope of envelope -envelope_envelope_cutoff: 5 +number_electrodes: 2 # number of electrodes to go over +minimum_electrodes: 1 # mimumun number of electrodes a chirp must be on -# Instantaneous frequency bandpass filter cutoff frequencies -instantaneous_lowf: 15 -instantaneous_highf: 8000 +# Feature extraction parameters ----------------------------------------------- -# Baseline envelope peak detection parameters -baseline_prominence_percentile: 90 +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 -# Search envelope peak detection parameters -search_prominence_percentile: 75 +# Feature processing parameters ----------------------------------------------- -# Instantaneous frequency peak detection parameters -instantaneous_prominence_percentile: 90 +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 -# search freq parameter -search_df_lower: 25 -search_df_upper: 100 -search_res: 1 -search_freq_percentiles: - - 5 - - 95 -default_search_freq: 50 +# 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/eventchirpsplots.py b/code/eventchirpsplots.py new file mode 100644 index 0000000..4ebaa66 --- /dev/null +++ b/code/eventchirpsplots.py @@ -0,0 +1,593 @@ +import os + +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, flatten + +logger = makeLogger(__name__) +ps = PlotStyle() + + +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: + 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 + 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) + + 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] + + 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) + 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 event_triggered_chirps( + event: np.ndarray, + chirps: np.ndarray, + time_before_event: int, + 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 + # timestamps of chirps around event centered on the event timepoint + centered_chirps = [] + + for event_timestamp in 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 + else: + centered_chirps.append(chirps_around_event - event_timestamp) + + time = np.arange(-time_before_event, time_after_event, dt) + + # Kernel density estimation with some if's + if len(centered_chirps) == 0: + centered_chirps = np.array([]) + centered_chirps_convolved = np.zeros(len(time)) + else: + # convert list of arrays to one array for plotting + centered_chirps = np.concatenate(centered_chirps, axis=0) + centered_chirps_convolved = (acausal_kde1d( + centered_chirps, time, width)) / len(event) + + return event_chirps, centered_chirps, centered_chirps_convolved + + +def main(datapath: str): + + foldernames = [ + datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath + x)] + + 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/': + continue + + bh = Behavior(folder) + + # Chirps are already sorted + category = bh.behavior + timestamps = bh.start_s + chirps = bh.chirps + nrecording_chirps.append(chirps) + chirps_fish_ids = bh.chirps_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 + # 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] + nrecording_chasing_onsets.append(chasing_onsets) + chasing_offsets = timestamps[category == 1] + nrecording_chasing_offsets.append(chasing_offsets) + physical_contacts = timestamps[category == 2] + nrecording_physicals.append(physical_contacts) + + # Define time window for chirps around event analysis + time_before_event = 30 + time_after_event = 60 + dt = 0.01 + width = 1.5 # width of kernel for all recordings, currently gaussian kernel + 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 ##### + # 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 = 100 + + 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, 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, 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, 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) + nrecording_centered_physical_chirps.append(centered_physical_chirps) + + ## Shuffled chirps ## + nshuffled_onset_chirps = [] + nshuffled_offset_chirps = [] + nshuffled_physical_chirps = [] + + # 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, 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, 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, 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([]) + # ######## median - q5, median + q95 + # 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) + # nrecording_shuffled_convolved_physical_chirps.append(nshuffled_physical_chirps) + + #### New shuffle approach #### + bootstrap_onset = [] + bootstrap_offset = [] + bootstrap_physical = [] + + # New bootstrapping approach + for n in range(nbootstrapping): + diff_onset = np.diff( + np.sort(flatten(nrecording_centered_onset_chirps))) + diff_offset = np.diff( + np.sort(flatten(nrecording_centered_offset_chirps))) + diff_physical = np.diff( + np.sort(flatten(nrecording_centered_physical_chirps))) + + np.random.shuffle(diff_onset) + shuffled_onset = np.cumsum(diff_onset) + np.random.shuffle(diff_offset) + shuffled_offset = np.cumsum(diff_offset) + np.random.shuffle(diff_physical) + shuffled_physical = np.cumsum(diff_physical) + + kde_onset (acausal_kde1d(shuffled_onset, time, width))/(27*100) + kde_offset = (acausal_kde1d(shuffled_offset, time, width))/(27*100) + kde_physical = (acausal_kde1d(shuffled_physical, time, width))/(27*100) + + bootstrap_onset.append(kde_onset) + bootstrap_offset.append(kde_offset) + bootstrap_physical.append(kde_physical) + + # New shuffle approach q5, q50, q95 + onset_q5, onset_median, onset_q95 = np.percentile( + bootstrap_onset, [5, 50, 95], axis=0) + offset_q5, offset_median, offset_q95 = np.percentile( + bootstrap_offset, [5, 50, 95], axis=0) + physical_q5, physical_median, physical_q95 = np.percentile( + bootstrap_physical, [5, 50, 95], axis=0) + + # 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( + # 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=( + 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.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) + ax[0].fill_between(time, onset_q5, onset_q95, color=ps.gray, alpha=0.5) + ax[0].plot(time, onset_median, 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.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) + ax[1].fill_between(time, offset_q5, offset_q95, color=ps.gray, alpha=0.5) + ax[1].plot(time, offset_median, 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.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) + ax[2].fill_between(time, physical_q5, physical_q95, + color=ps.gray, alpha=0.5) + ax[2].plot(time, physical_median, ps.black) + fig.suptitle('All recordings') + 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)] + + # Convolution over all recordings + # Rasterplot for each recording + + # #### Chirps around events, winner VS loser, one recording #### + # # Load file with fish ids and winner/loser info + # meta = pd.read_csv('../data/mount_data/order_meta.csv') + # current_recording = meta[meta.index == 43] + # fish1 = current_recording['rec_id1'].values + # fish2 = current_recording['rec_id2'].values + # # Implement check if fish_ids from meta and chirp detection are the same??? + # winner = current_recording['winner'].values + + # if winner == fish1: + # loser = fish2 + # elif winner == fish2: + # loser = fish1 + + # winner_chirps = chirps[chirps_fish_ids == winner] + # loser_chirps = chirps[chirps_fish_ids == loser] + + # # Event triggered winner chirps + # _, winner_centered_onset, winner_cc_onset = event_triggered_chirps(chasing_onsets, winner_chirps, time_before_event, time_after_event, dt, width) + # _, winner_centered_offset, winner_cc_offset = event_triggered_chirps(chasing_offsets, winner_chirps, time_before_event, time_after_event, dt, width) + # _, winner_centered_physical, winner_cc_physical = event_triggered_chirps(physical_contacts, winner_chirps, time_before_event, time_after_event, dt, width) + + # # Event triggered loser chirps + # _, loser_centered_onset, loser_cc_onset = event_triggered_chirps(chasing_onsets, loser_chirps, time_before_event, time_after_event, dt, width) + # _, loser_centered_offset, loser_cc_offset = event_triggered_chirps(chasing_offsets, loser_chirps, time_before_event, time_after_event, dt, width) + # _, loser_centered_physical, loser_cc_physical = event_triggered_chirps(physical_contacts, loser_chirps, time_before_event, time_after_event, dt, width) + + # ########## Winner VS Loser plot ########## + # fig, ax = plt.subplots(2, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='row') + # offset = [1.35] + # ax[1][0].set_xlabel('Time[s]') + # ax[1][1].set_xlabel('Time[s]') + # ax[1][2].set_xlabel('Time[s]') + # # Plot winner chasing onsets + # ax[0][0].set_ylabel('Chirp rate [Hz]') + # ax[0][0].plot(time, winner_cc_onset, color='tab:blue', zorder=100) + # ax0 = ax[0][0].twinx() + # ax0.eventplot(np.array([winner_centered_onset]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100) + # ax0.set_ylabel('Event') + # ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + # ax[0][0].set_zorder(ax0.get_zorder()+1) + # ax[0][0].patch.set_visible(False) + # ax0.set_yticklabels([]) + # ax0.set_yticks([]) + # # Plot winner chasing offets + # ax[0][1].plot(time, winner_cc_offset, color='tab:blue', zorder=100) + # ax1 = ax[0][1].twinx() + # ax1.eventplot(np.array([winner_centered_offset]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100) + # ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + # ax[0][1].set_zorder(ax1.get_zorder()+1) + # ax[0][1].patch.set_visible(False) + # ax1.set_yticklabels([]) + # ax1.set_yticks([]) + # # Plot winner physical contacts + # ax[0][2].plot(time, winner_cc_physical, color='tab:blue', zorder=100) + # ax2 = ax[0][2].twinx() + # ax2.eventplot(np.array([winner_centered_physical]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100) + # ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + # ax[0][2].set_zorder(ax2.get_zorder()+1) + # ax[0][2].patch.set_visible(False) + # ax2.set_yticklabels([]) + # ax2.set_yticks([]) + # # Plot loser chasing onsets + # ax[1][0].set_ylabel('Chirp rate [Hz]') + # ax[1][0].plot(time, loser_cc_onset, color='tab:blue', zorder=100) + # ax3 = ax[1][0].twinx() + # ax3.eventplot(np.array([loser_centered_onset]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100) + # ax3.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + # ax[1][0].set_zorder(ax3.get_zorder()+1) + # ax[1][0].patch.set_visible(False) + # ax3.set_yticklabels([]) + # ax3.set_yticks([]) + # # Plot loser chasing offsets + # ax[1][1].plot(time, loser_cc_offset, color='tab:blue', zorder=100) + # ax4 = ax[1][1].twinx() + # ax4.eventplot(np.array([loser_centered_offset]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100) + # ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + # ax[1][1].set_zorder(ax4.get_zorder()+1) + # ax[1][1].patch.set_visible(False) + # ax4.set_yticklabels([]) + # ax4.set_yticks([]) + # # Plot loser physical contacts + # ax[1][2].plot(time, loser_cc_physical, color='tab:blue', zorder=100) + # ax5 = ax[1][2].twinx() + # ax5.eventplot(np.array([loser_centered_physical]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100) + # ax5.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + # ax[1][2].set_zorder(ax5.get_zorder()+1) + # ax[1][2].patch.set_visible(False) + # ax5.set_yticklabels([]) + # ax5.set_yticks([]) + # plt.show() + # plt.close() + + # 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 #### + + +if __name__ == '__main__': + # Path to the data + datapath = '../data/mount_data/' + main(datapath) diff --git a/code/extract_chirps.py b/code/extract_chirps.py new file mode 100644 index 0000000..77e3e8d --- /dev/null +++ b/code/extract_chirps.py @@ -0,0 +1,58 @@ +import os +import pandas as pd +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 get_valid_datasets(dataroot): + + datasets = sorted([name for name in os.listdir(dataroot) if os.path.isdir( + os.path.join(dataroot, name))]) + + valid_datasets = [] + for dataset in datasets: + + path = os.path.join(dataroot, dataset) + csv_name = '-'.join(dataset.split('-')[:3]) + '.csv' + + if os.path.exists(os.path.join(path, csv_name)) is False: + continue + + if os.path.exists(os.path.join(path, 'ident_v.npy')) is False: + continue + + ident = np.load(os.path.join(path, 'ident_v.npy')) + number_of_fish = len(np.unique(ident[~np.isnan(ident)])) + if number_of_fish != 2: + continue + + valid_datasets.append(dataset) + + datapaths = [os.path.join(dataroot, dataset) + + '/' for dataset in valid_datasets] + + return datapaths, valid_datasets + + +def main(datapaths): + + for path in datapaths: + chirpdetection(path, plot='show') + + +if __name__ == '__main__': + + dataroot = '../data/mount_data/' + + + datapaths, valid_datasets= get_valid_datasets(dataroot) + + 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 diff --git a/code/get_behaviour.py b/code/get_behaviour.py new file mode 100644 index 0000000..36311ca --- /dev/null +++ b/code/get_behaviour.py @@ -0,0 +1,35 @@ +import os +from paramiko import SSHClient +from scp import SCPClient +from IPython import embed +from pandas import read_csv + +ssh = SSHClient() +ssh.load_system_host_keys() + +ssh.connect(hostname='kraken', + username='efish', + password='fwNix4U', + ) + + +# SCPCLient takes a paramiko transport as its only argument +scp = SCPClient(ssh.get_transport()) + +data = read_csv('../recs.csv') +foldernames = data['recording'].values + +directory = f'/Users/acfw/Documents/uni_tuebingen/chirpdetection/GP2023_chirp_detection/data/mount_data/' +for foldername in foldernames: + + if not os.path.exists(directory+foldername): + os.makedirs(directory+foldername) + + files = [('-').join(foldername.split('-')[:3])+'.csv','chirp_ids.npy', 'chirps.npy', 'fund_v.npy', 'ident_v.npy', 'idx_v.npy', 'times.npy', 'spec.npy', 'LED_on_time.npy', 'sign_v.npy'] + + + for f in files: + scp.get(f'/home/efish/behavior/2019_tube_competition/{foldername}/{f}', + directory+foldername) + +scp.close() diff --git a/code/modules/behaviour_handling.py b/code/modules/behaviour_handling.py new file mode 100644 index 0000000..94a0ca1 --- /dev/null +++ b/code/modules/behaviour_handling.py @@ -0,0 +1,169 @@ +import numpy as np +import os +from IPython import embed + +from pandas import read_csv +from modules.logger import makeLogger +from modules.datahandling import causal_kde1d, acausal_kde1d, flatten + + +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 = os.path.split(folder_path[:-1])[-1] + csv_filename = '-'.join(csv_filename.split('-')[:-1]) + '.csv' + # embed() + + # 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] + + wrong_bh = np.arange(len(category))[ + category != 2][:-1][np.diff(category[category != 2]) == 0] + + if category[category != 2][-1] == 0: + wrong_bh = np.append( + wrong_bh, + np.arange(len(category))[category != 2][-1]) + + 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) + + new_onset_ids = np.arange( + len(category))[category == 0] + new_offset_ids = np.arange( + len(category))[category == 1] + + # Check whether on- or offset is longer and calculate length difference + + if len(new_onset_ids) > len(new_offset_ids): + embed() + logger.warning('Onsets are greater than offsets') + elif len(new_onset_ids) < len(new_offset_ids): + logger.warning('Offsets are greater than onsets') + elif len(new_onset_ids) == len(new_offset_ids): + # logger.info('Chasing events are equal') + pass + + return category, timestamps + + +def center_chirps( + events: np.ndarray, + chirps: np.ndarray, + time_before_event: int, + 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 + # timestamps of chirps around event centered on the event timepoint + centered_chirps = [] + + for event_timestamp in events: + + 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)] + + if len(chirps_around_event) == 0: + continue + + centered_chirps.append(chirps_around_event - event_timestamp) + event_chirps.append(chirps_around_event) + + centered_chirps = np.sort(flatten(centered_chirps)) + event_chirps = np.sort(flatten(event_chirps)) + + if len(centered_chirps) != len(event_chirps): + raise ValueError( + 'Non centered chirps and centered chirps are not equal') + + # time = np.arange(-time_before_event, time_after_event, dt) + + # # Kernel density estimation with some if's + # if len(centered_chirps) == 0: + # centered_chirps = np.array([]) + # centered_chirps_convolved = np.zeros(len(time)) + # else: + # # convert list of arrays to one array for plotting + # centered_chirps = np.concatenate(centered_chirps, axis=0) + # centered_chirps_convolved = (acausal_kde1d( + # centered_chirps, time, width)) / len(event) + + return centered_chirps diff --git a/code/modules/datahandling.py b/code/modules/datahandling.py new file mode 100644 index 0000000..f375d44 --- /dev/null +++ b/code/modules/datahandling.py @@ -0,0 +1,338 @@ +import numpy as np +from typing import List, Any +from scipy.ndimage import gaussian_filter1d +from scipy.stats import gamma, norm + + +def minmaxnorm(data): + """ + Normalize data to [0, 1] + + Parameters + ---------- + data : np.ndarray + Data to normalize. + + Returns + ------- + np.ndarray + Normalized data. + + """ + return (data - np.min(data)) / (np.max(data) - np.min(data)) + + +def instantaneous_frequency( + signal: np.ndarray, + samplerate: int, + smoothing_window: int, +) -> tuple[np.ndarray, np.ndarray]: + """ + Compute the instantaneous frequency of a signal that is approximately + sinusoidal and symmetric around 0. + + Parameters + ---------- + signal : np.ndarray + Signal to compute the instantaneous frequency from. + samplerate : int + Samplerate of the signal. + smoothing_window : int + Window size for the gaussian filter. + + Returns + ------- + tuple[np.ndarray, np.ndarray] + + """ + # calculate instantaneous frequency with zero crossings + roll_signal = np.roll(signal, shift=1) + time_signal = np.arange(len(signal)) / samplerate + period_index = np.arange(len(signal))[(roll_signal < 0) & (signal >= 0)][ + 1:-1 + ] + + upper_bound = np.abs(signal[period_index]) + lower_bound = np.abs(signal[period_index - 1]) + upper_time = np.abs(time_signal[period_index]) + lower_time = np.abs(time_signal[period_index - 1]) + + # create ratio + lower_ratio = lower_bound / (lower_bound + upper_bound) + + # appy to time delta + time_delta = upper_time - lower_time + true_zero = lower_time + lower_ratio * time_delta + + # create new time array + instantaneous_frequency_time = true_zero[:-1] + 0.5 * np.diff(true_zero) + + # compute frequency + instantaneous_frequency = gaussian_filter1d( + 1 / np.diff(true_zero), smoothing_window + ) + + return instantaneous_frequency_time, instantaneous_frequency + + +def purge_duplicates( + timestamps: List[float], threshold: float = 0.5 +) -> List[float]: + """ + Compute the mean of groups of timestamps that are closer to the previous + or consecutive timestamp than the threshold, and return all timestamps that + are further apart from the previous or consecutive timestamp than the + threshold in a single list. + + Parameters + ---------- + timestamps : List[float] + A list of sorted timestamps + threshold : float, optional + The threshold to group the timestamps by, default is 0.5 + + Returns + ------- + List[float] + A list containing a list of timestamps that are further apart than + the threshold and a list of means of the groups of timestamps that + are closer to the previous or consecutive timestamp than the threshold. + """ + # Initialize an empty list to store the groups of timestamps that are + # closer to the previous or consecutive timestamp than the threshold + groups = [] + + # initialize the first group with the first timestamp + group = [timestamps[0]] + + for i in range(1, len(timestamps)): + + # check the difference between current timestamp and previous + # timestamp is less than the threshold + if timestamps[i] - timestamps[i - 1] < threshold: + # add the current timestamp to the current group + group.append(timestamps[i]) + else: + # if the difference is greater than the threshold + # append the current group to the groups list + groups.append(group) + + # start a new group with the current timestamp + group = [timestamps[i]] + + # after iterating through all the timestamps, add the last group to the + # groups list + groups.append(group) + + # get the mean of each group and only include the ones that have more + # than 1 timestamp + means = [np.mean(group) for group in groups if len(group) > 1] + + # get the timestamps that are outliers, i.e. the ones that are alone + # in a group + outliers = [ts for group in groups for ts in group if len(group) == 1] + + # return the outliers and means in a single list + return outliers + means + + +def group_timestamps( + sublists: List[List[float]], at_least_in: int, difference_threshold: float +) -> List[float]: + """ + Groups timestamps that are less than `threshold` milliseconds apart from + at least `n` other sublists. + Returns a list of the mean of each group. + If any of the sublists is empty, it will be ignored. + + Parameters + ---------- + sublists : List[List[float]] + a list of sublists, each containing timestamps + n : int + minimum number of sublists that a timestamp must be close to in order + to be grouped + threshold : float + the maximum difference in milliseconds between timestamps to be + considered a match + + Returns + ------- + List[float] + a list of the mean of each group. + + """ + # Flatten the sublists and sort the timestamps + timestamps = [ + timestamp for sublist in sublists if sublist for timestamp in sublist + ] + timestamps.sort() + + if len(timestamps) == 0: + return [] + + groups = [] + current_group = [timestamps[0]] + + # Group timestamps that are less than threshold milliseconds apart + for i in range(1, len(timestamps)): + if timestamps[i] - timestamps[i - 1] < difference_threshold: + current_group.append(timestamps[i]) + else: + groups.append(current_group) + current_group = [timestamps[i]] + + groups.append(current_group) + + # Retain only groups that contain at least n timestamps + final_groups = [] + for group in groups: + if len(group) >= at_least_in: + final_groups.append(group) + + # Calculate the mean of each group + means = [np.mean(group) for group in final_groups] + + return means + + +def flatten(list: List[List[Any]]) -> List: + """ + Flattens a list / array of lists. + + Parameters + ---------- + l : array or list of lists + The list to be flattened + + Returns + ------- + list + The flattened list + """ + return [item for sublist in list for item in sublist] + + +def causal_kde1d(spikes, time, width, shape=2): + """ + causalkde computes a kernel density estimate using a causal kernel (i.e. exponential or gamma distribution). + A shape of 1 turns the gamma distribution into an exponential. + + Parameters + ---------- + spikes : array-like + spike times + time : array-like + sampling time + width : float + kernel width + shape : int, optional + shape of gamma distribution, by default 1 + + Returns + ------- + rate : array-like + instantaneous firing rate + """ + + # compute dt + dt = time[1] - time[0] + + # time on which to compute kernel: + tmax = 10 * width + + # kernel not wider than time + if 2 * tmax > time[-1] - time[0]: + tmax = 0.5 * (time[-1] - time[0]) + + # kernel time + ktime = np.arange(-tmax, tmax, dt) + + # gamma kernel centered in ktime: + kernel = gamma.pdf( + x=ktime, + a=shape, + loc=0, + scale=width, + ) + + # indices of spikes in time array: + indices = np.asarray((spikes - time[0]) / dt, dtype=int) + + # binary spike train: + brate = np.zeros(len(time)) + brate[indices[(indices >= 0) & (indices < len(time))]] = 1.0 + + # convolution with kernel: + rate = np.convolve(brate, kernel, mode="same") + + return rate + + +def acausal_kde1d(spikes, time, width): + """ + causalkde computes a kernel density estimate using a causal kernel (i.e. exponential or gamma distribution). + A shape of 1 turns the gamma distribution into an exponential. + + Parameters + ---------- + spikes : array-like + spike times + time : array-like + sampling time + width : float + kernel width + shape : int, optional + shape of gamma distribution, by default 1 + + Returns + ------- + rate : array-like + instantaneous firing rate + """ + + # compute dt + dt = time[1] - time[0] + + # time on which to compute kernel: + tmax = 10 * width + + # kernel not wider than time + if 2 * tmax > time[-1] - time[0]: + tmax = 0.5 * (time[-1] - time[0]) + + # kernel time + ktime = np.arange(-tmax, tmax, dt) + + # gamma kernel centered in ktime: + kernel = norm.pdf( + x=ktime, + loc=0, + scale=width, + ) + + # indices of spikes in time array: + indices = np.asarray((spikes - time[0]) / dt, dtype=int) + + # binary spike train: + brate = np.zeros(len(time)) + brate[indices[(indices >= 0) & (indices < len(time))]] = 1.0 + + # convolution with kernel: + rate = np.convolve(brate, kernel, mode="same") + + return rate + + +if __name__ == "__main__": + + timestamps = [ + [1.2, 1.5, 1.3], + [], + [1.21, 1.51, 1.31], + [1.19, 1.49, 1.29], + [1.22, 1.52, 1.32], + [1.2, 1.5, 1.3], + ] + print(group_timestamps(timestamps, 2, 0.05)) + print(purge_duplicates([1, 2, 3, 4, 5, 6, 6.02, 7, 8, 8.02], 0.05)) diff --git a/code/modules/filehandling.py b/code/modules/filehandling.py index d25018d..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: @@ -36,6 +37,7 @@ class LoadData: def __init__(self, datapath: str) -> None: # load raw data + self.datapath = datapath self.file = os.path.join(datapath, "traces-grid1.raw") self.raw = DataLoader(self.file, 60.0, 0, channel=-1) self.raw_rate = self.raw.samplerate @@ -53,3 +55,23 @@ class LoadData: def __str__(self) -> str: return f"LoadData({self.file})" + + +def make_outputdir(path: str) -> str: + """ + Creates a new directory where the path leads if it does not already exist. + + Parameters + ---------- + path : string + path to the new output directory + + Returns + ------- + string + path of the newly created output directory + """ + + if os.path.isdir(path) == False: + os.mkdir(path) + return path diff --git a/code/modules/filters.py b/code/modules/filters.py index 5192cdc..e6d9896 100644 --- a/code/modules/filters.py +++ b/code/modules/filters.py @@ -3,8 +3,8 @@ import numpy as np def bandpass_filter( - data: np.ndarray, - rate: float, + signal: np.ndarray, + samplerate: float, lowf: float, highf: float, ) -> np.ndarray: @@ -12,7 +12,7 @@ def bandpass_filter( Parameters ---------- - data : np.ndarray + signal : np.ndarray The data to be filtered rate : float The sampling rate @@ -26,21 +26,22 @@ def bandpass_filter( np.ndarray The filtered data """ - sos = butter(2, (lowf, highf), "bandpass", fs=rate, output="sos") - fdata = sosfiltfilt(sos, data) - return fdata + sos = butter(2, (lowf, highf), "bandpass", fs=samplerate, output="sos") + filtered_signal = sosfiltfilt(sos, signal) + + return filtered_signal def highpass_filter( - data: np.ndarray, - rate: float, + signal: np.ndarray, + samplerate: float, cutoff: float, ) -> np.ndarray: """Highpass filter a signal. Parameters ---------- - data : np.ndarray + signal : np.ndarray The data to be filtered rate : float The sampling rate @@ -52,14 +53,15 @@ def highpass_filter( np.ndarray The filtered data """ - sos = butter(2, cutoff, "highpass", fs=rate, output="sos") - fdata = sosfiltfilt(sos, data) - return fdata + sos = butter(2, cutoff, "highpass", fs=samplerate, output="sos") + filtered_signal = sosfiltfilt(sos, signal) + + return filtered_signal def lowpass_filter( - data: np.ndarray, - rate: float, + signal: np.ndarray, + samplerate: float, cutoff: float ) -> np.ndarray: """Lowpass filter a signal. @@ -78,21 +80,25 @@ def lowpass_filter( np.ndarray The filtered data """ - sos = butter(2, cutoff, "lowpass", fs=rate, output="sos") - fdata = sosfiltfilt(sos, data) - return fdata + sos = butter(2, cutoff, "lowpass", fs=samplerate, output="sos") + filtered_signal = sosfiltfilt(sos, signal) + return filtered_signal -def envelope(data: np.ndarray, rate: float, freq: float) -> np.ndarray: + +def envelope(signal: np.ndarray, + samplerate: float, + cutoff_frequency: float + ) -> np.ndarray: """Calculate the envelope of a signal using a lowpass filter. Parameters ---------- - data : np.ndarray + signal : np.ndarray The signal to calculate the envelope of - rate : float + samplingrate : float The sampling rate of the signal - freq : float + cutoff_frequency : float The cutoff frequency of the lowpass filter Returns @@ -100,6 +106,7 @@ def envelope(data: np.ndarray, rate: float, freq: float) -> np.ndarray: np.ndarray The envelope of the signal """ - sos = butter(2, freq, "lowpass", fs=rate, output="sos") - envelope = np.sqrt(2) * sosfiltfilt(sos, np.abs(data)) + sos = butter(2, cutoff_frequency, "lowpass", fs=samplerate, output="sos") + envelope = np.sqrt(2) * sosfiltfilt(sos, np.abs(signal)) + return envelope diff --git a/code/modules/logger.py b/code/modules/logger.py new file mode 100644 index 0000000..5dabf80 --- /dev/null +++ b/code/modules/logger.py @@ -0,0 +1,41 @@ +import logging + + +def makeLogger(name: str): + + # create logger formats for file and terminal + file_formatter = logging.Formatter( + "[ %(levelname)s ] ~ %(asctime)s ~ %(module)s.%(funcName)s: %(message)s") + console_formatter = logging.Formatter( + "[ %(levelname)s ] in %(module)s.%(funcName)s: %(message)s") + + # create logging file if loglevel is debug + file_handler = logging.FileHandler(f"gridtools_log.log", mode="w") + file_handler.setLevel(logging.WARN) + file_handler.setFormatter(file_formatter) + + # create stream handler for terminal output + console_handler = logging.StreamHandler() + console_handler.setFormatter(console_formatter) + console_handler.setLevel(logging.INFO) + + # create script specific logger + logger = logging.getLogger(name) + logger.addHandler(file_handler) + logger.addHandler(console_handler) + logger.setLevel(logging.INFO) + + return logger + + +if __name__ == "__main__": + + # initiate logger + mylogger = makeLogger(__name__) + + # test logger levels + mylogger.debug("This is for debugging!") + mylogger.info("This is an info.") + mylogger.warning("This is a warning.") + mylogger.error("This is an error.") + mylogger.critical("This is a critical error!") diff --git a/code/modules/plotstyle.py b/code/modules/plotstyle.py index 9e382a7..61aedcc 100644 --- a/code/modules/plotstyle.py +++ b/code/modules/plotstyle.py @@ -23,17 +23,21 @@ 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 = "#89b4fa" + gblue2 = "#89dceb" + gblue3 = "#a6e3a1" @classmethod def lims(cls, track1, track2): - """Helper function to get frequency y axis limits from two fundamental frequency tracks. + """Helper function to get frequency y axis limits from two + fundamental frequency tracks. Args: track1 (array): First track @@ -91,12 +95,23 @@ def PlotStyle() -> None: ax.tick_params(left=False, labelleft=False) ax.patch.set_visible(False) + @classmethod + def hide_xax(cls, ax): + ax.xaxis.set_visible(False) + ax.spines["bottom"].set_visible(False) + + @classmethod + def hide_yax(cls, ax): + ax.yaxis.set_visible(False) + ax.spines["left"].set_visible(False) + @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) + plt.setp(bp["whiskers"], color=white) + plt.setp(bp["caps"], color=white) + plt.setp(bp["medians"], color=white) + @classmethod def label_subplots(cls, labels, axes, fig): @@ -215,9 +230,9 @@ 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["axes.xmargin"] = 0.1 - # plt.rcParams["axes.ymargin"] = 0.15 + plt.rcParams["image.cmap"] = "cmo.haline" + plt.rcParams["axes.xmargin"] = 0.05 + plt.rcParams["axes.ymargin"] = 0.1 plt.rcParams["axes.titlelocation"] = "left" plt.rcParams["axes.titlesize"] = BIGGER_SIZE # plt.rcParams["axes.titlepad"] = -10 @@ -230,9 +245,9 @@ def PlotStyle() -> None: plt.rcParams["legend.borderaxespad"] = 0.5 plt.rcParams["legend.fancybox"] = False - # specify the custom font to use - plt.rcParams["font.family"] = "sans-serif" - plt.rcParams["font.sans-serif"] = "Helvetica Now Text" + # # specify the custom font to use + # plt.rcParams["font.family"] = "sans-serif" + # plt.rcParams["font.sans-serif"] = "Helvetica Now Text" # dark mode modifications plt.rcParams["boxplot.flierprops.color"] = white @@ -247,31 +262,33 @@ def PlotStyle() -> None: # 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"] = "#555169" # 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 @@ -281,12 +298,11 @@ 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) @@ -294,14 +310,20 @@ 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() @@ -314,22 +336,21 @@ 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() @@ -341,24 +362,42 @@ 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_chirp_bodylegth(old).py b/code/plot_chirp_bodylegth(old).py new file mode 100644 index 0000000..68d31bd --- /dev/null +++ b/code/plot_chirp_bodylegth(old).py @@ -0,0 +1,277 @@ +import numpy as np +from extract_chirps import get_valid_datasets + +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 +from modules.behaviour_handling import Behavior, correct_chasing_events + +ps = PlotStyle() + +logger = makeLogger(__name__) + + +def get_chirp_winner_loser(folder_name, Behavior, order_meta_df): + + foldername = folder_name.split('/')[-2] + winner_row = order_meta_df[order_meta_df['recording'] == foldername] + 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 > 0: + 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] + + chirp_winner = len( + Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) + chirp_loser = len( + Behavior.chirps[Behavior.chirps_ids == loser_fish_id]) + + return chirp_winner, chirp_loser + else: + return np.nan, np.nan + + +def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df): + + foldername = folder_name.split('/')[-2] + folder_row = order_meta_df[order_meta_df['recording'] == foldername] + fish1 = folder_row['fish1'].values[0].astype(int) + fish2 = folder_row['fish2'].values[0].astype(int) + + groub = folder_row['group'].values[0].astype(int) + size_fish1_row = id_meta_df[(id_meta_df['group'] == groub) & ( + id_meta_df['fish'] == fish1)] + size_fish2_row = id_meta_df[(id_meta_df['group'] == groub) & ( + id_meta_df['fish'] == fish2)] + + size_winners = [size_fish1_row[col].values[0] + for col in ['l1', 'l2', 'l3']] + mean_size_winner = np.nanmean(size_winners) + + size_losers = [size_fish2_row[col].values[0] for col in ['l1', 'l2', 'l3']] + mean_size_loser = np.nanmean(size_losers) + + if mean_size_winner > mean_size_loser: + size_diff = mean_size_winner - mean_size_loser + winner_fish_id = folder_row['rec_id1'].values[0] + loser_fish_id = folder_row['rec_id2'].values[0] + + elif mean_size_winner < mean_size_loser: + size_diff = mean_size_loser - mean_size_winner + winner_fish_id = folder_row['rec_id2'].values[0] + loser_fish_id = folder_row['rec_id1'].values[0] + + else: + size_diff = np.nan + winner_fish_id = np.nan + loser_fish_id = np.nan + + chirp_diff = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) - len( + Behavior.chirps[Behavior.chirps_ids == loser_fish_id]) + + return size_diff, chirp_diff + + +def get_chirp_freq(folder_name, Behavior, order_meta_df): + + foldername = folder_name.split('/')[-2] + folder_row = order_meta_df[order_meta_df['recording'] == foldername] + fish1 = folder_row['rec_id1'].values[0].astype(int) + fish2 = folder_row['rec_id2'].values[0].astype(int) + chirp_freq_fish1 = np.nanmedian( + Behavior.freq[Behavior.ident == fish1]) + chirp_freq_fish2 = np.nanmedian( + Behavior.freq[Behavior.ident == fish2]) + + if chirp_freq_fish1 > chirp_freq_fish2: + freq_diff = chirp_freq_fish1 - chirp_freq_fish2 + winner_fish_id = folder_row['rec_id1'].values[0] + loser_fish_id = folder_row['rec_id2'].values[0] + + elif chirp_freq_fish1 < chirp_freq_fish2: + freq_diff = chirp_freq_fish2 - chirp_freq_fish1 + winner_fish_id = folder_row['rec_id2'].values[0] + loser_fish_id = folder_row['rec_id1'].values[0] + + chirp_diff = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) - len( + Behavior.chirps[Behavior.chirps_ids == loser_fish_id]) + + return freq_diff, chirp_diff + + +def main(datapath: str): + + foldernames = [ + datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)] + + foldernames, _ = get_valid_datasets(datapath) + path_order_meta = ( + '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv' + 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_diffs = [] + size_chirps_diffs = [] + chirps_loser = [] + freq_diffs = [] + freq_chirps_diffs = [] + + 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) + + # winner_chirp, loser_chirp = get_chirp_winner_loser( + # foldername, bh, order_meta_df) + # chirps_winner.append(winner_chirp) + # chirps_loser.append(loser_chirp) + # size_diff, chirp_diff = get_chirp_size( + # foldername, bh, order_meta_df, id_meta_df) + # size_diffs.append(size_diff) + # size_chirps_diffs.append(chirp_diff) + + # freq_diff, freq_chirps_diff = get_chirp_freq( + # foldername, bh, order_meta_df) + # freq_diffs.append(freq_diff) + # freq_chirps_diffs.append(freq_chirps_diff) + + folder_name = foldername.split('/')[-2] + 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_diffs.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_diffs.append(mean_size_winner - mean_size_loser) + else: + continue + + print(foldername) + all_fish_ids = np.unique(bh.chirps_ids) + 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) + + size_chirps_diffs.append(chirp_winner - chirp_loser) + freq_diffs.append(freq_winner - freq_loser) + + fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=( + 22*ps.cm, 12*ps.cm), width_ratios=[1.5, 1, 1]) + plt.subplots_adjust(left=0.098, right=0.945, top=0.94, wspace=0.343) + scatterwinner = 1.15 + scatterloser = 1.85 + chirps_winner = np.asarray(chirps_winner)[~np.isnan(chirps_winner)] + chirps_loser = np.asarray(chirps_loser)[~np.isnan(chirps_loser)] + + bplot1 = ax1.boxplot(chirps_winner, positions=[ + 1], showfliers=False, patch_artist=True) + bplot2 = ax1.boxplot(chirps_loser, positions=[ + 2], showfliers=False, patch_artist=True) + 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(0.1, 0.9, f'n = {len(chirps_winner)}', + transform=ax1.transAxes, color=ps.white) + + for w, l in zip(chirps_winner, chirps_loser): + ax1.plot([scatterwinner, scatterloser], [w, l], + color='r', alpha=0.5, linewidth=0.5) + ax1.set_ylabel('Chirps [n]', color=ps.white) + + colors1 = ps.red + ps.set_boxplot_color(bplot1, colors1) + colors1 = ps.orange + ps.set_boxplot_color(bplot2, colors1) + ax2.scatter(size_diffs, size_chirps_diffs, color='r') + ax2.set_xlabel('Size difference [mm]') + ax2.set_ylabel('Chirps [n]') + + ax3.scatter(freq_diffs, size_chirps_diffs, color='r') + # ax3.scatter(freq_diffs, freq_chirps_diffs, color='r') + ax3.set_xlabel('Frequency difference [Hz]') + ax3.set_yticklabels([]) + ax3.set + + plt.savefig('../poster/figs/chirps_winner_loser.pdf') + plt.show() + + +if __name__ == '__main__': + + # Path to the data + datapath = '../data/mount_data/' + + main(datapath) diff --git a/code/plot_chirp_size.py b/code/plot_chirp_size.py new file mode 100644 index 0000000..bcd36f4 --- /dev/null +++ b/code/plot_chirp_size.py @@ -0,0 +1,307 @@ +import numpy as np +from extract_chirps import get_valid_datasets + +import os + +import numpy as np +import matplotlib.pyplot as plt +from scipy.stats import pearsonr, spearmanr, wilcoxon +from thunderfish.powerspectrum import decibel + +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__) + + +def get_chirp_winner_loser(folder_name, Behavior, order_meta_df): + + foldername = folder_name.split('/')[-2] + winner_row = order_meta_df[order_meta_df['recording'] == foldername] + 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 > 0: + 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] + + chirp_winner = len( + Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) + chirp_loser = len( + Behavior.chirps[Behavior.chirps_ids == loser_fish_id]) + + return chirp_winner, chirp_loser + else: + return np.nan, np.nan + + +def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df): + + foldername = folder_name.split('/')[-2] + folder_row = order_meta_df[order_meta_df['recording'] == foldername] + fish1 = folder_row['fish1'].values[0].astype(int) + fish2 = folder_row['fish2'].values[0].astype(int) + winner = folder_row['winner'].values[0].astype(int) + + groub = folder_row['group'].values[0].astype(int) + size_fish1_row = id_meta_df[(id_meta_df['group'] == groub) & ( + id_meta_df['fish'] == fish1)] + size_fish2_row = id_meta_df[(id_meta_df['group'] == groub) & ( + id_meta_df['fish'] == fish2)] + + size_winners = [size_fish1_row[col].values[0] + for col in ['l1', 'l2', 'l3']] + size_fish1 = np.nanmean(size_winners) + + size_losers = [size_fish2_row[col].values[0] for col in ['l1', 'l2', 'l3']] + size_fish2 = np.nanmean(size_losers) + if winner == fish1: + if size_fish1 > size_fish2: + size_diff_bigger = size_fish1 - size_fish2 + size_diff_smaller = size_fish2 - size_fish1 + + elif size_fish1 < size_fish2: + size_diff_bigger = size_fish1 - size_fish2 + size_diff_smaller = size_fish2 - size_fish1 + else: + size_diff_bigger = np.nan + size_diff_smaller = np.nan + winner_fish_id = np.nan + loser_fish_id = np.nan + return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id + + winner_fish_id = folder_row['rec_id1'].values[0] + loser_fish_id = folder_row['rec_id2'].values[0] + + elif winner == fish2: + if size_fish2 > size_fish1: + size_diff_bigger = size_fish2 - size_fish1 + size_diff_smaller = size_fish1 - size_fish2 + + elif size_fish2 < size_fish1: + size_diff_bigger = size_fish2 - size_fish1 + size_diff_smaller = size_fish1 - size_fish2 + else: + size_diff_bigger = np.nan + size_diff_smaller = np.nan + winner_fish_id = np.nan + loser_fish_id = np.nan + return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id + + winner_fish_id = folder_row['rec_id2'].values[0] + loser_fish_id = folder_row['rec_id1'].values[0] + else: + size_diff_bigger = np.nan + size_diff_smaller = np.nan + winner_fish_id = np.nan + loser_fish_id = np.nan + return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id + + chirp_winner = len( + Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) + chirp_loser = len( + Behavior.chirps[Behavior.chirps_ids == loser_fish_id]) + + return size_diff_bigger, chirp_winner, size_diff_smaller, chirp_loser + + +def get_chirp_freq(folder_name, Behavior, order_meta_df): + + foldername = folder_name.split('/')[-2] + folder_row = order_meta_df[order_meta_df['recording'] == foldername] + fish1 = folder_row['fish1'].values[0].astype(int) + fish2 = folder_row['fish2'].values[0].astype(int) + + fish1_freq = folder_row['rec_id1'].values[0].astype(int) + fish2_freq = folder_row['rec_id2'].values[0].astype(int) + winner = folder_row['winner'].values[0].astype(int) + chirp_freq_fish1 = np.nanmedian( + Behavior.freq[Behavior.ident == fish1_freq]) + chirp_freq_fish2 = np.nanmedian( + Behavior.freq[Behavior.ident == fish2_freq]) + + if winner == fish1: + if chirp_freq_fish1 > chirp_freq_fish2: + freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2 + freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1 + + elif chirp_freq_fish1 < chirp_freq_fish2: + freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2 + freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1 + else: + freq_diff_higher = np.nan + freq_diff_lower = np.nan + winner_fish_id = np.nan + loser_fish_id = np.nan + + winner_fish_id = folder_row['rec_id1'].values[0] + loser_fish_id = folder_row['rec_id2'].values[0] + + elif winner == fish2: + if chirp_freq_fish2 > chirp_freq_fish1: + freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1 + freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2 + + elif chirp_freq_fish2 < chirp_freq_fish1: + freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1 + freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2 + else: + freq_diff_higher = np.nan + freq_diff_lower = np.nan + winner_fish_id = np.nan + loser_fish_id = np.nan + + winner_fish_id = folder_row['rec_id2'].values[0] + loser_fish_id = folder_row['rec_id1'].values[0] + else: + freq_diff_higher = np.nan + freq_diff_lower = np.nan + winner_fish_id = np.nan + loser_fish_id = np.nan + + chirp_winner = len( + Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) + chirp_loser = len( + Behavior.chirps[Behavior.chirps_ids == loser_fish_id]) + + return freq_diff_higher, chirp_winner, freq_diff_lower, chirp_loser + + +def main(datapath: str): + + foldernames = [ + datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)] + foldernames, _ = get_valid_datasets(datapath) + path_order_meta = ( + '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv' + 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_diffs_winner = [] + size_diffs_loser = [] + size_chirps_winner = [] + size_chirps_loser = [] + + freq_diffs_higher = [] + freq_diffs_lower = [] + freq_chirps_winner = [] + freq_chirps_loser = [] + + chirps_loser = [] + freq_diffs = [] + freq_chirps_diffs = [] + + 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) + + winner_chirp, loser_chirp = get_chirp_winner_loser( + foldername, bh, order_meta_df) + chirps_winner.append(winner_chirp) + chirps_loser.append(loser_chirp) + + size_diff_bigger, chirp_winner, size_diff_smaller, chirp_loser = get_chirp_size( + foldername, bh, order_meta_df, id_meta_df) + + freq_diff_higher, chirp_freq_winner, freq_diff_lower, chirp_freq_loser = get_chirp_freq( + foldername, bh, order_meta_df) + + freq_diffs_higher.append(freq_diff_higher) + freq_diffs_lower.append(freq_diff_lower) + freq_chirps_winner.append(chirp_freq_winner) + freq_chirps_loser.append(chirp_freq_loser) + + if np.isnan(size_diff_bigger): + continue + size_diffs_winner.append(size_diff_bigger) + size_diffs_loser.append(size_diff_smaller) + size_chirps_winner.append(chirp_winner) + size_chirps_loser.append(chirp_loser) + + size_winner_pearsonr = pearsonr(size_diffs_winner, size_chirps_winner) + size_loser_pearsonr = pearsonr(size_diffs_loser, size_chirps_loser) + + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=( + 13*ps.cm, 10*ps.cm), sharey=True) + plt.subplots_adjust(left=0.098, right=0.945, top=0.94, wspace=0.343) + scatterwinner = 1.15 + scatterloser = 1.85 + chirps_winner = np.asarray(chirps_winner)[~np.isnan(chirps_winner)] + chirps_loser = np.asarray(chirps_loser)[~np.isnan(chirps_loser)] + + stat = wilcoxon(chirps_winner, chirps_loser) + print(stat) + + bplot1 = ax1.boxplot(chirps_winner, positions=[ + 0.9], showfliers=False, patch_artist=True) + + bplot2 = ax1.boxplot(chirps_loser, positions=[ + 2.1], showfliers=False, patch_artist=True) + ax1.scatter(np.ones(len(chirps_winner)) * + scatterwinner, chirps_winner, color=ps.red) + ax1.scatter(np.ones(len(chirps_loser)) * + scatterloser, chirps_loser, color=ps.orange) + ax1.set_xticklabels(['winner', 'loser']) + ax1.text(0.1, 0.9, f'n = {len(chirps_winner)}', + transform=ax1.transAxes, color=ps.white) + + for w, l in zip(chirps_winner, chirps_loser): + ax1.plot([scatterwinner, scatterloser], [w, l], + color=ps.white, alpha=1, linewidth=0.5) + ax1.set_ylabel('chirps [n]', color=ps.white) + ax1.set_xlabel('outcome', color=ps.white) + + colors1 = ps.red + ps.set_boxplot_color(bplot1, colors1) + colors1 = ps.orange + ps.set_boxplot_color(bplot2, colors1) + + ax2.scatter(size_diffs_winner, size_chirps_winner, + color=ps.red, label='winner') + ax2.scatter(size_diffs_loser, size_chirps_loser, + color=ps.orange, label='loser') + + ax2.set_xlabel('size difference [cm]') + # ax2.set_xticks(np.arange(-10, 10.1, 2)) + + handles, labels = ax2.get_legend_handles_labels() + fig.legend(handles, labels, loc='upper center', ncol=2) + plt.subplots_adjust(left=0.162, right=0.97, top=0.85, bottom=0.176) + + # pearson r + plt.savefig('../poster/figs/chirps_winner_loser.pdf') + plt.show() + + +if __name__ == '__main__': + + # Path to the data + datapath = '../data/mount_data/' + + main(datapath) diff --git a/code/plot_chirps_in_chasing.py b/code/plot_chirps_in_chasing.py new file mode 100644 index 0000000..98894d8 --- /dev/null +++ b/code/plot_chirps_in_chasing.py @@ -0,0 +1,81 @@ +import numpy as np + +import os + +import numpy as np +import matplotlib.pyplot as plt +from scipy.stats import pearsonr, spearmanr +from thunderfish.powerspectrum import decibel + +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 +from modules.datahandling import flatten + + +ps = PlotStyle() + +logger = makeLogger(__name__) + + +def main(datapath: str): + + foldernames = [ + datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)] + time_precents = [] + chirps_percents = [] + 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) + + 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) + + chasing_onset = timestamps[category == 0] + chasing_offset = timestamps[category == 1] + if len(chasing_onset) != len(chasing_offset): + embed() + + chirps_in_chasings = [] + for onset, offset in zip(chasing_onset, chasing_offset): + chirps_in_chasing = [c for c in bh.chirps if (c > onset) & (c < offset)] + chirps_in_chasings.append(chirps_in_chasing) + + try: + time_chasing = np.sum(chasing_offset[chasing_offset<3*60*60] - chasing_onset[chasing_onset<3*60*60]) + except: + time_chasing = np.sum(chasing_offset[chasing_offset<3*60*60] - chasing_onset[chasing_onset<3*60*60][:-1]) + + + time_chasing_percent = (time_chasing/(3*60*60))*100 + chirps_chasing = np.asarray(flatten(chirps_in_chasings)) + chirps_chasing_new = chirps_chasing[chirps_chasing<3*60*60] + chirps_percent = (len(chirps_chasing_new)/len(bh.chirps))*100 + + time_precents.append(time_chasing_percent) + chirps_percents.append(chirps_percent) + + fig, ax = plt.subplots(1, 1, figsize=(14*ps.cm, 10*ps.cm)) + + ax.boxplot([time_precents, chirps_percents]) + ax.set_xticklabels(['Time Chasing', 'Chirps in Chasing']) + ax.set_ylabel('Percent') + ax.scatter(np.ones(len(time_precents))*1.25, time_precents, color=ps.white) + ax.scatter(np.ones(len(chirps_percents))*1.75, chirps_percents, color=ps.white) + plt.savefig('../poster/figs/chirps_in_chasing.pdf') + plt.show() + + +if __name__ == '__main__': + # Path to the data + datapath = '../data/mount_data/' + main(datapath) + + diff --git a/code/plot_event_timeline.py b/code/plot_event_timeline.py new file mode 100644 index 0000000..0b35484 --- /dev/null +++ b/code/plot_event_timeline.py @@ -0,0 +1,123 @@ +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 +from modules.behaviour_handling import Behavior, correct_chasing_events + +from extract_chirps import get_valid_datasets +ps = PlotStyle() + +logger = makeLogger(__name__) + + +def main(datapath: str): + + foldernames = [ + datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)] + foldernames, _ = get_valid_datasets(datapath) + for foldername in foldernames[1:2]: + # foldername = foldernames[0] + if foldername == '../data/mount_data/2020-05-12-10_00/': + continue + # behabvior is pandas dataframe with all the data + bh = Behavior(foldername) + # 2020-06-11-10 + 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.purple + fish2_color = ps.lavender + + fig, ax = plt.subplots(5, 1, figsize=( + 21*ps.cm, 10*ps.cm), height_ratios=[0.5, 0.5, 0.5, 0.2, 6], sharex=True) + # marker size + s = 80 + ax[0].scatter(physical_contact, np.ones( + len(physical_contact)), color=ps.maroon, marker='|', s=s) + ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), + color=ps.orange, 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[4].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[4].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[0].yaxis.set_label_coords(-0.1, 0.5) + + 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[4].axvspan(3, 6, 0, 5, facecolor='grey', alpha=0.5) + ax[4].set_xticks(np.arange(0, 6.1, 0.5)) + ps.hide_ax(ax[3]) + + labelpad = 30 + fsize = 12 + ax[0].set_ylabel('contact', rotation=0, + labelpad=labelpad, fontsize=fsize) + ax[1].set_ylabel('chasing', rotation=0, + labelpad=labelpad, fontsize=fsize) + ax[2].set_ylabel('chirps', rotation=0, + labelpad=labelpad, fontsize=fsize) + ax[4].set_ylabel('EODf') + + ax[4].set_xlabel('time [h]') + # ax[0].set_title(foldername.split('/')[-2]) + # 2020-03-31-9_59 + plt.subplots_adjust(left=0.158, right=0.987, top=0.918) + # plt.savefig('../poster/figs/timeline.pdf') + plt.show() + + # plot chirps + + +if __name__ == '__main__': + # Path to the data + datapath = '../data/mount_data/' + main(datapath) diff --git a/code/plot_introduction_specs.py b/code/plot_introduction_specs.py new file mode 100644 index 0000000..20fb562 --- /dev/null +++ b/code/plot_introduction_specs.py @@ -0,0 +1,121 @@ +import numpy as np +import matplotlib.pyplot as plt +from thunderfish.powerspectrum import spectrogram, decibel + +from modules.filehandling import LoadData +from modules.datahandling import instantaneous_frequency +from modules.filters import bandpass_filter +from modules.plotstyle import PlotStyle + +ps = PlotStyle() + + +def main(): + + # Load data + datapath = "../data/2022-06-02-10_00/" + data = LoadData(datapath) + + # good chirp times for data: 2022-06-02-10_00 + window_start_seconds = 3 * 60 * 60 + 6 * 60 + 43.5 + 9 + 6.25 + window_start_index = window_start_seconds * data.raw_rate + window_duration_seconds = 0.2 + window_duration_index = window_duration_seconds * data.raw_rate + + timescaler = 1000 + + raw = data.raw[window_start_index:window_start_index + + window_duration_index, 10] + + fig, (ax1, ax2, ax3) = plt.subplots( + 3, 1, figsize=(12 * ps.cm, 10*ps.cm), sharex=True, sharey=True) + + # plot instantaneous frequency + filtered1 = bandpass_filter( + signal=raw, lowf=750, highf=1200, samplerate=data.raw_rate) + filtered2 = bandpass_filter( + signal=raw, lowf=550, highf=700, samplerate=data.raw_rate) + + freqtime1, freq1 = instantaneous_frequency( + filtered1, data.raw_rate, smoothing_window=3) + freqtime2, freq2 = instantaneous_frequency( + filtered2, data.raw_rate, smoothing_window=3) + + ax1.plot(freqtime1*timescaler, freq1, color=ps.red, + lw=2, label=f"fish 1, {np.median(freq1):.0f} Hz") + ax1.plot(freqtime2*timescaler, freq2, color=ps.orange, + lw=2, label=f"fish 2, {np.median(freq2):.0f} Hz") + ax1.legend(bbox_to_anchor=(0, 1.02, 1, 0.2), loc="lower center", + mode="normal", borderaxespad=0, ncol=2) + ps.hide_xax(ax1) + + # plot fine spectrogram + spec_power, spec_freqs, spec_times = spectrogram( + raw, + ratetime=data.raw_rate, + freq_resolution=150, + overlap_frac=0.2, + ) + + ylims = [300, 1200] + fmask = np.zeros(spec_freqs.shape, dtype=bool) + fmask[(spec_freqs > ylims[0]) & (spec_freqs < ylims[1])] = True + + ax2.imshow( + decibel(spec_power[fmask, :]), + extent=[ + spec_times[0]*timescaler, + spec_times[-1]*timescaler, + spec_freqs[fmask][0], + spec_freqs[fmask][-1], + ], + aspect="auto", + origin="lower", + interpolation="gaussian", + alpha=1, + ) + ps.hide_xax(ax2) + + # plot coarse spectrogram + spec_power, spec_freqs, spec_times = spectrogram( + raw, + ratetime=data.raw_rate, + freq_resolution=10, + overlap_frac=0.3, + ) + fmask = np.zeros(spec_freqs.shape, dtype=bool) + fmask[(spec_freqs > ylims[0]) & (spec_freqs < ylims[1])] = True + ax3.imshow( + decibel(spec_power[fmask, :]), + extent=[ + spec_times[0]*timescaler, + spec_times[-1]*timescaler, + spec_freqs[fmask][0], + spec_freqs[fmask][-1], + ], + aspect="auto", + origin="lower", + interpolation="gaussian", + alpha=1, + ) + # ps.hide_xax(ax3) + + ax3.set_xlabel("time [ms]") + ax2.set_ylabel("frequency [Hz]") + + ax1.set_yticks(np.arange(400, 1201, 400)) + ax1.spines.left.set_bounds((400, 1200)) + ax2.set_yticks(np.arange(400, 1201, 400)) + ax2.spines.left.set_bounds((400, 1200)) + ax3.set_yticks(np.arange(400, 1201, 400)) + ax3.spines.left.set_bounds((400, 1200)) + + plt.subplots_adjust(left=0.17, right=0.98, top=0.9, + bottom=0.14, hspace=0.35) + + plt.savefig('../poster/figs/introplot.pdf') + plt.show() + + +if __name__ == '__main__': + main() diff --git a/code/plot_kdes.py b/code/plot_kdes.py new file mode 100644 index 0000000..5fd9cad --- /dev/null +++ b/code/plot_kdes.py @@ -0,0 +1,471 @@ +from extract_chirps import get_valid_datasets +import os + +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.datahandling import flatten, causal_kde1d, acausal_kde1d +from modules.behaviour_handling import ( + Behavior, correct_chasing_events, center_chirps) +from modules.plotstyle import PlotStyle + +logger = makeLogger(__name__) +ps = PlotStyle() + + +def bootstrap(data, nresamples, kde_time, kernel_width, event_times, time_before, time_after): + + bootstrapped_kdes = [] + data = data[data <= 3*60*60] # only night time + + # diff_data = np.diff(np.sort(data), prepend=0) + # if len(data) != 0: + # mean_chirprate = (len(data) - 1) / (data[-1] - data[0]) + + for i in tqdm(range(nresamples)): + + # np.random.shuffle(diff_data) + + # bootstrapped_data = np.cumsum(diff_data) + bootstrapped_data = data + np.random.randn(len(data)) * 10 + + bootstrap_data_centered = center_chirps( + bootstrapped_data, event_times, time_before, time_after) + + bootstrapped_kde = acausal_kde1d( + bootstrap_data_centered, time=kde_time, width=kernel_width) + + # bootstrapped_kdes = list(np.asarray( + # bootstrapped_kdes) / len(event_times)) + + bootstrapped_kdes.append(bootstrapped_kde) + + return bootstrapped_kdes + + +def jackknife(data, nresamples, subsetsize, kde_time, kernel_width, event_times, time_before, time_after): + + jackknife_kdes = [] + data = data[data <= 3*60*60] # only night time + subsetsize = int(len(data) * subsetsize) + + diff_data = np.diff(np.sort(data), prepend=0) + + for i in tqdm(range(nresamples)): + + bootstrapped_data = np.random.sample(data, subsetsize, replace=False) + + bootstrapped_data = np.cumsum(diff_data) + + bootstrap_data_centered = center_chirps( + bootstrapped_data, event_times, time_before, time_after) + + bootstrapped_kde = acausal_kde1d( + bootstrap_data_centered, time=kde_time, width=kernel_width) + + # bootstrapped_kdes = list(np.asarray( + # bootstrapped_kdes) / len(event_times)) + + jackknife_kdes.append(bootstrapped_kde) + + return jackknife_kdes + + +def get_chirp_winner_loser(folder_name, Behavior, order_meta_df): + + foldername = folder_name.split('/')[-2] + winner_row = order_meta_df[order_meta_df['recording'] == foldername] + 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 > 0: + 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] + + chirp_winner = Behavior.chirps[Behavior.chirps_ids == winner_fish_id] + chirp_loser = Behavior.chirps[Behavior.chirps_ids == loser_fish_id] + + return chirp_winner, chirp_loser + return None, None + + +def main(dataroot): + + foldernames, _ = get_valid_datasets(dataroot) + plot_all = True + time_before = 60 + time_after = 60 + dt = 0.001 + kernel_width = 1 + kde_time = np.arange(-time_before, time_after, dt) + nbootstraps = 2 + + meta_path = ( + '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv' + meta = pd.read_csv(meta_path) + meta['recording'] = meta['recording'].str[1:-1] + + winner_onsets = [] + winner_offsets = [] + winner_physicals = [] + + loser_onsets = [] + loser_offsets = [] + loser_physicals = [] + + winner_onsets_boot = [] + winner_offsets_boot = [] + winner_physicals_boot = [] + + loser_onsets_boot = [] + loser_offsets_boot = [] + loser_physicals_boot = [] + + onset_count = 0 + offset_count = 0 + physical_count = 0 + + # Iterate over all recordings and save chirp- and event-timestamps + for folder in tqdm(foldernames): + + foldername = folder.split('/')[-2] + # logger.info('Loading data from folder: {}'.format(foldername)) + + broken_folders = ['../data/mount_data/2020-05-12-10_00/'] + if folder in broken_folders: + continue + + bh = Behavior(folder) + category, timestamps = correct_chasing_events(bh.behavior, bh.start_s) + + category = category[timestamps < 3*60*60] # only night time + timestamps = timestamps[timestamps < 3*60*60] # only night time + + winner, loser = get_chirp_winner_loser(folder, bh, meta) + + if winner is None: + continue + + onsets = (timestamps[category == 0]) + offsets = (timestamps[category == 1]) + physicals = (timestamps[category == 2]) + + onset_count += len(onsets) + offset_count += len(offsets) + physical_count += len(physicals) + + winner_onsets.append(center_chirps( + winner, onsets, time_before, time_after)) + winner_offsets.append(center_chirps( + winner, offsets, time_before, time_after)) + winner_physicals.append(center_chirps( + winner, physicals, time_before, time_after)) + + loser_onsets.append(center_chirps( + loser, onsets, time_before, time_after)) + loser_offsets.append(center_chirps( + loser, offsets, time_before, time_after)) + loser_physicals.append(center_chirps( + loser, physicals, time_before, time_after)) + + # bootstrap + # chirps = [winner, winner, winner, loser, loser, loser] + + winner_onsets_boot.append(bootstrap( + winner, + nresamples=nbootstraps, + kde_time=kde_time, + kernel_width=kernel_width, + event_times=onsets, + time_before=time_before, + time_after=time_after)) + winner_offsets_boot.append(bootstrap( + winner, + nresamples=nbootstraps, + kde_time=kde_time, + kernel_width=kernel_width, + event_times=offsets, + time_before=time_before, + time_after=time_after)) + winner_physicals_boot.append(bootstrap( + winner, + nresamples=nbootstraps, + kde_time=kde_time, + kernel_width=kernel_width, + event_times=physicals, + time_before=time_before, + time_after=time_after)) + + loser_onsets_boot.append(bootstrap( + loser, + nresamples=nbootstraps, + kde_time=kde_time, + kernel_width=kernel_width, + event_times=onsets, + time_before=time_before, + time_after=time_after)) + loser_offsets_boot.append(bootstrap( + loser, + nresamples=nbootstraps, + kde_time=kde_time, + kernel_width=kernel_width, + event_times=offsets, + time_before=time_before, + time_after=time_after)) + loser_physicals_boot.append(bootstrap( + loser, + nresamples=nbootstraps, + kde_time=kde_time, + kernel_width=kernel_width, + event_times=physicals, + time_before=time_before, + time_after=time_after)) + + if plot_all: + + winner_onsets_conv = acausal_kde1d( + winner_onsets[-1], kde_time, kernel_width) + winner_offsets_conv = acausal_kde1d( + winner_offsets[-1], kde_time, kernel_width) + winner_physicals_conv = acausal_kde1d( + winner_physicals[-1], kde_time, kernel_width) + + loser_onsets_conv = acausal_kde1d( + loser_onsets[-1], kde_time, kernel_width) + loser_offsets_conv = acausal_kde1d( + loser_offsets[-1], kde_time, kernel_width) + loser_physicals_conv = acausal_kde1d( + loser_physicals[-1], kde_time, kernel_width) + + fig, ax = plt.subplots(2, 3, figsize=( + 21*ps.cm, 10*ps.cm), sharey=True, sharex=True) + ax[0, 0].set_title( + f"{foldername}, onsets {len(onsets)}, offsets {len(offsets)}, physicals {len(physicals)},winner {len(winner)}, looser {len(loser)} , onsets") + ax[0, 0].plot(kde_time, winner_onsets_conv/len(onsets)) + ax[0, 1].plot(kde_time, winner_offsets_conv/len(offsets)) + ax[0, 2].plot(kde_time, winner_physicals_conv/len(physicals)) + ax[1, 0].plot(kde_time, loser_onsets_conv/len(onsets)) + ax[1, 1].plot(kde_time, loser_offsets_conv/len(offsets)) + ax[1, 2].plot(kde_time, loser_physicals_conv/len(physicals)) + + # # plot bootstrap lines + for kde in winner_onsets_boot[-1]: + ax[0, 0].plot(kde_time, kde/len(onsets), + color='gray') + for kde in winner_offsets_boot[-1]: + ax[0, 1].plot(kde_time, kde/len(offsets), + color='gray') + for kde in winner_physicals_boot[-1]: + ax[0, 2].plot(kde_time, kde/len(physicals), + color='gray') + for kde in loser_onsets_boot[-1]: + ax[1, 0].plot(kde_time, kde/len(onsets), + color='gray') + for kde in loser_offsets_boot[-1]: + ax[1, 1].plot(kde_time, kde/len(offsets), + color='gray') + for kde in loser_physicals_boot[-1]: + ax[1, 2].plot(kde_time, kde/len(physicals), + color='gray') + + # plot bootstrap percentiles + # ax[0, 0].fill_between( + # kde_time, + # np.percentile(winner_onsets_boot[-1], 5, axis=0), + # np.percentile(winner_onsets_boot[-1], 95, axis=0), + # color='gray', + # alpha=0.5) + # ax[0, 1].fill_between( + # kde_time, + # np.percentile(winner_offsets_boot[-1], 5, axis=0), + # np.percentile( + # winner_offsets_boot[-1], 95, axis=0), + # color='gray', + # alpha=0.5) + # ax[0, 2].fill_between( + # kde_time, + # np.percentile( + # winner_physicals_boot[-1], 5, axis=0), + # np.percentile( + # winner_physicals_boot[-1], 95, axis=0), + # color='gray', + # alpha=0.5) + # ax[1, 0].fill_between( + # kde_time, + # np.percentile(loser_onsets_boot[-1], 5, axis=0), + # np.percentile(loser_onsets_boot[-1], 95, axis=0), + # color='gray', + # alpha=0.5) + # ax[1, 1].fill_between( + # kde_time, + # np.percentile(loser_offsets_boot[-1], 5, axis=0), + # np.percentile(loser_offsets_boot[-1], 95, axis=0), + # color='gray', + # alpha=0.5) + # ax[1, 2].fill_between( + # kde_time, + # np.percentile( + # loser_physicals_boot[-1], 5, axis=0), + # np.percentile( + # loser_physicals_boot[-1], 95, axis=0), + # color='gray', + # alpha=0.5) + + # ax[0, 0].plot(kde_time, np.median(winner_onsets_boot[-1], axis=0), + # color='black', linewidth=2) + # ax[0, 1].plot(kde_time, np.median(winner_offsets_boot[-1], axis=0), + # color='black', linewidth=2) + # ax[0, 2].plot(kde_time, np.median(winner_physicals_boot[-1], axis=0), + # color='black', linewidth=2) + # ax[1, 0].plot(kde_time, np.median(loser_onsets_boot[-1], axis=0), + # color='black', linewidth=2) + # ax[1, 1].plot(kde_time, np.median(loser_offsets_boot[-1], axis=0), + # color='black', linewidth=2) + # ax[1, 2].plot(kde_time, np.median(loser_physicals_boot[-1], axis=0), + # color='black', linewidth=2) + + ax[0, 0].set_xlim(-30, 30) + plt.show() + + winner_onsets = np.sort(flatten(winner_onsets)) + winner_offsets = np.sort(flatten(winner_offsets)) + winner_physicals = np.sort(flatten(winner_physicals)) + loser_onsets = np.sort(flatten(loser_onsets)) + loser_offsets = np.sort(flatten(loser_offsets)) + loser_physicals = np.sort(flatten(loser_physicals)) + + winner_onsets_conv = acausal_kde1d( + winner_onsets, kde_time, kernel_width) + winner_offsets_conv = acausal_kde1d( + winner_offsets, kde_time, kernel_width) + winner_physicals_conv = acausal_kde1d( + winner_physicals, kde_time, kernel_width) + loser_onsets_conv = acausal_kde1d( + loser_onsets, kde_time, kernel_width) + loser_offsets_conv = acausal_kde1d( + loser_offsets, kde_time, kernel_width) + loser_physicals_conv = acausal_kde1d( + loser_physicals, kde_time, kernel_width) + + winner_onsets_conv = winner_onsets_conv / onset_count + winner_offsets_conv = winner_offsets_conv / offset_count + winner_physicals_conv = winner_physicals_conv / physical_count + loser_onsets_conv = loser_onsets_conv / onset_count + loser_offsets_conv = loser_offsets_conv / offset_count + loser_physicals_conv = loser_physicals_conv / physical_count + + winner_onsets_boot = np.concatenate( + winner_onsets_boot) + winner_offsets_boot = np.concatenate( + winner_offsets_boot) + winner_physicals_boot = np.concatenate( + winner_physicals_boot) + loser_onsets_boot = np.concatenate( + loser_onsets_boot) + loser_offsets_boot = np.concatenate( + loser_offsets_boot) + loser_physicals_boot = np.concatenate( + loser_physicals_boot) + + percs = [5, 50, 95] + winner_onsets_boot_quarts = np.percentile( + winner_onsets_boot, percs, axis=0) + winner_offsets_boot_quarts = np.percentile( + winner_offsets_boot, percs, axis=0) + winner_physicals_boot_quarts = np.percentile( + winner_physicals_boot, percs, axis=0) + loser_onsets_boot_quarts = np.percentile( + loser_onsets_boot, percs, axis=0) + loser_offsets_boot_quarts = np.percentile( + loser_offsets_boot, percs, axis=0) + loser_physicals_boot_quarts = np.percentile( + loser_physicals_boot, percs, axis=0) + + fig, ax = plt.subplots(2, 3, figsize=( + 21*ps.cm, 10*ps.cm), sharey=True, sharex=True) + + ax[0, 0].plot(kde_time, winner_onsets_conv) + ax[0, 1].plot(kde_time, winner_offsets_conv) + ax[0, 2].plot(kde_time, winner_physicals_conv) + ax[1, 0].plot(kde_time, loser_onsets_conv) + ax[1, 1].plot(kde_time, loser_offsets_conv) + ax[1, 2].plot(kde_time, loser_physicals_conv) + + ax[0, 0].plot(kde_time, winner_onsets_boot_quarts[1], c=ps.black) + ax[0, 1].plot(kde_time, winner_offsets_boot_quarts[1], c=ps.black) + ax[0, 2].plot(kde_time, winner_physicals_boot_quarts[1], c=ps.black) + ax[1, 0].plot(kde_time, loser_onsets_boot_quarts[1], c=ps.black) + ax[1, 1].plot(kde_time, loser_offsets_boot_quarts[1], c=ps.black) + ax[1, 2].plot(kde_time, loser_physicals_boot_quarts[1], c=ps.black) + + # for kde in winner_onsets_boot: + # ax[0, 0].plot(kde_time, kde, + # color='gray') + # for kde in winner_offsets_boot: + # ax[0, 1].plot(kde_time, kde, + # color='gray') + # for kde in winner_physicals_boot: + # ax[0, 2].plot(kde_time, kde, + # color='gray') + # for kde in loser_onsets_boot: + # ax[1, 0].plot(kde_time, kde, + # color='gray') + # for kde in loser_offsets_boot: + # ax[1, 1].plot(kde_time, kde, + # color='gray') + # for kde in loser_physicals_boot: + # ax[1, 2].plot(kde_time, kde, + # color='gray') + + ax[0, 0].fill_between(kde_time, + winner_onsets_boot_quarts[0], + winner_onsets_boot_quarts[2], + color=ps.gray, + alpha=0.5) + + ax[0, 1].fill_between(kde_time, + winner_offsets_boot_quarts[0], + winner_offsets_boot_quarts[2], + color=ps.gray, + alpha=0.5) + + ax[0, 2].fill_between(kde_time, + loser_physicals_boot_quarts[0], + loser_physicals_boot_quarts[2], + color=ps.gray, + alpha=0.5) + + ax[1, 0].fill_between(kde_time, + loser_onsets_boot_quarts[0], + loser_onsets_boot_quarts[2], + color=ps.gray, + alpha=0.5) + + ax[1, 1].fill_between(kde_time, + loser_offsets_boot_quarts[0], + loser_offsets_boot_quarts[2], + color=ps.gray, + alpha=0.5) + + ax[1, 2].fill_between(kde_time, + loser_physicals_boot_quarts[0], + loser_physicals_boot_quarts[2], + color=ps.gray, + alpha=0.5) + + plt.show() + + 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This profoundly limits our current + understanding of chirps to experiments + with single - or physically separated - individuals. + % \begin{tikzfigure}[] + % \label{griddrawing} + % \includegraphics[width=0.8\linewidth]{figs/introplot} + % \end{tikzfigure} +} +\myblock[TranspBlock]{Chirp detection}{ \begin{tikzfigure}[] - \label{griddrawing} - \includegraphics[width=\linewidth]{example-image-a} + \label{fig:alg1} + \includegraphics[width=0.9\linewidth]{figs/algorithm1} \end{tikzfigure} -} - -\myblock[TranspBlock]{Methods}{ + \vspace{2cm} \begin{tikzfigure}[] - \label{detector} - \includegraphics[width=\linewidth]{example-image-b} + \label{fig:alg2} + \includegraphics[width=1\linewidth]{figs/algorithm} \end{tikzfigure} + \vspace{0cm} } -\column{0.4} -\myblock[TranspBlock]{Results}{ - \lipsum[3][1-5] +\column{0.6} +\myblock[TranspBlock]{Chirps during competition}{ \begin{tikzfigure}[] - \label{modulations} - \includegraphics[width=\linewidth]{example-image-c} + \label{fig:example_b} + \includegraphics[width=\linewidth]{figs/timeline.pdf} \end{tikzfigure} -} + \noindent + \begin{itemize} + \setlength\itemsep{0.5em} + \item Two fish compete for one hidding place in one tank, + \item Experiment had a 3 hour long darkphase and a 3 hour long light phase. + \end{itemize} -\myblock[TranspBlock]{More Stuff}{ - \lipsum[3][1-9] + \noindent + \begin{minipage}[c]{0.7\linewidth} + \begin{tikzfigure}[] + \label{fig:example_b} + \includegraphics[width=\linewidth]{figs/chirps_winner_loser.pdf} + \end{tikzfigure} + \end{minipage} % no space if you would like to put them side by side + \begin{minipage}[c]{0.2\linewidth} + \begin{itemize} + \setlength\itemsep{0.5em} + \item Fish who won the competition chirped more often than the fish who lost. + \item + \end{itemize} + \end{minipage} } -\column{0.3} -\myblock[TranspBlock]{More Results}{ +\myblock[TranspBlock]{Interactions at modulations}{ + \vspace{-1.2cm} \begin{tikzfigure}[] - \label{results} - \includegraphics[width=\linewidth]{example-image-a} + \label{fig:example_c} + \includegraphics[width=0.5\linewidth]{example-image-c} \end{tikzfigure} - \begin{multicols}{2} - \lipsum[5][1-8] - \end{multicols} - \vspace{-1cm} + } -\myblock[TranspBlock]{Conclusion}{ +\myblock[GrayBlock]{Conclusion}{ \begin{itemize} \setlength\itemsep{0.5em} - \item \lipsum[1][1] - \item \lipsum[1][1] - \item \lipsum[1][1] + \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} + \end{columns} -\node[ - above right, +\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..da09710 100644 --- a/poster/style.tex +++ b/poster/style.tex @@ -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=0.8\linewidth]{figs/efishlogo.pdf} \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_old/figs/Untitled.png b/poster_old/figs/Untitled.png new file mode 100644 index 0000000..3259ce2 Binary files /dev/null and b/poster_old/figs/Untitled.png differ diff --git a/poster_old/figs/algorithm.pdf b/poster_old/figs/algorithm.pdf new file mode 100644 index 0000000..2e2c453 Binary files /dev/null and b/poster_old/figs/algorithm.pdf differ diff --git a/poster_old/figs/introplot.pdf b/poster_old/figs/introplot.pdf new file mode 100644 index 0000000..cbead3e Binary files /dev/null and b/poster_old/figs/introplot.pdf differ diff --git a/poster_old/figs/logo.png b/poster_old/figs/logo.png new file mode 100644 index 0000000..234652f Binary files /dev/null and b/poster_old/figs/logo.png differ diff --git a/poster_old/figs/logo.svg b/poster_old/figs/logo.svg new file mode 100644 index 0000000..b34ed6c --- /dev/null +++ b/poster_old/figs/logo.svg @@ -0,0 +1,1184 @@ + + + + diff --git a/poster_old/figs/placeholder1.png b/poster_old/figs/placeholder1.png new file mode 100644 index 0000000..2dc3349 Binary files /dev/null and b/poster_old/figs/placeholder1.png differ diff --git a/poster_old/main.pdf b/poster_old/main.pdf new file mode 100644 index 0000000..4c1a7c1 Binary files /dev/null and b/poster_old/main.pdf differ diff --git a/poster_old/main.tex b/poster_old/main.tex new file mode 100644 index 0000000..ca20bb3 --- /dev/null +++ b/poster_old/main.tex @@ -0,0 +1,119 @@ +\documentclass[25pt, a0paper, 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}} diff --git a/recs.csv b/recs.csv new file mode 100644 index 0000000..92b9a0a --- /dev/null +++ b/recs.csv @@ -0,0 +1,29 @@ +recording +2020-03-13-10_00 +2020-03-16-10_00 +2020-03-19-10_00 +2020-03-20-10_00 +2020-03-23-09_58 +2020-03-24-10_00 +2020-03-25-10_00 +2020-03-31-09_59 +2020-05-11-10_00 +2020-05-12-10_00 +2020-05-13-10_00 +2020-05-14-10_00 +2020-05-15-10_00 +2020-05-18-10_00 +2020-05-19-10_00 +2020-05-21-10_00 +2020-05-25-10_00 +2020-05-27-10_00 +2020-05-28-10_00 +2020-05-29-10_00 +2020-06-02-10_00 +2020-06-03-10_10 +2020-06-04-10_00 +2020-06-05-10_00 +2020-06-08-10_00 +2020-06-09-10_00 +2020-06-10-10_00 +2020-06-11-10_00