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/chirpdetection.py b/code/chirpdetection.py index 2a48025..c7e1be8 100755 --- a/code/chirpdetection.py +++ b/code/chirpdetection.py @@ -2,7 +2,9 @@ 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 thunderfish.powerspectrum import spectrogram, decibel from sklearn.preprocessing import normalize @@ -40,9 +42,12 @@ class PlotBuffer: 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 @@ -57,84 +62,202 @@ class PlotBuffer: # 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 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.times[window_idx] + 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, + ) # get indices on raw data - start_idx = self.t0 * self.data.raw_rate - window_duration = self.dt * self.data.raw_rate + 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] - fig, axs = plt.subplots( - 7, - 1, - figsize=(20 / 2.54, 12 / 2.54), - constrained_layout=True, - sharex=True, - sharey="row", + self.time = self.time - self.t0 + self.frequency_time = self.frequency_time - self.t0 + chirps = np.asarray(chirps) - self.t0 + self.t0_old = self.t0 + self.t0 = 0 + + fig = plt.figure( + figsize=(14 / 2.54, 20 / 2.54) + ) + + 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(axs[0], data_oi, self.data.raw_rate, self.t0) + _ = plot_spectrogram( + ax0, + data_oi, + self.data.raw_rate, + self.t0 - 5, + [np.max(self.frequency) - 200, 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)) + ] + + # 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)) + if track_id == self.track_id: + ax0.plot(t[tmask]-self.t0_old, f, lw=lw, + zorder=10, color=ps.gblue1) + else: + ax0.plot(t[tmask]-self.t0_old, f, lw=lw, + zorder=10, color=ps.gray, alpha=0.5) + + ax0.fill_between( + np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate), + q50 - self.config.minimal_bandwidth / 2, + q50 + self.config.minimal_bandwidth / 2, + color=ps.gblue1, + lw=1, + ls="dashed", + alpha=0.5, + ) + + ax0.fill_between( + np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate), + search_lower, + search_upper, + color=ps.gblue2, + lw=1, + ls="dashed", + alpha=0.5, + ) + # ax0.axhline(q50, spec_times[0], spec_times[-1], + # color=ps.gblue1, lw=2, ls="dashed") + # ax0.axhline(q50 + self.search_frequency, + # spec_times[0], spec_times[-1], + # color=ps.gblue2, lw=2, ls="dashed") for chirp in chirps: - axs[0].scatter( - chirp, np.median(self.frequency), c=ps.black, marker="x" + ax0.scatter( + chirp, np.median(self.frequency) + 150, c=ps.black, marker="v" ) # plot waveform of filtered signal - axs[1].plot(self.time, self.baseline, c=ps.green) + 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 - axs[2].plot(self.time, self.search) + 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 - axs[3].plot(self.frequency_time, self.frequency) + ax3.plot(self.frequency_time, self.frequency, + c=ps.gblue3, lw=lw, label="baseline inst. freq.") # plot filtered and rectified envelope - axs[4].plot(self.time, self.baseline_envelope) - axs[4].scatter( + ax4.plot(self.time, self.baseline_envelope, c=ps.gblue1, lw=lw) + ax4.scatter( (self.time)[self.baseline_peaks], self.baseline_envelope[self.baseline_peaks], - c=ps.red, + edgecolors=ps.red, + zorder=10, + marker="o", + facecolors="none", ) # plot envelope of search signal - axs[5].plot(self.time, self.search_envelope) - axs[5].scatter( + ax5.plot(self.time, self.search_envelope, c=ps.gblue2, lw=lw) + ax5.scatter( (self.time)[self.search_peaks], self.search_envelope[self.search_peaks], - c=ps.red, + edgecolors=ps.red, + zorder=10, + marker="o", + facecolors="none", ) # plot filtered instantaneous frequency - axs[6].plot(self.frequency_time, self.frequency_filtered) - axs[6].scatter( + 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], - c=ps.red, - ) - axs[0].set_ylim( - np.max(self.frequency) - 200, top=np.max(self.frequency) + 200 + edgecolors=ps.red, + zorder=10, + marker="o", + facecolors="none", ) - axs[6].set_xlabel("Time [s]") - axs[0].set_title("Spectrogram") - axs[1].set_title("Fitered baseline") - axs[2].set_title("Fitered above") - axs[3].set_title("Fitered baseline instanenous frequency") - axs[4].set_title("Filtered envelope of baseline envelope") - axs[5].set_title("Search envelope") - axs[6].set_title("Filtered absolute instantaneous frequency") + + ax0.set_ylabel("frequency [Hz]") + ax1.set_ylabel("a.u.") + ax2.set_ylabel("a.u.") + ax3.set_ylabel("Hz") + ax5.set_ylabel("a.u.") + 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() @@ -144,13 +267,18 @@ class PlotBuffer: self.config.outputdir + self.data.datapath.split("/")[-2] + "/" ) - plt.savefig(f"{out}{self.track_id}_{self.t0}.pdf") + 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, window_start_seconds: float -) -> None: + axis, + signal: np.ndarray, + samplerate: float, + window_start_seconds: float, + ylims: list[float] +) -> np.ndarray: """ Plot a spectrogram of a signal. @@ -172,22 +300,28 @@ def plot_spectrogram( spec_power, spec_freqs, spec_times = spectrogram( signal, ratetime=samplerate, - freq_resolution=20, + freq_resolution=10, overlap_frac=0.5, ) + fmask = np.zeros(spec_freqs.shape, dtype=bool) + fmask[(spec_freqs > ylims[0]) & (spec_freqs < ylims[1])] = True + axis.imshow( - decibel(spec_power), + decibel(spec_power[fmask, :]), extent=[ spec_times[0] + window_start_seconds, spec_times[-1] + window_start_seconds, - spec_freqs[0], - spec_freqs[-1], + spec_freqs[fmask][0], + spec_freqs[fmask][-1], ], aspect="auto", origin="lower", interpolation="gaussian", + alpha=1, ) + # axis.use_sticky_edges = False + return spec_times def extract_frequency_bands( @@ -244,9 +378,16 @@ def extract_frequency_bands( def window_median_all_track_ids( data: LoadData, window_start_seconds: float, window_duration_seconds: float -) -> tuple[float, list[int]]: +) -> tuple[list[tuple[float, float, float]], list[int]]: """ - Calculate the median frequency of all fish in a given time window. + Calculate the median and quantiles of the frequency of all fish in a + given time window. + + 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. Parameters ---------- @@ -259,14 +400,16 @@ def window_median_all_track_ids( Returns ------- - tuple[float, list[int]] + tuple[list[tuple[float, float, float]], list[int]] """ - median_freq = [] + 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) @@ -277,20 +420,21 @@ def window_median_all_track_ids( ] if len(data.freq[window_idx]) > 0: - median_freq.append(np.median(data.freq[window_idx])) + frequency_percentiles.append( + np.percentile(data.freq[window_idx], [25, 50, 75])) track_ids.append(track_id) # convert to numpy array - median_freq = np.asarray(median_freq) + frequency_percentiles = np.asarray(frequency_percentiles) track_ids = np.asarray(track_ids) - return median_freq, track_ids + return frequency_percentiles, track_ids def find_searchband( - freq_temp: np.ndarray, - median_ids: np.ndarray, - median_freq: np.ndarray, + current_frequency: np.ndarray, + percentiles_ids: np.ndarray, + frequency_percentiles: np.ndarray, config: ConfLoader, data: LoadData, ) -> float: @@ -300,13 +444,13 @@ def find_searchband( Parameters ---------- - freq_temp : np.ndarray + current_frequency : np.ndarray Current EOD frequency array / the current fish of interest. - median_ids : np.ndarray + percentiles_ids : np.ndarray Array of track IDs of the medians of all other fish in the current window. - median_freq : np.ndarray - Array of median frequencies 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 @@ -317,19 +461,27 @@ def find_searchband( float """ - # frequency where second filter filters + # 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. + search_window = np.arange( - np.median(freq_temp) + config.search_df_lower, - np.median(freq_temp) + config.search_df_upper, + np.median(current_frequency) + config.search_df_lower, + np.median(current_frequency) + config.search_df_upper, config.search_res, ) # search window in boolean - search_window_bool = np.ones(len(search_window), dtype=bool) + search_window_bool = np.ones_like(len(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 = median_ids[ - (median_freq > search_window[0]) & (median_freq < search_window[-1]) + check_track_ids = percentiles_ids[ + (q25 > search_window[0]) & ( + q75 < search_window[-1]) ] # iterate through theses tracks @@ -337,19 +489,22 @@ def find_searchband( for j, check_track_id in enumerate(check_track_ids): - q1, q2 = np.percentile( - data.freq[data.ident == check_track_id], - config.search_freq_percentiles, - ) + q25_temp = q25[percentiles_ids == check_track_id] + q75_temp = q75[percentiles_ids == check_track_id] + + print(q25_temp, q75_temp) search_window_bool[ - (search_window > q1) & (search_window < q2) + (search_window > q25_temp) & (search_window < q75_temp) ] = 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)] @@ -385,14 +540,16 @@ def find_searchband( 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 = ( longest_search_window[-1] - longest_search_window[0] ) / 2 - else: - search_freq = config.default_search_freq + return search_freq - return search_freq + return config.default_search_freq def main(datapath: str, plot: str) -> None: @@ -432,16 +589,21 @@ def main(datapath: str, plot: str) -> None: raw_time = np.arange(data.raw.shape[0]) / data.raw_rate # good chirp times for data: 2022-06-02-10_00 - window_start_seconds = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate - window_duration_seconds = 60 * data.raw_rate + window_start_index = (3 * 60 * 60 + 6 * 60 + 43.5 + 5) * data.raw_rate + window_duration_index = 60 * data.raw_rate # t0 = 0 # dt = data.raw.shape[0] + # window_start_seconds = (23495 + ((28336-23495)/3)) * data.raw_rate + # window_duration_seconds = (28336 - 23495) * data.raw_rate + + # window_start_index = 0 + # window_duration_index = data.raw.shape[0] # generate starting points of rolling window window_start_indices = np.arange( - window_start_seconds, - window_start_seconds + window_duration_seconds, + window_start_index, + window_start_index + window_duration_index, window_duration - (window_overlap + 2 * window_edge), dtype=int, ) @@ -523,10 +685,10 @@ def main(datapath: str, plot: str) -> None: search_frequency = find_searchband( config=config, - freq_temp=current_frequencies, - median_ids=median_ids, + current_frequency=current_frequencies, + percentiles_ids=median_ids, data=data, - median_freq=median_freq, + frequency_percentiles=median_freq, ) # add all chirps that are detected on mulitple electrodes for one @@ -598,11 +760,12 @@ def main(datapath: str, plot: str) -> None: # band envelope correspond to troughs in the baseline envelope # during chirps - search_envelope = envelope( + 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 @@ -656,8 +819,10 @@ def main(datapath: str, plot: str) -> None: ) 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 @@ -709,7 +874,9 @@ def main(datapath: str, plot: str) -> None: baseline_peak_timestamps = current_raw_time[ baseline_peak_indices ] - search_peak_timestamps = current_raw_time[search_peak_indices] + search_peak_timestamps = current_raw_time[ + search_peak_indices] + frequency_peak_timestamps = baseline_frequency_time[ frequency_peak_indices ] @@ -770,10 +937,13 @@ def main(datapath: str, plot: str) -> None: 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, @@ -810,9 +980,9 @@ def main(datapath: str, plot: str) -> None: multiwindow_chirps.append(multielectrode_chirps_validated) multiwindow_ids.append(track_id) - logger.debug( - "Found %d chirps, starting plotting ... " - % len(multielectrode_chirps_validated) + 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 @@ -877,4 +1047,6 @@ def main(datapath: str, plot: str) -> None: 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, plot="show") + # 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/" + main(datapath, plot="save") diff --git a/code/chirpdetector_conf.yml b/code/chirpdetector_conf.yml index 0292fd6..2f4fc9a 100755 --- a/code/chirpdetector_conf.yml +++ b/code/chirpdetector_conf.yml @@ -3,7 +3,7 @@ dataroot: "../data/" outputdir: "../output/" # Duration and overlap of the analysis window in seconds -window: 5 +window: 10 overlap: 1 edge: 0.25 @@ -12,7 +12,7 @@ number_electrodes: 3 minimum_electrodes: 2 # Search window bandwidth and minimal baseline bandwidth -minimal_bandwidth: 10 +minimal_bandwidth: 20 # Instantaneous frequency smoothing usint a gaussian kernel of this width baseline_frequency_smoothing: 5 diff --git a/code/modules/datahandling.py b/code/modules/datahandling.py index 72e9caf..9460b5b 100644 --- a/code/modules/datahandling.py +++ b/code/modules/datahandling.py @@ -3,6 +3,24 @@ from typing import List, Any from scipy.ndimage import gaussian_filter1d +def norm(data): + """ + Normalize data to [0, 1] + + Parameters + ---------- + data : np.ndarray + Data to normalize. + + Returns + ------- + np.ndarray + Normalized data. + + """ + return (2*((data - np.min(data)) / (np.max(data) - np.min(data)))) - 1 + + def instantaneous_frequency( signal: np.ndarray, samplerate: int, diff --git a/code/modules/plotstyle.py b/code/modules/plotstyle.py index 79886dc..2325f62 100644 --- a/code/modules/plotstyle.py +++ b/code/modules/plotstyle.py @@ -30,10 +30,14 @@ def PlotStyle() -> None: purple = "#cba6f7" pink = "#f5c2e7" lavender = "#b4befe" + gblue1 = "#8cb8ff" + gblue2 = "#7cdcdc" + gblue3 = "#82e896" @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 @@ -91,6 +95,16 @@ 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) @@ -216,8 +230,8 @@ def PlotStyle() -> None: 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["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 +244,9 @@ 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 @@ -271,7 +285,7 @@ def PlotStyle() -> None: 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.edgecolor"] = black # figure edge color plt.rcParams["savefig.facecolor"] = black # figure face color when saving return style diff --git a/code/plot_introduction_specs.py b/code/plot_introduction_specs.py new file mode 100644 index 0000000..3f8395e --- /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.gblue1, + lw=2, label=f"fish 1, {np.median(freq1):.0f} Hz") + ax1.plot(freqtime2*timescaler, freq2, color=ps.gblue3, + 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/poster/figs/Untitled.png b/poster/figs/Untitled.png new file mode 100644 index 0000000..3259ce2 Binary files /dev/null and b/poster/figs/Untitled.png differ diff --git a/poster/figs/algorithm.pdf b/poster/figs/algorithm.pdf new file mode 100644 index 0000000..2e2c453 Binary files /dev/null and b/poster/figs/algorithm.pdf differ diff --git a/poster/figs/introplot.pdf b/poster/figs/introplot.pdf new file mode 100644 index 0000000..cbead3e Binary files /dev/null and b/poster/figs/introplot.pdf differ diff --git a/poster/figs/logo.png b/poster/figs/logo.png new file mode 100644 index 0000000..234652f Binary files /dev/null and b/poster/figs/logo.png differ diff --git a/poster/figs/logo.svg b/poster/figs/logo.svg new file mode 100644 index 0000000..b34ed6c --- /dev/null +++ b/poster/figs/logo.svg @@ -0,0 +1,1184 @@ + + + + diff --git a/poster/figs/placeholder1.png b/poster/figs/placeholder1.png new file mode 100644 index 0000000..2dc3349 Binary files /dev/null and b/poster/figs/placeholder1.png differ diff --git a/poster/main.pdf b/poster/main.pdf index 06827b3..4c1a7c1 100644 Binary files a/poster/main.pdf and b/poster/main.pdf differ diff --git a/poster/main.tex b/poster/main.tex index da4bff1..ca20bb3 100644 --- a/poster/main.tex +++ b/poster/main.tex @@ -7,65 +7,101 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val \begin{document} \renewcommand{\baselinestretch}{1} -\title{\parbox{1900pt}{A dark template to make colorful figures pop}} +\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} +\institute{Supervised by Till Raab \& Jan Benda, Neurothology Group, +University of Tübingen} \usetitlestyle[]{sampletitle} \maketitle \renewcommand{\baselinestretch}{1.4} \begin{columns} -\column{0.3} +\column{0.5} \myblock[TranspBlock]{Introduction}{ - \lipsum[1][1-5] + \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{griddrawing} - \includegraphics[width=\linewidth]{example-image-a} + \label{tradeoff} + \includegraphics[width=\linewidth]{figs/introplot} \end{tikzfigure} + \end{minipage} } -\myblock[TranspBlock]{Methods}{ - \begin{tikzfigure}[] - \label{detector} - \includegraphics[width=\linewidth]{example-image-b} - \end{tikzfigure} +\myblock[TranspBlock]{A chirp detection algorithm}{ + \begin{tikzfigure}[] + \label{modulations} + \includegraphics[width=\linewidth]{figs/algorithm} + \end{tikzfigure} } -\column{0.4} -\myblock[TranspBlock]{Results}{ - \lipsum[3][1-5] +\column{0.5} +\myblock[TranspBlock]{Chirps and diadic competitions}{ + \begin{minipage}[t]{0.7\linewidth} \begin{tikzfigure}[] \label{modulations} - \includegraphics[width=\linewidth]{example-image-c} + \includegraphics[width=\linewidth]{figs/placeholder1} \end{tikzfigure} -} + \end{minipage} \hfill + \begin{minipage}[t]{0.25\linewidth} + \lipsum[3][1-3] + \end{minipage} -\myblock[TranspBlock]{More Stuff}{ - \lipsum[3][1-9] -} + \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} -\column{0.3} -\myblock[TranspBlock]{More Results}{ + \begin{minipage}[t]{0.7\linewidth} \begin{tikzfigure}[] - \label{results} - \includegraphics[width=\linewidth]{example-image-a} + \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{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} - } + \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[ @@ -78,6 +114,6 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val fill=boxes, color=boxes, ] at (-0.51\paperwidth,-43.5) { - \textcolor{text}{\normalsize Contact: name.surname@student.uni-tuebingen.de}}; +\textcolor{text}{\normalsize Contact: \{name\}.\{surname\}@student.uni-tuebingen.de}}; \end{document}