Merge branch 'master' into chirp_simulation
This commit is contained in:
commit
7b74a7c024
4
.gitignore
vendored
4
.gitignore
vendored
@ -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
|
||||
|
||||
312
README.md
312
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 <branch>
|
||||
```
|
||||
- commit chances
|
||||
```shell
|
||||
git commit -m '<explaination>' file # commit one file
|
||||
git commit -a -m '<explaination>' # commit all files
|
||||
```
|
||||
- push commits
|
||||
```shell
|
||||
git push origin <branch>
|
||||
```
|
||||
|
||||
## 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 <existing branch>
|
||||
# switch into new branch
|
||||
git checkout master
|
||||
git checkout -b <new branch>
|
||||
```
|
||||
|
||||
|
||||
- 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 <branch>
|
||||
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 <branch>
|
||||
git push origin master
|
||||
```
|
||||
<!-- Improved compatibility of back to top link: See: https://github.com/othneildrew/Best-README-Template/pull/73 -->
|
||||
<a name="readme-top"></a>
|
||||
<!--
|
||||
*** Thanks for checking out the Best-README-Template. If you have a suggestion
|
||||
*** that would make this better, please fork the repo and create a pull request
|
||||
*** or simply open an issue with the tag "enhancement".
|
||||
*** Don't forget to give the project a star!
|
||||
*** Thanks again! Now go create something AMAZING! :D
|
||||
-->
|
||||
|
||||
|
||||
|
||||
<!-- PROJECT SHIELDS -->
|
||||
<!--
|
||||
*** I'm using markdown "reference style" links for readability.
|
||||
*** Reference links are enclosed in brackets [ ] instead of parentheses ( ).
|
||||
*** See the bottom of this document for the declaration of the reference variables
|
||||
*** for contributors-url, forks-url, etc. This is an optional, concise syntax you may use.
|
||||
*** https://www.markdownguide.org/basic-syntax/#reference-style-links
|
||||
-->
|
||||
<!-- [![Contributors][contributors-shield]][contributors-url] -->
|
||||
<!-- [![Forks][forks-shield]][forks-url] -->
|
||||
<!-- [![Stargazers][stars-shield]][stars-url] -->
|
||||
<!-- [![Issues][issues-shield]][issues-url] -->
|
||||
<!-- [![MIT License][license-shield]][license-url] -->
|
||||
<!-- [![LinkedIn][linkedin-shield]][linkedin-url] -->
|
||||
|
||||
|
||||
|
||||
<!-- PROJECT LOGO -->
|
||||
<br />
|
||||
<div align="center">
|
||||
<a href="https://github.com/github_username/repo_name">
|
||||
<img src="assets/logo.png" alt="Logo" width="150" height="100">
|
||||
</a>
|
||||
|
||||
<h3 align="center">chirpdetector</h3>
|
||||
|
||||
<p align="center">
|
||||
An algorithm to detect the chirps of weakly electric fish.
|
||||
<br />
|
||||
<a href="https://github.com/github_username/repo_name"><strong>Explore the docs »</strong></a>
|
||||
<br />
|
||||
<br />
|
||||
<a href="https://github.com/github_username/repo_name">View Demo</a>
|
||||
·
|
||||
<a href="https://github.com/github_username/repo_name/issues">Report Bug</a>
|
||||
·
|
||||
<a href="https://github.com/github_username/repo_name/issues">Request Feature</a>
|
||||
</p>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<!-- TABLE OF CONTENTS -->
|
||||
<details>
|
||||
<summary>Table of Contents</summary>
|
||||
<ol>
|
||||
<li>
|
||||
<a href="#about-the-project">About The Project</a>
|
||||
<ul>
|
||||
<li><a href="#built-with">Built With</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<a href="#getting-started">Getting Started</a>
|
||||
<ul>
|
||||
<li><a href="#prerequisites">Prerequisites</a></li>
|
||||
<li><a href="#installation">Installation</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><a href="#usage">Usage</a></li>
|
||||
<li><a href="#roadmap">Roadmap</a></li>
|
||||
<li><a href="#contributing">Contributing</a></li>
|
||||
<li><a href="#license">License</a></li>
|
||||
<li><a href="#contact">Contact</a></li>
|
||||
<li><a href="#acknowledgments">Acknowledgments</a></li>
|
||||
</ol>
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
<!-- ABOUT THE PROJECT -->
|
||||
## 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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|
||||
|
||||
|
||||
|
||||
<!-- ### Built With -->
|
||||
|
||||
<!-- * [![Next][Next.js]][Next-url] -->
|
||||
<!-- * [![React][React.js]][React-url] -->
|
||||
<!-- * [![Vue][Vue.js]][Vue-url] -->
|
||||
<!-- * [![Angular][Angular.io]][Angular-url] -->
|
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|
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|
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<!-- * [![Bootstrap][Bootstrap.com]][Bootstrap-url] -->
|
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|
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|
||||
<p align="right">(<a href="#readme-top">back to top</a>)</p>
|
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|
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|
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|
||||
<!-- GETTING STARTED -->
|
||||
## 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.
|
||||
|
||||
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||||
|
||||
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|
||||
<!-- * npm -->
|
||||
<!-- ```sh -->
|
||||
<!-- npm install npm@latest -g -->
|
||||
<!-- ``` -->
|
||||
|
||||
### 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';
|
||||
```
|
||||
|
||||
<p align="right">(<a href="#readme-top">back to top</a>)</p>
|
||||
|
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|
||||
|
||||
<!-- USAGE EXAMPLES -->
|
||||
## 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.
|
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|
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_For more examples, please refer to the [Documentation](https://example.com)_
|
||||
|
||||
<p align="right">(<a href="#readme-top">back to top</a>)</p>
|
||||
|
||||
|
||||
|
||||
<!-- ROADMAP -->
|
||||
## 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).
|
||||
|
||||
<p align="right">(<a href="#readme-top">back to top</a>)</p>
|
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|
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|
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|
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|
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<!-- ## Contributing -->
|
||||
|
||||
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||||
|
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
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|
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|
||||
<!-- <!-1- LICENSE -1-> -->
|
||||
<!-- ## License -->
|
||||
|
||||
<!-- Distributed under the MIT License. See `LICENSE.txt` for more information. -->
|
||||
|
||||
<!-- <p align="right">(<a href="#readme-top">back to top</a>)</p> -->
|
||||
|
||||
|
||||
|
||||
<!-- CONTACT -->
|
||||
## 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)
|
||||
|
||||
<p align="right">(<a href="#readme-top">back to top</a>)</p>
|
||||
|
||||
|
||||
|
||||
<!-- ACKNOWLEDGMENTS -->
|
||||
## Acknowledgments
|
||||
|
||||
* []()
|
||||
* []()
|
||||
* []()
|
||||
|
||||
<p align="right">(<a href="#readme-top">back to top</a>)</p>
|
||||
|
||||
|
||||
|
||||
<!-- MARKDOWN LINKS & IMAGES -->
|
||||
<!-- https://www.markdownguide.org/basic-syntax/#reference-style-links -->
|
||||
[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
|
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|
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||||
[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/
|
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[React.js]: https://img.shields.io/badge/React-20232A?style=for-the-badge&logo=react&logoColor=61DAFB
|
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[React-url]: https://reactjs.org/
|
||||
[Vue.js]: https://img.shields.io/badge/Vue.js-35495E?style=for-the-badge&logo=vuedotjs&logoColor=4FC08D
|
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[Vue-url]: https://vuejs.org/
|
||||
[Angular.io]: https://img.shields.io/badge/Angular-DD0031?style=for-the-badge&logo=angular&logoColor=white
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[Angular-url]: https://angular.io/
|
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|
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|
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|
||||
[JQuery-url]: https://jquery.com
|
||||
|
||||
|
||||
64
README1.md
Normal file
64
README1.md
Normal file
@ -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 <branch>
|
||||
```
|
||||
- commit chances
|
||||
```shell
|
||||
git commit -m '<explaination>' file # commit one file
|
||||
git commit -a -m '<explaination>' # commit all files
|
||||
```
|
||||
- push commits
|
||||
```shell
|
||||
git push origin <branch>
|
||||
```
|
||||
|
||||
## 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 <existing branch>
|
||||
# switch into new branch
|
||||
git checkout master
|
||||
git checkout -b <new branch>
|
||||
```
|
||||
|
||||
|
||||
- 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 <branch>
|
||||
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 <branch>
|
||||
git push origin master
|
||||
```
|
||||
BIN
assets/logo.png
Normal file
BIN
assets/logo.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 40 KiB |
1184
assets/logo.svg
Normal file
1184
assets/logo.svg
Normal file
File diff suppressed because it is too large
Load Diff
|
After Width: | Height: | Size: 84 KiB |
234
code/behavior.py
234
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:
|
||||
@ -20,22 +25,36 @@ 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:
|
||||
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:
|
||||
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]))
|
||||
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
|
||||
@ -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)
|
||||
|
||||
1268
code/chirpdetection.py
Normal file → Executable file
1268
code/chirpdetection.py
Normal file → Executable file
File diff suppressed because it is too large
Load Diff
61
code/chirpdetector_conf.yml
Normal file → Executable file
61
code/chirpdetector_conf.yml
Normal file → Executable file
@ -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
|
||||
|
||||
|
||||
593
code/eventchirpsplots.py
Normal file
593
code/eventchirpsplots.py
Normal file
@ -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)
|
||||
58
code/extract_chirps.py
Normal file
58
code/extract_chirps.py
Normal file
@ -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
|
||||
35
code/get_behaviour.py
Normal file
35
code/get_behaviour.py
Normal file
@ -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()
|
||||
169
code/modules/behaviour_handling.py
Normal file
169
code/modules/behaviour_handling.py
Normal file
@ -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
|
||||
338
code/modules/datahandling.py
Normal file
338
code/modules/datahandling.py
Normal file
@ -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))
|
||||
@ -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
|
||||
|
||||
@ -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
|
||||
|
||||
41
code/modules/logger.py
Normal file
41
code/modules/logger.py
Normal file
@ -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!")
|
||||
@ -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
|
||||
@ -253,25 +268,27 @@ def PlotStyle() -> None:
|
||||
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.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)
|
||||
|
||||
277
code/plot_chirp_bodylegth(old).py
Normal file
277
code/plot_chirp_bodylegth(old).py
Normal file
@ -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)
|
||||
307
code/plot_chirp_size.py
Normal file
307
code/plot_chirp_size.py
Normal file
@ -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)
|
||||
81
code/plot_chirps_in_chasing.py
Normal file
81
code/plot_chirps_in_chasing.py
Normal file
@ -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)
|
||||
|
||||
|
||||
123
code/plot_event_timeline.py
Normal file
123
code/plot_event_timeline.py
Normal file
@ -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)
|
||||
121
code/plot_introduction_specs.py
Normal file
121
code/plot_introduction_specs.py
Normal file
@ -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()
|
||||
471
code/plot_kdes.py
Normal file
471
code/plot_kdes.py
Normal file
@ -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()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main('../data/mount_data/')
|
||||
BIN
poster/figs/algorithm.pdf
Normal file
BIN
poster/figs/algorithm.pdf
Normal file
Binary file not shown.
BIN
poster/figs/algorithm1.pdf
Normal file
BIN
poster/figs/algorithm1.pdf
Normal file
Binary file not shown.
BIN
poster/figs/chirps_in_chasing.pdf
Normal file
BIN
poster/figs/chirps_in_chasing.pdf
Normal file
Binary file not shown.
BIN
poster/figs/chirps_winner_loser.pdf
Normal file
BIN
poster/figs/chirps_winner_loser.pdf
Normal file
Binary file not shown.
BIN
poster/figs/efishlogo.pdf
Normal file
BIN
poster/figs/efishlogo.pdf
Normal file
Binary file not shown.
BIN
poster/figs/introplot.pdf
Normal file
BIN
poster/figs/introplot.pdf
Normal file
Binary file not shown.
529
poster/figs/logo_all.pdf
Normal file
529
poster/figs/logo_all.pdf
Normal file
File diff suppressed because one or more lines are too long
BIN
poster/figs/timeline.pdf
Normal file
BIN
poster/figs/timeline.pdf
Normal file
Binary file not shown.
BIN
poster/main.pdf
BIN
poster/main.pdf
Binary file not shown.
101
poster/main.tex
101
poster/main.tex
@ -1,4 +1,4 @@
|
||||
\documentclass[25pt, a0paper, landscape, margin=0mm, innermargin=20mm,
|
||||
\documentclass[25pt, a0paper, portrait, margin=0mm, innermargin=20mm,
|
||||
blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default values for poster format options.
|
||||
|
||||
\input{packages}
|
||||
@ -7,77 +7,98 @@ 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}}
|
||||
\author{Sina Prause, Alexander Wendt, Patrick Weygoldt}
|
||||
\institute{Supervised by Till Raab \& Jan Benda}
|
||||
\title{\parbox{1500pt}{Bypassing time-frequency uncertainty in the detection of transient communication signals in weakly electric fish}}
|
||||
\author{Sina Prause, Alexander Wendt, and Patrick Weygoldt}
|
||||
\institute{Supervised by Till Raab \& Jan Benda, Neuroethology Lab, University of Tuebingen}
|
||||
\usetitlestyle[]{sampletitle}
|
||||
\maketitle
|
||||
\renewcommand{\baselinestretch}{1.4}
|
||||
|
||||
\begin{columns}
|
||||
\column{0.3}
|
||||
\myblock[TranspBlock]{Introduction}{
|
||||
\lipsum[1][1-5]
|
||||
\column{0.4}
|
||||
\myblock[GrayBlock]{Introduction}{
|
||||
The time-frequency tradeoff makes reliable signal detecion and simultaneous
|
||||
sender identification by simple Fourier decomposition in freely interacting
|
||||
weakly electric fish impossible. 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}
|
||||
|
||||
@ -8,4 +8,3 @@
|
||||
\setlength{\columnsep}{1.5cm}
|
||||
\usepackage{xspace}
|
||||
\usepackage{tikz}
|
||||
\usepackage{lipsum}
|
||||
|
||||
@ -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
|
||||
|
||||
BIN
poster_old/figs/Untitled.png
Normal file
BIN
poster_old/figs/Untitled.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 116 KiB |
BIN
poster_old/figs/algorithm.pdf
Normal file
BIN
poster_old/figs/algorithm.pdf
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Binary file not shown.
BIN
poster_old/figs/introplot.pdf
Normal file
BIN
poster_old/figs/introplot.pdf
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Binary file not shown.
BIN
poster_old/figs/logo.png
Normal file
BIN
poster_old/figs/logo.png
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Binary file not shown.
|
After Width: | Height: | Size: 40 KiB |
1184
poster_old/figs/logo.svg
Normal file
1184
poster_old/figs/logo.svg
Normal file
File diff suppressed because it is too large
Load Diff
|
After Width: | Height: | Size: 84 KiB |
BIN
poster_old/figs/placeholder1.png
Normal file
BIN
poster_old/figs/placeholder1.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 157 KiB |
BIN
poster_old/main.pdf
Normal file
BIN
poster_old/main.pdf
Normal file
Binary file not shown.
119
poster_old/main.tex
Normal file
119
poster_old/main.tex
Normal file
@ -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}
|
||||
11
poster_old/packages.tex
Normal file
11
poster_old/packages.tex
Normal file
@ -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}
|
||||
119
poster_old/style.tex
Normal file
119
poster_old/style.tex
Normal file
@ -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}}
|
||||
29
recs.csv
Normal file
29
recs.csv
Normal file
@ -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
|
||||
|
Loading…
Reference in New Issue
Block a user