ap-features

Package to compute features of traces from action potential models

https://github.com/computationalphysiology/ap_features

Science Score: 67.0%

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CITATION.cff file
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Low similarity (10.1%) to scientific vocabulary

Last synced: 6 months ago · JSON representation ·

Repository

Package to compute features of traces from action potential models

Basic Info

Host: GitHub
Owner: ComputationalPhysiology
License: lgpl-2.1
Language: Python
Default Branch: main
Homepage: https://computationalphysiology.github.io/ap_features/
Size: 5.06 MB

Statistics

Stars: 8
Watchers: 3
Forks: 3
Open Issues: 4
Releases: 7

Created almost 5 years ago · Last pushed 6 months ago

Metadata Files

Readme Contributing License Citation Authors

Action Potential features

ap_features is package for computing features of action potential traces. This includes chopping, background correction and feature calculations.

Parts of this library is written in numba and is therefore highly performant. This is useful if you want to do feature calculations on a large number of traces.

Quick start

```python import matplotlib.pyplot as plt import numpy as np from scipy.integrate import solve_ivp

import ap_features as apf

time = np.linspace(0, 999, 1000) res = solveivp( apf.testing.fitzhughnagumo, [0, 1000], [0.0, 0.0], teval=time, ) trace = apf.Beats(y=res.y[0, :], t=time) print(f"Number of beats: {trace.numbeats}") print(f"Beat rates: {trace.beat_rates}")

Get a list of beats

beats = trace.beats

Pick out the second beat

beat = beats[1]

Compute features

print(f"APD30: {beat.apd(30):.3f}s, APD80: {beat.apd(80):.3f}s") print(f"cAPD30: {beat.capd(30):.3f}s, cAPD80: {beat.capd(80):.3f}s") print(f"Time to peak: {beat.ttp():.3f}s") print(f"Decay time from max to 90%: {beat.tau(a=0.1):.3f}s") ```

Number of beats: 5 Beat rates: [779.2207792207793, 769.2307692307693, 779.2207792207793, 759.493670886076] APD30: 37.823s, APD80: 56.564s cAPD30: 88.525s, cAPD80: 132.387s Time to peak: 21.000s Decay time from max to 90%: 53.618s

Install

Install the package with pip python -m pip install ap_features See installation instructions for more options.

Available features

The list of currently implemented features are as follows - Action potential duration (APD) - Corrected action potential duration (cAPD) - Decay time (Time for the signal amplitude to go from maximum to (1 - a) * 100 % of maximum) - Time to peak (ttp) - Upstroke time (time from (1-a)*100 % signal amplitude to peak) - Beating frequency - APD up (The duration between first intersections of two APD lines) - Maximum relative upstroke velocity - Maximum upstroke velocity - APD integral (integral of the signals above the APD line)

Documentation

Documentation is hosted at GitHub pages: https://computationalphysiology.github.io/ap_features/

Note that the documentation is written using jupyterbook and contains an interactive demo

License

Free software: LGPLv2.1

Source Code

https://github.com/ComputationalPhysiology/ap_features

Owner

Name: Computational Physiology at Simula Research Laboratory
Login: ComputationalPhysiology
Kind: organization
Location: Fornebu, Norway

Website: https://computationalphysiology.github.io
Repositories: 51
Profile: https://github.com/ComputationalPhysiology

GitHub organization for the computational physiology department at Simula Research Laboratory

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- affiliation: Simula Research Laboratoy
  family-names: Finsberg
  given-names: Henrik
  orcid: "https://orcid.org/0000-0003-3766-2393"
- family-names: Hustad
  given-names: Kristian Gregorius
date-released: '2024-09-28'
doi: 10.5281/zenodo.13854740
repository-code: https://github.com/ComputationalPhysiology/ap_features/
title: 'ap_features'
type: software
version: v2024.0.0

GitHub Events

Total

Watch event: 1
Delete event: 1
Push event: 44
Pull request event: 28

Last Year

Watch event: 1
Delete event: 1
Push event: 44
Pull request event: 28

Committers

Last synced: almost 3 years ago

All Time

Total Commits: 283
Total Committers: 4
Avg Commits per committer: 70.75
Development Distribution Score (DDS): 0.318

Top Committers

Name	Email	Commits
Henrik Finsberg	h**f@s**o	193
pre-commit-ci[bot]	6**]@u**m	49
Henrik Finsberg	h**g@h**m	23
Kristian Gregorius Hustad	k**d@s**o	18

Committer Domains (Top 20 + Academic)

simula.no: 2

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 12
Total pull requests: 162
Average time to close issues: about 4 hours
Average time to close pull requests: 10 days
Total issue authors: 2
Total pull request authors: 4
Average comments per issue: 0.33
Average comments per pull request: 0.15
Merged pull requests: 148
Bot issues: 0
Bot pull requests: 123

Past Year

Issues: 3
Pull requests: 27
Average time to close issues: N/A
Average time to close pull requests: 11 days
Issue authors: 2
Pull request authors: 2
Average comments per issue: 0.0
Average comments per pull request: 0.0
Merged pull requests: 23
Bot issues: 0
Bot pull requests: 27

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Top Authors

Issue Authors

finsberg (11)
tk231 (1)

Pull Request Authors

pre-commit-ci[bot] (107)
finsberg (36)
dependabot[bot] (16)
KGHustad (3)

Top Labels

Issue Labels

enhancement (1)

Pull Request Labels

dependencies (16) github_actions (2)

Packages

Total packages: 1
Total downloads:
- pypi 2,960 last-month

Total dependent packages: 3
Total dependent repositories: 1
Total versions: 43
Total maintainers: 1

pypi.org: ap-features

Package to compute features of traces from action potential models

Homepage: https://computationalphysiology.github.io/ap_features
Documentation: https://computationalphysiology.github.io/ap_features
License: LGPL-2.1
Latest release: 2024.0.0
published over 1 year ago

Versions: 43
Dependent Packages: 3
Dependent Repositories: 1
Downloads: 2,960 Last month
Docker Downloads: 0

Rankings

Dependent packages count: 2.3%

Docker downloads count: 3.8%

Downloads: 7.1%

Average: 11.9%

Forks count: 16.9%

Stargazers count: 19.3%

Dependent repos count: 21.7%

Maintainers (1)

finsberg

Last synced: 6 months ago

Dependencies

.github/workflows/github-pages.yml actions

actions/checkout v3 composite
actions/configure-pages v3 composite
actions/deploy-pages v2 composite
actions/setup-python v4 composite
actions/upload-pages-artifact v1 composite

.github/workflows/main.yml actions

actions/checkout v3 composite
actions/setup-python v4 composite
actions/upload-artifact v3 composite
schneegans/dynamic-badges-action v1.6.0 composite

.github/workflows/pypi.yml actions

actions/checkout v3 composite
actions/setup-python v4 composite
pypa/gh-action-pypi-publish release/v1 composite

pyproject.toml pypi

numba <0.58
numpy *
scipy *
tqdm *

ap-features

Science Score: 67.0%

Repository

Basic Info

Statistics

Metadata Files

README.md

Action Potential features

Quick start

Get a list of beats

Pick out the second beat

Compute features

Install

Available features

Documentation

License

Source Code

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

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Issue Labels

Pull Request Labels

Packages

pypi.org: ap-features

Rankings

Maintainers (1)

Dependencies