| Name | ap-features JSON |
| Version |
2023.7.4
JSON |
| download |
| home_page | |
| Summary | Package to compute features of traces from action potential models |
| upload_time | 2023-10-18 10:05:35 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.7 |
| license | LGPL-2.1 |
| keywords |
action potential
cell
models
features
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://pypi.python.org/pypi/ap_features)
[](https://results.pre-commit.ci/latest/github/ComputationalPhysiology/ap_features/main)
[](https://github.com/ComputationalPhysiology/ap_features/actions/workflows/github-pages.yml)
[](https://github.com/ComputationalPhysiology/ap_features/actions/workflows/pypi.yml)
[](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/finsberg/a7290de789564f03eb6b1ee122fce423/raw/ap_features-coverage.json)
# 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 = solve_ivp(
apf.testing.fitzhugh_nagumo,
[0, 1000],
[0.0, 0.0],
t_eval=time,
)
trace = apf.Beats(y=res.y[0, :], t=time)
print(f"Number of beats: {trace.num_beats}")
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](https://computationalphysiology.github.io/ap_features/INSTALL.html) 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`](https://jupyterbook.org) and contains an [interactive demo](https://computationalphysiology.github.io/ap_features/demo_fitzhugh_nagumo.html)
## License
* Free software: LGPLv2.1
## Source Code
* <https://github.com/ComputationalPhysiology/ap_features>
Raw data
{
"_id": null,
"home_page": "",
"name": "ap-features",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "",
"keywords": "action potential,cell,models,features",
"author": "",
"author_email": "Henrik Finsberg <henriknf@simula.no>, \"Kristian G. Hustad\" <kghustad@simula.no>",
"download_url": "https://files.pythonhosted.org/packages/cb/30/0c22686181ea05b40839fc06d8e53ed9b95804db35ed2dbc3e051f9e5d22/ap_features-2023.7.4.tar.gz",
"platform": null,
"description": "[](https://pypi.python.org/pypi/ap_features)\n\n[](https://results.pre-commit.ci/latest/github/ComputationalPhysiology/ap_features/main)\n[](https://github.com/ComputationalPhysiology/ap_features/actions/workflows/github-pages.yml)\n[](https://github.com/ComputationalPhysiology/ap_features/actions/workflows/pypi.yml)\n[](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/finsberg/a7290de789564f03eb6b1ee122fce423/raw/ap_features-coverage.json)\n# Action Potential features\n\n`ap_features` is package for computing features of action potential traces. This includes chopping, background correction and feature calculations.\n\nParts 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.\n\n## Quick start\n\n```python\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.integrate import solve_ivp\n\nimport ap_features as apf\n\ntime = np.linspace(0, 999, 1000)\nres = solve_ivp(\n apf.testing.fitzhugh_nagumo,\n [0, 1000],\n [0.0, 0.0],\n t_eval=time,\n)\ntrace = apf.Beats(y=res.y[0, :], t=time)\nprint(f\"Number of beats: {trace.num_beats}\")\nprint(f\"Beat rates: {trace.beat_rates}\")\n\n# Get a list of beats\nbeats = trace.beats\n# Pick out the second beat\nbeat = beats[1]\n\n# Compute features\nprint(f\"APD30: {beat.apd(30):.3f}s, APD80: {beat.apd(80):.3f}s\")\nprint(f\"cAPD30: {beat.capd(30):.3f}s, cAPD80: {beat.capd(80):.3f}s\")\nprint(f\"Time to peak: {beat.ttp():.3f}s\")\nprint(f\"Decay time from max to 90%: {beat.tau(a=0.1):.3f}s\")\n```\n\n```\nNumber of beats: 5\nBeat rates: [779.2207792207793, 769.2307692307693, 779.2207792207793, 759.493670886076]\nAPD30: 37.823s, APD80: 56.564s\ncAPD30: 88.525s, cAPD80: 132.387s\nTime to peak: 21.000s\nDecay time from max to 90%: 53.618s\n```\n\n## Install\nInstall the package with pip\n```\npython -m pip install ap_features\n```\nSee [installation instructions](https://computationalphysiology.github.io/ap_features/INSTALL.html) for more options.\n\n\n## Available features\nThe list of currently implemented features are as follows\n- Action potential duration (APD)\n- Corrected action potential duration (cAPD)\n- Decay time (Time for the signal amplitude to go from maximum to (1 - a) * 100 % of maximum)\n- Time to peak (ttp)\n- Upstroke time (time from (1-a)*100 % signal amplitude to peak)\n- Beating frequency\n- APD up (The duration between first intersections of two APD lines)\n- Maximum relative upstroke velocity\n- Maximum upstroke velocity\n- APD integral (integral of the signals above the APD line)\n\n\n## Documentation\nDocumentation is hosted at GitHub pages: <https://computationalphysiology.github.io/ap_features/>\n\nNote that the documentation is written using [`jupyterbook`](https://jupyterbook.org) and contains an [interactive demo](https://computationalphysiology.github.io/ap_features/demo_fitzhugh_nagumo.html)\n\n\n## License\n* Free software: LGPLv2.1\n\n## Source Code\n* <https://github.com/ComputationalPhysiology/ap_features>\n",
"bugtrack_url": null,
"license": "LGPL-2.1",
"summary": "Package to compute features of traces from action potential models",
"version": "2023.7.4",
"project_urls": {
"Documentation": "https://computationalphysiology.github.io/ap_features",
"Homepage": "https://computationalphysiology.github.io/ap_features",
"Source": "https://github.com/ComputationalPhysiology/ap_features",
"Tracker": "https://github.com/ComputationalPhysiology/ap_features/issues"
},
"split_keywords": [
"action potential",
"cell",
"models",
"features"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "0e494f2f656d9fa77016fd110e4c517a30b6bbab11be559460b31a71d9dacac1",
"md5": "9727efa04c5e690ef6ced996da0485a1",
"sha256": "0bc6f1cfc2b34471679cae4505f03ed4231e7ee40206c8927f5053e299893f7e"
},
"downloads": -1,
"filename": "ap_features-2023.7.4-py3-none-any.whl",
"has_sig": false,
"md5_digest": "9727efa04c5e690ef6ced996da0485a1",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 49402,
"upload_time": "2023-10-18T10:05:33",
"upload_time_iso_8601": "2023-10-18T10:05:33.745232Z",
"url": "https://files.pythonhosted.org/packages/0e/49/4f2f656d9fa77016fd110e4c517a30b6bbab11be559460b31a71d9dacac1/ap_features-2023.7.4-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "cb300c22686181ea05b40839fc06d8e53ed9b95804db35ed2dbc3e051f9e5d22",
"md5": "ce4cd608a42f2a727784e266ff260012",
"sha256": "db655833ad48790014879741cbe3f10ae3d5ff236298d8772df52dfa96cdbaa6"
},
"downloads": -1,
"filename": "ap_features-2023.7.4.tar.gz",
"has_sig": false,
"md5_digest": "ce4cd608a42f2a727784e266ff260012",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 447934,
"upload_time": "2023-10-18T10:05:35",
"upload_time_iso_8601": "2023-10-18T10:05:35.782703Z",
"url": "https://files.pythonhosted.org/packages/cb/30/0c22686181ea05b40839fc06d8e53ed9b95804db35ed2dbc3e051f9e5d22/ap_features-2023.7.4.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-10-18 10:05:35",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "ComputationalPhysiology",
"github_project": "ap_features",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "ap-features"
}
