| Name | gaitalytics JSON |
| Version |
0.2.2
JSON |
| download |
| home_page | None |
| Summary | easy gait-metrics for everyone |
| upload_time | 2025-02-07 10:41:52 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | MIT License
Copyright (c) 2024 André Böni
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
gait-analysis
mocap
c3d
gait-metrics
biomechanics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
Gaitalytics
===========
.. start-badges
.. list-table::
:stub-columns: 1
* - docs
- |docs|
* - tests
- |github-actions|
* - package
- |pypi| |conda|
* - development
- |MIT| |last-commit| |commits-since| |pixi-badge|
.. |docs| image:: https://img.shields.io/readthedocs/python-gaitalytics?logo=readthedocs
:target: https://python-gaitalytics.readthedocs.io/
:alt: Documentation Status
.. |github-actions| image:: https://img.shields.io/github/actions/workflow/status/DART-Lab-LLUI/python-gaitalytics/on_push_test.yaml?logo=pytest
:alt: GitHub Actions Build Status
:target: https://github.com/DART-Lab-LLUI/python-gaitalytics/actions/
.. |last-commit| image:: https://img.shields.io/github/last-commit/DART-Lab-LLUI/python-gaitalytics
:alt: GitHub last commit
:target: https://github.com/DART-Lab-LLUI/python-gaitalytics
.. |commits-since| image:: https://img.shields.io/github/commits-since/DART-Lab-LLUI/python-gaitalytics/latest.svg
:alt: Commits since latest release
:target: https://github.com/DART-Lab-LLUI/python-gaitalytics/compare/
.. |pixi-badge| image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/prefix-dev/pixi/main/assets/badge/v0.json
:alt: Pixi Badge
:target: https://pixi.sh
.. |pypi| image:: https://img.shields.io/pypi/dm/gaitalytics?logo=pypi
:alt: PyPI - Downloads
:target: https://pypi.org/project/gaitalytics/
.. |conda| image:: https://img.shields.io/conda/dn/DartLab-LLUI/gaitalytics?logo=anaconda
:alt: Conda Downloads
:target: https://anaconda.org/dartlab-llui/gaitalytics
.. |MIT| image:: https://img.shields.io/github/license/DART-Lab-LLUI/python-gaitalytics?logo=opensourceinitiative
:alt: GitHub License
.. end-badges
This Python package provides a comprehensive set of tools and advanced algorithms for analyzing 3D motion capture data.
It is specifically designed to process gait data stored in c3d format. Prior to utilizing the features of gaitalytics,
it is necessary to perform data labeling, modeling, and filtering procedures.
The library's versatility allows it to be adaptable to various marker sets and modeling algorithms,
offering high configurability.
Quickstart
----------
Installation
^^^^^^^^^^^^
Fast install with anaconda:
.. code:: shell
conda install gaitalytics -c DartLab-LLUI
..
Or with pip:
.. code:: shell
pip install gaitalytics
..
Configuration
^^^^^^^^^^^^^
Gaitalytics can be used with any marker set, which at least includes
three or four hip markers (front left/right, back left/right or sacrum) and four foot
markers (left heel/toe, right heel/toe).
Additionally markers can be defined on which standard time-series features such as min max mean etc.
will be calculated.
All functionalities in the libraries only take points into account which
are configured in as specific yaml file.
Minimal requirements would look like this:
.. code-block:: yaml
analysis:
markers:
- "LHipAngles"
- "LKneeAngles"
- "LAnkleAngles"
- "LPelvisAngles"
- "LThoraxAngles"
mapping:
markers:
# Foot
l_heel: "LHEE"
r_heel: "RHEE"
l_toe: "LTOE"
r_toe: "RTOE"
# Hip
l_ant_hip: "LASI"
r_ant_hip: "RASI"
l_post_hip: "LPSI"
r_post_hip: "RPSI"
sacrum: "SACR"
..
Simple Pipeline
^^^^^^^^^^^^^^^^
.. code-block:: python
from gaitalytics import api
# Load configuration (yaml file from above)
config = api.load_config("./pig_config.yaml")
# Load trial from c3d file
trial = api.load_c3d_trial("./test_small.c3d", config)
# Detect events
events = api.detect_events(trial, config)
try:
# check events
api.check_events(events)
# write events to c3d in the same file
api.write_events_to_c3d("./test_small.c3d", events, './test.c3d')
# add events to trial
trial.events = events
# segment trial to gait cycles. (Events are already existing in the c3d file)
trial_segmented = api.segment_trial(trial)
# calculate features
features = api.calculate_features(trial_segmented, config)
# normalise time
trial_normalized = api.time_normalise_trial(trial_segmented)
# save features
features.to_netcdf("features.nc")
# export segmented trial to netcdf
api.export_trial(trial_segmented, "output_segments")
api.export_trial(trial_normalized, "output_norm")
except ValueError as e:
print(e)
..
Documentation
-------------
https://python-gaitalytics.readthedocs.org
https://github.com/DART-Lab-LLUI/python-gaitalytics
https://pypi.org/project/gaitalytics/
https://anaconda.org/dartlab-llui/gaitalytics
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"description": "Gaitalytics\n===========\n\n.. start-badges\n\n.. list-table::\n :stub-columns: 1\n\n * - docs\n - |docs|\n * - tests\n - |github-actions|\n * - package\n - |pypi| |conda|\n * - development\n - |MIT| |last-commit| |commits-since| |pixi-badge|\n\n.. |docs| image:: https://img.shields.io/readthedocs/python-gaitalytics?logo=readthedocs\n :target: https://python-gaitalytics.readthedocs.io/\n :alt: Documentation Status\n\n.. |github-actions| image:: https://img.shields.io/github/actions/workflow/status/DART-Lab-LLUI/python-gaitalytics/on_push_test.yaml?logo=pytest\n :alt: GitHub Actions Build Status\n :target: https://github.com/DART-Lab-LLUI/python-gaitalytics/actions/\n\n.. |last-commit| image:: https://img.shields.io/github/last-commit/DART-Lab-LLUI/python-gaitalytics\n :alt: GitHub last commit\n :target: https://github.com/DART-Lab-LLUI/python-gaitalytics\n\n.. |commits-since| image:: https://img.shields.io/github/commits-since/DART-Lab-LLUI/python-gaitalytics/latest.svg\n :alt: Commits since latest release\n :target: https://github.com/DART-Lab-LLUI/python-gaitalytics/compare/\n\n.. |pixi-badge| image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/prefix-dev/pixi/main/assets/badge/v0.json\n :alt: Pixi Badge\n :target: https://pixi.sh\n\n.. |pypi| image:: https://img.shields.io/pypi/dm/gaitalytics?logo=pypi\n :alt: PyPI - Downloads\n :target: https://pypi.org/project/gaitalytics/\n\n.. |conda| image:: https://img.shields.io/conda/dn/DartLab-LLUI/gaitalytics?logo=anaconda\n :alt: Conda Downloads\n :target: https://anaconda.org/dartlab-llui/gaitalytics\n\n.. |MIT| image:: https://img.shields.io/github/license/DART-Lab-LLUI/python-gaitalytics?logo=opensourceinitiative\n :alt: GitHub License\n\n\n.. end-badges\n\nThis Python package provides a comprehensive set of tools and advanced algorithms for analyzing 3D motion capture data.\nIt is specifically designed to process gait data stored in c3d format. Prior to utilizing the features of gaitalytics,\nit is necessary to perform data labeling, modeling, and filtering procedures.\n\nThe library's versatility allows it to be adaptable to various marker sets and modeling algorithms,\noffering high configurability.\n\nQuickstart\n----------\n\nInstallation\n^^^^^^^^^^^^\n\nFast install with anaconda:\n\n.. code:: shell\n\n conda install gaitalytics -c DartLab-LLUI\n..\n\nOr with pip:\n\n.. code:: shell\n\n pip install gaitalytics\n..\n\n\nConfiguration\n^^^^^^^^^^^^^\n\nGaitalytics can be used with any marker set, which at least includes\nthree or four hip markers (front left/right, back left/right or sacrum) and four foot\nmarkers (left heel/toe, right heel/toe).\n\nAdditionally markers can be defined on which standard time-series features such as min max mean etc.\nwill be calculated.\n\nAll functionalities in the libraries only take points into account which\nare configured in as specific yaml file.\n\n\n\nMinimal requirements would look like this:\n\n.. code-block:: yaml\n\n analysis:\n markers:\n - \"LHipAngles\"\n - \"LKneeAngles\"\n - \"LAnkleAngles\"\n - \"LPelvisAngles\"\n - \"LThoraxAngles\"\n mapping:\n markers:\n # Foot\n l_heel: \"LHEE\"\n r_heel: \"RHEE\"\n l_toe: \"LTOE\"\n r_toe: \"RTOE\"\n\n # Hip\n l_ant_hip: \"LASI\"\n r_ant_hip: \"RASI\"\n l_post_hip: \"LPSI\"\n r_post_hip: \"RPSI\"\n sacrum: \"SACR\"\n..\n\n\n\nSimple Pipeline\n^^^^^^^^^^^^^^^^\n.. code-block:: python\n\n from gaitalytics import api\n # Load configuration (yaml file from above)\n config = api.load_config(\"./pig_config.yaml\")\n\n # Load trial from c3d file\n trial = api.load_c3d_trial(\"./test_small.c3d\", config)\n\n # Detect events\n events = api.detect_events(trial, config)\n try:\n # check events\n api.check_events(events)\n\n # write events to c3d in the same file\n api.write_events_to_c3d(\"./test_small.c3d\", events, './test.c3d')\n\n # add events to trial\n trial.events = events\n\n # segment trial to gait cycles. (Events are already existing in the c3d file)\n trial_segmented = api.segment_trial(trial)\n\n # calculate features\n features = api.calculate_features(trial_segmented, config)\n\n # normalise time\n trial_normalized = api.time_normalise_trial(trial_segmented)\n\n # save features\n features.to_netcdf(\"features.nc\")\n\n # export segmented trial to netcdf\n api.export_trial(trial_segmented, \"output_segments\")\n api.export_trial(trial_normalized, \"output_norm\")\n\n except ValueError as e:\n print(e)\n..\n\nDocumentation\n-------------\nhttps://python-gaitalytics.readthedocs.org\n\nhttps://github.com/DART-Lab-LLUI/python-gaitalytics\n\nhttps://pypi.org/project/gaitalytics/\n\nhttps://anaconda.org/dartlab-llui/gaitalytics\n\n",
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