tscfat

https://github.com/argonsilicon/tscfat

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
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Academic publication links
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Committers with academic emails
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (18.1%) to scientific vocabulary

Last synced: 11 months ago · JSON representation

Repository

Basic Info

Host: GitHub
Owner: ArgonSilicon
License: mit
Language: Python
Default Branch: master
Homepage: https://tscfat.readthedocs.io/en/latest/
Size: 66.6 MB

Statistics

Stars: 1
Watchers: 2
Forks: 2
Open Issues: 46
Releases: 6

Created about 6 years ago · Last pushed about 1 year ago

Metadata Files

Readme Contributing License Citation

Time series co-fluctuation analysis toolbox.

A python Toolbox for time series exploratory data analysis.

What is it for?

The toolbox is designed for exploratory data analysis of Ecological Momentaty Assesment (EMA), Experience Sampling Methods (ESM), and Digital phenotyping data. The toolbox enables the inpection of time series interaction and dynamics by providing methods for summary statistics, trend and periodicity evaluation, and linear and nonlinear dependency assesment. The emphasis of the analysis is on the visualizations.

The toolbox has simple user interface, having a single configuration file for filenames, paths, and variables used in the analysis.

For the toolbox testing purposes, anomymized real life test data set is also provided.

Main Features

The toolbox features include: * Summary statistics * Rolling windows statistics * Time series decomposition * Similarity analysis * Similarity plot * Novelty score * Stability index * Clustering

Installation

Pip install

sh pip install tscfat

You may also clone the project.

The source code is currently hosted on GitHub at: https://github.com/ArgonSilicon/tscfat/

Dependencies

The project dependencies: * Pandas * Numpy * Matplotlib * Statsmodels * Sklearn * Tslearn * Nolds * Pytest * Seaborn

Usage example

A few motivating and useful examples of how your product can be used. Spice this up with code blocks and potentially more screenshots.

After cloning, make sure that pipenv is installed: sh pip install pipenv Activate the virtual environment: sh pipenv install Run the example file: sh pipenv run python ./Examples/example_one_subject.py Each analysis function can be used independently. Functions assume that the input data is expected to be in a CSV file, using the following format:

| Time | Y1 | Y2 | X1 | X2 | ... | X_n | | :-----------: |:-----:|:-----:|:-----:|:-----:|:-----:|:-----:| | 1472677200 | 3 | 5 | 56 | 0.1 | ... | 0.56 | | 1472763600 | 4 | 3 | 47 | 0.1 | ... | 0.41 | | : | : | : | : | : | : | : | | 1478037600 | 4 | 2 | 99 | 0.2 | ... | 0.71 |

The Time column contains timestamps in unix format.
Rest of the columns contain observations, which should be in numerical format. Each column represents one variable, rows correspond to the sampling timepoint.

For more examples and usage, please refer to the Docs. <!--

Development setup

Describe how to install all development dependencies and how to run an automated test-suite of some kind. Potentially do this for multiple platforms.

sh make install npm test -->

Release History

0.0.1
- Initial version, WIP

Contributing

Fork it (https://github.com/ArgonSilicon/tscfat/fork)
Create your feature branch (git checkout -b feature/fooBar)
Commit your changes (git commit -am 'Add some fooBar')
Push to the branch (git push origin feature/fooBar)
Create a new Pull Request

Citation (citation.cff)

GitHub Events

Total

Push event: 2
Pull request event: 2
Create event: 1

Last Year

Push event: 2
Pull request event: 2
Create event: 1

Committers

Last synced: over 3 years ago

All Time

Total Commits: 367
Total Committers: 7
Avg Commits per committer: 52.429
Development Distribution Score (DDS): 0.409

Top Committers

Name	Email	Commits
ArgonSilicon	a**n@g**m	217
Ikäheimonen Arsi	i**1@t**i	80
Arsi Ikäheimonen	6**n@u**m	56
snyk-bot	s**t@s**o	6
Arsi Ikaheimonen	a**n@a**i	5
AnaTriana	a**s@a**i	2
dependabot[bot]	4**]@u**m	1

Committer Domains (Top 20 + Academic)

aalto.fi: 2 snyk.io: 1 t31300-lr131.org.aalto.fi: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time

Total issues: 0
Total pull requests: 73
Average time to close issues: N/A
Average time to close pull requests: about 1 month
Total issue authors: 0
Total pull request authors: 4
Average comments per issue: 0
Average comments per pull request: 0.34
Merged pull requests: 25
Bot issues: 0
Bot pull requests: 8

Past Year

Issues: 0
Pull requests: 9
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 1
Average comments per issue: 0
Average comments per pull request: 0.0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

Pull Request Authors

ArgonSilicon (71)
dependabot[bot] (8)
snyk-bot (8)
AnaTomomi (2)

Top Labels

Issue Labels

Pull Request Labels

dependencies (8)

Packages

Total packages: 1
Total downloads:
- pypi 25 last-month

Total dependent packages: 0
Total dependent repositories: 1
Total versions: 5
Total maintainers: 1

pypi.org: tscfat

A time series co-fluctuation analysis toolbox

Homepage: https://github.com/ArgonSilicon/tscfat
Documentation: https://tscfat.readthedocs.io/
License: MIT
Latest release: 0.0.5
published over 5 years ago

Versions: 5
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 25 Last month

Rankings

Dependent packages count: 10.1%

Forks count: 19.1%

Average: 19.9%

Downloads: 21.0%

Dependent repos count: 21.6%

Stargazers count: 27.8%

Maintainers (1)

ArgonSilicon

Last synced: 11 months ago

Dependencies

Docs/requirements.txt pypi

matplotlib *
nolds *
numpy *
pandas *
pygments >=2.7.4
pytest *
scikit-learn >=0.24.2
seaborn *
sklearn *
sphinx ==3.2.1
sphinx_rtd_theme ==0.5.0
statsmodels *
tslearn *

Pipfile pypi

matplotlib *
nolds *
numpy *
pandas *
pytest *
seaborn *
sklearn *
statsmodels *
tscfat *
tslearn *

Pipfile.lock pypi

attrs ==21.4.0
cycler ==0.11.0
cython ==0.29.26
future ==0.18.2
iniconfig ==1.1.1
joblib ==1.1.0
kiwisolver ==1.3.2
llvmlite ==0.37.0
matplotlib ==3.4.1
nolds ==0.5.2
numba ==0.54.1
numpy ==1.20.2
packaging ==21.3
pandas ==1.2.3
patsy ==0.5.2
pillow ==9.0.0
pluggy ==0.13.1
py ==1.11.0
pyparsing ==3.0.6
pytest ==6.2.3
python-dateutil ==2.8.2
pytz ==2021.3
scikit-learn ==1.0.2
scipy ==1.7.3
seaborn ==0.11.1
setuptools ==60.5.0
six ==1.16.0
sklearn ==0.0
statsmodels ==0.12.2
threadpoolctl ==3.0.0
toml ==0.10.2
tscfat ==0.0.5
tslearn ==0.5.0.5

requirements.txt pypi

matplotlib *
nolds *
numpy *
pandas *
pytest *
scikit-learn >=0.24.2
seaborn *
sklearn *
statsmodels *
tscfat *
tslearn *

setup.py pypi

matplotlib *
nolds *
numpy *
pandas *
pytest *
seaborn *
sklearn *
statsmodels *
tslearn *

tscfat/tscfat.egg-info/requires.txt pypi

matplotlib *
nolds *
numpy *
pandas *
pytest *
scikit-learn >=0.24.2
seaborn *
sklearn *
statsmodels *
tslearn *

.github/workflows/python-app.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

tscfat

Science Score: 26.0%

Repository

Basic Info

Statistics

Metadata Files

README.md

Time series co-fluctuation analysis toolbox.

What is it for?

Main Features

Installation

Dependencies

Usage example

Development setup

Release History

Meta

Contributing

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

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: tscfat

Rankings

Maintainers (1)

Dependencies