fir
Python class that performs finite impulse response fitting on time series data.
Science Score: 33.0%
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✓DOI references
Found 4 DOI reference(s) in README -
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1 of 3 committers (33.3%) from academic institutions -
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Low similarity (8.4%) to scientific vocabulary
Repository
Python class that performs finite impulse response fitting on time series data.
Basic Info
- Host: GitHub
- Owner: tknapen
- License: mit
- Language: Jupyter Notebook
- Default Branch: master
- Homepage: tknapen.github.io/FIRDeconvolution
- Size: 10.4 MB
Statistics
- Stars: 21
- Watchers: 5
- Forks: 11
- Open Issues: 3
- Releases: 1
Metadata Files
README.md
FIRDeconvolution
FIRDeconvolution is a python class that performs finite impulse response fitting on time series data, in order to estimate event-related signals.
Example use cases are fMRI and pupil size analysis. The package performs the linear least squares analysis using numpy.linalg as a backend, but can switch between different backends, such as statsmodels (which is implemented). For very collinear design matrices ridge regression is implemented through the sklearn RidgeCV function. Bootstrap estimates of error regions are implemented through residual reshuffling.
It is possible to add covariates to the events to estimate not just the impulse response function, but also correlation timecourses with secondary variables. Furthermore, one can add the duration each event should have in the designmatrix, for designs in which the durations of the events vary.
In neuroscience, the inspection of the event-related signals such as those estimated by FIRDeconvolution is essential for a thorough understanding of one's data. Researchers may overlook essential patterns in their data when blindly running GLM analyses without looking at the impulse response shapes.
The test notebook explains how the package can be used for data analysis, by creating toy signals and then using FIRDeconvolution to fit the impulse response functions from the toy data.
Dependencies
numpy, scipy, matplotlib, statsmodels, sklearn
TODO - temporal autocorrelation correction
Owner
- Name: Tomas Knapen
- Login: tknapen
- Kind: user
- Location: Amsterdam
- Company: Vrije Universiteit & Spinoza Centre for Neuroimaging
- Website: tknapen.github.io
- Twitter: Tknapen
- Repositories: 9
- Profile: https://github.com/tknapen
GitHub Events
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- Watch event: 2
Last Year
- Watch event: 2
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Tomas Knapen | t****n@g****m | 84 |
| JvSlooten88 | j****n@g****m | 1 |
| Nicholas | n****h@u****k | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 11 months ago
All Time
- Total issues: 4
- Total pull requests: 4
- Average time to close issues: 2 months
- Average time to close pull requests: 2 days
- Total issue authors: 2
- Total pull request authors: 2
- Average comments per issue: 1.5
- Average comments per pull request: 0.25
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 3
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- ncfirth (2)
- tknapen (2)
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- dependabot[bot] (3)
- ncfirth (1)
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Packages
- Total packages: 1
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Total downloads:
- pypi 56 last-month
- Total dependent packages: 0
- Total dependent repositories: 3
- Total versions: 14
- Total maintainers: 1
pypi.org: fir
Finite Impulse Response package for time series analysis.
- Homepage: http://tknapen.github.io/FIRDeconvolution
- Documentation: https://fir.readthedocs.io/
- License: The MIT License (MIT)
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Latest release: 0.0.2
published over 10 years ago
Rankings
Maintainers (1)
Dependencies
- numpy ==1.9.3
- pandas ==0.16.1
- scikit-learn ==0.16.1
- scipy ==0.16.0
- seaborn ==0.5.1
- statsmodels ==0.5.0