Science Score: 57.0%
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○Scientific vocabulary similarity
Low similarity (11.6%) to scientific vocabulary
Keywords from Contributors
Repository
Denoising via adaptive binning.
Basic Info
- Host: GitHub
- Owner: maurosilber
- License: mit
- Language: Python
- Default Branch: master
- Size: 36.1 KB
Statistics
- Stars: 12
- Watchers: 1
- Forks: 2
- Open Issues: 1
- Releases: 0
Metadata Files
README.md
Binlets: denoising via adaptive binning
Usage
binlets requires an array of data,
and a test function to compare two data points.
Single-channel with Poisson statistics
For instance, for single-channel signal with Poisson statistics:
```python def chi2test(x: NDArray, y: NDArray) -> NDArray[bool]: """Compares two values with Poisson noise using a χ² test.""" diff = x - y vardiff = x + y # Poisson variance return diff**2 <= var_diff
denoised = binlets( data, test=chi2_test, levels=None, # max averaging area is 2**levels. By default, floor(log2(min(data.shape))) linear=True, # the transformation is linear (x - y) ) ```
We recomend wrapping this in a function, and providing an extra parameter to adjust the significance level:
```python def poissonbinlets(data: NDArray, *, nsigma: float, levels: int | None = None): def chi2test(x: NDArray, y: NDArray) -> NDArray[bool]: """Compares two values with Poisson noise using a χ² test.""" diff = x - y vardiff = x + y # Poisson variance return diff2 <= n_sigma2 * var_diff
denoised = binlets(
data,
test=chi2_test,
linear=True,
)
return denoised
```
Ratio of multichannel-data
For multichannel data,
binlets expects channels to be in the first dimension of the data array.
That is, data.shape should be (N_CHANNELS, *spatial_dimensions).
```python def ratio(channels): """Statistic of interest.""" return channels[1] / channels[0]
def test(x, y): # The test of your choice. For instance, a χ² test. diff = ratio(x) - ratio(y) vardiff = ratiovar(x) + ratiovar(y) return diff**2 <= vardiff
denoised = binlets(data, test=test, ...) # the same as before denoised_ratio = ratio(denoised) ```
Installation
Binlets can be installed from PyPI:
pip install binlets
or conda-forge:
conda installl -c conda-forge binlets
Development
To set up a development environment in a new conda environment, run the following commands:
git clone https://github.com/maurosilber/binlets
cd binlets
conda env create -f environment.yml
conda activate binlets
pre-commit install
Owner
- Name: Mauro Silberberg
- Login: maurosilber
- Kind: user
- Location: Argentina
- Twitter: maurosilber
- Repositories: 47
- Profile: https://github.com/maurosilber
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
Binlets: Data fusion-aware denoising enables accurate and unbiased quantification of multichannel signals
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Mauro
family-names: Silberberg
email: maurosilber@df.uba.ar
orcid: 'https://orcid.org/0000-0002-2402-1100'
affiliation: >-
1. Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física. Buenos Aires, Argentina.
2. CONICET - Universidad de Buenos Aires, Instituto de Física de Buenos Aires (IFIBA). Buenos Aires, Argentina
- given-names: Hernán Edgardo
family-names: Grecco
email: hgrecco@df.uba.ar
affiliation: >-
1. Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física. Buenos Aires, Argentina.
2. CONICET - Universidad de Buenos Aires, Instituto de Física de Buenos Aires (IFIBA). Buenos Aires, Argentina
orcid: 'https://orcid.org/0000-0002-1165-4320'
identifiers:
- type: doi
value: 10.1016/j.inffus.2023.101999
abstract: >-
As monitoring multiple signals becomes more cost-effective,
combining them through a data fusion-aware denoising method can produce a more robust estimation of the underlying process.
Here, we present a method based on the Haar wavelet transform
that trades off resolution against accuracy based on statistical significance.
By taking advantage of correlations between channels,
it offers a superior performance compared to denoising each channel separately.
It outperforms standard wavelet methods when the magnitude of interest in the data-fusion process involves a non-linear transformation or reduction of a multichannel signal.
We demonstrate its efficacy by benchmarking our method against standard wavelet thresholding for synthetic single and multichannel time series, and a multichannel two-dimensional image.
The method has a simple interpretation as an adaptive binning of the signal,
and neither requires training data nor specialized hardware to run fast.
In addition, a reference Python implementation is available on GitHub and PyPI,
making it simple to integrate into any analysis pipeline.
license: MIT
GitHub Events
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Last synced: over 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| Mauro Silberberg | m****r@g****m | 26 |
| pre-commit-ci[bot] | 6****] | 4 |
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 0
- Total pull requests: 5
- Average time to close issues: N/A
- Average time to close pull requests: 7 days
- Total issue authors: 0
- Total pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 5
Past Year
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- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
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- Average comments per issue: 0
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Top Authors
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- pre-commit-ci[bot] (5)
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Packages
- Total packages: 1
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Total downloads:
- pypi 1,780 last-month
- Total dependent packages: 1
- Total dependent repositories: 1
- Total versions: 3
- Total maintainers: 1
pypi.org: binlets
Denoising via adaptive binning.
- Homepage: https://github.com/maurosilber/binlets
- Documentation: https://binlets.readthedocs.io/
- License: MIT License Copyright (c) 2019 Mauro Silberberg 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.
-
Latest release: 1.0.0
published almost 3 years ago
Rankings
Maintainers (1)
Dependencies
- actions/checkout v3 composite
- actions/setup-python v4 composite
- pypa/gh-action-pypi-publish release/v1 composite
- actions/checkout v3 composite
- actions/setup-python v4 composite
- hypothesis * test
- pytest * test
- scipy * test
- numpy *
- typing_extensions *