Unable to verify the project's public source code repository.

Package Health Score

57 / 100

security
No known security issues
popularity
Limited
maintenance
Sustainable
community
Limited

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Popularity

Limited

GitHub Stars: ?
Forks: ?
Contributors: -

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package spcqe, we found that it has been starred ? times.

Security

No known security issues

Security and license risk for latest version

Release Date: Oct 28, 2024
Direct Vulnerabilities: 0
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0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

BSD 2-Clause License Copyright (c) 2023, Stanford University Convex Optimization Group Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Security Policy

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Maintenance

Sustainable

Commit Frequency

Unavailable commit data

Open Issues: ?
Open PR: ?
Last Release: 25 days ago
Last Commit: unknown

Further analysis of the maintenance status of spcqe based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Sustainable.

We found that spcqe demonstrates a positive version release cadence with at least one new version released in the past 3 months.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Limited

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 0
Funding: No

This project has seen only 10 or less contributors.

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Package

Python Versions Compatibility: >=3.9

Age: 8 months
Latest Release: 25 days ago
Dependencies: 5 Direct
Versions: 2
Maintainers: 1
Wheels: OS Independent

Readme

spcqe

Smooth (multi-) periodic consistent quantile estimation. We attempt to follow the sklearn "fit/transform" API, and the main class inherets TransformerMixin and BaseEstimator from sklearn.base.

Installation

The package is available on both PyPI and conda-forge.

pip installation:

pip install spcqe

conda installation:

conda install conda-forge::spcqe

You may also clone the repository to your local machine and install with pip by navigating to the project directory and running:

pip install .

If working on the files in this package (i.e. fixing bugs or adding features), it useful to install in editable mode:

pip install -e .

Usage

from spcqe.quantiles import SmoothPeriodicQuantiles

y1 = ... # some data with hourly measurement exhibiting daily, weekly, and yearly periodicities
P1 = int(365*24)
P2 = int(7*24)
P3 = int(24)
K = 3
l = 0.1
spq = SmoothPeriodicQuantiles(K, [P1, P2, P3], weight=l)
spq.fit(y1)

Examples

Many examples Jupyter notebooks are available in the notebooks folder.

Acknowledgement

This material is based upon work supported by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under the Solar Energy Technologies Office Award Number 38529, "PVInsight".

spcqe dependencies

cvxpy numpy scikit-learn sig-decomp tqdm

FAQs

What is spcqe?

Smooth periodic consistent quantile estimation. Visit Snyk Advisor to see a full health score report for spcqe, including popularity, security, maintenance & community analysis.

Is spcqe popular?

The python package spcqe receives a total of 378 weekly downloads. As such, spcqe popularity was classified as limited. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is spcqe safe to use?

The python package spcqe was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 21 November-2024, at 14:19 (UTC).

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spcqe

Smooth periodic consistent quantile estimation For more information about how to use this package see README

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Package

Readme

spcqe

Installation

Usage

Examples

Acknowledgement

spcqe dependencies

FAQs

What is spcqe?

Is spcqe popular?

Is spcqe safe to use?

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Readme

spcqe

Installation

Usage

Examples

Acknowledgement

spcqe dependencies

FAQs

What is spcqe?

Is spcqe popular?

Is spcqe safe to use?

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Source Code

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