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https://github.com/cvxgrp/spcqe

Smooth periodic consistent quantile estimation

https://github.com/cvxgrp/spcqe

Science Score: 26.0%

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    Scientific vocabulary similarity
    Low similarity (8.3%) to scientific vocabulary
Last synced: 6 months ago · JSON representation

Repository

Smooth periodic consistent quantile estimation

Basic Info
  • Host: GitHub
  • Owner: cvxgrp
  • License: bsd-2-clause
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 85.8 MB
Statistics
  • Stars: 8
  • Watchers: 5
  • Forks: 2
  • Open Issues: 2
  • Releases: 6
Created over 2 years ago · Last pushed 12 months ago
Metadata Files
Readme License

README.md

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 hourly data with daily, weekly, and yearly periodic statistics P1 = int(36524) P2 = int(724) P3 = int(24) K = 3 l = 0.1 spq = SmoothPeriodicQuantiles( num_harmonics=K, periods=[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".

Owner

  • Name: Stanford University Convex Optimization Group
  • Login: cvxgrp
  • Kind: organization
  • Location: Stanford, CA

GitHub Events

Total
  • Create event: 10
  • Release event: 4
  • Issues event: 7
  • Watch event: 1
  • Delete event: 6
  • Issue comment event: 11
  • Push event: 41
  • Pull request review comment event: 10
  • Pull request review event: 12
  • Pull request event: 9
Last Year
  • Create event: 10
  • Release event: 4
  • Issues event: 7
  • Watch event: 1
  • Delete event: 6
  • Issue comment event: 11
  • Push event: 41
  • Pull request review comment event: 10
  • Pull request review event: 12
  • Pull request event: 9

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 6
  • Total pull requests: 14
  • Average time to close issues: 15 days
  • Average time to close pull requests: 16 days
  • Total issue authors: 3
  • Total pull request authors: 4
  • Average comments per issue: 1.67
  • Average comments per pull request: 1.36
  • Merged pull requests: 14
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 4
  • Pull requests: 8
  • Average time to close issues: 20 days
  • Average time to close pull requests: 18 days
  • Issue authors: 1
  • Pull request authors: 3
  • Average comments per issue: 1.25
  • Average comments per pull request: 1.0
  • Merged pull requests: 8
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • bmeyers (4)
  • tschm (1)
  • AramisDuf (1)
Pull Request Authors
  • bmeyers (7)
  • AramisDuf (7)
  • pluflou (3)
  • giray98 (1)
Top Labels
Issue Labels
enhancement (1) good first issue (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 6,845 last-month
  • Total dependent packages: 1
  • Total dependent repositories: 0
  • Total versions: 5
  • Total maintainers: 1
pypi.org: spcqe

Smooth periodic consistent quantile estimation

  • Homepage: https://github.com/cvxgrp/spcqe
  • Documentation: https://spcqe.readthedocs.io/
  • 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.
  • Latest release: 0.3.0
    published about 1 year ago
  • Versions: 5
  • Dependent Packages: 1
  • Dependent Repositories: 0
  • Downloads: 6,845 Last month
Rankings
Dependent packages count: 9.7%
Average: 36.8%
Dependent repos count: 63.9%
Maintainers (1)
stanfordcvxgrp
Last synced: 7 months ago

Dependencies

pyproject.toml pypi
  • cvxpy *
  • numpy *
  • scikit-learn *
  • tqdm *
.github/workflows/build.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
  • actions/setup-python v4 composite
  • conda-incubator/setup-miniconda v2 composite
  • pypa/gh-action-pypi-publish release/v1 composite
.github/workflows/test.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
  • conda-incubator/setup-miniconda v2 composite