| Name | solar-data-tools JSON |
| Version |
1.6.5
JSON |
| download |
| home_page | None |
| Summary | Tools for performing common tasks on solar PV data signals |
| upload_time | 2025-01-22 00:33:36 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | BSD 2-Clause License Copyright (c) 2019, Bennet Meyers All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * 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. |
| keywords |
solar
pv
photovoltaic
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
scipy
numpy
pandas
scikit-learn
jupyter
matplotlib
seaborn
requests
pvlib
cvxpy
pykml
haversine
smart_open
sig-decomp
clarabel
qss
tqdm
spcqe
dask
distributed
dask-cloudprovider
graphviz
bokeh
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<!-- HEADER -->
<br />
<p align="center">
<a href="#">
<img src="docs/source/_static/SDT_v1_secondary_blue_text.png" width="600">
</a>
<p align="center">
<br />
<a href="https://solar-data-tools.readthedocs.io/"><strong>Explore our documentation </strong></a>
·
<a href="https://github.com/slacgismo/solar-data-tools/issues"><strong>Report Issue </strong></a>
<br />
<br />
</p>
</p>
<table align="center" >
<tr>
<td>Latest Release</td>
<td>
<a href="https://pypi.org/project/solar-data-tools/">
<img src="https://img.shields.io/pypi/v/solar-data-tools.svg" alt="latest release" />
</a>
<a href="https://anaconda.org/conda-forge/solar-data-tools">
<img src="https://anaconda.org/conda-forge/solar-data-tools/badges/version.svg" />
</a>
<a href="https://anaconda.org/conda-forge/solar-data-tools">
<img src="https://anaconda.org/conda-forge/solar-data-tools/badges/latest_release_date.svg" />
</a>
</tr>
<tr>
<td>License</td>
<td>
<a href="https://github.com/slacgismo/solar-data-tools/blob/main/LICENSE">
<img src="https://img.shields.io/pypi/l/solar-data-tools.svg" alt="license" />
</a>
</td>
</tr>
<tr>
<td>Build Status</td>
<td>
<a href="https://solar-data-tools.readthedocs.io/">
<img src="https://readthedocs.org/projects/solar-data-tools/badge/?version=stable" alt="documentation build status" />
</a>
<a href="https://github.com/slacgismo/solar-data-tools/actions/workflows/test.yml">
<img src="https://github.com/slacgismo/solar-data-tools/actions/workflows/test.yml/badge.svg?branch=main" alt="Actions build status" />
</a>
<a href="https://github.com/slacgismo/solar-data-tools/actions/workflows/build.yml">
<img src="https://github.com/slacgismo/solar-data-tools/actions/workflows/build.yml/badge.svg">
</a>
</td>
</tr>
<tr>
<td>Publications</td>
<td>
<a href="https://zenodo.org/badge/latestdoi/171066536">
<img src="https://zenodo.org/badge/171066536.svg" alt="DOI">
</a>
</td>
</tr>
<tr>
<td>PyPI Downloads</td>
<td>
<a href="https://pepy.tech/project/solar-data-tools">
<img src="https://img.shields.io/pypi/dm/solar-data-tools" alt="PyPI downloads" />
</a>
</td>
</tr>
<tr>
<td>Conda Downloads</td>
<td>
<a href="https://anaconda.org/conda-forge/solar-data-tools">
<img src="https://anaconda.org/conda-forge/solar-data-tools/badges/downloads.svg" alt="conda-forge downloads" />
</a>
</td>
</tr>
<tr>
<td>Test-Coverage</td>
<td>
<img src="https://img.shields.io/badge/test--coverage-45%25-yellowgreen" alt="test-coverage" />
</td>
</tr>
</table>
Solar Data Tools is an open-source Python library for analyzing PV power (and irradiance) time-series data. It
was developed to enable analysis of _unlabeled_ PV data, i.e. with no model, no meteorological data, and no performance index required,
by taking a statistical signal processing approach in the algorithms used in the package’s main data processing pipeline.
Solar Data Tools empowers PV system fleet owners or operators to analyze system performance a hundred times faster even when
they only have access to the most basic data stream—power output of the system.
Solar Data Tools provides methods for data I/O, cleaning, filtering, plotting, and analysis. These methods are largely automated and require little
to no input from the user regardless of system type—from utility tracking systems to multi-pitch rooftop systems.
Head over to our Getting Started pages in our [documentation](https://solar-data-tools.readthedocs.io/) for a demo! For an in-depth tutorial on Solar Data Tools, we recommend taking a look at the recent webinar
we did with the DOE's Solar Energy Technologies Office (SETO) with our colleagues at NREL, linked below:
<p align="center">
<a href="https://www.youtube.com/watch?v=XKbqIlAEwOQ">
<img src="https://img.youtube.com/vi/XKbqIlAEwOQ/hq1.jpg" />
</a>
<br/>
</img>
</p>
You can also check the [notebooks](https://github.com/slacgismo/solar-data-tools/blob/main/notebooks/examples) folder in this repo for more examples.
This work is 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.
## Install & Setup
#### Recommended: Install with pip
In a fresh Python virtual environment, simply run:
```bash
$ pip install solar-data-tools
```
or if you would like to use MOSEK, install the optional dependency as well:
```bash
$ pip install "solar-data-tools[mosek]"
```
#### Install with conda
>[!WARNING]
> `solar-data-tools` is now available on conda-forge! You can specify
> the channel using the `-c` flag as shown in the examples below.
> The use of the slacgismo channel is **deprecated** and packages
> on that channel will **not** be up-to-date with the latest releases.
Creating the environment and directly installing the package and its dependencies from the appropriate conda channels:
```bash
$ conda create -n pvi-user solar-data-tools -c conda-forge
```
Starting the environment:
```bash
$ conda activate pvi-user
```
Stopping the environment:
```bash
$ conda deactivate
```
Or alternatively install the package in an already existing environment:
```bash
$ conda install solar-data-tools -c conda-forge
```
### Solvers
#### CLARABEL
By default, the [CLARABEL](https://clarabel.org/stable/) solver is used to solve the signal decomposition problems. CLARABEL (as well as other solvers) is compatible with [OSD](https://github.com/cvxgrp/signal-decomposition/tree/main), the modeling language used to solve signal decomposition problems in Solar Data Tools. Both are open source and are dependencies of Solar Data Tools.
#### MOSEK
MOSEK is a commercial software package. Since it is more stable and offers faster solve times,
we provide continuing support for it (with signal decomposition problem formulations using CVXPY). However,
you will still need to obtain a license. If installing with pip, you can install the optional MOSEK dependency by running
`pip install "solar-data-tools[mosek]"`.
If installing from conda, you will have to manually install MOSEK if you desire to use it as
conda does not support optional dependencies like pip.
More information about MOSEK and how to obtain a license is available here:
* [mosek](https://www.mosek.com/resources/getting-started/)
* [Free 30-day trial](https://www.mosek.com/products/trial/)
* [Personal academic license](https://www.mosek.com/products/academic-licenses/)
## Usage
Users will primarily interact with this software through the `DataHandler` class. By default, Solar Data
Tools uses [CLARABEL](https://clarabel.org/stable/) as the solver all signal decomposition problems. If you would like
to specify another solver (such as MOSEK), just pass the keyword argument `solver` to `DataHandler.pipeline` with the solver of choice.
```python
from solardatatools import DataHandler
from solardatatools.dataio import get_pvdaq_data
pv_system_data = get_pvdaq_data(sysid=35, api_key='DEMO_KEY', year=[2011, 2012, 2013])
dh = DataHandler(pv_system_data)
dh.run_pipeline(power_col='dc_power')
```
If everything is working correctly, you should see a run summary like the following
```
total time: 25.99 seconds
--------------------------------
Breakdown
--------------------------------
Preprocessing 6.76s
Cleaning 0.41s
Filtering/Summarizing 18.83s
Data quality 0.21s
Clear day detect 0.44s
Clipping detect 15.51s
Capacity change detect 2.67s
```
You can also find more in-depth tutorials and guides in [our documentation](https://solar-data-tools.readthedocs.io/).
## Contributing
We welcome contributions of any form! Please see our [Contribution Guidelines](./CONTRIBUTING.md) for more information.
## Citing Solar Data Tools
If you use Solar Data Tools in your research, please cite:
**Recommended citations**
Bennet E. Meyers, Elpiniki Apostolaki-Iosifidou and Laura Schelhas, "Solar Data Tools: Automatic Solar
Data Processing Pipeline," 2020 47th IEEE Photovoltaic Specialists Conference (PVSC), Calgary, AB, Canada, 2020,
pp. 0655-0656, doi: [10.1109/PVSC45281.2020.9300847](https://doi.org/10.1109/PVSC45281.2020.9300847).
Bennet E. Meyers, Sara A. Miskovich, Duncan Ragsdale, Mitchell Victoriano, Aramis Dufour,
Nimish Telang, Nimish Yadav, Elpiniki Apostolaki-Iosifidou, Claire Berschauer, Chengcheng Ding,
Jonathan Goncalves, Victor-Haoyang Lian, Tristan Lin, Alejandro Londono-Hurtado, Junlin Luo, Xiao Ming,
David Jose Florez Rodriguez, Derin Serbetcioglu, Shixian Sheng, Jose St Louis, Tadatoshi Takahashi, and Haoxi Zhang. (2024).
slacgismo/solar-data-tools. Zenodo. doi: [10.5281/zenodo.5056959](https://zenodo.org/doi/10.5281/zenodo.5056959)
**Citing technical details (_e.g._, SDT algorithms)**
Bennet E. Meyers, PVInsight (Final Technical Report). United States. [https://doi.org/10.2172/1897181](https://doi.org/10.2172/1897181)
**Citing a specific version**
You can also cite the DOI corresponding to the specific version of
Solar Data Tools that you used. Solar Data Tools DOIs are listed at
[here](https://zenodo.org/search?q=parent.id%3A5056959&f=allversions%3Atrue&l=list&p=1&s=10&sort=version).
## Versioning
We use [Semantic Versioning](http://semver.org/) for versioning. For the versions available, see the [tags on this repository](https://github.com/slacgismo/solar-data-tools/tags).
## Authors
* **Bennet Meyers** - *Initial work and Main research work* - [Bennet Meyers GitHub](https://github.com/bmeyers)
See also the list of [contributors](https://github.com/bmeyers/solar-data-tools/contributors) who participated in this project.
Raw data
{
"_id": null,
"home_page": null,
"name": "solar-data-tools",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": "Sara Miskovich <smiskov@slac.stanford.edu>",
"keywords": "solar, pv, photovoltaic",
"author": null,
"author_email": "Bennet Meyers <bennetm@stanford.edu>",
"download_url": "https://files.pythonhosted.org/packages/8c/5b/e82bad53ab63beff482c157c9baf28c250e01db7653534302462c3d3a08a/solar_data_tools-1.6.5.tar.gz",
"platform": null,
"description": "<!-- HEADER -->\n<br />\n<p align=\"center\">\n <a href=\"#\">\n <img src=\"docs/source/_static/SDT_v1_secondary_blue_text.png\" width=\"600\">\n </a>\n\n <p align=\"center\">\n <br />\n <a href=\"https://solar-data-tools.readthedocs.io/\"><strong>Explore our documentation </strong></a>\n \u00b7\n <a href=\"https://github.com/slacgismo/solar-data-tools/issues\"><strong>Report Issue </strong></a>\n <br />\n <br />\n</p>\n</p> \n\n<table align=\"center\" >\n<tr>\n <td>Latest Release</td>\n <td>\n <a href=\"https://pypi.org/project/solar-data-tools/\">\n <img src=\"https://img.shields.io/pypi/v/solar-data-tools.svg\" alt=\"latest release\" />\n </a>\n <a href=\"https://anaconda.org/conda-forge/solar-data-tools\">\n <img src=\"https://anaconda.org/conda-forge/solar-data-tools/badges/version.svg\" />\n </a>\n <a href=\"https://anaconda.org/conda-forge/solar-data-tools\">\n <img src=\"https://anaconda.org/conda-forge/solar-data-tools/badges/latest_release_date.svg\" />\n </a>\n</tr>\n<tr>\n <td>License</td>\n <td>\n <a href=\"https://github.com/slacgismo/solar-data-tools/blob/main/LICENSE\">\n <img src=\"https://img.shields.io/pypi/l/solar-data-tools.svg\" alt=\"license\" />\n </a>\n</td>\n</tr>\n<tr>\n <td>Build Status</td>\n <td>\n <a href=\"https://solar-data-tools.readthedocs.io/\">\n <img src=\"https://readthedocs.org/projects/solar-data-tools/badge/?version=stable\" alt=\"documentation build status\" />\n </a>\n <a href=\"https://github.com/slacgismo/solar-data-tools/actions/workflows/test.yml\">\n <img src=\"https://github.com/slacgismo/solar-data-tools/actions/workflows/test.yml/badge.svg?branch=main\" alt=\"Actions build status\" />\n </a>\n <a href=\"https://github.com/slacgismo/solar-data-tools/actions/workflows/build.yml\">\n <img src=\"https://github.com/slacgismo/solar-data-tools/actions/workflows/build.yml/badge.svg\">\n </a> \n </td>\n</tr>\n<tr>\n <td>Publications</td>\n <td>\n <a href=\"https://zenodo.org/badge/latestdoi/171066536\">\n <img src=\"https://zenodo.org/badge/171066536.svg\" alt=\"DOI\">\n </a>\n </td>\n</tr>\n<tr>\n <td>PyPI Downloads</td>\n <td>\n <a href=\"https://pepy.tech/project/solar-data-tools\">\n <img src=\"https://img.shields.io/pypi/dm/solar-data-tools\" alt=\"PyPI downloads\" />\n </a>\n </td>\n</tr>\n<tr>\n <td>Conda Downloads</td>\n <td>\n <a href=\"https://anaconda.org/conda-forge/solar-data-tools\">\n <img src=\"https://anaconda.org/conda-forge/solar-data-tools/badges/downloads.svg\" alt=\"conda-forge downloads\" />\n </a>\n </td>\n</tr>\n<tr>\n <td>Test-Coverage</td>\n <td>\n <img src=\"https://img.shields.io/badge/test--coverage-45%25-yellowgreen\" alt=\"test-coverage\" />\n </td>\n</tr>\n</table>\n \n\nSolar Data Tools is an open-source Python library for analyzing PV power (and irradiance) time-series data. It\nwas developed to enable analysis of _unlabeled_ PV data, i.e. with no model, no meteorological data, and no performance index required,\nby taking a statistical signal processing approach in the algorithms used in the package\u2019s main data processing pipeline.\nSolar Data Tools empowers PV system fleet owners or operators to analyze system performance a hundred times faster even when \nthey only have access to the most basic data stream\u2014power output of the system.\n\nSolar Data Tools provides methods for data I/O, cleaning, filtering, plotting, and analysis. These methods are largely automated and require little\nto no input from the user regardless of system type\u2014from utility tracking systems to multi-pitch rooftop systems. \nHead over to our Getting Started pages in our [documentation](https://solar-data-tools.readthedocs.io/) for a demo! For an in-depth tutorial on Solar Data Tools, we recommend taking a look at the recent webinar \nwe did with the DOE's Solar Energy Technologies Office (SETO) with our colleagues at NREL, linked below:\n\n<p align=\"center\">\n <a href=\"https://www.youtube.com/watch?v=XKbqIlAEwOQ\">\n <img src=\"https://img.youtube.com/vi/XKbqIlAEwOQ/hq1.jpg\" />\n </a>\n <br/>\n </img>\n</p>\n\nYou can also check the [notebooks](https://github.com/slacgismo/solar-data-tools/blob/main/notebooks/examples) folder in this repo for more examples.\n\nThis work is supported by the U.S. Department of Energy\u2019s Office of Energy Efficiency and Renewable Energy (EERE) under the Solar Energy Technologies Office Award Number 38529.\n\n## Install & Setup\n\n#### Recommended: Install with pip\n\nIn a fresh Python virtual environment, simply run:\n\n```bash\n$ pip install solar-data-tools\n```\n\nor if you would like to use MOSEK, install the optional dependency as well:\n\n```bash\n$ pip install \"solar-data-tools[mosek]\"\n```\n\n#### Install with conda\n\n>[!WARNING]\n> `solar-data-tools` is now available on conda-forge! You can specify \n> the channel using the `-c` flag as shown in the examples below. \n> The use of the slacgismo channel is **deprecated** and packages \n> on that channel will **not** be up-to-date with the latest releases. \n\nCreating the environment and directly installing the package and its dependencies from the appropriate conda channels:\n\n```bash\n$ conda create -n pvi-user solar-data-tools -c conda-forge\n```\n\nStarting the environment:\n\n```bash\n$ conda activate pvi-user\n```\n\nStopping the environment:\n\n```bash\n$ conda deactivate\n```\n\nOr alternatively install the package in an already existing environment:\n\n```bash\n$ conda install solar-data-tools -c conda-forge\n```\n\n### Solvers\n\n#### CLARABEL\n\nBy default, the [CLARABEL](https://clarabel.org/stable/) solver is used to solve the signal decomposition problems. CLARABEL (as well as other solvers) is compatible with [OSD](https://github.com/cvxgrp/signal-decomposition/tree/main), the modeling language used to solve signal decomposition problems in Solar Data Tools. Both are open source and are dependencies of Solar Data Tools. \n\n#### MOSEK\n\nMOSEK is a commercial software package. Since it is more stable and offers faster solve times,\nwe provide continuing support for it (with signal decomposition problem formulations using CVXPY). However,\nyou will still need to obtain a license. If installing with pip, you can install the optional MOSEK dependency by running \n`pip install \"solar-data-tools[mosek]\"`. \nIf installing from conda, you will have to manually install MOSEK if you desire to use it as \nconda does not support optional dependencies like pip. \n\nMore information about MOSEK and how to obtain a license is available here:\n\n* [mosek](https://www.mosek.com/resources/getting-started/)\n* [Free 30-day trial](https://www.mosek.com/products/trial/)\n* [Personal academic license](https://www.mosek.com/products/academic-licenses/)\n\n## Usage\nUsers will primarily interact with this software through the `DataHandler` class. By default, Solar Data \nTools uses [CLARABEL](https://clarabel.org/stable/) as the solver all signal decomposition problems. If you would like \nto specify another solver (such as MOSEK), just pass the keyword argument `solver` to `DataHandler.pipeline` with the solver of choice.\n\n```python\nfrom solardatatools import DataHandler\nfrom solardatatools.dataio import get_pvdaq_data\n\npv_system_data = get_pvdaq_data(sysid=35, api_key='DEMO_KEY', year=[2011, 2012, 2013])\n\ndh = DataHandler(pv_system_data)\ndh.run_pipeline(power_col='dc_power')\n```\nIf everything is working correctly, you should see a run summary like the following\n\n```\ntotal time: 25.99 seconds\n--------------------------------\nBreakdown\n--------------------------------\nPreprocessing 6.76s\nCleaning 0.41s\nFiltering/Summarizing 18.83s\n Data quality 0.21s\n Clear day detect 0.44s\n Clipping detect 15.51s\n Capacity change detect 2.67s\n```\n\nYou can also find more in-depth tutorials and guides in [our documentation](https://solar-data-tools.readthedocs.io/).\n\n\n## Contributing\n\nWe welcome contributions of any form! Please see our [Contribution Guidelines](./CONTRIBUTING.md) for more information.\n\n## Citing Solar Data Tools\n\nIf you use Solar Data Tools in your research, please cite:\n\n**Recommended citations**\n\n Bennet E. Meyers, Elpiniki Apostolaki-Iosifidou and Laura Schelhas, \"Solar Data Tools: Automatic Solar \n Data Processing Pipeline,\" 2020 47th IEEE Photovoltaic Specialists Conference (PVSC), Calgary, AB, Canada, 2020,\n pp. 0655-0656, doi: [10.1109/PVSC45281.2020.9300847](https://doi.org/10.1109/PVSC45281.2020.9300847).\n\n Bennet E. Meyers, Sara A. Miskovich, Duncan Ragsdale, Mitchell Victoriano, Aramis Dufour, \n Nimish Telang, Nimish Yadav, Elpiniki Apostolaki-Iosifidou, Claire Berschauer, Chengcheng Ding, \n Jonathan Goncalves, Victor-Haoyang Lian, Tristan Lin, Alejandro Londono-Hurtado, Junlin Luo, Xiao Ming, \n David Jose Florez Rodriguez, Derin Serbetcioglu, Shixian Sheng, Jose St Louis, Tadatoshi Takahashi, and Haoxi Zhang. (2024). \n slacgismo/solar-data-tools. Zenodo. doi: [10.5281/zenodo.5056959](https://zenodo.org/doi/10.5281/zenodo.5056959)\n\n**Citing technical details (_e.g._, SDT algorithms)**\n\n Bennet E. Meyers, PVInsight (Final Technical Report). United States. [https://doi.org/10.2172/1897181](https://doi.org/10.2172/1897181)\n\n**Citing a specific version**\n\nYou can also cite the DOI corresponding to the specific version of\nSolar Data Tools that you used. Solar Data Tools DOIs are listed at\n[here](https://zenodo.org/search?q=parent.id%3A5056959&f=allversions%3Atrue&l=list&p=1&s=10&sort=version).\n\n\n## Versioning\n\nWe use [Semantic Versioning](http://semver.org/) for versioning. For the versions available, see the [tags on this repository](https://github.com/slacgismo/solar-data-tools/tags).\n\n## Authors\n\n* **Bennet Meyers** - *Initial work and Main research work* - [Bennet Meyers GitHub](https://github.com/bmeyers)\n\nSee also the list of [contributors](https://github.com/bmeyers/solar-data-tools/contributors) who participated in this project.\n",
"bugtrack_url": null,
"license": "BSD 2-Clause License Copyright (c) 2019, Bennet Meyers All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * 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. ",
"summary": "Tools for performing common tasks on solar PV data signals",
"version": "1.6.5",
"project_urls": {
"Bug Tracker": "https://github.com/slacgismo/solar-data-tools/issues",
"Discussions": "https://github.com/slacgismo/solar-data-tools/discussions",
"Documentation": "https://solar-data-tools.readthedocs.io/",
"Homepage": "https://github.com/slacgismo/solar-data-tools"
},
"split_keywords": [
"solar",
" pv",
" photovoltaic"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "81e55979d29c82dbe66720fcb5d07d1367cb7aaaaabf846773cdef7520ff2bdc",
"md5": "960bf68e8644ff923e1ad00f9d24ed5a",
"sha256": "9903589ee0fd08ce7b4ad46e1d13b8382f6068077b7b21813cf048945d38083f"
},
"downloads": -1,
"filename": "solar_data_tools-1.6.5-py3-none-any.whl",
"has_sig": false,
"md5_digest": "960bf68e8644ff923e1ad00f9d24ed5a",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 77030853,
"upload_time": "2025-01-22T00:33:23",
"upload_time_iso_8601": "2025-01-22T00:33:23.034324Z",
"url": "https://files.pythonhosted.org/packages/81/e5/5979d29c82dbe66720fcb5d07d1367cb7aaaaabf846773cdef7520ff2bdc/solar_data_tools-1.6.5-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "8c5be82bad53ab63beff482c157c9baf28c250e01db7653534302462c3d3a08a",
"md5": "dac173fefe3cb16f5a259bd812ed795a",
"sha256": "5d2660f7a10b7f1657baed5754b547a6522ddb4ff47148be5d1df0f792f92ec8"
},
"downloads": -1,
"filename": "solar_data_tools-1.6.5.tar.gz",
"has_sig": false,
"md5_digest": "dac173fefe3cb16f5a259bd812ed795a",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 76949369,
"upload_time": "2025-01-22T00:33:36",
"upload_time_iso_8601": "2025-01-22T00:33:36.189200Z",
"url": "https://files.pythonhosted.org/packages/8c/5b/e82bad53ab63beff482c157c9baf28c250e01db7653534302462c3d3a08a/solar_data_tools-1.6.5.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-01-22 00:33:36",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "slacgismo",
"github_project": "solar-data-tools",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "scipy",
"specs": []
},
{
"name": "numpy",
"specs": []
},
{
"name": "pandas",
"specs": []
},
{
"name": "scikit-learn",
"specs": []
},
{
"name": "jupyter",
"specs": []
},
{
"name": "matplotlib",
"specs": []
},
{
"name": "seaborn",
"specs": []
},
{
"name": "requests",
"specs": []
},
{
"name": "pvlib",
"specs": []
},
{
"name": "cvxpy",
"specs": []
},
{
"name": "pykml",
"specs": []
},
{
"name": "haversine",
"specs": []
},
{
"name": "smart_open",
"specs": []
},
{
"name": "sig-decomp",
"specs": []
},
{
"name": "clarabel",
"specs": []
},
{
"name": "qss",
"specs": []
},
{
"name": "tqdm",
"specs": []
},
{
"name": "spcqe",
"specs": []
},
{
"name": "dask",
"specs": []
},
{
"name": "distributed",
"specs": []
},
{
"name": "dask-cloudprovider",
"specs": []
},
{
"name": "graphviz",
"specs": []
},
{
"name": "bokeh",
"specs": []
}
],
"lcname": "solar-data-tools"
}
