| Name | tsdb JSON |
| Version |
0.6.1
JSON |
| download |
| home_page | None |
| Summary | TSDB (Time Series Data Beans): a Python toolbox helping load 172 open-source time-series datasets |
| upload_time | 2024-07-27 05:03:25 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | Copyright (c) 2023-present, Wenjie Du All rights reserved. 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. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. 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 OWNER 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 |
data mining
time series
time-series analysis
time-series database
time-series datasets
database
datasets
dataset downloading
imputation
classification
forecasting
partially observed
irregularly sampled
partially-observed time series
incomplete time series
missing data
missing values
pypots
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<a href='https://github.com/WenjieDu/TSDB'><img src="https://pypots.com/figs/pypots_logos/TSDB/logo_FFBG.svg" align='right' width='200'/></a>
<h3 align="center">Welcome to TSDB</h3>
*<p align='center'>load 172 public time-series datasets with a single line of code ;-)</p>*
<p align='center'>
<a href='https://github.com/WenjieDu/TSDB'>
<img alt='Python version' src='https://img.shields.io/badge/python-v3-E97040?logo=python&logoColor=white'>
</a>
<a href="https://github.com/WenjieDu/TSDB/releases">
<img alt="the latest release version" src="https://img.shields.io/github/v/release/wenjiedu/tsdb?color=EE781F&include_prereleases&label=Release&logo=github&logoColor=white">
</a>
<a href="https://github.com/WenjieDu/TSDB/blob/main/LICENSE">
<img alt="BSD-3 license" src="https://img.shields.io/badge/License-BSD--3-E9BB41?logo=opensourceinitiative&logoColor=white">
</a>
<a href="https://github.com/WenjieDu/PyPOTS/blob/main/README.md#-community">
<img alt="Community" src="https://img.shields.io/badge/join_us-community!-C8A062">
</a>
<a href="https://github.com/WenjieDu/TSDB/graphs/contributors">
<img alt="GitHub contributors" src="https://img.shields.io/github/contributors/wenjiedu/tsdb?color=D8E699&label=Contributors&logo=GitHub">
</a>
<a href="https://star-history.com/#wenjiedu/tsdb">
<img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/wenjiedu/tsdb?logo=None&color=6BB392&label=%E2%98%85%20Stars">
</a>
<a href="https://github.com/WenjieDu/TSDB/network/members">
<img alt="GitHub Repo forks" src="https://img.shields.io/github/forks/wenjiedu/tsdb?logo=forgejo&logoColor=black&label=Forks">
</a>
<a href="https://codeclimate.com/github/WenjieDu/TSDB">
<img alt="Code Climate maintainability" src="https://img.shields.io/codeclimate/maintainability-percentage/WenjieDu/TSDB?color=3C7699&label=Maintainability&logo=codeclimate">
</a>
<a href='https://coveralls.io/github/WenjieDu/TSDB'>
<img alt='Coveralls report' src='https://img.shields.io/coverallsCoverage/github/WenjieDu/TSDB?branch=main&logo=coveralls&color=75C1C4&label=Coverage'>
</a>
<a href='https://github.com/WenjieDu/TSDB/actions/workflows/testing_ci.yml'>
<img alt='GitHub Testing' src='https://img.shields.io/github/actions/workflow/status/wenjiedu/tsdb/testing_ci.yml?logo=github&color=C8D8E1&label=CI'>
</a>
<a href="https://arxiv.org/abs/2305.18811">
<img alt="arXiv DOI" src="https://img.shields.io/badge/DOI-10.48550/arXiv.2305.18811-F8F7F0">
</a>
<a href="https://anaconda.org/conda-forge/tsdb">
<img alt="Conda downloads" src="https://img.shields.io/endpoint?url=https://pypots.com/figs/downloads_badges/conda_tsdb_downloads.json">
</a>
<a href='https://pepy.tech/project/tsdb'>
<img alt='PyPI downloads' src='https://img.shields.io/endpoint?url=https://pypots.com/figs/downloads_badges/pypi_tsdb_downloads.json'>
</a>
</p>
> 📣 TSDB now supports a total of 1️⃣7️⃣2️⃣ time-series datasets ‼️
<a href='https://github.com/WenjieDu/PyPOTS'><img src='https://pypots.com/figs/pypots_logos/PyPOTS/logo_FFBG.svg' width='160' align='left' /></a>
TSDB is a part of
<a href="https://github.com/WenjieDu/PyPOTS">
PyPOTS <img align="center" src="https://img.shields.io/github/stars/WenjieDu/PyPOTS?style=social">
</a>
(a Python toolbox for data mining on Partially-Observed Time Series), and was separated from PyPOTS for decoupling datasets from learning algorithms.
TSDB is created to help researchers and engineers get rid of data collecting and downloading, and focus back on data processing details. TSDB provides all-in-one-stop convenience for downloading and loading open-source time-series datasets (available datasets listed [below](https://github.com/WenjieDu/TSDB#-list-of-available-datasets)).
❗️Please note that due to people have very different requirements for data processing, data-loading functions in TSDB only contain the most general steps (e.g. removing invalid samples) and won't process the data (not even normalize it). So, no worries, TSDB won't affect your data preprocessing. If you only want the raw datasets, TSDB can help you download and save raw datasets as well (take a look at [Usage Examples](https://github.com/WenjieDu/TSDB#-usage-example) below).
🤝 If you need TSDB to integrate an open-source dataset or want to add it into TSDB yourself, please feel free to request for it by creating an issue or make a PR to merge your code.
🤗 **Please** star this repo to help others notice TSDB if you think it is a useful toolkit.
**Please** properly [cite TSDB and PyPOTS](https://github.com/WenjieDu/TSDB#-citing-tsdbpypots) in your publications
if it helps with your research. This really means a lot to our open-source research. Thank you!
## ❖ Usage Examples
> [!IMPORTANT]
> TSDB is available on both <a alt='PyPI' href='https://pypi.python.org/pypi/tsdb'><img align='center' src='https://img.shields.io/badge/PyPI--lightgreen?style=social&logo=pypi'></a>
> and <a alt='Anaconda' href='https://anaconda.org/conda-forge/tsdb'><img align='center' src='https://img.shields.io/badge/Anaconda--lightgreen?style=social&logo=anaconda'></a>❗️
>
> Install via pip:
> > pip install tsdb
>
> or install from source code:
> > pip install `https://github.com/WenjieDu/TSDB/archive/main.zip`
>
> or install via conda:
> > conda install tsdb -c conda-forge
```python
import tsdb
# list all available datasets in TSDB
tsdb.list()
# ['physionet_2012',
# 'physionet_2019',
# 'electricity_load_diagrams',
# 'beijing_multisite_air_quality',
# 'italy_air_quality',
# 'vessel_ais',
# 'electricity_transformer_temperature',
# 'pems_traffic',
# 'solar_alabama',
# 'ucr_uea_ACSF1',
# 'ucr_uea_Adiac',
# ...
# select the dataset you need and load it, TSDB will download, extract, and process it automatically
data = tsdb.load('physionet_2012')
# if you need the raw data, use download_and_extract()
tsdb.download_and_extract('physionet_2012', './save_it_here')
# datasets you once loaded are cached, and you can check them with list_cached_data()
tsdb.list_cache()
# you can delete only one specific dataset's pickled cache
tsdb.delete_cache(dataset_name='physionet_2012', only_pickle=True)
# you can delete only one specific dataset raw files and preserve others
tsdb.delete_cache(dataset_name='physionet_2012')
# or you can delete all cache with delete_cached_data() to free disk space
tsdb.delete_cache()
# The default cache directory is ~/.pypots/tsdb under the user's home directory.
# To avoid taking up too much space if downloading many datasets ,
# TSDB cache directory can be migrated to an external disk
tsdb.migrate_cache("/mnt/external_disk/TSDB_cache")
```
That's all. Simple and efficient. Enjoy it! 😃
## ❖ List of Available Datasets
| Name | Main Tasks |
|---------------------------------------------------------------------------------------------------|-----------------------------------------|
| [PhysioNet Challenge 2012](dataset_profiles/physionet_2012) | Forecasting, Imputation, Classification |
| [PhysioNet Challenge 2019](dataset_profiles/physionet_2019) | Forecasting, Imputation, Classification |
| [Beijing Multi-Site Air-Quality](dataset_profiles/beijing_multisite_air_quality) | Forecasting, Imputation |
| [Italy Air Quality](dataset_profiles/italy_air_quality) | Forecasting, Imputation |
| [Electricity Load Diagrams](dataset_profiles/electricity_load_diagrams) | Forecasting, Imputation |
| [Electricity Transformer Temperature (ETT)](dataset_profiles/electricity_transformer_temperature) | Forecasting, Imputation |
| [Vessel AIS](dataset_profiles/vessel_ais) | Forecasting, Imputation, Classification |
| [PeMS Traffic](dataset_profiles/pems_traffic) | Forecasting, Imputation |
| [Solar Alabama](dataset_profiles/solar_alabama) | Forecasting, Imputation |
| [UCR & UEA Datasets](dataset_profiles/ucr_uea_datasets) (all 163 datasets) | Classification |
## ❖ Citing TSDB/PyPOTS
The paper introducing PyPOTS is available [on arXiv](https://arxiv.org/abs/2305.18811),
A short version of it is accepted by the 9th SIGKDD international workshop on Mining and Learning from Time Series ([MiLeTS'23](https://kdd-milets.github.io/milets2023/))).
**Additionally**, PyPOTS has been included as a [PyTorch Ecosystem](https://pytorch.org/ecosystem/) project.
We are pursuing to publish it in prestigious academic venues, e.g. JMLR (track for
[Machine Learning Open Source Software](https://www.jmlr.org/mloss/)). If you use PyPOTS in your work,
please cite it as below and 🌟star this repository to make others notice this library. 🤗
There are scientific research projects using PyPOTS and referencing in their papers.
Here is [an incomplete list of them](https://scholar.google.com/scholar?as_ylo=2022&q=%E2%80%9CPyPOTS%E2%80%9D&hl=en).
<p align="center">
<a href="https://github.com/WenjieDu/PyPOTS">
<img src="https://pypots.com/figs/pypots_logos/Ecosystem/PyPOTS_Ecosystem_Pipeline.png" width="95%"/>
</a>
</p>
``` bibtex
@article{du2023pypots,
title={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},
author={Wenjie Du},
journal={arXiv preprint arXiv:2305.18811},
year={2023},
}
```
or
> Wenjie Du.
> PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series.
> arXiv, abs/2305.18811, 2023.
<details>
<summary>🏠 Visits</summary>
<img align='left' src='https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FWenjieDu%2FTime_Series_Database&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=Visits+since+April+2022&edge_flat=false'>
</details>
Raw data
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"author": null,
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"download_url": "https://files.pythonhosted.org/packages/92/92/3e4ca31bc39d7611835dc81109449e3adc9130c1c321d500b86d2c53be7e/tsdb-0.6.1.tar.gz",
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"description": "<a href='https://github.com/WenjieDu/TSDB'><img src=\"https://pypots.com/figs/pypots_logos/TSDB/logo_FFBG.svg\" align='right' width='200'/></a>\n\n<h3 align=\"center\">Welcome to TSDB</h3>\n\n*<p align='center'>load 172 public time-series datasets with a single line of code ;-)</p>*\n\n<p align='center'>\n <a href='https://github.com/WenjieDu/TSDB'>\n <img alt='Python version' src='https://img.shields.io/badge/python-v3-E97040?logo=python&logoColor=white'>\n </a>\n <a href=\"https://github.com/WenjieDu/TSDB/releases\">\n <img alt=\"the latest release version\" src=\"https://img.shields.io/github/v/release/wenjiedu/tsdb?color=EE781F&include_prereleases&label=Release&logo=github&logoColor=white\">\n </a>\n <a href=\"https://github.com/WenjieDu/TSDB/blob/main/LICENSE\">\n <img alt=\"BSD-3 license\" src=\"https://img.shields.io/badge/License-BSD--3-E9BB41?logo=opensourceinitiative&logoColor=white\">\n </a>\n <a href=\"https://github.com/WenjieDu/PyPOTS/blob/main/README.md#-community\">\n <img alt=\"Community\" src=\"https://img.shields.io/badge/join_us-community!-C8A062\">\n </a>\n <a href=\"https://github.com/WenjieDu/TSDB/graphs/contributors\">\n <img alt=\"GitHub contributors\" src=\"https://img.shields.io/github/contributors/wenjiedu/tsdb?color=D8E699&label=Contributors&logo=GitHub\">\n </a>\n <a href=\"https://star-history.com/#wenjiedu/tsdb\">\n <img alt=\"GitHub Repo stars\" src=\"https://img.shields.io/github/stars/wenjiedu/tsdb?logo=None&color=6BB392&label=%E2%98%85%20Stars\">\n </a>\n <a href=\"https://github.com/WenjieDu/TSDB/network/members\">\n <img alt=\"GitHub Repo forks\" src=\"https://img.shields.io/github/forks/wenjiedu/tsdb?logo=forgejo&logoColor=black&label=Forks\">\n </a>\n <a href=\"https://codeclimate.com/github/WenjieDu/TSDB\">\n <img alt=\"Code Climate maintainability\" src=\"https://img.shields.io/codeclimate/maintainability-percentage/WenjieDu/TSDB?color=3C7699&label=Maintainability&logo=codeclimate\">\n </a>\n <a href='https://coveralls.io/github/WenjieDu/TSDB'>\n <img alt='Coveralls report' src='https://img.shields.io/coverallsCoverage/github/WenjieDu/TSDB?branch=main&logo=coveralls&color=75C1C4&label=Coverage'>\n </a>\n <a href='https://github.com/WenjieDu/TSDB/actions/workflows/testing_ci.yml'>\n <img alt='GitHub Testing' src='https://img.shields.io/github/actions/workflow/status/wenjiedu/tsdb/testing_ci.yml?logo=github&color=C8D8E1&label=CI'>\n </a>\n <a href=\"https://arxiv.org/abs/2305.18811\">\n <img alt=\"arXiv DOI\" src=\"https://img.shields.io/badge/DOI-10.48550/arXiv.2305.18811-F8F7F0\">\n </a>\n <a href=\"https://anaconda.org/conda-forge/tsdb\">\n <img alt=\"Conda downloads\" src=\"https://img.shields.io/endpoint?url=https://pypots.com/figs/downloads_badges/conda_tsdb_downloads.json\">\n </a>\n <a href='https://pepy.tech/project/tsdb'>\n <img alt='PyPI downloads' src='https://img.shields.io/endpoint?url=https://pypots.com/figs/downloads_badges/pypi_tsdb_downloads.json'>\n </a>\n</p>\n\n> \ud83d\udce3 TSDB now supports a total of 1\ufe0f\u20e37\ufe0f\u20e32\ufe0f\u20e3 time-series datasets \u203c\ufe0f\n\n<a href='https://github.com/WenjieDu/PyPOTS'><img src='https://pypots.com/figs/pypots_logos/PyPOTS/logo_FFBG.svg' width='160' align='left' /></a>\nTSDB is a part of\n<a href=\"https://github.com/WenjieDu/PyPOTS\">\nPyPOTS <img align=\"center\" src=\"https://img.shields.io/github/stars/WenjieDu/PyPOTS?style=social\">\n</a>\n(a Python toolbox for data mining on Partially-Observed Time Series), and was separated from PyPOTS for decoupling datasets from learning algorithms.\n\nTSDB is created to help researchers and engineers get rid of data collecting and downloading, and focus back on data processing details. TSDB provides all-in-one-stop convenience for downloading and loading open-source time-series datasets (available datasets listed [below](https://github.com/WenjieDu/TSDB#-list-of-available-datasets)).\n\n\u2757\ufe0fPlease note that due to people have very different requirements for data processing, data-loading functions in TSDB only contain the most general steps (e.g. removing invalid samples) and won't process the data (not even normalize it). So, no worries, TSDB won't affect your data preprocessing. If you only want the raw datasets, TSDB can help you download and save raw datasets as well (take a look at [Usage Examples](https://github.com/WenjieDu/TSDB#-usage-example) below).\n\n\ud83e\udd1d If you need TSDB to integrate an open-source dataset or want to add it into TSDB yourself, please feel free to request for it by creating an issue or make a PR to merge your code.\n\n\ud83e\udd17 **Please** star this repo to help others notice TSDB if you think it is a useful toolkit.\n**Please** properly [cite TSDB and PyPOTS](https://github.com/WenjieDu/TSDB#-citing-tsdbpypots) in your publications\nif it helps with your research. This really means a lot to our open-source research. Thank you!\n\n\n## \u2756 Usage Examples\n> [!IMPORTANT]\n> TSDB is available on both <a alt='PyPI' href='https://pypi.python.org/pypi/tsdb'><img align='center' src='https://img.shields.io/badge/PyPI--lightgreen?style=social&logo=pypi'></a>\n> and <a alt='Anaconda' href='https://anaconda.org/conda-forge/tsdb'><img align='center' src='https://img.shields.io/badge/Anaconda--lightgreen?style=social&logo=anaconda'></a>\u2757\ufe0f\n>\n> Install via pip:\n> > pip install tsdb\n>\n> or install from source code:\n> > pip install `https://github.com/WenjieDu/TSDB/archive/main.zip`\n>\n> or install via conda:\n> > conda install tsdb -c conda-forge\n\n\n```python\nimport tsdb\n\n# list all available datasets in TSDB\ntsdb.list()\n# ['physionet_2012',\n# 'physionet_2019',\n# 'electricity_load_diagrams',\n# 'beijing_multisite_air_quality',\n# 'italy_air_quality',\n# 'vessel_ais',\n# 'electricity_transformer_temperature',\n# 'pems_traffic',\n# 'solar_alabama',\n# 'ucr_uea_ACSF1',\n# 'ucr_uea_Adiac',\n# ...\n\n# select the dataset you need and load it, TSDB will download, extract, and process it automatically\ndata = tsdb.load('physionet_2012')\n# if you need the raw data, use download_and_extract()\ntsdb.download_and_extract('physionet_2012', './save_it_here')\n# datasets you once loaded are cached, and you can check them with list_cached_data()\ntsdb.list_cache()\n# you can delete only one specific dataset's pickled cache\ntsdb.delete_cache(dataset_name='physionet_2012', only_pickle=True)\n# you can delete only one specific dataset raw files and preserve others\ntsdb.delete_cache(dataset_name='physionet_2012')\n# or you can delete all cache with delete_cached_data() to free disk space\ntsdb.delete_cache()\n\n# The default cache directory is ~/.pypots/tsdb under the user's home directory.\n# To avoid taking up too much space if downloading many datasets ,\n# TSDB cache directory can be migrated to an external disk\ntsdb.migrate_cache(\"/mnt/external_disk/TSDB_cache\")\n```\n\nThat's all. Simple and efficient. Enjoy it! \ud83d\ude03\n\n\n## \u2756 List of Available Datasets\n\n| Name | Main Tasks |\n|---------------------------------------------------------------------------------------------------|-----------------------------------------|\n| [PhysioNet Challenge 2012](dataset_profiles/physionet_2012) | Forecasting, Imputation, Classification |\n| [PhysioNet Challenge 2019](dataset_profiles/physionet_2019) | Forecasting, Imputation, Classification |\n| [Beijing Multi-Site Air-Quality](dataset_profiles/beijing_multisite_air_quality) | Forecasting, Imputation |\n| [Italy Air Quality](dataset_profiles/italy_air_quality) | Forecasting, Imputation |\n| [Electricity Load Diagrams](dataset_profiles/electricity_load_diagrams) | Forecasting, Imputation |\n| [Electricity Transformer Temperature (ETT)](dataset_profiles/electricity_transformer_temperature) | Forecasting, Imputation |\n| [Vessel AIS](dataset_profiles/vessel_ais) | Forecasting, Imputation, Classification |\n| [PeMS Traffic](dataset_profiles/pems_traffic) | Forecasting, Imputation |\n| [Solar Alabama](dataset_profiles/solar_alabama) | Forecasting, Imputation |\n| [UCR & UEA Datasets](dataset_profiles/ucr_uea_datasets) (all 163 datasets) | Classification |\n\n\n## \u2756 Citing TSDB/PyPOTS\nThe paper introducing PyPOTS is available [on arXiv](https://arxiv.org/abs/2305.18811),\nA short version of it is accepted by the 9th SIGKDD international workshop on Mining and Learning from Time Series ([MiLeTS'23](https://kdd-milets.github.io/milets2023/))).\n**Additionally**, PyPOTS has been included as a [PyTorch Ecosystem](https://pytorch.org/ecosystem/) project.\nWe are pursuing to publish it in prestigious academic venues, e.g. JMLR (track for\n[Machine Learning Open Source Software](https://www.jmlr.org/mloss/)). If you use PyPOTS in your work,\nplease cite it as below and \ud83c\udf1fstar this repository to make others notice this library. \ud83e\udd17\n\nThere are scientific research projects using PyPOTS and referencing in their papers.\nHere is [an incomplete list of them](https://scholar.google.com/scholar?as_ylo=2022&q=%E2%80%9CPyPOTS%E2%80%9D&hl=en).\n\n<p align=\"center\">\n<a href=\"https://github.com/WenjieDu/PyPOTS\">\n <img src=\"https://pypots.com/figs/pypots_logos/Ecosystem/PyPOTS_Ecosystem_Pipeline.png\" width=\"95%\"/>\n</a>\n</p>\n\n``` bibtex\n@article{du2023pypots,\ntitle={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},\nauthor={Wenjie Du},\njournal={arXiv preprint arXiv:2305.18811},\nyear={2023},\n}\n```\nor\n> Wenjie Du.\n> PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series.\n> arXiv, abs/2305.18811, 2023.\n\n\n\n<details>\n<summary>\ud83c\udfe0 Visits</summary>\n<img align='left' src='https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FWenjieDu%2FTime_Series_Database&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=Visits+since+April+2022&edge_flat=false'>\n</details>\n",
"bugtrack_url": null,
"license": "Copyright (c) 2023-present, Wenjie Du All rights reserved. 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. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. 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 OWNER 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. ",
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