{
    "info": {
        "author": "SLAC National Accelerator Laboratory - Bennet Meyers",
        "author_email": "bennetm@stanford.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering"
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        "description": "# solar-data-tools\n\n[![PyPI release](https://img.shields.io/pypi/v/solar-data-tools.svg)](https://pypi.org/project/solar-data-tools/)\n[![Anaconda Cloud release](https://anaconda.org/slacgismo/solar-data-tools/badges/version.svg)](https://anaconda.org/slacgismo/solar-data-tools)\n\nTools for performing common tasks on solar PV data signals. These tasks include finding clear days in\na data set, common data transforms, and fixing time stamp issues. These tools are designed to be\nautomatic and require little if any input from the user. Libraries are included to help with data IO\nand plotting as well.  \n\nSee notebooks folder for examples.\n\n## Setup\n\n### Installing this project as PIP package\n\n```sh\n$ pip install solar-data-tools\n```\n\nAs of March 6, 2019, it fails because scs package installed as a dependency of cxvpy expects numpy to be already installed.\n[scs issue 85](https://github.com/cvxgrp/scs/issues/85) says, it is fixed.\nHowever, it doesn't seem to be reflected in its pip package.\nAlso, cvxpy doesn't work with numpy version less than 1.16.\nAs a work around, install numpy separatly first and then install this package.\ni.e.\n```sh\n$ pip install 'numpy>=1.16'\n$ pip install statistical-clear-sky\n```\n\n#### Solvers\n\nBy default, ECOS solver is used, which is supported by cvxpy because it is Open Source.\n\nHowever, it is found that Mosek solver is more stable. Thus, we encourage you to install it separately as below and obtain the license on your own.\n\n* [mosek](https://www.mosek.com/resources/getting-started/) - For using MOSEK solver.\n\n    ```sh\n    $ pip install -f https://download.mosek.com/stable/wheel/index.html Mosek\n    ```\n\n### Installing this project as Anaconda package\n\n```sh\n$ conda install -c slacgismo solar-data-tools\n```\n\nIf you are using Anaconda, the problem described in the section for PIP package above doesn't occur since numpy is already installed. And during solar-data-tools installation, numpy is upgraded above 1.16.\n\n#### Solvers\n\nBy default, ECOS solver is used, which is supported by cvxpy because it is Open Source.\n\nHowever, it is found that Mosek solver is more stable. Thus, we encourage you to install it separately as below and obtain the license on your own.\n\n* [mosek](https://www.mosek.com/resources/getting-started/) - For using MOSEK solver.\n\n    ```sh\n    $ conda install -c mosek mosek\n    ```\n\n### Using this project by cloning this GIT repository\n\nFrom a fresh `python` environment, run the following from the base project folder:\n\n```bash\n$ pip install -r requirements.txt\n```\n\nAs of March 6, 2019, it fails because scs package installed as a dependency of cxvpy expects numpy to be already installed.\n[scs issue 85](https://github.com/cvxgrp/scs/issues/85) says, it is fixed.\nHowever, it doesn't seem to be reflected in its pip package.\nAlso, cvxpy doesn't work with numpy version less than 1.16.\nAs a work around, install numpy separatly first and then install this package.\ni.e.\n```bash\n$ pip install 'numpy>=1.16'\n$ pip install -r requirements.txt\n```\n\nTo test that everything is working correctly, launch\n\n```bash\n$ jupyter notebook\n```\n\nand run the two notebooks in the `notebooks/` folder.\n\n## Usage\n\n#### Clear Day Detection\n\nThis algorithm estimates the clear days in a data set two ways and then combines the estimates for the final estimations. The first estimate is based on the \"smoothness\" of each daily power signal. The second estimate is based on the seasonally adjusted daily energy output of the system.\n\n```python\nimport numpy as np\nfrom solardatatools.clear_day_detection import find_clear_days\nfrom solardatatools.data_transforms import make_2d\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\npower_signals_d = make_2d(pv_system_data, key='dc_power')\n\nclear_days = find_clear_days(power_signals_d)\n```\n\n#### Time Shift Detection and Fixing\n\nThis algorithm determines if the time stamps provided with the data have \"shifted\" at any point and then corrects the shift if found. These shifts can often be caused by incorrect handling of daylight savings time, but can come from other sources as well.\n\n```python\nfrom solardatatools.data_transforms import fix_time_shifts, make_2d\nfrom solardatatools.dataio import get_pvdaq_data\nfrom solardatatools.plotting import plot_2d\n\npv_system_data = get_pvdaq_data(sysid=1199, year=[2015, 2016, 2017], api_key='DEMO_KEY')\n\npower_signals_d = make_2d(pv_system_data, key='dc_power')\n\nfixed_power_signals_d, time_shift_days_indices_ixs = fix_time_shifts(\n    power_signals_d, return_ixs=True)\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/bmeyers/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\n## License\n\nThis project is licensed under the BSD 2-Clause License - see the [LICENSE](LICENSE) file for details\n\n\n",
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