{
    "info": {
        "author": "Stefan Pfenninger",
        "author_email": "stefan@pfenninger.org",
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        "description": "[![Build Status](https://img.shields.io/travis/com/renewables-ninja/gsee/master.svg?style=flat-square)](https://travis-ci.com/renewables-ninja/gsee) [![Coverage](https://img.shields.io/coveralls/renewables-ninja/gsee.svg?style=flat-square)](https://coveralls.io/r/renewables-ninja/gsee) [![PyPI version](https://img.shields.io/pypi/v/gsee.svg?style=flat-square)](https://pypi.python.org/pypi/gsee)\n\n# GSEE: Global Solar Energy Estimator\n\n`GSEE` is a solar energy simulation library designed for rapid calculations and ease of use. [Renewables.ninja](https://www.renewables.ninja/) uses `GSEE`.\n\n## Requirements\n\nWorks only with Python 3. Required libraries:\n\n* [joblib](https://joblib.readthedocs.io/en/latest/)\n* [numpy](https://numpy.org/)\n* [pandas](https://pandas.pydata.org/)\n* [pyephem](https://pypi.org/project/ephem/)\n* [scipy](https://scipy.org/)\n* [xarray](https://xarray.pydata.org/)\n\n## Installation\n\nSimply install with `pip`:\n\n    pip install gsee\n\nThe recommended way to install the required scientific libraries is to use the [Anaconda Python distribution](https://www.continuum.io/downloads).\n\n**Known issue**: If you do not already have `numpy` installed, you will get a compiler error when pip tries to build to `climatedata_interface` Cython extension.\n\n## Functionality\n\nThe following submodules are available:\n\n* __``brl_model``__: an implementation of the BRL model, a method to derive the diffuse fraction of irradiance, based on Ridley et al. (2010)\n* __``climatedata_interface``__: an interface to use GSEE with annual, seasonal, monthly or daily data. See [docs/climatedata_interface](docs/climatedata_interface.md) for details.\n* __``pv``__: electric output from PV a panel\n* __``trigon``__: functions to calculate irradiance on an inclined plane\n\nA model can be imported like this: ``import gsee.pv``\n\nA plant simulation model implements a model class (e.g. ``PVPlant``) with the relevant settings, and a ``run_model()`` function that take time series data (a pandas Series) and runs a default instance of the model class, but can also take a ``model`` argument to specify a custom-configured model instance.\n\n## Examples\n\n### Power output from a PV system with fixed panels\n\nIn this example, ``data`` must be a pandas.DataFrame with columns ``global_horizontal`` (in W/m2), ``diffuse_fraction``, and optionally a ``temperature`` column for ambient air temperature (in degrees Celsius).\n\n```python\nresult = gsee.pv.run_model(\n    data,\n    coords=(22.78, 5.51),  # Latitude and longitude\n    tilt=30, # 30 degrees tilt angle\n    azim=180,  # facing towards equator,\n    tracking=0,  # fixed - no tracking\n    capacity=1000,  # 1000 W\n)\n```\n\n### Aperture irradiance on a panel with 2-axis tracking\n\n```python\nlocation = (22.78, 5.51)\nplane_irradiance = gsee.trigon.aperture_irradiance(\n    data['direct_horizontal'], data['diffuse_horizontal'],\n    location, tracking=2\n)\n```\n\n### Climate data Interface\n\nExample use directly reading NetCDF files with GHI, diffuse irradiance fraction, and temperature data:\n\n```python\nfrom gsee.climatedata_interface.interface import run_interface\n\nrun_interface(\n    ghi_data=('ghi_input.nc', 'ghi'),  # Tuple of (input file path, variable name)\n    diffuse_data=('diffuse_fraction_input.nc', 'diff_frac'),\n    temp_data=('temperature_input.nc', 't2m'),\n    outfile='output_file.nc',\n    params=dict(tilt=35, azim=180, tracking=0, capacity=1000),\n    frequency='detect'\n)\n```\n\nTilt can be given as a latitude-dependent function instead of static value:\n\n```python\nparams = dict(tilt=lambda lat: 0.35396 * lat + 16.84775, ...)\n```\n\nInstead of letting the climate data interface read and prepare data from NetCDF files, an `xarray.Dataset` can also be passed directly (e.g. when using the module in combination with a larger application):\n\n```python\nfrom gsee.climatedata_interface.interface import run_interface_from_dataset\n\nresult = run_interface_from_dataset(\n    data=my_dataset,  # my_dataset is an xarray.Dataset\n    params=dict(tilt=35, azim=180, tracking=0, capacity=1000)\n)\n```\n\nBy default, a built-in file with monthly probability density functions is automatically downloaded and used to generate synthetic daily irradiance.\n\nFor more information, see the [climate data interface documentation](docs/climatedata-interface.md).\n\n## Development\n\nTo install the latest development version directly from GitHub:\n\n    pip install -e git+https://github.com/renewables-ninja/gsee.git#egg=gsee\n\nTo build the `climatedata_interface` submodule [Cython >= 0.28.5](http://cython.org/) is required.\n\n## Credits and contact\n\nContact [Stefan Pfenninger](mailto:stefan.pfenninger@usys.ethz.ch) for questions about `GSEE`. `GSEE` is also a component of the [Renewables.ninja](https://www.renewables.ninja) project, developed by Stefan Pfenninger and Iain Staffell. Use the [contact page](https://www.renewables.ninja/about) there if you want more information about Renewables.ninja.\n\n## Citation\n\nIf you use `GSEE` or code derived from it in academic work, please cite:\n\nStefan Pfenninger and Iain Staffell (2016). Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data. *Energy* 114, pp. 1251-1265. [doi: 10.1016/j.energy.2016.08.060](https://doi.org/10.1016/j.energy.2016.08.060)\n\n## License\n\nBSD-3-Clause",
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