Metadata-Version: 2.1
Name: gsee
Version: 0.3.0
Summary: GSEE: Global Solar Energy Estimator
Home-page: https://github.com/renewables-ninja/gsee
Author: Stefan Pfenninger
Author-email: stefan@pfenninger.org
License: UNKNOWN
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)
        
        # GSEE: Global Solar Energy Estimator
        
        `GSEE` is a solar energy simulation library designed for rapid calculations and ease of use. [Renewables.ninja](https://www.renewables.ninja/) uses `GSEE`.
        
        ## Requirements
        
        Works only with Python 3. Required libraries:
        
        * [joblib](https://joblib.readthedocs.io/en/latest/)
        * [numpy](https://numpy.org/)
        * [pandas](https://pandas.pydata.org/)
        * [pyephem](https://pypi.org/project/ephem/)
        * [scipy](https://scipy.org/)
        * [xarray](https://xarray.pydata.org/)
        
        ## Installation
        
        Simply install with `pip`:
        
            pip install gsee
        
        The recommended way to install the required scientific libraries is to use the [Anaconda Python distribution](https://www.continuum.io/downloads).
        
        **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.
        
        ## Functionality
        
        The following submodules are available:
        
        * __``brl_model``__: an implementation of the BRL model, a method to derive the diffuse fraction of irradiance, based on Ridley et al. (2010)
        * __``climatedata_interface``__: an interface to use GSEE with annual, seasonal, monthly or daily data. See [docs/climatedata_interface](docs/climatedata_interface.md) for details.
        * __``pv``__: electric output from PV a panel
        * __``trigon``__: functions to calculate irradiance on an inclined plane
        
        A model can be imported like this: ``import gsee.pv``
        
        A 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.
        
        ## Examples
        
        ### Power output from a PV system with fixed panels
        
        In 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).
        
        ```python
        result = gsee.pv.run_model(
            data,
            coords=(22.78, 5.51),  # Latitude and longitude
            tilt=30, # 30 degrees tilt angle
            azim=180,  # facing towards equator,
            tracking=0,  # fixed - no tracking
            capacity=1000,  # 1000 W
        )
        ```
        
        ### Aperture irradiance on a panel with 2-axis tracking
        
        ```python
        location = (22.78, 5.51)
        plane_irradiance = gsee.trigon.aperture_irradiance(
            data['direct_horizontal'], data['diffuse_horizontal'],
            location, tracking=2
        )
        ```
        
        ### Climate data Interface
        
        Example use directly reading NetCDF files with GHI, diffuse irradiance fraction, and temperature data:
        
        ```python
        from gsee.climatedata_interface.interface import run_interface
        
        run_interface(
            ghi_data=('ghi_input.nc', 'ghi'),  # Tuple of (input file path, variable name)
            diffuse_data=('diffuse_fraction_input.nc', 'diff_frac'),
            temp_data=('temperature_input.nc', 't2m'),
            outfile='output_file.nc',
            params=dict(tilt=35, azim=180, tracking=0, capacity=1000),
            frequency='detect'
        )
        ```
        
        Tilt can be given as a latitude-dependent function instead of static value:
        
        ```python
        params = dict(tilt=lambda lat: 0.35396 * lat + 16.84775, ...)
        ```
        
        Instead 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):
        
        ```python
        from gsee.climatedata_interface.interface import run_interface_from_dataset
        
        result = run_interface_from_dataset(
            data=my_dataset,  # my_dataset is an xarray.Dataset
            params=dict(tilt=35, azim=180, tracking=0, capacity=1000)
        )
        ```
        
        By default, a built-in file with monthly probability density functions is automatically downloaded and used to generate synthetic daily irradiance.
        
        For more information, see the [climate data interface documentation](docs/climatedata-interface.md).
        
        ## Development
        
        To install the latest development version directly from GitHub:
        
            pip install -e git+https://github.com/renewables-ninja/gsee.git#egg=gsee
        
        To build the `climatedata_interface` submodule [Cython >= 0.28.5](http://cython.org/) is required.
        
        ## Credits and contact
        
        Contact [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.
        
        ## Citation
        
        If you use `GSEE` or code derived from it in academic work, please cite:
        
        Stefan 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)
        
        ## License
        
        BSD-3-Clause
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
Provides-Extra: generate_pdfs
