Metadata-Version: 2.1
Name: atlite
Version: 0.0.4
Summary: Library for fetching and converting weather data to power systems data
Home-page: https://github.com/PyPSA/atlite
Author: Jonas Hoersch (FIAS), Tom Brown (FIAS), Gorm Andresen (Aarhus University)
Author-email: jonas.hoersch@posteo.de
License: GPLv3
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: pandas (>=0.22)
Requires-Dist: bottleneck
Requires-Dist: numexpr
Requires-Dist: xarray (>=0.11.2)
Requires-Dist: dask (>=0.18.0)
Requires-Dist: rasterio (>=1.0)
Requires-Dist: shapely
Requires-Dist: progressbar2
Requires-Dist: geopandas

========
 Atlite
========

.. image:: https://img.shields.io/pypi/v/atlite.svg
    :target: https://pypi.python.org/pypi/atlite
    :alt: PyPI version

.. image:: https://img.shields.io/conda/vn/conda-forge/atlite.svg
    :target: https://anaconda.org/conda-forge/atlite
    :alt: Conda version

.. image:: https://img.shields.io/travis/PyPSA/atlite/master.svg
    :target: https://travis-ci.org/PyPSA/atlite
    :alt: Build status

.. image:: https://img.shields.io/pypi/l/atlite.svg
    :target: License

Atlite is a `free software
<http://www.gnu.org/philosophy/free-sw.en.html>`_, `xarray
<http://xarray.pydata.org/en/stable/>`_-based Python library for
converting weather data (such as wind speeds, solar radiation,
temperature and runoff) into power systems data (such as wind
power, solar power, hydro power and heating demand time series). It is
designed to work with big datasets, such as hourly global weather data
over several years at spatial resolutions down to e.g. 0.1 x 0.1
degree resolution.

Atlite was originally conceived as a light-weight version of the Aarhus
University RE Atlas, which produces wind and solar generation time
series from historical reanalysis data. It has since been extended to
use weather datasets simulated with projected climate change and to compute
other time series, such as hydro power, solar thermal collectors and
heating demand.

Atlite is designed to be modular, so that it can work with any weather
datasets. It currently has modules for the following datasets:

* `NCEP Climate Forecast System <http://rda.ucar.edu/datasets/ds094.1/>`_ hourly
  historical reanalysis weather data available on a 0.2 x 0.2 degree global grid
* `EURO-CORDEX Climate Change Projection <http://www.euro-cordex.net/>`_
  three-hourly up until 2100, available on a 0.11 x 0.11 degree grid for Europe
* `ECMWF ERA5
  <https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation>`_ hourly
  historical reanalysis weather data on an approximately 0.25 x 0.25 deg global
  grid
* `CMSAF SARAH-2
  <https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002>`_
  half-hourly historical surface radiation on a 0.05 x 0.05 deg grid available
  for Europe and Africa (automatically interpolated to a 0.2 deg grid and
  combined with ERA5 temperature).

It can process the following weather data fields:

* Temperature
* Downward short-wave radiation
* Upward short-wave radiation
* Wind 
* Runoff
* Surface roughness
* Height maps
* Soil temperature

The following power-system relevant time series can be produced for
all possible spatial distributions of assets:

* Wind power generation for a given turbine type
* Solar PV power generation for a given panel type
* Solar thermal collector heat output
* Hydroelectric inflow (simplified)
* Heating demand (based on the degree-day approximation)

Citation for Aarhus University RE
Atlas: G. B. Andresen, A. A. Søndergaard, M. Greiner, "Validation of
danish wind time series from a new global renewable energy atlas for
energy system analysis," Energy 93, Part 1 (2015) 1074 – 1088.
doi:http://dx.doi.org/10.1016/j.energy.2015.09.071.

Atlite was initially developed by the `Renewable Energy Group
<https://fias.uni-frankfurt.de/physics/schramm/renewable-energy-system-and-network-analysis/>`_
at `FIAS <https://fias.uni-frankfurt.de/>`_ to carry out simulations
for the `CoNDyNet project <http://condynet.de/>`_, financed by the
`German Federal Ministry for Education and Research (BMBF)
<https://www.bmbf.de/en/index.html>`_ as part of the `Stromnetze
Research Initiative
<http://forschung-stromnetze.info/projekte/grundlagen-und-konzepte-fuer-effiziente-dezentrale-stromnetze/>`_.

Getting started
===============

* Install atlite from conda-forge or pypi.
* Download one of the weather datasets listed above (ERA5 is downloaded
  automatically on-demand after the ECMWF
  `cdsapi<https://cds.climate.copernicus.eu/api-how-to>` client is 
  properly installed)
* Adjust the `atlite/config.py <atlite/config.py>`_ directory paths to
  point to the directory where you downloaded the dataset
* Create a cutout, i.e. a geographical rectangle and a selection of
  times, e.g. all hours in 2011 and 2012, to narrow down the scope -
  see `examples/create_cutout.py <examples/create_cutout.py>`_
* Select a sparse matrix of the geographical points inside the cutout
  you want to aggregate for your time series, and pass it to the
  appropriate converter function - see `examples/ <examples/>`_

Licence
=======


Copyright 2016-2017 Gorm Andresen (Aarhus University), Jonas Hörsch (FIAS), Tom Brown (FIAS), Markus Schlott (FIAS), David Schlachtberger (FIAS)


This program (atlite) is free software: you can redistribute it and/or
modify it under the terms of the GNU General Public License as
published by the Free Software Foundation; either `version 3 of the
License <LICENSE.txt>`_, or (at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
`GNU General Public License <LICENSE.txt>`_ for more details.


