8000 GitHub - sschiffel/pvlib-python at 1c8223a50e8ed304e8af2b7cf9b433315604190a
[go: up one dir, main page]
Skip to content

sschiffel/pvlib-python

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

509 Commits
docs
docs
 
 
pvlib
pvlib
 
 
.gitignore
.gitignore
 
 
.travis.yml
.travis.yml
 
 
LICENSE
LICENSE
 
 
MANIFEST.in
MANIFEST.in
 
 
README.md
README.md
 
 
setup.py
setup.py
 
 

Repository files navigation

pvlib-python

TravisCI Coverage Status Documentation Status

pvlib-python is a community supported tool that provides a set of documented functions for simulating the performance of photovoltaic energy systems. The toolbox was originally developed in MATLAB at Sandia National Laboratories and it implements many of the models and methods developed at the Labs. More information on Sandia Labs PV performance modeling programs can be found at https://pvpmc.sandia.gov/. We collaborate with the PVLIB-MATLAB project, but operate independently of it.

Documentation

Full documentation can be found at readthedocs.

Contributing

We need your help to make pvlib-python a great tool! Please see the Contributing to pvlib-python wiki for more on how you can contribute. The long-term success of pvlib-python requires substantial community support.

Installation

If you have Python

To obtain the most recent stable release, just use pip or conda:

pip install pvlib

conda install -c http://conda.anaconda.org/pvlib pvlib

If your system complains that you don't have access privileges or asks for a password then you're trying to install pvlib into your system's Python distribution. This is a very bad idea and you should instead follow the If you don't have Python instructions below.

If you don't have Python

There are many ways to install Python on your system, but the Anaconda Scientific Python distribution provides by far the easiest way for new users to get started. Anaconda includes all of the popular libraries that you'll need for pvlib, including Pandas, NumPy, and SciPy. "Anaconda installs cleanly into a single directory, does not require Administrator or root privileges, does not affect other Python installs on your system, or interfere with OSX Frameworks." -Anaconda Documentation.

  1. Install the full Anaconda Scientific Python distribution available here.

  2. Install pvlib: conda install -c http://conda.anaconda.org/pvlib pvlib

If you have trouble, see the Anaconda FAQ, Google your error messages, or make a new issue on our Issues page.

Working at the bleeding edge

We strongly recommend working in a virtual environment if you're going to use the development versions of the code. There are many ways to use virtual environments in Python, but Anaconda again provides the easiest solution:

  1. Create a new conda environment for pvlib and pre-install a handful of packages into the environment: conda create --name pvlibdev python pandas scipy ephem
  2. Activate the new environment: source activate pvlibdev
  3. Install the latest development version:
  4. If you don't plan to modify the source-code: pip install git+https://github.com/pvlib/pvlib-python.git
  5. If you do plan to modify the source code: Use the GitHub GUI application or git command-line tool to clone this repository to your computer, then navigate your command-line to the top-level pvlib-python directory, then pip install -e .
  6. You may also consider installing additional packages into your development environment: conda install ipython-notebook nose seaborn

The conda documentation has more information on how to use virtual environments.

NREL SPA algorithm

pvlib-python is distributed with several validated, high-precision, and high-performance solar position calculators. It also includes wrappers for the official NREL SPA algorithm. To use the NREL SPA algorithm, a pip install from the web cannot be used. Instead:

  1. Download the pvlib repository from https://github.com/pvlib/pvlib-python.git
  2. Download the SPA files from NREL
  3. Copy the SPA files into pvlib-python/pvlib/spa_c_files
  4. From the pvlib-python directory, run pip uninstall pvlib followed by pip install .

Usage

You're now ready to start some version of the Python interpreter and use pvlib. The easiest way to start is with one of our IPython notebook tutorials:

  1. Use the nbviewer website to choose a tutorial to experiment with. Go to our nbviewer tutorial page, click on e.g. pvsystem.ipynb, and then click on the download symbol.
  2. Start the IPython Notebook server: ipython notebook. This should open a web browser with the IPython Notebook's file/folder listing. If not, navigate to the url shown in the command line history, likely http://localhost:8888
  3. In IPython Notebook, navigate to the file that you downloaded in step one and open it.
  4. Use shift-enter to execute the notebook cell-by-cell. There is also a Play button that will execute all of the cells in the notebook.

You can also experiment with the following simple code in a new IPython notebook or any other Python interpreter:

# built-in imports
import sys
import datetime

# add-on imports
import pandas as pd

# pvlib imports
from pvlib.location import Location
import pvlib.solarposition
import pvlib.clearsky

# make a location
tus = Location(32.2, -111, 'MST', 700)

# make a pandas DatetimeIndex for some day
times = pd.date_range(start=datetime.datetime(2014,6,24), end=datetime.datetime(2014,6,25), freq='1Min')

# calculate the solar position
solpos = pvlib.solarposition.get_solarposition(times, tus)
solpos.plot()

# calculate clear sky data
tus_cs = pvlib.clearsky.ineichen(times, tus, airmass_model='young1994')
tus_cs.plot()

Many good online resources exist for getting started with scientific Python.

License

3 clause BSD.

Compatibility

pvlib-python is compatible with Python versions 2.7, 3.3, and 3.4, and pandas versions 0.13.1 through 0.16.2.

Testing

Testing can easily be accomplished by running nosetests on the pvlib directory:

nosetests -v pvlib

Unit test code should be placed in the corresponding test module in the pvlib/test directory. Use pip or conda to install nose. Developers must include comprehensive tests for any additions or modifications to pvlib.

Code Transition

Here are some of the major differences between the latest build and the original Sandia PVLIB_Python project.

Library wide changes:

  • Remove pvl_ from module names.
  • Consolidation of similar modules. For example, functions from pvl_clearsky_ineichen.py and pvl_clearsky_haurwitz.py have been consolidated into clearsky.py.
  • Removed Vars=Locals(); Expect...; var=pvl\_tools.Parse(Vars,Expect); pattern. Very few tests of input validitity remain. Garbage in, garbage or nan out.
  • Removing unnecssary and sometimes undesired behavior such as setting maximum zenith=90 or airmass=0. Instead, we make extensive use of nan values.
  • Adding logging calls, removing print calls.
  • Code in reviewed modules is mostly PEP8 compliant.
  • All code is Python 3 compatible (see testing).
  • Changing function and module names so that they do not conflict.
  • Added /pvlib/data for lookup tables, test, and tutorial data.
  • Return one DataFrame instead of a tuple of DataFrames.

More specific changes:

  • Add PyEphem option to solar position calculations.
  • irradiance.py has more AOI, projection, and irradiance sum and calculation functions
  • TMY data is not forced to 1987.
  • Locations are now pvlib.location.Location objects, not structs.
  • Specify time zones using a string from the standard IANA Time Zone Database naming conventions or using a pytz.timezone instead of an integer GMT offset. We may add dateutils support in the future.
  • clearsky.ineichen supports interpolating monthly Linke Turbidities to daily resolution.

Documentation:

  • Using readthedocs for documentation hosting.
  • Many typos and formatting errors corrected.
  • Documentation source code and tutorials live in / rather than /pvlib/docs.
  • Additional tutorials in /docs/tutorials.

Testing:

  • Tests are cleaner and more thorough. They are still no where near complete.
  • Using Coveralls to measure test coverage.
  • Using TravisCI for automated testing.
  • Using nosetests for more concise test code.

About

A set of documented functions for simulating the performance of photovoltaic energy systems.

Resources

Readme

License

BSD-3-Clause license
Activity

Stars

0 stars

Watchers

1 watching

Forks

0 forks
Report repository

Releases

4 tags

Packages

No packages published

Languages

  • Python 99.1%
  • PowerShell 0.9%
0