Pvfit 0.0.1

Description:

pvfit 0.0.1

pvfit
PVfit: Photovoltaic (PV) Device Performance Measurement and Modeling
IMPORTANT: This code is pre-release, and so the code organiztion and Application
Programming Interface (API) should be expected to change with minimal warning.
NOTICE: We are in the process of open-sourcing the single-diode equation (SDE) and
single-diode model (SDM) fitting algorithms (🎉), and thus moving the related code here.
The move is reasonably complete, but the code for SDM fitting using full I-V curves has
not yet been ported. Likewise, the documentation badly needs updating, so for now we
refer users to the demos/getting_started.py modules in the various subpackages.

So What Can PVfit Do for Me?
PVfit is currently focused on direct-current (DC) PV module performance measurement and
modeling. Following the standardized technical approach of most accredited PV
calibration laboratories for measuring current-voltage (I-V) curves using reference
devices, PVfit formulates it's DC performance models in terms of the effective
irradiance ratio (e.g., F = Isc / Isc0 = M * Isc,ref / Isc0,ref) to quantify the
effective irradiance on a PV device. This has benefits for both model calibration and
performane simulation. PVfit provides extensions for working with common
irradiance-based MET-station data, and PVfit also supports inference of
effective-irradiance ratio and cell temperature directly from I-V measurements, see
(poster). See
this paper for a more detailed introduction. Email
Mark Campanelli for support, etc. See the
demos/getting_started.py in individual subpackages to get started with specific
functionalities—

Measurement

Current-Voltage (I-V) Data/Curves

I-V Data/Curve Types
I-V Data/Curve Computations

Spectral Mismatch Correction

Quantum Efficiency/Spectral Response and Spectrum Types
Spectral Mismatch Correction Computations
Short-Circuit Current Calibration Using Absolute Spectral Response (FUTURE)

Modeling

Direct Current (DC)

Single Diode

Equation (single operating condition)

Simple Formulation

Parameter Fitting
Simulation

Model (variable operating conditions)

Simple Formulation

Parameter Fitting to IEC 61853-1 Performance Matrices
Parameter Fitting to Module Specification Datasheets
Inference of Operating Conditions from I-V Data
Auxiliary Equations (for simulation via simple SDE)

Photoconductive-Shunt Formulation

Parameter Fitting to IEC 61853-1 Performance Matrices
Auxiliary Equations (for simulation via simple SDE)

Alternating Current (AC)

Sandia Inverter Performance Model - Very experimental code here

Parameter Fitting
Simulation

We still need to improve test coverage for certain subpackages, esp.the simple SDM.
Up and Running in 5 Minutes
pvfit minimally requires python>=3.10,<3.13 with
numpy and scipy. It is tested with
CPython on recent versions of Ubuntu, macOS, and Windows. We suggest using a suitable
Python virtual environment that provides pip.
Download, Install, and Verify Package (non-editable mode)
This package is available at PyPI, but it is still pre-v1. With
sufficiently recent versions of pip and setuptools, install pvfit with the extra
packages needed for the demos using—
python -m pip install --upgrade pip setuptools
python -m pip install pvfit[demo]

Verify your installation—
python -c "from pvfit import __version__; print(__version__)"

which should print something similar to—
0.0.1

You should now be able to explore PVfit's functionality with the getting_started.py
modules in the various demos directories of the various subpackages.
NOTES:

You may want to install your own optimized versions of
numpy and scipy (e.g., using
conda), otherwise this setup will grab the default
versions from PyPI.
The demo option adds the matplotlib,
pandas, and
pvlib-python packages in order to run all the
provided demonstrations in the demos directories.
You can also run pvfit on the bleeding edge. If you have git installed, then
install from the main branch using—

python -m pip install --upgrade "pvfit[demo] @ git+https://github.com/markcampanelli/pvfit"

Developer Notes
Download, Install, and Verify Package with Developer and Testing Dependencies (editable mode)
Clone the repo at https://github.com/markcampanelli/pvfit using your preferred git
method, and go to the repo's root directory.
Install pvfit with all extras in editable (development) mode with pip—
python -m pip install --upgrade pip setuptools
python -m pip install -e .[demo,dev,docs,test]
python -m pip install --progress-bar off "ivcurves @ git+https://github.com/cwhanse/ivcurves@7ae47284b23cfff167932b8cccae53c10ebf9bf9"

This also installs the libraries needed to test, develop the code demonstrations, and
build documentation and source and wheel distributions.
Verify your installation—
python -c "from pvfit import __version__; print(__version__)"

which should print something similar to—
0.1.dev9+gadf7f38.d20190812

Next, make sure that the tests are passing.
Test with Coverage
From the root directory—
python -m pytest --doctest-modules --cov=pvfit --cov-report=html:htmlcov tests

The root of the generated coverage report is at artifacts/test/htmlcov/index.html (not
committed).
Build Documentation
From the docs subdirectory—
sphinx-apidoc -f -o . ../pvfit ../*_test.py

then—
make html

The root of the generated documentation is at docs/_build/html/pvfit.html (not
committed).
Distribute, inc. with Nuitka
PEP-517-compliant build is used to
generate distributions using setuptools as the
build backend (specified in pyproject.toml). From the repo root, execute--
python -m build

Pure-Python *.whl and *.tar.gz files are placed in the dist directory (not
committed).
Alternatively, nuitka can be used to transpile the
Python source code into faster-executing, compiled C code with the same Python
interface. With an appropriate setup for Nuitka (compilers, etc.), swap the
[build-system] table in the pyproject.toml, then--
python -m build

A platfrom-specific *.whl file is placed in the dist directory (not committed). The
included Python extension module has the same interface. Users may wish to remove
tests and demos before generating such wheel files.
Finally, the distribution manifests (cf. MANIFEST.in) are checked using--
python -m check_manifest

Dependencies
Currently, numpy and scipy are
the only runtime dependencies. In order to ensure a straightforward, consistent, and
well-tested API, the decision has been made to avoid any dependecy of the core code on
pandas. However, a design goal is for straightforward
integration with consumers that use pandas, e.g., integrating computations with
Series
and
DataFrame
objects. To avoid bloat, we also avoid dependency on plotting libraries such as
matplotlib. Any new dependencies or version ranges should
be appropriately recorded in pyproject.toml.
Coding Requirements and Style

Unit testing is a must, with a "collocation" scheme, i.e., module_test.py to test
module.py in the same directory. 100% code coverage is the goal.
Type hints should be used
throughout (WIP).
pylint is used for linting,
with black's default 88-character line limit (configured in
pyproject.toml). Check before committing code using--

python -m pylint .

Skip troublesome lines (sparingly) with the suffix # pylint: disable=<code>.

black is used to autoformat
code. Autoformat before committing
code, using--

python -m black .

About the Author and Maintainer
The author and maintainer of this code is
Mark Campanelli, the proprietor of
Intelligent Measurement Systems LLC (IMS),
in Bozeman, MT, USA. Your
suggestions/bug reports,
questions/discussions, and
contributions are welcome.

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