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pvcaptest

Collection of functions and Jupyter Notebooks to partially automate running a capacity test following ASTM E2848.
https://github.com/pvcaptest/pvcaptest

Category: Renewable Energy
Sub Category: Photovoltaics and Solar Energy

Keywords from Contributors

photovoltaic renewable-energy solar-energy

Last synced: about 19 hours ago
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Repository metadata

Collection of functions and jupyter notebooks to partially automate running a capacity test following ASTM E2848.

README.md

pvcaptest

What is pvcaptest?

pvcaptest is an open source python package created to facilitate capacity testing following the ASTM E2848 standard. The captest module contains a single class, CapData, which provides methods for loading, visualizing, filtering, and regressing capacity testing data. The module also includes functions that take CapData objects as arguments and provide summary data and capacity test results.

Documentation and examples are available on readthedocs including full examples in jupyter notebooks that can be run in the browser without installing anything.

Installation

These instructions assume that you are new to using conda and python, if that is not the case skip to the last section for users familiar with conda and pip.

The recommended method to install pvcaptest is to create a conda environment for pvcaptest. Installing Anaconda or miniconda will install both python and conda. There is no need to install python separately.

Easiest Option:

  1. Download and install the anaconda distribution. Follow the default installation settings.
  2. On Windows go to the start menu and open the Anaconda prompt under the newly installed Anaconda program. On OSX or Linux open a terminal window.
  3. Install pvcaptest by typing the command conda install -c conda-forge pvcaptest and pressing enter. The -c conda-forge option tells conda to install pvcaptest from the conda forge channel.

This will install the pvcaptest package in the base environment created when Anaconda is installed. This should work and provide you with jupyter notebook and jupyer lab to run pvcaptest in. If you think you will use your Anaconda installation to create and maintain additional environments, the following process for creating a stand alone option is likely a better option.

Better long term option:

  1. If you do not already have it installed, download and install the anaconda distribution or miniconda.
  2. Go to the project github page and download the project source to obtain a copy of the environment.yml file. Click the green code button and click 'Download ZIP'.
  3. On Windows go to the start menu and open the Anaconda prompt under the newly installed Anaconda program. On OSX or Linux open a terminal window. Note the path in the prompt for the next step. On Windows this should be something like C:\Users\username\.
  4. Unzip and move the environment.yml file to the folder identified by the path from the previous step.
  5. In your Anaconda prompt or terminal type conda env create -f environment.yml
    and hit enter. Wait for a few seconds while conda works to solve the environment. It should ask you if you want to proceed to install new packages including pvcaptest. Type y enter to proceed and wait for conda to finish installing pvcaptest and the other packages.
  6. Once the installation is complete conda will print out a command for activating the new environment. Run that command, which should be like conda activate captest_env.

The environment created will include jupyter lab and notebook for you to use pvcaptest in. You can start these using the commands jupyter lab or jupyter notebook.

See the conda documentation for more details on using conda to create and manage environments.

Install for users familiar with conda and pip:

Conda install into an existing environment:

conda install -c conda-forge pvcaptest

If you prefer, you can pip install pvcaptest, but the recommended approach is to use the conda package.

Note: The conda package is named pvcaptest and the pip package is named captest. The project is moving to consistent use of the pvcaptest name, but the package name on pypi will remain as captest.


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Last synced: 7 days ago

Total Commits: 884
Total Committers: 5
Avg Commits per committer: 176.8
Development Distribution Score (DDS): 0.026

Commits in past year: 143
Committers in past year: 2
Avg Commits per committer in past year: 71.5
Development Distribution Score (DDS) in past year: 0.077

Name Email Commits
bt- b****r@g****m 861
Kevin Anderson k****o@g****m 11
Ben Taylor b****r@g****m 8
Taylor B****2@g****m 3
Cliff Hansen c****e@s****v 1

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Issue and Pull Request metadata

Last synced: 2 days ago

Total issues: 69
Total pull requests: 56
Average time to close issues: about 1 year
Average time to close pull requests: about 1 month
Total issue authors: 13
Total pull request authors: 7
Average comments per issue: 1.65
Average comments per pull request: 0.38
Merged pull request: 46
Bot issues: 0
Bot pull requests: 0

Past year issues: 4
Past year pull requests: 13
Past year average time to close issues: 21 days
Past year average time to close pull requests: 7 minutes
Past year issue authors: 3
Past year pull request authors: 4
Past year average comments per issue: 3.5
Past year average comments per pull request: 0.08
Past year merged pull request: 7
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/pvcaptest/pvcaptest

Top Issue Authors

  • bt- (49)
  • UGuntupalli (4)
  • colmboreilly (3)
  • mibelle32 (2)
  • matsuobasho (2)
  • jforbess (2)
  • jplanger (1)
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  • bt- (47)
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Top Issue Labels

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  • bug (7)
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  • testing (2)
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Top Pull Request Labels


Package metadata

conda-forge.org: pvcaptest

  • Homepage: http://github.com/pvcaptest/pvcaptest
  • Licenses: MIT
  • Latest release: 0.10.0 (published over 3 years ago)
  • Last Synced: 2025-04-01T02:08:25.808Z (27 days ago)
  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Rankings:
    • Dependent repos count: 24.103%
    • Average: 43.319%
    • Forks count: 45.985%
    • Dependent packages count: 51.54%
    • Stargazers count: 51.65%

Dependencies

docs/requirements.txt pypi
  • bokeh *
  • captest *
  • docutils *
  • holoviews *
  • matplotlib *
  • nbsphinx *
  • notebook *
  • pandas *
  • pvlib *
  • recommonmark *
  • sphinx ==2.2.0
  • sphinx_rtd_theme *
  • statsmodels *
  • tables *
setup.py pypi
  • bokeh >=1
  • matplotlib >=2
  • numpy >=1.13.0
  • pandas >=1
  • python-dateutil >=2.5
  • scikit-learn >=0.19
  • statsmodels >=0.8
.github/workflows/publish.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
  • pypa/gh-action-pypi-publish release/v1 composite
.github/workflows/pytest.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
environment.yml pypi
pyproject.toml pypi

Score: 5.438079308923196