Metadata-Version: 2.1
Name: ampere
Version: 0.5.3
Summary: A Python package for working with battery discharge data and physics-based battery models
Home-page: https://github.com/nealde/Ampere
Author: Neal Dawson-Elli
Author-email: nealde@uw.edu
License: UNKNOWN
Description: 
        
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        Ampere - Advanced Model Package for ElectRochemical Experiments
        ------------
        
        `Ampere` is a Python module for working with battery models.
        
        Using a [scikit-learn-like API](https://arxiv.org/abs/1309.0238), we hope to make visualizing, fitting, and analyzing impedance spectra more intuitive and reproducible.
        
        <i>Ampere is currently in the alpha phase and new features are rapidly being added.</i>
        If you have a feature request or find a bug, please feel free to [file an issue](https://github.com/nealde/Ampere/issues) or, better yet, make the code improvements and [submit a pull request](https://help.github.com/articles/creating-a-pull-request-from-a-fork/)! The goal is to build an open-source tool that the entire electrochemical community can use and improve
        
        Ampere currently provides:
        - A simple API for fitting, predicting, and plotting discharge curves
        - A simple API for generating data, or fitting with arbitrary charge / discharge patterns.
        
        
        ## Installation
        ### Dependencies
        
        Ampere requires:
        
        - Python (>=3.5)
        - SciPy (>=1.0)
        - NumPy (>=1.14)
        - Matplotlib (>=2.0)
        - Cython (>=0.29)
        
        
        Several example notebooks are provided in the examples/ directory. Opening these will require Jupyter notebook or Jupyter lab.
        
        ### User Installation
        
        The easiest way to install Ampere is using pip:
        
        `pip install ampere`
        
        
        However, it depends on Cython and Microsoft c++ libraries in order to install (on windows). Those should be added as follows:
        
        `pip install --upgrade cython setuptools`
        
        follow [these instructions](https://docs.microsoft.com/en-us/answers/questions/136595/error-microsoft-visual-c-140-or-greater-is-require.html) to install the proper c++ libraries using Microsoft tools.
        
        That may or may not work, depending upon your system. An alternative method of installation that works is:
        
        `git clone https://github.com/nealde/ampere`
        
        I've recently added the Cython-generated c files back to the repo, so it may be as simple as:
        
        `cd ampere`
        `python setup.py install`
        
        However, if that doesn't work, the following will rebuild the files:
        
        `cd ampere/models/P2D`
        
        `python setup.py build_ext --inplace`
        
        `cd ../SPM`
        
        `python setup.py build_ext --inplace`
        
        This will build the local C code that is needed by the main compiler.  Then, you can cd back up to the main folder and
        
        `python setup.py install`
        
        That will typically work.  I'm still working on getting pip installation working, and it will likely require some package modifications,
        following SKLearn as a guide.
        
        ## Examples and Documentation
        
        Examples and documentation will be provided after my Defense, which is set for the end of May.
        
        ### On the Horizon
        
        - Currently, all models are solved with Finite Difference discretization.  I would love to use some higher order spatial discretizations.
        - Currently, the results have not been verified with external models. That is still on the to-do list, and to incorporate those values into the test suite would be excellent.
        - Some of my published work regarding surrogate models for solving and fitting will be implemented once they are fully fleshed out.
        
        - Add ability to serialize / deserialize models from disk, to save the result of an optimization
        - add ability to have custom Up / Un functions for different battery chemistries
        - add documentation / fix docstrings to be accurate
        - add Latex equations and node spacings
Keywords: battery numerical simulation modeling
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Cython
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Mathematics
Description-Content-Type: text/markdown
