https://github.com/materialsinnovation/pymks

Materials Knowledge System in Python
https://github.com/materialsinnovation/pymks

data-science machine-learning materials-science python

Last synced: 4 months ago
JSON representation

Materials Knowledge System in Python

• Host: GitHub
• URL: https://github.com/materialsinnovation/pymks
• Owner: materialsinnovation
• License: other
• Created: 2013-11-06T18:55:00.000Z (over 12 years ago)
• Default Branch: master
• Last Pushed: 2023-01-12T21:28:25.000Z (over 3 years ago)
• Last Synced: 2025-10-21T19:39:09.871Z (8 months ago)
• Topics: data-science, machine-learning, materials-science, python
• Language: Jupyter Notebook
• Homepage: https://pymks.readthedocs.io/
• Size: 251 MB
• Stars: 118
• Watchers: 17
• Forks: 79
• Open Issues: 34
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md
  • Citation: CITATION.md

Awesome Lists containing this project

README

          
# PyMKS Overview































PyMKS is an open source, Pythonic implementation of the methodologies

developed under the aegis of Materials Knowledge System (MKS) to build

salient process-structure-property linkages for materials science

applications.  PyMKS provides for efficient tools for obtaining a

digital, uniform grid representation of a materials internal structure

in terms of its local states, and computing hierarchical descriptors

of the structure that can be used to build efficient machine learning

based mappings to the relevant response space.

The various materials data analytics workflows developed under the MKS

paradigm confirm to the data transformation pipeline architecture

typical to most Data Science workflows. The workflows can be boiled

down to a data preprocessing step, followed by a feature generation

step (fingerprinting), and a model construction step (including hyper

parameter optimization). PyMKS, written in a functional programming

style and supporting distributed computation (multi-core,

multi-threaded, cluster), provides modular functionalities to address

each of these data transformation steps, while maximally leveraging

the capabilities of the underlying computing environment.

PyMKS consists of tools to compute 2-point statistics, tools for both homogenization

and localization linkages, and tools for discretizing the microstructure. In addition,

PyMKS has modules for generating synthetic data sets using conventional numerical

simulations.

To learn about PyMKS start with the [PyMKS examples][EXAMPLES], especially the

[introductory example](notebooks/intro.ipynb). To learn more about the

methods consult the [technical overview](notebooks/tech_overview.ipynb)

for an introduction.

The two principle objects that PyMKS provides are the

[TwoPointCorrelation][TwoPointCorrelation]

transformer and the

[LocalizationRegressor][LocalizationRegressor]

which provide the homogenization and localization functionality. The

objects provided by PyMKS all work as either transformers or

regressors in a Scikit-Learn pipeline and use both Numpy and Dask

arrays for out-of-memory, distributed or parallel computations. The

out-of-memory computations are still in an experimental stage as of

version 0.4 and some issues still need to be resolved.

This effort has been supported with grants from NIST and the Vannevar Bush Fellowship to Professor Kalidindi at Georgia Tech.

## Feedback

Please submit questions and issues on the [GitHub issue

tracker](https://github.com/materialsinnovation/pymks/issues).

## Installation

### Conda

To install using [Conda][conda],

    $ conda install -c conda-forge pymks

To create a development environment clone this repository and run

    $ conda env create -f environment.yml

    $ conda activate pymks

    $ python setup.py develop

in the base directory.

### Pip

Install a minimal version of PyMKS with

    $ pip install pymks

This is enough to run the tests, but not the examples. Some optional

packages are not available via Pip. To create a development

environment clone this repository and run

    $ pip install .

in the base directory.

### Nix

Follow the [Nix installation

guide](https://nixos.org/nix/manual/#chap-quick-start) and then run

    $ export NIX_VERSION=21.05

    $ export PYMKS_VERSION=0.4.1

    $ nix-shell \

        -I nixpkgs=https://github.com/NixOS/nixpkgs/archive/${NIX_VERSION}.tar.gz \

        -I pymks=https://github.com/materialsinnovation/pymks/archive/refs/tags/v${PYMKS_VERSION}.tar.gz \

        -E 'with (import  {}); mkShell { buildInputs = [ (python3Packages.callPackage  { graspi = null; }) ]; }'

to drop into a shell with PyMKS and all its requirements available. To

create a development environment with Nix clone this repository and

run

    $ nix-shell

in the base directory.

### Docker

PyMKS has a docker image avilable via

[docker.io](https://hub.docker.com/repository/docker/wd15/pymks). Assuming

that you have a working version of Docker, use

    $ docker pull docker.io/wd15/pymks

    $ docker run -i -t -p 8888:8888 wd15/fipy:latest

    # jupyter notebook --ip 0.0.0.0 --no-browser

The PyMKS example notebooks are available inside the image after

opening the Jupyter notebook from http://127.0.0.1:8888. See

[DOCKER.md](./DOCKER.md) for more details.

## Optional Packages

Packages that are optional when using PyMKS.

### Sfepy

[Sfepy](http://sfepy.org/doc-devel/index.html) is a python based

finite element solver. It's useful for generating data for PyMKS to

use for machine learning tasks. It's used in quite a few tests, but it

isn't strictly necessary to use PyMKS.  Sfepy will automatically

install when using Nix or Conda, but not when using Pip. See the

[Sfepy installation

instructions](http://sfepy.org/doc-devel/installation.html) to install

in your environment.

### GraSPI

GraSPI is a C++ library with a Python interface for creating materials

descriptors using graph theory. See the [API documentation][GRAPH]

for more details. Currently, only the Nix installation builds with

GraSPI by default To switch off GraSPI when using Nix use,

    $ nix-shell --arg withGraspi false

## Testing

To test a PyMKS installation use

    $ python -c "import pymks; pymks.test()"

## Citing

Please cite the following if you happen to use PyMKS for a

publication.

 - Brough, D.B., Wheeler, D. & Kalidindi, S.R. Materials Knowledge

   Systems in Python—a Data Science Framework for Accelerated

   Development of Hierarchical Materials. Integr Mater Manuf Innov 6,

   36–53 (2017). https://doi.org/10.1007/s40192-017-0089-0

   

[conda]: https://docs.conda.io/en/latest/

[EXAMPLES]: https://pymks.readthedocs.io/en/stable/EXAMPLES.html

[TwoPointCorrelation]: http://pymks.readthedocs.io/en/stable/API.html#pymks.TwoPointCorrelation

[LocalizationRegressor]: http://pymks.readthedocs.io/en/stable/API.html#pymks.LocalizationRegressor

[GRAPH]: http://pymks.readthedocs.io/en/stable/API.html#pymks.graph_descriptors