Skip to main content

Clean and add extra information to data ...

Project description

.. inclusion-marker-do-not-remove

Analysis of Imaging Data (DIC/SEM/TEM/Optical):
1. Folder Inp.
2. Operation
3. Folder Out.
4. Done.

Contents -->
1. Identification of Features (PCA/SEA/Correlation/Wavelet/blibla/... Analysis) --> Outp to Tmp folder, plots/feature-txt-files
2. Collection of Features to Library (from PCA, from Correlations, from Wavelets, from Images, from defects) -->
3. Reconstruction of Synthetic microstructure (EDX --> LAMMPS) -->
4. Interatomic Potential Finder/Machine-Learner (ASE) -->
5. Simulation of Synthetic Microstructure (LAMMPS, ASE) -->
6. Machine Training (given feature files) -->
7. Machine Predicting -->
==================================================================================================

**MATI** is a Python package that provides direct, easy-to-use black-box solutions for materials informatics based on imaging data that emerge in materials science.

Example
-------

The **example.py** runs over the given example data, producing a new file and a plot that compares the original and the prepared data. To run this
example, simply type: :code:`python example.py`.

Requirements and Installation
-----------------------------

This code has been developed in Python 3.7.1 and it is compatible with Python above 3.5 versions. The code has been tested to run in Windows, OSX and Linux operating systems.

This code uses numpy as specified in docs/requirements.txt. The ploting routine from the *example.py* also requires the use of matplotlib.

The code can be run directly from a cloned GitHub `repository`_ or it can also be installed as a python `package`_ through pip:

.. code::

pip install MaterialsInformatics

The software can be used in two ways:
- Either through the GUI, by using: python -m MaterialsInformatics
- Or through importing the library as
.. code-block:: python
import MaterialsInformatics as MATI

The code has been tested within Anaconda3

.. _compability:

Compatibility
-------------

This code has been tested with data generated by different versions of
the `ASE`_ and `LAMMPS`_ software. If you encounter issues running the code for
any version of these software report an issue. Note that an example
file will be needed in order to improve the code. List of the `ASE`_ and `LAMMPS`_
software is below. If you encounter issues running the code for any version of MATI software report an issue. Note that an example file will be needed in order to improve the code.
- 3.0.2.3
- 3.0.2.1
- TO
- 2.0.0.7
- 1.9.4.0

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

MaterialsInformatics-1.0.0.tar.gz (2.5 kB view details)

Uploaded Source

File details

Details for the file MaterialsInformatics-1.0.0.tar.gz.

File metadata

  • Download URL: MaterialsInformatics-1.0.0.tar.gz
  • Upload date:
  • Size: 2.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 importlib_metadata/4.5.0 pkginfo/1.5.0.1 requests/2.24.0 requests-toolbelt/0.9.1 tqdm/4.49.0 CPython/3.7.7

File hashes

Hashes for MaterialsInformatics-1.0.0.tar.gz
Algorithm Hash digest
SHA256 51fad76f63d4dbf9d8ab737f99f95a5f02367169c9a472bba9b585d0d7c4d6c1
MD5 7c4c569c29faf1c819d9bdf3632d0c38
BLAKE2b-256 4854bf3896d595c8342541821d2ef2895b3ac35106d0a076135cbb2cde171f79

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page