Skip to main content

Materials Morphology Python Package

Project description

m2py: Materials Morphology Python Package


  • Wesley Tatum, PhD Student at University of Washington MSE Department
  • Diego Torrejon, Staff Machine Learning Engineer at BlackSky
  • Patrick O’Neil, Director of Machine Learning and Artificial Intelligence at BlackSky


You need Python 3.6 or later to work with m2py.


Through github:

Clone or download this repository

pip install -r requirements.txt

Through pip:

pip install m2py


At the moment, we are offering two introductory tutorials found under the tutorials directory. There is a basic tutorial showing the main processing capabilities of m2py. There's also an advanced tutorial showing how users can use current m2py capabilities to make them suitable for their own applications.


SPM techniques have been pivotal in understanding surfaces, morphologies, and intermolecular interactions of matter from the atomic to millimeter scales. Probes interacting with the material surface produce 2-dimensional images of the topography with intensity scales representing any number of material properties (e.g. modulus, conductivity, or capacitance). In many experiments, multiple properties can be imaged simultaneously, producing a 3-dimensional stack of these images.

By utilizing different combinations of computer vision and machine learning techniques, m2py leverages differences in imaged material properties to recognize different material phases, as well as different domains and topographical features. First, outlier pixels or signals can be removed. Next, signals are compressed to only the most informative features via PCA. These signals can be deconvoluted via GMM, or some other semantic segmenter for phase labeling. Finally, an instance segmenter, such as Persistence Watershed Segmentation, clusters the phase-labeled pixels into domains, which receive another label.

Once labeled by m2py segmenters, quantitative descriptions of each domain and the total morphology are extracted and can be used to train supervised models to predict material properties or performance. Such supervised training has not been accessible to most SPM researchers, due to the labor-intensive nature of manual-labeling.

Project details

Download files

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

Files for m2py, version 0.0.4
Filename, size File type Python version Upload date Hashes
Filename, size m2py-0.0.4-py3-none-any.whl (23.7 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size m2py-0.0.4.tar.gz (2.4 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page