pycroscopy 0.0a47

A suite of Python libraries for high performance scientific computing of microscopy data.

0. Description

A python package for image processing and scientific analysis of imaging modalities such as multi-frequency scanning probe microscopy, scanning tunneling spectroscopy, x-ray diffraction microscopy, and transmission electron microscopy. Classes implemented here are ported to a high performance computing platform at Oak Ridge National Laboratory (ORNL).

1. Package Structure

The package structure is simple, with 4 main modules:

1. io: Input/Output from custom & proprietary microscope formats to HDF5.

2. processing: Multivariate Statistics, Machine Learning, and Filtering.

3. analysis: Model-dependent analysis of image information.

4. viz: Visualization and interactive slicing of high-dimensional data by lightweight Qt viewers.

Once a user converts their microscope’s data format into an HDF5 format, by simply extending some of the classes in io, the user gains access to the rest of the utilities present in pycroscopy.*.

2. Installation

Pycroscopy requires many commonly used python packages such as numpy, scipy etc. To simplify the installation process, we recommend the installation of Anaconda which contains most of the prerequisite packages as well as a development environment - Spyder. We are currently testing python 3 compatibility (see the cades_dev branch).

1. Recommended - uninstall existing Python distribution(s) if installed. Restart computer afterwards.

2. Install Anaconda 4.2 (Python 3.5) 64-bit - Mac / Windows / Linux

3. Install pycroscopy - Open a terminal (mac / linux) or command prompt (windows - if possible with administrator priveleges) and type:

   pip install pycroscopy

4. Enjoy pycroscopy!

If you would like to quickly view HDF5 files generated by and used in pycroscopy, we recommend HDF View

3. API and information

• See our homepage for more information

• Our api (documentation for our functions and classes) is available here

4. Examples and Resources

• Scientific workflows are available through jupyter notebooks over here. Many of these are tied to journal publications (see below).

• Videos and other tutorials are available at the Institute For Functional Imaging of Materials

5. Journal Papers using pycroscopy

1. Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography by S. Jesse et al., Scientific Reports (2015);

6. International conferences and workshops

• Aug 8 2017 @ 10:45 AM - Microscopy and Microanalysis conference - poster session

• Aug 9 2017 @ 8:30 - 10:00 AM - Microscopy and Microanalysis conference; X40 - Tutorial session on Large Scale Data Acquisition and Analysis for Materials Imaging and Spectroscopy by S. Jesse and S. V. Kalinin

• Oct 31 2017 @ 6:30 PM - American Vacuum Society conference; Session: SP-TuP1; poster 1641

• Dec 2017 - Materials Research Society conference

7. Pycroscopy news

• Apr 2017 - Lecture on atom finding

• Dec 2016 - Poster + abstract at the 2017 Spring Materials Research Society (MRS) conference