pycroscopy 0.0a38

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

See our homepage <https://pycroscopy.github.io/pycroscopy/> for more info. Our api documentation can be found at <http://pycroscopy.readthedocs.io/>.

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.

1. Uninstall existing Python 2.7 distribution(s) if installed. Restart computer afterwards.

2. Install Anaconda 4.2.13 Python 2.7 64-bit:

   1. Mac users: <https://repo.continuum.io/archive/Anaconda2-4.2.0-MacOSX-x86_64.pkg>

   2. Windows users: <https://repo.continuum.io/archive/Anaconda2-4.2.0-Windows-x86_64.exe>

   3. Linux users: <https://repo.continuum.io/archive/Anaconda2-4.2.0-Linux-x86_64.sh>

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 - available at <https://support.hdfgroup.org/products/java/hdfview/>