Not at all. We have ensured that the basic data and file formatting paradigm is general enough
that it can be extended to any other scientific domain so long as each experiment involves
N identical measurements of
Note that one of the major strengths of pycroscopy is that it can be science- and instrument-agnostic. For example, some of our scientific analysis algorithms such as curve-fitting for spectra, image denoising are written in a general enough manner that they can easily find applications in scientific domains beyond imaging and microscopy.
Our data and file format as well as programming framework can easily be extended to or adopted by other scientific domains such as neutron science, nuclear sciences, etc.
We are eager to hear about the many research domains that find our data format and package useful. Please send us an email at firstname.lastname@example.org
The Institute for Functional Imaging of Materials (IFIM) at Oak Ridge National Laboratory uses pycroscopy exclusively for in-house research as well as supporting the numerous users who visit IFIM to use their state-of-art scanning probe microscopy techniques.
Synchrotron Radiation Research at Lund University
Nuclear Engineering and Health Physics, Idaho State University
Prof. David Ginger’s group at Department of Chemistry, University of Washington
Idaho National Laboratory
Central Michigan University
Iowa State University
George Western University
University of Mons
and many more groups in universities and national labs.
Please get in touch with us if you would like your group / university to be added here.
Data sizes and dimensionality: ImageJ, FIJI, ImageSXM, WSxM, SpectraFox, and OpenFovea are all excellent software packages for dealing with conventional and popular microscopy data such as 2D images or a handful of (simple) spectra that are at best 1- 100 MB in size. We think that pycroscopy is complementary to these other software and packages. Pycroscopy was built from scratch to handle arbitrarily large datasets (gigabytes / terabytes) of datasets which regularly have a large number of position or spectroscopic dimensions (we have had absolutely no problem dealing with datasets with more than 9 unique dimensions - see Band Excitation Polarization Switching + First Order Reversal Curve probing).
Data centric: In addition, Pycroscopy takes a data centric approach aimed towards open science wherein all the data starting from the raw measurement from the instrument, all the way to the final data that is plotted in the resulting scientific publication, are contained in the same file. The processing steps applied to the data are completely transparent and traceable.
Data Processing framework: Most of the aforementioned packages are a collection of several popularly used algorithms applied to data. Again, most of these are applied to small datasets in memory. Perhaps more importantly, the algorithms are tied to a specific scientific domain / application. In contrast, the universal data format used by pycroscopy allows the development of a single version of an algorithm that can be applied to any data.
Scalable: Furthermore, pycroscopy was developed from the ground up to run on laptops while aiming towards compatibility with supercomputers. Nearly all the aforementioned software are applicable to laptops only. Supercomputer / cloud computing scaling in pycroscopy will arrive in the later part of 2018.
Flexibility / Customizable: Like ImageJ / FIJI, it is far easier to add features to pycroscopy when compared to WSxM or Gwyddion
User Interface: Pycroscopy relies on Jupyter notebooks + interactive widgets instead of graphical interface used in most other alternatives.
Other complimentary software:
GXSM is another software package that focuses more on the data acquisition from instruments rather than advanced data analysis.
xarray has many similar and more advanced features for handling scientific multidimensional data compared to pycroscopy. However, while pycroscopy is a file-based package, xarray enables the features for data in memory only. We see xarray as a package that is complementary to pycroscopy.
For simple data operations such as flattening, finding maximum, etc. on 2D images or spectra, ImageJ / Gwyddion / WSxM may be better alternatives to pycroscopy.
Traditional graphical interfaces are rather time consuming to develop. Instead, we provide jupyter widgets to interact with data wherever possible. Here are some great examples that use jupyter widgets to simplify interaction with the data:
Often, others may have encountered the same problem and may have brought up a similar issue. Try searching on google and trying out some suggested solutions.
If this does not work, get in touch with us on our Google group.
If something is indeed broken, please raise an
issue here and one of us will work with you to resolve the problem.
Not at all. Python has an excellent set of libraries for generating even complicated figures for journal papers. Pycroscopy has several functions that make it easier to quickly generate publication-ready figures. There are several publications that have only used pycroscopy and matplotlib to generate figures for papers. If you are still not convinced, you can always export your data to text / csv files and use conventional softwares like Origin Pro.
Please reference our Arxiv paper for now. This manuscript was submitted to Advanced Structural and Chemical Imaging recently and is currently being peer-reviewed.
Your contributions are very valuable to the imaging and scientific community at large. You can help even if you DON’T know how to program!
You can spread the word - tell anyone who you think may benefit from using pycroscopy.
Tell us what you think of our documentation or share your own.
Let us know what you would like to see in pycroscopy.
Put us in touch with others working on similar efforts so that we can join forces.
Guide us in developing data translators
Chances are that you are far better at python than you might think! Interesting tidbit - The (first version of the) first module of pycroscopy was written less than a week after we learnt how to write code in python. We weren’t great programmers when we began but we would like to think that we have gotten a lot better since then.
You can contribute in numerous ways including but not limited to:
translatorsto convert data from proprietary formats to the pycroscopy format
Writing image processing, signal processing code, functional fitting, etc.