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Tools for analysis of experimental rock mechanics data.

Project description


Data analysis tools for rock mechanics experiments in the spirit of the legacy XLook tool.

We follow the semantic versioning practice. That means that while the package is 0.X (as it is currently) we may be changing the API as design constraints and decisions are reached. We won't break your code for fun, but we also won't burden users for the next decade to maintain a bad design decision that was made. When you see a version number like 0.a.b it means that a is a feature release and b is a bug fix only release.

We only support Python 3 and you should too! It's not hard to switch from Python 2, has massive performance improvements, and is regularly updated.


Imposter syndrome disclaimer: We want your help. No, really.

There may be a little voice inside your head that is telling you that you're not ready to be an open source contributor; that your skills aren't nearly good enough to contribute. What could you possibly offer a project like this one?

We assure you - the little voice in your head is wrong. If you can write code at all, you can contribute code to open source. Contributing to open source projects is a fantastic way to advance one's coding skills. Writing perfect code isn't the measure of a good developer (that would disqualify all of us!); it's trying to create something, making mistakes, and learning from those mistakes. That's how we all improve, and we are happy to help others learn.

Being an open source contributor doesn't just mean writing code, either. You can help out by writing documentation, tests, or even giving feedback about the project (and yes - that includes giving feedback about the contribution process). Some of these contributions may be the most valuable to the project as a whole, because you're coming to the project with fresh eyes, so you can see the errors and assumptions that seasoned contributors have glossed over.

For more information, please read the see the contributing guide.


Data processing should have an audit trail - what did you do to the raw data to get to where you are when the data are published? This not only helps find and eliminate and errors, but also allows others in the future to reprocess the data with new techniques or query the raw data for some other purpose that you may never have thought of! If we only archive our processed data and do not document what happened to it - that utility is gone. It is your reponsibility as a researcher, likely funded by public funds, to make that data yeild every ounce of useful information it can. That includes making it useful for others in the future!

PyLook is growing to replace the tool "XLook" developed by Chris Marone and others as the primary tool for processing data from the experimental rock mechanics field. XLook is a very fast and versatile tool that had its own syntax for performing math and other operations. It can work with the proprietary binary format common to several labs as well as simple ASCII text. Python allows us to leverage modern tools and signal processing libraries as well as seamlessly pass data to data science tools.

What about your legacy files? Don't worry, your "r files" (short for reduction files) will still work. You can still run the old experiments in the modern tools, but with the idea that new experiments can be reduced in pure Python. We have provided examples of both!

What Can't PyLook Do

Quite a bit actually. We've got the basics for simple double direct shear experiments and a few other functions, but there is plenty to do. We need to implement the rest of the XLook syntax commands and continue to add more functionality! Rate and state inversion is also high on the list.

Thank You

A big thank you to Unidata's Python team for much of the excellent contributing and README text templates and for their efforts to maintain the scientific Python stack. All projects benefit from standing on the shoulders of these and the many other tireless volunteers and supportive companies.

Project details

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