Git is an amazing tool…but what is the best way to use it? Like any tool that gives you great power and flexibility, it’s up to you to use the tool in the best way to suit your purpose. A friend of mine who manages an enterprise-class software development team recommended the git branching strategy […]
A git branching strategy suitable for large projects Read More »
sed is a stream editor, which means that it accepts a stream of text, processes it, and spits out another stream of text. sed can process files that are too large to load into memory, and it is a completely command-line tool that can easily be integrated into shell scripts. This excellent sed tutorial was
Replacing text in place with GNU sed Read More »
I’ve just released a self-contained Fortran module that solves a system of linear equations using the LU decomposition. Download the Fortran linear solver from github This module is based on code that was implemented and released on the Web by Jean-Pierre Moreau. His implementation was based on one of the Numerical Recipes books. I updated
A self-contained Fortran linear equation solver Read More »
I have been optimizing some Fortran code that involved searching for an integer value in an unordered array (we know the value occurs only once). Since there is no intrinsic procedure to accomplish this, I thought I’d try a couple of approaches to see which was fastest. The simple answer is that, in this case,
Finding a value in an unordered Fortran array Read More »
I’ve recently gained a lot of experience with Sage, an open-source alternative to MATLAB, Mathematica, Maple, MuPAD, and Magma. Here are a couple of links to check out: Public notebook servers–try it online right now! Interactive examples with Sage Mathematical publications citing Sage
Sage: open-source mathematical software Read More »
Linear time-invariant (LTI) systems are widely used in the field of signal processing. Scipy contains powerful tools for simulating LTI systems in the scipy.signal package, but they are not well documented. I will provide a simple example that demonstrates how to use a few of the core classes and functions in scipy.signal for simulating LTI
Linear system simulation with Python Read More »
In a previous post, I described how Python’s Pickle module is fast and convenient for storing all sorts of data on disk. More recently, I showed how to profile the memory usage of Python code. In recent weeks, I’ve uncovered a serious limitation in the Pickle module when storing large amounts of data: Pickle requires
Storing large Numpy arrays on disk: Python Pickle vs. HDF5 Read More »
I have recently started using f2py to call Fortran from Python. I have found this useful for two reasons: speeding up Python scripts by calling compiled Fortran code, and using Python as a unit testing framework for Fortran modules. Unfortunately, the documentation for f2py is rather sparse, and may not be completely up to date.
f2py: binding Fortran and Python Read More »
In a previous post, I explained how to use the Python profiler. The profile is great for finding out which parts of the code run the slowest, or are called most often. However, the profiler doesn’t give any information about how much RAM is being consumed, or where it’s being consumed. If your program needs
Profiling memory usage of Python code Read More »
Lookup tables and spline fitting are widely used by scientific programmers. A particular function may not have an analytic solution–in other words, it can’t be expressed as an equation of elementary functions. This might happen if the function were empirically determined from experimental data, or if the equation can’t be algebraically solved for one variable.
Lookup tables and spline fitting in Python Read More »