Scientific computing, mathematical modeling, and computer simulation
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’m using GNU Fortran (gfortran) to build several shared libraries, and then dynamically linking to them from a Fortran program. The process is a little different than what I’m used to for C libraries, so I thought I’d explain it. Unlike C, there is no need to #include header files when compiling code that relies
Building and linking to a shared Fortran library 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 »
There are lots of good open-source tools that you can use to make high-resolution, publication-quality 2D plots. Personally, I like to use Python, numpy, and matplotlib. Unfortunately, it is much harder to find a good tools to make 3D plots. Older versions of matplotlib had rudimentary 3D support, but this was removed in version 0.98.
3D Plotting Software for Python::Part 1::PyX Read More »
I have released a new version of my Python class that generates VTK data files in the .vtu format, which is compatible with Paraview and other VTK applications. If you have downloaded the old one, please get the latest version, which incorporates some bug fixes and has been more thoroughly tested.
Updated Python class for writing Paraview (VTK) (.vtu) files Read More »
I recently found a solution to a problem that had been vexing me for about a year. In order to successfully import anything from scipy.integrate, I had edit the file scipy/integrate/__init__.py and comment out the line from odepack import * If not, I would get various import errors such as Traceback (most recent call last):
Scipy.integrate ODEPACK import error solved! Read More »
I spent some time today profiling a Brownian dynamics simulation written in Python to see how I could make it faster before starting some long runs on a Linux cluster. In the sections below, I have attempted to quantify the speed improvements due to various changes. Keep in mind that the actual speed improvement in
Optimizing Python code for fast math Read More »
Numerical programming frequently requires the use of look-up tables. A look-up table is a collection of pre-computed values. When given an “x” value, the table returns a pre-computed “y” value. Look-up tables can be used to speed up numerical codes, when it is faster to look up a value in the table than it is
A lookup table for fast Python math Read More »
NOTE: these instructions are obsolete. Please see Building NumPy on a 64-bit Red Hat Cluster with Intel MKL. In a previous post I described how I built Numpy with Intel compilers and the Math Kernel Library on a 64-bit cluster. Today I upgraded to Numpy-1.2.1 and I made a few improvements to my install process. Please
Update 2: building 64-bit Numpy with Intel compilers and MKL Read More »