You can find and submit more recipes at http://scipy-central.org/ (which eventually can supersede this wiki page)
NumPy / SciPy
NumPy arrays: tips and tricks:
Building Arrays Introduction to numerical arrays.
Indexing Indexing numpy arrays, from simple to complicated.
Multiplying multiple arguments Generalizing dot(a,b) to the case of N arguments.
Segment axis Devious trick using strides to allow general operations (like convolution) on successive, overlapping pieces of an array
Obarray Trick for avoiding object arrays when dealing with arrays of objects.
Recarray Accessing array columns with structured arrays and recarrays.
accumarray-like function A function that behaves like MATLAB's accumarray.
- Linear Algebra
Rank and nullspace of a matrix.
- Optimization and fitting techniques:
Optimization Quick example of fminbound with plot.
Optimization with fit Similar to above with spline fit and chaco plot.
Fitting Data Day to day work in the lab: fitting experimental data.
Linear regression Simple Linear regression example.
Fit statistics Estimates a multi-variate regression model and provides various fit statistics.
Least Squares Circle Fitting the least squares circle from a series of 2D points
RANSAC algorithm Implementation of the robust estimation method.
- Ordinary differential equations
A coupled spring-mass system Another example of solving differential equations.
Lotka-Volterra Tutorial Solving ordinary differential equations with Scipy
KdV Solving the Korteweg-deVries equation on a periodic domain using the pseudo-spectral method with the method of lines.
- Root finding
Data rebinning Examples of rebinning data to produce smaller arrays with and without interpolation.
Histograms 2D histograms with variable bin width
Convex Hull Finds the convex hull around a set of data points.
Minimum Point of a Convex Hull Finds the minimum point of the convex hull of a finite set of points.
Comm Theory Example of BPSK simulation.
Smoothing a signal Performing smoothing of 1D and 2D signals by convolving them with a window.
Butterworth Bandpass Filter Create and apply a Butterworth bandpass filter.
FIR Filter Design Design a low-pass FIR filter using the window method.
How to apply a FIR filter: signal.convolve, signal.fftconvolve, ndimage.convolve1d or signal.lfilter?
A zero phase delay filter Sample code for a null phase delay filter that processes the signal in the forward and backward direction removing the phase delay.
Savitzky Golay filtering of data Sample code for Savitzky Golay filtering.
Multithreading Easy multithreading for embarrassingly parallel problems
KDTree Searching multidimensional space using kd-trees.
Particle Filter A simple particle filter algorithm for tracking objects in a video sequence.
Brownian Motion Compute Brownian motion (i.e. the Wiener process).
Correlated Random Samples Generate correlated normally distributed random samples.
Large Markov Chains Find the stationary distribution of a large Markov chain; the M/M/1 tandem queue
Watershed algorithm Apply the watershed algorithm in order to split an array into distinct components (e.g. for the segmentation of an image into objects).
Linear Classification Fisher's discriminant function and Probabilistic Generative model
Plot an eye diagram using numpy and matplotlib.
Compiling Extensions on Windows A quick tutorial on how to compile extension modules on Windows using MinGW
Schrödinger's equation: a 1-d FDTD solver that animates the time evolution of a gaussian wave packet interacting with simple potentials.
DataFrames A useful class for storing alphanumerical data, similar to GNU R's data frames.
Data acquisition with PyUniversalLibrary A series of examples using an inexpensive USB data acquisition device from Measurement Computing.
Data acquisition with Ni-DAQmx A simple example of using ctypes and numpy to access data acquisition devices from National Instruments.
input/output Reading and writing a NumPy array from/to an ascii/binary file.
Fortran I/O Reading FORTRAN record-structured binary files (if you don't know what these are, thank your stars and you don't need this).
Reading SPE files Reading SPE binary files produced by CCD cameras (Princeton and like).
Reading LAS files Reading LAS (Log ASCII Standard) well log files.
There are several packages available to produce interactive screen graphics (use the mouse to zoom, orient, and fine-tune) and publication-quality printed plots, in 2D, 3D, and 4D (animations). These packages have releases more frequently than SciPy. Rather than bundling out-of-date packages with SciPy, the plotting packages are released separately. However, their developers work closely with the SciPy developers to ensure compatibility.
Matplotlib cookbook. Matplotlib is the preferred package for 2D graphics.
3D plotting with Mayavi. Advanced 3D data visualization with MayaVi2 (and TVTK): a very powerful interactive scientific data visualizer.
Python Imaging Library. Create/manipulate images as numpy array's.
Plotting with xplt. xplt is very fast but less flexible than matplotlib. It allows simple 3-d surface visualizations as well. It is based on pygist (included) and is available under the sandbox directory in SVN scipy.
Mat3d. Simple 3D plotting using an OpenGL backend.
Line Integral Convolution code in cython for visualizing vector fields
VTK volume rendering. This is a simple example that show how to use VTK to volume render your three dimensional numpy arrays.
Old Matplotlib recipes. The recipes on this page are being contributed to the Matplotlib project and will eventually be deleted from this Wiki.
Using NumPy With Other Languages (Advanced)
A comparison of Weave with NumPy, Pyrex, Psyco, Fortran and C++ using Laplace's equation as an example.
Using Pyrex and NumPy to share data between your Pyrex/C extension module and NumPy.
Using Pyrex and the array_struct interface to access array data without requiring a C dependency on Numeric, numarray, or NumPy.
Using SWIG and NumPy to access and modify NumPy arrays in C libraries.
numpy.i: A few SWIG and numpy.i basic examples.
numpy.i: Using SWIG and numpy.i to handle automatic C memory deallocation from Python (using a modified numpy.i).
Using f2py to wrap Fortran codes.
Using f2py and Numpy to wrap C codes.
Writing C Extensions.
Using ctypes with NumPy.
Using /Weave and iterators for fast, generalized code.
Using wxPython dialogs for simple user interaction.
Using TraitsUI to build interactive applications.
List of all pages in the category "Cookbook":