SciPy — Numerical Computing with Sage v4.3. Again I recommend this There are many useful SciPy modules, in particular scipy.optimize, scipy.stats, scipy.linalg, scipy.linsolve, scipy.sparse, scipy.integrate, scipy.fftpack, scipy.signal, scipy.special.

Most of these have relatively good documentation and often you can figure out what things do from the names of functions. I recommend exploring them. For example if you do sage: import scipysage: from scipy import optimize Then will show a list of available functions. You find it is a routine that uses the conjugate gradient algorithm to find the minima of a function. sage: scipy.special. will show all the special functions that SciPy has. Sage: from scipy import signal and then will show you a large number of functions for signal processing and filter design. Scipy.integrate This module has routines related to numerically solving ODE’s and numerical integration. X''(t) + ux'(t)(x(t)^2-1)+x(t)=0 which as a system reads The module we want to use is odeint in scipy.integrate.

Optimization. Signal processing in sagemath. I was experimenting with some tools for signal processing.

The first tools that come to mind are opensource tools scilab and octave. I spent a day looking at the tools, the UI. I figured I could do definite integrals quite easily. Having spent the day and not having the made the progress I would have liked to, I turned to sagemath. My first delight was to learn about piecewise functions.

I started with a function that looked like f1(x) = 1 f2(x) = 0 f = Piecewise([[(0,10),f1],[(10,20),f2]]) Plotting the function with plot(f) showed Which is exactly what I wanted. One particularly useful one is a plot fourier series partial sum. I started with f.plot_fourier_series_partial_sum(5,10,-20, 20) Signal processing. Chaos and Fractal Algorithms Applied to Signal Processing and Analysis. Bye Matlab, hello Python, thanks Sage « Bloody Fingers. For the past two months or so, I’ve been slowly migrating my scientific workflow (that’s a fancy way of saying “my chaotic data hacking”) from Matlab ((R) (TM) (C)) to Python.

The results are overwhelmingly positive, so I’d like to rant about it a bit. First, some background. My work typically involves the analysis of tons of remote sensing observations contained in files of various formats (netCDF if I’m very lucky, HDF if I’m lucky, some weird non-standard binary thing if I’m not); all these files span terabytes and terabytes of hard drive space stored in racks in a big temperature-controlled room somewhere high in the sky. I ssh to a central server on which all these drives are mounted; I then usually run there code in whatever language is the most convenient to analyze the data.

Why Matlab After a few years of this, Matlab emerged as the best solution for several reasons: Why ! Now, the problems. But somewhere I always hid a secret wish… to use Python. Then came SAGE Success. Sage vs enthought for sci computing. Sturlamolden wrote: > On 7 Jul, 22:35, jadamwils...

@gmail.com wrote: >> Hello, >> I have recently become interested in using python for scientific >> computing, and came across both sage and enthought. I am curious if >> anyone can tell me what the differences are between the two, since >> there seems to be a lot of overlap (from what I have seen). If my goal >> is to replace matlab (we do signal processing and stats on >> physiological data, with a lot of visualization), would sage or >> enthought get me going quicker? I realize that this is a pretty vague >> question, and I can probably accomplish the same with either, but what >> would lead me to choose one over the other? >> Thanks! I'm not entirely sure what is the advantage of a python wrapper over R, (compared with the stand-alone Rproject language), but presumably it would be to combine its functionality with that of some of the python libraries above.

The RPy is on sourceforge: --