# IPython HTML Notebook

Until recently I had never been a fan of IPython but with their HTML notebook they’ve finally won me over. What I like about this tool is that it makes it easy to go back and forth between interactive prototyping and a script. Being able to continuously edit and re-run code in an interactive session is a powerful tool.

The notebook also makes a great tutorial and demo tool. Here’s a PDF of my session developing a Python replacement for IDL’s GAUSSFIT function.

# Installation

The IPython notebook requires a few extra packages but if you have a setup like me it’s easy to get everything installed:

brew install zeromq
pip install pyzmq
pip install ipython

After doing this you may also want to locally install MathJax for JavaScript equation rendering:

from IPython.external.mathjax import install_mathjax
install_mathjax()

To launch the notebook from whatever directory you want to work in:

ipython notebook

This will launch the IPython notebook dashboard in your default browser, from which you can make new notebooks or resume working on existing ones.

See the docs for all you can do with the notebook, and enjoy!

# IDL’s GAUSSFIT in Python

A colleague recently asked for help getting the functionality of IDL’s GAUSSFIT function working in Python. This was a perfect opportunity to use the handy curve_fit function from SciPy. Here’s the code:

import numpy as np
from scipy.optimize import curve_fit

def fit_func(x, a0, a1, a2, a3, a4, a5):
z = (x - a1) / a2
y = a0 * np.exp(-z**2 / a2) + a3 + a4 * x + a5 * x**2
return y

parameters, covariance = curve_fit(fit_func, xdata, ydata)



The file focus_output.dat just contains some data in two columns of numbers. For more info on loadtxt see my post on reading text tables. fit_func defines the function we want to fit to the data. In this case it is a Gaussian plus a quadratic, the same as used in GAUSSFIT when NTERMS=6. Now, to plot the results:

import matplotlib.pyplot as plt

fitdata = fit_func(xdata, *parameters)

fig = plt.figure(figsize=(6,4), frameon=False)
ax = fig.add_axes([0, 0, 1, 1], axisbg='k')

ax.plot(xdata, ydata, 'c-', xdata, fitdata, 'm-', linewidth=3)

ax.set_ylim(0.38, 1.02)

fig.savefig('gauss_fit_demo.png')



# Reading Text Tables with Python

Reading tables is a pretty common thing to do and there are a number of ways to read tables besides writing a read function yourself. That’s not to say these are magic bullets. Every table is different and can have its own eccentricities. If you find yourself reading the same type of quirky file over and over again it could be worth your effort to write your own reader that does things just the way you like. That said, here are some other options.

# Install Python, NumPy, SciPy, and matplotlib on Mac OS X – Double Click

Update: These instructions are over a year old, though they may still work for you. See the “Install Python” page for the most up-to-date instructions.

I’ve already written a post about installing Python, NumPy, SciPy, and matplotlib on Lion, but it involves a lot of working at the command line, modifying your .bash_profile and dealing with compiler problems. That’s what I’ll call the compile-it-yourself (CIY) method. What I’ll describe below I’ll call the “double click” method.

I personally use the CIY method because it allows me to very easily control what’s installed. With Homebrew and pip I can uninstall and upgrade different things at will, or choose to install bleeding-edge versions. But it’s more hassle than everyone wants and there’s now an easier way using double-click installers.

Until recently the CIY was the only way to get everything working on Lion but now the developers of NumPy, SciPy, and matplotlib have all caught up and it’s possible to just download and double-click on a few DMG files to get a basic scientific Python installation working. Once you get to know Python, though, you will undoubtedly want to install some other packages and when that time comes I suggest you use pip.