# ipython: Future of Science¶

## Our Top Suggestions to You¶

The ipython notebook is a new workbook type of document. Think of it a bit like a mathematica notebook, but improved in a number of ways. There are also a lot of tools that have come online recently that make sharing and tweaking existing notebooks simple and useful.

This notebook + more will be available after the talk.

Run with:

ipython notebook --pylab inline

## $\LaTeX$ support¶

By the way, it supports inline $\LaTeX$ and full equations:

$$\alpha \equiv \frac{e^2}{\hbar c}$$

$$x = a_0 + \cfrac{1}{a_1  + \cfrac{1}{a_2 + \cfrac{1}{a_3 + \cfrac{1}{a_4} } } }$$

It's worth taking a look at the Wikipedia article on spectral lines if you're interested in getting an overview of the physics. It includes the helpful examples.

Continuous spectrum:

Emission spectrum:

Absorption spectrum:

Where the relationship between absorption and emission should be easily seen (they are the reverse of each other).

Of course, the real thing we see is much more complicated; it looks more like this:

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent cursus sapien lacus, eu molestie elit. Ut congue, turpis ut volutpat eleifend, massa velit lobortis ipsum, id ullamcorper metus erat nec elit. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Duis ut magna non turpis sollicitudin auctor dignissim vel nisi. Mauris lacinia turpis eget lacus convallis nec egestas neque faucibus. Nam rutrum cursus neque. Maecenas tortor felis, molestie et fermentum quis, vestibulum sed turpis. Suspendisse rutrum, lectus ut venenatis euismod, tortor risus cursus ante, a aliquam massa felis vel justo. Integer tincidunt quam at ante feugiat feugiat. Aenean facilisis facilisis arcu, quis laoreet nunc ornare sit amet. Nulla euismod est eget quam ultrices gravida. Nunc scelerisque tincidunt nisi, at faucibus orci sollicitudin a. Duis vitae pellentesque nisi.

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## Libraries!¶

In [46]:
import numpy as np
import pylab as pl

In [49]:
print "hello hawthorn!"

hello hawthorn!

In [154]:
from IPython.display import YouTubeVideo
# look below!


## Plotting!¶

In [155]:
#Here is a youtube video of Dave and I explaining the following code!


Out[155]:
In [88]:
def GaussFunc(x, amplitude, centroid, sigma):
"""Takes an array, and calculates a Gaussian with the following parameters. """
return amplitude * np.exp(-0.5 * ((x - centroid) / sigma)**2)

In [115]:
# Single physical cloud with following physical parameters

feature_centroid = 5315.3
feature_amplitude = 2.3
feature_sigma = 1.5

wavelength = np.linspace(5305., 5330., )
pl.plot(wavelength, GaussFunc(wavelength, feature_amplitude, feature_centroid, feature_sigma)) #show tab

Out[115]:
[<matplotlib.lines.Line2D at 0x10c7cbc50>]
In [135]:
# comments!
error = 0.05

exposure_one = {} # create a blank dictionary
exposure_one['wavelength'] = np.linspace(5305., 5330., num=40)
# smart line-continuation
exposure_one['flux'] = np.exp(-GaussFunc(exposure_one['wavelength'], feature_amplitude, feature_centroid,
feature_sigma)) + np.random.normal(0, error, len(exposure_one['wavelength']))
exposure_one['error'] = np.ones_like(exposure_one['wavelength']) * error
exposure_one['color'] = 'blue'
exposure_one['label'] = 'exp 01'

exposure_two = {}
exposure_two['wavelength'] = np.linspace(5305., 5330., num=40)
exposure_two['flux'] = np.exp(-GaussFunc(exposure_two['wavelength'], feature_amplitude, feature_centroid,
feature_sigma)) + np.random.normal(0, error, len(exposure_two['wavelength']))
exposure_two['error'] = np.ones_like(exposure_two['wavelength']) * error
exposure_two['color'] = 'green'
exposure_two['label'] = 'exp 02'

In [140]:
print exposure_two['flux']

[ 0.94499198  1.00954136  0.971235    1.01885901  0.98371828  1.01058178
0.90712515  0.97644162  1.01851115  0.93210774  0.97991725  0.90388535
0.52034844  0.27601661  0.24064394  0.05562805  0.1532537   0.09333654
0.25329119  0.3874541   0.5320797   0.80127639  0.80326568  1.03400306
1.07043673  0.89892248  1.00017245  1.05405606  0.94668394  0.98888399
1.06374898  1.09581885  1.03638447  0.97075836  1.05025257  0.9900755
0.97207472  1.00673306  1.03249482  0.96473522]

In [130]:
exposures = [exposure_one, exposure_two] # a list of dictionaries

In [141]:
exposures[0]

Out[141]:
{'color': 'blue',
'error': array([ 0.05,  0.05,  0.05,  0.05,  0.05,  0.05,  0.05,  0.05,  0.05,
0.05,  0.05,  0.05,  0.05,  0.05,  0.05,  0.05,  0.05,  0.05,
0.05,  0.05,  0.05,  0.05,  0.05,  0.05,  0.05,  0.05,  0.05,
0.05,  0.05,  0.05,  0.05,  0.05,  0.05,  0.05,  0.05,  0.05,
0.05,  0.05,  0.05,  0.05]),
'flux': array([ 1.05794236,  1.03830009,  1.03500467,  0.99047029,  1.02907917,
0.98033089,  0.96967209,  1.00930351,  1.04360832,  0.97732787,
0.91578568,  0.82660863,  0.62253641,  0.30535047,  0.26436108,
0.21307896,  0.08375021,  0.09149992,  0.14804059,  0.35441024,
0.54166221,  0.82174282,  0.92167278,  0.94021463,  1.11553511,
0.9480145 ,  0.98061066,  1.03047299,  1.0574776 ,  0.93308433,
0.94931708,  1.08303421,  0.94899156,  0.99525836,  0.95467019,
0.98808084,  0.99580691,  0.9336084 ,  0.95526332,  1.08318217]),
'label': 'exp 01',
'wavelength': array([ 5305.        ,  5305.64102564,  5306.28205128,  5306.92307692,
5307.56410256,  5308.20512821,  5308.84615385,  5309.48717949,
5310.12820513,  5310.76923077,  5311.41025641,  5312.05128205,
5312.69230769,  5313.33333333,  5313.97435897,  5314.61538462,
5315.25641026,  5315.8974359 ,  5316.53846154,  5317.17948718,
5317.82051282,  5318.46153846,  5319.1025641 ,  5319.74358974,
5320.38461538,  5321.02564103,  5321.66666667,  5322.30769231,
5322.94871795,  5323.58974359,  5324.23076923,  5324.87179487,
5325.51282051,  5326.15384615,  5326.79487179,  5327.43589744,
5328.07692308,  5328.71794872,  5329.35897436,  5330.        ])}
In [132]:
pl.rcParams['figure.figsize'] = 12, 8  # plotsize

In [152]:
for exposure in exposures:
pl.errorbar(exposure['wavelength'], exposure['flux'], yerr=exposure['error'], color=exposure['color'],
label=exposure['label'], linewidth=2.0)
pl.scatter(exposure['wavelength'], exposure['flux'], color=exposure['color'], linewidth=2.0)

pl.legend(loc=4)
pl.xlabel("Wavelength", fontsize=18)
pl.ylabel("Flux", fontsize=18)
pl.title("Cool Idea", fontsize=20)

Out[152]:
<matplotlib.text.Text at 0x10efa5990>

## Plotting inspiration¶

Various ways of plotting. Look here http://matplotlib.org/gallery.html and find plots that are similar to what you want to find.

In [54]:
%load http://matplotlib.org/mpl_examples/pylab_examples/polar_legend.py

In [57]:
#!/usr/bin/env python

import numpy as np
from matplotlib.pyplot import figure, show, rc

# radar green, solid grid lines
rc('grid', color='#316931', linewidth=1, linestyle='-')
rc('xtick', labelsize=15)
rc('ytick', labelsize=15)

# force square figure and square axes looks better for polar, IMO
fig = figure(figsize=(8,8))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True, axisbg='#d5de9c')

################ Edit Here ####################
r = np.arange(0, 8.0, 0.01) # 8

theta = 2*np.pi*r
ax.plot(theta, r, color='#ee8d18', lw=3, label='a line')
ax.plot(0.5*theta, r, color='blue', ls='--', lw=3, label='another line')
ax.legend()

show()


## Notes¶

To edit an ipython notebook, you need to have this stuff installed. However, you can download an ipython notebook and use nbconvert to convert into pure python

Sidenote: install packages via

pip install -U package_name



If you don't have pip use:

easy_install pip



If none of these work, you might have to do:

sudo pip install -U package_name