IPython-fu: el camino de la "nbconversión".¶

from IPython.nbformat import current
with open('IPython_fu_talk.ipynb') as f:
    nb = current.read(f,'json')
    
nb.worksheets[0].cells[15:20]

print "Hola PyConAr 2013"

from numpy.random import randn
data = {i : randn() for i in range(7)}
data

>>> from numpy.random import randn
>>> data = {i : randn() for i in range(7)}
>>> print data # pequeña trampa para verlo como se vería en consola

>>> the_world_is_flat = 1
>>> if the_world_is_flat:
...     print "Be careful not to fall off!"

In [5]: [x*x for x in range(5)]

%run non_existent_file

x = 1
y = 4
z = y/(1-x)

import scipy as sp

sp.

?

def suma(a, b):
    """
    Demo de una función que suma cosas...
    """
    return a + b

suma?

suma??

%magic

%lsmagic

%quickref

%%bash
mkdir temporal/
touch temporal/mi_programa.py
cat temporal/mi_programa.py

%%writefile temporal/mi_programa.py
def mi_funcion(x, y):
    """
    Demo
    """
    return x / y

x = 4
y = 2

resultado = mi_funcion(x, y)

%run temporal/mi_programa.py

resultado

!pwd

files = !ls
print "My current directory's files:"
print files

!echo $files

!echo {files[0].upper()}

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 3*np.pi, 500)
y = np.sin(x**2)

fig = plt.figure()

axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])

axes.plot(x, y, 'r')

axes.set_xlabel('x')
axes.set_ylabel('sin x^2')
axes.set_title('Una figura simple');

from IPython.display import display

from IPython.display import Image
Image(filename='pyconar2013_talks/figs/logo.png')

from IPython.display import SVG
SVG(filename='pyconar2013_talks/figs/python-logo.svg')

from IPython.display import YouTubeVideo
YouTubeVideo('MIAKOMzRl1I')

from IPython.display import HTML

s = """<table>
<tr>
<th>Header 1</th>
<th>Header 2</th>
</tr>
<tr>
<td>row 1, cell 1</td>
<td>row 1, cell 2</td>
</tr>
<tr>
<td>row 2, cell 1</td>
<td>row 2, cell 2</td>
</tr>
</table>"""

h = HTML(s)
display(h)

from IPython.display import IFrame
IFrame('http://www.damian.oquanta.info',1024,768)

%load soln/soundcloud.py

from IPython.display import HTML
h = HTML("""<iframe width="100%" height="166" scrolling="no" frameborder="no" src="https://w.soundcloud.com/player/?url=http%3A%2F%2Fapi.soundcloud.com%2Ftracks%2F94543639"></iframe>""")
display(h)

from IPython.display import Javascript

# fetch d3 from cloudflare
Javascript("""$.getScript('//cdnjs.cloudflare.com/ajax/libs/d3/3.2.2/d3.v3.min.js')""")

%%html
<style type="text/css">

circle {
  fill: rgb(31, 119, 180);
  fill-opacity: .25;
  stroke: rgb(31, 119, 180);
  stroke-width: 1px;
}

.leaf circle {
  fill: #ff7f0e;
  fill-opacity: 1;
}

text {
  font: 10px sans-serif;
}

</style>

%%javascript

// This unhides the output area
container.show();

// element is the jQuery element we will append to
var e = element.get(0);
    
var diameter = 600,
    format = d3.format(",d");

var pack = d3.layout.pack()
    .size([diameter - 4, diameter - 4])
    .value(function(d) { return d.size; });

var svg = d3.select(e).append("svg")
    .attr("width", diameter)
    .attr("height", diameter)
  .append("g")
    .attr("transform", "translate(2,2)");

d3.json("files/flare.json", function(error, root) {
  var node = svg.datum(root).selectAll(".node")
      .data(pack.nodes)
    .enter().append("g")
      .attr("class", function(d) { return d.children ? "node" : "leaf node"; })
      .attr("transform", function(d) { return "translate(" + d.x + "," + d.y + ")"; });

  node.append("title")
      .text(function(d) { return d.name + (d.children ? "" : ": " + format(d.size)); });

  node.append("circle")
      .attr("r", function(d) { return d.r; });

  node.filter(function(d) { return !d.children; }).append("text")
      .attr("dy", ".3em")
      .style("text-anchor", "middle")
      .text(function(d) { return d.name.substring(0, d.r / 3); });
});

d3.select(self.frameElement).style("height", diameter + "px");

%load http://matplotlib.sourceforge.net/mpl_examples/api/collections_demo.py

#!/usr/bin/env python
'''Demonstration of LineCollection, PolyCollection, and
RegularPolyCollection with autoscaling.

For the first two subplots, we will use spirals.  Their
size will be set in plot units, not data units.  Their positions
will be set in data units by using the "offsets" and "transOffset"
kwargs of the LineCollection and PolyCollection.

The third subplot will make regular polygons, with the same
type of scaling and positioning as in the first two.

The last subplot illustrates the use of "offsets=(xo,yo)",
that is, a single tuple instead of a list of tuples, to generate
successively offset curves, with the offset given in data
units.  This behavior is available only for the LineCollection.

'''

import matplotlib.pyplot as plt
from matplotlib import collections, transforms
from matplotlib.colors import colorConverter
import numpy as np

nverts = 50
npts = 100

# Make some spirals
r = np.array(range(nverts))
theta = np.array(range(nverts)) * (2*np.pi)/(nverts-1)
xx = r * np.sin(theta)
yy = r * np.cos(theta)
spiral = list(zip(xx,yy))

# Make some offsets
rs = np.random.RandomState([12345678])
xo = rs.randn(npts)
yo = rs.randn(npts)
xyo = list(zip(xo, yo))

# Make a list of colors cycling through the rgbcmyk series.
colors = [colorConverter.to_rgba(c) for c in ('r','g','b','c','y','m','k')]

fig, axes = plt.subplots(2,2)
((ax1, ax2), (ax3, ax4)) = axes # unpack the axes


col = collections.LineCollection([spiral], offsets=xyo,
                                transOffset=ax1.transData)
trans = fig.dpi_scale_trans + transforms.Affine2D().scale(1.0/72.0)
col.set_transform(trans)  # the points to pixels transform
    # Note: the first argument to the collection initializer
    # must be a list of sequences of x,y tuples; we have only
    # one sequence, but we still have to put it in a list.
ax1.add_collection(col, autolim=True)
    # autolim=True enables autoscaling.  For collections with
    # offsets like this, it is neither efficient nor accurate,
    # but it is good enough to generate a plot that you can use
    # as a starting point.  If you know beforehand the range of
    # x and y that you want to show, it is better to set them
    # explicitly, leave out the autolim kwarg (or set it to False),
    # and omit the 'ax1.autoscale_view()' call below.

# Make a transform for the line segments such that their size is
# given in points:
col.set_color(colors)

ax1.autoscale_view()  # See comment above, after ax1.add_collection.
ax1.set_title('LineCollection using offsets')


# The same data as above, but fill the curves.
col = collections.PolyCollection([spiral], offsets=xyo,
                                transOffset=ax2.transData)
trans = transforms.Affine2D().scale(fig.dpi/72.0)
col.set_transform(trans)  # the points to pixels transform
ax2.add_collection(col, autolim=True)
col.set_color(colors)


ax2.autoscale_view()
ax2.set_title('PolyCollection using offsets')

# 7-sided regular polygons

col = collections.RegularPolyCollection(7,
                                        sizes = np.fabs(xx)*10.0, offsets=xyo,
                                        transOffset=ax3.transData)
trans = transforms.Affine2D().scale(fig.dpi/72.0)
col.set_transform(trans)  # the points to pixels transform
ax3.add_collection(col, autolim=True)
col.set_color(colors)
ax3.autoscale_view()
ax3.set_title('RegularPolyCollection using offsets')


# Simulate a series of ocean current profiles, successively
# offset by 0.1 m/s so that they form what is sometimes called
# a "waterfall" plot or a "stagger" plot.

nverts = 60
ncurves = 20
offs = (0.1, 0.0)

yy = np.linspace(0, 2*np.pi, nverts)
ym = np.amax(yy)
xx = (0.2 + (ym-yy)/ym)**2 * np.cos(yy-0.4) * 0.5
segs = []
for i in range(ncurves):
    xxx = xx + 0.02*rs.randn(nverts)
    curve = list(zip(xxx, yy*100))
    segs.append(curve)

col = collections.LineCollection(segs, offsets=offs)
ax4.add_collection(col, autolim=True)
col.set_color(colors)
ax4.autoscale_view()
ax4.set_title('Successive data offsets')
ax4.set_xlabel('Zonal velocity component (m/s)')
ax4.set_ylabel('Depth (m)')
# Reverse the y-axis so depth increases downward
ax4.set_ylim(ax4.get_ylim()[::-1])


plt.show()

from IPython.display import Latex
Latex(r"""\begin{eqnarray}
\nabla \times \vec{\mathbf{B}} -\, \frac1c\, 
\frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
\nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
\nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, 
\frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
\nabla \cdot \vec{\mathbf{B}} & = 0 
\end{eqnarray}""")

import pandas
pandas.set_option('display.notebook_repr_html', True)

%%writefile data.csv
Date,Open,High,Low,Close,Volume,Adj Close
2012-06-01,569.16,590.00,548.50,584.00,14077000,581.50
2012-05-01,584.90,596.76,522.18,577.73,18827900,575.26
2012-04-02,601.83,644.00,555.00,583.98,28759100,581.48
2012-03-01,548.17,621.45,516.22,599.55,26486000,596.99
2012-02-01,458.41,547.61,453.98,542.44,22001000,540.12
2012-01-03,409.40,458.24,409.00,456.48,12949100,454.53

df = pandas.read_csv('data.csv')
df

%connect_info

%qtconsole

%%bash
ipython nbconvert  IPython_fu_talk.ipynb --to html

%%bash
ipython nbconvert IPython_fu_talk.ipynb --to slides