%pylab inline

from IPython.core.pylabtools import print_figure
from IPython.display import Image, SVG, Math

class Gaussian(object):
    """A simple object holding data sampled from a Gaussian distribution.
    """
    def __init__(self, mean=0, std=1, size=1000):
        self.data = np.random.normal(mean, std, size)
        self.mean = mean
        self.std = std
        self.size = size
        # For caching plots that may be expensive to compute
        self._png_data = None
        self._svg_data = None
        
    def _figure_data(self, format):
        fig, ax = plt.subplots()
        ax.plot(self.data, 'o')
        ax.set_title(self._repr_latex_())
        data = print_figure(fig, format)
        # We MUST close the figure, otherwise IPython's display machinery
        # will pick it up and send it as output, resulting in a double display
        plt.close(fig)
        return data
    
    # Here we define the special repr methods that provide the IPython display protocol
    # Note that for the two figures, we cache the figure data once computed.
    
    def _repr_png_(self):
        if self._png_data is None:
            self._png_data = self._figure_data('png')
        return self._png_data


    def _repr_svg_(self):
        if self._svg_data is None:
            self._svg_data = self._figure_data('svg')
        return self._svg_data
    
    def _repr_latex_(self):
        return r'$\mathcal{N}(\mu=%.2g, \sigma=%.2g),\ N=%d$' % (self.mean,
                                                                 self.std, self.size)
    
    # We expose as properties some of the above reprs, so that the user can see them
    # directly (since otherwise the client dictates which one it shows by default)
    @property
    def png(self):
        return Image(self._repr_png_(), embed=True)
    
    @property
    def svg(self):
        return SVG(self._repr_svg_())
        
    @property
    def latex(self):
        return Math(self._repr_svg_())
    
    # An example of using a property to display rich information, in this case
    # the histogram of the distribution.  We've hardcoded the format to be png
    # in this case, but in production code it would be trivial to make it an option
    @property
    def hist(self):
        fig, ax = plt.subplots()
        ax.hist(self.data, bins=100)
        ax.set_title(self._repr_latex_())
        data = print_figure(fig, 'png')
        plt.close(fig)
        return Image(data, embed=True)

x = Gaussian()
x

x.png

x.svg

display(x.png)
display(x.svg)

x2 = Gaussian(0.5, 0.2, 2000)
x2

display(x.hist)
display(x2.hist)

p = np.polynomial.Polynomial([1,2,3], [-10, 10])
p

def poly2latex(p):
    terms = ['%.2g' % p.coef[0]]
    if len(p) > 1:
        term = 'x'
        c = p.coef[1]
        if c!=1:
            term = ('%.2g ' % c) + term
        terms.append(term)
    if len(p) > 2:
        for i in range(2, len(p)):
            term = 'x^%d' % i
            c = p.coef[i]
            if c!=1:
                term = ('%.2g ' % c) + term
            terms.append(term)
    px = '$P(x)=%s$' % '+'.join(terms)
    dom = r', domain: $[%.2g,\ %.2g]$' % tuple(p.domain)
    return px+dom

poly2latex(p)

from IPython.display import Latex
Latex(poly2latex(p))

ip = get_ipython()
latex_formatter = ip.display_formatter.formatters['text/latex']
latex_formatter.for_type_by_name('numpy.polynomial.polynomial',
                                 'Polynomial', poly2latex)

p

p2 = np.polynomial.Polynomial([-20, 71, -15, 1])
p2