from IPython.display import HTML # use IPython tools for embedding

HTML('<center><iframe width="650" height="650" frameborder="0" seamless="seamless" scrolling="no" src="https://plot.ly/~cimar/38/.embed?width=650&height=650"></iframe></center>')

import plotly  
plotly.__version__

import matplotlib.pyplot as plt # side-stepping mpl's backend
import plotly.plotly as py
import plotly.tools as tls
from plotly.graph_objs import *
%matplotlib inline
py.sign_in("IPython.Demo", "1fw3zw2o13")

fig1 = plt.figure()

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-2.0, 2.0, 10000) # The x-values
sigma = np.linspace(0.4, 1.0, 4) # Some different values of sigma

# Evaluate a Gaussian for each sigma
gaussians = [(2*np.pi*s**2)**-0.5 * np.exp(-0.5*x**2/s**2) for s in sigma]

ax = plt.axes()

for s,y in zip(sigma, gaussians):
    ax.plot(x, y, lw=1.25, label=r"$\sigma = %3.2f$"%s)

formula = r"$y(x)=\frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{x^2}{2\sigma^2}}$"

ax.text(0.05, 0.80, formula, transform=ax.transAxes, fontsize=20)
ax.set_xlabel(r"$x$", fontsize=18)
ax.set_ylabel(r"$y(x)$", fontsize=18)
ax.legend()
plt.show()

py.iplot_mpl(fig1) # Translate figure to Plotly

from plotly.graph_objs import Data, Layout, Figure

tls.embed('MattSundquist', '1896')

# Convert MPL figure to Plotly
my_fig = tls.mpl_to_plotly(fig1)

print my_fig.to_string() # check out your graph.

help(Data) # check out data

# Strip out the data from the Figure
my_data = my_fig.get_data()
my_data

tls.embed('MattSundquist', 1891) # our MATLAB figure with a bit of styling 

tls.embed('MattSundquist', 1899)

from IPython.display import display, HTML
import urllib2
display(HTML( urllib2.urlopen('https://raw.githubusercontent.com/plotly/python-user-guide/master/custom.css').read()))