%%html

<div id="d3-example"></div>

<style>
.node {stroke: #fff; stroke-width: 1.5px;}
.link {stroke: #999; stroke-opacity: .6;}
</style>

%%javascript

require.config({paths: {d3: "http://d3js.org/d3.v3.min"}});

require(["d3"], function(d3) {
  var width = 600,
      height = 400;

  var color = d3.scale.category20();

  var force = d3.layout.force()
    .charge(-120)
    .linkDistance(30)
    .size([width, height]);

  var svg = d3.select("#d3-example").append("svg")
    .attr("width", width)
    .attr("height", height);

  d3.json("data/miserables.json", function(error, graph) {
    force.nodes(graph.nodes)
         .links(graph.links)
         .start();

    var link = svg.selectAll(".link")
        .data(graph.links)
        .enter().append("line")
        .attr("class", "link")
        .style("stroke-width", function(d) { return Math.sqrt(d.value); });

    var node = svg.selectAll(".node")
        .data(graph.nodes)
        .enter().append("circle")
        .attr("class", "node")
        .attr("r", 5)
        .style("fill", function(d) { return color(d.group); })
        .call(force.drag);

    node.append("title")
        .text(function(d) { return d.name; });

    force.on("tick", function() {
      link.attr("x1", function(d) { return d.source.x; })
          .attr("y1", function(d) { return d.source.y; })
          .attr("x2", function(d) { return d.target.x; })
          .attr("y2", function(d) { return d.target.y; });

      node.attr("cx", function(d) { return d.x; })
          .attr("cy", function(d) { return d.y; });
    });
  });
});

%matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
from mpld3 import enable_notebook, plugins
enable_notebook()

from sklearn.datasets import load_iris

data = load_iris()
X = data.data
y = data.target

# dither the data for clearer plotting
X += 0.1 * np.random.random(X.shape)

fig, ax = plt.subplots(4, 4, sharex="col", sharey="row", figsize=(8, 8))
fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95,
                    hspace=0.1, wspace=0.1)

for i in range(4):
    for j in range(4):
        ax[3 - i, j].scatter(X[:, j], X[:, i],
                             c=y, s=40, alpha=0.3)

# remove tick labels
for axi in ax.flat:
    for axis in [axi.xaxis, axi.yaxis]:
        axis.set_major_formatter(plt.NullFormatter())

# add a reset() button
plugins.connect(fig, plugins.ResetButton())
plt.show()

import plotly
py = plotly.plotly('IPython.Demo', '1fw3zw2o13')
nr = np.random

distributions = [nr.uniform, nr.normal , lambda size: nr.normal(0, 0.2, size=size),
                 lambda size: nr.beta(a=0.5, b=0.5, size=size),
                 lambda size: nr.beta(a=0.5, b=2, size=size)]

names = ['Uniform(0,1)', 'Normal(0,1)', 'Normal(0, 0.2)', 'beta(a=0.5, b=0.5)', 'beta(a=0.5, b=2)']

boxes = [{'y': dist(size=50), 'type': 'box', 'boxpoints': 'all', 'jitter': 0.5, 'pointpos': -1.8,
        'name': name} for dist, name in zip(distributions, names)]

layout = {'title': 'A few distributions',
          'showlegend': False,
          'xaxis': {'ticks': '', 'showgrid': False, 'showline': False},
          'yaxis': {'zeroline': False, 'ticks': '', 'showline': False},
          }

py.iplot(boxes, layout = layout, filename='Distributions', fileopt='overwrite', width=1000, height=650)