"""
Linked Brushing Example
=======================
This example shows a prototype of a linked brush plot, using the iris dataset
from scikit-learn.  Eventually, this plugin will be made a part of the mpld3
javascript source.  For now, this should be considered a proof-of-concept.

Click the paintbrush button at the bottom left to enable and disable the
brushing behavior.  The standard zoom and home buttons are available as well.
"""
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

import mpld3
from mpld3 import plugins, utils


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):
        points = ax[3 - i, j].scatter(X[:, j], X[:, i],
                                      c=y, s=40, alpha=0.6)

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

plugins.connect(fig, plugins.LinkedBrush(points))

mpld3.show()

"""
Linked Brushing Example
=======================
This example uses the standard Iris dataset and plots it with a linked brushing
tool for dynamically exploring the data. The paintbrush button at the bottom
left can be used to enable and disable the behavior.
"""

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

import mpld3
from mpld3 import plugins, utils


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):
        points = ax[3 - i, j].scatter(X[:, j], X[:, i],
                                      c=y, s=40, alpha=0.6)

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

# Here we connect the linked brush plugin
plugins.connect(fig, plugins.LinkedBrush(points))

mpld3.show()

fix documentation on linked brush example