Make dots in matplotlib plots selectable by mouse

Question

scikit-learn has a very nice demo that creates an outlier analysis tool. Here is the

import numpy as np
import pylab as pl
import matplotlib.font_manager
from scipy import stats

from sklearn import svm
from sklearn.covariance import EllipticEnvelope

# Example settings
n_samples = 200
outliers_fraction = 0.25
clusters_separation = [0, 1, 2]

# define two outlier detection tools to be compared
classifiers = {
    "One-Class SVM": svm.OneClassSVM(nu=0.95 * outliers_fraction + 0.05,
                                     kernel="rbf", gamma=0.1),
    "robust covariance estimator": EllipticEnvelope(contamination=.1)}

# Compare given classifiers under given settings
xx, yy = np.meshgrid(np.linspace(-7, 7, 500), np.linspace(-7, 7, 500))
n_inliers = int((1. - outliers_fraction) * n_samples)
n_outliers = int(outliers_fraction * n_samples)
ground_truth = np.ones(n_samples, dtype=int)
ground_truth[-n_outliers:] = 0

# Fit the problem with varying cluster separation
for i, offset in enumerate(clusters_separation):
    np.random.seed(42)
    # Data generation
    X1 = 0.3 * np.random.randn(0.5 * n_inliers, 2) - offset
    X2 = 0.3 * np.random.randn(0.5 * n_inliers, 2) + offset
    X = np.r_[X1, X2]
    # Add outliers
    X = np.r_[X, np.random.uniform(low=-6, high=6, size=(n_outliers, 2))]

    # Fit the model with the One-Class SVM
    pl.figure(figsize=(10, 5))
    for i, (clf_name, clf) in enumerate(classifiers.items()):
        # fit the data and tag outliers
        clf.fit(X)
        y_pred = clf.decision_function(X).ravel()
        threshold = stats.scoreatpercentile(y_pred,
                                            100 * outliers_fraction)
        y_pred = y_pred > threshold
        n_errors = (y_pred != ground_truth).sum()
        # plot the levels lines and the points
        Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
        Z = Z.reshape(xx.shape)
        subplot = pl.subplot(1, 2, i + 1)
        subplot.set_title("Outlier detection")
        subplot.contourf(xx, yy, Z, levels=np.linspace(Z.min(), threshold, 7),
                         cmap=pl.cm.Blues_r)
        a = subplot.contour(xx, yy, Z, levels=[threshold],
                            linewidths=2, colors='red')
        subplot.contourf(xx, yy, Z, levels=[threshold, Z.max()],
                         colors='orange')
        b = subplot.scatter(X[:-n_outliers, 0], X[:-n_outliers, 1], c='white')
        c = subplot.scatter(X[-n_outliers:, 0], X[-n_outliers:, 1], c='black')
        subplot.axis('tight')
        subplot.legend(
            [a.collections[0], b, c],
            ['learned decision function', 'true inliers', 'true outliers'],
            prop=matplotlib.font_manager.FontProperties(size=11))
        subplot.set_xlabel("%d. %s (errors: %d)" % (i + 1, clf_name, n_errors))
        subplot.set_xlim((-7, 7))
        subplot.set_ylim((-7, 7))
    pl.subplots_adjust(0.04, 0.1, 0.96, 0.94, 0.1, 0.26)

pl.show()

And here is what it looks like:

Is that cool or what?

However, I want the plot to be mouse-sensitive. That is, I want to be able to click on dots and find out what they are, with either a tool-tip or with a pop-up window, or something in a scroller. And I'd also like to be able to click-to-zoom, rather than zoom with a bounding box.

Is there any way to do this?

Answer 1

Not to plug my own project to much, but have a look at mpldatacursor. If you'd prefer, it's also quite easy to implement from scratch.

As a quick example:

import matplotlib.pyplot as plt
import numpy as np
from mpldatacursor import datacursor

x1, y1 = np.random.random((2, 5))
x2, y2 = np.random.random((2, 5))

fig, ax = plt.subplots()
ax.plot(x1, y1, 'ro', markersize=12, label='Series A')
ax.plot(x2, y2, 'bo', markersize=12, label='Series B')
ax.legend()

datacursor()
plt.show()

For this to work with the example code you posted, you'd need to change things slightly. As it is, the artist labels are set in the call to legend, instead of when the artist is created. This means that there's no way to retrieve what's displayed in the legend for a particular artist. All you'd need to do is just pass in the labels as a kwarg to scatter instead of as the second argument to legend, and things should work as you were wanting.