matplotlib · anntzer · Apr 2, 2019 · Mar 2, 2019 · Mar 3, 2019 · Mar 3, 2019
diff --git a/examples/statistics/confidence_ellipse.py b/examples/statistics/confidence_ellipse.py
@@ -0,0 +1,222 @@
+"""
+======================================================
+Plot a confidence ellipse of a two-dimensional dataset
+======================================================
+
+This example shows how to plot a confidence ellipse of a
+two-dimensional dataset, using its pearson correlation coefficient.
+
+The approach that is used to obtain the correct geometry is
+explained and proved here:
+
+https://carstenschelp.github.io/2018/09/14/Plot_Confidence_Ellipse_001.html
+
+The method avoids the use of an iterative eigen decomposition algorithm
+and makes use of the fact that a normalized covariance matrix (composed of
+pearson correlation coefficients and ones) is particularly easy to handle.
+"""
+
+#############################################################################
+#
+# The plotting function itself
+# """"""""""""""""""""""""""""
+#
+# This function plots the confidence ellipse of the covariance of the given
+# array-like variables x and y. The ellipse is plotted into the given
+# axes-object ax.
+#
+# The radiuses of the ellipse can be controlled by n_std which is the number
+# of standard deviations. The default value is 3 which makes the ellipse
+# enclose 99.7% of the points (given the data is normally distributed
+# like in these examples).
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Ellipse
+import matplotlib.transforms as transforms
+
+
+def
10000
 confidence_ellipse(x, y, ax, n_std=3.0, facecolor='none', **kwargs):
+    """
+    Create a plot of the covariance confidence ellipse of `x` and `y`
+
+    Parameters
+    ----------
+    x, y : array_like, shape (n, )
+        Input data.
+
+    ax : matplotlib.axes.Axes
+        The axes object to draw the ellipse into.
+
+    n_std : float
+        The number of standard deviations to determine the ellipse's radiuses.
+
+    Returns
+    -------
+    matplotlib.patches.Ellipse
+
+    Other parameters
+    ----------------
+    kwargs : `~matplotlib.patches.Patch` properties
+    """
+    if x.size != y.size:
+        raise ValueError("x and y must be the same size")
+
+    cov = np.cov(x, y)
+    pearson = cov[0, 1]/np.sqrt(cov[0, 0] * cov[1, 1])
+    # Using a special case to obtain the eigenvalues of this
+    # two-dimensionl dataset.
+    ell_radius_x = np.sqrt(1 + pearson)
+    ell_radius_y = np.sqrt(1 - pearson)
+    ellipse = Ellipse((0, 0),
+        width=ell_radius_x * 2,
+        height=ell_radius_y * 2,
+        facecolor=facecolor,
+        **kwargs)
+
+    # Calculating the stdandard deviation of x from
+    # the squareroot of the variance and multiplying
+    # with the given number of standard deviations.
+    scale_x = np.sqrt(cov[0, 0]) * n_std
+    mean_x = np.mean(x)
+
+    # calculating the stdandard deviation of y ...
+    scale_y = np.sqrt(cov[1, 1]) * n_std
+    mean_y = np.mean(y)
+
+    transf = transforms.Affine2D() \
+        .rotate_deg(45) \
+        .scale(scale_x, scale_y) \
+        .translate(mean_x, mean_y)
+
+    ellipse.set_transform(transf + ax.transData)
+    return ax.add_patch(ellipse)
+
+
+#############################################################################
+#
+# A helper function to create a correlated dataset
+# """"""""""""""""""""""""""""""""""""""""""""""""
+#
+# Creates a random two-dimesional dataset with the specified
+# two-dimensional mean (mu) and dimensions (scale).
+# The correlation can be controlled by the param 'dependency',
+# a 2x2 matrix.
+
+def get_correlated_dataset(n, dependency, mu, scale):
+    latent = np.random.randn(n, 2)
+    dependent = latent.dot(dependency)
+    scaled = dependent * scale
+    scaled_with_offset = scaled + mu
+    # return x and y of the new, correlated dataset
+    return scaled_with_offset[:, 0], scaled_with_offset[:, 1]
+
+
+#############################################################################
+#
+# Positive, negative and weak correlation
+# """""""""""""""""""""""""""""""""""""""
+#
+# Note that the shape for the weak correlation (right) is an ellipse,
+# not a circle because x and y are differently scaled.
+# However, the fact that x and y are uncorrelated is shown by
+# the axes of the ellipse being aligned with the x- and y-axis
+# of the coordinate system.
+
+np.random.seed(0)
+
+PARAMETERS = {
+    'Positive correlation': np.array([[0.85, 0.35],
+                                      [0.15, -0.65]]),
+    'Negative correlation': np.array([[0.9, -0.4],
+                                      [0.1, -0.6]]),
+    'Weak correlation': np.array([[1, 0],
+                                  [0, 1]]),
+}
+
+mu = 2, 4
+scale = 3, 5
+
+fig, axs = plt.subplots(1, 3, figsize=(9, 3))
+for ax, (title, dependency) in zip(axs, PARAMETERS.items()):
+    x, y = get_correlated_dataset(800, dependency, mu, scale)
+    ax.scatter(x, y, s=0.5)
+
+    ax.axvline(c='grey', lw=1)
+    ax.axhline(c='grey', lw=1)
+
+    confidence_ellipse(x, y, ax, edgecolor='red')
+
+    ax.scatter(mu[0], mu[1], c='red', s=3)
+    ax.set_title(title)
+
+plt.show()
+
+
+#############################################################################
+#
+# Different number of standard deviations
+# """""""""""""""""""""""""""""""""""""""
+#
+# A plot with n_std = 3 (blue), 2 (purple) and 1 (red)
+
+fig, ax_nstd = plt.subplots(figsize=(6, 6))
+
+dependency_nstd = np.array([
+    [0.8, 0.75],
+    [-0.2, 0.35]
+])
+mu = 0, 0
+scale = 8, 5
+
+ax_nstd.axvline(c='grey', lw=1)
+ax_nstd.axhline(c='grey', lw=1)
+
+x, y = get_correlated_dataset(500, dependency_nstd, mu, scale)
+ax_nstd.scatter(x, y, s=0.5)
+
+confidence_ellipse(x, y, ax_nstd, n_std=1,
+    label=r'$1\sigma$', edgecolor='firebrick')
+confidence_ellipse(x, y, ax_nstd, n_std=2,
+    label=r'$2\sigma$', edgecolor='fuchsia', linestyle='--')
+confidence_ellipse(x, y, ax_nstd, n_std=3,
+    label=r'$3\sigma$', edgecolor='blue', linestyle=':')
+
+ax_nstd.scatter(mu[0], mu[1], c='red', s=3)
+ax_nstd.set_title('Different standard deviations')
+ax_nstd.legend()
+plt.show()
+
+
+#############################################################################
+#
+# Using the keyword arguments
+# """""""""""""""""""""""""""
+#
+# Use the kwargs specified for matplotlib.patches.Patch in order
+# to have the ellipse rendered in different ways.
+
+fig, ax_kwargs = plt.subplots(figsize=(6, 6))
+dependency_kwargs = np.array([
+    [-0.8, 0.5],
+    [-0.2, 0.5]
+])
+mu = 2, -3
+scale = 6, 5
+
+ax_kwargs.axvline(c='grey', lw=1)
+ax_kwargs.axhline(c='grey', lw=1)
+
+x, y = get_correlated_dataset(500, dependency_kwargs, mu, scale)
+# Plot the ellipse with zorder=0 in order to demonstrate
+# its transparency (caused by the use of alpha).
+confidence_ellipse(x, y, ax_kwargs,
+    alpha=0.5, facecolor='pink', edgecolor='purple', zorder=0)
+
+ax_kwargs.scatter(x, y, s=0.5)
+ax_kwargs.scatter(mu[0], mu[1], c='red', s=3)
+ax_kwargs.set_title(f'Using kwargs')
+
+fig.subplots_adjust(hspace=0.25)
+plt.show()