- use the sphinx-gallery extension (https://github.com/sphinx-gallery…

…/sphinx-gallery)) in the doc

- make the examples compatible with sphinx-gallery (https://github.com/sphinx-gallery/sphinx-gallery)
- fix a dead link in xkcdify.py
- move examples/pyearth_vs_earth.py to benchmark/

doc/Makefile

@@ -40,6 +40,8 @@ help:
 
 clean:
 	-rm -rf $(BUILDDIR)/*
+	rm -rf auto_examples/
+	rm -rf modules/generated/*
 
 html:
 	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
@@ -151,3 +153,8 @@ doctest:
 	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
 	@echo "Testing of doctests in the sources finished, look at the " \
 	      "results in $(BUILDDIR)/doctest/output.txt."
+
+html-noplot:
+	$(SPHINXBUILD) -D plot_gallery=0 -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
+	@echo
+	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."

doc/conf.py

@@ -25,6 +25,8 @@
 # Create the figures
 import generate_figures
 
+import sphinxgallery
+
 # -- General configuration -----------------------------------------------
 
 # If your documentation needs a minimal Sphinx version, state it here.
@@ -35,7 +37,7 @@
 extensions = ['sphinx.ext.doctest', 'sphinx.ext.todo', 'sphinx.ext.coverage',
               'sphinx.ext.pngmath', 'sphinx.ext.mathjax', 'sphinx.ext.ifconfig',
               'sphinx.ext.viewcode', 'sphinx.ext.autodoc', 'numpydoc', 'sphinx.ext.autosummary',
-              'sphinxcontrib.bibtex']
+              'sphinxcontrib.bibtex', 'sphinxgallery.gen_gallery']
 
 autosummary_generate = True
 numpydoc_show_class_members = False
@@ -107,7 +109,7 @@
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
-html_theme = 'default'
+html_theme = 'sphinx_rtd_theme'
 
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
@@ -136,7 +138,7 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+html_static_path = ['_static', sphinxgallery.path_static()]
 
 # If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
 # using the given strftime format.

doc/generate_figures.py

@@ -1,5 +1,5 @@
-
-
+import matplotlib as mpl
+mpl.use('Agg')
 import numpy
 from pyearth import Earth
 from matplotlib import pyplot

doc/xkcdify.py

@@ -25,7 +25,7 @@
 import os
 import urllib2
 if not os.path.exists('Humor-Sans.ttf'):
-    fhandle = urllib2.urlopen('http://antiyawn.com/uploads/Humor-Sans.ttf')
+    fhandle = urllib2.urlopen('https://github.com/shreyankg/xkcd-desktop/raw/master/Humor-Sans.ttf')
     open('Humor-Sans.ttf', 'wb').write(fhandle.read())
 
 

examples/README.txt

@@ -0,0 +1 @@
+This is the gallery of examples.

examples/classifier_comp.py → examples/plot_classifier_comp.py

@@ -1,16 +1,19 @@
-'''
-This script recreates the scikit-learn classifier comparison example found at http://scikit-learn.org/dev/auto_examples/plot_classifier_comparison.html.
-It has been modified to include an Earth based classifier.
-'''
+"""
+===============================
+Plotting sckit-learn classifiers comparison with Earth
+===============================
 
+This script recreates the scikit-learn classifier comparison example found at http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html.
+It has been modified to include an Earth based classifier.
+"""
 # Code source: Gael Varoqueux
 #              Andreas Mueller
 # Modified for Documentation merge by Jaques Grobler
 # License: BSD 3 clause
 # Modified to include pyearth by Jason Rudy
 
 import numpy as np
-import pylab as pl
+import matplotlib.pyplot as plt
 from matplotlib.colors import ListedColormap
 from sklearn.cross_validation import train_test_split
 from sklearn.preprocessing import StandardScaler
@@ -25,10 +28,10 @@
 
 h = .02  # step size in the mesh
 
-from pyearth.earth import Earth
 from sklearn.linear_model.logistic import LogisticRegression
 from sklearn.metrics import accuracy_score
 from sklearn.pipeline import Pipeline
+from pyearth.earth import Earth
 
 np.random.seed(1)
 
@@ -60,7 +63,7 @@
             linearly_separable
             ]
 
-figure = pl.figure(figsize=(27, 9))
+figure = plt.figure(figsize=(27, 9))
 i = 1
 # iterate over datasets
 for ds in datasets:
@@ -75,9 +78,9 @@
                          np.arange(y_min, y_max, h))
 
     # just plot the dataset first
-    cm = pl.cm.RdBu
+    cm = plt.cm.RdBu
     cm_bright = ListedColormap(['#FF0000', '#0000FF'])
-    ax = pl.subplot(len(datasets), len(classifiers) + 1, i)
+    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
     # Plot the training points
     ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
     # and testing points
@@ -90,7 +93,7 @@
 
     # iterate over classifiers
     for name, clf in zip(names, classifiers):
-        ax = pl.subplot(len(datasets), len(classifiers) + 1, i)
+        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
         clf.fit(X_train, y_train)
         score = clf.score(X_test, y_test)
 
@@ -121,5 +124,5 @@
         i += 1
 
 figure.subplots_adjust(left=.02, right=.98)
-pl.savefig('classifier_comp.pdf', transparent=True)
-pl.show()
+plt.savefig('classifier_comp.pdf', transparent=True)
+plt.show()

examples/derivatives.py → examples/plot_derivatives.py

@@ -1,6 +1,15 @@
+"""
+===============================
+Plotting derivatives of simple sine function
+===============================
+
+A simple example plotting a fit of the sine function and
+the derivatives computed by Earth.
+"""
 import numpy
+import matplotlib.pyplot as plt
+
 from pyearth import Earth
-from matplotlib import pyplot
 
 # Create some fake data
 numpy.random.seed(2)
@@ -9,7 +18,7 @@
 X = 20 * numpy.random.uniform(size=(m, n)) - 10
 y = 10*numpy.sin(X[:, 6])  + \
     0.25*numpy.random.normal(size=m)
-    
+
 # Compute the known true derivative with respect to the predictive variable
 y_prime = 10*numpy.cos(X[:, 6])
 
@@ -18,20 +27,20 @@
 model.fit(X, y)
 
 # Print the model
-print model.trace()
-print model.summary()
+print(model.trace())
+print(model.summary())
 
 # Get the predicted values and derivatives
 y_hat = model.predict(X)
 y_prime_hat = model.predict_deriv(X, 'x6')
 
 # Plot true and predicted function values and derivatives for the predictive variable
-pyplot.subplot(211)
-pyplot.plot(X[:, 6], y, 'r.')
-pyplot.plot(X[:, 6], y_hat, 'b.')
-pyplot.ylabel('function')
-pyplot.subplot(212)
-pyplot.plot(X[:, 6], y_prime, 'r.')
-pyplot.plot(X[:, 6], y_prime_hat[:,0], 'b.')
-pyplot.ylabel('derivative')
-pyplot.show()
+plt.subplot(211)
+plt.plot(X[:, 6], y, 'r.')
+plt.plot(X[:, 6], y_hat, 'b.')
+plt.ylabel('function')
+plt.subplot(212)
+plt.plot(X[:, 6], y_prime, 'r.')
+plt.plot(X[:, 6], y_prime_hat[:,0], 'b.')
+plt.ylabel('derivative')
+plt.show()

examples/sine_wave.py → examples/plot_sine_wave.py

@@ -1,7 +1,14 @@
+"""
+===============================
+Plotting simple sine function
+===============================
 
+A simple example plotting a fit of the sine function.
+"""
 import numpy
+import matplotlib.pyplot as plt
+
 from pyearth import Earth
-from matplotlib import pyplot
 
 # Create some fake data
 numpy.random.seed(2)
@@ -17,12 +24,11 @@
 model.fit(X, y)
 
 # Print the model
-print model.trace()
-print model.summary()
+print(model.trace())
+print(model.summary())
 
 # Plot the model
 y_hat = model.predict(X)
-pyplot.figure()
-pyplot.plot(X[:, 6], y, 'r.')
-pyplot.plot(X[:, 6], y_hat, 'b.')
-pyplot.show()
+plt.plot(X[:, 6], y, 'r.')
+plt.plot(X[:, 6], y_hat, 'b.')
+plt.show()

examples/v_function.py → examples/plot_v_function.py

@@ -1,6 +1,13 @@
+"""
+===============================
+Plotting the absolute value function
+===============================
+
+A simple example plotting a fit of the absolute value function.
+"""
 
 import numpy
-from matplotlib import pyplot
+import matplotlib.pyplot as plt
 
 from pyearth import Earth
 
@@ -16,12 +23,12 @@
 model.fit(X, y)
 
 # Print the model
-print model.trace()
-print model.summary()
+print(model.trace())
+print(model.summary())
 
 # Plot the model
 y_hat = model.predict(X)
-pyplot.figure()
-pyplot.plot(X[:, 6], y, 'r.')
-pyplot.plot(X[:, 6], y_hat, 'b.')
-pyplot.show()
+plt.figure()
+plt.plot(X[:, 6], y, 'r.')
+plt.plot(X[:, 6], y_hat, 'b.')
+plt.show()