matplotlib
diff --git a/‎examples/pylab_examples/animation_demo.py renamed to ‎examples/animation/animation_demo.py b/‎examples/pylab_examples/animation_demo.py renamed to ‎examples/animation/animation_demo.py
diff --git a/‎examples/pylab_examples/color_by_yvalue.py renamed to ‎examples/color/color_by_yvalue.py b/‎examples/pylab_examples/color_by_yvalue.py renamed to ‎examples/color/color_by_yvalue.py
diff --git a/‎examples/pylab_examples/color_demo.py renamed to ‎examples/color/color_demo.py b/‎examples/pylab_examples/color_demo.py renamed to ‎examples/color/color_demo.py
diff --git a/‎examples/pylab_examples/colours_sgskip.py renamed to ‎examples/color/colors_sgskip.py b/‎examples/pylab_examples/colours_sgskip.py renamed to ‎examples/color/colors_sgskip.py
diff --git a/‎examples/pylab_examples/contour_corner_mask.py renamed to ‎examples/images_contours_and_fields/contour_corner_mask.py b/‎examples/pylab_examples/contour_corner_mask.py renamed to ‎examples/images_contours_and_fields/contour_corner_mask.py
diff --git a/‎examples/lines_bars_and_markers/barchart_demo.py
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Lines changed: 49 additions & 0 deletions
diff --git a/‎examples/statistics/barchart_demo.py renamed to ‎examples/lines_bars_and_markers/barchart_demo2.py
Lines changed: 11 additions & 55 deletions b/‎examples/statistics/barchart_demo.py renamed to ‎examples/lines_bars_and_markers/barchart_demo2.py
Lines changed: 11 additions & 55 deletions
diff --git a/‎examples/pylab_examples/broken_barh.py renamed to ‎examples/lines_bars_and_markers/broken_barh.py b/‎examples/pylab_examples/broken_barh.py renamed to ‎examples/lines_bars_and_markers/broken_barh.py
diff --git a/‎examples/pylab_examples/README
Lines changed: 0 additions & 13 deletions b/‎examples/pylab_examples/README
Lines changed: 0 additions & 13 deletions
@@ -0,0 +1,49 @@
+"""
+=============
+Barchart Demo
+=============
+
+Bar chart demo with pairs of bars grouped for easy comparison.
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+n_groups = 5
+
+means_men = (20, 35, 30, 35, 27)
+std_men = (2, 3, 4, 1, 2)
+
+means_women = (25, 32, 34, 20, 25)
+std_women = (3, 5, 2, 3, 3)
+
+fig, ax = plt.subplots()
+
+index = np.arange(n_groups)
+bar_width = 0.35
+
+opacity = 0.4
+error_config = {'ecolor': '0.3'}
+
+rects1 = plt.bar(index, means_men, bar_width,
+                 alpha=opacity,
+                 color='b',
+                 yerr=std_men,
+                 error_kw=error_config,
+                 label='Men')
+
+rects2 = plt.bar(index + bar_width, means_women, bar_width,
+                 alpha=opacity,
+                 color='r',
+                 yerr=std_women,
+                 error_kw=error_config,
+                 label='Women')
+
+plt.xlabel('Group')
+plt.ylabel('Scores')
+plt.title('Scores by group and gender')
+plt.xticks(index + bar_width / 2, ('A', 'B', 'C', 'D', 'E'))
+plt.legend()
+
+plt.tight_layout()
+plt.show()
@@ -1,69 +1,25 @@
 """
-=============
-Barchart Demo
-=============
+==============
+Barchart Demo2
+==============
 
-Bar charts of many shapes and sizes with Matplotlib.
+Thanks Josh Hemann for the example
 
-Bar charts are useful for visualizing counts, or summary statistics
-with error bars. These examples show a few ways to do this with Matplotlib.
+This examples comes from an application in which grade school gym
+teachers wanted to be able to show parents how their child did across
+a handful of fitness tests, and importantly, relative to how other
+children did. To extract the plotting code for demo purposes, we'll
+just make up some data for little Johnny Doe...
 
 """
-
-# Credit: Josh Hemann
-
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.ticker import MaxNLocator
 from collections import namedtuple
 
+# Fixing random state for reproducibility
+np.random.seed(19680801)
 
-n_groups = 5
-
-means_men = (20, 35, 30, 35, 27)
-std_men = (2, 3, 4, 1, 2)
-
-means_women = (25, 32, 34, 20, 25)
-std_women = (3, 5, 2, 3, 3)
-
-fig, ax = plt.subplots()
-
-index = np.arange(n_groups)
-bar_width = 0.35
-
-opacity = 0.4
-error_config = {'ecolor': '0.3'}
-
-rects1 = plt.bar(index, means_men, bar_width,
-                 alpha=opacity,
-                 color='b',
-                 yerr=std_men,
-                 error_kw=error_config,
-                 label='Men')
-
-rects2 = plt.bar(index + bar_width, means_women, bar_width,
-                 alpha=opacity,
-                 color='r',
-                 yerr=std_women,
-                 error_kw=error_config,
-                 label='Women')
-
-plt.xlabel('Group')
-plt.ylabel('Scores')
-plt.title('Scores by group and gender')
-plt.xticks(index + bar_width / 2, ('A', 'B', 'C', 'D', 'E'))
-plt.legend()
-
-plt.tight_layout()
-plt.show()
-
-###############################################################################
-# This example comes from an application in which grade school gym
-# teachers wanted to be able to show parents how their child did across
-# a handful of fitness tests, and importantly, relative to how other
-# children did. To extract the plotting code for demo purposes, we'll
-# just make up some data for little Johnny Doe...
 
 Student = namedtuple('Student', ['name', 'grade', 'gender'])
 Score = namedtuple('Score', ['score', 'percentile'])