Fix typos and remove pylab interface in favor of pyplot

README.html

@@ -161,7 +161,7 @@ <h2>Changing colors and line widths</h2>
 </div>
 <a class="reference external image-reference" href="scripts/exercice_3.py"><img alt="figures/exercice_3.png" class="align-right" src="figures/exercice_3.png" /></a>
 <p>First step, we want to have the cosine in blue and the sine in red and a
-slighty thicker line for both of them. We'll also slightly alter the figure
+slightly thicker line for both of them. We'll also slightly alter the figure
 size to make it more horizontal.</p>
 <pre class="literal-block">
 ...
@@ -414,8 +414,7 @@ <h2>Figures</h2>
 close. Depending on the argument it closes (1) the current figure (no
 argument), (2) a specific figure (figure number or figure instance as
 argument), or (3) all figures (all as argument).</p>
-<p>As with other objects, you can set figure properties also setp or with the
-set_something methods.</p>
+<p>As with other objects, you can set figure properties with the set_something methods.</p>
 </div>
 <div class="section" id="subplots">
 <h2>Subplots</h2>
@@ -504,10 +503,10 @@ <h3>Tick Locators</h3>
 </div>
 <div class="section" id="animation">
 <h1><a class="toc-backref" href="#id7">Animation</a></h1>
-<p>For quite a long time, animation in matplotlib was not an easy taks and was
+<p>For quite a long time, animation in matplotlib was not an easy task and was
 done mainly through clever hacks. However, things have started to change since
 version 1.1 and the introduction of tools for creating animation very
-intuitively, with the possiblity to save them in all kind of formats (but don't
+intuitively, with the possibility to save them in all kind of formats (but don't
 expect to be able to run very complex animation at 60 fps though).</p>
 <div class="admonition-documentation admonition">
 <p class="first admonition-title">Documentation</p>
@@ -610,7 +609,7 @@ <h2>Earthquakes</h2>
 the last 30 days. The USGS Earthquake Hazards Program is part of the National
 Earthquake Hazards Reduction Program (NEHRP) and provides several data on their
 <a class="reference external" href="http://earthquake.usgs.gov">website</a>. Those data are sorted according to
-eartquakes magnitude, ranging from significant only down to all earthquakes,
+earthquakes magnitude, ranging from significant only down to all earthquakes,
 major or minor. You would be surprised by the number of minor earthquakes
 happening every hour on the planet. Since this would represent too much data
 for us, we'll stick to earthquakes with magnitude &gt; 4.5. At the time of writing,
@@ -658,7 +657,7 @@ <h2>Earthquakes</h2>
 center is and to translate latitude/longitude in some coordinates matplotlib
 can handle. Fortunately, there is the <a class="reference external" href="http://matplotlib.org/basemap/">basemap</a> project (that tends to be replaced by the
 more complete <a class="reference external" href="http://scitools.org.uk/cartopy/">cartopy</a>) that is really
-simmple to install and to use. First step is to define a projection to draw the
+simple to install and to use. First step is to define a projection to draw the
 earth onto a screen (there exists many different projections) and we'll stick
 to the <cite>mill</cite> projection which is rather standard for non-specialist like me.</p>
 <pre class="code python literal-block">
@@ -709,7 +708,7 @@ <h2>Earthquakes</h2>
 <span class="name">animation</span> <span class="operator">=</span> <span class="name">FuncAnimation</span><span class="punctuation">(</span><span class="name">fig</span><span class="punctuation">,</span> <span class="name">update</span><span class="punctuation">,</span> <span class="name">interval</span><span class="operator">=</span><span class="literal number integer">10</span><span class="punctuation">)</span>
 <span class="name">plt</span><span class="operator">.</span><span class="name">show</span><span class="punctuation">()</span>
 </pre>
-<p>If eveything went well, you should obtain something like this (with animation):</p>
+<p>If everything went well, you should obtain something like this (with animation):</p>
 <a class="reference external image-reference" href="scripts/earthquakes.py"><img alt="figures/earthquakes.png" src="figures/earthquakes.png" style="width: 50%;" /></a>
 </div>
 </div>
@@ -738,15 +737,16 @@ <h2>Regular Plots</h2>
 <p>Starting from the code below, try to reproduce the graphic on the right taking
 care of filled areas:</p>
 <pre class="literal-block">
-from pylab import *
+import numpy as np
+import maplotlib.pyplot as plt
 
 n = 256
 X = np.linspace(-np.pi,np.pi,n,endpoint=True)
 Y = np.sin(2*X)
 
-plot (X, Y+1, color='blue', alpha=1.00)
-plot (X, Y-1, color='blue', alpha=1.00)
-show()
+plt.plot (X, Y+1, color='blue', alpha=1.00)
+plt.plot (X, Y-1, color='blue', alpha=1.00)
+plt.show()
 </pre>
 <p>Click on figure for solution.</p>
 </div>
@@ -760,14 +760,15 @@ <h2>Scatter Plots</h2>
 <p>Starting from the code below, try to reproduce the graphic on the right taking
 care of marker size, color and transparency.</p>
 <pre class="literal-block">
-from pylab import *
+import numpy as np
+import maplotlib.pyplot as plt
 
 n = 1024
 X = np.random.normal(0,1,n)
 Y = np.random.normal(0,1,n)
 
-scatter(X,Y)
-show()
+plt.scatter(X,Y)
+plt.show()
 </pre>
 <p>Click on figure for solution.</p>
 </div>
@@ -781,21 +782,22 @@ <h2>Bar Plots</h2>
 <p>Starting from the code below, try to reproduce the graphic on the right by
 adding labels for red bars.</p>
 <pre class="literal-block">
-from pylab import *
+import numpy as np
+import maplotlib.pyplot as plt
 
 n = 12
 X = np.arange(n)
 Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)
 Y2 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)
 
-bar(X, +Y1, facecolor='#9999ff', edgecolor='white')
-bar(X, -Y2, facecolor='#ff9999', edgecolor='white')
+plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')
+plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')
 
 for x,y in zip(X,Y1):
-    text(x+0.4, y+0.05, '%.2f' % y, ha='center', va= 'bottom')
+    plt.text(x+0.4, y+0.05, '%.2f' % y, ha='center', va= 'bottom')
 
-ylim(-1.25,+1.25)
-show()
+plt.ylim(-1.25,+1.25)
+plt.show()
 </pre>
 <p>Click on figure for solution.</p>
 </div>
@@ -810,7 +812,8 @@ <h2>Contour Plots</h2>
 <p>Starting from the code below, try to reproduce the graphic on the right taking
 care of the colormap (see <a class="reference internal" href="#colormaps">Colormaps</a> below).</p>
 <pre class="literal-block">
-from pylab import *
+import numpy as np
+import maplotlib.pyplot as plt
 
 def f(x,y): return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)
 
@@ -819,9 +822,9 @@ <h2>Contour Plots</h2>
 y = np.linspace(-3,3,n)
 X,Y = np.meshgrid(x,y)
 
-contourf(X, Y, f(X,Y), 8, alpha=.75, cmap='jet')
-C = contour(X, Y, f(X,Y), 8, colors='black', linewidth=.5)
-show()
+plt.contourf(X, Y, f(X,Y), 8, alpha=.75, cmap='jet')
+C = plt.contour(X, Y, f(X,Y), 8, colors='black', linewidth=.5)
+plt.show()
 </pre>
 <p>Click on figure for solution.</p>
 </div>
@@ -836,15 +839,17 @@ <h2>Imshow</h2>
 <p>Starting from the code below, try to reproduce the graphic on the right taking
 care of colormap, image interpolation and origin.</p>
 <pre class="literal-block">
-from pylab import *
+import numpy as np
+import maplotlib.pyplot as plt
 
 def f(x,y): return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)
 
 n = 10
 x = np.linspace(-3,3,4*n)
 y = np.linspace(-3,3,3*n)
 X,Y = np.meshgrid(x,y)
-imshow(f(X,Y)), show()
+plt.imshow(f(X,Y))
+plt.show()
 </pre>
 <p>Click on figure for solution.</p>
 </div>
@@ -858,11 +863,13 @@ <h2>Pie Charts</h2>
 <p>Starting from the code below, try to reproduce the graphic on the right taking
 care of colors and slices size.</p>
 <pre class="literal-block">
-from pylab import *
+import numpy as np
+import maplotlib.pyplot as plt
 
 n = 20
 Z = np.random.uniform(0,1,n)
-pie(Z), show()
+plt.pie(Z)
+plt.show()
 </pre>
 <p>Click on figure for solution.</p>
 </div>
@@ -876,11 +883,13 @@ <h2>Quiver Plots</h2>
 <p>Starting from the code above, try to reproduce the graphic on the right taking
 care of colors and orientations.</p>
 <pre class="literal-block">
-from pylab import *
+import numpy as np
+import maplotlib.pyplot as plt
 
 n = 8
 X,Y = np.mgrid[0:n,0:n]
-quiver(X,Y), show()
+plt.quiver(X,Y)
+plt.show()
 </pre>
 <p>Click on figure for solution.</p>
 </div>
@@ -890,15 +899,16 @@ <h2>Grids</h2>
 <p>Starting from the code below, try to reproduce the graphic on the right taking
 care of line styles.</p>
 <pre class="literal-block">
-from pylab import *
+import numpy as np
+import maplotlib.pyplot as plt
 
 axes = gca()
 axes.set_xlim(0,4)
 axes.set_ylim(0,3)
 axes.set_xticklabels([])
 axes.set_yticklabels([])
 
-show()
+plt.show()
 </pre>
 <p>Click on figure for solution.</p>
 </div>
@@ -911,13 +921,14 @@ <h2>Multi Plots</h2>
 </div>
 <p>Starting from the code below, try to reproduce the graphic on the right.</p>
 <pre class="literal-block">
-from pylab import *
+import numpy as np
+import maplotlib.pyplot as plt
 
-subplot(2,2,1)
-subplot(2,2,3)
-subplot(2,2,4)
+plt.subplot(2,2,1)
+plt.subplot(2,2,3)
+plt.subplot(2,2,4)
 
-show()
+plt.show()
 </pre>
 <p>Click on figure for solution.</p>
 </div>
@@ -930,21 +941,22 @@ <h2>Polar Axis</h2>
 </div>
 <p>Starting from the code below, try to reproduce the graphic on the right.</p>
 <pre class="literal-block">
-from pylab import *
+import numpy as np
+import maplotlib.pyplot as plt
 
-axes([0,0,1,1])
+plt.axes([0,0,1,1])
 
 N = 20
 theta = np.arange(0.0, 2*np.pi, 2*np.pi/N)
 radii = 10*np.random.rand(N)
 width = np.pi/4*np.random.rand(N)
-bars = bar(theta, radii, width=width, bottom=0.0)
+bars = plt.bar(theta, radii, width=width, bottom=0.0)
 
 for r,bar in zip(radii, bars):
     bar.set_facecolor( cm.jet(r/10.))
     bar.set_alpha(0.5)
 
-show()
+plt.show()
 </pre>
 <p>Click on figure for solution.</p>
 </div>
@@ -957,10 +969,11 @@ <h2>3D Plots</h2>
 </div>
 <p>Starting from the code below, try to reproduce the graphic on the right.</p>
 <pre class="literal-block">
-from pylab import *
+import numpy as np
+import maplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
 
-fig = figure()
+fig = plt.figure()
 ax = Axes3D(fig)
 X = np.arange(-4, 4, 0.25)
 Y = np.arange(-4, 4, 0.25)
@@ -970,7 +983,7 @@ <h2>3D Plots</h2>
 
 ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='hot')
 
-show()
+plt.show()
 </pre>
 <p>Click on figure for solution.</p>
 </div>