added binary colormap and barcode demo

jdh2358 · jdh2358 · commit 47af03d985d9 · 2007-02-13T20:45:55.000Z
svn path=/trunk/matplotlib/; revision=3018
diff --git a/CHANGELOG b/CHANGELOG
@@ -1,3 +1,7 @@
+2007-02-13 Added barcode demo- JDH
+
+2007-02-13 Added binary colormap to cm - JDH
+
 2007-02-13 Added twiny to pylab - JDH
 
 2007-02-12 Moved data files into lib/matplotlib so that setuptools'
diff --git a/examples/barcode_demo.py b/examples/barcode_demo.py
@@ -0,0 +1,25 @@
+from pylab import figure, show, cm, nx
+
+axprops = dict(xticks=[], yticks=[])
+barprops = dict(aspect='auto', cmap=cm.binary, interpolation='nearest')
+
+fig = figure()
+
+# a vertical barcode
+x = nx.mlab.rand(500,1)
+x[x>0.8] = 1.
+x[x<=0.8] = 0.
+ax = fig.add_axes([0.1, 0.3, 0.1, 0.6], **axprops)
+ax.imshow(x, **barprops)
+
+
+# a horizontal barcode
+x = nx.mlab.rand(1,500)
+x[x>0.8] = 1.
+x[x<=0.8] = 0.
+ax = fig.add_axes([0.3, 0.1, 0.6, 0.1], **axprops)
+ax.imshow(x, **barprops)
+
+fig.savefig('barcode.png', dpi=100)
+show()
+
diff --git a/lib/matplotlib/_cm.py b/lib/matplotlib/_cm.py
@@ -12,7 +12,11 @@
 from matplotlib import rcParams, colors
 LUTSIZE = rcParams['image.lut']
 
-
+_binary_data = {
+    'red'  :  ((0., 1., 1.), (1., 0., 0.)),
+    'green':  ((0., 1., 1.), (1., 0., 0.)),
+    'blue' :  ((0., 1., 1.), (1., 0., 0.))
+    }
 
 
 _bone_data = {'red':   ((0., 0., 0.),(1.0, 1.0, 1.0)),
@@ -376,6 +380,7 @@
 
 autumn = colors.LinearSegmentedColormap('autumn', _autumn_data, LUTSIZE)
 bone   = colors.LinearSegmentedColormap('bone  ', _bone_data, LUTSIZE)
+binary   = colors.LinearSegmentedColormap('binary  ', _binary_data, LUTSIZE)
 cool   = colors.LinearSegmentedColormap('cool',   _cool_data, LUTSIZE)
 copper = colors.LinearSegmentedColormap('copper', _copper_data, LUTSIZE)
 flag   = colors.LinearSegmentedColormap('flag',   _flag_data, LUTSIZE)
@@ -395,6 +400,7 @@
 datad = {
     'autumn': _autumn_data,
     'bone':   _bone_data,
+    'binary':   _binary_data,
     'cool':   _cool_data,
     'copper': _copper_data,
     'flag':   _flag_data,
diff --git a/lib/matplotlib/axes.py b/lib/matplotlib/axes.py
@@ -2554,7 +2554,7 @@ def xcorr(self, x, y, normed=False, detrend=detrend_none, **kwargs):
 
         data are plotted as plot(lags, c, **kwargs)
 
-        return value is lags, c, line where lags are a length
+        Return value is (lags, c, line) where lags are a length
         2*len(x)+1 lag vector, c is the 2*len(x)+1 cross correlation
         vector, and line is a Line2D instance returned by plot.  The
         default linestyle is None and the default marker is 'o',
