added some more example

svn path=/trunk/matplotlib/; revision=542

Author: jdh2358

examples/axhspan_demo.py

@@ -0,0 +1,42 @@
+from matplotlib.matlab import *
+figure(1)
+plot(10*rand(12), 'o')
+xlim(0,15)
+xticks([2, 4, 8, 12], ('John', 'Hunter', 'Was', 'Here'))
+
+ylim(-1,10)
+yticks(range(8))
+
+figure(2)
+t = arange(-1,2, .01)
+s = sin(2*pi*t)
+plot(t,s)
+# draw a thick red hline at y=0 that spans the xrange
+l = axhline(linewidth=4, color='r')
+
+# draw a default hline at y=1 that spans the xrange
+l = axhline(y=1)
+
+# draw a default vline at x=1 that spans the xrange
+l = axvline(x=1)
+
+# draw a thick blue vline at x=0 that spans the the upper quadrant of
+# the yrange
+l = axvline(x=0, ymin=0.75, linewidth=4, color='b')
+
+# draw a default hline at y=.5 that spans the the middle half of
+# the axes
+l = axhline(y=.5, xmin=0.25, xmax=0.75)
+
+p = axhspan(0.25, 0.75, facecolor=0.5, alpha=0.5)
+
+p = axvspan(1.25, 1.55, facecolor='g', alpha=0.5)
+
+axis([-1,2,-1,2])
+
+
+
+
+
+show()
+     

examples/date_demo_convert.py

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+import matplotlib
+matplotlib.use('TkAgg')
+from matplotlib import matlab
+ML = matlab
+from matplotlib.dates import DayLocator, HourLocator, \
+     drange, date2num, timezone
+import datetime
+
+matplotlib.rcParams['timezone'] = 'US/Pacific'
+tz = timezone('US/Pacific')
+
+
+date1 = datetime.datetime( 2000, 3, 2, tzinfo=tz)
+date2 = datetime.datetime( 2000, 3, 6, tzinfo=tz)
+delta = datetime.timedelta(hours=6)
+dates = drange(date1, date2, delta)
+    
+y = ML.arrayrange( len(dates)*1.0)
+ysq = y*y
+
+# note new constructor takes days or sequence of days you want to
+# tick, not the hour as before.  Default is to tick every day
+majorTick = DayLocator(tz=tz)
+
+# the hour locator takes the hour or sequence of hours you want to
+# tick, not the base multiple
+minorTick = HourLocator(range(0,25,6), tz=tz)
+ax = ML.subplot(111)
+ax.plot_date(dates, ysq, tz=tz)
+
+# this is superfluous, since the autoscaler should get it right, but
+# use date2num and num2date to to convert between dates and floats if
+# you want; both date2num and num2date convert an instance or sequence
+ML.xlim( dates[0], dates[-1] )
+ax.xaxis.set_major_locator(majorTick)
+ax.xaxis.set_minor_locator(minorTick)
+labels = ax.get_xticklabels()
+ML.set(labels,'rotation', 90)
+ML.show()

examples/image_interp.py

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+"""
+The same (small) array, interpolated with three different
+interpolation methods.
+
+The center of the pixel at A[i,j] is plotted at i+0.5, i+0.5.  If you
+are using interpolation='nearest', the region bounded by (i,j) and
+(i+1,j+1) will have the same color.  If you are using interpolation,
+the pixel center will have the same color as it does with nearest, but
+other pixels will be interpolated between the neighboring pixels.
+
+Earlier versions of matplotlib (<0.63) tried to hide the edge effects
+from you by setting the view limits so that they would not be visible.
+A recent bugfix in antigrain, and a new implementation in the
+matplotlib._image module which takes advantage of this fix, no longer
+makes this necessary.  To prevent edge effects, when doing
+interpolation, the matplotlib._image module now pads the input array
+with identical pixels around the edge.  Eg, if you have a 5x5 array
+with colors a-y as below
+
+
+  a b c d e 
+  f g h i j
+  k l m n o
+  p q r s t
+  u v w x y 
+
+the _image module creates the padded array,
+
+  a a b c d e e
+  a a b c d e e
+  f f g h i j j
+  k k l m n o o
+  p p q r s t t
+  o u v w x y y
+  o u v w x y y
+
+does the interpolation/resizing, and then extracts the central region.
+This allows you to plot the full range of your array w/o edge effects,
+and for example to layer multiple images of different sizes over one
+another with different interpolation methods - see
+examples/layer_images.py.  It also implies a performance hit, as this
+new temporary, padded array must be created.  Sophisticated
+interpolation also implies a performance hit, so if you need maximal
+performance or have very large images, interpolation='nearest' is
+suggested.
+
+"""
+from matplotlib.matlab import *
+A = rand(5,5)
+figure(1)
+imshow(A, interpolation='nearest')
+savefig('agg_nearest')
+grid(True)
+
+figure(2)
+imshow(A, interpolation='bilinear')
+savefig('agg_bilinear')
+grid(True)
+
+figure(3)
+imshow(A, interpolation='bicubic')
+savefig('agg_bicubic')
+grid(True)
+
+show()

examples/movie_demo.py

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+import os, sys
+from matplotlib.matlab import *
+
+files = []
+figure(figsize=(5,5))
+ax = subplot(111)
+for i in range(50):  # 50 frames
+    cla()
+    imshow(rand(5,5), interpolation='nearest')
+    fname = '_tmp%03d.png'%i
+    print 'Saving frame', fname
+    savefig(fname)
+    files.append(fname)
+
+print 'Making movie animation.mpg - this make take a while'
+#os.system("mencoder 'mf://_tmp*.png' -mf type=png:fps=10 -ovc lavc -lavcopts vcodec=wmv2 -oac copy -o animation.mpg")
+os.system("convert _tmp*.png animation.mpg")
+
+# cleanup
+for fname in files: os.remove(fname)