added spy and spy2

svn path=/trunk/matplotlib/; revision=681

Author: jdh2358

CHANGELOG

@@ -1,5 +1,11 @@
 New entries should be added at the top
 
+2004-11-12 Added new plotting functions spy, spy2 for sparse matrix
+           visualization - JDH
+
+2004-11-11 Added rgrids, thetragrids for customizing the grid
+           locations and labels for polar plots - JDH
+
 2004-11-11 make the Gtk backends build without an X-server connection - JV
 
 2004-11-10 matplotlib/__init__.py: Added FROZEN to signal we are running under

lib/matplotlib/axes.py

@@ -1,6 +1,6 @@
 from __future__ import division, generators
 
-import math, sys
+import math, sys, random
 
 from numerix import MLab, absolute, arange, array, asarray, ones, transpose, \
      log, log10, Float, ravel
@@ -11,7 +11,7 @@
 from cbook import iterable, is_string_like, flatten, enumerate, True, False,\
      allequal
 from collections import RegularPolyCollection, PolyCollection
-from colors import colorConverter, normalize, Colormap
+from colors import colorConverter, normalize, Colormap, LinearSegmentedColormap
 import cm
 from cm import ColormapJet, Grayscale
 import _image
@@ -2419,6 +2419,7 @@ def set_yticks(self, ticks):
         'Set the y ticks with list of ticks'
         return self.yaxis.set_ticks(ticks)
 
+
     def specgram(self, x, NFFT=256, Fs=2, detrend=mlab.detrend_none,
                  window=mlab.window_hanning, noverlap=128,
                  cmap = None, xextent=None):
@@ -2466,6 +2467,37 @@ def specgram(self, x, NFFT=256, Fs=2, detrend=mlab.detrend_none,
 
         return Pxx, freqs, bins, im
 
+    def spy(self, Z,  marker='s', markersize=10, **kwargs):
+        """
+        SPY(Z, **kwrags) plots the sparsity pattern of the matrix S
+        using plot markers.
+
+        kwargs give the marker properties - see help(plot) for more
+        information on marker properties
+
+        The line handles are returned
+        """
+        x,y,z = mlab.get_xyz_where(Z, Z>0)
+        return self.plot(x+0.5,y+0.5, linestyle='None',
+                         marker=marker,markersize=markersize, **kwargs)
+
+    def spy2(self, Z):
+        """
+SPY2(Z) plots the sparsity pattern of the matrix S as an image
+
+The image instance is returned
+        """
+
+        #binary colormap min white, max black
+        cmapdata = {
+             'red'  :  ((0., 1., 1.), (1., 0., 0.)),
+             'green':  ((0., 1., 1.), (1., 0., 0.)),
+             'blue' :  ((0., 1., 1.), (1., 0., 0.))
+             }
+        binary =  LinearSegmentedColormap('binary',  cmapdata, 2)
+        Z = where(Z>0,1.,0.)
+        return self.imshow(transpose(Z), interpolation='nearest', cmap=binary)
+
     def table(self,              
         cellText=None, cellColours=None,
         cellLoc='right', colWidths=None,

lib/matplotlib/matlab.py

@@ -1002,6 +1002,31 @@ def specgram(*args, **kwargs):
         return ret
 specgram.__doc__ = Axes.specgram.__doc__
 
+def spy(*args, **kwargs):
+    try: ret =  gca().spy(*args, **kwargs)
+    except ValueError, msg:
+        msg = raise_msg_to_str(msg)
+        error_msg(msg)
+    else:
+        Pxx, freqs, bins, im = ret
+        gci._current = im
+        draw_if_interactive()
+        return ret
+spy.__doc__ = Axes.spy.__doc__
+
+
+def spy2(*args, **kwargs):
+    try: ret =  gca().spy2(*args, **kwargs)
+    except ValueError, msg:
+        msg = raise_msg_to_str(msg)
+        error_msg(msg)
+    else:
+        Pxx, freqs, bins, im = ret
+        gci._current = im
+        draw_if_interactive()
+        return ret
+spy2.__doc__ = Axes.spy2.__doc__
+
 def subplot(*args, **kwargs):
     """
     Create a subplot command, creating axes with

lib/matplotlib/mlab.py

@@ -1141,6 +1141,40 @@ def bivariate_normal(X, Y, sigmax=1.0, sigmay=1.0,
 
 
 
+def get_xyz_where(Z, Cond):
+    """
+    Z and Cond are MxN matrices.  Z are data and Cond is a boolean
+    matrix where some condition is satisfied.  Return value is x,y,z
+    where x and y are the indices into Z and z are the values of Z at
+    those indices.  x,y,z are 1D arrays
+    """
+    
+    M,N = Z.shape
+    z = ravel(Z)
+    ind = nonzero( ravel(Cond) )
+
+    x = arange(M); x.shape = M,1
+    X = repeat(x, N, 1)
+    x = ravel(X)
+
+    y = arange(N); y.shape = 1,N
+    Y = repeat(y, M)
+    y = ravel(Y)
+
+    x = take(x, ind)
+    y = take(y, ind)
+    z = take(z, ind)
+    return x,y,z
+
+def get_sparse_matrix(M,N,frac=0.1):
+    'return a MxN sparse matrix with frac elements randomly filled'
+    data = zeros((M,N))*0.
+    for i in range(int(M*N*frac)):
+        x = random.randint(0,M-1)
+        y = random.randint(0,N-1)
+        data[x,y] = rand()
+    return data
+
 
 ### the following code was written and submitted by Fernando Perez
 ### from the ipython numutils package under a BSD license

unit/memleak_hawaii3.py

@@ -2,7 +2,8 @@
 
 import os, sys, time
 import matplotlib
-matplotlib.use('Cairo')
+#matplotlib.use('Cairo')
+matplotlib.use('Agg')
 from matplotlib.matlab import *