Added visualization code

hdnet/patterns.py

@@ -183,15 +183,27 @@ def counts_by_label(self):
     def patterns(self):
         """
         Returns the patterns encountered in the raw data
-        as 1d vectors.
+        as 01-strings.
         
+        Returns
+        -------
+        patterns : list of 01-strings
+        """
+        return self._patterns
+
+    @property
+    def patterns_as_binary(self):
+        """
+        Returns the patterns encountered in the raw data
+        as binary vectors.
+
         Returns
         -------
         patterns : 2d numpy array, int
             Binary array of patterns encountered in the
             raw data, as 1d vectors
         """
-        return self._patterns
+        return np.array([self.pattern_to_binary_matrix(i) for i in range(len(self._patterns))])
 
     @property
     def num_patterns(self):

hdnet/visualization.py

@@ -11,12 +11,15 @@
 
 """
 
+import os
 import numpy as np
 
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 from matplotlib.ticker import NullFormatter
 
+from hdnet.util import hdlog
+
 HAS_PRETTYPLOTLIB = False
 try:
     import prettyplotlib as ppl
@@ -50,9 +53,9 @@ def plot_matrix_whole_canvas(matrix, **kwargs):
     Value : Type
         Description
     """
-    plt.axis("off")
     ax = plt.axes([0, 0, 1, 1])
     ax.matshow(matrix, **kwargs)
+    plt.axis('off')
     return ax
 
 
@@ -77,7 +80,7 @@ def save_matrix_whole_canvas(matrix, fname, **kwargs):
     plt.figure()
     plot_matrix_whole_canvas(matrix, **kwargs)
     plt.savefig(fname)
-    plt.close
+    plt.close()
 
 
 def raster_plot_psth(spikes,
@@ -614,4 +617,56 @@ def plot_graph(g, nodeval=None, cmap_nodes='cool', cmap_edges='autumn',
     return fig
 
 
+def plot_network(network, filename = 'Jtheta.png', cmap = 'jet', axis = False, colorbar = True, overwrite = False):
+    if os.path.exists(filename) and not overwrite:
+        hdlog.error('plot_network: file name exists: {}, pass overwrite = True to overwrite'.format(filename))
+        return
+
+    plt.figure()
+    mat = network.J.copy()
+    mat[np.diag_indices(mat.shape[0])] = network.theta.ravel()
+    plt.matshow(mat, cmap = cmap)
+    if colorbar:
+        plt.colorbar()
+    if not axis:
+        plt.axis('off')
+    plt.savefig(filename)
+    plt.close()
+
+
+def plot_hopfield_patterns(patterns, path, format = 'png', window_size = 1, memories = True, mtas = True, overwrite = False):
+    if os.path.exists(path):
+        if not overwrite:
+            hdlog.error('plot_overview_hofield_patterns: path exists: {}, pass overwrite = True to overwrite'.format(path))
+            return
+    else:
+        os.makedirs(path)
+
+    def _save_mat(fn, mat):
+        plt.figure()
+        ax = plt.axes([0, 0, 1, 1])
+        ax.matshow(mat, cmap = 'gray')
+        ax.axes.get_xaxis().set_ticks([])
+        ax.axes.get_yaxis().set_ticks([])
+        plt.savefig(fn)
+        plt.close()
+
+    npats = len(patterns.patterns)
+    digits = int(np.ceil(np.log10(npats))) + 1
+    suffix  = '{{0:0>{}d}}.{}'.format(digits, format)
+    if memories:
+        fn = 'memory' + suffix
+        for i in range(npats):
+            pat = patterns.pattern_to_binary_matrix(i)
+            patmat = pat.reshape((len(pat) // window_size, window_size))
+            _save_mat(os.path.join(path, fn.format(i)), patmat)
+
+    if mtas:
+        fn = 'mta' + suffix
+        for i in range(npats):
+            pat = patterns.pattern_to_mta_matrix(i)
+            patmat = pat.reshape((len(pat) // window_size, window_size))
+            _save_mat(os.path.join(path, fn.format(i)), patmat)
+
+
 # end of source