Merge pull request #126 from DavidSilva98/master

Correct hex lattice and implement hex u-matrix

sompy/codebook.py

@@ -14,12 +14,12 @@ class InvalidMapsizeError(Exception):
     pass
 
 def generate_hex_lattice(n_rows, n_columns):
-    x_coord = []
-    y_coord = []
+    w, h = np.sqrt(3), 2
+    x_coord, y_coord = [], []
     for i in range(n_rows):
         for j in range(n_columns):
-            x_coord.append(i*1.5)
-            y_coord.append(np.sqrt(2/3)*(2*j+(1+i)%2))
+            x_coord.append(((j + 1) * w) - ((i % 2) * (0.5 * w)))
+            y_coord.append((i * h * (3 / 4)) + (0.5 * h))
     coordinates = np.column_stack([x_coord, y_coord])
     return coordinates
 

sompy/sompy.py

@@ -352,7 +352,8 @@ def _batchtrain(self, trainlen, radiusin, radiusfin, njob=1,
                 logging.info("nan quantization error, exit train\n")
 
                 #sys.exit("quantization error=nan, exit train")
-
+
+        bmu = self.find_bmu(data, njb=njob)
         bmu[1] = np.sqrt(bmu[1] + fixed_euclidean_x2)
         self._bmu = bmu
 

sompy/visualization/umatrix.py

@@ -1,95 +1,163 @@
-from .view import MatplotView
+from .mapview import MapView
+from .plot_tools import plot_hex_map
 from matplotlib import pyplot as plt
-from pylab import imshow, contour
+from matplotlib import cm
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+from skimage.feature import blob_log
+
+from warnings import warn
 from math import sqrt
 import numpy as np
 import scipy
 
 
-class UMatrixView(MatplotView):
+def rectxy_to_hexaxy(coord, X, Y):
+    """Convert rectangular grid xy coordinates to hexagonal grid xy coordinates.
+    Useful for plotting additional data on top of hexagonal grid.
+
+    Args:
+        coord (array): array with rectangular grid xy coordinates
+        X (array): mapsize shaped array with hexagonal grid x coordinates 
+        Y (array): mapsize shaped array with hexagonal grid y coordinates 
 
+    Returns:
+        [array]: array of coord's shape with hexagonal grid xy coordinates
+    """
+    out = np.vstack(([X[tuple(i)] for i in coord], [Y[tuple(i)] for i in coord])).T
+    return out
+
+class UMatrixView(MapView):
     def build_u_matrix(self, som, distance=1, row_normalized=False):
         UD2 = som.calculate_map_dist()
         Umatrix = np.zeros((som.codebook.nnodes, 1))
         codebook = som.codebook.matrix
         if row_normalized:
-            vector = som._normalizer.normalize_by(codebook.T, codebook.T,
-                                                  method='var').T
+            vector = som._normalizer.normalize_by(codebook.T, codebook.T).T
         else:
             vector = codebook
 
         for i in range(som.codebook.nnodes):
             codebook_i = vector[i][np.newaxis, :]
             neighborbor_ind = UD2[i][0:] <= distance
             neighborbor_codebooks = vector[neighborbor_ind]
-            Umatrix[i] = scipy.spatial.distance_matrix(
-                codebook_i, neighborbor_codebooks).mean()
+            neighborbor_dists = scipy.spatial.distance_matrix(
+                codebook_i, neighborbor_codebooks)
+            Umatrix[i] = np.sum(neighborbor_dists) / (neighborbor_dists.shape[1] - 1)
 
         return Umatrix.reshape(som.codebook.mapsize)
 
-    def show(self, som, distance2=1, row_normalized=False, show_data=True,
-             contooor=True, blob=False, labels=False):
-        umat = self.build_u_matrix(som, distance=distance2,
-                                   row_normalized=row_normalized)
-        msz = som.codebook.mapsize
-        proj = som.project_data(som.data_raw)
-        coord = som.bmu_ind_to_xy(proj)
+    def _set_contour(self, umat, ax, X=None, Y=None, hex=False):
+        mn = np.min(umat.flatten())
+        md = np.median(umat.flatten())
+        if hex:
+            ax.contour(X, Y, umat, np.linspace(mn, md, 15), 
+                linewidths=0.7, cmap=plt.cm.get_cmap('Blues'))
+        else:
+            ax.contour(umat, np.linspace(mn, md, 15), 
+                linewidths=0.7, cmap=plt.cm.get_cmap('Blues'))
+
+    def _set_show_data(self, X, Y, ax):
+        ax.scatter(X, Y, s=2, alpha=1., c='Gray',
+                marker='o', cmap='jet', linewidths=3, edgecolor='Gray')
+
+    def _set_labels(self, labels, X, Y, ax):
+        for label, x, y in zip(labels, X, Y):
+            ax.annotate(str(label), xy=(x, y),
+                horizontalalignment='center',
+                verticalalignment='center')
+
+    def _set_blob(self, umat, coord, ax, X=None, Y=None, hex=False):
+        # 'Laplacian of Gaussian'
+        image = 1 / umat
+        blobs = blob_log(image, max_sigma=5, num_sigma=4, threshold=.152)
+        blobs[:, 2] = blobs[:, 2] * sqrt(2)
+        if hex:
+            blobs[:, :2] = rectxy_to_hexaxy(blobs[:, :2].astype(int), X, Y)
+        else:
+            blobs[:, :2] = np.flip(blobs[:, :2], axis=1)
+        sel_points = list()
 
-        self._fig, ax = plt.subplots(1, 1)
-        imshow(umat, cmap=plt.cm.get_cmap('RdYlBu_r'), alpha=1)
+        for blob in blobs:
+            row, col, r = blob
+            c = plt.Circle((row, col), r, color='red', linewidth=2,
+                        fill=False)
+            ax.add_patch(c)
+
+            dist = scipy.spatial.distance_matrix(
+                coord, np.array([row, col])[np.newaxis, :])
+            sel_point = dist <= r
+            ax.plot(coord[:, 0][sel_point[:, 0]],
+                coord[:, 1][sel_point[:, 0]], '.r')
+            sel_points.append(sel_point[:, 0])
+            if hex:
+                ax.set_xlim([-0.5, umat.shape[1]])
+                ax.set_ylim([-(((umat.shape[0] - 1) * sqrt(3)/2) + 1/sqrt(3)), 1/sqrt(3)])
+            else:
+                ax.set_xlim([-0.5, umat.shape[1] - 0.5])
+                ax.set_ylim([umat.shape[0] - 0.5, -0.5])
+
+
+    def show(self, som, distance=1, row_normalized=False, show_data=False,
+            contour=False, blob=False, labels=False):
+        # Setting figure parameters
+        org_w = self.width
+        org_h = self.height
+        (self.width, self.height, indtoshow, no_row_in_plot, no_col_in_plot,
+            axis_num) = self._calculate_figure_params(som, 1, 1)
+        self.width /= (self.width/org_w) if self.width > self.height else (self.height/org_h)
+        self.height /= (self.width / org_w) if self.width > self.height else (self.height / org_h)
+        self.prepare()
+        plt.rc('figure', titlesize=self.text_size)
+        colormap = plt.get_cmap('RdYlBu_r')
+
+        # Setting figure data
+        if som.codebook.lattice == "hexa" and distance < sqrt(3):
+            warn("For hexagonal lattice, distance < sqrt(3) produces a null U-matrix.")
+        umat = self.build_u_matrix(som, distance=distance, row_normalized=row_normalized)
+        msz = som.codebook.mapsize
+        proj = som._bmu[0]
+        coord = som.bmu_ind_to_xy(proj)[:, :2]
+        sel_points = list()
 
-        if contooor:
-            mn = np.min(umat.flatten())
-            mx = np.max(umat.flatten())
-            std = np.std(umat.flatten())
-            md = np.median(umat.flatten())
-            mx = md + 0*std
-            contour(umat, np.linspace(mn, mx, 15), linewidths=0.7,
-                    cmap=plt.cm.get_cmap('Blues'))
+        if som.codebook.lattice == "rect":
+            ax = self._fig.add_subplot(111)
+            ax.imshow(umat, cmap=colormap, alpha=1)
+            divider = make_axes_locatable(ax)
+            cax = divider.append_axes("right", size="5%", pad=0.05)
+            plt.colorbar(cm.ScalarMappable(cmap=colormap), cax=cax, orientation='vertical')
+            coord = np.flip(coord, axis=1)
+
+            if contour:
+                self._set_contour(umat, ax, hex=False)
+
+            if blob:
+                self._set_blob(umat, coord, ax, hex=False)
+        elif som.codebook.lattice == "hexa":
+            ax, cents = plot_hex_map(umat, colormap=colormap, fig=self._fig, colorbar=True)
+            X = np.flip(np.array(cents)[:, 0].reshape(msz[0], msz[1]), axis=1)
+            Y = np.flip(np.array(cents)[:, 1].reshape(msz[0], msz[1]), axis=1)
+            coord = rectxy_to_hexaxy(coord, X, Y)
+
+            if contour:
+                self._set_contour(umat, ax, X, Y, hex=True)
+
+            if blob:
+                self._set_blob(umat, coord, ax, X, Y, hex=True)
+        else:
+            raise ValueError(
+                'lattice argument of SOM object should be either "rect" or "hexa".')
 
         if show_data:
-            plt.scatter(coord[:, 1], coord[:, 0], s=2, alpha=1., c='Gray',
-                        marker='o', cmap='jet', linewidths=3, edgecolor='Gray')
-            plt.axis('off')
+            self._set_show_data(coord[:, 0], coord[:, 1], ax)
 
         if labels:
-            if labels is True:
-                labels = som.build_data_labels()
-            for label, x, y in zip(labels, coord[:, 1], coord[:, 0]):
-                plt.annotate(str(label), xy=(x, y),
-                             horizontalalignment='center',
-                             verticalalignment='center')
-
+            labels = som.build_data_labels()
+            self._set_labels(labels, coord[:, 0], coord[:, 1], ax)
+
         ratio = float(msz[0])/(msz[0]+msz[1])
         self._fig.set_size_inches((1-ratio)*15, ratio*15)
         plt.tight_layout()
-        plt.subplots_adjust(hspace=.00, wspace=.000)
-        sel_points = list()
-
-        if blob:
-            from skimage.color import rgb2gray
-            from skimage.feature import blob_log
-
-            image = 1 / umat
-            rgb2gray(image)
-
-            # 'Laplacian of Gaussian'
-            blobs = blob_log(image, max_sigma=5, num_sigma=4, threshold=.152)
-            blobs[:, 2] = blobs[:, 2] * sqrt(2)
-            imshow(umat, cmap=plt.cm.get_cmap('RdYlBu_r'), alpha=1)
-            sel_points = list()
-
-            for blob in blobs:
-                row, col, r = blob
-                c = plt.Circle((col, row), r, color='red', linewidth=2,
-                               fill=False)
-                ax.add_patch(c)
-                dist = scipy.spatial.distance_matrix(
-                    coord[:, :2], np.array([row, col])[np.newaxis, :])
-                sel_point = dist <= r
-                plt.plot(coord[:, 1][sel_point[:, 0]],
-                         coord[:, 0][sel_point[:, 0]], '.r')
-                sel_points.append(sel_point[:, 0])
+        plt.subplots_adjust(top=0.90, hspace=.00, wspace=.000)
 
         plt.show()
         return sel_points, umat