Remove unrequired method

Author: lmcinnes

hdbscan/plots.py

@@ -210,72 +210,6 @@ def get_plot_data(self,
             'cluster_bounds': cluster_bounds
         }
 
-    def get_simple_plot_data(self, leaf_separation=1, log_size=False,
-                             max_rectangle_per_icicle=20):
-        """Generates simplified data for use in plotting the 'icicle plot' or
-        dendrogram plot of the condensed tree generated by HDBSCAN.
-
-        Parameters
-        ----------
-        leaf_separation : float, optional
-                          How far apart to space the final leaves of the
-                          dendrogram. (default 1)
-
-        log_size : boolean, optional
-                   Use log scale for the 'size' of clusters (i.e. number of
-                   points in the cluster at a given lambda value).
-                   (default False)
-
-        max_rectangles_per_icicle : int, optional
-            To simplify the plot this method will only emit
-            ``max_rectangles_per_icicle`` bars per branch of the dendrogram.
-            This ensures that we don't suffer from massive overplotting in
-            cases with a lot of data points.
-
-        Returns
-        -------
-        plot_data : dict
-                    Data associated to bars in a bar plot:
-                        `bar_centers` x coordinate centers for bars
-                        `bar_tops` heights of bars in lambda scale
-                        `bar_bottoms` y coordinate of bottoms of bars
-                        `bar_widths` widths of the bars (in x coord scale)
-                        `bar_bounds` a 4-tuple of [left, right, bottom, top]
-                                     giving the bounds on a full set of
-                                     cluster bars
-                    Data associates with cluster splits:
-                        `line_xs` x coordinates for horiontal dendrogram lines
-                        `line_ys` y coordinates for horiontal dendrogram lines
-        """
-        leaves = _get_leaves(self._raw_tree)
-        last_leaf = self._raw_tree['parent'].max()
-        root = self._raw_tree['parent'].min()
-
-        # We want to get the x and y coordinates for the start of each cluster
-        # Initialize the leaves, since we know where they go, the iterate
-        # through everything from the leaves back, setting coords as we go
-        cluster_x_coords = dict(zip(leaves, [leaf_separation * x
-                                             for x in range(len(leaves))]))
-        cluster_y_coords = {root: 0.0}
-
-        # We want to get the x and y coordinates for the start of each cluster
-        # Initialize the leaves, since we know where they go, the iterate
-        # through everything from the leaves back, setting coords as we go
-        cluster_x_coords = dict(zip(leaves, [leaf_separation * x
-                                             for x in range(len(leaves))]))
-        cluster_y_coords = {root: 0.0}
-
-        for cluster in range(last_leaf, root - 1, -1):
-            split = self._raw_tree[['child', 'lambda_val']]
-            split = split[(self._raw_tree['parent'] == cluster) &
-                          (self._raw_tree['child_size'] > 1)]
-            if len(split['child']) > 1:
-                left_child, right_child = split['child']
-                cluster_x_coords[cluster] = np.mean([cluster_x_coords[left_child],
-                                                     cluster_x_coords[right_child]])
-                cluster_y_coords[left_child] = split['lambda_val'][0]
-                cluster_y_coords[right_child] = split['lambda_val'][1]
-
     def _select_clusters(self):
         if self.cluster_selection_method == 'eom':
             stability = compute_stability(self._raw_tree)