diff --git a/model.py b/model.py
index a5d05af..a72380f 100644
--- a/model.py
+++ b/model.py
@@ -480,34 +480,42 @@ def calc_mutations_per_tree(self):
         mutations_per_tree[unique_values] = counts
         return mutations_per_tree
 
-    def calc_anc_spans(self, win_size_x=1, win_size_y=1):
+    def calc_anc_spans(self, win_size_x=1_000_000, win_size_y=5_000):
         edges_df = self.edges_df
-        num_x = int(np.ceil(edges_df.right.max() / win_size_x))
+        num_x = int(np.ceil(edges_df.right.max() - edges_df.right.min()) / win_size_x)
         num_y = int(np.ceil(edges_df.child_time.max() / win_size_y))
-        anc_spans=[]
-        x_start = 0 # redo start to ts.start, set span to min and max of win/span
-        x=[]
-        y=[]
+        anc_spans = np.zeros((num_x, num_y))
+        x_start = edges_df.left.min()
+        x = np.zeros((num_x, num_y))
+        y = np.zeros((num_x, num_y))
         for i in range(num_x):
             x_start = i * win_size_x
             x_end = x_start + win_size_x
-            
+
             for j in range(num_y):
                 y_start = j * win_size_y
                 y_end = y_start + win_size_y
-                x.append(x_end)
-                y.append(y_start)
-                anc_spans.append(edges_df[(((x_start >= edges_df.left) &
-                                    (x_start < edges_df.right)) |
-                                        ((x_end > edges_df.left) &
-                                        (x_end <= edges_df.right))) &
-                                    (edges_df.child_time >= y_start) &
-                                    (edges_df.child_time < y_end)].span.mean())
+                x[i][j] = x_end
+                y[i][j] = y_start
+                tmp_df = edges_df[
+                    (
+                        ((x_start >= edges_df.left) & (x_start < edges_df.right))
+                        | ((x_end > edges_df.left) & (x_end <= edges_df.right))
+                    )
+                    & (edges_df.child_time >= y_start)
+                    & (edges_df.child_time < y_end)
+                ]
+                n = np.unique(tmp_df.child).shape[0]
+                anc_spans[i][j] = np.sum(np.clip(tmp_df.span, None, win_size_x)) / n
+        anc_spans = anc_spans.flatten()
+        x = x.flatten()
+        y = y.flatten()
+
         df = pd.DataFrame(
             {
-                "Genomic position": x,
+                "Genomic_position": x,
                 "Time": y,
-                "Mean ancestor span": anc_spans,
+                "Mean_ancestor_span": anc_spans,
             }
         )
-        return df
\ No newline at end of file
+        return df
diff --git a/pages/nodes.py b/pages/nodes.py
index 83c4750..3c4de4a 100644
--- a/pages/nodes.py
+++ b/pages/nodes.py
@@ -10,7 +10,7 @@
 from plot_helpers import filter_points
 from plot_helpers import hover_points
 from plot_helpers import make_hist_matplotlib
-import model
+
 
 def page(tsm):
     hv.extension("matplotlib")
@@ -61,10 +61,12 @@ def page(tsm):
         log_y_checkbox,
     )
 
-    anc_span_data = tsm.calc_anc_spans(win_size_x=100_000, win_size_y=5_000)
-    #nc_span_data = tsm.calc_anc_spans(win_size_x=1, win_size_y=1)
-    heatmap = hv.HeatMap(
-        anc_span_data
-    ).opts(width=config.PLOT_WIDTH, height=config.PLOT_HEIGHT, tools=['hover'], colorbar=True)
+    anc_span_data = tsm.calc_anc_spans(win_size_x=1_000_000, win_size_y=5_000)
+    heatmap = hv.HeatMap(anc_span_data).opts(
+        width=config.PLOT_WIDTH,
+        height=config.PLOT_HEIGHT,
+        tools=["hover"],
+        colorbar=True,
+    )
 
     return pn.Column(main, hist_panel, heatmap, plot_options)