Merge pull request #13233 from anntzer/image-annotated-heatmap

NelleV · web-flow · commit 1c374ff46c96 · 2019-01-20T22:49:53.000-08:00
Remove warning in image_annotated_heatmap, and numpydocify it.
diff --git a/examples/images_contours_and_fields/image_annotated_heatmap.py b/examples/images_contours_and_fields/image_annotated_heatmap.py
@@ -104,20 +104,23 @@ def heatmap(data, row_labels, col_labels, ax=None,
     """
     Create a heatmap from a numpy array and two lists of labels.
 
-    Arguments:
-        data       : A 2D numpy array of shape (N,M)
-        row_labels : A list or array of length N with the labels
-                     for the rows
-        col_labels : A list or array of length M with the labels
-                     for the columns
-    Optional arguments:
-        ax         : A matplotlib.axes.Axes instance to which the heatmap
-                     is plotted. If not provided, use current axes or
-                     create a new one.
-        cbar_kw    : A dictionary with arguments to
-                     :meth:`matplotlib.Figure.colorbar`.
-        cbarlabel  : The label for the colorbar
-    All other arguments are directly passed on to the imshow call.
+    Parameters
+    ----------
+    data
+        A 2D numpy array of shape (N, M).
+    row_labels
+        A list or array of length N with the labels for the rows.
+    col_labels
+        A list or array of length M with the labels for the columns.
+    ax
+        A `matplotlib.axes.Axes` instance to which the heatmap is plotted.  If
+        not provided, use current axes or create a new one.  Optional.
+    cbar_kw
+        A dictionary with arguments to `matplotlib.Figure.colorbar`.  Optional.
+    cbarlabel
+        The label for the colorbar.  Optional.
+    **kwargs
+        All other arguments are forwarded to `imshow`.
     """
 
     if not ax:
@@ -163,21 +166,26 @@ def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
     """
     A function to annotate a heatmap.
 
-    Arguments:
-        im         : The AxesImage to be labeled.
-    Optional arguments:
-        data       : Data used to annotate. If None, the image's data is used.
-        valfmt     : The format of the annotations inside the heatmap.
-                     This should either use the string format method, e.g.
-                     "$ {x:.2f}", or be a :class:`matplotlib.ticker.Formatter`.
-        textcolors : A list or array of two color specifications. The first is
-                     used for values below a threshold, the second for those
-                     above.
-        threshold  : Value in data units according to which the colors from
-                     textcolors are applied. If None (the default) uses the
-                     middle of the colormap as separation.
-
-    Further arguments are passed on to the created text labels.
+    Parameters
+    ----------
+    im
+        The AxesImage to be labeled.
+    data
+        Data used to annotate.  If None, the image's data is used.  Optional.
+    valfmt
+        The format of the annotations inside the heatmap.  This should either
+        use the string format method, e.g. "$ {x:.2f}", or be a
+        `matplotlib.ticker.Formatter`.  Optional.
+    textcolors
+        A list or array of two color specifications.  The first is used for
+        values below a threshold, the second for those above.  Optional.
+    threshold
+        Value in data units according to which the colors from textcolors are
+        applied.  If None (the default) uses the middle of the colormap as
+        separation.  Optional.
+    **kwargs
+        All other arguments are forwarded to each call to `text` used to create
+        the text labels.
     """
 
     if not isinstance(data, (list, np.ndarray)):
@@ -204,7 +212,7 @@ def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
     texts = []
     for i in range(data.shape[0]):
         for j in range(data.shape[1]):
-            kw.update(color=textcolors[im.norm(data[i, j]) > threshold])
+            kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])
             text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)
             texts.append(text)
 
