resolve links to api

Author: cvanelteren

docs/first_figure.py

@@ -178,9 +178,10 @@
 series1 = np.sin(time) * np.exp(-time / 5)
 series2 = np.cos(time) * np.exp(-time / 5)
 
-# Use a Cartesian panel as the square reference. The map keeps its native
-# Robinson aspect while the right-hand plots remain readable in a tall figure.
-layout = [[1, 2], [1, 2], [1, 3], [1, 3]]
+# The map spans two rows while the right-hand panels stack beside it. With the
+# 4:1 column ratio below, the Robinson map keeps its native 2:1 aspect and the
+# right-hand panels receive approximately square plotting areas.
+layout = [[1, 2], [1, 3]]
 
 # 3. Create the figure
 # Apply a Robinson projection only to the first panel.
@@ -190,10 +191,8 @@
     share=0,
     refnum=2,
     refwidth=1.5,
-    wratios=(3.25, 1),
-    # The two right-hand axes span rows (0, 1) and (2, 3), respectively.
-    # Only the middle boundary separates them.
-    hspace=('0pt', '13em', '0pt'),
+    wratios=(4, 1),
+    hspace='13em',
 )
 
 # 4. Geographic data

docs/why_ultraplot.py

@@ -114,9 +114,14 @@
 # UltraPlot does not reinvent the wheel—it just makes it easier to steer. A good
 # mental model for your workflow looks like this:
 #
-# * Use standard Matplotlib axes methods (``plot``, ``imshow``, ``scatter``) to draw the data.
-# * Use ``axs.format()`` to apply consistent labels, ticks, and styling at the panel level.
-# * Use ``fig.format()`` for global aesthetics and unified figure guides (like shared colorbars).
+# * Use standard axes methods such as :meth:`~ultraplot.axes.PlotAxes.plot`,
+#   :meth:`~ultraplot.axes.PlotAxes.imshow`, and
+#   :meth:`~ultraplot.axes.PlotAxes.scatter` to draw data.
+# * Use :meth:`~ultraplot.axes.Axes.format` through ``axs.format()`` to apply
+#   consistent labels, ticks, and styling at the panel level.
+# * Use :meth:`~ultraplot.figure.Figure.format` through ``fig.format()`` for
+#   global aesthetics, and :meth:`~ultraplot.figure.Figure.colorbar` for a
+#   shared colorbar.
 #
 # For a single, fast plot, stick with Matplotlib. When layout scaling and repetitive
 # formatting become a chore, let UltraPlot handle the heavy lifting.