Taylor plot

DHI · Feb 20, 2024 · f303e03 · f303e03
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diff --git a/docs/user-guide/index.md b/docs/user-guide/index.md
@@ -3,7 +3,7 @@
 ModelSkill compares model results with observations. The workflow can be split in two phases:
 
 1. [Matching](matching.md) - making sure that observations and model results are in the same space and time
-2. Analysis - [plots](plotting.md) and [statistics](skill.md) of the matched data
+2. Analysis - [plots](plotting.qmd) and [statistics](skill.md) of the matched data
 
 If the observations and model results are already matched (i.e. are stored in the same data source), 
 the `from_matched()` function can be used to go directly to the analysis phase. 

diff --git a/docs/user-guide/plotting.qmd b/docs/user-guide/plotting.qmd
@@ -47,7 +47,7 @@ ms.plotting.temporal_coverage(obs=[o1, o2], mod=mr);
 The spatial coverage of observations and model results can be plotted using the [`spatial_overview`](`modelskill.plotting.spatial_overview`) function in the [`plotting`](`modelskill.plotting`) module:
 
 ```{python}
->>> ms.plotting.spatial_overview([o1, o2], mr);
+ms.plotting.spatial_overview([o1, o2], mr);
 ```
 
 
@@ -74,7 +74,7 @@ cmp.plot.scatter();
 A Taylor diagram shows how well a model result matches an observation in terms of correlation, standard deviation and root mean squared error. The `taylor` plot can be accessed through the Comparer [`plot`](`modelskill.comparison.ComparerPlotter`) accessor or the ComparerCollection [`plot`](`modelskill.comparison.ComparerCollectionPlotter`) accessor:
 
 ```{python}
-cmp.plot.taylor()
+cmp.plot.taylor();
 ```
 
 

diff --git a/modelskill/comparison/_comparer_plotter.py b/modelskill/comparison/_comparer_plotter.py
@@ -717,8 +717,17 @@ def taylor(
 
         Examples
         --------
-        >>> comparer.taylor()
-        >>> comparer.taylor(start="2017-10-28", figsize=(5,5))
+        ```{python}
+        #| echo: False
+        import modelskill as ms
+        o1 = ms.PointObservation('../data/SW/HKNA_Hm0.dfs0', item=0, x=4.2420, y=52.6887)
+        mr = ms.DfsuModelResult('../data/SW/HKZN_local_2017_DutchCoast.dfsu', item=0)
+        cmp = ms.match(obs=o1, mod=mr)
+        ```
+        ```{python}
+        cmp.plot.taylor()
+        ```
+
 
         Notes
         -----