review release changes (#597)

pastas · May 12, 2023 · a7b11cc · a7b11cc
1 parent c4f4c2d
commit a7b11cc
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Showing 5 changed files with 66 additions and 73 deletions.
diff --git a/doc/about/courses.rst b/doc/about/courses.rst
@@ -3,7 +3,7 @@ Courses
 
 Every now and then (on-site) Pastas courses and workshops are held. Please check the `GitHub Discussion <https://github
 .com/pastas/pastas/discussions>`_ page for announcements of any public Pastas courses to be held. We can also provide
-private courses on request. To organize such a workshop please contact of the following.
+private courses on request. To organize such a workshop please contact one of the following:
 
 - For courses in the Netherlands, please contact Artesia (info[AT]artesia-water.nl)
 - For international courses, please contact Raoul Collenteur (Raoul.Collenteur[AT]eawag.ch)
diff --git a/doc/benchmarks/autocorrelation.ipynb b/doc/benchmarks/autocorrelation.ipynb
diff --git a/doc/benchmarks/noisemodel.ipynb b/doc/benchmarks/noisemodel.ipynb
diff --git a/pastas/modelcompare.py b/pastas/modelcompare.py
@@ -317,13 +317,16 @@ def get_metrics(
         """
         if models is None:
             models = self.models
+            modelnames = self.modelnames
+        else:
+            modelnames = [iml.name for iml in models]
 
         metrics = concat(
             [ml.stats.summary(stats=metric_selection) for ml in models],
             axis=1,
             sort=False,
         )
-        metrics.columns = self.modelnames
+        metrics.columns = modelnames
         metrics.index.name = None
 
         return metrics
@@ -352,9 +355,12 @@ def get_parameters(
         """
         if models is None:
             models = self.models
+            modelnames = self.modelnames
+        else:
+            modelnames = [iml.name for iml in models]
 
         params = concat([ml.parameters[param_col] for ml in models], axis=1, sort=False)
-        params.columns = self.modelnames
+        params.columns = modelnames
 
         if param_selection:
             sel = np.array([])
@@ -378,11 +384,14 @@ def get_diagnostics(
         """
         if models is None:
             models = self.models
+            modelnames = self.modelnames
+        else:
+            modelnames = [iml.name for iml in models]
 
-        diags = DataFrame(index=self.modelnames)
+        diags = DataFrame(index=modelnames)
         for i, ml in enumerate(models):
             mldiag = ml.stats.diagnostics()
-            diags.loc[self.modelnames[i], mldiag.index] = mldiag[diag_col].values
+            diags.loc[modelnames[i], mldiag.index] = mldiag[diag_col].values
 
         return diags.transpose()
 
@@ -811,7 +820,7 @@ def plot_table_metrics(
                     index={met: f"\N{GREEK CAPITAL LETTER DELTA}{met.upper()}"}
                 )
         if "rsq" in metrics.index:
-            metrics = metrics.rename(index={"rsq": f"R\N{SUPERSCRIPT TWO}"})
+            metrics = metrics.rename(index={"rsq": "R\N{SUPERSCRIPT TWO}"})
 
         # add seperate column with parameter names
         metrics.loc[:, "Metrics"] = metrics.index
@@ -930,9 +939,11 @@ def plot(
                 if legend and not axn.startswith("rf"):
                     if legend_kwargs is None:
                         legend_kwargs = {}
-                    _, l = self.axes[axn].get_legend_handles_labels()
+                    _, labels = self.axes[axn].get_legend_handles_labels()
                     self.axes[axn].legend(
-                        ncol=legend_kwargs.pop("ncol", max([int(np.ceil(len(l))), 4])),
+                        ncol=legend_kwargs.pop(
+                            "ncol", max([int(np.ceil(len(labels))), 4])
+                        ),
                         loc=legend_kwargs.pop("loc", (0, 1)),
                         frameon=legend_kwargs.pop("frameon", False),
                         markerscale=legend_kwargs.pop("markerscale", 1.0),

diff --git a/pastas/version.py b/pastas/version.py
@@ -4,7 +4,7 @@
 
 logger = logging.getLogger(__name__)
 
-__version__ = "1.0.1"
+__version__ = "1.1.0"
 
 
 def check_numba_scipy() -> bool: