update to parameter correlation (#1009)

* added a save and show parameter. changed default colors to blue-white-red, added the option to return the list of parameters for other inspections.

* removing suggestions of autosave again.

pypesto/visualize/model_fit.py

@@ -289,7 +289,7 @@ def _time_trajectory_model_without_states(
         amici.plotting.plotObservableTrajectories(
             rdata=rdata,
             observable_indices=observable_indices,
-            ax=axes[i_cond],
+            ax=axes[i_cond] if len(rdatas) > 1 else axes,
             model=model,
         )
     return axes

pypesto/visualize/parameters.py

@@ -4,6 +4,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+from matplotlib.colors import Colormap
 from matplotlib.ticker import MaxNLocator
 
 from pypesto.util import delete_nan_inf
@@ -409,6 +410,8 @@ def parameters_correlation_matrix(
     start_indices: Optional[Union[int, Iterable[int]]] = None,
     method: Union[str, Callable] = 'pearson',
     cluster: bool = True,
+    cmap: Union[Colormap, str] = 'bwr',
+    return_table: bool = False,
 ) -> matplotlib.axes.Axes:
     """
     Plot correlation of optimized parameters.
@@ -427,6 +430,11 @@ def parameters_correlation_matrix(
         spearman` or a callable function.
     cluster:
         Whether to cluster the correlation matrix.
+    cmap:
+        Colormap to use for the heatmap. Defaults to 'bwr'.
+    return_table:
+        Whether to return the parameter table additionally for further
+        inspection.
 
     Returns
     -------
@@ -454,8 +462,12 @@ def parameters_correlation_matrix(
     corr_matrix = df.corr(method=method)
     if cluster:
         ax = sns.clustermap(
-            data=corr_matrix, yticklabels=True, vmin=-1, vmax=1
+            data=corr_matrix, yticklabels=True, vmin=-1, vmax=1, cmap=cmap
         )
     else:
-        ax = sns.heatmap(data=corr_matrix, yticklabels=True, vmin=-1, vmax=1)
+        ax = sns.heatmap(
+            data=corr_matrix, yticklabels=True, vmin=-1, vmax=1, cmap=cmap
+        )
+    if return_table:
+        return ax, df
     return ax