visualization of optimization result properties

doc/example/amici_import.ipynb

@@ -135,7 +135,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "constantParameters = {'ratio', 'specC17'}"
+    "constantParameters = ['ratio', 'specC17']"
    ]
   },
   {
@@ -530,7 +530,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,

pypesto/visualize/__init__.py

@@ -22,6 +22,9 @@
                          parameter_hist)
 from .optimizer_history import (optimizer_history,
                                 optimizer_history_lowlevel)
+from .optimization_stats import (optimization_run_properties_per_multistart,
+                                 optimization_run_property_per_multistart,
+                                 optimization_run_properties_one_plot)
 from .optimizer_convergence import optimizer_convergence
 from .profiles import (profiles,
                        profiles_lowlevel,

pypesto/visualize/clust_color.py

@@ -1,8 +1,11 @@
 from scipy import cluster
-from typing import Tuple
+from typing import List, Tuple, Optional, Union
 import matplotlib.cm as cm
 import numpy as np
 
+# for typehints
+RGBA = List[float]
+
 
 def assign_clusters(vals):
     """
@@ -207,32 +210,35 @@ def assign_colors(vals, colors=None, balance_alpha=True,
                'one single [r, g, b, alpha] color.')
 
 
-def assign_colors_for_result_list(num_results, colors=None):
+def assign_colors_for_list(
+        num_entries: int,
+        colors: Optional[Union[RGBA, List[RGBA], np.ndarray]] = None
+) -> Union[List[List[float]], np.ndarray]:
     """
-    Creates a list of colors for a list of pypesto.Result objects or checks
+    Creates a list of colors for a list of items or checks
     a user-provided list of colors and uses this if everything is ok
 
     Parameters
     ----------
 
-    num_results: int
+    num_entries:
         number of results in list
 
-    colors: list, or RGBA, optional
+    colors:
         list of colors, or single color
 
     Returns
     -------
 
-    colors: list of RGBA
-        One for each element in 'vals'.
+    colors:
+        List of RGBA, one for each element in 'vals'.
     """
 
     # if the user did not specify any colors:
     if colors is None:
         # default colors will be used, on for each entry in the result list.
         # Colors are created from assign_colors, which needs a dummy list
-        dummy_clusters = np.array(list(range(num_results)) * 2)
+        dummy_clusters = np.array(list(range(num_entries)) * 2)
 
         # we don't want alpha levels for all plotting routines in this case...
         colors = assign_colors(dummy_clusters, balance_alpha=False,
@@ -243,10 +249,10 @@ def assign_colors_for_result_list(num_results, colors=None):
         return colors[real_indices]
 
     # if the user specified color lies does not match the number of results
-    if len(colors) != num_results:
+    if len(colors) != num_entries:
         raise ('Incorrect color input. Colors must be specified either as '
                'list of [r, g, b, alpha] with length equal to function '
-               'values Number of function (here: ' + str(num_results) + '), '
+               'values Number of function (here: ' + str(num_entries) + '), '
                'or as one single [r, g, b, alpha] color.')
 
     return colors

pypesto/visualize/misc.py

@@ -1,9 +1,10 @@
 import numpy as np
 import warnings
 from .clust_color import assign_colors
-from .clust_color import assign_colors_for_result_list
+from .clust_color import assign_colors_for_list
 
-from typing import Optional
+from numbers import Number
+from typing import Iterable, List, Optional, Union
 
 
 def process_result_list(results, colors=None, legends=None):
@@ -57,7 +58,7 @@ def process_result_list(results, colors=None, legends=None):
             legends = [legends]
     else:
         # if more than one result is passed, we use one color per result
-        colors = assign_colors_for_result_list(len(results), colors)
+        colors = assign_colors_for_list(len(results), colors)
 
         # check whether list of legends has the correct length
         if legends is None:
@@ -75,8 +76,8 @@ def process_result_list(results, colors=None, legends=None):
 
     # size of legend list and size of results does not match
     if legend_error:
-        raise ('List of results passed and list of labels do not have the'
-               ' same length but should. Stopping.')
+        raise ValueError('List of results passed and list of labels do '
+                         'not have the same length but should. Stopping.')
 
     return results, colors, legends
 
@@ -141,9 +142,6 @@ def process_y_limits(ax, y_limits):
     y_limits: ndarray
        y_limits, minimum and maximum, for current axes object
 
-    min_val: float
-        Smallest value to be plotted
-
     Returns
     -------
 
@@ -202,3 +200,31 @@ def process_y_limits(ax, y_limits):
             ax.set_ylim(new_limits)
 
     return ax
+
+
+def process_start_indices(start_indices: Union[int, Iterable[int]],
+                          max_length: int) -> List[int]:
+    """
+    helper function that processes the start_indices and
+    creates an array of indices if a number was provided and checks that the
+    indices do not exceed the max_index
+
+    Parameters
+    ----------
+    start_indices:
+        list of indices or int specifying an endpoint of the sequence of
+        indices
+    max_length:
+        maximum possible index for the start_indices
+    """
+
+    if isinstance(start_indices, Number):
+        start_indices = range(int(start_indices))
+
+    start_indices = np.array(start_indices, dtype=int)
+
+    # check, whether index set is not too big
+    start_indices = [start_index for start_index in start_indices if
+                     start_index < max_length]
+
+    return start_indices