From 2bd54142400dffa616d268fefba72daaf0d15c6a Mon Sep 17 00:00:00 2001
From: delaossa <alberto.martinez.de.la.ossa@desy.de>
Date: Wed, 24 Jan 2024 17:09:23 +0100
Subject: [PATCH 01/15] Implement `plot_history` in `ExplorationDiagnostics`

---
 .../diagnostics/exploration_diagnostics.py    | 194 ++++++++++++++++++
 tests/test_exploration_diagnostics.py         |   4 +
 2 files changed, 198 insertions(+)

diff --git a/optimas/diagnostics/exploration_diagnostics.py b/optimas/diagnostics/exploration_diagnostics.py
index 9b8a1e96..a1dfb480 100644
--- a/optimas/diagnostics/exploration_diagnostics.py
+++ b/optimas/diagnostics/exploration_diagnostics.py
@@ -524,6 +524,200 @@ def plot_worker_timeline(
         ax.set_ylabel("Worker")
         ax.set_xlabel("Time (s)")
 
+    def get_df_with_selection(
+        self,
+        df: pd.DataFrame,
+        select: dict
+    ) -> pd.DataFrame:
+        """Return the DataFrame after applying selection criterium.
+
+        Parameters
+        ----------
+        df : DataFrame
+            The DataFrame object
+        select: dict
+            A dictionary containing the selection criteria to apply.
+            e.g. {'f' : [None, -10.]} (get data with f < -10)
+        """
+        condition = ''
+        for key in select:
+            if select[key][0] is not None:
+                if condition != '':
+                    condition += ' and '
+                condition += '%s > %f' % (key, select[key][0])
+            if select[key][1] is not None:
+                if condition != '':
+                    condition += ' and '
+                condition += '%s < %f' % (key, select[key][1])
+        print('Selecting according to the condition: ', condition)
+        return df.query(condition)
+
+    def plot_history(
+        self, 
+        parnames: Optional[list] = None,
+        xname: Optional[str] = None, 
+        select: Optional[dict] = None, 
+        sort: Optional[dict] = None, 
+        top: Optional[dict] = None
+    ) -> None:
+        """Print selected parameters versus simulation index.
+
+        Parameters
+        ----------
+        parnames: list of strings, optional
+            List with the names of the parameters to show.
+
+        xname: string, optional
+            Name of the parameter to plot in the x axis.
+            By default is the index of the history DataFrame.
+
+        select: dict, optional
+            Contains a set of rules to filter the dataframe, e.g.
+            'f' : [None, -10.] (get data with f < -10)
+
+        sort: dict, optional
+            A dict containing as keys the names of the parameres to sort by 
+            and, as values, a Bool indicating if ordering ascendingly (True)
+            or descendingly (False)
+            e.g. {'f': False} sort simulations according to `f` descendingly.
+
+        top: int, optional
+            Highight the top n simulations according to the objectives.
+        """
+
+        # Copy the history DataFrame
+        df = self.history.copy()
+
+        # Get lists of variable names            
+        objective_names = [obj.name for obj in self.objectives]
+        varpar_names = [var.name for var in self.varying_parameters]
+
+        # Order list of simulations and re-index
+        if sort is not None:
+            df = df.sort_values(by=list(sort.keys()), ascending=tuple(sort.values())).reset_index(drop=True)
+
+        # Define the quantity to plot in the x axis
+        if xname is not None:
+            xvalues = df[xname]
+        else:
+            xvalues = list(df.index)
+
+        # Apply selection to the history DataFrame
+        if select is not None:
+            df_select = self.get_df_with_selection(df, select)
+        else:
+            df_select = None
+
+        # Select top cases in separate DataFrames
+        if top is not None:
+            df_top = []
+            for obj_name in objective_names:
+                for o in self.objectives:
+                    if o.name == obj_name:
+                        ascending = o.minimize
+                index_list = list(df.sort_values(by=obj_name, ascending=ascending).index)
+                df_top.append(df.loc[index_list[:top]])
+        else:
+            df_top = None
+
+        # Default list of parameters to show
+        if parnames is None:
+            parnames = objective_names.copy()
+            parnames.extend(varpar_names)
+        
+        # Make figure
+        nplots = len(parnames)
+
+        # Definitions for the axes
+        l_margin, width1, width2 = 0.08, 0.72, 0.15
+        b_margin, t_margin = 0.1, 0.04
+        xspacing = 0.015
+        yspacing = 0.04
+        height = (1. - b_margin - t_margin - (nplots - 1) * yspacing) / nplots
+        nbins = 25
+    
+        # Actual plotting
+        ax_histy_list = []
+        histy_list = []
+        for i in range(nplots):
+            bottom1 = b_margin + (nplots - 1 - i) * (yspacing + height)
+            rect_scatter = [l_margin, bottom1, width1, height]
+            rect_histy = [l_margin + width1 + xspacing, bottom1, width2, height]
+            
+            h = df[parnames[i]]
+            ax_scatter = plt.axes(rect_scatter)
+            plt.grid(color='lightgray', linestyle='dotted')
+            plt.plot(xvalues, h, 'o')
+            if df_select is not None:
+                xvalues_cut = list(df_select.index)
+                if xname is not None:
+                    xvalues_cut = df_select[xname]
+                h_cut = df_select[parnames[i]]
+                plt.plot(xvalues_cut, h_cut, 'o')
+
+            if df_top is not None:
+                for df_t in df_top:
+                    if xname is not None:
+                        xvalues_top = df_t[xname]
+                    else:
+                        xvalues_top = list(df_t.index)
+                    h_top = df_t[parnames[i]]
+                    plt.plot(xvalues_top, h_top, 'o')
+
+            # Plot cummin only when proceeds
+            if (parnames[i] in objective_names) and (not sort) and (xname is None):
+                for o in self.objectives:
+                    if o.name == parnames[i]:
+                        minimize = o.minimize
+                if minimize:
+                    cum = df[parnames[i]].cummin().values
+                else:
+                    cum = df[parnames[i]].cummax().values
+                plt.plot(xvalues, cum, '-', c='black')
+            
+            plt.title(parnames[i].replace('_', ' '), fontdict={'fontsize': 'x-small'}, loc='right', pad=2)
+        
+            ax_histy = plt.axes(rect_histy)
+            plt.grid(color='lightgray', linestyle='dotted')
+            ymin, ymax = ax_scatter.get_ylim()
+            binwidth = (ymax - ymin) / nbins
+            bins = np.arange(ymin, ymax + binwidth, binwidth)
+            histy, _, _ = ax_histy.hist(h, bins=bins,
+                    weights=100. * np.ones(len(h)) / len(h), orientation='horizontal')
+
+            if df_select is not None:
+                h_cut = df_select[parnames[i]]
+                ax_histy.hist(h_cut, bins=bins,
+                              weights=100. * np.ones(len(h_cut)) / len(h), 
+                              orientation='horizontal')
+
+            if df_top is not None:
+                for df_t in df_top:
+                    h_top = df_t[parnames[i]]
+                    ax_histy.hist(h_top, bins=bins,
+                                  weights=100. * np.ones(len(h_top)) / len(h), 
+                                  orientation='horizontal')
+                    
+            ax_histy.set_ylim(ax_scatter.get_ylim())
+
+            ax_histy_list.append(ax_histy)
+            histy_list.append(histy)
+
+            if i != nplots - 1:
+                ax_scatter.tick_params(labelbottom=False)
+                ax_histy.tick_params(labelbottom=False, labelleft=False)
+                ax_histy.set_xticks([])
+            else:
+                ax_histy.tick_params(labelbottom=False, labelleft=False)
+                ax_histy.set_xticks([])
+                ax_scatter.set_xlabel('Evaluation number')
+                if xname is not None:
+                    ax_scatter.set_xlabel(xname.replace('_', ' '))
+            
+            histmax = 1.1 * max([h.max() for h in histy_list])
+            for i, ax_h in enumerate(ax_histy_list):
+                ax_h.set_xlim(-1, histmax)
+
     def _check_pareto_objectives(
         self,
         objectives: Optional[List[Union[str, Objective]]] = None,
diff --git a/tests/test_exploration_diagnostics.py b/tests/test_exploration_diagnostics.py
index 8e5aed54..be9fec7a 100644
--- a/tests/test_exploration_diagnostics.py
+++ b/tests/test_exploration_diagnostics.py
@@ -89,6 +89,10 @@ def test_exploration_diagnostics():
         diags.plot_worker_timeline()
         plt.savefig(os.path.join(exploration_dir_path, "timeline.png"))
 
+        plt.clf()
+        diags.plot_history(top=3)
+        plt.savefig(os.path.join(exploration_dir_path, "history.png"))
+
         # Check the simulation paths.
         delete_index = 10
         if i == 0:

From 73b2e0da767fbbfe6d8b7ee2f9a432a2f825f264 Mon Sep 17 00:00:00 2001
From: delaossa <alberto.martinez.de.la.ossa@desy.de>
Date: Thu, 25 Jan 2024 15:30:13 +0100
Subject: [PATCH 02/15] Fine tuning implementation of `plot_history`

---
 .../diagnostics/exploration_diagnostics.py    | 47 ++++++-------------
 optimas/utils/other.py                        | 28 +++++++++++
 tests/test_exploration_diagnostics.py         |  1 -
 3 files changed, 42 insertions(+), 34 deletions(-)

diff --git a/optimas/diagnostics/exploration_diagnostics.py b/optimas/diagnostics/exploration_diagnostics.py
index a1dfb480..f8db63d1 100644
--- a/optimas/diagnostics/exploration_diagnostics.py
+++ b/optimas/diagnostics/exploration_diagnostics.py
@@ -10,11 +10,13 @@
 import numpy.typing as npt
 import pandas as pd
 import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
 
 from optimas.core import VaryingParameter, Objective, Parameter
 from optimas.generators.base import Generator
 from optimas.evaluators.base import Evaluator
 from optimas.explorations import Exploration
+from optimas.utils.other import get_df_with_selection
 
 
 class ExplorationDiagnostics:
@@ -284,7 +286,7 @@ def plot_pareto_front(
                     str(id),
                     (x_pareto[i], y_pareto[i]),
                     (2, -2),
-                    fontsize=6,
+                    fontsize="xx-small",
                     va="top",
                     textcoords="offset points",
                 )
@@ -524,41 +526,14 @@ def plot_worker_timeline(
         ax.set_ylabel("Worker")
         ax.set_xlabel("Time (s)")
 
-    def get_df_with_selection(
-        self,
-        df: pd.DataFrame,
-        select: dict
-    ) -> pd.DataFrame:
-        """Return the DataFrame after applying selection criterium.
-
-        Parameters
-        ----------
-        df : DataFrame
-            The DataFrame object
-        select: dict
-            A dictionary containing the selection criteria to apply.
-            e.g. {'f' : [None, -10.]} (get data with f < -10)
-        """
-        condition = ''
-        for key in select:
-            if select[key][0] is not None:
-                if condition != '':
-                    condition += ' and '
-                condition += '%s > %f' % (key, select[key][0])
-            if select[key][1] is not None:
-                if condition != '':
-                    condition += ' and '
-                condition += '%s < %f' % (key, select[key][1])
-        print('Selecting according to the condition: ', condition)
-        return df.query(condition)
-
     def plot_history(
         self, 
         parnames: Optional[list] = None,
         xname: Optional[str] = None, 
         select: Optional[dict] = None, 
         sort: Optional[dict] = None, 
-        top: Optional[dict] = None
+        top: Optional[dict] = None,
+        fig: Optional[Figure] = None,
     ) -> None:
         """Print selected parameters versus simulation index.
 
@@ -583,6 +558,9 @@ def plot_history(
 
         top: int, optional
             Highight the top n simulations according to the objectives.
+
+        fig: Figure, optional
+            Matplotlib Figure object to use for this plot.
         """
 
         # Copy the history DataFrame
@@ -604,7 +582,7 @@ def plot_history(
 
         # Apply selection to the history DataFrame
         if select is not None:
-            df_select = self.get_df_with_selection(df, select)
+            df_select = get_df_with_selection(df, select)
         else:
             df_select = None
 
@@ -626,6 +604,9 @@ def plot_history(
             parnames.extend(varpar_names)
         
         # Make figure
+        if fig is None:
+            _ = plt.figure()
+
         nplots = len(parnames)
 
         # Definitions for the axes
@@ -664,7 +645,7 @@ def plot_history(
                     h_top = df_t[parnames[i]]
                     plt.plot(xvalues_top, h_top, 'o')
 
-            # Plot cummin only when proceeds
+            # Plot trace only when proceeds
             if (parnames[i] in objective_names) and (not sort) and (xname is None):
                 for o in self.objectives:
                     if o.name == parnames[i]:
@@ -673,7 +654,7 @@ def plot_history(
                     cum = df[parnames[i]].cummin().values
                 else:
                     cum = df[parnames[i]].cummax().values
-                plt.plot(xvalues, cum, '-', c='black')
+                plt.step(xvalues, cum, where='post', ls='-', c='black')
             
             plt.title(parnames[i].replace('_', ' '), fontdict={'fontsize': 'x-small'}, loc='right', pad=2)
         
diff --git a/optimas/utils/other.py b/optimas/utils/other.py
index 497da62a..6f3def80 100644
--- a/optimas/utils/other.py
+++ b/optimas/utils/other.py
@@ -65,3 +65,31 @@ def convert_to_dataframe(
         return pd.DataFrame(data)
     else:
         raise ValueError(f"Cannot convert {type(data)} to a pandas dataframe.")
+
+
+def get_df_with_selection(
+    df: pd.DataFrame,
+    select: dict
+) -> pd.DataFrame:
+    """Return the DataFrame after applying selection criterium.
+
+    Parameters
+    ----------
+    df : DataFrame
+        The DataFrame object
+    select: dict
+        A dictionary containing the selection criteria to apply.
+        e.g. {'f' : [None, -10.]} (get data with f < -10)
+    """
+    condition = ''
+    for key in select:
+        if select[key][0] is not None:
+            if condition != '':
+                condition += ' and '
+            condition += '%s > %f' % (key, select[key][0])
+        if select[key][1] is not None:
+            if condition != '':
+                condition += ' and '
+            condition += '%s < %f' % (key, select[key][1])
+    print('Selecting according to the condition: ', condition)
+    return df.query(condition)
\ No newline at end of file
diff --git a/tests/test_exploration_diagnostics.py b/tests/test_exploration_diagnostics.py
index be9fec7a..b2365136 100644
--- a/tests/test_exploration_diagnostics.py
+++ b/tests/test_exploration_diagnostics.py
@@ -89,7 +89,6 @@ def test_exploration_diagnostics():
         diags.plot_worker_timeline()
         plt.savefig(os.path.join(exploration_dir_path, "timeline.png"))
 
-        plt.clf()
         diags.plot_history(top=3)
         plt.savefig(os.path.join(exploration_dir_path, "history.png"))
 

From 4bbef2bfcb4b395f51f3eb40c1866a30e513e9e4 Mon Sep 17 00:00:00 2001
From: delaossa <alberto.martinez.de.la.ossa@desy.de>
Date: Fri, 26 Jan 2024 10:26:04 +0100
Subject: [PATCH 03/15] Add legend and comments

---
 .../diagnostics/exploration_diagnostics.py    | 107 ++++++++++--------
 optimas/utils/other.py                        |   2 +-
 tests/test_exploration_diagnostics.py         |   2 +-
 3 files changed, 64 insertions(+), 47 deletions(-)

diff --git a/optimas/diagnostics/exploration_diagnostics.py b/optimas/diagnostics/exploration_diagnostics.py
index f8db63d1..40eccbc5 100644
--- a/optimas/diagnostics/exploration_diagnostics.py
+++ b/optimas/diagnostics/exploration_diagnostics.py
@@ -533,9 +533,10 @@ def plot_history(
         select: Optional[dict] = None, 
         sort: Optional[dict] = None, 
         top: Optional[dict] = None,
-        fig: Optional[Figure] = None,
+        show_legend: Optional[bool] = False,
+        **subplots_kw
     ) -> None:
-        """Print selected parameters versus simulation index.
+        """Print selected parameters versus evaluation index.
 
         Parameters
         ----------
@@ -554,13 +555,16 @@ def plot_history(
             A dict containing as keys the names of the parameres to sort by 
             and, as values, a Bool indicating if ordering ascendingly (True)
             or descendingly (False)
-            e.g. {'f': False} sort simulations according to `f` descendingly.
+            e.g. {'f': False} sort simulations according to f descendingly.
 
         top: int, optional
-            Highight the top n simulations according to the objectives.
+            Highight the top n simulations of every objective.
 
-        fig: Figure, optional
-            Matplotlib Figure object to use for this plot.
+        show_legend : bool, optional
+            Whether to show the legend.
+
+        **subplots_kw
+            All additional keyword arguments are passed to the `pyplot.subplots` call. 
         """
 
         # Copy the history DataFrame
@@ -604,38 +608,33 @@ def plot_history(
             parnames.extend(varpar_names)
         
         # Make figure
-        if fig is None:
-            _ = plt.figure()
-
         nplots = len(parnames)
+        _, axs = plt.subplots(nplots, 2, 
+                              width_ratios=[0.8, 0.2], 
+                              **subplots_kw)
+        plt.subplots_adjust(wspace=0.05)
 
-        # Definitions for the axes
-        l_margin, width1, width2 = 0.08, 0.72, 0.15
-        b_margin, t_margin = 0.1, 0.04
-        xspacing = 0.015
-        yspacing = 0.04
-        height = (1. - b_margin - t_margin - (nplots - 1) * yspacing) / nplots
-        nbins = 25
-    
         # Actual plotting
         ax_histy_list = []
         histy_list = []
-        for i in range(nplots):
-            bottom1 = b_margin + (nplots - 1 - i) * (yspacing + height)
-            rect_scatter = [l_margin, bottom1, width1, height]
-            rect_histy = [l_margin + width1 + xspacing, bottom1, width2, height]
-            
+        for i in range(nplots):            
+
+            # Draw scatter plot
+            ax_scatter = axs[i][0]
+            ax_scatter.grid(color='lightgray', linestyle='dotted')
             h = df[parnames[i]]
-            ax_scatter = plt.axes(rect_scatter)
-            plt.grid(color='lightgray', linestyle='dotted')
-            plt.plot(xvalues, h, 'o')
+            ax_scatter.plot(xvalues, h, 'o')
+
+            # Draw selection
             if df_select is not None:
                 xvalues_cut = list(df_select.index)
                 if xname is not None:
                     xvalues_cut = df_select[xname]
                 h_cut = df_select[parnames[i]]
-                plt.plot(xvalues_cut, h_cut, 'o')
+                ax_scatter.plot(xvalues_cut, h_cut, 'o', 
+                                label='select')
 
+            # Draw top evaluations
             if df_top is not None:
                 for df_t in df_top:
                     if xname is not None:
@@ -643,9 +642,11 @@ def plot_history(
                     else:
                         xvalues_top = list(df_t.index)
                     h_top = df_t[parnames[i]]
-                    plt.plot(xvalues_top, h_top, 'o')
+                    label='top %i' % top
+                    ax_scatter.plot(xvalues_top, h_top, 'o', 
+                                    label=label)
 
-            # Plot trace only when proceeds
+            # Draw the trace only for `objective` parameters 
             if (parnames[i] in objective_names) and (not sort) and (xname is None):
                 for o in self.objectives:
                     if o.name == parnames[i]:
@@ -654,50 +655,66 @@ def plot_history(
                     cum = df[parnames[i]].cummin().values
                 else:
                     cum = df[parnames[i]].cummax().values
-                plt.step(xvalues, cum, where='post', ls='-', c='black')
+                ax_scatter.step(xvalues, cum, zorder=-1, where='post', c='black')
             
-            plt.title(parnames[i].replace('_', ' '), fontdict={'fontsize': 'x-small'}, loc='right', pad=2)
         
-            ax_histy = plt.axes(rect_histy)
-            plt.grid(color='lightgray', linestyle='dotted')
+            # Draw projected histogram
+            ax_histy = axs[i][1]
+            ax_histy.grid(color='lightgray', linestyle='dotted')
             ymin, ymax = ax_scatter.get_ylim()
+            nbins = 25
             binwidth = (ymax - ymin) / nbins
             bins = np.arange(ymin, ymax + binwidth, binwidth)
-            histy, _, _ = ax_histy.hist(h, bins=bins,
-                    weights=100. * np.ones(len(h)) / len(h), orientation='horizontal')
+            histy, *_ = ax_histy.hist(h, bins=bins,
+                                    weights=100. * np.ones(len(h)) / len(h), 
+                                    orientation='horizontal')
 
+            # Draw selection
             if df_select is not None:
                 h_cut = df_select[parnames[i]]
                 ax_histy.hist(h_cut, bins=bins,
                               weights=100. * np.ones(len(h_cut)) / len(h), 
-                              orientation='horizontal')
+                              orientation='horizontal',
+                              label='selection')
 
+            # Draw top evaluations
             if df_top is not None:
                 for df_t in df_top:
                     h_top = df_t[parnames[i]]
+                    label = 'top %i' % top
                     ax_histy.hist(h_top, bins=bins,
                                   weights=100. * np.ones(len(h_top)) / len(h), 
-                                  orientation='horizontal')
+                                  orientation='horizontal', 
+                                  label=label)
                     
             ax_histy.set_ylim(ax_scatter.get_ylim())
 
-            ax_histy_list.append(ax_histy)
-            histy_list.append(histy)
+            # Tuning axes and labels
+            ax_scatter.set_title(parnames[i].replace('_', ' '), 
+                                 fontdict={'fontsize': 'x-small'}, 
+                                 loc='right', pad=2)
 
             if i != nplots - 1:
                 ax_scatter.tick_params(labelbottom=False)
                 ax_histy.tick_params(labelbottom=False, labelleft=False)
-                ax_histy.set_xticks([])
             else:
-                ax_histy.tick_params(labelbottom=False, labelleft=False)
-                ax_histy.set_xticks([])
                 ax_scatter.set_xlabel('Evaluation number')
                 if xname is not None:
                     ax_scatter.set_xlabel(xname.replace('_', ' '))
-            
-            histmax = 1.1 * max([h.max() for h in histy_list])
-            for i, ax_h in enumerate(ax_histy_list):
-                ax_h.set_xlim(-1, histmax)
+                ax_histy.set_xlabel('%')
+                ax_histy.tick_params(labelbottom=True, labelleft=False)
+                if show_legend:
+                    ax_histy.legend(fontsize='xx-small')
+
+            # Make loist of histograms and axes for further manipulation 
+            # outside the loop
+            ax_histy_list.append(ax_histy)
+            histy_list.append(histy)
+
+        # Set the range of the histograms axes
+        histmax = 1.1 * max([h.max() for h in histy_list])
+        for i, ax_h in enumerate(ax_histy_list):
+            ax_h.set_xlim(-1, histmax)
 
     def _check_pareto_objectives(
         self,
diff --git a/optimas/utils/other.py b/optimas/utils/other.py
index 6f3def80..617e0d73 100644
--- a/optimas/utils/other.py
+++ b/optimas/utils/other.py
@@ -91,5 +91,5 @@ def get_df_with_selection(
             if condition != '':
                 condition += ' and '
             condition += '%s < %f' % (key, select[key][1])
-    print('Selecting according to the condition: ', condition)
+
     return df.query(condition)
\ No newline at end of file
diff --git a/tests/test_exploration_diagnostics.py b/tests/test_exploration_diagnostics.py
index b2365136..2f8b0b0b 100644
--- a/tests/test_exploration_diagnostics.py
+++ b/tests/test_exploration_diagnostics.py
@@ -89,7 +89,7 @@ def test_exploration_diagnostics():
         diags.plot_worker_timeline()
         plt.savefig(os.path.join(exploration_dir_path, "timeline.png"))
 
-        diags.plot_history(top=3)
+        diags.plot_history(top=5, show_legend=True)
         plt.savefig(os.path.join(exploration_dir_path, "history.png"))
 
         # Check the simulation paths.

From 3f1bc49e9147764c25c100e844a9198b8cfbdb65 Mon Sep 17 00:00:00 2001
From: delaossa <alberto.martinez.de.la.ossa@desy.de>
Date: Fri, 26 Jan 2024 11:47:20 +0100
Subject: [PATCH 04/15] Add `**subplots_kw` to other ploting functions.

---
 .../diagnostics/exploration_diagnostics.py    | 22 ++++++++++---------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/optimas/diagnostics/exploration_diagnostics.py b/optimas/diagnostics/exploration_diagnostics.py
index 40eccbc5..fdddd299 100644
--- a/optimas/diagnostics/exploration_diagnostics.py
+++ b/optimas/diagnostics/exploration_diagnostics.py
@@ -160,6 +160,7 @@ def plot_objective(
         show_trace: Optional[bool] = False,
         use_time_axis: Optional[bool] = False,
         relative_start_time: Optional[bool] = True,
+        **subplots_kw,
     ) -> None:
         """Plot the values that where reached during the optimization.
 
@@ -179,7 +180,8 @@ def plot_objective(
         relative_start_time : bool, optional
             Whether the time axis should be relative to the start time
             of the exploration. By default, True.
-
+        **subplots_kw
+            All additional keyword arguments are passed to the `pyplot.subplots` call. 
         """
         if fidelity_parameter is not None:
             fidelity = self.history[fidelity_parameter]
@@ -206,7 +208,7 @@ def plot_objective(
         else:
             x = history.trial_index
             xlabel = "Number of evaluations"
-        _, ax = plt.subplots()
+        _, ax = plt.subplots(**subplots_kw)
         ax.scatter(x, history[objective.name], c=fidelity)
         ax.set_ylabel(objective.name)
         ax.set_xlabel(xlabel)
@@ -224,6 +226,7 @@ def plot_pareto_front(
         self,
         objectives: Optional[List[Union[str, Objective]]] = None,
         show_best_evaluation_indices: Optional[bool] = False,
+        **subplots_kw,
     ) -> None:
         """Plot Pareto front of two optimization objectives.
 
@@ -235,6 +238,8 @@ def plot_pareto_front(
         show_best_evaluation_indices : bool, optional
             Whether to show the indices of the best evaluations. By default
             ``False``.
+        **subplots_kw
+            All additional keyword arguments are passed to the `pyplot.subplots` call. 
         """
         objectives = self._check_pareto_objectives(objectives)
         pareto_evals = self.get_pareto_front_evaluations(objectives)
@@ -244,7 +249,7 @@ def plot_pareto_front(
         y_pareto = pareto_evals[objectives[1].name].to_numpy()
 
         # Create figure
-        _, axes = plt.subplots()
+        _, axes = plt.subplots(**subplots_kw)
 
         # Plot all evaluations
         axes.scatter(
@@ -481,6 +486,7 @@ def plot_worker_timeline(
         self,
         fidelity_parameter: Optional[str] = None,
         relative_start_time: Optional[bool] = True,
+        **subplots_kw,
     ) -> None:
         """Plot the timeline of worker utilization.
 
@@ -492,6 +498,8 @@ def plot_worker_timeline(
         relative_start_time : bool, optional
             Whether the time axis should be relative to the start time
             of the exploration. By default, True.
+        **subplots_kw
+            All additional keyword arguments are passed to the `pyplot.subplots` call. 
         """
         df = self.history
         if fidelity_parameter is not None:
@@ -503,7 +511,7 @@ def plot_worker_timeline(
         if relative_start_time:
             sim_started_time = sim_started_time - df["gen_started_time"].min()
             sim_ended_time = sim_ended_time - df["gen_started_time"].min()
-        _, ax = plt.subplots()
+        _, ax = plt.subplots(**subplots_kw)
         for i in range(len(df)):
             start = sim_started_time.iloc[i]
             duration = sim_ended_time.iloc[i] - start
@@ -542,27 +550,21 @@ def plot_history(
         ----------
         parnames: list of strings, optional
             List with the names of the parameters to show.
-
         xname: string, optional
             Name of the parameter to plot in the x axis.
             By default is the index of the history DataFrame.
-
         select: dict, optional
             Contains a set of rules to filter the dataframe, e.g.
             'f' : [None, -10.] (get data with f < -10)
-
         sort: dict, optional
             A dict containing as keys the names of the parameres to sort by 
             and, as values, a Bool indicating if ordering ascendingly (True)
             or descendingly (False)
             e.g. {'f': False} sort simulations according to f descendingly.
-
         top: int, optional
             Highight the top n simulations of every objective.
-
         show_legend : bool, optional
             Whether to show the legend.
-
         **subplots_kw
             All additional keyword arguments are passed to the `pyplot.subplots` call. 
         """

From 684febaf2b02f481b6a5946106b5bcdb0536eabd Mon Sep 17 00:00:00 2001
From: delaossa <alberto.martinez.de.la.ossa@desy.de>
Date: Fri, 26 Jan 2024 12:49:18 +0100
Subject: [PATCH 05/15] Filter out non-simulated evaluations when doing plots
 vs running time

---
 optimas/diagnostics/exploration_diagnostics.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/optimas/diagnostics/exploration_diagnostics.py b/optimas/diagnostics/exploration_diagnostics.py
index fdddd299..c799c6bc 100644
--- a/optimas/diagnostics/exploration_diagnostics.py
+++ b/optimas/diagnostics/exploration_diagnostics.py
@@ -201,6 +201,7 @@ def plot_objective(
         history = self.history
         history = history[history.sim_ended]
         if use_time_axis:
+            history = history[history.sim_id >= 0]
             x = history.sim_ended_time
             xlabel = "Time (s)"
             if relative_start_time:
@@ -502,6 +503,8 @@ def plot_worker_timeline(
             All additional keyword arguments are passed to the `pyplot.subplots` call. 
         """
         df = self.history
+        df = df[df.sim_id >= 0]
+
         if fidelity_parameter is not None:
             min_fidelity = df[fidelity_parameter].min()
             max_fidelity = df[fidelity_parameter].max()

From dac9a932a39b517b60fa26265ffe8d2eb101deb0 Mon Sep 17 00:00:00 2001
From: delaossa <alberto.martinez.de.la.ossa@desy.de>
Date: Fri, 26 Jan 2024 13:09:30 +0100
Subject: [PATCH 06/15] Minor tweaks

---
 .../diagnostics/exploration_diagnostics.py    | 34 +++++++++----------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/optimas/diagnostics/exploration_diagnostics.py b/optimas/diagnostics/exploration_diagnostics.py
index c799c6bc..09482eab 100644
--- a/optimas/diagnostics/exploration_diagnostics.py
+++ b/optimas/diagnostics/exploration_diagnostics.py
@@ -587,7 +587,7 @@ def plot_history(
         if xname is not None:
             xvalues = df[xname]
         else:
-            xvalues = list(df.index)
+            xvalues = df.index
 
         # Apply selection to the history DataFrame
         if select is not None:
@@ -627,16 +627,16 @@ def plot_history(
             # Draw scatter plot
             ax_scatter = axs[i][0]
             ax_scatter.grid(color='lightgray', linestyle='dotted')
-            h = df[parnames[i]]
-            ax_scatter.plot(xvalues, h, 'o')
+            yvalues = df[parnames[i]]
+            ax_scatter.plot(xvalues, yvalues, 'o')
 
             # Draw selection
             if df_select is not None:
-                xvalues_cut = list(df_select.index)
+                xvalues_cut = df_select.index
                 if xname is not None:
                     xvalues_cut = df_select[xname]
-                h_cut = df_select[parnames[i]]
-                ax_scatter.plot(xvalues_cut, h_cut, 'o', 
+                yvalues_cut = df_select[parnames[i]]
+                ax_scatter.plot(xvalues_cut, yvalues_cut, 'o', 
                                 label='select')
 
             # Draw top evaluations
@@ -645,10 +645,10 @@ def plot_history(
                     if xname is not None:
                         xvalues_top = df_t[xname]
                     else:
-                        xvalues_top = list(df_t.index)
-                    h_top = df_t[parnames[i]]
+                        xvalues_top = df_t.index
+                    yvalues_top = df_t[parnames[i]]
                     label='top %i' % top
-                    ax_scatter.plot(xvalues_top, h_top, 'o', 
+                    ax_scatter.plot(xvalues_top, yvalues_top, 'o', 
                                     label=label)
 
             # Draw the trace only for `objective` parameters 
@@ -670,25 +670,25 @@ def plot_history(
             nbins = 25
             binwidth = (ymax - ymin) / nbins
             bins = np.arange(ymin, ymax + binwidth, binwidth)
-            histy, *_ = ax_histy.hist(h, bins=bins,
-                                    weights=100. * np.ones(len(h)) / len(h), 
+            histy, *_ = ax_histy.hist(yvalues, bins=bins,
+                                    weights=100. * np.ones(len(yvalues)) / len(yvalues), 
                                     orientation='horizontal')
 
             # Draw selection
             if df_select is not None:
-                h_cut = df_select[parnames[i]]
-                ax_histy.hist(h_cut, bins=bins,
-                              weights=100. * np.ones(len(h_cut)) / len(h), 
+                yvalues_cut = df_select[parnames[i]]
+                ax_histy.hist(yvalues_cut, bins=bins,
+                              weights=100. * np.ones(len(yvalues_cut)) / len(yvalues), 
                               orientation='horizontal',
                               label='selection')
 
             # Draw top evaluations
             if df_top is not None:
                 for df_t in df_top:
-                    h_top = df_t[parnames[i]]
+                    yvalues_top = df_t[parnames[i]]
                     label = 'top %i' % top
-                    ax_histy.hist(h_top, bins=bins,
-                                  weights=100. * np.ones(len(h_top)) / len(h), 
+                    ax_histy.hist(yvalues_top, bins=bins,
+                                  weights=100. * np.ones(len(yvalues_top)) / len(yvalues), 
                                   orientation='horizontal', 
                                   label=label)
                     

From 5d17cf4fa8629113f6711f114e77b98702ad5b07 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 26 Jan 2024 13:23:22 +0000
Subject: [PATCH 07/15] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../diagnostics/exploration_diagnostics.py    | 126 ++++++++++--------
 optimas/utils/other.py                        |  21 ++-
 2 files changed, 81 insertions(+), 66 deletions(-)

diff --git a/optimas/diagnostics/exploration_diagnostics.py b/optimas/diagnostics/exploration_diagnostics.py
index 09482eab..29d5727d 100644
--- a/optimas/diagnostics/exploration_diagnostics.py
+++ b/optimas/diagnostics/exploration_diagnostics.py
@@ -181,7 +181,7 @@ def plot_objective(
             Whether the time axis should be relative to the start time
             of the exploration. By default, True.
         **subplots_kw
-            All additional keyword arguments are passed to the `pyplot.subplots` call. 
+            All additional keyword arguments are passed to the `pyplot.subplots` call.
         """
         if fidelity_parameter is not None:
             fidelity = self.history[fidelity_parameter]
@@ -240,7 +240,7 @@ def plot_pareto_front(
             Whether to show the indices of the best evaluations. By default
             ``False``.
         **subplots_kw
-            All additional keyword arguments are passed to the `pyplot.subplots` call. 
+            All additional keyword arguments are passed to the `pyplot.subplots` call.
         """
         objectives = self._check_pareto_objectives(objectives)
         pareto_evals = self.get_pareto_front_evaluations(objectives)
@@ -500,7 +500,7 @@ def plot_worker_timeline(
             Whether the time axis should be relative to the start time
             of the exploration. By default, True.
         **subplots_kw
-            All additional keyword arguments are passed to the `pyplot.subplots` call. 
+            All additional keyword arguments are passed to the `pyplot.subplots` call.
         """
         df = self.history
         df = df[df.sim_id >= 0]
@@ -538,14 +538,14 @@ def plot_worker_timeline(
         ax.set_xlabel("Time (s)")
 
     def plot_history(
-        self, 
+        self,
         parnames: Optional[list] = None,
-        xname: Optional[str] = None, 
-        select: Optional[dict] = None, 
-        sort: Optional[dict] = None, 
+        xname: Optional[str] = None,
+        select: Optional[dict] = None,
+        sort: Optional[dict] = None,
         top: Optional[dict] = None,
         show_legend: Optional[bool] = False,
-        **subplots_kw
+        **subplots_kw,
     ) -> None:
         """Print selected parameters versus evaluation index.
 
@@ -560,7 +560,7 @@ def plot_history(
             Contains a set of rules to filter the dataframe, e.g.
             'f' : [None, -10.] (get data with f < -10)
         sort: dict, optional
-            A dict containing as keys the names of the parameres to sort by 
+            A dict containing as keys the names of the parameres to sort by
             and, as values, a Bool indicating if ordering ascendingly (True)
             or descendingly (False)
             e.g. {'f': False} sort simulations according to f descendingly.
@@ -569,19 +569,21 @@ def plot_history(
         show_legend : bool, optional
             Whether to show the legend.
         **subplots_kw
-            All additional keyword arguments are passed to the `pyplot.subplots` call. 
+            All additional keyword arguments are passed to the `pyplot.subplots` call.
         """
 
         # Copy the history DataFrame
         df = self.history.copy()
 
-        # Get lists of variable names            
+        # Get lists of variable names
         objective_names = [obj.name for obj in self.objectives]
         varpar_names = [var.name for var in self.varying_parameters]
 
         # Order list of simulations and re-index
         if sort is not None:
-            df = df.sort_values(by=list(sort.keys()), ascending=tuple(sort.values())).reset_index(drop=True)
+            df = df.sort_values(
+                by=list(sort.keys()), ascending=tuple(sort.values())
+            ).reset_index(drop=True)
 
         # Define the quantity to plot in the x axis
         if xname is not None:
@@ -602,7 +604,9 @@ def plot_history(
                 for o in self.objectives:
                     if o.name == obj_name:
                         ascending = o.minimize
-                index_list = list(df.sort_values(by=obj_name, ascending=ascending).index)
+                index_list = list(
+                    df.sort_values(by=obj_name, ascending=ascending).index
+                )
                 df_top.append(df.loc[index_list[:top]])
         else:
             df_top = None
@@ -611,24 +615,21 @@ def plot_history(
         if parnames is None:
             parnames = objective_names.copy()
             parnames.extend(varpar_names)
-        
+
         # Make figure
         nplots = len(parnames)
-        _, axs = plt.subplots(nplots, 2, 
-                              width_ratios=[0.8, 0.2], 
-                              **subplots_kw)
+        _, axs = plt.subplots(nplots, 2, width_ratios=[0.8, 0.2], **subplots_kw)
         plt.subplots_adjust(wspace=0.05)
 
         # Actual plotting
         ax_histy_list = []
         histy_list = []
-        for i in range(nplots):            
-
+        for i in range(nplots):
             # Draw scatter plot
             ax_scatter = axs[i][0]
-            ax_scatter.grid(color='lightgray', linestyle='dotted')
+            ax_scatter.grid(color="lightgray", linestyle="dotted")
             yvalues = df[parnames[i]]
-            ax_scatter.plot(xvalues, yvalues, 'o')
+            ax_scatter.plot(xvalues, yvalues, "o")
 
             # Draw selection
             if df_select is not None:
@@ -636,8 +637,7 @@ def plot_history(
                 if xname is not None:
                     xvalues_cut = df_select[xname]
                 yvalues_cut = df_select[parnames[i]]
-                ax_scatter.plot(xvalues_cut, yvalues_cut, 'o', 
-                                label='select')
+                ax_scatter.plot(xvalues_cut, yvalues_cut, "o", label="select")
 
             # Draw top evaluations
             if df_top is not None:
@@ -647,12 +647,15 @@ def plot_history(
                     else:
                         xvalues_top = df_t.index
                     yvalues_top = df_t[parnames[i]]
-                    label='top %i' % top
-                    ax_scatter.plot(xvalues_top, yvalues_top, 'o', 
-                                    label=label)
-
-            # Draw the trace only for `objective` parameters 
-            if (parnames[i] in objective_names) and (not sort) and (xname is None):
+                    label = "top %i" % top
+                    ax_scatter.plot(xvalues_top, yvalues_top, "o", label=label)
+
+            # Draw the trace only for `objective` parameters
+            if (
+                (parnames[i] in objective_names)
+                and (not sort)
+                and (xname is None)
+            ):
                 for o in self.objectives:
                     if o.name == parnames[i]:
                         minimize = o.minimize
@@ -660,58 +663,73 @@ def plot_history(
                     cum = df[parnames[i]].cummin().values
                 else:
                     cum = df[parnames[i]].cummax().values
-                ax_scatter.step(xvalues, cum, zorder=-1, where='post', c='black')
-            
-        
+                ax_scatter.step(
+                    xvalues, cum, zorder=-1, where="post", c="black"
+                )
+
             # Draw projected histogram
             ax_histy = axs[i][1]
-            ax_histy.grid(color='lightgray', linestyle='dotted')
+            ax_histy.grid(color="lightgray", linestyle="dotted")
             ymin, ymax = ax_scatter.get_ylim()
             nbins = 25
             binwidth = (ymax - ymin) / nbins
             bins = np.arange(ymin, ymax + binwidth, binwidth)
-            histy, *_ = ax_histy.hist(yvalues, bins=bins,
-                                    weights=100. * np.ones(len(yvalues)) / len(yvalues), 
-                                    orientation='horizontal')
+            histy, *_ = ax_histy.hist(
+                yvalues,
+                bins=bins,
+                weights=100.0 * np.ones(len(yvalues)) / len(yvalues),
+                orientation="horizontal",
+            )
 
             # Draw selection
             if df_select is not None:
                 yvalues_cut = df_select[parnames[i]]
-                ax_histy.hist(yvalues_cut, bins=bins,
-                              weights=100. * np.ones(len(yvalues_cut)) / len(yvalues), 
-                              orientation='horizontal',
-                              label='selection')
+                ax_histy.hist(
+                    yvalues_cut,
+                    bins=bins,
+                    weights=100.0 * np.ones(len(yvalues_cut)) / len(yvalues),
+                    orientation="horizontal",
+                    label="selection",
+                )
 
             # Draw top evaluations
             if df_top is not None:
                 for df_t in df_top:
                     yvalues_top = df_t[parnames[i]]
-                    label = 'top %i' % top
-                    ax_histy.hist(yvalues_top, bins=bins,
-                                  weights=100. * np.ones(len(yvalues_top)) / len(yvalues), 
-                                  orientation='horizontal', 
-                                  label=label)
-                    
+                    label = "top %i" % top
+                    ax_histy.hist(
+                        yvalues_top,
+                        bins=bins,
+                        weights=100.0
+                        * np.ones(len(yvalues_top))
+                        / len(yvalues),
+                        orientation="horizontal",
+                        label=label,
+                    )
+
             ax_histy.set_ylim(ax_scatter.get_ylim())
 
             # Tuning axes and labels
-            ax_scatter.set_title(parnames[i].replace('_', ' '), 
-                                 fontdict={'fontsize': 'x-small'}, 
-                                 loc='right', pad=2)
+            ax_scatter.set_title(
+                parnames[i].replace("_", " "),
+                fontdict={"fontsize": "x-small"},
+                loc="right",
+                pad=2,
+            )
 
             if i != nplots - 1:
                 ax_scatter.tick_params(labelbottom=False)
                 ax_histy.tick_params(labelbottom=False, labelleft=False)
             else:
-                ax_scatter.set_xlabel('Evaluation number')
+                ax_scatter.set_xlabel("Evaluation number")
                 if xname is not None:
-                    ax_scatter.set_xlabel(xname.replace('_', ' '))
-                ax_histy.set_xlabel('%')
+                    ax_scatter.set_xlabel(xname.replace("_", " "))
+                ax_histy.set_xlabel("%")
                 ax_histy.tick_params(labelbottom=True, labelleft=False)
                 if show_legend:
-                    ax_histy.legend(fontsize='xx-small')
+                    ax_histy.legend(fontsize="xx-small")
 
-            # Make loist of histograms and axes for further manipulation 
+            # Make loist of histograms and axes for further manipulation
             # outside the loop
             ax_histy_list.append(ax_histy)
             histy_list.append(histy)
diff --git a/optimas/utils/other.py b/optimas/utils/other.py
index 617e0d73..0747b03e 100644
--- a/optimas/utils/other.py
+++ b/optimas/utils/other.py
@@ -67,10 +67,7 @@ def convert_to_dataframe(
         raise ValueError(f"Cannot convert {type(data)} to a pandas dataframe.")
 
 
-def get_df_with_selection(
-    df: pd.DataFrame,
-    select: dict
-) -> pd.DataFrame:
+def get_df_with_selection(df: pd.DataFrame, select: dict) -> pd.DataFrame:
     """Return the DataFrame after applying selection criterium.
 
     Parameters
@@ -81,15 +78,15 @@ def get_df_with_selection(
         A dictionary containing the selection criteria to apply.
         e.g. {'f' : [None, -10.]} (get data with f < -10)
     """
-    condition = ''
+    condition = ""
     for key in select:
         if select[key][0] is not None:
-            if condition != '':
-                condition += ' and '
-            condition += '%s > %f' % (key, select[key][0])
+            if condition != "":
+                condition += " and "
+            condition += "%s > %f" % (key, select[key][0])
         if select[key][1] is not None:
-            if condition != '':
-                condition += ' and '
-            condition += '%s < %f' % (key, select[key][1])
+            if condition != "":
+                condition += " and "
+            condition += "%s < %f" % (key, select[key][1])
 
-    return df.query(condition)
\ No newline at end of file
+    return df.query(condition)

From 7847b68e6ee4f09bd860b9a207b6c1bb1b38f454 Mon Sep 17 00:00:00 2001
From: delaossa <alberto.martinez.de.la.ossa@desy.de>
Date: Fri, 26 Jan 2024 15:00:37 +0100
Subject: [PATCH 08/15] Remove a blank line causing pydocstyle test to fail

---
 optimas/diagnostics/exploration_diagnostics.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/optimas/diagnostics/exploration_diagnostics.py b/optimas/diagnostics/exploration_diagnostics.py
index 09482eab..b227f722 100644
--- a/optimas/diagnostics/exploration_diagnostics.py
+++ b/optimas/diagnostics/exploration_diagnostics.py
@@ -571,7 +571,6 @@ def plot_history(
         **subplots_kw
             All additional keyword arguments are passed to the `pyplot.subplots` call. 
         """
-
         # Copy the history DataFrame
         df = self.history.copy()
 

From 430501ec944178447a5daf00eb2e53d4bb3373bd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=C3=81ngel=20Ferran=20Pousa?= <angel.ferran.pousa@desy.de>
Date: Fri, 26 Jan 2024 17:00:16 +0100
Subject: [PATCH 09/15] Add missing data to history

---
 optimas/explorations/base.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/optimas/explorations/base.py b/optimas/explorations/base.py
index 7edcea89..bc7a636b 100644
--- a/optimas/explorations/base.py
+++ b/optimas/explorations/base.py
@@ -3,6 +3,7 @@
 import os
 import glob
 import json
+import time
 from typing import Optional, Union, Dict, List, Literal
 
 import numpy as np
@@ -396,6 +397,14 @@ def attach_evaluations(
         for field in fields:
             if field in history_new.dtype.names:
                 history_new[field] = evaluation_data[field]
+        
+        current_time = time.time()
+        history_new["gen_started_time"] = current_time
+        history_new["gen_ended_time"] = current_time
+        history_new["gen_informed_time"] = current_time
+        history_new["sim_started_time"] = current_time
+        history_new["sim_ended_time"] = current_time
+        history_new["gen_informed"] = True
         history_new["sim_started"] = True
         history_new["sim_ended"] = True
         history_new["trial_index"] = np.arange(

From 8dbbdff25c9816981121774c2137e6ff337fd9be Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 26 Jan 2024 16:00:35 +0000
Subject: [PATCH 10/15] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 optimas/explorations/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/optimas/explorations/base.py b/optimas/explorations/base.py
index bc7a636b..b1ea4850 100644
--- a/optimas/explorations/base.py
+++ b/optimas/explorations/base.py
@@ -397,7 +397,7 @@ def attach_evaluations(
         for field in fields:
             if field in history_new.dtype.names:
                 history_new[field] = evaluation_data[field]
-        
+
         current_time = time.time()
         history_new["gen_started_time"] = current_time
         history_new["gen_ended_time"] = current_time

From ab495b19e9325b9d9ea50894930118c765d48755 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=C3=81ngel=20Ferran=20Pousa?= <angel.ferran.pousa@desy.de>
Date: Fri, 26 Jan 2024 17:03:59 +0100
Subject: [PATCH 11/15] Remove unused import

---
 optimas/diagnostics/exploration_diagnostics.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/optimas/diagnostics/exploration_diagnostics.py b/optimas/diagnostics/exploration_diagnostics.py
index f1f04a6a..d17e49ac 100644
--- a/optimas/diagnostics/exploration_diagnostics.py
+++ b/optimas/diagnostics/exploration_diagnostics.py
@@ -10,7 +10,6 @@
 import numpy.typing as npt
 import pandas as pd
 import matplotlib.pyplot as plt
-from matplotlib.figure import Figure
 
 from optimas.core import VaryingParameter, Objective, Parameter
 from optimas.generators.base import Generator

From 926c67ea9bbccd2cbcede5145586c343d15af9ec Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=C3=81ngel=20Ferran=20Pousa?= <angel.ferran.pousa@desy.de>
Date: Fri, 26 Jan 2024 17:41:48 +0100
Subject: [PATCH 12/15] Fic bug when loading history

---
 optimas/explorations/base.py | 36 ++++++++++++++++++++++--------------
 1 file changed, 22 insertions(+), 14 deletions(-)

diff --git a/optimas/explorations/base.py b/optimas/explorations/base.py
index bc7a636b..e4fb851f 100644
--- a/optimas/explorations/base.py
+++ b/optimas/explorations/base.py
@@ -360,6 +360,13 @@ def attach_evaluations(
             a problem in the data. If set to `True`, the error will be ignored.
         """
         evaluation_data = convert_to_dataframe(evaluation_data)
+
+        # Determine if evaluations come from past history array and, if so,
+        # keep only those that finished.
+        is_history = "sim_ended" in evaluation_data
+        if is_history:
+            evaluation_data = evaluation_data[evaluation_data["sim_ended"]]
+
         n_evals = len(evaluation_data)
         if n_evals == 0:
             return
@@ -398,20 +405,21 @@ def attach_evaluations(
             if field in history_new.dtype.names:
                 history_new[field] = evaluation_data[field]
         
-        current_time = time.time()
-        history_new["gen_started_time"] = current_time
-        history_new["gen_ended_time"] = current_time
-        history_new["gen_informed_time"] = current_time
-        history_new["sim_started_time"] = current_time
-        history_new["sim_ended_time"] = current_time
-        history_new["gen_informed"] = True
-        history_new["sim_started"] = True
-        history_new["sim_ended"] = True
-        history_new["trial_index"] = np.arange(
-            self.generator._trial_count,
-            self.generator._trial_count + n_evals,
-            dtype=int,
-        )
+        if not is_history:
+            current_time = time.time()
+            history_new["gen_started_time"] = current_time
+            history_new["gen_ended_time"] = current_time
+            history_new["gen_informed_time"] = current_time
+            history_new["sim_started_time"] = current_time
+            history_new["sim_ended_time"] = current_time
+            history_new["gen_informed"] = True
+            history_new["sim_started"] = True
+            history_new["sim_ended"] = True
+            history_new["trial_index"] = np.arange(
+                self.generator._trial_count,
+                self.generator._trial_count + n_evals,
+                dtype=int,
+            )
 
         # Incorporate new history into generator.
         self.generator.incorporate_history(history_new)

From abac990ab9d018635d27362b6420bf2b419b1667 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=C3=81ngel=20Ferran=20Pousa?= <angel.ferran.pousa@desy.de>
Date: Fri, 26 Jan 2024 17:49:01 +0100
Subject: [PATCH 13/15] Plot also attached evaluations

---
 optimas/diagnostics/exploration_diagnostics.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/optimas/diagnostics/exploration_diagnostics.py b/optimas/diagnostics/exploration_diagnostics.py
index d17e49ac..d9018959 100644
--- a/optimas/diagnostics/exploration_diagnostics.py
+++ b/optimas/diagnostics/exploration_diagnostics.py
@@ -200,7 +200,6 @@ def plot_objective(
         history = self.history
         history = history[history.sim_ended]
         if use_time_axis:
-            history = history[history.sim_id >= 0]
             x = history.sim_ended_time
             xlabel = "Time (s)"
             if relative_start_time:

From f76c27f570c4504ec8722e0f541c6268af30e804 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 26 Jan 2024 16:53:24 +0000
Subject: [PATCH 14/15] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 optimas/explorations/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/optimas/explorations/base.py b/optimas/explorations/base.py
index e4fb851f..d52d28ad 100644
--- a/optimas/explorations/base.py
+++ b/optimas/explorations/base.py
@@ -404,7 +404,7 @@ def attach_evaluations(
         for field in fields:
             if field in history_new.dtype.names:
                 history_new[field] = evaluation_data[field]
-        
+
         if not is_history:
             current_time = time.time()
             history_new["gen_started_time"] = current_time

From f423a19d9ec55ba490b9ad7a0a3270378428fab0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=C3=81ngel=20Ferran=20Pousa?= <angel.ferran.pousa@desy.de>
Date: Fri, 26 Jan 2024 17:58:07 +0100
Subject: [PATCH 15/15] `dict` -> `Dict`

---
 optimas/diagnostics/exploration_diagnostics.py | 8 ++++----
 optimas/utils/other.py                         | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/optimas/diagnostics/exploration_diagnostics.py b/optimas/diagnostics/exploration_diagnostics.py
index d9018959..204248a4 100644
--- a/optimas/diagnostics/exploration_diagnostics.py
+++ b/optimas/diagnostics/exploration_diagnostics.py
@@ -4,7 +4,7 @@
 from warnings import warn
 import pathlib
 import json
-from typing import Optional, List, Tuple, Union
+from typing import Optional, List, Dict, Tuple, Union
 
 import numpy as np
 import numpy.typing as npt
@@ -539,9 +539,9 @@ def plot_history(
         self,
         parnames: Optional[list] = None,
         xname: Optional[str] = None,
-        select: Optional[dict] = None,
-        sort: Optional[dict] = None,
-        top: Optional[dict] = None,
+        select: Optional[Dict] = None,
+        sort: Optional[Dict] = None,
+        top: Optional[Dict] = None,
         show_legend: Optional[bool] = False,
         **subplots_kw,
     ) -> None:
diff --git a/optimas/utils/other.py b/optimas/utils/other.py
index 0747b03e..29e56206 100644
--- a/optimas/utils/other.py
+++ b/optimas/utils/other.py
@@ -67,7 +67,7 @@ def convert_to_dataframe(
         raise ValueError(f"Cannot convert {type(data)} to a pandas dataframe.")
 
 
-def get_df_with_selection(df: pd.DataFrame, select: dict) -> pd.DataFrame:
+def get_df_with_selection(df: pd.DataFrame, select: Dict) -> pd.DataFrame:
     """Return the DataFrame after applying selection criterium.
 
     Parameters