Update functions/PitchPlotFunctions.py

functions/PitchPlotFunctions.py

@@ -282,6 +282,8 @@ class PitchPlotFunctions:
                             ax:plt.Axes, 
                             n_std:float=3.0,
                             facecolor:str='none',
+                            linestyle:str='--',
+                            linewidth:float=2,
                             **kwargs):
         """
         Create a plot of the covariance confidence ellipse of *x* and *y*.
@@ -293,6 +295,10 @@ class PitchPlotFunctions:
             The axes object to draw the ellipse into.
         n_std : float
             The number of standard deviations to determine the ellipse's radiuses.
+        linestyle : str
+            The line style for the ellipse edge (default: '--').
+        linewidth : float
+            The line width for the ellipse edge (default: 2).
         **kwargs
             Forwarded to `~matplotlib.patches.Ellipse`
         Returns
@@ -310,7 +316,7 @@ class PitchPlotFunctions:
             ell_radius_x = np.sqrt(1 + pearson)
             ell_radius_y = np.sqrt(1 - pearson)
             ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,
-                            facecolor=facecolor,linewidth=2,linestyle='--', **kwargs)
+                            facecolor=facecolor, linewidth=linewidth, linestyle=linestyle, **kwargs)
             
 
             # Calculating the standard deviation of x from
@@ -339,7 +345,7 @@ class PitchPlotFunctions:
         return ax.add_patch(ellipse)
 
     
-    def break_plot_big(self, df: pl.DataFrame, ax: plt.Axes, sport_id: int):
+    def break_plot_big(self, df: pl.DataFrame, ax: plt.Axes, sport_id: int, compare_df: pl.DataFrame = None):
         """
         Plots a big break plot for the given DataFrame on the provided axis.
 
@@ -347,6 +353,7 @@ class PitchPlotFunctions:
         df (pl.DataFrame): The DataFrame containing pitch data.
         ax (plt.Axes): The matplotlib axis to plot on.
         sport_id (int): The sport ID to determine the plot title.
+        compare_df (pl.DataFrame): Optional DataFrame for comparison data (draws only ellipses, no points).
         """
         # Set font properties for different elements of the plot
         font_properties = {'size': 10}
@@ -355,26 +362,32 @@ class PitchPlotFunctions:
         
         # Get unique pitch types sorted by 'prop' and 'pitch_type'
         label_labels = df.sort(by=['prop', 'pitch_type'], descending=[False, True])['pitch_type'].unique()
-        j = 0
         dict_colour, dict_pitch = self.pitch_colours()
         custom_theme, colour_palette = self.sns_custom_theme()
         
-        # Loop through each pitch type and plot confidence ellipses
-        for label in label_labels:
-            subset = df.filter(pl.col('pitch_type') == label)
-            if len(subset) > 4:
-                try:
-                    if df['pitcher_hand'][0] == 'R':
-                        self.confidence_ellipse(subset['hb']* 1, subset['ivb'], ax=ax, edgecolor=dict_colour[label], n_std=2, facecolor=dict_colour[label], alpha=0.2)
-                    if df['pitcher_hand'][0] == 'L':
-                        self.confidence_ellipse(subset['hb'] * 1, subset['ivb'], ax=ax, edgecolor=dict_colour[label], n_std=2, facecolor=dict_colour[label], alpha=0.2)
-                except ValueError:
-                    return
-                j += 1
-            else:
-                j += 1
+        # Draw comparison ellipses first (if comparison data provided) - dashed lines, no fill
+        if compare_df is not None and len(compare_df) > 0:
+            compare_labels = compare_df['pitch_type'].unique()
+            for label in compare_labels:
+                subset = compare_df.filter(pl.col('pitch_type') == label)
+                if len(subset) > 4:
+                    try:
+                        # Draw comparison ellipses with dashed lines and lighter color
+                        self.confidence_ellipse(
+                            subset['hb'] * 1, 
+                            subset['ivb'], 
+                            ax=ax, 
+                            edgecolor=dict_colour.get(label, '#888888'), 
+                            n_std=2, 
+                            facecolor='none',  # No fill for comparison
+                            alpha=0.8,
+                            linestyle='--',
+                            linewidth=2.5
+                        )
+                    except ValueError:
+                        pass
         
-        # Plot scatter plot of pitch data
+        # Plot scatter plot of pitch data (no ellipses for original data)
         if df['pitcher_hand'][0] == 'R':
             sns.scatterplot(ax=ax, x=df['hb'] * 1, y=df['ivb'] * 1, hue=df['pitch_type'], palette=dict_colour, ec='black', alpha=1, zorder=2, s=35)
         if df['pitcher_hand'][0] == 'L':
@@ -572,7 +585,7 @@ class PitchPlotFunctions:
         )
         return df
     
-    def final_plot(self, df: pl.DataFrame, pitcher_id: str, plot_picker: str, sport_id: int,game_type: list = ['R']):
+    def final_plot(self, df: pl.DataFrame, pitcher_id: str, plot_picker: str, sport_id: int, game_type: list = ['R'], compare_df: pl.DataFrame = None):
         """
         Creates a final plot with player headshot, bio, logo, and pitch movement plots.
 
@@ -581,6 +594,7 @@ class PitchPlotFunctions:
         pitcher_id (str): The ID of the pitcher.
         plot_picker (str): The type of plot to create ('short_form_movement', 'long_form_movement', 'release_point').
         sport_id (int): The sport ID to determine the plot title.
+        compare_df (pl.DataFrame): Optional DataFrame for comparison data (draws only ellipses).
         """
         # Set the theme for seaborn plots
         sns.set_theme(style="whitegrid", rc=self.sns_custom_theme()[0])
@@ -602,7 +616,7 @@ class PitchPlotFunctions:
 
         # Plot player headshot, bio, and logo
         self.player_headshot(pitcher_id=pitcher_id, ax=ax_headshot, sport_id=sport_id)
-        self.player_bio(pitcher_id=pitcher_id, ax=ax_bio, start_date=start_date, end_date=end_date, batter_hand=batter_hand,game_type=game_type)
+        self.player_bio(pitcher_id=pitcher_id, ax=ax_bio, start_date=start_date, end_date=end_date, batter_hand=batter_hand, game_type=game_type)
         self.plot_logo(pitcher_id=pitcher_id, ax=ax_logo)
 
         # Create subplot for the main plot
@@ -617,7 +631,7 @@ class PitchPlotFunctions:
 
         # Plot the selected pitch movement plot
         if plot_picker == 'short_form_movement':
-            self.break_plot_big(df, ax_main_plot, sport_id=sport_id)
+            self.break_plot_big(df, ax_main_plot, sport_id=sport_id, compare_df=compare_df)
         elif plot_picker == 'long_form_movement':
             self.break_plot_big_long(df, ax_main_plot, sport_id=sport_id)
         elif plot_picker == 'release_point':
@@ -634,11 +648,18 @@ class PitchPlotFunctions:
         # Create custom legend handles with circles
         legend_handles = [mlines.Line2D([], [], color=color, marker='o', linestyle='None', markersize=5, label=label) for color, label in zip(ordered_colors, items_in_order)]
 
+        # Add comparison indicator to legend if comparison data is present
+        if compare_df is not None and len(compare_df) > 0:
+            # Add a dashed line legend entry to indicate comparison ellipses
+            compare_handle = mlines.Line2D([], [], color='gray', linestyle='--', linewidth=2, label='Comparison (dashed ellipse)')
+            legend_handles.append(compare_handle)
+
         # Add legend to ax_legend
+        ncols = min(len(legend_handles), 6)
         if len(items_in_order) <= 5: 
-            ax_legend.legend(handles=legend_handles, bbox_to_anchor=(0.1, 0, 0.8, 0.7), ncol=5, fancybox=True, loc='center', fontsize=10, framealpha=1.0, markerscale=2, prop={'size': 10})
+            ax_legend.legend(handles=legend_handles, bbox_to_anchor=(0.1, 0, 0.8, 0.7), ncol=ncols, fancybox=True, loc='center', fontsize=10, framealpha=1.0, markerscale=2, prop={'size': 10})
         else:
-            ax_legend.legend(handles=legend_handles, bbox_to_anchor=(0.1, 0, 0.8, 0.7), ncol=5, fancybox=True, loc='center', fontsize=10, framealpha=1.0, markerscale=2, prop={'size': 10})
+            ax_legend.legend(handles=legend_handles, bbox_to_anchor=(0.1, 0, 0.8, 0.7), ncol=ncols, fancybox=True, loc='center', fontsize=10, framealpha=1.0, markerscale=2, prop={'size': 10})
                     
         # Add footer text
         ax_footer.text(x=0.075, y=0, s='By: Thomas Nestico\n      @TJStats', fontname='Calibri', ha='left', fontsize=12, va='bottom')