Update app.py

app.py

@@ -1,123 +1,153 @@
-import polars as pl
-import numpy as np
-import joblib
-from shiny import App, reactive, render, ui
-import matplotlib.pyplot as plt
-import matplotlib.ticker as tkr
-import seaborn as sns
-import adjustText
-sns.set_style('whitegrid')
-
-
-import matplotlib
-cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#FFFFFF','#FFB000','#FE6100'])
-
-xwoba_model = joblib.load('joblib_model/xwoba_model.joblib')
-
-x = np.arange(-30,90.5,.5)
-y = np.arange(0,120.5,0.1)
-
-xx, yy = np.meshgrid(x, y)
-
-df = pl.DataFrame({'launch_angle': xx.ravel(), 'launch_speed': yy.ravel()})
-
-df = df.with_columns(
-    pl.Series('xwoba', xwoba_model.predict_proba(df) @ [0, 0.883, 1.244, 1.569, 2.004])
-)
-
-app_ui = ui.page_sidebar(
-    ui.sidebar(
-        ui.markdown("""
-        ### How to use this app
-        
-        1. Click anywhere on the plot to select a point, or manually enter coordinates
-        2. The selected point's coordinates will update automatically
-        3. The xwOBA value will be calculated based on these coordinates
-        """),
-        ui.hr(),
-        ui.input_numeric("x_select", "Launch Speed (mph)", value=110),
-        ui.input_numeric("y_select", "Launch Angle (°)", value=30),
-
-
-    ),
-    ui.output_plot("plot",width='900px',height='900px', click=True)
-)
-
-
-def server(input, output, session):
-    # Store the coordinates in reactive values
-    x_coord = reactive.value(110)
-    y_coord = reactive.value(30)
-    
-    @reactive.effect
-    @reactive.event(input.plot_click)
-    def _():
-        # Update reactive values when plot is clicked
-        click_data = input.plot_click()
-        if click_data is not None:
-            x_coord.set(click_data["x"])
-            y_coord.set(click_data["y"])
-            # Update the numeric inputs
-            ui.update_numeric("x_select", value=round(click_data["x"],1))
-            ui.update_numeric("y_select", value=round(click_data["y"],1))
-    
-    @reactive.effect
-    @reactive.event(input.x_select, input.y_select)
-    def _():
-        # Update reactive values when numeric inputs change
-        x_coord.set(round(input.x_select(),1))
-        y_coord.set(round(input.y_select(),1))
-
-
-    @render.plot
-    def plot():
-        fig, ax = plt.subplots(1, 1, figsize=(10, 10))
-
-        h = ax.hexbin(df['launch_speed'], 
-                    df['launch_angle'],
-                    C=df['xwoba'], 
-                    gridsize=(40,25), 
-                    cmap=cmap_sum,
-                    vmin=0.0,
-                    vmax=2.0,)
-        bounds=[0.0,0.4,0.8,1.2,1.6,2.0]
-        fig.colorbar(h, ax=ax, label='xwOBA',format=tkr.FormatStrFormatter('%.3f'),shrink=0.5,
-                    ticks=bounds)
-
-
-
-        ax.set_xlabel('Launch Speed')
-        ax.set_ylabel('Launch Angle')
-        ax.set_title('Exit Velocity vs Launch Angle\nExpected Weighted On Base Average (xwOBA)\nBy: @TJStats, Data:MLB')
-
-        ax.grid(False)
-        ax.axis('square')
-        ax.set_xlim(0, 120)
-        ax.set_ylim(-30, 90)
-
-        x_select = input.x_select()
-        y_select = input.y_select()
-
-
-        sns.scatterplot(x=[x_select],y=[y_select],color='#648FFF',s=50,ax=ax,edgecolor='k',zorder=100)
-
-        xwoba_value = (xwoba_model.predict_proba([[y_select,x_select]]) @ [0, 0.883, 1.244, 1.569, 2.004])[0]
-
-        texts = [ax.text(x_select+3, y_select+3, f'xwOBA: {xwoba_value:.3f}', color='black', fontsize=12, weight='bold',
-                        zorder=1000, bbox=dict(facecolor='white', alpha=0.5, edgecolor='black'))]
-
-
-
-        adjustText.adjust_text(texts,
-
-                                arrowprops=dict(arrowstyle='->', color='#DC267F'),avoid_self=True,
-                                min_arrow_len =5)
-        # xwoba_value = 
-
-        ax.axhline(y=y_select, color='k', linestyle='--',linewidth=1,alpha=0.5)
-        ax.axvline(x=x_select, color='k', linestyle='--',linewidth=1,alpha=0.5)
-
-        # ax.axis('square')
-
-
+import polars as pl
+import numpy as np
+import joblib
+from shiny import App, reactive, render, ui
+import matplotlib.pyplot as plt
+import matplotlib.ticker as tkr
+import seaborn as sns
+import adjustText
+sns.set_style('whitegrid')
+
+
+import matplotlib
+cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#FFFFFF','#FFB000','#FE6100'])
+
+xwoba_model = joblib.load('joblib_model/xwoba_model.joblib')
+
+x = np.arange(-30,90.5,.5)
+y = np.arange(0,120.5,0.1)
+
+xx, yy = np.meshgrid(x, y)
+
+df = pl.DataFrame({'launch_angle': xx.ravel(), 'launch_speed': yy.ravel()})
+
+df = df.with_columns(
+    pl.Series('xwoba', xwoba_model.predict_proba(df) @ [0, 0.883, 1.244, 1.569, 2.004])
+)
+
+df = df.with_columns(
+    pl.Series('xslg', xwoba_model.predict_proba(df) @ [0, 1, 2, 3, 4])
+)
+
+app_ui = ui.page_sidebar(
+    ui.sidebar(
+        ui.markdown("""
+        ### How to use this app
+        
+        1. Click anywhere on the plot to select a point, or manually enter coordinates
+        2. The selected point's coordinates will update automatically
+        3. The xwOBA value will be calculated based on these coordinates
+        """),
+        ui.hr(),
+        ui.input_numeric("x_select", "Launch Speed (mph)", value=110),
+        ui.input_numeric("y_select", "Launch Angle (°)", value=30),
+        ui.input_switch("flip_stat", "xwOBA", value=False),
+
+
+    ),
+    ui.output_plot("plot",width='900px',height='900px', click=True)
+)
+
+
+def server(input, output, session):
+    # Store the coordinates in reactive values
+    x_coord = reactive.value(110)
+    y_coord = reactive.value(30)
+    
+    @reactive.effect
+    @reactive.event(input.plot_click)
+    def _():
+        # Update reactive values when plot is clicked
+        click_data = input.plot_click()
+        if click_data is not None:
+            x_coord.set(click_data["x"])
+            y_coord.set(click_data["y"])
+            # Update the numeric inputs
+            ui.update_numeric("x_select", value=round(click_data["x"],1))
+            ui.update_numeric("y_select", value=round(click_data["y"],1))
+    
+    @reactive.effect
+    @reactive.event(input.x_select, input.y_select)
+    def _():
+        # Update reactive values when numeric inputs change
+        x_coord.set(round(input.x_select(),1))
+        y_coord.set(round(input.y_select(),1))
+
+
+    @render.plot
+    def plot():
+        switch = input.flip_stat()
+        fig, ax = plt.subplots(1, 1, figsize=(9, 9))
+
+
+        if switch:
+            h = ax.hexbin(df['launch_speed'], 
+                        df['launch_angle'],
+                        C=df['xwoba'], 
+                        gridsize=(40,25), 
+                        cmap=cmap_sum,
+                        vmin=0.0,
+                        vmax=2.0,)
+            bounds=[0.0,0.4,0.8,1.2,1.6,2.0]
+            fig.colorbar(h, ax=ax, label='xwOBA',format=tkr.FormatStrFormatter('%.3f'),shrink=0.5,
+                        ticks=bounds)
+            
+        if else:
+            h = ax.hexbin(df['launch_speed'], 
+                        df['launch_angle'],
+                        C=df['xslg'], 
+                        gridsize=(40,25), 
+                        cmap=cmap_sum,
+                        vmin=0.0,
+                        vmax=2.0,)
+            bounds=[0.0,0.5,1,1.5,2,2.5,3,3.5,4]
+            fig.colorbar(h, ax=ax, label='xSLG',format=tkr.FormatStrFormatter('%.3f'),shrink=0.5,
+                        ticks=bounds)
+
+
+
+        ax.set_xlabel('Launch Speed')
+        ax.set_ylabel('Launch Angle')
+        if switch:
+            ax.set_title('Exit Velocity vs Launch Angle\nExpected Weighted On Base Average (xwOBA)\nBy: @TJStats, Data:MLB')
+        else:
+            ax.set_title('Exit Velocity vs Launch Angle\nExpected Total Bases (xSLG)\nBy: @TJStats, Data:MLB')
+            
+        ax.grid(False)
+        ax.axis('square')
+        ax.set_xlim(0, 120)
+        ax.set_ylim(-30, 90)
+
+        x_select = input.x_select()
+        y_select = input.y_select()
+
+
+        sns.scatterplot(x=[x_select],y=[y_select],color='#648FFF',s=50,ax=ax,edgecolor='k',zorder=100)
+
+        
+        if switch:
+            xwoba_value = (xwoba_model.predict_proba([[y_select,x_select]]) @ [0, 0.883, 1.244, 1.569, 2.004])[0]
+            texts = [ax.text(x_select+3, y_select+3, f'xwOBA: {xwoba_value:.3f}', color='black', fontsize=12, weight='bold',
+                            zorder=1000, bbox=dict(facecolor='white', alpha=0.5, edgecolor='black'))]
+    
+        else switch:
+            xwoba_value = (xwoba_model.predict_proba([[y_select,x_select]]) @ [0, 1, 2, 3, 4])[0]
+            texts = [ax.text(x_select+3, y_select+3, f'xSLG: {xwoba_value:.3f}', color='black', fontsize=12, weight='bold',
+                            zorder=1000, bbox=dict(facecolor='white', alpha=0.5, edgecolor='black'))]
+    
+
+
+
+        adjustText.adjust_text(texts,
+
+                                arrowprops=dict(arrowstyle='->', color='#DC267F'),avoid_self=True,
+                                min_arrow_len =5)
+        # xwoba_value = 
+
+        ax.axhline(y=y_select, color='k', linestyle='--',linewidth=1,alpha=0.5)
+        ax.axvline(x=x_select, color='k', linestyle='--',linewidth=1,alpha=0.5)
+
+        # ax.axis('square')
+
+
 app = App(app_ui, server)