added line scores tiles

app/app.py

@@ -61,6 +61,7 @@ with ui.sidebar(open="open"):
     ui.input_action_button("reset", "Reset zoom")
 
 with ui.layout_columns(col_widths=[8, 4]):
+    
     with ui.card(full_screen=True):
         ui.card_header("Capital area")
 
@@ -69,179 +70,215 @@ with ui.layout_columns(col_widths=[8, 4]):
         def map():
             return Map(
                 basemap=basemaps.CartoDB.Positron)  
+    
+    
+    
+        
+
 
-    with ui.layout_column_wrap(width="250px"):
-        with ui.card(full_screen=False):
-            ui.card_header("Stop Data")
-            with ui.navset_pill(id="tab"):
-                with ui.nav_panel("Total Score"):
-                    "Total Score"
+    
+            
+    with ui.layout_column_wrap(width="450px"):
+            with ui.layout_columns(col_widths=(6, 6)):
+                with ui.value_box(theme="bg-gradient-red-yellow"):
+                    "Red line"
                     @render.text
-                    def totalScore():
-                        score = scores()
-                        return f"{round(float(score["total_score"]), 2)}"
+                    def render_line_score():
+                        return str(lineScore().get("red", 0))
+
+                with ui.value_box(theme="bg-gradient-blue-cyan"):
                     
-                    @render.plot(alt="A pie chart of score contributions from age, income, and density.")
-                    def contribution_pie_chart():
-                        print("Generating pie chart of contributions")
-                        
-                        # Get score components
-                        score = scores()
-                        age_contribution = score["age_score"]
-                        income_contribution = score["income_score"]
-                        density_contribution = score["density_score"]
-                        
-                        # Data for the pie chart
-                        contributions = [age_contribution, income_contribution, density_contribution]
-                        labels = ["Age", "Income", "Density"]
-                        colors = ["#FD4D86", "#36DEC2", "#704CB0"]  # Custom colors for the segments
-                        
-                        # Create a Matplotlib figure
-                        fig, ax = plt.subplots(figsize=(6, 6))
-                        
-                        # Create the pie chart
-                        ax.pie(
-                            contributions, 
-                            labels=labels, 
-                            autopct='%1.1f%%', 
-                            startangle=90, 
-                            colors=colors, 
-                            textprops={'fontsize': 12}
-                        )
-                        
-                        # Add a title
-                        ax.set_title("Score Contributions", fontsize=16)
-                        
-                        # Return the figure for rendering in Shiny
-                        return fig
-                
-                with ui.nav_panel("Income"):
-                    "Income Score"    
+                    "Blue line"
                     @render.text
-                    def incomeScore():
-                        score = scores()
-                        return f"{round(float(score["income_score"]), 2)}"
-                    @render.plot(alt="A chart of income distribution.")
-                    def income_plot():
-                        print("Generating income distribution bar chart")
-                        
-                        # Get selected stop coordinates
-                        x, y = stop.get()
-                        station_coord = (y, x)
-                        
-                        # Fetch income distribution data from the Data_provider instance
-                        income_data = initBackend.get_station_score(station_coord, radius=input.rad())['income_data']  # Assume this returns a dictionary
-                        
-                        # Example structure: {1: 150, 2: 200, 3: 180, ...}
-                        income_brackets = list(income_data.keys())
-                        populations = list(income_data.values())
-                        
-                        # Create a Matplotlib figure
-                        fig, ax = plt.subplots(figsize=(8, 4))
-                        
-                        # Create the bar chart
-                        ax.bar(income_brackets, populations, color='#36DEC2')
-                        
-                        # Customize the plot
-                        ax.set_title("Population by Income Bracket")
-                        ax.set_xlabel("Income Bracket")
-                        ax.set_ylabel("Population")
-                        ax.set_xticks(income_brackets)
-                        ax.set_xticklabels(income_brackets, rotation=45, ha="right")
-                        
-                        # Return the figure for rendering in Shiny
-                        return fig
+                    def render_line_score1():
+                        return str(lineScore().get("blue", 0))
 
-                
-                with ui.nav_panel("Age"):
-                    "Age Score" 
+                with ui.value_box(theme="bg-gradient-orange-yellow"):
+                    "Orange line"
                     @render.text
-                    def ageScore():
-                        score = scores()
-                        return f"{round(float(score["age_score"]), 2)}"
-                    
-                    @render.plot(alt="A bar chart of age distribution.")
-                    def age_plot():
-                        print("Generating age distribution bar chart")
-                        
-                        # Get selected stop coordinates
-                        x, y = stop.get()
-                        station_coord = (y, x)
-                        
-                        # Fetch age distribution data from the Data_provider instance
-                        age_data = initBackend.get_station_score(station_coord, radius=input.rad())['age_data']  # Assume this returns a dictionary
-                        
-                        # Example structure: {'0-4 ára': 120, '5-9 ára': 140, ...}
-                        age_brackets = list(age_data.keys())
-                        populations = list(age_data.values())
-                        
-                        # Create a Matplotlib figure
-                        fig, ax = plt.subplots(figsize=(8, 4))
-                        
-                        # Create the bar chart with custom colors
-                        ax.bar(age_brackets, populations, color='#FD4D86')
-                        
-                        # Customize the plot
-                        ax.set_title("Population by Age Bracket", fontsize=14)
-                        ax.set_xlabel("Age Bracket", fontsize=12)
-                        ax.set_ylabel("Population", fontsize=12)
-                        ax.set_xticks(range(len(age_brackets)))
-                        ax.set_xticklabels(age_brackets, rotation=45, ha="right", fontsize=10)
-                        
-                        # Return the figure for rendering in Shiny
-                        return fig
-                    
-                with ui.nav_panel("Density"):
-                    "Density" 
+                    def render_line_score2():
+                        return str(lineScore().get("orange", 0))
+
+                with ui.value_box(theme="bg-gradient-indigo-purple"):
+                    "Purple line"
                     @render.text
-                    def sensityScoer():
-                        score = scores()
-                        return f"{round(float(score["density_score"] * 1000000), 2)} Person / Kilometer"
-                
-                
-                        return f"{round(float(score["density_score"]), 2)}"
-                    @render.plot(alt="A bar chart of density scores for all areas within the radius.")
-                    def density_plot():
-                        print("Generating density score bar chart")
-                        
-                        # Get selected stop coordinates
-                        x, y = stop.get()
-                        station_coord = (y, x)
-                        
-                        # Fetch small area contributions from the Data_provider instance
-                        small_area_contributions = initBackend.get_station_score(
-                            station_coord, 
-                            radius=input.rad(),
-                            w_density=input.w_density(), 
-                            w_income=input.w_income(), 
-                            w_age=input.w_age()
-                        )['small_area_contributions']
-                        
-                        # Extract density scores for each small area
-                        area_ids = [area_id for area_id in small_area_contributions.keys()]
-                        density_scores = [area_data['density_score'] for area_data in small_area_contributions.values()]
+                    def render_line_score3():
+                        return str(lineScore().get("purple", 0))
+
+                with ui.value_box(theme="bg-gradient-green-teal"):
+                    "Green line"
+                    @render.text
+                    def render_line_score4():
+                        return str(lineScore().get("green", 0))
+
+                with ui.value_box():
+                    "Total score"
+                    @render.text
+                    def render_line_score5():
+                        return str(sum(lineScore().get(color, 0) for color in ["red", "blue", "orange", "purple", "green"]))
+
+            
+            with ui.card(min_height="600px"):
+                ui.card_header("Stop Data")
+                with ui.navset_pill(id="tab"):
+                    with ui.nav_panel("Score Contributions"):
                         
-                        # Create a Matplotlib figure
-                        fig, ax = plt.subplots(figsize=(8, 4))
                         
-                        # Create the bar chart
-                        ax.bar(area_ids, density_scores, color='#704CB0')
+                        @render.plot(alt="A pie chart of score contributions from age, income, and density.")
+                        def contribution_pie_chart():
+                            print("Generating pie chart of contributions")
+                            
+                            # Get score components
+                            score = scores()
+                            age_contribution = score["age_score"]
+                            income_contribution = score["income_score"]
+                            density_contribution = score["density_score"]
+                            
+                            # Data for the pie chart
+                            contributions = [age_contribution, income_contribution, density_contribution]
+                            labels = ["Age", "Income", "Density"]
+                            colors = ["#FD4D86", "#36DEC2", "#704CB0"]  # Custom colors for the segments
+                            
+                            # Create a Matplotlib figure
+                            fig, ax = plt.subplots(figsize=(6, 6))
+                            
+                            # Create the pie chart
+                            ax.pie(
+                                contributions, 
+                                labels=labels, 
+                                autopct='%1.1f%%', 
+                                startangle=90, 
+                                colors=colors, 
+                                textprops={'fontsize': 12}
+                            )
+                            
+                            # Add a title
+                            ax.set_title("Score Contributions", fontsize=16)
+                            
+                            # Return the figure for rendering in Shiny
+                            return fig
+                    
+                    with ui.nav_panel("Income"):
+                        "Income Score"    
+                        @render.text
+                        def incomeScore():
+                            score = scores()
+                            return score["income_score"]
+                        @render.plot(alt="A chart of income distribution.")
+                        def income_plot():
+                            print("Generating income distribution bar chart")
+                            
+                            # Get selected stop coordinates
+                            x, y = stop.get()
+                            station_coord = (y, x)
+                            
+                            # Fetch income distribution data from the Data_provider instance
+                            income_data = initBackend.get_station_score(station_coord, radius=input.rad())['income_data']  # Assume this returns a dictionary
+                            
+                            # Example structure: {1: 150, 2: 200, 3: 180, ...}
+                            income_brackets = list(income_data.keys())
+                            populations = list(income_data.values())
+                            
+                            # Create a Matplotlib figure
+                            fig, ax = plt.subplots(figsize=(8, 4))
+                            
+                            # Create the bar chart
+                            ax.bar(income_brackets, populations, color='#36DEC2')
+                            
+                            # Customize the plot
+                            ax.set_title("Population by Income Bracket")
+                            ax.set_xlabel("Income Bracket")
+                            ax.set_ylabel("Population")
+                            ax.set_xticks(income_brackets)
+                            ax.set_xticklabels(income_brackets, rotation=45, ha="right")
+                            
+                            # Return the figure for rendering in Shiny
+                            return fig
+
+                    
+                    with ui.nav_panel("Age"):
+                        "Age Score" 
+                        @render.text
+                        def ageScore():
+                            score = scores()
+                            return score["age_score"]                    
+                        @render.plot(alt="A bar chart of age distribution.")
+                        def age_plot():
+                            print("Generating age distribution bar chart")
+                            
+                            # Get selected stop coordinates
+                            x, y = stop.get()
+                            station_coord = (y, x)
+                            
+                            # Fetch age distribution data from the Data_provider instance
+                            age_data = initBackend.get_station_score(station_coord, radius=input.rad())['age_data']  # Assume this returns a dictionary
+                            
+                            # Example structure: {'0-4 ára': 120, '5-9 ára': 140, ...}
+                            age_brackets = list(age_data.keys())
+                            populations = list(age_data.values())
+                            
+                            # Create a Matplotlib figure
+                            fig, ax = plt.subplots(figsize=(8, 4))
+                            
+                            # Create the bar chart with custom colors
+                            ax.bar(age_brackets, populations, color='#FD4D86')
+                            
+                            # Customize the plot
+                            ax.set_title("Population by Age Bracket", fontsize=14)
+                            ax.set_xlabel("Age Bracket", fontsize=12)
+                            ax.set_ylabel("Population", fontsize=12)
+                            ax.set_xticks(range(len(age_brackets)))
+                            ax.set_xticklabels(age_brackets, rotation=45, ha="right", fontsize=10)
+                            
+                            # Return the figure for rendering in Shiny
+                            return fig
                         
-                        # Customize the plot
-                        ax.set_title("Density Scores of Small Areas", fontsize=14)
-                        ax.set_xlabel("Small Area ID", fontsize=12)
-                        ax.set_ylabel("Density Score", fontsize=12)
-                        ax.set_xticks(range(len(area_ids)))
-                        ax.set_xticklabels(area_ids, rotation=45, ha="right", fontsize=10)
+                    with ui.nav_panel("Density"):
+                        "Density" 
+                        @render.text
+                        def sensityScoer():
+                            score = scores()
+                            return float(score["density_score"] * 1000000)
+                    
+                    
+                        @render.plot(alt="A bar chart of density scores for all areas within the radius.")
+                        def density_plot():
+                            print("Generating density score bar chart")
+                            
+                            # Get selected stop coordinates
+                            x, y = stop.get()
+                            station_coord = (y, x)
+                            
+                            # Fetch small area contributions from the Data_provider instance
+                            small_area_contributions = initBackend.get_station_score(
+                                station_coord, 
+                                radius=input.rad(),
+                                w_density=input.w_density(), 
+                                w_income=input.w_income(), 
+                                w_age=input.w_age()
+                            )['small_area_contributions']
+                            
+                            # Extract density scores for each small area
+                            area_ids = [area_id for area_id in small_area_contributions.keys()]
+                            density_scores = [area_data['density_score'] for area_data in small_area_contributions.values()]
+                            
+                            # Create a Matplotlib figure
+                            fig, ax = plt.subplots(figsize=(8, 4))
+                            
+                            # Create the bar chart
+                            ax.bar(area_ids, density_scores, color='#704CB0')
+                            
+                            # Customize the plot
+                            ax.set_title("Density Scores of Small Areas", fontsize=14)
+                            ax.set_xlabel("Small Area ID", fontsize=12)
+                            ax.set_ylabel("Density Score", fontsize=12)
+                            ax.set_xticks(range(len(area_ids)))
+                            ax.set_xticklabels(area_ids, rotation=45, ha="right", fontsize=10)
+                            
+                            # Return the figure for rendering in Shiny
+                            return fig
                         
-                        # Return the figure for rendering in Shiny
-                        return fig
                     
-                with ui.nav_panel("The Line"):
-                        "The Total Score For Lines" 
-                        @render.text
-                        def line():
-                            return lineScore()
 
                         
 
@@ -324,27 +361,37 @@ def scores():
     score = initBackend.get_station_score(station_coord=(y, x), w_density=input.w_density(), w_income=input.w_income(), w_age=input. w_age(), radius=input.rad())
     return score
 
-
 @reactive.calc
 def lineScore():
+    # Generate stops based on the input year
     listOfStops = generateStops(input.year())
     listOflines = {}
+
+    # Handle stops with single and multiple colors
     for stop, color in listOfStops:
         x, y = stop
-        if color not in listOflines:
-            listOflines[color] = []
-        listOflines[color].append((y, x))
-        
-    
+        # If the color is a list (multiple colors), iterate through it
+        if isinstance(color, list):
+            for single_color in color:
+                if single_color not in listOflines:
+                    listOflines[single_color] = []
+                listOflines[single_color].append((y, x))
+        else:
+            # If it's a single color, process it normally
+            if color not in listOflines:
+                listOflines[color] = []
+            listOflines[color].append((y, x))
 
-    lines = []
+    # Calculate scores for each line
+    lines = {}
     for key, val in listOflines.items():
-        
-        score = initBackend.line_score(val, w_density=input.w_density(), w_income=input.w_income(), w_age=input. w_age(), radius=input.rad())
-        
-        lines.append((key, score["final_score"]))
-    
-    # Format the output
-    formatted_output = "\n".join(f"{key}: {float(value):.3f}" for key, value in lines)
-    print(formatted_output)
-    return formatted_output
+        score = initBackend.line_score(
+            val,
+            w_density=input.w_density(),
+            w_income=input.w_income(),
+            w_age=input.w_age(),
+            radius=input.rad()
+        )
+        lines[key] = score["final_score"]
+
+    return lines

app/data_processing/get_station_coverage.py

@@ -35,7 +35,7 @@ def get_station_coverage(
 
         # Validate and fix invalid geometries
         if not geometry.is_valid:
-            print(f"Invalid geometry detected for Area ID: {area['id']}. Attempting to fix.")
+            # print(f"Invalid geometry detected for Area ID: {area['id']}. Attempting to fix.")
             geometry = make_valid(geometry)
 
         # Simplify geometry to avoid potential issues with highly complex polygons