feat: add more 4 charts

Author: BMOit

charts/chart_07_age_group_distribution___interactions.png

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+"""Age distribution charts package."""

charts/chart_08_age_group_distribution___enrollments.png

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+"""
+Chart 7: Age Group Distribution - Interactions
+Type of Chart: Grouped vertical bar chart
+Data Points:
+  - demographic.csv: demo_age_5_17, demo_age_17_
+  - biometric.csv: bio_age_5_17, bio_age_17_
+  - 4 bars: 5-17 (Demo), 18+ (Demo), 5-17 (Bio), 18+ (Bio)
+"""
+import matplotlib.pyplot as plt
+import numpy as np
+
+from src.charts import register_chart
+from src.charts.base import BaseChart
+from src.processors import AgeGroupAggregator
+import config
+
+
+@register_chart
+class Chart07AgeGroupInteractions(BaseChart):
+
+    @property
+    def chart_id(self) -> str:
+        return "07"
+
+    @property
+    def title(self) -> str:
+        return "Age Group Distribution - Interactions"
+
+    def generate(self) -> plt.Figure:
+        data = self.data_loader.get_all_data()
+        processor = AgeGroupAggregator()
+        age_data = processor.process_interactions(data)
+
+        fig, ax = plt.subplots(figsize=(12, 8))
+
+        # Set up bar positions
+        x = np.arange(len(age_data))
+        width = 0.6
+
+        # Create bars with different colors for demo vs bio
+        colors = [
+            config.COLORS["demographic"],  # 5-17 (Demo)
+            config.COLORS["demographic"],  # 18+ (Demo)
+            config.COLORS["biometric"],    # 5-17 (Bio)
+            config.COLORS["biometric"],    # 18+ (Bio)
+        ]
+        
+        bars = ax.bar(
+            x,
+            age_data["total"],
+            width,
+            color=colors,
+            edgecolor="white",
+            linewidth=1.5,
+            alpha=0.8
+        )
+
+        # Add value labels on top of bars
+        for i, (bar, row) in enumerate(zip(bars, age_data.itertuples())):
+            height = bar.get_height()
+            # Format in millions
+            value_text = f"{row.total/1e6:.1f}M"
+            percentage_text = f"({row.percentage:.1f}%)"
+            
+            ax.text(
+                bar.get_x() + bar.get_width() / 2,
+                height,
+                f"{value_text}\n{percentage_text}",
+                ha="center",
+                va="bottom",
+                fontsize=10,
+                fontweight="bold"
+            )
+
+        self._apply_common_style(ax)
+        ax.set_xlabel("Age Group by Service Type", fontsize=12)
+        ax.set_ylabel("Total Interactions", fontsize=12)
+        ax.set_xticks(x)
+        ax.set_xticklabels(age_data["category"], fontsize=10)
+        ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f"{x/1e6:.0f}M"))
+        ax.grid(axis="y", alpha=0.3, linestyle="--")
+        
+        # Add legend
+        from matplotlib.patches import Patch
+        legend_elements = [
+            Patch(facecolor=config.COLORS["demographic"], label="Demographic", alpha=0.8),
+            Patch(facecolor=config.COLORS["biometric"], label="Biometric", alpha=0.8)
+        ]
+        ax.legend(handles=legend_elements, loc="upper right", fontsize=10)
+        
+        fig.tight_layout()
+
+        return fig

charts/chart_09_weekly_pattern___demographic_interactions.png

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+"""
+Chart 8: Age Group Distribution - Enrollments
+Type of Chart: Vertical bar chart
+Data Points:
+  - enrollment.csv: age_0_5, age_5_17, age_18_greater
+  - 3 bars: 0-5, 5-17, 18+
+"""
+import matplotlib.pyplot as plt
+import numpy as np
+
+from src.charts import register_chart
+from src.charts.base import BaseChart
+from src.processors import AgeGroupAggregator
+import config
+
+
+@register_chart
+class Chart08AgeGroupEnrollments(BaseChart):
+
+    @property
+    def chart_id(self) -> str:
+        return "08"
+
+    @property
+    def title(self) -> str:
+        return "Age Group Distribution - Enrollments"
+
+    def generate(self) -> plt.Figure:
+        data = self.data_loader.get_all_data()
+        processor = AgeGroupAggregator()
+        age_data = processor.process_enrollments(data)
+
+        fig, ax = plt.subplots(figsize=(10, 8))
+
+        # Create bars
+        bars = ax.bar(
+            age_data["age_group"],
+            age_data["total"],
+            color=config.COLORS["enrollment"],
+            edgecolor="white",
+            linewidth=1.5,
+            alpha=0.8,
+            width=0.6
+        )
+
+        # Add value labels on top of bars
+        for bar, row in zip(bars, age_data.itertuples()):
+            height = bar.get_height()
+            # Format in thousands or millions
+            if row.total >= 1e6:
+                value_text = f"{row.total/1e6:.2f}M"
+            else:
+                value_text = f"{row.total/1e3:.0f}K"
+            
+            percentage_text = f"({row.percentage:.1f}%)"
+            
+            ax.text(
+                bar.get_x() + bar.get_width() / 2,
+                height,
+                f"{value_text}\n{percentage_text}",
+                ha="center",
+                va="bottom",
+                fontsize=11,
+                fontweight="bold"
+            )
+
+        self._apply_common_style(ax)
+        ax.set_xlabel("Age Group", fontsize=12)
+        ax.set_ylabel("Total Enrollments", fontsize=12)
+        ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f"{x/1e6:.1f}M" if x >= 1e6 else f"{x/1e3:.0f}K"))
+        ax.grid(axis="y", alpha=0.3, linestyle="--")
+        
+        fig.tight_layout()
+
+        return fig