Create linePlot.py

linePlot.py

@@ -0,0 +1,103 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from typing import Dict, Tuple, Optional
+# from datetime import datetime
+
+def plot_stacked_time_series(time_series_df: pd.DataFrame, title: str) -> plt.Figure:
+    """
+    Create stacked time series plot with shared x-axis (cleaned up version)
+    """
+    n_series = len(time_series_df.columns)
+    
+    # Create figure with shared x-axis
+    fig, axes = plt.subplots(n_series, 1, figsize=(12, 2*n_series), sharex=True)
+    fig.suptitle(title, y=1.02, fontsize=14)
+    
+    # Calculate y-axis limits for consistent scaling
+    y_min = time_series_df.min().min()
+    y_max = time_series_df.max().max()
+    y_range = y_max - y_min
+    y_buffer = y_range * 0.1
+    
+    for idx, column in enumerate(time_series_df.columns):
+        ax = axes[idx] if n_series > 1 else axes
+        series = time_series_df[column]
+        
+        # Plot the line with smaller markers
+        ax.plot(range(len(series)), series, marker='o', markersize=3, linewidth=1.5)
+        
+        # Set title and labels
+        ax.set_title(column, pad=5, fontsize=10)
+        ax.set_ylabel('Popularity', fontsize=9)
+        
+        # Set consistent y-axis limits with buffer
+        ax.set_ylim(y_min - y_buffer, y_max + y_buffer)
+        
+        # Add grid with lower alpha
+        # ax.grid(True, linestyle='--', alpha=0.4)
+        
+        # Format x-axis (only for bottom subplot)
+        if idx == n_series-1:
+            ax.set_xlabel('Time Period', fontsize=10)
+            ax.set_xticks(range(len(series)))
+            ax.set_xticklabels(time_series_df.index, rotation=45, ha='right')
+        else:
+            ax.set_xticks(range(len(series)))
+            ax.set_xticklabels([])
+    
+    # Adjust spacing between subplots
+    plt.subplots_adjust(hspace=0.3)
+    
+    return fig
+
+
+def plot_emotion_topic_grid(pair_series: Dict[Tuple[str, str], pd.Series], 
+                          top_n: int) -> plt.Figure:
+    """
+    Create grid of time series plots for emotion-topic pairs with improved formatting
+    """
+    fig, axes = plt.subplots(top_n, top_n, figsize=(15, 15))
+    fig.suptitle('Emotion-Topic Pair Frequencies Over Time', y=1.02, fontsize=14)
+    
+    # Get unique emotions and topics
+    emotions = sorted(set(e for e, _ in pair_series.keys()))
+    topics = sorted(set(t for _, t in pair_series.keys()))
+    
+    # Calculate global y-axis limits for consistent scaling
+    all_values = [series.values for series in pair_series.values()]
+    y_min = min(min(values) for values in all_values)
+    y_max = max(max(values) for values in all_values)
+    y_range = y_max - y_min
+    y_buffer = y_range * 0.1
+    
+    for i, emotion in enumerate(emotions):
+        for j, topic in enumerate(topics):
+            ax = axes[i, j]
+            series = pair_series[(emotion, topic)]
+            
+            # Plot the line
+            ax.plot(range(len(series)), series, marker='o', markersize=3)
+            
+            # Set titles and labels
+            if i == 0:
+                ax.set_title(f'{topic}', fontsize=10)
+            if j == 0:
+                ax.set_ylabel(f'{emotion}', fontsize=10)
+            
+            # Set consistent y-axis limits
+            ax.set_ylim(y_min - y_buffer, y_max + y_buffer)
+            
+            # Add grid
+            ax.grid(True, linestyle='--', alpha=0.4)
+            
+            # Format ticks
+            if i == top_n-1:  # Only bottom row shows x-labels
+                ax.set_xticklabels(series.index, rotation=45, fontsize=8)
+            else:
+                ax.set_xticklabels([])
+            
+            ax.tick_params(axis='both', which='major', labelsize=8)
+    
+    plt.tight_layout()
+    return fig