Delete linePlot.py

linePlot.py

@@ -1,104 +0,0 @@
-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
-