feat: Create visualizer module with generalized functions

src/visualizer/__init__.py

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+from . import (  
+    visualization,
+)

src/visualizer/visualization.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+from matplotlib import gridspec
+
+
+
+
+def plot_mean_dwell_time(bus_stop_value, start, end, df_x , df_y, folder_path):
+    plt.figure(figsize=(13,5))
+    plt.plot(df_x, df_y, marker='o', linestyle='-')
+    plt.xlabel('Date')
+    plt.ylabel('Mean Dwell Time')
+    plt.title(f'Mean dwell time at the bus stop {bus_stop_value} on each day at [{start} - {end}) h')
+    plt.xticks(rotation=45)
+    plt.grid(True)
+    plt.tight_layout()
+    file_name = f'd{bus_stop_value}.png'  
+    save_path = f'{folder_path}/{file_name}'
+    plt.savefig(save_path,dpi=200)
+    plt.show()
+    return None
+
+def plot_mean_arrival_time(bus_stop_value, start, end, df_x , df_y, folder_path):
+    plt.figure(figsize=(13,5))
+    plt.plot(df_x, df_y, marker='o', linestyle='-')
+    myFmt = mdates.DateFormatter('%H:%M:%S') 
+    plt.gca().yaxis.set_major_formatter(myFmt)
+    plt.xlabel('Date')
+    plt.ylabel('Mean Arrival Time')
+    plt.title(f'Mean arrival time at the bus stop {bus_stop_value} on each day at [{start} - {end}) h')
+    plt.xticks(rotation=45)
+    plt.grid(True)
+    plt.tight_layout()
+    file_name = f'a{bus_stop_value}.png'  
+    save_path = f'{folder_path}/{file_name}'
+    plt.savefig(save_path,dpi=200)
+    plt.show()
+    return None
+
+def plot_data_drift_detections(stream, bus_stop, drifts = None):
+    fig = plt.figure(figsize=(7,3), tight_layout=True)
+    gs = gridspec.GridSpec(1, 2, width_ratios=[3, 1])
+    plt.grid()
+    plt.plot(stream, label='Stream Data Drift Detection')
+    if drifts is not None:
+        for drift_detected in drifts:
+            plt.axvline(drift_detected, color='red')
+    plt.ylabel(f'mean arrival time for {bus_stop} ')
+    plt.show()
+    return None
+
+
+
+# TEST CODE
+
+direction_value = 1 
+bus_stop_value  = 102 
+time_of_day_value = 10
+start  = '10:00:00'
+end = '10:15:00'
+csv_path = "src/datasets/_datasets/bus_dwell_times_654.csv"
+img_path = "./"
+
+df = pd.read_csv(csv_path)
+
+df['arrival_time'] = pd.to_datetime(df['arrival_time'])
+df['time_of_day'] = df['arrival_time'].dt.hour
+
+filtered_df = df[(df['direction'] == direction_value) & (df['bus_stop'] == bus_stop_value) & (df['time_of_day'] == time_of_day_value)]
+
+filtered_df['date'] = pd.to_datetime(filtered_df['date'])
+
+filtered_df['arrival_time'] = pd.to_datetime(filtered_df['arrival_time'])
+
+start_time = pd.Timestamp(start).time()
+end_time = pd.Timestamp(end).time()
+
+filtered_df = filtered_df[(filtered_df['arrival_time'].dt.time >= start_time) & (filtered_df['arrival_time'].dt.time <= end_time)]
+
+
+
+mean_dwell_times = filtered_df.groupby('date')['dwell_time_in_seconds'].mean().reset_index()
+sorted_mean_dwell_times = mean_dwell_times.sort_values(by='date')
+print(sorted_mean_dwell_times.head())
+
+df_x = sorted_mean_dwell_times['date']
+df_y = sorted_mean_dwell_times['dwell_time_in_seconds']
+plot_mean_dwell_time(bus_stop_value,start,end,df_x , df_y, img_path)
+
+mean_arrival_times = filtered_df.groupby('date')['arrival_time'].mean().reset_index()
+sorted_mean_arrival_times = mean_arrival_times.sort_values(by='date')
+print(sorted_mean_arrival_times.head())
+
+df_x = sorted_mean_arrival_times['date']
+df_y = sorted_mean_arrival_times['arrival_time']
+plot_mean_arrival_time(bus_stop_value,start,end,df_x , df_y, img_path)
+
+