added documentation

analyze.py

@@ -2,6 +2,21 @@ import pandas as pd
 import matplotlib.pyplot as plt
 
 def error(df, df_original, column_name):
+    """
+    Calculate the error between the values in a column of a DataFrame and the last value before each timestamp.
+
+    Args:
+        df (pandas.DataFrame): The DataFrame containing the values.
+        df_original (pandas.DataFrame): The original DataFrame containing the timestamps and values.
+        column_name (str): The name of the column to calculate the error for.
+
+    Returns:
+        list: A list of absolute differences between the values in the specified column and the last value before each timestamp.
+
+    Raises:
+        ValueError: If the specified column does not exist in the DataFrame.
+    """
+    
     diff = []
     # Check if the column exists in the DataFrame
     if column_name not in df.columns:
@@ -23,6 +38,19 @@ def error(df, df_original, column_name):
 
 
 def plot_histogram(data_series, bins=10, title="Distribution of Absolute Differences"):
+    """
+    Plots a histogram of the given data series.
+
+    Parameters:
+    - data_series (array-like): The data series to plot the histogram for.
+    - bins (int): The number of bins to use for the histogram. Default is 10.
+    - title (str): The title of the histogram plot. Default is "Distribution of Absolute Differences".
+
+    Returns:
+    None
+    """
+    import matplotlib.pyplot as plt
+
     plt.figure(figsize=(8, 4))  # Set the figure size for better readability
     plt.hist(data_series, bins=bins, color='blue', alpha=0.7, edgecolor='black')
     plt.title(title)
@@ -32,15 +60,40 @@ def plot_histogram(data_series, bins=10, title="Distribution of Absolute Differe
     plt.show()
 
 def compute_efficiency(df):
-    #compute the time differnece between the first and last point
+    """
+    Compute the efficiency of a data frame. i.e the time taken to collect each data point.
+
+    Parameters:
+    df (pandas.DataFrame): The input data frame.
+
+    Returns:
+    float: The efficiency value.
+
+    """
+    # compute the time difference between the first and last point
     time_diff = df["time"].iloc[-1] - df["time"].iloc[0]
-    #compute the number of points
+    # compute the number of points
     num_points = len(df)
-    #compute the efficiency
+    # compute the efficiency
     efficiency = time_diff.total_seconds() / num_points
     return efficiency
 
 def hourly_rate_of_change(df):
+    """
+    Calculate the average absolute rate of change per hour for a given DataFrame.
+
+    Args:
+        df (pandas.DataFrame): The DataFrame containing the data.
+
+    Returns:
+        pandas.Series: A Series containing the average absolute rate of change per hour.
+
+    Raises:
+        ValueError: If the DataFrame does not include 'time' and 'value' columns, or if it is empty.
+        ValueError: If the 'time' column is not of datetime type.
+
+    """
+    
     # Check if required columns exist
     if 'time' not in df.columns or 'value' not in df.columns:
         raise ValueError("DataFrame must include 'time' and 'value' columns.")