finished compare plot

analyze.py

@@ -1,18 +1,26 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 
-def distribution_of_differences(df, column_name):
+def error(df, df_original, column_name):
+    diff = []
     # Check if the column exists in the DataFrame
     if column_name not in df.columns:
         raise ValueError(f"The column '{column_name}' does not exist in the DataFrame.")
+
+    def last_value_before(timestamp):
+        if df[df['time'] <= timestamp].empty:
+            raise ValueError("No point before the date")
+        return df[df['time'] <= timestamp].iloc[-1]
     
-    # Calculate differences between consecutive rows for the specified column
-    differences = df[column_name].diff().abs()
+    for i in range(1, len(df_original)):
+        try:
+            diff.append(abs(df_original["value"].iloc[i] - last_value_before(df_original["time"].iloc[i])["value"]))
+        except ValueError:
+                    continue
+
+    return diff
 
-    # The first element of differences will be NaN since there's no previous element for the first row
-    differences = differences.dropna()  # Remove NaN values
 
-    return differences
 
 def plot_histogram(data_series, bins=10, title="Distribution of Absolute Differences"):
     plt.figure(figsize=(8, 4))  # Set the figure size for better readability
@@ -22,3 +30,40 @@ def plot_histogram(data_series, bins=10, title="Distribution of Absolute Differe
     plt.ylabel('Frequency')
     plt.grid(True)
     plt.show()
+
+def compute_efficiency(df):
+    #compute the time differnece between the first and last point
+    time_diff = df["time"].iloc[-1] - df["time"].iloc[0]
+    #compute the number of points
+    num_points = len(df)
+    #compute the efficiency
+    efficiency = time_diff.total_seconds() / num_points
+    return efficiency
+
+def hourly_rate_of_change(df):
+    # 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.")
+    
+    # Check if the DataFrame is empty
+    if df.empty:
+        raise ValueError("The DataFrame is empty.")
+    
+    # Ensure 'time' is of datetime type
+    if not pd.api.types.is_datetime64_any_dtype(df['time']):
+        raise ValueError("'time' column must be of datetime type.")
+
+    # Calculate the difference between consecutive entries
+    df['time_diff'] = df['time'].diff().dt.total_seconds() / 3600  # Convert time difference to hours
+    df['value_diff'] = df['value'].diff()
+
+    # Calculate the rate of change in degrees per hour, and take the absolute value
+    df['rate_of_change'] = (df['value_diff'] / df['time_diff']).abs()
+
+    # Extract the hour from each datetime
+    df['hour'] = df['time'].dt.hour
+
+    # Group by hour and calculate the average absolute rate of change for each hour
+    hourly_avg_abs_rate = df.groupby('hour')['rate_of_change'].mean()
+
+    return hourly_avg_abs_rate