ajout fonctions de base pour parse des données et les analyser

generate_data.py

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+import pandas as pd
+import numpy as np
+from opensimplex import OpenSimplex
+import datetime
+
+def generate_greenhouse_data(filepath):
+    # Read the CSV file into a DataFrame, parsing 'time' as datetime
+    df = pd.read_csv(filepath, parse_dates=["time"], dtype={"id": str, "value": float})
+    
+    # Compute the absolute differences between consecutive temperature readings
+    df['diff'] = df['value'].diff().abs()
+    
+    # Initial value for 'diff' will be NaN; we can fill it with 0 or a small number
+    df['diff'] = df['diff'].fillna(0)
+    
+    # Filter the DataFrame:
+    # 1. Exclude temperature values that are too high (>50) or too low (<-10)
+    # 2. Exclude rows where the difference from the previous reading is greater than 6
+    filtered_df = df[(df['value'] > 0) & (df['value'] < 50) & (df['diff'] <= 6)]
+    
+    # Drop the 'diff' column as it's no longer needed after filtering
+    filtered_df = filtered_df.drop(columns=['diff'])
+    
+    return filtered_df
+
+
+def generate_simplex(start_time=None, end_time=None, interval=600, max_temp=30, min_temp=10, frequency=10):
+    # Default time settings if none provided
+    if end_time is None:
+        end_time = datetime.datetime.now()
+    if start_time is None:
+        start_time = end_time - datetime.timedelta(days=1)
+    
+    # Calculate the number of samples needed based on the interval
+    total_seconds = int((end_time - start_time).total_seconds())
+    steps = total_seconds // interval
+    
+    # Time array
+    times = [start_time + datetime.timedelta(seconds=i * interval) for i in range(steps + 1)]
+    
+    # Simplex noise generator
+    simplex = OpenSimplex(seed=np.random.randint(0, 1000))
+    
+    # Generate noise values and scale them
+    temperatures = [simplex.noise2(x=i / frequency, y=0) for i in range(steps + 1)]
+    
+    # Map Simplex noise output (usually in range [-1, 1]) to the [min_temp, max_temp]
+    scaled_temperatures = min_temp + (np.array(temperatures) + 1) / 2 * (max_temp - min_temp)
+    
+    # Create DataFrame
+    df = pd.DataFrame({'time': times, 'value': scaled_temperatures})
+    return df