mirror of
https://git.roussel.pro/telecom-paris/GIN206.git
synced 2026-02-09 10:40:17 +01:00
70 lines
2.5 KiB
Python
70 lines
2.5 KiB
Python
import pandas as pd
|
|
import matplotlib.pyplot as plt
|
|
|
|
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]
|
|
|
|
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
|
|
|
|
|
|
|
|
def plot_histogram(data_series, bins=10, title="Distribution of Absolute Differences"):
|
|
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)
|
|
plt.xlabel('Absolute Difference')
|
|
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
|