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123 lines
4.1 KiB
Python
123 lines
4.1 KiB
Python
import pandas as pd
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import matplotlib.pyplot as plt
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def error(df, df_original, column_name):
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"""
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Calculate the error between the values in a column of a DataFrame and the last value before each timestamp.
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Args:
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df (pandas.DataFrame): The DataFrame containing the values.
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df_original (pandas.DataFrame): The original DataFrame containing the timestamps and values.
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column_name (str): The name of the column to calculate the error for.
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Returns:
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list: A list of absolute differences between the values in the specified column and the last value before each timestamp.
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Raises:
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ValueError: If the specified column does not exist in the DataFrame.
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"""
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diff = []
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# Check if the column exists in the DataFrame
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if column_name not in df.columns:
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raise ValueError(f"The column '{column_name}' does not exist in the DataFrame.")
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def last_value_before(timestamp):
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if df[df['time'] <= timestamp].empty:
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raise ValueError("No point before the date")
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return df[df['time'] <= timestamp].iloc[-1]
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for i in range(1, len(df_original)):
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try:
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diff.append(abs(df_original["value"].iloc[i] - last_value_before(df_original["time"].iloc[i])["value"]))
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except ValueError:
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continue
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return diff
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def plot_histogram(data_series, bins=10, title="Distribution of Absolute Differences"):
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"""
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Plots a histogram of the given data series.
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Parameters:
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- data_series (array-like): The data series to plot the histogram for.
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- bins (int): The number of bins to use for the histogram. Default is 10.
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- title (str): The title of the histogram plot. Default is "Distribution of Absolute Differences".
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Returns:
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None
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"""
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import matplotlib.pyplot as plt
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plt.figure(figsize=(8, 4)) # Set the figure size for better readability
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plt.hist(data_series, bins=bins, color='blue', alpha=0.7, edgecolor='black')
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plt.title(title)
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plt.xlabel('Absolute Difference')
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plt.ylabel('Frequency')
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plt.grid(True)
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plt.show()
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def compute_efficiency(df):
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"""
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Compute the efficiency of a data frame. i.e the time taken to collect each data point.
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Parameters:
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df (pandas.DataFrame): The input data frame.
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Returns:
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float: The efficiency value.
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"""
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# compute the time difference between the first and last point
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time_diff = df["time"].iloc[-1] - df["time"].iloc[0]
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# compute the number of points
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num_points = len(df)
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# compute the efficiency
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efficiency = time_diff.total_seconds() / num_points
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return efficiency
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def hourly_rate_of_change(df):
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"""
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Calculate the average absolute rate of change per hour for a given DataFrame.
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Args:
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df (pandas.DataFrame): The DataFrame containing the data.
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Returns:
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pandas.Series: A Series containing the average absolute rate of change per hour.
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Raises:
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ValueError: If the DataFrame does not include 'time' and 'value' columns, or if it is empty.
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ValueError: If the 'time' column is not of datetime type.
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"""
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# Check if required columns exist
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if 'time' not in df.columns or 'value' not in df.columns:
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raise ValueError("DataFrame must include 'time' and 'value' columns.")
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# Check if the DataFrame is empty
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if df.empty:
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raise ValueError("The DataFrame is empty.")
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# Ensure 'time' is of datetime type
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if not pd.api.types.is_datetime64_any_dtype(df['time']):
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raise ValueError("'time' column must be of datetime type.")
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# Calculate the difference between consecutive entries
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df['time_diff'] = df['time'].diff().dt.total_seconds() / 3600 # Convert time difference to hours
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df['value_diff'] = df['value'].diff()
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# Calculate the rate of change in degrees per hour, and take the absolute value
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df['rate_of_change'] = (df['value_diff'] / df['time_diff']).abs()
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# Extract the hour from each datetime
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df['hour'] = df['time'].dt.hour
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# Group by hour and calculate the average absolute rate of change for each hour
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hourly_avg_abs_rate = df.groupby('hour')['rate_of_change'].mean()
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return hourly_avg_abs_rate
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