mirror of
https://git.roussel.pro/telecom-paris/GIN206.git
synced 2026-02-09 02:30:17 +01:00
added documentation
This commit is contained in:
Quentin Roussel
185
main.py
185
main.py
@@ -4,11 +4,37 @@ from generate_data import *
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from analyze import *
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from poll import *
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# sort two lists based on the first list
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def sort(X,Y):
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return zip(*sorted(zip(X,Y)))
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def sort(X, Y):
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"""
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Sorts two lists X and Y in ascending order based on the values in X.
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Args:
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X (list): The first list to be sorted.
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Y (list): The second list to be sorted.
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Returns:
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tuple: A tuple containing the sorted X and Y lists.
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Example:
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X = [3, 1, 2]
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Y = ['c', 'a', 'b']
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sorted_X, sorted_Y = sort(X, Y)
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# sorted_X: [1, 2, 3]
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# sorted_Y: ['a', 'b', 'c']
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"""
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return zip(*sorted(zip(X, Y)))
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def plot_temperature_data(df, recent_count=None):
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"""
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Plots the temperature data from a DataFrame.
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Args:
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df (pandas.DataFrame): The DataFrame containing the temperature data.
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recent_count (int, optional): The number of recent data points to plot. If specified, only the last 'recent_count' rows will be plotted. Defaults to None.
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Returns:
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None
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"""
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plt.figure(figsize=(5, 5))
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# Check if recent_count is specified and valid
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@@ -25,6 +51,20 @@ def plot_temperature_data(df, recent_count=None):
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plt.show()
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def test_sample_every_kth_point(df):
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"""
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Test the sample_every_kth_point function with different values of k.
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Parameters:
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- df: The input DataFrame containing the data.
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Returns:
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- X: The array of values used for sampling.
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- EFFICIENCY: The efficiency values for each sampling.
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- MEAN: The mean error values for each sampling.
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- MEDIAN: The median error values for each sampling.
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- STD: The standard deviation of error values for each sampling.
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"""
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X = np.arange(1, 10, 1)
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MEAN = []
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STD = []
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@@ -46,24 +86,59 @@ def test_sample_every_kth_point(df):
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def example_sample_every_kth_point(k=10):
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"""
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Example function that demonstrates how to sample every kth point from a dataframe and plot the temperature data.
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Parameters:
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k (int): The sampling interval. Default is 10.
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Returns:
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None
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"""
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df = generate_greenhouse_data("datasets/greenhouse.csv")
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df = df.tail(150)
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df = sample_every_kth_point(df, k)
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plot_temperature_data(df)
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def example_sample_reglin():
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"""
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This function demonstrates the usage of the sample_reglin function.
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It generates greenhouse data, selects the last 150 rows, applies the sample_reglin function,
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and plots the temperature data.
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"""
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df = generate_greenhouse_data("datasets/greenhouse.csv")
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df = df.tail(150)
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df = sample_reglin(df)
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plot_temperature_data(df)
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def exaample_optimal_sample(dT = 0.3):
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def example_optimal_sample(dT = 0.3):
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"""
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Example function that demonstrates the usage of the optimal_sample function.
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Parameters:
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dT (float): The threshold value for temperature difference. Default is 0.3.
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Returns:
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None
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"""
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df = generate_greenhouse_data("datasets/greenhouse.csv")
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df = df.tail(150)
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df = optimal_sample(df, threshold_dT=dT)
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plot_temperature_data(df)
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def example_sample_avg_rate_of_change():
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"""
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This function demonstrates how to calculate the sample average rate of change for temperature data.
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It generates greenhouse data, calculates the hourly rate of change, selects the last 150 records,
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and then calculates the sample average rate of change based on the hourly rate of change.
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Finally, it plots the temperature data.
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Parameters:
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None
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Returns:
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None
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"""
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df = generate_greenhouse_data("datasets/greenhouse.csv")
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hroc = hourly_rate_of_change(df)
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df = df.tail(150)
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@@ -71,6 +146,25 @@ def example_sample_avg_rate_of_change():
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plot_temperature_data(df)
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def test_sample_reglin(df):
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"""
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Perform a test on the sample_reglin function with different values of max_dT.
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Parameters:
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- df: DataFrame
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The input DataFrame containing temperature data.
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Returns:
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- X: ndarray
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An array of values ranging from 0.4 to 3 with a step of 0.05.
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- EFFICIENCY: list
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A list of efficiency values calculated for each max_dT value.
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- MEAN: list
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A list of mean error values calculated for each max_dT value.
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- MEDIAN: list
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A list of median error values calculated for each max_dT value.
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- STD: list
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A list of standard deviation error values calculated for each max_dT value.
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"""
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X = np.arange(0.4, 3, 0.05)
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MEAN = []
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STD = []
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@@ -91,6 +185,20 @@ def test_sample_reglin(df):
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def test_optimal_sample(df):
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"""
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Test the optimal sample function with different threshold values.
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Args:
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df (pandas.DataFrame): The input DataFrame containing temperature data.
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Returns:
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tuple: A tuple containing the following lists:
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- X (numpy.ndarray): An array of threshold values.
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- EFFICIENCY (list): A list of efficiency values for each threshold.
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- MEAN (list): A list of mean error values for each threshold.
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- MEDIAN (list): A list of median error values for each threshold.
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- STD (list): A list of standard deviation error values for each threshold.
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"""
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X = np.arange(0.1, 3, 0.05)
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MEAN = []
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STD = []
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@@ -109,7 +217,24 @@ def test_optimal_sample(df):
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EFFICIENCY.append(compute_efficiency(df_sampeld))
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return X, EFFICIENCY, MEAN, MEDIAN, STD
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def test_sample_avg_rate_of_change(df,hourly_rate_of_change):
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def test_sample_avg_rate_of_change(df, hourly_rate_of_change):
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"""
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Test the sample average rate of change.
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This function takes a DataFrame `df` and the `hourly_rate_of_change` as input.
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It performs a series of calculations on the data and returns the results.
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Parameters:
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- df (pandas.DataFrame): The input DataFrame containing the data.
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- hourly_rate_of_change (float): The hourly rate of change.
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Returns:
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- X (numpy.ndarray): An array of values ranging from 0.01 to 3 with a step of 0.05.
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- EFFICIENCY (list): A list of efficiency values calculated for each sample.
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- MEAN (list): A list of mean values calculated for each sample.
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- MEDIAN (list): A list of median values calculated for each sample.
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- STD (list): A list of standard deviation values calculated for each sample.
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"""
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X = np.arange(0.01, 3, 0.05)
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MEAN = []
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STD = []
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@@ -128,35 +253,56 @@ def test_sample_avg_rate_of_change(df,hourly_rate_of_change):
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EFFICIENCY.append(compute_efficiency(df_sampled))
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return X, EFFICIENCY, MEAN, MEDIAN, STD
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def comparaison_mean(df,limit=1000):
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def comparaison_mean(df, limit=1000):
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"""
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Compare different sampling methods based on their mean and efficiency.
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Parameters:
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- df: DataFrame
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The input DataFrame containing the data.
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- limit: int, optional
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The number of rows to consider from the end of the DataFrame. Default is 1000.
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Returns:
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None
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"""
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plt.figure(figsize=(10, 5))
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hroc = hourly_rate_of_change(df)
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df = df.tail(limit)
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X, EFFICIENCY, MEAN, MEDIAN, STD = test_sample_every_kth_point(df)
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MEAN, EFFICIENCY = sort(MEAN, EFFICIENCY)
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plt.plot( MEAN,EFFICIENCY, label="Constant Polling Interval", marker='x')
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plt.plot(MEAN, EFFICIENCY, label="Constant Polling Interval", marker='x')
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X, EFFICIENCY, MEAN, MEDIAN, STD = test_sample_reglin(df)
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MEAN, EFFICIENCY = sort(MEAN, EFFICIENCY)
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plt.plot( MEAN,EFFICIENCY, label="Linear Regression", marker='x')
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plt.plot(MEAN, EFFICIENCY, label="Linear Regression", marker='x')
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X, EFFICIENCY, MEAN, MEDIAN, STD = test_optimal_sample(df)
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MEAN, EFFICIENCY = sort(MEAN, EFFICIENCY)
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plt.plot( MEAN,EFFICIENCY, label="Optimal Polling rate", marker='x')
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plt.plot(MEAN, EFFICIENCY, label="Optimal Polling rate", marker='x')
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X, EFFICIENCY, MEAN, MEDIAN, STD = test_sample_avg_rate_of_change(df,hroc)
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X, EFFICIENCY, MEAN, MEDIAN, STD = test_sample_avg_rate_of_change(df, hroc)
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MEAN, EFFICIENCY = sort(MEAN, EFFICIENCY)
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plt.plot( MEAN,EFFICIENCY, label="Hourly Rate of Change", marker='x')
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plt.plot(MEAN, EFFICIENCY, label="Hourly Rate of Change", marker='x')
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plt.ylabel("Average seconds between polls")
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plt.xlabel("Average error")
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plt.ylim(0, 8000)
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plt.xlim(0,1.3)
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plt.xlim(0, 1.3)
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plt.legend()
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plt.show()
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def example_optimal_sample(dT = 0.3):
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"""
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This function demonstrates how to use the `optimal_sample` function to generate an optimal sample of greenhouse data.
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Parameters:
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dT (float): The threshold value for temperature difference. Default is 0.3.
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Returns:
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None
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"""
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df = generate_greenhouse_data("datasets/greenhouse.csv")
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df = df.tail(1000)
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df = optimal_sample(df, threshold_dT=dT)
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@@ -169,6 +315,15 @@ def example_optimal_sample(dT = 0.3):
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plt.show()
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def histogram_sample_every_kth_point(k=10):
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"""
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Generate a histogram of the differences between the original data and the sampled data.
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Parameters:
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- k (int): The sampling interval. Only every kth point will be included in the sampled data.
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Returns:
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None
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"""
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df = generate_greenhouse_data("datasets/greenhouse.csv")
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df = df.tail(1000)
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df_sampled = sample_every_kth_point(df, k)
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@@ -192,9 +347,9 @@ def histogram_sample_every_kth_point(k=10):
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# comparaison_mean(df)
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# Temperature rate of change over the day
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df = generate_greenhouse_data("datasets/greenhouse.csv")
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hcor = hourly_rate_of_change(df)
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print(hcor)
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# df = generate_greenhouse_data("datasets/greenhouse.csv")
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# hcor = hourly_rate_of_change(df)
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# print(hcor)
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# hcor.plot()
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# plt.xlabel("Hour of the day")
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# plt.ylabel("Average absolute rate of change (°C/hour)")
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