TNO Intern

Commit 8e3981bf authored by Hen Brett's avatar Hen Brett 🐔
Browse files

Creating a new stochastic example

parent 9c0584c1

tests/test_doc_examples.py

+26 −24
Original line number Diff line number Diff line
@@ -273,7 +273,7 @@ def test_example8(): 
    from matplotlib import pyplot as plt



    # generate simulation samples across desired reservoir properties

    Nsamples = 1000

    Nsamples = 100

    thickness_samples = np.random.uniform(low=150, high=300, size=Nsamples)

    porosity_samples = np.random.uniform(low=0.1, high=0.8, size=Nsamples)

    ntg_samples = np.random.uniform(low=0.1, high=0.5, size=Nsamples)
@@ -300,28 +300,30 @@ def test_example8(): 
    results.plot.scatter(x="permeability", y="npv", ax=axes[1,1])

    plt.show()



    # Calculate Stochastics Across Samples

    def plot_survival_function(samples, xlabel='Parameter value (x)', ax=None):

        sorted_samples = np.sort(samples)

        cdf = np.arange(1, len(samples) + 1) / len(samples)



        # Compute P(θ > x)

        survival = 1 - cdf



        # Plot survival function (expectation curve as P(>x))

        if ax is None:

            fig, ax = plt.subplots(figsize=(8, 5))

        ax.plot(sorted_samples, survival, label=r'$P(\theta > x)$', color='blue')

        ax.set_xlabel(xlabel)

        ax.set_ylabel('Cumulative probability $P(\\theta > x)$')

        ax.grid(True)

        ax.legend()



    fig, axes = plt.subplots(2,2, figsize=(10,10))

    plt.suptitle("Stochastic Measures")

    plot_survival_function(results.permeability, xlabel="permeability", ax=axes[0,0])

    plot_survival_function(results.power, xlabel="power", ax=axes[0, 1])

    plot_survival_function(results.utc, xlabel="utc", ax=axes[1, 0])

    plot_survival_function(results.npv, xlabel="net-present-value", ax=axes[1, 1])

    plt.show()



    # # Calculate Stochastics Across Samples

    # def plot_survival_function(samples, xlabel='Parameter value (x)', ax=None):

    #     sorted_samples = np.sort(samples)

    #     cdf = np.arange(1, len(samples) + 1) / len(samples)

    #

    #     # Compute P(θ > x)

    #     survival = 1 - cdf

    #

    #     # Plot survival function (expectation curve as P(>x))

    #     if ax is None:

    #         fig, ax = plt.subplots(figsize=(8, 5))

    #     ax.plot(sorted_samples, survival, label=r'$P(\theta > x)$', color='blue')

    #     ax.set_xlabel(xlabel)

    #     ax.set_ylabel('Cumulative probability $P(\\theta > x)$')

    #     ax.grid(True)

    #     ax.legend()

    #

    # fig, axes = plt.subplots(2,2, figsize=(10,10))

    # plt.suptitle("Stochastic Measures")

    # plot_survival_function(results.permeability, xlabel="permeability", ax=axes[0,0])

    # plot_survival_function(results.power, xlabel="power", ax=axes[0, 1])

    # plot_survival_function(results.utc, xlabel="utc", ax=axes[1, 0])

    # plot_survival_function(results.npv, xlabel="net-present-value", ax=axes[1, 1])

    # plt.show()