creating a play based risk assessment test

import numpy as np

from matplotlib import pyplot as plt

def smooth_logit(x, midpoint=7.5, k=1.5):

    return 1 / (1 + np.exp(-k * (x - midpoint)))

def test_play_based_risk_single_location():

    # sigmoid functions

    temp_midpoint, temp_k = 80, 0.1

    perm_midpoint, perm_k = 250, 0.01

    thick_midpoint, thick_k = 50, 0.01

    ntg_midpoint, ntg_k = 0.5, 10

    temp_range = (0, 200)

    perm_range = (0, 500)

    thick_range = (0, 500)

    ntg_range = (0, 1)

    n_samples = 1000

    temperature = np.random.normal(100, 5, n_samples)

    permeability = np.random.normal(300, 50, n_samples)

    thickness = np.random.normal(200, 20, n_samples)

    ntg = np.random.normal(0.8, 0.1, n_samples)

    # chance of success factors

    temperature_cos = smooth_logit(temperature, midpoint=temp_midpoint, k=temp_k)

    permeability_cos = smooth_logit(permeability, midpoint=perm_midpoint, k=perm_k)

    thickness_cos = smooth_logit(thickness, midpoint=thick_midpoint, k=thick_k)

    ntg_cos = smooth_logit(ntg, midpoint=ntg_midpoint, k=ntg_k)

    # combined chance of success

    combined_cos = temperature_cos * permeability_cos * thickness_cos * ntg_cos

    # plot Alles

    fig, axd = plt.subplot_mosaic(

        [

            ["temp", "temp_sig", "temp_cos", "combined"],

            ["perm", "perm_sig", "perm_cos", "combined"],

            ["thick", "thick_sig", "thick_cos", "combined"],

            ["ntg", "ntg_sig", "ntg_cos", "combined"],

        ],

        figsize=(30, 10),

        constrained_layout=True,

    )

    # temperature

    axd["temp"].set_title("Temperature samples [C]")

    axd["temp"].hist(temperature, bins=20)

    axd["temp_sig"].set_title("Temperature Sigmoid")

    axd["temp_sig"].plot(

        np.linspace(temp_range[0], temp_range[1]),

        smooth_logit(

            np.linspace(temp_range[0], temp_range[1]), midpoint=temp_midpoint, k=temp_k

        ),

    )

    axd["temp_cos"].set_title("Temperature COS")

    axd["temp_cos"].hist(temperature_cos, bins=20)

    # thickness

    axd["thick"].set_title("thick samples [m]")

    axd["thick"].hist(thickness, bins=20)

    axd["thick_sig"].set_title("Thickness Sigmoid")

    axd["thick_sig"].plot(

        np.linspace(thick_range[0], thick_range[1]),

        smooth_logit(

            np.linspace(thick_range[0], thick_range[1]),

            midpoint=thick_midpoint,

            k=thick_k,

        ),

    )

    axd["thick_cos"].set_title("Thickness COS")

    axd["thick_cos"].hist(thickness_cos, bins=20)

    # permeability

    axd["perm"].set_title("permeability samples [mD]")

    axd["perm"].hist(permeability, bins=20)

    axd["perm_sig"].set_title("Permeability Sigmoid")

    axd["perm_sig"].plot(

        np.linspace(perm_range[0], perm_range[1]),

        smooth_logit(

            np.linspace(perm_range[0], perm_range[1]), midpoint=perm_midpoint, k=perm_k

        ),

    )

    axd["perm_cos"].set_title("Permeability COS")

    axd["perm_cos"].hist(permeability_cos, bins=20)

    # ntg

    axd["ntg"].set_title("ntg samples [0-1]")

    axd["ntg"].hist(ntg, bins=20)

    axd["ntg_sig"].set_title("ntg Sigmoid")

    axd["ntg_sig"].plot(

        np.linspace(ntg_range[0], ntg_range[1]),

        smooth_logit(

            np.linspace(ntg_range[0], ntg_range[1]), midpoint=ntg_midpoint, k=ntg_k

        ),

    )

    axd["ntg_cos"].set_title("ntg COS")

    axd["ntg_cos"].hist(ntg_cos, bins=20)

    # combined cos

    axd["combined"].set_title("Combined COS")

    axd["combined"].hist(combined_cos, bins=20)

    plt.show()