.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples_gallery/plot_loss_function_params.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_gallery_plot_loss_function_params.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_gallery_plot_loss_function_params.py:


========================
Loss function parameters
========================

Tweaking loss function parameters.

.. GENERATED FROM PYTHON SOURCE LINES 9-88



.. image-sg:: /examples_gallery/images/sphx_glr_plot_loss_function_params_001.png
   :alt: plot loss function params
   :srcset: /examples_gallery/images/sphx_glr_plot_loss_function_params_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np
    from scipy.optimize import minimize

    from dca.datasets import load_monthly_sodir_production
    from dca.decline_curve_analysis import Arps, CurveLoss

    # Load data to fit to
    df = (
        load_monthly_sodir_production()
        .loc[lambda df: df["prfInformationCarrier"] == "STATFJORD", :]
        .groupby("prfYear")["prfPrdOilNetMillSm3"]
        .sum()
    )

    # We fit the log-data to a log-model, and we shift the x-values
    rng = np.random.default_rng(42)
    x = df.index - df.index.min()
    x_eval = np.linspace(0, np.max(x) * 1.5)
    y = np.log(df.values) + rng.normal(loc=0, scale=0.3, size=len(x))
    plt.plot(x, y, "-o", label="Data")


    def log_arps(t, theta1, theta2, theta3):
        """Curve function to fit to."""
        return Arps(theta1, theta2, theta3).eval_log(t)


    def log_arps_grad(t, theta1, theta2, theta3):
        """Evaluate the gradient of the log of the Arps model."""
        return Arps(theta1, theta2, theta3).eval_grad_log(t)


    # Default parameters
    parameters = {"p": 2, "half_life": np.inf}

    parameter_diffs = [
        {},  # ALl default
        {"p": 1.2},  # Median regression
        {"p": 1.2, "half_life": 12},
    ]

    for parameter_diff in parameter_diffs:
        loss_params = {**parameters, **parameter_diff}
        mu = np.array([6, 2, 0])
        loss_function = CurveLoss(
            curve_func=log_arps,
            curve_func_grad=log_arps_grad,
            mu=mu,
            precision_matrix=np.eye(3) * 2e-1,
            **loss_params,
        )

        # Optimize the function
        optimization_result = minimize(
            loss_function,
            x0=mu,
            method="BFGS",
            jac=loss_function.grad,  # Use gradient information
            args=(
                x,
                y,
            ),
        )
        assert optimization_result.success
        optimal_parameters = optimization_result.x

        # Plot
        curve = Arps(*optimal_parameters)
        plt.plot(x_eval, curve.eval_log(x_eval), label=f"{parameter_diff}")


    # Plot the results
    plt.xlabel("Months since production start")
    plt.ylabel("log Production")
    plt.grid(True, ls="--", alpha=0.33)
    plt.legend()
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.119 seconds)


.. _sphx_glr_download_examples_gallery_plot_loss_function_params.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_loss_function_params.ipynb <plot_loss_function_params.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_loss_function_params.py <plot_loss_function_params.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_loss_function_params.zip <plot_loss_function_params.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_