.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples_gallery/plot_data_cleaning.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_data_cleaning.py>`
        to download the full example code.

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

.. _sphx_glr_examples_gallery_plot_data_cleaning.py:


=============
Data cleaning
=============

Shows how data can be cleaned before creating a Well.

.. GENERATED FROM PYTHON SOURCE LINES 9-92



.. image-sg:: /examples_gallery/images/sphx_glr_plot_data_cleaning_001.png
   :alt: Data with values missing at random, Cleaned data (interpolated)
   :srcset: /examples_gallery/images/sphx_glr_plot_data_cleaning_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np
    import scipy as sp

    from dca.adca.utils import clean_well_data
    from dca.decline_curve_analysis import Exponential

    rng = np.random.default_rng(42)

    n = 35
    # Create time_on
    noise = rng.normal(loc=0.5, scale=0.1, size=n)
    time_on = sp.special.expit(np.linspace(-0.5, 0.5, num=n) + noise)
    # time_on = np.ones_like(time_on)
    cumulative_time_on = np.cumsum(time_on)
    midpoints = cumulative_time_on - time_on / 2

    # Create production within each time period
    curve = Exponential.from_original_parametrization(C=10, k=0.1)
    production = curve.eval(midpoints) * time_on
    noise = rng.normal(scale=0.1, size=n)
    production = np.exp(np.log(production) + np.sqrt(time_on) * noise)

    # Create time
    time = midpoints

    # Add noise
    idx_time_on = rng.choice(np.arange(n), size=n // 3, replace=False)
    time_on[idx_time_on] = np.nan
    idx_production = rng.choice(np.arange(n), size=n // 3, replace=False)
    production[idx_production] = np.nan

    # Clean data
    cleaned = clean_well_data(production=production, time=time, time_on=time_on)

    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 5), sharex=True, sharey=True)
    ax1.set_title("Data with values missing at random")

    ax1.set_xlabel("Time")
    ax1.set_ylabel("Production")
    ax1.scatter(time, production, color="orange", label="Production", zorder=4)
    ax11 = ax1.twinx()
    ax11.set_ylabel("time_on")
    ax11.scatter(time, time_on, color="blue", label="Time on", zorder=6)
    ax11.tick_params(axis="y")
    ax1.grid(True, axis="x", which="both", zorder=0)
    ax1.set_xticks(time)
    ax1.scatter(
        time,
        production / time_on,
        color="green",
        zorder=8,
        label="Production rates for interpolation",
        marker="*",
    )

    lines, labels = ax1.get_legend_handles_labels()
    lines2, labels2 = ax11.get_legend_handles_labels()
    ax1.legend(lines + lines2, labels + labels2, loc="upper center", fontsize=6)

    ax2.set_title("Cleaned data (interpolated)")
    ax2.set_xlabel("Time")
    ax2.set_ylabel("Production")
    ax2.scatter(cleaned.time, cleaned.production, color="orange", zorder=4)
    ax22 = ax2.twinx()
    ax22.set_ylabel("time_on")
    ax22.scatter(cleaned.time, cleaned.time_on, color="blue", zorder=6)
    ax22.tick_params(axis="y")
    ax2.grid(True, axis="x", which="both", zorder=0)
    ax2.set_xticks(time)
    ax2.scatter(
        cleaned.time,
        cleaned.production / cleaned.time_on,
        color="green",
        zorder=8,
        label="Production rates after interpolation",
        marker="*",
    )
    ax2.set_xticklabels([])

    fig.tight_layout()  # otherwise the right y-label is slightly clipped
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.299 seconds)


.. _sphx_glr_download_examples_gallery_plot_data_cleaning.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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