.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/02-production/2.4-plot_sample_csfd.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_02-production_2.4-plot_sample_csfd.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_02-production_2.4-plot_sample_csfd.py:


Sample a power law and lunar Neukum Production Function
========================================================

.. rubric:: By Austin Blevins and David Minton

In this example, we use the :py:meth:`Production.sample` method to sample populations of craters from a production function. We will sample 1000 craters from each production function and repeat this 100 times. The population samples are then plotted against the expected value given by :py:meth:`Production.function`

.. GENERATED FROM PYTHON SOURCE LINES 9-72



.. image-sg:: /auto_examples/02-production/images/sphx_glr_2.4-plot_sample_csfd_001.png
   :alt: Power Law, NPF (Moon)
   :srcset: /auto_examples/02-production/images/sphx_glr_2.4-plot_sample_csfd_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import matplotlib.ticker as ticker
    import numpy as np

    from cratermaker import Production, Target

    fig, axs = plt.subplots(1, 2, figsize=(8, 7))
    axes = dict(zip(["Power Law", "NPF (Moon)"], axs))

    production = {
        "Power Law": Production.maker("powerlaw"),
        "NPF (Moon)": Production.maker("neukum", version="Moon"),
    }

    target = Target.maker("Moon")
    area = 4 * np.pi * target.radius**2
    age = 4100.0
    x_min = 1e0
    x_max = 1e5
    y_min = 1e0
    y_max = 1e4
    diameter_range = (2e3, 10000e3)  # Range of diameters to generate in m
    nD = 1000
    Dvals = np.logspace(np.log10(x_min), np.log10(x_max), num=nD)
    Nevaluations = 100
    for name, ax in axes.items():
        ax.title.set_text(name)
        ax.set_xscale("log")
        ax.set_yscale("log")
        ax.set_ylabel("$\\mathregular{N_{>D}}$")
        ax.set_xlabel("Diameter (km)")
        ax.set_xlim(x_min, x_max)
        ax.set_ylim(y_min, y_max)
        ax.yaxis.set_major_locator(ticker.LogLocator(base=10.0, numticks=20))
        ax.yaxis.set_minor_locator(ticker.LogLocator(base=10.0, subs=np.arange(2, 10), numticks=100))
        ax.xaxis.set_major_locator(ticker.LogLocator(base=10.0, numticks=20))
        ax.xaxis.set_minor_locator(ticker.LogLocator(base=10.0, subs=np.arange(2, 10), numticks=100))

        # Plot each sampled population Monte Carlo style
        for i in range(Nevaluations):
            Dsampled, _ = production[name].sample(age=age, diameter_range=diameter_range, area=area, compute_time=False)
            Dsampled = np.sort(Dsampled)[::-1]
            Nsampled = range(1, len(Dsampled) + 1)
            ax.plot(
                Dsampled * 1e-3,
                Nsampled,
                "-",
                color="cornflowerblue",
                linewidth=2.0,
                zorder=50,
                alpha=0.2,
            )

        # Plot the production function (the expected values)
        Nvals = production[name].function(diameter=Dvals * 1e3, age=age)
        Nvals *= area  # convert from per unit area to total number
        ax.plot(Dvals, Nvals, "-", color="black", linewidth=1.0, zorder=50)

    plt.tick_params(axis="y", which="minor")

    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 16.777 seconds)


.. _sphx_glr_download_auto_examples_02-production_2.4-plot_sample_csfd.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 2.4-plot_sample_csfd.ipynb <2.4-plot_sample_csfd.ipynb>`

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

      :download:`Download Python source code: 2.4-plot_sample_csfd.py <2.4-plot_sample_csfd.py>`

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

      :download:`Download zipped: 2.4-plot_sample_csfd.zip <2.4-plot_sample_csfd.zip>`


.. only:: html

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

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