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

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

.. _sphx_glr_auto_examples_plot_dom_hits.py:


==================
DOM hits.
==================

Estimate track/DOM distances using the number of hits per DOM.

.. GENERATED FROM PYTHON SOURCE LINES 9-88



.. image-sg:: /auto_examples/images/sphx_glr_plot_dom_hits_001.png
   :alt: plot dom hits
   :srcset: /auto_examples/images/sphx_glr_plot_dom_hits_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Detector: Parsing the DETX header
    Detector: Reading PMT information...
    Detector: Done.
    Pipeline and module initialisation took 0.015s (CPU 0.015s).
    --------------------------[ Blob     100 ]---------------------------
    --------------------------[ Blob     200 ]---------------------------
    --------------------------[ Blob     300 ]---------------------------
    --------------------------[ Blob     400 ]---------------------------
    --------------------------[ Blob     500 ]---------------------------
    ================================[ . ]================================
           n_hits    distance
    0           1  102.049055
    1           2   91.942233
    2           2  837.073030
    3           2   41.469520
    4           3   30.938144
    ...       ...         ...
    10028       4   35.125770
    10029       2   72.235454
    10030       2   51.261673
    10031       1   55.623002
    10032       2   61.987838

    [10033 rows x 2 columns]
    ============================================================
    500 cycles drained in 3.833711s (CPU 5.849016s). Memory peak: 701.18 MB
      wall  mean: 0.007496s  medi: 0.004076s  min: 0.001875s  max: 1.630178s  std: 0.072658s
      CPU   mean: 0.007497s  medi: 0.004072s  min: 0.001876s  max: 1.630059s  std: 0.072653s

    Blob([('HDF5Pump', None), ('StatusBar', None), ('DOMHits', None)])





|

.. code-block:: Python


    # Author: Tamas Gal <tgal@km3net.de>
    # License: BSD-3

    from collections import defaultdict, Counter

    import numpy as np
    import pandas as pd

    import matplotlib.pyplot as plt
    from matplotlib.colors import LogNorm

    import km3pipe as kp
    from km3pipe.dataclasses import Table
    from km3pipe.math import pld3
    from km3modules.common import StatusBar
    from km3net_testdata import data_path
    import km3pipe.style

    km3pipe.style.use("km3pipe")

    filename = data_path("hdf5/atmospheric_muons_sample.h5")
    cal = kp.calib.Calibration(filename=data_path("detx/KM3NeT_-00000001_20171212.detx"))


    def filter_muons(blob):
        """Write all muons from McTracks to Muons."""
        tracks = blob["McTracks"]
        muons = tracks[tracks.type == -13]  # PDG particle code
        blob["Muons"] = Table(muons)
        return blob


    class DOMHits(kp.Module):
        """Create histogram with n_hits and distance of hit to track."""

        def configure(self):
            self.hit_statistics = defaultdict(list)

        def process(self, blob):
            hits = blob["Hits"]
            muons = blob["Muons"]

            highest_energetic_muon = Table(muons[np.argmax(muons.energy)])
            muon = highest_energetic_muon

            triggered_hits = hits.triggered_rows

            dom_hits = Counter(triggered_hits.dom_id)
            for dom_id, n_hits in dom_hits.items():
                try:
                    distance = pld3(cal.detector.dom_positions[dom_id], muon.pos, muon.dir)
                except KeyError:
                    self.log.warning("DOM ID %s not found!" % dom_id)
                    continue
                self.hit_statistics["n_hits"].append(n_hits)
                self.hit_statistics["distance"].append(distance)
            return blob

        def finish(self):
            df = pd.DataFrame(self.hit_statistics)
            print(df)
            sdf = df[(df["distance"] < 200) & (df["n_hits"] < 50)]
            bins = (int(max(sdf["distance"])) - 1, int(max(sdf["n_hits"]) - 1))
            plt.hist2d(
                sdf["distance"], sdf["n_hits"], cmap="plasma", bins=bins, norm=LogNorm()
            )
            plt.xlabel("Distance between hit and muon track [m]")
            plt.ylabel("Number of hits on DOM")
            plt.tight_layout()
            plt.show()


    pipe = kp.Pipeline()
    pipe.attach(kp.io.HDF5Pump, filename=filename)
    pipe.attach(StatusBar, every=100)
    pipe.attach(filter_muons)
    pipe.attach(DOMHits)
    pipe.drain()


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

   **Total running time of the script:** (0 minutes 9.385 seconds)


.. _sphx_glr_download_auto_examples_plot_dom_hits.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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