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

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

.. _sphx_glr_auto_examples_tutorials_calibrated_data_exploration.py:


Explore Calibrated Data
=======================

.. GENERATED FROM PYTHON SOURCE LINES 6-25

.. code-block:: Python
   :lineno-start: 7


    import numpy as np
    from astropy import units as u
    from matplotlib import pyplot as plt

    import ctapipe
    from ctapipe.calib import CameraCalibrator
    from ctapipe.image import hillas_parameters, tailcuts_clean
    from ctapipe.io import EventSource
    from ctapipe.utils.datasets import get_dataset_path
    from ctapipe.visualization import ArrayDisplay, CameraDisplay

    # %matplotlib inline
    plt.style.use("ggplot")

    print(ctapipe.__version__)
    print(ctapipe.__file__)






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

 .. code-block:: none

    0.27.1.dev15+g1af6977ab
    /home/docs/checkouts/readthedocs.org/user_builds/ctapipe/envs/2868/lib/python3.10/site-packages/ctapipe/__init__.py




.. GENERATED FROM PYTHON SOURCE LINES 26-28

Let’s first open a raw event file and get an event out of it:


.. GENERATED FROM PYTHON SOURCE LINES 28-35

.. code-block:: Python
   :lineno-start: 29


    filename = get_dataset_path("gamma_prod5.simtel.zst")
    source = EventSource(filename, max_events=2)

    for event in source:
        print(event.index.event_id)





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

 .. code-block:: none


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    4009
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.. GENERATED FROM PYTHON SOURCE LINES 36-38

.. code-block:: Python
   :lineno-start: 36

    filename





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

 .. code-block:: none


    PosixPath('/home/docs/.cache/ctapipe/minio-cta.zeuthen.desy.de/dpps-testdata-public/data/ctapipe-test-data/v1.1.0/gamma_prod5.simtel.zst')



.. GENERATED FROM PYTHON SOURCE LINES 39-41

.. code-block:: Python
   :lineno-start: 39

    source






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div style="border:1px solid black; max-width: 700px; padding:2em; word-wrap:break-word;">
    <b>SimTelEventSource</b>
    <div class="document">
    <p>Read events from a SimTelArray data file (in EventIO format).</p>
    <p>ctapipe makes use of the sim_telarray metadata system to fill some
    information not directly accessible from the data inside the files itself.
    Make sure you set this parameters in the simulation configuration to fully
    make use of ctapipe. In future, ctapipe might require these metadata parameters.</p>
    <p>This includes:</p>
    <ul class="simple">
    <li><dl class="first docutils">
    <dt>Reference Point of the telescope coordinates. Make sure to include the</dt>
    <dd>user-defined parameters <tt class="docutils literal">LONGITUDE</tt> and <tt class="docutils literal">LATITUDE</tt> with the geodetic
    coordinates of the array reference point. Also make sure the <tt class="docutils literal">ALTITUDE</tt>
    config parameter is included in the global metadata.</dd>
    </dl>
    </li>
    <li><dl class="first docutils">
    <dt>Names of the optical structures and the cameras are read from</dt>
    <dd><tt class="docutils literal">OPTICS_CONFIG_NAME</tt> and <tt class="docutils literal">CAMERA_CONFIG_NAME</tt>, make sure to include
    these in the telescope meta.</dd>
    </dl>
    </li>
    <li><dl class="first docutils">
    <dt>The <tt class="docutils literal">MIRROR_CLASS</tt> should also be included in the telescope meta</dt>
    <dd>to correctly setup coordinate transforms.</dd>
    </dl>
    </li>
    </ul>
    <p>If these parameters are not included in the input data, ctapipe will
    fallback guesses these based on available data and the list of known telescopes
    for <cite>ctapipe.instrument.guess_telescope</cite>.</p>
    </div>

    <table>
        <colgroup>
            <col span='1' style=' '>
            <col span='1' style='width: 20em;'>
            <col span='1' >
        </colgroup>
        <tbody>
    <tr><th>allowed_tels</th>
    <td style="text-align: left;">None</td>
    <td style="text-align: left;"><i>list of allowed tel_ids, others will be ignored. If None, all telescopes in the input stream will be included (default: None)</i></td></tr>
    <tr><th>atmosphere_profile_choice</th>
    <td style="text-align: left;">AtmosphereProfileKind.AUTO</td>
    <td style="text-align: left;"><i>Which type of atmosphere density profile to load from the file, in case more than one exists.  If set to AUTO, TABLE will be attempted first and if missing, FIVELAYER will be loaded. (default: AtmosphereProfileKind.AUTO)</i></td></tr>
    <tr><th>back_seekable</th>
    <td style="text-align: left;">False</td>
    <td style="text-align: left;"><i>Require the event source to be backwards seekable. This will reduce in slower read speed for gzipped files and is not possible for zstd compressed files (default: False)</i></td></tr>
    <tr><th>calib_scale</th>
    <td style="text-align: left;">1.0</td>
    <td style="text-align: left;"><i>Factor to transform ADC counts into number of photoelectrons. Corrects the DC_to_PHE factor. (default: 1.0)</i></td></tr>
    <tr><th>calib_shift</th>
    <td style="text-align: left;">0.0</td>
    <td style="text-align: left;"><i>Factor to shift the R1 photoelectron samples. Can be used to simulate mis-calibration. (default: 0.0)</i></td></tr>
    <tr><th>focal_length_choice</th>
    <td style="text-align: left;">FocalLengthKind.EFFECTIVE</td>
    <td style="text-align: left;"><i>If both nominal and effective focal lengths are available in the SimTelArray file, which one to use for the `~ctapipe.coordinates.CameraFrame` attached to the `~ctapipe.instrument.CameraGeometry` instances in the `~ctapipe.instrument.SubarrayDescription`, which will be used in CameraFrame to TelescopeFrame coordinate transforms.  The &#x27;nominal&#x27; focal length is the one used during  the simulation, the &#x27;effective&#x27; focal length is computed using specialized  ray-tracing from a point light source (default: FocalLengthKind.EFFECTIVE)</i></td></tr>
    <tr><th>gain_selector_type</th>
    <td style="text-align: left;">ThresholdGainSelector</td>
    <td style="text-align: left;"><i>The name of a GainSelector subclass. Possible values: [&#x27;ManualGainSelector&#x27;, &#x27;ThresholdGainSelector&#x27;] (default: ThresholdGainSelector)</i></td></tr>
    <tr><th>input_url</th>
    <td style="text-align: left;"><span style="color:blue; max-width:30em;">/<wbr>home/<wbr>docs/<wbr>.cache/<wbr>ctapipe/<wbr>minio-cta.zeuthen.desy.de/<wbr>dpps-testdata-public/<wbr>data/<wbr>ctapipe-test-data/<wbr>v1.1.0/<wbr>gamma_<wbr>prod5.simtel.zst</span></td>
    <td style="text-align: left;"><i>Path to the input file containing events. (default: None)</i></td></tr>
    <tr><th>max_events</th>
    <td style="text-align: left;"><span style="color:blue; max-width:30em;">2</span></td>
    <td style="text-align: left;"><i>Maximum number of events that will be read from the file (default: None)</i></td></tr>
    <tr><th>override_obs_id</th>
    <td style="text-align: left;">None</td>
    <td style="text-align: left;"><i>Use the given obs_id instead of the run number from sim_telarray (default: None)</i></td></tr>
    <tr><th>select_gain</th>
    <td style="text-align: left;">None</td>
    <td style="text-align: left;"><i>Whether to perform gain selection. The default (None) means only select gain of physics events, not of calibration events. (default: None)</i></td></tr>
    <tr><th>skip_calibration_events</th>
    <td style="text-align: left;">True</td>
    <td style="text-align: left;"><i>Skip calibration events (default: True)</i></td></tr>
    <tr><th>skip_r1_calibration</th>
    <td style="text-align: left;">False</td>
    <td style="text-align: left;"><i>Skip the simtel category-A calibration. The R1 waveforms (in ADC counts) will be the same as the R0 waveforms. (default: False)</i></td></tr>
        </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 42-44

.. code-block:: Python
   :lineno-start: 42

    event





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

 .. code-block:: none


    ctapipe.containers.ArrayEventContainer:
                           index.*: event indexing information with default None
                              r0.*: Raw Data with default None
                              r1.*: R1 Calibrated Data with default None
                             dl0.*: DL0 Data Volume Reduced Data with default None
                             dl1.*: DL1 Calibrated image with default None
                             dl2.*: DL2 reconstruction info with default None
                      simulation.*: Simulated Event Information with default None
                                    with type <class
                                    'ctapipe.containers.SimulatedEventContainer'>
                         trigger.*: central trigger information with default None
                             count: number of events processed with default 0
                      monitoring.*: container for event-wise monitoring data (MON)
                                    with default None
                            muon.*: Container for muon analysis results with default
                                    None



.. GENERATED FROM PYTHON SOURCE LINES 45-48

.. code-block:: Python
   :lineno-start: 45

    print(event.r1)






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

 .. code-block:: none

    {'tel': {26: {'calibration_monitoring_id': None,
                  'event_time': <Time object: scale='tai' format='mjd' value=0.0>,
                  'event_type': <EventType.SUBARRAY: 32>,
                  'first_cell_id': None,
                  'module_hires_local_clock_counter': None,
                  'pedestal_intensity': None,
                  'pixel_status': array([4, 4, 4, ..., 4, 4, 4], shape=(1764,), dtype=uint8),
                  'selected_gain_channel': array([0, 0, 0, ..., 0, 0, 0], shape=(1764,), dtype=int8),
                  'waveform': array([[[ 0.5449172 ,  0.7336623 ,  0.85949236, ..., -0.08423293,
              0.10451212,  0.37714386],
            [ 0.26182497,  0.15617064,  0.26182497, ..., -0.66793317,
             -0.71019495, -0.71019495],
            [-0.5535298 , -0.15927683,  0.27647644, ...,  0.62922907,
              0.64997923,  0.64997923],
            ...,
            [ 0.00745648, -0.11931664, -0.28834748, ...,  0.00745648,
              0.21874502,  0.366647  ],
            [ 0.06618547, -0.12305467, -0.24921475, ...,  0.12926552,
              0.08721215,  0.12926552],
            [-0.23363174, -0.19188231, -0.15013288, ..., -0.08750875,
             -0.04575931, -0.12925817]]], shape=(1, 1764, 25), dtype=float32)},
             27: {'calibration_monitoring_id': None,
                  'event_time': <Time object: scale='tai' format='mjd' value=0.0>,
                  'event_type': <EventType.SUBARRAY: 32>,
                  'first_cell_id': None,
                  'module_hires_local_clock_counter': None,
                  'pedestal_intensity': None,
                  'pixel_status': array([4, 4, 4, ..., 4, 4, 4], shape=(1764,), dtype=uint8),
                  'selected_gain_channel': array([0, 0, 0, ..., 0, 0, 0], shape=(1764,), dtype=int8),
                  'waveform': array([[[ 0.8074397 ,  0.88952863,  0.78691745, ...,  0.31490615,
              0.04811714,  0.10968383],
            [-0.07342623, -0.07342623,  0.0933422 , ...,  0.00995799,
             -0.03173412, -0.19850256],
            [ 0.09717706,  0.05602828, -0.06741804, ..., -0.02626926,
             -0.21143875, -0.19086435],
            ...,
            [-0.09182719, -0.1328717 , -0.11234944, ...,  0.35966238,
              0.48279592,  0.5238404 ],
            [ 0.25864878,  0.31945735,  0.23837928, ...,  0.7045781 ,
              0.82619524,  0.7451171 ],
            [-0.16240191, -0.225794  , -0.18353261, ..., -0.07787914,
             -0.16240191, -0.2046633 ]]], shape=(1, 1764, 25), dtype=float32)},
             29: {'calibration_monitoring_id': None,
                  'event_time': <Time object: scale='tai' format='mjd' value=0.0>,
                  'event_type': <EventType.SUBARRAY: 32>,
                  'first_cell_id': None,
                  'module_hires_local_clock_counter': None,
                  'pedestal_intensity': None,
                  'pixel_status': array([4, 4, 4, ..., 4, 4, 4], shape=(1764,), dtype=uint8),
                  'selected_gain_channel': array([0, 0, 0, ..., 0, 0, 0], shape=(1764,), dtype=int8),
                  'waveform': array([[[-0.04938218,  0.03202369,  0.3372957 , ..., -0.13078806,
             -0.02903072, -0.06973365],
            [ 0.59601194,  0.32254168,  0.02803528, ..., -0.24543495,
             -0.24543495, -0.37165198],
            [-0.24841212, -0.1858834 , -0.01914014, ..., -0.35262665,
             -0.39431247, -0.4568412 ],
            ...,
            [ 0.05627692,  0.09927555,  0.12077487, ..., -0.5027053 ,
             -0.58870256, -0.61020184],
            [ 0.3565684 ,  0.47816813,  0.5592346 , ..., -0.17036383,
             -0.23116371, -0.3122302 ],
            [ 0.16858962,  0.08948522,  0.10926133, ...,  0.46523112,
              0.40590283,  0.36635062]]], shape=(1, 1764, 25), dtype=float32)},
             47: {'calibration_monitoring_id': None,
                  'event_time': <Time object: scale='tai' format='mjd' value=0.0>,
                  'event_type': <EventType.SUBARRAY: 32>,
                  'first_cell_id': None,
                  'module_hires_local_clock_counter': None,
                  'pedestal_intensity': None,
                  'pixel_status': array([4, 4, 4, ..., 4, 4, 4], shape=(2048,), dtype=uint8),
                  'selected_gain_channel': array([0, 0, 0, ..., 0, 0, 0], shape=(2048,), dtype=int8),
                  'waveform': array([[[-0.04045208,  0.04424386, -0.12514801, ..., -0.04045208,
              0.00189589,  0.00189589],
            [-0.07548809, -0.02983804, -0.02983804, ..., -0.02983804,
             -0.02983804, -0.12113815],
            [-0.00396833, -0.04704034, -0.17625633, ..., -0.17625633,
             -0.04704034, -0.13318433],
            ...,
            [-0.04439949, -0.04439949,  0.04724088, ..., -0.18186006,
             -0.22768025, -0.09021968],
            [-0.04938671,  0.04159823, -0.04938671, ..., -0.18586414,
             -0.00389424, -0.00389424],
            [-0.13006851, -0.17539828, -0.039409  , ..., -0.039409  ,
             -0.08473875, -0.08473875]]], shape=(1, 2048, 128), dtype=float32)},
             53: {'calibration_monitoring_id': None,
                  'event_time': <Time object: scale='tai' format='mjd' value=0.0>,
                  'event_type': <EventType.SUBARRAY: 32>,
                  'first_cell_id': None,
                  'module_hires_local_clock_counter': None,
                  'pedestal_intensity': None,
                  'pixel_status': array([4, 4, 4, ..., 4, 4, 4], shape=(2048,), dtype=uint8),
                  'selected_gain_channel': array([0, 0, 0, ..., 0, 0, 0], shape=(2048,), dtype=int8),
                  'waveform': array([[[-0.0082417 , -0.0082417 ,  0.03595864, ...,  0.03595864,
             -0.0082417 ,  0.08015899],
            [ 0.00310245,  0.00310245,  0.08867562, ...,  0.00310245,
              0.1314622 ,  0.04588903],
            [ 0.00668868,  0.09877883,  0.09877883, ...,  0.05273375,
             -0.13144656, -0.0393564 ],
            ...,
            [-0.0738365 , -0.16169758, -0.0738365 , ...,  0.10188565,
              0.10188565,  0.01402458],
            [ 0.19253932,  0.14814104,  0.14814104, ...,  0.14814104,
             -0.07385035,  0.10374276],
            [-0.16682361,  0.0083629 , -0.07923036, ..., -0.03543373,
             -0.03543373, -0.16682361]]], shape=(1, 2048, 128), dtype=float32)},
             59: {'calibration_monitoring_id': None,
                  'event_time': <Time object: scale='tai' format='mjd' value=0.0>,
                  'event_type': <EventType.SUBARRAY: 32>,
                  'first_cell_id': None,
                  'module_hires_local_clock_counter': None,
                  'pedestal_intensity': None,
                  'pixel_status': array([4, 4, 4, ..., 4, 4, 4], shape=(2048,), dtype=uint8),
                  'selected_gain_channel': array([0, 0, 0, ..., 0, 0, 0], shape=(2048,), dtype=int8),
                  'waveform': array([[[-0.1164972 , -0.03435972, -0.07542846, ...,  0.00670901,
              0.04777775,  0.17098396],
            [-0.19899125, -0.02344115, -0.11121619, ..., -0.11121619,
             -0.06732867, -0.06732867],
            [-0.0580824 ,  0.03068624,  0.11945489, ...,  0.07507057,
              0.03068624,  0.11945489],
            ...,
            [-0.02629879, -0.0717934 ,  0.06469042, ..., -0.117288  ,
             -0.02629879, -0.02629879],
            [-0.15135607, -0.05747958, -0.05747958, ...,  0.03639691,
              0.1302734 , -0.05747958],
            [-0.00660102, -0.14327039, -0.09771393, ...,  0.08451191,
             -0.00660102, -0.09771393]]], shape=(1, 2048, 128), dtype=float32)},
             69: {'calibration_monitoring_id': None,
                  'event_time': <Time object: scale='tai' format='mjd' value=0.0>,
                  'event_type': <EventType.SUBARRAY: 32>,
                  'first_cell_id': None,
                  'module_hires_local_clock_counter': None,
                  'pedestal_intensity': None,
                  'pixel_status': array([4, 4, 4, ..., 4, 4, 4], shape=(2048,), dtype=uint8),
                  'selected_gain_channel': array([0, 0, 0, ..., 0, 0, 0], shape=(2048,), dtype=int8),
                  'waveform': array([[[ 0.00841863,  0.04984551,  0.00841863, ...,  0.00841863,
              0.04984551,  0.09127238],
            [ 0.01125244, -0.06966043, -0.06966043, ..., -0.15057331,
              0.05170888, -0.029204  ],
            [ 0.2630286 ,  0.21625374,  0.21625374, ...,  0.12270404,
              0.07592919,  0.21625374],
            ...,
            [-0.03710477,  0.05580388,  0.00934956, ...,  0.24162118,
              0.14871253,  0.19516686],
            [-0.01875117, -0.06278115, -0.06278115, ..., -0.06278115,
             -0.01875117, -0.06278115],
            [-0.11524381, -0.11524381, -0.20668873, ...,  0.25053585,
              0.02192356,  0.15909094]]], shape=(1, 2048, 128), dtype=float32)},
             73: {'calibration_monitoring_id': None,
                  'event_time': <Time object: scale='tai' format='mjd' value=0.0>,
                  'event_type': <EventType.SUBARRAY: 32>,
                  'first_cell_id': None,
                  'module_hires_local_clock_counter': None,
                  'pedestal_intensity': None,
                  'pixel_status': array([4, 4, 4, ..., 4, 4, 4], shape=(2048,), dtype=uint8),
                  'selected_gain_channel': array([0, 0, 0, ..., 0, 0, 0], shape=(2048,), dtype=int8),
                  'waveform': array([[[-0.03057504,  0.01178788, -0.07293797, ...,  0.01178788,
              0.09651372,  0.01178788],
            [-0.08434547, -0.08434547, -0.1278464 , ...,  0.00265641,
             -0.08434547,  0.00265641],
            [ 0.09269284,  0.13629547,  0.00548759, ...,  0.00548759,
              0.04909021,  0.1798981 ],
            ...,
            [-0.05265228, -0.18097262, -0.09542572, ..., -0.09542572,
             -0.13819917, -0.05265228],
            [ 0.07313758,  0.07313758,  0.03256791, ..., -0.0891411 ,
             -0.00800176, -0.0891411 ],
            [-0.1561605 ,  0.06265412, -0.1561605 , ...,  0.06265412,
             -0.02487173, -0.06863465]]], shape=(1, 2048, 128), dtype=float32)},
             121: {'calibration_monitoring_id': None,
                   'event_time': <Time object: scale='tai' format='mjd' value=0.0>,
                   'event_type': <EventType.SUBARRAY: 32>,
                   'first_cell_id': None,
                   'module_hires_local_clock_counter': None,
                   'pedestal_intensity': None,
                   'pixel_status': array([4, 4, 4, ..., 4, 4, 4], shape=(1855,), dtype=uint8),
                   'selected_gain_channel': array([0, 0, 0, ..., 0, 0, 0], shape=(1855,), dtype=int8),
                   'waveform': array([[[-0.21347916, -0.25365788, -0.2938366 , ...,  0.52982706,
             -0.03267494, -0.11303237],
            [ 0.01376745,  0.0734081 ,  0.25233003, ..., -0.34407645,
             -0.18503472, -0.12539406],
            [-0.35374358, -0.25606352, -0.17791945, ..., -0.02163133,
              0.2909449 , -0.00209532],
            ...,
            [ 0.1588768 , -0.01432226,  0.35132024, ..., -0.22601001,
             -0.01432226,  0.4860306 ],
            [-0.18021354, -0.14094928,  0.19279698, ..., -0.29800636,
             -0.37653488, -0.396167  ],
            [-0.12698945, -0.0883472 , -0.06902608, ..., -0.32020068,
             -0.10766833,  0.06622178]]], shape=(1, 1855, 60), dtype=float32)},
             122: {'calibration_monitoring_id': None,
                   'event_time': <Time object: scale='tai' format='mjd' value=0.0>,
                   'event_type': <EventType.SUBARRAY: 32>,
                   'first_cell_id': None,
                   'module_hires_local_clock_counter': None,
                   'pedestal_intensity': None,
                   'pixel_status': array([4, 4, 4, ..., 4, 4, 4], shape=(1855,), dtype=uint8),
                   'selected_gain_channel': array([0, 0, 0, ..., 0, 0, 0], shape=(1855,), dtype=int8),
                   'waveform': array([[[ 0.0766639 ,  0.32452905,  0.3054625 , ..., -0.19026779,
             -0.11400159,  0.15293011],
            [-0.01299111,  0.04359313,  0.08131595, ..., -0.23932804,
             -0.1827438 , -0.14502099],
            [ 0.01621819, -0.1590356 ,  0.07463612, ..., -0.27587143,
             -0.23692615, -0.21745351],
            ...,
            [ 0.25392857,  0.34937772, -0.03241886, ..., -0.2614968 ,
             -0.1851375 , -0.29967648],
            [-0.19018087, -0.0539299 ,  0.00446337, ..., -0.19018087,
              0.04339222,  0.10178549],
            [ 0.13077052,  0.8790263 ,  1.5121658 , ..., -0.15702018,
             -0.31050855, -0.34888065]]], shape=(1, 1855, 60), dtype=float32)},
             124: {'calibration_monitoring_id': None,
                   'event_time': <Time object: scale='tai' format='mjd' value=0.0>,
                   'event_type': <EventType.SUBARRAY: 32>,
                   'first_cell_id': None,
                   'module_hires_local_clock_counter': None,
                   'pedestal_intensity': None,
                   'pixel_status': array([4, 4, 4, ..., 4, 4, 4], shape=(1855,), dtype=uint8),
                   'selected_gain_channel': array([0, 0, 0, ..., 0, 0, 0], shape=(1855,), dtype=int8),
                   'waveform': array([[[-0.11797108, -0.11797108, -0.09868825, ..., -0.0601226 ,
              0.11342283,  0.11342283],
            [-0.10063321,  0.10081094, -0.06400701, ..., -0.0273808 ,
              0.02755852, -0.10063321],
            [-0.19428314, -0.2135887 , -0.0591442 , ..., -0.23289426,
             -0.02053308, -0.09775533],
            ...,
            [-0.04796196, -0.04796196,  0.30063868, ...,  0.01013814,
              0.4362056 ,  0.33937207],
            [-0.10645148, -0.12572832, -0.12572832, ...,  0.31763902,
              0.39474636,  0.10559376],
            [ 0.50643456,  0.26373467,  0.35708076, ..., -0.128319  ,
             -0.14698821, -0.07231133]]], shape=(1, 1855, 60), dtype=float32)},
             148: {'calibration_monitoring_id': None,
                   'event_time': <Time object: scale='tai' format='mjd' value=0.0>,
                   'event_type': <EventType.SUBARRAY: 32>,
                   'first_cell_id': None,
                   'module_hires_local_clock_counter': None,
                   'pedestal_intensity': None,
                   'pixel_status': array([4, 4, 4, ..., 4, 4, 4], shape=(2048,), dtype=uint8),
                   'selected_gain_channel': array([0, 0, 0, ..., 0, 0, 0], shape=(2048,), dtype=int8),
                   'waveform': array([[[ 0.00710569, -0.03808828, -0.08328226, ...,  0.00710569,
              0.09749365,  0.00710569],
            [ 0.09487705,  0.09487705,  0.01112803, ..., -0.07262099,
             -0.03074648,  0.05300254],
            [-0.11470292, -0.02782934,  0.10248102, ..., -0.02782934,
             -0.02782934, -0.1581397 ],
            ...,
            [ 0.07964301,  0.0340572 , -0.05711443, ...,  0.07964301,
             -0.01152861,  0.07964301],
            [ 0.00197998,  0.00197998,  0.13376029, ...,  0.08983352,
              0.04590675, -0.08587357],
            [ 0.03801603, -0.00807014,  0.03801603, ..., -0.00807014,
             -0.1002425 , -0.1002425 ]]], shape=(1, 2048, 128), dtype=float32)},
             162: {'calibration_monitoring_id': None,
                   'event_time': <Time object: scale='tai' format='mjd' value=0.0>,
                   'event_type': <EventType.SUBARRAY: 32>,
                   'first_cell_id': None,
                   'module_hires_local_clock_counter': None,
                   'pedestal_intensity': None,
                   'pixel_status': array([4, 4, 4, ..., 4, 4, 4], shape=(2048,), dtype=uint8),
                   'selected_gain_channel': array([0, 0, 0, ..., 0, 0, 0], shape=(2048,), dtype=int8),
                   'waveform': array([[[-5.6908205e-03,  8.2228467e-02,  8.2228467e-02, ...,
             -4.9650464e-02, -5.6908205e-03,  3.8268823e-02],
            [-3.4884274e-02,  8.1552938e-03, -7.7923842e-02, ...,
             -1.2096341e-01, -7.7923842e-02,  8.1552938e-03],
            [-3.2151666e-02, -7.7254020e-02,  1.0315538e-01, ...,
              5.8053035e-02, -3.2151666e-02,  1.2950684e-02],
            ...,
            [-4.1048851e-02, -8.1529431e-02, -8.1529431e-02, ...,
             -5.6826987e-04, -5.6826987e-04, -1.6249059e-01],
            [ 4.2881422e-02, -2.7393538e-04,  4.2881422e-02, ...,
              8.6036779e-02,  2.1550286e-01,  2.5865820e-01],
            [-3.9726321e-02, -8.2472593e-02, -3.9726321e-02, ...,
              3.0224383e-01,  1.7400502e-01,  1.3125876e-01]]],
          shape=(1, 2048, 128), dtype=float32)},
             166: {'calibration_monitoring_id': None,
                   'event_time': <Time object: scale='tai' format='mjd' value=0.0>,
                   'event_type': <EventType.SUBARRAY: 32>,
                   'first_cell_id': None,
                   'module_hires_local_clock_counter': None,
                   'pedestal_intensity': None,
                   'pixel_status': array([4, 4, 4, ..., 4, 4, 4], shape=(2048,), dtype=uint8),
                   'selected_gain_channel': array([0, 0, 0, ..., 0, 0, 0], shape=(2048,), dtype=int8),
                   'waveform': array([[[-0.03838527, -0.08015627, -0.03838527, ...,  0.00338574,
              0.00338574, -0.03838527],
            [-0.08626862, -0.08626862, -0.04286342, ..., -0.08626862,
             -0.08626862, -0.12967381],
            [ 0.05237084, -0.21012151, -0.07887534, ..., -0.07887534,
             -0.03512662, -0.07887534],
            ...,
            [-0.11608443, -0.19859755, -0.07482787, ...,  0.00768524,
             -0.03357131, -0.15734099],
            [-0.00771839,  0.03798387, -0.14482519, ..., -0.09912293,
             -0.09912293, -0.05342066],
            [ 0.17404978,  0.1298405 ,  0.1298405 , ..., -0.04699665,
             -0.00278737, -0.04699665]]], shape=(1, 2048, 128), dtype=float32)}}}




.. GENERATED FROM PYTHON SOURCE LINES 49-65

Perform basic calibration:
--------------------------

Here we will use a ``CameraCalibrator`` which is just a simple wrapper
that runs the three calibraraton and trace-integration phases of the
pipeline, taking the data from levels:

**R0** → **R1** → **DL0** → **DL1**

You could of course do these each separately, by using the classes
``R1Calibrator``, ``DL0Reducer``, and ``DL1Calibrator``. Note that we
have not specified any configuration to the ``CameraCalibrator``, so it
will be using the default algorithms and thresholds, other than
specifying that the product is a “HESSIOR1Calibrator” (hopefully in the
near future that will be automatic).


.. GENERATED FROM PYTHON SOURCE LINES 65-71

.. code-block:: Python
   :lineno-start: 67



    calib = CameraCalibrator(subarray=source.subarray)
    calib(event)









.. GENERATED FROM PYTHON SOURCE LINES 72-83

Now the *r1*, *dl0* and *dl1* containers are filled in the event

-  **r1.tel[x]**: contains the “r1-calibrated” waveforms, after
   gain-selection, pedestal subtraciton, and gain-correction
-  **dl0.tel[x]**: is the same but with optional data volume reduction
   (some pixels not filled), in this case this is not performed by
   default, so it is the same as r1
-  **dl1.tel[x]**: contains the (possibly re-calibrated) waveforms as
   dl0, but also the time-integrated *image* that has been calculated
   using a ``ImageExtractor`` (a ``NeighborPeakWindowSum`` by default)


.. GENERATED FROM PYTHON SOURCE LINES 83-91

.. code-block:: Python
   :lineno-start: 84


    for tel_id in event.dl1.tel:
        print("TEL{:03}: {}".format(tel_id, source.subarray.tel[tel_id]))
        print("  - r0  wave shape  : {}".format(event.r0.tel[tel_id].waveform.shape))
        print("  - r1  wave shape  : {}".format(event.r1.tel[tel_id].waveform.shape))
        print("  - dl1 image shape : {}".format(event.dl1.tel[tel_id].image.shape))






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

 .. code-block:: none

    TEL026: MST_MST_FlashCam
      - r0  wave shape  : (1, 1764, 25)
      - r1  wave shape  : (1, 1764, 25)
      - dl1 image shape : (1764,)
    TEL027: MST_MST_FlashCam
      - r0  wave shape  : (1, 1764, 25)
      - r1  wave shape  : (1, 1764, 25)
      - dl1 image shape : (1764,)
    TEL029: MST_MST_FlashCam
      - r0  wave shape  : (1, 1764, 25)
      - r1  wave shape  : (1, 1764, 25)
      - dl1 image shape : (1764,)
    TEL047: SST_ASTRI_CHEC
      - r0  wave shape  : (1, 2048, 128)
      - r1  wave shape  : (1, 2048, 128)
      - dl1 image shape : (2048,)
    TEL053: SST_ASTRI_CHEC
      - r0  wave shape  : (1, 2048, 128)
      - r1  wave shape  : (1, 2048, 128)
      - dl1 image shape : (2048,)
    TEL059: SST_ASTRI_CHEC
      - r0  wave shape  : (1, 2048, 128)
      - r1  wave shape  : (1, 2048, 128)
      - dl1 image shape : (2048,)
    TEL069: SST_ASTRI_CHEC
      - r0  wave shape  : (1, 2048, 128)
      - r1  wave shape  : (1, 2048, 128)
      - dl1 image shape : (2048,)
    TEL073: SST_ASTRI_CHEC
      - r0  wave shape  : (1, 2048, 128)
      - r1  wave shape  : (1, 2048, 128)
      - dl1 image shape : (2048,)
    TEL121: MST_MST_NectarCam
      - r0  wave shape  : (2, 1855, 60)
      - r1  wave shape  : (1, 1855, 60)
      - dl1 image shape : (1855,)
    TEL122: MST_MST_NectarCam
      - r0  wave shape  : (2, 1855, 60)
      - r1  wave shape  : (1, 1855, 60)
      - dl1 image shape : (1855,)
    TEL124: MST_MST_NectarCam
      - r0  wave shape  : (2, 1855, 60)
      - r1  wave shape  : (1, 1855, 60)
      - dl1 image shape : (1855,)
    TEL148: SST_ASTRI_CHEC
      - r0  wave shape  : (1, 2048, 128)
      - r1  wave shape  : (1, 2048, 128)
      - dl1 image shape : (2048,)
    TEL162: SST_ASTRI_CHEC
      - r0  wave shape  : (1, 2048, 128)
      - r1  wave shape  : (1, 2048, 128)
      - dl1 image shape : (2048,)
    TEL166: SST_ASTRI_CHEC
      - r0  wave shape  : (1, 2048, 128)
      - r1  wave shape  : (1, 2048, 128)
      - dl1 image shape : (2048,)




.. GENERATED FROM PYTHON SOURCE LINES 92-97

Some image processing:
----------------------

Let’s look at the image


.. GENERATED FROM PYTHON SOURCE LINES 97-104

.. code-block:: Python
   :lineno-start: 99



    tel_id = sorted(event.r1.tel.keys())[1]
    sub = source.subarray
    geometry = sub.tel[tel_id].camera.geometry
    image = event.dl1.tel[tel_id].image








.. GENERATED FROM PYTHON SOURCE LINES 105-107

.. code-block:: Python
   :lineno-start: 105

    disp = CameraDisplay(geometry, image=image)




.. image-sg:: /auto_examples/tutorials/images/sphx_glr_calibrated_data_exploration_001.png
   :alt: FlashCam
   :srcset: /auto_examples/tutorials/images/sphx_glr_calibrated_data_exploration_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 108-119

.. code-block:: Python
   :lineno-start: 108

    mask = tailcuts_clean(
        geometry,
        image,
        picture_thresh=10,
        boundary_thresh=5,
        min_number_picture_neighbors=2,
    )
    cleaned = image.copy()
    cleaned[~mask] = 0
    disp = CameraDisplay(geometry, image=cleaned)




.. image-sg:: /auto_examples/tutorials/images/sphx_glr_calibrated_data_exploration_002.png
   :alt: FlashCam
   :srcset: /auto_examples/tutorials/images/sphx_glr_calibrated_data_exploration_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 120-124

.. code-block:: Python
   :lineno-start: 120

    params = hillas_parameters(geometry, cleaned)
    print(params)
    params





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

 .. code-block:: none

    {'intensity': np.float64(201.13648319244385),
     'kurtosis': np.float64(1.7664608543531792),
     'length': <Quantity 0.0862792 m>,
     'length_uncertainty': <Quantity 0.00266303 m>,
     'phi': <Angle -0.42975131 rad>,
     'psi': <Angle -0.30256932 rad>,
     'psi_uncertainty': <Angle 0.05077488 rad>,
     'r': <Quantity 0.78989421 m>,
     'skewness': np.float64(0.01211337763129691),
     'transverse_cog_uncertainty': <Quantity 0.00321526 m>,
     'width': <Quantity 0.02976784 m>,
     'width_uncertainty': <Quantity 0.00125979 m>,
     'x': <Quantity 0.71806864 m>,
     'y': <Quantity -0.32910527 m>}

    ctapipe.containers.CameraHillasParametersContainer:
                         intensity: total intensity (size) with default nan
                          skewness: measure of the asymmetry with default nan
                          kurtosis: measure of the tailedness with default nan
                                 x: centroid x coordinate with default nan m [m]
                                 y: centroid x coordinate with default nan m [m]
                                 r: radial coordinate of centroid with default nan m
                                    [m]
                               phi: polar coordinate of centroid with default nan
                                    deg [deg]
                            length: standard deviation along the major-axis with
                                    default nan m [m]
                length_uncertainty: uncertainty of length with default nan m [m]
                             width: standard spread along the minor-axis with
                                    default nan m [m]
                 width_uncertainty: uncertainty of width with default nan m [m]
                               psi: rotation angle of ellipse with default nan deg
                                    [deg]
                   psi_uncertainty: uncertainty of psi with default nan deg [deg]
        transverse_cog_uncertainty: uncertainty on the center of gravity along the
                                    transverse axis of the image with default nan m
                                    [m]



.. GENERATED FROM PYTHON SOURCE LINES 125-136

.. code-block:: Python
   :lineno-start: 125

    params = hillas_parameters(geometry, cleaned)

    plt.figure(figsize=(10, 10))
    disp = CameraDisplay(geometry, image=image)
    disp.add_colorbar()
    disp.overlay_moments(params, color="red", lw=3)
    disp.highlight_pixels(mask, color="white", alpha=0.3, linewidth=2)

    plt.xlim(params.x.to_value(u.m) - 0.5, params.x.to_value(u.m) + 0.5)
    plt.ylim(params.y.to_value(u.m) - 0.5, params.y.to_value(u.m) + 0.5)




.. image-sg:: /auto_examples/tutorials/images/sphx_glr_calibrated_data_exploration_003.png
   :alt: FlashCam
   :srcset: /auto_examples/tutorials/images/sphx_glr_calibrated_data_exploration_003.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    (-0.8291052663127615, 0.1708947336872385)



.. GENERATED FROM PYTHON SOURCE LINES 137-140

.. code-block:: Python
   :lineno-start: 137

    source.metadata






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

 .. code-block:: none


    {'is_simulation': False}



.. GENERATED FROM PYTHON SOURCE LINES 141-155

More complex image processing:
------------------------------

Let’s now explore how stereo reconstruction works.

first, look at a summed image from multiple telescopes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

For this, we want to use a ``CameraDisplay`` again, but since we can’t
sum and display images with different cameras, we’ll just sub-select
images from a particular camera type

These are the telescopes that are in this event:


.. GENERATED FROM PYTHON SOURCE LINES 155-161

.. code-block:: Python
   :lineno-start: 156


    tels_in_event = set(
        event.dl1.tel.keys()
    )  # use a set here, so we can intersect it later
    tels_in_event





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

 .. code-block:: none


    {162, 69, 122, 166, 59, 73, 47, 148, 53, 121, 26, 27, 124, 29}



.. GENERATED FROM PYTHON SOURCE LINES 162-165

.. code-block:: Python
   :lineno-start: 162

    cam_ids = set(sub.get_tel_ids_for_type("MST_MST_NectarCam"))
    cam_ids





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

 .. code-block:: none


    {128, 129, 130, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124}



.. GENERATED FROM PYTHON SOURCE LINES 166-172

.. code-block:: Python
   :lineno-start: 166

    cams_in_event = tels_in_event.intersection(cam_ids)
    first_tel_id = list(cams_in_event)[0]
    tel = sub.tel[first_tel_id]
    print("{}s in event: {}".format(tel, cams_in_event))






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

 .. code-block:: none

    MST_MST_NectarCams in event: {121, 122, 124}




.. GENERATED FROM PYTHON SOURCE LINES 173-175

Now, let’s sum those images:


.. GENERATED FROM PYTHON SOURCE LINES 175-184

.. code-block:: Python
   :lineno-start: 176


    image_sum = np.zeros_like(
        tel.camera.geometry.pix_x.value
    )  # just make an array of 0's in the same shape as the camera

    for tel_id in cams_in_event:
        image_sum += event.dl1.tel[tel_id].image









.. GENERATED FROM PYTHON SOURCE LINES 185-187

And finally display the sum of those images


.. GENERATED FROM PYTHON SOURCE LINES 187-195

.. code-block:: Python
   :lineno-start: 188


    plt.figure(figsize=(8, 8))

    disp = CameraDisplay(tel.camera.geometry, image=image_sum)
    disp.overlay_moments(params, with_label=False)
    plt.title("Sum of {}x {}".format(len(cams_in_event), tel))





.. image-sg:: /auto_examples/tutorials/images/sphx_glr_calibrated_data_exploration_004.png
   :alt: Sum of 3x MST_MST_NectarCam
   :srcset: /auto_examples/tutorials/images/sphx_glr_calibrated_data_exploration_004.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    Text(0.5, 1.0, 'Sum of 3x MST_MST_NectarCam')



.. GENERATED FROM PYTHON SOURCE LINES 196-201

let’s also show which telescopes those were. Note that currently
ArrayDisplay’s value field is a vector by ``tel_index``, not ``tel_id``,
so we have to convert to a tel_index. (this may change in a future
version to be more user-friendly)


.. GENERATED FROM PYTHON SOURCE LINES 201-214

.. code-block:: Python
   :lineno-start: 203



    nectarcam_subarray = sub.select_subarray(cam_ids, name="NectarCam")

    hit_pattern = np.zeros(shape=nectarcam_subarray.n_tels)
    hit_pattern[[nectarcam_subarray.tel_indices[x] for x in cams_in_event]] = 100

    plt.set_cmap(plt.cm.Accent)
    plt.figure(figsize=(8, 8))

    ad = ArrayDisplay(nectarcam_subarray)
    ad.values = hit_pattern
    ad.add_labels()



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/tutorials/images/sphx_glr_calibrated_data_exploration_005.png
         :alt: calibrated data exploration
         :srcset: /auto_examples/tutorials/images/sphx_glr_calibrated_data_exploration_005.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/tutorials/images/sphx_glr_calibrated_data_exploration_006.png
         :alt: NectarCam
         :srcset: /auto_examples/tutorials/images/sphx_glr_calibrated_data_exploration_006.png
         :class: sphx-glr-multi-img






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

   **Total running time of the script:** (0 minutes 5.538 seconds)


.. _sphx_glr_download_auto_examples_tutorials_calibrated_data_exploration.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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