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

    Click :ref:`here <sphx_glr_download_auto_examples_monitoring_pmt_rates.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_monitoring_pmt_rates.py:


======================
Mean PMT Rates Monitor
======================

The following script calculates the mean PMT rates and updates the plot.




.. code-block:: python

    from __future__ import absolute_import, print_function, division

    # Author: Tamas Gal <tgal@km3net.de>
    # License: MIT
    from datetime import datetime
    import io
    from collections import defaultdict
    import threading
    import time
    import km3pipe as kp
    from km3pipe.io.daq import TMCHData
    import numpy as np
    import matplotlib
    matplotlib.use('Agg')    # noqa
    import matplotlib.pyplot as plt
    import km3pipe.style as kpst
    kpst.use("km3pipe")

    __author__ = "Tamas Gal"
    __email__ = "tgal@km3net.de"
    VERSION = "1.0"

    log = kp.logger.get_logger("PMTrates")


    class PMTRates(kp.Module):
        def configure(self):
            self.detector = self.require("detector")
            self.du = self.require("du")
            self.interval = self.get("interval") or 10
            self.plot_path = self.get("plot_path") or "km3web/plots/pmtrates.png"
            self.max_x = 800
            self.index = 0
            self.rates = defaultdict(list)
            self.rates_matrix = np.full((18 * 31, self.max_x), np.nan)
            self.lock = threading.Lock()
            self.thread = threading.Thread(target=self.run, args=())
            self.thread.daemon = True
            self.thread.start()

        def run(self):
            interval = self.interval
            while True:
                time.sleep(interval)
                now = datetime.now()
                self.add_column()
                self.update_plot()
                with self.lock:
                    self.rates = defaultdict(list)
                delta_t = (datetime.now() - now).total_seconds()
                remaining_t = self.interval - delta_t
                log.info(
                    "Delta t: {} -> waiting for {}s".format(
                        delta_t, self.interval - delta_t
                    )
                )
                if (remaining_t < 0):
                    log.error(
                        "Can't keep up with plot production. "
                        "Increase the interval!"
                    )
                    interval = 1
                else:
                    interval = remaining_t

        def add_column(self):
            m = np.roll(self.rates_matrix, -1, 1)
            y_range = 18 * 31
            mean_rates = np.full(y_range, np.nan)
            for i in range(y_range):
                if i not in self.rates:
                    continue
                mean_rates[i] = np.mean(self.rates[i])

            m[:, self.max_x - 1] = mean_rates
            self.rates_matrix = m

        def update_plot(self):
            print("Updating plot at {}".format(self.plot_path))
            now = time.time()
            max_x = self.max_x
            interval = self.interval

            def xlabel_func(timestamp):
                return datetime.utcfromtimestamp(timestamp).strftime("%H:%M")

            m = self.rates_matrix
            m[m > 15000] = 15000
            m[m < 5000] = 5000
            fig, ax = plt.subplots(figsize=(10, 6))
            ax.imshow(m, origin='lower')
            ax.set_title(
                "Mean PMT Rates for DU{} (colours from 5kHz to 15kHz)\n{}".format(
                    self.du, datetime.utcnow()
                )
            )
            ax.set_xlabel("UTC time [{}s/px]".format(interval))
            plt.yticks([i * 31 for i in range(18)],
                       ["Floor {}".format(f) for f in range(1, 19)])
            xtics_int = range(0, max_x, int(max_x / 10))
            plt.xticks([i for i in xtics_int], [
                xlabel_func(now - (max_x - i) * interval) for i in xtics_int
            ])
            fig.tight_layout()
            plt.savefig(self.plot_path)
            plt.close('all')

        def process(self, blob):
            tmch_data = TMCHData(io.BytesIO(blob['CHData']))
            dom_id = tmch_data.dom_id

            if dom_id not in self.detector.doms:
                return blob

            du, floor, _ = self.detector.doms[dom_id]

            if du != self.du:
                return blob

            y_base = (floor - 1) * 31

            for channel_id, rate in enumerate(tmch_data.pmt_rates):
                idx = y_base + channel_id
                with self.lock:
                    self.rates[idx].append(rate)

            return blob


    def main():
        detector = kp.hardware.Detector(det_id=29)
        pipe = kp.Pipeline(timeit=True)
        pipe.attach(
            kp.io.CHPump,
            host='192.168.0.110',
            port=5553,
            tags='IO_MONIT',
            timeout=60 * 60 * 24 * 7,
            max_queue=1000
        )
        pipe.attach(PMTRates, detector=detector, du=2, interval=2)
        pipe.drain()


    if __name__ == "__main__":
        main()

**Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_monitoring_pmt_rates.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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