.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/live_imaging/viewing_the_sensor.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_live_imaging_viewing_the_sensor.py>`
        to download the full example code.

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

.. _sphx_glr_examples_live_imaging_viewing_the_sensor.py:


Viewing Sensor Data During Acquisition
======================================

This example shows how to view the sensor data during acquisition. This is useful for
monitoring the data during acquisition.

This example will:

1. Connect to the DE server
2. Start an acquisition
3. Continuously update a plot of the sensor data during acquisition
4. Continually update a plot of the virtual image 0 (The sum of the sensor data) during acquisition

Note: Using the qt matplotlib backend will make the plotting update.

.. GENERATED FROM PYTHON SOURCE LINES 17-39

.. code-block:: Python


    #  %matplotlib qt
    from deapi import Client
    import matplotlib.pyplot as plt
    import numpy as np

    client = Client()
    client.usingMmf = False

    client.connect(port=13241)  # connect to the running DE Server
    client["Frames Per Second"] = 500
    client.scan(size_x=64, size_y=64, enable="On")
    client.start_acquisition(1)


    fig, axs = plt.subplots(1, 2)
    data, _, _, _ = client.get_result("virtual_image0")
    live_im = axs[0].imshow(np.zeros_like(data))

    data2, _, _, _ = client.get_result("singleframe_integrated")
    live_virt_im = axs[1].imshow(np.zeros_like(data))




.. image-sg:: /examples/live_imaging/images/sphx_glr_viewing_the_sensor_001.png
   :alt: viewing the sensor
   :srcset: /examples/live_imaging/images/sphx_glr_viewing_the_sensor_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 40-45

Plotting the Sensor Data
------------------------
We will plot the sensor data during acquisition. Note that matplotlib will block unless you are
using the Qt backend, and you won't get a live view unless you initialize the plot first and then
update the data. If you have troubles with this please raise an issue on the github page.

.. GENERATED FROM PYTHON SOURCE LINES 45-58

.. code-block:: Python


    while client.acquiring:
        data, _, _, _ = client.get_result("singleframe_integrated")
        live_im.set_data(data)

        data, _, _, _ = client.get_result("virtual_image0")
        live_virt_im.set_data(data)
        plt.pause(
            0.02
        )  # allow the matplotlib event loop to run. ~50 fps. Anything faster we need to
        # use blitting in matplotlib. (up to ~500 fps)
        live_im.autoscale()
        live_virt_im.autoscale()








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

   **Total running time of the script:** (0 minutes 8.350 seconds)


.. _sphx_glr_download_examples_live_imaging_viewing_the_sensor.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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