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

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

.. _sphx_glr_auto_examples_micro_manager_wfs.py:

Micro-Manager simulated wavefront shaping
======================================================================
This script simulates a full wavefront shaping experiment in the Micro-Manager GUI.

To use it:

  * make sure  you have the PyDevice adapter installed in Micro-Manager (install the nightly build if you don't have it).
  * load the micro_manager_wfs.cfg hardware configuration in Micro-Manager,
  * locate the micro_manager_wfs.py in the file open dialog box that popps up.
  * take an initial snapshot, you may need to auto-adjust the color scale
  * to run the wavefront shaping algorithm, open the device properties, and use the 'wavefront' dropdown to select 'Optimized'
  * the Micro-Manager GUI will freeze while the algorithm is executed
  * afterwards, you can take another snapshot to see the optimized image with an improved contrast and resolution.
  * you can turn on live mode and switch between Flat and Optimized wavefronts.

You can also run this script from Python directly, in which case it will display the images in a matplotlib window.

.. GENERATED FROM PYTHON SOURCE LINES 18-122

.. code-block:: Python


    import astropy.units as u
    import numpy as np
    import skimage.data

    from openwfs.algorithms import FourierDualReference
    from openwfs.algorithms.utilities import WFSController
    from openwfs.processors import SingleRoi
    from openwfs.simulation import Microscope, StaticSource, Camera, SLM
    from openwfs.utilities.patterns import gaussian

    specimen_resolution = (512, 512)  # height × width in pixels of the specimen image
    specimen_pixel_size = 60 * u.nm  # resolution (pixel size) of the specimen image
    magnification = 40  # magnification from object plane to camera.
    numerical_aperture = 0.85  # numerical aperture of the microscope objective
    wavelength = 532.8 * u.nm  # wavelength of the light, for computing diffraction.
    camera_resolution = (256, 256)  # number of pixels on the camera
    camera_pixel_size = 6.45 * u.um  # Size of the pixels on the camera
    slm_shape = (100, 100)

    # Create a random noise image with a few bright spots, one in the center
    image_data = np.maximum(np.random.randint(-10000, 100, specimen_resolution, dtype=np.int16), 0)
    image_data[256, 256] = 100
    src = StaticSource(
        data=image_data,
        pixel_size=specimen_pixel_size,
    )

    # create an aberration pattern
    aberrations = StaticSource(data=skimage.data.camera() * (np.pi / 128), extent=2 * numerical_aperture)

    # simulate a spatial light modulator illuminated by a gaussian beam
    slm = SLM(slm_shape, field_amplitude=gaussian((100, 100), waist=1.0))

    # Create a microscope
    mic = Microscope(
        source=src,
        data_shape=specimen_resolution,
        magnification=magnification,
        numerical_aperture=numerical_aperture,
        wavelength=wavelength,
        aberrations=aberrations,
        incident_field=slm.field,
    )

    # simulate shot noise in a 16-bit camera:
    saturation = mic.read().mean() * 20
    cam = Camera(
        mic,
        analog_max=saturation,
        shot_noise=True,
        digital_max=0xFFFF,
    )

    # integrate the signal in a gaussian-weighted region of interest
    feedback = SingleRoi(source=cam, pos=(256, 256), mask_type="gaussian", radius=3.0, waist=1.0)

    # the WFS algorithm
    alg = WFSController(FourierDualReference, feedback=feedback, slm=slm, slm_shape=slm_shape, k_radius=3.5, phase_steps=5)

    # construct dictionary of objects to expose to Micro-Manager
    devices = {"camera": cam, "stage": mic.xy_stage, "algorithm": alg}

    if __name__ == "__main__":
        # run the algorithm
        # NOTE: the _psf field will later be replaced by a .psf field that returns a camera
        import matplotlib.pyplot as plt

        before = mic.read()
        before_psf = mic._psf
        plt.subplot(2, 3, 1)
        plt.imshow(before)
        plt.colorbar()
        plt.title("Image - no WFS")

        plt.subplot(2, 3, 2)
        plt.imshow(before_psf)
        plt.colorbar()
        plt.title("PSF - no WFS")

        plt.subplot(2, 3, 3)
        plt.title("Aberrations")
        plt.imshow(aberrations.read())
        plt.colorbar()

        alg.wavefront = WFSController.State.OPTIMIZED
        after = mic.read()
        after_psf = mic._psf

        plt.subplot(2, 3, 4)
        plt.imshow(after)
        plt.title("Image - WFS")
        plt.colorbar()

        plt.subplot(2, 3, 5)
        plt.imshow(after_psf)
        plt.colorbar()
        plt.title("PSF - WFS")

        plt.subplot(2, 3, 6)
        plt.imshow(-slm.phases.read())
        plt.colorbar()
        plt.title("Corrections × -1")
        plt.show(block=True)


.. _sphx_glr_download_auto_examples_micro_manager_wfs.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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