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

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

.. _sphx_glr_auto_examples_image_correlation_plot_imaging_ics_tttrlib.py:


=============================
Computing ICS data by tttrlib
=============================

Demonstrate the use of the tttrlib ICS features and compare to
numpy ICS implementation use CLSMImage as an input

.. GENERATED FROM PYTHON SOURCE LINES 9-99



.. image-sg:: /auto_examples/image_correlation/images/sphx_glr_plot_imaging_ics_tttrlib_001.png
   :alt: plot imaging ics tttrlib
   :srcset: /auto_examples/image_correlation/images/sphx_glr_plot_imaging_ics_tttrlib_001.png
   :class: sphx-glr-single-img





.. code-block:: Python

    import tttrlib
    import numpy as np
    import matplotlib.pylab as plt
    import matplotlib.patches


    def numpy_fft_ics(
            images: np.ndarray,
            subtract_average: bool = True
    ):
        if subtract_average:
            images = images - images.mean(axis=0) + images.mean()
        ics_list = list()
        _, nx, ny = images.shape
        N = nx * ny
        for im in images:
            img_flucc = im - im.mean()
            f = np.fft.fft2(img_flucc)
            ics = np.fft.ifft2(f*np.conj(f)).real / (np.mean(im)**2 * N)
            ics_list.append(ics)
        return np.array(ics_list)


    data = tttrlib.TTTR('../../tttr-data/imaging/leica/sp5/LSM_1.ptu', 'PTU')
    reading_parameter = {
        "tttr_data": data,
        "reading_routine": 'SP5',
        "channels": [0],
        "fill": True
    }
    clsm = tttrlib.CLSMImage(**reading_parameter)

    # # specifies a selection on the image
    #x_range = [80, 152]
    #y_range = [80, 182]

    # if nothing or the following ranges are specified
    # an ICS of the entire image is computed
    x_range = [0, -1]
    y_range = [0, -1]

    # the ICS routines computes the correlation between pairs or
    # images. This way the CCF between different images can be computed, e.g.,
    # to compute the correlation between different frames.
    # If no image pairs are specified the ACF of the images is computed.
    frames = np.arange(0, clsm.n_frames)
    frame_shift = 0

    img = clsm.intensity

    ics_parameter = {
        'tttr_data': data,
        'x_range': x_range,
        'y_range': y_range,
        'clsm': clsm,
        'frames_index_pairs': list(
            zip(
                frames.tolist(),
                np.roll(frames, frame_shift).tolist()
            )
        ),
        'subtract_average': "frame"
    }
    ics = tttrlib.CLSMImage.compute_ics(**ics_parameter)
    # ics = numpy_fft_ics(img[x_range[0]:x_range[1], y_range[0]:y_range[1], :])

    ics_mean = ics.mean(axis=0)
    ics_std = ics.std(axis=0)

    fig, ax = plt.subplots(ncols=2)
    ommit_center = True
    if ommit_center:
        nx, ny = ics_mean.shape
        data = ics_mean
        data[0, 0] = 0.0
    ax[0].imshow(img.sum(axis=0), cmap='inferno')
    rect = matplotlib.patches.Rectangle(
        xy=(x_range[0], y_range[0]),
        width=x_range[1]-x_range[0],
        height=y_range[1]-y_range[0],
        edgecolor='r',
        facecolor="none"
    )
    ax[0].add_patch(rect)
    ax[1].imshow(
        np.fft.fftshift(ics_mean),
        cmap='inferno',
        #vmax=0.00001
    )
    plt.show()


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

   **Total running time of the script:** (0 minutes 1.664 seconds)


.. _sphx_glr_download_auto_examples_image_correlation_plot_imaging_ics_tttrlib.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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