.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_tutorials/tutorials/orient3d_simulation.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_tutorials_tutorials_orient3d_simulation.py>`
        to download the full example code.

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

.. _sphx_glr_auto_tutorials_tutorials_orient3d_simulation.py:


3D Image Orientation
====================

This script illustrates the estimation of orientation angles using a synchronization
matrix and the voting method, based on simulated data projected from a 3D cryo-EM map.

.. GENERATED FROM PYTHON SOURCE LINES 8-28

.. code-block:: Python


    import os

    import numpy as np

    from aspire.abinitio import CLSyncVoting
    from aspire.operators import RadialCTFFilter
    from aspire.source import OrientedSource, Simulation
    from aspire.utils import mean_aligned_angular_distance
    from aspire.volume import Volume

    file_path = os.path.join(
        os.path.dirname(os.getcwd()), "data", "clean70SRibosome_vol_65p.mrc"
    )

    print(
        "This script illustrates orientation estimation using "
        "synchronization matrix and voting method"
    )





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

 .. code-block:: none

    This script illustrates orientation estimation using synchronization matrix and voting method




.. GENERATED FROM PYTHON SOURCE LINES 29-31

Initialize Simulation Object and CTF Filters
--------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 31-58

.. code-block:: Python


    # Define a precision for this experiment
    dtype = np.float32

    # Set the sizes of images
    img_size = 33

    # Set the total number of images generated from the 3D map
    num_imgs = 128

    # Specify the CTF parameters not used for this example
    # but necessary for initializing the simulation object
    pixel_size = 5  # Pixel size of the images (in angstroms)
    voltage = 200  # Voltage (in KV)
    defocus_min = 1.5e4  # Minimum defocus value (in angstroms)
    defocus_max = 2.5e4  # Maximum defocus value (in angstroms)
    defocus_ct = 7  # Number of defocus groups.
    Cs = 2.0  # Spherical aberration
    alpha = 0.1  # Amplitude contrast

    print("Initialize simulation object and CTF filters.")
    # Create CTF filters
    filters = [
        RadialCTFFilter(pixel_size, voltage, defocus=d, Cs=2.0, alpha=0.1)
        for d in np.linspace(defocus_min, defocus_max, defocus_ct)
    ]





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

 .. code-block:: none

    Initialize simulation object and CTF filters.




.. GENERATED FROM PYTHON SOURCE LINES 59-61

Downsampling
------------

.. GENERATED FROM PYTHON SOURCE LINES 61-71

.. code-block:: Python


    # Load the map file of a 70S Ribosome and downsample the 3D map to desired resolution.
    # The downsampling can be done by the internal function of Volume object.
    print(
        f"Load 3D map and downsample 3D map to desired grids "
        f"of {img_size} x {img_size} x {img_size}."
    )
    vols = Volume.load(file_path, dtype=dtype)
    vols = vols.downsample(img_size)





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

 .. code-block:: none

    Load 3D map and downsample 3D map to desired grids of 33 x 33 x 33.




.. GENERATED FROM PYTHON SOURCE LINES 72-74

Create Simulation Object and Obtain True Rotation Angles
--------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 74-82

.. code-block:: Python


    # Create a simulation object with specified filters and the downsampled 3D map
    print("Use downsampled map to creat simulation object.")
    sim = Simulation(L=img_size, n=num_imgs, vols=vols, unique_filters=filters, dtype=dtype)

    print("Get true rotation angles generated randomly by the simulation object.")
    rots_true = sim.rotations





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

 .. code-block:: none

    Use downsampled map to creat simulation object.
    Get true rotation angles generated randomly by the simulation object.




.. GENERATED FROM PYTHON SOURCE LINES 83-85

Estimate Orientation
--------------------

.. GENERATED FROM PYTHON SOURCE LINES 85-102

.. code-block:: Python


    # Initialize an orientation estimation object and create an
    # ``OrientedSource`` object to perform viewing angle and image offset
    # estimation. Here, because of the small image size of the
    # ``Simulation``, we customize the ``CLSyncVoting`` method to use
    # fewer theta values when searching for common-lines between pairs of
    # images. Additionally, since we are processing images with no noise,
    # we opt not to use a ``fuzzy_mask``, an option that improves
    # common-line detection in higher noise regimes.
    print(
        "Estimate rotation angles and offsets using synchronization matrix and voting method."
    )
    orient_est = CLSyncVoting(sim, n_theta=36, mask=False)
    oriented_src = OrientedSource(sim, orient_est)
    rots_est = oriented_src.rotations






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

 .. code-block:: none

    Estimate rotation angles and offsets using synchronization matrix and voting method.

    Searching over common line pairs:   0%|          | 0/8128 [00:00<?, ?it/s]
    Searching over common line pairs:   7%|▋         | 577/8128 [00:00<00:01, 5763.62it/s]
    Searching over common line pairs:  14%|█▍        | 1171/8128 [00:00<00:01, 5863.39it/s]
    Searching over common line pairs:  22%|██▏       | 1761/8128 [00:00<00:01, 5877.64it/s]
    Searching over common line pairs:  29%|██▉       | 2353/8128 [00:00<00:00, 5892.62it/s]
    Searching over common line pairs:  36%|███▌      | 2944/8128 [00:00<00:00, 5898.66it/s]
    Searching over common line pairs:  43%|████▎     | 3534/8128 [00:00<00:00, 5863.24it/s]
    Searching over common line pairs:  51%|█████     | 4126/8128 [00:00<00:00, 5879.16it/s]
    Searching over common line pairs:  58%|█████▊    | 4719/8128 [00:00<00:00, 5894.15it/s]
    Searching over common line pairs:  65%|██████▌   | 5312/8128 [00:00<00:00, 5903.42it/s]
    Searching over common line pairs:  73%|███████▎  | 5903/8128 [00:01<00:00, 5903.19it/s]
    Searching over common line pairs:  80%|███████▉  | 6494/8128 [00:01<00:00, 5892.56it/s]
    Searching over common line pairs:  87%|████████▋ | 7084/8128 [00:01<00:00, 5886.29it/s]
    Searching over common line pairs:  94%|█████████▍| 7673/8128 [00:01<00:00, 5877.70it/s]
    Searching over common line pairs: 100%|██████████| 8128/8128 [00:01<00:00, 5877.42it/s]
 
    LSQR            Least-squares solution of  Ax = b
    The matrix A has 8128 rows and 256 columns
    damp = 0.00000000000000e+00   calc_var =        0
    atol = 1.00e-06                 conlim = 1.00e+08
    btol = 1.00e-06               iter_lim =      512
 
       Itn      x[0]       r1norm     r2norm   Compatible    LS      Norm A   Cond A
         0  0.00000e+00   2.322e+02  2.322e+02    1.0e+00  2.2e-02
         1  1.06962e+00   1.656e+02  1.656e+02    7.1e-01  2.1e-01   7.4e+00  1.0e+00
         2  8.60900e-01   1.614e+02  1.614e+02    7.0e-01  4.6e-02   1.0e+01  2.1e+00
         3  8.97185e-01   1.609e+02  1.609e+02    6.9e-01  1.4e-02   1.2e+01  3.2e+00
         4  9.52016e-01   1.609e+02  1.609e+02    6.9e-01  5.8e-03   1.4e+01  4.4e+00
         5  9.33992e-01   1.609e+02  1.609e+02    6.9e-01  2.2e-03   1.5e+01  5.6e+00
         6  9.37907e-01   1.609e+02  1.609e+02    6.9e-01  9.5e-04   1.7e+01  6.8e+00
         7  9.34790e-01   1.609e+02  1.609e+02    6.9e-01  4.5e-04   1.8e+01  8.1e+00
         8  9.32633e-01   1.609e+02  1.609e+02    6.9e-01  1.9e-04   1.9e+01  9.4e+00
         9  9.33399e-01   1.609e+02  1.609e+02    6.9e-01  8.3e-05   2.0e+01  1.1e+01
        10  9.32934e-01   1.609e+02  1.609e+02    6.9e-01  3.8e-05   2.1e+01  1.2e+01
        12  9.32869e-01   1.609e+02  1.609e+02    6.9e-01  8.4e-06   2.3e+01  1.4e+01
        13  9.32768e-01   1.609e+02  1.609e+02    6.9e-01  2.8e-06   2.4e+01  1.6e+01
        14  9.32768e-01   1.609e+02  1.609e+02    6.9e-01  8.1e-07   2.5e+01  1.7e+01
 
    LSQR finished
    The least-squares solution is good enough, given atol     
 
    istop =       2   r1norm = 1.6e+02   anorm = 2.5e+01   arnorm = 3.3e-03
    itn   =      14   r2norm = 1.6e+02   acond = 1.7e+01   xnorm  = 2.4e+01
 




.. GENERATED FROM PYTHON SOURCE LINES 103-105

Mean Angular Distance
---------------------

.. GENERATED FROM PYTHON SOURCE LINES 105-116

.. code-block:: Python


    # ``mean_aligned_angular_distance`` will perform global alignment of the estimated rotations
    # to the ground truth and find the mean angular distance between them (in degrees).
    mean_ang_dist = mean_aligned_angular_distance(rots_est, rots_true)
    print(
        f"Mean angular distance between estimates and ground truth: {mean_ang_dist} degrees"
    )

    # Basic Check
    assert mean_ang_dist < 10





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

 .. code-block:: none

    Mean angular distance between estimates and ground truth: 8.61561393737793 degrees




.. GENERATED FROM PYTHON SOURCE LINES 117-119

Offsets Estimation
------------------

.. GENERATED FROM PYTHON SOURCE LINES 119-129

.. code-block:: Python


    # The ground truth offsets from the simulation can be compared to
    # those estimated by the commonlines method above.

    # Calculate Estimation error in pixels for each image.
    offs_diff = np.sqrt(np.sum((oriented_src.offsets - sim.offsets) ** 2, axis=1))

    # Calculate the mean error in pixels across all images.
    offs_err = offs_diff.mean()
    print(f"Mean offset error in pixels {offs_err}, approx {offs_err/img_size*100:.1f}%")




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

 .. code-block:: none

    Mean offset error in pixels 0.724621057510376, approx 2.2%





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

   **Total running time of the script:** (0 minutes 4.145 seconds)


.. _sphx_glr_download_auto_tutorials_tutorials_orient3d_simulation.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: orient3d_simulation.ipynb <orient3d_simulation.ipynb>`

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

      :download:`Download Python source code: orient3d_simulation.py <orient3d_simulation.py>`

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

      :download:`Download zipped: orient3d_simulation.zip <orient3d_simulation.zip>`


.. only:: html

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

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