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

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

.. _sphx_glr_auto_examples_plot_custom_ula.py:


Custom TDM ULA: ADC to point cloud
==================================

Define a radar from physical parameters, synthesize one moving target, and run
the same model-aware pipeline used for recorded ADC captures.

.. GENERATED FROM PYTHON SOURCE LINES 8-131



.. image-sg:: /auto_examples/images/sphx_glr_plot_custom_ula_001.png
   :alt: RD, RA, Point cloud
   :srcset: /auto_examples/images/sphx_glr_plot_custom_ula_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    from __future__ import annotations

    import numpy as np
    from matplotlib import pyplot as plt

    from pyradar.base import (
        FMCW,
        TDM,
        CFARConfig,
        DoAConfig,
        FFTConfig,
        ProcessingConfig,
        Radar,
        Sampler,
        Transceivers,
    )
    from pyradar.base.waveform import SPEED_OF_LIGHT
    from pyradar.sim import PointTarget


    def make_radar() -> Radar:
        """Create an eight-channel virtual ULA from two TX and four RX."""

        waveform = FMCW(
            startFrequency=77e9,
            slope=20e12,
            adcStartTime=2e-6,
            rampEndTime=40e-6,
            idleTime=10e-6,
        )
        sampler = Sampler(numSamples=64, numLoops=16, sampleRate=2e6)
        center = waveform.startFrequency + waveform.slope * (
            waveform.adcStartTime + sampler.captureDuration / 2
        )
        wavelength = SPEED_OF_LIGHT / center
        rx = np.column_stack((np.zeros(4), np.arange(4) * wavelength / 2, np.zeros(4)))
        tx = np.array([[0.0, 0.0, 0.0], [0.0, 2 * wavelength, 0.0]], dtype=float)
        processing = ProcessingConfig(
            fft=FFTConfig(
                rangeFftSize=64,
                dopplerFftSize=16,
                azimuthFftSize=128,
                elevationFftSize=16,
                rangeWindow="rectangular",
                dopplerWindow="rectangular",
                angleWindow="rectangular",
                removeDopplerMean=False,
            ),
            cfar=CFARConfig(
                method="ca",
                trainingCells=(3, 2),
                guardCells=(1, 1),
                pfa=1e-3,
                maxDetections=16,
            ),
            doa=DoAConfig(
                method="auto",
                azimuthFov=(-np.pi / 2, np.pi / 2),
                elevationFov=(-0.1, 0.1),
                azimuthBins=181,
                elevationBins=9,
            ),
            retainRangeAngle=True,
        )
        return Radar(
            waveform=waveform,
            sampler=sampler,
            transceivers=Transceivers(txPositions=tx, rxPositions=rx),
            mimo=TDM(numTx=2, numRx=4, txOrder=(1, 0)),
            processing=processing,
            name="custom_tdm_ula",
        )


    radar = make_radar()
    rangeBin = 18
    dopplerBin = -3
    azimuth = np.deg2rad(24)
    direction = np.array([np.cos(azimuth), np.sin(azimuth), 0.0])
    target = PointTarget(
        position=rangeBin * radar.rangeBinSize * direction,
        velocity=-dopplerBin * radar.velocityBinSize * direction,
        rcs=10_000.0,
    )
    adc = radar.simulate(target, noisePower=1e-3, seed=7, frameId=0)
    result = radar.process_adc(adc)

    rd = result.rangeDopplerCube.power.mean(axis=2)
    ra = result.rangeAngleCube.power
    points = result.pointCloud

    figure, axes = plt.subplots(1, 3, figsize=(12, 3.5), constrained_layout=True)
    axes[0].imshow(
        10 * np.log10(np.maximum(rd, np.finfo(float).tiny)),
        origin="lower",
        aspect="auto",
        extent=[
            radar.velocityAxis[0],
            radar.velocityAxis[-1],
            radar.rangeAxis[0],
            radar.rangeAxis[-1],
        ],
    )
    axes[0].set(xlabel="Radial velocity (m/s)", ylabel="Range (m)", title="RD")

    axes[1].imshow(
        10 * np.log10(np.maximum(ra, np.finfo(float).tiny)),
        origin="lower",
        aspect="auto",
        extent=[
            np.rad2deg(result.rangeAngleCube.coords["azimuth"][0]),
            np.rad2deg(result.rangeAngleCube.coords["azimuth"][-1]),
            radar.rangeAxis[0],
            radar.rangeAxis[-1],
        ],
    )
    axes[1].set(xlabel="Azimuth (deg)", ylabel="Range (m)", title="RA")

    axes[2].scatter(points.xyz[:, 0], points.xyz[:, 1], c=points.radialVelocity)
    axes[2].set(xlabel="Forward x (m)", ylabel="Left y (m)", title="Point cloud")
    axes[2].axis("equal")
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.361 seconds)


.. _sphx_glr_download_auto_examples_plot_custom_ula.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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