mach.io.must

mach.io.must#

Helper functions for reading PyMUST data.

Functions

download_pymust_doppler_data()

Download and load the PyMUST PWI_disk.mat test data.

extract_pymust_params(mat_data)

Extract PyMUST parameters from loaded MATLAB data.

linear_probe_positions(n_elements, pitch)

Generate element positions for a linear array.

scan_grid(*axes)

Return a flattened meshgrid of the given axes.
mach.io.must.download_pymust_doppler_data() → dict#

Download and load the PyMUST PWI_disk.mat test data.

This data contains ultrasound RF data from a rotating disk phantom, commonly used for benchmarking beamforming algorithms.

Returns:

MATLAB data structure containing RF data and parameters

Return type:

dict

mach.io.must.extract_pymust_params(mat_data: dict) → dict#

Extract PyMUST parameters from loaded MATLAB data.

Parameters:

mat_data – PyMUST data loaded from .mat file by download_pymust_doppler_data

Returns:

PyMUST acquisition parameters including:

  • Nelements: Number of array elements

  • pitch: Element spacing [m]

  • c: Speed of sound [m/s]

  • fs: Sampling frequency [Hz]

  • fc: Center frequency [Hz]

  • t0: Start time [s]

  • fnumber: F-number for beamforming

Return type:

dict

mach.io.must.linear_probe_positions(
n_elements: int,
pitch: float,
) → Float[ndarray, '{n_elements} xyz=3']#

Generate element positions for a linear array.

Parameters:

  • n_elements – Number of elements

  • pitch – Element spacing [m]

Returns:

Element positions [x, y, z] with shape (N_elements, 3)

mach.io.must.scan_grid(
*axes: ndarray,
) → Float[ndarray, 'points {len(axes)}']#

Return a flattened meshgrid of the given axes.

Example
>>> x = np.linspace(-1.25e-2, 1.25e-2, num=251, endpoint=True)
>>> y = np.array([0.0])
>>> z = np.linspace(1e-2, 3.5e-2, num=251, endpoint=True)
>>> grid = scan_grid(x, y, z)
>>> grid.shape
(63001, 3)
>>> grid
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