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‎examples/README.md‎

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- [Focused bowl transducer (axisymmetric)](at_focused_bowl_AS/) ([original example](http://www.k-wave.org/documentation/example_at_piston_and_bowl_transducers.php#heading6), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/waltsims/k-wave-python/blob/master/examples/at_focused_bowl_AS/at_focused_bowl_AS.ipynb))
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- [Focused annular array](at_focused_annular_array_3D/) ([original example](http://www.k-wave.org/documentation/example_at_piston_and_bowl_transducers.php#heading7), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/waltsims/k-wave-python/blob/master/examples/at_focused_annular_array_3D/at_focused_annular_array_3D.ipynb))
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- [2D FFT Reconstruction For A Line Sensor](pr_2D_FFT_line_sensor/) ([original example](http://www.k-wave.org/documentation/example_pr_2D_fft_line_sensor.php), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/waltsims/k-wave-python/blob/master/examples/pr_2D_FFT_line_sensor/pr_2D_FFT_line_sensor.ipynb))
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- [3D FFT Reconstruction For A Planar Sensor](pr_3D_FFT_planar_sensor/) ([original example](http://www.k-wave.org/documentation/example_pr_3D_fft_planar_sensor.php), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/waltsims/k-wave-python/blob/master/examples/pr_3D_FFT_planar_sensor/pr_3D_FFT_planar_sensor.ipynb))
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- [2D Time Reversal Reconstruction For A Line Sensor](pr_2D_TR_line_sensor/) ([original example](http://www.k-wave.org/documentation/example_pr_2D_tr_line_sensor.php), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/waltsims/k-wave-python/blob/master/examples/pr_2D_TR_line_sensor/pr_2D_TR_line_sensor.ipynb))
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- [3D Time Reversal Reconstruction For A Planar Sensor](pr_3D_TR_planar_sensor/) ([original example](http://www.k-wave.org/documentation/example_pr_3D_tr_planar_sensor.php), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/waltsims/k-wave-python/blob/master/examples/pr_3D_TR_planar_sensor/pr_3D_TR_planar_sensor.ipynb))
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- [Focussed Detector In 2D Example](sd_focussed_detector_2D/) ([original example](http://www.k-wave.org/documentation/example_sd_focussed_detector_2D.php), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/waltsims/k-wave-python/blob/master/examples/sd_focussed_detector_2D/sd_focussed_detector_2D.ipynb))
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‎examples/pr_2D_FFT_line_sensor/README.md‎

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# 2D FFT Reconstruction For A Line Sensor Example
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[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/waltsims/k-wave-python/blob/master/examples/pr_2D_FFT_line_sensor/pr_2D_FFT_line_sensor.ipynb)
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This example demonstrates the use of k-Wave for the reconstruction of a two-dimensional photoacoustic wave-field recorded over a linear array of sensor elements. The sensor data is simulated using [kspaceFirstOrder2D](https://k-wave-python.readthedocs.io/en/latest/kwave.kspaceFirstOrder2D.html) and reconstructed using kspaceLineRecon. It builds on the Homogeneous Propagation Medium and Heterogeneous Propagation Medium examples.
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To read more, visit the [original example page](http://www.k-wave.org/documentation/example_pr_2D_fft_line_sensor.php) or its [implementation](https://github.com/ucl-bug/k-wave/blob/main/k-Wave/examples/example_pr_2D_FFT_line_sensor.m).

‎examples/pr_2D_FFT_line_sensor/pr_2D_FFT_line_sensor.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"%%capture\n",
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"!pip install k-wave-python "
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"import matplotlib.pyplot as plt\n",
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"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
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"import numpy as np\n",
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"from scipy.interpolate import RegularGridInterpolator\n",
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"\n",
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"from kwave.data import Vector\n",
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"from kwave.kgrid import kWaveGrid\n",
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"from kwave.kmedium import kWaveMedium\n",
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"from kwave.ksensor import kSensor\n",
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"from kwave.ksource import kSource\n",
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"from kwave.kspaceFirstOrder2D import kspaceFirstOrder2D\n",
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"from kwave.kspaceLineRecon import kspaceLineRecon\n",
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"from kwave.options.simulation_execution_options import SimulationExecutionOptions\n",
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"from kwave.options.simulation_options import SimulationOptions\n",
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"from kwave.utils.colormap import get_color_map\n",
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"from kwave.utils.mapgen import make_disc\n",
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"from kwave.utils.filters import smooth"
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],
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"id": "48677f5b96e1511e",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## 2D FFT Reconstruction For A Line Sensor Example\n",
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"\n",
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"This example demonstrates the use of k-Wave for the reconstruction of a\n",
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"two-dimensional photoacoustic wave-field recorded over a linear array of\n",
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"sensor elements The sensor data is simulated using kspaceFirstOrder2D\n",
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"and reconstructed using kspaceLineRecon. It builds on the Homogeneous\n",
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"Propagation Medium and Heterogeneous Propagation Medium examples. "
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],
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"id": "262efa49a13b9574"
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "### SIMULATION",
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"id": "310bcfe8541609c2"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"# create the computational grid\n",
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"PML_size = 20 # size of the PML in grid points\n",
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"N = Vector([128, 256]) - 2 * PML_size # number of grid points\n",
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"d = Vector([0.1e-3, 0.1e-3]) # grid point spacing [m]\n",
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"kgrid = kWaveGrid(N, d)"
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],
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"id": "6d7c5328ae5dd3ee",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"# define the properties of the propagation medium\n",
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"medium = kWaveMedium(sound_speed=1500) # [m/s]"
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],
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"id": "bd5eece140453f0f",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"# create initial pressure distribution using makeDisc\n",
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"disc_magnitude = 5 # [Pa]\n",
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"disc_pos = Vector([60, 140])\n",
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"disc_radius = 5\n",
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"disc_2 = disc_magnitude * make_disc(N, disc_pos, disc_radius)\n",
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"\n",
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"disc_pos = Vector([30, 110])\n",
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"disc_radius = 8\n",
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"disc_1 = disc_magnitude * make_disc(N, disc_pos, disc_radius)\n",
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"\n",
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"# smooth the initial pressure distribution and restore the magnitude\n",
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"p0 = disc_1 + disc_2\n",
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"p0 = smooth(p0, restore_max=True)\n",
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"\n",
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"source = kSource()\n",
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"source.p0 = p0"
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],
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"id": "2ce3b8059a9ec319",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"# define a binary line sensor\n",
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"sensor = kSensor()\n",
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"sensor.mask = np.zeros(N)\n",
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"sensor.mask[0] = 1"
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],
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"id": "cf6961e373c6f8e8",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"%%capture\n",
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"\n",
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"# create the time array\n",
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"kgrid.makeTime(medium.sound_speed)"
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],
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"id": "5a278b1e9b253295",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"# set the input arguments: force the PML to be outside the computational grid\n",
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"simulation_options = SimulationOptions(\n",
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" save_to_disk=True,\n",
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" pml_inside=False,\n",
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" pml_size=PML_size,\n",
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" smooth_p0=False,\n",
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")\n",
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"execution_options = SimulationExecutionOptions(is_gpu_simulation=True)"
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],
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"id": "a3802e3e482dc77",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"# run the simulation\n",
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"sensor_data = kspaceFirstOrder2D(kgrid, source, sensor, medium, simulation_options, execution_options)\n",
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"sensor_data = sensor_data['p'].T\n",
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"\n",
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"# reconstruct the initial pressure\n",
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"p_xy = kspaceLineRecon(sensor_data.T, dy=d[1], dt=kgrid.dt.item(), c=medium.sound_speed.item(),\n",
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" pos_cond=True, interp='linear')\n",
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"\n",
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"# define a second k-space grid using the dimensions of p_xy\n",
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"N_recon = Vector(p_xy.shape)\n",
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"d_recon = Vector([kgrid.dt.item() * medium.sound_speed.item(), kgrid.dy])\n",
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"kgrid_recon = kWaveGrid(N_recon, d_recon)\n",
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"\n",
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"# resample p_xy to be the same size as source.p0\n",
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"interp_func = RegularGridInterpolator(\n",
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" (kgrid_recon.x_vec[:, 0] - kgrid_recon.x_vec.min(), kgrid_recon.y_vec[:, 0]),\n",
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" p_xy, method='linear'\n",
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")\n",
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"query_points = np.stack((kgrid.x - kgrid.x.min(), kgrid.y), axis=-1)\n",
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"p_xy_rs = interp_func(query_points)"
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],
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"id": "154c372469cd6063",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "### VISUALIZATION\n",
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"id": "b07f65d05feaf902"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": "cmap = get_color_map()",
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"id": "30edd081e39aa21b",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"# plot the initial pressure and sensor distribution\n",
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"fig, ax = plt.subplots(1, 1)\n",
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"im = ax.imshow(p0 + sensor.mask[PML_size:-PML_size, PML_size:-PML_size] * disc_magnitude,\n",
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" extent=[kgrid.y_vec.min() * 1e3, kgrid.y_vec.max() * 1e3, kgrid.x_vec.max() * 1e3, kgrid.x_vec.min() * 1e3],\n",
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" vmin=-disc_magnitude, vmax=disc_magnitude, cmap=cmap)\n",
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"divider = make_axes_locatable(ax)\n",
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"cax = divider.append_axes(\"right\", size=\"3%\", pad=\"2%\")\n",
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"ax.set_ylabel('x-position [mm]')\n",
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"ax.set_xlabel('y-position [mm]')\n",
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"fig.colorbar(im, cax=cax)\n",
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"plt.show()"
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],
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"id": "d02e73401dee274c",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"fig, ax = plt.subplots(1, 1)\n",
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"im = ax.imshow(sensor_data, vmin=-1, vmax=1, cmap=cmap, aspect='auto')\n",
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"divider = make_axes_locatable(ax)\n",
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"cax = divider.append_axes(\"right\", size=\"3%\", pad=\"2%\")\n",
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"ax.set_ylabel('Sensor Position')\n",
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"ax.set_xlabel('Time Step')\n",
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"fig.colorbar(im, cax=cax)\n",
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"plt.show()"
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],
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"id": "fc203d6e2a22dec6",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"# plot the reconstructed initial pressure\n",
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"fig, ax = plt.subplots(1, 1)\n",
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"im = ax.imshow(p_xy_rs,\n",
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" extent=[kgrid.y_vec.min() * 1e3, kgrid.y_vec.max() * 1e3, kgrid.x_vec.max() * 1e3, kgrid.x_vec.min() * 1e3],\n",
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" vmin=-disc_magnitude, vmax=disc_magnitude, cmap=cmap)\n",
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"divider = make_axes_locatable(ax)\n",
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"cax = divider.append_axes(\"right\", size=\"3%\", pad=\"2%\")\n",
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"ax.set_ylabel('x-position [mm]')\n",
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"ax.set_xlabel('y-position [mm]')\n",
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"fig.colorbar(im, cax=cax)\n",
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"plt.show()"
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],
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"id": "2da3a2a311fd1683",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"# plot a profile for comparison\n",
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"plt.plot(kgrid.y_vec[:, 0] * 1e3, p0[disc_pos[0], :], 'k-', label='Initial Pressure')\n",
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"plt.plot(kgrid.y_vec[:, 0] * 1e3, p_xy_rs[disc_pos[0], :], 'r--', label='Reconstructed Pressure')\n",
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"plt.xlabel('y-position [mm]')\n",
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"plt.ylabel('Pressure')\n",
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"plt.legend()\n",
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"plt.axis('tight')\n",
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"plt.ylim([0, 5.1])\n",
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"plt.show()"
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],
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"id": "f7ae5b0a2f5e147e",
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"outputs": [],
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"execution_count": null
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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