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| fetch-depth: 0 # otherwise, you will fail to push refs to dest repo | ||
| - uses: actions/setup-python@v5 | ||
| with: | ||
| python-version: '3.9' | ||
| python-version: '3.10' | ||
| cache: 'pip' | ||
| - name: Build and Commit | ||
| uses: waltsims/[email protected] | ||
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| # Modelling Sensor Directivity In 2D Example | ||
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| [](https://colab.research.google.com/github/waltsims/k-wave-python/blob/master/examples/sd_directivity_modelling_2D/sd_directivity_modelling_2D.ipynb) | ||
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| This example demonstrates how the sensitivity of a large single element detector varies with the angular position of a point-like source. | ||
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| To read more, visit the [original example page](http://www.k-wave.org/documentation/example_sd_directivity_modelling_2D.php). |
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examples/sd_directivity_modelling_2D/sd_directivity_modelling_2D.ipynb
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%pip install git+https://github.com/waltsims/k-wave-python " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Modelling Sensor Directivity In 2D Example\n", | ||
| "\n", | ||
| "This example demonstrates how the sensitivity of a large single element detector varies with the angular position of a point-like source." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%matplotlib inline\n", | ||
| "\n", | ||
| "import os\n", | ||
| "from copy import deepcopy\n", | ||
| "from tempfile import gettempdir\n", | ||
| "\n", | ||
| "import numpy as np\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "\n", | ||
| "from kwave.data import Vector\n", | ||
| "from kwave.kgrid import kWaveGrid\n", | ||
| "from kwave.kmedium import kWaveMedium\n", | ||
| "from kwave.ksensor import kSensor\n", | ||
| "from kwave.ksource import kSource\n", | ||
| "from kwave.kspaceFirstOrder2D import kspaceFirstOrder2DC\n", | ||
| "from kwave.options.simulation_execution_options import SimulationExecutionOptions\n", | ||
| "from kwave.options.simulation_options import SimulationOptions\n", | ||
| "from kwave.utils.conversion import cart2grid\n", | ||
| "from kwave.utils.filters import filter_time_series\n", | ||
| "from kwave.utils.mapgen import make_cart_circle\n", | ||
| "from kwave.utils.matlab import ind2sub, matlab_find, unflatten_matlab_mask\n", | ||
| "from kwave.utils.colormap import get_color_map\n", | ||
| "from kwave.utils.data import scale_SI" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Create the computational grid and medium" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "grid_size_points = Vector([128, 128]) # [grid points]\n", | ||
| "grid_size_meters = Vector([50e-3, 50e-3]) # [m]\n", | ||
| "grid_spacing_meters = grid_size_meters / grid_size_points # [m]\n", | ||
| "kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)\n", | ||
| "\n", | ||
| "# define the properties of the propagation medium\n", | ||
| "medium = kWaveMedium(sound_speed=1500)\n", | ||
| "\n", | ||
| "# define the array of time points [s]\n", | ||
| "Nt = 350\n", | ||
| "dt = 7e-8 # [s]\n", | ||
| "kgrid.setTime(Nt, dt)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Define a large area detector" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "sz = 20 # [grid points]\n", | ||
| "sensor_mask = np.zeros(grid_size_points)\n", | ||
| "sensor_mask[grid_size_points.x // 2, (grid_size_points.y // 2 - sz // 2) : (grid_size_points.y // 2 + sz // 2) + 1] = 1\n", | ||
| "sensor = kSensor(sensor_mask)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Define a time varying sinusoidal source" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# define equally spaced point sources lying on a circle centred at the\n", | ||
| "# centre of the detector face\n", | ||
| "radius = 30 # [grid points]\n", | ||
| "points = 11\n", | ||
| "circle = make_cart_circle(radius * grid_spacing_meters.x, points, Vector([0, 0]), np.pi)\n", | ||
| "\n", | ||
| "# find the binary sensor mask most closely corresponding to the Cartesian\n", | ||
| "# coordinates from makeCartCircle\n", | ||
| "circle, _, _ = cart2grid(kgrid, circle)\n", | ||
| "\n", | ||
| "# find the indices of the sources in the binary source mask\n", | ||
| "source_positions = matlab_find(circle, val=1, mode=\"eq\")\n", | ||
| "\n", | ||
| "source = kSource()\n", | ||
| "source_freq = 0.25e6 # [Hz]\n", | ||
| "source_mag = 1 # [Pa]\n", | ||
| "source.p = source_mag * np.sin(2 * np.pi * source_freq * kgrid.t_array)\n", | ||
| "\n", | ||
| "# filter the source to remove high frequencies not supported by the grid\n", | ||
| "source.p = filter_time_series(kgrid, medium, source.p)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Define simulation parameters and run simulations" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# pre-allocate array for storing the output time series\n", | ||
| "single_element_data = np.zeros((Nt, points)) # noqa: F841\n", | ||
| "\n", | ||
| "# run a simulation for each of these sources to see the effect that the\n", | ||
| "# angle from the detector has on the measured signal\n", | ||
| "for source_loop in range(points):\n", | ||
| " # select a point source\n", | ||
| " source.p_mask = np.zeros(grid_size_points)\n", | ||
| " source.p_mask[unflatten_matlab_mask(source.p_mask, source_positions[source_loop] - 1)] = 1\n", | ||
| "\n", | ||
| " # run the simulation\n", | ||
| "\n", | ||
| " input_filename = f\"example_input_{source_loop + 1}_input.h5\"\n", | ||
| " pathname = gettempdir()\n", | ||
| " input_file_full_path = os.path.join(pathname, input_filename)\n", | ||
| " simulation_options = SimulationOptions(save_to_disk=True, input_filename=input_filename, data_path=pathname)\n", | ||
| " # run the simulation\n", | ||
| " sensor_data = kspaceFirstOrder2DC(\n", | ||
| " medium=medium,\n", | ||
| " kgrid=kgrid,\n", | ||
| " source=deepcopy(source),\n", | ||
| " sensor=deepcopy(sensor),\n", | ||
| " simulation_options=simulation_options,\n", | ||
| " execution_options=SimulationExecutionOptions(),\n", | ||
| " )\n", | ||
| " single_element_data[:, source_loop] = sensor_data['p'].sum(axis=1)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Visualize recorded data" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "plt.figure()\n", | ||
| "plt.imshow(circle + sensor.mask, \n", | ||
| " extent=[\n", | ||
| " kgrid.y_vec.min() * 1e3, kgrid.y_vec.max() * 1e3, \n", | ||
| " kgrid.x_vec.max() * 1e3, kgrid.x_vec.min() * 1e3], vmin=-1, vmax=1, cmap=get_color_map())\n", | ||
| "plt.ylabel('x-position [mm]')\n", | ||
| "plt.xlabel('y-position [mm]')\n", | ||
| "plt.axis('image')\n", | ||
| "\n", | ||
| "_, t_scale, t_prefix, _ = scale_SI(kgrid.t_array[-1])\n", | ||
| "\n", | ||
| "# Plot the time series recorded for each of the sources\n", | ||
| "plt.figure()\n", | ||
| "# Loop through each source and plot individually\n", | ||
| "for i in range(single_element_data.shape[1]):\n", | ||
| " plt.plot((kgrid.t_array * t_scale).squeeze(), single_element_data[:, i], label=f'Source {i+1}')\n", | ||
| "\n", | ||
| "plt.xlabel(f'Time [{t_prefix}s]')\n", | ||
| "plt.ylabel('Pressure [au]')\n", | ||
| "plt.title('Time Series For Each Source Direction')\n", | ||
| "\n", | ||
| "\n", | ||
| "# Calculate angle between source and center of detector face\n", | ||
| "angles = []\n", | ||
| "for source_position in source_positions:\n", | ||
| " x, y = ind2sub(kgrid.y.shape, source_position)\n", | ||
| " angles.append(np.arctan(kgrid.y[x, y] / kgrid.x[x, y]))\n", | ||
| "\n", | ||
| "# Plot the maximum amplitudes for each of the sources\n", | ||
| "plt.figure()\n", | ||
| "plt.plot(angles, np.max(single_element_data[200:350, :], axis=0), 'o', mfc='none')\n", | ||
| "plt.xlabel('Angle Between Source and Centre of Detector Face [rad]')\n", | ||
| "plt.ylabel('Maximum Detected Pressure [au]')\n", | ||
| "plt.show()" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "env_kwave", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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