Add Focussed Detector In 3D Example

waltsims · Oct 28, 2024 · 1001de4 · 1001de4
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diff --git a/examples/README.md b/examples/README.md
@@ -28,7 +28,9 @@ Every example has a short readme.md file which briefly describes the purpose of
 
 - [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))
 
-- [Focussed Detector In 2D Example](at_focused_annular_array_3D/) ([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))
+- [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))
+
+- [Focussed Detector In 3D Example](sd_focussed_detector_3D/) ([original example](http://www.k-wave.org/documentation/example_sd_focussed_detector_3D.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_3D/sd_focussed_detector_3D.ipynb))
 
 
 ## Contributing new examples

diff --git a/examples/sd_focussed_detector_3D/README.md b/examples/sd_focussed_detector_3D/README.md
@@ -0,0 +1,7 @@
+# Focussed Detector In 3D Example
+
+[![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_3D/sd_focussed_detector_3D.ipynb)
+
+This example shows how k-Wave can be used to model the output of a focussed bowl detector where the directionality arises from spatially averaging across the detector surface.
+
+To read more, visit the [original example page](http://www.k-wave.org/documentation/example_sd_focussed_detector_3D.php).
diff --git a/examples/sd_focussed_detector_3D/sd_focussed_detector_3D.ipynb b/examples/sd_focussed_detector_3D/sd_focussed_detector_3D.ipynb
@@ -0,0 +1,276 @@
+{
+ "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": [
+    "# Focussed Detector In 3D Example\n",
+    "This example shows how k-Wave can be used to model the output of a focussed bowl detector where the directionality arises from spatially averaging across the detector surface."
+   ]
+  },
+  {
+   "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.ksource import kSource\n",
+    "from kwave.ktransducer import kSensor\n",
+    "from kwave.options.simulation_execution_options import SimulationExecutionOptions\n",
+    "from kwave.options.simulation_options import SimulationOptions\n",
+    "from kwave.utils.mapgen import make_bowl\n",
+    "from kwave.utils.data import scale_SI\n",
+    "\n",
+    "from kwave.kspaceFirstOrder3D import kspaceFirstOrder3D\n",
+    "from kwave.utils.filters import filter_time_series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the computational grid\n",
+    "grid_size = Vector([64, 64, 64])  # [grid points]\n",
+    "grid_spacing_single = 100e-3 / grid_size.x\n",
+    "grid_spacing = Vector([grid_spacing_single, grid_spacing_single, grid_spacing_single])  # [m]\n",
+    "kgrid = kWaveGrid(grid_size, grid_spacing)\n",
+    "\n",
+    "# define the properties of the propagation medium\n",
+    "medium = kWaveMedium(sound_speed=1500)\n",
+    "\n",
+    "# define the array of temporal points\n",
+    "_ = kgrid.makeTime(medium.sound_speed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define simulation parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_filename = \"example_sd_focused_3d_input.h5\"\n",
+    "pathname = gettempdir()\n",
+    "input_file_full_path = os.path.join(pathname, input_filename)\n",
+    "simulation_options = SimulationOptions(\n",
+    "    save_to_disk=True, \n",
+    "    input_filename=input_filename, \n",
+    "    data_path=pathname, \n",
+    "    pml_size=10, \n",
+    "    data_cast='single'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define a concave sensor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a concave sensor\n",
+    "sphere_offset = 10\n",
+    "diameter = grid_size.x / 2 + 1\n",
+    "radius = grid_size.x / 2\n",
+    "bowl_pos = Vector([1 + sphere_offset, grid_size.y / 2, grid_size.z / 2])\n",
+    "focus_pos = grid_size / 2\n",
+    "\n",
+    "sensor_mask = make_bowl(grid_size, bowl_pos, radius, diameter, focus_pos)\n",
+    "sensor = kSensor(sensor_mask)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run simulation with first source"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define a time varying sinusoidal source\n",
+    "source = kSource()\n",
+    "\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",
+    "source.p = filter_time_series(kgrid, medium, source.p)\n",
+    "\n",
+    "# place the first point source near the focus of the detector\n",
+    "source1 = np.zeros(grid_size)\n",
+    "source1[int(sphere_offset + radius), grid_size.y // 2 - 1, grid_size.z // 2 - 1] = 1\n",
+    "\n",
+    "# run the first simulation\n",
+    "source.p_mask = source1\n",
+    "\n",
+    "sensor_data1 = kspaceFirstOrder3D(\n",
+    "    medium=medium,\n",
+    "    kgrid=kgrid,\n",
+    "    source=deepcopy(source),\n",
+    "    sensor=deepcopy(sensor),\n",
+    "    simulation_options=simulation_options,\n",
+    "    execution_options=SimulationExecutionOptions(is_gpu_simulation=False),\n",
+    ")\n",
+    "\n",
+    "# average the data recorded at each grid point to simulate the measured signal from a single element focused detector\n",
+    "sensor_data1 = np.sum(sensor_data1['p'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run simulation with second source"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# place the second point source off axis\n",
+    "source2 = np.zeros(grid_size)\n",
+    "source2[int(1 + sphere_offset + radius), grid_size.y // 2 + 5, grid_size.z // 2 + 5] = 1\n",
+    "\n",
+    "\n",
+    "# run the second simulation\n",
+    "source.p_mask = source2\n",
+    "sensor_data2 = kspaceFirstOrder3D(\n",
+    "    medium=medium,\n",
+    "    kgrid=kgrid,\n",
+    "    source=source,\n",
+    "    sensor=sensor,\n",
+    "    simulation_options=simulation_options,\n",
+    "    execution_options=SimulationExecutionOptions(is_gpu_simulation=False),\n",
+    ")\n",
+    "\n",
+    "# average the data recorded at each grid point to simulate the measured signal from a single element focused detector\n",
+    "sensor_data2 = np.sum(sensor_data2['p'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize detector and sources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Combine arrays as in MATLAB: sensor.mask + source1 + source2\n",
+    "combined_array = sensor_mask + source1 + source2\n",
+    "\n",
+    "# Find the indices of non-zero elements\n",
+    "x, y, z = np.nonzero(combined_array)\n",
+    "\n",
+    "# Enable interactive mode\n",
+    "plt.ion()\n",
+    "\n",
+    "# Create an interactive 3D plot with matplotlib\n",
+    "fig = plt.figure(figsize=(8, 6))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "sc = ax.scatter(x, y, z, c='blue', marker='s', s=100, depthshade=True)  # 's' for square-like markers\n",
+    "\n",
+    "# Set the view angle to mimic MATLAB's `view([130, 40])`\n",
+    "ax.view_init(elev=40, azim=130)\n",
+    "\n",
+    "# Customize the axes\n",
+    "ax.set_xlabel('X')\n",
+    "ax.set_ylabel('Y')\n",
+    "ax.set_zlabel('Z')\n",
+    "ax.set_title('Interactive 3D Voxel Plot')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize recorded data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_sc, t_scale, t_prefix, _ = scale_SI(kgrid.t_array[-1])\n",
+    "t_array = kgrid.t_array.squeeze() * t_scale\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(t_array, sensor_data1)\n",
+    "plt.plot(t_array, sensor_data2, 'r')\n",
+    "\n",
+    "plt.xlabel('Time [' + t_prefix + 's]')\n",
+    "plt.ylabel('Average Pressure Measured By Focussed Detector [Pa]')\n",
+    "plt.legend(['Source on axis', 'Source off axis'])\n",
+    "\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
+}