1d demo

examples/ivp_1D_simulation/README.md

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+Simulations In One Dimension Example
+
+ [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/waltsims/k-wave-python/blob/HEAD/examples/ivp_1D_simulation/ivp_1D_simulation.ipynb)
+
+This example provides a simple demonstration for the simulation and detection of the pressure field generated by an initial pressure distribution within a one-dimensional heterogeneous propagation medium.
+
+To read more, visit the [original example page](http://www.k-wave.org/documentation/example_ivp_1D_simulation.php).

examples/ivp_1D_simulation/ivp_1D_simulation.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RQYRmOfG27FV"
+   },
+   "source": [
+    "First install the package using the latest version."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "5bq8_I2z29l6"
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install k-wave-python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7lktiAv5XtsS"
+   },
+   "source": [
+    "Now import dependencies, and parts of library which are required."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "VCcwZsQ2ASoj"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\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.kspaceFirstOrder1D import kspace_first_order_1D\n",
+    "from kwave.options.simulation_execution_options import SimulationExecutionOptions\n",
+    "from kwave.options.simulation_options import SimulationOptions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xUqsDgayASoj"
+   },
+   "source": [
+    "Set parameters to build the grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "FwbGsg_gASok"
+   },
+   "outputs": [],
+   "source": [
+    "# create the computational grid\n",
+    "Nx: int = 512         # number of grid points in the x (row) direction\n",
+    "dx: float = 0.05e-3   # grid point spacing in the x direction [m]\n",
+    "\n",
+    "grid_size_points = Vector([Nx, ])\n",
+    "grid_spacing_meters = Vector([dx, ])\n",
+    "\n",
+    "# create the k-space grid\n",
+    "kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)\n",
+    "\n",
+    "# define the properties of the propagation medium\n",
+    "sound_speed = 1500.0 * np.ones((Nx, 1))                    # [m/s]\n",
+    "sound_speed[:np.round(Nx / 3).astype(int) - 1] = 2000.0\t   # [m/s]\n",
+    "density = 1000.0 * np.ones((Nx, 1))                        # [kg/m^3]\n",
+    "density[np.round(4 * Nx / 5).astype(int) - 1:] = 1500.0    # [kg/m^3]\n",
+    "medium = kWaveMedium(sound_speed=sound_speed, density=density)\n",
+    "\n",
+    "# set the simulation time to capture the reflections\n",
+    "c_max = np.max(medium.sound_speed.flatten()) # [m/s]\n",
+    "t_end = 2.5 * kgrid.x_size / c_max           # [s]\n",
+    "\n",
+    "# define the time array\n",
+    "kgrid.makeTime(c_max, t_end=t_end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "misFfiLaASok"
+   },
+   "source": [
+    "Build source and sensor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "maEowzD6ASok"
+   },
+   "outputs": [],
+   "source": [
+    "# Create the source object\n",
+    "source = kSource()\n",
+    "\n",
+    "# create initial pressure distribution using a smoothly shaped sinusoid\n",
+    "x_pos: int = 280    # [grid points]\n",
+    "width: int = 100    # [grid points]\n",
+    "height: int = 1     # [au]\n",
+    "p0 = np.linspace(0.0, 2.0 * np.pi, width + 1)\n",
+    "\n",
+    "part1 = np.zeros(x_pos).astype(float)\n",
+    "part2 = (height / 2.0) * np.sin(p0 - np.pi / 2.0) + (height / 2.0)\n",
+    "part3 = np.zeros(Nx - x_pos - width - 1).astype(float)\n",
+    "source.p0 = np.concatenate([part1, part2, part3])\n",
+    "\n",
+    "# create a Cartesian sensor mask recording the pressure\n",
+    "sensor = kSensor()\n",
+    "sensor.record = [\"p\"]\n",
+    "\n",
+    "# this hack is needed to ensure that the sensor is in [1,2] dimensions\n",
+    "mask = np.array([-10e-3, 10e-3]) # [m]\n",
+    "mask = mask[:, np.newaxis].T\n",
+    "sensor.mask = mask"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZSmPpm6X3fm5"
+   },
+   "source": [
+    "Set options and run simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "BiFT0YEE3fyE"
+   },
+   "outputs": [],
+   "source": [
+    "# define the simulation options\n",
+    "simulation_options = SimulationOptions(data_cast=\"off\", save_to_disk=False)\n",
+    "execution_options = SimulationExecutionOptions(is_gpu_simulation=True)\n",
+    "\n",
+    "# run the simulation\n",
+    "sensor_data = kspace_first_order_1D(kgrid, source, sensor, medium, simulation_options=simulation_options, execution_options=execution_options)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "a4WRzAZ_ASol"
+   },
+   "source": [
+    "Visualisations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "s4ZO890kASol"
+   },
+   "outputs": [],
+   "source": [
+    "# plot the recorded time signals\n",
+    "_, ax1 = plt.subplots()\n",
+    "ax1.plot(sensor_data['p'][0, :], 'b-')\n",
+    "ax1.plot(sensor_data['p'][1, :], 'r-')\n",
+    "ax1.grid(True)\n",
+    "ax1.set_ylim(-0.1, 0.7)\n",
+    "ax1.set_ylabel('Pressure')\n",
+    "ax1.set_xlabel('Time Step')\n",
+    "ax1.legend(['Sensor Position 1', 'Sensor Position 2'])\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": 0
+}

examples/ivp_1D_simulation/ivp_1D_simulation.py

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+"""
+Simulations In One Dimension Example
+
+This example provides a simple demonstration of using k-Wave for the
+simulation and detection of the pressure field generated by an initial
+pressure distribution within a one-dimensional heterogeneous propagation
+medium. It builds on the Homogeneous Propagation Medium and Heterogeneous
+Propagation Medium examples.
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from kwave.data import Vector
+from kwave.kgrid import kWaveGrid
+from kwave.kmedium import kWaveMedium
+from kwave.ksensor import kSensor
+from kwave.ksource import kSource
+from kwave.kspaceFirstOrder1D import kspace_first_order_1D
+from kwave.options.simulation_execution_options import SimulationExecutionOptions
+from kwave.options.simulation_options import SimulationOptions
+
+# =========================================================================
+# SIMULATION
+# =========================================================================
+
+# create the computational grid
+Nx: int = 512         # number of grid points in the x (row) direction
+dx: float = 0.05e-3   # grid point spacing in the x direction [m]
+
+grid_size_points = Vector([Nx, ])
+grid_spacing_meters = Vector([dx, ])
+
+# create the k-space grid
+kgrid = kWaveGrid(grid_size_points, grid_spacing_meters)
+
+# define the properties of the propagation medium
+sound_speed = 1500.0 * np.ones((Nx, 1))                    # [m/s]
+sound_speed[:np.round(Nx / 3).astype(int) - 1] = 2000.0	   # [m/s]
+density = 1000.0 * np.ones((Nx, 1))                        # [kg/m^3]
+density[np.round(4 * Nx / 5).astype(int) - 1:] = 1500.0    # [kg/m^3]
+medium = kWaveMedium(sound_speed=sound_speed, density=density)
+
+# Create the source object
+source = kSource()
+
+# create initial pressure distribution using a smoothly shaped sinusoid
+x_pos: int = 280    # [grid points]
+width: int = 100    # [grid points]
+height: int = 1     # [au]
+p0 = np.linspace(0.0, 2.0 * np.pi, width + 1)
+
+part1 = np.zeros(x_pos).astype(float)
+part2 = (height / 2.0) * np.sin(p0 - np.pi / 2.0) + (height / 2.0)
+part3 = np.zeros(Nx - x_pos - width - 1).astype(float)
+source.p0 = np.concatenate([part1, part2, part3])
+
+# create a Cartesian sensor mask recording the pressure
+sensor = kSensor()
+sensor.record = ["p"]
+
+# this hack is needed to ensure that the sensor is in [1,2] dimensions
+mask = np.array([-10e-3, 10e-3]) # [m]
+mask = mask[:, np.newaxis].T
+sensor.mask = mask 
+
+# set the simulation time to capture the reflections
+c_max = np.max(medium.sound_speed.flatten()) # [m/s]
+t_end = 2.5 * kgrid.x_size / c_max           # [s]
+
+# define the time array
+kgrid.makeTime(c_max, t_end=t_end)
+
+# define the simulation options
+simulation_options = SimulationOptions(data_cast="off", save_to_disk=False)
+execution_options = SimulationExecutionOptions(is_gpu_simulation=True)
+
+# run the simulation
+sensor_data = kspace_first_order_1D(kgrid, source, sensor, medium, simulation_options=simulation_options, execution_options=execution_options)
+
+# =========================================================================
+# VISUALISATION
+# =========================================================================
+
+# plot the recorded time signals
+_, ax1 = plt.subplots()
+ax1.plot(sensor_data['p'][0, :], 'b-')
+ax1.plot(sensor_data['p'][1, :], 'r-')
+ax1.grid(True)
+ax1.set_ylim(-0.1, 0.7)
+ax1.set_ylabel('Pressure')
+ax1.set_xlabel('Time Step')
+ax1.legend(['Sensor Position 1', 'Sensor Position 2'])
+plt.show()