{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Emitter/Receiver Simulation with Signals" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from functools import partial\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Signal" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from lib.TravelSignal import TravelSignal\n", "\n", "####\n", "from scipy.stats import norm\n", "\n", "sample_rate = 3e2 # Hz\n", "interp_sample_rate = sample_rate * 1/10 # Hz\n", "\n", "t_offset = 8\n", "periodic = False\n", "\n", "time = t_offset + np.arange(0, 1, 1/sample_rate) #s\n", "time2 = t_offset + np.arange(-1.5, 1, 1/sample_rate) #s\n", "\n", "signal = norm.pdf(time, time[len(time)//2], (time[-1] - time[0])/10)\n", "\n", "if False:\n", " mysignal = TravelSignal(signal, sample_rate, t_0 = t_offset, periodic=True)\n", " mysignal2 = TravelSignal(signal, sample_rate, t_0 = t_offset, periodic=False)\n", "\n", " fig, ax = plt.subplots(1, 1, figsize=(16,4))\n", " ax.set_title(\"Raw and TravelSignal\")\n", " ax.set_ylabel(\"Amplitude\")\n", " ax.set_xlabel(\"Time\")\n", "\n", " ax.plot(time, signal, label='Raw signal')\n", " ax.plot(time2, mysignal(time2)+0.5, '.-', label='TravelSignal(periodic)+0.5')\n", " ax.plot(time2, mysignal2(time2)-0.5, '.-', label='TravelSignal-0.5')\n", "\n", " ax.legend()\n", "\n", " plt.show();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## New code" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "### Location\n", "class Location:\n", " \"\"\"\n", " A location is a point designated by a spatial coordinate x.\n", " \"\"\"\n", "\n", " def __init__(self, x):\n", " self.x = np.asarray(x) \n", "\n", " def __repr__(self):\n", " return \"Location({})\".format(repr(self.x))\n", "\n", " def __getitem__(self, key):\n", " return self.x[key]\n", "\n", " def __setitem__(self, key, val):\n", " self.x[key] = val\n", "\n", " def __add__(self, other):\n", " if isinstance(other, Location):\n", " other = other.x\n", "\n", " return self.__class__(self.x + other)\n", "\n", " def __sub__(self, other):\n", " if isinstance(other, Location):\n", " other = other.x\n", "\n", " return self.__class__(self.x - other)\n", " \n", " def __eq__(self, other):\n", " if isinstance(other, Location):\n", " other = other.x\n", "\n", " return np.all(self.x == other)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "### Receiver\n", "class Receiver(Location):\n", " \"\"\"\n", " A location able to trace a signal over time.\n", " \n", " Optionally applies a transformation to the traced signal.\n", " \"\"\"\n", " def __repr__(self):\n", " return \"Receiver({})\".format(repr(self.x))\n", " \n", " def recv(self, travel_signal: TravelSignal) -> TravelSignal:\n", " \"\"\"\n", " Return a function that traces the signal as a function of time\n", " at the receiver's location\n", " \"\"\"\n", " return partial(travel_signal, x_f=self.x) " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ " ### Emitter\n", "class Emitter(Location):\n", " \"\"\"\n", " Emit a signal from position x_0 (and time t_0)\n", " \"\"\"\n", " def emit(self, travel_signal: TravelSignal) -> TravelSignal:\n", " return partial(travel_signal, x_0=self.x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "if True:\n", " sample_rate = 3e2 # Hz\n", " periodic = False\n", " \n", " t_offset = 8\n", " t_start = 0\n", " t_end = 1\n", " time = t_offset + np.arange(t_start, t_end, 1/sample_rate) #s\n", " \n", " t_longstart = 0\n", " t_longend = 30*t_end\n", " longtime = np.arange(t_longstart, t_longend, 1/sample_rate) #s\n", "\n", "if False:\n", " if True:\n", " freq = sample_rate/8\n", " signal = np.cos(2*np.pi*freq*time)\n", " else: \n", " from scipy.stats import norm\n", " signal = norm.pdf(time, time[len(time)//2], (time[-1] - time[0])/10)\n", "\n", "\n", "#####\n", "# Setup Signal, Emitter and Antennae\n", "\n", "mysignal = TravelSignal(signal, sample_rate, t_0 = t_offset, periodic=periodic)\n", "\n", "source = Emitter([1,1])\n", "emitted = source.emit(mysignal)\n", "\n", "antennae = [\n", " Receiver([2,3]),\n", " Receiver([10,10]),\n", " Receiver([-2,-3]),\n", "]\n", " \n", "#####\n", "# Follow traces, and show geometry\n", "ylabel_kw = {\"rotation\": \"horizontal\", \"va\":\"center\", \"ha\":\"center\", \"labelpad\": 30}\n", "\n", "fig, axs = plt.subplots(1,1, figsize=(2,2))\n", "axs = [ axs ]\n", "\n", "### Geometry Plot\n", "i = 0\n", "axs[i].set_title(\"Geometry of Emitter(s) and Antennae\")\n", "axs[i].set_ylabel(\"y\", **ylabel_kw)\n", "axs[i].set_xlabel(\"x\")\n", "axs[i].plot(*source.x, '*', label=\"Emitter\")\n", "\n", "for j, ant in enumerate(antennae):\n", " axs[i].plot(*ant.x, '+', label=\"Antenna {}\".format(j))\n", "\n", "### Plot Traces\n", "fig, axs = plt.subplots(1+len(antennae),1, sharex=True, figsize=(12,6))\n", "axs[0].set_title(\"Traces of Emitter and Antenna\")\n", "\n", "# Emitter\n", "i = 0\n", "axs[i].set_ylabel(\"Emitter\\n at ({},{})\".format(*source.x), **ylabel_kw)\n", "axs[i].plot(time, emitted(time))\n", "\n", "# Antenna\n", "for j, ant in enumerate(antennae):\n", " i +=1\n", " axs[i].set_ylabel(\"Antenna {}\\n at ({},{})\".format(j, *ant.x), **ylabel_kw)\n", " axs[i].plot(longtime, ant.recv(emitted)(longtime), label=\"Antenna {}\".format(j))\n", " " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }