read_49E.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Universidade Federal do Rio Grande do Sul (UFRGS)   \n",
    "Programa de Pós-Graduação em Engenharia Civil (PPGEC)   \n",
    "\n",
    "# PEC00144: Experimental Methods in Civil Engineering\n",
    "\n",
    "### Reading a hall sensor 49E with Arduino analog input  \n",
    "\n",
    "---\n",
    "_Prof. Marcelo M. Rocha, Dr.techn._ [(ORCID)](https://orcid.org/0000-0001-5640-1020)  \n",
    "_Porto Alegre, RS, Brazil_ \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Python modules required for this notebook\n",
    "# (this cell must be executed with \"shift+enter\" before any other Python cell)\n",
    "\n",
    "import sys\n",
    "import time\n",
    "import serial\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from   MRPy  import MRPy\n",
    "\n",
    "#Ardn.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Setup serial communication\n",
    "\n",
    "In order to run this notebook, the Python module ``pyserial`` must be installed.\n",
    "To ensure the module availability, open a conda terminal and issue the command:\n",
    "\n",
    "    conda install -c anaconda pyserial\n",
    "\n",
    "Before openning the serial port, verify with Arduino IDE which USB identifier the \n",
    "board has be assigned (in Windows it has the form \"COMxx\", while in Linux it\n",
    "it is something like \"/dev/ttyXXXX\").\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#port  = '/dev/ttyUSB0' \n",
    "#baud  =  9600\n",
    "\n",
    "port   = 'COM4'           # change this address according to your computer\n",
    "baud   =  115200          # match this number with the Arduino's output baud rate\n",
    "\n",
    "Ardn  =  serial.Serial(port, baud, timeout=1)\n",
    "\n",
    "time.sleep(3)             # this is important to give time for serial settling\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Define function for reading one incoming line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ReadSerial(nchar, nvar, nlines=1):\n",
    "\n",
    "    Ardn.write(str(nlines).encode())\n",
    "    data = np.zeros((nlines,nvar))\n",
    "\n",
    "    for k in range(nlines):\n",
    "        \n",
    "        wait = True\n",
    "        while(wait):\n",
    "          \n",
    "            if (Ardn.inWaiting() >= nchar):\n",
    "                bdat = Ardn.readline() \n",
    "                sdat = bdat.decode()\n",
    "                sdat = sdat.replace('\\n',' ').split()\n",
    "                wait = False\n",
    "            \n",
    "                data[k, :] = np.array(sdat[0:nvar], dtype='int')\n",
    "        \n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Acquire data lines from serial port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acquiring...\n",
      "... acquisition ok!\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    print('Acquiring...')\n",
    "    \n",
    "    data = ReadSerial(17, 2, nlines=2048)   # approximately 24 seconds\n",
    "    t    = data[:,0]\n",
    "    dx   = data[:,1]\n",
    "\n",
    "    Ardn.close()\n",
    "    print('... acquisition ok!')\n",
    "    \n",
    "except:\n",
    "    Ardn.close()\n",
    "    sys.exit('... acquisition failure!')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Create ``MRPy`` instance and save to file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average sampling rate is  95.7Hz.\n",
      "[-0.14271009]\n"
     ]
    }
   ],
   "source": [
    "ti  = (t - t[0])/ 1000\n",
    "dx  = (dx - 502)/(1024 - 360)\n",
    " \n",
    "data = MRPy.resampling(ti, dx)\n",
    "data.to_file('Arduino/ReadSerial', form='excel')\n",
    "\n",
    "print('Average sampling rate is {0:5.1f}Hz.'.format(data.fs))\n",
    "\n",
    "print(data.mean(axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = data.plot_time(fig=1, figsize=(12,8), axis_t=[0, data.Td, -0.5, 0.5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}