reflow-oven-control-sw/measurement-data/Analog Measurement Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in Measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "constant_sampling = pd.read_csv(r'1000OhmSampling.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculation Function for $\\vartheta(R_{PT1000})$\n",
    "$\\vartheta(R_{PT1000}) = \\frac{-\\alpha R_0 + \\sqrt{\\alpha^2R_0^2 - 4\\beta R_0 \\left(R_0 - R_{PT1000}\\right)}}{2\\beta R_0}$\n",
    "\n",
    "with\n",
    "* $\\alpha = 3.9083 \\cdot 10^{-3}$\n",
    "* $\\beta = -5.7750 \\cdot 10^{-7}$\n",
    "* $R_0 = 1000~\\Omega$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "R_zero = 1000.0\n",
    "A = 3.9083E-3\n",
    "B = -5.7750E-7\n",
    "\n",
    "def calc_temp(resistance):\n",
    "    temp = (-R_zero * A + np.sqrt(R_zero*R_zero * A * A - 4* R_zero * B * (R_zero - resistance)))/(2*R_zero*B)\n",
    "    return temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description of ADC Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(constant_sampling['adc_results.pa2_raw'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------\n",
    "# Calculate Temperature from Resistance Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "constant_sampling['temp_calculated'] = constant_sampling.apply(lambda row: calc_temp(row['ext_lf_corr']) , axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histograms -- Starting from Index 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(28,6))\n",
    "signal_list = [('adc_results.pa2_raw', 25), ('ext_lf_corr', 25), ('temp_calculated', 25)]\n",
    "for ax,sig in zip(axes, signal_list):\n",
    "    n, bins, patches = ax.hist(constant_sampling[sig[0]][100:], 25, density=1)\n",
    "    mu = np.mean(constant_sampling[sig[0]][100:])\n",
    "    sigma = np.std(constant_sampling[sig[0]][100:])\n",
    "    y = ((1 / (np.sqrt(2 * np.pi) * sigma)) * np.exp(-0.5 * (1 / sigma * (bins - mu))**2))\n",
    "    ax.plot(bins, y)\n",
    "    ax.set_title('Histogram and Standard Deviation of '+sig[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Startup of Moving Average Filter with $\\alpha' = 0.005$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(28,6), sharex=True)\n",
    "data = constant_sampling['ext_lf_corr'][:20]\n",
    "ax[0].plot(constant_sampling['Time'][:20], data)\n",
    "ax[1].plot(constant_sampling['Time'][:20], constant_sampling['temp_calculated'][:20])\n",
    "plt.show()"
   ]
  }
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