Robotics-at-Home-with-Raspberry-Pi-Pico/ch-10/_figure_generator/integral_graph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A set of error values, assume time step of 1 unit\n",
    "error_values = [\n",
    "  # variation at the start\n",
    "  10, 8, 3, -5, -10, -8, -3, \n",
    "  2, 3, 5, 4, 2, -3, -2,\n",
    "  # the steady state \n",
    "  -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7faff88f2450>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the error values\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.plot(np.arange(len(error_values)), [x for x in error_values], label=\"error\")\n",
    "# draw a middle line\n",
    "plt.plot(np.arange(len(error_values)), [0 for x in error_values], label=\"zero\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fafe9984450>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot error values as a series of bars\n",
    "plt.bar(np.arange(len(error_values)), [x for x in error_values], label=\"error\")\n",
    "# draw a middle line\n",
    "plt.plot(np.arange(len(error_values)), [0 for x in error_values], label=\"zero\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make another set, accumulate the error values\n",
    "error_values_accumulated = [0]\n",
    "for i in range(1, len(error_values)):\n",
    "  error_values_accumulated.append(error_values_accumulated[-1] + error_values[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set plot to 300 dpi\n",
    "plt.figure(figsize=(10, 5), dpi=300)\n",
    "# set the bar style to white fill with hatched lines in black\n",
    "plt.bar(np.arange(len(error_values)), [x for x in error_values], label=\"error\", hatch=\"//\", color=\"white\", edgecolor=\"black\")\n",
    "# draw a middle line\n",
    "plt.plot(np.arange(len(error_values)), [0 for x in error_values], label=\"zero\")\n",
    "# plot the accumulated error values as a line, extra thick\n",
    "plt.plot(np.arange(len(error_values_accumulated)), [x for x in error_values_accumulated], \n",
    "  label=\"accumulated error\", linewidth=2, color=\"black\")\n",
    "# reduce plot margins\n",
    "plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)\n",
    "\n",
    "# export plot as a png\n",
    "plt.savefig(\"error_plot.png\", bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.4 ('.venv': venv)",
   "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": "3.7.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "97190208b4a6552dc9493965a1caf8d39c9d1897fe9495f2fe88af90c5a07bd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}