tracetools_analysis/analysis/Callback_duration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/boc7rng/ros2_ws/src/tracetools_analysis/tracetools_analysis\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from tracetools_analysis.analysis import load\n",
    "from tracetools_analysis.analysis import ros2_processor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load an process\n",
    "pickle_filename = '../../../the_pickle_file'\n",
    "events = load.load_pickle(pickle_filename)\n",
    "processor = ros2_processor.ros2_process(events)\n",
    "data_model = processor.get_data_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAAGeCAYAAACqz6bUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAIABJREFUeJzs3Xl8nGW5//HPlbVbmiYpLd2TLhRKCwWalrUtogiCIh4rIAJFVhUX5Kh49IeKG55zcMHlSJVdKCIqiyCySFpZu9BCW9p0TRe6JG26JG3TbNfvj3mCIWSZNJl5Zibf9+s1r8w8y9zfmbTPXHnmfu7b3B0REREREeleaWEHEBERERFJRSq0RURERERiQIW2iIiIiEgMqNAWEREREYkBFdoiIiIiIjGgQltEREREJAZUaIuIiIiIxIAKbRGRGDIz7+Rtdov988zsFjNbYGa7zeyQmW02s0fM7EPttFvW4nnrzGyXmS0zswfMbJaZZXWQvY+Z3Whm881sZ9D2NjN7MtjfuultEhFJSaYJa0REYsfMvtvK4q8AucAvgD0t1j3m7kuDfacDfwYGAiuBfwJVwDjgXKAP8Afganc/1KLdMmBUszbSgP7AeOAMoC+wBrjM3V9vJfexwJNAEbAR+AewCxgJnAcMAJ4DPuXuLV+DiIigQltEJO6aFcFF7l7WxjYTgAVAbyKF+a+82QHbzEYAjwEnAne7+1XRtmFmucD3gS8Ce4GT3X1Vs/VHAm8AQ4AfA7e4e32z9fnAQ8CHiRTb57h7YyffBhGRlKeuIyIiiekOImed/9vdf+ktzoq4+2bgfGA38FkzOy3aJ3b3ve7+JeB+ImfWb2uxyQ+IFNkPu/t/NS+yg/0rgf8A1gMfAi7p1CsTEekhVGiLiCQYMysCzgIOAf/d1nbuvg34ffDw2sNo6nvBz/PNrH/Qdm/gM8HyW9tpez9wexfaFhFJeSq0RUQSz+nBz8XuvruDbZ8LfkZ9RruJu68HtgDpwEnB4ilANrDV3VdG2fbJZpbe2fZFRFKdCm0RkcQzJPi5OYptm7YZephtvRP8PKILbWcBBYfZvohIylKhLSKSeJqGzYvmavXObBvN/of7fLqyXkSkBRXaIiKJZ1vwc2QU2w5vsU9nNZ0JrziMtkcEP2uBysNsX0QkZanQFhFJPC8FP08yswEdbPvB4OfLnW3EzMYSKdTrgcXB4oVELsIcambHRNn2a+7e0Nn2RURSnQptEZEEE1yk+CKRixK/1tZ2ZjYYuDp4OOcwmrol+Pmku1cFbR8kMkY2wLfbabs38NUutC0ikvJUaIuIJKYvAweAb5jZ51quNLNhwN+AfOAed4/6jLaZ9TezO4DLiMwaeXOLTb4N7AA+bWbfN7OMFvvnAY8CY4HngblRvyoRkR5EM0OKiMRZNDNDBtudSaSgzQdWEDnLXUWkwD2PyBTsDwJXRTEFu/HvKdinE5kMZzXwGXdf2ErbxwFPBM9RBjxDpB920xTseUSK7Fmagl1EpHUqtEVE4izaQjvYtoDIVOnnA+OAXkQuXHwV+J27P9tBG03qiRTp7wBLgceBJ9y9tp22+wLXAZ8AJgD9iBTbi4jMKvmnljNWiojIv6nQFhERERGJAfXRFhERERGJARXaIiIiIiIxoEJbRERERCQGVGiLiIiIiMSACm0RERERkRhQoS0iIiIiEgMqtEVEREREYkCFtoiIiIhIDKjQFhERERGJARXaIiIiIiIxoEJbRERERCQGVGhL3JmZm9nY4P69ZvaD4P5MM9sSg/bebeMw9v2umf2huzO1094ZZlYar/ZERBKRmf3dzK6IwfO2+3lgZtVmNrq725WeS4W2HBYz+7SZLQoOStuCg+LpYedKNs3/6ABw93+5+/gwM4lIz2VmZWZ20MyqzGyPmb1iZtebWczqhdZOaLj7ue5+X6zabIu793P39e1tE6uTQpKaVGhLp5nZV4GfAz8CBgMjgd8AF4SZK9GYWUbYGUREDsNH3T0HGAXcBnwDuOtwnkjHwc4zs/SwM0j3UaEtnWJmucCtwBfc/S/uvt/d69z9SXf/WrDNVDN7NTgbss3MfmVmWZ1o47/MbGdwZuXSZsvPM7MlZrbPzDab2Xdb7Hd6cPZlT7B+divPnWNmL5rZHWZmrawvMrN5wdmc54CBzda97yxGkPGDwf3vmtmjZvYHM9sHzG7vvTCz+cHTvBl8M3BRyzbM7BgzKwn2X2FmH2u27l4z+7WZPRXkfd3MxgTrzMx+ZmblZrbXzN4ys4nR/g5ERNx9r7s/AVwEXNF0DAmOSVc3bWdms83spWaP3cy+YGZrgDXBsl8Ex+V9ZrbYzM4Ilp8D/BdwUXAcfLNlG2aWZmbfNrONwTHt/uCzCDMrDNq7wsw2BZ8d3+rgpeW1dtxslr2pa+NHzOztYLt3zOw/zawv8HdgaJC32syGmlm2mf3czLYGt5+bWXaz5/168Bmw1cyutvd3ofw/M3vazPYDZ7b3edfsNV8ZrNttkW8dioNj/R4z+1XnftsSKyq0pbNOAXoBf21nmwbgRiJF6inAWcDno3z+I4P9hgFXAHPMrKkrxX7gcmAAcB7wOTP7OICZjSRy8PslcAQwGVja/InNrAB4AXjZ3b/k7t5K+w8Bi4MM3w8ydMYFwKNBxgdp571w9+nBPscHX1f+sUXeTOBJ4FlgEPBF4MFm7wfAJcD3gDxgLfDDYPnZwHTgqCDLRcCuTr4WERHcfQGwBTijE7t9HJgGTAgeLyRyXM4ncpz9k5n1cvdniHw7+sfgOHh8K881O7idCYwG+gEtC8nTgfFEjrG3mNkx7WRr67jZ0l3AdcHZ/YnAP919P3AusDXI28/dtwLfAk4OXuPxwFTg2/DuHxNfBT4IjAVmtNLWp4McOcBLtPN518w0YByR4/vPgwwfBI4FPmVmrbUjcaZCWzqrANjp7vVtbeDui939NXevd/cy4E5aP7C05f+5+yF3nwc8BXwqeN4Sd1/m7o3u/hYwt9nzXgo87+5zgzPsu9y9eaE9FJgH/Mndv91ao0GxXtys/flECt3OeNXdHwsyHuzie3EykQ+U29y91t3/CfyNyIdEk7+4+4Lg9/EgkYM8QB2RA/bRgLn7Snff1snXIiLSZCuRIjlaP3b3Snc/CODufwiOy/XufjuQTaQwjsalwE/dfb27VwPfBC6293ZL+V5wzH0TeJNIsduWto6bLdUBE8ysv7vvdvc3Osh4q7uXu3sFkUL+smDdp4B73H2Fux8I1rX0uLu/HHx21HTwedfk+8G2zxIpzOcG7b8D/As4oZ28EicqtKWzdgEDrZ1+d2Z2lJn9zcy2W6QLxY9o1gWjA7uDMwZNNhIpkjGzaRbp9lFhZnuB65s97whgXTvPex7QG/htO9sMbaP9ztjc/EEX34uhwGZ3b2yRZ1izx9ub3T9ApDAnKMp/Bfwa2GFmc8ysf+deiojIu4YBlZ3YvuWx8CYzWxl0ZdsD5NK5Y2HzY/FGIIPINUJNWj0WtiHabf8D+Aiw0SJdCk/pZMahzdY1fz/e8960tqyDz7smO5rdP9jK4/beA4kTFdrSWa8CNUS+FmzL/wGrgHHu3p9I/7v39YduQ17QB67JSCJnUiDydeMTwAh3zyVSNDc972ZgDG37HfAM8HSL529uWxvtN9kP9Gl6YJELVo5o8Rwtu6N05b3YCoyw917tPxJ4J5qd3f0Odz+JyNeIRwFfi7JdEZF3mVkxkUK7qR/2e46FRLr8tfTusTDoj/0NImd289x9ALCXfx8LW+vG19xWIhdmNhkJ1PPewrLbuftCd7+ASNe9x4BHmlZFmbHps2sbMLzZuhGtNdficXufd5JEVGhLp7j7XuAW4Ndm9nEz62NmmWZ2rpn9d7BZDrAPqDazo4HPdbKZ75lZVnBwPh/4U7PnrXT3GjObSqRPW5MHgQ+a2afMLMPMCsys5deBNwClwN/MrHcrr20jsKhZ+6cDH222yWqgV3CRSiaR/nfZLZ+nhY7eix1E+hy25nUiH2hfD97jmUGehztok+CimGlBzv1E/jhq6Gg/EZEmZtbfzM4
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7febf8b46be0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7febd6369438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7febd3d09748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "callback_instances = data_model.callback_instances\n",
    "callback_symbols = data_model.callback_symbols\n",
    "\n",
    "# Get a list of callback objects\n",
    "callback_objects = set(callback_instances['callback_object'])\n",
    "# Get their symbol\n",
    "symbols = {obj: callback_symbols.loc[obj, 'symbol'] for obj in callback_objects}\n",
    "\n",
    "# Plot durations\n",
    "for obj in callback_objects:\n",
    "    duration_ns = callback_instances.loc[callback_instances.loc[:, 'callback_object'] == obj, :]\n",
    "    duration_ms = duration_ns.apply(lambda d: d/1000000.0)\n",
    "\n",
    "    fig = plt.figure(figsize=(12, 6))\n",
    "    fig.suptitle('TODO', fontsize=20)\n",
    "\n",
    "    ax = fig.add_subplot(1, 2, 1)\n",
    "    duration_ms.plot(x='timestamp', y='duration', ax=ax)\n",
    "    ax.legend([str(symbols.get(obj, 'unknown'))])\n",
    "    ax.set_xlabel('start')\n",
    "    ax.set_ylabel('duration (ms)')\n",
    "    ax.title.set_text('Callback durations')\n",
    "    ax.grid()\n",
    "\n",
    "    ax = fig.add_subplot(1, 2, 2)\n",
    "    duration_ms.hist(column='duration', ax=ax)\n",
    "    ax.title.set_text('Duration histogram')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}