210907Benchmarking.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ec91dbd2",
   "metadata": {},
   "source": [
    "# Benchmarking a single image\n",
    "In this notebook, the template matching algorithm is benchmarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5481e98c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:hyperspy.api:The ipywidgets GUI elements are not available, probably because the hyperspy_gui_ipywidgets package is not installed.\n",
      "WARNING:hyperspy.api:The traitsui GUI elements are not available, probably because the hyperspy_gui_traitsui package is not installed.\n"
     ]
    }
   ],
   "source": [
    "import cupy as cp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import hyperspy.api as hs\n",
    "import pyxem.utils.indexation_utils as iutls\n",
    "import pyxem.utils.polar_transform_utils as ptu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0980e008",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we load a random image, it doesn't matter\n",
    "dt = hs.load(\"data/Cu-Ag_alloy-1000max.blo\", lazy=True)\n",
    "image = dt.inav[0, 0].data.compute()  # for the cpu\n",
    "image_cp = cp.asarray(image)          # for the gpu\n",
    "del dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4bfb9e60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512, 512)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "185b57c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4391bea7f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image, vmax=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cf24fde",
   "metadata": {},
   "source": [
    "# Benchmark on CPU\n",
    "Note: by default Pyxem's CPU implementation uses all available CPU cores on the system to index a pattern. To use only a single core, the source code must be edited.\n",
    "In the `_match_polar_to_polar_library_cpu` function on line 520 in `pyxem/utils/indexation_utils.py`, the option `parallel` must be set to `False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7aa776f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nkeep: 1, delta: 5\n",
      "1 2 3 4 5 (1, 10)\n",
      "nkeep: 500, delta: 5\n",
      "1 2 3 4 5 (500, 10)\n",
      "nkeep: 1000, delta: 5\n",
      "1 2 3 4 5 (1000, 10)\n",
      "nkeep: 3000, delta: 5\n",
      "1 2 3 4 5 (3000, 10)\n",
      "nkeep: 6000, delta: 5\n",
      "1 2 3 4 5 (6000, 10)\n",
      "nkeep: 10000, delta: 5\n",
      "1 2 3 4 5 (10000, 10)\n",
      "nkeep: 1, delta: 1\n",
      "1 2 3 4 5 (1, 10)\n",
      "nkeep: 500, delta: 1\n",
      "1 2 3 4 5 (500, 10)\n",
      "nkeep: 1000, delta: 1\n",
      "1 2 3 4 5 (1000, 10)\n",
      "nkeep: 3000, delta: 1\n",
      "1 2 3 4 5 (3000, 10)\n",
      "nkeep: 6000, delta: 1\n",
      "1 2 3 4 5 (6000, 10)\n",
      "nkeep: 10000, delta: 1\n",
      "1 2 3 4 5 (10000, 10)\n",
      "nkeep: 1, delta: 0.5\n",
      "1 2 3 4 5 (1, 10)\n",
      "nkeep: 500, delta: 0.5\n",
      "1 2 3 4 5 (500, 10)\n",
      "nkeep: 1000, delta: 0.5\n",
      "1 2 3 4 5 (1000, 10)\n",
      "nkeep: 3000, delta: 0.5\n",
      "1 2 3 4 5 (3000, 10)\n",
      "nkeep: 6000, delta: 0.5\n",
      "1 2 3 4 5 (6000, 10)\n",
      "nkeep: 10000, delta: 0.5\n",
      "1 2 3 4 5 (10000, 10)\n",
      "nkeep: 1, delta: 0.2\n",
      "1 2 3 4 5 (1, 10)\n",
      "nkeep: 500, delta: 0.2\n",
      "1 2 3 4 5 (500, 10)\n",
      "nkeep: 1000, delta: 0.2\n",
      "1 2 3 4 5 (1000, 10)\n",
      "nkeep: 3000, delta: 0.2\n",
      "1 2 3 4 5 (3000, 10)\n",
      "nkeep: 6000, delta: 0.2\n",
      "1 2 3 4 5 (6000, 10)\n",
      "nkeep: 10000, delta: 0.2\n",
      "1 2 3 4 5 (10000, 10)\n",
      "nkeep: 1, delta: 5\n",
      "1 2 3 4 5 (1, 50)\n",
      "nkeep: 500, delta: 5\n",
      "1 2 3 4 5 (500, 50)\n",
      "nkeep: 1000, delta: 5\n",
      "1 2 3 4 5 (1000, 50)\n",
      "nkeep: 3000, delta: 5\n",
      "1 2 3 4 5 (3000, 50)\n",
      "nkeep: 6000, delta: 5\n",
      "1 2 3 4 5 (6000, 50)\n",
      "nkeep: 10000, delta: 5\n",
      "1 2 3 4 5 (10000, 50)\n",
      "nkeep: 1, delta: 1\n",
      "1 2 3 4 5 (1, 50)\n",
      "nkeep: 500, delta: 1\n",
      "1 2 3 4 5 (500, 50)\n",
      "nkeep: 1000, delta: 1\n",
      "1 2 3 4 5 (1000, 50)\n",
      "nkeep: 3000, delta: 1\n",
      "1 2 3 4 5 (3000, 50)\n",
      "nkeep: 6000, delta: 1\n",
      "1 2 3 4 5 (6000, 50)\n",
      "nkeep: 10000, delta: 1\n",
      "1 2 3 4 5 (10000, 50)\n",
      "nkeep: 1, delta: 0.5\n",
      "1 2 3 4 5 (1, 50)\n",
      "nkeep: 500, delta: 0.5\n",
      "1 2 3 4 5 (500, 50)\n",
      "nkeep: 1000, delta: 0.5\n",
      "1 2 3 4 5 (1000, 50)\n",
      "nkeep: 3000, delta: 0.5\n",
      "1 2 3 4 5 (3000, 50)\n",
      "nkeep: 6000, delta: 0.5\n",
      "1 2 3 4 5 (6000, 50)\n",
      "nkeep: 10000, delta: 0.5\n",
      "1 2 3 4 5 (10000, 50)\n",
      "nkeep: 1, delta: 0.2\n",
      "1 2 3 4 5 (1, 50)\n",
      "nkeep: 500, delta: 0.2\n",
      "1 2 3 4 5 (500, 50)\n",
      "nkeep: 1000, delta: 0.2\n",
      "1 2 3 4 5 (1000, 50)\n",
      "nkeep: 3000, delta: 0.2\n",
      "1 2 3 4 5 (3000, 50)\n",
      "nkeep: 6000, delta: 0.2\n",
      "1 2 3 4 5 (6000, 50)\n",
      "nkeep: 10000, delta: 0.2\n",
      "1 2 3 4 5 (10000, 50)\n",
      "nkeep: 1, delta: 5\n",
      "1 2 3 4 5 (1, 100)\n",
      "nkeep: 500, delta: 5\n",
      "1 2 3 4 5 (500, 100)\n",
      "nkeep: 1000, delta: 5\n",
      "1 2 3 4 5 (1000, 100)\n",
      "nkeep: 3000, delta: 5\n",
      "1 2 3 4 5 (3000, 100)\n",
      "nkeep: 6000, delta: 5\n",
      "1 2 3 4 5 (6000, 100)\n",
      "nkeep: 10000, delta: 5\n",
      "1 2 3 4 5 (10000, 100)\n",
      "nkeep: 1, delta: 1\n",
      "1 2 3 4 5 (1, 100)\n",
      "nkeep: 500, delta: 1\n",
      "1 2 3 4 5 (500, 100)\n",
      "nkeep: 1000, delta: 1\n",
      "1 2 3 4 5 (1000, 100)\n",
      "nkeep: 3000, delta: 1\n",
      "1 2 3 4 5 (3000, 100)\n",
      "nkeep: 6000, delta: 1\n",
      "1 2 3 4 5 (6000, 100)\n",
      "nkeep: 10000, delta: 1\n",
      "1 2 3 4 5 (10000, 100)\n",
      "nkeep: 1, delta: 0.5\n",
      "1 2 3 4 5 (1, 100)\n",
      "nkeep: 500, delta: 0.5\n",
      "1 2 3 4 5 (500, 100)\n",
      "nkeep: 1000, delta: 0.5\n",
      "1 2 3 4 5 (1000, 100)\n",
      "nkeep: 3000, delta: 0.5\n",
      "1 2 3 4 5 (3000, 100)\n",
      "nkeep: 6000, delta: 0.5\n",
      "1 2 3 4 5 (6000, 100)\n",
      "nkeep: 10000, delta: 0.5\n",
      "1 2 3 4 5 (10000, 100)\n",
      "nkeep: 1, delta: 0.2\n",
      "1 2 3 4 5 (1, 100)\n",
      "nkeep: 500, delta: 0.2\n",
      "1 2 3 4 5 (500, 100)\n",
      "nkeep: 1000, delta: 0.2\n",
      "1 2 3 4 5 (1000, 100)\n",
      "nkeep: 3000, delta: 0.2\n",
      "1 2 3 4 5 (3000, 100)\n",
      "nkeep: 6000, delta: 0.2\n",
      "1 2 3 4 5 (6000, 100)\n",
      "nkeep: 10000, delta: 0.2\n",
      "1 2 3 4 5 (10000, 100)\n",
      "nkeep: 1, delta: 5\n",
      "1 2 3 4 5 (1, 300)\n",
      "nkeep: 500, delta: 5\n",
      "1 2 3 4 5 (500, 300)\n",
      "nkeep: 1000, delta: 5\n",
      "1 2 3 4 5 (1000, 300)\n",
      "nkeep: 3000, delta: 5\n",
      "1 2 3 4 5 (3000, 300)\n",
      "nkeep: 6000, delta: 5\n",
      "1 2 3 4 5 (6000, 300)\n",
      "nkeep: 10000, delta: 5\n",
      "1 2 3 4 5 (10000, 300)\n",
      "nkeep: 1, delta: 1\n",
      "1 2 3 4 5 (1, 300)\n",
      "nkeep: 500, delta: 1\n",
      "1 2 3 4 5 (500, 300)\n",
      "nkeep: 1000, delta: 1\n",
      "1 2 3 4 5 (1000, 300)\n",
      "nkeep: 3000, delta: 1\n",
      "1 2 3 4 5 (3000, 300)\n",
      "nkeep: 6000, delta: 1\n",
      "1 2 3 4 5 (6000, 300)\n",
      "nkeep: 10000, delta: 1\n",
      "1 2 3 4 5 (10000, 300)\n",
      "nkeep: 1, delta: 0.5\n",
      "1 2 3 4 5 (1, 300)\n",
      "nkeep: 500, delta: 0.5\n",
      "1 2 3 4 5 (500, 300)\n",
      "nkeep: 1000, delta: 0.5\n",
      "1 2 3 4 5 (1000, 300)\n",
      "nkeep: 3000, delta: 0.5\n",
      "1 2 3 4 5 (3000, 300)\n",
      "nkeep: 6000, delta: 0.5\n",
      "1 2 3 4 5 (6000, 300)\n",
      "nkeep: 10000, delta: 0.5\n",
      "1 2 3 4 5 (10000, 300)\n",
      "nkeep: 1, delta: 0.2\n",
      "1 2 3 4 5 (1, 300)\n",
      "nkeep: 500, delta: 0.2\n",
      "1 2 3 4 5 (500, 300)\n",
      "nkeep: 1000, delta: 0.2\n",
      "1 2 3 4 5 (1000, 300)\n",
      "nkeep: 3000, delta: 0.2\n",
      "1 2 3 4 5 (3000, 300)\n",
      "nkeep: 6000, delta: 0.2\n",
      "1 2 3 4 5 (6000, 300)\n",
      "nkeep: 10000, delta: 0.2\n",
      "1 2 3 4 5 (10000, 300)\n"
     ]
    }
   ],
   "source": [
    "from time import time, sleep\n",
    "from pyxem.utils import polar_transform_utils as ptutls\n",
    "n_keep = [1, 500, 1000, 3000, 6000, 10000]\n",
    "delta_theta = [5, 1, 0.5, 0.2]\n",
    "spots = [10, 50, 100, 300]\n",
    "\n",
    "grids = []\n",
    "\n",
    "max_r = 250\n",
    "for spot in spots:\n",
    "    for dt in delta_theta:\n",
    "        polar_image = ptutls.image_to_polar(image, delta_r=1, delta_theta=dt, max_r=max_r)\n",
    "        for nk in n_keep:\n",
    "            # we create some random templates which is easier to deal with\n",
    "            r = np.random.randint(1, max_r, size=(nk, spot))\n",
    "            theta = np.random.randint(0, int(360/dt), size=(nk, spot))\n",
    "            intensities = np.random.rand(nk, spot)\n",
    "            integrated_templates = iutls._get_integrated_polar_templates(\n",
    "                polar_image.shape[1],\n",
    "                r,\n",
    "                intensities,\n",
    "                normalize_templates=True,\n",
    "            )\n",
    "            print(f\"nkeep: {nk}, delta: {dt}\")\n",
    "            times = []\n",
    "            for i in range(5):\n",
    "                # things we want to time as it needs to be done each image: the time to convert a pattern to polar coordinates, the time to do a quick index, the time to do a full index\n",
    "                print(i+1, end=\" \")\n",
    "                start = time()\n",
    "                ptutls.image_to_polar(image, 1, dt, max_r, False, None)\n",
    "                #iutls._prefilter_templates(polar_image, r, theta, intensities, integrated_templates, None, nk)\n",
    "                #iutls._get_full_correlations(polar_image, r, theta, intensities)\n",
    "                iutls._mixed_matching_lib_to_polar(polar_image, integrated_templates, r, theta, intensities, nk, None, 1)\n",
    "                end = time()\n",
    "                times.append(end - start)\n",
    "            grids.append((nk, dt, spot, np.mean(times), np.std(times)))\n",
    "            print(r.shape)\n",
    "grids = np.array(grids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59c27d2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "grid_df = pd.DataFrame(grids, columns=[\"nk\", \"dt\", \"spot\", \"mean\", \"std\"])\n",
    "# grid_df.to_csv(\"pyxem_cpu_time_1_core.csv\")  # if the previous cell was run on a single core\n",
    "grid_df.to_csv(\"pyxem_cpu_time.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98d9ef12",
   "metadata": {},
   "source": [
    "# GPU benchmarking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "432259f5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nkeep: 1, delta: 5\n",
      "1 2 3 4 5 (1, 10)\n",
      "nkeep: 500, delta: 5\n",
      "1 2 3 4 5 (500, 10)\n",
      "nkeep: 1000, delta: 5\n",
      "1 2 3 4 5 (1000, 10)\n",
      "nkeep: 3000, delta: 5\n",
      "1 2 3 4 5 (3000, 10)\n",
      "nkeep: 6000, delta: 5\n",
      "1 2 3 4 5 (6000, 10)\n",
      "nkeep: 10000, delta: 5\n",
      "1 2 3 4 5 (10000, 10)\n",
      "nkeep: 1, delta: 1\n",
      "1 2 3 4 5 (1, 10)\n",
      "nkeep: 500, delta: 1\n",
      "1 2 3 4 5 (500, 10)\n",
      "nkeep: 1000, delta: 1\n",
      "1 2 3 4 5 (1000, 10)\n",
      "nkeep: 3000, delta: 1\n",
      "1 2 3 4 5 (3000, 10)\n",
      "nkeep: 6000, delta: 1\n",
      "1 2 3 4 5 (6000, 10)\n",
      "nkeep: 10000, delta: 1\n",
      "1 2 3 4 5 (10000, 10)\n",
      "nkeep: 1, delta: 0.5\n",
      "1 2 3 4 5 (1, 10)\n",
      "nkeep: 500, delta: 0.5\n",
      "1 2 3 4 5 (500, 10)\n",
      "nkeep: 1000, delta: 0.5\n",
      "1 2 3 4 5 (1000, 10)\n",
      "nkeep: 3000, delta: 0.5\n",
      "1 2 3 4 5 (3000, 10)\n",
      "nkeep: 6000, delta: 0.5\n",
      "1 2 3 4 5 (6000, 10)\n",
      "nkeep: 10000, delta: 0.5\n",
      "1 2 3 4 5 (10000, 10)\n",
      "nkeep: 1, delta: 0.2\n",
      "1 2 3 4 5 (1, 10)\n",
      "nkeep: 500, delta: 0.2\n",
      "1 2 3 4 5 (500, 10)\n",
      "nkeep: 1000, delta: 0.2\n",
      "1 2 3 4 5 (1000, 10)\n",
      "nkeep: 3000, delta: 0.2\n",
      "1 2 3 4 5 (3000, 10)\n",
      "nkeep: 6000, delta: 0.2\n",
      "1 2 3 4 5 (6000, 10)\n",
      "nkeep: 10000, delta: 0.2\n",
      "1 2 3 4 5 (10000, 10)\n",
      "nkeep: 1, delta: 5\n",
      "1 2 3 4 5 (1, 50)\n",
      "nkeep: 500, delta: 5\n",
      "1 2 3 4 5 (500, 50)\n",
      "nkeep: 1000, delta: 5\n",
      "1 2 3 4 5 (1000, 50)\n",
      "nkeep: 3000, delta: 5\n",
      "1 2 3 4 5 (3000, 50)\n",
      "nkeep: 6000, delta: 5\n",
      "1 2 3 4 5 (6000, 50)\n",
      "nkeep: 10000, delta: 5\n",
      "1 2 3 4 5 (10000, 50)\n",
      "nkeep: 1, delta: 1\n",
      "1 2 3 4 5 (1, 50)\n",
      "nkeep: 500, delta: 1\n",
      "1 2 3 4 5 (500, 50)\n",
      "nkeep: 1000, delta: 1\n",
      "1 2 3 4 5 (1000, 50)\n",
      "nkeep: 3000, delta: 1\n",
      "1 2 3 4 5 (3000, 50)\n",
      "nkeep: 6000, delta: 1\n",
      "1 2 3 4 5 (6000, 50)\n",
      "nkeep: 10000, delta: 1\n",
      "1 2 3 4 5 (10000, 50)\n",
      "nkeep: 1, delta: 0.5\n",
      "1 2 3 4 5 (1, 50)\n",
      "nkeep: 500, delta: 0.5\n",
      "1 2 3 4 5 (500, 50)\n",
      "nkeep: 1000, delta: 0.5\n",
      "1 2 3 4 5 (1000, 50)\n",
      "nkeep: 3000, delta: 0.5\n",
      "1 2 3 4 5 (3000, 50)\n",
      "nkeep: 6000, delta: 0.5\n",
      "1 2 3 4 5 (6000, 50)\n",
      "nkeep: 10000, delta: 0.5\n",
      "1 2 3 4 5 (10000, 50)\n",
      "nkeep: 1, delta: 0.2\n",
      "1 2 3 4 5 (1, 50)\n",
      "nkeep: 500, delta: 0.2\n",
      "1 2 3 4 5 (500, 50)\n",
      "nkeep: 1000, delta: 0.2\n",
      "1 2 3 4 5 (1000, 50)\n",
      "nkeep: 3000, delta: 0.2\n",
      "1 2 3 4 5 (3000, 50)\n",
      "nkeep: 6000, delta: 0.2\n",
      "1 2 3 4 5 (6000, 50)\n",
      "nkeep: 10000, delta: 0.2\n",
      "1 2 3 4 5 (10000, 50)\n",
      "nkeep: 1, delta: 5\n",
      "1 2 3 4 5 (1, 100)\n",
      "nkeep: 500, delta: 5\n",
      "1 2 3 4 5 (500, 100)\n",
      "nkeep: 1000, delta: 5\n",
      "1 2 3 4 5 (1000, 100)\n",
      "nkeep: 3000, delta: 5\n",
      "1 2 3 4 5 (3000, 100)\n",
      "nkeep: 6000, delta: 5\n",
      "1 2 3 4 5 (6000, 100)\n",
      "nkeep: 10000, delta: 5\n",
      "1 2 3 4 5 (10000, 100)\n",
      "nkeep: 1, delta: 1\n",
      "1 2 3 4 5 (1, 100)\n",
      "nkeep: 500, delta: 1\n",
      "1 2 3 4 5 (500, 100)\n",
      "nkeep: 1000, delta: 1\n",
      "1 2 3 4 5 (1000, 100)\n",
      "nkeep: 3000, delta: 1\n",
      "1 2 3 4 5 (3000, 100)\n",
      "nkeep: 6000, delta: 1\n",
      "1 2 3 4 5 (6000, 100)\n",
      "nkeep: 10000, delta: 1\n",
      "1 2 3 4 5 (10000, 100)\n",
      "nkeep: 1, delta: 0.5\n",
      "1 2 3 4 5 (1, 100)\n",
      "nkeep: 500, delta: 0.5\n",
      "1 2 3 4 5 (500, 100)\n",
      "nkeep: 1000, delta: 0.5\n",
      "1 2 3 4 5 (1000, 100)\n",
      "nkeep: 3000, delta: 0.5\n",
      "1 2 3 4 5 (3000, 100)\n",
      "nkeep: 6000, delta: 0.5\n",
      "1 2 3 4 5 (6000, 100)\n",
      "nkeep: 10000, delta: 0.5\n",
      "1 2 3 4 5 (10000, 100)\n",
      "nkeep: 1, delta: 0.2\n",
      "1 2 3 4 5 (1, 100)\n",
      "nkeep: 500, delta: 0.2\n",
      "1 2 3 4 5 (500, 100)\n",
      "nkeep: 1000, delta: 0.2\n",
      "1 2 3 4 5 (1000, 100)\n",
      "nkeep: 3000, delta: 0.2\n",
      "1 2 3 4 5 (3000, 100)\n",
      "nkeep: 6000, delta: 0.2\n",
      "1 2 3 4 5 (6000, 100)\n",
      "nkeep: 10000, delta: 0.2\n",
      "1 2 3 4 5 (10000, 100)\n",
      "nkeep: 1, delta: 5\n",
      "1 2 3 4 5 (1, 300)\n",
      "nkeep: 500, delta: 5\n",
      "1 2 3 4 5 (500, 300)\n",
      "nkeep: 1000, delta: 5\n",
      "1 2 3 4 5 (1000, 300)\n",
      "nkeep: 3000, delta: 5\n",
      "1 2 3 4 5 (3000, 300)\n",
      "nkeep: 6000, delta: 5\n",
      "1 2 3 4 5 (6000, 300)\n",
      "nkeep: 10000, delta: 5\n",
      "1 2 3 4 5 (10000, 300)\n",
      "nkeep: 1, delta: 1\n",
      "1 2 3 4 5 (1, 300)\n",
      "nkeep: 500, delta: 1\n",
      "1 2 3 4 5 (500, 300)\n",
      "nkeep: 1000, delta: 1\n",
      "1 2 3 4 5 (1000, 300)\n",
      "nkeep: 3000, delta: 1\n",
      "1 2 3 4 5 (3000, 300)\n",
      "nkeep: 6000, delta: 1\n",
      "1 2 3 4 5 (6000, 300)\n",
      "nkeep: 10000, delta: 1\n",
      "1 2 3 4 5 (10000, 300)\n",
      "nkeep: 1, delta: 0.5\n",
      "1 2 3 4 5 (1, 300)\n",
      "nkeep: 500, delta: 0.5\n",
      "1 2 3 4 5 (500, 300)\n",
      "nkeep: 1000, delta: 0.5\n",
      "1 2 3 4 5 (1000, 300)\n",
      "nkeep: 3000, delta: 0.5\n",
      "1 2 3 4 5 (3000, 300)\n",
      "nkeep: 6000, delta: 0.5\n",
      "1 2 3 4 5 (6000, 300)\n",
      "nkeep: 10000, delta: 0.5\n",
      "1 2 3 4 5 (10000, 300)\n",
      "nkeep: 1, delta: 0.2\n",
      "1 2 3 4 5 (1, 300)\n",
      "nkeep: 500, delta: 0.2\n",
      "1 2 3 4 5 (500, 300)\n",
      "nkeep: 1000, delta: 0.2\n",
      "1 2 3 4 5 (1000, 300)\n",
      "nkeep: 3000, delta: 0.2\n",
      "1 2 3 4 5 (3000, 300)\n",
      "nkeep: 6000, delta: 0.2\n",
      "1 2 3 4 5 (6000, 300)\n",
      "nkeep: 10000, delta: 0.2\n",
      "1 2 3 4 5 (10000, 300)\n"
     ]
    }
   ],
   "source": [
    "from time import time, sleep\n",
    "import cupy as cp\n",
    "from pyxem.utils import polar_transform_utils as ptutls\n",
    "from pyxem.utils.cuda_utils import TPB\n",
    "\n",
    "n_keep = [1, 500, 1000, 3000, 6000, 10000]\n",
    "delta_theta = [5, 1, 0.5, 0.2]\n",
    "spots = [10, 50, 100, 300]\n",
    "\n",
    "grids_gpu = []\n",
    "\n",
    "max_r = 250\n",
    "for spot in spots:\n",
    "    for dt in delta_theta:\n",
    "        polar_image = ptutls.image_to_polar(cp.asarray(image), delta_r=1, delta_theta=dt, max_r=max_r)\n",
    "        for nk in n_keep:\n",
    "            r = cp.random.randint(1, max_r, size=(nk, spot))\n",
    "            theta = cp.random.randint(0, int(360/dt), size=(nk, spot))\n",
    "            intensities = cp.random.rand(nk, spot)\n",
    "            integrated_templates = iutls._get_integrated_polar_templates(\n",
    "                polar_image.shape[1],\n",
    "                r,\n",
    "                intensities,\n",
    "                normalize_templates=True,\n",
    "            )\n",
    "            print(f\"nkeep: {nk}, delta: {dt}\")\n",
    "            times = []\n",
    "            for i in range(5):\n",
    "                # things we want to time as it needs to be done each image: the time to convert a pattern to polar coordinates, the time to do a quick index, the time to do a full index\n",
    "                print(i+1, end=\" \")\n",
    "                start = time()\n",
    "                image_cp = cp.asarray(image)          # for the gpu\n",
    "                polar_image = ptutls.image_to_polar(image_cp, 1, dt, 250, False, None)\n",
    "                answer = iutls._mixed_matching_lib_to_polar(polar_image, integrated_templates, r, theta, intensities, nk, None, 1)\n",
    "                cp.asnumpy(answer)\n",
    "                # answer = iutls._get_full_correlations(polar_image, r, theta, intensities)\n",
    "                end = time()\n",
    "                times.append(end - start)\n",
    "            grids_gpu.append((nk, dt, spot, np.mean(times), np.std(times)))\n",
    "            print(r.shape)\n",
    "grids_gpu = np.array(grids_gpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c0c0b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "grid_df_gpu = pd.DataFrame(grids_gpu, columns=[\"nk\", \"dt\", \"spot\", \"mean\", \"std\"])\n",
    "grid_df_gpu.to_csv(\"pyxem_gpu_time.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da3221e8",
   "metadata": {},
   "source": [
    "# Analysis of the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f03f21",
   "metadata": {},
   "source": [
    "We also compare to benchmark timings of py4DSTEM. Unfortunately these benchmarks can not be run in the same environment as py4DSTEM pins all its package versions. The script to obtain these timings is as follows:\n",
    "\n",
    "```python\n",
    "import py4DSTEM\n",
    "import numpy as np\n",
    "\n",
    "# Define gold structure using manual definition\n",
    "pos = np.array([\n",
    "    [0.0, 0.0, 0.0],\n",
    "    [0.0, 0.5, 0.5],\n",
    "    [0.5, 0.0, 0.5],\n",
    "    [0.5, 0.5, 0.0],\n",
    "])\n",
    "atom_num = 79\n",
    "a = 4.08\n",
    "cell = a\n",
    "\n",
    "crystal = py4DSTEM.process.diffraction.Crystal(\n",
    "    pos, \n",
    "    atom_num, \n",
    "    cell)\n",
    "\n",
    "crystal.calculate_structure_factors(1.5)\n",
    "\n",
    "q = np.linspace(0,crystal.k_max,100)\n",
    "\n",
    "atom_sf = py4DSTEM.process.utils.single_atom_scatter(\n",
    "    [crystal.numbers[0]],\n",
    "    [1],\n",
    "    q,\n",
    "    'A')\n",
    "atom_sf.get_scattering_factor(\n",
    "    [crystal.numbers[0]],\n",
    "    [1],\n",
    "    q,\n",
    "    'A');\n",
    "\n",
    "import time\n",
    "\n",
    "zone_steps = [3, 1, 0.5]\n",
    "in_plane_step = [5.0, 1.0, 0.2]\n",
    "\n",
    "times = []\n",
    "for z in zone_steps:\n",
    "    for ips in in_plane_step:\n",
    "        print(\"Zone_steps\", z, \"in_plane_diff\", ips)\n",
    "        # Create an orientation plan\n",
    "        crystal.orientation_plan(\n",
    "            angle_step_zone_axis = z,\n",
    "            angle_step_in_plane = ips,\n",
    "            accel_voltage = 300e3,\n",
    "            corr_kernel_size=0.08,\n",
    "        )\n",
    "        # measure the average number of spots\n",
    "        spots = []\n",
    "        for zv in crystal.orientation_vecs:\n",
    "            bp = crystal.generate_diffraction_pattern(zone_axis = zv)\n",
    "            spots.append(bp.data.shape[0])\n",
    "            \n",
    "        totalspots = int(round(sum(spots)/len(spots)))\n",
    "        # Testing some matches\n",
    "        zone_axis_test = np.array([1,3,5])\n",
    "\n",
    "        bragg_peaks = crystal.generate_diffraction_pattern(\n",
    "            zone_axis = zone_axis_test,\n",
    "            sigma_excitation_error=0.02)\n",
    "        \n",
    "        localtimes = []\n",
    "        for _ in range(5):\n",
    "            start = time.time()\n",
    "            orient = crystal.match_single_pattern(\n",
    "                bragg_peaks,\n",
    "            )\n",
    "            stop = time.time()\n",
    "            localtimes.append(stop-start)\n",
    "        meantime = np.mean(localtimes)\n",
    "        stdtime = np.std(localtimes)\n",
    "        times.append([z, totalspots, ips, meantime, stdtime])\n",
    "        \n",
    "import pandas as pd\n",
    "\n",
    "datatimes = pd.DataFrame(times, columns=[\"deltaz\", \"s\", \"deltaphi\", \"mean\", \"std\"])\n",
    "datatimes[\"N\"] = [190, 190, 190, 1596, 1596, 1596, 6105, 6105, 6105]\n",
    "\n",
    "datatimes.to_csv(\"timings_py4dstem.csv\")\n",
    "```\n",
    "\n",
    "It is based on the example in the `ACOM_01_basics` notebook which can be found [here](https://github.com/py4dstem/py4DSTEM_tutorials/tree/9fcc789/notebooks/acom) and times only the `match_single_pattern` function. To run the script, py4DSTEM must be installed as explained in the respository README.\n",
    "\n",
    "We also compare to one timing result from `Problematic` ([link](https://github.com/stefsmeets/problematic)). Specifically, we time the `indexer.find_orientation` method as used in the example provided [here](https://github.com/stefsmeets/problematic/blob/88cfe04ccdedb62d2bf5654d7fa3003048c8e76c/tutorial/erice_crystallography_school_2018.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b0758c73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>deltaz</th>\n",
       "      <th>s</th>\n",
       "      <th>deltaphi</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.004886</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022557</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.111324</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.046873</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>1596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.186239</td>\n",
       "      <td>0.001506</td>\n",
       "      <td>1596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.932287</td>\n",
       "      <td>0.009128</td>\n",
       "      <td>1596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>16</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.178323</td>\n",
       "      <td>0.000855</td>\n",
       "      <td>6105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.718836</td>\n",
       "      <td>0.002470</td>\n",
       "      <td>6105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.888544</td>\n",
       "      <td>2.645357</td>\n",
       "      <td>6105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  deltaz   s  deltaphi      mean       std     N\n",
       "0           0     3.0  16       5.0  0.004886  0.000275   190\n",
       "1           1     3.0  16       1.0  0.022557  0.000092   190\n",
       "2           2     3.0  16       0.2  0.111324  0.000421   190\n",
       "3           3     1.0  16       5.0  0.046873  0.000229  1596\n",
       "4           4     1.0  16       1.0  0.186239  0.001506  1596\n",
       "5           5     1.0  16       0.2  0.932287  0.009128  1596\n",
       "6           6     0.5  16       5.0  0.178323  0.000855  6105\n",
       "7           7     0.5  16       1.0  0.718836  0.002470  6105\n",
       "8           8     0.5  16       0.2  4.888544  2.645357  6105"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "grid_py4dstem = pd.read_csv(\"data/timings_py4dstem.csv\")\n",
    "grid_py4dstem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "56484018",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4dfd3cf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "grid_df = pd.read_csv(\"pyxem_cpu_time.csv\")\n",
    "grid_df_1core = pd.read_csv(\"pyxem_cpu_time_1_core.csv\")\n",
    "grid_df_gpu = pd.read_csv(\"pyxem_gpu_time.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7bde0f0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ae6d3a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
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       "\n",
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       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
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       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
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       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
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       "};\n",
       "\n",
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       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
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       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
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       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
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       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
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       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
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       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
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       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
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       "        for (var i = 0; i < nentries; i++) {\n",
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       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
    "\n",
    "line = lambda x, a, b: a*x + b\n",
    "\n",
    "c = \"C0\"\n",
    "x = grid_df_1core[\"nk\"] * grid_df_1core[\"spot\"] / grid_df_1core[\"dt\"]\n",
    "y = grid_df_1core[\"mean\"]\n",
    "a, b = curve_fit(line, x, y)[0]\n",
    "ax[0].errorbar(x, y, yerr=grid_df_1core[\"std\"], label=f\"Pyxem - CPU (1 core)\", marker=\"o\", ls=\"None\", markeredgecolor=\"white\", color=c)\n",
    "xx = np.linspace(0, 1.5e7)\n",
    "ax[0].plot(xx, line(xx, a, b), color=c)\n",
    "ax[0].text(0.1e7, 0.5, f\"{a:.1E} x {b:.1E}\", rotation=80, color=c)\n",
    "\n",
    "c = \"C1\"\n",
    "x = grid_df[\"nk\"] * grid_df[\"spot\"] / grid_df[\"dt\"]\n",
    "y = grid_df[\"mean\"]\n",
    "a, b = curve_fit(line, x, y)[0]\n",
    "ax[0].errorbar(x, y, yerr=grid_df[\"std\"], label=f\"Pyxem - CPU (16 cores)\", marker=\"o\", ls=\"None\", markeredgecolor=\"white\", color=c)\n",
    "xx = np.linspace(0, 1.5e7)\n",
    "ax[0].plot(xx, line(xx, a, b), color=c)\n",
    "ax[0].text(0.5e7, 0.33, f\"{a:.1E} x + {b:.1E}\", rotation=25, color=c)\n",
    "\n",
    "c = \"C2\"\n",
    "x = grid_df_gpu[\"nk\"] * grid_df_gpu[\"spot\"] / grid_df_gpu[\"dt\"]\n",
    "y = grid_df_gpu[\"mean\"]\n",
    "a, b = curve_fit(line, x, y)[0]\n",
    "ax[0].errorbar(x, y, yerr=grid_df_gpu[\"std\"], label=f\"Pyxem - GPU\", marker=\"s\", ls=\"None\", markeredgecolor=\"white\", color=c)\n",
    "xx = np.linspace(0, 1.5e7)\n",
    "ax[0].plot(xx, line(xx, a, b), color=c)\n",
    "ax[0].text(0.5e7, 0.1, f\"{a:.1E} x + {b:.1E}\", rotation=8, color=c)\n",
    "\n",
    "# py4dstem\n",
    "c = \"C4\"\n",
    "x = grid_py4dstem[\"N\"] * grid_py4dstem[\"s\"] / grid_py4dstem[\"deltaphi\"]\n",
    "y = grid_py4dstem[\"mean\"]\n",
    "a, b = curve_fit(line, x, y)[0]\n",
    "ax[0].errorbar(x, y, yerr=grid_py4dstem[\"std\"], label=f\"py4DSTEM\", marker=\"^\", ls=\"None\", markersize=8, markeredgecolor=\"white\", color=c)\n",
    "xx = np.linspace(0, 1.5e7)\n",
    "ax[0].plot(xx, line(xx, a, b), color=c)\n",
    "ax[0].text(0.05e7, 1.2, f\"{a:.1E} x - {-b:.1E}\", rotation=88, color=c)\n",
    "\n",
    "# add problematic\n",
    "x = [746*107/1.7] # N * Spots / delta phi\n",
    "y = [0.093]\n",
    "ax[0].scatter(x, y, color=\"red\",  marker=\"v\", label=\"Problematic\", zorder=10, edgecolor=\"white\", s=80)\n",
    "ax[1].scatter(x, y, color=\"red\",  marker=\"v\", label=\"1.7$^\\circ$ - Problematic\", zorder=10, edgecolor=\"white\", s=80)\n",
    "\n",
    "groups = grid_df_1core.groupby(\"dt\")\n",
    "colors = [\"C0\", \"None\", \"C1\", \"C2\"]\n",
    "for col, (name, group) in zip(colors, groups):\n",
    "    #print(group)\n",
    "    if np.all(0.5 == group[\"dt\"]):\n",
    "        continue\n",
    "    x = group[\"nk\"] * group[\"spot\"] / group[\"dt\"]\n",
    "    ax[1].errorbar(x, group[\"mean\"], yerr=group[\"std\"], label=f\"{name}\"+r\"$^\\circ$ - Pyxem CPU (1 core)\",\n",
    "                marker=\"o\", ls=\"None\", markeredgecolor=col, ecolor=col, color=\"None\",\n",
    "               )\n",
    "    \n",
    "groups = grid_df.groupby(\"dt\")\n",
    "for name, group in groups:\n",
    "    #print(group)\n",
    "    if np.all(0.5 == group[\"dt\"]):\n",
    "        continue\n",
    "    x = group[\"nk\"] * group[\"spot\"] / group[\"dt\"]\n",
    "    ax[1].errorbar(x, group[\"mean\"], yerr=group[\"std\"], label=f\"{name}\"+r\"$^\\circ$ - Pyxem CPU (16 cores)\",\n",
    "                marker=\"o\", ls=\"None\", markeredgecolor=\"white\",\n",
    "               )\n",
    "\n",
    "groups = grid_df_gpu.groupby(\"dt\")\n",
    "for i, (name, group) in enumerate(groups):\n",
    "    #print(group)\n",
    "    if np.all(0.5 == group[\"dt\"]):\n",
    "        continue\n",
    "    x = group[\"nk\"] * group[\"spot\"] / group[\"dt\"]\n",
    "    ax[1].errorbar(x, group[\"mean\"], yerr=group[\"std\"], label=f\"{name}\"+r\"$^\\circ$ - Pyxem GPU\",\n",
    "                marker=\"s\", ls=\"None\", color=f\"C{i+4}\", markeredgecolor=\"white\",\n",
    "               )\n",
    "    \n",
    "groups = grid_py4dstem.groupby(\"deltaphi\")\n",
    "colors = [\"C0\", \"C1\", \"C2\"]\n",
    "for color, (name, group) in zip(colors, groups):\n",
    "    x = group[\"N\"] * group[\"s\"] / group[\"deltaphi\"]\n",
    "    ax[1].errorbar(x, group[\"mean\"], yerr=group[\"std\"], label=f\"{name}\"+r\"$^\\circ$ - py4DSTEM\",\n",
    "                marker=\"^\", ls=\"None\", color=color, markeredgecolor=\"white\", markersize=8,\n",
    "               )\n",
    "    \n",
    "ax[0].set_ylabel(r\"Time (s)\")\n",
    "ax[0].legend(title=r\"Implementation\")\n",
    "ax[1].set_xscale(\"log\")\n",
    "ax[1].set_yscale(\"log\")\n",
    "ax[1].legend(title=r\"$\\Delta \\phi_1$ - Implementation\", fontsize=8)\n",
    "for i in ax:\n",
    "    i.set_xlabel(r\"$\\frac{NS}{\\Delta \\phi_1}$ (1/$^\\circ$)\")\n",
    "    \n",
    "#ax[0].set_ylim(-0.05, 1)\n",
    "ax[0].set_ylim(-0.05, 2)\n",
    "ax[1].set_ylim(None, 1e2)\n",
    "\n",
    "frac=0.98\n",
    "xx1, xx2 = ax[0].get_xlim()\n",
    "yy1, yy2 = ax[0].get_ylim()\n",
    "xx = frac*(xx2 - xx1) + xx1\n",
    "yy = frac*(yy2 - yy1) + yy1\n",
    "ax[0].text(xx, yy, \"a.\", va=\"top\", ha=\"right\", fontsize=20)\n",
    "xx1, xx2 = ax[1].get_xlim()\n",
    "yy1, yy2 = ax[1].get_ylim()\n",
    "xx = xx1*10**(frac*(np.log10(xx2) - np.log10(xx1)))\n",
    "yy = yy1*10**(frac*(np.log10(yy2) - np.log10(yy1)))\n",
    "#yy = frac*(yy2 - yy1) + yy1\n",
    "ax[1].text(xx, yy, \"b.\", va=\"top\", ha=\"right\", fontsize=20)\n",
    "\n",
    "# reorder legend entries\n",
    "handles,labels = ax[0].get_legend_handles_labels()\n",
    "order = [1, 2, 3, 4, 0]\n",
    "handles = [handles[o] for o in order]\n",
    "labels = [labels[o] for o in order]\n",
    "ax[0].legend(handles,labels, title=\"Implementation\", loc=9)\n",
    "\n",
    "handles,labels = ax[1].get_legend_handles_labels()\n",
    "handles = [*handles[1:], handles[0]]\n",
    "labels = [*labels[1:], labels[0]]\n",
    "ax[1].legend(handles,labels, title=r\"$\\Delta \\phi_1$ - Implementation\", fontsize=7)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32e50491",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"210903DataImages/benchmarking_2.png\", dpi=320)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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