cython dict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do\n",
    "\n",
    "- [ ] check if inheriting from `dict` like [here](https://github.com/cython/cython/wiki/ListExample) is not a better idea\n",
    "- [ ] should be use `double` or `float`?\n",
    "- [ ] what is `Pxy_DECREF` in `dot_dict`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "SMALL_SIZE = 12\n",
    "MEDIUM_SIZE = 16\n",
    "BIG_SIZE = 20\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=BIG_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=BIG_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=MEDIUM_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=MEDIUM_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to speed up the manipulation of Python dicts. The keys can be of any type, whilst the values are always floats. Let's first make a helper function to generate random dicts of a given size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.4224063210410265, 1: 0.30329846762413903}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "def rand_dict(size):\n",
    "    return {i: random.random() for i in range(size)}\n",
    "\n",
    "rand_dict(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dot product"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by benchmarking a dot product, because that's a pretty common operation in machine learning. We'll store the benchmarks in a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dot_bench = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're interested in dicts of all sizes, so we'll benchmark a few sizes that are fairly common to encounter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sizes = [2, 3, 5, 8, 10, 15, 20, 25, 30, 50, 75, 100, 125, 150, 175, 200]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first benchmark a basic Python implementation. This is the baseline which we want to improve on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 617 ns ± 2.14 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "3 775 ns ± 60.5 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "5 972 ns ± 123 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "8 1.12 µs ± 57.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "10 1.31 µs ± 88.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "15 2 µs ± 391 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "20 2.49 µs ± 255 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "25 2.85 µs ± 202 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "30 3.76 µs ± 133 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "50 4.41 µs ± 388 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "75 5.71 µs ± 179 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "100 7.5 µs ± 127 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "125 9.85 µs ± 1.49 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "150 12 µs ± 473 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "175 14 µs ± 805 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "200 15.7 µs ± 902 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def py_dot(x: dict, y: dict) -> float:\n",
    "    return sum(xi * y[i] for i, xi in x.items())\n",
    "\n",
    "dot_bench['Python'] = {}\n",
    "\n",
    "for size in sizes:\n",
    "    x = rand_dict(size)\n",
    "    y = rand_dict(size)\n",
    "    dot_bench['Python'][size] = %timeit -o -q py_dot(x, y)\n",
    "    print(size, dot_bench['Python'][size])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about numpy? It might be fast, but it requires converting the dictionary values to a numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 2.77 µs ± 331 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "3 2.55 µs ± 318 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "5 2.28 µs ± 134 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "8 2.5 µs ± 85 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "10 2.44 µs ± 40.8 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "15 2.9 µs ± 281 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "20 3.33 µs ± 456 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "25 3.4 µs ± 475 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "30 4.19 µs ± 479 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "50 4.93 µs ± 750 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "75 5.28 µs ± 336 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "100 6.47 µs ± 527 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "125 7.82 µs ± 981 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "150 8.57 µs ± 275 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "175 10.8 µs ± 1.67 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "200 11.4 µs ± 1.04 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def np_dot(x: dict, y: dict) -> float:\n",
    "    return np.dot(np.fromiter(x.values(), dtype=float), np.fromiter(y.values(), dtype=float))\n",
    "    \n",
    "dot_bench['Numpy'] = {}\n",
    "\n",
    "for size in sizes:\n",
    "    x = rand_dict(size)\n",
    "    y = rand_dict(size)\n",
    "    dot_bench['Numpy'][size] = %timeit -o -q np_dot(x, y)\n",
    "    print(size, dot_bench['Numpy'][size])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's implement a Cython dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html>\n",
       "<!-- Generated by Cython 0.29.13 -->\n",
       "<html>\n",
       "<head>\n",
       "    <meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n",
       "    <title>Cython: _cython_magic_e062e747d424c8c74d79c5fd3db6155e.pyx</title>\n",
       "    <style type=\"text/css\">\n",
       "    \n",
       "body.cython { font-family: courier; font-size: 12; }\n",
       "\n",
       ".cython.tag  {  }\n",
       ".cython.line { margin: 0em }\n",
       ".cython.code { font-size: 9; color: #444444; display: none; margin: 0px 0px 0px 8px; border-left: 8px none; }\n",
       "\n",
       ".cython.line .run { background-color: #B0FFB0; }\n",
       ".cython.line .mis { background-color: #FFB0B0; }\n",
       ".cython.code.run  { border-left: 8px solid #B0FFB0; }\n",
       ".cython.code.mis  { border-left: 8px solid #FFB0B0; }\n",
       "\n",
       ".cython.code .py_c_api  { color: red; }\n",
       ".cython.code .py_macro_api  { color: #FF7000; }\n",
       ".cython.code .pyx_c_api  { color: #FF3000; }\n",
       ".cython.code .pyx_macro_api  { color: #FF7000; }\n",
       ".cython.code .refnanny  { color: #FFA000; }\n",
       ".cython.code .trace  { color: #FFA000; }\n",
       ".cython.code .error_goto  { color: #FFA000; }\n",
       "\n",
       ".cython.code .coerce  { color: #008000; border: 1px dotted #008000 }\n",
       ".cython.code .py_attr { color: #FF0000; font-weight: bold; }\n",
       ".cython.code .c_attr  { color: #0000FF; }\n",
       ".cython.code .py_call { color: #FF0000; font-weight: bold; }\n",
       ".cython.code .c_call  { color: #0000FF; }\n",
       "\n",
       ".cython.score-0 {background-color: #FFFFff;}\n",
       ".cython.score-1 {background-color: #FFFFe7;}\n",
       ".cython.score-2 {background-color: #FFFFd4;}\n",
       ".cython.score-3 {background-color: #FFFFc4;}\n",
       ".cython.score-4 {background-color: #FFFFb6;}\n",
       ".cython.score-5 {background-color: #FFFFaa;}\n",
       ".cython.score-6 {background-color: #FFFF9f;}\n",
       ".cython.score-7 {background-color: #FFFF96;}\n",
       ".cython.score-8 {background-color: #FFFF8d;}\n",
       ".cython.score-9 {background-color: #FFFF86;}\n",
       ".cython.score-10 {background-color: #FFFF7f;}\n",
       ".cython.score-11 {background-color: #FFFF79;}\n",
       ".cython.score-12 {background-color: #FFFF73;}\n",
       ".cython.score-13 {background-color: #FFFF6e;}\n",
       ".cython.score-14 {background-color: #FFFF6a;}\n",
       ".cython.score-15 {background-color: #FFFF66;}\n",
       ".cython.score-16 {background-color: #FFFF62;}\n",
       ".cython.score-17 {background-color: #FFFF5e;}\n",
       ".cython.score-18 {background-color: #FFFF5b;}\n",
       ".cython.score-19 {background-color: #FFFF57;}\n",
       ".cython.score-20 {background-color: #FFFF55;}\n",
       ".cython.score-21 {background-color: #FFFF52;}\n",
       ".cython.score-22 {background-color: #FFFF4f;}\n",
       ".cython.score-23 {background-color: #FFFF4d;}\n",
       ".cython.score-24 {background-color: #FFFF4b;}\n",
       ".cython.score-25 {background-color: #FFFF48;}\n",
       ".cython.score-26 {background-color: #FFFF46;}\n",
       ".cython.score-27 {background-color: #FFFF44;}\n",
       ".cython.score-28 {background-color: #FFFF43;}\n",
       ".cython.score-29 {background-color: #FFFF41;}\n",
       ".cython.score-30 {background-color: #FFFF3f;}\n",
       ".cython.score-31 {background-color: #FFFF3e;}\n",
       ".cython.score-32 {background-color: #FFFF3c;}\n",
       ".cython.score-33 {background-color: #FFFF3b;}\n",
       ".cython.score-34 {background-color: #FFFF39;}\n",
       ".cython.score-35 {background-color: #FFFF38;}\n",
       ".cython.score-36 {background-color: #FFFF37;}\n",
       ".cython.score-37 {background-color: #FFFF36;}\n",
       ".cython.score-38 {background-color: #FFFF35;}\n",
       ".cython.score-39 {background-color: #FFFF34;}\n",
       ".cython.score-40 {background-color: #FFFF33;}\n",
       ".cython.score-41 {background-color: #FFFF32;}\n",
       ".cython.score-42 {background-color: #FFFF31;}\n",
       ".cython.score-43 {background-color: #FFFF30;}\n",
       ".cython.score-44 {background-color: #FFFF2f;}\n",
       ".cython.score-45 {background-color: #FFFF2e;}\n",
       ".cython.score-46 {background-color: #FFFF2d;}\n",
       ".cython.score-47 {background-color: #FFFF2c;}\n",
       ".cython.score-48 {background-color: #FFFF2b;}\n",
       ".cython.score-49 {background-color: #FFFF2b;}\n",
       ".cython.score-50 {background-color: #FFFF2a;}\n",
       ".cython.score-51 {background-color: #FFFF29;}\n",
       ".cython.score-52 {background-color: #FFFF29;}\n",
       ".cython.score-53 {background-color: #FFFF28;}\n",
       ".cython.score-54 {background-color: #FFFF27;}\n",
       ".cython.score-55 {background-color: #FFFF27;}\n",
       ".cython.score-56 {background-color: #FFFF26;}\n",
       ".cython.score-57 {background-color: #FFFF26;}\n",
       ".cython.score-58 {background-color: #FFFF25;}\n",
       ".cython.score-59 {background-color: #FFFF24;}\n",
       ".cython.score-60 {background-color: #FFFF24;}\n",
       ".cython.score-61 {background-color: #FFFF23;}\n",
       ".cython.score-62 {background-color: #FFFF23;}\n",
       ".cython.score-63 {background-color: #FFFF22;}\n",
       ".cython.score-64 {background-color: #FFFF22;}\n",
       ".cython.score-65 {background-color: #FFFF22;}\n",
       ".cython.score-66 {background-color: #FFFF21;}\n",
       ".cython.score-67 {background-color: #FFFF21;}\n",
       ".cython.score-68 {background-color: #FFFF20;}\n",
       ".cython.score-69 {background-color: #FFFF20;}\n",
       ".cython.score-70 {background-color: #FFFF1f;}\n",
       ".cython.score-71 {background-color: #FFFF1f;}\n",
       ".cython.score-72 {background-color: #FFFF1f;}\n",
       ".cython.score-73 {background-color: #FFFF1e;}\n",
       ".cython.score-74 {background-color: #FFFF1e;}\n",
       ".cython.score-75 {background-color: #FFFF1e;}\n",
       ".cython.score-76 {background-color: #FFFF1d;}\n",
       ".cython.score-77 {background-color: #FFFF1d;}\n",
       ".cython.score-78 {background-color: #FFFF1c;}\n",
       ".cython.score-79 {background-color: #FFFF1c;}\n",
       ".cython.score-80 {background-color: #FFFF1c;}\n",
       ".cython.score-81 {background-color: #FFFF1c;}\n",
       ".cython.score-82 {background-color: #FFFF1b;}\n",
       ".cython.score-83 {background-color: #FFFF1b;}\n",
       ".cython.score-84 {background-color: #FFFF1b;}\n",
       ".cython.score-85 {background-color: #FFFF1a;}\n",
       ".cython.score-86 {background-color: #FFFF1a;}\n",
       ".cython.score-87 {background-color: #FFFF1a;}\n",
       ".cython.score-88 {background-color: #FFFF1a;}\n",
       ".cython.score-89 {background-color: #FFFF19;}\n",
       ".cython.score-90 {background-color: #FFFF19;}\n",
       ".cython.score-91 {background-color: #FFFF19;}\n",
       ".cython.score-92 {background-color: #FFFF19;}\n",
       ".cython.score-93 {background-color: #FFFF18;}\n",
       ".cython.score-94 {background-color: #FFFF18;}\n",
       ".cython.score-95 {background-color: #FFFF18;}\n",
       ".cython.score-96 {background-color: #FFFF18;}\n",
       ".cython.score-97 {background-color: #FFFF17;}\n",
       ".cython.score-98 {background-color: #FFFF17;}\n",
       ".cython.score-99 {background-color: #FFFF17;}\n",
       ".cython.score-100 {background-color: #FFFF17;}\n",
       ".cython.score-101 {background-color: #FFFF16;}\n",
       ".cython.score-102 {background-color: #FFFF16;}\n",
       ".cython.score-103 {background-color: #FFFF16;}\n",
       ".cython.score-104 {background-color: #FFFF16;}\n",
       ".cython.score-105 {background-color: #FFFF16;}\n",
       ".cython.score-106 {background-color: #FFFF15;}\n",
       ".cython.score-107 {background-color: #FFFF15;}\n",
       ".cython.score-108 {background-color: #FFFF15;}\n",
       ".cython.score-109 {background-color: #FFFF15;}\n",
       ".cython.score-110 {background-color: #FFFF15;}\n",
       ".cython.score-111 {background-color: #FFFF15;}\n",
       ".cython.score-112 {background-color: #FFFF14;}\n",
       ".cython.score-113 {background-color: #FFFF14;}\n",
       ".cython.score-114 {background-color: #FFFF14;}\n",
       ".cython.score-115 {background-color: #FFFF14;}\n",
       ".cython.score-116 {background-color: #FFFF14;}\n",
       ".cython.score-117 {background-color: #FFFF14;}\n",
       ".cython.score-118 {background-color: #FFFF13;}\n",
       ".cython.score-119 {background-color: #FFFF13;}\n",
       ".cython.score-120 {background-color: #FFFF13;}\n",
       ".cython.score-121 {background-color: #FFFF13;}\n",
       ".cython.score-122 {background-color: #FFFF13;}\n",
       ".cython.score-123 {background-color: #FFFF13;}\n",
       ".cython.score-124 {background-color: #FFFF13;}\n",
       ".cython.score-125 {background-color: #FFFF12;}\n",
       ".cython.score-126 {background-color: #FFFF12;}\n",
       ".cython.score-127 {background-color: #FFFF12;}\n",
       ".cython.score-128 {background-color: #FFFF12;}\n",
       ".cython.score-129 {background-color: #FFFF12;}\n",
       ".cython.score-130 {background-color: #FFFF12;}\n",
       ".cython.score-131 {background-color: #FFFF12;}\n",
       ".cython.score-132 {background-color: #FFFF11;}\n",
       ".cython.score-133 {background-color: #FFFF11;}\n",
       ".cython.score-134 {background-color: #FFFF11;}\n",
       ".cython.score-135 {background-color: #FFFF11;}\n",
       ".cython.score-136 {background-color: #FFFF11;}\n",
       ".cython.score-137 {background-color: #FFFF11;}\n",
       ".cython.score-138 {background-color: #FFFF11;}\n",
       ".cython.score-139 {background-color: #FFFF11;}\n",
       ".cython.score-140 {background-color: #FFFF11;}\n",
       ".cython.score-141 {background-color: #FFFF10;}\n",
       ".cython.score-142 {background-color: #FFFF10;}\n",
       ".cython.score-143 {background-color: #FFFF10;}\n",
       ".cython.score-144 {background-color: #FFFF10;}\n",
       ".cython.score-145 {background-color: #FFFF10;}\n",
       ".cython.score-146 {background-color: #FFFF10;}\n",
       ".cython.score-147 {background-color: #FFFF10;}\n",
       ".cython.score-148 {background-color: #FFFF10;}\n",
       ".cython.score-149 {background-color: #FFFF10;}\n",
       ".cython.score-150 {background-color: #FFFF0f;}\n",
       ".cython.score-151 {background-color: #FFFF0f;}\n",
       ".cython.score-152 {background-color: #FFFF0f;}\n",
       ".cython.score-153 {background-color: #FFFF0f;}\n",
       ".cython.score-154 {background-color: #FFFF0f;}\n",
       ".cython.score-155 {background-color: #FFFF0f;}\n",
       ".cython.score-156 {background-color: #FFFF0f;}\n",
       ".cython.score-157 {background-color: #FFFF0f;}\n",
       ".cython.score-158 {background-color: #FFFF0f;}\n",
       ".cython.score-159 {background-color: #FFFF0f;}\n",
       ".cython.score-160 {background-color: #FFFF0f;}\n",
       ".cython.score-161 {background-color: #FFFF0e;}\n",
       ".cython.score-162 {background-color: #FFFF0e;}\n",
       ".cython.score-163 {background-color: #FFFF0e;}\n",
       ".cython.score-164 {background-color: #FFFF0e;}\n",
       ".cython.score-165 {background-color: #FFFF0e;}\n",
       ".cython.score-166 {background-color: #FFFF0e;}\n",
       ".cython.score-167 {background-color: #FFFF0e;}\n",
       ".cython.score-168 {background-color: #FFFF0e;}\n",
       ".cython.score-169 {background-color: #FFFF0e;}\n",
       ".cython.score-170 {background-color: #FFFF0e;}\n",
       ".cython.score-171 {background-color: #FFFF0e;}\n",
       ".cython.score-172 {background-color: #FFFF0e;}\n",
       ".cython.score-173 {background-color: #FFFF0d;}\n",
       ".cython.score-174 {background-color: #FFFF0d;}\n",
       ".cython.score-175 {background-color: #FFFF0d;}\n",
       ".cython.score-176 {background-color: #FFFF0d;}\n",
       ".cython.score-177 {background-color: #FFFF0d;}\n",
       ".cython.score-178 {background-color: #FFFF0d;}\n",
       ".cython.score-179 {background-color: #FFFF0d;}\n",
       ".cython.score-180 {background-color: #FFFF0d;}\n",
       ".cython.score-181 {background-color: #FFFF0d;}\n",
       ".cython.score-182 {background-color: #FFFF0d;}\n",
       ".cython.score-183 {background-color: #FFFF0d;}\n",
       ".cython.score-184 {background-color: #FFFF0d;}\n",
       ".cython.score-185 {background-color: #FFFF0d;}\n",
       ".cython.score-186 {background-color: #FFFF0d;}\n",
       ".cython.score-187 {background-color: #FFFF0c;}\n",
       ".cython.score-188 {background-color: #FFFF0c;}\n",
       ".cython.score-189 {background-color: #FFFF0c;}\n",
       ".cython.score-190 {background-color: #FFFF0c;}\n",
       ".cython.score-191 {background-color: #FFFF0c;}\n",
       ".cython.score-192 {background-color: #FFFF0c;}\n",
       ".cython.score-193 {background-color: #FFFF0c;}\n",
       ".cython.score-194 {background-color: #FFFF0c;}\n",
       ".cython.score-195 {background-color: #FFFF0c;}\n",
       ".cython.score-196 {background-color: #FFFF0c;}\n",
       ".cython.score-197 {background-color: #FFFF0c;}\n",
       ".cython.score-198 {background-color: #FFFF0c;}\n",
       ".cython.score-199 {background-color: #FFFF0c;}\n",
       ".cython.score-200 {background-color: #FFFF0c;}\n",
       ".cython.score-201 {background-color: #FFFF0c;}\n",
       ".cython.score-202 {background-color: #FFFF0c;}\n",
       ".cython.score-203 {background-color: #FFFF0b;}\n",
       ".cython.score-204 {background-color: #FFFF0b;}\n",
       ".cython.score-205 {background-color: #FFFF0b;}\n",
       ".cython.score-206 {background-color: #FFFF0b;}\n",
       ".cython.score-207 {background-color: #FFFF0b;}\n",
       ".cython.score-208 {background-color: #FFFF0b;}\n",
       ".cython.score-209 {background-color: #FFFF0b;}\n",
       ".cython.score-210 {background-color: #FFFF0b;}\n",
       ".cython.score-211 {background-color: #FFFF0b;}\n",
       ".cython.score-212 {background-color: #FFFF0b;}\n",
       ".cython.score-213 {background-color: #FFFF0b;}\n",
       ".cython.score-214 {background-color: #FFFF0b;}\n",
       ".cython.score-215 {background-color: #FFFF0b;}\n",
       ".cython.score-216 {background-color: #FFFF0b;}\n",
       ".cython.score-217 {background-color: #FFFF0b;}\n",
       ".cython.score-218 {background-color: #FFFF0b;}\n",
       ".cython.score-219 {background-color: #FFFF0b;}\n",
       ".cython.score-220 {background-color: #FFFF0b;}\n",
       ".cython.score-221 {background-color: #FFFF0b;}\n",
       ".cython.score-222 {background-color: #FFFF0a;}\n",
       ".cython.score-223 {background-color: #FFFF0a;}\n",
       ".cython.score-224 {background-color: #FFFF0a;}\n",
       ".cython.score-225 {background-color: #FFFF0a;}\n",
       ".cython.score-226 {background-color: #FFFF0a;}\n",
       ".cython.score-227 {background-color: #FFFF0a;}\n",
       ".cython.score-228 {background-color: #FFFF0a;}\n",
       ".cython.score-229 {background-color: #FFFF0a;}\n",
       ".cython.score-230 {background-color: #FFFF0a;}\n",
       ".cython.score-231 {background-color: #FFFF0a;}\n",
       ".cython.score-232 {background-color: #FFFF0a;}\n",
       ".cython.score-233 {background-color: #FFFF0a;}\n",
       ".cython.score-234 {background-color: #FFFF0a;}\n",
       ".cython.score-235 {background-color: #FFFF0a;}\n",
       ".cython.score-236 {background-color: #FFFF0a;}\n",
       ".cython.score-237 {background-color: #FFFF0a;}\n",
       ".cython.score-238 {background-color: #FFFF0a;}\n",
       ".cython.score-239 {background-color: #FFFF0a;}\n",
       ".cython.score-240 {background-color: #FFFF0a;}\n",
       ".cython.score-241 {background-color: #FFFF0a;}\n",
       ".cython.score-242 {background-color: #FFFF0a;}\n",
       ".cython.score-243 {background-color: #FFFF0a;}\n",
       ".cython.score-244 {background-color: #FFFF0a;}\n",
       ".cython.score-245 {background-color: #FFFF0a;}\n",
       ".cython.score-246 {background-color: #FFFF09;}\n",
       ".cython.score-247 {background-color: #FFFF09;}\n",
       ".cython.score-248 {background-color: #FFFF09;}\n",
       ".cython.score-249 {background-color: #FFFF09;}\n",
       ".cython.score-250 {background-color: #FFFF09;}\n",
       ".cython.score-251 {background-color: #FFFF09;}\n",
       ".cython.score-252 {background-color: #FFFF09;}\n",
       ".cython.score-253 {background-color: #FFFF09;}\n",
       ".cython.score-254 {background-color: #FFFF09;}\n",
       ".cython .hll { background-color: #ffffcc }\n",
       ".cython  { background: #f8f8f8; }\n",
       ".cython .c { color: #408080; font-style: italic } /* Comment */\n",
       ".cython .err { border: 1px solid #FF0000 } /* Error */\n",
       ".cython .k { color: #008000; font-weight: bold } /* Keyword */\n",
       ".cython .o { color: #666666 } /* Operator */\n",
       ".cython .ch { color: #408080; font-style: italic } /* Comment.Hashbang */\n",
       ".cython .cm { color: #408080; font-style: italic } /* Comment.Multiline */\n",
       ".cython .cp { color: #BC7A00 } /* Comment.Preproc */\n",
       ".cython .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */\n",
       ".cython .c1 { color: #408080; font-style: italic } /* Comment.Single */\n",
       ".cython .cs { color: #408080; font-style: italic } /* Comment.Special */\n",
       ".cython .gd { color: #A00000 } /* Generic.Deleted */\n",
       ".cython .ge { font-style: italic } /* Generic.Emph */\n",
       ".cython .gr { color: #FF0000 } /* Generic.Error */\n",
       ".cython .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
       ".cython .gi { color: #00A000 } /* Generic.Inserted */\n",
       ".cython .go { color: #888888 } /* Generic.Output */\n",
       ".cython .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
       ".cython .gs { font-weight: bold } /* Generic.Strong */\n",
       ".cython .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
       ".cython .gt { color: #0044DD } /* Generic.Traceback */\n",
       ".cython .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
       ".cython .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
       ".cython .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
       ".cython .kp { color: #008000 } /* Keyword.Pseudo */\n",
       ".cython .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
       ".cython .kt { color: #B00040 } /* Keyword.Type */\n",
       ".cython .m { color: #666666 } /* Literal.Number */\n",
       ".cython .s { color: #BA2121 } /* Literal.String */\n",
       ".cython .na { color: #7D9029 } /* Name.Attribute */\n",
       ".cython .nb { color: #008000 } /* Name.Builtin */\n",
       ".cython .nc { color: #0000FF; font-weight: bold } /* Name.Class */\n",
       ".cython .no { color: #880000 } /* Name.Constant */\n",
       ".cython .nd { color: #AA22FF } /* Name.Decorator */\n",
       ".cython .ni { color: #999999; font-weight: bold } /* Name.Entity */\n",
       ".cython .ne { color: #D2413A; font-weight: bold } /* Name.Exception */\n",
       ".cython .nf { color: #0000FF } /* Name.Function */\n",
       ".cython .nl { color: #A0A000 } /* Name.Label */\n",
       ".cython .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */\n",
       ".cython .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
       ".cython .nv { color: #19177C } /* Name.Variable */\n",
       ".cython .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */\n",
       ".cython .w { color: #bbbbbb } /* Text.Whitespace */\n",
       ".cython .mb { color: #666666 } /* Literal.Number.Bin */\n",
       ".cython .mf { color: #666666 } /* Literal.Number.Float */\n",
       ".cython .mh { color: #666666 } /* Literal.Number.Hex */\n",
       ".cython .mi { color: #666666 } /* Literal.Number.Integer */\n",
       ".cython .mo { color: #666666 } /* Literal.Number.Oct */\n",
       ".cython .sa { color: #BA2121 } /* Literal.String.Affix */\n",
       ".cython .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
       ".cython .sc { color: #BA2121 } /* Literal.String.Char */\n",
       ".cython .dl { color: #BA2121 } /* Literal.String.Delimiter */\n",
       ".cython .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
       ".cython .s2 { color: #BA2121 } /* Literal.String.Double */\n",
       ".cython .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */\n",
       ".cython .sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
       ".cython .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */\n",
       ".cython .sx { color: #008000 } /* Literal.String.Other */\n",
       ".cython .sr { color: #BB6688 } /* Literal.String.Regex */\n",
       ".cython .s1 { color: #BA2121 } /* Literal.String.Single */\n",
       ".cython .ss { color: #19177C } /* Literal.String.Symbol */\n",
       ".cython .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
       ".cython .fm { color: #0000FF } /* Name.Function.Magic */\n",
       ".cython .vc { color: #19177C } /* Name.Variable.Class */\n",
       ".cython .vg { color: #19177C } /* Name.Variable.Global */\n",
       ".cython .vi { color: #19177C } /* Name.Variable.Instance */\n",
       ".cython .vm { color: #19177C } /* Name.Variable.Magic */\n",
       ".cython .il { color: #666666 } /* Literal.Number.Integer.Long */\n",
       "    </style>\n",
       "</head>\n",
       "<body class=\"cython\">\n",
       "<p><span style=\"border-bottom: solid 1px grey;\">Generated by Cython 0.29.13</span></p>\n",
       "<p>\n",
       "    <span style=\"background-color: #FFFF00\">Yellow lines</span> hint at Python interaction.<br />\n",
       "    Click on a line that starts with a \"<code>+</code>\" to see the C code that Cython generated for it.\n",
       "</p>\n",
       "<div class=\"cython\"><pre class=\"cython line score-8\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">01</span>: <span class=\"c\"># distutils: language = c++</span></pre>\n",
       "<pre class='cython code score-8 '>  __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyDict_NewPresized</span>(0);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
       "  if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_test, __pyx_t_1) &lt; 0) <span class='error_goto'>__PYX_ERR(0, 1, __pyx_L1_error)</span>\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">02</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">03</span>: <span class=\"k\">cimport</span> <span class=\"nn\">cython</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">04</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">05</span>: <span class=\"c\"># C++</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">06</span>: <span class=\"k\">from</span> <span class=\"nn\">cython.operator</span> <span class=\"k\">cimport</span> <span class=\"n\">dereference</span> <span class=\"k\">as</span> <span class=\"n\">deref</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">07</span>: <span class=\"k\">from</span> <span class=\"nn\">cython.operator</span> <span class=\"k\">cimport</span> <span class=\"n\">preincrement</span> <span class=\"k\">as</span> <span class=\"n\">inc</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">08</span>: <span class=\"k\">from</span> <span class=\"nn\">libcpp.map</span> <span class=\"k\">cimport</span> <span class=\"nb\">map</span> <span class=\"k\">as</span> <span class=\"n\">cpp_map</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">09</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">10</span>: <span class=\"k\">ctypedef</span> <span class=\"nb\">int</span>   <span class=\"n\">KTYPE_t</span></pre>\n",
       "<pre class='cython code score-0 '>typedef int __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">11</span>: <span class=\"k\">ctypedef</span> <span class=\"nb\">float</span> <span class=\"n\">VTYPE_t</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">12</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">13</span>: <span class=\"k\">cdef</span> <span class=\"k\">class</span> <span class=\"nf\">IntFloatDict</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">14</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">15</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cpp_map</span>[<span class=\"nf\">KTYPE_t</span><span class=\"p\">,</span> <span class=\"kt\">VTYPE_t</span>] <span class=\"nf\">data</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">16</span>: </pre>\n",
       "<pre class=\"cython line score-28\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">17</span>:     <span class=\"k\">def</span> <span class=\"nf\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">d</span> <span class=\"ow\">not</span> <span class=\"bp\">None</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-28 '>/* Python wrapper */\n",
       "static int __pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_1__init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n",
       "static int __pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_1__init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
       "  PyObject *__pyx_v_d = 0;\n",
       "  int __pyx_r;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__init__ (wrapper)\", 0);\n",
       "  {\n",
       "    static PyObject **__pyx_pyargnames[] = {&amp;__pyx_n_s_d,0};\n",
       "    PyObject* values[1] = {0};\n",
       "    if (unlikely(__pyx_kwds)) {\n",
       "      Py_ssize_t kw_args;\n",
       "      const Py_ssize_t pos_args = <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args);\n",
       "      switch (pos_args) {\n",
       "        case  1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  0: break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
       "      }\n",
       "      kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n",
       "      switch (pos_args) {\n",
       "        case  0:\n",
       "        if (likely((values[0] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_d)) != 0)) kw_args--;\n",
       "        else goto __pyx_L5_argtuple_error;\n",
       "      }\n",
       "      if (unlikely(kw_args &gt; 0)) {\n",
       "        if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, \"__init__\") &lt; 0)) <span class='error_goto'>__PYX_ERR(0, 17, __pyx_L3_error)</span>\n",
       "      }\n",
       "    } else if (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args) != 1) {\n",
       "      goto __pyx_L5_argtuple_error;\n",
       "    } else {\n",
       "      values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "    }\n",
       "    __pyx_v_d = values[0];\n",
       "  }\n",
       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L5_argtuple_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"__init__\", 1, 1, 1, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); <span class='error_goto'>__PYX_ERR(0, 17, __pyx_L3_error)</span>\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_e062e747d424c8c74d79c5fd3db6155e.IntFloatDict.__init__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return -1;\n",
       "  __pyx_L4_argument_unpacking_done:;\n",
       "  if (unlikely(((PyObject *)__pyx_v_d) == Py_None)) {\n",
       "    <span class='py_c_api'>PyErr_Format</span>(PyExc_TypeError, \"Argument '%.200s' must not be None\", \"d\"); <span class='error_goto'>__PYX_ERR(0, 17, __pyx_L1_error)</span>\n",
       "  }\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict___init__(((struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *)__pyx_v_self), __pyx_v_d);\n",
       "\n",
       "  /* function exit code */\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  __pyx_r = -1;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static int __pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict___init__(struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *__pyx_v_self, PyObject *__pyx_v_d) {\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t __pyx_v_k;\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_VTYPE_t __pyx_v_v;\n",
       "  int __pyx_r;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__init__\", 0);\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_r = 0;\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_5);\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_6);\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_e062e747d424c8c74d79c5fd3db6155e.IntFloatDict.__init__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  __pyx_r = -1;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">18</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">19</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">KTYPE_t</span> <span class=\"nf\">k</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">20</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">VTYPE_t</span> <span class=\"nf\">v</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">21</span>: </pre>\n",
       "<pre class=\"cython line score-20\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">22</span>:         <span class=\"k\">for</span> <span class=\"n\">k</span><span class=\"p\">,</span> <span class=\"n\">v</span> <span class=\"ow\">in</span> <span class=\"n\">d</span><span class=\"o\">.</span><span class=\"n\">items</span><span class=\"p\">():</span></pre>\n",
       "<pre class='cython code score-20 '>  __pyx_t_2 = 0;\n",
       "  __pyx_t_5 = __Pyx_dict_iterator(__pyx_v_d, 0, __pyx_n_s_items, (&amp;__pyx_t_3), (&amp;__pyx_t_4));<span class='error_goto'> if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 22, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_5);\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n",
       "  __pyx_t_1 = __pyx_t_5;\n",
       "  __pyx_t_5 = 0;\n",
       "  while (1) {\n",
       "    __pyx_t_7 = __Pyx_dict_iter_next(__pyx_t_1, __pyx_t_3, &amp;__pyx_t_2, &amp;__pyx_t_5, &amp;__pyx_t_6, NULL, __pyx_t_4);\n",
       "    if (unlikely(__pyx_t_7 == 0)) break;\n",
       "    if (unlikely(__pyx_t_7 == -1)) <span class='error_goto'>__PYX_ERR(0, 22, __pyx_L1_error)</span>\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_5);\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_6);\n",
       "    __pyx_t_8 = <span class='pyx_c_api'>__Pyx_PyInt_As_int</span>(__pyx_t_5); if (unlikely((__pyx_t_8 == (int)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 22, __pyx_L1_error)</span>\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;\n",
       "    __pyx_t_9 = __pyx_<span class='py_c_api'>PyFloat_AsFloat</span>(__pyx_t_6); if (unlikely((__pyx_t_9 == (float)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 22, __pyx_L1_error)</span>\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_6); __pyx_t_6 = 0;\n",
       "    __pyx_v_k = __pyx_t_8;\n",
       "    __pyx_v_v = __pyx_t_9;\n",
       "</pre><pre class=\"cython line score-1\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">23</span>:             <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"n\">k</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">v</span></pre>\n",
       "<pre class='cython code score-1 '>    (__pyx_v_self-&gt;data[__pyx_v_k]) = __pyx_v_v;\n",
       "  }\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">24</span>: </pre>\n",
       "<pre class=\"cython line score-13\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">25</span>:     <span class=\"k\">def</span> <span class=\"nf\">__getitem__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">KTYPE_t</span> <span class=\"n\">k</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-13 '>/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_3__getitem__(PyObject *__pyx_v_self, PyObject *__pyx_arg_k); /*proto*/\n",
       "static PyObject *__pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_3__getitem__(PyObject *__pyx_v_self, PyObject *__pyx_arg_k) {\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t __pyx_v_k;\n",
       "  PyObject *__pyx_r = 0;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__getitem__ (wrapper)\", 0);\n",
       "  assert(__pyx_arg_k); {\n",
       "    __pyx_v_k = <span class='pyx_c_api'>__Pyx_PyInt_As_int</span>(__pyx_arg_k); if (unlikely((__pyx_v_k == (int)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 25, __pyx_L3_error)</span>\n",
       "  }\n",
       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_e062e747d424c8c74d79c5fd3db6155e.IntFloatDict.__getitem__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return NULL;\n",
       "  __pyx_L4_argument_unpacking_done:;\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_2__getitem__(((struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *)__pyx_v_self), ((__pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t)__pyx_v_k));\n",
       "\n",
       "  /* function exit code */\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static PyObject *__pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_2__getitem__(struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *__pyx_v_self, __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t __pyx_v_k) {\n",
       "  std::map&lt;__pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t,__pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_VTYPE_t&gt; ::iterator __pyx_v_it;\n",
       "  PyObject *__pyx_r = NULL;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__getitem__\", 0);\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_L1_error:;\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_2);\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_3);\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_e062e747d424c8c74d79c5fd3db6155e.IntFloatDict.__getitem__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">26</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">cpp_map</span>[<span class=\"nf\">KTYPE_t</span><span class=\"p\">,</span> <span class=\"kt\">VTYPE_t</span>].<span class=\"kt\">iterator</span> <span class=\"nf\">it</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">find</span><span class=\"p\">(</span><span class=\"n\">k</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_it = __pyx_v_self-&gt;data.find(__pyx_v_k);\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">27</span>:         <span class=\"k\">if</span> <span class=\"n\">it</span> <span class=\"o\">==</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">end</span><span class=\"p\">():</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_1 = ((__pyx_v_it == __pyx_v_self-&gt;data.end()) != 0);\n",
       "  if (unlikely(__pyx_t_1)) {\n",
       "/* … */\n",
       "  }\n",
       "</pre><pre class=\"cython line score-14\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">28</span>:             <span class=\"k\">raise</span> <span class=\"ne\">KeyError</span><span class=\"p\">(</span><span class=\"s\">&#39;</span><span class=\"si\">%i</span><span class=\"s\">&#39;</span> <span class=\"o\">%</span> <span class=\"n\">k</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-14 '>    __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyInt_From_int</span>(__pyx_v_k);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 28, __pyx_L1_error)</span>\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
       "    __pyx_t_3 = <span class='py_c_api'>PyUnicode_Format</span>(__pyx_kp_u_i, __pyx_t_2);<span class='error_goto'> if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 28, __pyx_L1_error)</span>\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_3);\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
       "    __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyObject_CallOneArg</span>(__pyx_builtin_KeyError, __pyx_t_3);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 28, __pyx_L1_error)</span>\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_3); __pyx_t_3 = 0;\n",
       "    <span class='pyx_c_api'>__Pyx_Raise</span>(__pyx_t_2, 0, 0, 0);\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
       "    <span class='error_goto'>__PYX_ERR(0, 28, __pyx_L1_error)</span>\n",
       "</pre><pre class=\"cython line score-6\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">29</span>:         <span class=\"k\">return</span> <span class=\"n\">deref</span><span class=\"p\">(</span><span class=\"n\">it</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">second</span></pre>\n",
       "<pre class='cython code score-6 '>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
       "  __pyx_t_2 = <span class='py_c_api'>PyFloat_FromDouble</span>((*__pyx_v_it).second);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 29, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
       "  __pyx_r = __pyx_t_2;\n",
       "  __pyx_t_2 = 0;\n",
       "  goto __pyx_L0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">30</span>: </pre>\n",
       "<pre class=\"cython line score-19\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">31</span>:     <span class=\"k\">def</span> <span class=\"nf\">__setitem__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">KTYPE_t</span> <span class=\"n\">k</span><span class=\"p\">,</span> <span class=\"n\">VTYPE_t</span> <span class=\"n\">v</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-19 '>/* Python wrapper */\n",
       "static int __pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_5__setitem__(PyObject *__pyx_v_self, PyObject *__pyx_arg_k, PyObject *__pyx_arg_v); /*proto*/\n",
       "static int __pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_5__setitem__(PyObject *__pyx_v_self, PyObject *__pyx_arg_k, PyObject *__pyx_arg_v) {\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t __pyx_v_k;\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_VTYPE_t __pyx_v_v;\n",
       "  int __pyx_r;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__setitem__ (wrapper)\", 0);\n",
       "  assert(__pyx_arg_k); {\n",
       "    __pyx_v_k = <span class='pyx_c_api'>__Pyx_PyInt_As_int</span>(__pyx_arg_k); if (unlikely((__pyx_v_k == (int)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 31, __pyx_L3_error)</span>\n",
       "  }\n",
       "  assert(__pyx_arg_v); {\n",
       "    __pyx_v_v = __pyx_<span class='py_c_api'>PyFloat_AsFloat</span>(__pyx_arg_v); if (unlikely((__pyx_v_v == (float)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 31, __pyx_L3_error)</span>\n",
       "  }\n",
       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_e062e747d424c8c74d79c5fd3db6155e.IntFloatDict.__setitem__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return -1;\n",
       "  __pyx_L4_argument_unpacking_done:;\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_4__setitem__(((struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *)__pyx_v_self), ((__pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t)__pyx_v_k), ((__pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_VTYPE_t)__pyx_v_v));\n",
       "\n",
       "  /* function exit code */\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static int __pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_4__setitem__(struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *__pyx_v_self, __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t __pyx_v_k, __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_VTYPE_t __pyx_v_v) {\n",
       "  int __pyx_r;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__setitem__\", 0);\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_r = 0;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">32</span>:         <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"n\">k</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">v</span></pre>\n",
       "<pre class='cython code score-0 '>  (__pyx_v_self-&gt;data[__pyx_v_k]) = __pyx_v_v;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">33</span>: </pre>\n",
       "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">34</span>:     <span class=\"k\">def</span> <span class=\"nf\">dot</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">IntFloatDict</span> <span class=\"n\">other</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-5 '>/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_7dot(PyObject *__pyx_v_self, PyObject *__pyx_v_other); /*proto*/\n",
       "static PyObject *__pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_7dot(PyObject *__pyx_v_self, PyObject *__pyx_v_other) {\n",
       "  PyObject *__pyx_r = 0;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"dot (wrapper)\", 0);\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_other), __pyx_ptype_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict, 1, \"other\", 0))) <span class='error_goto'>__PYX_ERR(0, 34, __pyx_L1_error)</span>\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_6dot(((struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *)__pyx_v_self), ((struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *)__pyx_v_other));\n",
       "\n",
       "  /* function exit code */\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static PyObject *__pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_6dot(struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *__pyx_v_self, struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *__pyx_v_other) {\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_VTYPE_t __pyx_v_total;\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t __pyx_v_k;\n",
       "  std::map&lt;__pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t,__pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_VTYPE_t&gt; ::iterator __pyx_v_it;\n",
       "  std::map&lt;__pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t,__pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_VTYPE_t&gt; ::iterator __pyx_v_end;\n",
       "  PyObject *__pyx_r = NULL;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"dot\", 0);\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_L1_error:;\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_3);\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_e062e747d424c8c74d79c5fd3db6155e.IntFloatDict.dot\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">35</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">VTYPE_t</span> <span class=\"nf\">total</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">36</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">KTYPE_t</span> <span class=\"nf\">k</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">37</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">cpp_map</span>[<span class=\"nf\">KTYPE_t</span><span class=\"p\">,</span> <span class=\"kt\">VTYPE_t</span>].<span class=\"kt\">iterator</span> <span class=\"nf\">it</span> <span class=\"o\">=</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">begin</span><span class=\"p\">()</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_it = __pyx_v_other-&gt;data.begin();\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">38</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">cpp_map</span>[<span class=\"nf\">KTYPE_t</span><span class=\"p\">,</span> <span class=\"kt\">VTYPE_t</span>].<span class=\"kt\">iterator</span> <span class=\"nf\">end</span> <span class=\"o\">=</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">end</span><span class=\"p\">()</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_end = __pyx_v_other-&gt;data.end();\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">39</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">40</span>:         <span class=\"k\">while</span> <span class=\"n\">it</span> <span class=\"o\">!=</span> <span class=\"n\">end</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  while (1) {\n",
       "    __pyx_t_1 = ((__pyx_v_it != __pyx_v_end) != 0);\n",
       "    if (!__pyx_t_1) break;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">41</span>:             <span class=\"n\">k</span> <span class=\"o\">=</span> <span class=\"n\">deref</span><span class=\"p\">(</span><span class=\"n\">it</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">first</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_2 = (*__pyx_v_it).first;\n",
       "    __pyx_v_k = __pyx_t_2;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">42</span>:             <span class=\"n\">total</span> <span class=\"o\">+=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"n\">k</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"n\">k</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_total = (__pyx_v_total + ((__pyx_v_self-&gt;data[__pyx_v_k]) * (__pyx_v_other-&gt;data[__pyx_v_k])));\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">43</span>:             <span class=\"n\">inc</span><span class=\"p\">(</span><span class=\"n\">it</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>    (void)((++__pyx_v_it));\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">44</span>: </pre>\n",
       "<pre class=\"cython line score-6\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">45</span>:         <span class=\"k\">return</span> <span class=\"n\">total</span></pre>\n",
       "<pre class='cython code score-6 '>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
       "  __pyx_t_3 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_total);<span class='error_goto'> if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 45, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_3);\n",
       "  __pyx_r = __pyx_t_3;\n",
       "  __pyx_t_3 = 0;\n",
       "  goto __pyx_L0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">46</span>: </pre>\n",
       "<pre class=\"cython line score-7\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">47</span>:     <span class=\"k\">def</span> <span class=\"nf\">dot_dict</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"nb\">dict</span> <span class=\"n\">other</span> <span class=\"ow\">not</span> <span class=\"bp\">None</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-7 '>/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_9dot_dict(PyObject *__pyx_v_self, PyObject *__pyx_v_other); /*proto*/\n",
       "static PyObject *__pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_9dot_dict(PyObject *__pyx_v_self, PyObject *__pyx_v_other) {\n",
       "  PyObject *__pyx_r = 0;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"dot_dict (wrapper)\", 0);\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_other), (&amp;PyDict_Type), 0, \"other\", 1))) <span class='error_goto'>__PYX_ERR(0, 47, __pyx_L1_error)</span>\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_8dot_dict(((struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *)__pyx_v_self), ((PyObject*)__pyx_v_other));\n",
       "\n",
       "  /* function exit code */\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static PyObject *__pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_8dot_dict(struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *__pyx_v_self, PyObject *__pyx_v_other) {\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_VTYPE_t __pyx_v_total;\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t __pyx_v_k;\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_VTYPE_t __pyx_v_v;\n",
       "  PyObject *__pyx_r = NULL;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"dot_dict\", 0);\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_L1_error:;\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_5);\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_6);\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_e062e747d424c8c74d79c5fd3db6155e.IntFloatDict.dot_dict\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">48</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">VTYPE_t</span> <span class=\"nf\">total</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">49</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">KTYPE_t</span> <span class=\"nf\">k</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">50</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">VTYPE_t</span> <span class=\"nf\">v</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">51</span>: </pre>\n",
       "<pre class=\"cython line score-20\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">52</span>:         <span class=\"k\">for</span> <span class=\"n\">k</span><span class=\"p\">,</span> <span class=\"n\">v</span> <span class=\"ow\">in</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">items</span><span class=\"p\">():</span></pre>\n",
       "<pre class='cython code score-20 '>  __pyx_t_2 = 0;\n",
       "  __pyx_t_5 = __Pyx_dict_iterator(__pyx_v_other, 1, __pyx_n_s_items, (&amp;__pyx_t_3), (&amp;__pyx_t_4));<span class='error_goto'> if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 52, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_5);\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n",
       "  __pyx_t_1 = __pyx_t_5;\n",
       "  __pyx_t_5 = 0;\n",
       "  while (1) {\n",
       "    __pyx_t_7 = __Pyx_dict_iter_next(__pyx_t_1, __pyx_t_3, &amp;__pyx_t_2, &amp;__pyx_t_5, &amp;__pyx_t_6, NULL, __pyx_t_4);\n",
       "    if (unlikely(__pyx_t_7 == 0)) break;\n",
       "    if (unlikely(__pyx_t_7 == -1)) <span class='error_goto'>__PYX_ERR(0, 52, __pyx_L1_error)</span>\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_5);\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_6);\n",
       "    __pyx_t_8 = <span class='pyx_c_api'>__Pyx_PyInt_As_int</span>(__pyx_t_5); if (unlikely((__pyx_t_8 == (int)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 52, __pyx_L1_error)</span>\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;\n",
       "    __pyx_t_9 = __pyx_<span class='py_c_api'>PyFloat_AsFloat</span>(__pyx_t_6); if (unlikely((__pyx_t_9 == (float)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 52, __pyx_L1_error)</span>\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_6); __pyx_t_6 = 0;\n",
       "    __pyx_v_k = __pyx_t_8;\n",
       "    __pyx_v_v = __pyx_t_9;\n",
       "</pre><pre class=\"cython line score-1\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">53</span>:             <span class=\"n\">total</span> <span class=\"o\">+=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"n\">k</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">v</span></pre>\n",
       "<pre class='cython code score-1 '>    __pyx_v_total = (__pyx_v_total + ((__pyx_v_self-&gt;data[__pyx_v_k]) * __pyx_v_v));\n",
       "  }\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">54</span>: </pre>\n",
       "<pre class=\"cython line score-6\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">55</span>:         <span class=\"k\">return</span> <span class=\"n\">total</span></pre>\n",
       "<pre class='cython code score-6 '>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
       "  __pyx_t_1 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_total);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 55, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
       "  __pyx_r = __pyx_t_1;\n",
       "  __pyx_t_1 = 0;\n",
       "  goto __pyx_L0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">56</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">57</span>:     <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">58</span>:     <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-31\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">59</span>:     <span class=\"k\">def</span> <span class=\"nf\">dot_list</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"nb\">list</span> <span class=\"n\">keys</span> <span class=\"ow\">not</span> <span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"nb\">list</span> <span class=\"n\">values</span> <span class=\"ow\">not</span> <span class=\"bp\">None</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-31 '>/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_11dot_list(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n",
       "static PyObject *__pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_11dot_list(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
       "  PyObject *__pyx_v_keys = 0;\n",
       "  PyObject *__pyx_v_values = 0;\n",
       "  PyObject *__pyx_r = 0;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"dot_list (wrapper)\", 0);\n",
       "  {\n",
       "    static PyObject **__pyx_pyargnames[] = {&amp;__pyx_n_s_keys,&amp;__pyx_n_s_values,0};\n",
       "    PyObject* values[2] = {0,0};\n",
       "    if (unlikely(__pyx_kwds)) {\n",
       "      Py_ssize_t kw_args;\n",
       "      const Py_ssize_t pos_args = <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args);\n",
       "      switch (pos_args) {\n",
       "        case  2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  0: break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
       "      }\n",
       "      kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n",
       "      switch (pos_args) {\n",
       "        case  0:\n",
       "        if (likely((values[0] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_keys)) != 0)) kw_args--;\n",
       "        else goto __pyx_L5_argtuple_error;\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  1:\n",
       "        if (likely((values[1] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_values)) != 0)) kw_args--;\n",
       "        else {\n",
       "          <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"dot_list\", 1, 2, 2, 1); <span class='error_goto'>__PYX_ERR(0, 59, __pyx_L3_error)</span>\n",
       "        }\n",
       "      }\n",
       "      if (unlikely(kw_args &gt; 0)) {\n",
       "        if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, \"dot_list\") &lt; 0)) <span class='error_goto'>__PYX_ERR(0, 59, __pyx_L3_error)</span>\n",
       "      }\n",
       "    } else if (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args) != 2) {\n",
       "      goto __pyx_L5_argtuple_error;\n",
       "    } else {\n",
       "      values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "      values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "    }\n",
       "    __pyx_v_keys = ((PyObject*)values[0]);\n",
       "    __pyx_v_values = ((PyObject*)values[1]);\n",
       "  }\n",
       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L5_argtuple_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"dot_list\", 1, 2, 2, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); <span class='error_goto'>__PYX_ERR(0, 59, __pyx_L3_error)</span>\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_e062e747d424c8c74d79c5fd3db6155e.IntFloatDict.dot_list\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return NULL;\n",
       "  __pyx_L4_argument_unpacking_done:;\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_keys), (&amp;PyList_Type), 0, \"keys\", 1))) <span class='error_goto'>__PYX_ERR(0, 59, __pyx_L1_error)</span>\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_values), (&amp;PyList_Type), 0, \"values\", 1))) <span class='error_goto'>__PYX_ERR(0, 59, __pyx_L1_error)</span>\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_10dot_list(((struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *)__pyx_v_self), __pyx_v_keys, __pyx_v_values);\n",
       "\n",
       "  /* function exit code */\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static PyObject *__pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_10dot_list(struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *__pyx_v_self, PyObject *__pyx_v_keys, PyObject *__pyx_v_values) {\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_VTYPE_t __pyx_v_total;\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t __pyx_v_k;\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_VTYPE_t __pyx_v_v;\n",
       "  int __pyx_v_i;\n",
       "  int __pyx_v_size;\n",
       "  PyObject *__pyx_r = NULL;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"dot_list\", 0);\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_L1_error:;\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_7);\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_e062e747d424c8c74d79c5fd3db6155e.IntFloatDict.dot_list\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">60</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">VTYPE_t</span> <span class=\"nf\">total</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">61</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">KTYPE_t</span> <span class=\"nf\">k</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">62</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">VTYPE_t</span> <span class=\"nf\">v</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">63</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">64</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">i</span></pre>\n",
       "<pre class=\"cython line score-1\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">65</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">size</span> <span class=\"o\">=</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">values</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-1 '>  __pyx_t_1 = <span class='py_macro_api'>PyList_GET_SIZE</span>(__pyx_v_values);<span class='error_goto'> if (unlikely(__pyx_t_1 == ((Py_ssize_t)-1))) __PYX_ERR(0, 65, __pyx_L1_error)</span>\n",
       "  __pyx_v_size = __pyx_t_1;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">66</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">67</span>:         <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">size</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_2 = __pyx_v_size;\n",
       "  __pyx_t_3 = __pyx_t_2;\n",
       "  for (__pyx_t_4 = 0; __pyx_t_4 &lt; __pyx_t_3; __pyx_t_4+=1) {\n",
       "    __pyx_v_i = __pyx_t_4;\n",
       "</pre><pre class=\"cython line score-8\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">68</span>:             <span class=\"n\">k</span> <span class=\"o\">=</span> <span class=\"n\">keys</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-8 '>    __pyx_t_5 = <span class='pyx_c_api'>__Pyx_PyInt_As_int</span>(<span class='py_macro_api'>PyList_GET_ITEM</span>(__pyx_v_keys, __pyx_v_i)); if (unlikely((__pyx_t_5 == (int)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 68, __pyx_L1_error)</span>\n",
       "    __pyx_v_k = __pyx_t_5;\n",
       "</pre><pre class=\"cython line score-11\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">69</span>:             <span class=\"n\">v</span> <span class=\"o\">=</span> <span class=\"n\">values</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-11 '>    __pyx_t_6 = __pyx_<span class='py_c_api'>PyFloat_AsFloat</span>(<span class='py_macro_api'>PyList_GET_ITEM</span>(__pyx_v_values, __pyx_v_i)); if (unlikely((__pyx_t_6 == (float)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 69, __pyx_L1_error)</span>\n",
       "    __pyx_v_v = __pyx_t_6;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">70</span>:             <span class=\"n\">total</span> <span class=\"o\">+=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"n\">k</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">v</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_total = (__pyx_v_total + ((__pyx_v_self-&gt;data[__pyx_v_k]) * __pyx_v_v));\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">71</span>: </pre>\n",
       "<pre class=\"cython line score-6\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">72</span>:         <span class=\"k\">return</span> <span class=\"n\">total</span></pre>\n",
       "<pre class='cython code score-6 '>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
       "  __pyx_t_7 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_total);<span class='error_goto'> if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 72, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_7);\n",
       "  __pyx_r = __pyx_t_7;\n",
       "  __pyx_t_7 = 0;\n",
       "  goto __pyx_L0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">73</span>: </pre>\n",
       "<pre class=\"cython line score-8\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">74</span>:     <span class=\"k\">def</span> <span class=\"nf\">sub_dict</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"nb\">dict</span> <span class=\"n\">other</span> <span class=\"ow\">not</span> <span class=\"bp\">None</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-8 '>/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_13sub_dict(PyObject *__pyx_v_self, PyObject *__pyx_v_other); /*proto*/\n",
       "static PyObject *__pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_13sub_dict(PyObject *__pyx_v_self, PyObject *__pyx_v_other) {\n",
       "  PyObject *__pyx_r = 0;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"sub_dict (wrapper)\", 0);\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_other), (&amp;PyDict_Type), 0, \"other\", 1))) <span class='error_goto'>__PYX_ERR(0, 74, __pyx_L1_error)</span>\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_12sub_dict(((struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *)__pyx_v_self), ((PyObject*)__pyx_v_other));\n",
       "\n",
       "  /* function exit code */\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static PyObject *__pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_12sub_dict(struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *__pyx_v_self, PyObject *__pyx_v_other) {\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t __pyx_v_k;\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_VTYPE_t __pyx_v_v;\n",
       "  PyObject *__pyx_r = NULL;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"sub_dict\", 0);\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_r = Py_None; <span class='pyx_macro_api'>__Pyx_INCREF</span>(Py_None);\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_5);\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_6);\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_e062e747d424c8c74d79c5fd3db6155e.IntFloatDict.sub_dict\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">75</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">KTYPE_t</span> <span class=\"nf\">k</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">76</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">VTYPE_t</span> <span class=\"nf\">v</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">77</span>: </pre>\n",
       "<pre class=\"cython line score-20\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">78</span>:         <span class=\"k\">for</span> <span class=\"n\">k</span><span class=\"p\">,</span> <span class=\"n\">v</span> <span class=\"ow\">in</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">items</span><span class=\"p\">():</span></pre>\n",
       "<pre class='cython code score-20 '>  __pyx_t_2 = 0;\n",
       "  __pyx_t_5 = __Pyx_dict_iterator(__pyx_v_other, 1, __pyx_n_s_items, (&amp;__pyx_t_3), (&amp;__pyx_t_4));<span class='error_goto'> if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 78, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_5);\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n",
       "  __pyx_t_1 = __pyx_t_5;\n",
       "  __pyx_t_5 = 0;\n",
       "  while (1) {\n",
       "    __pyx_t_7 = __Pyx_dict_iter_next(__pyx_t_1, __pyx_t_3, &amp;__pyx_t_2, &amp;__pyx_t_5, &amp;__pyx_t_6, NULL, __pyx_t_4);\n",
       "    if (unlikely(__pyx_t_7 == 0)) break;\n",
       "    if (unlikely(__pyx_t_7 == -1)) <span class='error_goto'>__PYX_ERR(0, 78, __pyx_L1_error)</span>\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_5);\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_6);\n",
       "    __pyx_t_8 = <span class='pyx_c_api'>__Pyx_PyInt_As_int</span>(__pyx_t_5); if (unlikely((__pyx_t_8 == (int)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 78, __pyx_L1_error)</span>\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;\n",
       "    __pyx_t_9 = __pyx_<span class='py_c_api'>PyFloat_AsFloat</span>(__pyx_t_6); if (unlikely((__pyx_t_9 == (float)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 78, __pyx_L1_error)</span>\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_6); __pyx_t_6 = 0;\n",
       "    __pyx_v_k = __pyx_t_8;\n",
       "    __pyx_v_v = __pyx_t_9;\n",
       "</pre><pre class=\"cython line score-1\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">79</span>:             <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"n\">k</span><span class=\"p\">]</span> <span class=\"o\">-=</span> <span class=\"n\">v</span></pre>\n",
       "<pre class='cython code score-1 '>    __pyx_t_8 = __pyx_v_k;\n",
       "    (__pyx_v_self-&gt;data[__pyx_t_8]) = ((__pyx_v_self-&gt;data[__pyx_t_8]) - __pyx_v_v);\n",
       "  }\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">80</span>: </pre>\n",
       "<pre class=\"cython line score-3\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">81</span>:     <span class=\"k\">def</span> <span class=\"nf\">sub</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">IntFloatDict</span> <span class=\"n\">other</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-3 '>/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_15sub(PyObject *__pyx_v_self, PyObject *__pyx_v_other); /*proto*/\n",
       "static PyObject *__pyx_pw_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_15sub(PyObject *__pyx_v_self, PyObject *__pyx_v_other) {\n",
       "  PyObject *__pyx_r = 0;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"sub (wrapper)\", 0);\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_other), __pyx_ptype_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict, 1, \"other\", 0))) <span class='error_goto'>__PYX_ERR(0, 81, __pyx_L1_error)</span>\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_14sub(((struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *)__pyx_v_self), ((struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *)__pyx_v_other));\n",
       "\n",
       "  /* function exit code */\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static PyObject *__pyx_pf_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_12IntFloatDict_14sub(struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *__pyx_v_self, struct __pyx_obj_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_IntFloatDict *__pyx_v_other) {\n",
       "  __pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t __pyx_v_k;\n",
       "  std::map&lt;__pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t,__pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_VTYPE_t&gt; ::iterator __pyx_v_it;\n",
       "  std::map&lt;__pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_KTYPE_t,__pyx_t_46_cython_magic_e062e747d424c8c74d79c5fd3db6155e_VTYPE_t&gt; ::iterator __pyx_v_end;\n",
       "  PyObject *__pyx_r = NULL;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"sub\", 0);\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_r = Py_None; <span class='pyx_macro_api'>__Pyx_INCREF</span>(Py_None);\n",
       "  <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">82</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">KTYPE_t</span> <span class=\"nf\">k</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">83</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">cpp_map</span>[<span class=\"nf\">KTYPE_t</span><span class=\"p\">,</span> <span class=\"kt\">VTYPE_t</span>].<span class=\"kt\">iterator</span> <span class=\"nf\">it</span> <span class=\"o\">=</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">begin</span><span class=\"p\">()</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_it = __pyx_v_other-&gt;data.begin();\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">84</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">cpp_map</span>[<span class=\"nf\">KTYPE_t</span><span class=\"p\">,</span> <span class=\"kt\">VTYPE_t</span>].<span class=\"kt\">iterator</span> <span class=\"nf\">end</span> <span class=\"o\">=</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">end</span><span class=\"p\">()</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_end = __pyx_v_other-&gt;data.end();\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">85</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">86</span>:         <span class=\"k\">while</span> <span class=\"n\">it</span> <span class=\"o\">!=</span> <span class=\"n\">end</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  while (1) {\n",
       "    __pyx_t_1 = ((__pyx_v_it != __pyx_v_end) != 0);\n",
       "    if (!__pyx_t_1) break;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">87</span>:             <span class=\"n\">k</span> <span class=\"o\">=</span> <span class=\"n\">deref</span><span class=\"p\">(</span><span class=\"n\">it</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">first</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_2 = (*__pyx_v_it).first;\n",
       "    __pyx_v_k = __pyx_t_2;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">88</span>:             <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"n\">k</span><span class=\"p\">]</span> <span class=\"o\">-=</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"n\">k</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_2 = __pyx_v_k;\n",
       "    (__pyx_v_self-&gt;data[__pyx_t_2]) = ((__pyx_v_self-&gt;data[__pyx_t_2]) - (__pyx_v_other-&gt;data[__pyx_v_k]));\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">89</span>:             <span class=\"n\">inc</span><span class=\"p\">(</span><span class=\"n\">it</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>    (void)((++__pyx_v_it));\n",
       "  }\n",
       "</pre></div></body></html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%cython --annotate\n",
    "# distutils: language = c++\n",
    "\n",
    "cimport cython\n",
    "\n",
    "# C++\n",
    "from cython.operator cimport dereference as deref\n",
    "from cython.operator cimport preincrement as inc\n",
    "from libcpp.map cimport map as cpp_map\n",
    "\n",
    "ctypedef int   KTYPE_t\n",
    "ctypedef float VTYPE_t\n",
    "\n",
    "cdef class IntFloatDict:\n",
    "    \n",
    "    cdef cpp_map[KTYPE_t, VTYPE_t] data\n",
    "\n",
    "    def __init__(self, d not None):\n",
    "        \n",
    "        cdef KTYPE_t k\n",
    "        cdef VTYPE_t v\n",
    "        \n",
    "        for k, v in d.items():\n",
    "            self.data[k] = v\n",
    "            \n",
    "    def __getitem__(self, KTYPE_t k):\n",
    "        cdef cpp_map[KTYPE_t, VTYPE_t].iterator it = self.data.find(k)\n",
    "        if it == self.data.end():\n",
    "            raise KeyError('%i' % k)\n",
    "        return deref(it).second\n",
    "            \n",
    "    def __setitem__(self, KTYPE_t k, VTYPE_t v):\n",
    "        self.data[k] = v\n",
    "            \n",
    "    def dot(self, IntFloatDict other):\n",
    "        cdef VTYPE_t total\n",
    "        cdef KTYPE_t k\n",
    "        cdef cpp_map[KTYPE_t, VTYPE_t].iterator it = other.data.begin()\n",
    "        cdef cpp_map[KTYPE_t, VTYPE_t].iterator end = other.data.end()\n",
    "        \n",
    "        while it != end:\n",
    "            k = deref(it).first\n",
    "            total += self.data[k] * other.data[k]\n",
    "            inc(it)\n",
    "        \n",
    "        return total\n",
    "    \n",
    "    def dot_dict(self, dict other not None):\n",
    "        cdef VTYPE_t total\n",
    "        cdef KTYPE_t k\n",
    "        cdef VTYPE_t v\n",
    "        \n",
    "        for k, v in other.items():\n",
    "            total += self.data[k] * v\n",
    "        \n",
    "        return total\n",
    "    \n",
    "    @cython.boundscheck(False)\n",
    "    @cython.wraparound(False)\n",
    "    def dot_list(self, list keys not None, list values not None):\n",
    "        cdef VTYPE_t total\n",
    "        cdef KTYPE_t k\n",
    "        cdef VTYPE_t v\n",
    "        \n",
    "        cdef int i\n",
    "        cdef int size = len(values)\n",
    "        \n",
    "        for i in range(size):\n",
    "            k = keys[i]\n",
    "            v = values[i]\n",
    "            total += self.data[k] * v\n",
    "        \n",
    "        return total\n",
    "    \n",
    "    def sub_dict(self, dict other not None):\n",
    "        cdef KTYPE_t k\n",
    "        cdef VTYPE_t v\n",
    "        \n",
    "        for k, v in other.items():\n",
    "            self.data[k] -= v\n",
    "            \n",
    "    def sub(self, IntFloatDict other):\n",
    "        cdef KTYPE_t k\n",
    "        cdef cpp_map[KTYPE_t, VTYPE_t].iterator it = other.data.begin()\n",
    "        cdef cpp_map[KTYPE_t, VTYPE_t].iterator end = other.data.end()\n",
    "        \n",
    "        while it != end:\n",
    "            k = deref(it).first\n",
    "            self.data[k] -= other.data[k]\n",
    "            inc(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "711 ns ± 7.74 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "x = IntFloatDict(rand_dict(30))\n",
    "y = rand_dict(30)\n",
    "%timeit x.dot_dict(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "433 ns ± 15.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 411 ns ± 1.21 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "3 561 ns ± 33.5 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "5 785 ns ± 5.75 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "8 1.42 µs ± 145 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "10 1.57 µs ± 57.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "15 2.25 µs ± 183 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "20 3.7 µs ± 440 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "25 3.78 µs ± 305 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "30 4.77 µs ± 444 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "50 9.59 µs ± 741 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "75 13.2 µs ± 1.12 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "100 17.8 µs ± 1.37 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "125 20.7 µs ± 312 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "150 28.5 µs ± 3.12 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "175 32.2 µs ± 2.72 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "200 38.7 µs ± 3.59 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def cython_dot(x: IntFloatDict, y: dict) -> float:\n",
    "    return x.dot(IntFloatDict(y))\n",
    "    \n",
    "dot_bench['Cython'] = {}\n",
    "\n",
    "for size in sizes:\n",
    "    x = IntFloatDict(rand_dict(size))\n",
    "    y = rand_dict(size)\n",
    "    dot_bench['Cython'][size] = %timeit -o -q cython_dot(x, y)\n",
    "    print(size, dot_bench['Cython'][size])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 144 ns ± 5.5 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n",
      "3 157 ns ± 7.58 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n",
      "5 187 ns ± 6.29 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n",
      "8 210 ns ± 1.86 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "10 245 ns ± 22.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "15 328 ns ± 40.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "20 353 ns ± 3.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "25 463 ns ± 36.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "30 518 ns ± 49.5 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "50 880 ns ± 70.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "75 1.14 µs ± 34.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "100 1.96 µs ± 110 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "125 2.36 µs ± 185 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "150 2.82 µs ± 114 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "175 3.17 µs ± 36.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "200 4.09 µs ± 61.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def cython_dot_from_dict(x: IntFloatDict, y: dict) -> float:\n",
    "    return x.dot_dict(y)\n",
    "    \n",
    "dot_bench['Cython from dict'] = {}\n",
    "\n",
    "for size in sizes:\n",
    "    x = IntFloatDict(rand_dict(size))\n",
    "    y = rand_dict(size)\n",
    "    dot_bench['Cython from dict'][size] = %timeit -o -q cython_dot_from_dict(x, y)\n",
    "    print(size, dot_bench['Cython from dict'][size])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 595 ns ± 26.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "3 663 ns ± 37.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "5 649 ns ± 46.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "8 692 ns ± 38.5 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "10 706 ns ± 48.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "15 774 ns ± 27.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "20 897 ns ± 60.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "25 962 ns ± 61.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "30 1.08 µs ± 77.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "50 1.36 µs ± 13.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "75 2.06 µs ± 21.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "100 2.49 µs ± 32.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "125 2.96 µs ± 19.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "150 3.46 µs ± 22.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "175 3.92 µs ± 81.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "200 5.32 µs ± 272 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def cython_dot_from_lists(x: IntFloatDict, y: dict) -> float:\n",
    "    return x.dot_list(list(y.keys()), list(y.values()))\n",
    "    \n",
    "dot_bench['Cython from lists'] = {}\n",
    "\n",
    "for size in sizes:\n",
    "    x = IntFloatDict(rand_dict(size))\n",
    "    y = rand_dict(size)\n",
    "    dot_bench['Cython from lists'][size] = %timeit -o -q cython_dot_from_lists(x, y)\n",
    "    print(size, dot_bench['Cython from lists'][size])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot these results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 477,
       "width": 749
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "\n",
    "for method, time_results in dot_bench.items():\n",
    "    avgs = [1e9 * tr.average for tr in time_results.values()]\n",
    "    stds = [1e9 * tr.stdev   for tr in time_results.values()]\n",
    "    lower = [avg - std for avg, std in zip(avgs, stds)]\n",
    "    upper = [avg + std for avg, std in zip(avgs, stds)]\n",
    "    ax.fill_between(list(time_results.keys()), lower, upper, interpolate=True, alpha=.2)\n",
    "    ax.plot(list(time_results.keys()), avgs, label=method)\n",
    "    \n",
    "ax.legend()\n",
    "ax.grid()\n",
    "ax.set_title('Dot product performance for different dict sizes', pad=15)\n",
    "ax.set_xlabel('Dict size', labelpad=10)\n",
    "ax.set_ylabel('Time in nanoseconds', labelpad=10)\n",
    "ax.set_yscale('log')\n",
    "fig.savefig('dotprod_perf.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subtract"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another common task is to subtract a gradient to a set weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_bench = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's give this a first go in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 328 ns ± 3.34 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "3 396 ns ± 2.59 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "5 581 ns ± 29.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "8 797 ns ± 44.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "10 902 ns ± 14.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "15 1.32 µs ± 125 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "20 1.54 µs ± 21 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "25 1.86 µs ± 7.91 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "30 2.26 µs ± 49.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "50 3.59 µs ± 47.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "75 5.16 µs ± 72.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "100 7.19 µs ± 56 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "125 9.07 µs ± 73.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "150 10.7 µs ± 36.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "175 13.1 µs ± 592 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "200 14.5 µs ± 377 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def py_sub(w: dict, g: dict):\n",
    "    for i, gi in g.items():\n",
    "        w[i] -= gi\n",
    "        \n",
    "sub_bench['Python'] = {}\n",
    "        \n",
    "for size in sizes:\n",
    "    w = rand_dict(size)\n",
    "    g = rand_dict(size)\n",
    "    sub_bench['Python'][size] = %timeit -o -q py_sub(w, g)\n",
    "    print(size, sub_bench['Python'][size])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see how our Cython dict performs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 318 ns ± 3.13 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "3 382 ns ± 3.52 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "5 530 ns ± 18.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "8 705 ns ± 12.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "10 851 ns ± 3.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "15 1.24 µs ± 9.79 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "20 1.56 µs ± 62.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "25 2.06 µs ± 186 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "30 2.47 µs ± 224 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "50 4.05 µs ± 242 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "75 6.08 µs ± 349 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "100 8.44 µs ± 501 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "125 11.2 µs ± 987 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "150 13.5 µs ± 372 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "175 16.6 µs ± 748 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "200 19.3 µs ± 1.12 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def cython_sub_py_loop(w: IntFloatDict, g: dict):\n",
    "    for i, gi in g.items():\n",
    "        w[i] -= gi\n",
    "        \n",
    "sub_bench['Cython w/ Python loop'] = {}\n",
    "        \n",
    "for size in sizes:\n",
    "    w = IntFloatDict(rand_dict(size))\n",
    "    g = rand_dict(size)\n",
    "    sub_bench['Cython w/ Python loop'][size] = %timeit -o -q cython_sub_py_loop(w, g)\n",
    "    print(size, sub_bench['Cython w/ Python loop'][size])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 151 ns ± 2.06 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "3 169 ns ± 4.48 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n",
      "5 199 ns ± 8.79 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "8 259 ns ± 20.5 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "10 278 ns ± 25.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "15 347 ns ± 2.56 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "20 416 ns ± 2.41 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "25 511 ns ± 1.39 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "30 627 ns ± 43.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "50 935 ns ± 8.98 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "75 1.42 µs ± 7.09 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "100 2.14 µs ± 80.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "125 2.69 µs ± 46.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "150 3.61 µs ± 374 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "175 4.1 µs ± 56.8 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "200 5.88 µs ± 429 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def cython_sub_from_dict(w: IntFloatDict, g: dict):\n",
    "    w.sub_dict(g)\n",
    "        \n",
    "sub_bench['Cython from dict'] = {}\n",
    "        \n",
    "for size in sizes:\n",
    "    w = IntFloatDict(rand_dict(size))\n",
    "    g = rand_dict(size)\n",
    "    sub_bench['Cython from dict'][size] = %timeit -o -q cython_sub_from_dict(w, g)\n",
    "    print(size, sub_bench['Cython from dict'][size])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 459 ns ± 32.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "3 648 ns ± 60.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "5 915 ns ± 48.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "8 1.34 µs ± 45.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "10 1.57 µs ± 46.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "15 2.28 µs ± 58 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "20 3.1 µs ± 29.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "25 3.82 µs ± 95.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "30 4.74 µs ± 31.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "50 8.36 µs ± 583 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "75 12.2 µs ± 151 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "100 18.3 µs ± 1.38 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "125 25 µs ± 1.65 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "150 30.3 µs ± 1.9 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "175 35.1 µs ± 338 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "200 42.3 µs ± 535 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def cython_sub(w: IntFloatDict, g: dict):\n",
    "    w.sub(IntFloatDict(g))\n",
    "        \n",
    "sub_bench['Cython'] = {}\n",
    "        \n",
    "for size in sizes:\n",
    "    w = IntFloatDict(rand_dict(size))\n",
    "    g = rand_dict(size)\n",
    "    sub_bench['Cython'][size] = %timeit -o -q cython_sub(w, g)\n",
    "    print(size, sub_bench['Cython'][size])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 477,
       "width": 749
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "\n",
    "for method, time_results in sub_bench.items():\n",
    "    avgs = [1e9 * tr.average for tr in time_results.values()]\n",
    "    stds = [1e9 * tr.stdev   for tr in time_results.values()]\n",
    "    lower = [avg - std for avg, std in zip(avgs, stds)]\n",
    "    upper = [avg + std for avg, std in zip(avgs, stds)]\n",
    "    ax.fill_between(list(time_results.keys()), lower, upper, interpolate=True, alpha=.2)\n",
    "    ax.plot(list(time_results.keys()), avgs, label=method)\n",
    "    \n",
    "ax.legend()\n",
    "ax.grid()\n",
    "ax.set_title('Subtraction performance for different dict sizes', pad=15)\n",
    "ax.set_xlabel('Dict size', labelpad=10)\n",
    "ax.set_ylabel('Time in nanoseconds', labelpad=10)\n",
    "ax.set_yscale('log')\n",
    "fig.savefig('subtract_perf.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html>\n",
       "<!-- Generated by Cython 0.29.14 -->\n",
       "<html>\n",
       "<head>\n",
       "    <meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n",
       "    <title>Cython: _cython_magic_ec2c52f31301d188c98415ab1d82f601.pyx</title>\n",
       "    <style type=\"text/css\">\n",
       "    \n",
       "body.cython { font-family: courier; font-size: 12; }\n",
       "\n",
       ".cython.tag  {  }\n",
       ".cython.line { margin: 0em }\n",
       ".cython.code { font-size: 9; color: #444444; display: none; margin: 0px 0px 0px 8px; border-left: 8px none; }\n",
       "\n",
       ".cython.line .run { background-color: #B0FFB0; }\n",
       ".cython.line .mis { background-color: #FFB0B0; }\n",
       ".cython.code.run  { border-left: 8px solid #B0FFB0; }\n",
       ".cython.code.mis  { border-left: 8px solid #FFB0B0; }\n",
       "\n",
       ".cython.code .py_c_api  { color: red; }\n",
       ".cython.code .py_macro_api  { color: #FF7000; }\n",
       ".cython.code .pyx_c_api  { color: #FF3000; }\n",
       ".cython.code .pyx_macro_api  { color: #FF7000; }\n",
       ".cython.code .refnanny  { color: #FFA000; }\n",
       ".cython.code .trace  { color: #FFA000; }\n",
       ".cython.code .error_goto  { color: #FFA000; }\n",
       "\n",
       ".cython.code .coerce  { color: #008000; border: 1px dotted #008000 }\n",
       ".cython.code .py_attr { color: #FF0000; font-weight: bold; }\n",
       ".cython.code .c_attr  { color: #0000FF; }\n",
       ".cython.code .py_call { color: #FF0000; font-weight: bold; }\n",
       ".cython.code .c_call  { color: #0000FF; }\n",
       "\n",
       ".cython.score-0 {background-color: #FFFFff;}\n",
       ".cython.score-1 {background-color: #FFFFe7;}\n",
       ".cython.score-2 {background-color: #FFFFd4;}\n",
       ".cython.score-3 {background-color: #FFFFc4;}\n",
       ".cython.score-4 {background-color: #FFFFb6;}\n",
       ".cython.score-5 {background-color: #FFFFaa;}\n",
       ".cython.score-6 {background-color: #FFFF9f;}\n",
       ".cython.score-7 {background-color: #FFFF96;}\n",
       ".cython.score-8 {background-color: #FFFF8d;}\n",
       ".cython.score-9 {background-color: #FFFF86;}\n",
       ".cython.score-10 {background-color: #FFFF7f;}\n",
       ".cython.score-11 {background-color: #FFFF79;}\n",
       ".cython.score-12 {background-color: #FFFF73;}\n",
       ".cython.score-13 {background-color: #FFFF6e;}\n",
       ".cython.score-14 {background-color: #FFFF6a;}\n",
       ".cython.score-15 {background-color: #FFFF66;}\n",
       ".cython.score-16 {background-color: #FFFF62;}\n",
       ".cython.score-17 {background-color: #FFFF5e;}\n",
       ".cython.score-18 {background-color: #FFFF5b;}\n",
       ".cython.score-19 {background-color: #FFFF57;}\n",
       ".cython.score-20 {background-color: #FFFF55;}\n",
       ".cython.score-21 {background-color: #FFFF52;}\n",
       ".cython.score-22 {background-color: #FFFF4f;}\n",
       ".cython.score-23 {background-color: #FFFF4d;}\n",
       ".cython.score-24 {background-color: #FFFF4b;}\n",
       ".cython.score-25 {background-color: #FFFF48;}\n",
       ".cython.score-26 {background-color: #FFFF46;}\n",
       ".cython.score-27 {background-color: #FFFF44;}\n",
       ".cython.score-28 {background-color: #FFFF43;}\n",
       ".cython.score-29 {background-color: #FFFF41;}\n",
       ".cython.score-30 {background-color: #FFFF3f;}\n",
       ".cython.score-31 {background-color: #FFFF3e;}\n",
       ".cython.score-32 {background-color: #FFFF3c;}\n",
       ".cython.score-33 {background-color: #FFFF3b;}\n",
       ".cython.score-34 {background-color: #FFFF39;}\n",
       ".cython.score-35 {background-color: #FFFF38;}\n",
       ".cython.score-36 {background-color: #FFFF37;}\n",
       ".cython.score-37 {background-color: #FFFF36;}\n",
       ".cython.score-38 {background-color: #FFFF35;}\n",
       ".cython.score-39 {background-color: #FFFF34;}\n",
       ".cython.score-40 {background-color: #FFFF33;}\n",
       ".cython.score-41 {background-color: #FFFF32;}\n",
       ".cython.score-42 {background-color: #FFFF31;}\n",
       ".cython.score-43 {background-color: #FFFF30;}\n",
       ".cython.score-44 {background-color: #FFFF2f;}\n",
       ".cython.score-45 {background-color: #FFFF2e;}\n",
       ".cython.score-46 {background-color: #FFFF2d;}\n",
       ".cython.score-47 {background-color: #FFFF2c;}\n",
       ".cython.score-48 {background-color: #FFFF2b;}\n",
       ".cython.score-49 {background-color: #FFFF2b;}\n",
       ".cython.score-50 {background-color: #FFFF2a;}\n",
       ".cython.score-51 {background-color: #FFFF29;}\n",
       ".cython.score-52 {background-color: #FFFF29;}\n",
       ".cython.score-53 {background-color: #FFFF28;}\n",
       ".cython.score-54 {background-color: #FFFF27;}\n",
       ".cython.score-55 {background-color: #FFFF27;}\n",
       ".cython.score-56 {background-color: #FFFF26;}\n",
       ".cython.score-57 {background-color: #FFFF26;}\n",
       ".cython.score-58 {background-color: #FFFF25;}\n",
       ".cython.score-59 {background-color: #FFFF24;}\n",
       ".cython.score-60 {background-color: #FFFF24;}\n",
       ".cython.score-61 {background-color: #FFFF23;}\n",
       ".cython.score-62 {background-color: #FFFF23;}\n",
       ".cython.score-63 {background-color: #FFFF22;}\n",
       ".cython.score-64 {background-color: #FFFF22;}\n",
       ".cython.score-65 {background-color: #FFFF22;}\n",
       ".cython.score-66 {background-color: #FFFF21;}\n",
       ".cython.score-67 {background-color: #FFFF21;}\n",
       ".cython.score-68 {background-color: #FFFF20;}\n",
       ".cython.score-69 {background-color: #FFFF20;}\n",
       ".cython.score-70 {background-color: #FFFF1f;}\n",
       ".cython.score-71 {background-color: #FFFF1f;}\n",
       ".cython.score-72 {background-color: #FFFF1f;}\n",
       ".cython.score-73 {background-color: #FFFF1e;}\n",
       ".cython.score-74 {background-color: #FFFF1e;}\n",
       ".cython.score-75 {background-color: #FFFF1e;}\n",
       ".cython.score-76 {background-color: #FFFF1d;}\n",
       ".cython.score-77 {background-color: #FFFF1d;}\n",
       ".cython.score-78 {background-color: #FFFF1c;}\n",
       ".cython.score-79 {background-color: #FFFF1c;}\n",
       ".cython.score-80 {background-color: #FFFF1c;}\n",
       ".cython.score-81 {background-color: #FFFF1c;}\n",
       ".cython.score-82 {background-color: #FFFF1b;}\n",
       ".cython.score-83 {background-color: #FFFF1b;}\n",
       ".cython.score-84 {background-color: #FFFF1b;}\n",
       ".cython.score-85 {background-color: #FFFF1a;}\n",
       ".cython.score-86 {background-color: #FFFF1a;}\n",
       ".cython.score-87 {background-color: #FFFF1a;}\n",
       ".cython.score-88 {background-color: #FFFF1a;}\n",
       ".cython.score-89 {background-color: #FFFF19;}\n",
       ".cython.score-90 {background-color: #FFFF19;}\n",
       ".cython.score-91 {background-color: #FFFF19;}\n",
       ".cython.score-92 {background-color: #FFFF19;}\n",
       ".cython.score-93 {background-color: #FFFF18;}\n",
       ".cython.score-94 {background-color: #FFFF18;}\n",
       ".cython.score-95 {background-color: #FFFF18;}\n",
       ".cython.score-96 {background-color: #FFFF18;}\n",
       ".cython.score-97 {background-color: #FFFF17;}\n",
       ".cython.score-98 {background-color: #FFFF17;}\n",
       ".cython.score-99 {background-color: #FFFF17;}\n",
       ".cython.score-100 {background-color: #FFFF17;}\n",
       ".cython.score-101 {background-color: #FFFF16;}\n",
       ".cython.score-102 {background-color: #FFFF16;}\n",
       ".cython.score-103 {background-color: #FFFF16;}\n",
       ".cython.score-104 {background-color: #FFFF16;}\n",
       ".cython.score-105 {background-color: #FFFF16;}\n",
       ".cython.score-106 {background-color: #FFFF15;}\n",
       ".cython.score-107 {background-color: #FFFF15;}\n",
       ".cython.score-108 {background-color: #FFFF15;}\n",
       ".cython.score-109 {background-color: #FFFF15;}\n",
       ".cython.score-110 {background-color: #FFFF15;}\n",
       ".cython.score-111 {background-color: #FFFF15;}\n",
       ".cython.score-112 {background-color: #FFFF14;}\n",
       ".cython.score-113 {background-color: #FFFF14;}\n",
       ".cython.score-114 {background-color: #FFFF14;}\n",
       ".cython.score-115 {background-color: #FFFF14;}\n",
       ".cython.score-116 {background-color: #FFFF14;}\n",
       ".cython.score-117 {background-color: #FFFF14;}\n",
       ".cython.score-118 {background-color: #FFFF13;}\n",
       ".cython.score-119 {background-color: #FFFF13;}\n",
       ".cython.score-120 {background-color: #FFFF13;}\n",
       ".cython.score-121 {background-color: #FFFF13;}\n",
       ".cython.score-122 {background-color: #FFFF13;}\n",
       ".cython.score-123 {background-color: #FFFF13;}\n",
       ".cython.score-124 {background-color: #FFFF13;}\n",
       ".cython.score-125 {background-color: #FFFF12;}\n",
       ".cython.score-126 {background-color: #FFFF12;}\n",
       ".cython.score-127 {background-color: #FFFF12;}\n",
       ".cython.score-128 {background-color: #FFFF12;}\n",
       ".cython.score-129 {background-color: #FFFF12;}\n",
       ".cython.score-130 {background-color: #FFFF12;}\n",
       ".cython.score-131 {background-color: #FFFF12;}\n",
       ".cython.score-132 {background-color: #FFFF11;}\n",
       ".cython.score-133 {background-color: #FFFF11;}\n",
       ".cython.score-134 {background-color: #FFFF11;}\n",
       ".cython.score-135 {background-color: #FFFF11;}\n",
       ".cython.score-136 {background-color: #FFFF11;}\n",
       ".cython.score-137 {background-color: #FFFF11;}\n",
       ".cython.score-138 {background-color: #FFFF11;}\n",
       ".cython.score-139 {background-color: #FFFF11;}\n",
       ".cython.score-140 {background-color: #FFFF11;}\n",
       ".cython.score-141 {background-color: #FFFF10;}\n",
       ".cython.score-142 {background-color: #FFFF10;}\n",
       ".cython.score-143 {background-color: #FFFF10;}\n",
       ".cython.score-144 {background-color: #FFFF10;}\n",
       ".cython.score-145 {background-color: #FFFF10;}\n",
       ".cython.score-146 {background-color: #FFFF10;}\n",
       ".cython.score-147 {background-color: #FFFF10;}\n",
       ".cython.score-148 {background-color: #FFFF10;}\n",
       ".cython.score-149 {background-color: #FFFF10;}\n",
       ".cython.score-150 {background-color: #FFFF0f;}\n",
       ".cython.score-151 {background-color: #FFFF0f;}\n",
       ".cython.score-152 {background-color: #FFFF0f;}\n",
       ".cython.score-153 {background-color: #FFFF0f;}\n",
       ".cython.score-154 {background-color: #FFFF0f;}\n",
       ".cython.score-155 {background-color: #FFFF0f;}\n",
       ".cython.score-156 {background-color: #FFFF0f;}\n",
       ".cython.score-157 {background-color: #FFFF0f;}\n",
       ".cython.score-158 {background-color: #FFFF0f;}\n",
       ".cython.score-159 {background-color: #FFFF0f;}\n",
       ".cython.score-160 {background-color: #FFFF0f;}\n",
       ".cython.score-161 {background-color: #FFFF0e;}\n",
       ".cython.score-162 {background-color: #FFFF0e;}\n",
       ".cython.score-163 {background-color: #FFFF0e;}\n",
       ".cython.score-164 {background-color: #FFFF0e;}\n",
       ".cython.score-165 {background-color: #FFFF0e;}\n",
       ".cython.score-166 {background-color: #FFFF0e;}\n",
       ".cython.score-167 {background-color: #FFFF0e;}\n",
       ".cython.score-168 {background-color: #FFFF0e;}\n",
       ".cython.score-169 {background-color: #FFFF0e;}\n",
       ".cython.score-170 {background-color: #FFFF0e;}\n",
       ".cython.score-171 {background-color: #FFFF0e;}\n",
       ".cython.score-172 {background-color: #FFFF0e;}\n",
       ".cython.score-173 {background-color: #FFFF0d;}\n",
       ".cython.score-174 {background-color: #FFFF0d;}\n",
       ".cython.score-175 {background-color: #FFFF0d;}\n",
       ".cython.score-176 {background-color: #FFFF0d;}\n",
       ".cython.score-177 {background-color: #FFFF0d;}\n",
       ".cython.score-178 {background-color: #FFFF0d;}\n",
       ".cython.score-179 {background-color: #FFFF0d;}\n",
       ".cython.score-180 {background-color: #FFFF0d;}\n",
       ".cython.score-181 {background-color: #FFFF0d;}\n",
       ".cython.score-182 {background-color: #FFFF0d;}\n",
       ".cython.score-183 {background-color: #FFFF0d;}\n",
       ".cython.score-184 {background-color: #FFFF0d;}\n",
       ".cython.score-185 {background-color: #FFFF0d;}\n",
       ".cython.score-186 {background-color: #FFFF0d;}\n",
       ".cython.score-187 {background-color: #FFFF0c;}\n",
       ".cython.score-188 {background-color: #FFFF0c;}\n",
       ".cython.score-189 {background-color: #FFFF0c;}\n",
       ".cython.score-190 {background-color: #FFFF0c;}\n",
       ".cython.score-191 {background-color: #FFFF0c;}\n",
       ".cython.score-192 {background-color: #FFFF0c;}\n",
       ".cython.score-193 {background-color: #FFFF0c;}\n",
       ".cython.score-194 {background-color: #FFFF0c;}\n",
       ".cython.score-195 {background-color: #FFFF0c;}\n",
       ".cython.score-196 {background-color: #FFFF0c;}\n",
       ".cython.score-197 {background-color: #FFFF0c;}\n",
       ".cython.score-198 {background-color: #FFFF0c;}\n",
       ".cython.score-199 {background-color: #FFFF0c;}\n",
       ".cython.score-200 {background-color: #FFFF0c;}\n",
       ".cython.score-201 {background-color: #FFFF0c;}\n",
       ".cython.score-202 {background-color: #FFFF0c;}\n",
       ".cython.score-203 {background-color: #FFFF0b;}\n",
       ".cython.score-204 {background-color: #FFFF0b;}\n",
       ".cython.score-205 {background-color: #FFFF0b;}\n",
       ".cython.score-206 {background-color: #FFFF0b;}\n",
       ".cython.score-207 {background-color: #FFFF0b;}\n",
       ".cython.score-208 {background-color: #FFFF0b;}\n",
       ".cython.score-209 {background-color: #FFFF0b;}\n",
       ".cython.score-210 {background-color: #FFFF0b;}\n",
       ".cython.score-211 {background-color: #FFFF0b;}\n",
       ".cython.score-212 {background-color: #FFFF0b;}\n",
       ".cython.score-213 {background-color: #FFFF0b;}\n",
       ".cython.score-214 {background-color: #FFFF0b;}\n",
       ".cython.score-215 {background-color: #FFFF0b;}\n",
       ".cython.score-216 {background-color: #FFFF0b;}\n",
       ".cython.score-217 {background-color: #FFFF0b;}\n",
       ".cython.score-218 {background-color: #FFFF0b;}\n",
       ".cython.score-219 {background-color: #FFFF0b;}\n",
       ".cython.score-220 {background-color: #FFFF0b;}\n",
       ".cython.score-221 {background-color: #FFFF0b;}\n",
       ".cython.score-222 {background-color: #FFFF0a;}\n",
       ".cython.score-223 {background-color: #FFFF0a;}\n",
       ".cython.score-224 {background-color: #FFFF0a;}\n",
       ".cython.score-225 {background-color: #FFFF0a;}\n",
       ".cython.score-226 {background-color: #FFFF0a;}\n",
       ".cython.score-227 {background-color: #FFFF0a;}\n",
       ".cython.score-228 {background-color: #FFFF0a;}\n",
       ".cython.score-229 {background-color: #FFFF0a;}\n",
       ".cython.score-230 {background-color: #FFFF0a;}\n",
       ".cython.score-231 {background-color: #FFFF0a;}\n",
       ".cython.score-232 {background-color: #FFFF0a;}\n",
       ".cython.score-233 {background-color: #FFFF0a;}\n",
       ".cython.score-234 {background-color: #FFFF0a;}\n",
       ".cython.score-235 {background-color: #FFFF0a;}\n",
       ".cython.score-236 {background-color: #FFFF0a;}\n",
       ".cython.score-237 {background-color: #FFFF0a;}\n",
       ".cython.score-238 {background-color: #FFFF0a;}\n",
       ".cython.score-239 {background-color: #FFFF0a;}\n",
       ".cython.score-240 {background-color: #FFFF0a;}\n",
       ".cython.score-241 {background-color: #FFFF0a;}\n",
       ".cython.score-242 {background-color: #FFFF0a;}\n",
       ".cython.score-243 {background-color: #FFFF0a;}\n",
       ".cython.score-244 {background-color: #FFFF0a;}\n",
       ".cython.score-245 {background-color: #FFFF0a;}\n",
       ".cython.score-246 {background-color: #FFFF09;}\n",
       ".cython.score-247 {background-color: #FFFF09;}\n",
       ".cython.score-248 {background-color: #FFFF09;}\n",
       ".cython.score-249 {background-color: #FFFF09;}\n",
       ".cython.score-250 {background-color: #FFFF09;}\n",
       ".cython.score-251 {background-color: #FFFF09;}\n",
       ".cython.score-252 {background-color: #FFFF09;}\n",
       ".cython.score-253 {background-color: #FFFF09;}\n",
       ".cython.score-254 {background-color: #FFFF09;}\n",
       ".cython .hll { background-color: #ffffcc }\n",
       ".cython  { background: #f8f8f8; }\n",
       ".cython .c { color: #408080; font-style: italic } /* Comment */\n",
       ".cython .err { border: 1px solid #FF0000 } /* Error */\n",
       ".cython .k { color: #008000; font-weight: bold } /* Keyword */\n",
       ".cython .o { color: #666666 } /* Operator */\n",
       ".cython .ch { color: #408080; font-style: italic } /* Comment.Hashbang */\n",
       ".cython .cm { color: #408080; font-style: italic } /* Comment.Multiline */\n",
       ".cython .cp { color: #BC7A00 } /* Comment.Preproc */\n",
       ".cython .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */\n",
       ".cython .c1 { color: #408080; font-style: italic } /* Comment.Single */\n",
       ".cython .cs { color: #408080; font-style: italic } /* Comment.Special */\n",
       ".cython .gd { color: #A00000 } /* Generic.Deleted */\n",
       ".cython .ge { font-style: italic } /* Generic.Emph */\n",
       ".cython .gr { color: #FF0000 } /* Generic.Error */\n",
       ".cython .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
       ".cython .gi { color: #00A000 } /* Generic.Inserted */\n",
       ".cython .go { color: #888888 } /* Generic.Output */\n",
       ".cython .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
       ".cython .gs { font-weight: bold } /* Generic.Strong */\n",
       ".cython .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
       ".cython .gt { color: #0044DD } /* Generic.Traceback */\n",
       ".cython .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
       ".cython .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
       ".cython .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
       ".cython .kp { color: #008000 } /* Keyword.Pseudo */\n",
       ".cython .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
       ".cython .kt { color: #B00040 } /* Keyword.Type */\n",
       ".cython .m { color: #666666 } /* Literal.Number */\n",
       ".cython .s { color: #BA2121 } /* Literal.String */\n",
       ".cython .na { color: #7D9029 } /* Name.Attribute */\n",
       ".cython .nb { color: #008000 } /* Name.Builtin */\n",
       ".cython .nc { color: #0000FF; font-weight: bold } /* Name.Class */\n",
       ".cython .no { color: #880000 } /* Name.Constant */\n",
       ".cython .nd { color: #AA22FF } /* Name.Decorator */\n",
       ".cython .ni { color: #999999; font-weight: bold } /* Name.Entity */\n",
       ".cython .ne { color: #D2413A; font-weight: bold } /* Name.Exception */\n",
       ".cython .nf { color: #0000FF } /* Name.Function */\n",
       ".cython .nl { color: #A0A000 } /* Name.Label */\n",
       ".cython .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */\n",
       ".cython .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
       ".cython .nv { color: #19177C } /* Name.Variable */\n",
       ".cython .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */\n",
       ".cython .w { color: #bbbbbb } /* Text.Whitespace */\n",
       ".cython .mb { color: #666666 } /* Literal.Number.Bin */\n",
       ".cython .mf { color: #666666 } /* Literal.Number.Float */\n",
       ".cython .mh { color: #666666 } /* Literal.Number.Hex */\n",
       ".cython .mi { color: #666666 } /* Literal.Number.Integer */\n",
       ".cython .mo { color: #666666 } /* Literal.Number.Oct */\n",
       ".cython .sa { color: #BA2121 } /* Literal.String.Affix */\n",
       ".cython .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
       ".cython .sc { color: #BA2121 } /* Literal.String.Char */\n",
       ".cython .dl { color: #BA2121 } /* Literal.String.Delimiter */\n",
       ".cython .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
       ".cython .s2 { color: #BA2121 } /* Literal.String.Double */\n",
       ".cython .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */\n",
       ".cython .sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
       ".cython .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */\n",
       ".cython .sx { color: #008000 } /* Literal.String.Other */\n",
       ".cython .sr { color: #BB6688 } /* Literal.String.Regex */\n",
       ".cython .s1 { color: #BA2121 } /* Literal.String.Single */\n",
       ".cython .ss { color: #19177C } /* Literal.String.Symbol */\n",
       ".cython .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
       ".cython .fm { color: #0000FF } /* Name.Function.Magic */\n",
       ".cython .vc { color: #19177C } /* Name.Variable.Class */\n",
       ".cython .vg { color: #19177C } /* Name.Variable.Global */\n",
       ".cython .vi { color: #19177C } /* Name.Variable.Instance */\n",
       ".cython .vm { color: #19177C } /* Name.Variable.Magic */\n",
       ".cython .il { color: #666666 } /* Literal.Number.Integer.Long */\n",
       "    </style>\n",
       "</head>\n",
       "<body class=\"cython\">\n",
       "<p><span style=\"border-bottom: solid 1px grey;\">Generated by Cython 0.29.14</span></p>\n",
       "<p>\n",
       "    <span style=\"background-color: #FFFF00\">Yellow lines</span> hint at Python interaction.<br />\n",
       "    Click on a line that starts with a \"<code>+</code>\" to see the C code that Cython generated for it.\n",
       "</p>\n",
       "<div class=\"cython\"><pre class=\"cython line score-8\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">01</span>: <span class=\"c\"># distutils: language = c++</span></pre>\n",
       "<pre class='cython code score-8 '>  __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyDict_NewPresized</span>(0);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
       "  if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_test, __pyx_t_1) &lt; 0) <span class='error_goto'>__PYX_ERR(0, 1, __pyx_L1_error)</span>\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">02</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">03</span>: <span class=\"k\">cimport</span> <span class=\"nn\">cython</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">04</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">05</span>: <span class=\"c\"># C++</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">06</span>: <span class=\"k\">from</span> <span class=\"nn\">cython.operator</span> <span class=\"k\">cimport</span> <span class=\"n\">dereference</span> <span class=\"k\">as</span> <span class=\"n\">deref</span><span class=\"p\">,</span> <span class=\"n\">preincrement</span> <span class=\"k\">as</span> <span class=\"n\">inc</span><span class=\"p\">,</span> \\</pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">07</span>:     <span class=\"n\">predecrement</span> <span class=\"k\">as</span> <span class=\"n\">dec</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">08</span>: <span class=\"k\">from</span> <span class=\"nn\">libcpp.utility</span> <span class=\"k\">cimport</span> <span class=\"n\">pair</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">09</span>: <span class=\"k\">from</span> <span class=\"nn\">libcpp.map</span> <span class=\"k\">cimport</span> <span class=\"nb\">map</span> <span class=\"k\">as</span> <span class=\"n\">cpp_map</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">10</span>: </pre>\n",
       "<pre class=\"cython line score-8\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">11</span>: <span class=\"k\">import</span> <span class=\"nn\">numpy</span> <span class=\"k\">as</span> <span class=\"nn\">np</span></pre>\n",
       "<pre class='cython code score-8 '>  __pyx_t_1 = <span class='pyx_c_api'>__Pyx_Import</span>(__pyx_n_s_numpy, 0, 0);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 11, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
       "  if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_np, __pyx_t_1) &lt; 0) <span class='error_goto'>__PYX_ERR(0, 11, __pyx_L1_error)</span>\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">12</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">13</span>: <span class=\"c\"># Import the C-level symbols of numpy</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">14</span>: <span class=\"k\">cimport</span> <span class=\"nn\">numpy</span> <span class=\"k\">as</span> <span class=\"nn\">np</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">15</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">16</span>: <span class=\"c\"># Numpy must be initialized. When using numpy from C or Cython you must</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">17</span>: <span class=\"c\"># _always_ do that, or you will have segfaults</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">18</span>: <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">import_array</span><span class=\"p\">()</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_2 = __pyx_f_5numpy_import_array();<span class='error_goto'> if (unlikely(__pyx_t_2 == ((int)-1))) __PYX_ERR(0, 18, __pyx_L1_error)</span>\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">19</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">20</span>: <span class=\"c\">#DTYPE = np.float64</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">21</span>: <span class=\"k\">ctypedef</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">float64_t</span> <span class=\"n\">DTYPE_t</span></pre>\n",
       "<pre class='cython code score-0 '>typedef __pyx_t_5numpy_float64_t __pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_DTYPE_t;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">22</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">23</span>: <span class=\"c\">#ITYPE = np.intp</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">24</span>: <span class=\"k\">ctypedef</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">intp_t</span> <span class=\"n\">ITYPE_t</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">25</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">26</span>: <span class=\"k\">cdef</span> <span class=\"k\">class</span> <span class=\"nf\">IntFloatDict</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">27</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">28</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cpp_map</span>[<span class=\"nf\">ITYPE_t</span><span class=\"p\">,</span> <span class=\"kt\">DTYPE_t</span>] <span class=\"nf\">my_map</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">29</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">30</span>:     <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">31</span>:     <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-47\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">32</span>:     <span class=\"k\">def</span> <span class=\"nf\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">[</span><span class=\"n\">ITYPE_t</span><span class=\"p\">,</span> <span class=\"n\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">keys</span><span class=\"p\">,</span></pre>\n",
       "<pre class='cython code score-47 '>/* Python wrapper */\n",
       "static int __pyx_pw_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict_1__init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n",
       "static int __pyx_pw_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict_1__init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
       "  PyArrayObject *__pyx_v_keys = 0;\n",
       "  PyArrayObject *__pyx_v_values = 0;\n",
       "  int __pyx_r;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__init__ (wrapper)\", 0);\n",
       "  {\n",
       "    static PyObject **__pyx_pyargnames[] = {&amp;__pyx_n_s_keys,&amp;__pyx_n_s_values,0};\n",
       "    PyObject* values[2] = {0,0};\n",
       "    if (unlikely(__pyx_kwds)) {\n",
       "      Py_ssize_t kw_args;\n",
       "      const Py_ssize_t pos_args = <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args);\n",
       "      switch (pos_args) {\n",
       "        case  2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  0: break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
       "      }\n",
       "      kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n",
       "      switch (pos_args) {\n",
       "        case  0:\n",
       "        if (likely((values[0] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_keys)) != 0)) kw_args--;\n",
       "        else goto __pyx_L5_argtuple_error;\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  1:\n",
       "        if (likely((values[1] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_values)) != 0)) kw_args--;\n",
       "        else {\n",
       "          <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"__init__\", 1, 2, 2, 1); <span class='error_goto'>__PYX_ERR(0, 32, __pyx_L3_error)</span>\n",
       "        }\n",
       "      }\n",
       "      if (unlikely(kw_args &gt; 0)) {\n",
       "        if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, \"__init__\") &lt; 0)) <span class='error_goto'>__PYX_ERR(0, 32, __pyx_L3_error)</span>\n",
       "      }\n",
       "    } else if (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args) != 2) {\n",
       "      goto __pyx_L5_argtuple_error;\n",
       "    } else {\n",
       "      values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "      values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "    }\n",
       "    __pyx_v_keys = ((PyArrayObject *)values[0]);\n",
       "    __pyx_v_values = ((PyArrayObject *)values[1]);\n",
       "  }\n",
       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L5_argtuple_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"__init__\", 1, 2, 2, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); <span class='error_goto'>__PYX_ERR(0, 32, __pyx_L3_error)</span>\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_ec2c52f31301d188c98415ab1d82f601.IntFloatDict.__init__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return -1;\n",
       "  __pyx_L4_argument_unpacking_done:;\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_keys), __pyx_ptype_5numpy_ndarray, 1, \"keys\", 0))) <span class='error_goto'>__PYX_ERR(0, 32, __pyx_L1_error)</span>\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_values), __pyx_ptype_5numpy_ndarray, 1, \"values\", 0))) <span class='error_goto'>__PYX_ERR(0, 33, __pyx_L1_error)</span>\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict___init__(((struct __pyx_obj_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_IntFloatDict *)__pyx_v_self), __pyx_v_keys, __pyx_v_values);\n",
       "\n",
       "  /* function exit code */\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  __pyx_r = -1;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static int __pyx_pf_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict___init__(struct __pyx_obj_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_IntFloatDict *__pyx_v_self, PyArrayObject *__pyx_v_keys, PyArrayObject *__pyx_v_values) {\n",
       "  int __pyx_v_i;\n",
       "  int __pyx_v_size;\n",
       "  __Pyx_LocalBuf_ND __pyx_pybuffernd_keys;\n",
       "  __Pyx_Buffer __pyx_pybuffer_keys;\n",
       "  __Pyx_LocalBuf_ND __pyx_pybuffernd_values;\n",
       "  __Pyx_Buffer __pyx_pybuffer_values;\n",
       "  int __pyx_r;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__init__\", 0);\n",
       "  __pyx_pybuffer_keys.pybuffer.buf = NULL;\n",
       "  __pyx_pybuffer_keys.refcount = 0;\n",
       "  __pyx_pybuffernd_keys.data = NULL;\n",
       "  __pyx_pybuffernd_keys.rcbuffer = &amp;__pyx_pybuffer_keys;\n",
       "  __pyx_pybuffer_values.pybuffer.buf = NULL;\n",
       "  __pyx_pybuffer_values.refcount = 0;\n",
       "  __pyx_pybuffernd_values.data = NULL;\n",
       "  __pyx_pybuffernd_values.rcbuffer = &amp;__pyx_pybuffer_values;\n",
       "  {\n",
       "    __Pyx_BufFmt_StackElem __pyx_stack[1];\n",
       "    if (unlikely(<span class='pyx_c_api'>__Pyx_GetBufferAndValidate</span>(&amp;__pyx_pybuffernd_keys.rcbuffer-&gt;pybuffer, (PyObject*)__pyx_v_keys, &amp;__Pyx_TypeInfo_nn___pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_ITYPE_t, PyBUF_FORMAT| PyBUF_STRIDES, 1, 0, __pyx_stack) == -1)) <span class='error_goto'>__PYX_ERR(0, 32, __pyx_L1_error)</span>\n",
       "  }\n",
       "  __pyx_pybuffernd_keys.diminfo[0].strides = __pyx_pybuffernd_keys.rcbuffer-&gt;pybuffer.strides[0]; __pyx_pybuffernd_keys.diminfo[0].shape = __pyx_pybuffernd_keys.rcbuffer-&gt;pybuffer.shape[0];\n",
       "  {\n",
       "    __Pyx_BufFmt_StackElem __pyx_stack[1];\n",
       "    if (unlikely(<span class='pyx_c_api'>__Pyx_GetBufferAndValidate</span>(&amp;__pyx_pybuffernd_values.rcbuffer-&gt;pybuffer, (PyObject*)__pyx_v_values, &amp;__Pyx_TypeInfo_nn___pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES, 1, 0, __pyx_stack) == -1)) <span class='error_goto'>__PYX_ERR(0, 32, __pyx_L1_error)</span>\n",
       "  }\n",
       "  __pyx_pybuffernd_values.diminfo[0].strides = __pyx_pybuffernd_values.rcbuffer-&gt;pybuffer.strides[0]; __pyx_pybuffernd_values.diminfo[0].shape = __pyx_pybuffernd_values.rcbuffer-&gt;pybuffer.shape[0];\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_r = 0;\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n",
       "  { PyObject *__pyx_type, *__pyx_value, *__pyx_tb;\n",
       "    __Pyx_PyThreadState_declare\n",
       "    __Pyx_PyThreadState_assign\n",
       "    <span class='pyx_c_api'>__Pyx_ErrFetch</span>(&amp;__pyx_type, &amp;__pyx_value, &amp;__pyx_tb);\n",
       "    <span class='pyx_c_api'>__Pyx_SafeReleaseBuffer</span>(&amp;__pyx_pybuffernd_keys.rcbuffer-&gt;pybuffer);\n",
       "    <span class='pyx_c_api'>__Pyx_SafeReleaseBuffer</span>(&amp;__pyx_pybuffernd_values.rcbuffer-&gt;pybuffer);\n",
       "  <span class='pyx_c_api'>__Pyx_ErrRestore</span>(__pyx_type, __pyx_value, __pyx_tb);}\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_ec2c52f31301d188c98415ab1d82f601.IntFloatDict.__init__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  __pyx_r = -1;\n",
       "  goto __pyx_L2;\n",
       "  __pyx_L0:;\n",
       "  <span class='pyx_c_api'>__Pyx_SafeReleaseBuffer</span>(&amp;__pyx_pybuffernd_keys.rcbuffer-&gt;pybuffer);\n",
       "  <span class='pyx_c_api'>__Pyx_SafeReleaseBuffer</span>(&amp;__pyx_pybuffernd_values.rcbuffer-&gt;pybuffer);\n",
       "  __pyx_L2:;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">33</span>:                        <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">[</span><span class=\"n\">DTYPE_t</span><span class=\"p\">,</span> <span class=\"n\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">values</span><span class=\"p\">):</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">34</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">i</span></pre>\n",
       "<pre class=\"cython line score-10\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">35</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">size</span> <span class=\"o\">=</span> <span class=\"n\">values</span><span class=\"o\">.</span><span class=\"n\">size</span></pre>\n",
       "<pre class='cython code score-10 '>  __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(((PyObject *)__pyx_v_values), __pyx_n_s_size);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 35, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
       "  __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyInt_As_int</span>(__pyx_t_1); if (unlikely((__pyx_t_2 == (int)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 35, __pyx_L1_error)</span>\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
       "  __pyx_v_size = __pyx_t_2;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">36</span>:         <span class=\"c\"># Should check that sizes for keys and values are equal, and</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">37</span>:         <span class=\"c\"># after should boundcheck(False)</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">38</span>:         <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">size</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_2 = __pyx_v_size;\n",
       "  __pyx_t_3 = __pyx_t_2;\n",
       "  for (__pyx_t_4 = 0; __pyx_t_4 &lt; __pyx_t_3; __pyx_t_4+=1) {\n",
       "    __pyx_v_i = __pyx_t_4;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">39</span>:             <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"p\">[</span><span class=\"n\">keys</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]]</span> <span class=\"o\">=</span> <span class=\"n\">values</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_5 = __pyx_v_i;\n",
       "    __pyx_t_6 = __pyx_v_i;\n",
       "    (__pyx_v_self-&gt;my_map[(*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_ITYPE_t *, __pyx_pybuffernd_keys.rcbuffer-&gt;pybuffer.buf, __pyx_t_6, __pyx_pybuffernd_keys.diminfo[0].strides))]) = (*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_DTYPE_t *, __pyx_pybuffernd_values.rcbuffer-&gt;pybuffer.buf, __pyx_t_5, __pyx_pybuffernd_values.diminfo[0].strides));\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">40</span>: </pre>\n",
       "<pre class=\"cython line score-13\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">41</span>:     <span class=\"k\">def</span> <span class=\"nf\">__getitem__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"nb\">int</span> <span class=\"n\">key</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-13 '>/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict_3__getitem__(PyObject *__pyx_v_self, PyObject *__pyx_arg_key); /*proto*/\n",
       "static PyObject *__pyx_pw_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict_3__getitem__(PyObject *__pyx_v_self, PyObject *__pyx_arg_key) {\n",
       "  int __pyx_v_key;\n",
       "  PyObject *__pyx_r = 0;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__getitem__ (wrapper)\", 0);\n",
       "  assert(__pyx_arg_key); {\n",
       "    __pyx_v_key = <span class='pyx_c_api'>__Pyx_PyInt_As_int</span>(__pyx_arg_key); if (unlikely((__pyx_v_key == (int)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 41, __pyx_L3_error)</span>\n",
       "  }\n",
       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_ec2c52f31301d188c98415ab1d82f601.IntFloatDict.__getitem__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return NULL;\n",
       "  __pyx_L4_argument_unpacking_done:;\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict_2__getitem__(((struct __pyx_obj_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_IntFloatDict *)__pyx_v_self), ((int)__pyx_v_key));\n",
       "\n",
       "  /* function exit code */\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static PyObject *__pyx_pf_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict_2__getitem__(struct __pyx_obj_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_IntFloatDict *__pyx_v_self, int __pyx_v_key) {\n",
       "  std::map&lt;__pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_ITYPE_t,__pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_DTYPE_t&gt; ::iterator __pyx_v_it;\n",
       "  PyObject *__pyx_r = NULL;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__getitem__\", 0);\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_L1_error:;\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_2);\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_3);\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_ec2c52f31301d188c98415ab1d82f601.IntFloatDict.__getitem__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">42</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">cpp_map</span>[<span class=\"nf\">ITYPE_t</span><span class=\"p\">,</span> <span class=\"kt\">DTYPE_t</span>].<span class=\"kt\">iterator</span> <span class=\"nf\">it</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"o\">.</span><span class=\"n\">find</span><span class=\"p\">(</span><span class=\"n\">key</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_it = __pyx_v_self-&gt;my_map.find(__pyx_v_key);\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">43</span>:         <span class=\"k\">if</span> <span class=\"n\">it</span> <span class=\"o\">==</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"o\">.</span><span class=\"n\">end</span><span class=\"p\">():</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_1 = ((__pyx_v_it == __pyx_v_self-&gt;my_map.end()) != 0);\n",
       "  if (unlikely(__pyx_t_1)) {\n",
       "/* … */\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">44</span>:             <span class=\"c\"># The key is not in the dict</span></pre>\n",
       "<pre class=\"cython line score-14\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">45</span>:             <span class=\"k\">raise</span> <span class=\"ne\">KeyError</span><span class=\"p\">(</span><span class=\"s\">&#39;</span><span class=\"si\">%i</span><span class=\"s\">&#39;</span> <span class=\"o\">%</span> <span class=\"n\">key</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-14 '>    __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyInt_From_int</span>(__pyx_v_key);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 45, __pyx_L1_error)</span>\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
       "    __pyx_t_3 = <span class='py_c_api'>PyUnicode_Format</span>(__pyx_kp_u_i, __pyx_t_2);<span class='error_goto'> if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 45, __pyx_L1_error)</span>\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_3);\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
       "    __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyObject_CallOneArg</span>(__pyx_builtin_KeyError, __pyx_t_3);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 45, __pyx_L1_error)</span>\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_3); __pyx_t_3 = 0;\n",
       "    <span class='pyx_c_api'>__Pyx_Raise</span>(__pyx_t_2, 0, 0, 0);\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
       "    <span class='error_goto'>__PYX_ERR(0, 45, __pyx_L1_error)</span>\n",
       "</pre><pre class=\"cython line score-6\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">46</span>:         <span class=\"k\">return</span> <span class=\"n\">deref</span><span class=\"p\">(</span><span class=\"n\">it</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">second</span></pre>\n",
       "<pre class='cython code score-6 '>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
       "  __pyx_t_2 = <span class='py_c_api'>PyFloat_FromDouble</span>((*__pyx_v_it).second);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 46, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
       "  __pyx_r = __pyx_t_2;\n",
       "  __pyx_t_2 = 0;\n",
       "  goto __pyx_L0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">47</span>: </pre>\n",
       "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">48</span>:     <span class=\"k\">def</span> <span class=\"nf\">dot</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">IntFloatDict</span> <span class=\"n\">other</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-5 '>/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict_5dot(PyObject *__pyx_v_self, PyObject *__pyx_v_other); /*proto*/\n",
       "static PyObject *__pyx_pw_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict_5dot(PyObject *__pyx_v_self, PyObject *__pyx_v_other) {\n",
       "  PyObject *__pyx_r = 0;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"dot (wrapper)\", 0);\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_other), __pyx_ptype_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_IntFloatDict, 1, \"other\", 0))) <span class='error_goto'>__PYX_ERR(0, 48, __pyx_L1_error)</span>\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict_4dot(((struct __pyx_obj_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_IntFloatDict *)__pyx_v_self), ((struct __pyx_obj_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_IntFloatDict *)__pyx_v_other));\n",
       "\n",
       "  /* function exit code */\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static PyObject *__pyx_pf_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict_4dot(struct __pyx_obj_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_IntFloatDict *__pyx_v_self, struct __pyx_obj_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_IntFloatDict *__pyx_v_other) {\n",
       "  __pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_DTYPE_t __pyx_v_total;\n",
       "  __pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_ITYPE_t __pyx_v_key;\n",
       "  std::map&lt;__pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_ITYPE_t,__pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_DTYPE_t&gt; ::iterator __pyx_v_it;\n",
       "  std::map&lt;__pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_ITYPE_t,__pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_DTYPE_t&gt; ::iterator __pyx_v_end;\n",
       "  PyObject *__pyx_r = NULL;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"dot\", 0);\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_L1_error:;\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_3);\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_ec2c52f31301d188c98415ab1d82f601.IntFloatDict.dot\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">49</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">DTYPE_t</span> <span class=\"nf\">total</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">50</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">ITYPE_t</span> <span class=\"nf\">key</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">51</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">52</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">cpp_map</span>[<span class=\"nf\">ITYPE_t</span><span class=\"p\">,</span> <span class=\"kt\">DTYPE_t</span>].<span class=\"kt\">iterator</span> <span class=\"nf\">it</span> <span class=\"o\">=</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"o\">.</span><span class=\"n\">begin</span><span class=\"p\">()</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_it = __pyx_v_other-&gt;my_map.begin();\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">53</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">cpp_map</span>[<span class=\"nf\">ITYPE_t</span><span class=\"p\">,</span> <span class=\"kt\">DTYPE_t</span>].<span class=\"kt\">iterator</span> <span class=\"nf\">end</span> <span class=\"o\">=</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"o\">.</span><span class=\"n\">end</span><span class=\"p\">()</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_end = __pyx_v_other-&gt;my_map.end();\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">54</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">55</span>:         <span class=\"k\">while</span> <span class=\"n\">it</span> <span class=\"o\">!=</span> <span class=\"n\">end</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  while (1) {\n",
       "    __pyx_t_1 = ((__pyx_v_it != __pyx_v_end) != 0);\n",
       "    if (!__pyx_t_1) break;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">56</span>:             <span class=\"n\">key</span> <span class=\"o\">=</span> <span class=\"n\">deref</span><span class=\"p\">(</span><span class=\"n\">it</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">first</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_2 = (*__pyx_v_it).first;\n",
       "    __pyx_v_key = __pyx_t_2;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">57</span>:             <span class=\"n\">total</span> <span class=\"o\">+=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"p\">[</span><span class=\"n\">key</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"p\">[</span><span class=\"n\">key</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_total = (__pyx_v_total + ((__pyx_v_self-&gt;my_map[__pyx_v_key]) * (__pyx_v_other-&gt;my_map[__pyx_v_key])));\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">58</span>:             <span class=\"n\">inc</span><span class=\"p\">(</span><span class=\"n\">it</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>    (void)((++__pyx_v_it));\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">59</span>: </pre>\n",
       "<pre class=\"cython line score-6\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">60</span>:         <span class=\"k\">return</span> <span class=\"n\">total</span></pre>\n",
       "<pre class='cython code score-6 '>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
       "  __pyx_t_3 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_total);<span class='error_goto'> if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 60, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_3);\n",
       "  __pyx_r = __pyx_t_3;\n",
       "  __pyx_t_3 = 0;\n",
       "  goto __pyx_L0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">61</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">62</span>:     <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">63</span>:     <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-47\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">64</span>:     <span class=\"k\">def</span> <span class=\"nf\">dot_numpy</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">[</span><span class=\"n\">ITYPE_t</span><span class=\"p\">,</span> <span class=\"n\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">keys</span><span class=\"p\">,</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">[</span><span class=\"n\">DTYPE_t</span><span class=\"p\">,</span> <span class=\"n\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">values</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-47 '>/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict_7dot_numpy(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n",
       "static PyObject *__pyx_pw_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict_7dot_numpy(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
       "  PyArrayObject *__pyx_v_keys = 0;\n",
       "  PyArrayObject *__pyx_v_values = 0;\n",
       "  PyObject *__pyx_r = 0;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"dot_numpy (wrapper)\", 0);\n",
       "  {\n",
       "    static PyObject **__pyx_pyargnames[] = {&amp;__pyx_n_s_keys,&amp;__pyx_n_s_values,0};\n",
       "    PyObject* values[2] = {0,0};\n",
       "    if (unlikely(__pyx_kwds)) {\n",
       "      Py_ssize_t kw_args;\n",
       "      const Py_ssize_t pos_args = <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args);\n",
       "      switch (pos_args) {\n",
       "        case  2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  0: break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
       "      }\n",
       "      kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n",
       "      switch (pos_args) {\n",
       "        case  0:\n",
       "        if (likely((values[0] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_keys)) != 0)) kw_args--;\n",
       "        else goto __pyx_L5_argtuple_error;\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  1:\n",
       "        if (likely((values[1] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_values)) != 0)) kw_args--;\n",
       "        else {\n",
       "          <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"dot_numpy\", 1, 2, 2, 1); <span class='error_goto'>__PYX_ERR(0, 64, __pyx_L3_error)</span>\n",
       "        }\n",
       "      }\n",
       "      if (unlikely(kw_args &gt; 0)) {\n",
       "        if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, \"dot_numpy\") &lt; 0)) <span class='error_goto'>__PYX_ERR(0, 64, __pyx_L3_error)</span>\n",
       "      }\n",
       "    } else if (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args) != 2) {\n",
       "      goto __pyx_L5_argtuple_error;\n",
       "    } else {\n",
       "      values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "      values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "    }\n",
       "    __pyx_v_keys = ((PyArrayObject *)values[0]);\n",
       "    __pyx_v_values = ((PyArrayObject *)values[1]);\n",
       "  }\n",
       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L5_argtuple_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"dot_numpy\", 1, 2, 2, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); <span class='error_goto'>__PYX_ERR(0, 64, __pyx_L3_error)</span>\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_ec2c52f31301d188c98415ab1d82f601.IntFloatDict.dot_numpy\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return NULL;\n",
       "  __pyx_L4_argument_unpacking_done:;\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_keys), __pyx_ptype_5numpy_ndarray, 1, \"keys\", 0))) <span class='error_goto'>__PYX_ERR(0, 64, __pyx_L1_error)</span>\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_values), __pyx_ptype_5numpy_ndarray, 1, \"values\", 0))) <span class='error_goto'>__PYX_ERR(0, 64, __pyx_L1_error)</span>\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict_6dot_numpy(((struct __pyx_obj_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_IntFloatDict *)__pyx_v_self), __pyx_v_keys, __pyx_v_values);\n",
       "\n",
       "  /* function exit code */\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static PyObject *__pyx_pf_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_12IntFloatDict_6dot_numpy(struct __pyx_obj_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_IntFloatDict *__pyx_v_self, PyArrayObject *__pyx_v_keys, PyArrayObject *__pyx_v_values) {\n",
       "  __pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_DTYPE_t __pyx_v_total;\n",
       "  int __pyx_v_i;\n",
       "  int __pyx_v_size;\n",
       "  __Pyx_LocalBuf_ND __pyx_pybuffernd_keys;\n",
       "  __Pyx_Buffer __pyx_pybuffer_keys;\n",
       "  __Pyx_LocalBuf_ND __pyx_pybuffernd_values;\n",
       "  __Pyx_Buffer __pyx_pybuffer_values;\n",
       "  PyObject *__pyx_r = NULL;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"dot_numpy\", 0);\n",
       "  __pyx_pybuffer_keys.pybuffer.buf = NULL;\n",
       "  __pyx_pybuffer_keys.refcount = 0;\n",
       "  __pyx_pybuffernd_keys.data = NULL;\n",
       "  __pyx_pybuffernd_keys.rcbuffer = &amp;__pyx_pybuffer_keys;\n",
       "  __pyx_pybuffer_values.pybuffer.buf = NULL;\n",
       "  __pyx_pybuffer_values.refcount = 0;\n",
       "  __pyx_pybuffernd_values.data = NULL;\n",
       "  __pyx_pybuffernd_values.rcbuffer = &amp;__pyx_pybuffer_values;\n",
       "  {\n",
       "    __Pyx_BufFmt_StackElem __pyx_stack[1];\n",
       "    if (unlikely(<span class='pyx_c_api'>__Pyx_GetBufferAndValidate</span>(&amp;__pyx_pybuffernd_keys.rcbuffer-&gt;pybuffer, (PyObject*)__pyx_v_keys, &amp;__Pyx_TypeInfo_nn___pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_ITYPE_t, PyBUF_FORMAT| PyBUF_STRIDES, 1, 0, __pyx_stack) == -1)) <span class='error_goto'>__PYX_ERR(0, 64, __pyx_L1_error)</span>\n",
       "  }\n",
       "  __pyx_pybuffernd_keys.diminfo[0].strides = __pyx_pybuffernd_keys.rcbuffer-&gt;pybuffer.strides[0]; __pyx_pybuffernd_keys.diminfo[0].shape = __pyx_pybuffernd_keys.rcbuffer-&gt;pybuffer.shape[0];\n",
       "  {\n",
       "    __Pyx_BufFmt_StackElem __pyx_stack[1];\n",
       "    if (unlikely(<span class='pyx_c_api'>__Pyx_GetBufferAndValidate</span>(&amp;__pyx_pybuffernd_values.rcbuffer-&gt;pybuffer, (PyObject*)__pyx_v_values, &amp;__Pyx_TypeInfo_nn___pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES, 1, 0, __pyx_stack) == -1)) <span class='error_goto'>__PYX_ERR(0, 64, __pyx_L1_error)</span>\n",
       "  }\n",
       "  __pyx_pybuffernd_values.diminfo[0].strides = __pyx_pybuffernd_values.rcbuffer-&gt;pybuffer.strides[0]; __pyx_pybuffernd_values.diminfo[0].shape = __pyx_pybuffernd_values.rcbuffer-&gt;pybuffer.shape[0];\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_L1_error:;\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n",
       "  { PyObject *__pyx_type, *__pyx_value, *__pyx_tb;\n",
       "    __Pyx_PyThreadState_declare\n",
       "    __Pyx_PyThreadState_assign\n",
       "    <span class='pyx_c_api'>__Pyx_ErrFetch</span>(&amp;__pyx_type, &amp;__pyx_value, &amp;__pyx_tb);\n",
       "    <span class='pyx_c_api'>__Pyx_SafeReleaseBuffer</span>(&amp;__pyx_pybuffernd_keys.rcbuffer-&gt;pybuffer);\n",
       "    <span class='pyx_c_api'>__Pyx_SafeReleaseBuffer</span>(&amp;__pyx_pybuffernd_values.rcbuffer-&gt;pybuffer);\n",
       "  <span class='pyx_c_api'>__Pyx_ErrRestore</span>(__pyx_type, __pyx_value, __pyx_tb);}\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_ec2c52f31301d188c98415ab1d82f601.IntFloatDict.dot_numpy\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  __pyx_r = NULL;\n",
       "  goto __pyx_L2;\n",
       "  __pyx_L0:;\n",
       "  <span class='pyx_c_api'>__Pyx_SafeReleaseBuffer</span>(&amp;__pyx_pybuffernd_keys.rcbuffer-&gt;pybuffer);\n",
       "  <span class='pyx_c_api'>__Pyx_SafeReleaseBuffer</span>(&amp;__pyx_pybuffernd_values.rcbuffer-&gt;pybuffer);\n",
       "  __pyx_L2:;\n",
       "  <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">65</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">DTYPE_t</span> <span class=\"nf\">total</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">66</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">67</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">i</span></pre>\n",
       "<pre class=\"cython line score-10\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">68</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">size</span> <span class=\"o\">=</span> <span class=\"n\">values</span><span class=\"o\">.</span><span class=\"n\">size</span></pre>\n",
       "<pre class='cython code score-10 '>  __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(((PyObject *)__pyx_v_values), __pyx_n_s_size);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 68, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
       "  __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyInt_As_int</span>(__pyx_t_1); if (unlikely((__pyx_t_2 == (int)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 68, __pyx_L1_error)</span>\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
       "  __pyx_v_size = __pyx_t_2;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">69</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">70</span>:         <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">size</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_2 = __pyx_v_size;\n",
       "  __pyx_t_3 = __pyx_t_2;\n",
       "  for (__pyx_t_4 = 0; __pyx_t_4 &lt; __pyx_t_3; __pyx_t_4+=1) {\n",
       "    __pyx_v_i = __pyx_t_4;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">71</span>:             <span class=\"n\">total</span> <span class=\"o\">+=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"p\">[</span><span class=\"n\">keys</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]]</span> <span class=\"o\">*</span> <span class=\"n\">values</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_5 = __pyx_v_i;\n",
       "    __pyx_t_6 = __pyx_v_i;\n",
       "    __pyx_v_total = (__pyx_v_total + ((__pyx_v_self-&gt;my_map[(*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_ITYPE_t *, __pyx_pybuffernd_keys.rcbuffer-&gt;pybuffer.buf, __pyx_t_5, __pyx_pybuffernd_keys.diminfo[0].strides))]) * (*__Pyx_BufPtrStrided1d(__pyx_t_46_cython_magic_ec2c52f31301d188c98415ab1d82f601_DTYPE_t *, __pyx_pybuffernd_values.rcbuffer-&gt;pybuffer.buf, __pyx_t_6, __pyx_pybuffernd_values.diminfo[0].strides))));\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">72</span>: </pre>\n",
       "<pre class=\"cython line score-6\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">73</span>:         <span class=\"k\">return</span> <span class=\"n\">total</span></pre>\n",
       "<pre class='cython code score-6 '>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
       "  __pyx_t_1 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_total);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 73, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
       "  __pyx_r = __pyx_t_1;\n",
       "  __pyx_t_1 = 0;\n",
       "  goto __pyx_L0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">74</span>: </pre>\n",
       "</div></body></html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%cython --annotate\n",
    "# distutils: language = c++\n",
    "\n",
    "cimport cython\n",
    "\n",
    "# C++\n",
    "from cython.operator cimport dereference as deref, preincrement as inc, \\\n",
    "    predecrement as dec\n",
    "from libcpp.utility cimport pair\n",
    "from libcpp.map cimport map as cpp_map\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# Import the C-level symbols of numpy\n",
    "cimport numpy as np\n",
    "\n",
    "# Numpy must be initialized. When using numpy from C or Cython you must\n",
    "# _always_ do that, or you will have segfaults\n",
    "np.import_array()\n",
    "\n",
    "#DTYPE = np.float64\n",
    "ctypedef np.float64_t DTYPE_t\n",
    "\n",
    "#ITYPE = np.intp\n",
    "ctypedef np.intp_t ITYPE_t\n",
    "\n",
    "cdef class IntFloatDict:\n",
    "    \n",
    "    cdef cpp_map[ITYPE_t, DTYPE_t] my_map\n",
    "\n",
    "    @cython.boundscheck(False)\n",
    "    @cython.wraparound(False)\n",
    "    def __init__(self, np.ndarray[ITYPE_t, ndim=1] keys,\n",
    "                       np.ndarray[DTYPE_t, ndim=1] values):\n",
    "        cdef int i\n",
    "        cdef int size = values.size\n",
    "        # Should check that sizes for keys and values are equal, and\n",
    "        # after should boundcheck(False)\n",
    "        for i in range(size):\n",
    "            self.my_map[keys[i]] = values[i]\n",
    "            \n",
    "    def __getitem__(self, int key):\n",
    "        cdef cpp_map[ITYPE_t, DTYPE_t].iterator it = self.my_map.find(key)\n",
    "        if it == self.my_map.end():\n",
    "            # The key is not in the dict\n",
    "            raise KeyError('%i' % key)\n",
    "        return deref(it).second\n",
    "    \n",
    "    def __setitem__(self, int key, float value):\n",
    "        self.my_map[key] = value\n",
    "    \n",
    "    def dot(self, IntFloatDict other):\n",
    "        cdef DTYPE_t total\n",
    "        cdef ITYPE_t key\n",
    "        \n",
    "        cdef cpp_map[ITYPE_t, DTYPE_t].iterator it = other.my_map.begin()\n",
    "        cdef cpp_map[ITYPE_t, DTYPE_t].iterator end = other.my_map.end()\n",
    "        \n",
    "        while it != end:\n",
    "            key = deref(it).first\n",
    "            total += self.my_map[key] * other.my_map[key]\n",
    "            inc(it)\n",
    "        \n",
    "        return total\n",
    "    \n",
    "    @cython.boundscheck(False)\n",
    "    @cython.wraparound(False)\n",
    "    def dot_numpy(self, np.ndarray[ITYPE_t, ndim=1] keys, np.ndarray[DTYPE_t, ndim=1] values):\n",
    "        cdef DTYPE_t total\n",
    "        \n",
    "        cdef int i\n",
    "        cdef int size = values.size\n",
    "        \n",
    "        for i in range(size):\n",
    "            total += self.my_map[keys[i]] * values[i]\n",
    "        \n",
    "        return total\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy dot product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "591 ns ± 145 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "ks = np.arange(30)\n",
    "vs = np.arange(30).astype(float)\n",
    "%timeit vs.dot(vs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cython dict dot product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "332 ns ± 2.56 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "a = IntFloatDict(ks, vs)\n",
    "b = IntFloatDict(ks, vs)\n",
    "%timeit a.dot(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.11 µs ± 138 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "a = IntFloatDict(ks, vs)\n",
    "%timeit a.dot_numpy(ks, vs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialization cost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.11 µs ± 1.41 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit IntFloatDict(ks, vs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python dict dot product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.14 µs ± 1.38 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "a = dict(zip(ks, vs))\n",
    "b = dict(zip(ks, vs))\n",
    "%timeit sum(a[k] * b[k] for k in a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html>\n",
       "<!-- Generated by Cython 0.29.14 -->\n",
       "<html>\n",
       "<head>\n",
       "    <meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n",
       "    <title>Cython: _cython_magic_d874df24fd6facfa28d5b0d69cbd3167.pyx</title>\n",
       "    <style type=\"text/css\">\n",
       "    \n",
       "body.cython { font-family: courier; font-size: 12; }\n",
       "\n",
       ".cython.tag  {  }\n",
       ".cython.line { margin: 0em }\n",
       ".cython.code { font-size: 9; color: #444444; display: none; margin: 0px 0px 0px 8px; border-left: 8px none; }\n",
       "\n",
       ".cython.line .run { background-color: #B0FFB0; }\n",
       ".cython.line .mis { background-color: #FFB0B0; }\n",
       ".cython.code.run  { border-left: 8px solid #B0FFB0; }\n",
       ".cython.code.mis  { border-left: 8px solid #FFB0B0; }\n",
       "\n",
       ".cython.code .py_c_api  { color: red; }\n",
       ".cython.code .py_macro_api  { color: #FF7000; }\n",
       ".cython.code .pyx_c_api  { color: #FF3000; }\n",
       ".cython.code .pyx_macro_api  { color: #FF7000; }\n",
       ".cython.code .refnanny  { color: #FFA000; }\n",
       ".cython.code .trace  { color: #FFA000; }\n",
       ".cython.code .error_goto  { color: #FFA000; }\n",
       "\n",
       ".cython.code .coerce  { color: #008000; border: 1px dotted #008000 }\n",
       ".cython.code .py_attr { color: #FF0000; font-weight: bold; }\n",
       ".cython.code .c_attr  { color: #0000FF; }\n",
       ".cython.code .py_call { color: #FF0000; font-weight: bold; }\n",
       ".cython.code .c_call  { color: #0000FF; }\n",
       "\n",
       ".cython.score-0 {background-color: #FFFFff;}\n",
       ".cython.score-1 {background-color: #FFFFe7;}\n",
       ".cython.score-2 {background-color: #FFFFd4;}\n",
       ".cython.score-3 {background-color: #FFFFc4;}\n",
       ".cython.score-4 {background-color: #FFFFb6;}\n",
       ".cython.score-5 {background-color: #FFFFaa;}\n",
       ".cython.score-6 {background-color: #FFFF9f;}\n",
       ".cython.score-7 {background-color: #FFFF96;}\n",
       ".cython.score-8 {background-color: #FFFF8d;}\n",
       ".cython.score-9 {background-color: #FFFF86;}\n",
       ".cython.score-10 {background-color: #FFFF7f;}\n",
       ".cython.score-11 {background-color: #FFFF79;}\n",
       ".cython.score-12 {background-color: #FFFF73;}\n",
       ".cython.score-13 {background-color: #FFFF6e;}\n",
       ".cython.score-14 {background-color: #FFFF6a;}\n",
       ".cython.score-15 {background-color: #FFFF66;}\n",
       ".cython.score-16 {background-color: #FFFF62;}\n",
       ".cython.score-17 {background-color: #FFFF5e;}\n",
       ".cython.score-18 {background-color: #FFFF5b;}\n",
       ".cython.score-19 {background-color: #FFFF57;}\n",
       ".cython.score-20 {background-color: #FFFF55;}\n",
       ".cython.score-21 {background-color: #FFFF52;}\n",
       ".cython.score-22 {background-color: #FFFF4f;}\n",
       ".cython.score-23 {background-color: #FFFF4d;}\n",
       ".cython.score-24 {background-color: #FFFF4b;}\n",
       ".cython.score-25 {background-color: #FFFF48;}\n",
       ".cython.score-26 {background-color: #FFFF46;}\n",
       ".cython.score-27 {background-color: #FFFF44;}\n",
       ".cython.score-28 {background-color: #FFFF43;}\n",
       ".cython.score-29 {background-color: #FFFF41;}\n",
       ".cython.score-30 {background-color: #FFFF3f;}\n",
       ".cython.score-31 {background-color: #FFFF3e;}\n",
       ".cython.score-32 {background-color: #FFFF3c;}\n",
       ".cython.score-33 {background-color: #FFFF3b;}\n",
       ".cython.score-34 {background-color: #FFFF39;}\n",
       ".cython.score-35 {background-color: #FFFF38;}\n",
       ".cython.score-36 {background-color: #FFFF37;}\n",
       ".cython.score-37 {background-color: #FFFF36;}\n",
       ".cython.score-38 {background-color: #FFFF35;}\n",
       ".cython.score-39 {background-color: #FFFF34;}\n",
       ".cython.score-40 {background-color: #FFFF33;}\n",
       ".cython.score-41 {background-color: #FFFF32;}\n",
       ".cython.score-42 {background-color: #FFFF31;}\n",
       ".cython.score-43 {background-color: #FFFF30;}\n",
       ".cython.score-44 {background-color: #FFFF2f;}\n",
       ".cython.score-45 {background-color: #FFFF2e;}\n",
       ".cython.score-46 {background-color: #FFFF2d;}\n",
       ".cython.score-47 {background-color: #FFFF2c;}\n",
       ".cython.score-48 {background-color: #FFFF2b;}\n",
       ".cython.score-49 {background-color: #FFFF2b;}\n",
       ".cython.score-50 {background-color: #FFFF2a;}\n",
       ".cython.score-51 {background-color: #FFFF29;}\n",
       ".cython.score-52 {background-color: #FFFF29;}\n",
       ".cython.score-53 {background-color: #FFFF28;}\n",
       ".cython.score-54 {background-color: #FFFF27;}\n",
       ".cython.score-55 {background-color: #FFFF27;}\n",
       ".cython.score-56 {background-color: #FFFF26;}\n",
       ".cython.score-57 {background-color: #FFFF26;}\n",
       ".cython.score-58 {background-color: #FFFF25;}\n",
       ".cython.score-59 {background-color: #FFFF24;}\n",
       ".cython.score-60 {background-color: #FFFF24;}\n",
       ".cython.score-61 {background-color: #FFFF23;}\n",
       ".cython.score-62 {background-color: #FFFF23;}\n",
       ".cython.score-63 {background-color: #FFFF22;}\n",
       ".cython.score-64 {background-color: #FFFF22;}\n",
       ".cython.score-65 {background-color: #FFFF22;}\n",
       ".cython.score-66 {background-color: #FFFF21;}\n",
       ".cython.score-67 {background-color: #FFFF21;}\n",
       ".cython.score-68 {background-color: #FFFF20;}\n",
       ".cython.score-69 {background-color: #FFFF20;}\n",
       ".cython.score-70 {background-color: #FFFF1f;}\n",
       ".cython.score-71 {background-color: #FFFF1f;}\n",
       ".cython.score-72 {background-color: #FFFF1f;}\n",
       ".cython.score-73 {background-color: #FFFF1e;}\n",
       ".cython.score-74 {background-color: #FFFF1e;}\n",
       ".cython.score-75 {background-color: #FFFF1e;}\n",
       ".cython.score-76 {background-color: #FFFF1d;}\n",
       ".cython.score-77 {background-color: #FFFF1d;}\n",
       ".cython.score-78 {background-color: #FFFF1c;}\n",
       ".cython.score-79 {background-color: #FFFF1c;}\n",
       ".cython.score-80 {background-color: #FFFF1c;}\n",
       ".cython.score-81 {background-color: #FFFF1c;}\n",
       ".cython.score-82 {background-color: #FFFF1b;}\n",
       ".cython.score-83 {background-color: #FFFF1b;}\n",
       ".cython.score-84 {background-color: #FFFF1b;}\n",
       ".cython.score-85 {background-color: #FFFF1a;}\n",
       ".cython.score-86 {background-color: #FFFF1a;}\n",
       ".cython.score-87 {background-color: #FFFF1a;}\n",
       ".cython.score-88 {background-color: #FFFF1a;}\n",
       ".cython.score-89 {background-color: #FFFF19;}\n",
       ".cython.score-90 {background-color: #FFFF19;}\n",
       ".cython.score-91 {background-color: #FFFF19;}\n",
       ".cython.score-92 {background-color: #FFFF19;}\n",
       ".cython.score-93 {background-color: #FFFF18;}\n",
       ".cython.score-94 {background-color: #FFFF18;}\n",
       ".cython.score-95 {background-color: #FFFF18;}\n",
       ".cython.score-96 {background-color: #FFFF18;}\n",
       ".cython.score-97 {background-color: #FFFF17;}\n",
       ".cython.score-98 {background-color: #FFFF17;}\n",
       ".cython.score-99 {background-color: #FFFF17;}\n",
       ".cython.score-100 {background-color: #FFFF17;}\n",
       ".cython.score-101 {background-color: #FFFF16;}\n",
       ".cython.score-102 {background-color: #FFFF16;}\n",
       ".cython.score-103 {background-color: #FFFF16;}\n",
       ".cython.score-104 {background-color: #FFFF16;}\n",
       ".cython.score-105 {background-color: #FFFF16;}\n",
       ".cython.score-106 {background-color: #FFFF15;}\n",
       ".cython.score-107 {background-color: #FFFF15;}\n",
       ".cython.score-108 {background-color: #FFFF15;}\n",
       ".cython.score-109 {background-color: #FFFF15;}\n",
       ".cython.score-110 {background-color: #FFFF15;}\n",
       ".cython.score-111 {background-color: #FFFF15;}\n",
       ".cython.score-112 {background-color: #FFFF14;}\n",
       ".cython.score-113 {background-color: #FFFF14;}\n",
       ".cython.score-114 {background-color: #FFFF14;}\n",
       ".cython.score-115 {background-color: #FFFF14;}\n",
       ".cython.score-116 {background-color: #FFFF14;}\n",
       ".cython.score-117 {background-color: #FFFF14;}\n",
       ".cython.score-118 {background-color: #FFFF13;}\n",
       ".cython.score-119 {background-color: #FFFF13;}\n",
       ".cython.score-120 {background-color: #FFFF13;}\n",
       ".cython.score-121 {background-color: #FFFF13;}\n",
       ".cython.score-122 {background-color: #FFFF13;}\n",
       ".cython.score-123 {background-color: #FFFF13;}\n",
       ".cython.score-124 {background-color: #FFFF13;}\n",
       ".cython.score-125 {background-color: #FFFF12;}\n",
       ".cython.score-126 {background-color: #FFFF12;}\n",
       ".cython.score-127 {background-color: #FFFF12;}\n",
       ".cython.score-128 {background-color: #FFFF12;}\n",
       ".cython.score-129 {background-color: #FFFF12;}\n",
       ".cython.score-130 {background-color: #FFFF12;}\n",
       ".cython.score-131 {background-color: #FFFF12;}\n",
       ".cython.score-132 {background-color: #FFFF11;}\n",
       ".cython.score-133 {background-color: #FFFF11;}\n",
       ".cython.score-134 {background-color: #FFFF11;}\n",
       ".cython.score-135 {background-color: #FFFF11;}\n",
       ".cython.score-136 {background-color: #FFFF11;}\n",
       ".cython.score-137 {background-color: #FFFF11;}\n",
       ".cython.score-138 {background-color: #FFFF11;}\n",
       ".cython.score-139 {background-color: #FFFF11;}\n",
       ".cython.score-140 {background-color: #FFFF11;}\n",
       ".cython.score-141 {background-color: #FFFF10;}\n",
       ".cython.score-142 {background-color: #FFFF10;}\n",
       ".cython.score-143 {background-color: #FFFF10;}\n",
       ".cython.score-144 {background-color: #FFFF10;}\n",
       ".cython.score-145 {background-color: #FFFF10;}\n",
       ".cython.score-146 {background-color: #FFFF10;}\n",
       ".cython.score-147 {background-color: #FFFF10;}\n",
       ".cython.score-148 {background-color: #FFFF10;}\n",
       ".cython.score-149 {background-color: #FFFF10;}\n",
       ".cython.score-150 {background-color: #FFFF0f;}\n",
       ".cython.score-151 {background-color: #FFFF0f;}\n",
       ".cython.score-152 {background-color: #FFFF0f;}\n",
       ".cython.score-153 {background-color: #FFFF0f;}\n",
       ".cython.score-154 {background-color: #FFFF0f;}\n",
       ".cython.score-155 {background-color: #FFFF0f;}\n",
       ".cython.score-156 {background-color: #FFFF0f;}\n",
       ".cython.score-157 {background-color: #FFFF0f;}\n",
       ".cython.score-158 {background-color: #FFFF0f;}\n",
       ".cython.score-159 {background-color: #FFFF0f;}\n",
       ".cython.score-160 {background-color: #FFFF0f;}\n",
       ".cython.score-161 {background-color: #FFFF0e;}\n",
       ".cython.score-162 {background-color: #FFFF0e;}\n",
       ".cython.score-163 {background-color: #FFFF0e;}\n",
       ".cython.score-164 {background-color: #FFFF0e;}\n",
       ".cython.score-165 {background-color: #FFFF0e;}\n",
       ".cython.score-166 {background-color: #FFFF0e;}\n",
       ".cython.score-167 {background-color: #FFFF0e;}\n",
       ".cython.score-168 {background-color: #FFFF0e;}\n",
       ".cython.score-169 {background-color: #FFFF0e;}\n",
       ".cython.score-170 {background-color: #FFFF0e;}\n",
       ".cython.score-171 {background-color: #FFFF0e;}\n",
       ".cython.score-172 {background-color: #FFFF0e;}\n",
       ".cython.score-173 {background-color: #FFFF0d;}\n",
       ".cython.score-174 {background-color: #FFFF0d;}\n",
       ".cython.score-175 {background-color: #FFFF0d;}\n",
       ".cython.score-176 {background-color: #FFFF0d;}\n",
       ".cython.score-177 {background-color: #FFFF0d;}\n",
       ".cython.score-178 {background-color: #FFFF0d;}\n",
       ".cython.score-179 {background-color: #FFFF0d;}\n",
       ".cython.score-180 {background-color: #FFFF0d;}\n",
       ".cython.score-181 {background-color: #FFFF0d;}\n",
       ".cython.score-182 {background-color: #FFFF0d;}\n",
       ".cython.score-183 {background-color: #FFFF0d;}\n",
       ".cython.score-184 {background-color: #FFFF0d;}\n",
       ".cython.score-185 {background-color: #FFFF0d;}\n",
       ".cython.score-186 {background-color: #FFFF0d;}\n",
       ".cython.score-187 {background-color: #FFFF0c;}\n",
       ".cython.score-188 {background-color: #FFFF0c;}\n",
       ".cython.score-189 {background-color: #FFFF0c;}\n",
       ".cython.score-190 {background-color: #FFFF0c;}\n",
       ".cython.score-191 {background-color: #FFFF0c;}\n",
       ".cython.score-192 {background-color: #FFFF0c;}\n",
       ".cython.score-193 {background-color: #FFFF0c;}\n",
       ".cython.score-194 {background-color: #FFFF0c;}\n",
       ".cython.score-195 {background-color: #FFFF0c;}\n",
       ".cython.score-196 {background-color: #FFFF0c;}\n",
       ".cython.score-197 {background-color: #FFFF0c;}\n",
       ".cython.score-198 {background-color: #FFFF0c;}\n",
       ".cython.score-199 {background-color: #FFFF0c;}\n",
       ".cython.score-200 {background-color: #FFFF0c;}\n",
       ".cython.score-201 {background-color: #FFFF0c;}\n",
       ".cython.score-202 {background-color: #FFFF0c;}\n",
       ".cython.score-203 {background-color: #FFFF0b;}\n",
       ".cython.score-204 {background-color: #FFFF0b;}\n",
       ".cython.score-205 {background-color: #FFFF0b;}\n",
       ".cython.score-206 {background-color: #FFFF0b;}\n",
       ".cython.score-207 {background-color: #FFFF0b;}\n",
       ".cython.score-208 {background-color: #FFFF0b;}\n",
       ".cython.score-209 {background-color: #FFFF0b;}\n",
       ".cython.score-210 {background-color: #FFFF0b;}\n",
       ".cython.score-211 {background-color: #FFFF0b;}\n",
       ".cython.score-212 {background-color: #FFFF0b;}\n",
       ".cython.score-213 {background-color: #FFFF0b;}\n",
       ".cython.score-214 {background-color: #FFFF0b;}\n",
       ".cython.score-215 {background-color: #FFFF0b;}\n",
       ".cython.score-216 {background-color: #FFFF0b;}\n",
       ".cython.score-217 {background-color: #FFFF0b;}\n",
       ".cython.score-218 {background-color: #FFFF0b;}\n",
       ".cython.score-219 {background-color: #FFFF0b;}\n",
       ".cython.score-220 {background-color: #FFFF0b;}\n",
       ".cython.score-221 {background-color: #FFFF0b;}\n",
       ".cython.score-222 {background-color: #FFFF0a;}\n",
       ".cython.score-223 {background-color: #FFFF0a;}\n",
       ".cython.score-224 {background-color: #FFFF0a;}\n",
       ".cython.score-225 {background-color: #FFFF0a;}\n",
       ".cython.score-226 {background-color: #FFFF0a;}\n",
       ".cython.score-227 {background-color: #FFFF0a;}\n",
       ".cython.score-228 {background-color: #FFFF0a;}\n",
       ".cython.score-229 {background-color: #FFFF0a;}\n",
       ".cython.score-230 {background-color: #FFFF0a;}\n",
       ".cython.score-231 {background-color: #FFFF0a;}\n",
       ".cython.score-232 {background-color: #FFFF0a;}\n",
       ".cython.score-233 {background-color: #FFFF0a;}\n",
       ".cython.score-234 {background-color: #FFFF0a;}\n",
       ".cython.score-235 {background-color: #FFFF0a;}\n",
       ".cython.score-236 {background-color: #FFFF0a;}\n",
       ".cython.score-237 {background-color: #FFFF0a;}\n",
       ".cython.score-238 {background-color: #FFFF0a;}\n",
       ".cython.score-239 {background-color: #FFFF0a;}\n",
       ".cython.score-240 {background-color: #FFFF0a;}\n",
       ".cython.score-241 {background-color: #FFFF0a;}\n",
       ".cython.score-242 {background-color: #FFFF0a;}\n",
       ".cython.score-243 {background-color: #FFFF0a;}\n",
       ".cython.score-244 {background-color: #FFFF0a;}\n",
       ".cython.score-245 {background-color: #FFFF0a;}\n",
       ".cython.score-246 {background-color: #FFFF09;}\n",
       ".cython.score-247 {background-color: #FFFF09;}\n",
       ".cython.score-248 {background-color: #FFFF09;}\n",
       ".cython.score-249 {background-color: #FFFF09;}\n",
       ".cython.score-250 {background-color: #FFFF09;}\n",
       ".cython.score-251 {background-color: #FFFF09;}\n",
       ".cython.score-252 {background-color: #FFFF09;}\n",
       ".cython.score-253 {background-color: #FFFF09;}\n",
       ".cython.score-254 {background-color: #FFFF09;}\n",
       ".cython .hll { background-color: #ffffcc }\n",
       ".cython  { background: #f8f8f8; }\n",
       ".cython .c { color: #408080; font-style: italic } /* Comment */\n",
       ".cython .err { border: 1px solid #FF0000 } /* Error */\n",
       ".cython .k { color: #008000; font-weight: bold } /* Keyword */\n",
       ".cython .o { color: #666666 } /* Operator */\n",
       ".cython .ch { color: #408080; font-style: italic } /* Comment.Hashbang */\n",
       ".cython .cm { color: #408080; font-style: italic } /* Comment.Multiline */\n",
       ".cython .cp { color: #BC7A00 } /* Comment.Preproc */\n",
       ".cython .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */\n",
       ".cython .c1 { color: #408080; font-style: italic } /* Comment.Single */\n",
       ".cython .cs { color: #408080; font-style: italic } /* Comment.Special */\n",
       ".cython .gd { color: #A00000 } /* Generic.Deleted */\n",
       ".cython .ge { font-style: italic } /* Generic.Emph */\n",
       ".cython .gr { color: #FF0000 } /* Generic.Error */\n",
       ".cython .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
       ".cython .gi { color: #00A000 } /* Generic.Inserted */\n",
       ".cython .go { color: #888888 } /* Generic.Output */\n",
       ".cython .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
       ".cython .gs { font-weight: bold } /* Generic.Strong */\n",
       ".cython .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
       ".cython .gt { color: #0044DD } /* Generic.Traceback */\n",
       ".cython .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
       ".cython .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
       ".cython .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
       ".cython .kp { color: #008000 } /* Keyword.Pseudo */\n",
       ".cython .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
       ".cython .kt { color: #B00040 } /* Keyword.Type */\n",
       ".cython .m { color: #666666 } /* Literal.Number */\n",
       ".cython .s { color: #BA2121 } /* Literal.String */\n",
       ".cython .na { color: #7D9029 } /* Name.Attribute */\n",
       ".cython .nb { color: #008000 } /* Name.Builtin */\n",
       ".cython .nc { color: #0000FF; font-weight: bold } /* Name.Class */\n",
       ".cython .no { color: #880000 } /* Name.Constant */\n",
       ".cython .nd { color: #AA22FF } /* Name.Decorator */\n",
       ".cython .ni { color: #999999; font-weight: bold } /* Name.Entity */\n",
       ".cython .ne { color: #D2413A; font-weight: bold } /* Name.Exception */\n",
       ".cython .nf { color: #0000FF } /* Name.Function */\n",
       ".cython .nl { color: #A0A000 } /* Name.Label */\n",
       ".cython .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */\n",
       ".cython .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
       ".cython .nv { color: #19177C } /* Name.Variable */\n",
       ".cython .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */\n",
       ".cython .w { color: #bbbbbb } /* Text.Whitespace */\n",
       ".cython .mb { color: #666666 } /* Literal.Number.Bin */\n",
       ".cython .mf { color: #666666 } /* Literal.Number.Float */\n",
       ".cython .mh { color: #666666 } /* Literal.Number.Hex */\n",
       ".cython .mi { color: #666666 } /* Literal.Number.Integer */\n",
       ".cython .mo { color: #666666 } /* Literal.Number.Oct */\n",
       ".cython .sa { color: #BA2121 } /* Literal.String.Affix */\n",
       ".cython .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
       ".cython .sc { color: #BA2121 } /* Literal.String.Char */\n",
       ".cython .dl { color: #BA2121 } /* Literal.String.Delimiter */\n",
       ".cython .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
       ".cython .s2 { color: #BA2121 } /* Literal.String.Double */\n",
       ".cython .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */\n",
       ".cython .sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
       ".cython .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */\n",
       ".cython .sx { color: #008000 } /* Literal.String.Other */\n",
       ".cython .sr { color: #BB6688 } /* Literal.String.Regex */\n",
       ".cython .s1 { color: #BA2121 } /* Literal.String.Single */\n",
       ".cython .ss { color: #19177C } /* Literal.String.Symbol */\n",
       ".cython .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
       ".cython .fm { color: #0000FF } /* Name.Function.Magic */\n",
       ".cython .vc { color: #19177C } /* Name.Variable.Class */\n",
       ".cython .vg { color: #19177C } /* Name.Variable.Global */\n",
       ".cython .vi { color: #19177C } /* Name.Variable.Instance */\n",
       ".cython .vm { color: #19177C } /* Name.Variable.Magic */\n",
       ".cython .il { color: #666666 } /* Literal.Number.Integer.Long */\n",
       "    </style>\n",
       "</head>\n",
       "<body class=\"cython\">\n",
       "<p><span style=\"border-bottom: solid 1px grey;\">Generated by Cython 0.29.14</span></p>\n",
       "<p>\n",
       "    <span style=\"background-color: #FFFF00\">Yellow lines</span> hint at Python interaction.<br />\n",
       "    Click on a line that starts with a \"<code>+</code>\" to see the C code that Cython generated for it.\n",
       "</p>\n",
       "<div class=\"cython\"><pre class=\"cython line score-8\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">01</span>: <span class=\"c\"># distutils: language = c++</span></pre>\n",
       "<pre class='cython code score-8 '>  __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyDict_NewPresized</span>(0);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
       "  if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_test, __pyx_t_1) &lt; 0) <span class='error_goto'>__PYX_ERR(0, 1, __pyx_L1_error)</span>\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">02</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">03</span>: <span class=\"k\">cimport</span> <span class=\"nn\">cython</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">04</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">05</span>: <span class=\"c\"># C++</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">06</span>: <span class=\"k\">from</span> <span class=\"nn\">cython.operator</span> <span class=\"k\">cimport</span> <span class=\"n\">dereference</span> <span class=\"k\">as</span> <span class=\"n\">deref</span><span class=\"p\">,</span> <span class=\"n\">preincrement</span> <span class=\"k\">as</span> <span class=\"n\">inc</span><span class=\"p\">,</span> \\</pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">07</span>:     <span class=\"n\">predecrement</span> <span class=\"k\">as</span> <span class=\"n\">dec</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">08</span>: <span class=\"k\">from</span> <span class=\"nn\">libcpp.utility</span> <span class=\"k\">cimport</span> <span class=\"n\">pair</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">09</span>: <span class=\"k\">from</span> <span class=\"nn\">libcpp.map</span> <span class=\"k\">cimport</span> <span class=\"nb\">map</span> <span class=\"k\">as</span> <span class=\"n\">cpp_map</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">10</span>: </pre>\n",
       "<pre class=\"cython line score-8\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">11</span>: <span class=\"k\">import</span> <span class=\"nn\">numpy</span> <span class=\"k\">as</span> <span class=\"nn\">np</span></pre>\n",
       "<pre class='cython code score-8 '>  __pyx_t_1 = <span class='pyx_c_api'>__Pyx_Import</span>(__pyx_n_s_numpy, 0, 0);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 11, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
       "  if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_np, __pyx_t_1) &lt; 0) <span class='error_goto'>__PYX_ERR(0, 11, __pyx_L1_error)</span>\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">12</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">13</span>: <span class=\"c\"># Import the C-level symbols of numpy</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">14</span>: <span class=\"k\">cimport</span> <span class=\"nn\">numpy</span> <span class=\"k\">as</span> <span class=\"nn\">np</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">15</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">16</span>: <span class=\"c\"># Numpy must be initialized. When using numpy from C or Cython you must</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">17</span>: <span class=\"c\"># _always_ do that, or you will have segfaults</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">18</span>: <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">import_array</span><span class=\"p\">()</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_2 = __pyx_f_5numpy_import_array();<span class='error_goto'> if (unlikely(__pyx_t_2 == ((int)-1))) __PYX_ERR(0, 18, __pyx_L1_error)</span>\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">19</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">20</span>: <span class=\"c\">#DTYPE = np.float64</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">21</span>: <span class=\"k\">ctypedef</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">float64_t</span> <span class=\"n\">DTYPE_t</span></pre>\n",
       "<pre class='cython code score-0 '>typedef __pyx_t_5numpy_float64_t __pyx_t_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_DTYPE_t;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">22</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">23</span>: <span class=\"c\">#ITYPE = np.intp</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">24</span>: <span class=\"k\">ctypedef</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">intp_t</span> <span class=\"n\">ITYPE_t</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">25</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">26</span>: <span class=\"k\">cdef</span> <span class=\"k\">class</span> <span class=\"nf\">IntFloatDict</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">27</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">28</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cpp_map</span>[<span class=\"nf\">ITYPE_t</span><span class=\"p\">,</span> <span class=\"kt\">DTYPE_t</span>] <span class=\"nf\">my_map</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">29</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">30</span>:     <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">31</span>:     <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-30\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">32</span>:     <span class=\"k\">def</span> <span class=\"nf\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"nb\">list</span> <span class=\"n\">keys</span> <span class=\"ow\">not</span> <span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"nb\">list</span> <span class=\"n\">values</span> <span class=\"ow\">not</span> <span class=\"bp\">None</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-30 '>/* Python wrapper */\n",
       "static int __pyx_pw_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_1__init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n",
       "static int __pyx_pw_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_1__init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
       "  PyObject *__pyx_v_keys = 0;\n",
       "  PyObject *__pyx_v_values = 0;\n",
       "  int __pyx_r;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__init__ (wrapper)\", 0);\n",
       "  {\n",
       "    static PyObject **__pyx_pyargnames[] = {&amp;__pyx_n_s_keys,&amp;__pyx_n_s_values,0};\n",
       "    PyObject* values[2] = {0,0};\n",
       "    if (unlikely(__pyx_kwds)) {\n",
       "      Py_ssize_t kw_args;\n",
       "      const Py_ssize_t pos_args = <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args);\n",
       "      switch (pos_args) {\n",
       "        case  2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  0: break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
       "      }\n",
       "      kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n",
       "      switch (pos_args) {\n",
       "        case  0:\n",
       "        if (likely((values[0] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_keys)) != 0)) kw_args--;\n",
       "        else goto __pyx_L5_argtuple_error;\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  1:\n",
       "        if (likely((values[1] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_values)) != 0)) kw_args--;\n",
       "        else {\n",
       "          <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"__init__\", 1, 2, 2, 1); <span class='error_goto'>__PYX_ERR(0, 32, __pyx_L3_error)</span>\n",
       "        }\n",
       "      }\n",
       "      if (unlikely(kw_args &gt; 0)) {\n",
       "        if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, \"__init__\") &lt; 0)) <span class='error_goto'>__PYX_ERR(0, 32, __pyx_L3_error)</span>\n",
       "      }\n",
       "    } else if (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args) != 2) {\n",
       "      goto __pyx_L5_argtuple_error;\n",
       "    } else {\n",
       "      values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "      values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "    }\n",
       "    __pyx_v_keys = ((PyObject*)values[0]);\n",
       "    __pyx_v_values = ((PyObject*)values[1]);\n",
       "  }\n",
       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L5_argtuple_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"__init__\", 1, 2, 2, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); <span class='error_goto'>__PYX_ERR(0, 32, __pyx_L3_error)</span>\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_d874df24fd6facfa28d5b0d69cbd3167.IntFloatDict.__init__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return -1;\n",
       "  __pyx_L4_argument_unpacking_done:;\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_keys), (&amp;PyList_Type), 0, \"keys\", 1))) <span class='error_goto'>__PYX_ERR(0, 32, __pyx_L1_error)</span>\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_values), (&amp;PyList_Type), 0, \"values\", 1))) <span class='error_goto'>__PYX_ERR(0, 32, __pyx_L1_error)</span>\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict___init__(((struct __pyx_obj_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_IntFloatDict *)__pyx_v_self), __pyx_v_keys, __pyx_v_values);\n",
       "\n",
       "  /* function exit code */\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  __pyx_r = -1;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static int __pyx_pf_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict___init__(struct __pyx_obj_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_IntFloatDict *__pyx_v_self, PyObject *__pyx_v_keys, PyObject *__pyx_v_values) {\n",
       "  int __pyx_v_i;\n",
       "  int __pyx_v_size;\n",
       "  int __pyx_r;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__init__\", 0);\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_r = 0;\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_d874df24fd6facfa28d5b0d69cbd3167.IntFloatDict.__init__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  __pyx_r = -1;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">33</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">i</span></pre>\n",
       "<pre class=\"cython line score-1\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">34</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">size</span> <span class=\"o\">=</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">keys</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-1 '>  __pyx_t_1 = <span class='py_macro_api'>PyList_GET_SIZE</span>(__pyx_v_keys);<span class='error_goto'> if (unlikely(__pyx_t_1 == ((Py_ssize_t)-1))) __PYX_ERR(0, 34, __pyx_L1_error)</span>\n",
       "  __pyx_v_size = __pyx_t_1;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">35</span>:         <span class=\"c\"># Should check that sizes for keys and values are equal, and</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">36</span>:         <span class=\"c\"># after should boundcheck(False)</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">37</span>:         <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">size</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_2 = __pyx_v_size;\n",
       "  __pyx_t_3 = __pyx_t_2;\n",
       "  for (__pyx_t_4 = 0; __pyx_t_4 &lt; __pyx_t_3; __pyx_t_4+=1) {\n",
       "    __pyx_v_i = __pyx_t_4;\n",
       "</pre><pre class=\"cython line score-19\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">38</span>:             <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"p\">[</span><span class=\"n\">keys</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]]</span> <span class=\"o\">=</span> <span class=\"n\">values</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-19 '>    __pyx_t_5 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(<span class='py_macro_api'>PyList_GET_ITEM</span>(__pyx_v_values, __pyx_v_i)); if (unlikely((__pyx_t_5 == ((npy_float64)-1)) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 38, __pyx_L1_error)</span>\n",
       "    __pyx_t_6 = <span class='pyx_c_api'>__Pyx_PyInt_As_Py_intptr_t</span>(<span class='py_macro_api'>PyList_GET_ITEM</span>(__pyx_v_keys, __pyx_v_i)); if (unlikely((__pyx_t_6 == ((npy_intp)-1)) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 38, __pyx_L1_error)</span>\n",
       "    (__pyx_v_self-&gt;my_map[__pyx_t_6]) = __pyx_t_5;\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">39</span>: </pre>\n",
       "<pre class=\"cython line score-13\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">40</span>:     <span class=\"k\">def</span> <span class=\"nf\">__getitem__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"nb\">int</span> <span class=\"n\">key</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-13 '>/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_3__getitem__(PyObject *__pyx_v_self, PyObject *__pyx_arg_key); /*proto*/\n",
       "static PyObject *__pyx_pw_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_3__getitem__(PyObject *__pyx_v_self, PyObject *__pyx_arg_key) {\n",
       "  int __pyx_v_key;\n",
       "  PyObject *__pyx_r = 0;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__getitem__ (wrapper)\", 0);\n",
       "  assert(__pyx_arg_key); {\n",
       "    __pyx_v_key = <span class='pyx_c_api'>__Pyx_PyInt_As_int</span>(__pyx_arg_key); if (unlikely((__pyx_v_key == (int)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 40, __pyx_L3_error)</span>\n",
       "  }\n",
       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_d874df24fd6facfa28d5b0d69cbd3167.IntFloatDict.__getitem__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return NULL;\n",
       "  __pyx_L4_argument_unpacking_done:;\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_2__getitem__(((struct __pyx_obj_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_IntFloatDict *)__pyx_v_self), ((int)__pyx_v_key));\n",
       "\n",
       "  /* function exit code */\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static PyObject *__pyx_pf_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_2__getitem__(struct __pyx_obj_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_IntFloatDict *__pyx_v_self, int __pyx_v_key) {\n",
       "  std::map&lt;__pyx_t_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_ITYPE_t,__pyx_t_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_DTYPE_t&gt; ::iterator __pyx_v_it;\n",
       "  PyObject *__pyx_r = NULL;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__getitem__\", 0);\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_L1_error:;\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_2);\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_3);\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_d874df24fd6facfa28d5b0d69cbd3167.IntFloatDict.__getitem__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">41</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">cpp_map</span>[<span class=\"nf\">ITYPE_t</span><span class=\"p\">,</span> <span class=\"kt\">DTYPE_t</span>].<span class=\"kt\">iterator</span> <span class=\"nf\">it</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"o\">.</span><span class=\"n\">find</span><span class=\"p\">(</span><span class=\"n\">key</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_it = __pyx_v_self-&gt;my_map.find(__pyx_v_key);\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">42</span>:         <span class=\"k\">if</span> <span class=\"n\">it</span> <span class=\"o\">==</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"o\">.</span><span class=\"n\">end</span><span class=\"p\">():</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_1 = ((__pyx_v_it == __pyx_v_self-&gt;my_map.end()) != 0);\n",
       "  if (unlikely(__pyx_t_1)) {\n",
       "/* … */\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">43</span>:             <span class=\"c\"># The key is not in the dict</span></pre>\n",
       "<pre class=\"cython line score-14\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">44</span>:             <span class=\"k\">raise</span> <span class=\"ne\">KeyError</span><span class=\"p\">(</span><span class=\"s\">&#39;</span><span class=\"si\">%i</span><span class=\"s\">&#39;</span> <span class=\"o\">%</span> <span class=\"n\">key</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-14 '>    __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyInt_From_int</span>(__pyx_v_key);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 44, __pyx_L1_error)</span>\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
       "    __pyx_t_3 = <span class='py_c_api'>PyUnicode_Format</span>(__pyx_kp_u_i, __pyx_t_2);<span class='error_goto'> if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 44, __pyx_L1_error)</span>\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_3);\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
       "    __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyObject_CallOneArg</span>(__pyx_builtin_KeyError, __pyx_t_3);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 44, __pyx_L1_error)</span>\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_3); __pyx_t_3 = 0;\n",
       "    <span class='pyx_c_api'>__Pyx_Raise</span>(__pyx_t_2, 0, 0, 0);\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
       "    <span class='error_goto'>__PYX_ERR(0, 44, __pyx_L1_error)</span>\n",
       "</pre><pre class=\"cython line score-6\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">45</span>:         <span class=\"k\">return</span> <span class=\"n\">deref</span><span class=\"p\">(</span><span class=\"n\">it</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">second</span></pre>\n",
       "<pre class='cython code score-6 '>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
       "  __pyx_t_2 = <span class='py_c_api'>PyFloat_FromDouble</span>((*__pyx_v_it).second);<span class='error_goto'> if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 45, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
       "  __pyx_r = __pyx_t_2;\n",
       "  __pyx_t_2 = 0;\n",
       "  goto __pyx_L0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">46</span>: </pre>\n",
       "<pre class=\"cython line score-19\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">47</span>:     <span class=\"k\">def</span> <span class=\"nf\">__setitem__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"nb\">int</span> <span class=\"n\">key</span><span class=\"p\">,</span> <span class=\"nb\">float</span> <span class=\"n\">value</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-19 '>/* Python wrapper */\n",
       "static int __pyx_pw_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_5__setitem__(PyObject *__pyx_v_self, PyObject *__pyx_arg_key, PyObject *__pyx_arg_value); /*proto*/\n",
       "static int __pyx_pw_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_5__setitem__(PyObject *__pyx_v_self, PyObject *__pyx_arg_key, PyObject *__pyx_arg_value) {\n",
       "  int __pyx_v_key;\n",
       "  float __pyx_v_value;\n",
       "  int __pyx_r;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__setitem__ (wrapper)\", 0);\n",
       "  assert(__pyx_arg_key); {\n",
       "    __pyx_v_key = <span class='pyx_c_api'>__Pyx_PyInt_As_int</span>(__pyx_arg_key); if (unlikely((__pyx_v_key == (int)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 47, __pyx_L3_error)</span>\n",
       "  }\n",
       "  assert(__pyx_arg_value); {\n",
       "    __pyx_v_value = __pyx_<span class='py_c_api'>PyFloat_AsFloat</span>(__pyx_arg_value); if (unlikely((__pyx_v_value == (float)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 47, __pyx_L3_error)</span>\n",
       "  }\n",
       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_d874df24fd6facfa28d5b0d69cbd3167.IntFloatDict.__setitem__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return -1;\n",
       "  __pyx_L4_argument_unpacking_done:;\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_4__setitem__(((struct __pyx_obj_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_IntFloatDict *)__pyx_v_self), ((int)__pyx_v_key), ((float)__pyx_v_value));\n",
       "\n",
       "  /* function exit code */\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static int __pyx_pf_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_4__setitem__(struct __pyx_obj_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_IntFloatDict *__pyx_v_self, int __pyx_v_key, float __pyx_v_value) {\n",
       "  int __pyx_r;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"__setitem__\", 0);\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_r = 0;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">48</span>:         <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"p\">[</span><span class=\"n\">key</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">value</span></pre>\n",
       "<pre class='cython code score-0 '>  (__pyx_v_self-&gt;my_map[__pyx_v_key]) = __pyx_v_value;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">49</span>: </pre>\n",
       "<pre class=\"cython line score-5\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">50</span>:     <span class=\"k\">def</span> <span class=\"nf\">dot</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">IntFloatDict</span> <span class=\"n\">other</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-5 '>/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_7dot(PyObject *__pyx_v_self, PyObject *__pyx_v_other); /*proto*/\n",
       "static PyObject *__pyx_pw_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_7dot(PyObject *__pyx_v_self, PyObject *__pyx_v_other) {\n",
       "  PyObject *__pyx_r = 0;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"dot (wrapper)\", 0);\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_other), __pyx_ptype_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_IntFloatDict, 1, \"other\", 0))) <span class='error_goto'>__PYX_ERR(0, 50, __pyx_L1_error)</span>\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_6dot(((struct __pyx_obj_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_IntFloatDict *)__pyx_v_self), ((struct __pyx_obj_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_IntFloatDict *)__pyx_v_other));\n",
       "\n",
       "  /* function exit code */\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static PyObject *__pyx_pf_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_6dot(struct __pyx_obj_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_IntFloatDict *__pyx_v_self, struct __pyx_obj_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_IntFloatDict *__pyx_v_other) {\n",
       "  __pyx_t_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_DTYPE_t __pyx_v_total;\n",
       "  __pyx_t_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_ITYPE_t __pyx_v_key;\n",
       "  std::map&lt;__pyx_t_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_ITYPE_t,__pyx_t_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_DTYPE_t&gt; ::iterator __pyx_v_it;\n",
       "  std::map&lt;__pyx_t_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_ITYPE_t,__pyx_t_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_DTYPE_t&gt; ::iterator __pyx_v_end;\n",
       "  PyObject *__pyx_r = NULL;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"dot\", 0);\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_L1_error:;\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_3);\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_d874df24fd6facfa28d5b0d69cbd3167.IntFloatDict.dot\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">51</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">DTYPE_t</span> <span class=\"nf\">total</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">52</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">ITYPE_t</span> <span class=\"nf\">key</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">53</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">54</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">cpp_map</span>[<span class=\"nf\">ITYPE_t</span><span class=\"p\">,</span> <span class=\"kt\">DTYPE_t</span>].<span class=\"kt\">iterator</span> <span class=\"nf\">it</span> <span class=\"o\">=</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"o\">.</span><span class=\"n\">begin</span><span class=\"p\">()</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_it = __pyx_v_other-&gt;my_map.begin();\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">55</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">cpp_map</span>[<span class=\"nf\">ITYPE_t</span><span class=\"p\">,</span> <span class=\"kt\">DTYPE_t</span>].<span class=\"kt\">iterator</span> <span class=\"nf\">end</span> <span class=\"o\">=</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"o\">.</span><span class=\"n\">end</span><span class=\"p\">()</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_end = __pyx_v_other-&gt;my_map.end();\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">56</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">57</span>:         <span class=\"k\">while</span> <span class=\"n\">it</span> <span class=\"o\">!=</span> <span class=\"n\">end</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  while (1) {\n",
       "    __pyx_t_1 = ((__pyx_v_it != __pyx_v_end) != 0);\n",
       "    if (!__pyx_t_1) break;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">58</span>:             <span class=\"n\">key</span> <span class=\"o\">=</span> <span class=\"n\">deref</span><span class=\"p\">(</span><span class=\"n\">it</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">first</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_2 = (*__pyx_v_it).first;\n",
       "    __pyx_v_key = __pyx_t_2;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">59</span>:             <span class=\"n\">total</span> <span class=\"o\">+=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"p\">[</span><span class=\"n\">key</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"p\">[</span><span class=\"n\">key</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_total = (__pyx_v_total + ((__pyx_v_self-&gt;my_map[__pyx_v_key]) * (__pyx_v_other-&gt;my_map[__pyx_v_key])));\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">60</span>:             <span class=\"n\">inc</span><span class=\"p\">(</span><span class=\"n\">it</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>    (void)((++__pyx_v_it));\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">61</span>: </pre>\n",
       "<pre class=\"cython line score-6\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">62</span>:         <span class=\"k\">return</span> <span class=\"n\">total</span></pre>\n",
       "<pre class='cython code score-6 '>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
       "  __pyx_t_3 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_total);<span class='error_goto'> if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 62, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_3);\n",
       "  __pyx_r = __pyx_t_3;\n",
       "  __pyx_t_3 = 0;\n",
       "  goto __pyx_L0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">63</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">64</span>:     <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">65</span>:     <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-31\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">66</span>:     <span class=\"k\">def</span> <span class=\"nf\">dot_list</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"nb\">list</span> <span class=\"n\">keys</span> <span class=\"ow\">not</span> <span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"nb\">list</span> <span class=\"n\">values</span> <span class=\"ow\">not</span> <span class=\"bp\">None</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-31 '>/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_9dot_list(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n",
       "static PyObject *__pyx_pw_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_9dot_list(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
       "  PyObject *__pyx_v_keys = 0;\n",
       "  PyObject *__pyx_v_values = 0;\n",
       "  PyObject *__pyx_r = 0;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"dot_list (wrapper)\", 0);\n",
       "  {\n",
       "    static PyObject **__pyx_pyargnames[] = {&amp;__pyx_n_s_keys,&amp;__pyx_n_s_values,0};\n",
       "    PyObject* values[2] = {0,0};\n",
       "    if (unlikely(__pyx_kwds)) {\n",
       "      Py_ssize_t kw_args;\n",
       "      const Py_ssize_t pos_args = <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args);\n",
       "      switch (pos_args) {\n",
       "        case  2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  0: break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
       "      }\n",
       "      kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n",
       "      switch (pos_args) {\n",
       "        case  0:\n",
       "        if (likely((values[0] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_keys)) != 0)) kw_args--;\n",
       "        else goto __pyx_L5_argtuple_error;\n",
       "        CYTHON_FALLTHROUGH;\n",
       "        case  1:\n",
       "        if (likely((values[1] = <span class='pyx_c_api'>__Pyx_PyDict_GetItemStr</span>(__pyx_kwds, __pyx_n_s_values)) != 0)) kw_args--;\n",
       "        else {\n",
       "          <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"dot_list\", 1, 2, 2, 1); <span class='error_goto'>__PYX_ERR(0, 66, __pyx_L3_error)</span>\n",
       "        }\n",
       "      }\n",
       "      if (unlikely(kw_args &gt; 0)) {\n",
       "        if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, \"dot_list\") &lt; 0)) <span class='error_goto'>__PYX_ERR(0, 66, __pyx_L3_error)</span>\n",
       "      }\n",
       "    } else if (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args) != 2) {\n",
       "      goto __pyx_L5_argtuple_error;\n",
       "    } else {\n",
       "      values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "      values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "    }\n",
       "    __pyx_v_keys = ((PyObject*)values[0]);\n",
       "    __pyx_v_values = ((PyObject*)values[1]);\n",
       "  }\n",
       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L5_argtuple_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"dot_list\", 1, 2, 2, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); <span class='error_goto'>__PYX_ERR(0, 66, __pyx_L3_error)</span>\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_d874df24fd6facfa28d5b0d69cbd3167.IntFloatDict.dot_list\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return NULL;\n",
       "  __pyx_L4_argument_unpacking_done:;\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_keys), (&amp;PyList_Type), 0, \"keys\", 1))) <span class='error_goto'>__PYX_ERR(0, 66, __pyx_L1_error)</span>\n",
       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_values), (&amp;PyList_Type), 0, \"values\", 1))) <span class='error_goto'>__PYX_ERR(0, 66, __pyx_L1_error)</span>\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_8dot_list(((struct __pyx_obj_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_IntFloatDict *)__pyx_v_self), __pyx_v_keys, __pyx_v_values);\n",
       "\n",
       "  /* function exit code */\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "\n",
       "static PyObject *__pyx_pf_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_12IntFloatDict_8dot_list(struct __pyx_obj_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_IntFloatDict *__pyx_v_self, PyObject *__pyx_v_keys, PyObject *__pyx_v_values) {\n",
       "  __pyx_t_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_DTYPE_t __pyx_v_total;\n",
       "  __pyx_t_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_ITYPE_t __pyx_v_k;\n",
       "  __pyx_t_46_cython_magic_d874df24fd6facfa28d5b0d69cbd3167_DTYPE_t __pyx_v_v;\n",
       "  int __pyx_v_i;\n",
       "  int __pyx_v_size;\n",
       "  PyObject *__pyx_r = NULL;\n",
       "  <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"dot_list\", 0);\n",
       "/* … */\n",
       "  /* function exit code */\n",
       "  __pyx_L1_error:;\n",
       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_7);\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_d874df24fd6facfa28d5b0d69cbd3167.IntFloatDict.dot_list\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
       "  <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return __pyx_r;\n",
       "}\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">67</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">DTYPE_t</span> <span class=\"nf\">total</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">68</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">ITYPE_t</span> <span class=\"nf\">k</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">69</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">DTYPE_t</span> <span class=\"nf\">v</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">70</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">71</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">i</span></pre>\n",
       "<pre class=\"cython line score-1\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">72</span>:         <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">size</span> <span class=\"o\">=</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">values</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-1 '>  __pyx_t_1 = <span class='py_macro_api'>PyList_GET_SIZE</span>(__pyx_v_values);<span class='error_goto'> if (unlikely(__pyx_t_1 == ((Py_ssize_t)-1))) __PYX_ERR(0, 72, __pyx_L1_error)</span>\n",
       "  __pyx_v_size = __pyx_t_1;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">73</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">74</span>:         <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">size</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_2 = __pyx_v_size;\n",
       "  __pyx_t_3 = __pyx_t_2;\n",
       "  for (__pyx_t_4 = 0; __pyx_t_4 &lt; __pyx_t_3; __pyx_t_4+=1) {\n",
       "    __pyx_v_i = __pyx_t_4;\n",
       "</pre><pre class=\"cython line score-8\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">75</span>:             <span class=\"n\">k</span> <span class=\"o\">=</span> <span class=\"n\">keys</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-8 '>    __pyx_t_5 = <span class='pyx_c_api'>__Pyx_PyInt_As_Py_intptr_t</span>(<span class='py_macro_api'>PyList_GET_ITEM</span>(__pyx_v_keys, __pyx_v_i)); if (unlikely((__pyx_t_5 == ((npy_intp)-1)) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 75, __pyx_L1_error)</span>\n",
       "    __pyx_v_k = __pyx_t_5;\n",
       "</pre><pre class=\"cython line score-11\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">76</span>:             <span class=\"n\">v</span> <span class=\"o\">=</span> <span class=\"n\">values</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-11 '>    __pyx_t_6 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(<span class='py_macro_api'>PyList_GET_ITEM</span>(__pyx_v_values, __pyx_v_i)); if (unlikely((__pyx_t_6 == ((npy_float64)-1)) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) <span class='error_goto'>__PYX_ERR(0, 76, __pyx_L1_error)</span>\n",
       "    __pyx_v_v = __pyx_t_6;\n",
       "</pre><pre class=\"cython line score-0\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">77</span>:             <span class=\"n\">total</span> <span class=\"o\">+=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">my_map</span><span class=\"p\">[</span><span class=\"n\">k</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">v</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_total = (__pyx_v_total + ((__pyx_v_self-&gt;my_map[__pyx_v_k]) * __pyx_v_v));\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">78</span>: </pre>\n",
       "<pre class=\"cython line score-6\" onclick=\"(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)\">+<span class=\"\">79</span>:         <span class=\"k\">return</span> <span class=\"n\">total</span></pre>\n",
       "<pre class='cython code score-6 '>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
       "  __pyx_t_7 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_total);<span class='error_goto'> if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 79, __pyx_L1_error)</span>\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_7);\n",
       "  __pyx_r = __pyx_t_7;\n",
       "  __pyx_t_7 = 0;\n",
       "  goto __pyx_L0;\n",
       "</pre></div></body></html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%cython --annotate\n",
    "# distutils: language = c++\n",
    "\n",
    "cimport cython\n",
    "\n",
    "# C++\n",
    "from cython.operator cimport dereference as deref, preincrement as inc, \\\n",
    "    predecrement as dec\n",
    "from libcpp.utility cimport pair\n",
    "from libcpp.map cimport map as cpp_map\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# Import the C-level symbols of numpy\n",
    "cimport numpy as np\n",
    "\n",
    "# Numpy must be initialized. When using numpy from C or Cython you must\n",
    "# _always_ do that, or you will have segfaults\n",
    "np.import_array()\n",
    "\n",
    "#DTYPE = np.float64\n",
    "ctypedef np.float64_t DTYPE_t\n",
    "\n",
    "#ITYPE = np.intp\n",
    "ctypedef np.intp_t ITYPE_t\n",
    "\n",
    "cdef class IntFloatDict:\n",
    "    \n",
    "    cdef cpp_map[ITYPE_t, DTYPE_t] my_map\n",
    "\n",
    "    @cython.boundscheck(False)\n",
    "    @cython.wraparound(False)\n",
    "    def __init__(self, list keys not None, list values not None):\n",
    "        cdef int i\n",
    "        cdef int size = len(keys)\n",
    "        # Should check that sizes for keys and values are equal, and\n",
    "        # after should boundcheck(False)\n",
    "        for i in range(size):\n",
    "            self.my_map[keys[i]] = values[i]\n",
    "            \n",
    "    def __getitem__(self, int key):\n",
    "        cdef cpp_map[ITYPE_t, DTYPE_t].iterator it = self.my_map.find(key)\n",
    "        if it == self.my_map.end():\n",
    "            # The key is not in the dict\n",
    "            raise KeyError('%i' % key)\n",
    "        return deref(it).second\n",
    "    \n",
    "    def __setitem__(self, int key, float value):\n",
    "        self.my_map[key] = value\n",
    "            \n",
    "    def dot(self, IntFloatDict other):\n",
    "        cdef DTYPE_t total\n",
    "        cdef ITYPE_t key\n",
    "        \n",
    "        cdef cpp_map[ITYPE_t, DTYPE_t].iterator it = other.my_map.begin()\n",
    "        cdef cpp_map[ITYPE_t, DTYPE_t].iterator end = other.my_map.end()\n",
    "        \n",
    "        while it != end:\n",
    "            key = deref(it).first\n",
    "            total += self.my_map[key] * other.my_map[key]\n",
    "            inc(it)\n",
    "        \n",
    "        return total\n",
    "    \n",
    "    @cython.boundscheck(False)\n",
    "    @cython.wraparound(False)\n",
    "    def dot_list(self, list keys not None, list values not None):\n",
    "        cdef DTYPE_t total\n",
    "        cdef ITYPE_t k\n",
    "        cdef DTYPE_t v\n",
    "        \n",
    "        cdef int i\n",
    "        cdef int size = len(values)\n",
    "        \n",
    "        for i in range(size):\n",
    "            k = keys[i]\n",
    "            v = values[i]\n",
    "            total += self.my_map[k] * v\n",
    "        \n",
    "        return total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## sklearn example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 38 µs, sys: 0 ns, total: 38 µs\n",
      "Wall time: 41 µs\n",
      "CPU times: user 33 µs, sys: 0 ns, total: 33 µs\n",
      "Wall time: 36.2 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "328350.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.utils._fast_dict import IntFloatDict, argmin\n",
    "\n",
    "keys = np.arange(100)\n",
    "values = np.arange(100).astype(float)\n",
    "\n",
    "cd = IntFloatDict(keys, values)\n",
    "%time sum(v * v for _, v in cd)\n",
    "\n",
    "d = dict(zip(keys, values))\n",
    "%time sum(v * v for k, v in d.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "444 ns ± 4.27 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit argmin(cd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.29 µs ± 169 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit min(d, key=d.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "707 ns ± 126 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.empty(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89.6 ns ± 5.75 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit cd[50] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "433 ns ± 138 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit d[50] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}