performance.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "id": "90db6855-6a68-4852-85df-0ca08b010895",
   "metadata": {},
   "outputs": [],
   "source": [
    "import awkward as ak\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a7fcfcd-4e89-4501-8a38-68451d1a673b",
   "metadata": {},
   "source": [
    "## Define lookup table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c3e58475-fbba-4689-8b82-a7167216385d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup = ak.zip(\n",
    "    {\n",
    "        \"x\": np.random.random(size=(4, 4, 64)),\n",
    "        \"y\": np.random.random(size=(4, 4, 64)),\n",
    "    }\n",
    ")\n",
    "lookup_var = lookup\n",
    "for i in range(3):\n",
    "    lookup = ak.to_regular(lookup, axis=i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "547e7dda-18da-4e13-8555-2be52df18005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4 * 4 * 64 * {\"x\": float64, \"y\": float64}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lookup.type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a0ce8216-6717-42b5-90ed-94d69bcadd2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4 * var * var * {\"x\": float64, \"y\": float64}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lookup_var.type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d20c45e2-e6dc-4f41-912c-8a93f52d8623",
   "metadata": {},
   "source": [
    "## Define indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0291c174-7cae-4cf9-8fd8-95036c1ffedb",
   "metadata": {},
   "outputs": [],
   "source": [
    "u, v, w = np.ascontiguousarray(\n",
    "    np.random.randint([0, 0, 0], [4, 4, 64], size=(100_000, 3)).T\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5d03509-ebdf-415c-823a-a85e4e965d48",
   "metadata": {},
   "source": [
    "## Record benchmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ad4bb663-7a3c-4092-b65e-4b79258f85bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128 ms ± 2.12 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit lookup[u, v, w]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a96ea862-fbad-4e93-973a-e0211c5564dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.04 ms ± 95.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit lookup_var[u, v, w]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7595156d-d096-48f7-aa3a-c4360f5afcdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.45 ms ± 85.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "lookup_np = ak.to_numpy(lookup)\n",
    "%timeit lookup_np[u, v, w]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03a53da5-f03c-4f00-8497-032e9838df5d",
   "metadata": {},
   "source": [
    "## Field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fb2be723-0ae1-4689-9d7a-ac28a0509a09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "278 ms ± 3.07 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit lookup.x[u, v, w]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e510ca30-9cb8-42c7-9304-ce57c109f471",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.36 ms ± 61.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit lookup_var.x[u, v, w]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c359ed9c-f23d-44ae-8eaa-c94a33ddbb36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "551 µs ± 4.47 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "lookup_x_np = ak.to_numpy(lookup.x)\n",
    "%timeit lookup_x_np[u, v, w]"
   ]
  }
 ],
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requirements.txt

awkward
numpy
jupyterlab