ut_optimizations_perf_tests.ipynb

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   "source": [
    "import numpy as np\n",
    "import pyfesom2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-20T22:12:00.877724Z",
     "start_time": "2021-02-20T22:11:56.976843Z"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/suvarchal/AWI/fesom2/AO1_MESH/pickle_mesh_py3_fesom2\n",
      "The usepickle == True)\n",
      "The pickle file for FESOM2 exists.\n",
      "The mesh will be loaded from /home/suvarchal/AWI/fesom2/AO1_MESH/pickle_mesh_py3_fesom2\n"
     ]
    }
   ],
   "source": [
    "mesh = pyfesom2.load_mesh(\"../../../fesom2/AO1_MESH/\") # load AO1 mesh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check perf of opt get_no_cyclic "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-20T22:12:00.881742Z",
     "start_time": "2021-02-20T22:12:00.879451Z"
    }
   },
   "outputs": [],
   "source": [
    "from pyfesom2.ut import get_no_cyclic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-20T22:12:01.062848Z",
     "start_time": "2021-02-20T22:12:00.883528Z"
    }
   },
   "outputs": [],
   "source": [
    "def opt_get_no_cyclic(mesh, elem_no_nan):\n",
    "    \"\"\"Compute non cyclic elements of the mesh.\"\"\"\n",
    "    d = mesh.x2[elem_no_nan].max(axis=1) - mesh.x2[elem_no_nan].min(axis=1)\n",
    "    no_cyclic_elem = np.argwhere(d < 100)\n",
    "    return no_cyclic_elem.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-20T22:12:01.075033Z",
     "start_time": "2021-02-20T22:12:01.064541Z"
    }
   },
   "outputs": [],
   "source": [
    "## dont use time it becuse get_no_cyclic in current mster is too slow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2021-02-20T22:12:01.076350Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.41 s, sys: 925 ms, total: 10.3 s\n",
      "Wall time: 10.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "non_cyclic_elem = get_no_cyclic(mesh, mesh.elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-20T22:12:13.583381Z",
     "start_time": "2021-02-20T22:12:11.435161Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.07 s, sys: 1.06 s, total: 2.13 s\n",
      "Wall time: 2.15 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "opt_non_cyclic_elem = opt_get_no_cyclic(mesh, mesh.elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-20T22:12:15.368827Z",
     "start_time": "2021-02-20T22:12:13.585587Z"
    }
   },
   "outputs": [],
   "source": [
    "assert np.all(np.asarray(non_cyclic_elem) == opt_non_cyclic_elem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## check perf of opt cut_region "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-20T22:12:15.372929Z",
     "start_time": "2021-02-20T22:12:15.370345Z"
    }
   },
   "outputs": [],
   "source": [
    "from pyfesom2.ut import cut_region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-20T22:12:15.379036Z",
     "start_time": "2021-02-20T22:12:15.374620Z"
    }
   },
   "outputs": [],
   "source": [
    "def opt_cut_region(mesh, box):\n",
    "    left, right, down, up = box\n",
    "    \n",
    "    selection = (\n",
    "        (mesh.x2 >= left)\n",
    "        & (mesh.x2 <= right)\n",
    "        & (mesh.y2 >= down)\n",
    "        & (mesh.y2 <= up)\n",
    "    )\n",
    "\n",
    "    elem_selection = selection[mesh.elem]    \n",
    "    \n",
    "    no_nan_triangles = np.all(elem_selection, axis=1) \n",
    "    \n",
    "    elem_no_nan = mesh.elem[no_nan_triangles]\n",
    "    \n",
    "    return elem_no_nan, no_nan_triangles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-20T22:12:15.385796Z",
     "start_time": "2021-02-20T22:12:15.380565Z"
    }
   },
   "outputs": [],
   "source": [
    "# a box for selection, bigger domains \n",
    "bbox = [10,40,0,30] # [-180,180, 0, 90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-20T22:12:15.391523Z",
     "start_time": "2021-02-20T22:12:15.387226Z"
    }
   },
   "outputs": [],
   "source": [
    "### timeit can be used here because it is fast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-20T22:12:29.488649Z",
     "start_time": "2021-02-20T22:12:15.393010Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.74 s ± 77.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "tri,ind_tri =cut_region(mesh, bbox)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-20T22:12:45.576114Z",
     "start_time": "2021-02-20T22:12:29.490557Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "198 ms ± 6.18 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "opt_tri, opt_ind_tri = opt_cut_region(mesh, bbox)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-20T22:12:47.677333Z",
     "start_time": "2021-02-20T22:12:45.577761Z"
    }
   },
   "outputs": [],
   "source": [
    "tri,ind_tri =cut_region(mesh, bbox)\n",
    "opt_tri, opt_ind_tri = opt_cut_region(mesh, bbox)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-20T22:12:47.714040Z",
     "start_time": "2021-02-20T22:12:47.678716Z"
    }
   },
   "outputs": [],
   "source": [
    "# a quick test both old and opt version give same result\n",
    "assert np.all(ind_tri == opt_ind_tri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-20T22:12:48.067073Z",
     "start_time": "2021-02-20T22:12:47.715364Z"
    }
   },
   "outputs": [],
   "source": [
    "assert np.all(tri.ravel() == opt_tri.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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