JiaweiZhuang/timing_interp_vs_xESMF.ipynb

## timing_interp_vs_xESMF.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import xesmf as xe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# xESMF timing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mostly copied from http://xesmf.readthedocs.io/en/latest/Reuse_regridder.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (x: 600, x_b: 601, y: 400, y_b: 401)\n",
       "Coordinates:\n",
       "    lon      (y, x) float64 -119.8 -119.4 -119.0 -118.6 -118.2 -117.8 -117.4 ...\n",
       "    lat      (y, x) float64 -59.85 -59.85 -59.85 -59.85 -59.85 -59.85 -59.85 ...\n",
       "    lon_b    (y_b, x_b) float64 -120.0 -119.6 -119.2 -118.8 -118.4 -118.0 ...\n",
       "    lat_b    (y_b, x_b) float64 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 ...\n",
       "Dimensions without coordinates: x, x_b, y, y_b\n",
       "Data variables:\n",
       "    *empty*"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_in = xe.util.grid_2d(-120, 120, 0.4,  # longitude range and resolution\n",
    "                        -60, 60, 0.3)  # latitude range and resolution\n",
    "ds_in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (x: 400, x_b: 401, y: 300, y_b: 301)\n",
       "Coordinates:\n",
       "    lon      (y, x) float64 -119.7 -119.1 -118.5 -117.9 -117.3 -116.7 -116.1 ...\n",
       "    lat      (y, x) float64 -59.8 -59.8 -59.8 -59.8 -59.8 -59.8 -59.8 -59.8 ...\n",
       "    lon_b    (y_b, x_b) float64 -120.0 -119.4 -118.8 -118.2 -117.6 -117.0 ...\n",
       "    lat_b    (y_b, x_b) float64 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 ...\n",
       "Dimensions without coordinates: x, x_b, y, y_b\n",
       "Data variables:\n",
       "    *empty*"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_out = xe.util.grid_2d(-120, 120, 0.6,\n",
    "                         -60, 60, 0.4)\n",
    "ds_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lev: 50, time: 10, x: 600, x_b: 601, y: 400, y_b: 401)\n",
       "Coordinates:\n",
       "    lon      (y, x) float64 -119.8 -119.4 -119.0 -118.6 -118.2 -117.8 -117.4 ...\n",
       "    lat      (y, x) float64 -59.85 -59.85 -59.85 -59.85 -59.85 -59.85 -59.85 ...\n",
       "    lon_b    (y_b, x_b) float64 -120.0 -119.6 -119.2 -118.8 -118.4 -118.0 ...\n",
       "    lat_b    (y_b, x_b) float64 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 ...\n",
       "  * time     (time) int64 1 2 3 4 5 6 7 8 9 10\n",
       "  * lev      (lev) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...\n",
       "Dimensions without coordinates: x, x_b, y, y_b\n",
       "Data variables:\n",
       "    data2D   (y, x) float64 1.872 1.869 1.866 1.863 1.86 1.857 1.855 1.852 ...\n",
       "    data4D   (time, lev, y, x) float64 1.872 1.869 1.866 1.863 1.86 1.857 ..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_in.coords['time'] = np.arange(1, 11)\n",
    "ds_in.coords['lev'] = np.arange(1, 51)\n",
    "ds_in['data2D'] = xe.data.wave_smooth(ds_in['lon'], ds_in['lat'])\n",
    "ds_in['data4D'] = ds_in['time'] * ds_in['lev'] * ds_in['data2D']\n",
    "ds_in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwrite existing file: bilinear_400x600_300x400.nc \n",
      " You can set reuse_weights=True to save computing time.\n",
      "CPU times: user 5.7 s, sys: 409 ms, total: 6.11 s\n",
      "Wall time: 6.23 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# slow but only needs to be done once\n",
    "regridder = xe.Regridder(ds_in, ds_out, 'bilinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 417 ms, sys: 152 ms, total: 569 ms\n",
      "Wall time: 585 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# fast\n",
    "dr_out = regridder(ds_in['data4D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'data4D' (time: 10, lev: 50, y: 300, x: 400)>\n",
       "array([[[[  1.871198, ...,   1.871198],\n",
       "         ...,\n",
       "         [  1.871198, ...,   1.871198]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 93.559913, ...,  93.559913],\n",
       "         ...,\n",
       "         [ 93.559913, ...,  93.559913]]],\n",
       "\n",
       "\n",
       "       ...,\n",
       "\n",
       "\n",
       "       [[[ 18.711983, ...,  18.711983],\n",
       "         ...,\n",
       "         [ 18.711983, ...,  18.711983]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[935.599126, ..., 935.599126],\n",
       "         ...,\n",
       "         [935.599126, ..., 935.599126]]]])\n",
       "Coordinates:\n",
       "    lon      (y, x) float64 -119.7 -119.1 -118.5 -117.9 -117.3 -116.7 -116.1 ...\n",
       "    lat      (y, x) float64 -59.8 -59.8 -59.8 -59.8 -59.8 -59.8 -59.8 -59.8 ...\n",
       "  * time     (time) int64 1 2 3 4 5 6 7 8 9 10\n",
       "  * lev      (lev) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...\n",
       "Dimensions without coordinates: y, x\n",
       "Attributes:\n",
       "    regrid_method:  bilinear"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dr_out # shape is correct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# interp() timing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interp doesn't like 2D coordinates. Reduce to 1D. (https://github.com/pydata/xarray/issues/2281)\n",
    "ds_in['lon'] = ds_in['lon'].isel(y=0)\n",
    "ds_in['lat'] = ds_in['lat'].isel(x=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'data4D' (time: 10, lev: 50, lat: 400, lon: 600)>\n",
       "array([[[[  1.872343, ...,   1.872343],\n",
       "         ...,\n",
       "         [  1.872343, ...,   1.872343]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 93.617126, ...,  93.617126],\n",
       "         ...,\n",
       "         [ 93.617126, ...,  93.617126]]],\n",
       "\n",
       "\n",
       "       ...,\n",
       "\n",
       "\n",
       "       [[[ 18.723425, ...,  18.723425],\n",
       "         ...,\n",
       "         [ 18.723425, ...,  18.723425]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[936.171263, ..., 936.171263],\n",
       "         ...,\n",
       "         [936.171263, ..., 936.171263]]]])\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 -119.8 -119.4 -119.0 -118.6 -118.2 -117.8 -117.4 ...\n",
       "  * lat      (lat) float64 -59.85 -59.55 -59.25 -58.95 -58.65 -58.35 -58.05 ...\n",
       "  * time     (time) int64 1 2 3 4 5 6 7 8 9 10\n",
       "  * lev      (lev) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make the dimension naming easier for interp() to understand\n",
    "dr = ds_in['data4D']\n",
    "dr = dr.rename({'x': 'lon', 'y': 'lat'})\n",
    "dr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# output grid as pure numpy arrays\n",
    "lon_out = ds_out['lon'].isel(y=0).values\n",
    "lat_out = ds_out['lat'].isel(x=0).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.76 s, sys: 2.55 s, total: 11.3 s\n",
      "Wall time: 9.96 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "# 16x slower than xESMF...\n",
    "dr_out_interp = dr.interp(lon=lon_out, lat=lat_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'data4D' (time: 10, lev: 50, lat: 300, lon: 400)>\n",
       "array([[[[  1.871199, ...,   1.871199],\n",
       "         ...,\n",
       "         [  1.871199, ...,   1.871199]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 93.559957, ...,  93.559957],\n",
       "         ...,\n",
       "         [ 93.559957, ...,  93.559957]]],\n",
       "\n",
       "\n",
       "       ...,\n",
       "\n",
       "\n",
       "       [[[ 18.711991, ...,  18.711991],\n",
       "         ...,\n",
       "         [ 18.711991, ...,  18.711991]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[935.599568, ..., 935.599568],\n",
       "         ...,\n",
       "         [935.599568, ..., 935.599568]]]])\n",
       "Coordinates:\n",
       "  * time     (time) int64 1 2 3 4 5 6 7 8 9 10\n",
       "  * lev      (lev) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...\n",
       "  * lon      (lon) float64 -119.7 -119.1 -118.5 -117.9 -117.3 -116.7 -116.1 ...\n",
       "  * lat      (lat) float64 -59.8 -59.4 -59.0 -58.6 -58.2 -57.8 -57.4 -57.0 ..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dr_out_interp  # shape is correct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x355211f28>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=[8, 2])\n",
    "dr_out[0,0].plot(ax=axes[0])\n",
    "dr_out_interp[0,0].plot(ax=axes[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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}