ESMPy_periodic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ESMPy periodic grid test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import ESMF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background\n",
    "\n",
    "From [ESMPy documentation](http://www.earthsystemmodeling.org/esmf_releases/last_built/esmpy_doc/html/grid.html#ESMF.api.grid.Grid), we should be able to set periodic dimension\n",
    "\n",
    "    periodic_dim\n",
    "    Return type:\tint\n",
    "    Returns:\tThe periodic dimension of the Grid (e.g. 0 for x or longitude, 1 for y or latitude, etc.)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make fake grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume both input and output grids are periodic in longitude. Thus `lon=180` is omitted as it is the same as `lon=-180`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-180, -170, -160, -150, -140, -130, -120, -110, -100,  -90,  -80,\n",
       "         -70,  -60,  -50,  -40,  -30,  -20,  -10,    0,   10,   20,   30,\n",
       "          40,   50,   60,   70,   80,   90,  100,  110,  120,  130,  140,\n",
       "         150,  160,  170]),\n",
       " array([-90., -86., -82., -78., -74., -70., -66., -62., -58., -54., -50.,\n",
       "        -46., -42., -38., -34., -30., -26., -22., -18., -14., -10.,  -6.,\n",
       "         -2.,   2.,   6.,  10.,  14.,  18.,  22.,  26.,  30.,  34.,  38.,\n",
       "         42.,  46.,  50.,  54.,  58.,  62.,  66.,  70.,  74.,  78.,  82.,\n",
       "         86.,  90.]))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lon_in = np.arange(-180, 180, 10)\n",
    "lat_in = np.arange(-90, 90.1, 4)\n",
    "lon_in, lat_in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-180, -175, -170, -165, -160, -155, -150, -145, -140, -135, -130,\n",
       "        -125, -120, -115, -110, -105, -100,  -95,  -90,  -85,  -80,  -75,\n",
       "         -70,  -65,  -60,  -55,  -50,  -45,  -40,  -35,  -30,  -25,  -20,\n",
       "         -15,  -10,   -5,    0,    5,   10,   15,   20,   25,   30,   35,\n",
       "          40,   45,   50,   55,   60,   65,   70,   75,   80,   85,   90,\n",
       "          95,  100,  105,  110,  115,  120,  125,  130,  135,  140,  145,\n",
       "         150,  155,  160,  165,  170,  175]),\n",
       " array([-90., -86., -82., -78., -74., -70., -66., -62., -58., -54., -50.,\n",
       "        -46., -42., -38., -34., -30., -26., -22., -18., -14., -10.,  -6.,\n",
       "         -2.,   2.,   6.,  10.,  14.,  18.,  22.,  26.,  30.,  34.,  38.,\n",
       "         42.,  46.,  50.,  54.,  58.,  62.,  66.,  70.,  74.,  78.,  82.,\n",
       "         86.,  90.]))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lon_out = np.arange(-180, 180, 5)\n",
    "lat_out = np.arange(-90, 90.1, 4)\n",
    "lon_out, lat_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Grid & Regrid object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sourcegrid = ESMF.Grid(np.array([lat_in.size, lon_in.size]), \n",
    "                       staggerloc = ESMF.StaggerLoc.CENTER,\n",
    "                       coord_sys = ESMF.CoordSys.SPH_DEG,\n",
    "                       periodic_dim = 0)\n",
    "\n",
    "source_lon = sourcegrid.get_coords(0)\n",
    "source_lat = sourcegrid.get_coords(1)\n",
    "source_lon[...], source_lat[...] = np.meshgrid(lon_in, lat_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sourcegrid.periodic_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "destgrid = ESMF.Grid(np.array([lat_out.size, lon_out.size]), \n",
    "                       staggerloc = ESMF.StaggerLoc.CENTER,\n",
    "                       coord_sys = ESMF.CoordSys.SPH_DEG,\n",
    "                       periodic_dim = 0)\n",
    "\n",
    "dest_lon = destgrid.get_coords(0)\n",
    "dest_lat = destgrid.get_coords(1)\n",
    "dest_lon[...], dest_lat[...] = np.meshgrid(lon_out, lat_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sourcefield = ESMF.Field(sourcegrid)\n",
    "destfield = ESMF.Field(destgrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regrid_bi = ESMF.Regrid(sourcefield, destfield, \n",
    "                        regrid_method = ESMF.RegridMethod.BILINEAR,  \n",
    "                        unmapped_action = ESMF.UnmappedAction.IGNORE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "wave = lambda x,k:  np.sin(x*k*np.pi/180.0)\n",
    "sourcefield.data[...] = np.outer(wave(lat_in,3), wave(lon_in,3)) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fb73ceb4d30>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p11TGkkZluPszZvYF4FLgdDMbKVrNpTYYFEKIlWSQR2WcaWanF69HgVcBe4HP09rnCuAa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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb740cabcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(sourcefield.data, vmin=0, vmax=2)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply regridding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "destfield = regrid_bi(sourcefield, destfield)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fb73cd12da0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XtaligxVx+sDklii9uWedohgJEmyoLtpPvDZnA1OVo9FT+/PteuhTNk822IEE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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb73ce83d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(destfield.data, vmin=0, vmax=2)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output data at `lon=175` is empty. However, if ESMPy understood periodic grids, it should have been able to interpolate between `lon=180` and `1on=-170`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.25095496,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,  1.        ])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "destfield.data[:,-1] # almost all 0"
   ]
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}