JiaweiZhuang/ESMPy_stretched_grid_bug.ipynb

## ESMPy_stretched_grid_bug.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Debugging ESMPy with strongly stretched grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import ESMF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# packages in environment at /opt/conda:\n",
      "#\n",
      "esmpy                     7.1.0.dev46              py27_1    nesii/channel/dev-esmf\n"
     ]
    }
   ],
   "source": [
    "!conda list esmpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input grid and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# cell centers\n",
    "lon_in, lat_in = np.meshgrid(np.linspace(-20, 20, 5), np.linspace(-15, 15, 4))\n",
    "# cell bounds\n",
    "lon_in_b, lat_in_b = np.meshgrid(np.linspace(-25, 25, 6), np.linspace(-20, 20, 5))\n",
    "\n",
    "# stretch the grid in diagonal direction?\n",
    "# Switch to False will remove the bug\n",
    "stretch = True\n",
    "if stretch:\n",
    "    lon_in += lat_in  \n",
    "    lat_in += lon_in\n",
    "    lon_in_b += lat_in_b\n",
    "    lat_in_b += lon_in_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'input data and grid')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oPI2zq2lKX+x3b3wEu5qmxAa/1NjHhR4KT+FsmLFnX+wj3jSCDTP2LBh8xV7S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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22900dcfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.arange(20).reshape(4, 5) # a boring data\n",
    "\n",
    "plt.pcolormesh(lon_in_b, lat_in_b, data)\n",
    "plt.scatter(lon_in, lat_in, label='cell center')\n",
    "plt.legend()\n",
    "plt.title('input data and grid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output Grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just a simple rectilinear grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lon_out_b = np.linspace(-50, 50, 51)\n",
    "lon_out = 0.5*(lon_out_b[1:]+lon_out_b[:-1])\n",
    "\n",
    "lat_out_b = np.linspace(-80, 80, 51)\n",
    "lat_out = 0.5*(lat_out_b[1:]+lat_out_b[:-1])\n",
    "\n",
    "# 1D -> 2D\n",
    "lon_out, lat_out = np.meshgrid(lon_out, lat_out)\n",
    "lon_out_b, lat_out_b = np.meshgrid(lon_out_b, lat_out_b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regridding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convenience function\n",
    "Adapted from https://github.com/JiaweiZhuang/xESMF/blob/master/xesmf/backend.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def esmf_grid(lon, lat):\n",
    "    '''\n",
    "    Create an ESMF.Grid object, for contrusting ESMF.Field and ESMF.Regrid\n",
    "    '''\n",
    "    staggerloc = ESMF.StaggerLoc.CENTER\n",
    "    \n",
    "    grid = ESMF.Grid(np.array(lon.shape), staggerloc=staggerloc,\n",
    "                     coord_sys=ESMF.CoordSys.SPH_DEG)\n",
    "    lon_pointer = grid.get_coords(coord_dim=0, staggerloc=staggerloc)\n",
    "    lat_pointer = grid.get_coords(coord_dim=1, staggerloc=staggerloc)\n",
    "\n",
    "    lon_pointer[...] = lon\n",
    "    lat_pointer[...] = lat\n",
    "\n",
    "    return grid\n",
    "\n",
    "def add_corner(grid, lon_b, lat_b):\n",
    "    '''\n",
    "    Add corner information to ESMF.Grid for conservative regridding.\n",
    "    '''\n",
    "    staggerloc = ESMF.StaggerLoc.CORNER \n",
    "    \n",
    "    grid.add_coords(staggerloc=staggerloc)\n",
    "    lon_b_pointer = grid.get_coords(coord_dim=0, staggerloc=staggerloc)\n",
    "    lat_b_pointer = grid.get_coords(coord_dim=1, staggerloc=staggerloc)\n",
    "\n",
    "    lon_b_pointer[...] = lon_b\n",
    "    lat_b_pointer[...] = lat_b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The regridding process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# make ESMF.Grid object\n",
    "\n",
    "grid_in = esmf_grid(lon_in, lat_in)\n",
    "add_corner(grid_in, lon_in_b, lat_in_b)\n",
    "\n",
    "grid_out = esmf_grid(lon_out, lat_out)\n",
    "add_corner(grid_out, lon_out_b, lat_out_b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`line_type=ESMF.LineType.GREAT_CIRCLE` fixes the previous \"diffusion\" issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# make ESMF.Regrid object\n",
    "\n",
    "sourcefield = ESMF.Field(grid_in)\n",
    "destfield = ESMF.Field(grid_out)\n",
    "regrid = ESMF.Regrid(sourcefield, destfield, \n",
    "                     regrid_method=ESMF.RegridMethod.BILINEAR,\n",
    "                     unmapped_action=ESMF.UnmappedAction.IGNORE,\n",
    "                     line_type=ESMF.LineType.GREAT_CIRCLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Apply regridding\n",
    "\n",
    "sourcefield.data[...] = data\n",
    "destfield = regrid(sourcefield, destfield)\n",
    "data_out = destfield.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check output data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the result is correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f227b1a75d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a5YAj/CQ3ACf1VF0GfAj4+b7Desp6V8etqg3ABoCVOd4VdCVpgRww8Kvq3L7yJD8BnAbc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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22900dcb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(lon_out_b, lat_out_b, data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conservative method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# make ESMF.Regrid object\n",
    "regrid_con = ESMF.Regrid(sourcefield, destfield, \n",
    "                        regrid_method=ESMF.RegridMethod.CONSERVE,\n",
    "                        unmapped_action=ESMF.UnmappedAction.IGNORE,\n",
    "                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Apply regridding\n",
    "sourcefield.data[...] = data\n",
    "destfield = regrid_con(sourcefield, destfield)\n",
    "data_out_con = destfield.data.copy() # to avoid being overwritten by the next operation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is correct in 7.1.0.dev46."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f227b127110>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f227b1be150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(lon_out_b, lat_out_b, data_out_con)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use fractional normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f227b09c910>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f227b16f190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make ESMF.Regrid object\n",
    "regrid_frac = ESMF.Regrid(sourcefield, destfield, \n",
    "                          regrid_method=ESMF.RegridMethod.CONSERVE,\n",
    "                          unmapped_action=ESMF.UnmappedAction.IGNORE,\n",
    "                          norm_type=ESMF.NormType.FRACAREA)\n",
    "\n",
    "# Apply regridding\n",
    "sourcefield.data[...] = data\n",
    "destfield = regrid_frac(sourcefield, destfield)\n",
    "data_out_frac = destfield.data\n",
    "\n",
    "plt.pcolormesh(lon_out_b, lat_out_b, data_out_frac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f227afdc950>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f227b102bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Difference at the edge\n",
    "plt.pcolormesh(lon_out_b, lat_out_b, data_out_frac-data_out_con, \n",
    "               cmap='RdBu_r', vmin=-10, vmax=10)\n",
    "plt.title('fraction area norm. - destination area norm.')\n",
    "plt.colorbar()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
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   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}