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JiaweiZhuang/ESMPy_periodic_fix_ordering.ipynb

Last active December 1, 2017 01:10
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ESMPy_periodic_fix_ordering
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ESMPy_periodic_fix_ordering.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ESMPy periodic grid test\n",
"\n",
"Fix the memory layout in https://gist.github.com/rokuingh/7157d0c41c88cc1a5335b857dd4702dd"
]
},
{
"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([-88., -84., -80., -76., -72., -68., -64., -60., -56., -52., -48.,\n",
" -44., -40., -36., -32., -28., -24., -20., -16., -12., -8., -4.,\n",
" 0., 4., 8., 12., 16., 20., 24., 28., 32., 36., 40.,\n",
" 44., 48., 52., 56., 60., 64., 68., 72., 76., 80., 84.,\n",
" 88.]))"
]
},
"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",
"lat_in = np.arange(-88, 88.1, 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([-88., -84., -80., -76., -72., -68., -64., -60., -56., -52., -48.,\n",
" -44., -40., -36., -32., -28., -24., -20., -16., -12., -8., -4.,\n",
" 0., 4., 8., 12., 16., 20., 24., 28., 32., 36., 40.,\n",
" 44., 48., 52., 56., 60., 64., 68., 72., 76., 80., 84.,\n",
" 88.]))"
]
},
"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",
"lat_out = np.arange(-88, 88.1, 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([lon_in.size, lat_in.size]), \n",
" staggerloc = ESMF.StaggerLoc.CENTER,\n",
" coord_sys = ESMF.CoordSys.SPH_DEG,\n",
" num_peri_dims = 1)\n",
"\n",
"source_lon = sourcegrid.get_coords(0)\n",
"source_lat = sourcegrid.get_coords(1)\n",
"\n",
"lons_in, lats_in = np.meshgrid(lon_in, lat_in)\n",
"\n",
"source_lon[...], source_lat[...] = [lons_in.T, lats_in.T]"
]
},
{
"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([lon_out.size, lat_out.size]), \n",
" staggerloc = ESMF.StaggerLoc.CENTER,\n",
" coord_sys = ESMF.CoordSys.SPH_DEG,\n",
" num_peri_dims = 1)\n",
"\n",
"dest_lon = destgrid.get_coords(0)\n",
"dest_lat = destgrid.get_coords(1)\n",
"\n",
"lons_out, lats_out = np.meshgrid(lon_out, lat_out)\n",
"\n",
"dest_lon[...], dest_lat[...] = [lons_out.T, lats_out.T]"
]
},
{
"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": {
"collapsed": true
},
"outputs": [],
"source": [
"wave = lambda x,k: np.sin(x*k*np.pi/180.0)\n",
"sourcefield.data[...] = np.outer(wave(lon_in,3), wave(lat_in,3)) + 1"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7fa1f5594da0>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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U0cS6nlN4zyu+MC02UvNHlH9Q8Z8qsX/iQje+98w6N/7MSX/+Q9Xl/kZGWRnj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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa1f9388940>"
]
},
"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 0x7fa1f53f9278>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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YMVP+krItP7dgm4ZbInsWnTFHVhsca652WxwunGvuG4n9DBYb9rEXFsjKBwJbGMKiL8qz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\n4zgDhnfMjuM4A4Z3zI7jOAPG/wdgw2EiRuw55gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa1f5504ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.pcolormesh(destfield.data, vmin=0, vmax=2)\n",
"plt.colorbar()"
]
}
],
"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
}
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