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python-awips/examples/notebooks/Gridded_Data.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## EDEX Grid Inventory"
]
},
{
"cell_type": "code",
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"execution_count": 45,
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"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"AUTOSPE\n",
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"AVN211\n",
"AVN225\n",
"DGEX\n",
"ECMF-Global\n",
"ECMF1\n",
"ECMF10\n",
"ECMF11\n",
"ECMF12\n",
"ECMF2\n",
"ECMF3\n",
"ECMF4\n",
"ECMF5\n",
"ECMF6\n",
"ECMF7\n",
"ECMF8\n",
"ECMF9\n",
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"ESTOFS\n",
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"ETA\n",
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"FFG-ALR\n",
"FFG-FWR\n",
"FFG-KRF\n",
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"FFG-MSR\n",
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"FFG-ORN\n",
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"FFG-RHA\n",
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"FFG-RSA\n",
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"FFG-TAR\n",
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"FFG-TIR\n",
"FFG-TUA\n",
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"GFS\n",
"GFS40\n",
"GFSGuide\n",
"GFSLAMP5\n",
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"GribModel:9:151:172\n",
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"HFR-EAST_6KM\n",
"HFR-EAST_PR_6KM\n",
"HFR-US_EAST_DELAWARE_1KM\n",
"HFR-US_EAST_FLORIDA_2KM\n",
"HFR-US_EAST_NORTH_2KM\n",
"HFR-US_EAST_SOUTH_2KM\n",
"HFR-US_EAST_VIRGINIA_1KM\n",
"HFR-US_HAWAII_1KM\n",
"HFR-US_HAWAII_2KM\n",
"HFR-US_HAWAII_6KM\n",
"HFR-US_WEST_500M\n",
"HFR-US_WEST_CENCAL_2KM\n",
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"HFR-US_WEST_LOSANGELES_1KM\n",
"HFR-US_WEST_LOSOSOS_1KM\n",
"HFR-US_WEST_NORTH_2KM\n",
"HFR-US_WEST_SANFRAN_1KM\n",
"HFR-US_WEST_SOCAL_2KM\n",
"HFR-US_WEST_WASHINGTON_1KM\n",
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"HFR-WEST_6KM\n",
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"HPCGuide\n",
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"HPCqpf\n",
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"HPCqpfNDFD\n",
"HRRR\n",
"LAMP2p5\n",
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"MPE-Local-ORN\n",
"MPE-Local-RHA\n",
"MPE-Local-RSA\n",
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"MPE-Local-TAR\n",
"MPE-Local-TIR\n",
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"MPE-Mosaic-MSR\n",
"MPE-Mosaic-ORN\n",
"MPE-Mosaic-RHA\n",
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"MPE-Mosaic-TAR\n",
"MPE-Mosaic-TIR\n",
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"MRMS_1000\n",
"NAM12\n",
"NAM40\n",
"NCWF\n",
"NOHRSC-SNOW\n",
"NamDNG\n",
"NamDNG5\n",
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"QPE-ALR\n",
"QPE-Auto-TUA\n",
"QPE-FWR\n",
"QPE-KRF\n",
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"QPE-MSR\n",
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"QPE-RFC-RSA\n",
"QPE-RFC-STR\n",
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"QPE-TIR\n",
"QPE-TUA\n",
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"QPE-XNAV-ALR\n",
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"QPE-XNAV-FWR\n",
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"QPE-XNAV-KRF\n",
"QPE-XNAV-MSR\n",
"QPE-XNAV-RHA\n",
"QPE-XNAV-SJU\n",
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"QPE-XNAV-TAR\n",
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"QPE-XNAV-TIR\n",
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"QPE-XNAV-TUA\n",
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"RAP13\n",
"RAP40\n",
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"RCM\n",
"RFCqpf\n",
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"RTMA\n",
"RTMA5\n",
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"UKMET-Global\n",
"UKMET37\n",
"UKMET38\n",
"UKMET39\n",
"UKMET40\n",
"UKMET41\n",
"UKMET42\n",
"UKMET43\n",
"UKMET44\n",
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"URMA25\n",
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"estofsPR\n",
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"fnmocWave\n"
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]
}
],
"source": [
"from awips.dataaccess import DataAccessLayer\n",
"\n",
"# Set host\n",
"DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n",
"\n",
"# Init data request\n",
"request = DataAccessLayer.newDataRequest()\n",
"\n",
"# Set datatype\n",
"request.setDatatype(\"grid\")\n",
"\n",
"# Get a list of all available models\n",
"available_grids = DataAccessLayer.getAvailableLocationNames(request)\n",
"\n",
"# Sort\n",
"available_grids.sort()\n",
"\n",
"for grid in available_grids:\n",
" print grid"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**LocationNames** is different for different plugins\n",
"- radar is icao\n",
"- satellite is sector\n",
"\n",
"\n",
"## Requesting a Grid"
]
},
{
"cell_type": "code",
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"execution_count": 46,
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"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Time : 2016-02-23 15:00:00 (12)\n",
"Model: RAP40\n",
"Unit : %\n",
"Parm : RH\n",
"(151, 113)\n",
"[[ 93.05456543 93.05456543 87.05456543 ..., 73.05456543 72.05456543\n",
" 71.05456543]\n",
" [ 70.05456543 70.05456543 67.05456543 ..., 69.05456543 46.05456924\n",
" 37.05456924]\n",
" [ 40.05456924 56.05456924 68.05456543 ..., 51.05456924 73.05456543\n",
" 74.05456543]\n",
" ..., \n",
" [ 65.05456543 62.05456924 63.05456924 ..., 67.05456543 65.05456543\n",
" 46.05456924]\n",
" [ 48.05456924 59.05456924 62.05456924 ..., 4.05456877 5.05456877\n",
" 5.05456877]\n",
" [ 7.05456877 8.05456829 10.05456829 ..., 91.05456543 95.05456543\n",
" 95.05456543]]\n",
"lat array = [[ 54.24940109 54.35071945 54.45080566 ..., 57.9545517 57.91926193\n",
" 57.88272858]\n",
" [ 57.84495163 57.80593109 57.76566696 ..., 58.07667542 58.08861542\n",
" 58.09931183]\n",
" [ 58.10876846 58.11697769 58.12394714 ..., 56.40270996 56.46187973\n",
" 56.51980972]\n",
" ..., \n",
" [ 19.93209648 19.89832115 19.86351395 ..., 20.054636 20.06362152\n",
" 20.07156372]\n",
" [ 20.0784626 20.08431816 20.08912849 ..., 18.58354759 18.63155174\n",
" 18.67854691]\n",
" [ 18.72453308 18.76950836 18.81346893 ..., 17.49624634 17.42861557\n",
" 17.36001205]]\n",
"lon array = [[-139.83120728 -139.32348633 -138.81448364 ..., -79.26060486\n",
" -78.70166016 -78.14326477]\n",
" [ -77.58544922 -77.02822876 -76.47161865 ..., -100.70157623\n",
" -100.13801575 -99.57427216]\n",
" [ -99.01037598 -98.44634247 -97.88218689 ..., -121.69165039\n",
" -121.15060425 -120.60871887]\n",
" ..., \n",
" [ -82.65139008 -82.26644897 -81.88170624 ..., -98.52494049\n",
" -98.13802338 -97.75105286]\n",
" [ -97.36403656 -96.97698212 -96.58989716 ..., -113.07767487\n",
" -112.69831085 -112.31866455]\n",
" [-111.93874359 -111.5585556 -111.17810822 ..., -69.85433197\n",
" -69.48160553 -69.10926819]]\n"
]
}
],
"source": [
"# Grid request\n",
"request.setLocationNames('RAP40')\n",
"request.setParameters(\"RH\")\n",
"request.setLevels(\"850MB\")\n",
"\n",
"# Get available times\n",
"t = DataAccessLayer.getAvailableTimes(request)\n",
"\n",
"# Select last available time [-1]\n",
"response = DataAccessLayer.getGridData(request, [t[0]])\n",
"data = response[0]\n",
"lon,lat = data.getLatLonCoords()\n",
"\n",
"# Print info\n",
"print 'Time :', t[-1]\n",
"print 'Model:', data.getLocationName()\n",
"print 'Unit :', data.getUnit()\n",
"print 'Parm :', data.getParameter()\n",
"\n",
"# Print data array\n",
"print data.getRawData().shape\n",
"print data.getRawData()\n",
"print \"lat array =\", lat\n",
"print \"lon array =\", lon\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting a Grid with Basemap\n",
"\n",
"Using **matplotlib**, **numpy**, and **basemap**:"
]
},
{
"cell_type": "code",
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"execution_count": 47,
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"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
Gridded Data example cleanup
2016-03-17 12:09:04 -05:00
"<matplotlib.figure.Figure at 0x7f5b11b7b3d0>"
docs ready
2016-03-15 20:27:25 -05:00
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.tri as mtri\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.transforms import offset_copy\n",
"from mpl_toolkits.basemap import Basemap, cm\n",
"import numpy as np\n",
"from numpy import linspace, transpose\n",
"from numpy import meshgrid\n",
"\n",
"\n",
"plt.figure(figsize=(12, 12), dpi=100)\n",
"lons,lats = data.getLatLonCoords()\n",
"\n",
"map = Basemap(projection='cyl',\n",
" resolution = 'c',\n",
" llcrnrlon = lons.min(), llcrnrlat = lats.min(),\n",
" urcrnrlon =lons.max(), urcrnrlat = lats.max()\n",
")\n",
"map.drawcoastlines()\n",
"map.drawstates()\n",
"map.drawcountries()\n",
"\n",
"# \n",
"# We have to reproject our grid, see https://stackoverflow.com/questions/31822553/m\n",
"#\n",
"x = linspace(0, map.urcrnrx, data.getRawData().shape[1])\n",
"y = linspace(0, map.urcrnry, data.getRawData().shape[0])\n",
"xx, yy = meshgrid(x, y)\n",
"ngrid = len(x)\n",
"rlons = np.repeat(np.linspace(np.min(lons), np.max(lons), ngrid),\n",
" ngrid).reshape(ngrid, ngrid)\n",
"rlats = np.repeat(np.linspace(np.min(lats), np.max(lats), ngrid),\n",
" ngrid).reshape(ngrid, ngrid).T\n",
"tli = mtri.LinearTriInterpolator(mtri.Triangulation(lons.flatten(),\n",
" lats.flatten()), data.getRawData().flatten())\n",
"rdata = tli(rlons, rlats)\n",
"cs = map.contourf(rlons, rlats, rdata, latlon=True, vmin=0, vmax=100, cmap='YlGn')\n",
"\n",
"# add colorbar.\n",
"cbar = map.colorbar(cs,location='bottom',pad=\"5%\")\n",
"cbar.set_label(data.getParameter() + data.getUnit() )\n",
"\n",
"# Show plot\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or use **pcolormesh** rather than **contourf**"
]
},
{
"cell_type": "code",
Gridded Data example cleanup
2016-03-17 12:09:04 -05:00
"execution_count": 48,
docs ready
2016-03-15 20:27:25 -05:00
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAACXYAAATxCAYAAACREFnnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/nuphroAwNraGhs3blS
"text/plain": [
Gridded Data example cleanup
2016-03-17 12:09:04 -05:00
"<matplotlib.figure.Figure at 0x7f5b11b7fcd0>"
docs ready
2016-03-15 20:27:25 -05:00
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12, 12), dpi=100)\n",
"map = Basemap(projection='cyl',\n",
" resolution = 'c',\n",
" llcrnrlon = lons.min(), llcrnrlat = lats.min(),\n",
" urcrnrlon =lons.max(), urcrnrlat = lats.max()\n",
")\n",
"map.drawcoastlines()\n",
"map.drawstates()\n",
"map.drawcountries()\n",
"cs = map.pcolormesh(rlons, rlats, rdata, latlon=True, vmin=0, vmax=100, cmap='YlGn')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting a Grid with Cartopy"
]
},
{
"cell_type": "code",
Gridded Data example cleanup
2016-03-17 12:09:04 -05:00
"execution_count": 49,
docs ready
2016-03-15 20:27:25 -05:00
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAACYEAAAcvCAYAAAB6XKnFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmhnMp6UqnZseoT8eVOu
"text/plain": [
Gridded Data example cleanup
2016-03-17 12:09:04 -05:00
"<matplotlib.figure.Figure at 0x7f5b113e7590>"
docs ready
2016-03-15 20:27:25 -05:00
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import iris\n",
"import cartopy.crs as ccrs\n",
"from cartopy import config\n",
"\n",
"lon,lat = data.getLatLonCoords()\n",
"plt.figure(figsize=(12, 12), dpi=100)\n",
"ax = plt.axes(projection=ccrs.PlateCarree())\n",
"cs = plt.contourf(rlons, rlats, rdata, 60, transform=ccrs.PlateCarree(), vmin=0, vmax=100, cmap='YlGn')\n",
"ax.coastlines()\n",
"ax.gridlines()\n",
"\n",
"# add colorbar\n",
"cbar = plt.colorbar(orientation='horizontal')\n",
"cbar.set_label(data.getParameter() + data.getUnit() )\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
Reference in a new issue Copy permalink