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python-awips/examples/notebooks/Satellite_Imagery.ipynb

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new Warning notebook, new Satellite notebook, expanded Radar notebook
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"Satellite images are returned by Python AWIPS as grids, and can be rendered with Cartopy pcolormesh the same as gridded forecast models in other python-awips examples. \n",
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"\n",
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"### Available Sectors and Products"
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]
},
{
"cell_type": "code",
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"execution_count": 44,
"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"AREA7201\n",
" - Unknown-1\n",
"Arctic\n",
" - Unknown-2\n",
" - Unknown-4\n",
" - Unknown-5\n",
"ECONUS\n",
" - ACTP\n",
" - ADP\n",
" - AOD\n",
" - CAPE\n",
" - CH-01-0.47um\n",
" - CH-02-0.64um\n",
" - CH-03-0.87um\n",
" - CH-04-1.38um\n",
" - CH-05-1.61um\n",
" - CH-06-2.25um\n",
" - CH-07-3.90um\n",
" - CH-08-6.19um\n",
" - CH-09-6.95um\n",
" - CH-10-7.34um\n",
" - CH-11-8.50um\n",
" - CH-12-9.61um\n",
" - CH-13-10.35um\n",
" - CH-14-11.20um\n",
" - CH-15-12.30um\n",
" - CH-16-13.30um\n",
" - CSM\n",
" - CTH\n",
" - FDC Area\n",
" - FDC Power\n",
" - FDC Temp\n",
" - KI\n",
" - LI\n",
" - LST\n",
" - SI\n",
" - TPW\n",
" - TT\n",
"EFD\n",
" - ACTP\n",
" - ADP\n",
" - AOD\n",
" - CAPE\n",
" - CH-01-0.47um\n",
" - CH-02-0.64um\n",
" - CH-03-0.87um\n",
" - CH-04-1.38um\n",
" - CH-05-1.61um\n",
" - CH-06-2.25um\n",
" - CH-07-3.90um\n",
" - CH-08-6.19um\n",
" - CH-09-6.95um\n",
" - CH-10-7.34um\n",
" - CH-11-8.50um\n",
" - CH-12-9.61um\n",
" - CH-13-10.35um\n",
" - CH-14-11.20um\n",
" - CH-15-12.30um\n",
" - CH-16-13.30um\n",
" - CSM\n",
" - CTH\n",
" - CTT\n",
" - FDC Area\n",
" - FDC Power\n",
" - FDC Temp\n",
" - KI\n",
" - LI\n",
" - LST\n",
" - RRQPE\n",
" - SI\n",
" - SST\n",
" - TPW\n",
" - TT\n",
" - VAH\n",
" - VAML\n",
"EMESO-1\n",
" - ACTP\n",
" - ADP\n",
" - CAPE\n",
" - CH-01-0.47um\n",
" - CH-02-0.64um\n",
" - CH-03-0.87um\n",
" - CH-04-1.38um\n",
" - CH-05-1.61um\n",
" - CH-06-2.25um\n",
" - CH-07-3.90um\n",
" - CH-08-6.19um\n",
" - CH-09-6.95um\n",
" - CH-10-7.34um\n",
" - CH-11-8.50um\n",
" - CH-12-9.61um\n",
" - CH-13-10.35um\n",
" - CH-14-11.20um\n",
" - CH-15-12.30um\n",
" - CH-16-13.30um\n",
" - CSM\n",
" - CTH\n",
" - CTT\n",
" - KI\n",
" - LI\n",
" - LST\n",
" - SI\n",
" - TPW\n",
" - TT\n",
"EMESO-2\n",
" - ACTP\n",
" - ADP\n",
" - CAPE\n",
" - CH-01-0.47um\n",
" - CH-02-0.64um\n",
" - CH-03-0.87um\n",
" - CH-04-1.38um\n",
" - CH-05-1.61um\n",
" - CH-06-2.25um\n",
" - CH-07-3.90um\n",
" - CH-08-6.19um\n",
" - CH-09-6.95um\n",
" - CH-10-7.34um\n",
" - CH-11-8.50um\n",
" - CH-12-9.61um\n",
" - CH-13-10.35um\n",
" - CH-14-11.20um\n",
" - CH-15-12.30um\n",
" - CH-16-13.30um\n",
" - CSM\n",
" - CTH\n",
" - CTT\n",
" - KI\n",
" - LI\n",
" - LST\n",
" - SI\n",
" - TPW\n",
" - TT\n",
"East CONUS\n",
" - Low cloud base imagery\n",
"GOES-East\n",
" - Imager 11 micron IR\n",
" - Imager 13 micron IR\n",
" - Imager 3.5-4.0 micron IR (Fog)\n",
" - Imager 6.7-6.5 micron IR (WV)\n",
" - Imager Visible\n",
"GOES-West\n",
" - Imager 11 micron IR\n",
" - Imager 13 micron IR\n",
" - Imager 3.5-4.0 micron IR (Fog)\n",
" - Imager 6.7-6.5 micron IR (WV)\n",
" - Imager Visible\n",
"NEXRCOMP\n",
" - DHR\n",
" - DVL\n",
" - EET\n",
" - HHC\n",
" - N0R\n",
" - N1P\n",
" - NTP\n",
"NH Composite - Meteosat-GOES E-GOES W-GMS\n",
" - Imager 11 micron IR\n",
" - Imager 6.7-6.5 micron IR (WV)\n",
" - Imager Visible\n",
"Northern Hemisphere Composite\n",
" - Imager 11 micron IR\n",
" - Imager 6.7-6.5 micron IR (WV)\n",
" - Imager Visible\n",
"PRREGI\n",
" - CH-01-0.47um\n",
" - CH-02-0.64um\n",
" - CH-03-0.87um\n",
" - CH-04-1.38um\n",
" - CH-05-1.61um\n",
" - CH-06-2.25um\n",
" - CH-07-3.90um\n",
" - CH-08-6.19um\n",
" - CH-09-6.95um\n",
" - CH-10-7.34um\n",
" - CH-11-8.50um\n",
" - CH-12-9.61um\n",
" - CH-13-10.35um\n",
" - CH-14-11.20um\n",
" - CH-15-12.30um\n",
" - CH-16-13.30um\n",
"Supernational\n",
" - Gridded Cloud Amount\n",
" - Gridded Cloud Top Pressure or Height\n",
" - Imager 11 micron IR\n",
" - Imager 6.7-6.5 micron IR (WV)\n",
" - Imager Visible\n",
" - Percent of Normal TPW\n",
" - Rain fall rate\n",
" - Sounder Based Derived Lifted Index (LI)\n",
" - Sounder Based Derived Precipitable Water (PW)\n",
" - Sounder Based Derived Surface Skin Temp (SFC Skin)\n",
"West CONUS\n",
" - Imager 11 micron IR\n",
" - Imager 13 micron (IR)\n",
" - Imager 13 micron IR\n",
" - Imager 3.9 micron IR\n",
" - Imager 6.7-6.5 micron IR (WV)\n",
" - Imager Visible\n",
" - Low cloud base imagery\n",
" - Sounder 11.03 micron imagery\n",
" - Sounder 14.06 micron imagery\n",
" - Sounder 3.98 micron imagery\n",
" - Sounder 4.45 micron imagery\n",
" - Sounder 6.51 micron imagery\n",
" - Sounder 7.02 micron imagery\n",
" - Sounder 7.43 micron imagery\n",
" - Sounder Visible imagery\n"
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]
}
],
"source": [
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"from awips.dataaccess import DataAccessLayer\n",
"\n",
"DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n",
"request = DataAccessLayer.newDataRequest()\n",
"request.setDatatype(\"satellite\")\n",
"\n",
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"availableSectors = DataAccessLayer.getAvailableLocationNames(request)\n",
"availableSectors.sort()\n",
"\n",
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"for sector in availableSectors:\n",
" print sector\n",
" request.setLocationNames(sector)\n",
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" availableProducts = DataAccessLayer.getAvailableParameters(request)\n",
" availableProducts.sort()\n",
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" for product in availableProducts:\n",
" print \" - \" + product"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"### GOES 16 Mesoscale Sectors Plot"
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]
},
{
"cell_type": "code",
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"execution_count": 52,
"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Latest image available: 2018-02-11 20:04:28 (44m ago)\n",
"Image grid size: (727, 513)\n",
"Image grid extent: [-81.83214, -68.16786, 32.143394, 45.88625]\n",
"Latest image available: 2018-02-11 20:04:58 (44m ago)\n",
"Image grid size: (703, 606)\n",
"Image grid extent: [-95.21105, -78.94757, 30.756178, 44.556156]\n"
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]
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},
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{
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"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x1242d8210>"
]
},
"metadata": {},
"output_type": "display_data"
},
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{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgcAAAJsCAYAAABpkgCiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npQ0oVjyTGEKQDgXQQQE
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"text/plain": [
jupyter notebook cleanup for 0.9.11
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"<matplotlib.figure.Figure at 0x11a05a5d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x124313690>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
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"%matplotlib inline\n",
"import cartopy.crs as ccrs\n",
"import cartopy.feature as cfeat\n",
"import matplotlib.pyplot as plt\n",
"from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
"import numpy as np\n",
"import datetime\n",
"\n",
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"def make_map(bbox, projection=ccrs.PlateCarree()):\n",
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" fig, ax = plt.subplots(figsize=(10,12),\n",
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" subplot_kw=dict(projection=projection))\n",
" ax.set_extent(bbox)\n",
" ax.coastlines(resolution='50m')\n",
" gl = ax.gridlines(draw_labels=True)\n",
" gl.xlabels_top = gl.ylabels_right = False\n",
" gl.xformatter = LONGITUDE_FORMATTER\n",
" gl.yformatter = LATITUDE_FORMATTER\n",
" return fig, ax\n",
"\n",
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"sectors = [\"EMESO-1\",\"EMESO-2\"]\n",
"fig = plt.figure(figsize=(16,7*len(sectors)))\n",
"\n",
"for i, sector in enumerate(sectors):\n",
"\n",
" request = DataAccessLayer.newDataRequest()\n",
" request.setDatatype(\"satellite\")\n",
" request.setLocationNames(sector)\n",
" request.setParameters(\"CH-13-10.35um\")\n",
"\n",
" utc = datetime.datetime.utcnow()\n",
" times = DataAccessLayer.getAvailableTimes(request)\n",
" hourdiff = utc - datetime.datetime.strptime(str(times[-1]),'%Y-%m-%d %H:%M:%S')\n",
" hours,days = hourdiff.seconds/3600,hourdiff.days\n",
" minute = str((hourdiff.seconds - (3600 * hours)) / 60)\n",
" offsetStr = ''\n",
" if hours > 0:\n",
" offsetStr += str(hours) + \"hr \"\n",
" offsetStr += str(minute) + \"m ago\"\n",
" if days > 1:\n",
" offsetStr = str(days) + \" days ago\"\n",
"\n",
" response = DataAccessLayer.getGridData(request, [times[-1]])\n",
" grid = response[0]\n",
" data = grid.getRawData()\n",
" lons,lats = grid.getLatLonCoords()\n",
" bbox = [lons.min(), lons.max(), lats.min(), lats.max()]\n",
"\n",
" print(\"Latest image available: \"+str(times[-1]) + \" (\"+offsetStr+\")\")\n",
" print(\"Image grid size: \" + str(data.shape))\n",
" print(\"Image grid extent: \" + str(list(bbox)))\n",
" \n",
" fig, ax = make_map(bbox=bbox)\n",
" states = cfeat.NaturalEarthFeature(category='cultural',\n",
" name='admin_1_states_provinces_lines',\n",
" scale='50m', facecolor='none')\n",
" ax.add_feature(states, linestyle=':')\n",
" cs = ax.pcolormesh(lons, lats, data, cmap='Greys_r')\n",
" cbar = fig.colorbar(cs, shrink=0.6, orientation='horizontal')\n",
" cbar.set_label(str(grid.getLocationName())+\" \" \\\n",
" +str(grid.getParameter())+\" \" \\\n",
" +str(grid.getDataTime().getRefTime()))"
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]
}
],
"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",
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"version": "2.7.14"
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}
},
"nbformat": 4,
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"nbformat_minor": 1
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}
Reference in a new issue Copy permalink