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<h1>Satellite Imagery</h1>
<p>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. </p>
<pre><code class="python">%matplotlib inline
from awips.dataaccess import DataAccessLayer
import cartopy.crs as ccrs
import cartopy.feature as cfeat
import matplotlib.pyplot as plt
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import numpy as np
import datetime
DataAccessLayer.changeEDEXHost(&quot;edex-cloud.unidata.ucar.edu&quot;)
request = DataAccessLayer.newDataRequest()
request.setDatatype(&quot;satellite&quot;)
</code></pre>
<h3 id="available-satellite-sectors-and-products">Available Satellite Sectors and Products<a class="headerlink" href="#available-satellite-sectors-and-products" title="Permanent link"></a></h3>
<pre><code class="python">availableSectors = DataAccessLayer.getAvailableLocationNames(request)
availableSectors.sort()
print(&quot;\nAvailable sectors and products\n&quot;)
for sect in availableSectors:
request.setLocationNames(sect)
availableProducts = DataAccessLayer.getAvailableParameters(request)
availableProducts.sort()
print(sect + &quot;:&quot;)
for prod in availableProducts:
print(&quot; - &quot;+prod)
</code></pre>
<p>Available sectors and products</p>
<ul>
<li>Alaska National:<ul>
<li>Imager 11 micron IR</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
<li>Imager Visible</li>
<li>Percent of Normal TPW</li>
<li>Rain fall rate</li>
<li>Sounder Based Derived Precipitable Water (PW)</li>
</ul>
</li>
<li>Alaska Regional:<ul>
<li>Imager 11 micron IR</li>
<li>Imager 3.9 micron IR</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
<li>Imager Visible</li>
</ul>
</li>
<li>East CONUS:<ul>
<li>Imager 11 micron IR</li>
<li>Imager 13 micron IR</li>
<li>Imager 3.9 micron IR</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
<li>Imager Visible</li>
<li>Low cloud base imagery</li>
</ul>
</li>
<li>GOES-East:<ul>
<li>Imager 11 micron IR</li>
<li>Imager 13 micron IR</li>
<li>Imager 3.5-4.0 micron IR (Fog)</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
<li>Imager Visible</li>
</ul>
</li>
<li>GOES-East-West:<ul>
<li>Imager 11 micron IR</li>
<li>Imager 13 micron IR</li>
<li>Imager 3.5-4.0 micron IR (Fog)</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
<li>Imager Visible</li>
</ul>
</li>
<li>GOES-Sounder:<ul>
<li>CAPE</li>
<li>Sounder Based Derived Lifted Index (LI)</li>
<li>Sounder Based Derived Precipitable Water (PW)</li>
<li>Sounder Based Derived Surface Skin Temp (SFC Skin)</li>
<li>Sounder Based Total Column Ozone</li>
</ul>
</li>
<li>GOES-West:<ul>
<li>Imager 11 micron IR</li>
<li>Imager 13 micron IR</li>
<li>Imager 3.5-4.0 micron IR (Fog)</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
<li>Imager Visible</li>
</ul>
</li>
<li>Global:<ul>
<li>Imager 11 micron IR</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
</ul>
</li>
<li>Hawaii National:<ul>
<li>Gridded Cloud Amount</li>
<li>Gridded Cloud Top Pressure or Height</li>
<li>Imager 11 micron IR</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
<li>Imager Visible</li>
<li>Percent of Normal TPW</li>
<li>Rain fall rate</li>
<li>Sounder 11.03 micron imagery</li>
<li>Sounder 14.06 micron imagery</li>
<li>Sounder 3.98 micron imagery</li>
<li>Sounder 4.45 micron imagery</li>
<li>Sounder 6.51 micron imagery</li>
<li>Sounder 7.02 micron imagery</li>
<li>Sounder 7.43 micron imagery</li>
<li>Sounder Based Derived Lifted Index (LI)</li>
<li>Sounder Based Derived Precipitable Water (PW)</li>
<li>Sounder Based Derived Surface Skin Temp (SFC Skin)</li>
<li>Sounder Visible imagery</li>
</ul>
</li>
<li>Hawaii Regional:<ul>
<li>Imager 11 micron IR</li>
<li>Imager 13 micron IR</li>
<li>Imager 3.9 micron IR</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
<li>Imager Visible</li>
</ul>
</li>
<li>Mollweide:<ul>
<li>Imager 11 micron IR</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
</ul>
</li>
<li>NEXRCOMP:<ul>
<li>DHR</li>
<li>DVL</li>
<li>EET</li>
<li>HHC</li>
<li>N0R</li>
<li>N1P</li>
<li>NTP</li>
</ul>
</li>
<li>NH Composite - Meteosat-GOES E-GOES W-GMS:<ul>
<li>Imager 11 micron IR</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
<li>Imager Visible</li>
</ul>
</li>
<li>Northern Hemisphere Composite:<ul>
<li>Imager 11 micron IR</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
<li>Imager Visible</li>
</ul>
</li>
<li>Puerto Rico National:<ul>
<li>Imager 11 micron IR</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
<li>Imager Visible</li>
<li>Percent of Normal TPW</li>
<li>Rain fall rate</li>
<li>Sounder Based Derived Precipitable Water (PW)</li>
</ul>
</li>
<li>Puerto Rico Regional:<ul>
<li>Imager 11 micron IR</li>
<li>Imager 13 micron IR</li>
<li>Imager 3.9 micron IR</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
<li>Imager Visible</li>
</ul>
</li>
<li>Supernational:<ul>
<li>Gridded Cloud Amount</li>
<li>Gridded Cloud Top Pressure or Height</li>
<li>Imager 11 micron IR</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
<li>Imager Visible</li>
<li>Percent of Normal TPW</li>
<li>Rain fall rate</li>
<li>Sounder Based Derived Lifted Index (LI)</li>
<li>Sounder Based Derived Precipitable Water (PW)</li>
<li>Sounder Based Derived Surface Skin Temp (SFC Skin)</li>
</ul>
</li>
<li>West CONUS:<ul>
<li>Imager 11 micron IR</li>
<li>Imager 13 micron IR</li>
<li>Imager 3.9 micron IR</li>
<li>Imager 6.7-6.5 micron IR (WV)</li>
<li>Imager Visible</li>
<li>Low cloud base imagery</li>
<li>Sounder 11.03 micron imagery</li>
<li>Sounder 14.06 micron imagery</li>
<li>Sounder 3.98 micron imagery</li>
<li>Sounder 4.45 micron imagery</li>
<li>Sounder 6.51 micron imagery</li>
<li>Sounder 7.02 micron imagery</li>
<li>Sounder 7.43 micron imagery</li>
<li>Sounder Visible imagery</li>
</ul>
</li>
</ul>
<h3 id="plot-global-water-vapor-composite">Plot Global Water Vapor Composite<a class="headerlink" href="#plot-global-water-vapor-composite" title="Permanent link"></a></h3>
<pre><code class="python">request.setLocationNames(&quot;Global&quot;)
availableProducts = DataAccessLayer.getAvailableParameters(request)
availableProducts.sort()
request.setParameters(availableProducts[0])
utc = datetime.datetime.utcnow()
times = DataAccessLayer.getAvailableTimes(request)
hourdiff = utc - datetime.datetime.strptime(str(times[-1]),'%Y-%m-%d %H:%M:%S')
hours,days = hourdiff.seconds/3600,hourdiff.days
minute = str((hourdiff.seconds - (3600 * hours)) / 60)
offsetStr = ''
if hours &gt; 0:
offsetStr += str(hours) + &quot;hr &quot;
offsetStr += str(minute) + &quot;m ago&quot;
if days &gt; 1:
offsetStr = str(days) + &quot; days ago&quot;
print(&quot;Found &quot;+ str(len(times)) +&quot; available times&quot;)
print(&quot; &quot;+str(times[0]) + &quot;\n to\n &quot; + str(times[-1]))
print(&quot;Using &quot;+str(times[-1]) + &quot; (&quot;+offsetStr+&quot;)&quot;)
</code></pre>
<blockquote>
<pre><code> Found 96 available times
2017-01-23 00:00:00
to
2017-02-03 21:00:00
Using 2017-02-03 21:00:00 (2hr 3m ago)
</code></pre>
</blockquote>
<pre><code class="python">response = DataAccessLayer.getGridData(request, [times[-1]])
grid = response[0]
data = grid.getRawData()
lons,lats = grid.getLatLonCoords()
bbox = [lons.min(), lons.max(), lats.min(), lats.max()]
print(&quot;grid size &quot; + str(data.shape))
print(&quot;grid extent &quot; + str(list(bbox)))
</code></pre>
<blockquote>
<pre><code> grid size (1024, 2048)
grid extent [-179.91191, 179.99982, -89.977936, 89.890022]
</code></pre>
</blockquote>
<pre><code class="python">def make_map(bbox, projection=ccrs.PlateCarree()):
fig, ax = plt.subplots(figsize=(18,14),
subplot_kw=dict(projection=projection))
ax.set_extent(bbox)
ax.coastlines(resolution='50m')
gl = ax.gridlines(draw_labels=True)
gl.xlabels_top = gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
return fig, ax
fig, ax = make_map(bbox=bbox)
# State boundaries
states = cfeat.NaturalEarthFeature(category='cultural',
name='admin_1_states_provinces_lines',
scale='50m', facecolor='none')
ax.add_feature(states, linestyle=':')
cs = ax.pcolormesh(lons, lats, data, cmap='Greys_r')
cbar = fig.colorbar(cs, shrink=0.9, orientation='horizontal')
cbar.set_label(str(grid.getLocationName())+&quot; &quot; \
+str(grid.getParameter())+&quot; &quot; \
+str(grid.getDataTime().getRefTime()))
plt.tight_layout()
</code></pre>
<p><img alt="png" src="../../images/output_7_0.png" /></p>
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