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.
%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("edex-cloud.unidata.ucar.edu")
request = DataAccessLayer.newDataRequest()
request.setDatatype("satellite")
availableSectors = DataAccessLayer.getAvailableLocationNames(request)
availableSectors.sort()
print("\nAvailable sectors and products\n")
for sect in availableSectors:
request.setLocationNames(sect)
availableProducts = DataAccessLayer.getAvailableParameters(request)
availableProducts.sort()
print(sect + ":")
for prod in availableProducts:
print(" - "+prod)
Available sectors and products Alaska National: - Imager 11 micron IR - Imager 6.7-6.5 micron IR (WV) - Imager Visible - Percent of Normal TPW - Rain fall rate - Sounder Based Derived Precipitable Water (PW) Alaska Regional: - Imager 11 micron IR - Imager 3.9 micron IR - Imager 6.7-6.5 micron IR (WV) - Imager Visible East CONUS: - Imager 11 micron IR - Imager 13 micron (IR) - Imager 3.9 micron IR - Imager 6.7-6.5 micron IR (WV) - Imager Visible - Low cloud base imagery GOES-East: - Imager 11 micron IR - Imager 13 micron IR - Imager 3.5-4.0 micron IR (Fog) - Imager 6.7-6.5 micron IR (WV) - Imager Visible GOES-East-West: - Imager 11 micron IR - Imager 13 micron IR - Imager 3.5-4.0 micron IR (Fog) - Imager 6.7-6.5 micron IR (WV) - Imager Visible GOES-Sounder: - CAPE - Sounder Based Derived Lifted Index (LI) - Sounder Based Derived Precipitable Water (PW) - Sounder Based Derived Surface Skin Temp (SFC Skin) - Sounder Based Total Column Ozone GOES-West: - Imager 11 micron IR - Imager 13 micron IR - Imager 3.5-4.0 micron IR (Fog) - Imager 6.7-6.5 micron IR (WV) - Imager Visible Global: - Imager 11 micron IR - Imager 6.7-6.5 micron IR (WV) Hawaii National: - Gridded Cloud Amount - Gridded Cloud Top Pressure or Height - Imager 11 micron IR - Imager 6.7-6.5 micron IR (WV) - Imager Visible - Percent of Normal TPW - Rain fall rate - Sounder 11.03 micron imagery - Sounder 14.06 micron imagery - Sounder 3.98 micron imagery - Sounder 4.45 micron imagery - Sounder 6.51 micron imagery - Sounder 7.02 micron imagery - Sounder 7.43 micron imagery - Sounder Based Derived Lifted Index (LI) - Sounder Based Derived Precipitable Water (PW) - Sounder Based Derived Surface Skin Temp (SFC Skin) - Sounder Visible imagery Hawaii Regional: - Imager 11 micron IR - Imager 13 micron (IR) - Imager 3.9 micron IR - Imager 6.7-6.5 micron IR (WV) - Imager Visible Mollweide: - Imager 11 micron IR - Imager 6.7-6.5 micron IR (WV) NEXRCOMP: - DHR - DVL - EET - HHC - N0R - N1P - NTP NH Composite - Meteosat-GOES E-GOES W-GMS: - Imager 11 micron IR - Imager 6.7-6.5 micron IR (WV) - Imager Visible Northern Hemisphere Composite: - Imager 11 micron IR - Imager 6.7-6.5 micron IR (WV) - Imager Visible Puerto Rico National: - Imager 11 micron IR - Imager 6.7-6.5 micron IR (WV) - Imager Visible - Percent of Normal TPW - Rain fall rate - Sounder Based Derived Precipitable Water (PW) Puerto Rico Regional: - Imager 11 micron IR - Imager 13 micron (IR) - Imager 3.9 micron IR - Imager 6.7-6.5 micron IR (WV) - Imager Visible Supernational: - Gridded Cloud Amount - Gridded Cloud Top Pressure or Height - Imager 11 micron IR - Imager 6.7-6.5 micron IR (WV) - Imager Visible - Percent of Normal TPW - Rain fall rate - Sounder Based Derived Lifted Index (LI) - Sounder Based Derived Precipitable Water (PW) - Sounder Based Derived Surface Skin Temp (SFC Skin) West CONUS: - Imager 11 micron IR - Imager 13 micron (IR) - Imager 3.9 micron IR - Imager 6.7-6.5 micron IR (WV) - Imager Visible - Low cloud base imagery - Sounder 11.03 micron imagery - Sounder 14.06 micron imagery - Sounder 3.98 micron imagery - Sounder 4.45 micron imagery - Sounder 6.51 micron imagery - Sounder 7.02 micron imagery - Sounder 7.43 micron imagery - Sounder Visible imagery
request.setLocationNames("Global")
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 > 0:
offsetStr += str(hours) + "hr "
offsetStr += str(minute) + "m ago"
if days > 1:
offsetStr = str(days) + " days ago"
print("Found "+ str(len(times)) +" available times")
print(" "+str(times[0]) + "\n to\n " + str(times[-1]))
print("Using "+str(times[-1]) + " ("+offsetStr+")")
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)
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("grid size " + str(data.shape))
print("grid extent " + str(list(bbox)))
grid size (1024, 2048) grid extent [-179.91191, 179.99982, -89.977936, 89.890022]
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())+" " \
+str(grid.getParameter())+" " \
+str(grid.getDataTime().getRefTime()))
plt.tight_layout()