def test_gini_mercator_upper_corner():
"""Test that the upper corner of the Mercator coordinates is correct."""
f = GiniFile(get_test_data('HI-REGIONAL_4km_3.9_20160616_1715.gini'))
ds = f.to_dataset()
lat = ds.variables['lat']
lon = ds.variables['lon']
# 2nd corner lat/lon are at the "upper right" corner of the pixel, so need to add one
# more grid point
assert_almost_equal(lon[-1, -1] + (lon[-1, -1] - lon[-1, -2]), f.proj_info.lo2, 4)
assert_almost_equal(lat[-1, -1] + (lat[-1, -1] - lat[-2, -1]), f.proj_info.la2, 4)
def test_gini_dataset():
'Test the dataset interface for GINI'
f = GiniFile(get_test_data('WEST-CONUS_4km_WV_20151208_2200.gini'))
ds = f.to_dataset()
assert 'x' in ds.variables
assert 'y' in ds.variables
assert 'WV' in ds.variables
assert hasattr(ds.variables['WV'], 'grid_mapping')
assert ds.variables['WV'].grid_mapping in ds.variables
assert_almost_equal(ds.variables['lon'][0, 0], f.prod_desc.lo1, 4)
assert_almost_equal(ds.variables['lat'][0, 0], f.prod_desc.la1, 4)
def test_dataset():
'Test the dataset interface for GINI'
f = GiniFile(get_test_data('WEST-CONUS_4km_WV_20151208_2200.gini'))
ds = f.to_dataset()
assert 'x' in ds.variables
assert 'y' in ds.variables
assert 'WV' in ds.variables
assert hasattr(ds.variables['WV'], 'grid_mapping')
assert ds.variables['WV'].grid_mapping in ds.variables
assert_almost_equal(ds.variables['lon'][0, 0], f.prod_desc.lo1, 4)
assert_almost_equal(ds.variables['lat'][0, 0], f.prod_desc.la1, 4)
def test_gini_ak_regional_dataset():
'Test the dataset interface for GINI of a AK REGIONAL file'
f = GiniFile(get_test_data('AK-REGIONAL_8km_3.9_20160408_1445.gini'))
ds = f.to_dataset()
assert 'x' in ds.variables
assert 'y' in ds.variables
assert 'IR' in ds.variables
assert hasattr(ds.variables['IR'], 'grid_mapping')
assert ds.variables['IR'].grid_mapping in ds.variables
assert_almost_equal(ds.variables['lon'][0, 0], f.prod_desc.lo1, 4)
assert_almost_equal(ds.variables['lat'][0, 0], f.prod_desc.la1, 4)
def test_gini_dataset():
"Test the dataset interface for GINI"
f = GiniFile(get_test_data("WEST-CONUS_4km_WV_20151208_2200.gini"))
ds = f.to_dataset()
assert "x" in ds.variables
assert "y" in ds.variables
assert "WV" in ds.variables
assert hasattr(ds.variables["WV"], "grid_mapping")
assert ds.variables["WV"].grid_mapping in ds.variables
assert_almost_equal(ds.variables["lon"][0, 0], f.prod_desc.lo1, 4)
assert_almost_equal(ds.variables["lat"][0, 0], f.prod_desc.la1, 4)
def test_gini_ak_regional_dataset():
'Test the dataset interface for GINI of a AK REGIONAL file'
f = GiniFile(get_test_data('AK-REGIONAL_8km_3.9_20160408_1445.gini'))
ds = f.to_dataset()
assert 'x' in ds.variables
assert 'y' in ds.variables
assert 'IR' in ds.variables
assert hasattr(ds.variables['IR'], 'grid_mapping')
assert ds.variables['IR'].grid_mapping in ds.variables
assert_almost_equal(ds.variables['lon'][0, 0], f.prod_desc.lo1, 4)
assert_almost_equal(ds.variables['lat'][0, 0], f.prod_desc.la1, 4)
assert_almost_equal(ds.variables['Polar_Stereographic'].longitude_of_projection_origin,
210.0)
assert_almost_equal(ds.variables['Polar_Stereographic'].latitude_of_projection_origin,
90.0)
def test_gini_ak_regional():
'Test reading of AK Regional Gini file'
f = GiniFile(get_test_data('AK-REGIONAL_8km_3.9_20160408_1445.gini'))
pdb = f.prod_desc
assert pdb.source == 1
assert pdb.creating_entity == 'GOES-15'
assert pdb.sector_id == 'Alaska Regional'
assert pdb.channel == 'IR (3.9 micron)'
assert pdb.num_records == 408
assert pdb.record_len == 576
assert pdb.datetime, datetime(2016, 4, 8, 14, 45, 20 == 0)
assert pdb.projection == GiniProjection.polar_stereographic
assert pdb.nx == 576
assert pdb.ny == 408
assert_almost_equal(pdb.la1, 42.0846, 4)
assert_almost_equal(pdb.lo1, -175.641, 4)
proj = f.proj_info
assert proj.reserved == 0
assert_almost_equal(proj.lov, 210.0, 1)
assert_almost_equal(proj.dx, 7.9375, 4)
assert_almost_equal(proj.dy, 7.9375, 4)
assert proj.proj_center == 0
pdb2 = f.prod_desc2
assert pdb2.scanning_mode == [False, False, False]
assert_almost_equal(pdb2.lat_in, 0.0, 4)
assert pdb2.resolution == 8
assert pdb2.compression == 0
assert pdb2.version == 1
assert pdb2.pdb_size == 512
assert pdb2.nav_cal == 0
assert f.data.shape, (pdb.num_records == pdb.record_len)
def test_gini_basic():
'Basic test of GINI reading'
f = GiniFile(get_test_data('WEST-CONUS_4km_WV_20151208_2200.gini'))
pdb = f.prod_desc
assert pdb.source == 1
assert pdb.creating_entity == 'GOES-15'
assert pdb.sector_id == 'West CONUS'
assert pdb.channel == 'WV (6.5/6.7 micron)'
assert pdb.num_records == 1280
assert pdb.record_len == 1100
assert pdb.datetime, datetime(2015, 12, 8, 22, 0, 19 == 0)
assert pdb.projection == GiniProjection.lambert_conformal
assert pdb.nx == 1100
assert pdb.ny == 1280
assert_almost_equal(pdb.la1, 12.19, 4)
assert_almost_equal(pdb.lo1, -133.4588, 4)
proj = f.proj_info
assert proj.reserved == 0
assert_almost_equal(proj.lov, -95.0, 4)
assert_almost_equal(proj.dx, 4.0635, 4)
assert_almost_equal(proj.dy, 4.0635, 4)
assert proj.proj_center == 0
pdb2 = f.prod_desc2
assert pdb2.scanning_mode == [False, False, False]
assert_almost_equal(pdb2.lat_in, 25.0, 4)
assert pdb2.resolution == 4
assert pdb2.compression == 0
assert pdb2.version == 1
assert pdb2.pdb_size == 512
assert f.data.shape, (pdb.num_records == pdb.record_len)
def test_raw_gini(filename, pdb, pdb2, proj_info):
"""Test raw GINI parsing."""
f = GiniFile(get_test_data(filename))
assert f.prod_desc == pdb
assert f.prod_desc2 == pdb2
assert f.proj_info == proj_info
assert f.data.shape == (pdb.num_records, pdb.record_len)
def test_gini_mercator():
'Test reading of GINI file with Mercator projection (from HI)'
f = GiniFile(get_test_data('HI-REGIONAL_4km_3.9_20160616_1715.gini'))
pdb = f.prod_desc
assert pdb.source == 1
assert pdb.creating_entity == 'GOES-15'
assert pdb.sector_id == 'Hawaii Regional'
assert pdb.channel == 'IR (3.9 micron)'
assert pdb.num_records == 520
assert pdb.record_len == 560
assert pdb.datetime == datetime(2016, 6, 16, 17, 15, 18)
assert pdb.projection == GiniProjection.mercator
assert pdb.nx == 560
assert pdb.ny == 520
assert_almost_equal(pdb.la1, 9.343, 4)
assert_almost_equal(pdb.lo1, -167.315, 4)
proj = f.proj_info
assert proj.resolution == 0
assert_almost_equal(proj.la2, 28.0922, 4)
assert_almost_equal(proj.lo2, -145.878, 4)
assert proj.di == 0
assert proj.dj == 0
pdb2 = f.prod_desc2
assert pdb2.scanning_mode == [False, False, False]
assert_almost_equal(pdb2.lat_in, 20.0, 4)
assert pdb2.resolution == 4
assert pdb2.compression == 0
assert pdb2.version == 1
assert pdb2.pdb_size == 512
assert pdb2.nav_cal == 0
assert f.data.shape, (pdb.num_records == pdb.record_len)
def test_gini_str():
"""Test the str representation of GiniFile."""
f = GiniFile(get_test_data('WEST-CONUS_4km_WV_20151208_2200.gini'))
truth = ('GiniFile: GOES-15 West CONUS WV (6.5/6.7 micron)\n'
'\tTime: 2015-12-08 22:00:19\n\tSize: 1280x1100\n'
'\tProjection: lambert_conformal\n'
'\tLower Left Corner (Lon, Lat): (-133.4588, 12.19)\n\tResolution: 4km')
assert str(f) == truth
def test_gini_mercator_dataset():
'Test the dataset interface for a GINI file with Mercator projection'
f = GiniFile(get_test_data('HI-REGIONAL_4km_3.9_20160616_1715.gini'))
ds = f.to_dataset()
assert 'x' in ds.variables
assert 'y' in ds.variables
assert 'IR' in ds.variables
assert hasattr(ds.variables['IR'], 'grid_mapping')
assert ds.variables['IR'].grid_mapping in ds.variables
lat = ds.variables['lat']
lon = ds.variables['lon']
assert_almost_equal(lon[0, 0], f.prod_desc.lo1, 4)
assert_almost_equal(lat[0, 0], f.prod_desc.la1, 4)
# 2nd corner lat/lon are at the "upper right" corner of the pixel, so need to add one
# more grid point
assert_almost_equal(lon[-1, -1] + (lon[-1, -1] - lon[-1, -2]), f.proj_info.lo2, 4)
assert_almost_equal(lat[-1, -1] + (lat[-1, -1] - lat[-2, -1]), f.proj_info.la2, 4)
assert_almost_equal(ds.variables['Mercator'].longitude_of_projection_origin, -167.315)
assert_almost_equal(ds.variables['Mercator'].latitude_of_projection_origin, 9.343)
def test_gini_mercator_dataset():
'Test the dataset interface for a GINI file with Mercator projection'
f = GiniFile(get_test_data('HI-REGIONAL_4km_3.9_20160616_1715.gini'))
ds = f.to_dataset()
assert 'x' in ds.variables
assert 'y' in ds.variables
assert 'IR' in ds.variables
assert hasattr(ds.variables['IR'], 'grid_mapping')
assert ds.variables['IR'].grid_mapping in ds.variables
lat = ds.variables['lat']
lon = ds.variables['lon']
assert_almost_equal(lon[0, 0], f.prod_desc.lo1, 4)
assert_almost_equal(lat[0, 0], f.prod_desc.la1, 4)
# 2nd corner lat/lon are at the "upper right" corner of the pixel, so need to add one
# more grid point
assert_almost_equal(lon[-1, -1] + (lon[-1, -1] - lon[-1, -2]),
f.proj_info.lo2, 4)
assert_almost_equal(lat[-1, -1] + (lat[-1, -1] - lat[-2, -1]),
f.proj_info.la2, 4)
assert_almost_equal(
ds.variables['Mercator'].longitude_of_projection_origin, -167.315)
assert_almost_equal(ds.variables['Mercator'].latitude_of_projection_origin,
9.343)
def test_gini_dataset(filename, bounds, data_var, proj_attrs):
"""Test the dataset interface for GINI."""
f = GiniFile(get_test_data(filename))
ds = f.to_dataset()
# Check our calculated x and y arrays
x0, x1, y0, y1 = bounds
x = ds.variables['x']
assert_almost_equal(x[0], x0, 4)
assert_almost_equal(x[-1], x1, 4)
y = ds.variables['y']
assert_almost_equal(y[0], y0, 4)
assert_almost_equal(y[-1], y1, 4)
# Check the projection metadata
proj_name = ds.variables[data_var].grid_mapping
proj_var = ds.variables[proj_name]
for attr, val in proj_attrs.items():
assert getattr(proj_var, attr) == val, 'Values mismatch for ' + attr
# Check the lon/lat corner
assert_almost_equal(ds.variables['lon'][0, 0], f.prod_desc.lo1, 4)
assert_almost_equal(ds.variables['lat'][0, 0], f.prod_desc.la1, 4)
def mapimagery():
link = [
'http://thredds.ucar.edu/thredds/catalog/satellite/WV/EAST-CONUS_4km/current/catalog.xml',
'http://thredds.ucar.edu/thredds/catalog/satellite/IR/EAST-CONUS_4km/current/catalog.xml',
'http://thredds.ucar.edu/thredds/catalog/satellite/VIS/EAST-CONUS_1km/current/catalog.xml'
]
keywords = ['WV', 'IR', 'Visible']
descriptors = ['Water Vapor', 'Infrared', 'Visible']
index = random.randint(
0,
len(link) -
1) #random index that picks either wv, ir, or visible image to plot
image = []
for i in range(
1, 14, 4
): #loops through the first 4 hours, pulls out image from every hour
fig = plt.figure(figsize=(10, 10))
plt.subplots_adjust(hspace=0.03,
wspace=0,
top=1,
left=0,
right=0.85,
bottom=0.0)
plt.style.use('ggplot')
plt.axis('off')
figuretext = """Satellite Imagery from GOES East Satellite (Imagery retrieved from UCAR/Unidata)
To explore more, check out the following links:
http://www.weather.gov/satellite http://www.goes.noaa.gov/
http://www.ssec.wisc.edu/data/geo-new/#/animation
To learn how to interpret satellite imagery, check out the following links:
https://www.wunderground.com/about/satellite.asp
http://www.weather.cod.edu/labs/satellitelab/satlab.ppt"""
plt.figtext(0.05,
0.20,
figuretext,
horizontalalignment='left',
verticalalignment='top',
fontsize=16,
color='white',
bbox=dict(facecolor='gray', alpha=0.85))
cat = TDSCatalog(link[index]) ##current wv image
dataset_name = sorted(cat.datasets.keys())[
-i] #pulls the last one in the list above (most recent)
dataset = cat.datasets[
dataset_name] #saves that file into a dataset object
remote_gini_file = urllib2.urlopen(dataset.access_urls['HTTPServer'])
gini = GiniFile(
remote_gini_file
) ## MetPy's built in function to read gini files and parse them for you
gini_ds = gini.to_dataset() ## converts to more comprehensive dataset
x = gini_ds.variables['x'][:]
y = gini_ds.variables['y'][:]
dat = gini_ds.variables[str(keywords[index])]
proj_var = gini_ds.variables[
dat.grid_mapping] #pulls out the grid information (projection)
#print(proj_var)
# Create CartoPy projection information for the file
globe = ccrs.Globe(ellipse='sphere',
semimajor_axis=proj_var.earth_radius,
semiminor_axis=proj_var.earth_radius)
proj = ccrs.LambertConformal(
central_longitude=proj_var.longitude_of_central_meridian,
central_latitude=proj_var.latitude_of_projection_origin,
standard_parallels=[proj_var.standard_parallel],
globe=globe)
# Create a feature for States/Admin 1 regions at 1:50m from Natural Earth
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
lakes = cfeature.NaturalEarthFeature(category='physical',
name='lakes',
scale='110m',
facecolor='none')
ax = fig.add_subplot(1, 1, 1, projection=proj)
ax.add_feature(states_provinces, edgecolor='black', linewidth=2.0)
ax.add_feature(lakes, edgecolor='black', linewidth=2.0)
ax.coastlines(resolution='50m', zorder=2, color='black', linewidth=2.0)
ax.add_feature(cfeature.BORDERS,
linewidth=2.0) #find way to make higher res
if 'WV' in link[index]: #for wv color
map_norm, map_cmap = registry.get_with_steps('WVCIMSS', 0,
1) #color ramp
im = ax.imshow(dat[:],
cmap=map_cmap,
norm=map_norm,
zorder=0,
extent=(x.min(), x.max(), y.min(), y.max()),
origin='upper')
else: #for vis/ir color
im = ax.imshow(dat[:],
extent=(x.min(), x.max(), y.min(), y.max()),
origin='upper',
cmap='gray',
norm=plt.Normalize(0, 255))
time_var = gini_ds.variables['time']
timestamp = num2date(time_var[:].squeeze(), time_var.units)
text = ax.text(0.99,
0.95,
timestamp.strftime('%d %B %Y %H%MZ'),
horizontalalignment='right',
transform=ax.transAxes,
color='white',
fontsize=20,
weight='bold')
title = descriptors[index] + ' Satellite Image'
title_text = ax.text(0.99,
0.9,
title,
horizontalalignment='right',
transform=ax.transAxes,
color='white',
fontsize=20,
weight='bold')
title_text.set_path_effects([
patheffects.Stroke(linewidth=2, foreground='black'),
patheffects.Normal()
])
text.set_path_effects([
patheffects.Stroke(linewidth=2, foreground='black'),
patheffects.Normal()
])
fig.tight_layout()
#plt.show()
plt.savefig('Satellite_Figure_' + str(i) + '.png',
bbox_inches='tight',
pad_inches=0)
image.append('Satellite_Figure_' + str(i) + '.png')
plt.close()
return image
def test_gini_bad_size():
'Test reading a GINI file that reports a bad header size'
f = GiniFile(get_test_data('NHEM-MULTICOMP_1km_IR_20151208_2100.gini'))
pdb2 = f.prod_desc2
assert pdb2.pdb_size == 512 # Catching bad size
def mapimagery():
link = ['http://thredds.ucar.edu/thredds/catalog/satellite/WV/EAST-CONUS_4km/current/catalog.xml',
'http://thredds.ucar.edu/thredds/catalog/satellite/IR/EAST-CONUS_4km/current/catalog.xml',
'http://thredds.ucar.edu/thredds/catalog/satellite/VIS/EAST-CONUS_1km/current/catalog.xml']
keywords = ['WV', 'IR', 'Visible']
descriptors = ['Water Vapor', 'Infrared', 'Visible']
index = random.randint(0, len(link)-1) #random index that picks either wv, ir, or visible image to plot
image = []
for i in range(1,14,4): #loops through the first 4 hours, pulls out image from every hour
fig = plt.figure(figsize = (10, 10))
plt.subplots_adjust(hspace=0.03, wspace=0, top = 1, left = 0, right = 0.85, bottom = 0.0)
plt.style.use('ggplot')
plt.axis('off')
figuretext ="""Satellite Imagery from GOES East Satellite (Imagery retrieved from UCAR/Unidata)
To explore more, check out the following links:
http://www.weather.gov/satellite http://www.goes.noaa.gov/
http://www.ssec.wisc.edu/data/geo-new/#/animation
To learn how to interpret satellite imagery, check out the following links:
https://www.wunderground.com/about/satellite.asp
http://www.weather.cod.edu/labs/satellitelab/satlab.ppt"""
plt.figtext(0.05, 0.20, figuretext, horizontalalignment='left', verticalalignment='top',
fontsize = 16, color = 'white', bbox=dict(facecolor='gray', alpha = 0.85))
cat = TDSCatalog(link[index]) ##current wv image
dataset_name = sorted(cat.datasets.keys())[-i] #pulls the last one in the list above (most recent)
dataset = cat.datasets[dataset_name] #saves that file into a dataset object
remote_gini_file = urllib2.urlopen(dataset.access_urls['HTTPServer'])
gini = GiniFile(remote_gini_file) ## MetPy's built in function to read gini files and parse them for you
gini_ds = gini.to_dataset() ## converts to more comprehensive dataset
x = gini_ds.variables['x'][:]
y = gini_ds.variables['y'][:]
dat = gini_ds.variables[str(keywords[index])]
proj_var = gini_ds.variables[dat.grid_mapping] #pulls out the grid information (projection)
#print(proj_var)
# Create CartoPy projection information for the file
globe = ccrs.Globe(ellipse='sphere', semimajor_axis=proj_var.earth_radius,
semiminor_axis=proj_var.earth_radius)
proj = ccrs.LambertConformal(central_longitude=proj_var.longitude_of_central_meridian,
central_latitude=proj_var.latitude_of_projection_origin,
standard_parallels=[proj_var.standard_parallel], globe=globe)
# Create a feature for States/Admin 1 regions at 1:50m from Natural Earth
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
lakes = cfeature.NaturalEarthFeature(
category='physical',
name='lakes',
scale='110m',
facecolor='none')
ax = fig.add_subplot(1,1,1, projection=proj)
ax.add_feature(states_provinces, edgecolor='black', linewidth=2.0)
ax.add_feature(lakes, edgecolor='black', linewidth=2.0)
ax.coastlines(resolution='50m', zorder=2, color='black', linewidth=2.0)
ax.add_feature(cfeature.BORDERS, linewidth=2.0) #find way to make higher res
if 'WV' in link[index]: #for wv color
map_norm, map_cmap = registry.get_with_steps('WVCIMSS', 0, 1) #color ramp
im = ax.imshow(dat[:], cmap=map_cmap, norm=map_norm, zorder=0,
extent=(x.min(), x.max(), y.min(), y.max()), origin='upper')
else: #for vis/ir color
im = ax.imshow(dat[:], extent=(x.min(), x.max(), y.min(), y.max()), origin='upper',
cmap='gray', norm=plt.Normalize(0, 255))
time_var = gini_ds.variables['time']
timestamp = num2date(time_var[:].squeeze(), time_var.units)
text = ax.text(0.99, 0.95, timestamp.strftime('%d %B %Y %H%MZ'),
horizontalalignment='right', transform=ax.transAxes,
color='white', fontsize=20, weight='bold')
title = descriptors[index] + ' Satellite Image'
title_text = ax.text(0.99, 0.9, title, horizontalalignment='right', transform=ax.transAxes,
color='white', fontsize=20, weight='bold')
title_text.set_path_effects([patheffects.Stroke(linewidth=2, foreground='black'), patheffects.Normal()])
text.set_path_effects([patheffects.Stroke(linewidth=2, foreground='black'), patheffects.Normal()])
fig.tight_layout()
#plt.show()
plt.savefig('Satellite_Figure_' + str(i) + '.png', bbox_inches='tight', pad_inches=0)
image.append('Satellite_Figure_' + str(i) + '.png')
plt.close()
return image