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)
# Because the actual data raster has the top row first, the maximum y value is y[0],
# while the minimum y value is y[-1]
y = ds.variables['y']
assert_almost_equal(y[-1], y0, 4)
assert_almost_equal(y[0], y1, 4)
xmin, xmax, ymin, ymax = ds.img_extent
assert_almost_equal(xmin, x0, 4)
assert_almost_equal(xmax, x1, 4)
assert_almost_equal(ymin, y0, 4)
assert_almost_equal(ymax, 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 lower left lon/lat corner
assert_almost_equal(ds.variables['lon'][-1, 0], f.prod_desc.lo1, 4)
assert_almost_equal(ds.variables['lat'][-1, 0], f.prod_desc.la1, 4)
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_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[0, -1] + (lon[0, -1] - lon[0, -2]), f.proj_info.lo2, 4)
assert_almost_equal(lat[0, -1] + (lat[0, -1] - lat[1, -1]), f.proj_info.la2, 4)
def get_closest_satellite_vis(time):
"""
Get the super national 8 km visible satellite image. The image closest to
the requested time will be returned.
time : datetime object of image
"""
catalog = TDSCatalog(
'http://thredds.ucar.edu/thredds/catalog/satellite/VIS/'
'SUPER-NATIONAL_8km/current/catalog.xml')
# Figure out the closest image to the requested time in the catalog
datasets = list(catalog.datasets)
dt_stamps = []
for dataset in datasets:
fmt = 'SUPER-NATIONAL_8km_VIS_%Y%m%d_%H%M.gini'
dt_stamps.append(datetime.strptime(dataset, fmt))
closest_time = min(dt_stamps, key=lambda x: abs(x - time))
index = dt_stamps.index(closest_time)
# Request that image and convert it to a netCDF like dataset
satellite = list(catalog.datasets.values())[index]
sat_data = GiniFile(
urllib.request.urlopen(satellite.access_urls['HTTPServer']))
ds = sat_data.to_dataset()
# Pull out the variables we need
x = ds.variables['x'][:]
y = ds.variables['y'][:]
channel_data = ds.variables['Visible']
time_var = ds.variables['time']
proj_var = ds.variables[channel_data.grid_mapping]
# Make a globe and projection for this dataset
globe = ccrs.Globe(ellipse='sphere',
semimajor_axis=proj_var.earth_radius,
semiminor_axis=proj_var.earth_radius)
proj = ccrs.Stereographic(
central_longitude=proj_var.straight_vertical_longitude_from_pole,
central_latitude=proj_var.latitude_of_projection_origin,
true_scale_latitude=proj_var.standard_parallel,
globe=globe)
timestamp = netCDF4.num2date(time_var[:].squeeze(), time_var.units)
return timestamp, globe, proj, x, y, channel_data
def get_closest_satellite_vis(time):
"""
Get the super national 8 km visible satellite image. The image closest to
the requested time will be returned.
time : datetime object of image
"""
catalog = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/satellite/VIS/'
'SUPER-NATIONAL_8km/current/catalog.xml')
# Figure out the closest image to the requested time in the catalog
datasets = list(catalog.datasets)
dt_stamps = []
for dataset in datasets:
fmt = 'SUPER-NATIONAL_8km_VIS_%Y%m%d_%H%M.gini'
dt_stamps.append(datetime.strptime(dataset, fmt))
closest_time = min(dt_stamps, key=lambda x: abs(x - time))
index = dt_stamps.index(closest_time)
# Request that image and convert it to a netCDF like dataset
satellite = list(catalog.datasets.values())[index]
sat_data = GiniFile(urllib.request.urlopen(satellite.access_urls['HTTPServer']))
ds = sat_data.to_dataset()
# Pull out the variables we need
x = ds.variables['x'][:]
y = ds.variables['y'][:]
channel_data = ds.variables['Visible']
time_var = ds.variables['time']
proj_var = ds.variables[channel_data.grid_mapping]
# Make a globe and projection for this dataset
globe = ccrs.Globe(ellipse='sphere', semimajor_axis=proj_var.earth_radius,
semiminor_axis=proj_var.earth_radius)
proj = ccrs.Stereographic(central_longitude=proj_var.straight_vertical_longitude_from_pole,
central_latitude=proj_var.latitude_of_projection_origin,
true_scale_latitude=proj_var.standard_parallel, globe=globe)
timestamp = netCDF4.num2date(time_var[:].squeeze(), time_var.units)
return timestamp, globe, proj, x, y, channel_data
def test_gini_dataset(filename, bounds, data_var, proj_attrs, image, dt):
"""Test the dataset interface for GINI."""
f = GiniFile(get_test_data(filename))
with pytest.warns(MetpyDeprecationWarning):
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)
# Because the actual data raster has the top row first, the maximum y value is y[0],
# while the minimum y value is y[-1]
y = ds.variables['y']
assert_almost_equal(y[-1], y0, 4)
assert_almost_equal(y[0], y1, 4)
xmin, xmax, ymin, ymax = ds.img_extent
assert_almost_equal(xmin, x0, 4)
assert_almost_equal(xmax, x1, 4)
assert_almost_equal(ymin, y0, 4)
assert_almost_equal(ymax, 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 lower left lon/lat corner
assert_almost_equal(ds.variables['lon'][-1, 0], f.prod_desc.lo1, 4)
assert_almost_equal(ds.variables['lat'][-1, 0], f.prod_desc.la1, 4)
# Check a pixel of the image to make sure we're decoding correctly
x_ind, y_ind, val = image
assert val == ds.variables[data_var][x_ind, y_ind]
# Check that the decoded date time will work properly
assert f.prod_desc.datetime == dt
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)
from metpy.cbook import get_test_data
from metpy.io import GiniFile
from metpy.plots import add_metpy_logo, add_timestamp, ctables
###########################################
# Open the GINI file from the test data
f = GiniFile(get_test_data('WEST-CONUS_4km_WV_20151208_2200.gini'))
print(f)
###########################################
# Get a Dataset view of the data (essentially a NetCDF-like interface to the
# underlying data). Pull out the data, (x, y) coordinates, and the projection
# information.
ds = f.to_dataset()
x = ds.variables['x'][:]
y = ds.variables['y'][:]
dat = ds.variables['WV']
proj_var = ds.variables[dat.grid_mapping]
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,
from metpy.cbook import get_test_data
from metpy.io import GiniFile
from metpy.plots import ctables
###########################################
# Open the GINI file from the test data
f = GiniFile(get_test_data('WEST-CONUS_4km_WV_20151208_2200.gini'))
print(f)
###########################################
# Get a Dataset view of the data (essentially a NetCDF-like interface to the
# underlying data). Pull out the data, (x, y) coordinates, and the projection
# information.
ds = f.to_dataset()
x = ds.variables['x'][:]
y = ds.variables['y'][:]
dat = ds.variables['WV']
proj_var = ds.variables[dat.grid_mapping]
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)
