def main():
fname = iris.sample_data_path('NAME_output.txt')
boundary_volc_ash_constraint = iris.Constraint(
'VOLCANIC_ASH_AIR_CONCENTRATION', flight_level='From FL000 - FL200')
# Callback shown as None to illustrate where a cube-level callback function would be used if required
cube = iris.load_strict(fname, boundary_volc_ash_constraint, callback=None)
map = iplt.map_setup(lon_range=[-70, 20],
lat_range=[20, 75],
resolution='i')
map.drawcoastlines()
iplt.contourf(cube,
levels=(0.0002, 0.002, 0.004, 1),
colors=('#80ffff', '#939598', '#e00404'),
extend='max')
time = cube.coord('time')
time_date = time.units.num2date(time.points[0]).strftime(UTC_format)
plt.title('Volcanic ash concentration forecast\nvalid at %s' % time_date)
plt.show()
def test_simple(self):
# First sub-plot
plt.subplot(221)
plt.title('Default')
iplt.contourf(self.cube)
map = iplt.gcm()
map.drawcoastlines()
# Second sub-plot
plt.subplot(222)
plt.title('Molleweide')
iplt.map_setup(projection='moll', lon_0=120)
iplt.contourf(self.cube)
map = iplt.gcm()
map.drawcoastlines()
# Third sub-plot
plt.subplot(223)
plt.title('Native')
iplt.map_setup(cube=self.cube)
iplt.contourf(self.cube)
map = iplt.gcm()
map.drawcoastlines()
# Fourth sub-plot
plt.subplot(224)
plt.title('Three/six level')
iplt.contourf(self.cube, 3)
iplt.contour(self.cube, 6)
map = iplt.gcm()
map.drawcoastlines()
self.check_graphic()
def main():
fname = iris.sample_data_path('rotated_pole.nc')
temperature = iris.load_strict(fname)
# Calculate the lat lon range and buffer it by 10 degrees
lat_range, lon_range = iris.analysis.cartography.lat_lon_range(temperature)
lat_range = lat_range[0] - 10, lat_range[1] + 10
lon_range = lon_range[0] - 10, lon_range[1] + 10
# Plot #1: Point plot showing data values & a colorbar
plt.figure()
iplt.map_setup(temperature, lat_range=lat_range, lon_range=lon_range)
points = qplt.points(temperature, c=temperature.data)
cb = plt.colorbar(points, orientation='horizontal')
cb.set_label(temperature.units)
iplt.gcm().drawcoastlines()
plt.show()
# Plot #2: Contourf of the point based data
plt.figure()
iplt.map_setup(temperature, lat_range=lat_range, lon_range=lon_range)
qplt.contourf(temperature, 15)
iplt.gcm().drawcoastlines()
plt.show()
# Plot #3: Contourf overlayed by coloured point data
plt.figure()
iplt.map_setup(temperature, lat_range=lat_range, lon_range=lon_range)
qplt.contourf(temperature)
iplt.points(temperature, c=temperature.data)
iplt.gcm().drawcoastlines()
plt.show()
# For the purposes of this example, add some bounds to the latitude and longitude
temperature.coord('grid_latitude').guess_bounds()
temperature.coord('grid_longitude').guess_bounds()
# Plot #4: Block plot
plt.figure()
iplt.map_setup(temperature, lat_range=lat_range, lon_range=lon_range)
iplt.pcolormesh(temperature)
iplt.gcm().bluemarble()
iplt.gcm().drawcoastlines()
plt.show()
def main():
fname = iris.sample_data_path('rotated_pole.nc')
temperature = iris.load_strict(fname)
# Calculate the lat lon range and buffer it by 10 degrees
lat_range, lon_range = iris.analysis.cartography.lat_lon_range(temperature)
lat_range = lat_range[0] - 10, lat_range[1] + 10
lon_range = lon_range[0] - 10, lon_range[1] + 10
# Plot #1: Point plot showing data values & a colorbar
plt.figure()
iplt.map_setup(temperature, lat_range=lat_range, lon_range=lon_range)
points = qplt.points(temperature, c=temperature.data)
cb = plt.colorbar(points, orientation='horizontal')
cb.set_label(temperature.units)
iplt.gcm().drawcoastlines()
plt.show()
# Plot #2: Contourf of the point based data
plt.figure()
iplt.map_setup(temperature, lat_range=lat_range, lon_range=lon_range)
qplt.contourf(temperature, 15)
iplt.gcm().drawcoastlines()
plt.show()
# Plot #3: Contourf overlayed by coloured point data
plt.figure()
iplt.map_setup(temperature, lat_range=lat_range, lon_range=lon_range)
qplt.contourf(temperature)
iplt.points(temperature, c=temperature.data)
iplt.gcm().drawcoastlines()
plt.show()
# For the purposes of this example, add some bounds to the latitude and longitude
temperature.coord('grid_latitude').guess_bounds()
temperature.coord('grid_longitude').guess_bounds()
# Plot #4: Block plot
plt.figure()
iplt.map_setup(temperature, lat_range=lat_range, lon_range=lon_range)
iplt.pcolormesh(temperature)
iplt.gcm().bluemarble()
iplt.gcm().drawcoastlines()
plt.show()
def main():
fname = iris.sample_data_path('NAME_output.txt')
boundary_volc_ash_constraint = iris.Constraint('VOLCANIC_ASH_AIR_CONCENTRATION', flight_level='From FL000 - FL200')
# Callback shown as None to illustrate where a cube-level callback function would be used if required
cube = iris.load_strict(fname, boundary_volc_ash_constraint, callback=None)
map = iplt.map_setup(lon_range=[-70, 20], lat_range=[20, 75], resolution='i')
map.drawcoastlines()
iplt.contourf(cube,
levels=(0.0002, 0.002, 0.004, 1),
colors=('#80ffff', '#939598', '#e00404'),
extend='max'
)
time = cube.coord('time')
time_date = time.units.num2date(time.points[0]).strftime(UTC_format)
plt.title('Volcanic ash concentration forecast\nvalid at %s' % time_date)
plt.show()
def test_yx(self):
cube = self.wind[0, 0, :, :]
iplt.map_setup(cube=cube, mode=coords.POINT_MODE)
self.draw_method(cube)
self.check_graphic()
