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():
# Enable a future option, to ensure that the netcdf load works the same way
# as in future Iris versions.
iris.FUTURE.netcdf_promote = True
# Load some test data.
fname = iris.sample_data_path('rotated_pole.nc')
air_pressure = iris.load_cube(fname)
# Plot #1: Point plot showing data values & a colorbar
plt.figure()
points = qplt.points(air_pressure, c=air_pressure.data)
cb = plt.colorbar(points, orientation='horizontal')
cb.set_label(air_pressure.units)
plt.gca().coastlines()
iplt.show()
# Plot #2: Contourf of the point based data
plt.figure()
qplt.contourf(air_pressure, 15)
plt.gca().coastlines()
iplt.show()
# Plot #3: Contourf overlayed by coloured point data
plt.figure()
qplt.contourf(air_pressure)
iplt.points(air_pressure, c=air_pressure.data)
plt.gca().coastlines()
iplt.show()
# For the purposes of this example, add some bounds to the latitude
# and longitude
air_pressure.coord('grid_latitude').guess_bounds()
air_pressure.coord('grid_longitude').guess_bounds()
# Plot #4: Block plot
plt.figure()
plt.axes(projection=ccrs.PlateCarree())
iplt.pcolormesh(air_pressure)
plt.gca().stock_img()
plt.gca().coastlines()
iplt.show()
def main():
fname = iris.sample_data_path('rotated_pole.nc')
temperature = iris.load_cube(fname)
# Plot #1: Point plot showing data values & a colorbar
plt.figure()
points = qplt.points(temperature, c=temperature.data)
cb = plt.colorbar(points, orientation='horizontal')
cb.set_label(temperature.units)
plt.gca().coastlines()
plt.show()
# Plot #2: Contourf of the point based data
plt.figure()
qplt.contourf(temperature, 15)
plt.gca().coastlines()
plt.show()
# Plot #3: Contourf overlayed by coloured point data
plt.figure()
qplt.contourf(temperature)
iplt.points(temperature, c=temperature.data)
plt.gca().coastlines()
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()
ax = plt.axes(projection=ccrs.PlateCarree())
iplt.pcolormesh(temperature)
plt.gca().stock_img()
plt.gca().coastlines()
plt.show()
def test_xaxis_labels(self):
qplt.points(self.cube, coords=("str_coord", "bar"))
self.assertBoundsTickLabels("xaxis")
def test_alignment(self):
cube = self._small()
qplt.contourf(cube)
#qplt.outline(cube)
qplt.points(cube)
self.check_graphic()
def test_alignment(self):
cube = self._small()
qplt.contourf(cube)
#qplt.outline(cube)
qplt.points(cube)
self.check_graphic()
def test_xaxis_labels(self):
qplt.points(self.cube, coords=('str_coord', 'bar'))
self.assertBoundsTickLabels('xaxis')
def test_xaxis_labels(self):
qplt.points(self.cube, coords=('str_coord', 'bar'))
self.assertBoundsTickLabels('xaxis')
