def test_relative_vorticity_distance(self):
x_min = 0.0
x_max = 100.0
dx = 1.0
x_1d = numpy.arange(x_min, x_max, dx)
size = x_1d.size
data_1d = x_1d * 2.0 + 1.0
data_2d = numpy.broadcast_to(data_1d[numpy.newaxis, :], (size, size))
dim_x = cf.DimensionCoordinate(
data=cf.Data(x_1d, "m"), properties={"axis": "X"}
)
dim_y = cf.DimensionCoordinate(
data=cf.Data(x_1d, "m"), properties={"axis": "Y"}
)
u = cf.Field()
X = u.set_construct(cf.DomainAxis(size=dim_x.data.size))
Y = u.set_construct(cf.DomainAxis(size=dim_y.data.size))
u.set_construct(dim_x, axes=[X])
u.set_construct(dim_y, axes=[Y])
u.set_data(cf.Data(data_2d, "m/s"), axes=("Y", "X"))
v = cf.Field()
v.set_construct(cf.DomainAxis(size=dim_x.data.size))
v.set_construct(cf.DomainAxis(size=dim_y.data.size))
v.set_construct(dim_x, axes=[X])
v.set_construct(dim_y, axes=[Y])
v.set_data(cf.Data(data_2d, "m/s"), axes=("X", "Y"))
rv = cf.relative_vorticity(u, v, one_sided_at_boundary=True)
self.assertTrue((rv.array == 0.0).all())
def test_relative_vorticity_distance(self):
if self.test_only and inspect.stack()[0][3] not in self.test_only:
returncf
x_min = 0.0
x_max = 100.0
dx = 1.0
x_1d = numpy.arange(x_min, x_max, dx)
size = x_1d.size
data_1d = x_1d * 2.0 + 1.0
data_2d = numpy.broadcast_to(data_1d[numpy.newaxis, :], (size, size))
dim_x = cf.DimensionCoordinate(data=cf.Data(x_1d, 'm'),
properties={'axis': 'X'})
dim_y = cf.DimensionCoordinate(data=cf.Data(x_1d, 'm'),
properties={'axis': 'Y'})
u = cf.Field()
X = u.set_construct(cf.DomainAxis(size=dim_x.data.size))
Y = u.set_construct(cf.DomainAxis(size=dim_y.data.size))
u.set_construct(dim_x, axes=[X])
u.set_construct(dim_y, axes=[Y])
u.set_data(cf.Data(data_2d, 'm/s'), axes=('Y', 'X'))
v = cf.Field()
v.set_construct(cf.DomainAxis(size=dim_x.data.size))
v.set_construct(cf.DomainAxis(size=dim_y.data.size))
v.set_construct(dim_x, axes=[X])
v.set_construct(dim_y, axes=[Y])
v.set_data(cf.Data(data_2d, 'm/s'), axes=('X', 'Y'))
rv = cf.relative_vorticity(u, v, one_sided_at_boundary=True)
self.assertTrue((rv.array == 0.0).all())
def test_relative_vorticity_latlong(self):
lat_min = -90.0
lat_max = 90.0
dlat = 1.0
lat_1d = numpy.arange(lat_min, lat_max, dlat)
lat_size = lat_1d.size
lon_min = 0.0
lon_max = 359.0
dlon = 1.0
lon_1d = numpy.arange(lon_min, lon_max, dlon)
lon_size = lon_1d.size
u_1d = lat_1d * 2.0 + 1.0
u_2d = numpy.broadcast_to(u_1d[numpy.newaxis, :], (lon_size, lat_size))
v_1d = lon_1d * 2.0 + 1.0
v_2d = numpy.broadcast_to(v_1d[:, numpy.newaxis], (lon_size, lat_size))
v_2d = v_2d * numpy.cos(lat_1d * numpy.pi / 180.0)[numpy.newaxis, :]
rv_array = (
u_2d
/ cf.Data(6371229.0, "meters")
* numpy.tan(lat_1d * numpy.pi / 180.0)[numpy.newaxis, :]
)
dim_x = cf.DimensionCoordinate(
data=cf.Data(lon_1d, "degrees_east"), properties={"axis": "X"}
)
dim_y = cf.DimensionCoordinate(
data=cf.Data(lat_1d, "degrees_north"), properties={"axis": "Y"}
)
u = cf.Field()
u.set_construct(cf.DomainAxis(size=lon_1d.size))
u.set_construct(cf.DomainAxis(size=lat_1d.size))
u.set_construct(dim_x)
u.set_construct(dim_y)
u.set_data(cf.Data(u_2d, "m/s"), axes=("X", "Y"))
u.cyclic("X", period=360.0)
v = cf.Field()
v.set_construct(cf.DomainAxis(size=lon_1d.size))
v.set_construct(cf.DomainAxis(size=lat_1d.size))
v.set_construct(dim_x)
v.set_construct(dim_y)
v.set_data(cf.Data(v_2d, "m/s"), axes=("X", "Y"))
v.cyclic("X", period=360.0)
rv = cf.relative_vorticity(u, v, wrap=False)
self.assertTrue(numpy.allclose(rv.array, rv_array))
def test_relative_vorticity_latlong(self):
if self.test_only and inspect.stack()[0][3] not in self.test_only:
return
lat_min = -90.0
lat_max = 90.0
dlat = 1.0
lat_1d = numpy.arange(lat_min, lat_max, dlat)
lat_size = lat_1d.size
lon_min = 0.0
lon_max = 359.0
dlon = 1.0
lon_1d = numpy.arange(lon_min, lon_max, dlon)
lon_size = lon_1d.size
u_1d = lat_1d * 2.0 + 1.0
u_2d = numpy.broadcast_to(lat_1d[numpy.newaxis, :],
(lon_size, lat_size))
v_1d = lon_1d * 2.0 + 1.0
v_2d = numpy.broadcast_to(lon_1d[:, numpy.newaxis],
(lon_size, lat_size))
v_2d = v_2d * numpy.cos(lat_1d * numpy.pi / 180.0)[numpy.newaxis, :]
rv_array = (u_2d / cf.Data(6371229.0, 'meters') *
numpy.tan(lat_1d * numpy.pi / 180.0)[numpy.newaxis, :])
dim_x = cf.DimensionCoordinate(data=cf.Data(lon_1d, 'degrees_east'),
properties={'axis': 'X'})
dim_y = cf.DimensionCoordinate(data=cf.Data(lat_1d, 'degrees_north'),
properties={'axis': 'Y'})
u = cf.Field()
u.set_construct(cf.DomainAxis(size=lon_1d.size))
u.set_construct(cf.DomainAxis(size=lat_1d.size))
u.set_construct(dim_x)
u.set_construct(dim_y)
u.set_data(cf.Data(u_2d, 'm/s'), axes=('X', 'Y'))
u.cyclic('X', period=360.0)
v = cf.Field()
v.set_construct(cf.DomainAxis(size=lon_1d.size))
v.set_construct(cf.DomainAxis(size=lat_1d.size))
v.set_construct(dim_x)
v.set_construct(dim_y)
v.set_data(cf.Data(v_2d, 'm/s'), axes=('X', 'Y'))
v.cyclic('X', period=360.0)
rv = cf.relative_vorticity(u, v, wrap=True)
self.assertTrue(numpy.allclose(rv.array, rv_array))
print(q)
q.iscyclic('X')
r = q.convolution_filter([0.1, 0.15, 0.5, 0.15, 0.1], axis='X')
print(r)
print(q.dimension_coordinate('X').bounds.array)
print(r.dimension_coordinate('X').bounds.array)
from scipy.signal import windows
exponential_weights = windows.exponential(3)
print(exponential_weights)
r = q.convolution_filter(exponential_weights, axis='Y')
print(r.array)
r = q.derivative('X')
r = q.derivative('Y', one_sided_at_boundary=True)
u, v = cf.read('wind_components.nc')
zeta = cf.relative_vorticity(u, v)
print(zeta)
print(zeta.array.round(8))
print("\n**Aggregation**\n")
a = cf.read('air_temperature.nc')[0]
a
a_parts = [a[0, :, 0:30], a[0, :, 30:96], a[1, :, 0:30], a[1, :, 30:96]]
a_parts
for i, f in enumerate(a_parts):
cf.write(f, str(i) + '_air_temperature.nc')
x = cf.read('[0-3]_air_temperature.nc')
y = cf.read('[0-3]_air_temperature.nc', aggregate=False)
z = cf.aggregate(y)