def test_first_derivative_xarray_pint_conversion(test_da_lonlat):
"""Test first derivative with implicit xarray to pint quantity conversion."""
dx, _ = grid_deltas_from_dataarray(test_da_lonlat)
deriv = first_derivative(test_da_lonlat, delta=dx, axis=-1)
truth = np.array([[[-3.30782978e-06] * 4, [-3.42816074e-06] * 4, [-3.57012948e-06] * 4,
[-3.73759364e-06] * 4]] * 3) * units('kelvin / meter')
assert_array_almost_equal(deriv, truth, 12)
def test_grid_deltas_from_dataarray_xy(test_da_xy):
"""Test grid_deltas_from_dataarray with a xy grid."""
dx, dy = grid_deltas_from_dataarray(test_da_xy)
true_dx = np.array([[[[500] * 3]]]) * units('km')
true_dy = np.array([[[[500]] * 3]]) * units('km')
assert_array_almost_equal(dx, true_dx, 5)
assert_array_almost_equal(dy, true_dy, 5)
def test_grid_deltas_from_dataarray_xy(test_da_xy):
"""Test grid_deltas_from_dataarray with a xy grid."""
dx, dy = grid_deltas_from_dataarray(test_da_xy)
true_dx = np.array([[[[500] * 3]]]) * units('km')
true_dy = np.array([[[[500]] * 3]]) * units('km')
assert_array_almost_equal(dx, true_dx, 5)
assert_array_almost_equal(dy, true_dy, 5)
def test_first_derivative_xarray_pint_conversion(test_da_lonlat):
"""Test first derivative with implicit xarray to pint quantity conversion."""
dx, _ = grid_deltas_from_dataarray(test_da_lonlat)
deriv = first_derivative(test_da_lonlat, delta=dx, axis=-1)
truth = np.array([[[-3.30782978e-06] * 4, [-3.42816074e-06] * 4, [-3.57012948e-06] * 4,
[-3.73759364e-06] * 4]] * 3) * units('kelvin / meter')
assert_array_almost_equal(deriv, truth, 12)
def test_grid_deltas_from_dataarray_lonlat(test_da_lonlat):
"""Test grid_deltas_from_dataarray with a lonlat grid."""
dx, dy = grid_deltas_from_dataarray(test_da_lonlat)
true_dx = np.array([[[321609.59212064, 321609.59212065, 321609.59212064],
[310320.85961483, 310320.85961483, 310320.85961483],
[297980.72966733, 297980.72966733, 297980.72966733],
[284629.6008561, 284629.6008561, 284629.6008561]]]) * units.m
true_dy = np.array([[[369603.78775948, 369603.78775948, 369603.78775948, 369603.78775948],
[369802.28173967, 369802.28173967, 369802.28173967, 369802.28173967],
[370009.56291098, 370009.56291098, 370009.56291098,
370009.56291098]]]) * units.m
assert_array_almost_equal(dx, true_dx, 5)
assert_array_almost_equal(dy, true_dy, 5)
def test_grid_deltas_from_dataarray_lonlat(test_da_lonlat):
"""Test grid_deltas_from_dataarray with a lonlat grid."""
dx, dy = grid_deltas_from_dataarray(test_da_lonlat)
true_dx = np.array([[[321609.59212064, 321609.59212065, 321609.59212064],
[310320.85961483, 310320.85961483, 310320.85961483],
[297980.72966733, 297980.72966733, 297980.72966733],
[284629.6008561, 284629.6008561, 284629.6008561]]]) * units.m
true_dy = np.array([[[369603.78775948, 369603.78775948, 369603.78775948, 369603.78775948],
[369802.28173967, 369802.28173967, 369802.28173967, 369802.28173967],
[370009.56291098, 370009.56291098, 370009.56291098,
370009.56291098]]]) * units.m
assert_array_almost_equal(dx, true_dx, 5)
assert_array_almost_equal(dy, true_dy, 5)
# - ``normal_component``
# - ``tangential_component``
# - ``absolute_momentum``
#
# More details can be found by looking at the documentation for the specific function of
# interest.
#########################################################################
# There is also the special case of the helper function, ``grid_deltas_from_dataarray``, which
# takes a ``DataArray`` input, but returns unit arrays for use in other calculations. We could
# rewrite the above geostrophic wind example using this helper function as follows:
heights = data['height'].loc[time[0]].loc[{vertical.name: 500.}]
lat, lon = xr.broadcast(y, x)
f = mpcalc.coriolis_parameter(lat)
dx, dy = mpcalc.grid_deltas_from_dataarray(heights)
u_geo, v_geo = mpcalc.geostrophic_wind(heights, f, dx, dy)
print(u_geo)
print(v_geo)
#########################################################################
# Plotting
# --------
#
# Like most meteorological data, we want to be able to plot these data. DataArrays can be used
# like normal numpy arrays in plotting code, which is the recommended process at the current
# point in time, or we can use some of xarray's plotting functionality for quick inspection of
# the data.
#
# (More detail beyond the following can be found at `xarray's plotting reference
# <http://xarray.pydata.org/en/stable/plotting.html>`_.)
# - ``cross_section_components``
# - ``normal_component``
# - ``tangential_component``
# - ``absolute_momentum``
#
# More details can be found by looking at the documentation for the specific function.
#########################################################################
# There is also the special case of the helper function, ``grid_deltas_from_dataarray``, which
# takes a DataArray input, but returns unit arrays for use in other calculations. We could
# rewrite the above geostrophic wind example using this helper function as follows:
heights = data['height'].loc[time[0]].loc[{vertical.name: 500.}]
lat, lon = xr.broadcast(y, x)
f = mpcalc.coriolis_parameter(lat)
dx, dy = mpcalc.grid_deltas_from_dataarray(heights)
u_geo, v_geo = mpcalc.geostrophic_wind(heights, f, dx, dy)
print(u_geo)
print(v_geo)
#########################################################################
# Plotting
# --------
#
# Like most meteorological data, we want to be able to plot these data. DataArrays can be used
# like normal numpy arrays in plotting code, or we can use some of xarray's plotting
# functionality.
#
# (More detail beyond the following can be found at `xarray's plotting reference
# <http://xarray.pydata.org/en/stable/plotting.html>`_.)
