def test_isotropic_ps_slope(chunk, N=512, dL=1.0, amp=1e1, s=-3.0):
"""Test the spectral slope of isotropic power spectrum."""
theta = synthetic_field_xr(
N,
dL,
amp,
s,
other_dim_sizes=[10],
dim_order=True,
)
if chunk:
theta = theta.chunk({"d0": 2})
iso_ps = xrft.isotropic_power_spectrum(
theta, dim=["y", "x"], detrend="constant", density=True
).mean("d0")
npt.assert_almost_equal(np.ma.masked_invalid(iso_ps).mask.sum(), 0.0)
y_fit, a, b = xrft.fit_loglog(iso_ps.freq_r.values[4:], iso_ps.values[4:])
npt.assert_allclose(a, s, atol=0.1)
iso_ps_sequal = np.zeros((len(theta.d0), int(N / 4)))
for i in range(len(theta.d0)):
iso_ps_sequal[i] = xrft.isotropic_power_spectrum(
theta.isel(d0=i), detrend="constant", density=True
)
npt.assert_almost_equal(iso_ps.values, iso_ps_sequal.mean(axis=0))
def test_isotropic_ps():
"""Test data with extra coordinates"""
da = xr.DataArray(np.random.rand(2,5,16,32),
dims=['time','z','y','x'],
coords={'time': np.array(['2019-04-18', '2019-04-19'],
dtype='datetime64'),
'zz': ('z',np.arange(5)), 'z': np.arange(5),
'y': np.arange(16), 'x': np.arange(32)})
with pytest.raises(ValueError):
xrft.isotropic_power_spectrum(da, dim=['z','y','x'])
iso_ps = xrft.isotropic_power_spectrum(da, dim=['y','x'])
npt.assert_equal(
np.ma.masked_invalid(iso_ps.isel(freq_r=slice(1,None))).mask.sum(),
0.)
def test_isotropic_ps():
"""Test data with extra coordinates"""
da = xr.DataArray(
np.random.rand(2, 5, 16, 32),
dims=["time", "z", "y", "x"],
coords={
"time": np.array(["2019-04-18", "2019-04-19"], dtype="datetime64"),
"zz": ("z", np.arange(5)),
"z": np.arange(5),
"y": np.arange(16),
"x": np.arange(32),
},
)
with pytest.raises(ValueError):
xrft.isotropic_power_spectrum(da, dim=["z", "y", "x"])
iso_ps = xrft.isotropic_power_spectrum(da, dim=["y", "x"])
npt.assert_equal(
np.ma.masked_invalid(iso_ps.isel(freq_r=slice(1, None))).mask.sum(),
0.0)
def test_isotropic_ps_slope(N=512, dL=1., amp=1e1, s=-3.):
"""Test the spectral slope of isotropic power spectrum."""
theta = xr.DataArray(synthetic_field(N, dL, amp, s),
dims=['y', 'x'],
coords={'y':range(N), 'x':range(N)})
iso_ps = xrft.isotropic_power_spectrum(theta, detrend='constant',
density=True)
npt.assert_almost_equal(np.ma.masked_invalid(iso_ps[1:]).mask.sum(), 0.)
y_fit, a, b = xrft.fit_loglog(iso_ps.freq_r.values[4:],
iso_ps.values[4:])
npt.assert_allclose(a, s, atol=.1)