def test_good_dtypes():
# FIXME: Using `np.asfarray` will prevent from using complex dtypes.
# NOTE: There is probably a more "numpy" efficient way to test this.
q, p, x, y = data()
dtypes = [int, float, np.integer, np.float16, np.float32,
np.float64, np.float128, np.floating]
for dtype in dtypes:
zslice(np.empty_like(q, dtype=dtype), p, p0=0)
def test_good_dtypes(data):
# FIXME: Using `np.asfarray` will prevent from using complex dtypes.
# NOTE: There is probably a more "numpy" efficient way to test this.
dtypes = [
int,
float,
np.integer,
np.float16,
np.float32,
np.float64,
np.float128,
np.floating,
]
for dtype in dtypes:
zslice(np.empty_like(data["q"], dtype=dtype), data["p"], p0=0)
def test_bad_dtypes(data):
# FIXME: Boolean array are converted to float! Only str fails correctly.
with pytest.raises(ValueError):
zslice(np.empty_like(data["q"], dtype=np.str_), data["p"], p0=0)
def test_p0_wrong_shape(data):
with pytest.raises(ValueError):
zslice(data["q"], data["p"], p0=np.zeros((2, 2)))
def test_mismatch_shapes(data):
with pytest.raises(ValueError):
zslice(data["q"], data["p"][0], p0=0)
def test_mismatch_shapes():
q, p, x, y = data()
with pytest.raises(ValueError):
zslice(q, p[0], p0=0)
def test_corret_results_2D(data, expected_results):
K, I, J = data["q"].shape
s50 = zslice(data["q"].reshape(K, -1), data["p"].reshape(K, -1), p0=-50)
np.testing.assert_almost_equal(s50, expected_results.ravel())
def test_gt_3D_input(data):
with pytest.raises(ValueError):
zslice(data["q"][np.newaxis, ...], data["p"][np.newaxis, ...], p0=0)
def test_2D_input(data):
K, I, J = data["q"].shape
s50 = zslice(data["q"].reshape(K, -1), data["p"].reshape(K, -1), p0=-50)
assert s50.shape == (I * J, )
def test_gt_3D_input():
q, p, x, y = data()
with pytest.raises(ValueError):
zslice(q[np.newaxis, ...], p[np.newaxis, ...], p0=0)
def test_1D_input():
q, p, x, y = data()
with pytest.raises(ValueError):
zslice(q.ravel(), p.ravel(), p0=0)
def test_2D_input():
q, p, x, y = data()
K, I, J = q.shape
s50 = zslice(q.reshape(K, -1), p.reshape(K, -1), p0=-50)
assert s50.shape == (I*J,)
def test_3D_input():
q, p, x, y = data()
K, I, J = q.shape
s50 = zslice(q, p, p0=-50)
assert s50.shape == (I, J)
def test_bad_dtypes():
# FIXME: Boolean array are converted to float! Only str fails correctly.
q, p, x, y = data()
with pytest.raises(ValueError):
zslice(np.empty_like(q, dtype=np.str_), p, p0=0)
def test_p0_wrong_shape():
q, p, x, y = data()
with pytest.raises(ValueError):
zslice(q, p, p0=np.zeros((2, 2)))
def test_corret_results_3D():
q, p, x, y = data()
s50 = zslice(q, p, p0=-50)
f50 = expected_results()
np.testing.assert_almost_equal(s50, f50)
def test_3D_input(data):
K, I, J = data["q"].shape
s50 = zslice(data["q"], data["p"], p0=-50)
assert s50.shape == (I, J)
def test_corret_results_2D():
q, p, x, y = data()
K, I, J = q.shape
s50 = zslice(q.reshape(K, -1), p.reshape(K, -1), p0=-50)
f50 = expected_results()
np.testing.assert_almost_equal(s50, f50.ravel())
def test_1D_input(data):
with pytest.raises(ValueError):
zslice(data["q"].ravel(), data["p"].ravel(), p0=0)
def test_p0_outside_bounds():
with pytest.raises(ValueError):
q, p, x, y = data()
K, I, J = q.shape
zslice(q, p, p0=50)
def test_corret_results_3D(data, expected_results):
s50 = zslice(data["q"], data["p"], p0=-50)
np.testing.assert_almost_equal(s50, expected_results)
# In[7]:
# calculate the 3D z values using formula terms by specifying this derived vertical coordinate
# with a terrible name
z3d = var.coord('sea_surface_height_above_reference_ellipsoid').points
# In[8]:
# read the 3D chuck of data
var3d = var.data
# In[9]:
# specify depth for fixed z slice
z0 = -25
isoslice = zslice(var3d, z3d, z0)
# In[10]:
# For some reason I cannot tricontourf with NaNs.
isoslice = ma.masked_invalid(isoslice)
vmin, vmax = isoslice.min(), isoslice.max()
isoslice = isoslice.filled(fill_value=-999)
# In[11]:
def make_map(projection=ccrs.PlateCarree()):
fig, ax = plt.subplots(figsize=(9, 13),
subplot_kw=dict(projection=projection))
gl = ax.gridlines(draw_labels=True)
def test_p0_outside_bounds(data):
with pytest.raises(ValueError):
K, I, J = data["q"].shape
zslice(data["q"], data["p"], p0=50)
gl.xlabels_top = gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
ax.coastlines('50m')
return fig, ax
cube = iris.load_cube(url, "sea_water_potential_temperature")
cube = cube[-1, ...] # last time step
lon = cube.coord(axis='X').points
lat = cube.coord(axis='Y').points
p = cube.coord("sea_surface_height_above_reference_ellipsoid").points
p0 = -250
isoslice = zslice(cube.data, p, p0)
fig, ax = make_map()
ax.set_extent(
[lon.min(), lon.max(),
lat.min(), lat.max()]
)
cs = ax.pcolormesh(
lon, lat,
ma.masked_invalid(isoslice),
)
kw = {"shrink": 0.65, "orientation": "horizontal", "extend": "both"}
cbar = fig.colorbar(cs, **kw)
