def test_get_transect_uv():
mesh_path = os.path.join(my_data_folder, "pi-grid")
mesh = load_mesh(mesh_path,
abg=[50, 15, -90],
usepickle=False,
usejoblib=False)
data_path = os.path.join(my_data_folder, "pi-results")
u = get_data(data_path, "u", [1948, 1949], mesh, compute=False, depth=0)
v = get_data(data_path, "v", [1948, 1949], mesh, compute=False, depth=0)
u_int, v_int, lonreg2, latreg2 = get_vector_forplot(u, v, mesh)
assert u_int.max() == pytest.approx(0.1808899530319614)
assert u_int.min() == pytest.approx(-0.2817374262365221)
assert u_int.mean() == pytest.approx(0.0033813037887997395)
assert v_int.max() == pytest.approx(0.22638858037707627)
assert v_int.min() == pytest.approx(-0.23170293870157044)
assert v_int.mean() == pytest.approx(0.00045745072385952796)
u = get_data(data_path, "u", [1948, 1949], mesh, compute=False, depth=1000)
v = get_data(data_path, "v", [1948, 1949], mesh, compute=False, depth=1000)
u_int, v_int, lonreg2, latreg2 = get_vector_forplot(u, v, mesh)
assert u_int.max() == pytest.approx(0.11594848468259907)
assert u_int.min() == pytest.approx(-0.06621269500377537)
assert u_int.mean() == pytest.approx(0.0023697001377188153)
assert v_int.max() == pytest.approx(0.07796926296747467)
assert v_int.min() == pytest.approx(-0.08086355433299418)
assert v_int.mean() == pytest.approx(-0.0004927562442740134)
def test_get_transect_uv():
mesh_path = os.path.join(my_data_folder, "pi-grid")
mesh = load_mesh(mesh_path, abg=[50, 15, -90], usepickle=False, usejoblib=False)
data_path = os.path.join(my_data_folder, "pi-results")
u = get_data(data_path, "u", [1948, 1949], mesh)
v = get_data(data_path, "v", [1948, 1949], mesh)
u_nodes = tonodes3d(u, mesh)
v_nodes = tonodes3d(v, mesh)
lon_start = 120
lat_start = 75
lon_end = 120
lat_end = 80
lonlat = transect_get_lonlat(lon_start, lat_start, lon_end, lat_end, 30)
dist, rot_u, rot_v = get_transect_uv(u_nodes, v_nodes, mesh, lonlat, myangle=0)
assert dist.shape == (30.,)
assert rot_u.shape == (30,47)
assert rot_v.shape == (30,47)
assert rot_u.max() == pytest.approx(0.012118130919170302)
assert rot_u.min() == pytest.approx(-0.010595729181566588)
assert rot_v.max() == pytest.approx(0.014261195850716241)
assert rot_v.min() == pytest.approx(-0.0024032835331608696)
dist, rot_u_90, rot_v_90 = get_transect_uv(u_nodes, v_nodes, mesh, lonlat, myangle=90)
assert rot_u_90.max() == pytest.approx(0.014261195850716241)
assert rot_u_90.min() == pytest.approx(-0.0024032835331608696)
assert rot_v_90.max() == pytest.approx(0.012118130919170302)
assert rot_v_90.min() == pytest.approx(-0.010595729181566588)
def test_get_plot_levels():
mesh_path = os.path.join(my_data_folder, "pi-grid")
data_path = os.path.join(my_data_folder, "pi-results")
mesh = load_mesh(mesh_path,
abg=[50, 15, -90],
usepickle=False,
usejoblib=False)
data = get_data(data_path, "temp", 1948, mesh, depth=0)
data_levels = get_plot_levels([0, 1, 5], data, lev_to_data=False)
assert data_levels[3] == 0.75
data_levels = get_plot_levels([0, 1, 5, 10], data, lev_to_data=False)
assert data_levels[3] == 10
data_levels = get_plot_levels([-100, 100, 5], data, lev_to_data=True)
assert data_levels[3] == pytest.approx(21.13924074)
data_levels = get_plot_levels([-100, 100, 5], data, lev_to_data=False)
assert data_levels[3] == 50
def test_plot():
mesh_path = os.path.join(my_data_folder, "pi-grid")
data_path = os.path.join(my_data_folder, "pi-results")
figure_path = os.path.join(my_data_folder, "baseline_images")
mesh = load_mesh(mesh_path,
abg=[50, 15, -90],
usepickle=False,
usejoblib=False)
data = get_data(data_path, "temp", 1948, mesh, depth=0)
# standard plot
ax = plot(mesh, data, influence=800000)
plt.savefig("./out.png")
baseline_image = os.path.join(figure_path, "plot_temp_basic.png")
compare_images("./out.png", baseline_image, tol=10)
os.remove("./out.png")
# inverce distance interpolation
ax = plot(mesh, data, influence=800000, interp="idist")
plt.savefig("./out.png")
baseline_image = os.path.join(figure_path, "plot_temp_idist.png")
compare_images("./out.png", baseline_image, tol=10)
os.remove("./out.png")
# pc projection
ax = plot(mesh, data, influence=800000, mapproj="pc")
plt.savefig("./out.png")
baseline_image = os.path.join(figure_path, "plot_temp_pc.png")
compare_images("./out.png", baseline_image, tol=10)
os.remove("./out.png")
# pc projection
ax = plot(mesh, data, influence=800000, mapproj="pc")
plt.savefig("./out.png")
baseline_image = os.path.join(figure_path, "plot_temp_pc.png")
compare_images("./out.png", baseline_image, tol=10)
os.remove("./out.png")
# np projection
ax = plot(mesh, data, influence=800000, mapproj="np")
plt.savefig("./out.png")
baseline_image = os.path.join(figure_path, "plot_temp_np.png")
compare_images("./out.png", baseline_image, tol=10)
os.remove("./out.png")
def test_plot():
mesh_path = os.path.join(my_data_folder, 'pi-grid')
data_path = os.path.join(my_data_folder, 'pi-results')
figure_path = os.path.join(my_data_folder, 'baseline_images')
mesh = load_mesh(mesh_path,
abg=[50, 15, -90],
usepickle=False,
usejoblib=False)
data = get_data(data_path, 'temp', 1948, mesh, depth=0)
# standard plot
ax = plot(mesh, data, influence=800000)
plt.savefig('./out.png')
baseline_image = os.path.join(figure_path, 'plot_temp_basic.png')
compare_images('./out.png', baseline_image, tol=10)
os.remove('./out.png')
# inverce distance interpolation
ax = plot(mesh, data, influence=800000, interp='idist')
plt.savefig('./out.png')
baseline_image = os.path.join(figure_path, 'plot_temp_idist.png')
compare_images('./out.png', baseline_image, tol=10)
os.remove('./out.png')
# pc projection
ax = plot(mesh, data, influence=800000, mapproj='pc')
plt.savefig('./out.png')
baseline_image = os.path.join(figure_path, 'plot_temp_pc.png')
compare_images('./out.png', baseline_image, tol=10)
os.remove('./out.png')
# pc projection
ax = plot(mesh, data, influence=800000, mapproj='pc')
plt.savefig('./out.png')
baseline_image = os.path.join(figure_path, 'plot_temp_pc.png')
compare_images('./out.png', baseline_image, tol=10)
os.remove('./out.png')
# np projection
ax = plot(mesh, data, influence=800000, mapproj='np')
plt.savefig('./out.png')
baseline_image = os.path.join(figure_path, 'plot_temp_np.png')
compare_images('./out.png', baseline_image, tol=10)
os.remove('./out.png')
def test_regriding():
mesh_path = os.path.join(my_data_folder, 'pi-grid')
data_path = os.path.join(my_data_folder, 'pi-results')
mesh = load_mesh(mesh_path, usepickle=False, usejoblib=False)
lons = range(0, 360)
lats = range(-90, 90)
lons, lats = np.meshgrid(lons, lats)
data = get_data(data_path, 'temp', 1948, mesh, depth=0)
# default nn interpolation
data_inter = fesom2regular(data, mesh, lons, lats)
mmean = data_inter.mean()
assert mmean == pytest.approx(6.309350409986763)
assert isinstance(data_inter, np.ma.core.MaskedArray)
# idist method
data_inter = fesom2regular(data, mesh, lons, lats, how='idist')
mmean = data_inter.mean()
assert mmean == pytest.approx(6.308561066202526)
assert isinstance(data_inter, np.ma.core.MaskedArray)
# linear method from scipy
data_inter = fesom2regular(data, mesh, lons, lats, how='linear')
mmean = data_inter.mean()
assert mmean == pytest.approx(13.582933890477655)
assert isinstance(data_inter, np.ma.core.MaskedArray)
# cubic method from scipy
data_inter = fesom2regular(data, mesh, lons, lats, how='cubic')
mmean = data_inter.mean()
assert mmean == pytest.approx(13.39402615207708)
assert isinstance(data_inter, np.ma.core.MaskedArray)
# clean up
os.remove(os.path.join(mesh_path,
'distances_3140_0_359_-90_-90_360_180_1'))
os.remove(os.path.join(mesh_path,
'distances_3140_0_359_-90_-90_360_180_5'))
os.remove(os.path.join(mesh_path, 'inds_3140_0_359_-90_-90_360_180_1'))
os.remove(os.path.join(mesh_path, 'inds_3140_0_359_-90_-90_360_180_5'))
os.remove(os.path.join(mesh_path, 'qhull_3140'))
def test_get_transect():
mesh_path = os.path.join(my_data_folder, "pi-grid")
mesh = load_mesh(mesh_path, abg=[50, 15, -90], usepickle=False, usejoblib=False)
data_path = os.path.join(my_data_folder, "pi-results")
data = get_data(data_path, "temp", [1948, 1949], mesh)
lon_start = -149
lat_start = 70.52
lon_end = -149
lat_end = 73
lonlat = transect_get_lonlat(lon_start, lat_start, lon_end, lat_end)
dist, transect_data = get_transect(data, mesh, lonlat)
assert dist.shape[0] == 30
assert dist.max() == pytest.approx(258.87327988009883)
assert dist.min() == 0
assert transect_data.shape == (30, 47)
assert transect_data.min() == pytest.approx(-1.446849)
assert transect_data.max() == pytest.approx(0.38322645)
def test_xmoc_data():
mesh_path = os.path.join(my_data_folder, "pi-grid")
data_path = os.path.join(my_data_folder, "pi-results")
mesh = load_mesh(mesh_path, usepickle=False, usejoblib=False)
# xarray as input
data = get_data(data_path, "w", [1948], mesh, how="mean", compute=False)
lats, moc = xmoc_data(mesh, data)
assert moc.mean() == pytest.approx(-5.283107985611987)
assert moc.max() == pytest.approx(32.49306582121843)
assert moc.min() == pytest.approx(-79.29266240207812)
# numpy as input
data = get_data(data_path, "w", [1948], mesh, how="mean", compute=True)
lats, moc = xmoc_data(mesh, data)
assert moc.mean() == pytest.approx(-5.283107985611987)
assert moc.max() == pytest.approx(32.49306582121843)
assert moc.min() == pytest.approx(-79.29266240207812)
# masked
# xarray as input
data = get_data(data_path, "w", [1948], mesh, how="mean", compute=False)
lats, moc = xmoc_data(mesh, data, mask="Atlantic_MOC")
assert moc.mean() == pytest.approx(-6.4900976496575975)
assert moc.max() == pytest.approx(19.88617032403639)
assert moc.min() == pytest.approx(-49.8639074605277)
# numpy as input
data = get_data(data_path, "w", [1948], mesh, how="mean", compute=True)
lats, moc = xmoc_data(mesh, data, mask="Atlantic_MOC")
assert moc.mean() == pytest.approx(-6.4900976496575975)
assert moc.max() == pytest.approx(19.88617032403639)
assert moc.min() == pytest.approx(-49.8639074605277)
# different nlats
# xarray as input
data = get_data(data_path, "w", [1948], mesh, how="mean", compute=False)
lats, moc = xmoc_data(mesh, data, nlats=30)
assert moc.mean() == pytest.approx(-5.5225348525691)
assert moc.max() == pytest.approx(32.37976644734857)
assert moc.min() == pytest.approx(-80.2349660966104)
# numpy as input
data = get_data(data_path, "w", [1948], mesh, how="mean", compute=True)
lats, moc = xmoc_data(mesh, data, nlats=30)
assert moc.mean() == pytest.approx(-5.5225348525691)
assert moc.max() == pytest.approx(32.37976644734857)
assert moc.min() == pytest.approx(-80.2349660966104)
def test_ice_integrals():
mesh_path = os.path.join(my_data_folder, "pi-grid")
data_path = os.path.join(my_data_folder, "pi-results")
mesh = load_mesh(mesh_path, usepickle=False, usejoblib=False)
# default get_data (with how='mean') should work.
data = get_data(data_path, "a_ice", 1948, mesh, depth=0, compute=False)
ext = ice_ext(data, mesh)
assert ext.data[0] == pytest.approx(12710587600895.246)
data = get_data(data_path, "a_ice", 1948, mesh, depth=0, compute=False)
area = ice_area(data, mesh)
assert area.data[0] == pytest.approx(9066097785122.738)
data = get_data(data_path, "m_ice", 1948, mesh, depth=0, compute=False)
vol = ice_vol(data, mesh)
assert vol.data[0] == pytest.approx(13403821068217.506)
# work with xarray as input
data = get_data(data_path,
"a_ice",
1948,
mesh,
depth=0,
how="ori",
compute=False)
ext = ice_ext(data, mesh)
area = ice_area(data, mesh)
assert ext.data[0] == pytest.approx(12710587600895.246)
assert area.data[0] == pytest.approx(9066097785122.738)
data = get_data(data_path,
"m_ice",
1948,
mesh,
depth=0,
how="ori",
compute=False)
vol = ice_vol(data, mesh)
assert vol.data[0] == pytest.approx(13403821068217.506)
# work with numpy array as input
data = get_data(data_path,
"a_ice",
1948,
mesh,
depth=0,
how="ori",
compute=True)
ext = ice_ext(data, mesh)
# have to load data once again, since ice_ext actually modify numpy array.
# I don't want to add .copy to the `ice_ext` function.
data = get_data(data_path,
"a_ice",
1948,
mesh,
depth=0,
how="ori",
compute=True)
area = ice_area(data, mesh)
assert ext.data[0] == pytest.approx(12710587600895.246)
assert area.data[0] == pytest.approx(9066097785122.738)
data = get_data(data_path,
"m_ice",
1948,
mesh,
depth=0,
how="ori",
compute=True)
vol = ice_vol(data, mesh)
assert vol.data[0] == pytest.approx(13403821068217.506)
def test_volmean_data():
mesh_path = os.path.join(my_data_folder, "pi-grid")
data_path = os.path.join(my_data_folder, "pi-results")
mesh = load_mesh(mesh_path, usepickle=False, usejoblib=False)
# xarray as input
# time series
data = get_data(data_path,
"temp", [1948, 1949],
mesh,
how="ori",
compute=False)
result = volmean_data(data, mesh)
assert np.nanmean(result) == pytest.approx(3.3736459180632776)
# one point
data = get_data(data_path, "temp", [1948], mesh, how="ori", compute=False)
result = volmean_data(data, mesh)
assert np.nanmean(result) == pytest.approx(3.4039440198518953)
data = get_data(data_path,
"temp", [1948, 1949],
mesh,
how="mean",
compute=False)
result = volmean_data(data, mesh)
assert np.nanmean(result) == pytest.approx(3.3736459180632776)
# numpy array as input
# time series
data = get_data(data_path,
"temp", [1948, 1949],
mesh,
how="ori",
compute=True)
result = volmean_data(data, mesh)
assert np.nanmean(result) == pytest.approx(3.3736459180632776)
# one point
data = get_data(data_path, "temp", [1948], mesh, how="ori", compute=True)
result = volmean_data(data, mesh)
assert np.nanmean(result) == pytest.approx(3.4039440198518953)
data = get_data(data_path,
"temp", [1948, 1949],
mesh,
how="mean",
compute=True)
result = volmean_data(data, mesh)
assert np.nanmean(result) == pytest.approx(3.3736459180632776)
# different depth ranges
data = get_data(data_path, "temp", [1948], mesh, how="ori", compute=False)
result = volmean_data(data, mesh, [0, 100])
assert np.nanmean(result) == pytest.approx(16.26645462001221)
data = get_data(data_path, "temp", [1948], mesh, how="ori", compute=False)
result = volmean_data(data, mesh, [0, 'bottom'])
assert np.nanmean(result) == pytest.approx(3.4039440198518953)
data = get_data(data_path, "temp", [1948], mesh, how="ori", compute=False)
result = volmean_data(data, mesh, [500, 500])
assert np.nanmean(result) == pytest.approx(6.339069486142839)
def test_hovm_data():
mesh_path = os.path.join(my_data_folder, "pi-grid")
data_path = os.path.join(my_data_folder, "pi-results")
mesh = load_mesh(mesh_path, usepickle=False, usejoblib=False)
# work on xarray
# mean first, the hovm
data = get_data(data_path,
"temp", [1948, 1949],
mesh,
how="mean",
compute=False)
hovm = hovm_data(data, mesh)
assert hovm.shape == (1, 47)
assert np.nanmean(hovm) == pytest.approx(7.446110751429013)
# hovm first, then mean
data = get_data(data_path,
"temp", [1948, 1949],
mesh,
how="ori",
compute=False)
hovm = hovm_data(data, mesh)
assert hovm.shape == (2, 47)
assert np.nanmean(hovm) == pytest.approx(7.446110751429013)
# work on numpy array
# mean first, the hovm
data = get_data(data_path,
"temp", [1948, 1949],
mesh,
how="mean",
compute=True)
hovm = hovm_data(data, mesh)
assert hovm.shape == (1, 47)
assert np.nanmean(hovm) == pytest.approx(7.446110751429013)
# hovm first, then mean
data = get_data(data_path,
"temp", [1948, 1949],
mesh,
how="ori",
compute=True)
hovm = hovm_data(data, mesh)
assert hovm.shape == (2, 47)
assert np.nanmean(hovm) == pytest.approx(7.446110751429013)
# test when only 1 time step of 3d field is in input
data = get_data(data_path, "temp", [1948], mesh, how="mean", compute=False)
hovm = hovm_data(data, mesh)
assert hovm.shape == (1, 47)
assert np.nanmean(hovm) == pytest.approx(7.440160989229884)
data = get_data(data_path, "temp", [1948], mesh, how="mean", compute=True)
hovm = hovm_data(data, mesh)
assert hovm.shape == (1, 47)
assert np.nanmean(hovm) == pytest.approx(7.440160989229884)
# if we try to supplu 2d variable
with pytest.raises(ValueError):
data = get_data(data_path,
"a_ice", [1948, 1949],
mesh,
how="mean",
compute=True)
hovm = hovm_data(data, mesh)
def test_get_data():
mesh_path = os.path.join(my_data_folder, 'pi-grid')
data_path = os.path.join(my_data_folder, 'pi-results')
mesh = load_mesh(mesh_path, usepickle=False, usejoblib=False)
# variable on vertices
temp = get_data(data_path, 'temp', 1948, mesh, depth=0)
assert type(temp) == np.ndarray
mmin = temp.min()
assert mmin == pytest.approx(-1.8924446)
mmax = temp.max()
assert mmax == pytest.approx(28.816469)
# variable on elements
u = get_data(data_path, 'u', 1948, mesh, depth=0)
assert type(u) == np.ndarray
mmin = u.min()
assert mmin == pytest.approx(-0.51486444)
mmax = u.max()
assert mmax == pytest.approx(0.27181712)
# 2d variable on vertices
ice = get_data(data_path, 'a_ice', 1948, mesh, depth=0)
assert type(u) == np.ndarray
mmean = ice.mean()
assert mmean == pytest.approx(0.27451384)
# get multiple years
temp = get_data(data_path, 'temp', [1948, 1949], mesh, depth=0)
mmean = temp.mean()
assert mmean == pytest.approx(8.5541878)
# get one record from multiple files
temp = get_data(data_path,
'temp', [1948, 1949],
mesh,
records=slice(0, 1),
depth=0)
mmean = temp.mean()
assert mmean == pytest.approx(8.4580183)
# get different depth
temp = get_data(data_path, 'temp', [1948, 1949], mesh, depth=200)
mmean = temp.mean()
assert mmean == pytest.approx(5.3856239)
# get different depth and different aggregation
temp = get_data(data_path,
'temp', [1948, 1949],
mesh,
depth=200,
how='max')
mmean = temp.mean()
assert mmean == pytest.approx(5.6487503)
# directly open ncfile (in data 1948, but we directly request 1949)
temp = get_data(data_path,
'temp', [1948],
mesh,
depth=200,
how='max',
ncfile='{}/{}'.format(data_path, "temp.fesom.1949.nc"))
mmean = temp.mean()
assert mmean == pytest.approx(5.3057818)
# return dask array
temp = get_data(data_path,
'temp', [1948, 1949],
mesh,
depth=200,
how='max',
compute=False)
assert isinstance(temp, xr.DataArray)
# use range as argument
temp = get_data(data_path,
'temp',
range(1948, 1950),
mesh,
depth=200,
how='max')
mmean = temp.mean()
assert mmean == pytest.approx(5.6487503)