def test_obs_ncobsplatform_xylocsa():
# Load platform
obs = NcObsPlatform(NCFILE_OBS_PROFILES, name='profiles')
# Orig
lons_orig = obs.lons.copy()
lats_orig = obs.lats.copy()
# Init output
saout = obs.xylocsa_init_pert_output()
assert saout.shape == (4, len(lons_orig))
# Loop on indices
for i in obs.xylocsa_get_pert_indices_iter():
# X
# - activate
obs.xylocsa_activate_pert('+x', i)
assert abs(obs.lons - lons_orig).ptp() > 0.0001
# - deactivate
obs.xylocsa_deactivate_pert()
assert_allclose(obs.lons, lons_orig)
# Y
# - activate
obs.xylocsa_activate_pert('+y', i)
assert abs(obs.lats - lats_orig).ptp() > 0.0001
# - deactivate
obs.xylocsa_deactivate_pert()
assert_allclose(obs.lats, lats_orig)
return
def test_fcore_computeensstats():
"""Test the :func:`sonat._fcore.f_computeens` function"""
# Inits
nfiles = 5
nstate = 4
inpath = 'inputs'
infile = 'fieldA_{it}.txt'
states = N.zeros((nstate, nfiles), order='f')
element = 0
# Read states
for it in range(1, nfiles + 1):
states[:, it - 1] = N.loadtxt(
os.path.join(THIS_DIR, inpath, infile).format(**locals()))
# Anomaly
meanstate = states.mean(axis=1)
states -= meanstate.reshape(1, nstate).T
# Compute stats
skewness, kurtosis, status = f_computeensstats(element, meanstate, states)
# Checks
assert_allclose(status, 0)
assert_allclose([skewness, kurtosis], [-1.2529E+00, -1.3728E+00],
rtol=1e-4)
def test_arm_arm_indirect_spectrum():
# Load ARM
arm = get_arm()
# Indirect spectrum
pcs = N.dot(arm.S.T, arm.raw_arm)
spect = (pcs**2).sum(axis=0)
# Compare with direct spectrum
assert_allclose(spect, arm.raw_spect, atol=1e-7)
def test_obs_obsmanager_project_model():
# Load manager
manager = test_obs_obsmanager_init_surf()
# Load model
f = DS(NCFILE_MANGA0, 'mars', level='surf', logger_level='error')
temp = f('temp')
f.close()
temp.id += '_surf'
# Interpolate model
otemp = manager.project_model(temp)
assert_allclose(otemp[1][0], [12.91558515, 10.58179214])
return manager
def test_fcore_eofcovar():
"""Test the :func:`sonat._fcore.f_eofcovar` function"""
# Inits
nfiles = 5
nstate = 4
inpath = 'inputs'
infile = 'fieldA_{it}.txt'
outfile_eof = 'sangoma_eofcovar_uv_eof_{}.txt' # Files holding EOFs
outfile_svals = 'sangoma_eofcovar_uv_svals.txt' # Files holding singular values
outfile_mstate = 'sangoma_eofcovar_uv_meanstate.txt' # Files holding mean state
states = N.zeros((nstate, nfiles), order='f')
dim_fields = N.array([1])
offsets = 1
remove_mstate = 1
do_mv = 0
meanstate = N.zeros(states.shape[0], order='f')
# Read states
for it in range(1, nfiles + 1):
states[:, it - 1] = N.loadtxt(
os.path.join(THIS_DIR, inpath, infile).format(**locals()))
# EOF decomposition
stddev, svals, svec, status = f_eofcovar(dim_fields, offsets,
remove_mstate, do_mv, states,
meanstate)
# True values
stddev_true = 1.
svals_true = N.loadtxt(os.path.join(THIS_DIR, outfile_svals))
svec_true = N.array([
N.loadtxt(os.path.join(THIS_DIR, outfile_eof.format(i + 1)))
for i in xrange(nfiles - 1)
]).T
meanstate_true = N.loadtxt(os.path.join(THIS_DIR, outfile_mstate))
# Checks
assert_allclose(status, 0)
assert_allclose(stddev, stddev_true)
assert_allclose(svals[:nfiles - 1], svals_true)
assert_allclose(svec[:, :nfiles - 1], svec_true)
assert_allclose(meanstate, meanstate_true)
def test_arm_arm_init():
# Load ensemble
ens = Ensemble.from_file(ENS_NCFILE, checkvars=True, logger=LOGGER)
# Load observations
obs0 = NcObsPlatform(NCFILE_OBS_HFRADARS)
obs1 = NcObsPlatform(NCFILE_OBS_PROFILES)
obs2 = NcObsPlatform(NCFILE_OBS_SATSST)
obsmanager = ObsManager([obs0, obs1, obs2])
# Bathymetry
bathy = get_bathy()[::2, ::2]
# Init ARM
arm = ARM(ens, obsmanager, syncnorms=True, bathy=bathy)
# Checks
assert_allclose(arm.obsmanager[2].norms + arm.obsmanager[1].norms[:1],
arm.ens.norms[arm.ens.varnames.index('temp')])
return arm
def test_ens_ensemble_init():
# Load file from previous routine
ncfile = ENS_NCFILE
varnames = ['sal', 'temp', 'temp_surf', 'u_surf', 'v_surf']
vars = []
f = cdms2.open(ncfile)
for vname in f.listvariables():
if vname in varnames:
vars.append(f(vname))
f.close()
varnames = [v.id for v in vars]
# Init from variables
ensv = Ensemble(vars, checkvars=True)
# Init from file
ensf = Ensemble.from_file(ncfile, checkvars=True, logger=LOGGER)
# Checks
assert [v.id for v in ensv.variables] == varnames
assert [v.id for v in ensf.variables] == varnames
assert_allclose(ensv.stacked_data, ensf.stacked_data)
def test_obs_obsmanager_init_surf():
# Load and stack surface obs
obs_surf0 = NcObsPlatform(NCFILE_OBS_SURF,
lon=(-7, -5),
varnames=['temp'],
norms=0.2,
name='surf_west')
obs_surf1 = NcObsPlatform(NCFILE_OBS_SURF,
lon=(-5, 0),
varnames=['temp', 'sal'],
norms=[0.2, 0.1],
name='surf_east')
# Setup manager
manager = ObsManager([obs_surf0, obs_surf1])
# Checks
stacked = manager.stacked_data
assert_allclose(stacked, [1., 2.5, 1.5, 4., 1.5, 4.])
assert_allclose(manager.lons, [-5.8, -5.7, -4.6, -2.8])
assert_allclose(manager.lats, [48.1, 47.5, 47.4, 47.3])
assert manager.varnames == ['temp', 'sal']
model_specs = manager.get_model_specs()
assert sorted(model_specs.keys()) == ['depths', 'lat', 'lon', 'varnames']
assert model_specs['varnames'] == ['temp', 'sal']
assert model_specs['depths']['temp'] == ('surf', )
assert_allclose(model_specs['lat'][:2], (47.3, 48.1))
assert_allclose(model_specs['lon'][:2], (-5.8, -2.8))
# Renorm by name
manager.set_named_norms(temp=0.1)
assert manager.stacked_data[0] == 2 * stacked[0]
CACHE['manager_surf'] = manager
return manager
def test_fcore_arm():
"""Test the :func:`sonat._fcore.f_arm` function"""
# Sample states
ssamples = N.array([[0.24, -0.38, 0.14], [0.51, -0.75, 0.24]], order='F')
nens = ssamples.shape[1]
# Observation operator
H = N.array([[0.0, 1.0], [1.0, 0.0]], order='F')
# Generate data-space samples
dsamples = N.dot(H, ssamples)
# Observation error
R = N.asfortranarray(N.diag([0.1, 0.1]))
# Call ARM
ndof = min(dsamples.shape)
arm_spect, arm, arm_rep, status = f_arm(ndof, ssamples, dsamples, R)
# PCs
pcs = N.dot(dsamples.T / N.sqrt(0.1 * (nens - 1)), arm)
# True values
arm_spect_true = N.loadtxt(os.path.join(
THIS_DIR, 'sangoma_example_arm_output--RM_spectrum'),
usecols=[1])
arm_true = N.array([
N.loadtxt(os.path.join(
THIS_DIR, 'sangoma_example_arm_output--mode{:04d}'.format(i + 1)),
usecols=[1]) for i in xrange(dsamples.shape[0])
]).T
arm_rep_true = N.array([
N.loadtxt(os.path.join(
THIS_DIR,
'sangoma_example_arm_output--modrep{:04d}'.format(i + 1)),
usecols=[1]) for i in xrange(ssamples.shape[0])
]).T
# Checks
assert_allclose(status, 0)
assert_allclose(arm_spect, arm_spect_true)
assert_allclose(arm, arm_true)
assert_allclose(arm_rep, arm_rep_true)
assert_allclose(N.dot(arm.T, arm), N.diag(N.ones(ndof)), atol=1e-7)
assert_allclose((pcs**2).sum(axis=0), arm_spect)
def test_obs_ncobsplatform_surf():
# Load and stack obs
obs = NcObsPlatform(NCFILE_OBS_SURF,
logger=LOGGER,
lat=(45, 47.8),
norms=[0.2, 0.1])
stacked = obs.stacked_data.copy()
assert obs.lons.shape == (3, )
assert obs.ns == 6
assert obs.ndim == 1
assert_allclose(obs.means, [0, 0], atol=1e-7)
assert obs.depths == 'surf'
assert_allclose(stacked, [2.5, 1.5, 4., 1., 1.5, 4.])
# Named norms
notnormed = obs.set_named_norms(temp=0.1)
assert obs.norms == [0.1, 0.1]
assert_allclose(obs.stacked_data[:3], 2 * stacked[:3])
assert_allclose(obs.stacked_data[3:], stacked[3:])
obs.set_named_norms(temp=0.2)
# Interpolate model
f = DS(NCFILE_MANGA0, 'mars', level=obs.depths, logger_level='error')
temp = f('temp')
sal = f('sal')
f.close()
temp.id += '_surf'
sal.id += '_surf'
otemp = obs.project_model(temp)
osal = obs.project_model(sal)
otem_true = [12.97311556, 12.91558515, 10.58179214]
assert_allclose(otemp[0], otem_true)
# Stack model
otemp[:] -= 11.5
osal[:] -= 35.5
stacked_data = obs.restack([otemp, osal])
assert stacked_data.shape == (6, 15)
assert_allclose(stacked_data[:3, 0] * obs.norms[0] + 11.5, otem_true)
return obs
def test_stack_mv2_scattered_without_record_fixed_norm():
# Fake data
# - first array
norm0 = 20.
lons, lats, data0 = create_mv2_scattered_xyzt(nt=0)
data0[:, 3:5] = MV2.masked
# - second array
norm1 = 8.
lons, lats, data1 = create_mv2_scattered_xyzt(nt=0, np=20, nz=0)
data1[10:12] = MV2.masked
# Stack: fixed norm, no record dim, no anomaly
stacker = Stacker([data0, data1], norms=[norm0, norm1], nordim=True,
mean=False, logger=LOGGER)
stacked = stacker.stacked_data
# Unstack
unstacked0, unstacked1 = stacker.unstack(stacker.stacked_data)
# Restack
restacked = stacker.restack([data0, data1])
# Renorm and norm back
norms = stacker.norms
stacker.set_norms([(norm*2) for norm in norms])
renormed = stacker.stacked_data.copy()
stacker.set_norms(norms)
backnormed = stacker.stacked_data.copy()
# Checks
assert_allclose(stacked.shape,
((~data0.mask).sum()+(~data1.mask).sum(),))
assert_allclose(stacked,
N.concatenate((data0.compressed()/norm0, data1.compressed()/norm1)))
assert_allclose(unstacked0, data0)
assert_allclose(unstacked1, data1)
assert_allclose(restacked, stacked)
assert_allclose(renormed, stacked/2)
assert_allclose(backnormed, stacked)
return stacker
def test_stack_mv2_with_record():
# Fake data
# - first array
nt = 5
data0 = create_mv2_gridder_xyzt(nt=nt, rotate=30)
data0.id = 'data0'
data0.units = 'units0'
data0[:, :, 3:5, 2:4] = MV2.masked
raxis = data0.getTime()
del raxis.axis
del raxis.units
raxis.id = 'member'
# - second array
data1 = create_mv2_gridder_xyzt(rotate=0, nx=10, ny=9, nz=4)
data1.id = 'data1'
data1.long_name = 'long_name1'
data1[:, :, 5:7, 6:7] = MV2.masked
data1.setAxis(0, raxis)
# Stack
stacker = Stacker([data0, data1], nordim=False, logger=LOGGER)
stacked = stacker.stacked_data
# Unstack
unstacked0, unstacked1 = stacker.unstack(stacker.stacked_data, format=2)
unstacked0b, unstacked1b = stacker.unstack(stacker.stacked_data[:, :nt/2])
# Restack
restacked = stacker.restack([data0, data1])
# Renorm and norm back
norms = stacker.norms
stacker.set_norms([(norm*2) for norm in norms])
renormed = stacker.stacked_data.copy()
stacker.set_norms(norms)
backnormed = stacker.stacked_data.copy()
# Checks
assert_allclose(stacked.shape,
((~data0[0].mask).sum()+(~data1[0].mask).sum(), data0.shape[0]))
assert_allclose(unstacked0, data0)
assert unstacked0.id == 'data0'
assert unstacked0.units == 'units0'
assert_allclose(unstacked1, data1)
assert_allclose(unstacked0[:nt/2], unstacked0b)
assert_allclose(unstacked1[:nt/2], unstacked1b)
assert_allclose(restacked, stacked)
assert_allclose(renormed, stacked/2)
assert_allclose(backnormed, stacked)
return stacker
def test_ens_generate_pseudo_ensemble():
# Specs
ncpat = NCPAT_MANGA
varnames = ['temp', 'sal', 'u', 'v']
time = ('2014-01-01 13', '2014-01-25 12')
nrens = 14
# nrens = 2
enrich = 1.5
dtfile = (15, 'day')
ncfile = ENS_NCFILE
level = {'temp':('3d', 'surf'), 'u':'surf', 'v':'surf'}
depths = create_dep([-40., -30, -20, -10, 0.])
# Direct
enrich = 0 # <= 1
(temp, temp_surf, sal, u_surf, v_surf) = generate_pseudo_ensemble(ncpat,
nrens=nrens, enrich=enrich,
time=time, varnames=varnames, dtfile=dtfile, logger=LOGGER, anomaly=False,
level=level, depths=depths)
assert temp.shape[0]==nrens
assert sal.shape[0]==nrens
assert temp.ndim==4
assert temp.shape[1]==len(depths)
assert temp_surf.ndim==3
assert v_surf.ndim==3
f = cdms2.open(NCFILE_MANGA0)
temp0 = f('TEMP', time=slice(1, 2), level=slice(-1, None), squeeze=1)
f.close()
assert_allclose(temp0, temp_surf[0])
tsum = temp.sum()
# Enrichment
enrich = 1.5
ens = generate_pseudo_ensemble(ncpat, nrens=nrens, enrich=enrich,
varnames=varnames, time=time, dtfile=dtfile, logger=LOGGER, getmodes=True,
level=level, depths=depths)
(temp, temp_surf, sal, u_surf, v_surf), modes = ens
(temp_eof, temp_surf_eof, sal_eof, u_surf_eof, v_surf_eof) = modes['eofs']
ev = modes['eigenvalues']
temp_var, temp_surf_var, sal_var, u_surf_var, v_surf_var = modes['variance']
assert tsum!=temp.sum()
assert temp.shape[0]==nrens
assert sal.shape[0]==nrens
eof0 = N.concatenate( (temp_eof[0].compressed(), temp_surf_eof[0].compressed(),
sal_eof[0].compressed(),
u_surf_eof[0].compressed(), v_surf_eof[0].compressed()))
assert_allclose((eof0**2).sum(), 1)
eof1 = N.concatenate( (temp_eof[1].compressed(), temp_surf_eof[1].compressed(),
sal_eof[1].compressed(),
u_surf_eof[1].compressed(), v_surf_eof[1].compressed()))
assert_allclose((eof0*eof1).sum(), 0, atol=1e-7)
assert_allclose(ev.total_variance, eof0.size)
expv = (ev**2).sum()/ev.total_variance
assert expv > .8 and expv < 1
expvm = temp.var(axis=0).mean()/temp_var.mean()
assert expvm > .8 and expvm < 1
# Save ensemble
f = cdms2.open(ncfile, 'w')
for var in (
temp, temp_surf, sal, u_surf, v_surf,
temp_eof, temp_surf_eof, sal_eof, u_surf_eof, v_surf_eof,
temp_var, temp_surf_var, sal_var, u_surf_var, v_surf_var,
ev
):
f.write(var)
f.close()
def test_pack_mv2_scattered_without_record_fixed_norm():
# Fake data
lons, lats, data = create_mv2_scattered_xyzt(nt=0)
data[:, 3:5] = MV2.masked
data[:] *= data
norm = data.std()*2.
# Pack
packer = Packer(data, nordim=True, logger=LOGGER, mean=False, norm=norm)
packed = packer.packed_data.copy()
# Unpacked
unpacked = packer.unpack(packer.packed_data)
# Repack
repacked = packer.repack(data)
# Renorm and norm back
norm = packer.norm
packer.set_norm(norm*2)
renormed = packer.packed_data.copy()
packer.set_norm(norm)
backnormed = packer.packed_data.copy()
# Checks
svalid = ~data.mask
cdata = data.compressed()
cnorm = cdata.std()*2
cdata /= cnorm
assert_allclose(packer.good, svalid)
assert_allclose(packer.sshape, data.shape)
assert_allclose(packer.norm, data.asma().std()*2)
assert_allclose(packer.mean, 0)
assert_allclose(packed.shape, svalid.sum())
assert_allclose(packed, cdata)
assert_allclose(unpacked, data)
assert_allclose(repacked, packer.packed_data)
assert_allclose(renormed, packed/2)
assert_allclose(backnormed, packed)
return packer
def test_pack_mv2_curved_with_record():
# Fake data
nt = 5
data = create_mv2_gridder_xyzt(nt=nt, rotate=30)
data.id = 'mydata'
data.long_name = 'My long name'
data[:, :, 3:5, 2:4] = MV2.masked
raxis = data.getTime()
del raxis.axis
del raxis.units
raxis.id = 'member'
# Pack
packer = Packer(data, nordim=False, logger=LOGGER)
packed = packer.packed_data.copy()
# Unpacked
unpacked = packer.unpack(packed, format=2)
unpacked2 = packer.unpack(packed[:, :nt/2])
# Repack
repacked = packer.repack(data)
# Renorm and norm back
norm = packer.norm
packer.set_norm(norm*2)
renormed = packer.packed_data.copy()
packer.set_norm(norm)
backnormed = packer.packed_data.copy()
# Checks
svalid = ~data[0].mask
cdata = data.asma().reshape(data.shape[0], -1).compress(svalid.ravel(), axis=1)
cdata -= cdata.mean(axis=0)
cnorm = cdata.std()
cdata /= cnorm
dmean = data.asma().mean(axis=0)
assert_allclose(packer.good, svalid)
assert_allclose(packer.sshape, data.shape[1:])
assert_allclose(packer.mean, dmean)
assert_allclose(packer.norm, (data.asma()-dmean).std())
assert_allclose(packed.shape, (svalid.sum(), data.shape[0]))
assert_allclose(packed, cdata.T)
assert_allclose(unpacked, data)
assert unpacked.id == 'mydata'
assert unpacked.long_name == 'My long name'
assert_allclose(unpacked2, unpacked[:nt/2])
assert_allclose(repacked, packer.packed_data)
assert_allclose(renormed, packed/2)
assert_allclose(backnormed, packed)
return packer
