def test_interp_end_point():
"""Test interpolation with point at data endpoints."""
x = np.array([1., 2., 3., 4.])
y = x
x_interp = np.array([1.0, 4.0])
y_interp_truth = np.array([1.0, 4.0])
y_interp = interp(x_interp, x, y)
assert_array_almost_equal(y_interp, y_interp_truth, 7)
def test_interp_decrease():
"""Test interpolation with decreasing interpolation points."""
x = np.array([1., 2., 3., 4.])
y = x
x_interp = np.array([3.5000000, 2.5000000])
y_interp_truth = np.array([3.5000000, 2.5000000])
y_interp = interp(x_interp, x, y)
assert_array_almost_equal(y_interp, y_interp_truth, 7)
def test_interp_decrease_xp():
"""Test interpolation with decreasing order."""
x = np.array([4., 3., 2., 1.])
y = x
x_interp = np.array([3.5000000, 2.5000000])
y_interp_truth = np.array([3.5000000, 2.5000000])
y_interp = interp(x_interp, x, y)
assert_array_almost_equal(y_interp, y_interp_truth, 7)
def test_interp():
"""Test deprecated interp function."""
x = np.array([1., 2., 3., 4.])
y = x
x_interp = np.array([3.5000000, 2.5000000])
y_interp_truth = np.array([3.5000000, 2.5000000])
y_interp = interp(x_interp, x, y)
assert_array_almost_equal(y_interp, y_interp_truth, 7)
def test_interp_masked_units():
"""Test interpolating with masked arrays with units."""
x = np.ma.array([1., 2., 3., 4.]) * units.m
y = np.ma.array([50., 60., 70., 80.]) * units.degC
x_interp = np.array([250., 350.]) * units.cm
y_interp_truth = np.array([65., 75.]) * units.degC
y_interp = interp(x_interp, x, y)
assert_array_almost_equal(y_interp, y_interp_truth, 7)
def test_interp():
"""Test deprecated interp function."""
x = np.array([1., 2., 3., 4.])
y = x
x_interp = np.array([3.5000000, 2.5000000])
y_interp_truth = np.array([3.5000000, 2.5000000])
with pytest.warns(MetpyDeprecationWarning):
y_interp = interp(x_interp, x, y)
assert_array_almost_equal(y_interp, y_interp_truth, 7)
def test_interp_2args():
"""Test interpolation with 2 arguments."""
x = np.array([1., 2., 3., 4.])
y = x
y2 = x
x_interp = np.array([2.5000000, 3.5000000])
y_interp_truth = np.array([2.5000000, 3.5000000])
y_interp = interp(x_interp, x, y, y2)
assert_array_almost_equal(y_interp[0], y_interp_truth, 7)
assert_array_almost_equal(y_interp[1], y_interp_truth, 7)
def upper_air(config,
station_id,
sounding_station_id,
date,
use_nan_sounding=False,
use_existing=True,
save=True):
"""
Retrieves upper-air data and interpolates to pressure levels. If use_nan_sounding is True, then if a retrieval
error occurs, a blank sounding will be returned instead of an error.
:param config:
:param station_id: station ID of surface station used
:param sounding_station_id: station ID of sounding station to use
:param date: datetime
:param use_nan_sounding: bool: if True, use sounding of NaNs instead of raising an error
:param use_existing: bool: preferentially use existing soundings in sounding_data_dir
:param save: bool: if True, save processed soundings to sounding_data_dir
:return:
"""
variables = ['height', 'temperature', 'dewpoint', 'u_wind', 'v_wind']
# Define levels for interpolation: same as model data, except omitting lowest_p_level
plevs = [600, 750, 850, 925]
pres_interp = np.array([p for p in plevs if p <= config['lowest_p_level']])
# Try retrieving the sounding, first checking for existing
if config['verbose']:
print('upper_air: retrieving sounding for %s' %
datetime.strftime(date, '%Y%m%d%H'))
nan_sounding = False
retrieve_sounding = False
sndg_data_dir = config['Obs']['sounding_data_dir']
if not (os.path.isdir(sndg_data_dir)):
os.makedirs(sndg_data_dir)
sndg_file = '%s/%s_SNDG_%s.pkl' % (sndg_data_dir, station_id,
datetime.strftime(date, '%Y%m%d%H'))
if use_existing:
try:
data = read_pkl(sndg_file)
if config['verbose']:
print(' Read from file.')
except:
retrieve_sounding = True
else:
retrieve_sounding = True
if retrieve_sounding:
try:
dset = WyomingUpperAir.request_data(
date, config['Obs']['sounding_station_id'])
except:
# Try again
try:
dset = WyomingUpperAir.request_data(
date, config['Obs']['sounding_station_id'])
except:
if use_nan_sounding:
if config['verbose']:
print(
'upper_air: warning: unable to retrieve sounding; using nan.'
)
nan_sounding = True
else:
raise ValueError('error retrieving sounding for %s' % date)
# Retrieve pressure for interpolation to fixed levels
if not nan_sounding:
pressure = dset.variables['pressure']
pres = np.array([p.magnitude
for p in list(pressure)]) # units are hPa
# Get variables and interpolate; add to dictionary
data = OrderedDict()
for var in variables:
if not nan_sounding:
var_data = dset.variables[var]
var_array = np.array([v.magnitude for v in list(var_data)])
var_interp = interp(pres_interp, pres, var_array)
data[var] = var_interp.tolist()
else:
data[var] = [np.nan] * len(pres_interp)
# Save
if save and not nan_sounding:
with open(sndg_file, 'wb') as handle:
pickle.dump(data, handle, protocol=2)
return data
