def tcol_error(df):
"""Triple collocation error estimate
In this case df has to have exactly 3 columns, since triple wise
application of a function is not yet implemented and would probably return a
complicated structure
Returns
-------
result : namedtuple
with column names of df
See Also
--------
pytesmo.metrics.tcol_error
"""
if len(df.columns) != 3:
raise DataFrameDimensionError("DataFrame has to have 3 columns")
tcol_result = namedtuple('triple_collocation_error',df.columns)
return tcol_result._make(metrics.tcol_error(df.ix[:,0].values, df.ix[:,1].values, df.ix[:,2].values))
def test_tcol_error():
"""
Test the triple collocation error estimation based on
a random signal and error.
Also compare the results to the other method
"""
n = 1000000
signal = np.sin(np.linspace(0, 2 * np.pi, n))
sig_err_x = 0.02
sig_err_y = 0.07
sig_err_z = 0.04
err_pred = np.array((sig_err_x, sig_err_y, sig_err_z))
err_x = np.random.normal(0, sig_err_x, n)
err_y = np.random.normal(0, sig_err_y, n)
err_z = np.random.normal(0, sig_err_z, n)
alpha_y = 0.2
alpha_z = 0.5
beta_y = 0.9
beta_z = 1.6
x = signal + err_x
y = alpha_y + beta_y * (signal + err_y)
z = alpha_z + beta_z * (signal + err_z)
snr, err, beta = met.tcol_snr(x, y, z, ref_ind=0)
# classical triple collocation errors use scaled (removed alpha and beta)
# input arrays
ex, ey, ez = met.tcol_error(signal + err_x, signal + err_y, signal + err_z)
nptest.assert_almost_equal(err, np.array([ex, ey, ez]), decimal=2)
nptest.assert_almost_equal(err_pred, np.array([ex, ey, ez]), decimal=2)
def test_tcol_error():
"""
Test the triple collocation error estimation based on
a random signal and error.
Also compare the results to the other method
"""
n = 1000000
signal = np.sin(np.linspace(0, 2 * np.pi, n))
sig_err_x = 0.02
sig_err_y = 0.07
sig_err_z = 0.04
err_pred = np.array((sig_err_x, sig_err_y, sig_err_z))
err_x = np.random.normal(0, sig_err_x, n)
err_y = np.random.normal(0, sig_err_y, n)
err_z = np.random.normal(0, sig_err_z, n)
alpha_y = 0.2
alpha_z = 0.5
beta_y = 0.9
beta_z = 1.6
x = signal + err_x
y = alpha_y + beta_y * (signal + err_y)
z = alpha_z + beta_z * (signal + err_z)
snr, err, beta = met.tcol_snr(x, y, z, ref_ind=0)
# classical triple collocation errors use scaled (removed alpha and beta)
# input arrays
ex, ey, ez = met.tcol_error(signal + err_x, signal + err_y, signal + err_z)
nptest.assert_almost_equal(err, np.array([ex, ey, ez]), decimal=2)
nptest.assert_almost_equal(err_pred, np.array([ex, ey, ez]), decimal=2)
def tcol_error(df):
"""Triple collocation error estimate
In this case df has to have exactly 3 columns, since triple wise
application of a function is not yet implemented and
would probably return a complicated structure
Returns
-------
result : namedtuple
with column names of df
See Also
--------
pytesmo.metrics.tcol_error
"""
if len(df.columns) != 3:
raise DataFrameDimensionError("DataFrame has to have 3 columns")
tcol_result = namedtuple('triple_collocation_error', df.columns)
return tcol_result._make(metrics.tcol_error(df.ix[:, 0].values,
df.ix[:, 1].values,
df.ix[:, 2].values))
