def compute_correction_factors(data, true_conductivity, elem_file, elec_file):
"""Compute correction factors for 2D rhizotron geometries, following
Weigand and Kemna, 2017, Biogeosciences
https://doi.org/10.5194/bg-14-921-2017
Parameters
----------
data : :py:class:`pandas.DataFrame`
measured data
true_conductivity : float
Conductivity in S/m
elem_file : string
path to CRTomo FE mesh file (elem.dat)
elec_file : string
path to CRTomo FE electrode file (elec.dat)
Returns
-------
correction_factors : Nx5 :py:class.`numpy.ndarray`
measurement configurations and correction factors
(a,b,m,n,correction_factor)
"""
settings = {
'rho': 100,
'pha': 0,
'elem': 'elem.dat',
'elec': 'elec.dat',
'2D': True,
'sink_node': 100,
}
K = geometric_factors.compute_K_numerical(data, settings=settings)
data = geometric_factors.apply_K(data, K)
data = fixK.fix_sign_with_K(data)
frequency = 100
data_onef = data.query('frequency == {}'.format(frequency))
rho_measured = data_onef['r'] * data_onef['k']
rho_true = 1 / true_conductivity * 1e4
correction_factors = rho_true / rho_measured
collection = np.hstack(
(data_onef[['a', 'b', 'm',
'n']].values, np.abs(correction_factors)[:, np.newaxis]))
return collection
###############################################################################
# data import
seit = reda.sEIT()
seit.import_eit_fzj('data/bnk_raps_20130408_1715_03_einzel.mat',
'data/configs.dat')
print(seit.data[['a', 'b', 'm', 'n']].iloc[0:10])
###############################################################################
# compute geometric factors and correct for signs/phase shifts by pi
settings = {
'rho': 100,
'elem': 'data/elem.dat',
'elec': 'data/elec.dat',
'sink_node': '6467',
'2D': True,
}
k = geom_facs.compute_K_numerical(seit.data, settings)
seit.data = geom_facs.apply_K(seit.data, k)
# input('nr 2, press enter to continue')
fix_sign_with_K(seit.data)
###############################################################################
# apply correction factors for 2D rhizotron tank
corr_facs_nor = np.loadtxt('data/corr_fac_avg_nor.dat')
corr_facs_rec = np.loadtxt('data/corr_fac_avg_rec.dat')
corr_facs = np.vstack((corr_facs_nor, corr_facs_rec))
seit.data, cfacs = eit_fzj.apply_correction_factors(seit.data, corr_facs)
###############################################################################
# apply data filters
seit.filter('r < 0')
seit.filter('rho_a < 15 or rho_a > 35')