def compute_K_analytical(self, spacing, **kwargs):
"""Compute geometrical factors over the homogeneous half-space with a
constant electrode spacing
"""
K = redaK.compute_K_analytical(self.data, spacing=spacing, **kwargs)
self.data = redaK.apply_K(self.data, K, **kwargs)
redafixK.fix_sign_with_K(self.data, **kwargs)
def compute_K_analytical(self, spacing):
"""Assuming an equal electrode spacing, compute the K-factor over a
homogeneous half-space.
For more complex grids, please refer to the module:
reda.utils.geometric_factors
Parameters
----------
spacing : float
Electrode spacing
"""
assert isinstance(spacing, Number)
K = geometric_factors.compute_K_analytical(self.data, spacing)
self.data = geometric_factors.apply_K(self.data, K)
fix_sign_with_K(self.data)
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
def compute_K_analytical(self, spacing):
"""Compute geometrical factors over the homogeneous half-space with a
constant electrode spacing
"""
redaK.compute_K_analytical(self.data, spacing=spacing)
redafixK.fix_sign_with_K(self.data)
def fix_sign_with_K(self):
""" """
fix_sign_with_K(self.data)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Import MPT DAS-1 data
=====================
"""
import reda
###############################################################################
# normal loading of tdip data
ip = reda.TDIP()
# with profiler():
ip.import_mpt('data_mpt_das1/TD_2000ms.Data')
print(ip.data[['a', 'b', 'm', 'n', 'id', 'norrec']])
# import IPython
# IPython.embed()
# exit()
###############################################################################
#
import reda.utils.geometric_factors as geomK
K = geomK.compute_K_analytical(ip.data, spacing=2)
geomK.apply_K(ip.data, K)
import reda.utils.fix_sign_with_K as fixK
ip.data = fixK.fix_sign_with_K(ip.data)
###############################################################################
'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')
seit.filter('rpha < - 40 or rpha > 3')
seit.filter('rphadiff < -5 or rphadiff > 5')
seit.filter('k > 400')
seit.filter('rho_a < 0')
# initialize an sEIT container
seit_not_used = reda.sEIT()
# import the data
seit_not_used.import_eit_fzj(filename='data_EIT40_v_EZ-2017/eit_data_mnu0.mat',
configfile=MD_ConfigsPermutate)
##############################################################################
# Compute geometric factors
import reda.utils.geometric_factors as redaK
import reda.utils.fix_sign_with_K as redafixK
K = redaK.compute_K_analytical(seit.data, spacing=0.25)
redaK.apply_K(seit.data, K)
redafixK.fix_sign_with_K(seit.data)
##############################################################################
# compute normal and reciprocal pairs
# note that this is usually done on import once.
import reda.utils.norrec as norrec
seit.data = norrec.assign_norrec_to_df(seit.data)
##############################################################################
# quadrupoles can be directly accessed using a pandas grouper
print(seit.abmn)
quadpole_data = seit.abmn.get_group((10, 29, 15, 34))
print(quadpole_data[['a', 'b', 'm', 'n', 'frequency', 'r', 'rpha']])
##############################################################################
# filter data
