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 _add_rhoa(df, spacing):
"""a simple wrapper to compute K factors and add rhoa
"""
df['K'] = redaK.compute_K_analytical(df, spacing=spacing)
df['rho_a'] = df['R'] * df['K']
if 'Zt' in df.columns:
df['rho_a_complex'] = df['Zt'] * df['K']
return df
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_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)
#!/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)
###############################################################################
# generate configs, by hand
a = 1
b = 2
quads = []
for m in range(3, 40):
quads.append((a, b, m, m + 1))
config.add_to_configs(quads)
# config.compute_K_factors(spacing=1)
fig, ax = plt.subplots(figsize=(15 / 2.54, 10 / 2.54))
for spacing in np.arange(0.5, 4, 0.5):
dipole_separation = (config.configs[:, 2] - config.configs[:, 1]) * spacing
print(spacing, dipole_separation)
K = compute_K_analytical(config.configs, spacing=spacing)
ax.plot(dipole_separation,
np.abs(K),
'.-',
label='spacing {0}m'.format(spacing))
ax.set_xlabel('dipole separation [m]')
ax.set_ylabel('K [m]')
ax.set_title(
'geometric factor for different electrode distances ' +
'(dipole-dipole skip-0)',
fontsize=10.0,
)
ax.axhline(y=5000, color='k', linestyle='dashed')
ax.annotate(
# from the current injection dipoles:
from reda.importers.eit_fzj import MD_ConfigsPermutate
# 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']])
import IPython
IPython
import sys
import IPython.core.ultratb as ultratb
sys.excepthook = ultratb.VerboseTB(call_pdb=True, )
import pandas as pd
pd.set_option('display.width', 1000)
import edf
container = reda.ERT()
container.import_syscal_dat('data_normal.txt')
container.import_syscal_dat('data_reciprocal.txt', reciprocals=48)
import reda.utils.geometric_factors as edfK
K = edfK.compute_K_analytical(container.df, spacing=0.25)
edfK.apply_K(container.df, K)
import reda.plotters as plotters
plotters.histograms.plot_histograms(container, [
'R',
'rho_a',
'Iab',
])
from reda.utils.geometric_factors import compute_K_analytical
grid = CRGrid.crt_grid.create_surface_grid(
nr_electrodes=50,
spacing=0.5,
depth=25,
right=20,
left=20,
char_lengths=[0.1, 5, 5, 5],
lines=[-3, ],
)
print(grid)
man = CRman.tdMan(grid=grid)
man.configs.gen_schlumberger(M=20, N=21)
K = compute_K_analytical(man.configs.configs, spacing=0.5)
print(man.configs.configs)
# pseudo depth after Knödel et al for Schlumberger configurations
pdepth = np.abs(
np.max(
man.configs.configs, axis=1
) - np.min(
man.configs.configs, axis=1
)
) * 0.19
fig, axes = plt.subplots(2, 1, figsize=(12 / 2.54, 10 / 2.54))
ax = axes[0]
for contrast in (2, 5, 10):