def _get_tdm(self, m):
"""For a given model of resistivity magnitudes and phases, return a
tdMan instance
Parameters
----------
m : Nx1|Nx2 ndarray
N Model parameters, first column linear magnitudes [ohm m], second
column phase values [mrad]
Returns
-------
tdm : crtomo.tdMan
td manager
"""
if len(m.shape) == 1:
m = m[:, np.newaxis]
assert len(m.shape) == 2
# print('gettm')
# import IPython
# IPython.embed()
tdm = crtomo.tdMan(grid=self.grid, tempdir=self.tempdir)
tdm.configs.add_to_configs(self.configs)
pid_mag = tdm.parman.add_data(m[:, 0])
tdm.register_magnitude_model(pid_mag)
if m.shape[1] == 2:
pid_pha = tdm.parman.add_data(m[:, 1])
else:
pid_pha = tdm.parman.add_data(np.zeros(m.shape[0]))
tdm.register_phase_model(pid_pha)
return tdm
def export_to_crtomo_td_manager(self, grid, norrec='norrec'):
"""Return a ready-initialized tdman object from the CRTomo tools.
WARNING: Not timestep aware!
Parameters
----------
grid : crtomo.crt_grid
A CRTomo grid instance
norrec : str (nor|rec|norrec)
Which data to export. Default: norrec (all)
"""
subdata = self.data.query('norrec == "{}"'.format(norrec))
import crtomo
data = subdata[['a', 'b', 'm', 'n', 'r', 'rpha']]
tdman = crtomo.tdMan(grid=grid, volt_data=data)
return tdman
noise level. As such it **may** be possible/useful to sometimes reduce a given noise estimate below the actual noise level added to synthetic data. For further reading, see: https://en.wikipedia.org/wiki/Pseudorandom_number_generator """ ############################################################################### # Imports import numpy as np import crtomo ############################################################################### # Setup: Generate some synthetic data mesh = crtomo.crt_grid.create_surface_grid(nr_electrodes=10, spacing=1) tdm = crtomo.tdMan(grid=mesh) tdm.add_homogeneous_model(100, 0) tdm.configs.gen_dipole_dipole(skipc=0) rmag = tdm.measurements()[:, 0] rpha = tdm.measurements()[:, 1] ############################################################################### # Generate data noise # ------------------- # For synthetic studies a good starting point is that the structure of the # actual noise should be the same as used in the inversion error model. As such # we use a linear model for the magnitude noise components, and an absolute # standard deviation for the phase values. # Important: ALWAYS initialize the random number generator using a seed!
#!/usr/bin/env python3 # *-* coding: utf-8 *-* """ Plot inversion results from a tomodir ===================================== """ import crtomo import numpy as np tdm = crtomo.tdMan(tomodir='tomodir') ############################################################################### # Plot the last magnitude and phase iteration the quick and dirty way. # Note that all iterations are stored in the tdm.a['inversion'][KEY] list tdm.show_parset(tdm.a['inversion']['rmag'][-1]) tdm.show_parset(tdm.a['inversion']['rpha'][-1]) ############################################################################### # Let's do this the nice way: We want to plot the magnitude, real and imaginary # part of the complex conductivity as well as the phase into one plot result. import matplotlib.pylab as plt # extract parameter set ids pid_rmag = tdm.a['inversion']['rmag'][-1] pid_rpha = tdm.a['inversion']['rpha'][-1] pid_cre = tdm.a['inversion']['cre'][-1] pid_cim = tdm.a['inversion']['cim'][-1] # Note that we can switch out the resistivity magnitude with the conductivity # magnitude by accessing the parset and taking the inverse values. rmag = tdm.parman.parsets[pid_rmag] cmag = 1 / rmag
###############################################################################
# Datenfile in CRTomo Format ausgeben (volt.dat)
# ----------------------------------------------
crto.write_files_to_directory(ert.data, '.')
###############################################################################
# Arbeiten im Tomodir
# -------------------
###############################################################################
# Gitter und Daten einlesen
# -------------------------
td_obj = crtomo.tdMan(elem_file='elem.dat', elec_file='elec.dat')
td_obj.read_voltages('volt.dat')
###############################################################################
# Inversionseinstellungen
# -----------------------
td_obj.crtomo_cfg['robust_inv'] = 'F'
td_obj.crtomo_cfg['dc_inv'] = 'F'
td_obj.crtomo_cfg['cells_z'] = '-1'
td_obj.crtomo_cfg['mag_rel'] = '10'
td_obj.crtomo_cfg['mag_abs'] = '0.5'
td_obj.crtomo_cfg['fpi_inv'] = 'F'
# td_obj.crtomo_cfg['pha_a1'] = '10'
# td_obj.crtomo_cfg['pha_b'] = '-1.5'
# td_obj.crtomo_cfg['pha_rel'] = '10'
#!/usr/bin/env python3
# *-* coding: utf-8 *-*
"""
Plot a potential distribution, computed with CRMod
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
"""
###############################################################################
# create a tomodir object
import crtomo
grid = crtomo.crt_grid('grid_surface/elem.dat', 'grid_surface/elec.dat')
td = crtomo.tdMan(grid=grid)
###############################################################################
# define configurations
import numpy as np
td.configs.add_to_configs(np.array((
(1, 10, 5, 7),
(1, 3, 10, 7),
)))
###############################################################################
# add a homogeneous forward model
td.add_homogeneous_model(100, 0)
###############################################################################
# compute FEM solution using CRMod
td.model(potentials=True)
###############################################################################
###############################################################################
# compute correction factors using data from this frequency
target_frequency = 70
###############################################################################
# dataset 1
seit1 = reda.sEIT()
seit1.import_eit_fzj(
'data/CalibrationData/bnk_water_blubber_20130428_1810_34_einzel.mat',
'data/configs.dat')
configurations = seit1.data.query(
'frequency == {}'.format(target_frequency))[['a', 'b', 'm', 'n']].values
# extract configurations for one frequency
###############################################################################
# synthetic forward modeling
grid = crtomo.crt_grid('data/elem.dat', 'data/elec.dat')
tdman = crtomo.tdMan(grid=grid)
# add configuration added from the measurement data
tdman.configs.add_to_configs(configurations)
# true water conductivity 37.5 mS/m
tdman.add_homogeneous_model(1 / (37.5 / 1000), 0)
tdman.crmod_cfg['2D'] = '0'
tdman.crmod_cfg['fictitious_sink'] = 'T'
tdman.crmod_cfg['sink_node'] = '6467'
R_mod = tdman.measurements()
df_mod = pd.concat(
(pd.DataFrame(tdman.configs.configs), pd.DataFrame(R_mod[:, 0])),
axis=1,