# -*- coding: utf-8 -*-
""" Copyright 2014-2017 Maximilian Weigand
This program is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
version.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import os
import numpy as np
import logging
import NDimInv.plot_helper
import sip_formats.convert as sip_convert
plt, mpl = NDimInv.plot_helper.setup()
[docs]class plot_iteration():
""" This class defines an override function for the default plot function
of the Iteration class. The new plot function is aware of the Cole-Cole
Decomposition approach and will plot more information (i.e. the RTD)
In addition, it will renormalize data if necessary.
"""
[docs] def plot(self, it, norm_factors=None):
try:
self._plot(it)
except Exception as e:
logging.info('Exception in plot routine', e)
return self.fig
[docs] def finalize_fig(self):
ax = self.axes[0, 0]
title = 'Cole-Cole decomposition, iteration {0}'.format(self.it.nr)
ax.annotate(
title,
xy=(0.0, 1.00),
xytext=(15, -30),
textcoords='offset points',
xycoords='figure fraction'
)
dd_c = 'c = {0}'.format(os.environ.get('DD_C', '1.0'))
if 'DD_COND' in os.environ and os.environ['DD_COND'] == '1':
model_title = 'conductivity model'
else:
model_title = 'resistivity model'
logging.info('dd_c: {0}'.format(dd_c))
model_title += ', {0}'.format(dd_c)
ax.annotate(
model_title,
xy=(1.0, 1.0),
xytext=(-160, -30),
textcoords='offset points',
xycoords='figure fraction',
)
# self.fig.tight_layout()
self.fig.subplots_adjust(
left=0.04,
right=0.98,
wspace=0.4,
hspace=0.3,
top=self.top_margin,
bottom=self.bottom_margin,
)
def _plot(self, it):
"""Plot one or more spectra
"""
self.it = it
D = it.Data.D
M = it.Model.convert_to_M(it.m)
# renormalize here? why do we compute the forward solution again?
F = it.Model.F(M)
extra_size = int(
np.sum([x[1][1] for x in it.Data.extra_dims.items()]))
self.nr_spectra = max(1, extra_size)
fig, axes = self.create_figure()
# iterate over spectra
for nr, (d, m) in enumerate(it.Model.DM_iterator()):
self._plot_rre_rim(nr, axes[nr, 0:2], D[d], F[d], it)
self._plot_rmag_rpha(nr, axes[nr, 2:4], D[d], F[d], it)
self._plot_rtd(nr, axes[nr, 4], M[m], it)
ax1 = axes[nr, 0].twinx()
ax2 = axes[nr, 1].twinx()
for ax in (ax1, ax2):
ax.grid(None)
self._plot_cre_cim(nr, [ax1, ax2], D[d], F[d], it)
self.finalize_fig()
def _plot_rtd(self, nr, ax, m, it):
ax.semilogx(it.Data.obj.tau, m[1:], '.-', color='k')
ax.set_xlim(it.Data.obj.tau.min(), it.Data.obj.tau.max())
ax.xaxis.set_major_locator(mpl.ticker.LogLocator(numticks=5))
ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(5))
self._mark_tau_parameters_tau(nr, ax, it)
ax.invert_xaxis()
# mark limits of data frequencies, converted into the tau space
tau_min = np.min(it.Model.obj.tau)
tau_max = np.max(it.Model.obj.tau)
d_fmin = np.min(it.Data.obj.frequencies)
d_fmax = np.max(it.Data.obj.frequencies)
t_fmin = 1 / (2 * np.pi * d_fmin)
t_fmax = 1 / (2 * np.pi * d_fmax)
ax.axvline(t_fmin, c='y', alpha=0.7)
ax.axvline(t_fmax, c='y', alpha=0.7)
ax.axvspan(tau_min, t_fmax, hatch='/', color='gray', alpha=0.5)
ax.axvspan(t_fmin, tau_max, hatch='/', color='gray', alpha=0.5,
label='area outside data range')
ax.set_xlabel(r'$\tau~[s]$')
ax.set_ylabel(r'$log_{10}(m)$')
title_string = r'$\lambda:$ '
for lam in it.lams:
if(type(lam) == list):
lam = lam[0]
if(isinstance(lam, float) or isinstance(lam, int)):
title_string += '{0} '.format(lam)
else:
pass
# individual lambdas
# title_string += '{0} '.format(
# lam[m_indices[nr], m_indices[nr]])
ax.set_title(title_string)
def _plot_rmag_rpha(self, nr, axes, orig_data, fit_data, it):
rmag_rpha_orig = sip_convert.convert(
it.Data.obj.data_format,
'rmag_rpha',
orig_data
)
rmag_rpha_fit = sip_convert.convert(
it.Data.obj.data_format,
'rmag_rpha',
fit_data
)
frequencies = it.Data.obj.frequencies
ax = axes[0]
ax.semilogx(frequencies, rmag_rpha_orig[:, 0], '.', color='k')
ax.semilogx(frequencies, rmag_rpha_fit[:, 0], '-', color='k')
ax.set_xlabel('frequency [Hz]')
ax.set_ylabel(r'$|\rho|~[\Omega m]$')
ax.xaxis.set_major_locator(mpl.ticker.LogLocator(numticks=4))
ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(5))
ax = axes[1]
ax.semilogx(frequencies, -rmag_rpha_orig[:, 1], '.', color='k')
ax.semilogx(frequencies, -rmag_rpha_fit[:, 1], '-', color='k')
ax.set_xlabel('frequency [Hz]')
ax.set_ylabel(r'$-\phi~[mrad]$')
ax.xaxis.set_major_locator(mpl.ticker.LogLocator(numticks=4))
ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(5))
self._mark_tau_parameters_f(nr, ax, it)
def _plot_cre_cim(self, nr, axes, orig_data, fit_data, it):
cre_cim_orig = sip_convert.convert(it.Data.obj.data_format,
'cre_cim',
orig_data)
cre_cim_fit = sip_convert.convert(it.Data.obj.data_format,
'cre_cim',
fit_data)
frequencies = it.Data.obj.frequencies
ax = axes[0]
ax.semilogx(frequencies, cre_cim_orig[:, 0], '.', color='gray')
ax.semilogx(frequencies, cre_cim_fit[:, 0], '-', color='gray')
# ax.set_xlabel('frequency [Hz]')
ax.set_ylabel(r"$-\sigma'~[S/m]$", color='gray')
ax.xaxis.set_major_locator(mpl.ticker.LogLocator(numticks=4))
ax = axes[1]
ax.semilogx(frequencies, cre_cim_orig[:, 1], '.', color='gray',
label='data')
ax.semilogx(frequencies, cre_cim_fit[:, 1], '-', color='gray',
label='fit')
# ax.set_xlabel('frequency [Hz]')
ax.set_ylabel(r"$-\sigma''~[S/m]$", color='gray')
ax.xaxis.set_major_locator(mpl.ticker.LogLocator(numticks=4))
def _plot_rre_rim(self, nr, axes, orig_data, fit_data, it):
rre_rim_orig = sip_convert.convert(it.Data.obj.data_format,
'rre_rim',
orig_data)
rre_rim_fit = sip_convert.convert(it.Data.obj.data_format,
'rre_rim',
fit_data)
frequencies = it.Data.obj.frequencies
ax = axes[0]
ax.semilogx(frequencies, rre_rim_orig[:, 0], '.', color='k')
ax.semilogx(frequencies, rre_rim_fit[:, 0], '-', color='k')
ax.set_xlabel('frequency [Hz]')
ax.set_ylabel(r"$\rho'~[\Omega m]$")
ax.xaxis.set_major_locator(mpl.ticker.LogLocator(numticks=4))
ax = axes[1]
ax.semilogx(frequencies, -rre_rim_orig[:, 1], '.', color='k',
label='data')
ax.semilogx(frequencies, -rre_rim_fit[:, 1], '-', color='k',
label='fit')
ax.set_xlabel('frequency [Hz]')
ax.set_ylabel(r"$\rho''~[\Omega m]$")
ax.xaxis.set_major_locator(mpl.ticker.LogLocator(numticks=4))
self._mark_tau_parameters_f(nr, ax, it)
# legend is created from first plot
if nr == 0:
ax.legend(loc="upper center", ncol=5,
bbox_to_anchor=(0, 0, 1, 1),
bbox_transform=ax.get_figure().transFigure)
leg = ax.get_legend()
ltext = leg.get_texts()
plt.setp(ltext, fontsize='6')
def _mark_tau_parameters_tau(self, nr, ax, it):
# mark relaxation time parameters
# mark tau_peak
for index in range(1, 3):
try:
tpeak = it.stat_pars['tau_peak{0}'.format(index)][nr]
if(not np.isnan(tpeak)):
ax.axvline(x=10**tpeak, color='k', label=r'$\tau_{peak}^' +
'{0}'.format(index) + '$',
linestyle='dashed')
except Exception:
pass
try:
ax.axvline(x=10**it.stat_pars['tau_50'][nr], color='g',
label=r'$\tau_{50}$')
except:
pass
try:
ax.axvline(x=10**it.stat_pars['tau_mean'][nr], color='c',
label=r'$\tau_{mean}$')
except:
pass
def _mark_tau_parameters_f(self, nr, ax, it):
# mark relaxation time parameters
# mark tau_peak
for index in range(1, 3):
try:
fpeak = it.stat_pars['f_peak{0}'.format(index)][nr]
if(not np.isnan(fpeak)):
ax.axvline(x=fpeak, color='k', label=r'$\tau_{peak}^' +
'{0}'.format(index) + '$',
linestyle='dashed')
except:
pass
try:
ax.axvline(x=it.stat_pars['f_50'][nr], color='g',
label=r'$\tau_{50}$')
except:
pass
try:
ax.axvline(x=it.stat_pars['f_mean'][nr], color='c',
label=r'$\tau_{mean}$')
except:
pass