Provide Python classes for the Cole-Cole model, both in resistivity and conductivity formulations.
This package is licenced under the GPL-3 licence.
-
implemented: sip_models.res.cc
This is the resistivity formulation of the Cole-Cole model, including derivatives and Jacobian matrix
-
implemented: sip_models.sip_response
Hold one spectrum and return it in various formats
- implement conductivity models
- unit tests
- Planned is also the implementation of derived models such as the Debye decomposition or the Warburg model.
We suggest to run in a virtualenv:
mkvirtualenv --python /usr/bin/python3 sip_models
workon sip_models
pip install -U pip
pip install -U numpy matplotlib scipy pandas shapely
For now you also need to install crtomo_tools from the git-repository
Then install with:
pip install .
For the documentation to build, the following package are also required: ::
pip install sphinx sphinxcontrib-napoleon
import numpy as np
frequencies = np.logspace(-3, 4, 30)
import sip_models.res.cc as cc_res
cc_obj = cc_res.cc(frequencies)
cc_paramers = {
'rho0': 100,
'm': 0.1,
'tau': 0.04,
'c': 0.6
}
# return an SIP spectrum object
response = cc_obj.response(cc_parameters)
# resistivity magnitude
response.rmag
# resistivity phase
response.rpha
# resistivity real part
response.rre
# resistivity imaginary part
response.rim
# resistivity magnitude and phase
response.rmag_rpha
# for other formats, please look at sip_models.sip_response.py
These snippets represent the planned usage of this library. When its finished...
# resistivity formulation
import sip_models.res.ccd
import sip_models.res.cc
import sip_models.res.dd
import sip_models.res.warburg
import sip_models.res.ccd_em
import sip_models.res.dd_em
import sip_models.res.dd_spline
import sip_models.res.ccd_spline
# conductivity formulation
import sip_models.cond.cc
import sip_models.cond.ccd
import sip_models.cond.dd
import sip_models.cond.warburg
import sip_models.cond.ccd_em
import sip_models.cond.dd_em
import sip_models.cond.dd_spline
import sip_models.cond.ccd_spline
frequencies = np.logspace(-4, 4, 30)
parameters = [
100, # rho0
0.1, # m
0.04, # tau
0.5, # c
]
# also possible:
parameters = {
'rho0': 100,
'm': 0.1,
'tau': 0.04,
'c': 0.5,
}
# this also holds true for multiple pol. terms
parameters = {
'rho0': 100,
'm': [0.1, 0.2, 0.3],
'tau': [0.4, 0.04, 0.004],
'c': [0.4, 0.6, 0.8],
}
response = sip_models.res.cc.forward(frequencies, parameters)
# response is a numpy.ndarray...
print response
# with added functionality
print response.rmag
print response.rpha
print response.cre
print response.cim
print response.frequencies
Jacobian = sip_models.res.cc.Jacobian(frequencies, parameters)
# or individual derivatives
dcre_drho0 = sip_models.res.cc.dcre_drho0(frequencies, parameters)
# derivatives can also be found in this dict
sip_models.res.cc.derivatives
