https://peterdemin.github.io/script-to-pex.html

I think during the last restructuring of ccd_single, some (or all) plot functionality of ccd_time broke. This should be checked.

does it make sense to record some command line usage?

https://github.com/nbedos/termtosvg

Currently the binder example produces garbled plots.

It would be nice to provide read-to-use jupyter examples, perhaps even using mybinder.

this prevents errors caused by outdated geccoinv versions

Hi!
I was successfully using your in my previous research based in python 3.6.
Recently I had to update to 3.7 since the main circuit simulator required this. When I tried to install your package via pip I get the following:

ERROR: Cannot install ccd-tools==0.8.10, ccd-tools==0.8.11, ccd-tools==0.8.12, ccd-tools==0.8.14, ccd-tools==0.8.15, ccd-tools==0.8.16, ccd-tools==0.8.17, ccd-tools==0.8.4, ccd-tools==0.8.5, ccd-tools==0.8.6, ccd-tools==0.8.7, ccd-tools==0.8.8 and ccd-tools==0.8.9 because these package versions have conflicting dependencies.

The conflict is caused by:
ccd-tools 0.8.17 depends on sip-models
ccd-tools 0.8.16 depends on sip-models
ccd-tools 0.8.15 depends on sip_models
ccd-tools 0.8.14 depends on sip_models
ccd-tools 0.8.12 depends on sip_models
ccd-tools 0.8.11 depends on sip_models
ccd-tools 0.8.10 depends on sip_models
ccd-tools 0.8.9 depends on sip_models
ccd-tools 0.8.8 depends on sip_models
ccd-tools 0.8.7 depends on sip_models
ccd-tools 0.8.6 depends on sip_models
ccd-tools 0.8.5 depends on sip_models
ccd-tools 0.8.4 depends on sip_models

To fix this you could try to:

1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict

ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/user_guide/#fixing-conflicting-dependencies

Upon viewing your setup.py file I suspect that you last version works in 3.6 and not in 3.7.
Any chance of an update?

Cheers!

We need a few detailed examples on the homepage.

• dual Cole-Cole response
• time-regularization
• perhaps an application: imaging of crop roots

I'm using your tools in an EIS environment and your DRFT plotter is perfect. Sadly there seems to be an issue when using the latest version of Numpy (18.0) and Python 3.6.

Error:

File "C:\Anaconda3\envs\py36\lib\site-packages\numpy\core\function_base.py", line 117, in linspace
num = operator.index(num)
TypeError: 'numpy.float64' object cannot be interpreted as an integer

During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Cloud\PyCode\EIS_2\run.py", line 153, in
ccd_obj.fit_data()
File "C:\Anaconda3\envs\py36\lib\site-packages\lib_dd\decomposition\ccd_single.py", line 42, in fit_data
results = list(map(decomp_single_sl.fit_one_spectrum, fit_datas))
File "C:\Anaconda3\envs\py36\lib\site-packages\lib_dd\decomposition\ccd_single_stateless.py", line 159, in fit_one_spectrum
ND = _prepare_ND_object(fit_data)
File "C:\Anaconda3\envs\py36\lib\site-packages\lib_dd\decomposition\ccd_single_stateless.py", line 96, in _prepare_ND_object
model = ccd_res.decomposition_resistivity(fit_data['inv_opts'])
File "C:\Anaconda3\envs\py36\lib\site-packages\lib_dd\models\ccd_res.py", line 31, in init
self.set_settings(settings)
File "C:\Anaconda3\envs\py36\lib\site-packages\lib_dd\models\ccd_res.py", line 50, in set_settings
settings
File "C:\Anaconda3\envs\py36\lib\site-packages\lib_dd\base_class.py", line 79, in determine_tau_range
factor_right=factor_right)
File "C:\Anaconda3\envs\py36\lib\site-packages\lib_dd\base_class.py", line 121, in get_tau_values_for_data
g_tau = np.logspace(np.log10(g_tau_fmin), np.log10(g_tau_fmax), N)
File "<array_function internals>", line 6, in logspace
File "C:\Anaconda3\envs\py36\lib\site-packages\numpy\core\function_base.py", line 272, in logspace
y = linspace(start, stop, num=num, endpoint=endpoint, axis=axis)
File "<array_function internals>", line 6, in linspace
File "C:\Anaconda3\envs\py36\lib\site-packages\numpy\core\function_base.py", line 121, in linspace
.format(type(num)))
TypeError: object of type <class 'numpy.float64'> cannot be safely interpreted as an integer.

For replication:
data
Out[2]:
array([111.5240459 , 111.27922201, 110.89255334, 110.28070184,
109.31029894, 107.76799022, 105.31651814, 101.44401546,
95.45213796, 86.61517417, 74.69325881, 60.64844596,
46.6560329 , 34.85849103, 26.157732 , 20.27772108,
16.48595453, 14.0900904 , 12.58484767, 11.63704805,
11.03784258, 10.65836886, -0.56018419, -0.87687646,
-1.36883975, -2.12754477, -3.28406441, -5.01405951,
-7.52322821, -10.9805576 , -15.34865668, -20.09620679,
-23.99015799, -25.48622686, -23.86026477, -19.89417354,
-15.13940556, -10.80141968, -7.38468767, -4.91328416,
-3.21486398, -2.08293414, -1.34115994, -0.85980969])

frequencies
Out[3]:
array([1.00000000e-02, 2.15443469e-02, 4.64158883e-02, 1.00000000e-01,
2.15443469e-01, 4.64158883e-01, 1.00000000e+00, 2.15443469e+00,
4.64158883e+00, 1.00000000e+01, 2.15443469e+01, 4.64158883e+01,
1.00000000e+02, 2.15443469e+02, 4.64158883e+02, 1.00000000e+03,
2.15443469e+03, 4.64158883e+03, 1.00000000e+04, 2.15443469e+04,
4.64158883e+04, 1.00000000e+05])

data = np.array(tmp)
frequencies = algi.bData['f_real']

set options using this dict-like object

config = cfg_single.cfg_single()
config['frequency_file'] = frequencies
config['data_file'] = data
config['fixed_lambda'] = 10
config['data_format'] = "rre_rim"

generate a ccd object

ccd_obj = ccd_single.ccd_single(config)

commence with the actual fitting

ccd_obj.fit_data()

extract the last iteration

last_it = ccd_obj.results[0].iterations[-1]

print(dir(last_it))
print('fit parameters', last_it.m)
print('stat_pars', last_it.stat_pars)
last_it.plot()

plt.figure()
ts = last_it.Data.obj.tau
dft = last_it.stat_pars['m_i'][0]
plt.plot(ts,dft)

Reverting to Numpy 1.17.4 solves the problems but this is probably not a good long term solution.