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%matplotlib inline


    

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import lib_dd.decomposition.ccd_single as ccd_single
import lib_dd.config.cfg_single as cfg_single
import numpy as np

frequencies = np.loadtxt('frequencies.dat')
data = np.loadtxt('data.dat')

# 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['norm'] = 10

# 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)

# save to directory
ccd_obj.save_to_directory()


    

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ccd_obj.results[-1].iterations[-1].plot()


    

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_ = last_it.plot()


    

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ccd_obj.data