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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 We start by adding some data from an hdf file.
You will also need the python package h5py.
The data object keeps the loaded data, electrode geometry, ground truth, and offers some pre-processing functions.
Step1:  Next we define the inverse problem parameters as a dictionary. Parameter's not specified will be filled with default values.
Step2:  Now we are ready to use different source localization methods.
Step3:  And visualize the results
Step4:  Alternatively we can use the optimization methods built on top of CasADi package. And initialize the problem.
Step5:  And use the optimization method as described in the thesis "Source localization for high-density microelectrode arrays" by Cem Uran.
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Python Code:
# Data path/filename
t_ind = 38
data_path = '../data/'
file_name = data_path + 'data_sim_low.hdf5'
data_options = {'flag_cell': True, 'flag_electode': False}
data = data_in(file_name, **data_options)
Explanation: We start by adding some data from an hdf file.
You will also need the python package h5py.
The data object keeps the loaded data, electrode geometry, ground truth, and offers some pre-processing functions.
End of explanation
localization_options = {'p_vres':20, 'p_jlen':0, 'p_erad': 5, 't_ind': 38, 'flag_depthweighted': False}
loc = data_out(data, **localization_options)
Explanation: Next we define the inverse problem parameters as a dictionary. Parameter's not specified will be filled with default values.
End of explanation
loc.cmp_sloreta()
Explanation: Now we are ready to use different source localization methods.
End of explanation
loc.xres = loc.res[:, t_ind]
vis = visualize(data=data, loc=loc)
vis.show_snapshot()
Explanation: And visualize the results
End of explanation
optimization_options = {'p_vres':10, 'p_jlen':0, 'p_erad': 10,
                        'solver': p_solver,
                        'hessian': p_hessian,
                        'linsol': p_linsol,
                        'method': p_method,
                        't_ind': 35, 't_int': 1, 
                        'sigma': float(p_sparse),
                        'flag_depthweighted': bool(int(p_norm)),
                        'flag_parallel': False,
                        'datafile_name': 'output_file',
                        'flag_lift_mask': False,
                        'flag_data_mask': True,
                        'flag_callback': True,
                        'flag_callback_plot': True,
                        'callback_steps': 40,
                        'p_dyn': float(p_dynamic)
                        }
opt = opt_out(data, **optimization_options)
Explanation: Alternatively we can use the optimization methods built on top of CasADi package. And initialize the problem.
End of explanation
opt.set_optimization_variables_thesis()
Explanation: And use the optimization method as described in the thesis "Source localization for high-density microelectrode arrays" by Cem Uran.
End of explanation
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