Allright, I found a fix.
# Make Carlo Simulations number
mc_number_list = range(5, 100, 20)
sim_attr_list = ['chi2_sim', 'f_count_sim', 'g_count_sim',
'h_count_sim', 'i0_sim', 'iter_sim', 'peak_intensity_sim',
'r2eff_sim', 'select_sim', 'warning_sim']
# Loop over the Monte Carlo simulations:
for number in mc_number_list:
# First delete old simulations.
for cur_spin, mol_name, resi, resn, spin_id in
spin_loop(full_info=True, return_id=True, skip_desel=True):
# Loop over the simulated attributes.
for sim_attr in sim_attr_list:
if hasattr(cur_spin, sim_attr):
delattr(cur_spin, sim_attr)
self.interpreter.monte_carlo.setup(number=number)
self.interpreter.monte_carlo.create_data()
self.interpreter.monte_carlo.initial_values()
self.interpreter.minimise.execute(min_algor=min_algor,
constraints=constraints)
self.interpreter.eliminate()
self.interpreter.monte_carlo.error_analysis()
est_key = 'mc_%s'%number
est_keys.append(est_key)
# Collect data.
for cur_spin, mol_name, resi, resn, spin_id in
spin_loop(full_info=True, return_id=True, skip_desel=True):
# Add key for estimate.
my_dic[spin_id][est_key] = {}
for exp_type, frq, offset, point, ei, mi, oi, di in
loop_exp_frq_offset_point(return_indices=True):
# Generate the param_key.
param_key = return_param_key_from_data(exp_type=exp_type,
frq=frq, offset=offset, point=point)
# Add key to dic.
my_dic[spin_id][est_key][param_key] = {}
# Get the value.
r2eff = getattr(cur_spin, 'r2eff')[param_key]
r2eff_err = getattr(cur_spin, 'r2eff_err')[param_key]
i0 = getattr(cur_spin, 'i0')[param_key]
i0_err = getattr(cur_spin, 'i0_err')[param_key]
# Save to dic.
my_dic[spin_id][est_key][param_key]['r2eff'] = r2eff
my_dic[spin_id][est_key][param_key]['r2eff_err'] = r2eff_err
my_dic[spin_id][est_key][param_key]['i0'] = i0
my_dic[spin_id][est_key][param_key]['i0_err'] = i0_err
2014-08-28 11:20 GMT+02:00 Edward d'Auvergne <edw...@nmr-relax.com>:
> Hi,
>
> Could you describe a situation that covers this? What do you mean by
> the Monte Carlo simulation data key? In the data pipe and spin
> containers, the Monte Carlo simulation optimisation results are
> usually stored in the "*_sim" data structures as lists, and the errors
> from the simulations in the "*_err" data structures which are simple
> floats. But this is dependent on the analysis and data type - some
> model parameters can be single values, lists of values, or
> dictionaries of values, and this is preserved in the Monte Carlo
> simulation structures as well.
>
> Regards,
>
> Edward
>
>
> On 28 August 2014 11:14, Troels Emtekær Linnet <tlin...@nmr-relax.com> wrote:
>> Dear Edward.
>>
>> Is there a way to clear the Monte Carlo simulation data key?
>>
>> I try to run some data with increasing number of Monte Carlo simulations.
>>
>> Thank you.
>> Best
>> Troels
>>
>> _______________________________________________
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