threeML.minimizer.minimization module
- exception threeML.minimizer.minimization.BetterMinimumDuringProfiling[source]
Bases:
RuntimeWarning
- class threeML.minimizer.minimization.FunctionWrapper(function, all_parameters, fixed_parameters)[source]
Bases:
object
- class threeML.minimizer.minimization.GlobalMinimization(minimizer_type)[source]
Bases:
_Minimization
- class threeML.minimizer.minimization.GlobalMinimizer(function, parameters, verbosity=1, setup_dict=None)[source]
Bases:
Minimizer
- class threeML.minimizer.minimization.LocalMinimization(minimizer_type)[source]
Bases:
_Minimization
- class threeML.minimizer.minimization.LocalMinimizer(function, parameters, verbosity=1, setup_dict=None)[source]
Bases:
Minimizer
- class threeML.minimizer.minimization.Minimizer(function, parameters, verbosity=1, setup_dict=None)[source]
Bases:
object- property Npar
- property algorithm_name
- contours(param_1, param_1_minimum, param_1_maximum, param_1_n_steps, param_2=None, param_2_minimum=None, param_2_maximum=None, param_2_n_steps=None, progress=True, **options)[source]
Generate confidence contours for the given parameters by stepping for the given number of steps between the given boundaries. Call it specifying only source_1, param_1, param_1_minimum and param_1_maximum to generate the profile of the likelihood for parameter 1. Specify all parameters to obtain instead a 2d contour of param_1 vs param_2
- Parameters:
param_1 – name of the first parameter
param_1_minimum – lower bound for the range for the first parameter
param_1_maximum – upper bound for the range for the first parameter
param_1_n_steps – number of steps for the first parameter
param_2 – name of the second parameter
param_2_minimum – lower bound for the range for the second parameter
param_2_maximum – upper bound for the range for the second parameter
param_2_n_steps – number of steps for the second parameter
progress – (True or False) whether to display progress or not
log – by default the steps are taken linearly. With this optional parameter you can provide a tuple of
booleans which specify whether the steps are to be taken logarithmically. For example, ‘log=(True,False)’ specify that the steps for the first parameter are to be taken logarithmically, while they are linear for the second parameter. If you are generating the profile for only one parameter, you can specify
‘log=(True,)’ or ‘log=(False,)’ (optional)
- Param:
parallel: whether to use or not parallel computation (default:False)
- Returns:
a : an array corresponding to the steps for the first parameter b : an array corresponding to the steps for the second parameter (or None if stepping only in one direction) contour : a matrix of size param_1_steps x param_2_steps containing the value of the function at the corresponding points in the grid. If param_2_steps is None (only one parameter), then this reduces to an array of size param_1_steps.
- property correlation_matrix
- property covariance_matrix
- property fit_results
- property function
- get_errors()[source]
Compute asymmetric errors using the profile likelihood method (slow, but accurate).
- Returns:
a dictionary with asymmetric errors for each parameter
- minimize(compute_covar=True)[source]
Minimize objective function. This call _minimize, which is implemented by each subclass.
- Parameters:
compute_covar –
- Returns:
best fit values (in external reference) and minimum of the objective function
- property parameters
- restore_best_fit()[source]
Reset all the parameters to their best fit value (from the last run fit)
- Returns:
none
- property verbosity