ajustador.optimize
-
ajustador.optimize.filtereddict(**kwargs)[source]
-
ajustador.optimize.exe_map(single=False, async=False)[source]
-
ajustador.optimize.iv_filename(injection_current)[source]
-
ajustador.optimize.iv_filename_to_current(ivfile)[source]
-
ajustador.optimize.execute(p)[source]
-
ajustador.optimize.load_simulation(ivfile, simtime, junction_potential, features)[source]
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class
ajustador.optimize.Simulation(dir, junction_potential=0, currents=None, simtime=None, baseline=None, morph_file=None, single=False, async=False, features=None, **params)[source]
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execute_for(injection_currents, junction_potential, single, async)[source]
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wait()[source]
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class
ajustador.optimize.SimulationResult(dirname, features)[source]
-
report()[source]
-
static
find_global(param, p_file)[source]
-
wait()[source]
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class
ajustador.optimize.SimulationResults(dirname, features)[source]
-
load(last=None)[source]
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ordered(measurement, *, fitness=<class 'ajustador.fitnesses.combined_fitness'>)[source]
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class
ajustador.optimize.Param(name, value)[source]
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min = None
-
max = None
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fixed = True
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static
make(args)[source]
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valid(val)[source]
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class
ajustador.optimize.AjuParam(name, value, min=None, max=None)[source]
-
fixed = False
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scaled
-
scale(val)[source]
-
unscale(val)[source]
-
valid(val)[source]
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class
ajustador.optimize.ParamSet(*params)[source]
-
scaled
-
scale(values)[source]
-
scale_dict(values)[source]
-
unscale(scaled_values)[source]
-
unscaled_dict(scaled_values)[source]
-
update(**kwargs)[source]
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scaled_bounds
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class
ajustador.optimize.Fit(dirname, measurement, model, neuron_type, fitness_func, params, feature_list=None)[source]
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fitness_max = 200
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load(last=None)[source]
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param_names()[source]
-
sim(scaled_params)[source]
-
sim_fitness(sim, full=False, max_fitness=None)[source]
-
name
-
fitness(scaled_params)[source]
-
fitness_full(scaled_params)[source]
-
fitness_multi(many_values)[source]
-
finished()[source]
-
param_values(*what)[source]
-
do_fit(count, params=None, sigma=1, popsize=8, seed=123)[source]