def to_string(self):
#0 : C:\Users\bartl_000\Desktop\Work\Modular\chemicallite\output : ensemble_output : none : all
if self.label.startswith('Simulation'): numb = '0'
else:
procs = lfu.grab_mobj_names(self.parent.parent.post_processes)
numb = str(procs.index(self.parent.label) + 1)
plot_types = [
item for item, out_item in zip(['vtk', 'pkl', 'txt', 'plt'], [
self.output_vtk, self.output_pkl, self.output_txt,
self.output_plt
]) if out_item
]
if not plot_types: plots = 'none'
else: plots = ', '.join(plot_types)
targs = self.get_target_labels()
if not self.targeted: targs = 'none'
elif self.targeted == targs: targs = 'all'
else: targs = ', '.join(self.targeted)
return '\t' + ' : '.join(
[numb, self.save_directory, self.save_filename, plots, targs])
def finalize(self, *args, **kwargs):
def get_interped_y(label, data, x, x_to):
run_y = lfu.grab_mobj_by_name(label, data)
run_interped = lgeo.scalars(
label = 'interpolated best result - ' + label,
scalars = lm.linear_interpolation(
x.scalars, run_y.scalars,
x_to.scalars, 'linear'))
return run_interped
self.best_fits = [(met.best_measure,
met.data[0].scalars[met.best_measure])
for met in self.metrics]
self.handle_fitting_key()
self.parameter_space.set_current_position(
self.p_sp_trajectory[self.best_fits[
self.prime_metric][0]])
best_run_data = lis.run_system(self.ensemble)
best_run_data = [lgeo.scalars(label = lab, scalars = dat)
for lab, dat in zip(self.run_targets, best_run_data)]
best_run_data_x = lfu.grab_mobj_by_name(
self.data_to_fit_to[0].label, best_run_data)
best_run_data_ys = [get_interped_y(
lab, best_run_data, best_run_data_x, self.data_to_fit_to[0])
for lab in lfu.grab_mobj_names(self.data_to_fit_to[1:])]
print 'fit routine:', self.label, 'best fit:', self.best_fits
print '\tran using regime:', self.regime
best_data = self.data_to_fit_to + best_run_data_ys
lgd.quick_plot_display(best_data[0], best_data[1:], delay = 5)
self.ensemble.data_pool.pool_names =\
[dat.label for dat in best_data]
self.ensemble.data_pool.batch_pool.append(best_data)
if self.regime.startswith('coarse'):
self.impose_coarse_result_to_p_space()
self.ensemble.cartographer_plan.parameter_space.\
set_current_position(self.p_sp_trajectory[
self.best_fits[self.prime_metric][0]])
def generate_add_remove_select_inspect_box_template(window, key, labels, wheres, parent, selector_handle, memory_handle, base_class, function_handles = None, verbosities = None): if wheres[0] is wheres[1]: whars = wheres[:-1] else: whars = wheres _add_func_ = lgb.generate_add_function(base_class, parent = parent, wheres = whars) _sel_func_ = lgb.generate_selected_function(wheres[1], lfu.grab_mobj_names(wheres[1]), selector_handle, offset = 1) _rem_func_ = lgb.generate_remove_function(_sel_func_, parent = parent, wheres = wheres) template = interface_template_gui( layout = 'grid', widg_positions = [(0, 0), (0, 2), (1, 2)], widg_spans = [(3, 2), None, None], grid_spacing = 10, widgets = ['mobj_catalog', 'button_set'], verbosities = verbosities, instances = [[wheres[1], memory_handle[0]], None], keys = [[None, memory_handle[1]], None], handles = [selector_handle, None], labels = [None, labels], initials = [[memory_handle[0].__dict__[ memory_handle[1]]], None], bindings = [None, [lgb.create_reset_widgets_wrapper( window, _add_func_), lgb.create_reset_widgets_wrapper( window, _rem_func_)]]) if function_handles: for dex, func in zip(range(3), [_add_func_, _sel_func_, _rem_func_]): if function_handles[dex]: for hand in function_handles[dex]: hand[0].__dict__[hand[1]] = func return template
