def __init__(self, *args, **kwargs):
self.impose_default('capture_targets',[],**kwargs)
self.impose_default('replicate_reduced',False,**kwargs)
blks = args[0]
raw = blks[0].raw + blks[1].raw
data_block.__init__(self, *(raw,), **kwargs)
if not hasattr(self, 'label'): self.label = kwargs['label']
self.output = lo.output_plan(
label = self.label + ' Output', parent = self)
self.output.flat_data = True
self._children_ = [self.output]
self.output.output_plt = False
def __init__(self, *args, **kwargs):
self.impose_default('parameter_space', None, **kwargs)
self.impose_default('many_steps', 1, **kwargs)
self.impose_default('p_sp_trajectory', [], **kwargs)
self.impose_default('p_sp_step_factor', 1.0, **kwargs)
self.impose_default('capture_targets', [], **kwargs)
self.impose_default('bAbort', False, **kwargs)
self.impose_default('brand_new', True, **kwargs)
self.impose_default('iteration', 0, **kwargs)
self.impose_default('auto_overwrite_key', True, **kwargs)
self.impose_default('initial_creep_factor', 20, **kwargs)
self.impose_default('display_frequency', 500, **kwargs)
self.impose_default('max_sim_wait_time', 1.0, **kwargs)
self.impose_default('last_best', 0, **kwargs)
self.impose_default('timeouts', 0, **kwargs)
self.impose_default('use_time_out', True, **kwargs)
self.impose_default('use_genetics', False, **kwargs)
self.impose_default('use_mean_fitting', False, **kwargs)
self.impose_default('regime', 'fine', **kwargs)
self.impose_default('valid_regimes',
['fine', 'coarse-magnitude', 'coarse-decimate'], **kwargs)
self.impose_default('metrics', [], **kwargs)
self.metrics.append(
lgeo.metric_avg_ptwise_diff_on_domain(
parent = self, acceptance_weight = 1.0))
self.metrics.append(
lgeo.metric_slope_1st_derivative(
parent = self, acceptance_weight = 0.9))
self.metrics.append(
lgeo.metric_slope_2nd_derivative(
parent = self, acceptance_weight = 0.75))
self.metrics.append(
lgeo.metric_slope_3rd_derivative(
parent = self, acceptance_weight = 0.5))
self.impose_default('metric_weights',
[met.acceptance_weight for met
in self.metrics], **kwargs)
self.metric_rulers = [lm.differences,
lm.deriv_first_differences,
lm.deriv_second_differences,
lm.deriv_third_differences]
self.impose_default('prime_metric', 0, **kwargs)
self.prime_metric =\
self.metric_weights.index(max(self.metric_weights))
self.metrics[self.prime_metric].is_heaviest = True
self.impose_default('fitted_criteria', [], **kwargs)
self.fitted_criteria.append(lc.criterion_iteration(
parent = self, max_iterations = 5000))
self.fitted_criteria.append(criterion_impatient(
parent = self, max_timeouts = 50,
max_last_best = 2000))
self.impose_default('fitter_criteria', [], **kwargs)
self.fitter_criteria.append(
criterion_minimize_measures(parent = self))
self.impose_default('data_to_fit_to', None, **kwargs)
self.impose_default('input_data_file', '', **kwargs)
self.impose_default('input_data_domain', '', **kwargs)
self.impose_default('input_data_codomains', [], **kwargs)
self.impose_default('input_data_targets', [], **kwargs)
self.impose_default('input_data_aliases', {}, **kwargs)
self.input_data_file = os.path.join(os.getcwd(),
'chemicallite', 'output', 'mm_fit_input.0.pkl')
# 'imager', 'fit_data.pkl')
#self.input_data_domain = 'time'
#self.input_data_codomains = ['ES_Complex', 'Product',
# 'Substrate', 'Enzyme']
#self.input_data_aliases = {'time':'time',
# 'ES_Complex mean':'ES_Complex',
# 'Product mean':'Product',
# 'Enzyme mean':'Enzyme',
# 'Substrate mean':'Substrate'}
#self.input_data_codomains = ['ES_Complex', 'Product']
#self.input_data_codomains = ['ES_Complex']
#self.input_data_codomains = ['lacI', 'tetR', 'cl']
if not 'visible_attributes' in kwargs.keys():
kwargs['visible_attributes'] = None
if not 'valid_base_classes' in kwargs.keys():
kwargs['valid_base_classes'] =\
valid_fit_routine_base_classes
if not 'base_class' in kwargs.keys():
kwargs['base_class'] = lfu.interface_template_class(
object, 'fit routine abstract')
lfu.modular_object_qt.__init__(self, *args, **kwargs)
self.output = lo.output_plan(label = ' '.join(
[self.label, 'output']), parent = self)
#self.output.flat_data = False
#self._children_ = [self.output] + self.metrics +\
# self.fitted_criteria + self.fitter_criteria
self._children_ = [self.output]
def __init__(self, *args, **kwargs):
#self.impose_default('current_tab_index', 0, **kwargs)
#self.current_tab_index = 0
self.aborted = False
self.data_pool = lfu.data_container(
data = [], postproc_data = [])
self.data_scheme = None
self.__dict__ = lfu.dictionary()
if 'parent' in kwargs.keys(): self.parent = kwargs['parent']
self.cancel_make = False
self.skip_simulation = False
self.mcfg_path = ''
#self.num_trajectories = 1
num_traj = lset.get_setting('trajectory_count')
if num_traj: self.num_trajectories = num_traj
else: self.num_trajectories = 1
self.data_pool_descr = ''
self.treebook_memory = [0, [], []]
self._module_memory_ = []
self.simulation_plan = simulation_plan(parent = self)
self.output_plan = lo.output_plan(
label = 'Simulation', parent = self)
self.fitting_plan = lfr.fit_routine_plan(parent = self)
self.cartographer_plan = lgeo.cartographer_plan(
label = 'Parameter Scan', parent = self)
self.postprocess_plan = lpp.post_process_plan(
label = 'Post Process Plan', parent = self,
_always_sourceable_ = ['simulation'])
self.multiprocess_plan = lmp.multiprocess_plan(parent = self)
self.run_params = lfu.dictionary(parent = self)
self.run_params['end_criteria'] = \
self.simulation_plan.end_criteria
self.run_params['capture_criteria'] = \
self.simulation_plan.capture_criteria
self.run_params['plot_targets'] = \
self.simulation_plan.plot_targets
self.run_params['output_plans'] = {
'Simulation' : self.output_plan}
self.run_params['fit_routines'] = \
self.fitting_plan.routines
self.run_params['post_processes'] = \
self.postprocess_plan.post_processes
self.run_params['p_space_map'] = None
self.run_params['multiprocessing'] = None
self.__dict__.create_partition('template owners',
['output_plan', 'fitting_plan', 'cartographer_plan',
'postprocess_plan', 'multiprocess_plan', 'run_params',
'simulation_plan'])
if 'label' not in kwargs.keys(): kwargs['label'] = 'ensemble'
if 'module_options' in kwargs.keys():
opts = kwargs['module_options']
else:
print 'no modules detected; requesting from manager'
opts = self.parent.find_module_options()
if len(opts) == 0:
lgd.message_dialog(None,
'No module options detected!', 'Problem')
self.cancel_make = True
return
elif len(opts) == 1: module = opts[0]
else:
module_choice_container = lfu.data_container(data = opts[0])
module_options_templates = [lgm.interface_template_gui(
layout = 'horizontal',
widgets = ['radio'],
verbosities = [0],
labels = [opts],
initials = [[module_choice_container.data]],
instances = [[module_choice_container]],
keys = [['data']],
box_labels = ['Ensemble Module'],
minimum_sizes = [[(250, 50)]])]
mod_dlg = lgd.create_dialog(title = 'Choose Ensemble Module',
templates = module_options_templates, variety = 'templated')
module = mod_dlg()
if module: module = module_choice_container.data
else:
self.cancel_make = True
return
self.impose_default('module', module, **kwargs)
self._children_ = [self.simulation_plan, self.output_plan,
self.fitting_plan, self.cartographer_plan,
self.postprocess_plan, self.multiprocess_plan]
self.load_module(reset_params = True)
self.mcfg_dir = os.path.join(os.getcwd(), self.module)
if not os.path.isdir(self.mcfg_dir): self.mcfg_dir = os.getcwd()
lfu.modular_object_qt.__init__(self, *args, **kwargs)
self.data_pool_id = lfu.get_new_pool_id()
self.data_pool_pkl = os.path.join(os.getcwd(), 'data_pools',
'.'.join(['data_pool', self.data_pool_id, 'pkl']))
