def __init__(self,
parent=None,
verify_pass_func=None,
base_class=None,
bRelevant=True,
label='criterion',
valid_base_classes=None,
visible_attributes=['label', 'base_class', 'bRelevant'],
**kwargs):
if valid_base_classes is None:
global valid_criterion_base_classes
valid_base_classes = valid_criterion_base_classes
if base_class is None:
base_class = lfu.interface_template_class(object,
'criterion abstract')
if verify_pass_func is not None:
self.verify_pass = verify_pass_func
self.bRelevant = bRelevant #checking relevance is the
#responsibility of the owner of the criterion!!
lfu.modular_object_qt.__init__(self,
parent=parent,
label=label,
visible_attributes=visible_attributes,
valid_base_classes=valid_base_classes,
base_class=base_class)
self._children_ = []
def __init__(self, parent = None, max_captures = 100.0, base_class =\ lfu.interface_template_class(object, 'capture limit'), label = 'capture limit criterion', visible_attributes =\ ['label', 'base_class', 'bRelevant', 'max_captures']): criterion.__init__(self, parent = parent, label = label, base_class = base_class) self.max_captures = max_captures
def __init__(self, *args, **kwargs):
if not 'base_class' in kwargs.keys():
kwargs['base_class'] = lfu.interface_template_class(
trajectory_criterion_ceiling, 'ceiling limit')
self.impose_default('ceiling', 2500, **kwargs)
trajectory_criterion.__init__(self, *args, **kwargs)
self._children_ = []
def __init__(self, *args, **kwargs):
if not 'base_class' in kwargs.keys():
kwargs['base_class'] = lfu.interface_template_class(
trajectory_criterion_ceiling, 'ceiling limit')
self.impose_default('ceiling', 2500, **kwargs)
trajectory_criterion.__init__(self, *args, **kwargs)
self._children_ = []
def __init__(self, parent = None, max_captures = 100.0, base_class =\
lfu.interface_template_class(object, 'capture limit'),
label = 'capture limit criterion', visible_attributes =\
['label', 'base_class', 'bRelevant', 'max_captures']):
criterion.__init__(self,
parent=parent,
label=label,
base_class=base_class)
self.max_captures = max_captures
def __init__(self, parent = None, max_iterations = 1000, base_class = None, label = 'iteration criterion', visible_attributes =\ ['label', 'base_class', 'bRelevant', 'max_iterations'], **kwargs): if base_class is None: base_class = lfu.interface_template_class( criterion_iteration, 'iteration limit') criterion.__init__(self, parent = parent, label = label, base_class = base_class) self.max_iterations = max_iterations self._children_ = []
def __init__(self, *args, **kwargs):
if not 'base_class' in kwargs.keys():
kwargs['base_class'] =\
lfu.interface_template_class(
object, 'trajectory criterion abstract')
if not 'label' in kwargs.keys():
kwargs['label'] = 'trajectory criterion'
global valid_trajectory_criterion_base_classes
kwargs['valid_base_classes'] =\
valid_trajectory_criterion_base_classes
criterion.__init__(self, *args, **kwargs)
def __init__(self, *args, **kwargs):
if not 'label' in kwargs.keys():
kwargs['label'] = 'timeout criterion'
if not 'base_class' in kwargs.keys():
kwargs['base_class'] = lfu.interface_template_class(
criterion_impatient, 'timeout limit')
self.impose_default('max_timeouts', 100, **kwargs)
self.impose_default('max_last_best', 100, **kwargs)
lc.criterion.__init__(self, *args, **kwargs)
def __init__(self,
parent=None,
filenames=[],
label='another csv output writer',
base_class=lfu.interface_template_class(
object, 'txt writer object')):
self.filenames = filenames
writer.__init__(self,
label=label,
parent=parent,
base_class=base_class)
def __init__(self, *args, **kwargs): if not 'base_class' in kwargs.keys(): kwargs['base_class'] =\ lfu.interface_template_class( object, 'trajectory criterion abstract') if not 'label' in kwargs.keys(): kwargs['label'] = 'trajectory criterion' global valid_trajectory_criterion_base_classes kwargs['valid_base_classes'] =\ valid_trajectory_criterion_base_classes criterion.__init__(self, *args, **kwargs)
def __init__(self, parent = None, max_iterations = 1000, base_class = None,
label = 'iteration criterion', visible_attributes =\
['label', 'base_class', 'bRelevant', 'max_iterations'], **kwargs):
if base_class is None:
base_class = lfu.interface_template_class(criterion_iteration,
'iteration limit')
criterion.__init__(self,
parent=parent,
label=label,
base_class=base_class)
self.max_iterations = max_iterations
self._children_ = []
def __init__(self, parent = None, increment = 10.0, key = 'time', keys = ['iteration', 'time'], base_class = lfu.interface_template_class( object, 'scalar increment'), label = 'scalar increment criterion', visible_attributes =\ ['label', 'base_class', 'bRelevant', 'key', 'increment']): criterion.__init__(self, parent = parent, label = label, base_class = base_class) #self.key expected to match dater.label self.key = key self.keys = keys self.increment = increment
def __init__(self, *args, **kwargs):
if not 'label' in kwargs.keys():
kwargs['label'] = 'simulated annealing routine'
if not 'base_class' in kwargs.keys():
kwargs['base_class'] = lfu.interface_template_class(
object, 'simulated annealing')
self.impose_default('cooling_curve', None, **kwargs)
self.impose_default('max_temperature', 1000, **kwargs)
self.impose_default('temperature', None, **kwargs)
fit_routine.__init__(self, *args, **kwargs)
def __init__(self, parent = None, filenames = [],
label = 'another output writer',
base_class = lfu.interface_template_class(
object, 'writer object'),
valid_base_classes = None, visible_attributes =\
['label', 'base_class', 'filenames']):
global valid_writers_base_classes
if valid_base_classes is None:
valid_base_classes = valid_writers_base_classes
if base_class is None:
base_class = lfu.interface_template_class(object, 'writer')
self.filenames = filenames
#lfu.modular_object_qt.__init__(self, label = label,
lfu.modular_object_qt.__init__(self,
visible_attributes=visible_attributes,
valid_base_classes=valid_base_classes,
parent=parent,
base_class=base_class)
self.axes_manager = plot_axes_manager(parent=self)
self._children_ = [self.axes_manager]
def __init__(self,
parent=None,
filenames=[],
iteration_resolution=1,
label='another plt output writer',
base_class=lfu.interface_template_class(
object, 'plt writer object')):
self.filenames = filenames
self.iteration_resolution = iteration_resolution
writer.__init__(self,
label=label,
parent=parent,
base_class=base_class)
def __init__(self, *args, **kwargs):
if not 'base_class' in kwargs.keys():
kwargs['base_class'] = lfu.interface_template_class(
object, 'layout reduction')
if not 'label' in kwargs.keys():
kwargs['label'] = 'layout reduction'
if not 'valid_regimes' in kwargs.keys():
kwargs['valid_regimes'] = ['all trajectories']
if not 'regime' in kwargs.keys():
kwargs['regime'] = 'all trajectories'
#self.impose_default('function_of', None, **kwargs)
post_process_plate_reader.__init__(self, *args, **kwargs)
def __init__(self, *args, **kwargs):
if not 'base_class' in kwargs.keys():
kwargs['base_class'] = lfu.interface_template_class(
object, 'layout reduction')
if not 'label' in kwargs.keys():
kwargs['label'] = 'layout reduction'
if not 'valid_regimes' in kwargs.keys():
kwargs['valid_regimes'] = ['all trajectories']
if not 'regime' in kwargs.keys():
kwargs['regime'] = 'all trajectories'
#self.impose_default('function_of', None, **kwargs)
post_process_plate_reader.__init__(self, *args, **kwargs)
def __init__(self, parent = None, increment = 10.0, key = 'time',
keys = ['iteration', 'time'],
base_class = lfu.interface_template_class(
object, 'scalar increment'),
label = 'scalar increment criterion', visible_attributes =\
['label', 'base_class', 'bRelevant', 'key', 'increment']):
criterion.__init__(self,
parent=parent,
label=label,
base_class=base_class)
#self.key expected to match dater.label
self.key = key
self.keys = keys
self.increment = increment
def __init__(self, *args, **kwargs):
if not 'valid_base_classes' in kwargs.keys():
global valid_metric_base_classes
kwargs['valid_base_classes'] = valid_metric_base_classes
if not 'base_class' in kwargs.keys():
kwargs['base_class'] = lfu.interface_template_class(
object, 'abstract metric')
self.impose_default('best_measure', 0, **kwargs)
self.impose_default('display_threshold', 0, **kwargs)
self.impose_default('display_time', 1.0, **kwargs)
self.impose_default('acceptance_weight', 1.0, **kwargs)
self.impose_default('best_advantage', 2.0, **kwargs)
self.impose_default('best_flag', False, **kwargs)
self.impose_default('is_heaviest', False, **kwargs)
lfu.modular_object_qt.__init__(self, *args, **kwargs)
self._children_ = []
def __init__( self, parent = None, verify_pass_func = None, base_class = None, bRelevant = True, label = 'criterion', valid_base_classes = None, visible_attributes = ['label', 'base_class', 'bRelevant'], **kwargs ): if valid_base_classes is None: global valid_criterion_base_classes valid_base_classes = valid_criterion_base_classes if base_class is None: base_class = lfu.interface_template_class( object, 'criterion abstract') if verify_pass_func is not None: self.verify_pass = verify_pass_func self.bRelevant = bRelevant #checking relevance is the #responsibility of the owner of the criterion!! lfu.modular_object_qt.__init__(self, parent= parent, label = label, visible_attributes = visible_attributes, valid_base_classes = valid_base_classes, base_class = base_class) self._children_ = []
npts = xdim*ydim*zdim flat = ['0']*npts for j in range(len(Q)): try: flat[int(z[j])*xdim*ydim + int(y[j])*xdim + int(x[j])]=str(Q[j]) except IndexError: print 'Youve got an indexing problem' pdb.set_trace() string = array_to_string(flat) return string valid_metric_base_classes = [ lfu.interface_template_class( metric_slope_1st_derivative, 'slope-1 comparison metric'), lfu.interface_template_class( metric_slope_2nd_derivative, 'slope-2 comparison metric'), lfu.interface_template_class( metric_slope_3rd_derivative, 'slope-3 comparison metric'), lfu.interface_template_class( metric_avg_ptwise_diff_on_domain, 'pointwise difference metric')]
means = [mean(val) for val in vals] data[dex + 1].scalars = means data[0].scalars = bins return data def set_settables(self, *args, **kwargs): self.valid_regimes = ['all trajectories'] self.valid_inputs = self.get_valid_inputs(*args, **kwargs) self.handle_widget_inheritance(*args, from_sub = False) post_process_plate_reader.set_settables( self, *args, from_sub = True) valid_postproc_base_classes = [ lfu.interface_template_class( post_process_layout_reduction, 'layout reduction')] lpp.valid_postproc_base_classes = valid_postproc_base_classes if __name__ == 'libs.plate_reader_analyzer.libplatereaderprocesses': if lfu.gui_pack is None: lfu.find_gui_pack() lgm = lfu.gui_pack.lgm lgd = lfu.gui_pack.lgd lgb = lfu.gui_pack.lgb if __name__ == '__main__': print 'this is a library!'
top_templates.append(
lgm.interface_template_gui(
panel_position=(0, 2),
widgets=['check_set'],
append_instead=[False],
instances=[[self] * 4],
keys=[['output_vtk', 'output_pkl', 'output_txt',
'output_plt']],
labels=[[
'Output .vtk files', 'Output .pkl files',
'Output .csv files', 'Output .plt files'
]],
box_labels=['Output Types']))
top_template = lgm.interface_template_gui(widgets=['panel'],
scrollable=[True],
templates=[top_templates])
self.widg_templates.append(
lgm.interface_template_gui(
widgets=['splitter'],
orientations=[['vertical']],
templates=[[top_template, writers_splitter_template]]))
lfu.modular_object_qt.set_settables(self, *args, from_sub=True)
valid_writers_base_classes = [
lfu.interface_template_class(writer_vtk, 'vtk'),
lfu.interface_template_class(writer_pkl, 'pickle'),
lfu.interface_template_class(writer_txt, 'text'),
lfu.interface_template_class(writer_plt, 'plot')
]
improves.append(sca[-1] - min(sca) <=\ (np.mean(sca[-100:]) - min(sca))/20.0) weights = [met.acceptance_weight for met in metrics] weights = [we/sum(weights) for we, imp in zip(weights, improves) if imp] weight = sum(weights) if weight >= self.reject_probability: #if improves.count(True) > int(len(improves)/2): #if improves.count(True) == len(improves): self.accepts += 1 return True self.rejects += 1 return False valid_fit_routine_base_classes = [ lfu.interface_template_class( fit_routine_simulated_annealing, 'simulated annealing')]
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]
self.handle_widget_inheritance(*args, from_sub = False) self.widg_templates.append( lgm.interface_template_gui( widgets = ['spin'], doubles = [[False]], initials = [[int(self.ceiling)]], minimum_values = [[0]], maximum_values = [[sys.maxint]], instances = [[self]], keys = [['ceiling']], box_labels = ['Ceiling Limit'])) trajectory_criterion.set_settables(self, *args, from_sub = True) ''' valid_criterion_base_classes = [ lfu.interface_template_class( criterion_sim_time, 'time limit'), lfu.interface_template_class( criterion_iteration, 'iteration limit'), lfu.interface_template_class( criterion_capture_count, 'capture limit'), lfu.interface_template_class( criterion_scalar_increment, 'scalar increment')] valid_trajectory_criterion_base_classes = [ lfu.interface_template_class( trajectory_criterion_ceiling, 'ceiling limit')] if __name__ == 'libs.modular_core.libcriterion': if lfu.gui_pack is None: lfu.find_gui_pack() lgm = lfu.gui_pack.lgm lgd = lfu.gui_pack.lgd
self.widg_templates.append(
lgm.interface_template_gui(
widgets = ['spin'],
doubles = [[False]],
initials = [[int(self.ceiling)]],
minimum_values = [[0]],
maximum_values = [[sys.maxint]],
instances = [[self]],
keys = [['ceiling']],
box_labels = ['Ceiling Limit']))
trajectory_criterion.set_settables(self, *args, from_sub = True)
'''
valid_criterion_base_classes = [
lfu.interface_template_class(criterion_sim_time, 'time limit'),
lfu.interface_template_class(criterion_iteration, 'iteration limit'),
lfu.interface_template_class(criterion_capture_count, 'capture limit'),
lfu.interface_template_class(criterion_scalar_increment,
'scalar increment')
]
valid_trajectory_criterion_base_classes = [
lfu.interface_template_class(trajectory_criterion_ceiling, 'ceiling limit')
]
if __name__ == 'libs.modular_core.libcriterion':
if lfu.gui_pack is None: lfu.find_gui_pack()
lgm = lfu.gui_pack.lgm
lgd = lfu.gui_pack.lgd
lgb = lfu.gui_pack.lgb
mean_of)
bins, vals = bin_scalars(bin_axes, mean_axes, self.bin_count,
self.ordered)
means = [mean(val) for val in vals]
data[dex + 1].scalars = means
data[0].scalars = bins
return data
def set_settables(self, *args, **kwargs):
self.valid_regimes = ['all trajectories']
self.valid_inputs = self.get_valid_inputs(*args, **kwargs)
self.handle_widget_inheritance(*args, from_sub=False)
post_process_plate_reader.set_settables(self, *args, from_sub=True)
valid_postproc_base_classes = [
lfu.interface_template_class(post_process_layout_reduction,
'layout reduction')
]
lpp.valid_postproc_base_classes = valid_postproc_base_classes
if __name__ == 'libs.plate_reader_analyzer.libplatereaderprocesses':
if lfu.gui_pack is None: lfu.find_gui_pack()
lgm = lfu.gui_pack.lgm
lgd = lfu.gui_pack.lgd
lgb = lfu.gui_pack.lgb
if __name__ == '__main__':
print 'this is a library!'