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#$Id: traitedDockingParameters.py 258 2015-11-14 17:04:46Z sarkiss $
from enthought.traits.api import HasTraits, List, Trait, Int, Bool, Float, Enum, File, Str, TraitHandler
from enthought.traits.ui.api import Item, Group, View, CheckListEditor, VGroup, HGroup, spring, EnumEditor
from enthought.traits.ui.menu import LiveButtons, UndoButton, RevertButton, OKButton, CancelButton, HelpButton
from AutoDockTools.DockingParameters import DockingParameters
from miscTraits import PositiveInt, TraitPositiveInteger
Buttons = [ UndoButton, RevertButton, OKButton, CancelButton ]
class TraitedDockingParameters( HasTraits ):
dpo = DockingParameters()
randomLibraryTuple = ('Platform-Independent library', 'Built-In library')
randomLibrary = Trait( randomLibraryTuple[0], randomLibraryTuple[1])
seedType1 = Trait('time', 'PID', 'custom')
seedType1.default_value(0,"PID")
seedType2 = Trait('time', 'PID', 'custom')
seedInt1 = PositiveInt()
seedInt2 = PositiveInt()
external_grid_energy = Float(dpo['extnrg']['value'])
e0max_0 = Float(dpo['e0max']['value'][0])
e0max_1 = Trait(dpo['e0max']['value'][1], TraitPositiveInteger(), Int)
calculate_internal_electrostatic_energy = Bool(dpo['intelec']['value'])
dstep = Float(dpo['dstep']['value'])
try:
tstep = Float(dpo['tstep']['value'][0]) #TODO: remove this after PyRx 1.0 release
except:
tstep = Float(dpo['tstep']['value'])
qstep = Float(dpo['qstep']['value'])
outputLevelList = ["No output", "Minimal output",
"Full state output at end of each cycle",
"Detailed output for each step"]
# The view includes one group per column formation. These will be displayed
# on separate tabbed panels.
outputLevelEnum = Enum(outputLevelList, editor = EnumEditor(values = outputLevelList, cols=1))
outputLevelEnum.default_value = "Minimal output"
write_all_flag = Bool(dpo['write_all_flag']['value'])
analysis_flag = Bool(dpo['analysis']['value'])
rmstol = Float(dpo['rmstol']['value'])
rmsatoms = Enum("ligand only",'all')
rmsref = File
custom_parameter_file = Bool(dpo['custom_parameter_file']['value'])
parameter_file = File(dpo['parameter_file']['value'])
include_1_4_interactions_flag = Bool(dpo['include_1_4_interactions_flag']['value'])
include_1_4_interactions = Float(dpo['include_1_4_interactions']['value'])
unbound_model_flag = Bool(True)
unbound_model = Trait("bound", "extended")
epdb_flag = Bool(dpo['epdb_flag']['value'])
epdb = File(dpo['epdb']['value'])
def __init__(self):
seedType1_group = HGroup(Item(name='seedType1', style='custom', show_label=False),
Group(Item(name='seedInt1', show_label=False),
enabled_when='object.seedType1 == "custom"'),
)
seedType2_group = HGroup(Item(name='seedType2', style='custom', show_label=False),
Group(Item(name='seedInt1', show_label=False),
enabled_when='object.seedType2 == "custom"'),
visible_when = 'randomLibrary == "Platform-Independent library"',
)
seedsGroup = Group( seedType1_group, seedType2_group,
show_border = True,
label = 'Random Number Generator Seeds'
)
# CheckListEditor display with four columns
random_group = Group(Group(
Item('randomLibrary',
style='custom', show_label=False),
seedsGroup,
),
label='Random Numbers'
)
energy_group = Group( Item('external_grid_energy'),
Item('e0max_0', label= "Maximum allowable energy to start a run"),
Item('e0max_1', label="Maximum number of retries"),
Item('calculate_internal_electrostatic_energy'),
label='Energy Parameters', show_border = True,
)
step_group = Group( Item('tstep', label="Translation (Angstrom/step)"),
Item('qstep', label="Quaternion (Degree/step)"),
Item('dstep', label="Torsion (Degree/step)"),
label='Step Size', show_border = True,
)
miscOutputGroup = Group(Item('analysis_flag', label = "Perform cluster analysis:"),
Item('write_all_flag', label = "Write all conformations in a cluster:"),
Item('rmstol', label = "RMS Cluster Tolerance (Angstrom):"),
Item(name='rmsatoms', style='custom', label="For RMS calculation use:"),
Item('rmsref', label = 'Reference structure file for RMS'),
)
output_group = VGroup( Item('outputLevelEnum', style='custom', show_label=False),
miscOutputGroup,
label='Output Options'
)
custom_parameter_group = Group(Item(name='custom_parameter_file', label = "Use custom parameter library"),
Item(name='parameter_file', enabled_when='object.custom_parameter_file'))
include_1_4_interactions_group = Group(
Item(name='include_1_4_interactions_flag', label='Include internal 1-4 interactions'),
Item(name='include_1_4_interactions', label='Scaling factor for 1-4 interactions',
enabled_when='object.include_1_4_interactions_flag'),
)
# unbound_group = Group(Item(name='unbound_model_flag', label = "Include unbound ligand energy"),
# Item(name='unbound_model', enabled_when='object.unbound_model_flag == True', label="Unbound ligand state"),
# label='Unbound Ligand Parameters', show_border = True,
# )
#
epdb_group = Group(Item(name='epdb_flag', label = "Compute binding energy without docking (epdb)"),
Item(name='epdb', enabled_when='object.epdb_flag', label = "Enter filename for epdb calculation"))
autodock4_specific_group = Group(custom_parameter_group, include_1_4_interactions_group,
epdb_group,
label='New in AutoDock 4.2'
)
self.docking_parameters_group = Group( output_group,
Group(
energy_group,
step_group, label="Energy and Steps"),
random_group,
autodock4_specific_group,
layout='tabbed',
label = 'Docking Parameters',
)
self.docking_parameters_view = View( output_group,
Group(
energy_group,
step_group, label="Energy and Steps"),
random_group,
autodock4_specific_group,
title = 'AutoDock Parameters', buttons = Buttons
)
def _randomLibrary_changed(self, old, new):
if new == self.randomLibraryTuple[0]:
if self.seedType1 == 'custom':
seed1 = self.seedInt1
else:
seed1 = self.seedType1.lower()
if self.seedType2 == 'custom':
seed2 = self.seedInt2
else:
seed2 = self.seedType2.lower()
self.dpo['seed']['value'] = [seed1, seed2]
else:
if self.seedType1 == 'custom':
seed1 = self.seedInt1
else:
seed1 = self.seedType2.lower()
self.dpo['seed']['value'] = [seed1]
def _seedType1_changed(self):
self._randomLibrary_changed(None, self.randomLibrary)
def _seedType2_changed(self):
self._randomLibrary_changed(None, self.randomLibrary)
def _external_grid_energy_changed(self, new):
self.dpo['extnrg']['value'] = new
def _e0max_0_changed(self, new):
self.dpo['e0max']['value'][0] = new
def _e0max_1_changed(self, new):
self.dpo['e0max']['value'][1] = new
def _calculate_internal_electrostatic_energy_changed(self, new):
self.dpo['intelec']['value'] = new
def _dstep_changed(self, new):
self.dpo['dstep']['value'] = new
def _tstep_changed(self, new):
self.dpo['tstep']['value'] = [new]
def _qstep_changed(self, new):
self.dpo['qstep']['value'] = new
def _outputLevelEnum_changed(self, new):
index = self.outputLevelList.index(new)
self.dpo['outlev']['value'] = index
def _write_all_flag_changed(self, new):
self.dpo['write_all_flag']['value'] = new
def _analysis_changed(self, new):
self.dpo['analysis']['value'] = new
def _rmstol_changed(self, new):
self.dpo['rmstol']['value'] = new
def _rmsatoms_changed(self, new):
if new:
self.dpo['rmsatoms_flag']['value'] = True
self.dpo['rmsatoms']['value'] = new
else:
self.dpo['rmsatoms_flag']['value'] = False
self.dpo['rmsatoms']['value'] = new
def _custom_parameter_file_changed(self, new):
self.dpo['custom_parameter_file']['value'] = new
def _parameter_file_changed(self, new):
self.dpo['parameter_file']['value'] = new
def _include_1_4_interactions_flag_changed(self, new):
self.dpo['include_1_4_interactions_flag']['value'] = new
def _include_1_4_interactions_changed(self, new):
self.dpo['include_1_4_interactions']['value'] = new
def _unbound_model_flag_changed(self, new):
self.dpo['unbound_model_flag']['value'] = new
def _unbound_model_changed(self, new):
self.dpo['unbound_model']['value'] = new
def _epdb_flag_changed(self, new):
self.dpo['epdb_flag']['value'] = new
def _epdb_changed(self, new):
self.dpo['epdb']['value'] = new
class TraitedGeneticAlgorithmParameters(TraitedDockingParameters):
ga_run = PositiveInt()
ga_pop_size = PositiveInt()
ga_num_evals = PositiveInt()
ga_evals_type = Trait('short', 'medium', 'long', 'custom')
ga_evals_type.default_value(0,"short")
ga_num_generations = PositiveInt()
ga_elitism = PositiveInt()
ga_mutation_rate = Float
ga_crossover_rate = Float
ga_crossover_mode = Enum('twopt', 'arithmetic', 'uniform')
ga_cauchy_alpha = Float
ga_cauchy_beta = Float
ga_window_size = Float
def __init__(self):
super(TraitedGeneticAlgorithmParameters, self).__init__()
self.ga_run = self.dpo['ga_run']['value']
self.ga_pop_size = self.dpo['ga_pop_size']['value']
self.ga_num_evals = 250000 #self.dpo['ga_num_evals']['value']
self.ga_num_generations = self.dpo['ga_num_generations']['value']
self.ga_elitism = self.dpo['ga_elitism']['value']
self.ga_mutation_rate = self.dpo['ga_mutation_rate']['value']
self.ga_crossover_rate = self.dpo['ga_crossover_rate']['value']
self.ga_crossover_mode = self.dpo['ga_crossover_mode']['value']
self.ga_cauchy_alpha = self.dpo['ga_cauchy_alpha']['value']
self.ga_cauchy_beta = self.dpo['ga_cauchy_beta']['value']
self.ga_window_size = self.dpo['ga_window_size']['value']
ga_evals_group = HGroup(spring, Item(name='ga_evals_type', style='simple', label="Maximum number of energy evaluations"),
Item(name='ga_num_evals', show_label=False, enabled_when='object.ga_evals_type == "custom"'),
)
self.genetic_algorithm_group = Group(Item(name='ga_run', label="Number of GA runs"),
Item(name='ga_pop_size', label="Number of individuals in population"),
#Item(name='ga_num_evals', label="Maximum number of energy evaluations"),
ga_evals_group,
Item(name='ga_num_generations', label="Maximum number of generations"),
Item(name='ga_elitism', label="Number of top individuals to survive to next generation"),
Item(name='ga_mutation_rate', label="Rate of gene mutation"),
Item(name='ga_crossover_rate', label="Rate of crossover"),
Item(name='ga_crossover_mode', label="GA crossover mode"),
Item(name="ga_cauchy_alpha", label="Mean of Cauchy distribution for gene mutation (Cauchy alpha)"),
Item(name="ga_cauchy_beta", label="Variance of Cauchy distribution for gene mutation (Cauchy beta)"),
Item(name='ga_window_size', label="Number of generations for picking worst individual (GA window size)"),
label = 'Genetic Algorithm Parameters',
)
self.genetic_algorithm_view = View(self.genetic_algorithm_group, buttons = Buttons)
def _ga_run_changed(self, new):
self.dpo['ga_run']['value'] = new
def _ga_pop_size_changed(self, new):
self.dpo['ga_pop_size']['value'] = new
def _ga_num_evals_changed(self, new):
self.dpo['ga_num_evals']['value'] = new
def _ga_evals_type_changed(self, new):
if new == 'short':
self.ga_num_evals = 250000
elif new == 'medium':
self.ga_num_evals = 2500000
elif new == 'long':
self.ga_num_evals = 25000000
def _ga_num_generations_changed(self, new):
self.dpo['ga_num_generations']['value'] = new
def _ga_elitism_changed(self, new):
self.dpo['ga_elitism']['value'] = new
def _ga_mutation_rate_changed(self, new):
self.dpo['ga_mutation_rate']['value'] = new
def _ga_crossover_rate_changed(self, new):
self.dpo['ga_crossover_rate']['value'] = new
def _ga_cauchy_alpha_changed(self, new):
self.dpo['ga_cauchy_alpha']['value'] = new
def _ga_cauchy_beta_changed(self, new):
self.dpo['ga_cauchy_beta']['value'] = new
def _ga_window_size_changed(self, new):
self.dpo['ga_window_size']['value'] = new
class TraitedSimulatedAnnealingParameters(TraitedDockingParameters):
runs = PositiveInt()
cycles = PositiveInt()
accs = PositiveInt()
rejs = PositiveInt()
select = Enum("Minimum state","Last state")
linear_schedule = Enum("Linear","Geometric")
trnrf = Float
quarf = Float
dihrf = Float
rtrf = Float
rt0 = Float
def __init__(self):
super(TraitedSimulatedAnnealingParameters, self).__init__()
self.runs = self.dpo['runs']['value']
self.cycles = self.dpo['cycles']['value']
self.accs = self.dpo['accs']['value']
self.rejs = self.dpo['rejs']['value']
self.trnrf = self.dpo['trnrf']['value']
self.quarf = self.dpo['quarf']['value']
self.dihrf = self.dpo['dihrf']['value']
self.rtrf = self.dpo['rtrf']['value']
self.rt0 = self.dpo['rt0']['value']
#self.ga_pop_size =
self.simulated_annealing_group = Group(Item(name='runs', label="Number of runs"),
Item(name='cycles', label="Number of temperature reduction cycles"),
Item(name='accs', label="Maximum number of accepted steps per cycle"),
Item(name='rejs', label="Maximum number of rejected steps per cycle"),
Item(name='select', label="State selection for the next cycle", style='custom'),
Item(name='linear_schedule', label="Temperature reduction schedule type", style='custom'),
Item(name='trnrf', label="Per cycle reduction factor for translation"),
Item(name='quarf', label="Per cycle reduction factor for quaternion"),
Item(name='dihrf', label="Per cycle reduction factor for dihedral"),
Item(name='rtrf', label="Per cycle reduction factor for temperature"),
Item(name='rt0', label="Initial annealing temperature"),
label = 'Simulated Annealing Parameters',
)
self.simulated_annealing_view = View(self.simulated_annealing_group, buttons = Buttons)
def _runs_changed(self, new):
self.dpo['runs']['value'] = new
def _cycles_changed(self, new):
self.dpo['cycles']['value'] = new
def _accs_changed(self, new):
self.dpo['accs']['value'] = new
def _rejs_changed(self, new):
self.dpo['rejs']['value'] = new
def _select_changed(self, new):
if new == "Minimum state":
self.dpo['select']['value'] = 'm'
else:
self.dpo['select']['value'] = 'l' #(l)ast state
def _linear_schedule_changed(self, new):
if new == "Linear":
self.dpo['linear_schedule']['value'] = 1
else:
self.dpo['linear_schedule']['value'] = 0
def _trnrf_changed(self, new):
self.dpo['trnrf']['value'] = new
def _quarf_changed(self, new):
self.dpo['quarf']['value'] = new
def _dihrf_changed(self, new):
self.dpo['dihrf']['value'] = new
def _rtrf_changed(self, new):
self.dpo['rtrf']['value'] = new
def _rt0_changed(self, new):
self.dpo['rt0']['value'] = new
class TraitedLocalSearchParameters(TraitedDockingParameters):
do_local_only = PositiveInt()
sw_max_its = PositiveInt()
sw_max_succ = PositiveInt()
sw_max_fail = PositiveInt()
sw_rho = Float
sw_lb_rho = Float
ls_search_freq = Float
sw_type = Enum("Uniform","Relative")
def __init__(self):
super(TraitedLocalSearchParameters, self).__init__()
self.do_local_only = self.dpo['do_local_only']['value']
self.sw_max_its = self.dpo['sw_max_its']['value']
self.sw_max_succ = self.dpo['sw_max_succ']['value']
self.sw_max_fail = self.dpo['sw_max_fail']['value']
self.sw_rho = self.dpo['sw_rho']['value']
self.sw_lb_rho = self.dpo['sw_lb_rho']['value']
self.ls_search_freq = self.dpo['ls_search_freq']['value']
#self.ga_pop_size =
self.local_search_group = Group(Item(name='do_local_only', label="Number of LS runs"),
Item(name='sw_max_its', label="Maximum number of iterations"),
Item(name='sw_max_succ', label="Maximum number of successes in a row before changing rho"),
Item(name='sw_max_fail', label="Maximum number of failures in a row before changing rho"),
Item(name='sw_rho', label="Solis and Wets parameter defining initial variance \nand size of local space to sample (rho)"),
Item(name='sw_lb_rho', label="Lower bound on rho"),
Item(name='ls_search_freq', label="Probability of any particular phenotype being\nsubjected to local search"),
Item(name='sw_type', label="Solis and Wets variances", style='custom'),
label = 'Local Search Parameters',
)
self.local_search_view = View(self.local_search_group, buttons = Buttons)
def _do_local_only_changed(self, new):
self.dpo['do_local_only']['value'] = new
def _sw_max_its_changed(self, new):
self.dpo['sw_max_its']['value'] = new
def _sw_max_succ_changed(self, new):
self.dpo['sw_max_succ']['value'] = new
def _sw_max_fail_changed(self, new):
self.dpo['sw_max_fail']['value'] = new
def _sw_rho_changed(self, new):
self.dpo['sw_rho']['value'] = new
def _sw_lb_rho_changed(self, new):
self.dpo['sw_lb_rho']['value'] = new
def _dihrf_changed(self, new):
self.dpo['dihrf']['value'] = new
def _rtrf_changed(self, new):
self.dpo['rtrf']['value'] = new
def _rt0_changed(self, new):
self.dpo['rt0']['value'] = new
def _sw_type_changed(self, new):
if new == "Uniform":
self.dpo['set_sw1']['value'] = 1
self.dpo['set_psw1']['value'] = 0
else:
self.dpo['set_sw1']['value'] = 0
self.dpo['set_psw1']['value'] = 1
class TraitedLamarckianGAParameters(TraitedGeneticAlgorithmParameters, TraitedLocalSearchParameters):
pass
import wx
class BookDialog(wx.Dialog):
def __init__(self, parent, parameters, title):
wx.Dialog.__init__(self, parent, -1, title)
sizer = wx.BoxSizer(wx.VERTICAL)
self.book = wx.Treebook(self, -1, style=
wx.BK_DEFAULT
#wx.BK_TOP
#wx.BK_BOTTOM
#wx.BK_LEFT
#wx.BK_RIGHT
)
sizer.Add(self.book)
self.pages = []
self.parameters = parameters
self.sizer = sizer
def finish_layout(self):
line = wx.StaticLine(self, -1, size=(20,-1), style=wx.LI_HORIZONTAL)
self.sizer.Add(line, 0, wx.GROW|wx.ALIGN_CENTER_VERTICAL, 5)
self.okButton = wx.Button(self, wx.ID_OK, "")
self.cancelButton = wx.Button(self, wx.ID_CANCEL, "")
buttonSizer = wx.BoxSizer(wx.HORIZONTAL)
lin = wx.StaticLine(self)
buttonSizer.Add((10, -1), 1, flag=wx.EXPAND | wx.ALIGN_RIGHT)
buttonSizer.Add(self.okButton, 0, wx.ALIGN_RIGHT|wx.ALL, 5)
buttonSizer.Add(self.cancelButton, 0, wx.ALIGN_RIGHT|wx.ALL, 5)
self.sizer.Add(lin,0,wx.EXPAND)
self.sizer.Add(buttonSizer, 0, wx.EXPAND|wx.ALIGN_BOTTOM)
self.SetSizer(self.sizer)
self.sizer.Fit(self)
self.Bind(wx.EVT_BUTTON, self._on_ok, self.okButton)
self.Bind(wx.EVT_BUTTON, self._on_cancel, self.cancelButton)
def _on_error(self, errors):
""" Handles editing errors.
"""
self.okButton.Enable(errors == 0)
def _on_ok(self, event=None):
""" Handles the user clicking the **OK** button.
"""
for page in self.pages:
if page.handler.close(page.info, True):
page.finish()
self.EndModal(wx.ID_OK)
def _on_cancel (self, event):
""" Handles a request to cancel all changes.
"""
for page in self.pages:
page.handler.close(page.info, False)
page.finish()
self.EndModal(wx.ID_CANCEL)
class GeneticAlgorithmParametersGUI(BookDialog):
def __init__(self, parent, parameters):
BookDialog.__init__(self, parent, parameters, 'Genetic Algorithm Parameters')
page1 = parameters.genetic_algorithm_view.ui(parameters, self.book, kind='subpanel')
page1.on_trait_change( self._on_error, 'errors', dispatch = 'ui' )
self.pages.append(page1)
self.book.AddPage(page1.control, "Genetic Algorithm")
page2 = parameters.docking_parameters_view.ui(parameters, self.book, kind='subpanel')
page2.on_trait_change( self._on_error, 'errors', dispatch = 'ui' )
self.pages.append(page2)
self.book.AddPage(page2.control, "Docking Parameters")
self.finish_layout()
class SimulatedAnnealingParametersGUI(BookDialog):
def __init__(self, parent, parameters):
BookDialog.__init__(self, parent, parameters, 'Simulated Annealing Parameters')
page1 = parameters.simulated_annealing_view.ui(parameters, self.book, kind='subpanel')
page1.on_trait_change( self._on_error, 'errors', dispatch = 'ui' )
self.pages.append(page1)
self.book.AddPage(page1.control, "Simulated Annealing")
page2 = parameters.docking_parameters_view.ui(parameters, self.book, kind='subpanel')
page2.on_trait_change( self._on_error, 'errors', dispatch = 'ui' )
self.pages.append(page2)
self.book.AddPage(page2.control, "Docking Parameters")
self.finish_layout()
class LocalSearchParametersGUI(BookDialog):
def __init__(self, parent, parameters):
BookDialog.__init__(self, parent, parameters, 'Local Search Parameters')
page1 = parameters.local_search_view.ui(parameters, self.book, kind='subpanel')
page1.on_trait_change( self._on_error, 'errors', dispatch = 'ui' )
self.pages.append(page1)
self.book.AddPage(page1.control, "Local Search")
page2 = parameters.docking_parameters_view.ui(parameters, self.book, kind='subpanel')
page2.on_trait_change( self._on_error, 'errors', dispatch = 'ui' )
self.pages.append(page2)
self.book.AddPage(page2.control, "Docking Parameters")
self.finish_layout()
class LamarckianGAParametersGUI(BookDialog):
def __init__(self, parent, parameters):
BookDialog.__init__(self, parent, parameters, 'Lamarckian Genetic Algorithm Parameters')
page1 = parameters.genetic_algorithm_view.ui(parameters, self.book, kind='subpanel')
page1.on_trait_change( self._on_error, 'errors', dispatch = 'ui' )
self.pages.append(page1)
self.book.AddPage(page1.control, "Genetic Algorithm")
page2 = parameters.local_search_view.ui(parameters, self.book, kind='subpanel')
page2.on_trait_change( self._on_error, 'errors', dispatch = 'ui' )
self.pages.append(page2)
self.book.AddPage(page2.control, "Local Search")
page3 = parameters.docking_parameters_view.ui(parameters, self.book, kind='subpanel')
page3.on_trait_change( self._on_error, 'errors', dispatch = 'ui' )
self.pages.append(page3)
self.book.AddPage(page3.control, "Docking Parameters")
self.finish_layout()
#TODO: pass file to configure_traits to save the state
if __name__ == "__main__":
demo = TraitedDockingParameters()
demo.configure_traits(view=demo.docking_parameters_view)
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