class StochasticInteractorTestCase(unittest.TestCase):
""" Unit testing for StochasticInteractor
"""
def setUp(self):
code = "from blockcanvas.debug.my_operator import add\n"\
"c = add(a,b)"
self.block = Block(code)
# Context setup
self.context = MultiContext(DataContext(name='Data'), {})
self.context['a'] = 1
self.context['b'] = 2
def test_attributes(self):
""" Test if creation of attributes is working correctly.
"""
interactor = StochasticInteractor(context=self.context,
block=self.block,
distribution='constant',
inputs=['b'])
self.assertTrue(hasattr(interactor, interactor._input_prefix + 'b'))
# Check if the attribute is correct
attribute_b = getattr(interactor, interactor._input_prefix + 'b')
distribution_b = attribute_b.distribution
self.assertEqual(distribution_b.value, self.context['b'])
desired = self.context['b'] * numpy.ones(attribute_b.samples)
self.assertTrue((desired == distribution_b.values).all())
def test_create_shadows(self):
""" Test if shadows are working correctly.
"""
raise nose.SkipTest("shadows not implemented")
interactor = StochasticInteractor(context=self.context,
block=self.block,
distribution='uniform',
inputs=['b'])
# Change attributes
attribute_b = getattr(interactor, interactor._input_prefix + 'b')
distribution_b = attribute_b.distribution
distribution_b.low = 20.0
distribution_b.high = 30.0
# Create shadows
interactor._execute_button_changed()
# Check if the shadows were created correctly.
self.assertEqual(len(distribution_b.values), len(self.context.shadows))
# Check if the shadow context gives desired effects.
self.block.execute(self.context.shadows[-1])
self.assertEqual(self.context.shadows[-1]['c'],
self.context['a'] + self.context.shadows[-1]['b'])
def setUp(self):
code = "from blockcanvas.debug.my_operator import add\n"\
"c = add(a,b)"
self.block = Block(code)
# Context setup
self.context = MultiContext(DataContext(name='Data'), {})
self.context['a'] = 1
self.context['b'] = 2
def setUp(self):
code = "from blockcanvas.debug.my_operator import add\n"\
"c = add(a,b)"
self.block = Block(code)
# Context setup
self.context = MultiContext(DataContext(name='Data'), {})
self.context['a'] = 1
self.context['b'] = 2
class Interactor(HasPrivateTraits):
'Interacts with a computational model'
#---------------------------------------------------------------------------
# Interactor interface:
#---------------------------------------------------------------------------
# The persistence id associated with this interactor (empty string means
# no persistence):
id = Str
# Whole block
block = Any(Block(''))
# Namespace in which block executes
context = Any({})
# A dict to hold high and how bounds. Should be of the form {'input_var' : (low, high, format)}
ranges = Dict
# Subset of the block's inputs to display
display_inputs = List(Str)
# When block or context change
_updated = Event
# Cached restricted blocks that need to execute when an input is changed
# (one for each of block.inputs)
_blocks = Dict
# List of actual inputs:
_block_inputs = List
# Does the interactor need to run any updates?
_needs_update = Bool(False)
#---------------------------------------------------------------------------
# Interactor interface:
#---------------------------------------------------------------------------
def view_items(self):
""" Returns the contents of the view based upon the current block.
"""
visible_inputs = self._block_inputs
if visible_inputs is None:
return []
# Only display requested inputs (if requested at all)
if self.display_inputs != []:
visible_inputs = set(visible_inputs) & set(self.display_inputs)
items = []
for input in visible_inputs:
if input != 'clear':
if self.ranges.has_key(input):
low, high, format = self.ranges[input]
range = RangeEditor(high=high, low=low, format=format)
else:
range = RangeEditor()
items.append(
Item(name='var_' + input,
label=input,
_type='Range',
editor=range))
return items
#-- Trait Listeners --------------------------------------------------------
def _block_changed(self, old, new):
# Get the old and new inputs:
old_inputs = new_inputs = []
if old is not None:
old_inputs = old.inputs
if new is not None:
new_inputs = new.inputs
# Filter out inputs already bound to arrays in the current context:
context = self.context
if context is not None:
new_inputs = [
input for input in new_inputs
if not isinstance(context.get(input), ndarray)
]
# Save the list of actual inputs:
self._block_inputs = new_inputs
# Update the trait definitions as needed:
[
self.remove_trait('var_' + input) for input in old_inputs
if input not in new_inputs
]
[
self.add_trait('var_' + input, Any) for input in new_inputs
if input not in old_inputs
]
# Cache restricted blocks, one for each input:
self._blocks = dict([(input, new.restrict(inputs=[input]))
for input in new_inputs])
self._updated = True
def _context_changed(self, old, new):
self._updated = True
def _anytrait_changed(self, name, old, new):
if name[:4] == 'var_':
name = name[4:]
if (not self._no_block_update) and (self.block is not None):
if isinstance(old, ArangeGenerator):
old.on_trait_change(self._array_changed,
'array',
remove=True)
if isinstance(new, ArangeGenerator):
new.on_trait_change(self._array_changed, 'array')
new = new.array
self.context[name] = new
self._needs_update = True
#print "Adding update func"
def update_func():
if self._needs_update:
self._blocks[name].execute(self.context)
self._needs_update = False
GUI.invoke_after(10, update_func)
# try:
# self._blocks[ name ].execute( self.context )
# except:
# from traceback import print_exc
# print_exc()
def _array_changed(self, object, name, old, new):
name = object.name[4:]
self.context[name] = new
try:
self._blocks[name].execute(self.context)
except:
pass
#-- Event Interface --------------------------------------------------------
def __updated_fired(self):
'When block or context change'
# The block or context changed, so put them back in sync:
# - Run the whole block once to initialize the intermediate variables
# within the context
# Be robust when block and context disagree:
block, context = self.block, self.context
if (block is not None) and (context is not None):
## Calculate the list of 'real' block inputs:
#self._block_inputs = [
# input for input in block.inputs
# if isinstance( context.get( input, 0 ), ValidTypes )
#]
self._no_block_update = True
#for input in block.inputs:
for input in self._block_inputs:
var_input = 'var_' + input
try:
value = context[input]
setattr(self, var_input, value)
except:
context[input] = value = 0
setattr(self, var_input, value)
self._no_block_update = False
try:
self.block.execute(context)
except:
pass
# Remove unreferenced values from the data portion of the context:
if 'clear' in block.inputs:
try:
names = context.keys()
all = block.inputs + block.outputs
for name in names:
if name not in all:
del context[name]
except:
pass
#-- HasTraits Interface ----------------------------------------------------
def trait_view(self, name=None, view_element=None):
'Returns the View object for the specified name and view_element.'
return View(
Group(*self.view_items()),
id='blockcanvas.numerical_modeling.ui.interactor.Interactor',
title='Parametric Controls',
width=400,
height=200,
buttons=menu.NoButtons,
resizable=True,
handler=InteractorHandler)
class StochasticInteractorTestCase(unittest.TestCase):
""" Unit testing for StochasticInteractor
"""
def setUp(self):
code = "from blockcanvas.debug.my_operator import add\n"\
"c = add(a,b)"
self.block = Block(code)
# Context setup
self.context = MultiContext(DataContext(name='Data'), {})
self.context['a'] = 1
self.context['b'] = 2
def test_attributes(self):
""" Test if creation of attributes is working correctly.
"""
interactor = StochasticInteractor(context = self.context,
block = self.block,
distribution = 'constant',
inputs = ['b'])
self.assertTrue(hasattr(interactor, interactor._input_prefix + 'b'))
# Check if the attribute is correct
attribute_b = getattr(interactor, interactor._input_prefix + 'b')
distribution_b = attribute_b.distribution
self.assertEqual(distribution_b.value, self.context['b'])
desired = self.context['b']*numpy.ones(attribute_b.samples)
self.assertTrue((desired == distribution_b.values).all())
def test_create_shadows(self):
""" Test if shadows are working correctly.
"""
raise nose.SkipTest("shadows not implemented")
interactor = StochasticInteractor(context = self.context,
block = self.block,
distribution = 'uniform',
inputs = ['b'])
# Change attributes
attribute_b = getattr(interactor, interactor._input_prefix+'b')
distribution_b = attribute_b.distribution
distribution_b.low = 20.0
distribution_b.high = 30.0
# Create shadows
interactor._execute_button_changed()
# Check if the shadows were created correctly.
self.assertEqual(len(distribution_b.values), len(self.context.shadows))
# Check if the shadow context gives desired effects.
self.block.execute(self.context.shadows[-1])
self.assertEqual(self.context.shadows[-1]['c'],
self.context['a']+self.context.shadows[-1]['b'])
dist_val = stochastic_item.distribution
value_list = []
for attr_name in attr_dict[self.distribution]:
value_list.append(getattr(dist_val, attr_name))
range_dict[input_name] = tuple(value_list)
return range_dict
# Test
if __name__ == '__main__':
from blockcanvas.numerical_modeling.workflow.api import Block
from codetools.contexts.api import DataContext, MultiContext
code = "from blockcanvas.debug.my_operator import add, mul\n"\
"c = add(a,b)\n"\
"d = mul(c,2)\n"\
"e = mul(z,3)\n"\
"f = add(d,e)"
block = Block(code)
context = MultiContext(DataContext(name='Data'), {})
context['a'] = 35
context['b'] = 33
context['z'] = 30
interactor = StochasticInteractor(context=context, block=block,
inputs=['b','z'], distribution='gaussian')
interactor.edit_traits(kind='livemodal')
### EOF ------------------------------------------------------------------------
class Displayer(HasTraits):
""" Displays the results of a computational model.
"""
#---------------------------------------------------------------------------
# Displayer interface:
#---------------------------------------------------------------------------
# The persistence id associated with this displayer (empty string means no
# persistence):
id = Str
# The whole block:
block = Any(Block(''))
# The namespace in which the block executes:
context = Instance(ANumericContext)
# Subset of the block's outputs to display
display_outputs = List(Str)
# Event fired when the block or context changes:
_updated = Event
#---------------------------------------------------------------------------
# Displayer interface:
#---------------------------------------------------------------------------
def view_items(self):
""" Returns the contents of the view based upon the current block.
"""
return [
Item(name='var_' + output,
label=output,
style='readonly',
editor=TextEditor()) for output in self.block.outputs
if output in self.display_outputs or self.display_outputs == []
]
#-- Trait Event Handlers ---------------------------------------------------
def _block_changed(self, old, new):
# Remove the old outputs:
if old is not None:
[self.remove_trait('var_' + output) for output in old.outputs]
# Add the new inputs:
if new is not None:
[self.add_trait('var_' + output, Any) for output in new.outputs]
self._updated = True
def _context_changed(self, old, new):
self._updated = True
def _context_modified_changed_for_context(self, event):
if len(event.removed) > 0:
block = self.block
if block is not None:
context = self.context
outputs = block.outputs
for name in event.removed:
if name in outputs:
self._set_var(name, context[name])
#-- Event Interface --------------------------------------------------------
def __updated_fired(self):
""" Handles the 'block' or 'context' being changed.
"""
# The block or context changed, so put them back in sync:
# - Run the whole block once to initialize the intermediate variables
# within the context.
# Be robust when block and context disagree:
block, context = self.block, self.context
if (block is not None) and (context is not None):
for output in block.outputs:
try:
value = context[output]
except:
context[output] = value = 0.0
self._set_var(output, value)
#-- Private Methods --------------------------------------------------------
def _set_var(self, name, value):
""" Set a 'legal' value for the specified output variable.
"""
try:
n = len(value)
except:
n = 0
max_len = 10
if isinstance(value, basestring):
max_len = 80
if n > max_len:
value = '%s[%d]' % (value.__class__.__name__, n)
setattr(self, 'var_' + name, value)
#---------------------------------------------------------------------------
# HasTraits interface:
#---------------------------------------------------------------------------
def trait_view(self, name=None, view_element=None):
return View(Group(*self.view_items()),
id='blockcanvas.numerical_modeling.ui.displayer.Displayer',
width=250,
buttons=menu.NoButtons,
resizable=True,
handler=DisplayerHandler)
