def _adaptee_changed ( self, context ):
""" Handles being bound to a IContext object.
"""
self.data_context_name = context.name
values = []
contexts = []
for name in context.keys():
value = context[ name ]
try:
adapt( value, IContext )
except AdaptationError:
# Is not a subcontext.
values.append( name )
else:
# Is a subcontext.
contexts.append( name )
self.data_context_values = values
self.data_contexts = contexts
def _adaptee_changed(self, context):
""" Handles being bound to a IContext object.
"""
self.data_context_name = context.name
values = []
contexts = []
for name in context.keys():
value = context[name]
try:
adapt(value, IContext)
except AdaptationError:
# Is not a subcontext.
values.append(name)
else:
# Is a subcontext.
contexts.append(name)
self.data_context_values = values
self.data_contexts = contexts
def validate(self, value):
""" Validates a value. """
if value is None:
if not self.allow_none:
raise TraitError
elif self.default_value == value:
pass
else:
value = adapt(value, self.klass, Undefined)
if value is Undefined:
raise TraitError
return value
def _unpack_drag_event ( self, event ):
""" Inspect object and see if it can be dropped here. If so, return
the droppable object. Otherwise, return None.
"""
# fixme: poor man's interface checking for IBasicFunctionInfo.
# fixme: This will not work for a LocalFunctionInfo, and it should...
# fixme: This was for plotting...
#elif isinstance( event.obj, BlockVariable ):
# result = event.obj
# If it supports the IMinimalFunctionInfo interface, we're good.
try:
result = adapt(event.obj, IMinimalFunctionInfo)
except NotImplementedError:
result = None
return result
def _create_variable(self, key, value):
""" Create variables from key, value applied here.
Parameters:
-----------
key : Str
value : Float/Str/Array/IContext
Returns:
--------
result: List(Tuple)
tuple is ordered as (key, value, datatype)
"""
if isinstance(value, int) or isinstance(value, float):
return [(key, value, 'scalar')]
if isinstance(value, basestring):
return [(key, value, 'str')]
if isinstance(value, numpy.ndarray):
return [(key, value, 'array')]
try:
value = adapt(value, IContext)
variables = []
for sub_key in value.keys():
sub_value = value[sub_key]
sub_key = "%s[%r]" % (key, sub_key)
variable_list = self._create_variable(sub_key, sub_value)
if variable_list:
variables += variable_list
return variables
except AdaptationFailure:
pass
return None