def visit_neuron(self, node):
"""
Private method: Used to visit a single neuron and create the corresponding global as well as local scopes.
:return: a single neuron.
:rtype: ast_neuron
"""
# set current processed neuron
Logger.set_current_neuron(node)
code, message = Messages.get_start_building_symbol_table()
Logger.log_message(neuron=node,
code=code,
error_position=node.get_source_position(),
message=message,
log_level=LoggingLevel.INFO)
# before starting the work on the neuron, make everything which was implicit explicit
# but if we have a model without an equations block, just skip this step
if node.get_equations_blocks() is not None:
make_implicit_odes_explicit(node.get_equations_blocks())
scope = Scope(scope_type=ScopeType.GLOBAL,
source_position=node.get_source_position())
node.update_scope(scope)
node.get_body().update_scope(scope)
# now first, we add all predefined elements to the scope
variables = PredefinedVariables.get_variables()
functions = PredefinedFunctions.get_function_symbols()
types = PredefinedTypes.get_types()
for symbol in variables.keys():
node.get_scope().add_symbol(variables[symbol])
for symbol in functions.keys():
node.get_scope().add_symbol(functions[symbol])
for symbol in types.keys():
node.get_scope().add_symbol(types[symbol])
def endvisit_neuron(self, node):
# before following checks occur, we need to ensure several simple properties
CoCosManager.post_symbol_table_builder_checks(
node, skip_check_correct_usage_of_shapes=self.after_ast_rewrite_)
# the following part is done in order to mark conductance based buffers as such.
if node.get_input_blocks() is not None and node.get_equations_blocks() is not None and \
len(node.get_equations_blocks().get_declarations()) > 0:
# this case should be prevented, since several input blocks result in a incorrect model
if isinstance(node.get_input_blocks(), list):
buffers = (buffer for bufferA in node.get_input_blocks()
for buffer in bufferA.get_input_lines())
else:
buffers = (
buffer
for buffer in node.get_input_blocks().get_input_lines())
from pynestml.meta_model.ast_ode_shape import ASTOdeShape
# todo by KP: ode declarations are not used, is this correct?
# ode_declarations = (decl for decl in node.get_equations_blocks().get_declarations() if
# not isinstance(decl, ASTOdeShape))
mark_conductance_based_buffers(input_lines=buffers)
# now update the equations
if node.get_equations_blocks() is not None and len(
node.get_equations_blocks().get_declarations()) > 0:
equation_block = node.get_equations_blocks()
assign_ode_to_variables(equation_block)
if not self.after_ast_rewrite_:
CoCosManager.post_ode_specification_checks(node)
Logger.set_current_neuron(None)
return
def visit_neuron(self, node):
"""
Private method: Used to visit a single neuron and create the corresponding global as well as local scopes.
:return: a single neuron.
:rtype: ast_neuron
"""
# set current processed neuron
Logger.set_current_neuron(node)
code, message = Messages.get_start_building_symbol_table()
Logger.log_message(neuron=node, code=code, error_position=node.get_source_position(),
message=message, log_level=LoggingLevel.INFO)
# before starting the work on the neuron, make everything which was implicit explicit
# but if we have a model without an equations block, just skip this step
if node.get_equations_blocks() is not None:
make_implicit_odes_explicit(node.get_equations_blocks())
scope = Scope(scope_type=ScopeType.GLOBAL, source_position=node.get_source_position())
node.update_scope(scope)
node.get_body().update_scope(scope)
# now first, we add all predefined elements to the scope
variables = PredefinedVariables.get_variables()
functions = PredefinedFunctions.get_function_symbols()
types = PredefinedTypes.get_types()
for symbol in variables.keys():
node.get_scope().add_symbol(variables[symbol])
for symbol in functions.keys():
node.get_scope().add_symbol(functions[symbol])
for symbol in types.keys():
node.get_scope().add_symbol(types[symbol])
def visit_neuron(self, node):
"""
Private method: Used to visit a single neuron and create the corresponding global as well as local scopes.
:return: a single neuron.
:rtype: ast_neuron
"""
# set current processed neuron
Logger.set_current_neuron(node)
code, message = Messages.get_start_building_symbol_table()
Logger.log_message(neuron=node,
code=code,
error_position=node.get_source_position(),
message=message,
log_level=LoggingLevel.INFO)
scope = Scope(scope_type=ScopeType.GLOBAL,
source_position=node.get_source_position())
node.update_scope(scope)
node.get_body().update_scope(scope)
# now first, we add all predefined elements to the scope
variables = PredefinedVariables.get_variables()
functions = PredefinedFunctions.get_function_symbols()
types = PredefinedTypes.get_types()
for symbol in variables.keys():
node.get_scope().add_symbol(variables[symbol])
for symbol in functions.keys():
node.get_scope().add_symbol(functions[symbol])
for symbol in types.keys():
node.get_scope().add_symbol(types[symbol])
def test(self):
Logger.init_logger(LoggingLevel.INFO)
model = ModelParser.parse_model(
os.path.join(os.path.realpath(os.path.join(os.path.dirname(__file__),
'resources', 'ExpressionTypeTest.nestml'))))
Logger.set_current_neuron(model.get_neuron_list()[0])
model.accept(ExpressionTestVisitor())
# ExpressionTestVisitor().handle(model)
Logger.set_current_neuron(None)
self.assertEqual(len(Logger.get_all_messages_of_level_and_or_neuron(model.get_neuron_list()[0],
LoggingLevel.ERROR)), 2)
def test(self):
Logger.init_logger(LoggingLevel.INFO)
model = ModelParser.parse_model(
os.path.join(os.path.realpath(os.path.join(os.path.dirname(__file__),
'resources', 'ExpressionTypeTest.nestml'))))
Logger.set_current_neuron(model.get_neuron_list()[0])
model.accept(ExpressionTestVisitor())
# ExpressionTestVisitor().handle(model)
Logger.set_current_neuron(None)
self.assertEqual(len(Logger.get_all_messages_of_level_and_or_neuron(model.get_neuron_list()[0],
LoggingLevel.ERROR)), 2)
def visitNeuron(self, ctx):
name = str(ctx.NAME()) if ctx.NAME() is not None else None
body = self.visit(ctx.body()) if ctx.body() is not None else None
# after we have constructed the meta_model of the neuron,
# we can ensure some basic properties which should always hold
# we have to check if each type of block is defined at most once (except for function), and that input,output
# and update are defined once
if hasattr(ctx.start.source[1], 'fileName'):
artifact_name = ntpath.basename(ctx.start.source[1].fileName)
else:
artifact_name = 'parsed from string'
neuron = ASTNodeFactory.create_ast_neuron(name=name, body=body, source_position=create_source_pos(ctx),
artifact_name=artifact_name)
# update the comments
update_node_comments(neuron, self.__comments.visit(ctx))
# in order to enable the logger to print correct messages set as the source the corresponding neuron
Logger.set_current_neuron(neuron)
CoCoEachBlockUniqueAndDefined.check_co_co(node=neuron)
Logger.set_current_neuron(neuron)
# now the meta_model seems to be correct, return it
return neuron
def endvisit_neuron(self, node):
# before following checks occur, we need to ensure several simple properties
CoCosManager.post_symbol_table_builder_checks(node, skip_check_correct_usage_of_shapes=self.after_ast_rewrite_)
# the following part is done in order to mark conductance based buffers as such.
if node.get_input_blocks() is not None and node.get_equations_blocks() is not None and \
len(node.get_equations_blocks().get_declarations()) > 0:
# this case should be prevented, since several input blocks result in a incorrect model
if isinstance(node.get_input_blocks(), list):
buffers = (buffer for bufferA in node.get_input_blocks() for buffer in bufferA.get_input_lines())
else:
buffers = (buffer for buffer in node.get_input_blocks().get_input_lines())
from pynestml.meta_model.ast_ode_shape import ASTOdeShape
# todo by KP: ode declarations are not used, is this correct?
# ode_declarations = (decl for decl in node.get_equations_blocks().get_declarations() if
# not isinstance(decl, ASTOdeShape))
mark_conductance_based_buffers(input_lines=buffers)
# now update the equations
if node.get_equations_blocks() is not None and len(node.get_equations_blocks().get_declarations()) > 0:
equation_block = node.get_equations_blocks()
assign_ode_to_variables(equation_block)
if not self.after_ast_rewrite_:
CoCosManager.post_ode_specification_checks(node)
Logger.set_current_neuron(None)
return