def setup_generation_helpers(neuron):
"""
Returns a standard namespace with often required functionality.
:param neuron: a single neuron instance
:type neuron: ASTNeuron
:return: a map from name to functionality.
:rtype: dict
"""
gsl_converter = GSLReferenceConverter()
gsl_printer = LegacyExpressionPrinter(gsl_converter)
# helper classes and objects
converter = NESTReferenceConverter(False)
legacy_pretty_printer = LegacyExpressionPrinter(converter)
namespace = dict()
namespace['neuronName'] = neuron.get_name()
namespace['neuron'] = neuron
namespace['moduleName'] = FrontendConfiguration.get_module_name()
namespace['printer'] = NestPrinter(legacy_pretty_printer)
namespace['assignments'] = NestAssignmentsHelper()
namespace['names'] = NestNamesConverter()
namespace['declarations'] = NestDeclarationsHelper()
namespace['utils'] = ASTUtils()
namespace['idemPrinter'] = LegacyExpressionPrinter()
namespace['outputEvent'] = namespace['printer'].print_output_event(neuron.get_body())
namespace['is_spike_input'] = ASTUtils.is_spike_input(neuron.get_body())
namespace['is_current_input'] = ASTUtils.is_current_input(neuron.get_body())
namespace['odeTransformer'] = OdeTransformer()
namespace['printerGSL'] = gsl_printer
namespace['now'] = datetime.datetime.utcnow()
define_solver_type(neuron, namespace)
return namespace
def define_solver_type(neuron, namespace):
# type: (ASTNeuron, dict) -> None
"""
For a handed over neuron this method enriches the namespace by methods which are used to solve
odes.
:param namespace: a single namespace dict.
:param neuron: a single neuron
"""
namespace['useGSL'] = False
if neuron.get_equations_block() is not None and len(neuron.get_equations_block().get_declarations()) > 0:
if (not is_functional_shape_present(neuron.get_equations_block().get_ode_shapes())) or \
len(neuron.get_equations_block().get_ode_equations()) > 1:
namespace['names'] = GSLNamesConverter()
namespace['useGSL'] = True
converter = NESTReferenceConverter(True)
legacy_pretty_printer = LegacyExpressionPrinter(converter)
namespace['printer'] = NestPrinter(legacy_pretty_printer)
return
def setup_generation_helpers(self, neuron):
"""
Returns a standard namespace with often required functionality.
:param neuron: a single neuron instance
:type neuron: ASTNeuron
:return: a map from name to functionality.
:rtype: dict
"""
gsl_converter = GSLReferenceConverter()
gsl_printer = LegacyExpressionPrinter(gsl_converter)
# helper classes and objects
converter = NESTReferenceConverter(False)
legacy_pretty_printer = LegacyExpressionPrinter(converter)
namespace = dict()
namespace['neuronName'] = neuron.get_name()
namespace['neuron'] = neuron
namespace['moduleName'] = FrontendConfiguration.get_module_name()
namespace['printer'] = NestPrinter(legacy_pretty_printer)
namespace['assignments'] = NestAssignmentsHelper()
namespace['names'] = NestNamesConverter()
namespace['declarations'] = NestDeclarationsHelper()
namespace['utils'] = ASTUtils()
namespace['idemPrinter'] = LegacyExpressionPrinter()
namespace['outputEvent'] = namespace['printer'].print_output_event(neuron.get_body())
namespace['is_spike_input'] = ASTUtils.is_spike_input(neuron.get_body())
namespace['is_current_input'] = ASTUtils.is_current_input(neuron.get_body())
namespace['odeTransformer'] = OdeTransformer()
namespace['printerGSL'] = gsl_printer
namespace['now'] = datetime.datetime.utcnow()
namespace['tracing'] = FrontendConfiguration.is_dev
namespace['PredefinedUnits'] = pynestml.symbols.predefined_units.PredefinedUnits
namespace['UnitTypeSymbol'] = pynestml.symbols.unit_type_symbol.UnitTypeSymbol
rng_visitor = ASTRandomNumberGeneratorVisitor()
neuron.accept(rng_visitor)
namespace['norm_rng'] = rng_visitor._norm_rng_is_used
self.define_solver_type(neuron, namespace)
return namespace
def convert_encapsulated(self):
return NESTReferenceConverter.convert_encapsulated()
def convert_bit_operator(self, op):
return NESTReferenceConverter.convert_bit_operator(op)
def convert_comparison_operator(self, op):
return NESTReferenceConverter.convert_comparison_operator(op)
def convert_logical_operator(self, op):
return NESTReferenceConverter.convert_logical_operator(op)
def convert_logical_not(self):
return NESTReferenceConverter.convert_logical_not()
def setup_generation_helpers(self, neuron: ASTNeuron) -> Dict:
"""
Returns a standard namespace with often required functionality.
:param neuron: a single neuron instance
:type neuron: ASTNeuron
:return: a map from name to functionality.
:rtype: dict
"""
gsl_converter = GSLReferenceConverter()
gsl_printer = UnitlessExpressionPrinter(gsl_converter)
# helper classes and objects
converter = NESTReferenceConverter(False)
unitless_pretty_printer = UnitlessExpressionPrinter(converter)
namespace = dict()
namespace['neuronName'] = neuron.get_name()
namespace['neuron'] = neuron
namespace['moduleName'] = FrontendConfiguration.get_module_name()
namespace['printer'] = NestPrinter(unitless_pretty_printer)
namespace['assignments'] = NestAssignmentsHelper()
namespace['names'] = NestNamesConverter()
namespace['declarations'] = NestDeclarationsHelper()
namespace['utils'] = ASTUtils()
namespace['idemPrinter'] = UnitlessExpressionPrinter()
namespace['outputEvent'] = namespace['printer'].print_output_event(
neuron.get_body())
namespace['is_spike_input'] = ASTUtils.is_spike_input(
neuron.get_body())
namespace['is_current_input'] = ASTUtils.is_current_input(
neuron.get_body())
namespace['odeTransformer'] = OdeTransformer()
namespace['printerGSL'] = gsl_printer
namespace['now'] = datetime.datetime.utcnow()
namespace['tracing'] = FrontendConfiguration.is_dev
namespace[
'PredefinedUnits'] = pynestml.symbols.predefined_units.PredefinedUnits
namespace[
'UnitTypeSymbol'] = pynestml.symbols.unit_type_symbol.UnitTypeSymbol
namespace['initial_values'] = {}
namespace['uses_analytic_solver'] = neuron.get_name() in self.analytic_solver.keys() \
and self.analytic_solver[neuron.get_name()] is not None
if namespace['uses_analytic_solver']:
namespace['analytic_state_variables'] = self.analytic_solver[
neuron.get_name()]["state_variables"]
namespace['analytic_variable_symbols'] = {
sym:
neuron.get_equations_block().get_scope().resolve_to_symbol(
sym, SymbolKind.VARIABLE)
for sym in namespace['analytic_state_variables']
}
namespace['update_expressions'] = {}
for sym, expr in self.analytic_solver[
neuron.get_name()]["initial_values"].items():
namespace['initial_values'][sym] = expr
for sym in namespace['analytic_state_variables']:
expr_str = self.analytic_solver[
neuron.get_name()]["update_expressions"][sym]
expr_ast = ModelParser.parse_expression(expr_str)
# pretend that update expressions are in "equations" block, which should always be present, as differential equations must have been defined to get here
expr_ast.update_scope(
neuron.get_equations_blocks().get_scope())
expr_ast.accept(ASTSymbolTableVisitor())
namespace['update_expressions'][sym] = expr_ast
namespace['propagators'] = self.analytic_solver[
neuron.get_name()]["propagators"]
namespace['uses_numeric_solver'] = neuron.get_name() in self.analytic_solver.keys() \
and self.numeric_solver[neuron.get_name()] is not None
if namespace['uses_numeric_solver']:
namespace['numeric_state_variables'] = self.numeric_solver[
neuron.get_name()]["state_variables"]
namespace['numeric_variable_symbols'] = {
sym:
neuron.get_equations_block().get_scope().resolve_to_symbol(
sym, SymbolKind.VARIABLE)
for sym in namespace['numeric_state_variables']
}
assert not any([
sym is None
for sym in namespace['numeric_variable_symbols'].values()
])
namespace['numeric_update_expressions'] = {}
for sym, expr in self.numeric_solver[
neuron.get_name()]["initial_values"].items():
namespace['initial_values'][sym] = expr
for sym in namespace['numeric_state_variables']:
expr_str = self.numeric_solver[
neuron.get_name()]["update_expressions"][sym]
expr_ast = ModelParser.parse_expression(expr_str)
# pretend that update expressions are in "equations" block, which should always be present, as differential equations must have been defined to get here
expr_ast.update_scope(
neuron.get_equations_blocks().get_scope())
expr_ast.accept(ASTSymbolTableVisitor())
namespace['numeric_update_expressions'][sym] = expr_ast
namespace['useGSL'] = namespace['uses_numeric_solver']
namespace['names'] = GSLNamesConverter()
converter = NESTReferenceConverter(True)
unitless_pretty_printer = UnitlessExpressionPrinter(converter)
namespace['printer'] = NestPrinter(unitless_pretty_printer)
namespace["spike_updates"] = neuron.spike_updates
rng_visitor = ASTRandomNumberGeneratorVisitor()
neuron.accept(rng_visitor)
namespace['norm_rng'] = rng_visitor._norm_rng_is_used
return namespace
def convert_encapsulated(self):
return NESTReferenceConverter.convert_encapsulated()
def convert_bit_operator(self, op):
return NESTReferenceConverter.convert_bit_operator(op)
def convert_comparison_operator(self, op):
return NESTReferenceConverter.convert_comparison_operator(op)
def convert_logical_operator(self, op):
return NESTReferenceConverter.convert_logical_operator(op)
def convert_logical_not(self):
return NESTReferenceConverter.convert_logical_not()
def convert_ternary_operator(self):
return NESTReferenceConverter.convert_ternary_operator()
def convert_arithmetic_operator(self, op):
return NESTReferenceConverter.convert_arithmetic_operator(op)
def convert_ternary_operator(self):
return NESTReferenceConverter.convert_ternary_operator()
from pynestml.symbol_table.symbol_table import SymbolTable
from pynestml.symbols.predefined_functions import PredefinedFunctions
from pynestml.symbols.predefined_types import PredefinedTypes
from pynestml.symbols.predefined_units import PredefinedUnits
from pynestml.symbols.predefined_variables import PredefinedVariables
from pynestml.utils.logger import Logger, LoggingLevel
from pynestml.utils.model_parser import ModelParser
SymbolTable.initialize_symbol_table(
ASTSourceLocation(start_line=0, start_column=0, end_line=0, end_column=0))
PredefinedUnits.register_units()
PredefinedTypes.register_types()
PredefinedVariables.register_variables()
PredefinedFunctions.register_functions()
Logger.init_logger(LoggingLevel.INFO)
printer = NestPrinter(ExpressionsPrettyPrinter(), NESTReferenceConverter())
def get_first_statement_in_update_block(model):
if model.get_neuron_list()[0].get_update_blocks():
return model.get_neuron_list()[0].get_update_blocks().get_block(
).get_stmts()[0]
return None
def get_first_declaration_in_state_block(model):
return model.get_neuron_list()[0].get_state_blocks().get_declarations()[0]
def get_first_declared_function(model):
return model.get_neuron_list()[0].get_functions()[0]
def convert_arithmetic_operator(self, op):
return NESTReferenceConverter.convert_arithmetic_operator(op)
