def analyse_and_generate_neuron(neuron):
# type: (ASTNeuron) -> None
"""
Analysis a single neuron, solves it and generates the corresponding code.
:param neuron: a single neuron.
"""
code, message = Messages.get_start_processing_neuron(neuron.get_name())
Logger.log_message(neuron, code, message, neuron.get_source_position(), LoggingLevel.INFO)
# make normalization
# apply spikes to buffers
# get rid of convolve, store them and apply then at the end
equations_block = neuron.get_equations_block()
shape_to_buffers = {}
if neuron.get_equations_block() is not None:
# extract function names and corresponding incoming buffers
convolve_calls = OdeTransformer.get_sum_function_calls(equations_block)
for convolve in convolve_calls:
shape_to_buffers[str(convolve.get_args()[0])] = str(convolve.get_args()[1])
OdeTransformer.refactor_convolve_call(neuron.get_equations_block())
make_functions_self_contained(equations_block.get_ode_functions())
replace_functions_through_defining_expressions(equations_block.get_ode_equations(),
equations_block.get_ode_functions())
# transform everything into gsl processable (e.g. no functional shapes) or exact form.
transform_shapes_and_odes(neuron, shape_to_buffers)
# update the symbol table
neuron.accept(ASTSymbolTableVisitor())
generate_nest_code(neuron)
# now store the transformed model
store_transformed_model(neuron)
# at that point all shapes are transformed into the ODE form and spikes can be applied
code, message = Messages.get_code_generated(neuron.get_name(), FrontendConfiguration.get_target_path())
Logger.log_message(neuron, code, message, neuron.get_source_position(), LoggingLevel.INFO)
def generate_neurons(self, neurons: List[ASTNode]):
"""
Generate code for the given neurons.
:param neurons: a list of neurons.
:type neurons: List[ASTNode]
"""
from pynestml.frontend.frontend_configuration import FrontendConfiguration
for neuron in neurons:
self.generate_neuron_code(neuron)
if not Logger.has_errors(neuron):
code, message = Messages.get_code_generated(neuron.get_name(), FrontendConfiguration.get_target_path())
Logger.log_message(neuron, code, message, neuron.get_source_position(), LoggingLevel.INFO)
def generate_neurons(self, neurons):
# type: (list(ASTNeuron)) -> None
"""
Analyse a list of neurons, solve them and generate the corresponding code.
:param neurons: a list of neurons.
"""
from pynestml.frontend.frontend_configuration import FrontendConfiguration
for neuron in neurons:
self.generate_neuron_code(neuron)
if not Logger.has_errors(neuron):
code, message = Messages.get_code_generated(
neuron.get_name(), FrontendConfiguration.get_target_path())
Logger.log_message(neuron, code, message,
neuron.get_source_position(),
LoggingLevel.INFO)
def generate_synapses(self, synapses: Sequence[ASTSynapse]) -> None:
"""
Generates code for a list of synapses.
:param synapses: a list of synapses.
"""
from pynestml.frontend.frontend_configuration import FrontendConfiguration
for synapse in synapses:
if Logger.logging_level == LoggingLevel.INFO:
print("Generating code for the synapse {}.".format(
synapse.get_name()))
self.generate_synapse_code(synapse)
code, message = Messages.get_code_generated(
synapse.get_name(), FrontendConfiguration.get_target_path())
Logger.log_message(synapse, code, message,
synapse.get_source_position(),
LoggingLevel.INFO)
