def translate_expression(self, expr):
for varname, var in self.variables.iteritems():
if isinstance(var, Function):
impl_name = var.implementations[self.codeobj_class].name
if impl_name is not None:
expr = word_substitute(expr, {varname: impl_name})
return NumpyNodeRenderer().render_expr(expr, self.variables).strip()
def ufunc_at_vectorisation(self, statement, variables, indices,
conditional_write_vars, created_vars,
used_variables):
if not self._use_ufunc_at_vectorisation:
raise VectorisationError()
# Avoids circular import
from brian2.devices.device import device
# See https://github.com/brian-team/brian2/pull/531 for explanation
used = set(get_identifiers(statement.expr))
used = used.intersection(k for k in list(variables.keys())
if k in indices and indices[k] != '_idx')
used_variables.update(used)
if statement.var in used_variables:
raise VectorisationError()
expr = NumpyNodeRenderer(
auto_vectorise=self.auto_vectorise).render_expr(statement.expr)
if statement.op == ':=' or indices[
statement.var] == '_idx' or not statement.inplace:
if statement.op == ':=':
op = '='
else:
op = statement.op
line = '{var} {op} {expr}'.format(var=statement.var,
op=op,
expr=expr)
elif statement.inplace:
if statement.op == '+=':
ufunc_name = '_numpy.add'
elif statement.op == '*=':
ufunc_name = '_numpy.multiply'
elif statement.op == '/=':
ufunc_name = '_numpy.divide'
elif statement.op == '-=':
ufunc_name = '_numpy.subtract'
else:
raise VectorisationError()
line = '{ufunc_name}.at({array_name}, {idx}, {expr})'.format(
ufunc_name=ufunc_name,
array_name=device.get_array_name(variables[statement.var]),
idx=indices[statement.var],
expr=expr)
line = self.conditional_write(
line,
statement,
variables,
conditional_write_vars=conditional_write_vars,
created_vars=created_vars)
else:
raise VectorisationError()
if len(statement.comment):
line += ' # ' + statement.comment
return line
def ufunc_at_vectorisation(self, statement, variables, indices,
conditional_write_vars, created_vars, index):
'''
'''
# Avoids circular import
from brian2.devices.device import device
# We assume that the code has passed the test for synapse order independence
expr = NumpyNodeRenderer().render_expr(statement.expr)
if statement.op == ':=' or indices[
statement.var] == '_idx' or not statement.inplace:
if statement.op == ':=':
op = '='
else:
op = statement.op
line = '{var} {op} {expr}'.format(var=statement.var,
op=op,
expr=expr)
elif statement.inplace:
if statement.op == '+=':
ufunc_name = '_numpy.add'
elif statement.op == '*=':
ufunc_name = '_numpy.multiply'
elif statement.op == '/=':
ufunc_name = '_numpy.divide'
elif statement.op == '-=':
ufunc_name = '_numpy.subtract'
else:
raise VectorisationError()
line = '{ufunc_name}.at({array_name}, {idx}, {expr})'.format(
ufunc_name=ufunc_name,
array_name=device.get_array_name(variables[statement.var]),
idx=indices[statement.var],
expr=expr)
line = self.conditional_write(
line,
statement,
variables,
conditional_write_vars=conditional_write_vars,
created_vars=created_vars)
else:
raise VectorisationError()
if len(statement.comment):
line += ' # ' + statement.comment
return line
def translate_expression(self, expr):
expr = word_substitute(expr, self.func_name_replacements)
return NumpyNodeRenderer(
auto_vectorise=self.auto_vectorise).render_expr(
expr, self.variables).strip()
def test_parse_expressions_numpy():
parse_expressions(NumpyNodeRenderer(), numpy_evaluator)
def translate_expression(self, expr):
return NumpyNodeRenderer().render_expr(expr).strip()