'''
Brian AST representation
This is a standard Python AST representation with additional information added.
'''
import ast
import numpy
from __builtin__ import all as logical_all # defensive programming against numpy import
__all__ = ['brian_ast', 'BrianASTRenderer', 'dtype_hierarchy']
# This codifies the idea that operations involving e.g. boolean and integer will end up
# as integer. In general the output type will be the max of the hierarchy values here.
dtype_hierarchy = {'boolean': 0,
'integer': 1,
'float': 2,
}
# This is just so you can invert from number to string
for tc, i in dtype_hierarchy.items():
dtype_hierarchy[i] = tc
[docs]def is_boolean(value):
return isinstance(value, bool)
[docs]def is_integer(value):
return isinstance(value, (int, numpy.integer))
[docs]def is_float(value):
return isinstance(value, (float, numpy.float))
[docs]def brian_dtype_from_value(value):
'''
Returns 'boolean', 'integer' or 'float'
'''
if is_float(value):
return 'float'
elif is_integer(value):
return 'integer'
elif is_boolean(value):
return 'boolean'
raise TypeError("Unknown dtype for value "+str(value))
# The following functions are called very often during the optimisation process
# so we don't use numpy.issubdtype but instead a precalculated list of all
# standard types
bool_dtype =numpy.dtype(numpy.bool)
[docs]def is_boolean_dtype(obj):
return numpy.dtype(obj) is bool_dtype
integer_dtypes = {numpy.dtype(c) for c in numpy.typecodes['AllInteger']}
[docs]def is_integer_dtype(obj):
return numpy.dtype(obj) in integer_dtypes
float_dtypes = {numpy.dtype(c) for c in numpy.typecodes['AllFloat']}
[docs]def is_float_dtype(obj):
return numpy.dtype(obj) in float_dtypes
[docs]def brian_dtype_from_dtype(dtype):
'''
Returns 'boolean', 'integer' or 'float'
'''
if is_float_dtype(dtype):
return 'float'
elif is_integer_dtype(dtype):
return 'integer'
elif is_boolean_dtype(dtype):
return 'boolean'
raise TypeError("Unknown dtype: "+str(dtype))
[docs]def brian_ast(expr, variables):
'''
Returns an AST tree representation with additional information
Each node will be a standard Python ``ast`` node with the
following additional attributes:
``dtype``
One of ``'boolean'``, ``'integer'`` or ``'float'``, referring to the data type
of the value of this node.
``scalar``
Either ``True`` or ``False`` if the node uses any vector-valued variables.
``complexity``
An integer representation of the computational complexity of the node. This
is a very rough representation used for things like ``2*(x+y)`` is less
complex than ``2*x+2*y`` and ``exp(x)`` is more complex than ``2*x`` but
shouldn't be relied on for fine distinctions between expressions.
Parameters
----------
expr : str
The expression to convert into an AST representation
variables : dict
The dictionary of `Variable` objects used in the expression.
'''
node = ast.parse(expr, mode='eval').body
renderer = BrianASTRenderer(variables)
return renderer.render_node(node)
[docs]class BrianASTRenderer(object):
'''
This class is modelled after `NodeRenderer` - see there for details.
'''
def __init__(self, variables, copy_variables=True):
if copy_variables:
self.variables = variables.copy()
else:
self.variables = variables
[docs] def render_node(self, node):
nodename = node.__class__.__name__
methname = 'render_'+nodename
try:
return getattr(self, methname)(node)
except AttributeError:
raise SyntaxError("Unknown syntax: " + nodename)
[docs] def render_NameConstant(self, node):
if node.value is not True and node.value is not False:
raise SyntaxError("Unknown NameConstant "+str(node.value))
# NameConstant only used for True and False and None, and we don't support None
node.dtype = 'boolean'
node.scalar = True
node.complexity = 0
node.stateless = True
return node
[docs] def render_Name(self, node):
node.complexity = 0
if node.id=='True' or node.id=='False':
node.dtype = 'boolean'
node.scalar = True
elif node.id in self.variables:
var = self.variables[node.id]
dtype = var.dtype
node.dtype = brian_dtype_from_dtype(dtype)
node.scalar = var.scalar
else: # don't think we need to handle other names (pi, e, inf)?
node.dtype = 'float'
node.scalar = True # I think this assumption is OK, but not certain
node.stateless = True
return node
[docs] def render_Num(self, node):
node.complexity = 0
node.dtype = brian_dtype_from_value(node.n)
node.scalar = True
node.stateless = True
return node
[docs] def render_Call(self, node):
if len(node.keywords):
raise ValueError("Keyword arguments not supported.")
elif getattr(node, 'starargs', None) is not None:
raise ValueError("Variable number of arguments not supported")
elif getattr(node, 'kwargs', None) is not None:
raise ValueError("Keyword arguments not supported")
node.args = [self.render_node(subnode) for subnode in node.args]
node.dtype = 'float' # default dtype
# Condition for scalarity of function call: stateless and arguments are scalar
node.scalar = False
if node.func.id in self.variables:
funcvar = self.variables[node.func.id]
# sometimes this attribute doesn't exist, if so assume it's not stateless
node.stateless = getattr(funcvar, 'stateless', False)
if node.stateless:
node.scalar = logical_all(subnode.scalar for subnode in node.args)
# check that argument types are valid
node_arg_types = [subnode.dtype for subnode in node.args]
for subnode, argtype in zip(node.args, funcvar._arg_types):
if argtype!='any' and argtype!=subnode.dtype:
raise TypeError("Function %s takes arguments with types %s but "
"received %s" % (node.func.id, funcvar._arg_types, node_arg_types))
# compute return type
return_type = funcvar._return_type
if return_type=='highest':
return_type = dtype_hierarchy[max(dtype_hierarchy[nat] for nat in node_arg_types)]
node.dtype = return_type
else:
node.stateless = False
# we leave node.func because it is an ast.Name object that doesn't have a dtype
# TODO: variable complexity for function calls?
node.complexity = 20+sum(subnode.complexity for subnode in node.args)
return node
[docs] def render_BinOp(self, node):
node.left = self.render_node(node.left)
node.right = self.render_node(node.right)
# TODO: we could capture some syntax errors here, e.g. bool+bool
# captures, e.g. int+float->float
newdtype = dtype_hierarchy[max(dtype_hierarchy[subnode.dtype] for subnode in [node.left, node.right])]
node.dtype = newdtype
node.scalar = node.left.scalar and node.right.scalar
node.complexity = 1+node.left.complexity+node.right.complexity
node.stateless = node.left.stateless and node.right.stateless
return node
[docs] def render_BoolOp(self, node):
node.values = [self.render_node(subnode) for subnode in node.values]
node.dtype = 'boolean'
for subnode in node.values:
if subnode.dtype!='boolean':
raise TypeError("Boolean operator acting on non-booleans")
node.scalar = logical_all(subnode.scalar for subnode in node.values)
node.complexity = 1+sum(subnode.complexity for subnode in node.values)
node.stateless = logical_all(subnode.stateless
for subnode in node.values)
return node
[docs] def render_Compare(self, node):
node.left = self.render_node(node.left)
node.comparators = [self.render_node(subnode) for subnode in node.comparators]
node.dtype = 'boolean'
comparators = [node.left]+node.comparators
node.scalar = logical_all(subnode.scalar for subnode in comparators)
node.complexity = 1+sum(subnode.complexity for subnode in comparators)
node.stateless = node.left.stateless and all(c.stateless
for c in node.comparators)
return node
[docs] def render_UnaryOp(self, node):
node.operand = self.render_node(node.operand)
node.dtype = node.operand.dtype
if node.dtype=='boolean' and node.op.__class__.__name__ != 'Not':
raise TypeError("Unary operator %s does not apply to boolean types" % node.op.__class__.__name__)
node.scalar = node.operand.scalar
node.complexity = 1+node.operand.complexity
node.stateless = node.operand.stateless
return node
if __name__=='__main__':
import brian2
eqs = '''
x : 1
y : 1 (shared)
a : integer
b : boolean
c : integer (shared)
'''
expr = 'x<3.0+1.0'
G = brian2.NeuronGroup(2, eqs)
variables = {}
variables.update(**brian2.DEFAULT_FUNCTIONS)
variables.update(**brian2.DEFAULT_CONSTANTS)
variables.update(**G.variables)
node = brian_ast(expr, variables)
print node.dtype, node.scalar, node.complexity