def from_primitives(expr, var_order):
from pymbolic import get_dependencies, evaluate
deps = get_dependencies(expr)
var_deps = [dep for dep in deps if dep in var_order]
context = dict((vd, Polynomial(vd, var_order=var_order)) for vd in var_deps)
# FIXME not fast, but works
# (and exercises multivariate polynomial code)
return evaluate(expr, context)
def from_primitives(expr, var_order):
from pymbolic import get_dependencies, evaluate
deps = get_dependencies(expr)
var_deps = [dep for dep in deps if dep in var_order]
context = dict(
(vd, Polynomial(vd, var_order=var_order)) for vd in var_deps)
# FIXME not fast, but works
# (and exercises multivariate polynomial code)
return evaluate(expr, context)
def as_primitives(self):
deps = pymbolic.get_dependencies(self)
context = dict((dep, dep) for dep in deps)
return pymbolic.evaluate(self, context)
def _get_expr_dep_data(self, parsed):
from pymbolic import get_dependencies
deps = get_dependencies(parsed)
# gather information on aggregation expressions
dep_data = []
from pymbolic.primitives import Variable, Lookup, Subscript
for dep_idx, dep in enumerate(deps):
if isinstance(dep, Variable):
name = dep.name
agg_func = self.quantity_data[name].default_aggregator
if agg_func is None:
if self.is_parallel:
raise ValueError, "must specify explicit aggregator for '%s'" % name
else:
agg_func = lambda lst: lst[0]
elif isinstance(dep, Lookup):
assert isinstance(dep.aggregate, Variable)
name = dep.aggregate.name
agg_name = dep.name
if agg_name == "min":
agg_func = min
elif agg_name == "max":
agg_func = max
elif agg_name == "avg":
from pytools import average
agg_func = average
elif agg_name == "sum":
agg_func = sum
elif agg_name == "norm2":
from math import sqrt
agg_func = lambda iterable: sqrt(
sum(entry**2 for entry in iterable))
else:
raise ValueError, "invalid rank aggregator '%s'" % agg_name
elif isinstance(dep, Subscript):
assert isinstance(dep.aggregate, Variable)
name = dep.aggregate.name
class Nth:
def __init__(self, n):
self.n = n
def __call__(self, lst):
return lst[self.n]
from pymbolic import evaluate
agg_func = Nth(evaluate(dep.index))
qdat = self.quantity_data[name]
from pytools import Record
this_dep_data = Record(name=name, qdat=qdat, agg_func=agg_func,
varname="logvar%d" % dep_idx, expr=dep)
dep_data.append(this_dep_data)
# substitute in the "logvar" variable names
from pymbolic import var, substitute
parsed = substitute(parsed,
dict((dd.expr, var(dd.varname)) for dd in dep_data))
return parsed, dep_data
def as_primitives(self):
deps = pymbolic.get_dependencies(self)
context = dict((dep, dep) for dep in deps)
return pymbolic.evaluate(self, context)