def test_numeric_evaluation(self):
symbolic = Broadcast(a, (b, ))
arguments = {'a': (1, 2., 3), 'b': 3}
expected = np.asarray([1, 2., 3])
result, _ = evaluate_lambdified(symbolic, ['a', 'b'], arguments, None)
np.testing.assert_array_equal(expected, result)
with self.assertRaises(ValueError):
arguments = {'a': (1, 2., 3), 'b': 4}
evaluate_lambdified(symbolic, ['a', 'b'], arguments, None)
arguments = {'a': 1, 'b': 3}
expected = np.asarray([1, 1, 1])
result, _ = evaluate_lambdified(symbolic, ['a', 'b'], arguments, None)
np.testing.assert_array_equal(expected, result)
def evaluate_numeric(self, **kwargs) -> Union[Number, numpy.ndarray]:
parsed_kwargs = self._parse_evaluate_numeric_arguments(kwargs)
result, self._expression_lambda = evaluate_lambdified(self.underlying_expression, self.variables,
parsed_kwargs, lambdified=self._expression_lambda)
return self._parse_evaluate_numeric_result(result, kwargs)
def evaluate(self, expression: Union[sympy.Expr, np.ndarray], parameters):
if isinstance(expression, np.ndarray):
variables = set.union(
*map(set, map(get_variables, expression.flat)))
else:
variables = get_variables(expression)
return evaluate_lambdified(expression,
variables=list(variables),
parameters=parameters,
lambdified=None)[0]
def evaluate_numeric(self, **kwargs) -> Union[numpy.ndarray, Number]:
parsed_kwargs = self._parse_evaluate_numeric_arguments(kwargs)
result, self._expression_lambda = evaluate_lambdified(self.underlying_expression, self.variables,
parsed_kwargs, lambdified=self._expression_lambda)
if isinstance(result, (list, tuple)):
result = numpy.array(result)
return self._parse_evaluate_numeric_result(numpy.array(result), kwargs)
def test_numeric_evaluation(self):
with self.assertWarns(UnsupportedBroadcastArgumentWarning):
# use c (and not b as above)
# here because sympy caches function call results and we want the warning everytime
symbolic = Broadcast(a, (c, ))
arguments = {'a': (1, 2., 3), 'c': 3}
expected = np.asarray([1, 2., 3])
result, _ = evaluate_lambdified(symbolic, ['a', 'c'], arguments, None)
np.testing.assert_array_equal(expected, result)
with self.assertRaises(ValueError):
arguments = {'a': (1, 2., 3), 'c': 4}
evaluate_lambdified(symbolic, ['a', 'c'], arguments, None)
arguments = {'a': 1, 'c': 3}
expected = np.asarray([1, 1, 1])
result, _ = evaluate_lambdified(symbolic, ['a', 'c'], arguments, None)
np.testing.assert_array_equal(expected, result)
def evaluate_in_scope(self, scope: Mapping) -> Union[Number, numpy.ndarray]:
"""Evaluate the expression by taking the variables from the given scope (typically of type Scope but it can be
any mapping.)
Args:
scope:
Returns:
"""
parsed_kwargs = self._parse_evaluate_numeric_arguments(scope)
result, self._expression_lambda = evaluate_lambdified(self.underlying_expression, self.variables,
parsed_kwargs, lambdified=self._expression_lambda)
return self._parse_evaluate_numeric_result(result, scope)
