def get_constant(self, precision, evaluation, preference=None):
## print("XXX", self, preference)
if preference is None:
preference = (
evaluation.parse("Settings`$PreferredBackendMethod")
.evaluate(evaluation)
.get_string_value()
)
# TODO: validate PreferredBackendMethod is in "mpmath", "numpy", "sympy"
try:
d = get_precision(precision, evaluation)
except PrecisionValueError:
d = None
conversion_fn = MachineReal if d is None else PrecisionReal
# print("XXX1", self, preference, conversion_fn)
if preference == "sympy" and hasattr(self, "sympy_name"):
value = sympy_constant(self.sympy_name, d)
elif preference == "mpmath" and hasattr(self, "mpmath_name"):
value = mp_constant(self.mpmath_name, d)
elif preference == "numpy" and hasattr(self, "numpy_name"):
value = numpy_constant(self.numpy_name)
elif hasattr(self, "mpmath_name"):
value = mp_constant(self.mpmath_name, d)
elif hasattr(self, "sympy_name"):
value = sympy_constant(self.sympy_name, d)
elif hasattr(self, "numpy_name"):
value = numpy_constant(self.numpy_name)
return conversion_fn(value)
def apply_other(self, expr, prec, evaluation):
"N[expr_, prec_]"
try:
d = get_precision(prec, evaluation)
except PrecisionValueError:
return
if expr.get_head_name() in ("System`List", "System`Rule"):
return Expression(
expr.head,
*[self.apply_other(leaf, prec, evaluation) for leaf in expr.leaves]
)
# Special case for the Root builtin
if expr.has_form("Root", 2):
return from_sympy(sympy.N(expr.to_sympy(), d))
if isinstance(expr, Number):
return expr.round(d)
name = expr.get_lookup_name()
if name != "":
nexpr = Expression("N", expr, prec)
result = evaluation.definitions.get_value(
name, "System`NValues", nexpr, evaluation
)
if result is not None:
if not result.same(nexpr):
result = Expression("N", result, prec).evaluate(evaluation)
return result
if expr.is_atom():
return expr
else:
attributes = expr.head.get_attributes(evaluation.definitions)
if "System`NHoldAll" in attributes:
eval_range = ()
elif "System`NHoldFirst" in attributes:
eval_range = range(1, len(expr.leaves))
elif "System`NHoldRest" in attributes:
if len(expr.leaves) > 0:
eval_range = (0,)
else:
eval_range = ()
else:
eval_range = range(len(expr.leaves))
head = Expression('N', expr.head, prec).evaluate(evaluation)
leaves = expr.get_mutable_leaves()
for index in eval_range:
leaves[index] = Expression("N", leaves[index], prec).evaluate(
evaluation
)
return Expression(head, *leaves)
def apply_N(self, precision, evaluation):
'N[E, precision_]'
try:
d = get_precision(precision, evaluation)
except PrecisionValueError:
return
if d is None:
return MachineReal(math.e)
else:
return PrecisionReal(sympy.E.n(d))
def apply_N(self, precision, evaluation):
'N[E, precision_]'
try:
d = get_precision(precision, evaluation)
except PrecisionValueError:
return
if d is None:
return MachineReal(math.e)
else:
return PrecisionReal(sympy.E.n(d))
def get_constant(self, precision, evaluation, have_mpmath=False):
try:
d = get_precision(precision, evaluation)
except PrecisionValueError:
return
sympy_fn = self.to_sympy()
if d is None:
result = self.get_mpmath_function() if have_mpmath else sympy_fn()
return MachineReal(result)
else:
return PrecisionReal(sympy_fn.n(d))
def apply_N(self, precision, evaluation, options={}):
"N[Degree, precision_, OptionsPattern[%(name)s]]"
try:
if precision:
d = get_precision(precision, evaluation)
else:
d = get_precision(Symbol("System`MachinePrecision"),
evaluation)
except PrecisionValueError:
return
# FIXME: There are all sorts of interactions between in the trig functions,
# that are expected to work out right. Until we have convertion between
# mpmath and sympy worked out so that values can be made the to the same
# precision and compared. we have to not use mpmath right now.
# return self.get_constant(precision, evaluation, preference="mpmath")
if d is None:
return MachineReal(math.pi / 180)
else:
return PrecisionReal((sympy.pi / 180).n(d))
def apply_N(self, precision, evaluation):
"N[Pi, precision_]"
try:
d = get_precision(precision, evaluation)
except PrecisionValueError:
return
if d is None:
return MachineReal(math.pi)
else:
return PrecisionReal(sympy.pi.n(d))
def apply_N(self, precision, evaluation):
"N[Catalan, precision_]"
try:
d = get_precision(precision, evaluation)
except PrecisionValueError:
return
if d is None:
return MachineReal(mpmath.catalan)
else:
return PrecisionReal(sympy.Catalan.n(d))
def get_constant(self, precision, evaluation, preference=None):
# first, determine the precision
machine_d = int(0.30103 * machine_precision)
d = None
if precision:
try:
d = get_precision(precision, evaluation)
except PrecisionValueError:
pass
if d is None:
d = machine_d
# If preference not especified, determine it
# from the precision.
if preference is None:
if d <= machine_d:
preference = "numpy"
else:
preference = "mpmath"
# If preference is not valid, send a message and return.
if not (preference in ("sympy", "numpy", "mpmath")):
evaluation.message(
f'{preference} not in ("sympy", "numpy", "mpmath")')
return
# Try to determine the numeric value
value = None
if preference == "mpmath" and not hasattr(self, "mpmath_name"):
preference = "numpy"
elif preference == "sympy" and not hasattr(self, "sympy_name"):
preference = "numpy"
if preference == "numpy" and not hasattr(self, "numpy_name"):
if hasattr(self, "sympy_name"):
preference = "sympy"
elif hasattr(self, "mpmath_name"):
preference = "mpmath"
else:
preference = ""
if preference == "numpy":
value = numpy_constant(self.numpy_name)
if d == machine_d:
return MachineReal(value)
if preference == "sympy":
value = sympy_constant(self.sympy_name, d + 2)
if preference == "mpmath":
value = mp_constant(self.mpmath_name, d * 2)
if value:
return PrecisionReal(sympy.Float(str(value), d))
# If the value is not available, return none
# and keep it unevaluated.
return
def apply_other(self, expr, prec, evaluation):
'N[expr_, prec_]'
try:
d = get_precision(prec, evaluation)
except PrecisionValueError:
return
if expr.get_head_name() in ('System`List', 'System`Rule'):
return Expression(
expr.head, *[
self.apply_other(leaf, prec, evaluation)
for leaf in expr.leaves
])
if isinstance(expr, Number):
return expr.round(d)
name = expr.get_lookup_name()
if name != '':
nexpr = Expression('N', expr, prec)
result = evaluation.definitions.get_value(name, 'System`NValues',
nexpr, evaluation)
if result is not None:
if not result.same(nexpr):
result = Expression('N', result, prec).evaluate(evaluation)
return result
if expr.is_atom():
return expr
else:
attributes = expr.head.get_attributes(evaluation.definitions)
if 'System`NHoldAll' in attributes:
eval_range = ()
elif 'System`NHoldFirst' in attributes:
eval_range = range(1, len(expr.leaves))
elif 'System`NHoldRest' in attributes:
if len(expr.leaves) > 0:
eval_range = (0, )
else:
eval_range = ()
else:
eval_range = range(len(expr.leaves))
head = Expression('N', expr.head, prec).evaluate(evaluation)
leaves = expr.leaves[:]
for index in eval_range:
leaves[index] = Expression('N', leaves[index],
prec).evaluate(evaluation)
return Expression(head, *leaves)
def apply_other(self, expr, prec, evaluation):
'N[expr_, prec_]'
try:
d = get_precision(prec, evaluation)
except PrecisionValueError:
return
if expr.get_head_name() in ('System`List', 'System`Rule'):
return Expression(
expr.head, *[self.apply_other(leaf, prec, evaluation)
for leaf in expr.leaves])
if isinstance(expr, Number):
return expr.round(d)
name = expr.get_lookup_name()
if name != '':
nexpr = Expression('N', expr, prec)
result = evaluation.definitions.get_value(
name, 'System`NValues', nexpr, evaluation)
if result is not None:
if not result.same(nexpr):
result = Expression('N', result, prec).evaluate(evaluation)
return result
if expr.is_atom():
return expr
else:
attributes = expr.head.get_attributes(evaluation.definitions)
if 'System`NHoldAll' in attributes:
eval_range = ()
elif 'System`NHoldFirst' in attributes:
eval_range = range(1, len(expr.leaves))
elif 'System`NHoldRest' in attributes:
if len(expr.leaves) > 0:
eval_range = (0,)
else:
eval_range = ()
else:
eval_range = range(len(expr.leaves))
head = Expression('N', expr.head, prec).evaluate(evaluation)
leaves = expr.leaves[:]
for index in eval_range:
leaves[index] = Expression(
'N', leaves[index], prec).evaluate(evaluation)
return Expression(head, *leaves)
def apply_N(self, k, precision, evaluation):
'N[AiryBiZero[k_Integer], precision_]'
try:
d = get_precision(precision, evaluation)
except PrecisionValueError:
return
if d is None:
p = machine_precision
else:
p = _prec(d)
k_int = k.get_int_value()
with mpmath.workprec(p):
result = mpmath.airybizero(k_int)
return Number.from_mpmath(result, d)
def apply_N(self, k, precision, evaluation):
"N[AiryBiZero[k_Integer], precision_]"
try:
d = get_precision(precision, evaluation)
except PrecisionValueError:
return
if d is None:
p = machine_precision
else:
p = _prec(d)
k_int = k.get_int_value()
with mpmath.workprec(p):
result = mpmath.airybizero(k_int)
return from_mpmath(result, d)
def get_constant(self, precision, evaluation, preference="mpmath"):
try:
d = get_precision(precision, evaluation)
except PrecisionValueError:
return
mpmath_name = self.mpmath_name
if d is None:
return MachineReal(mp_fn(mpmath_name))
elif preference == "mpmath":
result = mp_fn(mpmath_name, d)
else:
sympy_fn = self.to_sympy()
try:
result = sympy_fn.n(d)
except:
from trepan.api import debug; debug()
pass
return PrecisionReal(result)
def get_constant(self, precision, evaluation, preference="sympy"):
try:
d = get_precision(precision, evaluation)
except PrecisionValueError:
return
sympy_fn = self.to_sympy()
if d is None:
if hasattr(self, "mpmath_name"):
# Prefer mpmath when precision is not given.
result = mp_fn(self.mpmath_name)
else:
result = sympy_fn()
return MachineReal(result)
if preference == "sympy":
result = sympy_fn_n(d)
elif hasattr(self, "mpmath_name"):
result = mp_fn(self.mpmath_name, d)
return PrecisionReal(result)
