def __init__(self, base, parent_approximation, models=None, exact=Infinity,
category=None, element_constructor=None, gens=None, names=None, normalize=True, facade=None):
Parent.__init__(self,base,category=category,element_constructor=element_constructor,
gens=gens, names=names, normalize=normalize, facade=facade)
self._approximation = parent_approximation
self._precision = ParentBigOh(self)
if models is None:
dimension = self.dimension()
if dimension == 1:
self._precision.add_model(ValuationBigOh)
else:
if self.dimension() < Infinity:
self._precision.add_model(FlatBigOh)
self._precision.add_model(JaggedBigOh)
try:
if self.indices_basis() is None:
self._precision.add_model(FlatIntervalBigOh)
self._precision.add_modelconversion(FlatIntervalBigOh,JaggedBigOh)
except NotImplementedError:
pass
self._precision.add_modelconversion(FlatBigOh,JaggedBigOh,safemode=True)
self._precision.add_modelconversion(FlatBigOh,FlatIntervalBigOh,safemode=True)
else:
for model in models.vertices():
self._precision.add_model(model)
for (mfrom,mto,map) in models.edges():
self._precision.add_modelconversion(mfrom,mto,map)
try:
self._exact_precision = self._precision(exact)
except PrecisionError:
self._exact_precision = ExactBigOh(self._precision)
class Parent_inexact(Parent,UniqueRepresentation):
def __init__(self, base, parent_approximation, models=None, exact=Infinity,
category=None, element_constructor=None, gens=None, names=None, normalize=True, facade=None):
Parent.__init__(self,base,category=category,element_constructor=element_constructor,
gens=gens, names=names, normalize=normalize, facade=facade)
self._approximation = parent_approximation
self._precision = ParentBigOh(self)
if models is None:
dimension = self.dimension()
if dimension == 1:
self._precision.add_model(ValuationBigOh)
else:
if self.dimension() < Infinity:
self._precision.add_model(FlatBigOh)
self._precision.add_model(JaggedBigOh)
try:
if self.indices_basis() is None:
self._precision.add_model(FlatIntervalBigOh)
self._precision.add_modelconversion(FlatIntervalBigOh,JaggedBigOh)
except NotImplementedError:
pass
self._precision.add_modelconversion(FlatBigOh,JaggedBigOh,safemode=True)
self._precision.add_modelconversion(FlatBigOh,FlatIntervalBigOh,safemode=True)
else:
for model in models.vertices():
self._precision.add_model(model)
for (mfrom,mto,map) in models.edges():
self._precision.add_modelconversion(mfrom,mto,map)
try:
self._exact_precision = self._precision(exact)
except PrecisionError:
self._exact_precision = ExactBigOh(self._precision)
def __hash__(self):
return id(self)
def uniformizer_name(self):
base = self._base
if hasattr(base, "uniformizer_name"):
return base.uniformizer_name()
elif hasattr(base, "uniformizer"):
import re
return re.sub("\s*\+\s*O(.*)", "", base.uniformizer().__repr__())
else:
raise NotImplementedError
def _repr_(self):
s = self._approximation.__repr__()
i = s.rfind("over ")
if i == -1: s = ""
else: s = s[:i+5]
s += self._base.__repr__()
return s
def approximation(self):
return self._approximation
def default_working_precision(self):
return self._default_workprec
def is_exact(self):
return False
def random_element(self,*args,**kwargs):
dimension = self.dimension()
if dimension is Infinity:
raise NotImplementedError
base = self.base_ring()
coefficients = [ base.random_element(*args,**kwargs) for _ in range(dimension) ]
return self(coefficients)
def dimension(self):
try:
return self._approximation.dimension()
except AttributeError:
return Infinity
def indices_basis(self):
"""
None: the basis is indexed by usual natural integers (including 0, as always in Python)
"""
pass
def precision_models(self,graph=False):
return self._precision.models(graph)
def precision(self):
return self._precision
