def __new__(cls, *args, **kwargs):
options = kwargs.get('options', {})
key = cls._cache_key(*args, **kwargs)
obj = cls._cache_get(key)
if obj is not None:
newobj = sympy.Matrix.__new__(cls, *args, **options)
newobj.__init_cached__(key)
return newobj
name = kwargs.get('name')
# Number of dimensions
indices, _ = cls.__indices_setup__(**kwargs)
# Create new, unique type instance from cls and the symbol name
newcls = type(name, (cls,), dict(cls.__dict__))
# Create the new Function object and invoke __init__
comps = cls.__subfunc_setup__(*args, **kwargs)
newobj = sympy.ImmutableDenseMatrix.__new__(newcls, comps)
# Initialization. The following attributes must be available
newobj._indices = indices
newobj._name = name
newobj._dtype = cls.__dtype_setup__(**kwargs)
newobj.__init_finalize__(*args, **kwargs)
# Store new instance in symbol cache
Cached.__init__(newobj, newcls)
return newobj
def __new__(cls, *args, **kwargs):
key = cls._cache_key(*args, **kwargs)
obj = cls._cache_get(key)
if obj is not None:
return obj
# Not in cache. Create a new Symbol via sympy.Symbol
name = kwargs.get('name') or args[0]
assumptions, kwargs = cls._filter_assumptions(**kwargs)
# Create new, unique type instance from cls and the symbol name
newcls = type(name, (cls,), dict(cls.__dict__))
# Create the new Symbol and invoke __init__
newobj = sympy.Symbol.__new__(newcls, name, **assumptions)
# Initialization
newobj._dtype = cls.__dtype_setup__(**kwargs)
newobj.__init_finalize__(*args, **kwargs)
# Store new instance in symbol cache
Cached.__init__(newobj, newcls)
return newobj
def __new__(cls, *args, **kwargs):
options = kwargs.get('options', {'evaluate': False})
# Is the object already in cache (e.g., f(x), f(x+1)) ?
key = cls._cache_key(*args, **kwargs)
obj = cls._cache_get(key)
if obj is not None:
return obj
# Does the base object exist at least (e.g. f(x))?
obj = cls._cache_get(cls)
if obj is not None:
with sympy_mutex:
newobj = sympy.Function.__new__(cls, *args, **options)
newobj.__init_cached__(obj)
Cached.__init__(newobj, key)
return newobj
# Preprocess arguments
args, kwargs = cls.__args_setup__(*args, **kwargs)
# Not in cache. Create a new Function via sympy.Function
name = kwargs.get('name')
dimensions, indices = cls.__indices_setup__(**kwargs)
# Create new, unique type instance from cls and the symbol name
newcls = type(name, (cls, ), dict(cls.__dict__))
# Create the new Function object and invoke __init__
with sympy_mutex:
newobj = sympy.Function.__new__(newcls, *indices, **options)
# Initialization. The following attributes must be available
# when executing __init_finalize__
newobj._name = name
newobj._dimensions = dimensions
newobj._shape = cls.__shape_setup__(**kwargs)
newobj._dtype = cls.__dtype_setup__(**kwargs)
newobj.__init_finalize__(*args, **kwargs)
# All objects cached on the AbstractFunction `newobj` keep a reference
# to `newobj` through the `function` field. Thus, all indexified
# object will point to `newobj`, the "actual Function".
newobj.function = newobj
# Store new instance in symbol cache
key = (newcls, indices)
Cached.__init__(newobj, key, newcls)
return newobj
def _cache_get(cls, *args, **kwargs):
# Is the object already in cache (e.g., f(x), f(x+1)) ?
key = cls._cache_key(*args, **kwargs)
obj = super()._cache_get(key)
if obj is not None:
return obj
# Does the base object exist at least (e.g. f(x))?
obj = super()._cache_get(cls)
if obj is not None:
options = kwargs.get('options', {'evaluate': False})
with sympy_mutex:
newobj = sympy.Function.__new__(cls, *args, **options)
newobj.__init_cached__(obj)
Cached.__init__(newobj, key)
return newobj
# Not in cache
return None
def __new__(cls, *args, **kwargs):
key = cls._cache_key(*args, **kwargs)
obj = cls._cache_get(key)
if obj is not None:
return obj
# Not in cache. Create a new Symbol via sympy.Symbol
name = kwargs.get('name') or args[0]
assumptions, kwargs = cls._filter_assumptions(**kwargs)
# Note: use __xnew__ to bypass sympy caching
newobj = sympy.Symbol.__xnew__(cls, name, **assumptions)
# Initialization
newobj._dtype = cls.__dtype_setup__(**kwargs)
newobj.__init_finalize__(*args, **kwargs)
# Store new instance in symbol cache
Cached.__init__(newobj, key)
return newobj
def __new__(cls, *args, **kwargs):
options = kwargs.get('options', {})
key = cls._cache_key(*args, **kwargs)
obj = cls._cache_get(key)
if obj is not None:
newobj = sympy.Function.__new__(cls, *args, **options)
newobj.__init_cached__(key)
return newobj
# Not in cache. Create a new Function via sympy.Function
name = kwargs.get('name')
dimensions, indices = cls.__indices_setup__(**kwargs)
# Create new, unique type instance from cls and the symbol name
newcls = type(name, (cls,), dict(cls.__dict__))
# Create the new Function object and invoke __init__
newobj = sympy.Function.__new__(newcls, *indices, **options)
# Initialization. The following attributes must be available
# when executing __init_finalize__
newobj._name = name
newobj._dimensions = dimensions
newobj._shape = cls.__shape_setup__(**kwargs)
newobj._dtype = cls.__dtype_setup__(**kwargs)
newobj.__init_finalize__(*args, **kwargs)
# All objects cached on the AbstractFunction `newobj` keep a reference
# to `newobj` through the `function` field. Thus, all indexified
# object will point to `newobj`, the "actual Function".
newobj.function = newobj
# Store new instance in symbol cache
Cached.__init__(newobj, newcls)
return newobj