diff --git a/pystencils/data_types.py b/pystencils/data_types.py
index 5d553006bcca4c1cccbddb681d8710a92b75c437..37b4f9479635f7661528c15275c59a4b81e89310 100644
--- a/pystencils/data_types.py
+++ b/pystencils/data_types.py
@@ -7,6 +7,7 @@ import numpy as np
import sympy as sp
import sympy.codegen.ast
from sympy.core.cache import cacheit
+from sympy.core.logic import fuzzy_not, fuzzy_or
from sympy.logic.boolalg import Boolean, BooleanFunction
import pystencils
@@ -20,6 +21,105 @@ except ImportError as e:
_ir_importerror = e
+class Type(sp.Basic):
+ is_Atom = True
+
+ def __new__(cls, *args, **kwargs):
+ return sp.Basic.__new__(cls)
+
+ def _sympystr(self, *args, **kwargs):
+ return str(self)
+
+
+class BasicType(Type):
+ @staticmethod
+ def numpy_name_to_c(name):
+ if name == 'float64':
+ return 'double'
+ elif name == 'float32':
+ return 'float'
+ elif name == 'complex64':
+ return 'ComplexFloat'
+ elif name == 'complex128':
+ return 'ComplexDouble'
+ elif name.startswith('int'):
+ width = int(name[len("int"):])
+ return "int%d_t" % (width,)
+ elif name.startswith('uint'):
+ width = int(name[len("uint"):])
+ return "uint%d_t" % (width,)
+ elif name == 'bool':
+ return 'bool'
+ else:
+ raise NotImplementedError("Can map numpy to C name for %s" % (name,))
+
+ def __init__(self, dtype, const=False):
+ self.const = const
+ if isinstance(dtype, Type):
+ self._dtype = dtype.numpy_dtype
+ else:
+ self._dtype = np.dtype(dtype)
+ assert self._dtype.fields is None, "Tried to initialize NativeType with a structured type"
+ assert self._dtype.hasobject is False
+ assert self._dtype.subdtype is None
+
+ def __getnewargs__(self):
+ return self.numpy_dtype, self.const
+
+ @property
+ def base_type(self):
+ return None
+
+ @property
+ def numpy_dtype(self):
+ return self._dtype
+
+ @property
+ def sympy_dtype(self):
+ return getattr(sympy.codegen.ast, str(self.numpy_dtype))
+
+ @property
+ def item_size(self):
+ return 1
+
+ def is_int(self):
+ return self.numpy_dtype in np.sctypes['int'] or self.numpy_dtype in np.sctypes['uint']
+
+ def is_float(self):
+ return self.numpy_dtype in np.sctypes['float']
+
+ def is_uint(self):
+ return self.numpy_dtype in np.sctypes['uint']
+
+ def is_complex(self):
+ return self.numpy_dtype in np.sctypes['complex']
+
+ def is_other(self):
+ return self.numpy_dtype in np.sctypes['others']
+
+ @property
+ def base_name(self):
+ return BasicType.numpy_name_to_c(str(self._dtype))
+
+ def __str__(self):
+ result = BasicType.numpy_name_to_c(str(self._dtype))
+ if self.const:
+ result += " const"
+ return result
+
+ def __repr__(self):
+ return str(self)
+
+ def __eq__(self, other):
+ if not isinstance(other, BasicType):
+ return False
+ else:
+ return (self.numpy_dtype, self.const) == (other.numpy_dtype, other.const)
+
+ def __hash__(self):
+ return hash(str(self))
+
+
def typed_symbols(names, dtype, *args):
symbols = sp.symbols(names, *args)
if isinstance(symbols, Tuple):
@@ -144,48 +244,48 @@ class cast_func(sp.Function):
@property
def is_integer(self):
- """
- Uses Numpy type hierarchy to determine :func:`sympy.Expr.is_integer` predicate
-
- For reference: Numpy type hierarchy https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.scalars.html
- """
- if hasattr(self.dtype, 'numpy_dtype'):
- return np.issubdtype(self.dtype.numpy_dtype, np.integer) or super().is_integer
- else:
- return super().is_integer
+ try:
+ dtype_assumptions = assumptions_from_dtype(self.args[1].numpy_dtype)
+ except Exception:
+ dtype_assumptions = {}
+ # we do not want a float to be integer even if it numerically is
+ return dtype_assumptions.get('integer', None)
@property
def is_negative(self):
- """
- See :func:`.TypedSymbol.is_integer`
- """
- if hasattr(self.dtype, 'numpy_dtype'):
- if np.issubdtype(self.dtype.numpy_dtype, np.unsignedinteger):
- return False
-
- return super().is_negative
+ try:
+ dtype_assumptions = assumptions_from_dtype(self.args[1].numpy_dtype)
+ except Exception:
+ dtype_assumptions = {}
+ return fuzzy_or((self.args[0].is_negative, dtype_assumptions.get('negative', None)))
@property
def is_nonnegative(self):
- """
- See :func:`.TypedSymbol.is_integer`
- """
- if self.is_negative is False:
- return True
- else:
- return super().is_nonnegative
+ try:
+ dtype_assumptions = assumptions_from_dtype(self.args[1].numpy_dtype)
+ except Exception:
+ dtype_assumptions = {}
+ return fuzzy_or((self.args[0].is_nonnegative, fuzzy_not(dtype_assumptions.get('negative', None))))
@property
def is_real(self):
- """
- See :func:`.TypedSymbol.is_integer`
- """
- if hasattr(self.dtype, 'numpy_dtype'):
- return np.issubdtype(self.dtype.numpy_dtype, np.integer) or \
- np.issubdtype(self.dtype.numpy_dtype, np.floating) or \
- super().is_real
- else:
- return super().is_real
+ try:
+ dtype_assumptions = assumptions_from_dtype(self.args[1].numpy_dtype)
+ except Exception:
+ dtype_assumptions = {}
+ return fuzzy_or((self.args[0].is_real, dtype_assumptions.get('real', None)))
+
+ @property
+ def is_extended_real(self):
+ try:
+ dtype_assumptions = assumptions_from_dtype(self.args[1].numpy_dtype)
+ except Exception:
+ dtype_assumptions = {}
+ return fuzzy_or((self.args[0].is_extended_real, dtype_assumptions.get('real', None)))
+
+ @property
+ def is_imaginary(self):
+ return self.args[0].is_imaginary
# noinspection PyPep8Naming
@@ -214,7 +314,27 @@ class pointer_arithmetic_func(sp.Function, Boolean):
raise NotImplementedError()
+def create_type(specification):
+ """Creates a subclass of Type according to a string or an object of subclass Type.
+
+ Args:
+ specification: Type object, or a string
+
+ Returns:
+ Type object, or a new Type object parsed from the string
+ """
+ if isinstance(specification, Type):
+ return specification
+ else:
+ numpy_dtype = np.dtype(specification)
+ if numpy_dtype.fields is None:
+ return BasicType(numpy_dtype, const=False)
+ else:
+ return StructType(numpy_dtype, const=False)
+
+
class TypedSymbol(sp.Symbol):
+
def __new__(cls, *args, **kwds):
obj = TypedSymbol.__xnew_cached_(cls, *args, **kwds)
return obj
@@ -267,25 +387,6 @@ class TypedSymbol(sp.Symbol):
return headers
-def create_type(specification):
- """Creates a subclass of Type according to a string or an object of subclass Type.
-
- Args:
- specification: Type object, or a string
-
- Returns:
- Type object, or a new Type object parsed from the string
- """
- if isinstance(specification, Type):
- return specification
- else:
- numpy_dtype = np.dtype(specification)
- if numpy_dtype.fields is None:
- return BasicType(numpy_dtype, const=False)
- else:
- return StructType(numpy_dtype, const=False)
-
-
@memorycache(maxsize=64)
def create_composite_type_from_string(specification):
"""Creates a new Type object from a c-like string specification.
@@ -575,105 +676,6 @@ def get_type_of_expression(expr,
raise NotImplementedError("Could not determine type for", expr, type(expr))
-class Type(sp.Basic):
- is_Atom = True
-
- def __new__(cls, *args, **kwargs):
- return sp.Basic.__new__(cls)
-
- def _sympystr(self, *args, **kwargs):
- return str(self)
-
-
-class BasicType(Type):
- @staticmethod
- def numpy_name_to_c(name):
- if name == 'float64':
- return 'double'
- elif name == 'float32':
- return 'float'
- elif name == 'complex64':
- return 'ComplexFloat'
- elif name == 'complex128':
- return 'ComplexDouble'
- elif name.startswith('int'):
- width = int(name[len("int"):])
- return "int%d_t" % (width,)
- elif name.startswith('uint'):
- width = int(name[len("uint"):])
- return "uint%d_t" % (width,)
- elif name == 'bool':
- return 'bool'
- else:
- raise NotImplementedError("Can map numpy to C name for %s" % (name,))
-
- def __init__(self, dtype, const=False):
- self.const = const
- if isinstance(dtype, Type):
- self._dtype = dtype.numpy_dtype
- else:
- self._dtype = np.dtype(dtype)
- assert self._dtype.fields is None, "Tried to initialize NativeType with a structured type"
- assert self._dtype.hasobject is False
- assert self._dtype.subdtype is None
-
- def __getnewargs__(self):
- return self.numpy_dtype, self.const
-
- @property
- def base_type(self):
- return None
-
- @property
- def numpy_dtype(self):
- return self._dtype
-
- @property
- def sympy_dtype(self):
- return getattr(sympy.codegen.ast, str(self.numpy_dtype))
-
- @property
- def item_size(self):
- return 1
-
- def is_int(self):
- return self.numpy_dtype in np.sctypes['int'] or self.numpy_dtype in np.sctypes['uint']
-
- def is_float(self):
- return self.numpy_dtype in np.sctypes['float']
-
- def is_uint(self):
- return self.numpy_dtype in np.sctypes['uint']
-
- def is_complex(self):
- return self.numpy_dtype in np.sctypes['complex']
-
- def is_other(self):
- return self.numpy_dtype in np.sctypes['others']
-
- @property
- def base_name(self):
- return BasicType.numpy_name_to_c(str(self._dtype))
-
- def __str__(self):
- result = BasicType.numpy_name_to_c(str(self._dtype))
- if self.const:
- result += " const"
- return result
-
- def __repr__(self):
- return str(self)
-
- def __eq__(self, other):
- if not isinstance(other, BasicType):
- return False
- else:
- return (self.numpy_dtype, self.const) == (other.numpy_dtype, other.const)
-
- def __hash__(self):
- return hash(str(self))
-
-
class VectorType(Type):
instruction_set = None