def as_dtype(nbtype):
"""
Return a numpy dtype instance corresponding to the given Numba type.
NotImplementedError is if no correspondence is known.
"""
nbtype = types.unliteral(nbtype)
if isinstance(nbtype, (types.Complex, types.Integer, types.Float)):
return np.dtype(str(nbtype))
if nbtype is types.bool_:
return np.dtype('?')
if isinstance(nbtype, (types.NPDatetime, types.NPTimedelta)):
letter = _as_dtype_letters[type(nbtype)]
if nbtype.unit:
return np.dtype('%s[%s]' % (letter, nbtype.unit))
else:
return np.dtype(letter)
if isinstance(nbtype, (types.CharSeq, types.UnicodeCharSeq)):
letter = _as_dtype_letters[type(nbtype)]
return np.dtype('%s%d' % (letter, nbtype.count))
if isinstance(nbtype, types.Record):
return as_struct_dtype(nbtype)
if isinstance(nbtype, types.EnumMember):
return as_dtype(nbtype.dtype)
if isinstance(nbtype, types.npytypes.DType):
return as_dtype(nbtype.dtype)
if isinstance(nbtype, types.NumberClass):
return as_dtype(nbtype.dtype)
if isinstance(nbtype, types.NestedArray):
spec = (as_dtype(nbtype.dtype), tuple(nbtype.shape))
return np.dtype(spec)
if isinstance(nbtype, types.PyObject):
return np.dtype(object)
raise NotImplementedError("%r cannot be represented as a Numpy dtype" %
(nbtype, ))
def _prepare_argument(ctxt, bld, inp, tyinp, where='input operand'):
"""returns an instance of the appropriate Helper (either
_ScalarHelper or _ArrayHelper) class to handle the argument.
using the polymorphic interface of the Helper classes, scalar
and array cases can be handled with the same code"""
# first un-Optional Optionals
if isinstance(tyinp, types.Optional):
oty = tyinp
tyinp = tyinp.type
inp = ctxt.cast(bld, inp, oty, tyinp)
# then prepare the arg for a concrete instance
if isinstance(tyinp, types.ArrayCompatible):
ary = ctxt.make_array(tyinp)(ctxt, bld, inp)
shape = cgutils.unpack_tuple(bld, ary.shape, tyinp.ndim)
strides = cgutils.unpack_tuple(bld, ary.strides, tyinp.ndim)
return _ArrayHelper(ctxt, bld, shape, strides, ary.data, tyinp.layout,
tyinp.dtype, tyinp.ndim, inp)
elif (types.unliteral(tyinp) in types.number_domain | {types.boolean}
or isinstance(tyinp, types.scalars._NPDatetimeBase)):
return _ScalarHelper(ctxt, bld, inp, tyinp)
else:
raise NotImplementedError('unsupported type for {0}: {1}'.format(
where, str(tyinp)))
def generic(self, args, kws):
assert not kws
if len(args) == 1:
# One-argument type() -> return the __class__
# Avoid literal types
arg = types.unliteral(args[0])
classty = self.context.resolve_getattr(arg, "__class__")
if classty is not None:
return signature(classty, *args)
def unify_pairs(self, first, second):
"""
Try to unify the two given types. A third type is returned,
or None in case of failure.
"""
if first == second:
return first
if first is types.undefined:
return second
elif second is types.undefined:
return first
# Types with special unification rules
unified = first.unify(self, second)
if unified is not None:
return unified
unified = second.unify(self, first)
if unified is not None:
return unified
# Other types with simple conversion rules
conv = self.can_convert(fromty=first, toty=second)
if conv is not None and conv <= Conversion.safe:
# Can convert from first to second
return second
conv = self.can_convert(fromty=second, toty=first)
if conv is not None and conv <= Conversion.safe:
# Can convert from second to first
return first
if isinstance(first, types.Literal) or \
isinstance(second, types.Literal):
first = types.unliteral(first)
second = types.unliteral(second)
return self.unify_pairs(first, second)
# Cannot unify
return None
def _resolve_builtin_function_type(self, func, args, kws):
# NOTE: we should reduce usage of this
if func in self._functions:
# Note: Duplicating code with types.Function.get_call_type().
# *defns* are CallTemplates.
defns = self._functions[func]
for defn in defns:
for support_literals in [True, False]:
if support_literals:
res = defn.apply(args, kws)
else:
fixedargs = [types.unliteral(a) for a in args]
res = defn.apply(fixedargs, kws)
if res is not None:
return res
def _do_work_getattr(self, state, work_list, block, i, expr):
recv_type = state.type_annotation.typemap[expr.value.name]
recv_type = types.unliteral(recv_type)
matched = state.typingctx.find_matching_getattr_template(
recv_type,
expr.attr,
)
if not matched:
return False
template = matched['template']
if getattr(template, 'is_method', False):
# The attribute template is representing a method.
# Don't inline the getattr.
return False
inline_type = getattr(template, '_inline', None)
if inline_type is None:
# inline not defined
return False
sig = typing.signature(matched['return_type'], recv_type)
arg_typs = sig.args
if not inline_type.is_never_inline:
try:
impl = template._overload_func(recv_type)
if impl is None:
raise Exception # abort for this template
except Exception:
return False
else:
return False
is_method = False
return self._run_inliner(
state,
inline_type,
sig,
template,
arg_typs,
expr,
i,
impl,
block,
work_list,
is_method,
)
def generic(self, args, kws):
assert not kws
assert len(args) == 1
column = types.unliteral(args[0])
ret_typ = column
if (isinstance(column, types.List)
and (isinstance(column.dtype, types.Number)
or column.dtype == types.boolean)):
ret_typ = types.Array(column.dtype, 1, 'C')
if (isinstance(column, types.List)
and (column.dtype == string_type
or isinstance(column.dtype, types.Optional)
and column.dtype.type == string_type)):
ret_typ = string_array_type
if isinstance(column, SeriesType):
ret_typ = column.data
# TODO: add other types
return signature(ret_typ, column)
def _get_attr_info(self, state, expr):
recv_type = state.type_annotation.typemap[expr.value.name]
recv_type = types.unliteral(recv_type)
matched = state.typingctx.find_matching_getattr_template(
recv_type, expr.attr,
)
if not matched:
return None
template = matched['template']
if getattr(template, 'is_method', False):
# The attribute template is representing a method.
# Don't inline the getattr.
return None
templates = [template]
sig = typing.signature(matched['return_type'], recv_type)
arg_typs = sig.args
is_method = False
return templates, sig, arg_typs, is_method
def resolve_getattr(self, typ, attr):
"""
Resolve getting the attribute *attr* (a string) on the Numba type.
The attribute's type is returned, or None if resolution failed.
"""
def core(typ):
out = self.find_matching_getattr_template(typ, attr)
if out:
return out['return_type']
out = core(typ)
if out is not None:
return out
# Try again without literals
out = core(types.unliteral(typ))
if out is not None:
return out
if isinstance(typ, types.Module):
attrty = self.resolve_module_constants(typ, attr)
if attrty is not None:
return attrty
def __unliteral__(self):
return signature(types.unliteral(self.return_type),
*map(types.unliteral, self.args))
def unliteral_all(args):
return tuple(types.unliteral(a) for a in args)
def resolve_split(self, dict, args, kws):
assert not kws
assert len(args) == 1
return signature(types.List(std_str_type), types.unliteral(args[0]))
def preprocess_fields(self, fields):
# This method is called by the type constructor for additional
# preprocessing on the fields.
# Here, we don't want the struct to take Literal types.
return tuple((name, types.unliteral(typ)) for name, typ in fields)
def preprocess_fields(self, fields):
return tuple((name, types.unliteral(typ)) for name, typ in fields)
