numba.targets package
Submodules
numba.targets.arrayobj module
Implementation of operations on Array objects.
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numba.targets.arrayobj.array_len(context, builder, sig, args)
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numba.targets.arrayobj.array_ndim(context, builder, typ, value, attr)
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numba.targets.arrayobj.array_prod(context, builder, sig, args)
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numba.targets.arrayobj.array_record_getattr(context, builder, typ, value, attr)
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numba.targets.arrayobj.array_shape(context, builder, typ, value, attr)
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numba.targets.arrayobj.array_size(context, builder, typ, value, attr)
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numba.targets.arrayobj.array_strides(context, builder, typ, value, attr)
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numba.targets.arrayobj.array_sum(context, builder, sig, args)
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numba.targets.arrayobj.getitem_array1d_intp(context, builder, sig, args)
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numba.targets.arrayobj.getitem_array1d_slice(context, builder, sig, args)
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numba.targets.arrayobj.getitem_array_tuple(context, builder, sig, args)
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numba.targets.arrayobj.getitem_array_unituple(context, builder, sig, args)
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numba.targets.arrayobj.getiter_array(context, builder, sig, args)
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numba.targets.arrayobj.make_array(array_type)
Return the Structure representation of the given array_type
(an instance of types.Array).
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numba.targets.arrayobj.make_array_flat_cls(flatiterty)
Return the Structure representation of the given enum_type (an
instance of types.EnumerateType).
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numba.targets.arrayobj.make_array_flatiter(context, builder, typ, value, attr)
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numba.targets.arrayobj.make_arrayiter_cls(iterator_type)
Return the Structure representation of the given iterator_type (an
instance of types.ArrayIteratorType).
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numba.targets.arrayobj.numpy_prod(context, builder, sig, args)
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numba.targets.arrayobj.numpy_sum(context, builder, sig, args)
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numba.targets.arrayobj.setitem_array1d(context, builder, sig, args)
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numba.targets.arrayobj.setitem_array1d_slice(context, builder, sig, args)
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numba.targets.arrayobj.setitem_array_tuple(context, builder, sig, args)
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numba.targets.arrayobj.setitem_array_unituple(context, builder, sig, args)
numba.targets.base module
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class numba.targets.base.BaseContext(typing_context)
Bases: object
Most objects are lowered as plain-old-data structure in the generated
llvm. They are passed around by reference (a pointer to the structure).
Only POD structure can life across function boundaries by copying the
data.
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add_exception(exc)
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add_libs(libs)
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call_class_method(builder, func, signature, args)
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call_external_function(builder, callee, argtys, args)
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call_function(builder, callee, resty, argtys, args, env=None)
Call the Numba-compiled callee, using the same calling
convention as in get_function_type().
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call_function_pointer(builder, funcptr, signature, args)
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cast(builder, val, fromty, toty)
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compile_internal(builder, impl, sig, args, locals={}, cache_key=None)
Invoke compiler to implement a function for a nopython function
- cache_key : hashable
- A hashable object to use as the key for caching.
If it is None, no caching is performed.
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debug_print(builder, text)
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declare_external_function(module, fndesc)
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declare_function(module, fndesc)
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finalize(func, fndesc)
Perform any necessary work to complete the compilation.
An implementation of get_executable() should call finalize().
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get_abi_sizeof(lty)
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get_argument_type(ty)
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get_argument_value(builder, ty, val)
Argument representation to local value representation
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get_arguments(func)
Get the Python-level arguments of LLVM func.
See get_function_type() for the calling convention.
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get_attribute(val, typ, attr)
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get_bound_function(builder, obj, ty)
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get_constant(ty, val)
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get_constant_generic(builder, ty, val)
Return a LLVM constant representing value val of Numba type ty.
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get_constant_null(ty)
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get_constant_struct(builder, ty, val)
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get_constant_undef(ty)
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get_data_type(ty)
Get a data representation of the type that is safe for storage.
Record data are stored as byte array.
Returns None if it is an opaque pointer
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get_dummy_type()
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get_dummy_value()
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get_executable(func, fndesc)
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get_external_function_type(fndesc)
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get_function(fn, sig)
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get_function_type(fndesc)
Get the implemented Function type for the high-level fndesc.
Some parameters can be added or shuffled around.
This is kept in sync with call_function() and get_arguments().
- Returns: -2 for return none in native function;
- -1 for failure with python exception set;
- 0 for success;
>0 for user error code.
Return value is passed by reference as the first argument.
Actual arguments starts at the 2rd argument position.
Caller is responsible to allocate space for return value.
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get_python_api(builder)
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get_return_status(builder, code)
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get_return_type(ty)
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get_return_value(builder, ty, val)
Local value representation to return type representation
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get_setattr(attr, sig)
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get_struct_member_type(member_type)
Get the LLVM type for struct member of type member_type.
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get_struct_member_value(builder, ty, val)
Local value representation to struct member representation
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get_struct_type(struct)
Get the LLVM struct type for the given Structure class struct.
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get_user_function(func)
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get_value_as_argument(builder, ty, val)
Prepare local value representation as argument type representation
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get_value_type(ty)
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implement_pow_as_math_call = False
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implement_powi_as_math_call = False
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init()
For subclasses to add initializer
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insert_attr_defn(defns)
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insert_class(cls, attrs)
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insert_const_string(mod, string)
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insert_func_defn(defns)
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insert_user_function(func, fndesc, libs=())
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is_struct_type(ty)
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is_true(builder, typ, val)
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link_dependencies(module, depends)
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localized()
Returns a localized context that contains extra environment information
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make_array(typ)
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make_complex(typ)
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make_constant_array(builder, typ, ary)
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make_pair(first_type, second_type)
Create a heterogenous pair class parametered for the given types.
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mangler = None
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optimize(module)
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optimize_function(func)
Perform function-level optimization.
This may improve generated code and reduce memory usage.
Note: This is called at the end of lowering.
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pack_value(builder, ty, value, ptr)
Pack data for array storage
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pair_first(builder, val, ty)
Extract the first element of a heterogenous pair.
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pair_second(builder, val, ty)
Extract the second element of a heterogenous pair.
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print_string(builder, text)
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remove_user_function(func)
Remove user function func.
KeyError is raised if the function isn’t known to us.
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return_errcode(builder, code)
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return_errcode_propagate(builder, code)
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return_exc(builder)
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return_native_none(builder)
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return_user_exc(builder, code)
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return_value(builder, retval)
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sentry_record_alignment(rectyp, attr)
Assumes offset starts from a properly aligned location
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strict_alignment = False
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unpack_value(builder, ty, ptr)
Unpack data from array storage
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class numba.targets.base.Overloads
Bases: object
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append(impl)
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find(sig)
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class numba.targets.base.Status
Bases: tuple
Status(code, ok, err, exc, none)
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code
Alias for field number 0
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err
Alias for field number 2
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exc
Alias for field number 3
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none
Alias for field number 4
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ok
Alias for field number 1
numba.targets.builtins module
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class numba.targets.builtins.Complex128(context, builder, value=None, ref=None, cast_ref=False)
Bases: numba.cgutils.Structure
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class numba.targets.builtins.Complex64(context, builder, value=None, ref=None, cast_ref=False)
Bases: numba.cgutils.Structure
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class numba.targets.builtins.Slice(context, builder, value=None, ref=None, cast_ref=False)
Bases: numba.cgutils.Structure
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numba.targets.builtins.array_ravel_impl(context, builder, sig, args)
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numba.targets.builtins.bool_invert_impl(context, builder, sig, args)
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numba.targets.builtins.caster(restype)
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numba.targets.builtins.cls
alias of Complex128
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numba.targets.builtins.complex128_imag_impl(context, builder, typ, value, attr)
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numba.targets.builtins.complex128_power_impl(context, builder, sig, args)
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numba.targets.builtins.complex128_real_impl(context, builder, typ, value, attr)
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numba.targets.builtins.complex64_imag_impl(context, builder, typ, value, attr)
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numba.targets.builtins.complex64_real_impl(context, builder, typ, value, attr)
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numba.targets.builtins.complex_abs_impl(context, builder, sig, args)
abs(z) := hypot(z.real, z.imag)
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numba.targets.builtins.complex_add_impl(context, builder, sig, args)
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numba.targets.builtins.complex_div_impl(context, builder, sig, args)
z = c^2 + d^2
(a+bi)/(c+di) = (ac + bd) / z, (bc - ad) / z
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numba.targets.builtins.complex_eq_impl(context, builder, sig, args)
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numba.targets.builtins.complex_impl(context, builder, sig, args)
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numba.targets.builtins.complex_mul_impl(context, builder, sig, args)
(a+bi)(c+di)=(ac-bd)+i(ad+bc)
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numba.targets.builtins.complex_ne_impl(context, builder, sig, args)
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numba.targets.builtins.complex_negate_impl(context, builder, sig, args)
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numba.targets.builtins.complex_positive_impl(context, builder, sig, args)
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numba.targets.builtins.complex_sub_impl(context, builder, sig, args)
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numba.targets.builtins.float_impl(context, builder, sig, args)
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numba.targets.builtins.get_complex_info(ty)
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numba.targets.builtins.getitem_unituple(context, builder, sig, args)
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numba.targets.builtins.getiter_unituple(context, builder, sig, args)
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numba.targets.builtins.int_abs_impl(context, builder, sig, args)
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numba.targets.builtins.int_add_impl(context, builder, sig, args)
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numba.targets.builtins.int_and_impl(context, builder, sig, args)
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numba.targets.builtins.int_ashr_impl(context, builder, sig, args)
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numba.targets.builtins.int_divmod(context, builder, x, y)
Reference Objects/intobject.c
xdivy = x / y;
xmody = (long)(x - (unsigned long)xdivy * y);
/* If the signs of x and y differ, and the remainder is non-0,
- C89 doesn’t define whether xdivy is now the floor or the
- ceiling of the infinitely precise quotient. We want the floor,
- and we have it iff the remainder’s sign matches y’s.
*/
- if (xmody && ((y ^ xmody) < 0) /* i.e. and signs differ */) {
- xmody += y;
–xdivy;
assert(xmody && ((y ^ xmody) >= 0));
}
*p_xdivy = xdivy;
*p_xmody = xmody;
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numba.targets.builtins.int_eq_impl(context, builder, sig, args)
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numba.targets.builtins.int_impl(context, builder, sig, args)
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numba.targets.builtins.int_invert_impl(context, builder, sig, args)
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numba.targets.builtins.int_lshr_impl(context, builder, sig, args)
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numba.targets.builtins.int_mul_impl(context, builder, sig, args)
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numba.targets.builtins.int_ne_impl(context, builder, sig, args)
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numba.targets.builtins.int_negate_impl(context, builder, sig, args)
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numba.targets.builtins.int_or_impl(context, builder, sig, args)
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numba.targets.builtins.int_positive_impl(context, builder, sig, args)
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numba.targets.builtins.int_power_func_body(context, builder, x, y)
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numba.targets.builtins.int_sdiv_impl(context, builder, sig, args)
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numba.targets.builtins.int_sfloordiv_impl(context, builder, sig, args)
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numba.targets.builtins.int_sge_impl(context, builder, sig, args)
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numba.targets.builtins.int_sgt_impl(context, builder, sig, args)
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numba.targets.builtins.int_shl_impl(context, builder, sig, args)
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numba.targets.builtins.int_sign_impl(context, builder, sig, args)
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numba.targets.builtins.int_sle_impl(context, builder, sig, args)
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numba.targets.builtins.int_slt_impl(context, builder, sig, args)
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numba.targets.builtins.int_spower_impl(context, builder, sig, args)
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numba.targets.builtins.int_srem_impl(context, builder, sig, args)
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numba.targets.builtins.int_struediv_impl(context, builder, sig, args)
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numba.targets.builtins.int_sub_impl(context, builder, sig, args)
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numba.targets.builtins.int_udiv_impl(context, builder, sig, args)
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numba.targets.builtins.int_ufloordiv_impl(context, builder, sig, args)
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numba.targets.builtins.int_uge_impl(context, builder, sig, args)
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numba.targets.builtins.int_ugt_impl(context, builder, sig, args)
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numba.targets.builtins.int_ule_impl(context, builder, sig, args)
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numba.targets.builtins.int_ult_impl(context, builder, sig, args)
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numba.targets.builtins.int_upower_impl(context, builder, sig, args)
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numba.targets.builtins.int_urem_impl(context, builder, sig, args)
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numba.targets.builtins.int_utruediv_impl(context, builder, sig, args)
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numba.targets.builtins.int_xor_impl(context, builder, sig, args)
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numba.targets.builtins.make_pair(first_type, second_type)
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numba.targets.builtins.make_unituple_iter(tupiter)
Return the Structure representation of the given tupiter (an
instance of types.UniTupleIter).
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numba.targets.builtins.math_e_impl(context, builder, typ, value, attr)
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numba.targets.builtins.math_pi_impl(context, builder, typ, value, attr)
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numba.targets.builtins.max_impl(context, builder, sig, args)
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numba.targets.builtins.min_impl(context, builder, sig, args)
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numba.targets.builtins.number_as_bool_impl(context, builder, sig, args)
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numba.targets.builtins.number_not_impl(context, builder, sig, args)
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numba.targets.builtins.real_abs_impl(context, builder, sig, args)
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numba.targets.builtins.real_add_impl(context, builder, sig, args)
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numba.targets.builtins.real_div_impl(context, builder, sig, args)
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numba.targets.builtins.real_divmod(context, builder, x, y)
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numba.targets.builtins.real_divmod_func_body(context, builder, vx, wx)
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numba.targets.builtins.real_eq_impl(context, builder, sig, args)
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numba.targets.builtins.real_floordiv_impl(context, builder, sig, args)
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numba.targets.builtins.real_ge_impl(context, builder, sig, args)
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numba.targets.builtins.real_gt_impl(context, builder, sig, args)
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numba.targets.builtins.real_le_impl(context, builder, sig, args)
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numba.targets.builtins.real_lt_impl(context, builder, sig, args)
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numba.targets.builtins.real_mod_impl(context, builder, sig, args)
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numba.targets.builtins.real_mul_impl(context, builder, sig, args)
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numba.targets.builtins.real_ne_impl(context, builder, sig, args)
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numba.targets.builtins.real_negate_impl(context, builder, sig, args)
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numba.targets.builtins.real_positive_impl(context, builder, sig, args)
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numba.targets.builtins.real_power_impl(context, builder, sig, args)
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numba.targets.builtins.real_sign_impl(context, builder, sig, args)
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numba.targets.builtins.real_sub_impl(context, builder, sig, args)
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numba.targets.builtins.round_impl_f32(context, builder, sig, args)
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numba.targets.builtins.round_impl_f64(context, builder, sig, args)
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numba.targets.builtins.slice0_empty_impl(context, builder, sig, args)
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numba.targets.builtins.slice0_none_none_impl(context, builder, sig, args)
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numba.targets.builtins.slice1_start_impl(context, builder, sig, args)
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numba.targets.builtins.slice1_stop_impl(context, builder, sig, args)
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numba.targets.builtins.slice2_impl(context, builder, sig, args)
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numba.targets.builtins.slice3_impl(context, builder, sig, args)
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numba.targets.builtins.uint_abs_impl(context, builder, sig, args)
numba.targets.cpu module
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class numba.targets.cpu.CPUContext(typing_context)
Bases: numba.targets.base.BaseContext
Changes BaseContext calling convention
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build_pass_manager()
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call_function(builder, callee, resty, argtys, args, env=None)
Call the Numba-compiled callee, using the same calling
convention as in get_function_type().
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declare_function(module, fndesc)
Override parent to handle get_env_argument
-
dynamic_map_function(func)
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finalize(func, fndesc)
Finalize the compilation. Called by get_executable().
- Rewrite intrinsics
- Fix div & rem instructions on 32bit platform
- Optimize python API calls
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get_abi_sizeof(lty)
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get_arguments(func)
Override parent to handle enviroment argument
Get the Python-level arguments of LLVM func.
See get_function_type() for the calling convention.
-
get_env_argument(func)
Get the environment argument of LLVM func (which can be
a declaration).
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get_env_body(builder, envptr)
From the given envptr (a pointer to a _dynfunc.Environment object),
get a EnvBody allowing structured access to environment fields.
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get_env_from_closure(builder, clo)
From the pointer clo to a _dynfunc.Closure, get a pointer
to the enclosed _dynfunc.Environment.
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get_executable(func, fndesc, env)
(cfunc, fnptr)
- cfunc
callable function (Can be None)
- fnptr
callable function address
- env
an execution environment (from _dynfunc)
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get_function_type(fndesc)
Get the implemented Function type for the high-level fndesc.
Some parameters can be added or shuffled around.
This is kept in sync with call_function() and get_arguments().
(Same return value convention as BaseContext target.)
Returns: -2 for return none in native function;
- -1 for failure with python exception set;
- 0 for success;
>0 for user error code.
Return value is passed by reference as the first argument.
The 2nd argument is a _dynfunc.Environment object.
It MUST NOT be used if the function is in nopython mode.
Actual arguments starts at the 3rd argument position.
Caller is responsible to allocate space for return value.
-
init()
-
map_math_functions()
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map_numpy_math_functions()
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optimize(module)
-
optimize_function(func)
Run O1 function passes
-
optimize_pythonapi(func)
-
prepare_for_call(func, fndesc, env)
-
remove_native_function(func)
Remove internal references to nonpython mode function func.
KeyError is raised if the function isn’t known to us.
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class numba.targets.cpu.CPUTargetOptions
Bases: numba.targets.options.TargetOptions
-
OPTIONS = {'forceobj': <type 'bool'>, 'boundcheck': <type 'bool'>, 'nopython': <type 'bool'>, 'looplift': <type 'bool'>, 'wraparound': <type 'bool'>}
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class numba.targets.cpu.ClosureBody(context, builder, value=None, ref=None, cast_ref=False)
Bases: numba.cgutils.Structure
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class numba.targets.cpu.EnvBody(context, builder, value=None, ref=None, cast_ref=False)
Bases: numba.cgutils.Structure
-
numba.targets.cpu.remove_null_refct_call(bb)
Remove refct api calls to NULL pointer
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numba.targets.cpu.remove_refct_calls(func)
Remove redundant incref/decref within on a per block basis
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numba.targets.cpu.remove_refct_pairs(bb)
Remove incref decref pairs on the same variable
numba.targets.descriptors module
Target Descriptors
-
class numba.targets.descriptors.TargetDescriptor
Bases: object
numba.targets.imputils module
Utilities to simplify the boilerplate for native lowering.
-
class numba.targets.imputils.Registry
Bases: object
-
register(item)
-
register_attr(item)
-
numba.targets.imputils.call_getiter(context, builder, iterable_type, val)
Call the getiter() implementation for the given iterable_type
of value val, and return the corresponding LLVM inst.
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numba.targets.imputils.call_iternext(context, builder, iterator_type, val)
Call the iternext() implementation for the given iterator_type
of value val, and return a convenience _IternextResult() object
reflecting the results.
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numba.targets.imputils.impl_attribute(ty, attr, rtype)
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numba.targets.imputils.impl_attribute_generic(ty)
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numba.targets.imputils.implement(func, *argtys)
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numba.targets.imputils.iterator_impl(iterable_type, iterator_type)
Decorator a given class as implementing iterator_type
(by providing an iternext() method).
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numba.targets.imputils.iternext_impl(func)
Wrap the given iternext() implementation so that it gets passed
an _IternextResult() object easing the returning of the iternext()
result pair.
- The wrapped function will be called with the following signature:
- (context, builder, sig, args, iternext_result)
-
numba.targets.imputils.python_attr_impl(cls, attr, atyp)
-
numba.targets.imputils.user_function(func, fndesc, libs)
numba.targets.intrinsics module
LLVM pass that converts intrinsic into other math calls
-
class numba.targets.intrinsics.DivmodFixer
Bases: object
Fix 64-bit div/mod on 32-bit machines
-
I64 = <llvm.core.IntegerType object at 0x106e62e90>
-
NAMES = ('sdiv', 'udiv', 'srem', 'urem')
-
declare(module, fname)
-
run(module)
-
run_on_func(func)
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class numba.targets.intrinsics.IntrinsicMapping(context, mapping=None, availintr=None)
Bases: object
-
apply_mapping(module)
-
run(module)
-
translate_intrinsic_to_cmath(module)
-
numba.targets.intrinsics.powi_as_pow(context, fn)
numba.targets.iterators module
Implementation of various iterable and iterator types.
-
numba.targets.iterators.iterator_getiter(context, builder, sig, args)
-
numba.targets.iterators.make_enumerate_cls(enum_type)
Return the Structure representation of the given enum_type (an
instance of types.EnumerateType).
-
numba.targets.iterators.make_enumerate_object(context, builder, sig, args)
-
numba.targets.iterators.make_zip_cls(zip_type)
Return the Structure representation of the given zip_type (an
instance of types.ZipType).
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numba.targets.iterators.make_zip_object(context, builder, sig, args)
numba.targets.mathimpl module
Provide math calls that uses intrinsics or libc math functions.
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numba.targets.mathimpl.atan2_f32_impl(context, builder, sig, args)
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numba.targets.mathimpl.atan2_f64_impl(context, builder, sig, args)
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numba.targets.mathimpl.atan2_s64_impl(context, builder, sig, args)
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numba.targets.mathimpl.atan2_u64_impl(context, builder, sig, args)
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numba.targets.mathimpl.degrees_f32_impl(context, builder, sig, args)
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numba.targets.mathimpl.degrees_f64_impl(context, builder, sig, args)
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numba.targets.mathimpl.hypot_f32_impl(context, builder, sig, args)
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numba.targets.mathimpl.hypot_f64_impl(context, builder, sig, args)
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numba.targets.mathimpl.hypot_s64_impl(context, builder, sig, args)
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numba.targets.mathimpl.hypot_u64_impl(context, builder, sig, args)
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numba.targets.mathimpl.isinf_f32_impl(context, builder, sig, args)
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numba.targets.mathimpl.isinf_f64_impl(context, builder, sig, args)
-
numba.targets.mathimpl.isinf_s64_impl(context, builder, sig, args)
-
numba.targets.mathimpl.isinf_u64_impl(context, builder, sig, args)
-
numba.targets.mathimpl.isnan_f32_impl(context, builder, sig, args)
-
numba.targets.mathimpl.isnan_f64_impl(context, builder, sig, args)
-
numba.targets.mathimpl.isnan_s64_impl(context, builder, sig, args)
-
numba.targets.mathimpl.isnan_u64_impl(context, builder, sig, args)
-
numba.targets.mathimpl.radians_f32_impl(context, builder, sig, args)
-
numba.targets.mathimpl.radians_f64_impl(context, builder, sig, args)
-
numba.targets.mathimpl.unary_math_extern(fn, f32extern, f64extern, int_restype=False)
Register implementations of Python function fn using the
external function named f32extern and f64extern (for float32
and float64 inputs, respectively).
If int_restype is true, then the function’s return value should be
integral, otherwise floating-point.
-
numba.targets.mathimpl.unary_math_int_impl(fn, f64impl)
-
numba.targets.mathimpl.unary_math_intr(fn, intrcode)
numba.targets.npdatetime module
Implementation of operations on numpy timedelta64.
-
numba.targets.npdatetime.add_constant(builder, val, const)
Add constant const to val.
-
numba.targets.npdatetime.alloc_boolean_result(builder, name='ret')
Allocate an uninitialized boolean result slot.
-
numba.targets.npdatetime.alloc_timedelta_result(builder, name='ret')
Allocate a NaT-initialized datetime64 (or timedelta64) result slot.
-
numba.targets.npdatetime.are_not_nat(builder, vals)
Return a predicate which is true if all of vals are not NaT.
-
numba.targets.npdatetime.convert_datetime_for_arith(builder, dt_val, src_unit, dest_unit)
Convert datetime dt_val from src_unit to dest_unit.
-
numba.targets.npdatetime.datetime_eq_datetime_impl(context, builder, sig, args)
-
numba.targets.npdatetime.datetime_ge_datetime_impl(context, builder, sig, args)
-
numba.targets.npdatetime.datetime_gt_datetime_impl(context, builder, sig, args)
-
numba.targets.npdatetime.datetime_le_datetime_impl(context, builder, sig, args)
-
numba.targets.npdatetime.datetime_lt_datetime_impl(context, builder, sig, args)
-
numba.targets.npdatetime.datetime_max_impl(context, builder, sig, args)
-
numba.targets.npdatetime.datetime_min_impl(context, builder, sig, args)
-
numba.targets.npdatetime.datetime_minus_datetime(context, builder, sig, args)
-
numba.targets.npdatetime.datetime_minus_timedelta(context, builder, sig, args)
-
numba.targets.npdatetime.datetime_ne_datetime_impl(context, builder, sig, args)
-
numba.targets.npdatetime.datetime_plus_timedelta(context, builder, sig, args)
-
numba.targets.npdatetime.func(context, builder, sig, args)
-
numba.targets.npdatetime.is_leap_year(builder, year_val)
Return a predicate indicating whether year_val (offset by 1970) is a
leap year.
-
numba.targets.npdatetime.is_not_nat(builder, val)
Return a predicate which is true if val is not NaT.
-
numba.targets.npdatetime.llvm_datetime_type(context, tp)
-
numba.targets.npdatetime.llvm_timedelta_type(context, tp)
-
numba.targets.npdatetime.make_constant_array(vals)
-
numba.targets.npdatetime.normalize_timedeltas(context, builder, left, right, leftty, rightty)
Scale either left or right to the other’s unit, in order to have
homogenous units.
-
numba.targets.npdatetime.number_times_timedelta(context, builder, sig, args)
-
numba.targets.npdatetime.reduce_datetime_for_unit(builder, dt_val, src_unit, dest_unit)
-
numba.targets.npdatetime.scale_by_constant(builder, val, factor)
Multiply val by the constant factor.
-
numba.targets.npdatetime.scale_timedelta(context, builder, val, srcty, destty)
Scale the timedelta64 val from srcty to destty
(both numba.types.NPTimedelta instances)
-
numba.targets.npdatetime.timedelta_abs_impl(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_add_impl(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_eq_timedelta_impl(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_ge_timedelta_impl(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_gt_timedelta_impl(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_le_timedelta_impl(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_lt_timedelta_impl(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_max_impl(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_min_impl(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_ne_timedelta_impl(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_neg_impl(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_over_number(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_over_timedelta(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_plus_datetime(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_pos_impl(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_sign_impl(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_sub_impl(context, builder, sig, args)
-
numba.targets.npdatetime.timedelta_times_number(context, builder, sig, args)
-
numba.targets.npdatetime.unscale_by_constant(builder, val, factor)
Divide val by the constant factor.
-
numba.targets.npdatetime.year_to_days(builder, year_val)
Given a year year_val (offset to 1970), return the number of days
since the 1970 epoch.
numba.targets.npyfuncs module
Codegen for functions used as kernels in NumPy functions
Typically, the kernels of several ufuncs that can’t map directly to
Python builtins
-
numba.targets.npyfuncs.np_complex_acos_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_acosh_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_asin_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_asinh_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_atan_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_atanh_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_conjugate_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_cos_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_cosh_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_div_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_eq_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_exp2_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_exp_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_expm1_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_floor_div_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_fmax_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_fmin_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_ge_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_gt_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_isfinite_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_isinf_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_isnan_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_le_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_log10_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_log1p_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_log2_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_log_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_logical_and_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_logical_not_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_logical_or_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_logical_xor_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_lt_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_maximum_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_minimum_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_ne_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_power_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_reciprocal_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_rint_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_sign_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_sin_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_sinh_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_sqrt_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_square_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_tan_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_complex_tanh_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_dummy_return_arg(context, builder, sig, args)
-
numba.targets.npyfuncs.np_int_fmod_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_int_power_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_int_reciprocal_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_int_sdiv_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_int_smax_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_int_smin_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_int_square_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_int_srem_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_int_truediv_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_int_udiv_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_int_umax_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_int_umin_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_int_urem_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_logical_and_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_logical_not_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_logical_or_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_logical_xor_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_acos_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_acosh_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_asin_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_asinh_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_atan2_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_atan_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_atanh_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_ceil_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_copysign_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_cos_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_cosh_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_deg2rad_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_div_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_exp2_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_exp_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_expm1_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_fabs_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_floor_div_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_floor_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_fmax_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_fmin_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_fmod_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_hypot_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_isfinite_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_isinf_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_isnan_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_ldexp_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_log10_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_log1p_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_log2_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_log_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_logaddexp2_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_logaddexp_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_maximum_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_minimum_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_mod_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_nextafter_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_power_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_rad2deg_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_reciprocal_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_rint_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_signbit_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_sin_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_sinh_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_spacing_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_sqrt_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_square_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_tan_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_tanh_impl(context, builder, sig, args)
-
numba.targets.npyfuncs.np_real_trunc_impl(context, builder, sig, args)
numba.targets.npyimpl module
-
class numba.targets.npyimpl.npy
This will be used as an index of the npy_* functions
-
numba.targets.npyimpl.numpy_ufunc_kernel(context, builder, sig, args, kernel_class, explicit_output=True)
-
numba.targets.npyimpl.register_binary_ufunc_kernel(ufunc, kernel)
-
numba.targets.npyimpl.register_unary_ufunc_kernel(ufunc, kernel)
numba.targets.operatorimpl module
Definition of implementations for the operator module.
numba.targets.options module
Target Options
-
class numba.targets.options.TargetOptions
Bases: object
-
OPTIONS = {}
-
from_dict(dic)
-
classmethod parse_as_flags(flags, options)
-
set_flags(flags)
Provide default flags setting logic.
Subclass can override.
numba.targets.printimpl module
This file implements print functionality for the CPU.
-
numba.targets.printimpl.int_print_impl(context, builder, sig, args)
-
numba.targets.printimpl.print_charseq(context, builder, sig, args)
-
numba.targets.printimpl.print_varargs(context, builder, sig, args)
-
numba.targets.printimpl.real_print_impl(context, builder, sig, args)
numba.targets.rangeobj module
Implementation of the range object for fixed-size integers.
-
numba.targets.rangeobj.make_range_impl(range_state_type, range_iter_type, int_type)
-
numba.targets.rangeobj.make_range_iterator(typ)
Return the Structure representation of the given typ (an
instance of types.RangeIteratorType).
numba.targets.registry module
-
class numba.targets.registry.CPUOverloaded(py_func, locals={}, targetoptions={})
Bases: numba.dispatcher.Overloaded
-
targetdescr = <numba.targets.registry.CPUTarget object at 0x108296c90>
-
class numba.targets.registry.CPUTarget
Bases: numba.targets.descriptors.TargetDescriptor
-
options
alias of CPUTargetOptions
-
target_context = <numba.targets.cpu.CPUContext object at 0x10828c610>
-
typing_context = <numba.typing.context.Context object at 0x10828c5d0>
-
class numba.targets.registry.TargetRegistry(*args, **kws)
Bases: numba.utils.UniqueDict
ondemand:
A dictionary of target-name -> function, where function is executed
the first time a target is used. It is used for deferred
initialization for some targets (e.g. gpu).
numba.targets.ufunc_db module
This file contains information on how to translate different ufuncs
into numba. It is a database of different ufuncs and how each of its
loops maps to a function that implements the inner kernel of that ufunc
(the inner kernel being the per-element function).
Use the function get_ufunc_info to get the information related to the
ufunc
-
numba.targets.ufunc_db.get_ufunc_info(ufunc_key)
get the lowering information for the ufunc with key ufunc_key.
The lowering information is a dictionary that maps from a numpy
loop string (as given by the ufunc types attribute) to a function
that handles code generation for a scalar version of the ufunc
(that is, generates the “per element” operation”).
raises a KeyError if the ufunc is not in the ufunc_db
-
numba.targets.ufunc_db.get_ufuncs()
obtain a list of supported ufuncs in the db
Module contents