optinsn_t Class Reference

Proxy of C++ optinsn_t class.

Inheritance diagram for optinsn_t:

Public Member Functions
"int"	func (self, *args)
	func(self, blk, ins, optflags) -> int Optimize an instruction.
"void"	install (self, *args)
	install(self)
"bool"	remove (self, *args)
	remove(self) -> bool
	__init__ (self, *args)
	init(self) -> optinsn_t
	__disown__ (self)

Properties
	thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")

Constructor & Destructor Documentation

__init__()

__init__	(		self,
		*	args )

Parameters

self	PyObject *

Member Function Documentation

__disown__()

__disown__

(

self

)

func()

"int" func	(		self,
		*	args )

Parameters

blk	(C++: mblock_t *) current basic block. maybe nullptr, which means that the instruction must be optimized without context
ins	(C++: minsn_t *) instruction to optimize; it is always a top-level instruction. the callback may not delete the instruction but may convert it into nop (see mblock_t.make_nop). to optimize sub-instructions, visit them using minsn_visitor_t. sub-instructions may not be converted into nop but can be converted to "mov x,x". for example: add x,0,x => mov x,x this callback may change other instructions in the block, but should do this with care, e.g. to no break the propagation algorithm if called with OPTI_NO_LDXOPT.
optflags	(C++: int) combination of optimization flags bits

Returns

: number of changes made to the instruction. if after this call the instruction's use/def lists have changed, you must mark the block level lists as dirty (see mark_lists_dirty)

install()

"void" install	(		self,
		*	args )

remove()

"bool" remove	(		self,
		*	args )

Property Documentation

thisown

thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")

static

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The documentation for this class was generated from the following file:

• ida_hexrays.py