# The Object Space

## Introduction

The object space creates all objects in PyPy, and knows how to perform operations on them. It may be helpful to think of an object space as being a library offering a fixed API: a set of operations, along with implementations that correspond to the known semantics of Python objects.

For example, `add()` is an operation, with implementations in the object space that perform numeric addition (when `add()` is operating on numbers), concatenation (when `add()` is operating on sequences), and so on.

We have some working object spaces which can be plugged into the bytecode interpreter:

- The Standard Object Space is a complete implementation of the various built-in types and objects of Python. The Standard Object Space, together with the bytecode interpreter, is the foundation of our Python implementation. Internally, it is a set of interpreter-level classes implementing the various application-level objects -- integers, strings, lists, types, etc. To draw a comparison with CPython, the Standard Object Space provides the equivalent of the C structures `PyIntObject`, `PyListObject`, etc.

- various Object Space proxies wrap another object space (e.g. the standard one) and adds new capabilities, like lazily computed objects (computed only when an operation is performed on them), security-checking objects, distributed objects living on several machines, etc.

The various object spaces documented here can be found in source pypy/objspace.

Note that most object-space operations take and return application-level objects, which are treated as opaque "black boxes" by the interpreter. Only a very few operations allow the bytecode interpreter to gain some knowledge about the value of an application-level object.

## Object Space Interface

This is the public API that all Object Spaces implement:

### Administrative Functions

getexecutioncontext()

  Return the currently active execution context. (source pypy/interpreter/executioncontext.py).

getbuiltinmodule(name)

  Return a `Module` object for the built-in module given by `name`. (source pypy/interpreter/module.py).

### Operations on Objects in the Object Space

These functions both take and return "wrapped" (i.e. application-level) objects.

The following functions implement operations with straightforward semantics that directly correspond to language-level constructs:

> `id, type, issubtype, iter, next, repr, str, len, hash,`

> `getattr, setattr, delattr, getitem, setitem, delitem,`

> `pos, neg, abs, invert, add, sub, mul, truediv, floordiv, div, mod, divmod, pow, lshift, rshift, and_, or_, xor,`

> `nonzero, hex, oct, int, float, long, ord,`

> `lt, le, eq, ne, gt, ge, cmp, coerce, contains,`

> `inplace_add, inplace_sub, inplace_mul, inplace_truediv, inplace_floordiv, inplace_div, inplace_mod, inplace_pow, inplace_lshift, inplace_rshift, inplace_and, inplace_or, inplace_xor,`

> `get, set, delete, userdel`

call(w_callable, w_args, w_kwds)

  Calls a function with the given positional (`w_args`) and keyword (`w_kwds`) arguments.

index(w_obj)

  Implements index lookup (as introduced in CPython 2.5) using `w_obj`. Will return a wrapped integer or long, or raise a `TypeError` if the object doesn't have an `__index__()` special method.

is_(w_x, w_y)

  Implements `w_x is w_y`.

isinstance(w_obj, w_type)

  Implements `issubtype()` with `type(w_obj)` and `w_type` as arguments.

### Convenience Functions

The following functions are used so often that it was worthwhile to introduce them as shortcuts -- however, they are not strictly necessary since they can be expressed using several other object space methods.

eq_w(w_obj1, w_obj2)

  Returns `True` when `w_obj1` and `w_obj2` are equal. Shortcut for `space.is_true(space.eq(w_obj1, w_obj2))`.

is_w(w_obj1, w_obj2)

  Shortcut for `space.is_true(space.is_(w_obj1, w_obj2))`.

hash_w(w_obj)

  Shortcut for `space.int_w(space.hash(w_obj))`.

len_w(w_obj)

  Shortcut for `space.int_w(space.len(w_obj))`.

NOTE that the above four functions return interpreter-level objects, not application-level ones!

not_(w_obj)

  Shortcut for `space.newbool(not space.is_true(w_obj))`.

finditem(w_obj, w_key)

  Equivalent to `getitem(w_obj, w_key)` but returns an interpreter-level None instead of raising a KeyError if the key is not found.

call_function(w_callable, *args_w, **kw_w)

  Collects the arguments in a wrapped tuple and dict and invokes `space.call(w_callable, ...)`.

call_method(w_object, 'method', ...)

  Uses `space.getattr()` to get the method object, and then `space.call_function()` to invoke it.

unpackiterable(w_iterable[, expected_length=-1])

  Iterates over `w_x` (using `space.iter()` and `space.next()`) and collects the resulting wrapped objects in a list. If `expected_length` is given and the length does not match, raises an exception.

  Of course, in cases where iterating directly is better than collecting the elements in a list first, you should use `space.iter()` and `space.next()` directly.

unpacktuple(w_tuple[, expected_length=None])

  Equivalent to `unpackiterable()`, but only for tuples.

callable(w_obj)

  Implements the built-in `callable()`.

### Creation of Application Level objects

wrap(x)

  Deprecated! Eventually this method should disappear. Returns a wrapped object that is a reference to the interpreter-level object `x`. This can be used either on simple immutable objects (integers, strings, etc.) to create a new wrapped object, or on instances of `W_Root` to obtain an application-level-visible reference to them. For example, most classes of the bytecode interpreter subclass `W_Root` and can be directly exposed to application-level code in this way - functions, frames, code objects, etc.

newint(i)

  Creates a wrapped object holding an integral value. `newint` creates an object of type `W_IntObject`.

newlong(l)

  Creates a wrapped object holding an integral value. The main difference to `newint` is the type of the argument (which is `rpython.rlib.rbigint.rbigint`). On PyPy3 this method will return an `int` (PyPy2 it returns a `long`).

newbytes(t)

  The given argument is a rpython bytestring. Creates a wrapped object of type `bytes` (both on PyPy2 and PyPy3).

newtext(t)

  The given argument is a rpython bytestring. Creates a wrapped object of type `str`. On PyPy3 this will return a wrapped unicode object. The object will hold a utf-8-nosg decoded value of t. The "utf-8-nosg" codec used here is slightly different from the "utf-8" implemented in Python 2 or Python 3: it is defined as utf-8 without any special handling of surrogate characters. They are encoded using the same three-bytes sequence that encodes any char in the range from `'\u0800'` to `'\uffff'`.

  PyPy2 will return a bytestring object. No encoding/decoding steps will be applied.

newbool(b)

  Creates a wrapped `bool` object from an interpreter-level object.

newtuple([w_x, w_y, w_z, ...])

  Creates a new wrapped tuple out of an interpreter-level list of wrapped objects.

newlist([..])

  Creates a wrapped `list` from an interpreter-level list of wrapped objects.

newdict()

  Returns a new empty dictionary.

newslice(w_start, w_end, w_step)

  Creates a new slice object.

newunicode(ustr)

  Creates a Unicode string from an rpython unicode string. This method may disappear soon and be replaced by :py:function:`newutf8`.

newutf8(bytestr)

  Creates a Unicode string from an rpython byte string, decoded as "utf-8-nosg". On PyPy3 it is the same as :py:function:`newtext`.

Many more space operations can be found in source pypy/interpreter/baseobjspace.py and source pypy/objspace/std/objspace.py.

### Conversions from Application Level to Interpreter Level

unwrap(w_x)

  Returns the interpreter-level equivalent of `w_x` -- use this ONLY for testing, because this method is not RPython and thus cannot be translated! In most circumstances you should use the functions described below instead.

is_true(w_x)

  Returns an interpreter-level boolean (`True` or `False`) that gives the truth value of the wrapped object `w_x`.

  This is a particularly important operation because it is necessary to implement, for example, if-statements in the language (or rather, to be pedantic, to implement the conditional-branching bytecodes into which if-statements are compiled).

int_w(w_x)

  If `w_x` is an application-level integer or long which can be converted without overflow to an integer, return an interpreter-level integer. Otherwise raise `TypeError` or `OverflowError`.

bigint_w(w_x)

  If `w_x` is an application-level integer or long, return an interpreter-level `rbigint`. Otherwise raise `TypeError`.

bytes_w(w_x)

  Takes an application level `bytes` (on PyPy2 this equals str) and returns a rpython byte string.

text_w(w_x)

  PyPy2 takes either a `str` and returns a rpython byte string, or it takes an `unicode` and uses the systems default encoding to return a rpython byte string.

str_w(w_x)

  Deprecated. use text_w or bytes_w instead If `w_x` is an application-level string, return an interpreter-level string. Otherwise raise `TypeError`.

unicode_w(w_x)

  Takes an application level `unicode` and return an interpreter-level unicode string. This method may disappear soon and be replaced by :py:function:`text_w`.

float_w(w_x)

  If `w_x` is an application-level float, integer or long, return an interpreter-level float. Otherwise raise `TypeError` (or:py:exc:OverflowError in the case of very large longs).

getindex_w(w_obj[, w_exception=None])

  Call `index(w_obj)`. If the resulting integer or long object can be converted to an interpreter-level `int`, return that. If not, return a clamped result if `w_exception` is None, otherwise raise the exception at the application level.

  (If `w_obj` can't be converted to an index, `index()` will raise an application-level `TypeError`.)

interp_w(RequiredClass, w_x[, can_be_None=False])

  If `w_x` is a wrapped instance of the given bytecode interpreter class, unwrap it and return it. If `can_be_None` is `True`, a wrapped `None` is also accepted and returns an interpreter-level `None`. Otherwise, raises an `OperationError` encapsulating a `TypeError` with a nice error message.

interpclass_w(w_x)

  If `w_x` is a wrapped instance of an bytecode interpreter class -- for example `Function`, `Frame`, `Cell`, etc. -- return it unwrapped. Otherwise return `None`.

### Data Members

space.builtin

  The `Module` containing the builtins.

space.sys

  The `sys` `Module`.

space.w_None

  The ObjSpace's instance of `None`.

space.w_True

  The ObjSpace's instance of `True`.

space.w_False

  The ObjSpace's instance of `False`.

space.w_Ellipsis

  The ObjSpace's instance of `Ellipsis`.

space.w_NotImplemented

  The ObjSpace's instance of `NotImplemented`.

space.w_int
space.w_float
space.w_long
space.w_tuple
space.w_str
space.w_unicode
space.w_type
space.w_instance
space.w_slice

  Python's most common basic type objects.

space.w_[XYZ]Error

  Python's built-in exception classes (`KeyError`, `IndexError`, etc).

ObjSpace.MethodTable

  List of tuples containing `(method_name, symbol, number_of_arguments, list_of_special_names)` for the regular part of the interface.

  NOTE that tuples are interpreter-level.

ObjSpace.BuiltinModuleTable

  List of names of built-in modules.

ObjSpace.ConstantTable

  List of names of the constants that the object space should define.

ObjSpace.ExceptionTable

  List of names of exception classes.

ObjSpace.IrregularOpTable

  List of names of methods that have an irregular API (take and/or return non-wrapped objects).

## The Standard Object Space

### Introduction

The Standard Object Space (source pypy/objspace/std/) is the direct equivalent of CPython's object library (the `Objects/` subdirectory in the distribution). It is an implementation of the common Python types in a lower-level language.

The Standard Object Space defines an abstract parent class, `W_Object` as well as subclasses like `W_IntObject`, `W_ListObject`, and so on. A wrapped object (a "black box" for the bytecode interpreter's main loop) is an instance of one of these classes. When the main loop invokes an operation (such as addition), between two wrapped objects `w1` and `w2`, the Standard Object Space does some internal dispatching (similar to `Object/abstract.c` in CPython) and invokes a method of the proper `W_XYZObject` class that can perform the operation.

The operation itself is done with the primitives allowed by RPython, and the result is constructed as a wrapped object. For example, compare the following implementation of integer addition with the function `int_add()` in `Object/intobject.c`:

    def add__Int_Int(space, w_int1, w_int2):
        x = w_int1.intval
        y = w_int2.intval
        try:
            z = ovfcheck(x + y)
        except OverflowError:
            raise FailedToImplementArgs(space.w_OverflowError,
                                    space.wrap("integer addition"))
        return W_IntObject(space, z)

This may seem like a lot of work just for integer objects (why wrap them into `W_IntObject` instances instead of using plain integers?), but the code is kept simple and readable by wrapping all objects (from simple integers to more complex types) in the same way.

(Interestingly, the obvious optimization above has actually been made in PyPy, but isn't hard-coded at this level -- see Standard Interpreter Optimizations.)

### Object types

The larger part of the source pypy/objspace/std/ package defines and implements the library of Python's standard built-in object types. Each type `xxx` (`int`, `float`, `list`, `tuple`, `str`, `type`, etc.) is typically implemented in the module `xxxobject.py`.

The `W_AbstractXxxObject` class, when present, is the abstract base class, which mainly defines what appears on the Python-level type object. There are then actual implementations as subclasses, which are called `W_XxxObject` or some variant for the cases where we have several different implementations. For example, source pypy/objspace/std/bytesobject.py defines `W_AbstractBytesObject`, which contains everything needed to build the `str` app-level type; and there are subclasses `W_BytesObject` (the usual string) and `W_Buffer` (a special implementation tweaked for repeated additions, in source pypy/objspace/std/bufferobject.py). For mutable data types like lists and dictionaries, we have a single class `W_ListObject` or `W_DictMultiObject` which has an indirection to the real data and a strategy; the strategy can change as the content of the object changes.

From the user's point of view, even when there are several `W_AbstractXxxObject` subclasses, this is not visible: at the app-level, they are still all instances of exactly the same Python type. PyPy knows that (e.g.) the application-level type of its interpreter-level `W_BytesObject` instances is str because there is a `typedef` class attribute in `W_BytesObject` which points back to the string type specification from source pypy/objspace/std/bytesobject.py; all other implementations of strings use the same `typedef` from source pypy/objspace/std/bytesobject.py.

For other examples of multiple implementations of the same Python type, see Standard Interpreter Optimizations.

## Object Space proxies

We have implemented several proxy object spaces, which wrap another object space (typically the standard one) and add some capabilities to all objects. To find out more, see Transparent Proxies (DEPRECATED).