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# Extending and Embedding the Python Interpreter
Guido van Rossum
Dept. AA, CWI, P.O. Box 94079
1090 GB Amsterdam, The Netherlands
E-mail: guido@cwi.nl
10 April 1995
Release 1.2
Copyright © 1991-1995 by Stichting Mathematisch Centrum,
Amsterdam, The Netherlands.
All Rights Reserved
Permission to use, copy, modify, and distribute this software and its
documentation for any purpose and without fee is hereby granted,
provided that the above copyright notice appear in all copies and that
both that copyright notice and this permission notice appear in
supporting documentation, and that the names of Stichting Mathematisch
Centrum or CWI not be used in advertising or publicity pertaining to
distribution of the software without specific, written prior permission.
STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO
THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE
FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
### Abstract:
Python is an interpreted, object-oriented programming language. This
document describes how to write modules in C or C++ to extend the
Python interpreter with new modules. Those modules can define new
functions but also new object types and their methods. The document
also describes how to embed the Python interpreter in another
application, for use as an extension language. Finally, it shows how
to compile and link extension modules so that they can be loaded
dynamically (at run time) into the interpreter, if the underlying
operating system supports this feature.
This document assumes basic knowledge about Python. For an informal
introduction to the language, see the Python Tutorial. The Python
Reference Manual gives a more formal definition of the language. The
Python Library Reference documents the existing object types,
functions and modules (both built-in and written in Python) that give
the language its wide application range.
---
- Contents (node1.html#SECTION00100000000000000000)
Extending Python with C or C++ code (node2.html#SECTION00200000000000000000)
- Introduction (node3.html#SECTION00210000000000000000)
A Simple Example (node4.html#SECTION00220000000000000000)
Intermezzo: Errors and Exceptions (node5.html#SECTION00230000000000000000)
Back to the Example (node6.html#SECTION00240000000000000000)
The Module's Method Table and Initialization Function (node7.html#SECTION00250000000000000000)
Compilation and Linkage (node8.html#SECTION00260000000000000000)
Calling Python Functions From C (node9.html#SECTION00270000000000000000)
Format Strings for PyArg_ParseTuple() (node10.html#SECTION00280000000000000000)
The Py_BuildValue() Function (node11.html#SECTION00290000000000000000)
Reference Counts (node12.html#SECTION002100000000000000000)
- Introduction (node13.html#SECTION002101000000000000000)
Reference Counting in Python (node14.html#SECTION002102000000000000000)
Ownership Rules (node15.html#SECTION002103000000000000000)
Thin Ice (node16.html#SECTION002104000000000000000)
NULL Pointers (node17.html#SECTION002105000000000000000)
Writing Extensions in C++ (node18.html#SECTION002110000000000000000)
Embedding Python in another application (node19.html#SECTION00300000000000000000)
- Embedding Python in C++ (node20.html#SECTION00310000000000000000)
Dynamic Loading (node21.html#SECTION00400000000000000000)
- Configuring and Building the Interpreter for Dynamic Loading (node22.html#SECTION00410000000000000000)
- Shared Libraries (node23.html#SECTION00411000000000000000)
SGI IRIX 4 Dynamic Loading (node24.html#SECTION00412000000000000000)
GNU Dynamic Loading (node25.html#SECTION00413000000000000000)
Building a Dynamically Loadable Module (node26.html#SECTION00420000000000000000)
- Shared Libraries (node27.html#SECTION00421000000000000000)
SGI IRIX 4 Dynamic Loading (node28.html#SECTION00422000000000000000)
GNU Dynamic Loading (node29.html#SECTION00423000000000000000)
About this document ... (node30.html#SECTION00500000000000000000)
---
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guido@cwi.nl


Footnotes

guido@cwi.nl


Contents

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## Contents
- Extending Python with C or C++ code (node2.html#SECTION00200000000000000000)
- Introduction (node3.html#SECTION00210000000000000000)
A Simple Example (node4.html#SECTION00220000000000000000)
Intermezzo: Errors and Exceptions (node5.html#SECTION00230000000000000000)
Back to the Example (node6.html#SECTION00240000000000000000)
The Module's Method Table and Initialization Function (node7.html#SECTION00250000000000000000)
Compilation and Linkage (node8.html#SECTION00260000000000000000)
Calling Python Functions From C (node9.html#SECTION00270000000000000000)
Format Strings for PyArg_ParseTuple() (node10.html#SECTION00280000000000000000)
The Py_BuildValue() Function (node11.html#SECTION00290000000000000000)
Reference Counts (node12.html#SECTION002100000000000000000)
- Introduction (node13.html#SECTION002101000000000000000)
Reference Counting in Python (node14.html#SECTION002102000000000000000)
Ownership Rules (node15.html#SECTION002103000000000000000)
Thin Ice (node16.html#SECTION002104000000000000000)
NULL Pointers (node17.html#SECTION002105000000000000000)
Writing Extensions in C++ (node18.html#SECTION002110000000000000000)
Embedding Python in another application (node19.html#SECTION00300000000000000000)
- Embedding Python in C++ (node20.html#SECTION00310000000000000000)
Dynamic Loading (node21.html#SECTION00400000000000000000)
- Configuring and Building the Interpreter for Dynamic Loading (node22.html#SECTION00410000000000000000)
- Shared Libraries (node23.html#SECTION00411000000000000000)
SGI IRIX 4 Dynamic Loading (node24.html#SECTION00412000000000000000)
GNU Dynamic Loading (node25.html#SECTION00413000000000000000)
Building a Dynamically Loadable Module (node26.html#SECTION00420000000000000000)
- Shared Libraries (node27.html#SECTION00421000000000000000)
SGI IRIX 4 Dynamic Loading (node28.html#SECTION00422000000000000000)
GNU Dynamic Loading (node29.html#SECTION00423000000000000000)
About this document ... (node30.html#SECTION00500000000000000000)
---
guido@cwi.nl


Format Strings for PyArg_ParseTuple()

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# Format Strings for PyArg_ParseTuple()
The `PyArg_ParseTuple()` function is declared as follows:
```text
int PyArg_ParseTuple(PyObject *arg, char *format, ...);
```
The arg argument must be a tuple object containing an argument
list passed from Python to a C function. The format argument
must be a format string, whose syntax is explained below. The
remaining arguments must be addresses of variables whose type is
determined by the format string. For the conversion to succeed, the
arg object must match the format and the format must be
exhausted.
Note that while `PyArg_ParseTuple()` checks that the Python
arguments have the required types, it cannot check the validity of the
addresses of C variables passed to the call: if you make mistakes
there, your code will probably crash or at least overwrite random bits
in memory. So be careful!
A format string consists of zero or more ``format units''. A format
unit describes one Python object; it is usually a single character or
a parenthesized sequence of format units. With a few exceptions, a
format unit that is not a parenthesized sequence normally corresponds
to a single address argument to `PyArg_ParseTuple()`. In the
following description, the quoted form is the format unit; the entry
in (round) parentheses is the Python object type that matches the
format unit; and the entry in [square] brackets is the type of the C
variable(s) whose address should be passed. (Use the `&'
operator to pass a variable's address.)
It is possible to pass Python long integers where integers are
requested; however no proper range checking is done -- the most
significant bits are silently truncated when the receiving field is
too small to receive the value (actually, the semantics are inherited
from downcasts in C --- your milage may vary).
A few other characters have a meaning in a format string. These may
not occur inside nested parentheses. They are:
Some example calls:
```text
int ok;
int i, j;
long k, l;
char *s;
int size;

ok = PyArg_ParseTuple(args, ""); /* No arguments */
/* Python call: f() */

ok = PyArg_ParseTuple(args, "s", &s); /* A string */
/* Possible Python call: f('whoops!') */

ok = PyArg_ParseTuple(args, "lls", &k, &l, &s); /* Two longs and a string */
/* Possible Python call: f(1, 2, 'three') */

ok = PyArg_ParseTuple(args, "(ii)s#", &i, &j, &s, &size);
/* A pair of ints and a string, whose size is also returned */
/* Possible Python call: f(1, 2, 'three') */

{
char *file;
char *mode = "r";
int bufsize = 0;
ok = PyArg_ParseTuple(args, "s|si", &file, &mode, &bufsize);
/* A string, and optionally another string and an integer */
/* Possible Python calls:
f('spam')
f('spam', 'w')
f('spam', 'wb', 100000) */
}

{
int left, top, right, bottom, h, v;
ok = PyArg_ParseTuple(args, "((ii)(ii))(ii)",
&left, &top, &right, &bottom, &h, &v);
/* A rectangle and a point */
/* Possible Python call:
f(((0, 0), (400, 300)), (10, 10)) */
}
```
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The Py_BuildValue() Function

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# The Py_BuildValue() Function
This function is the counterpart to `PyArg_ParseTuple()`. It is
declared as follows:
```text
PyObject *Py_BuildValue(char *format, ...);
```
It recognizes a set of format units similar to the ones recognized by
`PyArg_ParseTuple()`, but the arguments (which are input to the
function, not output) must not be pointers, just values. It returns a
new Python object, suitable for returning from a C function called
from Python.
One difference with `PyArg_ParseTuple()`: while the latter
requires its first argument to be a tuple (since Python argument lists
are always represented as tuples internally), `BuildValue()` does
not always build a tuple. It builds a tuple only if its format string
contains two or more format units. If the format string is empty, it
returns `None`; if it contains exactly one format unit, it
returns whatever object is described by that format unit. To force it
to return a tuple of size 0 or one, parenthesize the format string.
In the following description, the quoted form is the format unit; the
entry in (round) parentheses is the Python object type that the format
unit will return; and the entry in [square] brackets is the type of
the C value(s) to be passed.
The characters space, tab, colon and comma are ignored in format
strings (but not within format units such as `s#'). This can be
used to make long format strings a tad more readable.
If there is an error in the format string, the
`PyExc_SystemError` exception is raised and `NULL` returned.
Examples (to the left the call, to the right the resulting Python value):
```text
Py_BuildValue("") None
Py_BuildValue("i", 123) 123
Py_BuildValue("iii", 123, 456, 789) (123, 456, 789)
Py_BuildValue("s", "hello") 'hello'
Py_BuildValue("ss", "hello", "world") ('hello', 'world')
Py_BuildValue("s#", "hello", 4) 'hell'
Py_BuildValue("()") ()
Py_BuildValue("(i)", 123) (123,)
Py_BuildValue("(ii)", 123, 456) (123, 456)
Py_BuildValue("(i,i)", 123, 456) (123, 456)
Py_BuildValue("[i,i]", 123, 456) [123, 456]
Py_BuildValue("{s:i,s:i}",
"abc", 123, "def", 456) {'abc': 123, 'def': 456}
Py_BuildValue("((ii)(ii)) (ii)",
1, 2, 3, 4, 5, 6) (((1, 2), (3, 4)), (5, 6))
```
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Reference Counts

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# Reference Counts
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- Introduction (node13.html#SECTION002101000000000000000)
Reference Counting in Python (node14.html#SECTION002102000000000000000)
Ownership Rules (node15.html#SECTION002103000000000000000)
Thin Ice (node16.html#SECTION002104000000000000000)
NULL Pointers (node17.html#SECTION002105000000000000000)
---
guido@cwi.nl


Introduction

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## Introduction
In languages like C or C++, the programmer is responsible for
dynamic allocation and deallocation of memory on the heap. In C, this
is done using the functions `malloc()` and `free()`. In
C++, the operators `new` and `delete` are used with
essentially the same meaning; they are actually implemented using
`malloc()` and `free()`, so we'll restrict the following
discussion to the latter.
Every block of memory allocated with `malloc()` should eventually
be returned to the pool of available memory by exactly one call to
`free()`. It is important to call `free()` at the right
time. If a block's address is forgotten but `free()` is not
called for it, the memory it occupies cannot be reused until the
program terminates. This is called a memory leak. On the other
hand, if a program calls `free()` for a block and then continues
to use the block, it creates a conflict with re-use of the block
through another `malloc()` call. This is called using freed
memory has the same bad consequences as referencing uninitialized
data --- core dumps, wrong results, mysterious crashes.
Common causes of memory leaks are unusual paths through the code. For
instance, a function may allocate a block of memory, do some
calculation, and then free the block again. Now a change in the
requirements for the function may add a test to the calculation that
detects an error condition and can return prematurely from the
function. It's easy to forget to free the allocated memory block when
taking this premature exit, especially when it is added later to the
code. Such leaks, once introduced, often go undetected for a long
time: the error exit is taken only in a small fraction of all calls,
and most modern machines have plenty of virtual memory, so the leak
only becomes apparent in a long-running process that uses the leaking
function frequently. Therefore, it's important to prevent leaks from
happening by having a coding convention or strategy that minimizes
this kind of errors.
Since Python makes heavy use of malloc() and `free()`, it
needs a strategy to avoid memory leaks as well as the use of freed
memory. The chosen method is called `reference counting`. The
principle is simple: every object contains a counter, which is
incremented when a reference to the object is stored somewhere, and
which is decremented when a reference to it is deleted. When the
counter reaches zero, the last reference to the object has been
deleted and the object is freed.
An alternative strategy is called automatic garbage collection.
(Sometimes, reference counting is also referred to as a garbage
collection strategy, hence my use of ``automatic'' to distinguish the
two.) The big advantage of automatic garbage collection is that the
user doesn't need to call free() explicitly. (Another claimed
advantage is an improvement in speed or memory usage --- this is no
hard fact however.) The disadvantage is that for C, there is no
truly portable automatic garbage collector, while reference counting
can be implemented portably (as long as the functions `malloc()`
and `free()` are available --- which the C Standard guarantees).
Maybe some day a sufficiently portable automatic garbage collector
will be available for C. Until then, we'll have to live with
reference counts.
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Reference Counting in Python

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## Reference Counting in Python
There are two macros, `Py_INCREF(x)` and `Py_DECREF(x)`,
which handle the incrementing and decrementing of the reference count.
`Py_DECREF()` also frees the object when the count reaches zero.
For flexibility, it doesn't call `free()` directly --- rather, it
makes a call through a function pointer in the object's type
object. For this purpose (and others), every object also contains a
pointer to its type object.
The big question now remains: when to use Py_INCREF(x) and
`Py_DECREF(x)`? Let's first introduce some terms. Nobody
``owns'' an object; however, you can `own a reference` to an
object. An object's reference count is now defined as the number of
owned references to it. The owner of a reference is responsible for
calling Py_DECREF() when the reference is no longer needed.
Ownership of a reference can be transferred. There are three ways to
dispose of an owned reference: pass it on, store it, or call
`Py_DECREF()`. Forgetting to dispose of an owned reference creates
a memory leak.
It is also possible to `borrow`footnode.html#371 a reference to an object. The borrower
of a reference should not call Py_DECREF(). The borrower must
not hold on to the object longer than the owner from which it was
borrowed. Using a borrowed reference after the owner has disposed of
it risks using freed memory and should be avoided
completely.footnode.html#538
The advantage of borrowing over owning a reference is that you don't
need to take care of disposing of the reference on all possible paths
through the code --- in other words, with a borrowed reference you
don't run the risk of leaking when a premature exit is taken. The
disadvantage of borrowing over leaking is that there are some subtle
situations where in seemingly correct code a borrowed reference can be
used after the owner from which it was borrowed has in fact disposed
of it.
A borrowed reference can be changed into an owned reference by calling
`Py_INCREF()`. This does not affect the status of the owner from
which the reference was borrowed --- it creates a new owned reference,
and gives full owner responsibilities (i.e., the new owner must
dispose of the reference properly, as well as the previous owner).
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Ownership Rules

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## Ownership Rules
Whenever an object reference is passed into or out of a function, it
is part of the function's interface specification whether ownership is
transferred with the reference or not.
Most functions that return a reference to an object pass on ownership
with the reference. In particular, all functions whose function it is
to create a new object, e.g. `PyInt_FromLong()` and
`Py_BuildValue()`, pass ownership to the receiver. Even if in
fact, in some cases, you don't receive a reference to a brand new
object, you still receive ownership of the reference. For instance,
`PyInt_FromLong()` maintains a cache of popular values and can
return a reference to a cached item.
Many functions that extract objects from other objects also transfer
ownership with the reference, for instance
`PyObject_GetAttrString()`. The picture is less clear, here,
however, since a few common routines are exceptions:
`PyTuple_GetItem()`, `PyList_GetItem()` and
`PyDict_GetItem()` (and `PyDict_GetItemString()`) all return
references that you borrow from the tuple, list or dictionary.
The function `PyImport_AddModule()` also returns a borrowed
reference, even though it may actually create the object it returns:
this is possible because an owned reference to the object is stored in
`sys.modules`.
When you pass an object reference into another function, in general,
the function borrows the reference from you --- if it needs to store
it, it will use `Py_INCREF()` to become an independent owner.
There are exactly two important exceptions to this rule:
`PyTuple_SetItem()` and `PyList_SetItem()`. These functions
take over ownership of the item passed to them --- even if they fail!
(Note that `PyDict_SetItem()` and friends don't take over
ownership --- they are ``normal''.)
When a C function is called from Python, it borrows references to its
arguments from the caller. The caller owns a reference to the object,
so the borrowed reference's lifetime is guaranteed until the function
returns. Only when such a borrowed reference must be stored or passed
on, it must be turned into an owned reference by calling
`Py_INCREF()`.
The object reference returned from a C function that is called from
Python must be an owned reference --- ownership is tranferred from the
function to its caller.
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Thin Ice

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## Thin Ice
There are a few situations where seemingly harmless use of a borrowed
reference can lead to problems. These all have to do with implicit
invocations of the interpreter, which can cause the owner of a
reference to dispose of it.
The first and most important case to know about is using
`Py_DECREF()` on an unrelated object while borrowing a reference
to a list item. For instance:
```text
bug(PyObject *list) {
PyObject *item = PyList_GetItem(list, 0);
PyList_SetItem(list, 1, PyInt_FromLong(0L));
PyObject_Print(item, stdout, 0); /* BUG! */
}
```
This function first borrows a reference to `list[0]`, then
replaces `list[1]` with the value `0`, and finally prints
the borrowed reference. Looks harmless, right? But it's not!
Let's follow the control flow into `PyList_SetItem()`. The list
owns references to all its items, so when item 1 is replaced, it has
to dispose of the original item 1. Now let's suppose the original
item 1 was an instance of a user-defined class, and let's further
suppose that the class defined a `__del__()` method. If this
class instance has a reference count of 1, disposing of it will call
its `__del__()` method.
Since it is written in Python, the `__del__()` method can execute
arbitrary Python code. Could it perhaps do something to invalidate
the reference to `item` in `bug()`? You bet! Assuming that
the list passed into `bug()` is accessible to the
`__del__()` method, it could execute a statement to the effect of
`del list[0]`, and assuming this was the last reference to that
object, it would free the memory associated with it, thereby
invalidating `item`.
The solution, once you know the source of the problem, is easy:
temporarily increment the reference count. The correct version of the
function reads:
```text
no_bug(PyObject *list) {
PyObject *item = PyList_GetItem(list, 0);
Py_INCREF(item);
PyList_SetItem(list, 1, PyInt_FromLong(0L));
PyObject_Print(item, stdout, 0);
Py_DECREF(item);
}
```
This is a true story. An older version of Python contained variants
of this bug and someone spent a considerable amount of time in a C
debugger to figure out why his `__del__()` methods would fail...
The second case of problems with a borrowed reference is a variant
involving threads. Normally, multiple threads in the Python
interpreter can't get in each other's way, because there is a global
lock protecting Python's entire object space. However, it is possible
to temporarily release this lock using the macro
`Py_BEGIN_ALLOW_THREADS`, and to re-acquire it using
`Py_END_ALLOW_THREADS`. This is common around blocking I/O
calls, to let other threads use the CPU while waiting for the I/O to
complete. Obviously, the following function has the same problem as
the previous one:
```text
bug(PyObject *list) {
PyObject *item = PyList_GetItem(list, 0);
Py_BEGIN_ALLOW_THREADS
...some blocking I/O call...
Py_END_ALLOW_THREADS
PyObject_Print(item, stdout, 0); /* BUG! */
}
```
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NULL Pointers

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## NULL Pointers
In general, functions that take object references as arguments don't
expect you to pass them `NULL` pointers, and will dump core (or
cause later core dumps) if you do so. Functions that return object
references generally return `NULL` only to indicate that an
exception occurred. The reason for not testing for `NULL`
arguments is that functions often pass the objects they receive on to
other function --- if each function were to test for `NULL`,
there would be a lot of redundant tests and the code would run slower.
It is better to test for `NULL` only at the ``source'', i.e.\
when a pointer that may be `NULL` is received, e.g. from
`malloc()` or from a function that may raise an exception.
The macros `Py_INCREF()` and `Py_DECREF()`
don't check for `NULL` pointers --- however, their variants
`Py_XINCREF()` and `Py_XDECREF()` do.
The macros for checking for a particular object type
(`Py type _Check()`) don't check for `NULL` pointers ---
again, there is much code that calls several of these in a row to test
an object against various different expected types, and this would
generate redundant tests. There are no variants with `NULL`
checking.
The C function calling mechanism guarantees that the argument list
passed to C functions (`args` in the examples) is never
`NULL` --- in fact it guarantees that it is always a tuple.
footnode.html#427
It is a severe error to ever let a `NULL` pointer ``escape'' to
the Python user.
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Writing Extensions in C++

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# Writing Extensions in C++
It is possible to write extension modules in C++. Some restrictions
apply: since the main program (the Python interpreter) is compiled and
linked by the C compiler, global or static objects with constructors
cannot be used. All functions that will be called directly or
indirectly (i.e. via function pointers) by the Python interpreter will
have to be declared using `extern "C"`; this applies to all
``methods'' as well as to the module's initialization function.
It is unnecessary to enclose the Python header files in
`extern "C" {...}` --- they use this form already if the symbol
`__cplusplus' is defined (all recent C++ compilers define this
symbol).
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Embedding Python in another application

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# Embedding Python in another application
Embedding Python is similar to extending it, but not quite. The
difference is that when you extend Python, the main program of the
application is still the Python interpreter, while if you embed
Python, the main program may have nothing to do with Python ---
instead, some parts of the application occasionally call the Python
interpreter to run some Python code.
So if you are embedding Python, you are providing your own main
program. One of the things this main program has to do is initialize
the Python interpreter. At the very least, you have to call the
function `Py_Initialize()`. There are optional calls to pass command
line arguments to Python. Then later you can call the interpreter
from any part of the application.
There are several different ways to call the interpreter: you can pass
a string containing Python statements to `PyRun_SimpleString()`,
or you can pass a stdio file pointer and a file name (for
identification in error messages only) to `PyRun_SimpleFile()`. You
can also call the lower-level operations described in the previous
chapters to construct and use Python objects.
A simple demo of embedding Python can be found in the directory
``Demo/embed`'.
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- Embedding Python in C++ (node20.html#SECTION00310000000000000000)
---
guido@cwi.nl


Extending Python with C or C++ code

---
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---
# Extending Python with C or C++ code
---
- Introduction (node3.html#SECTION00210000000000000000)
A Simple Example (node4.html#SECTION00220000000000000000)
Intermezzo: Errors and Exceptions (node5.html#SECTION00230000000000000000)
Back to the Example (node6.html#SECTION00240000000000000000)
The Module's Method Table and Initialization Function (node7.html#SECTION00250000000000000000)
Compilation and Linkage (node8.html#SECTION00260000000000000000)
Calling Python Functions From C (node9.html#SECTION00270000000000000000)
Format Strings for PyArg_ParseTuple() (node10.html#SECTION00280000000000000000)
The Py_BuildValue() Function (node11.html#SECTION00290000000000000000)
Reference Counts (node12.html#SECTION002100000000000000000)
- Introduction (node13.html#SECTION002101000000000000000)
Reference Counting in Python (node14.html#SECTION002102000000000000000)
Ownership Rules (node15.html#SECTION002103000000000000000)
Thin Ice (node16.html#SECTION002104000000000000000)
NULL Pointers (node17.html#SECTION002105000000000000000)
Writing Extensions in C++ (node18.html#SECTION002110000000000000000)
---
guido@cwi.nl


Embedding Python in C++

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# Embedding Python in C++
It is also possible to embed Python in a C++ program; precisely how this
is done will depend on the details of the C++ system used; in general you
will need to write the main program in C++, and use the C++ compiler
to compile and link your program. There is no need to recompile Python
itself using C++.
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Dynamic Loading

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# Dynamic Loading
On most modern systems it is possible to configure Python to support
dynamic loading of extension modules implemented in C. When shared
libraries are used dynamic loading is configured automatically;
otherwise you have to select it as a build option (see below). Once
configured, dynamic loading is trivial to use: when a Python program
executes `import spam`, the search for modules tries to find a
file ``spammodule.o`' (``spammodule.so`' when using shared
libraries) in the module search path, and if one is found, it is
loaded into the executing binary and executed. Once loaded, the
module acts just like a built-in extension module.
The advantages of dynamic loading are twofold: the ``core'' Python
binary gets smaller, and users can extend Python with their own
modules implemented in C without having to build and maintain their
own copy of the Python interpreter. There are also disadvantages:
dynamic loading isn't available on all systems (this just means that
on some systems you have to use static loading), and dynamically
loading a module that was compiled for a different version of Python
(e.g. with a different representation of objects) may dump core.
---
- Configuring and Building the Interpreter for Dynamic Loading (node22.html#SECTION00410000000000000000)
- Shared Libraries (node23.html#SECTION00411000000000000000)
SGI IRIX 4 Dynamic Loading (node24.html#SECTION00412000000000000000)
GNU Dynamic Loading (node25.html#SECTION00413000000000000000)
Building a Dynamically Loadable Module (node26.html#SECTION00420000000000000000)
- Shared Libraries (node27.html#SECTION00421000000000000000)
SGI IRIX 4 Dynamic Loading (node28.html#SECTION00422000000000000000)
GNU Dynamic Loading (node29.html#SECTION00423000000000000000)
---
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# Configuring and Building the Interpreter for Dynamic Loading
There are three styles of dynamic loading: one using shared libraries,
one using SGI IRIX 4 dynamic loading, and one using GNU dynamic
loading.
---
- Shared Libraries (node23.html#SECTION00411000000000000000)
SGI IRIX 4 Dynamic Loading (node24.html#SECTION00412000000000000000)
GNU Dynamic Loading (node25.html#SECTION00413000000000000000)
---
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Shared Libraries

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## Shared Libraries
The following systems support dynamic loading using shared libraries:
SunOS 4; Solaris 2; SGI IRIX 5 (but not SGI IRIX 4!); and probably all
systems derived from SVR4, or at least those SVR4 derivatives that
support shared libraries (are there any that don't?).
You don't need to do anything to configure dynamic loading on these
systems --- the ``configure`' detects the presence of the
``<dlfcn.h>`' header file and automatically configures dynamic
loading.
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SGI IRIX 4 Dynamic Loading

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## SGI IRIX 4 Dynamic Loading
Only SGI IRIX 4 supports dynamic loading of modules using SGI dynamic
loading. (SGI IRIX 5 might also support it but it is inferior to
using shared libraries so there is no reason to; a small test didn't
work right away so I gave up trying to support it.)
Before you build Python, you first need to fetch and build the `dl`
package written by Jack Jansen. This is available by anonymous ftp
from host ``ftp.cwi.nl`', directory ``pub/dynload`', file
``dl-1.6.tar.Z`'. (The version number may change.) Follow the
instructions in the package's ``README`' file to build it.
Once you have built `dl`, you can configure Python to use it. To
this end, you run the ``configure`' script with the option
`--with-dl= directory` where directory is the absolute
pathname of the `dl` directory.
Now build and install Python as you normally would (see the
``README`' file in the toplevel Python directory.)
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GNU Dynamic Loading

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## GNU Dynamic Loading
GNU dynamic loading supports (according to its ``README`' file) the
following hardware and software combinations: VAX (Ultrix), Sun 3
(SunOS 3.4 and 4.0), Sparc (SunOS 4.0), Sequent Symmetry (Dynix), and
Atari ST. There is no reason to use it on a Sparc; I haven't seen a
Sun 3 for years so I don't know if these have shared libraries or not.
You need to fetch and build two packages. One is GNU DLD 3.2.3,
available by anonymous ftp from host ``ftp.cwi.nl`', directory
``pub/dynload`', file ``dld-3.2.3.tar.Z`'. (As far as I know,
no further development on GNU DLD is being done.) The other is an
emulation of Jack Jansen's `dl` package that I wrote on top of
GNU DLD 3.2.3. This is available from the same host and directory,
file dl-dld-1.1.tar.Z. (The version number may change --- but I doubt
it will.) Follow the instructions in each package's ``README`'
file to configure build them.
Now configure Python. Run the ``configure`' script with the option
`--with-dl-dld= dl-directory , dld-directory` where
dl-directory is the absolute pathname of the directory where you
have built the ``dl-dld`' package, and dld-directory is that
of the GNU DLD package. The Python interpreter you build hereafter
will support GNU dynamic loading.
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Building a Dynamically Loadable Module

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# Building a Dynamically Loadable Module
Since there are three styles of dynamic loading, there are also three
groups of instructions for building a dynamically loadable module.
Instructions common for all three styles are given first. Assuming
your module is called `spam`, the source filename must be
``spammodule.c`', so the object name is ``spammodule.o`'. The
module must be written as a normal Python extension module (as
described earlier).
Note that in all cases you will have to create your own Makefile that
compiles your module file(s). This Makefile will have to pass two
`-I' arguments to the C compiler which will make it find the
Python header files. If the Make variable PYTHONTOP points to
the toplevel Python directory, your CFLAGS Make variable should
contain the options `-I$(PYTHONTOP) -I$(PYTHONTOP)/Include'.
(Most header files are in the ``Include`' subdirectory, but the
``config.h`' header lives in the toplevel directory.) You must
also add `-DHAVE_CONFIG_H' to the definition of CFLAGS to
direct the Python headers to include ``config.h`'.
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SGI IRIX 4 Dynamic Loading (node28.html#SECTION00422000000000000000)
GNU Dynamic Loading (node29.html#SECTION00423000000000000000)
---
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Shared Libraries

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## Shared Libraries
You must link the `.o' file to produce a shared library. This is
done using a special invocation of the Unix loader/linker,
ld(1). Unfortunately the invocation differs slightly per system.
On SunOS 4, use
```text
ld spammodule.o -o spammodule.so
```
On Solaris 2, use
```text
ld -G spammodule.o -o spammodule.so
```
On SGI IRIX 5, use
```text
ld -shared spammodule.o -o spammodule.so
```
On other systems, consult the manual page for `ld`(1) to find what
flags, if any, must be used.
If your extension module uses system libraries that haven't already
been linked with Python (e.g. a windowing system), these must be
passed to the `ld` command as `-l' options after the
`.o' file.
The resulting file ``spammodule.so`' must be copied into a directory
along the Python module search path.
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SGI IRIX 4 Dynamic Loading

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## SGI IRIX 4 Dynamic Loading
IMPORTANT: You must compile your extension module with the
additional C flag `-G0' (or `-G 0'). This instruct the
assembler to generate position-independent code.
You don't need to link the resulting ``spammodule.o`' file; just
copy it into a directory along the Python module search path.
The first time your extension is loaded, it takes some extra time and
a few messages may be printed. This creates a file
``spammodule.ld`' which is an image that can be loaded quickly into
the Python interpreter process. When a new Python interpreter is
installed, the `dl` package detects this and rebuilds
``spammodule.ld`'. The file ``spammodule.ld`' is placed in the
directory where ``spammodule.o`' was found, unless this directory is
unwritable; in that case it is placed in a temporary
directory.footnode.html#544
If your extension modules uses additional system libraries, you must
create a file ``spammodule.libs`' in the same directory as the
``spammodule.o`'. This file should contain one or more lines with
whitespace-separated options that will be passed to the linker ---
normally only `-l' options or absolute pathnames of libraries
(`.a' files) should be used.
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GNU Dynamic Loading

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## GNU Dynamic Loading
Just copy ``spammodule.o`' into a directory along the Python module
search path.
If your extension modules uses additional system libraries, you must
create a file ``spammodule.libs`' in the same directory as the
``spammodule.o`'. This file should contain one or more lines with
whitespace-separated absolute pathnames of libraries (`.a'
files). No `-l' options can be used.
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Introduction

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# Introduction
It is quite easy to add new built-in modules to Python, if you know
how to program in C. Such extension modules can do two things
that can't be done directly in Python: they can implement new built-in
object types, and they can call C library functions and system calls.
To support extensions, the Python API (Application Programmers
Interface) defines a set of functions, macros and variables that
provide access to most aspects of the Python run-time system. The
Python API is incorporated in a C source file by including the header
`"Python.h"`.
The compilation of an extension module depends on its intended use as
well as on your system setup; details are given in a later section.
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About this document ...

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# About this document ...
Extending and Embedding the Python Interpreter
This document was generated using the LaTeX2HTML (http://cbl.leeds.ac.uk/nikos/tex2html/doc/latex2html/latex2html.html) translator Version 95.1 (Fri Jan 20 1995) Copyright © 1993, 1994, Nikos Drakos (http://cbl.leeds.ac.uk/nikos/personal.html), Computer Based Learning Unit, University of Leeds.
The command line arguments were:
latex2html -address guido@cwi.nl -dont_include myformat ext.tex.
The translation was initiated by Guido van Rossum on Tue Apr 11 00:22:00 MDT 1995
---
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A Simple Example

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# A Simple Example
Let's create an extension module called `spam' (the favorite food
of Monty Python fans...) and let's say we want to create a Python
interface to the C library function `system()`.footnode.html#522
This function takes a null-terminated character string as argument and
returns an integer. We want this function to be callable from Python
as follows:
```text
>>> import spam
>>> status = spam.system("ls -l")
```
Begin by creating a file `spammodule.c'. (In general, if a
module is called `spam', the C file containing its implementation
is called ``spammodule.c`'; if the module name is very long, like
`spammify', the module name can be just ``spammify.c`'.)
The first line of our file can be:
```text
#include "Python.h"
```
which pulls in the Python API (you can add a comment describing the
purpose of the module and a copyright notice if you like).
All user-visible symbols defined by `"Python.h"` have a prefix of
`Py' or `PY', except those defined in standard header files.
For convenience, and since they are used extensively by the Python
interpreter, `"Python.h"` includes a few standard header files:
`<stdio.h>`, `<string.h>`, `<errno.h>`, and
`<stdlib.h>`. If the latter header file does not exist on your
system, it declares the functions `malloc()`, `free()` and
`realloc()` directly.
The next thing we add to our module file is the C function that will
be called when the Python expression `spam.system(string)'
is evaluated (we'll see shortly how it ends up being called):
```text
static PyObject *
spam_system(self, args)
PyObject *self;
PyObject *args;
{
char *command;
int sts;
if (!PyArg_ParseTuple(args, "s", &command))
return NULL;
sts = system(command);
return Py_BuildValue("i", sts);
}
```
There is a straightforward translation from the argument list in
Python (e.g. the single expression `"ls -l"`) to the arguments
passed to the C function. The C function always has two arguments,
conventionally named self and args.
The self argument is only used when the C function implements a
builtin method. This will be discussed later. In the example,
self will always be a `NULL` pointer, since we are defining
a function, not a method. (This is done so that the interpreter
doesn't have to understand two different types of C functions.)
The args argument will be a pointer to a Python tuple object
containing the arguments. Each item of the tuple corresponds to an
argument in the call's argument list. The arguments are Python
objects -- in order to do anything with them in our C function we have
to convert them to C values. The function `PyArg_ParseTuple()`
in the Python API checks the argument types and converts them to C
values. It uses a template string to determine the required types of
the arguments as well as the types of the C variables into which to
store the converted values. More about this later.
`PyArg_ParseTuple()` returns true (nonzero) if all arguments have
the right type and its components have been stored in the variables
whose addresses are passed. It returns false (zero) if an invalid
argument list was passed. In the latter case it also raises an
appropriate exception by so the calling function can return
`NULL` immediately (as we saw in the example).
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Intermezzo: Errors and Exceptions

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# Intermezzo: Errors and Exceptions
An important convention throughout the Python interpreter is the
following: when a function fails, it should set an exception condition
and return an error value (usually a `NULL` pointer). Exceptions
are stored in a static global variable inside the interpreter; if this
variable is `NULL` no exception has occurred. A second global
variable stores the ``associated value'' of the exception (the second
argument to `raise`). A third variable contains the stack
traceback in case the error originated in Python code. These three
variables are the C equivalents of the Python variables
`sys.exc_type`, `sys.exc_value` and `sys.exc_traceback`
(see the section on module `sys` in the Library Reference
Manual). It is important to know about them to understand how errors
are passed around.
The Python API defines a number of functions to set various types of
exceptions.
The most common one is `PyErr_SetString()`. Its arguments are an
exception object and a C string. The exception object is usually a
predefined object like `PyExc_ZeroDivisionError`. The C string
indicates the cause of the error and is converted to a Python string
object and stored as the ``associated value'' of the exception.
Another useful function is `PyErr_SetFromErrno()`, which only
takes an exception argument and constructs the associated value by
inspection of the (Unix) global variable `errno`. The most
general function is `PyErr_SetObject()`, which takes two object
arguments, the exception and its associated value. You don't need to
`Py_INCREF()` the objects passed to any of these functions.
You can test non-destructively whether an exception has been set with
`PyErr_Occurred()`. This returns the current exception object,
or `NULL` if no exception has occurred. You normally don't need
to call `PyErr_Occurred()` to see whether an error occurred in a
function call, since you should be able to tell from the return value.
When a function f that calls another function varg detects
that the latter fails, f should itself return an error value
(e.g. `NULL` or `-1`). It should not call one of the
`PyErr_*()` functions --- one has already been called by g.
f's caller is then supposed to also return an error indication
to its caller, again without calling `PyErr_*()`,
and so on --- the most detailed cause of the error was already
reported by the function that first detected it. Once the error
reaches the Python interpreter's main loop, this aborts the currently
executing Python code and tries to find an exception handler specified
by the Python programmer.
(There are situations where a module can actually give a more detailed
error message by calling another `PyErr_*()` function, and in
such cases it is fine to do so. As a general rule, however, this is
not necessary, and can cause information about the cause of the error
to be lost: most operations can fail for a variety of reasons.)
To ignore an exception set by a function call that failed, the exception
condition must be cleared explicitly by calling `PyErr_Clear()`.
The only time C code should call `PyErr_Clear()` is if it doesn't
want to pass the error on to the interpreter but wants to handle it
completely by itself (e.g. by trying something else or pretending
nothing happened).
Note that a failing `malloc()` call must be turned into an
exception --- the direct caller of `malloc()` (or
`realloc()`) must call `PyErr_NoMemory()` and return a
failure indicator itself. All the object-creating functions
(`PyInt_FromLong()` etc.) already do this, so only if you call
`malloc()` directly this note is of importance.
Also note that, with the important exception of
`PyArg_ParseTuple()` and friends, functions that return an
integer status usually return a positive value or zero for success and
`-1` for failure, like Unix system calls.
Finally, be careful to clean up garbage (by making `Py_XDECREF()`
or `Py_DECREF()` calls for objects you have already created) when
you return an error indicator!
The choice of which exception to raise is entirely yours. There are
predeclared C objects corresponding to all built-in Python exceptions,
e.g. `PyExc_ZeroDevisionError` which you can use directly. Of
course, you should choose exceptions wisely --- don't use
`PyExc_TypeError` to mean that a file couldn't be opened (that
should probably be `PyExc_IOError`). If something's wrong with
the argument list, the `PyArg_ParseTuple()` function usually
raises `PyExc_TypeError`. If you have an argument whose value
which must be in a particular range or must satisfy other conditions,
`PyExc_ValueError` is appropriate.
You can also define a new exception that is unique to your module.
For this, you usually declare a static object variable at the
beginning of your file, e.g.
```text
static PyObject *SpamError;
```
and initialize it in your module's initialization function
(`initspam()`) with a string object, e.g. (leaving out the error
checking for now):
```text
void
initspam()
{
PyObject *m, *d;
m = Py_InitModule("spam", SpamMethods);
d = PyModule_GetDict(m);
SpamError = PyString_FromString("spam.error");
PyDict_SetItemString(d, "error", SpamError);
}
```
Note that the Python name for the exception object is
`spam.error`. It is conventional for module and exception names
to be spelled in lower case. It is also conventional that the
value of the exception object is the same as its name, e.g.\
the string `"spam.error"`.
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Back to the Example

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# Back to the Example
Going back to our example function, you should now be able to
understand this statement:
```text
if (!PyArg_ParseTuple(args, "s", &command))
return NULL;
```
It returns `NULL` (the error indicator for functions returning
object pointers) if an error is detected in the argument list, relying
on the exception set by `PyArg_ParseTuple()`. Otherwise the
string value of the argument has been copied to the local variable
`command`. This is a pointer assignment and you are not supposed
to modify the string to which it points (so in Standard C, the variable
`command` should properly be declared as const char
*command).
The next statement is a call to the ' function `system()`,
passing it the string we just got from `PyArg_ParseTuple()`:
```text
sts = system(command);
```
Our `spam.system()` function must return the value of `sys`
as a Python object. This is done using the function
`Py_BuildValue()`, which is something like the inverse of
`PyArg_ParseTuple()`: it takes a format string and an arbitrary
number of C values, and returns a new Python object. More info on
`Py_BuildValue()` is given later.
```text
return Py_BuildValue("i", sts);
```
In this case, it will return an integer object. (Yes, even integers
are objects on the heap in Python!)
If you have a C function that returns no useful argument (a function
returning `void`), the corresponding Python function must return
`None`. You need this idiom to do so:
```text
Py_INCREF(Py_None);
return Py_None;
```
`Py_None` is the C name for the special Python object
`None`. It is a genuine Python object (not a `NULL`
pointer, which means ``error'' in most contexts, as we have seen).
---
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# The Module's Method Table and Initialization Function
I promised to show how `spam_system()` is called from Python
programs. First, we need to list its name and address in a ``method
table'':
```text
static PyMethodDef SpamMethods[] = {
...
{"system", spam_system, 1},
...
{NULL, NULL} /* Sentinel */
};
```
Note the third entry (`1'). This is a flag telling the
interpreter the calling convention to be used for the C function. It
should normally always be `1'; a value of `0' means that an
obsolete variant of `PyArg_ParseTuple()` is used.
The method table must be passed to the interpreter in the module's
initialization function (which should be the only non-`static`
item defined in the module file):
```text
void
initspam()
{
(void) Py_InitModule("spam", SpamMethods);
}
```
When the Python program imports module `spam` for the first time,
`initspam()` is called. It calls `Py_InitModule()`, which
creates a ``module object'' (which is inserted in the dictionary
`sys.modules` under the key `"spam"`), and inserts built-in
function objects into the newly created module based upon the table
(an array of `PyMethodDef` structures) that was passed as its
second argument. `Py_InitModule()` returns a pointer to the
module object that it creates (which is unused here). It aborts with
a fatal error if the module could not be initialized satisfactorily,
so the caller doesn't need to check for errors.
---
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Compilation and Linkage

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# Compilation and Linkage
There are two more things to do before you can use your new extension:
compiling and linking it with the Python system. If you use dynamic
loading, the details depend on the style of dynamic loading your
system uses; see the chapter on Dynamic Loading for more info about
this.
If you can't use dynamic loading, or if you want to make your module a
permanent part of the Python interpreter, you will have to change the
configuration setup and rebuild the interpreter. Luckily, this is
very simple: just place your file (``spammodule.c`' for example) in
the ``Modules`' directory, add a line to the file
``Modules/Setup`' describing your file:
```text
spam spammodule.o
```
and rebuild the interpreter by running `make` in the toplevel
directory. You can also run `make` in the ``Modules`'
subdirectory, but then you must first rebuilt the ``Makefile`'
there by running `make Makefile`. (This is necessary each time
you change the ``Setup`' file.)
If your module requires additional libraries to link with, these can
be listed on the line in the ``Setup`' file as well, for instance:
```text
spam spammodule.o -lX11
```
---
guido@cwi.nl


Calling Python Functions From C

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# Calling Python Functions From C
So far we have concentrated on making C functions callable from
Python. The reverse is also useful: calling Python functions from C.
This is especially the case for libraries that support so-called
``callback'' functions. If a C interface makes use of callbacks, the
equivalent Python often needs to provide a callback mechanism to the
Python programmer; the implementation will require calling the Python
callback functions from a C callback. Other uses are also imaginable.
Fortunately, the Python interpreter is easily called recursively, and
there is a standard interface to call a Python function. (I won't
dwell on how to call the Python parser with a particular string as
input --- if you're interested, have a look at the implementation of
the `-c' command line option in ``Python/pythonmain.c`'.)
Calling a Python function is easy. First, the Python program must
somehow pass you the Python function object. You should provide a
function (or some other interface) to do this. When this function is
called, save a pointer to the Python function object (be careful to
`Py_INCREF()` it!) in a global variable --- or whereever you see fit.
For example, the following function might be part of a module
definition:
```text
static PyObject *my_callback = NULL;

static PyObject *
my_set_callback(dummy, arg)
PyObject *dummy, *arg;
{
Py_XDECREF(my_callback); /* Dispose of previous callback */
Py_XINCREF(arg); /* Add a reference to new callback */
my_callback = arg; /* Remember new callback */
/* Boilerplate to return "None" */
Py_INCREF(Py_None);
return Py_None;
}
```
The macros `Py_XINCREF()` and `Py_XDECREF()` increment/decrement
the reference count of an object and are safe in the presence of
`NULL` pointers. More info on them in the section on Reference
Counts below.
Later, when it is time to call the function, you call the C function
`PyEval_CallObject()`. This function has two arguments, both
pointers to arbitrary Python objects: the Python function, and the
argument list. The argument list must always be a tuple object, whose
length is the number of arguments. To call the Python function with
no arguments, pass an empty tuple; to call it with one argument, pass
a singleton tuple. `Py_BuildValue()` returns a tuple when its
format string consists of zero or more format codes between
parentheses. For example:
```text
int arg;
PyObject *arglist;
PyObject *result;
...
arg = 123;
...
/* Time to call the callback */
arglist = Py_BuildValue("(i)", arg);
result = PyEval_CallObject(my_callback, arglist);
Py_DECREF(arglist);
```
`PyEval_CallObject()` returns a Python object pointer: this is
the return value of the Python function. `PyEval_CallObject()` is
``reference-count-neutral'' with respect to its arguments. In the
example a new tuple was created to serve as the argument list, which
is `Py_DECREF()`-ed immediately after the call.
The return value of `PyEval_CallObject()` is ``new'': either it
is a brand new object, or it is an existing object whose reference
count has been incremented. So, unless you want to save it in a
global variable, you should somehow `Py_DECREF()` the result,
even (especially!) if you are not interested in its value.
Before you do this, however, it is important to check that the return
value isn't `NULL`. If it is, the Python function terminated by raising
an exception. If the C code that called `PyEval_CallObject()` is
called from Python, it should now return an error indication to its
Python caller, so the interpreter can print a stack trace, or the
calling Python code can handle the exception. If this is not possible
or desirable, the exception should be cleared by calling
`PyErr_Clear()`. For example:
```text
if (result == NULL)
return NULL; /* Pass error back */
...use result...
Py_DECREF(result);
```
Depending on the desired interface to the Python callback function,
you may also have to provide an argument list to `PyEval_CallObject()`.
In some cases the argument list is also provided by the Python
program, through the same interface that specified the callback
function. It can then be saved and used in the same manner as the
function object. In other cases, you may have to construct a new
tuple to pass as the argument list. The simplest way to do this is to
call `Py_BuildValue()`. For example, if you want to pass an integral
event code, you might use the following code:
```text
PyObject *arglist;
...
arglist = Py_BuildValue("(l)", eventcode);
result = PyEval_CallObject(my_callback, arglist);
Py_DECREF(arglist);
if (result == NULL)
return NULL; /* Pass error back */
/* Here maybe use the result */
Py_DECREF(result);
```
Note the placement of `Py_DECREF(argument)` immediately after the call,
before the error check! Also note that strictly spoken this code is
not complete: `Py_BuildValue()` may run out of memory, and this should
be checked.
---
node10.html node2.html node8.html node1.html
---
---
guido@cwi.nl


__builtin__ -- Python library reference

__builtin__ -- Python library reference
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# 3.9. Built-in Module `__builtin__`
This module provides direct access to all `built-in' identifiers of
Python; e.g. `__builtin__.open` is the full name for the built-in
function `open`.  See the section on Built-in Functions in the
previous chapter.


__main__ -- Python library reference

__main__ -- Python library reference
Prev: __builtin__ (__builtin__.html)
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# 3.10. Built-in Module `__main__`
This module represents the (otherwise anonymous) scope in which the
interpreter's main program executes --- commands read either from
standard input or from a script file.


aifc -- Python library reference

aifc -- Python library reference
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# 12.3. Standard Module `aifc`
This module provides support for reading and writing AIFF and AIFF-C
files.  AIFF is Audio Interchange File Format, a format for storing
digital audio samples in a file.  AIFF-C is a newer version of the
format that includes the ability to compress the audio data.
Audio files have a number of parameters that describe the audio data.
The sampling rate or frame rate is the number of times per second the
sound is sampled. The number of channels indicate if the audio is
mono, stereo, or quadro. Each frame consists of one sample per
channel. The sample size is the size in bytes of each sample. Thus a
frame consists of nchannels*samplesize bytes, and a
second's worth of audio consists of
nchannels*samplesize*framerate bytes.
For example, CD quality audio has a sample size of two bytes (16
bits), uses two channels (stereo) and has a frame rate of 44,100
frames/second. This gives a frame size of 4 bytes (2*2), and a
second's worth occupies 2*2*44100 bytes, i.e. 176,400 bytes.
Module `aifc` defines the following function:
Objects returned by `aifc.open()` when a file is opened for
reading have the following methods:
Objects returned by `aifc.open()` when a file is opened for
writing have all the above methods, except for `readframes` and
`setpos`. In addition the following methods exist. The
`get` methods can only be called after the corresponding
`set` methods have been called. Before the first
`writeframes` or `writeframesraw`, all parameters except for
the number of frames must be filled in.


al -- Python library reference

al -- Python library reference
Prev: SGI IRIX Specific Services (sgi_irix_specific_services.html)
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# 16.1. Built-in Module `al`
This module provides access to the audio facilities of the SGI Indy
and Indigo workstations.  See section 3A of the IRIX man pages for
details.  You'll need to read those man pages to understand what these
functions do!  Some of the functions are not available in IRIX
releases before 4.0.5.  Again, see the manual to check whether a
specific function is available on your platform.
All functions and methods defined in this module are equivalent to
the C functions with `AL' prefixed to their name.
Symbolic constants from the C header file <audio.h> are defined
in the standard module `AL`, see below.
Warning: the current version of the audio library may dump core
when bad argument values are passed rather than returning an error
status. Unfortunately, since the precise circumstances under which
this may happen are undocumented and hard to check, the Python
interface can provide no protection against this kind of problems.
(One example is specifying an excessive queue size --- there is no
documented upper limit.)
The module defines the following functions:
## Menu


AL (uppercase) -- Python library reference
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# 16.2. Standard Module `AL`
This module defines symbolic constants needed to use the built-in
module `al` (see above); they are equivalent to those defined in
the C header file <audio.h> except that the name prefix
`AL_' is omitted.  Read the module source for a complete list of
the defined names.  Suggested use:


alias objects -- Python library reference
Prev: FSSpec objects (fsspec_objects.html)
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## 14.6.2. alias objects
Note that it is currently not possible to directly manipulate a resource
as an alias object. Hence, after calling Update or after
Resolve indicates that the alias has changed the Python program
is responsible for getting the data from the alias object and
modifying the resource.


array -- Python library reference

array -- Python library reference
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# 5.4. Built-in Module `array`
This module defines a new object type which can efficiently represent
an array of basic values: characters, integers, floating point
numbers.  Arrays are sequence types and behave very much like lists,
except that the type of objects stored in them is constrained.  The
type is specified at object creation time by using a type code,
which is a single character.  The following type codes are defined:
The actual representation of values is determined by the machine
architecture (strictly speaking, by the C implementation). The actual
size can be accessed through the itemsize attribute.
See also built-in module `struct`.
The module defines the following function:
Array objects support the following data items and methods:
When an array object is printed or converted to a string, it is
represented as `array( typecode , initializer )`. The
initializer is omitted if the array is empty, otherwise it is a
string if the typecode is `'c'`, otherwise it is a list of
numbers. The string is guaranteed to be able to be converted back to
an array with the same type and value using reverse quotes
(````). Examples:


Audio Device Objects -- Python library reference
Prev: sunaudiodev (sunaudiodev.html)
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## 17.1.1. Audio Device Objects
The audio device objects are returned by `open` define the
following methods (except `control` objects which only provide
getinfo, setinfo and drain):
There is a companion module, `SUNAUDIODEV`, which defines useful
symbolic constants like `MIN_GAIN`, `MAX_GAIN`,
`SPEAKER`, etc. The names of
the constants are the same names as used in the C include file
<sun/audioio.h>, with the leading string `AUDIO_' stripped.
Useability of the control device is limited at the moment, since there
is no way to use the ``wait for something to happen'' feature the
device provides.


audioop -- Python library reference

audioop -- Python library reference
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# 12.1. Built-in Module `audioop`
The `audioop` module contains some useful operations on sound fragments.
It operates on sound fragments consisting of signed integer samples
8, 16 or 32 bits wide, stored in Python strings.  This is the same
format as used by the `al` and `sunaudiodev` modules.  All
scalar items are integers, unless specified otherwise.
A few of the more complicated operations only take 16-bit samples,
otherwise the sample size (in bytes) is always a parameter of the operation.
The module defines the following variables and functions:
Note that operations such as `mul` or `max` make no
distinction between mono and stereo fragments, i.e. all samples are
treated equal. If this is a problem the stereo fragment should be split
into two mono fragments first and recombined later. Here is an example
of how to do that:
If you use the ADPCM coder to build network packets and you want your
protocol to be stateless (i.e. to be able to tolerate packet loss)
you should not only transmit the data but also the state. Note that
you should send the initial state (the one you passed to
`lin2adpcm`) along to the decoder, not the final state (as returned by
the coder). If you want to use `struct` to store the state in
binary you can code the first element (the predicted value) in 16 bits
and the second (the delta index) in 8.
The ADPCM coders have never been tried against other ADPCM coders,
only against themselves. It could well be that I misinterpreted the
standards in which case they will not be interoperable with the
respective standards.
The `find...` routines might look a bit funny at first sight.
They are primarily meant to do echo cancellation. A reasonably
fast way to do this is to pick the most energetic piece of the output
sample, locate that in the input sample and subtract the whole output
sample from the input sample:


Bit-string Operations -- Python library reference
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### 2.1.4.1. Bit-string Operations on Integer Types
Plain and long integer types support additional operations that make
sense only for bit-strings.  Negative numbers are treated as their 2's
complement value (for long integers, this assumes a sufficiently large
number of bits that no overflow occurs during the operation).
The priorities of the binary bit-wise operations are all lower than
the numeric operations and higher than the comparisons; the unary
operation `~' has the same priority as the other unary numeric
operations (`+' and `-').
This table lists the bit-string operations sorted in ascending
priority (operations in the same box have the same priority):
Notes:


Bitmap Objects -- Python library reference
Prev: Menu Objects (menu_objects.html)
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## 15.1.5. Bitmap Objects
A bitmap represents a rectangular array of bits.
The top left bit has coordinate (0, 0).
A bitmap can be drawn with the `bitmap` method of a drawing object.
Bitmaps are currently not available on the Macintosh.
The following methods are defined:


Boolean Operations -- Python library reference
Prev: Truth Value Testing (truth_value_testing.html)
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## 2.1.2. Boolean Operations
These are the Boolean operations, ordered by ascending priority:
 Notes:


Built-in Functions -- Python library reference
Prev: Exceptions (exceptions.html)
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# 2.3. Built-in Functions
The Python interpreter has a number of functions built into it that
are always available.  They are listed here in alphabetical order.
## ---------- Footnotes ----------
(1) (#footnoteref1)
It is used relatively rarely
so does not warrant being made into a statement.
(2) (#footnoteref2)
This is ugly --- the
language definition should require truncation towards zero.
(3) (#footnoteref3)
Specifying a buffer size currently has no effect on systems
that don't have `setvbuf()`. The interface to specify the buffer
size is not done using a method that calls `setvbuf()`, because
that may dump core when called after any I/O has been performed, and
there's no reliable way to determine whether this is the case.
(4) (#footnoteref4)
In the current implementation, local variable bindings
cannot normally be affected this way, but variables retrieved from
other scopes (e.g. modules) can be. This may change.


Built-in Objects -- Python library reference
Prev: Introduction (introduction.html)
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# 2. Built-in Types, Exceptions and Functions
Names for built-in exceptions and functions are found in a separate
symbol table.  This table is searched last when the interpreter looks
up the meaning of a name, so local and global
user-defined names can override built-in names.  Built-in types are
described together here for easy reference.(1) (#footnotetext1)
The tables in this chapter document the priorities of operators by
listing them in order of ascending priority (within a table) and
grouping operators that have the same priority in the same box.
Binary operators of the same priority group from left to right.
(Unary operators group from right to left, but there you have no real
choice.)  See Chapter 5 of the Python Reference Manual for the
complete picture on operator priorities.
## Menu
## ---------- Footnotes ----------
(1) (#footnoteref1)
Most descriptions sorely lack explanations of the exceptions
that may be raised --- this will be fixed in a future version of
this manual.


Calibration -- Python library reference

Calibration -- Python library reference
Prev: Limitations (limitations.html)
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# 10.7. Calibration
The profiler class has a hard coded constant that is added to each
event handling time to compensate for the overhead of calling the time
function, and socking away the results.  The following procedure can
be used to obtain this constant for a given platform (see discussion
in section Limitations above).
The argument to calibrate() is the number of times to try to do the
sample calls to get the CPU times. If your computer is very
fast, you might have to do:
or even:
The object of this exercise is to get a fairly consistent result.
When you have a consistent answer, you are ready to use that number in
the source code. For a Sun Sparcstation 1000 running Solaris 2.3, the
magical number is about .00053. If you have a choice, you are better
off with a smaller constant, and your results will ``less often'' show
up as negative in profile statistics.
The following shows how the trace_dispatch() method in the Profile
class should be modified to install the calibration constant on a Sun
Sparcstation 1000:
Note that if there is no calibration constant, then the line
containing the callibration constant should simply say:
You can also achieve the same results using a derived class (and the
profiler will actually run equally fast!!), but the above method is
the simplest to use. I could have made the profiler ``self
calibrating'', but it would have made the initialization of the
profiler class slower, and would have required some very fancy
coding, or else the use of a variable where the constant `.00053'
was placed in the code shown. This is a VERY critical
performance section, and there is no reason to use a variable lookup
at this point, when a constant can be used.


cd -- Python library reference

cd -- Python library reference
Prev: AL (uppercase) (al__uppercase_.html)
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# 16.3. Built-in Module `cd`
This module provides an interface to the Silicon Graphics CD library.
It is available only on Silicon Graphics systems.
The way the library works is as follows. A program opens the CD-ROM
device with `cd.open()` and creates a parser to parse the data
from the CD with `cd.createparser()`. The object returned by
`cd.open()` can be used to read data from the CD, but also to get
status information for the CD-ROM device, and to get information about
the CD, such as the table of contents. Data from the CD is passed to
the parser, which parses the frames, and calls any callback
functions that have previously been added.
An audio CD is divided into tracks or programs (the terms
are used interchangeably). Tracks can be subdivided into
indices. An audio CD contains a table of contents which
gives the starts of the tracks on the CD. Index 0 is usually the
pause before the start of a track. The start of the track as given by
the table of contents is normally the start of index 1.
Positions on a CD can be represented in two ways. Either a frame
number or a tuple of three values, minutes, seconds and frames. Most
functions use the latter representation. Positions can be both
relative to the beginning of the CD, and to the beginning of the
track.
Module `cd` defines the following functions and constants:
The module defines the following variables:
The following variables are states as returned by `getstatus`:
Player objects (returned by `cd.open()`) have the following
methods:
Parser objects (returned by `cd.createparser()`) have the
following methods:


cgi -- Python library reference

cgi -- Python library reference
Prev: Internet and WWW (internet_and_www.html)
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# 11.1. Standard Module `cgi`
This module makes it easy to write Python scripts that run in a WWW
server using the Common Gateway Interface.  It was written by Michael
McLay and subsequently modified by Steve Majewski and Guido van
Rossum.
When a WWW server finds that a URL contains a reference to a file in a
particular subdirectory (usually `/cgibin`), it runs the file as
a subprocess. Information about the request such as the full URL, the
originating host etc., is passed to the subprocess in the shell
environment; additional input from the client may be read from
standard input. Standard output from the subprocess is sent back
across the network to the client as the response from the request.
The CGI protocol describes what the environment variables passed to
the subprocess mean and how the output should be formatted. The
official reference documentation for the CGI protocol can be found on
the World-Wide Web at
`<URL:http://hoohoo.ncsa.uiuc.edu/cgi/overview.html>`. The
`cgi` module was based on version 1.1 of the protocol and should
also work with version 1.0.
The `cgi` module defines several classes that make it easy to
access the information passed to the subprocess from a Python script;
in particular, it knows how to parse the input sent by an HTML
``form'' using either a POST or a GET request (these are alternatives
for submitting forms in the HTTP protocol).
The formatting of the output is so trivial that no additional support
is needed. All you need to do is print a minimal set of MIME headers
describing the output format, followed by a blank line and your actual
output. E.g. if you want to generate HTML, your script could start as
follows:
The server will add some header lines of its own, but it won't touch
the output following the header.
The `cgi` module defines the following functions:
The module defines the following classes. Since the base class
initializes itself by calling `parse()`, at most one instance of
at most one of these classes should be created per script invocation:
(It currently defines some more classes, but these are experimental
and/or obsolescent, and are thus not documented --- see the source for
more informations.)
The module defines the following variable:
## Menu


CGI Example -- Python library reference

CGI Example -- Python library reference
Prev: cgi (cgi.html)
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## 11.1.1. Example
This example assumes that you have a WWW server up and running,
e.g. NCSA's `httpd`.
Place the following file in a convenient spot in the WWW server's
directory tree. E.g., if you place it in the subdirectory test
of the root directory and call it test.html, its URL will be
http://yourservername/test/test.html.
Selecting this file's URL from a forms-capable browser such as Mosaic
or Netscape will bring up a simple form with two text input fields and
a ``submit'' button.
But wait. Before pressing ``submit'', a script that responds to the
form must also be installed. The test file as shown assumes that the
script is called test.py and lives in the server's
`cgi-bin` directory. Here's the test script:
The script should be made executable (`chmod +x script').
If the Python interpreter is not located at
/usr/local/bin/python but somewhere else, the first line of the
script should be modified accordingly.
Now that everything is installed correctly, we can try out the form.
Bring up the test form in your WWW browser, fill in a name and address
in the form, and press the ``submit'' button. The script should now
run and its output is sent back to your browser. This should roughly
look as follows:
Test Form Output
- • Name: the name you entered
• Address: the address you entered
If you didn't enter a name or address, the corresponding line will be
missing (since the browser doesn't send empty form fields to the
server).


Classes and Instances -- Python library reference
Prev: Modules (modules.html)
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### 2.1.7.2. Classes and Class Instances
(See Chapters 3 and 7 of the Python Reference Manual for these.)


Code Objects -- Python library reference
Prev: Methods (methods.html)
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### 2.1.7.5. Code Objects
Code objects are used by the implementation to represent
``pseudo-compiled'' executable Python code such as a function body.
They differ from function objects because they don't contain a
reference to their global execution environment.  Code objects are
returned by the built-in `compile()` function and can be
extracted from function objects through their `func_code`
attribute.
A code object can be executed or evaluated by passing it (instead of a
source string) to the `exec` statement or the built-in
`eval()` function.
(See the Python Reference Manual for more info.)


Comparisons -- Python library reference

Comparisons -- Python library reference
Prev: Boolean Operations (boolean_operations.html)
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## 2.1.3. Comparisons
Comparison operations are supported by all objects.  They all have the
same priority (which is higher than that of the Boolean operations).
Comparisons can be chained arbitrarily, e.g. `x < y <= z` is
equivalent to `x < y and y <= z`, except that `y` is
evaluated only once (but in both cases `z` is not evaluated at
all when `x < y` is found to be false).
This table summarizes the comparison operations:
Notes:
Objects of different types, except different numeric types, never
compare equal; such objects are ordered consistently but arbitrarily
(so that sorting a heterogeneous array yields a consistent result).
Furthermore, some types (e.g., windows) support only a degenerate
notion of comparison where any two objects of that type are unequal.
Again, such objects are ordered arbitrarily but consistently.
(Implementation note: objects of different types except numbers are
ordered by their type names; objects of the same types that don't
support proper comparison are ordered by their address.)
Two more operations with the same syntactic priority, `in` and
`not in`, are supported only by sequence types (below).


Concept Index -- Python library reference
Prev: Module Index (module_index.html)
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# Concept Index


Configuration Objects -- Python library reference
Prev: al (al.html)
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## 16.1.1. Configuration Objects
Configuration objects (returned by `al.newconfig()` have the
following methods:


connection object -- Python library reference
Prev: ctb (ctb.html)
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## 14.3.1. connection object
For all connection methods that take a timeout argument, a value
of `-1` is indefinite, meaning that the command runs to completion.


console window object -- Python library reference
Prev: macconsole options object (macconsole_options_object.html)
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## 14.4.2. console window object


copy -- Python library reference

copy -- Python library reference
Prev: shelve (shelve.html)
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# 3.6. Standard Module `copy`
This module provides generic (shallow and deep) copying operations.
Interface summary:
For module specific errors, `copy.error` is raised.
The difference between shallow and deep copying is only relevant for
compound objects (objects that contain other objects, like lists or
class instances):
- • A shallow copy constructs a new compound object and then (to the
extent possible) inserts references into it to the objects found
in the original.
• A deep copy constructs a new compound object and then,
recursively, inserts copies into it of the objects found in the
original.
Two problems often exist with deep copy operations that don't exist
with shallow copy operations:
- • Recursive objects (compound objects that, directly or indirectly,
contain a reference to themselves) may cause a recursive loop.
• Because deep copy copies everything it may copy too much, e.g.
administrative data structures that should be shared even between
copies.
Python's `deepcopy()` operation avoids these problems by:
- • keeping a table of objects already copied during the current
copying pass; and
• letting user-defined classes override the copying operation or the
set of components copied.
This version does not copy types like module, class, function, method,
nor stack trace, stack frame, nor file, socket, window, nor array, nor
any similar types.
Classes can use the same interfaces to control copying that they use
to control pickling: they can define methods called
`__getinitargs__()`, `__getstate__()` and
`__setstate__()`. See the description of module `pickle`
for information on these methods.


Cryptographic Services -- Python library reference
Prev: Multimedia Services (multimedia_services.html)
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# 13. Cryptographic Services
The modules described in this chapter implement various algorithms of
a cryptographic nature.  They are available at the discretion of the
installation.  Here's an overview:
Hardcore cypherpunks will probably find the Python Cryptography Kit of
further interest; the package adds built-in modules for DES and IDEA
encryption, and provides a Python module for reading and decrypting
PGP files. The Python Cryptography Kit is not distributed with Python
but available separately. See the URL
http://www.cs.mcgill.ca/%7Efnord/crypt.html for more information.
## Menu


ctb -- Python library reference

ctb -- Python library reference
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# 14.3. Built-in Module `ctb`
This module provides a partial interface to the Macintosh
Communications Toolbox. Currently, only Connection Manager tools are
supported.  It may not be available in all Mac Python versions.
## Menu


dbm -- Python library reference

dbm -- Python library reference
Prev: grp (grp.html)
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# 8.5. Built-in Module `dbm`
Dbm provides python programs with an interface to the unix `ndbm`
database library.  Dbm objects are of the mapping type, so they can be
handled just like objects of the built-in dictionary type,
except that keys and values are always strings, and printing a dbm
object doesn't print the keys and values.
The module defines the following constant and functions:


Debugger Commands -- Python library reference
Prev: The Python Debugger (the_python_debugger.html)
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# 9.1. Debugger Commands
The debugger recognizes the following commands.  Most commands can be
abbreviated to one or two letters; e.g. ```h(elp)`'' means that
either ```h`'' or ```help`'' can be used to enter the help
command (but not ```he`'' or ```hel`'', nor ```H`'' or
```Help` or ```HELP`'').  Arguments to commands must be
separated by whitespace (spaces or tabs).  Optional arguments are
enclosed in square brackets (```[]`'') in the command syntax; the
square brackets must not be typed.  Alternatives in the command syntax
are separated by a vertical bar (```|`'').

Entering a blank line repeats the last command entered. Exception: if
the last command was a ```list`'' command, the next 11 lines are
listed.

Commands that the debugger doesn't recognize are assumed to be Python
statements and are executed in the context of the program being
debugged. Python statements can also be prefixed with an exclamation
point (```!`''). This is a powerful way to inspect the program
being debugged; it is even possible to change a variable or call a
function. When an
exception occurs in such a statement, the exception name is printed
but the debugger's state is not changed.


Deterministic Profiling -- Python library reference
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# 10.4. What Is Deterministic Profiling?
Deterministic profiling is meant to reflect the fact that all
function call, function return, and exception events
are monitored, and precise timings are made for the intervals between
these events (during which time the user's code is executing).  In
contrast, statistical profiling (which is not done by this
module) randomly samples the effective instruction pointer, and
deduces where time is being spent.  The latter technique traditionally
involves less overhead (as the code does not need to be instrumented),
but provides only relative indications of where time is being spent.
In Python, since there is an interpreter active during execution, the
presence of instrumented code is not required to do deterministic
profiling. Python automatically provides a hook (optional
callback) for each event. In addition, the interpreted nature of
Python tends to add so much overhead to execution, that deterministic
profiling tends to only add small processing overhead in typical
applications. The result is that deterministic profiling is not that
expensive, yet provides extensive run time statistics about the
execution of a Python program.
Call count statistics can be used to identify bugs in code (surprising
counts), and to identify possible inline-expansion points (high call
counts). Internal time statistics can be used to identify ``hot
loops'' that should be carefully optimized. Cumulative time
statistics should be used to identify high level errors in the
selection of algorithms. Note that the unusual handling of cumulative
times in this profiler allows statistics for recursive implementations
of algorithms to be directly compared to iterative implementations.


dnr result object -- Python library reference
Prev: macdnr (macdnr.html)
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## 14.5.1. dnr result object
Since the DNR calls all execute asynchronously you do not get the
results back immediately.  Instead, you get a dnr result object.  You
can check this object to see whether the query is complete, and access
its attributes to obtain the information when it is.
Alternatively, you can also reference the result attributes directly,
this will result in an implicit wait for the query to complete.
The rtnCode and cname attributes are always available, the
others depend on the type of query (address, hinfo or mx).
The simplest way to use the module to convert names to dotted-decimal
strings, without worrying about idle time, etc:


Drawing Objects -- Python library reference
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## 15.1.3. Drawing Objects
Drawing objects are created exclusively by the window method
`begindrawing()`.
Only one drawing object can exist at any given time; the drawing object
must be deleted to finish drawing.
No drawing object may exist when
`stdwin.getevent()`
is called.
Drawing objects have the following methods:


Examples -- Python library reference

Examples -- Python library reference
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## 3.8.1. Examples
The following function emulates the default import statement:


Exceptions -- Python library reference

Exceptions -- Python library reference
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# 2.2. Built-in Exceptions
Exceptions are string objects.  Two distinct string objects with the
same value are different exceptions.  This is done to force programmers
to use exception names rather than their string value when specifying
exception handlers.  The string value of all built-in exceptions is
their name, but this is not a requirement for user-defined exceptions
or exceptions defined by library modules.
The following exceptions can be generated by the interpreter or
built-in functions. Except where mentioned, they have an `associated
value' indicating the detailed cause of the error. This may be a
string or a tuple containing several items of information (e.g., an
error code and a string explaining the code).
User code can raise built-in exceptions. This can be used to test an
exception handler or to report an error condition `just like' the
situation in which the interpreter raises the same exception; but
beware that there is nothing to prevent user code from raising an
inappropriate error.


fcntl -- Python library reference

fcntl -- Python library reference
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# 8.9. Built-in Module `fcntl`
This module performs file control and I/O control on file descriptors.
It is an interface to the fcntl() and ioctl() UNIX routines.
File descriptors can be obtained with the fileno() method of a
file or socket object.
The module defines the following functions:
If the library modules `FCNTL` or `IOCTL` are missing, you
can find the opcodes in the C include files `sys/fcntl` and
`sys/ioctl`. You can create the modules yourself with the h2py
script, found in the `Demo/scripts` directory.
Examples (all on a SVR4 compliant system):
Note that in the first example the return value variable `rv` will
hold an integer value; in the second example it will hold a string
value.


File Objects -- Python library reference
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### 2.1.7.8. File Objects
File objects are implemented using C's `stdio` package and can be
created with the built-in function `open()` described under
Built-in Functions below.  They are also returned by some other
built-in functions and methods, e.g. `posix.popen()` and
`posix.fdopen()` and the `makefile()` method of socket
objects.
When a file operation fails for an I/O-related reason, the exception
`IOError` is raised.  This includes situations where the
operation is not defined for some reason, like `seek()` on a tty
device or writing a file opened for reading.
Files have the following methods:
## ---------- Footnotes ----------
(1) (#footnoteref1)
The advantage of leaving the newline on is that an empty string
can be returned to mean EOF without being ambiguous. Another
advantage is that (in cases where it might matter, e.g. if you
want to make an exact copy of a file while scanning its lines)
you can tell whether the last line of a file ended in a newline
or not (yes this happens!).


fl -- Python library reference

fl -- Python library reference
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# 16.4. Built-in Module `fl`
This module provides an interface to the FORMS Library by Mark
Overmars.  The source for the library can be retrieved by anonymous
ftp from host `ftp.cs.ruu.nl', directory SGI/FORMS.  It
was last tested with version 2.0b.
Most functions are literal translations of their C equivalents,
dropping the initial `fl_' from their name. Constants used by
the library are defined in module `FL` described below.
The creation of objects is a little different in Python than in C:
instead of the `current form' maintained by the library to which new
FORMS objects are added, all functions that add a FORMS object to a
form are methods of the Python object representing the form.
Consequently, there are no Python equivalents for the C functions
`fl_addto_form` and `fl_end_form`, and the equivalent of
`fl_bgn_form` is called `fl.make_form`.
Watch out for the somewhat confusing terminology: FORMS uses the word
object for the buttons, sliders etc. that you can place in a form.
In Python, `object' means any value. The Python interface to FORMS
introduces two new Python object types: form objects (representing an
entire form) and FORMS objects (representing one button, slider etc.).
Hopefully this isn't too confusing...
There are no `free objects' in the Python interface to FORMS, nor is
there an easy way to add object classes written in Python. The FORMS
interface to GL event handling is available, though, so you can mix
FORMS with pure GL windows.
Please note: importing `fl` implies a call to the GL function
`foreground()` and to the FORMS routine `fl_init()`.
## Menu


FL (uppercase) -- Python library reference
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# 16.5. Standard Module `FL`
This module defines symbolic constants needed to use the built-in
module `fl` (see above); they are equivalent to those defined in
the C header file <forms.h> except that the name prefix
`FL_' is omitted.  Read the module source for a complete list of
the defined names.  Suggested use:


FL Functions -- Python library reference
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## 16.4.1. Functions Defined in Module `fl`
Module `fl` defines the following functions.  For more information
about what they do, see the description of the equivalent C function
in the FORMS documentation:


flp -- Python library reference

flp -- Python library reference
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# 16.6. Standard Module `flp`
This module defines functions that can read form definitions created
by the `form designer' (`fdesign`) program that comes with the
FORMS library (see module `fl` above).
For now, see the file flp.doc in the Python library source
directory for a description.
XXX A complete description should be inserted here!


fm -- Python library reference

fm -- Python library reference
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# 16.7. Built-in Module `fm`
This module provides access to the IRIS Font Manager library.
It is available only on Silicon Graphics machines.
See also: 4Sight User's Guide, Section 1, Chapter 5: Using the IRIS
Font Manager.
This is not yet a full interface to the IRIS Font Manager.
Among the unsupported features are: matrix operations; cache
operations; character operations (use string operations instead); some
details of font info; individual glyph metrics; and printer matching.
It supports the following operations:
Font handle objects support the following operations:


Form Objects -- Python library reference
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## 16.4.2. Form Objects
Form objects (returned by `fl.make_form()` above) have the
following methods.  Each method corresponds to a C function whose name
is prefixed with `fl_'; and whose first argument is a form
pointer; please refer to the official FORMS documentation for
descriptions.
All the `add_...' functions return a Python object representing
the FORMS object. Methods of FORMS objects are described below. Most
kinds of FORMS object also have some methods specific to that kind;
these methods are listed here.
Form objects have the following data attributes; see the FORMS
documentation:


FORMS Objects -- Python library reference
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## 16.4.3. FORMS Objects
Besides methods specific to particular kinds of FORMS objects, all
FORMS objects also have the following methods:
FORMS objects have these data attributes; see the FORMS documentation:


FSSpec objects -- Python library reference
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## 14.6.1. FSSpec objects


FTP Objects -- Python library reference

FTP Objects -- Python library reference
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## 11.4.1. FTP Objects
FTP instances have the following methods:


ftplib -- Python library reference

ftplib -- Python library reference
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# 11.4. Standard Module `ftplib`
This module defines the class `FTP` and a few related items.  The
`FTP` class implements the client side of the FTP protocol.  You
can use this to write Python programs that perform a variety of
automated FTP jobs, such as mirroring other ftp servers.  It is also
used bu the module `urllib` to handle URLs that use FTP.  For
more information on FTP (File Transfer Protocol), see Internet RFC
959.
Here's a sample session using the `ftplib` module:
The module defines the following items:
## Menu


Functions -- Python library reference

Functions -- Python library reference
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### 2.1.7.3. Functions
Function objects are created by function definitions.  The only
operation on a function object is to call it:
`func ( argument-list )`.
There are really two flavors of function objects: built-in functions
and user-defined functions. Both support the same operation (to call
the function), but the implementation is different, hence the
different object types.
The implementation adds two special read-only attributes:
`f .func_code` is a function's code object (see below) and
`f .func_globals` is the dictionary used as the function's
global name space (this is the same as `m .__dict__` where
m is the module in which the function f was defined).


gdbm -- Python library reference

gdbm -- Python library reference
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# 8.6. Built-in Module `gdbm`
Gdbm provides python programs with an interface to the GNU `gdbm`
database library.  Gdbm objects are of the mapping type, so they can be
handled just like objects of the built-in dictionary type,
except that keys and values are always strings, and printing a gdbm
object doesn't print the keys and values.
The module is based on the Dbm module, modified to use GDBM instead.
The module defines the following constant and functions:


Generic Operating System Services -- Python library reference
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# 6. Generic Operating System Services
The modules described in this chapter provide interfaces to operating
system features that are available on (almost) all operating systems,
such as files and a clock.  The interfaces are generally modelled
after the UNIX or C interfaces but they are available on most other
systems as well.  Here's an overview:
## Menu